From 8645089eae989d721dbabb18b47c0f21e0adaf38 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 15 May 2020 20:20:07 -0400
Subject: [PATCH 001/144] Adding image sources

---
 book/figs/image_sources.txt | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 book/figs/image_sources.txt

diff --git a/book/figs/image_sources.txt b/book/figs/image_sources.txt
new file mode 100644
index 00000000..7e2136df
--- /dev/null
+++ b/book/figs/image_sources.txt
@@ -0,0 +1,5 @@
+globe, public domain, http://www.freestockphotos.biz/stockphoto/16911
+sphere, CC0, https://www.kissclipart.com/blue-sphere-clipart-clip-art-spxa3o/
+chart, CC0, https://openclipart.org/detail/215430/chart
+empire state building, public domain, https://freesvg.org/boort-art-deco-empire-state-building
+other building, public domain, https://freesvg.org/building-vector-silhouette

From f4df9a66c0bcef94a7268e040ace2d0e27601ce5 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 2 Jun 2020 09:34:10 -0400
Subject: [PATCH 002/144] Run on Colab

---
 soln/jump2_soln.ipynb | 496 ++++++++++++------------------------------
 1 file changed, 136 insertions(+), 360 deletions(-)

diff --git a/soln/jump2_soln.ipynb b/soln/jump2_soln.ipynb
index 87ebc5d4..5fa97f6e 100644
--- a/soln/jump2_soln.ipynb
+++ b/soln/jump2_soln.ipynb
@@ -18,13 +18,26 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "# If we're running on Colab, install modsimpy\n",
+    "# https://pypi.org/project/modsimpy/\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Configure Jupyter so figures appear in the notebook\n",
     "%matplotlib inline\n",
     "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
     "# import functions from the modsim.py module\n",
     "from modsim import *"
    ]
@@ -75,26 +88,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "newton"
-      ],
-      "text/latex": [
-       "$\\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "<Unit('newton')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "m = UNITS.meter\n",
     "s = UNITS.second\n",
@@ -104,92 +100,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>v_init</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>M</th>\n",
-       "      <td>75 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>m_cord</th>\n",
-       "      <td>75 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>1 meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>60.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>L</th>\n",
-       "      <td>25 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k</th>\n",
-       "      <td>40.0 newton / meter</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "v_init           0.0 meter / second\n",
-       "g           9.8 meter / second ** 2\n",
-       "M                       75 kilogram\n",
-       "m_cord                  75 kilogram\n",
-       "area                   1 meter ** 2\n",
-       "rho       1.2 kilogram / meter ** 3\n",
-       "v_term          60.0 meter / second\n",
-       "L                          25 meter\n",
-       "k               40.0 newton / meter\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "params = Params(v_init = 0 * m / s,\n",
     "                g = 9.8 * m/s**2,\n",
@@ -215,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -250,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -353,13 +266,14 @@
        "dtype: object"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "system = make_system(params)"
+    "system = make_system(params)\n",
+    "system"
    ]
   },
   {
@@ -371,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -397,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -412,7 +326,7 @@
        "-81.66666666666667 <Unit('kilogram * meter / second ** 2')>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -432,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -459,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -474,7 +388,7 @@
        "-3.6363636363636362 <Unit('meter / second ** 2')>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -494,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -527,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -536,7 +450,7 @@
        "(0.0 <Unit('meter / second')>, -9.8 <Unit('meter / second ** 2')>)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -554,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -581,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -596,7 +510,7 @@
        "25 <Unit('meter')>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -614,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -623,7 +537,7 @@
        "'A termination event occurred.'"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -642,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -651,13 +565,14 @@
        "2.2118255911654763"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "t_final = get_last_label(results)"
+    "t_final = get_last_label(results)\n",
+    "t_final"
    ]
   },
   {
@@ -669,12 +584,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -703,7 +618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -718,7 +633,7 @@
        "-25.0 <Unit('meter')>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -736,14 +651,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -773,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -788,7 +703,7 @@
        "-23.876881009672466 <Unit('meter / second')>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -808,12 +723,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -840,26 +755,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "1.4537935759242928 dimensionless"
-      ],
-      "text/latex": [
-       "$1.4537935759242928\\ dimensionless$"
-      ],
-      "text/plain": [
-       "1.4537935759242928 <Unit('dimensionless')>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "max_acceleration = max(abs(a)) * m/s**2 / params.g"
    ]
@@ -873,7 +771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -888,7 +786,7 @@
        "1.625 <Unit('dimensionless')>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -919,7 +817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -937,152 +835,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-21.43444396611346 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>-21.778799793406385 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>-22.1185904356283 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>-22.453924142289296 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>-22.78486198074548 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>-23.111415158472745 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>61</th>\n",
-       "      <td>-23.4335416886762 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>71</th>\n",
-       "      <td>-23.751142173575644 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>81</th>\n",
-       "      <td>-24.064054490941942 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>91</th>\n",
-       "      <td>-24.372047155067598 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101</th>\n",
-       "      <td>-24.67679574004737 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>111</th>\n",
-       "      <td>-24.97875199285987 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>121</th>\n",
-       "      <td>-25.27786764272998 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>131</th>\n",
-       "      <td>-25.57421210507495 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>141</th>\n",
-       "      <td>-25.867837825239334 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>151</th>\n",
-       "      <td>-26.158780104872434 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>161</th>\n",
-       "      <td>-26.44705675559671 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>171</th>\n",
-       "      <td>-26.732667571098236 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>181</th>\n",
-       "      <td>-27.0155936062971 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>191</th>\n",
-       "      <td>-27.295796249246642 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>201</th>\n",
-       "      <td>-27.57321606785513 meter ** 2 / second ** 2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "1       -21.43444396611346 meter ** 2 / second ** 2\n",
-       "11     -21.778799793406385 meter ** 2 / second ** 2\n",
-       "21       -22.1185904356283 meter ** 2 / second ** 2\n",
-       "31     -22.453924142289296 meter ** 2 / second ** 2\n",
-       "41      -22.78486198074548 meter ** 2 / second ** 2\n",
-       "51     -23.111415158472745 meter ** 2 / second ** 2\n",
-       "61       -23.4335416886762 meter ** 2 / second ** 2\n",
-       "71     -23.751142173575644 meter ** 2 / second ** 2\n",
-       "81     -24.064054490941942 meter ** 2 / second ** 2\n",
-       "91     -24.372047155067598 meter ** 2 / second ** 2\n",
-       "101     -24.67679574004737 meter ** 2 / second ** 2\n",
-       "111     -24.97875199285987 meter ** 2 / second ** 2\n",
-       "121     -25.27786764272998 meter ** 2 / second ** 2\n",
-       "131     -25.57421210507495 meter ** 2 / second ** 2\n",
-       "141    -25.867837825239334 meter ** 2 / second ** 2\n",
-       "151    -26.158780104872434 meter ** 2 / second ** 2\n",
-       "161     -26.44705675559671 meter ** 2 / second ** 2\n",
-       "171    -26.732667571098236 meter ** 2 / second ** 2\n",
-       "181      -27.0155936062971 meter ** 2 / second ** 2\n",
-       "191    -27.295796249246642 meter ** 2 / second ** 2\n",
-       "201     -27.57321606785513 meter ** 2 / second ** 2\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "m_cord_array = linspace(1, 201, 21) * kg\n",
     "sweep = sweep_m_cord(m_cord_array, params)"
@@ -1099,12 +854,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1140,7 +895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1173,7 +928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1188,7 +943,7 @@
        "0.0 <Unit('newton')>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1199,7 +954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1214,7 +969,7 @@
        "40.0 <Unit('newton')>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1232,7 +987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1265,7 +1020,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1368,32 +1123,36 @@
        "dtype: object"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "system1 = make_system(params)"
+    "system1 = make_system(params)\n",
+    "system1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "A termination event occurred.\n"
-     ]
+     "data": {
+      "text/plain": [
+       "'A termination event occurred.'"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "event_func.direction=-1\n",
     "results1, details1 = run_ode_solver(system1, slope_func1, events=event_func)\n",
-    "print(details1.message)"
+    "details1.message"
    ]
   },
   {
@@ -1405,7 +1164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1414,18 +1173,19 @@
        "2.2118255911654763"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "t_final = get_last_label(results1)"
+    "t_final = get_last_label(results1)\n",
+    "t_final"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1471,13 +1231,14 @@
        "Name: 2.2118255911654763, dtype: object"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "init2 = results1.row[t_final]"
+    "init2 = results1.row[t_final]\n",
+    "init2"
    ]
   },
   {
@@ -1489,7 +1250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1597,13 +1358,14 @@
        "dtype: object"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "system2 = System(system1, t_0=t_final, init=init2)"
+    "system2 = System(system1, t_0=t_final, init=init2)\n",
+    "system2"
    ]
   },
   {
@@ -1615,23 +1377,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "A termination event occurred.\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "8.09952482949752"
+       "'A termination event occurred.'"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1639,8 +1394,28 @@
    "source": [
     "event_func.direction=+1\n",
     "results2, details2 = run_ode_solver(system2, slope_func2, events=event_func)\n",
-    "print(details2.message)\n",
-    "t_final = get_last_label(results2)"
+    "details2.message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8.09952482949752"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t_final = get_last_label(results2)\n",
+    "t_final"
    ]
   },
   {
@@ -1652,12 +1427,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1682,7 +1457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -1697,7 +1472,7 @@
        "-77.04609533183535 <Unit('meter')>"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1715,7 +1490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1743,7 +1518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1752,12 +1527,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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1RKQcLV63m4+/cxZFPHxND+LqVa/Q1y9rKXweWGqMGQkkAcdckGutHVTOuUS46ZKOLFq3mwVrUli0NoVeHfx/UlZEHHsOZvLix0vweuGaC9rRo13DCs9wIiOodOBrnEURS4rdRMpd3ZrVuPr8dgD8d+IqcrSZrEiFyMsv5LmPFpOWmUePdg24wte/raKVdQTVCzjdWrvSn2FEirv4jJb8uGgbScmH+eLHjYy8oJ3bkUQqvQ++XovdepB6tarxwNU9XNt6rKwjKAvU8mcQkZKEhoZw27AEAL6cuZFd+9JdTiRSuc1duYvJsxMJDfHwyLU9ia0e4VqWso6gngHeN8a8DiQCeUXvtNZ+U97BRI7o2Koug3o246fF23ln8mqeGNXH7UgilVLyvgxe+XQZADcM6Ui7+Dqu5ilrgfrE999/l3CfF2d3cxG/ueGiDsxblcyitbu1YELED/LyC3l+3GIys/Pp2zmOSwe0cjtS2QqUtVaNDcVVtWOjuPr8drw7ZTVvT15N17b1CQ/T5yKR8jLu23Vs3J5K/drVuKcCL8Y9nt9rt3FCjDFabi5+M+SMljRrWIPkfRlM+lk7TIiUl6V2D1/N2kRIiIeHR/YkJtq9805FHW9kdL8x5ltjzGBjTHhpDzLGhBljhhpjfsBpbCjiF2GhIdw6tDMAn/6wgb0Hs1xOJBL8DqZlH90E9urzDe1bunveqahSp/istUONMZcBzwItjDGzgDXAPsAD1MfpstsX2Ab83Vr7hd8TS5XWpW19+ndpzK8rdvHe1NU8cl0vtyOJBK3CQi8vfbyU1LQcOreux/BB7lzvVJrjnoOy1k4EJvo65V6IU4wa4uwkkYJzke4z1to5fs4pctRNF3dk0drd/LJiFxdu2kfnNvXcjiQSlCbPTmTZhr3UiI7gwZHd/dYZ92SVdZHELNQpVwJEg9rRjDjnNMZNX8+YSat4+f6zCA3VOh6RE7Fl1yE+/GYdAPf9sRt1a1ZzOdFv6bdagtLQgW1oUCeapOTDTJ+vTfZFTkROXgH/Hr+E/IJCBveNr5DmgydDBUqCUmR4KKMu7gjA+OnrSMvMdTmRSPD44Ou1bEtJo0n9GG66pKPbcUqlAiVBq2/nOBLa1CMtM4/x09e7HUckKCxdv4epczYTGuLhoZE9iIqouP5OJ0oFSoKWx+PhlqGdCQnx8O3cLSQlH3Y7kkhAO5Sew8sTnCXlIy9oR5tmgb3FaplLpzGmAZAAhOMsMz9Ke/GJW1rExXJh33im/bqFMRNX8fTt/QLiCniRQOP1evnPFys4mJZDx1Z1GXb2aW5H+l1lKlDGmFHAGzjFqTjtxSeuuvqCdvy8bCerEvcxd2Uy/bs0djuSSMCZuWQH81YlUy0ylPuvCrwl5SUp6wjqYeBt4C/W2jQ/5hE5YTWiI7hmcDve/HIl701bQ68ODYkI12cmkSP2HsxizESnnd/oSzvTsE60y4nKpqznoJoBr6g4SaA6//QWxMfFsudApvbpEymisNDLK58uJSM7n94dGnFu7+ZuRyqzshaoGcA5/gwicipCQ0O4+dJOAHz+4wb2H9I+fSIA38zdwoqN+4itHsFdI7oE1Tnask7xrQBeNMZcAmwAjrnoxFr7p/IOJnKiupxWn76d45i3KpkPvl7LA1f3cDuSiKt27k1n7LS1ANwxvAu1a0S5nOjElHUEdRawAKiGs0FsryK3nv6JJnLibrq4I+FhIcxcsoP1Ww+4HUfENQWFXl76ZCm5eQWc3aMp/ROCb/FQWffiO9vfQUTKQ6O61Rl6Vms+/3Ej70xazXN3n0lIEKxWEilvk3/ehN16kDqxUdzia1MTbE7kOqiGwF1AR5yR1zrgbWvtZj9lEzkpV5zTlh8XbcNuO8ispTsY1LOZ25FEKtS2lMOM8+2ucveIrgHTgPBElWmKzxjTG+fc02U4/aD2AkOAlcYYTfFJQKkWGcb1F3UAnD3HsnLyXU4kUnEKCgp5ecIy8vIL+UPv5vRs39DtSCetrOegXgA+ATpba2+x1o621nYG3gee91c4kZM1sHsz2javxYHD2Xzx00a344hUmK9mbWLj9lTq1arGqEs6uR3nlJS1QPUEXrLWeosdfw1noUS5McacbozJM8bEFznW3BjzvTEmzRiTaIy5sDxfUyqfkBAPoy915t0nztrE7gOZLicS8b+tyYf5+Dtnau+eEV2pXq2kzX+CR1kLVDIQX8LxVkC5XbxrjIkBPuS358YmACuBusBoYIIxplV5va5UTu3i63BWt6bk5Rcydtoat+OI+JUztbeU/AIvF/SNp5tp4HakU1bWAvURMMYYc5kxJs53Gwa85buvvLwGfFX0gDGmLc4I7glrba619idgCjCqHF9XKqnrL+pARHgov67YxerEfW7HEfGbr2ZtYtOOQ9SvXY0bh3RwO065KOsqvqeBxsBnOEXNA+ThFJTHyvIDjDERQJ0S7vJaa3cbY4YDrYEHgT8Xub8DsM1am1Hk2HqgdxmzSxVWv3Y1hp/dho9nWN6evJoX7zsrKDbJFDkR21IO8/F3FoC7r+hKdFRwT+0dUdbroHKB0caYhwADZAGbrLUnsp9MP2BmCccLjDEtcBZbnA0UFrs/Bih+AiETCI7dDsV1l53dhhkLt7F55yF+WLiN8/u0cDuSSLkpKCjklU+XkV9QyHmnt6gUU3tHlFqgfAsRvrfW5pWyKKGZMQYoWz8oa+0sivWR8r2OB/gB+Ku1NskYU7yDVgbODhZFRQPpv/eaIgBREWHccFEH/j1+CeO+XceZXRtXmk+YIpNnJ7JhWyr1akZx08WB2779ZBzvHNQ0oHaRr0u7TT3FDM2A/sCrxphUYKvv+EpjzNXAWqC5MaZokWrnOy5SJgO6NaF9fB1S03P49PsNbscRKRc79qQdvSD3ziuCf9VecaWOoKy1ISV9Xd6stduAozsY+kZQB4EEa22S79gK4GljzF9wpgovBfr6K5NUPh6Ph9FDO/HAy7OZMieR8/u2oHG9GLdjiZy0gkIvr366nLz8Qgb1bBbUF+SWpqw7SfxUwtQbxpj6xpgl5R/rNy4H2gN7gHeAUdba1RXwulKJnNasNuf0akZ+gZf3pmjZuQS3r3/dzLqkA9SuEcnoS4P7gtzSHO8c1ECcFXTg7GZ+qzGm+DVP7XFW3pUba20qxc5VWWu3A4PL83Wkarruwg7MXbmLBWtSWL5hD13bVp4TylJ1pOzP4MNv1gFw++Vdgnavvd9zvFV8+4GHcIqFB7gTKChyvxdnocKDfksnUs7qxEZxxTlt+fCbdbw9eTWvPjCQ0FC/zWCLlDuv18vrny8nJ7eAM7s2oW/nOLcj+c3xzkGtwtkpAmPMTGCYtfZgRQUT8ZdLB7Tmu/lb2ZaSxvR5SVx0hjYlkeAxY8E2VmzcR43oCG69LDjbaJRVqR8djTFFrzO6CMgxxkSXdPN/TJHyExEeyqhLnOW4479bT1pm7u88QyQw7D+UxXtTndPvt17WmZoxkS4n8q/jzW2kGXP0iq90nD33it+OHBcJKn06xZHQph5pmXlHN9cUCWRer5c3vlhJZnY+vTs0YkC3Jm5H8rvjnYMaBBzpma2OulKpOMvOO3PvCzP5Zm4SF/SNp0WjWLdjiZRq9rKdLFybQnRUGHcMT8Djqfxbdh3vHNTPJX0NR/fVSwA2WGsP+y+eiP/Ex8VyQd94vpmbxDuTV/PULX2rxC+9BJ9D6TmMmbQKgJsu7kTdmsU316mcynodVBtjzM/GmD6+c04Lfbetxpg+fk0o4kcjL2hPTLVwlm/Yy8I1KW7HESnRmEmrOJyRS0Kbepx3enO341SYsq6vfQ3nXFMScC3QFGfT2DeBF/2STKQCxFaPYOQF7QB4d8oa8vILfucZIhVr4ZoUZi/bSWREKHeP6FqlRvllLVBnAvdba1OAocDX1tqNwNtAV3+FE6kIg/vG07xRDZL3ZzDp50S344gclZGVxxtfrgDg2sHtaVS3usuJKlZZC1Q2EG6MqY6zq8S3vuONgEP+CCZSUUJDQ45uFfPZDxvYf+hEusiI+M/YaWvYfygb06I2Q6rg9XplLVDf4YyWvsTpxTTVGHOO79gUP2UTqTBd2zagb+c4snMLeP9rbZQv7luxcS/fzd9KWGgI94zoWiUbbZa1QN0KLMYZSV3k627bC5gF3OefaCIV66aLOxIeFsKsJTtYu2W/23GkCsvOzef1z5cD8Mc/tKV5Fb0EoqwdddOBewGMMbHGmFrW2mf9mkykgjWqW51hZ7fh0+83MGbSKl64V+3hxR3jp68nZX8m8XGxDDv7NLfjuKbMu2QaY243xmzH6dW03xiTbIz5s/+iiVS84YNOo16taiTuOMQPC7f+/hNEypndeoApsxMJ8cA9V3YlPKzqbmZc1uugHgKexVlufiYwAHgJ+JMx5l7/xROpWFERYUfbZn/4zTrStU+fVKC8/AJe/Ww5hV64bGAbTmtW+/efVImVaYoPp9XGbdbaT4oc+9UYsxX4B/BKuScTcckZXRrzzdy6rE7cz/jp67l1WILbkaSK+PzHjWxLSSOuXnWuOr+d23FcV9axY31gUQnHl+BctCtSaXg8Hm4Z2pmQEA/fzN3Cll26kkL8b8uuQ3z2wwYA7h7RlcjwUJcTua+sBWo1cEUJx68EtBW0VDotG9fkov4tKfTCW1+txOv1uh1JKrGCgkJe/XQZBYVeBveLp3Prem5HCghlneJ7AvjaGNMXmOc71he4ABjmj2Aibrv6/HbMWbaTtVsO8PPSHQzs0cztSFJJTfo5kU07DlGvVjVuuKiD23ECRplGUNbaGcA5QA7OXnzDgcNAL2vtNP/FE3FPTLVwrve9Wbw3dQ2Z2XkuJ5LKaMeeNMb7epLddUUXoqPCXU4UOMo6gsJaOxuY7ccsIgFnUM9mTJ+fhN16kE9mWEZd0sntSFKJFBZ6ee2z5eTlFzKoZzN6tGvodqSActyW78aYMcaYA75rnt4wxlTNy5mlygoJ8XDbsAQ8Hpg6ZzPbUtT+TMrPN3O3sHbLAWrViOTmS/Xhp7jjTfE9CVwMPIfTUuMinL33RKqUNk1rcUGfeAoKvbz11SotmJBykbI/gw98+z7ePiyBGtERLicKPMcrUMOBq621z1prn8dZxXepMUYTpFLlXHthe2KrR7AqcR8/L93hdhwJcl6vM7WXnVvAGV0a0y+hsduRAtLxClRTjl1Cvsj3eE2SSpVTIzqCG4c4O0y8O3UN6VlaMCEnb/r8razctI/Y6hHcpgvBS3W8AhUKHG0vaq314qzi0zhUqqRBPZvRoWUdUtNyGD99ndtxJEjtOZDJ2KmrAbhtWAI1YyJdThS4qu4uhCInKCTEw+2Xd3F2mPh1C5t2pLodSYKM1+vltc+Xk5VTQN/OcZzRRVN7x/N7y8xvMMakF3v8NcaYfUUfZK19o9yTiQSg+LhYLjmzFZN+TuTNL1fw/N0DCFFLDimj7xduY/mGvdSIDuf2yxPwePT/zvEcr0BtA24vdiwFuLHYMS+gAiVVxlXnGWYv28mGbal8t2Arg/vGux1JgsDeg1m8O8WZ2rvlsgRq14hyOVHgK7VAWWvjKzCHSNCIjgpn9NBO/OvDxXwwbQ19OjaidqzebKR0zqq9ZWRm53N6x0ac1a2J25GCgs5BiZyE/gmN6dm+IRnZ+bwzebXbcSTAzViwlWW+qb07h3fR1F4ZqUCJnASPx9lhIjIilNnLd7Jk/W63I0mA2nMg8+jU3m3DEjTaPgEqUCInqWGdaK4+z2kq98aXK8nOzXc5kQSawkIvr3y6jKycAvolxHFmV03tnQgVKJFTcMmAVrRsHMueA5lMmGHdjiMBZvr8pKMX5N4+TFN7J0oFSuQUhIWGcNcVXfF4YOLPieq+K0cl78tg7NQ1ANx+eQK1auiC3BOlAiVyito2r81F/VoebZ1QUKjNZKu6gkIvL32ylOzcAgZ0bcIZXTS1dzJUoETKwbUXtqdezSg2bk9l6pxEt+OIyybN2sS6pAPUiY3ktsu1197JUoESKQfRUeHcMbwLAB99u57kfRkuJxK3bNl1iHHTnX2277mym9ponAIVKJFy0qtDIwZ2b0puXgGvf75cfaOqoLz8Al78eCn5BYUM7huvDpSHyS0AABY/SURBVLmnSAVKpBzdfGknYqtHsHLTPr5fuM3tOFLBPplhSUo+TFzd6tx4cUe34wQ9FSiRclQzJpJbhnYG4L0pq9l/KMvlRFJR1mzez5c/bcTjgfuu6ka1yN/bi1t+jwqUSDkb0K0JvTo42yC98cVKTfVVARlZebz48RIKvTB80Gl0aFnX7UiVggqUSDnzeDzccXkXqkeFsXBtCjOXbHc7kvjZWxNXsudgFm2a1uQq3+4icupUoET8oF6taoz2TfWNmbiKfama6qusZi/bwawlO4iMCOXBkT0ID9PbankJmL9JY0w/Y8wiY0y6MWaDMWZ4kfuaG2O+N8akGWMSjTEXuplVpCwG9WxG7w6NyMjO57XPtKqvMtpzMJM3vlgBwKhLOtG0QQ2XE1UuAVGgjDFxwNfA60AN4E5gnDGmue8hE4CVQF1gNDDBGNPKjawiZeXxeLjzii7EVAtnqd3DjAVa1VeZHNktIsPX4+mCPi3cjlTpBESBAq4DZltrP7DWeq213wO9gYPGmLZAT+AJa22utfYnYAowysW8ImVSJzaK24Y5Owm8O2U1ew5kupxIyssXP25gdeJ+atWI5O4RXbURrB9U2DpIY0wEUKeEu7xADyDJGDMBOBfYDjxirV1pjDkH2GatLXpp/nqcAiYS8AZ0a8LcVbuYuzKZlycs4++39SM0RG9mwWzN5v18/J2zW8QDV3WnZow2gvWHihxB9QOSS7jtxClco4FxQBzwDDDRGNMaiAGKf+zMBKIrJrbIqTmyqq9WjUhWJe5j0qxNbkeSU5CWmcu/x/9vSXk308DtSJVWhY2grLWzgBI/NhpjvgamW2un+Q59Zoy5GxiMU8CqFXtKNJDup6gi5a5mTCT3XtmNJ9+Zz7jp6+jStj5tmtZyO5acIK/Xy6ufLmNfahamRW1GXqAl5f4UKOeg1gO1ix07UjzXAs2NMUWLVDvfcZGg0bN9Qy7q35L8Ai8vjF+iDrxB6JtftzB/dQrRUWE8NLIHYaGB8hZaOQXK3+6HQD9jzDXGmBBjzJVAAjDZWmuBFcDTxphIY8zZwKXAxy7mFTkpN17ckWYNY9ixJ/1oMzsJDpt2pPKu79/s7hFdaVS3usuJKr+AKFDW2hXAhcB9QCrwODDMWnvkEvzLgfbAHuAdYJS1drUbWUVORWR4KA+N7ElYqIdv5iaxaG2K25GkDNIzc3n2g0Xk5RdyQd94NSCsIAGzm6G19kec5eQl3bcd53yUSNBr1aQm1w5uz9hpa3l5wjJefXAgdWsWP80qgcLr9fLyhGXsPpBJ66Y1GX1pJ7cjVRkBMYISqWqGntWGbm3rczgjl+fHLaGgoNDtSFKKibMSWbAmhepRYfz5ul5EhIe6HanKUIEScUFIiIcHru5BndhI1mzezyczrNuRpARrNu/ng2+c9Vj3X9Vd550qmAqUiEtq1YjkoZE9CfHAZz9uYPmGPW5HkiIOHs7muY8WU1joZdjANpzeKc7tSFWOCpSIizq3qccfz2uH1wsvjF/KgcPZbkcSIC+/kGc+WMSBw9l0bFWXay9s73akKkkFSsRlI85tS0KbeqSm5/D8uMU6HxUAxkxaxbqkA9SrGcUj1/XU9U4u0d+6iMtCQzw8NNI5H7U6cT9jp+kadDd9Oy+J6fOSCA8L4dEbe1O7RpTbkaosFSiRAFA7Noo/X9ebsFAPk2cnMmvpDrcjVUlrt+xnzMSVANx1RRdOa1Z8gxupSCpQIgGifcs6R7vwvvbZcjbvPORyoqpl78EsnvlgEfkFXi4Z0IpBPZv//pPEr1SgRALI4L7xnNurObl5BTz9/kIOZ+S6HalKyMzO46l355OalkNCm3rcNKSj25EEFSiRgOLxeLj98gTaNKvFngOZPPfRIvK1aMKvCgoKee6jxSQlH6ZJ/Rj+cn0vQrUoIiDoX0EkwESEh/Lo9b2pFRPJio37eOurlXi9XrdjVVrvTF7NkvV7qBEdwV9v7kNMdITbkcRHBUokANWvXY3HbupNRFgI383fyqSfE92OVClNmZPItF+3EBYawmM39iaunnaKCCQqUCIBql2LOtx3VXcAxk5bw7xVyS4nqlzmrtzFu5Odpgj3/rEbHVvVdTmRFKcCJRLAzuzahGsHt8frhX+PX8Km7aluR6oUVm7ay/PjnLbtIy9ox8DuTd2OJCVQgRIJcFeccxrn9GpGbl4BT747n1370t2OFNQ27UjlH+8tJL+gkCH9W3LluW3djiSlUIESCXAej4c7h3ely2n1SE3L4fH/zmP/oSy3YwWlXXvT+dvb88jKyWdA1yaMHtoZj8fjdiwphQqUSBAIDwvh0Rt6c5pv+fkTY+aRlqlrpE7E3oNZPD5mHofSc+nWtj73XdWdkBAVp0CmAiUSJKKjwvnb6L40axjDtpQ0nnxnPtk5+W7HCgr7UrN47M1f2XMgk7bNa/GXG3oTHqa3v0CnfyGRIBJbPYKnbulH/drVsFsP8vTYheTkFbgdK6DtP5TFo2/+SvL+DNo0rcmTo/tSLTLM7VhSBipQIkGmXq1q/P3WftSKiWT5xr38/d35ZOdqJFWS/YeyePSNX0nel0HrpjX5+639dCFuEFGBEglCTerH8M87+lO7hrPbxFPvLCBL033HODKtt2tfBq0aqzgFIxUokSDVrGEN/nlHf+rERrEqcR9PvjOfzOw8t2MFhO2703j4tTns3JtBy8ax/P22ftRQcQo6KlAiQaxpgxo8c2d/6tWMYs3m/TwxZh6H0nPcjuWqDdsO8sjrv7AvNYv28XV4+vb+xFZXcQpGKlAiQa5xvRieufMMGvgWTjzy+hxS9me4HcsVy+weHnvzV9Iyc+nZviFP3dpXI6cgpgIlUgk0qlud5+4+k1aNa7JzbwYPvzqHDdsOuh2rQn2/YCtPvTuf7NwCzu7RlMdu7E1UhFbrBTMVKJFKom7NajxzZ3+6ta1PanoOj775KwvXprgdy+8KCgoZM2kVr362nPwCL0PPas19f+xOmHo6BT39C4pUItFR4Txxcx/O6dWMnNwC/vHeAiZ8byksrJz9pNIyc/nb2/OZOmczYaEe7h7RlVGXdNIOEZWExr8ilUxYaAj3XtmNuLrVGf/desZPX4/depAHru5eqc7HJO5I5V8fLiZ5fwa1YiL5yw296NBSLTMqE42gRCohj8fDlX8w/O3mvtSIDmfxut3c99LPbNoR/O06Cgu9TJy1iYdenU3yfucC3BfuG6DiVAmpQIlUYt3bNeDl+wfSxrfJ7MOvzubTHywFBYVuRzsp+w9l8de35/He1DXkF3i5sF88/7rrTBrUjnY7mviBpvhEKrkGdaJ57q4zeHfKGr7+dQvjvl3P/FXJ3PfH7rSIi3U7Xpl4vV5mLd3B25NWk5aZS2z1CO69shu9OzZyO5r4kQqUSBUQHhbKbcMS6Nspjlc/W8amHYe476VZXPkHw2UD2xAZHup2xFJtTTnMW1+tZHXifoCjrTLqxEa5nEz8TQVKpArp0rY+rz10Nu9PW8u385IYP309MxZs5brB7RnQrWlArX7LyMrj8x83MOnnRAoKvdSMieDGIR0Z1LOZmgxWESpQIlVMdFQ4dwzvwhldG/P2pNUkJR/mhY+XMnl2IjcM6UhCm3quFoD0rDymzk5k8pzNZGTl4fHA4L7xXHth+0q1ClF+nwqUSBWV0KY+Lz8wkJmLt/PRt+vYtOMQ//fWXNo0rcmlA1rTv0uTCm3qdzAtm2/nJjFldiIZ2c7O7J1a1+XGIR1p27x2heWQwKECJVKFhYZ4OLd3c87o0pjJsxOZMmczm3Yc4oWPlzJ22loG94vnzK5NaFI/xi+vX1BQyJL1e/h+4VYWrd1Nge+C4oQ29fjjeYbOrev55XUlOKhAiQhRkWFc+QfD0IFtmLVkB5NnJ7J9dxrjpzsX+rZoVIN+CY3p0ymOFnGxhJ7CuapD6Tms3LiPZRv2sHjdbg6mObuvh4R4OL1jI4ae1ZpOKkyCCpSIFBEZHsr5fVpw3unNWbZhL7OWbGfhmhS2pqSxNcXyyQxLZEQorZvU5LRmtWnZOJY6sVHUqhFJrZhIqlcLJ7+gkNy8QvLyC8nIzmPX3nR2+m5bdh5m865Dx7xmk/oxnHd6c87u0YzaWpknRahAichveDweupsGdDcNyMsvZOWmvcxdmczyjXvZcyCTtVsOsHbLgZP62eFhIXRsWZcubevTtW19WjepqVV5UiIVKBE5rvCwEHq0a0iPdg0BZ4pu4/ZUNm5PZcfuNFLTcziYlsOh9Bwys/MIDwshPCyU8LAQoiLCiKtXnaYNYmhcP4amDWJo27x2QF93JYFDBUpETkjNmEh6tm9Iz/YN3Y4ilZz24hMRkYCkAiUiIgEpYKb4jDE3Av8H1APWAw9aa3/x3dcceBfoA+wB7rbWfuNWVhER8b+AGEEZYxKAF4FLgVrAOGCSMeZIvgnASqAuMBqYYIxp5UZWERGpGAFRoIDT+F8WD1AAZAEYY9oCPYEnrLW51tqfgCnAKDeCiohIxaiwKT5jTARQp4S7vMB3wGpgFf8rTudbawuNMR2AbdbajCLPWQ/09nNkERFxUUWOoPoBySXcdgJRgMU5x1QdeARniq8REANkFvtZmYBaaIqIVGIVNoKy1s7Cmb77DWPM60CKtXaB79AbxpgbgCuAHUC1Yk+JBtLL+NKhACkpKSeYWERE/K3Ie/Nvrt4OlFV8zYBNxY7lA3nAWqC5MaaatTbLd1873/GyiAMYOXJkeeQUERH/iAMSix4IlAI1DXjOGPMZsAgYCXQEvrbWbjfGrACeNsb8BWeq8FKgbxl/9iLgTJzpxIJyTy4iIqciFKc4LSp+h8fr9VZ8nBIYYx4Cbse5Dmot8JC19lfffc2AMTjFaR/wF2vtZ25lFRER/wuYAiUiIlJUoFwHJSIicgwVKBERCUgqUCIiEpBUoEREJCCpQImISEBSgRIRkYCkAiUiIgEpUHaSCEjGmC7AW0ACsBm4yVr7m6udA4ExpjcwzVrbwO0sRRlj/gA8i9NSZQ/wvLX2v+6mOpYxZgjwT6AlTsbnAi0jgDGmFk5ftCeste+7HOcYxpibgP8COUUO32mt/cClSMcwxsQBbwJnA9nAGGvt4+6m+h9jzEicv7+iqgE/WmvPcyHSbxhj+gCvAgbYCzxrrX3Hn6+pEVQpfO1BJgOf4jRRfBqYYYyJdTVYMcYYjzHmZmAGEOF2nqJ8O4B8CfwD5+/wKuAZY8z5rgYrwvfG9QXwiLW2Bs4GxS8bY7q7m6xEbwFN3A5Riu7AC9bamCK3gChOPpNxtjtriNM14XpjzNXuRvofa+34on93ONuzHQAedjkaAL7msZOBV621NXF+l1/3fYj3GxWo0g0Ewq21L1tr86y1E4A1wJXuxvqNJ3G2iPqH20FKEA98bK2daK0t9I0+ZwH9XU1VhLU2Gahvrf3W90tYF2ej4jR3kx3LGHM9EIvTMy0Q9QCWux2iJMaY04FWwD3W2mxr7Rac3++ZrgYrhTEmHBgP/M1au8LtPD61gQaAxxjjwenjlw/k+vNFVaBK1wFYV+zYeqCzC1mO5y1rbQ9gsdtBirPWzrHW3nbke2NMHZxPhsvcS/Vb1to0Y0w0zvTUDOA/1tqNLsc6yhjTEvgrcJPbWUpijAnFmQa/1hizyxizyRjzZ98bWSDogVPY/2aM2WmMSQQu8304CUR34jRtfcPtIEdYa/cDrwMf4HSZWAQ8aq0t/h5ZrlSgShcUjRKttbvczlAWxpiawBRgAc5UQaDJxmmW2Qu4yRgzyuU8wNE3/3E4mycHalOz+jgfkD7AOY83HGdUf7uboYo48sEoD2ckNQx4KJCm+I7wnVp4GGf0FDAbpfpmF7KBq3HOjQ0E/mqM8ev5MS2SKF0Gp9YoUXyMMW1xitJaYKS1ttDlSL/hy5QLLDbGjMFp6fKuu6kAeByw1tqv3A5SGl/hPKvIoeXGmNeAywmMUUAOcNha+zff9yuMMe/gFKqPXUtVsguAQuBrt4MUMwzob609ck7sZ2PMu8CtOLMOfqERVOnW4qxWKepEGiUKYIwZgDNqmgQMt9ZmuxzpGMaYs4wxS4odjgRS3chTgj8Cw40xqcaYVJwp5jeMMYHwxg+AMaajMebJYocjcD5xB4L1QLRvdHJEoH44vxT4LAA/xDXD+b0o6khTWb8J1H+kQDAT54Tg/Thzr5fjzLNPdDVVEDHGtMZpRvmYtfY1t/OUYjnQxBjzAPAKcDowCrjM1VQ+1tp2Rb83xiwHXg6wZeapwIPGmB04o85uwD3AXa6m+p/vcZZFv2CMeRDng+coAmcKsqg+OKPmQDMDZwXuLcDbOKs2RwM3+/NFNYIqhbU2FxiMU5gOAI8BQ621e10NFlzuBGrg/I+dXuT2L7eDHWGtPQRciDOFcQCnMebN1tqfXQ0WRKy1O4FLcKZ7DuNcWvB3a+0Xrgbz8Y3az8I5/5QMTMe51u1LV4OVLB4IuPPK1to1OL8jt+J8IPkY+LO11q/nk9WwUEREApJGUCIiEpBUoEREJCCpQImISEBSgRIRkYCkAiUiIgFJBUpERAKSLtQVKSfGmPeB64/zkCdxdnOfCdSw1lbItlm+/fx+Ba6z1m44zuNCgPnAtdZaWxHZRI5HIyiR8nMvEOe7DfQd613k2L+Bub6vMyow1z3AiuMVJzi6H+FTOH2nRFynC3VF/MAY0wmnxUNLa22SizmigG3AIGvt6jI+JxEYZa2d5c9sIr9HU3wiFcgYM5AiU3zGGC9Od9K/4OwRtxi4BqflwrU4Wwf9xVr7ke/5NYAXcFpaeIGfgHuP03blj0Bq0eJkjHkcuAWnTcY6nL4+3xZ5zkSc0eCscvgji5w0TfGJuO9Z4D6cjUKbA0txClMv4Cvgv8aYGN9jx+AUsvNx9pfzAt8ZY0r7sHkRzt5zABhjLvO91jU4u/N/DXxujIkt8pzpwLnH+ZkiFUIFSsR9/7HWzrTWLsfZ/T0dZ1RjgRdx+pK1NMa0whkRXW2tXeQbFV2Ls8HoBaX87J7AmiLfx+P0R9rqm3p8CmcT0KJtE9biNOw8Zid1kYqmT0gi7ttU5OtMIKlIN9UjPZUigRa+r60xx7Qqi8YZVU0r4Wc3BPYV+X4czkrDzb4+WFOAsdbarCKP2e/7b4MT/HOIlCuNoETcV7zpW2nN6sJ8j+0GdC1yawuMLeU5hYDnyDe+djE9cEZcc4EbgJW+RR1HHHlfKCjzn0DED1SgRILHOiAcqG6t3WSt3YTT3+h5nCJVkhScxRAAGGOGAbdaa2dYa+/FGXml4fTEOqJ+keeKuEZTfCJBwlprjTFTgA+NMXfidIl9GmdxxfpSnrYE6FLk+1DgeWPMbpwVg32ARr6vj+gCHOTYqUeRCqcRlEhwuR6nmEwCFgE1gT9Ya1NLefzXOKv9ALDWfg78FWfUtQH4B3CXtfanIs8ZAEy31mqKT1ylC3VFKjFjTDSQBFxgrV1ahseHAFtxVgrO8XM8kePSCEqkErPWZuKMlu4s41MuBTarOEkgUIESqfxeAhJMsbXpxflGT48Bt1VIKpHfoSk+EREJSBpBiYhIQFKBEhGRgKQCJSIiAUkFSkREApIKlIiIBKT/BxWQ/S3gTJ8sAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1774,7 +1549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1784,7 +1559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 44,
    "metadata": {
     "scrolled": true
    },
@@ -1798,7 +1573,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1818,7 +1593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1833,7 +1608,7 @@
        "-74.62920556283255 <Unit('meter')>"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1844,7 +1619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -1859,13 +1634,14 @@
        "-2.4168897690027933 <Unit('meter')>"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "diff = min(results.y) - min(results_no_cord.y)"
+    "diff = min(results.y) - min(results_no_cord.y)\n",
+    "diff"
    ]
   },
   {

From 32f2a1156a84725d36d3ee2983266c28bdcf2dad Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 2 Jun 2020 09:48:26 -0400
Subject: [PATCH 003/144] Run on Colab

---
 soln/chap08soln.ipynb | 356 ++++++++++++------------------------------
 1 file changed, 97 insertions(+), 259 deletions(-)

diff --git a/soln/chap08soln.ipynb b/soln/chap08soln.ipynb
index 7893a8f9..f32e3d1e 100644
--- a/soln/chap08soln.ipynb
+++ b/soln/chap08soln.ipynb
@@ -18,13 +18,27 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "# If we're running on Colab, install modsimpy\n",
+    "# https://pypi.org/project/modsimpy/\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install pint\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Configure Jupyter so figures appear in the notebook\n",
     "%matplotlib inline\n",
     "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
     "# import functions from the modsim.py module\n",
     "from modsim import *"
    ]
@@ -38,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -91,11 +105,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def read_table2(filename = 'data/World_population_estimates.html'):\n",
+    "# Get the data file\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates2.csv'\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/notebooks/data/World_population_estimates2.csv\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_table2(filename):\n",
     "    tables = pd.read_html(filename, header=0, index_col=0, decimal='M')\n",
     "    table2 = tables[2]\n",
     "    table2.columns = ['census', 'prb', 'un', 'maddison', \n",
@@ -106,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -275,25 +304,25 @@
        "1954    NaN           NaN  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "table2 = pd.read_csv('data/World_population_estimates2.csv')\n",
+    "table2 = pd.read_csv('World_population_estimates2.csv')\n",
     "table2.index = table2.Year\n",
     "table2.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -331,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -355,20 +384,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.557628654"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "t_0 = get_first_label(census)\n",
     "t_end = get_last_label(census)\n",
@@ -384,74 +402,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>1950.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>2016.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p_0</th>\n",
-       "      <td>2.557629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>alpha</th>\n",
-       "      <td>0.025000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>beta</th>\n",
-       "      <td>-0.001800</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "t_0      1950.000000\n",
-       "t_end    2016.000000\n",
-       "p_0         2.557629\n",
-       "alpha       0.025000\n",
-       "beta       -0.001800\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "system = System(t_0=t_0, \n",
     "                t_end=t_end,\n",
@@ -469,12 +424,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -506,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {
     "scrolled": false
    },
@@ -520,7 +475,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -547,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -556,7 +511,7 @@
        "13.856665141368708"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -567,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -576,7 +531,7 @@
        "13.88888888888889"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -594,12 +549,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -640,7 +595,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the data file\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates3.csv'\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/notebooks/data/World_population_estimates3.csv\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -653,7 +623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -663,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -750,13 +720,13 @@
        "2020  2020  7.643402e+09  NaN  7.758157e+09"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "table3 = pd.read_csv('data/World_population_estimates3.csv')\n",
+    "table3 = pd.read_csv('World_population_estimates3.csv')\n",
     "table3.index = table3.Year\n",
     "table3.head()"
    ]
@@ -777,7 +747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -802,72 +772,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>1950.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>2100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p_0</th>\n",
-       "      <td>2.557629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>alpha</th>\n",
-       "      <td>0.025000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>beta</th>\n",
-       "      <td>-0.001800</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "t_0      1950.000000\n",
-       "t_end    2100.000000\n",
-       "p_0         2.557629\n",
-       "alpha       0.025000\n",
-       "beta       -0.001800\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "system = System(t_0=t_0, \n",
     "                t_end=2100,\n",
@@ -878,7 +785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -890,7 +797,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -932,12 +839,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -981,7 +888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -995,12 +902,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1025,20 +932,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.043001508"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Solution\n",
     "\n",
@@ -1049,67 +945,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>1960</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>2100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p_0</th>\n",
-       "      <td>3.043</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>alpha_func</th>\n",
-       "      <td>&lt;function alpha_func at 0x7ff92564ac80&gt;</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "t_0                                              1960\n",
-       "t_end                                            2100\n",
-       "p_0                                             3.043\n",
-       "alpha_func    <function alpha_func at 0x7ff92564ac80>\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Solution\n",
     "\n",
@@ -1121,7 +959,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1140,7 +978,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1149,7 +987,7 @@
        "3.1102518413268"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1162,7 +1000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1173,12 +1011,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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CBQv47LPPmD17Ni+++CJBQUFMmjSJP/3pT2f1qElLS6NJkyZ4enpWed05c+YwdepU2rQxf4U99thjxMTEcPDgQQAsFgtTpkzB09OTQYMGERISwqFDhwgLC8NmszFv3jxGjRrF2LFj+d3vfnfOnj12u/2CzhOioSnKOk3uwW3kHtxG3qGdFGWcAsBisxMYfQ0Wqw2L1UbwsAlY/ZriHdEZW2CzWo760miUCWrRG6PP2vbsXf3O2jaifyQj+keW2dasiU+F57/556FnbfvDtZ3PKy673U5RUflGTXA4HHh5eZW8btu2LU8++SRgJqDly5fzyiuvEBQUxPjx48ucGxISQnp6OoWFhWclqaysLLy8vLDb7SQlJfHUU0/x7LPPluy32WwcO3YMm82Gv79/mRg8PT1xOp34+/vz8ccf89577zFx4kS8vb2ZNGkSkydPrvK9VnWeJCnRkJUeN5S140dOLfxPmf0WL1+8W3XCO6IzhqMQi9X8mm7S9/pLHmtta5QJqq5q0aIFSUlJdO/evWRbUVERSUlJJasF/+EPf+Caa67h9ttvByA4OJjx48ezfft2tNZnXbN37954e3uzZs2aMm1QAK+99hqHDh3i448/JjQ0lGeffZYrrriiZH9CQgKRkZFs3ry50pgzMjJwOBxMnz6dwsJC1q5dy9SpU4mJicFqtVJYWFhyrGEYZGRknPM86bQhGhLDUUR+0l5yDmwj98A2fNp0JXjYBAC8WnbE4umNd5uu+LTrgU9kd+whrbF4WGs56rqhUbZB1VXXX389b7/9Nvv37wcgPT2dV199lZCQEHr06AHAyJEjmT59OitWrKCgoICCggI2bNjAmjVruOqqq866pt1u59FHH+XZZ59l5cqVFBUVkZOTw6xZs1iwYAH3338/AGPGjOGdd97h+PHjOBwO3n//fSZMmEBeXl6VMaelpXHXXXcRFxeHp6cnYWFhWCwWmjRpQps2bSgoKGDJkiU4HA4+/vhjzpw5c87zhKjvirJPk7l5BclzXubgvyaR9MnTpP80h/yje8hJ/K1N2zO4JZGPfETL8U/R9PIb8AqLlORUipSg6pD7778fq9XK5MmTSU1NxcvLi379+vHRRx+VVM/98Y9/xNvbm2nTpvH4449jGAYdOnTg2WefZdCgQRVe99ZbbyUgIID33nuPJ554AsMw6Nq1KzNnzqRv374A3HvvvRQVFTFhwgTS09Pp1KkTM2fOJDAwsMqY27Vrx3PPPcczzzzDyZMnCQoK4umnnyYqKgqAp59+mtdee42///3vjB49mujoaLfOE6I+MQwDDGdJcjm95kuytqwo2e/ZPAKfyB5mKantb+MVLRYLWKtuH27MLPW915RSKhI4sHLlSiIiImo7HCFEI2EUFZJ7eCc5ezeSkxBP0NDfE9B9KAA5+zeTuXEpvlEx+Hbs02g6NVyIo0ePFtf+tNNaHyy9T0pQQgjhJmd+Dmf2biRHx5GTuAWjILdkX96hnSUJyrdDb3w79K6lKBsOSVBCCOGm5NkvkXdkd8lre2hbs5QUFYNX+NnjEMXFkQQlhBDlOAvzydm3iTO71xI06Bbsoeb4QL/O/cBiwa9zf3w7xeDZJLSWI23YJEEJIQTgLCogd/8WsnevJSdhI0ah2YPVMzic4NA/ABDY93qaxI6qzTAbFUlQQohGL2XZB2Tt+BGj1Bx3Xi074td1IP5dB5Rsk0Hkl5ZbCUopZQX6ADFAKOAAkoF4rfWWqs4VQoi6pjD9BFb/IDxsdsCs0jPyc7CHtcO/60D8ug7As2lYLUcpqkxQSqkg4EHgPqAZkAikAlagOdBWKXUcmA68o7VOr+xaQghRm5wFeZzZ8wtZ21aTd2gnoTf9Bf+uAwEIGvQ7mg4Yi71ZeC1HKUqrNEEppSYCzwMrgbuAFVrr/HLHBAJXABOAHUqpp7TWH9dgvEII4TbDMMg7spusras5s2cdRoHZrmSx2SnKTC05zjOoRW2FKKpQVQmqL9BXa32qsgO01pnAt8C3SqmWwFOAJCghRJ1wYu4r5OyNL3ntFaEI6HEl/l3641EHV5AVZVU6F5/WempVyamC449rrR+onrAaJ6UUu3fvPmv76NGjmT9/PgBvvfUWPXr0YN++fWWO2bBhQ6WTrM6fP58uXbrQu3dvevfuTc+ePRk5ciQzZ84ss/7SlVdeSY8ePUqOK35ce+1vq24eOnSI+++/n759+9K7d29GjRrF//73vwrve9111zF48OAyE8YKUVMMwyDv6B6KstJKtnm37YY1IJimA8YS8ae3aDXpJQJ7D5fkVE+43YtPKRUK9AA8gTJdWbTWSyo8qfJrxQKLtdahrtd24G3gd5gdMP6ltf6/87lmY5Kfn8+jjz7KnDlzsNvtbp3TqVMnFi40F2k0DIPNmzfz6KOPkpmZWWZhwn/9619nzXpezOl0cs899zBq1Chef/11vL292bJlC/fffz92u51bbrml5NjiSWBDQkJYvnw511/f+JYKEJeGszCfM7vWkrFxKQXJiTQdMLZktvDAPtfSpO91MgFrPeVuL767gGmYyak8A7PThDvXsWC2Z71ebtdzgAI6AE2AZUqpY1rrT9y5bmPTv39/kpKSePPNN3n88cfP+3yLxUJ0dDQvvPAC9957L3fdddc5J4UFOH36NIcOHWLUqFH4+Jirdfbu3ZvHH3+cgoKCMsfOnj2bq6++mmbNmvHpp59KghLVrijjFJmbviNz8wqcuVkAePgGlikdFffSE/WTuyWox4APgCe01lkXcb/ngOuBF4CnS22fBNyutT4NnFZKvQ5MBmokQSW+eHOl+5qPnExg9DUAZG5aTsrS9yo9tv1TX5U8PzrzMQqSE6s8prr4+vry6quvcttttzFkyBAuv/zyC7pO//798fDwYPPmzRUuCV9es2bNiI2N5Y477uDGG28sqeYbM2ZMmePS0tJYuXIlS5cuJSAggFdffZXt27eXWedKiIuRvv4b0lZ9CoYTAHuLDjTpOxK/rgMlKTUg7q4H1Rr4z0UmJ4DpWus+wMbiDUqppkBLYFep4/YA8m1WhV69ejF58mT+9re/kZmZeUHXKF5/KTs7u2Tbo48+WrJoYPHj7bffLtk/c+ZM7rnnHrZt28bUqVPp168fU6ZMITk5ueSYr7/+mv79+9OyZUv8/f258cYbK22nEsIdhqOIouzfRrF4hXcAiwf+3a4gfNJLtLrzFQJ6DJPk1MC4W4JaDlwF7L2Ym2mtkyrY7O/6M6fUthzA92LuVRV3SzWB0deUlKbOJeKu1y4mJMD9Jd+LTZkyhZ9//pnnnnuuTPuPuxwOB5mZmbRs2bJk2+uvv15pG1RxjLfddhu33XYbBQUF/Prrr7z55ps89NBDzJ49G8MwmDNnDidPnmTgQHOMSX5+Pvn5+Tz++OM0aybLDgj3OQvzydq6ioxfFmAPbUuLW58EwLt1V9o++D5WP1ngsiFzN0FtBf6llLoRSADKNDhorc+/IeQ3Z1x/+pTa5gtkV3Bsg+bOku+lWa1WXnvtNUaPHo2v7/nn87i4OAzDoFOnTm4dP3v2bL788ku+/vprwExW/fv3x9PTk7vvvhuA9evXk56ezrJly/Dw+K2APnnyZL788suSFXyFqIozP5fMTd+RsWERjjNmycli98ZZkIuH3QeLxSLJqRFwt4pvCLABM4n0xDVGyvWouG+zm1ztTsmYnSSKdaZslV+j4M6S7+W1adOGJ598kjlz5rh9H6fTSVxcHM888wyTJ0/G39//3CcBQ4YM4fDhw7zyyiukpqZiGAZHjhxh1qxZJcvNz549mxEjRhAWFkZISEjJ46abbuLLL7+ULueiSo68M6T9OJvDb/+JtFWf4jiTjj2sHaE3P0rEvf/Gw+5z7ouIBsOtEpTWelgNx/Ep8Hel1DbMKr9Hgf/U8D3rHHeWfK/IuHHjWLNmDevXr6/0mISEBHr3NhdQs9lstGrVirvvvpvx48eXOe4vf/kLVuvZnTKXLVtGixYt+Pzzz3nrrbcYNWoUubm5BAUFMWLECB588EFSU1NZsWIFH3300Vnnjxo1ildeeYXvvvuOUaNkNmhRMaMwn/R188FRhHfrLjQdeDM+7XvVyCSthmFQUFCA0+ks8wDz/4jVasVqtWKz2WSS2Fri9pLvSqkw4AGgG2bJazfwgdb67K5r577WUGCB1rqp67U38AbmOCgP4H3gaa31OYOTJd+FqL+KMlPI3Pw9QVfcUjJWKWPjMuyhrfFp0+28r2cYBllZWZw+fZrTp0+TlZVFdnZ2ySMnJ6ekTbT80IiqeHl54e3tjbe3Nz4+Pvj6+hIQEEBgYGDJIzg4GD8/P0lm5+mil3x3Daz9HjgCrMMcqDsKmKqUGqq13ljV+eVprX8AmpZ6nQfc73oIIRo4R04W6evmk7lxKYajEHvz1vh3GwRAk5gR5zzf6XSSmprKyZMnSx4pKSmkp6dX2NGoMp6enlitVjw8PEoehmHgcDhwOBwUFRXhcDhKklpGRkaV17Pb7TRr1ozg4GBCQkJo0aIFYWFhNGnSRBLXBXC3k8QbwBfAfaVLNUqpt4HXgJquAhRCNADOgjwy4haTvn5hydpLfl0GYA+LrPQcwzBIT0/n2LFjJY/jx49Xmoj8/PwICgoiKCiIgIAA/P398ff3JyAgAF9fX7y8vPDy8sJut5fpyFPV/fPz88nNzSUvL4/c3Fyys7PJysoqeWRkZJCamkpeXh7Hjx/n+PHjZa7h7e1NixYtiIiIICIigtatW19Qx6bGxt0EFQPcXUGV21vAr9UbkhCiIcra/gNpKz8t6ZXn074nwUNvw6tl+zLHGYbB6dOnOXjwYMkjK+vsIZhNmzYlNDS05BESEkJQUFCFQzIuhsViKaneq4phGOTm5pKamlpSujtx4gTJycnk5OSUvJdiwcHBtG3blnbt2tGuXTu3Oys1Ju4mqONAJKDLbW8PXOzgXSFEI+DMO4PjTDpe4VEED5uAT+RvwykKCgpITExk79697Nu376zB5z4+PkRERNCqVauSR/F0W3WFxWLB19cXX19fWrduXbLdMAyys7NJSkri6NGjHDlyhGPHjpGWlkZaWhqbN28GIDQ0lPbt29OpUyfatGlTYWelxsbdBPUp8L5S6mGguKtYf+Dfrn1CCFHCMAxyE7fgOJNOQA+zBSCw9zXYAkPw7dQXi8VCVlYWu3fvRmvNoUOHcDgcJef7+PgQGRlZ8ggJCam3bTgWi4WAgACUUihljqZxOBwkJydz8OBBEhMTOXz4cElb2vr16/Hy8qJjx4506tSJTp06nbP01lC5m6BeBMKBOZi97CxAIWYV31M1E5oQoj7KT9pH6qpPyTu0A4uXL74dY7D6BmCxeeJo2ZkNGzawe/duDh8+XOa8iIgIOnbsSFRUFC1btqy3CckdVqu1pCQ4cOBAioqKOHLkCPv27SMhIYGUlBR27tzJzp07sVqtdOjQgW7duqGUqvYqzLrM3XFQBcA9SqlHMQfU5gL7tNa5NRmcEKL+KMpO5/QPn5G1dRUAHt5+NB0wliLDwo6tW9m6dSsHDhwoOd5qtdKxY0c6d+5MVFQUfn6Nd40mm81W0hZ19dVXk5aWRkJCQknpMiEhgYSEBKxWK1FRUfTs2ZOoqKgGXw1Y1ZLv1wHfa60LXc/La11cXD3f9aCEEA2H4XSQEf8tp3+aa/bM87DRpO91ZLbuy5o9e9n137dKxhyZX7Cd8AhoS99eUUSGB9dy9HVTcHAw/fr1o1+/fmRnZ7Nr1y527tzJ4cOH2bNnD3v27MHX15fu3bvTq1evCqdCawiqKkEtBloAJ13PK+P2elBCiAbIYuHMrnUY+TnYI3tyuFlPFu08SPrauSWHRERE0LNnT7p168bc1QeYu3Ivp3K9mXKzJKhz8ff3JzY2ltjYWDIzM9m+fTtbt27l1KlTbNiwgQ0bNhAeHk7fvn257LLLsNncXoe2zqv0nWitPSp6Luqft956i927dzNt2rQqjyse0R0fH+/WAoai8SpMO47F5oktsDkWiwfOPmNItMaz/kAGjn1bAAgMDKRHjx60ad8Z/4CmtGxuVuENjY7gx83HiGwp/8bOV2BgIAMHDmTAgAEcP36cLVu2sH37dpKSkli4cKPTXNUAACAASURBVCHLly8nOjqamJgYmjZteu4L1nENJ9UKIWqcsyCX9LVfkb5hET4dojmlRhIXF8exY8dKjlFK0bdvX9q1a8f6Hck8/u6v9O0axpO3xwLQpkUg7z8xHA+PhtsJoqZZLBbCw8MJDw/n6quvZseOHcTHx3P8+HHWrl3L2rVr6dSpE7GxsbRv377edjipqg3qFGb13TlprUOrLaJG7OjRo4wZM4aHHnqI6dOnU1hYyNSpU/Hw8GD69OkUFBRw3333cfvtt7Nu3TreeOMNDhw4QKtWrbj//vsZMWJEyXWeeuoptm3bRvv27WnfvuxAyDlz5jBz5kzS0tLo0aMH//jHP8qM2xCiPMMwyN7xI2mr/ocjOw2APfsO8NOu+RiYA1mLf7n7+Abg7WV+tXSJDMbDw4LVw4LDaWB1JSVJTtXH09OT3r1706tXL44dO0Z8fDw7d+4s6VgRFhbGwIED6datm1szZ9QlVZWgHsPNBFWffP755+zde1HrLp6XqKgo/vCHP7h9fFZWFjt27GD16tWsWLGCRx99lNGjR7Ny5Up++uknpk6dSvfu3Zk8eTKvvPIK11xzDfHx8UyZMoWQkBD69OnDQw89RNeuXfnggw/QWnPXXXcRE2OuirJ8+XL++9//8t577xEVFcWsWbO45557WLy4qmZG0ZgVnDzMqaXvkX90DwCp+BFX1JIUfAkLCyM2Npbu3buTllXIv+ZsoaDQwSsPXAFAUKA3s565hkA/Wem2plkslpKplK655ho2bdpEXFwcJ06cYP78+axatYoBAwbQq1evKldHqEuqaoP66BLGIUqZMmVKyWKADoeDiRMnYrfbGTZsGA6Hgw8//JDLL7+c664zO1f279+fG264ga+//prQ0FB27NjBjBkzsNvtdO/endGjR5dUwcyZM4eJEyfSrZs5U/S9997Lp59+yoYNG2jbtm2tvWdRNznOZHD0w8fBUUiuYWOTswWJRhDt2rVn5KBBtGvXrqT6KMAX9h1Jx+F0cjIth9Bgc645SU6Xnp+fH1dccQX9+/dn27ZtrF27lrS0NJYsWcKaNWvo168fMTExdX4AcFVVfG6vgKe1Pv/1xmvJ+ZRmaktx42bxGIeAgACAkuJ5Xl4erVq1KnNOREQEGzZs4NSpU3h5eREUFFRmX3GCSkpKYtq0abz//vsl+wsLC0lKSpIEJcpIS0tjzZo12AqbYsPJZmcLOnTuxt2DBhEeHs7Wvaf4z+zNPHhLbzw8LPh6e/Lk7bFEhgcS4CtJqS6w2WxER0fTq1cv9uzZw88//8zx48dZuXIla9eupX///vTr1w+7vW7+fVVVxXemin2iBp2rQTMmJoaNG8uucHLkyBGaN29OWFgY+fn5pKam0qxZMwBOnDhRclxoaCgTJ04ss1Dh/v37CQ8PJzU1tRrfhaiPHGcySF76PnvzfPgxMQ3DMLBYWtKjR0/uGTiQkJAQAAqLnPzny82kZOQR0yWMQT3NH0zdOzavzfBFJTw8POjatStdunQhMTGRn3/+mYMHD7J69Wo2bNjAoEGDiImJqXNVf1VV8d1xKQMR7hs0aBDTp09nyZIlXHvttcTFxbFo0SLefvttWrVqRWxsLK+88grPPfcchw4dYv78+URHRwMwZswY3n33Xfr27Uv79u1ZvHgxTzzxBIsXL25Q4yfE+TEMJylxS0lf/RlWRz5NDTvQmV69ejF48GCCgoJIOHyapkVOPG0eeNo8mHh9V1LSc4lW0keqvrBYLHTo0IEOHTpw4MABVq1axdGjR1m+fDm//PILV1xxBdHR0XVmhoqqqvheBZ7TWp9xPa+MobX+a/WHJipjs9mYNm0ab7zxBk899RRhYWE899xzDBpkLvj273//m6eeeooBAwYQHh7O8OHDSUsze16NGTOGzMxMpkyZwsmTJ2nTpg3vvPMOkZGRHD16tDbflqglWcf2c2T+m3hnJmEFkpz+pHUYxpRrRtG8uVki+mDhdr75MZEpN/dg5IB2AAzrIz0/67N27dpx5513sm/fPlatWkVycjJLlixh3bp1DBs2jO7du9d69/RKl3xXSq0GbtJap7ueV8bQWl9ZI9G5QZZ8F+LCFBXksWfeO3gf+AUPDHING0dDo+lxw0RatmxZ5ti1W5N488tNTBjRhTFDOtRSxKKmGIbB7t27Wb16NSkpKQCEh4dz7bXX0qZNmxq9d1VLvleaoOoLSVBCnL99+/axYtkSBqSvw99SSJJvG1qPmkzbqM5knilgzooEmjf1KUlGhmGQeaaAJv6NZybtxsjpdLJt2zZWrVpVskhk165dGT58eJmOV9WpqgTldqODUsoPGA90AwqAncBs10znQoh64OTxY6xcsZKERHNW8e0BXejTN5aBg0aUVOfsO5LOwh/34+/jyYj+bfG227BYLJKcGgEPDw969epF165dWbduHWvXrmXXrl1orYmNjWXw4MGXtGu6WwlKKdUd+AEzMW3DXBPqDuB5pdRIrfWeGotQCHHRcnJyWL/oC4L3riDACMTLqx2DBw8mNjYWq9XK4RNZtG1hzo0X3TmUW4Z3YkD3lnjbpeNMY2S32xk6dCjR0dGsWrWKrVu38ssvv7BlyxauuuoqoqOjL0n7lLv/+j4AFgB/0loXAiilfIAZwHvAkJoJTwhxMQzDYPPGOE6t+ISOjmQsFmjv42To3ZPxbxJEXkERf5v2EweSMnn/iato1sRcRv2PI7vUcuSiLggMDGTMmDHExsayfPlyDh06xOLFi9m0aRPXXXfdWeMxq5u7EzP1BF4pTk4ArsUK/wnE1kRgQoiLk5yczFfTX8fjuzeJciaDxYJn75F0eXga/k3M9gRvu41mTbzx9baRlCJDH0XFwsPDmTRpEjfffDMBAQEkJSUxY8YMFi1aRE5OTo3d190S1CbgCiCh3PYYYFe1RiSEuCh5eXmsXrUS56/f0NuSgsUCzsAwIn73CB7NI5m7ej+De7eiRTNz+Ys/je2Bl6cVX++6NUhT1C0Wi4XLLruMqKgofvzxR9avX8+mTZvIzMxkwoQJNXLPqsZBTSn1Mg54WykVA6wHHEAP4D7glRqJTAhxXgzDYMeOHSxfvpzs7CwGWwvBYiGg32iaDx2PxerJBwu2881Piew7ml6y/EVQQN2ej03ULV5eXlx99dX06tWLn376qUZ7T59rNvPSkoERrkexFOBO4IVqjksIcR7S09NZumghxxITOIOdiIjWdL5yAsHeHthb/LYe0NhhHUk4fJpRg9rVcsSivgsJCWHs2LE1eo+qpjqSf8FC1HGGYRAfH8/G77+hv5FIJ5sVy7UP0rtPDAVFTuat3Mvh5fH8bWJfLBYLzZr48NqDg2s7bCHcUmknCaXUpPO5kFLKopS68+JDEkK4IyUlhVkffsihZR9ztbGHJpZ8mgc3pUcns8R0JreQb37az7ptx9l/LKO2wxXivFVVxddbKfUIMB34Wmt9vKKDlFKhwB+Ae4EV1R+iEKI0h8PB2rVr2fjjCvoZB2lhNXvfBcaMxN5/PNYAs/NDcKA3D/yuF8FNvOkY0bQ2QxbiglRVxfewq1PEs8CbSqldmLNHpAAWIASz+3knYClwu9Y6ruZDFqLxOnHiBAsWLMB+Yg8jPY7h5eHAw7cJoTc8wC+nm/PBK2t4aHxv+ncPB+CK3jU7TkWImlRlN3Ot9UbgRqVUO2AkZrfyjoATs9PEf4ElWuvDNR2oEI2Z0+lk3bp1rF69GqfTSS9fO14FDnyjYgi5fgpWvybk/ZzImbwiNu4+WZKghKjP3BoHpbU+AEyr4ViEEBVITU1lwYIFnDx6ECc2YmJiGD58OPkHtpIR1BmrnzlF0YgB7WjR3E/WZxINhky0JUQdZRgGcXFxrFzxPd2cxxhkS8V2/aN06BVLWmYef198hqycX3jnsSvx8/HE6mGhT+ew2g5biGojCUqIOigjI4OFCxdy6sAehlsP09wjF7DQ3MgEoIm/F16eVvJsHqRk5OLnI7NAiIanziQopVQ/zDYtBZwCXtZaz6jdqIS49Hbu3MmiRYsIKzjJKNtRPHFgC2xOQf878ezaBwCrh4W/TuxLgK8n3l515r+xENXK3clia5RSygNYCPxXa90E+D3m1Eo9azcyIS6dgoICFi5cyPx5c+hRmMgQ6yE8ceDXuR97ej7MI7NP8PG3v019GRLkI8lJNGjns2Bhb6AP4InZzbyE1vpiO1AEAaGARSllAQygCHP9KSEavKSkJL766ivS0tJobitCWdLBYqPZ8EkExoykY1ImVo992KweGIZxSdbiEaK2ubtg4VOYS2ukAVnldhtcZA8/rXWqUupt4GNgFmAFHtJa776Y6wpR1xmGwbp161i1ahVOp5PQ0FBuvvlmvI/v4miBP036xgDQvlUTPnhyeMl6TUI0Bu6WoO4GntFav1gTQbiq+PIwZ6T4ChgAzFdK7dFaL6+JewpR27Kysvj66685eCCRaI/jNInqSf9b7sFi8eDv8z3Zvu8YLzdvT5d2wQCSnESj426CagbMqcE4xgIDtdbFM6ivUUrNBCYDkqBEg6O1ZuHChVhyMxjpeZRmRjYeyRvwcN6Bh92HjhFNOXQ8i7yCotoOVYha426CmgdMAP5RQ3G0BrzKbSsCCis4Voh6q6ioiO+//564uDjCLZkMth/D01mIh38w9qsfwMNulpImjOjCTUM70sS//H8LIRoPdxNULvCEUmocsJdynRe01rdcZBzLgf9TSt0LfABEA/dgVi0K0SCcPn2aefPmcTzpGNHWE3SznAQnWFr34N9HonEuSeONKAd2TyueNg9JTqLRc7ebuS/wOebKuqeBM+UeF0VrvROzmm8ykO6619+01gsv9tpC1AV79uzhvffeIykpiSHeruRk8SB42ARCx/2NXA8fLBbIPCMdV4Uo5u5cfHfUdCBa6yXAkpq+jxCXksPhYMWKFaxfvx4ApRS9+40jbdF/CblhKn6R3QB4/t7+NGvijafNWpvhClGnnM84qO7A40A3zJLXHsyBtetqKDYh6rWMjAzmzp3LsWPHaOaRR5/hN9KvXz+270/hjdQbGbXfk3GR5rEtmvnVaqxC1EVuVfEppUYCmzB7880D5gIBmL3trqm58ISonxISEpg+fTpJx44ywDuF6zwS6B7owGKxUFjkJC2rkG37UnA6jdoOVYg6y90S1IvAC1rr50pvVEo9jTmAV7qCC4FZpbdq1SrWrVuHF0Xc4H+KJnmnwMOGszAPgD6dw/jn5P507xiCh4fMCCFEZdztJNEF+KyC7V8C3asvHCHqr+zsbD799FPWrVtHM0suY/2O0CTvFE7vQD41RpEZfnnJsb06hWKV5CREldxNUIeB3hVs7wOcrL5whKifjhw5wvvvv8+hQ4fo7JPLdfYD2PKz8AqPYmmziWxMDeSbnxJrO0wh6hV3q/jeAaYrpSKA9a5t/YGngFdrIjAh6gPDMIiPj+e7777D6XQS2boVl5/ZiDOziICeV9F8xD3cVQSt2x3ihis61Ha4QtQr7nYz/69SKgB4Amju2pwE/F1r/XZNBSdEXVZYWMjixYvZtm0bAP369WP48OHs3x7Lnvh4rh95BxarB742GDOkYy1HK0T943Y3c9dEsS8qpUKBXK11+VnNhWg00tLSmDNnDidOnMDT05NR143gsssuY/ehdF5dmERaZnO84g9zbb/I2g5ViHqr0gSllJoCfKi1znM9L7+/5Hk1rAclRL2xd+9e5s+fT15eHsHBwdx6662EhoZSlJfDjG920L5VUwZ09+XKmDa1HaoQ9VpVJajHgNmYy2A8VsVxF70elBD1gWEYrFmzhjVr1gDQqWN7runelmahoQDYvH157LY+rIo/woQRnWVRQSEuUqUJSmvdrqLn5blWwBWiQcvLy+Prr78mISEBgMExPWm3bwkZi79h08E8rr7RHK8e3tyf20Z2qc1QhWgw3J1JIlEpFVzB9nDgRLVHJUQdkpqaysyZM0lISMDb25uxA7oTuf1LnNmpHCxqzqwf09isZbSFENWtqjaoG4FBrpeRwPNKqZxyh0nXJNGg7d27l6+++or8/HxCQ0K4trUN4j7DAAJ6X02G1xD6peTRtX2z2g5ViAanqjaorcDDQHEVXm/KrgNlANnApJoJTYjaYxgGa9euZeXKlQB0jupIf8deirZtB4uV5iPuJjD6GsYYhrQ1CVFDqmqDOgRcCaCUmgU8pLXOvFSBCVFbCgsL+eabb9ixYwcAQ4cOpX8PxZEZ35FteLOAa3mkyzAASU5C1CC314NSStmUUq2A4gVrLJjLtPfRWn9RUwEKcSmlp6cze/ZskpOTsdvt3HTTTXTu3BmA0HF/48N5+/FrHoZMoydEzXMrQSmlRgGzgLM6SmCusCsJStR7Bw8eZO7cueTk5BAcHMSIdv74J++kKKoTNqsHAZFdeWJKR/x8PKXkJMQl4O5ksf+HuaRGLJAFDAN+DyQDD9RMaEJcGoZhEBcXx6effkpOTg7tI9tyQ3AG1q3fkvXzl8xesL7kWH9fuyQnIS4RdxNUFPC81vpXzIUL/bTWczCTU1WDeIWo04qKili0aBFLly7F6XRyea/LuCI7HufBLRhe/ryffRV7TkGRw1nboQrR6Lg7F18uUPw/NAHoCSwFfgU61UBcQtS4M2fOMHv2bI4cOYLNZuOaPl1ovm0ezsJ87KGRhI37K5NSPOjavpms3SRELXC3BPUj8IxSqimwEbhJKeUJDAWkZ5+od06cOMEHH3zAkSNHCAgIYFz/LjT79TOMwnyMyMsJv/0lPJuG0r1jc0lOQtQSd0tQjwCLgDuB6ZjjozIBO+aaUELUGwkJCXz11VcUFBQQHh7O+PHj8THySYhbyLIsRRP/kXTw9KrtMIVo9NztZr4P6KKU8tFa5yqlYjE7SqRorTfUaIRCVBPDMPjll1/4/vvvAega1YHRvxuH3e4FBNDhwWlE705jSHRE7QYqhACqnurI9xzbVxe/1lqXnwJJiDrF4XCwePFitmzZAsCALpG027+YJe+lMGrKg9isHnj5+jG0j18tRyqEKFZVCSobczojd1jPfYgQtePMmTPMmTOHw4cPY7PZuCrSn9C93wCQdTKZpWsPcMNgWY5diLqmqgQ17JJFIUQNOXnyJF988QXp6en4+/kxPCCVgIObwGqjKOb3OPKiuG5gpavJCCFqUVVz8a25lIEIUd327t3LvHnzKCgooEVwUwbmbsU3JQNbYHPCbn4Mr/COMkZCiDrM3amO4qmiuk9rHVttEQlxkQzDYP369Xz//fcYhkHXrl3pnbEZj8wMdhe2ovPIx/EKl44QQtR17nYzX1zBee2B64F/VGdAQlwMh8PBt99+y+bNmwEYMmQIQ4YMwZF1JSu+nM3+wH4Mb9uylqMUQrjD3W7mz1W0XSl1J3Aj8GZ1BiXEhcjJyWHOnDkcOnQIm9VK/xZ2uvboi8ViwRbYjKvvvo8RMuhWiHrD3ZkkKrMKuLo6AhHiYpw6dYoZM2Zw6NAh/LxsDCeByBMbWPThxxQWmbN0yYwQQtQv7rZBVTQmqgnwNHC8WiMS4jyVXpa9ubeFIYXb8LUU8YvRg9NhfXA4nHjaLva3mBDiUnO3DaqyMVF5wB3VF44Q7jMMgw0bNrB8+XIMw6CtZy79C/fh5eNLyKhHuC68B039vWR5DCHqKXcTVPkxUQZQAOzUWmdVb0hCnJvD4WDJkiVs2rQJgO4eJ+npTCYvsDXtJz6BZ9MwZE4IIeo3dztJrAFQSvkDCnCYm3VudQWilGoJvIuZDPOA97XWz1TX9UXDkZOTw9y5czl48CBWq5XRN96Abe1nrEnqQnrwjXRtElrbIQohqoG7bVBewDRgAuYM5gC5SqkPgEe01o5qiGUh5vpSYUBLYI1SarfW+vNquLZoIFJSUvj88885ffo0fr6+jP/974mIiMDo2pWigxlc1qGZVOkJ0UC4W8X3X8y1n24F4jB7/10OvA7kA3+9mCCUUpdjjqsaqLUuBA4opYZiLpQoBAD79+9n7ty55OfnE0QuHfLT8fRpCoDF5kn3js1rOUIhRHVyN0HdAtygtf651Lb5Sqk0YA4XmaCAPsB24B9Kqdsxq/imaa3fuMjrigbAMAzi4uL47rvvMAyDNpYMBngcZl2+YsWGg0y47rLaDlEIUQPOZ8n3wgq2Z1RTHMHAFcAazJJUZ2CZUuq4VPE1bg6Hg6VLl/Lrr78CcJnlBNE+Wfhf8zAtzrRiRP/I2g1QCFFj3E1QTwAzlFKTgfVaa6dS6jLMdqkXS4+TusC1ofKBTK31P1yvtyqlZgBjAUlQjVRubi5z587lwIEDeOCkv8dROocHEzb2WTyDWjCytgMUQtQodxPUm4A/8BPgUEo5AU/AAsQC/yp17IWsDbUH8FVK2bXWBecZm2iAUlJS+OKLL0hLS8PH04Mhzn3syW+NX5/JRAS1qO3whBCXgLtJYEyNRgHfA6eAN5RSj2B2Zb8LuK+G7yvqoMTERObOnUteXh5hYWGMv/UW9v66GW8iuLxHm9oOTwhxiZzvOCgfIAqzF9/+6hqkq7XOU0oNAd7CnDopD3hVa/1VdVxf1B9xcXEsW7YMwzBo2TKC22//I3a7nb7Dr6JvbQcnhLik3B0HZQVeAh7it6q9AqXUR8ADWuuiiw1Ea52IuXyHaIQcDgfLli1l48bizhAnOZlk43R2EWHB9nOcLYRoiNydQfNF4DZgItDa9ZgIXAc8WzOhicYiNzeXzz79hI0bf8UDJwOtRzGaKoq6XEdwoFdthyeEqCXutkFNBO7WWi8ptW2OUioLeB9JUuICpaam8vn/PiUtPQNvChnme5LLbn4AzzbdZQZyIRo5dxOUP7Cvgu2JgAzfFxckMTGRuXPmkOeaGSLaK4tedz2PPVh66Qkh3K/iiwfur2D7A5jz5wlxXuLj4/nf//5HXn4+rZv60poClvnfTJFvs9oOTQhRR7hbgvor8INrfrz1rm39gEhgRPWHJRoqp9PJsqVLiHd1hhg4cCBXXjmMQ8lZ3BAWiM0q1XpCCJNb3wZa641ANOZ4pdaY1XqLgM5a619qLjzRkOTm5vK/Tz4hfuOveGDQs9flDB8+HA8PK+3Cm0pyEkKU4fZsDVrrBOBRpVQzwKG1Tq+5sERDU74zRF/LcdbsUtwwyolVEpMQogJuJyil1DOYMzuEuV4fAd7UWr9ZQ7GJBiIxMZE5s2eTX1BAELlcHVLAjlZ38cDg3pKchBCVcneg7ovAvcALlF0P6mmllKfW+rWaC1HUZ/Hx8SxduhTDMIiwZDC8XSAR456hi7csyC6EqJq7Jai7gUnlxkGtVUrtA94BJEGJMpxOJ8uWLSM+Ph6AbpaTNPENpvXvn8TDJjNDCCHOzd0EZQOOVLB9HxBQfeGIhiA3N5d58+aRmJiI1WplQPswjiV60PLqOyU5CSHc5m6Ceg2YppT6g9b6CIBSKhh4GXPZdyEAszPEF198TmpqGr6+vowfP57WrVtTWOTA03YhK7EIIRordxPUrUAXINHVOaIIaAPYgX5KqQeLD9Rah1Z7lKJeKL1MRiD59Iq+htatWwNIchJCnLfzWbBQiAoZhsH69ev5/vvvSzpDxFiOs14fZeiVBhaLpbZDFELUQ+6uB/VxTQci6qeioiIWL17M1q1bAehuOUFvv1yO9XqAu4b2l+QkhLhgsqy6uGBZWVnMnj2bY8eOYcXJQI8jdAiy0+L3L9KuWXhthyeEqOckQYkLcvToUWbPnk12dja+FDLMegA8Awif9BK2gODaDk8I0QBIghLnbcuWLSxevBiHw0Hbtm0ZGBFAUnwmLcY+LslJCFFtJEEJtzmdTpYvX86GDRsA6Nu3L9deey1Wq5UOw0bjYZV/TkKI6lPpN4pS6kN3L6K1vrN6whF1VenBtxagl+U4gX6hWK1m93FJTkKI6lbVt0rpGSLswA3AHszFCwswl9/oCXxeY9GJOuHkyZN8+eWXnD59Gh8rDGYfzcjl6KlDQExthyeEaKAqTVBa63HFz5VS72POXP6X0scopV4AOtdceKK27dmzh6+//pqCggKaeToY4kwgwMcL55BHGRbTr7bDE0I0YO7Wy/wes8RU3sfAluoLR9QVhmHw448/8sMPPwDQ2pLJIOchfJq3pMUtT+AZLN3IhRA1y90ElQxcCewtt30UcKhaIxK1Lj8/n4ULF7J7924AenqcoLvlBFnBig63P4VVlsoQQlwC7iao54GZSqkrgU2ABXM9qOuAcVWdKOqXU6dOMXv2bFJTU/Hy8uLmm2/G/9RejiUmEv37P0lnCCHEJePuVEefKqWOApOBP7o2bwMGa6031FRw4tLatWsXCxcupKCgAB8vb+6+9x6Cg4MhKoqWA2o7OiFEY+P2z2Gt9WpgdQ3GImqJ0+lk1apVrF27FjDbm2IKd7Nvz0FiB8jAWyFE7ahqHNSr7l5Ea/149YQjLrWcnBzmzZvHgQMHsAB9PJLobEnhhF8n+vToWNvhCSEasapKUH3dvMb/t3fn4VXVdx7H3/eGGAgBJWwGiAuCXwVFKQgGKmqVVsVWiuvU3bbTfVo7Tlv3OrXV1taljk47rdZ2mNaihrRCtW1UFhF4rMWt6FdRJFiCUQuEJSBJ7vzxO3m8jTTNdu85Jp/X8+QhOeeecz+c3F++Z/39Mt0RRPJvw4YNzJs3jy1bttA3nWFG6lWGpxsYdOy5jJ42m1QqHXdEEenF2noO6viW783sfOAhd38rL6kk51atWsXChQtpampiEA0cn3qNkv7F7DvnWvrtf1jc8URE2n0N6ofA0YAK1PtcY2MjDz/8ME899RQARxwyFnv5N2wrKWfMhZfTb9DgmBOKiATtLVArgY8DN+Ywi+RYfX098+bNC+M3FRQwa9YsJk6cyPb1kykeeRCptIZlF5HkaG+Baga+Y2ZXAWuBhuyZ7j6lu4NJ93rllVeorKxkx44d9GM33AHyoAAAD+VJREFUI4aMYuLEiQD0Lz845nQiIu/VkSMoPe/0PtTc3MySJUtYvHgxAPumtnJMuoaGdBGZTEZDsotIYrX3Qd3rWr43s4FA2t035yKQme1DeAj4Gne/Jxfv0Vts376dBx6oZO3aV4EME1J1HN7nbQYeey5Dp31MxUlEEq3dD+qa2eeAK4AR0c91wG3u3t3XpX4EjOzmdfY6NTU13H///WzdupW9aOSYdA37DS5h+OxvUzRCzzeJSPK1q0CZ2WXA1cC3gccJffFNB75hZg3uflt3hDGzC4GBwHPdsb7eKJPJsHz5cqqrq8lkMgxO7+LY1KsU7X8Eo876Mumi4rgjioi0S3uPoL4AfNbdf5U1bZmZrQOuB7pcoMzsQOBaYBrwcFfX1xvt3LmTyvnzefmllwCoqKig4tADafjrGoZOPUmn9ETkfaW9BWooYSTd1p4CRnU1hJkVAHOBy9x9o5l1dZW9Tm1tLffe+2vq67eQBmaffiaHHzYOgAHlY+MNJyLSCe0tUM8ThtW4odX0swnDwHfV1YC7e2U3rKtXyWQyrFy5kurqapqamiilgRkF6yhlV9zRRES6pL0F6hpgoZlVAMujaRXAScCcbshxDjDCzFrWNQC408ymuPvnu2H9PdKOHTuYP7+KNWvCOJIHp95icuFblJ54EaXjj4w5nYhI17T3NvM/mNkJwJcI40E1AC8AR7n7M10N4e6HZP9sZk8Dt+o2839s3bp1zLvvfnZs30YhTUxLr+egISUMm3MDRcMPiDueiEiXtTXcxknAUnffDuDuS4Al+Qome9bc3MzSpUtZvHgxmUyGoWzngwU1DBs/lWGnfJZ0Ub+4I4qIdIu2jqAWAo1m9ifg0ejrCXfP+cUNd9f5qT2or6+nsrKSdevWATC9YiojX1jIsGkXUzr5w7pLT0R6lLYK1BDCs04VwAeBS4G0ma3g3YK10t2bcp5SeOmll6icP59dO3fSr18xZ5xxOqNHjyZz4kx18ioiPVJb40FtAhZEXy23gn+AULAqgEuAIWa21N1n5SFrr9TY2Eh1dTUrV4auEMtSWynrk2L06NEAKk4i0mO1u6sjd28ys9eBWqAOqAGGAaNzlK3Xe+ONN6isrKSuro4UGSamN2IFWxh83ClxRxMRybk2C5SZlQDHAScCM4FDgPXAY8BdwCfcfUOOM/Y6mUyGFStWUF1dTXNzMwPZxfSCGkaUlTH849dQWDoi7ogiIjnX1l18S4EpwCZgEaE7o0fdfU1+ovVO9fX1VFVVsXbtWgDGpt5mUnoDQ6bOovT480j1KYw5oYhIfrR1BDUdeB24m3BDxHJ3352XVL3U6tWrefDBB9m5cyfFxcVMLNnFqPrNjJhzOQPGToo7nohIXrVVoEYTTu2dAHwO6G9mjwOPEArWn909k/uIPd+uXbv43e8e4tlnwzPPY8aM4bTTTqNfAdDcREH/veMNKCISg7bu4nsN+Gn0hZkdAXyIULCuBnab2SLgEXe/M+dJe6iamhqqqqrYtGkTaZoZyxZO/uhsSkr6xx1NRCRWHbmL7xngGTO7DZgEXEzo9mg2oALVQbt37+axxx5j+fLQteEgGvhgQQ0lww+gpI8eLRMR+acFysxGAVOBo6N/PwA0Ak8QxoJalMN8PdLrr79OZeV8Nm36GykyjE/VcWS/rQydeQkDjjhBPUKIiND2XXwPEApSGbAVWAY8CFwGPKUeJDqusbGRxYsXs2zZMjKZDHuzk2kF69l/3JEMnvlJ+gwYFHdEEZHEaOsIqgi4lXCE9Gd3b85Loh6qtraWqqoq6urqAJiw/3AOqH2SsllfZPiEo2NOJyKSPG3dJHFqPoP0VE1NTVT/8Y+siLoqKi0tZfbs2YwaOYJM8yWk++wVc0IRkWRq900S0nH1WzYz9+6f8mb9diA8dDvl5EsoLy8H1I+eiEhbVKC62e7GZv6wfC1/fXo5BzY+y5v1/SlhF5MHvsOIGeczZkx53BFFRN4XVKC6WePO7fRddAsnpmtpyqQoHjCSQ0/4OIMmHKu780REOkAFqhv85dW3OWT/QRQUpOnbv4QRQ/rTvLWYITPOZMzkk9V/nohIJ6hAddE9v3qUvi88RN2MORx/YgWpVIox51xKut8ACvqqNwgRkc5Sgeqk3X+rZdOySo5Zu4hUUTP1r/6eMI4jFA7aN95wIiI9gApUB7y9pYGFC59g/PYVDHrzacg0k0qlKRp/POOPOzPueCIiPYoKVAfULn+Iaa/+H+kUkEpTMuFDDJo+h8LSsrijiYj0OCpQbXhtwxZq1tUyo+IQAA6eOp11q+4jNbaCUSeeTeE+w2NOKCLSc6lA7UGmuYkNf1rEuofupU+qmc2H3cE+A/qy16DhHPTVu0gXFccdUUSkx1OBimx4cxt/eXkDUwpfZsvKBTRuqaO8D+wq6E/j5joYsB+AipOISJ6oQAE7tm7jt7ffwvTC1bydfgeAwtIyBk75KAMmHEe6sCjmhCIivY8KFNC3byHHFr9MUdM7pIYdxLAZp1N88FGkUum4o4mI9FoqUEC6sIjy0z5HQf+B9C0fpy6JREQSQAUqUnJoRdwRREQki85hiYhIIqlAiYhIIqlAiYhIIqlAiYhIIqlAiYhIIqlAiYhIIvWE28wLADZu3Bh3DhER6aCsv90Fref1hAJVBnDuuefGnUNERDqvDHgle0JPKFBPAscAtUBTzFlERKRjCgjF6cnWM1KZTCb/cURERP4J3SQhIiKJpAIlIiKJpAIlIiKJpAIlIiKJpAIlIiKJpAIlIiKJpAIlIiKJpAIlIiKJ1BN6ksg7M5sCLHD3YdHPQ4HbgI8Au4C7gWvdvSmaPy2afyiwAbjC3e+P5u0H3AUcDdQBX3L33yUg88XAVcAQ4EXg39398VxnNrOZwI3A2GjdN7n7j81sL+C/gDMIPYbc7O43ZC13FvAdwhPpi4GL3L0u13m7mPnLwJeBwYATtvHSJGfOWn4q8Dgw1t1fy3XmLmzj2NpeFzInqu1lze8LLAJudPeqrOk5a3s6guoAM0uZ2aeAPwB7Zc36OTCM0AgOA6YA/xktUwYsJHwgBwBfAOZGvziAe4FnCX+kPg3ca2ajY848AbgZOA3YB5gLVJlZy+clJ5nNrBx4ALg+et9/AW4ws48A1wEGHAQcBVxoZhdEy40jNIKLokwvRxlb5GwbdyHzHOBrwKnAIOC/gQXRjkMiM2ctXwL8gvfu4CbtcxFb2+tC5iS2vZZci4CprZbLadvTEVTHXAfMIvwSrwIws2LgJOCIrL2Gq4EHzewq4AJgibv/PFrHH6OjmU1mdjAwGZjp7u8Aj5rZb4FPAlfGmHks7+68pAh7eQ3R63KZ+QDgl+4+P/r5STNbBEwHLiTsmW0ibLvvA58h/KE8D3gway/z8ug1Y6P8udzGnc1cBnzH3VdHy/3MzH4AHG5mryc0c4vbgUrgGy0TEvq5iLPtdTZz4tqema0FHuHdo6RsOW17OoLqmB+5+yTgT1nTWrbh9qxpTcBQwp7IJOA1M7vXzN4ys1XAvu6+FRgH1Lh79rIvAofHnPn3wPPAc8A7wHeBs929OZeZ3X2pu3+25WczKyV0BLyK0DBWZ708+z3HZc9z9x3A+mh+TrdxZzO7+x3ufkfWcjOAEuAvSc0cvfYMwp7/Ta1Wm8TPRWxtrwuZk9j2NgCj3f0WoHXnrTlteypQHeDuG/YwbRvh9Nn3zKzUzAYD10Sz+wGlhEPbuYQP5g3AfDM7iPAHaUerVe4AimPO3JdwTeRooD/wdcJphn3zkRnAzPYGfgusBJ7Kep89vWdbmfKSFzqcOXu5w4BfA1e5+xtJzWxmIwmF6QKgudWqkvi5iLXtdTJz0treb9x9W1TU9ySnbU8FqnucT9jbeYFwkbDlAuJmwg0ID7v7Anff7e7zgD8DJxOOYPq1WlcxsC3mzN8ENrr7Snff5e53Aq8BZ+Yjc3QqYwXwBuFCckvjyH7f7PdsK1NetnEnMrcsdyqwFLjV3b8XTU5cZjNLEU5BXdtyU0QrSfxcxN72OpH5mySo7UVHbm3JadtTgeoeZcBn3H24ux8G/BV4ITrcfZFwETxby7W/1cB+Zpb9SzyEvz/8z5W2MpcDRa1e3wjsznXm6FTXSkLBPMPdd0bn6jcSLizv6T1XZ8+LrrHtF03P+TbuZOaWu/h+BXzK3b+b9bokZi4nXD/5oZltBtZF8581s0/kOnMnt3Gsba+TmRPV9tqxWE7bnm6S6B43A8+b2WWEX853CXcOQdjr/KKZnQf8krAnNAE4y93Xm9kzwLeji4vTCHfvVMSceQHh9N88wiBi5wLjgYW5zBydelkAXOnut7ea/b/AtWb2LOHUwWWE24chbNfHzew4YDnhVM4qd38pWm/OtnFnM9u7t+Z+yN1XZi/k7p60zO5eQzj91LKOfYBNwISs28yT9rmIre11IXMS215bctr2VKC6x6eBnwB/I5wiuz06NMfdnzGzUwgF4E6gBpjj7uujZU8H/ofwjMBbwCfd/fmYM/8kOg/9S8KzGKuBU/KQ+QuE24FvMLPsZ2/uIFwj+wHhJoJ09P4/ivI+Z2aXRD+PJOwFnpm1fC63cacyE+6AKwIeMcvemeYcd1+Q0Mz/TNI+F3G2vc5mTlzbc/ev/6OFct32NKKuiIgkkq5BiYhIIqlAiYhIIqlAiYhIIqlAiYhIIqlAiYhIIqlAiYhIIqlAieRB1GHpm1EnnK3nXWlm28zsgBiiiSSWCpRIfnyF8GD83w3+Z2YHEoYeuPIf9HEn0mupQInkgbtvBC4HPmVhNNoWtwNPR/+KSBb1JCGSJ1GP4MsIIxsfBXwMmAdM9GjgQgvDfV9B6DbmBcKR1cPRvD7At4BPACOAtwmdzV7m7k1mNpcwDMahhDGb5rj7orz9B0W6mY6gRPLE3TPAvxI6LL0Y+D5wfVZxmkXoxPfK6DV3EcYvmhKt4nLCUNznE0ZevRz4N8Kw8S3OI3Q8egJh2ASR9y11FiuSR+7+vJndDPyYcIR0Y9bsK4Abo3GLANaY2WTgq8A5hFFWL3L3JdH8n5nZ1wgjl/4mmrba3efm+v8hkg8qUCL5dx1hpNRvufvurOnjgElmdnXWtEKi8XPcvcrMTjCzm4CDCUdZBwAFWa9/JZfBRfJJp/hE8szdG6JvG1rN6gP8B3Bk1td4whg6mNn1wH2Ednsf8GGg9dAFrdcp8r6lIyiR5HgR2N/d17RMiI6mdgI3AZ8HvuLuv4jm9SMMNpmKIatIzqlAiSTH94BfmJkDjwIzgWsJ158gDC55qpk9AexNOFU4kPcOES7SI+gUn0hCuPt9wKXA1wjXnS4FPuPu90cvuYBw995zQCWwBrgHmJT3sCJ5oOegREQkkXQEJSIiiaQCJSIiiaQCJSIiiaQCJSIiiaQCJSIiiaQCJSIiiaQCJSIiiaQCJSIiifT/ogGfvt3JpfMAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]

From 2035172e09dba52f5c4a7e8f3663b5c9bc69d6bf Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 2 Jun 2020 09:51:53 -0400
Subject: [PATCH 004/144] Run on Colab

---
 soln/chap08soln.ipynb | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/soln/chap08soln.ipynb b/soln/chap08soln.ipynb
index f32e3d1e..4ce822af 100644
--- a/soln/chap08soln.ipynb
+++ b/soln/chap08soln.ipynb
@@ -27,7 +27,8 @@
     "\n",
     "if IN_COLAB:\n",
     "    !pip install pint\n",
-    "    !pip install modsimpy"
+    "    !pip install modsimpy\n",
+    "    !mkdir figs"
    ]
   },
   {

From 34ee4334ff3b3cf85f06d6450efd38fb6142c414 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 2 Jun 2020 11:01:40 -0400
Subject: [PATCH 005/144] Run on Colab

---
 soln/jump2_soln.ipynb | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/soln/jump2_soln.ipynb b/soln/jump2_soln.ipynb
index 5fa97f6e..fadfae6d 100644
--- a/soln/jump2_soln.ipynb
+++ b/soln/jump2_soln.ipynb
@@ -26,7 +26,9 @@
     "IN_COLAB = 'google.colab' in sys.modules\n",
     "\n",
     "if IN_COLAB:\n",
-    "    !pip install modsimpy"
+    "    !pip install pint==0.9\n",
+    "    !pip install modsimpy\n",
+    "    !mkdir figs"
    ]
   },
   {

From c36a476a20042acb33773e47d12aea5b0c413e60 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 2 Jun 2020 15:47:02 -0400
Subject: [PATCH 006/144] Run on Colab

---
 soln/chap09soln.ipynb | 139 +++++++++++++++++++++---------------------
 1 file changed, 69 insertions(+), 70 deletions(-)

diff --git a/soln/chap09soln.ipynb b/soln/chap09soln.ipynb
index 9f679158..aa6929e0 100644
--- a/soln/chap09soln.ipynb
+++ b/soln/chap09soln.ipynb
@@ -19,9 +19,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
     "# import everything from SymPy.\n",
     "from sympy import *\n",
     "\n",
@@ -76,7 +73,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAcAAAANCAYAAABlyXS1AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAhElEQVQYGV2QgRGAIAwDWydwBmbBDXQFl9QZ2ABXwUSNUrnL0fZpA1hrzXqZWYUSa38wA6B+NwxI+jUhKSr8YQbYBR0BCwuUnpjwgOrriST40bcfG/xwOMDgR3iNxTYyhLKewV2QXci/D2GDPIOfu/NySZBjeX0DYMzvKxrLN27QSmn8CWOLY8q7tsXYAAAAAElFTkSuQmCC\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAcAAAANCAYAAABlyXS1AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnElEQVQYGV2QCxHCMBAFGxSgIRqQQB3UAzIaG2ABCcUBQyWgAQdhNyRAmpnX++xd7tKQcx7aSSnt8R9oxH/uGqj2iI0C4y0cya0CzxbaeSuET5jn2cQJOa/BF/59cCFF0YRwP7H2/9puHp0dbFeaL6d01vdFMkvN/yDewYii76Y2tJndPMBEbWzQZ5S/YocAuwZXxnHeGV2R8UX7Bq+TPY8f0jS0AAAAAElFTkSuQmCC\n",
       "text/latex": [
        "$\\displaystyle t$"
       ],
@@ -90,7 +87,8 @@
     }
    ],
    "source": [
-    "t = symbols('t')"
+    "t = symbols('t')\n",
+    "t"
    ]
   },
   {
@@ -107,7 +105,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle t + 1$"
       ],
@@ -121,7 +119,8 @@
     }
    ],
    "source": [
-    "expr = t + 1"
+    "expr = t + 1\n",
+    "expr"
    ]
   },
   {
@@ -165,7 +164,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOCAYAAAAWo42rAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVQoFW2RARHCMAxFVw4BQwJIQEMlDAvMAWiYhHnAAUjgcDAN4KD816Yju5G7vyQ//5ImCymlBgshHOT6nJTPUW5U/ZZTEyKCbCqUdwJdzlljwouRXRUaj3Ai3ijAXsLHQO4NXr3dOB+rtB7tBcSyVpiEodYWHVVgqcFEo3z7V1hJ68qCbyGSByOVr023RYjt89Yc2w5e6N/3qZD3xq1xPJy/s9OEcg4rzM5GM+JO7CGOBpyhrR2vSham7tyRK/RMmZdRIYo8OXU+lUQPuC82XK6BdbIUdQAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOCAYAAAAWo42rAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVQoFXWR0RGCQAxEwbEALUFKsAZKkBagBH/5cygBW5ASKAHpAFvQDvBtuDDHjGYmbLLZy11COs9zIqvrOgMqS5bPGWjhO6WphEF0BVch8YX6A6+I7zupMZFlKBpBbJ1IriJcOBJ/gotPEB4sWPhkH8gePIaCQx6CVmhv9Ipj6PYk74jtausYCXxyvVm33Lz2s6OKdGqAEi+I+7/CIH4LsZNNzYlMvnCb70Cm6XNfz0QyIfaVbNRKfBjtcEAojM1vGV1oK4gVHNLkEurXvtZhSLTgIhJL1MBrTckXilpU0lDNGcIAAAAASUVORK5CYII=\n",
       "text/latex": [
        "$\\displaystyle 3$"
       ],
@@ -206,7 +205,8 @@
     }
    ],
    "source": [
-    "f = Function('f')"
+    "f = Function('f')\n",
+    "f"
    ]
   },
   {
@@ -250,7 +250,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle f{\\left(t \\right)}$"
       ],
@@ -281,7 +281,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)}$"
       ],
@@ -297,7 +297,8 @@
     }
    ],
    "source": [
-    "dfdt = diff(f(t), t)"
+    "dfdt = diff(f(t), t)\n",
+    "dfdt"
    ]
   },
   {
@@ -334,7 +335,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAA0AAAAJCAYAAADpeqZqAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVQYGXWQAQ3CQAxF707BJTgYDggSwAEakIAFMhwMC+BgOCDBAThYmIPt/UtLLiRr0v32t//aLk7TFGqLMbbkg3Fr8EbPo+4JEpmwofDGdxWXyb94Yz3KD7VIgtYFjnA93pnoRJwTn8BKHaBJZ+V/9iHfGrdCPPpqWqH312uE142qC7NqiSnaU/7Cl0z1Z5lCkDwg1k1LNtJ392K5ieSK7510ZIujxZqk2zcFtaMR2ln2m0hNj/mPGgkHuMsMhpRwj4BD4poAAAAASUVORK5CYII=\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAA0AAAAJCAYAAADpeqZqAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVQYGXWQ2w2CQBREhViAiR1gB2gLdmAsQUvwd/+MdqAtSAfSgY8OwApI7GA9s+4lG6KTDHOfc4HMez9K4Zw7kHexNkMv1Op0JrMlGgWNK9zaEDohf8E5cRtnypyC4UFQ2YKKxG/kDnfKwQrWYYnmiUSuezhES2ERi1MZ2aU1xTo6D5d0raCnbw2mOYkuiE/4D+rfzFRLchKar/x+MldZZxyDM7qE0h4Mbkh0JYC8JGjTX653FvqLDAUTVD9Kb9QRHz9smkzvIogUTAAAAABJRU5ErkJggg==\n",
       "text/latex": [
        "$\\displaystyle \\alpha$"
       ],
@@ -348,7 +349,8 @@
     }
    ],
    "source": [
-    "alpha = symbols('alpha')"
+    "alpha = symbols('alpha')\n",
+    "alpha"
    ]
   },
   {
@@ -365,7 +367,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)} = \\alpha f{\\left(t \\right)}$"
       ],
@@ -381,7 +383,8 @@
     }
    ],
    "source": [
-    "eq1 = Eq(dfdt, alpha*f(t))"
+    "eq1 = Eq(dfdt, alpha*f(t))\n",
+    "eq1"
    ]
   },
   {
@@ -398,7 +401,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle f{\\left(t \\right)} = C_{1} e^{\\alpha t}$"
       ],
@@ -413,7 +416,8 @@
     }
    ],
    "source": [
-    "solution_eq = dsolve(eq1)"
+    "solution_eq = dsolve(eq1)\n",
+    "solution_eq"
    ]
   },
   {
@@ -441,7 +445,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle f{\\left(t \\right)} = p_{0} e^{\\alpha t}$"
       ],
@@ -456,7 +460,8 @@
     }
    ],
    "source": [
-    "particular = solution_eq.subs(C1, p_0)"
+    "particular = solution_eq.subs(C1, p_0)\n",
+    "particular"
    ]
   },
   {
@@ -498,7 +503,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)} = r \\left(1 - \\frac{f{\\left(t \\right)}}{K}\\right) f{\\left(t \\right)}$"
       ],
@@ -514,7 +519,8 @@
     }
    ],
    "source": [
-    "eq2 = Eq(diff(f(t), t), r * f(t) * (1 - f(t)/K))"
+    "eq2 = Eq(diff(f(t), t), r * f(t) * (1 - f(t)/K))\n",
+    "eq2"
    ]
   },
   {
@@ -531,7 +537,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle f{\\left(t \\right)} = \\frac{K e^{C_{1} K + r t}}{e^{C_{1} K + r t} - 1}$"
       ],
@@ -549,7 +555,8 @@
     }
    ],
    "source": [
-    "solution_eq = dsolve(eq2)"
+    "solution_eq = dsolve(eq2)\n",
+    "solution_eq"
    ]
   },
   {
@@ -566,7 +573,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle \\frac{K e^{C_{1} K + r t}}{e^{C_{1} K + r t} - 1}$"
       ],
@@ -584,7 +591,8 @@
     }
    ],
    "source": [
-    "general = solution_eq.rhs"
+    "general = solution_eq.rhs\n",
+    "general"
    ]
   },
   {
@@ -601,7 +609,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle \\frac{K e^{C_{1} K}}{e^{C_{1} K} - 1}$"
       ],
@@ -619,7 +627,8 @@
     }
    ],
    "source": [
-    "at_0 = general.subs(t, 0)"
+    "at_0 = general.subs(t, 0)\n",
+    "at_0"
    ]
   },
   {
@@ -661,7 +670,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle \\frac{\\log{\\left(- \\frac{p_{0}}{K - p_{0}} \\right)}}{K}$"
       ],
@@ -679,7 +688,8 @@
     }
    ],
    "source": [
-    "value_of_C1 = solutions[0]"
+    "value_of_C1 = solutions[0]\n",
+    "value_of_C1"
    ]
   },
   {
@@ -696,7 +706,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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dRoE4YG/cn7aicLMb2agZlQfzPaxD5UdAN7dfEfl3rn6mtXFpRLhQlqiy4jgxlrXXjfq5j/rE+pB3VRsf3o+CyLEMeTlS4AwzaFL5NwGRd/SzdUnnQrkJdv6DpBjLZhG7sLM+SPAPBuP61B9MYPqEQB7yJYdxERu5NvUTs7yXleNCOTLx56hODMaU45zu6B91m4Peh7YhfrEm53iJdXLnOtg3eg6l+DLlOSPknxu2Q7oMFt5qJwXEI9a/TKJYAZ0CSYWuKaHCBiHOvjB7888Yb5D8vVEWn+ARm1Kt68i8QhfKnBob80fB5NGzylnixrpxY9GNWpJ3Ufs8P53o8H8YJ+3ekIhQcQAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle - \\frac{K p_{0} e^{r t}}{\\left(K - p_{0}\\right) \\left(- \\frac{p_{0} e^{r t}}{K - p_{0}} - 1\\right)}$"
       ],
@@ -716,7 +726,8 @@
     }
    ],
    "source": [
-    "particular = general.subs(C1, value_of_C1)"
+    "particular = general.subs(C1, value_of_C1)\n",
+    "particular"
    ]
   },
   {
@@ -733,7 +744,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle \\frac{K p_{0} e^{r t}}{K + p_{0} e^{r t} - p_{0}}$"
       ],
@@ -751,7 +762,8 @@
     }
    ],
    "source": [
-    "particular = simplify(particular)"
+    "particular = simplify(particular)\n",
+    "particular"
    ]
   },
   {
@@ -770,7 +782,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle p_{0}$"
       ],
@@ -809,7 +821,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle \\frac{K - p_{0}}{p_{0}}$"
       ],
@@ -825,7 +837,8 @@
     }
    ],
    "source": [
-    "A = (K - p_0) / p_0"
+    "A = (K - p_0) / p_0\n",
+    "A"
    ]
   },
   {
@@ -835,7 +848,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle \\frac{K}{1 + \\frac{\\left(K - p_{0}\\right) e^{- r t}}{p_{0}}}$"
       ],
@@ -854,7 +867,8 @@
     }
    ],
    "source": [
-    "logistic = K / (1 + A * exp(-r*t))"
+    "logistic = K / (1 + A * exp(-r*t))\n",
+    "logistic"
    ]
   },
   {
@@ -873,7 +887,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOCAYAAAAWo42rAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVQoFW1SiRGCMBAkGQtgtAPswKcCKYEa7MESLMWhBGkBOtAOEDuA3XDL3Cg3c9lkby+bEMI4jpkihHDHvEd+kXvkA/UOmGUUmrjFsnLrHOsXskh1E11BDBIJwdGh9cIniFoCIbgKybPlEQOjnOFv5FkZZcQFeBbGZ4bVseCOWyup2yuXZlmzqJ29UPMdhUuXWIdy6yNuKEuRTre4vGXdoLpmreZGwhrCk9/K5nzGLrm6j8vn+n3CAdyBmo11Eo7IG75rYdwZeIEo/RQTNRRqzYwqZmYAAAAASUVORK5CYII=\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOCAYAAAAWo42rAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVQoFW2S3Q2CQBCEkVgA0Q7ODvypQEpQO9AWjG+8GVuwA4MdCBWY0IF2ILED/AZvyYWwyTKze7M75GDUNE2kyLIsAS7kl3yRM/JGvwKjWA8fJfjg4Ehe4Wcyhzudt0KKA9yBdzUVcG1Wnau2jVt4oUYvntRzhhITpj2BldqqSGOp/zyqPQ6B08aJP7HpUGjDU7PWoW0OhR2X0Ka6ZkDM7aN3NEtrBrrO5W3WupohaxsuTKhLXYarPF+AlVxbIUSfrAY3JobLYUfu1Rvr4UPTJwTO1ytwTd3+FD9d+zu+IBCKGAAAAABJRU5ErkJggg==\n",
       "text/latex": [
        "$\\displaystyle 0$"
       ],
@@ -928,7 +942,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)} = \\alpha f{\\left(t \\right)} + \\beta f^{2}{\\left(t \\right)}$"
       ],
@@ -946,7 +960,8 @@
    "source": [
     "# Solution\n",
     "\n",
-    "eq3 = Eq(diff(f(t), t), alpha*f(t) + beta*f(t)**2)"
+    "eq3 = Eq(diff(f(t), t), alpha*f(t) + beta*f(t)**2)\n",
+    "eq3"
    ]
   },
   {
@@ -956,7 +971,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle f{\\left(t \\right)} = \\frac{\\alpha e^{\\alpha \\left(C_{1} + t\\right)}}{\\beta \\left(1 - e^{\\alpha \\left(C_{1} + t\\right)}\\right)}$"
       ],
@@ -976,7 +991,8 @@
    "source": [
     "# Solution\n",
     "\n",
-    "solution_eq = dsolve(eq3)"
+    "solution_eq = dsolve(eq3)\n",
+    "solution_eq"
    ]
   },
   {
@@ -986,7 +1002,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJEAAAA5CAYAAAA2l8mgAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAKBElEQVR4Ae2d7ZXcNBSGhz0UsIQKCB2ElLB0AEkFSToIJ7+Sfzmhg0AFAToIVBBCB4QKWLaD8DyKpZVla8YzOx7PeH3P0err6uvq1dW1RvauPn78uFpcWwbPnz+/e+wyoY/3jqWPn68WakngxYsXr3A/tBKbCOmPCX6Pu8JdNv5LfNN/I/8D/qHoLu3dwf1+qAZr7Xwmmhf6JAEm5FdCP5RgIH6XdPPeEP7xE/dqRfi8Sb9P+IuYPoZP/e+p9xH+X7F+wq8JC/pDgjc2n/yzFLrlASbiHiL4UE4IcQH0N05wJQApLuJqJMH1p/FdiDosv5bgEayhfwWjGvNVkXbw6AKia5E/I/jmOppCbwn9yETWtg0BJM+uJEA20QUMAlzQJmribmsCfTKavU2EgF2p/+K+xJ0Tf6K08Q3nk3KPeNoqGp6n+E6Qdk+N3Ep+qmXeJJ3+2LbaRhBdEXf7el30U+CbP0ofqHcjzRpECNtt6Al+0CL4v+Ke4tyW1DzBgCZeW8kC7nfyc7CRdE19eaS5RWm/VMtd11APUV6A2n/toZf4v/Vwy/MQt4CoRzg3SkLgrlq3gHwbcttxUtQ4+RYkiPqMU9Pz8kTrRL1qhG9x+ndwNwIR5SNpD7W0ZMzAt43zLH7w4Jw10QOk+aiQqEARGN8z4WFby/L7Jlz+vvRQjDqs6wI/aAF8Aafm+i4wFH9IV0NZJidtGjVNTukpkDwB6VbWB3LLXOIE7GQ0ZxC5OsvVq8BND9tYJvWYniWFoKBQC9RIrVbWVeNdwesZU4tIe4tTe9XI9oM2hM++ezaUA0oA5fFaPaOlHwWIEEo0fh3o1zhtl7XbSCNQ7RqNZsly+ZmJAHLVBwE3/NoOK8Ku7NywlqfUEPIJkr9xj3HJ5iDsZNq2W+bY5LjeNY08o+0StGmMY3ekVv+kIEIgCiDaKflq+4e8b3DaNPL45JSMSsKqeCfwW3nwV/jyuUVFIbvqPdtxJV/hBI1xfUHrxIQ6iQuqFS4HFtmh3q9JF5xuRWos6xK4GrqGxybbFcw+KfokVtI3JMgzGU0KIkatLfATAkpax4nBefYiGASF9kWuBQSLQvPpJwJIzSB/0gxNXmn3rEjvpFFOsqzgTGA1UaJMBOanhAP+pW1lk+TT07Sn5bUx9bDvP2kyEDFwJ83Jf9kzLMFxv0n/Et58xatFJG0DV6dkvlom5wsZQ/9Q1gNFtWIHRFvUodYTiALd/mjv7Fzfpnap2wWWFs4m/rHyJwMRA/LpqXYGIxh8ahEwJcgUnD92Ju1EfF/kQV7L/tmmYspqh+laP49sqGMnLUJb59Trdr5T+Q192ir7bCvuPTE3AlAICrxG5r+DV0CVFLaxMvGmcdpSa1ziq0kOQrS161i8OTDZNpsLZyoQRWB4olylZlLL/Fi2TF/BH7e3Tt7QBNvE7TqxQ5u5MR99dPutyuLGDWxRwZTbmduR5yOtbQnBuMLO4xiIa2fkPz66vT2M+frwyO/WF+0lkxc6kAQmvU/E5MdJTxqJtAAqfA1GV9q/hFs2BnE1jucnlgurMZYjvtCBJTApiA481qW5kSQwiU000liWaieSwAKiiQQ/p2YXEM1pNicaywKiiQQ/p2YXEM1pNicaywKiiQQ/p2YXEM1pNicaywKiiQQ/p2Y/453u5RXYOc3oBGNZTqwnEPrcmly2s7nN6ATjGR1E/DDq/Whv+91KYux3j33gztG6PpK/9orNqFdBms55yX3dKzHr+n/SeYzbsfdeHCP9lD5T8xf99Q2czitPTtDYmuhn2uht+KTRMaDzCh22zv1n0uPLir5Z4vXW+CKl96Qs42tBo16Ko/73uKR9CHuj0370ak3SfVHAO10Cv0NrQWSlFmxcbwOdGpsEyijAN/jhvk+Nb47pjDlepGuBgXRl6B0oL/GXd6SUkyD6E7cTUafl1xI85zCE/hWMasx4v6vISm+8hFeuyswqiGjMfVB7xktiv+C8xJ7QW1aUx+FTWKK2dWsx55l5+Bnje9MzRt8mOeXP1KgYOkDrtYkAgQDwTYxwkR7fd8EsrBti3/gGgu+TVbUQeb614Xb31To+8o+GGhn44uJt/UyN256atPWGSQdECCpsW/jlmxiqwZCHv4kE4aOSiTqtQ+Bc4u7gjJ8E0fcgPPzwIiH+rftMDWPWLgq2EX7aZTogYka/g6G1Xzez7OX4ElgdAFDWLU9wdN7aJO+K9GBoE3a7VBsdPdFX1bjCy8fk1uTrzcrEcCQXWssWajJMz8tH/l6fei/IUOvru+CU3T7I+anNo204d+vIMTiHa0HUqaAZkIPxve9NdB8Gt799DXpTe4fIf0AjpWYVKAIjPl3l/egbu/x96aEc8rKuC/wwOfhO1jF+pkaNrDwStTQRHRcodtwBaa1bQA0kOofaLgKtbyWSfJzEeB2fxnDtCyPml6v3kjTTlVNOMT1PMywo1AI1UquVddV4V/B2jk5IO8RnapSDxxO6Kzt4VvTSpzGZFIQMGpA+cqpOBdYQks/yJ0GM14XjhyV8+vTJya3cB4jceFQmafzwCB4X14qwWtd4pKihYjz48FhfODLJMyyLsz23zLGp/ExNudgdY5lW9inObZJHSxPBLWhWDEoApVVBXI2kQTxkOxNwmzoCy/TEuBSEZytrvzBCvqveMxI1ibJx4sOZCb4AeIfzyWVFXFDpyyNvIuJz+ExNHJPzHKgEUVTnMT/6rkSFNYTOYYpoHcI/iMdJgfEPnP4QcrCCw77XKI5p7RdGqMNFkWumUB/pnbSmIbWKGi4Aq0kLHmXS4szTDxGm7WBnrWlrm8/UpHlIIKIBB20jfeSKdVKGkHwJpUMKDOGhf9Y7RBMOqS7yjPKFEfp6Gz5Tk/CQQIRU/R2ntkp81KwBLE7Iqfpjbb1z/UzNeTPRSW5n2cyriToEsNRC5tUAVpax8thQmXds8bSayo4xbs+xdibKu5XN8TM1cZdJJkuuiRzwY1w6RCKsIanh6VlIQt4GyWpk+rh8CvSSTj7MO8o4XQDaStFeyrO3ClNXxybaqoIDMdPPvsPlWuvBtKFMWoDheiwJahqR5UGhj4Hx9yGC233gsqnLE9wv8oasSCJNo1M026YTpuErQD3jSAAmfhCiTTXO8oWRgdJu5s+jCk2cT+S/quKy/qt9/gM26vsPd7HPOpe62v/Ybyp5MK/vcU/z9nObKOJqH75XR66Ruo8alzomlwDax51DE6e1Y5w1GbXzoV077pbVewtu1wqXckchAX8z83OEyR6yV2oi7aC9Pr7TiHZO9cdD8hY6TQn4hO7DSIs+Z8L3CqCsdn/19oT5JJ5Qsn4vwR4JgBN3FrWQCqJFo768SIOeCHuIWft5oNWZJXKcEmD+fKz38LTXzh3LsA7SoFG1kI/uPs4vdLoScBvrXD2JwxlVE8VGFn/eEvgfbzZ3sGoIEekAAAAASUVORK5CYII=\n",
       "text/latex": [
        "$\\displaystyle \\frac{\\alpha e^{\\alpha \\left(C_{1} + t\\right)}}{\\beta \\left(1 - e^{\\alpha \\left(C_{1} + t\\right)}\\right)}$"
       ],
@@ -1006,33 +1022,15 @@
    "source": [
     "# Solution\n",
     "\n",
-    "general = solution_eq.rhs"
+    "general = solution_eq.rhs\n",
+    "general"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{\\alpha e^{C_{1} \\alpha}}{\\beta \\left(1 - e^{C_{1} \\alpha}\\right)}$"
-      ],
-      "text/plain": [
-       "      C₁⋅α   \n",
-       "   α⋅ℯ       \n",
-       "─────────────\n",
-       "  ⎛     C₁⋅α⎞\n",
-       "β⋅⎝1 - ℯ    ⎠"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Solution\n",
     "\n",
@@ -1046,7 +1044,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle \\frac{\\log{\\left(\\frac{\\beta p_{0}}{\\alpha + \\beta p_{0}} \\right)}}{\\alpha}$"
       ],
@@ -1067,7 +1065,8 @@
     "# Solution\n",
     "\n",
     "solutions = solve(Eq(at_0, p_0), C1)\n",
-    "value_of_C1 = solutions[0]"
+    "value_of_C1 = solutions[0]\n",
+    "value_of_C1"
    ]
   },
   {
@@ -1077,7 +1076,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/latex": [
        "$\\displaystyle \\frac{\\alpha p_{0} e^{\\alpha t}}{\\alpha - \\beta p_{0} e^{\\alpha t} + \\beta p_{0}}$"
       ],
@@ -1140,7 +1139,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,

From d47ab8edafceb6efd1aa51d849c23626c4ec516d Mon Sep 17 00:00:00 2001
From: Abhishek Jaisingh <abhi2254015@gmail.com>
Date: Tue, 15 Dec 2020 12:40:35 +0530
Subject: [PATCH 007/144] Fix Broken Book Zip Files Link

In Chapter 0, Section 0.6 - Copying my files, there is still reference to a broken link (http://modsimpy.com/zip). Probably the domain name itself is expired.
The best thing is to use the Github zip link itself (https://github.com/AllenDowney/ModSimPy/archive/master.zip), which is guaranteed to be working.
---
 book/book.tex | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/book/book.tex b/book/book.tex
index 75656585..0b0274e3 100644
--- a/book/book.tex
+++ b/book/book.tex
@@ -448,7 +448,7 @@ \section{Installing Python}
 
 \section{Copying my files}
 
-The simplest way to get the files for this book is to download a Zip archive from \url{http://modsimpy.com/zip}.  You will need a program like WinZip or gzip to unpack the Zip file.  Make a note of the location of the files you unpack.
+The simplest way to get the files for this book is to download a Zip archive from \url{https://github.com/AllenDowney/ModSimPy/archive/master.zip}.  You will need a program like WinZip or gzip to unpack the Zip file.  Make a note of the location of the files you unpack.
 
 If you download the Zip file, you can skip the rest of this section, which explains how to use Git.
 

From aaa56907ca877e42ce4ccdfce426ded54cadfd72 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 22 Jan 2021 15:47:41 -0500
Subject: [PATCH 008/144] Updating notebooks

---
 colab/chap01soln.ipynb |  522 +++++++++++++++++
 colab/chap02soln.ipynb |  917 +++++++++++++++++++++++++++++
 colab/chap03soln.ipynb |  595 +++++++++++++++++++
 colab/chap04soln.ipynb |  667 ++++++++++++++++++++++
 colab/chap05soln.ipynb |  594 +++++++++++++++++++
 colab/chap06soln.ipynb |  527 +++++++++++++++++
 colab/chap07soln.ipynb |  523 +++++++++++++++++
 colab/chap08soln.ipynb |  617 ++++++++++++++++++++
 colab/chap09soln.ipynb |  638 +++++++++++++++++++++
 colab/chap10soln.ipynb |  447 +++++++++++++++
 colab/chap11soln.ipynb |  463 +++++++++++++++
 colab/chap12soln.ipynb |  877 ++++++++++++++++++++++++++++
 colab/chap13ode.ipynb  |  180 ++++++
 colab/chap13soln.ipynb |  517 +++++++++++++++++
 colab/chap14soln.ipynb |  475 +++++++++++++++
 colab/chap15soln.ipynb |  318 +++++++++++
 colab/chap16soln.ipynb |  842 +++++++++++++++++++++++++++
 colab/chap17soln.ipynb |  273 +++++++++
 colab/chap18soln.ipynb |  533 +++++++++++++++++
 colab/chap20soln.ipynb |  643 +++++++++++++++++++++
 colab/chap21soln.ipynb |  514 +++++++++++++++++
 colab/chap22soln.ipynb | 1238 ++++++++++++++++++++++++++++++++++++++++
 colab/chap23soln.ipynb |  571 ++++++++++++++++++
 colab/chap24soln.ipynb |  640 +++++++++++++++++++++
 colab/chap25soln.ipynb |  944 ++++++++++++++++++++++++++++++
 25 files changed, 15075 insertions(+)
 create mode 100644 colab/chap01soln.ipynb
 create mode 100644 colab/chap02soln.ipynb
 create mode 100644 colab/chap03soln.ipynb
 create mode 100644 colab/chap04soln.ipynb
 create mode 100644 colab/chap05soln.ipynb
 create mode 100644 colab/chap06soln.ipynb
 create mode 100644 colab/chap07soln.ipynb
 create mode 100644 colab/chap08soln.ipynb
 create mode 100644 colab/chap09soln.ipynb
 create mode 100644 colab/chap10soln.ipynb
 create mode 100644 colab/chap11soln.ipynb
 create mode 100644 colab/chap12soln.ipynb
 create mode 100644 colab/chap13ode.ipynb
 create mode 100644 colab/chap13soln.ipynb
 create mode 100644 colab/chap14soln.ipynb
 create mode 100644 colab/chap15soln.ipynb
 create mode 100644 colab/chap16soln.ipynb
 create mode 100644 colab/chap17soln.ipynb
 create mode 100644 colab/chap18soln.ipynb
 create mode 100644 colab/chap20soln.ipynb
 create mode 100644 colab/chap21soln.ipynb
 create mode 100644 colab/chap22soln.ipynb
 create mode 100644 colab/chap23soln.ipynb
 create mode 100644 colab/chap24soln.ipynb
 create mode 100644 colab/chap25soln.ipynb

diff --git a/colab/chap01soln.ipynb b/colab/chap01soln.ipynb
new file mode 100644
index 00000000..0dfd8650
--- /dev/null
+++ b/colab/chap01soln.ipynb
@@ -0,0 +1,522 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Modeling and Simulation in Python\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Jupyter\n",
+    "\n",
+    "Welcome to *Modeling and Simulation in Python*, and welcome to Jupyter.\n",
+    "\n",
+    "This is a Jupyter notebook, which is a development environment where you can write and run Python code.  Each notebook is divided into cells.  Each cell contains either text (like this cell) or Python code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Selecting and running cells\n",
+    "\n",
+    "To select a cell, click in the left margin next to the cell.  You should see a blue frame surrounding the selected cell.\n",
+    "\n",
+    "To edit a code cell, click inside the cell.  You should see a green frame around the selected cell, and you should see a cursor inside the cell.\n",
+    "\n",
+    "To edit a text cell, double-click inside the cell.  Again, you should see a green frame around the selected cell, and you should see a cursor inside the cell.\n",
+    "\n",
+    "To run a cell, hold down SHIFT and press ENTER.  \n",
+    "\n",
+    "* If you run a text cell, Jupyter formats the text and displays the result.\n",
+    "\n",
+    "* If you run a code cell, Jupyter runs the Python code in the cell and displays the result, if any.\n",
+    "\n",
+    "To try it out, edit this cell, change some of the text, and then press SHIFT-ENTER to format it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding and removing cells\n",
+    "\n",
+    "You can add and remove cells from a notebook using the buttons in the toolbar and the items in the menu, both of which you should see at the top of this notebook.\n",
+    "\n",
+    "Try the following exercises:\n",
+    "\n",
+    "1.  From the Insert menu select \"Insert cell below\" to add a cell below this one.  By default, you get a code cell, as you can see in the pulldown menu that says \"Code\".\n",
+    "\n",
+    "2.  In the new cell, add a print statement like `print('Hello')`, and run it.\n",
+    "\n",
+    "3.  Add another cell, select the new cell, and then click on the pulldown menu that says \"Code\" and select \"Markdown\".  This makes the new cell a text cell.\n",
+    "\n",
+    "4.  In the new cell, type some text, and then run it.\n",
+    "\n",
+    "5.  Use the arrow buttons in the toolbar to move cells up and down.\n",
+    "\n",
+    "6.  Use the cut, copy, and paste buttons to delete, add, and move cells.\n",
+    "\n",
+    "7.  As you make changes, Jupyter saves your notebook automatically, but if you want to make sure, you can press the save button, which looks like a floppy disk from the 1990s.\n",
+    "\n",
+    "8.  Finally, when you are done with a notebook, select \"Close and Halt\" from the File menu."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using the notebooks\n",
+    "\n",
+    "The notebooks for each chapter contain the code from the chapter along with additional examples, explanatory text, and exercises.  I recommend you \n",
+    "\n",
+    "1. Read the chapter first to understand the concepts and vocabulary, \n",
+    "2. Run the notebook to review what you learned and see it in action, and then\n",
+    "3. Attempt the exercises.\n",
+    "\n",
+    "If you try to work through the notebooks without reading the book, you're gonna have a bad time.  The notebooks contain some explanatory text, but it is probably not enough to make sense if you have not read the book.  If you are working through a notebook and you get stuck, you might want to re-read (or read!) the corresponding section of the book."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installing modules\n",
+    "\n",
+    "These notebooks use standard Python modules like NumPy and SciPy.  I assume you already have them installed in your environment.\n",
+    "\n",
+    "They also use two less common modules: Pint, which provides units, and modsim, which contains code I wrote specifically for this book.\n",
+    "\n",
+    "The following cells check whether you have these modules already and tries to install them if you don't."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first time you run this on a new installation of Python, it might produce a warning message in pink.  That's probably ok, but if you get a message that says `modsim.py depends on Python 3.7 features`, that means you have an older version of Python, and some features in `modsim.py` won't work correctly.\n",
+    "\n",
+    "If you need a newer version of Python, I recommend installing Anaconda.  You'll find more information in the preface of the book.\n",
+    "\n",
+    "You can find out what version of Python and Jupyter you have by running the following cells."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python --version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!jupyter-notebook --version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The penny myth\n",
+    "\n",
+    "The following cells contain code from the beginning of Chapter 1.\n",
+    "\n",
+    "`modsim` defines `UNITS`, which contains variables representing pretty much every unit you've ever heard of.  It uses [Pint](https://pint.readthedocs.io/en/latest/), which is a Python library that provides tools for computing with units.\n",
+    "\n",
+    "The following lines create new variables named `meter` and `second`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meter = UNITS.meter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "second = UNITS.second"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To find out what other units are defined, type `UNITS.` (including the period) in the next cell and then press TAB.  You should see a pop-up menu with a list of units."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a variable named `a` and give it the value of acceleration due to gravity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 9.8 * meter / second**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create `t` and give it the value 4 seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = 4 * second"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute the distance a penny would fall after `t` seconds with constant acceleration `a`.  Notice that the units of the result are correct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a * t**2 / 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise**: Compute the velocity of the penny after `t` seconds.  Check that the units of the result are correct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise**: Why would it be nonsensical to add `a` and `t`? What happens if you try?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The error messages you get from Python are big and scary, but if you read them carefully, they contain a lot of useful information.\n",
+    "\n",
+    "1.  Start from the bottom and read up.\n",
+    "2.  The last line usually tells you what type of error happened, and sometimes additional information.\n",
+    "3.  The previous lines are a \"traceback\" of what was happening when the error occurred.  The first section of the traceback shows the code you wrote.  The following sections are often from Python libraries.\n",
+    "\n",
+    "In this example, you should get a `DimensionalityError`, which is defined by Pint to indicate that you have violated a rules of dimensional analysis: you cannot add quantities with different dimensions.\n",
+    "\n",
+    "Before you go on, you might want to delete the erroneous code so the notebook can run without errors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Falling pennies\n",
+    "\n",
+    "Now let's solve the falling penny problem.\n",
+    "\n",
+    "Set `h` to the height of the Empire State Building:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = 381 * meter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute the time it would take a penny to fall, assuming constant acceleration.\n",
+    "\n",
+    "$ a t^2 / 2 = h $\n",
+    "\n",
+    "$ t = \\sqrt{2 h / a}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = sqrt(2 * h / a)\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given `t`, we can compute the velocity of the penny when it lands.\n",
+    "\n",
+    "$v = a t$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = a * t\n",
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can convert from one set of units to another like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mile = UNITS.mile\n",
+    "hour = UNITS.hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v.to(mile/hour)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose you bring a 10 foot pole to the top of the Empire State Building and use it to drop the penny from `h` plus 10 feet.\n",
+    "\n",
+    "Define a variable named `foot` that contains the unit `foot` provided by `UNITS`.  Define a variable named `pole_height` and give it the value 10 feet.\n",
+    "\n",
+    "What happens if you add `h`, which is in units of meters, to `pole_height`, which is in units of feet?  What happens if you write the addition the other way around?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** In reality, air resistance limits the velocity of the penny.  At about 18 m/s, the force of air resistance equals the force of gravity and the penny stops accelerating.\n",
+    "\n",
+    "As a simplification, let's assume that the acceleration of the penny is `a` until the penny reaches 18 m/s, and then 0 afterwards.  What is the total time for the penny to fall 381 m?\n",
+    "\n",
+    "You can break this question into three parts:\n",
+    "\n",
+    "1. How long until the penny reaches 18 m/s with constant acceleration `a`.\n",
+    "2. How far would the penny fall during that time?\n",
+    "3. How long to fall the remaining distance with constant velocity 18 m/s?\n",
+    "\n",
+    "Suggestion: Assign each intermediate result to a variable with a meaningful name.  And assign units to all quantities!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Restart and run all\n",
+    "\n",
+    "When you change the contents of a cell, you have to run it again for those changes to have an effect.  If you forget to do that, the results can be confusing, because the code you are looking at is not the code you ran.\n",
+    "\n",
+    "If you ever lose track of which cells have run, and in what order, you should go to the Kernel menu and select \"Restart & Run All\".  Restarting the kernel means that all of your variables get deleted, and running all the cells means all of your code will run again, in the right order.\n",
+    "\n",
+    "**Exercise:**  Select \"Restart & Run All\" now and confirm that it does what you want."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap02soln.ipynb b/colab/chap02soln.ipynb
new file mode 100644
index 00000000..f6b97493
--- /dev/null
+++ b/colab/chap02soln.ipynb
@@ -0,0 +1,917 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 2\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim library\n",
+    "from modsim import *\n",
+    "\n",
+    "# set the random number generator\n",
+    "np.random.seed(7)\n",
+    "\n",
+    "# If this cell runs successfully, it produces no output."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modeling a bikeshare system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll start with a `State` object that represents the number of bikes at each station.\n",
+    "\n",
+    "When you display a `State` object, it lists the state variables and their values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=10, wellesley=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can access the state variables using dot notation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare.olin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "bikeshare.wellesley"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** What happens if you spell the name of a state variable wrong?  Edit the previous cell, change the spelling of `wellesley`, and run the cell again.\n",
+    "\n",
+    "The error message uses the word \"attribute\", which is another name for what we are calling a state variable. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Add a third attribute called `babson` with initial value 0, and display the state of `bikeshare` again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Updating\n",
+    "\n",
+    "We can use the update operators `+=` and `-=` to change state variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare.olin -= 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we display `bikeshare`, we should see the change."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, if we subtract a bike from `olin`, we should add it to `wellesley`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare.wellesley += 1\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Functions\n",
+    "\n",
+    "We can take the code we've written so far and encapsulate it in a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley():\n",
+    "    bikeshare.olin -= 1\n",
+    "    bikeshare.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you define a function, it doesn't run the statements inside the function, yet.  When you call the function, it runs the statements inside."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bike_to_wellesley()\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "One common error is to omit the parentheses, which has the effect of looking up the function, but not calling it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bike_to_wellesley"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output indicates that `bike_to_wellesley` is a function defined in a \"namespace\" called `__main__`, but you don't have to understand what that means."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Define a function called `bike_to_olin` that moves a bike from Wellesley to Olin.  Call the new function and display `bikeshare`  to confirm that it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conditionals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`modsim.py` provides `flip`, which takes a probability and returns either `True` or `False`, which are special values defined by Python.\n",
+    "\n",
+    "The Python function `help` looks up a function and displays its documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(flip)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following example, the probability is 0.7 or 70%.  If you run this cell several times, you should get `True` about 70% of the time and `False` about 30%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flip(0.7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following example, we use `flip` as part of an if statement.  If the result from `flip` is `True`, we print `heads`; otherwise we do nothing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if flip(0.7):\n",
+    "    print('heads')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With an else clause, we can print heads or tails depending on whether `flip` returns `True` or `False`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if flip(0.7):\n",
+    "    print('heads')\n",
+    "else:\n",
+    "    print('tails')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step\n",
+    "\n",
+    "Now let's get back to the bikeshare state.  Again let's start with a new `State` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=10, wellesley=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Suppose that in any given minute, there is a 50% chance that a student picks up a bike at Olin and rides to Wellesley.  We can simulate that like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if flip(0.5):\n",
+    "    bike_to_wellesley()\n",
+    "    print('Moving a bike to Wellesley')\n",
+    "\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And maybe at the same time, there is also a 40% chance that a student at Wellesley rides to Olin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if flip(0.4):\n",
+    "    bike_to_olin()\n",
+    "    print('Moving a bike to Olin')\n",
+    "\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can wrap that code in a function called `step` that simulates one time step.  In any given minute, a student might ride from Olin to Wellesley, from Wellesley to Olin, or both, or neither, depending on the results of `flip`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def step():\n",
+    "    if flip(0.5):\n",
+    "        bike_to_wellesley()\n",
+    "        print('Moving a bike to Wellesley')\n",
+    "        \n",
+    "    if flip(0.4):\n",
+    "        bike_to_olin()\n",
+    "        print('Moving a bike to Olin')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since this function takes no parameters, we call it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step()\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parameters\n",
+    "\n",
+    "As defined in the previous section, `step` is not as useful as it could be, because the probabilities `0.5` and `0.4` are \"hard coded\".\n",
+    "\n",
+    "It would be better to generalize this function so it takes the probabilities `p1` and `p2` as parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def step(p1, p2):\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley()\n",
+    "        print('Moving a bike to Wellesley')\n",
+    "        \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin()\n",
+    "        print('Moving a bike to Olin')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can call it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step(0.5, 0.4)\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** At the beginning of `step`, add a print statement that displays the values of `p1` and `p2`.  Call it again with values `0.3`, and `0.2`, and confirm that the values of the parameters are what you expect. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## For loop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we go on, I'll redefine `step` without the print statements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def step(p1, p2):\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley()\n",
+    "    \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And let's start again with a new `State` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=10, wellesley=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use a `for` loop to move 4 bikes from Olin to Wellesley."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(4):\n",
+    "    bike_to_wellesley()\n",
+    "    \n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or we can simulate 4 random time steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(4):\n",
+    "    step(0.3, 0.2)\n",
+    "    \n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If each step corresponds to a minute, we can simulate an entire hour like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(60):\n",
+    "    step(0.3, 0.2)\n",
+    "\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After 60 minutes, you might see that the number of bike at Olin is negative.  We'll fix that problem in the next notebook.\n",
+    "\n",
+    "But first, we want to plot the results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TimeSeries\n",
+    "\n",
+    "`modsim.py` provides an object called a `TimeSeries` that can contain a sequence of values changing over time.\n",
+    "\n",
+    "We can create a new, empty `TimeSeries` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = TimeSeries()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can add a value to the `TimeSeries` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results[0] = bikeshare.olin\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `0` in brackets is an `index` that indicates that this value is associated with time step 0.\n",
+    "\n",
+    "Now we'll use a for loop to save the results of the simulation.  I'll start one more time with a new `State` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=10, wellesley=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a for loop that runs 10 steps and stores the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(10):\n",
+    "    step(0.3, 0.2)\n",
+    "    results[i] = bikeshare.olin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can display the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `TimeSeries` is a specialized version of a Pandas `Series`, so we can use any of the functions provided by `Series`, including several that compute summary statistics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can read the documentation of `Series` [here](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting\n",
+    "\n",
+    "We can also plot the results like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(results, label='Olin')\n",
+    "\n",
+    "decorate(title='Olin-Wellesley Bikeshare',\n",
+    "         xlabel='Time step (min)', \n",
+    "         ylabel='Number of bikes')\n",
+    "\n",
+    "savefig('figs/chap02-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`decorate`, which is defined in the `modsim` library, adds a title and labels the axes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(decorate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`savefig()` saves a figure in a file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(savefig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The suffix of the filename indicates the format you want.  This example saves the current figure in a PDF file named `chap01-fig01.pdf`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Wrap the code from this section in a function named `run_simulation` that takes three parameters, named `p1`, `p2`, and `num_steps`.\n",
+    "\n",
+    "It should:\n",
+    "\n",
+    "1. Create a `TimeSeries` object to hold the results.\n",
+    "2. Use a for loop to run `step` the number of times specified by `num_steps`, passing along the specified values of `p1` and `p2`.\n",
+    "3. After each step, it should save the number of bikes at Olin in the `TimeSeries`.\n",
+    "4. After the for loop, it should plot the results and\n",
+    "5. Decorate the axes.\n",
+    "\n",
+    "To test your function:\n",
+    "\n",
+    "1. Create a `State` object with the initial state of the system.\n",
+    "2. Call `run_simulation` with appropriate parameters.\n",
+    "3. Save the resulting figure.\n",
+    "\n",
+    "Optional:\n",
+    "\n",
+    "1. Extend your solution so it creates two `TimeSeries` objects, keeps track of the number of bikes at Olin *and* at Wellesley, and plots both series at the end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Opening the hood\n",
+    "\n",
+    "The functions in `modsim.py` are built on top of several widely-used Python libraries, especially NumPy, SciPy, and Pandas.  These libraries are powerful but can be hard to use.  The intent of `modsim.py` is to give you the power of these libraries while making it easy to get started.\n",
+    "\n",
+    "In the future, you might want to use these libraries directly, rather than using `modsim.py`.  So we will pause occasionally to open the hood and let you see how `modsim.py` works.\n",
+    "\n",
+    "You don't need to know anything in these sections, so if you are already feeling overwhelmed, you might want to skip them.  But if you are curious, read on."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pandas\n",
+    "\n",
+    "This chapter introduces two objects, `State` and `TimeSeries`.  Both are based on the `Series` object defined by Pandas, which is a library primarily used for data science.\n",
+    "\n",
+    "You can read the documentation of the `Series` object [here](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html)\n",
+    "\n",
+    "The primary differences between `TimeSeries` and `Series` are:\n",
+    "\n",
+    "1. I made it easier to create a new, empty `Series` while avoiding a [confusing inconsistency](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html).\n",
+    "\n",
+    "2. I provide a function so the `Series` looks good when displayed in Jupyter.\n",
+    "\n",
+    "3. I provide a function called `set` that we'll use later.\n",
+    "\n",
+    "`State` has all of those capabilities; in addition, it provides an easier way to initialize state variables, and it provides functions called `T` and `dt`, which will help us avoid a confusing error later."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pyplot\n",
+    "\n",
+    "The `plot` function in `modsim.py` is based on the `plot` function in Pyplot, which is part of Matplotlib.  You can read the documentation of `plot` [here](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html).\n",
+    "\n",
+    "`decorate` provides a convenient way to call the `pyplot` functions `title`, `xlabel`, and `ylabel`, and `legend`.  It also avoids an annoying warning message if you try to make a legend when you don't have any labelled lines."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(decorate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### NumPy\n",
+    "\n",
+    "The `flip` function in `modsim.py` uses NumPy's `random` function to generate a random number between 0 and 1.\n",
+    "\n",
+    "You can get the source code for `flip` by running the following cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_code(flip)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap03soln.ipynb b/colab/chap03soln.ipynb
new file mode 100644
index 00000000..82a9d53a
--- /dev/null
+++ b/colab/chap03soln.ipynb
@@ -0,0 +1,595 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 3\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim library\n",
+    "from modsim import *\n",
+    "\n",
+    "# set the random number generator\n",
+    "np.random.seed(7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More than one State object\n",
+    "\n",
+    "Here's the code from the previous chapter, with two changes:\n",
+    "\n",
+    "1. I've added DocStrings that explain what each function does, and what parameters it takes.\n",
+    "\n",
+    "2. I've added a parameter named `state` to the functions so they work with whatever `State` object we give them, instead of always using `bikeshare`.  That makes it possible to work with more than one `State` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def step(state, p1, p2):\n",
+    "    \"\"\"Simulate one minute of time.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    p1: probability of an Olin->Wellesley customer arrival\n",
+    "    p2: probability of a Wellesley->Olin customer arrival\n",
+    "    \"\"\"\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley(state)\n",
+    "    \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin(state)\n",
+    "        \n",
+    "def bike_to_wellesley(state):\n",
+    "    \"\"\"Move one bike from Olin to Wellesley.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    state.olin -= 1\n",
+    "    state.wellesley += 1\n",
+    "    \n",
+    "def bike_to_olin(state):\n",
+    "    \"\"\"Move one bike from Wellesley to Olin.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    state.wellesley -= 1\n",
+    "    state.olin += 1\n",
+    "    \n",
+    "def decorate_bikeshare():\n",
+    "    \"\"\"Add a title and label the axes.\"\"\"\n",
+    "    decorate(title='Olin-Wellesley Bikeshare',\n",
+    "             xlabel='Time step (min)', \n",
+    "             ylabel='Number of bikes')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's `run_simulation`, which is a solution to the exercise at the end of the previous notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(state, p1, p2, num_steps):\n",
+    "    \"\"\"Simulate the given number of time steps.\n",
+    "    \n",
+    "    state: State object\n",
+    "    p1: probability of an Olin->Wellesley customer arrival\n",
+    "    p2: probability of a Wellesley->Olin customer arrival\n",
+    "    num_steps: number of time steps\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()    \n",
+    "    for i in range(num_steps):\n",
+    "        step(state, p1, p2)\n",
+    "        results[i] = state.olin\n",
+    "        \n",
+    "    plot(results, label='Olin')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create more than one `State` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare1 = State(olin=10, wellesley=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare2 = State(olin=2, wellesley=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whenever we call a function, we indicate which `State` object to work with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bike_to_olin(bikeshare1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bike_to_wellesley(bikeshare2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And you can confirm that the different objects are getting updated independently:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Negative bikes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the code we have so far, the number of bikes at one of the locations can go negative, and the number of bikes at the other location can exceed the actual number of bikes in the system.\n",
+    "\n",
+    "If you run this simulation a few times, it happens often."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=10, wellesley=2)\n",
+    "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
+    "decorate_bikeshare()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can fix this problem using the `return` statement to exit the function early if an update would cause negative bikes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley(state):\n",
+    "    \"\"\"Move one bike from Olin to Wellesley.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    if state.olin == 0:\n",
+    "        return\n",
+    "    state.olin -= 1\n",
+    "    state.wellesley += 1\n",
+    "    \n",
+    "def bike_to_olin(state):\n",
+    "    \"\"\"Move one bike from Wellesley to Olin.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    if state.wellesley == 0:\n",
+    "        return\n",
+    "    state.wellesley -= 1\n",
+    "    state.olin += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now if you run the simulation again, it should behave."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=10, wellesley=2)\n",
+    "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
+    "decorate_bikeshare()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparison operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `if` statements in the previous section used the comparison operator `==`.  The other comparison operators are listed in the book.\n",
+    "\n",
+    "It is easy to confuse the comparison operator `==` with the assignment operator `=`.\n",
+    "\n",
+    "Remember that `=` creates a variable or gives an existing variable a new value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whereas `==` compares two values and returns `True` if they are equal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x == 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use `==` in an `if` statement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if x == 5:\n",
+    "    print('yes, x is 5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But if you use `=` in an `if` statement, you get an error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If you remove the # from the if statement and run it, you'll get\n",
+    "# SyntaxError: invalid syntax\n",
+    "\n",
+    "#if x = 5:\n",
+    "#    print('yes, x is 5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Add an `else` clause to the `if` statement above, and print an appropriate message.\n",
+    "\n",
+    "Replace the `==` operator with one or two of the other comparison operators, and confirm they do what you expect."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have a working simulation, we'll use it to evaluate alternative designs and see how good or bad they are.  The metric we'll use is the number of customers who arrive and find no bikes available, which might indicate a design problem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we'll make a new `State` object that creates and initializes additional state variables to keep track of the metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=10, wellesley=2, \n",
+    "                  olin_empty=0, wellesley_empty=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we need versions of `bike_to_wellesley` and `bike_to_olin` that update the metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley(state):\n",
+    "    \"\"\"Move one bike from Olin to Wellesley.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    if state.olin == 0:\n",
+    "        state.olin_empty += 1\n",
+    "        return\n",
+    "    state.olin -= 1\n",
+    "    state.wellesley += 1\n",
+    "    \n",
+    "def bike_to_olin(state):\n",
+    "    \"\"\"Move one bike from Wellesley to Olin.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    if state.wellesley == 0:\n",
+    "        state.wellesley_empty += 1\n",
+    "        return\n",
+    "    state.wellesley -= 1\n",
+    "    state.olin += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now when we run a simulation, it keeps track of unhappy customers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
+    "decorate_bikeshare()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the simulation, we can print the number of unhappy customers at each location."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare.olin_empty"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare.wellesley_empty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "**Exercise:** As another metric, we might be interested in the time until the first customer arrives and doesn't find a bike.  To make that work, we have to add a \"clock\" to keep track of how many time steps have elapsed:\n",
+    "\n",
+    "1. Create a new `State` object with an additional state variable, `clock`, initialized to 0. \n",
+    "\n",
+    "2. Write a modified version of `step` that adds one to the clock each time it is invoked.\n",
+    "\n",
+    "Test your code by running the simulation and check the value of `clock` at the end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=10, wellesley=2, \n",
+    "                  olin_empty=0, wellesley_empty=0,\n",
+    "                  clock=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Continuing the previous exercise, let's record the time when the first customer arrives and doesn't find a bike.\n",
+    "\n",
+    "1. Create a new `State` object with an additional state variable, `t_first_empty`, initialized to -1 as a special value to indicate that it has not been set. \n",
+    "\n",
+    "2. Write a modified version of `step` that checks whether`olin_empty` and `wellesley_empty` are 0.  If not, it should set `t_first_empty` to `clock` (but only if `t_first_empty` has not already been set).\n",
+    "\n",
+    "Test your code by running the simulation and printing the values of `olin_empty`, `wellesley_empty`, and `t_first_empty` at the end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap04soln.ipynb b/colab/chap04soln.ipynb
new file mode 100644
index 00000000..1945f357
--- /dev/null
+++ b/colab/chap04soln.ipynb
@@ -0,0 +1,667 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 4\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim library\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Returning values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a simple function that returns a value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_five(x):\n",
+    "    return x + 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's how we call it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = add_five(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you run a function on the last line of a cell, Jupyter displays the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "add_five(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But that can be a bad habit, because usually if you call a function and don't assign the result in a variable, the result gets discarded.\n",
+    "\n",
+    "In the following example, Jupyter shows the second result, but the first result just disappears."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "add_five(3)\n",
+    "add_five(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you call a function that returns a variable, it is generally a good idea to assign the result to a variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y1 = add_five(3)\n",
+    "y2 = add_five(5)\n",
+    "\n",
+    "print(y1, y2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a function called `make_state` that creates a `State` object with the state variables `olin=10` and `wellesley=2`, and then returns the new `State` object.\n",
+    "\n",
+    "Write a line of code that calls `make_state` and assigns the result to a variable named `init`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running simulations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the code from the previous notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def step(state, p1, p2):\n",
+    "    \"\"\"Simulate one minute of time.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    p1: probability of an Olin->Wellesley customer arrival\n",
+    "    p2: probability of a Wellesley->Olin customer arrival\n",
+    "    \"\"\"\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley(state)\n",
+    "    \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin(state)\n",
+    "        \n",
+    "def bike_to_wellesley(state):\n",
+    "    \"\"\"Move one bike from Olin to Wellesley.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    if state.olin == 0:\n",
+    "        state.olin_empty += 1\n",
+    "        return\n",
+    "    state.olin -= 1\n",
+    "    state.wellesley += 1\n",
+    "    \n",
+    "def bike_to_olin(state):\n",
+    "    \"\"\"Move one bike from Wellesley to Olin.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    if state.wellesley == 0:\n",
+    "        state.wellesley_empty += 1\n",
+    "        return\n",
+    "    state.wellesley -= 1\n",
+    "    state.olin += 1\n",
+    "    \n",
+    "def decorate_bikeshare():\n",
+    "    \"\"\"Add a title and label the axes.\"\"\"\n",
+    "    decorate(title='Olin-Wellesley Bikeshare',\n",
+    "             xlabel='Time step (min)', \n",
+    "             ylabel='Number of bikes')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a modified version of `run_simulation` that creates a `State` object, runs the simulation, and returns the `State` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(p1, p2, num_steps):\n",
+    "    \"\"\"Simulate the given number of time steps.\n",
+    "    \n",
+    "    p1: probability of an Olin->Wellesley customer arrival\n",
+    "    p2: probability of a Wellesley->Olin customer arrival\n",
+    "    num_steps: number of time steps\n",
+    "    \"\"\"\n",
+    "    state = State(olin=10, wellesley=2, \n",
+    "                  olin_empty=0, wellesley_empty=0)\n",
+    "                    \n",
+    "    for i in range(num_steps):\n",
+    "        step(state, p1, p2)\n",
+    "        \n",
+    "    return state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now `run_simulation` doesn't plot anything:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state = run_simulation(0.4, 0.2, 60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But after the simulation, we can read the metrics from the `State` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state.olin_empty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run simulations with different values for the parameters.  When `p1` is small, we probably don't run out of bikes at Olin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state = run_simulation(0.2, 0.2, 60)\n",
+    "state.olin_empty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When `p1` is large, we probably do."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state = run_simulation(0.6, 0.2, 60)\n",
+    "state.olin_empty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More for loops"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`linspace` creates a NumPy array of equally spaced numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p1_array = linspace(0, 1, 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use an array in a `for` loop, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for p1 in p1_array:\n",
+    "    print(p1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will come in handy in the next section.\n",
+    "\n",
+    "`linspace` is defined in `modsim.py`.  You can get the documentation using `help`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(linspace)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`linspace` is based on a NumPy function with the same name.  [Click here](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html) to read more about how to use it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** \n",
+    "Use `linspace` to make an array of 10 equally spaced numbers from 1 to 10 (including both)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The `modsim` library provides a related function called `linrange`.  You can view the documentation by running the following cell:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(linrange)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use `linrange` to make an array of numbers from 1 to 11 with a step size of 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sweeping parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`p1_array` contains a range of values for `p1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p2 = 0.2\n",
+    "num_steps = 60\n",
+    "p1_array = linspace(0, 1, 11)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following loop runs a simulation for each value of `p1` in `p1_array`; after each simulation, it prints the number of unhappy customers at the Olin station:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for p1 in p1_array:\n",
+    "    state = run_simulation(p1, p2, num_steps)\n",
+    "    print(p1, state.olin_empty)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can do the same thing, but storing the results in a `SweepSeries` instead of printing them.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sweep = SweepSeries()\n",
+    "\n",
+    "for p1 in p1_array:\n",
+    "    state = run_simulation(p1, p2, num_steps)\n",
+    "    sweep[p1] = state.olin_empty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then we can plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(sweep, label='Olin')\n",
+    "\n",
+    "decorate(title='Olin-Wellesley Bikeshare',\n",
+    "         xlabel='Arrival rate at Olin (p1 in customers/min)', \n",
+    "         ylabel='Number of unhappy customers')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "**Exercise:** Wrap this code in a function named `sweep_p1` that takes an array called `p1_array` as a parameter.  It should create a new `SweepSeries`, run a simulation for each value of `p1` in `p1_array`, store the results in the `SweepSeries`, and return the `SweepSeries`.\n",
+    "\n",
+    "Use your function to plot the number of unhappy customers at Olin as a function of `p1`.  Label the axes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a function called `sweep_p2` that runs simulations with `p1=0.5` and a range of values for `p2`.  It should store the results in a `SweepSeries` and return the `SweepSeries`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optional Exercises\n",
+    "\n",
+    "The following two exercises are a little more challenging.  If you are comfortable with what you have learned so far, you should give them a try.  If you feel like you have your hands full, you might want to skip them for now.\n",
+    "\n",
+    "**Exercise:** Because our simulations are random, the results vary from one run to another, and the results of a parameter sweep tend to be noisy.  We can get a clearer picture of the relationship between a parameter and a metric by running multiple simulations with the same parameter and taking the average of the results.\n",
+    "\n",
+    "Write a function called `run_multiple_simulations` that takes as parameters `p1`, `p2`, `num_steps`, and `num_runs`.\n",
+    "\n",
+    "`num_runs` specifies how many times it should call `run_simulation`.\n",
+    "\n",
+    "After each run, it should store the total number of unhappy customers (at Olin or Wellesley) in a `TimeSeries`.  At the end, it should return the `TimeSeries`.\n",
+    "\n",
+    "Test your function with parameters\n",
+    "\n",
+    "```\n",
+    "p1 = 0.3\n",
+    "p2 = 0.3\n",
+    "num_steps = 60\n",
+    "num_runs = 10\n",
+    "```\n",
+    "\n",
+    "Display the resulting `TimeSeries` and use the `mean` function provided by the `TimeSeries` object to compute the average number of unhappy customers (see Section 2.7)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Continuting the previous exercise, use `run_multiple_simulations` to run simulations with a range of values for `p1` and\n",
+    "\n",
+    "```\n",
+    "p2 = 0.3\n",
+    "num_steps = 60\n",
+    "num_runs = 20\n",
+    "```\n",
+    "\n",
+    "Store the results in a `SweepSeries`, then plot the average number of unhappy customers as a function of `p1`.  Label the axes.\n",
+    "\n",
+    "What value of `p1` minimizes the average number of unhappy customers?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap05soln.ipynb b/colab/chap05soln.ipynb
new file mode 100644
index 00000000..32632ff6
--- /dev/null
+++ b/colab/chap05soln.ipynb
@@ -0,0 +1,594 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 5\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading data\n",
+    "\n",
+    "Pandas is a library that provides tools for reading and processing data.  `read_html` reads a web page from a file or the Internet and creates one `DataFrame` for each table on the page."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data directory contains a downloaded copy of https://en.wikipedia.org/wiki/World_population_estimates\n",
+    "\n",
+    "The arguments of `read_html` specify the file to read and how to interpret the tables in the file.  The result, `tables`, is a sequence of `DataFrame` objects; `len(tables)` reports the length of the sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'data/World_population_estimates.html'\n",
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
+    "len(tables)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can select the `DataFrame` we want using the bracket operator.  The tables are numbered from 0, so `tables[2]` is actually the third table on the page.\n",
+    "\n",
+    "`head` selects the header and the first five rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "table2 = tables[2]\n",
+    "table2.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`tail` selects the last five rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table2.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Long column names are awkard to work with, but we can replace them with abbreviated names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the DataFrame looks like now.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table2.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first column, which is labeled `Year`, is special.  It is the **index** for this `DataFrame`, which means it contains the labels for the rows.\n",
+    "\n",
+    "Some of the values use scientific notation; for example, `2.544000e+09` is shorthand for $2.544 \\cdot 10^9$ or 2.544 billion.\n",
+    "\n",
+    "`NaN` is a special value that indicates missing data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Series\n",
+    "\n",
+    "We can use dot notation to select a column from a `DataFrame`.  The result is a `Series`, which is like a `DataFrame` with a single column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census = table2.census\n",
+    "census.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Like a `DataFrame`, a `Series` contains an index, which labels the rows.\n",
+    "\n",
+    "`1e9` is scientific notation for $1 \\cdot 10^9$ or 1 billion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From here on, we will work in units of billions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "un = table2.un / 1e9\n",
+    "un.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census = table2.census / 1e9\n",
+    "census.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what these estimates look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "plot(census, ':', label='US Census')\n",
+    "plot(un, '--', label='UN DESA')\n",
+    "    \n",
+    "decorate(xlabel='Year',\n",
+    "         ylabel='World population (billion)')\n",
+    "\n",
+    "savefig('figs/chap05-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following expression computes the elementwise differences between the two series, then divides through by the UN value to produce [relative errors](https://en.wikipedia.org/wiki/Approximation_error), then finds the largest element.\n",
+    "\n",
+    "So the largest relative error between the estimates is about 1.3%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max(abs(census - un) / un) * 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Break down that expression into smaller steps and display the intermediate results, to make sure you understand how it works.\n",
+    "\n",
+    "1.  Compute the elementwise differences, `census - un`\n",
+    "2.  Compute the absolute differences, `abs(census - un)`\n",
+    "3.  Compute the relative differences, `abs(census - un) / un`\n",
+    "4.  Compute the percent differences, `abs(census - un) / un * 100`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`max` and `abs` are built-in functions provided by Python, but NumPy also provides version that are a little more general.  When you import `modsim`, you get the NumPy versions of these functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Constant growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can select a value from a `Series` using bracket notation.  Here's the first element:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census[1950]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the last value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census[2016]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But rather than \"hard code\" those dates, we can get the first and last labels from the `Series`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_0 = get_first_label(census)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_end = get_last_label(census)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "elapsed_time = t_end - t_0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can get the first and last values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p_0 = get_first_value(census)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p_end = get_last_value(census)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we can compute the average annual growth in billions of people per year."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_growth = p_end - p_0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "annual_growth = total_growth / elapsed_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### TimeSeries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's create a `TimeSeries` to contain values generated by a linear growth model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = TimeSeries()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initially the `TimeSeries` is empty, but we can initialize it so the starting value, in 1950, is the 1950 population estimated by the US Census."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results[t_0] = census[t_0]\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After that, the population in the model grows by a constant amount each year."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for t in linrange(t_0, t_end):\n",
+    "    results[t+1] = results[t] + annual_growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results looks like, compared to the actual data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(census, ':', label='US Census')\n",
+    "plot(un, '--', label='UN DESA')\n",
+    "plot(results, color='gray', label='model')\n",
+    "\n",
+    "decorate(xlabel='Year', \n",
+    "         ylabel='World population (billion)',\n",
+    "         title='Constant growth')\n",
+    "\n",
+    "savefig('figs/chap05-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model fits the data pretty well after 1990, but not so well before."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercises\n",
+    "\n",
+    "**Optional Exercise:**  Try fitting the model using data from 1970 to the present, and see if that does a better job.\n",
+    "\n",
+    "Hint: \n",
+    "\n",
+    "1. Copy the code from above and make a few changes.  Test your code after each small change.\n",
+    "\n",
+    "2. Make sure your `TimeSeries` starts in 1950, even though the estimated annual growth is based on later data.\n",
+    "\n",
+    "3. You might want to add a constant to the starting value to match the data better."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census.loc[1960:1970]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap06soln.ipynb b/colab/chap06soln.ipynb
new file mode 100644
index 00000000..1af43b6b
--- /dev/null
+++ b/colab/chap06soln.ipynb
@@ -0,0 +1,527 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 6\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *\n",
+    "\n",
+    "from pandas import read_html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Code from the previous chapter\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'data/World_population_estimates.html'\n",
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
+    "table2 = tables[2]\n",
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "un = table2.un / 1e9\n",
+    "un.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census = table2.census / 1e9\n",
+    "census.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_0 = get_first_label(census)\n",
+    "t_end = get_last_label(census)\n",
+    "elapsed_time = t_end - t_0\n",
+    "\n",
+    "p_0 = get_first_value(census)\n",
+    "p_end = get_last_value(census)\n",
+    "total_growth = p_end - p_0\n",
+    "\n",
+    "annual_growth = total_growth / elapsed_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### System objects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can rewrite the code from the previous chapter using system objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = System(t_0=t_0, \n",
+    "                t_end=t_end,\n",
+    "                p_0=p_0,\n",
+    "                annual_growth=annual_growth)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can encapsulate the code that runs the model in a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation1(system):\n",
+    "    \"\"\"Runs the constant growth model.\n",
+    "    \n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: TimeSeries\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.p_0\n",
+    "    \n",
+    "    for t in linrange(system.t_0, system.t_end):\n",
+    "        results[t+1] = results[t] + system.annual_growth\n",
+    "    \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also encapsulate the code that plots the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(census, un, timeseries, title):\n",
+    "    \"\"\"Plot the estimates and the model.\n",
+    "    \n",
+    "    census: TimeSeries of population estimates\n",
+    "    un: TimeSeries of population estimates\n",
+    "    timeseries: TimeSeries of simulation results\n",
+    "    title: string\n",
+    "    \"\"\"\n",
+    "    plot(census, ':', label='US Census')\n",
+    "    plot(un, '--', label='UN DESA')\n",
+    "    plot(timeseries, color='gray', label='model')\n",
+    "    \n",
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)',\n",
+    "             title=title)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation1(system)\n",
+    "plot_results(census, un, results, 'Constant growth model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Proportional growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a more realistic model where the number of births and deaths is proportional to the current population."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation2(system):\n",
+    "    \"\"\"Run a model with proportional birth and death.\n",
+    "    \n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: TimeSeries\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.p_0\n",
+    "    \n",
+    "    for t in linrange(system.t_0, system.t_end):\n",
+    "        births = system.birth_rate * results[t]\n",
+    "        deaths = system.death_rate * results[t]\n",
+    "        results[t+1] = results[t] + births - deaths\n",
+    "        \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I picked a death rate that seemed reasonable and then adjusted the birth rate to fit the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.death_rate = 0.01\n",
+    "system.birth_rate = 0.027"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation2(system)\n",
+    "plot_results(census, un, results, 'Proportional model')\n",
+    "savefig('figs/chap06-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model fits the data pretty well for the first 20 years, but not so well after that."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Factoring out the update function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`run_simulation1` and `run_simulation2` are nearly identical except the body of the loop.  So we can factor that part out into a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func1(pop, t, system):\n",
+    "    \"\"\"Compute the population next year.\n",
+    "    \n",
+    "    pop: current population\n",
+    "    t: current year\n",
+    "    system: system object containing parameters of the model\n",
+    "    \n",
+    "    returns: population next year\n",
+    "    \"\"\"\n",
+    "    births = system.birth_rate * pop\n",
+    "    deaths = system.death_rate * pop\n",
+    "    return pop + births - deaths"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The name `update_func` refers to a function object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "update_func1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Which we can confirm by checking its type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(update_func1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`run_simulation` takes the update function as a parameter and calls it just like any other function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Simulate the system using any update function.\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that computes the population next year\n",
+    "    \n",
+    "    returns: TimeSeries\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.p_0\n",
+    "    \n",
+    "    for t in linrange(system.t_0, system.t_end):\n",
+    "        results[t+1] = update_func(results[t], t, system)\n",
+    "        \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we use it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_0 = get_first_label(census)\n",
+    "t_end = get_last_label(census)\n",
+    "p_0 = census[t_0]\n",
+    "\n",
+    "system = System(t_0=t_0, \n",
+    "                t_end=t_end,\n",
+    "                p_0=p_0,\n",
+    "                birth_rate=0.027,\n",
+    "                death_rate=0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, update_func1)\n",
+    "plot_results(census, un, results, 'Proportional model, factored')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember not to put parentheses after `update_func1`.  What happens if you try?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** When you run `run_simulation`, it runs `update_func1` once for each year between `t_0` and `t_end`.  To see that for yourself, add a print statement at the beginning of `update_func1` that prints the values of `t` and `pop`, then run `run_simulation` again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Combining birth and death"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since births and deaths get added up, we don't have to compute them separately.  We can combine the birth and death rates into a single net growth rate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func2(pop, t, system):\n",
+    "    \"\"\"Compute the population next year.\n",
+    "    \n",
+    "    pop: current population\n",
+    "    t: current year\n",
+    "    system: system object containing parameters of the model\n",
+    "    \n",
+    "    returns: population next year\n",
+    "    \"\"\"\n",
+    "    net_growth = system.alpha  * pop\n",
+    "    return pop + net_growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how it works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.alpha = system.birth_rate - system.death_rate\n",
+    "\n",
+    "results = run_simulation(system, update_func2)\n",
+    "plot_results(census, un, results, 'Proportional model, combined birth and death')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercises\n",
+    "\n",
+    "**Exercise:** Maybe the reason the proportional model doesn't work very well is that the growth rate, `alpha`, is changing over time.  So let's try a model with different growth rates before and after 1980 (as an arbitrary choice).\n",
+    "\n",
+    "Write an update function that takes `pop`, `t`, and `system` as parameters.  The system object, `system`, should contain two parameters: the growth rate before 1980, `alpha1`, and the growth rate after 1980, `alpha2`.  It should use `t` to determine which growth rate to use.  Note: Don't forget the `return` statement.\n",
+    "\n",
+    "Test your function by calling it directly, then pass it to `run_simulation`.  Plot the results.  Adjust the parameters `alpha1` and `alpha2` to fit the data as well as you can.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap07soln.ipynb b/colab/chap07soln.ipynb
new file mode 100644
index 00000000..fb1ed01b
--- /dev/null
+++ b/colab/chap07soln.ipynb
@@ -0,0 +1,523 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 7\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *\n",
+    "\n",
+    "from pandas import read_html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Code from the previous chapter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'data/World_population_estimates.html'\n",
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
+    "table2 = tables[2]\n",
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "un = table2.un / 1e9\n",
+    "un.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census = table2.census / 1e9\n",
+    "census.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(census, un, timeseries, title):\n",
+    "    \"\"\"Plot the estimates and the model.\n",
+    "    \n",
+    "    census: TimeSeries of population estimates\n",
+    "    un: TimeSeries of population estimates\n",
+    "    timeseries: TimeSeries of simulation results\n",
+    "    title: string\n",
+    "    \"\"\"\n",
+    "    plot(census, ':', label='US Census')\n",
+    "    plot(un, '--', label='UN DESA')\n",
+    "    plot(timeseries, color='gray', label='model')\n",
+    "    \n",
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)',\n",
+    "             title=title)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Simulate the system using any update function.\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that computes the population next year\n",
+    "    \n",
+    "    returns: TimeSeries\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.p_0\n",
+    "    \n",
+    "    for t in linrange(system.t_0, system.t_end):\n",
+    "        results[t+1] = update_func(results[t], t, system)\n",
+    "        \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Quadratic growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the implementation of the quadratic growth model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func_quad(pop, t, system):\n",
+    "    \"\"\"Compute the population next year with a quadratic model.\n",
+    "    \n",
+    "    pop: current population\n",
+    "    t: current year\n",
+    "    system: system object containing parameters of the model\n",
+    "    \n",
+    "    returns: population next year\n",
+    "    \"\"\"\n",
+    "    net_growth = system.alpha * pop + system.beta * pop**2\n",
+    "    return pop + net_growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a `System` object with the parameters `alpha` and `beta`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_0 = get_first_label(census)\n",
+    "t_end = get_last_label(census)\n",
+    "p_0 = census[t_0]\n",
+    "\n",
+    "system = System(t_0=t_0, \n",
+    "                t_end=t_end,\n",
+    "                p_0=p_0,\n",
+    "                alpha=0.025,\n",
+    "                beta=-0.0018)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, update_func_quad)\n",
+    "plot_results(census, un, results, 'Quadratic model')\n",
+    "savefig('figs/chap07-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Can you find values for the parameters that make the model fit better?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Equilibrium\n",
+    "\n",
+    "To understand the quadratic model better, let's plot net growth as a function of population."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pop_array = linspace(0, 15, 100)\n",
+    "net_growth_array = system.alpha * pop_array + system.beta * pop_array**2\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.set_style('whitegrid')\n",
+    "\n",
+    "plot(pop_array, net_growth_array)\n",
+    "decorate(xlabel='Population (billions)',\n",
+    "         ylabel='Net growth (billions)')\n",
+    "\n",
+    "sns.set_style('white')\n",
+    "\n",
+    "savefig('figs/chap07-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like.  Remember that the x axis is population now, not time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It looks like the growth rate passes through 0 when the population is a little less than 14 billion.\n",
+    "\n",
+    "In the book we found that the net growth is 0 when the population is $-\\alpha/\\beta$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "-system.alpha / system.beta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the equilibrium the population tends toward."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`sns` is a library called Seaborn which provides functions that control the appearance of plots.  In this case I want a grid to make it easier to estimate the population where the growth rate crosses through 0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dysfunctions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When people first learn about functions, there are a few things they often find confusing.  In this section I present and explain some common problems with functions.\n",
+    "\n",
+    "As an example, suppose you want a function that takes a `System` object, with variables `alpha` and `beta`, as a parameter and computes the carrying capacity, `-alpha/beta`.  Here's a good solution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def carrying_capacity(system):\n",
+    "    K = -system.alpha / system.beta\n",
+    "    return K\n",
+    "    \n",
+    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
+    "pop = carrying_capacity(sys1)\n",
+    "print(pop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's see all the ways that can go wrong.\n",
+    "\n",
+    "**Dysfunction #1:** Not using parameters.  In the following version, the function doesn't take any parameters; when `sys1` appears inside the function, it refers to the object we created outside the function.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def carrying_capacity():\n",
+    "    K = -sys1.alpha / sys1.beta\n",
+    "    return K\n",
+    "    \n",
+    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
+    "pop = carrying_capacity()\n",
+    "print(pop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This version actually works, but it is not as versatile as it could be.  If there are several `System` objects, this function can only work with one of them, and only if it is named `system`.\n",
+    "\n",
+    "**Dysfunction #2:** Clobbering the parameters.  When people first learn about parameters, they often write functions like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def carrying_capacity(system):\n",
+    "    system = System(alpha=0.025, beta=-0.0018)\n",
+    "    K = -system.alpha / system.beta\n",
+    "    return K\n",
+    "    \n",
+    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
+    "pop = carrying_capacity(sys1)\n",
+    "print(pop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example, we have a `System` object named `sys1` that gets passed as an argument to `carrying_capacity`.  But when the function runs, it ignores the argument and immediately replaces it with a new `System` object.  As a result, this function always returns the same value, no matter what argument is passed.\n",
+    "\n",
+    "When you write a function, you generally don't know what the values of the parameters will be.  Your job is to write a function that works for any valid values.  If you assign your own values to the parameters, you defeat the whole purpose of functions.\n",
+    "\n",
+    "\n",
+    "**Dysfunction #3:** No return value.  Here's a version that computes the value of `K` but doesn't return it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def carrying_capacity(system):\n",
+    "    K = -system.alpha / system.beta\n",
+    "    \n",
+    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
+    "pop = carrying_capacity(sys1)\n",
+    "print(pop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A function that doesn't have a return statement always returns a special value called `None`, so in this example the value of `pop` is `None`.  If you are debugging a program and find that the value of a variable is `None` when it shouldn't be, a function without a return statement is a likely cause.\n",
+    "\n",
+    "**Dysfunction #4:** Ignoring the return value.  Finally, here's a version where the function is correct, but the way it's used is not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def carrying_capacity(system):\n",
+    "    K = -system.alpha / system.beta\n",
+    "    return K\n",
+    "    \n",
+    "sys2 = System(alpha=0.025, beta=-0.0018)\n",
+    "carrying_capacity(sys2)\n",
+    "\n",
+    "# print(K)     This line won't work because K only exists inside the function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example, `carrying_capacity` runs and returns `K`, but the return value is dropped.\n",
+    "\n",
+    "When you call a function that returns a value, you should do something with the result.  Often you assign it to a variable, as in the previous examples, but you can also use it as part of an expression.\n",
+    "\n",
+    "For example, you could eliminate the temporary variable `pop` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(carrying_capacity(sys1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or if you had more than one system, you could compute the total carrying capacity like this:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total = carrying_capacity(sys1) + carrying_capacity(sys2)\n",
+    "total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "**Exercise:** In the book, I present a different way to parameterize the quadratic model:\n",
+    "\n",
+    "$ \\Delta p = r p (1 - p / K) $\n",
+    "\n",
+    "where $r=\\alpha$ and $K=-\\alpha/\\beta$.  Write a version of `update_func` that implements this version of the model.  Test it by computing the values of `r` and `K` that correspond to `alpha=0.025, beta=-0.0018`, and confirm that you get the same results. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap08soln.ipynb b/colab/chap08soln.ipynb
new file mode 100644
index 00000000..3c31a82b
--- /dev/null
+++ b/colab/chap08soln.ipynb
@@ -0,0 +1,617 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 8\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install modsimpy\n",
+    "# https://pypi.org/project/modsimpy/\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install pint\n",
+    "    !pip install modsimpy\n",
+    "    !mkdir figs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Functions from the previous chapter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(census, un, timeseries, title):\n",
+    "    \"\"\"Plot the estimates and the model.\n",
+    "    \n",
+    "    census: TimeSeries of population estimates\n",
+    "    un: TimeSeries of population estimates\n",
+    "    timeseries: TimeSeries of simulation results\n",
+    "    title: string\n",
+    "    \"\"\"\n",
+    "    plot(census, ':', label='US Census')\n",
+    "    plot(un, '--', label='UN DESA')\n",
+    "    plot(timeseries, color='gray', label='model')\n",
+    "    \n",
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)',\n",
+    "             title=title)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Simulate the system using any update function.\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that computes the population next year\n",
+    "    \n",
+    "    returns: TimeSeries\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.p_0\n",
+    "    \n",
+    "    for t in linrange(system.t_0, system.t_end):\n",
+    "        results[t+1] = update_func(results[t], t, system)\n",
+    "        \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reading the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the data file\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates2.csv'\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/notebooks/data/World_population_estimates2.csv\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_table2(filename):\n",
+    "    tables = pd.read_html(filename, header=0, index_col=0, decimal='M')\n",
+    "    table2 = tables[2]\n",
+    "    table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']\n",
+    "    return table2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#table2 = read_table2()\n",
+    "#table2.to_csv('data/World_population_estimates2.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table2 = pd.read_csv('World_population_estimates2.csv')\n",
+    "table2.index = table2.Year\n",
+    "table2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "un = table2.un / 1e9\n",
+    "census = table2.census / 1e9\n",
+    "plot(census, ':', label='US Census')\n",
+    "plot(un, '--', label='UN DESA')\n",
+    "    \n",
+    "decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)',\n",
+    "             title='Estimated world population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the quadratic model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the update function for the quadratic growth model with parameters `alpha` and `beta`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func_quad(pop, t, system):\n",
+    "    \"\"\"Update population based on a quadratic model.\n",
+    "    \n",
+    "    pop: current population in billions\n",
+    "    t: what year it is\n",
+    "    system: system object with model parameters\n",
+    "    \"\"\"\n",
+    "    net_growth = system.alpha * pop + system.beta * pop**2\n",
+    "    return pop + net_growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract the starting time and population."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_0 = get_first_label(census)\n",
+    "t_end = get_last_label(census)\n",
+    "p_0 = get_first_value(census)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialize the system object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "system = System(t_0=t_0, \n",
+    "                t_end=t_end,\n",
+    "                p_0=p_0,\n",
+    "                alpha=0.025,\n",
+    "                beta=-0.0018)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the model and plot results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, update_func_quad)\n",
+    "plot_results(census, un, results, 'Quadratic model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generating projections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To generate projections, all we have to do is change `t_end`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "system.t_end = 2250\n",
+    "results = run_simulation(system, update_func_quad)\n",
+    "plot_results(census, un, results, 'World population projection')\n",
+    "savefig('figs/chap08-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The population in the model converges on the equilibrium population, `-alpha/beta`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results[system.t_end]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "-system.alpha / system.beta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  What happens if we start with an initial population above the carrying capacity, like 20 billion?  Run the model with initial populations between 1 and 20 billion, and plot the results on the same axes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing projections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can compare the projection from our model with projections produced by people who know what they are doing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the data file\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates3.csv'\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/notebooks/data/World_population_estimates3.csv\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_table3(filename = 'data/World_population_estimates.html'):\n",
+    "    tables = pd.read_html(filename, header=0, index_col=0, decimal='M')\n",
+    "    table3 = tables[3]\n",
+    "    table3.columns = ['census', 'prb', 'un']\n",
+    "    return table3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#table3 = read_table3()\n",
+    "#table3.to_csv('data/World_population_estimates3.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table3 = pd.read_csv('World_population_estimates3.csv')\n",
+    "table3.index = table3.Year\n",
+    "table3.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`NaN` is a special value that represents missing data, in this case because some agencies did not publish projections for some years."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function plots projections from the UN DESA and U.S. Census.  It uses `dropna` to remove the `NaN` values from each series before plotting it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_projections(table):\n",
+    "    \"\"\"Plot world population projections.\n",
+    "    \n",
+    "    table: DataFrame with columns 'un' and 'census'\n",
+    "    \"\"\"\n",
+    "    census_proj = table.census / 1e9\n",
+    "    un_proj = table.un / 1e9\n",
+    "    \n",
+    "    plot(census_proj.dropna(), ':', color='C0', label='US Census')\n",
+    "    plot(un_proj.dropna(), '--', color='C1', label='UN DESA')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the model until 2100, which is as far as the other projections go."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = System(t_0=t_0, \n",
+    "                t_end=2100,\n",
+    "                p_0=p_0,\n",
+    "                alpha=0.025,\n",
+    "                beta=-0.0018)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, update_func_quad)\n",
+    "\n",
+    "plt.axvspan(1950, 2016, color='C0', alpha=0.05)\n",
+    "plot_results(census, un, results, 'World population projections')\n",
+    "plot_projections(table3)\n",
+    "savefig('figs/chap08-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "People who know what they are doing expect the growth rate to decline more sharply than our model projects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "**Exercise:** The net growth rate of world population has been declining for several decades.  That observation suggests one more way to generate projections, by extrapolating observed changes in growth rate.\n",
+    "\n",
+    "The `modsim` library provides a function, `compute_rel_diff`, that computes relative differences of the elements in a sequence.\n",
+    "\n",
+    "Here's how we can use it to compute the relative differences in the `census` and `un` estimates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha_census = compute_rel_diff(census)\n",
+    "plot(alpha_census, label='US Census')\n",
+    "\n",
+    "alpha_un = compute_rel_diff(un)\n",
+    "plot(alpha_un, label='UN DESA')\n",
+    "\n",
+    "decorate(xlabel='Year', label='Net growth rate')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Other than a bump around 1990, net growth rate has been declining roughly linearly since 1965.  As an exercise, you can use this data to make a projection of world population until 2100.\n",
+    "\n",
+    "1. Define a function, `alpha_func`, that takes `t` as a parameter and returns an estimate of the net growth rate at time `t`, based on a linear function `alpha = intercept + slope * t`.  Choose values of `slope` and `intercept` to fit the observed net growth rates since 1965.\n",
+    "\n",
+    "2. Call your function with a range of `ts` from 1960 to 2020 and plot the results.\n",
+    "\n",
+    "3. Create a `System` object that includes `alpha_func` as a system variable.\n",
+    "\n",
+    "4. Define an update function that uses `alpha_func` to compute the net growth rate at the given time `t`.\n",
+    "\n",
+    "5. Test your update function with `t_0 = 1960` and `p_0 = census[t_0]`.\n",
+    "\n",
+    "6. Run a simulation from 1960 to 2100 with your update function, and plot the results.\n",
+    "\n",
+    "7. Compare your projections with those from the US Census and UN."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Related viewing:** You might be interested in this [video by Hans Rosling about the demographic changes we expect in this century](https://www.youtube.com/watch?v=ezVk1ahRF78)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap09soln.ipynb b/colab/chap09soln.ipynb
new file mode 100644
index 00000000..fe1d4db2
--- /dev/null
+++ b/colab/chap09soln.ipynb
@@ -0,0 +1,638 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 9\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import everything from SymPy.\n",
+    "from sympy import *\n",
+    "\n",
+    "# Set up Jupyter notebook to display math.\n",
+    "init_printing() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following displays SymPy expressions and provides the option of showing results in LaTeX format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sympy.printing import latex\n",
+    "\n",
+    "def show(expr, show_latex=False):\n",
+    "    \"\"\"Display a SymPy expression.\n",
+    "    \n",
+    "    expr: SymPy expression\n",
+    "    show_latex: boolean\n",
+    "    \"\"\"\n",
+    "    if show_latex:\n",
+    "        print(latex(expr))\n",
+    "    return expr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis with SymPy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a symbol for time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = symbols('t')\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you combine symbols and numbers, you get symbolic expressions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "expr = t + 1\n",
+    "expr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an `Add` object, which just represents the sum without trying to compute it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(expr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`subs` can be used to replace a symbol with a number, which allows the addition to proceed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "expr.subs(t, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`f` is a special class of symbol that represents a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = Function('f')\n",
+    "f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The type of `f` is `UndefinedFunction`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SymPy understands that `f(t)` means `f` evaluated at `t`, but it doesn't try to evaluate it yet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f(t)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`diff` returns a `Derivative` object that represents the time derivative of `f`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfdt = diff(f(t), t)\n",
+    "dfdt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(dfdt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need a symbol for `alpha`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha = symbols('alpha')\n",
+    "alpha"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can write the differential equation for proportional growth."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eq1 = Eq(dfdt, alpha*f(t))\n",
+    "eq1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And use `dsolve` to solve it.  The result is the general solution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "solution_eq = dsolve(eq1)\n",
+    "solution_eq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can tell it's a general solution because it contains an unspecified constant, `C1`.\n",
+    "\n",
+    "In this example, finding the particular solution is easy: we just replace `C1` with `p_0`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C1, p_0 = symbols('C1 p_0')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "particular = solution_eq.subs(C1, p_0)\n",
+    "particular"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the next example, we have to work a little harder to find the particular solution."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Solving the quadratic growth equation \n",
+    "\n",
+    "We'll use the (r, K) parameterization, so we'll need two more symbols:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r, K = symbols('r K')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can write the differential equation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eq2 = Eq(diff(f(t), t), r * f(t) * (1 - f(t)/K))\n",
+    "eq2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And solve it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "solution_eq = dsolve(eq2)\n",
+    "solution_eq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result, `solution_eq`, contains `rhs`, which is the right-hand side of the solution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "general = solution_eq.rhs\n",
+    "general"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can evaluate the right-hand side at $t=0$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "at_0 = general.subs(t, 0)\n",
+    "at_0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we want to find the value of `C1` that makes `f(0) = p_0`.\n",
+    "\n",
+    "So we'll create the equation `at_0 = p_0` and solve for `C1`.  Because this is just an algebraic identity, not a differential equation, we use `solve`, not `dsolve`.\n",
+    "\n",
+    "The result from `solve` is a list of solutions.  In this case, [we have reason to expect only one solution](https://en.wikipedia.org/wiki/Picard%E2%80%93Lindel%C3%B6f_theorem), but we still get a list, so we have to use the bracket operator, `[0]`, to select the first one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "solutions = solve(Eq(at_0, p_0), C1)\n",
+    "type(solutions), len(solutions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "value_of_C1 = solutions[0]\n",
+    "value_of_C1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now in the general solution, we want to replace `C1` with the value of `C1` we just figured out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "particular = general.subs(C1, value_of_C1)\n",
+    "particular"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is complicated, but SymPy provides a method that tries to simplify it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "particular = simplify(particular)\n",
+    "particular"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Often simplicity is in the eye of the beholder, but that's about as simple as this expression gets.\n",
+    "\n",
+    "Just to double-check, we can evaluate it at `t=0` and confirm that we get `p_0`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "particular.subs(t, 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This solution is called the [logistic function](https://en.wikipedia.org/wiki/Population_growth#Logistic_equation).\n",
+    "\n",
+    "In some places you'll see it written in a different form:\n",
+    "\n",
+    "$f(t) = \\frac{K}{1 + A e^{-rt}}$\n",
+    "\n",
+    "where $A = (K - p_0) / p_0$.\n",
+    "\n",
+    "We can use SymPy to confirm that these two forms are equivalent.  First we represent the alternative version of the logistic function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = (K - p_0) / p_0\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logistic = K / (1 + A * exp(-r*t))\n",
+    "logistic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To see whether two expressions are equivalent, we can check whether their difference simplifies to 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "simplify(particular - logistic)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This test only works one way: if SymPy says the difference reduces to 0, the expressions are definitely equivalent (and not just numerically close).\n",
+    "\n",
+    "But if SymPy can't find a way to simplify the result to 0, that doesn't necessarily mean there isn't one.  Testing whether two expressions are equivalent is a surprisingly hard problem; in fact, there is no algorithm that can solve it in general."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercises\n",
+    "\n",
+    "**Exercise:** Solve the quadratic growth equation using the alternative parameterization\n",
+    "\n",
+    "$\\frac{df(t)}{dt} = \\alpha f(t) + \\beta f^2(t) $"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Use [WolframAlpha](https://www.wolframalpha.com/) to solve the quadratic growth model, using either or both forms of parameterization:\n",
+    "\n",
+    "    df(t) / dt = alpha f(t) + beta f(t)^2\n",
+    "\n",
+    "or\n",
+    "\n",
+    "    df(t) / dt = r f(t) (1 - f(t)/K)\n",
+    "\n",
+    "Find the general solution and also the particular solution where `f(0) = p_0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap10soln.ipynb b/colab/chap10soln.ipynb
new file mode 100644
index 00000000..e7167c43
--- /dev/null
+++ b/colab/chap10soln.ipynb
@@ -0,0 +1,447 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 10\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *\n",
+    "\n",
+    "from pandas import read_html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Under the hood\n",
+    "\n",
+    "To get a `DataFrame` and a `Series`, I'll read the world population data and select a column.\n",
+    "\n",
+    "`DataFrame` and `Series` contain a variable called `shape` that indicates the number of rows and columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'data/World_population_estimates.html'\n",
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
+    "table2 = tables[2]\n",
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']\n",
+    "table2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census = table2.census / 1e9\n",
+    "census.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "un = table2.un / 1e9\n",
+    "un.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `DataFrame` contains `index`, which labels the rows.  It is an `Int64Index`, which is similar to a NumPy array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "table2.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And `columns`, which labels the columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "table2.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And `values`, which is an array of values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "table2.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `Series` does not have `columns`, but it does have `name`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "census.name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It contains `values`, which is an array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And it contains `index`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you ever wonder what kind of object a variable refers to, you can use the `type` function.  The result indicates what type the object is, and the module where that type is defined.\n",
+    "\n",
+    "`DataFrame`, `Int64Index`, `Index`, and `Series` are defined by Pandas.\n",
+    "\n",
+    "`ndarray` is defined by NumPy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(table2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(table2.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(table2.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(table2.values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(census)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(census.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "type(census.values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optional exercise\n",
+    "\n",
+    "The following exercise provides a chance to practice what you have learned so far, and maybe develop a different growth model.  If you feel comfortable with what we have done so far, you might want to give it a try.\n",
+    "\n",
+    "**Optional Exercise:** On the Wikipedia page about world population estimates, the first table contains estimates for prehistoric populations.  The following cells process this table and plot some of the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'data/World_population_estimates.html'\n",
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
+    "len(tables)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select `tables[1]`, which is the second table on the page."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table1 = tables[1]\n",
+    "table1.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Not all agencies and researchers provided estimates for the same dates.  Again `NaN` is the special value that indicates missing data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table1.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, we'll replace the long column names with more convenient abbreviations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table1.columns = ['PRB', 'UN', 'Maddison', 'HYDE', 'Tanton', \n",
+    "                  'Biraben', 'McEvedy & Jones', 'Thomlinson', 'Durand', 'Clark']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some of the estimates are in a form Pandas doesn't recognize as numbers, but we can coerce them to be numeric."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in table1.columns:\n",
+    "    table1[col] = pd.to_numeric(table1[col], errors='coerce')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the results.  Notice that we are working in millions now, not billions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "table1.plot()\n",
+    "decorate(xlim=[-10000, 2000], xlabel='Year', \n",
+    "         ylabel='World population (millions)',\n",
+    "         title='Prehistoric population estimates')\n",
+    "plt.legend(fontsize='small');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `xlim` to zoom in on everything after Year 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table1.plot()\n",
+    "decorate(xlim=[0, 2000], xlabel='Year', \n",
+    "         ylabel='World population (millions)',\n",
+    "         title='CE population estimates')\n",
+    "plt.legend(fontsize='small');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "See if you can find a model that fits these data well from Year 0 to 1950.\n",
+    "\n",
+    "How well does your best model predict actual population growth from 1950 to the present?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap11soln.ipynb b/colab/chap11soln.ipynb
new file mode 100644
index 00000000..e75bc7e8
--- /dev/null
+++ b/colab/chap11soln.ipynb
@@ -0,0 +1,463 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 11\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### SIR implementation\n",
+    "\n",
+    "We'll use a `State` object to represent the number (or fraction) of people in each compartment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init = State(S=89, I=1, R=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To convert from number of people to fractions, we divide through by the total."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init /= sum(init)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`make_system` creates a `System` object with the given parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(beta, gamma):\n",
+    "    \"\"\"Make a system object for the SIR model.\n",
+    "    \n",
+    "    beta: contact rate in days\n",
+    "    gamma: recovery rate in days\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(S=89, I=1, R=0)\n",
+    "    init /= sum(init)\n",
+    "\n",
+    "    t0 = 0\n",
+    "    t_end = 7 * 14\n",
+    "\n",
+    "    return System(init=init, t0=t0, t_end=t_end,\n",
+    "                  beta=beta, gamma=gamma)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's an example with hypothetical values for `beta` and `gamma`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc = 3      # time between contacts in days \n",
+    "tr = 4      # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "system = make_system(beta, gamma)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The update function takes the state during the current time step and returns the state during the next time step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func(state, t, system):\n",
+    "    \"\"\"Update the SIR model.\n",
+    "    \n",
+    "    state: State with variables S, I, R\n",
+    "    t: time step\n",
+    "    system: System with beta and gamma\n",
+    "    \n",
+    "    returns: State object\n",
+    "    \"\"\"\n",
+    "    s, i, r = state\n",
+    "\n",
+    "    infected = system.beta * i * s    \n",
+    "    recovered = system.gamma * i\n",
+    "    \n",
+    "    s -= infected\n",
+    "    i += infected - recovered\n",
+    "    r += recovered\n",
+    "    \n",
+    "    return State(S=s, I=i, R=r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run a single time step, we call it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state = update_func(init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run a simulation by calling the update function for each time step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \n",
+    "    returns: State object for final state\n",
+    "    \"\"\"\n",
+    "    state = system.init\n",
+    "    \n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        state = update_func(state, t, system)\n",
+    "        \n",
+    "    return state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is the state of the system at `t_end`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "run_simulation(system, update_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise**  Suppose the time between contacts is 4 days and the recovery time is 5 days.  After 14 weeks, how many students, total, have been infected?\n",
+    "\n",
+    "Hint: what is the change in `S` between the beginning and the end of the simulation?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using TimeSeries objects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we want to store the state of the system at each time step, we can use one `TimeSeries` object for each state variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "    \n",
+    "    Add three Series objects to the System: S, I, R\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \"\"\"\n",
+    "    S = TimeSeries()\n",
+    "    I = TimeSeries()\n",
+    "    R = TimeSeries()\n",
+    "\n",
+    "    state = system.init\n",
+    "    t0 = system.t0\n",
+    "    S[t0], I[t0], R[t0] = state\n",
+    "    \n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        state = update_func(state, t, system)\n",
+    "        S[t+1], I[t+1], R[t+1] = state\n",
+    "    \n",
+    "    return S, I, R"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we call it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc = 3      # time between contacts in days \n",
+    "tr = 4      # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "system = make_system(beta, gamma)\n",
+    "S, I, R = run_simulation(system, update_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then we can plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(S, I, R):\n",
+    "    \"\"\"Plot the results of a SIR model.\n",
+    "    \n",
+    "    S: TimeSeries\n",
+    "    I: TimeSeries\n",
+    "    R: TimeSeries\n",
+    "    \"\"\"\n",
+    "    plot(S, '--', label='Susceptible')\n",
+    "    plot(I, '-', label='Infected')\n",
+    "    plot(R, ':', label='Recovered')\n",
+    "    decorate(xlabel='Time (days)',\n",
+    "             ylabel='Fraction of population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what they look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_results(S, I, R)\n",
+    "savefig('figs/chap11-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using a DataFrame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of making three `TimeSeries` objects, we can use one `DataFrame`.\n",
+    "\n",
+    "We have to use `row` to selects rows, rather than columns.  But then Pandas does the right thing, matching up the state variables with the columns of the `DataFrame`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "        \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \n",
+    "    returns: TimeFrame\n",
+    "    \"\"\"\n",
+    "    frame = TimeFrame(columns=system.init.index)\n",
+    "    frame.row[system.t0] = system.init\n",
+    "    \n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
+    "    \n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run it, and what the result looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc = 3      # time between contacts in days \n",
+    "tr = 4      # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "system = make_system(beta, gamma)\n",
+    "results = run_simulation(system, update_func)\n",
+    "results.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can extract the results and plot them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_results(results.S, results.I, results.R)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "**Exercise**  Suppose the time between contacts is 4 days and the recovery time is 5 days.  Simulate this scenario for 14 weeks and plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap12soln.ipynb b/colab/chap12soln.ipynb
new file mode 100644
index 00000000..50a9c232
--- /dev/null
+++ b/colab/chap12soln.ipynb
@@ -0,0 +1,877 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 12\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Code\n",
+    "\n",
+    "Here's the code from the previous notebook that we'll need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(beta, gamma):\n",
+    "    \"\"\"Make a system object for the SIR model.\n",
+    "    \n",
+    "    beta: contact rate in days\n",
+    "    gamma: recovery rate in days\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(S=89, I=1, R=0)\n",
+    "    init /= sum(init)\n",
+    "\n",
+    "    t0 = 0\n",
+    "    t_end = 7 * 14\n",
+    "\n",
+    "    return System(init=init, t0=t0, t_end=t_end,\n",
+    "                  beta=beta, gamma=gamma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func(state, t, system):\n",
+    "    \"\"\"Update the SIR model.\n",
+    "    \n",
+    "    state: State with variables S, I, R\n",
+    "    t: time step\n",
+    "    system: System with beta and gamma\n",
+    "    \n",
+    "    returns: State object\n",
+    "    \"\"\"\n",
+    "    s, i, r = state\n",
+    "\n",
+    "    infected = system.beta * i * s    \n",
+    "    recovered = system.gamma * i\n",
+    "    \n",
+    "    s -= infected\n",
+    "    i += infected - recovered\n",
+    "    r += recovered\n",
+    "    \n",
+    "    return State(S=s, I=i, R=r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "        \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \n",
+    "    returns: TimeFrame\n",
+    "    \"\"\"\n",
+    "    frame = TimeFrame(columns=system.init.index)\n",
+    "    frame.row[system.t0] = system.init\n",
+    "    \n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
+    "    \n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given the results, we can compute metrics that quantify whatever we are interested in, like the total number of sick students, for example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_total_infected(results):\n",
+    "    \"\"\"Fraction of population infected during the simulation.\n",
+    "    \n",
+    "    results: DataFrame with columns S, I, R\n",
+    "    \n",
+    "    returns: fraction of population\n",
+    "    \"\"\"\n",
+    "    return get_first_value(results.S) - get_last_value(results.S)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's an example.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beta = 0.333\n",
+    "gamma = 0.25\n",
+    "system = make_system(beta, gamma)\n",
+    "\n",
+    "results = run_simulation(system, update_func)\n",
+    "print(beta, gamma, calc_total_infected(results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write functions that take a `TimeFrame` object as a parameter and compute the other metrics mentioned in the book:\n",
+    "\n",
+    "1.  The fraction of students who are sick at the peak of the outbreak.\n",
+    "\n",
+    "2.  The day the outbreak peaks.\n",
+    "\n",
+    "3.  The fraction of students who are sick at the end of the semester.\n",
+    "\n",
+    "Note: Not all of these functions require the `System` object, but when you write a set of related functons, it is often convenient if they all take the same parameters.\n",
+    "\n",
+    "Hint:  If you have a `TimeSeries` called `I`, you can compute the largest value of the series like this:\n",
+    "\n",
+    "    I.max()\n",
+    "\n",
+    "And the index of the largest value like this:\n",
+    "\n",
+    "    I.idxmax()\n",
+    "\n",
+    "You can read about these functions in the `Series` [documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What if?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use this model to evaluate \"what if\" scenarios.  For example, this function models the effect of immunization by moving some fraction of the population from S to R before the simulation starts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_immunization(system, fraction):\n",
+    "    \"\"\"Immunize a fraction of the population.\n",
+    "    \n",
+    "    Moves the given fraction from S to R.\n",
+    "    \n",
+    "    system: System object\n",
+    "    fraction: number from 0 to 1\n",
+    "    \"\"\"\n",
+    "    system.init.S -= fraction\n",
+    "    system.init.R += fraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start again with the system we used in the previous sections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc = 3      # time between contacts in days \n",
+    "tr = 4      # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "system = make_system(beta, gamma)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And run the model without immunization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, update_func)\n",
+    "calc_total_infected(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now with 10% immunization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system2 = make_system(beta, gamma)\n",
+    "add_immunization(system2, 0.1)\n",
+    "results2 = run_simulation(system2, update_func)\n",
+    "calc_total_infected(results2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "10% immunization leads to a drop in infections of 16 percentage points.\n",
+    "\n",
+    "Here's what the time series looks like for S, with and without immunization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(results.S, '-', label='No immunization')\n",
+    "plot(results2.S, '--', label='10% immunization')\n",
+    "\n",
+    "decorate(xlabel='Time (days)',\n",
+    "         ylabel='Fraction susceptible')\n",
+    "\n",
+    "savefig('figs/chap12-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can sweep through a range of values for the fraction of the population who are immunized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "immunize_array = linspace(0, 1, 11)\n",
+    "for fraction in immunize_array:\n",
+    "    system = make_system(beta, gamma)\n",
+    "    add_immunization(system, fraction)\n",
+    "    results = run_simulation(system, update_func)\n",
+    "    print(fraction, calc_total_infected(results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function does the same thing and stores the results in a `Sweep` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_immunity(immunize_array):\n",
+    "    \"\"\"Sweeps a range of values for immunity.\n",
+    "    \n",
+    "    immunize_array: array of fraction immunized\n",
+    "    \n",
+    "    returns: Sweep object\n",
+    "    \"\"\"\n",
+    "    sweep = SweepSeries()\n",
+    "    \n",
+    "    for fraction in immunize_array:\n",
+    "        system = make_system(beta, gamma)\n",
+    "        add_immunization(system, fraction)\n",
+    "        results = run_simulation(system, update_func)\n",
+    "        sweep[fraction] = calc_total_infected(results)\n",
+    "        \n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "immunize_array = linspace(0, 1, 21)\n",
+    "infected_sweep = sweep_immunity(immunize_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(infected_sweep)\n",
+    "\n",
+    "decorate(xlabel='Fraction immunized',\n",
+    "         ylabel='Total fraction infected',\n",
+    "         title='Fraction infected vs. immunization rate',\n",
+    "         legend=False)\n",
+    "\n",
+    "savefig('figs/chap12-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If 40% of the population is immunized, less than 4% of the population gets sick."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Logistic function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To model the effect of a hand-washing campaign, I'll use a [generalized logistic function](https://en.wikipedia.org/wiki/Generalised_logistic_function) (GLF), which is a convenient function for modeling curves that have a generally sigmoid shape.  The parameters of the GLF correspond to various features of the curve in a way that makes it easy to find a function that has the shape you want, based on data or background information about the scenario."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def logistic(x, A=0, B=1, C=1, M=0, K=1, Q=1, nu=1):\n",
+    "    \"\"\"Computes the generalize logistic function.\n",
+    "    \n",
+    "    A: controls the lower bound\n",
+    "    B: controls the steepness of the transition \n",
+    "    C: not all that useful, AFAIK\n",
+    "    M: controls the location of the transition\n",
+    "    K: controls the upper bound\n",
+    "    Q: shift the transition left or right\n",
+    "    nu: affects the symmetry of the transition\n",
+    "    \n",
+    "    returns: float or array\n",
+    "    \"\"\"\n",
+    "    exponent = -B * (x - M)\n",
+    "    denom = C + Q * exp(exponent)\n",
+    "    return A + (K-A) / denom ** (1/nu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following array represents the range of possible spending."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spending = linspace(0, 1200, 21)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`compute_factor` computes the reduction in `beta` for a given level of campaign spending.\n",
+    "\n",
+    "`M` is chosen so the transition happens around \\$500.\n",
+    "\n",
+    "`K` is the maximum reduction in `beta`, 20%.\n",
+    "\n",
+    "`B` is chosen by trial and error to yield a curve that seems feasible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_factor(spending):\n",
+    "    \"\"\"Reduction factor as a function of spending.\n",
+    "    \n",
+    "    spending: dollars from 0 to 1200\n",
+    "    \n",
+    "    returns: fractional reduction in beta\n",
+    "    \"\"\"\n",
+    "    return logistic(spending, M=500, K=0.2, B=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "percent_reduction = compute_factor(spending) * 100\n",
+    "\n",
+    "plot(spending, percent_reduction)\n",
+    "\n",
+    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
+    "         ylabel='Percent reduction in infection rate',\n",
+    "         title='Effect of hand washing on infection rate',\n",
+    "         legend=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Modify the parameters `M`, `K`, and `B`, and see what effect they have on the shape of the curve.  Read about the [generalized logistic function on Wikipedia](https://en.wikipedia.org/wiki/Generalised_logistic_function).  Modify the other parameters and see what effect they have."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hand washing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can model the effect of a hand-washing campaign by modifying `beta`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_hand_washing(system, spending):\n",
+    "    \"\"\"Modifies system to model the effect of hand washing.\n",
+    "    \n",
+    "    system: System object\n",
+    "    spending: campaign spending in USD\n",
+    "    \"\"\"\n",
+    "    factor = compute_factor(spending)\n",
+    "    system.beta *= (1 - factor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start with the same values of `beta` and `gamma` we've been using."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc = 3      # time between contacts in days \n",
+    "tr = 4      # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "beta, gamma"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can sweep different levels of campaign spending."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spending_array = linspace(0, 1200, 13)\n",
+    "\n",
+    "for spending in spending_array:\n",
+    "    system = make_system(beta, gamma)\n",
+    "    add_hand_washing(system, spending)\n",
+    "    results = run_simulation(system, update_func)\n",
+    "    print(spending, system.beta, calc_total_infected(results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a function that sweeps a range of spending and stores the results in a `SweepSeries`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_hand_washing(spending_array):\n",
+    "    \"\"\"Run simulations with a range of spending.\n",
+    "    \n",
+    "    spending_array: array of dollars from 0 to 1200\n",
+    "    \n",
+    "    returns: Sweep object\n",
+    "    \"\"\"\n",
+    "    sweep = SweepSeries()\n",
+    "    \n",
+    "    for spending in spending_array:\n",
+    "        system = make_system(beta, gamma)\n",
+    "        add_hand_washing(system, spending)\n",
+    "        results = run_simulation(system, update_func)\n",
+    "        sweep[spending] = calc_total_infected(results)\n",
+    "        \n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spending_array = linspace(0, 1200, 20)\n",
+    "infected_sweep = sweep_hand_washing(spending_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(infected_sweep)\n",
+    "\n",
+    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
+    "         ylabel='Total fraction infected',\n",
+    "         title='Effect of hand washing on total infections',\n",
+    "         legend=False)\n",
+    "\n",
+    "savefig('figs/chap12-fig03.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's put it all together to make some public health spending decisions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optimization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Suppose we have \\$1200 to spend on any combination of vaccines and a hand-washing campaign."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_students = 90\n",
+    "budget = 1200\n",
+    "price_per_dose = 100\n",
+    "max_doses = int(budget / price_per_dose)\n",
+    "dose_array = linrange(max_doses, endpoint=True)\n",
+    "max_doses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can sweep through a range of doses from, 0 to `max_doses`, model the effects of immunization and the hand-washing campaign, and run simulations.\n",
+    "\n",
+    "For each scenario, we compute the fraction of students who get sick."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for doses in dose_array:\n",
+    "    fraction = doses / num_students\n",
+    "    spending = budget - doses * price_per_dose\n",
+    "    \n",
+    "    system = make_system(beta, gamma)\n",
+    "    add_immunization(system, fraction)\n",
+    "    add_hand_washing(system, spending)\n",
+    "    \n",
+    "    results = run_simulation(system, update_func)\n",
+    "    print(doses, system.init.S, system.beta, calc_total_infected(results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function wraps that loop and stores the results in a `Sweep` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_doses(dose_array):\n",
+    "    \"\"\"Runs simulations with different doses and campaign spending.\n",
+    "    \n",
+    "    dose_array: range of values for number of vaccinations\n",
+    "    \n",
+    "    return: Sweep object with total number of infections \n",
+    "    \"\"\"\n",
+    "    sweep = SweepSeries()\n",
+    "    \n",
+    "    for doses in dose_array:\n",
+    "        fraction = doses / num_students\n",
+    "        spending = budget - doses * price_per_dose\n",
+    "        \n",
+    "        system = make_system(beta, gamma)\n",
+    "        add_immunization(system, fraction)\n",
+    "        add_hand_washing(system, spending)\n",
+    "        \n",
+    "        results = run_simulation(system, update_func)\n",
+    "        sweep[doses] = calc_total_infected(results)\n",
+    "\n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can compute the number of infected students for each possible allocation of the budget."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infected_sweep = sweep_doses(dose_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(infected_sweep)\n",
+    "\n",
+    "decorate(xlabel='Doses of vaccine',\n",
+    "         ylabel='Total fraction infected',\n",
+    "         title='Total infections vs. doses',\n",
+    "         legend=False)\n",
+    "\n",
+    "savefig('figs/chap12-fig04.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercises\n",
+    "\n",
+    "**Exercise:** Suppose the price of the vaccine drops to $50 per dose.  How does that affect the optimal allocation of the spending?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose we have the option to quarantine infected students.  For example, a student who feels ill might be moved to an infirmary, or a private dorm room, until they are no longer infectious.\n",
+    "\n",
+    "How might you incorporate the effect of quarantine in the SIR model?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap13ode.ipynb b/colab/chap13ode.ipynb
new file mode 100644
index 00000000..28d11d48
--- /dev/null
+++ b/colab/chap13ode.ipynb
@@ -0,0 +1,180 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 13\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Code from previous chapters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`make_system`, `plot_results`, and `calc_total_infected` are unchanged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(beta, gamma):\n",
+    "    \"\"\"Make a system object for the SIR model.\n",
+    "    \n",
+    "    beta: contact rate in days\n",
+    "    gamma: recovery rate in days\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(S=89, I=1, R=0)\n",
+    "    init /= np.sum(init)\n",
+    "\n",
+    "    t0 = 0\n",
+    "    t_end = 7 * 14\n",
+    "\n",
+    "    return System(init=init, t0=t0, t_end=t_end,\n",
+    "                  beta=beta, gamma=gamma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(S, I, R):\n",
+    "    \"\"\"Plot the results of a SIR model.\n",
+    "    \n",
+    "    S: TimeSeries\n",
+    "    I: TimeSeries\n",
+    "    R: TimeSeries\n",
+    "    \"\"\"\n",
+    "    plot(S, '--', label='Susceptible')\n",
+    "    plot(I, '-', label='Infected')\n",
+    "    plot(R, ':', label='Recovered')\n",
+    "    decorate(xlabel='Time (days)',\n",
+    "             ylabel='Fraction of population')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_total_infected(results):\n",
+    "    \"\"\"Fraction of population infected during the simulation.\n",
+    "    \n",
+    "    results: DataFrame with columns S, I, R\n",
+    "    \n",
+    "    returns: fraction of population\n",
+    "    \"\"\"\n",
+    "    return get_first_value(results.S) - get_last_value(results.S)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Write a slope function for the SIR model and test it with `run_ode_solver`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(0.333, 0.25)\n",
+    "slope_func(system.init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_ode_solver(system, slope_func, max_step=3)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_results(results.S, results.I, results.R)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap13soln.ipynb b/colab/chap13soln.ipynb
new file mode 100644
index 00000000..d368dd4c
--- /dev/null
+++ b/colab/chap13soln.ipynb
@@ -0,0 +1,517 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 13\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Code from previous chapters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`make_system`, `plot_results`, and `calc_total_infected` are unchanged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(beta, gamma):\n",
+    "    \"\"\"Make a system object for the SIR model.\n",
+    "    \n",
+    "    beta: contact rate in days\n",
+    "    gamma: recovery rate in days\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(S=89, I=1, R=0)\n",
+    "    init /= np.sum(init)\n",
+    "\n",
+    "    t0 = 0\n",
+    "    t_end = 7 * 14\n",
+    "\n",
+    "    return System(init=init, t0=t0, t_end=t_end,\n",
+    "                  beta=beta, gamma=gamma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(S, I, R):\n",
+    "    \"\"\"Plot the results of a SIR model.\n",
+    "    \n",
+    "    S: TimeSeries\n",
+    "    I: TimeSeries\n",
+    "    R: TimeSeries\n",
+    "    \"\"\"\n",
+    "    plot(S, '--', label='Susceptible')\n",
+    "    plot(I, '-', label='Infected')\n",
+    "    plot(R, ':', label='Recovered')\n",
+    "    decorate(xlabel='Time (days)',\n",
+    "             ylabel='Fraction of population')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_total_infected(results):\n",
+    "    \"\"\"Fraction of population infected during the simulation.\n",
+    "    \n",
+    "    results: DataFrame with columns S, I, R\n",
+    "    \n",
+    "    returns: fraction of population\n",
+    "    \"\"\"\n",
+    "    return get_first_value(results.S) - get_last_value(results.S)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "        \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \n",
+    "    returns: TimeFrame\n",
+    "    \"\"\"\n",
+    "    init, t0, t_end = system.init, system.t0, system.t_end\n",
+    "    \n",
+    "    frame = TimeFrame(columns=init.index)\n",
+    "    frame.row[t0] = init\n",
+    "    \n",
+    "    for t in linrange(t0, t_end):\n",
+    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
+    "    \n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func(state, t, system):\n",
+    "    \"\"\"Update the SIR model.\n",
+    "    \n",
+    "    state: State (s, i, r)\n",
+    "    t: time\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: State (sir)\n",
+    "    \"\"\"\n",
+    "    beta, gamma = system.beta, system.gamma\n",
+    "    s, i, r = state\n",
+    "\n",
+    "    infected = beta * i * s    \n",
+    "    recovered = gamma * i\n",
+    "    \n",
+    "    s -= infected\n",
+    "    i += infected - recovered\n",
+    "    r += recovered\n",
+    "    \n",
+    "    return State(S=s, I=i, R=r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sweeping beta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a range of values for `beta`, with constant `gamma`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beta_array = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 , 1.1]\n",
+    "gamma = 0.2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the simulation once for each value of `beta` and print total infections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for beta in beta_array:\n",
+    "    system = make_system(beta, gamma)\n",
+    "    results = run_simulation(system, update_func)\n",
+    "    print(system.beta, calc_total_infected(results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wrap that loop in a function and return a `SweepSeries` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_beta(beta_array, gamma):\n",
+    "    \"\"\"Sweep a range of values for beta.\n",
+    "    \n",
+    "    beta_array: array of beta values\n",
+    "    gamma: recovery rate\n",
+    "    \n",
+    "    returns: SweepSeries that maps from beta to total infected\n",
+    "    \"\"\"\n",
+    "    sweep = SweepSeries()\n",
+    "    for beta in beta_array:\n",
+    "        system = make_system(beta, gamma)\n",
+    "        results = run_simulation(system, update_func)\n",
+    "        sweep[system.beta] = calc_total_infected(results)\n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sweep `beta` and plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infected_sweep = sweep_beta(beta_array, gamma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "label = 'gamma = ' + str(gamma)\n",
+    "plot(infected_sweep, label=label)\n",
+    "\n",
+    "decorate(xlabel='Contact rate (beta)',\n",
+    "         ylabel='Fraction infected')\n",
+    "\n",
+    "savefig('figs/chap13-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sweeping gamma"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the same array of values for `beta`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beta_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And now an array of values for `gamma`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamma_array = [0.2, 0.4, 0.6, 0.8]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For each value of `gamma`, sweep `beta` and plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(7, 4))\n",
+    "\n",
+    "for gamma in gamma_array:\n",
+    "    infected_sweep = sweep_beta(beta_array, gamma)\n",
+    "    label = 'gamma = ' + str(gamma)\n",
+    "    plot(infected_sweep, label=label)\n",
+    "    \n",
+    "decorate(xlabel='Contact rate (beta)',\n",
+    "         ylabel='Fraction infected',\n",
+    "         loc='upper left')\n",
+    "\n",
+    "plt.legend(bbox_to_anchor=(1.02, 1.02))\n",
+    "plt.tight_layout()\n",
+    "savefig('figs/chap13-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Suppose the infectious period for the Freshman Plague is known to be 2 days on average, and suppose during one particularly bad year, 40% of the class is infected at some point.  Estimate the time between contacts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SweepFrame\n",
+    "\n",
+    "The following sweeps two parameters and stores the results in a `SweepFrame`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_parameters(beta_array, gamma_array):\n",
+    "    \"\"\"Sweep a range of values for beta and gamma.\n",
+    "    \n",
+    "    beta_array: array of infection rates\n",
+    "    gamma_array: array of recovery rates\n",
+    "    \n",
+    "    returns: SweepFrame with one row for each beta\n",
+    "             and one column for each gamma\n",
+    "    \"\"\"\n",
+    "    frame = SweepFrame(columns=gamma_array)\n",
+    "    for gamma in gamma_array:\n",
+    "        frame[gamma] = sweep_beta(beta_array, gamma)\n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the `SweepFrame` look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frame = sweep_parameters(beta_array, gamma_array)\n",
+    "frame.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's how we can plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for gamma in gamma_array:\n",
+    "    label = 'gamma = ' + str(gamma)\n",
+    "    plot(frame[gamma], label=label)\n",
+    "    \n",
+    "decorate(xlabel='Contact rate (beta)',\n",
+    "         ylabel='Fraction infected',\n",
+    "         title='',\n",
+    "         loc='upper left')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also plot one line for each value of `beta`, although there are a lot of them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(7, 4))\n",
+    "\n",
+    "\n",
+    "for beta in [1.1, 0.9, 0.7, 0.5, 0.3]:\n",
+    "    label = 'beta = ' + str(beta)\n",
+    "    plot(frame.row[beta], label=label)\n",
+    "    \n",
+    "decorate(xlabel='Recovery rate (gamma)',\n",
+    "         ylabel='Fraction infected')\n",
+    "\n",
+    "plt.legend(bbox_to_anchor=(1.02, 1.02))\n",
+    "plt.tight_layout()\n",
+    "savefig('figs/chap13-fig03.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's often useful to separate the code that generates results from the code that plots the results, so we can run the simulations once, save the results, and then use them for different analysis, visualization, etc.\n",
+    "\n",
+    "After running `sweep_parameters`, we have a `SweepFrame` with one row for each value of `beta` and one column for each value of `gamma`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "contour(frame)\n",
+    "\n",
+    "decorate(xlabel='Recovery rate (gamma)',\n",
+    "         ylabel='Contact rate (beta)',\n",
+    "         title='Fraction infected, contour plot')\n",
+    "\n",
+    "savefig('figs/chap13-fig04.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap14soln.ipynb b/colab/chap14soln.ipynb
new file mode 100644
index 00000000..c9624be2
--- /dev/null
+++ b/colab/chap14soln.ipynb
@@ -0,0 +1,475 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 14\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Code from previous chapters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(beta, gamma):\n",
+    "    \"\"\"Make a system object for the SIR model.\n",
+    "    \n",
+    "    beta: contact rate in days\n",
+    "    gamma: recovery rate in days\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(S=89, I=1, R=0)\n",
+    "    init /= np.sum(init)\n",
+    "\n",
+    "    t0 = 0\n",
+    "    t_end = 7 * 14\n",
+    "\n",
+    "    return System(init=init, t0=t0, t_end=t_end,\n",
+    "                  beta=beta, gamma=gamma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func(state, t, system):\n",
+    "    \"\"\"Update the SIR model.\n",
+    "    \n",
+    "    state: State (s, i, r)\n",
+    "    t: time\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: State (sir)\n",
+    "    \"\"\"\n",
+    "    s, i, r = state\n",
+    "\n",
+    "    infected = system.beta * i * s    \n",
+    "    recovered = system.gamma * i\n",
+    "    \n",
+    "    s -= infected\n",
+    "    i += infected - recovered\n",
+    "    r += recovered\n",
+    "    \n",
+    "    return State(S=s, I=i, R=r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "        \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \n",
+    "    returns: TimeFrame\n",
+    "    \"\"\"\n",
+    "    init, t0, t_end = system.init, system.t0, system.t_end\n",
+    "    \n",
+    "    frame = TimeFrame(columns=init.index)\n",
+    "    frame.row[t0] = init\n",
+    "    \n",
+    "    for t in linrange(t0, t_end):\n",
+    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
+    "    \n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_total_infected(results):\n",
+    "    \"\"\"Fraction of population infected during the simulation.\n",
+    "    \n",
+    "    results: DataFrame with columns S, I, R\n",
+    "    \n",
+    "    returns: fraction of population\n",
+    "    \"\"\"\n",
+    "    return get_first_value(results.S) - get_last_value(results.S)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_beta(beta_array, gamma):\n",
+    "    \"\"\"Sweep a range of values for beta.\n",
+    "    \n",
+    "    beta_array: array of beta values\n",
+    "    gamma: recovery rate\n",
+    "    \n",
+    "    returns: SweepSeries that maps from beta to total infected\n",
+    "    \"\"\"\n",
+    "    sweep = SweepSeries()\n",
+    "    for beta in beta_array:\n",
+    "        system = make_system(beta, gamma)\n",
+    "        results = run_simulation(system, update_func)\n",
+    "        sweep[system.beta] = calc_total_infected(results)\n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_parameters(beta_array, gamma_array):\n",
+    "    \"\"\"Sweep a range of values for beta and gamma.\n",
+    "    \n",
+    "    beta_array: array of infection rates\n",
+    "    gamma_array: array of recovery rates\n",
+    "    \n",
+    "    returns: SweepFrame with one row for each beta\n",
+    "             and one column for each gamma\n",
+    "    \"\"\"\n",
+    "    frame = SweepFrame(columns=gamma_array)\n",
+    "    for gamma in gamma_array:\n",
+    "        frame[gamma] = sweep_beta(beta_array, gamma)\n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Contact number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the `SweepFrame` from the previous chapter, with one row for each value of `beta` and one column for each value of `gamma`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beta_array = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 , 1.1]\n",
+    "gamma_array = [0.2, 0.4, 0.6, 0.8]\n",
+    "frame = sweep_parameters(beta_array, gamma_array)\n",
+    "frame.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frame.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following loop shows how we can loop through the columns and rows of the `SweepFrame`.  With 11 rows and 4 columns, there are 44 elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for gamma in frame.columns:\n",
+    "    column = frame[gamma]\n",
+    "    for beta in column.index:\n",
+    "        frac_infected = column[beta]\n",
+    "        print(beta, gamma, frac_infected)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can wrap that loop in a function and plot the results.  For each element of the `SweepFrame`, we have `beta`, `gamma`, and `frac_infected`, and we plot `beta/gamma` on the x-axis and `frac_infected` on the y-axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_sweep_frame(frame):\n",
+    "    \"\"\"Plot the values from a SweepFrame.\n",
+    "    \n",
+    "    For each (beta, gamma), compute the contact number,\n",
+    "    beta/gamma\n",
+    "    \n",
+    "    frame: SweepFrame with one row per beta, one column per gamma\n",
+    "    \"\"\"\n",
+    "    for gamma in frame.columns:\n",
+    "        column = frame[gamma]\n",
+    "        for beta in column.index:\n",
+    "            frac_infected = column[beta]\n",
+    "            plot(beta/gamma, frac_infected, 'ro')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_sweep_frame(frame)\n",
+    "\n",
+    "decorate(xlabel='Contact number (beta/gamma)',\n",
+    "         ylabel='Fraction infected')\n",
+    "\n",
+    "savefig('figs/chap14-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It turns out that the ratio `beta/gamma`, called the \"contact number\" is sufficient to predict the total number of infections; we don't have to know `beta` and `gamma` separately.\n",
+    "\n",
+    "We can see that in the previous plot: when we plot the fraction infected versus the contact number, the results fall close to a curve."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the book we figured out the relationship between $c$ and $s_{\\infty}$ analytically.  Now we can compute it for a range of values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s_inf_array = linspace(0.0001, 0.9999, 101);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c_array = log(s_inf_array) / (s_inf_array - 1);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`total_infected` is the change in $s$ from the beginning to the end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frac_infected = 1 - s_inf_array\n",
+    "frac_infected_series = Series(frac_infected, index=c_array);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can plot the analytic results and compare them to the simulations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_sweep_frame(frame)\n",
+    "plot(frac_infected_series, label='Analysis')\n",
+    "\n",
+    "decorate(xlabel='Contact number (c)',\n",
+    "         ylabel='Fraction infected')\n",
+    "\n",
+    "savefig('figs/chap14-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The agreement is generally good, except for values of `c` less than 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  If we didn't know about contact numbers, we might have explored other possibilities, like the difference between `beta` and `gamma`, rather than their ratio.\n",
+    "\n",
+    "Write a version of `plot_sweep_frame`, called `plot_sweep_frame_difference`, that plots the fraction infected versus the difference `beta-gamma`.\n",
+    "\n",
+    "What do the results look like, and what does that imply? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose you run a survey at the end of the semester and find that 26% of students had the Freshman Plague at some point.\n",
+    "\n",
+    "What is your best estimate of `c`?\n",
+    "\n",
+    "Hint: if you print `frac_infected_series`, you can read off the answer. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap15soln.ipynb b/colab/chap15soln.ipynb
new file mode 100644
index 00000000..4e0f0cdb
--- /dev/null
+++ b/colab/chap15soln.ipynb
@@ -0,0 +1,318 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 15\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The coffee cooling problem\n",
+    "\n",
+    "I'll use a `State` object to store the initial temperature.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init = State(T=90)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And a `System` object to contain the system parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee = System(init=init,\n",
+    "                volume=300,\n",
+    "                r=0.01,\n",
+    "                T_env=22,\n",
+    "                t_0=0,\n",
+    "                t_end=30,\n",
+    "                dt=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The update function implements Newton's law of cooling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func(state, t, system):\n",
+    "    \"\"\"Update the thermal transfer model.\n",
+    "    \n",
+    "    state: State (temp)\n",
+    "    t: time\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: State (temp)\n",
+    "    \"\"\"\n",
+    "    r, T_env, dt = system.r, system.T_env, system.dt\n",
+    "    \n",
+    "    T = state.T\n",
+    "    T += -r * (T - T_env) * dt\n",
+    "    \n",
+    "    return State(T=T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "update_func(init, 0, coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a version of `run_simulation` that uses `linrange` to make an array of time steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "    \n",
+    "    Add a TimeFrame to the System: results\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \"\"\"\n",
+    "    init = system.init\n",
+    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
+    "    \n",
+    "    frame = TimeFrame(columns=init.index)\n",
+    "    frame.row[t_0] = init\n",
+    "    ts = linrange(t_0, t_end, dt)\n",
+    "    \n",
+    "    for t in ts:\n",
+    "        frame.row[t+dt] = update_func(frame.row[t], t, system)\n",
+    "    \n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's how it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(coffee, update_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(results.T, label='coffee')\n",
+    "decorate(xlabel='Time (minutes)',\n",
+    "         ylabel='Temperature (C)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the final temperature:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee.T_final = get_last_value(results.T)\n",
+    "T_final = get_last_value(results.T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Encapsulation\n",
+    "\n",
+    "Before we go on, let's define a function to initialize `System` objects with relevant parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(T_init, r, volume, t_end):\n",
+    "    \"\"\"Makes a System object with the given parameters.\n",
+    "\n",
+    "    T_init: initial temperature in degC\n",
+    "    r: heat transfer rate, in 1/min\n",
+    "    volume: volume of liquid in mL\n",
+    "    t_end: end time of simulation\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(T=T_init)\n",
+    "                   \n",
+    "    return System(init=init,\n",
+    "                  r=r, \n",
+    "                  volume=volume,\n",
+    "                  temp=T_init,\n",
+    "                  t_0=0, \n",
+    "                  t_end=t_end, \n",
+    "                  dt=1,\n",
+    "                  T_env=22)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we use it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee = make_system(T_init=90, r=0.01, volume=300, t_end=30)\n",
+    "results = run_simulation(coffee, update_func)\n",
+    "T_final = get_last_value(results.T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "**Exercise:**  Simulate the temperature of 50 mL of milk with a starting temperature of 5 degC, in a vessel with the same insulation, for 15 minutes, and plot the results.\n",
+    "\n",
+    "By trial and error, find a value for `r` that makes the final temperature close to 20 C."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(results.T, label='milk')\n",
+    "decorate(xlabel='Time (minutes)',\n",
+    "         ylabel='Temperature (C)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap16soln.ipynb b/colab/chap16soln.ipynb
new file mode 100644
index 00000000..b359b505
--- /dev/null
+++ b/colab/chap16soln.ipynb
@@ -0,0 +1,842 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 16\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Code from previous notebooks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func(state, t, system):\n",
+    "    \"\"\"Update the thermal transfer model.\n",
+    "    \n",
+    "    state: State (temp)\n",
+    "    t: time\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: State (temp)\n",
+    "    \"\"\"\n",
+    "    r, T_env, dt = system.r, system.T_env, system.dt\n",
+    "    \n",
+    "    T = state.T\n",
+    "    T += -r * (T - T_env) * dt\n",
+    "    \n",
+    "    return State(T=T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "    \n",
+    "    Add a TimeFrame to the System: results\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \"\"\"\n",
+    "    init = system.init\n",
+    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
+    "    \n",
+    "    frame = TimeFrame(columns=init.index)\n",
+    "    frame.row[t_0] = init\n",
+    "    ts = linrange(t_0, t_end, dt)\n",
+    "    \n",
+    "    for t in ts:\n",
+    "        frame.row[t+dt] = update_func(frame.row[t], t, system)\n",
+    "    \n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(T_init, r, volume, t_end):\n",
+    "    \"\"\"Makes a System object with the given parameters.\n",
+    "\n",
+    "    T_init: initial temperature in degC\n",
+    "    r: heat transfer rate, in 1/min\n",
+    "    volume: volume of liquid in mL\n",
+    "    t_end: end time of simulation\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(T=T_init)\n",
+    "                   \n",
+    "    return System(init=init,\n",
+    "                  r=r, \n",
+    "                  volume=volume,\n",
+    "                  temp=T_init,\n",
+    "                  t_0=0, \n",
+    "                  t_end=t_end, \n",
+    "                  dt=1,\n",
+    "                  T_env=22)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using `root_bisect`\n",
+    "\n",
+    "As a simple example, let's find the roots of this function; that is, the values of `x` that make the result 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def func(x):\n",
+    "    return (x-1) * (x-2) * (x-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`modsim.py` provides `root_bisect`, which searches for a root by bisection.  The first argument is the function whose roots we want.  The second argument is an interval that contains a root."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = root_bisect(func, [0.5, 1.5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an object that contains the root that was found and other information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res.root"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we provide a different interval, we find a different root."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = root_bisect(func, [1.5, 2.5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res.root"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the interval doesn't contain a root, the results explain the error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = root_bisect(func, [4, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to find the value of `r` that makes the final temperature 70, so we define an \"error function\" that takes `r` as a parameter and returns the difference between the final temperature and the goal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def error_func1(r):\n",
+    "    \"\"\"Runs a simulation and returns the `error`.\n",
+    "    \n",
+    "    r: heat transfer rate, in 1/min\n",
+    "    \n",
+    "    returns: difference between final temp and 70 C\n",
+    "    \"\"\"\n",
+    "    system = make_system(T_init=90, r=r, volume=300, t_end=30)\n",
+    "    results = run_simulation(system, update_func)\n",
+    "    T_final = get_last_value(results.T)\n",
+    "    return T_final - 70"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With `r=0.01`, we end up a little too warm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error_func1(r=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With `r=0.02`, we end up too cold."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error_func1(r=0.02)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The return value from `root_bisect` is an array with a single element, the estimated value of `r`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = root_bisect(error_func1, [0.01, 0.02])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r_coffee = res.root"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we run the simulation with the estimated value of `r`, the final temperature is 70 C, as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=30)\n",
+    "results = run_simulation(coffee, update_func)\n",
+    "T_final = get_last_value(results.T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  When you call `root_bisect`, it calls `error_func1` several times.  To see how this works, add a print statement to `error_func1` and run `root_bisect` again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Repeat this process to estimate `r_milk`, given that it starts at 5 C and reaches 20 C after 15 minutes.  \n",
+    "\n",
+    "Before you use `root_bisect`, you might want to try a few values for `r_milk` and see how close you can get by trial and error.  Here's an initial guess to get you started:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r_milk = 0.1\n",
+    "milk = make_system(T_init=5, r=r_milk, volume=50, t_end=15)\n",
+    "results = run_simulation(milk, update_func)\n",
+    "T_final = get_last_value(results.T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mixing liquids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function takes `System` objects that represent two liquids, computes the temperature of the mixture, and returns a new `System` object that represents the mixture."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mix(system1, system2):\n",
+    "    \"\"\"Simulates the mixture of two liquids.\n",
+    "    \n",
+    "    system1: System representing coffee\n",
+    "    system2: System representing milk\n",
+    "    \n",
+    "    returns: System representing the mixture\n",
+    "    \"\"\"\n",
+    "    assert system1.t_end == system2.t_end\n",
+    "    \n",
+    "    V1, V2 = system1.volume, system2.volume\n",
+    "    T1, T2 = system1.temp, system2.temp\n",
+    "    \n",
+    "    V_mix = V1 + V2\n",
+    "    T_mix = (V1 * T1 + V2 * T2) / V_mix\n",
+    "    \n",
+    "    return make_system(T_init=T_mix,\n",
+    "                       r=system1.r,\n",
+    "                       volume=V_mix,\n",
+    "                       t_end=30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`mix` requires the `System` objects to have `temp` as a system variable.  `make_system` initializes this variable;\n",
+    "the following function makes sure it gets updated when we run a simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_and_set(system):\n",
+    "    \"\"\"Run a simulation and set the final temperature.\n",
+    "    \n",
+    "    system: System\n",
+    "    \n",
+    "    returns: TimeFrame\n",
+    "    \"\"\"\n",
+    "    results = run_simulation(system, update_func)\n",
+    "    system.temp = get_last_value(results.T)\n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mixing immediately\n",
+    "\n",
+    "Next here's what we get if we add the milk immediately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=30)\n",
+    "coffee.temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "milk = make_system(T_init=5, r=r_milk, volume=50, t_end=30)\n",
+    "milk.temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mix_first = mix(coffee, milk)\n",
+    "mix_first.temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mix_results = run_and_set(mix_first)\n",
+    "mix_first.temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mixing at the end\n",
+    "\n",
+    "First we'll see what happens if we add the milk at the end.  We'll simulate the coffee and the milk separately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee_results = run_and_set(coffee)\n",
+    "coffee.temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "milk_results = run_and_set(milk)\n",
+    "milk.temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(coffee_results.T, label='coffee')\n",
+    "plot(milk_results.T, '--', label='milk')\n",
+    "\n",
+    "decorate(xlabel='Time (minutes)',\n",
+    "         ylabel='Temperature (C)',\n",
+    "         loc='center left')\n",
+    "\n",
+    "savefig('figs/chap16-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what happens when we mix them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mix_last = mix(coffee, milk)\n",
+    "mix_last.temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mix_last.temp - mix_first.temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function takes `t_add`, which is the time when the milk is added, and returns the final temperature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_and_mix(t_add, t_total):\n",
+    "    \"\"\"Simulates two liquids and them mixes them at t_add.\n",
+    "    \n",
+    "    t_add: time in minutes\n",
+    "    t_total: total time to simulate, min\n",
+    "    \n",
+    "    returns: final temperature\n",
+    "    \"\"\"\n",
+    "    coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=t_add)\n",
+    "    coffee_results = run_and_set(coffee)\n",
+    "    \n",
+    "    milk = make_system(T_init=5, r=r_milk, volume=50, t_end=t_add)\n",
+    "    milk_results = run_and_set(milk)\n",
+    "    \n",
+    "    mixture = mix(coffee, milk)\n",
+    "    mixture.t_end = t_total - t_add\n",
+    "    results = run_and_set(mixture)\n",
+    "\n",
+    "    return mixture.temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can try it out with a few values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_and_mix(t_add=0, t_total=30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_and_mix(t_add=15, t_total=30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_and_mix(t_add=30, t_total=30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then sweep a range of values for `t_add`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sweep = SweepSeries()\n",
+    "for t_add in linspace(0, 30, 11):\n",
+    "    sweep[t_add] = run_and_mix(t_add, 30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the result looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(sweep, label='final temp', color='C2')\n",
+    "decorate(xlabel='Time added (min)',\n",
+    "         ylabel='Final temperature (C)')\n",
+    "\n",
+    "savefig('figs/chap16-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use the analytic result to compute temperature as a function of time.  The following function is similar to `run_simulation`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_analysis(system):\n",
+    "    \"\"\"Computes temperature using the analytic solution.\n",
+    "        \n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: TimeFrame\n",
+    "    \"\"\"\n",
+    "    T_env, r = system.T_env, system.r\n",
+    "    \n",
+    "    T_init = system.init.T    \n",
+    "    ts = linspace(0, system.t_end)\n",
+    "    \n",
+    "    T_array = T_env + (T_init - T_env) * exp(-r * ts)\n",
+    "    \n",
+    "    # to be consistent with run_simulation,\n",
+    "    # we put the array into a TimeFrame\n",
+    "    return TimeFrame(T_array, index=ts, columns=['T'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run it.  From the analysis (see `chap16sympy.ipynb`), we have the computed value of `r_coffee2`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r_coffee2 = 0.011610223142273859\n",
+    "coffee2 = make_system(T_init=90, r=r_coffee2, volume=300, t_end=30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_analysis(coffee2)\n",
+    "T_final_analysis = get_last_value(results.T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can compare to the results from simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=30)\n",
+    "results = run_simulation(coffee, update_func)\n",
+    "T_final_simulation = get_last_value(results.T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "They are identical except for a small roundoff error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "T_final_analysis - T_final_simulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "**Exercise:**  Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
+    "\n",
+    "Hint: Think about the simplest way to represent the behavior of a refrigerator in this model.  The change you make to test this variation of the problem should be very small!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap17soln.ipynb b/colab/chap17soln.ipynb
new file mode 100644
index 00000000..834f0306
--- /dev/null
+++ b/colab/chap17soln.ipynb
@@ -0,0 +1,273 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 17\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data\n",
+    "\n",
+    "We have data from Pacini and Bergman (1986), \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 23: 113-122.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('data/glucose_insulin.csv', index_col='time')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the glucose time series looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(data.glucose, 'bo', label='glucose')\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration (mg/dL)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the insulin time series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(data.insulin, 'go', label='insulin')\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration ($\\mu$U/mL)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the book, I put them in a single figure, using `subplot`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subplot(2, 1, 1)\n",
+    "plot(data.glucose, 'bo', label='glucose')\n",
+    "decorate(ylabel='Concentration (mg/dL)')\n",
+    "\n",
+    "subplot(2, 1, 2)\n",
+    "plot(data.insulin, 'go', label='insulin')\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration ($\\mu$U/mL)')\n",
+    "\n",
+    "savefig('figs/chap17-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpolation\n",
+    "\n",
+    "We have measurements of insulin concentration at discrete points in time, but we need to estimate it at intervening points.  We'll use `interpolate`, which takes a `Series` and returns a function:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The return value from `interpolate` is a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "I = interpolate(data.insulin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use the result, `I`, to estimate the insulin level at any point in time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "I(7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`I` can also take an array of time and return an array of estimates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_0 = get_first_label(data)\n",
+    "t_end = get_last_label(data)\n",
+    "ts = linrange(t_0, t_end, endpoint=True)\n",
+    "I(ts)\n",
+    "type(ts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the interpolated values look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(data.insulin, 'go', label='insulin data')\n",
+    "plot(ts, I(ts), color='green', label='interpolated')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration ($\\mu$U/mL)')\n",
+    "\n",
+    "savefig('figs/chap17-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** [Read the documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html) of `scipy.interpolate.interp1d`.  Pass a keyword argument to `interpolate` to specify one of the other kinds of interpolation, and run the code again to see what it looks like. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Interpolate the glucose data and generate a plot, similar to the previous one, that shows the data points and the interpolated curve evaluated at the time values in `ts`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Under the hood"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_code(interpolate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap18soln.ipynb b/colab/chap18soln.ipynb
new file mode 100644
index 00000000..e8e6df35
--- /dev/null
+++ b/colab/chap18soln.ipynb
@@ -0,0 +1,533 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 18\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Code from the previous chapter\n",
+    "\n",
+    "Read the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('data/glucose_insulin.csv', index_col='time');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Interpolate the insulin data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "I = interpolate(data.insulin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The glucose minimal model\n",
+    "\n",
+    "I'll cheat by starting with parameters that fit the data roughly; then we'll see how to improve them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = Params(G0 = 290,\n",
+    "                k1 = 0.03,\n",
+    "                k2 = 0.02,\n",
+    "                k3 = 1e-05)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a version of `make_system` that takes the parameters and data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(params, data):\n",
+    "    \"\"\"Makes a System object with the given parameters.\n",
+    "    \n",
+    "    params: sequence of G0, k1, k2, k3\n",
+    "    data: DataFrame with `glucose` and `insulin`\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    G0, k1, k2, k3 = params\n",
+    "    \n",
+    "    Gb = data.glucose[0]\n",
+    "    Ib = data.insulin[0]\n",
+    "    I = interpolate(data.insulin)\n",
+    "    \n",
+    "    t_0 = get_first_label(data)\n",
+    "    t_end = get_last_label(data)\n",
+    "\n",
+    "    init = State(G=G0, X=0)\n",
+    "    \n",
+    "    return System(params,\n",
+    "                  init=init, Gb=Gb, Ib=Ib, I=I,\n",
+    "                  t_0=t_0, t_end=t_end, dt=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the update function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func(state, t, system):\n",
+    "    \"\"\"Updates the glucose minimal model.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time in min\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: State object\n",
+    "    \"\"\"\n",
+    "    G, X = state\n",
+    "    k1, k2, k3 = system.k1, system.k2, system.k3 \n",
+    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
+    "    dt = system.dt\n",
+    "        \n",
+    "    dGdt = -k1 * (G - Gb) - X*G\n",
+    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
+    "    \n",
+    "    G += dGdt * dt\n",
+    "    X += dXdt * dt\n",
+    "\n",
+    "    return State(G=G, X=X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before running the simulation, it is always a good idea to test the update function using the initial conditions.  In this case we can veryify that the results are at least qualitatively correct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "update_func(system.init, system.t_0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now `run_simulation` is pretty much the same as it always is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "        \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \n",
+    "    returns: TimeFrame\n",
+    "    \"\"\"\n",
+    "    init = system.init\n",
+    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
+    "    \n",
+    "    frame = TimeFrame(columns=init.index)\n",
+    "    frame.row[t_0] = init\n",
+    "    ts = linrange(t_0, t_end, dt)\n",
+    "    \n",
+    "    for t in ts:\n",
+    "        frame.row[t+dt] = update_func(frame.row[t], t, system)\n",
+    "    \n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, update_func);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are in a `TimeFrame` object with one column per state variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following plot shows the results of the simulation along with the actual glucose data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subplot(2, 1, 1)\n",
+    "\n",
+    "plot(results.G, 'b-', label='simulation')\n",
+    "plot(data.glucose, 'bo', label='glucose data')\n",
+    "decorate(ylabel='Concentration (mg/dL)')\n",
+    "\n",
+    "subplot(2, 1, 2)\n",
+    "\n",
+    "plot(results.X, 'C1', label='remote insulin')\n",
+    "\n",
+    "decorate(xlabel='Time (min)', \n",
+    "         ylabel='Concentration (arbitrary units)')\n",
+    "\n",
+    "savefig('figs/chap18-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Numerical solution\n",
+    "\n",
+    "Now let's solve the differential equation numerically using `run_ode_solver`, which is an implementation of Ralston's method.\n",
+    "\n",
+    "Instead of an update function, we provide a slope function that evaluates the right-hand side of the differential equations.\n",
+    "\n",
+    "We don't have to do the update part; the solver does it for us."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Computes derivatives of the glucose minimal model.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time in min\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: derivatives of G and X\n",
+    "    \"\"\"\n",
+    "    G, X = state\n",
+    "    k1, k2, k3 = system.k1, system.k2, system.k3 \n",
+    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
+    "    \n",
+    "    dGdt = -k1 * (G - Gb) - X*G\n",
+    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
+    "    \n",
+    "    return dGdt, dXdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(system.init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run the ODE solver."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results2, details = run_ode_solver(system, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`details` is a `ModSimSeries` object with information about how the solver worked."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`results` is a `TimeFrame` with one row for each time step and one column for each state variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting the results from `run_simulation` and `run_ode_solver`, we can see that they are not very different."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(results.G, 'C0', label='run_simulation')\n",
+    "plot(results2.G, 'C2--', label='run_ode_solver')\n",
+    "\n",
+    "decorate(xlabel='Time (min)', ylabel='Concentration (mg/dL)')\n",
+    "\n",
+    "savefig('figs/chap18-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The differences in `G` are less than 2%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diff = results.G - results2.G\n",
+    "percent_diff = diff / results2.G * 100\n",
+    "percent_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max(abs(percent_diff))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercises\n",
+    "\n",
+    "**Exercise:**  Our solution to the differential equations is only approximate because we used a finite step size, `dt=2` minutes.\n",
+    "\n",
+    "If we make the step size smaller, we expect the solution to be more accurate.  Run the simulation with `dt=1` and compare the results.  What is the largest relative error between the two solutions?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Under the hood\n",
+    "\n",
+    "Here's the source code for `run_ode_solver` if you'd like to know how it works.\n",
+    "\n",
+    "Notice that `run_ode_solver` is another name for `run_ralston`, which implements [Ralston's method](https://en.wikipedia.org/wiki/List_of_Runge–Kutta_methods)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_code(run_ode_solver)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Related reading:** You might be interested in this article about [people making a DIY artificial pancreas](https://www.bloomberg.com/news/features/2018-08-08/the-250-biohack-that-s-revolutionizing-life-with-diabetes)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap20soln.ipynb b/colab/chap20soln.ipynb
new file mode 100644
index 00000000..46fc858c
--- /dev/null
+++ b/colab/chap20soln.ipynb
@@ -0,0 +1,643 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 20\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dropping pennies\n",
+    "\n",
+    "I'll start by getting the units we need from Pint."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = UNITS.meter\n",
+    "s = UNITS.second"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And defining the initial state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "init = State(y=381 * m, \n",
+    "             v=0 * m/s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Acceleration due to gravity is about 9.8 m / s$^2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g = 9.8 * m/s**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I'll start with a duration of 10 seconds and step size 0.1 second."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_end = 10 * s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt = 0.1 * s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we make a `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = System(init=init, g=g, t_end=t_end, dt=dt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And define the slope function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Compute derivatives of the state.\n",
+    "    \n",
+    "    state: position, velocity\n",
+    "    t: time\n",
+    "    system: System object containing `g`\n",
+    "    \n",
+    "    returns: derivatives of y and v\n",
+    "    \"\"\"\n",
+    "    y, v = state\n",
+    "    g = system.g    \n",
+    "\n",
+    "    dydt = v\n",
+    "    dvdt = -g\n",
+    "    \n",
+    "    return dydt, dvdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's always a good idea to test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dydt, dvdt = slope_func(system.init, 0, system)\n",
+    "print(dydt)\n",
+    "print(dvdt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we're ready to call `run_ode_solver`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_ode_solver(system, slope_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's position as a function of time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_position(results):\n",
+    "    plot(results.y, label='y')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Position (m)')\n",
+    "\n",
+    "plot_position(results)\n",
+    "savefig('figs/chap20-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Onto the sidewalk\n",
+    "\n",
+    "To figure out when the penny hit the sidewalk, we can use `crossings`, which finds the times where a `Series` passes through a given value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_crossings = crossings(results.y, 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this example there should be just one crossing, the time when the penny hits the sidewalk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_sidewalk = t_crossings[0] * s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can compare that to the exact result.  Without air resistance, we have\n",
+    "\n",
+    "$v = -g t$\n",
+    "\n",
+    "and\n",
+    "\n",
+    "$y = 381 - g t^2 / 2$\n",
+    "\n",
+    "Setting $y=0$ and solving for $t$ yields\n",
+    "\n",
+    "$t = \\sqrt{\\frac{2 y_{init}}{g}}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sqrt(2 * init.y / g)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The estimate is accurate to about 9 decimal places."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Events\n",
+    "\n",
+    "Instead of running the simulation until the penny goes through the sidewalk, it would be better to detect the point where the penny hits the sidewalk and stop.  `run_ode_solver` provides exactly the tool we need, **event functions**.\n",
+    "\n",
+    "Here's an event function that returns the height of the penny above the sidewalk:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(state, t, system):\n",
+    "    \"\"\"Return the height of the penny above the sidewalk.\n",
+    "    \"\"\"\n",
+    "    y, v = state\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's how we pass it to `run_ode_solver`.  The solver should run until the event function returns 0, and then terminate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The message from the solver indicates the solver stopped because the event we wanted to detect happened.\n",
+    "\n",
+    "Here are the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the `events` option, the solver returns the actual time steps it computed, which are not necessarily equally spaced. \n",
+    "\n",
+    "The last time step is when the event occurred:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_sidewalk = get_last_label(results) * s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is accurate to about 4 decimal places.\n",
+    "\n",
+    "We can also check the velocity of the penny when it hits the sidewalk:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_sidewalk = get_last_value(results.v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And convert to kilometers per hour."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "km = UNITS.kilometer\n",
+    "h = UNITS.hour\n",
+    "v_sidewalk.to(km / h)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If there were no air resistance, the penny would hit the sidewalk (or someone's head) at more than 300 km/h.\n",
+    "\n",
+    "So it's a good thing there is air resistance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Under the hood\n",
+    "\n",
+    "Here is the source code for `crossings` so you can see what's happening under the hood:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_code(crossings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [documentation of InterpolatedUnivariateSpline is here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.InterpolatedUnivariateSpline.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercises\n",
+    "\n",
+    "**Exercise:** Here's a question from the web site [Ask an Astronomer](http://curious.astro.cornell.edu/about-us/39-our-solar-system/the-earth/other-catastrophes/57-how-long-would-it-take-the-earth-to-fall-into-the-sun-intermediate):\n",
+    "\n",
+    "\"If the Earth suddenly stopped orbiting the Sun, I know eventually it would be pulled in by the Sun's gravity and hit it. How long would it take the Earth to hit the Sun? I imagine it would go slowly at first and then pick up speed.\"\n",
+    "\n",
+    "Use `run_ode_solver` to answer this question.\n",
+    "\n",
+    "Here are some suggestions about how to proceed:\n",
+    "\n",
+    "1.  Look up the Law of Universal Gravitation and any constants you need.  I suggest you work entirely in SI units: meters, kilograms, and Newtons.\n",
+    "\n",
+    "2.  When the distance between the Earth and the Sun gets small, this system behaves badly, so you should use an event function to stop when the surface of Earth reaches the surface of the Sun.\n",
+    "\n",
+    "3. Express your answer in days, and plot the results as millions of kilometers versus days.\n",
+    "\n",
+    "If you read the reply by Dave Rothstein, you will see other ways to solve the problem, and a good discussion of the modeling decisions behind them.\n",
+    "\n",
+    "You might also be interested to know that [it's actually not that easy to get to the Sun](https://www.theatlantic.com/science/archive/2018/08/parker-solar-probe-launch-nasa/567197/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap21soln.ipynb b/colab/chap21soln.ipynb
new file mode 100644
index 00000000..54539d62
--- /dev/null
+++ b/colab/chap21soln.ipynb
@@ -0,0 +1,514 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 21\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### With air resistance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we'll add air resistance using the [drag equation](https://en.wikipedia.org/wiki/Drag_equation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I'll start by getting the units we'll need from Pint."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = UNITS.meter\n",
+    "s = UNITS.second\n",
+    "kg = UNITS.kilogram"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now I'll create a `Params` object to contain the quantities we need.  Using a Params object is convenient for grouping the system parameters in a way that's easy to read (and double-check)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = Params(height = 381 * m,\n",
+    "                v_init = 0 * m / s,\n",
+    "                g = 9.8 * m/s**2,\n",
+    "                mass = 2.5e-3 * kg,\n",
+    "                diameter = 19e-3 * m,\n",
+    "                rho = 1.2 * kg/m**3,\n",
+    "                v_term = 18 * m / s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can pass the `Params` object `make_system` which computes some additional parameters and defines `init`.\n",
+    "\n",
+    "`make_system` uses the given radius to compute `area` and the given `v_term` to compute the drag coefficient `C_d`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(params):\n",
+    "    \"\"\"Makes a System object for the given conditions.\n",
+    "    \n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    diameter, mass = params.diameter, params.mass\n",
+    "    g, rho = params.g, params.rho, \n",
+    "    v_init, v_term = params.v_init, params.v_term\n",
+    "    height = params.height\n",
+    "    \n",
+    "    area = np.pi * (diameter/2)**2\n",
+    "    C_d = 2 * mass * g / (rho * area * v_term**2)\n",
+    "    init = State(y=height, v=v_init)\n",
+    "    t_end = 30 * s\n",
+    "    dt = t_end / 100\n",
+    "    \n",
+    "    return System(params, area=area, C_d=C_d, \n",
+    "                  init=init, t_end=t_end, dt=dt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's make a `System`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the slope function, including acceleration due to gravity and drag."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Compute derivatives of the state.\n",
+    "    \n",
+    "    state: position, velocity\n",
+    "    t: time\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: derivatives of y and v\n",
+    "    \"\"\"\n",
+    "    y, v = state\n",
+    "    rho, C_d, g = system.rho, system.C_d, system.g\n",
+    "    area, mass = system.area, system.mass\n",
+    "    \n",
+    "    f_drag = rho * v**2 * C_d * area / 2\n",
+    "    a_drag = f_drag / mass\n",
+    "    \n",
+    "    dydt = v\n",
+    "    dvdt = -g + a_drag\n",
+    "    \n",
+    "    return dydt, dvdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, let's test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(system.init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use the same event function as in the previous chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(state, t, system):\n",
+    "    \"\"\"Return the height of the penny above the sidewalk.\n",
+    "    \"\"\"\n",
+    "    y, v = state\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final height is close to 0, as expected.\n",
+    "\n",
+    "Interestingly, the final velocity is not exactly terminal velocity, which suggests that there are some numerical errors.\n",
+    "\n",
+    "We can get the flight time from `results`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_sidewalk = get_last_label(results) * s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the plot of position as a function of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_position(results):\n",
+    "    plot(results.y)\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Position (m)')\n",
+    "    \n",
+    "plot_position(results)\n",
+    "savefig('figs/chap21-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And velocity as a function of time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_velocity(results):\n",
+    "    plot(results.v, color='C1', label='v')\n",
+    "        \n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Velocity (m/s)')\n",
+    "    \n",
+    "plot_velocity(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From an initial velocity of 0, the penny accelerates downward until it reaches terminal velocity; after that, velocity is constant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Run the simulation with an initial velocity, downward, that exceeds the penny's terminal velocity.  Hint: You can create a new `Params` object based on an existing one, like this:\n",
+    "\n",
+    "`params2 = Params(params, v_init=-30 * m/s)`\n",
+    "\n",
+    "What do you expect to happen?  Plot velocity and position as a function of time, and see if they are consistent with your prediction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_position(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose we drop a quarter from the Empire State Building and find that its flight time is 19.1 seconds.  Use this measurement to estimate the terminal velocity.\n",
+    "\n",
+    "1. You can get the relevant dimensions of a quarter from https://en.wikipedia.org/wiki/Quarter_(United_States_coin).\n",
+    "\n",
+    "2. Create a `Params` object with the system parameters.  We don't know `v_term`, so we'll start with the inital guess `v_term = 18 * m / s`.\n",
+    "\n",
+    "3. Use `make_system` to create a `System` object.\n",
+    "\n",
+    "4. Call `run_ode_solver` to simulate the system.  How does the flight time of the simulation compare to the measurement?\n",
+    "\n",
+    "5. Try a few different values of `t_term` and see if you can get the simulated flight time close to 19.1 seconds.\n",
+    "\n",
+    "6. Optionally, write an error function and use `root_scalar` to improve your estimate.\n",
+    "\n",
+    "7. Use your best estimate of `v_term` to compute `C_d`.\n",
+    "\n",
+    "Note: I fabricated the observed flight time, so don't take the results of this exercise too seriously."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap22soln.ipynb b/colab/chap22soln.ipynb
new file mode 100644
index 00000000..5a41edee
--- /dev/null
+++ b/colab/chap22soln.ipynb
@@ -0,0 +1,1238 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 22\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Vectors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `Vector` object represents a vector quantity.  In the context of mechanics, vector quantities include position, velocity, acceleration, and force, all of which might be in 2D or 3D.\n",
+    "\n",
+    "You can define a `Vector` object without units, but if it represents a physical quantity, you will often want to attach units to it.\n",
+    "\n",
+    "I'll start by grabbing the units we'll need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = UNITS.meter\n",
+    "s = UNITS.second\n",
+    "kg = UNITS.kilogram"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a two dimensional `Vector` in meters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = Vector(3, 4) * m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can access the elements by name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A.x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The magnitude is the length of the vector."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A.mag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The angle is the number of radians between the vector and the positive x axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A.angle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we make another `Vector` with the same units,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "B = Vector(1, 2) * m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can add `Vector` objects like this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A + B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And subtract like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A - B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can compute the Euclidean distance between two Vectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A.dist(B)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the difference in angle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A.diff_angle(B)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we are given the magnitude and angle of a vector, what we have is the representation of the vector in polar coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mag = A.mag\n",
+    "angle = A.angle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `pol2cart` to convert from polar to Cartesian coordinates, and then use the Cartesian coordinates to make a `Vector` object.\n",
+    "\n",
+    "In this example, the `Vector` we get should have the same components as `A`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = pol2cart(angle, mag)\n",
+    "Vector(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another way to represent the direction of `A` is a unit vector, which is a vector with magnitude 1 that points in the same direction as `A`.  You can compute a unit vector by dividing a vector by its magnitude:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A / A.mag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or by using the `hat` function, so named because unit vectors are conventionally decorated with a hat, like this: $\\hat{A}$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A.hat()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Create a `Vector` named `a_grav` that represents acceleration due to gravity, with x component 0 and y component $-9.8$ meters / second$^2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Degrees and radians\n",
+    "\n",
+    "Pint provides units to represent degree and radians."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "degree = UNITS.degree\n",
+    "radian = UNITS.radian"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you have an angle in degrees,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "angle = 45 * degree\n",
+    "angle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can convert to radians."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "angle_rad = angle.to(radian)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If it's already in radians, `to` does the right thing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "angle_rad.to(radian)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also convert from radians to degrees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "angle_rad.to(degree)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an alterative, you can use `np.deg2rad`, which works with Pint quantities, but it also works with simple numbers and NumPy arrays:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.deg2rad(angle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Create a `Vector` named `a_force` that represents acceleration due to a force of 0.5 Newton applied to an object with mass 0.3 kilograms, in a direction 45 degrees up from the positive x-axis.\n",
+    "\n",
+    "Add `a_force` to `a_grav` from the previous exercise.  If that addition succeeds, that means that the units are compatible.  Confirm that the total acceleration seems to make sense."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Baseball"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a `Params` object that contains parameters for the flight of a baseball."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_end = 10 * s\n",
+    "dt = t_end / 100\n",
+    "\n",
+    "params = Params(x = 0 * m, \n",
+    "                y = 1 * m,\n",
+    "                g = 9.8 * m/s**2,\n",
+    "                mass = 145e-3 * kg,\n",
+    "                diameter = 73e-3 * m,\n",
+    "                rho = 1.2 * kg/m**3,\n",
+    "                C_d = 0.33,\n",
+    "                angle = 45 * degree,\n",
+    "                velocity = 40 * m / s,\n",
+    "                t_end=t_end, dt=dt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the function that uses the `Params` object to make a `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(params):\n",
+    "    \"\"\"Make a system object.\n",
+    "    \n",
+    "    params: Params object with angle, velocity, x, y,\n",
+    "               diameter, duration, g, mass, rho, and C_d\n",
+    "               \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    angle, velocity = params.angle, params.velocity\n",
+    "    \n",
+    "    # convert angle to degrees\n",
+    "    theta = np.deg2rad(angle)\n",
+    "    \n",
+    "    # compute x and y components of velocity\n",
+    "    vx, vy = pol2cart(theta, velocity)\n",
+    "    \n",
+    "    # make the initial state\n",
+    "    R = Vector(params.x, params.y)\n",
+    "    V = Vector(vx, vy)\n",
+    "    init = State(R=R, V=V)\n",
+    "    \n",
+    "    # compute area from diameter\n",
+    "    diameter = params.diameter\n",
+    "    area = np.pi * (diameter/2)**2\n",
+    "    \n",
+    "    return System(params, init=init, area=area)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we use it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a function that computes drag force using vectors:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drag_force(V, system):\n",
+    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
+    "    \n",
+    "    V: velocity Vector\n",
+    "    system: System object with rho, C_d, area\n",
+    "    \n",
+    "    returns: Vector drag force\n",
+    "    \"\"\"\n",
+    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
+    "    \n",
+    "    mag = rho * V.mag**2 * C_d * area / 2\n",
+    "    direction = -V.hat()\n",
+    "    f_drag = direction * mag\n",
+    "    return f_drag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can test it like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "V_test = Vector(10, 10) * m/s\n",
+    "drag_force(V_test, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the slope function that computes acceleration due to gravity and drag."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Computes derivatives of the state variables.\n",
+    "    \n",
+    "    state: State (x, y, x velocity, y velocity)\n",
+    "    t: time\n",
+    "    system: System object with g, rho, C_d, area, mass\n",
+    "    \n",
+    "    returns: sequence (vx, vy, ax, ay)\n",
+    "    \"\"\"\n",
+    "    R, V = state\n",
+    "    mass, g = system.mass, system.g\n",
+    "    \n",
+    "    a_drag = drag_force(V, system) / mass\n",
+    "    a_grav = Vector(0, -g)\n",
+    "    \n",
+    "    A = a_grav + a_drag\n",
+    "    \n",
+    "    return V, A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Always test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(system.init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use an event function to stop the simulation when the ball hits the ground:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(state, t, system):\n",
+    "    \"\"\"Stop when the y coordinate is 0.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: y coordinate\n",
+    "    \"\"\"\n",
+    "    R, V = state\n",
+    "    return R.y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_func(system.init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can call `run_ode_solver`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final label tells us the flight time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flight_time = get_last_label(results) * s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final value of `x` tells us the how far the ball landed from home plate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "R_final = get_last_value(results.R)\n",
+    "x_dist = R_final.x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualizing the results\n",
+    "\n",
+    "The simplest way to visualize the results is to plot x and y as functions of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xs = results.R.extract('x')\n",
+    "ys = results.R.extract('y')\n",
+    "\n",
+    "xs.plot()\n",
+    "ys.plot()\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Position (m)')\n",
+    "\n",
+    "savefig('figs/chap22-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the velocities the same way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vx = results.V.extract('x')\n",
+    "vy = results.V.extract('y')\n",
+    "\n",
+    "vx.plot(label='vx')\n",
+    "vy.plot(label='vy')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Velocity (m/s)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The x velocity slows down due to drag.\n",
+    "\n",
+    "The y velocity drops quickly while drag and gravity are in the same direction, then more slowly after the ball starts to fall.\n",
+    "\n",
+    "Another way to visualize the results is to plot y versus x.  The result is the trajectory of the ball through its plane of motion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_trajectory(results):\n",
+    "    xs = results.R.extract('x')\n",
+    "    ys = results.R.extract('y')\n",
+    "    plot(xs, ys, color='C2', label='trajectory')\n",
+    "\n",
+    "    decorate(xlabel='x position (m)',\n",
+    "             ylabel='y position (m)')\n",
+    "\n",
+    "plot_trajectory(results)\n",
+    "savefig('figs/chap22-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Animation\n",
+    "\n",
+    "One of the best ways to visualize the results of a physical model is animation.  If there are problems with the model, animation can make them apparent.\n",
+    "\n",
+    "The ModSimPy library provides `animate`, which takes as parameters a `TimeSeries` and a draw function.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The draw function should take as parameters a `State` object and the time.  It should draw a single frame of the animation.\n",
+    "\n",
+    "Inside the draw function, you almost always have to call `set_xlim` and `set_ylim`.  Otherwise `matplotlib` auto-scales the axes, which is usually not what you want."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xs = results.R.extract('x')\n",
+    "ys = results.R.extract('y')\n",
+    "\n",
+    "def draw_func(state, t):\n",
+    "    set_xlim(xs)\n",
+    "    set_ylim(ys)\n",
+    "    x, y = state.R\n",
+    "    plot(x, y, 'bo')\n",
+    "    decorate(xlabel='x position (m)',\n",
+    "             ylabel='y position (m)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "animate(results, draw_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Delete the lines that set the x and y axes (or [comment them out](https://en.wiktionary.org/wiki/comment_out)) and see what the animation does."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Under the hood\n",
+    "\n",
+    "`Vector` is a function that returns a `ModSimVector` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "V = Vector(3, 4)\n",
+    "type(V)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `ModSimVector` is a specialized kind of Pint `Quantity`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "isinstance(V, Quantity)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's one gotcha you might run into with Vectors and Quantities.  If you multiply a `ModSimVector` and a `Quantity`, you get a `ModSimVector`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "V1 = V * m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(V1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But if you multiply a `Quantity` and a `Vector`, you get a `Quantity`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "V2 = m * V"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(V2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With a `ModSimVector` you can get the coordinates using dot notation, as well as `mag`, `mag2`, and `angle`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "V1.x, V1.y, V1.mag, V1.angle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With a `Quantity`, you can't.  But you can use indexing to get the coordinates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "V2[0], V2[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And you can use vector functions to get the magnitude and angle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vector_mag(V2), vector_angle(V2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And often you can avoid the whole issue by doing the multiplication with the `ModSimVector` on the left."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Run the simulation with and without air resistance.  How wrong would we be if we ignored drag?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hint\n",
+    "\n",
+    "system_no_drag = System(system, C_d=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / meter$^3$.  How much farther would a ball hit with the same velocity and launch angle travel?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hint\n",
+    "\n",
+    "system2 = System(system, rho=1.0*kg/m**3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The model so far is based on the assumption that coefficient of drag does not depend on velocity, but in reality it does.  The following figure, from Adair, [*The Physics of Baseball*](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC), shows coefficient of drag as a function of velocity.\n",
+    "\n",
+    "<img src=\"data/baseball_drag.png\" width=\"400\">\n",
+    "\n",
+    "\n",
+    "I used [an online graph digitizer](https://automeris.io/WebPlotDigitizer/) to extract the data and save it in a CSV file.  Here's how we can read it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "baseball_drag = pd.read_csv('data/baseball_drag.csv')\n",
+    "mph = Quantity(baseball_drag['Velocity in mph'], UNITS.mph)\n",
+    "mps = mph.to(m/s)\n",
+    "baseball_drag.index = magnitude(mps)\n",
+    "baseball_drag.index.name = 'Velocity in meters per second'\n",
+    "baseball_drag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Modify the model to include the dependence of `C_d` on velocity, and see how much it affects the results.  Hint: use `interpolate`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C_d = drag_interp(43 * m / s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap23soln.ipynb b/colab/chap23soln.ipynb
new file mode 100644
index 00000000..0eca5394
--- /dev/null
+++ b/colab/chap23soln.ipynb
@@ -0,0 +1,571 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 23\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Code from the previous chapter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = UNITS.meter\n",
+    "s = UNITS.second\n",
+    "kg = UNITS.kilogram\n",
+    "degree = UNITS.degree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_end = 20 * s\n",
+    "dt = t_end / 100\n",
+    "\n",
+    "params = Params(x = 0 * m, \n",
+    "                y = 1 * m,\n",
+    "                g = 9.8 * m/s**2,\n",
+    "                mass = 145e-3 * kg,\n",
+    "                diameter = 73e-3 * m,\n",
+    "                rho = 1.2 * kg/m**3,\n",
+    "                C_d = 0.3,\n",
+    "                angle = 45 * degree,\n",
+    "                velocity = 40 * m / s,\n",
+    "                t_end=t_end,\n",
+    "                dt=dt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(params):\n",
+    "    \"\"\"Make a system object.\n",
+    "    \n",
+    "    params: Params object with angle, velocity, x, y,\n",
+    "               diameter, duration, g, mass, rho, and C_d\n",
+    "               \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    angle, velocity = params.angle, params.velocity\n",
+    "    \n",
+    "    # convert angle to degrees\n",
+    "    theta = np.deg2rad(angle)\n",
+    "    \n",
+    "    # compute x and y components of velocity\n",
+    "    vx, vy = pol2cart(theta, velocity)\n",
+    "    \n",
+    "    # make the initial state\n",
+    "    R = Vector(params.x, params.y)\n",
+    "    V = Vector(vx, vy)\n",
+    "    init = State(R=R, V=V)\n",
+    "    \n",
+    "    # compute area from diameter\n",
+    "    diameter = params.diameter\n",
+    "    area = np.pi * (diameter/2)**2\n",
+    "    \n",
+    "    return System(params, init=init, area=area)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drag_force(V, system):\n",
+    "    \"\"\"Computes drag force in the opposite direction of `V`.\n",
+    "    \n",
+    "    V: velocity Vector\n",
+    "    system: System object with rho, C_d, area\n",
+    "    \n",
+    "    returns: Vector drag force\n",
+    "    \"\"\"\n",
+    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
+    "    \n",
+    "    mag = rho * V.mag**2 * C_d * area / 2\n",
+    "    direction = -V.hat()\n",
+    "    f_drag = direction * mag\n",
+    "    return f_drag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Computes derivatives of the state variables.\n",
+    "    \n",
+    "    state: State (x, y, x velocity, y velocity)\n",
+    "    t: time\n",
+    "    system: System object with g, rho, C_d, area, mass\n",
+    "    \n",
+    "    returns: sequence (vx, vy, ax, ay)\n",
+    "    \"\"\"\n",
+    "    R, V = state\n",
+    "    mass, g = system.mass, system.g\n",
+    "    \n",
+    "    a_drag = drag_force(V, system) / mass\n",
+    "    a_grav = Vector(0, -g)\n",
+    "    \n",
+    "    A = a_grav + a_drag\n",
+    "    \n",
+    "    return V, A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(state, t, system):\n",
+    "    \"\"\"Stop when the y coordinate is 0.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: y coordinate\n",
+    "    \"\"\"\n",
+    "    R, V = state\n",
+    "    return R.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optimal launch angle\n",
+    "\n",
+    "To find the launch angle that maximizes distance from home plate, we need a function that takes launch angle and returns range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def range_func(angle, params):  \n",
+    "    \"\"\"Computes range for a given launch angle.\n",
+    "    \n",
+    "    angle: launch angle in degrees\n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: distance in meters\n",
+    "    \"\"\"\n",
+    "    params = Params(params, angle=angle)\n",
+    "    system = make_system(params)\n",
+    "    results, details = run_ode_solver(system, slope_func, events=event_func)\n",
+    "    x_dist = get_last_value(results.R).x\n",
+    "    print(angle, x_dist)\n",
+    "    return x_dist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's test `range_func`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "range_func(45, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And sweep through a range of angles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "angles = linspace(20, 80, 21)\n",
+    "sweep = SweepSeries()\n",
+    "\n",
+    "for angle in angles:\n",
+    "    x_dist = range_func(angle, params)\n",
+    "    sweep[angle] = x_dist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting the `Sweep` object, it looks like the peak is between 40 and 45 degrees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(sweep, color='C2')\n",
+    "decorate(xlabel='Launch angle (degree)',\n",
+    "         ylabel='Range (m)',\n",
+    "         title='Range as a function of launch angle',\n",
+    "         legend=False)\n",
+    "\n",
+    "savefig('figs/chap23-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `maximize` to search for the peak efficiently."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bounds = [0, 90] * degree\n",
+    "res = maximize(range_func, bounds, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`res` is an `ModSimSeries` object with detailed results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`x` is the optimal angle and `fun` the optional range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimal_angle = res.x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_x_dist = res.fun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Under the hood\n",
+    "\n",
+    "Read the source code for `maximize` and `minimize_scalar`, below.\n",
+    "\n",
+    "Add a print statement to `range_func` that prints `angle`.  Then run `maximize` again so you can see how many times it calls `range_func` and what the arguments are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_code(maximize)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_code(minimize_scalar)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Manny Ramirez problem\n",
+    "\n",
+    "Finally, let's solve the Manny Ramirez problem:\n",
+    "\n",
+    "*What is the minimum effort required to hit a home run in Fenway Park?*\n",
+    "\n",
+    "Fenway Park is a baseball stadium in Boston, Massachusetts.  One of its most famous features is the \"Green Monster\", which is a wall in left field that is unusually close to home plate, only 310 feet along the left field line.  To compensate for the short distance, the wall is unusually high, at 37 feet.\n",
+    "\n",
+    "Although the problem asks for a minimum, it is not an optimization problem.  Rather, we want to solve for the initial velocity that just barely gets the ball to the top of the wall, given that it is launched at the optimal angle.\n",
+    "\n",
+    "And we have to be careful about what we mean by \"optimal\".  For this problem, we don't want the longest range, we want the maximum height at the point where it reaches the wall.\n",
+    "\n",
+    "If you are ready to solve the problem on your own, go ahead.  Otherwise I will walk you through the process with an outline and some starter code.\n",
+    "\n",
+    "As a first step, write a function called `height_func` that takes a launch angle and a params as parameters, simulates the flights of a baseball, and returns the height of the baseball when it reaches a point 94.5 meters (310 feet) from home plate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Always test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your function with a launch angle of 45 degrees:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now use `maximize` to find the optimal angle.  Is it higher or lower than the angle that maximizes range?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With initial velocity 40 m/s and an optimal launch angle, the ball clears the Green Monster with a little room to spare.\n",
+    "\n",
+    "Which means we can get over the wall with a lower initial velocity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Finding the minimum velocity\n",
+    "\n",
+    "Even though we are finding the \"minimum\" velocity, we are not really solving a minimization problem.  Rather, we want to find the velocity that makes the height at the wall exactly 11 m, given given that it's launched at the optimal angle.  And that's a job for `root_bisect`.\n",
+    "\n",
+    "Write an error function that takes a velocity and a `Params` object as parameters.  It should use `maximize` to find the highest possible height of the ball at the wall, for the given velocity.  Then it should return the difference between that optimal height and 11 meters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your error function before you call `root_bisect`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then use `root_bisect` to find the answer to the problem, the minimum velocity that gets the ball out of the park."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And just to check, run `error_func` with the value you found."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap24soln.ipynb b/colab/chap24soln.ipynb
new file mode 100644
index 00000000..6d7e6b52
--- /dev/null
+++ b/colab/chap24soln.ipynb
@@ -0,0 +1,640 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 24\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rolling paper\n",
+    "\n",
+    "We'll start by loading the units we need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radian = UNITS.radian\n",
+    "m = UNITS.meter\n",
+    "s = UNITS.second"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And creating a `Params` object with the system parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = Params(Rmin = 0.02 * m,\n",
+    "                Rmax = 0.055 * m,\n",
+    "                L = 47 * m,\n",
+    "                omega = 10 * radian / s,\n",
+    "                t_end = 130 * s,\n",
+    "                dt = 1*s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function estimates the parameter `k`, which is the increase in the radius of the roll for each radian of rotation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def estimate_k(params):\n",
+    "    \"\"\"Estimates the parameter `k`.\n",
+    "    \n",
+    "    params: Params with Rmin, Rmax, and L\n",
+    "    \n",
+    "    returns: k in meters per radian\n",
+    "    \"\"\"\n",
+    "    Rmin, Rmax, L = params.Rmin, params.Rmax, params.L\n",
+    "    \n",
+    "    Ravg = (Rmax + Rmin) / 2\n",
+    "    Cavg = 2 * pi * Ravg\n",
+    "    revs = L / Cavg\n",
+    "    rads = 2 * pi * revs\n",
+    "    k = (Rmax - Rmin) / rads\n",
+    "    return k"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As usual, `make_system` takes a `Params` object and returns a `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(params):\n",
+    "    \"\"\"Make a system object.\n",
+    "    \n",
+    "    params: Params with Rmin, Rmax, and L\n",
+    "    \n",
+    "    returns: System with init, k, and ts\n",
+    "    \"\"\"\n",
+    "    init = State(theta = 0 * radian,\n",
+    "                 y = 0 * m,\n",
+    "                 r = params.Rmin)\n",
+    "    \n",
+    "    k = estimate_k(params)\n",
+    "\n",
+    "    return System(params, init=init, k=k)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Testing `make_system`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can write a slope function based on the differential equations\n",
+    "\n",
+    "$\\omega = \\frac{d\\theta}{dt} = 10$\n",
+    "\n",
+    "$\\frac{dy}{dt} = r \\frac{d\\theta}{dt}$\n",
+    "\n",
+    "$\\frac{dr}{dt} = k \\frac{d\\theta}{dt}$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Computes the derivatives of the state variables.\n",
+    "    \n",
+    "    state: State object with theta, y, r\n",
+    "    t: time\n",
+    "    system: System object with r, k\n",
+    "    \n",
+    "    returns: sequence of derivatives\n",
+    "    \"\"\"\n",
+    "    theta, y, r = state\n",
+    "    k, omega = system.k, system.omega\n",
+    "    \n",
+    "    dydt = r * omega\n",
+    "    drdt = k * omega\n",
+    "    \n",
+    "    return omega, dydt, drdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Testing `slope_func`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(system.init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll use an event function to stop when `y=L`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(state, t, system):\n",
+    "    \"\"\"Detects when we've rolled length `L`.\n",
+    "    \n",
+    "    state: State object with theta, y, r\n",
+    "    t: time\n",
+    "    system: System object with L\n",
+    "    \n",
+    "    returns: difference between `y` and `L`\n",
+    "    \"\"\"\n",
+    "    theta, y, r = state\n",
+    "    \n",
+    "    return y - system.L"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_func(system.init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And look at the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final value of `y` is 47 meters, as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unrolled = get_last_value(results.y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final value of radius is `R_max`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radius = get_last_value(results.r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The total number of rotations is close to 200, which seems plausible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radians = get_last_value(results.theta) \n",
+    "rotations = magnitude(radians) / 2 / np.pi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The elapsed time is about 2 minutes, which is also plausible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_final = get_last_label(results) * s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting `theta`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_theta(results):\n",
+    "    plot(results.theta, color='C0', label='theta')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Angle (rad)')\n",
+    "    \n",
+    "plot_theta(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting `y`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_y(results):\n",
+    "    plot(results.y, color='C1', label='y')\n",
+    "\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Length (m)')\n",
+    "    \n",
+    "plot_y(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting `r`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_r(results):\n",
+    "    plot(results.r, color='C2', label='r')\n",
+    "\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Radius (m)')\n",
+    "    \n",
+    "plot_r(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also see the relationship between `y` and `r`, which I derive analytically in the book."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(results.r, results.y, color='C3')\n",
+    "\n",
+    "decorate(xlabel='Radius (m)',\n",
+    "         ylabel='Length (m)',\n",
+    "         legend=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the figure from the book."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_three(results):\n",
+    "    subplot(3, 1, 1)\n",
+    "    plot_theta(results)\n",
+    "\n",
+    "    subplot(3, 1, 2)\n",
+    "    plot_y(results)\n",
+    "\n",
+    "    subplot(3, 1, 3)\n",
+    "    plot_r(results)\n",
+    "\n",
+    "plot_three(results)\n",
+    "savefig('figs/chap24-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Animation\n",
+    "\n",
+    "Here's a draw function that animates the results using `matplotlib` patches."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.patches import Circle\n",
+    "from matplotlib.patches import Arrow\n",
+    "\n",
+    "def draw_func(state, t):\n",
+    "    # get radius in mm\n",
+    "    theta, y, r = state\n",
+    "    radius = r.magnitude * 1000\n",
+    "    \n",
+    "    # draw a circle with\n",
+    "    circle = Circle([0, 0], radius, fill=True)\n",
+    "    plt.gca().add_patch(circle)\n",
+    "    \n",
+    "    # draw an arrow to show rotation\n",
+    "    dx, dy = pol2cart(theta, radius)\n",
+    "    arrow = Arrow(0, 0, dx, dy)\n",
+    "    plt.gca().add_patch(arrow)\n",
+    "\n",
+    "    # make the aspect ratio 1\n",
+    "    plt.axis('equal')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "animate(results, draw_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Run the simulation again with a smaller step size to smooth out the animation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercises\n",
+    "\n",
+    "**Exercise:** Since we keep `omega` constant, the linear velocity of the paper increases with radius.  Use `gradient` to estimate the derivative of `results.y`.  What is the peak linear velocity?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(dydt, label='dydt')\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Linear velocity (m/s)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now suppose the peak velocity is the limit; that is, we can't move the paper any faster than that.\n",
+    "\n",
+    "Nevertheless, we might be able to speed up the process by keeping the linear velocity at the maximum all the time.\n",
+    "\n",
+    "Write a slope function that keeps the linear velocity, `dydt`, constant, and computes the angular velocity, `omega`, accordingly.\n",
+    "\n",
+    "Run the simulation and see how much faster we could finish rolling the paper."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/colab/chap25soln.ipynb b/colab/chap25soln.ipynb
new file mode 100644
index 00000000..5c74611e
--- /dev/null
+++ b/colab/chap25soln.ipynb
@@ -0,0 +1,944 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 25\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Teapots and Turntables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tables in Chinese restaurants often have a rotating tray or turntable that makes it easy for customers to share dishes.  These turntables are supported by low-friction bearings that allow them to turn easily and glide.  However, they can be heavy, especially when they are loaded with food, so they have a high moment of inertia.\n",
+    "\n",
+    "Suppose I am sitting at a table with a pot of tea on the turntable directly in front of me, and the person sitting directly opposite asks me to pass the tea.  I push on the edge of the turntable with 1 Newton of force until it has turned 0.5 radians, then let go.  The turntable glides until it comes to a stop 1.5 radians from the starting position.  How much force should I apply for a second push so the teapot glides to a stop directly opposite me?\n",
+    "\n",
+    "The following figure shows the scenario, where `F` is the force I apply to the turntable at the perimeter, perpendicular to the moment arm, `r`, and `tau` is the resulting torque.  The blue circle near the bottom is the teapot.\n",
+    "\n",
+    "![](diagrams/teapot.png)\n",
+    "\n",
+    "We'll answer this question in these steps:\n",
+    "\n",
+    "1.  We'll use the results from the first push to estimate the coefficient of friction for the turntable.\n",
+    "\n",
+    "2.  We'll use that coefficient of friction to estimate the force needed to rotate the turntable through the remaining angle.\n",
+    "\n",
+    "Our simulation will use the following parameters:\n",
+    "\n",
+    "1.  The radius of the turntable is 0.5 meters, and its weight is 7 kg.\n",
+    "\n",
+    "2.  The teapot weights 0.3 kg, and it sits 0.4 meters from the center of the turntable.\n",
+    "\n",
+    "As usual, I'll get units from Pint."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radian = UNITS.radian\n",
+    "m = UNITS.meter\n",
+    "s = UNITS.second\n",
+    "kg = UNITS.kilogram\n",
+    "N = UNITS.newton"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And store the parameters in a `Params` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = Params(radius_disk=0.5*m,\n",
+    "                mass_disk=7*kg,\n",
+    "                radius_pot=0.4*m,\n",
+    "                mass_pot=0.3*kg,\n",
+    "                force=1*N,\n",
+    "                torque_friction=0.2*N*m,\n",
+    "                theta_end=0.5*radian,\n",
+    "                t_end=20*s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`make_system` creates the initial state, `init`, and computes the total moment of inertia for the turntable and the teapot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(params):\n",
+    "    \"\"\"Make a system object.\n",
+    "    \n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    mass_disk, mass_pot = params.mass_disk, params.mass_pot\n",
+    "    radius_disk, radius_pot = params.radius_disk, params.radius_pot\n",
+    "    \n",
+    "    init = State(theta=0*radian, omega=0*radian/s)\n",
+    "    \n",
+    "    I_disk = mass_disk * radius_disk**2 / 2\n",
+    "    I_pot = mass_pot * radius_pot**2\n",
+    "    \n",
+    "    return System(params, init=init, I=I_disk+I_pot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the `System` object we'll use for the first phase of the simulation, while I am pushing the turntable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system1 = make_system(params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simulation\n",
+    "\n",
+    "When I stop pushing on the turntable, the angular acceleration changes abruptly.  We could implement the slope function with an `if` statement that checks the value of `theta` and sets `force` accordingly.  And for a coarse model like this one, that might be fine.  But we will get more accurate results if we simulate the system in two phases:\n",
+    "\n",
+    "1.  During the first phase, force is constant, and we run until `theta` is 0.5 radians.\n",
+    "\n",
+    "2.  During the second phase, force is 0, and we run until `omega` is 0.\n",
+    "\n",
+    "Then we can combine the results of the two phases into a single `TimeFrame`.\n",
+    "\n",
+    "Here's the slope function we'll use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Computes the derivatives of the state variables.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time\n",
+    "    system: System object \n",
+    "    \n",
+    "    returns: sequence of derivatives\n",
+    "    \"\"\"\n",
+    "    theta, omega = state\n",
+    "    radius_disk, force = system.radius_disk, system.force\n",
+    "    torque_friction, I = system.torque_friction, system.I\n",
+    "    \n",
+    "    torque = radius_disk * force - torque_friction\n",
+    "    alpha = torque / I\n",
+    "    \n",
+    "    return omega, alpha "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, we'll test the slope function before running the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(system1.init, 0, system1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's an event function that stops the simulation when `theta` reaches `theta_end`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func1(state, t, system):\n",
+    "    \"\"\"Stops when theta reaches theta_end.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time\n",
+    "    system: System object \n",
+    "    \n",
+    "    returns: difference from target\n",
+    "    \"\"\"\n",
+    "    theta, omega = state\n",
+    "    return theta - system.theta_end "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_func1(system1.init, 0, system1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run the first phase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results1, details1 = run_ode_solver(system1, slope_func, events=event_func1)\n",
+    "details1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And look at the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results1.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Phase 2\n",
+    "\n",
+    "Before we run the second phase, we have to extract the final time and state of the first phase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_0 = results1.last_label() * s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And make an initial `State` object for Phase 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init2 = results1.last_row()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And a new `System` object with zero force."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system2 = System(system1, t_0=t_0, init=init2, force=0*N)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's an event function that stops when angular velocity is 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func2(state, t, system):\n",
+    "    \"\"\"Stops when omega is 0.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time\n",
+    "    system: System object \n",
+    "    \n",
+    "    returns: omega\n",
+    "    \"\"\"\n",
+    "    theta, omega = state\n",
+    "    return omega"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_func2(system2.init, 0, system2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run the second phase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(system2.init, system2.t_0, system2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results2, details2 = run_ode_solver(system2, slope_func, events=event_func2)\n",
+    "details2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And check the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results2.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas provides `combine_first`, which combines `results1` and `results2`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = results1.combine_first(results2)\n",
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can plot `theta` for both phases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_theta(results):\n",
+    "    plot(results.theta, label='theta')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Angle (rad)')\n",
+    "    \n",
+    "plot_theta(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And `omega`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_omega(results):\n",
+    "    plot(results.omega, label='omega', color='C1')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Angular velocity (rad/s)')\n",
+    "    \n",
+    "plot_omega(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subplot(2, 1, 1)\n",
+    "plot_theta(results)\n",
+    "subplot(2, 1, 2)\n",
+    "plot_omega(results)\n",
+    "savefig('figs/chap25-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Estimating friction\n",
+    "\n",
+    "Let's take the code from the previous section and wrap it in a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_two_phases(force, torque_friction, params):\n",
+    "    \"\"\"Run both phases.\n",
+    "    \n",
+    "    force: force applied to the turntable\n",
+    "    torque_friction: friction due to torque\n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: TimeFrame of simulation results\n",
+    "    \"\"\"\n",
+    "    # put the specified parameters into the Params object\n",
+    "    params = Params(params, force=force, torque_friction=torque_friction)\n",
+    "\n",
+    "    # run phase 1\n",
+    "    system1 = make_system(params)\n",
+    "    results1, details1 = run_ode_solver(system1, slope_func, \n",
+    "                                          events=event_func1)\n",
+    "\n",
+    "    # get the final state from phase 1\n",
+    "    t_0 = results1.last_label() * s\n",
+    "    init2 = results1.last_row()\n",
+    "    \n",
+    "    # run phase 2\n",
+    "    system2 = System(system1, t_0=t_0, init=init2, force=0*N)\n",
+    "    results2, details2 = run_ode_solver(system2, slope_func, \n",
+    "                                        events=event_func2)\n",
+    "    \n",
+    "    # combine and return the results\n",
+    "    results = results1.combine_first(results2)\n",
+    "    return TimeFrame(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's test it with the same parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "force = 1*N\n",
+    "torque_friction = 0.2*N*m\n",
+    "results = run_two_phases(force, torque_friction, params)\n",
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And check the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "theta_final = results.last_row().theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the error function we'll use with `root_bisect`.\n",
+    "\n",
+    "It takes a hypothetical value for `torque_friction` and returns the difference between `theta_final` and the observed duration of the first push, 1.5 radian."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def error_func1(torque_friction, params):\n",
+    "    \"\"\"Error function for root_scalar.\n",
+    "    \n",
+    "    torque_friction: hypothetical value\n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: offset from target value\n",
+    "    \"\"\"\n",
+    "    force = 1 * N\n",
+    "    results = run_two_phases(force, torque_friction, params)\n",
+    "    theta_final = results.last_row().theta\n",
+    "    print(torque_friction, theta_final)\n",
+    "    return theta_final - 1.5 * radian"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Testing the error function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guess1 = 0.1*N*m\n",
+    "error_func1(guess1, params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guess2 = 0.3*N*m\n",
+    "error_func1(guess2, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And running `root_scalar`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = root_bisect(error_func1, [guess1, guess2], params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is the coefficient of friction that yields a total rotation of 1.5 radian."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torque_friction = res.root"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a test run with the estimated value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "force = 1 * N\n",
+    "results = run_two_phases(force, torque_friction, params)\n",
+    "theta_final = get_last_value(results.theta)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks good."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Animation\n",
+    "\n",
+    "\n",
+    "Here's a draw function we can use to animate the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.patches import Circle\n",
+    "from matplotlib.patches import Arrow\n",
+    "\n",
+    "def draw_func(state, t):\n",
+    "    theta, omega = state\n",
+    "    \n",
+    "    # draw a circle for the table\n",
+    "    radius_disk = magnitude(params.radius_disk)\n",
+    "    circle1 = Circle([0, 0], radius_disk)\n",
+    "    plt.gca().add_patch(circle1)\n",
+    "    \n",
+    "    # draw a circle for the teapot\n",
+    "    radius_pot = magnitude(params.radius_pot)\n",
+    "    center = pol2cart(theta, radius_pot)\n",
+    "    circle2 = Circle(center, 0.05, color='C1')\n",
+    "    plt.gca().add_patch(circle2)\n",
+    "\n",
+    "    # make the aspect ratio 1\n",
+    "    plt.axis('equal')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state = results.first_row()\n",
+    "draw_func(state, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "animate(results, draw_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Exercises\n",
+    "\n",
+    "Now finish off the example by estimating the force that delivers the teapot to the desired position.\n",
+    "\n",
+    "Write an error function that takes `force` and `params` and returns the offset from the desired angle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test the error function with `force=1`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And run `root_bisect` to find the desired force."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "force = res.root\n",
+    "results = run_two_phases(force, torque_friction, params)\n",
+    "theta_final = get_last_value(results.theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remaining_angle = np.pi - 1.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Now suppose my friend pours 0.1 kg of tea and puts the teapot back on the turntable at distance 0.3 meters from the center.  If I ask for the tea back, how much force should they apply, over an arc of 0.5 radians, to make the teapot glide to a stop back in front of me?  You can assume that torque due to friction is proportional to the total mass of the teapot and the turntable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From ed53a2cd9f59ecd91a43d389c389821750204fd8 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 22 Jan 2021 15:51:48 -0500
Subject: [PATCH 009/144] Updating notebooks

---
 colab/chap01soln.ipynb | 17 +----------
 colab/chap02soln.ipynb | 64 ++++++++++++++++++++++++++++--------------
 2 files changed, 44 insertions(+), 37 deletions(-)

diff --git a/colab/chap01soln.ipynb b/colab/chap01soln.ipynb
index 0dfd8650..7f892bfe 100644
--- a/colab/chap01soln.ipynb
+++ b/colab/chap01soln.ipynb
@@ -77,21 +77,6 @@
     "8.  Finally, when you are done with a notebook, select \"Close and Halt\" from the File menu."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using the notebooks\n",
-    "\n",
-    "The notebooks for each chapter contain the code from the chapter along with additional examples, explanatory text, and exercises.  I recommend you \n",
-    "\n",
-    "1. Read the chapter first to understand the concepts and vocabulary, \n",
-    "2. Run the notebook to review what you learned and see it in action, and then\n",
-    "3. Attempt the exercises.\n",
-    "\n",
-    "If you try to work through the notebooks without reading the book, you're gonna have a bad time.  The notebooks contain some explanatory text, but it is probably not enough to make sense if you have not read the book.  If you are working through a notebook and you get stuck, you might want to re-read (or read!) the corresponding section of the book."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -100,7 +85,7 @@
     "\n",
     "These notebooks use standard Python modules like NumPy and SciPy.  I assume you already have them installed in your environment.\n",
     "\n",
-    "They also use two less common modules: Pint, which provides units, and modsim, which contains code I wrote specifically for this book.\n",
+    "They also use two less common modules: Pint, which provides units, and `modsimpy`, which contains code I wrote specifically for this book.\n",
     "\n",
     "The following cells check whether you have these modules already and tries to install them if you don't."
    ]
diff --git a/colab/chap02soln.ipynb b/colab/chap02soln.ipynb
index f6b97493..ba819318 100644
--- a/colab/chap02soln.ipynb
+++ b/colab/chap02soln.ipynb
@@ -4,34 +4,55 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 2\n",
+    "# Chapter 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim library\n",
-    "from modsim import *\n",
+    "# check if the libraries we need are installed\n",
     "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
     "# set the random number generator\n",
-    "np.random.seed(7)\n",
-    "\n",
-    "# If this cell runs successfully, it produces no output."
+    "np.random.seed(7)"
    ]
   },
   {
@@ -52,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -77,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
@@ -894,6 +915,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -909,7 +931,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

From e6a2dc83cbbcd6eb5fc1d0e85c519c00ae6f3084 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 22 Jan 2021 15:58:26 -0500
Subject: [PATCH 010/144] Updating notebooks

---
 colab/{chap01soln.ipynb => chap01.ipynb} | 0
 colab/{chap02soln.ipynb => chap02.ipynb} | 0
 colab/{chap03soln.ipynb => chap03.ipynb} | 0
 colab/{chap04soln.ipynb => chap04.ipynb} | 0
 colab/{chap05soln.ipynb => chap05.ipynb} | 0
 colab/{chap06soln.ipynb => chap06.ipynb} | 0
 colab/{chap07soln.ipynb => chap07.ipynb} | 0
 colab/{chap08soln.ipynb => chap08.ipynb} | 0
 colab/{chap09soln.ipynb => chap09.ipynb} | 0
 colab/{chap10soln.ipynb => chap10.ipynb} | 0
 colab/{chap11soln.ipynb => chap11.ipynb} | 0
 colab/{chap12soln.ipynb => chap12.ipynb} | 0
 colab/{chap13soln.ipynb => chap13.ipynb} | 0
 colab/{chap14soln.ipynb => chap14.ipynb} | 0
 colab/{chap15soln.ipynb => chap15.ipynb} | 0
 colab/{chap16soln.ipynb => chap16.ipynb} | 0
 colab/{chap17soln.ipynb => chap17.ipynb} | 0
 colab/{chap18soln.ipynb => chap18.ipynb} | 0
 colab/{chap20soln.ipynb => chap20.ipynb} | 0
 colab/{chap21soln.ipynb => chap21.ipynb} | 0
 colab/{chap22soln.ipynb => chap22.ipynb} | 0
 colab/{chap23soln.ipynb => chap23.ipynb} | 0
 colab/{chap24soln.ipynb => chap24.ipynb} | 0
 colab/{chap25soln.ipynb => chap25.ipynb} | 0
 24 files changed, 0 insertions(+), 0 deletions(-)
 rename colab/{chap01soln.ipynb => chap01.ipynb} (100%)
 rename colab/{chap02soln.ipynb => chap02.ipynb} (100%)
 rename colab/{chap03soln.ipynb => chap03.ipynb} (100%)
 rename colab/{chap04soln.ipynb => chap04.ipynb} (100%)
 rename colab/{chap05soln.ipynb => chap05.ipynb} (100%)
 rename colab/{chap06soln.ipynb => chap06.ipynb} (100%)
 rename colab/{chap07soln.ipynb => chap07.ipynb} (100%)
 rename colab/{chap08soln.ipynb => chap08.ipynb} (100%)
 rename colab/{chap09soln.ipynb => chap09.ipynb} (100%)
 rename colab/{chap10soln.ipynb => chap10.ipynb} (100%)
 rename colab/{chap11soln.ipynb => chap11.ipynb} (100%)
 rename colab/{chap12soln.ipynb => chap12.ipynb} (100%)
 rename colab/{chap13soln.ipynb => chap13.ipynb} (100%)
 rename colab/{chap14soln.ipynb => chap14.ipynb} (100%)
 rename colab/{chap15soln.ipynb => chap15.ipynb} (100%)
 rename colab/{chap16soln.ipynb => chap16.ipynb} (100%)
 rename colab/{chap17soln.ipynb => chap17.ipynb} (100%)
 rename colab/{chap18soln.ipynb => chap18.ipynb} (100%)
 rename colab/{chap20soln.ipynb => chap20.ipynb} (100%)
 rename colab/{chap21soln.ipynb => chap21.ipynb} (100%)
 rename colab/{chap22soln.ipynb => chap22.ipynb} (100%)
 rename colab/{chap23soln.ipynb => chap23.ipynb} (100%)
 rename colab/{chap24soln.ipynb => chap24.ipynb} (100%)
 rename colab/{chap25soln.ipynb => chap25.ipynb} (100%)

diff --git a/colab/chap01soln.ipynb b/colab/chap01.ipynb
similarity index 100%
rename from colab/chap01soln.ipynb
rename to colab/chap01.ipynb
diff --git a/colab/chap02soln.ipynb b/colab/chap02.ipynb
similarity index 100%
rename from colab/chap02soln.ipynb
rename to colab/chap02.ipynb
diff --git a/colab/chap03soln.ipynb b/colab/chap03.ipynb
similarity index 100%
rename from colab/chap03soln.ipynb
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diff --git a/colab/chap04soln.ipynb b/colab/chap04.ipynb
similarity index 100%
rename from colab/chap04soln.ipynb
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diff --git a/colab/chap05soln.ipynb b/colab/chap05.ipynb
similarity index 100%
rename from colab/chap05soln.ipynb
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diff --git a/colab/chap06soln.ipynb b/colab/chap06.ipynb
similarity index 100%
rename from colab/chap06soln.ipynb
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diff --git a/colab/chap07soln.ipynb b/colab/chap07.ipynb
similarity index 100%
rename from colab/chap07soln.ipynb
rename to colab/chap07.ipynb
diff --git a/colab/chap08soln.ipynb b/colab/chap08.ipynb
similarity index 100%
rename from colab/chap08soln.ipynb
rename to colab/chap08.ipynb
diff --git a/colab/chap09soln.ipynb b/colab/chap09.ipynb
similarity index 100%
rename from colab/chap09soln.ipynb
rename to colab/chap09.ipynb
diff --git a/colab/chap10soln.ipynb b/colab/chap10.ipynb
similarity index 100%
rename from colab/chap10soln.ipynb
rename to colab/chap10.ipynb
diff --git a/colab/chap11soln.ipynb b/colab/chap11.ipynb
similarity index 100%
rename from colab/chap11soln.ipynb
rename to colab/chap11.ipynb
diff --git a/colab/chap12soln.ipynb b/colab/chap12.ipynb
similarity index 100%
rename from colab/chap12soln.ipynb
rename to colab/chap12.ipynb
diff --git a/colab/chap13soln.ipynb b/colab/chap13.ipynb
similarity index 100%
rename from colab/chap13soln.ipynb
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diff --git a/colab/chap14soln.ipynb b/colab/chap14.ipynb
similarity index 100%
rename from colab/chap14soln.ipynb
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diff --git a/colab/chap15soln.ipynb b/colab/chap15.ipynb
similarity index 100%
rename from colab/chap15soln.ipynb
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diff --git a/colab/chap16soln.ipynb b/colab/chap16.ipynb
similarity index 100%
rename from colab/chap16soln.ipynb
rename to colab/chap16.ipynb
diff --git a/colab/chap17soln.ipynb b/colab/chap17.ipynb
similarity index 100%
rename from colab/chap17soln.ipynb
rename to colab/chap17.ipynb
diff --git a/colab/chap18soln.ipynb b/colab/chap18.ipynb
similarity index 100%
rename from colab/chap18soln.ipynb
rename to colab/chap18.ipynb
diff --git a/colab/chap20soln.ipynb b/colab/chap20.ipynb
similarity index 100%
rename from colab/chap20soln.ipynb
rename to colab/chap20.ipynb
diff --git a/colab/chap21soln.ipynb b/colab/chap21.ipynb
similarity index 100%
rename from colab/chap21soln.ipynb
rename to colab/chap21.ipynb
diff --git a/colab/chap22soln.ipynb b/colab/chap22.ipynb
similarity index 100%
rename from colab/chap22soln.ipynb
rename to colab/chap22.ipynb
diff --git a/colab/chap23soln.ipynb b/colab/chap23.ipynb
similarity index 100%
rename from colab/chap23soln.ipynb
rename to colab/chap23.ipynb
diff --git a/colab/chap24soln.ipynb b/colab/chap24.ipynb
similarity index 100%
rename from colab/chap24soln.ipynb
rename to colab/chap24.ipynb
diff --git a/colab/chap25soln.ipynb b/colab/chap25.ipynb
similarity index 100%
rename from colab/chap25soln.ipynb
rename to colab/chap25.ipynb

From d3c34aeb4c6bb9276717901b46a3a9397f4be3f3 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 22 Jan 2021 19:29:56 -0500
Subject: [PATCH 011/144] Adding data

---
 data/DataOWall.csv                   |  867 +++++++++
 data/World_population_estimates.html | 2634 ++++++++++++++++++++++++++
 data/glucose_insulin.csv             |   25 +
 3 files changed, 3526 insertions(+)
 create mode 100644 data/DataOWall.csv
 create mode 100644 data/World_population_estimates.html
 create mode 100644 data/glucose_insulin.csv

diff --git a/data/DataOWall.csv b/data/DataOWall.csv
new file mode 100644
index 00000000..040eabf1
--- /dev/null
+++ b/data/DataOWall.csv
@@ -0,0 +1,867 @@
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+2014-10-08 13:45:00,25.896,-14.647,19.33,16.37
+2014-10-08 13:50:00,25.476,-13.359,19.37,16.42
+2014-10-08 13:55:00,25.364,-14.678,19.38,16.48
+2014-10-08 14:00:00,25.672,-16.733,19.41,16.63
+2014-10-08 14:05:00,25.532,-17.485,19.44,16.73
+2014-10-08 14:10:00,25.42,-10.245,19.45,16.73
+2014-10-08 14:15:00,24.804,-6.702,19.47,16.6
+2014-10-08 14:20:00,24.748,-3.313,19.47,16.53
+2014-10-08 14:25:00,25.14,-4.218,19.51,16.66
+2014-10-08 14:30:00,25.042,-4.939,19.54,16.66
+2014-10-08 14:35:00,24.832,-9.387,19.54,16.77
+2014-10-08 14:40:00,24.692,-6.227,19.56,16.78
+2014-10-08 14:45:00,24.566,-5.936,19.57,16.73
+2014-10-08 14:50:00,24.538,-9.954,19.59,16.83
+2014-10-08 14:55:00,24.594,-6.365,19.6,16.84
+2014-10-08 15:00:00,24.65,-8.436,19.63,16.87
+2014-10-08 15:05:00,24.468,-2.469,19.65,16.81
+2014-10-08 15:10:00,24.328,1.258,19.67,16.67
+2014-10-08 15:15:00,24.216,2.393,19.67,16.54
+2014-10-08 15:20:00,25.182,0.859,19.72,16.51
+2014-10-08 15:25:00,25.378,-2.101,19.75,16.59
+2014-10-08 15:30:00,25.308,0.598,19.79,16.52
+2014-10-08 15:35:00,25.252,-0.123,19.82,16.59
+2014-10-08 15:40:00,25.28,3.804,19.83,16.39
+2014-10-08 15:45:00,25.294,-1.656,19.86,16.45
+2014-10-08 15:50:00,25.182,-4.003,19.87,16.52
+2014-10-08 15:55:00,25.098,-0.982,19.9,16.47
+2014-10-08 16:00:00,24.972,-0.092,19.92,16.42
+2014-10-08 16:05:00,24.902,0.936,19.93,16.36
+2014-10-08 16:10:00,24.524,0.752,19.94,16.33
+2014-10-08 16:15:00,24.076,1.242,19.95,16.32
+2014-10-08 16:20:00,23.529,4.693,19.94,16.2
+2014-10-08 16:25:00,23.725,2.331,19.96,16.22
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+<h1 id="firstHeading" class="firstHeading" lang="en">World population estimates</h1>			<div id="bodyContent" class="mw-body-content">
+				<div id="siteSub" class="noprint">From Wikipedia, the free encyclopedia</div>				<div id="contentSub"></div>
+				<div id="jump-to-nav"></div>				<a class="mw-jump-link" href="#mw-head">Jump to navigation</a>
+				<a class="mw-jump-link" href="#p-search">Jump to search</a>
+				<div id="mw-content-text" lang="en" dir="ltr" class="mw-content-ltr"><div class="mw-parser-output"><p>This article lists estimates of <a href="/wiki/World_population" title="World population">world population</a>, as well as projections of <a href="/wiki/Projections_of_population_growth" title="Projections of population growth">future developments</a>. In summary, estimates for the progression of world population since the late medieval period are in the following ranges:</p>
+<table class="wikitable">
+<tr>
+<td>year</td>
+<td>1400</td>
+<td>1500</td>
+<td>1600</td>
+<td>1700</td>
+<td>1800</td>
+<td>1900</td>
+<td>2000</td>
+<td>2100<sup id="cite_ref-UNProspect2017_1-0" class="reference"><a href="#cite_note-UNProspect2017-1">[1]</a></sup></td>
+</tr>
+<tr>
+<td>population<br />
+(in billions)</td>
+<td>0.35–0.40</td>
+<td>0.42–0.50</td>
+<td>0.50–0.58</td>
+<td>0.60–0.68</td>
+<td>0.89–0.98</td>
+<td>1.56–1.71</td>
+<td>6.06–6.15</td>
+<td>ca. 10–13</td>
+</tr>
+<tr>
+<td>growth p.a.<sup id="cite_ref-2" class="reference"><a href="#cite_note-2">[2]</a></sup></td>
+<td>&lt;0%</td>
+<td>&lt;0.12%</td>
+<td>0.15%–0.3%</td>
+<td>0.1%–0.15%</td>
+<td>0.3%–0.5%</td>
+<td>0.5%–0.6%</td>
+<td>1.3%–1.4%</td>
+<td>0.7%–0.8%</td>
+</tr>
+</table>
+<p>Estimates for pre-modern times are necessarily fraught with great uncertainties, and few of the published estimates have <a href="/wiki/Confidence_interval" title="Confidence interval">confidence intervals</a>; in the absence of a straightforward means to assess the error of such estimates, a rough idea of expert consensus can be gained by comparing the values given in independent publications. Population estimates cannot be considered accurate to more than two decimal digits; for example, world population for the year 2012 was estimated at 7.02, 7.06 and 7.08 billion by the <a href="/wiki/United_States_Census_Bureau" title="United States Census Bureau">United States Census Bureau</a>, the <a href="/wiki/Population_Reference_Bureau" title="Population Reference Bureau">Population Reference Bureau</a> and the <a href="/wiki/United_Nations_Department_of_Economic_and_Social_Affairs" title="United Nations Department of Economic and Social Affairs">United Nations Department of Economic and Social Affairs</a>, respectively, corresponding to a spread of estimates of the order of 0.8%.</p>
+<p></p>
+<div id="toc" class="toc">
+<div class="toctitle" lang="en" dir="ltr">
+<h2>Contents</h2>
+</div>
+<ul>
+<li class="toclevel-1 tocsection-1"><a href="#Deep_prehistory"><span class="tocnumber">1</span> <span class="toctext">Deep prehistory</span></a></li>
+<li class="toclevel-1 tocsection-2"><a href="#Historical_population"><span class="tocnumber">2</span> <span class="toctext">Historical population</span></a>
+<ul>
+<li class="toclevel-2 tocsection-3"><a href="#Before_1950"><span class="tocnumber">2.1</span> <span class="toctext">Before 1950</span></a></li>
+<li class="toclevel-2 tocsection-4"><a href="#1950_to_present"><span class="tocnumber">2.2</span> <span class="toctext">1950 to present</span></a></li>
+</ul>
+</li>
+<li class="toclevel-1 tocsection-5"><a href="#Projections"><span class="tocnumber">3</span> <span class="toctext">Projections</span></a></li>
+<li class="toclevel-1 tocsection-6"><a href="#By_world_region"><span class="tocnumber">4</span> <span class="toctext">By world region</span></a></li>
+<li class="toclevel-1"><a href="#See_also"><span class="tocnumber">5</span> <span class="toctext">See also</span></a></li>
+<li class="toclevel-1 tocsection-7"><a href="#References"><span class="tocnumber">6</span> <span class="toctext">References</span></a></li>
+<li class="toclevel-1 tocsection-8"><a href="#Further_reading"><span class="tocnumber">7</span> <span class="toctext">Further reading</span></a></li>
+<li class="toclevel-1 tocsection-9"><a href="#External_links"><span class="tocnumber">8</span> <span class="toctext">External links</span></a></li>
+</ul>
+</div>
+<p></p>
+<h2><span class="mw-headline" id="Deep_prehistory">Deep prehistory</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=1" title="Edit section: Deep prehistory">edit</a><span class="mw-editsection-bracket">]</span></span></h2>
+<div role="note" class="hatnote navigation-not-searchable">Further information: <a href="/wiki/Paleodemography" title="Paleodemography">Paleodemography</a></div>
+<div class="thumb tright">
+<div class="thumbinner" style="width:352px;"><a href="/wiki/File:Population_curve.svg" class="image"><img alt="" src="//upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Population_curve.svg/350px-Population_curve.svg.png" width="350" height="212" class="thumbimage" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Population_curve.svg/525px-Population_curve.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Population_curve.svg/700px-Population_curve.svg.png 2x" data-file-width="547" data-file-height="332" /></a>
+<div class="thumbcaption">
+<div class="magnify"><a href="/wiki/File:Population_curve.svg" class="internal" title="Enlarge"></a></div>
+Graph of world population over the past 12,000 years (<a href="/wiki/Holocene" title="Holocene">Holocene</a>).</div>
+</div>
+</div>
+<p>As a general rule, the confidence of estimates on historical world population decreases for the more distant past. Robust population data only exists for the last two or three centuries. Until the late 18th century, few governments had ever performed an accurate census. In many early attempts, such as in <a href="/wiki/Census_in_Egypt" class="mw-redirect" title="Census in Egypt">Ancient Egypt</a> and the <a href="/wiki/Achaemenid_Empire" title="Achaemenid Empire">Persian Empire</a>, the focus was on counting merely a subset of the population for purposes of taxation or military service.<sup id="cite_ref-3" class="reference"><a href="#cite_note-3">[3]</a></sup> Published estimates for the 1st century ("<a href="/wiki/AD_1" title="AD 1">AD 1</a>") suggest an uncertainty of the order of 50% (estimates range between 150 and 330 million). Some estimates extend their timeline into deep prehistory, to "<a href="/wiki/10,000_BC" class="mw-redirect" title="10,000 BC">10,000 BC</a>", i.e. the <a href="/wiki/Holocene" title="Holocene">early Holocene</a>, when world population estimates range roughly between one and ten million (with an uncertainty of up to an order of magnitude).<sup id="cite_ref-4" class="reference"><a href="#cite_note-4">[4]</a></sup><sup id="cite_ref-5" class="reference"><a href="#cite_note-5">[5]</a></sup></p>
+<p>Estimates for yet deeper prehistory, into the <a href="/wiki/Paleolithic" title="Paleolithic">Paleolithic</a>, are of a different nature. At this time human populations consisted entirely of non-sedentary <a href="/wiki/Hunter-gatherer" title="Hunter-gatherer">hunter-gatherer</a> populations, with <a href="/wiki/Anatomically_modern_humans" class="mw-redirect" title="Anatomically modern humans">anatomically modern humans</a> existing alongside <a href="/wiki/Archaic_humans" title="Archaic humans">archaic human varieties</a>, some of which are still ancestral to the modern human population due to <a href="/wiki/Archaic_human_admixture_with_modern_humans" title="Archaic human admixture with modern humans">interbreeding with modern humans</a> during the <a href="/wiki/Upper_Paleolithic" title="Upper Paleolithic">Upper Paleolithic</a>. Estimates of the size of these populations are a topic of <a href="/wiki/Paleoanthropology" title="Paleoanthropology">paleoanthropology</a>. A late human <a href="/wiki/Population_bottleneck" title="Population bottleneck">population bottleneck</a> is postulated by some scholars at approximately 70,000 years ago, during the <a href="/wiki/Toba_catastrophe_theory" title="Toba catastrophe theory">Toba catastrophe</a>, when <i><a href="/wiki/Homo_sapiens" title="Homo sapiens">Homo sapiens</a></i> population may have dropped to as low as between 1,000 and 10,000 individuals.<sup id="cite_ref-ambrose1998_6-0" class="reference"><a href="#cite_note-ambrose1998-6">[6]</a></sup><sup id="cite_ref-robock2009_7-0" class="reference"><a href="#cite_note-robock2009-7">[7]</a></sup> For the time of speciation of <i><a href="/wiki/Homo_sapiens" title="Homo sapiens">Homo sapiens</a></i>, some 200,000 years ago, an <a href="/wiki/Effective_population_size" title="Effective population size">effective population size</a> of the order of 10,000 to 30,000 individuals has been estimated, with an actual "census population" of early <i>Homo sapiens</i> of roughly 100,000 to 300,000 individuals.<sup id="cite_ref-8" class="reference"><a href="#cite_note-8">[8]</a></sup></p>
+<p><span id="Cumulative_population"></span><span id="Number_of_humans_who_have_ever_lived"></span> The question of "how many people have ever lived?" or "what percentage of people who have ever lived are alive today" can be traced to the 1970s.<sup id="cite_ref-9" class="reference"><a href="#cite_note-9">[9]</a></sup> The more dramatic phrasing of "the living outnumber the dead" also dates to the 1970s, a time of <a href="/wiki/Population_explosion" class="mw-redirect" title="Population explosion">population explosion</a> and growing fears of <a href="/wiki/Human_overpopulation" title="Human overpopulation">human overpopulation</a> in the wake of <a href="/wiki/Decolonization" title="Decolonization">decolonization</a> and before the adoption of China's <a href="/wiki/One-child_policy" title="One-child policy">One-child policy</a>. The claim that "the living outnumber the dead" was never accurate (although it may be roughly accurate if only <a href="/wiki/Effective_population_size" title="Effective population size">ancestral population</a> is considered). <a href="/wiki/Arthur_C._Clarke" title="Arthur C. Clarke">Arthur C. Clarke</a> in <i><a href="/wiki/2001:_A_Space_Odyssey" title="2001: A Space Odyssey">2001: A Space Odyssey</a></i> (1968) has the claim that "Behind every man now alive stand 30 ghosts, for that is the ratio by which the dead outnumber the living", which was roughly accurate at the time of writing.<sup id="cite_ref-10" class="reference"><a href="#cite_note-10">[10]</a></sup></p>
+<p>Estimates of the "total number of people who have ever lived" is 107.6 billion as of 2011.<sup id="cite_ref-11" class="reference"><a href="#cite_note-11">[11]</a></sup> The answer naturally depends on the definition of "people", i.e. is only <i><a href="/wiki/Homo_sapiens" title="Homo sapiens">Homo sapiens</a></i> to be counted, or all of genus <i><a href="/wiki/Homo" title="Homo">Homo</a></i>, but due to the small population sizes in the Lower Paleolithic, the order of magnitude of the estimate is not affected by the choice of cut-off date substantially more than by the uncertainty of estimates throughout the Neolithic to Iron Age.<sup id="cite_ref-12" class="reference"><a href="#cite_note-12">[12]</a></sup> The estimate is more crucially affected by the estimate of <a href="/wiki/Infant_mortality" title="Infant mortality">infant mortalitys</a> vs. <a href="/wiki/Stillborn" class="mw-redirect" title="Stillborn">stillborn</a> infants, due to the very high infant mortality throughout the pre-modern period. An estimate on the "total number of people who have ever lived" as of 1995 was calculated by Haub (1995) at "about 105 billion births since the dawn of the human race" with a cut-off date at 50,000 BC (beginning of the Upper Paleolithic), and an inclusion of a high infant mortality rate throughout pre-modern history.<sup id="cite_ref-13" class="reference"><a href="#cite_note-13">[13]</a></sup></p>
+<h2><span class="mw-headline" id="Historical_population">Historical population</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=2" title="Edit section: Historical population">edit</a><span class="mw-editsection-bracket">]</span></span></h2>
+<h3><span class="mw-headline" id="Before_1950">Before 1950</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=3" title="Edit section: Before 1950">edit</a><span class="mw-editsection-bracket">]</span></span></h3>
+<p>The following table uses <a href="/wiki/Astronomical_year_numbering" title="Astronomical year numbering">astronomical year numbering</a> for dates, negative numbers corresponding roughly to the corresponding year BC (i.e. -10000 = 10,001 BC, etc.). The table starts counting around the <a href="/wiki/Late_Glacial_Maximum" class="mw-redirect" title="Late Glacial Maximum">Late Glacial Maximum</a> period, in which ice retreated and humans started to spread into the northern hemisphere.</p>
+<p>From the beginning of the <a href="/wiki/Early_modern_period" title="Early modern period">early modern period</a> until the 20th century, world population has been characterized by a faster-than-exponential growth. For the period of <a href="/wiki/Classical_Antiquity" class="mw-redirect" title="Classical Antiquity">Classical Antiquity</a> to the <a href="/wiki/Middle_Ages" title="Middle Ages">Middle Ages</a>, roughly 500 BC to AD 1500, there has also been a general tendency of growth (estimated at roughly a factor 4 to 5 over the 2,000 year period), but not strictily <a href="/wiki/Monotonic_function" title="Monotonic function">monotonic</a>: A noticeable dip in world population is assumed due to the <a href="/wiki/Black_Death" title="Black Death">Black Death</a> in the mid-14th century.<sup id="cite_ref-Haub1650_14-0" class="reference"><a href="#cite_note-Haub1650-14">[14]</a></sup></p>
+<table class="wikitable" style="text-align:right;font-size:small;">
+<tr>
+<th>Year</th>
+<th><a href="/wiki/Population_Reference_Bureau" title="Population Reference Bureau">Population Reference Bureau</a>
+<p>(1973–2016)<sup id="cite_ref-Population_Reference_Bureau_15-0" class="reference"><a href="#cite_note-Population_Reference_Bureau-15">[15]</a></sup></p>
+</th>
+<th><a href="/wiki/United_Nations_Department_of_Economic_and_Social_Affairs" title="United Nations Department of Economic and Social Affairs">United Nations Department of Economic and Social Affairs</a>
+<p>(2015)<sup id="cite_ref-The_World_at_Six_Billion,_1999_16-0" class="reference"><a href="#cite_note-The_World_at_Six_Billion,_1999-16">[16]</a></sup></p>
+</th>
+<th><a href="/wiki/Angus_Maddison" title="Angus Maddison">Maddison</a>
+<p>(2008)<sup id="cite_ref-www.ggdc.net/maddison/Historical_Statistics_17-0" class="reference"><a href="#cite_note-www.ggdc.net/maddison/Historical_Statistics-17">[17]</a></sup></p>
+</th>
+<th>HYDE
+<p>(2010)<sup class="noprint Inline-Template Template-Fact" style="white-space:nowrap;">[<i><a href="/wiki/Wikipedia:Citation_needed" title="Wikipedia:Citation needed"><span title="This claim needs references to reliable sources. (January 2017)">citation needed</span></a></i>]</sup></p>
+</th>
+<th><a href="/wiki/John_Tanton" title="John Tanton">Tanton</a>
+<p>(1994)<sup id="cite_ref-thesocialcontract.com_18-0" class="reference"><a href="#cite_note-thesocialcontract.com-18">[18]</a></sup></p>
+</th>
+<th>Biraben
+<p>(1980)<sup id="cite_ref-French_1980,_pp._1_19-0" class="reference"><a href="#cite_note-French_1980,_pp._1-19">[19]</a></sup></p>
+</th>
+<th><a href="/wiki/Colin_McEvedy" title="Colin McEvedy">McEvedy</a> &amp;
+<p>Jones (1978)<sup id="cite_ref-Richard_Jones_1978_20-0" class="reference"><a href="#cite_note-Richard_Jones_1978-20">[20]</a></sup></p>
+</th>
+<th>Thomlinson
+<p>(1975)<sup id="cite_ref-ReferenceA_21-0" class="reference"><a href="#cite_note-ReferenceA-21">[21]</a></sup></p>
+</th>
+<th>Durand
+<p>(1974)<sup id="cite_ref-John_D._Durand_1974_22-0" class="reference"><a href="#cite_note-John_D._Durand_1974-22">[22]</a></sup></p>
+</th>
+<th><a href="/wiki/Colin_Clark_(economist)" title="Colin Clark (economist)">Clark</a>
+<p>(1967)<sup id="cite_ref-Colin_Clark_1967_23-0" class="reference"><a href="#cite_note-Colin_Clark_1967-23">[23]</a></sup></p>
+</th>
+</tr>
+<tr>
+<td>-10000</td>
+<td></td>
+<td></td>
+<td></td>
+<td>2M<sup id="cite_ref-mnp.nl_24-0" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
+<td></td>
+<td></td>
+<td>4M</td>
+<td>1–10M</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>-9000</td>
+<td></td>
+<td></td>
+<td></td>
+<td>4M</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>-8000</td>
+<td>5M</td>
+<td></td>
+<td></td>
+<td>5M</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>5–10M</td>
+<td></td>
+</tr>
+<tr>
+<td>-7000</td>
+<td></td>
+<td></td>
+<td></td>
+<td>8M</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>-6000</td>
+<td></td>
+<td></td>
+<td></td>
+<td>11M</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>-5000</td>
+<td></td>
+<td></td>
+<td></td>
+<td>18M<sup id="cite_ref-mnp.nl_24-1" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
+<td></td>
+<td></td>
+<td>5M</td>
+<td>5–20M</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>-4000</td>
+<td></td>
+<td></td>
+<td></td>
+<td>28M</td>
+<td></td>
+<td></td>
+<td>7M</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>-3000</td>
+<td></td>
+<td></td>
+<td></td>
+<td>45M</td>
+<td></td>
+<td></td>
+<td>14M</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>-2000</td>
+<td></td>
+<td></td>
+<td></td>
+<td>72M</td>
+<td></td>
+<td></td>
+<td>27M</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>-1000</td>
+<td></td>
+<td></td>
+<td></td>
+<td>115M</td>
+<td></td>
+<td></td>
+<td>50M</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>-200</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>150M</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1</td>
+<td>300M<sup id="cite_ref-25" class="reference"><a href="#cite_note-25">[25]</a></sup></td>
+<td>300M</td>
+<td>231M<sup id="cite_ref-26" class="reference"><a href="#cite_note-26">[26]</a></sup></td>
+<td>188M<sup id="cite_ref-mnp.nl_24-2" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
+<td>150M</td>
+<td>255M</td>
+<td>170M</td>
+<td>200M</td>
+<td>270–330M</td>
+<td>256M<sup id="cite_ref-27" class="reference"><a href="#cite_note-27">[27]</a></sup></td>
+</tr>
+<tr>
+<td>100</td>
+<td></td>
+<td></td>
+<td></td>
+<td>195M</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>200</td>
+<td></td>
+<td></td>
+<td></td>
+<td>202M</td>
+<td></td>
+<td>256M</td>
+<td>190M</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>300</td>
+<td></td>
+<td></td>
+<td></td>
+<td>205M</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>350</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>254M</td>
+</tr>
+<tr>
+<td>400</td>
+<td></td>
+<td></td>
+<td></td>
+<td>209M</td>
+<td></td>
+<td>206M</td>
+<td>190M</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>500</td>
+<td></td>
+<td></td>
+<td></td>
+<td>210M<sup id="cite_ref-mnp.nl_24-3" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
+<td></td>
+<td>206M</td>
+<td>190M</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>600</td>
+<td></td>
+<td></td>
+<td></td>
+<td>213M</td>
+<td></td>
+<td>206M</td>
+<td>200M</td>
+<td></td>
+<td></td>
+<td>237M</td>
+</tr>
+<tr>
+<td>700</td>
+<td></td>
+<td></td>
+<td></td>
+<td>226M</td>
+<td></td>
+<td>207M</td>
+<td>210M</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>800</td>
+<td></td>
+<td></td>
+<td></td>
+<td>240M</td>
+<td></td>
+<td>224M</td>
+<td>220M</td>
+<td></td>
+<td></td>
+<td>261M</td>
+</tr>
+<tr>
+<td>900</td>
+<td></td>
+<td></td>
+<td></td>
+<td>269M</td>
+<td></td>
+<td>226M</td>
+<td>240M</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1000</td>
+<td></td>
+<td>310M</td>
+<td>267M</td>
+<td>295M<sup id="cite_ref-mnp.nl_24-4" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
+<td></td>
+<td>254M</td>
+<td>265M</td>
+<td></td>
+<td>275–345M</td>
+<td>280M</td>
+</tr>
+<tr>
+<td>1100</td>
+<td></td>
+<td></td>
+<td></td>
+<td>353M</td>
+<td></td>
+<td>301M</td>
+<td>320M</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1200</td>
+<td></td>
+<td></td>
+<td></td>
+<td>393M</td>
+<td></td>
+<td>400M</td>
+<td>360M</td>
+<td></td>
+<td></td>
+<td>384M</td>
+</tr>
+<tr>
+<td>1250</td>
+<td></td>
+<td>400M</td>
+<td></td>
+<td></td>
+<td></td>
+<td>416M</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1300</td>
+<td></td>
+<td></td>
+<td></td>
+<td>392M</td>
+<td>300M</td>
+<td>432M</td>
+<td>360M</td>
+<td>400M</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1340</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>443M</td>
+<td></td>
+<td></td>
+<td></td>
+<td>378M</td>
+</tr>
+<tr>
+<td>1400</td>
+<td></td>
+<td></td>
+<td></td>
+<td>390M</td>
+<td></td>
+<td>374M</td>
+<td>350M</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1500</td>
+<td></td>
+<td>500M</td>
+<td>438M</td>
+<td>461M<sup id="cite_ref-mnp.nl_24-5" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
+<td>460M</td>
+<td>425M</td>
+<td></td>
+<td></td>
+<td>440–540M</td>
+<td>427M</td>
+</tr>
+<tr>
+<td>1600</td>
+<td></td>
+<td></td>
+<td>556M</td>
+<td>554M<sup id="cite_ref-mnp.nl_24-6" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
+<td></td>
+<td>579M</td>
+<td>545M</td>
+<td></td>
+<td></td>
+<td>498M</td>
+</tr>
+<tr>
+<td>1650</td>
+<td>(&lt;700M)<sup id="cite_ref-Haub1650_14-1" class="reference"><a href="#cite_note-Haub1650-14">[14]</a></sup></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>545M</td>
+<td>500M</td>
+<td></td>
+<td>516M</td>
+</tr>
+<tr>
+<td>1700</td>
+<td></td>
+<td></td>
+<td>603M</td>
+<td>603M<sup id="cite_ref-mnp.nl_24-7" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
+<td>600M</td>
+<td>679M</td>
+<td>610M</td>
+<td>600M</td>
+<td></td>
+<td>641M</td>
+</tr>
+<tr>
+<td>1750</td>
+<td></td>
+<td>791M</td>
+<td></td>
+<td>814M</td>
+<td></td>
+<td>770M</td>
+<td>720M</td>
+<td>700M</td>
+<td>735–805M</td>
+<td>731M</td>
+</tr>
+<tr>
+<td>1800</td>
+<td>1,000M</td>
+<td>978M</td>
+<td></td>
+<td>989M<sup id="cite_ref-mnp.nl_24-8" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
+<td>900M</td>
+<td>954M</td>
+<td>900M</td>
+<td>900M</td>
+<td></td>
+<td>890M</td>
+</tr>
+<tr>
+<td>1820</td>
+<td></td>
+<td></td>
+<td>1,042M</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1850</td>
+<td>1,265M</td>
+<td>1,262M</td>
+<td></td>
+<td>1,263M</td>
+<td></td>
+<td>1,241M</td>
+<td>1,200M</td>
+<td>1,200M</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1870</td>
+<td></td>
+<td></td>
+<td>1,276M</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1875</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>1,325M</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1900</td>
+<td>1,656M</td>
+<td>1,650M</td>
+<td>1,563M</td>
+<td>1,654M<sup id="cite_ref-mnp.nl_24-9" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
+<td>1,600M</td>
+<td>1,633M</td>
+<td>1,625M</td>
+<td>1,600M</td>
+<td>1,650–1,710M</td>
+<td>1,668M</td>
+</tr>
+<tr>
+<td>1910</td>
+<td></td>
+<td>1,750M</td>
+<td></td>
+<td>1,777M</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1913</td>
+<td></td>
+<td></td>
+<td>1,793M</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1920</td>
+<td></td>
+<td>1,860M</td>
+<td>1,863M</td>
+<td>1,912M</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>1,968M</td>
+</tr>
+<tr>
+<td>1925</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>2,000M</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1930</td>
+<td></td>
+<td>2,070M</td>
+<td></td>
+<td>2,092M</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>2,145M</td>
+</tr>
+<tr>
+<td>1940</td>
+<td></td>
+<td>2,300M</td>
+<td>2,299M</td>
+<td>2,307M</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>2,340M</td>
+</tr>
+</table>
+<h3><span class="mw-headline" id="1950_to_present">1950 to present</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=4" title="Edit section: 1950 to present">edit</a><span class="mw-editsection-bracket">]</span></span></h3>
+<p>For times after <a href="/wiki/World_War_II" title="World War II">World War II</a>, demographic data of some accuracy becomes available for a significant number of countries, and population estimates are often given as grand totals of numbers (typically given by country) of widely diverging accuracies. Some sources give these numbers rounded to the nearest million or the nearest thousand, while others give them without any rounding.</p>
+<p>Taking these numbers at face value would be <a href="/wiki/False_precision" title="False precision">false precision</a>; in spite of being stated to four, seven or even ten digits, they should not be interpreted as accurate to more than three digits at best (estimates by the <a href="/wiki/United_States_Census_Bureau" title="United States Census Bureau">United States Census Bureau</a> and by the <a href="/wiki/United_Nations_Department_of_Economic_and_Social_Affairs" title="United Nations Department of Economic and Social Affairs">United Nations</a> differ by about 0.5%–1.5%).</p>
+<table class="wikitable" style="text-align:right;font-size:small;">
+<tr>
+<th>Year</th>
+<th><a href="/wiki/United_States_Census_Bureau" title="United States Census Bureau">United States Census Bureau</a>
+<p>(2017)<sup id="cite_ref-census.gov_28-0" class="reference"><a href="#cite_note-census.gov-28">[28]</a></sup></p>
+</th>
+<th><a href="/wiki/Population_Reference_Bureau" title="Population Reference Bureau">Population Reference Bureau</a>
+<p>(1973–2016)<sup id="cite_ref-Population_Reference_Bureau_15-1" class="reference"><a href="#cite_note-Population_Reference_Bureau-15">[15]</a></sup></p>
+</th>
+<th><a href="/wiki/United_Nations_Department_of_Economic_and_Social_Affairs" title="United Nations Department of Economic and Social Affairs">United Nations Department of Economic and Social Affairs</a>
+<p>(2015)<sup id="cite_ref-The_World_at_Six_Billion,_1999_16-1" class="reference"><a href="#cite_note-The_World_at_Six_Billion,_1999-16">[16]</a></sup></p>
+</th>
+<th><a href="/wiki/Angus_Maddison" title="Angus Maddison">Maddison</a>
+<p>(2008)<sup id="cite_ref-www.ggdc.net/maddison/Historical_Statistics_17-1" class="reference"><a href="#cite_note-www.ggdc.net/maddison/Historical_Statistics-17">[17]</a></sup></p>
+</th>
+<th>HYDE
+<p>(2007)<sup id="cite_ref-mnp.nl_24-10" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></p>
+</th>
+<th><a href="/wiki/John_Tanton" title="John Tanton">Tanton</a>
+<p>(1994)<sup id="cite_ref-thesocialcontract.com_18-1" class="reference"><a href="#cite_note-thesocialcontract.com-18">[18]</a></sup></p>
+</th>
+<th>Biraben
+<p>(1980)<sup id="cite_ref-French_1980,_pp._1_19-1" class="reference"><a href="#cite_note-French_1980,_pp._1-19">[19]</a></sup></p>
+</th>
+<th><a href="/wiki/Colin_McEvedy" title="Colin McEvedy">McEvedy</a> &amp;
+<p>Jones (1978)<sup id="cite_ref-Richard_Jones_1978_20-1" class="reference"><a href="#cite_note-Richard_Jones_1978-20">[20]</a></sup></p>
+</th>
+<th>Thomlinson
+<p>(1975)<sup id="cite_ref-ReferenceA_21-1" class="reference"><a href="#cite_note-ReferenceA-21">[21]</a></sup></p>
+</th>
+<th>Durand
+<p>(1974)<sup id="cite_ref-John_D._Durand_1974_22-1" class="reference"><a href="#cite_note-John_D._Durand_1974-22">[22]</a></sup></p>
+</th>
+<th><a href="/wiki/Colin_Clark_(economist)" title="Colin Clark (economist)">Clark</a>
+<p>(1967)<sup id="cite_ref-Colin_Clark_1967_23-1" class="reference"><a href="#cite_note-Colin_Clark_1967-23">[23]</a></sup></p>
+</th>
+</tr>
+<tr>
+<td>1950</td>
+<td>2,557,628,654</td>
+<td>2,516,000,000</td>
+<td>2,525,149,000</td>
+<td>2,544,000,000</td>
+<td>2,527,960,000</td>
+<td>2,400,000,000</td>
+<td>2,527,000,000</td>
+<td>2,500,000,000</td>
+<td>2,400,000,000</td>
+<td></td>
+<td>2,486,000,000</td>
+</tr>
+<tr>
+<td>1951</td>
+<td>2,594,939,877</td>
+<td></td>
+<td>2,572,850,917</td>
+<td>2,571,663,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1952</td>
+<td>2,636,772,306</td>
+<td></td>
+<td>2,619,292,068</td>
+<td>2,617,949,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1953</td>
+<td>2,682,053,389</td>
+<td></td>
+<td>2,665,865,392</td>
+<td>2,665,959,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1954</td>
+<td>2,730,228,104</td>
+<td></td>
+<td>2,713,172,027</td>
+<td>2,716,927,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1955</td>
+<td>2,782,098,943</td>
+<td></td>
+<td>2,761,650,981</td>
+<td>2,769,074,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1956</td>
+<td>2,835,299,673</td>
+<td></td>
+<td>2,811,572,031</td>
+<td>2,822,502,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1957</td>
+<td>2,891,349,717</td>
+<td></td>
+<td>2,863,042,795</td>
+<td>2,879,934,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1958</td>
+<td>2,948,137,248</td>
+<td></td>
+<td>2,916,030,167</td>
+<td>2,939,254,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1959</td>
+<td>3,000,716,593</td>
+<td></td>
+<td>2,970,395,814</td>
+<td>2,995,909,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1960</td>
+<td>3,043,001,508</td>
+<td></td>
+<td>3,026,002,942</td>
+<td>3,041,507,000</td>
+<td>3,042,000,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1961</td>
+<td>3,083,966,929</td>
+<td></td>
+<td>3,082,830,266</td>
+<td>3,082,161,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1962</td>
+<td>3,140,093,217</td>
+<td></td>
+<td>3,141,071,531</td>
+<td>3,135,787,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>3,036,000,000</td>
+</tr>
+<tr>
+<td>1963</td>
+<td>3,209,827,882</td>
+<td></td>
+<td>3,201,178,277</td>
+<td>3,201,354,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1964</td>
+<td>3,281,201,306</td>
+<td></td>
+<td>3,263,738,832</td>
+<td>3,266,477,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1965</td>
+<td>3,350,425,793</td>
+<td></td>
+<td>3,329,122,479</td>
+<td>3,333,138,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1966</td>
+<td>3,420,677,923</td>
+<td></td>
+<td>3,397,475,247</td>
+<td>3,402,224,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>3,288,000,000</td>
+</tr>
+<tr>
+<td>1967</td>
+<td>3,490,333,715</td>
+<td></td>
+<td>3,468,521,724</td>
+<td>3,471,464,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1968</td>
+<td>3,562,313,822</td>
+<td></td>
+<td>3,541,674,891</td>
+<td>3,543,086,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1969</td>
+<td>3,637,159,050</td>
+<td></td>
+<td>3,616,108,749</td>
+<td>3,615,743,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1970</td>
+<td>3,712,697,742</td>
+<td></td>
+<td>3,691,172,616</td>
+<td>3,691,157,000</td>
+<td>3,710,000,000</td>
+<td></td>
+<td>3,637,000,000</td>
+<td></td>
+<td>3,600,000,000</td>
+<td>3,600,000,000– 3,700,000,000</td>
+<td>3,632,000,000</td>
+</tr>
+<tr>
+<td>1971</td>
+<td>3,790,326,948</td>
+<td></td>
+<td>3,766,754,345</td>
+<td>3,769,818,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1972</td>
+<td>3,866,568,653</td>
+<td></td>
+<td>3,842,873,611</td>
+<td>3,846,499,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1973</td>
+<td>3,942,096,442</td>
+<td></td>
+<td>3,919,182,332</td>
+<td>3,922,793,000</td>
+<td>3,923,000,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td>3,860,000,000</td>
+</tr>
+<tr>
+<td>1974</td>
+<td>4,016,608,813</td>
+<td></td>
+<td>3,995,304,922</td>
+<td>3,997,677,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1975</td>
+<td>4,089,083,233</td>
+<td></td>
+<td>4,071,020,434</td>
+<td>4,070,671,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td>3,900,000,000</td>
+<td>4,000,000,000</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1976</td>
+<td>4,160,185,010</td>
+<td></td>
+<td>4,146,135,850</td>
+<td>4,141,445,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1977</td>
+<td>4,232,084,578</td>
+<td></td>
+<td>4,220,816,737</td>
+<td>4,213,539,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1978</td>
+<td>4,304,105,753</td>
+<td></td>
+<td>4,295,664,825</td>
+<td>4,286,317,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1979</td>
+<td>4,379,013,942</td>
+<td></td>
+<td>4,371,527,871</td>
+<td>4,363,144,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1980</td>
+<td>4,451,362,735</td>
+<td></td>
+<td>4,449,048,798</td>
+<td>4,439,529,000</td>
+<td>4,461,000,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1981</td>
+<td>4,534,410,125</td>
+<td></td>
+<td>4,528,234,634</td>
+<td>4,514,838,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1982</td>
+<td>4,614,566,561</td>
+<td></td>
+<td>4,608,962,418</td>
+<td>4,587,307,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1983</td>
+<td>4,695,736,743</td>
+<td></td>
+<td>4,691,559,840</td>
+<td>4,676,388,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1984</td>
+<td>4,774,569,391</td>
+<td></td>
+<td>4,776,392,828</td>
+<td>4,756,521,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1985</td>
+<td>4,856,462,699</td>
+<td></td>
+<td>4,863,601,517</td>
+<td>4,837,719,000</td>
+<td></td>
+<td>5,000,000,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1986</td>
+<td>4,940,571,232</td>
+<td></td>
+<td>4,953,376,710</td>
+<td>4,920,968,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1987</td>
+<td>5,027,200,492</td>
+<td></td>
+<td>5,045,315,871</td>
+<td>5,006,672,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1988</td>
+<td>5,114,557,167</td>
+<td></td>
+<td>5,138,214,688</td>
+<td>5,093,306,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1989</td>
+<td>5,201,440,110</td>
+<td></td>
+<td>5,230,000,000</td>
+<td>5,180,540,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1990</td>
+<td>5,288,955,934</td>
+<td></td>
+<td>5,320,816,667</td>
+<td>5,269,029,000</td>
+<td>5,308,000,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1991</td>
+<td>5,371,585,922</td>
+<td></td>
+<td>5,408,908,724</td>
+<td>5,351,922,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1992</td>
+<td>5,456,136,278</td>
+<td></td>
+<td>5,494,899,570</td>
+<td>5,435,722,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1993</td>
+<td>5,538,268,316</td>
+<td></td>
+<td>5,578,865,109</td>
+<td>5,518,127,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1994</td>
+<td>5,618,682,132</td>
+<td></td>
+<td>5,661,086,346</td>
+<td>5,599,396,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1995</td>
+<td>5,699,202,985</td>
+<td>5,760,000,000</td>
+<td>5,741,822,412</td>
+<td>5,681,575,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1996</td>
+<td>5,779,440,593</td>
+<td></td>
+<td>5,821,016,750</td>
+<td>5,762,212,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1997</td>
+<td>5,857,972,543</td>
+<td>5,840,000,000</td>
+<td>5,898,688,337</td>
+<td>5,842,122,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1998</td>
+<td>5,935,213,248</td>
+<td></td>
+<td>5,975,303,657</td>
+<td>5,921,366,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>1999</td>
+<td>6,012,074,922</td>
+<td></td>
+<td>6,051,478,010</td>
+<td>5,999,622,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2000</td>
+<td>6,088,571,383</td>
+<td>6,067,000,000</td>
+<td>6,127,700,428</td>
+<td>6,076,558,000</td>
+<td>6,145,000,000</td>
+<td></td>
+<td></td>
+<td>5,750,000,000</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2001</td>
+<td>6,165,219,247</td>
+<td>6,137,000,000</td>
+<td>6,204,147,026</td>
+<td>6,154,791,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2002</td>
+<td>6,242,016,348</td>
+<td>6,215,000,000</td>
+<td>6,280,853,817</td>
+<td>6,231,704,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2003</td>
+<td>6,318,590,956</td>
+<td>6,314,000,000</td>
+<td>6,357,991,749</td>
+<td>6,308,364,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2004</td>
+<td>6,395,699,509</td>
+<td>6,396,000,000</td>
+<td>6,435,705,595</td>
+<td>6,374,056,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2005</td>
+<td>6,473,044,732</td>
+<td>6,477,000,000</td>
+<td>6,514,094,605</td>
+<td>6,462,987,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2006</td>
+<td>6,551,263,534</td>
+<td>6,555,000,000</td>
+<td>6,593,227,977</td>
+<td>6,540,214,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2007</td>
+<td>6,629,913,759</td>
+<td>6,625,000,000</td>
+<td>6,673,105,937</td>
+<td>6,616,689,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2008</td>
+<td>6,709,049,780</td>
+<td>6,705,000,000</td>
+<td>6,753,649,228</td>
+<td>6,694,832,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2009</td>
+<td>6,788,214,394</td>
+<td>6,809,972,000</td>
+<td>6,834,721,933</td>
+<td>6,764,086,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2010</td>
+<td>6,858,584,755</td>
+<td>6,892,319,000</td>
+<td>6,916,183,482</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2011</td>
+<td>6,935,999,491</td>
+<td>6,986,951,000</td>
+<td>6,997,998,760</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2012</td>
+<td>7,013,871,313</td>
+<td>7,057,075,000</td>
+<td>7,080,072,417</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2013</td>
+<td>7,092,128,094</td>
+<td>7,136,796,000</td>
+<td>7,162,119,434</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2014</td>
+<td>7,169,968,185</td>
+<td>7,238,184,000</td>
+<td>7,243,784,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2015</td>
+<td>7,247,892,788</td>
+<td>7,336,435,000</td>
+<td>7,349,472,000</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>2016</td>
+<td>7,325,996,709</td>
+<td>7,418,151,841</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+</table>
+<h2><span class="mw-headline" id="Projections">Projections</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=5" title="Edit section: Projections">edit</a><span class="mw-editsection-bracket">]</span></span></h2>
+<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Projections_of_population_growth" title="Projections of population growth">Projections of population growth</a></div>
+<div class="thumb tright">
+<div class="thumbinner" style="width:302px;"><a href="/wiki/File:World-Population-1800-2100.svg" class="image"><img alt="" src="//upload.wikimedia.org/wikipedia/commons/thumb/5/56/World-Population-1800-2100.svg/300px-World-Population-1800-2100.svg.png" width="300" height="306" class="thumbimage" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/5/56/World-Population-1800-2100.svg/450px-World-Population-1800-2100.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/56/World-Population-1800-2100.svg/600px-World-Population-1800-2100.svg.png 2x" data-file-width="1003" data-file-height="1024" /></a>
+<div class="thumbcaption">
+<div class="magnify"><a href="/wiki/File:World-Population-1800-2100.svg" class="internal" title="Enlarge"></a></div>
+World population estimates from 1800 to 2100, based on "high", "medium" and "low" <a rel="nofollow" class="external text" href="http://esa.un.org/wpp/unpp/panel_population.htm">United Nations projections in 2010</a> (colored red, orange and green) and <a rel="nofollow" class="external text" href="https://www.census.gov/ipc/www/worldhis.html">US Census Bureau historical estimates</a> (in black). Actual recorded population figures (as of 2010) are colored in blue. According to the highest estimate, the world population may rise to 16 <a href="/wiki/Thousand_million" class="mw-redirect" title="Thousand million">billion</a> by 2100; according to the lowest estimate, it may decline to 6 billion.</div>
+</div>
+</div>
+<p>As of 2015<sup class="plainlinks noexcerpt noprint asof-tag update" style="display:none;"><a class="external text" href="//en.wikipedia.org/w/index.php?title=World_population_estimates&amp;action=edit">[update]</a></sup>, the population of the world is projected to reach 8 billion in 2025, and 9 billion by about 2040/42. Kapitza (1996) estimated an asymptotic limit of population growth of 14 billion, 90% of which (12.6 billion) expected to be reached by 2135.<sup id="cite_ref-29" class="reference"><a href="#cite_note-29">[29]</a></sup></p>
+<p>Reasonable predictions of population development are possible for the next 30 years or so, representing the period of fertility of the children alive today. Projections of population reaching more than one generation into the future are highly speculative: Thus, the United Nations Department of Economic and Social Affairs report of 2004 projected the world population to peak at 9.22 billion in 2075 and then stabilise at a value close to 9 billion;<sup id="cite_ref-un-esa1_30-0" class="reference"><a href="#cite_note-un-esa1-30">[30]</a></sup> By contrast, a 2014 projection by the United Nations Population Division predicts a population close to 11 billion by 2100 without any declining trend in the foreseeable future.<sup id="cite_ref-Science_31-0" class="reference"><a href="#cite_note-Science-31">[31]</a></sup> On the other hand, a conservative scenario published in 2012 assumes that a maximum of 8 billion will be reached before 2040.<sup id="cite_ref-32" class="reference"><a href="#cite_note-32">[32]</a></sup></p>
+<p>The following table shows projections of world population for the 21st century.</p>
+<table class="wikitable" style="text-align:right;font-size:small;">
+<tr>
+<th>Year</th>
+<th><a href="/wiki/United_States_Census_Bureau" title="United States Census Bureau">United States Census Bureau</a>
+<p>(2015)<sup id="cite_ref-census.gov_28-1" class="reference"><a href="#cite_note-census.gov-28">[28]</a></sup></p>
+</th>
+<th><a href="/wiki/Population_Reference_Bureau" title="Population Reference Bureau">Population Reference Bureau</a>
+<p>(1973-2015)<sup id="cite_ref-Population_Reference_Bureau_15-2" class="reference"><a href="#cite_note-Population_Reference_Bureau-15">[15]</a></sup></p>
+</th>
+<th><a href="/wiki/United_Nations_Department_of_Economic_and_Social_Affairs" title="United Nations Department of Economic and Social Affairs">United Nations Department of Economic and Social Affairs</a>
+<p>(2015)<sup id="cite_ref-The_World_at_Six_Billion,_1999_16-2" class="reference"><a href="#cite_note-The_World_at_Six_Billion,_1999-16">[16]</a></sup></p>
+</th>
+</tr>
+<tr>
+<td><span id="2016"></span>2016</td>
+<td>7,334,771,614</td>
+<td></td>
+<td>7,432,663,280</td>
+</tr>
+<tr>
+<td><span id="2017"></span>2017</td>
+<td>7,412,778,971</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2018"></span>2018</td>
+<td>7,490,427,640</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2019"></span>2019</td>
+<td>7,567,402,977</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2020"></span>2020</td>
+<td>7,643,402,123</td>
+<td></td>
+<td>7,758,157,000</td>
+</tr>
+<tr>
+<td><span id="2021"></span>2021</td>
+<td>7,718,256,830</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2022"></span>2022</td>
+<td>7,792,021,317</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2023"></span>2023</td>
+<td>7,864,725,370</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2024"></span>2024</td>
+<td>7,936,271,554</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2025"></span>2025</td>
+<td>8,006,580,553</td>
+<td>8,000,000,000</td>
+<td>8,141,661,000</td>
+</tr>
+<tr>
+<td><span id="2026"></span>2026</td>
+<td>8,075,716,000</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2027"></span>2027</td>
+<td>8,143,729,466</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2028"></span>2028</td>
+<td>8,210,559,895</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2029"></span>2029</td>
+<td>8,276,190,519</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2030"></span>2030</td>
+<td>8,340,606,590</td>
+<td>8,505,000,000</td>
+<td>8,500,766,000</td>
+</tr>
+<tr>
+<td><span id="2031"></span>2031</td>
+<td>8,403,880,343</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2032"></span>2032</td>
+<td>8,466,094,022</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2033"></span>2033</td>
+<td>8,527,246,205</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2034"></span>2034</td>
+<td>8,587,325,154</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2035"></span>2035</td>
+<td>8,646,304,704</td>
+<td></td>
+<td>8,838,908,000</td>
+</tr>
+<tr>
+<td><span id="2036"></span>2036</td>
+<td>8,704,239,274</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2037"></span>2037</td>
+<td>8,761,189,197</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2038"></span>2038</td>
+<td>8,817,138,785</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2039"></span>2039</td>
+<td>8,872,066,537</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2040"></span>2040</td>
+<td>8,925,949,679</td>
+<td></td>
+<td>9,157,234,000</td>
+</tr>
+<tr>
+<td><span id="2041"></span>2041</td>
+<td>8,978,822,945</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2042"></span>2042</td>
+<td>9,030,723,366</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2043"></span>2043</td>
+<td>9,081,617,002</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2044"></span>2044</td>
+<td>9,131,462,326</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2045"></span>2045</td>
+<td>9,180,225,214</td>
+<td></td>
+<td>9,453,892,000</td>
+</tr>
+<tr>
+<td><span id="2046"></span>2046</td>
+<td>9,227,935,007</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2047"></span>2047</td>
+<td>9,274,616,811</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2048"></span>2048</td>
+<td>9,320,232,984</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2049"></span>2049</td>
+<td>9,364,750,182</td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td><span id="2050"></span>2050</td>
+<td>9,408,141,302</td>
+<td>9,804,000,000</td>
+<td>9,725,148,000</td>
+</tr>
+<tr>
+<td><span id="2055"></span>2055</td>
+<td></td>
+<td></td>
+<td>9,968,809,000</td>
+</tr>
+<tr>
+<td><span id="2060"></span>2060</td>
+<td></td>
+<td></td>
+<td>10,184,290,000</td>
+</tr>
+<tr>
+<td><span id="2065"></span>2065</td>
+<td></td>
+<td></td>
+<td>10,375,719,000</td>
+</tr>
+<tr>
+<td><span id="2070"></span>2070</td>
+<td></td>
+<td></td>
+<td>10,547,989,000</td>
+</tr>
+<tr>
+<td><span id="2075"></span>2075</td>
+<td></td>
+<td></td>
+<td>10,701,653,000</td>
+</tr>
+<tr>
+<td><span id="2080"></span>2080</td>
+<td></td>
+<td></td>
+<td>10,836,635,000</td>
+</tr>
+<tr>
+<td><span id="2085"></span>2085</td>
+<td></td>
+<td></td>
+<td>10,953,525,000</td>
+</tr>
+<tr>
+<td><span id="2090"></span>2090</td>
+<td></td>
+<td></td>
+<td>11,055,270,000</td>
+</tr>
+<tr>
+<td><span id="2095"></span>2095</td>
+<td></td>
+<td></td>
+<td>11,142,461,000</td>
+</tr>
+<tr>
+<td><span id="2100"></span>2100</td>
+<td></td>
+<td></td>
+<td>11,213,317,000</td>
+</tr>
+</table>
+<p>Other, historical projections include</p>
+<ul>
+<li><a href="/wiki/John_Tanton" title="John Tanton">Tanton</a> (1994):<sup id="cite_ref-thesocialcontract.com_18-2" class="reference"><a href="#cite_note-thesocialcontract.com-18">[18]</a></sup> 8 billion for the year 2020;</li>
+<li><a href="/wiki/Colin_McEvedy" title="Colin McEvedy">McEvedy</a> &amp; Jones (1978):<sup id="cite_ref-Richard_Jones_1978_20-2" class="reference"><a href="#cite_note-Richard_Jones_1978-20">[20]</a></sup> 5.75 billion for the year 2000, 8.25 billion for the year 2200.</li>
+</ul>
+<h2><span class="mw-headline" id="By_world_region">By world region</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=6" title="Edit section: By world region">edit</a><span class="mw-editsection-bracket">]</span></span></h2>
+<div class="thumb tright">
+<div class="thumbinner" style="width:252px;"><a href="/wiki/File:Pop_continents_2000_2050.png" class="image"><img alt="" src="//upload.wikimedia.org/wikipedia/commons/thumb/0/0c/Pop_continents_2000_2050.png/250px-Pop_continents_2000_2050.png" width="250" height="134" class="thumbimage" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/0/0c/Pop_continents_2000_2050.png/375px-Pop_continents_2000_2050.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/0/0c/Pop_continents_2000_2050.png/500px-Pop_continents_2000_2050.png 2x" data-file-width="825" data-file-height="443" /></a>
+<div class="thumbcaption">
+<div class="magnify"><a href="/wiki/File:Pop_continents_2000_2050.png" class="internal" title="Enlarge"></a></div>
+UN estimates (as of 2017) for world population by continent in 2000 and in 2050 (pie chart size to scale).<br />
+<span style="margin:0px; font-size:90%;"><span style="border:none; background-color:yellow; color:yellow;">&#160;&#160;&#160;&#160;</span>&#160;Asia</span> <span style="margin:0px; font-size:90%;"><span style="border:none; background-color:black; color:black;">&#160;&#160;&#160;&#160;</span>&#160;Africa</span> <span style="margin:0px; font-size:90%;"><span style="border:none; background-color:blue; color:blue;">&#160;&#160;&#160;&#160;</span>&#160;Europe</span> <span style="margin:0px; font-size:90%;"><span style="border:none; background-color:red; color:red;">&#160;&#160;&#160;&#160;</span>&#160;South/Central America</span> <span style="margin:0px; font-size:90%;"><span style="border:none; background-color:brown; color:brown;">&#160;&#160;&#160;&#160;</span>&#160;North America</span> <span style="margin:0px; font-size:90%;"><span style="border:none; background-color:green; color:green;">&#160;&#160;&#160;&#160;</span>&#160;Oceania</span></div>
+</div>
+</div>
+<p>Population estimates for world regions based on Maddison (2007),<sup id="cite_ref-33" class="reference"><a href="#cite_note-33">[33]</a></sup> in millions. The row showing total world population includes the average growth rate per year over the period separating each column from the preceding one.</p>
+<table class="wikitable">
+<tr>
+<th>year</th>
+<th>1</th>
+<th>1000</th>
+<th>1500</th>
+<th>1600</th>
+<th>1700</th>
+<th>1820</th>
+<th>1913</th>
+<th>2000</th>
+<th>2030</th>
+</tr>
+<tr>
+<td><a href="/wiki/East_Asia" title="East Asia">East</a>/<a href="/wiki/Southeast_Asia" title="Southeast Asia">Southeast Asia</a></td>
+<td>74 (33%)</td>
+<td>88 (33%)</td>
+<td>166 (38%)</td>
+<td>223 (40%)</td>
+<td>216 (36%)</td>
+<td>469 (45%)</td>
+<td>613 (34%)</td>
+<td>1,996 (33%)</td>
+<td>2,417 (30%)</td>
+</tr>
+<tr>
+<td><a href="/wiki/Ethnic_groups_of_South_Asia" class="mw-redirect" title="Ethnic groups of South Asia">South Asia</a></td>
+<td>75 (33%)</td>
+<td>75 (28%)</td>
+<td>110 (25%)</td>
+<td>135 (24%)</td>
+<td>165 (27%)</td>
+<td>216 (21%)</td>
+<td>326 (18%)</td>
+<td>1,372 (23%)</td>
+<td>2,003 (25%)</td>
+</tr>
+<tr>
+<td><a href="/wiki/Demographics_of_Europe" title="Demographics of Europe">Europe</a><sup id="cite_ref-34" class="reference"><a href="#cite_note-34">[34]</a></sup></td>
+<td>34 (15%)</td>
+<td>40 (15%)</td>
+<td>78 (18%)</td>
+<td>112 (20%)</td>
+<td>127 (21%)</td>
+<td>224 (21%)</td>
+<td>498 (28%)</td>
+<td>742 (13%)</td>
+<td>829 (11%)</td>
+</tr>
+<tr>
+<td><a href="/wiki/Western_Asia#Demographics" title="Western Asia">West Asia</a></td>
+<td>19 (8%)</td>
+<td>20 (7%)</td>
+<td>18 (3%)</td>
+<td>21 (3%)</td>
+<td>21 (3%)</td>
+<td>25 (2%)</td>
+<td>39 (2%)</td>
+<td>237 (4%)</td>
+<td>370 (5%)</td>
+</tr>
+<tr>
+<td><a href="/wiki/Demographics_of_Africa" title="Demographics of Africa">Africa</a></td>
+<td>17 (8%)</td>
+<td>32 (12%)</td>
+<td>47 (11%)</td>
+<td>55 (10%)</td>
+<td>61 (10%)</td>
+<td>74 (7%)</td>
+<td>125 (7%)</td>
+<td>798 (13%)</td>
+<td>1,449 (18%)</td>
+</tr>
+<tr>
+<td><a href="/wiki/Demographics_of_Latin_America" class="mw-redirect" title="Demographics of Latin America">South/Central America</a></td>
+<td>6 (3%)</td>
+<td>11 (4%)</td>
+<td>18 (4%)</td>
+<td>9 (2%)</td>
+<td>12 (2%)</td>
+<td>22 (2%)</td>
+<td>81 (5%)</td>
+<td>520 (9%)</td>
+<td>702 (9%)</td>
+</tr>
+<tr>
+<td><a href="/wiki/North_America" title="North America">North America</a></td>
+<td>1 (0%)</td>
+<td>1 (0%)</td>
+<td>2 (0%)</td>
+<td>2 (0%)</td>
+<td>1 (0%)</td>
+<td>11 (1%)</td>
+<td>105 (6%)</td>
+<td>314 (5%)</td>
+<td>413 (5%)</td>
+</tr>
+<tr>
+<th>World</th>
+<th>226</th>
+<th>267<br />
+<small>(+ 0.02% p.a.)</small></th>
+<th>438<br />
+<small>(+ 0.1% p.a.)</small></th>
+<th>556<br />
+<small>(+ 0.2% p.a.)</small></th>
+<th>603<br />
+<small>(+ 0.1% p.a.)</small></th>
+<th>1,041<br />
+<small>(+ 0.5% p.a.)</small></th>
+<th>1,791<br />
+<small>(+ 0.6% p.a.)</small></th>
+<th>6,062<br />
+<small>(+ 1.4% p.a.)</small></th>
+<th>8,175<br />
+<small>(+ 1.0% p.a.)</small></th>
+</tr>
+</table>
+<p>When considering population estimates by world region, it is worth noting that <a href="/wiki/Population_history_of_the_indigenous_peoples_of_the_Americas" class="mw-redirect" title="Population history of the indigenous peoples of the Americas">population history of the indigenous peoples of the Americas</a> before the 1492 voyage of Christopher Columbus has proven difficult to establish, with many historians arguing for an estimate of 50 million people throughout the Americas, and some estimating that populations may have reached 100 million people or more.<sup id="cite_ref-35" class="reference"><a href="#cite_note-35">[35]</a></sup> It is therefore estimated by some that populations in Mexico, Central, and South America could have reached 37 million by 1492.<sup id="cite_ref-36" class="reference"><a href="#cite_note-36">[36]</a></sup> Additionally, the population estimate of 2 million for North America for the same time period represents the low end of modern estimates, and some estimate the population to have been as high as 18 million.<sup id="cite_ref-37" class="reference"><a href="#cite_note-37">[37]</a></sup></p>
+<h2><span class="mw-headline" id="See_also">See also</span></h2>
+<ul>
+<li><a href="/wiki/Projections_of_population_growth" title="Projections of population growth">Projections of population growth</a></li>
+<li><a href="/wiki/Population_growth" title="Population growth">Population growth</a></li>
+</ul>
+<h2><span class="mw-headline" id="References">References</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=7" title="Edit section: References">edit</a><span class="mw-editsection-bracket">]</span></span></h2>
+<div class="reflist columns references-column-width" style="-moz-column-width: 30em; -webkit-column-width: 30em; column-width: 30em; list-style-type: decimal;">
+<ol class="references">
+<li id="cite_note-UNProspect2017-1"><span class="mw-cite-backlink"><b><a href="#cite_ref-UNProspect2017_1-0">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="https://esa.un.org/unpd/wpp/DataQuery/">UN World Population Prospect 2017</a> gives a "median" estimate of 11.2 billion for 2100, with a "95% confidence interval" between 10 and 13 billion.</span></li>
+<li id="cite_note-2"><span class="mw-cite-backlink"><b><a href="#cite_ref-2">^</a></b></span> <span class="reference-text">range of estimates for average growth rates over the preceding century according to the data cited under <a href="#Historical_population">#Historical_population</a>; The average growth rate for the 14th century is negative as a consequence of the <a href="/wiki/Black_Death" title="Black Death">Black Death</a>.</span></li>
+<li id="cite_note-3"><span class="mw-cite-backlink"><b><a href="#cite_ref-3">^</a></b></span> <span class="reference-text">Kuhrt, A. (1995). <i>The Ancient Near East, c. 3000–330 BCE</i>. Vol. 2. London: Routledge. p. 695.</span></li>
+<li id="cite_note-4"><span class="mw-cite-backlink"><b><a href="#cite_ref-4">^</a></b></span> <span class="reference-text">Thomlinson (1975): "one to ten million".</span></li>
+<li id="cite_note-5"><span class="mw-cite-backlink"><b><a href="#cite_ref-5">^</a></b></span> <span class="reference-text"><cite class="citation journal">Pala, M; Olivieri, A; Achilli, A; Accetturo, M; Metspalu, E; Reidla, M; Tamm, E; Karmin, M; Reisberg, T; Hooshiar Kashani, B; Perego, UA; Carossa, V; Gandini, F; Pereira, JB; Soares, P; Angerhofer, N; Rychkov, S; Al-Zahery, N; Carelli, V; Sanati, MH; Houshmand, M; Hatina, J; Macaulay, V; Pereira, L; Woodward, SR; Davies, W; Gamble, C; Baird, D; Semino, O; Villems, R; Torroni, A; Richards, MB (2012). <a rel="nofollow" class="external text" href="//www.ncbi.nlm.nih.gov/pmc/articles/PMC3376494">"Mitochondrial DNA signals of late glacial recolonization of Europe from near eastern refugia"</a>. <i>Am. J. Hum. Genet</i>. <b>90</b>: 915–24. <a href="/wiki/Digital_object_identifier" title="Digital object identifier">doi</a>:<a rel="nofollow" class="external text" href="//doi.org/10.1016%2Fj.ajhg.2012.04.003">10.1016/j.ajhg.2012.04.003</a>. <a href="/wiki/PubMed_Central" title="PubMed Central">PMC</a>&#160;<span class="plainlinks"><a rel="nofollow" class="external text" href="//www.ncbi.nlm.nih.gov/pmc/articles/PMC3376494">3376494</a> <img alt="Freely accessible" src="//upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/9px-Lock-green.svg.png" title="Freely accessible" width="9" height="14" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/14px-Lock-green.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/18px-Lock-green.svg.png 2x" data-file-width="512" data-file-height="813" /></span>. <a href="/wiki/PubMed_Identifier" class="mw-redirect" title="PubMed Identifier">PMID</a>&#160;<a rel="nofollow" class="external text" href="//www.ncbi.nlm.nih.gov/pubmed/22560092">22560092</a>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Am.+J.+Hum.+Genet.&amp;rft.atitle=Mitochondrial+DNA+signals+of+late+glacial+recolonization+of+Europe+from+near+eastern+refugia&amp;rft.volume=90&amp;rft.pages=915-24&amp;rft.date=2012&amp;rft_id=%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC3376494&amp;rft_id=info%3Apmid%2F22560092&amp;rft_id=info%3Adoi%2F10.1016%2Fj.ajhg.2012.04.003&amp;rft.aulast=Pala&amp;rft.aufirst=M&amp;rft.au=Olivieri%2C+A&amp;rft.au=Achilli%2C+A&amp;rft.au=Accetturo%2C+M&amp;rft.au=Metspalu%2C+E&amp;rft.au=Reidla%2C+M&amp;rft.au=Tamm%2C+E&amp;rft.au=Karmin%2C+M&amp;rft.au=Reisberg%2C+T&amp;rft.au=Hooshiar+Kashani%2C+B&amp;rft.au=Perego%2C+UA&amp;rft.au=Carossa%2C+V&amp;rft.au=Gandini%2C+F&amp;rft.au=Pereira%2C+JB&amp;rft.au=Soares%2C+P&amp;rft.au=Angerhofer%2C+N&amp;rft.au=Rychkov%2C+S&amp;rft.au=Al-Zahery%2C+N&amp;rft.au=Carelli%2C+V&amp;rft.au=Sanati%2C+MH&amp;rft.au=Houshmand%2C+M&amp;rft.au=Hatina%2C+J&amp;rft.au=Macaulay%2C+V&amp;rft.au=Pereira%2C+L&amp;rft.au=Woodward%2C+SR&amp;rft.au=Davies%2C+W&amp;rft.au=Gamble%2C+C&amp;rft.au=Baird%2C+D&amp;rft.au=Semino%2C+O&amp;rft.au=Villems%2C+R&amp;rft.au=Torroni%2C+A&amp;rft.au=Richards%2C+MB&amp;rft_id=%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC3376494&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span></span></li>
+<li id="cite_note-ambrose1998-6"><span class="mw-cite-backlink"><b><a href="#cite_ref-ambrose1998_6-0">^</a></b></span> <span class="reference-text"><cite class="citation journal">Stanley H. Ambrose (1998). "Late Pleistocene human population bottlenecks, volcanic winter, and differentiation of modern humans". <i><a href="/wiki/Journal_of_Human_Evolution" title="Journal of Human Evolution">Journal of Human Evolution</a></i>. <b>34</b> (6): 623–651. <a href="/wiki/Digital_object_identifier" title="Digital object identifier">doi</a>:<a rel="nofollow" class="external text" href="//doi.org/10.1006%2Fjhev.1998.0219">10.1006/jhev.1998.0219</a>. <a href="/wiki/PubMed_Identifier" class="mw-redirect" title="PubMed Identifier">PMID</a>&#160;<a rel="nofollow" class="external text" href="//www.ncbi.nlm.nih.gov/pubmed/9650103">9650103</a>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Journal+of+Human+Evolution&amp;rft.atitle=Late+Pleistocene+human+population+bottlenecks%2C+volcanic+winter%2C+and+differentiation+of+modern+humans&amp;rft.volume=34&amp;rft.issue=6&amp;rft.pages=623-651&amp;rft.date=1998&amp;rft_id=info%3Adoi%2F10.1006%2Fjhev.1998.0219&amp;rft_id=info%3Apmid%2F9650103&amp;rft.au=Stanley+H.+Ambrose&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span> <cite class="citation web">Ambrose, Stanley H. (2005). <a rel="nofollow" class="external text" href="http://www.bradshawfoundation.com/evolution/">"Volcanic Winter, and Differentiation of Modern Humans"</a>. <i>Bradshaw Foundation</i><span class="reference-accessdate">. Retrieved <span class="nowrap">2006-04-08</span></span>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=Bradshaw+Foundation&amp;rft.atitle=Volcanic+Winter%2C+and+Differentiation+of+Modern+Humans&amp;rft.date=2005&amp;rft.au=Ambrose%2C+Stanley+H.&amp;rft_id=http%3A%2F%2Fwww.bradshawfoundation.com%2Fevolution%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span></span></li>
+<li id="cite_note-robock2009-7"><span class="mw-cite-backlink"><b><a href="#cite_ref-robock2009_7-0">^</a></b></span> <span class="reference-text"><cite class="citation journal">Robock, A., C.M. Ammann, L. Oman, D. Shindell, S. Levis, and G. Stenchikov (2009). "Did the Toba volcanic eruption of ~74k BP produce widespread glaciation?". <i><a href="/wiki/Journal_of_Geophysical_Research" title="Journal of Geophysical Research">Journal of Geophysical Research</a></i>. <b>114</b> (D10): D10107. <a href="/wiki/Bibcode" title="Bibcode">Bibcode</a>:<a rel="nofollow" class="external text" href="http://adsabs.harvard.edu/abs/2009JGRD..11410107R">2009JGRD..11410107R</a>. <a href="/wiki/Digital_object_identifier" title="Digital object identifier">doi</a>:<a rel="nofollow" class="external text" href="//doi.org/10.1029%2F2008JD011652">10.1029/2008JD011652</a>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Journal+of+Geophysical+Research&amp;rft.atitle=Did+the+Toba+volcanic+eruption+of+~74k+BP+produce+widespread+glaciation%3F&amp;rft.volume=114&amp;rft.issue=D10&amp;rft.pages=D10107&amp;rft.date=2009&amp;rft_id=info%3Adoi%2F10.1029%2F2008JD011652&amp;rft_id=info%3Abibcode%2F2009JGRD..11410107R&amp;rft.au=Robock%2C+A.%2C+C.M.+Ammann%2C+L.+Oman%2C+D.+Shindell%2C+S.+Levis%2C+and+G.+Stenchikov&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span><span class="citation-comment" style="display:none; color:#33aa33; margin-left:0.3em">CS1 maint: Multiple names: authors list (<a href="/wiki/Category:CS1_maint:_Multiple_names:_authors_list" title="Category:CS1 maint: Multiple names: authors list">link</a>)</span></span></li>
+<li id="cite_note-8"><span class="mw-cite-backlink"><b><a href="#cite_ref-8">^</a></b></span> <span class="reference-text">Per Sjödin, Agnès E Sjöstrand, Mattias Jakobsson and Michael G B Blum, "Resequencing data provide no evidence for a human bottleneck in Africa during the penultimate glacial period" Mol <i>Biol Evol</i> (2012) doi: 10.1093/molbev/mss061. "A small human effective population size, on the order of 10,000 individuals, which is smaller than the effective population size of most great apes, has been interpreted as a result of a very long history, starting ∼ 2 mya, of a small population size, coined as the long-necked bottle model (Harpending et al. 1998; Hawks et al. 2000). Our findings are consistent with this hypothesis, but, depending on the mutation rate, we find either an effective population size of NA = 12,000 (95% C.I. = 9,000−15,500 when averaging over all three demographic models) using the mutation rate calibrated with the human-chimp divergence or an effective population size of NA = 32,500 individuals (95% C.I. = 27,500−34,500) using the mutation rate given by whole-genome trio analysis (The 1000 Genomes Project Consortium 2010) (supplementary figure 4 and table 6, Supplementary Material online). Not surprisingly, the estimated effective mutation rates θ = 4NAµ are comparable for the two mutation rates we considered, and are equal to 1.4 × 10<sup>−3</sup>/bp/generation (95% C.I. = (1.1−1.7) × 10<sup>−3</sup>). Relating the estimated effective population size to the census population size during the Pleistocene is a difficult task because there are many factors affecting the effective population size (Charlesworth 2009). Nevertheless, based on published estimates of the ratio between effective and census population size, a comprehensive value on the order of 10% has been found by Frankham (1995). This 10% rule roughly predicts that 120,000−325,[0]00 individuals (depending on the assumed mutation rate)"</span></li>
+<li id="cite_note-9"><span class="mw-cite-backlink"><b><a href="#cite_ref-9">^</a></b></span> <span class="reference-text">Haub (1995): "at some time back in the 1970s, some now-forgotten writer made the statement that 75 percent of the people who had ever been born were alive at that moment." Haub (1995) is the basis of a 2007 article in Scientific American, <cite class="citation web"><a rel="nofollow" class="external text" href="http://www.scientificamerican.com/article/fact-or-fiction-living-outnumber-dead/">"Fact or Fiction"</a>. <i>Scientificamerican.com</i>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=Scientificamerican.com&amp;rft.atitle=Fact+or+Fiction&amp;rft_id=http%3A%2F%2Fwww.scientificamerican.com%2Farticle%2Ffact-or-fiction-living-outnumber-dead%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span></span></li>
+<li id="cite_note-10"><span class="mw-cite-backlink"><b><a href="#cite_ref-10">^</a></b></span> <span class="reference-text">Wesley Stephenson, <a rel="nofollow" class="external text" href="http://www.bbc.com/news/magazine-16870579">Do the dead outnumber the living?</a>, BBC News 4 February 2012. Ciara Curtin, <a rel="nofollow" class="external text" href="https://www.scientificamerican.com/article/fact-or-fiction-living-outnumber-dead/">Fact or Fiction?: Living People Outnumber the Dead</a>, Scientific American, 1 March 2007.</span></li>
+<li id="cite_note-11"><span class="mw-cite-backlink"><b><a href="#cite_ref-11">^</a></b></span> <span class="reference-text">Kapitza, 'The phenomenological theory of world population growth', <i>Physics-Uspekhi</i> 39(1) 57-71 (1996) cites estimates ranging between 80 and 150 billion (Sergei P Kapitza, 'The phenomenological theory of world population growth', <i>Physics-Uspekhi</i> 39(1) 57-71, 1996), citing K. M. Weiss, <i>Human Biology</i> 56637, 1984, and N. Keyfitz, <i>Applied Mathematical Demography</i>, New York: Wiley, 1977). Haub (1995) cited 105 billion, updated to 107 billion as of 2011 in <cite class="citation web">Haub, Carl (October 2011). <a rel="nofollow" class="external text" href="http://www.prb.org/Articles/2002/HowManyPeopleHaveEverLivedonEarth.aspx">"How Many People Have Ever Lived on Earth?"</a>. <a href="/wiki/Population_Reference_Bureau" title="Population Reference Bureau">Population Reference Bureau</a><span class="reference-accessdate">. Retrieved <span class="nowrap">April 29,</span> 2013</span>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=unknown&amp;rft.btitle=How+Many+People+Have+Ever+Lived+on+Earth%3F&amp;rft.pub=Population+Reference+Bureau&amp;rft.date=2011-10&amp;rft.aulast=Haub&amp;rft.aufirst=Carl&amp;rft_id=http%3A%2F%2Fwww.prb.org%2FArticles%2F2002%2FHowManyPeopleHaveEverLivedonEarth.aspx&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span></span></li>
+<li id="cite_note-12"><span class="mw-cite-backlink"><b><a href="#cite_ref-12">^</a></b></span> <span class="reference-text">Haub (1995): "Clearly, the period 8000 B.C. to 1 A.D. is key to the magnitude of our number, but, unfortunately, little is known about that era. [...] of course, pushing the date of humanity's arrival on the planet before 50,000 B.C. would also raise the number, although perhaps not by terribly much."</span></li>
+<li id="cite_note-13"><span class="mw-cite-backlink"><b><a href="#cite_ref-13">^</a></b></span> <span class="reference-text">Haub (1995): "Life expectancy at birth probably averaged only about 10 years for most of human history. Estimates of average life expectancy in Iron Age France have been put at only 10 or 12 years. Under these conditions, the birth rate would have to be about 80 per 1,000 people just for the species to survive. [...] Our birth rate assumption will greatly affect the estimate of the number of persons ever born. Infant mortality in the human race's earliest days is thought to have been very high--perhaps 500 infant deaths per 1,000 births, or even higher. [...] Birth rates were set at 80 per 1,000 per year through 1 A.D. and at 60 per 1,000 from 2 A.D. to 1750. Rates then declined to the low 30s by the modern period. (For a brief bibliography of sources consulted in the course of this alchemy, see [Colin McEvedy and Richard Jones 1978])." "So, our estimate here is that about 5.5 percent of all people ever born are alive today." Using the UN estimates for birth rates (<cite class="citation web"><a rel="nofollow" class="external text" href="http://data.un.org/Data.aspx?q=world+population&amp;d=PopDiv&amp;f=variableID%3A53%3BcrID%3A900">"UNdata: Crude birth rate"</a>. United Nations. 25 August 2011.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=unknown&amp;rft.btitle=UNdata%3A+Crude+birth+rate&amp;rft.pub=United+Nations&amp;rft.date=2011-08-25&amp;rft_id=http%3A%2F%2Fdata.un.org%2FData.aspx%3Fq%3Dworld%2Bpopulation%26d%3DPopDiv%26f%3DvariableID%253A53%253BcrID%253A900&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span>) and world population (U.S. Census Bureau, International Data Base), there were an estimated 3.0 billion births during 1995–2016, so that based on the estimate by Haub (1995), the figures for 2017 would be "about 108 billion births" and "about 7 percent of all people ever born are alive today."</span></li>
+<li id="cite_note-Haub1650-14"><span class="mw-cite-backlink">^ <a href="#cite_ref-Haub1650_14-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-Haub1650_14-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text">Haub (1995): "The average annual rate of growth was actually lower from 1 A.D. to 1650 than the rate suggested above for the 8000 B.C. to 1 A.D. period. One reason for this abnormally slow growth was the Black Plague. This dreaded scourge was not limited to 14th century Europe. The epidemic may have begun about 542 A.D. in Western Asia, spreading from there. It is believed that half the Byzantine Empire was destroyed in the 6th century, a total of 100 million deaths."</span></li>
+<li id="cite_note-Population_Reference_Bureau-15"><span class="mw-cite-backlink">^ <a href="#cite_ref-Population_Reference_Bureau_15-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-Population_Reference_Bureau_15-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-Population_Reference_Bureau_15-2"><sup><i><b>c</b></i></sup></a></span> <span class="reference-text">Data from <a rel="nofollow" class="external text" href="http://www.prb.org/Home.aspx">Population Reference Bureau</a>.<br />
+2016 estimate: (a) <a rel="nofollow" class="external text" href="http://www.prb.org/pdf16/prb-wpds2016-web-2016.pdf">"2016 World Population Data Sheet"</a><br />
+2015 estimate: (b) Toshiko Kaneda, 2015, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf15/2015-world-population-data-sheet_eng.pdf">"2015 World Population Data Sheet"</a>.<br />
+2014 estimate: (c) Carl Haub, 2014, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf14/2014-world-population-data-sheet_eng.pdf">"2014 World Population Data Sheet"</a>.<br />
+2013 estimate: (d) Carl Haub, 2013, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf13/2013-population-data-sheet_eng.pdf">"2013 World Population Data Sheet"</a>.<br />
+2012 estimate: (e) Carl Haub, 2012, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf12/2012-population-data-sheet_eng.pdf">"2012 World Population Data Sheet"</a>.<br />
+2011 estimate: (f) Carl Haub, 2011, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf11/2011population-data-sheet_eng.pdf">"2011 World Population Data Sheet"</a>.<br />
+2010 estimate: (g) Carl Haub, 2010, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf10/10wpds_eng.pdf">"2010 World Population Data Sheet"</a>.<br />
+2009 estimate: (h) Carl Haub, 2009, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf09/09wpds_eng.pdf">"2009 World Population Data Sheet"</a>.<br />
+2008 estimate: (i) Carl Haub, 2008, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf08/08WPDS_Eng.pdf">"2008 World Population Data Sheet"</a>.<br />
+2007 estimate: (j) Carl Haub, 2007, <a rel="nofollow" class="external text" href="http://www.prb.org/Publications/Datasheets/2007/2007WorldPopulationDataSheet.aspx">"2007 World Population Data Sheet"</a>.<br />
+2006 estimate: (k) Carl Haub, 2006, <a rel="nofollow" class="external text" href="http://www.prb.org/Publications/Datasheets/2006/2006WorldPopulationDataSheet.aspx">"2006 World Population Data Sheet"</a>.<br />
+2005 estimate: (l) Carl Haub, 2005, <a rel="nofollow" class="external text" href="http://www.prb.org/Publications/Datasheets/2005/2005WorldPopulationDataSheet.aspx">"2005 World Population Data Sheet"</a>.<br />
+2004 estimate: (m) Carl Haub, 2004, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf04/04WorldDataSheet_ENG.pdf">"2004 World Population Data Sheet"</a>.<br />
+2003 estimate: (n) Carl Haub, 2003, <a rel="nofollow" class="external text" href="http://homepage.ntu.edu.tw/~psc/WorldPopulationDS03_Eng.pdf">"2003 World Population Data Sheet"</a>.<br />
+2002 estimate: (o) Carl Haub, 2002, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf/worldpopulationds02_eng.pdf">"2002 World Population Data Sheet"</a>.<br />
+2001 estimate: (p) Carl Haub, 2001, <a rel="nofollow" class="external text" href="http://www.ined.fr/fichier/s_rubrique/18790/publi_pdf2_pop_and_soc_english_370.en.pdf">"2001 World Population Data Sheet"</a>.<br />
+2000 estimate: (q) 2000, <a rel="nofollow" class="external text" href="http://www.prb.org/Publications/Articles/2000/9BillionWorldPopulationby2050.aspx">"9 Billion World Population by 2050"</a>.<br />
+1997 estimate: (r) 1997, <a rel="nofollow" class="external text" href="http://www.epidemiolog.net/evolving/DemographyBasics.pdf">"Studying Populations"</a>.<br />
+Estimates for 1995 and prior: (s) Carl Haub, 1995, <a rel="nofollow" class="external text" href="https://www.un.org/popin/popis/journals/poptoday/today0295.html">"How Many People Have Ever Lived on Earth?"</a> <i>Population Today</i>, Vol. 23 (no. 2), pp. 5–6.</span></li>
+<li id="cite_note-The_World_at_Six_Billion,_1999-16"><span class="mw-cite-backlink">^ <a href="#cite_ref-The_World_at_Six_Billion,_1999_16-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-The_World_at_Six_Billion,_1999_16-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-The_World_at_Six_Billion,_1999_16-2"><sup><i><b>c</b></i></sup></a></span> <span class="reference-text">Data from <a rel="nofollow" class="external text" href="http://esa.un.org/unpd/wpp/unpp/panel_population.htm">United Nations Department of Economic and Social Affairs, Population Division.</a><br />
+1950–2100 estimates (only medium variants shown): (a) <a rel="nofollow" class="external text" href="http://esa.un.org/unpp/">World Population Prospects: The 2008 Revision.</a><br />
+Estimates prior to 1950: (b) <a rel="nofollow" class="external text" href="https://www.un.org/esa/population/publications/sixbillion/sixbillion.htm">"The World at Six Billion", 1999.</a><br />
+Estimates from 1950 to 2100: (c) <a rel="nofollow" class="external text" href="http://www.geohive.com/earth/his_history3.aspx">"Population of the entire world, yearly, 1950 - 2100", 2013.</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20161119132504/http://www.geohive.com/earth/his_history3.aspx">Archived</a> November 19, 2016, at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>.<br />
+2014: (d) <a rel="nofollow" class="external free" href="http://esa.un.org/unpd/wup/Highlights/WUP2014-Highlights.pdf">http://esa.un.org/unpd/wup/Highlights/WUP2014-Highlights.pdf</a> "2014 World Urbanization Prospects", 2014.]<br />
+2015: (e) <a rel="nofollow" class="external free" href="http://esa.un.org/unpd/wpp/Publications/Files/Key_Findings_WPP_2015.pdf">http://esa.un.org/unpd/wpp/Publications/Files/Key_Findings_WPP_2015.pdf</a> "2015 World Urbanization Prospects", 2015.] <a rel="nofollow" class="external text" href="https://web.archive.org/web/20140320035709/http://esa.un.org/unpd/wpp/unpp/panel_population.htm">Archived</a> March 20, 2014, at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>.</span></li>
+<li id="cite_note-www.ggdc.net/maddison/Historical_Statistics-17"><span class="mw-cite-backlink">^ <a href="#cite_ref-www.ggdc.net/maddison/Historical_Statistics_17-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-www.ggdc.net/maddison/Historical_Statistics_17-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><a rel="nofollow" class="external text" href="http://www.theworldeconomy.org/publications/worldeconomy/">Angus Maddison, 2003, <i>The World Economy: Historical Statistics</i>, Vol. 2, OECD, Paris</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20080513232041/http://www.theworldeconomy.org/publications/worldeconomy/">Archived</a> May 13, 2008, at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>. <a href="/wiki/International_Standard_Book_Number" title="International Standard Book Number">ISBN</a>&#160;<a href="/wiki/Special:BookSources/92-64-10412-7" title="Special:BookSources/92-64-10412-7">92-64-10412-7</a>.<br />
+<a rel="nofollow" class="external text" href="http://www.ggdc.net/maddison/Historical_Statistics/horizontal-file_02-2010.xls">"Statistical Appendix"</a> (2008, ggdc.net) "The historical data were originally developed in three books: Monitoring the World Economy 1820-1992, OECD, Paris 1995; The World Economy: A Millennial Perspective, OECD Development Centre, Paris 2001; The World Economy: Historical Statistics, OECD Development Centre, Paris 2003. All these contain detailed source notes. Figures for 1820 onwards are annual, wherever possible. For earlier years, benchmark figures are shown for 1 AD, 1000 AD, 1500, 1600 and 1700." "OECD countries GDP revised and updated 1991-2003 from National Accounts for OECD Countries, vol. I, 2006. Norway 1820-1990 GDP from Ola Grytten (2004), “The Gross Domestic Product for Norway, 1830-2003” in Eitrheim, Klovland and Qvigstad (eds), Historical Monetary Statistics for Norway, 1819-2003, Norges Bank, Oslo. Latin American GDP 2000-2003 revised and updated from ECLAC, Statistical Yearbook 2004 and preliminary version of the 2005 Yearbook supplied by Andre Hofman. For Chile, GDP 1820-2003 from Rolf Lűders (1998), “The Comparative Economic Performance of Chile 1810-1995”, Estudios de Economia, vol. 25, no. 2, with revised population estimates from Diaz, J., R. Lűders, and G. Wagner (2005) Chili 1810-2000: la Republica en Cifras, mimeo, Instituto de Economia, Universidad Católica de Chile. For Peru, GDP 1896-1990 and population 1896-1949 from Bruno Seminario and Arlette Beltran, Crecimiento Economico en el Peru 1896-1995, Universidad del Pacifico, 1998. " "For Asia there are amendments to the GDP estimates for South and North Korea, 1911-74, to correct an error in Maddison (2003). Estimates for the Philippines, 1902-1940 were amended in line with Richard Hooley (2005), 'American Economic Policy in the Philippines, 1902-1940', Journal of Asian Economics, 16. 1820 estimates were amended for Hong Kong, the Philippines, Singapore, Sri Lanka, Taiwan and Thailand." "Asian countries GDP revised and updated 1998-2003 from Asian Development Bank, Key Indicators 2005, except for South Korea and Japan, where OECD sources were used for 1991-2003. GDP for African countries updated 2000-2003 from IMF, World Economic Outlook, April 2005. Population estimates for all countries except China and Indonesia revised and updated 1950-2008 and 2030 from International Data Base, International Programs Center, Population Division, US Bureau of the Census, April 2005 version. China’s population 1990-2003 from China Statistical Yearbook 2005, China Statistics Press, Beijing. Indonesian population 1950-2003 kindly supplied by Pierre van der Eng. The figures now include three countries previously omitted: Cook Islands, Nauru and Tuvalu."</span></li>
+<li id="cite_note-thesocialcontract.com-18"><span class="mw-cite-backlink">^ <a href="#cite_ref-thesocialcontract.com_18-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-thesocialcontract.com_18-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-thesocialcontract.com_18-2"><sup><i><b>c</b></i></sup></a></span> <span class="reference-text"><a rel="nofollow" class="external text" href="http://www.thesocialcontract.com/artman2/publish/tsc0403/article_329.shtml">John H. Tanton, 1994, "End of the Migration Epoch? Time For a New Paradigm", The Social Contract, Vol. 4 (no 3), pp. 162–173</a>.</span></li>
+<li id="cite_note-French_1980,_pp._1-19"><span class="mw-cite-backlink">^ <a href="#cite_ref-French_1980,_pp._1_19-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-French_1980,_pp._1_19-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text">Slightly updated data from original paper in French: (a) Jean-Noël Biraben, 1980, "An Essay Concerning Mankind's Evolution", Population, Selected Papers, Vol. 4, pp. 1–13. Original paper in French: (b) Jean-Noël Biraben, 1979, "Essai sur l'évolution du nombre des hommes", Population, Vol. 34 (no. 1), pp. 13–25.</span></li>
+<li id="cite_note-Richard_Jones_1978-20"><span class="mw-cite-backlink">^ <a href="#cite_ref-Richard_Jones_1978_20-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-Richard_Jones_1978_20-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-Richard_Jones_1978_20-2"><sup><i><b>c</b></i></sup></a></span> <span class="reference-text">Colin McEvedy and Richard Jones, 1978, <i>Atlas of World Population History</i>, Facts on File, New York, <a href="/wiki/International_Standard_Book_Number" title="International Standard Book Number">ISBN</a>&#160;<a href="/wiki/Special:BookSources/0-7139-1031-3" title="Special:BookSources/0-7139-1031-3">0-7139-1031-3</a>.</span></li>
+<li id="cite_note-ReferenceA-21"><span class="mw-cite-backlink">^ <a href="#cite_ref-ReferenceA_21-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-ReferenceA_21-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><a href="/wiki/Ralph_Thomlinson" title="Ralph Thomlinson">Ralph Thomlinson</a>, 1975, <i>Demographic Problems: Controversy over population control</i>, 2nd Ed., Dickenson Publishing Company, Ecino, CA, <a href="/wiki/International_Standard_Book_Number" title="International Standard Book Number">ISBN</a>&#160;<a href="/wiki/Special:BookSources/0-8221-0166-1" title="Special:BookSources/0-8221-0166-1">0-8221-0166-1</a>.</span></li>
+<li id="cite_note-John_D._Durand_1974-22"><span class="mw-cite-backlink">^ <a href="#cite_ref-John_D._Durand_1974_22-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-John_D._Durand_1974_22-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text">John D. Durand, 1974, "Historical Estimates of World Population: An Evaluation", University of Pennsylvania, Population Center, Analytical and Technical Reports, Number 10.</span></li>
+<li id="cite_note-Colin_Clark_1967-23"><span class="mw-cite-backlink">^ <a href="#cite_ref-Colin_Clark_1967_23-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-Colin_Clark_1967_23-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text">Colin Clark, 1967, <i>Population Growth and Land Use</i>, St. Martin's Press, New York, <a href="/wiki/International_Standard_Book_Number" title="International Standard Book Number">ISBN</a>&#160;<a href="/wiki/Special:BookSources/0-333-01126-0" title="Special:BookSources/0-333-01126-0">0-333-01126-0</a>.</span></li>
+<li id="cite_note-mnp.nl-24"><span class="mw-cite-backlink">^ <a href="#cite_ref-mnp.nl_24-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-2"><sup><i><b>c</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-3"><sup><i><b>d</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-4"><sup><i><b>e</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-5"><sup><i><b>f</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-6"><sup><i><b>g</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-7"><sup><i><b>h</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-8"><sup><i><b>i</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-9"><sup><i><b>j</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-10"><sup><i><b>k</b></i></sup></a></span> <span class="reference-text"><a rel="nofollow" class="external text" href="http://journals.sagepub.com/doi/pdf/10.1177/0959683609356587">Data</a> from <a rel="nofollow" class="external text" href="http://themasites.pbl.nl/tridion/en/themasites/hyde/index.html">History Database of the Global Environment.</a> K. Klein Goldewijk, A. Beusen and P. Janssen, "HYDE 3.1: Long-term dynamic modeling of global population and built-up area in a spatially explicit way", from table on pg. 2, Netherlands Environmental Assessment Agency (MNP), Bilthoven, The Netherlands.</span></li>
+<li id="cite_note-25"><span class="mw-cite-backlink"><b><a href="#cite_ref-25">^</a></b></span> <span class="reference-text">Haub (1995): "By 1 A.D., the world may have held about 300 million people. One estimate of the population of the Roman Empire, from Spain to Asia Minor, in 14 A.D. is 45 million. However, other historians set the figure twice as high, suggesting how imprecise population estimates of early historical periods can be."</span></li>
+<li id="cite_note-26"><span class="mw-cite-backlink"><b><a href="#cite_ref-26">^</a></b></span> <span class="reference-text">"The present figures are a revision and update of those presented on this website in 2003. The most significant changes are in the entries for the year 1, where gaps in previous tables have been filled with the new estimates for the Roman Empire in Maddison (2007). The estimates are in fact for 14 AD"</span></li>
+<li id="cite_note-27"><span class="mw-cite-backlink"><b><a href="#cite_ref-27">^</a></b></span> <span class="reference-text">The estimates are in fact for 14 AD"</span></li>
+<li id="cite_note-census.gov-28"><span class="mw-cite-backlink">^ <a href="#cite_ref-census.gov_28-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-census.gov_28-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text">Data from <a rel="nofollow" class="external text" href="https://www.census.gov/population/international/data/worldpop/table_population.php">U.S. Census Bureau, International Data Base</a> Retrieved on 28 Oct, 2017</span></li>
+<li id="cite_note-29"><span class="mw-cite-backlink"><b><a href="#cite_ref-29">^</a></b></span> <span class="reference-text">"The expression for growth (6) indicates a limit for world population N=πK<sup>2</sup>=14×10<sup>9</sup>, in the foreseeable future. Of this asymptotic limit 90% will be reached for Model III by year 2135, or in 3T years after T1 = 2007." Sergei P. Kapitza, 'The phenomenological theory of world population growth', <i><a href="/wiki/Physics-Uspekhi" title="Physics-Uspekhi">Physics-Uspekhi</a></i> 39(1) 57-71 (1996).</span></li>
+<li id="cite_note-un-esa1-30"><span class="mw-cite-backlink"><b><a href="#cite_ref-un-esa1_30-0">^</a></b></span> <span class="reference-text">United Nations Department of Economic and Social Affairs. <i><a rel="nofollow" class="external text" href="https://www.un.org/esa/population/publications/longrange2/WorldPop2300final.pdf">World Population to 2300</a></i>. 2004. Executive Summary, Page 2.</span></li>
+<li id="cite_note-Science-31"><span class="mw-cite-backlink"><b><a href="#cite_ref-Science_31-0">^</a></b></span> <span class="reference-text"><cite class="citation journal">Gerland, P.; Raftery, A. E.; Ev Ikova, H.; Li, N.; Gu, D.; Spoorenberg, T.; Alkema, L.; Fosdick, B. K.; Chunn, J.; Lalic, N.; Bay, G.; Buettner, T.; Heilig, G. K.; Wilmoth, J. (September 14, 2014). <a rel="nofollow" class="external text" href="http://www.sciencemag.org/content/early/2014/09/17/science.1257469">"World population stabilization unlikely this century"</a>. <i>Science</i>. AAAS. <b>346</b> (6206): 234–7. <a href="/wiki/Digital_object_identifier" title="Digital object identifier">doi</a>:<a rel="nofollow" class="external text" href="//doi.org/10.1126%2Fscience.1257469">10.1126/science.1257469</a>. <a href="/wiki/International_Standard_Serial_Number" title="International Standard Serial Number">ISSN</a>&#160;<a rel="nofollow" class="external text" href="//www.worldcat.org/issn/1095-9203">1095-9203</a>. <a href="/wiki/PubMed_Central" title="PubMed Central">PMC</a>&#160;<span class="plainlinks"><a rel="nofollow" class="external text" href="//www.ncbi.nlm.nih.gov/pmc/articles/PMC4230924">4230924</a> <img alt="Freely accessible" src="//upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/9px-Lock-green.svg.png" title="Freely accessible" width="9" height="14" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/14px-Lock-green.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/18px-Lock-green.svg.png 2x" data-file-width="512" data-file-height="813" /></span>. <a href="/wiki/PubMed_Identifier" class="mw-redirect" title="PubMed Identifier">PMID</a>&#160;<a rel="nofollow" class="external text" href="//www.ncbi.nlm.nih.gov/pubmed/25301627">25301627</a><span class="reference-accessdate">. Retrieved <span class="nowrap">September 21,</span> 2014</span>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Science&amp;rft.atitle=World+population+stabilization+unlikely+this+century&amp;rft.volume=346&amp;rft.issue=6206&amp;rft.pages=234-7&amp;rft.date=2014-09-14&amp;rft_id=%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC4230924&amp;rft.issn=1095-9203&amp;rft_id=info%3Apmid%2F25301627&amp;rft_id=info%3Adoi%2F10.1126%2Fscience.1257469&amp;rft.aulast=Gerland&amp;rft.aufirst=P.&amp;rft.au=Raftery%2C+A.+E.&amp;rft.au=Ev+Ikova%2C+H.&amp;rft.au=Li%2C+N.&amp;rft.au=Gu%2C+D.&amp;rft.au=Spoorenberg%2C+T.&amp;rft.au=Alkema%2C+L.&amp;rft.au=Fosdick%2C+B.+K.&amp;rft.au=Chunn%2C+J.&amp;rft.au=Lalic%2C+N.&amp;rft.au=Bay%2C+G.&amp;rft.au=Buettner%2C+T.&amp;rft.au=Heilig%2C+G.+K.&amp;rft.au=Wilmoth%2C+J.&amp;rft_id=http%3A%2F%2Fwww.sciencemag.org%2Fcontent%2Fearly%2F2014%2F09%2F17%2Fscience.1257469&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span></span></li>
+<li id="cite_note-32"><span class="mw-cite-backlink"><b><a href="#cite_ref-32">^</a></b></span> <span class="reference-text">Randers, Jorgen (2012). 2052: A Global Forecast for the Next Forty Years. Vermont: Chelsea Green Publishing. p. 62.</span></li>
+<li id="cite_note-33"><span class="mw-cite-backlink"><b><a href="#cite_ref-33">^</a></b></span> <span class="reference-text">Angus Maddison, The World Economy: Historical Statistics, Statistical Appendix (2007, ggdc.net). Estimates cited are for the beginning of the 1st millennium ("year 0"), the beginning of the 2nd millennium ("year 1000"), and for the beginning each century since the 16th (years 1820 and 1913 are given for the 19th and 20th century, respectively, as Maddison presents detailed estimates for these years), and a projection for the year 2030.</span></li>
+<li id="cite_note-34"><span class="mw-cite-backlink"><b><a href="#cite_ref-34">^</a></b></span> <span class="reference-text">includes <a href="/wiki/Demographics_of_Central_Asia" title="Demographics of Central Asia">Central Asia</a> (listed under "<a href="/wiki/Former_USSR" class="mw-redirect" title="Former USSR">former USSR</a>")</span></li>
+<li id="cite_note-35"><span class="mw-cite-backlink"><b><a href="#cite_ref-35">^</a></b></span> <span class="reference-text"><cite class="citation book">Taylor, Alan (2002). <a rel="nofollow" class="external text" href="https://books.google.com/books?id=NPoAQRgkrOcC&amp;pg=PA40&amp;dq=pre-Columbian+population+million&amp;cd=6#v=onepage&amp;q=pre-Columbianpopulationmillion&amp;f=false"><i>American Colonies</i></a>. Penguin. <a href="/wiki/International_Standard_Book_Number" title="International Standard Book Number">ISBN</a>&#160;<a href="/wiki/Special:BookSources/9780142002100" title="Special:BookSources/9780142002100">9780142002100</a>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=book&amp;rft.btitle=American+Colonies&amp;rft.pub=Penguin&amp;rft.date=2002&amp;rft.isbn=9780142002100&amp;rft.aulast=Taylor&amp;rft.aufirst=Alan&amp;rft_id=https%3A%2F%2Fbooks.google.com%2Fbooks%3Fid%3DNPoAQRgkrOcC%26pg%3DPA40%26dq%3Dpre-Columbian%2Bpopulation%2Bmillion%26cd%3D6%23v%3Donepage%26q%3Dpre-Columbianpopulationmillion%26f%3Dfalse&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span></span></li>
+<li id="cite_note-36"><span class="mw-cite-backlink"><b><a href="#cite_ref-36">^</a></b></span> <span class="reference-text">"La catastrophe démographique" (The Demographic Catastrophe"), <i><a href="/wiki/L%27Histoire" title="L'Histoire">L'Histoire</a></i> n°322, July–August 2007, p. 17.</span></li>
+<li id="cite_note-37"><span class="mw-cite-backlink"><b><a href="#cite_ref-37">^</a></b></span> <span class="reference-text">Dobyns, Henry (1983). <i>Their Number Become Thinned: Native American Dynamics in Eastern North America</i>. Knoxville: University of Tennessee Press.</span></li>
+</ol>
+</div>
+<h2><span class="mw-headline" id="Further_reading">Further reading</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=8" title="Edit section: Further reading">edit</a><span class="mw-editsection-bracket">]</span></span></h2>
+<ul>
+<li><cite class="citation web">Haub, Carl (October 2011). <a rel="nofollow" class="external text" href="http://www.prb.org/Articles/2002/HowManyPeopleHaveEverLivedonEarth.aspx">"How Many People Have Ever Lived on Earth?"</a>. <a href="/wiki/Population_Reference_Bureau" title="Population Reference Bureau">Population Reference Bureau</a><span class="reference-accessdate">. Retrieved <span class="nowrap">21 July</span> 2013</span>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=unknown&amp;rft.btitle=How+Many+People+Have+Ever+Lived+on+Earth%3F&amp;rft.pub=Population+Reference+Bureau&amp;rft.date=2011-10&amp;rft.aulast=Haub&amp;rft.aufirst=Carl&amp;rft_id=http%3A%2F%2Fwww.prb.org%2FArticles%2F2002%2FHowManyPeopleHaveEverLivedonEarth.aspx&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span></li>
+</ul>
+<h2><span class="mw-headline" id="External_links">External links</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=9" title="Edit section: External links">edit</a><span class="mw-editsection-bracket">]</span></span></h2>
+<ul>
+<li><a rel="nofollow" class="external text" href="https://www.census.gov/population/international/data/worldpop/table_history.php">Historical Estimates of World Population</a>—<a href="/wiki/United_States_Census_Bureau" title="United States Census Bureau">United States Census Bureau</a></li>
+<li><a rel="nofollow" class="external text" href="http://itbulk.org/population/world-population-history/">World Population Estimation Graps 10000 BC - 1950 AD</a> World population estimation graphs; estimations by HYDE, <a href="/wiki/Colin_McEvedy" title="Colin McEvedy">Colin McEvedy</a>, <a href="/wiki/Massimo_Livi_Bacci" title="Massimo Livi Bacci">Massimo Livi Bacci</a></li>
+</ul>
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+<ul>
+<li class="nv-view"><a href="/wiki/Template:Population" title="Template:Population"><abbr title="View this template" style=";background:#e6e6e6;;background:none transparent;border:none;-moz-box-shadow:none;-webkit-box-shadow:none;box-shadow:none;">v</abbr></a></li>
+<li class="nv-talk"><a href="/wiki/Template_talk:Population" title="Template talk:Population"><abbr title="Discuss this template" style=";background:#e6e6e6;;background:none transparent;border:none;-moz-box-shadow:none;-webkit-box-shadow:none;box-shadow:none;">t</abbr></a></li>
+<li class="nv-edit"><a class="external text" href="//en.wikipedia.org/w/index.php?title=Template:Population&amp;action=edit"><abbr title="Edit this template" style=";background:#e6e6e6;;background:none transparent;border:none;-moz-box-shadow:none;-webkit-box-shadow:none;box-shadow:none;">e</abbr></a></li>
+</ul>
+</div>
+<div id="Global_human_population" style="font-size:114%;margin:0 4em"><a href="/wiki/World_population" title="World population">Global human population</a></div>
+</th>
+</tr>
+<tr>
+<th scope="row" class="navbox-group" style="width:1%;background:#eee;">Major topics</th>
+<td class="navbox-list navbox-odd hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px">
+<div style="padding:0em 0.25em">
+<ul>
+<li><a href="/wiki/Biocapacity" title="Biocapacity">Biocapacity</a></li>
+<li><a href="/wiki/Optimum_population" title="Optimum population">Optimum population</a></li>
+<li><a href="/wiki/Human_overpopulation" title="Human overpopulation">Overpopulation</a>
+<ul>
+<li><a href="/wiki/Malthusian_catastrophe" title="Malthusian catastrophe">Malthusian catastrophe</a></li>
+</ul>
+</li>
+<li><a href="/wiki/Population" title="Population">Population</a></li>
+<li><a href="/wiki/Population_ethics" title="Population ethics">Population ethics</a></li>
+<li><a href="/wiki/Population_momentum" title="Population momentum">Population momentum</a></li>
+<li><a href="/wiki/Sustainable_development" title="Sustainable development">Sustainable development</a></li>
+<li><a href="/wiki/Reproductive_rights" title="Reproductive rights">Women's reproductive rights</a></li>
+<li><a href="/wiki/Zero_population_growth" title="Zero population growth">Zero population growth</a></li>
+</ul>
+</div>
+</td>
+</tr>
+<tr>
+<th scope="row" class="navbox-group" style="width:1%;background:#eee;">Biological and<br />
+related topics</th>
+<td class="navbox-list navbox-even hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px">
+<div style="padding:0em 0.25em">
+<ul>
+<li><a href="/wiki/Family_planning" title="Family planning">Family planning</a>
+<ul>
+<li><a href="/wiki/Population_Matters#Pledge_two_or_fewer" title="Population Matters">Pledge two or fewer</a></li>
+</ul>
+</li>
+<li><a href="/wiki/Human_population_planning" title="Human population planning">Human population planning</a>
+<ul>
+<li><a href="/wiki/One-child_policy" title="One-child policy">One-child policy</a></li>
+<li><a href="/wiki/Two-child_policy" title="Two-child policy">Two-child policy</a></li>
+</ul>
+</li>
+<li><a href="/wiki/Population_biology" title="Population biology">Population biology</a></li>
+<li><a href="/wiki/Population_decline" title="Population decline">Population decline</a></li>
+<li><a href="/wiki/Population_density" title="Population density">Population density</a>
+<ul>
+<li><a href="/wiki/Physiological_density" title="Physiological density">Physiological density</a></li>
+</ul>
+</li>
+<li><a href="/wiki/Population_dynamics" title="Population dynamics">Population dynamics</a></li>
+<li><a href="/wiki/Population_growth" title="Population growth">Population growth</a></li>
+<li><a href="/wiki/Population_model" title="Population model">Population model</a></li>
+<li><a href="/wiki/Population_pyramid" title="Population pyramid">Population pyramid</a></li>
+<li><a href="/wiki/Projections_of_population_growth" title="Projections of population growth">Projections of population growth</a></li>
+</ul>
+</div>
+</td>
+</tr>
+<tr>
+<th scope="row" class="navbox-group" style="width:1%;background:#eee;"><a href="/wiki/Human_impact_on_the_environment" title="Human impact on the environment">Human impact on<br />
+the environment</a></th>
+<td class="navbox-list navbox-odd hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px">
+<div style="padding:0em 0.25em">
+<ul>
+<li><a href="/wiki/Deforestation" title="Deforestation">Deforestation</a></li>
+<li><a href="/wiki/Desalination" title="Desalination">Desalination</a></li>
+<li><a href="/wiki/Desertification" title="Desertification">Desertification</a></li>
+<li><a href="/wiki/Human_impact_on_the_environment" title="Human impact on the environment">Environmental impact</a>
+<ul>
+<li><a href="/wiki/Environmental_impact_of_agriculture" title="Environmental impact of agriculture">of agriculture</a></li>
+<li><a href="/wiki/Environmental_impact_of_aviation" title="Environmental impact of aviation">of aviation</a></li>
+<li><a href="/wiki/Environmental_impact_of_biodiesel" title="Environmental impact of biodiesel">of biodiesel</a></li>
+<li><a href="/wiki/Environmental_impact_of_concrete" title="Environmental impact of concrete">of concrete</a></li>
+<li><a href="/wiki/Environmental_impact_of_electricity_generation" title="Environmental impact of electricity generation">of electricity generation</a></li>
+<li><a href="/wiki/Environmental_impact_of_the_energy_industry" title="Environmental impact of the energy industry">of the energy industry</a></li>
+<li><a href="/wiki/Environmental_impact_of_fishing" title="Environmental impact of fishing">of fishing</a></li>
+<li><a href="/wiki/Environmental_impact_of_irrigation" title="Environmental impact of irrigation">of irrigation</a></li>
+<li><a href="/wiki/Environmental_impact_of_mining" title="Environmental impact of mining">of mining</a></li>
+<li><a href="/wiki/Off-roading#Environmental_impact" title="Off-roading">of off-roading</a></li>
+<li><a href="/wiki/Environmental_impact_of_the_oil_shale_industry" title="Environmental impact of the oil shale industry">of oil shale industry</a></li>
+<li><a href="/wiki/Social_and_environmental_impact_of_palm_oil" title="Social and environmental impact of palm oil">of palm oil</a></li>
+<li><a href="/wiki/Environmental_impact_of_paper" title="Environmental impact of paper">of paper</a></li>
+<li><a href="/wiki/Environmental_impact_of_the_petroleum_industry" title="Environmental impact of the petroleum industry">of the petroleum industry</a></li>
+<li><a href="/wiki/Environmental_impact_of_reservoirs" title="Environmental impact of reservoirs">of reservoirs</a></li>
+<li><a href="/wiki/Environmental_impact_of_shipping" title="Environmental impact of shipping">of shipping</a></li>
+<li><a href="/wiki/Environmental_impact_of_war" title="Environmental impact of war">of war</a></li>
+</ul>
+</li>
+<li><a href="/wiki/Industrialisation" title="Industrialisation">Industrialisation</a></li>
+<li><a href="/wiki/Land_degradation" title="Land degradation">Land degradation</a></li>
+<li><a href="/wiki/Land_reclamation" title="Land reclamation">Land reclamation</a></li>
+<li><a href="/wiki/Overconsumption" title="Overconsumption">Overconsumption</a></li>
+<li><a href="/wiki/Pollution" title="Pollution">Pollution</a></li>
+<li><a href="/wiki/Quarry" title="Quarry">Quarrying</a></li>
+<li><a href="/wiki/Urbanization" title="Urbanization">Urbanization</a>
+<ul>
+<li><a href="/wiki/Green_belt" title="Green belt">Loss of green belts</a></li>
+<li><a href="/wiki/Urban_sprawl" title="Urban sprawl">Urban sprawl</a></li>
+</ul>
+</li>
+<li><a href="/wiki/Waste" title="Waste">Waste</a></li>
+<li><a href="/wiki/Water_scarcity" title="Water scarcity">Water scarcity</a>
+<ul>
+<li><a href="/wiki/Overdrafting" title="Overdrafting">Overdrafting</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</td>
+</tr>
+<tr>
+<th scope="row" class="navbox-group" style="width:1%;background:#eee;">
+<div style="padding:0.1em 0;line-height:1.2em;"><a href="/wiki/Population_ecology" title="Population ecology">Population<br />
+ecology</a></div>
+</th>
+<td class="navbox-list navbox-even hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px">
+<div style="padding:0em 0.25em">
+<ul>
+<li><a href="/wiki/Carrying_capacity#Humans" title="Carrying capacity">Carrying capacity</a></li>
+<li><a href="/wiki/Deep_ecology" title="Deep ecology">Deep ecology</a></li>
+<li><a href="/wiki/Earth%27s_energy_budget" title="Earth's energy budget">Earth's energy budget</a></li>
+<li><a href="/wiki/Food_security" title="Food security">Food security</a></li>
+<li><a href="/wiki/Habitat_destruction" title="Habitat destruction">Habitat destruction</a></li>
+<li><span class="nowrap"><a href="/wiki/I_%3D_PAT" title="I = PAT">I = P × A  × T</a></span></li>
+<li><a href="/wiki/Malthusian_growth_model" title="Malthusian growth model">Malthusian growth model</a></li>
+<li><a href="/wiki/Overshoot_(population)" title="Overshoot (population)">Overshoot (population)</a></li>
+<li><a href="/wiki/World_energy_consumption" title="World energy consumption">World energy consumption</a></li>
+<li><a href="/wiki/World_energy_resources" title="World energy resources">World energy resources</a></li>
+<li><a href="/wiki/World3" title="World3">World3 model</a></li>
+</ul>
+</div>
+</td>
+</tr>
+<tr>
+<th scope="row" class="navbox-group" style="width:1%;background:#eee;">Literature</th>
+<td class="navbox-list navbox-odd hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px">
+<div style="padding:0em 0.25em">
+<ul>
+<li><i><a href="/wiki/A_Modest_Proposal" title="A Modest Proposal">A Modest Proposal</a></i></li>
+<li><i><a href="/wiki/Observations_Concerning_the_Increase_of_Mankind,_Peopling_of_Countries,_etc." title="Observations Concerning the Increase of Mankind, Peopling of Countries, etc.">Observations Concerning the Increase of Mankind, Peopling of Countries, etc.</a></i></li>
+<li><i><a href="/wiki/An_Essay_on_the_Principle_of_Population" title="An Essay on the Principle of Population">An Essay on the Principle of Population</a></i></li>
+<li>"<a href="/wiki/How_Much_Land_Does_a_Man_Need%3F" title="How Much Land Does a Man Need?">How Much Land Does a Man Need?</a>"</li>
+<li><i><a href="/wiki/Operating_Manual_for_Spaceship_Earth" title="Operating Manual for Spaceship Earth">Operating Manual for Spaceship Earth</a></i></li>
+<li><i><a href="/wiki/Population_Control:_Real_Costs,_Illusory_Benefits" title="Population Control: Real Costs, Illusory Benefits">Population Control: Real Costs, Illusory Benefits</a></i></li>
+<li><i><a href="/wiki/The_Limits_to_Growth" title="The Limits to Growth">The Limits to Growth</a></i></li>
+<li><i><a href="/wiki/The_Population_Bomb" title="The Population Bomb">The Population Bomb</a></i></li>
+<li><i><a href="/wiki/The_Skeptical_Environmentalist" title="The Skeptical Environmentalist">The Skeptical Environmentalist</a></i></li>
+<li><i><a href="/wiki/The_Ultimate_Resource" title="The Ultimate Resource">The Ultimate Resource</a></i></li>
+</ul>
+</div>
+</td>
+</tr>
+<tr>
+<th scope="row" class="navbox-group" style="width:1%;background:#eee;">Publications</th>
+<td class="navbox-list navbox-even hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px;font-style:italic;">
+<div style="padding:0em 0.25em">
+<ul>
+<li><a href="/wiki/Population_and_Environment" title="Population and Environment">Population and Environment</a></li>
+<li><a href="/wiki/Population_and_Development_Review" title="Population and Development Review">Population and Development Review</a></li>
+</ul>
+</div>
+</td>
+</tr>
+<tr>
+<th scope="row" class="navbox-group" style="width:1%;background:#eee;">Lists</th>
+<td class="navbox-list navbox-odd hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px">
+<div style="padding:0em 0.25em">
+<ul>
+<li><a href="/wiki/Population_and_housing_censuses_by_country" title="Population and housing censuses by country">Population and housing censuses by country</a></li>
+<li><a href="/wiki/List_of_metropolitan_areas_by_population" title="List of metropolitan areas by population">Metropolitan areas by population</a></li>
+<li><a href="/wiki/World_population_milestones" title="World population milestones">Population milestone babies</a></li>
+</ul>
+</div>
+</td>
+</tr>
+<tr>
+<th scope="row" class="navbox-group" style="width:1%;background:#eee;">
+<div style="padding:0.1em 0;line-height:1.2em;">Events and<br />
+organizations</div>
+</th>
+<td class="navbox-list navbox-even hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px">
+<div style="padding:0em 0.25em">
+<ul>
+<li><a href="/wiki/7_Billion_Actions" title="7 Billion Actions">7 Billion Actions</a></li>
+<li><a href="/wiki/International_Conference_on_Population_and_Development" title="International Conference on Population and Development">International Conference on Population and Development</a></li>
+<li><a href="/wiki/Population_Action_International" title="Population Action International">Population Action International</a></li>
+<li><a href="/wiki/Population_Connection" title="Population Connection">Population Connection</a></li>
+<li><a href="/wiki/Population_Matters" title="Population Matters">Population Matters</a></li>
+<li><a href="/wiki/Population_Research_Institute" title="Population Research Institute">Population Research Institute</a></li>
+<li><a href="/wiki/United_Nations_Population_Fund" title="United Nations Population Fund">United Nations Population Fund</a></li>
+<li><a href="/wiki/Voluntary_Human_Extinction_Movement" title="Voluntary Human Extinction Movement">Voluntary Human Extinction Movement</a></li>
+<li><a href="/wiki/World_Population_Day" title="World Population Day">World Population Day</a></li>
+<li><a href="/wiki/World_Population_Foundation" title="World Population Foundation">World Population Foundation</a></li>
+</ul>
+</div>
+</td>
+</tr>
+<tr>
+<th scope="row" class="navbox-group" style="width:1%;background:#eee;">Related topics</th>
+<td class="navbox-list navbox-odd hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px">
+<div style="padding:0em 0.25em">
+<ul>
+<li><a href="/wiki/Classic_Maya_collapse" title="Classic Maya collapse">Classic Maya collapse</a></li>
+<li><a href="/wiki/Fertility_and_intelligence" title="Fertility and intelligence">Fertility and intelligence</a></li>
+<li><a href="/wiki/Green_Revolution" title="Green Revolution">Green Revolution</a></li>
+<li><a href="/wiki/Holocene_extinction" title="Holocene extinction">Holocene extinction</a></li>
+<li><a href="/wiki/Human_migration" title="Human migration">Migration</a></li>
+</ul>
+</div>
+</td>
+</tr>
+<tr>
+<td class="navbox-abovebelow hlist" colspan="2" style="background:#e6e6e6;">
+<div>
+<ul>
+<li><img alt="Commons page" src="//upload.wikimedia.org/wikipedia/en/thumb/4/4a/Commons-logo.svg/12px-Commons-logo.svg.png" title="Commons page" width="12" height="16" srcset="//upload.wikimedia.org/wikipedia/en/thumb/4/4a/Commons-logo.svg/18px-Commons-logo.svg.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/4/4a/Commons-logo.svg/24px-Commons-logo.svg.png 2x" data-file-width="1024" data-file-height="1376" /> <b><a href="https://commons.wikimedia.org/wiki/Category:Human_overpopulation" class="extiw" title="commons:Category:Human overpopulation">Commons</a></b></li>
+<li><img alt="Category" src="//upload.wikimedia.org/wikipedia/en/thumb/4/48/Folder_Hexagonal_Icon.svg/16px-Folder_Hexagonal_Icon.svg.png" title="Category" width="16" height="14" srcset="//upload.wikimedia.org/wikipedia/en/thumb/4/48/Folder_Hexagonal_Icon.svg/24px-Folder_Hexagonal_Icon.svg.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/4/48/Folder_Hexagonal_Icon.svg/32px-Folder_Hexagonal_Icon.svg.png 2x" data-file-width="36" data-file-height="31" /> <a href="/wiki/Category:Human_overpopulation" title="Category:Human overpopulation">Human overpopulation</a></li>
+<li><a href="/wiki/Category:Human_activities_with_impact_on_the_environment" title="Category:Human activities with impact on the environment">Human activities with impact on the environment</a></li>
+<li><a href="/wiki/Category:Human_migration" title="Category:Human migration">Human migration</a></li>
+</ul>
+</div>
+</td>
+</tr>
+</table>
+</div>
+
+
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+				<div id="catlinks" class="catlinks" data-mw="interface"><div id="mw-normal-catlinks" class="mw-normal-catlinks"><a href="/wiki/Help:Category" title="Help:Category">Categories</a>: <ul><li><a href="/wiki/Category:World_population" title="Category:World population">World population</a></li><li><a href="/wiki/Category:Demographic_history" title="Category:Demographic history">Demographic history</a></li></ul></div><div id="mw-hidden-catlinks" class="mw-hidden-catlinks mw-hidden-cats-hidden">Hidden categories: <ul><li><a href="/wiki/Category:CS1_maint:_Multiple_names:_authors_list" title="Category:CS1 maint: Multiple names: authors list">CS1 maint: Multiple names: authors list</a></li><li><a href="/wiki/Category:Webarchive_template_wayback_links" title="Category:Webarchive template wayback links">Webarchive template wayback links</a></li><li><a href="/wiki/Category:All_articles_with_unsourced_statements" title="Category:All articles with unsourced statements">All articles with unsourced statements</a></li><li><a href="/wiki/Category:Articles_with_unsourced_statements_from_January_2017" title="Category:Articles with unsourced statements from January 2017">Articles with unsourced statements from January 2017</a></li><li><a href="/wiki/Category:Use_mdy_dates_from_July_2013" title="Category:Use mdy dates from July 2013">Use mdy dates from July 2013</a></li><li><a href="/wiki/Category:Articles_containing_potentially_dated_statements_from_2015" title="Category:Articles containing potentially dated statements from 2015">Articles containing potentially dated statements from 2015</a></li><li><a href="/wiki/Category:All_articles_containing_potentially_dated_statements" title="Category:All articles containing potentially dated statements">All articles containing potentially dated statements</a></li></ul></div></div>				<div class="visualClear"></div>
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diff --git a/data/glucose_insulin.csv b/data/glucose_insulin.csv
new file mode 100644
index 00000000..91688b17
--- /dev/null
+++ b/data/glucose_insulin.csv
@@ -0,0 +1,25 @@
+time,glucose,insulin
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\ No newline at end of file

From 28506e74ead64dea2321b5f7b6c62232d7d0d245 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 22 Jan 2021 19:36:09 -0500
Subject: [PATCH 012/144] Adding data

---
 data/World_population_estimates.csv  | 68 ++++++++++++++++++++++++++++
 data/World_population_estimates2.csv | 68 ++++++++++++++++++++++++++++
 data/World_population_estimates3.csv | 46 +++++++++++++++++++
 data/baseball_drag.csv               | 17 +++++++
 4 files changed, 199 insertions(+)
 create mode 100644 data/World_population_estimates.csv
 create mode 100644 data/World_population_estimates2.csv
 create mode 100644 data/World_population_estimates3.csv
 create mode 100644 data/baseball_drag.csv

diff --git a/data/World_population_estimates.csv b/data/World_population_estimates.csv
new file mode 100644
index 00000000..ed7de057
--- /dev/null
+++ b/data/World_population_estimates.csv
@@ -0,0 +1,68 @@
+Year,census,prb,un,maddison,hyde,tanton,biraben,mj,thomlinson,durand,clark
+1950,2557628654,2516000000.0,2525149000.0,2544000000.0,2527960000.0,2400000000.0,2527000000.0,2500000000.0,2400000000.0,,2486000000.0
+1951,2594939877,,2572850917.0,2571663000.0,,,,,,,
+1952,2636772306,,2619292068.0,2617949000.0,,,,,,,
+1953,2682053389,,2665865392.0,2665959000.0,,,,,,,
+1954,2730228104,,2713172027.0,2716927000.0,,,,,,,
+1955,2782098943,,2761650981.0,2769074000.0,,,,,,,
+1956,2835299673,,2811572031.0,2822502000.0,,,,,,,
+1957,2891349717,,2863042795.0,2879934000.0,,,,,,,
+1958,2948137248,,2916030167.0,2939254000.0,,,,,,,
+1959,3000716593,,2970395814.0,2995909000.0,,,,,,,
+1960,3043001508,,3026002942.0,3041507000.0,3042000000.0,,,,,,
+1961,3083966929,,3082830266.0,3082161000.0,,,,,,,
+1962,3140093217,,3141071531.0,3135787000.0,,,,,,,3036000000.0
+1963,3209827882,,3201178277.0,3201354000.0,,,,,,,
+1964,3281201306,,3263738832.0,3266477000.0,,,,,,,
+1965,3350425793,,3329122479.0,3333138000.0,,,,,,,
+1966,3420677923,,3397475247.0,3402224000.0,,,,,,,3288000000.0
+1967,3490333715,,3468521724.0,3471464000.0,,,,,,,
+1968,3562313822,,3541674891.0,3543086000.0,,,,,,,
+1969,3637159050,,3616108749.0,3615743000.0,,,,,,,
+1970,3712697742,,3691172616.0,3691157000.0,3710000000.0,,3637000000.0,,3600000000.0,"3,600,000,000– 3,700,000,000",3632000000.0
+1971,3790326948,,3766754345.0,3769818000.0,,,,,,,
+1972,3866568653,,3842873611.0,3846499000.0,,,,,,,
+1973,3942096442,,3919182332.0,3922793000.0,3923000000.0,,,,,,3860000000.0
+1974,4016608813,,3995304922.0,3997677000.0,,,,,,,
+1975,4089083233,,4071020434.0,4070671000.0,,,,3900000000.0,4000000000.0,,
+1976,4160185010,,4146135850.0,4141445000.0,,,,,,,
+1977,4232084578,,4220816737.0,4213539000.0,,,,,,,
+1978,4304105753,,4295664825.0,4286317000.0,,,,,,,
+1979,4379013942,,4371527871.0,4363144000.0,,,,,,,
+1980,4451362735,,4449048798.0,4439529000.0,4461000000.0,,,,,,
+1981,4534410125,,4528234634.0,4514838000.0,,,,,,,
+1982,4614566561,,4608962418.0,4587307000.0,,,,,,,
+1983,4695736743,,4691559840.0,4676388000.0,,,,,,,
+1984,4774569391,,4776392828.0,4756521000.0,,,,,,,
+1985,4856462699,,4863601517.0,4837719000.0,,5000000000.0,,,,,
+1986,4940571232,,4953376710.0,4920968000.0,,,,,,,
+1987,5027200492,,5045315871.0,5006672000.0,,,,,,,
+1988,5114557167,,5138214688.0,5093306000.0,,,,,,,
+1989,5201440110,,5230000000.0,5180540000.0,,,,,,,
+1990,5288955934,,5320816667.0,5269029000.0,5308000000.0,,,,,,
+1991,5371585922,,5408908724.0,5351922000.0,,,,,,,
+1992,5456136278,,5494899570.0,5435722000.0,,,,,,,
+1993,5538268316,,5578865109.0,5518127000.0,,,,,,,
+1994,5618682132,,5661086346.0,5599396000.0,,,,,,,
+1995,5699202985,5760000000.0,5741822412.0,5681575000.0,,,,,,,
+1996,5779440593,,5821016750.0,5762212000.0,,,,,,,
+1997,5857972543,5840000000.0,5898688337.0,5842122000.0,,,,,,,
+1998,5935213248,,5975303657.0,5921366000.0,,,,,,,
+1999,6012074922,,6051478010.0,5999622000.0,,,,,,,
+2000,6088571383,6067000000.0,6127700428.0,6076558000.0,6145000000.0,,,5750000000.0,,,
+2001,6165219247,6137000000.0,6204147026.0,6154791000.0,,,,,,,
+2002,6242016348,6215000000.0,6280853817.0,6231704000.0,,,,,,,
+2003,6318590956,6314000000.0,6357991749.0,6308364000.0,,,,,,,
+2004,6395699509,6396000000.0,6435705595.0,6374056000.0,,,,,,,
+2005,6473044732,6477000000.0,6514094605.0,6462987000.0,,,,,,,
+2006,6551263534,6555000000.0,6593227977.0,6540214000.0,,,,,,,
+2007,6629913759,6625000000.0,6673105937.0,6616689000.0,,,,,,,
+2008,6709049780,6705000000.0,6753649228.0,6694832000.0,,,,,,,
+2009,6788214394,6809972000.0,6834721933.0,6764086000.0,,,,,,,
+2010,6858584755,6892319000.0,6916183482.0,,,,,,,,
+2011,6935999491,6986951000.0,6997998760.0,,,,,,,,
+2012,7013871313,7057075000.0,7080072417.0,,,,,,,,
+2013,7092128094,7136796000.0,7162119434.0,,,,,,,,
+2014,7169968185,7238184000.0,7243784000.0,,,,,,,,
+2015,7247892788,7336435000.0,7349472000.0,,,,,,,,
+2016,7325996709,7418151841.0,,,,,,,,,
diff --git a/data/World_population_estimates2.csv b/data/World_population_estimates2.csv
new file mode 100644
index 00000000..ed7de057
--- /dev/null
+++ b/data/World_population_estimates2.csv
@@ -0,0 +1,68 @@
+Year,census,prb,un,maddison,hyde,tanton,biraben,mj,thomlinson,durand,clark
+1950,2557628654,2516000000.0,2525149000.0,2544000000.0,2527960000.0,2400000000.0,2527000000.0,2500000000.0,2400000000.0,,2486000000.0
+1951,2594939877,,2572850917.0,2571663000.0,,,,,,,
+1952,2636772306,,2619292068.0,2617949000.0,,,,,,,
+1953,2682053389,,2665865392.0,2665959000.0,,,,,,,
+1954,2730228104,,2713172027.0,2716927000.0,,,,,,,
+1955,2782098943,,2761650981.0,2769074000.0,,,,,,,
+1956,2835299673,,2811572031.0,2822502000.0,,,,,,,
+1957,2891349717,,2863042795.0,2879934000.0,,,,,,,
+1958,2948137248,,2916030167.0,2939254000.0,,,,,,,
+1959,3000716593,,2970395814.0,2995909000.0,,,,,,,
+1960,3043001508,,3026002942.0,3041507000.0,3042000000.0,,,,,,
+1961,3083966929,,3082830266.0,3082161000.0,,,,,,,
+1962,3140093217,,3141071531.0,3135787000.0,,,,,,,3036000000.0
+1963,3209827882,,3201178277.0,3201354000.0,,,,,,,
+1964,3281201306,,3263738832.0,3266477000.0,,,,,,,
+1965,3350425793,,3329122479.0,3333138000.0,,,,,,,
+1966,3420677923,,3397475247.0,3402224000.0,,,,,,,3288000000.0
+1967,3490333715,,3468521724.0,3471464000.0,,,,,,,
+1968,3562313822,,3541674891.0,3543086000.0,,,,,,,
+1969,3637159050,,3616108749.0,3615743000.0,,,,,,,
+1970,3712697742,,3691172616.0,3691157000.0,3710000000.0,,3637000000.0,,3600000000.0,"3,600,000,000– 3,700,000,000",3632000000.0
+1971,3790326948,,3766754345.0,3769818000.0,,,,,,,
+1972,3866568653,,3842873611.0,3846499000.0,,,,,,,
+1973,3942096442,,3919182332.0,3922793000.0,3923000000.0,,,,,,3860000000.0
+1974,4016608813,,3995304922.0,3997677000.0,,,,,,,
+1975,4089083233,,4071020434.0,4070671000.0,,,,3900000000.0,4000000000.0,,
+1976,4160185010,,4146135850.0,4141445000.0,,,,,,,
+1977,4232084578,,4220816737.0,4213539000.0,,,,,,,
+1978,4304105753,,4295664825.0,4286317000.0,,,,,,,
+1979,4379013942,,4371527871.0,4363144000.0,,,,,,,
+1980,4451362735,,4449048798.0,4439529000.0,4461000000.0,,,,,,
+1981,4534410125,,4528234634.0,4514838000.0,,,,,,,
+1982,4614566561,,4608962418.0,4587307000.0,,,,,,,
+1983,4695736743,,4691559840.0,4676388000.0,,,,,,,
+1984,4774569391,,4776392828.0,4756521000.0,,,,,,,
+1985,4856462699,,4863601517.0,4837719000.0,,5000000000.0,,,,,
+1986,4940571232,,4953376710.0,4920968000.0,,,,,,,
+1987,5027200492,,5045315871.0,5006672000.0,,,,,,,
+1988,5114557167,,5138214688.0,5093306000.0,,,,,,,
+1989,5201440110,,5230000000.0,5180540000.0,,,,,,,
+1990,5288955934,,5320816667.0,5269029000.0,5308000000.0,,,,,,
+1991,5371585922,,5408908724.0,5351922000.0,,,,,,,
+1992,5456136278,,5494899570.0,5435722000.0,,,,,,,
+1993,5538268316,,5578865109.0,5518127000.0,,,,,,,
+1994,5618682132,,5661086346.0,5599396000.0,,,,,,,
+1995,5699202985,5760000000.0,5741822412.0,5681575000.0,,,,,,,
+1996,5779440593,,5821016750.0,5762212000.0,,,,,,,
+1997,5857972543,5840000000.0,5898688337.0,5842122000.0,,,,,,,
+1998,5935213248,,5975303657.0,5921366000.0,,,,,,,
+1999,6012074922,,6051478010.0,5999622000.0,,,,,,,
+2000,6088571383,6067000000.0,6127700428.0,6076558000.0,6145000000.0,,,5750000000.0,,,
+2001,6165219247,6137000000.0,6204147026.0,6154791000.0,,,,,,,
+2002,6242016348,6215000000.0,6280853817.0,6231704000.0,,,,,,,
+2003,6318590956,6314000000.0,6357991749.0,6308364000.0,,,,,,,
+2004,6395699509,6396000000.0,6435705595.0,6374056000.0,,,,,,,
+2005,6473044732,6477000000.0,6514094605.0,6462987000.0,,,,,,,
+2006,6551263534,6555000000.0,6593227977.0,6540214000.0,,,,,,,
+2007,6629913759,6625000000.0,6673105937.0,6616689000.0,,,,,,,
+2008,6709049780,6705000000.0,6753649228.0,6694832000.0,,,,,,,
+2009,6788214394,6809972000.0,6834721933.0,6764086000.0,,,,,,,
+2010,6858584755,6892319000.0,6916183482.0,,,,,,,,
+2011,6935999491,6986951000.0,6997998760.0,,,,,,,,
+2012,7013871313,7057075000.0,7080072417.0,,,,,,,,
+2013,7092128094,7136796000.0,7162119434.0,,,,,,,,
+2014,7169968185,7238184000.0,7243784000.0,,,,,,,,
+2015,7247892788,7336435000.0,7349472000.0,,,,,,,,
+2016,7325996709,7418151841.0,,,,,,,,,
diff --git a/data/World_population_estimates3.csv b/data/World_population_estimates3.csv
new file mode 100644
index 00000000..2ab604fe
--- /dev/null
+++ b/data/World_population_estimates3.csv
@@ -0,0 +1,46 @@
+Year,census,prb,un
+2016,7334771614.0,,7432663280.0
+2017,7412778971.0,,
+2018,7490427640.0,,
+2019,7567402977.0,,
+2020,7643402123.0,,7758157000.0
+2021,7718256830.0,,
+2022,7792021317.0,,
+2023,7864725370.0,,
+2024,7936271554.0,,
+2025,8006580553.0,8000000000.0,8141661000.0
+2026,8075716000.0,,
+2027,8143729466.0,,
+2028,8210559895.0,,
+2029,8276190519.0,,
+2030,8340606590.0,8505000000.0,8500766000.0
+2031,8403880343.0,,
+2032,8466094022.0,,
+2033,8527246205.0,,
+2034,8587325154.0,,
+2035,8646304704.0,,8838908000.0
+2036,8704239274.0,,
+2037,8761189197.0,,
+2038,8817138785.0,,
+2039,8872066537.0,,
+2040,8925949679.0,,9157234000.0
+2041,8978822945.0,,
+2042,9030723366.0,,
+2043,9081617002.0,,
+2044,9131462326.0,,
+2045,9180225214.0,,9453892000.0
+2046,9227935007.0,,
+2047,9274616811.0,,
+2048,9320232984.0,,
+2049,9364750182.0,,
+2050,9408141302.0,9804000000.0,9725148000.0
+2055,,,9968809000.0
+2060,,,10184290000.0
+2065,,,10375719000.0
+2070,,,10547989000.0
+2075,,,10701653000.0
+2080,,,10836635000.0
+2085,,,10953525000.0
+2090,,,11055270000.0
+2095,,,11142461000.0
+2100,,,11213317000.0
diff --git a/data/baseball_drag.csv b/data/baseball_drag.csv
new file mode 100644
index 00000000..7de5138a
--- /dev/null
+++ b/data/baseball_drag.csv
@@ -0,0 +1,17 @@
+Velocity in mph,Drag coefficient
+0.058486,0.49965
+19.845,0.49878
+39.476,0.49704
+50.181,0.48225
+60.134,0.45004
+68.533,0.40914
+73.769,0.38042
+77.408,0.36562
+83.879,0.34822
+90.507,0.33081
+97.29,0.31427
+104.54,0.30035
+113.83,0.28816
+120.9,0.28381
+127.34,0.28033
+134.41,0.28207

From 2f7f3d5e1e42319511a53a900cc5f6e510a6549d Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sat, 23 Jan 2021 11:42:22 -0500
Subject: [PATCH 013/144] Updating notebooks

---
 colab/chap01.ipynb    |  75 ++++++++++++++++--------
 colab/chap02.ipynb    |  64 +++------------------
 colab/chap03.ipynb    |  45 +++++++++------
 colab/chap04.ipynb    |  42 +++++++++-----
 colab/chap05.ipynb    | 129 +++++++++++++++++++++++++-----------------
 colab/chap06.ipynb    | 113 +++++++++++++++++++++++-------------
 colab/chap07.ipynb    | 110 +++++++++++++++++++++--------------
 colab/chap08.ipynb    | 120 ++++++++++++++++++---------------------
 colab/chap09.ipynb    |  69 ++++++++++++++--------
 colab/chap10.ipynb    | 113 ++++++++++++++++++++++--------------
 colab/chap11.ipynb    |  45 +++++++++------
 colab/chap12.ipynb    |  54 +++++++++++-------
 colab/chap13.ipynb    |  99 +++++++++++++++++---------------
 colab/chap13ode.ipynb |  42 +++++++++-----
 colab/chap14.ipynb    |  48 ++++++++++------
 colab/chap15.ipynb    |  42 +++++++++-----
 colab/chap16.ipynb    |  46 +++++++++------
 colab/chap17.ipynb    |  46 +++++++++------
 colab/chap18.ipynb    |  46 +++++++++------
 colab/chap20.ipynb    |  43 +++++++++-----
 colab/chap21.ipynb    |  43 +++++++++-----
 colab/chap22.ipynb    |  46 +++++++++------
 colab/chap23.ipynb    |  43 +++++++++-----
 colab/chap24.ipynb    |  43 +++++++++-----
 colab/chap25.ipynb    |  43 +++++++++-----
 25 files changed, 958 insertions(+), 651 deletions(-)

diff --git a/colab/chap01.ipynb b/colab/chap01.ipynb
index 7f892bfe..7a39ec16 100644
--- a/colab/chap01.ipynb
+++ b/colab/chap01.ipynb
@@ -9,15 +9,40 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "Modeling and Simulation in Python\n",
+    "*Modeling and Simulation in Python*\n",
     "\n",
     "Copyright 2021 Allen Downey\n",
     "\n",
     "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -92,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -199,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -231,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -247,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -263,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -318,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -337,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -354,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -364,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -384,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -393,7 +418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -419,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -428,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -437,7 +462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -446,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -455,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/colab/chap02.ipynb b/colab/chap02.ipynb
index ba819318..52250e76 100644
--- a/colab/chap02.ipynb
+++ b/colab/chap02.ipynb
@@ -43,18 +43,6 @@
     "    from modsim import *"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# set the random number generator\n",
-    "np.random.seed(7)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -73,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "scrolled": true
    },
@@ -737,7 +725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -745,9 +733,7 @@
     "\n",
     "decorate(title='Olin-Wellesley Bikeshare',\n",
     "         xlabel='Time step (min)', \n",
-    "         ylabel='Number of bikes')\n",
-    "\n",
-    "savefig('figs/chap02-fig01.pdf')"
+    "         ylabel='Number of bikes')"
    ]
   },
   {
@@ -757,38 +743,6 @@
     "`decorate`, which is defined in the `modsim` library, adds a title and labels the axes."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(decorate)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`savefig()` saves a figure in a file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(savefig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The suffix of the filename indicates the format you want.  This example saves the current figure in a PDF file named `chap01-fig01.pdf`."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -816,7 +770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -825,7 +779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -879,7 +833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -899,7 +853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/colab/chap03.ipynb b/colab/chap03.ipynb
index 82a9d53a..9aae1691 100644
--- a/colab/chap03.ipynb
+++ b/colab/chap03.ipynb
@@ -4,32 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 3\n",
+    "# Chapter 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim library\n",
-    "from modsim import *\n",
-    "\n",
-    "# set the random number generator\n",
-    "np.random.seed(7)"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -587,7 +598,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap04.ipynb b/colab/chap04.ipynb
index 1945f357..5a790b56 100644
--- a/colab/chap04.ipynb
+++ b/colab/chap04.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 4\n",
+    "# Chapter 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# import functions from the modsim library\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -659,7 +673,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap05.ipynb b/colab/chap05.ipynb
index 32632ff6..2dca482e 100644
--- a/colab/chap05.ipynb
+++ b/colab/chap05.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 5\n",
+    "# Chapter 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -51,18 +65,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The data directory contains a downloaded copy of https://en.wikipedia.org/wiki/World_population_estimates\n",
+    "The following cell downloads a copy of https://en.wikipedia.org/wiki/World_population_estimates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
     "\n",
-    "The arguments of `read_html` specify the file to read and how to interpret the tables in the file.  The result, `tables`, is a sequence of `DataFrame` objects; `len(tables)` reports the length of the sequence."
+    "filename = 'World_population_estimates.html'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "filename = 'data/World_population_estimates.html'\n",
     "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
     "len(tables)"
    ]
@@ -71,6 +96,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "The arguments of `read_html` specify the file to read and how to interpret the tables in the file.  The result, `tables`, is a sequence of `DataFrame` objects; `len(tables)` reports the length of the sequence.\n",
+    "\n",
     "We can select the `DataFrame` we want using the bracket operator.  The tables are numbered from 0, so `tables[2]` is actually the third table on the page.\n",
     "\n",
     "`head` selects the header and the first five rows."
@@ -78,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
@@ -97,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -195,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,7 +232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {
     "scrolled": false
    },
@@ -232,9 +259,7 @@
     "plot(un, '--', label='UN DESA')\n",
     "    \n",
     "decorate(xlabel='Year',\n",
-    "         ylabel='World population (billion)')\n",
-    "\n",
-    "savefig('figs/chap05-fig01.pdf')"
+    "         ylabel='World population (billion)')"
    ]
   },
   {
@@ -248,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -269,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {
     "scrolled": true
    },
@@ -280,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {
     "scrolled": true
    },
@@ -291,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {
     "scrolled": true
    },
@@ -302,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -332,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -348,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -364,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -373,7 +398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -382,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -398,7 +423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -407,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -423,7 +448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -432,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -455,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -471,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -488,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -505,7 +530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -515,9 +540,7 @@
     "\n",
     "decorate(xlabel='Year', \n",
     "         ylabel='World population (billion)',\n",
-    "         title='Constant growth')\n",
-    "\n",
-    "savefig('figs/chap05-fig02.pdf')"
+    "         title='Constant growth')"
    ]
   },
   {
@@ -546,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -555,7 +578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -586,7 +609,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap06.ipynb b/colab/chap06.ipynb
index 1af43b6b..6d32eff3 100644
--- a/colab/chap06.ipynb
+++ b/colab/chap06.ipynb
@@ -4,31 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 6\n",
+    "# Chapter 6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *\n",
-    "\n",
-    "from pandas import read_html"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -41,11 +53,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "filename = 'data/World_population_estimates.html'\n",
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates.html'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_html\n",
+    "\n",
     "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
     "table2 = tables[2]\n",
     "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
@@ -55,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -184,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -208,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -239,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,13 +283,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
     "results = run_simulation2(system)\n",
-    "plot_results(census, un, results, 'Proportional model')\n",
-    "savefig('figs/chap06-fig01.pdf')"
+    "plot_results(census, un, results, 'Proportional model')"
    ]
   },
   {
@@ -288,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -315,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -331,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -347,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -377,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -394,7 +420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -432,7 +458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -458,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -484,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {
     "scrolled": false
    },
@@ -495,12 +521,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Solution goes here"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -519,7 +552,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap07.ipynb b/colab/chap07.ipynb
index fb1ed01b..e8ed937c 100644
--- a/colab/chap07.ipynb
+++ b/colab/chap07.ipynb
@@ -4,31 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 7\n",
+    "# Chapter 7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *\n",
-    "\n",
-    "from pandas import read_html"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -40,11 +52,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates.html'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "filename = 'data/World_population_estimates.html'\n",
+    "from pandas import read_html\n",
+    "\n",
     "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
     "table2 = tables[2]\n",
     "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
@@ -54,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -134,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -184,13 +211,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
     "results = run_simulation(system, update_func_quad)\n",
-    "plot_results(census, un, results, 'Quadratic model')\n",
-    "savefig('figs/chap07-fig01.pdf')"
+    "plot_results(census, un, results, 'Quadratic model')"
    ]
   },
   {
@@ -211,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -229,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -239,9 +265,7 @@
     "decorate(xlabel='Population (billions)',\n",
     "         ylabel='Net growth (billions)')\n",
     "\n",
-    "sns.set_style('white')\n",
-    "\n",
-    "savefig('figs/chap07-fig02.pdf')"
+    "sns.set_style('white')"
    ]
   },
   {
@@ -262,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -325,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -349,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -377,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -400,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -427,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -443,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -466,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -475,7 +499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -484,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -515,7 +539,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap08.ipynb b/colab/chap08.ipynb
index 3c31a82b..a77508ed 100644
--- a/colab/chap08.ipynb
+++ b/colab/chap08.ipynb
@@ -4,31 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 8\n",
+    "# Chapter 8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# If we're running on Colab, install modsimpy\n",
-    "# https://pypi.org/project/modsimpy/\n",
-    "\n",
-    "import sys\n",
-    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "if IN_COLAB:\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
     "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
     "    !pip install modsimpy\n",
-    "    !mkdir figs"
+    "    from modsim import *"
    ]
   },
   {
@@ -116,7 +128,7 @@
     "\n",
     "filename = 'World_population_estimates2.csv'\n",
     "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/notebooks/data/World_population_estimates2.csv\n"
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates2.csv"
    ]
   },
   {
@@ -124,31 +136,6 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "def read_table2(filename):\n",
-    "    tables = pd.read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "    table2 = tables[2]\n",
-    "    table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']\n",
-    "    return table2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#table2 = read_table2()\n",
-    "#table2.to_csv('data/World_population_estimates2.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "table2 = pd.read_csv('World_population_estimates2.csv')\n",
     "table2.index = table2.Year\n",
@@ -157,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -211,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -229,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {
     "scrolled": true
    },
@@ -251,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -275,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "scrolled": false
    },
@@ -283,8 +270,7 @@
    "source": [
     "system.t_end = 2250\n",
     "results = run_simulation(system, update_func_quad)\n",
-    "plot_results(census, un, results, 'World population projection')\n",
-    "savefig('figs/chap08-fig01.pdf')"
+    "plot_results(census, un, results, 'World population projection')"
    ]
   },
   {
@@ -296,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -321,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -344,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -354,12 +340,12 @@
     "\n",
     "filename = 'World_population_estimates3.csv'\n",
     "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/notebooks/data/World_population_estimates3.csv\n"
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates3.csv\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -372,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -382,7 +368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -407,7 +393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -432,7 +418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -445,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,8 +439,7 @@
     "\n",
     "plt.axvspan(1950, 2016, color='C0', alpha=0.05)\n",
     "plot_results(census, un, results, 'World population projections')\n",
-    "plot_projections(table3)\n",
-    "savefig('figs/chap08-fig02.pdf')"
+    "plot_projections(table3)"
    ]
   },
   {
@@ -479,7 +464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -515,7 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -524,7 +509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -591,6 +576,13 @@
    "source": [
     "**Related viewing:** You might be interested in this [video by Hans Rosling about the demographic changes we expect in this century](https://www.youtube.com/watch?v=ezVk1ahRF78)."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -609,7 +601,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap09.ipynb b/colab/chap09.ipynb
index fe1d4db2..9e3b0cd5 100644
--- a/colab/chap09.ipynb
+++ b/colab/chap09.ipynb
@@ -4,26 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 9\n",
+    "# Chapter 9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# import everything from SymPy.\n",
-    "from sympy import *\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# Set up Jupyter notebook to display math.\n",
-    "init_printing() "
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -72,7 +89,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "t = symbols('t')\n",
+    "import sympy as sp\n",
+    "\n",
+    "t = sp.symbols('t')\n",
     "t"
    ]
   },
@@ -138,7 +157,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "f = Function('f')\n",
+    "f = sp.Function('f')\n",
     "f"
    ]
   },
@@ -187,7 +206,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dfdt = diff(f(t), t)\n",
+    "dfdt = sp.diff(f(t), t)\n",
     "dfdt"
    ]
   },
@@ -213,7 +232,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "alpha = symbols('alpha')\n",
+    "alpha = sp.symbols('alpha')\n",
     "alpha"
    ]
   },
@@ -230,7 +249,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "eq1 = Eq(dfdt, alpha*f(t))\n",
+    "eq1 = sp.Eq(dfdt, alpha*f(t))\n",
     "eq1"
    ]
   },
@@ -247,7 +266,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "solution_eq = dsolve(eq1)\n",
+    "solution_eq = sp.dsolve(eq1)\n",
     "solution_eq"
    ]
   },
@@ -266,7 +285,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "C1, p_0 = symbols('C1 p_0')"
+    "C1, p_0 = sp.symbols('C1 p_0')"
    ]
   },
   {
@@ -301,7 +320,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "r, K = symbols('r K')"
+    "r, K = sp.symbols('r K')"
    ]
   },
   {
@@ -317,7 +336,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "eq2 = Eq(diff(f(t), t), r * f(t) * (1 - f(t)/K))\n",
+    "eq2 = sp.Eq(sp.diff(f(t), t), r * f(t) * (1 - f(t)/K))\n",
     "eq2"
    ]
   },
@@ -334,7 +353,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "solution_eq = dsolve(eq2)\n",
+    "solution_eq = sp.dsolve(eq2)\n",
     "solution_eq"
    ]
   },
@@ -389,7 +408,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "solutions = solve(Eq(at_0, p_0), C1)\n",
+    "solutions = sp.solve(sp.Eq(at_0, p_0), C1)\n",
     "type(solutions), len(solutions)"
    ]
   },
@@ -433,7 +452,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "particular = simplify(particular)\n",
+    "particular = sp.simplify(particular)\n",
     "particular"
    ]
   },
@@ -486,7 +505,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "logistic = K / (1 + A * exp(-r*t))\n",
+    "logistic = K / (1 + A * sp.exp(-r*t))\n",
     "logistic"
    ]
   },
@@ -505,7 +524,7 @@
    },
    "outputs": [],
    "source": [
-    "simplify(particular - logistic)"
+    "sp.simplify(particular - logistic)"
    ]
   },
   {
@@ -630,7 +649,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap10.ipynb b/colab/chap10.ipynb
index e7167c43..f2e903ab 100644
--- a/colab/chap10.ipynb
+++ b/colab/chap10.ipynb
@@ -4,31 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 10\n",
+    "# Chapter 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "from pandas import read_html"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -44,11 +56,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates.html'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "filename = 'data/World_population_estimates.html'\n",
+    "from pandas import read_html\n",
+    "\n",
     "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
     "table2 = tables[2]\n",
     "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
@@ -59,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {
     "scrolled": true
    },
@@ -104,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {
     "scrolled": true
    },
@@ -122,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {
     "scrolled": false
    },
@@ -140,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {
     "scrolled": true
    },
@@ -158,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -194,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -212,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -221,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -230,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -239,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -248,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {
     "scrolled": true
    },
@@ -270,11 +297,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "filename = 'data/World_population_estimates.html'\n",
+    "filename = 'World_population_estimates.html'\n",
     "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
     "len(tables)"
    ]
@@ -288,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -321,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -338,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -355,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 27,
    "metadata": {
     "scrolled": false
    },
@@ -377,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -399,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -408,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -439,7 +466,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap11.ipynb b/colab/chap11.ipynb
index e75bc7e8..887d7df7 100644
--- a/colab/chap11.ipynb
+++ b/colab/chap11.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 11\n",
+    "# Chapter 11"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -337,8 +351,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_results(S, I, R)\n",
-    "savefig('figs/chap11-fig01.pdf')"
+    "plot_results(S, I, R)\n"
    ]
   },
   {
@@ -455,7 +468,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap12.ipynb b/colab/chap12.ipynb
index 50a9c232..ef6f4478 100644
--- a/colab/chap12.ipynb
+++ b/colab/chap12.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 12\n",
+    "# Chapter 12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -325,8 +339,7 @@
     "\n",
     "decorate(xlabel='Time (days)',\n",
     "         ylabel='Fraction susceptible')\n",
-    "\n",
-    "savefig('figs/chap12-fig01.pdf')"
+    "\n"
    ]
   },
   {
@@ -419,8 +432,7 @@
     "         ylabel='Total fraction infected',\n",
     "         title='Fraction infected vs. immunization rate',\n",
     "         legend=False)\n",
-    "\n",
-    "savefig('figs/chap12-fig02.pdf')"
+    "\n"
    ]
   },
   {
@@ -684,8 +696,7 @@
     "         ylabel='Total fraction infected',\n",
     "         title='Effect of hand washing on total infections',\n",
     "         legend=False)\n",
-    "\n",
-    "savefig('figs/chap12-fig03.pdf')"
+    "\n"
    ]
   },
   {
@@ -821,8 +832,7 @@
     "         ylabel='Total fraction infected',\n",
     "         title='Total infections vs. doses',\n",
     "         legend=False)\n",
-    "\n",
-    "savefig('figs/chap12-fig04.pdf')"
+    "\n"
    ]
   },
   {
@@ -869,7 +879,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap13.ipynb b/colab/chap13.ipynb
index d368dd4c..bff8f833 100644
--- a/colab/chap13.ipynb
+++ b/colab/chap13.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 13\n",
+    "# Chapter 13"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -45,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -172,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -189,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -208,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -237,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -255,8 +269,7 @@
     "\n",
     "decorate(xlabel='Contact rate (beta)',\n",
     "         ylabel='Fraction infected')\n",
-    "\n",
-    "savefig('figs/chap13-fig01.pdf')"
+    "\n"
    ]
   },
   {
@@ -275,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -291,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -307,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -323,8 +336,7 @@
     "         loc='upper left')\n",
     "\n",
     "plt.legend(bbox_to_anchor=(1.02, 1.02))\n",
-    "plt.tight_layout()\n",
-    "savefig('figs/chap13-fig02.pdf')"
+    "plt.tight_layout()\n"
    ]
   },
   {
@@ -336,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -345,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -354,7 +366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -372,7 +384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -400,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -417,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -440,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -455,8 +467,7 @@
     "         ylabel='Fraction infected')\n",
     "\n",
     "plt.legend(bbox_to_anchor=(1.02, 1.02))\n",
-    "plt.tight_layout()\n",
-    "savefig('figs/chap13-fig03.pdf')"
+    "plt.tight_layout()\n"
    ]
   },
   {
@@ -470,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {
     "scrolled": true
    },
@@ -480,9 +491,7 @@
     "\n",
     "decorate(xlabel='Recovery rate (gamma)',\n",
     "         ylabel='Contact rate (beta)',\n",
-    "         title='Fraction infected, contour plot')\n",
-    "\n",
-    "savefig('figs/chap13-fig04.pdf')"
+    "         title='Fraction infected, contour plot')"
    ]
   },
   {
@@ -509,7 +518,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap13ode.ipynb b/colab/chap13ode.ipynb
index 28d11d48..c376d88b 100644
--- a/colab/chap13ode.ipynb
+++ b/colab/chap13ode.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 13\n",
+    "# Chapter 13"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -172,7 +186,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap14.ipynb b/colab/chap14.ipynb
index c9624be2..c8522091 100644
--- a/colab/chap14.ipynb
+++ b/colab/chap14.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 14\n",
+    "# Chapter 14"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -276,8 +290,7 @@
     "\n",
     "decorate(xlabel='Contact number (beta/gamma)',\n",
     "         ylabel='Fraction infected')\n",
-    "\n",
-    "savefig('figs/chap14-fig01.pdf')"
+    "\n"
    ]
   },
   {
@@ -356,8 +369,7 @@
     "\n",
     "decorate(xlabel='Contact number (c)',\n",
     "         ylabel='Fraction infected')\n",
-    "\n",
-    "savefig('figs/chap14-fig02.pdf')"
+    "\n"
    ]
   },
   {
@@ -467,7 +479,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap15.ipynb b/colab/chap15.ipynb
index 4e0f0cdb..66c8b36a 100644
--- a/colab/chap15.ipynb
+++ b/colab/chap15.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 15\n",
+    "# Chapter 15"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -310,7 +324,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap16.ipynb b/colab/chap16.ipynb
index b359b505..f87abcbc 100644
--- a/colab/chap16.ipynb
+++ b/colab/chap16.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 16\n",
+    "# Chapter xx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -554,8 +568,7 @@
     "decorate(xlabel='Time (minutes)',\n",
     "         ylabel='Temperature (C)',\n",
     "         loc='center left')\n",
-    "\n",
-    "savefig('figs/chap16-fig01.pdf')"
+    "\n"
    ]
   },
   {
@@ -686,8 +699,7 @@
     "plot(sweep, label='final temp', color='C2')\n",
     "decorate(xlabel='Time added (min)',\n",
     "         ylabel='Final temperature (C)')\n",
-    "\n",
-    "savefig('figs/chap16-fig02.pdf')"
+    "\n"
    ]
   },
   {
diff --git a/colab/chap17.ipynb b/colab/chap17.ipynb
index 834f0306..1ead820b 100644
--- a/colab/chap17.ipynb
+++ b/colab/chap17.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 17\n",
+    "# Chapter xx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -104,8 +118,7 @@
     "plot(data.insulin, 'go', label='insulin')\n",
     "decorate(xlabel='Time (min)',\n",
     "         ylabel='Concentration ($\\mu$U/mL)')\n",
-    "\n",
-    "savefig('figs/chap17-fig01.pdf')"
+    "\n"
    ]
   },
   {
@@ -189,8 +202,7 @@
     "\n",
     "decorate(xlabel='Time (min)',\n",
     "         ylabel='Concentration ($\\mu$U/mL)')\n",
-    "\n",
-    "savefig('figs/chap17-fig02.pdf')"
+    "\n"
    ]
   },
   {
diff --git a/colab/chap18.ipynb b/colab/chap18.ipynb
index e8e6df35..70f215fa 100644
--- a/colab/chap18.ipynb
+++ b/colab/chap18.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 18\n",
+    "# Chapter xx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -273,8 +287,7 @@
     "\n",
     "decorate(xlabel='Time (min)', \n",
     "         ylabel='Concentration (arbitrary units)')\n",
-    "\n",
-    "savefig('figs/chap18-fig01.pdf')"
+    "\n"
    ]
   },
   {
@@ -396,8 +409,7 @@
     "plot(results2.G, 'C2--', label='run_ode_solver')\n",
     "\n",
     "decorate(xlabel='Time (min)', ylabel='Concentration (mg/dL)')\n",
-    "\n",
-    "savefig('figs/chap18-fig02.pdf')"
+    "\n"
    ]
   },
   {
diff --git a/colab/chap20.ipynb b/colab/chap20.ipynb
index 46fc858c..3327ed2e 100644
--- a/colab/chap20.ipynb
+++ b/colab/chap20.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 20\n",
+    "# Chapter xx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -233,8 +247,7 @@
     "    decorate(xlabel='Time (s)',\n",
     "             ylabel='Position (m)')\n",
     "\n",
-    "plot_position(results)\n",
-    "savefig('figs/chap20-fig01.pdf')"
+    "plot_position(results)\n"
    ]
   },
   {
diff --git a/colab/chap21.ipynb b/colab/chap21.ipynb
index 54539d62..f644024c 100644
--- a/colab/chap21.ipynb
+++ b/colab/chap21.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 21\n",
+    "# Chapter xx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -287,8 +301,7 @@
     "    decorate(xlabel='Time (s)',\n",
     "             ylabel='Position (m)')\n",
     "    \n",
-    "plot_position(results)\n",
-    "savefig('figs/chap21-fig01.pdf')"
+    "plot_position(results)\n"
    ]
   },
   {
diff --git a/colab/chap22.ipynb b/colab/chap22.ipynb
index 5a41edee..8464813a 100644
--- a/colab/chap22.ipynb
+++ b/colab/chap22.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 22\n",
+    "# Chapter xx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -717,8 +731,7 @@
     "\n",
     "decorate(xlabel='Time (s)',\n",
     "         ylabel='Position (m)')\n",
-    "\n",
-    "savefig('figs/chap22-fig01.pdf')"
+    "\n"
    ]
   },
   {
@@ -769,8 +782,7 @@
     "    decorate(xlabel='x position (m)',\n",
     "             ylabel='y position (m)')\n",
     "\n",
-    "plot_trajectory(results)\n",
-    "savefig('figs/chap22-fig02.pdf')"
+    "plot_trajectory(results)\n"
    ]
   },
   {
diff --git a/colab/chap23.ipynb b/colab/chap23.ipynb
index 0eca5394..918d2da3 100644
--- a/colab/chap23.ipynb
+++ b/colab/chap23.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 23\n",
+    "# Chapter xx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -257,8 +271,7 @@
     "         ylabel='Range (m)',\n",
     "         title='Range as a function of launch angle',\n",
     "         legend=False)\n",
-    "\n",
-    "savefig('figs/chap23-fig01.pdf')"
+    "\n"
    ]
   },
   {
diff --git a/colab/chap24.ipynb b/colab/chap24.ipynb
index 6d7e6b52..9052b971 100644
--- a/colab/chap24.ipynb
+++ b/colab/chap24.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 24\n",
+    "# Chapter xx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -456,8 +470,7 @@
     "    subplot(3, 1, 3)\n",
     "    plot_r(results)\n",
     "\n",
-    "plot_three(results)\n",
-    "savefig('figs/chap24-fig01.pdf')"
+    "plot_three(results)\n"
    ]
   },
   {
diff --git a/colab/chap25.ipynb b/colab/chap25.ipynb
index 5c74611e..96cb2878 100644
--- a/colab/chap25.ipynb
+++ b/colab/chap25.ipynb
@@ -4,29 +4,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 25\n",
+    "# Chapter xx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
     "\n",
-    "Copyright 2017 Allen Downey\n",
+    "Copyright 2021 Allen Downey\n",
     "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
+    "# check if the libraries we need are installed\n",
     "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
    ]
   },
   {
@@ -467,8 +481,7 @@
     "subplot(2, 1, 1)\n",
     "plot_theta(results)\n",
     "subplot(2, 1, 2)\n",
-    "plot_omega(results)\n",
-    "savefig('figs/chap25-fig01.pdf')"
+    "plot_omega(results)\n"
    ]
   },
   {

From 57aaa2a46a27eb99bcae1e92ced22b698bfba8f8 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sat, 23 Jan 2021 11:52:35 -0500
Subject: [PATCH 014/144] Updating README

---
 README.md | 95 +++++++++++++++++++++++++++++++++++++++++++++----------
 1 file changed, 79 insertions(+), 16 deletions(-)

diff --git a/README.md b/README.md
index 969d35d2..b0c6fe6c 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,6 @@
 # ModSimPython
-Text and supporting code for Modeling and Simulation in Python
 
-You can run the code from the repository in a browser by pressing the Binder button below.
-
-[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/AllenDowney/ModSimPy/master?filepath=notebooks)
+Text and supporting code for *Modeling and Simulation in Python*.
 
 
 *Modeling and Simulation in Python* is an introduction to physical modeling using a computational approach.  It is organized in three parts:
@@ -23,8 +20,78 @@ Python is an ideal programming language for this material.  It is a good first l
 [This](http://greenteapress.com/wp/modsimpy/) and other Free Books by Allen Downey are available from [Green Tea Press](http://greenteapress.com/wp).
 
 
-Getting started
----------------
+## Getting started
+
+There are three ways to work with the code:
+
+* You can run the notebooks on Colab.
+
+* You can run them on Binder.
+
+* You can download them and run them in your own Jupyter environment.
+
+### Colab
+
+Use these links to run the notebooks on Colab:
+
+* [chap01.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap01.ipynb)
+
+* [chap02.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap02.ipynb)
+
+* [chap03.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap03.ipynb)
+
+* [chap04.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap04.ipynb)
+
+* [chap05.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap05.ipynb)
+
+* [chap06.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap06.ipynb)
+
+* [chap07.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap07.ipynb)
+
+* [chap08.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap08.ipynb)
+
+* [chap09.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap09.ipynb)
+
+* [chap10.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap10.ipynb)
+
+* [chap11.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap11.ipynb)
+
+* [chap12.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap12.ipynb)
+
+* [chap13.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap13.ipynb)
+
+* [chap13ode.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap13ode.ipynb)
+
+* [chap14.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap14.ipynb)
+
+* [chap15.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap15.ipynb)
+
+* [chap16.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap16.ipynb)
+
+* [chap17.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap17.ipynb)
+
+* [chap18.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap18.ipynb)
+
+* [chap20.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap20.ipynb)
+
+* [chap21.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap21.ipynb)
+
+* [chap22.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap22.ipynb)
+
+* [chap23.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap23.ipynb)
+
+* [chap24.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap24.ipynb)
+
+* [chap25.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap25.ipynb)
+
+### Binder
+
+You can run the code from the repository in a browser by pressing the Binder button below.
+
+[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/AllenDowney/ModSimPy/master?filepath=notebooks)
+
+
+### Install Jupyter
 
 To run the examples and work on the exercises in this book, you have to:
 
@@ -38,11 +105,9 @@ To run the examples and work on the exercises in this book, you have to:
     text.
 
 The next three sections provide details for these steps. I wish there
-were an easier way to get started; it’s regrettable that you have to do
-so much work before you write your first program. Be persistent!
+were an easier way to get started; it’s regrettable that you have to do so much work before you write your first program. Be persistent!
 
-Installing Python
------------------
+#### Installing Python
 
 You might already have Python installed on your computer, but you might
 not have the latest version. To use the code in this book, you need
@@ -59,7 +124,7 @@ all files in your home directory, so you don’t need administrator (root)
 permission to install it, and if you have a version of Python already,
 Anaconda will not remove or modify it.
 
-Start at [the Anaconda download page](https://www.anaconda.com/distribution/#download-section). 
+Start at [the Anaconda download page](https://www.anaconda.com/distribution/#download-section).
 Download the installer for
 your system and run it. You don’t need administrative privileges to
 install Anaconda, so I recommend you run the installer as a normal user,
@@ -90,11 +155,10 @@ pip install modsimpy
 ```
 That should be everything you need.
 
-Copying my files
-----------------
+#### Copying my files
 
 The simplest way to get the files for this book is to download a [Zip
-archive from GitHub](https://github.com/AllenDowney/ModSimPy/archive/master.zip). 
+archive from GitHub](https://github.com/AllenDowney/ModSimPy/archive/master.zip).
 You will need a program like
 WinZip or gzip to unpack the Zip file. Make a note of the location of
 the files you download.
@@ -153,8 +217,7 @@ git clone https://github.com/YourGitHubUserName/ModSimPy
 Of course, you should replace with your GitHub user name. After cloning,
 you should have a new directory called .
 
-Running Jupyter
----------------
+#### Running Jupyter
 
 The code for each chapter, and starter code for the exercises, is in
 Jupyter notebooks. If you have not used Jupyter before, you can read

From ed2ea29a86ad1dbf3e98231b4b87d3b3d09a1dbc Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sat, 23 Jan 2021 15:44:23 -0500
Subject: [PATCH 015/144] Updating notebooks

---
 colab/chap16.ipynb |  4 +--
 colab/chap17.ipynb | 42 ++++++++++++++++++----------
 colab/chap18.ipynb | 70 +++++++++++++++++++++++++++-------------------
 colab/chap20.ipynb |  4 +--
 colab/chap21.ipynb |  4 +--
 colab/chap22.ipynb | 44 +++++++++++++++++++----------
 colab/chap23.ipynb |  4 +--
 colab/chap24.ipynb |  4 +--
 colab/chap25.ipynb |  4 +--
 9 files changed, 111 insertions(+), 69 deletions(-)

diff --git a/colab/chap16.ipynb b/colab/chap16.ipynb
index f87abcbc..163d9611 100644
--- a/colab/chap16.ipynb
+++ b/colab/chap16.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter xx"
+    "# Chapter 16"
    ]
   },
   {
@@ -846,7 +846,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap17.ipynb b/colab/chap17.ipynb
index 1ead820b..4e65e064 100644
--- a/colab/chap17.ipynb
+++ b/colab/chap17.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter xx"
+    "# Chapter 17"
    ]
   },
   {
@@ -54,11 +54,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = pd.read_csv('data/glucose_insulin.csv', index_col='time')"
+    "import os\n",
+    "\n",
+    "filename = 'glucose_insulin.csv'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/glucose_insulin.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(filename, index_col='time')"
    ]
   },
   {
@@ -70,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -155,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {
     "scrolled": true
    },
@@ -173,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -230,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -277,7 +291,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap18.ipynb b/colab/chap18.ipynb
index 70f215fa..089fb4b3 100644
--- a/colab/chap18.ipynb
+++ b/colab/chap18.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter xx"
+    "# Chapter 18"
    ]
   },
   {
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {
     "tags": []
    },
@@ -54,11 +54,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'glucose_insulin.csv'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/glucose_insulin.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = pd.read_csv('data/glucose_insulin.csv', index_col='time');"
+    "data = pd.read_csv(filename, index_col='time');"
    ]
   },
   {
@@ -70,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -153,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -189,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -239,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -255,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -337,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -353,7 +367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -369,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -385,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -401,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -421,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -432,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -452,7 +466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -461,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -470,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -479,7 +493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -499,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -537,7 +551,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap20.ipynb b/colab/chap20.ipynb
index 3327ed2e..f1968b01 100644
--- a/colab/chap20.ipynb
+++ b/colab/chap20.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter xx"
+    "# Chapter 20"
    ]
   },
   {
@@ -648,7 +648,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap21.ipynb b/colab/chap21.ipynb
index f644024c..ae52a9b9 100644
--- a/colab/chap21.ipynb
+++ b/colab/chap21.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter xx"
+    "# Chapter 21"
    ]
   },
   {
@@ -519,7 +519,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap22.ipynb b/colab/chap22.ipynb
index 8464813a..10bc4bcf 100644
--- a/colab/chap22.ipynb
+++ b/colab/chap22.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter xx"
+    "# Chapter 22"
    ]
   },
   {
@@ -1100,11 +1100,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'baseball_drag.csv'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/baseball_drag.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
-    "baseball_drag = pd.read_csv('data/baseball_drag.csv')\n",
+    "baseball_drag = pd.read_csv(filename)\n",
     "mph = Quantity(baseball_drag['Velocity in mph'], UNITS.mph)\n",
     "mps = mph.to(m/s)\n",
     "baseball_drag.index = magnitude(mps)\n",
@@ -1121,7 +1135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1130,7 +1144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1139,7 +1153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1148,7 +1162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1157,7 +1171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1166,7 +1180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1175,7 +1189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1184,7 +1198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1193,7 +1207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1202,7 +1216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1211,7 +1225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1242,7 +1256,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap23.ipynb b/colab/chap23.ipynb
index 918d2da3..713d90ae 100644
--- a/colab/chap23.ipynb
+++ b/colab/chap23.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter xx"
+    "# Chapter 23"
    ]
   },
   {
@@ -576,7 +576,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap24.ipynb b/colab/chap24.ipynb
index 9052b971..a2273574 100644
--- a/colab/chap24.ipynb
+++ b/colab/chap24.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter xx"
+    "# Chapter 24"
    ]
   },
   {
@@ -645,7 +645,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/colab/chap25.ipynb b/colab/chap25.ipynb
index 96cb2878..1516eaff 100644
--- a/colab/chap25.ipynb
+++ b/colab/chap25.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter xx"
+    "# Chapter 25"
    ]
   },
   {
@@ -949,7 +949,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

From c81c2cf5ce83aa6f99803766e757861311cb4a71 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 25 Jan 2021 08:49:03 -0500
Subject: [PATCH 016/144] Update README.md

---
 README.md | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/README.md b/README.md
index b0c6fe6c..5d66e33b 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,4 @@
-# ModSimPython
-
-Text and supporting code for *Modeling and Simulation in Python*.
-
+# Modeling and Simulation in Python
 
 *Modeling and Simulation in Python* is an introduction to physical modeling using a computational approach.  It is organized in three parts:
 

From 2dc75bd7b37ccaac6cf5edfb62a08d7b85718523 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Wed, 27 Jan 2021 11:07:43 -0500
Subject: [PATCH 017/144] Revising chapters

---
 jupyter/chap01.ipynb | 1736 +++++++++++++++++++++++++++++++++++++++++
 jupyter/chap02.ipynb | 1744 ++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 3480 insertions(+)
 create mode 100644 jupyter/chap01.ipynb
 create mode 100644 jupyter/chap02.ipynb

diff --git a/jupyter/chap01.ipynb b/jupyter/chap01.ipynb
new file mode 100644
index 00000000..a00e67d7
--- /dev/null
+++ b/jupyter/chap01.ipynb
@@ -0,0 +1,1736 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "legislative-somalia",
+   "metadata": {},
+   "source": [
+    "# Chapter 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "official-adrian",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "intermediate-monte",
+   "metadata": {},
+   "source": [
+    "## Jupyter\n",
+    "\n",
+    "Welcome to *Modeling and Simulation in Python*, and welcome to Jupyter.\n",
+    "\n",
+    "This is a Jupyter notebook, which is a development environment where you can write and run Python code.  Each notebook is divided into cells.  Each cell contains either text (like this cell) or Python code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "natural-locking",
+   "metadata": {},
+   "source": [
+    "To run a cell, hold down SHIFT and press ENTER.  \n",
+    "\n",
+    "* If you run a text cell, Jupyter formats the text and displays the result.\n",
+    "\n",
+    "* If you run a code cell, Jupyter runs the Python code in the cell and displays the result, if any.\n",
+    "\n",
+    "If you run the following cell, it checks whether the libraries you need are installed.  If so, the cell produces no output.  If not, you'll see updates from the installer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "private-paste",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim as ms\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "demographic-athletics",
+   "metadata": {},
+   "source": [
+    "If the previous cell runs without producing any error messages, you are all set.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "worthy-singer",
+   "metadata": {},
+   "source": [
+    "## Modeling\n",
+    "\n",
+    "This book is about modeling and simulation of physical systems. The\n",
+    "following diagram shows what I mean by \"modeling\":\n",
+    "\n",
+    "<img src =\"https://github.com/AllenDowney/ModSimPy/raw/master/book/figs/modeling_framework.png\" width=\"400\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fantastic-transition",
+   "metadata": {},
+   "source": [
+    "Starting in the lower left, the **system** is something in the real\n",
+    "world we are interested in. \n",
+    "To model the system, we have to decide which elements of the real world to include and which we can leave out.\n",
+    "This process is called **abstraction**.\n",
+    "\n",
+    "The result of abstraction is a **model**, which is a description of the system that includes only the features we think are essential. A model\n",
+    "can be represented in the form of diagrams and equations, which can be\n",
+    "used for mathematical **analysis**. It can also be implemented in the\n",
+    "form of a computer program, which can run **simulations**.\n",
+    "\n",
+    "The result of analysis and simulation might be a **prediction** about\n",
+    "what the system will do, an **explanation** of why it behaves the way it\n",
+    "does, or a **design** intended to achieve a purpose.\n",
+    "\n",
+    "We can **validate** predictions and test designs by taking\n",
+    "**measurements** from the real world and comparing the **data** we get\n",
+    "with the results from analysis and simulation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "original-startup",
+   "metadata": {},
+   "source": [
+    "For any physical system, there are many possible models, each one\n",
+    "including and excluding different features, or including different\n",
+    "levels of detail. The goal of the modeling process is to find the model\n",
+    "best suited to its purpose (prediction, explanation, or design).\n",
+    "\n",
+    "Sometimes the best model is the most detailed. If we include more\n",
+    "features, the model is more realistic, and we expect its predictions to\n",
+    "be more accurate.\n",
+    "\n",
+    "But often a simpler model is better. If we include only the essential\n",
+    "features and leave out the rest, we get models that are easier to work\n",
+    "with, and the explanations they provide can be clearer and more\n",
+    "compelling."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "talented-plastic",
+   "metadata": {},
+   "source": [
+    "As an example, suppose someone asks you why the orbit of the Earth is\n",
+    "elliptical. If you model the Earth and Sun as point masses (ignoring\n",
+    "their actual size), compute the gravitational force between them using\n",
+    "Newton's law of universal gravitation, and compute the resulting orbit\n",
+    "using Newton's laws of motion, you can show that the result is an\n",
+    "ellipse.\n",
+    "\n",
+    "Of course, the actual orbit of Earth is not a perfect ellipse, because\n",
+    "of the gravitational forces of the Moon, Jupiter, and other objects in\n",
+    "the solar system, and because Newton's laws of motion are only\n",
+    "approximately true (they don't take into account relativistic effects).\n",
+    "\n",
+    "But adding these features to the model would not improve the\n",
+    "explanation; more detail would only be a distraction from the\n",
+    "fundamental cause. However, if the goal is to predict the position of\n",
+    "the Earth with great precision, including more details might be\n",
+    "necessary."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tamil-conducting",
+   "metadata": {},
+   "source": [
+    "Choosing the best model depends on what the model is for. It is usually\n",
+    "a good idea to start with a simple model, even if it is likely to be too\n",
+    "simple, and test whether it is good enough for its purpose. Then you can\n",
+    "add features gradually, starting with the ones you expect to be most\n",
+    "essential. This process is called **iterative modeling**.\n",
+    "\n",
+    "Comparing results of successive models provides a form of **internal\n",
+    "validation**, so you can catch conceptual, mathematical, and software\n",
+    "errors. And by adding and removing features, you can tell which ones\n",
+    "have the biggest effect on the results, and which can be ignored.\n",
+    "\n",
+    "Comparing results to data from the real world provides **external\n",
+    "validation**, which is generally the strongest test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "frequent-initial",
+   "metadata": {},
+   "source": [
+    "## The falling penny myth\n",
+    "\n",
+    "Let's see an example of how models are used. You might have heard that a\n",
+    "penny dropped from the top of the Empire State Building would be going\n",
+    "so fast when it hit the pavement that it would be embedded in the\n",
+    "concrete; or if it hit a person, it would break their skull.\n",
+    "\n",
+    "We can test this myth by making and analyzing a model. To get started,\n",
+    "we'll assume that the effect of air resistance is small. This will turn out to be a bad assumption, but bear with me.\n",
+    "If air resistance is negligible, the primary force acting on the penny\n",
+    "is gravity, which causes the penny to accelerate downward.\n",
+    "\n",
+    "If the initial velocity is 0, the velocity after $t$ seconds is \n",
+    "\n",
+    "$$v = a t$$\n",
+    "\n",
+    "and the distance the penny has dropped is \n",
+    "\n",
+    "$$x = a t^2 / 2$$ \n",
+    "\n",
+    "To find the time until the penny reaches the sidewalk, we can solve for $t$:\n",
+    "\n",
+    "$$t = \\sqrt{ 2 x / a}$$ \n",
+    "\n",
+    "Plugging in the acceleration of gravity, $a = 9.8$ m/s$^2$, and the height of the Empire State Building, $x = 381$ m, we get $t = 8.8$ s. \n",
+    "\n",
+    "Then computing $v = a t$ we get a velocity on impact of $86$ m/s, which is about 190 miles per hour. That sounds like it could hurt."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "covered-yacht",
+   "metadata": {},
+   "source": [
+    "Of course, these results are not exact because the model is based on\n",
+    "simplifications. For example, we assume that gravity is constant. In\n",
+    "fact, the force of gravity is different on different parts of the globe, and it gets weaker as you move away from the surface. But these differences are small, so ignoring them is probably a good choice for this scenario.\n",
+    "\n",
+    "On the other hand, ignoring air resistance is not a good choice. Once\n",
+    "the penny gets to about 29 m/s, the upward force of air resistance\n",
+    "equals the downward force of gravity, so the penny stops accelerating.\n",
+    "This is the **terminal velocity** of the penny in air.\n",
+    "\n",
+    "Once the penny reaches terminal velocity, it doesn't matter how much farther it falls; it hits the sidewalk at about 29 m/s.\n",
+    "That's much less than 86 m/s, as the simple model predicts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "basic-cincinnati",
+   "metadata": {},
+   "source": [
+    "The statistician George Box famously said \"All models are wrong, but\n",
+    "some are useful.\" He was talking about statistical models, but his wise words apply to all kinds of models. Our first model, which ignores air resistance, is very wrong, and probably not useful. The second model, which takes air resistance into account, is still wrong, but it's better, and it's good enough to refute the myth.\n",
+    "\n",
+    "The television show *Mythbusters* has tested the myth of the falling\n",
+    "penny more carefully; you can view the results at\n",
+    "<http://modsimpy.com/myth>. Their work is based on a mathematical model of motion, measurements to determine the force of air resistance on a penny, and a physical model of a human head."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "accessory-therapist",
+   "metadata": {},
+   "source": [
+    "## Computation\n",
+    "\n",
+    "Let me show you how I computed the results from the\n",
+    "previous section using Python.\n",
+    "First we'll create a variable to represent acceleration due to gravity in meters per second squared (m/s^2)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "going-steps",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 9.8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "shared-acquisition",
+   "metadata": {},
+   "source": [
+    "A **variable** is a name that corresponds to a value. In this example, the name is `a` and the value is the number `9.8`.\n",
+    "\n",
+    "Suppose we let the penny drop for $3.4$ seconds (s).  I'll create a variable to represent this time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "comparative-investigation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = 3.4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "disturbed-village",
+   "metadata": {},
+   "source": [
+    "Now we can compute the velocity of the penny after `t` seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "featured-procurement",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = a * t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "convinced-differential",
+   "metadata": {},
+   "source": [
+    "Python uses the symbol `*` for multiplication.  The other arithmetic operators are `+` for addition, `-` for subtraction, `/` for division, and `**` for exponentiation.\n",
+    "\n",
+    "When you assign a value to a variable, Jupyter does not show the result automatically, but you can display the value of a variable like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "joined-solomon",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33.32"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "excess-valuation",
+   "metadata": {},
+   "source": [
+    "After $3.4$ s, the velocity of the penny is about $33$ m/s (ignoring air resistance).  Now let's see how far it would travel during that time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "norwegian-vitamin",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "56.644"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = a * t**2 / 2\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "discrete-stable",
+   "metadata": {},
+   "source": [
+    "It would travel about $56$ m.  Now, going in the other direction, let's compute the time it takes to fall 381 m, the height of the Empire State Building."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "solar-contest",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = 381"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "welcome-article",
+   "metadata": {},
+   "source": [
+    "For this computation, we need the square root function, which is provided by a library called NumPy.\n",
+    "Before we can use it, we have to import it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "irish-deposit",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import sqrt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "residential-victorian",
+   "metadata": {},
+   "source": [
+    "Now we can use it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "blond-graham",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8.817885349720552"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t = sqrt(2 * h / a)\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adjacent-reading",
+   "metadata": {},
+   "source": [
+    "With no air resistance, it would take about $8.8$ s for the penny to reach the sidewalk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "early-qualification",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "86.41527642726142"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v = a * t\n",
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hungarian-tongue",
+   "metadata": {},
+   "source": [
+    "And its velocity on impact would be about $86$ m/s.\n",
+    "\n",
+    "Python displays results with about 16 digits, which gives the impression that they are very precise, but don't be fooled.\n",
+    "The numbers we get out are only as good as the numbers we put in.\n",
+    "\n",
+    "For example, the \"roof height\" of the Empire State Building is $380$ m [according to Wikipedia](https://en.wikipedia.org/wiki/Empire_State_Building).\n",
+    "I chose $h=381$ m for this example on the assumption that the observation deck is on the roof and you drop the penny from a 1 meter railing.\n",
+    "But that's probably not right, so we should treat this value as an approximation where only the first two digits are likely to be right.\n",
+    "\n",
+    "If the precision of the inputs is two digits, the precision of the outputs is two digits, *at most*.\n",
+    "That's why, if the output is `86.41527642726142`, I report it as \"about 86\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "gothic-muscle",
+   "metadata": {},
+   "source": [
+    "## Computation with units\n",
+    "\n",
+    "The computation in the previous section uses numbers without units.  \n",
+    "When we set `h=381`, we left out the meters, and when we set `a=9.8`, we left out the meters per second squared.\n",
+    "And, when we got the result `v=86`, we added back the meters per second.\n",
+    "\n",
+    "Leaving units of out computation is common practice, but it tends to cause errors, including the very expensive failure of the [Mars Climate Orbiter](https://en.wikipedia.org/wiki/Mars_Climate_Orbiter) in 1999.\n",
+    "When possible, it is better to include units in the computation.\n",
+    "\n",
+    "To represent units, we'll use a Python library called [Pint](https://pint.readthedocs.io/en/latest/).\n",
+    "The ModSim library initializes Pint and provides an object called `units` that contains variables representing pretty much every unit you've ever heard of.\n",
+    "\n",
+    "We can import it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "satellite-consultancy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import units"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "conscious-motor",
+   "metadata": {},
+   "source": [
+    "Now we can create variables named `meter` and `second`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "contrary-robin",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "meter"
+      ],
+      "text/latex": [
+       "$\\mathrm{meter}$"
+      ],
+      "text/plain": [
+       "<Unit('meter')>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meter = units.meter\n",
+    "meter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "relative-aircraft",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "second"
+      ],
+      "text/latex": [
+       "$\\mathrm{second}$"
+      ],
+      "text/plain": [
+       "<Unit('second')>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "second = units.second\n",
+    "second"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "identified-saskatchewan",
+   "metadata": {},
+   "source": [
+    "To find out what other units are defined, type `units.` (including the period) in the next cell and then press TAB.  You should see a pop-up menu with a list of units."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "relevant-musician",
+   "metadata": {},
+   "source": [
+    "We can use `meter` and `second` to create a variable named `a` and give it the value of acceleration due to gravity. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "patent-logistics",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "9.8 meter/second<sup>2</sup>"
+      ],
+      "text/latex": [
+       "$9.8\\ \\frac{\\mathrm{meter}}{\\mathrm{second}^{2}}$"
+      ],
+      "text/plain": [
+       "9.8 <Unit('meter / second ** 2')>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = 9.8 * meter / second**2\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "judicial-bradley",
+   "metadata": {},
+   "source": [
+    "The result is a **quantity** with two parts, called `magnitude` and `units`, which we can access like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "nearby-ministry",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.8"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.magnitude"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "express-adelaide",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "meter/second<sup>2</sup>"
+      ],
+      "text/latex": [
+       "$\\frac{\\mathrm{meter}}{\\mathrm{second}^{2}}$"
+      ],
+      "text/plain": [
+       "<Unit('meter / second ** 2')>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.units"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "assumed-finland",
+   "metadata": {},
+   "source": [
+    "Now we can create a quantity that represents $3.4$ s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "formal-twenty",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "3.4 second"
+      ],
+      "text/latex": [
+       "$3.4\\ \\mathrm{second}$"
+      ],
+      "text/plain": [
+       "3.4 <Unit('second')>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t = 3.4 * second\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "strong-patch",
+   "metadata": {},
+   "source": [
+    "And use it to compute the distance a penny would fall after `t` seconds with constant acceleration `a`.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "wanted-wells",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "56.644 meter"
+      ],
+      "text/latex": [
+       "$56.644\\ \\mathrm{meter}$"
+      ],
+      "text/plain": [
+       "56.644 <Unit('meter')>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a * t**2 / 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "known-relationship",
+   "metadata": {},
+   "source": [
+    "Notice that the units of the result are correct.\n",
+    "If we create a quantity to represent the height of the Empire State Building:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "certified-sperm",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = 381 * meter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "married-mexico",
+   "metadata": {},
+   "source": [
+    "We can use it to compute the time to reach the sidewalk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "fossil-syria",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "8.817885349720552 second"
+      ],
+      "text/latex": [
+       "$8.817885349720552\\ \\mathrm{second}$"
+      ],
+      "text/plain": [
+       "8.817885349720552 <Unit('second')>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t = sqrt(2 * h / a)\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "superior-packet",
+   "metadata": {},
+   "source": [
+    "And the velocity of the penny on impact:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "oriental-vulnerability",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "86.41527642726142 meter/second"
+      ],
+      "text/latex": [
+       "$86.41527642726142\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
+      ],
+      "text/plain": [
+       "86.41527642726142 <Unit('meter / second')>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v = a * t\n",
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "complicated-welcome",
+   "metadata": {},
+   "source": [
+    "As in the previous section, the result is about $86$, but now it has the correct units, m/s.\n",
+    "\n",
+    "With Pint quantities, we can convert from one set of units to another like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "adolescent-layout",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mile = units.mile\n",
+    "hour = units.hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "extra-rendering",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "193.30546802805438 mile/hour"
+      ],
+      "text/latex": [
+       "$193.30546802805438\\ \\frac{\\mathrm{mile}}{\\mathrm{hour}}$"
+      ],
+      "text/plain": [
+       "193.30546802805438 <Unit('mile / hour')>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v.to(mile/hour)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "silver-learning",
+   "metadata": {},
+   "source": [
+    "If you are more familiar with miles per hour, this result might be easier to interpret.\n",
+    "And it might give you a sense that this model is not realistic."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hazardous-input",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This chapter introduces a modeling framework that consists of three steps:\n",
+    "\n",
+    "* Abstraction is the process of defining a model, deciding which elements of the real world to include and which can be left out.\n",
+    "\n",
+    "* Analysis and simulation are ways to use a model to generate predictions, explain why things behave as they to, and design things that behave as we want.\n",
+    "\n",
+    "* Validation is how we test whether the model is right, often by comparing predictions with measurements from the real world.\n",
+    "\n",
+    "As a first example, we modeled a penny dropped from the Empire State building, including gravity but ignoring air resistance.\n",
+    "In the exercises, you'll have a chance to try a better model, including air resistance.\n",
+    "\n",
+    "This chapter also presents Pint, a library for doing computation with units, which is convenient for converting between different units and important for avoiding catastrophic errors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "organized-senate",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aging-offense",
+   "metadata": {},
+   "source": [
+    "**Exercise** In mathematical notation, we can write an equation like $v = a t$ and it's understood that we are multiplying $a$ and $t$.\n",
+    "But that doesn't work in Python.  If you put two variables side-by-side, like this:\n",
+    "\n",
+    "```\n",
+    "v = a t\n",
+    "```\n",
+    "\n",
+    "you'll get a **syntax error**, which means that something is wrong with the punctuation of the program.\n",
+    "\n",
+    "Try it out in the next cell so you see what the error message looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "macro-constitution",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "33.32 meter/second"
+      ],
+      "text/latex": [
+       "$33.32\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
+      ],
+      "text/plain": [
+       "33.32 <Unit('meter / second')>"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = 9.8 * meter / second**2\n",
+    "t = 3.4 * second\n",
+    "\n",
+    "v = a * t\n",
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "entitled-carrier",
+   "metadata": {},
+   "source": [
+    "**Exercise:** In this chapter we used the `sqrt` function from the NumPy library.  NumPy also provides a variable named `pi` that contains an approximation of the mathematical constant $\\pi$.\n",
+    "We can import it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "continuous-catalyst",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.141592653589793"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from numpy import pi\n",
+    "pi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "medieval-milan",
+   "metadata": {},
+   "source": [
+    "NumPy provides other functions we'll use, including `log`, `exp`, `sin`, and `cos`.\n",
+    "Import `sin` and `cos` from NumPy and compute\n",
+    "\n",
+    "$$sin^2 \\pi/4 + cos^2 \\pi/4$$\n",
+    "\n",
+    "Note: A mathematical identity tells us that the answer should be $1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "interested-graduate",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "from numpy import sin, cos\n",
+    "\n",
+    "sin(pi/4)**2 + cos(pi/4)**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fewer-collector",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose you bring a 10 foot pole to the top of the Empire State Building and use it to drop the penny from `h` plus 10 feet.\n",
+    "\n",
+    "Define a variable named `foot` that contains the unit `foot` provided by `UNITS`.  Define a variable named `pole_height` and give it the value 10 feet.\n",
+    "\n",
+    "What happens if you add `h`, which is in units of meters, to `pole_height`, which is in units of feet?  What happens if you write the addition the other way around?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "damaged-glenn",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = 381 * meter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "baking-maldives",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "384.048 meter"
+      ],
+      "text/latex": [
+       "$384.048\\ \\mathrm{meter}$"
+      ],
+      "text/plain": [
+       "384.048 <Unit('meter')>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "foot = units.foot\n",
+    "pole_height = 10 * foot\n",
+    "\n",
+    "h + pole_height"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "lovely-acrylic",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "1260.0 foot"
+      ],
+      "text/latex": [
+       "$1260.0\\ \\mathrm{foot}$"
+      ],
+      "text/plain": [
+       "1260.0 <Unit('foot')>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "pole_height + h"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cooperative-bradley",
+   "metadata": {},
+   "source": [
+    "**Exercise**: Why would it be nonsensical to add `a` and `t`? What happens if you try?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "environmental-cover",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 9.8 * meter / second**2\n",
+    "t = 3.4 * second"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hindu-possession",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "continued-pharmacy",
+   "metadata": {},
+   "source": [
+    "In this example, you should get a `DimensionalityError`, which is defined by Pint to indicate that you have violated a rules of dimensional analysis: you cannot add quantities with different dimensions.\n",
+    "\n",
+    "The error messages you get from Python are big and scary, but if you read them carefully, they contain a lot of useful information.\n",
+    "\n",
+    "1.  Start from the bottom and read up.\n",
+    "2.  The last line usually tells you what type of error happened, and sometimes additional information.\n",
+    "3.  The previous lines are a \"traceback\" of what was happening when the error occurred.  The first section of the traceback shows the code you wrote.  The following sections are often from Python libraries.\n",
+    "\n",
+    "Before you go on, you might want to delete the erroneous code so the notebook can run without errors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "angry-artist",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose instead of dropping the penny, you throw it downward at its terminal velocity, $29$ m/s.  How long would it take to fall $381$ m?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "dressed-harrison",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "13.137931034482758 second"
+      ],
+      "text/latex": [
+       "$13.137931034482758\\ \\mathrm{second}$"
+      ],
+      "text/plain": [
+       "13.137931034482758 <Unit('second')>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "h = 381 * meter\n",
+    "v = 29 * meter / second\n",
+    "\n",
+    "t = h / v\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "electrical-jersey",
+   "metadata": {},
+   "source": [
+    "**Exercise:** So far we have considered two models of a falling penny:\n",
+    "\n",
+    "* If we ignore air resistance, the penny falls with constant acceleration, and we can compute the time to reach the sidewalk and the velocity of the penny when it gets there.\n",
+    "\n",
+    "* If we take air resistance into account, and drop the penny at its terminal velocity, it falls with constant velocity. \n",
+    "\n",
+    "Now let's consider a third model that includes elements of the first two: let's assume that the acceleration of the penny is `a` until the penny reaches $29$ m/s, and then $0$ m/s afterwards.  What is the total time for the penny to fall $381$ m?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "economic-bahrain",
+   "metadata": {},
+   "source": [
+    "You can break this question into three parts:\n",
+    "\n",
+    "1. How long would the penny take to reach $29$ m/s with constant acceleration `a`.\n",
+    "2. How far would it fall during that time?\n",
+    "3. How long would it take to fall the remaining distance with constant velocity $29$ m/s?\n",
+    "\n",
+    "Suggestion: Assign each intermediate result to a variable with a meaningful name.  And assign units to all quantities!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "packed-salem",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 9.8 * meter / second**2\n",
+    "h = 381 * meter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "understanding-point",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "v_terminal = 29 * meter / second "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "baking-cooling",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time to reach terminal velocity 2.9591836734693877 second\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "t1 = v_terminal / a\n",
+    "print('Time to reach terminal velocity', t1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "instructional-parliament",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Height fallen in t1 42.90816326530612 meter\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "h1 = a * t1**2 / 2\n",
+    "print('Height fallen in t1', h1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "breeding-sport",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time to fall remaining distance 11.658339197748065 second\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "t2 = (h - h1) / v_terminal\n",
+    "print('Time to fall remaining distance', t2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "bulgarian-excuse",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total falling time 14.617522871217453 second\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "t_total = t1 + t2\n",
+    "print('Total falling time', t_total)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "regional-monitoring",
+   "metadata": {},
+   "source": [
+    "**Exercise:** When I was in high school, the pitcher on the baseball team claimed that, when he threw a fastball, he was throwing the ball down; that is, the ball was leaving his hand at a downward angle.\n",
+    "I was skeptical; watching from the side, I thought the ball was leaving his hand at an upward angle.\n",
+    "\n",
+    "Can you think of a simple model you could use to settle the argument?  What factors would you include and what could you ignore?  What quantities would you have to look up or estimate?\n",
+    "\n",
+    "I suggest you convert all quantities to [SI units](https://en.wikipedia.org/wiki/International_System_of_Units): meters and seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "statutory-music",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# I suggest the following model: \n",
+    "\n",
+    "# 1. Let's ignore the motion of the ball toward home plate and\n",
+    "#    think about how far the ball would drop while it's in flight.\n",
+    "\n",
+    "# 2. Let's ignore air resistance.  Since we are only thinking \n",
+    "#    about the relatively slow motion in the vertical direction, \n",
+    "#    this is probably a good assumption.\n",
+    "\n",
+    "# 3. Let's also ignore the effect of spin.  This is probably a\n",
+    "#    less good assumption.\n",
+    "\n",
+    "# The distance from the pitcher's mound to home plate is about 60\n",
+    "# feet, but the point where the ball is released is a bit closer.\n",
+    "\n",
+    "# An average pitcher in high school might be able to throw a ball\n",
+    "# at 80 mph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "antique-discussion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "v = (80 * mile / hour).to(meter/second)\n",
+    "x = (60 * foot).to(meter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "directed-manchester",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.5113636363636364 second"
+      ],
+      "text/latex": [
+       "$0.5113636363636364\\ \\mathrm{second}$"
+      ],
+      "text/plain": [
+       "0.5113636363636364 <Unit('second')>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "t = x / v\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "ideal-version",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "1.2813145661157026 meter"
+      ],
+      "text/latex": [
+       "$1.2813145661157026\\ \\mathrm{meter}$"
+      ],
+      "text/plain": [
+       "1.2813145661157026 <Unit('meter')>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "a = 9.8 * meter / second**2\n",
+    "h = a * t**2 / 2\n",
+    "h"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "contrary-technique",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# In the time it takes the ball to reach home plate, it drops\n",
+    "# about 1.3 m.  If the release point is at 2 m, which is plausible,\n",
+    "# it would cross the plate at 0.7 m, which is in the strike zone.\n",
+    "\n",
+    "# So I could be wrong -- it is plausible that the ball leaves\n",
+    "# the pitcher's hand at a downward angle, at least for some pitches."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "upset-harvey",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose I run a 10K race in 44:52.  What is my average page in minutes per mile?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "alert-negative",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mile = units.mile\n",
+    "kilometer = units.kilometer\n",
+    "minute = units.minute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "correct-cuisine",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "2692.0 second"
+      ],
+      "text/latex": [
+       "$2692.0\\ \\mathrm{second}$"
+      ],
+      "text/plain": [
+       "2692.0 <Unit('second')>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "t = 52 * second + 44 * minute\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "reflected-accuracy",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.003714710252600297 kilometer/second"
+      ],
+      "text/latex": [
+       "$0.003714710252600297\\ \\frac{\\mathrm{kilometer}}{\\mathrm{second}}$"
+      ],
+      "text/plain": [
+       "0.003714710252600297 <Unit('kilometer / second')>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "v = 10 * kilometer / t\n",
+    "v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "inner-river",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "7.220590079999998 minute/mile"
+      ],
+      "text/latex": [
+       "$7.220590079999998\\ \\frac{\\mathrm{minute}}{\\mathrm{mile}}$"
+      ],
+      "text/plain": [
+       "7.220590079999998 <Unit('minute / mile')>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "pace = (1 / v).to(minute / mile)\n",
+    "pace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "abstract-musical",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "7.0 minute/mile"
+      ],
+      "text/latex": [
+       "$7.0\\ \\frac{\\mathrm{minute}}{\\mathrm{mile}}$"
+      ],
+      "text/plain": [
+       "7.0 <Unit('minute / mile')>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# To convert to minutes and seconds, we can use np.round.\n",
+    "# But we haven't covered that yet.\n",
+    "\n",
+    "from numpy import round\n",
+    "\n",
+    "round(pace)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "radical-junior",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "13.235404799999895 second/mile"
+      ],
+      "text/latex": [
+       "$13.235404799999895\\ \\frac{\\mathrm{second}}{\\mathrm{mile}}$"
+      ],
+      "text/plain": [
+       "13.235404799999895 <Unit('second / mile')>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "remainder = pace - round(pace)\n",
+    "remainder.to(second/mile)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "banned-surname",
+   "metadata": {},
+   "source": [
+    "## Jupyter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "american-rainbow",
+   "metadata": {},
+   "source": [
+    "### Adding and removing cells\n",
+    "\n",
+    "You can add and remove cells from a notebook using the buttons in the toolbar and the items in the menu, both of which you should see at the top of this notebook.\n",
+    "\n",
+    "Try the following exercises:\n",
+    "\n",
+    "1.  From the Insert menu select \"Insert cell below\" to add a cell below this one.  By default, you get a code cell, as you can see in the pulldown menu that says \"Code\".\n",
+    "\n",
+    "2.  In the new cell, add a print statement like `print('Hello')`, and run it.\n",
+    "\n",
+    "3.  Add another cell, select the new cell, and then click on the pulldown menu that says \"Code\" and select \"Markdown\".  This makes the new cell a text cell.\n",
+    "\n",
+    "4.  In the new cell, type some text, and then run it.\n",
+    "\n",
+    "5.  Use the arrow buttons in the toolbar to move cells up and down.\n",
+    "\n",
+    "6.  Use the cut, copy, and paste buttons to delete, add, and move cells.\n",
+    "\n",
+    "7.  As you make changes, Jupyter saves your notebook automatically, but if you want to make sure, you can press the save button, which looks like a floppy disk from the 1990s.\n",
+    "\n",
+    "8.  Finally, when you are done with a notebook, select \"Close and Halt\" from the File menu."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "radical-kenya",
+   "metadata": {},
+   "source": [
+    "### Restart and run all\n",
+    "\n",
+    "When you change the contents of a cell, you have to run it again for those changes to have an effect.  If you forget to do that, the results can be confusing, because the code you are looking at is not the code you ran.\n",
+    "\n",
+    "If you ever lose track of which cells have run, and in what order, you should go to the Kernel menu and select \"Restart & Run All\".  Restarting the kernel means that all of your variables get deleted, and running all the cells means all of your code will run again, in the right order.\n",
+    "\n",
+    "**Exercise:**  Select \"Restart & Run All\" now and confirm that it does what you want."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "growing-jumping",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/chap02.ipynb b/jupyter/chap02.ipynb
new file mode 100644
index 00000000..90a93b05
--- /dev/null
+++ b/jupyter/chap02.ipynb
@@ -0,0 +1,1744 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "british-hampshire",
+   "metadata": {},
+   "source": [
+    "# Chapter 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-target",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "confirmed-melbourne",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "    \n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "parliamentary-bailey",
+   "metadata": {},
+   "source": [
+    "# Bike share\n",
+    "\n",
+    "This chapter presents a simple model of a bike share system and\n",
+    "demonstrates the features of Python we'll use to develop simulations of\n",
+    "real-world systems.\n",
+    "\n",
+    "Along the way, we'll make decisions about how to model the system. In\n",
+    "the next chapter we'll review these decisions and gradually improve the\n",
+    "model.\n",
+    "\n",
+    "## Modeling\n",
+    "\n",
+    "Imagine a bike share system for students traveling between Olin College\n",
+    "and Wellesley College, which are about 3 miles apart in eastern\n",
+    "Massachusetts.\n",
+    "\n",
+    "Suppose the system contains 12 bikes and two bike racks, one at Olin and\n",
+    "one at Wellesley, each with the capacity to hold 12 bikes.\n",
+    "\n",
+    "As students arrive, check out a bike, and ride to the other campus, the\n",
+    "number of bikes in each location changes. In the simulation, we'll need\n",
+    "to keep track of where the bikes are. To do that, we'll use a function called `State`, which is defined in the ModSim library.\n",
+    "We can import it like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "after-publisher",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import State"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "encouraging-input",
+   "metadata": {},
+   "source": [
+    "Now we can call it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "organic-consequence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=10, wellesley=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "varied-croatia",
+   "metadata": {},
+   "source": [
+    "The expressions in parentheses are **keyword arguments**.\n",
+    "They create two variables, `olin` and `wellesley`, and give them values.\n",
+    "\n",
+    "Then we call the `State` function.\n",
+    "The result is a `State` object, which is a collection of **state variables**.\n",
+    "\n",
+    "In this example, the state variables represent the number of\n",
+    "bikes at each location. The initial values are 10 and 2, indicating that there are 10 bikes at Olin and 2 at Wellesley. \n",
+    "\n",
+    "The `State` object is assigned to a new variable named `bikeshare`.\n",
+    "We can read the variables inside a `State` object using the **dot\n",
+    "operator**, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "above-woman",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare.olin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unnecessary-jewelry",
+   "metadata": {},
+   "source": [
+    "And this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "sacred-hours",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare.wellesley"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "organizational-husband",
+   "metadata": {},
+   "source": [
+    "Or, to display the state variables and their values, you can just type the name of the object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "primary-carrier",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "namespace(olin=10, wellesley=2)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "american-worship",
+   "metadata": {},
+   "source": [
+    "These values make up the **state** of the system.\n",
+    "We can update the state by assigning new values to the variables. \n",
+    "For example, if a student moves a bike from Olin to Wellesley, we can figure out the new values and assign them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "beautiful-priority",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare.olin = 9\n",
+    "bikeshare.wellesley = 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adjusted-baghdad",
+   "metadata": {},
+   "source": [
+    "Or we can use **update operators**, `-=` and `+=`, to subtract 1 from\n",
+    "`olin` and add 1 to `wellesley`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "another-boating",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare.olin -= 1\n",
+    "bikeshare.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "finite-taste",
+   "metadata": {},
+   "source": [
+    "The result is the same either way."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fiscal-addition",
+   "metadata": {},
+   "source": [
+    "## Defining functions\n",
+    "\n",
+    "So far we have used functions defined in NumPy and ModSim. Now we're going to define our own function.\n",
+    "\n",
+    "When you are developing code in Jupyter, it is often efficient to write a few lines of code, test them to confirm they do what you intend, and then use them to define a new function. For example, these lines move a bike from Olin to Wellesley:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "miniature-milton",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare.olin -= 1\n",
+    "bikeshare.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "imperial-account",
+   "metadata": {},
+   "source": [
+    "Rather than repeat them every time a bike moves, we can define a new\n",
+    "function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "peaceful-scroll",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley():\n",
+    "    bikeshare.olin -= 1\n",
+    "    bikeshare.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "illegal-newfoundland",
+   "metadata": {},
+   "source": [
+    "`def` is a special word in Python that indicates we are defining a new\n",
+    "function. The name of the function is `bike_to_wellesley`. The empty\n",
+    "parentheses indicate that this function requires no additional\n",
+    "information when it runs. The colon indicates the beginning of an\n",
+    "indented **code block**.\n",
+    "\n",
+    "The next two lines are the **body** of the function. They have to be\n",
+    "indented; by convention, the indentation is 4 spaces.\n",
+    "\n",
+    "When you define a function, it has no immediate effect. The body of the\n",
+    "function doesn't run until you **call** the function. Here's how to call\n",
+    "this function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cordless-portrait",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bike_to_wellesley()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "potential-fourth",
+   "metadata": {},
+   "source": [
+    "When you call the function, it runs the statements in the body, which\n",
+    "update the variables of the `bikeshare` object; you can check by\n",
+    "displaying the new state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "fabulous-hopkins",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "namespace(olin=6, wellesley=6)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "coated-pathology",
+   "metadata": {},
+   "source": [
+    "When you call a function, you have to include the parentheses. If you\n",
+    "leave them out, you get this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "alternate-province",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.bike_to_wellesley()>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bike_to_wellesley"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "living-national",
+   "metadata": {},
+   "source": [
+    "This result indicates that `bike_to_wellesley` is a function. You don't\n",
+    "have to know what `__main__` means, but if you see something like this,\n",
+    "it probably means that you looked up a function but you didn't actually\n",
+    "call it. So don't forget the parentheses."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "impossible-scheme",
+   "metadata": {},
+   "source": [
+    "## Print statements\n",
+    "\n",
+    "As you write more complicated programs, it is easy to lose track of what\n",
+    "is going on. One of the most useful tools for debugging is the **print\n",
+    "statement**, which displays text in the Jupyter notebook.\n",
+    "\n",
+    "Normally when Jupyter runs the code in a cell, it displays the value of\n",
+    "the last line of code. For example, if you run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "functional-handbook",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare.olin\n",
+    "bikeshare.wellesley"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "naval-architect",
+   "metadata": {},
+   "source": [
+    "Jupyter runs both lines, but it only displays the value of the\n",
+    "second. If you want to display more than one value, you can use\n",
+    "print statements:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "fleet-marathon",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6\n",
+      "6\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(bikeshare.olin)\n",
+    "print(bikeshare.wellesley)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "literary-integrity",
+   "metadata": {},
+   "source": [
+    "When you call the `print` function, you can put a variable name in\n",
+    "parentheses, as in the previous example, or you can provide a sequence\n",
+    "of variables separated by commas, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "accessory-porcelain",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(bikeshare.olin, bikeshare.wellesley)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sustained-dallas",
+   "metadata": {},
+   "source": [
+    "Python looks up the values of the variables and displays them; in this\n",
+    "example, it displays two values on the same line, with a space between\n",
+    "them.\n",
+    "\n",
+    "Print statements are useful for debugging functions. For example, we can\n",
+    "add a print statement to `move_bike`, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "stone-oliver",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley():\n",
+    "    print('Moving a bike to Wellesley')\n",
+    "    bikeshare.olin -= 1\n",
+    "    bikeshare.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pharmaceutical-combat",
+   "metadata": {},
+   "source": [
+    "Each time we call this version of the function, it displays a message,\n",
+    "which can help us keep track of what the program is doing.\n",
+    "The message in this example is a **string**, which is a sequence of\n",
+    "letters and other symbols in quotes.\n",
+    "\n",
+    "Just like `bike_to_wellesley`, we can define a function that moves a\n",
+    "bike from Wellesley to Olin:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "loose-arlington",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_olin():\n",
+    "    print('Moving a bike to Olin')\n",
+    "    bikeshare.wellesley -= 1\n",
+    "    bikeshare.olin += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prostate-launch",
+   "metadata": {},
+   "source": [
+    "And call it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "compatible-transcript",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moving a bike to Olin\n"
+     ]
+    }
+   ],
+   "source": [
+    "bike_to_olin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "clean-bleeding",
+   "metadata": {},
+   "source": [
+    "One benefit of defining functions is that you avoid repeating chunks of\n",
+    "code, which makes programs smaller. Another benefit is that the name you\n",
+    "give the function documents what it does, which makes programs more\n",
+    "readable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prescribed-observer",
+   "metadata": {},
+   "source": [
+    "## If statements\n",
+    "\n",
+    "The ModSim library provides a function called `flip`, which we can import like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "elegant-doubt",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import flip"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "steady-arrow",
+   "metadata": {},
+   "source": [
+    "When you call it, you provide a probability between 0 and 1, like `0.7`, as an example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "improving-simulation",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flip(0.7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "brown-header",
+   "metadata": {},
+   "source": [
+    "The result is one of two values: `True` with probability 0.7 or `False`\n",
+    "with probability 0.3. If you run `flip` like this 100 times, you should\n",
+    "get `True` about 70 times and `False` about 30 times. But the results\n",
+    "are random, so they might differ from these expectations.\n",
+    "\n",
+    "`True` and `False` are special values defined by Python. Note that they\n",
+    "are not strings. There is a difference between `True`, which is a\n",
+    "special value, and `'True'`, which is a string.\n",
+    "\n",
+    "`True` and `False` are called **boolean** values because they are\n",
+    "related to Boolean algebra (<https://modsimpy.com/boolean>).\n",
+    "\n",
+    "We can use boolean values to control the behavior of the program, using\n",
+    "an **if statement**:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "organizational-portable",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if flip(0.5):\n",
+    "    print('heads')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "covered-freedom",
+   "metadata": {},
+   "source": [
+    "If the result from `flip` is `True`, the program displays the string\n",
+    "`'heads'`. Otherwise it does nothing.\n",
+    "\n",
+    "The syntax for `if` statements is similar to the syntax for\n",
+    "function definitions: the first line has to end with a colon, and the\n",
+    "lines inside the `if` statement have to be indented.\n",
+    "\n",
+    "Optionally, you can add an **else clause** to indicate what should\n",
+    "happen if the result is `False`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "auburn-sphere",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tails\n"
+     ]
+    }
+   ],
+   "source": [
+    "if flip(0.5):\n",
+    "    print('heads')\n",
+    "else:\n",
+    "    print('tails')    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "collected-venice",
+   "metadata": {},
+   "source": [
+    "Now we can use `flip` to simulate the arrival of students who want to\n",
+    "borrow a bike. Suppose students arrive at the Olin station every 2\n",
+    "minutes, on average. In that case, the chance of an arrival during any\n",
+    "one-minute period is 50%, and we can simulate it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "incident-somalia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if flip(0.5):\n",
+    "    bike_to_wellesley()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "twenty-brand",
+   "metadata": {},
+   "source": [
+    "If students arrive at the Wellesley station every 3 minutes, on average,\n",
+    "the chance of an arrival during any one-minute period is 33%, and we can\n",
+    "simulate it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "variable-drain",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if flip(0.33):\n",
+    "    bike_to_olin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "loved-kentucky",
+   "metadata": {},
+   "source": [
+    "We can combine these snippets into a function that simulates a **time\n",
+    "step**, which is an interval of time, in this case one minute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "smoking-spectrum",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def step():\n",
+    "    if flip(0.5):\n",
+    "        bike_to_wellesley()\n",
+    "    \n",
+    "    if flip(0.33):\n",
+    "        bike_to_olin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "educational-color",
+   "metadata": {},
+   "source": [
+    "Then we can simulate a time step like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "offensive-meter",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Olin\n"
+     ]
+    }
+   ],
+   "source": [
+    "step()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bridal-stage",
+   "metadata": {},
+   "source": [
+    "Even though there are no values in parentheses, we have to include them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adult-window",
+   "metadata": {},
+   "source": [
+    "## Parameters\n",
+    "\n",
+    "The previous version of `step` is fine if the arrival probabilities\n",
+    "never change, but in reality, these probabilities vary over time.\n",
+    "\n",
+    "So instead of putting the constant values 0.5 and 0.33 in `step` we can replace them with **parameters**. Parameters are variables whose values are set when a function is called.\n",
+    "\n",
+    "Here's a version of `step` that takes two parameters, `p1` and `p2`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "funky-macintosh",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def step(p1, p2):\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley()\n",
+    "    \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "swiss-distributor",
+   "metadata": {},
+   "source": [
+    "The values of `p1` and `p2` are not set inside this function; instead,\n",
+    "they are provided when the function is called, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "pleased-vision",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moving a bike to Wellesley\n"
+     ]
+    }
+   ],
+   "source": [
+    "step(0.5, 0.33)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "located-inspector",
+   "metadata": {},
+   "source": [
+    "The values you provide when you call the function are called\n",
+    "**arguments**. The arguments, `0.5` and `0.33` in this example, get\n",
+    "assigned to the parameters, `p1` and `p2`, in order. So running this\n",
+    "function has the same effect as:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "known-edgar",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moving a bike to Wellesley\n"
+     ]
+    }
+   ],
+   "source": [
+    "p1 = 0.5\n",
+    "p2 = 0.33\n",
+    "\n",
+    "if flip(p1):\n",
+    "    bike_to_wellesley()\n",
+    "    \n",
+    "if flip(p2):\n",
+    "    bike_to_olin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "periodic-heath",
+   "metadata": {},
+   "source": [
+    "The advantage of using parameters is that you can call the same function many times, providing different arguments each time.\n",
+    "\n",
+    "Adding parameters to a function is called **generalization**, because it makes the function more general, that is, less specialized."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "indonesian-shelter",
+   "metadata": {},
+   "source": [
+    "## For loops\n",
+    "\n",
+    "At some point you will get sick of running cells over and over.\n",
+    "Fortunately, there is an easy way to repeat a chunk of code, the **for\n",
+    "loop**. Here's an example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "welcome-crossing",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "Moving a bike to Wellesley\n",
+      "2\n",
+      "Moving a bike to Wellesley\n",
+      "3\n",
+      "Moving a bike to Wellesley\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(1, 4):\n",
+    "    print(i)\n",
+    "    bike_to_wellesley()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "motivated-study",
+   "metadata": {},
+   "source": [
+    "The syntax here should look familiar; the first line ends with a\n",
+    "colon, and the lines inside the `for` loop are indented. The other\n",
+    "elements of the loop are:\n",
+    "\n",
+    "-   The words `for` and `in` are special words we have to use in a for\n",
+    "    loop.\n",
+    "\n",
+    "-   `range` is a Python function we're using here to control the number of times the loop runs.\n",
+    "\n",
+    "-   `i` is a **loop variable** that gets created when the for loop runs.\n",
+    "\n",
+    "When this loop runs, it runs the statements inside the loop three times.  The first time, the value of `i` is `1`; the second time, it is `2`; the third time, it is `3`.\n",
+    "\n",
+    "Each time through the loop, it prints the value of `i` and moves one bike Olin to Wellesley.\n",
+    "\n",
+    "It might seem strange that the loop stops when `i` is `3`, but that's how `range` works: it includes the first argument, `1`, but not the second, `4`.\n",
+    "\n",
+    "Now, since we have moved a number of bikes around, let's start again with a new `State` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "passing-notification",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=10, wellesley=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "linear-cleaner",
+   "metadata": {},
+   "source": [
+    "We'll use this `bikeshare` in the next section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "successful-rough",
+   "metadata": {},
+   "source": [
+    "## TimeSeries\n",
+    "\n",
+    "When we run a simulation, we often want to save the results for later analysis. The ModSim library provides a `TimeSeries` object for this purpose. A `TimeSeries` contains a sequence of time stamps and a\n",
+    "corresponding sequence of values. In this example, the time stamps are\n",
+    "integers representing minutes, and the values are the number of bikes at one location.\n",
+    "\n",
+    "We can create a new, empty `TimeSeries` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "approved-bruce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import TimeSeries\n",
+    "\n",
+    "results = TimeSeries()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "extensive-uniform",
+   "metadata": {},
+   "source": [
+    "And we can add a value to a `TimeSeries` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "affected-syntax",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results[0] = bikeshare.olin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "rational-assault",
+   "metadata": {},
+   "source": [
+    "The number in brackets is the time stamp, also called a **label**.\n",
+    "\n",
+    "We can use a `TimeSeries` inside a for loop to store the results of the\n",
+    "simulation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "optical-ordinary",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "Moving a bike to Wellesley\n",
+      "2\n",
+      "3\n",
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(1, 5):\n",
+    "    print(i)\n",
+    "    step(0.3, 0.2)\n",
+    "    results[i] = bikeshare.olin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dutch-sheep",
+   "metadata": {},
+   "source": [
+    "Each time through the loop, we print the value of `i` and call `step`, which updates `bikeshare`.\n",
+    "Then we store the number of bikes at Olin in `results`. \n",
+    "We use the loop variable, `i`, as the time stamp.\n",
+    "\n",
+    "When the loop exits, `results` contains 5 time stamps, from 0 through\n",
+    "4, and the number of bikes at Olin at the end of each time step.\n",
+    "\n",
+    "We can display the `TimeSeries` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "allied-hobby",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    10\n",
+       "1     9\n",
+       "2     9\n",
+       "3     9\n",
+       "4     9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "found-indonesia",
+   "metadata": {},
+   "source": [
+    "The left column displays the timestamps; the right column displays the values (which might be negative, depending on the state of the system).\n",
+    "At the bottom, `dtype` is the type of the data in the `TimeSeries`; you can ignore this for now.\n",
+    "\n",
+    "ModSim provides a function called `display` that displays the `TimeSeries` as a table:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "compound-globe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   values\n",
+       "0      10\n",
+       "1       9\n",
+       "2       9\n",
+       "3       9\n",
+       "4       9"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import display\n",
+    "\n",
+    "display(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dated-canyon",
+   "metadata": {},
+   "source": [
+    "You don't have to use `display`, but I think it looks better."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "senior-mexico",
+   "metadata": {},
+   "source": [
+    "## Plotting\n",
+    "\n",
+    "ModSim provides a function called `plot` we can use to plot\n",
+    "the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "persistent-replication",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import plot\n",
+    "\n",
+    "plot(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mexican-recipe",
+   "metadata": {},
+   "source": [
+    "The legend for this figure says \"None\" because we have not provided a label.  Here's how we can do that:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "hollow-george",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(results, label='Olin')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "romantic-measure",
+   "metadata": {},
+   "source": [
+    "Whenever you make a figure, you should label the axes. The ModSim\n",
+    "library provides `decorate`, which we can use to labels the axes and give the figure a title:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "occupied-claim",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "plot(results, label='Olin')\n",
+    "\n",
+    "decorate(title='Olin-Wellesley Bikeshare',\n",
+    "         xlabel='Time step (min)', \n",
+    "         ylabel='Number of bikes')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "practical-fruit",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This chapter introduces the tools we need to run simulations, record the results, and plot them.\n",
+    "\n",
+    "We used a `State` object to represent the state of the system.\n",
+    "Then we used the `flip` function and an `if` statement to simulate a single time step.\n",
+    "\n",
+    "We used `for` loop to simulate a series of steps, and a `TimeSeries` to record the results.\n",
+    "\n",
+    "Finally, we used `plot` and `decorate` to plot the results.\n",
+    "\n",
+    "In the next chapter, we will extend this simulation to make it a little more realistic."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bronze-adventure",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "headed-invite",
+   "metadata": {},
+   "source": [
+    "**Exercise:** What happens if you spell the name of a state variable wrong?  Edit the following cell, change the spelling of `wellesley`, and run it.\n",
+    "\n",
+    "The error message uses the word \"attribute\", which is another name for what we are calling a state variable. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "compressed-utilization",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare = State(olin=10, wellesley=2)\n",
+    "\n",
+    "bikeshare.wellesley"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cheap-panama",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Make a `State` object with a third state variable, called `babson`, with initial value 0, and display the state of the system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "elect-makeup",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>babson</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "namespace(olin=10, wellesley=2, babson=0)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "bikeshare = State(olin=10, wellesley=2, babson=0)\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prescribed-white",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Wrap the code in the chapter in a function named `run_simulation` that takes three parameters, named `p1`, `p2`, and `num_steps`.\n",
+    "\n",
+    "It should:\n",
+    "\n",
+    "1. Create a `TimeSeries` object to hold the results.\n",
+    "\n",
+    "2. Use a for loop to run `step` the number of times specified by `num_steps`, passing along the specified values of `p1` and `p2`.\n",
+    "\n",
+    "3. After each step, it should save the number of bikes at Olin in the `TimeSeries`.\n",
+    "\n",
+    "4. After the for loop, it should plot the results and\n",
+    "\n",
+    "5. Decorate the axes.\n",
+    "\n",
+    "To test your function:\n",
+    "\n",
+    "1. Create a `State` object with the initial state of the system.\n",
+    "\n",
+    "2. Call `run_simulation` with appropriate parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "strategic-reporter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def run_simulation(p1, p2, num_steps):\n",
+    "    results = TimeSeries()\n",
+    "    results[0] = bikeshare.olin\n",
+    "    \n",
+    "    for i in range(1, num_steps):\n",
+    "        step(p1, p2)\n",
+    "        results[i] = bikeshare.olin\n",
+    "        \n",
+    "    plot(results, label='Olin')\n",
+    "    decorate(title='Olin-Wellesley Bikeshare',\n",
+    "             xlabel='Time step (min)', \n",
+    "             ylabel='Number of bikes')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "adjustable-zoning",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moving a bike to Olin\n",
+      "Moving a bike to Olin\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Olin\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Olin\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Olin\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Olin\n",
+      "Moving a bike to Olin\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Olin\n",
+      "Moving a bike to Olin\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Olin\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "bikeshare = State(olin=10, wellesley=2)\n",
+    "run_simulation(0.3, 0.2, 60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eleven-listening",
+   "metadata": {},
+   "source": [
+    "## Opening the hood\n",
+    "\n",
+    "This section contains additional information about the functions we've used an pointers to their documentation.\n",
+    "\n",
+    "You don't need to know anything in these sections, so if you are already feeling overwhelmed, you might want to skip them.  But if you are curious, read on."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "liable-reliance",
+   "metadata": {},
+   "source": [
+    "The `State` object defined in the ModSim library, is based on the `SimpleNamespace` object defined in a standard Python library called `types`; the documentation is at <https://docs.python.org/3.7/library/types.html#types.SimpleNamespace>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "naked-department",
+   "metadata": {},
+   "source": [
+    "The `TimeSeries` object is based on the `Series` object defined by a library called Pandas.\n",
+    "The documentation is at <https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html>.\n",
+    "\n",
+    "`display` works by creating another Pandas object, called a `DataFrame`, which can be displayed as a table.\n",
+    "We'll use `DataFrame` objects in future chapters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "derived-province",
+   "metadata": {},
+   "source": [
+    "`Series` objects provide their own `plot` function, but the syntax is different from the other functions we've used, so ModSim provides a function called `plot` just to make it consistent.\n",
+    "\n",
+    "`decorate` is based on Matplotlib, which is most widely-used plotting function for Python.  Matplotlib provides separate functions for `title`, `xlabel`, and `ylabel`.\n",
+    "`decorate` makes them a little easier to use."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "southeast-advertiser",
+   "metadata": {},
+   "source": [
+    "The `flip` function uses NumPy's `random` function to generate a random number between 0 and 1, then returns `True` or `False` with the given probability.\n",
+    "\n",
+    "You can get the source code for `flip` by running the following cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "tested-blond",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "def flip(p=0.5):\n",
+      "    \"\"\"Flips a coin with the given probability.\n",
+      "\n",
+      "    p: float 0-1\n",
+      "\n",
+      "    returns: boolean (True or False)\n",
+      "    \"\"\"\n",
+      "    return np.random.random() < p\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from modsim import source_code\n",
+    "\n",
+    "source_code(flip)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "rational-taiwan",
+   "metadata": {},
+   "source": [
+    "You might not understand everything in this function yet, but you will."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "significant-defense",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 7ac719da92c6d9a88c61eb4f10d21cdecbfae0b8 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 28 Jan 2021 10:49:23 -0500
Subject: [PATCH 018/144] Revising chapters

---
 jupyter/chap01.ipynb |  202 +++---
 jupyter/chap02.ipynb |  443 +++++++------
 jupyter/chap03.ipynb | 1077 +++++++++++++++++++++++++++++++
 jupyter/chap04.ipynb | 1208 +++++++++++++++++++++++++++++++++++
 jupyter/chap05.ipynb | 1450 ++++++++++++++++++++++++++++++++++++++++++
 5 files changed, 4049 insertions(+), 331 deletions(-)
 create mode 100644 jupyter/chap03.ipynb
 create mode 100644 jupyter/chap04.ipynb
 create mode 100644 jupyter/chap05.ipynb

diff --git a/jupyter/chap01.ipynb b/jupyter/chap01.ipynb
index a00e67d7..c1d987fc 100644
--- a/jupyter/chap01.ipynb
+++ b/jupyter/chap01.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "legislative-somalia",
+   "id": "uniform-persian",
    "metadata": {},
    "source": [
     "# Chapter 1"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "official-adrian",
+   "id": "biblical-enhancement",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "intermediate-monte",
+   "id": "confirmed-budapest",
    "metadata": {},
    "source": [
     "## Jupyter\n",
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "natural-locking",
+   "id": "danish-scope",
    "metadata": {},
    "source": [
     "To run a cell, hold down SHIFT and press ENTER.  \n",
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "private-paste",
+   "id": "asian-america",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "demographic-athletics",
+   "id": "copyrighted-desperate",
    "metadata": {},
    "source": [
     "If the previous cell runs without producing any error messages, you are all set.\n"
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "worthy-singer",
+   "id": "hourly-financing",
    "metadata": {},
    "source": [
     "## Modeling\n",
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fantastic-transition",
+   "id": "structured-receiver",
    "metadata": {},
    "source": [
     "Starting in the lower left, the **system** is something in the real\n",
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "original-startup",
+   "id": "center-accommodation",
    "metadata": {},
    "source": [
     "For any physical system, there are many possible models, each one\n",
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "talented-plastic",
+   "id": "confirmed-highlight",
    "metadata": {},
    "source": [
     "As an example, suppose someone asks you why the orbit of the Earth is\n",
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "tamil-conducting",
+   "id": "stretch-geneva",
    "metadata": {},
    "source": [
     "Choosing the best model depends on what the model is for. It is usually\n",
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "frequent-initial",
+   "id": "criminal-lunch",
    "metadata": {},
    "source": [
     "## The falling penny myth\n",
@@ -221,7 +221,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "covered-yacht",
+   "id": "documentary-diagnosis",
    "metadata": {},
    "source": [
     "Of course, these results are not exact because the model is based on\n",
@@ -239,7 +239,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "basic-cincinnati",
+   "id": "recent-appliance",
    "metadata": {},
    "source": [
     "The statistician George Box famously said \"All models are wrong, but\n",
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "accessory-therapist",
+   "id": "brief-zoning",
    "metadata": {},
    "source": [
     "## Computation\n",
@@ -265,7 +265,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "going-steps",
+   "id": "eleven-marine",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -274,7 +274,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "shared-acquisition",
+   "id": "upset-myanmar",
    "metadata": {},
    "source": [
     "A **variable** is a name that corresponds to a value. In this example, the name is `a` and the value is the number `9.8`.\n",
@@ -285,7 +285,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "comparative-investigation",
+   "id": "following-launch",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -294,7 +294,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "disturbed-village",
+   "id": "greek-heritage",
    "metadata": {},
    "source": [
     "Now we can compute the velocity of the penny after `t` seconds."
@@ -303,7 +303,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "featured-procurement",
+   "id": "mature-duration",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,7 +312,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "convinced-differential",
+   "id": "qualified-diabetes",
    "metadata": {},
    "source": [
     "Python uses the symbol `*` for multiplication.  The other arithmetic operators are `+` for addition, `-` for subtraction, `/` for division, and `**` for exponentiation.\n",
@@ -323,7 +323,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "joined-solomon",
+   "id": "considered-inclusion",
    "metadata": {},
    "outputs": [
     {
@@ -343,7 +343,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "excess-valuation",
+   "id": "northern-saturday",
    "metadata": {},
    "source": [
     "After $3.4$ s, the velocity of the penny is about $33$ m/s (ignoring air resistance).  Now let's see how far it would travel during that time:"
@@ -352,7 +352,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "norwegian-vitamin",
+   "id": "valued-electricity",
    "metadata": {},
    "outputs": [
     {
@@ -373,7 +373,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "discrete-stable",
+   "id": "yellow-business",
    "metadata": {},
    "source": [
     "It would travel about $56$ m.  Now, going in the other direction, let's compute the time it takes to fall 381 m, the height of the Empire State Building."
@@ -382,7 +382,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "solar-contest",
+   "id": "closed-month",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -391,7 +391,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "welcome-article",
+   "id": "fuzzy-lease",
    "metadata": {},
    "source": [
     "For this computation, we need the square root function, which is provided by a library called NumPy.\n",
@@ -401,7 +401,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "irish-deposit",
+   "id": "nuclear-clone",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -410,7 +410,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "residential-victorian",
+   "id": "unlimited-swiss",
    "metadata": {},
    "source": [
     "Now we can use it like this:"
@@ -419,7 +419,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "blond-graham",
+   "id": "quarterly-nightmare",
    "metadata": {},
    "outputs": [
     {
@@ -440,7 +440,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adjacent-reading",
+   "id": "velvet-oklahoma",
    "metadata": {},
    "source": [
     "With no air resistance, it would take about $8.8$ s for the penny to reach the sidewalk."
@@ -449,7 +449,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "early-qualification",
+   "id": "quality-external",
    "metadata": {},
    "outputs": [
     {
@@ -470,7 +470,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hungarian-tongue",
+   "id": "human-phase",
    "metadata": {},
    "source": [
     "And its velocity on impact would be about $86$ m/s.\n",
@@ -488,7 +488,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "gothic-muscle",
+   "id": "consecutive-sleeve",
    "metadata": {},
    "source": [
     "## Computation with units\n",
@@ -509,7 +509,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "satellite-consultancy",
+   "id": "based-belief",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -518,7 +518,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "conscious-motor",
+   "id": "unlike-opera",
    "metadata": {},
    "source": [
     "Now we can create variables named `meter` and `second`."
@@ -527,7 +527,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "contrary-robin",
+   "id": "russian-popularity",
    "metadata": {},
    "outputs": [
     {
@@ -555,7 +555,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "relative-aircraft",
+   "id": "endless-paint",
    "metadata": {},
    "outputs": [
     {
@@ -582,7 +582,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "identified-saskatchewan",
+   "id": "spiritual-scenario",
    "metadata": {},
    "source": [
     "To find out what other units are defined, type `units.` (including the period) in the next cell and then press TAB.  You should see a pop-up menu with a list of units."
@@ -590,7 +590,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "relevant-musician",
+   "id": "everyday-location",
    "metadata": {},
    "source": [
     "We can use `meter` and `second` to create a variable named `a` and give it the value of acceleration due to gravity. "
@@ -599,7 +599,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "patent-logistics",
+   "id": "earlier-bandwidth",
    "metadata": {},
    "outputs": [
     {
@@ -626,7 +626,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "judicial-bradley",
+   "id": "generic-bowling",
    "metadata": {},
    "source": [
     "The result is a **quantity** with two parts, called `magnitude` and `units`, which we can access like this:"
@@ -635,7 +635,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "nearby-ministry",
+   "id": "missing-privilege",
    "metadata": {},
    "outputs": [
     {
@@ -656,7 +656,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "express-adelaide",
+   "id": "fourth-swedish",
    "metadata": {},
    "outputs": [
     {
@@ -682,7 +682,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "assumed-finland",
+   "id": "realistic-techno",
    "metadata": {},
    "source": [
     "Now we can create a quantity that represents $3.4$ s."
@@ -691,7 +691,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "formal-twenty",
+   "id": "apart-france",
    "metadata": {},
    "outputs": [
     {
@@ -718,7 +718,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "strong-patch",
+   "id": "severe-share",
    "metadata": {},
    "source": [
     "And use it to compute the distance a penny would fall after `t` seconds with constant acceleration `a`.  "
@@ -727,7 +727,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "wanted-wells",
+   "id": "alien-scout",
    "metadata": {},
    "outputs": [
     {
@@ -753,7 +753,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "known-relationship",
+   "id": "wicked-indianapolis",
    "metadata": {},
    "source": [
     "Notice that the units of the result are correct.\n",
@@ -763,7 +763,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "certified-sperm",
+   "id": "confidential-costs",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -772,7 +772,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "married-mexico",
+   "id": "hollow-programmer",
    "metadata": {},
    "source": [
     "We can use it to compute the time to reach the sidewalk."
@@ -781,7 +781,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "fossil-syria",
+   "id": "studied-opera",
    "metadata": {},
    "outputs": [
     {
@@ -808,7 +808,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "superior-packet",
+   "id": "seasonal-laser",
    "metadata": {},
    "source": [
     "And the velocity of the penny on impact:"
@@ -817,7 +817,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "oriental-vulnerability",
+   "id": "exterior-greek",
    "metadata": {},
    "outputs": [
     {
@@ -844,7 +844,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "complicated-welcome",
+   "id": "superior-biography",
    "metadata": {},
    "source": [
     "As in the previous section, the result is about $86$, but now it has the correct units, m/s.\n",
@@ -855,7 +855,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "adolescent-layout",
+   "id": "antique-landing",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -866,7 +866,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "extra-rendering",
+   "id": "included-failure",
    "metadata": {},
    "outputs": [
     {
@@ -892,7 +892,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "silver-learning",
+   "id": "progressive-charleston",
    "metadata": {},
    "source": [
     "If you are more familiar with miles per hour, this result might be easier to interpret.\n",
@@ -901,7 +901,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hazardous-input",
+   "id": "continuing-democrat",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -922,7 +922,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "organized-senate",
+   "id": "thousand-equation",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -930,7 +930,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aging-offense",
+   "id": "handmade-zoning",
    "metadata": {},
    "source": [
     "**Exercise** In mathematical notation, we can write an equation like $v = a t$ and it's understood that we are multiplying $a$ and $t$.\n",
@@ -948,7 +948,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "macro-constitution",
+   "id": "postal-marking",
    "metadata": {},
    "outputs": [
     {
@@ -978,7 +978,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "entitled-carrier",
+   "id": "pressing-sugar",
    "metadata": {},
    "source": [
     "**Exercise:** In this chapter we used the `sqrt` function from the NumPy library.  NumPy also provides a variable named `pi` that contains an approximation of the mathematical constant $\\pi$.\n",
@@ -988,7 +988,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "continuous-catalyst",
+   "id": "legal-observer",
    "metadata": {},
    "outputs": [
     {
@@ -1009,7 +1009,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "medieval-milan",
+   "id": "aggregate-mambo",
    "metadata": {},
    "source": [
     "NumPy provides other functions we'll use, including `log`, `exp`, `sin`, and `cos`.\n",
@@ -1023,7 +1023,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "interested-graduate",
+   "id": "pressing-belgium",
    "metadata": {},
    "outputs": [
     {
@@ -1047,7 +1047,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fewer-collector",
+   "id": "unlimited-delivery",
    "metadata": {},
    "source": [
     "**Exercise:** Suppose you bring a 10 foot pole to the top of the Empire State Building and use it to drop the penny from `h` plus 10 feet.\n",
@@ -1060,7 +1060,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "damaged-glenn",
+   "id": "inner-equivalent",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1070,7 +1070,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "baking-maldives",
+   "id": "nuclear-thirty",
    "metadata": {},
    "outputs": [
     {
@@ -1102,7 +1102,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "lovely-acrylic",
+   "id": "available-steering",
    "metadata": {},
    "outputs": [
     {
@@ -1130,7 +1130,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cooperative-bradley",
+   "id": "aggressive-climate",
    "metadata": {},
    "source": [
     "**Exercise**: Why would it be nonsensical to add `a` and `t`? What happens if you try?"
@@ -1139,7 +1139,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "environmental-cover",
+   "id": "documentary-doctrine",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1150,14 +1150,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "hindu-possession",
+   "id": "primary-partner",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "markdown",
-   "id": "continued-pharmacy",
+   "id": "steady-chancellor",
    "metadata": {},
    "source": [
     "In this example, you should get a `DimensionalityError`, which is defined by Pint to indicate that you have violated a rules of dimensional analysis: you cannot add quantities with different dimensions.\n",
@@ -1173,7 +1173,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "angry-artist",
+   "id": "valid-empire",
    "metadata": {},
    "source": [
     "**Exercise:** Suppose instead of dropping the penny, you throw it downward at its terminal velocity, $29$ m/s.  How long would it take to fall $381$ m?"
@@ -1182,7 +1182,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "dressed-harrison",
+   "id": "greenhouse-reason",
    "metadata": {},
    "outputs": [
     {
@@ -1214,7 +1214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "electrical-jersey",
+   "id": "asian-murray",
    "metadata": {},
    "source": [
     "**Exercise:** So far we have considered two models of a falling penny:\n",
@@ -1228,7 +1228,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "economic-bahrain",
+   "id": "detailed-minnesota",
    "metadata": {},
    "source": [
     "You can break this question into three parts:\n",
@@ -1243,7 +1243,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "packed-salem",
+   "id": "secure-crowd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1254,7 +1254,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "understanding-point",
+   "id": "thirty-minneapolis",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1266,7 +1266,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "baking-cooling",
+   "id": "premier-seeking",
    "metadata": {},
    "outputs": [
     {
@@ -1287,7 +1287,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "instructional-parliament",
+   "id": "brave-laundry",
    "metadata": {},
    "outputs": [
     {
@@ -1308,7 +1308,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "breeding-sport",
+   "id": "adjusted-consultation",
    "metadata": {},
    "outputs": [
     {
@@ -1329,7 +1329,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "bulgarian-excuse",
+   "id": "understanding-consortium",
    "metadata": {},
    "outputs": [
     {
@@ -1349,7 +1349,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "regional-monitoring",
+   "id": "composed-tunnel",
    "metadata": {},
    "source": [
     "**Exercise:** When I was in high school, the pitcher on the baseball team claimed that, when he threw a fastball, he was throwing the ball down; that is, the ball was leaving his hand at a downward angle.\n",
@@ -1363,7 +1363,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "statutory-music",
+   "id": "about-complex",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1391,7 +1391,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "antique-discussion",
+   "id": "unique-owner",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1404,7 +1404,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "directed-manchester",
+   "id": "minus-batman",
    "metadata": {},
    "outputs": [
     {
@@ -1434,7 +1434,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "ideal-version",
+   "id": "exact-vegetable",
    "metadata": {},
    "outputs": [
     {
@@ -1465,7 +1465,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "contrary-technique",
+   "id": "neutral-lightning",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1481,7 +1481,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "upset-harvey",
+   "id": "jewish-secret",
    "metadata": {},
    "source": [
     "**Exercise:** Suppose I run a 10K race in 44:52.  What is my average page in minutes per mile?"
@@ -1490,7 +1490,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "alert-negative",
+   "id": "virgin-cambodia",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1502,7 +1502,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "correct-cuisine",
+   "id": "right-intention",
    "metadata": {},
    "outputs": [
     {
@@ -1532,7 +1532,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "reflected-accuracy",
+   "id": "mineral-sally",
    "metadata": {},
    "outputs": [
     {
@@ -1562,7 +1562,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "inner-river",
+   "id": "preceding-cricket",
    "metadata": {},
    "outputs": [
     {
@@ -1592,7 +1592,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "abstract-musical",
+   "id": "effective-rendering",
    "metadata": {},
    "outputs": [
     {
@@ -1626,7 +1626,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "radical-junior",
+   "id": "printable-reply",
    "metadata": {},
    "outputs": [
     {
@@ -1655,7 +1655,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "banned-surname",
+   "id": "environmental-wallet",
    "metadata": {},
    "source": [
     "## Jupyter"
@@ -1663,7 +1663,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "american-rainbow",
+   "id": "quality-probe",
    "metadata": {},
    "source": [
     "### Adding and removing cells\n",
@@ -1691,7 +1691,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "radical-kenya",
+   "id": "suffering-intro",
    "metadata": {},
    "source": [
     "### Restart and run all\n",
@@ -1706,7 +1706,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "growing-jumping",
+   "id": "weighted-quebec",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap02.ipynb b/jupyter/chap02.ipynb
index 90a93b05..7b9b4c9f 100644
--- a/jupyter/chap02.ipynb
+++ b/jupyter/chap02.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "british-hampshire",
+   "id": "million-filter",
    "metadata": {},
    "source": [
     "# Chapter 2"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "plastic-target",
+   "id": "expanded-rwanda",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "confirmed-melbourne",
+   "id": "unknown-introduction",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "parliamentary-bailey",
+   "id": "narrative-parcel",
    "metadata": {},
    "source": [
     "# Bike share\n",
@@ -83,7 +83,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "after-publisher",
+   "id": "prospective-appreciation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "encouraging-input",
+   "id": "average-damages",
    "metadata": {},
    "source": [
     "Now we can call it like this:"
@@ -101,7 +101,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "organic-consequence",
+   "id": "distinguished-legislation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "varied-croatia",
+   "id": "requested-wonder",
    "metadata": {},
    "source": [
     "The expressions in parentheses are **keyword arguments**.\n",
@@ -130,7 +130,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "above-woman",
+   "id": "distinguished-black",
    "metadata": {},
    "outputs": [
     {
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "unnecessary-jewelry",
+   "id": "increased-footage",
    "metadata": {},
    "source": [
     "And this:"
@@ -159,7 +159,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "sacred-hours",
+   "id": "becoming-anatomy",
    "metadata": {},
    "outputs": [
     {
@@ -179,7 +179,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "organizational-husband",
+   "id": "accurate-exemption",
    "metadata": {},
    "source": [
     "Or, to display the state variables and their values, you can just type the name of the object:"
@@ -188,7 +188,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "primary-carrier",
+   "id": "incomplete-prediction",
    "metadata": {},
    "outputs": [
     {
@@ -243,7 +243,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "american-worship",
+   "id": "noble-reviewer",
    "metadata": {},
    "source": [
     "These values make up the **state** of the system.\n",
@@ -254,7 +254,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "beautiful-priority",
+   "id": "signal-satisfaction",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adjusted-baghdad",
+   "id": "continuing-sperm",
    "metadata": {},
    "source": [
     "Or we can use **update operators**, `-=` and `+=`, to subtract 1 from\n",
@@ -274,7 +274,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "another-boating",
+   "id": "caring-richmond",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,7 +284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "finite-taste",
+   "id": "antique-remains",
    "metadata": {},
    "source": [
     "The result is the same either way."
@@ -292,7 +292,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fiscal-addition",
+   "id": "dynamic-pepper",
    "metadata": {},
    "source": [
     "## Defining functions\n",
@@ -305,7 +305,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "miniature-milton",
+   "id": "maritime-paste",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -315,7 +315,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "imperial-account",
+   "id": "united-flexibility",
    "metadata": {},
    "source": [
     "Rather than repeat them every time a bike moves, we can define a new\n",
@@ -325,7 +325,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "peaceful-scroll",
+   "id": "narrow-marshall",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "illegal-newfoundland",
+   "id": "amended-territory",
    "metadata": {},
    "source": [
     "`def` is a special word in Python that indicates we are defining a new\n",
@@ -356,7 +356,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "cordless-portrait",
+   "id": "occupational-leeds",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -365,7 +365,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "potential-fourth",
+   "id": "governing-australia",
    "metadata": {},
    "source": [
     "When you call the function, it runs the statements in the body, which\n",
@@ -376,7 +376,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "fabulous-hopkins",
+   "id": "played-chambers",
    "metadata": {},
    "outputs": [
     {
@@ -431,7 +431,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "coated-pathology",
+   "id": "conventional-terminal",
    "metadata": {},
    "source": [
     "When you call a function, you have to include the parentheses. If you\n",
@@ -441,7 +441,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "alternate-province",
+   "id": "determined-wednesday",
    "metadata": {},
    "outputs": [
     {
@@ -461,7 +461,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "living-national",
+   "id": "heated-retail",
    "metadata": {},
    "source": [
     "This result indicates that `bike_to_wellesley` is a function. You don't\n",
@@ -472,7 +472,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "impossible-scheme",
+   "id": "cutting-coating",
    "metadata": {},
    "source": [
     "## Print statements\n",
@@ -488,7 +488,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "functional-handbook",
+   "id": "spread-majority",
    "metadata": {},
    "outputs": [
     {
@@ -509,7 +509,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "naval-architect",
+   "id": "swedish-sequence",
    "metadata": {},
    "source": [
     "Jupyter runs both lines, but it only displays the value of the\n",
@@ -520,7 +520,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "fleet-marathon",
+   "id": "liked-volunteer",
    "metadata": {},
    "outputs": [
     {
@@ -539,7 +539,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "literary-integrity",
+   "id": "north-paradise",
    "metadata": {},
    "source": [
     "When you call the `print` function, you can put a variable name in\n",
@@ -550,7 +550,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "accessory-porcelain",
+   "id": "double-washer",
    "metadata": {},
    "outputs": [
     {
@@ -567,7 +567,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sustained-dallas",
+   "id": "abstract-muslim",
    "metadata": {},
    "source": [
     "Python looks up the values of the variables and displays them; in this\n",
@@ -581,7 +581,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "stone-oliver",
+   "id": "mediterranean-rogers",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -593,7 +593,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "pharmaceutical-combat",
+   "id": "cooperative-kuwait",
    "metadata": {},
    "source": [
     "Each time we call this version of the function, it displays a message,\n",
@@ -608,7 +608,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "loose-arlington",
+   "id": "floral-velvet",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -620,7 +620,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "prostate-launch",
+   "id": "chicken-feedback",
    "metadata": {},
    "source": [
     "And call it like this:"
@@ -629,7 +629,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "compatible-transcript",
+   "id": "fitting-adventure",
    "metadata": {},
    "outputs": [
     {
@@ -646,7 +646,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "clean-bleeding",
+   "id": "conditional-detector",
    "metadata": {},
    "source": [
     "One benefit of defining functions is that you avoid repeating chunks of\n",
@@ -657,7 +657,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "prescribed-observer",
+   "id": "threaded-taylor",
    "metadata": {},
    "source": [
     "## If statements\n",
@@ -668,7 +668,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "elegant-doubt",
+   "id": "english-reply",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -677,7 +677,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "steady-arrow",
+   "id": "eleven-hanging",
    "metadata": {},
    "source": [
     "When you call it, you provide a probability between 0 and 1, like `0.7`, as an example:"
@@ -686,13 +686,13 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "improving-simulation",
+   "id": "emerging-mystery",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "False"
+       "True"
       ]
      },
      "execution_count": 21,
@@ -706,7 +706,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "brown-header",
+   "id": "compact-football",
    "metadata": {},
    "source": [
     "The result is one of two values: `True` with probability 0.7 or `False`\n",
@@ -728,9 +728,17 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "organizational-portable",
+   "id": "potential-denmark",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "heads\n"
+     ]
+    }
+   ],
    "source": [
     "if flip(0.5):\n",
     "    print('heads')"
@@ -738,7 +746,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "covered-freedom",
+   "id": "changed-portable",
    "metadata": {},
    "source": [
     "If the result from `flip` is `True`, the program displays the string\n",
@@ -755,14 +763,14 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "auburn-sphere",
+   "id": "activated-butter",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tails\n"
+      "heads\n"
      ]
     }
    ],
@@ -775,7 +783,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "collected-venice",
+   "id": "sexual-neighborhood",
    "metadata": {},
    "source": [
     "Now we can use `flip` to simulate the arrival of students who want to\n",
@@ -787,9 +795,17 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "incident-somalia",
+   "id": "multiple-manor",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moving a bike to Wellesley\n"
+     ]
+    }
+   ],
    "source": [
     "if flip(0.5):\n",
     "    bike_to_wellesley()"
@@ -797,7 +813,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "twenty-brand",
+   "id": "thrown-casting",
    "metadata": {},
    "source": [
     "If students arrive at the Wellesley station every 3 minutes, on average,\n",
@@ -808,7 +824,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "variable-drain",
+   "id": "occupied-indianapolis",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -818,7 +834,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "loved-kentucky",
+   "id": "coordinated-census",
    "metadata": {},
    "source": [
     "We can combine these snippets into a function that simulates a **time\n",
@@ -828,7 +844,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "smoking-spectrum",
+   "id": "perceived-horizontal",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -842,7 +858,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "educational-color",
+   "id": "metric-classroom",
    "metadata": {},
    "source": [
     "Then we can simulate a time step like this:"
@@ -851,25 +867,16 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "offensive-meter",
+   "id": "stable-jamaica",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "step()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bridal-stage",
+   "id": "black-glance",
    "metadata": {},
    "source": [
     "Even though there are no values in parentheses, we have to include them."
@@ -877,7 +884,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adult-window",
+   "id": "respective-speed",
    "metadata": {},
    "source": [
     "## Parameters\n",
@@ -893,7 +900,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "funky-macintosh",
+   "id": "competent-owner",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -907,7 +914,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "swiss-distributor",
+   "id": "written-trust",
    "metadata": {},
    "source": [
     "The values of `p1` and `p2` are not set inside this function; instead,\n",
@@ -917,24 +924,16 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "pleased-vision",
+   "id": "familiar-skiing",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Moving a bike to Wellesley\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "step(0.5, 0.33)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "located-inspector",
+   "id": "tropical-termination",
    "metadata": {},
    "source": [
     "The values you provide when you call the function are called\n",
@@ -946,17 +945,9 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "known-edgar",
+   "id": "religious-failing",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Moving a bike to Wellesley\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "p1 = 0.5\n",
     "p2 = 0.33\n",
@@ -970,7 +961,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "periodic-heath",
+   "id": "distinguished-ridge",
    "metadata": {},
    "source": [
     "The advantage of using parameters is that you can call the same function many times, providing different arguments each time.\n",
@@ -980,7 +971,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "indonesian-shelter",
+   "id": "laden-wisdom",
    "metadata": {},
    "source": [
     "## For loops\n",
@@ -992,32 +983,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "welcome-crossing",
+   "execution_count": 62,
+   "id": "natural-roads",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "0\n",
+      "Moving a bike to Wellesley\n",
       "1\n",
       "Moving a bike to Wellesley\n",
       "2\n",
-      "Moving a bike to Wellesley\n",
-      "3\n",
       "Moving a bike to Wellesley\n"
      ]
     }
    ],
    "source": [
-    "for i in range(1, 4):\n",
+    "for i in range(3):\n",
     "    print(i)\n",
     "    bike_to_wellesley()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "motivated-study",
+   "id": "hybrid-activation",
    "metadata": {},
    "source": [
     "The syntax here should look familiar; the first line ends with a\n",
@@ -1031,51 +1022,54 @@
     "\n",
     "-   `i` is a **loop variable** that gets created when the for loop runs.\n",
     "\n",
-    "When this loop runs, it runs the statements inside the loop three times.  The first time, the value of `i` is `1`; the second time, it is `2`; the third time, it is `3`.\n",
+    "When this loop runs, it runs the statements inside the loop three times.  The first time, the value of `i` is `0`; the second time, it is `1`; the third time, it is `2`.\n",
     "\n",
-    "Each time through the loop, it prints the value of `i` and moves one bike Olin to Wellesley.\n",
-    "\n",
-    "It might seem strange that the loop stops when `i` is `3`, but that's how `range` works: it includes the first argument, `1`, but not the second, `4`.\n",
-    "\n",
-    "Now, since we have moved a number of bikes around, let's start again with a new `State` object."
+    "Each time through the loop, it prints the value of `i` and moves one bike Olin to Wellesley."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "passing-notification",
+   "cell_type": "markdown",
+   "id": "urban-brisbane",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
+    "## TimeSeries"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "linear-cleaner",
+   "id": "greenhouse-caribbean",
+   "metadata": {},
+   "source": [
+    "When we run a simulation, we often want to save the results for later analysis. The ModSim library provides a `TimeSeries` object for this purpose. A `TimeSeries` contains a sequence of time stamps and a\n",
+    "corresponding sequence of quantities.\n",
+    "\n",
+    "In this example, the time stamps will be integers representing minutes, and the quantities will be the number of bikes at one location.\n",
+    "\n",
+    "Since we have moved a number of bikes around, let's start again with a new `State` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "linear-magnet",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "We'll use this `bikeshare` in the next section."
+    "bikeshare = State(olin=10, wellesley=2)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "successful-rough",
+   "id": "crucial-tract",
    "metadata": {},
    "source": [
-    "## TimeSeries\n",
-    "\n",
-    "When we run a simulation, we often want to save the results for later analysis. The ModSim library provides a `TimeSeries` object for this purpose. A `TimeSeries` contains a sequence of time stamps and a\n",
-    "corresponding sequence of values. In this example, the time stamps are\n",
-    "integers representing minutes, and the values are the number of bikes at one location.\n",
-    "\n",
     "We can create a new, empty `TimeSeries` like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "approved-bruce",
+   "execution_count": 64,
+   "id": "pressed-brooks",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1086,16 +1080,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "extensive-uniform",
+   "id": "established-click",
    "metadata": {},
    "source": [
-    "And we can add a value to a `TimeSeries` like this:"
+    "And we can add a quantity like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "affected-syntax",
+   "execution_count": 65,
+   "id": "eligible-hormone",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1104,73 +1098,75 @@
   },
   {
    "cell_type": "markdown",
-   "id": "rational-assault",
+   "id": "based-dealer",
    "metadata": {},
    "source": [
     "The number in brackets is the time stamp, also called a **label**.\n",
+    "In this case, label is `0` and the quantity is the number of bikes at Olin.\n",
     "\n",
-    "We can use a `TimeSeries` inside a for loop to store the results of the\n",
-    "simulation:"
+    "We can use a `TimeSeries` inside a for loop to store the results of the simulation:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "id": "optical-ordinary",
+   "execution_count": 66,
+   "id": "renewable-coating",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "0\n",
       "1\n",
-      "Moving a bike to Wellesley\n",
-      "2\n",
-      "3\n",
-      "4\n"
+      "Moving a bike to Olin\n",
+      "2\n"
      ]
     }
    ],
    "source": [
-    "for i in range(1, 5):\n",
+    "for i in range(3):\n",
     "    print(i)\n",
     "    step(0.3, 0.2)\n",
-    "    results[i] = bikeshare.olin"
+    "    results[i+1] = bikeshare.olin"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dutch-sheep",
+   "id": "common-senegal",
    "metadata": {},
    "source": [
     "Each time through the loop, we print the value of `i` and call `step`, which updates `bikeshare`.\n",
     "Then we store the number of bikes at Olin in `results`. \n",
-    "We use the loop variable, `i`, as the time stamp.\n",
+    "We use the loop variable, `i`, to compute the time stamp, `i+1`.\n",
     "\n",
-    "When the loop exits, `results` contains 5 time stamps, from 0 through\n",
-    "4, and the number of bikes at Olin at the end of each time step.\n",
+    "The first time through the loop, the value of `i` is `0`, so the time stamp is `1`.\n",
+    "The last time, the value of `i` is `2`, so the time stamp is `3`.\n",
+    "\n",
+    "When the loop exits, `results` contains 4 time stamps, from 0 through\n",
+    "3, and the number of bikes at Olin at the end of each time step.\n",
     "\n",
     "We can display the `TimeSeries` like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "allied-hobby",
+   "execution_count": 67,
+   "id": "fewer-blocking",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "Time\n",
        "0    10\n",
-       "1     9\n",
-       "2     9\n",
-       "3     9\n",
-       "4     9\n",
-       "dtype: int64"
+       "1    10\n",
+       "2    11\n",
+       "3    11\n",
+       "Name: Quantity, dtype: int64"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1181,19 +1177,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "found-indonesia",
+   "id": "prescription-induction",
    "metadata": {},
    "source": [
-    "The left column displays the timestamps; the right column displays the values (which might be negative, depending on the state of the system).\n",
+    "The left column is the time stamps; the right column is the quantities (which might be negative, depending on the state of the system).\n",
+    "\n",
     "At the bottom, `dtype` is the type of the data in the `TimeSeries`; you can ignore this for now.\n",
     "\n",
-    "ModSim provides a function called `display` that displays the `TimeSeries` as a table:"
+    "ModSim provides a function called `show` that displays the `TimeSeries` as a table:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "compound-globe",
+   "execution_count": 68,
+   "id": "divine-local",
    "metadata": {},
    "outputs": [
     {
@@ -1217,7 +1214,11 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>values</th>\n",
+       "      <th>Quantity</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Time</th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1227,55 +1228,51 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>9</td>\n",
+       "      <td>10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>9</td>\n",
+       "      <td>11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>9</td>\n",
+       "      <td>11</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   values\n",
-       "0      10\n",
-       "1       9\n",
-       "2       9\n",
-       "3       9\n",
-       "4       9"
+       "      Quantity\n",
+       "Time          \n",
+       "0           10\n",
+       "1           10\n",
+       "2           11\n",
+       "3           11"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from modsim import display\n",
+    "from modsim import show\n",
     "\n",
-    "display(results)"
+    "show(results)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dated-canyon",
+   "id": "conditional-sandwich",
    "metadata": {},
    "source": [
-    "You don't have to use `display`, but I think it looks better."
+    "You don't have to use `show`, but I think it looks better."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "senior-mexico",
+   "id": "parental-treasure",
    "metadata": {},
    "source": [
     "## Plotting\n",
@@ -1286,13 +1283,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "persistent-replication",
+   "execution_count": 69,
+   "id": "weekly-producer",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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l/n9ex7/uOMDCC7Lj+zURkcGmlSSGwLWzJjBmeIEWkBUR6QMV1BAoys9j8bwKXt6ylyPNrUHHERHJCCqoIbKkKkpreyfPbtgddBQRkYygghoisytGM2tSia6JEhFJkQpqCNXEo2xuPMa7u48FHUVEJPRUUEPotnkVFOZFqNU1USIi56SCGkJjRxRy7cUTWLmukdPtWkBWRORsVFBDrLoqyuHmNl7dmjMLu4uI9IsKaohdMaOMiSXFOllCROQcVFBDLC9iLKmKsuZPTew92hJ0HBGR0FJBBWBJVZROh+VrtbKEiEhvVFABmDp+BAumjaO2bhfuWkBWRCQZFVRAauIxPjrYzDsfHQ46iohIKKmgAnLTZycyojCPWp0sISKSlAoqIMML87l17mRe2LSHE6fbg44jIhI6KqgAVcdjNLd2sGrjnqCjiIiEjgoqQJVTxjC9bISuiRIRSUIFFSAzoyYeo+7jw7zfdCLoOCIioaKCCtgd8yvIixjLdLddEZFPUEEFrLykmKsuLGN5fQPtHZ1BxxERCQ0VVAgsqYqx//hp1mxvCjqKiEhoqKBC4OqZ5ZSOKGTpOzrMJyLyZyqoECjMj3D7/Ape3baPgydOBx1HRCQUVFAhUR2P0dbhrFy/O+goIiKhoIIKiQsnjmJudLQWkBURSVBBhUh1PMa2vcfZ1Hg06CgiIoFTQYXIrXMnU5Qf0coSIiKooEJl9LACbpw9kWfX76alrSPoOCIigVJBhUxNPMaxlnZe3rI36CgiIoFSQYXMpdNLiY4dRm2drokSkdymggqZSMRYUhXlX98/QMPh5qDjiIgERgUVQkuqogAsr28MOImISHBUUCEUHTucz583ntr6XXR26pooEclNKqiQqo5HaTh8irc+OBh0FBGRQKigQur6iycyqjhf10SJSM5SQYVUcUEet82bzIub93L0VFvQcUREhpwKKsRq4jFOt3fy/EYtICsiuSetgjKzB81ss5ltMbOHzjLuEjPrMLMl6Wwv13y2YjQzJ45iqa6JEpEc1O+CMrPZwDeABcBc4BYzm5FkXB7wI+Dl/m4rV5l1XRO1YdcR3tt7POg4IiJDKp09qIuAt9y92d3bgdeB25OM+4/AcmB/GtvKWbfPryA/YtTqZAkRyTHpFNRmYKGZlZrZcOAmINZ9gJlV0FVaj53rw8zsHjOrM7O6pqamNGJll9KRRXzhogn8dl0jbR2dQccRERky/S4od99K16G71cBLwAagvcewfwC+7e7nXJrb3R9397i7x8vKyvobKyvVXBLl4MlWXtumnVARyR1pnSTh7k+4e6W7LwQOAdt7DIkDT5vZR8AS4GdmtjidbeaihTPKKB9VpMN8IpJT0j2LrzzxcwpwB/BU99fdfZq7T3X3qcAy4G/cfWU628xF+XkR7qiM8rv3mth/rCXoOCIiQyLd66CWm9m7wHPAfe5+2MzuNbN7ByCbdFMdj9LR6axYpwVkRSQ35KfzZne/IslzSU+IcPevpbOtXHde2UjinxlLbd0uvrlwOmYWdCQRkUGllSQySE08xvtNJ1m780jQUUREBp0KKoPcNGcSwwrydLKEiOQEFVQGGVmUz81zJvHcht00t/Y8o19EJLuooDJMTTzGydYOXty0N+goIiKDSgWVYS6ZOpappcN1nygRyXoqqAxjZlTHY/zxw0N8dOBk0HFERAaNCioD3VFZQcRgWb1uwyEi2UsFlYEmjR7GwgvKWFbfQEenBx1HRGRQqKAyVE08xt5jLbyx40DQUUREBoUKKkNdc1E5Y4cX6GQJEclaKqgMVZSfx23zKli9ZR+HT7YGHUdEZMCpoDJYTTxGa0cnz6zXArIikn1UUBls1uQSZleUUKuz+UQkC6mgMlxNPMaW3cfY3Hg06CgiIgNKBZXhFs2dTGFeRNdEiUjWUUFluDHDC7nu4gn8dl0jLW0dQccRERkwKqgsUBOPcfRUG/+ydV/QUUREBowKKgt8/vzxTB5dTG2dDvOJSPZQQWWBvIixpCrKmu1N7D5yKug4IiIDQgWVJZZUxXCHFWu1FyUi2UEFlSWmlA7n0unjqK1vwF0LyIpI5lNBZZGaeIyPDzbz9oeHgo4iIpI2FVQWuXH2JEYW5bNUJ0uISBZQQWWRYYV53Dp3Eqs27eF4S1vQcURE0qKCyjLV8Rin2jp4YeOeoKOIiKRFBZVl5sfGcH75SN0nSkQyngoqy5gZNfEoa3ceYcf+E0HHERHpNxVUFrp9fpS8iFFbr70oEclcKqgsVDaqiKsuLGd5fSNtHZ1BxxER6RcVVJaqiUc5cOI0r7/XFHQUEZF+UUFlqatmljN+ZKEO84lIxlJBZamCvAh3VEZ5det+Dpw4HXQcEZE+U0FlseqqKO2dzsp1jUFHERHpMxVUFpsxYRTzYmP4zTu7tICsiGQcFVSWq4nH2L7/BBsajgYdRUSkT1RQWe6WuZMoLohQq5UlRCTDpFVQZvagmW02sy1m9lCS1/+dmW1M/HnTzOamsz3pu5LiAm6aPYln1+/mVGtH0HFERFLW74Iys9nAN4AFwFzgFjOb0WPYh8Bfuvsc4IfA4/3dnvTfkniU46fbeXnL3qCjiIikLJ09qIuAt9y92d3bgdeB27sPcPc33f1w4uFbQDSN7Uk/XTqtlNi4YVpAVkQySjoFtRlYaGalZjYcuAmInWX8XwEv9vaimd1jZnVmVtfUpNUPBlIkYlRXxXjz/YPsOtQcdBwRkZT0u6DcfSvwI2A18BKwAWhPNtbMrqKroL59ls973N3j7h4vKyvrbyzpxZ1VUcxgWb3utisimSGtkyTc/Ql3r3T3hcAhYHvPMWY2B/g5cJu7H0xne9J/FWOGcfn541lW30Bnp66JEpHwS/csvvLEzynAHcBTPV6fAqwAvuLuf0pnW5K+6niMxiOnePN9/TtBRMIvP833LzezUqANuM/dD5vZvQDu/hjwPaAU+JmZAbS7ezzNbUo/XTdrAiXF+Syt28XlM8YHHUdE5KzSKih3vyLJc491+/2vgb9OZxsycIoL8lg8v4Kn39nF0eY2Rg8vCDqSiEivtJJEjqmJx2ht7+TZjbuDjiIiclYqqBxz8eQSZk4cpaWPRCT0VFA5xsyoicfY2HCUbXuPBR1HRKRXKqgctHh+BQV5Rm2drokSkfBSQeWgcSMKuXbWBH67rpHW9s6g44iIJKWCylHVVTEOnWzltW37go4iIpKUCipHXTFjPBNKiliqw3wiElIqqByVnxfhzsoov39vP/uOtQQdR0TkU1RQOaw6HqPTYcXaxqCjiIh8igoqh00bP4IFU8dRW7cLdy0gKyLhooLKcUviUT44cJL6jw+fe7CIyBBSQeW4mz87ieGFebrbroiEjgoqx40oyueWOZN4YeMeTp5Oer9JEZFAqKCEmniMk60drNq0J+goIiJnqKCEqs+MZfr4EVr6SERCRQUlmBlL4lHe/ugQHzSdCDqOiAiggpKEOyujRAyW1WsvSkTCQQUlAEwoKebKC8tZvraBjk5dEyUiwVNByRk18Sj7jp1mzfamoKOIiKig5N9cPXMC40YU6m67IhIKKig5ozA/wuJ5Fax+dx+HTrYGHUdEcpwKSj6h5pIobR3OM+u1gKyIBEsFJZ8wc2IJc6Kj+c07WkBWRIKlgpJPqa6Ksm3vcbbsPhZ0FBHJYSoo+ZRFcysozI9oAVkRCZQKSj5l9PACbrh4IivXNdLS1hF0HBHJUSooSaomHuNYSzur390XdBQRyVEqKEnqsvNKqRgzTIf5RCQwKihJKhIx7qyK8saOAzQeORV0HBHJQSoo6VV1VRR3WK4FZEUkACoo6VVs3HAuO6+UZfUNdGoBWREZYiooOauaeIydh5r544eHgo4iIjlGBSVndf3FExlVlK8FZEVkyKmg5KyGFeZx67zJrNq8h2MtbUHHEZEcooKSc6qJx2hp6+SFjXuCjiIiOUQFJec0NzqaCyaM1DVRIjKk0iooM3vQzDab2RYzeyjJ62Zmj5rZDjPbaGaV6WxPgmFm1MRjrNt5hO37jgcdR0RyRL8LysxmA98AFgBzgVvMbEaPYTcCMxJ/7gH+X3+3J8FaPL+C/IhRq2uiRGSI5Kfx3ouAt9y9GcDMXgduB/5XtzG3Af/kXTcWesvMxpjZJHfXlxkZZvzIIq6eWc6KtQ18bnopWNCJRCQMFs4oIy8yOH8hpFNQm4G/M7NS4BRwE1DXY0wF0P2Li4bEc58qKDO7h669LKZMmZJGLBksX1owhVfe3cfd//hO0FFEJCS2/fAG8iJ5g/LZ/S4od99qZj8CVgMngA1Ae49hyWo16ZIE7v448DhAPB7XsgUhdNXMcl566ApOteoWHCLSpTBv8M61S2cPCnd/AngCwMz+O117SN01ALFuj6PA7nS2KcGaObEk6AgikiPSPYuvPPFzCnAH8FSPIc8CdyXO5rsUOKrvn0REJBVp7UEByxPfQbUB97n7YTO7F8DdHwNW0fXd1A6gGbg7ze2JiEiOSPcQ3xVJnnus2+8O3JfONkREJDdpJQkREQklFZSIiISSCkpEREJJBSUiIqGkghIRkVBSQYmISChZ15ng4WJmTcDHaX7MeODAAMQZCso6eDIpr7IOnkzKm4tZP+PuZT2fDGVBDQQzq3P3eNA5UqGsgyeT8irr4MmkvMr6b3SIT0REQkkFJSIioZTNBfV40AH6QFkHTyblVdbBk0l5lTUha7+DEhGRzJbNe1AiIpLBVFAiIhJKGV9QZnaDmb1nZjvM7DtJXjczezTx+kYzqwwiZyLLubJeaWZHzWx94s/3gsiZyPKkme03s829vB6meT1X1jDNa8zMfmdmW81si5k9mGRMKOY2xayhmFszKzazt81sQyLr3yYZE4p5TWRJJW8o5rZbnjwzW2dmzyd5bXDm1t0z9g+QB7wPTAcKgQ3ArB5jbgJeBAy4FPhjiLNeCTwf9LwmsiwEKoHNvbweinlNMWuY5nUSUJn4fRTwpxD/bzaVrKGY28RcjUz8XgD8Ebg0jPPah7yhmNtuef4z8M/JMg3W3Gb6HtQCYIe7f+DurcDTwG09xtwG/JN3eQsYY2aThjooqWUNDXdfAxw6y5CwzGsqWUPD3fe4+9rE78eBrUBFj2GhmNsUs4ZCYq5OJB4WJP70PAMsFPMKKecNDTOLAjcDP+9lyKDMbaYXVAWwq9vjBj79/0CpjBkKqeb4XGK3/0Uzu3hoovVLWOY1VaGbVzObCsyn61/P3YVubs+SFUIyt4lDUOuB/cBqdw/1vKaQF0Iyt8A/AN8COnt5fVDmNtMLypI81/NfIamMGQqp5FhL15pUc4H/A6wc7FBpCMu8piJ082pmI4HlwEPufqzny0neEtjcniNraObW3TvcfR4QBRaY2eweQ0I1rynkDcXcmtktwH53rz/bsCTPpT23mV5QDUCs2+MosLsfY4bCOXO4+7E/7/a7+yqgwMzGD13EPgnLvJ5T2ObVzAro+gv/1+6+IsmQ0MztubKGbW4TOY4Avwdu6PFSaOa1u97yhmhuPw8sMrOP6Ppq4moz+1WPMYMyt5leUO8AM8xsmpkVAl8Enu0x5lngrsRZJpcCR919z1AHJYWsZjbRzCzx+wK6/u9zcMiTpiYs83pOYZrXRI4ngK3u/ve9DAvF3KaSNSxza2ZlZjYm8fsw4AvAth7DQjGvkFresMytu3/X3aPuPpWuv7dec/d/32PYoMxtfrofECR3bzez+4GX6TpL7kl332Jm9yZefwxYRdcZJjuAZuDuEGddAvwHM2sHTgFf9MQpMkPNzJ6i6yyi8WbWAHyfri9yQzWvkFLW0MwrXf8a/QqwKfH9A8B/AaZA6OY2laxhmdtJwC/NLI+uv8iXuvvzYfy7ICGVvGGZ26SGYm611JGIiIRSph/iExGRLKWCEhGRUFJBiYhIKKmgREQklFRQIiISSiooEREJJRWUiIiE0v8HXt9JK1JZmQkAAAAASUVORK5CYII=\n",
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mItPHaiCsRKYaC8rM5plZgZltrbCsnZmtMLPcwGXbah7bxsxeN7MsM9thZtc1ZHiRaHX0zAXmrN3J2Ms7Mah7lT9+Ir5XmyOo+UBmpWXTgZXOuXRgZeB2VZ4Bljrn+gFXATuCzCkiFcxelUdRSRlTx2ggrESuGgvKObcWOF5p8W3AgsD1BcD3Kz/OzFoBNwAvBr7ORefciXpkFRFg77GzvPzxXn70nW70Sk70Oo5IyAT7HlRH59whgMBlVR/Z2RMoBF4ys0/NbK6ZtajuC5rZJDPbaGYbCwsLg4wlEvlmLc8htkkTHhqR7nUUkZAK5UkSscBg4Hnn3CDgLNW/FIhzbo5zLsM5l5GcrDOSRKryRf5J3vnsID+9Po0OrTQQViJbsAV1xMw6AwQuC6pYJx/Id859HLj9OuWFJSJBemxpFm2bxzHphp5eRxEJuWAL6m1gQuD6BOCtyis45w4D+83sq3dxRwDbg3w+kai3LreQD/KO8uDN6bRM0EBYiXy1Oc38FeBDoK+Z5ZvZfcAMYJSZ5QKjArcxsxQzW1zh4Q8CL5vZ58BA4P82cH6RqFBW5pixJIuubZtx17XdvY4j0ihia1rBOTe+mrtGVLHuQWBchdtbgIxgw4lIuXc+P8i2g6d4+kcDaRqrgbASHTRJQiTMXSwpY9bybPp3bsXfXZXidRyRRqOCEglzL3+8l/3HzzN9bD+aaCCsRBEVlEgYO11UzLOr8hjWuz03pCd5HUekUamgRMLYf67dxfGzF3kkUwNhJfqooETCVMHpIv5z3W6+d2Vnruzaxus4Io1OBSUSpn6/Mpfi0jKmjtZAWIlOKiiRMLSr8AyvfLKff7ymO6lJ1Y6wFIloKiiRMPTE8hyaxjbhwZs1EFailwpKJMxs2X+Cv31xiJ9d35Pklk29jiPiGRWUSBhxzjFjyQ6SEuP5mQbCSpRTQYmEkTU5hXy06zj/PCKdxKY1TiITiWgqKJEwUVrmeGxJFj3aN+fO72ggrIgKSiRMvLXlAFmHTzN1dF/iY/WjKaKfApEwUFRcyhPLc7iiS2tuuaKz13FEwoIKSiQMLPxoLwdOaCCsSEUqKBGPnSoqZvbqPK5PT2JYbw2EFfmKCkrEY//x/k5OnCvmkcx+XkcRCSsqKBEPHTlVxIsf7Oa2gSlc3qW113FEwooKSsRDT7+XQ2mZ00BYkSqooEQ8kldwhj9v2M+Pr+1Bt3bNvY4jEnZUUCIeeXxZFs3jY5lyU2+vo4iEJRWUiAc27f2SZduO8E839KR9ogbCilRFBSXSyJwrH2mUlNiU+65P8zqOSNhSQYk0slVZBXyy5zgPjUynebwGwopURwUl0ohKyxyPLc0iLakFP/pON6/jiIQ1FZRII3pzcz45R87w6zF9iYvRj5/IpegnRKSRFBWX8uSKHK7q1oaxl3fyOo5I2FNBiTSSBev3cOhkEf8yth9mGggrUhMVlEgjOHmumD+szuOmvslc27O913FEfEEFJdIInns/j9MXSpimgbAitaaCEgmxgyfO89J/7eHvB3Whf+dWXscR8Q0VlEiIPf1eDjj41ag+XkcR8RUVlEgI5Rw5zeub8vnJdT3o2lYDYUXqosaCMrN5ZlZgZlsrLGtnZivMLDdw2fYSj48xs0/N7N2GCi3iFzOXZtMiPpYHNBBWpM5qcwQ1H8istGw6sNI5lw6sDNyuzi+AHUGlE/GxDXuO896OI0we3ou2LeK9jiPiOzUWlHNuLXC80uLbgAWB6wuA71f1WDPrCtwCzA0+ooj/OOf498U76NiqKfcO00BYkWAE+x5UR+fcIYDAZYdq1nsamAaUBfk8Ir60fPsRNu87wS9H9qFZfIzXcUR8KWQnSZjZ94AC59ymWq4/ycw2mtnGwsLCUMUSCbmS0jJmLs2iV3IL7hjS1es4Ir4VbEEdMbPOAIHLgirWGQb8nZntAV4FbjazhdV9QefcHOdchnMuIzk5OchYIt57fVM+OwvPMi2zH7EaCCsStGB/et4GJgSuTwDeqryCc+5fnHNdnXOpwJ3AKufcj4N8PhFfOH+xlKfey2Fw9zaMHtDR6zgivlab08xfAT4E+ppZvpndB8wARplZLjAqcBszSzGzxaEMLBLOXlq/myOnLjB9bH8NhBWppxo/ztM5N76au0ZUse5BYFwVy9cAa+qYTcRXvjx7kefX7GRk/w5cndbO6zgivqcXyEUayHNr8jirgbAiDUYFJdIA8r88x4L1e7ljSFf6dGzpdRyRiKCCEmkAT67IwQweGqmBsCINRQUlUk87Dp1i0acHmDgslZQ2zbyOIxIxVFAi9TRzaRYtm8by8xs1EFakIamgROrhw53HWJ1dyAM39aZ18ziv44hEFBWUSJCcc8xYmkXn1glMGJrqdRyRiKOCEgnS0q2H+Wz/CX45qg8JcRoIK9LQVFAiQSguLePxZdn06ZjIPwzWQFiRUFBBiQThzxv2s+voWR7J7EdME400EgkFFZRIHZ29UMLT7+VydWo7bu5X3UehiUh9qaBE6mjeB7s5euYCj4ztp4GwIiGkghKpg2NnLvAfa3cx5rKODOnR1us4IhFNBSVSB7NX53HuYgm/HqOBsCKhpoISqaX9x8+x8KO9/Og73ejdIdHrOCIRTwUlUktPLM8mponxixEaCCvSGFRQIrWw9cBJ/rrlIPcOS6NT6wSv44hEBRWUSC08tjSLNs3jmDy8l9dRRKKGCkqkBh/kHmVd7lGm3NSbVgkaCCvSWFRQIpdQVuZ4bGkWXdo04+7rengdRySqqKBELuFvXxziiwMneXh0H5rGaiCsSGNSQYlU42JJGbOWZ9OvU0tuG9jF6zgiUUcFJVKNVzfsY++xczwyVgNhRbygghKpwpkLJfx+ZS7X9mzH8D7JXscRiUoqKJEqzF23i6NnLjJ9bH8NhBXxiApKpJLC0xeYs3YXt1zRmYHd2ngdRyRqqaBEKnl2VS4XSsqYOqav11FEopoKSqSCPUfP8v8+3sf4q7uRltTC6zgiUU0FJVLBrOXZxMU04Z9HpHsdRSTqqaBEAj7PP8G7nx/iZ9en0aGlBsKKeE0FJQI455ixJIt2LeL52Q09vY4jIqigRABYl3uU9TuP8eDNvWmpgbAiYUEFJVGvrKz86Klbu2b84zXdvY4jIgEqKIl673x+kO2HTjF1dF8NhBUJIzUWlJnNM7MCM9taYVk7M1thZrmBy7ZVPK6bma02sx1mts3MftHQ4UXq60JJKY8vy+aylFbcemWK13FEpILaHEHNBzIrLZsOrHTOpQMrA7crKwEeds71B64FHjCzAfXIKtLgXv5oH/lfnmf62H400UBYkbBSY0E559YCxystvg1YELi+APh+FY875JzbHLh+GtgB6DMLJGycKirm2VW5fLd3EtenayCsSLgJ9j2ojs65Q1BeRECHS61sZqnAIODjS6wzycw2mtnGwsLCIGOJ1N5/rt3Fl+eKeSSzn9dRRKQKIT9JwswSgTeAh5xzp6pbzzk3xzmX4ZzLSE7W/2YltApOFTF33W5uvSqFK7q29jqOiFQh2II6YmadAQKXBVWtZGZxlJfTy865N4N8LpEG98zKXIpLy5g6uo/XUUSkGsEW1NvAhMD1CcBblVew8g/ReRHY4Zx7MsjnEWlwuwrP8OqG/dx1TXd6tNdAWJFwVZvTzF8BPgT6mlm+md0HzABGmVkuMCpwGzNLMbPFgYcOA+4GbjazLYE/40KyFSJ1MGt5NgmxTXhQA2FFwlpsTSs458ZXc9eIKtY9CIwLXP8A0Hm7ElY+3fcli784zC9H9iEpsanXcUTkEjRJQqKGc45/X5JFUmI8P70+zes4IlIDFZREjTXZhXyy+zi/GJFOi6Y1vnggIh5TQUlUKC1zPLY0i9T2zbnzag2EFfEDFZREhb9+eoCsw6eZOqYvcTH6thfxA/2kSsQrKi7lyRU5XNm1NeMu7+x1HBGpJRWURLyFH+3lwInzTM/UQFgRP1FBSUQ7eb6Y2avzuLFPMkN7J3kdR0TqQAUlEe2F93dy8rwGwor4kQpKItbhk0XM+2A33x/YhQEprbyOIyJ1pIKSiPX0ezk4B78apYGwIn6kgpKIlFdwmtc27ufH1/agW7vmXscRkSCooCQizVyaTfP4WKbc3NvrKCISJBWURJxNe4+zfPsRJt/Yk3Yt4r2OIyJBUkFJRHHOMWNJFh1aNuXe72ogrIifqaAkoqzcUcCGPV/y0Mg+NI/XQFgRP1NBScQoKS3jsaVZ9ExqwQ8zunodR0TqSQUlEePNzQfILTjDtMy+xGogrIjv6adYIsJXA2EHdmvDmMs6eR1HRBqACkoiwvz1ezh8qojpY/thpoGwIpFABSW+d+LcRZ5bncfN/Tpwbc/2XscRkQaighLfe37NTk5fKGFaZl+vo4hIA1JBia8dPHGel9bv4fZBXenXSQNhRSKJCkp87akVOQD8arQGwopEGhWU+Fb24dO8sTmfiUNT6dKmmddxRKSBqaDEt2YuzaJF01h+PryX11FEJARUUOJLH+86xsqsAn4+vDdtmmsgrEgkUkGJ7zjnmLE0i06tErhnWKrXcUQkRFRQ4jvLth3h030n+OWodBLiYryOIyIhooISXykpLWPmsix6d0jkHwZrIKxIJFNBia/8ZVM+uwrPMm2MBsKKRDr9hItvnL9YylMrcsjo0ZZRAzp6HUdEQkwFJb4x7792U3D6ggbCikQJFZT4wvGzF3lhzU5GDehIRmo7r+OISCNQQYkv/GF1HmcvljBtjAbCikSLGgvKzOaZWYGZba2wrJ2ZrTCz3MBl22oem2lm2WaWZ2bTGzK4RI/9x8/xpw/38oMh3Ujv2NLrOCLSSGpzBDUfyKy0bDqw0jmXDqwM3P4GM4sB/gCMBQYA481sQL3SSlR6akUOZvDQqHSvo4hII4qtaQXn3FozS620+DZgeOD6AmAN8Eilda4G8pxzuwDM7NXA47YHH7d2/vc72zl65kKon0YaQZlz/O2LQ/zTDb3o3FoDYUWiSY0FVY2OzrlDAM65Q2bWoYp1ugD7K9zOB66p7gua2SRgEkD37t2DjFUu58hpDpw4X6+vIeFjWK8k7r9RA2FFok2wBVUbVZ0H7Kpb2Tk3B5gDkJGRUe16tbHwp9X2oIiI+ESwZ/EdMbPOAIHLgirWyQe6VbjdFTgY5POJiEiUCbag3gYmBK5PAN6qYp0NQLqZpZlZPHBn4HEiIiI1qs1p5q8AHwJ9zSzfzO4DZgCjzCwXGBW4jZmlmNliAOdcCTAFWAbsAF5zzm0LzWaIiEikqc1ZfOOruWtEFeseBMZVuL0YWBx0OhERiVqaJCEiImFJBSUiImFJBSUiImFJBSUiImFJBSUiImHJnKvX0IaQMLNCYG89v0wScLQB4oS7aNjOaNhG0HZGmmjYzobaxh7OueTKC8OyoBqCmW10zmV4nSPUomE7o2EbQdsZaaJhO0O9jXqJT0REwpIKSkREwlIkF9QcrwM0kmjYzmjYRtB2Rppo2M6QbmPEvgclIiL+FslHUCIi4mMqKBERCUu+LygzyzSzbDPLM7PpVdxvZvb7wP2fm9lgL3LWRy22cbiZnTSzLYE//8uLnPVhZvPMrMDMtlZzv+/3I9RqO32/LwHMrJuZrTazHWa2zcx+UcU6vt6ntdxG3+9PM0sws0/M7LPAdv62inVCsy+dc779A8QAO4GeQDzwGTCg0jrjgCWUfwT9tcDHXucOwTYOB971Oms9t/MGYDCwtZr7fb0f67Cdvt+Xge3oDAwOXG8J5ETgz2ZtttH3+zOwfxID1+OAj4FrG2Nf+v0I6mogzzm3yzl3EXgVuK3SOrcBf3TlPgLafPVx9T5Rm230PefcWuD4JVbx+34EarWdEcE5d8g5tzlw/TTlH1rapdJqvt6ntdxG3wvsnzOBm3GBP5XPrgvJvvR7QXUB9le4nc+3v0Fqs044q23+6wKH4EvM7LLGidao/L4f6yKi9qWZpQKDKP+fd0URs08vsY0QAfvTzGLMbAtQAKxwzjXKvqzxE3XDnFWxrHKz12adcFab/Jspn2V1xszGAX8F0kMdrJH5fT/WVkTtSzNLBN4AHnLOnap8dxUP8d0+rWEbI2J/OudKgYFm1gZYZGaXO+cqvo8akn3p9yOofKBbhdtdgYNBrBPOaszvnDv11SG4c24xEGdmSY0XsVH4fT/WSiTtSzOLo/wf7pedc29WsYrv92lN2xhJ+xPAOXcCWANkVrorJPvS7wW1AUg3szQziwfuBN6utM7bwE8CZ5lcC5x0zh1q7KD1UOM2mlknM7PA9asp36/HGj1paPl9P9ZKpOzLwDa8COxwzj1ZzWq+3qe12cZI2J9mlhw4csLMmgEjgaxKq4VkX/r6JT7nXImZTQGWUX622zzn3DYzmxy4/wVgMeVnmOQB54B7vMobjFpu4x3A/WZWApwH7nSBU2v8wsxeofyMpyQzywd+Q/mbsRGxH79Si+30/b4MGAbcDXwReO8C4H8A3SFi9mlttjES9mdnYIGZxVBesK85595tjH9nNepIRETCkt9f4hMRkQilghIRkbCkghIRkbCkghIRkbCkghIRkbCkghIJETNrX2GK9WEzOxC4fsbMnvM6n0i402nmIo3AzP4NOOOcm+V1FhG/0BGUSCMLfEbQu4Hr/2ZmC8xsuZntMbPbzWymmX1hZksDo3QwsyFm9r6ZbTKzZX6a+i0SLBWUiPd6AbdQ/pEFC4HVzrkrKJ88cEugpJ4F7nDODQHmAY96FVaksfh61JFIhFjinCs2sy8oH2e1NLD8CyAV6AtcDqwIjHWLAXwzs04kWCooEe9dAHDOlZlZcYVZbWWU/4wasM05d51XAUW8oJf4RMJfNpBsZtdB+Uc8+PWD70TqQgUlEuaccxcpn4r9mJl9BmwBhnoaSqQR6DRzEREJSzqCEhGRsKSCEhGRsKSCEhGRsKSCEhGRsKSCEhGRsKSCEhGRsKSCEhGRsPTfOtEJpeGD/3AAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1311,7 +1308,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "mexican-recipe",
+   "id": "serial-report",
    "metadata": {},
    "source": [
     "The legend for this figure says \"None\" because we have not provided a label.  Here's how we can do that:"
@@ -1319,13 +1316,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "id": "hollow-george",
+   "execution_count": 70,
+   "id": "sublime-treatment",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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XQFhJDZokIZLgjtfUMXtRMWMH9ODqMwcGHUek3aigRBLc029tYdveo0yfMoYOGggrKUQFJZLADlZV88jSUi4e2YdL87KCjiPSrlRQIgns18s3svfwce4v0EBYST0qKJEEVXGwil+/vonPnTGAM3J6BR1HpN2poEQS1M+XlFBdW8e9V2ogrKQmFZRIAtpYeYhn3t7GV84fQm5WsyMsRZKaCkokAf100Xo6pXfgris0EFZSlwpKJMF8sG0ff/24nG9eMpzs7p2CjiMSGBWUSAJxd2YsWEdWt458UwNhJcWpoEQSyKvrK3lz416+OyGPbp1anEQmktRUUCIJorbOmbmgiKF9unDDZzQQVkQFJZIgXvxgO0U7D3LvlaPpmK5fTRH9FogkgKrqWn66aD2nD+rJVacPCDqOSEJQQYkkgKfe3ML2fRoIK9KQCkokYAeqqnl0WSmX5GVx8UgNhBX5lApKJGD/89oG9h2p5v6CMUFHEUkoKiiRAO06UMUTKzZxzVkDOW1Qz6DjiCQUFZRIgB5+ZT21da6BsCJNUEGJBKS04hC/f2cbX7tgKIN7dwk6jkjCUUGJBOSBhUV06ZjOnZePDDqKSEJSQYkE4N0tn7BwzS6+delw+nTTQFiRpqigRNqZe/1Io6xunbjtkmFBxxFJWCookXa2tKiCtzfv5Z6JeXTpqIGwIs1RQYm0o9o6Z2ZhEcOyuvLlzwwOOo5IQlNBibSj598rY/2uQ/xw8mgy0vTrJ3Ii+g0RaSdV1bU8uHg9Zw7uxZTT+gcdRyThqaBE2sm8lZsp31/Fj6aMwUwDYUVaooISaQf7j1Tzi2WlXD46mwuG9wk6jkgoqKBE2sFjr5Vy8FgN92kgrEjUVFAicbZj31F+87fN/MvZgxg7oEfQcURCQwUlEmcPv7IeHL4/aVTQUURCRQUlEkfrdx3kT++W8a8XDiXnFA2EFTkZLRaUmc01swozW91gW28zW2xmJZHLU07w+DQze9/MXm6r0CJhMauwmK4d07lDA2FFTlo0Z1BPAgWNtk0Hlrh7HrAkcrs5dwPrYkonEmLvbN7LK+t2cfv4EZzStWPQcURCp8WCcvflwN5Gm68B5kWuzwOubeqxZpYDXAU8HntEkfBxd/57/jr69ejE1y/WQFiRWMT6GlQ/dy8HiFz2bWbdw8B9QF2M30cklBat3cV7W/fxvYmj6NwxLeg4IqEUtzdJmNnngAp3fzfK9dPMbJWZraqsrIxXLJG4q6mtY1ZhESOyu3L9uTlBxxEJrVgLapeZDQCIXFY0seZi4Goz2ww8C1xhZk819wXdfY6757t7fnZ2doyxRIL3p3fL2FB5mPsKxpCugbAiMYv1t+cl4ObI9ZuBFxsvcPcfuXuOu+cCNwBL3f1rMX4/kVA4eryWh15ZzzlDenHluH5BxxEJtWjeZv4M8AYw2szKzOw2YAYwycxKgEmR25jZQDObH8/AIonsNys3sevAMaZPGauBsCKt1OLHebr7jc3cNaGJtTuAqU1sfxV49SSziYTKJ4eP88tXNzBxbF/OG9Y76DgioacnyEXayGOvlnJYA2FF2owKSqQNlH1yhHkrt3D9uTmM6tc96DgiSUEFJdIGHly8HjO4Z6IGwoq0FRWUSCutKz/AC+9v55aLcxnYq3PQcUSShgpKpJVmFRbRvVM637lMA2FF2pIKSqQV3tiwh2XFldxx+Uh6dskIOo5IUlFBicTI3ZlRWMSAnpncfFFu0HFEko4KSiRGhat38uG2fXxv0igyMzQQVqStqaBEYlBdW8cDC4sZ1a8bXzhHA2FF4kEFJRKD37+zjY27D3N/wRjSOmikkUg8qKBETtLhYzU8/EoJ5+X25ooxzX0Umoi0lgpK5CTNXbGJ3YeOcf+UMRoIKxJHKiiRk7Dn0DH+Z/lGJp/aj3OHnhJ0HJGkpoISOQmPLivlyPEafjhZA2FF4k0FJRKlbXuP8NSbW/jyZwYzsm+3oOOIJD0VlEiUfrqomLQOxt0TNBBWpD2ooESisHr7fv78wQ6+fvEw+vfMDDqOSEpQQYlEYWZhEb26ZHD7+BFBRxFJGSookRasKNnN6yW7ufPykfTI1EBYkfaighI5gbo6Z2ZhEYN6deamC4cGHUckpaigRE7grx+X8/H2/fzgylF0StdAWJH2pIISacbxmjpmLypmTP/uXHPWoKDjiKQcFZRIM559Zytb9hzh/ikaCCsSBBWUSBMOHavh50tKuGB4b8aPyg46jkhKUkGJNOHx1zey+9Bxpk8Zq4GwIgFRQYk0UnnwGHOWb+Sq0wdw1uBeQccRSVkqKJFGHllawrGaOu6dPDroKCIpTQUl0sDm3Yf5f29t5cbzBjMsq2vQcURSmgpKpIHZi4rJSOvAdyfkBR1FJOWpoEQiPirbx8sflfPNS4bRt7sGwooETQUlArg7MxYU0btrR7556fCg44gIKigRAF4v2c3KDXu464qRdNdAWJGEoIKSlFdXV3/2NLh3Z75y/pCg44hIhApKUt5fPtrB2vID3HvlaA2EFUkgLRaUmc01swozW91gW28zW2xmJZHLU5p43GAzW2Zm68xsjZnd3dbhRVrrWE0tDyws5tSBPfj8GQODjiMiDURzBvUkUNBo23RgibvnAUsitxurAX7g7mOBC4A7zGxcK7KKtLmn39xK2SdHmT5lDB00EFYkobRYUO6+HNjbaPM1wLzI9XnAtU08rtzd34tcPwisA/SZBZIwDlRV88jSEj47MotL8jQQViTRxPoaVD93L4f6IgL6nmixmeUCZwNvnWDNNDNbZWarKisrY4wlEr1fL9/IJ0equb9gTNBRRKQJcX+ThJl1A54D7nH3A82tc/c57p7v7vnZ2frXrMRXxYEqHn99E58/cyCn5/QMOo6INCHWgtplZgMAIpcVTS0yswzqy+lpd38+xu8l0uZ+tqSE6to67r1yVNBRRKQZsRbUS8DNkes3Ay82XmD1H6LzBLDO3R+M8fuItLmNlYd49p1tfPX8IQzto4GwIokqmreZPwO8AYw2szIzuw2YAUwysxJgUuQ2ZjbQzOZHHnoxcBNwhZl9EPkzNS57IXISZi8qJjO9A3dpIKxIQktvaYG739jMXROaWLsDmBq5vgLQ+3Yloby/9RPmf7yT700cRVa3TkHHEZET0CQJSRnuzn8vKCKrW0e+ccmwoOOISAtUUJIyXi2u5O1Ne7l7Qh5dO7X45IGIBEwFJSmhts6ZWVhEbp8u3HCeBsKKhIEKSlLCn9/fTtHOg9w7eTQZafqxFwkD/aZK0quqruXBxes5I6cnU08bEHQcEYmSCkqS3lNvbmH7vqNML9BAWJEwUUFJUtt/tJpHl5Vy2ahsLhqZFXQcETkJKihJar96bQP7j2ogrEgYqaAkae3cX8XcFZu49qxBjBvYI+g4InKSVFCStB5+ZT3u8P1JGggrEkYqKElKpRUH+cOqbXztgqEM7t0l6DgiEgMVlCSlWYXFdOmYzp1XjAw6iojESAUlSefdLXtZtHYXt182nN5dOwYdR0RipIKSpOLuzFhQRN/unfj6ZzUQViTMVFCSVJasq+CdzZ9wz8RRdOmogbAiYaaCkqRRU1vHzMIihmd15Uv5OUHHEZFWUkFJ0nj+ve2UVBzivoLRpGsgrEjo6bdYksKnA2HPGtyLyaf2DzqOiLQBFZQkhSdXbmbngSqmTxmDmQbCiiQDFZSE3r4jx3lsWSlXjOnLBcP7BB1HRNqICkpC75evbuDgsRruKxgddBQRaUMqKAm1HfuO8puVm7nu7BzG9NdAWJFkooKSUHto8XoAvn+lBsKKJBsVlIRW8c6DPPdeGbdclMugXp2DjiMibUwFJaE1q7CIrp3S+c74EUFHEZE4UEFJKL21cQ9Liir4zviR9OqigbAiyUgFJaHj7swoLKJ/j0xuvTg36DgiEicqKAmdhWt28f7WfXxvUh6ZGWlBxxGROFFBSajU1NYxa2ERI/t24wvnaCCsSDJTQUmo/PHdMjZWHua+yRoIK5Ls9BsuoXH0eC0PLV5P/tBTmDSuX9BxRCTOVFASGnP/tomKg8c0EFYkRaigJBT2Hj7Or17dwKRx/cjP7R10HBFpByooCYVfLCvl8PEa7pusgbAiqaLFgjKzuWZWYWarG2zrbWaLzawkcnlKM48tMLNiMys1s+ltGVxSx7a9R/jdG1v44rmDyevXPeg4ItJOojmDehIoaLRtOrDE3fOAJZHb/8DM0oBfAFOAccCNZjauVWklJT20eD1mcM+kvKCjiEg7Sm9pgbsvN7PcRpuvAcZHrs8DXgXub7TmPKDU3TcCmNmzkcetjT1udP7zL2vZfehYvL+NtIM6d/76cTnfunQEA3pqIKxIKmmxoJrRz93LAdy93Mz6NrFmELCtwe0y4PzmvqCZTQOmAQwZMiTGWPXW7zrI9n1HW/U1JHFcPCKLb1+mgbAiqSbWgopGU+8D9uYWu/scYA5Afn5+s+ui8dQ3mu1BEREJiVjfxbfLzAYARC4rmlhTBgxucDsH2BHj9xMRkRQTa0G9BNwcuX4z8GITa94B8sxsmJl1BG6IPE5ERKRF0bzN/BngDWC0mZWZ2W3ADGCSmZUAkyK3MbOBZjYfwN1rgDuBhcA64A/uviY+uyEiIskmmnfx3djMXROaWLsDmNrg9nxgfszpREQkZWmShIiIJCQVlIiIJCQVlIiIJCQVlIiIJCQVlIiIJCRzb9XQhrgws0pgSyu/TBawuw3iJLpU2M9U2EfQfiabVNjPttrHoe6e3XhjQhZUWzCzVe6eH3SOeEuF/UyFfQTtZ7JJhf2M9z7qKT4REUlIKigREUlIyVxQc4IO0E5SYT9TYR9B+5lsUmE/47qPSfsalIiIhFsyn0GJiEiIqaBERCQhhb6gzKzAzIrNrNTMpjdxv5nZzyP3f2Rm5wSRszWi2MfxZrbfzD6I/Pk/QeRsDTOba2YVZra6mftDfxwhqv0M/bEEMLPBZrbMzNaZ2Rozu7uJNaE+plHuY+iPp5llmtnbZvZhZD//o4k18TmW7h7aP0AasAEYDnQEPgTGNVozFVhA/UfQXwC8FXTuOOzjeODloLO2cj8vBc4BVjdzf6iP40nsZ+iPZWQ/BgDnRK53B9Yn4e9mNPsY+uMZOT7dItczgLeAC9rjWIb9DOo8oNTdN7r7ceBZ4JpGa64Bfuv13gR6ffpx9SERzT6GnrsvB/aeYEnYjyMQ1X4mBXcvd/f3ItcPUv+hpYMaLQv1MY1yH0MvcnwORW5mRP40fnddXI5l2AtqELCtwe0y/vkHJJo1iSza/BdGTsEXmNmp7ROtXYX9OJ6MpDqWZpYLnE39v7wbSppjeoJ9hCQ4nmaWZmYfABXAYndvl2PZ4ifqJjhrYlvjZo9mTSKLJv971M+yOmRmU4E/A3nxDtbOwn4co5VUx9LMugHPAfe4+4HGdzfxkNAd0xb2MSmOp7vXAmeZWS/gBTM7zd0bvo4al2MZ9jOoMmBwg9s5wI4Y1iSyFvO7+4FPT8HdfT6QYWZZ7RexXYT9OEYlmY6lmWVQ/xf30+7+fBNLQn9MW9rHZDqeAO6+D3gVKGh0V1yOZdgL6h0gz8yGmVlH4AbgpUZrXgL+NfIukwuA/e5e3t5BW6HFfTSz/mZmkevnUX9c97R70vgK+3GMSrIcy8g+PAGsc/cHm1kW6mMazT4mw/E0s+zImRNm1hmYCBQ1WhaXYxnqp/jcvcbM7gQWUv9ut7nuvsbMbo/c/ytgPvXvMCkFjgC3BpU3FlHu4/XAt82sBjgK3OCRt9aEhZk9Q/07nrLMrAz4MfUvxibFcfxUFPsZ+mMZcTFwE/Bx5LULgP8FDIGkOabR7GMyHM8BwDwzS6O+YP/g7i+3x9+zGnUkIiIJKexP8YmISJJSQYmISEJSQYmISEJSQYmISEJSQYmISEJSQYnEiZn1aTDFeqeZbY9cP2RmjwWdTyTR6W3mIu3AzP4dOOTus4POIhIWOoMSaWeRzwh6OXL9381snpktMrPNZnadmc0ys4/NrDAySgczO9fMXjOzd81sYZimfovESgUlErwRwFXUf2TBU8Aydz+d+skDV0VK6hHgenc/F5gL/FdQYUXaS6hHHYkkiQXuXm1mH1M/zqowsv1jIBcYDZwGLI6MdUsDQjOzTiRWKiiR4B0DcPc6M6tuMKutjvrfUQPWuPuFQQUUCYKe4hNJfMVAtpldCPUf8RDWD74TORkqKJEE5+7HqZ+KPdPMPgQ+AC4KNJRIO9DbzEVEJCHpDEpERBKSCkpERBKSCkpERBKSCkpERBKSCkpERBKSCkpERBKSCkpERBLS/wfEBGRrQIHfsgAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1342,7 +1339,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "romantic-measure",
+   "id": "julian-security",
    "metadata": {},
    "source": [
     "Whenever you make a figure, you should label the axes. The ModSim\n",
@@ -1351,13 +1348,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "occupied-claim",
+   "execution_count": 71,
+   "id": "helpful-pilot",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1380,7 +1377,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "practical-fruit",
+   "id": "false-thumb",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -1399,7 +1396,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bronze-adventure",
+   "id": "sweet-cartoon",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -1407,7 +1404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "headed-invite",
+   "id": "destroyed-trainer",
    "metadata": {},
    "source": [
     "**Exercise:** What happens if you spell the name of a state variable wrong?  Edit the following cell, change the spelling of `wellesley`, and run it.\n",
@@ -1418,7 +1415,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "compressed-utilization",
+   "id": "olympic-adjustment",
    "metadata": {},
    "outputs": [
     {
@@ -1440,7 +1437,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cheap-panama",
+   "id": "aquatic-taxation",
    "metadata": {},
    "source": [
     "**Exercise:** Make a `State` object with a third state variable, called `babson`, with initial value 0, and display the state of the system."
@@ -1449,7 +1446,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "elect-makeup",
+   "id": "crucial-myanmar",
    "metadata": {},
    "outputs": [
     {
@@ -1511,7 +1508,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "prescribed-white",
+   "id": "parliamentary-cherry",
    "metadata": {},
    "source": [
     "**Exercise:** Wrap the code in the chapter in a function named `run_simulation` that takes three parameters, named `p1`, `p2`, and `num_steps`.\n",
@@ -1537,8 +1534,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "id": "strategic-reporter",
+   "execution_count": 72,
+   "id": "overall-command",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1548,9 +1545,9 @@
     "    results = TimeSeries()\n",
     "    results[0] = bikeshare.olin\n",
     "    \n",
-    "    for i in range(1, num_steps):\n",
+    "    for i in range(num_steps):\n",
     "        step(p1, p2)\n",
-    "        results[i] = bikeshare.olin\n",
+    "        results[i+1] = bikeshare.olin\n",
     "        \n",
     "    plot(results, label='Olin')\n",
     "    decorate(title='Olin-Wellesley Bikeshare',\n",
@@ -1560,51 +1557,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "id": "adjustable-zoning",
+   "execution_count": 73,
+   "id": "forbidden-salem",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Moving a bike to Olin\n",
       "Moving a bike to Olin\n",
       "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
       "Moving a bike to Olin\n",
       "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
       "Moving a bike to Olin\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
       "Moving a bike to Olin\n",
       "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
       "Moving a bike to Olin\n",
+      "Moving a bike to Wellesley\n",
       "Moving a bike to Olin\n",
       "Moving a bike to Wellesley\n",
       "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n",
-      "Moving a bike to Olin\n",
+      "Moving a bike to Wellesley\n",
       "Moving a bike to Wellesley\n",
       "Moving a bike to Wellesley\n",
       "Moving a bike to Olin\n",
       "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n"
+      "Moving a bike to Olin\n"
      ]
     },
     {
      "data": {
-      "image/png": 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ur5nZ4Aa7ZwAfCt7/AngFeCDRsZQt3ErvLjlcO7pfoi/VrNIpRXz2seVc9J2XyIxxiqUxA7vzvx+bmLApmURE2puwJovt5+47Adx9p5n1bepAM7sXuBcgEmnbfaMLIz2ZPKQXuVnJHRzR0HWj+3PvZUPZfzS2h3Z3HTrBi+t2s6ziQJMr/oqIpBtLxjM5QQtqvruPCbYPuHuPep/vd/cWf/MWFxf7kiVLEhZne3X0ZA0l33qJa0b34wd3jA87HBGRuDKzpe5e3HB/WKP4dpvZAIDg656Q4kgJXXKzuPnCgZoqSUQ6lLAS1Dzg7uD93cDckOJIGTNLIpoqSUQ6lGQMM38MeAs4z8wqzeyvge8AV5vZRuDqYFuacf6AfCZoqiQR6UCSMYrvriY+mproa6eb0pIi7n9qJQs37+OiYb3DDkdEJKE63EwSqeyGsQPo3ilbUyWJSIegBJVC8rIzuXVCoaZKEpEOQQkqxWiqJBHpKJSgUsy5fbsyZWgvZi3aqqmSRCStKUGloJklRWzbd5zXN+0NOxQRkYRRgkpB147uR+8uOZQt1GAJEUlfSlApKDcrk9uLB/HShj3sOngi7HBERBJCCSpFzZwc4XSt8/jiirBDERFJCCWoFBXp3ZlLh5/DE4u3UXO6NuxwRETiTgkqhZWWFLHz4An+8HZV2KGIiMSdElQKm3p+X/rl5zJLM0uISBpSgkph2ZkZfGRShFfeqWLbvmNhhyMiEldKUCnuzkmDMNBgCRFJO0pQKa6gRyeuHNmXJxZXUq3BEiKSRpSg0kBpSRF7j5xkwbrdYYciIhI3SlBp4LIRfRjYo5OW4RCRtKIElQYyM4y7Jg/izU3vs7nqSNjhiIjEhRJUmrijeBBZGcZjizRYQkTSgxJUmuibn8c1o/sxe2klJ6pPhx2OiEibhZqgzOwfzGytma0xs8fMLC/MeFJdaUkR+49V8/yaXWGHIiLSZqElKDMbCHwWKHb3MUAmcGdY8aSDi4b2ZnDvzhosISJpIewuviygk5llAZ2BHSHHk9IyMoyZJREWb9nPO7sPhx2OiEibhJag3H078H2gAtgJHHT3FxseZ2b3mtkSM1tSVaVJUVty28RB5GRmaDFDEUl5YXbx9QRmAEOAAqCLmX204XHu/rC7F7t7cZ8+fZIdZsrp1SWH6y/oz9PLt3PsVE3Y4YiInLUwu/iuAt5z9yp3rwaeBi4OMZ60MbOkiMMnapi/cmfYoYiInLUwE1QFMMXMOpuZAVOB9SHGkzYmDe7J8L5dNVhCRFJaqxKUmfU0s7HxuLC7lwOzgWXA6iCWh+NRdkdnZpSWRFhZeZA12w+GHY6IyFlpMUGZ2Stmlm9mvYCVwM/N7AfxuLi7f9XdR7r7GHf/mLufjEe5AjdPKCQvO4Oycs0sISKpKZYWVHd3PwTcAvzc3ScSvX8k7Vj3TtncOLaAuSu2c/hEddjhiIi0WiwJKsvMBgB3APMTHI/EUemUIo6dOs3cFXq8TERSTywJ6l+BF4B33X2xmQ0FNiY2LImHcYXdGV2QT1l5Be4edjgiIq3SYoJy96fcfay7/22wvdndb018aNJW0cESRazfeYjl2w6EHY6ISKvEMkhihJm9ZGZrgu2xZvbPiQ9N4mH6+AK65GRStlCDJUQktcTSxfdT4ItANYC7r0KTuqaMrrlZ3HThQOav2sHBYxosISKpI5YE1dndFzXYpzl0UkhpSREna2qZs6wy7FBERGKWFcMxe81sGOAAZnYb0cldJUWMKsjnwkgPysq3csXIvm0qy4BBvTqTmWHxCU5EpAmxJKhPE53hYaSZbQfeA0oTGpXEXWlJEV94aiVXfP+VNpd172VD+dL157c9KBGRZsSSoHq6+1Vm1gXIcPfDZnYjoIneUsjNFw6ka24mJ6pr21TO7KWVPL6ogs9fPYK87Mw4RScicqZYEtRPzexud18NYGZ3Av8A/CahkUlcZWYY140Z0OZy+nfP486HFzJ/1U5um1gYh8hERBoXyyCJ24BfmNn5ZvYJol1+1yQ2LGmvSob0YlifLpopXUQSLpYHdTcTHVY+h2iyusbdNUV2B2VmzCwpYnnFAdbtOBR2OCKSxppMUGa22sxWmdkqosti9AIGA+XBPumgbp0wkNysDGYtUitKRBKnuXtQ05IWhaSUHp1zmDa2gGeWbefBD59P19xYbmWKiLROc118+919K3C4iZd0YDNLIhw9dZp5mildRBKkuQQ1K/i6FFgSfF1ab1s6sAmRHozs342y8q2aKV1EEqLJBOXu04KvQ9x9aPC17jU0eSFKe2RmlE4pYu2OQ6yq1JgZEYm/WIaZY2a3mNkPzOwhM7spwTFJirhpfAGdczI15FxEEiKW5Tb+C/gUsBpYA3zKzP4z0YFJ+9ctL5sZ4wuYt3IHB49rpnQRia9YWlCXA9e6+8/d/efA9cCH4nFxM+thZrPNbIOZrTezi+JRriTPzMlFnKiu5RnNlC4icRZLgnobiNTbHgTE6zmoHwHPu/tIYBywPk7lSpJcUNidcYXdmbVIy8qLSHw1+QCLmf2G6BIb3YH1ZrYo2C4B/tjWC5tZPnAZcA+Au58CTrW1XEm+mSURHpizmknffIn2sArH8H5d+dVflZDRHoIRkbPW3BOW30/wtYcCVcDPzWwc0eHr97n70foHmdm9wL0AkUjkjEIkfDPGD+TdqqMcPhH+faiqw6f4/frdvL5pL5eP6BN2OCLSBhZWt4yZFQMLgUvcvdzMfgQccvd/aeqc4uJiX7JEj2BJ007V1HLRt19iYlFPHv7L4rDDEZEYmNlSdz/jBzamYeYJUglUunt5sD0bmBBiPJIGcrIyuL14EC9t2MOugyfCDkdE2iC0BOXuu4BtZnZesGsqsC6seCR9zJwc4XSt88TibWGHIiJt0Nxs5i8FX7+bwOv/PVAWzI4+HvhWAq8lHUSkd2cuG9GHxxdXUHO6bSsIi0h4mmtBDTCzy4HpZnahmU2o/4rHxd19hbsXu/tYd7/J3ffHo1yRmZMj7Dx4gj+8XRV2KCJylpobxfcV4EGgEPhBg88cuDJRQYm01dTz+9IvP5ey8q1cPapf2OGIyFloMkG5+2xgtpn9i7t/I4kxibRZdmYGH5kU4ccvb2TbvmMM6tU57JBEpJViWfL9G2Y23cy+H7y0kKGkhDsnDcKAxxdXhB2KiJyFWCaL/TZwH9ERduuA+4J9Iu1aQY9OXDmyL08sruRUjQZLiKSaWIaZ3wBc7e6PuPsjwHXBPpF2r7SkiL1HTrJg3e6wQxGRVor1Oage9d53T0AcIglx2Yg+DOzRiVmLtGaVSKqJJUF9G1huZo+a2S+Izpmn55UkJWRmGHdNHsSbm95nc9WRsMMRkVaIZZDEY8AU4OngdZG7P57owETi5Y7iQWRlGLPKNVhCJJXE1MXn7jvdfZ67zw2mKBJJGX3z87hmdD9mL6vkRPXpsMMRkRiFOVmsSNLMnFzEgWPVPL9Gf1+JpAolKOkQLh7Wm8G9O1NWrsESIqmi2QRlZhlmtiZZwYgkSkaGMbMkwuIt+3ln9+GwwxGRGDSboNy9FlhpZlrKVlLebRMHkZOZocESIikili6+AcBaM3vJzObVvRIdmEi89eqSw4cv6M+cZZUcO1UTdjgi0oLmZjOv8/WERyGSJKUlRcxdsYP5K3dyx6RBYYcjIs2I5TmoV4EtQHbwfjGwLMFxiSTEpME9Gd63K2WL1M0n0t7FMlnsJ4DZwP8GuwYCzyYwJpGEMTNKSyKs3HaANdsPhh2OiDQjlntQnwYuAQ4BuPtGoG8igxJJpJsnFJKXnUGZBkuItGuxJKiT7n6qbsPMsoiuqCuSkrp3yubGsQXMW7GdIyc1WEKkvYolQb1qZl8COpnZ1cBTwG/iFYCZZZrZcjObH68yRVpSOqWIo6dO8+zy7WGHIiJNiCVBPQhUAauBTwLPAf8cxxjuA9bHsTyRFo0r7M7ognzKyitwV4eASHvU4jBzd68NltkoJ9q197bH6SfazAqJLn74TeDz8ShTJBZm0ZklvvzMGn7y8iZ6dc1J2rWzMzKYNm4AnXNiecpDpONq8SfEzG4A/gd4FzBgiJl90t1/F4fr/zvwT0C3Zq5/L3AvQCSiCS0kfmaMH8hDL77DQwveSfq1dx06wWenDk/6dUVSibXUGDKzDcA0d98UbA8DfuvuI9t0YbNpwPXu/ndm9iHgC+4+rblziouLfcmSJW25rMgHHDtVw5ETyR0o8bknVrBl71Fef+BKMjMsqdcWaY/MbKm7FzfcH0sfw5665BTYDOyJQ0yXANPN7HogD8g3s1+7+0fjULZITDrnZCW9q+1jU4r427JlvPL2Hqae3y+p1xZJJU0OkjCzW8zsFqLz8D1nZveY2d1ER/AtbuuF3f2L7l7o7oOBO4GXlZykI7hqVD/6dMvVc1giLWjuT8cb673fDVwevK8CeiYsIpE0l52ZwZ2TBvGTP2yicv8xCnt2DjskkXapyQTl7h9PVhDu/grwSrKuJxK2jwQJ6onF27j/mvPCDkekXYplFN8Q4O+BwfWPd/fpiQtLJL0V9uzMFef15fHF2/js1OFkZ2pxa5GGYvmpeJbobOY/Bh6q9xKRNigtiVB1+CS/X7c77FBE2qVYhi+dcPf/SHgkIh3Mh87rS0H3PGYtquDDFwwIOxyRdieWFtSPzOyrZnaRmU2oeyU8MpE0l5lh3DU5wusb97Jl79GwwxFpd2JJUBcAnwC+w5+7976fyKBEOoo7Jg0iM8N4TAsoipwhli6+m4Gh9ZfcEJH46Jefx9Xn9+OppZV8/poR5GZlhh2SSLsRSwtqJdAjwXGIdFilUyLsO3qK59fsCjsUkXYllhZUP2CDmS0GTtbt1DBzkfi4ZNg5FPXuzC/f2srogu5nfN6nay7dO2eHEJlIuGJJUF9NeBQiHVhGhjFzcoRv/24DV/3g1TM+75+fx2v/dAU5WXpWSjqWWNaDOvMnRkTi6p5LBhPp1Znq2g+uLrB171EeWvAOL67bxbSxBSFFJxKOWGaSOEx0oUKAHCAbOOru+YkMTKQjyc3KbPRZqNpa54kl2yhbWKEEJR1Oi30G7t7N3fODVx5wK/CTxIcmIhnBs1JvbX6fd6uOhB2OSFK1ulPb3Z8Frox/KCLSmDuKB5GVYTym5Tmkg4mli++WepsZQDF/7vITkQTr0y2Xa0f3Z/aySr5w7XnkZetZKekYYmlB3VjvdS1wGJiRyKBE5INKSyIcOFbNc6t3hh2KSNLEMoovaetCiUjjLhrWm6HndGFWeQW3TCgMOxyRpGgyQZnZV5o5z939GwmIR0QaYWbMLInwb79dz4ZdhxjZX4NoJf0118V3tJEXwF8DDyQ4LhFp4NYJheRkZTBLgyWkg2gyQbn7Q3Uv4GGgE/Bx4HFgaJLiE5FAzy453HDBAJ5Ztp1jp2rCDkck4ZodJGFmvczs34BVRLsDJ7j7A+6+JynRicgHlJZEOHyyht+s3BF2KCIJ12SCMrPvAYuJjtq7wN2/5u7743VhMxtkZn8ws/VmttbM7otX2SLpamJRT0b060qZuvmkA2iuBXU/UAD8M7DDzA4Fr8NmdigO164B7nf384EpwKfNbFQcyhVJW2ZGaUkRqyoPsqryQNjhiCRUc/egMty9U4OpjvLrttt6YXff6e7LgveHgfXAwLaWK5Lubp4wkE7ZmRosIWmvXczfb2aDgQuB8kY+u9fMlpjZkqqqqqTHJtLe5OdlM31cAfNW7uDQieqwwxFJmNATlJl1BeYAn3P3M7oO3f1hdy929+I+ffokP0CRdmhmSYRjp04zd/n2sEMRSZhQE5SZZRNNTmXu/nSYsYikkrGF3RkzMJ+y8grcNTWmpKfQEpSZGfB/wHp3/0FYcYikorrBEht2HWZZxYGwwxFJiDBbUJcAHwOuNLMVwev6EOMRSSnTxxXQNTeLsvKtYYcikhChJSh3f8Pdzd3Huvv44PVcWPGIpJouuVncdGEB81ft5MCxU2GHIxJ3oQ+SEJGzN3NyEadqapm9tDLsUETiTglKJIWNKshnQqQHsxZpsISkHyUokRRXWlLE5qqjLNy8L+xQROJKCUokxd0wdgDdO2VrsISkHSUokRSXl53JrRMKeWHtLvYeORl2OCJxowQlkgZmlkSoPu08tUSDJSR9KEGJpIFz+3ZlytBezFq0ldpaDZaQ9JAVdgAiEh8zS4r47GPL+fiji+mam4AfbYN7Lh7MpMG94l92krk7P3ppI1NH9uOCwu5xL/+/X3mXNdsPxnx8j87Z/Mu0UeRlZ8Y9llSmBCWSJq4d3Y9Lh5/D9gPHE1L+zgPH2XngOE//3SUJKT+ZllXs599/v5ElW/bz678piWvZW/Ye5bvPb6B/fh5d81r+FXu61nlv71EmFvXklgmFcY0l1SlBiaSJ3KxMfvXX8f1lW9/PXt/Mv/12Pet3HuL8AW1eEi5UZQuja2m9sWkvW/YeZfA5XeJW9mOLKsjKMOZ95hL65ue1eLy7M/WhVykrr1CCakD3oEQkJrdNLCQnKyPlF0o8cOwU81fv5JpR/cjMMB5bFL/6nKw5zZNLtnH1qH4xJSeITvw7syTC0q372bArHouVpw8lKBGJSY/OOUwbO4Bnlm/n6MmasMM5a7OXVnKqppbPXTWCq8/vx5NLtnGy5nRcyn5+zS72H6umtKSoVefdOiE9kn+8KUGJSMxKSyIcOVnDvJU7wg7lrLg7s8ormBDpwaiCfEqnRNh/rJrn1+yKS/llCyso6t2Zi4f1btV5PbvkcMMFA3h6WWon/3hTghKRmE2I9GRk/24pO2vFW5vfZ/Peo39q4Vwy7BwivTpTFoeWy8bdh1m0ZR8zJ0fIyLBWn1+X/H+Tosk/EZSgRCRm0YUSI6zZfohVlQfCDqfVysor6N4pmxvGDgAgIyN6/2fRe/vYuPtwm8vOyczgtolnN9BhYlFPzuvXLS7JMl0oQYlIq8y4cCCdsjP/NBIuVVQdPsmLa3dx64TCDzxvdPvEQrIzrU2J4fip08xZVsl1Y/rTu2vuWZVRN1hi9faDKZn8E0EJSkRaJT8vmxnjC5i3cgeHTlSHHU7Mnlq6jerTzsySyAf29+6ay3VjBjBnWSXHT53dYInfrNrB4RM1lDYou7VunhBN/hosEaUEJSKtVlpSxPHq0zy7fHvYocSktjY6OGLK0F6c27frGZ+XlkQ4fKKG+avO7v7PrPIKzu3blclD2jbLRn5eNtPHFTB3RWol/0RRghKRVrugsDsXDOxO2cLUWCjxtY1VVO4/zswmhn+XDOnFsD5dzqqbb832g6zYdoCZkyOYtX5wREMzSyIplfwTKdQEZWbXmdnbZrbJzB4MMxYRaZ3Skghv7z7M0q37ww6lRWXlFfTuksO1o/s1+nn0/k8RK7YdYO2O2OfQA5i1qILcrAxujdMsEGMLuzNmYH7KJP9ECi1BmVkm8J/Ah4FRwF1mNiqseESkdW4cV0C33Kx2P+ps58HjvLxhD7cXDyI3q+nJWG+dMJDcVj4se+RkDXOXb+fGcQV075wdj3CDkZJFvL37MMsq2n/yT6Qw5+KbDGxy980AZvY4MANYF2JMIhKjLrlZ3DxhII8v3sZXpo2iZ5ecuF/judU727wIY/l7+zhd69w1eVCzx0Vnyijg2eXbGdGvG7H01q3bcYijp063eXBEQ9PHFfDN367noRff4box/eNadksmRHoyZmD8Z3g/G2EmqIHAtnrblcAZM12a2b3AvQCRSHz/E4hI28wsifDLt7Yye2kln7hsaFzLXlaxn78rWxaXsq46vx9FvVueEPbui4t4ZnklX523Nuayxw/qwfhBPdoQ3Zm65GZx56RB/OyN9/jju+/HteyW9M/P440HriArM/whCmEmqMb+Pjmjw9XdHwYeBiguLu7YHbIi7czI/vlMLOrJrEUV/M2lQ+IySKBO2cIKuuRk8uLnLycvq22/LHt0jq11N7awB6u+di0nq2Mfbp7fKTuu9a7z5RvO5++uODep96Fe37iXzz2xgj+8XcXVoxq/X5dMYSaoSqB+m7sQ0BwfIimmtCTC559cyVvvvs/F554TlzIPHqtm/qod3DaxkIE9OsWlzFh1zc1KzIKPrWRm9EpAt2lzpo0dwLd/t56y8q3tIkGF2YZbDAw3syFmlgPcCcwLMR4ROQvXXzCA7p2yKYvjshVzllVysqb2jIdqJbGyMjP4yKQIr75TxbZ9x8IOJ7wE5e41wGeAF4D1wJPuHnvHr4i0C3nZmdw2sZAX1uyi6nDbBjRAdMbxsvKtjB/Ug9EF7eNmfUdy56RBGPD44vBHZ4Z6F8zdn3P3Ee4+zN2/GWYsInL2ZpZEqKl1nlyyreWDW1D+3j7erToa95FxEpuCHp24cmRfnlgcXTcrTOEP0xCRlDesT1cuGtqbxxZVUFvbtpv6s8oryM/LYtrYgjhFJ61VWlLE3iMnWbBud6hxKEGJSFzMLIlQuf84r22sOusy9h45ye/W7OSWCYV0ymn6oVpJrMtG9GFgj07MWhTuul9KUCISF9eO7k/vLjltmlli9tJKqk+7uvdClhmsk/XmpvfZXHUktDiUoEQkLnKyMrhj0iBe3rCHnQePt/r82lrnsUUVTB7Si+H9uiUgQmmN24sLycowHovj6MzWUoISkbi5a1KEWneeWNz6wRJvvruXre8fU+upnejbLY9rRvdj9tJKTrTiweV4UoISkbiJ9O7MpcP78PiibdScbt0IsLKFFfTqkpP0ueekaaUlRew/Vs3za3aFcn0lKBGJq9KSCLsOneDlDXtiPmf3oRMsWL+b2ycWNjvjuCTXRUN7M7h3Z8rKwxksEf58HiKSVqaO7Eu//Fx+8dYWBp/T8gStEB0cEZ1xXN177UlGMFjiW89t4J3dhxmR5HuDSlAiEldZmRncNTnCv/9+I9f88LWYz7t0+DkxJzRJntsmDuL7L7xD2cKtfH3GmKReWwlKROLuU5cPY2T/fE634qHdSUN6JjAiOVu9uuTw4Qv68/Ty7Tzw4ZF0zkle2lCCEpG4y8vO1GCHNFJaUsTcFTuYv3Ind0xqfuHHeNIgCRERadakwT0Z3rdr0gdLKEGJiEizzIzSkggrKw+yZvvBpF1XCUpERFp084RC8rIz2jSVVWspQYmISIu6d8rmxrEFzF2xncMnqpNyTSUoERGJSemUIo6dOs3cFTuScj0lKBERicm4wu6MLsinrLwC97at+xULJSgREYmJWXRmifU7D7F824GEX08JSkREYjZj/EC65GRStjDxgyWUoEREJGZdc7O46cKBzF+1g4PHEjtYIpQEZWbfM7MNZrbKzJ4xsx5hxCEiIq1XWlLEyZpa5iyrTOh1wmpBLQDGuPtY4B3giyHFISIirTSqIJ/xg3pQVr41oYMlQklQ7v6iu9cEmwuBwjDiEBGRs1NaEuHdqqOUv7cvYddoD5PF/hXwRFMfmtm9wL0AkYjWihERaQ+mjS3grXffJz8vO2HXsEQ1z8zs90Bj0xl/2d3nBsd8GSgGbvEYAikuLvYlS5bEN1AREQmVmS119+KG+xPWgnL3q1oI6G5gGjA1luQkIiIdSyhdfGZ2HfAAcLm7HwsjBhERad/CGsX3E6AbsMDMVpjZ/4QUh4iItFOhtKDc/dwwrisiIqlDM0mIiEi7pAQlIiLtkhKUiIi0S0pQIiLSLiXsQd1EMLMqYGsbizkH2BuHcNq7jlDPjlBHUD3TTUeoZ2vrWOTufRruTKkEFQ9mtqSxJ5bTTUeoZ0eoI6ie6aYj1DNedVQXn4iItEtKUCIi0i51xAT1cNgBJElHqGdHqCOonummI9QzLnXscPegREQkNXTEFpSIiKQAJSgREWmXOkyCMrPrzOxtM9tkZg+GHU+8mNkjZrbHzNbU29fLzBaY2cbga88wY4wHMxtkZn8ws/VmttbM7gv2p1VdzSzPzBaZ2cqgnl8P9qdVPQHMLNPMlpvZ/GA7Heu4xcxWB6s2LAn2pWM9e5jZbDPbEPyMXhSPenaIBGVmmcB/Ah8GRgF3mdmocKOKm0eB6xrsexB4yd2HAy8F26muBrjf3c8HpgCfDv4N062uJ4Er3X0cMB64zsymkH71BLgPWF9vOx3rCHCFu4+v91xQOtbzR8Dz7j4SGEf037Xt9XT3tH8BFwEv1Nv+IvDFsOOKY/0GA2vqbb8NDAjeDwDeDjvGBNR5LnB1OtcV6AwsA0rSrZ5AYfBL60pgfrAvreoY1GMLcE6DfWlVTyAfeI9g0F0869khWlDAQGBbve3KYF+66ufuOwGCr31DjieuzGwwcCFQThrWNej6WgHsARa4ezrW89+BfwJq6+1LtzoCOPCimS01s3uDfelWz6FAFfDzoMv2Z2bWhTjUs6MkKGtkn8bXpyAz6wrMAT7n7ofCjicR3P20u48n2sqYbGZjQg4prsxsGrDH3ZeGHUsSXOLuE4jeXvi0mV0WdkAJkAVMAP7b3S8EjhKnbsuOkqAqgUH1tguBHSHFkgy7zWwAQPB1T8jxxIWZZRNNTmXu/nSwOy3rCuDuB4BXiN5jTKd6XgJMN7MtwOPAlWb2a9KrjgC4+47g6x7gGWAy6VfPSqAyaOkDzCaasNpcz46SoBYDw81siJnlAHcC80KOKZHmAXcH7+8mer8mpZmZAf8HrHf3H9T7KK3qamZ9zKxH8L4TcBWwgTSqp7t/0d0L3X0w0Z/Fl939o6RRHQHMrIuZdat7D1wDrCHN6unuu4BtZnZesGsqsI441LPDzCRhZtcT7ffOBB5x92+GG1F8mNljwIeITm+/G/gq8CzwJBABKoDb3X1fSCHGhZn9BfA6sJo/37f4EtH7UGlTVzMbC/yC6P/TDOBJd/9XM+tNGtWzjpl9CPiCu09Ltzqa2VCirSaIdoPNcvdvpls9AcxsPPAzIAfYDHyc4P8vbahnh0lQIiKSWjpKF5+IiKQYJSgREWmXlKBERKRdUoISEZF2SQlKRETaJSUo6bDMrHcwy/QKM9tlZtuD90fM7L+SFMP44BGIeJVnZvaymeW34pzpLc3wHzyf9XzbIxSJXVbYAYiExd3fJzpjOGb2NeCIu38/yWGMB4qB5+JU3vXAytZMA+Xu82jhwXV3rzKznWZ2ibu/2dYgRWKhFpRIA2b2oXprFH3NzH5hZi8Ga/vcYmb/L1jj5/lg+iXMbKKZvRpMCvpC3RQvDcq93czWWHStp9eCWU3+FfhI0HL7SDD7wCNmtjiYeHNGcO49ZjY3uObbZvbVJsIvJXhi38wGB+vz/Cy4bpmZXWVmbwZr9EyuV/ZPgvePmtl/mNkfzWyzmd1Wr+xng/JFkkIJSqRlw4AbgBnAr4E/uPsFwHHghiBJ/Ri4zd0nAo8Ajc1U8hXgWo+u9TTd3U8F+57w6HpBTwBfJjr1zyTgCuB7wTQ5EJ3HrZRoq+t2Mys+4wrRee7qT8J6LtG1esYCI4GZwF8AXyA6E0djBgTHTAO+U2//EuDSJs4RiTt18Ym07HfuXm1mq4lOQVR3L2Y10bW4zgPGAAuiUwaSCexspJw3gUfN7Eng6UY+h+h8bdPN7AvBdh7RqWIguvTG+wBm9jTRJLKkwfm93P1wve333H11cM5aogvIeVCXwU3E8Ky71wLrzKxfvf17gIImzhGJOyUokZadBHD3WjOr9j/PD1ZL9GfIgLXuflFzhbj7p8yshGhrbEUwf1lDBtzq7m9/YGf0vIbzkjU2T1mNmWUECeZPsdeL92S99039/Nc/p/5SNXlEW40iSaEuPpG2exvoY2YXQXRZEDMb3fAgMxvm7uXu/hVgL9ElYA4D3eod9gLw98Hs7ZjZhfU+u9rMegWznN9EtEXWWCxD41CnxowgOhu3SFIoQYm0UXAv6Tbgu2a2ElgBXNzIod8LBlesAV4DVgJ/AEbVDZIAvgFkA6uC475R7/w3gF8F5c9x94bdewC/JTq7fSJcEZQvkhSazVwkBZjZPUCxu3+mheMGAL9096sTEMNrwAx33x/vskUaoxaUSBpx953AT1vzoG4szKwP8AMlJ0kmtaBERKRdUgtKRETaJSUoERFpl5SgRESkXVKCEhGRdkkJSkRE2qX/D3fJ55cT2CUOAAAAAElFTkSuQmCC\n",
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FzCMjZycYy31UUnmKmkstUIGSUXp6B1jQ3lpUxDwycnaCdN9AMIv5dEXM42rkdUORuFGBklGKmcV8pJFR8yhibqaIeVx15ZhDUSQuVKBkL0NDQcR8rOm7kUNG3eMoclJZnYqaS8ypQMle1m8JIubjiYdHsxMEEfMBRcxjLoqaa/l3iSsVKNlL93CCb+w32EazE7y4aRuDu31cbUjlaNJYiTsVKNlLzwRmII9mJ3jqpS3BY51BxVr0/VFQQuJKBUr2Mp6IeSQaMrrv6Q3BY12DirUZrZNpb5uiMyiJLRUo2Uswi/nYIuaRqCDdtWo9rZObFTGvAZ3JRNbFJkXiQAVK9tLTNzDum2ujArX65W10JhOKmNeArmSbzqAktlSgZFgUMR/v9ERR1Bw0vFcrOpNtrFXUXGKqbAXKzC4zs/Vmtjxj2zlmtsLMhsxsUbn2LeOzbsv2cUfMI9F7FZCoDYqaS5yV8wzqcuC0EduWA2cB95ZxvzJO0bWIicxAHr13LEt1SPVEw7ma1VziaFK5Gnb3e80sNWLbk0DdX5twd678Qw8bBwZHPTdnRgvnLFpQ8n129/az9pVtvOnAjnG3sWcG8vEXl2h4ULOY14bo+3TNI8/zp3Vbqtybyps6qYkPv7GTfaaW7VehTEBsvytmdh5wHsDChQur3JuxeXLtFv7fTStyPv/Wg2ez3/SWku7z+3f+iXuf3sDjF7593G2kJxAxjxx3YJJrl7Zy2P7Tx92GVM6M1skcPmc6d63awF2rNlS7O1WR3GcqZx8zv9rdkCxiW6Dc/VLgUoBFixZ5lbszJtFwyS2fOZ7D5uz5RX3f0xtY8rNH6O7tL3mBeq53K5sGBtnYv5NZbVPG1Ua6t5+FycS4IuaRY7vaeeCCk8b9fqm8W//qeIZq6iesNHYNDXH4hbfT3bu12l2RHGJboGpZNFR2wOy2vX7ZHzh7n+D53n7eeECyZPtz9+HrR919/RMoUAOanqgBmRnN9T3qnlVzUzMLZrXqPrAYU8y8DNK9/ew7bSqJKXvX/zkzWpjcbKRLnJjq69/J1h27gPGvkDo05PS8rBnIpbF06j6wWCtnzPwq4PfAIWa2xsw+bmbvNbM1wHHArWZ2e7n2X025lquY1NzEgvZEyec+y2xvvCukRhHzTsXDpYF0hYs2ujfgGGcNKGeK70M5nrqhXPuMi+7eAU4+dN+sz5Xjzv3ojGxKc9O4z6BKETEXqTWpZIL+nbvp3bqT2dM0NVfcaIivxLbu2EXv1h105rgPqDPZRk/fQEk/saV7+2luMl67cOa4z86iopnS/UvSQKIRAw3zxZMKVIlFBSLXmUhXR4Jtg7tZt3lHyfbZ3dfP/FmtvGrffege53BFujeImM+ZMf6IuUitiX5OdaNyPKlAldiem12zF6hUGT6x9YTLq3d1tLF5+y42ZblBuJB038Qj5iK1Zt6sYOb+8Q6NS3mpQJVYNKdZrqGy4VVMS/SJLYqYp5KJ4ba7x/HDFrSh60/SWCY3NylqHmMqUCUW3IQ7OmIemTuzlcnNNq4ikk3v1iBinupoGy6KYy1+0SzmugdKGlGqo01DfDGlAlViPQWWq2huMha0J+gp0Se24SXaO9pY0J7AjDHfZ7Vuy3Z27JrYLOYitSqVbKOnT1HzOFKBKrHu3oGCUe1SRs2jT36pZBtTJzUzd0brmM+gMtsQaTRR1HzD1tIFl6Q0VKBKaMv2wbwR80iqIyhQpfjE1tM3QHOTMX9WkL7r6mgb8wXfQtfNROpZFDXXmljxowJVQtF/8EJnUKlkgu2DQyWJmkcR88nNwbcy1ZEYc9Q83dvPlElNzFXEXBqQoubxpQJVQumM60H5pEq4SFy6d+/581LJIGqebS2qXLp7+1nYnqBJEXNpQPNntTKpyUo+BZlMnApUCUX/wQst+BcVlInee+Hu9PQNDK+Kmtn2WK5x9fQpYi6Na1JzE/NntWqIL4ZUoEoo3TeQN2IemTuzlSnNTROOmkcR88yCOHwjcJGfBqOIuZZol0amqHk8qUCVULo3f8Q8EkTNWyccNe/JMqS4oL2VpjFEzaOIuZZol0amqHk8qUCVULqvv+jZwLs6Jh41zxYPnzqpmbkzi4+aR2106R4oaWCKmseTClSJBBHznUXf7BotlDY0gbW20339e0XMI6kx3GcVTfFS6LqZSD3bMzSu61BxMqYCZWazzOyocnWmlg3fS1TkL/pURxvbB4dYv2X8n9jSfQMsyIiY72m7+Kh5T58i5iLjCRdJ+RUsUGZ2t5lNN7N24AngZ2b2vfJ3rbYUGzGPRIVsIhdmc13zSiXb2FJk1Ly7t59ORcylwSlqHk/FnEHNcPfNwFnAz9z9GOCU8nar9hQbMY9MNGqeLWI+su1iPg329A0oICENb1JzUzBHpqLmsVJMgZpkZnOA9wO3lLk/Nau7t7iIeWSiUfNsEfNIsVFzRcxF9uhMJhQ1j5liCtTXgNuBZ939ETM7AHi6vN2qPdGigcWKouYTX6J99D6Ho+YF2n5ps2YxF4lE4SJFzeOjYIFy9+vc/Sh3/3T4+Dl3f1/5u1Zb0mMsUBBN7Dq+IYV8S8sPR80LtD1c5DTEJ0IqmWBAUfNYKSYkcbCZ/dbMloePjzKzvy9/12rHWCPmkdQEouZRxHzerOzpu2Lus4oitTqDElHUPI6KGeL7MfBFYBDA3f8IfLCcnao1w7OYj/FaTmcYNV+3ZfuY95nuzR4xH247WThqng4j5nOmt4x5/yL1pmuM04RJ+RVToBLu/vCIbbvK0Zla1T2c4BvjEF9y/J/Y0gVW7i0map5WxFxk2LyZYdRc90LFRjEFqtfMDgQcwMzOBtaWtVc1ZqwR80i0QOBYfyDcnXRvf97pibqKWNIj3dev4T2RUBQ1V4GKj2IK1PnAj4BDzewF4HPAp8rZqVpT7CzmI82ZEUTNx/oD0bt1J/07d+edtaKzwH1WQ0MeLrOhiLlIpDOZ0DWoGCmmQM1y91OA2cCh7n48oOmOMownwQdB1HxhMjHmMe+ooHXmOftZ2J7IGzVXxFxkNEXN46WokISZvdrd+919i5l9EFCKL0NPX/7htnxS4/jE1p0nYh6ZMqmJebNa6c4RNc8XUxdpVF0dbUHUfAJzZErpFFOgzgauMLPDzOwTBEN+by9vt2pHFDEf73RBqWQbPS+PLWreUyBivlfbOYb4onuk8p2FiTSa6DpyseupSXkVc6PucwSx8usJitXb3f2VcnesVkRnP+OdLig1jqh5oYj5cNvJtpxRc0XMRUZT1Dxecl7VN7NlhMm9UDvQDDxkZri7rkORcT1oAmdQEBSdOUUueVFs+q4zmRiOmre3Tdm7DUXMRUZR1Dxe8sXOzphIw2Z2WdjGenc/MtzWDlwDpIA08H533ziR/VRbOsuqtmORGTU/7sBkwddHEfPXp9oLvjYzaj6qQCliLjKKoubxkm+MaKO79wBbcvwp5HLgtBHbLgB+6+4HAb8NH9e07r5+9p/eQuuU5nG9f+6MVqZMaip6SGHD1h0FI+aRXLOaRxFzLfMuMloqmaBbUfNYyHcG9QuCM6BHCYb6MseCHDggX8Pufq+ZpUZsPhM4Ifz6CuBu4AtF93acrvxDDyvXbi5L2w8+0zeh5dKbmoyF7QluX/ESW3YUnqBjY/9OoLhww4JZQdT853/o4dHn95yo7hgcYseuIS3zLpJFZ7KNB5/t40s3LBv13CmH7ctJh+5XhV41ppwFyt3PCP/uKuH+9nP3tWG7a81s31wvNLPzgPMAFi5cOKGdPrF6E3et2jChNvI59fCJ/Yd955H7c9XDq7ljxbqiXn/A7DaOmjej4OumTGriHUfszyPpjbywcdtez82f1cobugoPE4o0mrcdMpvblr806udx8/ZBnli9SQWqgqyYG9LM7CzgeIIzp/vc/caiGg/OoG7JuAa1yd1nZjy/0d1nFWpn0aJFvnTp0mJ2KSJSFhfdvIJfPrqGZRe9HTOFi0rJzB5190Ujtxez3Ma/EUxttAxYDnzKzP51nP1YF67OS/j3+nG2IyJSUalkgq07dtG7dWe1u9Iwipk87m3AkR6eapnZFQTFajxuBj4GfDP8+6ZxtiMiUlHRdd+evn5mT5ta5d40hmJmklgFZF4EWgD8sdCbzOwq4PfAIWa2xsw+TlCYTjWzp4FTw8ciIrEXTQuWb4UAKa18N+r+muCa0wzgSTN7OHz8BuDBQg27+4dyPHXyOPopIlJV82a10qybeCsq3xDfJRXrhYhIzE1ubmLBrFbN01dB+WLm91SyIyIicZfqaNM8fRVUzDUoEREhXC8qxwTMUnoqUCIiRUolE/Tv3K2oeYXkLFBm9tvw729VrjsiIvEVRc0VlKiMfCGJOWb2NmCxmV3N3nPx4e6PlbVnIiIx05XcMwFzMSsKyMTkK1AXEsw2Ph/43ojnHDipXJ0SEYmj+bO0XlQl5Uvx/RL4pZn9P3e/uIJ9EhGJpUnNTcyf1Tq8kraUV8Gpjtz9YjNbDLw13HS3u99S3m6JiMRTqqNNZ1AVUsxksf8IfBZYGf75bLhNRKThKGpeOcVMFns6cLS7D8HwZLH/C3yxnB0TEYmjKGq+YesO9p3WUu3u1LVi74OamfF14ZXyRETqVGp4VnNdhyq3Ys6g/hH4XzO7iyBq/lZ09iQiDSqVMau5oublVUxI4iozuxt4PUGB+oK7v1TujomIxNFw1Fxz8pVdMWdQuPtagsUGRUQa2qTmJha0JzTEVwGai09EZIw6kwktXFgBKlAiImOUSrbR06eoebnlLVBm1mRmyyvVGRGRWpAZNZfyyVugwnufnjCzhRXqj4hI7EVRc015VF7FhCTmACvM7GFgeNDV3ReXrVciIjHWlbHsxrFdipqXSzEF6qtl74WISA2ZN1NR80oo5j6oe8ysEzjI3f/HzBJAc/m7JiIST1HUXJPGllcxk8V+Avgl8KNw0zzgxjL2SUQk9lLJhK5BlVkxMfPzgTcDmwHc/Wlg33J2SkQk7jqTwbIbipqXTzEFaoe774wemNkkghV1RUQaVldHGwM7d7Nhi6Lm5VJMgbrHzL4EtJrZqcB1wK/L2y0RkXjrTCYASGvKo7IppkBdAGwAlgGfBH4D/H05OyUiEnfDUXMl+cqmmBTfULhI4UMEQ3urXIOuItLghqPmSvKVTcECZWanA/8BPEuw3EaXmX3S3f+73J0TEYkrRc3Lr5gbdb8LnOjuzwCY2YHArYAKlIg0tFQyQbei5mVTzDWo9VFxCj0HrC9Tf0REakanZjUvq5xnUGZ2VvjlCjP7DXAtwTWoc4BHJrJTM/ss8AmCIcMfu/s/TaQ9EZFqyIya7zu9pdrdqTv5hvjenfH1OuBt4dcbgFnj3aGZHUlQnI4FdgK3mdmt4Q3AIiI1I5rVvLu3XwWqDHIWKHc/t0z7PAz4g7sPAJjZPcB7gW+XaX8iImWRCu+F+totK9mvDAWqdXIzXz3zCDr2mVrytmtBMSm+LuAzQCrz9RNYbmM58HUzSwLbgHcBS7Ps9zzgPICFC7UclYjEz/xZCd555P6s2bit5DNKDO4e4qmXtvD2I/bjzKPnlbTtWlFMiu9G4KcEs0cMTXSH7v6kmX0LuBPYCjwB7MryukuBSwEWLVqkK5AiEjvNTca/f+SYsrS9fXA3h114G90NfCNwMQVqu7v/Syl36u4/JSh6mNk3gDWlbF9EpNa1TG5m7ozWhp6popgC9c9m9hXgDmD4HNbdHxvvTs1sX3dfHy4lfxZw3HjbEhGpV6mOREPP9VdMgXo18FHgJPYM8Xn4eLyuD69BDQLnu/vGCbQlIlKXUsk2bl22ttrdqJpiCtR7gQMyl9yYKHd/S6naEhGpV6lkG5sGBtk0sJOZiSnV7k7FFTOTxBPAzDL3Q0RERojus2rUYb5izqD2A54ys0fY+xrUeGPmIiJShK6OcM2p3n6OXjCzup2pgmIK1FfK3gsRERll/qwEZjRs1LyY9aDuqURHRERkb1HUvKdBl/QoZiaJLQSpPYApwGSg392nl7NjIiISRM27dQ0qO3eflvnYzN5DMNGriIiUWSNHzYtJ8e3F3W9kYvdAiYhIkbo69kTNG00xQ3xnZTxsAhaxZ8hPRETKqDO5Z0mP1y5srHuhiknxZa4LtQtIA2eWpTciIrKXKGre0zfAaxeOeym+mlTMNahyrQslIiIFLGhv3Kh5viXfL8zzPnf3i8vQHxERyTB1UjireQNGzfOdQWX712gDPg4kARUoEZEK6Opoa8jpjvIt+f7d6GszmwZ8FjgXuBr4bq73iYhIaXUmE9zyx8aLmueNmZtZu5n9A/BHgmL2Onf/gruvr0jvRESEro42Xtk2yMb+xoqa5yxQZvYd4BFgC/Bqd79I6zaJiFReKhnNat5Y16HynUF9HpgL/D3wopltDv9sMbPNlemeiIikolnNG6xA5bsGNeZZJkREpPSiqHm6t7GCEipCIiIx16hRcxUoEZEa0NXRRrrBbtZVgRIRqQGpjkTD3QulAiUiUgNSycaLmqtAiYjUgChq3t1A16FUoEREakCqIyhQjbT8uwqUiEgNWNDeSpNBdwNFzVWgRERqwNRJzcyd2dpQST4VKBGRGpFKtmmIT0RE4ifVkaC7tx93r3ZXKkIFSkSkRqSSbWzevotNA4PV7kpFqECJiNSIRouaq0CJiNSIKGreKEEJFSgRkRoRRc0bZcqjqhQoM/trM1thZsvN7Coza6lGP0REakmjRc0rXqDMbB7wV8Aidz8SaAY+WOl+iIjUoq6OtoZZdiPngoUV2G+rmQ0CCeDFKvVDRKSmdCYTXP3wat75z/eNeu7Pjl3AR49LFWxj+Quv8OUblrFz98Tj6peccxRHzJ0x4XayqXiBcvcXzOwS4HlgG3CHu98x8nVmdh5wHsDChQsr20kRkZg6+5gFbNiyg6ERteXx1Zu4/rEXiipQd69azxNrXuGUw/bDbGL9mTqpfANxFS9QZjYLOBPoAjYB15nZR9z9yszXufulwKUAixYtaoy70kRECjh6wUx+9NFFo7Z/+YZl3LpsbVFtdPcOsP/0Fn7ysdHtxEk1QhKnAN3uvsHdB4FfAW+qQj9EROpGV0cbmwYG2TRQeL2odF8/qY5EBXo1MdUoUM8DbzSzhJkZcDLwZBX6ISJSNzrDm3iLiaD39PUP3/QbZxUvUO7+EPBL4DFgWdiHSyvdDxGRetIVnhEViqBv2T5I79adwzf9xllVUnzu/hXgK9XYt4hIPZo/K4EZBSPoPeEZViqpIT4REamAlsnNzJ1R+Cbe7vD5WjiDUoESEakTXR1tdBe4BhUVsM52FSgREamQzmSi4IKG6b4gYt46pblCvRo/FSgRkTpRTNS8ViLmoAIlIlI3oqh5d57rUOne2oiYgwqUiEjdiKLmPTmuQ23ePkhff21EzEEFSkSkbixoD6Lmuc6genqjiLkKlIiIVNDUSUHUPFdQIrpHStegRESk4vJFzWspYg4qUCIidSXVkch5s253Xz9zZtRGxBxUoERE6koq2cYr27JHzXv6BuisgSmOIipQIiJ1JJUnap7u7aerRhJ8oAIlIlJXogDEyEljo4h5Z40k+EAFSkSkrixoT9BkkO7dOyhRaxFzUIESEakrUyc1M3dm66gzqO7wsYb4RESkalLJtlEr6/aE16QWtiskISIiVZItal5rEXNQgRIRqTtR1Hxj/56oebq3v6Yi5qACJSJSd6IgROZ1qJ6+gZq6/gQqUCIidSearTwqUMOzmNdQgg9UoERE6s6C9laaDLrDaPnwHHwqUCIiUk1R1Dya1TxK9GmIT0REqq6ro234zGnPGZRCEiIiUmWdyQTdvf24O+neIGLeMrl2IuagAiUiUpdSyTY2b9/FpoFB0n39NReQABUoEZG6NDyreV8/6b6BmllFN5MKlIhIHYqi5svWvMLLNRgxBxUoEZG6FEXN7/nTBmBPwaolKlAiInUoipo/+GwvUFvLbERUoERE6lRXRxvbB4eA2ouYgwqUiEjdiopSLUbMoQoFyswOMbPHM/5sNrPPVbofIiL1LhrWq8XhPYBJld6hu68CjgYws2bgBeCGSvdDRKTeRVMb1WJAAqo/xHcy8Ky791S5HyIidScqTKkavP4E1S9QHwSuyvaEmZ1nZkvNbOmGDRsq3C0Rkdp3QEcbF737cN53zPxqd2VczN2rs2OzKcCLwBHuvi7faxctWuRLly6tTMdERKSizOxRd180cns1z6DeCTxWqDiJiEhjqmaB+hA5hvdERESqUqDMLAGcCvyqGvsXEZH4q3jMHMDdB4BkNfYtIiK1odopPhERkaxUoEREJJZUoEREJJZUoEREJJaqdqPuWJjZBmCi0yF1AL0l6E6c6Rjrg46xPugYi9fp7rNHbqyJAlUKZrY0253K9UTHWB90jPVBxzhxGuITEZFYUoESEZFYaqQCdWm1O1ABOsb6oGOsDzrGCWqYa1AiIlJbGukMSkREaogKlIiIxFJDFCgzO83MVpnZM2Z2QbX7UwpmdpmZrTez5Rnb2s3sTjN7Ovx7VjX7OFFmtsDM7jKzJ81shZl9NtxeN8dpZi1m9rCZPREe41fD7XVzjABm1mxm/2tmt4SP6+r4AMwsbWbLzOxxM1sabqur4zSzmWb2SzN7Kvy5PK6cx1j3BcrMmoF/JVgg8XDgQ2Z2eHV7VRKXA6eN2HYB8Ft3Pwj4bfi4lu0CPu/uhwFvBM4Pv3f1dJw7gJPc/TXA0cBpZvZG6usYAT4LPJnxuN6OL3Kiux+dcW9QvR3nPwO3ufuhwGsIvqflO0Z3r+s/wHHA7RmPvwh8sdr9KtGxpYDlGY9XAXPCr+cAq6rdxxIf700E64jV5XECCeAx4A31dIzA/PAX10nALeG2ujm+jONMAx0jttXNcQLTgW7CcF0ljrHuz6CAecDqjMdrwm31aD93XwsQ/r1vlftTMmaWAl4LPESdHWc4/PU4sB64093r7Rj/Cfg7YChjWz0dX8SBO8zsUTM7L9xWT8d5ALAB+Fk4XPsTM2ujjMfYCAXKsmxTtr6GmNk+wPXA59x9c7X7U2ruvtvdjyY40zjWzI6scpdKxszOANa7+6PV7ksFvNndX0dwOeF8M3trtTtUYpOA1wH/7u6vBfop85BlIxSoNcCCjMfzgRer1JdyW2dmcwDCv9dXuT8TZmaTCYrTf7n7r8LNdXecAO6+Cbib4NpivRzjm4HFZpYGrgZOMrMrqZ/jG+buL4Z/rwduAI6lvo5zDbAmPMMH+CVBwSrbMTZCgXoEOMjMusxsCvBB4OYq96lcbgY+Fn79MYJrNjXLzAz4KfCku38v46m6OU4zm21mM8OvW4FTgKeok2N09y+6+3x3TxH87P3O3T9CnRxfxMzazGxa9DXwdmA5dXSc7v4SsNrMDgk3nQyspIzH2BAzSZjZuwjGwZuBy9z969Xt0cSZ2VXACQTT3a8DvgLcCFwLLASeB85x95er1MUJM7PjgfuAZey5fvElgutQdXGcZnYUcAXB/80m4Fp3/5qZJamTY4yY2QnA37j7GfV2fGZ2AMFZEwRDYb9w96/X4XEeDfwEmAI8B5xL+P+WMhxjQxQoERGpPY0wxCciIjVIBUpERGJJBUpERGJJBUpERGJJBUpERGJJBUoakpklw1mnHzezl8zshfDrrWb2bxXqw9HhLRClas/M7HdmNn0M71lcaIb/8F6t2ybeQ5GxmVTtDohUg7v3EcwejpldBGx190sq3I2jgUXAb0rU3ruAJ8YyHZS730yBG9fdfYOZrTWzN7v7AxPtpEixdAYlksHMTshYs+giM7vCzO4I1/o5y8y+Ha75c1s4DRNmdoyZ3RNOEnp7NO3LiHbPMbPlFqz7dG84q8nXgA+EZ24fCGcjuMzMHgkn4zwzfO8SM7sp3OcqM/tKju5/mPAufjNLhWv2/CTc73+Z2Slm9kC4bs+xGW3/MPz6cjP7FzN70MyeM7OzM9q+MWxfpGJUoETyOxA4HTgTuBK4y91fDWwDTg+L1A+As939GOAyINtMJRcC7/Bg3afF7r4z3HaNB+sHXQN8mWAqoNcDJwLfCafNgWBetw8TnHWdY2aLRu0hmPcuc1LWVxGs33MUcCjwZ8DxwN8QzMiRzZzwNWcA38zYvhR4S473iJSFhvhE8vtvdx80s2UE0xFF12KWEazHdQhwJHBnMHUgzcDaLO08AFxuZtcCv8ryPATzty02s78JH7cQTB8DwTIcfQBm9iuCIrJ0xPvb3X1LxuNud18WvmcFwaJyHh5LKkcfbnT3IWClme2XsX09MDfHe0TKQgVKJL8dAO4+ZGaDvmdusCGCnx8DVrj7cfkacfdPmdkbCM7GHg/nNBvJgPe5+6q9NgbvGzknWbY5ynaZWVNYYIb7ntHfHRlf5/rZz3xP5lI1LQRnjSIVoyE+kYlZBcw2s+MgWB7EzI4Y+SIzO9DdH3L3C4FegiVgtgDTMl52O/CZcBZ3zOy1Gc+dambt4Yzn7yE4I8vWlwNKcEzZHEwwO7dIxahAiUxAeC3pbOBbZvYE8Djwpiwv/U4YrlgO3As8AdwFHB6FJICLgcnAH8PXXZzx/vuB/wzbv97dRw7vAdxKMMN9OZwYti9SMZrNXCTmzGwJsMjd/7LA6+YAP3f3U8vQh3uBM919Y6nbFslFZ1AidcLd1wI/HsuNusUws9nA91ScpNJ0BiUiIrGkMygREYklFSgREYklFSgREYklFSgREYklFSgREYml/w/0GZJGr9w7BAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1624,7 +1607,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eleven-listening",
+   "id": "sensitive-insertion",
    "metadata": {},
    "source": [
     "## Opening the hood\n",
@@ -1636,7 +1619,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "liable-reliance",
+   "id": "rolled-fundamental",
    "metadata": {},
    "source": [
     "The `State` object defined in the ModSim library, is based on the `SimpleNamespace` object defined in a standard Python library called `types`; the documentation is at <https://docs.python.org/3.7/library/types.html#types.SimpleNamespace>."
@@ -1644,19 +1627,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "naked-department",
+   "id": "indonesian-wiring",
    "metadata": {},
    "source": [
     "The `TimeSeries` object is based on the `Series` object defined by a library called Pandas.\n",
     "The documentation is at <https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html>.\n",
     "\n",
-    "`display` works by creating another Pandas object, called a `DataFrame`, which can be displayed as a table.\n",
+    "`show` works by creating another Pandas object, called a `DataFrame`, which can be displayed as a table.\n",
     "We'll use `DataFrame` objects in future chapters."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "derived-province",
+   "id": "embedded-narrative",
    "metadata": {},
    "source": [
     "`Series` objects provide their own `plot` function, but the syntax is different from the other functions we've used, so ModSim provides a function called `plot` just to make it consistent.\n",
@@ -1667,7 +1650,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "southeast-advertiser",
+   "id": "tested-appearance",
    "metadata": {},
    "source": [
     "The `flip` function uses NumPy's `random` function to generate a random number between 0 and 1, then returns `True` or `False` with the given probability.\n",
@@ -1678,7 +1661,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "tested-blond",
+   "id": "relative-oliver",
    "metadata": {},
    "outputs": [
     {
@@ -1705,7 +1688,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "rational-taiwan",
+   "id": "sharp-bottle",
    "metadata": {},
    "source": [
     "You might not understand everything in this function yet, but you will."
@@ -1714,7 +1697,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "significant-defense",
+   "id": "smooth-causing",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap03.ipynb b/jupyter/chap03.ipynb
new file mode 100644
index 00000000..57f108f3
--- /dev/null
+++ b/jupyter/chap03.ipynb
@@ -0,0 +1,1077 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "missing-intention",
+   "metadata": {},
+   "source": [
+    "# Chapter 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "comprehensive-extreme",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "electrical-charlotte",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "handy-occurrence",
+   "metadata": {},
+   "source": [
+    "To paraphrase two Georges, \"All models are wrong, but some models are\n",
+    "more wrong than others.\" In this chapter, I demonstrate the process we\n",
+    "use to make models less wrong.\n",
+    "\n",
+    "As an example, we'll review the bikeshare model from the previous\n",
+    "chapter, consider its strengths and weaknesses, and gradually improve\n",
+    "it. We'll also see ways to use the model to understand the behavior of\n",
+    "the system and evaluate designs intended to make it work better."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "authentic-supplier",
+   "metadata": {},
+   "source": [
+    "## Iterative modeling\n",
+    "\n",
+    "The model we have so far is simple, but it is based on unrealistic\n",
+    "assumptions. Before you go on, take a minute to review the model from\n",
+    "the previous chapter. What assumptions is it based on? Make a list of\n",
+    "ways this model might be unrealistic; that is, what are the differences between the model and the real world?\n",
+    "\n",
+    "Here are some of the differences on my list:\n",
+    "\n",
+    "-   In the model, a student is equally likely to arrive during any\n",
+    "    one-minute period. In reality, this probability varies depending on time of day, day of the week, etc.\n",
+    "\n",
+    "-   The model does not account for travel time from one bike station to another.\n",
+    "\n",
+    "-   The model does not check whether a bike is available, so it's\n",
+    "    possible for the number of bikes to be negative (as you might have\n",
+    "    noticed in some of your simulations)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "constant-kennedy",
+   "metadata": {},
+   "source": [
+    "Some of these modeling decisions are better than others. For example,\n",
+    "the first assumption might be reasonable if we simulate the system for a short period of time, like one hour.\n",
+    "\n",
+    "The second assumption is not very realistic, but it might not affect the results very much, depending on what we use the model for.\n",
+    "\n",
+    "On the other hand, the third assumption seems problematic, and it is\n",
+    "relatively easy to fix. In this chapter, we will.\n",
+    "\n",
+    "This process, starting with a simple model, identifying the most\n",
+    "important problems, and making gradual improvements, is called\n",
+    "**iterative modeling**.\n",
+    "\n",
+    "For any physical system, there are many possible models, based on\n",
+    "different assumptions and simplifications. It often takes several\n",
+    "iterations to develop a model that is good enough for the intended\n",
+    "purpose, but no more complicated than necessary."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "arranged-bahamas",
+   "metadata": {},
+   "source": [
+    "## More than one State object\n",
+    "\n",
+    "Before we go on, I want to make a few changes to the code from the\n",
+    "previous chapter. First I'll generalize the functions we wrote so they\n",
+    "take a `State` object as a parameter. Then, I'll make the code more\n",
+    "readable by adding documentation.\n",
+    "\n",
+    "Here is one of the functions from the previous chapter, `bike_to_wellesley`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "outside-darkness",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley():\n",
+    "    bikeshare.olin -= 1\n",
+    "    bikeshare.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "widespread-monitor",
+   "metadata": {},
+   "source": [
+    "When this function is called, it modifies `bikeshare`. As long as there\n",
+    "is only one `State` object, that's fine, but what if there is more than\n",
+    "one bike share system in the world? Or what if we want to run more than\n",
+    "one simulation?\n",
+    "\n",
+    "This function would be more flexible if it took a `State` object as a\n",
+    "parameter. Here's what that looks like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "central-vegetable",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley(state):\n",
+    "    state.olin -= 1\n",
+    "    state.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "finite-silence",
+   "metadata": {},
+   "source": [
+    "The name of the parameter is `state` rather than `bikeshare` as a\n",
+    "reminder that the value of `state` could be any `State` object, not just\n",
+    "`bikeshare`.\n",
+    "\n",
+    "This version of `bike_to_wellesley` requires a `State` object as a\n",
+    "parameter, so we have to provide one when we call it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "statistical-conviction",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import State\n",
+    "\n",
+    "bikeshare = State(olin=10, wellesley=2)\n",
+    "bike_to_wellesley(bikeshare)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "still-commons",
+   "metadata": {},
+   "source": [
+    "Again, the argument we provide gets assigned to the parameter, so this\n",
+    "function call has the same effect as:\n",
+    "\n",
+    "```\n",
+    "state = bikeshare \n",
+    "state.olin -= 1 \n",
+    "state.wellesley += 1\n",
+    "```\n",
+    "\n",
+    "Now we can create as many `State` objects as we want:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "facial-tunnel",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare1 = State(olin=10, wellesley=2)\n",
+    "bikeshare2 = State(olin=2, wellesley=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "active-gilbert",
+   "metadata": {},
+   "source": [
+    "And update them independently:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "static-african",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bike_to_wellesley(bikeshare1)\n",
+    "bike_to_wellesley(bikeshare2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "honey-elder",
+   "metadata": {},
+   "source": [
+    "Changes in `bikeshare1` do not affect `bikeshare2`, and vice versa. So\n",
+    "we can simulate different bike share systems, or run multiple\n",
+    "simulations of the same system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "animated-parliament",
+   "metadata": {},
+   "source": [
+    "## Documentation\n",
+    "\n",
+    "Another problem with the code we have so far is that it contains no\n",
+    "**documentation**. Documentation is text we add to a program to help\n",
+    "other programmers read and understand it. It has no effect on the\n",
+    "program when it runs.\n",
+    "\n",
+    "There are two forms of documentation, **docstrings** and **comments**:\n",
+    "\n",
+    "* A docstring is a string in triple-quotes that appears at the beginning of a function.\n",
+    "\n",
+    "* A comment is a line of text that begins with a hash symbol, `#`.\n",
+    "\n",
+    "Here's a version of `bike_to_olin` with a docstring and a comment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "sticky-macedonia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_olin(state):\n",
+    "    \"\"\"Move one bike from Wellesley to Olin.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    # We decrease one state variable and increase the\n",
+    "    # other, so the total number of bikes is unchanged.\n",
+    "    state.wellesley -= 1\n",
+    "    state.olin += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "returning-faith",
+   "metadata": {},
+   "source": [
+    "Docstrings follow a conventional format:\n",
+    "\n",
+    "-   The first line is a single sentence that describes what the function does.\n",
+    "\n",
+    "-   The following lines explain what the parameters are.\n",
+    "\n",
+    "A function's docstring should include the information someone needs to\n",
+    "know to *use* the function; it should not include details about how the function works.\n",
+    "\n",
+    "Comments provide details about how the function works, especially if there is something that would not be obvious to someone reading the program."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "frank-influence",
+   "metadata": {},
+   "source": [
+    "## Negative bikes\n",
+    "\n",
+    "The changes we've made so far improve the quality of the code, but we\n",
+    "haven't done anything to improve the quality of the model yet. Let's do that now.\n",
+    "\n",
+    "Currently the simulation does not check whether a bike is available when a customer arrives, so the number of bikes at a location can be\n",
+    "negative. That's not very realistic. Here's a version of\n",
+    "`bike_to_olin` that fixes the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fitted-carnival",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_olin(state):\n",
+    "    if state.wellesley == 0:\n",
+    "        return\n",
+    "    state.wellesley -= 1\n",
+    "    state.olin += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "signal-baking",
+   "metadata": {},
+   "source": [
+    "The first line checks whether the number of bikes at Wellesley is zero. If so, it uses a **return statement**, which causes the function to end immediately, without running the rest of the statements. So if there are no bikes at Wellesley, we \"return\" from `bike_to_olin` without changing the state.\n",
+    "\n",
+    "We can test it by initializing the state with no bikes at Wellesley And calling `bike_to_olin`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "moral-bench",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=12, wellesley=0)\n",
+    "bike_to_olin(bikeshare)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "imperial-disposal",
+   "metadata": {},
+   "source": [
+    "The state of the system should be unchanged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "latest-boards",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "namespace(olin=12, wellesley=0)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "substantial-english",
+   "metadata": {},
+   "source": [
+    "No more negative bikes (at least at Wellesley)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "marked-talent",
+   "metadata": {},
+   "source": [
+    "## Comparison operators\n",
+    "\n",
+    "The updated version of `bike_to_olin` uses the equals operator, `==`, which compares two values and returns `True` if they are equal and `False` otherwise.\n",
+    "\n",
+    "It is easy to confuse the equals operators with the assignment operator, `=`, which assigns a value to a variable. For example, the following statement creates a variable, `x`, if it doesn't already exist, and gives it the value `5`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "typical-characterization",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dependent-athens",
+   "metadata": {},
+   "source": [
+    "On the other hand, the following statement checks whether `x` is `5` and\n",
+    "returns `True` or `False`. It does not create `x` or change its value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "living-baseball",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x == 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "small-adventure",
+   "metadata": {},
+   "source": [
+    "You can use the equals operator in an `if` statement, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "cordless-consideration",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "yes, x is 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "if x == 5:\n",
+    "    print('yes, x is 5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mineral-azerbaijan",
+   "metadata": {},
+   "source": [
+    "If you make a mistake and use `=` in an `if` statement, like this:\n",
+    "\n",
+    "```\n",
+    "if x = 5:\n",
+    "    print('yes, x is 5')\n",
+    "```\n",
+    "\n",
+    "That's a **syntax error**, which means that the structure of the program is invalid. Python will print an error message and the program won't run."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "empty-lecture",
+   "metadata": {},
+   "source": [
+    "The equals operator is one of the **comparison operators**. The others\n",
+    "are:\n",
+    "\n",
+    "| Operation             \t| Symbol \t|\n",
+    "|-----------------------\t|--------\t|\n",
+    "| Less than             \t| `<`      \t|\n",
+    "| Greater than          \t| `>`      \t|\n",
+    "| Less than or equal    \t| `<=`     \t|\n",
+    "| Greater than or equal \t| `>=`     \t|\n",
+    "| Equal                 \t| `==`     \t|\n",
+    "| Not equal             \t| `!=`     \t|"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "caroline-advocacy",
+   "metadata": {},
+   "source": [
+    "## Metrics\n",
+    "\n",
+    "Getting back to the bike share system, at this point we have the ability to simulate the behavior of the system. Since the arrival of customers is random, the state of the system is different each time we run a\n",
+    "simulation. Models like this are called random or **stochastic**; models\n",
+    "that do the same thing every time they run are **deterministic**.\n",
+    "\n",
+    "Suppose we want to use our model to predict how well the bike share\n",
+    "system will work, or to design a system that works better. First, we\n",
+    "have to decide what we mean by \"how well\" and \"better\".\n",
+    "\n",
+    "From the customer's point of view, we might like to know the probability of finding an available bike. From the system-owner's point of view, we\n",
+    "might want to minimize the number of customers who don't get a bike when\n",
+    "they want one, or maximize the number of bikes in use. Statistics like\n",
+    "these that quantify how well the system works are called **metrics**.\n",
+    "\n",
+    "As a simple example, let's measure the number of unhappy customers.\n",
+    "Here's a version of `bike_to_olin` that keeps track of the number of\n",
+    "customers who arrive at a station with no bikes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "champion-gibson",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_olin(state):\n",
+    "    if state.wellesley == 0:\n",
+    "        state.wellesley_empty += 1\n",
+    "        return\n",
+    "    state.wellesley -= 1\n",
+    "    state.olin += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "living-leather",
+   "metadata": {},
+   "source": [
+    "If a customer arrives at the Wellesley station and finds no bike\n",
+    "available, `bike_to_olin` updates `wellesley_empty` which counts the\n",
+    "number of unhappy customers.\n",
+    "\n",
+    "This function only works if we initialize `wellesley_empty` when we\n",
+    "create the `State` object, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "aging-evolution",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=12, wellesley=0, \n",
+    "                  wellesley_empty=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "painted-province",
+   "metadata": {},
+   "source": [
+    "We can test it by calling `bike_to_olin`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "exciting-feature",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "bike_to_olin(bikeshare)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "integral-wheat",
+   "metadata": {},
+   "source": [
+    "There should be 12 bikes at Olin, no bikes at Wellesley, and one unhappy customer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "aware-technology",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley_empty</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "namespace(olin=12, wellesley=0, wellesley_empty=1)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "appropriate-steps",
+   "metadata": {},
+   "source": [
+    "Looks good!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "harmful-criminal",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this chapter, we wrote several versions of `bike_to_olin`:\n",
+    "\n",
+    "* We added a parameter, `state`, so we can work with more than one `State` object.\n",
+    "\n",
+    "* We added a docstring that explains how to use the function and a comment that explains how it works.\n",
+    "\n",
+    "* We used a conditional operator, `==`, to check whether a bike is available, in order to avoid negative bikes.\n",
+    "\n",
+    "* We added a state variable, `wellesley_empty`, to count the number of unhappy customers, which is a metric we'll use to quantify how well the system works.\n",
+    "\n",
+    "In the exercises, you'll update `bike_to_wellesley` the same way and test it by running a simulation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "gothic-backing",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "limited-tamil",
+   "metadata": {},
+   "source": [
+    "Here's the code we have so far, with docstrings, all in one place."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "standing-garbage",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import TimeSeries, plot, decorate\n",
+    "\n",
+    "def run_simulation(state, p1, p2, num_steps):\n",
+    "    \"\"\"Simulate the given number of time steps.\n",
+    "    \n",
+    "    state: State object\n",
+    "    p1: probability of an Olin->Wellesley customer arrival\n",
+    "    p2: probability of a Wellesley->Olin customer arrival\n",
+    "    num_steps: number of time steps\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[0] = state.olin\n",
+    "    \n",
+    "    for i in range(num_steps):\n",
+    "        step(state, p1, p2)\n",
+    "        results[i+1] = state.olin\n",
+    "        \n",
+    "    plot(results, label='Olin')\n",
+    "    decorate(title='Olin-Wellesley Bikeshare',\n",
+    "             xlabel='Time step (min)', \n",
+    "             ylabel='Number of bikes')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "durable-vacuum",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import flip\n",
+    "\n",
+    "def step(state, p1, p2):\n",
+    "    \"\"\"Simulate one time step.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    p1: probability of an Olin->Wellesley ride\n",
+    "    p2: probability of a Wellesley->Olin ride\n",
+    "    \"\"\"\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley(state)\n",
+    "    \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin(state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "peripheral-maximum",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_olin(state):\n",
+    "    \"\"\"Move one bike from Wellesley to Olin.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    if state.wellesley == 0:\n",
+    "        state.wellesley_empty += 1\n",
+    "        return\n",
+    "    state.wellesley -= 1\n",
+    "    state.olin += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "exclusive-audio",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley(state):\n",
+    "    \"\"\"Move one bike from Olin to Wellesley.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    state.olin -= 1\n",
+    "    state.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "useful-gabriel",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Modify `bike_to_wellesley` so it checks whether a bike is available at Olin.  If not, it should add one to `olin_empty`.\n",
+    "\n",
+    "To test it, create a `State` that initializes `olin` and `olin_empty` to `0`, run `bike_to_wellesley`, and check the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "violent-grant",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def bike_to_wellesley(state):\n",
+    "    \"\"\"Move one bike from Olin to Wellesley.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    if state.olin == 0:\n",
+    "        state.olin_empty += 1\n",
+    "        return\n",
+    "    state.olin -= 1\n",
+    "    state.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "acceptable-stress",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "bikeshare = State(olin=0, wellesley=12,\n",
+    "                  olin_empty=0, wellesley_empty=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "measured-suggestion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "bike_to_wellesley(bikeshare)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "collected-column",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>olin_empty</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley_empty</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "namespace(olin=0, wellesley=12, olin_empty=1, wellesley_empty=0)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "understanding-humor",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Now run the simulation a few times and confirm that the number of bikes is never negative.  What's the final state of the system?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "quantitative-threat",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "bikeshare = State(olin=10, wellesley=2,\n",
+    "                  olin_empty=0, wellesley_empty=0)\n",
+    "\n",
+    "run_simulation(bikeshare, 0.3, 0.2, 60)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "hindu-reverse",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>olin_empty</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley_empty</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "namespace(olin=0, wellesley=12, olin_empty=2, wellesley_empty=0)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "institutional-dublin",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/chap04.ipynb b/jupyter/chap04.ipynb
new file mode 100644
index 00000000..d9ca99f7
--- /dev/null
+++ b/jupyter/chap04.ipynb
@@ -0,0 +1,1208 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "killing-desire",
+   "metadata": {},
+   "source": [
+    "# Chapter 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "younger-hobby",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "satellite-sociology",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "concrete-jackson",
+   "metadata": {},
+   "source": [
+    "Here the code from previous chapters we'll reuse."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fifth-mongolia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import flip\n",
+    "\n",
+    "def step(state, p1, p2):\n",
+    "    \"\"\"Simulate one time step.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    p1: probability of an Olin->Wellesley ride\n",
+    "    p2: probability of a Wellesley->Olin ride\n",
+    "    \"\"\"\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley(state)\n",
+    "    \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin(state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "duplicate-evolution",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_olin(state):\n",
+    "    \"\"\"Move one bike from Wellesley to Olin.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    if state.wellesley == 0:\n",
+    "        state.wellesley_empty += 1\n",
+    "        return\n",
+    "    state.wellesley -= 1\n",
+    "    state.olin += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "parental-veteran",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley(state):\n",
+    "    \"\"\"Move one bike from Olin to Wellesley.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    if state.olin == 0:\n",
+    "        state.olin_empty += 1\n",
+    "        return\n",
+    "    state.olin -= 1\n",
+    "    state.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "outer-delivery",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we defined metrics that quantify the performance of bike sharing this system. In this chapter we see how those metrics depend on the parameters of the system, like the arrival rate of customers at bike stations.\n",
+    "\n",
+    "We also discuss a program development strategy, called incremental\n",
+    "development, that might help you write programs faster and spend less\n",
+    "time debugging."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aggressive-cambridge",
+   "metadata": {},
+   "source": [
+    "## Functions that return values\n",
+    "\n",
+    "We have seen several functions that return values; for example, when you run `sqrt`, it returns a number you can assign to a variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "assisted-assault",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.4142135623730951"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from numpy import sqrt\n",
+    "\n",
+    "root_2 = sqrt(2)\n",
+    "root_2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "polar-marshall",
+   "metadata": {},
+   "source": [
+    "When you run `State`, it returns a new `State` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "upper-closer",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "namespace(olin=10, wellesley=2)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import State\n",
+    "\n",
+    "bikeshare = State(olin=10, wellesley=2)\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "qualified-complaint",
+   "metadata": {},
+   "source": [
+    "Not all functions have return values. For example, when you run `step`,\n",
+    "it updates a `State` object, but it doesn't return a value.\n",
+    "\n",
+    "To write functions that return values, we can use a `return` statement, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "exceptional-philosophy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_five(x):\n",
+    "    return x + 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sporting-japan",
+   "metadata": {},
+   "source": [
+    "`add_five` takes a parameter, `x`, which could be any number. It\n",
+    "computes `x + 5` and returns the result. So if we run it like this, the\n",
+    "result is `8`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "differential-cooperation",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "add_five(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pending-detail",
+   "metadata": {},
+   "source": [
+    "As a more useful example, here's a version of `run_simulation` that\n",
+    "creates a `State` object, runs a simulation, and then returns the\n",
+    "`State` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "historic-hollow",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import TimeSeries\n",
+    "\n",
+    "def run_simulation(p1, p2, num_steps):\n",
+    "    state = State(olin=10, wellesley=2,\n",
+    "                  olin_empty=0, wellesley_empty=0)\n",
+    "    \n",
+    "    for i in range(num_steps):\n",
+    "        step(state, p1, p2)\n",
+    "        \n",
+    "    return state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "corporate-watershed",
+   "metadata": {},
+   "source": [
+    "We can call `run_simulation` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "sustained-centre",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_state = run_simulation(0.3, 0.2, 60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "assumed-bosnia",
+   "metadata": {},
+   "source": [
+    "The result is a `State` object that represents the final state of the system, including the metrics we'll use to evaluate the performance of the system:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "relevant-colonial",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(final_state.olin_empty, \n",
+    "      final_state.wellesley_empty)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eight-singapore",
+   "metadata": {},
+   "source": [
+    "The simulation we just ran starts with `olin=10` and `wellesley=2`, and uses the values `p1=0.3`, `p2=0.2`, and `num_steps=60`. \n",
+    "These five values are **parameters of the model**, which are quantities that determine the behavior of the system.\n",
+    "\n",
+    "It is easy to get the parameters of a model confused with the parameters of a function. \n",
+    "In fact, it is common for the parameters of the model to appear as parameters in functions. \n",
+    "\n",
+    "For example, the previous version of `run_simulation` takes `p1`, `p2`, and `num_steps` as parameters.\n",
+    "So we can call `run_simulation` with different parameters and see how\n",
+    "the metrics, like the number of unhappy customers, depend on the\n",
+    "parameters. But before we do that, we need a new version of a `for` loop."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "better-violation",
+   "metadata": {},
+   "source": [
+    "## Loops and arrays\n",
+    "\n",
+    "In `run_simulation`, we use this `for` loop:\n",
+    "\n",
+    "```\n",
+    "    for i in range(num_steps):\n",
+    "        step(state, p1, p2)\n",
+    "```\n",
+    "\n",
+    "In this example, `range` creates a sequence of numbers from 0 to `num_steps` (including `0` but not `num_steps`). \n",
+    "Each time through the loop, the next number in the sequence gets assigned to the loop variable, `i`.\n",
+    "\n",
+    "But `range` only works with integers; to get a sequence of non-integer\n",
+    "values, we can use `linspace`, which is defined NumPy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "mexican-cocktail",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.  , 0.25, 0.5 , 0.75, 1.  ])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from numpy import linspace\n",
+    "\n",
+    "p1_array = linspace(0, 1, 5)\n",
+    "p1_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "upper-christianity",
+   "metadata": {},
+   "source": [
+    "The arguments indicate where the sequence should start and stop, and how\n",
+    "many elements it should contain. In this example, the sequence contains\n",
+    "`5` equally-spaced numbers, starting at `0` and ending at `1`.\n",
+    "\n",
+    "The result is a NumPy **array**, which is a new kind of object we have\n",
+    "not seen before. An array is a container for a sequence of numbers.\n",
+    "\n",
+    "We can use an array in a `for` loop like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "broadband-prairie",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n",
+      "0.25\n",
+      "0.5\n",
+      "0.75\n",
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "for p1 in p1_array:\n",
+    "    print(p1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hired-community",
+   "metadata": {},
+   "source": [
+    "When this loop runs, it\n",
+    "\n",
+    "1.  Gets the first value from the array and assigns it to `p1`.\n",
+    "\n",
+    "2.  Runs the body of the loop, which prints `p1`.\n",
+    "\n",
+    "3.  Gets the next value from the array and assigns it to `p1`.\n",
+    "\n",
+    "4.  Runs the body of the loop, which prints `p1`.\n",
+    "\n",
+    "And so on, until it gets to the end of the array. This will come in handy in the next section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fuzzy-blend",
+   "metadata": {},
+   "source": [
+    "## Sweeping parameters\n",
+    "\n",
+    "If we know the actual values of parameters like `p1` and `p2`, we can\n",
+    "use them to make specific predictions, like how many bikes will be at\n",
+    "Olin after one hour.\n",
+    "\n",
+    "But prediction is not the only goal; models like this are also used to\n",
+    "explain why systems behave as they do and to evaluate alternative\n",
+    "designs. For example, if we observe the system and notice that we often run out of bikes at a particular time, we could use the model to figure out why that happens. And if we are considering adding more bikes, or another station, we could evaluate the effect of various \"what if\" scenarios.\n",
+    "\n",
+    "As an example, suppose we have enough data to estimate that `p2` is\n",
+    "about `0.2`, but we don't have any information about `p1`. We could run simulations with a range of values for `p1` and see how the results vary. This process is called **sweeping** a parameter, in the sense that the value of the parameter \"sweeps\" through a range of possible values.\n",
+    "\n",
+    "Now that we know about loops and arrays, we can use them like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "oriented-mistress",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 0\n",
+      "0.12 0\n",
+      "0.24 0\n",
+      "0.36 0\n",
+      "0.48 7\n",
+      "0.6 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "p1_array = linspace(0, 0.6, 6)\n",
+    "p2 = 0.2\n",
+    "num_steps = 60\n",
+    "\n",
+    "for p1 in p1_array:\n",
+    "    final_state = run_simulation(p1, p2, num_steps)\n",
+    "    print(p1, final_state.olin_empty)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "terminal-warren",
+   "metadata": {},
+   "source": [
+    "Each time through the loop, we run a simulation with a different value\n",
+    "of `p1` and the same value of `p2`, `0.2`. Then we print `p1` and the\n",
+    "number of unhappy customers at Olin.\n",
+    "\n",
+    "To save and plot the results, we can use a `SweepSeries` object, which\n",
+    "is similar to a `TimeSeries`; the difference is that the labels in a\n",
+    "`SweepSeries` are parameter values rather than time values.\n",
+    "\n",
+    "We can create an empty `SweepSeries` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "foster-forestry",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import SweepSeries\n",
+    "\n",
+    "sweep = SweepSeries()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "crazy-storm",
+   "metadata": {},
+   "source": [
+    "And add values like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "hollywood-municipality",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for p1 in p1_array:\n",
+    "    final_state = run_simulation(p1, p2, num_steps)\n",
+    "    sweep[p1] = final_state.olin_empty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daily-recipient",
+   "metadata": {},
+   "source": [
+    "The result is a `SweepSeries` that maps from each value of `p1` to the\n",
+    "resulting number of unhappy customers. Then we can plot the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "cooked-intent",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import plot\n",
+    "\n",
+    "plot(sweep, label='Olin')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opened-medicare",
+   "metadata": {},
+   "source": [
+    "NumPy provides functions that compute a variety of summary statistics, like `mean`, `median`, and `std` (which computes standard deviation).\n",
+    "\n",
+    "We can use `mean` to compute the average of the values in a series, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "rural-shark",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.166666666666667"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from numpy import mean\n",
+    "\n",
+    "mean(sweep)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hydraulic-baseball",
+   "metadata": {},
+   "source": [
+    "In this example, computing the mean might not be useful, but in the exercises below, it will be."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "portuguese-selection",
+   "metadata": {},
+   "source": [
+    "## Incremental development\n",
+    "\n",
+    "When you start writing programs that are more than a few lines, you\n",
+    "might find yourself spending more and more time debugging. The more code you write before you start debugging, the harder it is to find the problem.\n",
+    "\n",
+    "**Incremental development** is a way of programming that tries to\n",
+    "minimize the pain of debugging. The fundamental steps are:\n",
+    "\n",
+    "1.  Always start with a working program. If you have an example from a\n",
+    "    book, or a program you wrote that is similar to what you are working\n",
+    "    on, start with that. Otherwise, start with something you *know* is\n",
+    "    correct, like `x=5`. Run the program and confirm that it does what\n",
+    "    you expect.\n",
+    "\n",
+    "2.  Make one small, testable change at a time. A \"testable\" change is\n",
+    "    one that displays something or has some other effect you can check.\n",
+    "    Ideally, you should know what the correct answer is, or be able to\n",
+    "    check it by performing another computation.\n",
+    "\n",
+    "3.  Run the program and see if the change worked. If so, go back to\n",
+    "    Step 2. If not, you will have to do some debugging, but if the\n",
+    "    change you made was small, it shouldn't take long to find the\n",
+    "    problem.\n",
+    "\n",
+    "When this process works, your changes usually work the first time, or if they don't, the problem is obvious. In practice, there are two problems with incremental development:\n",
+    "\n",
+    "-   Sometimes you have to write extra code to generate visible output\n",
+    "    that you can check. This extra code is called **scaffolding**\n",
+    "    because you use it to build the program and then remove it when you\n",
+    "    are done. That might seem like a waste, but time you spend on\n",
+    "    scaffolding is almost always time you save on debugging.\n",
+    "\n",
+    "-   When you are getting started, it might not be obvious how to choose\n",
+    "    the steps that get from `x=5` to the program you are trying to\n",
+    "    write. You will see more examples of this process as we go along,\n",
+    "    and you will get better with experience.\n",
+    "\n",
+    "If you find yourself writing more than a few lines of code before you\n",
+    "start testing, and you are spending a lot of time debugging, try\n",
+    "incremental development."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "indonesian-assumption",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "attempted-confidence",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prerequisite-sunglasses",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a function called `make_state` that creates a `State` object with the state variables `olin=10` and `wellesley=2`, and then returns the new `State` object.\n",
+    "\n",
+    "Write a line of code that calls `make_state` and assigns the result to a variable named `init`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "restricted-destruction",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def make_state():\n",
+    "    state = State(olin=10, wellesley=2)\n",
+    "    return state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "fallen-jungle",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "init = make_state()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "classified-clerk",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Read the documentation of `linspace` at <>.\n",
+    "Then use it to make an array of 101 equally spaced points between 0 and 1 (including both)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "dimensional-gentleman",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.  , 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ,\n",
+       "       0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 , 0.21,\n",
+       "       0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 , 0.31, 0.32,\n",
+       "       0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 , 0.41, 0.42, 0.43,\n",
+       "       0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 , 0.51, 0.52, 0.53, 0.54,\n",
+       "       0.55, 0.56, 0.57, 0.58, 0.59, 0.6 , 0.61, 0.62, 0.63, 0.64, 0.65,\n",
+       "       0.66, 0.67, 0.68, 0.69, 0.7 , 0.71, 0.72, 0.73, 0.74, 0.75, 0.76,\n",
+       "       0.77, 0.78, 0.79, 0.8 , 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87,\n",
+       "       0.88, 0.89, 0.9 , 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98,\n",
+       "       0.99, 1.  ])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "p1_array = linspace(0, 1, 101)\n",
+    "p1_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "local-stretch",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Wrap the code from this chapter in a function named `sweep_p1` that takes an array called `p1_array` as a parameter.  It should create a new `SweepSeries`, run a simulation for each value of `p1` in `p1_array`, store the results in the `SweepSeries`, and return the `SweepSeries`.\n",
+    "\n",
+    "Use your function to plot the number of unhappy customers at Olin as a function of `p1`.  Label the axes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "underlying-samoa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def sweep_p1(p1_array):\n",
+    "    p2 = 0.2\n",
+    "    num_steps = 60\n",
+    "    sweep = SweepSeries()\n",
+    "    \n",
+    "    for p1 in p1_array:\n",
+    "        state = run_simulation(p1, p2, num_steps)\n",
+    "        sweep[p1] = state.olin_empty\n",
+    "        \n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "auburn-junction",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "from modsim import decorate\n",
+    "\n",
+    "p1_array = linspace(0, 1, 101)\n",
+    "sweep = sweep_p1(p1_array)\n",
+    "plot(sweep, label='Olin')\n",
+    "decorate(title='Olin-Wellesley Bikeshare',\n",
+    "         xlabel='Arrival rate at Olin (p1 in customers/min)', \n",
+    "         ylabel='Number of unhappy customers')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mineral-missile",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a function called `sweep_p2` that runs simulations with `p1=0.5` and a range of values for `p2`.  It should store the results in a `SweepSeries` and return the `SweepSeries`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "false-lightning",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def sweep_p2(p2_array):\n",
+    "    p1 = 0.5\n",
+    "    num_steps = 60\n",
+    "    sweep = SweepSeries()\n",
+    "    \n",
+    "    for p2 in p2_array:\n",
+    "        state = run_simulation(p1, p2, num_steps)\n",
+    "        sweep[p2] = state.olin_empty\n",
+    "        \n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "neural-detail",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "p2_array = linspace(0, 1, 101)\n",
+    "sweep = sweep_p2(p2_array)\n",
+    "plot(sweep, label='Olin')\n",
+    "\n",
+    "decorate(title='Olin-Wellesley Bikeshare',\n",
+    "         xlabel='Arrival rate at Wellesley (p2 in customers/min)', \n",
+    "         ylabel='Number of unhappy customers')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "junior-neighbor",
+   "metadata": {},
+   "source": [
+    "## Optional Exercises\n",
+    "\n",
+    "The following two exercises are a little more challenging.  If you are comfortable with what you have learned so far, you should give them a try.  If you feel like you have your hands full, you might want to skip them for now."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "marked-scale",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Because our simulations are random, the results vary from one run to another, and the results of a parameter sweep tend to be noisy.  We can get a clearer picture of the relationship between a parameter and a metric by running multiple simulations with the same parameter and taking the average of the results.\n",
+    "\n",
+    "Write a function called `run_multiple_simulations` that takes as parameters `p1`, `p2`, `num_steps`, and `num_runs`.\n",
+    "\n",
+    "`num_runs` specifies how many times it should call `run_simulation`.\n",
+    "\n",
+    "After each run, it should store the total number of unhappy customers (at Olin or Wellesley) in a `TimeSeries`.\n",
+    "At the end, it should return the `TimeSeries`.\n",
+    "\n",
+    "Test your function with parameters\n",
+    "\n",
+    "```\n",
+    "p1 = 0.3\n",
+    "p2 = 0.3\n",
+    "num_steps = 60\n",
+    "num_runs = 10\n",
+    "```\n",
+    "\n",
+    "Display the resulting `TimeSeries` and use the `mean` function from NumPy to compute the average number of unhappy customers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "facial-grass",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def run_multiple_simulations(p1, p2, num_steps, num_runs):\n",
+    "    totals = TimeSeries()\n",
+    "    \n",
+    "    for i in range(num_runs):\n",
+    "        state = run_simulation(p1, p2, num_steps)\n",
+    "        totals[i] = state.olin_empty + state.wellesley_empty\n",
+    "        \n",
+    "    return totals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "tropical-stream",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Quantity</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Time</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Quantity\n",
+       "Time          \n",
+       "0            7\n",
+       "1            3\n",
+       "2            1\n",
+       "3            3\n",
+       "4            4\n",
+       "5            5\n",
+       "6            2\n",
+       "7            3\n",
+       "8            0\n",
+       "9            0"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "from modsim import show\n",
+    "\n",
+    "p1 = 0.3\n",
+    "p2 = 0.3\n",
+    "num_steps = 60\n",
+    "num_runs = 10\n",
+    "totals = run_multiple_simulations(p1, p2, num_steps, num_runs)\n",
+    "show(totals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "documentary-murder",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.8"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "mean(totals)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecological-andrews",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Continuting the previous exercise, use `run_multiple_simulations` to run simulations with a range of values for `p1` and\n",
+    "\n",
+    "```\n",
+    "p2 = 0.3\n",
+    "num_steps = 60\n",
+    "num_runs = 20\n",
+    "```\n",
+    "\n",
+    "Store the results in a `SweepSeries`, then plot the average number of unhappy customers as a function of `p1`.  Label the axes.\n",
+    "\n",
+    "What value of `p1` minimizes the average number of unhappy customers?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "urban-parts",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "p1_array = linspace(0, 1, 20)\n",
+    "p2 = 0.3\n",
+    "num_steps = 60\n",
+    "num_runs = 20\n",
+    "\n",
+    "sweep = SweepSeries()\n",
+    "for p1 in p1_array:\n",
+    "    totals = run_multiple_simulations(p1, p2, num_steps, num_runs)\n",
+    "    sweep[p1] = mean(totals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "inappropriate-cooperation",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "plot(sweep, label='total', color='green')\n",
+    "    \n",
+    "decorate(title='Olin-Wellesley Bikeshare',\n",
+    "         xlabel='Arrival rate at Olin (p1 in customers/min)', \n",
+    "         ylabel='Average total unhappy customers')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "premier-complaint",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/chap05.ipynb b/jupyter/chap05.ipynb
new file mode 100644
index 00000000..00bb7e44
--- /dev/null
+++ b/jupyter/chap05.ipynb
@@ -0,0 +1,1450 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "manual-spirit",
+   "metadata": {},
+   "source": [
+    "# Chapter 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "intimate-lambda",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "respected-auditor",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "motivated-cleaner",
+   "metadata": {},
+   "source": [
+    "In this chapter..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "modern-holder",
+   "metadata": {},
+   "source": [
+    "# World population\n",
+    "\n",
+    "In 1968 Paul Erlich published *The Population Bomb*, in which he\n",
+    "predicted that world population would grow quickly during the 1970s,\n",
+    "that agricultural production could not keep up, and that mass starvation\n",
+    "in the next two decades was inevitable (see\n",
+    "<https://modsimpy.com/popbomb>). As someone who grew up during those\n",
+    "decades, I am happy to report that those predictions were wrong.\n",
+    "\n",
+    "But world population growth is still a topic of concern, and it is an\n",
+    "open question how many people the earth can sustain while maintaining\n",
+    "and improving our quality of life.\n",
+    "\n",
+    "In this chapter and the next, we use tools from the previous chapters to\n",
+    "explain world population growth since 1950 and generate predictions for\n",
+    "the next 50-100 years.\n",
+    "\n",
+    "For background on world population growth, watch this video from the\n",
+    "American Museum of Natural History <https://modsimpy.com/human>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lyric-norwegian",
+   "metadata": {},
+   "source": [
+    "## World Population Data\n",
+    "\n",
+    "The Wikipedia article on world population contains tables with estimates of world population from prehistory to the present, and projections for the future (<https://modsimpy.com/worldpop>)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sharp-study",
+   "metadata": {},
+   "source": [
+    "The following cell downloads a copy of https://en.wikipedia.org/wiki/World_population_estimates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "false-future",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates.html'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tender-ethics",
+   "metadata": {},
+   "source": [
+    "To read this data, we will use Pandas, which provides functions for\n",
+    "working with data. The function we'll use is `read_html`, which can read a web page and extract data from any tables it contains. Before we can use it, we have to import it. You have already seen this import\n",
+    "statement:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "rising-trader",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "higher-warner",
+   "metadata": {},
+   "source": [
+    "Now we can use it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "emerging-clause",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'World_population_estimates.html'\n",
+    "tables = read_html(filename,\n",
+    "                   header=0, \n",
+    "                   index_col=0,\n",
+    "                   decimal='M')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "recovered-philadelphia",
+   "metadata": {},
+   "source": [
+    "The arguments are:\n",
+    "\n",
+    "-   `filename`: The name of the file (including the directory it's in)\n",
+    "    as a string. This argument can also be a URL starting with `http`.\n",
+    "\n",
+    "-   `header`: Indicates which row of each table should be considered the\n",
+    "    header, that is, the set of labels that identify the columns. In\n",
+    "    this case it is the first row (numbered 0).\n",
+    "\n",
+    "-   `index_col`: Indicates which column of each table should be\n",
+    "    considered the **index**, that is, the set of labels that identify\n",
+    "    the rows. In this case it is the first column, which contains the\n",
+    "    years.\n",
+    "\n",
+    "-   `decimal`: Normally this argument is used to indicate which\n",
+    "    character should be considered a decimal point, because some\n",
+    "    conventions use a period and some use a comma. In this case I am\n",
+    "    abusing the feature by treating `M` as a decimal point, which allows\n",
+    "    some of the estimates, which are expressed in millions, to be read\n",
+    "    as numbers.\n",
+    "\n",
+    "The result, which is assigned to `tables`, is a sequence that contains\n",
+    "one `DataFrame` for each table. A `DataFrame` is an object, defined by\n",
+    "Pandas, that represents tabular data.\n",
+    "\n",
+    "To select a `DataFrame` from `tables`, we can use the bracket operator\n",
+    "like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "judicial-samba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table2 = tables[2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ethical-proportion",
+   "metadata": {},
+   "source": [
+    "This line selects the third table (numbered 2), which contains\n",
+    "population estimates from 1950 to 2016.\n",
+    "\n",
+    "We can use `head` to display the first few lines of the table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "superior-colombia",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>United States Census Bureau (2017)[28]</th>\n",
+       "      <th>Population Reference Bureau (1973–2016)[15]</th>\n",
+       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
+       "      <th>Maddison (2008)[17]</th>\n",
+       "      <th>HYDE (2007)[24]</th>\n",
+       "      <th>Tanton (1994)[18]</th>\n",
+       "      <th>Biraben (1980)[19]</th>\n",
+       "      <th>McEvedy &amp; Jones (1978)[20]</th>\n",
+       "      <th>Thomlinson (1975)[21]</th>\n",
+       "      <th>Durand (1974)[22]</th>\n",
+       "      <th>Clark (1967)[23]</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1950</th>\n",
+       "      <td>2557628654</td>\n",
+       "      <td>2.516000e+09</td>\n",
+       "      <td>2.525149e+09</td>\n",
+       "      <td>2.544000e+09</td>\n",
+       "      <td>2.527960e+09</td>\n",
+       "      <td>2.400000e+09</td>\n",
+       "      <td>2.527000e+09</td>\n",
+       "      <td>2.500000e+09</td>\n",
+       "      <td>2.400000e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.486000e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1951</th>\n",
+       "      <td>2594939877</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.572851e+09</td>\n",
+       "      <td>2.571663e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1952</th>\n",
+       "      <td>2636772306</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.619292e+09</td>\n",
+       "      <td>2.617949e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1953</th>\n",
+       "      <td>2682053389</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.665865e+09</td>\n",
+       "      <td>2.665959e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954</th>\n",
+       "      <td>2730228104</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.713172e+09</td>\n",
+       "      <td>2.716927e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      United States Census Bureau (2017)[28]  \\\n",
+       "Year                                           \n",
+       "1950                              2557628654   \n",
+       "1951                              2594939877   \n",
+       "1952                              2636772306   \n",
+       "1953                              2682053389   \n",
+       "1954                              2730228104   \n",
+       "\n",
+       "      Population Reference Bureau (1973–2016)[15]  \\\n",
+       "Year                                                \n",
+       "1950                                 2.516000e+09   \n",
+       "1951                                          NaN   \n",
+       "1952                                          NaN   \n",
+       "1953                                          NaN   \n",
+       "1954                                          NaN   \n",
+       "\n",
+       "      United Nations Department of Economic and Social Affairs (2015)[16]  \\\n",
+       "Year                                                                        \n",
+       "1950                                       2.525149e+09                     \n",
+       "1951                                       2.572851e+09                     \n",
+       "1952                                       2.619292e+09                     \n",
+       "1953                                       2.665865e+09                     \n",
+       "1954                                       2.713172e+09                     \n",
+       "\n",
+       "      Maddison (2008)[17]  HYDE (2007)[24]  Tanton (1994)[18]  \\\n",
+       "Year                                                            \n",
+       "1950         2.544000e+09     2.527960e+09       2.400000e+09   \n",
+       "1951         2.571663e+09              NaN                NaN   \n",
+       "1952         2.617949e+09              NaN                NaN   \n",
+       "1953         2.665959e+09              NaN                NaN   \n",
+       "1954         2.716927e+09              NaN                NaN   \n",
+       "\n",
+       "      Biraben (1980)[19]  McEvedy & Jones (1978)[20]  Thomlinson (1975)[21]  \\\n",
+       "Year                                                                          \n",
+       "1950        2.527000e+09                2.500000e+09           2.400000e+09   \n",
+       "1951                 NaN                         NaN                    NaN   \n",
+       "1952                 NaN                         NaN                    NaN   \n",
+       "1953                 NaN                         NaN                    NaN   \n",
+       "1954                 NaN                         NaN                    NaN   \n",
+       "\n",
+       "     Durand (1974)[22]  Clark (1967)[23]  \n",
+       "Year                                      \n",
+       "1950               NaN      2.486000e+09  \n",
+       "1951               NaN               NaN  \n",
+       "1952               NaN               NaN  \n",
+       "1953               NaN               NaN  \n",
+       "1954               NaN               NaN  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table2.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "collectible-chapter",
+   "metadata": {},
+   "source": [
+    "The first column, which is labeled `Year`, is special.  It is the **index** for this `DataFrame`, which means it contains the labels for the rows.\n",
+    "\n",
+    "Some of the values use scientific notation; for example, `2.544000e+09` is shorthand for $2.544 \\cdot 10^9$ or 2.544 billion.\n",
+    "\n",
+    "`NaN` is a special value that indicates missing data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "illegal-hollow",
+   "metadata": {},
+   "source": [
+    "The column labels are long strings, which makes them hard to work with.\n",
+    "We can replace them with shorter strings like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "interim-trouble",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "intense-mileage",
+   "metadata": {},
+   "source": [
+    "Now we can select a column from the `DataFrame` using the dot operator,\n",
+    "like selecting a state variable from a `State` object.\n",
+    "\n",
+    "Here are the estimates from the United States Census Bureau:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "resident-engineer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census = table2.census / 1e9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "disciplinary-advocacy",
+   "metadata": {},
+   "source": [
+    "The result is a Pandas `Series`, which is similar to the `TimeSeries` and `SweepSeries` objects we've been using.\n",
+    "\n",
+    "The number `1e9` is a shorter way to write `1000000000` or one billion.\n",
+    "When we divide a `Series` by a number, it divides all of the elements of the `Series`.\n",
+    "From here on, we'll express population estimates in terms of billions.\n",
+    "\n",
+    "We can use `tail` to see the last few elements of the `Series`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "african-calvin",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "2012    7.013871\n",
+       "2013    7.092128\n",
+       "2014    7.169968\n",
+       "2015    7.247893\n",
+       "2016    7.325997\n",
+       "Name: census, dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "census.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "waiting-stack",
+   "metadata": {},
+   "source": [
+    "The left column is the **index** of the `Series`; in this example it contains the dates.\n",
+    "The right column contains the **values**, which are population estimates.\n",
+    "In 2016 the world population was about 7.3 billion.\n",
+    "\n",
+    "Here are the estimates from the United Nations\n",
+    "Department of Economic and Social Affairs (U.N. DESA):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ruled-hearing",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "2012    7.080072\n",
+       "2013    7.162119\n",
+       "2014    7.243784\n",
+       "2015    7.349472\n",
+       "2016         NaN\n",
+       "Name: un, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "un = table2.un / 1e9\n",
+    "un.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "incoming-summit",
+   "metadata": {},
+   "source": [
+    "The most recent estimate we have from the U.N. is for 2015, so the value for 2016 is `NaN`.\n",
+    "\n",
+    "Now we can plot the estimates like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "marine-sudan",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RaN/jeqeRPTidB3ecz+TlOwAwK/6TuxY29aBERE6Ec7DkE/jiblzWHj7cXJdLT29DXPmajBpUhaSEmFAnjFjqQYmIHK89m+Cja2F0PyhXm4XdP+G+SX8yacmfACpOJ0g9KBGR45G9D4aejsvYxrp2f6PWefeQ6olmQvWdtKyZFOp0xYJ6UCIix2LvVt99TAKc/QjP1n+Tc35sy879viWBVJwKj3pQIiKB8Hph/jsw6UEyzn+J7EYXkNS6J71rZ9Dx5AySEnU4r7CpQImIFGTr7zB+CKycTm7dM7juy0xqL/uZZ3qlUKt8oiZ3DRIVKBGR/Pz4Jnz1d5wnBuvxIp62fbhozmpa1NChvGDTOSgRkfzElmZHjU6cl/MMP1W9CMy4ukMdUmuXC3WyYk8FSkQkr5z98N1jMOe/OOegdS+irhxJct0GJMZqEcGilG+BMrOOZvaKmf1kZpvNbLWZfWFmN5uZ+rciUrysng2vnwbTnuK3xXN8k7s6KJsQy9Br02hYpUyoE5YoRy1QZvYlMAD4CugOVAeaA/8A4oFxZnZhUYQUEQmq/Xvgi3vgrXMgay9cPZq5LR8g2+vYm5UT6nQlljnnjvyCWSXn3JZ8PxzAe45FWlqamzt3bmHtTkQkMKtm4t45j4XVLmfbyffSJaUBB343av684DOzec65tMO3H7UHFUjhKcziJCJSpDK2/W9y1zodybl5Hvdl9mH66kxAk7uGgwKHmZvZpcCTQBXA/DfnnCsb5GwiIoXPOVg8Br78G27/bt5ZX4sru5xEfKX6fHxjMqXjdPVNuAhkFN9TwIXOuSTnXFnnXBkVJxGJSDvWwAe9YMz1UL4O6d0/4V9TtjJ9ue9gkIpTeAnku/Gnc+6XoCcREQmmrAwY1hmXncGKdv+gwfl30CbKwzd199CgculQp5MjCKRAzTWzUcCnwP4DG51zY4MVSkSk0OxYA+VqQ2winP80j8yPZ/S8KH7o5igdh4pTGAukQJUFMoBuebY5QAVKRMJXzn6Y/ixMf4bt5w+DphdQvsUlXF9zH5dlZOlwXgQo8DvknOtXFEFERArNmjnw2S2weSk5zS/n8s+9tPvjF566PIWa5RKoWS4h1AklAIGM4qsFvASciq/n9D0wxDm3NoDPlgPeAFr6P9vfOTfzRAKLiOTru8dg2n/wlqlB1FUfE924GzfPX0u7OuVDnUyOUSCj+N4GPgNqADWB8f5tgXgBmOicawqkABpsISLBcWDSgUqNWdu4D+13PsbChPYAXNq2FnUqlgphODkegRSoys65t51zOf7bO0Dlgj5kZmWB04E3AZxzWc65HScSVkTkL/Zsgo/7wazXyPU6aN2TpEuf4ezUhlRPig91OjkBgRSoLWZ2jZl5/LdrgK0BfK4+sBl428wWmNkbZvaXP2HMbKCZzTWzuZs3bz7G+CJSYjkH6R/AyyfB0gnM+HUDfd6ajdfrKBMfw78vbUWVsipQkSyQAtUf6AVsBDYAl/u3FSQaaAu85pxrA+wF7j38Tc65Yc65NOdcWuXKBXbMRERg+0oYcQl8ehNUbgo3fs/a5jdQq1wiWbneUKeTQhLIKL7VwPHMWr4WWOucm+1/PpojFCgRkWO2cx1u3Vw+q3kHce1voHvlGvSuDL1PSg51MilERy1QZnaPc+4pM3sJ3wi8Qzjnbs1vx865jWa2xsyaOOeWAV2Bn084sYiUTH8ugZUzoMNAqHsqObcu4r9vLebcLRmhTiZBkl8P6sCIuxNZ/+IW4H0ziwVWALqmSkSOTU4WTH8Gpj+DN74cz25qy+DubYgvVZ6xN51KbLQWBi+ujlqgnHPj/ffDj3fnzrl04C9rfIiIBGTdPBg3GDb9DK16Mafx3Qz98Dc6NtvOqQ0rqTgVc/kd4hvPEQ7tHeCc02q6IhI8+7bDOz3IjSvL4tOGktL1Ck4GpicnU03Dx0uE/A7xPV1kKUREDti8DCo3gYTy0Ptd/jYzlm9nZDLjtBwSY6NVnEqQ/A7xTS3KICJSwmVlwHePwKzX2N7jLd/krg3P4s7K+7gpK5fEWE3uWtLkd4hvEfkf4msdlEQiUvKsmgnj/g+2rSC7bX96fB7NySt+4emeKVRP0sSuJVV+f5JcUGQpRKTkmvIETHkCb7lkovqOJ6be6dxZey1tamty15Iuv0N8q4oyiIiUUJUasanZtVy67Gxe9LSiLXBJm1qhTiVh4KhjNM3se//9bjPbdfh90UUUkWIlZz98/RDMet33vOVlJFz0LK0b1KRcQkxos0lYOWqBcs518t+Xcc6VPfy+6CKKSLGxPh2GdYYZz7P6t8Xc/P78g5O7vnp1O+pr+XXJI6BhMWbWFuiEf8FC59yCoKYSkeIlNwe+fxamPgmJleCqj5m1qxmrZq5kW0YWlUrHhTqhhKFAVtR9EOgJjPVvesfMPnbOPRrUZCJSfGxIh8mPszH5fJa3e4jTGjemp3Nc0qYmMR7NBiFHFkgP6kqgjXMuE8DMngDmAypQInJ0zsGaOZDcAWqlkTtoOtd/vINyP27jtBQwM2I8FuqUEsYCKVArgXgg0/88Dvg9WIFEpBjYtR7G3Qy/T+anC8bTOOUU4qu34o2++6hQKjbU6SRC5Heh7oFlNvYDS8zsa//zs4HviyaeiEScJZ/C+CGQm8Wm0x/nojE7uXvPH/xf54a66FaOSX49qAPLbMwDPsmzfUrQ0ohIZBt/G8x7m+xqbYnp+QZVKjbg5Uob6NqsSqiTSQTK70Ld415mQ0RKqMpNWNLoRq5adhrjXDXqAue3rh7qVBKhClpuYxgw0TmXfdhr9YHrgJXOubeCmlBEwlduDkx7Cqq2wDW7EDv5Jio2y+SysiuoXEZDx+XE5HeI7wbgDuB5M9sGbMY3WKIe8BvwsnNuXPAjikhY2r4KxgyAtXNYVOtK3llcl2d6pVAtKZ4HezQPdTopBvI7xLcRuAe4x8zqAtWBfcCvzrmMooknImFp0WiYcLvv8WVvMnlTCpkbd5OV49Uqt1JoAppJwjm3Et9wcxEp6VbPhjHXs7V8Kn+e/RLNm7dmsNdh5ru2SaSw6E8dEQlM5k7ffXIH9l/yNpdk/IO3lviWjIuKMhUnKXRaolJE8ucczB4KUx5n+fmjadDiJOJSLuX92hnUKKfrmiR41IMSkaPL2AajroGJf2NX5TR6fbCCkT+uBqB2hUQ8Ueo1SfAEMlnsqcDDQB3/+w1wzrn6wY0mIiG1Zg6M7o/bvRE753HKdLiJ22ev5uLUmqFOJiVEIIf43gRuxzejRG5w44hI2Ph5HHuzHbdGP8ZTra6jYlQUfTrWDXUqKUECKVA7nXNfBj2JiITevu2weyNUaQZdH2Jd05uI/36TBkBISARSoCab2X/wrQe1/8BG59z8oKUSkaK3bj583Je9OcY7bUZxc9dmNK5Ti1fq1Ap1MimhAilQHfz3aXm2OaBL4ccRkSLnHMx9EybeB6WrMrza/Uxauo0bztBFtxJaBRYo59yZRRFEREIgex98dgss+ph9dbqQ0PtN+sckMcBMxUlCrsD/gWaWZGbPmtlc/+0ZM0sqinAiEmSeWLx7tzDUcyU3u79BYgXiYzwqThIWAjnE9xawGOjlf34t8DZwabBCiUiQ/fwZmdXaEV+hJlHXjKHVH9u5qFLpUKcSOUQgBaqBc+6yPM//aWbpQcojIsGUmw1fPwizXmVC9AXE9fgPPVJqcEqDSqFOJvIXgfTj95lZpwNP/Bfu7gteJBEJil3r4Z0LYNar5LYfxLR6t1ItKT7UqUSOKpAe1E3AcP95JwO24VusUEQixYaFeEdcSnbmXrj4DeJSe/JiqDOJFCCQUXzpQIqZlfU/3xXsUCJSyMols7NcC65Z04P7y5zJKaHOIxKA/JZ8v8Y5956Z3XHYdgCcc88GOZuInIj9u/FOf46FDQbSpl41yg/8jHf37KdiaS3FLpEhvx5UKf99mSO85gLZuZmtBHbjm8MvxzmXlv8nRKRQbF7mm4V8y2+8MDWOx++8lRrlElScJKLkt+T7UP/Db5xzM/K+5h8oEagznXNbjieciByHxWNx4wZjMQns7jWay3Oaad0miUiBjOJ7KcBtIhJqM16E0f1YEVUHN2gaSc27ckHrGqFOJXJc8jsH1RE4Bah82HmosoAnwP07YJKZOWCoc27YEb7OQGAgQHJycqC5ReRIGnXjp2XL+aLKDdxVurqWzJaIlt//31igtP89ec9D7QIuD3D/pzrn1ptZFeBrM1vqnJuW9w3+ojUMIC0tLaBzWyLyP27FFJZOG83u0/9J+/pNad3/JVqHOpRIIcjvHNRUYKqZveOcW3U8O3fOrfffbzKzT4D2wLT8PyUiAfF6YfrTMOXfJFCDkXOuoH39TgV/TiRCBHIEIMO/HlQL4OBl5865fJfbMLNSQJRzbrf/cTfgXycSVkT89mxm1wf9KLt+OtaqF4lnPMHDFSqEOpVIoQpkkMT7wFKgHvBPYCXwYwCfqwp8b2YLgTnA5865iceZU0QO8HrJfPM84tbN4ofmD8Glw6hSqSJRUVr1VoqXQHpQFZ1zb5rZkDyH/aYW9CHn3Aog5YQTioiP10tmTi7xsTHEnf8Ek1Z7Ob1TF9By7FJMBVKgsv33G8zsfGA9oDWgRYrSnk1sfrcv729txBW3PkW1hl05p2GoQ4kEVyAF6lH/RLF34rv+qSxwe1BTicj//P4djB1ExcxdVKp0CtEe9ZikZAhkstgJ/oc7AS3/LlJUcrNZOvJemvz2Jla5CVF9xnFN1eahTiVSZPK7UPcl8plzzzl3a1ASiYjPhp9o/NtbfJfYndP6v0Fsgla8lZIlvx7U3CJLISIAOOeYOvUbajQ7mca12pEzaAZnVm2mEXpSIuV3oe7wogwiUuJl7iT7s9s5/eexDF37Go2vuZLY6jqkJyVXgeegzGwyRzjUV9CFuiISGOcci374ilZz7iJ213q2tr+Tgd0CnU1MpPgKZBTfXXkexwOXATnBiSNS8iwf/TAtFr/AvtK1SOz/FRVrnxTqSCJhIZBRfPMO2zQjkAt1RSR/2/dmUb5ULA1rVWXtrouoedWLkJAU6lgiYSOQQ3x5J/iKAtoB1YKWSKS483r5+t3HmLYe/nbnfZQ++SaSO/5fqFOJhJ1ADvHNw3cOyvAd2vsDuD6YoUSKK7dzHTbuZs5eOZnalc8lPjpKUxWJHEUgh/jqFUUQkeIsOyeXMe88x8UbnyfecuGC52jarp+Kk0g+AjnEFw/8H9AJX0/qe+A151xmkLOJFBsx63/kirWPsL5MK2pcNxwqNgh1JJGwF8hyG+/iWwvqJeBloBkwIpihRIqDvftzeHn0JP7clQnJJ+N6v0+N26eqOIkEKJBzUE2cc3mXzZjsX+NJRI4mYxtu3F3csPQzvqs0lnM7n441uyDUqUQiSiA9qAVmdvKBJ2bWAZgRvEgikSsrx0v6NyPh1ZMpvXwcOafexrmndQx1LJGIFEgPqgPQx8xW+58nA7+Y2SLAOedaBy2dSCRxjj/e6EPqxvFkVmxO/NWjKVVdPx4ixyuQAtU96ClEIpjX69i5L5vypWJpUL8hKyvdQt2LH4To2FBHE4logQwzX2VmKcBp/k3TnXM6ByUCsHcrc18fyCfWlX8NuZGYbg9TN9SZRIqJAs9BmdkQ4H2giv/2npndEuxgIuFsb2Y2btFoeOUk0vZM5fLae4jWkhgihSqQQ3zXAx2cc3sBzOxJYCa+YeciJc661Sv4/e2BnO5+hBptier7Mu2qtgh1LJFiJ5ACZUBunue5/m0iJUpOrpdoTxTVN3xDFRay7qS/U7P7neAJ5MdIRI5VID9ZbwOzzewTfIXpIuDNoKYSCTNfTZ3Bt7Nm89Btt1LqpAFENT6HmuXrhDqWSLEWyCCJZ81sCr6pjgD6OecWBDWVSLjIzYYfXuTsaU/Qxiqxb/+NlIpLBBUnkaA7lmMTBnjR4T0pAbxexxujRnPp2ieptHc5Uc0vosq5T0GZxFBHEykxAhnF9yAwHCgPVALeNrN/BDuYSChFbVnGgGUDic3aAVd8AL3ehTJaBk2kKAXSg7oSaHNg9nIzewKYDzwazGAiRW3llr28/skkhvTqTvUqTeGC5ynb8mKI1yq3IqEQyFx8K4H4PM/jgN+DkkYkVHatp8qX1/OvdQNY99siAKLS+qo4iYRQID2o/cASM/sa33pQZwPfm9mLAM65W4OYTySoJi5aS9Tct+m2YSiJ3mxyzvw7aaltQh1LRAisQH3ivx0wJThRRIpYbg4tJ11Jrd0/kVuvM54ezxFdoX6oU4mIXyDDzIcXRRCRopCZnctr3y6la8uatK5VjqrtLiS33GA8Kb20/LpImAnkHJRIsZG7/Bt6zbqY32d/AUBM57vxpPZWcRIJQ5qjRYq9P7bsZcIP6QzOeotSi0cTX6ERl7RvGOpYIlIAFSgp9jZMH06f9EfBkw2d/46n020QHRfqWCJSgKMWKDMbj2/U3hE55y4MSiKRE+ScY9LPfxIXHUXnJlU4ubqHnC2tsItfgsqNQx1PRAKUXw/qaf/9pUA14D3/8yvxXRsVEDPzAHOBdc65C44jo8gx8eZksXH8I+wvVZPOTf5OVPsbiG1/A0TplKtIJDlqgXLOTQUws0ecc6fneWm8mU07hq8xBPgFKHt8EUUKtj8nl/dnrebqmhuJ++J2+mb+Qm7z63wvqjCJRKRAfnIrm9nBi0PMrB5QOZCdm1kt4HzgjeOLJxKYBb+tIWriPcQOPxf274IrR+G58IVQxxKRExDIIInbgSlmtsL/vC4wKMD9Pw/cA5Q55mQiBdi2N4sl63dyWqPKnBz1Kx2iv8baD4SuD0Cc/suJRLpALtSdaGaNgKb+TUudc/sL+pyZXQBscs7NM7PO+bxvIDAQIDk5OZDMIgA8M3Y6+1b8QLv77iOxcTds8FyopOHjIsWFOXfkgXpmdml+H3TOjc13x2b/Bq4FcvBNNlsWGOucu+Zon0lLS3Nz584tKLOUYCs276FcQgwVln+Md+L9OG8OnjuWQEK5UEcTkeNkZvOcc2mHb8+vB9Ujn9cckG+Bcs7dB9zn/+KdgbvyK04iBdmZkc3/vTSaV8q+S4U984hKPgV6vKDiJFJM5TeKr5+ZRQGXO+c+KsJMIodYv2MfNcolkMRuJsTeR1SWBy54DtpepxF6IsVYvj/dzjkvMPhEv4hzboqugZLjMX7heno/NZpFa3dCYgWiezxP1OA5kNZfxUmkmAvkJ/xrM7vLzGqbWYUDt6AnkxJtX1YuZO+j2/pXmRI3hIZ75/leSOkNZWuENpyIFIlAhpn399/fnGebA7RwjgTFfWMXUWrdDO53Q4nbtgLa9iEhWYsIipQ0gQwzr1cUQaRkc85h/iUv+u0eSuOtI3AV6kPf8VDv9AI+LSLFUYEFysxigJuAA78lpgBDnXPZQcwlJci2vVnc8sE8+nasS7eW1Wncoi3UqIh1vhdiEkIdT0RCJJBDfK8BMcCr/ufX+rcNCFYoKVnK7N/IbVsexvN7D2g52DcAQkRKvEAK1EnOuZQ8z78zs4XBCiQlw2+b9vDad8t4IvlHYqY8Qpo3F6vRK9SxRCSMBFKgcs2sgXPudwD/xLG5wY0lxd32lQvps3QIMUuXQ4Mu2AXPQfm6oY4lImEkkAJ1NzDZP1msAXWAfkFNJcVS+podrNmWQY+UGpyUtBuXsA3O/S+06gn+ARIiIgfkt6LubcAMYCrQCGiCr0AFNFmsyOEmTfiY2B2/kdPy30Q36Y7dtlCzjovIUeXXg6oFvIBvFvOfgB/wFaw1gAqUBGTy0k2kVvJS/vtHuOfP98it2BgPuUCUipOI5Cu/ufjuAjCzWCANOAXfRbv/NbMdzrnmRRNRItX67RlMeO85OsR/AN490OkOPGfcA56YUEcTkQgQyDmoBHxLZST5b+uBRcEMJZHL63UsWLOddnUqUMO7gadjhuKqtoULX4CqLUIdT0QiSH7noIYBLYDdwGx8h/iedc5tL6JsEoHemraM2V9/xD233k6jqg2wfl9itdIgyhPqaCISYfLrQSUDccByYB2wFthRBJkkwuzLymVXZjZVty+g3+IhDIhZhvNeDLSG5A6hjiciESq/c1DdzTc5Wgt855/uBFqa2TZgpnPuoSLKKGHMOUe/175iwP4RVM34Ak9SbbjyQ6x661BHE5EIl+85KOdbD36xme0AdvpvFwDtARWoEmzTrkwql4nDnJc3s+4lcd9a6DgYOt8HcaVDHU9EioGjrgdlZrea2YdmtgaYhq8wLQMuBbQeVAk2f/V2rnpqJF8v2QhRHkqd/yg2aBqc85iKk4gUmvx6UHWB0cDtzrkNRRNHwpVzjm17s6gYm0PKshf4KuZFdu+KAvpC8wtDHU9EiqH8zkHdUZRBJLzd/8kiYn6dwMOx7+HZtQ5SrqJc6/NCHUtEirFAroOSEmpXZjYJMR5iPFHcsvclqmd+hCvbAi57E+p0DHU8ESnmjnoOSkq2jTszueA/X/L+978CUP3kXnDuU75zTSpOIlIE1IOSQ+zMyCYp3kPVFaP5IupBMnZeC/wTGp0d6mgiUsKoQMlBb33/B9O/m8B/q4wmemM6pWumUbptj1DHEpESSgWqhMvO9ZKd6yUxNpoeuz6kv/cJvHuqwSXDfOs0RekosIiEhgpUCbY/J5crXpxExzpluOeyTlRucx7E5xJ16m26nklEQk4FqgTalZlN2WhH3Ny3+CDjcbbv7gJ0guopvpuISBhQgSphvlq8nkkfD+XfSZ8Su2slCfXOIKHb7aGOJSLyFypQJYBzjsxsLwmxHjpteIdz7HlyYpvC1WOgYVcwC3VEEZG/UIEqAZ59awS7c2N5eOAVlGrfFyrXI7p1L63RJCJhTQWqmNqfk0vcpkUw+XHuXPMVK6qchdfbm6ikmpB6ZajjiYgUSAWqGFq5eDarxv6DM7xzID4Juj5E/Q6DIEqH8kQkcqhAFSNZOV5io6OouWUaVVjCxrZ3UK3bbb4iJSISYVSgioMNP7F89EOM2X8Sd91+LzGn/B8xHW4gMaFcqJOJiBw3FagItnT+VBovfY2oX7+kbnRpUmq2IzvXER1bKtTRREROmApUhNr04S00XfouWTFlie38d2I6DOJc9ZhEpBhRgYoQ3lwvb414m+jk9lzXpRWVW3VlEeWpf95txJatEOp4IiKFLmgFyszigWlAnP/rjHbOPRSsr1ccpa/ZwfKNO+lZehFR059hwPr5fGd3Aq2wFhfTqsXFoY4oIhI0wexB7Qe6OOf2mFkM8L2ZfemcmxXErxnx1mzLoHaFRHCOX78aRru1w4G1UL4u9HiBLim6hklESoagraXgfPb4n8b4by5YX684+HDOajo/9Q2rt2aAGRczmbqVy8Clb8DgedDuOoiOC3VMEZEiEdRzUGbmAeYBDYFXnHOzj/CegcBAgOTk5GDGCTu7M7P577QVdG5ahbYVvZy3bTg9koaTm/MdkEzsle9BYgXNlSciJVJQC5RzLhdINbNywCdm1tI5t/iw9wwDhgGkpaWViB5WZnYu8TEePFHGlFlz6LZyKmyaQNmcfdD4XIjO9r2xVMXQBhURCaEiGcXnnNthZlOA7sDiAt5erD04bjFL1u9i9I0dSczeyThuxzYCrXvDKYOhSrNQRxQRCQvBHMVXGcj2F6cE4CzgyWB9vXDlnGPuqu20Sy5PFF66R8/ngph55HpPJrpUReziV6HuaVC2eqijioiElWD2oKoDw/3noaKAj5xzE4L49cLS1F83c9Pb0xlz8kqar36PU7atgHLJkLMXPGWgda9QRxQRCUtBK1DOuZ+ANsHaf7jyeh2fLFhHqTgP3VtW57SYpSwscxux6bugZhr0fBCa9gCPrpEWEcmPfksWEq/XERVlGI450z6nYmIM3Vv2x1OtJZ7GZ8HJN0Ht9qGOKSISMVSgCsGnC9bx2rdLGH/mZmLnDuXJnQtxFc8E+vuGifd8O9QRRUQijgrUcVqyfic1khIoXyqWlA0fMzrjWWLH74RKTeD8Z7GUK0IdUUQkoqlAHYe12/by2Muvc/oZ53DjOanUq1oeGnWC9gOg/pm6sFZEpBCoQAVoxKxV7NuxmYFJs6k19y0+iP2NfWUrAKnQrq/vJiIihUYFKh/rduyjZrkEyMmi2ax7aL3zO3BZULsDnH43Cc0vDnVEEZFiSwXqKEbPWMxnn4/nsbuGULtCIm0rZhPVqA+06wfVWoY6nohIsacC5bcjI4uXv11Oz+obaLJ2LJctHsslcTnssQFAIlHXjNG5JRGRIlSiC9TOfdls2bOfBpVLk7DhR66YN4iGrIHY0ljKFXja9SWpfGXfm1WcRESKVIkrUM45zAy8Xp545XW88Uk8ObgPceWqUb9GFWh3F7S8HOJKhzqqiEiJVqIK1PuzVzFp5gLebvMbUekj+PeelWyvehHQByo2IGrgd6GOKCIifsW6QK3emsGIWSsZfGYjkhJjOG3pY1y542OiJnt9M4h3eYDyTS8IdUwRETmCYlWgsnK8zPhtCw0qlya5YiK7N/5G6VkvsqTRI5zSuDrJTdpCzZrQ5hqo2CDUcUVEJB/FqkDtzsxm0PCZPJOygeTsSTT//TuaRxsWPwCoDiffGOqIIiISoGJVoCpmb2RJuTuJWboZytbEOt/r6y0l1Qp1NBEROUbFqkBRLpmYZudBk3Oh4dlac0lEJIIVr9/gZnDhi6FOISIihSAq1AFERESORAVKRETCkgqUiIiEJRUoEREJSypQIiISllSgREQkLKlAiYhIWFKBEhGRsKQCJSIiYcmcc6HOcJCZbQZWneBuKgFbCiFOOFGbwl9xaw+oTZGiOLSpjnOu8uEbw6pAFQYzm+ucSwt1jsKkNoW/4tYeUJsiRXFs0wE6xCciImFJBUpERMJScSxQw0IdIAjUpvBX3NoDalOkKI5tAorhOSgRESkeimMPSkREigEVKBERCUthX6DM7C0z22Rmi/NsSzGzmWa2yMzGm1lZ//a6ZrbPzNL9t9fzfKad//2/mdmLZmahaI8/S8Bt8r/W2v/aEv/r8f7tEdkmM7s6z/co3cy8Zpbqfy1S2xRjZsP9238xs/vyfCYs2nSM7Yk1s7f92xeaWec8nwmL9viz1Dazyf5/8yVmNsS/vYKZfW1my/335fN85j5/9mVmdk6e7WHRrmNtk5lV9L9/j5m9fNi+wqJNx805F9Y34HSgLbA4z7YfgTP8j/sDj/gf1837vsP2MwfoCBjwJXBuhLQpGvgJSPE/rwh4IrlNh32uFbCiGHyfrgI+9D9OBFYCdcOpTcfYnpuBt/2PqwDzgKhwao8/S3Wgrf9xGeBXoDnwFHCvf/u9wJP+x82BhUAcUA/4Pdx+no6jTaWATsCNwMuH7Sss2nS8t7DvQTnnpgHbDtvcBJjmf/w1cFl++zCz6kBZ59xM5/uuvQtcXMhRA3aMbeoG/OScW+j/7FbnXG6EtymvK4GREPHfJweUMrNoIAHIAnaFU5uOsT3NgW/9n9sE7ADSwqk9/mwbnHPz/Y93A78ANYGLgOH+tw3nfxkvwveHxH7n3B/Ab0D7cGrXsbbJObfXOfc9kJl3P+HUpuMV9gXqKBYDF/of9wRq53mtnpktMLOpZnaaf1tNYG2e96z1bwsnR2tTY8CZ2VdmNt/M7vFvj+Q25dUbf4Eists0GtgLbABWA08757YR/m06WnsWAheZWbSZ1QPa+V8L2/aYWV2gDTAbqOqc2wC+X/j4eoHgy7omz8cO5A/LdgXYpqMJyzYdi0gtUP2Bm81sHr4ucJZ/+wYg2TnXBrgD+MB/TP1Ix13DbXz90doUja/7frX//hIz60pktwkAM+sAZDjnDpwTieQ2tQdygRr4Dh3daWb1Cf82Ha09b+H7hTYXeB74AcghTNtjZqWBMcBtzrld+b31CNtcPttD5hjadNRdHGFbyL9XxyI61AGOh3NuKb5DX5hZY+B8//b9wH7/43lm9ju+HshaoFaeXdQC1hdl5oIcrU34sk91zm3xv/YFvvMI7xG5bTrgCv7Xe4LI/j5dBUx0zmUDm8xsBpAGTCeM25TPz1IOcPuB95nZD8ByYDth1h4zi8H3i/x959xY/+Y/zay6c26D/1DXJv/2tRzakz+QP6z+7x1jm44mrNp0PCKyB2VmVfz3UcA/gNf9zyubmcf/uD7QCN8J+A3AbjM72T+KpQ8wLiThj+JobQK+AlqbWaL//MYZwM8R3qYD23oCHx7YFuFtWg10MZ9SwMnA0nBvUz4/S4n+dmBmZwM5zrmw+3/nz/Am8Itz7tk8L30G9PU/7sv/Mn4GXGFmcf5Dl42AOeHUruNo0xGFU5uOW6hHaRR0w/cX9gYgG99fBNcDQ/CNbPkVeIL/zYhxGbAE3/Hz+UCPPPtJw3e8/Xfg5QOfCfc2+d9/jb9di4GnikmbOgOzjrCfiGwTUBr42P99+hm4O9zadIztqQssw3eC/ht8yyGEVXv8WTrhO2z1E5Duv52Hb7Trt/h6fd8CFfJ85n5/9mXkGdUWLu06zjatxDcAZo//e9s8nNp0vDdNdSQiImEpIg/xiYhI8acCJSIiYUkFSkREwpIKlIiIhCUVKBERCUsqUCJB5L8u6nszOzfPtl5mNjGUuUQigYaZiwSZmbXEd41UG8CD77qW7s65349jXx7nXG7hJhQJTypQIkXAzJ7CN5lsKf99HXxLjUQDDzvnxvknBh3hfw/AYOfcD+Zbi+khfBfZpjrnmhdtepHQUIESKQL+aYPm45uMdQKwxDn3npmVw7dmTxt8swd4nXOZZtYIGOmcS/MXqM+Bls63RIRIiRCRk8WKRBrn3F4zG4VvKppeQA8zu8v/cjyQjG8iz5fNt7pwLr6Jjg+Yo+IkJY0KlEjR8fpvBlzmnFuW90Uzexj4E0jBN4Ap7wJ0e4soo0jY0Cg+kaL3FXCLf4ZpzKyNf3sSsME55wWuxTegQqTEUoESKXqPADHAT2a22P8c4FWgr5nNwnd4T70mKdE0SEJERMKSelAiIhKWVKBERCQsqUCJiEhYUoESEZGwpAIlIiJhSQVKRETCkgqUiIiEpf8HUpJplJBrFekAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import plot, decorate\n",
+    "\n",
+    "plot(census, style=':', label='US Census')\n",
+    "plot(un, style='--', label='UN DESA')\n",
+    "\n",
+    "decorate(ylabel='World population (billion)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "czech-fitness",
+   "metadata": {},
+   "source": [
+    "The keyword argument `style=':'` specifies a dotted line; `style='--'` specifies a dashed line.\n",
+    "\n",
+    "The `label` argument provides the strings that appear in the legend.\n",
+    "\n",
+    "The lines overlap almost completely, but it looks like the most recent estimates diverge slightly.\n",
+    "In the next section, we'll quantify these differences."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adolescent-graduate",
+   "metadata": {},
+   "source": [
+    "## Absolute and relative errors\n",
+    "\n",
+    "Estimates of world population from the U.S. Census and the U.N. DESA differ slightly.\n",
+    "One way to characterize this difference is **absolute error**, which is the absolute value of the difference between the two.\n",
+    "\n",
+    "To compute absolute value, we can import `abs` from NumPy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "resident-matthew",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import abs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "resident-bonus",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "2012    0.066201\n",
+       "2013    0.069991\n",
+       "2014    0.073816\n",
+       "2015    0.101579\n",
+       "2016         NaN\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs_error = abs(un - census)\n",
+    "abs_error.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "removed-friendly",
+   "metadata": {},
+   "source": [
+    "When you subtract two `Series` objects, the result is a new `Series`.\n",
+    "Because one of the estimates from 2016 is `NaN`, the result is `NaN`.\n",
+    "\n",
+    "To summarize the results, we can compute the **mean absolute error**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "executive-luxury",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.029034508242424265"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from numpy import mean\n",
+    "\n",
+    "mean(abs_error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "empty-kuwait",
+   "metadata": {},
+   "source": [
+    "On average, the estimates differ by about 0.029 billion.\n",
+    "But we can also use `max` to compute the maximum absolute error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "wicked-paste",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.10157921199999986"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from numpy import max\n",
+    "\n",
+    "max(abs_error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "vulnerable-olympus",
+   "metadata": {},
+   "source": [
+    "In the worst case, they differ by about 0.1 billion.\n",
+    "\n",
+    "Now 0.1 billion is a lot of people, so that might sound like a serious discrepancy.\n",
+    "But counting everyone is the world is hard, and we should not expect the estimates to be exact.\n",
+    "\n",
+    "Another way to quantify the magnitude of the difference is **relative error**, which is the size of the error divided by the estimates themselves."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "negative-championship",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "2012    0.943860\n",
+       "2013    0.986888\n",
+       "2014    1.029514\n",
+       "2015    1.401500\n",
+       "2016         NaN\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rel_error = abs_error / census * 100\n",
+    "rel_error.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "supposed-canberra",
+   "metadata": {},
+   "source": [
+    "I multiplied by 100 so we can interpret the results as a percentage.  In 2015, the difference between the estimates is about 1.4%, and that happens to be the maximum.\n",
+    "\n",
+    "Again, we can summarize the results by computing the mean."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "statutory-leadership",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5946585816022846"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean(rel_error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fluid-syria",
+   "metadata": {},
+   "source": [
+    "The mean relative error is about 0.6%.\n",
+    "So that's not bad.\n",
+    "\n",
+    "You might wonder why I divided by `census` rather than `un`.\n",
+    "In general, if you think one estimate is better than the other, you put the better one in the denominator.\n",
+    "In this case, I don't know which is better, so I put the smaller one in the denominator, which makes the computed errors a little bigger."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "confident-people",
+   "metadata": {},
+   "source": [
+    "## Modeling Population Growth\n",
+    "\n",
+    "Suppose we want to predict world population growth over the next 50 or\n",
+    "100 years. We can do that by developing a model that describes how\n",
+    "populations grow, fitting the model to the data we have so far, and then using the model to generate predictions.\n",
+    "\n",
+    "In the next few sections I demonstrate this process starting with simple models and gradually improving them.\n",
+    "\n",
+    "Although there is some curvature in the plotted estimates, it looks like world population growth has been close to linear since 1960 or so. So we'll start with a model that has constant growth.\n",
+    "\n",
+    "To fit the model to the data, we'll compute the average annual growth\n",
+    "from 1950 to 2016. Since the UN and Census data are so close, we'll use the Census data.\n",
+    "\n",
+    "We can select a value from a `Series` using the bracket operator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "afraid-tennessee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.557628654"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "census[1950]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "trained-canyon",
+   "metadata": {},
+   "source": [
+    "So we can get the total growth during the interval like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "artistic-suicide",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_growth = census[2016] - census[1950]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "advisory-mortality",
+   "metadata": {},
+   "source": [
+    "The numbers in brackets are called **labels**, because they label the\n",
+    "rows of the `Series`.\n",
+    "\n",
+    "In this example, the labels 2016 and 1950 are part of the data, so it\n",
+    "would be better not to make them part of the program. \n",
+    "Putting values like these in the program is called **hard coding**; it is considered bad practice because if the data change in the future, we have to change the program (see <https://modsimpy.com/hardcode>).\n",
+    "\n",
+    "It would be better to get the labels from the `Series`.\n",
+    "We can do that by selecting the index from `census` and then selecting the first element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "authorized-rough",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1950"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t_0 = census.index[0]\n",
+    "t_0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "concerned-belgium",
+   "metadata": {},
+   "source": [
+    "So `t_0` is the label of the first element, which is 1950.\n",
+    "We can get the label of the last element like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "forced-trunk",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t_end = census.index[-1]\n",
+    "t_end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "driving-humor",
+   "metadata": {},
+   "source": [
+    "The value `-1` indicates the last element; `-2` indicates the second to last element, and so on.\n",
+    "\n",
+    "The difference between `t_0` and `t_end` is the elapsed time between them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "vocal-weather",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "66"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "elapsed_time = t_end - t_0\n",
+    "elapsed_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "accurate-interaction",
+   "metadata": {},
+   "source": [
+    "Now we can use `t_0` and `t_end` to select the population at the beginning and end of the interval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "finnish-sentence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p_0 = census[t_0]\n",
+    "p_end = census[t_end]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "looking-board",
+   "metadata": {},
+   "source": [
+    "And compute the total growth during the interval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "viral-indian",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.768368055"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_growth = p_end - p_0\n",
+    "total_growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "active-pierce",
+   "metadata": {},
+   "source": [
+    "Finally, we can compute average annual growth."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "vietnamese-adrian",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.07224800083333333"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "annual_growth = total_growth / elapsed_time\n",
+    "annual_growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "occupied-indiana",
+   "metadata": {},
+   "source": [
+    "From 1950 to 2016, world population grew by about 0.07 billion people per year, on average.\n",
+    "The next step is to use this estimate to simulate population growth."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "underlying-oliver",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "Our simulation will start with the observed population in 1950, `p_0`,\n",
+    "and add `annual_growth` each year. To store the results, we'll use a\n",
+    "`TimeSeries` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "opposed-above",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import TimeSeries\n",
+    "\n",
+    "results = TimeSeries(time='Year', quantity='Population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "motivated-point",
+   "metadata": {},
+   "source": [
+    "In this example, I use the keyword arguments `time` and `quantity` to give names to the index and the values.\n",
+    "These names don't affect the computation, but they appear when we display or plot the `TimeSeries`.\n",
+    "\n",
+    "We can set the first value in the new `TimeSeries` like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "architectural-techno",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results[t_0] = p_0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "absent-anthony",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like so far."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "every-arkansas",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Population</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1950</th>\n",
+       "      <td>2.557629</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Population\n",
+       "Year            \n",
+       "1950    2.557629"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import show\n",
+    "\n",
+    "show(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "diagnostic-testimony",
+   "metadata": {},
+   "source": [
+    "Now we set the rest of the values by simulating annual growth:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "dedicated-system",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for t in range(t_0, t_end):\n",
+    "    results[t+1] = results[t] + annual_growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "classical-uncertainty",
+   "metadata": {},
+   "source": [
+    "The values of `t` go from from `t_0` to `t_end`, including the first but not the last.\n",
+    "\n",
+    "Inside the loop, we compute the population for the next year by adding the population for the current year and `annual_growth`. \n",
+    "\n",
+    "The last time through the loop, the value of `t` is 2015, so the last label in `results` is 2016.\n",
+    "\n",
+    "Here's what the results look like, compared to the estimates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "trained-canvas",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Dv1tMa5wsu/3CY3f90OiQQkNDiYmJ4dNPP2Xq1KkWXxcREYHRaCQnJweANWvWEB0dff74V199RWxsLH/84x/p2rUrEyZMYO7cuYwePRqA8vJyEhMTef/99yksLGTJkiXn15wSQrRuZRWVJB92IF3dgYNyZuLEMQwZMoTx2SWEB1h3GYzmYtUWlFLKHZgIfG3Nz7GmKy23cc5vf/tbXnvttfP3lCxVt/U0cuRIMjIyzr86d+6Mp6cn6enpLFiwgMDAQObOncsnn3wCwIoVKxg7dizu7u7MnDmTb775htra2quroBCi5SjOpmbZ3ayL/4a3336b9AIvAiOi+LS4JxV+nTEYDPTq0AYPl9bxiKtVa6G1LgP8r3ji1aivxePsXv9xD/+rbjFZstwGQGRkJNHR0Sxbtszisg8fPozBYCAoKIg9e/Zc9jyDwcCYMWMYM2YMvXv3ZuHChdx5550sWbKEtWvXnh+UkZeXR3JyMhMmTLC8gkII+6c1On0he+P+w8qaQRSwncAOnZg9bQr+/gF025/D2G5Bto6yyclyG1dwpeU26vrd7353fnTfleTm5nLffffx0EMP1TuwYd++fRcs056RkUGnTp04e/YsqampHDt2jKNHj3L06FHeffddlixput5UIYQdOHOAUwvmsHBFKstqJuDoF8pRn/58URiGv38ADg6Kcd2DW+UAqdbRDrSy+pbbqCsqKor+/fufX6PpYuXl5URHR1NdXY2joyO33XYbTzzxxPnjF9+D+v3vf094eDgPP/wwhYWFODo6EhkZyYIFC/j6668ZN27cBYsaTps2jWeeeYbKysoL9gshWqbi4mKSvviMjJwonBwdmDh+EkOHDGLs6RI8Xex7FoimIMttCPkeC2Fnqo+sZ92OQ6zdeYza2lrCI7vyx22uvDJ7ALMGdLR1eE1OltsQQgg7p8uL2PHFqyQeruGs8iIwJIKbp0/Bz8+P3jGF9OnYxtYhNitJUEIIYQeOr1lCXMo6ThgDaOdhJNdvAO9lOnGbuxcAfUN8bBugDUiCEkIIGyosLCThm0XsOpaHp/Jk4tAohsXO5GRRBbeUVeHl6mTrEG1GEpQQQthAZXkZqSu/Z/32AyilGBbpy3P7wzhTGsZwpejg40aHFjZ3XlOTBCWEEM3IaDSSsWoFSakbKTU6E9E5ghun3kibNm2o2XGKgWG+tg7RbkiCEkKIZnJk327ili/ldJkDIQ6F9Onanfu2+9J3jKZPG7i+dztbh2hXJEE1o7CwMNLS0ggICGjUOUKIliUvL4+VP//AvoNHaKOLuTFUEX3znyhx8OLJtpl0DfaydYh2SRKUEEJYSXl5OauSEtm8ZSuOjo6M66RYUx7Fq1U9Wermi5dSPDg20tZh2i1JUFdw9OhRJk+eTExMDBs2bKBv377cddddvPDCC+Tk5LB48WIiIyO5++67OXz4MO7u7ixYsIA+ffqQl5fHvHnzyM3NZfDgwRdMDLto0SLefvttqqqqGDJkCP/6178wGAw2rKkQoqnU1taStnkTq5JWUlFdS59uEYyfMgMvLy/yM07Qw44mSLBnLSpB/fzzz2RnZzdpmW3btmXy5Mn1nnPw4EG++OILFixYwKBBg/jss89ITU1l+fLlvPLKK4SEhNCvXz++/fZbkpKSuP3228nIyOCll14iJiaG559/nh9++IEFCxYAppkbli5dytq1a3FycuKBBx5g8eLF3H777fXGIYSwb1prDhw4QPyPy8krKiVcZzKqbRmPH+qGy9ESruvtxbToDrYOs8VoUQnKVsLDw+nduzdgmm9v/PjxKKXo3bs3R48eJTMzk6+++gqAcePGkZeXR1FREatXr+brr00rjUyZMgVfX9PonMTERNLT0xk0aBBg6gYICmp9MxELcS05ffo08fHxHD58GH/yudllO12nPERtj+mEfbWjxS23bg9aVIK6UkvHWupOvOrg4HB+28HBgZqaGhwdf/ltPDez8KVmGNZac8cdd/Dqq69aKWIhRHMpLS0lKSmJrVu34uLiwuQIRZvqCp4reobPusfi6mjgjbnRtg6zRZLlNprAqFGjWLx4MQApKSkEBATg7e19wf6ffvrp/LpS48eP58svvzy/km5+fj6ZmZm2CV4I0SA1NTWsXbuWt996i4wt6Qzu2paHH36YIbf+gbxRL9MuKJiKallAtDFaVAvKXr344ovcdddd9OnTB3d3dxYuXAiYluWYN28e/fv3Z/To0YSGhgLQs2dP/vznPxMbG4vRaMTJyYl3332XTp062bIaQggLaK3ZvXs3CQkrKSwsoitHmOC0mRUFd7Ao7RT3jurMiMgARkTKoyKNJcttCPkeC2GhEydOEB8fz7FjxwhyKGJS7Uoieg2GSS/z4PKTRIf4cM+oCFuH2eLIchtCCNFAZ8+eJTExke3bt+Ph4cENvf3ocfQ7XjfczwOTf0WApwvvzG+dq9raUr0JSik1DLgVGAm0A8qBncAPwCKtdZHVIxRCCBupqqpi7dq1rFu3Dl1bw4hIH0bOug8XJycOnp5H8uKdTC8sJ8DTRZKTFVw2QSmlfgJOAt8BLwM5gCvQFRgLfKeU+ofWerm1g9Rayw/fSuypi1cIe6G1Zvv27SQmJlJcXEyU80nGV/9IXukY3kw+wm8mdyeyXQCJT4xu9cuu21J9LajbtNZnLtpXAmwxv/6ulLL6XUBXV1fy8vLw9/eXJNXEtNbk5eXh6upq61CEsBuZmZnEx8dz8uRJ2rtVMUt/TaibI8xYwJt7Q8g8dZZao8bgoCQ5WdllE9QlklODzmmsjh07kpWVRW5urrU/6prk6upKx44dbR2GEDZXUFBAQkICu3fvxtvbm5tGdKPXuodJCpxH5bTf06VjEL/rbMTJoOSP5WZyxUESSqkZwF+BIECZX1pr7W3l2ABwcnIiPDy8OT5KCHENqqioYM2aNWzcuBEHBWMivRg+5yGcnJwo6D2cZz/YzzOnKunSEZwd5dHR5mTJKL6/AVO11nusHYwQQjQXo9HIli1bSE5OpqysjGjfMsblf4pztj+v/TSW56ZG4xscyupn2uPuLAOebcGS7/ppSU5CiNbk0KFDxMXFkZubSydfZyZVfUu7wuMQ8zCfOM7i+/XZ/HpsJUFerpKcbMiS73yaUmop8C1QeW6n1vprawUlhBDWkJubS3x8PAcPHsTX15fZk0fS/efZZPsNZMvw9+k/YDC31BqZPawbHi6SmGzNkp+AN1AGxNbZpwFJUEKIFqGsrIyUlBTS0tJwdnJiYndvBs98AEdHR6pCUpi/KJdhma70HwBOBgecDHKvyR5cMUFpre9qjkCEEKKp1dbWsmnTJlavXk1lZSUDOroyJvtD3Pfl858f+3LHlDE4t+/NF/dXEuDpbOtwxUUsGcXXEfgnMAJTyykVeFRrnWXl2IQQokG01uzbt4+VK1eSn59P5/b+xFYkEXRsA3Qex5ouT/OX5YX06V3I4HA/Ar1crlyoaHaWdPF9DHwGzDZv32reN9FaQQkhRENlZ2cTFxfH0aNHCQgIYP6cGXRZPpUqBze2Dn2bfpNuJwZI7lpOiJ+7rcMV9bAkQQVqrT+us/2JUuoxK8UjhBANUlxcTFJSEhkZGbi5uXFdnyAGTr0HB0dH8PyCe38oJXuvAz/GgoODkuTUAliSoM4opW4Flpi35wF5lhSulPIBPgR6YeoevFtrvb4BcQohxCVVV1ezfv16UlNTqa2tZVjPUEad/gjXbdtZ5ehJ9ISbaRMyiFduLsfbzUmmJ2pBLElQdwPvAG9gSjLrzPss8Rbws9Z6llLKGZA/WYQQTUJrza5du0hISKCoqIjukWFMZB1+u16HNqFkTfqAO5e787xfFneNCKe9j5utQxZXyZJRfMeAG6+2YKWUNzAKuNNcThVQdbXlCCHExbKysoiLiyMrK4u2bdsyfdo0wuLvQOfs5UCPB+hy0x/o6OzO8pAienVollnZhBXUt9zGM1rrvyml/omp5XQBrfUjVyg7AsgFPlZK9QXSMY3+K73oc+4F7gXOL4kuhBCXUlRUREJCAjt37sTT05Mbh3en78jrcXD1gilv8MbaXD7YCeunOuLjDL07trF1yKIRLrvku1Jqqtb6e6XUHZc6rrVeWG/BSg0ENgAjtNYblVJvAWe11n+43DWXWvJdCCGqqqpITU1l/XrTLexhA3oTU/Qtznu+4uTA31A7/DFC/NzJK6kkv7SKLsFeNo5YXI2rXvJda/29+Wu9iageWUCW1nqjeftL4NkGliWEuAYZjUa2bdtGUlISJSUl9IrqyQTfLNpsuA90LZUxv+GGNb0ZW3aAv8/pi7+nC/6e8kxTa1FfF9/3XKJr7xytdb33pbTW2Uqp40qpblrrfcB4YHeDIxVCXFOOHj1KXFwc2dnZdOzYkblz59Ix/VVYs4i8jhPwn/l3XHzDeDc8T+4ztVL1DZJ4vQnKfxhYbB7BdxiQaZOEEPXKz89n5cqV7N27lzZt2jDz+vFEde+C8goG54dJcRjKnWv9+Lq4Df19YVhnf1uHLKykvi6+VY0tXGudAfyiX1EIIS5WUVHBqlWr2LRpE46OjowbM4ahjrtxSphH1dFJnBj7FuFB3Rk6uQv/DD1NvxAfW4csrKy+Lr4d1N/F18cqEQkhrim1tbWkp6eTkpJCeXk5/fr1Y1w3PzyTn4XsHejO43nkZCzHP9vCiodjcHUyMLVve1uHLZpBfV18NzRbFEKIa9KBAweIj4/nzJkzhIeHExsbS9vsJFhyOzUebXGYtRCHqGncfigPT1dHlJJZIK4l9XXxZTZnIEKIa0dOTg7x8fEcOnQIPz8/bp47l66hwSh3X/CcyJnoB5iYNpDflPXnZqUYHhlg65CFDdTXxZeqtY5RShVj6upTdb9qrWXYjBDiqpSWlpKcnMyWLVtwcXFh0qRJDIrww/Dz05BaStH8H2jjGYj/tJe5y+sg47oH2TpkYUOXfVDXFuRBXSFap5qamvMLB1ZVVTFo0CBGjxiG+9YPYPVr4OjCqtD7+c3RgcQ/ORZvVydbhyya0VU/qHvRxf2BGMwLFmqttzZxfEKIVkhrzZ49e0hISKCgoIAuXbowceJEAg0l8OkkyN2Lscd0HK7/K75FrkzzO4WTgyy3LkwsWVH3eUyLFX5t3vWJUuoLrfWfrRqZEKJFO3nyJPHx8WRmZhIUFMStt95K54gIUAqqPTF6BPGGno/RZxJPe7Wljxf06ehj67CFHbGkBTUP6Ke1rgBQSv0F2AJIghJC/EJxcTGJiYls27YNd3d3pkyZQv/+/XHY9wN8/CDc+hU4e+Bw5/ec/W4nXdrIMhji0ixJUEcBV6DCvO0CHLJWQEKIlqm6upp169axdu1ajEYjw4cPZ+TIkbhWF8IXt8Oe76kIiOLxBXH8dv5kQvzceWlaL1uHLexYfaP4zi2zUQnsUkqtNG9PBFKbJzwhhL3TWrN9+3YSExMpLi6mZ8+eTJgwAV8fH9j6KcT/HmoqYcKL5Pf4Ffs+2cKpogpZcl1cUX0tqHPD6dKBb+rsT7FaNEKIFuXYsWPExcVx8uRJ2rdvz6xZs/63rpvRCBmfUeDVjSVtn+KBmEm0BxIeHy3LrguL1PegbkOX2RBCtHIFBQUkJCSwe/duvLy8mD59On369EFpI2z4N0TdBF7BcPNnfJKaQ9zuHG6vrMHTxVGSk7DYlZbbWAD8rLWuvuhYBKal3I9qrT+yaoRCCLtRWVnJmjVr2LBhAw4ODowePZrhw4fj7OwMufvhuwchaxPph07iMPJx+oX68eA4Hx4a3xUngwwfF1envi6+e4AngDeVUvmYlm93BcKBg8A7WuvvrB+iEMLWjEYjW7duJTk5mdLSUvr27cu4cePw9vaG2hpY8w9I+Qs4u1Mx9d88+HMgkzxP0C/UF2dHSUyiYerr4ssGngGeUUqFAe2AcmC/1rqsecITQtja4cOHiYuLIycnh5CQEObNm0eHDh3+d0LKq7DmdbLaTqD9/Hdx9W7LN5HltPV2tV3QolWwaCYJrfVRTMPNhRDXiDNnzrBy5Ur279+Pj48Ps2fPpkePHqYZxWtroLwAPANh6P2kV3Vk5qog/pttYJQ3tJNnm0QTsChBCSGuHWVlZaxatYq0tDQcHR2ZMGECQ4YMwdHR/Osidz98ez9GbWTflK/p0SGA/pPv5IseBQwK87Nt8KJVkQQlhABMCwdu3ryZVatWUVlZSf/+/Rk7diweHh6mE4y1sOE9SPoTOLnxcZsHef/jNFb/ZhyuTgZJTqLJSYIS4hqntWb//v3Ex8eTn59P586diY2NJSiozlIXxafhy7sgcy21Xa7DcONbDCt2o1tpFa5OBtsFL1o1SyaLHQG8CHQyn39uPagI64YmhLC27Oxs4uPjOXLkCP7+/syfP5/IyMhfrlzr4kVtVTmvuTyK8pvHb7yC6ellm5jFtcOSFtR/gMcxzShRa91whBDNoaSkhKSkJLZu3YqbmxuTJ09m4MCBGAx1WkMlOZDyF4wTXsLB1QvDvUk4rdzPsM7+tgtcXFMsSVBFWuufrB6JEMLqampq2LBhA2vWrKGmpoYhQ4YwevRo3NwuGnW3ZwV8/wjGymKe3tWJx+65hxA/d56M7WabwMU1yZIElayUeg3TelCV53ZqrbdYLSohRJPSWrN7924SEhIoLCykW7duTJw4EX//i1pDVaXw83OwZSG07cOZiW9zJK6M0qoa2wQurmmWJKgh5q91l+PVwLimD0cI0dROnDhBXFwcx48fJzg4mNtvv53w8PBLn7ziCfT2pawLvo0Rv/4HQY7OfP1A88YrxDlXTFBa67HNEYgQomkVFRWRmJjIjh078PDwYOrUqURHR+Nw8ZLqxlqoLgMXLxjzLF/WjuaTUyEsrXXAU8b5ChuyZBRfG+AFYJR51yrgj1rrImsGJoRomKqqKlJTU1m/fj1aa2JiYoiJicHFxeWXJxedgG/+j/xqZ05M/ojeIeFMn9GJaRqZQ0/YnCV/H30E7ATmmLdvAz4GZlgrKCHE1dNak5GRQVJSEiUlJfTq1Yvx48fj4+Nz6Qt2L4flD6Nrq3lP38mRxAN8eOcgmXVc2A1LElRnrfXMOtsvKaUyrBSPEKIBjh49SlxcHNnZ2XTo0IE5c+YQEhJy6ZOrSiHut5D+Cbp9P9TM/zC7JogQX1nhVtgXSxJUuVIqRmudCucf3C23blhCCEvk5+eTkJDAnj178Pb2ZsaMGfTq1euXD9rWVV1O9Z6f+E/NVHz7vMRc/850bb6QhbCYJQnqfmCh+V6UAvIxLVYohLCRiooKVq9ezcaNGzEYDIwdO5Zhw4bh5OR06Qu0hp1fUdF1Kq4eATg+shnntHyuj+7YvIELcRUsGcWXAfRVSnmbt89aOyghxKUZjUbS09NJSUmhrKyM6Ohoxo0bh5dXPfMOlZ6Bbx+AA3H81elRHnns9/h6tOHumDbNF7gQDVDfku+3aq0XKaWeuGg/AFrrf1g5NiFEHQcPHiQ+Pp7c3FzCwsKIjY2lXbt29V90eBV8fS+U53Nq+Evk542ivt4/IexJfS0o8xz7XOpPM21J4Uqpo0Axpjn8arTWA+u/QghxsdzcXOLj4zl48CC+vr7MmTOH7t2713+fCWDDe+ifn6PArRN+9yTRrm1v3mqekIVoEvUt+f6++W2C1npt3WPmgRKWGqu1PtOQ4IS4lpWWlpKSkkJ6ejrOzs7ExsYyePDgCyd0rU/IEDb5TuUTr3t4J6gXsiiGaGksGSTxT6C/BfuEEE2gpqaGTZs2sXr1aqqqqhg4cCBjxozB3f3Kw8D19mXs27Ye9+tfJrRDf6If/IR/GRyu3NoSwg7Vdw9qGDAcCLzoPpQ3WPzHmAbilVIaeF9rvaDBkQrRymmt2bt3LytXrqSgoIDIyEhiY2MJDAy88sUVZ+HHp1HbP6dUd+ebtft57sZoXByl3SRarvpaUM6Ap/mcuvehzgKzLCx/hNb6pFIqCFiplNqrtV5d9wSl1L3AvQChoaEWBy5Ea3Lq1Cni4uLIzMwkMDCQW265hcjISIuu1VlpVC29C5eSLBjzHN7d/o/fBPtYN2AhmoHSuv7xDkqpTlrrzEZ/kFIvAiVa69cvd87AgQN1WlpaYz9KiBajuLiYpKQkMjIycHd3Z8yYMQwYMOCXE7peTsVZqv4eRU6VM3mx79J3xGTrBiyEFSil0i81iM6Se1Bl5vWgogDXczu11vUut6GU8gActNbF5vexwB+vLmwhWqfq6mrWr19PamoqRqORYcOGMWrUKFxdXa98MaDPnqLQwQ9fT28cbl7EulN+zBgaZeWohWheliSoxcBS4AbgPuAOINeC64KBb8w3Zx2Bz7TWPzcwTiFaBa01O3fuJCEhgbNnz9KjRw8mTJiAn5+fpQVAxmKqvn+KD5x/zSNPvoRr59HM6WzduIWwBUsSlL/W+j9KqUe11quAVUqpVVe6SGt9GOjb6AiFaCWOHz9OXFwcJ06coF27dsyYMYNOnTpZfL0uyYUVj6P2fk950BA6RE6WmcdFq2ZJgqo2fz2llJoCnARkAi8hLFRYWEhCQgK7du3Cy8uLadOm0bdv36sa+l2xO47KL+/FU5dgmPASPsMf5hYHGaEnWjdLEtSfzRPFPonp+Sdv4HGrRiVEK1BZWXl+4UClFKNGjWLEiBE4OztfdVkuBiO5jv6sG/wh18VMtEK0QtifK47ia04yik+0Bkaj8fzCgaWlpfTp04dx48bRps3VTc56YsvPJKemMuVXz+Pr4Wxaml1aTaIVuupRfEqpf1LPnHta60eaKDYhWo3Dhw8THx/P6dOnCQkJYd68eXTo0OHqCqksgcSX6LBpAUNVKEdOP4RvRFtJTuKaU18XnzRlhLBQXl4e8fHx7N+/Hx8fH2bNmkXPnj2veoqhrauW03n9s3hXnIQh9xM65vc4u3laKWoh7Ft9k8UubM5AhGiJysvLWbVqFZs3b8bR0ZHx48czdOhQHB0tub17kaIT9Em+k2yHIFxv/wHniBFc/d0qIVqPK/4vUkolc4muvis9qCtEa1ZbW0taWhqrVq2ioqKCfv36MXbsWDw9r661U2vUfJeQTHS/wUQEdqB69mICI0ZKq0kILBvF91Sd967ATKDGOuEIYd+01hw4cID4+Hjy8vKIiIggNjaW4ODgqy+svJCqH55jxs7PWFb0LhGzb8U16rqmD1qIFsqSJd/TL9q11pIHdYVobU6fPk18fDyHDx/G39+fefPm0aVLl6u+z1RRXcu2+EUM2fMKbqW5FPV/iNmTZ1gpaiFaLku6+OrOweIADADaWi0iIexMSUkJycnJbN26FRcXFyZNmsSgQYMsXzjwIsc/uZshJ76l3D8Kt/lLadM+umkDFqKVsKSLLx3TPSiFqWvvCPArawYlhD2oqalhw4YNrFmzhpqaGgYPHszo0aNxc3O76rIKSirJLamka1tvwvuN43hwF0Km/AYMTlaIXIjWwZIuvvDmCEQIe6G1Zvfu3SQkJFBYWEjXrl2ZOHEiAQEBDSsvdz9ZC+5mpeNYHn/mjzgOvIOQJo5ZiNbIki4+V+ABIAZTSyoVeE9rXWHl2IRodidOnCA+Pp5jx44RHBzMbbfdRkRERIPKOp5bSIdd7+Ow5nV6GFzxHxYpS68LcRUs6eL7L1CMaR4+gHnAp8BsawUlRHM7e/YsiYmJbN++HQ8PD6ZOnUp0dLTlCwde5Pi2FCq+fhAHlQVRM3Cc/BfaezVgpJ8Q1zBLElQ3rXXdZTOSlVLbrBWQEM2pqqqKdevWsXbtWrTWxMTEEBMTg4uLS4PKK6+qxc3ZQEe3Sopdaym4bhG+0VObOGohrg2WJKitSqmhWusNAEqpIcBa64YlhHVprdm+fTuJiYkUFxcTFRXFhAkT8PHxaWiBbPjmHTbuOsBtT7yOX9dJeD+9DRwbluiEEJYlqCHA7UqpY+btUGCPUmoHoLXWfawWnRBWkJmZSXx8PCdPnqRDhw7Mnj2bkJCGD1uozdmH4ccnGXp0DQHu/TCcm3hFkpMQjWJJgpps9SiEaAYFBQUkJCSwe/duvL29uemmm+jdu3eDBy4YK0pY+f7TTChcBi4ecMObRPa/Axp430oIcSFLhplnKqX6AiPNu9ZoreUelGgxKioqWLNmDRs3bsTBwYExY8YwfPhwnJwa9gxSda0RJ4MDDkWZTChYyoHg6+l+2xvgGdjEkQtxbbNkmPmjwD3A1+Zdi5RSC7TW/6znMiFszmg0smXLFpKTkykrKyM6OpqxY8fi7e3d4DLTM7aw+vuFTLvvZSKCozA8upXuvp2aMGohxDmWdPH9ChiitS4FUEr9FVjP/4adC2F3Dh06RFxcHLm5uXTq1IlJkybRrl27BpdXXVGK0/q36J/6JlFGB06VPAyBkSDJSQirsSRBKaC2znateZ8Qdic3N5f4+HgOHjyIr68vc+bMoXv37g1/QFZrPv3kX1x34m0CarJRvWbhGvsnwr3bN23gQohfsCRBfQxsVEp9gykxTQP+Y9WohLhKZWVlpKSkkJaWhrOzMxMnTmTw4MENWziQ/91noryAuVmvcNYlGOP8FThEjLzyxUKIJmHJIIl/KKVSME11BHCX1nqrVaMSwkK1tbVs2rSJ1atXU1lZyYABAxgzZgweHh4NLvPg8VOs+O9rDJ77LMMjg3D+9U8EBPWQiV2FaGZX8+elAoxI956wA1pr9u3bx8qVK8nPz6dz587ExsYSFBTU4DIrqqpx3fMVneOf57Hq0+wruA6YCO3kUT8hbMGSUXzPY5p37ytMyeljpdQXWus/Wzs4IS4lOzubuLg4jh49SkBAALfccguRkZGNKvOjL75h2L6/0KNmL6rDAJi3hG4dBzRRxEKIhrCkBTUP6Hdu9nKl1F+ALYAkKNGsiouLzy8c6ObmxvXXX8+AAQMaPKFrSWUNbk4GDBiZe/QFUMVU3/BPnPrfKg/bCmEHLElQRwFX4NzyGi7AIWsFJMTFqqurWb9+PampqdTW1jJ06FBGjx6Nq6trg8s8nlvEp++9TLdJv2bmkK543PYZ+ISAa5smjFwI0RiWJKhKYJdSaiWm9aAmAqlKqbcBtNaPWDE+cQ3TWrNz504SExMpKiqie/fuTJw4ET8/vwaXeaakkoDT6+j487P81riX46Vdga7QtlfTBS6EaBKWJKhvzK9zUqwTihD/c/z4ceLj48nKyqJt27ZMnz6dsLCwRpX53rdJdNv2F8bpjSjfMLh5CSHdrmuSeIUQTc+SYeYLmyMQIQAKCwtJTExk586deHp6cuONNxIdHd3gB21ziytxdzbg4eLILTl/x01to2b073Ac8Qg4NbyLUAhhfQ17ilGIJlZZWUlqaiobNmwAYNSoUYwYMQJnZ+cGl3mmuIKXX/8b3QeN574bRuA94y1TUmrTsanCFkJYkSQoYVNGo5GMjAySk5MpKSmhd+/ejB8/njZtGjZYQWvNgZwSuqoTBPz8G95UKRToKmAEBDRuKLoQonlZPUEppQxAGnBCa32DtT9PtBxHjhwhPj6e7OxsOnbsyNy5c+nYsXGtm3d/3or7+tfp4hiHcvaA617Dd+DdTRSxEKI5XTZBKaW+h3NLg/6S1vpGCz/jUWAP0PA1DkSrkpeXx8qVK9m3bx9t2rRh5syZREVFNfg+06micgwOiiAvV+4s/y8eDj+i+96GmvACeAQ0cfRCiOZSXwvqdfPXGUBbYJF5ex6mZ6OuSCnVEZgCvAw80bAQRWtRXl7O6tWr2bRpE46OjowbN46hQ4c2eOFAgLKqGh5781P6RQTx7G3T8Zz4LAy+DSWzQAjR4l02QWmtVwEopf6ktR5V59D3SqnVFpb/JvAM4HW5E5RS9wL3AoSGhlpYrGhJamtrSU9PJyUlhfLycvr168e4cePw9PRsUHlaa3adPEsvP4178st8rj+kXE8EpoNXW9NLCNHiWXIPKlApFaG1PgyglAoHrri2tVLqBiBHa52ulBpzufO01guABQADBw68bJeiaHm01hw4cICVK1dy5swZwsPDiY2NpW3bxiWQzzdlsmX5u7zq9RWOlQWoQb/CfexvmyhqIYS9sCRBPQ6kKKUOm7fDgP+z4LoRwI1KqesxTZXkrZRapLW+tUGRihYlJyeH+Ph4Dh06hJ+fHzfffDNdu3Zt8H2m4opqCsuqCfFz5yZjPPOcFmAMHALXvy6zjQvRSimtr9xoUUq5AN3Nm3u11pVX9SGmFtRTVxrFN3DgQJ2WlnY1RQs7U1paSnJyMlu2bMHFxYXRo0czaNAgDAZDg8vUWjPnzZ/oZMjjtYdvRVWXw74foddMaOhKuUIIu6GUStdaD7x4f32j+GZc5lBnpRRa66+bLDrR4tXU1LBx40bWrFlDVVUVgwYNYvTo0bi7uze4zEO5JUT4u6G2fsri8hcwuvmj9C3g7A69ZzVh9EIIe1RfF9/Ueo5pwOIEpbVOQebwa5W01uzZs4eEhAQKCgro0qULsbGxBAQ0bnj3piP5vPLBIj4JXoZPwQ6cO42A61+TZTCEuIbUN4rvLqWUAzBLa72sGWMSLcTJkyeJi4vj2LFjBAUFceutt9K5c+cGl2c0ak6draCDjxsD1D6+cX4eXRUIMz6A3rOlO0+Ia0y9gyS01kal1EOAJChx3tmzZ0lKSmLbtm24u7szZcoU+vfv3+CFA8955outnD68jQ+euh3X0CEw6RVUv1vBVZ7xFuJaZMkovpVKqaeApUDpuZ1a63yrRSXsUnV1NevWrWPt2rUYjUZGjBhBTExMoxYOLCqrxs3ZgPOpdF46/RhOtZk4Vd0IHv4w7IEmjF4I0dJYkqDOTWT2YJ19Goho+nCEPdJas337dhITEykuLqZnz55MmDABX1/fRpWbW1zJ/DeW83bgd/Q4/T0eXu1h+lvg3vAFCYUQrYcl60GFN0cgwj4dO3aMuLg4Tp48Sfv27Zk1a1ajZ/woqazB08WRQApZoR7FKbcKRjwGo54Gl4bNLiGEaH2umKCUUk7A/cC56Y5SgPe11tVWjEvYWEFBAQkJCezevRsvLy+mT59Onz59Gvyg7TnL0o7z3x9S+Ojx2QR5B+My7jfQbYoshSGE+AVLuvjeA5yAf5m3bzPv+7W1ghK2U1lZyerVq9m4cSMODg6MHj2a4cOHN2rhQKNRU1VrxLX0JNfvfY6Z+kdKC6PBuzeMeLTpghdCtCqWJKhBWuu+dbaTlFLbrBWQsA2j0cjWrVtJTk6mtLSUvn37Mm7cOLy9GzeCrtaouX3BKu7Uy5mY9xmeAGN+g3c7aTEJIepnSYKqVUp11lofAlBKRQC11g1LNKfDhw8TFxdHTk4OoaGhzJ8/n/bt2zeqzIrqWlydDBh0Df8sehS/8qPQczrE/gl8ZNZ6IcSVWZKgngaSzZPFKqATcJdVoxLN4syZM6xcuZL9+/fj4+PD7Nmz6dGjR6PvM204nMdLi+J5494pdG/rjd/YhyCgK0SMbqLIhRDXgvrm4nsMWAusAroA3TAlqKueLFbYl/LyclJSUkhLS8PJyYkJEyYwZMgQHB0t+Xvl8ipranGpLiZ6119ZYfyQnBNe0PYGGHxPE0UuhLiW1PcbqSPwFqZZzLcD6zAlrOOAJKgWqLa2ls2bN7Nq1SoqKyvp378/Y8eOxcPDo9Fl/+7rDCKOf8PdlYtwLcuDAXfQrvvQJohaCHGtqm8uvqcAlFLOwEBgOKaHdj9QShVqrXs2T4iisbTW7N+/n/j4ePLz84mIiCA2Npbg4OBGlVtaWYO7swGlFA+efI72BevQIUPhtq+hXd8rFyCEEPWwpE/HDfAG2phfJ4Ed1gxKNJ3s7Gzi4+M5cuQIAQEBzJ8/n8jIyEbfZ9qbfZYnF6zgyVljGNezPe1H3Q3qVyhZo0kI0UTquwe1AIgCioGNmLr4/qG1Lmim2EQjlJSUkJSUxNatW3Fzc+O6665jwIABjV44ML+0Cn+narrseINv9TvknnwJej4AfWY3YfRCCFF/CyoUcAEOACeALKCwGWISjVBdXc2GDRtITU2lpqaGoUOHMmrUKNzc3Bpd9m+/ysDv4Fc85fgFhpJsDL3n0H7QtCaIWgghfqm+e1CTlakfKArT/acngV5KqXxgvdb6hWaKUVhAa82uXbtISEigqKiIbt26MXHiRPz9/RtV7rG8Mtr5uOJkcODxs68RVPEDusMgmLsIQgY1UfRCCPFLV1oPSgM7lVKFQJH5dQMwGJAEZSeysrKIi4sjKyuL4OBgpk2bRnh44+f4PZhTzMNvfcbtk4Yzb1RvgsbeD6WzUFE3yX0mIYTV1XcP6hFMLacRQDWmIebrgY+QQRJ2oaioiMTERHbs2IGHhwdTp04lOjq6UQsHVlTXcuB0Cb29Sui8/lV+cFpMeemjQG8IH9l0wQshxBXU14IKA74EHtdan2qecIQlqqqqSE1NZf369WitiYmJISYmBhcXl0aX/acv1tJl/wf0coxHaSNq6H14jHqkCaIWQoirU989qCeaMxBxZVprMjIySEpKoqSkhF69ejF+/Hh8fHwaVW7qgTP06uCNj7szv6n9AC++R0XdDGOeA99OTRO8EEJcpcbNbSOazdGjR4mLiyM7O5sOHTowZ84cQkJCGl3usdP5JHzyRw4Onc6dU8fhfd0LUPssBEc1QdRCCNFwkqDsXH5+PitXrmTv3r14e3szY8YMevXq1agHbY/nl7HlyGmm6WRCV7/Oi04nqPEKAcbJwoFCCLshCcpOVVRUnF840GAwMHbsWIYNG4aTk1Ojy1737XuMOPYekAsdB8P0f+EYLjONCyHsiyQoO2M0GklPTyclJYWysjL69evH2LFj8fLyanCZ1bVGlqw/xMju7QkP8GCaXxYOFe1g4nsQOUGGjAsh7JIkKDty8OBB4uPjyc3NJSwsjEmTJtG2bdvGFVpdQcWGj5mY8Bprsl8hfNbNuE55BRxdJTEJIeyaJCg7kJubS3x8PAcPHsTPz4+5c+fSrVu3Rt1n2nEoi/w1Cxidtwyv4lM4tR/E7CGdTQedGj/tkRBCWJskKBsqKysjOTmZ9PR0XFxciI2NZfDgwY2a0BUAren45fX0Ls+kOjQGpxkLcA0bKS0mIUSLIgnKBmpqati0aROrV6+mqqqKgQMHMmbMGNzd3RtcZlnOUTZ98xau459jaGQQHte/RJlne9zDhzRh5EII0XwkQTUjrTV79+5l5cqVFBQUEBkZSWxsLIGBgQ0us/RoOh7p7+G282tiNHy/ZzRETse59004N2HsQgjR3CRBNZNTp04RFxdHZmYmgYGB3HLLLURGNuKZo9IzHHxvDpEl6WhnL9TQ+6nqfy83BcrMD0KI1kESlJUVFxeTlJRERkYG7u7uTJkyhf79+zdoQteDJ8+QsHo1d86chqubL75uBjYGP0L/GU/g5OFLwzsIhRDC/lgtQSmlXIHVmBY9dAS+vJbWkKqurmbdunWsXbsWo9HI8OHDGTlyJK6urhaXUVVjJPVgLlE+NQTv+4zQ9f9mbnkl+7KG0Te8Hf4PrqRxqz0JIYT9smYLqhIYp7UuUUo5AalKqZ+01hus+Jk2p7Vmx44dJCYmcvbsWXr06MHEiRPx9fW16PqK6lpKK2vw93ThzPF9ZC/6LaOc14KxEsfICbgPeoC+YY18NkoIIVoAqyUo82KHJeZNJ/NLW+vz7MHx48eJi4vjxIkTtGvXjhkzZtCpk+X3hGqNmrF/SyK2qxcvzR5Ke8NZbnZZi+49F4Y/iENQdxq/oIYQQrQMVr0HpZQyAOlAJPCu1nrjJc65F7gXIDQ01JrhWE1hYSEJCQns2rULLy8vpk2bRt++fS160PabrVlsySzkT9eFYdi2hDjndyirHg4MhZDBODy5F9z9rF8JIYSwM1ZNUFrrWiBaKeUDfKOU6qW13nnROQuABQADBw5sUS2syspK1qxZw4YNG1BKMWrUKEaMGIGzc/0DvA/mFNM50BOlFGdP7GPwvoXoPUmoymK82/fHu2+s6USlJDkJIa5ZzTKKT2tdqJRKASYDO69wut0zGo3nFw4sLS2lT58+jB8/Hm9v7ytem7jnNL9euInFvx7K8MhAbtPf41D5A/ScDkP+DzoOkhkfhBAC647iCwSqzcnJDZgA/NVan9dcjhw5QlxcHKdPnyYkJIR58+bRoUOHy55fU2tkWVoW7XxcGRviyKi8pWT4LsCg3wcCcRj9DIx5Frxk4IMQQtRlzRZUO2Ch+T6UA7BMa73Cip9nVXl5ecTHx7N//358fHyYNWsWPXv2vOx9pqoaI86ODjgoxaqUeH7lkgglSTjVVNAmZCg4m5+D8m7fjLUQQoiWw5qj+LYD/axVfnMpLy9n1apVbN68GUdHR8aPH8/QoUNxdLz8t+69lEMs23yMlU+MxlFX8x4vo0oroe88GPQraNu7GWsghBAtk8wkcRm1tbWkpaWRkpJCZWUl/fv3Z8yYMXh6ev7i3OpaI3G7shndNRCvov1MPfEvphs3UV6VipebCw7zlkBQD3BtY4OaCCFEyyQJ6iJaa/bv38/KlSvJy8sjIiKC2NhYgoODL3muUoo9mdmkLH2ToUEb8SrIoKPBBXpOA8oBFwgd2uz1EEKIlk4SVB2nT58mLi6OI0eO4O/vz7x58+jSpcsv7jMZjZpHP99KZz8XHpscRe/KLbzu9D7aoQvE/hn6zgcPmYRICCEaQxIUUFJSQnJyMlu3bsXFxYXJkyczcODACxYOLCyrYuuxQsaGOuKw4wuePfYBJytGA39HdZ0Ed/2MCh0qQ8SFEKKJXNMJqqamhg0bNrBmzRpqamoYPHgwo0ePxs3NtCT6uS48gO+/XkTA/s/RTltRtVV0aNeXDoOGmQoyOEGnYbaqhhBCtErXZILSWrN7924SEhIoLCykW7duTJgwgYCAgPPnbDteyKuf/cyLd1xP97bezCQBZ/cDqOhfQ/Qt0LaXDWsghBCt3zWXoE6cOEFcXBzHjx8nODiY2267jYiICKprjXyXcYJObpVEFyYQtWUxn5dnsOdMMrTtj/u0N8DNFxxlnVohhGgO10yCOnv2LImJiWzfvh0PDw+mTp1Kr959yCmpAkAVZOL13b30YgvoGhyDe8OkV+gREWYqwOuXo/iEEEJYT6tPUFVVVaxdu5Z169ahtWbEiBGMHDkSF2dn/vjvhZSUlfO3J+/H0cOHUZ4nMPS8F/rNl4dphRDCxlptgtJas23bNpKSkiguLiYqKorq4Cg+2bidsYY3YcdSns8/RH7gYLS+D+Xmi+MTu2QUnhBC2IlWmaAyMzOJi4vj1KlT1Lr6ctPsW+jXM5KsxQ8xr+RTWAWEjYSRT+DX48b/JSVJTkIIYTdaVYI6np3DZ1//QEXuMby9vBjTJ5TSbcuoNF4PQMd+EyE0HHrPAZ8QG0crhBCiPq0mQWmtWf71V5Tm5NKzTSkzaz7HadtJtLs/yisXiDRPPySEEKIlaDUJSinFTeOH4LXkBryKK6HLJIh+DdUlVoaGCyFEC9RqEhRA+279YebbEDEGPAKueL4QQgj71aoSFAC9Z9k6AiGEEE3AwdYBCCGEEJciCUoIIYRdkgQlhBDCLkmCEkIIYZckQQkhhLBLkqCEEELYJUlQQggh7JIkKCGEEHZJaa1tHcN5SqlcILORxQQAZ5ogHHsidbJ/ra0+IHVqKVpDnTpprQMv3mlXCaopKKXStNYDbR1HU5I62b/WVh+QOrUUrbFO50gXnxBCCLskCUoIIYRdao0JaoGtA7ACqZP9a231AalTS9Ea6wS0wntQQgghWofW2IISQgjRCkiCEkIIYZfsPkEppT5SSuUopXbW2ddXKbVeKbVDKfW9UsrbvD9MKVWulMowv/5d55oB5vMPKqXeVkopW9THHIvFdTIf62M+tst83NW8v0XWSSl1S52fUYZSyqiUijYfa6l1clJKLTTv36OUeq7ONXZRp6usj7NS6mPz/m1KqTF1rrGL+phjCVFKJZu/57uUUo+a9/sppVYqpQ6Yv/rWueY5c+z7lFKT6uy3i3pdbZ2UUv7m80uUUu9cVJZd1KnBtNZ2/QJGAf2BnXX2bQZGm9/fDfzJ/D6s7nkXlbMJGAYo4CfguhZSJ0dgO9DXvO0PGFpynS66rjdwuBX8nOYDn5vfuwNHgTB7qtNV1udB4GPz+yAgHXCwp/qYY2kH9De/9wL2Az2BvwHPmvc/C/zV/L4nsA1wAcKBQ/b2/6kBdfIAYoD7gHcuKssu6tTQl923oLTWq4H8i3Z3A1ab368EZtZXhlKqHeCttV6vTT+1/wLTmzhUi11lnWKB7VrrbeZr87TWtS28TnXNA5ZAi/85acBDKeUIuAFVwFl7qtNV1qcnkGi+LgcoBAbaU33MsZ3SWm8xvy8G9gAdgGnAQvNpC/lfjNMw/SFRqbU+AhwEBttTva62TlrrUq11KlBRtxx7qlND2X2CuoydwI3m97OBkDrHwpVSW5VSq5RSI837OgBZdc7JMu+zJ5erU1dAK6XilFJblFLPmPe35DrVNRdzgqJl1+lLoBQ4BRwDXtda52P/dbpcfbYB05RSjkqpcGCA+Zjd1kcpFQb0AzYCwVrrU2D6hY+pFQimWI/Xuexc/HZZLwvrdDl2Waer0VIT1N3Ag0qpdExN4Crz/lNAqNa6H/AE8Jm5T/1S/a72Nr7+cnVyxNR8v8X89Sal1Hhadp0AUEoNAcq01ufuibTkOg0GaoH2mLqOnlRKRWD/dbpcfT7C9AstDXgTWAfUYKf1UUp5Al8Bj2mtz9Z36iX26Xr228xV1OmyRVxin81/VlfD0dYBNITWei+mri+UUl2BKeb9lUCl+X26UuoQphZIFtCxThEdgZPNGfOVXK5OmGJfpbU+Yz72I6b7CItouXU652b+13qClv1zmg/8rLWuBnKUUmuBgcAa7LhO9fxfqgEeP3eeUmodcAAowM7qo5RywvSLfLHW+mvz7tNKqXZa61Pmrq4c8/4sLmzJn4vfrv7tXWWdLseu6tQQLbIFpZQKMn91AH4P/Nu8HaiUMpjfRwBdMN2APwUUK6WGmkex3A58Z5PgL+NydQLigD5KKXfz/Y3RwO4WXqdz+2YDn5/b18LrdAwYp0w8gKHAXnuvUz3/l9zN9UApNRGo0Vrb3b87cwz/AfZorf9R59By4A7z+zv4X4zLgZuVUi7mrssuwCZ7qlcD6nRJ9lSnBrP1KI0rvTD9hX0KqMb0F8GvgEcxjWzZD/yF/82IMRPYhan/fAswtU45AzH1tx8C3jl3jb3XyXz+reZ67QT+1krqNAbYcIlyWmSdAE/gC/PPaTfwtL3V6SrrEwbsw3SDPgHTcgh2VR9zLDGYuq22Axnm1/WYRrsmYmr1JQJ+da75nTn2fdQZ1WYv9WpgnY5iGgBTYv7Z9rSnOjX0JVMdCSGEsEstsotPCCFE6ycJSgghhF2SBCWEEMIuSYISQghhlyRBCSGEsEuSoISwIvNzUalKqevq7JujlPrZlnEJ0RLIMHMhrEwp1QvTM1L9AAOm51oma60PNaAsg9a6tmkjFMI+SYISohkopf6GaTJZD/PXTpiWGnEEXtRaf2eeGPRT8zkAD2mt1ynTWkwvYHrINlpr3bN5oxfCNiRBCdEMzNMGbcE0GesKYJfWepFSygfTmj39MM0eYNRaVyilugBLtNYDzQnqB6CXNi0RIcQ1oUVOFitES6O1LlVKLcU0Fc0cYKpS6inzYVcgFNNEnu8o0+rCtZgmOj5nkyQnca2RBCVE8zGaXwqYqbXeV/egUupF4DTQF9MAproL0JU2U4xC2A0ZxSdE84sDHjbPMI1Sqp95fxvglNbaCNyGaUCFENcsSVBCNL8/AU7AdqXUTvM2wL+AO5RSGzB170mrSVzTZJCEEEIIuyQtKCGEEHZJEpQQQgi7JAlKCCGEXZIEJYQQwi5JghJCCGGXJEEJIYSwS5KghBBC2KX/BxAo6E+EzPpPAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "plot(census, style=':', label='US Census')\n",
+    "plot(un, style='--', label='UN DESA')\n",
+    "plot(results, color='gray', label='model')\n",
+    "\n",
+    "decorate(xlabel='Year', \n",
+    "         ylabel='World population (billion)',\n",
+    "         title='Constant growth')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opening-fabric",
+   "metadata": {},
+   "source": [
+    "The keyword argument `color='gray'` specifies the color of the line.\n",
+    "\n",
+    "From 1950 to 1990, the model does not fit the data particularly well; after that, it's pretty good."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "portuguese-party",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This chapter is a first step toward modeling changes in world population growth during the last 70 years.\n",
+    "\n",
+    "We used Pandas to read data from a web page and store the results in a `DataFrame`.\n",
+    "From the `DataFrame` we selected two `Series` objects and used them to compute absolute and relative errors.\n",
+    "\n",
+    "Then we computed average population growth and used it to build a simple model with constant annual growth.\n",
+    "The model fits recent data pretty well; nevertheless, there are two reasons we should be skeptical:\n",
+    "\n",
+    "* There is no obvious mechanism that could cause population growth to be constant from year to year. Changes in population are determined by the fraction of people who die and the fraction of people who give birth, so we expect them to depend on the current population.\n",
+    "\n",
+    "* According to this model, world population would keep growing at the same rate forever, and that does not seem reasonable.\n",
+    "\n",
+    "In the next chapter we'll consider other models that might fit the data better and make more credible predictions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "terminal-house",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "gross-puzzle",
+   "metadata": {},
+   "source": [
+    "Here's the code from this chapter all in one place."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "binding-monitoring",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_0 = census.index[0]\n",
+    "t_end = census.index[-1]\n",
+    "elapsed_time = t_end - t_0\n",
+    "\n",
+    "p_0 = census[t_0]\n",
+    "p_end = census[t_end]\n",
+    "\n",
+    "total_growth = p_end - p_0\n",
+    "annual_growth = total_growth / elapsed_time\n",
+    "\n",
+    "results = TimeSeries(time='Year', quantity='Population')\n",
+    "results[t_0] = p_0\n",
+    "\n",
+    "for t in range(t_0, t_end):\n",
+    "    results[t+1] = results[t] + annual_growth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "another-concentration",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Dv1tMa5wsu/3CY3f90OiQQkNDiYmJ4dNPP2Xq1KkWXxcREYHRaCQnJweANWvWEB0dff74V199RWxsLH/84x/p2rUrEyZMYO7cuYwePRqA8vJyEhMTef/99yksLGTJkiXn15wSQrRuZRWVJB92IF3dgYNyZuLEMQwZMoTx2SWEB1h3GYzmYtUWlFLKHZgIfG3Nz7GmKy23cc5vf/tbXnvttfP3lCxVt/U0cuRIMjIyzr86d+6Mp6cn6enpLFiwgMDAQObOncsnn3wCwIoVKxg7dizu7u7MnDmTb775htra2quroBCi5SjOpmbZ3ayL/4a3336b9AIvAiOi+LS4JxV+nTEYDPTq0AYPl9bxiKtVa6G1LgP8r3ji1aivxePsXv9xD/+rbjFZstwGQGRkJNHR0Sxbtszisg8fPozBYCAoKIg9e/Zc9jyDwcCYMWMYM2YMvXv3ZuHChdx5550sWbKEtWvXnh+UkZeXR3JyMhMmTLC8gkII+6c1On0he+P+w8qaQRSwncAOnZg9bQr+/gF025/D2G5Bto6yyclyG1dwpeU26vrd7353fnTfleTm5nLffffx0EMP1TuwYd++fRcs056RkUGnTp04e/YsqampHDt2jKNHj3L06FHeffddlixput5UIYQdOHOAUwvmsHBFKstqJuDoF8pRn/58URiGv38ADg6Kcd2DW+UAqdbRDrSy+pbbqCsqKor+/fufX6PpYuXl5URHR1NdXY2joyO33XYbTzzxxPnjF9+D+v3vf094eDgPP/wwhYWFODo6EhkZyYIFC/j6668ZN27cBYsaTps2jWeeeYbKysoL9gshWqbi4mKSvviMjJwonBwdmDh+EkOHDGLs6RI8Xex7FoimIMttCPkeC2Fnqo+sZ92OQ6zdeYza2lrCI7vyx22uvDJ7ALMGdLR1eE1OltsQQgg7p8uL2PHFqyQeruGs8iIwJIKbp0/Bz8+P3jGF9OnYxtYhNitJUEIIYQeOr1lCXMo6ThgDaOdhJNdvAO9lOnGbuxcAfUN8bBugDUiCEkIIGyosLCThm0XsOpaHp/Jk4tAohsXO5GRRBbeUVeHl6mTrEG1GEpQQQthAZXkZqSu/Z/32AyilGBbpy3P7wzhTGsZwpejg40aHFjZ3XlOTBCWEEM3IaDSSsWoFSakbKTU6E9E5ghun3kibNm2o2XGKgWG+tg7RbkiCEkKIZnJk327ili/ldJkDIQ6F9Onanfu2+9J3jKZPG7i+dztbh2hXJEE1o7CwMNLS0ggICGjUOUKIliUvL4+VP//AvoNHaKOLuTFUEX3znyhx8OLJtpl0DfaydYh2SRKUEEJYSXl5OauSEtm8ZSuOjo6M66RYUx7Fq1U9Wermi5dSPDg20tZh2i1JUFdw9OhRJk+eTExMDBs2bKBv377cddddvPDCC+Tk5LB48WIiIyO5++67OXz4MO7u7ixYsIA+ffqQl5fHvHnzyM3NZfDgwRdMDLto0SLefvttqqqqGDJkCP/6178wGAw2rKkQoqnU1taStnkTq5JWUlFdS59uEYyfMgMvLy/yM07Qw44mSLBnLSpB/fzzz2RnZzdpmW3btmXy5Mn1nnPw4EG++OILFixYwKBBg/jss89ITU1l+fLlvPLKK4SEhNCvXz++/fZbkpKSuP3228nIyOCll14iJiaG559/nh9++IEFCxYAppkbli5dytq1a3FycuKBBx5g8eLF3H777fXGIYSwb1prDhw4QPyPy8krKiVcZzKqbRmPH+qGy9ESruvtxbToDrYOs8VoUQnKVsLDw+nduzdgmm9v/PjxKKXo3bs3R48eJTMzk6+++gqAcePGkZeXR1FREatXr+brr00rjUyZMgVfX9PonMTERNLT0xk0aBBg6gYICmp9MxELcS05ffo08fHxHD58GH/yudllO12nPERtj+mEfbWjxS23bg9aVIK6UkvHWupOvOrg4HB+28HBgZqaGhwdf/ltPDez8KVmGNZac8cdd/Dqq69aKWIhRHMpLS0lKSmJrVu34uLiwuQIRZvqCp4reobPusfi6mjgjbnRtg6zRZLlNprAqFGjWLx4MQApKSkEBATg7e19wf6ffvrp/LpS48eP58svvzy/km5+fj6ZmZm2CV4I0SA1NTWsXbuWt996i4wt6Qzu2paHH36YIbf+gbxRL9MuKJiKallAtDFaVAvKXr344ovcdddd9OnTB3d3dxYuXAiYluWYN28e/fv3Z/To0YSGhgLQs2dP/vznPxMbG4vRaMTJyYl3332XTp062bIaQggLaK3ZvXs3CQkrKSwsoitHmOC0mRUFd7Ao7RT3jurMiMgARkTKoyKNJcttCPkeC2GhEydOEB8fz7FjxwhyKGJS7Uoieg2GSS/z4PKTRIf4cM+oCFuH2eLIchtCCNFAZ8+eJTExke3bt+Ph4cENvf3ocfQ7XjfczwOTf0WApwvvzG+dq9raUr0JSik1DLgVGAm0A8qBncAPwCKtdZHVIxRCCBupqqpi7dq1rFu3Dl1bw4hIH0bOug8XJycOnp5H8uKdTC8sJ8DTRZKTFVw2QSmlfgJOAt8BLwM5gCvQFRgLfKeU+ofWerm1g9Rayw/fSuypi1cIe6G1Zvv27SQmJlJcXEyU80nGV/9IXukY3kw+wm8mdyeyXQCJT4xu9cuu21J9LajbtNZnLtpXAmwxv/6ulLL6XUBXV1fy8vLw9/eXJNXEtNbk5eXh6upq61CEsBuZmZnEx8dz8uRJ2rtVMUt/TaibI8xYwJt7Q8g8dZZao8bgoCQ5WdllE9QlklODzmmsjh07kpWVRW5urrU/6prk6upKx44dbR2GEDZXUFBAQkICu3fvxtvbm5tGdKPXuodJCpxH5bTf06VjEL/rbMTJoOSP5WZyxUESSqkZwF+BIECZX1pr7W3l2ABwcnIiPDy8OT5KCHENqqioYM2aNWzcuBEHBWMivRg+5yGcnJwo6D2cZz/YzzOnKunSEZwd5dHR5mTJKL6/AVO11nusHYwQQjQXo9HIli1bSE5OpqysjGjfMsblf4pztj+v/TSW56ZG4xscyupn2uPuLAOebcGS7/ppSU5CiNbk0KFDxMXFkZubSydfZyZVfUu7wuMQ8zCfOM7i+/XZ/HpsJUFerpKcbMiS73yaUmop8C1QeW6n1vprawUlhBDWkJubS3x8PAcPHsTX15fZk0fS/efZZPsNZMvw9+k/YDC31BqZPawbHi6SmGzNkp+AN1AGxNbZpwFJUEKIFqGsrIyUlBTS0tJwdnJiYndvBs98AEdHR6pCUpi/KJdhma70HwBOBgecDHKvyR5cMUFpre9qjkCEEKKp1dbWsmnTJlavXk1lZSUDOroyJvtD3Pfl858f+3LHlDE4t+/NF/dXEuDpbOtwxUUsGcXXEfgnMAJTyykVeFRrnWXl2IQQokG01uzbt4+VK1eSn59P5/b+xFYkEXRsA3Qex5ouT/OX5YX06V3I4HA/Ar1crlyoaHaWdPF9DHwGzDZv32reN9FaQQkhRENlZ2cTFxfH0aNHCQgIYP6cGXRZPpUqBze2Dn2bfpNuJwZI7lpOiJ+7rcMV9bAkQQVqrT+us/2JUuoxK8UjhBANUlxcTFJSEhkZGbi5uXFdnyAGTr0HB0dH8PyCe38oJXuvAz/GgoODkuTUAliSoM4opW4Flpi35wF5lhSulPIBPgR6YeoevFtrvb4BcQohxCVVV1ezfv16UlNTqa2tZVjPUEad/gjXbdtZ5ehJ9ISbaRMyiFduLsfbzUmmJ2pBLElQdwPvAG9gSjLrzPss8Rbws9Z6llLKGZA/WYQQTUJrza5du0hISKCoqIjukWFMZB1+u16HNqFkTfqAO5e787xfFneNCKe9j5utQxZXyZJRfMeAG6+2YKWUNzAKuNNcThVQdbXlCCHExbKysoiLiyMrK4u2bdsyfdo0wuLvQOfs5UCPB+hy0x/o6OzO8pAienVollnZhBXUt9zGM1rrvyml/omp5XQBrfUjVyg7AsgFPlZK9QXSMY3+K73oc+4F7gXOL4kuhBCXUlRUREJCAjt37sTT05Mbh3en78jrcXD1gilv8MbaXD7YCeunOuLjDL07trF1yKIRLrvku1Jqqtb6e6XUHZc6rrVeWG/BSg0ENgAjtNYblVJvAWe11n+43DWXWvJdCCGqqqpITU1l/XrTLexhA3oTU/Qtznu+4uTA31A7/DFC/NzJK6kkv7SKLsFeNo5YXI2rXvJda/29+Wu9iageWUCW1nqjeftL4NkGliWEuAYZjUa2bdtGUlISJSUl9IrqyQTfLNpsuA90LZUxv+GGNb0ZW3aAv8/pi7+nC/6e8kxTa1FfF9/3XKJr7xytdb33pbTW2Uqp40qpblrrfcB4YHeDIxVCXFOOHj1KXFwc2dnZdOzYkblz59Ix/VVYs4i8jhPwn/l3XHzDeDc8T+4ztVL1DZJ4vQnKfxhYbB7BdxiQaZOEEPXKz89n5cqV7N27lzZt2jDz+vFEde+C8goG54dJcRjKnWv9+Lq4Df19YVhnf1uHLKykvi6+VY0tXGudAfyiX1EIIS5WUVHBqlWr2LRpE46OjowbM4ahjrtxSphH1dFJnBj7FuFB3Rk6uQv/DD1NvxAfW4csrKy+Lr4d1N/F18cqEQkhrim1tbWkp6eTkpJCeXk5/fr1Y1w3PzyTn4XsHejO43nkZCzHP9vCiodjcHUyMLVve1uHLZpBfV18NzRbFEKIa9KBAweIj4/nzJkzhIeHExsbS9vsJFhyOzUebXGYtRCHqGncfigPT1dHlJJZIK4l9XXxZTZnIEKIa0dOTg7x8fEcOnQIPz8/bp47l66hwSh3X/CcyJnoB5iYNpDflPXnZqUYHhlg65CFDdTXxZeqtY5RShVj6upTdb9qrWXYjBDiqpSWlpKcnMyWLVtwcXFh0qRJDIrww/Dz05BaStH8H2jjGYj/tJe5y+sg47oH2TpkYUOXfVDXFuRBXSFap5qamvMLB1ZVVTFo0CBGjxiG+9YPYPVr4OjCqtD7+c3RgcQ/ORZvVydbhyya0VU/qHvRxf2BGMwLFmqttzZxfEKIVkhrzZ49e0hISKCgoIAuXbowceJEAg0l8OkkyN2Lscd0HK7/K75FrkzzO4WTgyy3LkwsWVH3eUyLFX5t3vWJUuoLrfWfrRqZEKJFO3nyJPHx8WRmZhIUFMStt95K54gIUAqqPTF6BPGGno/RZxJPe7Wljxf06ehj67CFHbGkBTUP6Ke1rgBQSv0F2AJIghJC/EJxcTGJiYls27YNd3d3pkyZQv/+/XHY9wN8/CDc+hU4e+Bw5/ec/W4nXdrIMhji0ixJUEcBV6DCvO0CHLJWQEKIlqm6upp169axdu1ajEYjw4cPZ+TIkbhWF8IXt8Oe76kIiOLxBXH8dv5kQvzceWlaL1uHLexYfaP4zi2zUQnsUkqtNG9PBFKbJzwhhL3TWrN9+3YSExMpLi6mZ8+eTJgwAV8fH9j6KcT/HmoqYcKL5Pf4Ffs+2cKpogpZcl1cUX0tqHPD6dKBb+rsT7FaNEKIFuXYsWPExcVx8uRJ2rdvz6xZs/63rpvRCBmfUeDVjSVtn+KBmEm0BxIeHy3LrguL1PegbkOX2RBCtHIFBQUkJCSwe/duvLy8mD59On369EFpI2z4N0TdBF7BcPNnfJKaQ9zuHG6vrMHTxVGSk7DYlZbbWAD8rLWuvuhYBKal3I9qrT+yaoRCCLtRWVnJmjVr2LBhAw4ODowePZrhw4fj7OwMufvhuwchaxPph07iMPJx+oX68eA4Hx4a3xUngwwfF1envi6+e4AngDeVUvmYlm93BcKBg8A7WuvvrB+iEMLWjEYjW7duJTk5mdLSUvr27cu4cePw9vaG2hpY8w9I+Qs4u1Mx9d88+HMgkzxP0C/UF2dHSUyiYerr4ssGngGeUUqFAe2AcmC/1rqsecITQtja4cOHiYuLIycnh5CQEObNm0eHDh3+d0LKq7DmdbLaTqD9/Hdx9W7LN5HltPV2tV3QolWwaCYJrfVRTMPNhRDXiDNnzrBy5Ur279+Pj48Ps2fPpkePHqYZxWtroLwAPANh6P2kV3Vk5qog/pttYJQ3tJNnm0QTsChBCSGuHWVlZaxatYq0tDQcHR2ZMGECQ4YMwdHR/Osidz98ez9GbWTflK/p0SGA/pPv5IseBQwK87Nt8KJVkQQlhABMCwdu3ryZVatWUVlZSf/+/Rk7diweHh6mE4y1sOE9SPoTOLnxcZsHef/jNFb/ZhyuTgZJTqLJSYIS4hqntWb//v3Ex8eTn59P586diY2NJSiozlIXxafhy7sgcy21Xa7DcONbDCt2o1tpFa5OBtsFL1o1SyaLHQG8CHQyn39uPagI64YmhLC27Oxs4uPjOXLkCP7+/syfP5/IyMhfrlzr4kVtVTmvuTyK8pvHb7yC6ellm5jFtcOSFtR/gMcxzShRa91whBDNoaSkhKSkJLZu3YqbmxuTJ09m4MCBGAx1WkMlOZDyF4wTXsLB1QvDvUk4rdzPsM7+tgtcXFMsSVBFWuufrB6JEMLqampq2LBhA2vWrKGmpoYhQ4YwevRo3NwuGnW3ZwV8/wjGymKe3tWJx+65hxA/d56M7WabwMU1yZIElayUeg3TelCV53ZqrbdYLSohRJPSWrN7924SEhIoLCykW7duTJw4EX//i1pDVaXw83OwZSG07cOZiW9zJK6M0qoa2wQurmmWJKgh5q91l+PVwLimD0cI0dROnDhBXFwcx48fJzg4mNtvv53w8PBLn7ziCfT2pawLvo0Rv/4HQY7OfP1A88YrxDlXTFBa67HNEYgQomkVFRWRmJjIjh078PDwYOrUqURHR+Nw8ZLqxlqoLgMXLxjzLF/WjuaTUyEsrXXAU8b5ChuyZBRfG+AFYJR51yrgj1rrImsGJoRomKqqKlJTU1m/fj1aa2JiYoiJicHFxeWXJxedgG/+j/xqZ05M/ojeIeFMn9GJaRqZQ0/YnCV/H30E7ATmmLdvAz4GZlgrKCHE1dNak5GRQVJSEiUlJfTq1Yvx48fj4+Nz6Qt2L4flD6Nrq3lP38mRxAN8eOcgmXVc2A1LElRnrfXMOtsvKaUyrBSPEKIBjh49SlxcHNnZ2XTo0IE5c+YQEhJy6ZOrSiHut5D+Cbp9P9TM/zC7JogQX1nhVtgXSxJUuVIqRmudCucf3C23blhCCEvk5+eTkJDAnj178Pb2ZsaMGfTq1euXD9rWVV1O9Z6f+E/NVHz7vMRc/850bb6QhbCYJQnqfmCh+V6UAvIxLVYohLCRiooKVq9ezcaNGzEYDIwdO5Zhw4bh5OR06Qu0hp1fUdF1Kq4eATg+shnntHyuj+7YvIELcRUsGcWXAfRVSnmbt89aOyghxKUZjUbS09NJSUmhrKyM6Ohoxo0bh5dXPfMOlZ6Bbx+AA3H81elRHnns9/h6tOHumDbNF7gQDVDfku+3aq0XKaWeuGg/AFrrf1g5NiFEHQcPHiQ+Pp7c3FzCwsKIjY2lXbt29V90eBV8fS+U53Nq+Evk542ivt4/IexJfS0o8xz7XOpPM21J4Uqpo0Axpjn8arTWA+u/QghxsdzcXOLj4zl48CC+vr7MmTOH7t2713+fCWDDe+ifn6PArRN+9yTRrm1v3mqekIVoEvUt+f6++W2C1npt3WPmgRKWGqu1PtOQ4IS4lpWWlpKSkkJ6ejrOzs7ExsYyePDgCyd0rU/IEDb5TuUTr3t4J6gXsiiGaGksGSTxT6C/BfuEEE2gpqaGTZs2sXr1aqqqqhg4cCBjxozB3f3Kw8D19mXs27Ye9+tfJrRDf6If/IR/GRyu3NoSwg7Vdw9qGDAcCLzoPpQ3WPzHmAbilVIaeF9rvaDBkQrRymmt2bt3LytXrqSgoIDIyEhiY2MJDAy88sUVZ+HHp1HbP6dUd+ebtft57sZoXByl3SRarvpaUM6Ap/mcuvehzgKzLCx/hNb6pFIqCFiplNqrtV5d9wSl1L3AvQChoaEWBy5Ea3Lq1Cni4uLIzMwkMDCQW265hcjISIuu1VlpVC29C5eSLBjzHN7d/o/fBPtYN2AhmoHSuv7xDkqpTlrrzEZ/kFIvAiVa69cvd87AgQN1WlpaYz9KiBajuLiYpKQkMjIycHd3Z8yYMQwYMOCXE7peTsVZqv4eRU6VM3mx79J3xGTrBiyEFSil0i81iM6Se1Bl5vWgogDXczu11vUut6GU8gActNbF5vexwB+vLmwhWqfq6mrWr19PamoqRqORYcOGMWrUKFxdXa98MaDPnqLQwQ9fT28cbl7EulN+zBgaZeWohWheliSoxcBS4AbgPuAOINeC64KBb8w3Zx2Bz7TWPzcwTiFaBa01O3fuJCEhgbNnz9KjRw8mTJiAn5+fpQVAxmKqvn+KD5x/zSNPvoRr59HM6WzduIWwBUsSlL/W+j9KqUe11quAVUqpVVe6SGt9GOjb6AiFaCWOHz9OXFwcJ06coF27dsyYMYNOnTpZfL0uyYUVj6P2fk950BA6RE6WmcdFq2ZJgqo2fz2llJoCnARkAi8hLFRYWEhCQgK7du3Cy8uLadOm0bdv36sa+l2xO47KL+/FU5dgmPASPsMf5hYHGaEnWjdLEtSfzRPFPonp+Sdv4HGrRiVEK1BZWXl+4UClFKNGjWLEiBE4OztfdVkuBiO5jv6sG/wh18VMtEK0QtifK47ia04yik+0Bkaj8fzCgaWlpfTp04dx48bRps3VTc56YsvPJKemMuVXz+Pr4Wxaml1aTaIVuupRfEqpf1LPnHta60eaKDYhWo3Dhw8THx/P6dOnCQkJYd68eXTo0OHqCqksgcSX6LBpAUNVKEdOP4RvRFtJTuKaU18XnzRlhLBQXl4e8fHx7N+/Hx8fH2bNmkXPnj2veoqhrauW03n9s3hXnIQh9xM65vc4u3laKWoh7Ft9k8UubM5AhGiJysvLWbVqFZs3b8bR0ZHx48czdOhQHB0tub17kaIT9Em+k2yHIFxv/wHniBFc/d0qIVqPK/4vUkolc4muvis9qCtEa1ZbW0taWhqrVq2ioqKCfv36MXbsWDw9r661U2vUfJeQTHS/wUQEdqB69mICI0ZKq0kILBvF91Sd967ATKDGOuEIYd+01hw4cID4+Hjy8vKIiIggNjaW4ODgqy+svJCqH55jxs7PWFb0LhGzb8U16rqmD1qIFsqSJd/TL9q11pIHdYVobU6fPk18fDyHDx/G39+fefPm0aVLl6u+z1RRXcu2+EUM2fMKbqW5FPV/iNmTZ1gpaiFaLku6+OrOweIADADaWi0iIexMSUkJycnJbN26FRcXFyZNmsSgQYMsXzjwIsc/uZshJ76l3D8Kt/lLadM+umkDFqKVsKSLLx3TPSiFqWvvCPArawYlhD2oqalhw4YNrFmzhpqaGgYPHszo0aNxc3O76rIKSirJLamka1tvwvuN43hwF0Km/AYMTlaIXIjWwZIuvvDmCEQIe6G1Zvfu3SQkJFBYWEjXrl2ZOHEiAQEBDSsvdz9ZC+5mpeNYHn/mjzgOvIOQJo5ZiNbIki4+V+ABIAZTSyoVeE9rXWHl2IRodidOnCA+Pp5jx44RHBzMbbfdRkRERIPKOp5bSIdd7+Ow5nV6GFzxHxYpS68LcRUs6eL7L1CMaR4+gHnAp8BsawUlRHM7e/YsiYmJbN++HQ8PD6ZOnUp0dLTlCwde5Pi2FCq+fhAHlQVRM3Cc/BfaezVgpJ8Q1zBLElQ3rXXdZTOSlVLbrBWQEM2pqqqKdevWsXbtWrTWxMTEEBMTg4uLS4PKK6+qxc3ZQEe3Sopdaym4bhG+0VObOGohrg2WJKitSqmhWusNAEqpIcBa64YlhHVprdm+fTuJiYkUFxcTFRXFhAkT8PHxaWiBbPjmHTbuOsBtT7yOX9dJeD+9DRwbluiEEJYlqCHA7UqpY+btUGCPUmoHoLXWfawWnRBWkJmZSXx8PCdPnqRDhw7Mnj2bkJCGD1uozdmH4ccnGXp0DQHu/TCcm3hFkpMQjWJJgpps9SiEaAYFBQUkJCSwe/duvL29uemmm+jdu3eDBy4YK0pY+f7TTChcBi4ecMObRPa/Axp430oIcSFLhplnKqX6AiPNu9ZoreUelGgxKioqWLNmDRs3bsTBwYExY8YwfPhwnJwa9gxSda0RJ4MDDkWZTChYyoHg6+l+2xvgGdjEkQtxbbNkmPmjwD3A1+Zdi5RSC7TW/6znMiFszmg0smXLFpKTkykrKyM6OpqxY8fi7e3d4DLTM7aw+vuFTLvvZSKCozA8upXuvp2aMGohxDmWdPH9ChiitS4FUEr9FVjP/4adC2F3Dh06RFxcHLm5uXTq1IlJkybRrl27BpdXXVGK0/q36J/6JlFGB06VPAyBkSDJSQirsSRBKaC2znateZ8Qdic3N5f4+HgOHjyIr68vc+bMoXv37g1/QFZrPv3kX1x34m0CarJRvWbhGvsnwr3bN23gQohfsCRBfQxsVEp9gykxTQP+Y9WohLhKZWVlpKSkkJaWhrOzMxMnTmTw4MENWziQ/91noryAuVmvcNYlGOP8FThEjLzyxUKIJmHJIIl/KKVSME11BHCX1nqrVaMSwkK1tbVs2rSJ1atXU1lZyYABAxgzZgweHh4NLvPg8VOs+O9rDJ77LMMjg3D+9U8EBPWQiV2FaGZX8+elAoxI956wA1pr9u3bx8qVK8nPz6dz587ExsYSFBTU4DIrqqpx3fMVneOf57Hq0+wruA6YCO3kUT8hbMGSUXzPY5p37ytMyeljpdQXWus/Wzs4IS4lOzubuLg4jh49SkBAALfccguRkZGNKvOjL75h2L6/0KNmL6rDAJi3hG4dBzRRxEKIhrCkBTUP6Hdu9nKl1F+ALYAkKNGsiouLzy8c6ObmxvXXX8+AAQMaPKFrSWUNbk4GDBiZe/QFUMVU3/BPnPrfKg/bCmEHLElQRwFX4NzyGi7AIWsFJMTFqqurWb9+PampqdTW1jJ06FBGjx6Nq6trg8s8nlvEp++9TLdJv2bmkK543PYZ+ISAa5smjFwI0RiWJKhKYJdSaiWm9aAmAqlKqbcBtNaPWDE+cQ3TWrNz504SExMpKiqie/fuTJw4ET8/vwaXeaakkoDT6+j487P81riX46Vdga7QtlfTBS6EaBKWJKhvzK9zUqwTihD/c/z4ceLj48nKyqJt27ZMnz6dsLCwRpX53rdJdNv2F8bpjSjfMLh5CSHdrmuSeIUQTc+SYeYLmyMQIQAKCwtJTExk586deHp6cuONNxIdHd3gB21ziytxdzbg4eLILTl/x01to2b073Ac8Qg4NbyLUAhhfQ17ilGIJlZZWUlqaiobNmwAYNSoUYwYMQJnZ+cGl3mmuIKXX/8b3QeN574bRuA94y1TUmrTsanCFkJYkSQoYVNGo5GMjAySk5MpKSmhd+/ejB8/njZtGjZYQWvNgZwSuqoTBPz8G95UKRToKmAEBDRuKLoQonlZPUEppQxAGnBCa32DtT9PtBxHjhwhPj6e7OxsOnbsyNy5c+nYsXGtm3d/3or7+tfp4hiHcvaA617Dd+DdTRSxEKI5XTZBKaW+h3NLg/6S1vpGCz/jUWAP0PA1DkSrkpeXx8qVK9m3bx9t2rRh5syZREVFNfg+06micgwOiiAvV+4s/y8eDj+i+96GmvACeAQ0cfRCiOZSXwvqdfPXGUBbYJF5ex6mZ6OuSCnVEZgCvAw80bAQRWtRXl7O6tWr2bRpE46OjowbN46hQ4c2eOFAgLKqGh5781P6RQTx7G3T8Zz4LAy+DSWzQAjR4l02QWmtVwEopf6ktR5V59D3SqnVFpb/JvAM4HW5E5RS9wL3AoSGhlpYrGhJamtrSU9PJyUlhfLycvr168e4cePw9PRsUHlaa3adPEsvP4178st8rj+kXE8EpoNXW9NLCNHiWXIPKlApFaG1PgyglAoHrri2tVLqBiBHa52ulBpzufO01guABQADBw68bJeiaHm01hw4cICVK1dy5swZwsPDiY2NpW3bxiWQzzdlsmX5u7zq9RWOlQWoQb/CfexvmyhqIYS9sCRBPQ6kKKUOm7fDgP+z4LoRwI1KqesxTZXkrZRapLW+tUGRihYlJyeH+Ph4Dh06hJ+fHzfffDNdu3Zt8H2m4opqCsuqCfFz5yZjPPOcFmAMHALXvy6zjQvRSimtr9xoUUq5AN3Nm3u11pVX9SGmFtRTVxrFN3DgQJ2WlnY1RQs7U1paSnJyMlu2bMHFxYXRo0czaNAgDAZDg8vUWjPnzZ/oZMjjtYdvRVWXw74foddMaOhKuUIIu6GUStdaD7x4f32j+GZc5lBnpRRa66+bLDrR4tXU1LBx40bWrFlDVVUVgwYNYvTo0bi7uze4zEO5JUT4u6G2fsri8hcwuvmj9C3g7A69ZzVh9EIIe1RfF9/Ueo5pwOIEpbVOQebwa5W01uzZs4eEhAQKCgro0qULsbGxBAQ0bnj3piP5vPLBIj4JXoZPwQ6cO42A61+TZTCEuIbUN4rvLqWUAzBLa72sGWMSLcTJkyeJi4vj2LFjBAUFceutt9K5c+cGl2c0ak6draCDjxsD1D6+cX4eXRUIMz6A3rOlO0+Ia0y9gyS01kal1EOAJChx3tmzZ0lKSmLbtm24u7szZcoU+vfv3+CFA8955outnD68jQ+euh3X0CEw6RVUv1vBVZ7xFuJaZMkovpVKqaeApUDpuZ1a63yrRSXsUnV1NevWrWPt2rUYjUZGjBhBTExMoxYOLCqrxs3ZgPOpdF46/RhOtZk4Vd0IHv4w7IEmjF4I0dJYkqDOTWT2YJ19Goho+nCEPdJas337dhITEykuLqZnz55MmDABX1/fRpWbW1zJ/DeW83bgd/Q4/T0eXu1h+lvg3vAFCYUQrYcl60GFN0cgwj4dO3aMuLg4Tp48Sfv27Zk1a1ajZ/woqazB08WRQApZoR7FKbcKRjwGo54Gl4bNLiGEaH2umKCUUk7A/cC56Y5SgPe11tVWjEvYWEFBAQkJCezevRsvLy+mT59Onz59Gvyg7TnL0o7z3x9S+Ojx2QR5B+My7jfQbYoshSGE+AVLuvjeA5yAf5m3bzPv+7W1ghK2U1lZyerVq9m4cSMODg6MHj2a4cOHN2rhQKNRU1VrxLX0JNfvfY6Z+kdKC6PBuzeMeLTpghdCtCqWJKhBWuu+dbaTlFLbrBWQsA2j0cjWrVtJTk6mtLSUvn37Mm7cOLy9GzeCrtaouX3BKu7Uy5mY9xmeAGN+g3c7aTEJIepnSYKqVUp11lofAlBKRQC11g1LNKfDhw8TFxdHTk4OoaGhzJ8/n/bt2zeqzIrqWlydDBh0Df8sehS/8qPQczrE/gl8ZNZ6IcSVWZKgngaSzZPFKqATcJdVoxLN4syZM6xcuZL9+/fj4+PD7Nmz6dGjR6PvM204nMdLi+J5494pdG/rjd/YhyCgK0SMbqLIhRDXgvrm4nsMWAusAroA3TAlqKueLFbYl/LyclJSUkhLS8PJyYkJEyYwZMgQHB0t+Xvl8ipranGpLiZ6119ZYfyQnBNe0PYGGHxPE0UuhLiW1PcbqSPwFqZZzLcD6zAlrOOAJKgWqLa2ls2bN7Nq1SoqKyvp378/Y8eOxcPDo9Fl/+7rDCKOf8PdlYtwLcuDAXfQrvvQJohaCHGtqm8uvqcAlFLOwEBgOKaHdj9QShVqrXs2T4iisbTW7N+/n/j4ePLz84mIiCA2Npbg4OBGlVtaWYO7swGlFA+efI72BevQIUPhtq+hXd8rFyCEEPWwpE/HDfAG2phfJ4Ed1gxKNJ3s7Gzi4+M5cuQIAQEBzJ8/n8jIyEbfZ9qbfZYnF6zgyVljGNezPe1H3Q3qVyhZo0kI0UTquwe1AIgCioGNmLr4/qG1Lmim2EQjlJSUkJSUxNatW3Fzc+O6665jwIABjV44ML+0Cn+narrseINv9TvknnwJej4AfWY3YfRCCFF/CyoUcAEOACeALKCwGWISjVBdXc2GDRtITU2lpqaGoUOHMmrUKNzc3Bpd9m+/ysDv4Fc85fgFhpJsDL3n0H7QtCaIWgghfqm+e1CTlakfKArT/acngV5KqXxgvdb6hWaKUVhAa82uXbtISEigqKiIbt26MXHiRPz9/RtV7rG8Mtr5uOJkcODxs68RVPEDusMgmLsIQgY1UfRCCPFLV1oPSgM7lVKFQJH5dQMwGJAEZSeysrKIi4sjKyuL4OBgpk2bRnh44+f4PZhTzMNvfcbtk4Yzb1RvgsbeD6WzUFE3yX0mIYTV1XcP6hFMLacRQDWmIebrgY+QQRJ2oaioiMTERHbs2IGHhwdTp04lOjq6UQsHVlTXcuB0Cb29Sui8/lV+cFpMeemjQG8IH9l0wQshxBXU14IKA74EHtdan2qecIQlqqqqSE1NZf369WitiYmJISYmBhcXl0aX/acv1tJl/wf0coxHaSNq6H14jHqkCaIWQoirU989qCeaMxBxZVprMjIySEpKoqSkhF69ejF+/Hh8fHwaVW7qgTP06uCNj7szv6n9AC++R0XdDGOeA99OTRO8EEJcpcbNbSOazdGjR4mLiyM7O5sOHTowZ84cQkJCGl3usdP5JHzyRw4Onc6dU8fhfd0LUPssBEc1QdRCCNFwkqDsXH5+PitXrmTv3r14e3szY8YMevXq1agHbY/nl7HlyGmm6WRCV7/Oi04nqPEKAcbJwoFCCLshCcpOVVRUnF840GAwMHbsWIYNG4aTk1Ojy1737XuMOPYekAsdB8P0f+EYLjONCyHsiyQoO2M0GklPTyclJYWysjL69evH2LFj8fLyanCZ1bVGlqw/xMju7QkP8GCaXxYOFe1g4nsQOUGGjAsh7JIkKDty8OBB4uPjyc3NJSwsjEmTJtG2bdvGFVpdQcWGj5mY8Bprsl8hfNbNuE55BRxdJTEJIeyaJCg7kJubS3x8PAcPHsTPz4+5c+fSrVu3Rt1n2nEoi/w1Cxidtwyv4lM4tR/E7CGdTQedGj/tkRBCWJskKBsqKysjOTmZ9PR0XFxciI2NZfDgwY2a0BUAren45fX0Ls+kOjQGpxkLcA0bKS0mIUSLIgnKBmpqati0aROrV6+mqqqKgQMHMmbMGNzd3RtcZlnOUTZ98xau459jaGQQHte/RJlne9zDhzRh5EII0XwkQTUjrTV79+5l5cqVFBQUEBkZSWxsLIGBgQ0us/RoOh7p7+G282tiNHy/ZzRETse59004N2HsQgjR3CRBNZNTp04RFxdHZmYmgYGB3HLLLURGNuKZo9IzHHxvDpEl6WhnL9TQ+6nqfy83BcrMD0KI1kESlJUVFxeTlJRERkYG7u7uTJkyhf79+zdoQteDJ8+QsHo1d86chqubL75uBjYGP0L/GU/g5OFLwzsIhRDC/lgtQSmlXIHVmBY9dAS+vJbWkKqurmbdunWsXbsWo9HI8OHDGTlyJK6urhaXUVVjJPVgLlE+NQTv+4zQ9f9mbnkl+7KG0Te8Hf4PrqRxqz0JIYT9smYLqhIYp7UuUUo5AalKqZ+01hus+Jk2p7Vmx44dJCYmcvbsWXr06MHEiRPx9fW16PqK6lpKK2vw93ThzPF9ZC/6LaOc14KxEsfICbgPeoC+YY18NkoIIVoAqyUo82KHJeZNJ/NLW+vz7MHx48eJi4vjxIkTtGvXjhkzZtCpk+X3hGqNmrF/SyK2qxcvzR5Ke8NZbnZZi+49F4Y/iENQdxq/oIYQQrQMVr0HpZQyAOlAJPCu1nrjJc65F7gXIDQ01JrhWE1hYSEJCQns2rULLy8vpk2bRt++fS160PabrVlsySzkT9eFYdi2hDjndyirHg4MhZDBODy5F9z9rF8JIYSwM1ZNUFrrWiBaKeUDfKOU6qW13nnROQuABQADBw5sUS2syspK1qxZw4YNG1BKMWrUKEaMGIGzc/0DvA/mFNM50BOlFGdP7GPwvoXoPUmoymK82/fHu2+s6USlJDkJIa5ZzTKKT2tdqJRKASYDO69wut0zGo3nFw4sLS2lT58+jB8/Hm9v7ytem7jnNL9euInFvx7K8MhAbtPf41D5A/ScDkP+DzoOkhkfhBAC647iCwSqzcnJDZgA/NVan9dcjhw5QlxcHKdPnyYkJIR58+bRoUOHy55fU2tkWVoW7XxcGRviyKi8pWT4LsCg3wcCcRj9DIx5Frxk4IMQQtRlzRZUO2Ch+T6UA7BMa73Cip9nVXl5ecTHx7N//358fHyYNWsWPXv2vOx9pqoaI86ODjgoxaqUeH7lkgglSTjVVNAmZCg4m5+D8m7fjLUQQoiWw5qj+LYD/axVfnMpLy9n1apVbN68GUdHR8aPH8/QoUNxdLz8t+69lEMs23yMlU+MxlFX8x4vo0oroe88GPQraNu7GWsghBAtk8wkcRm1tbWkpaWRkpJCZWUl/fv3Z8yYMXh6ev7i3OpaI3G7shndNRCvov1MPfEvphs3UV6VipebCw7zlkBQD3BtY4OaCCFEyyQJ6iJaa/bv38/KlSvJy8sjIiKC2NhYgoODL3muUoo9mdmkLH2ToUEb8SrIoKPBBXpOA8oBFwgd2uz1EEKIlk4SVB2nT58mLi6OI0eO4O/vz7x58+jSpcsv7jMZjZpHP99KZz8XHpscRe/KLbzu9D7aoQvE/hn6zgcPmYRICCEaQxIUUFJSQnJyMlu3bsXFxYXJkyczcODACxYOLCyrYuuxQsaGOuKw4wuePfYBJytGA39HdZ0Ed/2MCh0qQ8SFEKKJXNMJqqamhg0bNrBmzRpqamoYPHgwo0ePxs3NtCT6uS48gO+/XkTA/s/RTltRtVV0aNeXDoOGmQoyOEGnYbaqhhBCtErXZILSWrN7924SEhIoLCykW7duTJgwgYCAgPPnbDteyKuf/cyLd1xP97bezCQBZ/cDqOhfQ/Qt0LaXDWsghBCt3zWXoE6cOEFcXBzHjx8nODiY2267jYiICKprjXyXcYJObpVEFyYQtWUxn5dnsOdMMrTtj/u0N8DNFxxlnVohhGgO10yCOnv2LImJiWzfvh0PDw+mTp1Kr959yCmpAkAVZOL13b30YgvoGhyDe8OkV+gREWYqwOuXo/iEEEJYT6tPUFVVVaxdu5Z169ahtWbEiBGMHDkSF2dn/vjvhZSUlfO3J+/H0cOHUZ4nMPS8F/rNl4dphRDCxlptgtJas23bNpKSkiguLiYqKorq4Cg+2bidsYY3YcdSns8/RH7gYLS+D+Xmi+MTu2QUnhBC2IlWmaAyMzOJi4vj1KlT1Lr6ctPsW+jXM5KsxQ8xr+RTWAWEjYSRT+DX48b/JSVJTkIIYTdaVYI6np3DZ1//QEXuMby9vBjTJ5TSbcuoNF4PQMd+EyE0HHrPAZ8QG0crhBCiPq0mQWmtWf71V5Tm5NKzTSkzaz7HadtJtLs/yisXiDRPPySEEKIlaDUJSinFTeOH4LXkBryKK6HLJIh+DdUlVoaGCyFEC9RqEhRA+279YebbEDEGPAKueL4QQgj71aoSFAC9Z9k6AiGEEE3AwdYBCCGEEJciCUoIIYRdkgQlhBDCLkmCEkIIYZckQQkhhLBLkqCEEELYJUlQQggh7JIkKCGEEHZJaa1tHcN5SqlcILORxQQAZ5ogHHsidbJ/ra0+IHVqKVpDnTpprQMv3mlXCaopKKXStNYDbR1HU5I62b/WVh+QOrUUrbFO50gXnxBCCLskCUoIIYRdao0JaoGtA7ACqZP9a231AalTS9Ea6wS0wntQQgghWofW2IISQgjRCkiCEkIIYZfsPkEppT5SSuUopXbW2ddXKbVeKbVDKfW9UsrbvD9MKVWulMowv/5d55oB5vMPKqXeVkopW9THHIvFdTIf62M+tst83NW8v0XWSSl1S52fUYZSyqiUijYfa6l1clJKLTTv36OUeq7ONXZRp6usj7NS6mPz/m1KqTF1rrGL+phjCVFKJZu/57uUUo+a9/sppVYqpQ6Yv/rWueY5c+z7lFKT6uy3i3pdbZ2UUv7m80uUUu9cVJZd1KnBtNZ2/QJGAf2BnXX2bQZGm9/fDfzJ/D6s7nkXlbMJGAYo4CfguhZSJ0dgO9DXvO0PGFpynS66rjdwuBX8nOYDn5vfuwNHgTB7qtNV1udB4GPz+yAgHXCwp/qYY2kH9De/9wL2Az2BvwHPmvc/C/zV/L4nsA1wAcKBQ/b2/6kBdfIAYoD7gHcuKssu6tTQl923oLTWq4H8i3Z3A1ab368EZtZXhlKqHeCttV6vTT+1/wLTmzhUi11lnWKB7VrrbeZr87TWtS28TnXNA5ZAi/85acBDKeUIuAFVwFl7qtNV1qcnkGi+LgcoBAbaU33MsZ3SWm8xvy8G9gAdgGnAQvNpC/lfjNMw/SFRqbU+AhwEBttTva62TlrrUq11KlBRtxx7qlND2X2CuoydwI3m97OBkDrHwpVSW5VSq5RSI837OgBZdc7JMu+zJ5erU1dAK6XilFJblFLPmPe35DrVNRdzgqJl1+lLoBQ4BRwDXtda52P/dbpcfbYB05RSjkqpcGCA+Zjd1kcpFQb0AzYCwVrrU2D6hY+pFQimWI/Xuexc/HZZLwvrdDl2Waer0VIT1N3Ag0qpdExN4Crz/lNAqNa6H/AE8Jm5T/1S/a72Nr7+cnVyxNR8v8X89Sal1Hhadp0AUEoNAcq01ufuibTkOg0GaoH2mLqOnlRKRWD/dbpcfT7C9AstDXgTWAfUYKf1UUp5Al8Bj2mtz9Z36iX26Xr228xV1OmyRVxin81/VlfD0dYBNITWei+mri+UUl2BKeb9lUCl+X26UuoQphZIFtCxThEdgZPNGfOVXK5OmGJfpbU+Yz72I6b7CItouXU652b+13qClv1zmg/8rLWuBnKUUmuBgcAa7LhO9fxfqgEeP3eeUmodcAAowM7qo5RywvSLfLHW+mvz7tNKqXZa61Pmrq4c8/4sLmzJn4vfrv7tXWWdLseu6tQQLbIFpZQKMn91AH4P/Nu8HaiUMpjfRwBdMN2APwUUK6WGmkex3A58Z5PgL+NydQLigD5KKXfz/Y3RwO4WXqdz+2YDn5/b18LrdAwYp0w8gKHAXnuvUz3/l9zN9UApNRGo0Vrb3b87cwz/AfZorf9R59By4A7z+zv4X4zLgZuVUi7mrssuwCZ7qlcD6nRJ9lSnBrP1KI0rvTD9hX0KqMb0F8GvgEcxjWzZD/yF/82IMRPYhan/fAswtU45AzH1tx8C3jl3jb3XyXz+reZ67QT+1krqNAbYcIlyWmSdAE/gC/PPaTfwtL3V6SrrEwbsw3SDPgHTcgh2VR9zLDGYuq22Axnm1/WYRrsmYmr1JQJ+da75nTn2fdQZ1WYv9WpgnY5iGgBTYv7Z9rSnOjX0JVMdCSGEsEstsotPCCFE6ycJSgghhF2SBCWEEMIuSYISQghhlyRBCSGEsEuSoISwIvNzUalKqevq7JujlPrZlnEJ0RLIMHMhrEwp1QvTM1L9AAOm51oma60PNaAsg9a6tmkjFMI+SYISohkopf6GaTJZD/PXTpiWGnEEXtRaf2eeGPRT8zkAD2mt1ynTWkwvYHrINlpr3bN5oxfCNiRBCdEMzNMGbcE0GesKYJfWepFSygfTmj39MM0eYNRaVyilugBLtNYDzQnqB6CXNi0RIcQ1oUVOFitES6O1LlVKLcU0Fc0cYKpS6inzYVcgFNNEnu8o0+rCtZgmOj5nkyQnca2RBCVE8zGaXwqYqbXeV/egUupF4DTQF9MAproL0JU2U4xC2A0ZxSdE84sDHjbPMI1Sqp95fxvglNbaCNyGaUCFENcsSVBCNL8/AU7AdqXUTvM2wL+AO5RSGzB170mrSVzTZJCEEEIIuyQtKCGEEHZJEpQQQgi7JAlKCCGEXZIEJYQQwi5JghJCCGGXJEEJIYSwS5KghBBC2KX/BxAo6E+EzPpPAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(census, style=':', label='US Census')\n",
+    "plot(un, style='--', label='UN DESA')\n",
+    "plot(results, color='gray', label='model')\n",
+    "\n",
+    "decorate(xlabel='Year', \n",
+    "         ylabel='World population (billion)',\n",
+    "         title='Constant growth')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fiscal-edwards",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Try fitting the model using data from 1970 to the present, and see if that does a better job.\n",
+    "\n",
+    "Suggestions: \n",
+    "\n",
+    "1. Define `t_1` to be 1970 and `p_1` to be the population in 1970.  Use `t_1` and `p_1` to compute annual growth, but use `t_0` and `p_0` to run the simulation. \n",
+    "\n",
+    "2. You might want to add a constant to the starting value to match the data better."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "spanish-number",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.07854997754347826"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "t_0 = census.index[0]\n",
+    "t_1 = 1970\n",
+    "t_end = census.index[-1]\n",
+    "elapsed_time = t_end - t_1\n",
+    "\n",
+    "p_0 = census[t_0]\n",
+    "p_1 = census[t_1]\n",
+    "p_end = census[t_end]\n",
+    "\n",
+    "total_growth = p_end - p_1\n",
+    "annual_growth = total_growth / elapsed_time\n",
+    "annual_growth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "optimum-material",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results = TimeSeries(time='Year', quantity='Population')\n",
+    "results[t_0] = p_0 - 0.45\n",
+    "\n",
+    "for t in range(t_0, t_end):\n",
+    "    results[t+1] = results[t] + annual_growth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "formal-shade",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "plot(census, style=':', label='US Census')\n",
+    "plot(un, style='--', label='UN DESA')\n",
+    "plot(results, color='gray', label='model')\n",
+    "\n",
+    "decorate(xlabel='Year', \n",
+    "         ylabel='World population (billion)',\n",
+    "         title='Constant growth')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "close-villa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From d9c52ef8eb6854d90589fa323c5607fdea36a57d Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 28 Jan 2021 19:16:53 -0500
Subject: [PATCH 019/144] Revising chapters

---
 jupyter/chap02.ipynb |  212 ++++-----
 jupyter/chap05.ipynb |  160 +++----
 jupyter/chap06.ipynb |  783 +++++++++++++++++++++++++++++++
 jupyter/chap07.ipynb |  907 ++++++++++++++++++++++++++++++++++++
 jupyter/chap08.ipynb | 1049 ++++++++++++++++++++++++++++++++++++++++++
 5 files changed, 2925 insertions(+), 186 deletions(-)
 create mode 100644 jupyter/chap06.ipynb
 create mode 100644 jupyter/chap07.ipynb
 create mode 100644 jupyter/chap08.ipynb

diff --git a/jupyter/chap02.ipynb b/jupyter/chap02.ipynb
index 7b9b4c9f..41748cef 100644
--- a/jupyter/chap02.ipynb
+++ b/jupyter/chap02.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "million-filter",
+   "id": "friendly-reserve",
    "metadata": {},
    "source": [
     "# Chapter 2"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "expanded-rwanda",
+   "id": "rental-baptist",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "unknown-introduction",
+   "id": "framed-placement",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "narrative-parcel",
+   "id": "disabled-nurse",
    "metadata": {},
    "source": [
     "# Bike share\n",
@@ -83,7 +83,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "prospective-appreciation",
+   "id": "aggregate-carbon",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "average-damages",
+   "id": "scientific-connectivity",
    "metadata": {},
    "source": [
     "Now we can call it like this:"
@@ -101,7 +101,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "distinguished-legislation",
+   "id": "reflected-tumor",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "requested-wonder",
+   "id": "pressed-fighter",
    "metadata": {},
    "source": [
     "The expressions in parentheses are **keyword arguments**.\n",
@@ -130,7 +130,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "distinguished-black",
+   "id": "sixth-wilderness",
    "metadata": {},
    "outputs": [
     {
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "increased-footage",
+   "id": "military-credits",
    "metadata": {},
    "source": [
     "And this:"
@@ -159,7 +159,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "becoming-anatomy",
+   "id": "surprising-beverage",
    "metadata": {},
    "outputs": [
     {
@@ -179,7 +179,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "accurate-exemption",
+   "id": "mathematical-daughter",
    "metadata": {},
    "source": [
     "Or, to display the state variables and their values, you can just type the name of the object:"
@@ -188,7 +188,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "incomplete-prediction",
+   "id": "yellow-consultancy",
    "metadata": {},
    "outputs": [
     {
@@ -243,7 +243,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "noble-reviewer",
+   "id": "artificial-strand",
    "metadata": {},
    "source": [
     "These values make up the **state** of the system.\n",
@@ -254,7 +254,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "signal-satisfaction",
+   "id": "documented-coach",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "continuing-sperm",
+   "id": "blank-pittsburgh",
    "metadata": {},
    "source": [
     "Or we can use **update operators**, `-=` and `+=`, to subtract 1 from\n",
@@ -274,7 +274,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "caring-richmond",
+   "id": "ahead-intro",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,7 +284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "antique-remains",
+   "id": "suited-laser",
    "metadata": {},
    "source": [
     "The result is the same either way."
@@ -292,7 +292,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dynamic-pepper",
+   "id": "retained-sauce",
    "metadata": {},
    "source": [
     "## Defining functions\n",
@@ -305,7 +305,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "maritime-paste",
+   "id": "raised-conviction",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -315,7 +315,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "united-flexibility",
+   "id": "impaired-teddy",
    "metadata": {},
    "source": [
     "Rather than repeat them every time a bike moves, we can define a new\n",
@@ -325,7 +325,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "narrow-marshall",
+   "id": "covered-algorithm",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "amended-territory",
+   "id": "marine-smith",
    "metadata": {},
    "source": [
     "`def` is a special word in Python that indicates we are defining a new\n",
@@ -356,7 +356,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "occupational-leeds",
+   "id": "sublime-question",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -365,7 +365,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "governing-australia",
+   "id": "lucky-scope",
    "metadata": {},
    "source": [
     "When you call the function, it runs the statements in the body, which\n",
@@ -376,7 +376,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "played-chambers",
+   "id": "divine-algeria",
    "metadata": {},
    "outputs": [
     {
@@ -431,7 +431,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "conventional-terminal",
+   "id": "interracial-farmer",
    "metadata": {},
    "source": [
     "When you call a function, you have to include the parentheses. If you\n",
@@ -441,7 +441,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "determined-wednesday",
+   "id": "worst-tomato",
    "metadata": {},
    "outputs": [
     {
@@ -461,7 +461,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "heated-retail",
+   "id": "racial-france",
    "metadata": {},
    "source": [
     "This result indicates that `bike_to_wellesley` is a function. You don't\n",
@@ -472,7 +472,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cutting-coating",
+   "id": "deluxe-scoop",
    "metadata": {},
    "source": [
     "## Print statements\n",
@@ -488,7 +488,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "spread-majority",
+   "id": "awful-laundry",
    "metadata": {},
    "outputs": [
     {
@@ -509,7 +509,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "swedish-sequence",
+   "id": "ignored-yugoslavia",
    "metadata": {},
    "source": [
     "Jupyter runs both lines, but it only displays the value of the\n",
@@ -520,7 +520,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "liked-volunteer",
+   "id": "infectious-hayes",
    "metadata": {},
    "outputs": [
     {
@@ -539,7 +539,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "north-paradise",
+   "id": "valued-religious",
    "metadata": {},
    "source": [
     "When you call the `print` function, you can put a variable name in\n",
@@ -550,7 +550,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "double-washer",
+   "id": "foreign-collins",
    "metadata": {},
    "outputs": [
     {
@@ -567,7 +567,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abstract-muslim",
+   "id": "controversial-overall",
    "metadata": {},
    "source": [
     "Python looks up the values of the variables and displays them; in this\n",
@@ -581,7 +581,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "mediterranean-rogers",
+   "id": "polish-outdoors",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -593,7 +593,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cooperative-kuwait",
+   "id": "sufficient-stone",
    "metadata": {},
    "source": [
     "Each time we call this version of the function, it displays a message,\n",
@@ -608,7 +608,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "floral-velvet",
+   "id": "scientific-edinburgh",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -620,7 +620,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "chicken-feedback",
+   "id": "flying-evaluation",
    "metadata": {},
    "source": [
     "And call it like this:"
@@ -629,7 +629,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "fitting-adventure",
+   "id": "rotary-carry",
    "metadata": {},
    "outputs": [
     {
@@ -646,7 +646,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "conditional-detector",
+   "id": "active-rocket",
    "metadata": {},
    "source": [
     "One benefit of defining functions is that you avoid repeating chunks of\n",
@@ -657,7 +657,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "threaded-taylor",
+   "id": "wanted-facing",
    "metadata": {},
    "source": [
     "## If statements\n",
@@ -668,7 +668,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "english-reply",
+   "id": "protected-stewart",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -677,7 +677,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eleven-hanging",
+   "id": "finished-attention",
    "metadata": {},
    "source": [
     "When you call it, you provide a probability between 0 and 1, like `0.7`, as an example:"
@@ -686,7 +686,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "emerging-mystery",
+   "id": "substantial-azerbaijan",
    "metadata": {},
    "outputs": [
     {
@@ -706,7 +706,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "compact-football",
+   "id": "progressive-objective",
    "metadata": {},
    "source": [
     "The result is one of two values: `True` with probability 0.7 or `False`\n",
@@ -728,7 +728,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "potential-denmark",
+   "id": "blond-prophet",
    "metadata": {},
    "outputs": [
     {
@@ -746,7 +746,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "changed-portable",
+   "id": "fallen-hurricane",
    "metadata": {},
    "source": [
     "If the result from `flip` is `True`, the program displays the string\n",
@@ -763,7 +763,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "activated-butter",
+   "id": "experimental-dispute",
    "metadata": {},
    "outputs": [
     {
@@ -783,7 +783,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sexual-neighborhood",
+   "id": "statistical-tooth",
    "metadata": {},
    "source": [
     "Now we can use `flip` to simulate the arrival of students who want to\n",
@@ -795,7 +795,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "multiple-manor",
+   "id": "protective-information",
    "metadata": {},
    "outputs": [
     {
@@ -813,7 +813,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "thrown-casting",
+   "id": "breathing-joseph",
    "metadata": {},
    "source": [
     "If students arrive at the Wellesley station every 3 minutes, on average,\n",
@@ -824,7 +824,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "occupied-indianapolis",
+   "id": "better-suggestion",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -834,7 +834,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "coordinated-census",
+   "id": "smooth-doctrine",
    "metadata": {},
    "source": [
     "We can combine these snippets into a function that simulates a **time\n",
@@ -844,7 +844,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "perceived-horizontal",
+   "id": "reserved-polls",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -858,7 +858,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "metric-classroom",
+   "id": "asian-absorption",
    "metadata": {},
    "source": [
     "Then we can simulate a time step like this:"
@@ -867,7 +867,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "stable-jamaica",
+   "id": "hired-colors",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -876,7 +876,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "black-glance",
+   "id": "isolated-principle",
    "metadata": {},
    "source": [
     "Even though there are no values in parentheses, we have to include them."
@@ -884,7 +884,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "respective-speed",
+   "id": "accompanied-ceremony",
    "metadata": {},
    "source": [
     "## Parameters\n",
@@ -900,7 +900,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "competent-owner",
+   "id": "accomplished-incentive",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -914,7 +914,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "written-trust",
+   "id": "governmental-intelligence",
    "metadata": {},
    "source": [
     "The values of `p1` and `p2` are not set inside this function; instead,\n",
@@ -924,7 +924,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "familiar-skiing",
+   "id": "bound-welsh",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -933,7 +933,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "tropical-termination",
+   "id": "simplified-popularity",
    "metadata": {},
    "source": [
     "The values you provide when you call the function are called\n",
@@ -945,7 +945,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "religious-failing",
+   "id": "potential-hardware",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -961,7 +961,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "distinguished-ridge",
+   "id": "severe-bride",
    "metadata": {},
    "source": [
     "The advantage of using parameters is that you can call the same function many times, providing different arguments each time.\n",
@@ -971,7 +971,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "laden-wisdom",
+   "id": "naughty-natural",
    "metadata": {},
    "source": [
     "## For loops\n",
@@ -984,7 +984,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "natural-roads",
+   "id": "posted-bruce",
    "metadata": {},
    "outputs": [
     {
@@ -1008,7 +1008,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hybrid-activation",
+   "id": "combined-handle",
    "metadata": {},
    "source": [
     "The syntax here should look familiar; the first line ends with a\n",
@@ -1029,7 +1029,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "urban-brisbane",
+   "id": "spanish-turner",
    "metadata": {},
    "source": [
     "## TimeSeries"
@@ -1037,7 +1037,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "greenhouse-caribbean",
+   "id": "arabic-parts",
    "metadata": {},
    "source": [
     "When we run a simulation, we often want to save the results for later analysis. The ModSim library provides a `TimeSeries` object for this purpose. A `TimeSeries` contains a sequence of time stamps and a\n",
@@ -1051,7 +1051,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "linear-magnet",
+   "id": "naval-earthquake",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1060,7 +1060,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "crucial-tract",
+   "id": "everyday-mechanics",
    "metadata": {},
    "source": [
     "We can create a new, empty `TimeSeries` like this:"
@@ -1069,7 +1069,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "pressed-brooks",
+   "id": "hispanic-honolulu",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1080,7 +1080,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "established-click",
+   "id": "encouraging-auckland",
    "metadata": {},
    "source": [
     "And we can add a quantity like this:"
@@ -1089,7 +1089,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "eligible-hormone",
+   "id": "colonial-fireplace",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1098,7 +1098,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "based-dealer",
+   "id": "racial-arrival",
    "metadata": {},
    "source": [
     "The number in brackets is the time stamp, also called a **label**.\n",
@@ -1110,7 +1110,7 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "renewable-coating",
+   "id": "impressive-hours",
    "metadata": {},
    "outputs": [
     {
@@ -1133,7 +1133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "common-senegal",
+   "id": "minimal-credits",
    "metadata": {},
    "source": [
     "Each time through the loop, we print the value of `i` and call `step`, which updates `bikeshare`.\n",
@@ -1152,7 +1152,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "id": "fewer-blocking",
+   "id": "finnish-lighting",
    "metadata": {},
    "outputs": [
     {
@@ -1177,7 +1177,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "prescription-induction",
+   "id": "expensive-territory",
    "metadata": {},
    "source": [
     "The left column is the time stamps; the right column is the quantities (which might be negative, depending on the state of the system).\n",
@@ -1190,7 +1190,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "divine-local",
+   "id": "right-bulletin",
    "metadata": {},
    "outputs": [
     {
@@ -1264,7 +1264,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "conditional-sandwich",
+   "id": "statutory-copyright",
    "metadata": {},
    "source": [
     "You don't have to use `show`, but I think it looks better."
@@ -1272,7 +1272,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "parental-treasure",
+   "id": "widespread-clinic",
    "metadata": {},
    "source": [
     "## Plotting\n",
@@ -1284,7 +1284,7 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "id": "weekly-producer",
+   "id": "computational-council",
    "metadata": {},
    "outputs": [
     {
@@ -1308,7 +1308,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "serial-report",
+   "id": "asian-sword",
    "metadata": {},
    "source": [
     "The legend for this figure says \"None\" because we have not provided a label.  Here's how we can do that:"
@@ -1317,7 +1317,7 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "sublime-treatment",
+   "id": "touched-retailer",
    "metadata": {},
    "outputs": [
     {
@@ -1339,7 +1339,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "julian-security",
+   "id": "expired-state",
    "metadata": {},
    "source": [
     "Whenever you make a figure, you should label the axes. The ModSim\n",
@@ -1349,7 +1349,7 @@
   {
    "cell_type": "code",
    "execution_count": 71,
-   "id": "helpful-pilot",
+   "id": "nominated-sister",
    "metadata": {},
    "outputs": [
     {
@@ -1377,7 +1377,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "false-thumb",
+   "id": "aboriginal-audio",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -1396,7 +1396,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sweet-cartoon",
+   "id": "protective-hearts",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -1404,7 +1404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "destroyed-trainer",
+   "id": "meaning-honduras",
    "metadata": {},
    "source": [
     "**Exercise:** What happens if you spell the name of a state variable wrong?  Edit the following cell, change the spelling of `wellesley`, and run it.\n",
@@ -1415,7 +1415,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "olympic-adjustment",
+   "id": "occasional-phone",
    "metadata": {},
    "outputs": [
     {
@@ -1437,7 +1437,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aquatic-taxation",
+   "id": "enormous-hacker",
    "metadata": {},
    "source": [
     "**Exercise:** Make a `State` object with a third state variable, called `babson`, with initial value 0, and display the state of the system."
@@ -1446,7 +1446,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "crucial-myanmar",
+   "id": "atlantic-newman",
    "metadata": {},
    "outputs": [
     {
@@ -1508,7 +1508,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "parliamentary-cherry",
+   "id": "ignored-landscape",
    "metadata": {},
    "source": [
     "**Exercise:** Wrap the code in the chapter in a function named `run_simulation` that takes three parameters, named `p1`, `p2`, and `num_steps`.\n",
@@ -1535,7 +1535,7 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "id": "overall-command",
+   "id": "generic-works",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1558,7 +1558,7 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "id": "forbidden-salem",
+   "id": "distributed-tampa",
    "metadata": {},
    "outputs": [
     {
@@ -1607,7 +1607,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sensitive-insertion",
+   "id": "caroline-labor",
    "metadata": {},
    "source": [
     "## Opening the hood\n",
@@ -1619,7 +1619,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "rolled-fundamental",
+   "id": "undefined-harrison",
    "metadata": {},
    "source": [
     "The `State` object defined in the ModSim library, is based on the `SimpleNamespace` object defined in a standard Python library called `types`; the documentation is at <https://docs.python.org/3.7/library/types.html#types.SimpleNamespace>."
@@ -1627,7 +1627,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "indonesian-wiring",
+   "id": "august-functionality",
    "metadata": {},
    "source": [
     "The `TimeSeries` object is based on the `Series` object defined by a library called Pandas.\n",
@@ -1639,7 +1639,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "embedded-narrative",
+   "id": "numerical-cliff",
    "metadata": {},
    "source": [
     "`Series` objects provide their own `plot` function, but the syntax is different from the other functions we've used, so ModSim provides a function called `plot` just to make it consistent.\n",
@@ -1650,7 +1650,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "tested-appearance",
+   "id": "overall-novel",
    "metadata": {},
    "source": [
     "The `flip` function uses NumPy's `random` function to generate a random number between 0 and 1, then returns `True` or `False` with the given probability.\n",
@@ -1661,7 +1661,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "relative-oliver",
+   "id": "offshore-czech",
    "metadata": {},
    "outputs": [
     {
@@ -1688,7 +1688,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sharp-bottle",
+   "id": "european-cooling",
    "metadata": {},
    "source": [
     "You might not understand everything in this function yet, but you will."
@@ -1697,7 +1697,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "smooth-causing",
+   "id": "familiar-future",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap05.ipynb b/jupyter/chap05.ipynb
index 00bb7e44..2d73f78a 100644
--- a/jupyter/chap05.ipynb
+++ b/jupyter/chap05.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "manual-spirit",
+   "id": "virtual-fireplace",
    "metadata": {},
    "source": [
     "# Chapter 5"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "intimate-lambda",
+   "id": "valid-islam",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "respected-auditor",
+   "id": "driven-transcript",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "motivated-cleaner",
+   "id": "imposed-israel",
    "metadata": {},
    "source": [
     "In this chapter..."
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "modern-holder",
+   "id": "handmade-direction",
    "metadata": {},
    "source": [
     "# World population\n",
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "lyric-norwegian",
+   "id": "guilty-kenya",
    "metadata": {},
    "source": [
     "## World Population Data\n",
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sharp-study",
+   "id": "raised-calendar",
    "metadata": {},
    "source": [
     "The following cell downloads a copy of https://en.wikipedia.org/wiki/World_population_estimates"
@@ -105,7 +105,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "false-future",
+   "id": "thrown-violence",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "tender-ethics",
+   "id": "representative-canal",
    "metadata": {},
    "source": [
     "To read this data, we will use Pandas, which provides functions for\n",
@@ -130,7 +130,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "rising-trader",
+   "id": "lined-footage",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,7 +139,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "higher-warner",
+   "id": "amber-batman",
    "metadata": {},
    "source": [
     "Now we can use it like this:"
@@ -148,7 +148,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "emerging-clause",
+   "id": "authentic-singer",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -161,7 +161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "recovered-philadelphia",
+   "id": "julian-intranet",
    "metadata": {},
    "source": [
     "The arguments are:\n",
@@ -196,7 +196,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "judicial-samba",
+   "id": "recreational-albania",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,7 +205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ethical-proportion",
+   "id": "postal-breeding",
    "metadata": {},
    "source": [
     "This line selects the third table (numbered 2), which contains\n",
@@ -217,7 +217,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "superior-colombia",
+   "id": "conservative-investigation",
    "metadata": {},
    "outputs": [
     {
@@ -404,7 +404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "collectible-chapter",
+   "id": "adolescent-password",
    "metadata": {},
    "source": [
     "The first column, which is labeled `Year`, is special.  It is the **index** for this `DataFrame`, which means it contains the labels for the rows.\n",
@@ -416,7 +416,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "illegal-hollow",
+   "id": "naughty-macintosh",
    "metadata": {},
    "source": [
     "The column labels are long strings, which makes them hard to work with.\n",
@@ -426,7 +426,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "interim-trouble",
+   "id": "suspected-integer",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -437,7 +437,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "intense-mileage",
+   "id": "about-mistress",
    "metadata": {},
    "source": [
     "Now we can select a column from the `DataFrame` using the dot operator,\n",
@@ -449,7 +449,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "resident-engineer",
+   "id": "cleared-protection",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -458,7 +458,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "disciplinary-advocacy",
+   "id": "rational-sweden",
    "metadata": {},
    "source": [
     "The result is a Pandas `Series`, which is similar to the `TimeSeries` and `SweepSeries` objects we've been using.\n",
@@ -473,7 +473,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "african-calvin",
+   "id": "portable-knowing",
    "metadata": {},
    "outputs": [
     {
@@ -499,7 +499,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "waiting-stack",
+   "id": "contemporary-telephone",
    "metadata": {},
    "source": [
     "The left column is the **index** of the `Series`; in this example it contains the dates.\n",
@@ -513,7 +513,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "ruled-hearing",
+   "id": "revolutionary-prescription",
    "metadata": {},
    "outputs": [
     {
@@ -540,7 +540,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "incoming-summit",
+   "id": "cleared-institution",
    "metadata": {},
    "source": [
     "The most recent estimate we have from the U.N. is for 2015, so the value for 2016 is `NaN`.\n",
@@ -551,7 +551,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "marine-sudan",
+   "id": "sharp-woman",
    "metadata": {},
    "outputs": [
     {
@@ -578,7 +578,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "czech-fitness",
+   "id": "detected-evidence",
    "metadata": {},
    "source": [
     "The keyword argument `style=':'` specifies a dotted line; `style='--'` specifies a dashed line.\n",
@@ -591,7 +591,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adolescent-graduate",
+   "id": "afraid-patio",
    "metadata": {},
    "source": [
     "## Absolute and relative errors\n",
@@ -605,7 +605,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "resident-matthew",
+   "id": "norwegian-spank",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -615,7 +615,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "resident-bonus",
+   "id": "married-account",
    "metadata": {},
    "outputs": [
     {
@@ -642,7 +642,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "removed-friendly",
+   "id": "polyphonic-agency",
    "metadata": {},
    "source": [
     "When you subtract two `Series` objects, the result is a new `Series`.\n",
@@ -654,7 +654,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "executive-luxury",
+   "id": "british-dayton",
    "metadata": {},
    "outputs": [
     {
@@ -676,7 +676,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "empty-kuwait",
+   "id": "spectacular-distance",
    "metadata": {},
    "source": [
     "On average, the estimates differ by about 0.029 billion.\n",
@@ -686,7 +686,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "wicked-paste",
+   "id": "administrative-store",
    "metadata": {
     "scrolled": true
    },
@@ -710,7 +710,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "vulnerable-olympus",
+   "id": "saved-smell",
    "metadata": {},
    "source": [
     "In the worst case, they differ by about 0.1 billion.\n",
@@ -724,7 +724,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "negative-championship",
+   "id": "embedded-procurement",
    "metadata": {
     "scrolled": true
    },
@@ -753,7 +753,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "supposed-canberra",
+   "id": "geographic-ordering",
    "metadata": {},
    "source": [
     "I multiplied by 100 so we can interpret the results as a percentage.  In 2015, the difference between the estimates is about 1.4%, and that happens to be the maximum.\n",
@@ -764,7 +764,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "statutory-leadership",
+   "id": "compressed-survivor",
    "metadata": {},
    "outputs": [
     {
@@ -784,7 +784,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fluid-syria",
+   "id": "statistical-violence",
    "metadata": {},
    "source": [
     "The mean relative error is about 0.6%.\n",
@@ -797,7 +797,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "confident-people",
+   "id": "insured-swing",
    "metadata": {},
    "source": [
     "## Modeling Population Growth\n",
@@ -819,7 +819,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "afraid-tennessee",
+   "id": "absent-minimum",
    "metadata": {},
    "outputs": [
     {
@@ -839,7 +839,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "trained-canyon",
+   "id": "cooked-beast",
    "metadata": {},
    "source": [
     "So we can get the total growth during the interval like this:"
@@ -848,7 +848,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "artistic-suicide",
+   "id": "historic-buddy",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -857,7 +857,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "advisory-mortality",
+   "id": "sufficient-oracle",
    "metadata": {},
    "source": [
     "The numbers in brackets are called **labels**, because they label the\n",
@@ -874,7 +874,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "authorized-rough",
+   "id": "focused-maryland",
    "metadata": {},
    "outputs": [
     {
@@ -895,7 +895,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "concerned-belgium",
+   "id": "special-fireplace",
    "metadata": {},
    "source": [
     "So `t_0` is the label of the first element, which is 1950.\n",
@@ -905,7 +905,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "forced-trunk",
+   "id": "employed-commission",
    "metadata": {},
    "outputs": [
     {
@@ -926,7 +926,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "driving-humor",
+   "id": "supreme-finder",
    "metadata": {},
    "source": [
     "The value `-1` indicates the last element; `-2` indicates the second to last element, and so on.\n",
@@ -937,7 +937,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "vocal-weather",
+   "id": "critical-carrier",
    "metadata": {},
    "outputs": [
     {
@@ -958,7 +958,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "accurate-interaction",
+   "id": "difficult-banks",
    "metadata": {},
    "source": [
     "Now we can use `t_0` and `t_end` to select the population at the beginning and end of the interval."
@@ -967,7 +967,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "finnish-sentence",
+   "id": "executive-pressure",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -977,7 +977,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "looking-board",
+   "id": "unnecessary-pontiac",
    "metadata": {},
    "source": [
     "And compute the total growth during the interval."
@@ -986,7 +986,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "viral-indian",
+   "id": "classical-proportion",
    "metadata": {},
    "outputs": [
     {
@@ -1007,7 +1007,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "active-pierce",
+   "id": "acknowledged-tenant",
    "metadata": {},
    "source": [
     "Finally, we can compute average annual growth."
@@ -1016,7 +1016,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "vietnamese-adrian",
+   "id": "double-short",
    "metadata": {},
    "outputs": [
     {
@@ -1037,7 +1037,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "occupied-indiana",
+   "id": "compound-linux",
    "metadata": {},
    "source": [
     "From 1950 to 2016, world population grew by about 0.07 billion people per year, on average.\n",
@@ -1046,7 +1046,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "underlying-oliver",
+   "id": "moved-spotlight",
    "metadata": {},
    "source": [
     "## Simulation\n",
@@ -1059,7 +1059,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "opposed-above",
+   "id": "imperial-gardening",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1070,7 +1070,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "motivated-point",
+   "id": "virgin-network",
    "metadata": {},
    "source": [
     "In this example, I use the keyword arguments `time` and `quantity` to give names to the index and the values.\n",
@@ -1082,7 +1082,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "architectural-techno",
+   "id": "suburban-deficit",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1091,7 +1091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "absent-anthony",
+   "id": "checked-neutral",
    "metadata": {},
    "source": [
     "Here's what it looks like so far."
@@ -1100,7 +1100,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "every-arkansas",
+   "id": "defensive-fifty",
    "metadata": {},
    "outputs": [
     {
@@ -1159,7 +1159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "diagnostic-testimony",
+   "id": "economic-profession",
    "metadata": {},
    "source": [
     "Now we set the rest of the values by simulating annual growth:"
@@ -1168,7 +1168,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "dedicated-system",
+   "id": "upset-amsterdam",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1178,7 +1178,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "classical-uncertainty",
+   "id": "underlying-biology",
    "metadata": {},
    "source": [
     "The values of `t` go from from `t_0` to `t_end`, including the first but not the last.\n",
@@ -1193,7 +1193,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "trained-canvas",
+   "id": "experimental-investigation",
    "metadata": {},
    "outputs": [
     {
@@ -1223,17 +1223,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "opening-fabric",
+   "id": "younger-abuse",
    "metadata": {},
    "source": [
-    "The keyword argument `color='gray'` specifies the color of the line.\n",
+    "The keyword argument `color='gray'` specifies the color of the line.  For details on color specification see <https://modsimpy.com/color>.\n",
     "\n",
-    "From 1950 to 1990, the model does not fit the data particularly well; after that, it's pretty good."
+    "From 1950 to 1990, the model does not fit the data particularly well, but after that, it's pretty good."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "portuguese-party",
+   "id": "similar-characterization",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -1255,7 +1255,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "terminal-house",
+   "id": "equivalent-louis",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -1263,7 +1263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "gross-puzzle",
+   "id": "excessive-cream",
    "metadata": {},
    "source": [
     "Here's the code from this chapter all in one place."
@@ -1272,7 +1272,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "binding-monitoring",
+   "id": "foreign-support",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1296,7 +1296,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "another-concentration",
+   "id": "inner-harvest",
    "metadata": {},
    "outputs": [
     {
@@ -1324,7 +1324,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fiscal-edwards",
+   "id": "narrative-state",
    "metadata": {},
    "source": [
     "**Exercise:**  Try fitting the model using data from 1970 to the present, and see if that does a better job.\n",
@@ -1339,7 +1339,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "spanish-number",
+   "id": "happy-empire",
    "metadata": {},
    "outputs": [
     {
@@ -1373,7 +1373,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "optimum-material",
+   "id": "tutorial-fundamentals",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1389,7 +1389,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "formal-shade",
+   "id": "beautiful-harvest",
    "metadata": {},
    "outputs": [
     {
@@ -1420,7 +1420,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "close-villa",
+   "id": "liberal-escape",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap06.ipynb b/jupyter/chap06.ipynb
new file mode 100644
index 00000000..b049a5c5
--- /dev/null
+++ b/jupyter/chap06.ipynb
@@ -0,0 +1,783 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "plain-cincinnati",
+   "metadata": {},
+   "source": [
+    "# Chapter 6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "corrected-chart",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "operational-landscape",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "statutory-baking",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we simulated a model of world population with\n",
+    "constant growth. In this chapter we see if we can make a better model\n",
+    "with growth proportional to the population.\n",
+    "\n",
+    "But first, we'll improve the code from the previous chapter by\n",
+    "encapsulating it in a function and using `System` objects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "handled-seating",
+   "metadata": {},
+   "source": [
+    "Here's the code that reads the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "lovely-blake",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates.html'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "alleged-manufacturer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_html\n",
+    "\n",
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
+    "table2 = tables[2]\n",
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "negative-shakespeare",
+   "metadata": {},
+   "source": [
+    "## System objects\n",
+    "\n",
+    "Like a `State` object, a `System` object contains variables and their\n",
+    "values. The difference is:\n",
+    "\n",
+    "-   `State` objects contain state variables that get updated in the course of a simulation.\n",
+    "\n",
+    "-   `System` objects contain **system parameters**, which usually don't get updated over the course of a simulation.\n",
+    "\n",
+    "For example, in the bike share model, state variables include the number of bikes at each location, which get updated whenever a customer moves a bike. System parameters include the number of locations, total number of bikes, and arrival rates at each location.\n",
+    "\n",
+    "In the population model, the only state variable is the population.\n",
+    "System parameters include the annual growth rate, the initial time and\n",
+    "population, and the end time.\n",
+    "\n",
+    "Suppose we have the following variables, as computed in the previous\n",
+    "chapter (assuming `table2` is the `DataFrame` we read from the file):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "complimentary-drain",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "un = table2.un / 1e9\n",
+    "census = table2.census / 1e9\n",
+    "\n",
+    "t_0 = census.index[0]\n",
+    "t_end = census.index[-1]\n",
+    "elapsed_time = t_end - t_0\n",
+    "\n",
+    "p_0 = census[t_0]\n",
+    "p_end = census[t_end]\n",
+    "\n",
+    "total_growth = p_end - p_0\n",
+    "annual_growth = total_growth / elapsed_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exotic-proportion",
+   "metadata": {},
+   "source": [
+    "Some of these are parameters we need to simulate the system; others are temporary values we can discard. \n",
+    "To distinguish between them, we'll put the parameters we need into a `System` object like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "european-southeast",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import System\n",
+    "\n",
+    "system = System(t_0=t_0, \n",
+    "                t_end=t_end,\n",
+    "                p_0=p_0,\n",
+    "                annual_growth=annual_growth)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "innocent-chapter",
+   "metadata": {},
+   "source": [
+    "`t0` and `t_end` are the first and last years; `p_0` is the initial\n",
+    "population, and `annual_growth` is the estimated annual growth.\n",
+    "\n",
+    "Here's what `system` looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "corresponding-chapter",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>System</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>t_0</th>\n",
+       "      <td>1950.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>2016.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p_0</th>\n",
+       "      <td>2.557629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>annual_growth</th>\n",
+       "      <td>0.072248</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "namespace(t_0=1950,\n",
+       "          t_end=2016,\n",
+       "          p_0=2.557628654,\n",
+       "          annual_growth=0.07224800083333333)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "modified-observation",
+   "metadata": {},
+   "source": [
+    "Next we'll wrap the code from the previous chapter in a function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "patient-adobe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import TimeSeries\n",
+    "\n",
+    "def run_simulation1(system):\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.p_0\n",
+    "    \n",
+    "    for t in range(system.t_0, system.t_end):\n",
+    "        results[t+1] = results[t] + system.annual_growth\n",
+    "    \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "realistic-switzerland",
+   "metadata": {},
+   "source": [
+    "`run_simulation1` takes a `System` object and uses the parameters in it to determine `t_0`, `t_end`, and `annual_growth`.\n",
+    "\n",
+    "Inside the loop, it stores the results in a `TimeSeries` which it returns at the end.\n",
+    "\n",
+    "The following function plots the results along with the estimates\n",
+    "`census` and `un`:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "durable-convertible",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "Here's how we call it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "critical-cooper",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation1(system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "under-dealing",
+   "metadata": {},
+   "source": [
+    "Here's the code we used in the previous chapter to plot the results, wrapped in a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "sublime-desperate",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import plot, decorate\n",
+    "\n",
+    "def plot_results(results, title):\n",
+    "    plot(census, style=':', label='US Census')\n",
+    "    plot(un, style='--', label='UN DESA')\n",
+    "    plot(results, color='gray', label='model')\n",
+    "    \n",
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)',\n",
+    "             title=title)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "worst-muslim",
+   "metadata": {},
+   "source": [
+    "And here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "announced-appendix",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_results(results, 'Constant growth model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "competitive-immigration",
+   "metadata": {},
+   "source": [
+    "It might not be obvious that using functions and `System` objects is a\n",
+    "big improvement, and for a simple model that we run only once, maybe\n",
+    "it's not. But as we work with more complex models, and when we run many simulations with different parameters, we'll see that the organization of the code makes a big difference.\n",
+    "\n",
+    "Now let's see if we can improve the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "european-massachusetts",
+   "metadata": {},
+   "source": [
+    "## Proportional growth model\n",
+    "\n",
+    "The biggest problem with the constant growth model is that it doesn't\n",
+    "make any sense. It is hard to imagine how people all over the world\n",
+    "could conspire to keep population growth constant from year to year.\n",
+    "\n",
+    "On the other hand, if some fraction of the population dies each year,\n",
+    "and some fraction gives birth, we can compute the net change in the\n",
+    "population like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "biblical-evans",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation2(system):\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.p_0\n",
+    "    \n",
+    "    for t in range(system.t_0, system.t_end):\n",
+    "        births = system.birth_rate * results[t]\n",
+    "        deaths = system.death_rate * results[t]\n",
+    "        results[t+1] = results[t] + births - deaths\n",
+    "        \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "neutral-singapore",
+   "metadata": {},
+   "source": [
+    "Now we can choose the values of `birth_rate` and `death_rate` that best fit the data. \n",
+    "\n",
+    "For the death rate, I'll use 7.7 deaths per 1000 people, which was roughly the global death rate in 2020 (see <https://www.indexmundi.com/world/death_rate.html>).\n",
+    "I chose the birth rate by hand to fit the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "alert-enzyme",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.death_rate = 7.7 / 1000\n",
+    "system.birth_rate = 25 / 1000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bacterial-convergence",
+   "metadata": {},
+   "source": [
+    "Then I ran the simulation and plotted the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "mechanical-phone",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results = run_simulation2(system)\n",
+    "plot_results(results, 'Proportional model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "optical-republican",
+   "metadata": {},
+   "source": [
+    "The proportional model fits\n",
+    "the data well from 1950 to 1965, but not so well after that. Overall,\n",
+    "the **quality of fit** is not as good as the constant growth model,\n",
+    "which is surprising, because it seems like the proportional model is\n",
+    "more realistic.\n",
+    "\n",
+    "In the next chapter we'll try one more time to find a model that makes\n",
+    "sense and fits the data. But first, I want to make a few more\n",
+    "improvements to the code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "requested-package",
+   "metadata": {},
+   "source": [
+    "## Factoring out the update function\n",
+    "\n",
+    "`run_simulation1` and `run_simulation2` are nearly identical except for the body of the `for` loop, where we compute the population for the next year.\n",
+    "\n",
+    "Rather than repeat identical code, we can separate the things that\n",
+    "change from the things that don't. First, I'll pull out the update code from `run_simulation2` and make it a function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "found-prediction",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def growth_func1(pop, t, system):\n",
+    "    births = system.birth_rate * pop\n",
+    "    deaths = system.death_rate * pop\n",
+    "    return births - deaths"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "excellent-trinity",
+   "metadata": {},
+   "source": [
+    "This function takes as arguments the current population, current year,\n",
+    "and a `System` object; it returns the net population growth during the current year.\n",
+    "\n",
+    "This update function does not use `t`, so we could leave it out. But we will see other functions that need it, and it is convenient if they all take the same parameters, used or not.\n",
+    "\n",
+    "Now we can write a function that runs any model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "extended-investing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, growth_func):\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.p_0\n",
+    "    \n",
+    "    for t in range(system.t_0, system.t_end):\n",
+    "        growth = growth_func(results[t], t, system)\n",
+    "        results[t+1] = results[t] + growth\n",
+    "        \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "comprehensive-proposal",
+   "metadata": {},
+   "source": [
+    "This function demonstrates a feature we have not seen before: it takes a\n",
+    "function as a parameter! When we call `run_simulation`, the second\n",
+    "parameter is a function, like `growth_func1`, that computes the\n",
+    "population for the next year.\n",
+    "\n",
+    "Here's how we call it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "arbitrary-python",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, growth_func1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "incident-cancellation",
+   "metadata": {},
+   "source": [
+    "Passing a function as an argument is the same as passing any other\n",
+    "value. The argument, which is `growth_func1` in this example, gets\n",
+    "assigned to the parameter, which is called `growth_func`. Inside\n",
+    "`run_simulation`, we can run `growth_func` just like any other function.\n",
+    "\n",
+    "Each time through the loop, `run_simulation` calls `growth_func1` to compute net growth, and uses it to compute the population during the next year."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acknowledged-short",
+   "metadata": {},
+   "source": [
+    "## Combining birth and death\n",
+    "\n",
+    "We can simplify the code slightly by combining births and deaths to compute the net growth rate. \n",
+    "Instead of two parameters, `birth_rate` and `death_rate`, we can write the update function in terms of a single parameter that represents the difference:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "impressed-laundry",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.alpha = system.birth_rate - system.death_rate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "veterinary-heading",
+   "metadata": {},
+   "source": [
+    "The name of this parameter, `alpha`, is the conventional name for a\n",
+    "proportional growth rate.\n",
+    "\n",
+    "Here's the modified version of `growth_func1`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "floating-singer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def growth_func2(pop, t, system):\n",
+    "    return system.alpha * pop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "instant-motion",
+   "metadata": {},
+   "source": [
+    "And here's how we run it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "professional-business",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, growth_func2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ordinary-exhibition",
+   "metadata": {},
+   "source": [
+    "The results are the same as the previous versions, but now the code is organized in a way that makes it easy to explore other models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "satellite-money",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this chapter, we wrapped the code from the previous chapter in functions and used a `System` object to store the parameters of the system.\n",
+    "\n",
+    "We explored a new model of population growth, where the number of births and deaths is proportional to the current population.  This model seems more realistic, but it turns out not to fit the data particularly well.\n",
+    "\n",
+    "In the next chapter, we'll try one more model, which is based on the assumption that the population can't keep growing forever.\n",
+    "But first, you might want to work on some exercises."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "north-bicycle",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "communist-cycle",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Maybe the reason the proportional model doesn't work very well is that the growth rate, `alpha`, is changing over time.  So let's try a model with different growth rates before and after 1980 (as an arbitrary choice).\n",
+    "\n",
+    "Write an update function that takes `pop`, `t`, and `system` as parameters.  The system object, `system`, should contain two parameters: the growth rate before 1980, `alpha1`, and the growth rate after 1980, `alpha2`.  It should use `t` to determine which growth rate to use.\n",
+    "\n",
+    "Test your function by calling it directly, then pass it to `run_simulation`.  Plot the results.  Adjust the parameters `alpha1` and `alpha2` to fit the data as well as you can."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "silver-farming",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def growth_func3(pop, t, system):\n",
+    "    \"\"\"Compute the population next year.\n",
+    "    \n",
+    "    pop: current population\n",
+    "    t: current year\n",
+    "    system: system object containing parameters of the model\n",
+    "    \n",
+    "    returns: population next year\n",
+    "    \"\"\"\n",
+    "    if t < 1980:\n",
+    "        return system.alpha1 * pop\n",
+    "    else:\n",
+    "        return system.alpha2 * pop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "capable-texas",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "system.alpha1 = 19 / 1000\n",
+    "system.alpha2 = 15 / 1000\n",
+    "\n",
+    "results = run_simulation(system, growth_func3)\n",
+    "plot_results(results, \n",
+    "             'Proportional model, parameter changes over time')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "popular-ireland",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# Using two parameters, we can make the model fit the data better.\n",
+    "# But it still seems like the shape of the function is not right."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "indian-sitting",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/chap07.ipynb b/jupyter/chap07.ipynb
new file mode 100644
index 00000000..648f2181
--- /dev/null
+++ b/jupyter/chap07.ipynb
@@ -0,0 +1,907 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dutch-anime",
+   "metadata": {},
+   "source": [
+    "# Chapter 7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "legitimate-middle",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "smart-vault",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "nervous-associate",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we developed a population model where net growth during each time step is proportional to the current population. This model seems more realistic than the constant growth model, but it does not fit the data as well.\n",
+    "\n",
+    "There are a few things we could try to improve the model:\n",
+    "\n",
+    "-   Maybe net growth depends on the current population, but the\n",
+    "    relationship is quadratic, not linear.\n",
+    "\n",
+    "-   Maybe the net growth rate varies over time.\n",
+    "\n",
+    "In this chapter, we'll explore the first option.\n",
+    "In the exercises, you will have a chance to try the second. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "configured-custom",
+   "metadata": {},
+   "source": [
+    "Here's the code that reads the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "retired-essence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates.html'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "italic-remains",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_html\n",
+    "\n",
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
+    "table2 = tables[2]\n",
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "automated-impossible",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "un = table2.un / 1e9\n",
+    "census = table2.census / 1e9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "chinese-bundle",
+   "metadata": {},
+   "source": [
+    "And here are the functions from the previous chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "incoming-clause",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import TimeSeries\n",
+    "\n",
+    "def run_simulation(system, growth_func):\n",
+    "    \"\"\"Simulate the system using any update function.\n",
+    "    \n",
+    "    system: System object\n",
+    "    growth_func: function that computes the population next year\n",
+    "    \n",
+    "    returns: TimeSeries\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.p_0\n",
+    "    \n",
+    "    for t in range(system.t_0, system.t_end):\n",
+    "        growth = growth_func(results[t], t, system)\n",
+    "        results[t+1] = results[t] + growth\n",
+    "        \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "worst-referral",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import plot, decorate\n",
+    "\n",
+    "def plot_results(timeseries, title):\n",
+    "    \"\"\"Plot the estimates and the model.\n",
+    "    \n",
+    "    timeseries: TimeSeries of simulation results\n",
+    "    title: string\n",
+    "    \"\"\"\n",
+    "    plot(census, style=':', label='US Census')\n",
+    "    plot(un, style='--', label='UN DESA')\n",
+    "    plot(timeseries, color='gray', label='model')\n",
+    "    \n",
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)',\n",
+    "             title=title)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "funky-bristol",
+   "metadata": {},
+   "source": [
+    "## Quadratic growth\n",
+    "\n",
+    "It makes sense that net growth should depend on the current population, but maybe it's not a linear relationship, like this:\n",
+    "\n",
+    "```\n",
+    "net_growth = system.alpha * pop\n",
+    "```\n",
+    "\n",
+    "Maybe it's a quadratic relationship, like this:\n",
+    "\n",
+    "```\n",
+    "net_growth = system.alpha * pop + system.beta * pop**2\n",
+    "```\n",
+    "\n",
+    "We can test that conjecture with a new update function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "coastal-crazy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def growth_func_quad(pop, t, system):\n",
+    "    return system.alpha * pop + system.beta * pop**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "closing-wrist",
+   "metadata": {},
+   "source": [
+    "Here's the `System` object we'll use, initialized with `t_0`, `p_0`, and `t_end`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "formed-touch",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import System\n",
+    "\n",
+    "t_0 = census.index[0]\n",
+    "p_0 = census[t_0]\n",
+    "t_end = census.index[-1]\n",
+    "\n",
+    "system = System(t_0=t_0,\n",
+    "                p_0=p_0,\n",
+    "                t_end=t_end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "single-punishment",
+   "metadata": {},
+   "source": [
+    "Now we have to add the parameters `alpha` and `beta` .\n",
+    "I chose the following values by trial and error; we'll see better ways to do it later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "equivalent-monster",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.alpha = 25 / 1000\n",
+    "system.beta = -1.8 / 1000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "incoming-cradle",
+   "metadata": {},
+   "source": [
+    "And here's how we run it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "prescription-assurance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, growth_func_quad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "toxic-european",
+   "metadata": {},
+   "source": [
+    "And here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "instant-malawi",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_results(results, 'Quadratic model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unexpected-davis",
+   "metadata": {},
+   "source": [
+    "The model fits the data well over the whole range, with just a bit of space between them in the 1960s.\n",
+    "\n",
+    "Of course, we should expect the quadratic model to fit better than the\n",
+    "constant and proportional models because it has two parameters we can\n",
+    "choose, where the other models have only one. In general, the more\n",
+    "parameters you have to play with, the better you should expect the model\n",
+    "to fit.\n",
+    "\n",
+    "But fitting the data is not the only reason to think the quadratic model\n",
+    "might be a good choice. It also makes sense; that is, there is a\n",
+    "legitimate reason to expect the relationship between growth and\n",
+    "population to have this form.\n",
+    "\n",
+    "To understand it, let's look at net growth as a function of population."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opened-paint",
+   "metadata": {},
+   "source": [
+    "## Net growth\n",
+    "\n",
+    "Let's plot the relationship between growth and population in the quadratic model.\n",
+    "\n",
+    "I'll use `linspace` to make an array of 101 populations from 0 to 15 billion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "wanted-malta",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import linspace\n",
+    "\n",
+    "pop_array = linspace(0, 15, 101)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exempt-techno",
+   "metadata": {},
+   "source": [
+    "Now I'll use the quadratic model to compute net growth for each population."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "reduced-creature",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "growth_array = (system.alpha * pop_array + \n",
+    "                system.beta * pop_array**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "color-surfing",
+   "metadata": {},
+   "source": [
+    "I'll put the results in a `Series` with `pop_array` and the index and `growth_array` as the values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "powered-productivity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import Series\n",
+    "\n",
+    "growth_series = Series(growth_array, index=pop_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "spare-complexity",
+   "metadata": {},
+   "source": [
+    "Now we can plot the `Series` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "growing-butler",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(growth_series, label='growth')\n",
+    "\n",
+    "decorate(xlabel='Population (billions)',\n",
+    "         ylabel='Net growth (billions)',\n",
+    "         title='Growth vs. Population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "placed-conservative",
+   "metadata": {},
+   "source": [
+    "Note that the x-axis is not time, as in the previous figures, but population. We can divide this curve into four regimes of behavior:\n",
+    "\n",
+    "-   When the population is less than 3-4 billion, net growth is\n",
+    "    proportional to population, as in the proportional model. In this\n",
+    "    regime, the population grows slowly because the population is small.\n",
+    "\n",
+    "-   Between 4 billion and 10 billion, the population grows quickly\n",
+    "    because there are a lot of people.\n",
+    "\n",
+    "-   Above 10 billion, population grows more slowly; this behavior models\n",
+    "    the effect of resource limitations that decrease birth rates or\n",
+    "    increase death rates.\n",
+    "\n",
+    "-   Above 14 billion, resources are so limited that the death rate\n",
+    "    exceeds the birth rate and net growth becomes negative.\n",
+    "\n",
+    "Just below 14 billion, there is a point where net growth is 0, which\n",
+    "means that the population does not change. At this point, the birth and death rates are equal, so the population is in **equilibrium**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "spread-ranking",
+   "metadata": {},
+   "source": [
+    "## Equilibrium\n",
+    "\n",
+    "To find the equilibrium point, we can find the roots, or zeros, of this equation: \n",
+    "\n",
+    "$$\\Delta p = \\alpha p + \\beta p^2$$ \n",
+    "\n",
+    "where $\\Delta p$ is net\n",
+    "population growth, $p$ is current population, and $\\alpha$ and $\\beta$\n",
+    "are the parameters of the model. We can rewrite the right hand side like\n",
+    "this: \n",
+    "\n",
+    "$$\\Delta p = p (\\alpha + \\beta p)$$ \n",
+    "\n",
+    "which is $0$ when $p=0$ or $p=-\\alpha/\\beta$.\n",
+    "We can use the parameters of the system to compute the second equilibrium point:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "foreign-emperor",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13.88888888888889"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-system.alpha / system.beta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "enormous-casting",
+   "metadata": {},
+   "source": [
+    "With these parameters, net growth is 0 when the population is about 13.9 billion.\n",
+    "\n",
+    "In the context of population modeling, the quadratic model is more\n",
+    "conventionally written like this: \n",
+    "\n",
+    "$$\\Delta p = r p (1 - p / K)$$ \n",
+    "\n",
+    "This is the same model; it's just a different way to **parameterize** it. Given $\\alpha$ and $\\beta$, we can compute $r=\\alpha$ and $K=-\\alpha/\\beta$.\n",
+    "\n",
+    "In this version, it is easier to interpret the parameters: $r$ is the\n",
+    "maximum growth rate, observed when $p$ is small, and $K$ is the\n",
+    "equilibrium point. $K$ is also called the **carrying capacity**, since\n",
+    "it indicates the maximum population the environment can sustain."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opened-possession",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this chapter we implemented a quadratic growth model where net growth depends on the current population and the population squared.\n",
+    "\n",
+    "This model fits the data well, and we saw one reason why: it is based on the assumption that there is a limit to the number of people the Earth can support.\n",
+    "\n",
+    "In the next chapter we'll use the models we have developed to generate\n",
+    "predictions.\n",
+    "But first, I want to warn you about a few things that can go wrong when you write functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "baking-ability",
+   "metadata": {},
+   "source": [
+    "## Dysfunctions\n",
+    "\n",
+    "When people learn about functions, there are a few things they often\n",
+    "find confusing. In this section I present and explain some common\n",
+    "problems.\n",
+    "\n",
+    "As an example, suppose you want a function that takes a\n",
+    "`System` object, with variables `alpha` and `beta`, and computes the\n",
+    "carrying capacity, `-alpha/beta`. \n",
+    "Here's a good solution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "interracial-speed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13.88888888888889\n"
+     ]
+    }
+   ],
+   "source": [
+    "def carrying_capacity(system):\n",
+    "    K = -system.alpha / system.beta\n",
+    "    return K\n",
+    "    \n",
+    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
+    "pop = carrying_capacity(sys1)\n",
+    "print(pop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "attempted-infection",
+   "metadata": {},
+   "source": [
+    "Now let's see all the ways that can go wrong."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "detailed-trainer",
+   "metadata": {},
+   "source": [
+    "Dysfunction \\#1: Not using parameters. In the following version, the\n",
+    "function doesn't take any parameters; when `sys1` appears inside the\n",
+    "function, it refers to the object we create outside the function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "wooden-collect",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13.88888888888889\n"
+     ]
+    }
+   ],
+   "source": [
+    "def carrying_capacity():\n",
+    "    K = -sys1.alpha / sys1.beta\n",
+    "    return K\n",
+    "    \n",
+    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
+    "pop = carrying_capacity()\n",
+    "print(pop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "settled-scale",
+   "metadata": {},
+   "source": [
+    "This version actually works, but it is not as versatile as it could be.\n",
+    "If there are several `System` objects, this function can only work with one of them, and only if it is named `sys1`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unnecessary-stack",
+   "metadata": {},
+   "source": [
+    "Dysfunction \\#2: Clobbering the parameters. When people first learn\n",
+    "about parameters, they often write functions like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "committed-better",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13.88888888888889\n"
+     ]
+    }
+   ],
+   "source": [
+    "# WRONG\n",
+    "def carrying_capacity(system):\n",
+    "    system = System(alpha=0.025, beta=-0.0018)\n",
+    "    K = -system.alpha / system.beta\n",
+    "    return K\n",
+    "    \n",
+    "sys1 = System(alpha=0.03, beta=-0.002)\n",
+    "pop = carrying_capacity(sys1)\n",
+    "print(pop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "terminal-wheat",
+   "metadata": {},
+   "source": [
+    "In this example, we have a `System` object named `sys1` that gets passed\n",
+    "as an argument to `carrying_capacity`. But when the function runs, it\n",
+    "ignores the argument and immediately replaces it with a new `System`\n",
+    "object. As a result, this function always returns the same value, no\n",
+    "matter what argument is passed.\n",
+    "\n",
+    "When you write a function, you generally don't know what the values of\n",
+    "the parameters will be. Your job is to write a function that works for\n",
+    "any valid values. If you assign your own values to the parameters, you\n",
+    "defeat the whole purpose of functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "interpreted-perfume",
+   "metadata": {},
+   "source": [
+    "Dysfunction \\#3: No return value. Here's a version that computes the\n",
+    "value of `K` but doesn't return it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "crude-window",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "# WRONG\n",
+    "def carrying_capacity(system):\n",
+    "    K = -system.alpha / system.beta\n",
+    "    \n",
+    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
+    "pop = carrying_capacity(sys1)\n",
+    "print(pop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "logical-enemy",
+   "metadata": {},
+   "source": [
+    "A function that doesn't have a return statement actually returns a special value called `None`, so in this example the value of `pop` is `None`. If you are debugging a program and find that the value of a variable is `None` when it shouldn't be, a function without a return statement is a likely cause."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "concerned-space",
+   "metadata": {},
+   "source": [
+    "Dysfunction \\#4: Ignoring the return value. Finally, here's a version\n",
+    "where the function is correct, but the way it's used is not.\n",
+    "\n",
+    "```\n",
+    "def carrying_capacity(system):\n",
+    "    K = -system.alpha / system.beta\n",
+    "    return K\n",
+    "    \n",
+    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
+    "carrying_capacity(sys1)\n",
+    "print(K)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "better-karen",
+   "metadata": {},
+   "source": [
+    "In this example, `carrying_capacity` runs and returns `K`, but the\n",
+    "return value doesn't get displayed or assigned to a variable.\n",
+    "If we try to print `K`, we get a `NameError`, because `K` only exists inside the function.\n",
+    "\n",
+    "When you call a function that returns a value, you should do something\n",
+    "with the result."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "metropolitan-blackjack",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "vital-seven",
+   "metadata": {},
+   "source": [
+    "**Exercise:** In a previous section, we saw a different way to parameterize the quadratic model:\n",
+    "\n",
+    "$$ \\Delta p = r p (1 - p / K) $$\n",
+    "\n",
+    "where $r=\\alpha$ and $K=-\\alpha/\\beta$.  \n",
+    "\n",
+    "Write a version of `growth_func` that implements this version of the model.  Test it by computing the values of `r` and `K` that correspond to `alpha=0.025, beta=-0.0018`, and confirm that you get the same results. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "twelve-amplifier",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.025, 13.88888888888889)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "system.r = system.alpha\n",
+    "system.K = -system.alpha/system.beta\n",
+    "\n",
+    "system.r, system.K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "yellow-horse",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def growth_func_quad2(pop, t, system):\n",
+    "    return system.r * pop * (1 - pop / system.K)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "animal-drink",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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5UUpVDSdPnmTOnDkcP36cyHbtGFl7HwHxT0HTbjBhGtRqTEGRDR8vD2r4eNIxpDZHz+bSrJ7zeiJLyX/u7iA6OtpcOmHhzp07adeunYsiqh70HCtVfZXUmtauXUuNGjUYOXIkkbv/BVtmQNS9MOpd8PZj1e5T/Hr2Vr57vC9N69zYa9kikmSMib50udaglFKqmjp+/Dhz5szh5MmTdOzYkREjRtjvzfS6DRpHQc9HKDbgCbRqWJOOTWtjs1VcpUYTlFJKVTNFRUWsWbOG2NhYAgICuOu2W2hz+EtIOgj9X4DWwzHG8OvZWyksNrw3IYqmdWrwycTuFRqnJiillKpGjh07xg8//EBaWhqdO3ViePN8aiyeADmnoe8z2GwGDw9BRAip609hsQ1jjEsGltYEpZRS1UBRURGrVq1i3bp1BAYGcvctg2m9412Yt9zenHfvN+zzasHj/1zDX2/vTFSzOjw9uJVLY9YEpZRSVVxqaipz5szh9OnTREVFMXz4cPzSd8DiRLj5r5joXyKeXjTKL6JODR/yCotdHTKgCUoppaqswsJCVq5cSUJCAjVr1uTeYdG0LNoFfmPt3cef38HUpHSWfZ7EFw/2INDXi1mP9r7yjiuI3qh7BVeabuOBBx6gadOm5OfnAxcP1HopT09PoqKiaN++PZ07d+bdd9+9cD/TqlWrqF27NlFRURcey5YtA+zj9bVv355OnToRFRV10Q2/RUVF1K9fn1deeeVGF10pVYkdPnyYyZMnEx8fT5dO7Xk84jAtF98DiZ9D3nn74AC+NfHz9iDQ14vsAveoNZWmNagbwNPTk08//ZTHHnus3O1q1KhBcnIyYB8F/Z577uHcuXP84Q9/AKB///4/u0k3Pj6e+fPns2nTJnx9fTl9+jQFBQUX1i9ZsoQ2bdowa9Ys3n77bZ0hV6lqrrCwkOXLl7N+/Xpq167Nff1Cidj0G3sniF5PcK7XS7wwcze3dQlhVKfG3BndjAndm7s67DI5rQYlIm1EJLnU47yIPOus47nSs88+y3vvvUdRUZHD72nQoAFTpkzhgw8+oLybpY8fP079+vUvjMtXv359mjRpcmH9jBkzeOaZZ2jevDkJCQnXXgilVKV36NAhPv74Y9avX090dDSP3TeeiIRXoVYT+NVKGPE2gbXqcD63iMy8QuDyA2K7A6fVoIwxu4EoABHxBI4C31/3jj8b9fNl7W+FHr+CghyYfsfP10fdA13uhex0mHX/xesmLbjukJo3b06/fv348ssvueWWWxx+X0REBDabjVOnTgGwdu1aoqKiLqyfPXs2w4YN449//COtW7dmyJAhTJgwgYEDBwKQm5vL8uXLmTx5MmfPnmXGjBkX5pxSSlUfBQUFLF++nA0bNlCnTh3u7xdK+KCR9sFdH1xIXGYjPlxwkE8fKMbP25OZj/Ry68RUoqKuQQ0G9htjDlXQ8W6YK023UeLVV1/lb3/724VrSo4qXXvq378/ycnJFx4tWrQgMDCQpKQkpkyZQnBwMBMmTODzzz8HYP78+dx00034+/szfvx4vv/+e4qL3a8dWSnlPCkpKXz88cds2LCBHpGhPOY1m/C1z0DKWvsGjTuDhyens/I5ed4+00FlSE5Qcdeg7gJmlLVCRB4GHgZ7TeSKyqvx+PiXvz4g6KprTI5MtwHQsmVLoqKimDVrlsP7PnDgAJ6enjRo0ICdO3dedjtPT09iYmKIiYmhY8eOTJ06lQceeIAZM2YQFxd3oVNGeno6K1euZMiQIY4XUClVKRUUFLB06VISExOpW6c2D7TOInTHs1CrCcUTpvP33Q0ISj3AQ/0j6NuyPgufGYCnR+VITCWcXoMSER9gDPBNWeuNMVOMMdHGmOjg4GBnh3PVrjTdRmm//e1vL/Tuu5K0tDQeffRRnnzyyXL/m9m9e/dF07QnJycTGhrK+fPniY2N5fDhw6SkpJCSksKHH37IjBll/h+glKpCDhw4wEcffURiYiK9evbkMb+5hO75BHo9Bk+sx7PdaPadyuJQ+v+mw6hsyQkqpgZ1M7DJGHOyAo7lFOVNt1Fa+/bt6dq164U5mi6Vm5tLVFQUhYWFeHl5cd999/H8889fWH/pNajXXnuN8PBwnnrqKc6ePYuXlxctW7ZkypQpfPfddwwaNOiiSQ3Hjh3Lyy+/TH5+/kXLlVJVQ35+PkuWLGHTpk0E1anFgxPvo1lYBBzwIzXXhzcSvflzgQ/BvvDve7vi5Vm57yRy+nQbIvI1sNgY89mVttXpNlxDz7FS7m/fvn3MmzePzMxMejfzIib1A7xjXoT+9n9y96dlcdeUBP55VxR9WtR3cbRXxyXTbYiIPzAUeMSZx1FKqaoqLy+PxYsXk5ycTP3aATxYK46QQwnQbgzzZSA7F+/ipeFtaREcSNyvB+HjVblrTaU5NUEZY3KAIGceQymlqqo9e/Ywf/58srKy6BvqQ0zK23jVbgJ3z4Q2I9g07ye2HztDYbENb0+PKpWcQEeSUEopt5Obm8vixYvZsmULDYLrc9ddd9HEHOdc4jmeOjacV2p1ox3w8og2+Hp5VJpu41dLE5RSSrmR3bt3M3/+fLKzs+lf6ygDmqTj2bgxSBOo24ljH6/jxPk82jWuhZ+3p6vDdSpNUEop5QZycnJYtGgR27Zto6E/3GO+pnF+DitznmTa1EQ+mRhNbX9vlj43oMrWmC6lCUoppVzsp59+4scffyQ3J4eBfj/RP3sJnp0nwNA3Obw1m9qpZ8kvsuHn7VltkhNogqpQYWFhJCYmUr/+5buAOrKNUqpqyM7O5scff+Snn36iUaNG/GLsEOovW8bfa71D93ZjGRQYzP296yMS5upQXUITlFJKVTBjDDt27GDhwoXk5eZwU/3T9P3lb/H08qIgbCXrpiTQItv9Rxt3tqrVJ9EJUlJSaNu2LQ899BAdOnTg3nvvZdmyZfTt25dWrVqxYcMGMjIyuPXWW+nUqRO9evVi69atgH1svGHDhtGlSxceeeSRiwaGnTZtGj169CAqKopHHnlEB3lVqprIyspi1qxZzJ49mzoFx3nENpUO3od58ot1FBTZ8PH25IfH+3B7txBXh+pylaoGtWjRIk6cOHFD99moUSNGjBhR7jb79u3jm2++YcqUKXTv3p2vvvqK2NhY5s6dy9tvv02zZs3o0qULP/zwAytWrOD+++8nOTmZP/zhD/Tr14/XX3+dBQsWMGXKFMA+csPMmTOJi4vD29ubxx9/nOnTp3P//feXG4dSqvIyxrBt2zYWLVxIQX4ug00sfXyP4XHbeyynJ3sW7uLk+Tya1fOv1rWm0ipVgnKV8PBwOnbsCNjH2xs8eDAiQseOHUlJSeHQoUPMnj0bgEGDBpGens65c+dYs2YN3333HQCjRo2ibt26ACxfvpykpCS6d+8O2O95aNCggQtKppSqCJmZmcyfP589e/YQ0qQxY879wOE63Zgd+QF3RLZlMDCwTYNKP3bejVapEtSVajrOUnrgVQ8PjwuvPTw8KCoqwsvr56ex5D+gsv4TMsYwceJE/vznPzspYqWUOzDGkJyczOJFCykuLGDYkCH07N0HKfoFz32xjZDjBZRMsarJ6ef0jNwAAwYMYPr06QCsWrWK+vXrU6tWrYuWL1y48MK8UoMHD+bbb7+9MJNuRkYGhw5VurkclVLlOHfuHF99OZW5c+fSsOAgD3t/T+z+k5zPK0J8/PlkYjR/Gd/J1WG6tUpVg3JXb7zxBpMmTaJTp074+/szdepUwD4tx913303Xrl0ZOHDghQkZIyMj+dOf/sSwYcOw2Wx4e3vz4YcfEhoa6spiKKVuAGMMm5KSWLJ4IaaogBGspUe3aHa3n8cXn++kZ+o5BrQOrvKjQNwITp9u42rodBuuoedYqRvjzJkzzJs3j4MHDxLmncFN/jvZ2Oppxo8aDUBWfhGBvlovuJRLpttQSqnqwBjDxnVrWLZiJeLlw6hRo+jWOoTfLj3Bpr3nuaXIho+Xhyanq1Tu2RKR3sAvgP5AYyAX2A4sAKYZY845PUKllHJjGenpzJ3xXw6l59LCHMK/eWeCwtohtQJ4dXR9fKrgNBgV5bIJSkQWAseAOcBbwCnAD2gN3ATMEZF3jTFznR2kMUbvC3ASd2riVaoysdlsrF/2AysSkvG0FTK27iGa3fwUMdPP88zOkzzUP0JrTNepvLN3nzHm9CXLsoBN1uMfIuL0AeP8/PxIT08nKChIk9QNZowhPT0dPz8/V4eiVKWSlpbG3LlzSU1NpZXHUWqHtyHqvrfAw4PlL+bRoKb+Tt0Il01QZSSna9rmeoWEhJCamkpaWpqzD1Ut+fn5ERKiQ6oo5QhbURFxsz9k9Z7z+Pj6Mu7mQSSc8eedhHSiswpoWMtPk9MNdMX6p4iMA94BGgBiPYwxppaTYwPA29ub8PDwijiUUkpd1oktK5k7fwHHi2oSElBA65GT6BgZQuuiYsb3K6ZegI+rQ6xyHGkg/StwizFmp7ODUUopd1N87jhrZ7zH2hN+1BAvbotuwmNbI+j3Uwb9I0Pw9fLE10vvaXIGRxLUSU1OSqnq6NixY8yZPoVTOQE08c/jroeeo2a9hnzTN5fGtbUpz9kcSVCJIjIT+AHIL1lojPnOWUEppZQrFe5Zzurtx1i3/SCBATWJ7NyCVzf6EJ3lQ5d60KRODVeHWC04kqBqATnAsFLLDKAJSilVtZw5xOEf3mTuoQDSpR4Nwtsy6c6x+Pr60mtALs3q+bs6wmrlignKGDOpIgJRSimXKcihYM17LF+3iQ2mI7X9PDjk24EdOY151NcXEdHk5AKO9OILAd4H+mKvOcUCzxhjUp0cm1JKVYgDK6YyLyGds9KRLh0jGTH6VjJybdQN8Nb7L13IkSa+z4Cv4MK0Jb+wlg11VlBKKeV0J7aTl36EJfsL2bz5FDUDG/FjRnO6teyHj48PjbTXuMs5kqCCjTGflXr9uYg866R4lFLKuXIyYMWf2J20mvkylCzjT58+fYiJiWH8+QJCgwJcHaGyODKC4WkR+YWIeFqPXwDpzg5MKaVuqOIi2PAfsv/Zm9lJJ/iaMZz3rk+Cd2cG3DQIb29vTU5uxpEa1IPAB8B72K9BrbOWKaVUpWFS1rLjx/+wwOMOCjx8iBkwkHZR3Xnex1tvtHVTjvTiOwyMqYBYlFLqxjpzCI4mkhk6jAUbT7JbRpFeHECL7jEMHPiz+fGUmylvuo2XjTF/FZH3sdecLmKMedqpkSml1LUqyIbY9zCx/yLJszML2Y0HhqFDh9KgRQdaNKjp6giVA8qrQZUMb5RYzjblEpE6wCdAB+xJ7kFjTPy17k8ppcplDGyfDUtf58z5LOYF/pKD2X6cMn48++DdtGre2NURqqtQ3nQb86yfU69j//8EFhljbhcRH0DvdFNKOU/6foq/e5jVvsNY59kez0Ivho0YTGibDjSpo39+KpvymvjmUUbTXgljTLnXpUSkFjAAeMDavgAouKYolVLqcrLSYPeP0G0ip2y1+KH+7zh++jzFgcG8+Kt7qFWrQmYGUk5QXhPf369z3xFAGvCZiHQGkrCPQJFdeiMReRh4GKB58+bXeUilVLVRXAgbpsCqdygqyOPfycLZY4fw8/Oj39BRDOzZFS9PR+6kUe5KjLlsJen6diwSDSQAfY0x60Xkn8B5Y8zvLvee6Ohok5h4zZe8lFLVxb7lsOg3cHoPR5uOZvrZLuTmZBPSog133XYLAQF6P1NlIiJJxpifdassr4lvG+U38XW6wjFTgVRjzHrr9bfAbxyIVSmlLi/3LMyayHmfYOaHvMm+Y2cJDPRg4M23EtOjs6ujUzdQeU18o69nx8aYEyJyRETaGGN2A4OBn65nn0qpaqogG5K/gu4PQY067B/8Cf9dvAXf7DN069aNoUOH4uvr6+oo1Q1WXi++Qzdg/08B060efAcAnbpDKeU4Y2DH97DkNTh/lDnH61Bs82bb1i0E16nLqFF30LplhKujVE5SXhNfrDGmn4hkYm/qk9I/jTFX7BpjjEkG9HZtpdTVO7UTfnwJUtZiGnZkccRrrNu8B3+PYvr168fAgQPx8nJktDZVWZVXg+pn/dRbrpVSFctmg6/vpTgng+QOv2dfURN2bdlNk/rBTLh9HI0aNXJ1hKoCOPTvh4h0BfphTVhojNns1KiUUtWPMfDTHGg9Arz9MOM/4bXvd+K54yA1vPYzZMgQevfujYeHdh2vLhyZUfd17JMVfmct+lxEvjHG/MmpkSmlqo9Tu+DHFyFlLXu6/wGPjhOIXbEVv/RDNA5pzu23jaVevXqujlJVMEdqUHcDXYwxeQAi8hdgE6AJSil1ffKzYPU7kPAR+ASScdM7/HqZJ1FJn+Dn68OYMWOIiorSaderKUcSVArgB+RZr32B/c4KSClVjfzwKOycx8kWd1DU+znmLVtLlNcx2rRpx6iRN1Ozpl4Cr87K68VXMs1GPrBDRJZar4cCsRUTnlKqyjlzCHwCIKA+xLzKLK8xfJucQYeDswkMDGTChAm0bdvW1VEqN1BeDapkzKEk4PtSy1c5LRqlVNVVVADxH8Dqv5IfOZ60m/5GXqYPR48co73XObp27caQIUPw8/NzdaTKTZTXzfx6ptlQSqn/OZwA856FtJ2YtrcwaV8/Gu+bRq3c4wQFBfHAAw8QGhrq6iiVm7nSdBtTsM/nVHjJugjs02ikGGM+dWqESqnKLWkqzHuaoppN8bhrBtvym9L+4CJsRQX0GzCA/v376w23qkzlfSt+BTwP/J+IZGCfOsMPCAf2AR8YY+Y4P0SlVKVjDORngl8taDWMYx0eYfymrkxYeZxzpzYSEhLCLbfcQoMGDVwdqXJj5TXxnQBeBl4WkTCgMZAL7DHG5FRMeEqpSufsYZj/PBTmcuaO76gd2JD9jcYwosYqcs56MnLkSKKjo7XruLoih+rVxpgU7N3NlVKqbLZi+wSCy98EYGHDh/j3B4u4uc5xTp08SZs2bRg5cqTOcKscpg2/Sqnrd/YIfDMRjiZhazmUomF/5fiKrXQ7uoWc7ADuvPNO2rVr5+ooVSWjCUopdf3861FkM/w94CUC/ftT9NVczp07R3R0NIMHD9au4+qa6KiLSqlrk5oIX9+LKcgBnwBy755Dtlcj0retwsfHh0mTJjFq1ChNTuqaOTJYbF/gDSDU2r5kPiidJUyp6qggB1a+BQkfkV+jIU/9azb39mlN/JqV+BYVMeCmm+jbty+enp6ujlRVco408f0XeA77iBLFzg1HKeXWUmJhzpNw5iBEP8j25g9Re95SVi1dRFhYGKNHjyYoKMjVUaoqwpEEdc4Ys9DpkSil3JsxsOwNsvKLmB/5EQ38GxM35zuCfX0ZNmosnTt31q7j6oZyJEGtFJG/YZ8PKr9koTFmk9OiUkq5j5Q4CG4LAUFwx+f844dt5O/Zju/OfXTu3JmhQ4cSEBDg6ihVFeRIgupp/YwutcwAg258OEopt1GQA8v/COv/zdaQu/EY+kf2btoIKVtpXK8eo0aNJyJCL0Ur57ligjLG3FQRgSil3Mjh9fDDY5Cxn5yoX/LH5I50nPZfPGxFDNDx81QFcaQXX23g98AAa9Fq4I/GmHPODEwp5SJbZmJ+eJRsv0bkjJnBgi0n6SiHad6kOaNHjyY4ONjVEapqwpF/gT4FtgN3Wq/vAz4DxjkrKKWUCxgDIhARw/amE3jrUEciFyTh5+urU68rl3AkQbUwxowv9foPIpLspHiUUhXNZoOEjyjau4yU4Z8j2VmszImknddpOnXUThDKdRxJULki0s8YEwsXbtzNdW5YSqkKcfYw/PA4pKwl3ncA33zyJUHFGQQFBXH//fcTHh7u6ghVNeZIgnoMmGpdixIgA/tkhUqpysoY2PYNZsHz2GyQ1PEt1uzOJJhzDIiJoW/fvtoJQrmcI734koHOIlLLen3e2UEppZysKI/i5X8itqAdCQE3k7c9nYiICEaOHKkjQSi3Ud6U778wxkwTkecvWQ6AMeZdJ8emlLrRDq3DNOlCgc2DFeGvsWHLTnyKixg3bhwdOnTQThDKrZRXgyq5KlqzjHXGCbEopZylKB+W/xET/wH/9n6IbN8m5GRn6XQYyq2VN+X7ZOvpMmNMXOl1VkcJpVRlcGonzH6IjJOpzK35JGlZPtSt4889d99F06ZNXR2dUpflyFXQ94GuDixTSrmb7d+R/90TrJGerPccjmeBN8OH30SPHj3w8NDp4JR7K+8aVG+gDxB8yXWoWoBDE72ISAqQiX2ajiJjTHT571BK3Uj78+vxgzxAls2PdpHtuHnECGrWLKvVXin3U14NygcItLYp/Y0+D9x+Fce4yRhz+hpiU0pdi10L2L1+EfGefTi0fw916zZhwogRtG3dytWRKXVVyrsGtRpYLSKfG2MOVWBMSqlrkZ9F8cLfsCF5ByvoS6HHPm7Se5pUJebItzbHmg+qPXChq48xxpHpNgywREQMMNkYM+XSDUTkYeBhgObNmzsUtFLqEkc2sG/Gr1ma055TEkOjkOaMHTOaRjqwq6rEHElQ04GZwGjgUWAikObg/vsaY46JSANgqYjsMsasKb2BlbSmAERHR2v3daWuUvbZdBZ//j7bbDHU8PNhwq230aZNG72nSVV6jiSoIGPMf0XkmVLNfqsd2bkx5pj185SIfA/0ANaU/y6llCOK0/aydtcp1q+LpYBWBIW2ZtKEWwmoofc0qarBkQRVaP08LiKjgGNAyJXeJCIBgIcxJtN6Pgz44zVHqpSysxWTuvhfLNiwjxM0oHloGKNHjdR5mlSV40iC+pM1UOwL2O9/qgU858D7GgLfW80MXsBXxphF1xqoUgqyUney4uv32ZzdkACPujRs1YX77hiNl6fe06SqHkcGi51vPT0HODz9uzHmAND5GuNSSpVis9lYP+c/rNmaQgHB9G5Zh4HjH8FXhyhSVVh5N+q+Tzlj7hljnnZKREqpixw5coSFCxdy/PgJGnjk0bjnUIYNc6QTrVKVW3k1qMQKi0Ip9TPZmZnM+fID9qYVEBAQyPjx42nfvr32zlPVRnk36k6tyECUUnY2m43ENYtZuXYdBcVChMdROo9/mw7hDV0dmlIV6orXoERkJWU09Tl4o65S6iocTjnItzO+ILMAwuUkN8d0p/6ANxAd2FVVQ4704nux1HM/YDxQ5JxwlKqeMjMzWbZsGVu3bqUWmXT3Psawx97Bq24zV4emlMs40osv6ZJFcY7eqKuUKl9xcTErVq0mPm4tIp7079+fnq0b4N8kUmtNqtpzpImvXqmXHkA3oJHTIlKqmti/fz8L535H+vkcWpkD1Ow6lkGDtOVcqRKONPElYb8GJdib9g4Cv3RmUEpVZWfOnOHb77/n2JEj1DNnuNt/B2G3vIBPuxGuDk0pt+JIE194RQSiVFVXUFBAXFwccXFxeBTnc5NJoFOXrtS5+QfwCXB1eEq5HUea+PyAx4F+2GtSscC/jTF5To5NqSrBGMP27dtZ8OOP5Ofl0aFDBwZF+OBV51ZqhndzdXhKuS1Hmvi+wD5t+/vW67uBL4E7nBWUUlXFiRMnWPjjAg4fSaWhOYVfrTqMHz/e1WEpVSk4kqDaGGNKj6m3UkS2OCsgpaqCnJwcli5bTvLmTfhLPqPNGtq0CMVv9CuuDk2pSsORBLVZRHoZYxIARKQnEOfcsJSqnIqLi9m4cSOrV68mPy+XnmYzPWocot5tf4NWQ10dnlKViiMJqidwv4gctl43B3aKyDbAGGM6OS06pSqRffv28ePChZzJyKBFixYM6xCMSSmk3sgZ4OPv6vCUqnQcSVDa91WpcqSnp7NkyRL27NlDTbJozFnG3/lbavh4QdRwV4enVKXlSDfzQyLSGehvLVprjNFrUKray8vLY/Xq1WzYsAEvihhiYunmf4zTfX5nT05KqeviSDfzZ4BfAd9Zi6aJyBRjzPvlvE2pKstms7Fp0yZWrlxJTk4O7c1OhhJL7T4PwMBfE+Jb09UhKlUlOPJv3i+BnsaYbAAReQeI53/dzpWqNg4ePMjCRYtIO3WK0NBQhvdqj6zYQNHN8yE8ytXhKVWlOJKgBCgu9brYWqZUtZGens7SpUvZvXs3/h4FtCney8BbX6ZxnRrQdoGrw1OqSnIkQX0GrBeR77EnprHAf50alVJuouQ60/oNG/DCxmCP9fQ0mznReRKNa/m4OjylqjRHOkm8KyKrsA91BDDJGLPZqVEp5WI2m42kpCRWrlxJbm4urc1ebmE5gW0HwbB1NKunQ1Qq5WxX09VIABvavKeqMGMM+/btY9HiJWSknyYsLIzhA3vjv+gpPG+aBm0GuzpEpaoNR3rxvY593L3Z2JPTZyLyjTHmT84OTqmKdPLkSZYsWcKBAwcI9CykV3EizYc8RaOm9eDRxa4OT6lqx5Ea1N1Al5LRy0XkL8AmQBOUqhKysrJYuXIlmzZtxsfTMMIznm7FiWR0uJcGwX6uDk+pasuRBJUC+AEl02v4AvudFZBSFaWwsJB169YRFxdHcXER7dnJqKIV1GjZH4bH0SC4jatDVKpacyRB5QM7RGQp9vmghgKxIvIvAGPM006MT6kbzhjDli1bWLpsOTnZWbRt25Yhg2Lw+/FpvHt9AW10eCKl3IEjCep761FilXNCUcr5Dh48yJIlSzhx4gT1fQoYYluGb4+HCApuCBNnujo8pVQpjnQzn1oRgSjlTKdOnWLJ0qXs37cPf28Y57mC9gXbyes2Cf/GOjSRUu5IR7RUVVpmZiYrV64kOTkZHx9vespmhhSswav1UBj2Cf71W7k6RKXUZWiCUlVSfn4+8fHxxMbFUVRUTLfo7gyOGYD38tfwinwKWur9TEq5O01QqkopLi5m06ZNrF69muzsbFrUzGPQ+W/JbX0H/gEBMOY9V4eolHLQZROUiMzD3muvTMaYMY4cQEQ8gUTgqDFm9FVHqJQDjDHs2rWLZcuWk5GRTm1fwy+95hKSdQhbz4fwCGnk6hCVUlepvBrU362f44BGwDTr9d3Y741y1DPATqDW1QanlCMOHz7M0qVLSU1NpX5QECO81tEjLwFpORSGf41HcGtXh6iUugaXTVDGmNUAIvKmMWZAqVXzRGSNIzsXkRBgFPAW8Pz1BKrUpdLS0li+fDm7d++mAC8GDR7OTX164BGbC41egNbDXB2iUuo6OHINKlhEIowxBwBEJBwIdnD//we8DFy2H6+IPAw8DNC8eXMHd6uqs/Pnz7Nq1Sp7zzxvb/o1LqDPsY84U6c/Hh4eMOAlV4eolLoBHElQzwGrROSA9ToMeORKbxKR0cApY0ySiMRcbjtjzBRgCkB0dPRlr3kplZubS1xcHOvXr6ewqJi6NQy/Ml/if+wYdL2PGmHtXB2iUuoGcuRG3UUi0gpoay3aZYzJd2DffYExIjIS+1h+tURkmjHmF9cerqqOCgsLWb9+PbFxceTn5dGpY0ciD/6HNpnrMM17w80zoHFnV4eplLrByuvFN+4yq1qICMaY78rbsTHmFeAVa18xwIuanNTVsNlsbN68mdWrV5OZmUkGAdw25m5iurSGxHPg+0ukw3gQnaJMqaqovBrULeWsM0C5CUqpa2WM4aeffmLlypWkp6cT0rQJY8LyaL7tr2TkNAZaQ/QkV4eplHKy8nrxTRIRD+B2Y8ys6zmIMWYVOsisugJjDPv372fFihUcP34cm29N/Py8mZT5LzxSD0PkWBpF9nN1mEqpClLuNShjjE1EngSuK0EpdSWpqaksX76clJQU6tSpw6233krtjf8gLHUOpmYkTJwH4QOuvCOlVJXhSC++pSLyIjATyC5ZaIzJcFpUqto4efIkK1asYM+ePfjV8GefNOHOvjfRuXNL8J0A5/sj3SaBp47KpVR148hv/YPWzydKLTNAxI0PR1UX6enprFq1iu3bt+Pr58egm2Lo7rkT24o/cO5kGvAqtB3l6jCVUi7kSDfz8IoIRFUP586dY/Xq1SQnJ+Pl5UWNZu05fuIIfbf9Go+0nRA+AP/oW10dplLKDVwxQYmIN/AYUHIBYBUw2RhT6MS4VBWTmZlJbGwsSUlJAHSL7s7AAf05u+htQg5/iK0wFCZMg7ajtdu4UgpwrInv34A38JH1+j5r2UPOCkpVHTk5OcTFxbFhwwaKi4vp0Kkzsw4IjbwaEhgYSGCvcdCwPh69HgdvP1eHq5RyI44kqO7GmNK36a8QkS3OCkhVDXl5ecTHx5OQkEBBQQGR7TswOGYg9Q4vZOj21zl3fAjwHwiJtj+UUuoSjiSoYhFpYYzZDyAiEUCxc8NSlVV+fj4JCQnEx8eTn59Pu3btsDWKZPGq5dx66lY4tY2aIT2oOfhRV4eqlHJzjiSol4CV1mCxAoQCehu/ukhBQQEbNmxg3bp15Obm0rp1a3r3G0BYs6akL/8nd9lepzinCYz/L+jwREopB5Q3Ft+zQBywGmgFtMGeoBwdLFZVA4WFhWzcuJG4uDhycnJo2bIlAwYO5DdzdpESt4tX7mpKULdx4JmHZ5+nwMff1SErpSqJ8mpQIcA/sY9ivhVYhz1hHQE0QVVzhYWFJCYmEhcXR3Z2NhEREUT37ke7iOaQPJ3/nH2DLGkLDIY6zSDm164OWSlVyZQ3Ft+LACLiA0QDfbDftPsfETlrjImsmBCVOyksLCQpKYm4uDiysrIIDw8nJiaG/bl+vPPFF3wc/B1+6Tvwb9YT/+FvujpcpVQl5sg1qBpALaC29TgGbHNmUMr9XJqYQkNDGXvbOGoHNyG4pi9BG6dzk8ebFBWGwO2fQvtxep1JKXVdyrsGNQVoD2QC67E38b1rjDlTQbEpN3BpYgoLC2P8+PGEhobywIcLaeqZwNuP3U1ApzFQmIFX94fAu4arw1ZKVQHl1aCaA77AXuAokAqcrYCYlBsoKCggKSmJdevWXZSYsrzrEtrAB1n3Lz45+zfyAxpjbBMQ35rQ5ylXh62UqkLKuwY1QkQEey2qD/AC0EFEMoB4Y8zvKyhGVYEKCgrYuHEj69atIycnh/DwcG6//XZCQ0NZuuM486a/wzt151AjOxXvlkPxHvYmeHi4OmylVBV0pfmgDLBdRM4C56zHaKAHoAmqCsnPz2fDhg3Ex8eTm5tLixYtGDBgAATUJz07n1AgxpbAUJ8PsQV2hHHvQ4tBrg5bKVWFlXcN6mnsNae+QCH2LubxwKdoJ4kqIzc3l/Xr17N+/Xry8vJo1aoVAwYMICQkBGMMT/7jcxrLaaKefxHv9mPA80s82o7WWpNSyunKq0GFAd8CzxljjldMOKqiZGdnEx8fz8aNGykoKKBt27b079+fmvWCmbHhMJN89uC95i98mDWborotEfMCeHhC5BhXh66UqibKuwb1fEUGoirG+fPnWbduHUlJSRQVFdG+fXv69+9Pw4YNAViXtIVaS17Ha+Va8PKD/i/g1edprTEppSqczqNdTZw5c4bY2Fi2bNmCzWajU6dO9OvXj6CgIL5NSkVSU7m9Wwi9656ll886pPvD0P95CGzg6tCVUtWUJqgqLi0tjdjYWLZt24aHhwdRUVH07duXunXr2jfIPEngqtexeXhDtylIxEDk+R2amJRSLqcJqoo6evQosbGx7Nq1C29vb3r16kXv3r2pWbMm21LP8btvfuQfjVfhu2UqI4oLoev9YIx99AdNTkopN6AJqgoxxnDw4EFiY2M5ePAgfn5+DBgwgJ49e+Lv74/9rgGod3AO/zj2Aj7Hi6HzXUj/FyCohYujV0qpi2mCqgKMMezatYu4uDiOHj1KYGAgQ4cOpVu3bvj6+mKM4S/TFhDo58WTtw+nadte2E7fjgx4UROTUsptaYKqxIqKiti2bRtxcXGkp6dTt25dRo8eTefOnfHy8iK/qBhObEdi3+Xlfd+zJ2gwMByCW+Nx279dHb5SSpVLE1QllJ+fT1JSEgkJCWRmZtKoUSPGjx9PZGQkHlZ38OTYhWQte4d+bAafQDz6PkXbXk+4OHKllHKcJqhKJCsri/Xr17Nx40by8/MJDw9n7NixREREICIUFhZwNsdGvUA/Wp1fR7FnCmeiX6ZuzONQo66rw1dKqauiCaoSOH36NPHx8WzZsoXi4mIiIyPp06cPTZs2tW+Qn4XZPI3TS97j2wZP8dSjTxIw6CUY+qpOfaGUqrQ0QbmxI0eOsG7dOnbt2oWnpydRUVH06dOHevXqAXD88F7Slr9PpxM/IPnn8K7Tmb6RYfY3+9VyXeBKKXUDaIJyMzabjd27dxMfH8+RI0fw8/Ojf//+9OjRg8DAQPKLiikosuHjKQTMuI0GOalktxpNwMBnqN+sO/VdXQCllLpBNEG5icLCQrZs2UJ8fDwZGRnUqVOHESNG0KVLF3x8fKC4kONx09m95BPyb/uc4VGheI/7kNO+jWnYvLWrw1dKqRvOaQlKRPyANdhn5fUCvtVJDn8uKyuLjRs3kpiYSE5ODk2aNOH222+nXbt2FNnguc+W84DvKrqnfUfjzGP4+IWQ7n0aCKVGq4HoFSalVFXlzBpUPjDIGJMlIt5ArIgsNMYkOPGYlcapU6dISEhg69atFBcX06ZNG3r16kXCKSH2dBHtPTzwObOf947dg48pgIgYuOX/CGo5lCAdWVwpVQ04LUFZs/FmWS+9rYdx1vEqA2MMBw4cICEhgX379uHl5UWnzlEENmvLoKiWUFTA6iX/wifrNAz8C9SLwGfgCxA5Fhq0c3X4SilVoZx6DUpEPIEkoCXwoTFmfRnbPAw8DNC8eXNnhuMyhYWFbNu2jYSEBNLS0ggMDOSmm24iOjqaT+KP8tXMeJYcm06tn6YzMeskNOoENpt9DqaY37g6fKWUcgmnJihjTDEQJSJ1gO9FpIMxZvsl20wBpgBER0dXqRpWZmYmiYmJF64vNWrUiP5DbuaL3cXEhLbF39+fid7LedLvVWSDDVoOhh4fQMshOkGgUqraq5BefMaYsyKyChgBbL/C5pXesWPHWL9+Pdu3b8dms9GiZStadehKj05tyD6Xzs41f6bgyCgIHUTt1v0g7wmIngT1IlwdulJKuQ1n9uILBgqt5FQDGAK846zjuZrNZmPXrl0kJCRw5MgRfHx8iI6OpmfPnvzii+10XbOOngf+SeCO73iiKA8K6wODoFEH+0MppdRFnFmDagxMta5DeQCzjDHznXg8l8jJyWHTpk1s3LiR8+fPU7duXdr36M+m7DoMH94FDw9hms9b1DuVAJmBEHUPdJsEjTu5OnSllHJrzuzFtxXo4qz9u9qJEycuNOMVFRXRPDSMIcOG075tG+JWzidyx385EvMxofVrUq/rbeA1ATreAb41XR26UkpVCjqSxFUoLi5m165dbNiwgcOHD+Pl5UXnzp1p1roTT01bRxe/b/FYMZf+6Xvp5xOIcAKoCb0edXXoSilV6WiCckBWVhZJSUkkJSWRmZlJnTp1aNSuB7VCWjG6T0vMkY2s9Hwcz13F0Kwn9PsQaX8b+AS4OnSllKq0NEFdhjGGI0eOkJiYyI4dO7DZbISEhjN69Gha1ixk8Vfvkn40CPr8FWkShWf/5+1NeMFtXB26UkpVCZqgLlFQUMC2bdvYuHEjJ0+exNfXl+joaA7l+3Fi03e0yP0PHie3MsLDy97ZAcDTGwa95trAlVKqitEEZTl9+jSJiYkkJyeTn59P3aBgchq0Z8KYwbRtWpfzX9xLLY/5FEtnGPEO0vF2CNDJLZRSylmqdYIq6fSQmJhISkoKHh4eNItoxaBWNal/eAFF239gy5HZtG3am1ojXgPzWzwbRro6bKWUqhaqZYI6d+4cSUlJbN68maysLGrXrk1Mv57sXjuLWw5+SdDek+AdgK3jaPq3DLa/SQdrVUqpClVtEpTNZmPfvn0kJSWxd+9ejDHgX5dM/4b87umH8cg/R7/4BygO6Q1d34K2I/HQXnhKKeUyVT5BZWZmsmnTJjZt2sT58+cp9vBmQERNonPXUPvoSk7V6gg8AjXq4vnSXjz1RlqllHILVTJB2Ww29u/fT2z8Bg4f3A8YIiIiiApMo/fRD/DbVwBBLSHmFRp2vB08xP5GTU5KKeU2qlSCOpGWwazFa8k7sZ/c7Ez8fLwIsh2n1bBfMrxvNGz9Bo49ZL9fqUkXEHF1yEoppS6jSiWo776ZyZm0U9T3LWS03wba5G7Ew9cfaXK3fYNOd9gfSiml3F6VSlC3xHQncNY46uRnIS0HQ6f/QJuR4OPv6tCUUkpdpSqVoJpFRsO4f0DEQAhs4OpwlFJKXYcqlaAAbcJTSqkqwsPVASillFJl0QSllFLKLWmCUkop5ZY0QSmllHJLmqCUUkq5JU1QSiml3JImKKWUUm5JE5RSSim3JMYYV8dwgYikAYeuczf1gdM3IBx3omVyf1WtPKBlqiyqQplCjTHBly50qwR1I4hIojEm2tVx3EhaJvdX1coDWqbKoiqWqYQ28SmllHJLmqCUUkq5paqYoKa4OgAn0DK5v6pWHtAyVRZVsUxAFbwGpZRSqmqoijUopZRSVYAmKKWUUm7J7ROUiHwqIqdEZHupZZ1FJF5EtonIPBGpZS0PE5FcEUm2Hh+Xek83a/t9IvIvERFXlMeKxeEyWes6Wet2WOv9rOWVskwicm+pzyhZRGwiEmWtq6xl8haRqdbynSLySqn3uEWZrrI8PiLymbV8i4jElHqPW5THiqWZiKy0zvkOEXnGWl5PRJaKyF7rZ91S73nFin23iAwvtdwtynW1ZRKRIGv7LBH54JJ9uUWZrpkxxq0fwACgK7C91LKNwEDr+YPAm9bzsNLbXbKfDUBvQICFwM2VpExewFags/U6CPCszGW65H0dgQNV4HO6B/jaeu4PpABh7lSmqyzPE8Bn1vMGQBLg4U7lsWJpDHS1ntcE9gCRwF+B31jLfwO8Yz2PBLYAvkA4sN/dfp+uoUwBQD/gUeCDS/blFmW61ofb16CMMWuAjEsWtwHWWM+XAuPL24eINAZqGWPijf1T+wK49QaH6rCrLNMwYKsxZov13nRjTHElL1NpdwMzoNJ/TgYIEBEvoAZQAJx3pzJdZXkigeXW+04BZ4FodyqPFdtxY8wm63kmsBNoCowFplqbTeV/MY7F/o9EvjHmILAP6OFO5braMhljso0xsUBe6f24U5muldsnqMvYDoyxnt8BNCu1LlxENovIahHpby1rCqSW2ibVWuZOLlem1oARkcUisklEXraWV+YylTYBK0FRucv0LZANHAcOA383xmTg/mW6XHm2AGNFxEtEwoFu1jq3LY+IhAFdgPVAQ2PMcbD/wcdeCwR7rEdKva0kfrcsl4Nluhy3LNPVqKwJ6kHgCRFJwl4FLrCWHweaG2O6AM8DX1lt6mW1u7pb//rLlckLe/X9XuvnbSIymMpdJgBEpCeQY4wpuSZSmcvUAygGmmBvOnpBRCJw/zJdrjyfYv+Dlgj8H7AOKMJNyyMigcBs4FljzPnyNi1jmSlnuctcRZkuu4sylrn8s7oaXq4O4FoYY3Zhb/pCRFoDo6zl+UC+9TxJRPZjr4GkAiGldhECHKvImK/kcmXCHvtqY8xpa92P2K8jTKPylqnEXfyv9gSV+3O6B1hkjCkETolIHBANrMWNy1TO71IR8FzJdiKyDtgLnMHNyiMi3tj/kE83xnxnLT4pIo2NMcetpq5T1vJULq7Jl8TvVt+9qyzT5bhVma5FpaxBiUgD66cH8BrwsfU6WEQ8recRQCvsF+CPA5ki0svqxXI/MMclwV/G5coELAY6iYi/dX1jIPBTJS9TybI7gK9LllXyMh0GBoldANAL2OXuZSrnd8nfKgciMhQoMsa43ffOiuG/wE5jzLulVs0FJlrPJ/K/GOcCd4mIr9V02QrY4E7luoYylcmdynTNXN1L40oP7P9hHwcKsf9H8EvgGew9W/YAf+F/I2KMB3Zgbz/fBNxSaj/R2Nvb9wMflLzH3ctkbf8Lq1zbgb9WkTLFAAll7KdSlgkIBL6xPqefgJfcrUxXWZ4wYDf2C/TLsE+H4FblsWLph73ZaiuQbD1GYu/tuhx7rW85UK/Ue35rxb6bUr3a3KVc11imFOwdYLKszzbSncp0rQ8d6kgppZRbqpRNfEoppao+TVBKKaXckiYopZRSbkkTlFJKKbekCUoppZRb0gSllBNZ90XFisjNpZbdKSKLXBmXUpWBdjNXyslEpAP2e6S6AJ7Y72sZYYzZfw378jTGFN/YCJVyT5qglKoAIvJX7IPJBlg/Q7FPNeIFvGGMmWMNDPqltQ3Ak8aYdWKfi+n32G+yjTLGRFZs9Eq5hiYopSqANWzQJuyDsc4HdhhjpolIHexz9nTBPnqAzRiTJyKtgBnGmGgrQS0AOhj7FBFKVQuVcrBYpSobY0y2iMzEPhTNncAtIvKitdoPaI59IM8PxD67cDH2gY5LbNDkpKobTVBKVRyb9RBgvDFmd+mVIvIGcBLojL0DU+kJ6LIrKEal3Ib24lOq4i0GnrJGmEZEuljLawPHjTE24D7sHSqUqrY0QSlV8d4EvIGtIrLdeg3wETBRRBKwN+9prUlVa9pJQimllFvSGpRSSim3pAlKKaWUW9IEpZRSyi1pglJKKeWWNEEppZRyS5qglFJKuSVNUEoppdzS/wNnUETn2UvetQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results = run_simulation(system, growth_func_quad2)\n",
+    "plot_results(results, 'Quadratic model, alternate parameters')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "early-mortgage",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  What happens if we start with an initial population above the carrying capacity, like 20 billion?  Run the model with initial populations between 1 and 20 billion, and plot the results on the same axes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "domestic-ukraine",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "p0_array = linspace(1, 25, 11)\n",
+    "\n",
+    "for p_0 in p0_array:\n",
+    "    system.p_0 = p_0\n",
+    "    results = run_simulation(system, growth_func_quad)\n",
+    "    plot(results, label='_nolegend')\n",
+    "\n",
+    "decorate(xlabel='Year',\n",
+    "         ylabel='Population (billions)',\n",
+    "         title='Projections with hypothetical starting populations')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "commercial-respondent",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/chap08.ipynb b/jupyter/chap08.ipynb
new file mode 100644
index 00000000..49664fb6
--- /dev/null
+++ b/jupyter/chap08.ipynb
@@ -0,0 +1,1049 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "sophisticated-translation",
+   "metadata": {},
+   "source": [
+    "# Chapter 8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "amino-creation",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "korean-spine",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    import pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n",
+    "    from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "rental-constitution",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we developed a quadratic model of world\n",
+    "population growth from 1950 to 2016. It is a simple model, but it fits\n",
+    "the data well and the mechanisms it's based on are plausible.\n",
+    "\n",
+    "In this chapter we'll use the quadratic model to generate projections of future growth, and compare our results to projections from actual\n",
+    "demographers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ambient-concentration",
+   "metadata": {},
+   "source": [
+    "Here's the code that downloads the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "portable-heather",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates.html'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "brave-championship",
+   "metadata": {},
+   "source": [
+    "And reads `table2`, which contains world populations estimates from the U.S. Census and U.N. DESA, among other organizations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "apart-waste",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_html\n",
+    "\n",
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "powerful-heather",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table2 = tables[2]\n",
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pending-alliance",
+   "metadata": {},
+   "source": [
+    "## Generating Projections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "colored-framework",
+   "metadata": {},
+   "source": [
+    "Now let's run the quadratic model, extending the results until 2100, and see how our projections compare to the professionals'.\n",
+    "\n",
+    "Here's the code we'll need from the previous chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "acute-compiler",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import TimeSeries\n",
+    "\n",
+    "def run_simulation(system, growth_func):\n",
+    "    \"\"\"Simulate the system using any update function.\n",
+    "    \n",
+    "    system: System object\n",
+    "    growth_func: function that computes the population next year\n",
+    "    \n",
+    "    returns: TimeSeries\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.p_0\n",
+    "    \n",
+    "    for t in range(system.t_0, system.t_end):\n",
+    "        growth = growth_func(results[t], t, system)\n",
+    "        results[t+1] = results[t] + growth\n",
+    "        \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "northern-seafood",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def growth_func_quad(pop, t, system):\n",
+    "    return system.alpha * pop + system.beta * pop**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "stuck-influence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census = table2.census / 1e9\n",
+    "un = table2.un / 1e9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "covered-suicide",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import System\n",
+    "\n",
+    "t_0 = census.index[0]\n",
+    "p_0 = census[t_0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "occasional-denmark",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = System(t_0=t_0,\n",
+    "                p_0=p_0,\n",
+    "                t_end=2100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "available-microwave",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.alpha = 25 / 1000\n",
+    "system.beta = -1.8 / 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "arranged-sailing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, growth_func_quad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "generous-renewal",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Quantity</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Time</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2096</th>\n",
+       "      <td>12.462519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2097</th>\n",
+       "      <td>12.494516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2098</th>\n",
+       "      <td>12.525875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2099</th>\n",
+       "      <td>12.556607</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2100</th>\n",
+       "      <td>12.586719</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Quantity\n",
+       "Time           \n",
+       "2096  12.462519\n",
+       "2097  12.494516\n",
+       "2098  12.525875\n",
+       "2099  12.556607\n",
+       "2100  12.586719"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import show\n",
+    "\n",
+    "show(results.tail())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "promising-fantasy",
+   "metadata": {},
+   "source": [
+    "And here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "prostate-brush",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import plot\n",
+    "\n",
+    "plot(results, color='gray', label='model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "killing-relief",
+   "metadata": {},
+   "source": [
+    "According to the model, population growth will slow gradually after 2020, approaching 12.5 billion by 2100.\n",
+    "\n",
+    "I am using the word \"projection\" deliberately, rather than\n",
+    "\"prediction\", with the following distinction: \"prediction\" implies\n",
+    "something like \"this is what we should reasonably expect to happen, at\n",
+    "least approximately\"; \"projection\" implies something like \"if this\n",
+    "model is actually a good description of what is happening in this\n",
+    "system, and if nothing in the future causes the parameters of the model to change, this is what would happen.\"\n",
+    "\n",
+    "Using \"projection\" leaves open the possibility that there are important things in the real world that are not captured in the model. It also suggests that, even if the model is good, the parameters we estimate based on the past might be different in the future.\n",
+    "\n",
+    "The quadratic model we've been working with is based on the assumption\n",
+    "that population growth is limited by the availability of resources; in\n",
+    "that scenario, as the population approaches carrying capacity, birth\n",
+    "rates fall and death rates rise because resources become scarce.\n",
+    "\n",
+    "If that assumption is valid, we might be able to use actual population\n",
+    "growth to estimate carrying capacity, especially if we observe the\n",
+    "transition into the regime where the growth rate starts to fall.\n",
+    "\n",
+    "But in the case of world population growth, those conditions don't\n",
+    "apply. Over the last 50 years, the net growth rate has leveled off, but not yet started to fall, so we don't have enough data to make a credible estimate of carrying capacity. And resource limitations are probably *not* the primary reason growth has slowed. As evidence, consider:\n",
+    "\n",
+    "-   First, the death rate is not increasing; rather, it has declined\n",
+    "    from 1.9% in 1950 to 0.8% now (see <http://modsimpy.com/mortality>).\n",
+    "    So the decrease in net growth is due entirely to declining birth\n",
+    "    rates.\n",
+    "\n",
+    "-   Second, the relationship between resources and birth rate is the\n",
+    "    opposite of what the model assumes; as nations develop and people\n",
+    "    become more wealthy, birth rates tend to fall.\n",
+    "\n",
+    "We should not take too seriously the idea that this model can estimate\n",
+    "carrying capacity. But the predictions of a model can be credible even\n",
+    "if the assumptions of the model are not strictly true. For example,\n",
+    "population growth might behave *as if* it is resource limited, even if\n",
+    "the actual mechanism is something else.\n",
+    "\n",
+    "In fact, demographers who study population growth often use models\n",
+    "similar to ours. In the next section, we'll compare our projections to\n",
+    "theirs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opposed-folks",
+   "metadata": {},
+   "source": [
+    "## Projections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "advisory-sheffield",
+   "metadata": {},
+   "source": [
+    "From the same page where we got the past population estimates, we'll read `table3`, which contains predictions for population growth over the next 50-100 years, generated by the U.S. Census, U.N. DESA, and the Population Reference Bureau."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "spoken-calculation",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>United States Census Bureau (2015)[28]</th>\n",
+       "      <th>Population Reference Bureau (1973-2015)[15]</th>\n",
+       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2016</th>\n",
+       "      <td>7.334772e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.432663e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017</th>\n",
+       "      <td>7.412779e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018</th>\n",
+       "      <td>7.490428e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019</th>\n",
+       "      <td>7.567403e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020</th>\n",
+       "      <td>7.643402e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.758157e+09</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      United States Census Bureau (2015)[28]  \\\n",
+       "Year                                           \n",
+       "2016                            7.334772e+09   \n",
+       "2017                            7.412779e+09   \n",
+       "2018                            7.490428e+09   \n",
+       "2019                            7.567403e+09   \n",
+       "2020                            7.643402e+09   \n",
+       "\n",
+       "      Population Reference Bureau (1973-2015)[15]  \\\n",
+       "Year                                                \n",
+       "2016                                          NaN   \n",
+       "2017                                          NaN   \n",
+       "2018                                          NaN   \n",
+       "2019                                          NaN   \n",
+       "2020                                          NaN   \n",
+       "\n",
+       "      United Nations Department of Economic and Social Affairs (2015)[16]  \n",
+       "Year                                                                       \n",
+       "2016                                       7.432663e+09                    \n",
+       "2017                                                NaN                    \n",
+       "2018                                                NaN                    \n",
+       "2019                                                NaN                    \n",
+       "2020                                       7.758157e+09                    "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table3 = tables[3]\n",
+    "table3.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hispanic-episode",
+   "metadata": {},
+   "source": [
+    "Some values are `NaN`, which indicates missing data, because some organizations did not publish projections for some years.\n",
+    "\n",
+    "The column names are long strings; for convenience, I'll replace them with abbreviations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "corrected-denial",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table3.columns = ['census', 'prb', 'un']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "explicit-tiffany",
+   "metadata": {},
+   "source": [
+    "The following function plots projections from the U.N. DESA and U.S. Census.  It uses `dropna` to remove the `NaN` values from each series before plotting it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "lyric-cinema",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import plot, decorate\n",
+    "\n",
+    "def plot_projections(table):\n",
+    "    \"\"\"Plot world population projections.\n",
+    "    \n",
+    "    table: DataFrame with columns 'un' and 'census'\n",
+    "    \"\"\"\n",
+    "    census_proj = table.census / 1e9\n",
+    "    un_proj = table.un / 1e9\n",
+    "    prb_proj = table.prb / 1e9\n",
+    "    \n",
+    "    plot(census_proj.dropna(), style=':', label='US Census')\n",
+    "    plot(un_proj.dropna(), style='--', label='UN DESA')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "conscious-method",
+   "metadata": {},
+   "source": [
+    "Here are their projections compared to the results of the logistic model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "neutral-material",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_projections(table3)\n",
+    "plot(results, color='gray', label='model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "rough-patent",
+   "metadata": {},
+   "source": [
+    "The U.N. DESA expects the world population to reach 11 billion around 2100, and then level off.\n",
+    "Projections by U.S. Census are a little lower, and they only go until 2050.\n",
+    "\n",
+    "Real demographers expect world population to grow more slowly than our model projects, probably because their models are broken down by region and country, where conditions are different, and they take into account expected economic development.\n",
+    "\n",
+    "Nevertheless, their projections are qualitatively similar to ours, and\n",
+    "theirs differ from each other almost as much as they differ from ours.\n",
+    "So the results from this model, simple as it is, are not entirely unreasonable.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "rising-procurement",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "\n",
+    "You might be interested in this [video by Hans Rosling about the demographic changes we expect in this century](https://www.youtube.com/watch?v=ezVk1ahRF78)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "complex-forwarding",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "**Exercise:** The net growth rate of world population has been declining for several decades.  That observation suggests one more way to generate more realistic projections, by extrapolating observed changes in growth rate.\n",
+    "\n",
+    "To compute past growth rates, we'll use a function called `diff`, which computes the difference between successive elements in a `Series`.  For example, here are the changes from one year to the next in `census`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "involved-delivery",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950         NaN\n",
+       "1951    0.037311\n",
+       "1952    0.041832\n",
+       "1953    0.045281\n",
+       "1954    0.048175\n",
+       "Name: census, dtype: float64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diff = census.diff()\n",
+    "diff.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "executed-cheat",
+   "metadata": {},
+   "source": [
+    "The first element is `NaN` because we don't have the data for 1945, so we can't compute the first difference.\n",
+    "\n",
+    "If we divide these differences by the populations, the result is an estimate of the growth rate during each year: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "promotional-poker",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950         NaN\n",
+       "1951    0.014378\n",
+       "1952    0.015865\n",
+       "1953    0.016883\n",
+       "1954    0.017645\n",
+       "Name: census, dtype: float64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "alpha = census.diff() / census\n",
+    "alpha.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "incoming-tribute",
+   "metadata": {},
+   "source": [
+    "The following function computes and plots the growth rates for the `census` and `un` estimates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "historical-program",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_alpha():\n",
+    "    alpha_census = census.diff() / census\n",
+    "    plot(alpha_census, style='.', label='US Census')\n",
+    "\n",
+    "    alpha_un = un.diff() / un\n",
+    "    plot(alpha_un, style='.', label='UN DESA')\n",
+    "\n",
+    "    decorate(xlabel='Year', label='Net growth rate')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "horizontal-consensus",
+   "metadata": {},
+   "source": [
+    "And here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "rough-manual",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_alpha()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acute-ratio",
+   "metadata": {},
+   "source": [
+    "Other than a bump around 1990, net growth rate has been declining roughly linearly since 1970.\n",
+    "\n",
+    "We can model the decline by fitting a line to this data and extrapolating into the future.\n",
+    "\n",
+    "Here's a function that takes a time stamp and computes a line that roughly fits the growth rates since 1970."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "color-pricing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def alpha_func(t):\n",
+    "    intercept = 0.02\n",
+    "    slope = -0.00021\n",
+    "    return intercept + slope * (t - 1970)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "duplicate-parliament",
+   "metadata": {},
+   "source": [
+    "To see what it looks like, I'll create an array of time stamps from 1960 to 2020 and use `alpha_func` to compute the corresponding growth rates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "approximate-calcium",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import linspace\n",
+    "\n",
+    "t_array = linspace(1960, 2020, 5)\n",
+    "alpha_array = alpha_func(t_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "transparent-madison",
+   "metadata": {},
+   "source": [
+    "To plot the results, I'll put them into a `Series` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "looking-yield",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import Series\n",
+    "\n",
+    "alpha_series = Series(alpha_array, index=t_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "graphic-persian",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like, compared to the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "focal-luther",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rV3Ly5EkeeOABatWqZQ9K+Zr1ktMv88qCRYSZw7yysTO/mTXNeRBUqpoTEZ577jleffVVAP7xj39w7do1/vjHP7o3Y26iAUo5VbBWlTdrPm1bXnNePeAXv/gFO3fuZOPGjZw9e5bIyEhatmx5271O7d/EW5b/wocsMkng0/3BTBo/8c4gqFQ1V6NGDZYtW8ZLL710xxJH1ZGO4lOuCw6H/s/fNnN+3+nvOXCrGf3GPIjVauXtt99m8+bNt82Z6ms9go9j4IQPWfS1HgHsQfCJwW01OCnlYLPZmDNnDv/617/uOJeens7QoUPp2rUrQ4cO5fTp04B9I8Onn36afv360bp1az7++OO8a1555RV69epF165d+cMf/lBp5SgvWoNSzp3ZfVttyZmCAx3eiXqYr7/cyZYtW0hLS2PChAk0aNCAwG4jyDnwb3KyM7HYfAjsNqKSC6NUyaxevZpvvvmmXO/ZvHlzRo0aVWy6J554gq5du/LCCy/cdvzJJ59k2rRpREVFERcXx9NPP83y5csB+15O27dv59ixY0RERDBp0iTWrl3LiRMn2L17N8YYIiIi2Lp1KwMGDCjXclUkl2pQIjJKRI6LSIqIvOjkvIjIa47zB0Wkh+N4sIhsEpGjInJYRJ7Jd00jEVknIiccj/o12lO4uM9NwYEOe89cZcKECUyYMIFz584RExPD0aNHITgcy/RPsAx9Gcv0T34OeLoKhVJ3qF+/PtOmTeO111677fiuXbt49NFHAZg6dSrbt2/POzd+/HgsFgudOnXi/PnzgH0H3LVr19K9e3d69OjBsWPHvG7ft2JrUCJiBeYBw4EMYI+IJBpjjuRLNhpo5/jpDbzheMwCnjfG7BORekCyiKxzXPsisMEY81dH0HsR+G05lk2VlrN9bpzUogob6NC1a1eCgoKIj4/nww8/JCwsjJEjR+KT/x6FrEKhlCdwpaZTkZ599ll69OiRt9yYM/lXJs+/YGvu+qrGGF566SXmzp1bcRmtYK7UoMKBFGNMqjHmFrAUGFcgzThgkbFLAhqKSIAx5pwxZh+AMeYqcBQIzHfNO47n7wDjy1YUVW4K2eem4Jp9uaP9nhvR4Y55TI0aNWLGjBn069eP5ORk3nzzzbxvdkC5b/amVFXSqFEjHnroIRYuXJh3rF+/fixduhSAJUuWON2GI7+RI0cSFxfHtWvXADh79mzejrrewpU+qEDgTL7XGdhrR8WlCQTO5R4QkVCgO/C541AzY8w5AGPMORFxupOWiMwB5gB3jA5TFcTJiL3CJtbeMdovH6vVyvDhw2ndujUJCQksWLCAESNG0LNnTyT09mHrZd3sTamq5vnnn+f111/Pe/3aa68xY8YMXnnlFfz9/XnrrbeKvH7EiBEcPXqUvn37Avb9phYvXlzspoWexJUA5WyHq4J7dBSZRkTqAvHAs8aYK65nD4wxsUAs2LfbKMm1qgwK7HNTlom1bdq04fHHH2f58uWsWrWKkydPEhERQe0CQVCp6i63tgPQrFkzrl+/nvc6NDSUjRs33nFN7rbszu7xzDPP8Mwzz+CtXGniywCC870OAr52NY2I+GAPTkuMMcvypTkvIgGONAGAd9U9q5nc/iarUKqJtXXq1OHRRx9l5MiRnDhxgpiYGNKym94xbF0HTiilcrkSoPYA7USklYj4Ao8AiQXSJALTHKP5+gA/OJrtBFgIHDXG/NPJNVGO51HAilKXQlW4ovqbXCUi9OnTh1mzZuHr68s777zDxo0bf54zdWY3OW8/QM6G/yLn7Qc0SClVzRXbxGeMyRKRJ4E1gBWIM8YcFpFox/kYYBUwBkgBrgO5Q0/uBaYCX4rIAcex3xljVgF/BT4UkZnAaeDBciuVqhBF9TeVREBAAHPmzOGzzz5j27ZtnDp1isjISH48sJZmWbewSQ5ZWbc4d2Atgdr0pyqRMea20XGq/JRm93bd8l25NCm3ohw6dIiVK1cC4N+8KY+e+q1jSSQbn3afz6TxEys1P6r6OnXqFPXq1aNx48YapMqZMYZLly5x9epVWrVqddu5orZ815Ukqjs3z0fq0qULgYGBLFu2jIz0M/yO5wjITmOfdOI33QdXWj6UCgoKIiMjg4sXL7o7K1VSzZo1CQoKKtE1GqCqOxcn5VYkPz8/pk+fzubNm9m+fTu36nRkzrDRukafqlQ+Pj53fLtX7qWLxVZ3hUzKrWxWq5WhQ4cybdo0atsMO1d9yOeff25vt9aRfUpVS9oHVQk8fmtzN/ZBOXP9+nVWrFjBV199Rbtgf8ad/Tt1cq7okkhKVUFF9UFpgKpgurV56Rhj2LNnD2vXfEatnGtMMJ/RWr6GIS/b504ppaqEogKUNvFVMGcrMMCd69qp24kI4eHhzHrgXmpyi3dlEuulP9kt73V31pRSlUQHSVQwZyt+a63Kdc27DWNOgzqsXr+RHV/3IG3dISZObE+jRo3cnTWlVAXTGlQFc7YCQ2G1qkrhhQMODlo6crrtw/QYNJpLly4xf/58Dh486O5sKaUqmNagKkHBFRgK20epwnnhHkwFa5sLHpnMid0bSEhIIDU1ldGjR9+2F45SqurQAOUGubWqSh/Z5wFznkqqYG3ziwu3eDwqiq2fvM/WA19w5tQJIh+eQosWLdydVaVUOdMmPjcJC/HjicFtK7fvyUPmPJWEs1XULWf3MujL54niI7KunGfhwgXs3LmzVGt9KaU8lw4zr248bM6TK+6YR7btVdj4ZzDZ/CS1+cT/CY5ezKZNmzaMHz+eunXrujvLSikX6TwoVbUU6Esz01aQfMHKmjVrqFGjBuPGjaNdu3buzqVSygUaoFTV46QmeOHCBeLj47lw4QJ9+vRh6NCh2GzazaqUJ9MAVYk8alkjL2zOK6vMzEzWrVvHnj17CAgIIDIyksaNK2mUpFKqxDRAVRKPmoDrhUPKy9OxY8dITEwkKyuLMWPGcM899+geP0p5IF3qqIIUXK7IrRNwC3I2pLwa6dixI9HR0bRo0YIVK1awbNkybty44e5sKaVKQBvoS8lZbcltE3CdyR1SnluD8oIh5SXhtCm1QJNm/fr1mTZtGtu3b2fz5s1kZGQQGRlZ4k3TlFLuUb0CVDn2yTirLT0xuK17JuDCnWULDrc361XBPiinTamWE06bNC0WCwMGDKBVq1bEx8cTFxfH4MGDuffee7FYtAFBKU9WfQJUOffJFFZbKrisUYUoGIwKK1vuTxXj7MtBmK3oVTKCg4OJjo5m5cqVbNy4kVOnTjFhwgTq1avnxpIopYpSfQJUOS/zU2nLFbkSjLxwCaOycPrlwFJ8k2bNmjWJjIykdevWrF69mjfeeIPx48fTvn17N5RCKVWc6hOgKqBPpsJrS64Goyre31SQ8y8HrjVpigg9evSgZcuWfPzxx7z//vuEh4czfPjwUs+Z8qipBUpVIdUnQHlDn0zB2pKrwcgbylbOnH45KEGTZpMmTZg1axbr16/n888/Jz09ncjISPz9/UuUD4+aWqBUFVN9AhR4dp+Ms9pSSYKRJ5fNQ9lsNkaNGkXr1q1ZsWIFsbGxjB49mu7du7s8Z8ppf1hlBahqOBFbVS/VK0B5EldqS/2f12BUCdq3b090dDQJCQl88sknnDx5kgceeICaNWsWe63u7aVUxdEA5Q6u1pZAg1FZuVjLqFevHlOnTmXHjh1s2rSJs2fPEhkZSXBwcJG3d9tgmWo2MEZVTy4FKBEZBfwvYAUWGGP+WuC8OM6PAa4D040x+xzn4oCxwAVjTJd813QDYoCaQBbwS2OM9+xDXhYlqS2p0ithLUNEuO+++/LmTL311lsMHDiQ/v37Fzlnyi2DZQr7QqPNfqoKKXamoohYgXnAaKATMFlEOhVINhpo5/iZA7yR79zbwCgnt/478CdjTDfg947XVdOZ3fY9jM444m9hGwcGh9sDlX6wlI9SLvcUGBjI3Llz6dKlC5s3b2bRokVcuXKlgjNbhMJqS1GJMOTlnwNvbiDb+Gf745nq8X1PVV2u1KDCgRRjTCqAiCwFxgFH8qUZBywy9pVnk0SkoYgEGGPOGWO2ikiok/saoL7jeQPg69IWwqMV9i1ea0sVrwzD72vUqMHEiRNp06YNq1atIiYmhoiICDp27FiBGXYoWAtytflXm/1UFeNKgAoEzuR7nQH0diFNIHCuiPs+C6wRkX9gr8n1c5ZIROZgr5XRsmVLF7LrYQr70CjnviWdi+NEOXwRuOeeewgKCiI+Pp4PPviAnj17MmLECHx8fCogw5TtC001mw+nqj5XApSz8bYF9+hwJU1BjwO/MsbEi8hDwEJg2B03MSYWiAX7dhvFZ9fDVMKHhs7FKUI5fBFo3LgxM2fOZMOGDezatYv09HQmTZpE06ZNyymT+ZTlC43WzFUV48pqmRlA/qFMQdzZHOdKmoKigGWO5x9hb0r0fgX7m5z1FZQzj9rmo4qyWq2MGDGCKVOmcP36dd5880327NlDmfdTc7V/0lXaj6mqEFdqUHuAdiLSCjgLPAI8WiBNIvCko3+qN/CDMaao5j2wB7CBwGZgCHCiBPkuP+U56slNi7Z61DYfbuRyM2cZ/s/btm1LdHQ0K1asYNWqVaSmphIREUGtWrVKnmHtn1SqSMUGKGNMlog8CazBPsw8zhhzWESiHedjgFXYh5inYB9m/lju9SLyPjAIaCIiGcAfjDELgdnA/4qIDbiBo5+pUpVgGLJLH35u6qSutLk4HszlZs5ymOBat25dHn30UZKSkli/fj0xMTFMmDCB0NDQkmW6kvonlfJWLs2DMsaswh6E8h+LyffcAE8Ucu3kQo5vB8JczmlFcDGguPzh58ZO6krZ5sODubzkUDl9iRAR+vbtS2hoKB9//DGLFi2if//+DBw4sPA5U66OzlNKAdV9JQkXPyAK/fAr500CdSRe6bnczFnOQSEgIIC5c+fy2WefsXXrVk6dOsXEiRNpePUr1/bsqozmPJ28q7yUlLmTtxL17NnT7N27t3xv6uyPt8Cx3BpU7odfUTu4lpaOxCu7yuiDKup+X2a3YeWuowg5PJD1KZ1zjt2+TcrGP9trbmK1D5rp/3zZ39uVvOmafcqDiUiyMaans3PVuwYFd7b3O/mDDgsJZ3mED5ePbMSv0xA6hvjBtvLtb3LrqthVhMvNnM76eFwNWkVsIHm31ZegCUuJX7uNj6+M5KRpwajsrfi6c88unbyrvJgGqIIKWR6n45pf2F+fiYPmRayFVko6Es+NCqtllGI3Y7/vknls4nA2vf0XdtCD0wQxqd49NHfX6Dzt51JeTANUQc7+oCthcVcdiedGha3ZV8rdjA/ktGNB1lC6kcI3Vn/e/GQXI27Ww9qsHZ9nNaJPTuPKGx2kw9aVF9MAVVBhf9CVsBVGdR+J5zaufilxcQPJpE0p7M5qS5JpS+2sTH7R4iKrV6/mbM4utt0K5d+2GuXSx+hyn5sOW1deSgOUMwX/oPVbaNXm6pcSF3czzt9cm2OrwYgHItm8fRfNU5KJqHGE7ZmtytzHqINqVHWgAcpV+i20anPypeTYyMU/D4wpwW7GzpprRfrxxPEb9LWkMNznKxpcrkd2diusVmupsquDalR1oAFKVWmlnVuWnH6ZKYmZ3Mq6F9+UTJY0vVyi6ws214aF+DFv1hB2nehMnYuHOPllMm999w0TJ06kUaNGJSoTFD6oxm1D7ZWqABqgVJVVWDOYKx/iFVFD+TlodeTw4bv45JNPmD9/PmPHjuXuu+8utiz58+ysllaZyz0pVRk0QKkqq7BV3l35EK/oYf+dO3cmMDCQZcuWsWzZMk6ePMno0aOpUaPGHWkLCzwFa2mVvdyTUhXNle02qp3k9MvM25RCcvpld2dFlUFukLEKeUHG1a1Jcmsoz43oUGEDEBo2bMj06dMZMGAABw8eJDY2lq+/vnOXGlfz7Ky8TpV1Sw+lKonWoArQ0VFVR2Fzy1ytGVXGsH+LxcLgwYNp1aoVy5YtY+HChXTo0Y8LtVrSt00TwkL8XK7NuTyXTkelKi+ha/EVMG9TCq+uPU6OAavAcyM68MTgthX6nqpyeeqivNevX2fRB/GcP53K2ez67DZtWDirv8v9Zkp5I12LrwR0yaGqz1nNyBMCQO3atckK6UPSySx62s4wii/ZurchYSEDylSb84SyKVUaGqAK0CWHqh9Patbt26YJr29qzoVb9Rjgm8qVQ5tYW+8GQ4cOLdWcKU8qm1IlpQHKCV1yqHrxpEmv+b8g9Wo5kIvHdrNr1y7S0tKIjIykceOS1eg9qWxKlZQGKFXteVqz7m1fkNrcT5s2bVixYgXz589nzJgx3HPPPYiIS/cq84RepdxIB0kohfs+sF193ytXrrBs2TLS09O5++67uf/++53OmXLlPbTZT3kSHSShVDHc0axbkkBRv359pk2bxvbt29m8eTMZGRlERkYSGBhY7PuUekKvUm6mE3WVchNXJ+DmslgsDBgwgOnTp5OTk0NcXBzbt2+npK0gLk/oVcrNtAallJuUtu+rZcuWREdH88knn7BhwwZOnTrF+PHjqVevnkvX60hV5S20D0qpEijvvqqy3M8Yw/79+1m9ejU+Pj6MGzeO9u3blz4zusK5coOi+qA0QCnlIk8dXPDtt9/y8ccfc/78ecLDwxk+fDg2WwkbR3SFc+UmRQUo7YNSykUl7TOqLE2aNGHWrFn07t2b3bt3s2DBAr799tuS3cTZCudKuZlLAUpERonIcRFJEZEXnZwXEXnNcf6giPTIdy5ORC6IyCEn1z3luO9hEfl72YqiVMXy5MEFNpuNUaNGMXnyZK5evUpsbCz79u1zfQCFrnCuPFCxTXwiYgW+AoYDGcAeYLIx5ki+NGOAp4AxQG/gf40xvR3nBgDXgEXGmC75rhkMvAzcb4y5KSJNjTEXisqLNvEpd/OGCa5Xr14lISGBU6dO0alTJx544AFq1qxZ/IXaB6XcoKzzoMKBFGNMquNmS4FxwJF8acZhD0AGSBKRhiISYIw5Z4zZKiKhTu77OPBXY8xNgOKCk1KewBuWwapXrx5Tp05lx44dbNy4kbNnzxIZGUlwcHDRFwaHa2BSHsWVJr5A4Ey+1xmOYyVNU1B7oL+IfC4iW0Skl7NEIjJHRPaKyN6LFy+6kF2llIhw3333MWPGDESEt956i61bt5KTk+PurCnlMlcClLNFvwq2C7qSpiAb4Af0AX4DfChOFhgzxsQaY3oaY3r6+/u7kN2S0d1zVVUWFBTE3Llz6dy5M5s2bWLRokVcuXLF3dlSyiWuBKgMIH/bQBBQcF9qV9I4u+8yY7cbyAGauJCfcpM7bPjVtceZsiBJg5SqkmrWrMnEiRMZN24cX3/9NTExMRw7dszd2VKqWK4EqD1AOxFpJSK+wCNAYoE0icA0x2i+PsAPxphzxdx3OTAEQETaA75ACcfGlo2nDhtWqryJCN26dWPu3Lk0bNiQDz74gE8//ZTMzEx3Z02pQhUboIwxWcCTwBrgKPChMeawiESLSLQj2SogFUgB3gR+mXu9iLwP7AI6iEiGiMx0nIoDWjuGny8Fokwlzxr25GHDSlWExo0bM3PmTPr27cvevXtZsGABFy7o+CTlmar9ShLeMGxYqYqQkpLC8uXLuXnzJiNHjiQsLOzOfaZ06LmqYLrUkVLKqWvXrrF8+XJOnjxJx44diYiIoFatWvaTuvyRqgS61JFSyqm6desyZcoUhg8fzldffUVMTAzp6en2k2nbMNk3wWRjdPkj5QYaoJSq5kSEfv36MXPmTGw2G++88w6bNm3iiG9XbuTYyDIWbuRYOVbzHndnVVUzuh+UUgqAc5m1oOMwAs9/ydatW7HV9+fLzBfpIcfZbe5i8LVQOro7k6pa0QCllCqwlUg9/j5kJF/u2kgb3xw+y+zPOWsTXtJRrqqSaROfUuqOOYFnaMIvH3+cpv5NGOSbym87/UjXFnXdnU1VzWiAUko5nRPo5+fHk9Gzuffeezn91WFiY2P55ptv3J1VVY3oMHOlFFD0nMDU1FQSEhL46aefGD58OOHh4XfOmVKqFHQelFKqzH788UdWrFjBiRMnaN++PePGjaN27druzpbycjoPSilVZnXq1GHy5MmM7H0XJ098xRuvv8apU6fcnS1VhWmAUkq5TDL20Gfv08wyS6jx0zcsWrSIDRs2kJ2d7e6sqSpIA5RSynVp2yD7Fs3NeebwHt0DbGzfvp233nqLy5d1uxpVvjRAKaVcF9rfvi6fWPG1WogYPZxJkybx7bffMn/+fL788kt351BVITpRVynluuBwjo1czOUjG/HrNISOweF0BgIDA1m2bBnLli0jNTWV0aNH4+vr6+7cKi+nAUop5bLk9MtMSczkVta9+KZksqTpZcJC/GjYsCHTp09ny5YtbN26ldOnTzNp0iQCAgLcnWXlxbSJTynlsqJ2obZYLAwePJioqCgyMzNZsGABu3btwpumsijPogFKKeUyV3ahDg0NJTo6mvbt27N27Vree+89rl275obcKm+nE3WVUiXi6i7Uxhj27t3LmjVrqFmzJhMmTKBNmzaVmFPlDXQlCaVUxSpia/jz588THx/PxYsX6devH0OGDMFqtbopo8rTFBWgdJCEUqpsitkavlmzZsyePZs1a9awc+dO0tLSmDhxIo0b6/YdqmjVqg8qOf0y8zalkJyuEwqVKjeOybuYbPujk63hfXx8GDt2LA899BDfffcdsbGxfPHFF27IbDV2Zjdse9X+6CWqTQ3q9g3ZLCyZ1afI9nOllItyJ+/m1qBC+xea9K677qJFixYsW7aM5cuXc/LkSe6//35q1KhRiRmuhoqp5XqqalODKmp4rFKqDByTd3eFRnNs5OJiP/gaNGhAVFQUgwYN4tChQ8yfP5+zZ89WUmarKRdquZ6o2gQoV4bHKqVKLjn9MuMTM5ly7F7GJ2a61IRusVgYOHAg06dPJycnh7i4OHbs2KFzpipKviWqiqvlepJq08QXFuLHkll9XBoeq5RynbPWCVf/vlq2bMncuXNZuXIl69evJzU1lfHjx1OvXr0KznU1Exxub9YrZKSlp9Jh5kqpMsnt383MysEnX/+uq/OlwD5nav/+/Xz22Wf4+voyfvx42rVrV0klUO5U5g0LRWSUiBwXkRQRedHJeRGR1xznD4pIj3zn4kTkgogcKuTevxYRIyJNXC2QUspz5LZOPDeiw23B6ZUFi7i+4e+8smBRsc1+IkKPHj2YM2cO9erV47333mP16tVkZWVVUimUJyq2iU9ErMA8YDiQAewRkURjzJF8yUYD7Rw/vYE3HI8AbwOvA4uc3DvYcd/TpS+CUsrdwkL8bqslndq/ibcs/4UPWWSSwKf7gwkLmVjsffz9/Zk1axbr1q3j888/Jz09ncjISJo00e+v1ZErNahwIMUYk2qMuQUsBcYVSDMOWGTskoCGIhIAYIzZCnxXyL3/BbwAeE87o1KqWH2tR/AhC5vk4EMWfa1Hir/IwWazMXr0aB555BF++OEHYmNj2b9/vw6gqIZcCVCBwJl8rzMcx0qa5jYiEgGcNcYUOVtPROaIyF4R2Xvx4kUXslu16ORi5Y0Cu43AYvMlBysWmy+B3UYAJft97tChA9HR0QQGBpKYmEh8fDw3btyo6KwrD+LKKD5xcqzgVxlX0vycWKQ28DIworg3N8bEArFgHyRRXPqqRCcXK68VHI5l+ie3jRorze9z/fr1mTp1Kjt27GDTpk1kZGQQGRlJcHBwJRVEuZMrNagMIP9vQxDwdSnS5NcGaAV8ISJpjvT7RKS5C/mpNnRysfJqweHQ//m8Ic2l/X22WCz079+fGTNmICK89dZbbN26lZycnIrMvfIArgSoPUA7EWklIr7AI0BigTSJwDTHaL4+wA/GmHOF3dAY86UxpqkxJtQYE4o9wPUwxnxTumJUTTq5WFUlZf19DgoKYu7cuXTu3JlNmzbx7rvvcuXKlQrKrfIELs2DEpExwP8AViDOGPNnEYkGMMbEiIhgH6k3CrgOPGaM2eu49n1gENAEOA/8wRizsMD904Cexphvi8pHdZwHVZK5JEp5uvL4fTbG8MUXX7Bq1SpsNhsRERF07NixnHOqKovuB6WU8gxF7BtVUpcuXeLjjz/mm2++oVevXgwfPhwfH59yyqiqLLoflFLK/cp5Re3GjRszc+ZMNmzYQFJSEunp6UyaNAl/f/9yzLRyp2qzWKxSys0qYEVtm83GyJEjmTJlCj/++COxsbHs3btX50xVERqglFKVo5AVtctjrl/btm2Jjo4mJCSETz/9lI8++oiffvqpvHKu3ET7oJRSladAH1R5z/UzxrBr1y42bNhA3bp1mThxIiEhIeVYAFXeyrxYrPIsurqE8lrlNDeqMCJCv379mDlzJjabjXfeeYfNmzfrnCkvpYMkvIyuLqGqkty5UblbdZTXXL8WLVowZ84cPvvsM7Zs2cKpU6eYOHEiDRo0KJf7q8qhNSgvo6tLqKokLMSP5RE+LO64g+URPuX6ZatGjRqMHz+eCRMm8M033xATE8ORI64vWqvcT2tQXqaivnEq5RZndtNxzS/so/rOxEHzsg09d6Zr164EBQURHx/PRx99RI8ePRg1apTOmfICGqC8jG5dr6oUZ0PPK2A78kaNGjFjxgw2bdrEjh07OH36NJMmTaJZs2bl/l6q/GgTnxcKC/HjicFtNTgp71eBQ88LslqtDBs2jKlTp3Ljxg3efPNNdu/erXOmPJgOM1dKuVcFDz135scff2TFihWcOHGC9u3bM27cOGrXrl2u76Fco8PMlVKeq4KHnjtTp04dJk+ezMiRIzl58iQxMTGcOnWq3N9HlY0GKKWUR+nTujHhthSesK2gly2lwgYCiQh9+vRh5syZ+Pr6smjRIjZs2EB2dnaFvJ8qOR0koZTyKGGWE7zn+xfHorIrsFj6AuEVtvVMQEAAc+bMYfXq1Wzfvp20tDQmTpyIn5/28bqb1qCUUp4lbRuWnEws5GDJyYS0bXn9Uq+uPc6UBUnlvoqKr68vERERREZGcvHiRebPn8+hQ4fK9T1UyWmAUkp5Ficj+yprgnqXLl2Ijo7G39+f+Ph4VqxYwa1btyrkvVTxtIlPKeVZgsPte0XlG9nXJ+cy4bYUwsxhkqUzfVr3q7C3b9iwIY899hibN29m27ZtnDlzhsjISAICAirsPZVzOsxcKeX5zuwm5+0H8jY7tEz/JG9IekVOWk9LS2PZsmVcv36dYcOG0bt3b0Sk3N+nOtMddZVS3s3RLwU5kNsvldOuwudLhYaGEh0dTWJiImvWrCE1NZVx48ZRp06dcn0f5Zz2QSmlPF8h/VKds48RbVlBl+xjef1S5b0KRe3atXn44YcZM2YMqampxMTEcPLkyXK5tyqaNvEppbxDgRUnju1ZT8jKyfiQRSY20se+z49Nwyq0VnX+/Hni4+O5ePEi/fr1Y8iQIVit1nK7f3WkK0kopbxfgRUnOt74gpqWLGySQ01LNh1vfFHhtapmzZoxe/ZswsLC2LlzJ3FxcXz33XdlLppyTvuglFLeKbQ/Yq0B2bcQR7Pf0G/SmOHzl59rVXXvJjm9cbnWqnx8fBg7dixt2rQhMTGR+fPnc//999O1a9dyLJwCrUEppbxV7nD0IS/bH4PDS1SrKqu77rqL6OhomjdvTkJCAgkJCdy8ebNc7q3stAallPJeweG37x/lYq0K2nJsz3ouH9mIX6chdOw1rFRv36BBA6Kioti6dStbt27NmzMVGBhYPuWr5lyqQYnIKBE5LiIpIvKik/MiIq85zh8UkR75zsWJyAUROVTgmldE5JgjfYKINCxzaZRS1ZuLtarcARa9Ut8gZOVkju1Zb7/+zG7Y9qr90UUWi4VBgwYxffp0srOziYuLY8eOHbrPVDkoNkCJiBWYB4wGOgGTRaRTgWSjgXaOnznAG/nOvQ2McnLrdUAXY0xX4CvgpZJmXiml7lBgMEVerUqsebWqy0c24oM9aPmQxeUjG/MmA+ds+C/7pODcIOVi0GrZsiXR0dF06NCB9evXs3jxYq5evVrBha3aXKlBhQMpxphUY8wtYCkwrkCaccAiY5cENBSRAABjzFbgjmEuxpi1xpgsx8skIKi0hVBKqUI5qVX5dRpCJjayjIVMbPh1GsLZA2vJybqFhRxysm5x9sDakgWtM7uptff/eLBPCGPHjuX06dPExMRw4sQJ95S7CnClDyoQOJPvdQbQ24U0gcA5F/MxA/jAxbRKKVUyBfqqOvYaxjHev60P6uPlV7gfGxh7X9Wu7E70PbCWZlm3sEkOWVm3OHdgLYFw57JLAO9E5PV9hUUl0nLOHD5+/13ee+89+nQOZej4Kdhs2u1fEq78azlbeKpg46oraZzfXORlIAtYUsj5OdibDWnZsqUrt1RKqWJ17DUM8g2OaNV9MI8l/0fegrS/6T6YXftxLWg1rIXJvomYHEz2LSRtG/6hMPuHV1lLOEmHIe2bfxP5yFSa/JR624RjVThXAlQGEJzvdRDwdSnS3EFEooCxwFBTSI+iMSYWiAX7ShIu5FcppUosLMSP38yaRlLqJX6Tt/isa0GrS836hOTY7CMFjZX0mvfQMW0b1pzrjDEbaS2nSfwhgtj5MYzOXkc386W9P8zR5FhwlQxl50qA2gO0E5FWwFngEeDRAmkSgSdFZCn25r8fjDFFNu+JyCjgt8BAY8z1EudcKaXKWViI322TeF0NWhtSL7Ex83f0lqPsNncx+Foo1CUvaIWY0wwb0JMv9+0n8fshpJoW3J+9iZpp2+xv5GgeJH/QUsUHKGNMlog8CawBrECcMeawiEQ7zscAq4AxQApwHXgs93oReR8YBDQRkQzgD8aYhcDrQA1gnWP5+iRjTHQ5lk0ppcrMtaAF/7Z25EBWe3xsFl5q3ZgNqdwetLI68Pj4Jmx7509sMb3IoAWRtbsQlLbNHpxMtv0xbZvWqhx0sVillCoHBfemyt2mPjMrBx/HEksAryxYRDdOcNXajBoWw+Ae7bh3/3NYcm7+XIOCalOr0v2glFKqgjmraS2Z1ee2oDVvUwq7s9qSZNpSMyuLqODLbEz+itSA3zGh9Q3qdxhoD0TbXtVaFRqglFKqwhQMWn1aN8bXZiEzKwdsvgwfE4H1cjqfffYZMd/bGBfcgA7w8/5XuTWo0P724FRNalW5NEAppVQlcVarIrQRwcHBxMfHs3TpUnr16sWIESOwRSXeXluqhrUq7YNSSikPkJWVxYYNG0hKSqJp06ZMmjQJf3//nxM4q0GB19eqdMNCpZTycDabjZEjR/Loo49y7do1YmNjSU5O/nnRWSdLNuFsBGAVogFKKaU8SLt27YiOjqZly5asXLmSjz76iJ9++sl+0slCuFh9Qaw/91VBqVZl90TaxKeUUh7IGMPOnTvZuHEjdevWJTIy0vlybwX7oLxsMIU28SmllJfZd/p7DtxqRr8xD2K1Wnn77bfZvHkzOTk5tycsWKuqQs1+OopPKaU8TO4k31tZOfjaLLwT9TBff7mTLVu2sGP/EQaOGMt9nQtZPNvZEHUvpTUopZTyMEmpl7iVlUOOgcysHPaeuUrLHoPYld2an364xGcfLWLllj2APZjN25RCcvpl+8XOBlN4Ka1BKaWUh8k/odfHZqFP68YkpV7iq8xGnKU2g3xTSd68iotfn+YfR2rwU5bg61hOKSzE7479r7yVBiillPIwTif0Ar42C9ezarIhpxO/62o4eSiZEZaabKY1V7Jqk5R66baVK7ydBiillPJArqzttzogiM1rV/FAjaPsz2lJ71Z9gTsXrvVWGqCUUspLFAxao/p1o34jfzav/ZSel9NJSVpL5s0BPLb4i7wBFnnNfl5IA5RSSnmxfh0D6dthNklJSaxfv54Tp07jlxPMOVOfzKycvGY/b6xVaYBSSikvJyL07duX0NBQliz9kJE3v+JLSwBHCKJP68Z3DFv3llqVDjNXSqkqIiAggKefeJyW7TrR1XaOxwPO0KaB3DFsPSn1EuBkiLqH0RqUUkpVIb6+vsyY8iCHDh1i5cqVxMTE0LnP4DuGrXtDrUoDlFJKVUFdunQhMDCQZcuWsW/Lal64qxPXm95N33bN8nb3LVir8rQApU18SilVRfn5+TF9+nTuu+8+zpw4AsfWE1jjJvDzZGCrkFer8jS6mrlSSlUDp06dIiEhgevXrzNs2DB69+7NvtPfu31kX1GrmWuAUkqpauL69eusWLGCr776inbt2jFu3Djq1Knj1jzpdhtKKaWoXbs2jzzyCKNHjyY1NZWYmBhSU1Pdna1CaYBSSik3q8zh3iJCeHg4s2fPpmbNmrz77rusX7+e7OzsCn/vktJRfEop5UbuGu7drFkz5syZw5o1a9ixYwdpaWlMnDiRRo0aVfh7u0prUEop5UaFTaKtDD4+PowdO5YHH3yQS5cuMX/+fA4ePFhp718clwKUiIwSkeMikiIiLzo5LyLymuP8QRHpke9cnIhcEJFDBa5pJCLrROSE49GzBuArpVQl8ITh3p06dSI6OprmzZuTkJDA8uXLuXnzZqXno6BiR/GJiBX4ChgOZAB7gMnGmCP50owBngLGAL2B/zXG9HacGwBcAxYZY7rku+bvwHfGmL86gp6fMea3ReVFR/EppaoiT1nINScnh61bt7J161b8/PyIjIykRYsWFfqeZR3FFw6kGGNSjTG3gKXAuAJpxmEPQMYYkwQ0FJEAAGPMVuA7J/cdB7zjeP4OMN6FvCilVJUTFuLHE4Pbun0lB4vFwqBBg4iKiiIrK4uFCxeyc+dO3DUdyZUAFQicyfc6w3GspGkKamaMOQfgeGzqLJGIzBGRvSKy9+LFiy5kVymlVFmEhIQQHR1Nhw4dWLduHUuWLOHatWuVng9XApQ4OVYwnLqSplSMMbHGmJ7GmJ7+/v7lcUullFLFqFWrFg8++CD3338/6enpxMTEkJKSUql5cCVAZQDB+V4HAV+XIk1B53ObAR2PF1zIi1JKqUoiIvTs2ZPZs2dTp04dlixZwpo1a8jKyqqU93clQO0B2olIKxHxBR4BEgukSQSmOUbz9QF+yG2+K0IiEOV4HgWsKEG+lVJKVZKmTZsya9YsevXqRVJSEnFxcVy6VPHD4YsNUMaYLOBJYA1wFPjQGHNYRKJFJNqRbBWQCqQAbwK/zL1eRN4HdgEdRCRDRGY6Tv0VGC4iJ7CPEPxrOZVJKaVUOfPx8WHMmDE8/PDDfP/998yfP58DBw5U6HvqYrFKKaVK5MqVKyQkJBAcHMyQIUPKdK+ihpnrUkdKKaVKpH79+kydOrXC30cDlFJKqRKzWCp+pTxdi08ppZRH0gCllFLKI2mAUkop5ZE0QCmllPJIGqCUUkp5JA1QSimlPJIGKKWUUh5JA5RSSimP5FVLHYnIRSC9BJc0Ab6toOxUJi2HZ9FyeBYth2cpaTlCjDFO91LyqgBVUiKyt7A1nryJlsOzaDk8i5bDs5RnObSJTymllEfSAKWUUsojVfUAFevuDJQTLYdn0XJ4Fi2HZym3clTpPiillFLeq6rXoJRSSnkpDVBKKaU8klcFKBGJE5ELInIo37F7RGSXiHwpIp+ISH3H8VAR+UlEDjh+YvJdE+ZInyIir4mIeGo5HOe6Os4ddpyv6W3lEJEp+f4vDohIjoh088Jy+IjIO47jR0XkpXzXeFM5fEXkLcfxL0RkkAeVI1hENjn+fQ+LyDOO441EZJ2InHA8+uW75iVHfo+LyEhPKEtJyyEijR3pr4nI6wXu5U3lGC4iyY78JovIkHz3Klk5jDFe8wMMAHoAh/Id2wMMdDyfAfyn43lo/nQF7rMb6AsI8Bkw2oPLYQMOAvc4XjcGrN5WjgLX3Q2keun/x6PAUsfz2kAaEOqF5XgCeMvxvCmQDFg8pBwBQA/H83rAV0An4O/Ai47jLwJ/czzvBHwB1ABaASc94W+kFOWoA9wHRAOvF7iXN5WjO9DC8bwLcLa05ai0X7py/McKLfAHeIWfB3sEA0ecpSvwj30s3+vJwHwPLscYYLG3l6PANX8B/uyN5XDk7xPsXxwaO/5YG3lhOeYBv8iXbgMQ7inlKFCmFcBw4DgQkO/35rjj+UvAS/nSr3F8CHpUWYorR75008kXoLy1HI7jAlzC/uWhxOXwqia+QhwCIhzPH8T+R5irlYjsF5EtItLfcSwQyMiXJsNxzN0KK0d7wIjIGhHZJyIvOI57Wznyexh43/Hc28rxMfAjcA44DfzDGPMd3leOL4BxImITkVZAmOOcR5VDREKxfyP/HGhmjDkH4Hhs6kgWCJzJd1lunj2mLC6WozDeXI5IYL8x5ialKEdVCFAzgCdEJBl79fOW4/g5oKUxpjvwHPCeo/3dWZunJ4y1L6wcNuzV/imOxwkiMhTvKwcAItIbuG6Mye0n8bZyhAPZQAvszUnPi0hrvK8ccdg/IPYC/wPsBLLwoHKISF0gHnjWGHOlqKROjpkijleqEpSj0Fs4Oebx5RCRzsDfgLm5h5wkK7IctpJm0tMYY44BIwBEpD1wv+P4TeCm43myiJzEXhvJAILy3SII+Loy8+xMYeXAnt8txphvHedWYe9nWIx3lSPXI/xcewLv+/94FFhtjMkELojIDqAnsA0vKocxJgv4VW46EdkJnAAu4wHlEBEf7B+GS4wxyxyHz4tIgDHmnIgEABccxzO4vaaem2e3/26VsByF8bpyiEgQkABMM8acdBwucTm8vgYlIk0djxbgP4AYx2t/EbE6nrcG2mHvmD8HXBWRPo4RJNOwt6m6VWHlwN6e3lVEaouIDRiIvR/B28qRe+xBYGnuMS8sx2lgiNjVAfpgb1f3qnI4fp/qOJ4PB7KMMR7xe+V434XAUWPMP/OdSgSiHM+j8uUrEXhERGo4mivbAbvdXZZSlMMpbyuHiDQEPsXeL7gjN3GpyuGujrZSds69j73pLhN7NJ4JPIO9o/or4K/83CEcCRzG3ta+D3gg3316Ym+bPwm8nnuNJ5bDkf4XjrIcAv7uxeUYBCQ5uY/XlAOoC3zk+P84AvzGS8sRir2T+yiwHvuWB55SjvuwN/0cBA44fsZgH5SyAXtNbwPQKN81Lzvye5x8I8PcWZZSliMN+A645vg/7ORt5cD+RejHfGkPAE1LUw5d6kgppZRH8vomPqWUUlWTBiillFIeSQOUUkopj6QBSimllEfSAKWUUsojaYBSqpI45k5tF5HR+Y49JCKr3ZkvpTyVDjNXqhKJSBfs86i6A1bsc0RGmZ9n25fkXlZjTHb55lApz6EBSqlKJiJ/xz6RsY7jMQT7FiQ24I/GmBWORTnfdaQBeNIYs1Ps+zb9AfuE3G7GmE6Vm3ulKo8GKKUqmWOJoX3YF25dCRw2xix2LBGzG3vtygA5xpgbItIOeN8Y09MRoD4FuhhjTrkj/0pVFq9fLFYpb2OM+VFEPsC+nM1DwAMi8mvH6ZpAS+yLaL4u9l2Hs7EvdJxrtwYnVR1ogFLKPXIcPwJEGmOO5z8pIn8EzgP3YB/MdCPf6R8rKY9KuZWO4lPKvdYATzlWd0ZEujuONwDOGWNygKnYB1QoVa1ogFLKvf4T8AEOisghx2uA/wOiRCQJe/Oe1ppUtaODJJRSSnkkrUEppZTySBqglFJKeSQNUEoppTySBiillFIeSQOUUkopj6QBSimllEfSAKWUUsoj/X8O6TsrKH8M1AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_alpha()\n",
+    "plot(alpha_series, color='gray')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "raising-anthony",
+   "metadata": {},
+   "source": [
+    "If you don't like the `slope` and `intercept` I chose, feel free to adjust them.\n",
+    "\n",
+    "Now, as an exercise, you can use this function to make a projection of world population until 2100.\n",
+    "\n",
+    "1. Create a `System` object that includes `alpha_func` as a system variable.\n",
+    "\n",
+    "2. Define a growth function that uses `alpha_func` to compute the net growth rate at the given time `t`.\n",
+    "\n",
+    "3. Run a simulation from 1960 to 2100 with your update function, and plot the results.\n",
+    "\n",
+    "4. Compare your projections with those from the US Census and UN."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "bigger-crystal",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "t_0 = 1960\n",
+    "t_end = 2100\n",
+    "p_0 = census[t_0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "overhead-ethics",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "system = System(t_0=t_0, \n",
+    "                t_end=t_end,\n",
+    "                p_0=p_0,\n",
+    "                alpha_func=alpha_func)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "verified-friday",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def growth_func_alpha(pop, t, system):\n",
+    "    return system.alpha_func(t) * pop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "fatal-status",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.06725033332680001"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "growth_func_alpha(p_0, t_0, system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "balanced-pierre",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results = run_simulation(system, growth_func_alpha);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "relevant-sessions",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "plot_projections(table3)\n",
+    "plot(results, color='gray', label='model')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "informal-myrtle",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# If the net growth rate continues to decrease linearly,\n",
+    "# world population will peak around 2065 at about 9.8 billion,\n",
+    "# and then start to decline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "bridal-spread",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2066, 9.75910331482456)"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results.idxmax(), results.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "annual-carbon",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 66f233e8e40e45b3794e7576246cff234f6ec6d5 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 29 Jan 2021 15:01:31 -0500
Subject: [PATCH 020/144] Revising chapters

---
 jupyter/chap02.ipynb |  387 ++++++------
 jupyter/chap03.ipynb |  194 +++---
 jupyter/chap04.ipynb |  156 ++---
 jupyter/chap05.ipynb |  311 +++++-----
 jupyter/chap06.ipynb |  144 ++---
 jupyter/chap07.ipynb |  182 +++---
 jupyter/chap08.ipynb |  256 ++++----
 jupyter/chap09.ipynb | 1378 ++++++++++++++++++++++++++++++++++++++++++
 jupyter/chap10.ipynb |  656 ++++++++++++++++++++
 9 files changed, 2833 insertions(+), 831 deletions(-)
 create mode 100644 jupyter/chap09.ipynb
 create mode 100644 jupyter/chap10.ipynb

diff --git a/jupyter/chap02.ipynb b/jupyter/chap02.ipynb
index 41748cef..3c23b290 100644
--- a/jupyter/chap02.ipynb
+++ b/jupyter/chap02.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "friendly-reserve",
+   "id": "victorian-latitude",
    "metadata": {},
    "source": [
     "# Chapter 2"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "rental-baptist",
+   "id": "cathedral-glasgow",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "framed-placement",
+   "id": "burning-chorus",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "disabled-nurse",
+   "id": "above-denial",
    "metadata": {},
    "source": [
     "# Bike share\n",
@@ -83,7 +83,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "aggregate-carbon",
+   "id": "wrapped-workplace",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "scientific-connectivity",
+   "id": "accredited-evanescence",
    "metadata": {},
    "source": [
     "Now we can call it like this:"
@@ -101,7 +101,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "reflected-tumor",
+   "id": "incorrect-comparison",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "pressed-fighter",
+   "id": "living-wayne",
    "metadata": {},
    "source": [
     "The expressions in parentheses are **keyword arguments**.\n",
@@ -130,7 +130,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "sixth-wilderness",
+   "id": "brief-diversity",
    "metadata": {},
    "outputs": [
     {
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "military-credits",
+   "id": "intermediate-midwest",
    "metadata": {},
    "source": [
     "And this:"
@@ -159,7 +159,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "surprising-beverage",
+   "id": "designed-brazilian",
    "metadata": {},
    "outputs": [
     {
@@ -179,7 +179,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "mathematical-daughter",
+   "id": "phantom-oklahoma",
    "metadata": {},
    "source": [
     "Or, to display the state variables and their values, you can just type the name of the object:"
@@ -188,7 +188,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "yellow-consultancy",
+   "id": "impaired-potter",
    "metadata": {},
    "outputs": [
     {
@@ -212,7 +212,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>values</th>\n",
+       "      <th>State</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -243,7 +243,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "artificial-strand",
+   "id": "fleet-beijing",
    "metadata": {},
    "source": [
     "These values make up the **state** of the system.\n",
@@ -254,7 +254,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "documented-coach",
+   "id": "floppy-trainer",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "blank-pittsburgh",
+   "id": "natural-gossip",
    "metadata": {},
    "source": [
     "Or we can use **update operators**, `-=` and `+=`, to subtract 1 from\n",
@@ -274,7 +274,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "ahead-intro",
+   "id": "hungarian-bride",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,7 +284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "suited-laser",
+   "id": "radical-mills",
    "metadata": {},
    "source": [
     "The result is the same either way."
@@ -292,7 +292,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "retained-sauce",
+   "id": "controversial-opportunity",
    "metadata": {},
    "source": [
     "## Defining functions\n",
@@ -305,7 +305,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "raised-conviction",
+   "id": "vertical-drawing",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -315,7 +315,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "impaired-teddy",
+   "id": "approximate-rolling",
    "metadata": {},
    "source": [
     "Rather than repeat them every time a bike moves, we can define a new\n",
@@ -325,7 +325,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "covered-algorithm",
+   "id": "significant-nutrition",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "marine-smith",
+   "id": "generous-tracker",
    "metadata": {},
    "source": [
     "`def` is a special word in Python that indicates we are defining a new\n",
@@ -356,7 +356,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "sublime-question",
+   "id": "moving-jurisdiction",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -365,7 +365,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "lucky-scope",
+   "id": "meaningful-christmas",
    "metadata": {},
    "source": [
     "When you call the function, it runs the statements in the body, which\n",
@@ -376,7 +376,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "divine-algeria",
+   "id": "proper-symposium",
    "metadata": {},
    "outputs": [
     {
@@ -400,7 +400,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>values</th>\n",
+       "      <th>State</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -431,7 +431,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "interracial-farmer",
+   "id": "eleven-brook",
    "metadata": {},
    "source": [
     "When you call a function, you have to include the parentheses. If you\n",
@@ -441,7 +441,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "worst-tomato",
+   "id": "identical-yacht",
    "metadata": {},
    "outputs": [
     {
@@ -461,7 +461,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "racial-france",
+   "id": "premier-youth",
    "metadata": {},
    "source": [
     "This result indicates that `bike_to_wellesley` is a function. You don't\n",
@@ -472,7 +472,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "deluxe-scoop",
+   "id": "brazilian-medicare",
    "metadata": {},
    "source": [
     "## Print statements\n",
@@ -488,7 +488,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "awful-laundry",
+   "id": "heavy-patrol",
    "metadata": {},
    "outputs": [
     {
@@ -509,7 +509,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ignored-yugoslavia",
+   "id": "ancient-projection",
    "metadata": {},
    "source": [
     "Jupyter runs both lines, but it only displays the value of the\n",
@@ -520,7 +520,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "infectious-hayes",
+   "id": "french-preference",
    "metadata": {},
    "outputs": [
     {
@@ -539,7 +539,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "valued-religious",
+   "id": "original-hollywood",
    "metadata": {},
    "source": [
     "When you call the `print` function, you can put a variable name in\n",
@@ -550,7 +550,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "foreign-collins",
+   "id": "alternative-keyboard",
    "metadata": {},
    "outputs": [
     {
@@ -567,7 +567,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "controversial-overall",
+   "id": "described-produce",
    "metadata": {},
    "source": [
     "Python looks up the values of the variables and displays them; in this\n",
@@ -581,7 +581,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "polish-outdoors",
+   "id": "robust-holly",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -593,7 +593,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sufficient-stone",
+   "id": "vital-lender",
    "metadata": {},
    "source": [
     "Each time we call this version of the function, it displays a message,\n",
@@ -608,7 +608,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "scientific-edinburgh",
+   "id": "fifteen-atmosphere",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -620,7 +620,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "flying-evaluation",
+   "id": "requested-glasgow",
    "metadata": {},
    "source": [
     "And call it like this:"
@@ -629,7 +629,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "rotary-carry",
+   "id": "matched-narrow",
    "metadata": {},
    "outputs": [
     {
@@ -646,7 +646,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "active-rocket",
+   "id": "sitting-semiconductor",
    "metadata": {},
    "source": [
     "One benefit of defining functions is that you avoid repeating chunks of\n",
@@ -657,7 +657,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "wanted-facing",
+   "id": "enhanced-maintenance",
    "metadata": {},
    "source": [
     "## If statements\n",
@@ -668,7 +668,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "protected-stewart",
+   "id": "active-graduate",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -677,7 +677,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "finished-attention",
+   "id": "adopted-train",
    "metadata": {},
    "source": [
     "When you call it, you provide a probability between 0 and 1, like `0.7`, as an example:"
@@ -686,7 +686,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "substantial-azerbaijan",
+   "id": "illegal-metropolitan",
    "metadata": {},
    "outputs": [
     {
@@ -706,7 +706,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "progressive-objective",
+   "id": "appropriate-funds",
    "metadata": {},
    "source": [
     "The result is one of two values: `True` with probability 0.7 or `False`\n",
@@ -728,17 +728,9 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "blond-prophet",
+   "id": "excessive-murder",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "heads\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "if flip(0.5):\n",
     "    print('heads')"
@@ -746,7 +738,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fallen-hurricane",
+   "id": "seventh-profile",
    "metadata": {},
    "source": [
     "If the result from `flip` is `True`, the program displays the string\n",
@@ -763,14 +755,14 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "experimental-dispute",
+   "id": "fundamental-nursing",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "heads\n"
+      "tails\n"
      ]
     }
    ],
@@ -783,7 +775,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "statistical-tooth",
+   "id": "recovered-chemical",
    "metadata": {},
    "source": [
     "Now we can use `flip` to simulate the arrival of students who want to\n",
@@ -795,7 +787,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "protective-information",
+   "id": "twenty-health",
    "metadata": {},
    "outputs": [
     {
@@ -813,7 +805,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "breathing-joseph",
+   "id": "difficult-construction",
    "metadata": {},
    "source": [
     "If students arrive at the Wellesley station every 3 minutes, on average,\n",
@@ -824,9 +816,17 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "better-suggestion",
+   "id": "played-character",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moving a bike to Olin\n"
+     ]
+    }
+   ],
    "source": [
     "if flip(0.33):\n",
     "    bike_to_olin()"
@@ -834,7 +834,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "smooth-doctrine",
+   "id": "standard-party",
    "metadata": {},
    "source": [
     "We can combine these snippets into a function that simulates a **time\n",
@@ -844,7 +844,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "reserved-polls",
+   "id": "ecological-colon",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -858,7 +858,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "asian-absorption",
+   "id": "amateur-exposure",
    "metadata": {},
    "source": [
     "Then we can simulate a time step like this:"
@@ -867,7 +867,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "hired-colors",
+   "id": "mediterranean-german",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -876,7 +876,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "isolated-principle",
+   "id": "sought-mobile",
    "metadata": {},
    "source": [
     "Even though there are no values in parentheses, we have to include them."
@@ -884,7 +884,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "accompanied-ceremony",
+   "id": "organic-proportion",
    "metadata": {},
    "source": [
     "## Parameters\n",
@@ -900,7 +900,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "accomplished-incentive",
+   "id": "hollywood-shopping",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -914,7 +914,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "governmental-intelligence",
+   "id": "encouraging-arkansas",
    "metadata": {},
    "source": [
     "The values of `p1` and `p2` are not set inside this function; instead,\n",
@@ -924,16 +924,25 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "bound-welsh",
+   "id": "buried-alert",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Olin\n"
+     ]
+    }
+   ],
    "source": [
     "step(0.5, 0.33)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "simplified-popularity",
+   "id": "aggregate-dynamics",
    "metadata": {},
    "source": [
     "The values you provide when you call the function are called\n",
@@ -945,9 +954,17 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "potential-hardware",
+   "id": "recognized-denmark",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moving a bike to Wellesley\n"
+     ]
+    }
+   ],
    "source": [
     "p1 = 0.5\n",
     "p2 = 0.33\n",
@@ -961,7 +978,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "severe-bride",
+   "id": "raised-museum",
    "metadata": {},
    "source": [
     "The advantage of using parameters is that you can call the same function many times, providing different arguments each time.\n",
@@ -971,7 +988,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "naughty-natural",
+   "id": "scenic-african",
    "metadata": {},
    "source": [
     "## For loops\n",
@@ -983,8 +1000,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
-   "id": "posted-bruce",
+   "execution_count": 31,
+   "id": "polish-river",
    "metadata": {},
    "outputs": [
     {
@@ -1008,7 +1025,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "combined-handle",
+   "id": "compatible-conspiracy",
    "metadata": {},
    "source": [
     "The syntax here should look familiar; the first line ends with a\n",
@@ -1029,7 +1046,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "spanish-turner",
+   "id": "breeding-groove",
    "metadata": {},
    "source": [
     "## TimeSeries"
@@ -1037,21 +1054,21 @@
   },
   {
    "cell_type": "markdown",
-   "id": "arabic-parts",
+   "id": "flexible-projection",
    "metadata": {},
    "source": [
     "When we run a simulation, we often want to save the results for later analysis. The ModSim library provides a `TimeSeries` object for this purpose. A `TimeSeries` contains a sequence of time stamps and a\n",
     "corresponding sequence of quantities.\n",
     "\n",
-    "In this example, the time stamps will be integers representing minutes, and the quantities will be the number of bikes at one location.\n",
+    "In this example, the time stamps are integers representing minutes, and the quantities are the number of bikes at one location.\n",
     "\n",
     "Since we have moved a number of bikes around, let's start again with a new `State` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
-   "id": "naval-earthquake",
+   "execution_count": 32,
+   "id": "every-consultation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1060,7 +1077,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "everyday-mechanics",
+   "id": "cross-sharp",
    "metadata": {},
    "source": [
     "We can create a new, empty `TimeSeries` like this:"
@@ -1068,8 +1085,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "id": "hispanic-honolulu",
+   "execution_count": 33,
+   "id": "changing-planet",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1080,7 +1097,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "encouraging-auckland",
+   "id": "attractive-revision",
    "metadata": {},
    "source": [
     "And we can add a quantity like this:"
@@ -1088,8 +1105,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "id": "colonial-fireplace",
+   "execution_count": 34,
+   "id": "aquatic-richardson",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1098,19 +1115,18 @@
   },
   {
    "cell_type": "markdown",
-   "id": "racial-arrival",
+   "id": "searching-funeral",
    "metadata": {},
    "source": [
     "The number in brackets is the time stamp, also called a **label**.\n",
-    "In this case, label is `0` and the quantity is the number of bikes at Olin.\n",
     "\n",
     "We can use a `TimeSeries` inside a for loop to store the results of the simulation:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
-   "id": "impressive-hours",
+   "execution_count": 35,
+   "id": "english-titanium",
    "metadata": {},
    "outputs": [
     {
@@ -1118,6 +1134,8 @@
      "output_type": "stream",
      "text": [
       "0\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Olin\n",
       "1\n",
       "Moving a bike to Olin\n",
       "2\n"
@@ -1127,13 +1145,13 @@
    "source": [
     "for i in range(3):\n",
     "    print(i)\n",
-    "    step(0.3, 0.2)\n",
+    "    step(0.6, 0.6)\n",
     "    results[i+1] = bikeshare.olin"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "minimal-credits",
+   "id": "prospective-joining",
    "metadata": {},
    "source": [
     "Each time through the loop, we print the value of `i` and call `step`, which updates `bikeshare`.\n",
@@ -1151,8 +1169,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
-   "id": "finnish-lighting",
+   "execution_count": 36,
+   "id": "indonesian-singing",
    "metadata": {},
    "outputs": [
     {
@@ -1166,7 +1184,7 @@
        "Name: Quantity, dtype: int64"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1177,7 +1195,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "expensive-territory",
+   "id": "small-encoding",
    "metadata": {},
    "source": [
     "The left column is the time stamps; the right column is the quantities (which might be negative, depending on the state of the system).\n",
@@ -1189,8 +1207,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
-   "id": "right-bulletin",
+   "execution_count": 37,
+   "id": "verbal-bikini",
    "metadata": {},
    "outputs": [
     {
@@ -1251,7 +1269,7 @@
        "3           11"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1264,7 +1282,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "statutory-copyright",
+   "id": "visible-montgomery",
    "metadata": {},
    "source": [
     "You don't have to use `show`, but I think it looks better."
@@ -1272,89 +1290,24 @@
   },
   {
    "cell_type": "markdown",
-   "id": "widespread-clinic",
+   "id": "following-contrary",
    "metadata": {},
    "source": [
     "## Plotting\n",
     "\n",
-    "ModSim provides a function called `plot` we can use to plot\n",
-    "the results:"
+    "`results` provides a function called `plot` we can use to plot\n",
+    "the results, and the ModSim library provides `decorate`, which we can use to label the axes and give the figure a title:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
-   "id": "computational-council",
+   "execution_count": 38,
+   "id": "saved-hands",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import plot\n",
-    "\n",
-    "plot(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "asian-sword",
-   "metadata": {},
-   "source": [
-    "The legend for this figure says \"None\" because we have not provided a label.  Here's how we can do that:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "touched-retailer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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XQFhJDZokIZLgjtfUMXtRMWMH9ODqMwcGHUek3aigRBLc029tYdveo0yfMoYOGggrKUQFJZLADlZV88jSUi4e2YdL87KCjiPSrlRQIgns18s3svfwce4v0EBYST0qKJEEVXGwil+/vonPnTGAM3J6BR1HpN2poEQS1M+XlFBdW8e9V2ogrKQmFZRIAtpYeYhn3t7GV84fQm5WsyMsRZKaCkokAf100Xo6pXfgris0EFZSlwpKJMF8sG0ff/24nG9eMpzs7p2CjiMSGBWUSAJxd2YsWEdWt458UwNhJcWpoEQSyKvrK3lz416+OyGPbp1anEQmktRUUCIJorbOmbmgiKF9unDDZzQQVkQFJZIgXvxgO0U7D3LvlaPpmK5fTRH9FogkgKrqWn66aD2nD+rJVacPCDqOSEJQQYkkgKfe3ML2fRoIK9KQCkokYAeqqnl0WSmX5GVx8UgNhBX5lApKJGD/89oG9h2p5v6CMUFHEUkoKiiRAO06UMUTKzZxzVkDOW1Qz6DjiCQUFZRIgB5+ZT21da6BsCJNUEGJBKS04hC/f2cbX7tgKIN7dwk6jkjCUUGJBOSBhUV06ZjOnZePDDqKSEJSQYkE4N0tn7BwzS6+delw+nTTQFiRpqigRNqZe/1Io6xunbjtkmFBxxFJWCookXa2tKiCtzfv5Z6JeXTpqIGwIs1RQYm0o9o6Z2ZhEcOyuvLlzwwOOo5IQlNBibSj598rY/2uQ/xw8mgy0vTrJ3Ii+g0RaSdV1bU8uHg9Zw7uxZTT+gcdRyThqaBE2sm8lZsp31/Fj6aMwUwDYUVaooISaQf7j1Tzi2WlXD46mwuG9wk6jkgoqKBE2sFjr5Vy8FgN92kgrEjUVFAicbZj31F+87fN/MvZgxg7oEfQcURCQwUlEmcPv7IeHL4/aVTQUURCRQUlEkfrdx3kT++W8a8XDiXnFA2EFTkZLRaUmc01swozW91gW28zW2xmJZHLU07w+DQze9/MXm6r0CJhMauwmK4d07lDA2FFTlo0Z1BPAgWNtk0Hlrh7HrAkcrs5dwPrYkonEmLvbN7LK+t2cfv4EZzStWPQcURCp8WCcvflwN5Gm68B5kWuzwOubeqxZpYDXAU8HntEkfBxd/57/jr69ejE1y/WQFiRWMT6GlQ/dy8HiFz2bWbdw8B9QF2M30cklBat3cV7W/fxvYmj6NwxLeg4IqEUtzdJmNnngAp3fzfK9dPMbJWZraqsrIxXLJG4q6mtY1ZhESOyu3L9uTlBxxEJrVgLapeZDQCIXFY0seZi4Goz2ww8C1xhZk819wXdfY6757t7fnZ2doyxRIL3p3fL2FB5mPsKxpCugbAiMYv1t+cl4ObI9ZuBFxsvcPcfuXuOu+cCNwBL3f1rMX4/kVA4eryWh15ZzzlDenHluH5BxxEJtWjeZv4M8AYw2szKzOw2YAYwycxKgEmR25jZQDObH8/AIonsNys3sevAMaZPGauBsCKt1OLHebr7jc3cNaGJtTuAqU1sfxV49SSziYTKJ4eP88tXNzBxbF/OG9Y76DgioacnyEXayGOvlnJYA2FF2owKSqQNlH1yhHkrt3D9uTmM6tc96DgiSUEFJdIGHly8HjO4Z6IGwoq0FRWUSCutKz/AC+9v55aLcxnYq3PQcUSShgpKpJVmFRbRvVM637lMA2FF2pIKSqQV3tiwh2XFldxx+Uh6dskIOo5IUlFBicTI3ZlRWMSAnpncfFFu0HFEko4KSiRGhat38uG2fXxv0igyMzQQVqStqaBEYlBdW8cDC4sZ1a8bXzhHA2FF4kEFJRKD37+zjY27D3N/wRjSOmikkUg8qKBETtLhYzU8/EoJ5+X25ooxzX0Umoi0lgpK5CTNXbGJ3YeOcf+UMRoIKxJHKiiRk7Dn0DH+Z/lGJp/aj3OHnhJ0HJGkpoISOQmPLivlyPEafjhZA2FF4k0FJRKlbXuP8NSbW/jyZwYzsm+3oOOIJD0VlEiUfrqomLQOxt0TNBBWpD2ooESisHr7fv78wQ6+fvEw+vfMDDqOSEpQQYlEYWZhEb26ZHD7+BFBRxFJGSookRasKNnN6yW7ufPykfTI1EBYkfaighI5gbo6Z2ZhEYN6deamC4cGHUckpaigRE7grx+X8/H2/fzgylF0StdAWJH2pIISacbxmjpmLypmTP/uXHPWoKDjiKQcFZRIM559Zytb9hzh/ikaCCsSBBWUSBMOHavh50tKuGB4b8aPyg46jkhKUkGJNOHx1zey+9Bxpk8Zq4GwIgFRQYk0UnnwGHOWb+Sq0wdw1uBeQccRSVkqKJFGHllawrGaOu6dPDroKCIpTQUl0sDm3Yf5f29t5cbzBjMsq2vQcURSmgpKpIHZi4rJSOvAdyfkBR1FJOWpoEQiPirbx8sflfPNS4bRt7sGwooETQUlArg7MxYU0btrR7556fCg44gIKigRAF4v2c3KDXu464qRdNdAWJGEoIKSlFdXV3/2NLh3Z75y/pCg44hIhApKUt5fPtrB2vID3HvlaA2EFUkgLRaUmc01swozW91gW28zW2xmJZHLU5p43GAzW2Zm68xsjZnd3dbhRVrrWE0tDyws5tSBPfj8GQODjiMiDURzBvUkUNBo23RgibvnAUsitxurAX7g7mOBC4A7zGxcK7KKtLmn39xK2SdHmT5lDB00EFYkobRYUO6+HNjbaPM1wLzI9XnAtU08rtzd34tcPwisA/SZBZIwDlRV88jSEj47MotL8jQQViTRxPoaVD93L4f6IgL6nmixmeUCZwNvnWDNNDNbZWarKisrY4wlEr1fL9/IJ0equb9gTNBRRKQJcX+ThJl1A54D7nH3A82tc/c57p7v7vnZ2frXrMRXxYEqHn99E58/cyCn5/QMOo6INCHWgtplZgMAIpcVTS0yswzqy+lpd38+xu8l0uZ+tqSE6to67r1yVNBRRKQZsRbUS8DNkes3Ay82XmD1H6LzBLDO3R+M8fuItLmNlYd49p1tfPX8IQzto4GwIokqmreZPwO8AYw2szIzuw2YAUwysxJgUuQ2ZjbQzOZHHnoxcBNwhZl9EPkzNS57IXISZi8qJjO9A3dpIKxIQktvaYG739jMXROaWLsDmBq5vgLQ+3Yloby/9RPmf7yT700cRVa3TkHHEZET0CQJSRnuzn8vKCKrW0e+ccmwoOOISAtUUJIyXi2u5O1Ne7l7Qh5dO7X45IGIBEwFJSmhts6ZWVhEbp8u3HCeBsKKhIEKSlLCn9/fTtHOg9w7eTQZafqxFwkD/aZK0quqruXBxes5I6cnU08bEHQcEYmSCkqS3lNvbmH7vqNML9BAWJEwUUFJUtt/tJpHl5Vy2ahsLhqZFXQcETkJKihJar96bQP7j2ogrEgYqaAkae3cX8XcFZu49qxBjBvYI+g4InKSVFCStB5+ZT3u8P1JGggrEkYqKElKpRUH+cOqbXztgqEM7t0l6DgiEgMVlCSlWYXFdOmYzp1XjAw6iojESAUlSefdLXtZtHYXt182nN5dOwYdR0RipIKSpOLuzFhQRN/unfj6ZzUQViTMVFCSVJasq+CdzZ9wz8RRdOmogbAiYaaCkqRRU1vHzMIihmd15Uv5OUHHEZFWUkFJ0nj+ve2UVBzivoLRpGsgrEjo6bdYksKnA2HPGtyLyaf2DzqOiLQBFZQkhSdXbmbngSqmTxmDmQbCiiQDFZSE3r4jx3lsWSlXjOnLBcP7BB1HRNqICkpC75evbuDgsRruKxgddBQRaUMqKAm1HfuO8puVm7nu7BzG9NdAWJFkooKSUHto8XoAvn+lBsKKJBsVlIRW8c6DPPdeGbdclMugXp2DjiMibUwFJaE1q7CIrp3S+c74EUFHEZE4UEFJKL21cQ9Liir4zviR9OqigbAiyUgFJaHj7swoLKJ/j0xuvTg36DgiEicqKAmdhWt28f7WfXxvUh6ZGWlBxxGROFFBSajU1NYxa2ERI/t24wvnaCCsSDJTQUmo/PHdMjZWHua+yRoIK5Ls9BsuoXH0eC0PLV5P/tBTmDSuX9BxRCTOVFASGnP/tomKg8c0EFYkRaigJBT2Hj7Or17dwKRx/cjP7R10HBFpByooCYVfLCvl8PEa7pusgbAiqaLFgjKzuWZWYWarG2zrbWaLzawkcnlKM48tMLNiMys1s+ltGVxSx7a9R/jdG1v44rmDyevXPeg4ItJOojmDehIoaLRtOrDE3fOAJZHb/8DM0oBfAFOAccCNZjauVWklJT20eD1mcM+kvKCjiEg7Sm9pgbsvN7PcRpuvAcZHrs8DXgXub7TmPKDU3TcCmNmzkcetjT1udP7zL2vZfehYvL+NtIM6d/76cTnfunQEA3pqIKxIKmmxoJrRz93LAdy93Mz6NrFmELCtwe0y4PzmvqCZTQOmAQwZMiTGWPXW7zrI9n1HW/U1JHFcPCKLb1+mgbAiqSbWgopGU+8D9uYWu/scYA5Afn5+s+ui8dQ3mu1BEREJiVjfxbfLzAYARC4rmlhTBgxucDsH2BHj9xMRkRQTa0G9BNwcuX4z8GITa94B8sxsmJl1BG6IPE5ERKRF0bzN/BngDWC0mZWZ2W3ADGCSmZUAkyK3MbOBZjYfwN1rgDuBhcA64A/uviY+uyEiIskmmnfx3djMXROaWLsDmNrg9nxgfszpREQkZWmShIiIJCQVlIiIJCQVlIiIJCQVlIiIJCQVlIiIJCRzb9XQhrgws0pgSyu/TBawuw3iJLpU2M9U2EfQfiabVNjPttrHoe6e3XhjQhZUWzCzVe6eH3SOeEuF/UyFfQTtZ7JJhf2M9z7qKT4REUlIKigREUlIyVxQc4IO0E5SYT9TYR9B+5lsUmE/47qPSfsalIiIhFsyn0GJiEiIqaBERCQhhb6gzKzAzIrNrNTMpjdxv5nZzyP3f2Rm5wSRszWi2MfxZrbfzD6I/Pk/QeRsDTOba2YVZra6mftDfxwhqv0M/bEEMLPBZrbMzNaZ2Rozu7uJNaE+plHuY+iPp5llmtnbZvZhZD//o4k18TmW7h7aP0AasAEYDnQEPgTGNVozFVhA/UfQXwC8FXTuOOzjeODloLO2cj8vBc4BVjdzf6iP40nsZ+iPZWQ/BgDnRK53B9Yn4e9mNPsY+uMZOT7dItczgLeAC9rjWIb9DOo8oNTdN7r7ceBZ4JpGa64Bfuv13gR6ffpx9SERzT6GnrsvB/aeYEnYjyMQ1X4mBXcvd/f3ItcPUv+hpYMaLQv1MY1yH0MvcnwORW5mRP40fnddXI5l2AtqELCtwe0y/vkHJJo1iSza/BdGTsEXmNmp7ROtXYX9OJ6MpDqWZpYLnE39v7wbSppjeoJ9hCQ4nmaWZmYfABXAYndvl2PZ4ifqJjhrYlvjZo9mTSKLJv971M+yOmRmU4E/A3nxDtbOwn4co5VUx9LMugHPAfe4+4HGdzfxkNAd0xb2MSmOp7vXAmeZWS/gBTM7zd0bvo4al2MZ9jOoMmBwg9s5wI4Y1iSyFvO7+4FPT8HdfT6QYWZZ7RexXYT9OEYlmY6lmWVQ/xf30+7+fBNLQn9MW9rHZDqeAO6+D3gVKGh0V1yOZdgL6h0gz8yGmVlH4AbgpUZrXgL+NfIukwuA/e5e3t5BW6HFfTSz/mZmkevnUX9c97R70vgK+3GMSrIcy8g+PAGsc/cHm1kW6mMazT4mw/E0s+zImRNm1hmYCBQ1WhaXYxnqp/jcvcbM7gQWUv9ut7nuvsbMbo/c/ytgPvXvMCkFjgC3BpU3FlHu4/XAt82sBjgK3OCRt9aEhZk9Q/07nrLMrAz4MfUvxibFcfxUFPsZ+mMZcTFwE/Bx5LULgP8FDIGkOabR7GMyHM8BwDwzS6O+YP/g7i+3x9+zGnUkIiIJKexP8YmISJJSQYmISEJSQYmISEJSQYmISEJSQYmISEJSQYnEiZn1aTDFeqeZbY9cP2RmjwWdTyTR6W3mIu3AzP4dOOTus4POIhIWOoMSaWeRzwh6OXL9381snpktMrPNZnadmc0ys4/NrDAySgczO9fMXjOzd81sYZimfovESgUlErwRwFXUf2TBU8Aydz+d+skDV0VK6hHgenc/F5gL/FdQYUXaS6hHHYkkiQXuXm1mH1M/zqowsv1jIBcYDZwGLI6MdUsDQjOzTiRWKiiR4B0DcPc6M6tuMKutjvrfUQPWuPuFQQUUCYKe4hNJfMVAtpldCPUf8RDWD74TORkqKJEE5+7HqZ+KPdPMPgQ+AC4KNJRIO9DbzEVEJCHpDEpERBKSCkpERBKSCkpERBKSCkpERBKSCkpERBKSCkpERBKSCkpERBLS/wfEBGRrQIHfsgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(results, label='Olin')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "expired-state",
-   "metadata": {},
-   "source": [
-    "Whenever you make a figure, you should label the axes. The ModSim\n",
-    "library provides `decorate`, which we can use to labels the axes and give the figure a title:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "nominated-sister",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1368,7 +1321,7 @@
    "source": [
     "from modsim import decorate\n",
     "\n",
-    "plot(results, label='Olin')\n",
+    "results.plot()\n",
     "\n",
     "decorate(title='Olin-Wellesley Bikeshare',\n",
     "         xlabel='Time step (min)', \n",
@@ -1377,7 +1330,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aboriginal-audio",
+   "id": "limited-interstate",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -1396,7 +1349,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "protective-hearts",
+   "id": "fallen-surprise",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -1404,7 +1357,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "meaning-honduras",
+   "id": "capital-internship",
    "metadata": {},
    "source": [
     "**Exercise:** What happens if you spell the name of a state variable wrong?  Edit the following cell, change the spelling of `wellesley`, and run it.\n",
@@ -1414,8 +1367,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "id": "occasional-phone",
+   "execution_count": 39,
+   "id": "helpful-zambia",
    "metadata": {},
    "outputs": [
     {
@@ -1424,7 +1377,7 @@
        "2"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1437,7 +1390,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "enormous-hacker",
+   "id": "dirty-multiple",
    "metadata": {},
    "source": [
     "**Exercise:** Make a `State` object with a third state variable, called `babson`, with initial value 0, and display the state of the system."
@@ -1445,8 +1398,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "id": "atlantic-newman",
+   "execution_count": 40,
+   "id": "beneficial-mainland",
    "metadata": {},
    "outputs": [
     {
@@ -1470,7 +1423,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>values</th>\n",
+       "      <th>State</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1494,7 +1447,7 @@
        "namespace(olin=10, wellesley=2, babson=0)"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1508,7 +1461,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ignored-landscape",
+   "id": "christian-madrid",
    "metadata": {},
    "source": [
     "**Exercise:** Wrap the code in the chapter in a function named `run_simulation` that takes three parameters, named `p1`, `p2`, and `num_steps`.\n",
@@ -1534,8 +1487,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "id": "generic-works",
+   "execution_count": 41,
+   "id": "former-frost",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1549,7 +1502,7 @@
     "        step(p1, p2)\n",
     "        results[i+1] = bikeshare.olin\n",
     "        \n",
-    "    plot(results, label='Olin')\n",
+    "    results.plot()\n",
     "    decorate(title='Olin-Wellesley Bikeshare',\n",
     "             xlabel='Time step (min)', \n",
     "             ylabel='Number of bikes')"
@@ -1557,37 +1510,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
-   "id": "distributed-tampa",
+   "execution_count": 42,
+   "id": "spare-honduras",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Olin\n",
+      "Moving a bike to Wellesley\n",
       "Moving a bike to Olin\n",
       "Moving a bike to Wellesley\n",
       "Moving a bike to Olin\n",
       "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
       "Moving a bike to Olin\n",
       "Moving a bike to Olin\n",
+      "Moving a bike to Olin\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Olin\n",
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Wellesley\n",
       "Moving a bike to Wellesley\n",
       "Moving a bike to Olin\n",
       "Moving a bike to Wellesley\n",
       "Moving a bike to Olin\n",
+      "Moving a bike to Olin\n",
+      "Moving a bike to Wellesley\n",
       "Moving a bike to Wellesley\n",
+      "Moving a bike to Olin\n",
       "Moving a bike to Wellesley\n",
       "Moving a bike to Wellesley\n",
       "Moving a bike to Wellesley\n",
       "Moving a bike to Wellesley\n",
       "Moving a bike to Olin\n",
       "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n"
+      "Moving a bike to Wellesley\n",
+      "Moving a bike to Olin\n",
+      "Moving a bike to Wellesley\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1607,7 +1574,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "caroline-labor",
+   "id": "instructional-finnish",
    "metadata": {},
    "source": [
     "## Opening the hood\n",
@@ -1619,7 +1586,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "undefined-harrison",
+   "id": "digital-stretch",
    "metadata": {},
    "source": [
     "The `State` object defined in the ModSim library, is based on the `SimpleNamespace` object defined in a standard Python library called `types`; the documentation is at <https://docs.python.org/3.7/library/types.html#types.SimpleNamespace>."
@@ -1627,7 +1594,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "august-functionality",
+   "id": "declared-collar",
    "metadata": {},
    "source": [
     "The `TimeSeries` object is based on the `Series` object defined by a library called Pandas.\n",
@@ -1639,7 +1606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "numerical-cliff",
+   "id": "comfortable-pillow",
    "metadata": {},
    "source": [
     "`Series` objects provide their own `plot` function, but the syntax is different from the other functions we've used, so ModSim provides a function called `plot` just to make it consistent.\n",
@@ -1650,7 +1617,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "overall-novel",
+   "id": "banner-policy",
    "metadata": {},
    "source": [
     "The `flip` function uses NumPy's `random` function to generate a random number between 0 and 1, then returns `True` or `False` with the given probability.\n",
@@ -1660,8 +1627,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "id": "offshore-czech",
+   "execution_count": 43,
+   "id": "agricultural-midwest",
    "metadata": {},
    "outputs": [
     {
@@ -1688,7 +1655,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "european-cooling",
+   "id": "running-membership",
    "metadata": {},
    "source": [
     "You might not understand everything in this function yet, but you will."
@@ -1697,7 +1664,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "familiar-future",
+   "id": "junior-lindsay",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap03.ipynb b/jupyter/chap03.ipynb
index 57f108f3..3854e10c 100644
--- a/jupyter/chap03.ipynb
+++ b/jupyter/chap03.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "missing-intention",
+   "id": "signal-format",
    "metadata": {},
    "source": [
     "# Chapter 3"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "comprehensive-extreme",
+   "id": "welcome-gentleman",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -26,8 +26,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "electrical-charlotte",
+   "execution_count": 62,
+   "id": "backed-christianity",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -41,18 +41,16 @@
     "    import pint\n",
     "except ImportError:\n",
     "    !pip install pint\n",
-    "    import pint\n",
     "    \n",
     "try:\n",
     "    import modsim\n",
     "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
+    "    !pip install modsimpy"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "handy-occurrence",
+   "id": "coral-steering",
    "metadata": {},
    "source": [
     "To paraphrase two Georges, \"All models are wrong, but some models are\n",
@@ -67,7 +65,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "authentic-supplier",
+   "id": "twelve-length",
    "metadata": {},
    "source": [
     "## Iterative modeling\n",
@@ -91,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "constant-kennedy",
+   "id": "sweet-heater",
    "metadata": {},
    "source": [
     "Some of these modeling decisions are better than others. For example,\n",
@@ -114,7 +112,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "arranged-bahamas",
+   "id": "female-salem",
    "metadata": {},
    "source": [
     "## More than one State object\n",
@@ -129,8 +127,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "outside-darkness",
+   "execution_count": 63,
+   "id": "embedded-heavy",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,7 +139,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "widespread-monitor",
+   "id": "artificial-remains",
    "metadata": {},
    "source": [
     "When this function is called, it modifies `bikeshare`. As long as there\n",
@@ -155,8 +153,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "central-vegetable",
+   "execution_count": 64,
+   "id": "unusual-advancement",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,7 +165,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "finite-silence",
+   "id": "serial-mortality",
    "metadata": {},
    "source": [
     "The name of the parameter is `state` rather than `bikeshare` as a\n",
@@ -180,8 +178,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "statistical-conviction",
+   "execution_count": 65,
+   "id": "packed-hungarian",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,7 +191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "still-commons",
+   "id": "fewer-rhythm",
    "metadata": {},
    "source": [
     "Again, the argument we provide gets assigned to the parameter, so this\n",
@@ -210,8 +208,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "facial-tunnel",
+   "execution_count": 66,
+   "id": "right-assessment",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -221,7 +219,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "active-gilbert",
+   "id": "occupied-navigator",
    "metadata": {},
    "source": [
     "And update them independently:"
@@ -229,8 +227,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "static-african",
+   "execution_count": 67,
+   "id": "cleared-advocacy",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,7 +238,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "honey-elder",
+   "id": "brown-ferry",
    "metadata": {},
    "source": [
     "Changes in `bikeshare1` do not affect `bikeshare2`, and vice versa. So\n",
@@ -250,7 +248,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "animated-parliament",
+   "id": "magnetic-packing",
    "metadata": {},
    "source": [
     "## Documentation\n",
@@ -271,8 +269,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "sticky-macedonia",
+   "execution_count": 68,
+   "id": "moral-parallel",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -289,7 +287,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "returning-faith",
+   "id": "therapeutic-utility",
    "metadata": {},
    "source": [
     "Docstrings follow a conventional format:\n",
@@ -306,7 +304,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "frank-influence",
+   "id": "american-clear",
    "metadata": {},
    "source": [
     "## Negative bikes\n",
@@ -321,8 +319,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "fitted-carnival",
+   "execution_count": 69,
+   "id": "divine-leisure",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -335,7 +333,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "signal-baking",
+   "id": "decimal-denver",
    "metadata": {},
    "source": [
     "The first line checks whether the number of bikes at Wellesley is zero. If so, it uses a **return statement**, which causes the function to end immediately, without running the rest of the statements. So if there are no bikes at Wellesley, we \"return\" from `bike_to_olin` without changing the state.\n",
@@ -345,8 +343,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "moral-bench",
+   "execution_count": 70,
+   "id": "choice-cooking",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -356,7 +354,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "imperial-disposal",
+   "id": "persistent-denmark",
    "metadata": {},
    "source": [
     "The state of the system should be unchanged."
@@ -364,8 +362,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "latest-boards",
+   "execution_count": 71,
+   "id": "twelve-moderator",
    "metadata": {},
    "outputs": [
     {
@@ -409,7 +407,7 @@
        "namespace(olin=12, wellesley=0)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -420,7 +418,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "substantial-english",
+   "id": "criminal-general",
    "metadata": {},
    "source": [
     "No more negative bikes (at least at Wellesley)."
@@ -428,7 +426,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "marked-talent",
+   "id": "gorgeous-found",
    "metadata": {},
    "source": [
     "## Comparison operators\n",
@@ -440,8 +438,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "typical-characterization",
+   "execution_count": 72,
+   "id": "level-burns",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -450,7 +448,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dependent-athens",
+   "id": "weighted-monster",
    "metadata": {},
    "source": [
     "On the other hand, the following statement checks whether `x` is `5` and\n",
@@ -459,8 +457,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "living-baseball",
+   "execution_count": 73,
+   "id": "civic-remains",
    "metadata": {},
    "outputs": [
     {
@@ -469,7 +467,7 @@
        "True"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -480,7 +478,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "small-adventure",
+   "id": "forward-perth",
    "metadata": {},
    "source": [
     "You can use the equals operator in an `if` statement, like this:"
@@ -488,8 +486,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "cordless-consideration",
+   "execution_count": 74,
+   "id": "affecting-naples",
    "metadata": {},
    "outputs": [
     {
@@ -507,7 +505,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "mineral-azerbaijan",
+   "id": "consolidated-anatomy",
    "metadata": {},
    "source": [
     "If you make a mistake and use `=` in an `if` statement, like this:\n",
@@ -522,7 +520,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "empty-lecture",
+   "id": "twelve-defensive",
    "metadata": {},
    "source": [
     "The equals operator is one of the **comparison operators**. The others\n",
@@ -540,7 +538,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "caroline-advocacy",
+   "id": "center-sequence",
    "metadata": {},
    "source": [
     "## Metrics\n",
@@ -565,8 +563,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "champion-gibson",
+   "execution_count": 75,
+   "id": "arbitrary-ferry",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -580,7 +578,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "living-leather",
+   "id": "severe-contact",
    "metadata": {},
    "source": [
     "If a customer arrives at the Wellesley station and finds no bike\n",
@@ -593,8 +591,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "aging-evolution",
+   "execution_count": 76,
+   "id": "cardiovascular-montgomery",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -604,7 +602,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "painted-province",
+   "id": "computational-prior",
    "metadata": {},
    "source": [
     "We can test it by calling `bike_to_olin`:"
@@ -612,8 +610,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "exciting-feature",
+   "execution_count": 77,
+   "id": "cosmetic-above",
    "metadata": {
     "scrolled": true
    },
@@ -624,7 +622,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "integral-wheat",
+   "id": "pleased-gasoline",
    "metadata": {},
    "source": [
     "There should be 12 bikes at Olin, no bikes at Wellesley, and one unhappy customer."
@@ -632,8 +630,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "aware-technology",
+   "execution_count": 78,
+   "id": "bulgarian-palestine",
    "metadata": {},
    "outputs": [
     {
@@ -681,7 +679,7 @@
        "namespace(olin=12, wellesley=0, wellesley_empty=1)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -692,7 +690,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "appropriate-steps",
+   "id": "revised-associate",
    "metadata": {},
    "source": [
     "Looks good!"
@@ -700,7 +698,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "harmful-criminal",
+   "id": "native-kidney",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -720,7 +718,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "gothic-backing",
+   "id": "impaired-cyprus",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -728,7 +726,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "limited-tamil",
+   "id": "careful-hacker",
    "metadata": {},
    "source": [
     "Here's the code we have so far, with docstrings, all in one place."
@@ -736,12 +734,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "id": "standing-garbage",
+   "execution_count": 79,
+   "id": "wrong-internet",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from modsim import TimeSeries, plot, decorate\n",
+    "from modsim import TimeSeries, decorate\n",
     "\n",
     "def run_simulation(state, p1, p2, num_steps):\n",
     "    \"\"\"Simulate the given number of time steps.\n",
@@ -758,7 +756,7 @@
     "        step(state, p1, p2)\n",
     "        results[i+1] = state.olin\n",
     "        \n",
-    "    plot(results, label='Olin')\n",
+    "    results.plot(label='Olin')\n",
     "    decorate(title='Olin-Wellesley Bikeshare',\n",
     "             xlabel='Time step (min)', \n",
     "             ylabel='Number of bikes')"
@@ -766,8 +764,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "id": "durable-vacuum",
+   "execution_count": 80,
+   "id": "instrumental-copyright",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -789,8 +787,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "id": "peripheral-maximum",
+   "execution_count": 81,
+   "id": "improved-renaissance",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -808,8 +806,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "id": "exclusive-audio",
+   "execution_count": 82,
+   "id": "unavailable-maker",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -824,7 +822,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "useful-gabriel",
+   "id": "bigger-rapid",
    "metadata": {},
    "source": [
     "**Exercise:** Modify `bike_to_wellesley` so it checks whether a bike is available at Olin.  If not, it should add one to `olin_empty`.\n",
@@ -834,8 +832,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "id": "violent-grant",
+   "execution_count": 83,
+   "id": "phantom-carter",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -855,8 +853,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "id": "acceptable-stress",
+   "execution_count": 84,
+   "id": "adopted-contrary",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -868,8 +866,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "id": "measured-suggestion",
+   "execution_count": 85,
+   "id": "comparable-natural",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -880,8 +878,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "id": "collected-column",
+   "execution_count": 86,
+   "id": "attractive-amendment",
    "metadata": {},
    "outputs": [
     {
@@ -933,7 +931,7 @@
        "namespace(olin=0, wellesley=12, olin_empty=1, wellesley_empty=0)"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -946,7 +944,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "understanding-humor",
+   "id": "possible-initial",
    "metadata": {},
    "source": [
     "**Exercise:** Now run the simulation a few times and confirm that the number of bikes is never negative.  What's the final state of the system?"
@@ -954,13 +952,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
-   "id": "quantitative-threat",
+   "execution_count": 87,
+   "id": "immune-shock",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -982,8 +980,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
-   "id": "hindu-reverse",
+   "execution_count": 88,
+   "id": "sporting-collectible",
    "metadata": {},
    "outputs": [
     {
@@ -1035,7 +1033,7 @@
        "namespace(olin=0, wellesley=12, olin_empty=2, wellesley_empty=0)"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1047,7 +1045,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "institutional-dublin",
+   "id": "owned-fitting",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap04.ipynb b/jupyter/chap04.ipynb
index d9ca99f7..45528b03 100644
--- a/jupyter/chap04.ipynb
+++ b/jupyter/chap04.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "killing-desire",
+   "id": "existing-guidance",
    "metadata": {},
    "source": [
     "# Chapter 4"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "younger-hobby",
+   "id": "hawaiian-detective",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "satellite-sociology",
+   "id": "informed-defendant",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -41,18 +41,16 @@
     "    import pint\n",
     "except ImportError:\n",
     "    !pip install pint\n",
-    "    import pint\n",
     "    \n",
     "try:\n",
     "    import modsim\n",
     "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
+    "    !pip install modsimpy"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "concrete-jackson",
+   "id": "caring-gnome",
    "metadata": {},
    "source": [
     "Here the code from previous chapters we'll reuse."
@@ -61,7 +59,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "fifth-mongolia",
+   "id": "stylish-raising",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,7 +82,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "duplicate-evolution",
+   "id": "wound-bottle",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,7 +101,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "parental-veteran",
+   "id": "prescribed-affect",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,10 +119,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "outer-delivery",
+   "id": "atlantic-collectible",
    "metadata": {},
    "source": [
-    "In the previous chapter we defined metrics that quantify the performance of bike sharing this system. In this chapter we see how those metrics depend on the parameters of the system, like the arrival rate of customers at bike stations.\n",
+    "In the previous chapter we defined metrics that quantify the performance of bike sharing this system. In this chapter we see how those metrics depend on the parameters of the system, like the Customer rate of customers at bike stations.\n",
     "\n",
     "We also discuss a program development strategy, called incremental\n",
     "development, that might help you write programs faster and spend less\n",
@@ -133,7 +131,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aggressive-cambridge",
+   "id": "strategic-newspaper",
    "metadata": {},
    "source": [
     "## Functions that return values\n",
@@ -144,7 +142,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "assisted-assault",
+   "id": "imposed-pregnancy",
    "metadata": {},
    "outputs": [
     {
@@ -167,7 +165,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "polar-marshall",
+   "id": "unsigned-recipe",
    "metadata": {},
    "source": [
     "When you run `State`, it returns a new `State` object:"
@@ -176,7 +174,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "upper-closer",
+   "id": "accessible-wallace",
    "metadata": {},
    "outputs": [
     {
@@ -234,7 +232,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "qualified-complaint",
+   "id": "missing-pendant",
    "metadata": {},
    "source": [
     "Not all functions have return values. For example, when you run `step`,\n",
@@ -246,7 +244,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "exceptional-philosophy",
+   "id": "minimal-supervisor",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,7 +254,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sporting-japan",
+   "id": "sized-intensity",
    "metadata": {},
    "source": [
     "`add_five` takes a parameter, `x`, which could be any number. It\n",
@@ -267,7 +265,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "differential-cooperation",
+   "id": "warming-program",
    "metadata": {},
    "outputs": [
     {
@@ -287,7 +285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "pending-detail",
+   "id": "rental-representation",
    "metadata": {},
    "source": [
     "As a more useful example, here's a version of `run_simulation` that\n",
@@ -298,7 +296,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "historic-hollow",
+   "id": "sitting-cleveland",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -316,7 +314,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "corporate-watershed",
+   "id": "minimal-ability",
    "metadata": {},
    "source": [
     "We can call `run_simulation` like this:"
@@ -325,7 +323,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "sustained-centre",
+   "id": "difficult-shepherd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -334,7 +332,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "assumed-bosnia",
+   "id": "charming-wheel",
    "metadata": {},
    "source": [
     "The result is a `State` object that represents the final state of the system, including the metrics we'll use to evaluate the performance of the system:"
@@ -343,7 +341,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "relevant-colonial",
+   "id": "tough-sweet",
    "metadata": {},
    "outputs": [
     {
@@ -361,7 +359,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eight-singapore",
+   "id": "aggregate-lightweight",
    "metadata": {},
    "source": [
     "The simulation we just ran starts with `olin=10` and `wellesley=2`, and uses the values `p1=0.3`, `p2=0.2`, and `num_steps=60`. \n",
@@ -378,7 +376,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "better-violation",
+   "id": "valuable-aircraft",
    "metadata": {},
    "source": [
     "## Loops and arrays\n",
@@ -400,7 +398,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "mexican-cocktail",
+   "id": "bound-juice",
    "metadata": {},
    "outputs": [
     {
@@ -423,7 +421,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "upper-christianity",
+   "id": "ordered-colleague",
    "metadata": {},
    "source": [
     "The arguments indicate where the sequence should start and stop, and how\n",
@@ -439,7 +437,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "broadband-prairie",
+   "id": "commercial-methodology",
    "metadata": {},
    "outputs": [
     {
@@ -461,7 +459,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hired-community",
+   "id": "finnish-budapest",
    "metadata": {},
    "source": [
     "When this loop runs, it\n",
@@ -479,7 +477,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fuzzy-blend",
+   "id": "crazy-belize",
    "metadata": {},
    "source": [
     "## Sweeping parameters\n",
@@ -501,7 +499,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "oriented-mistress",
+   "id": "working-chair",
    "metadata": {},
    "outputs": [
     {
@@ -529,7 +527,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "terminal-warren",
+   "id": "chicken-mainstream",
    "metadata": {},
    "source": [
     "Each time through the loop, we run a simulation with a different value\n",
@@ -546,7 +544,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "foster-forestry",
+   "id": "instrumental-session",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -557,7 +555,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "crazy-storm",
+   "id": "listed-orleans",
    "metadata": {},
    "source": [
     "And add values like this:"
@@ -566,7 +564,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "hollywood-municipality",
+   "id": "hollywood-technical",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -577,7 +575,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "daily-recipient",
+   "id": "instructional-showcase",
    "metadata": {},
    "source": [
     "The result is a `SweepSeries` that maps from each value of `p1` to the\n",
@@ -586,13 +584,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "cooked-intent",
+   "execution_count": 49,
+   "id": "hollywood-spirit",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -604,14 +602,18 @@
     }
    ],
    "source": [
-    "from modsim import plot\n",
+    "from modsim import decorate\n",
     "\n",
-    "plot(sweep, label='Olin')"
+    "sweep.plot(label='Olin')\n",
+    "\n",
+    "decorate(title='Olin-Wellesley Bikeshare',\n",
+    "         xlabel='Customer rate at Olin (p1 in customers/min)', \n",
+    "         ylabel='Number of unhappy customers')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "opened-medicare",
+   "id": "intense-start",
    "metadata": {},
    "source": [
     "NumPy provides functions that compute a variety of summary statistics, like `mean`, `median`, and `std` (which computes standard deviation).\n",
@@ -622,7 +624,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "rural-shark",
+   "id": "contemporary-enough",
    "metadata": {},
    "outputs": [
     {
@@ -644,7 +646,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hydraulic-baseball",
+   "id": "owned-senior",
    "metadata": {},
    "source": [
     "In this example, computing the mean might not be useful, but in the exercises below, it will be."
@@ -652,7 +654,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "portuguese-selection",
+   "id": "korean-christianity",
    "metadata": {},
    "source": [
     "## Incremental development\n",
@@ -699,7 +701,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "indonesian-assumption",
+   "id": "nominated-assault",
    "metadata": {},
    "source": [
     "## Summary"
@@ -707,7 +709,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "attempted-confidence",
+   "id": "appreciated-preview",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -715,7 +717,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "prerequisite-sunglasses",
+   "id": "primary-quest",
    "metadata": {},
    "source": [
     "**Exercise:** Write a function called `make_state` that creates a `State` object with the state variables `olin=10` and `wellesley=2`, and then returns the new `State` object.\n",
@@ -726,7 +728,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "restricted-destruction",
+   "id": "reflected-freedom",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -740,7 +742,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "fallen-jungle",
+   "id": "north-formation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -751,7 +753,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "classified-clerk",
+   "id": "robust-blair",
    "metadata": {},
    "source": [
     "**Exercise:** Read the documentation of `linspace` at <>.\n",
@@ -761,7 +763,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "dimensional-gentleman",
+   "id": "collected-butter",
    "metadata": {},
    "outputs": [
     {
@@ -793,7 +795,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "local-stretch",
+   "id": "fleet-debut",
    "metadata": {},
    "source": [
     "**Exercise:** Wrap the code from this chapter in a function named `sweep_p1` that takes an array called `p1_array` as a parameter.  It should create a new `SweepSeries`, run a simulation for each value of `p1` in `p1_array`, store the results in the `SweepSeries`, and return the `SweepSeries`.\n",
@@ -804,7 +806,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "underlying-samoa",
+   "id": "authorized-sarah",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -825,7 +827,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "auburn-junction",
+   "id": "romance-wisdom",
    "metadata": {},
    "outputs": [
     {
@@ -844,19 +846,17 @@
    "source": [
     "# Solution\n",
     "\n",
-    "from modsim import decorate\n",
-    "\n",
     "p1_array = linspace(0, 1, 101)\n",
     "sweep = sweep_p1(p1_array)\n",
-    "plot(sweep, label='Olin')\n",
+    "sweep.plot(label='Olin')\n",
     "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Arrival rate at Olin (p1 in customers/min)', \n",
+    "         xlabel='Customer rate at Olin (p1 in customers/min)', \n",
     "         ylabel='Number of unhappy customers')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "mineral-missile",
+   "id": "developmental-broad",
    "metadata": {},
    "source": [
     "**Exercise:** Write a function called `sweep_p2` that runs simulations with `p1=0.5` and a range of values for `p2`.  It should store the results in a `SweepSeries` and return the `SweepSeries`.\n"
@@ -865,7 +865,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "false-lightning",
+   "id": "norman-banana",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -886,7 +886,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "neural-detail",
+   "id": "mexican-robert",
    "metadata": {},
    "outputs": [
     {
@@ -907,16 +907,16 @@
     "\n",
     "p2_array = linspace(0, 1, 101)\n",
     "sweep = sweep_p2(p2_array)\n",
-    "plot(sweep, label='Olin')\n",
+    "sweep.plot(label='Olin')\n",
     "\n",
     "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Arrival rate at Wellesley (p2 in customers/min)', \n",
+    "         xlabel='Customer rate at Wellesley (p2 in customers/min)', \n",
     "         ylabel='Number of unhappy customers')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "junior-neighbor",
+   "id": "separate-mention",
    "metadata": {},
    "source": [
     "## Optional Exercises\n",
@@ -926,7 +926,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "marked-scale",
+   "id": "bearing-orbit",
    "metadata": {},
    "source": [
     "**Exercise:** Because our simulations are random, the results vary from one run to another, and the results of a parameter sweep tend to be noisy.  We can get a clearer picture of the relationship between a parameter and a metric by running multiple simulations with the same parameter and taking the average of the results.\n",
@@ -953,7 +953,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "facial-grass",
+   "id": "accredited-salmon",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -972,7 +972,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "tropical-stream",
+   "id": "visible-allowance",
    "metadata": {},
    "outputs": [
     {
@@ -1084,7 +1084,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "documentary-murder",
+   "id": "spatial-fundamentals",
    "metadata": {},
    "outputs": [
     {
@@ -1106,7 +1106,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ecological-andrews",
+   "id": "structural-expense",
    "metadata": {},
    "source": [
     "**Exercise:**  Continuting the previous exercise, use `run_multiple_simulations` to run simulations with a range of values for `p1` and\n",
@@ -1125,7 +1125,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "urban-parts",
+   "id": "reverse-emphasis",
    "metadata": {
     "scrolled": true
    },
@@ -1147,7 +1147,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "inappropriate-cooperation",
+   "id": "broad-latitude",
    "metadata": {
     "scrolled": true
    },
@@ -1168,17 +1168,17 @@
    "source": [
     "# Solution\n",
     "\n",
-    "plot(sweep, label='total', color='green')\n",
+    "sweep.plot(label='total', color='green')\n",
     "    \n",
     "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Arrival rate at Olin (p1 in customers/min)', \n",
+    "         xlabel='Customer rate at Olin (p1 in customers/min)', \n",
     "         ylabel='Average total unhappy customers')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "premier-complaint",
+   "id": "powerful-planner",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap05.ipynb b/jupyter/chap05.ipynb
index 2d73f78a..39cea07e 100644
--- a/jupyter/chap05.ipynb
+++ b/jupyter/chap05.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "virtual-fireplace",
+   "id": "wicked-mozambique",
    "metadata": {},
    "source": [
     "# Chapter 5"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "valid-islam",
+   "id": "inclusive-theater",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "driven-transcript",
+   "id": "legislative-rating",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "imposed-israel",
+   "id": "therapeutic-genealogy",
    "metadata": {},
    "source": [
     "In this chapter..."
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "handmade-direction",
+   "id": "informal-contractor",
    "metadata": {},
    "source": [
     "# World population\n",
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "guilty-kenya",
+   "id": "dying-browse",
    "metadata": {},
    "source": [
     "## World Population Data\n",
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "raised-calendar",
+   "id": "continuing-cancellation",
    "metadata": {},
    "source": [
     "The following cell downloads a copy of https://en.wikipedia.org/wiki/World_population_estimates"
@@ -105,7 +105,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "thrown-violence",
+   "id": "convenient-stroke",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "representative-canal",
+   "id": "acknowledged-bracelet",
    "metadata": {},
    "source": [
     "To read this data, we will use Pandas, which provides functions for\n",
@@ -130,7 +130,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "lined-footage",
+   "id": "israeli-finding",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,7 +139,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "amber-batman",
+   "id": "regulation-parade",
    "metadata": {},
    "source": [
     "Now we can use it like this:"
@@ -148,7 +148,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "authentic-singer",
+   "id": "instant-beverage",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -161,7 +161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "julian-intranet",
+   "id": "dried-immunology",
    "metadata": {},
    "source": [
     "The arguments are:\n",
@@ -196,7 +196,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "recreational-albania",
+   "id": "driving-wrapping",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,7 +205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "postal-breeding",
+   "id": "simplified-convert",
    "metadata": {},
    "source": [
     "This line selects the third table (numbered 2), which contains\n",
@@ -217,7 +217,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "conservative-investigation",
+   "id": "running-alcohol",
    "metadata": {},
    "outputs": [
     {
@@ -404,7 +404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adolescent-password",
+   "id": "valid-editing",
    "metadata": {},
    "source": [
     "The first column, which is labeled `Year`, is special.  It is the **index** for this `DataFrame`, which means it contains the labels for the rows.\n",
@@ -416,7 +416,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "naughty-macintosh",
+   "id": "plastic-senate",
    "metadata": {},
    "source": [
     "The column labels are long strings, which makes them hard to work with.\n",
@@ -426,7 +426,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "suspected-integer",
+   "id": "engaging-regular",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -437,7 +437,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "about-mistress",
+   "id": "communist-crowd",
    "metadata": {},
    "source": [
     "Now we can select a column from the `DataFrame` using the dot operator,\n",
@@ -449,7 +449,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "cleared-protection",
+   "id": "amended-negative",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -458,7 +458,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "rational-sweden",
+   "id": "absent-heart",
    "metadata": {},
    "source": [
     "The result is a Pandas `Series`, which is similar to the `TimeSeries` and `SweepSeries` objects we've been using.\n",
@@ -473,7 +473,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "portable-knowing",
+   "id": "graduate-specialist",
    "metadata": {},
    "outputs": [
     {
@@ -499,7 +499,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "contemporary-telephone",
+   "id": "brilliant-brook",
    "metadata": {},
    "source": [
     "The left column is the **index** of the `Series`; in this example it contains the dates.\n",
@@ -513,7 +513,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "revolutionary-prescription",
+   "id": "linear-admission",
    "metadata": {},
    "outputs": [
     {
@@ -540,7 +540,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cleared-institution",
+   "id": "mental-sussex",
    "metadata": {},
    "source": [
     "The most recent estimate we have from the U.N. is for 2015, so the value for 2016 is `NaN`.\n",
@@ -551,12 +551,39 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "sharp-woman",
+   "id": "ordered-garage",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "def plot_estimates():\n",
+    "    census.plot(style=':', label='US Census')\n",
+    "    un.plot(style='--', label='UN DESA')\n",
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)') "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "invisible-mouse",
+   "metadata": {},
+   "source": [
+    "The keyword argument `style=':'` specifies a dotted line; `style='--'` specifies a dashed line.\n",
+    "The `label` argument provides the string that appears in the legend.\n",
+    "\n",
+    "And here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "periodic-weekend",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -568,30 +595,22 @@
     }
    ],
    "source": [
-    "from modsim import plot, decorate\n",
-    "\n",
-    "plot(census, style=':', label='US Census')\n",
-    "plot(un, style='--', label='UN DESA')\n",
-    "\n",
-    "decorate(ylabel='World population (billion)')"
+    "plot_estimates()\n",
+    "decorate(title='World Population Estimates')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "detected-evidence",
+   "id": "labeled-magic",
    "metadata": {},
    "source": [
-    "The keyword argument `style=':'` specifies a dotted line; `style='--'` specifies a dashed line.\n",
-    "\n",
-    "The `label` argument provides the strings that appear in the legend.\n",
-    "\n",
-    "The lines overlap almost completely, but it looks like the most recent estimates diverge slightly.\n",
+    "The lines overlap almost completely, but the most recent estimates diverge slightly.\n",
     "In the next section, we'll quantify these differences."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "afraid-patio",
+   "id": "tender-summer",
    "metadata": {},
    "source": [
     "## Absolute and relative errors\n",
@@ -604,8 +623,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "norwegian-spank",
+   "execution_count": 13,
+   "id": "secondary-player",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -614,8 +633,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "married-account",
+   "execution_count": 14,
+   "id": "caring-garlic",
    "metadata": {},
    "outputs": [
     {
@@ -630,7 +649,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -642,7 +661,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "polyphonic-agency",
+   "id": "bridal-extraction",
    "metadata": {},
    "source": [
     "When you subtract two `Series` objects, the result is a new `Series`.\n",
@@ -653,8 +672,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "british-dayton",
+   "execution_count": 15,
+   "id": "corresponding-emerald",
    "metadata": {},
    "outputs": [
     {
@@ -663,7 +682,7 @@
        "0.029034508242424265"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -676,7 +695,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "spectacular-distance",
+   "id": "identical-archive",
    "metadata": {},
    "source": [
     "On average, the estimates differ by about 0.029 billion.\n",
@@ -685,8 +704,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "administrative-store",
+   "execution_count": 16,
+   "id": "headed-witch",
    "metadata": {
     "scrolled": true
    },
@@ -697,7 +716,7 @@
        "0.10157921199999986"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -710,7 +729,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "saved-smell",
+   "id": "composite-partnership",
    "metadata": {},
    "source": [
     "In the worst case, they differ by about 0.1 billion.\n",
@@ -723,8 +742,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "embedded-procurement",
+   "execution_count": 17,
+   "id": "advisory-complex",
    "metadata": {
     "scrolled": true
    },
@@ -741,7 +760,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -753,7 +772,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "geographic-ordering",
+   "id": "greater-register",
    "metadata": {},
    "source": [
     "I multiplied by 100 so we can interpret the results as a percentage.  In 2015, the difference between the estimates is about 1.4%, and that happens to be the maximum.\n",
@@ -763,8 +782,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "compressed-survivor",
+   "execution_count": 18,
+   "id": "frank-owner",
    "metadata": {},
    "outputs": [
     {
@@ -773,7 +792,7 @@
        "0.5946585816022846"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -784,7 +803,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "statistical-violence",
+   "id": "peaceful-fleece",
    "metadata": {},
    "source": [
     "The mean relative error is about 0.6%.\n",
@@ -797,7 +816,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "insured-swing",
+   "id": "vietnamese-excuse",
    "metadata": {},
    "source": [
     "## Modeling Population Growth\n",
@@ -818,8 +837,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "absent-minimum",
+   "execution_count": 19,
+   "id": "undefined-sauce",
    "metadata": {},
    "outputs": [
     {
@@ -828,7 +847,7 @@
        "2.557628654"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -839,7 +858,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cooked-beast",
+   "id": "precise-correlation",
    "metadata": {},
    "source": [
     "So we can get the total growth during the interval like this:"
@@ -847,8 +866,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "historic-buddy",
+   "execution_count": 20,
+   "id": "postal-debate",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -857,7 +876,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sufficient-oracle",
+   "id": "dutch-sample",
    "metadata": {},
    "source": [
     "The numbers in brackets are called **labels**, because they label the\n",
@@ -873,8 +892,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "focused-maryland",
+   "execution_count": 21,
+   "id": "functional-trick",
    "metadata": {},
    "outputs": [
     {
@@ -883,7 +902,7 @@
        "1950"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -895,7 +914,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "special-fireplace",
+   "id": "empty-siemens",
    "metadata": {},
    "source": [
     "So `t_0` is the label of the first element, which is 1950.\n",
@@ -904,8 +923,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "employed-commission",
+   "execution_count": 22,
+   "id": "acoustic-south",
    "metadata": {},
    "outputs": [
     {
@@ -914,7 +933,7 @@
        "2016"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -926,7 +945,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "supreme-finder",
+   "id": "express-metallic",
    "metadata": {},
    "source": [
     "The value `-1` indicates the last element; `-2` indicates the second to last element, and so on.\n",
@@ -936,8 +955,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "critical-carrier",
+   "execution_count": 23,
+   "id": "planned-remains",
    "metadata": {},
    "outputs": [
     {
@@ -946,7 +965,7 @@
        "66"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -958,7 +977,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "difficult-banks",
+   "id": "bridal-royal",
    "metadata": {},
    "source": [
     "Now we can use `t_0` and `t_end` to select the population at the beginning and end of the interval."
@@ -966,8 +985,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "executive-pressure",
+   "execution_count": 24,
+   "id": "quarterly-aggregate",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -977,7 +996,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "unnecessary-pontiac",
+   "id": "driven-castle",
    "metadata": {},
    "source": [
     "And compute the total growth during the interval."
@@ -985,8 +1004,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "classical-proportion",
+   "execution_count": 25,
+   "id": "prescription-optics",
    "metadata": {},
    "outputs": [
     {
@@ -995,7 +1014,7 @@
        "4.768368055"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1007,7 +1026,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "acknowledged-tenant",
+   "id": "accepted-auditor",
    "metadata": {},
    "source": [
     "Finally, we can compute average annual growth."
@@ -1015,8 +1034,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "double-short",
+   "execution_count": 26,
+   "id": "convertible-patch",
    "metadata": {},
    "outputs": [
     {
@@ -1025,7 +1044,7 @@
        "0.07224800083333333"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1037,7 +1056,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "compound-linux",
+   "id": "japanese-merit",
    "metadata": {},
    "source": [
     "From 1950 to 2016, world population grew by about 0.07 billion people per year, on average.\n",
@@ -1046,7 +1065,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "moved-spotlight",
+   "id": "public-verification",
    "metadata": {},
    "source": [
     "## Simulation\n",
@@ -1058,8 +1077,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "imperial-gardening",
+   "execution_count": 27,
+   "id": "duplicate-leave",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1070,7 +1089,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "virgin-network",
+   "id": "expired-salmon",
    "metadata": {},
    "source": [
     "In this example, I use the keyword arguments `time` and `quantity` to give names to the index and the values.\n",
@@ -1081,8 +1100,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "suburban-deficit",
+   "execution_count": 28,
+   "id": "convenient-thong",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1091,7 +1110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "checked-neutral",
+   "id": "constant-casino",
    "metadata": {},
    "source": [
     "Here's what it looks like so far."
@@ -1099,8 +1118,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "defensive-fifty",
+   "execution_count": 29,
+   "id": "israeli-surveillance",
    "metadata": {},
    "outputs": [
     {
@@ -1146,7 +1165,7 @@
        "1950    2.557629"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1159,7 +1178,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "economic-profession",
+   "id": "stretch-snapshot",
    "metadata": {},
    "source": [
     "Now we set the rest of the values by simulating annual growth:"
@@ -1167,8 +1186,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "id": "upset-amsterdam",
+   "execution_count": 30,
+   "id": "attended-morris",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1178,7 +1197,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "underlying-biology",
+   "id": "signed-colleague",
    "metadata": {},
    "source": [
     "The values of `t` go from from `t_0` to `t_end`, including the first but not the last.\n",
@@ -1192,13 +1211,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "id": "experimental-investigation",
+   "execution_count": 31,
+   "id": "wrong-brooks",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1210,20 +1229,14 @@
     }
    ],
    "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "plot(census, style=':', label='US Census')\n",
-    "plot(un, style='--', label='UN DESA')\n",
-    "plot(results, color='gray', label='model')\n",
-    "\n",
-    "decorate(xlabel='Year', \n",
-    "         ylabel='World population (billion)',\n",
-    "         title='Constant growth')"
+    "results.plot(color='gray', label='model')\n",
+    "plot_estimates()\n",
+    "decorate(title='Constant Growth Model')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "younger-abuse",
+   "id": "automated-albany",
    "metadata": {},
    "source": [
     "The keyword argument `color='gray'` specifies the color of the line.  For details on color specification see <https://modsimpy.com/color>.\n",
@@ -1233,7 +1246,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "similar-characterization",
+   "id": "unnecessary-million",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -1255,7 +1268,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "equivalent-louis",
+   "id": "advanced-ivory",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -1263,7 +1276,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "excessive-cream",
+   "id": "hearing-today",
    "metadata": {},
    "source": [
     "Here's the code from this chapter all in one place."
@@ -1271,8 +1284,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "foreign-support",
+   "execution_count": 32,
+   "id": "terminal-reynolds",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1295,13 +1308,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "inner-harvest",
+   "execution_count": 33,
+   "id": "organizational-memphis",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1313,18 +1326,14 @@
     }
    ],
    "source": [
-    "plot(census, style=':', label='US Census')\n",
-    "plot(un, style='--', label='UN DESA')\n",
-    "plot(results, color='gray', label='model')\n",
-    "\n",
-    "decorate(xlabel='Year', \n",
-    "         ylabel='World population (billion)',\n",
-    "         title='Constant growth')"
+    "results.plot(color='gray', label='model')\n",
+    "plot_estimates()\n",
+    "decorate(title='Constant Growth Model')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "narrative-state",
+   "id": "ecological-welsh",
    "metadata": {},
    "source": [
     "**Exercise:**  Try fitting the model using data from 1970 to the present, and see if that does a better job.\n",
@@ -1338,8 +1347,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "happy-empire",
+   "execution_count": 34,
+   "id": "rising-anger",
    "metadata": {},
    "outputs": [
     {
@@ -1348,7 +1357,7 @@
        "0.07854997754347826"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1372,8 +1381,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "tutorial-fundamentals",
+   "execution_count": 35,
+   "id": "false-handbook",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1388,13 +1397,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "id": "beautiful-harvest",
+   "execution_count": 36,
+   "id": "political-loading",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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BodMFxMfH0717dxwOh3djvgSPJygRqQIMA/5wkfV3AncC56ZhK6WUKoWjW6zhvP6PQas4iH2CeduPc9f763m8xxFO71pFYV4e1/SIZuDAgYSEhHg7YreUxYm61wAbjDHHL7TSGDPFGBNtjIkOCwsrg3BKZ//+/XTq1Om8ZU899RR///vfAVi9ejU9evQgMjKS9u3b89RTT11wP2vXrqV///60bdv23MVns7KyPB2+UqoiyzoJcx6BKQMgdQ9nTmew7UgGBmjESeJqnuDY5qU0bdqUe+65h2uvvbbcJCcomyG+cVTg4b2JEycya9YsunTpQmFhIbt27frFNsePH2fMmDF8+umn9OrVC2MMX3zxBWfOnClXfyxKKR+y+VOY+wfIyYDud8LAPzDh/W2czlrHhNCDHDiQTNewMOLjb6JVq1bejvayeLQHJSIhwGDgS0++jjelpKTQsGFDABwOBx06dPjFNm+++SYTJ06kV69egHV9vtGjR1O/fn0yMzO57bbb6NatG1FRUXz99dcAfPjhh4wcOZIhQ4bQunVrHn/8cQAKCwuZNGkSnTp1onPnzvzzn/8EYODAgRSd5Jyamkp4eDgA27ZtO1fWIyIigj179nj0/VBKlZH8LEy9DiyP+5K8wS9wpsDBoJqpdDm7jtTUE1x77bXcfffd5TY5gYd7UMaYLKDuldzn2HdWMfrqJoyJbkp+oZOb313Djd2b8puoJmTnFTLpg7Xc3LM513dpxOmcfO6Yuo5b+4QzpFNDTmbmcc+09dzRryVxHeqTciaHetWDflU8Dz/8MG3btmXgwIEMGTKEiRMnEhR0/j63bt3KxIkTL/j8v/71r8TExPD++++Tnp5O9+7diYuLA2DTpk1s3LiRwMBA2rZtywMPPEBKSgqHDx9m69atAKSnp5cY39tvv83kyZMZP348eXl5WndKqfLq9BFIfAKa94bo26DrJFbXGsbN767hzuTFsH8NhYWFXNOnB/379//F51B5pBeLvYSLTb8sWv7EE0+wbt064uPj+eSTT0p9xfX58+fz4osvEhkZycCBA8nJyeHAgQOAdVHZmjVrEhQURIcOHUhOTqZly5YkJSXxwAMPMHfuXGrUqFHi/nv16sXzzz/PSy+9RHJyMsHBwaWKTynlZQW5sOwVeD0atn/D/iMpzNlyBCNC1awjDKt1mLzdy2nVqhX33Xcf8fHxFSI5QTm81NHMu3qdux/g8DvvcXAVx3mPawQFnPe4TtUq5z12p/dUt27dc1chL3Ly5ElatGhx7vFVV13FPffcwx133EFYWBhpaWnUrfu/jmPHjh1Zv349w4cP/8X+i45HtW3b9rzla9asOa/Mh8PhoKCggNq1a7N582bmzZvHm2++yaxZs3j//ffx9/fH6bRKMefk5Jx73k033USPHj347rvvSEhI4N133yUmJuaS7VZK+YCkpfDtZDj1M7QdCgl/5cmvTnB41w6OrvkvR44cpmODBiQkTDw3rF+RaA/qEqpVq0bDhg1ZuHAhYCWnuXPn0rdvXwC+++67c6Xc9+zZg8PhoFatWuft4/7772fq1KmsWbPm3LJp06Zx7NgxEhISeP3118/tY+PGjSXGk5qaitPpZNSoUTz77LNs2LABgPDwcNavXw/A559/fm77pKQkWrZsyYMPPsiwYcPOlaNXSpUDzgLycfBmk7+RPvxD0v1qMyD4IL0yV3D6dAbDhg3jjjvuqJDJCcphD8obPvroI+677z4effRRwCqjcdVVVwHw8ccf8/DDDxMSEoK/vz/Tp0//xclv9evX59NPP+Wxxx4jJSUFPz8/+vfvz8iRI/nLX/7CQw89REREBMYYwsPDmTNnzkVjOXz4MLfeeuu53lJR0cPHHnuMG264gY8//vi8HtLMmTOZNm0aAQEBNGjQgCeeeOKKvjdKqSsoJwOWvmxdASLmz9AqlqQxC5jyn7WY75aSsXstADED+tGnT58yrc3kDVpuQ+l7rJS3OZ2waRosfAYyU9nVdAz/bfoYk+Nas2nTJuYvWkJu5hk6d+587th0RaLlNpRSyhcd+wm+vh+OboKmPWD8Z0xd48/ePUcJTlrC8ePHaNKkCQk33uC1woHeoglKKaW8yVGFgsw03g/7I7HX30stRx4dshOpcmIXObVqMmrUqDIrHOhrNEEppVRZys+Gla/Dqf0w4i0Ia8up29fwwZurODV/OXk/r8Pf35/YWN8pHOgtmqCUUqosGAPbvrSKB2Yc5ECDwbwxaz0vjIrk513bGObYQM6+bKKifK9woLdoglJKKU87mQRf3QcHVkL9zvCbt/lufwP2bjnI62+9Q8bJE7Ro0YL4+HifLBzoLZqglFLKU4pqNAXWoOD0MabWfoh2sffSOkSoenA+ESf34ahbhxtvvJE2bdpUyuNMJdETdS/hUuU2Jk2aROPGjcnNzQXOv1BrcQ6Hg8jISDp27EiXLl145ZVXzp3PtGTJEmrWrElkZOS524IFCwDren0dO3YkIiKCyMjI8074LSgoIDQ0lD/84YLltpRS3pCfA8v+AR8NwzgLoWooBfeu5QsTQ+KKH3n77bc5euQwCQnx3HvvvT5d1dabtAd1BTgcDt5//33uueeeErcLDg5m06ZNgHUV9JtuuomMjAyefvppAPr16/eLk3RXrVrFnDlz2LBhA4GBgaSmppKXl3du/fz582nbti2zZs3i+eef1z9ypbzJGNg22z7OdIBD9WP409uLmfLbAWxY9yP9sleRfzqP6OjyVTjQW7QHdQU89NBD/POf/6SgoMDt59SrV48pU6bwxhtvUNLJ0kePHiU0NPTcdflCQ0Np1KjRufUzZsxg8uTJNGvWjNWrV19+I5RSv07GYXg/AT6/FRNUHW75ho293yTX6eDVt94hMTGR5s2blcvCgd5S/npQHwz95bKOI6D7HZCXBdPH/HJ95E0QNR4y02DWLeevu/W7Xx1Ss2bN6Nu3Lx9//DHXX3+9289r2bIlTqeTlJQUAJYtW0ZkZOS59V988QXx8fE888wztGnThri4OMaOHcuAAQMAyM7OZuHChbzzzjukp6czY8aMczWnlFJlpLAAHP7WMJ7TMKX6g4RefRv9goNIXTqP9mnJVA8LY9T48eW6NpM3aA/qEi5VbqPIH//4R/72t7+dO6bkLtfeU79+/di0adO521VXXUW1atVYv349U6ZMISwsjLFjx/Lhhx8CMGfOHAYNGkRISAijRo1i9uzZWu9JqbKSexYW/RXe6kFu9hnwD8Rx+zw21LmObT/9xJQpU0hNPcHQoUPLfeFAbyl/PaiSejxVQkpeX7VuqXtM7pTbAGjVqhWRkZHMmjXL7X0nJSXhcDioV68eO3bsuOh2DoeDgQMHMnDgQDp37szUqVOZNGkSM2bMYMWKFecmZaSlpbF48eJzBQ+VUh7gLIRN02HRc3D2OPsbXsO9ry5k+gMJbFm/lpZHV+B0OunRuzf9+vWrMLWZvKH8Jagy5lpuIzY29ly5jcmTJ/9i2z/96U8MHXqBIcgLOHHiBHfffTf3339/iRMbdu3ahZ+fH61btwasKrvNmzfn9OnTLF++nIMHD547PvXBBx8wY8YMTVBKeUpmKnw0HI5vxdm4G35jp3PS2YqG8zbz73emkH82nfbt2xMXF0edOnW8HW25pwnKDSWV23DVsWNHunbteq5GU3HZ2dlERkaSn5+Pv78/EyZM4JFHHjm3vvgxqD//+c+0aNGCBx54gPT0dPz9/WnVqhVTpkzhyy+/JCYm5ryihsOHD+fxxx8nNzf3vOVKqV8pOx2Ca0FIXfLDOvB8+jVUbTqasVRlQ+IXNDt6mNCGDUkYPYLmzZt7O9oKQ8ttKH2PlbqYsymw+HnY+gWpk5YR2jAcgOe+2Qwn9lF46CeqVatGbGwsXbp00dM8LpOW21BKKXflZcHqN2H5q1CQw84mNzDxrR/59P7aJG1dB1tWISL079+/UhQO9JYSE5SI9AJuBvoBDYFsYCvwHTDNGJPh8QiVUqos5Z6Ft3pCxkFyW11L4JBnqerXkP5z1jFj6nvkZ58hIiKCmJiYClc40NdcNEGJyPfAEeBr4K9AChAEtAEGAV+LyCvGmG88HaQxRrvOHuJLQ7xKedWJXRDWFgKrURA1kfuXBxLk34/JZxzMm/cx1Y4fp0HTpiQkjKVx48bejrZSKKkHNcEYk1ps2Vlgg337h4iEeiwyW1BQEGlpadStW1eT1BVmjCEtLU2nwarKLWUHJD6B2ZPIjmFz6NC1L/4Df0dvv52k7dvCRx/toGbNyl040FsumqAukJwua5tfq0mTJhw6dIgTJ054+qUqpaCgoEpXRlopAM4csyZAbPwYqlRnQ5uHuWnWCabXSiF19waSf/wRf39/YmJi6NWrF/7+esi+rF3yHReRkcBLQD1A7JsxxtTwcGwABAQE/OKkWKWU+lXyc+DtvpjsdNI73Urta/5MK6pyu/9qFnz2Ibk5WjjQF7jzleBl4HpjzMUvdaCUUr6usAB2fw/troOAIAqHvMy4b7OpcqoVTxxKJTFxBtmpqVo40Ie4k6COa3JSSpVbxsDuebDgSTixk8U932dgwkgcnUdye/4+kjavZsaMvdSpo4UDfY07CWqdiMwEvgJyixYaY770VFBKKXVFHN4AiU/A/mVQ5yrWdHuVW5cEMrXZEc4kbTxXZy0hIYFu3brhcDi8HbFy4U6CqgFkAfEuywygCUop5bsK82HmzeTnZrM/+klaX/MAkYXwhHMlq775mLy8PLp168aAAQO0NpOPumSCMsbcerk7F5FawLtAJ6ykdpsxZtXl7k8ppUqUdRJ+fBf6TAb/QJw3TOfGz1NwHK7BU7v2kJiYSHp6Om3atGHw4MGEhnr8TBn1K7gzi68J8DrQByvJLAcmG2MOubH/fwFzjTGjRaQKoF9TlFJXXkEurJ0CP/wdk5PBzCP1GDF6AkFNovh9ws9sWrmEzz47QL169ZgwYQItW7b0dsTKDe4M8X0AfAIUlaq92V42uKQniUgNoD8wCcAYkwfkXW6gSin1C04nbP0CFj0D6QegVRyb2z7Cn2dnUKfDIbJ+3sjmzZsJCQnhuuuuIyoqCj8/rdNaXriToMKMMR+4PP5QRB5y43ktgRPAByLSBViP1fPKdN1IRO4E7gSrdLpSSpWGWfU66c4Q1kdPIe66sXTMz+fvJ5ez7rvpOJ1OemvhwHLLnQSVKiI3AzPsx+OANDf33RV4wBizRkT+Bfwe+IvrRsaYKcAUsMptuBu4UqqSOrELlr4E1/4dQuogN83i/748QPZxQ9jmzSxatIjTp0/ToUMH4uLiqF27trcjVpfJnb7ubcANwDHgKDDaXnYph4BDxpg19uPPsRKWUkqV3tkTMOcReKsXzt3zefXjzzlxJheqN+DhPmH0K9zCV199RdWqVZk0aRJjxozR5FTOuTOL7wAwrLQ7NsYcE5GDItLWGLMLiAW2X0aMSqnKzBhY/gos+ycUZEO333Kg0318PHU37fYd5ezedWzbto3q1aszfPhwLRxYgZRUbuNxY8zLIvI61uy98xhjHnRj/w8A0+0ZfEnAZU9ZV0pVUiJwZCNJNaKZ2+Bu7r12CA1zc3mux27WzZmmhQMrsJJ6UEWXN1pXwjYlMsZsAn5RxlcppUqUvMq6NNGwNyCsDYx6j4+/38vxjBx+XLeOpUuWkJmZSUREBLGxsdSoUSbXrlZlrKRyG9/aP6eWXThKqUrt1H5IfBK2f0VB1YY8+8kCxo1tQLsGNbipfRALFyzjv98dp2nTpowbN04LB1ZwJQ3xfcsFhvaKGGNKfVxKKaUuavELsPyfGD8HMvCPnI26i9Xvb6HboRNsXPwdu3fvplatWowePZoOHTrocaZKoKQhvr+XWRRKqcrJGOsYE0B+JnvC4vhPlQm8NGAIgTk5PNQuk3Xff4K/vz+xsbH07NlTCwdWIiUN8S0ty0CUUpXMgTXw/eOYuKeQqwbB4GdZtmI/mcknWb5qDauW/0BOTg5RUVEMGjRICwdWQiUN8f1EyUN8ER6JSClVsZ0+Yh1n+mkWhdUa8tKczfRK6MjAtmH0Ccsja9MqFu1No0WLFiQkJFC/fn1vR6y8pKS+8nVlFoVSqnJYMwUWPIVxFiD9HqOw12Q2fbSN8BNpTPtxHklJSdStW5dx48bRunVrPc5UyZU0xJdcloEopSqwomNNIhyu24vn8sfx2sCR5Odkc3OjVDYtTiQwMJAhQ4YQHR2thQMVUPIQ33JjTF8ROYM11CeuP40xeuKBUqpkaftg7h+g/XWYqAlIt9+yo9owMlftJ3HJcjavXUFBQQHdu3dnwIABBAcHezti5UNK6kH1tX9WL7twlFIVQl4mLPsHrHwd46jCRyfbI7nJTOjZnAbOE3Q9s5ofl2vhQFUyt+ZrikhXoC92wUJjzEaPRqWUKr/2LoBvJsPpQxBxI8Q9xYqvDhORkcGHH37IgQNaOFC5x52Kuk9gFSv80l70oYh8Zox5zqORKaXKJ2M461eNJ6q+yJNDfosUZDOk2kF+WrOFnKpVtXCgcps7PahxQJQxJgdARF4ENgCaoJRSkJ8Ny18FPwem/++Q1oNJHn01yd/sYN6SZezZuApjDH369KFfv34EBgZ6O2JVTriToPYDQUCO/TgQ2OepgJRS5cieBfDfR+HUftbVvpbEszv4/bXtKUg7SJ+cNez48YwWDlSXraRZfEVlNnKBbSKSaD8eDCwvm/CUUj7pzDGY+3vYNhtC28DEb5m9qTbm7Fneffddjhw5QqNGjRg9ejTNmjXzdrSqnCqpB1VUZmM9MNtl+RKPRaOUKh8yU3Huns+nVW+h95inqVlF6JK/gB07t3OmenVGjBhBRESEnmirfpWSpplrmQ2l1P8c3Qy758OA30GDTqTdsZH3Pt7BqR/WcWrXWvz8/BgwYAC9e/fWwoHqirhUuY0pwFxjTH6xdS2BScB+Y8z7Ho1QKeVdeVmw5HlY9RY5VWrxt+O9+NPo3hw4eIghznWkbs+kS5cuxMTEaOFAdUWVNMR3B/AI8KqInAROYE2WaAHsBd4wxnzt+RCVUl6zdyHMeRjSk6HrRKYFTWL1tnRe//cU0lOP06xZMxISbqJRo0bejlRVQCUN8R0DHgceF5FwoCGQDew2xmSVTXhKKa/JycB8Nok0apISP4N6bboTND+Rbhm7oXYtxowZQ/v27fU4k/IYt64kYYzZjzXdXClVkRkD+xZBy0EQVJPccV8y9tNUIrcaai38N/7+/gweHEePHj20cKDyOP0LU0pZTh+F7x6FXd8xo9nTjJpwP5uOFRLDTxQeyyGqa1cGDRpE1apVvR2pqiQ0QSlV2RkDmz6BeX+Agly2dnyMv21rzZ433sOcPkbLli2Jj4/XwoGqzGmCUqqy+/ZB2PARp0KjKRz8POtX72IIG6kTUIt4LRyovMidi8X2AZ4CmtvbF9WD0ssQK1VeGQPOQnD4YzqOZMrOYGam92XAp/MIDg5i+DWDtXCg8jp3elDvAQ9jXVGi0LPhKKU87lQy5tsH2e3flgbDn+GnowEcyK9HP+cWevTQwoHKd7iToDKMMd97PBKllGc5nbDuPUh8EifwQVYrju3/lBb5yXRq04b4+Hjq1q3r7SiVOsedBLVYRP6GVQ8qt2ihMWaDx6JSSl1Z6Qdwzr4Xv+Rl5DTuzdcmloxDQo/aOSQkaOFA5ZvcSVA97J/RLssMEHPlw1FKeUTOabIPbeb5/NspOFyHsGpwx4hBREZGauFA5bMumaCMMYPKIhCl1BWWfoDszV+R3vk2du1MZYm5k9SAGtzQrxN9+/bVwoHK57kzi68m8CTQ3160FHjGGJPhxnP3A2ewJlcUGGOiS36GUupXMwbWf4CZ/xeceQU8vMif9n4n6NSxIw/FxVGrVi1vR6iUW9wZ4nsf2ArcYD+eAHwAjHTzNQYZY1IvIzalVGmdSiZ/9v0EHPiBI1Va8Y/C4XRuEMItw26jadOm3o5OqVJxJ0FdZYwZ5fL4aRHZ5KF4lFKXqyCPnCmDKcjKYJYZSkpQNJOui6Nz5856oq0ql9xJUNki0tcYsxzOnbib7eb+DTBfRAzwjjFmSvENRORO4E5AS0MrdTkyDnParyZrVq/iaG5/Fjo7MqJPJPfH9iUgIMDb0Sl12dxJUPcAU+1jUQKcxCpW6I4+xpgjIlIPSBSRncaYH1w3sJPWFIDo6GjjduRKVXbOQpxr3iF//lO8VXAD2Y4wukZdzxsxMVSvXt3b0Sn1q7kzi28T0EVEatiPT7u7c2PMEftniojMBroDP5T8LKXUpTiPbSfv8zsISt3KDtOGgyGdeWz8UFo0a+Lt0JS6Ykoq+X6zMWaaiDxSbDkAxphXStqxiFQF/IwxZ+z78cAzvz5kpSq3lHn/oPaqv5JvAllSbTTNrn2ENzp00ONMqsIpqQdVVPTlQmMF7gzF1Qdm2/80/sAnxpi5pQtPKVUk8+xZfli2jLS122ng7Mqe9vdzx5jrtHCgqrBKKvn+jn13gTFmhes6e6JEiYwxSUCXXxeeUqow8xTb37+HtSl+7PfvQI+rf0OvQYOI08KBqoJz56vX60BXN5Yppa4g43RyZOG/qbnyBTo6z7LVMYTR4yfSpZWez6Qqh5KOQfUCegNhxY5D1QC0SIxSHnRsz0YOzZhMtHMzJxwNSYt/g7E9hul181SlUlIPqgpQzd7G9TjUaWC0J4NSqrI6c+YMixcv5tCGRCaY3XxdZxLX3fsyYQF63TxV+ZR0DGopsFREPjTGJJdhTEpVOgUFBcyf9TbHdqxgt38XBvYciqPnEwyvFebt0JTyGneOQWXZ9aA6AkFFC40xWm5DqV/JGMPO9csx8//MtXmbOOIIpePwP9IjsrO3Q1PK69xJUNOBmcB1wN3AROCEJ4NSqjI4fDCZuVNf5Ib82YRIHumdb6fh9c/SqIrOzlMK3EtQdY0x74nIZJdhv6WeDkypiio9PZ1Fixaxb8sa7mE2h4Lb0PrWd6hVv723Q1PKp7iToPLtn0dFZChwBNDrqShVSnl5ecyZ8zWpG79mhyOS6/rFE9DpdtrVaw16FQilfsGdBPWcfaHYR7HOf6oBPOzRqJSqQIwxbN7wIxnzX+La3B/wczjZMHA8PQfGejs0pXyaOxeLnWPfzQC0/LtSpZCcnMzsT95mZM7nREoK2U0HEDD8FXqGtvJ2aEr5vJJO1H2dEq65Z4x50CMRKVUBnDx5ksTEBezesZU7mUWhfyA5o2YR3CHB26EpVW6U1INaV2ZRKFVB5OTkMPf7/5K24XM2OKK5YVAsddqNJ6BuC/Cv4u3wlCpXSjpRd2pZBqJUeeZ0Olm3dg0Zi/7F4LwlBDtyaH91HL0HDPB2aEqVW5c8BiUii7nAUJ+eqKuUZd++fcz/7D8Mzvqa7n5HyG3UHb/r/0HvhhHeDk2pcs2dWXyPudwPAkYBBZ4JR6nyIyUlhXnzE0nau4fxfvOo6ijk5ND3qHP1KJ02rtQV4M4svvXFFq3QE3VVZZaVlcXiuV9xduMsVjr6MSE+nvD2t+OoFoYEBF16B0opt7gzxFfH5aEfcDXQwGMRKeWjCgsLWbTkBwJWvUpcwUr8HQU07RRD7969vR2aUhWSO0N867GOQQnW0N7PwO2eDEopX2KMYdeuXSz+7C2GFcyhsaSRGx6DY+hL9A5r4+3wlKqw3Bnia1EWgSjli7YnHWTp4sWkHPqZYQHbwb82WSPfJaStzhFSytPcGeILAu4F+mL1pJYD/zbG5Hg4NqW85uzZsyz/+kNCds5kt18C44ZeQ0Tnh3AEVgetaqtUmXBniO8j4AzWdfgAxgEfA2M8FZRS3nImK4fZX39N1J7XSHBuJTugKi0GhtO0e3dvh6ZUpeNOgmprjOni8nixiGz2VEBKeYMxhm3btrHzi2cZ61yAww9yut5JyOA/EhJc29vhKVUpuZOgNopIT2PMagAR6QGs8GxYSpWNgkInHy3YwPF928g7sZ/YYMOxWkNoPvYl/GtqVRmlvMmdBNUDuEVEDtiPmwE7ROQnwBhj9HR5VS5lnEpjx6xnGHrkaz6QkVw/7GYiu/wZP4fD26EppXAvQQ3xeBRKlaFF246wfd673Hj6A3pykhPV2vDwqNEEtuzq7dCUUi7cmWaeLCJdgH72omXGGD0GpcoVY4xVOHDzZkK+eYj7zSbSqzTkbMJ/COs6Ri9NpJQPcmea+WTgDuBLe9E0EZlijHm9hKcp5TOOn87hmXdnUp18gs8cplftLpxo9xvCYu8HhzuDCEopb3Dnv/N2oIcxJhNARF4CVvG/aedK+aQzOfkUHN/N2S8f47WMVXzpfz3tRj5Op06dEO0xKeXz3ElQAhS6PC60lynls174fAWNtr7NTfyX6hiONhvOiNEvE1CzvrdDU0q5yZ0E9QGwRkRmYyWm4cB77r6AiDiwqvMeNsZcd1lRKuWGn1MzaVgjkJ82b2TYtodob/ZwsE4v6o76B02adPB2eEqpUnJnksQrIrIE61JHALcaYzaW4jUmAzuAGqUPTyn37D6WwZuvv4yjeiNq5qbSteFw6vXqRfPOsd4OTSl1mUpzhFgAJ6UY3hORJsBQ4K/AI6ULTamSZWTls+v4GdpkrKDu93/kXwEHWGoGU3/sc7Rt21aPMylVzrkzi+8JrOvufYGVnD4Qkc+MMc+5sf9XgceB6iXs/07gToBmzZq5sUulLK9Mn03MobeoJRvJoAZ7Oj1Gn+H/h39AFW+HppS6AtzpQY0DooquXi4iLwIbgBITlIhcB6QYY9aLyMCLbWeMmQJMAYiOjjbuha0qo0Kn4csNh4hpG8rurZsYeeRV2shedjQZR7Mxf6V1zbreDlEpdQW5k6D2A0FAUXmNQGCfG8/rAwwTkWvt59cQkWnGmJsvJ1Clkg4d5ehXf+Efwe0ILMilc/h4GscMpn1TLRqoVEXkToLKBbaJSCJWPajBwHIReQ3AGPPghZ5kjPkD8AcAuwf1mCYnVVqbD6az9WAa12V/Q5MV/+BB/zOsqHIN9cc+R6tWrbwdnlLKg9xJULPtW5ElnglFqV/6cd50Yg6+QU05wkG/ppzp/Ty94m7GT4sGKlXhuTPNfOqvfRFjzBI0sSk3ZGTl89qiPYy9uhHH9myh7eFvqC45bGz3OO2HP0LT4GBvh6iUKiN6ITLlU/JT93H12keYs7EzZ5zV6NhmPB3jEogK0ytAKFXZaIJSXjd361E27krifjOD2ls/Ic7Pj2rVGtLsN/cRHh7u7fCUUl6iCUp5nd+697h//5tUJZutAZHIoD/Rt2esHmdSqpK7aIISkW+xZu1dkDFmmEciUhVe2tlcnvhqMzde3Yj8o7vJP7CZo9KQU10fJCrhJqpU0RNtlVIl96D+bv8cCTQAptmPx2GdG6VUqRhjEKDqgcU8su93rEjqxs+mOZ073Uj9uDja1Kzp7RCVUj7kognKGLMUQESeNcb0d1n1rYj84PHIVIXy6doDbFm7mL/4f0zwsR+pRU0a1K1FzIjbadKkibfDU0r5IHeOQYWJSEtjTBKAiLQAwjwblqoICp3WCLHDT+i445/cmPo+ZwlhcfA11Lvm/xjcOVIv6KqUuih3EtTDwBIRSbIfhwN3eSwiVSGknMnh/inzGdOtOTWyj3Fi/2kyHH3w6zuZvn1jCAgI8HaISikf586JunNFpDXQzl600xiT69mwVHmVkZVPTUcOoT++xtSzr/HD4h6s5mqiooYTGRNDtWrVvB2iUqqcKGkW38iLrLpKRDDGfOmhmFQ59a9528hf8x4PBXyJf+4pkmhNVuO+3HX9LTRo0MDb4SmlypmSelDXl7DOAJqgFAdPZlErJIDqQQGMOvJ3mji/JCm3KetqjCXi2lsZoYUDlVKXqaRZfLeKiB8w2hgzqwxjUuXEsfRsnn3lFbpd3Y1GVQpIPlCDugE30GzQrYzq0QOHw+HtEJVS5ViJx6CMMU4RuR/QBKUASD2by5ZD6cQE7aH+gqeZ4r+WlT91ZwH9iO6WwMCBAwkJCfF2mEqpCsCdWXyJIvIYMBPILFpojDnpsaiUz5o++2u67n0TZBOZUp0lxHG65XDuSbiGsDA9+0ApdeW4k6Bus3/e57LMAC2vfDjK12Rk5fPvpfsY36MZTeuEMM6xmOp+u0k0/UgKHUxswlAtHKiU8gh3ppm3KItAlG/KO7adLqseZ2veJDYQws7dtQgKuY/egxK44+qr9YKuSimPuWSCEpEA4B6g6HJHS4B3jDH5HoxLedG/l+wj+/BWHgn8htCtXzDYP5C5W75mCx3p0XsQ/fv3JygoyNthKqUqOHeG+P4NBABv2Y8n2Mt+66mgVNnLziskuIo1667nzhfocuxzCh1BbKjSm8V5EYR36Mp9cXHUqVPHy5EqpSoLdxJUN2NMF5fHi0Rks6cCUmVvffJJXvzgM34/cSRXtwijeXhrNmfGknimNbXqt+SG+HgtHKiUKnPuJKhCEbnKGLMPQERaAoWeDUt5WkZ2PmlncmiZuYkuP/yDz1jM/j2Gz9eHsm1bDtWq9SZ+RCxdunTRE22VUl7hToL6HbDYvlisAM2BWz0alfIo43TyzzdeZVze55C/E0dIKHvDxzN7zWHy/NLo378/ffr00cKBSimvKulafA8BK4ClQGugLVaC0ovFlkNZeQV8teEwN3Zvhh/wO/9PCZAcDrSbzJdJwWQk59G5c2diY2OpqYUDlVI+oKQeVBPgX1hXMd8CrMRKWAcBTVDlSdZJkue8So+ts/ix1jx6tGvOydh/MXflFo5sPUGTJvUYPS5BCwcqpXxKSdfiewxARKoA0UBvrJN2/yMi6caYDmUTorochU7DN4uWEnVkJuEHv6J9fhanmw2gTmA6n366il27dlGzZk1GjRpFx44d9TiTUsrnuHMMKhioAdS0b0eAnzwZlPr1/E4l8ZvlwynAHyJvJPfq37Jqewprp32Dv78/MTEx9OzZUwsHKqV8VknHoKYAHYEzwBqsIb5XjDGnyig2VRp5meyc/y4bt+9kxENvEFz3KrISXiGwwzWs3XWIJTMSyc7OJioqihgtHKiUKgdK6kE1AwKBPcBh4BCQXgYxqVI4sGcL9XZOI2jrp7TLzYCA9qSezqJpaHUOh/Zn/rTZpKamEh4eTkJCghYOVEqVGyUdgxoi1oGJjljHnx4FOonISWCVMebJMopRXUTGsik0W/g7CsUBHUdA9ztp26Q7J1JTmTbta/bt20edOnUYO3YsbbVwoFKqnLlUPSgDbBWRdCDDvl0HdAc0QZW1syeYN+1vHKoewe3jb6Zmxzi2H7qPegPuIrRRczIzM1n83/+yYcMGAgMDiY+Pp3v37lo4UClVLpV0DOpBrJ5THyAfa4r5KuB93JgkISJBwA9Yw4T+wOfa6yq9L9cf5MS2xdwVsgS2f0OCM5/lfrcBN0OdlnQY9zwFBQWsXLmSH374gby8PKKjo7VwoFKq3CupBxUOfA48bIw5ehn7zgVijDFn7SuiLxeR740xqy9jX5XGzmOnmb3xMI8ntMPhJ0Qv/y3NTq3GBNZAuv0Wom+jb1gbAIwx7NixgwULFnDq1Clat27N4MGDtXCgUqpCKOkY1CO/Zsf28OBZ+2GAfTO/Zp8VkTGGTQfTuapeNWoE+pO2YxlNV33Ivi7v0KZRHZr2Gw/cjHQaBVX+1yM6cuQI8+fPJzk5mbCwMG6++Wauuuoq7zVEKaWuMHfOg7psIuIA1gOtgDeNMWsusM2dwJ0AzZo182Q4PsUYg4iw4+gZJr01n/ei9hGd9i19UrbTO6ga4ncYqIN0veW85505c4aFCxeyefNmQkJCGDp0KF27dtXCgUqpCkesjo6HX0SkFjAbeMAYs/Vi20VHR5t169Z5PB5vKnQaHpixgZah1XgsoS3m+Hac7wzA4cyDRlFw9a3QaRQEnn+eUn5+PitXrmTFihU4nU569OhBv379tHCgUqrcE5H1xpjo4ss92oMqYoxJF5ElwBDgogmqojqZmcfGA6eIbV8fx9mjXH/6U2o5Q4CnkbB2OPo8aE0Tb9D5F881xvDTTz+xcOFCTp8+Tfv27Rk8eDC1a9cu83YopVRZ8liCEpEwIN9OTsFAHPCSp17P1xQN4QG8mbiNlPVf0b/1VgJ+XsQ1xgl1hlkb+vlB7F8uuI+DBw8yb948Dh8+TMOGDRk5ciTNmzcvqyYopZRXebIH1RCYah+H8gNmGWPmePD1fMbWwxk8MnMjb9zUlTYNavCw8wOqOaZCamPo+whE3gR1Lz6hIT09nQULFrBt2zaqV6/O8OHDtXCgUqrS8ViCMsZsAaI8tX9fYoxhffIpggIcdKqRTYvdn/CfrKlkHn4DGvSjWp+7IWIYtBwEfhc/aTY3N5dly5axevVqREQLByqlKrUyOQZVUeUXOglw+FGQl8Psj/7FTcGrIOtHqhonVRtHQ6j99tbvYN0uwul0snHjRhYvXkxmZiYRERHExsZSo0aNMmqJUkr5Hk1Ql+mF77ayd+9u3ps8kgAxPOP4D+KoBX0esobwQlu7tZ+ff/6ZefPmcfz4cZo2bcq4ceNo3LixR2NXSqnyQBOUm5JOnGXm2gM81jmbgO1f8NCWWZz1q05+wXACqoTguGsp1LnKmvTghrS0NBITE88VDhw9ejQdOnTQ40xKKWXTBFWCw+nZVKviT82QALI3zOTGtS8T8OMxcFQhuNVggiNuAD87objZY8rOzmbp0qX8+OOP5woH9urVC39//VUopZQr/VQspmh6+NH9O5n2n3/SfNAkbozrRbsGNTDhbaDLn6D99RBcuvOQCgsLWb9+PUuWLNHCgUop5QZNUDZjDL//cC4xhStIMCtpeHg9/xcAacG9gF44uoyBLmMua7979uwhMTGR1NRUWrRoQXx8vBYOVEqpS6jUCerbzUfYcegkjw/thORk8HzyeBwUQsMuEPc0dPwNdWtf/omxKSkpzJ8//1zhwBtvvJE2bdrocSallHJDpUpQyWmZJG4/zu1dgpEd3xK5fDqNsiF/yGICgmvh+M1b0DgaQlv9qtfJzMxk8eLF5woHJiQk0K1bNy0cqJRSpVChE1R2XiGrk9KIDq9N9aAADq2YSed178DCXYChcVh7mnYb/b+JDl1u/FWvV1BQwJo1a1i2bBl5eXl069aNAQMGaOFApZS6DBUqQRU6DVsOpVO/RhCNagWz++ckFn78Jjkj7+Oaq1sTXes0jjCDdP49dBiBX712V+R1L1Q4MD4+ntDQ0Cuyf6WUqowqVIJKz8pj4luJvNL5II2cy4lIWkqXgELyqgwEWhPY9wHo/9AVfc0jR44wb948Dhw4QL169bRwoFJKXSEVKkHVLUxlU8i9+O3Jh9rhSN+HoNMoqtSzLzNUwnXwSksLByqllGdVqARFzcb4DfoDtBgIjbuCB2bLFS8c2Lt3by0cqJRSHlCxEhRAv0c9stvihQM7dOhAXFycFg5USikPqXgJygMOHDjAvHnzOHLkiBYOVEqpMqIJqgTFCweOGDGCiIgIPdFWKaXKgCaoCyheOHDAgAH07t1bCwcqpVQZ0gTlQgsHKqWU79AEZUtKSmLevHmkpKRo4UCllPIBlT5BpaamkpiYyO7du6lVq5YWDlRKKR9RaRNU8cKBsbGx9OzZUwsHKqWUj6h0n8aFhYWsW7eOpUuXkpOTQ1RUFIMGDdLCgUop5WMqTYIqKhw4f/580tLSaNGiBQkJCdSvX9/boSmllLqASpGgjh8/zvz580lKSqJu3bqMGzeO1q1b63EmpZTyYRU6QRUvHDhkyBCio6O1cKBSSpUDFTJBFRQUsHr1apYtW0ZBQQHdunVj4MCBBAcHezs0pZRSbqpQCcoYw/bt21mwYAHp6em0adOGwYMHa+FApZQqhypMgjLGMH36dPbt20e9evWYMGECLVu29HZYSimlLlOFSVAiQqtWrWjfvj1RUVFaOFAppco5jyUoEWkKfAQ0AJzAFGPMvzz1egA9e/b05O6VUkqVIU/2oAqAR40xG0SkOrBeRBKNMds9+JpKKaUqCI+NgxljjhpjNtj3zwA7AL36qlJKKbeUyYEaEQkHooA1F1h3p4isE5F1J06cKItwlFJKlQMeT1AiUg34AnjIGHO6+HpjzBRjTLQxJjosLMzT4SillConPJqgRCQAKzlNN8Z86cnXUkopVbF4LEGJdaG794AdxphXPPU6SimlKiZP9qD6ABOAGBHZZN+u9eDrKaWUqkA8Ns3cGLMc0MuFK6WUuixijPF2DOeIyAkg+VfuJhRIvQLh+BJtk++raO0BbVN5URHa1NwY84tZcj6VoK4EEVlnjIn2dhxXkrbJ91W09oC2qbyoiG0qohesU0op5ZM0QSmllPJJFTFBTfF2AB6gbfJ9Fa09oG0qLypim4AKeAxKKaVUxVARe1BKKaUqAE1QSimlfJLPJygReV9EUkRkq8uyLiKySkR+EpFvRaSGvTxcRLJdrlzxtstzrra33ysir9mXYvKK0rTJXhdhr9tmrw+yl5fLNonIeJff0SYRcYpIpL2uvLYpQESm2st3iMgfXJ7jE20qZXuqiMgH9vLNIjLQ5Tk+0R47lqYisth+z7eJyGR7eR0RSRSRPfbP2i7P+YMd+y4RSXBZ7hPtKm2bRKSuvf1ZEXmj2L58ok2XzRjj0zegP9AV2Oqy7EdggH3/NuBZ+36463bF9rMW6IV1dYvvgWvKSZv8gS1AF/txXcBRnttU7HmdgaQK8Hu6CfjUvh8C7AfCfalNpWzPfcAH9v16wHrAz5faY8fSEOhq368O7AY6AC8Dv7eX/x54yb7fAdgMBAItgH2+9v90GW2qCvQF7gbeKLYvn2jT5d58vgdljPkBOFlscVvgB/t+IjCqpH2ISEOghjFmlbF+ax8BI65wqG4rZZvigS3GmM32c9OMMYXlvE2uxgEzoNz/ngxQVUT8gWAgDzjtS20qZXs6AAvt56UA6UC0L7XHju1ihVGHA1PtzabyvxiHY32RyDXG/AzsBbr7UrtK2yZjTKaxLi2X47ofX2rT5fL5BHURW4Fh9v0xQFOXdS1EZKOILBWRfvayxsAhl20O4XvVfS/WpjaAEZF5IrJBRB63l5fnNrkai52gKN9t+hzIBI4CB4C/G2NO4vttulh7NgPDRcRfRFoAV9vrfLY9cn5h1PrGmKNgfeBj9QLBivWgy9OK4vfJdrnZpovxyTaVRnlNULcB94nIeqwucJ69/CjQzBgTBTwCfGKPqV9o3NXX5tdfrE3+WN338fbP34hILOW7TQCISA8gyxhTdEykPLepO1AINMIaOnpURFri+226WHvex/pAWwe8CqwECvDR9sglCqO6bnqBZaaE5V5TijZddBcXWOb131VpeOxq5p5kjNmJNfSFiLQBhtrLc4Fc+/56EdmH1QM5BDRx2UUT4EhZxnwpF2sTVuxLjTGp9rr/Yh1HmEb5bVORG/lf7wnK9+/pJmCuMSYfSBGRFUA0sAwfblMJ/0sFwMNF24nISmAPcAofa49cuDDqcRFpaIw5ag91pdjLD3F+T74ofp/62ytlmy7Gp9p0OcplD0pE6tk//YA/A2/bj8NExGHfbwm0xjoAfxQ4IyI97VkstwBfeyX4i7hYm4B5QISIhNjHNwYA28t5m4qWjQE+LVpWztt0AKv2mYhIVaAnsNPX21TC/1KI3Q5EZDBQYIzxub87O4YLFUb9Bpho35/I/2L8BrhRRALtocvWwFpfatdltOmCfKlNl83bszQudcP6hn0UyMf6RnA7MBlrZstu4EX+d0WMUcA2rPHzDcD1LvuJxhpv3we8UfQcX2+Tvf3Ndru2Ai9XkDYNBFZfYD/lsk1ANeAz+/e0Hfidr7WplO0JB3ZhHaBfgFUOwafaY8fSF2vYaguwyb5dizXbdSFWr28hUMflOX+yY9+Fy6w2X2nXZbZpP9YEmLP277aDL7Xpcm96qSOllFI+qVwO8SmllKr4NEEppZTySZqglFJK+SRNUEoppXySJiillFI+SROUUh5knxe1XESucVl2g4jM9WZcSpUHOs1cKQ8TkU5Y50hFAQ6s81qGGGP2Xca+HMaYwisboVK+SROUUmVARF7GuphsVftnc6xSI/7AU8aYr+0Lg35sbwNwvzFmpVi1mJ7EOsk20hjToWyjV8o7NEEpVQbsywZtwLoY6xxgmzFmmojUwqrZE4V19QCnMSZHRFoDM4wx0XaC+g7oZKwSEUpVCuXyYrFKlTfGmEwRmYl1KZobgOtF5DF7dRDQDOtCnm+IVV24EOtCx0XWanJSlY0mKKXKjtO+CTDKGLPLdaWIPAUcB7pgTWByLUCXWUYxKuUzdBafUmVvHvCAfYVpRCTKXl4TOGqMcQITsCZUKFVpaYJSquw9CwQAW0Rkq/0Y4C1gooisxhre016TqtR0koRSSimfpD0opZRSPkkTlFJKKZ+kCUoppZRP0gSllFLKJ2mCUkop5ZM0QSmllPJJmqCUUkr5pP8HDQO+gWBQRYQAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1408,19 +1417,15 @@
    "source": [
     "# Solution\n",
     "\n",
-    "plot(census, style=':', label='US Census')\n",
-    "plot(un, style='--', label='UN DESA')\n",
-    "plot(results, color='gray', label='model')\n",
-    "\n",
-    "decorate(xlabel='Year', \n",
-    "         ylabel='World population (billion)',\n",
-    "         title='Constant growth')"
+    "results.plot(color='gray', label='model')\n",
+    "plot_estimates()\n",
+    "decorate(title='Constant Growth Model, t1=1970')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "liberal-escape",
+   "id": "improved-sigma",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap06.ipynb b/jupyter/chap06.ipynb
index b049a5c5..26cdf959 100644
--- a/jupyter/chap06.ipynb
+++ b/jupyter/chap06.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "plain-cincinnati",
+   "id": "creative-motel",
    "metadata": {},
    "source": [
     "# Chapter 6"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "corrected-chart",
+   "id": "parental-symposium",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "operational-landscape",
+   "id": "compound-church",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "statutory-baking",
+   "id": "quality-spectrum",
    "metadata": {},
    "source": [
     "In the previous chapter we simulated a model of world population with\n",
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "handled-seating",
+   "id": "ranking-tamil",
    "metadata": {},
    "source": [
     "Here's the code that reads the data."
@@ -74,7 +74,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "lovely-blake",
+   "id": "annoying-herald",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,7 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "alleged-manufacturer",
+   "id": "shared-offense",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "negative-shakespeare",
+   "id": "therapeutic-merchant",
    "metadata": {},
    "source": [
     "## System objects\n",
@@ -129,7 +129,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "complimentary-drain",
+   "id": "numerous-university",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "exotic-proportion",
+   "id": "starting-cooling",
    "metadata": {},
    "source": [
     "Some of these are parameters we need to simulate the system; others are temporary values we can discard. \n",
@@ -159,7 +159,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "european-southeast",
+   "id": "colonial-domestic",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "innocent-chapter",
+   "id": "fleet-beaver",
    "metadata": {},
    "source": [
     "`t0` and `t_end` are the first and last years; `p_0` is the initial\n",
@@ -185,7 +185,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "corresponding-chapter",
+   "id": "floral-routine",
    "metadata": {},
    "outputs": [
     {
@@ -251,7 +251,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "modified-observation",
+   "id": "combined-banking",
    "metadata": {},
    "source": [
     "Next we'll wrap the code from the previous chapter in a function:"
@@ -260,7 +260,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "patient-adobe",
+   "id": "pacific-challenge",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "realistic-switzerland",
+   "id": "technological-medicine",
    "metadata": {},
    "source": [
     "`run_simulation1` takes a `System` object and uses the parameters in it to determine `t_0`, `t_end`, and `annual_growth`.\n",
@@ -291,7 +291,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "durable-convertible",
+   "id": "grateful-terror",
    "metadata": {},
    "source": [
     "\n",
@@ -302,43 +302,40 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "critical-cooper",
+   "id": "electoral-breach",
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = run_simulation1(system)"
+    "results1 = run_simulation1(system)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "under-dealing",
+   "id": "ordinary-sound",
    "metadata": {},
    "source": [
-    "Here's the code we used in the previous chapter to plot the results, wrapped in a function."
+    "Here's the function we used in the previous chapter to plot the estimates."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "sublime-desperate",
+   "id": "peripheral-cassette",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from modsim import plot, decorate\n",
+    "from modsim import decorate\n",
     "\n",
-    "def plot_results(results, title):\n",
-    "    plot(census, style=':', label='US Census')\n",
-    "    plot(un, style='--', label='UN DESA')\n",
-    "    plot(results, color='gray', label='model')\n",
-    "    \n",
+    "def plot_estimates():\n",
+    "    census.plot(style=':', label='US Census')\n",
+    "    un.plot(style='--', label='UN DESA')\n",
     "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)',\n",
-    "             title=title)"
+    "             ylabel='World population (billion)') "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "worst-muslim",
+   "id": "warming-audience",
    "metadata": {},
    "source": [
     "And here are the results."
@@ -347,12 +344,12 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "announced-appendix",
+   "id": "capable-diana",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -364,12 +361,14 @@
     }
    ],
    "source": [
-    "plot_results(results, 'Constant growth model')"
+    "results1.plot(label='model', color='gray')\n",
+    "plot_estimates()\n",
+    "decorate(title='Constant Growth Model')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "competitive-immigration",
+   "id": "exposed-witness",
    "metadata": {},
    "source": [
     "It might not be obvious that using functions and `System` objects is a\n",
@@ -381,7 +380,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "european-massachusetts",
+   "id": "geographic-hormone",
    "metadata": {},
    "source": [
     "## Proportional growth model\n",
@@ -398,7 +397,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "biblical-evans",
+   "id": "laughing-wesley",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -416,7 +415,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "neutral-singapore",
+   "id": "educated-portugal",
    "metadata": {},
    "source": [
     "Now we can choose the values of `birth_rate` and `death_rate` that best fit the data. \n",
@@ -428,7 +427,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "alert-enzyme",
+   "id": "wired-brief",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -438,7 +437,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bacterial-convergence",
+   "id": "sufficient-contest",
    "metadata": {},
    "source": [
     "Then I ran the simulation and plotted the results:"
@@ -447,12 +446,12 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "mechanical-phone",
+   "id": "looking-trace",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -464,13 +463,15 @@
     }
    ],
    "source": [
-    "results = run_simulation2(system)\n",
-    "plot_results(results, 'Proportional model')"
+    "results2 = run_simulation2(system)\n",
+    "results2.plot(label='model', color='gray')\n",
+    "plot_estimates()\n",
+    "decorate(title='Proportional Growth Model')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "optical-republican",
+   "id": "suited-costs",
    "metadata": {},
    "source": [
     "The proportional model fits\n",
@@ -486,7 +487,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "requested-package",
+   "id": "appropriate-checkout",
    "metadata": {},
    "source": [
     "## Factoring out the update function\n",
@@ -500,7 +501,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "found-prediction",
+   "id": "handmade-permit",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -512,7 +513,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "excellent-trinity",
+   "id": "fabulous-bankruptcy",
    "metadata": {},
    "source": [
     "This function takes as arguments the current population, current year,\n",
@@ -526,7 +527,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "extended-investing",
+   "id": "civilian-accused",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -543,7 +544,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "comprehensive-proposal",
+   "id": "failing-assist",
    "metadata": {},
    "source": [
     "This function demonstrates a feature we have not seen before: it takes a\n",
@@ -557,7 +558,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "arbitrary-python",
+   "id": "wicked-seeking",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -566,7 +567,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "incident-cancellation",
+   "id": "simple-camel",
    "metadata": {},
    "source": [
     "Passing a function as an argument is the same as passing any other\n",
@@ -579,7 +580,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "acknowledged-short",
+   "id": "spectacular-paradise",
    "metadata": {},
    "source": [
     "## Combining birth and death\n",
@@ -591,7 +592,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "impressed-laundry",
+   "id": "impressive-model",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -600,7 +601,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "veterinary-heading",
+   "id": "modern-uncertainty",
    "metadata": {},
    "source": [
     "The name of this parameter, `alpha`, is the conventional name for a\n",
@@ -612,7 +613,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "floating-singer",
+   "id": "familiar-helena",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -622,7 +623,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "instant-motion",
+   "id": "understanding-typing",
    "metadata": {},
    "source": [
     "And here's how we run it:"
@@ -631,7 +632,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "professional-business",
+   "id": "independent-effectiveness",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -640,7 +641,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ordinary-exhibition",
+   "id": "everyday-delicious",
    "metadata": {},
    "source": [
     "The results are the same as the previous versions, but now the code is organized in a way that makes it easy to explore other models."
@@ -648,7 +649,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "satellite-money",
+   "id": "attractive-steps",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -663,7 +664,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "north-bicycle",
+   "id": "looking-douglas",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -671,7 +672,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "communist-cycle",
+   "id": "compliant-preserve",
    "metadata": {},
    "source": [
     "**Exercise:** Maybe the reason the proportional model doesn't work very well is that the growth rate, `alpha`, is changing over time.  So let's try a model with different growth rates before and after 1980 (as an arbitrary choice).\n",
@@ -684,7 +685,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "silver-farming",
+   "id": "minus-recommendation",
    "metadata": {
     "scrolled": false
    },
@@ -710,12 +711,12 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "capable-texas",
+   "id": "headed-amsterdam",
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
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G2267jWbNmpXr/vUc1FUwYMAAPv/8cwBWrlxJaGgoNWvWvGD6okWLOHnyJABDhw7lq6++IjU1FYATJ05w4ECxd5tXSqlyZ4xh8+bNfPbZZwQFBfGb3/ym3JMTVLAalKd64YUXmDZtGh07diQwMJBPPvkEgD/96U/ccsstdOnShYEDBxIREQFA27Zteemllxg+fDiFhYX4+vry9ttv06RJE3cWQymlKCgoYNGiRcTGxtKyZUsmTpx4QStSeRLHnmXu1q1bN3PxgIU7duzgmmuucVNEVYO+x0opgMzMTObOncuBAwfo27cvQ4YMwcvL9Q1tIhJrjOl28XStQSmllOLo0aPMmjWLc+fOMWHCBDp27OjukDRBKaVUVZeYmMh3331HYGAg06ZNo0GDBu4OCdAEpZRSVVZhYSHLli1j3bp1REREMGnSJIKCgtwd1nmaoJRSqgrKyspi3rx57Nmzp+yLbwvywLv8BzXVbuZKKVXFHDt2jPfee499+/YxduxYxowZU3Jyiv0E/tsHzp24YPLxMzkuj1MTlFJKVSFJSUl88MEH5OXlceedd9K1a9fSVzgaDzUbgkOP76TDp+j72nLW7nbtyAuaoMqwf/9+2rdvf8G0F154gTfeeAOADRs20LNnT6Kiorjmmmt44YUXit3Opk2bGDBgAK1btz5/89lz5865OnyllAKs801Llixh3rx51K9fn3vvvZfGjRsXv/CJfXBsu/V85Gtw2zwKA2qTnG59Z7UNr8l9A5rRPKy6S2PWc1BX6I477mDOnDl06tSJgoICdu3a9atljh07xk033cSsWbPo3bs3xhjmzZvHmTNnCAwMdEPUSqmq5Ny5c3z11Vfs27eP7t27M2LEiJKb9HYtgq+nQ51m8JsV4OMHwNNzt7J2dxrLnxhEgJ83jw9v7fK4tQZ1hVJTUwkPDwfA29ubtm3b/mqZt99+mzvuuIPevXsD1v35brzxRurVq0dmZiZ33XUX3bt3p3Pnznz77bcAfPzxx9xwww2MHDmSli1b8vTTTwPWVd533nkn7du3p0OHDvzjH/8AYNCgQRRd5JyWlkZkZCQA27ZtOz+sR8eOHfn5559d+n4opTzLkSNHmDFjBsnJyYwbN47Ro0cXn5xOH4Wv7oYvb4bakXDTxxw8mUV2njUU0C09IvjdqDb4+5Zf2qhwCWryu+uZG3MQgLyCQia/u575Ww4BkJVbwOR31/P91iMAnM7OY/K761mcdBSAE5m5TH53PUu3HwMg9cyVjwT52GOP0bp1ayZMmMC7775b7OiSSUlJJbbzvvzyywwZMoTNmzezYsUKnnrqKTIzMwFrYLDZs2eTmJjI7NmzOXjwIPHx8Rw+fJikpCQSExOZNm1aqfG98847PPLII8THxxMTE0OjRo2uuMxKqYohLi6ODz/8EIC77rqLqKio4hdMSYK3usGO72HA03DXEg5LPYa+uYr31+wFoGuTWoyLaljsDbBdpcIlqPJW0odRNP35558nJiaG4cOH88UXX1zyHdeXLFnCq6++SlRUFIMGDSI7O5vk5GTAuqlscHAw/v7+tG3blgMHDtCsWTP27t3LQw89xOLFi6lZs2ap2+/duzevvPIKr732GgcOHCAgIOCS4lNKVTz5+fl8//33fP/99zRp0oR77723+Itvzx63/ta9BrrcQcH9G9je5iHw9adhSAB/GH0NN3Yt4TxVOahwCWr29N7c1M16w3y9vZg9vTcTOlu1ggA/b2ZP7811nawPoqa/L7On92Zke6sJrnaQH7On92ZY23oA1K1R9lDFderUOX8X8iInTpy4YCTJ5s2bc//997Ns2TK2bt1Kenr6Bcu3a9eO2NjYYrdfdD4qPj6e+Ph4kpOTz98Xz/EGjd7e3uTn51OrVi22bt3KoEGDePvtt7nnnnsA8PHxobCwEOCCWtytt97Kd999R0BAACNGjGD58uVlllkpVXGdOnWKjz76iLi4OPr168eUKVN+fa779BH46i54u4fVfdzLG0a+wmubcrjxnXXnu5Df0SeS+sGuG9K9LBUuQZW36tWrEx4ezrJlywArOS1evJh+/foBsHDhwvNDuf/88894e3sTEhJywTYefPBBPvnkEzZu3Hh+2syZM0lJSWHEiBH8+9//Pr+NLVu2lBpPWloahYWFTJw4kRdffJG4uDgAIiMjzyfBr7766vzye/fupVmzZjz88MNcf/3154ejV0pVPnv27GHGjBmkp6czefJkhg4deuHNXgvyYO3/wVvdYedC6Dmdg2fM+YQ0pWcEr07sSGh1PzeV4ELai88Jn376Kb/97W954oknAGsYjebNmwPw2Wef8dhjjxEYGIiPjw+ff/75r05A1qtXj1mzZvHkk0+SmpqKl5cXAwYM4IYbbuCPf/wjjz76KB07dsQYQ2RkJAsWLCgxlsOHDzNt2rTztaWiQQ+ffPJJJk2axGeffcaQIUPOLz979mxmzpyJr68v9evX5/nnn7+q741Syv2MMaxevZqVK1dSt25dJk2aRJ06dS5cKPsUvH8tpO2CViNh5KucDmzEqL8u5/qoBrwyoQNN6gTRpI7n3OpIh9tQ+h4rVYFlZWXx9ddfs3v3bjp27MiYMWPw83OoAeVlga997nnRMxREDiTWvxc9mtYGYEHCEbpH1qZeTfc15ZU03IbLmvhEpLWIxDs8TovIo67an1JKVTVHjhzh3XffZd++fYwZM4bx48f/kpwK8mDdv+HNtpC225o26jXeT23N5Bnr2Z16BoCxHRu4NTmVxmVNfMaYXUAUgIh4A4eB+a7an1JKVRXGGGJjY1m8eDHVq1dn2rRpNGzY8JcF9q2B/z0Jx3dCyxEcOVtAtjlLs7Dq3Nwjgoa1Alx+F4irobzOQQ0F9hhjDpTT/pRSqlLKzc1lwYIFJCYm0qJFCyZMmPBLLz1j4JsHYOsXEBIBt8wir8UIbnhtBdeEn+KjaT0IDvBlbEfPGO+pLOWVoG4GvixuhojcC9wLEBERUU7hKKVUxXP8+HHmzJlDeno6gwcPpn///tY1mYUFVldxEahRn8L+T7EibCpDWkXgK8LfJ3WqEDWmi7m8m7mI+AHXA3OLm2+MmWGM6WaM6RYWFubqcJRSqkLaunUr7733HllZWUydOpUBAwZYyWnfGvhvX9i32lpw2J/4ptad3P1FEuv3WNdk9m0R6tbrmS5XedSgRgFxxphj5bAvpZSqVPLy8li0aBFbtmyhSZMmTJw4kRo1algX2y55DpLmQUgTUk5lcST5JF0ianFdpwbU8Peld/M6Ze/Ag5XHhbq3UELzXkVQ1nAbd955Jw0bNiQnx7rQzfFGrRfz9vYmKiqKdu3a0alTJ958883z1zOtXLmS4OBgoqKizj+WLl0KWPfra9euHR07diQqKuqCC37z8/MJDQ3l2WefvdpFV0q5WVpaGu+//z5btmyhX79+3H777VZy2vSedbHtjgUw6FnMAxuYtiqQ579NwhiDr7cX17atV673zXMFl9agRCQQuBaY7sr9uJu3tzcffvgh999/f6nLBQQEEB8fD1h3Qb/11ls5deoUf/7znwHo37//ry7SXb9+PQsWLCAuLo5q1aqRlpZGbm7u+flLliyhdevWzJkzh1deeaXCH5BKKUtCQgILFizA19eXKVOm0KJFi18GDTSFFDbpx+JGjzCsby/8fLx4/caO1K1RrVJ9B7i0BmWMOWeMqWOMOeXK/bjbo48+yj/+8Q/y8/OdXqdu3brMmDGDt956i9Iulj569CihoaHn78sXGhp6wU0fv/zySx555BEiIiLYsGHD5RdCKeUR8vLy+O6775g/fz7h4eFMnz6dFqHVYM7tEP+5tVCPe1nf820eWHSSxdtSAGjfMJi6Hno90+WqeLc6+mjMr6e1Gw89fgO55+Dzm349P+pW6DwFMtOtD9nRtIVXHFJERAT9+vXjs88+47rrrnN6vWbNmlFYWEhqaioAa9asueB2+PPmzWP48OH85S9/oVWrVgwbNozJkyczcOBAwLqCfNmyZbz77rtkZGTw5Zdfnh9zSilV8Rw/fpyvvvqK1NRU+vXrx+D+vfHa8Das/jsG2Obbnl2Fh5jYtRF9mtdhzvTedI+s5e6wXUZvFluGsobbKPL73/+e119//fw5JWc51p769+9//q7m8fHxNG/enOrVqxMbG8uMGTMICwtj8uTJfPzxxwAsWLCAwYMHExgYyMSJE5k/fz4FBQWXVkCllNsZY9iyZQvvvfceZ8+eZcqUKQxt6oPXf/vA8peg5bXIg5t4/eQg5sQcxBiDiNCjae1K1aR3sYpXgyqtxuMXWPr8oDqXXGMqabiNpk2bXjCtRYsWREVFMWfOHKe3vXfvXry9valbty47duwocTlvb28GDRrEoEGD6NChA5988gl33nknX375JWvXrj3fKSM9PZ0VK1YwbNgw5wuolHKrnJwcFi5cSGJiIk2bNmXChAlWR4if9pBnvHi30RtMGXsHtYL8+Mfk+gQH+FbqpORIa1BlKGu4DUd/+MMfzvfuK8vx48e57777ePDBB0s92Hbt2nXBMO3x8fE0adKE06dPEx0dTXJyMvv372f//v28/fbbfPllhe0wqVSVc/ToUWbMmEFSUhJD+vdiasMD1Ih7x5rZagR7Jy3jvcNN2HbkNGCNaeftVTWSE1TEGpQblDbchqN27drRpUuX82M0XSwrK4uoqCjy8vLw8fFh6tSpPP744+fnX3wO6rnnnqNp06Y89NBDZGRk4OPjQ4sWLZgxYwZff/01Q4YMuWBQw3HjxvH000+Tk5NzwXSllGcxxrBp0yZ+/PFHAgMCuH9AfcLiHofTh9jbeCLf5/7EI9e2onWDWqx/dgiBflXzq1qH21D6HitVjjIzM/n222/5+eef6RpZk1H5S/A+uA7qd4DRb/CH2CB+Tj3LF/f0xMe7ajRylTTcRtVMy0op5QZ79+5l/vz5ZGVlMWrUKLpHBGA+fYu59R+ny4RHaV4vmD+GF1DNx6vKnGcqTakJSkR6A7cB/YFwIAtIAhYCMyv79U1KKXU1FBQUsHLlStauWc2A6vvo3qYmQT16AJB+bxyvvbWZ546cpXm9YPx9vcvYWtVRYoISkUXAEeBb4GUgFfAHWgGDgW9F5E1jzHeuDrKoS6W6+jypiVepyujEiRN8/fXXeB3axCMBGwg+e4ATx3rwt69j+fMNXQkNCSb6mcGamIpRWg1qqjEm7aJpZ4E4+/F3EQl1WWQ2f39/0tPTqVOnjiapq8wYQ3p6Ov7+levqc6U8gTGGhIQEVi2cw7CC5bRlO/g2gjEfMietExvjj3A6O4+a/r6anEpQYoIqJjld1jJXqlGjRhw6dIjjx4+7eldVkr+/P40aNXJ3GEpVKtnZ2SxcuJCkpCTaNKxH6/RjLKtxB4FDnqR3mwjuLijkNwOaV6ku45ejzE4SInID8BpQFxD7YYwxNV0cGwC+vr6/uihWKaU8VfKBAyTMfonW57YSNvh1+vXvT272Xbz8nximpBXQG/CtIr3zrpQzvfj+BlxnjCn5VgdKKVXFFRQUELvwI+rG/YOxHCKjegs+3ptL3/6Cf2B1Fj86AD8fTUyXwpkEdUyTk1JKlezEkX2kzpxO93MbyfWpQd7QvxEdOJqE5Xs5djqbBiEBmpwugzMJKkZEZgPfADlFE40xX7sqKKWUqgiMMcTFxbFs8QLuzv+ZpdXHc7bP00zo3Y4xxjCyfcMqc7GtKziToGoC54DhDtMMoAlKKVU1GUN2/DwyfnydxeeuJaJ5a3xGb+TDeXvpk+UHWCMe+HhrJ4grUWaCMsZMK49AlFKqQjiawLn5jxKYGkshYfxUrTkP33QzNfx9mXlPmPbMu4rKrHuKSCMRmS8iqSJyTETmiYj2S1ZKVS255yiY/wDm3QGQuo3VNcfz07j/cTywBcfPWGc/NDldXc408X0EfAEUDVV7mz3tWlcFpZRSHsMYEOFgShqybS3rCgaxMXwqr907Dh8fHwZGtdSbCLiIMwkqzBjzkcPrj0XkURfFo5RSnsEYSJqHWfN3Vkc+waqY7QTXuIOfGveidaMwfHysr09NTq7jTIJKE5HbgKKR8G4B0l0XklJKudnBzfDDs3BoM0e9GvLmoaOMj+rEreNG6lhr5ciZBHUX8BbwD6zee+vsaUopVbkU5MM390HiXHL8arHEaySxfj2R4Hb0H9Rdk1M5c6YXXzJwfTnEopRS7pGfCz5+4O1DdgF8LWOZmTmIOzsF89SYMQQFBbk7wiqptOE2njbG/E1E/o1Vc7qAMeZhl0amlFKuVpAPWz6Fla+ROWkO21JyWLq7GVtNXXp1vIYbb+yNl5deaOsupdWgim5vFFPKMkopVTH9vBSWPAfHd3AytCtT311HE59MereM4MHrr6dmzXK5H7YqRWnDbXxv//2k/MJRSikXMwZmTYFdCykIiWR/txf4fGsOuV7VGTm4C2MH9tCeeR6itCa+7ymmaa+IMUbPSymlKo5zJyCgFohA4+58mdqIGSd7MjAmiaZNI3l43HWEhIS4O0rloLQmvjfKLQqllHKV3HOw/i2I/icnxrxLSMcxxAcOZFnGBhpznJGjRtKzh9aaPFFpTXyryjMQpZS6qgoLIWEWLHsRzhzhVOQoJs09TpfouQSl76R/ZCTXX389tWrVcnekqgSlNfElUnoTX8eyNi4iIcD7QHt7W3cZY9ZfephKKXWJvpwMPy8hp24UfhPf56c0f2rs24pPRjKjR4+mW7duWmvycKU18Y29Ctv/F7DYGHOjiPgBgVdhm0opVby03VCrCXj7Qqdb+PhMd95N78Q9K/dw6MA+xjeL5Prr79FaUwVRWhPfgSvZsIjUBAYAd9rbywVyr2SbSilVrMw0WPlXiPmIlL5/JmTgA1RrNwGv5LV0OBbPsSNnGDNmDF27dtVaUwVSWhNftDGmn4icwWqeE8e/xpiyLhJoBhwHPhKRTkAs8IgxJvPqhK6UqvLysmHjf2HNm5Cbyal2tzF2WShT8rYTkhJDcnIyA1u0YOzYqQQHB7s7WnWJSqtB9bP/1riCbXcBHjLGbBSRfwG/A/7ouJCI3AvcCxAREXGZu1JKVUlz74CfFpPReCgh1/+V6rVbMD53FSdiFpDr58W4cePo1KmT1poqKGduFouIdAH6YdWgoo0xW5xY7RBwyBiz0X79FVaCuoAxZgYwA6Bbt24ldspQSikAkjdCWCvrmqZ+j/Ne3kj+uSec+ZmBrPj6fQpSUmjfpg2jR4+mRo3L/X2tPIEzI+o+D3wC1AFCscaDeq6s9YwxKcBBEWltTxoKbL+CWJVSVdnJ/TD3TvhwOEcW/53UM9kQ0ZPBI2/ggQ5ezPr0I86cOcNNN93E5MmTNTlVAs7UoG4BOhtjsgFE5FUgDnjJiXUfAj63e/DtBaZdbqBKqSoq+zSs+Tts+A94+XC291OMXN2WSb57mdohiMXff096ejpRUVEMHz6cgIAAd0esrhJnEtR+wB/Itl9XA/Y4s3FjTDzQ7XICU0opAP73JCTM5nCT8TSc+Feq12zAGw0OcXrPFj7+OI6QkBCmTp1Ks2bN3B2puspK68VXNMxGDrBNRH60X18LRJdPeEqpKmnvKgiJgNpNYeAzfGZG8kKsP6sLQji1fTuJSxaRmZlJnz59GDhwIH5+fu6OWLlAaTWoomE2YoH5DtNXuiwapVTVdmKfNQTGzgWktrqV08P+Rou6zbl+VATN2qWy5ofv2LVrF+Hh4dx6662Eh4e7O2LlQqV1M9dhNpRS5SPnLES/CeveAi8fcgf+gbGr29JLdvPPyZ3YmRjH6uXLMcZw7bXX0qtXLx1IsAooa7iNGVi3Ksq7aF4zrDtE7DfGfOjSCJVSld+av0P0mxxsdB2NbnoNv+CGvNP8JCFk8sEHH3DkyBFatGjB6NGj9TZFVUhpTXy/AR4H/ikiJ7DuCuEPNAV2A28ZY751fYhKqUrpSLz1t0EU9HmIJfmduXelN/NPBdIuIJe0nZtZsGEDgYGBTJw4kXbt2ukFt1WMGFP2tbEiEgmEA1nAT8aYc64Iplu3biYmRkeYV6pSO3cClv0FYj/mXOMB7Lr2EzpH1CKvoJC4AycJyUtj0aJFnDp1ii5dujBs2DDtOl7JiUisMeZXPb6dupOEMWY/VndzpZS6PIUFEPsxLH8Rsk9jet7HbTsGUPD9dr55oA/nzp5h/+al7Ny5k7CwMKZNm6a3P6vinEpQSil1xeK/gIWPkx7Wk5q3v4lveHv+0vEUYdV9Wb9+PStXrsQYw9ChQ+nduzfe3t7ujli5mSYopZTrZKbBib3QuAd0upkdp30Ztbg6bx4N4YZwCC48zfwvF3Ls2DFatmzJ6NGjCQkJcXfUykNoglJKXX2FBRD7ESx7kQK/6my+fjm9WtSjzcDJfFT/ON0bB/Hdd9+xZcsWatasyaRJk2jTpo12glAXKDNBiUhf4AWgib180XhQel8RpdSvHY6DBY/B0XiI7M9f8u9k4awEop8ZQjUfL4LPHeI/by8lJydH7wShSuVMDeoD4DGsO0oUuDYcpVSFdjQB3htCbkAo+de9Q2CXm7k9LZNbCwwZ6cdZuHAhhw4dokmTJowePZq6deu6O2LlwZxJUKeMMYtcHolSqmIyBtJ+tsZoqt+Bk4NeYdAPYdye3pEnRGhQ3ZsVK1YQExNDYGAg48ePp2PHjtqcp8rkTIJaISKvA19j3TgWAGNMnMuiUkpVDOl7YOHjmOSNJI7/kY7tO1Br0AP8ve4x+jSvw5YtW1i6dClZWVl0796dwYMH4+/v7+6oVQXhTILqaf91vIjKAEOufjhKqQohPxfW/R+s+hv4VGNZ44d44IsDLHuyOY1rB9KuViFfzvyEQ4cO0bhxY0aPHk39+vXdHbWqYMpMUMaYweURiFKqgsjLhveGQOo2slteh/91rxPlVZv/23+C2tUMCxYsIDY2lsDAQMaNG0enTp20OU9dFmd68QUDfwIG2JNWAX8xxpxyZWBKKQ+Tnws+fuDrT8E11/O7k+PIKBjKezXDqV1YSGj2Yd5++zOys7Pp0aOHNuepK+ZME9+HQBIwyX49FfgIuMFVQSmlPMzOhfC/p0gf9S51rumP9+DfMajOUVrUrU5ycjKLFi0iJSWFyMhIRo4cSb169dwdsaoEnElQzY0xEx1e/1lE4l0Uj1LKk5xNhf89Bdu/ITOkDdNmJvLUnW3o3zKM/pFBLF36I4mJidSsWZMbb7yRtm3banOeumqcSVBZItLPGBMN5y/czXJtWEopt0uYg/nfU5CXhQz5I949HqTX8n20qhvE6tWriY6OprCwkP79+9OvXz+92FZddc4kqPuBT+xzUQKcwBqsUClVmZ0+wn6vCF4OvJ93+t5MNS9hQlOY9fF7ZGRk0KZNG4YPH64DCCqXcaYXXzzQSURq2q9PuzoopZQbGANxn5DnH4r3NWPw6vMQP4dMotfJbI6lprLsxyXs27ePsLAwpk6dSrNmercz5VqlDfl+mzFmpog8ftF0AIwxb7o4NqVUeclIhu8ehr0riPbpz7HMjtzcI4K+TYPJ3R/HB+/FUa1aNUaOHEn37t3x8vJyd8SqCiitBhVk/61RzLyyh+FVSnk+YyD2I8ySP1p3gR7zJgv2dmZsTT/Wr1/PqlWryM3NpXv37gwaNEhHtlXlqsQEZYx513661Biz1nGe3VFCKVXR7V0BCx4jwTeKZnd/SPV6zbi35k/8+ONXpKen07x5c0aMGEFYWJi7I1VVkDOdJP4NdHFimlKqIjAGju+Cum2g2WD2jfiIZzbU4S8nvdm55DP27dtHaGgot9xyCy1bttRu48ptSjsH1RvoA4RddB6qJqBjMStVEZ06DN8/TP7eaGZ1/4rbRvUnrMNw7ktdzqI5nxAQEMCoUaPo2rWrDrmu3K60GpQfUN1exvE81GngRlcGpZS6yoyBrbNg0TNQmMd3daezKNmLRqtWsX7dOvLz8+nVqxcDBgzQ80zKY5R2DmoVsEpEPjbGHCjHmJRSV1NhAYWzb8dr1wJyGvTAb+I7NNx3ii6rVrJq5RmuueYahg0bRu3atd0dqVIXcOYc1Dl7PKh2wPk7PxpjdLgNpSoCL28yqzfhv4W3URA6ldpfLSUlJYWGDRty0003EhER4e4IlSqWMwnqc2A2MBa4D7gDOO7MxkVkP3AGa6j4fGNMt9LXUEpdFedOkL/wKdbVnsCAoWPJ6vkoQenLSEv8gazgYCZOnEi7du20A4TyaM4kqDrGmA9E5BGHZr9Vl7CPwcaYtMuMTyl1qXYthu8fQTLT+CG3JjuPF5LyUzx+fn4MGzaMnj174uPjzL++Uu7lzFGaZ/89KiJjgCNAI9eFpJS6LFkZZC94Bv9tsygMu4aNLZ7kTMJZUncn0LNnT/r3709gYKC7o1TKac4kqJfsG8U+gXX9U03gMSe3b4AlImKAd40xMy5eQETuBe4FtC1cqStgts7Cd9scZnrfwLGzrTi79QgD27dnyJAhekNXVSGJMa67a5GINDDGHBGRusCPwEPGmNUlLd+tWzcTExPjsniUqnSyT3FsXxKhrXqzfVsCP/7vGw5mBRDVrD7Dhg2jYcOG7o5QqTKJSGxxfRRKu1D335Ryzz1jzMNl7dQYc8T+myoi84EeQIkJSil1CX5aQt63DyNns3is+gvUOZdMvXr1+e2Nw2jevLl2gFAVXmlNfFdUlRGRIMDLGHPGfj4c+MuVbFMpBZw7Qe7CZ/DbNoezvuG8UTCVet6ZXDdhAh06dNDEpCqN0i7U/eQKt10PmG//s/gAXxhjFl/hNpWq2s6kcPZfvfHPy2CF9GKL7yAmDhtC165dtWeeqnTKPKJFZAXFNPWVdaGuMWYv0OnyQ1NKFcnPyeLk2WzWR28kIK8Ni+nNyAED+G3f3lSrVs3d4SnlEs785HrS4bk/MBHId004SqkLFBZyZu0Mcpf9lecL7qdhtXy69X6Ev/TvT1BQUNnrK1WBOTPke+xFk9Ze4oW6SqnLcDY5kbx506l1ahsJhc25pnEod0y+ieDgYHeHplS5cKaJz/EOkl5AV6C+yyJSqorLzc0l9sPH6XJ0Fl54EdPwTiInPMfDOmigqmKcaeKLxToHJVhNe/uAu10ZlFJVUX5+Phs2xbBhXTQ9z2xng1dnQq7/K9066y0sVdXkTBNf0/IIRKmqqqCggMSNK0n54XUWFfSgS8vmNLnxAyIi9V9PVW3ONPH5Aw8A/bBqUtHAf40x2S6OTalKrbCwkNi4LZxa8X/0yVxMe8nFq0k3rr39dr2WSSmca+L7FGvIjH/br28BPgNuclVQSlVmhYWFJCUlsXrxPPqeXcAwr91khXXC+8Z3GF6vrbvDU8pjOJOgWhtjHK9nWiEiW10VkFKVlTGGbdu2sWLlKk6kpzEoaA/NvI9zeMDfaTjwLvDycneISnkUZxLUFhHpZYzZACAiPYG1rg1LqcrDGMP27dtZtXIlOSmJpHnV4pZJt9GuTSsk9yzBAXqncaWK40yC6gncLiLJ9usIYIeIJALGGNPRZdEpVYEVJaYVK1fhc3wb433W0MD7ADtr9iWy1UuItzdoclKqRM4kqJEuj0KpSqQoMa1evZrUY0doV5jIDV6rEL/aMPrftIm6TZvzlHKCM93MD4hIJ6C/PWmNMUbPQSl1kcLCwl8SU+pxwsJCmdornCYb3uJQy9uIuOFFCAhxd5hKVRjOdDN/BPgN8LU9aaaIzDDG/LuU1ZSqMop65a1Zs4a0tDRqVsujum8ed959L0HVfKDHKCJq6zVNSl0qZ5r47gZ6GmMyAUTkNWA9v3Q7V6rKOnToEAsXLiQlJYWmtf24rdFWgg8u44hPYzKznyPIvwZoclLqsjiToAQocHhdYE9TqsrKyspi2bJlxMbGUiOwGl2qH2HUyfn4nPKCQb+nQZ+HwC/Q3WEqVaE5k6A+AjbaQ7YLMA74wKVRKeWhjDEkJiayZMkSzmaeo1fPngxpVxefD4byU52htJn6Dwhp7O4wlaoUnOkk8aaIrMS61RHANGPMFpdGpZQHSk1NZdGiRezfv5+w6lDd6yhd+w7Gr0Y1zEMxtKnT3N0hKlWpXMoY0QIUos17qorJyclh1apVrN+wkVo+2TwUnkTtIys54R3K2TMnoEY4oslJqavOmV58z2Pdd28eVnL6SETmGmNecnVwSrlT0fVMP/zwA9mn02gpB7gp7wd8U31g4DPU7vsItf10VFulXMWZGtQtQOeiu5eLyKtAHKAJSlVax48fZ9GiRSTuOUSLBnW4eexg6swey7GGo2k08RUIbuTuEJWq9JxJUPsBf6BoeI1qwB5XBaSUO+Xk5LBy5Uo2bdxILY7Tu3AvrcfMpEGjWvB4Ao2C6rg7RKWqDGcSVA6wTUR+xBoP6logWkT+D8AY87AL41OqXBhjSEhI4JsflhOSuZcHqm+gTuZPpFdvim+1TKAWaHJSqlw5k6Dm248iK10TilLuceTIERYvXszx5F30LoxluNdmoC6M/Sd1Ok8F70vpS6SUulqc6Wb+SXkEolR5O3v2LIt+XM53cfvpUCOHEWMn0HL1j5xs8wS1hj4O1aq7O0SlqjT9aaiqnIKCAjZu3Mi6VctonJXI72UHZ8f9j6jWjaHLZoK8vN0dolIKTVCqCjHGsOunn/i/+WvpeW4FD/itJtArg4yIawlp6G8tpMlJKY+hCUpVCampqSxZsoSjuxN4zCykpdchqN8drn2RkCa93R2eUqoYJSYoEfkeq9desYwx17skIqWuonPnzvH+NytYuu0gvWqcZsiICTTcd4zCji/j1W4ciN4YRSlPVVoN6g377w1AfWCm/foWrGujlPJYReeZ4ld9T/fsaKb57ObwhJW0ad0aemuNSamKoMQEZYxZBSAiLxpjBjjM+l5EVrs8MqUugzGGLYnbeH5eDL8xXzPdey1ePt4U9nyANk3C3R2eUuoSOHMOKkxEmhlj9gKISFMgzNkdiIg3EAMcNsaMvbwwlSrb7v3JrFmxjOMHdjFTPiNYMpGON8OQ5/DWWxMpVeE4k6AeA1aKyF77dSQw/RL28QiwA6h5aaEp5ZyMjAxe/nIFu5OT6VqrgFHX3UTNzDCk5TAI7+Tu8JRSl8mZC3UXi0hLoI09aacxJseZjYtII2AM8DLw+GVHqVQx0k6dIXrtOk7Efsv9BauJrHaI9InLCWveBejq7vCUUleotF58N5Qwq7mIYIz52ont/xN4GqhRyn7uBe4FiIiIcGKTqqrLz89n9bqNvLpoO7/3/ZzxXlsoDAzDa+g/CYvs6O7wlFJXSWk1qOtKmWeAUhOUiIwFUo0xsSIyqMQNGTMDmAHQrVu3Eru1K1VYWMiC6Dj2xkVzLuM43/h/QDUvA32fxqvvw1CtxN9BSqkKqLRefNNExAu40Rgz5zK23Re4XkRGYw3XUVNEZhpjbrvMWFUVZYxhz549vPbVWrxP7yUivDE33349gVndoVF3CG7o7hCVUi5Q6jkoY0yhiDwIXHKCMsY8CzwLYNegntTkpC5VzI69RK+Jpv6RH3nVax11/NLIH7sIn6bNgGbuDk8p5ULO9OL7UUSeBGYDmUUTjTEnXBaVqvLS0tL4cdkKvoo/xB/9viBKfsLUagnD38InUi+0VaoqcCZB3WX//a3DNMMl/Hw1xqxEx5FSTkg7cZJ3vl1D4cF4qvl48WnQe/j7eMGQN5Cud4K3r7tDVEqVE2e6mTctj0BU1ZaZmUl0dDSz1+2kWf4u6nccxZSxQwg6MQLCWoF/sLtDVEqVszITlIj4AvcDRbc7Wgm8a4zJc2FcqorIzs5m1g9rSdwaz2Cznv/4bsJfTmG6TkSCgiCou7tDVEq5iTNNfP8FfIH/2K+n2tPucVVQqvLLzc1l48aNREevJflsFk/7zqUph6FBbxj+EtKom7tDVEq5mTMJqrsxxvF+MctFZKurAlKVW35+Pus2buaD5duILDhM65bNeejYPwjw9oNrP4NrrtMhMJRSgHMJqkBEmhtj9gCISDOgwLVhqcomPz+fLVu2sGbNGg5nnOXawnhqDHuGcYN6wsn+UCMcfPzcHaZSyoM4k6CeAlbYN4sVoAkwzaVRqUqjoKCA+Ph4vlgaQ3ZmBveGbKKD7xoAvBrcZy1Uq4kbI1RKearS7sX3KLAWWAW0BFpjJSinbxarqq6CggISEhJYvXo1J0+epBHHmeb9HSGnT0OHSTD0jxCi915USpWstBpUI+BfWHcxTwDWYSWsg4AmKFWsosS0aMVa1p4IYFTjIKZMGU2jtb/DjygY/iI0iHJ3mEqpCqC0e/E9CSAifkA3oA/WRbvviUiGMaZt+YSoKgLHGlNGRgaNa+TzJ68l5HX+Ny1btoQmn4JvoHaAUEo5zZlzUAFYgw0G248jQKIrg1IVR9E5pujoaDal+RAZlM+zDWOpfmgVBTXr4x2aZS3oF+TeQJVSFU5p56BmAO2AM8BGrCa+N40xJ8spNuXB8vPziYuLY+3atZw+fZqGDRpwV80VDM5chKQGwZA/4t3rAfALdHeoSqkKqrQaVARQDfgZOAwcAjLKISblwXJzc88npuTTBSRJBC+OH0XvTq0pWLQH4TfIwKchKNTdoSqlKrjSzkGNFBHBqkX1AZ4A2ovICWC9MeZP5RSj8gDZ2dls3ryZDRs2cDYzixZNGnBT5EGCE5/kcPb7iLTBZ9Rf9RyTUuqqKWs8KAMkiUgGcMp+jAV6AJqgqoBz586xYcMGNm3aRE5ODlt823NdrQSmnvwHHDiEaT6E4KZ2d3FNTkqpq6i0c1APY9Wc+gJ5WF3M1wMfop0kKr3Tp0+zbt064uLiOJEj9GjXnH59+yJzbiX8ZDymQWdk/H+QZgPdHapSqpIqrQYVCXwFPGaMOVo+4Sh3S09PJzo6moSEBIwxmAYdSNm7i5Y9htCgYSgMvg/8ghC9Z55SysVKOwf1eHkGotzryJEjrF27lu3bt5PpFUiLVp259RoI2PgvvP02kXmiBUTeCFG3uDtUpVQV4cx1UKqSMsawd+9e1q5dy759+6hWrRp9+vRhwaYkBux5heo7tkGNBjD2nwR1GufucJVSVYwmqCqosLCQbdu2sW7dOlJSUiAghDMR/Xhqch8C/f3ol/g0foVZMOwN6HI7+FRzd8hKqSpIE1QVkpuby5YtW9iwYQMZGRnUqVOH66+7jnNHt5Oz8UX2p35B28gGBNw+F0Iaa2JSSrmVJqgq4MyZM2zatImYmBiys7MJb9iY5FpRjKy9m86bHoaURPJrNsCn2nGgAYS2cHfISimlCaoyS01NZf369SQmJlJQUEDrNtfQt09vGtWpzuG/96PR3kNQpwWM+w8+HW7SAQOVUh5FE1QlU9TxYcOGDezevRsfHx86d+5MZlB9tmxazbgJDRA/b8KjroWmA6DtOPDydnfYSin1K5qgKon8/HwSExPZsGEDqampVK9enb4DBtG1ZX1qbf+M/LUfMyE/m4yTNxNQLxzv6/7p7pCVUqpUmqAquLNnzxITE0NMTAyZmZnUq1ePcePG0TTUn3UfPk3N1WuBQnzaTYA+DxFWL9zdISullFM0QVVQKSkpbNiwgaSkJAoKCmjVqhVdu3YlG286tmoO6XsYLLEcb3Ub9Uc8BrUi3R2yUkpdEk1QFUhhYSG7du1i48aNHDhwAF9fX7p06UKvqGuovX8hJ78aR1xOOGlPLSK0TnP8f7eb+r4B7g5bKaUuiyaoCiArK4u4uDg2b97MqVOnCA4OZtiwYUTW8ePQj28REvcD5GcREN6D0KZTqRNk98bT5KSUqsA0QXmwlJQUNm3aRGJiIvn5+URGRtK93yDCG0fSrF4IZ5e8TGjaQg42uY4mox7FP7wTndwdtFJKXSUuS1Ai4g+sxhqV1wf4Sgc5LFtBQQE7d+5k06ZNJCcn4+3tQ9M2HRjetTmhyQtJXXAD34c/RLPpj1G9/2/J7vkbmgTXdXfYSil11bmyBpUDDDHGnBURXyBaRBYZYza4cJ8V1pkzZ4iLiyMmJoYzZ85Su3Ytrh02jDVJu2j003uEbV8PhXn41OvL8B4drJUCQvDXVjylVCXlsgRlj8Z71n7paz+Mq/ZXERljOHDgAJs3b2bnzp0UFhZyonZbNnsHsfKBEfh5e9Fp/QP4FqZjut+DdL+bOqEtqePuwJVSqhy49ByUiHgDsUAL4G1jzMZilrkXuBcgIiLCleF4jOzsbBISEti8eTP7Uk+zg0bc2a0Hw1sGcG7zZwScWknm2Rj8QoIJmvol1GoCfkHuDlsppcqVSxOUMaYAiBKREGC+iLQ3xiRdtMwMYAZAt27dKnUN68iRI9ZFtQk7yMkvoGWjugwd2IfAld8xYu8GasYmEewbCB0mgk+utVK9tu4NWiml3KRcevEZYzJEZCUwEkgqY/FKJTc3l6SkJGJjYzly5Aji48dX2e0Z0zqEe6b2h8OxjFr9Pvi2g1GvQ8dJEBDi7rCVUsrtXNmLLwzIs5NTADAMeM1V+/M0KSkpxMbGkpiYyK5zgWT6h/HMsH50ZCcPb3wJfDsD/aFBF5i+Gup3BBF3h62UUh7DlTWocOAT+zyUFzDHGLPAhftzO8fa0q5DaYT4FtK+fTvCMtJpdvgLuizfiBTkEBLeCVr3s1YSgXC9ekkppS7myl58CUBnV23fUxhjOHLkCHFxcSQlJZGbm8vp6k3YkBPO67eOoE/rcAoWPIlXylak8x3QeSqEd3R32Eop5fH0ThKXKSsri4SEBOLi4jhy7DhJhY3o3aQ5DzZNJXj/XHzORJNR+C0QjveQ38OIF/XWQ0opdQk0QV0CYwz79u1jy5YtJG7fyal8H9o2qs2Ea/vQZ/lbDD28Eb+D56BWUxj8HCGN2lgrBtZ2b+BKKVUBaYJyQkZGBvHx8cTHx3Pq1Cn8/f1J9Q3Fz+ssd999D16FeRTGPIlX5A0QNQUiemuHB6WUukKaoEqQl5fHjh07iI+PZ9++fRwuqEmadwj/aptKo/SlSEoCWTWaIDwGPn54PRQHXl7uDlsppSoNTVAOjDEcPHiQ+Ph4tiZtZ092IM1DvBk0aBARxxbTZPuf8dpmoEFnGPEKAe1v/CUpaXJSSqmrShMUVhNeQkICW+K3cuzEKUJ8C+hfN4fuyUs50eZZBg4cCHsNhNWADjdBaEt3h6yUUpVelU1QOTk5bN++nYSEBPbv34+YAk4UejE5MI5ehTHI4UzyatTFq4V996Vmg6yHUkqpclGlElRhYSF79+4lISGBnTt3suVcCN4+Xtw/eBBRrSOp/m5XCgsDkfY3Qocb8W3SF7y83R22UkpVSZU+QRljSElJYevWrfy4ZQ/bzgYwNXAzU0JSeLQwlhSfRkT0i8bH2wvu+QHveh3Ax8/dYSulVJVXaRPUyZMnWROzlfmxB6mfnUygdyEDA4/yO9+FhOVlQEYgtBlFs/YTwdvu4NCwq1tjVkop9YtKlaDST53h02Xx5B7bS/axfQSaU/QuOAZRtzF1zGB8t8/F++fT0P4GaDVCx1hSSikPVqkS1MzPv2DBQeGBgBWMCtxK4LnDGD9fpNM0CAiArrdbD6WUUh6vUiWo8f078Mi8UZhCQer2gw5PI9dcr7caUkqpCqhSJagmHfpA4QykaX+o2cDd4SillLoClSpBAdBpsrsjUEopdRXo/XmUUkp5JE1QSimlPJImKKWUUh5JE5RSSimPpAlKKaWUR9IEpZRSyiNpglJKKeWRNEEppZTySGKMcXcM54nIceDAFW4mFEi7CuF4Ei2T56ts5QEtU0VRGcrUxBgTdvFEj0pQV4OIxBhjurk7jqtJy+T5Klt5QMtUUVTGMhXRJj6llFIeSROUUkopj1QZE9QMdwfgAlomz1fZygNapoqiMpYJqITnoJRSSlUOlbEGpZRSqhLQBKWUUsojeXyCEpEPRSRVRJIcpnUSkfUikigi34tITXt6pIhkiUi8/XjHYZ2u9vK7ReT/RETcUR47FqfLZM/raM/bZs/3t6dXyDKJyBSHzyheRApFJMqeV1HL5Csin9jTd4jIsw7reESZLrE8fiLykT19q4gMcljHI8pjx9JYRFbY7/k2EXnEnl5bRH4UkZ/tv7Uc1nnWjn2XiIxwmO4R5brUMolIHXv5syLy1kXb8ogyXTZjjEc/gAFAFyDJYdpmYKD9/C7gRft5pONyF21nE9AbEGARMKqClMkHSAA62a/rAN4VuUwXrdcB2FsJPqdbgVn280BgPxDpSWW6xPL8FvjIfl4XiAW8PKk8dizhQBf7eQ3gJ6At8Dfgd/b03wGv2c/bAluBakBTYI+n/T9dRpmCgH7AfcBbF23LI8p0uQ+Pr0EZY1YDJy6a3BpYbT//EZhY2jZEJByoaYxZb6xP7VNg/FUO1WmXWKbhQIIxZqu9broxpqCCl8nRLcCXUOE/JwMEiYgPEADkAqc9qUyXWJ62wDJ7vVQgA+jmSeWxYztqjImzn58BdgANgXHAJ/Zin/BLjOOwfkjkGGP2AbuBHp5UrkstkzEm0xgTDWQ7bseTynS5PD5BlSAJuN5+fhPQ2GFeUxHZIiKrRKS/Pa0hcMhhmUP2NE9SUplaAUZEfhCROBF52p5ekcvkaDJ2gqJil+krIBM4CiQDbxhjTuD5ZSqpPFuBcSLiIyJNga72PI8tj4hEAp2BjUA9Y8xRsL7wsWqBYMV60GG1ovg9slxOlqkkHlmmS1FRE9RdwG9FJBarCpxrTz8KRBhjOgOPA1/YberFtbt6Wv/6ksrkg1V9n2L/nSAiQ6nYZQJARHoC54wxRedEKnKZegAFQAOspqMnRKQZnl+mksrzIdYXWgzwT2AdkI+HlkdEqgPzgEeNMadLW7SYaaaU6W5zCWUqcRPFTHP7Z3UpfNwdwOUwxuzEavpCRFoBY+zpOUCO/TxWRPZg1UAOAY0cNtEIOFKeMZelpDJhxb7KGJNmz/sf1nmEmVTcMhW5mV9qT1CxP6dbgcXGmDwgVUTWAt2ANXhwmUr5X8oHHitaTkTWAT8DJ/Gw8oiIL9YX+efGmK/tycdEJNwYc9Ru6kq1px/iwpp8UfwedexdYplK4lFluhwVsgYlInXtv17Ac8A79uswEfG2nzcDWmKdgD8KnBGRXnYvltuBb90SfAlKKhPwA9BRRALt8xsDge0VvExF024CZhVNq+BlSgaGiCUI6AXs9PQylfK/FGiXAxG5Fsg3xnjccWfH8AGwwxjzpsOs74A77Od38EuM3wE3i0g1u+myJbDJk8p1GWUqlieV6bK5u5dGWQ+sX9hHgTysXwR3A49g9Wz5CXiVX+6IMRHYhtV+Hgdc57Cdbljt7XuAt4rW8fQy2cvfZpcrCfhbJSnTIGBDMdupkGUCqgNz7c9pO/CUp5XpEssTCezCOkG/FGs4BI8qjx1LP6xmqwQg3n6Mxurtugyr1rcMqO2wzh/s2Hfh0KvNU8p1mWXaj9UB5qz92bb1pDJd7kNvdaSUUsojVcgmPqWUUpWfJiillFIeSROUUkopj6QJSimllEfSBKWUUsojaYJSyoXs66KiRWSUw7RJIrLYnXEpVRFoN3OlXExE2mNdI9UZ8Ma6rmWkMWbPZWzL2xhTcHUjVMozaYJSqhyIyN+wbiYbZP9tgjXUiA/wgjHmW/vGoJ/ZywA8aIxZJ9ZYTH/Cusg2yhjTtnyjV8o9NEEpVQ7s2wbFYd2MdQGwzRgzU0RCsMbs6Yx194BCY0y2iLQEvjTGdLMT1EKgvbGGiFCqSqiQN4tVqqIxxmSKyGysW9FMAq4TkSft2f5ABNaNPN8Sa3ThAqwbHRfZpMlJVTWaoJQqP4X2Q4CJxphdjjNF5AXgGNAJqwOT4wB0meUUo1IeQ3vxKVX+fgAesu8wjYh0tqcHA0eNMYXAVKwOFUpVWZqglCp/LwK+QIKIJNmvAf4D3CEiG7Ca97TWpKo07SShlFLKI2kNSimllEfSBKWUUsojaYJSSinlkTRBKaWU8kiaoJRSSnkkTVBKKaU8kiYopZRSHun/ASS1/xdfdDvJAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -732,15 +733,16 @@
     "system.alpha1 = 19 / 1000\n",
     "system.alpha2 = 15 / 1000\n",
     "\n",
-    "results = run_simulation(system, growth_func3)\n",
-    "plot_results(results, \n",
-    "             'Proportional model, parameter changes over time')"
+    "results3 = run_simulation(system, growth_func3)\n",
+    "results3.plot(label='model', color='gray')\n",
+    "plot_estimates()\n",
+    "decorate(title='Proportional growth, parameter changes over time')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "popular-ireland",
+   "execution_count": 22,
+   "id": "preliminary-carnival",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -753,7 +755,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "indian-sitting",
+   "id": "median-policy",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap07.ipynb b/jupyter/chap07.ipynb
index 648f2181..e331b338 100644
--- a/jupyter/chap07.ipynb
+++ b/jupyter/chap07.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "dutch-anime",
+   "id": "regular-director",
    "metadata": {},
    "source": [
     "# Chapter 7"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "legitimate-middle",
+   "id": "disciplinary-occasion",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "smart-vault",
+   "id": "similar-wellington",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -41,18 +41,16 @@
     "    import pint\n",
     "except ImportError:\n",
     "    !pip install pint\n",
-    "    import pint\n",
     "    \n",
     "try:\n",
     "    import modsim\n",
     "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
+    "    !pip install modsimpy"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "nervous-associate",
+   "id": "indie-scoop",
    "metadata": {},
    "source": [
     "In the previous chapter we developed a population model where net growth during each time step is proportional to the current population. This model seems more realistic than the constant growth model, but it does not fit the data as well.\n",
@@ -70,7 +68,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "configured-custom",
+   "id": "qualified-houston",
    "metadata": {},
    "source": [
     "Here's the code that reads the data."
@@ -79,7 +77,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "retired-essence",
+   "id": "major-arkansas",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,7 +92,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "italic-remains",
+   "id": "intense-columbus",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "automated-impossible",
+   "id": "wound-necklace",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -120,7 +118,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "chinese-bundle",
+   "id": "intended-matrix",
    "metadata": {},
    "source": [
     "And here are the functions from the previous chapter."
@@ -129,7 +127,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "incoming-clause",
+   "id": "former-accuracy",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,30 +154,22 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "worst-referral",
+   "id": "duplicate-insert",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from modsim import plot, decorate\n",
+    "from modsim import decorate\n",
     "\n",
-    "def plot_results(timeseries, title):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    timeseries: TimeSeries of simulation results\n",
-    "    title: string\n",
-    "    \"\"\"\n",
-    "    plot(census, style=':', label='US Census')\n",
-    "    plot(un, style='--', label='UN DESA')\n",
-    "    plot(timeseries, color='gray', label='model')\n",
-    "    \n",
+    "def plot_estimates():\n",
+    "    census.plot(style=':', label='US Census')\n",
+    "    un.plot(style='--', label='UN DESA')\n",
     "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)',\n",
-    "             title=title)"
+    "             ylabel='World population (billion)')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "funky-bristol",
+   "id": "champion-retreat",
    "metadata": {},
    "source": [
     "## Quadratic growth\n",
@@ -202,7 +192,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "coastal-crazy",
+   "id": "sized-hawaiian",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -212,7 +202,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "closing-wrist",
+   "id": "amateur-strand",
    "metadata": {},
    "source": [
     "Here's the `System` object we'll use, initialized with `t_0`, `p_0`, and `t_end`."
@@ -221,7 +211,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "formed-touch",
+   "id": "interstate-canal",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -238,7 +228,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "single-punishment",
+   "id": "acting-transportation",
    "metadata": {},
    "source": [
     "Now we have to add the parameters `alpha` and `beta` .\n",
@@ -248,7 +238,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "equivalent-monster",
+   "id": "banned-dallas",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +248,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "incoming-cradle",
+   "id": "supreme-wellington",
    "metadata": {},
    "source": [
     "And here's how we run it:"
@@ -267,7 +257,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "prescription-assurance",
+   "id": "positive-scheme",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -276,7 +266,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "toxic-european",
+   "id": "satisfied-density",
    "metadata": {},
    "source": [
     "And here are the results."
@@ -285,12 +275,12 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "instant-malawi",
+   "id": "flush-fellowship",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -302,12 +292,14 @@
     }
    ],
    "source": [
-    "plot_results(results, 'Quadratic model')"
+    "results.plot(color='gray', label='model')\n",
+    "plot_estimates()\n",
+    "decorate(title='Quadratic Growth Model')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "unexpected-davis",
+   "id": "organized-combining",
    "metadata": {},
    "source": [
     "The model fits the data well over the whole range, with just a bit of space between them in the 1960s.\n",
@@ -328,7 +320,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "opened-paint",
+   "id": "powerful-century",
    "metadata": {},
    "source": [
     "## Net growth\n",
@@ -341,7 +333,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "wanted-malta",
+   "id": "excited-catch",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -352,7 +344,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "exempt-techno",
+   "id": "tracked-guide",
    "metadata": {},
    "source": [
     "Now I'll use the quadratic model to compute net growth for each population."
@@ -361,7 +353,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "reduced-creature",
+   "id": "classical-badge",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -371,36 +363,34 @@
   },
   {
    "cell_type": "markdown",
-   "id": "color-surfing",
+   "id": "quantitative-differential",
    "metadata": {},
    "source": [
-    "I'll put the results in a `Series` with `pop_array` and the index and `growth_array` as the values."
+    "To plot the growth rate versus population, we can import the `plot` function from Matplotlib:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "powered-productivity",
+   "id": "interim-consent",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pandas import Series\n",
-    "\n",
-    "growth_series = Series(growth_array, index=pop_array)"
+    "from matplotlib.pyplot import plot"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "spare-complexity",
+   "id": "published-average",
    "metadata": {},
    "source": [
-    "Now we can plot the `Series` like this:"
+    "And use it like this."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "growing-butler",
+   "id": "received-crossing",
    "metadata": {},
    "outputs": [
     {
@@ -417,7 +407,7 @@
     }
    ],
    "source": [
-    "plot(growth_series, label='growth')\n",
+    "plot(pop_array, growth_array, label='growth')\n",
     "\n",
     "decorate(xlabel='Population (billions)',\n",
     "         ylabel='Net growth (billions)',\n",
@@ -426,7 +416,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "placed-conservative",
+   "id": "plastic-wisdom",
    "metadata": {},
    "source": [
     "Note that the x-axis is not time, as in the previous figures, but population. We can divide this curve into four regimes of behavior:\n",
@@ -451,7 +441,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "spread-ranking",
+   "id": "relevant-detection",
    "metadata": {},
    "source": [
     "## Equilibrium\n",
@@ -474,7 +464,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "foreign-emperor",
+   "id": "lightweight-entrepreneur",
    "metadata": {},
    "outputs": [
     {
@@ -494,7 +484,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "enormous-casting",
+   "id": "stuck-flood",
    "metadata": {},
    "source": [
     "With these parameters, net growth is 0 when the population is about 13.9 billion.\n",
@@ -514,7 +504,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "opened-possession",
+   "id": "derived-oliver",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -530,7 +520,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "baking-ability",
+   "id": "bibliographic-handy",
    "metadata": {},
    "source": [
     "## Dysfunctions\n",
@@ -548,7 +538,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "interracial-speed",
+   "id": "lucky-insurance",
    "metadata": {},
    "outputs": [
     {
@@ -571,7 +561,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "attempted-infection",
+   "id": "accredited-fitting",
    "metadata": {},
    "source": [
     "Now let's see all the ways that can go wrong."
@@ -579,7 +569,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "detailed-trainer",
+   "id": "transparent-cotton",
    "metadata": {},
    "source": [
     "Dysfunction \\#1: Not using parameters. In the following version, the\n",
@@ -590,7 +580,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "wooden-collect",
+   "id": "aggregate-baker",
    "metadata": {},
    "outputs": [
     {
@@ -613,7 +603,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "settled-scale",
+   "id": "otherwise-belarus",
    "metadata": {},
    "source": [
     "This version actually works, but it is not as versatile as it could be.\n",
@@ -622,7 +612,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "unnecessary-stack",
+   "id": "naughty-crazy",
    "metadata": {},
    "source": [
     "Dysfunction \\#2: Clobbering the parameters. When people first learn\n",
@@ -632,7 +622,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "committed-better",
+   "id": "outdoor-petroleum",
    "metadata": {},
    "outputs": [
     {
@@ -657,7 +647,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "terminal-wheat",
+   "id": "afraid-street",
    "metadata": {},
    "source": [
     "In this example, we have a `System` object named `sys1` that gets passed\n",
@@ -674,7 +664,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "interpreted-perfume",
+   "id": "expired-detail",
    "metadata": {},
    "source": [
     "Dysfunction \\#3: No return value. Here's a version that computes the\n",
@@ -684,7 +674,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "crude-window",
+   "id": "increasing-database",
    "metadata": {},
    "outputs": [
     {
@@ -707,7 +697,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "logical-enemy",
+   "id": "brutal-chile",
    "metadata": {},
    "source": [
     "A function that doesn't have a return statement actually returns a special value called `None`, so in this example the value of `pop` is `None`. If you are debugging a program and find that the value of a variable is `None` when it shouldn't be, a function without a return statement is a likely cause."
@@ -715,7 +705,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "concerned-space",
+   "id": "meaning-thriller",
    "metadata": {},
    "source": [
     "Dysfunction \\#4: Ignoring the return value. Finally, here's a version\n",
@@ -734,7 +724,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "better-karen",
+   "id": "opposed-vessel",
    "metadata": {},
    "source": [
     "In this example, `carrying_capacity` runs and returns `K`, but the\n",
@@ -747,7 +737,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "metropolitan-blackjack",
+   "id": "quarterly-ethiopia",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -755,7 +745,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "vital-seven",
+   "id": "painted-samuel",
    "metadata": {},
    "source": [
     "**Exercise:** In a previous section, we saw a different way to parameterize the quadratic model:\n",
@@ -770,7 +760,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "twelve-amplifier",
+   "id": "unauthorized-settle",
    "metadata": {},
    "outputs": [
     {
@@ -796,7 +786,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "yellow-horse",
+   "id": "primary-roman",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -809,12 +799,12 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "animal-drink",
+   "id": "ordinary-solid",
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -828,22 +818,30 @@
    "source": [
     "# Solution\n",
     "\n",
-    "results = run_simulation(system, growth_func_quad2)\n",
-    "plot_results(results, 'Quadratic model, alternate parameters')"
+    "results2 = run_simulation(system, growth_func_quad2)\n",
+    "results2.plot(color='gray', label='model')\n",
+    "plot_estimates()\n",
+    "decorate(title='Quadratic Growth Model, alternate parameters')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "early-mortgage",
+   "id": "ignored-architect",
    "metadata": {},
    "source": [
-    "**Exercise:**  What happens if we start with an initial population above the carrying capacity, like 20 billion?  Run the model with initial populations between 1 and 20 billion, and plot the results on the same axes."
+    "**Exercise:**  What happens if we start with an initial population above the carrying capacity, like 20 billion?  Run the model with initial populations between 1 and 20 billion, and plot the results on the same axes.\n",
+    "\n",
+    "Hint: If there are too many labels in the legend, you can plot results like this:\n",
+    "\n",
+    "```\n",
+    "    results.plot(label='_nolegend')\n",
+    "```\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "domestic-ukraine",
+   "id": "intermediate-kazakhstan",
    "metadata": {},
    "outputs": [
     {
@@ -866,18 +864,28 @@
     "\n",
     "for p_0 in p0_array:\n",
     "    system.p_0 = p_0\n",
-    "    results = run_simulation(system, growth_func_quad)\n",
-    "    plot(results, label='_nolegend')\n",
+    "    results3 = run_simulation(system, growth_func_quad)\n",
+    "    results3.plot(label='_nolegend')\n",
     "\n",
     "decorate(xlabel='Year',\n",
     "         ylabel='Population (billions)',\n",
     "         title='Projections with hypothetical starting populations')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "positive-enforcement",
+   "metadata": {},
+   "source": [
+    "## Under the hood\n",
+    "\n",
+    "Explain the difference between the Pandas and Matplotlib `plot` functions."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "commercial-respondent",
+   "id": "described-funds",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap08.ipynb b/jupyter/chap08.ipynb
index 49664fb6..92c864d6 100644
--- a/jupyter/chap08.ipynb
+++ b/jupyter/chap08.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "sophisticated-translation",
+   "id": "external-reward",
    "metadata": {},
    "source": [
     "# Chapter 8"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "amino-creation",
+   "id": "compound-announcement",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "korean-spine",
+   "id": "tropical-medicine",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -41,18 +41,16 @@
     "    import pint\n",
     "except ImportError:\n",
     "    !pip install pint\n",
-    "    import pint\n",
     "    \n",
     "try:\n",
     "    import modsim\n",
     "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
+    "    !pip install modsimpy"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "rental-constitution",
+   "id": "chicken-emphasis",
    "metadata": {},
    "source": [
     "In the previous chapter we developed a quadratic model of world\n",
@@ -65,7 +63,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ambient-concentration",
+   "id": "global-international",
    "metadata": {},
    "source": [
     "Here's the code that downloads the data."
@@ -74,7 +72,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "portable-heather",
+   "id": "necessary-factor",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,16 +86,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "brave-championship",
+   "id": "designed-entrance",
    "metadata": {},
    "source": [
-    "And reads `table2`, which contains world populations estimates from the U.S. Census and U.N. DESA, among other organizations."
+    "And here's the code that reads `table2`, which contains world populations estimates from the U.S. Census and U.N. DESA, among other organizations."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "apart-waste",
+   "id": "changed-desktop",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,7 +107,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "powerful-heather",
+   "id": "metallic-inventory",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +119,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "pending-alliance",
+   "id": "further-armstrong",
    "metadata": {},
    "source": [
     "## Generating Projections"
@@ -129,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "colored-framework",
+   "id": "concrete-lightning",
    "metadata": {},
    "source": [
     "Now let's run the quadratic model, extending the results until 2100, and see how our projections compare to the professionals'.\n",
@@ -140,20 +138,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "acute-compiler",
+   "id": "extraordinary-shade",
    "metadata": {},
    "outputs": [],
    "source": [
     "from modsim import TimeSeries\n",
     "\n",
     "def run_simulation(system, growth_func):\n",
-    "    \"\"\"Simulate the system using any update function.\n",
-    "    \n",
-    "    system: System object\n",
-    "    growth_func: function that computes the population next year\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
     "    results = TimeSeries()\n",
     "    results[system.t_0] = system.p_0\n",
     "    \n",
@@ -167,7 +158,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "northern-seafood",
+   "id": "indirect-russia",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -178,7 +169,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "stuck-influence",
+   "id": "collaborative-schedule",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -189,7 +180,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "covered-suicide",
+   "id": "comfortable-compression",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -202,7 +193,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "occasional-denmark",
+   "id": "working-cleveland",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,7 +205,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "available-microwave",
+   "id": "sophisticated-adult",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -225,7 +216,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "arranged-sailing",
+   "id": "broken-windsor",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,7 +226,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "generous-renewal",
+   "id": "latest-function",
    "metadata": {},
    "outputs": [
     {
@@ -314,7 +305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "promising-fantasy",
+   "id": "outer-ensemble",
    "metadata": {},
    "source": [
     "And here are the results."
@@ -322,13 +313,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "prostate-brush",
+   "execution_count": 13,
+   "id": "portable-pottery",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -340,14 +331,17 @@
     }
    ],
    "source": [
-    "from modsim import plot\n",
+    "from modsim import decorate\n",
     "\n",
-    "plot(results, color='gray', label='model')"
+    "results.plot(color='gray', label='model')\n",
+    "decorate(xlabel='Year', \n",
+    "         ylabel='World population (billion)',\n",
+    "         title='Quadratic Model Projection')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "killing-relief",
+   "id": "macro-carroll",
    "metadata": {},
    "source": [
     "According to the model, population growth will slow gradually after 2020, approaching 12.5 billion by 2100.\n",
@@ -395,7 +389,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "opposed-folks",
+   "id": "small-seminar",
    "metadata": {},
    "source": [
     "## Projections"
@@ -403,7 +397,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "advisory-sheffield",
+   "id": "greater-illness",
    "metadata": {},
    "source": [
     "From the same page where we got the past population estimates, we'll read `table3`, which contains predictions for population growth over the next 50-100 years, generated by the U.S. Census, U.N. DESA, and the Population Reference Bureau."
@@ -411,8 +405,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "spoken-calculation",
+   "execution_count": 14,
+   "id": "precious-contribution",
    "metadata": {},
    "outputs": [
     {
@@ -508,7 +502,7 @@
        "2020                                       7.758157e+09                    "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -520,7 +514,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hispanic-episode",
+   "id": "coated-smoke",
    "metadata": {},
    "source": [
     "Some values are `NaN`, which indicates missing data, because some organizations did not publish projections for some years.\n",
@@ -530,8 +524,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "corrected-denial",
+   "execution_count": 15,
+   "id": "headed-tuner",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -540,7 +534,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "explicit-tiffany",
+   "id": "ceramic-scroll",
    "metadata": {},
    "source": [
     "The following function plots projections from the U.N. DESA and U.S. Census.  It uses `dropna` to remove the `NaN` values from each series before plotting it."
@@ -548,43 +542,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "lyric-cinema",
+   "execution_count": 16,
+   "id": "paperback-delay",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from modsim import plot, decorate\n",
-    "\n",
     "def plot_projections(table):\n",
     "    \"\"\"Plot world population projections.\n",
     "    \n",
     "    table: DataFrame with columns 'un' and 'census'\n",
     "    \"\"\"\n",
-    "    census_proj = table.census / 1e9\n",
-    "    un_proj = table.un / 1e9\n",
-    "    prb_proj = table.prb / 1e9\n",
+    "    census_proj = table.census.dropna() / 1e9\n",
+    "    un_proj = table.un.dropna() / 1e9\n",
+    "    \n",
+    "    census_proj.plot(style=':', label='US Census')\n",
+    "    un_proj.plot(style='--', label='UN DESA')\n",
     "    \n",
-    "    plot(census_proj.dropna(), style=':', label='US Census')\n",
-    "    plot(un_proj.dropna(), style='--', label='UN DESA')"
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "conscious-method",
+   "id": "prostate-matrix",
    "metadata": {},
    "source": [
-    "Here are their projections compared to the results of the logistic model."
+    "Here are their projections compared to the results of the quadratic model."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "neutral-material",
+   "execution_count": 17,
+   "id": "billion-dynamics",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -597,12 +591,13 @@
    ],
    "source": [
     "plot_projections(table3)\n",
-    "plot(results, color='gray', label='model')"
+    "results.plot(color='gray', label='model')\n",
+    "decorate(title='Quadratic Model Projection')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "rough-patent",
+   "id": "integrated-there",
    "metadata": {},
    "source": [
     "The U.N. DESA expects the world population to reach 11 billion around 2100, and then level off.\n",
@@ -617,7 +612,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "rising-procurement",
+   "id": "serial-binary",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -628,7 +623,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "complex-forwarding",
+   "id": "authorized-suggestion",
    "metadata": {},
    "source": [
     "## Exercises\n",
@@ -640,8 +635,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "involved-delivery",
+   "execution_count": 18,
+   "id": "handmade-funeral",
    "metadata": {},
    "outputs": [
     {
@@ -656,7 +651,7 @@
        "Name: census, dtype: float64"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -668,7 +663,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "executed-cheat",
+   "id": "discrete-scanner",
    "metadata": {},
    "source": [
     "The first element is `NaN` because we don't have the data for 1945, so we can't compute the first difference.\n",
@@ -678,8 +673,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "promotional-poker",
+   "execution_count": 19,
+   "id": "objective-accused",
    "metadata": {},
    "outputs": [
     {
@@ -694,7 +689,7 @@
        "Name: census, dtype: float64"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -706,7 +701,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "incoming-tribute",
+   "id": "weighted-trouble",
    "metadata": {},
    "source": [
     "The following function computes and plots the growth rates for the `census` and `un` estimates:"
@@ -714,24 +709,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "historical-program",
+   "execution_count": 20,
+   "id": "unique-matrix",
    "metadata": {},
    "outputs": [],
    "source": [
     "def plot_alpha():\n",
     "    alpha_census = census.diff() / census\n",
-    "    plot(alpha_census, style='.', label='US Census')\n",
+    "    alpha_census.plot(style='.', label='US Census')\n",
     "\n",
     "    alpha_un = un.diff() / un\n",
-    "    plot(alpha_un, style='.', label='UN DESA')\n",
+    "    alpha_un.plot(style='.', label='UN DESA')\n",
     "\n",
     "    decorate(xlabel='Year', label='Net growth rate')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "horizontal-consensus",
+   "id": "flexible-amateur",
    "metadata": {},
    "source": [
     "And here's what it looks like."
@@ -739,8 +734,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "id": "rough-manual",
+   "execution_count": 21,
+   "id": "pressing-proceeding",
    "metadata": {},
    "outputs": [
     {
@@ -762,7 +757,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "acute-ratio",
+   "id": "cross-reducing",
    "metadata": {},
    "source": [
     "Other than a bump around 1990, net growth rate has been declining roughly linearly since 1970.\n",
@@ -774,8 +769,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "color-pricing",
+   "execution_count": 22,
+   "id": "mexican-denver",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -787,7 +782,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "duplicate-parliament",
+   "id": "handy-dubai",
    "metadata": {},
    "source": [
     "To see what it looks like, I'll create an array of time stamps from 1960 to 2020 and use `alpha_func` to compute the corresponding growth rates."
@@ -795,8 +790,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "approximate-calcium",
+   "execution_count": 23,
+   "id": "addressed-worker",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -808,27 +803,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "transparent-madison",
-   "metadata": {},
-   "source": [
-    "To plot the results, I'll put them into a `Series` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "looking-yield",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas import Series\n",
-    "\n",
-    "alpha_series = Series(alpha_array, index=t_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "graphic-persian",
+   "id": "jewish-operations",
    "metadata": {},
    "source": [
     "Here's what it looks like, compared to the data."
@@ -836,13 +811,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "id": "focal-luther",
+   "execution_count": 24,
+   "id": "cathedral-shakespeare",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f968672d910>]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -854,13 +839,15 @@
     }
    ],
    "source": [
+    "from matplotlib.pyplot import plot\n",
+    "\n",
     "plot_alpha()\n",
-    "plot(alpha_series, color='gray')"
+    "plot(t_array, alpha_array, color='gray')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "raising-anthony",
+   "id": "expensive-viewer",
    "metadata": {},
    "source": [
     "If you don't like the `slope` and `intercept` I chose, feel free to adjust them.\n",
@@ -878,8 +865,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "bigger-crystal",
+   "execution_count": 25,
+   "id": "common-april",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -892,8 +879,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "id": "overhead-ethics",
+   "execution_count": 26,
+   "id": "ignored-chain",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -907,8 +894,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "id": "verified-friday",
+   "execution_count": 27,
+   "id": "color-accountability",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -920,8 +907,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "id": "fatal-status",
+   "execution_count": 28,
+   "id": "numeric-raise",
    "metadata": {},
    "outputs": [
     {
@@ -930,7 +917,7 @@
        "0.06725033332680001"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -943,25 +930,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "id": "balanced-pierre",
+   "execution_count": 29,
+   "id": "included-vehicle",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Solution\n",
     "\n",
-    "results = run_simulation(system, growth_func_alpha);"
+    "results2 = run_simulation(system, growth_func_alpha);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "id": "relevant-sessions",
+   "execution_count": 30,
+   "id": "brown-rating",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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9635cXBMqq2pzDmrO0zdn2vb19rrlfqCf9y336wT43nK/fi2/W+47U3sKDQ3l0qVLtyy7ePHiLRe2RkdH88wzz/Cd73yHBg0acOHCBUJDQwvXx8bGsmPHDkaPHn3b/gvOR7Vt2/aW5Vu2bMHf37/wvre3N7m5udSrV4+vv/6azz//nLfeeou5c+cyY8YMfHx8yM+33qyO1zU99thj9OzZk2XLljF8+HDeeecdBg0adMdyK6XcJPcGpOyDM4lWIjqzCxp3gtFvgW8gnN4BIc2h3Sho0sX6q2OdZsAvCLpOdmv4ZeGxvfhE5CkR2S4i21NTU90dzm1q165N48aNWbNmDWAlpxUrVtC3b18Ali1bRsFsxYcPH8bb25u6deveso8f/OAHvP/++2zZsqVw2UcffcS5c+cYPnw4//znPwv3sWvXrlLjSUtLIz8/n4cffpjf/va37Ny5E7CmKtmxYwcA8+fPL9w+KSmJqKgonnvuOR588EF2795djmdDKVXhMi9ZSafAO4Ng+kBY+gLsXwxBodCoo7VOxDpH9Ph8GPQLaHf/zeRUhXlsDcoYMx2YDhAfH++R89J/8MEHfP/73+dHP/oRAL/61a+Ijo4G4MMPP+TFF18kKCgIHx8fPv74Y7y9vW95fKNGjZg9ezYvvfQSKSkpeHl50b9/f8aOHcsvf/lLXnjhBTp16oQxhsjISJYuXVpiLKdPn2bq1KmFtaXf//73ALz00ktMmDCBDz/88JYa0pw5c/joo4/w9fUlPDycV199tUKfG6XUXco4DSc2wslN1jVGKfshoC68fAy8vKDvD63tmnSxziFVg1MldyIFv9BdsnPrHNRSY0yHIssTgJeMMU7NQhgfH2+KTlh44MAB2rdvX0GRquLoc6yUi+Tnw/m9ViLqOhl8A2DVq7Dx7+BfB5p1h4heEHEPtOgNXt533mcVJiI7jDG39ex2WQ1KRGYBA4EwEUkGfgVcBP4JNACWiUiiMWa4q2JQSimPce0CHFoGR9fCsXVw/YK1vHFnqyND/DToOB4axlT7hOQsV/bim1jCqoWuOqZSSnmMrMtw/Euo1xIaxVgXwS55FmqHW926o++FFn2gbnNr+3qRbg3XE3nsOSillKpS8vMheatVQ0paC8nbweRBrx9YF7c2i4dnNkHD9jXi/FFF0ASllFJldf2iNQpD067W/VkTISvd6sjQ90WrltSsu7XO29eqSSmnaYJSSqm7cTEJDi23/k58BSHN4PmvrZ52k+ZBaDQE1nN3lNWCJiillCpNfr7VJCdys6cdWJ0Z+r4Ibe+/uW2zaj3EaKXz2At1Pd2dptuYMmUKTZs2JTs7G7h1oNaivL29iYuLIzY2ls6dO/Pmm28WXs+UkJBASEgIcXFxhX+rV68GrPH6YmNj6dSpE3Fxcbdc8Jubm0tYWBg/+9nPKrroSlV/OVnwzefw6fPW1BKp9mwErYfD8N/Dc4nwvU0w+JfQrJueU3IRrUG5kLe3NzNmzOCZZ54pdbvAwEASExMBaxT0xx57jIyMDH79618D0K9fv9su0t20aRNLly5l586d+Pv7k5aWxo0bNwrXr1y5krZt2zJ37lzeeOONajH+oVIud/kMrPkNHFwG2ZfBrza0GnJzbLvIPtafqhRag3KhF154gb/+9a/k5uY6/ZiGDRsyffp0/vWvf1HaRdRnz54lLCyscFy+sLAwmjRpUrh+1qxZPP/880RERLB5s+tnvlSqSsrPg2PrISnBuu9XC45+ATEPwqQF8HISTHhfOze4SfWpQf1v5O3LYsdAj+9YI/1+PP729XGPQZdJ1gV0c4sMpDh1WblDioiIoG/fvnz44Yc88MADTj8uKiqK/Px8UlJSAPjyyy+Ji4srXL9gwQKGDRvGb37zG9q0acOQIUN45JFHGDBgAACZmZmsWbOGt99+m/T0dGbNmlU455RSNZ4xkLwN9i6AfQvh6nlo0ReiBkJACPzwgF4o6yG0BlVGd5puo8DPf/5z/vznPxeeU3KWY+2pX79+JCYmFv5FR0dTu3ZtduzYwfTp02nQoAGPPPII7733HgBLly7l3nvvJSgoiIcffpiFCxfqfE9KFVjwJLw7FLb/D5r3gPHvWb3vCmhy8hjVpwZVWo3HL6j09bVC77rG5Mx0GwCtWrUiLi6OuXPnOr3vpKQkvL29adiwIQcOHChxO29vbwYOHMjAgQPp2LEj77//PlOmTGHWrFls3LixsFPGhQsXWLt2beGEh0rVGFfOwdezYfccmLwEajeAzhOt80rtRkJAHXdHqEpRfRJUJXOcbmPw4MGF0208//zzt237i1/8gpEji2mCLEZqairf/e53+cEPflBqx4ZDhw7h5eVVONFgYmIiLVq04PLly2zYsIFTp04Vnp/63//+x6xZszRBqZohL8fqgbfrIzi80hrNofk91th3tRtAa/0cVBWaoMqhtOk2HMXGxtK1a9fCOZqKyszMJC4ujpycHHx8fPjWt77FD3/4w8L1Rc9BvfLKK7Rs2ZJnn32W9PR0fHx8aNWqFdOnT+eTTz5h0KBBt0xqOHr0aF5++WWys7NvWa5UtZKTaU3cd/k0zJlkjXnX5zmImwRhOmN0VeTS6TYqik634R76HCuPl5VhdXbY9RHUagiPzbaWn9wCTbuBt/4GrwoqfboNpZRymZNbYPu71syyuVnWqA7R995cH9HTfbGpCqMJSilVNdy4Bt7+Vq3o2Do4tMJqvuvyuDU4q16MXu1oglJKebZLJ2Dbf2HnB/DA3yH2Ibjne9D7Weuck6q2XDmj7gxgFJBSMOW7iNQH5gCRwHFggjHmUkn7UErVUMbA8Q2w5T9w6DNAoP0DUD/KWu9f263hqcrhygt13wNGFFn2U2CNMaY1sMa+r5RSloIL2o2BpS9Y01n0eQFe2G0NOdS4szujU5XMlVO+rxeRyCKLRwMD7dvvAwnAT1wVg1Kqikg/BdvegQNL4LsbrDHxHp0JdSO0Ga8Gq+yhjhoZY84C2P8blrShiDwlIttFZHtqamqlBegukZGRpKWllXsbpaqU1EPwyVPw987w1T+gUazVdRygQVtNTjWcx3aSMMZMB6aDdR2Um8NRSlW01G/grZ5WErrnGej5tFVjUspW2TWo8yLSGMD+n1LJx69Qx48fp127djz55JN06NCBSZMmsXr1avr06UPr1q3ZunUrFy9eZMyYMXTq1Il77rmH3bt3A9b4eMOGDaNLly48/fTTtwwO+9FHH9GjRw/i4uJ4+umndaBXVX2c3gE73rNuN2gDo96EF/bC8Nc1OanbVHYNagnwBPAH+//iitjpihUrOHfuXEXsqlB4eDgjRhTt43G7I0eOMG/ePKZPn0737t2ZOXMmGzZsYMmSJbzxxhs0b96cLl26sGjRIr744gsmT55MYmIiv/71r+nbty+vvvoqy5YtY/r06YA1esOcOXPYuHEjvr6+fO973+Pjjz9m8uTJd4hEKQ92cjOs+xMcXWMNQdTpUfANgPhp7o5MeTBXdjOfhdUhIkxEkoFfYSWmuSLybeAkUMwkTVVLy5Yt6dixI2CNuTd48GBEhI4dO3L8+HFOnDjBggULABg0aBAXLlwgIyOD9evX88knnwAwcuRI6tWrB8CaNWvYsWMH3bt3B6xx+ho2LPFUnVKe7fx+WP4yHP8SgsJgyGvQ/UkrOSl1B67sxTexhFWDK/pYztR0XMVx8FUvL6/C+15eXuTm5uLjc/tTXDBKeXGjlRtjeOKJJ/j973/vooiVcjFjIPuKNZWFjz9cTILhb0C3KVbvvAqQm5vL5cuXycjI4MqVK1y/fp3MzMzC/9nZ2eTk5JCbm1v4v6AZXUQKP3u+vr74+fnd8hcYGEitWrUK/2rXrk1ISAiBgYGlzjCgKp7HdpKoLvr378/HH3/ML3/5SxISEggLC6NOnTqFy1955RWWL19eOLfU4MGDGT16NC+++CINGzbk4sWLXLlyhRYtWri5JEo54cRXsOpVa+DWiTMhNBpe2FOmSQBzc3O5cOECaWlppKWlceHChcIWiGvXrhX7mMDAQIKCgvD398fHx4eAgABq166Nr69vYXIpSFTGGHJzc7lx4waZmZlkZGSQnZ3N9evXyc3NvW3ffn5+1K1bl3r16lG3bl0aNGhAgwYNaNiwIQEBWiN0BU1QLvbaa68xdepUOnXqRFBQEO+//z5gTc0xceJEunbtyoABA4iIsE4Qx8TE8Lvf/Y5hw4aRn5+Pr68vb731liYo5dlSDsDq1+CbFRDcGLp86+Y6J5JTdnY2586d48yZM5w7d46zZ8+SlpZ2S+ehunXrUr9+fRo1akRISEjhX3BwMEFBQQQEBODlVf5+X8YYbty4wbVr17h27RpXr14lIyODS5cukZ6ezqVLl0hKSiInJ6fwMcHBwTRs2JAmTZrQpEkTmjZtSnBwcLljqel0ug1VIn2OlVP2LYT508CvNvR9EXp+15rFuhQZGRmcPHmy8C8l5WaH3uDgYBo3bkx4eDgNGjQgLCyM0NBQfH19XV0SpxljyMjIICUlhdTUVFJTUzl37hwpKSmFSTU4OJimTZvSokULIiMjadSokTYRlkCn21BKVZzMdLiaYnUVjxoIvZ+DPs9DUP3iN8/MJCkpiaNHj3Ls2DHS09MBq9msefPmxMTE0KRJExo3bkzt2p4/zp6IULduXerWrUubNm0Kl+fk5HDu3DlOnz7NmTNnOHXqFAcPHgQgICCgMFm1atWK0NBQTVh3UGqCEpFewONAP6AxkAnsBZYBHxljMlweoVLKc+RkWSOLr/8L1GsBT62DwHow9Ne3bGaM4dy5cxw6dIgjR45w5swZjDH4+/vTsmVLevbsSUREBOHh4RXSLOcpfH19ad68Oc2bNy9clpGRUdij98SJExw6dIjPP/+cevXq0apVK1q1akXLli09qoboKUpMUCKyHDiDda3S61gX1QYAbYB7gcUi8qYxZkllBFocY4z+AnGRqtD0qypRfh7sngtrX4eMUxA92EpKDp+//Px8Tpw4wcGDBzl06BAZGRmICE2bNqV///5ER0fTtGnTapWQnBESEkLnzp3p3Nka6PbSpUscOXKEI0eOkJiYyLZt2/D19aVVq1a0b9+eNm3a3NI7uCYr8RyUiIQZY0od+M2ZbSpCceegjh07RnBwsFaTXcAYw4ULF7hy5QotW7Z0dzjKE3w9BxY+BY3jrMQUNRCw3isnT55kz5497N+/n8zMTHx8fIiOjqZdu3a0adOGoKDSz0fVZLm5uYVJ/eDBg1y9ehVvb2+ioqKIjY2lffv2+Pn5uTtMlyvpHFSV7SSRk5NDcnIyWVlZboqqegsICKBZs2ba7FCTXTpuTRYYNQDycuDQcmg3Cry8OHfuHHv27GHv3r1cvnwZX19f2rZtS0xMDNHR0TXiS7WiGWM4deoUBw4c4MCBA2RkZODr60u7du3o1KkTUVFR1bb2WeYEJSJjgT9ijTwu9p8xxtRxRaDFKS5BKaVcJCcTNvwNNv7N6jL+7A7w8iYrK4s9e/awa9cuzp49i5eXF61ataJDhw60bduWPLz5yYLd9IoOZWIPHVevPAqS1e7du9m3bx9ZWVnUqlWLTp060a1bN0JDQ90dYoUqTy++PwEPGGMOVHxYSimPYQwcXAqf/xzST0KHhzFDfsOJk6fYuXMnBw4cIDc3l/DwcO677z46dOiAf0Agx9Ku4ufnZzUNX8vmatbtF7mquyMiREREEBERwYgRIzh8+DC7d+9my5YtbNq0icjISLp27Ur79u2LHa2munCmBrXRGNOnkuIpltaglKoEJzfDjOHQMIYbQ95g9+Vgtm3bRkpKCv7+/nTs2JGuXbvSuHHjwof8dMFuVu4/z1c/HUSAr7d2XHKxK1eukJiYyK5du7h06RKBgYF06dKFHj16EBIS4u7wyqw8TXx/B8KBRUB2wXJjzCcVHGOJNEEp5SLZVyB5G0QPAmO4uH0BW9Nqkfj112RnZxMeHk6PHj3o0KEDvr6+pF+/wXtfHWdyr0jq1/JjT3IGp9OvMzQmHG8vTUyVxRjDsWPH2L59e+F1Vu3bt6dnz540b968yv1IKE8TXx3gOjDMYZkBKi1BKaUqmDGwZx6s/CVkZZD86Bd8tXMvBw4cwMvLi5iYGHr06EGzZs0QkcLLDlKuZPOPNYeJDK3FmC5N6dgshI7Nqu4v96pKRIiKiiIqKoqMjAy2bt3Kzp072b9/P40bN6ZXr17ExsZW+U4VVbYXn1KqjM7tgc9+jDm5iW/qD+Erv4GcPH8Rf39/4uPj6dmzZ+E4csYYfrl4L/4+3vxyVAwAZ9IzaVJXp2L3NDdu3Cg8T5WWlkbdunXp3bs3cXFxHt8bt8w1KBFpBvwT6INVc9oAPG+MSa7wKJVSrnX5LGb6IPb7dmB9nZ+QcimHkJA8hg8fTpcuXQovEE27mk1YbX9EBG8RHFvvNDl5Jj8/P+Lj4+nWrRuHDh1iw4YNfPbZZ6xbt46ePXvSo0ePKncBsDPnoFYBM4EP7UWPA5OMMUPLfFCR54HvYHVZ/68x5m+lba81KKXKKfUQJqwN+/fvZ93KT0m9nE1YWBj9+vUjNjYWb++bI44v2nWaH8//mpUvDqBlWMXM36QqnzGGEydOsHHjRo4cOUJgYCC9e/emR48eHnedWnk6SSQaY+LutOwuAukAzAZ6ADeAFcAzxpjDJT1GE5RSZXQtjfwVP2f/nl2sr/coqelXCQsLY8CAAcTExBSeozieZs2vFBlWi9Qr2byzIYmn+0dTv5ZnfZGpsjlz5gwJCQkcPnyYoKAg+vTpQ/fu3T2m6a88CWo18B4wy140EZhqjCnTzLgiMh4Ybox50r7/SyDbGPOnkh6jCUqpu2QMJnEW+5b/l3U5nUijPg3CQuk/YOAtiQkgOzePXr//gl5Robw1qasbg1aulpycTEJCAkePHqVWrVr07duX+Ph4t19LVZ4EFQH8C+iFdQ7qK6xzUCfKGEh7rAFoe2GNjr4G2G6MebbIdk8BTwFERER0O3GiTIdTquYxhmMzvsOqU76clXAa1KvDgMHDiImJKex+nHolm8/2nOWJ3pEArP8mlXaNg2kYrDPD1gQnT54kISGhcEzT/v3707VrV7f1+vOosfhE5NvA94GrwH4g0xjzYknbaw1KKSfk5XI+NY3Va9Zw5MgR6gR4M2j4KDp17nzbdTHvfJnE75cfZNWL/Ylq4PnzLynXOH78OF988QWnTp2iQYMGDB06lFatWlX6dVR3naBE5GVjzJ9E5J9YNadbGGOeq6DA3gCSjTH/LmkbTVBKlS7j0EYSFr1PYlZTAgIC6NevHz169ChsurmRm88Hm47TLrwOfVuHkZWTx9mMLO0EoTDGcPDgQVavXs3Fixdp2bIlw4YNIzw8vNJiKEs384Kx9yo8M4hIQ2NMit18OBaruU8pdZeyr1ziy9l/ZcsZgyGcXm3D6Td6MoGBt3cF/2DTCQa1a0jf1mEE+HprclKAddFvwTxU27dvZ926dbz99tvExcVx7733UqdOpY0Lfntsbmri+xIIBXKAHxpj1pS2vdaglLqVMYav1y5k9YZtXDMBdKp/g3vHP03d8JujiO84cYkPNh3n/43vjI+3Fxev3dBeeeqOMjMz+fLLL9m6dSsiQu/evenbt69Le/zddQ1KRD6lmKa9AsaYB8sajDGmX1kfq1RNd+bMGZYvX05ycjJNva8zcXhfmnYfedt2qVey2HbsIsmXMokMq6XJSTklMDCQYcOG0b17d9asWcP69ev5+uuvGTFiBG3btq3U81OlnYMaUNoDjTHrXBJRMbQGpRRcv36dNUvmsPPQSWrVqsXgwYOJi22H+FnNeTdy8/njioO0aVSbR7pHYIwhOzefAF/vO+xZqZIdP36czz77jNTUVFq3bs2IESOoX79+hR7jrmtQlZmAlFIly8/PZ/vWzaxdvYrsvHzu8U9iwHf/RUDtWwdp9fUW9p3JwNfb6iosIpqcVLlFRkby9NNPs3XrVhISEvj3v/9Nnz59XN7sB6XXoPZQehNfJ1cFVZTWoFRNdebMGT79ZC7nLmTQ0pzgvna1aTD6NxBYF4CvT6Xz588P8e/Hu1InwJecvPzCBKVURbty5QqrVq1iz5491K1bt7DZr7zK0otvVLmPqpQqkxs3bvDFF1+wdetWapmrjPPfScyYHyHt7rtlOy8RTly8xqmL14ltEqLJSblUcHAwY8eOpWvXrnz22WfMnj2bxx57jNatW7vkeDrdhlIe5ptvvuGzpZ+SceUq8fHxDG5yjYC2QyCoPsYY3l6fxI3cfJ4bbH0p5Obl46OJSVWyvLw89uzZQ+diLgS/W2XpxbfBGNNXRK5gNfWJ439jjPs6xytVDV25coUVy5ez/8ABGnCBaSOG0rznrb3zRITD56+SlZtXOL26JiflDt7e3sTFxbn0GKV1kuhr/w92aQRK1XDGGHbu3MmqlZ+TeyObQeYrerdvjHcH6/r11CvZ/GH5QV4Y0prm9YP4/diO+PloUlLVn1ND2IpIV6Av9oSFxphdLo1KqRoiPT2dJUuWcOzYMVrKaUb6biR01K+g43iwm01y8vL54uB5BrRtQPP6QZqcVI3hzIy6rwLjgU/sRe+JyDxjzO9cGplS1Zgxhh07drBq1SoARsWG0DV9I/LwMqgXyYGzl1m9/zzPDm5Nk7qBbPzpIIL83DslglKVzZl3/ESgizEmC0BE/gDsBDRBKVUGjrWmqEZ1eODRqdQNCYH8H4C39ZFcsfccH285wWM9Iwit7a/JSdVIzrzrjwMBQJZ93x846qqAlKqubqk15d1gJGvolp+H1HkeRPjqeDrB/r50bBbC9+6NZkrvSOrp8ESqBiutF1/BNBvZwD4RWWXfHwpsqJzwlKoeMjIyWLJkCUlJSbT0z+DB3LnU7TAMRr4JXl5k5+bxo7lf06FpCP+dHI+/jzf+PjoKhKrZSqtBFVx4tANY6LA8wWXRKFUN7d27l2XLlpGXl8tIn410y9uHjPkLdH6ULccu0j3S4O/jzXtTe9AiNMjd4SrlMUrrZv5+ZQaiVHWTlZXF8uXL2b17N82aNeOhhx6i/r5AiPk/CI3mqyNpPPbOFv76SGce6tKMtuF6RYdSju403cZ0YIUxJqfIuihgCnDcGDPDpREqVQWdOHGChQsXcvnyZQYGHaHffQ/hVb8+9PuhNS8T0Cs6lL+M78zIjk3cHa5SHqm0Jr7vAD8E/iYiF4FUrM4SLYEjwL+MMYtdH6JSVUdeXh5r165l48aN1A/yZhpzaeaVB7mZAPx99WE+3nKCz1/oT71afozr1szNESvluUpr4jsHvAy8LCKRQGMgE/jGGHO9PAcVkReBJ7E6XewBphZ0Y1eqqkpLS2PBggWcO3eOrvWuMvzi//BrNRDGTscE1kOAITENycvPp5a/dhtX6k6c+pQYY45jdTcvNxFpCjwHxBhjMkVkLvAo8F5F7F+pymaMITExkeXLl+Pr68sjnYJo9/Vf4d5fkN/3h/xm2UFq+6fy0vC2xDYJIbZJyJ13qpRyLkG56LiBIpIDBAFn3BSHUuWSnZ3N0qVL2bt3Ly0jmvHQuAkEBwVA/ABo3gMvIPNGHj5eUji4q1LKOZWeoIwxp0XkL8BJrCbDlcaYlUW3E5GngKcAIiIiKjdIpZxw+vRpFixYQHp6OoOaZtPn4pt4eT9EntTmv0mhjKx1vXBwVy8vTUxK3a1KH3VSROoBo7E6WzQBaonI40W3M8ZMN8bEG2PiGzRoUNlhKlUiYwxfffUVM2bMID/3BlPq76Bf8lt4dRgD/sGkXMnirS+OsDjxNIAmJ6XKyJnBYvsArwEt7O0L5oOKKuMxhwDHjDGp9v4/AXoDH5Vxf0pVmmvXrrFo0SKOHDlCu2b1eTDlHwRmZ8EjH7E7uB+dvH1pHOLLZ8/3o1m9QHeHq1SV5kwN6l3gTazpNroD8fb/sjoJ3CMiQWI1yA8GDpRjf0pVihMnTvCf//yHY8eOcf/99zPBdy2BdRvBUwl8lhvPg//ayLpvUgFoXj9IzzcpVU7OnIPKMMYsr6gDGmO2iMh8rBHRc4FdWBcEK+WRjDFs2rSJ1atXU69uHSY99BDhUbEQ+w553gF4+wcxtG4+vxkdS+/oUHeHq1S1IcaY0jewptfwxpoPKrtguTFmp2tDuyk+Pt5s3779zhsqVcGysrJYvHgxBw8eJCayMQ+m/gP/hq1g8iLm70jm/a+OM/fpXgT66cCuSpWViOwwxsQXXe5MDaqn/d/xwQYYVBGBKeWpzp49y7x588jIyGB4hzB67nsFqdMIhv0WgIbB/jQI9ic7N08TlFIucMcEZYy5tzICUcpTGGPYuXMny5cvp1ZQEFPaZdJ8z88hsh/7+v6Do+cDeDAc+rdpQL/WYXquSSkXcaYXXwjwK6C/vWgd8BtjTIYrA1PKHW7cuMFnn33G119/TXR0NA/dN4haH4+Ent+FYb/jzQ8TOXbhGvd1CMfX20uTk1Iu5EwT3wxgLzDBvv8t4H/AWFcFpZQ7pKWlMW/ePFJSUhjQvSP9h9yHl18gVyavxgTUoY63L38e3xkvAV/vSr+EUKkax5kEFW2Medjh/q9FJNFF8SjlFvv27WPJkiX4+PjweP/WRG96FryncGPwbxk7Yy9tGgXz1qSu1Ncp2JWqNM4kqEwR6WuM2QCFF+5mujYspSpHfn4+a9as4auvvqJZ06aMa5ZGyLpnoElX6PV9/Hy8mNa3JVFhtdwdqlI1jjMJ6hngfftclAAXsSYrVKpKu379OgsWLCApKYn4Lp0ZcX0+3ps/Ja/jo7zu9RQjLwXQrQ5M7KFjQSrlDs704ksEOotIHfv+ZVcHpZSrnT17lrlz53LlyhUefPBBujSvDe88D8N/z9XOT/LFWxtpWP8S3VrUd3eoStVYpU35/rgx5iMR+WGR5QAYY950cWxKucTu3bv59NNPCQwMZOrUqTRt2hSAPePWExMVQYiXsOy5fjqpoFJuVtonsKDRPbiYdaUPP6GUB8rLy2PVqlVs2bKFFi1aMG7cOGrXrg3A3tMZPPjuXl4dlc/UPi01OSnlAUqb8v1t++ZqY8xGx3V2Rwmlqoxr164xb948Tpw4Qc+ePRk6dCje3jdHf4htUoc3HurI6LgmboxSKeXImZ+J/wS6OrFMKY90+vRp5s6dy/Xr13nooYfo1KnTbduIiHaGUMrDlHYOqhfWPE0NipyHqoM1eKxSHi8xMZGlS5dSu3Ztpk2bRuPGjd0dklLKSaXVoPyA2vY2juehLgPjXBmUUuWVn5/PqlWr2Lx5My1btmTcuHEEBQW5Oyyl1F0o7RzUOmCdiLxnjDlRiTEpVS5ZWVnMnz+fo0eP0qNHD4YNG3bL+SalVNXgzDmo6yLyZyAWCChYaIwp03QbItIWmOOwKAp41Rjzt7LsTylHaWlpzJ49m0uXLjFq1Ci6devm7pCUUmXkTIL6GCuhjAK+CzwBpJb1gMaYQ0AcgIh4A6eBhWXdn1IFjhw5wvz58/H29mby5Mm0aNHC3SEppcrBmQQVaox5V0Sed2j2W1dBxx8MHNUmRFUexhg2b97MqlWraNiwIY8++ih169Z1d1hKqXJyJkHl2P/PishI4AzQrIKO/ygwq7gVIvIU8BRARIR2/1XFy83NZdmyZSQmJtK+fXvGjBmDn5+OOK5UdSDGlD4ohIiMAr4EmmNd/1QH+LUxZkm5Dizih5XsYo0x50vbNj4+3mzfvr08h1PV0NWrV5kzZw7JyckMGDCAAQMG6ASCSlVBIrLDGBNfdLkzg8UutW9mABU5/ft9wM47JSelinPmzBnmzJlDZmYm48ePJyYmxt0hKaUqWGkX6v6TUsbcM8Y8V85jT6SE5j2lSrN3714WL15MrVq1mDZtGuHh4e4OSSnlAqXVoFzWpiYiQcBQ4GlXHUNVP8YY1q5dy5dffknz5s155JFHqFVLJxJUqroq7ULd9111UGPMdSDUVftX1U9OTg6LFi1i//79xMXFMWrUKL34Vqlq7o7noERkLcU09ZX1Ql2l7tbVq1eZPXs2p0+fZujQofTq1Us7QyhVAzjTzfwlh9sBwMNArmvCUepW58+fZ9asWVy/fp1HHnmEdu3auTskpVQlcaYX344iizZW4IW6SpXom2++YcGCBfj7+zN16lQdiVypGsaZJr76Dne9gG6AdptSLmOMYcuWLaxcuZLw8HAeffRR6tSp4+6wlFKVzJkmvh1Y56AEq2nvGPBtVwalaq78/HyWL1/O9u3badeuHQ899JCODKFUDeVME1/LyghEKcdpMnr37s2QIUO0M4RSNZgzTXwBwPeAvlg1qQ3A/xljslwcm6pBLl26xKxZs7hw4QIPPPAAXbt2dXdISik3c6aJ7wPgCtY4fGCNAPEhMN5VQama5eTJk8yZM4f8/Hwef/xxWrbUSrtSyrkE1dYY09nh/loR+dpVAamaZffu3SxZsoSQkBAee+wxQkP1+m2llMWZBLVLRO4xxmwGEJGewEbXhqWqO2MMCQkJrF+/nhYtWjBhwgSCgoLcHZZSyoM4k6B6ApNF5KR9PwI4ICJ7AGOM6eSy6FS1lJOTw5IlS9i7d68OW6SUKpEzCWqEy6NQNYbjHE6DBw+mT58+2lNPKVUsZ7qZnxCRzkA/e9GXxhg9B6XuWkpKCjNnzuTatWtMmDCB9u3buzskpZQH87rTBiLyPPAx0ND++0hEnnV1YKp6OXLkCO+++y55eXlMnTpVk5NS6o6caeL7NtDTGHMNQET+CGziZrdzpUq1detWVqxYQaNGjZg4caIOW6SUcoozCUqAPIf7efayMhORusA7QAesi3+nGWM2lWefyvPk5+ezYsUKtm3bRtu2bRk7dqwOW6SUcpozCep/wBYRWYiVmEYD75bzuH8HVhhjxomIH6D9i6uZ7Oxs5s+fz5EjR+jVqxdDhgzBy+uOLcpKKVXImU4Sb4pIAtZQRwBTjTG7ynpAEakD9Aem2Pu/Adwo6/6U50lPT2fmzJlcuHCBUaNG0a1bN3eHpJSqgpypQRUQIJ9yNu8BUUAq8D+7d+AO4PmCc1yqaktOTmb27Nnk5uYyadIkoqKi3B2SUqqKcqYX36vA+0A9IAwrsbxSjmP6AF2xBpztAlwDflrMcZ8Ske0isj01NbUch1OVZe/evbz33nv4+fnx5JNPanJSSpWLGGNK30DkANClYPRyEQkEdhpjytRPWETCgc3GmEj7fj/gp8aYkSU9Jj4+3mzfvr0sh1OVwBjD+vXrSUhIICIigkceeUSHLVJKOU1Edhhj4osud6aJ7zgQABRMr+EPHC1rIMaYcyJySkTaGmMOAYOB/WXdn3Kv3NxclixZwp49e+jcuTOjRo3Cx+duWo6VUqp4znyTZAP7RGQVVpfwocAGEfkHgDHmuTIc91ngY7sHXxIwtQz7UG527do15syZw6lTpxg0aBB9+/bVYYuUUhXGmQS10P4rkFDegxpjEoHbqnOq6khNTWXmzJlcvXqV8ePHExMT4+6QlFLVjDPdzN+vjEBU1XH06FHmzZuHr68vU6ZMoWnTpu4OSSlVDenJAnVXtm3bxvLly2nYsCETJ04kJCTE3SEppaopTVDKKfn5+axcuZItW7bQunVrHn74Yfz9/d0dllKqGtMEpe4oOzubBQsWcPjwYe655x6GDh2qwxYppVyuxAQlIp9i9dorljHmQZdEpDxKeno6s2bNIjU1lZEjRxIfr31blFKVo7Qa1F/s/2OBcOAj+/5ErGujVDVXdNii6Ohod4eklKpBSkxQxph1ACLyW2NMf4dVn4rIepdHptxqz549LF68mDp16jBlyhTCwsLcHZJSqoZx5hxUAxGJMsYkAYhIS6CBa8NS7mKMISEhgfXr19OiRQsmTJigwxYppdzCmQT1IpAgIkn2/UjgaZdFpNwmJyeHxYsXs2/fPuLi4hg1ahTe3t7uDkspVUM5c6HuChFpDbSzFx00xmS7NixV2a5evcrs2bM5ffo0Q4YMoXfv3jpskVLKrUrrxTe2hFXRIoIx5hMXxaQq2fnz55k5cyaZmZlMmDCB9u3LNFC9UkpVqNJqUA+Uss4AmqCqgUOHDrFgwQICAgKYOnUqjRs3dndISikFlN6Lb6qIeAHjjDFzKzEmVQmMMWzevJmVK1fSuHFjJk6cSHBwsLvDUkqpQqWegzLG5IvIDwBNUNVIXl4ey5YtY9euXcTExDBmzBh8fX3dHZZSSt3CmV58q0TkJWAO1vTsABhjLrosKuUymZmZzJ07l+PHj9OvXz/uvfde7QyhlPJIziSoafb/7zssM0BUxYejXOnChQvMnDmTjIwMxowZQ+fOnd0dklJKlciZbuYtK/qgInIcuALkAbnFzUWvKtaxY8eYO3cuXl5eTJ48mYiICHeHpJRSpbpjghIRX+AZoGC4owTgbWNMTjmPfa8xJq2c+1BO2LlzJ8uWLSM0NJSJEydSr149d4eklFJ35EwT3/8BvsC/7fvfspc96aqgVMVwnMMpOjqacePGERAQ4O6wlFLKKc4kqO7GGMeTFV+IyNflPK4BVoqIwaqNTS+6gYg8BTwFaHNUGWRmZjJ//nySkpLo2bMnw4YN0zmclFJVijMJKk9Eoo0xRwFEJArr3FF59DHGnBGRhli9BA8aY24ZId1OWtMB4uPjS5yXSt0uLS2NWbNmkZ6ezoMPPkiXLl3cHZJSSt01ZxLUj4G19mCxArQAppbnoMaYM/b/FBFZCPQAdAqPCnDkyBHmz5+Pt7c3TzzxhNY+lVJVVmlj8b0AbATWAa2BtlgJqlyDxYpILcDLGHPFvj0M+E1Z96csBSNDrFq1ioYNGzJx4kRCQkLcHZZSSpVZaTWoZsDfsUYx3w18hZWwTgHlGc28EbDQvjjUB5hpjFlRjv3VeLm5uSxbtozExETat2/PmDFj8PPzc3dYSilVLqWNxfcSgIj4AfFAb6yLdv8rIunGmJiyHNCe+FCvEK0gV69eZc6cOSQnJzNgwAAGDBigI0MopaoFZ85BBQJ1gBD77wywx5VBKeecPXuW2bNnk5mZyfjx44mJKdNvBqWU8kilnYOaDsRijfiwBauJ701jzKVKik2VYt++fSxatIigoCCmTZtGeHi4u0NSSqkKVVoNKgLwBw4Dp4FkIL0SYlKlMMaQkJDA+vXrad68ORMmTKB27druDksppSpcaeegRoh1MiMW6/zTj4AOInIR2GSM+VUlxahs2dnZLFy4kEOHDhEXF8fIkSPx8XGmlVYppaqeO80HZYC9IpIOZNh/o7CuW9IEVYnS0tKYPXs2Fy9eZMSIEfTo0UM7QyilqrXSzkE9h1Vz6gPkYHUx3wTMQDtJVKpDhw7xySef4OPjw+TJk4mMjHR3SEop5XKl1aAigfnAi8aYs5UTjnJkjGHdunWsW7eOJk2aMGHCBL34VilVY5R2DuqHlRmIulVWVhYLFy7km2++0fNNSqkaSb/xPFBqaipz5szh0qVL3HfffXTv3l3PNymlahxNUB7m4MGDLFy4EF9fXyZPnkyLFi3cHZJSSrmFJigP4Xh9k55vUkopTVAeISsri08++YTDhw/r+SallLLpt6CbpaamMnv2bNLT07n//vuJj4/X801KKYUmKLfas2cPn376KX5+fnq+SSmlitAE5QZ5eXl8/vnnbNu2jebNmzNu3Djq1Knj7rCUUsqjuC1BiYg3sB04bYwZ5a44KltGRgbz588nOTmZe+65hyFDhuDt7e3usJRSyuO4swb1PHAAa66pGiEpKYkFCxaQm5vLuHHjiI2NdXdISinlsdySoESkGTASeB2o9iNWGGP48ssvWbt2LQ0aNGDChAmEhYW5OyyllPJo7qpB/Q14GQguaQMReQp4CiAiIqJyonKBzMxMFi5cyOHDh+nYsSOjRo3Cz8/P3WEppZTHq/QEJSKjgBRjzA4RGVjSdsaY6cB0gPj4eFM50VWsM2fOMG/ePC5fvqxdyJVS6i65owbVB3hQRO4HAoA6IvKRMeZxN8TiEsYYduzYwYoVK6hVqxZTp06lWbNm7g5LKaWqlEpPUMaYnwE/A7BrUC9Vp+SUlZXF0qVL2bdvH9HR0YwdO5agoCB3h6WUUlWOXgdVgc6cOcP8+fNJT09n8ODB9OnTR5v0lFKqjNyaoIwxCUCCO2OoCMYYtm7dyqpVq6hVqxZTpkyp0h07lFLKE2gNqpyysrJYsmQJBw4coE2bNowePVqb9JRSqgJogiqH06dPM3/+fC5fvszQoUPp1auXNukppVQF0QRVBsYYNm/ezOrVqwkODtZeekop5QKaoO7S9evXWbJkCYcOHaJdu3Y8+OCDBAYGujsspZSqdjRB3YWkpCQWLlxIZmYmI0aMoEePHtqkp5RSLqIJygl5eXmsWbOGTZs2ERYWxqRJkwgPD3d3WEopVa1pgrqDtLQ0FixYwLlz5+jWrRvDhw/H19fX3WEppVS1pwmqBMYYdu7cyYoVK/D19eWRRx6hXbt27g5LKaVqDE1Qxbh+/TqffvopBw8eJCoqijFjxhAcXOLA60oppVxAE1QRSUlJLFq0iGvXrum1TUop5UaaoGy5ubmsXbuWr776itDQUCZOnEjjxo3dHZZSStVYmqCAc+fOsXDhQlJSUujatSvDhw/XSQWVUsrNanSCys/PZ8OGDaxbt46goCAmTpxImzZt3B2WUkopanCCSktLY9GiRZw+fZrY2Fjuv/9+HeRVKaU8SI1LUAVTY6xevRpfX18efvhhOnTo4O6wlFJKFVGjElR6ejqLFy/m+PHjtG7dmgceeEC7jyullIeq9AQlIgHAesDfPv58Y8yvXHlMYwyJiYmsWLECgAceeIAuXbpo93GllPJg7qhBZQODjDFXRcQX2CAiy40xm111wEWLFrF7925atGjB6NGjqVevnqsOpZRSqoJUeoIyxhjgqn3X1/4zrjxmVFQU4eHh3HPPPVprUkqpKsIt56BExBvYAbQC3jLGbClmm6eApwAiIiLKdbzOnTuX6/FKKaUqn5c7DmqMyTPGxAHNgB4icls3OmPMdGNMvDEmvkGDBpUeo1JKKfdyS4IqYIxJBxKAEe6MQymllOep9AQlIg1EpK59OxAYAhys7DiUUkp5Nnecg2oMvG+fh/IC5hpjlrohDqWUUh7MHb34dgNdKvu4Simlqha3noNSSimlSqIJSimllEfSBKWUUsojiTWwg2cTkVTgRDl3EwakVUA47qRl8AxaBs+gZXC/ioq/hTHmtgteq0SCqggist0YE+/uOMpDy+AZtAyeQcvgfq6OX5v4lFJKeSRNUEoppTxSTUpQ090dQAXQMngGLYNn0DK4n0vjrzHnoJRSSlUtNakGpZRSqgrRBKWUUsojVdkEJSIzRCRFRPY6LOssIptEZI+IfCoidRzWdbLX7bPXB9jLu9n3j4jIP6QSp9y9mzKIiK+IvG8vPyAiP3N4jDvL0FxE1tox7ROR5+3l9UVklYgctv/Xc3jMz+xYD4nIcHeX427LICJDRWSHHesOERlU1crg8LgIEbkqIi9VxTJ42ue6DO8lj/pclxL/ePt+vojEF3mM6z7Pxpgq+Qf0B7oCex2WbQMG2LenAb+1b/sAu4HO9v1QwNu+vRXoBQiwHLjPQ8vwGDDbvh0EHAciPaAMjYGu9u1g4BsgBvgT8FN7+U+BP9q3Y4CvAX+gJXDU3a9FGcrQBWhi3+4AnHbYV5Uog8PjFgDzgJeqWhk88XNdhjJ41Oe6lPjbA22x5u+Ld9jepZ9nl79gLn4yI7n1y/0yNzt+NAf227fvBz4q4cU46HB/IvC2h5ZhIvCp/aEMtd849T2hDEXKsxgYChwCGjs8z4fs2z8Dfuaw/ef2m9hjynGnMhTZVoAL9ge0SpUBGAP8GXgNO0FVpTJ48uf6Lsrg0Z/rgvgd7idwa4Jy6ee5yjbxlWAv8KB9ezzWFzxAG8CIyOcislNEXraXNwWSHR6fbC9zp5LKMB+4BpwFTgJ/McZcxIPKICKRWLWLLUAjY8xZAPt/Q3uzpsAph4cVxOsR5XCyDI4eBnYZY7KpQmUQkVrAT4BfF3l4lSkDHv65drIMHvu5LhJ/SVz6eXbHhIWuNA34h4i8CiwBbtjLfYC+QHfgOrBGRHZg1VaKcne/+5LK0APIA5oA9YAvRWQ11i/4oiq9DCJSG6u56AVjzOVSmptLitft5biLMhRsHwv8ERhWsKiYzTy1DL8G/mqMuVpkm6pUBo/9XN9FGTzyc100/tI2LWZZhX2eq1WCMsYcxP6yEJE2wEh7VTKwzhiTZq/7DOvcz0dAM4ddNAPOVFrAxSilDI8BK4wxOUCKiGwE4oEvcXMZRMQX6838sTHmE3vxeRFpbIw5KyKNgRR7eTI3a4VwM95k3FiOuywDItIMWAhMNsYctRdXpTL0BMaJyJ+AukC+iGTZj68qZfDIz/VdlsHjPtclxF8Sl36eq1UTn4gUNF94Aa8A/7FXfQ50EpEgEfEBBmCd2zkLXBGRe+weJpOx2lzdppQynAQGiaUWcA9WG69by2Af813ggDHmTYdVS4An7NtPOMS0BHhURPxFpCXQGtjqznLcbRlEpC6wDKvtfWPBxlWpDMaYfsaYSGNMJPA34A1jzL+qUhnwwM91GcrgUZ/rUuIviWs/z5V90q0CT97Nwmq3zcHK1t8Gnsc6yfgN8Afszgb29o8D+7DO8fzJYXm8vewo8C/Hx3hSGYDaWL2t9gH7gR97SBn6YlXddwOJ9t/9WCd81wCH7f/1HR7zCzvWQzj07HFXOe62DFg/HK45bJsINKxKZSjy2Ne4tRdflSmDp32uy/Be8qjPdSnxP4T1HZUNnAc+d3iMyz7POtSRUkopj1StmviUUkpVH5qglFJKeSRNUEoppTySJiillFIeSROUUkopj6QJSikXE5FQEUm0/86JyGn79lUR+be741PKU2k3c6UqkYi8Blw1xvzF3bEo5em0BqWUm4jIQBFZat9+Tax5gVaKyHERGSsifxJrPp0V9vAzBXPsrBNrHqrP7WFzlKqWNEEp5TmiscZeHI01ntxaY0xHIBMYaSepfwLjjDHdgBnA6+4KVilXq1aDxSpVxS03xuSIyB7AG1hhL9+DNW9YW6wJElfZo2N7Yw2VpVS1pAlKKc+RDWCMyReRHHPzBHE+1mdVgH3GmF7uClCpyqRNfEpVHYeABiLSC6xpEew5qZSqljRBKVVFGGNuAOOAP4rI11gjTfd2a1BKuZB2M1dKKeWRtAallFLKI2mCUkop5ZE0QSmllPJImqCUUkp5JE1QSimlPJImKKWUUh5JE5RSSimP9P8BUsWYZ+r5wgIAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -976,13 +963,14 @@
     "# Solution\n",
     "\n",
     "plot_projections(table3)\n",
-    "plot(results, color='gray', label='model')"
+    "results2.plot(color='gray', label='model')\n",
+    "decorate(title='Proportional model, linearly decreasing rate')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "id": "informal-myrtle",
+   "execution_count": 31,
+   "id": "laughing-cylinder",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -995,17 +983,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "id": "bridal-spread",
+   "execution_count": 32,
+   "id": "personalized-parking",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(2066, 9.75910331482456)"
+       "(2100, 12.58671867833247)"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1019,7 +1007,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "annual-carbon",
+   "id": "simple-version",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap09.ipynb b/jupyter/chap09.ipynb
new file mode 100644
index 00000000..87d7b420
--- /dev/null
+++ b/jupyter/chap09.ipynb
@@ -0,0 +1,1378 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "asian-customer",
+   "metadata": {},
+   "source": [
+    "# Chapter 9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "gentle-purchase",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "appropriate-burns",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "biblical-murder",
+   "metadata": {},
+   "source": [
+    "In this chapter we express the models from previous chapters as\n",
+    "difference equations and differential equations, solve the equations,\n",
+    "and derive the functional forms of the solutions. We also discuss the\n",
+    "complementary roles of mathematical analysis and simulation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hybrid-retrieval",
+   "metadata": {},
+   "source": [
+    "## Recurrence relations\n",
+    "\n",
+    "The population models in the previous chapter and this one are simple\n",
+    "enough that we didn't really need to run simulations. We could have\n",
+    "solved them mathematically. For example, we wrote the constant growth\n",
+    "model like this:\n",
+    "\n",
+    "```\n",
+    "results[t+1] = results[t] + annual_growth\n",
+    "```\n",
+    "\n",
+    "In mathematical notation, we would write the same model like this:\n",
+    "\n",
+    "$$x_{n+1} = x_n + c$$ \n",
+    "\n",
+    "where $x_n$ is the population during year $n$,\n",
+    "$x_0$ is a given initial population, and $c$ is constant annual growth.\n",
+    "This way of representing the model is a **recurrence relation**; see\n",
+    "<http://modsimpy.com/recur>.\n",
+    "\n",
+    "Sometimes it is possible to solve a recurrence relation by writing an\n",
+    "equation that computes $x_n$, for a given value of $n$, directly; that\n",
+    "is, without computing the intervening values from $x_1$ through\n",
+    "$x_{n-1}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "specified-mexican",
+   "metadata": {},
+   "source": [
+    "In the case of constant growth we can see that $x_1 = x_0 + c$, and\n",
+    "$x_2 = x_1 + c$. Combining these, we get $x_2 = x_0 + 2c$, then\n",
+    "$x_3 = x_0 + 3c$, and it is not hard to conclude that in general\n",
+    "\n",
+    "$$x_n = x_0 + nc$$ \n",
+    "\n",
+    "So if we want to know $x_{100}$ and we don't care\n",
+    "about the other values, we can compute it with one multiplication and\n",
+    "one addition."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "motivated-consumer",
+   "metadata": {},
+   "source": [
+    "We can also write the proportional model as a recurrence relation:\n",
+    "\n",
+    "$$x_{n+1} = x_n + \\alpha x_n$$ \n",
+    "\n",
+    "Or more conventionally as:\n",
+    "\n",
+    "$$x_{n+1} = x_n (1 + \\alpha)$$ \n",
+    "\n",
+    "Now we can see that\n",
+    "\n",
+    "$x_1 = x_0 (1 + \\alpha)$, and $x_2 = x_0 (1 + \\alpha)^2$, and in general\n",
+    "\n",
+    "$$x_n = x_0 (1 + \\alpha)^n$$ \n",
+    "\n",
+    "This result is a **geometric progression**;\n",
+    "see <http://modsimpy.com/geom>. When $\\alpha$ is positive, the factor\n",
+    "$1+\\alpha$ is greater than 1, so the elements of the sequence grow\n",
+    "without bound.\n",
+    "\n",
+    "Finally, we can write the quadratic model like this:\n",
+    "\n",
+    "$$x_{n+1} = x_n + \\alpha x_n + \\beta x_n^2$$ \n",
+    "\n",
+    "or with the more\n",
+    "conventional parameterization like this:\n",
+    "\n",
+    "$$x_{n+1} = x_n + r x_n (1 - x_n / K)$$ \n",
+    "\n",
+    "There is no analytic solution to\n",
+    "this equation, but we can approximate it with a differential equation\n",
+    "and solve that, which is what we'll do in the next section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "decreased-location",
+   "metadata": {},
+   "source": [
+    "## Differential equations\n",
+    "\n",
+    "Starting again with the constant growth model \n",
+    "\n",
+    "$$x_{n+1} = x_n + c$$ \n",
+    "\n",
+    "If we define $\\Delta x$ to be the change in $x$ from one time step to the next, we can write: \n",
+    "\n",
+    "$$\\Delta x = x_{n+1} - x_n = c$$ \n",
+    "\n",
+    "If we define\n",
+    "$\\Delta t$ to be the time step, which is one year in the example, we can\n",
+    "write the rate of change per unit of time like this:\n",
+    "\n",
+    "$$\\frac{\\Delta x}{\\Delta t} = c$$ \n",
+    "\n",
+    "This model is **discrete**, which\n",
+    "means it is only defined at integer values of $n$ and not in between.\n",
+    "But in reality, people are born and die all the time, not once a year,\n",
+    "so a **continuous** model might be more realistic."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "covered-defense",
+   "metadata": {},
+   "source": [
+    "We can make this model continuous by writing the rate of change in the\n",
+    "form of a derivative: \n",
+    "\n",
+    "$$\\frac{dx}{dt} = c$$ \n",
+    "\n",
+    "This way of representing the model is a **differential equation**; see <http://modsimpy.com/diffeq>.\n",
+    "\n",
+    "We can solve this differential equation if we multiply both sides by\n",
+    "$dt$: \n",
+    "\n",
+    "$$dx = c dt$$ \n",
+    "\n",
+    "And then integrate both sides: \n",
+    "\n",
+    "$$x(t) = c t + x_0$$\n",
+    "\n",
+    "Similarly, we can write the proportional growth model like this:\n",
+    "\n",
+    "$$\\frac{\\Delta x}{\\Delta t} = \\alpha x$$ \n",
+    "\n",
+    "And as a differential equation\n",
+    "like this: \n",
+    "\n",
+    "$$\\frac{dx}{dt} = \\alpha x$$ \n",
+    "\n",
+    "If we multiply both sides by\n",
+    "$dt$ and divide by $x$, we get \n",
+    "\n",
+    "$$\\frac{1}{x}~dx = \\alpha~dt$$ \n",
+    "\n",
+    "Now we\n",
+    "integrate both sides, yielding: \n",
+    "\n",
+    "$$\\ln x = \\alpha t + K$$ \n",
+    "\n",
+    "where $\\ln$ is the natural logarithm and $K$ is the constant of integration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "underlying-visitor",
+   "metadata": {},
+   "source": [
+    "Exponentiating both sides, we have\n",
+    "\n",
+    "$$\\exp(\\ln(x)) = \\exp(\\alpha t + K)$$ \n",
+    "\n",
+    "The exponential function can be written $\\exp(x)$ or $e^x$. In this book I use the first form because it resembles the Python code.\n",
+    "\n",
+    "We can rewrite the previous equation as\n",
+    "\n",
+    "$$x = \\exp(\\alpha t) \\exp(K)$$ \n",
+    "\n",
+    "Since $K$ is an arbitrary constant,\n",
+    "$\\exp(K)$ is also an arbitrary constant, so we can write\n",
+    "\n",
+    "$$x = C \\exp(\\alpha t)$$ \n",
+    "\n",
+    "where $C = \\exp(K)$. There are many solutions\n",
+    "to this differential equation, with different values of $C$. The\n",
+    "particular solution we want is the one that has the value $x_0$ when\n",
+    "$t=0$.\n",
+    "\n",
+    "When $t=0$, $x(t) = C$, so $C = x_0$ and the solution we want is\n",
+    "$$x(t) = x_0 \\exp(\\alpha t)$$ If you would like to see this derivation\n",
+    "done more carefully, you might like this video:\n",
+    "<http://modsimpy.com/khan1>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "czech-scratch",
+   "metadata": {},
+   "source": [
+    "## Analysis and simulation\n",
+    "\n",
+    "Once you have designed a model, there are generally two ways to proceed: simulation and analysis. Simulation often comes in the form of a computer program that models changes in a system over time, like births and deaths, or bikes moving from place to place. Analysis often comes in the form of algebra; that is, symbolic manipulation using mathematical notation.\n",
+    "\n",
+    "Analysis and simulation have different capabilities and limitations.\n",
+    "Simulation is generally more versatile; it is easy to add and remove\n",
+    "parts of a program and test many versions of a model, as we have done in the previous examples.\n",
+    "\n",
+    "But there are several things we can do with analysis that are harder or impossible with simulations:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "embedded-miniature",
+   "metadata": {},
+   "source": [
+    "-   With analysis we can sometimes compute, exactly and efficiently, a\n",
+    "    value that we could only approximate, less efficiently, with\n",
+    "    simulation. For example, in the quadratic model we plotted growth rate versus population and saw net crossed through zero when the population is\n",
+    "    near 14 billion.  We could estimate the crossing point using a\n",
+    "    numerical search algorithm (more about that later). But with the\n",
+    "    analysis in Section xxx, we get the general result that\n",
+    "    $K=-\\alpha/\\beta$.\n",
+    "\n",
+    "-   Analysis often provides \"computational shortcuts\", that is, the\n",
+    "    ability to jump forward in time to compute the state of a system\n",
+    "    many time steps in the future without computing the intervening\n",
+    "    states.\n",
+    "\n",
+    "-   We can use analysis to state and prove generalizations about models;\n",
+    "    for example, we might prove that certain results will always or\n",
+    "    never occur. With simulations, we can show examples and sometimes\n",
+    "    find counterexamples, but it is hard to write proofs.\n",
+    "\n",
+    "-   Analysis can provide insight into models and the systems they\n",
+    "    describe; for example, sometimes we can identify regimes of\n",
+    "    qualitatively different behavior and key parameters that control\n",
+    "    those behaviors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "impressive-planning",
+   "metadata": {},
+   "source": [
+    "When people see what analysis can do, they sometimes get drunk with\n",
+    "power, and imagine that it gives them a special ability to see past the veil of the material world and discern the laws of mathematics that govern the universe. When they analyze a model of a physical system, they talk about \"the math behind it\" as if our world is the mere shadow of a world of ideal mathematical entities (I am not making this up; see <http://modsimpy.com/plato>.).\n",
+    "\n",
+    "This is, of course, nonsense. Mathematical notation is a language\n",
+    "designed by humans for a purpose, specifically to facilitate symbolic\n",
+    "manipulations like algebra. Similarly, programming languages are\n",
+    "designed for a purpose, specifically to represent computational ideas\n",
+    "and run programs.\n",
+    "\n",
+    "Each of these languages is good for the purposes it was designed for and less good for other purposes. But they are often complementary, and one of the goals of this book is to show how they can be used together."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "graduate-conducting",
+   "metadata": {},
+   "source": [
+    "## Analysis with WolframAlpha\n",
+    "\n",
+    "Until recently, most analysis was done by rubbing graphite on wood\n",
+    "pulp, a process that is laborious and error-prone. A useful\n",
+    "alternative is symbolic computation. If you have used services like\n",
+    "WolframAlpha, you have used symbolic computation.\n",
+    "\n",
+    "For example, if you go to <https://www.wolframalpha.com/> and type\n",
+    "\n",
+    "```\n",
+    "df(t) / dt = alpha f(t)\n",
+    "```\n",
+    "\n",
+    "WolframAlpha infers that `f(t)` is a function of `t` and `alpha` is a\n",
+    "parameter; it classifies the query as a \"first-order linear ordinary\n",
+    "differential equation\\\", and reports the general solution:\n",
+    "\n",
+    "$$f(t) = c_1 \\exp(\\alpha t)$$ \n",
+    "\n",
+    "If you add a second equation to specify\n",
+    "the initial condition:\n",
+    "\n",
+    "```\n",
+    "df(t) / dt = alpha f(t),  f(0) = p_0\n",
+    "```\n",
+    "\n",
+    "WolframAlpha reports the particular solution:\n",
+    "\n",
+    "$$f(t) = p_0 \\exp(\\alpha t)$$\n",
+    "\n",
+    "WolframAlpha is based on Mathematica, a powerful programming language\n",
+    "designed specifically for symbolic computation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beginning-tamil",
+   "metadata": {},
+   "source": [
+    "## Analysis with SymPy\n",
+    "\n",
+    "Python has a library called SymPy that provides symbolic computation\n",
+    "tools similar to Mathematica. They are not as easy to use as\n",
+    "WolframAlpha, but they have some other advantages.\n",
+    "\n",
+    "Before we can use SymPy, we have to import it:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "civic-payday",
+   "metadata": {},
+   "source": [
+    "SymPy defines a `Symbol` object that represents symbolic variable names,\n",
+    "functions, and other mathematical entities.\n",
+    "\n",
+    "The `symbols` function takes a string and returns `Symbol` objects. So\n",
+    "if we run this assignment:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "flush-balance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sympy import symbols\n",
+    "\n",
+    "t = symbols('t')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faced-praise",
+   "metadata": {},
+   "source": [
+    "Python understands that `t` is a symbol, not a numerical value. If we\n",
+    "now run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ancient-anderson",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle t + 1$"
+      ],
+      "text/plain": [
+       "t + 1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr = t + 1\n",
+    "expr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "industrial-confidence",
+   "metadata": {},
+   "source": [
+    "Python doesn't try to perform numerical addition; rather, it creates a\n",
+    "new `Symbol` that represents the sum of `t` and `1`. We can evaluate the\n",
+    "sum using `subs`, which substitutes a value for a symbol. This example\n",
+    "substitutes 2 for `t`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "familiar-oakland",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 3$"
+      ],
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr.subs(t, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "progressive-celebration",
+   "metadata": {},
+   "source": [
+    "Functions in SymPy are represented by a special kind of `Symbol`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "incomplete-sleep",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "f"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sympy import Function\n",
+    "\n",
+    "f = Function('f')\n",
+    "f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "legislative-dispatch",
+   "metadata": {},
+   "source": [
+    "Now if we write `f(t)`, we get an object that represents the evaluation of a function, $f$, at a value, $t$. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "inside-sally",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle f{\\left(t \\right)}$"
+      ],
+      "text/plain": [
+       "f(t)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f(t)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "interior-index",
+   "metadata": {},
+   "source": [
+    "But again SymPy doesn't actually\n",
+    "try to evaluate it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "indian-trout",
+   "metadata": {},
+   "source": [
+    "## Differential equations in SymPy\n",
+    "\n",
+    "SymPy provides a function, `diff`, that can differentiate a function. We can apply it to `f(t)` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "requested-program",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)}$"
+      ],
+      "text/plain": [
+       "Derivative(f(t), t)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sympy import diff\n",
+    "\n",
+    "dfdt = diff(f(t), t)\n",
+    "dfdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "treated-commissioner",
+   "metadata": {},
+   "source": [
+    "The result is a `Symbol` that represents the derivative of `f` with\n",
+    "respect to `t`. But again, SymPy doesn't try to compute the derivative\n",
+    "yet.\n",
+    "\n",
+    "To represent a differential equation, we use `Eq`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "incomplete-parker",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)} = \\alpha f{\\left(t \\right)}$"
+      ],
+      "text/plain": [
+       "Eq(Derivative(f(t), t), alpha*f(t))"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sympy import Eq\n",
+    "\n",
+    "alpha = symbols('alpha')\n",
+    "eq1 = Eq(dfdt, alpha*f(t))\n",
+    "eq1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "concerned-asthma",
+   "metadata": {},
+   "source": [
+    "The result is an object that represents an equation.  Now\n",
+    "we can use `dsolve` to solve this differential equation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "described-overhead",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle f{\\left(t \\right)} = C_{1} e^{\\alpha t}$"
+      ],
+      "text/plain": [
+       "Eq(f(t), C1*exp(alpha*t))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sympy import dsolve\n",
+    "\n",
+    "solution_eq = dsolve(eq1)\n",
+    "solution_eq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "inside-medicine",
+   "metadata": {},
+   "source": [
+    "The result is the **general\n",
+    "solution**, which still contains an unspecified constant, $C_1$. To get the **particular solution** where $f(0) = p_0$, we substitute `p_0` for `C1`. First, we have to create two more symbols:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "material-voice",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C1, p_0 = symbols('C1 p_0')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "serious-license",
+   "metadata": {},
+   "source": [
+    "Now we can perform the substitution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "supposed-domestic",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle f{\\left(t \\right)} = p_{0} e^{\\alpha t}$"
+      ],
+      "text/plain": [
+       "Eq(f(t), p_0*exp(alpha*t))"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "particular = solution_eq.subs(C1, p_0)\n",
+    "particular"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "modern-raleigh",
+   "metadata": {},
+   "source": [
+    "The result is called the **exponential growth curve**; see\n",
+    "<http://modsimpy.com/expo>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "negative-chance",
+   "metadata": {},
+   "source": [
+    "## Solving the quadratic growth model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "arabic-victorian",
+   "metadata": {},
+   "source": [
+    "We'll use the (r, K) parameterization, so we'll need two more symbols:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "political-chinese",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r, K = symbols('r K')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "virtual-temperature",
+   "metadata": {},
+   "source": [
+    "Now we can write the differential equation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "lined-queen",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)} = r \\left(1 - \\frac{f{\\left(t \\right)}}{K}\\right) f{\\left(t \\right)}$"
+      ],
+      "text/plain": [
+       "Eq(Derivative(f(t), t), r*(1 - f(t)/K)*f(t))"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eq2 = Eq(diff(f(t), t), r * f(t) * (1 - f(t)/K))\n",
+    "eq2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "relative-zoning",
+   "metadata": {},
+   "source": [
+    "And solve it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "printable-typing",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle f{\\left(t \\right)} = \\frac{K e^{C_{1} K + r t}}{e^{C_{1} K + r t} - 1}$"
+      ],
+      "text/plain": [
+       "Eq(f(t), K*exp(C1*K + r*t)/(exp(C1*K + r*t) - 1))"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution_eq = dsolve(eq2)\n",
+    "solution_eq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "convertible-simple",
+   "metadata": {},
+   "source": [
+    "The result, `solution_eq`, contains `rhs`, which is the right-hand side of the solution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "final-treatment",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{K e^{C_{1} K + r t}}{e^{C_{1} K + r t} - 1}$"
+      ],
+      "text/plain": [
+       "K*exp(C1*K + r*t)/(exp(C1*K + r*t) - 1)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "general = solution_eq.rhs\n",
+    "general"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "phantom-melbourne",
+   "metadata": {},
+   "source": [
+    "We can evaluate the right-hand side at $t=0$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "freelance-admission",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{K e^{C_{1} K}}{e^{C_{1} K} - 1}$"
+      ],
+      "text/plain": [
+       "K*exp(C1*K)/(exp(C1*K) - 1)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "at_0 = general.subs(t, 0)\n",
+    "at_0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "precise-radar",
+   "metadata": {},
+   "source": [
+    "Now we want to find the value of `C1` that makes `f(0) = p_0`.\n",
+    "\n",
+    "So we'll create the equation `at_0 = p_0` and solve for `C1`.  Because this is just an algebraic identity, not a differential equation, we use `solve`, not `dsolve`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "orange-glasgow",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sympy import solve\n",
+    "\n",
+    "solutions = solve(Eq(at_0, p_0), C1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "periodic-bikini",
+   "metadata": {},
+   "source": [
+    "The result from `solve` is a list of solutions.  In this case, [we have reason to expect only one solution](https://en.wikipedia.org/wiki/Picard%E2%80%93Lindel%C3%B6f_theorem), but we still get a list, so we have to use the bracket operator, `[0]`, to select the first one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "expensive-palace",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(list, 1)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(solutions), len(solutions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "worthy-sleeve",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{\\log{\\left(- \\frac{p_{0}}{K - p_{0}} \\right)}}{K}$"
+      ],
+      "text/plain": [
+       "log(-p_0/(K - p_0))/K"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "value_of_C1 = solutions[0]\n",
+    "value_of_C1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lightweight-pickup",
+   "metadata": {},
+   "source": [
+    "Now in the general solution, we want to replace `C1` with the value of `C1` we just figured out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "aggressive-queue",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle - \\frac{K p_{0} e^{r t}}{\\left(K - p_{0}\\right) \\left(- \\frac{p_{0} e^{r t}}{K - p_{0}} - 1\\right)}$"
+      ],
+      "text/plain": [
+       "-K*p_0*exp(r*t)/((K - p_0)*(-p_0*exp(r*t)/(K - p_0) - 1))"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "particular = general.subs(C1, value_of_C1)\n",
+    "particular"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "electric-albania",
+   "metadata": {},
+   "source": [
+    "The result is complicated, but SymPy provides a function that tries to simplify it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "funded-tolerance",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{K p_{0} e^{r t}}{K + p_{0} e^{r t} - p_{0}}$"
+      ],
+      "text/plain": [
+       "K*p_0*exp(r*t)/(K + p_0*exp(r*t) - p_0)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "particular.simplify()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "scenic-preparation",
+   "metadata": {},
+   "source": [
+    "This function is called the **logistic growth curve**; see\n",
+    "<http://modsimpy.com/logistic>. In the context of growth models, the\n",
+    "logistic function is often written like this:\n",
+    "\n",
+    "$$f(t) = \\frac{K}{1 + A \\exp(-rt)}$$ \n",
+    "\n",
+    "where $A = (K - p_0) / p_0$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "burning-sherman",
+   "metadata": {},
+   "source": [
+    "We can use SymPy to confirm that these two forms are equivalent.  First we represent the alternative version of the logistic function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "institutional-finish",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{K - p_{0}}{p_{0}}$"
+      ],
+      "text/plain": [
+       "(K - p_0)/p_0"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = (K - p_0) / p_0\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "noble-auditor",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{K}{1 + \\frac{\\left(K - p_{0}\\right) e^{- r t}}{p_{0}}}$"
+      ],
+      "text/plain": [
+       "K/(1 + (K - p_0)*exp(-r*t)/p_0)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sympy import exp\n",
+    "\n",
+    "logistic = K / (1 + A * exp(-r*t))\n",
+    "logistic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "brave-conditions",
+   "metadata": {},
+   "source": [
+    "To see whether two expressions are equivalent, we can check whether their difference simplifies to 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "industrial-administrator",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 0$"
+      ],
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(particular - logistic).simplify()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "therapeutic-topic",
+   "metadata": {},
+   "source": [
+    "This test only works one way: if SymPy says the difference reduces to 0, the expressions are definitely equivalent (and not just numerically close).\n",
+    "\n",
+    "But if SymPy can't find a way to simplify the result to 0, that doesn't necessarily mean there isn't one.  Testing whether two expressions are equivalent is a surprisingly hard problem; in fact, there is no algorithm that can solve it in general."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cutting-access",
+   "metadata": {},
+   "source": [
+    "If you would like to see this differential equation solved by hand, you might like this video: <http://modsimpy.com/khan2>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "outstanding-launch",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this chapter we wrote the growth models from the previous chapters in terms of difference and differential equations.\n",
+    "\n",
+    "What I called the constant growth model is more commonly called **linear growth** because the solution is a line.  If we model time as continuous, the solution is\n",
+    "\n",
+    "$$f(t) = p_0 + \\alpha t$$\n",
+    "\n",
+    "where $\\alpha$ is the growth rate.\n",
+    "\n",
+    "Similarly, the proportional growth model is usually called **exponential growth** because the solution is exponential:\n",
+    "\n",
+    "$$f(t) = p_0 \\exp{\\alpha t}$$\n",
+    "\n",
+    "Finally, the quadratic growth model is called **logistic growth** because the solution is the logistic function:\n",
+    "\n",
+    "$$f(t) = \\frac{K}{1 + A \\exp(-rt)}$$ \n",
+    "\n",
+    "where $A = (K - p_0) / p_0$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adjacent-tracy",
+   "metadata": {},
+   "source": [
+    "I avoided these terms until now because they are based on results we had not derived yet."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "offshore-passage",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ideal-ecology",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Solve the quadratic growth equation using the alternative parameterization\n",
+    "\n",
+    "$\\frac{df(t)}{dt} = \\alpha f(t) + \\beta f^2(t) $"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "internal-knock",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "alpha, beta = symbols('alpha beta')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "smooth-sunglasses",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)} = \\alpha f{\\left(t \\right)} + \\beta f^{2}{\\left(t \\right)}$"
+      ],
+      "text/plain": [
+       "Eq(Derivative(f(t), t), alpha*f(t) + beta*f(t)**2)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "eq3 = Eq(diff(f(t), t), alpha*f(t) + beta*f(t)**2)\n",
+    "eq3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "fundamental-arlington",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle f{\\left(t \\right)} = \\frac{\\alpha e^{\\alpha \\left(C_{1} + t\\right)}}{\\beta \\left(1 - e^{\\alpha \\left(C_{1} + t\\right)}\\right)}$"
+      ],
+      "text/plain": [
+       "Eq(f(t), alpha*exp(alpha*(C1 + t))/(beta*(1 - exp(alpha*(C1 + t)))))"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "solution_eq = dsolve(eq3)\n",
+    "solution_eq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "desirable-alpha",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{\\alpha e^{\\alpha \\left(C_{1} + t\\right)}}{\\beta \\left(1 - e^{\\alpha \\left(C_{1} + t\\right)}\\right)}$"
+      ],
+      "text/plain": [
+       "alpha*exp(alpha*(C1 + t))/(beta*(1 - exp(alpha*(C1 + t))))"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "general = solution_eq.rhs\n",
+    "general"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "coupled-grounds",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "at_0 = general.subs(t, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "looking-ground",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{\\log{\\left(\\frac{\\beta p_{0}}{\\alpha + \\beta p_{0}} \\right)}}{\\alpha}$"
+      ],
+      "text/plain": [
+       "log(beta*p_0/(alpha + beta*p_0))/alpha"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "solutions = solve(Eq(at_0, p_0), C1)\n",
+    "value_of_C1 = solutions[0]\n",
+    "value_of_C1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "anticipated-paragraph",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{\\alpha p_{0} e^{\\alpha t}}{\\alpha - \\beta p_{0} e^{\\alpha t} + \\beta p_{0}}$"
+      ],
+      "text/plain": [
+       "alpha*p_0*exp(alpha*t)/(alpha - beta*p_0*exp(alpha*t) + beta*p_0)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "particular = general.subs(C1, value_of_C1)\n",
+    "particular.simplify()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "residential-spanking",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Use [WolframAlpha](https://www.wolframalpha.com/) to solve the quadratic growth model, using either or both forms of parameterization:\n",
+    "\n",
+    "    df(t) / dt = alpha f(t) + beta f(t)^2\n",
+    "\n",
+    "or\n",
+    "\n",
+    "    df(t) / dt = r f(t) (1 - f(t)/K)\n",
+    "\n",
+    "Find the general solution and also the particular solution where `f(0) = p_0`."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/chap10.ipynb b/jupyter/chap10.ipynb
new file mode 100644
index 00000000..8898834d
--- /dev/null
+++ b/jupyter/chap10.ipynb
@@ -0,0 +1,656 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "negative-distributor",
+   "metadata": {},
+   "source": [
+    "# Chapter 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "organized-stone",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "exciting-width",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "graphic-subject",
+   "metadata": {},
+   "source": [
+    "# Case studies\n",
+    "\n",
+    "This chapter presents case studies where you can practice the tools we have learned so far."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cleared-greene",
+   "metadata": {},
+   "source": [
+    "## Prehistoric World Population\n",
+    "\n",
+    "On the Wikipedia page about world population estimates, the first table contains estimates for prehistoric populations.  The following cells process this table and plot some of the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "manufactured-color",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates.html'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "intellectual-mortality",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pandas import read_html\n",
+    "\n",
+    "filename = 'World_population_estimates.html'\n",
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
+    "len(tables)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bound-giving",
+   "metadata": {},
+   "source": [
+    "Select `tables[1]`, which is the second table on the page."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "voluntary-definition",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Population Reference Bureau (1973–2016)[15]</th>\n",
+       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
+       "      <th>Maddison (2008)[17]</th>\n",
+       "      <th>HYDE (2010)[citation needed]</th>\n",
+       "      <th>Tanton (1994)[18]</th>\n",
+       "      <th>Biraben (1980)[19]</th>\n",
+       "      <th>McEvedy &amp; Jones (1978)[20]</th>\n",
+       "      <th>Thomlinson (1975)[21]</th>\n",
+       "      <th>Durand (1974)[22]</th>\n",
+       "      <th>Clark (1967)[23]</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>-10000</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2M[24]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1–10M</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-9000</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-8000</th>\n",
+       "      <td>5.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5–10M</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-7000</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-6000</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Population Reference Bureau (1973–2016)[15]  \\\n",
+       "Year                                                 \n",
+       "-10000                                         NaN   \n",
+       "-9000                                          NaN   \n",
+       "-8000                                           5.   \n",
+       "-7000                                          NaN   \n",
+       "-6000                                          NaN   \n",
+       "\n",
+       "        United Nations Department of Economic and Social Affairs (2015)[16]  \\\n",
+       "Year                                                                          \n",
+       "-10000                                                NaN                     \n",
+       "-9000                                                 NaN                     \n",
+       "-8000                                                 NaN                     \n",
+       "-7000                                                 NaN                     \n",
+       "-6000                                                 NaN                     \n",
+       "\n",
+       "       Maddison (2008)[17] HYDE (2010)[citation needed]  Tanton (1994)[18]  \\\n",
+       "Year                                                                         \n",
+       "-10000                 NaN                       2M[24]                NaN   \n",
+       "-9000                  NaN                           4.                NaN   \n",
+       "-8000                  NaN                           5.                NaN   \n",
+       "-7000                  NaN                           8.                NaN   \n",
+       "-6000                  NaN                          11.                NaN   \n",
+       "\n",
+       "        Biraben (1980)[19]  McEvedy & Jones (1978)[20] Thomlinson (1975)[21]  \\\n",
+       "Year                                                                           \n",
+       "-10000                 NaN                         4.0                 1–10M   \n",
+       "-9000                  NaN                         NaN                   NaN   \n",
+       "-8000                  NaN                         NaN                   NaN   \n",
+       "-7000                  NaN                         NaN                   NaN   \n",
+       "-6000                  NaN                         NaN                   NaN   \n",
+       "\n",
+       "       Durand (1974)[22] Clark (1967)[23]  \n",
+       "Year                                       \n",
+       "-10000               NaN              NaN  \n",
+       "-9000                NaN              NaN  \n",
+       "-8000              5–10M              NaN  \n",
+       "-7000                NaN              NaN  \n",
+       "-6000                NaN              NaN  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table1 = tables[1]\n",
+    "table1.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tracked-stylus",
+   "metadata": {},
+   "source": [
+    "Not all researchers provide estimates for the same dates.\n",
+    "\n",
+    "Again, we'll replace the long column names with more convenient abbreviations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "exempt-headline",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table1.columns = ['PRB', 'UN', 'Maddison', 'HYDE', 'Tanton', \n",
+    "                  'Biraben', 'McEvedy & Jones', 'Thomlinson', 'Durand', 'Clark']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "professional-watch",
+   "metadata": {},
+   "source": [
+    "Some of the estimates are in a form Pandas doesn't recognize as numbers, but we can coerce them to be numeric."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "sapphire-excuse",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in table1.columns:\n",
+    "    table1[col] = pd.to_numeric(table1[col], errors='coerce')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "amended-contribution",
+   "metadata": {},
+   "source": [
+    "Here are the results.  Notice that we are working in millions now, not billions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "right-wrong",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OnMh9993HzJkzqdhhLC4uZuXKlcybN4958+bRtm1bJk+ezB/+8Afmzp1Ljx49eP3118v2feedd5g0aRIvvfRSZN8AY4ypgYgMkhCRdkAf4CuguapuBieJiUgzd7dWOD2kUhvdsmJ3vWJ5Zee5FqenVeXjLKrr9YTayJEjGT16NJdffjkAPp+PO++8k6VLl1JYWEiPHj3IycmhTZs2JCYmkpiYSMeOHQFYvXo1/fr1A+C4444rV6/X6+Xmm2/mqquuokmTJjz88MOsWbOmbOaKAQMGsHDhQjIyMujduzcAbdq0ITc3N0ItN8aYmgv7IAkRaQi8Cdyiqruq27WSMq2m/MBC1cmqmqmqmWlpaYcebIh17tyZwYMHc/755wOQl5fH1q1bmT9/Pvfccw+qSlpaGhs3bqSoqIjc3FzWrFkDQKdOnfj222+B8lMkgZPoLrjgAl566SWaNWvGW2+9RadOncpmSP/qq6/K7nkF9r40DNPhG2NMuIS1ByUiXpzk9B9Vfcst3ioi6W7vKR3Y5pZvBNoEHN4a2OSWt66kvFZ4+umny9aTk5PZvHkzw4cPp3v37gB4PB5uueUWjj/+eLp160ZGRgbgzHh+8cUXM2nSJJKTk8vVuXv3bs466yz8fj9xcXFMmzaNBg0acMkll/Daa6/RokUL7rrrLj7//POItdMYY0JNwvVXtTvS7mVgh6reElD+OLA9YJBEqqqOFZEewGvAcTiDJD4FOquqT0QWAX/CuUQ4E/i7qs6s7vyZmZka2PNYuXIlRx99dEjbaGrOPhdj6hYR+UZVM0NRVzh7UCcAlwE/iMhSt+xuYAIwXUSuBtYDFwCo6nIRmQ6swBkBeIM7gg/gOmAKUA9n9J5NBW6MMXVc2BKUqi6g8vtHAMOqOGY8ML6S8sXAMQceYYwxpq46aIISkUzgRJzLbgXAMmC2qob+8YnGGGOMq8pRfCIyRkSWAHfhXFpbhTOgYTAwS0ReFpHKx3IbY4wxNVRdD6oBcIKqFlS2UUR6A51x7iMZY4wxIVVlD0pV/1FVcnK3L1XVT8MTVu2WnZ1d9t0ngI8++oguXbrw5z//uaxsxIgRrFy5kqFDhzJkyBBOPfVURo4cWfY9qClTptC5c2eGDh3K0KFDmTFjRsTbYYwx0RTMPaiJwMM4958+AnrhfOl2aphjq1MuvvhiFixYwNq1a/n+++85+uijy4ZXz5w5k4YNG/L9999z6aWXsmDBAgBuvvlmbrzxxmiGbYwxURPMKL7T3O8pnYvzpdkLgDlArUxQWx55hMKVNZ8sNvHobrS4++5DOubxxx/n1ltvJScnh5kzD/wa17HHHkvnzp356aefahyfMcZEwtqda3lu6XNcc+w1dEnpEtK6g5nqyOv+/D3wuo3eC87cuXPLLs+NHTsWgD59+pCcnMxFF110wOwQpVq2bMmmTc5EGU899VRZHaW9KmOMiSU5+Tl8lP0ROwt3hrzuYHpQ74nIjziX+K4XkTRgX8gjiZBD7fUcrpNOOqnsvtFHH33El1868+C2b9+e9u3bV3ncpk2bSE9PZ8OGDXaJzxgT8wp9hQAkeZJCXvdBe1CqOg4YBGSqajGwF+fRGCbEli1bxk8//UTXrl2jHYoxxgRlX4nTX0mMTwx53cHOJHE00E5EAvd/JeTRHKF+//vf4/V6adKkCa+++ioejwdwLvGV9sLGjBnDmDFjohilMcYcqLQHlegJfYI66GSxIvIq0BFYCpTOjaeqelPIowkhmyy2drDPxZja7f9++j8e+uIhZp0/ixYNWkR8sthMoLvaw4SMMcZUUOQrAqJ0Dwpn7r0WIT+zMcaYWq+BtwEdm3QkKT70CSqYHlRTYIWIfA0Ulhaq6lkhj8YYY0ytck6nczin0zlhqTuYBPVAWM5sjDGm9vP7QOJAqnq60uELZpj5XOBHoJG7rHTLjDHGHOk+fQgebh6Wqg+aoERkFPA1zhRHo4CvROT86o86chUVFZXN/tCoUaOy9T179lR7XF5eHtOnT49QlMYYEyIlhRCG70BBcJf4/gz0V9VtAO5MErMBm167EgkJCWRlZQGQmZlZtn4wpQlq1KhR4QvOGGNCraQAwjBAAoIbxRdXmpxc24M8zgCvvvoqw4YNo2/fvrz66qsAPPDAA1xyySWcfvrpDBkyhPz8fJ577rmy+ftWrVrFa6+9xoABAxgwYAAfffQRAEOHDuWWW25hyJAhXH/99dFsljHGOIr3gTc8CSqYHtRHIvIx8Lr7ejRw4FTctcT86T/x24bqL7cFo2mbhpw46uAz944cOZLLLruMwsJCBg8ezGWXXQZA165due+++/jzn//M7Nmzue6661izZg0zZszA5/NxwQUXsGjRIgoKCjjllFMYMWIEAOeddx5PPvkkgwcPZufOnTRp0qTGbTHGmMMWxh7UQROUqv6viIwETgAEmKyqb4clmjpo1qxZPPHEEwDlHqPRp08fANq0aUNubm65Y3JycsjIyCAxMbFsKSkpKXdc69atycvLswRljImuksLoJSgAVX0TeDMsEURYML2eUHrwwQeZM2cOiYmJdOzYsaxcAoZkqiperxefz5lJKi0tjXXr1lFYWEhBQQFFRUXEx8dXepwxxkRVcQF464Wl6ioTlIgsUNXBIrIbCPxNKDhz8TUOS0R1zMiRIzn55JPp06cPKSkpVe6Xnp5OQUEB559/PhMnTmTcuHEMGTIEgPHjx0cqXGOMOTQl+8LWgzroZLG1lU0WWzvY52JMLffPE6FROlzifE0mIpPFikhqdQfak3WNMcZQUhiVUXzf4Fzaq2z+CgU6hCUiY4wxtca+Ye8jCUI4vqpbZYJS1aqfS26MMcYAeR9uwNusHokd00Jed3WX+PpWd6CqLgl5NMYYY2oV9fkhPjxzN1R3ie+v1WxT4JQQx2KMMaa2KfYjkU5QqnpyWM5ojDGmzlBfFBKUiJyiqp+JyHmVBqX6VlgiqgOys7Pp378/PXr0YM+ePdxxxx3s27ePrl27MmjQoIMe/8ADD5CZmckZZ5wRgWiNMebwaYkintA/Cwqqv8R3EvAZcGZlMQGWoKpx0kknMWPGDPLz8xk8eDBLlhx4y87v9xMXZ/PuGmNqLy3xI97IX+K73/15ZVjOHCVzpkxm27q1Na6nWUYHTh5z7UH3y8/Pp379+mW9omOOOYbLLruMFi1a0L9/f9LT05kyZQq5ubnceuutZZPJTp8+naeffpqEhARmzJhBYmIiN910E8uWLSM+Pp6XXnqJ1q1bc/TRR9O3b19WrFjBbbfdVna8McaEm/oVfAqeyA+SAEBEkoHLgXaB+6vqTWGJqI4ofXTGTz/9xF/+8hc2bNhQtm3Tpk18+umnJCQkkJ+fX+ls561bt+aVV17hkUceYdq0aTRt2pSUlBTmzJnDN998w4QJE3jmmWfYsmULzz33HHFxcQwfPtwSlDEmcnzOTEQRvwcVYCbwJfAD4A+2YhH5N3AGsE1Vj3HLHgCuAXLc3e5W1ZnutruAqwEfcJOqfuyW9wOmAPXcWG7WGszPFEyvJxRKL/EVFRUxbNgwjj/++LJtvXr1IiEhAah6tvN+/fqV/VywYAHbtm3j7bffZt68eagqbdq0AaBDhw40buxMi1hXp60yxsQm9TkpQeIjfw+qVJKq3nYYdU8BngFeqVD+N1WdFFggIt2BC4EeQEtgtoh0UVUf8BxwLU6SnAmMAD48jHiiIiEhAVWlqKiorCzwvlNVs51/++23jBw5km+//ZaOHTuSmprKqFGjuPfeewEoLi4Gys9ubowxkaTFpQkqej2oV0XkGuB9oLAssIPMxaeq80SkXZBxnA1MU9VC4BcRWQ0cJyLZQGNV/QJARF4BzqEWJKjSS3yFhYWcccYZZQmloqpmO8/Ozmb48OEkJiaW3YP67LPPOPlkZ/T/pZdeytVXXx2RthhjTGX296DCk6AOOpu5iNwAjAfy2P/YDVXVg87F5yao9ytc4hsD7AIWA7eraq6IPAN8qapT3f1exElC2cAEVT3VLT8RuFNVKx1/LSLX4vS2aNu2bb9169aVbbNZs2OTfS7G1F7FvxWwddJiUkZ3pUGfZkBoZzMPJu3dBnRS1Xaq2t5dDnei2OeAjkBvYDP7Z6uoakLaqsorpaqTVTVTVTPT0kI/L5QxxpgAJeG9BxVMgloO5IfiZKq6VVV9quoHXgCOczdtBNoE7Noa2OSWt66k3BhjTJRpaYKK1jBznFF1S0VkDuXvQR3yMHMRSVfVze7Lc4Fl7vq7wGsi8gTOIInOwNeq6hOR3SIyEPgKZ7j73w/1vMYYY0KvLEFFcZDEf93lkIjI68BQoKmIbATuB4aKSG+cy3TZwP8DUNXlIjIdWAGUADe4I/gArmP/MPMPqQUDJIwx5kgQ9QSlqi8fTsWqelElxS9Ws/94nMEYFcsXA8ccTgzGGGPCR0vcIQGRvgclIu+JyJki4q1kWwcReUhErgpLVLVcdnY2IsKcOXMAKCoqIiUlhWeeeabS/bOysmjTpg1Dhw5l6NChVe4XjIEDBwa13/Llyxk4cCADBw7ks88+q3SfzMyQDMQxxtRVUexBXYMzgu9JEdmBM/tDEtAeWA08o6rvhCWqOiAzM5O33nqLk08+mdmzZ9O5c+dq9x89ejSTJk2qdp9QevbZZ/nHP/5B3759yc3Njdh5jTF1R7i/B1XdZLFbgLHAWPf7TOlAAfCTqoZkVF805L23hqJNe2tcT0LLBiSf2bHK7RkZGaxfvx5V5e233+bcc88t2/bvf/+byZMnk5CQwAMPPFDpjOaPP/44GRkZjBo1ilWrVjF+/HhefvnlSieMfeSRR3jvvffo168fPp+P4uJiTj75ZBYsWAA4ye/RRx+lQ4f93w7wer3MmjWLfv36kZqaWm1bN2zYwBVXXEFxcTE9e/bk2WefZcqUKbz77rsUFhaybds23nnnHVq2bMmUKVN48cUX8fl8PPzww5xyyimMGTOGNWvWICK88sortGvX7hDfbWNMLNLi8M7FF1Stqpqtql+o6tLanJwibdCgQcybN4+cnBzS09MByMnJ4cUXX2T+/PnMmzePoUOHAvDGG2+UXeKbMWMGo0ePZvr06WXbRo8ezQcffFA2YeyECROYMGECW7Zs4eOPP+bzzz/npptuYvv27Xi9Xvr06cPixYvZtWsXO3bsKJecAFJSUvjvf//Lp59+etB2TJgwgbFjxzJ//nwKCgqYO3cuAMnJyXzwwQdce+21zJgxg99++43XX3+defPmMXv2bMaPH09xcTErV65k3rx5zJs3j7Zt24bwHTbGRFMszMVXp1TX6wm1kSNHMnr0aC6//PKysrVr19KnTx+8XufWXmnvqbJLfLm5uezZs4fZs2dz11138be//e2ACWOzs7M59thjERG6dOlCkyZNALj88suZOnUqvXr1YuTIkeXqXbhwIYWFhcycOZOzzz6bjIwMbrzxRj744AM8Hs8B7VizZg39+/cHYMCAAaxevRqPx0OfPn0AaNOmDV999RVr165lxYoVZdMx5eTk4PV6ufnmm7nqqqto0qQJDz/8MA0bNgzF22uMibLSUXzhetyGPS0vjDp37szgwYM5//zzy8o6dOjA0qVLKSkpAZyHFlblrLPO4tFHH6Vr1654vV66devGqFGjyMrKYu7cubz00ku0a9eOZcuWoaqsXr2anTt3AtC/f3++//57pk2bxqhRo8rVu2vXLrZs2UJqairTpk3jvPPO49RTT600OQF06tSJRYsWAfDVV1+V3U8LnKhWVenQoQPHHnssc+bMISsri6VLl+Lz+bjgggt46aWXaNasGW+9Zc+5NKbOKB0kEekHFprQePrpp8u9TktL48orr+SEE06gQYMG3HPPPcTFxfHGG2+wePFiAEaMGMG4ceO44IILaNeuHR9+6Hz168wzz6x0wtjhw4czaNAg+vbty1FHHVV2ruHDh/P1118fcI/pd7/7He+//z4DBw6kUaNGXHnllbz11ltceeWV5Y4vTUB33nknV1xxBQ8//DDHHnssQ4YMYe3aAx/62LRpUy688EJOOukkPB4PPXv25KGHHuKss84qe3rwtGnTQvCuGmNiQekw83DNJBHMZLEnAA8AGTgJTQhysthoyszM1NJf+HBkTkr62GOP0aVLl3IDNIJVUFDAsGHD+Pzzz8MQ2X5H4udiTF2x8+NsdmdtoPWjJ5aVhXKy2GB6UC8CtwLf4Ex7ZGqBhx56iIULF3L77bcf8rFbtmzhjDPOOKxjjTFHDi3xh20EHwSXoHaqqk0vVMvcd999h31sixYtCOx9GmNMZTyNE0lo0yhs9QeToOaIyOPAW5SfLHZJ2KIyxhgT8xqd2IpGJ7YKW/3BJKgB7s/Aa4oKnBL6cIwxxhhHMJPFnhyJQIwxxphAB727JSJNROQJEVnsLn8VkSaRCK42KioqKpsRolGjRmXrd9xxR0jqHzNmDMuWLWPp0qU899xzIanTGGNiUTCX+P6N82DB0m97Xga8BJwXrqBqs4SEBLKysgBnwtisrCyysrJ4//33Q3qe3r1707t375DWaYwxsSSYBNVRVQPnynlQRJaGKZ6w+/DDD9myZUuN62nRogWnn3560PsvW7aMs88+m+zsbKZOnUrPnj157bXXeOqppwB48MEHGTFiBEOHDqVXr158+eWXnHXWWWzdupWvv/6akSNH8r//+79l9ZUmvUmTJtG3b18GDBjAkiVLOOecc7jrrrt47rnn+Pe//039+vW55ZZbOPfcc7n11ltZtGhR2USz7du35+ijj6Zv376sWLGC2267jcsuu6zG740xxoRCMAPYC0RkcOkL94u7BeELqW4qLi7mnXfeYdKkSbz00kv4fD4mTJjAvHnz+Pjjj7n77rvL9h01ahRffvklL7zwAldddRULFy5k6tSpVdadl5fHuHHj+OKLL8pmanjjjTeYPXs2c+fO5eyzz2bRokVs3ryZBQsW8OCDD/LQQw8BzneennvuOebPn8+zzz4b3jfBGGMOQTA9qOuAl937TgLsAMaEM6hwOpReTyiVXo5r06YNubm55OTkkJGRQWJiYtlSOj9f6eSvLVq0oFevXohI2eSylUlJSSEjIwOAevXqATBx4kTuuOMOSkpKGDdu3AETvt5zzz2AMzdg48aNAWc+PWOMiRXBjOJbCvQSkcbu613hDqouqjixalpaGuvWraOwsJCCggKKioqIj48/YN/A9WDqLtWzZ09eeOEFPv/8cx577DGuv/56/vvf/wJVT/hqjDGxpMoEJSKXqupUEbmtQjkAqvpEmGOr0zweD+PGjWPIkCEAjB8/PqT1//GPfyQ7O5s9e/bw+OOPk5mZSXp6OoMHDy67B2WMMbGsysliReT/qerzInJ/JZtVVR8Kb2g1Y5PF1g72uRhTt0RkslhVfd5dna2qCysEcEIoTm6MMcZUJZhRfH8PsswYY4wJmeruQQ0CjgfSKtyHagxU/uhVY4wxJkSqG8WXADR09wmcT30XcH6lRxhjjDEhUt09qLnAXBGZoqrrIhiTMcYYE9QXdfPd50H1AJJKC1XVHrdRhezsbPr370/Pnj1RVYYMGcI999xT7Zdta+Kf//wnSUlJjBkzJiz1G2NMNAQzSOI/wI9Ae+BBIBtYFMaY6oSTTjqJzz77jM8++wy/38+TTz550GP8fn/4AzPGmFoimB7UUar6oojcHHDZb264AwuXn376C7v3rKxxPY0aHk2XLvcedD8R4Z577mHYsGG88cYbZY9SHzhwIF9++SUPPPAAv/zyC9u2bWPixIk89thjbNy4kZKSEl577TXatm1b6WSw69ev5+KLL6Zx48YkJydz2mmn1bhNxhgTS4LpQRW7PzeLyB9EpA/QOowx1TmJiYkUFhZWub1t27Z8+OGH9OzZk8mTJ5OVlcXYsWN5/nnnq2iVTQY7ceJE7rvvPmbOnGnTFRlj6qRgelAPuxPF3o7z/afGwK1hjSqMgun1hFpRUREJCQnlklTgDB6lk7j6fD7uvPNOli5dSmFhIT169AAqnwx29erV9OvXD4DjjjsuIu0wxphIOmgPSlXfV9WdqrpMVU9W1X6q+m4kgqsrHnnkEc4++2w8Hg+7du1i165d/Pzzz2Xb4+Kcj2Hp0qVs3bqV+fPnc88995Qlscp6SJ06deLbb78FIHBKJ2OMqSuq+6Lu34Eqn7+gqjeFJaI6Yu7cuZxyyin4/X4GDx7MLbfcQnp6OkOGDKFHjx60bNnygGO6devG5s2bGT58ON27d6+2/rFjx3LxxRczadIkkpOTw9QKY4yJnuomi72iugNV9eWwRBQiNlls7WCfizF1S6Qmi61RAhKRfwNnANtU9Ri3LBV4A2iHM1x9lKrmutvuAq4GfMBNqvqxW94PmALUA2YCN6s9Wc8YY+q8g96DEpE5IvJZxSWIuqcAIyqUjQM+VdXOwKfua0SkO3AhzpeBRwDPikjpfH/PAdcCnd2lYp3GGGPqoGBG8d0RsJ4EjARKDnaQqs4TkXYVis8GhrrrLwNZwJ1u+TRVLQR+EZHVwHEikg00VtUvAETkFeAc4MMg4jbGGFOLBfPI928qFC2swRd1m6vqZrfezSLSzC1vBXwZsN9Gt6zYXa9YXikRuRant0Xbtm0PM0RjjDHV2pMD3nqQ2DCspwnmEl9qwNJURH4HtAhxHJV901SrKa+Uqk5W1UxVzUxLSwtZcMYYYwLMuBKmjgz7aYK5xPcN+5NFCfALzmCGw7FVRNLd3lM6sM0t3wi0CdivNbDJLW9dSXnMmz9/Pvfeey9+vx+v18u5554LwI033njQY6dMmcKePXuC2tcYYyKtKC+H+Badg5qKqCaC+aJue1Xt4P7srKqnqeqCwzzfu0Dp8PUrgHcCyi8UkUQRaY8zGOJr93LgbhEZKM63VS8POCZmbd++neuvv55p06Yxb948pk+fTrADD23CWGNMrHtnWT3eWFD19G2hctAelIgkAdcDg3F6UguA51R130GOex1nQERTEdkI3A9MAKaLyNXAeuACAFVdLiLTgRU4vbQbVNXnVnUd+4eZf0gNB0jc+/NGlu0pqEkVABzTsB5/6Vz5lIQffPAB5513Hi1aOFdCjzrqKBo1asSePXsAuPTSSyudEPb4449n586dDBs2DIAdO3Zw6aWX8thjj9GzZ88ax2yMMTVWUsTeQiE1rUHYTxXMJb5XgN048/ABXAS8iptcqqKqF1WxaVgV+48HxldSvhg4Jog4Y8bmzZsrnSmi1OTJk6lfvz7vvvsuzz//POPHjyc3N5dbbrmFTp06MWXKFHJycrjkkkt44okn7IusxpjYsTeHvb4EWjduHPZTBZOguqpqr4DXc0Tku3AFFG5V9XpCqWXLluXm2gtU3YSwnTp1KtvvX//6F7fddpslJ2NMTPHt3MQ+n5cGKU3Dfq5g7nF9KyIDS1+IyABgYfhCqv3+8Ic/8Pbbb7NlyxbAuVRXenmvqglhSyeMLTV27FgWLVrE+++/H9ngjTGmGvlb19O4aQ/2FDUP+7mC6UENAC4XkfXu67bAShH5AVBVPTZs0dVSqampPPvss4wePRqAhIQEzj33XPx+f9ATwno8Hl555RVGjx5N/fr1OeWUUyIVvjHGVGnvto3MPbY3n3viGR7mcwWToGxqocNw4oknMndu5d9nnj9//gFlgRPbjhkzpmz97bffDnlsxhhzuPZu38wPLXqRHIEBx8HMJLFORHoBJ7pF81W11t6DMsYYc/j27tjB7mZJdNybH/ZzBTOTxM3Af4Bm7jJVRP4U7sCMMcbEnrydheQnJNLc4zn4zjUUzCW+q4EBqroXQEQeA75g/7DzWkNVK306rYkOe2qKMbXPr74mIEILrzfs5wpmFJ/gPKOplI/K58iLaUlJSWzfvt1+KcYIVWX79u0kJSVFOxRjzCHY7EkFoHX9xLCfK5ge1EvAVyLyNk5iOht4MaxRhUHr1q3ZuHEjOTk50Q7FuJKSkmjdOvzfSzPGhE5OUgoAGU3CO5M5BDdI4gkRycKZ6gjgSlX9NqxRhYHX66V9+/bRDsMYY2qv4n1sr58MQPu0JmE/3aFMRitU/QgMY4wxdd3eHHLrNSLeV0KrtOSwny6YUXz34Tz9NgVoCrwkIveEOzBjjDGxpWj7BnbWq0eDwn0kJsbGPaiLgD6ls5eLyARgCfBwOAMzxhgTW/KlCTvrFdK4qDAiI6KDucSXDQQOtUoE1oQlGmOMMTFrV76PnUlJNCkqjsj5gulBFQLLRWQWzj2o4cACEXkaQFVvCmN8xhhjYsS29b+wK7ENR+2MzINVg0lQb7tLqazwhGKMMSaWrV2/EX/XDNIiNFYumGHmL0ciEGOMMbFt3W+7oCs0jw//NEdwaMPMjTHGHKF8JcVku8MROiaEf5ojsARljDEmCNs3bmBb03TifSV0TmkQkXNagjLGGHNQ27LXsik1jaP27CS1RWpEzlnlPSgReQ9n1F6lVPWssERkjDEm5mzNXsuvLfvSYdt60lo0i8g5qxskMcn9eR7QApjqvr4I57tRxhhjjhAbCovZ540nPX8PjRo1isg5q0xQqjoXQET+oqpDAja9JyLzwh6ZMcaYmHHUORfDinV0Koncc/WCuQeVJiIdSl+ISHsgLXwhGWOMiTXf5+xG1E+PevUjds5gvqh7K5AlImvd1+2A/xe2iIwxxsSc73N3k5y/h9YRuv8EwX1R9yMR6Qx0c4t+VNXC8IZljDEmlvxSWEiTgj206NklYuesbhTfeVVs6igiqOpbYYrJGGNMDPGrslmE7gV7ad4mPWLnra4HdWY12xSwBGWMMUeALYXFFMcJTQuLaNAgMl/ShepH8V0pInHA+ao6PWIRGWOMiQmF+fl4kxJZu2MvAG0lMnPwlap2FJ+q+oEbIxSLMcaYGLJg2stMvv5Kfl6fB0CXlMYRPX8ww8xnicgdItJGRFJLl7BHZowxJqo2/Lic5PSW/JiTR5zfT4+2zSN6/mCGmV/l/rwhoEyBDpXsa4wxpg4o3rePXRu3sLyj8su+Ahp6/KS3yIhoDMEMM28fiUCMMcbEjs1rfqJXylDOK+7F+V5oXLCXtLTIztFw0Et8IuIVkZtEZIa73CgiNXoYiIhki8gPIrJURBa7ZakiMktEfnZ/pgTsf5eIrBaRVSLyu5qc2xhjzMEt/+Fzsnf/wM/9f2NbwwTS/CUkJSVFNIZg7kE9B/QDnnWXfm5ZTZ2sqr1VNdN9PQ74VFU7A5+6rxGR7sCFQA9gBPCsSISHkhhjzBHm5+XfsMaTTbNhx5Hviae1N/K/doO5B9VfVXsFvP5MRL4LQyxnA0Pd9ZeBLOBOt3yaO3vFLyKyGjgO+CIMMRhjzBFPVSlYv4U9rRty15q9xPn9HNcgMeJxBNOD8olIx9IX7sSxvhqeV4FPROQbEbnWLWuuqpsB3J+lEz61AjYEHLvRLTuAiFwrIotFZHFOTk4NQzTGmCPTpo1riN+nLDvuQhbvyueUHxdzXLPID94Opgf1v8Acd7JYATKAK2t43hNUdZOINMMZxv5jNftWNq97pQ9SVNXJwGSAzMzMKh+2aIwxpmoLF3/IzkbJLG/ciSvSU7m+0+mkpsZQghKRW4CFwFygM9AVJ1nUeLJYVd3k/twmIm/jXLLbKiLpqrpZRNKBbe7uG4E2AYe3BjbV5PzGGGOqtnrFN3zZ50TiRPhTuxa0TEqIShzVXeJrDTyFkyg+xhmo0Bqo0URMItJARBqVrgOnAcuAd4Er3N2uAN5x198FLhSRRPdZVJ2Br2sSgzHGmMr51c9v2/JZ3rUfF6YfFbXkBNXPxXcHgIgkAJnA8Thf2n1BRPJUtfthnrM58Lb7RMZ44DX3kR6LgOkicjWwHrjAjWO5iEwHVgAlwA2qWtN7YMYYc8TzlRSz7oeltOvVl7g4Z5Tess3fs6r9EDQujhvbRu7ZT5UJ5h5UPaAx0MRdNgE/HO4JVXUt0KuS8u3AsCqOGQ+MP9xzGmOMKW9vXi7v/vURNv20ktNvvJ3uJ54MwMfffML33YdysqeYjHqRH7kXqLp7UJNxvnu0G/gK+Bx4QlVzIxSbMcaYMNiVs40Z4+9h9/btJNZvwJrFX5UlqB+KO1LcIJ5xR7eNcpTV34NqCyQCW4BfcQYr5EUgJmOMMWGy/dcNvH7/WPJ37uT8P/+FLgNPIPu7JfhKisnd/Cut5n3GyA0r6NmsabRDrfYe1AhxbhT1wLn/dDtwjIjsAL5Q1fsjFKMxxpgQyN2ymWn33EGc18uo+x+lWbsOFOzexQ+ffcKGFcv4YsbrtNr1G38+8/Rohwoc5B6UqiqwTETygJ3ucgbOsHBLUMYYU4ssevJxCvfuYfRVN9CsnfNAioyevfF4vXzyz6fZvT2H02+8nYapR0U5UkeVl/jcCWKnicgGYB5OYloFnAfY86CMMaYW2fnxx6xetZzmSQ1oedqIsnJvUhJtexzL7u05dMwcyNGDh0YvyAqq60G1A2YAt5ZOQWSMMab2KfjhB1bcfy/7Mlow7NRrQcpP0NNj6Knkbd3C8GtuQKSyyXuio7p7ULdFMhBjjDGhV/zrr2y47nq2NEulf9oIGqxIpOiXnSR2SC7bp+ugE+k66MToBVmFYL4HZYwxppby7d5NXHITGmQMoX2DnjQ6pU255BTLgpnN3BhjTC2V1K0b3j/eT/cGgyhpBY2HR/ax7TVhCcoYY+qw4i17kYX5bC/aRMsr+8XUPaaDsQRljDF1lPqVHW/+RLGvkC1tN5PQsH60QzoklqCMMaaO2rtoC8Ub9rB0+6d0PumEaIdzyCxBGWNMHeTbXUTeB2vYtm89nq6NaN87M9ohHTJLUMYYUwf4CwvZ/OCDFK1fD8COd37CX1jCipKv+N11N9Wqe0+lbJi5McbUciXbt7PxhhspWLqUxG7dYXEhhctyWZH3JSfdfA31GjWOdoiHxRKUMcbUYvt++omN111PyfbttBj/NIXrUijZtoWVeV/S6JQ2tOneM9ohHjZLUMYYU8uoKgVLl5I3bRq7PvyIuJSmpN3+PHuXFFHky+Xzze+QMaI/x19wUbRDrRFLUMYYU0uU7NjBrvfeI++ttylctYo9TZtTMOQCWjU8joLlhWwo+JHv8uYy9P9dS7fjh0Q73BqzBGWMMVHmLyjAv2cP8Wlp7Ny2leJ9BeW2q9/P1scmsmfxIuKS0qjfbQjS5RKax6URJx72yE7WNlqJr61y7ukP0rx9xyi1JLQsQRljTISUbN9OwXffs2/ZMgqWL6No7S/4tm/Hn59PQvv2dPxwJrP/9Q+yv1tSdoxH4mmW1Jb0+keTPuQPNPQmA7Cz+Df2tNpLm9/1o1XX5nSLq32j9A7GEpQxxkTIjldeZfvzz0NcHIkdO1KvZ0/i05riOaop3vR0AAakn0F/GV52jOwG8YN6wN9M8LWKR9ok0alnPxLr166ZIQ6VJShjjAkjVaVk+z68TeuRfN65NDxpCEnduhFXRXJp1Lo5xXF7yl7HpySR1C2VxPZNEO+R9dVVS1DGGBNi/iIfhavz2LdqB/t+zMW3s5D0uweQkJFBQkb1s4k3qUWzjYebJShjjAkB364i8n/IYd+POyhcuxN8iiR4SOycTONhbZGEI6v3EwqWoIwx5jD5i3zsW76dvUu2Urg6DxTi0+rRcFBLkrqlkNiuCRJvielwWYIyxphDoH6lcG0e+Uu2UbBsO1rkw5OcSKOT21C/TzO8aXV74EIkWYIyxtRpqopvxw6Kf/2V4o0bQYTGp59+yPUUb91L/pJt5H+7Dd+uIiTRQ71jm9Kgb3MS2jVG6uAw72izBGWMqdXKJSB3KSpb30Txr7+i+/aV7Z/QsWPQCcq3u4j873LI/3Ybxb/ugThI6pJKkzOaUe/oVMTrCVezDJagjDExTlXx5eaWS0AVk5AWlJ95wdOkCd5WrUjs0IGGJ56It1UrZ2ndCm/LVtWfr9hHwYrt5C/Zxr6fc8EP3lYNaXJmB+r3SsPTMCGczTUBLEEZY6JKVfHl5VG88dcDklDxpl8p+nUTmp9f7pi4Jk3wtmpJYvv2NDxhMN7WrfcnoVYt8TRseGgx+JXCX3aS/+02Cn74DS304WmSQKMhrZ37Ss0bhLLJJkiWoIwxIac+H76dO/Hl5uLLzaXE/enb4f7My6VkRy4lWzZXnoAaN3aSTUYGDY4/YX/vp1UrvC1b4mnUKCRxFm/LJ/9b975SXiGS4KFez6bU79vM+WKs3VeKKktQxphqqSr+vXvdBLPDTTZ5AclnR7nXvtxcfDt3gmql9cXVr48nJQVPcrKbgI4P6P24Cahx+B6w59tTRMF3Oez9dhvFG/eAQGLnFJqMaEdS96OIS7D7SrHCEpQxdZyqosXF+PfuDVjy96/n5+PfswdfXp7Tswns6eTmUpKXB8XFlVfu9RKfnIwnNRVPSgqJ3boSn5KCJznFSUKpKc7r0iU5mbikpNC1zaf495XgLyjBn1+MFrjrgUv+/nUtKKF4Wz74FW96A5r8oT31ezXD09juK8UiS1DGxBhVRQsLyyeU/PyDrvv27kXz8/GV2+Zsp6Tk4CcWwdOkSVky8bZtQ71ex+5PNikpxKcGJJuUFOIaNECkZpfB1K9lyWN/Yik+IMlUlny00Fd9k7xxxNWLR+rFE1cvHk9qEkldU5z7Si3svlKsqzUJSkRGAE8BHuBfqjohyiEZE3K/vfACOU8+Bb7qf/GWkoQE4urXJ65BA2epXx9Pw0Z4m7coex24rdL1Bs66p3FjxFP+8pb6FS3xo0U+tNgfsPgo3lSMFu9Ai91tRT60xI+/aP8+Zfu727SofHkwSYZ4J8nE1Ysnrn48nuREvOkN9pfVi0fqe8u9Liu3WRxqtVqRoETEA/wDGA5sBBaJyLuquiK6kUWHqu6/vq8KPp9T5veD348G/izd7lfQ8tuddQW/b/++fj/q81e9r/pRnw9K18tt9zvl/ornCzhHVXW453T2ddd97vHqHNdg4AAaDBoUuffZr6AA7k913/vS990tQ3X/7ZbActxyDSzXst3KygKO82b0JGXMn4hLqo8kJiGJ9YhLrEdcYhLiTUASksCbgMQ7CwjqU/Cr89PnR33qxO5T5z30adk+vn1+Sva62/wKvn2orwB8v5UlFX9AEqKk8vtIBxUf5/RevHFIggfxxpUtcfUS968n7e/dBCYhZ91JOkfaDN5mv1qRoIDjgNWquhZARKYBZwMxk6DWX3UVBctXlE8cFdbL/qmXbjvY9sr2PRLFxUFcHJLgjWiC+u1fPziTfkZc90rK/MA+dzkIATyCxMUh8QJxgngEPHHOqDRP+dfiEfDGEdcoYX/iSPA4Zd44xBuQYBLikHiP8zNwW0LAenycjX4zIVFbElQrYEPA643AgIo7ici1wLUAbdu2jUxkrgYnnEBC+w4g4iwAQsD1eamwTcpvF9m/T8D20nUp3V62bf928XhA4hBPHIj7y7xs3flFRZxn/3rpvqXrcVJ+e2lCiAuoo/QccVJ++wH17a+jXF0iEGQdIhW2R0n9zOYkdkx23meh7LOR0vdecP5T9tFVUw77PzO3Lgn4fAOPk9IE44nbn0zinNdOYhHnPYwPSDBuQirbZkwdUFsSVGX/4g7oUqjqZGAyQGZmZkS7HEddfXUkT2cioEHf5tEOwZgjWm25uLsRaBPwujWwKUqxGGOMiYDakqAWAZ1FpL2IJAAXAu9GOSZjjDFhVCsu8alqiYjcCHyMM8z836q6PMphGWOMCaNakaAAVHUmMDPacRhjjImM2nKJzxhjzBHGEpQxxpiYZAnKGGNMTLIEZYwxJiaJ1tEpdERkN7Aq2nFEQFPgt2gHESFHSluPlHbCkdPWI6WdAF1VNSRPlKw1o/gOwypVzYx2EOEmIouPhHbCkdPWI6WdcOS09UhpJzhtDVVddonPGGNMTLIEZYwxJibV5QQ1OdoBRMiR0k44ctp6pLQTjpy2HinthBC2tc4OkjDGGFO71eUelDHGmFrMEpQxxpiYVGsSlIhcICLLRcQvIpkVtt0lIqtFZJWI/C6gvJ+I/OBue1rcR5uKSKKIvOGWfyUi7QKOuUJEfnaXKyLWwEqISG8R+VJElorIYhE5LmBbyNocK0TkT257lovIxIDyutjWO0RERaRpQFmdaqeIPC4iP4rI9yLytogkB2yrU22tjoiMcNu5WkTGRTueQyUibURkjoisdP9t3uyWp4rILPd35SwRSQk45pA+3yqpaq1YgKOBrkAWkBlQ3h34DkgE2gNrAI+77WtgEM4TeT8ETnfLrwf+6a5fCLzhrqcCa92fKe56ShTb/ElAzL8HskLd5lhZgJOB2UCi+7pZHW5rG5xHx6wDmtbhdp4GxLvrjwGP1dW2VvMeeNz2dQAS3HZ3j3Zch9iGdKCvu94I+Mn9DCcC49zycTX5fKtaak0PSlVXqmplM0OcDUxT1UJV/QVYDRwnIulAY1X9Qp135hXgnIBjXnbXZwDD3Ez+O2CWqu5Q1VxgFjAifK06KAUau+tN2P8U4VC2OVZcB0xQ1UIAVd3mltfFtv4NGIvz+Zaqc+1U1U9UtcR9+SXOk7ChDra1GscBq1V1raoWAdNw2lJrqOpmVV3iru8GVgKtKP+ZvEz5z+pQP99K1ZoEVY1WwIaA1xvdslbuesXycse4/4B2AkdVU1e03AI8LiIbgEnAXW55KNscK7oAJ7qXb+aKSH+3vE61VUTOAn5V1e8qbKpT7azEVTh/MUPdb2ugWPudUiPupdU+wFdAc1XdDE4SA5q5ux3O51upmJrqSERmAy0q2fRnVX2nqsMqKdNqyg/3mLCors3AMOBWVX1TREYBLwKnEto2R8xB2hqPc1l1INAfmC4iHaiFbT1IO+/GufR1wGGVlMV0OyG4f7Mi8megBPhP6WGV7B/zbT1MtTXuA4hIQ+BN4BZV3VVNBzZkv19jKkGp6qmHcdhGnGv6pVrjXArbyP5LCoHlgcdsFJF4nMtnO9zyoRWOyTqMmIJWXZtF5BXgZvfl/wH/ctdD2eaIOUhbrwPecrv+X4uIH2eCzVrX1qraKSI9ca7Jf+f+424NLBFn8Eutaycc/N+sOAONzgCGuZ8t1NK2Hqaq2lqriIgXJzn9R1Xfcou3iki6qm52L9+VXpY/nM+3ctG+AXcYN+yyKD9Iogflb8itZf8NuUU4f5GX3pD7vVt+A+VvuE5311OBX3D+kk9x11Oj2NaVwFB3fRjwTajbHCsL8EfgIXe9C84lAqmLbQ1oczb7B0nUuXbi3L9dAaRVKK9zba3mPYh329ee/YMkekQ7rkNsg+DcL3qyQvnjlB8kMfFwP98qzx3txh/Cm3QuTgYuBLYCHwds+zPOSJFVBIwKATKBZe62Z9g/c0YSTo9kNc6okg4Bx1zllq8GroxymwcD37gf9ldAv3C0ORYW9x/vVDf2JcApdbWtAfFn4yaouthON64NwFJ3+WddbetB3off44x8W4Nz6TPqMR1i/INxLsV9H/BZ/h7nHuCnwM/uz9SAYw7p861qsamOjDHGxKS6MIrPGGNMHWQJyhhjTEyyBGWMMSYmWYIyxhgTkyxBGWOMiUmWoIyJEHEsEJHTA8pGichH0YzLmFhlw8yNiSAROQbn+zx9cGa6XgqMUNU1h1GXR1V9oY3QmNhhCcqYCBPnWVd7gQbuzwygJ86sAw+o6jvupJyvuvsA3Kiqn4vIUOB+YDPQW1W7RzZ6YyLHEpQxESYiDXBmyygC3geWq+pU94F+X+P0rhTwq+o+EekMvK6qmW6C+gA4Rp1HGRhTZ8XUZLHGHAlUda+IvAHsAUYBZ4rIHe7mJKAtziSaz4hIb8CHMz9hqa8tOZkjgSUoY6LD7y4CjNQKD+MUkQdw5pzshTOYaV/A5r0RitGYqLJRfMZE18fAn0qfDisifdzyJsBmVfUDl+EMqDDmiGIJypjo+gvgBb4XkWXua4BngStE5Eucy3vWazJHHBskYYwxJiZZD8oYY0xMsgRljDEmJlmCMsYYE5MsQRljjIlJlqCMMcbEJEtQxhhjYpIlKGOMMTHp/wMLDKTKUa5u+AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table1.plot()\n",
+    "decorate(xlim=[-10000, 2000], xlabel='Year', \n",
+    "         ylabel='World population (millions)',\n",
+    "         title='Prehistoric population estimates')\n",
+    "plt.legend(fontsize='small');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "later-community",
+   "metadata": {},
+   "source": [
+    "We can use `xlim` to zoom in on everything after Year 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "champion-stage",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table1.plot()\n",
+    "decorate(xlim=[0, 2000], xlabel='Year', \n",
+    "         ylabel='World population (millions)',\n",
+    "         title='CE population estimates')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "medium-aircraft",
+   "metadata": {},
+   "source": [
+    "See if you can find a model that fits these data well from Year 0 to 1950.\n",
+    "\n",
+    "How well does your best model predict actual population growth from 1950 to the present?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "nervous-anatomy",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# The function I found that best matches the data has the form\n",
+    "# a + b / (c - x)\n",
+    "\n",
+    "# This function is hard to explain physically; that is, it doesn't\n",
+    "# correspond to a growth model that makes sense in terms of human \n",
+    "# behavior.\n",
+    "\n",
+    "# And it implies that the population goes to infinity in 2040.\n",
+    "\n",
+    "from numpy import linspace\n",
+    "from matplotlib.pyplot import plot\n",
+    "\n",
+    "xs = linspace(100, 1950)\n",
+    "ys = 110 + 200000 / (2040 - xs)\n",
+    "table1.plot()\n",
+    "plot(xs, ys, color='gray', alpha=0.5, label='model')\n",
+    "\n",
+    "decorate(xlim=[0, 2000], xlabel='Year', \n",
+    "         ylabel='World population (millions)',\n",
+    "         title='CE population estimates')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "trained-valuable",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# And it doesn't do a particularly good job of predicting\n",
+    "# actual growth from 1940 to the present.\n",
+    "\n",
+    "# TODO: load these series from table2\n",
+    "#plot(census, ':', label='US Census')\n",
+    "#plot(un, '--', label='UN DESA')\n",
+    "\n",
+    "xs = linspace(1940, 2020)\n",
+    "ys = 110 + 200000 / (2040 - xs)\n",
+    "plot(xs, ys/1000, color='gray', label='model')\n",
+    "\n",
+    "decorate(xlim=[1950, 2016], xlabel='Year', \n",
+    "         ylabel='World population (billions)',\n",
+    "         title='Prehistoric population estimates')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "surprised-designation",
+   "metadata": {},
+   "source": [
+    "## One queue or two?\n",
+    "\n",
+    "This case study is related to **queueing theory**, which is the study of systems that involve waiting in lines, also known as \"queues\".\n",
+    "\n",
+    "Suppose you are designing the checkout area for a new store. There is\n",
+    "enough room in the store for two checkout counters and a waiting area\n",
+    "for customers. You can make two lines, one for each counter, or one line that feeds both counters.\n",
+    "\n",
+    "In theory, you might expect a single line to be better, but it has some practical drawbacks: in order to maintain a single line, you might have to install barriers, and customers might be put off by what seems to be a longer line, even if it moves faster.\n",
+    "\n",
+    "So you'd like to check whether the single line is really better and by\n",
+    "how much. Simulation can help answer this question.\n",
+    "\n",
+    "This figure shows the three scenarios we'll consider:\n",
+    "\n",
+    "![One queue, one server (left), one queue, two servers (middle), two\n",
+    "queues, two servers (right).](figs/queue.pdf){width=\"4.5in\"}\n",
+    "\n",
+    "As we did in the bike share model, we'll assume that a customer is equally likely to arrive during any time step. I'll denote this probability using the Greek letter lambda, $\\lambda$, or the variable name `lam`. The value of $\\lambda$ probably varies from day to day, so we'll have to consider a range of possibilities.\n",
+    "\n",
+    "Based on data from other stores, you know that it takes 5 minutes for a customer to check out, on average. But checkout times are variable: most customers take less than 5 minutes, but some take substantially more. A simple way to model this variability is to assume that when a customer is checking out, they always have the same probability of finishing during the next time step, regardless of how long they have been checking out. I'll denote this probability using the Greek letter mu, $\\mu$, or the variable name `mu`.\n",
+    "\n",
+    "If we choose $\\mu=1/5$ per minute, the average time for each checkout\n",
+    "will be 5 minutes, which is consistent with the data. Most people takes less than 5 minutes, but a few take substantially longer, which is probably not a bad model of the distribution in real stores.\n",
+    "\n",
+    "Now we're ready to get started. In the repository for this book, you'll find a notebook called `queue.ipynb` that contains some code to get you started and instructions.\n",
+    "\n",
+    "As always, you should practice incremental development: write no more\n",
+    "than one or two lines of code a time, and test as you go!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "practical-arrest",
+   "metadata": {},
+   "source": [
+    "## Predicting salmon populations\n",
+    "\n",
+    "Each year the U.S. Atlantic Salmon Assessment Committee reports\n",
+    "estimates of salmon populations in oceans and rivers in the northeastern United States. The reports are useful for monitoring changes in these populations, but they generally do not include predictions.\n",
+    "\n",
+    "The goal of this case study is to model year-to-year changes in\n",
+    "population, evaluate how predictable these changes are, and estimate the probability that a particular population will increase or decrease in the next 10 years.\n",
+    "\n",
+    "As an example, I use data from page 18 of the 2017 report, which\n",
+    "provides population estimates for the Narraguagus and Sheepscot Rivers\n",
+    "in Maine.\n",
+    "\n",
+    "In the repository for this book, you'll find a notebook called\n",
+    "`salmon.ipynb` that contains some code to get you started and\n",
+    "instructions.\n",
+    "\n",
+    "You should take my instructions as suggestions; if you want to try\n",
+    "something different, please do!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hungarian-cliff",
+   "metadata": {},
+   "source": [
+    "## Tree growth\n",
+    "\n",
+    "This case study is based on \"Height-Age Curves for Planted Stands of\n",
+    "Douglas Fir, with Adjustments for Density\", a working paper by\n",
+    "Flewelling, Collier, Gonyea, Marshall, and Turnblom.\n",
+    "\n",
+    "It provides \"site index curves\", which are curves that show the\n",
+    "expected height of the tallest tree in a stand of Douglas firs as a\n",
+    "function of age, for a stand where the trees are the same age.\n",
+    "\n",
+    "Depending on the quality of the site, the trees might grow more quickly or slowing. So each curve is identified by a \"site index\\\" that indicates the quality of the site.\n",
+    "\n",
+    "The following figure shows site curves for three different site\n",
+    "indices. \n",
+    "\n",
+    "![Site index curves for tree\n",
+    "growth.](figs/trees-fig01.pdf){height=\"3in\"}\n",
+    "\n",
+    "The goal of this case study is to explain the shape of these\n",
+    "curves, that is, why trees grow the way they do.\n",
+    "\n",
+    "As a starting place, let's assume that the ability of the tree to gain\n",
+    "mass is limited by the area it exposes to sunlight, and that the growth rate (in mass) is proportional to that area. In that case we can write:\n",
+    "\n",
+    "$$m_{n+1} = m_n + \\alpha A$$\n",
+    "\n",
+    "where $m_n$ is the mass of the at time step $n$, $A$ is the area exposed to sunlight, and $\\alpha$ is an unknown growth parameter.\n",
+    "\n",
+    "To get from $m$ to $A$, I'll make the additional assumption that mass is proportional to height raised to an unknown power: $m = \\beta h^D$ where $h$ is height, $\\beta$ is an unknown constant of proportionality, and $D$ is the dimension that relates height and mass. I start by assuming $D=3$, but then revisit that assumption.\n",
+    "\n",
+    "Finally, we'll assume that area is proportional to height squared:\n",
+    "\n",
+    "$$A = \\gamma h^2$$\n",
+    "\n",
+    "I specify height in feet, and choose units for mass and area so that\n",
+    "$\\beta=1$ and $\\gamma=1$. Putting all that together, we can write a\n",
+    "difference equation for height:\n",
+    "\n",
+    "$$h_{n+1}^D = h_n^D + \\alpha h_n^2$$\n",
+    "\n",
+    "With $D=3$, the solution to this equation is close to a straight line,\n",
+    "which is not a bad model for the growth curves. But the model implies\n",
+    "that trees can grow forever, and we know that's not true. As trees get\n",
+    "taller, it gets harder for them to move water and nutrients against the force of gravity, and their growth slows.\n",
+    "\n",
+    "We can model this effect by adding a factor to the model similar to what we saw in the logistic model of population growth. Instead of assuming:\n",
+    "\n",
+    "$$m_{n+1} = m_n + \\alpha A$$\n",
+    "\n",
+    "Let's assume\n",
+    "\n",
+    "$$m_{n+1} = m_n + \\alpha A (1 - h / K)$$\n",
+    "\n",
+    "where $K$ is similar to the carrying capacity of the logistic model. As $h$ approaches $K$, the factor $(1 - h/K)$ goes to 0, causing growth to level off.\n",
+    "\n",
+    "In the repository for this book, you'll find a notebook called\n",
+    "`trees.ipynb` that implements both models and uses them to fit the data.\n",
+    "\n",
+    "There are no exercises in this case study; it is mostly meant as an\n",
+    "example of what you can do with the tools we have so far, and a preview of what we will be able to do with the tools in the next few chapters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "solar-turtle",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 7b8587510b59b2fd84f2579dac19517918cc2ce3 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 29 Jan 2021 16:47:18 -0500
Subject: [PATCH 021/144] Revising chapters

---
 jupyter/chap10.ipynb |   56 +--
 jupyter/chap11.ipynb |  914 +++++++++++++++++++++++++++++++++++++
 jupyter/chap12.ipynb | 1036 ++++++++++++++++++++++++++++++++++++++++++
 jupyter/chap13.ipynb |  738 ++++++++++++++++++++++++++++++
 4 files changed, 2716 insertions(+), 28 deletions(-)
 create mode 100644 jupyter/chap11.ipynb
 create mode 100644 jupyter/chap12.ipynb
 create mode 100644 jupyter/chap13.ipynb

diff --git a/jupyter/chap10.ipynb b/jupyter/chap10.ipynb
index 8898834d..e404b395 100644
--- a/jupyter/chap10.ipynb
+++ b/jupyter/chap10.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "negative-distributor",
+   "id": "dense-storm",
    "metadata": {},
    "source": [
     "# Chapter 10"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "organized-stone",
+   "id": "confused-bargain",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -26,8 +26,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "exciting-width",
+   "execution_count": 16,
+   "id": "inner-scott",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -45,12 +45,12 @@
     "try:\n",
     "    from modsim import *\n",
     "except ImportError:\n",
-    "    !pip install modsimpy\n"
+    "    !pip install modsimpy"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "graphic-subject",
+   "id": "thirty-medication",
    "metadata": {},
    "source": [
     "# Case studies\n",
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cleared-greene",
+   "id": "industrial-mercy",
    "metadata": {},
    "source": [
     "## Prehistoric World Population\n",
@@ -71,7 +71,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "manufactured-color",
+   "id": "usual-penguin",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +86,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "intellectual-mortality",
+   "id": "thick-lincoln",
    "metadata": {},
    "outputs": [
     {
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bound-giving",
+   "id": "disciplinary-chosen",
    "metadata": {},
    "source": [
     "Select `tables[1]`, which is the second table on the page."
@@ -119,7 +119,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "voluntary-definition",
+   "id": "conscious-orange",
    "metadata": {},
    "outputs": [
     {
@@ -292,7 +292,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "tracked-stylus",
+   "id": "hungry-monster",
    "metadata": {},
    "source": [
     "Not all researchers provide estimates for the same dates.\n",
@@ -303,7 +303,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "exempt-headline",
+   "id": "thick-blanket",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -313,7 +313,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "professional-watch",
+   "id": "substantial-implement",
    "metadata": {},
    "source": [
     "Some of the estimates are in a form Pandas doesn't recognize as numbers, but we can coerce them to be numeric."
@@ -322,7 +322,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "sapphire-excuse",
+   "id": "ranking-prescription",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "amended-contribution",
+   "id": "amazing-difference",
    "metadata": {},
    "source": [
     "Here are the results.  Notice that we are working in millions now, not billions."
@@ -341,7 +341,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "right-wrong",
+   "id": "metallic-offense",
    "metadata": {
     "scrolled": false
    },
@@ -369,7 +369,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "later-community",
+   "id": "human-conservation",
    "metadata": {},
    "source": [
     "We can use `xlim` to zoom in on everything after Year 0."
@@ -377,13 +377,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "champion-stage",
+   "execution_count": 15,
+   "id": "solar-action",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -403,7 +403,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "medium-aircraft",
+   "id": "wanted-fantasy",
    "metadata": {},
    "source": [
     "See if you can find a model that fits these data well from Year 0 to 1950.\n",
@@ -414,7 +414,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "nervous-anatomy",
+   "id": "together-jackson",
    "metadata": {},
    "outputs": [
     {
@@ -458,7 +458,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "trained-valuable",
+   "id": "simple-verse",
    "metadata": {
     "scrolled": true
    },
@@ -497,7 +497,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "surprised-designation",
+   "id": "ruled-spain",
    "metadata": {},
    "source": [
     "## One queue or two?\n",
@@ -533,7 +533,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "practical-arrest",
+   "id": "forward-point",
    "metadata": {},
    "source": [
     "## Predicting salmon populations\n",
@@ -558,7 +558,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hungarian-cliff",
+   "id": "limiting-moore",
    "metadata": {},
    "source": [
     "## Tree growth\n",
@@ -626,7 +626,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "solar-turtle",
+   "id": "australian-gregory",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap11.ipynb b/jupyter/chap11.ipynb
new file mode 100644
index 00000000..8f018cc3
--- /dev/null
+++ b/jupyter/chap11.ipynb
@@ -0,0 +1,914 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "steady-bridge",
+   "metadata": {},
+   "source": [
+    "# Chapter 11"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "endless-enclosure",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "drawn-population",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "chicken-tamil",
+   "metadata": {},
+   "source": [
+    "In this chapter, we develop a model of an epidemic as it spreads in a\n",
+    "susceptible population, and use it to evaluate the effectiveness of\n",
+    "possible interventions.\n",
+    "\n",
+    "My presentation of the model in the next few chapters is based on an excellent article by David Smith and Lang Moore, [^1]: Smith and Moore, \"The SIR Model for Spread of Disease,\" Journal of Online Mathematics and its Applications, December 2001, available at <http://modsimpy.com/sir>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "developed-asset",
+   "metadata": {},
+   "source": [
+    "## The Freshman Plague\n",
+    "\n",
+    "Every year at Olin College, about 90 new students come to campus from\n",
+    "around the country and the world. Most of them arrive healthy and happy, but usually at least one brings with them some kind of infectious disease. A few weeks later, predictably, some fraction of the incoming class comes down with what we call \"The Freshman Plague\".\n",
+    "\n",
+    "In this chapter we introduce a well-known model of infectious disease,\n",
+    "the Kermack-McKendrick model, and use it to explain the progression of\n",
+    "the disease over the course of the semester, predict the effect of\n",
+    "possible interventions (like immunization) and design the most effective intervention campaign.\n",
+    "\n",
+    "So far we have done our own modeling; that is, we've chosen physical\n",
+    "systems, identified factors that seem important, and made decisions\n",
+    "about how to represent them. In this chapter we start with an existing\n",
+    "model and reverse-engineer it. Along the way, we consider the modeling\n",
+    "decisions that went into it and identify its capabilities and\n",
+    "limitations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "respected-cleveland",
+   "metadata": {},
+   "source": [
+    "## The SIR model\n",
+    "\n",
+    "The Kermack-McKendrick model is a simple version of an **SIR model**,\n",
+    "so-named because it considers three categories of people:\n",
+    "\n",
+    "-   **S**: People who are \"susceptible\\\", that is, capable of\n",
+    "    contracting the disease if they come into contact with someone who\n",
+    "    is infected.\n",
+    "\n",
+    "-   **I**: People who are \"infectious\\\", that is, capable of passing\n",
+    "    along the disease if they come into contact with someone\n",
+    "    susceptible.\n",
+    "\n",
+    "-   **R**: People who are \"recovered\\\". In the basic version of the\n",
+    "    model, people who have recovered are considered to be immune to\n",
+    "    reinfection. That is a reasonable model for some diseases, but not\n",
+    "    for others, so it should be on the list of assumptions to reconsider\n",
+    "    later."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "arabic-flight",
+   "metadata": {},
+   "source": [
+    "Let's think about how the number of people in each category changes over time. Suppose we know that people with the disease are infectious for a period of 4 days, on average. If 100 people are infectious at a\n",
+    "particular point in time, and we ignore the particular time each one\n",
+    "became infected, we expect about 1 out of 4 to recover on any particular day.\n",
+    "\n",
+    "Putting that a different way, if the time between recoveries is 4 days, the recovery rate is about 0.25 recoveries per day, which we'll denote with the Greek letter gamma, $\\gamma$, or the variable name `gamma`.\n",
+    "\n",
+    "If the total number of people in the population is $N$, and the fraction currently infectious is $i$, the total number of recoveries we expect per day is $\\gamma i N$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "emotional-ranch",
+   "metadata": {},
+   "source": [
+    "Now let's think about the number of new infections. Suppose we know that each susceptible person comes into contact with 1 person every 3 days, on average, in a way that would cause them to become infected if the other person is infected. We'll denote this contact rate with the Greek letter beta, $\\beta$.\n",
+    "\n",
+    "It's probably not reasonable to assume that we know $\\beta$ ahead of\n",
+    "time, but later we'll see how to estimate it based on data from previous outbreaks.\n",
+    "\n",
+    "If $s$ is the fraction of the population that's susceptible, $s N$ is\n",
+    "the number of susceptible people, $\\beta s N$ is the number of contacts per day, and $\\beta s i N$ is the number of those contacts where the other person is infectious."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "greek-reference",
+   "metadata": {},
+   "source": [
+    "In summary:\n",
+    "\n",
+    "-   The number of recoveries we expect per day is $\\gamma i N$; dividing by $N$ yields the fraction of the population that recovers in a day, which is $\\gamma i$.\n",
+    "\n",
+    "-   The number of new infections we expect per day is $\\beta s i N$;\n",
+    "    dividing by $N$ yields the fraction of the population that gets\n",
+    "    infected in a day, which is $\\beta s i$.\n",
+    "\n",
+    "This model assumes that the population is closed; that is, no one\n",
+    "arrives or departs, so the size of the population, $N$, is constant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afraid-consultation",
+   "metadata": {},
+   "source": [
+    "## The SIR equations\n",
+    "\n",
+    "If we treat time as a continuous quantity, we can write differential\n",
+    "equations that describe the rates of change for $s$, $i$, and $r$ (where $r$ is the fraction of the population that has recovered):\n",
+    "\n",
+    "$$\\frac{ds}{dt} = -\\beta s i$$\n",
+    "\n",
+    "$$\\frac{di}{dt} = \\beta s i - \\gamma i$$\n",
+    "\n",
+    "$$\\frac{dr}{dt} = \\gamma i$$ \n",
+    "\n",
+    "To avoid cluttering the equations, I leave it implied that $s$ is a function of time, $s(t)$, and likewise for $i$ and $r$.\n",
+    "\n",
+    "SIR models are examples of **compartment models**, so-called because\n",
+    "they divide the world into discrete categories, or compartments, and\n",
+    "describe transitions from one compartment to another. Compartments are\n",
+    "also called **stocks** and transitions between them are called\n",
+    "**flows**.\n",
+    "\n",
+    "In this example, there are three stocks---susceptible, infectious, and\n",
+    "recovered---and two flows---new infections and recoveries. Compartment\n",
+    "models are often represented visually using stock and flow diagrams (see <http://modsimpy.com/stock>).\n",
+    "\n",
+    "The following figure shows the stock and flow diagram for an SIR\n",
+    "model.\n",
+    "\n",
+    "![Stock and flow diagram for an SIR\n",
+    "model.](figs/stock_flow1.pdf){width=\"4in\"}\n",
+    "\n",
+    "Stocks are represented by rectangles, flows by arrows. The widget in the middle of the arrows represents a valve that controls the rate of flow; the diagram shows the parameters that control the valves."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "respected-bangkok",
+   "metadata": {},
+   "source": [
+    "## Implementation\n",
+    "\n",
+    "For a given physical system, there are many possible models, and for a\n",
+    "given model, there are many ways to represent it. For example, we can\n",
+    "represent an SIR model as a stock-and-flow diagram, as a set of\n",
+    "differential equations, or as a Python program. The process of\n",
+    "representing a model in these forms is called **implementation**. In\n",
+    "this section, we implement the SIR model in Python.\n",
+    "\n",
+    "I'll represent the initial state of the system using a `State` object\n",
+    "with state variables `S`, `I`, and `R`; they represent the fraction of\n",
+    "the population in each compartment.\n",
+    "\n",
+    "We can initialize the `State` object with the *number* of people in each compartment, assuming there is one infected student in a class of 90:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "british-operations",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "S    89\n",
+       "I     1\n",
+       "R     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "init = State(S=89, I=1, R=0)\n",
+    "init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "laden-strain",
+   "metadata": {},
+   "source": [
+    "And then convert the numbers to fractions by dividing by the total:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "complete-disposition",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "S    0.988889\n",
+       "I    0.011111\n",
+       "R    0.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from numpy import sum\n",
+    "\n",
+    "init /= sum(init)\n",
+    "init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "curious-commerce",
+   "metadata": {},
+   "source": [
+    "For now, let's assume we know the time between contacts and time between\n",
+    "recoveries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "palestinian-gender",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc = 3             # time between contacts in days \n",
+    "tr = 4             # recovery time in days"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "innocent-bacon",
+   "metadata": {},
+   "source": [
+    "We can use them to compute the parameters of the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "double-nickname",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "human-halifax",
+   "metadata": {},
+   "source": [
+    "Now we need a `System` object to store the parameters and initial\n",
+    "conditions. The following function takes the system parameters and returns a new `System` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "missing-animal",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(beta, gamma):\n",
+    "    init = State(S=89, I=1, R=0)\n",
+    "    init /= sum(init)\n",
+    "\n",
+    "    t0 = 0\n",
+    "    t_end = 7 * 14\n",
+    "\n",
+    "    return System(init=init, t0=t0, t_end=t_end,\n",
+    "                  beta=beta, gamma=gamma)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "impressive-moderator",
+   "metadata": {},
+   "source": [
+    "The default value for `t_end` is 14 weeks, about the length of a\n",
+    "semester.\n",
+    "\n",
+    "Here's what the `System` object looks like. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "artistic-bunch",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "namespace(init=S    0.988889\n",
+       "               I    0.011111\n",
+       "               R    0.000000\n",
+       "               dtype: float64,\n",
+       "          t0=0,\n",
+       "          t_end=98,\n",
+       "          beta=0.3333333333333333,\n",
+       "          gamma=0.25)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = make_system(beta, gamma)\n",
+    "system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "latter-respondent",
+   "metadata": {},
+   "source": [
+    "## The update function\n",
+    "\n",
+    "At any point in time, the state of the system is represented by a\n",
+    "`State` object with three variables, `S`, `I` and `R`. So I'll define an\n",
+    "update function that takes as parameters a `State` object, the current\n",
+    "time, and a `System` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "forbidden-shelter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func(state, t, system):\n",
+    "    s, i, r = state\n",
+    "\n",
+    "    infected = system.beta * i * s    \n",
+    "    recovered = system.gamma * i\n",
+    "    \n",
+    "    s -= infected\n",
+    "    i += infected - recovered\n",
+    "    r += recovered\n",
+    "    \n",
+    "    return State(S=s, I=i, R=r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "subsequent-duplicate",
+   "metadata": {},
+   "source": [
+    "The first line uses a feature we have not seen before, **multiple\n",
+    "assignment**. The value on the right side is a `State` object that\n",
+    "contains three values. The left side is a sequence of three variable\n",
+    "names. The assignment does just what we want: it assigns the three\n",
+    "values from the `State` object to the three variables, in order.\n",
+    "\n",
+    "The variables `s`, `i` and `r`, are lowercase to distinguish them\n",
+    "from the state variables, `S`, `I` and `R`.\n",
+    "\n",
+    "The update function computes `infected` and `recovered` as a fraction of the population, then updates `s`, `i` and `r`. The return value is a `State` that contains the updated values.\n",
+    "\n",
+    "When we call `update_func` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "complimentary-force",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "S    0.985226\n",
+       "I    0.011996\n",
+       "R    0.002778\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state = update_func(init, 0, system)\n",
+    "state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "affected-presentation",
+   "metadata": {},
+   "source": [
+    "You might notice that this version of `update_func` does not use one of its parameters, `t`. I include it anyway because update functions\n",
+    "sometimes depend on time, and it is convenient if they all take the same parameters, whether they need them or not."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "certain-clear",
+   "metadata": {},
+   "source": [
+    "## Running the simulation\n",
+    "\n",
+    "Now we can simulate the model over a sequence of time steps:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "emotional-thomas",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import arange\n",
+    "\n",
+    "def run_simulation(system, update_func):\n",
+    "    state = system.init\n",
+    "\n",
+    "    for t in arange(system.t0, system.t_end):\n",
+    "        state = update_func(state, t, system)\n",
+    "\n",
+    "    return state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "controversial-magic",
+   "metadata": {},
+   "source": [
+    "The parameters of `run_simulation` are the `System` object and the\n",
+    "update function. The `System` object contains the parameters, initial\n",
+    "conditions, and values of `t0` and `t_end`.\n",
+    "\n",
+    "We can call `run_simulation` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "theoretical-ideal",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(beta, gamma)\n",
+    "final_state = run_simulation(system, update_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "working-austin",
+   "metadata": {},
+   "source": [
+    "The result is the final state of the system:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "russian-professor",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "S    0.520568\n",
+       "I    0.000666\n",
+       "R    0.478766\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "informed-drunk",
+   "metadata": {},
+   "source": [
+    "This result indicates that after 14 weeks (98 days), about 52% of the\n",
+    "population is still susceptible, which means they were never infected,\n",
+    "less than 1% are actively infected, and 48% have recovered, which means they were infected at some point."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "funded-civilian",
+   "metadata": {},
+   "source": [
+    "## Collecting the results\n",
+    "\n",
+    "The previous version of `run_simulation` only returns the final state,\n",
+    "but we might want to see how the state changes over time. We'll consider two ways to do that: first, using three `TimeSeries` objects, then using a new object called a `TimeFrame`.\n",
+    "\n",
+    "Here's the first version:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "directed-entertainment",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import TimeSeries\n",
+    "\n",
+    "def run_simulation(system, update_func):\n",
+    "    S = TimeSeries()\n",
+    "    I = TimeSeries()\n",
+    "    R = TimeSeries()\n",
+    "\n",
+    "    state = system.init\n",
+    "    t0 = system.t0\n",
+    "    S[t0], I[t0], R[t0] = state\n",
+    "    \n",
+    "    for t in arange(system.t0, system.t_end):\n",
+    "        state = update_func(state, t, system)\n",
+    "        S[t+1], I[t+1], R[t+1] = state\n",
+    "    \n",
+    "    return S, I, R"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "underlying-crime",
+   "metadata": {},
+   "source": [
+    "First, we create `TimeSeries` objects to store the results. Notice that\n",
+    "the variables `S`, `I`, and `R` are `TimeSeries` objects now.\n",
+    "\n",
+    "Next we initialize `state`, `t0`, and the first elements of `S`, `I` and\n",
+    "`R`.\n",
+    "\n",
+    "Inside the loop, we use `update_func` to compute the state of the system\n",
+    "at the next time step, then use multiple assignment to unpack the\n",
+    "elements of `state`, assigning each to the corresponding `TimeSeries`.\n",
+    "\n",
+    "At the end of the function, we return the values `S`, `I`, and `R`. This\n",
+    "is the first example we have seen where a function returns more than one\n",
+    "value.\n",
+    "\n",
+    "Now we can run the function like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "hired-miller",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(beta, gamma)\n",
+    "S, I, R = run_simulation(system, update_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "normal-sodium",
+   "metadata": {},
+   "source": [
+    "We'll use the following function to plot the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "aboriginal-myrtle",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "def plot_results(S, I, R):\n",
+    "    S.plot(style='--', label='Susceptible')\n",
+    "    I.plot(style='-', label='Infected')\n",
+    "    R.plot(style=':', label='Resistant')\n",
+    "    decorate(xlabel='Time (days)',\n",
+    "             ylabel='Fraction of population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "waiting-blues",
+   "metadata": {},
+   "source": [
+    "And run it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "timely-dream",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_results(S, I, R)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "moral-barcelona",
+   "metadata": {},
+   "source": [
+    "Notice that it takes about three weeks (21 days) for the outbreak to get going, and about six weeks (42 days) before it peaks. The fraction of the population that's infected is never very high, but it adds up. In total, almost half the population gets sick."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "academic-mileage",
+   "metadata": {},
+   "source": [
+    "## Now with a TimeFrame\n",
+    "\n",
+    "If the number of state variables is small, storing them as separate\n",
+    "`TimeSeries` objects might not be so bad. But a better alternative is to use a `TimeFrame`, which is another object defined in the ModSim\n",
+    "library.\n",
+    "\n",
+    "A `TimeFrame` is a kind of a `DataFrame`, which we used in.\n",
+    "\n",
+    "Here's a more concise version of `run_simulation` using a `TimeFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "assured-phenomenon",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import TimeFrame\n",
+    "\n",
+    "def run_simulation(system, update_func):\n",
+    "    frame = TimeFrame(columns=system.init.index)\n",
+    "    frame.loc[system.t0] = system.init\n",
+    "    \n",
+    "    for t in arange(system.t0, system.t_end):\n",
+    "        frame.loc[t+1] = update_func(frame.loc[t], t, system)\n",
+    "    \n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "inner-brave",
+   "metadata": {},
+   "source": [
+    "The first line creates an empty `TimeFrame` with one column for each\n",
+    "state variable. Then, before the loop starts, we store the initial\n",
+    "conditions in the `TimeFrame` at `t0`. Based on the way we've been using\n",
+    "`TimeSeries` objects, it is tempting to write:\n",
+    "\n",
+    "```\n",
+    "frame[system.t0] = system.init\n",
+    "```\n",
+    "\n",
+    "But when you use the bracket operator with a `TimeFrame` or `DataFrame`, it selects a column, not a row. \n",
+    "\n",
+    "To select a row, we have to use `loc`, like this:\n",
+    "\n",
+    "```\n",
+    "frame.loc[system.t0] = system.init\n",
+    "```\n",
+    "\n",
+    "Since the value on the right side is a `State`, the assignment matches\n",
+    "up the index of the `State` with the columns of the `TimeFrame`; that\n",
+    "is, it assigns the `S` value from `system.init` to the `S` column of\n",
+    "`frame`, and likewise with `I` and `R`.\n",
+    "\n",
+    "We use the same feature to write the loop more concisely, assigning the `State` we get from `update_func` directly to the next row of\n",
+    "`frame`.\n",
+    "\n",
+    "Finally, we return `frame`. We can call this version of `run_simulation` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "equal-thinking",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, update_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "framed-dairy",
+   "metadata": {},
+   "source": [
+    "And plot the results like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "neutral-baker",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_results(results.S, results.I, results.R)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "warming-beauty",
+   "metadata": {},
+   "source": [
+    "As with a `DataFrame`, we can use the dot operator to select columns\n",
+    "from a `TimeFrame`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "indie-ancient",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "removed-stevens",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "listed-istanbul",
+   "metadata": {},
+   "source": [
+    "**Exercise**  Suppose the time between contacts is 4 days and the recovery time is 5 days.  After 14 weeks, how many students, total, have been infected?\n",
+    "\n",
+    "Hint: what is the change in `S` between the beginning and the end of the simulation?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "strange-soviet",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3787177442414792"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "tc = 4      # time between contacts in days \n",
+    "tr = 5      # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "system = make_system(beta, gamma)\n",
+    "s_0 = system.init.S\n",
+    "\n",
+    "final = run_simulation(system, update_func)\n",
+    "s_end = final.S[system.t_end]\n",
+    "\n",
+    "s_0 - s_end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "growing-clear",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "polar-edgar",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/chap12.ipynb b/jupyter/chap12.ipynb
new file mode 100644
index 00000000..74d236fb
--- /dev/null
+++ b/jupyter/chap12.ipynb
@@ -0,0 +1,1036 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "noble-particle",
+   "metadata": {},
+   "source": [
+    "# Chapter 12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "conscious-partner",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "governing-nerve",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dominican-johns",
+   "metadata": {},
+   "source": [
+    "### Code\n",
+    "\n",
+    "Here's the code from the previous notebook that we'll need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "changed-netherlands",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import State, System\n",
+    "\n",
+    "def make_system(beta, gamma):\n",
+    "    \"\"\"Make a system object for the SIR model.\n",
+    "    \n",
+    "    beta: contact rate in days\n",
+    "    gamma: recovery rate in days\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(S=89, I=1, R=0)\n",
+    "    init /= sum(init)\n",
+    "\n",
+    "    t0 = 0\n",
+    "    t_end = 7 * 14\n",
+    "\n",
+    "    return System(init=init, t0=t0, t_end=t_end,\n",
+    "                  beta=beta, gamma=gamma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "thorough-heading",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func(state, t, system):\n",
+    "    \"\"\"Update the SIR model.\n",
+    "    \n",
+    "    state: State with variables S, I, R\n",
+    "    t: time step\n",
+    "    system: System with beta and gamma\n",
+    "    \n",
+    "    returns: State object\n",
+    "    \"\"\"\n",
+    "    s, i, r = state\n",
+    "\n",
+    "    infected = system.beta * i * s    \n",
+    "    recovered = system.gamma * i\n",
+    "    \n",
+    "    s -= infected\n",
+    "    i += infected - recovered\n",
+    "    r += recovered\n",
+    "    \n",
+    "    return State(S=s, I=i, R=r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "peaceful-collective",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import arange\n",
+    "\n",
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "        \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \n",
+    "    returns: TimeFrame\n",
+    "    \"\"\"\n",
+    "    frame = TimeFrame(columns=system.init.index)\n",
+    "    frame.loc[system.t0] = system.init\n",
+    "    \n",
+    "    for t in arange(system.t0, system.t_end):\n",
+    "        frame.loc[t+1] = update_func(frame.loc[t], t, system)\n",
+    "    \n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "patent-madagascar",
+   "metadata": {},
+   "source": [
+    "In the previous chapter I presented the SIR model of infectious disease and used it to model the Freshman Plague at Olin. In this chapter we'll consider metrics intended to quantify the effects of the disease and interventions intended to reduce those effects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "knowing-connecticut",
+   "metadata": {},
+   "source": [
+    "## Immunization\n",
+    "\n",
+    "Models like this are useful for testing \"what if?\" scenarios. As an\n",
+    "example, we'll consider the effect of immunization.\n",
+    "\n",
+    "Suppose there is a vaccine that causes a student to become immune to the Freshman Plague without being infected. How might you modify the model to capture this effect?\n",
+    "\n",
+    "One option is to treat immunization as a shortcut from susceptible to\n",
+    "recovered without going through infectious. We can implement this\n",
+    "feature like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "catholic-walker",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_immunization(system, fraction):\n",
+    "    system.init.S -= fraction\n",
+    "    system.init.R += fraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "logical-mailman",
+   "metadata": {},
+   "source": [
+    "`add_immunization` moves the given fraction of the population from `S`\n",
+    "to `R`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "sophisticated-occasions",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc = 3             # time between contacts in days \n",
+    "tr = 4             # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "system = make_system(beta, gamma)\n",
+    "results = run_simulation(system, update_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "postal-haiti",
+   "metadata": {},
+   "source": [
+    "If we assume that 10% of students are vaccinated at the\n",
+    "beginning of the semester, and the vaccine is 100% effective, we can\n",
+    "simulate the effect like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "abandoned-piece",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system2 = make_system(beta, gamma)\n",
+    "add_immunization(system2, 0.1)\n",
+    "results2 = run_simulation(system2, update_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "robust-forestry",
+   "metadata": {},
+   "source": [
+    "The following figure shows `S` as a function of time, with and\n",
+    "without immunization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "suffering-coupon",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results.S.plot(label='No immunization')\n",
+    "results2.S.plot(label='10% immunization')\n",
+    "\n",
+    "decorate(xlabel='Time (days)',\n",
+    "         ylabel='Fraction of population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "shaped-assets",
+   "metadata": {},
+   "source": [
+    "## Metrics\n",
+    "\n",
+    "When we plot a time series, we get a view of everything that happened\n",
+    "when the model ran, but often we want to boil it down to a few numbers\n",
+    "that summarize the outcome. These summary statistics are called\n",
+    "**metrics**, as we saw in Section xxx.\n",
+    "\n",
+    "In the SIR model, we might want to know the time until the peak of the\n",
+    "outbreak, the number of people who are sick at the peak, the number of\n",
+    "students who will still be sick at the end of the semester, or the total number of students who get sick at any point.\n",
+    "\n",
+    "As an example, I will focus on the last one --- the total number of sick students --- and we will consider interventions intended to minimize it.\n",
+    "\n",
+    "When a person gets infected, they move from `S` to `I`, so we can get\n",
+    "the total number of infections by computing the difference in `S` at the beginning and the end:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "unable-joshua",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_total_infected(results, system):\n",
+    "    s_0 = results.S[system.t0]\n",
+    "    s_end = results.S[system.t_end]\n",
+    "    return s_0 - s_end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "floral-transcription",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.468320811028781"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calc_total_infected(results, system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "cardiovascular-acting",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.30650802853979753"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calc_total_infected(results2, system2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reliable-fisher",
+   "metadata": {},
+   "source": [
+    "Without immunization, almost 47% of the population gets infected at some point. With 10% immunization, only 31% get infected. That's pretty good."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "multiple-springfield",
+   "metadata": {},
+   "source": [
+    "## Sweeping Immunization\n",
+    "\n",
+    "Now let's see what happens if we administer more vaccines. This\n",
+    "following function sweeps a range of immunization rates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "funny-batman",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_immunity(immunize_array):\n",
+    "    sweep = SweepSeries()\n",
+    "\n",
+    "    for fraction in immunize_array:\n",
+    "        sir = make_system(beta, gamma)\n",
+    "        add_immunization(sir, fraction)\n",
+    "        results = run_simulation(sir, update_func)\n",
+    "        sweep[fraction] = calc_total_infected(results, sir)\n",
+    "\n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "normal-blowing",
+   "metadata": {},
+   "source": [
+    "The parameter of `sweep_immunity` is an array of immunization rates. The\n",
+    "result is a `SweepSeries` object that maps from each immunization rate\n",
+    "to the resulting fraction of students ever infected.\n",
+    "\n",
+    "The following figure shows a plot of the `SweepSeries`. Notice that\n",
+    "the x-axis is the immunization rate, not time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "severe-oklahoma",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "immunize_array = linspace(0, 1, 21)\n",
+    "infected_sweep = sweep_immunity(immunize_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "sudden-worker",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "infected_sweep.plot()\n",
+    "\n",
+    "decorate(xlabel='Fraction immunized',\n",
+    "         ylabel='Total fraction infected',\n",
+    "         title='Fraction infected vs. immunization rate')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fantastic-violence",
+   "metadata": {},
+   "source": [
+    "As the immunization rate increases, the number of infections drops\n",
+    "steeply. If 40% of the students are immunized, fewer than 4% get sick.\n",
+    "That's because immunization has two effects: it protects the people who get immunized (of course) but it also protects the rest of the\n",
+    "population.\n",
+    "\n",
+    "Reducing the number of \"susceptibles\" and increasing the number of\n",
+    "\"resistants\" makes it harder for the disease to spread, because some\n",
+    "fraction of contacts are wasted on people who cannot be infected. This\n",
+    "phenomenon is called **herd immunity**, and it is an important element\n",
+    "of public health (see <http://modsimpy.com/herd>)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "completed-stand",
+   "metadata": {},
+   "source": [
+    "The steepness of the curve is a blessing and a curse. It's a blessing\n",
+    "because it means we don't have to immunize everyone, and vaccines can\n",
+    "protect the \"herd\" even if they are not 100% effective.\n",
+    "\n",
+    "But it's a curse because a small decrease in immunization can cause a\n",
+    "big increase in infections. In this example, if we drop from 80%\n",
+    "immunization to 60%, that might not be too bad. But if we drop from 40% to 20%, that would trigger a major outbreak, affecting more than 15% of the population. For a serious disease like measles, just to name one, that would be a public health catastrophe.\n",
+    "\n",
+    "One use of models like this is to demonstrate phenomena like herd\n",
+    "immunity and to predict the effect of interventions like vaccination.\n",
+    "Another use is to evaluate alternatives and guide decision making. We'll see an example in the next section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "known-classic",
+   "metadata": {},
+   "source": [
+    "## Hand washing\n",
+    "\n",
+    "Suppose you are the Dean of Student Life, and you have a budget of just \\$1200 to combat the Freshman Plague. You have two options for spending this money:\n",
+    "\n",
+    "1.  You can pay for vaccinations, at a rate of \\$100 per dose.\n",
+    "\n",
+    "2.  You can spend money on a campaign to remind students to wash hands\n",
+    "    frequently.\n",
+    "\n",
+    "We have already seen how we can model the effect of vaccination. Now\n",
+    "let's think about the hand-washing campaign. We'll have to answer two\n",
+    "questions:\n",
+    "\n",
+    "1.  How should we incorporate the effect of hand washing in the model?\n",
+    "\n",
+    "2.  How should we quantify the effect of the money we spend on a\n",
+    "    hand-washing campaign?\n",
+    "\n",
+    "For the sake of simplicity, let's assume that we have data from a\n",
+    "similar campaign at another school showing that a well-funded campaign\n",
+    "can change student behavior enough to reduce the infection rate by 20%.\n",
+    "\n",
+    "In terms of the model, hand washing has the effect of reducing `beta`.\n",
+    "That's not the only way we could incorporate the effect, but it seems\n",
+    "reasonable and it's easy to implement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "suspected-chosen",
+   "metadata": {},
+   "source": [
+    "Now we have to model the relationship between the money we spend and the\n",
+    "effectiveness of the campaign. Again, let's suppose we have data from\n",
+    "another school that suggests:\n",
+    "\n",
+    "-   If we spend \\$500 on posters, materials, and staff time, we can\n",
+    "    change student behavior in a way that decreases the effective value of `beta` by 10%.\n",
+    "\n",
+    "-   If we spend \\$1000, the total decrease in `beta` is almost 20%.\n",
+    "\n",
+    "-   Above \\$1000, additional spending has little additional benefit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ultimate-leader",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tutorial-terrace",
+   "metadata": {},
+   "source": [
+    "### Logistic function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abroad-birmingham",
+   "metadata": {},
+   "source": [
+    "To model the effect of a hand-washing campaign, I'll use a [generalized logistic function](https://en.wikipedia.org/wiki/Generalised_logistic_function) (GLF), which is a convenient function for modeling curves that have a generally sigmoid shape.  The parameters of the GLF correspond to various features of the curve in a way that makes it easy to find a function that has the shape you want, based on data or background information about the scenario."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "fossil-adolescent",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import exp\n",
+    "\n",
+    "def logistic(x, A=0, B=1, C=1, M=0, K=1, Q=1, nu=1):\n",
+    "    \"\"\"Computes the generalize logistic function.\n",
+    "    \n",
+    "    A: controls the lower bound\n",
+    "    B: controls the steepness of the transition \n",
+    "    C: not all that useful, AFAIK\n",
+    "    M: controls the location of the transition\n",
+    "    K: controls the upper bound\n",
+    "    Q: shift the transition left or right\n",
+    "    nu: affects the symmetry of the transition\n",
+    "    \n",
+    "    returns: float or array\n",
+    "    \"\"\"\n",
+    "    exponent = -B * (x - M)\n",
+    "    denom = C + Q * exp(exponent)\n",
+    "    return A + (K-A) / denom ** (1/nu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cloudy-canvas",
+   "metadata": {},
+   "source": [
+    "The following array represents the range of possible spending."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "equal-lesson",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spending = linspace(0, 1200, 21)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "competitive-dylan",
+   "metadata": {},
+   "source": [
+    "`compute_factor` computes the reduction in `beta` for a given level of campaign spending.\n",
+    "\n",
+    "`M` is chosen so the transition happens around \\$500.\n",
+    "\n",
+    "`K` is the maximum reduction in `beta`, 20%.\n",
+    "\n",
+    "`B` is chosen by trial and error to yield a curve that seems feasible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "ecological-puppy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_factor(spending):\n",
+    "    \"\"\"Reduction factor as a function of spending.\n",
+    "    \n",
+    "    spending: dollars from 0 to 1200\n",
+    "    \n",
+    "    returns: fractional reduction in beta\n",
+    "    \"\"\"\n",
+    "    return logistic(spending, M=500, K=0.2, B=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "informed-maple",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "diverse-ensemble",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "percent_reduction = compute_factor(spending) * 100\n",
+    "\n",
+    "plot(spending, percent_reduction)\n",
+    "\n",
+    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
+    "         ylabel='Percent reduction in infection rate',\n",
+    "         title='Effect of hand washing on infection rate')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "published-decade",
+   "metadata": {},
+   "source": [
+    "The result is the following function, which\n",
+    "takes spending as a parameter and returns `factor`, which is the factor\n",
+    "by which `beta` is reduced:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "atomic-guatemala",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_factor(spending):\n",
+    "    return logistic(spending, M=500, K=0.2, B=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reflected-details",
+   "metadata": {},
+   "source": [
+    "I use `compute_factor` to write `add_hand_washing`, which takes a\n",
+    "`System` object and a budget, and modifies `system.beta` to model the\n",
+    "effect of hand washing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "presidential-omaha",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_hand_washing(system, spending):\n",
+    "    factor = compute_factor(spending)\n",
+    "    system.beta *= (1 - factor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "perfect-promotion",
+   "metadata": {},
+   "source": [
+    "Now we can sweep a range of values for `spending` and use the simulation\n",
+    "to compute the effect:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "outer-nutrition",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_hand_washing(spending_array):\n",
+    "    sweep = SweepSeries()\n",
+    "    \n",
+    "    for spending in spending_array:\n",
+    "        system = make_system(beta, gamma)\n",
+    "        add_hand_washing(system, spending)\n",
+    "        results = run_simulation(system, update_func)\n",
+    "        sweep[spending] = calc_total_infected(results, system)\n",
+    "        \n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "agricultural-interpretation",
+   "metadata": {},
+   "source": [
+    "Here's how we run it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "first-termination",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import linspace\n",
+    "\n",
+    "spending_array = linspace(0, 1200, 20)\n",
+    "infected_sweep2 = sweep_hand_washing(spending_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cardiac-shopper",
+   "metadata": {},
+   "source": [
+    "The following figure shows the result. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "cardiovascular-coach",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "infected_sweep2.plot()\n",
+    "\n",
+    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
+    "         ylabel='Total fraction infected',\n",
+    "         title='Effect of hand washing on total infections')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fifth-highlight",
+   "metadata": {},
+   "source": [
+    "Below \\$200, the campaign has little effect. \n",
+    "\n",
+    "At \\$800 it has a substantial effect, reducing total infections from more than 45% to about 20%. \n",
+    "\n",
+    "Above \\$800, the additional benefit is small."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ruled-disease",
+   "metadata": {},
+   "source": [
+    "## Optimization\n",
+    "\n",
+    "Let's put it all together. With a fixed budget of \\$1200, we have to\n",
+    "decide how many doses of vaccine to buy and how much to spend on the\n",
+    "hand-washing campaign.\n",
+    "\n",
+    "Here are the parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "representative-brook",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_students = 90\n",
+    "budget = 1200\n",
+    "price_per_dose = 100\n",
+    "max_doses = int(budget / price_per_dose)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "another-idaho",
+   "metadata": {},
+   "source": [
+    "The fraction `budget/price_per_dose` might not be an integer. `int` is a\n",
+    "built-in function that converts numbers to integers, rounding down.\n",
+    "\n",
+    "We'll sweep the range of possible doses:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "historic-inclusion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dose_array = arange(max_doses+1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "straight-equation",
+   "metadata": {},
+   "source": [
+    "In this example we call `linrange` with only one argument; it returns a\n",
+    "NumPy array with the integers from 0 to `max_doses`. With the argument\n",
+    "`endpoint=True`, the result includes both endpoints.\n",
+    "\n",
+    "Then we run the simulation for each element of `dose_array`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "received-swiss",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_doses(dose_array):\n",
+    "    sweep = SweepSeries()\n",
+    "    \n",
+    "    for doses in dose_array:\n",
+    "        fraction = doses / num_students\n",
+    "        spending = budget - doses * price_per_dose\n",
+    "        \n",
+    "        system = make_system(beta, gamma)\n",
+    "        add_immunization(system, fraction)\n",
+    "        add_hand_washing(system, spending)\n",
+    "        \n",
+    "        results = run_simulation(system, update_func)\n",
+    "        sweep[doses] = calc_total_infected(results, system)\n",
+    "\n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "appropriate-design",
+   "metadata": {},
+   "source": [
+    "For each number of doses, we compute the fraction of students we can\n",
+    "immunize, `fraction` and the remaining budget we can spend on the\n",
+    "campaign, `spending`. Then we run the simulation with those quantities\n",
+    "and store the number of infections.\n",
+    "\n",
+    "The following figure shows the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "original-browse",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infected_sweep3 = sweep_doses(dose_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "technical-violation",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "infected_sweep3.plot()\n",
+    "\n",
+    "decorate(xlabel='Doses of vaccine',\n",
+    "         ylabel='Total fraction infected',\n",
+    "         title='Total infections vs. doses')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "economic-newspaper",
+   "metadata": {},
+   "source": [
+    "If we buy no doses of vaccine and spend the entire budget on the campaign, the fraction infected is around 19%. At 4 doses, we have \\$800 left for the campaign, and this is the optimal point that minimizes the number of students who get sick.\n",
+    "\n",
+    "As we increase the number of doses, we have to cut campaign spending,\n",
+    "which turns out to make things worse. But interestingly, when we get\n",
+    "above 10 doses, the effect of herd immunity starts to kick in, and the\n",
+    "number of sick students goes down again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sharp-difficulty",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "quick-rotation",
+   "metadata": {},
+   "source": [
+    "### Exercises\n",
+    "\n",
+    "**Exercise:** Suppose the price of the vaccine drops to $50 per dose.  How does that affect the optimal allocation of the spending?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fresh-costa",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose we have the option to quarantine infected students.  For example, a student who feels ill might be moved to an infirmary, or a private dorm room, until they are no longer infectious.\n",
+    "\n",
+    "How might you incorporate the effect of quarantine in the SIR model?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "transparent-entry",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "\"\"\"There is no unique best answer to this question,\n",
+    "but one simple option is to model quarantine as an\n",
+    "effective reduction in gamma, on the assumption that\n",
+    "quarantine reduces the number of infectious contacts\n",
+    "per infected student.\n",
+    "\n",
+    "Another option would be to add a fourth compartment\n",
+    "to the model to track the fraction of the population\n",
+    "in quarantine at each point in time.  This approach\n",
+    "would be more complex, and it is not obvious that it\n",
+    "is substantially better.\n",
+    "\n",
+    "The following function could be used, like \n",
+    "add_immunization and add_hand_washing, to adjust the\n",
+    "parameters in order to model various interventions.\n",
+    "\n",
+    "In this example, `high` is the highest duration of\n",
+    "the infection period, with no quarantine.  `low` is\n",
+    "the lowest duration, on the assumption that it takes\n",
+    "some time to identify infectious students.\n",
+    "\n",
+    "`fraction` is the fraction of infected students who \n",
+    "are quarantined as soon as they are identified.\n",
+    "\"\"\"\n",
+    "\n",
+    "def add_quarantine(system, fraction):\n",
+    "    \"\"\"Model the effect of quarantine by adjusting gamma.\n",
+    "    \n",
+    "    system: System object\n",
+    "    fraction: fraction of students quarantined\n",
+    "    \"\"\"\n",
+    "    # `low` represents the number of days a student \n",
+    "    # is infectious if quarantined.\n",
+    "    # `high` is the number of days they are infectious\n",
+    "    # if not quarantined\n",
+    "    low = 1\n",
+    "    high = 4\n",
+    "    tr = high - fraction * (high-low)\n",
+    "    system.gamma = 1 / tr"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/chap13.ipynb b/jupyter/chap13.ipynb
new file mode 100644
index 00000000..16d36724
--- /dev/null
+++ b/jupyter/chap13.ipynb
@@ -0,0 +1,738 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fifty-repair",
+   "metadata": {},
+   "source": [
+    "# Chapter 13"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pharmaceutical-constant",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "aware-blade",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    \n",
+    "try:\n",
+    "    from modsim import *\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "increased-happiness",
+   "metadata": {},
+   "source": [
+    "### Code from previous chapters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "honey-backup",
+   "metadata": {},
+   "source": [
+    "`make_system`, `plot_results`, and `calc_total_infected` are unchanged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "experimental-preparation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import State, System\n",
+    "\n",
+    "def make_system(beta, gamma):\n",
+    "    \"\"\"Make a system object for the SIR model.\n",
+    "    \n",
+    "    beta: contact rate in days\n",
+    "    gamma: recovery rate in days\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(S=89, I=1, R=0)\n",
+    "    init /= sum(init)\n",
+    "\n",
+    "    t0 = 0\n",
+    "    t_end = 7 * 14\n",
+    "\n",
+    "    return System(init=init, t0=t0, t_end=t_end,\n",
+    "                  beta=beta, gamma=gamma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "arbitrary-newspaper",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func(state, t, system):\n",
+    "    \"\"\"Update the SIR model.\n",
+    "    \n",
+    "    state: State with variables S, I, R\n",
+    "    t: time step\n",
+    "    system: System with beta and gamma\n",
+    "    \n",
+    "    returns: State object\n",
+    "    \"\"\"\n",
+    "    s, i, r = state\n",
+    "\n",
+    "    infected = system.beta * i * s    \n",
+    "    recovered = system.gamma * i\n",
+    "    \n",
+    "    s -= infected\n",
+    "    i += infected - recovered\n",
+    "    r += recovered\n",
+    "    \n",
+    "    return State(S=s, I=i, R=r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "attended-sherman",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import arange\n",
+    "\n",
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "        \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \n",
+    "    returns: TimeFrame\n",
+    "    \"\"\"\n",
+    "    frame = TimeFrame(columns=system.init.index)\n",
+    "    frame.loc[system.t0] = system.init\n",
+    "    \n",
+    "    for t in arange(system.t0, system.t_end):\n",
+    "        frame.loc[t+1] = update_func(frame.loc[t], t, system)\n",
+    "    \n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "genetic-nylon",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_total_infected(results, system):\n",
+    "    s_0 = results.S[system.t0]\n",
+    "    s_end = results.S[system.t_end]\n",
+    "    return s_0 - s_end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "descending-benjamin",
+   "metadata": {},
+   "source": [
+    "In the previous chapters I presented an SIR model of infectious disease, specifically the Kermack-McKendrick model. We extended the model to include vaccination and the effect of a hand-washing campaign, and used the extended model to allocate a limited budget optimally, that is, to minimize the number of infections.\n",
+    "\n",
+    "But we assumed that the parameters of the model, contact rate and\n",
+    "recovery rate, were known. In this chapter, we explore the behavior of\n",
+    "the model as we vary these parameters, use analysis to understand these relationships better, and propose a method for using data to estimate parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "italian-halifax",
+   "metadata": {},
+   "source": [
+    "## Sweeping beta\n",
+    "\n",
+    "Recall that $\\beta$ is the contact rate, which captures both the\n",
+    "frequency of interaction between people and the fraction of those\n",
+    "interactions that result in a new infection. If $N$ is the size of the\n",
+    "population and $s$ is the fraction that's susceptible, $s N$ is the\n",
+    "number of susceptibles, $\\beta s N$ is the number of contacts per day\n",
+    "between susceptibles and other people, and $\\beta s i N$ is the number\n",
+    "of those contacts where the other person is infectious.\n",
+    "\n",
+    "As $\\beta$ increases, we expect the total number of infections to\n",
+    "increase. To quantify that relationship, I'll create a range of values\n",
+    "for $\\beta$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "pleased-virus",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import linspace\n",
+    "\n",
+    "beta_array = linspace(0.1, 1.1, 11)\n",
+    "gamma = 0.25"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "operational-continuity",
+   "metadata": {},
+   "source": [
+    "Then run the simulation for each value and print the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "functional-bennett",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.1 0.0072309016649785285\n",
+      "0.2 0.038410532615067994\n",
+      "0.30000000000000004 0.33703425948982\n",
+      "0.4 0.6502429153895082\n",
+      "0.5 0.8045061124629623\n",
+      "0.6 0.8862866308018508\n",
+      "0.7000000000000001 0.9316695082755875\n",
+      "0.8 0.9574278300784942\n",
+      "0.9 0.9720993156325133\n",
+      "1.0 0.9803437149675784\n",
+      "1.1 0.9848347293510136\n"
+     ]
+    }
+   ],
+   "source": [
+    "for beta in beta_array:\n",
+    "    system = make_system(beta, gamma)\n",
+    "    results = run_simulation(system, update_func)\n",
+    "    print(beta, calc_total_infected(results, system))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "indian-islam",
+   "metadata": {},
+   "source": [
+    "We can wrap that code in a function and store the results in a\n",
+    "`SweepSeries` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "successful-holocaust",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_beta(beta_array, gamma):\n",
+    "    sweep = SweepSeries()\n",
+    "    for beta in beta_array:\n",
+    "        system = make_system(beta, gamma)\n",
+    "        results = run_simulation(system, update_func)\n",
+    "        sweep[beta] = calc_total_infected(results, system)\n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "british-turtle",
+   "metadata": {},
+   "source": [
+    "Now we can run `sweep_beta` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "varied-attention",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infected_sweep = sweep_beta(beta_array, gamma)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "played-waters",
+   "metadata": {},
+   "source": [
+    "And plot the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "possible-valley",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'gamma = 0.25'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "label = f'gamma = {gamma}'\n",
+    "label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "defensive-river",
+   "metadata": {},
+   "source": [
+    "The first line uses string operations to assemble a label for the\n",
+    "plotted line:\n",
+    "\n",
+    "-   When the `+` operator is applied to strings, it joins them\n",
+    "    end-to-end, which is called **concatenation**.\n",
+    "\n",
+    "-   The function `str` converts any type of object to a String\n",
+    "    representation. In this case, `gamma` is a number, so we have to\n",
+    "    convert it to a string before trying to concatenate it.\n",
+    "\n",
+    "If the value of `gamma` is `0.25`, the value of `label` is the string\n",
+    "`'gamma = 0.25'`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "composite-salad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "infected_sweep.plot(label=label)\n",
+    "\n",
+    "decorate(xlabel='Contact rate (beta)',\n",
+    "         ylabel='Fraction infected')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fuzzy-thesis",
+   "metadata": {},
+   "source": [
+    "Remember that this figure\n",
+    "is a parameter sweep, not a time series, so the x-axis is the parameter\n",
+    "`beta`, not time.\n",
+    "\n",
+    "When `beta` is small, the contact rate is low and the outbreak never\n",
+    "really takes off; the total number of infected students is near zero. As\n",
+    "`beta` increases, it reaches a threshold near 0.3 where the fraction of\n",
+    "infected students increases quickly. When `beta` exceeds 0.5, more than\n",
+    "80% of the population gets sick."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "competent-melissa",
+   "metadata": {},
+   "source": [
+    "## Sweeping gamma\n",
+    "\n",
+    "Let's see what that looks like for a few different values of `gamma`.\n",
+    "Again, we'll use `linspace` to make an array of values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "traditional-spirit",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamma_array = linspace(0.1, 0.7, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "precise-review",
+   "metadata": {},
+   "source": [
+    "And run `sweep_beta` for each value of `gamma`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "laughing-tonight",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for gamma in gamma_array:\n",
+    "    infected_sweep = sweep_beta(beta_array, gamma)\n",
+    "    label = 'gamma = ' + str(gamma)\n",
+    "    plot(infected_sweep, label=label)\n",
+    "    \n",
+    "decorate()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "oriental-north",
+   "metadata": {},
+   "source": [
+    "The following figure shows the results. When `gamma` is low, the\n",
+    "recovery rate is low, which means people are infectious longer. In that\n",
+    "case, even a low contact rate (`beta`) results in an epidemic.\n",
+    "\n",
+    "When `gamma` is high, `beta` has to be even higher to get things going."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "large-radical",
+   "metadata": {},
+   "source": [
+    "## SweepFrame\n",
+    "\n",
+    "In the previous section, we swept a range of values for `gamma`, and for\n",
+    "each value, we swept a range of values for `beta`. This process is a\n",
+    "**two-dimensional sweep**.\n",
+    "\n",
+    "If we want to store the results, rather than plot them, we can use a\n",
+    "`SweepFrame`, which is a kind of `DataFrame` where the rows sweep one\n",
+    "parameter, the columns sweep another parameter, and the values contain\n",
+    "metrics from a simulation.\n",
+    "\n",
+    "This function shows how it works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "patient-navigation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_parameters(beta_array, gamma_array):\n",
+    "    frame = SweepFrame(columns=gamma_array)\n",
+    "    for gamma in gamma_array:\n",
+    "        frame[gamma] = sweep_beta(beta_array, gamma)\n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "minus-meaning",
+   "metadata": {},
+   "source": [
+    "`sweep_parameters` takes as parameters an array of values for `beta` and\n",
+    "an array of values for `gamma`.\n",
+    "\n",
+    "It creates a `SweepFrame` to store the results, with one column for each\n",
+    "value of `gamma` and one row for each value of `beta`.\n",
+    "\n",
+    "Each time through the loop, we run `sweep_beta`. The result is a\n",
+    "`SweepSeries` object with one element for each value of `beta`. The\n",
+    "assignment inside the loop stores the `SweepSeries` as a new column in\n",
+    "the `SweepFrame`, corresponding to the current value of `gamma`.\n",
+    "\n",
+    "At the end, the `SweepFrame` stores the fraction of students infected\n",
+    "for each pair of parameters, `beta` and `gamma`.\n",
+    "\n",
+    "We can run `sweep_parameters` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "polish-excellence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frame = sweep_parameters(beta_array, gamma_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cultural-olympus",
+   "metadata": {},
+   "source": [
+    "With the results in a `SweepFrame`, we can plot each column like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "religious-placement",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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naQ1EF6YGVnto+COXdjA7jlZj2yKYcz8c2AkRiXDOSDj1JohpbnYyTfM7ujA1IKUUI79YR3Gllem399FTDnmCymJY8ASsnmosnvevT6D9pRCgV/1tCEopKu2VhAWGmR1Fc4Gry1707t27fW5ublBoaKgDYOHChZuTk5MPm0/vSHRhakAf/7aTRRl5PH1FJ9onRh77BZp7bVsEs++F4r3Q917oNwaC9BdkQ7A77CzctZBpG6aRHJnMS+e8ZHYkzQWuLnsB8PHHH2eec845JzQ8Xg8paiAZ2SWMnb+Rfu3juVkPDTdXVQnMeQA+uQoCQ+C27+Hi53VRagDl1nJmbJzBgK8H8NDPD1FYVUivhGOeK+u1Ro4cmZSWlta5b9++ba+44oq0J598MgFgwoQJcV26dOnYvn37TpdccknrkpISC8DgwYNThw4d2qJPnz7tUlJSus6bNy9iyJAhqa1ateo8ePDg1Jr3DQ8P73HXXXcld+7cuWPfvn3bLVq0KLx3797tU1JSuk6fPj0aICMjI7hnz57tO3Xq1LFTp04dFyxYcNJLXM+dOzfmtttu2w/GshfLli2LdDhOekKJw+iOqQHUDA2PCg3k5SF6aLiparqkoj26S2pAeeV5fLbpMz7P+Jzi6mK6x3fn4V4P0695PwIaYFDJE78+0Xxr4dZ6XfaiTWyb8ufOfE4ve3GEZS9uv/32VIvFwhVXXFE4fvz4fYfOqXc0ujA1gHHfbiIjp4Qpt55GXIQeGm6KqhL44QlYPQWatIH//ADNe5udyudtO7CNaRumMTdzLjaHjQtaXMDNnW/mlKanmB3N7fx52YvPP/88My0tzVpYWGi5/PLLW0+aNKnJ8OHD97v6ObowudmijFymLnMODW+vh4abonaXdMZw43wk3SW5jVKKldkrmbphKkv3LiU0IJSr217NTZ1uokVUC1MyHa2zcRd/XvYiLS3NChAbG+u49tprC1asWNEI0IXJE+SVVDHyi3V0SNRDw01RVQILnjTOS2rSxjiW1KKP2al8ltVhZcGOBUzdMJWNBRtpHNqYe065h2vbX0tsqP9NaOuvy15YrVby8/MDk5KSbFVVVTJ//vzo888/Xy974QmUUoz6ch3FlTam3366Hhre0DIXw//uhaLduktys9LqUmZtmcX0jdPZV7aP1KhUnjrjKa5ofQUhAf6769pfl72oqKiwXHjhhW2tVqs4HA45++yzi0eMGKGXvfAEU3/dztNz/uKZgZ31KLyGdGiXdOUk3SW5SXZZNjM2zuCLzV9Qai2lZ0JPbul8C+eknINF3DfgVy974Rv0shcNbFN2MS98u4nzOzTlpjNamh3Hf+guqUFkFGQwbcM0vt3+LQ4cXNzyYm7ufDNd4rqYHc3j6GUvTowuTPWs0mrn/s/WEhUaxEvXdNNDwxuCPpbkdkopfsv6jakbpvLbvt8ICwzjug7XMbTjUFIiU8yO57H0shcnRhemelYzNHyqHhreMDJ/hv8N112SmziUg3mZ85iyYQpbCrcQHxbP/afez5B2Q4gOiTY7nuajdGGqR7WHhp+nh4a7V+0uqXFruO07aHG62al8yubCzTz727Osy1tHm5g2PHfmc1yWdhnBAcFmR9N8nC5M9UQPDW9Ah3ZJ/cZAcL2e1O/Xyq3lvPvnu3yy4RMigyN5/sznGdh6oN4trTUYtxYmEekPTAQCgA+VUuMOeT4a+BRo4czyilJqijszuYMeGt5ArBXw/RhY9ZHuktxkyZ4lvLD8BfaW7mVQm0GM6DmCmNAYs2NpfsZthUlEAoC3gYuAPcBKEZmtlPqr1mb3AH8ppa4QkXggQ0SmK6UOm0bdk01btoNFGXk8M7CznjXcXRx2+PI/kDEfTr/HOJaku6R6k1uey7gV41iwcwGtolsx5ZIp9Er03clVtRPjyrIXhYWFljPOOOPgbqOcnJygQYMGFUyePNnl2Tfc2TH1BrYqpTIBRGQmcCVQuzApIFKMfQQRQAHg8podnkAPDW8ASsG3oyBjHlz6MvQZZnYin2F32Pk843Pe+OMNbA4b9/a4l1s730qQXo9Kq4Mry17ExsY6Nm3adPB7vnPnzh2HDBlSeDyf485lL5KB2hVyj/Ox2t4COgJZQDpwv1LqsLOTRWSYiKwSkVV5ecd1ArFb6aHhDeTX12Hlh9D3Pl2U6tHG/Ru5cf6NvLjiRbrHd+frgV8zrNswXZTqkb8ve5Genh6yf//+oEsuuaT0eD7HnR1TXd/Sh04zcQmwFjgfaA0sEJGlSqnif7xIqfeB98GY+aH+o54YPTS8Afz5f/Dj09BlMFz4jNlpfEK5tZy31r7F9I3TiQ2JZfzZ47k07VKf/sUq67Exzau2bKnXfb8hbduWN3thrF724gjLXgBMmzat8cCBAwuOZ8kLcG9h2gM0r3U/BaMzqu1WYJwy5kXaKiLbgQ7ACjfmqhc/b85j6rId3HqmHhruNpk/wzd3Q+rZcNU7cJw/3Nrhftr1Ey8sf4Gc8hyGtBvC/afer89HchN/Xvaixtdff9146tSpx32SsTsL00qgrYikAXuB64AbDtlmF3ABsFREEoD2QJ3L9HqaWav3EB8Zwuj+emi4W+RsgM9vNGZyuPZTY6VZ7YRll2XzwvIXWLR7EW1j2/LKua/4xZpINY7W2biLPy97AfDbb7+F2e12Ofvss497Gia3FSallE1EhgPfYwwXn6yU2iAidzqffxd4DpgqIukYu/5GK6VMnXTRVZn5pXRIjNRDw92haC98eg0EN4Ibv4SwGLMTeS2bw8aMjTN4a+1bKKUY0XMEN3a6kSCLPo7kbv667EWNTz75pPGgQYMKTiS7W89jUkrNB+Yf8ti7tW5nARe7M4M7KKXIzCvjX70amx3F91QcgOnXGDM73PYdROt52E7U+vz1PPvbs2ws2Mg5KefwWJ/HSI44dPyR5i7+uuxFzf3Zs2c3njNnzpYT+Ry97MUJ2FdUwRkv/sRzV3bm32ekmprFp9iq4NPBsOt3o1NqdZ7ZibxSSXUJb/7xJjM3zSQ+LJ5H+jzChS0u9JnBDXrZC9+gl72oZ5l5ZQC0io8wOYkPcTiMgQ47lsLVH+iidAKUUizYuYBxK8aRX5HP9R2u594e9xIRrH9OzaKXvTgxujCdgMw8Y0h+q/iTPi1Aq7HwGVj/JVzwFHT7l9lpvM7e0r2M/X0sS/cupWPjjrxx/ht6fSQPoJe9ODG6MJ2AbXllhAcHkBgVanYU37DiA+Mk2l7/gbMeNDuN15mzbQ7P/vYsFrEw6rRRXN/hegIt+r+25r30T+8J2JZXSqv4Rj6zz95UG+fC/JHQ/jK47GXQf6cuU0rxzrp3eGfdO/RO7M3Ys8aS2CjR7FiexOFwOMRisXjXgXQ/4HA4BDjiYAx9xuIJyMwro1Wc3m9/0navgFn/geSeMPgjsOih966y2q2M+WUM76x7hytbX8m7F76ri9Lh1ufl5UU7vwQ1D+FwOCQvLy8aWH+kbXTHdJwqrXayiir4V3zzY2+sHVn+VphxLUQ1gxs+1zOFH4eiqiIeXPwgK7NXMvyU4QzrNkx373Ww2Wy3Z2dnf5idnd0F/Uu4J3EA62022+1H2kAXpuO0Pb8MpfTAh5NSmgufXg1igRtnQaM4sxN5jT0le7h74d3sKdnDi2e/yOWtLjc7ksfq2bNnLjDQ7Bza8dOF6Tht0yPyTk51Gcz4F5Tlwc1zoXErsxN5jfS8dIb/NBybw8Z7F73HaYmnmR1J09xCt7fHqeYcprQ4XZiOm90GX9wC+9bBNVMgpc6pu7Q6LNy5kNu+v42wwDA+vexTXZQ0n6YL03HKzCslOSaM8GDdbB4XpWDeCNjyAwyYAO37m53IKyil+HjDxzy4+EHaxbZj+mXTSYtOMzuWprmV/nY9TtvyyvRuvBOx5BVYMw3Ofhh63WZ2Gq9gc9gYv2I8MzNmclHLi3jhrBcIDdTnzmm+T3dMx8GYvLWUVno33vH5Yzoseh66Xw/nP252Gq9Qbi3ngUUPMDNjJrd0voVXzn1FFyXNb+iO6TjkllRRVm3Xc+Qdj60LYc59xtx3V7yhT6B1QW55LsMXDiejMIPH+zzOtR2uNTuSpjUoXZiOQ82IvNa6MLlm3zr4v5sgviP86xMIDDY7kcfbXLiZexbeQ1FVEW+e/ybnpJxjdiRNa3C6MB2HbQdnFde78o7pwC6YPgRCY2DoFxAaZXYij7csaxkPLX6I8MBwpvWfRscmHc2OpGmm0MeYjkNmXilhQXry1mMqLzBWoLVVGusqRSWZncjjfbXlK+758R6SIpKYPmC6LkqaX9Md03HIdI7Is1j0cZIjslbCzKFQuB3+/TU01V+wR+NQDt764y0+SP+AM5udySvnvqLXT9L8ni5MxyEzv5RTmseaHcOzff8Y7FpmnECbepbZaTxatb2ax399nG+3f8vgtoMZc/oYgixBZsfSNNPpwuSiSqudPYUVXN0jxewonqtwh3Gu0ml3QJerzU7j0Q5UHuD+RfezJncND5z6ALd1uU1PxKppTrowuWjHfj156zEtnQASAGePMDuJR9tVvIu7F97NvtJ9vHzOy/RP07NgaFptRyxMIlICHHGBLaWUXw2zqpkjTw8VP4LCHbB2hrEKbVQzs9N4rLW5a7nvp/tQKD685EN6NO1hdiRN8zhHLExKqUgAEXkWyAY+AQQYCkQ2SDoPsi1Xzyp+VDXd0lkPmJ3EY32/43seW/oYiY0SmXThJFpGtTQ7kqZ5JFeGi1+ilJqklCpRShUrpd4BBrs7mKfJzC8jKTpUT95al5puqectuls6gukbp/Pwzw/TOa4zn172qS5KmnYUrhQmu4gMFZEAEbGIyFDA7u5gniYzr1R3S0eydIKx6J/ulur08+6fGb9iPOc3P58PLv6A2FA9srNG9c6dVO/caXYMzcO4UphuAP4F5DgvQ5yP+Q1j8tYyfXypLoU7dbd0FNsObGP00tF0bNKRceeMIyQgxOxIHqMiPZ0d19/A3pGjUOqIh7M1P3TM/VJKqR3Ale6P4rnySqooqbLpWcXrcrBbetDsJB6nqKqIe3+6l9CAUCb2m0hYYJjZkTxG6ZIl7Ln/AQIbN6bZuHF6qLz2D8fsmESknYgsFJH1zvvdRMSv1i74e4483TH9Q+FOWDtdd0t1sDqsPPTzQ2SXZfN6v9dJbJRodiSPcWDWV+y+626C01JJnfkZIa30wofaP7myK+8D4FHACqCU+hO4zp2hPE1mvh6RVyfdLR3RKytfYfm+5Tx1xlOc0vQUs+N4BKUU+e+8w74xY2jUpw8tP/6EwPh4s2NpHsiVIWbhSqkVh7TaNjfl8UiZeWWEBlloFq13xRxU0y31uk13S4f4cvOXzNg0g5s73cyVbfx6L/hBymYj+7nnOfD550QNvIJmzz+PBOtlULS6uVKY8kWkNc6TbUXkGmCfW1N5mG15paTFRejJW2ur6ZbOfMDsJB5ldc5qxi4fy5nJZ/JgT91JAjgqKtj70MOU/vQTTe64g/gRD+pjStpRuVKY7gHeBzqIyF5gO8ZJtn4jM6+MrinRZsfwHAd2OY8t3QrRyWan8RhZpVmMWDyClIgUXjrnJQIsAWZHMp2tsJA9d91Nxbp1JDz+OI1v9KuvDu0EuVKYlFLqQhFpBFiUUiUi4jdHK6tsdvYUlnNVD/0FfJA+tnSYcms59/10H1a7lTfOf4OoYL+asatO1Xv2svuOO7Du3UvyxNeJuvhisyNpXsKVwQ+zAJRSZUqpEudjX7ovkmfZub8ch4LWeuCD4cAu+ONTOPVm3S05OZSDx399nC0HtvDyuS+TFu03v7cdUeVff7Hj+uuw7d9PiymTdVHSjsvRJnHtAHQGokWk9hoGUYBLS7iKSH9gIhAAfKiUGlfHNucBrwNBQL5S6lwXszeIg3Pkxemh4oDulurw3rr3WLBzAQ/3epgzk880O47pSn/9lb333oclOpqWU6YQ0qaN2ZE0L3O0XXntgcuBGOCKWo+XAHcc641FJAB4G7gI2AOsFJHZSqm/am0TA0wC+iuldolI0+P9A7hbZr5xDlOa7pj+7pb0saWDFuxcwKR1k7iy9ZXc1Okms+OYrmj2bLIeG0NIq1Y0/+B9ghISzI6keaGjzS7+P+B/InKGUuq3E3jv3sBWpVQmgIjMxJhB4q9a29wAfKWU2uX8zNwT+By32pZXSmJUKBEhevJWlr6qu6VaMgoyGPPLGLrHd+fJM57065FmSikKPvqI3FcmEN6nDylvvUlApN8tQqDVE1eOMd3p7GwAEJFYEZnswuuSgd217u9xPlZbOyBWRBaLyGoRqfNXThEZJiKrRGRVXl6eCx9df7bllekTa6HWsaWbdLcE7K/Yz70/3UtUcBSv93ud4AD/PSdH2e3kjH2B3FcmEHXZZTT/4H1dlLST4kph6qaUOlBzRylVCLiyulldvz4eOlNjINATGABcAjwhIu0Oe5FS7yuleimlesU34JnixuStelZxwNktie6WAKvdyojFIyioLGDi+ROJC4szO5JpHFVV7H1wBIWffkrjW26h2SsvY9EnzmonyZX9UxYRiXUWJESksYuv2wM0r3U/BciqY5t8pVQZUCYiS4DuwGYX3t/t8kurKam06YEPB48t3QzRKWanMZVSirHLx7Imdw0vnfMSnZt0NjuSaexFRey+5x4qVq2m6SOjaXLLLWZH0nyEKwVmArBMRL7E6Hj+BYx14XUrgbbOc572Ysyvd+hyGf8D3hKRQCAY6AO85mJ2t8vMM0bktW7q54VJd0sHfbbpM2ZtmcUdXe/g0rRLzY5jGmtWFruGDcO6cxfJrxq78DStvriy7MXHIrIKOB9j99zVtUfWHeV1NhEZDnyPMVx8slJqg4jc6Xz+XaXURhH5DvgTcGAMKV9/En+eenVwVnF/Xu7iwG7dLTn9vu93Xlr5Ev2a92N4j+FmxzFNZcZmdg8bhqOsjOYffECj0/uYHUnzMa4ONWsMlCmlpohIvIikKaW2H+tFSqn5wPxDHnv3kPsvAy+7GrghZeaVEhJoITnGjydv/eVV49rPu6Xdxbt5aPFDpEWn8eLZL2IRVw7P+p6y35ezZ/hwLOHhtJw+ndD2hx0S1rST5sp6TE8BozGWvgDjRNhP3RnKU2Tml5EW18h/J289sBvWfOIciee/3VJpdSnDfxqOiPDG+W/QKMg/O+ji+fPZfccdBCYmkDrzM12UNLdx5de+QcBAoAxAKZUF+MVY0My8Uv9eTr2mWzp7hLk5TGR32Bm9dDQ7i3fy6rmv0jyy+bFf5IP2T53K3hEPEdq9G6nTpxPUTC91ormPK4WpWiml+HvZC7/4dbHKZmdXQbn/DhXX3RIAb/7xJkv2LOGR3o/QO6m32XEanFKKnPEvkTtuPJEXX0yLjz4iIFrPtK+5lyuF6f9E5D0gRkTuAH7EWNXWp+1yTt7qt4VJd0vMy5zHR+s/Yki7IVzb/lqz45gi/823KJgyhdihQ0l+7VUsISFmR9L8wNEmcQ1RSlUppV4RkYuAYoz5855USi1osIQmqRmR55e78nS3xPr89Ty17Cl6JfTi0d6P+uV0Q4VffEH+pElED76ahMfH+OXfgWaOo43K+w04VUQ+UUr9G/D5YlRbZr5xDlOaPw4V/8V5KpmfjsTLK8/j/p/uJy4sjgnnTSAoIMjsSA2udMkSsp9+hkZnnUXS00/roqQ1qKMVpmARuRnoe8iyFwAopb5yXyzzbcsto2lkCJGhfvalVLQH1nwMp/4bYvzvQH+VvYr7F91PibWETy78hMahjc2O1OAq1m9gzwMPEtK+Hcmvv44E+dn/Ac10RytMd2IsoR7DP5e9AGMghE8Xpsx8P50jb2nNeUv+d2xJKcUzy54hPT+d1897nfaN25sdqcFV79nD7jvvJDAmhubvvktAhB/+H9BMd7RlL34BfhGRVUqpjxowk+mMyVvLuLxbktlRGpafd0vTNkxjTuYc7jnlHi5oeYHZcRqc/cABdt8xDGW10nzaVIKaetzyaJqfcGVKoo9EpC+QWnt7pdTHbsxlqv1l1RRVWGnlbwMf/LhbyijI4LU1r3FRy4v4b7f/mh2nwTmqqth99z1Y9+yhxZTJhLRubXYkzY8dszCJyCdAa2AtYHc+rACfLUyZNXPk+dOuPD/ulpRSjFsxjqjgKJ464ym/O9CvHA6yRo6iYs0akl9/jfBevcyOpPk5V+bK6wV0cp5k6xcOziruT8tdHByJ53/d0vc7v2dVziqeOP0JokP87+TR3PHjKfnhB5qOHk1U//5mx9E0l06wXQ8kujuIJ8nMLyM40EJyrJ9M3lrTLfW40e+6pQpbBRNWTaBD4w4MbjvY7DgNbv/UqRRM+5jYm/5N41tuNjuOpgGudUxxwF8isgKoqnlQKTXQbalMti23lLQmjQjwl8lbf3kNlPLLWR4mr59Mdlk2484eR4AlwOw4Dar4u+/IHf8SkRdfTMLo0X63C1PzXK4UpqfdHcLTZOaX0SHRL+apPaRbamF2mga1p2QPk9Mnc2napfRM6Gl2nAZVvno1WaNGE9ajB81eGo8E+FdR1jybK6Pyfm6IIJ6i2uZgV0E5A7r6yVBxP+6WJqyaQIAlgBE9/evPXpWZye677yEoOZmUt9/CEhpqdiRN+4cjHmMSkV+c1yUiUlzrUiIixQ0XsWHtKijH7lD+MSKvaK+zWxrqd93S7/t+58ddP3JH1ztIbOQ/h1BteXnsvv0OJDCQ5h+8T2BsrNmRNO0wRzvB9izntZ/s0zJsc47I84tzmH55DZQDzn7I7CQNyuqwMn7FeFIiUrip801mx2kw9tIydv/3TmwHDtBy2jSCU/xzgl7N87m6tLrf8JtzmIr2wpppfnls6f8y/o+tB7Yysd9EQgL8YxkHZbWy98EHqczIoPmktwnr2sXsSJp2RK4MF/crmXmlxEeGEOXrk7f6abdUUFnA23+8Td9mfenXvJ/ZcRqEUop9zzxD2dKlJD71JBHnnmt2JE07Kl2YDrEtr5RWvr7UhR93S2/+8SYVtgpGn+Y/w6PzJ02i6MtZxN19F7H/+pfZcTTtmHRhOkRmfpnvH1/y027pr/1/MWvzLK7veD2tYlqZHadBHJj1FflvvkX0VVcRd++9ZsfRNJccszCJyNUiskVEinx9VF5BWTUHyq209uXjS1Wl8Mcn0P16v+qWlFK8uPxFYkNjuav7XWbHaRClS39h31NP0ahvX5KefcZvOkTN+7nSMb0EDFRKRSulopRSkUqpKHcHM8PBOfJ8uWPa+iPYKo3C5EfmbZ/H2ry1PHDqA0QG+/5A08q//mLv/fcT0qYNyW9MRIKDzY6kaS5zpTDlKKU2uj2JB/h7qLgPd0yb5kF4E2hxutlJGky5tZzXVr1G5yadubLNlWbHcTvr3r3s+u9/sURH0/y99wiI8OFftDSf5Mpw8VUi8jnwDf+cK8/nVrDNzCsjOMBCSmy42VHcw1YNm7+HTleAH80L90H6B+RW5PJqv1exiG8fVrUXFbFr2H9RlVW0nDGZoAS92J/mfVwpTFFAOXBxrcd8cmn1bXlltGwS7ruTt+5YClVF0OFys5M0mF3Fu5i2YRoDWw+ke3x3s+O4laOqij33DMe6axfNP/yQkLZtzY6kaSfElbnybm2IIJ4gM7+Udk19+PjDpnkQ1AhanWd2kgbz8sqXCbIE8cCpD5gdxa2Uw0HWI49QvmoVzSa8QqM+vc2OpGknzJVReSki8rWI5IpIjojMEhGfm8vEanewa3+57x5fcjiMwtTmAgjyj3Wmftn7C4v3LObO7ncSHx5vdhy3yn35FUq+/Y6mIx8mesAAs+No2klxZYf7FGA20AxIBuY4H/MpuwrKsTmU757DlLUGSrOh4xVmJ2kQVrsxH17LqJbc2PFGs+O41YFZsyiYMoXYoUNpfNttZsfRtJPmSmGKV0pNUUrZnJepgM/9+lkzR57PnsO0cQ5YAqHtRWYnaRAzNs1gR/EORp02iqAA351eqipzO9nPjyX8jNNJeOxRfa6S5hNcKUz5InKjiAQ4LzcC+90drKFl+vqs4pvmQerZEOb7yxzkV+Tzzrp3OCflHM5JOcfsOG6jqqvJGjkSS3AwzcaN04v9aT7DlcJ0G/AvIBvYB1zjfMynbMsrJS4imOgwH/ztOi8D9m+BDv5x7GHimolU2asYddoos6O4Vd6bb1K5YQOJzz9HUEKC2XE0rd64MipvFzCwAbKYKjOvjFZxvtotzTWu/aAwpeel883Wb7ity220jGppdhy3Kft9Ofs//IiYIUOIusg/ds9q/uOIhUlERimlXhKRNzHOW/oHpdR9bk3WwDLzy7iks4/+1rlxLiT3hKhmZidxK4dy8OKKF4kPi2dYt2Fmx3Eb+4EDZI0eTXBqKgmPPmJ2HE2rd0fblVczDdEqYHUdl2MSkf4ikiEiW0XkiP+DROQ0EbGLyDUu5q5XhWXVFJRV+2bHVLTXGJHnByfVzt42m/T8dB7s+SCNgnxzEItSin1PPoWtoIBmr7yMJdxHZynR/NrRllaf47xZrpT6ovZzIjLkWG8sIgHA28BFwB5gpYjMVkr9Vcd244HvjzN7vcnM9+E58jLmG9c+XphKq0t5ffXrdI/vzoBWvrvLsmjWLEp++IGmDz9EWOfOZsfRNLdwZfDDoy4+dqjewFalVKZSqhqYCdQ1g+a9wCwg14X3dIttB5dT98GOadNciGsH8e3MTuJW7/35HgWVBTza+1GfnQ+vavt2sse+QPjpp+vzlTSfdrRjTJcClwHJIvJGraeiAJsL750M7K51fw/Q55DPSAYGAecDpx0lyzBgGECLFvW/hlBmXhlBAULzWB+bEaGiEHb8An19e4G4zKJMPv3rUwa1HUTnON/sIoyh4aOQ4GCajXsRsfhm8dU0OHrHlIVxfKmSfx5bmg1c4sJ713Wm36GDKF4HRiul7Ed7I6XU+0qpXkqpXvHx9X9u77a8Ulo2aURggI/9Z9/8Azhs0MF3Z3tQSvHSypcIDQzlvh4+NR7nH/LeepvK9etJeu5ZghITzY6jaW51tGNM64B1IvI1UFZTPJzHhEJceO89QPNa91Mwil1tvYCZzrPV44DLRMSmlPrG5T9BPcjMK/XNxQE3zYHIJGjWw+wkbrNkzxJ+3fsro04bRZOwJmbHcYuy5SvY/8EHxAy5hqiLLz72CzTNy7nSIvwA1N7HFQb86MLrVgJtRSRNRIKB6zC6rYOUUmlKqVSlVCrwJXB3Qxclm93BroJy3zu+ZK2ArQuNc5d8dLdPtb2a8SvH0yq6Fdd1uM7sOG5xcGh4ixYkPKKHhmv+wZX1mEKVUqU1d5RSpSJyzDGqSimbiAzHGG0XAExWSm0QkTudz797oqHr0+7CCqx25Xtz5G1bBNZynz6p9uO/PmZ3yW7eu+g9giy+N2OHUop9Tz2NLT+f1M8+w9LIx35GNe0IXClMZSJyqlJqDYCI9AQqXHlzpdR8YP4hj9VZkJRSt7jynvVtW66PzpG3aR6ERBvz4/mg3PJc3v/zfc5vfj59m/U1O45bFH31NSXff0/8QyMI69rF7Dia1mBcKUwPAF+ISM3xoSTgWrclamA15zD5VMdktxnnL7W7BHx0Zu3XVr+G3WHn4dMeNjuKW1Tv3En22LGE9+5NEz00XPMzrsyVt1JEOgDtMUbabVJKWd2erIFk5pXRpFEwMeHBZkepP7t+g4oC6OibJ9X+kfsHczPnMqzbMJpHNj/2C7yMslrZ+/BIJCiIZuP1rOGa/3GlYwKjKHUCQoEeIoJS6mP3xWo4mXllvjfjw6Z5EBgKbS40O0m9szvsvLj8RRLCE/hPl/+YHcct8t56m8r0dJJff52gpCSz42hag3NlafWngDedl37AS/jQbOPb8kp9a448pYzZHlr1g2AfK7jAN1u/YWPBRh7q9RDhQb43T1z5ypXsf/99ogdfTVR/V04X1DTf48o44muAC4BspdStQHdcO4/J4xWVW9lfVu1bHVP2n1C02yd34xVXFzNxzURObXoq/VP7mx2n3tmLitg7ajRBLZqT+NhjZsfRNNO4siuvQinlEBGbiERhzGnXys25GsS2gwMffKhj2jgXxALtfO+Le+r6qRyoOsCjfXxvCXGlFPuefhpbXh6pn83QQ8M1v+ZKYVolIjHABxhTEpUCK9wZqqH8PVTch74ENs2DFn2hUZzZSerVgcoDTN84nUtSL6FD4w5mx6l3Rd/8j5JvvyP+wQcJ69rV7DiaZqqjFiYxfi19USl1AHhXRL4DopRSfzZEOHfLzC8j0CI0b+wjxyoKMiF3A1zyotlJ6t3Hf31Mha2CO7vfaXaUele9cyc5zz1H+Gmn0eR23xzQoWnH46jHmJRSCvim1v0dvlKUwJgjr0WTcIJ8ZfLWTfOMax+b7aGwspDpG6fTP7U/rWNamx2nXimrlb2jRkFgIM1eGq+Hhmsarg1++F1EjrgkhTfLzCvzveNLiV0htqXZSepVTbf03+7/NTtKvcubNInKdX+S9Owzemi4pjm5Upj6YRSnbSLyp4iki4jXd002u4Md+33oHKbSXNi93OeWuCisLGTGxhk+2S2Vr1rF/vfeJ3rQIKL6+95gFU07UUdbKLCFUmoXcGkD5mkwe2omb/WVc5gy5gPK53bjTdswzSePLdmLi9k7ahRBKSkkjBljdhxN8yhHG/zwDXCqUmqniMxSSg1uoEwN4uAceU19pGPaNA9iUyHBd1ZwLawsZMamGfRP60+rGJ84QwEwhoZnP/0MtpxcUmdMJyDCR34GNa2eHG1XXu0TRXznW8EpM68MwDdmfagshszF0OFy8KHze6ZumEqlrdLnuqXi2bMpnj+f+HuHE9a9u9lxNM3jHK0wqSPc9gnb8kqJDQ8itpEPTN669UewVxuFyUcUVBbw2abPuDTtUlpF+87vRdW7d5P97HOE9epJkzvuMDuOpnmko+3K6y4ixRidU5jzNs77SikV5fZ0brQtr8x31mDaNBfC46B5b7OT1JupG6ZSZa/yqZF4ymYj6+GRYLGQ/NJLemi4ph3BEQuTUsqn/9dk5pVxfod4s2OcPFsVbP4BugwCi2/8kxVUFjBz00yf65byJ71Dxbp1JL86gaBmzcyOo2key0fOLD0+RRVW8kurfKNj2r4Uqkt8ajfe1PXObqmb73RL5WvWkP/uu0RfdRVRl11mdhxN82h+WZgy85xz5MX5wGioTXMgOALSzjU7Sb3YX7GfmRkzuSztMtKi08yOUy/spaVkjRxFUHIyCY8/bnYcTfN4ri4U6FMOjsjz9o7J4YBN840FAYNCzU5TL6ZtmOZz3VLO82Ox7ttHy+mf6qHhmuYC/+yY8ksJtAgtm3j55K17VkJZLnT0jdkearqlAWkDSI1ONTtOvSj+7juKvvmGuDvvJLxHD7PjaJpX8MvCtC23jBaNfWDy1k1zwRIEbS8yO0m9qBmJN6zbMLOj1AtrTg77nnqa0G7diLvLt87F0jR38vJv5hOTmV/q/XPk1SyhnnYOhEabneak5VfkM3PTTC5vdblPdEvK4WDfo4+iqqtJfmk8EhRkdiRN8xp+V5jsDsWO/eXef3wpb5Ox/pKPzI03df1Uqh3VPtMtFX7yCWXLfiPh0UcITk01O46meRW/K0x7Cyuotjlo7e0d08a5gPhEYcqvyOfzjM+5vNXltIzy/iU7KjM2kzvhVSLOP5+YIUPMjqNpXsfvCtO2mqHi3t4xbZoLKadBZKLZSU7alPVTsDqsPjESz1FVRdbIkViiokh6/jnEh+Yu1LSG4r+FyZvPYTqwG/at9Zlu6f8y/o8BrQbQIqqF2XFOWt7rE6navJlmY58nsHFjs+Nomlfyu8KUmV9GTHgQjb158taM+ca1DwwTn7x+ss90S2W//UbBlCnE3nA9Eef6xgnPmmYG/ytMeaW0imvk3btYNs6B+A7QxLtXdK3pli5vdbnXd0v2oiKyHnmU4LQ0mo4caXYcTfNqfleYvH5W8fIC2LnMJ+bG+yj9I2wOm9d3S0opsp95Btv+/TR7+WUsYWFmR9I0r+ZXhamk0kpeSZV3n8O0+TtQdq8/vpRXnscXm7/gitZX0DyqudlxTkrx3LkUz/+W+OHDCeviOysIa5pZ/Kow1cyR19qbO6ZN8yAqGZp59/Q2k9dPxuawMayrd5+3ZN27l+xnniXs1FNpcsftZsfRNJ/gV4WpZkSe157DVF0OWxca3ZIXHyOr6ZYGth7o1d2SstvJGv0IKEWzl8brhf80rZ741ezimXllBFiEFo29tDBt+wlsFV5/fOmj9R9hd9i5o5t3Ly2+f/JkyletImnciwSnpJgdR9N8hls7JhHpLyIZIrJVRB6p4/mhIvKn87JMRLq7M09mfinNY8MIDvTSRnHTXAiNgZZ9zU5ywnLLc/ki4wsGthlI80jv7ZYqNmwg7403iezfn+grrzQ7jqb5FLd9Q4tIAPA2cCnQCbheRDodstl24FylVDfgOeB9d+UBo2Py2uNLdhtkfAvtL4UA750QdPL6yTiUgzu6em+35KioIGvkKAJjY0l6+invPvVA0zyQO1uH3sBWpVSmUqoamAn841dLpdQypVSh8+7vgNv2h9gdisz8Mu8dkbfzV6g84NWj8XLKcvgi4wuubHMlKZHeu+sr95UJVGdm0mzciwTExJgdR9N8jjsLUzKwu9b9Pc7HjuQ/wLd1PSEiw0RklYisysvLO6EwWQeMyVu99hymTXMhMAxaX2B2khNW0y3d3tV7R6+VLllC4fTpNL75Zhr19d5dqprmydxZmOrav6Hq3FCkH0ZhGl3X80qp95VSvZRSveLj408ozN8j8rywMCllDBNvfT4Ee+equzllOXy5+Uuv7pZsBQVkjRlDSNu2xI940Ow4muaz3FmY9gC1j26nAFmHbiQi3YAPgSuVUvvdFabmHCav3JWX9QcU74WO3jsa76P1HxnHlrx0JJ5Sin1PPonjQBHNXnkZS0iI2ZE0zWe5szCtBNqKSJqIBAPXAbNrbyAiLYCvgH8rpTa7MQvb8kqJCg2kiTdO3rppHkgAtOtvdpITkl2WfbBbSo442t5cz1X01VeU/riQ+AcfJLR9e7PjaJpPc9t5TEopm4gMB74HAoDJSqkNInKn8/l3gSeBJsAk58gmm1KqlzvyZDrnyPPKEVSb5hpDxMO9cxmFj9I/QinltavTVu/aRfbYFwjv04fGt9xsdhxN83luPcFWKTUfmH/IY+/Wun070CBHwjPzSzmrzYkdnzJV/lZjGfWet5qd5IRkl2Uza8ssrmp7Fc0impkd57gpm42skaOQwECajXsRsXjpOXCa5kX8YuaH0iobOcVeOnnrprnGtZcOE/8w/UMUymvPW8p/7z0q1q0j+dUJBCUlmR1H0/yCX/z6l+nNc+RtmgdJ3SHG+2ZJyC7L5qstXzGozSCv7JYq1q0jf9I7RA28gqjLLjM7jqb5DT8pTDUj8rxsqHhJNuxZAR28c6Vab+6WHGVl7B01isCEpiQ+8YTZcTTNr/jFrrzMvFIsAi2beNk5QDVLqHvhbryaY0tXt7mapAjv2wWWM2481l27afnxNAIiI82Oo2l+xS86pm15ZTRvHE5IoJctS7BxLjRuBU07mp3kuH3w5wcAXjnLQ8nChRz44gua3H474aedZnYcTfM7flKYSmkV52XHlyqLYPsSY4kLLxvivq90H19t/YrBbQd7Xbdky8tj3+NPENKpI/H3Djc7jqb5JZ8vTA6HYsf+Mu87vrRlATisXrn20ofpHyKI13VLSimyxozBUV5O8ssvI8FeeDK2pvkAny9MWUUVVFod3jdH3qa50KgppHjXrqSs0iy+2voVV7e9msRGiWbHOS6Fn31G2ZKlNB01kpDWrc2Oo2l+y+cL0zZvnCPPWml0TB0uAy86obPCVsFzvz/ndd2SUoqCGTPIfXEcjc4+m9gbbjA7kqb5NZ8flVdzDpPXFCalYPELUF0KHb1nmHhBZQH3LryX9Px0xvQZ4zXdkr2khH2PP0HJ99/T6NxzSB4/3junrdI0H+IHhamMyNBA4iO8YDZopWDhs/DrRGMKIi9Ze2lH0Q7uXng3ueW5vHbea1zQ0jtyV6zfwN4HH8SalUXTkQ/T+NZb9ZRDmuYBfL8w5Zd6x+StSsFPz8Evr0LPW2DAq14xGu+P3D+496d7CZAAJl8ymW7x3cyOdExKKQqnzyB3/HgC4uJo+cknhJ/aw+xYmqY5+Xxh2pZbRt/WTcyOcXRKwaKxsHQCnHozDHjNK44tfb/jex5b+hhJEUm8c8E7NI/y/GmT7MXF7BvzOCULFhBx3nkkvfgCgbGxZsfSNK0Wny5MZVU2sosrPfv4klKw6AVY8jKcehNc/rrHFyWlFNM2TGPC6gn0aNqDN/q9QUxojNmxjqkiPZ29D47Amp1N01GjaHzrLZ7fSWuaH/LpwrQ93xiR59FDxRe/CEtechaliR5flOwOO+NWjGNmxkwubnkxL5z9AiEBnn38TilF4SefkPPyKwTGx5H66SeEnXKK2bE0TTsCny5M2w6OyPPQwrToRfh5PPT4t1cUpXJrOaOXjGbxnsXc2vlWHuj5ABbx7Mz2oiKyxoyh9MeFRJx/Ps1eGEtATIzZsTRNOwofL0xliKdO3rp4HPw8DnrcCFe84fFFKb8in+ELh7OxYCNj+ozhug7XmR3pmCr+/JO9DzyINS+Ppo+MpvHNN+tdd5rmBXy6MGXmlZISG0ZokIdN3rp4vLEL75ShcMWbHl+UMg9kcvfCuymoLGBiv4mc1/w8syMdlVKKgqnTyJ0wgaCEBFKnf0pYN88fLahpmsHHC1OZ5x1f+vkl4wTa7jfAQM8vSiuzV3L/ovsJtgQz5ZIpdI7rbHako7IfOEDWY2Mo/eknIi68gGZjxxIQHW12LE3TjoNPF6YAi9A+0YPW0vn5ZWNYePfr4cq3wOJhndwh5mXO44lfnyAlMoV3LnyH5IhksyMdVcXatewZMQJbXj4Jjz1G7L9v1LvuNM0L+XRhmnPvWWZH+NuSl2HR89DtOrjybY8uSkopPlr/ERPXTKRXQi9e7/c60SGe23Uoh4OCKVPJfe01ghITSZ0xnbCuXc2OpWnaCfLpwuQxlrwCPzmL0lWTPLoo2Rw2nv/9eWZtmcVlaZfx3JnPERzgucs/2AoL2ffoY5QuXkzkxReT9PxzBERFmR1L07SToAuTuy2dYEw11O1ajy9KZdYyHv75YX7Z+wt3dL2D4T2Ge/Rw8PI1f7D3oYew5+eT8MTjxN5wg951p2k+QBcmd1r6qjEpa9chcNU7Hl2UcstzGb5wOJsLN/PUGU9xTbtrzI50RMrhoGDyZHJfe52g5GRafvYZYV08e1CGpmmu04XJXX55HRY+A12ugave9eiitKVwC3cvvJviqmLePP9Nzk452+xIR2QrLCRr9GjKliwlsn9/kp57loBIDxrgomnaSdOFyR1+nQg/PgVdBsOg9yDAc/+af9/3Ow8uepCwwDCm9p9KxyYdzY50GEdlJRVr11G+YjkHZn2FvaCAxKeeJOa66/SuO03zQZ77jemtfn0DFjzpLErve3RRmr1tNk/9+hSp0alMumASSRFJZkcCwFFdTcXatZQvX0H5ihVUrFuHqq4Gi4Ww7t1p/s4kQjt1Mjumpmlu4rnfmt5o2Zuw4AnofLVHFyWlFO/++S6T1k6iT2IfXu33KlHB5o1kU9XVVKSnU7Z8OeXLV1Cxdi2qqgpECO3UidihQwnv05vwnj31bjtN8wOe+c3pjZa9BT88Dp0HwdUfeFxRcigHO4p2kJ6fzqLdi1i4ayEDWw/k6TOeJiggqEGzKKuVivT1lK9YTvmKFZSv+QNVWQkihHToQOx11xmFqFcvPfRb0/yQZ317eqvf3oYfxkCnq+DqDz2iKOWU5bA+fz3p+emsz1/Phv0bKLUas603CmrEPafcw3+7/bdBjtEom43KDRsoW76C8uXLKf/jD1R5OQAh7dsTM2QIjWoKkZ75W9P8nvnfoN7ut7fh+8eg05Uw2JyiVFJdwob9G4xClGcUotyKXAACLYG0j23PgFYD6BrXla5xXUmNTnXr+UnKZqNy40bKV6ygbPlyKlatxlFTiNq2IWbQIMJ79ya892l69VhN0w6jC9PJ+G2SUZQ6DoTBH0ED7BKrtlezuXDzwU4oPT+d7UXbDz7fMqolvZN60yWuC13jutK+cft6X8jPUV2NLTcXW3Y21pwcbDm52HJynLdzqNqyBUep0Z0Ft25N1JUDadSnD+GnnUZgEw9f5l7TNNPpwuQKhx2K98L+bVCwDfZnQv5m2LoAOl4B10x2S1FyKAc7inewIX/DwUK0qWATVocVgCahTega35UBaUY31Dmu80nNaaeUwlFc/Hexyc3Bmp19WOGxFxYe9loJDyeoaVMCExKIGjCA8N6n0ah3bwLj4084j6Zp/kkXphoOh1F8CrY5C1Cmcdm/DQp3gL3q720Dw6BxK+hzF1z83HEXJYdyUFJdQmFlIYVVhRRUFlBYWciBqgMHb+eW57Jx/0ZKrCUAhAWG0blJZ27seCNd4rrQLb4bCeEJRz1GpJRClZdjLy3DUVqCo7QUe0kpjtISbHn5RuHJycGWbRQca24uqqLisPcJaNyYwMQEghITCevencCEpgQlJBKYkEBQQlMCExOxREToc4o0TasXbi1MItIfmAgEAB8qpcYd8rw4n78MKAduUUqtcVsghwNKsmoVHmf3U7ANCrYfUnxCITYN4tpCu0ugSWto3NooSJFJ/1hHyeqwcqDSKCoHqg5QWOksNlWFRvFxFqCa2weqDmBX9jojhgWG0TgklrigWAbG9aNLaBrtglNIIhrKK3DsLcGekYej5H/klZZgLy3FUVJqFJ1S49pRUnLwNg7Hkf8+goIIio8nMDGRkE4diTjvPAITE41ik5BAYEIigU3jsQR77iSumqb5HrcVJhEJAN4GLgL2ACtFZLZS6q9am10KtHVe+gDvOK/rx5pPjF1uzu7HUZBJpb2KchEqLUJFYAgV0clUxjSjonlXKhrFUREWQ0VYFBWWIKqsFVRVlFFVVY41O53qncuprirHWlmBvbqS6soyKsuLsVWUE2yDYCvGdc3FCpEqmAQVQoQjmHBHIGH2IELtCYTYhGCbItCqCKi2Y6m2IdXVqKpqVOUecOwC1gFgd/4FHvZ3HBSEJTISS0QEARERWCIiCEpJOXjbElnzuHObyAhj+0YRBMY1ISA2FvHwhQo1TfM/7uyYegNblVKZACIyE7gSqF2YrgQ+Vkop4HcRiRGRJKXUvvoIMH38i0iFQhyCOMBib0KgXRFoh0AHxrW9jED7FoLsWwir9XhQ3Q3N8QuyYwmxI6EKS4gFCQ3BEhKChIZiiQpBQkKdj9W+DsUSGvLPguIsMAERjYziEhmpOxlN03ySOwtTMrC71v09HN4N1bVNMvCPwiQiw4BhAC1atHA5QIuyxgSWVUNQoHEJDUSCgpDgICQomICgYCzBIQQ4L4HBoQSGGJeg4DCCQsKwBAcbrznkQlCQ8VyIUUQkNBQJCcES6iwsISFISAgS4LmTt2qapnkidxamuo6EqxPYBqXU+8D7AL169Trs+SM5e+4vrm6qaZqmeQh3HmDYAzSvdT8FyDqBbTRN0zQ/4s7CtBJoKyJpIhIMXAfMPmSb2cBNYjgdKKqv40uapmmad3LbrjyllE1EhgPfYwwXn6yU2iAidzqffxeYjzFUfCvGcPFb3ZVH0zRN8w5uPY9JKTUfo/jUfuzdWrcVcI87M2iapmneRZ/EommapnkUXZg0TdM0j6ILk6ZpmuZRdGHSNE3TPIouTJqmaZpHEWNgnPcQkTxgp9k5nOKAfLNDnCBvze6tuUFnry8tlVJ6oS8f5nWFyZOIyCqlVC+zc5wIb83urblBZ9c0V+ldeZqmaZpH0YVJ0zRN8yi6MJ2c980OcBK8Nbu35gadXdNcoo8xaZqmaR5Fd0yapmmaR9GFSdM0TfMoujC5QET6i0iGiGwVkUfqeH6oiPzpvCwTke5m5DzUsXLX2u40EbGLyDUNme9oXMkuIueJyFoR2SAiPzd0xiNx4eclWkTmiMg6Z3aPWO5FRCaLSK6IrD/C8yIibzj/XH+KyKkNnVHzE0opfTnKBWMtqW1AKyAYWAd0OmSbvkCs8/alwHJvyF1ru58wlie5xuzcx/F3HgP8BbRw3m9qdu7jyP4YMN55Ox4oAII9IPs5wKnA+iM8fxnwLSDA6Z7wc64vvnnRHdOx9Qa2KqUylVLVwEzgytobKKWWKaUKnXd/x1gi3mzHzO10LzALyG3IcMfgSvYbgK+UUrsAlFKekt+V7AqIFBEBIjAKk61hYx5OKbXEmeVIrgQ+VobfgRgRSWqYdJo/0YXp2JKB3bXu73E+diT/wfit0mzHzC0iycAg4F08iyt/5+2AWBFZLCKrReSmBkt3dK5kfwvoCGQB6cD9SilHw8Q7Kcf7f0HTTohbV7D1EVLHY3WOsReRfhiF6Sy3JnKNK7lfB0YrpezGL+8ew5XsgUBP4AIgDPhNRH5XSm12d7hjcCX7JcBa4HygNbBARJYqpYrdnO1kufx/QdNOhi5Mx7YHaF7rfgrGb7r/ICLdgA+BS5VS+xso29G4krsXMNNZlOKAy0TEppT6pkESHpkr2fcA+UqpMqBMRJYA3QGzC5Mr2W8FximlFLBVRLYDHYAVDRPxhLn0f0HTTpbelXdsK4G2IpImIsHAdcDs2huISAvgK+DfHvAbe41j5lZKpSmlUpVSqcCXwN0eUJTAhezA/4CzRSRQRMKBPsDGBs5ZF1ey78Lo9BCRBKA9kNmgKU/MbOAm5+i804EipdQ+s0Npvkd3TMeglLKJyHDge4wRV5OVUhtE5E7n8+8CTwJNgEnO7sOmTJ6J2cXcHsmV7EqpjSLyHfAn4AA+VErVOcy5Ibn49/4cMFVE0jF2j41WSpm+pISIfAacB8SJyB7gKSAIDuaejzEybytQjtH5aVq901MSaZqmaR5F78rTNE3TPIouTJqmaZpH0YVJ0zRN8yi6MGmapmkeRRcmTdM0zaPowuSnRCRRRGaKyDYR+UtE5otIuxN8rwec5xKdyGtPEZHLTuS1td4jRkTuPoHXiYj8JCJRIpJ6pFm1j/L6W0SkmQvbvSIi5x9vPk3zV7ow+SHn5KFfA4uVUq2VUp0wZrxOOMG3fAA4ocIEnIJxbsxRicjRzrmLAY67MDk/d91JTAV0C3DMwgS8CRxx2RFN0/5JFyb/1A+w1j7JVim1Vim11NlFvCwi60UkXUSuhYNrHy0WkS9FZJOITHduex/Gl/MiEVnk3PYdEVnlXGvomZrPEGPdp2XOdYhWiEg08CxwrRjrKl1bO6SzI/lCROYAP4hIhIgsFJE1zmw1s3aPA1o73+Nl52tHishK57pBz1C3oRgzSNQIFJFpztd8WdMFikhPEfnZOVns9yKSJMbaVb2A6c7PDRORJ52fuV5E3nf+AoBSaifQREQST+QfS9P8jtnrbuhLw1+A+4DXjvDcYGABxqwFCRjT5yRhzAhQhDE/mgX4DTjL+ZodQFyt92jsvA4AFgPdMNYmygROcz4XhTHzyC3AW0fIcgvG/Gw17xcIRDlvx2HMQCBAKrXWEAIuBt53PmcB5gLn1PH+O4FI5+1UjAlJz3Tenww8jDHzwTIg3vn4tRizOeD8s/U69M/tvP0JcEWt+x8Ag83+t9cXffGGi56SSDvUWcBnSik7kCPGyrCnAcXACqXUHgARWYvxZf5LHe/xLxEZhlFIkoBOGF/6+5RSKwGUc/eZHHtW8wVKqZo1ggR4QUTOwZiGKJm6dz9e7Lz84bwfAbQFlhyyXWOlVEmt+7uVUr86b3+KUcC/A7pgzAAORrE90vxw/URkFMZuzcbABmCO87lcXNvtp2l+Txcm/7QBONIy6kerFFW1btup4+dHRNIwOo3TlFKFIjIVCHW+74nMf1VW6/ZQjBVfeyqlrCKyw/neh8UAXlRKvXeM97aJiEX9vRbSofmU8702KKXOONobiUgoMAmjg9otIk8fki0UqDhGHk3T0MeY/NVPQIiI3FHzgPP4z7kYXcW1IhIgIvEYy20fazmGEiDSeTsKo5gUiTFz9qXOxzcBzUTkNOfnRToHNNR+7bFEA7nOotQPaFnH54MxgeptIhLh/KxkEWlax/tlYCyBXqOFiNQUoOsxusEMIL7mcREJEpHOdXxuTRHKd37uoYW/HWD6JLOa5g10YfJDSimFsXLtRc7h4huApzHW1vkaY8budRgFbJRSKvsYb/k+8K2ILFJKrcPYhbYB4zjNr87PrMY4PvOmiKzDOI4VCiwCOtU1+KEO04FeIrIKo3va5Hzv/cCvzkEHLyulfgBmYCwemI6xpEddxW8exrGzGhuBm0XkT4xdce84c18DjHfmXgv0dW4/FXjXuVuzCuM4UjrwDcbyF4BRzIA2wKpj/Pk0TUPPLq75MRFJAj5WSl3k5s8ZBJyqlHrCnZ+jab5Cd0ya31LGIncfiEiUmz8qEJjg5s/QNJ+hOyZN0zTNo+iOSdM0TfMoujBpmqZpHkUXJk3TNM2j6MKkaZqmeRRdmDRN0zSP8v8wiPYUXQUQlAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for gamma in gamma_array:\n",
+    "    label = f'gamma = {gamma}'\n",
+    "    plot(frame[gamma], label=label)\n",
+    "\n",
+    "decorate(xlabel='Contact rate (beta)',\n",
+    "         ylabel='Fraction infected')\n",
+    "\n",
+    "plt.legend(bbox_to_anchor=(1.02, 1.02))\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "familiar-appliance",
+   "metadata": {},
+   "source": [
+    "Alternatively, we can plot each row like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "instructional-navigator",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for beta in beta_array:\n",
+    "    label = f'beta = {beta}'\n",
+    "    plot(frame.loc[beta], label=label)\n",
+    "    \n",
+    "decorate(xlabel='Recovery rate (gamma)',\n",
+    "         ylabel='Fraction infected')\n",
+    "\n",
+    "plt.legend(bbox_to_anchor=(1.02, 1.02))\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "improving-montreal",
+   "metadata": {},
+   "source": [
+    "This example demonstrates one use of a `SweepFrame`: we can run the analysis once, save the results, and then generate different visualizations.\n",
+    "\n",
+    "Another way to visualize the results of a two-dimensional sweep is a\n",
+    "**contour plot**, which shows the parameters on the axes and contour\n",
+    "lines, that is, lines of constant value. In this example, the value is\n",
+    "the fraction of students infected.\n",
+    "\n",
+    "The ModSim library provides `contour`, which takes a `SweepFrame` as a\n",
+    "parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "filled-therapist",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import contour\n",
+    "\n",
+    "contour(frame)\n",
+    "\n",
+    "decorate(xlabel='Recovery rate (gamma)',\n",
+    "         ylabel='Contact rate (beta)',\n",
+    "         title='Fraction infected, contour plot')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "polished-favor",
+   "metadata": {},
+   "source": [
+    "Infection rates are lowest in the lower right, where the contact rate is and the recovery rate is high. They increase as we move to the upper left, where the contact rate is high and the recovery rate is low.\n",
+    "\n",
+    "This figure suggests that there might be a relationship between `beta`\n",
+    "and `gamma` that determines the outcome of the model. In fact, there is.\n",
+    "In the next chapter we'll explore it by running simulations, then derive it by analysis.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "level-barrel",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "addressed-moscow",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bored-bennett",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Suppose the infectious period for the Freshman Plague is known to be 2 days on average, and suppose during one particularly bad year, 40% of the class is infected at some point.  Estimate the time between contacts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "raised-notification",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# Sweep beta with fixed gamma\n",
+    "gamma = 1/2\n",
+    "infected_sweep = sweep_beta(beta_array, gamma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "infectious-winner",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# Interpolating by eye, we can see that the infection rate passes through 0.4\n",
+    "# when beta is between 0.6 and 0.7\n",
+    "# We can use the `crossings` function to interpolate more precisely\n",
+    "# (although we don't know about it yet :)\n",
+    "beta_estimate = crossings(infected_sweep, 0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "automatic-consultancy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# Time between contacts is 1/beta\n",
+    "time_between_contacts = 1/beta_estimate"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From b27e64183e31945571bbb3f757a11d0d2d63b18f Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sat, 30 Jan 2021 18:57:16 -0500
Subject: [PATCH 022/144] Revising chapters

---
 jupyter/chap11.ipynb |  128 ++--
 jupyter/chap12.ipynb |  133 ++---
 jupyter/chap13.ipynb |   93 +--
 jupyter/chap14.ipynb |  921 +++++++++++++++++++++++++++++
 jupyter/chap15.ipynb |  570 ++++++++++++++++++
 jupyter/chap16.ipynb | 1339 ++++++++++++++++++++++++++++++++++++++++++
 6 files changed, 3010 insertions(+), 174 deletions(-)
 create mode 100644 jupyter/chap14.ipynb
 create mode 100644 jupyter/chap15.ipynb
 create mode 100644 jupyter/chap16.ipynb

diff --git a/jupyter/chap11.ipynb b/jupyter/chap11.ipynb
index 8f018cc3..040c405c 100644
--- a/jupyter/chap11.ipynb
+++ b/jupyter/chap11.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "steady-bridge",
+   "id": "pressing-munich",
    "metadata": {},
    "source": [
     "# Chapter 11"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "endless-enclosure",
+   "id": "detected-creation",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "drawn-population",
+   "id": "breathing-commonwealth",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -43,14 +43,14 @@
     "    !pip install pint\n",
     "    \n",
     "try:\n",
-    "    from modsim import *\n",
+    "    import modsim\n",
     "except ImportError:\n",
     "    !pip install modsimpy"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "chicken-tamil",
+   "id": "persistent-carbon",
    "metadata": {},
    "source": [
     "In this chapter, we develop a model of an epidemic as it spreads in a\n",
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "developed-asset",
+   "id": "working-patent",
    "metadata": {},
    "source": [
     "## The Freshman Plague\n",
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "respected-cleveland",
+   "id": "distant-expense",
    "metadata": {},
    "source": [
     "## The SIR model\n",
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "arabic-flight",
+   "id": "reasonable-kitchen",
    "metadata": {},
    "source": [
     "Let's think about how the number of people in each category changes over time. Suppose we know that people with the disease are infectious for a period of 4 days, on average. If 100 people are infectious at a\n",
@@ -124,7 +124,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "emotional-ranch",
+   "id": "important-yugoslavia",
    "metadata": {},
    "source": [
     "Now let's think about the number of new infections. Suppose we know that each susceptible person comes into contact with 1 person every 3 days, on average, in a way that would cause them to become infected if the other person is infected. We'll denote this contact rate with the Greek letter beta, $\\beta$.\n",
@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "greek-reference",
+   "id": "virgin-cambodia",
    "metadata": {},
    "source": [
     "In summary:\n",
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "afraid-consultation",
+   "id": "fourth-celtic",
    "metadata": {},
    "source": [
     "## The SIR equations\n",
@@ -163,11 +163,10 @@
     "If we treat time as a continuous quantity, we can write differential\n",
     "equations that describe the rates of change for $s$, $i$, and $r$ (where $r$ is the fraction of the population that has recovered):\n",
     "\n",
-    "$$\\frac{ds}{dt} = -\\beta s i$$\n",
-    "\n",
-    "$$\\frac{di}{dt} = \\beta s i - \\gamma i$$\n",
-    "\n",
-    "$$\\frac{dr}{dt} = \\gamma i$$ \n",
+    "$$\\begin{aligned}\n",
+    "\\frac{ds}{dt} &= -\\beta s i \\\\\n",
+    "\\frac{di}{dt} &= \\beta s i - \\gamma i\\\\\n",
+    "\\frac{dr}{dt} &= \\gamma i\\end{aligned}$$ \n",
     "\n",
     "To avoid cluttering the equations, I leave it implied that $s$ is a function of time, $s(t)$, and likewise for $i$ and $r$.\n",
     "\n",
@@ -192,7 +191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "respected-bangkok",
+   "id": "martial-details",
    "metadata": {},
    "source": [
     "## Implementation\n",
@@ -214,7 +213,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "british-operations",
+   "id": "criminal-change",
    "metadata": {},
    "outputs": [
     {
@@ -238,7 +237,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "laden-strain",
+   "id": "chronic-jonathan",
    "metadata": {},
    "source": [
     "And then convert the numbers to fractions by dividing by the total:"
@@ -247,7 +246,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "complete-disposition",
+   "id": "pediatric-ratio",
    "metadata": {},
    "outputs": [
     {
@@ -273,7 +272,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "curious-commerce",
+   "id": "ordinary-scottish",
    "metadata": {},
    "source": [
     "For now, let's assume we know the time between contacts and time between\n",
@@ -283,7 +282,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "palestinian-gender",
+   "id": "little-stylus",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -293,7 +292,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "innocent-bacon",
+   "id": "covered-avenue",
    "metadata": {},
    "source": [
     "We can use them to compute the parameters of the model:"
@@ -302,7 +301,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "double-nickname",
+   "id": "veterinary-aerospace",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,7 +311,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "human-halifax",
+   "id": "lightweight-delta",
    "metadata": {},
    "source": [
     "Now we need a `System` object to store the parameters and initial\n",
@@ -322,10 +321,11 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "missing-animal",
+   "id": "nasty-sherman",
    "metadata": {},
    "outputs": [],
    "source": [
+    "#export\n",
     "def make_system(beta, gamma):\n",
     "    init = State(S=89, I=1, R=0)\n",
     "    init /= sum(init)\n",
@@ -339,7 +339,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "impressive-moderator",
+   "id": "victorian-blogger",
    "metadata": {},
    "source": [
     "The default value for `t_end` is 14 weeks, about the length of a\n",
@@ -351,7 +351,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "artistic-bunch",
+   "id": "supported-shadow",
    "metadata": {
     "scrolled": true
    },
@@ -381,7 +381,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "latter-respondent",
+   "id": "alone-desire",
    "metadata": {},
    "source": [
     "## The update function\n",
@@ -395,10 +395,11 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "forbidden-shelter",
+   "id": "wound-wayne",
    "metadata": {},
    "outputs": [],
    "source": [
+    "#export\n",
     "def update_func(state, t, system):\n",
     "    s, i, r = state\n",
     "\n",
@@ -414,7 +415,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "subsequent-duplicate",
+   "id": "aboriginal-malpractice",
    "metadata": {},
    "source": [
     "The first line uses a feature we have not seen before, **multiple\n",
@@ -434,7 +435,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "complimentary-force",
+   "id": "assigned-feelings",
    "metadata": {},
    "outputs": [
     {
@@ -458,7 +459,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "affected-presentation",
+   "id": "standard-search",
    "metadata": {},
    "source": [
     "You might notice that this version of `update_func` does not use one of its parameters, `t`. I include it anyway because update functions\n",
@@ -467,7 +468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "certain-clear",
+   "id": "informational-cisco",
    "metadata": {},
    "source": [
     "## Running the simulation\n",
@@ -478,10 +479,11 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "emotional-thomas",
+   "id": "occasional-pottery",
    "metadata": {},
    "outputs": [],
    "source": [
+    "#export\n",
     "from numpy import arange\n",
     "\n",
     "def run_simulation(system, update_func):\n",
@@ -495,7 +497,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "controversial-magic",
+   "id": "fifteen-metallic",
    "metadata": {},
    "source": [
     "The parameters of `run_simulation` are the `System` object and the\n",
@@ -508,7 +510,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "theoretical-ideal",
+   "id": "differential-difference",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -518,7 +520,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "working-austin",
+   "id": "incredible-marsh",
    "metadata": {},
    "source": [
     "The result is the final state of the system:"
@@ -527,7 +529,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "russian-professor",
+   "id": "weekly-acrobat",
    "metadata": {},
    "outputs": [
     {
@@ -550,7 +552,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "informed-drunk",
+   "id": "behind-removal",
    "metadata": {},
    "source": [
     "This result indicates that after 14 weeks (98 days), about 52% of the\n",
@@ -560,7 +562,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "funded-civilian",
+   "id": "serial-genius",
    "metadata": {},
    "source": [
     "## Collecting the results\n",
@@ -574,7 +576,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "directed-entertainment",
+   "id": "advanced-recommendation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -598,7 +600,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "underlying-crime",
+   "id": "behind-breathing",
    "metadata": {},
    "source": [
     "First, we create `TimeSeries` objects to store the results. Notice that\n",
@@ -621,7 +623,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "hired-miller",
+   "id": "subtle-zambia",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -631,7 +633,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "normal-sodium",
+   "id": "smoking-toddler",
    "metadata": {},
    "source": [
     "We'll use the following function to plot the results:"
@@ -640,10 +642,11 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "aboriginal-myrtle",
+   "id": "welcome-tractor",
    "metadata": {},
    "outputs": [],
    "source": [
+    "#export\n",
     "from modsim import decorate\n",
     "\n",
     "def plot_results(S, I, R):\n",
@@ -656,7 +659,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "waiting-blues",
+   "id": "important-command",
    "metadata": {},
    "source": [
     "And run it like this:"
@@ -665,7 +668,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "timely-dream",
+   "id": "agricultural-joining",
    "metadata": {},
    "outputs": [
     {
@@ -687,7 +690,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "moral-barcelona",
+   "id": "civic-pharmacy",
    "metadata": {},
    "source": [
     "Notice that it takes about three weeks (21 days) for the outbreak to get going, and about six weeks (42 days) before it peaks. The fraction of the population that's infected is never very high, but it adds up. In total, almost half the population gets sick."
@@ -695,7 +698,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "academic-mileage",
+   "id": "unexpected-empire",
    "metadata": {},
    "source": [
     "## Now with a TimeFrame\n",
@@ -712,10 +715,11 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "assured-phenomenon",
+   "id": "native-central",
    "metadata": {},
    "outputs": [],
    "source": [
+    "#export\n",
     "from modsim import TimeFrame\n",
     "\n",
     "def run_simulation(system, update_func):\n",
@@ -730,7 +734,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "inner-brave",
+   "id": "beginning-secret",
    "metadata": {},
    "source": [
     "The first line creates an empty `TimeFrame` with one column for each\n",
@@ -764,7 +768,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "equal-thinking",
+   "id": "quick-fifty",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -773,7 +777,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "framed-dairy",
+   "id": "global-complement",
    "metadata": {},
    "source": [
     "And plot the results like this:"
@@ -782,7 +786,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "neutral-baker",
+   "id": "listed-enough",
    "metadata": {},
    "outputs": [
     {
@@ -804,7 +808,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "warming-beauty",
+   "id": "welsh-distinction",
    "metadata": {},
    "source": [
     "As with a `DataFrame`, we can use the dot operator to select columns\n",
@@ -813,7 +817,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "indie-ancient",
+   "id": "neutral-replacement",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -822,7 +826,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "removed-stevens",
+   "id": "induced-tunnel",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -830,7 +834,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "listed-istanbul",
+   "id": "floral-remove",
    "metadata": {},
    "source": [
     "**Exercise**  Suppose the time between contacts is 4 days and the recovery time is 5 days.  After 14 weeks, how many students, total, have been infected?\n",
@@ -841,7 +845,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "strange-soviet",
+   "id": "recovered-freeze",
    "metadata": {},
    "outputs": [
     {
@@ -876,7 +880,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "growing-clear",
+   "id": "interpreted-colony",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -884,7 +888,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "polar-edgar",
+   "id": "given-canberra",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap12.ipynb b/jupyter/chap12.ipynb
index 74d236fb..c515d65c 100644
--- a/jupyter/chap12.ipynb
+++ b/jupyter/chap12.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "noble-particle",
+   "id": "combined-semiconductor",
    "metadata": {},
    "source": [
     "# Chapter 12"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "conscious-partner",
+   "id": "excellent-orchestra",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "governing-nerve",
+   "id": "absolute-density",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -43,14 +43,14 @@
     "    !pip install pint\n",
     "    \n",
     "try:\n",
-    "    from modsim import *\n",
+    "    import modsim\n",
     "except ImportError:\n",
     "    !pip install modsimpy"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dominican-johns",
+   "id": "athletic-diameter",
    "metadata": {},
    "source": [
     "### Code\n",
@@ -61,7 +61,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "changed-netherlands",
+   "id": "expensive-venice",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +88,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "thorough-heading",
+   "id": "satellite-fleet",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,11 +116,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "peaceful-collective",
+   "id": "affiliated-metabolism",
    "metadata": {},
    "outputs": [],
    "source": [
     "from numpy import arange\n",
+    "from modsim import TimeFrame\n",
     "\n",
     "def run_simulation(system, update_func):\n",
     "    \"\"\"Runs a simulation of the system.\n",
@@ -141,7 +142,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "patent-madagascar",
+   "id": "identical-steam",
    "metadata": {},
    "source": [
     "In the previous chapter I presented the SIR model of infectious disease and used it to model the Freshman Plague at Olin. In this chapter we'll consider metrics intended to quantify the effects of the disease and interventions intended to reduce those effects."
@@ -149,7 +150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "knowing-connecticut",
+   "id": "complex-renewal",
    "metadata": {},
    "source": [
     "## Immunization\n",
@@ -167,7 +168,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "catholic-walker",
+   "id": "recent-cooper",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -178,7 +179,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "logical-mailman",
+   "id": "arranged-screening",
    "metadata": {},
    "source": [
     "`add_immunization` moves the given fraction of the population from `S`\n",
@@ -188,7 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "sophisticated-occasions",
+   "id": "found-learning",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -204,7 +205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "postal-haiti",
+   "id": "unsigned-joseph",
    "metadata": {},
    "source": [
     "If we assume that 10% of students are vaccinated at the\n",
@@ -215,7 +216,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "abandoned-piece",
+   "id": "funny-copper",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,7 +227,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "robust-forestry",
+   "id": "bronze-techno",
    "metadata": {},
    "source": [
     "The following figure shows `S` as a function of time, with and\n",
@@ -236,7 +237,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "suffering-coupon",
+   "id": "divided-biotechnology",
    "metadata": {},
    "outputs": [
     {
@@ -262,7 +263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "shaped-assets",
+   "id": "postal-cemetery",
    "metadata": {},
    "source": [
     "## Metrics\n",
@@ -285,7 +286,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "unable-joshua",
+   "id": "synthetic-element",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,7 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "floral-transcription",
+   "id": "recovered-picnic",
    "metadata": {},
    "outputs": [
     {
@@ -319,7 +320,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "cardiovascular-acting",
+   "id": "american-transfer",
    "metadata": {},
    "outputs": [
     {
@@ -339,7 +340,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "reliable-fisher",
+   "id": "adverse-trance",
    "metadata": {},
    "source": [
     "Without immunization, almost 47% of the population gets infected at some point. With 10% immunization, only 31% get infected. That's pretty good."
@@ -347,7 +348,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "multiple-springfield",
+   "id": "eight-maximum",
    "metadata": {},
    "source": [
     "## Sweeping Immunization\n",
@@ -359,7 +360,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "funny-batman",
+   "id": "progressive-architect",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -377,7 +378,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "normal-blowing",
+   "id": "indie-seeker",
    "metadata": {},
    "source": [
     "The parameter of `sweep_immunity` is an array of immunization rates. The\n",
@@ -391,7 +392,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "severe-oklahoma",
+   "id": "measured-pavilion",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -402,7 +403,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "sudden-worker",
+   "id": "interior-humanitarian",
    "metadata": {},
    "outputs": [
     {
@@ -428,7 +429,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fantastic-violence",
+   "id": "turkish-mumbai",
    "metadata": {},
    "source": [
     "As the immunization rate increases, the number of infections drops\n",
@@ -445,7 +446,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "completed-stand",
+   "id": "french-spouse",
    "metadata": {},
    "source": [
     "The steepness of the curve is a blessing and a curse. It's a blessing\n",
@@ -463,7 +464,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "known-classic",
+   "id": "changed-capability",
    "metadata": {},
    "source": [
     "## Hand washing\n",
@@ -495,7 +496,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "suspected-chosen",
+   "id": "alpine-guest",
    "metadata": {},
    "source": [
     "Now we have to model the relationship between the money we spend and the\n",
@@ -513,14 +514,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ultimate-leader",
+   "id": "closed-feature",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "markdown",
-   "id": "tutorial-terrace",
+   "id": "agricultural-trinidad",
    "metadata": {},
    "source": [
     "### Logistic function"
@@ -528,7 +529,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abroad-birmingham",
+   "id": "cheap-structure",
    "metadata": {},
    "source": [
     "To model the effect of a hand-washing campaign, I'll use a [generalized logistic function](https://en.wikipedia.org/wiki/Generalised_logistic_function) (GLF), which is a convenient function for modeling curves that have a generally sigmoid shape.  The parameters of the GLF correspond to various features of the curve in a way that makes it easy to find a function that has the shape you want, based on data or background information about the scenario."
@@ -537,7 +538,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "fossil-adolescent",
+   "id": "blond-armstrong",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -563,7 +564,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cloudy-canvas",
+   "id": "seven-budget",
    "metadata": {},
    "source": [
     "The following array represents the range of possible spending."
@@ -572,7 +573,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "equal-lesson",
+   "id": "disturbed-amount",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -581,7 +582,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "competitive-dylan",
+   "id": "supreme-nutrition",
    "metadata": {},
    "source": [
     "`compute_factor` computes the reduction in `beta` for a given level of campaign spending.\n",
@@ -596,7 +597,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "ecological-puppy",
+   "id": "otherwise-answer",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -612,7 +613,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "informed-maple",
+   "id": "expected-venture",
    "metadata": {},
    "source": [
     "Here's what it looks like."
@@ -621,7 +622,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "diverse-ensemble",
+   "id": "seventh-strike",
    "metadata": {},
    "outputs": [
     {
@@ -649,7 +650,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "published-decade",
+   "id": "legal-michigan",
    "metadata": {},
    "source": [
     "The result is the following function, which\n",
@@ -660,7 +661,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "atomic-guatemala",
+   "id": "obvious-congress",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -670,7 +671,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "reflected-details",
+   "id": "sunset-investing",
    "metadata": {},
    "source": [
     "I use `compute_factor` to write `add_hand_washing`, which takes a\n",
@@ -681,7 +682,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "presidential-omaha",
+   "id": "polish-multiple",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -692,7 +693,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "perfect-promotion",
+   "id": "worthy-fellowship",
    "metadata": {},
    "source": [
     "Now we can sweep a range of values for `spending` and use the simulation\n",
@@ -702,7 +703,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "outer-nutrition",
+   "id": "statistical-garden",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -720,7 +721,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "agricultural-interpretation",
+   "id": "clinical-surge",
    "metadata": {},
    "source": [
     "Here's how we run it:"
@@ -729,7 +730,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "id": "first-termination",
+   "id": "joined-graduation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -741,7 +742,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cardiac-shopper",
+   "id": "designing-smile",
    "metadata": {},
    "source": [
     "The following figure shows the result. "
@@ -750,7 +751,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "cardiovascular-coach",
+   "id": "cheap-decision",
    "metadata": {},
    "outputs": [
     {
@@ -776,7 +777,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fifth-highlight",
+   "id": "selective-right",
    "metadata": {},
    "source": [
     "Below \\$200, the campaign has little effect. \n",
@@ -788,7 +789,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ruled-disease",
+   "id": "lucky-boulder",
    "metadata": {},
    "source": [
     "## Optimization\n",
@@ -803,7 +804,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "representative-brook",
+   "id": "surrounded-copying",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -815,7 +816,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "another-idaho",
+   "id": "expired-conditioning",
    "metadata": {},
    "source": [
     "The fraction `budget/price_per_dose` might not be an integer. `int` is a\n",
@@ -827,7 +828,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "historic-inclusion",
+   "id": "shaped-utility",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -836,7 +837,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "straight-equation",
+   "id": "occupational-reply",
    "metadata": {},
    "source": [
     "In this example we call `linrange` with only one argument; it returns a\n",
@@ -849,7 +850,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "received-swiss",
+   "id": "recognized-release",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -872,7 +873,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "appropriate-design",
+   "id": "cardiac-mitchell",
    "metadata": {},
    "source": [
     "For each number of doses, we compute the fraction of students we can\n",
@@ -886,7 +887,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "original-browse",
+   "id": "worth-mounting",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -896,7 +897,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "technical-violation",
+   "id": "adjusted-highlight",
    "metadata": {},
    "outputs": [
     {
@@ -922,7 +923,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "economic-newspaper",
+   "id": "dynamic-easter",
    "metadata": {},
    "source": [
     "If we buy no doses of vaccine and spend the entire budget on the campaign, the fraction infected is around 19%. At 4 doses, we have \\$800 left for the campaign, and this is the optimal point that minimizes the number of students who get sick.\n",
@@ -935,7 +936,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sharp-difficulty",
+   "id": "amino-excess",
    "metadata": {},
    "source": [
     "## Summary"
@@ -943,7 +944,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "quick-rotation",
+   "id": "timely-bottom",
    "metadata": {},
    "source": [
     "### Exercises\n",
@@ -953,7 +954,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fresh-costa",
+   "id": "drawn-hindu",
    "metadata": {},
    "source": [
     "**Exercise:** Suppose we have the option to quarantine infected students.  For example, a student who feels ill might be moved to an infirmary, or a private dorm room, until they are no longer infectious.\n",
@@ -964,7 +965,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "transparent-entry",
+   "id": "impressive-librarian",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/jupyter/chap13.ipynb b/jupyter/chap13.ipynb
index 16d36724..02d03388 100644
--- a/jupyter/chap13.ipynb
+++ b/jupyter/chap13.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "fifty-repair",
+   "id": "artistic-wells",
    "metadata": {},
    "source": [
     "# Chapter 13"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "pharmaceutical-constant",
+   "id": "ceramic-rendering",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "aware-blade",
+   "id": "upper-basic",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -43,14 +43,14 @@
     "    !pip install pint\n",
     "    \n",
     "try:\n",
-    "    from modsim import *\n",
+    "    import modsim\n",
     "except ImportError:\n",
     "    !pip install modsimpy"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "increased-happiness",
+   "id": "extraordinary-router",
    "metadata": {},
    "source": [
     "### Code from previous chapters"
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "honey-backup",
+   "id": "spiritual-product",
    "metadata": {},
    "source": [
     "`make_system`, `plot_results`, and `calc_total_infected` are unchanged."
@@ -67,7 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "experimental-preparation",
+   "id": "entitled-playlist",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,7 +94,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "arbitrary-newspaper",
+   "id": "monthly-width",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,11 +122,12 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "attended-sherman",
+   "id": "antique-timer",
    "metadata": {},
    "outputs": [],
    "source": [
     "from numpy import arange\n",
+    "from modsim import TimeFrame\n",
     "\n",
     "def run_simulation(system, update_func):\n",
     "    \"\"\"Runs a simulation of the system.\n",
@@ -148,7 +149,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "genetic-nylon",
+   "id": "swiss-system",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "descending-benjamin",
+   "id": "electoral-montgomery",
    "metadata": {},
    "source": [
     "In the previous chapters I presented an SIR model of infectious disease, specifically the Kermack-McKendrick model. We extended the model to include vaccination and the effect of a hand-washing campaign, and used the extended model to allocate a limited budget optimally, that is, to minimize the number of infections.\n",
@@ -172,7 +173,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "italian-halifax",
+   "id": "nervous-spread",
    "metadata": {},
    "source": [
     "## Sweeping beta\n",
@@ -193,7 +194,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "pleased-virus",
+   "id": "amazing-operations",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,7 +206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "operational-continuity",
+   "id": "adopted-selling",
    "metadata": {},
    "source": [
     "Then run the simulation for each value and print the results."
@@ -214,7 +215,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "functional-bennett",
+   "id": "brief-minutes",
    "metadata": {},
    "outputs": [
     {
@@ -244,7 +245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "indian-islam",
+   "id": "understanding-teach",
    "metadata": {},
    "source": [
     "We can wrap that code in a function and store the results in a\n",
@@ -254,7 +255,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "successful-holocaust",
+   "id": "single-burke",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -269,7 +270,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "british-turtle",
+   "id": "wired-wrist",
    "metadata": {},
    "source": [
     "Now we can run `sweep_beta` like this:"
@@ -278,7 +279,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "varied-attention",
+   "id": "collected-practice",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -287,7 +288,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "played-waters",
+   "id": "exempt-binary",
    "metadata": {},
    "source": [
     "And plot the results:"
@@ -296,7 +297,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "possible-valley",
+   "id": "competitive-amount",
    "metadata": {},
    "outputs": [
     {
@@ -317,7 +318,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "defensive-river",
+   "id": "imported-huntington",
    "metadata": {},
    "source": [
     "The first line uses string operations to assemble a label for the\n",
@@ -337,7 +338,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "composite-salad",
+   "id": "certain-second",
    "metadata": {},
    "outputs": [
     {
@@ -362,7 +363,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fuzzy-thesis",
+   "id": "affiliated-honduras",
    "metadata": {},
    "source": [
     "Remember that this figure\n",
@@ -378,7 +379,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "competent-melissa",
+   "id": "worst-alexandria",
    "metadata": {},
    "source": [
     "## Sweeping gamma\n",
@@ -390,7 +391,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "traditional-spirit",
+   "id": "advised-venice",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -399,7 +400,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "precise-review",
+   "id": "sensitive-folder",
    "metadata": {},
    "source": [
     "And run `sweep_beta` for each value of `gamma`:"
@@ -408,7 +409,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "laughing-tonight",
+   "id": "determined-consent",
    "metadata": {},
    "outputs": [
     {
@@ -435,7 +436,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "oriental-north",
+   "id": "tutorial-venture",
    "metadata": {},
    "source": [
     "The following figure shows the results. When `gamma` is low, the\n",
@@ -447,7 +448,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "large-radical",
+   "id": "rising-flour",
    "metadata": {},
    "source": [
     "## SweepFrame\n",
@@ -467,7 +468,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "patient-navigation",
+   "id": "aquatic-federal",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -480,7 +481,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "minus-meaning",
+   "id": "level-distinction",
    "metadata": {},
    "source": [
     "`sweep_parameters` takes as parameters an array of values for `beta` and\n",
@@ -503,7 +504,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "polish-excellence",
+   "id": "julian-liberia",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -512,7 +513,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cultural-olympus",
+   "id": "sharing-contemporary",
    "metadata": {},
    "source": [
     "With the results in a `SweepFrame`, we can plot each column like this:"
@@ -521,7 +522,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "religious-placement",
+   "id": "superior-redhead",
    "metadata": {},
    "outputs": [
     {
@@ -551,7 +552,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "familiar-appliance",
+   "id": "english-photographer",
    "metadata": {},
    "source": [
     "Alternatively, we can plot each row like this:"
@@ -560,7 +561,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "instructional-navigator",
+   "id": "regular-drama",
    "metadata": {},
    "outputs": [
     {
@@ -590,7 +591,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "improving-montreal",
+   "id": "selective-evaluation",
    "metadata": {},
    "source": [
     "This example demonstrates one use of a `SweepFrame`: we can run the analysis once, save the results, and then generate different visualizations.\n",
@@ -607,7 +608,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "filled-therapist",
+   "id": "logical-method",
    "metadata": {},
    "outputs": [
     {
@@ -635,7 +636,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "polished-favor",
+   "id": "corresponding-headline",
    "metadata": {},
    "source": [
     "Infection rates are lowest in the lower right, where the contact rate is and the recovery rate is high. They increase as we move to the upper left, where the contact rate is high and the recovery rate is low.\n",
@@ -648,7 +649,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "level-barrel",
+   "id": "located-double",
    "metadata": {},
    "source": [
     "## Summary"
@@ -656,7 +657,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "addressed-moscow",
+   "id": "flying-tracker",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -664,7 +665,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bored-bennett",
+   "id": "stopped-breathing",
    "metadata": {},
    "source": [
     "**Exercise:**  Suppose the infectious period for the Freshman Plague is known to be 2 days on average, and suppose during one particularly bad year, 40% of the class is infected at some point.  Estimate the time between contacts."
@@ -673,7 +674,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "raised-notification",
+   "id": "talented-crack",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -687,7 +688,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "infectious-winner",
+   "id": "pressed-isaac",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -703,7 +704,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "automatic-consultancy",
+   "id": "equal-crazy",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/jupyter/chap14.ipynb b/jupyter/chap14.ipynb
new file mode 100644
index 00000000..81ead454
--- /dev/null
+++ b/jupyter/chap14.ipynb
@@ -0,0 +1,921 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "happy-minutes",
+   "metadata": {},
+   "source": [
+    "# Chapter 14"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "impressed-headline",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "conditional-complaint",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "therapeutic-scanning",
+   "metadata": {},
+   "source": [
+    "### Code from previous chapters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "level-struggle",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import State, System\n",
+    "\n",
+    "def make_system(beta, gamma):\n",
+    "    \"\"\"Make a system object for the SIR model.\n",
+    "    \n",
+    "    beta: contact rate in days\n",
+    "    gamma: recovery rate in days\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(S=89, I=1, R=0)\n",
+    "    init /= sum(init)\n",
+    "\n",
+    "    t0 = 0\n",
+    "    t_end = 7 * 14\n",
+    "\n",
+    "    return System(init=init, t0=t0, t_end=t_end,\n",
+    "                  beta=beta, gamma=gamma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "offshore-remainder",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func(state, t, system):\n",
+    "    \"\"\"Update the SIR model.\n",
+    "    \n",
+    "    state: State with variables S, I, R\n",
+    "    t: time step\n",
+    "    system: System with beta and gamma\n",
+    "    \n",
+    "    returns: State object\n",
+    "    \"\"\"\n",
+    "    s, i, r = state\n",
+    "\n",
+    "    infected = system.beta * i * s    \n",
+    "    recovered = system.gamma * i\n",
+    "    \n",
+    "    s -= infected\n",
+    "    i += infected - recovered\n",
+    "    r += recovered\n",
+    "    \n",
+    "    return State(S=s, I=i, R=r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ordinary-elephant",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import arange\n",
+    "from modsim import TimeFrame\n",
+    "\n",
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "        \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \n",
+    "    returns: TimeFrame\n",
+    "    \"\"\"\n",
+    "    frame = TimeFrame(columns=system.init.index)\n",
+    "    frame.loc[system.t0] = system.init\n",
+    "    \n",
+    "    for t in arange(system.t0, system.t_end):\n",
+    "        frame.loc[t+1] = update_func(frame.loc[t], t, system)\n",
+    "    \n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "fiscal-testament",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_total_infected(results, system):\n",
+    "    s_0 = results.S[system.t0]\n",
+    "    s_end = results.S[system.t_end]\n",
+    "    return s_0 - s_end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "engaging-consequence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import SweepSeries\n",
+    "\n",
+    "def sweep_beta(beta_array, gamma):\n",
+    "    \"\"\"Sweep a range of values for beta.\n",
+    "    \n",
+    "    beta_array: array of beta values\n",
+    "    gamma: recovery rate\n",
+    "    \n",
+    "    returns: SweepSeries that maps from beta to total infected\n",
+    "    \"\"\"\n",
+    "    sweep = SweepSeries()\n",
+    "    for beta in beta_array:\n",
+    "        system = make_system(beta, gamma)\n",
+    "        results = run_simulation(system, update_func)\n",
+    "        sweep[beta] = calc_total_infected(results, system)\n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "proud-ability",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import SweepFrame\n",
+    "\n",
+    "def sweep_parameters(beta_array, gamma_array):\n",
+    "    \"\"\"Sweep a range of values for beta and gamma.\n",
+    "    \n",
+    "    beta_array: array of infection rates\n",
+    "    gamma_array: array of recovery rates\n",
+    "    \n",
+    "    returns: SweepFrame with one row for each beta\n",
+    "             and one column for each gamma\n",
+    "    \"\"\"\n",
+    "    frame = SweepFrame(columns=gamma_array)\n",
+    "    for gamma in gamma_array:\n",
+    "        frame[gamma] = sweep_beta(beta_array, gamma)\n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "flying-chosen",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0.2</th>\n",
+       "      <th>0.4</th>\n",
+       "      <th>0.6</th>\n",
+       "      <th>0.8</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Parameter</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.1</th>\n",
+       "      <td>0.010756</td>\n",
+       "      <td>0.003642</td>\n",
+       "      <td>0.002191</td>\n",
+       "      <td>0.001567</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.2</th>\n",
+       "      <td>0.118984</td>\n",
+       "      <td>0.010763</td>\n",
+       "      <td>0.005447</td>\n",
+       "      <td>0.003644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.3</th>\n",
+       "      <td>0.589095</td>\n",
+       "      <td>0.030185</td>\n",
+       "      <td>0.010771</td>\n",
+       "      <td>0.006526</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.4</th>\n",
+       "      <td>0.801339</td>\n",
+       "      <td>0.131563</td>\n",
+       "      <td>0.020917</td>\n",
+       "      <td>0.010780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.5</th>\n",
+       "      <td>0.896577</td>\n",
+       "      <td>0.396409</td>\n",
+       "      <td>0.046140</td>\n",
+       "      <td>0.017640</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                0.2       0.4       0.6       0.8\n",
+       "Parameter                                        \n",
+       "0.1        0.010756  0.003642  0.002191  0.001567\n",
+       "0.2        0.118984  0.010763  0.005447  0.003644\n",
+       "0.3        0.589095  0.030185  0.010771  0.006526\n",
+       "0.4        0.801339  0.131563  0.020917  0.010780\n",
+       "0.5        0.896577  0.396409  0.046140  0.017640"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "beta_array = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 , 1.1]\n",
+    "gamma_array = [0.2, 0.4, 0.6, 0.8]\n",
+    "frame = sweep_parameters(beta_array, gamma_array)\n",
+    "frame.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "disturbed-joshua",
+   "metadata": {},
+   "source": [
+    "In the previous chapters we used simulation to predict the effect of an infectious disease in a susceptible population and to design\n",
+    "interventions that would minimize the effect.\n",
+    "\n",
+    "In this chapter we use analysis to investigate the relationship between the parameters, `beta` and `gamma`, and the outcome of the simulation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "respiratory-twelve",
+   "metadata": {},
+   "source": [
+    "## Nondimensionalization\n",
+    "\n",
+    "The figures in\n",
+    "Section [\\[sweepframe\\]](#sweepframe){reference-type=\"ref\"\n",
+    "reference=\"sweepframe\"} suggest that there is a relationship between the parameters of the SIR model, `beta` and `gamma`, that determines the outcome of the simulation, the fraction of students infected. Let's think what that relationship might be.\n",
+    "\n",
+    "-   When `beta` exceeds `gamma`, that means there are more contacts\n",
+    "    (that is, potential infections) than recoveries during each day (or other unit of time). The difference between `beta` and `gamma` might be called the \"excess contact rate\\\", in units of contacts per day.\n",
+    "\n",
+    "-   As an alternative, we might consider the ratio `beta/gamma`, which\n",
+    "    is the number of contacts per recovery. Because the numerator and\n",
+    "    denominator are in the same units, this ratio is **dimensionless**, which means it has no units.\n",
+    "\n",
+    "Describing physical systems using dimensionless parameters is often a\n",
+    "useful move in the modeling and simulation game. It is so useful, in\n",
+    "fact, that it has a name: **nondimensionalization** (see\n",
+    "<http://modsimpy.com/nondim>).\n",
+    "\n",
+    "So we'll try the second option first."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "caroline-compiler",
+   "metadata": {},
+   "source": [
+    "## Exploring the results\n",
+    "\n",
+    "Suppose we have a `SweepFrame` with one row for each value of `beta` and one column for each value of `gamma`. Each element in the `SweepFrame` is the fraction of students infected in a simulation with a given pair of parameters.\n",
+    "\n",
+    "We can print the values in the `SweepFrame` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "satisfied-japan",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.1 0.2 0.010756340768063644\n",
+      "0.2 0.2 0.11898421353185373\n",
+      "0.3 0.2 0.5890954199973404\n",
+      "0.4 0.2 0.8013385277185551\n",
+      "0.5 0.2 0.8965769637207062\n",
+      "0.6 0.2 0.942929291399791\n",
+      "0.7 0.2 0.966299311298026\n",
+      "0.8 0.2 0.9781518959989762\n",
+      "0.9 0.2 0.9840568957948106\n",
+      "1.0 0.2 0.9868823507202488\n",
+      "1.1 0.2 0.988148177093735\n",
+      "0.1 0.4 0.0036416926514175607\n",
+      "0.2 0.4 0.010763463373360094\n",
+      "0.3 0.4 0.030184952469116566\n",
+      "0.4 0.4 0.131562924303259\n",
+      "0.5 0.4 0.3964094037932606\n",
+      "0.6 0.4 0.5979016626615987\n",
+      "0.7 0.4 0.7284704154876106\n",
+      "0.8 0.4 0.8144604459153759\n",
+      "0.9 0.4 0.8722697237137128\n",
+      "1.0 0.4 0.9116692168795855\n",
+      "1.1 0.4 0.9386802509510287\n",
+      "0.1 0.6 0.002190722188881611\n",
+      "0.2 0.6 0.005446688837466351\n",
+      "0.3 0.6 0.010771139974975585\n",
+      "0.4 0.6 0.020916599304195316\n",
+      "0.5 0.6 0.04614035896610047\n",
+      "0.6 0.6 0.13288938996079536\n",
+      "0.7 0.6 0.3118432512847451\n",
+      "0.8 0.6 0.47832565854255393\n",
+      "0.9 0.6 0.605687582114665\n",
+      "1.0 0.6 0.7014254793376209\n",
+      "1.1 0.6 0.7738176405451065\n",
+      "0.1 0.8 0.0015665254038139675\n",
+      "0.2 0.8 0.003643953969662994\n",
+      "0.3 0.8 0.006526163529085194\n",
+      "0.4 0.8 0.010779807499500693\n",
+      "0.5 0.8 0.017639902596349066\n",
+      "0.6 0.8 0.030291868201986594\n",
+      "0.7 0.8 0.05882382948158804\n",
+      "0.8 0.8 0.13358889291095588\n",
+      "0.9 0.8 0.2668895539427739\n",
+      "1.0 0.8 0.40375121210421994\n",
+      "1.1 0.8 0.519583469821867\n"
+     ]
+    }
+   ],
+   "source": [
+    "for gamma in frame.columns:\n",
+    "    column = frame[gamma]\n",
+    "    for beta in column.index:\n",
+    "        frac_infected = column[beta]\n",
+    "        print(beta, gamma, frac_infected)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "automotive-solution",
+   "metadata": {},
+   "source": [
+    "This is the first example we've seen with one `for` loop inside another:\n",
+    "\n",
+    "-   Each time the outer loop runs, it selects a value of `gamma` from\n",
+    "    the columns of the `DataFrame` and extracts the corresponding\n",
+    "    column.\n",
+    "\n",
+    "-   Each time the inner loop runs, it selects a value of `beta` from the\n",
+    "    column and selects the corresponding element, which is the fraction\n",
+    "    of students infected.\n",
+    "\n",
+    "In the example from the previous chapter, `frame` has 4 columns, one for\n",
+    "each value of `gamma`, and 11 rows, one for each value of `beta`. So\n",
+    "these loops print 44 lines, one for each pair of parameters.\n",
+    "\n",
+    "The following function encapulates the previous loop and plots the\n",
+    "fraction infected as a function of the ratio `beta/gamma`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "baking-tactics",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.pyplot import plot\n",
+    "\n",
+    "def plot_sweep_frame(frame):\n",
+    "    for gamma in frame.columns:\n",
+    "        series = frame[gamma]\n",
+    "        for beta in series.index:\n",
+    "            frac_infected = series[beta]\n",
+    "            plot(beta/gamma, frac_infected, 'o', \n",
+    "                 color='C1', alpha=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "dental-outreach",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "plot_sweep_frame(frame)\n",
+    "\n",
+    "decorate(xlabel='Contact number (beta/gamma)',\n",
+    "         ylabel='Fraction infected')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "systematic-gossip",
+   "metadata": {},
+   "source": [
+    "The results fall on a single curve, at least approximately. That means that we can predict the fraction of students who will be infected based on a single parameter, the ratio `beta/gamma`. We don't need to know the values of `beta` and `gamma` separately."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "theoretical-arrow",
+   "metadata": {},
+   "source": [
+    "## Contact number\n",
+    "\n",
+    "From Section xxx, recall that the number of new infections in a\n",
+    "given day is $\\beta s i N$, and the number of recoveries is\n",
+    "$\\gamma i N$. If we divide these quantities, the result is\n",
+    "$\\beta s / \\gamma$, which is the number of new infections per recovery\n",
+    "(as a fraction of the population).\n",
+    "\n",
+    "When a new disease is introduced to a susceptible population, $s$ is\n",
+    "approximately 1, so the number of people infected by each sick person is $\\beta / \\gamma$. This ratio is called the \"contact number\\\" or \"basic reproduction number\\\" (see <http://modsimpy.com/contact>). By convention it is usually denoted $R_0$, but in the context of an SIR model, this notation is confusing, so we'll use $c$ instead."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "intimate-romance",
+   "metadata": {},
+   "source": [
+    "The results in the previous section suggest that there is a relationship between $c$ and the total number of infections. We can derive this relationship by analyzing the differential equations from\n",
+    "Section xxx:\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\frac{ds}{dt} &= -\\beta s i \\\\\n",
+    "\\frac{di}{dt} &= \\beta s i - \\gamma i\\\\\n",
+    "\\frac{dr}{dt} &= \\gamma i\\end{aligned}$$ \n",
+    "\n",
+    "In the same way we divided the\n",
+    "contact rate by the infection rate to get the dimensionless quantity\n",
+    "$c$, now we'll divide $di/dt$ by $ds/dt$ to get a ratio of rates:\n",
+    "\n",
+    "$$\\frac{di}{ds} = -1 + \\frac{1}{cs}$$ \n",
+    "\n",
+    "Dividing one differential equation by another is not an obvious move, but in this case it is useful because it gives us a relationship between $i$, $s$ and $c$ that does not depend on time. From that relationship, we can derive an equation that relates $c$ to the final value of $s$. In theory, this equation makes it possible to infer $c$ by observing the course of an epidemic."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "revolutionary-motorcycle",
+   "metadata": {},
+   "source": [
+    "Here's how the derivation goes. We multiply both sides of the previous\n",
+    "equation by $ds$: \n",
+    "\n",
+    "$$di = \\left( -1 + \\frac{1}{cs} \\right) ds$$ \n",
+    "\n",
+    "And then integrate both sides: \n",
+    "\n",
+    "$$i = -s + \\frac{1}{c} \\log s + q$$ \n",
+    "\n",
+    "where $q$ is a constant of integration. Rearranging terms yields:\n",
+    "\n",
+    "$$q = i + s - \\frac{1}{c} \\log s$$ \n",
+    "\n",
+    "Now let's see if we can figure out what $q$ is. At the beginning of an epidemic, if the fraction infected is small and nearly everyone is susceptible, we can use the approximations $i(0) = 0$ and $s(0) = 1$ to compute $q$:\n",
+    "\n",
+    "$$q = 0 + 1 + \\frac{1}{c} \\log 1$$ \n",
+    "\n",
+    "Since $\\log 1 = 0$, we get $q = 1$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "spread-convert",
+   "metadata": {},
+   "source": [
+    "Now, at the end of the epidemic, let's assume that $i(\\infty) = 0$, and $s(\\infty)$ is an unknown quantity, $s_{\\infty}$. Now we have:\n",
+    "\n",
+    "$$q = 1 = 0 + s_{\\infty}- \\frac{1}{c} \\log s_{\\infty}$$ \n",
+    "\n",
+    "Solving for $c$, we get $$c = \\frac{\\log s_{\\infty}}{s_{\\infty}- 1}$$ By relating $c$ and $s_{\\infty}$, this equation makes it possible to estimate $c$ based on data, and possibly predict the behavior of future epidemics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "intended-worker",
+   "metadata": {},
+   "source": [
+    "## Analysis and simulation\n",
+    "\n",
+    "Let's compare this analytic result to the results from simulation. I'll create an array of values for $s_{\\infty}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "previous-schedule",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import linspace\n",
+    "\n",
+    "s_inf_array = linspace(0.0001, 0.999, 31)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "positive-float",
+   "metadata": {},
+   "source": [
+    "And compute the corresponding values of $c$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "valuable-landscape",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import log\n",
+    "\n",
+    "c_array = log(s_inf_array) / (s_inf_array - 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "premium-carry",
+   "metadata": {},
+   "source": [
+    "To get the total infected, we compute the difference between $s(0)$ and\n",
+    "$s(\\infty)$, then store the results in a `Series`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "composite-cabinet",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frac_infected = 1 - s_inf_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bottom-print",
+   "metadata": {},
+   "source": [
+    "Recall from Section [\\[dataframe\\]](#dataframe){reference-type=\"ref\"\n",
+    "reference=\"dataframe\"} that a `Series` object contains an index and a\n",
+    "corresponding sequence of values. In this case, the index is `c_array`\n",
+    "and the values are from `frac_infected`.\n",
+    "\n",
+    "Now we can plot the results:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "active-street",
+   "metadata": {},
+   "source": [
+    " compares the analytic results from this\n",
+    "section with the simulation results from\n",
+    "Section [\\[nondim\\]](#nondim){reference-type=\"ref\" reference=\"nondim\"}."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "integral-reading",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_sweep_frame(frame)\n",
+    "plot(c_array, frac_infected, label='analysis')\n",
+    "\n",
+    "decorate(xlabel='Contact number (c)',\n",
+    "         ylabel='Fraction infected')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "peaceful-interest",
+   "metadata": {},
+   "source": [
+    "When the contact number exceeds 1, analysis and simulation agree. When\n",
+    "the contact number is less than 1, they do not: analysis indicates there should be no infections; in the simulations there are a small number of infections.\n",
+    "\n",
+    "The reason for the discrepancy is that the simulation divides time into a discrete series of days, whereas the analysis treats time as a\n",
+    "continuous quantity. In other words, the two methods are actually based on different models. So which model is better?\n",
+    "\n",
+    "Probably neither. When the contact number is small, the early progress\n",
+    "of the epidemic depends on details of the scenario. If we are lucky, the original infected person, \"patient zero\", infects no one and there is no epidemic. If we are unlucky, patient zero might have a large number of close friends, or might work in the dining hall (and fail to observe safe food handling procedures).\n",
+    "\n",
+    "For contact numbers near or less than 1, we might need a more detailed\n",
+    "model. But for higher contact numbers the SIR model might be good\n",
+    "enough."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beginning-capital",
+   "metadata": {},
+   "source": [
+    "## Estimating contact number\n",
+    "\n",
+    "Figure xxx shows that if we know the contact number, we can compute the fraction infected. But we can also read the figure the other way; that is, at the end of an epidemic, if we can estimate the fraction of the population that was ever infected, we can use it to estimate the contact number.\n",
+    "\n",
+    "Well, in theory we can. In practice, it might not work very well,\n",
+    "because of the shape of the curve. When the contact number is near 2,\n",
+    "the curve is quite steep, which means that small changes in $c$ yield\n",
+    "big changes in the number of infections. If we observe that the total\n",
+    "fraction infected is anywhere from 20% to 80%, we would conclude that\n",
+    "$c$ is near 2.\n",
+    "\n",
+    "On the other hand, for larger contact numbers, nearly the entire\n",
+    "population is infected, so the curve is nearly flat. In that case we\n",
+    "would not be able to estimate $c$ precisely, because any value greater\n",
+    "than 3 would yield effectively the same results. Fortunately, this is\n",
+    "unlikely to happen in the real world; very few epidemics affect anything close to 90% of the population.\n",
+    "\n",
+    "So the SIR model has limitations; nevertheless, it provides insight into the behavior of infectious disease, especially the phenomenon of herd immunity. As we saw in Chapter xxx, if we know the parameters of the model, we can use it to evaluate possible interventions. And as we saw in this chapter, we might be able to use data from earlier outbreaks to estimate the parameters.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "olive-niger",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "upper-interim",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  If we didn't know about contact numbers, we might have explored other possibilities, like the difference between `beta` and `gamma`, rather than their ratio.\n",
+    "\n",
+    "Write a version of `plot_sweep_frame`, called `plot_sweep_frame_difference`, that plots the fraction infected versus the difference `beta-gamma`.\n",
+    "\n",
+    "What do the results look like, and what does that imply? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "talented-discovery",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def plot_sweep_frame_difference(frame):\n",
+    "    for gamma in frame.columns:\n",
+    "        column = frame[gamma]\n",
+    "        for beta in column.index:\n",
+    "            frac_infected = column[beta]\n",
+    "            plot(beta - gamma, frac_infected, 'ro', \n",
+    "                 color='C1', alpha=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "bright-label",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "plot_sweep_frame_difference(frame)\n",
+    "\n",
+    "decorate(xlabel='Excess infection rate (infections-recoveries per day)',\n",
+    "         ylabel='Fraction infected')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "suspended-louisiana",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# The results don't fall on a line, which means that if we \n",
+    "# know the difference between `beta` and `gamma`, \n",
+    "# but not their ratio, that's not enough to predict \n",
+    "# the fraction infected."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "understanding-fault",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose you run a survey at the end of the semester and find that 26% of students had the Freshman Plague at some point.\n",
+    "\n",
+    "What is your best estimate of `c`?\n",
+    "\n",
+    "Hint: if you print `frac_infected_series`, you can read off the answer. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "another-timing",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.211261    0.999900\n",
+       "3.516747    0.966603\n",
+       "2.901137    0.933307\n",
+       "2.558511    0.900010\n",
+       "2.325167    0.866713\n",
+       "2.150496    0.833417\n",
+       "2.012246    0.800120\n",
+       "1.898689    0.766823\n",
+       "1.802908    0.733527\n",
+       "1.720492    0.700230\n",
+       "1.648460    0.666933\n",
+       "1.584709    0.633637\n",
+       "1.527703    0.600340\n",
+       "1.476285    0.567043\n",
+       "1.429566    0.533747\n",
+       "1.386847    0.500450\n",
+       "1.347569    0.467153\n",
+       "1.311281    0.433857\n",
+       "1.277610    0.400560\n",
+       "1.246247    0.367263\n",
+       "1.216935    0.333967\n",
+       "1.189452    0.300670\n",
+       "1.163613    0.267373\n",
+       "1.139256    0.234077\n",
+       "1.116242    0.200780\n",
+       "1.094449    0.167483\n",
+       "1.073772    0.134187\n",
+       "1.054117    0.100890\n",
+       "1.035401    0.067593\n",
+       "1.017551    0.034297\n",
+       "1.000500    0.001000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "from pandas import Series\n",
+    "\n",
+    "Series(frac_infected, index=c_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "gross-terry",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# It looks like the fraction infected is 0.26 when the contact \n",
+    "# number is about 1.16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "stopped-zealand",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/chap15.ipynb b/jupyter/chap15.ipynb
new file mode 100644
index 00000000..fc9b5f85
--- /dev/null
+++ b/jupyter/chap15.ipynb
@@ -0,0 +1,570 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "third-grass",
+   "metadata": {},
+   "source": [
+    "# Chapter 15"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "driving-terrain",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "administrative-process",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "willing-twist",
+   "metadata": {},
+   "source": [
+    "So far the systems we have studied have been physical in the sense that they exist in the world, but they have not been physics, in the sense of what physics classes are usually about. In the next few chapters, we'll do some physics, starting with **thermal systems**, that is, systems where the temperature of objects changes as heat transfers from one to another."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "graduate-yahoo",
+   "metadata": {},
+   "source": [
+    "## The coffee cooling problem\n",
+    "\n",
+    "The coffee cooling problem was discussed by Jearl Walker in \n",
+    "\"The Amateur Scientist\", *Scientific American*, Volume 237, Issue 5, November 1977. Since then it has become a standard example of modeling and simulation.\n",
+    "\n",
+    "Here is my version of the problem:\n",
+    "\n",
+    "> Suppose I stop on the way to work to pick up a cup of coffee, which I take with milk. Assuming that I want the coffee to be as hot as possible when I arrive at work, should I add the milk at the coffee shop, wait until I get to work, or add the milk at some point in between?\n",
+    "\n",
+    "To help answer this question, I made a trial run with the milk and\n",
+    "coffee in separate containers and took some measurements (not really):\n",
+    "\n",
+    "-   When served, the temperature of the coffee is 90 °C. The volume is\n",
+    "    300 mL.\n",
+    "\n",
+    "-   The milk is at an initial temperature of 5 °C, and I take about\n",
+    "    50 mL.\n",
+    "\n",
+    "-   The ambient temperature in my car is 22 °C.\n",
+    "\n",
+    "-   The coffee is served in a well insulated cup. When I arrive at work after 30 minutes, the temperature of the coffee has fallen to 70 °C.\n",
+    "\n",
+    "-   The milk container is not as well insulated. After 15 minutes, it\n",
+    "    warms up to 20 °C, nearly the ambient temperature.\n",
+    "\n",
+    "To use this data and answer the question, we have to know something\n",
+    "about temperature and heat, and we have to make some modeling decisions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "technological-invention",
+   "metadata": {},
+   "source": [
+    "## Temperature and heat\n",
+    "\n",
+    "To understand how coffee cools (and milk warms), we need a model of\n",
+    "temperature and heat. **Temperature** is a property of an object or a\n",
+    "system; in SI units it is measured in degrees Celsius (°C). Temperature quantifies how hot or cold the object is, which is related to the average velocity of the particles that make up the object.\n",
+    "\n",
+    "When particles in a hot object contact particles in a cold object, the\n",
+    "hot object gets cooler and the cold object gets warmer as energy is\n",
+    "transferred from one to the other. The transferred energy is called\n",
+    "**heat**; in SI units it is measured in joules (J).\n",
+    "\n",
+    "Heat is related to temperature by the following equation (see\n",
+    "<http://modsimpy.com/thermass>): \n",
+    "\n",
+    "$$Q = C~\\Delta T$$ \n",
+    "\n",
+    "where $Q$ is the\n",
+    "amount of heat transferred to an object, $\\Delta T$ is resulting change in temperature, and $C$ is the **thermal mass** of the object, which quantifies how much energy it takes to heat or cool it. In SI units, thermal mass is measured in joules per degree Celsius (J/°C).\n",
+    "\n",
+    "For objects made primarily from one material, thermal mass can be\n",
+    "computed like this: \n",
+    "\n",
+    "$$C = m c_p$$ \n",
+    "\n",
+    "where $m$ is the mass of the object and $c_p$ is the **specific heat capacity** of the material (see <http://modsimpy.com/specheat>).\n",
+    "\n",
+    "We can use these equations to estimate the thermal mass of a cup of\n",
+    "coffee. The specific heat capacity of coffee is probably close to that\n",
+    "of water, which is 4.2 J/g/°C. Assuming that the density of coffee is\n",
+    "close to that of water, which is 1 g/mL, the mass of 300 mL of coffee is 300 g, and the thermal mass is 1260 J/°C.\n",
+    "\n",
+    "So when a cup of coffee cools from 90 °C to 70 °C, the change in\n",
+    "temperature, $\\Delta T$ is 20 °C, which means that 25 200 J of heat\n",
+    "energy was transferred from the coffee to the surrounding environment\n",
+    "(the cup holder and air in my car).\n",
+    "\n",
+    "To give you a sense of how much energy that is, if you were able to\n",
+    "harness all of that heat to do work (which you cannot), you could\n",
+    "use it to lift a cup of coffee from sea level to 8571 m, just shy of the height of Mount Everest, 8848 m.\n",
+    "\n",
+    "Assuming that the cup has less mass than the coffee, and is made from a material with lower specific heat, we can ignore the thermal mass of the cup. For a cup with substantial thermal mass, like a ceramic mug, we might consider a model that computes the temperature of coffee and cup separately."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hidden-sandwich",
+   "metadata": {},
+   "source": [
+    "## Heat transfer\n",
+    "\n",
+    "In a situation like the coffee cooling problem, there are three ways\n",
+    "heat transfers from one object to another (see <http://modsimpy.com/transfer>):\n",
+    "\n",
+    "-   Conduction: When objects at different temperatures come into\n",
+    "    contact, the faster-moving particles of the higher-temperature\n",
+    "    object transfer kinetic energy to the slower-moving particles of the\n",
+    "    lower-temperature object.\n",
+    "\n",
+    "-   Convection: When particles in a gas or liquid flow from place to\n",
+    "    place, they carry heat energy with them. Fluid flows can be caused\n",
+    "    by external action, like stirring, or by internal differences in\n",
+    "    temperature. For example, you might have heard that hot air rises,\n",
+    "    which is a form of \"natural convection\\\".\n",
+    "\n",
+    "-   Radiation: As the particles in an object move due to thermal energy,\n",
+    "    they emit electromagnetic radiation. The energy carried by this\n",
+    "    radiation depends on the object's temperature and surface properties\n",
+    "    (see <http://modsimpy.com/thermrad>).\n",
+    "\n",
+    "For objects like coffee in a car, the effect of radiation is much\n",
+    "smaller than the effects of conduction and convection, so we will ignore it.\n",
+    "\n",
+    "Convection can be a complex topic, since it often depends on details of fluid flow in three dimensions. But for this problem we will be able to get away with a simple model called \"Newton's law of cooling\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "precious-phone",
+   "metadata": {},
+   "source": [
+    "## Newton's law of cooling\n",
+    "\n",
+    "Newton's law of cooling asserts that the temperature rate of change for an object is proportional to the difference in temperature between the\n",
+    "object and the surrounding environment:\n",
+    "\n",
+    "$$\\frac{dT}{dt} = -r (T - T_{env})$$ \n",
+    "\n",
+    "where $T$, the temperature of the object, is a function of time, $t$; $T_{env}$ is the temperature of the environment, and $r$ is a constant that characterizes how quickly heat is transferred between the system and the environment.\n",
+    "\n",
+    "Newton's so-called \"law \" is really a model: it is a good approximation in some conditions and less good in others.\n",
+    "\n",
+    "For example, if the primary mechanism of heat transfer is conduction,\n",
+    "Newton's law is \"true\", which is to say that $r$ is constant over a\n",
+    "wide range of temperatures. And sometimes we can estimate $r$ based on\n",
+    "the material properties and shape of the object.\n",
+    "\n",
+    "When convection contributes a non-negligible fraction of heat transfer, $r$ depends on temperature, but Newton's law is often accurate enough, at least over a narrow range of temperatures. In this case $r$ usually has to be estimated experimentally, since it depends on details of surface shape, air flow, evaporation, etc.\n",
+    "\n",
+    "When radiation makes up a substantial part of heat transfer, Newton's\n",
+    "law is not a good model at all. This is the case for objects in space or in a vacuum, and for objects at high temperatures (more than a few\n",
+    "hundred degrees Celsius, say).\n",
+    "\n",
+    "However, for a situation like the coffee cooling problem, we expect\n",
+    "Newton's model to be quite good."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "complex-statement",
+   "metadata": {},
+   "source": [
+    "## Implementation\n",
+    "\n",
+    "To get started, let's forget about the milk temporarily and focus on the coffee. \n",
+    "\n",
+    "Here's a `System` object that contains the parameters of the system:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "advisory-stations",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import System\n",
+    "\n",
+    "def make_system(T_init, volume, r, t_end):\n",
+    "    return System(T_init=T_init,\n",
+    "                  volume=volume,\n",
+    "                  r=r,\n",
+    "                  t_end=t_end,\n",
+    "                  T_env=22,\n",
+    "                  t_0=0,\n",
+    "                  dt=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "simple-shelter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee = make_system(T_init=90, volume=300, r=0.01, t_end=30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "legal-shift",
+   "metadata": {},
+   "source": [
+    "The values of `volume`, `T_env`, and `t_end` come from the statement of the problem. I chose the value of `r` arbitrarily for now; we will\n",
+    "figure out how to estimate it soon.\n",
+    "\n",
+    "`dt` is the time step we use to simulate the cooling process. Strictly\n",
+    "speaking, Newton's law is a differential equation, but over a short\n",
+    "period of time we can approximate it with a difference equation:\n",
+    "\n",
+    "$$\\Delta T = -r (T - T_{env}) dt$$ \n",
+    "\n",
+    "where $dt$ is a small time step and\n",
+    "$\\Delta T$ is the change in temperature during that time step.\n",
+    "\n",
+    "Note: I use $\\Delta T$ to denote a change in temperature over time, but in the context of heat transfer, you might also see $\\Delta T$ used to denote the difference in temperature between an object and its\n",
+    "environment, $T - T_{env}$. To minimize confusion, I avoid this second\n",
+    "use.\n",
+    "\n",
+    "Now we can write an update function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "organic-morrison",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def change_func(T, t, system):\n",
+    "    r, T_env, dt = system.r, system.T_env, system.dt    \n",
+    "    return -r * (T - T_env) * dt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "manufactured-alfred",
+   "metadata": {},
+   "source": [
+    "Like previous update functions, this one takes a `State` object, a time,\n",
+    "and a `System` object.\n",
+    "\n",
+    "Now if we run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "olympic-bailey",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.68"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "change_func(coffee.T_init, 0, coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "synthetic-montana",
+   "metadata": {},
+   "source": [
+    "we see that the temperature after one minute is 89.3 °C, so the\n",
+    "temperature drops by about 0.7 °C/min, at least for this value of `r`.\n",
+    "\n",
+    "Here's a version of `run_simulation` that simulates a series of time\n",
+    "steps from `t_0` to `t_end`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "synthetic-danish",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import linrange\n",
+    "from pandas import Series\n",
+    "from numpy import empty\n",
+    "\n",
+    "def run_simulation(system, change_func):\n",
+    "    t_array = linrange(system.t_0, system.t_end, system.dt)\n",
+    "    n = len(t_array)\n",
+    "    \n",
+    "    T_array = empty(n)\n",
+    "    T_array[0] = system.T_init\n",
+    "    \n",
+    "    for i in range(n-1):\n",
+    "        t = t_array[i]\n",
+    "        T = T_array[i]\n",
+    "        T_array[i+1] = T + change_func(T, t, system)\n",
+    "    \n",
+    "    system.T_final = T_array[-1]\n",
+    "    return Series(T_array, index=t_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "finite-paraguay",
+   "metadata": {},
+   "source": [
+    "This function is similar to previous versions of `run_simulation`.\n",
+    "\n",
+    "One difference is that it uses `linrange` to make an array of values\n",
+    "from `t_0` to `t_end` with time step `dt`.\n",
+    "\n",
+    "We can run it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "virtual-notebook",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(coffee, change_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "impressed-provincial",
+   "metadata": {},
+   "source": [
+    "The result is a `TimeSeries` with one row per time step. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "realistic-imagination",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26.0    74.362934\n",
+       "27.0    73.839305\n",
+       "28.0    73.320912\n",
+       "29.0    72.807702\n",
+       "30.0    72.299625\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "seven-poker",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "72.2996253904031"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee.T_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "seven-costume",
+   "metadata": {},
+   "source": [
+    "The temperature after 30 minutes is 72.3 °C, which is a little higher than stated in the problem, 70 °C. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acoustic-provider",
+   "metadata": {},
+   "source": [
+    "We can adjust `r` and find the right value by trial and error, but we'll see a better way in the next chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "demanding-spending",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "results.plot(label='coffee')\n",
+    "\n",
+    "decorate(xlabel='Time (minute)',\n",
+    "         ylabel='Temperature (C)',\n",
+    "         title='Coffee Cooling')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "automatic-standard",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "**Exercise:**  Simulate the temperature of 50 mL of milk with a starting temperature of 5 degC, in a vessel with the same insulation, for 15 minutes, and plot the results.\n",
+    "\n",
+    "By trial and error, find a value for `r` that makes the final temperature close to 20 C."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "maritime-pillow",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.00135627897414"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "milk = make_system(T_init=5, t_end=15, r=0.133, volume=50)\n",
+    "results_milk = run_simulation(milk, change_func)\n",
+    "milk.T_final"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "killing-kitchen",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results_milk.plot(color='C1', label='milk')\n",
+    "\n",
+    "decorate(xlabel='Time (minutes)',\n",
+    "         ylabel='Temperature (C)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "buried-sweden",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/chap16.ipynb b/jupyter/chap16.ipynb
new file mode 100644
index 00000000..6aacefe6
--- /dev/null
+++ b/jupyter/chap16.ipynb
@@ -0,0 +1,1339 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "nearby-contemporary",
+   "metadata": {},
+   "source": [
+    "# Chapter 16"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "boxed-certificate",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "veterinary-timer",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "coral-content",
+   "metadata": {},
+   "source": [
+    "## Code from previous notebooks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "romantic-edgar",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def change_func(T, t, system):\n",
+    "    r, T_env, dt = system.r, system.T_env, system.dt\n",
+    "    \n",
+    "    return -r * (T - T_env) * dt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "id": "unlikely-protest",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import TimeSeries\n",
+    "from numpy import arange\n",
+    "\n",
+    "def run_simulation(system, change_func):\n",
+    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
+    "    \n",
+    "    results = TimeSeries(quantity='Temperature')\n",
+    "    results[t_0] = system.T_init\n",
+    "    \n",
+    "    for t in arange(t_0, t_end, dt):\n",
+    "        T = results[t]\n",
+    "        results[t+dt] = T + change_func(T, t, system)\n",
+    "    \n",
+    "    system.t_end = results.index[-1]\n",
+    "    system.T_final = results[system.t_end]\n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "id": "rubber-norway",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import System\n",
+    "\n",
+    "def make_system(T_init, volume, r, t_end):\n",
+    "    return System(T_init=T_init,\n",
+    "                  T_final=T_init,\n",
+    "                  volume=volume,\n",
+    "                  r=r,\n",
+    "                  t_end=t_end,\n",
+    "                  T_env=22,\n",
+    "                  t_0=0,\n",
+    "                  dt=0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tamil-mobile",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we wrote a simulation of a cooling cup of\n",
+    "coffee. Given the initial temperature of the coffee, the temperature of the atmosphere, and the rate parameter, `r`, we can predict how the\n",
+    "temperature of the coffee will change over time.\n",
+    "\n",
+    "In general, we don't know the value of `r`, but we can use measurements to estimate it. Given an initial temperature, a final temperature, and the time in between, we can find `r` by trial and error.\n",
+    "\n",
+    "In this chapter, we'll see a better way to find `r`, using a **bisection search**.\n",
+    "\n",
+    "And then we'll get back to solving the coffee cooling problem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "requested-pantyhose",
+   "metadata": {},
+   "source": [
+    "## Finding roots\n",
+    "\n",
+    "The ModSim library provides a method called `root_bisect` that finds the roots of non-linear equations. As a simple example, suppose you want to find the roots of the polynomial \n",
+    "\n",
+    "$$f(x) = (x - 1)(x - 2)(x - 3)$$ \n",
+    "\n",
+    "where **root** means a value of $x$ that makes $f(x)=0$. Because of the way I wrote the polynomial, we can see that if $x=1$, the first factor is 0; if $x=2$, the second factor is 0; and if $x=3$, the third factor is 0, so those are the roots.\n",
+    "\n",
+    "I'll use this example to demonstrate `root_bisect`. First, we have to\n",
+    "write a function that evaluates $f$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "id": "three-reporter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def func(x):\n",
+    "    return (x-1) * (x-2) * (x-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sharing-transcript",
+   "metadata": {},
+   "source": [
+    "Now we call `root_bisect` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "id": "stable-costa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "      converged: True\n",
+       "           flag: 'converged'\n",
+       " function_calls: 3\n",
+       "     iterations: 2\n",
+       "           root: 2.0"
+      ]
+     },
+     "execution_count": 257,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.optimize import root_scalar\n",
+    "\n",
+    "res = root_scalar(func, bracket=[1.5, 2.5])\n",
+    "res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "secondary-sapphire",
+   "metadata": {},
+   "source": [
+    "The first argument is the function whose roots we want. The second\n",
+    "argument is an interval that contains a root. The result is an object\n",
+    "that contains several variables, including `root`, which stores the root\n",
+    "that was found."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "nonprofit-jordan",
+   "metadata": {},
+   "source": [
+    "If we provide a different interval, we find a different root."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "id": "earned-norfolk",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 258,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res = root_scalar(func, bracket=[2.5, 3.5])\n",
+    "\n",
+    "res.root"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "assisted-oregon",
+   "metadata": {},
+   "source": [
+    "If the interval doesn't contain a root, you'll get a `ValueError`:\n",
+    "\n",
+    "```\n",
+    "res = root_scalar(func, bracket=[4, 5])\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "selective-sector",
+   "metadata": {},
+   "source": [
+    "## Estimating `r`\n",
+    "\n",
+    "So how can we use `root_scalar` to estimate `r`?\n",
+    "\n",
+    "What we want is the value of `r` that yields a final temperature of\n",
+    "70 °C. To work with `root_scalar`, we need a function that takes `r` as a parameter and returns the difference between the final temperature and the goal:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "id": "empirical-leisure",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def error_func(r, system):\n",
+    "    system.r = r\n",
+    "    results = run_simulation(system, change_func)\n",
+    "    return system.T_final - 70"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "green-manitoba",
+   "metadata": {},
+   "source": [
+    "I call a function like this an \"error function\\\" because it returns the\n",
+    "difference between what we got and what we wanted, that is, the error.\n",
+    "When we find the right value of `r`, this error will be 0.\n",
+    "\n",
+    "We can test `error_func` like this, using the initial guess `r=0.01`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "id": "cubic-shopping",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "0.6000000000000001",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m/home/downey/anaconda3/envs/ModSimPy/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 0.6000000000000001",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-260-038f0cf9b128>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcoffee\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_system\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mT_init\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m90\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvolume\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_end\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0merror_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoffee\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-259-97c7981df9e1>\u001b[0m in \u001b[0;36merror_func\u001b[0;34m(r, system)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0merror_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msystem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0msystem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_simulation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchange_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0msystem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT_final\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m70\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-254-7a85a112ecb2>\u001b[0m in \u001b[0;36mrun_simulation\u001b[0;34m(system, change_func)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_end\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m         \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mT\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mchange_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msystem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/downey/anaconda3/envs/ModSimPy/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    822\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    823\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 824\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    825\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    826\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/downey/anaconda3/envs/ModSimPy/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m    930\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 932\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    933\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    934\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/downey/anaconda3/envs/ModSimPy/lib/python3.7/site-packages/pandas/core/indexes/numeric.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    393\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mnan_idxs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 395\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    396\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    397\u001b[0m     \u001b[0;31m# ----------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/downey/anaconda3/envs/ModSimPy/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 0.6000000000000001"
+     ]
+    }
+   ],
+   "source": [
+    "coffee = make_system(T_init=90, volume=300, r=0.01, t_end=30)\n",
+    "error_func(0.01, coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "floating-trace",
+   "metadata": {},
+   "source": [
+    "The result is an error of 2.3 °C, because the final temperature with\n",
+    "this value of `r` is too high."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "conscious-closer",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-10.907066281994297"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error_func(0.02, coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "compliant-brake",
+   "metadata": {},
+   "source": [
+    "With `r=0.02`, the error is  about -11°C, which means that the final temperature is too low. So we know that the correct value must be in between.\n",
+    "\n",
+    "Now we can call `root_scalar` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "global-directory",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = root_scalar(error_func, coffee, bracket=[0.01, 0.02])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "referenced-virgin",
+   "metadata": {},
+   "source": [
+    "The first argument is the error function.\n",
+    "The second argument is the `System` object, which `root_scalar` will pass as an argument to `error_func`.\n",
+    "The third argument is an interval that brackets the root.\n",
+    "\n",
+    "Here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "swedish-separate",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "      converged: True\n",
+       "           flag: 'converged'\n",
+       " function_calls: 7\n",
+       "     iterations: 6\n",
+       "           root: 0.011543084584004043"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "complimentary-remainder",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.011543084584004043"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r_coffee = res.root\n",
+    "r_coffee"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "liable-natural",
+   "metadata": {},
+   "source": [
+    "In this example, `r_coffee` turns out to be about `0.0115`, in units of min$^{-1}$ (inverse minutes).\n",
+    "\n",
+    "We can confirm that this value is correct by setting `r` to the root we found and running the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "threaded-neutral",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "69.99999999996255"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee.r = res.root\n",
+    "results = run_simulation(coffee, change_func)\n",
+    "coffee.T_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "smaller-experience",
+   "metadata": {},
+   "source": [
+    "The result is very close to 70 C."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "obvious-integer",
+   "metadata": {},
+   "source": [
+    "## Mixing liquids\n",
+    "\n",
+    "When we mix two liquids, the temperature of the mixture depends on the\n",
+    "temperatures of the ingredients, but it might not be obvious how to\n",
+    "compute it.\n",
+    "\n",
+    "Assuming there are no chemical reactions that either produce or consume heat, the total thermal energy of the system is the same before and after mixing; in other words, thermal energy is **conserved**.\n",
+    "\n",
+    "If the temperature of the first liquid is $T_1$, the temperature of the second liquid is $T_2$, and the final temperature of the mixture is $T$, the heat transfer into the first liquid is $C_1 (T - T_1)$ and the heat transfer into the second liquid is $C_2 (T - T_2)$, where $C_1$ and $C_2$ are the thermal masses of the liquids.\n",
+    "\n",
+    "In order to conserve energy, these heat transfers must add up to 0:\n",
+    "\n",
+    "$$C_1 (T - T_1) + C_2 (T - T_2) = 0$$ \n",
+    "\n",
+    "We can solve this equation for T:\n",
+    "\n",
+    "$$T = \\frac{C_1 T_1 + C_2 T_2}{C_1 + C_2}$$ \n",
+    "\n",
+    "For the coffee cooling problem, we have the volume of each liquid; if we also know the density, $\\rho$, and the specific heat capacity, $c_p$, we can compute thermal mass: \n",
+    "\n",
+    "$$C = \\rho V c_p$$ \n",
+    "\n",
+    "If the liquids have the same density and heat capacity, they drop out of the equation, and we can write:\n",
+    "\n",
+    "$$T = \\frac{V_1 T_1 + V_2 T_2}{V_1 + V_2}$$ \n",
+    "\n",
+    "where $V_1$ and $V_2$ are the volumes of the liquids.\n",
+    "\n",
+    "As an approximation, I'll assume that milk and coffee have the same\n",
+    "density and specific heat. As an exercise, you can look up these\n",
+    "quantities and see how good this assumption is.\n",
+    "\n",
+    "The following function takes two `System` objects that represent the\n",
+    "coffee and milk, and creates a new `System` to represent the mixture:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "going-error",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mix(system1, system2):\n",
+    "    \n",
+    "    V1, V2 = system1.volume, system2.volume\n",
+    "    T1, T2 = system1.T_final, system2.T_final\n",
+    "    \n",
+    "    V_mix = V1 + V2\n",
+    "    T_mix = (V1 * T1 + V2 * T2) / V_mix\n",
+    "    \n",
+    "    return make_system(T_init=T_mix,\n",
+    "                       volume=V_mix,\n",
+    "                       r=system1.r,\n",
+    "                       t_end=30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "quick-frederick",
+   "metadata": {},
+   "source": [
+    "The first two lines extract volume and temperature from the two `System` objects. Then the following two lines compute the volume and temperature of the mixture. Finally, `mix` makes a new `System` object and returns it.\n",
+    "\n",
+    "This function uses the value of `r` from `system1` as the value of `r`\n",
+    "for the mixture. If `system1` represents the coffee, and we are adding\n",
+    "the milk to the coffee, this is probably a reasonable choice. On the\n",
+    "other hand, when we increase the amount of liquid in the coffee cup,\n",
+    "that might change `r`. So this is an assumption we might want to\n",
+    "revisit.\n",
+    "\n",
+    "Now we have everything we need to solve the problem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "composite-antibody",
+   "metadata": {},
+   "source": [
+    "## Mix first or last?\n",
+    "\n",
+    "First I'll create objects to represent the coffee and cream.\n",
+    "For `r_coffee`, I'll use the value we just computed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "id": "latin-herald",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee = make_system(T_init=90, volume=300, r=r_coffee, t_end=30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "trying-perception",
+   "metadata": {},
+   "source": [
+    "For `r_milk`, I'll use the value I estimated in the exercise from the previous chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "id": "proud-opportunity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r_milk = 0.133\n",
+    "milk = make_system(T_init=5, volume=50, r=r_milk, t_end=15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "changing-newport",
+   "metadata": {},
+   "source": [
+    "Now we can mix them and simulate 30 minutes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "id": "powerful-cradle",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "61.428571428540685"
+      ]
+     },
+     "execution_count": 237,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mix_first = mix(coffee, milk)\n",
+    "run_simulation(mix_first, change_func)\n",
+    "\n",
+    "mix_first.T_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ordered-maple",
+   "metadata": {},
+   "source": [
+    "The final temperature is 61.4 °C which is still warm enough to be\n",
+    "enjoyable. Would we do any better if we added the milk last?\n",
+    "\n",
+    "I'll simulate the coffee and milk separately, and then mix them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "painted-politics",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "63.10928927119819"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee_results = run_simulation(coffee, change_func)\n",
+    "run_simulation(milk, change_func)\n",
+    "mix_last = mix(coffee, milk)\n",
+    "mix_last.T_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "executive-transcription",
+   "metadata": {},
+   "source": [
+    "After mixing, the temperature is 63.1 °C, so it looks like adding the\n",
+    "milk at the end is better by about 1.7 °C. But is that the best we can\n",
+    "do?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "appointed-reach",
+   "metadata": {},
+   "source": [
+    "## Optimization\n",
+    "\n",
+    "Adding the milk after 30 minutes is better than adding it immediately, but maybe there's something in between that's even better. To find out, I'll use the following function, which takes the time to add the milk, `t_add`, as a parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "shared-disco",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_and_mix(t_add, t_total):\n",
+    "    coffee.t_end = t_add\n",
+    "    coffee_results = run_simulation(coffee, change_func)\n",
+    "    \n",
+    "    milk.t_end = t_add\n",
+    "    milk_results = run_simulation(milk, change_func)\n",
+    "    \n",
+    "    mixture = mix(coffee, milk)\n",
+    "    mixture.t_end = t_total - t_add\n",
+    "    results = run_simulation(mixture, change_func)\n",
+    "\n",
+    "    return mixture.T_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pleasant-webster",
+   "metadata": {},
+   "source": [
+    "When `t_add=0`, we add the milk immediately; when `t_add=30`, we add it\n",
+    "at the end. Now we can sweep the range of values in between:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "id": "authentic-barrier",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee = make_system(T_init=90, volume=300, r=r_coffee, t_end=30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "id": "tutorial-momentum",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "milk = make_system(T_init=5, volume=50, r=0.133, t_end=15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "id": "split-dance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import SweepSeries\n",
+    "from numpy import linspace\n",
+    "\n",
+    "sweep = SweepSeries()\n",
+    "for t_add in linspace(0, 30, 11):\n",
+    "    sweep[t_add] = run_and_mix(t_add, 30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "measured-consideration",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "id": "impaired-partnership",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "sweep.plot(label='mixture')\n",
+    "\n",
+    "decorate(xlabel='Time until mixing (minutes)',\n",
+    "         ylabel='Final emperature (C)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "conservative-phrase",
+   "metadata": {},
+   "source": [
+    "Note that this is a parameter sweep, not a time series. The x-axis is the time when we add the milk, not the index of a `TimeSeries`.\n",
+    "\n",
+    "The final temperature is maximized when `t_add=30`, so adding the milk\n",
+    "at the end is optimal.\n",
+    "\n",
+    "As an exercise you will have a chance to explore this solution and try some variations. For example, suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office. In that case is it better to add the milk at the coffee shop, or wait until I get to the office?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "accomplished-tulsa",
+   "metadata": {},
+   "source": [
+    "## Analysis\n",
+    "\n",
+    "Simulating Newton's law of cooling is almost silly, because we can solve the differential equation analytically. If\n",
+    "\n",
+    "$$\\frac{dT}{dt} = -r (T - T_{env})$$ \n",
+    "\n",
+    "the general solution is\n",
+    "\n",
+    "$$T{\\left (t \\right )} = C \\exp(-r t) + T_{env}$$ \n",
+    "\n",
+    "and the particular solution where $T(0) = T_{init}$ is\n",
+    "\n",
+    "$$T_{env} + \\left(- T_{env} + T_{init}\\right) \\exp(-r t)$$ \n",
+    "\n",
+    "If you would like to see this solution done by hand, you can watch this video: <http://modsimpy.com/khan3>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opponent-trace",
+   "metadata": {},
+   "source": [
+    "Now we can use the observed data to estimate the parameter $r$. If we\n",
+    "observe the that temperature at $t_{end}$ is $T_{final}$, we can plug these values into the particular solution and solve for $r$. The result is:\n",
+    "\n",
+    "$$r = \\frac{1}{t_{end}} \\log{\\left (\\frac{T_{init} - T_{env}}{T_{final} - T_{env}} \\right )}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "electoral-archives",
+   "metadata": {},
+   "source": [
+    "The following function takes a `System` object with a simulated `T_final` and computes `r`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "id": "little-bahrain",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import log\n",
+    "\n",
+    "def compute_r(system):\n",
+    "    t_end = system.t_end\n",
+    "    T_init = system.T_init\n",
+    "    T_final = system.T_final\n",
+    "    T_env = system.T_env\n",
+    "    \n",
+    "    r = log((T_init - T_env) / (T_final - T_env)) / t_end\n",
+    "    return r"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "superior-harmony",
+   "metadata": {},
+   "source": [
+    "Can can use this function to compute `r` for the coffee, given the parameters of the problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "id": "executive-queensland",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.01161022314227386"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee = make_system(T_init=90, volume=300, r=0, t_end=30)\n",
+    "coffee.T_final = 70\n",
+    "r_coffee2 = compute_r(coffee)\n",
+    "r_coffee2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "arctic-retention",
+   "metadata": {},
+   "source": [
+    "Notice that this value is not the same as `r_coffee` in the simulations.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "id": "defined-wound",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.011543084584004043"
+      ]
+     },
+     "execution_count": 199,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r_coffee"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "passive-impression",
+   "metadata": {},
+   "source": [
+    "That's because the simulations use discrete time steps, and the analysis uses continuous time.\n",
+    "\n",
+    "Nevertheless, the results of any analysis are consistent with the simulation.\n",
+    "To check, we'll use the following function, which takes a `System` object and uses the analytic result to compute a time series:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "id": "mediterranean-arbitration",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import exp\n",
+    "from pandas import Series\n",
+    "\n",
+    "def run_analysis(system):\n",
+    "    T_env, T_init, r = system.T_env, system.T_init, system.r\n",
+    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
+    "    \n",
+    "    t_array = arange(t_0, t_end+dt, dt)    \n",
+    "    T_array = T_env + (T_init - T_env) * exp(-r * t_array)\n",
+    "    \n",
+    "    system.T_final = T_array[-1]\n",
+    "    return Series(T_array, index=t_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efficient-sheffield",
+   "metadata": {},
+   "source": [
+    "The first two lines unpack the system variables.\n",
+    "`t_array` is a NumPy array of time stamps.\n",
+    "`T_array` is and array of the corresponding temperatures.\n",
+    "The result is a Pandas `Series`.\n",
+    "\n",
+    "We can run it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "id": "steady-customs",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee2 = make_system(T_init=90, r=r_coffee2, \n",
+    "                      volume=300, t_end=30)\n",
+    "results2 = run_analysis(coffee2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "id": "suspected-birthday",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "70.0"
+      ]
+     },
+     "execution_count": 206,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee2.T_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "alpha-casino",
+   "metadata": {},
+   "source": [
+    "The final temperature is 70 °C, as it should be. In fact, the results\n",
+    "are identical to what we got by simulation, with a small difference due to rounding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "laden-lloyd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee = make_system(T_init=90, volume=300, r=r_coffee, t_end=30)\n",
+    "results = run_simulation(coffee, change_func)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "id": "mighty-imperial",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from numpy import allclose\n",
+    "\n",
+    "allclose(results, results2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "common-profile",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "greatest-warrant",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Use `root_scalar` to estimate `r_milk`, given that it starts at 5 °C and reaches 20 °C after 15 minutes.  \n",
+    "\n",
+    "1. Write an error function that takes a possible value of `r`, runs a simulation, and returns the difference between `T_final` and 20 °C.\n",
+    "\n",
+    "2. Try your error function and find two values of `r` that bracket a root.\n",
+    "\n",
+    "3. Use `root_scalar` to compute `r_milk`.\n",
+    "\n",
+    "4. Run the simulation with the computed value and confirm that `T_final` is 20 °C."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "id": "powered-deficit",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def error_func2(r):\n",
+    "    system = make_system(T_init=5, volume=50, r=r, t_end=15)\n",
+    "    run_simulation(system, change_func)\n",
+    "    return system.T_final - 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "id": "after-novelty",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1.500149245609034"
+      ]
+     },
+     "execution_count": 218,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "error_func2(r=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "id": "universal-steel",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.4018656744898585"
+      ]
+     },
+     "execution_count": 219,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "error_func2(r=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "id": "frequent-albuquerque",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "      converged: True\n",
+       "           flag: 'converged'\n",
+       " function_calls: 9\n",
+       "     iterations: 8\n",
+       "           root: 0.13296078935466452"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "res = root_scalar(error_func2, bracket=[0.1, 0.2])\n",
+    "res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "id": "future-pregnancy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "r_milk = res.root"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "id": "arctic-wireless",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.999999999999996"
+      ]
+     },
+     "execution_count": 224,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "milk = make_system(T_init=5, volume=50, r=r_milk, t_end=15)\n",
+    "results = run_simulation(milk, change_func)\n",
+    "milk.T_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "competitive-kingdom",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Use `compute_r` to compute `r_milk` according to the analytic solution.  Run the analysis with this value of `r_milk` and confirm that the results are consistent with the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "id": "twenty-andrew",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.14267107756641806"
+      ]
+     },
+     "execution_count": 229,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "milk = make_system(T_init=5, volume=50, r=0, t_end=15)\n",
+    "milk.T_final = 20\n",
+    "r_milk2 = compute_r(milk)\n",
+    "r_milk2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "id": "automated-premium",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "milk2 = make_system(T_init=5, volume=50, r=r_milk2, t_end=15)\n",
+    "results2 = run_analysis(milk2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "id": "naughty-communication",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.0"
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "milk2.T_final"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "id": "architectural-hierarchy",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 232,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "from numpy import allclose\n",
+    "\n",
+    "allclose(results, results2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "regulated-liberty",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
+    "\n",
+    "Hint: Think about the simplest way to represent the behavior of a refrigerator in this model.  The change you make to test this variation of the problem should be very small!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "significant-corps",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# A refrigerator keeps the milk at a constant temperature,\n",
+    "# so it is like a container with r = 0.\n",
+    "\n",
+    "# With T_init=5 and r_milk = 0, \n",
+    "# it is best to add the milk at the beginning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "vocal-illinois",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 0c96f2bbb2d09e6238d9f3a6e43f367102828937 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sun, 31 Jan 2021 20:04:07 -0500
Subject: [PATCH 023/144] Revising chapters

---
 jupyter/chap15.ipynb |  656 ++++++++++++++++++++----
 jupyter/chap16.ipynb |  828 ++++++------------------------
 jupyter/chap17.ipynb |  665 ++++++++++++++++++++++++
 jupyter/chap18.ipynb |  869 +++++++++++++++++++++++++++++++
 jupyter/modsim.py    | 1153 ++++++++++++++++++++++++++++++++++++++++++
 5 files changed, 3390 insertions(+), 781 deletions(-)
 create mode 100644 jupyter/chap17.ipynb
 create mode 100644 jupyter/chap18.ipynb
 create mode 100644 jupyter/modsim.py

diff --git a/jupyter/chap15.ipynb b/jupyter/chap15.ipynb
index fc9b5f85..cd848e13 100644
--- a/jupyter/chap15.ipynb
+++ b/jupyter/chap15.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "third-grass",
+   "id": "located-subscription",
    "metadata": {},
    "source": [
     "# Chapter 15"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "driving-terrain",
+   "id": "american-article",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "administrative-process",
+   "id": "adjusted-motivation",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "willing-twist",
+   "id": "italic-murder",
    "metadata": {},
    "source": [
     "So far the systems we have studied have been physical in the sense that they exist in the world, but they have not been physics, in the sense of what physics classes are usually about. In the next few chapters, we'll do some physics, starting with **thermal systems**, that is, systems where the temperature of objects changes as heat transfers from one to another."
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "graduate-yahoo",
+   "id": "honey-rolling",
    "metadata": {},
    "source": [
     "## The coffee cooling problem\n",
@@ -68,8 +68,14 @@
     "\n",
     "Here is my version of the problem:\n",
     "\n",
-    "> Suppose I stop on the way to work to pick up a cup of coffee, which I take with milk. Assuming that I want the coffee to be as hot as possible when I arrive at work, should I add the milk at the coffee shop, wait until I get to work, or add the milk at some point in between?\n",
-    "\n",
+    "> Suppose I stop on the way to work to pick up a cup of coffee, which I take with milk. Assuming that I want the coffee to be as hot as possible when I arrive at work, should I add the milk at the coffee shop, wait until I get to work, or add the milk at some point in between?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "appreciated-ukraine",
+   "metadata": {},
+   "source": [
     "To help answer this question, I made a trial run with the milk and\n",
     "coffee in separate containers and took some measurements (not really):\n",
     "\n",
@@ -92,7 +98,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "technological-invention",
+   "id": "juvenile-republican",
    "metadata": {},
    "source": [
     "## Temperature and heat\n",
@@ -111,9 +117,14 @@
     "\n",
     "$$Q = C~\\Delta T$$ \n",
     "\n",
-    "where $Q$ is the\n",
-    "amount of heat transferred to an object, $\\Delta T$ is resulting change in temperature, and $C$ is the **thermal mass** of the object, which quantifies how much energy it takes to heat or cool it. In SI units, thermal mass is measured in joules per degree Celsius (J/°C).\n",
-    "\n",
+    "where $Q$ is the amount of heat transferred to an object, $\\Delta T$ is resulting change in temperature, and $C$ is the **thermal mass** of the object, which quantifies how much energy it takes to heat or cool it. In SI units, thermal mass is measured in joules per degree Celsius (J/°C)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "purple-edward",
+   "metadata": {},
+   "source": [
     "For objects made primarily from one material, thermal mass can be\n",
     "computed like this: \n",
     "\n",
@@ -140,7 +151,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hidden-sandwich",
+   "id": "running-metropolitan",
    "metadata": {},
    "source": [
     "## Heat transfer\n",
@@ -150,8 +161,7 @@
     "\n",
     "-   Conduction: When objects at different temperatures come into\n",
     "    contact, the faster-moving particles of the higher-temperature\n",
-    "    object transfer kinetic energy to the slower-moving particles of the\n",
-    "    lower-temperature object.\n",
+    "    object transfer kinetic energy to the slower-moving particles of the lower-temperature object.\n",
     "\n",
     "-   Convection: When particles in a gas or liquid flow from place to\n",
     "    place, they carry heat energy with them. Fluid flows can be caused\n",
@@ -162,8 +172,14 @@
     "-   Radiation: As the particles in an object move due to thermal energy,\n",
     "    they emit electromagnetic radiation. The energy carried by this\n",
     "    radiation depends on the object's temperature and surface properties\n",
-    "    (see <http://modsimpy.com/thermrad>).\n",
-    "\n",
+    "    (see <http://modsimpy.com/thermrad>)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "convertible-alabama",
+   "metadata": {},
+   "source": [
     "For objects like coffee in a car, the effect of radiation is much\n",
     "smaller than the effects of conduction and convection, so we will ignore it.\n",
     "\n",
@@ -172,7 +188,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "precious-phone",
+   "id": "expected-implementation",
    "metadata": {},
    "source": [
     "## Newton's law of cooling\n",
@@ -182,8 +198,14 @@
     "\n",
     "$$\\frac{dT}{dt} = -r (T - T_{env})$$ \n",
     "\n",
-    "where $T$, the temperature of the object, is a function of time, $t$; $T_{env}$ is the temperature of the environment, and $r$ is a constant that characterizes how quickly heat is transferred between the system and the environment.\n",
-    "\n",
+    "where $T$, the temperature of the object, is a function of time, $t$; $T_{env}$ is the temperature of the environment, and $r$ is a constant that characterizes how quickly heat is transferred between the system and the environment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "educated-accommodation",
+   "metadata": {},
+   "source": [
     "Newton's so-called \"law \" is really a model: it is a good approximation in some conditions and less good in others.\n",
     "\n",
     "For example, if the primary mechanism of heat transfer is conduction,\n",
@@ -203,20 +225,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "complex-statement",
+   "id": "determined-seventh",
    "metadata": {},
    "source": [
     "## Implementation\n",
     "\n",
     "To get started, let's forget about the milk temporarily and focus on the coffee. \n",
     "\n",
-    "Here's a `System` object that contains the parameters of the system:"
+    "Here's a function that takes the parameters of the system and makes a `System` object:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "advisory-stations",
+   "id": "geological-graph",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -224,6 +246,7 @@
     "\n",
     "def make_system(T_init, volume, r, t_end):\n",
     "    return System(T_init=T_init,\n",
+    "                  T_final=T_init,\n",
     "                  volume=volume,\n",
     "                  r=r,\n",
     "                  t_end=t_end,\n",
@@ -232,10 +255,18 @@
     "                  dt=1)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "further-allen",
+   "metadata": {},
+   "source": [
+    "The values of `T_init`, `volume`, and `t_end` come from the statement of the problem:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "simple-shelter",
+   "id": "eastern-alignment",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -244,32 +275,30 @@
   },
   {
    "cell_type": "markdown",
-   "id": "legal-shift",
+   "id": "corrected-cross",
    "metadata": {},
    "source": [
-    "The values of `volume`, `T_env`, and `t_end` come from the statement of the problem. I chose the value of `r` arbitrarily for now; we will\n",
-    "figure out how to estimate it soon.\n",
+    "I chose the value of `r` arbitrarily for now; we will figure out how to estimate it soon.\n",
+    "\n",
+    "In addition, `make_system` sets the temperature of the environment, `T_env`, and the time step, `dt`, which we will use use to simulate the cooling process.\n",
     "\n",
-    "`dt` is the time step we use to simulate the cooling process. Strictly\n",
-    "speaking, Newton's law is a differential equation, but over a short\n",
-    "period of time we can approximate it with a difference equation:\n",
+    "Strictly speaking, Newton's law is a differential equation, but over a short period of time we can approximate it with a difference equation:\n",
     "\n",
     "$$\\Delta T = -r (T - T_{env}) dt$$ \n",
     "\n",
-    "where $dt$ is a small time step and\n",
-    "$\\Delta T$ is the change in temperature during that time step.\n",
+    "where $dt$ is the time step and $\\Delta T$ is the change in temperature during that time step.\n",
     "\n",
     "Note: I use $\\Delta T$ to denote a change in temperature over time, but in the context of heat transfer, you might also see $\\Delta T$ used to denote the difference in temperature between an object and its\n",
     "environment, $T - T_{env}$. To minimize confusion, I avoid this second\n",
     "use.\n",
     "\n",
-    "Now we can write an update function:"
+    "The following function takes the current temperature, `T`, the current time `t`, and a `System` object, and computes the change in temperature during a time step:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "organic-morrison",
+   "id": "vulnerable-sarah",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -280,19 +309,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "manufactured-alfred",
+   "id": "unknown-programming",
    "metadata": {},
    "source": [
-    "Like previous update functions, this one takes a `State` object, a time,\n",
-    "and a `System` object.\n",
-    "\n",
-    "Now if we run"
+    "We can test it with the initial temperature of the coffee, like this:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "olympic-bailey",
+   "id": "killing-tragedy",
    "metadata": {},
    "outputs": [
     {
@@ -312,46 +338,44 @@
   },
   {
    "cell_type": "markdown",
-   "id": "synthetic-montana",
+   "id": "geographic-female",
    "metadata": {},
    "source": [
-    "we see that the temperature after one minute is 89.3 °C, so the\n",
-    "temperature drops by about 0.7 °C/min, at least for this value of `r`.\n",
+    "With `dt=1` minute, the temperature drops by about 0.7 °C/min, at least for this value of `r`.\n",
     "\n",
-    "Here's a version of `run_simulation` that simulates a series of time\n",
-    "steps from `t_0` to `t_end`:"
+    "Now here's a version of `run_simulation` that simulates a series of time steps from `t_0` to `t_end`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "synthetic-danish",
+   "id": "basic-extra",
    "metadata": {},
    "outputs": [],
    "source": [
     "from modsim import linrange\n",
-    "from pandas import Series\n",
-    "from numpy import empty\n",
+    "from modsim import TimeSeries\n",
     "\n",
     "def run_simulation(system, change_func):\n",
     "    t_array = linrange(system.t_0, system.t_end, system.dt)\n",
     "    n = len(t_array)\n",
     "    \n",
-    "    T_array = empty(n)\n",
-    "    T_array[0] = system.T_init\n",
+    "    series = TimeSeries(index=t_array)\n",
+    "    series.iloc[0] = system.T_init\n",
     "    \n",
     "    for i in range(n-1):\n",
     "        t = t_array[i]\n",
-    "        T = T_array[i]\n",
-    "        T_array[i+1] = T + change_func(T, t, system)\n",
+    "        T = series.iloc[i]\n",
+    "        series.iloc[i+1] = T + change_func(T, t, system)\n",
     "    \n",
-    "    system.T_final = T_array[-1]\n",
-    "    return Series(T_array, index=t_array)"
+    "    system.t_end = t_array[-1]\n",
+    "    system.T_final = series.iloc[-1]\n",
+    "    return series"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "finite-paraguay",
+   "id": "voluntary-velvet",
    "metadata": {},
    "source": [
     "This function is similar to previous versions of `run_simulation`.\n",
@@ -365,7 +389,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "virtual-notebook",
+   "id": "after-championship",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -374,7 +398,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "impressed-provincial",
+   "id": "consecutive-party",
    "metadata": {},
    "source": [
     "The result is a `TimeSeries` with one row per time step. "
@@ -383,18 +407,19 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "realistic-imagination",
+   "id": "proof-guard",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "26.0    74.362934\n",
-       "27.0    73.839305\n",
-       "28.0    73.320912\n",
-       "29.0    72.807702\n",
-       "30.0    72.299625\n",
-       "dtype: float64"
+       "Time\n",
+       "0.0    90.000000\n",
+       "1.0    89.320000\n",
+       "2.0    88.646800\n",
+       "3.0    87.980332\n",
+       "4.0    87.320529\n",
+       "Name: Quantity, dtype: float64"
       ]
      },
      "execution_count": 8,
@@ -403,13 +428,58 @@
     }
    ],
    "source": [
-    "results.tail()"
+    "results.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "limiting-legislation",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "seven-poker",
+   "id": "expected-company",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "results.plot(label='coffee')\n",
+    "\n",
+    "decorate(xlabel='Time (minute)',\n",
+    "         ylabel='Temperature (C)',\n",
+    "         title='Coffee Cooling')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "alien-score",
+   "metadata": {},
+   "source": [
+    "The temperature after 30 minutes is 72.3 °C, which is a little higher than what's stated in the problem, 70 °C. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "protecting-picture",
    "metadata": {},
    "outputs": [
     {
@@ -418,7 +488,7 @@
        "72.2996253904031"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -429,74 +499,390 @@
   },
   {
    "cell_type": "markdown",
-   "id": "seven-costume",
+   "id": "tight-operation",
    "metadata": {},
    "source": [
-    "The temperature after 30 minutes is 72.3 °C, which is a little higher than stated in the problem, 70 °C. "
+    "By trial and error, we could find the value of `r` where the final temperature is precisely 70 °C.\n",
+    "But it is more efficient to use a root-finding algorithm."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "acoustic-provider",
+   "id": "equipped-blackberry",
    "metadata": {},
    "source": [
-    "We can adjust `r` and find the right value by trial and error, but we'll see a better way in the next chapter."
+    "## Finding roots\n",
+    "\n",
+    "The SciPy library provides a function called `root_scalar` that finds the roots of non-linear equations. As a simple example, suppose you want to find the roots of the polynomial \n",
+    "\n",
+    "$$f(x) = (x - 1)(x - 2)(x - 3)$$ \n",
+    "\n",
+    "A **root** is a value of $x$ that makes $f(x)=0$. Because of the way I wrote the polynomial, we can see that if $x=1$, the first factor is 0; if $x=2$, the second factor is 0; and if $x=3$, the third factor is 0, so those are the roots.\n",
+    "\n",
+    "I'll use this example to demonstrate `root_scalar`. First, we have to\n",
+    "write a function that evaluates $f$:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "demanding-spending",
+   "execution_count": 11,
+   "id": "round-budapest",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def func(x):\n",
+    "    return (x-1) * (x-2) * (x-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "injured-limitation",
+   "metadata": {},
+   "source": [
+    "Now we call `root_scalar` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "regulated-cloud",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "      converged: True\n",
+       "           flag: 'converged'\n",
+       " function_calls: 3\n",
+       "     iterations: 2\n",
+       "           root: 2.0"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.optimize import root_scalar\n",
+    "\n",
+    "res = root_scalar(func, bracket=[1.5, 2.5])\n",
+    "res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "regulated-weekend",
+   "metadata": {},
+   "source": [
+    "The first argument is the function whose roots we want. The second\n",
+    "argument is an interval that contains or \"brackets\" a root. The result is an object that contains several variables, including `root`, which stores the root that was found."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "advised-climate",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0"
+      ]
      },
-     "output_type": "display_data"
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "from modsim import decorate\n",
+    "res.root"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "changing-winter",
+   "metadata": {},
+   "source": [
+    "If we provide a different interval, we find a different root."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "authentic-hunter",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res = root_scalar(func, bracket=[2.5, 3.5])\n",
+    "res.root"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bound-freight",
+   "metadata": {},
+   "source": [
+    "If the interval doesn't contain a root, you'll get a `ValueError`:\n",
     "\n",
-    "results.plot(label='coffee')\n",
+    "```\n",
+    "res = root_scalar(func, bracket=[4, 5])\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "caroline-divide",
+   "metadata": {},
+   "source": [
+    "Now we can use `root_scalar` to estimate `r`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "imperial-engineering",
+   "metadata": {},
+   "source": [
+    "## Estimating `r`\n",
     "\n",
-    "decorate(xlabel='Time (minute)',\n",
-    "         ylabel='Temperature (C)',\n",
-    "         title='Coffee Cooling')"
+    "What we want is the value of `r` that yields a final temperature of\n",
+    "70 °C. To use `root_scalar`, we need a function that takes `r` as a parameter and returns the difference between the final temperature and the goal:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "quality-colors",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def error_func(r, system):\n",
+    "    system.r = r\n",
+    "    results = run_simulation(system, change_func)\n",
+    "    return system.T_final - 70"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "random-loading",
+   "metadata": {},
+   "source": [
+    "This is called an \"error function\" because it returns the\n",
+    "difference between what we got and what we wanted, that is, the error.\n",
+    "With the right value of `r`, the error is 0.\n",
+    "\n",
+    "We can test `error_func` like this, using the initial guess `r=0.01`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "unknown-pharmacy",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.2996253904030937"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee = make_system(T_init=90, volume=300, r=0.01, t_end=30)\n",
+    "error_func(0.01, coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "moderate-monkey",
+   "metadata": {},
+   "source": [
+    "The result is an error of 2.3 °C, because the final temperature with\n",
+    "this value of `r` is too high."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "fossil-israel",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-10.907066281994297"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error_func(0.02, coffee)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "automatic-standard",
+   "id": "south-fabric",
+   "metadata": {},
+   "source": [
+    "With `r=0.02`, the error is  about -11°C, which means that the final temperature is too low. So we know that the correct value must be in between.\n",
+    "\n",
+    "So we can call `root_scalar` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "backed-while",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = root_scalar(error_func, coffee, bracket=[0.01, 0.02])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exotic-development",
+   "metadata": {},
+   "source": [
+    "The first argument is the error function.\n",
+    "The second argument is the `System` object, which `root_scalar` passes as an argument to `error_func`.\n",
+    "The third argument is an interval that brackets the root.\n",
+    "\n",
+    "Here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "crazy-panic",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "      converged: True\n",
+       "           flag: 'converged'\n",
+       " function_calls: 7\n",
+       "     iterations: 6\n",
+       "           root: 0.011543084584004043"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "early-twelve",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.011543084584004043"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r_coffee = res.root\n",
+    "r_coffee"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "greenhouse-designer",
+   "metadata": {},
+   "source": [
+    "In this example, `r_coffee` turns out to be about `0.0115`, in units of min$^{-1}$ (inverse minutes).\n",
+    "\n",
+    "We can confirm that this value is correct by setting `r` to the root we found and running the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "foster-lodging",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "69.99999999996255"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee.r = res.root\n",
+    "run_simulation(coffee, change_func)\n",
+    "coffee.T_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sweet-horizontal",
+   "metadata": {},
+   "source": [
+    "The final temperature is very close to 70 °C."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "imposed-complement",
    "metadata": {},
    "source": [
     "## Exercises\n",
     "\n",
-    "**Exercise:**  Simulate the temperature of 50 mL of milk with a starting temperature of 5 degC, in a vessel with the same insulation, for 15 minutes, and plot the results.\n",
+    "**Exercise:**  Simulate the temperature of 50 mL of milk with a starting temperature of 5 °C, in a vessel with `r=0.1`, for 15 minutes, and plot the results.\n",
     "\n",
-    "By trial and error, find a value for `r` that makes the final temperature close to 20 C."
+    "By trial and error, find a value for `r` that makes the final temperature close to 20 °C."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
-   "id": "maritime-pillow",
+   "execution_count": 22,
+   "id": "radical-accident",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "20.00135627897414"
+       "18.499850754390966"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -504,20 +890,20 @@
    "source": [
     "# Solution\n",
     "\n",
-    "milk = make_system(T_init=5, t_end=15, r=0.133, volume=50)\n",
+    "milk = make_system(T_init=5, t_end=15, r=0.1, volume=50)\n",
     "results_milk = run_simulation(milk, change_func)\n",
     "milk.T_final"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
-   "id": "killing-kitchen",
+   "execution_count": 23,
+   "id": "extraordinary-andrew",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -537,10 +923,92 @@
     "         ylabel='Temperature (C)')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "hispanic-ghana",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write an error function that simulates the temperature of the milk and returns the difference between the final temperature and 20 °C.  Use it to estimate the value of `r` for the milk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "naughty-organ",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def error_func2(r, system):\n",
+    "    system.r = r\n",
+    "    results = run_simulation(system, change_func)\n",
+    "    return system.T_final - 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "separate-amateur",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "      converged: True\n",
+       "           flag: 'converged'\n",
+       " function_calls: 9\n",
+       "     iterations: 8\n",
+       "           root: 0.13296078935466452"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "root_scalar(error_func2, milk, bracket=[0.1, 0.2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "cloudy-reminder",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.999999999999996"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "run_simulation(milk, change_func)\n",
+    "milk.T_final"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "present-french",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "buried-sweden",
+   "id": "mighty-transport",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap16.ipynb b/jupyter/chap16.ipynb
index 6aacefe6..d704de50 100644
--- a/jupyter/chap16.ipynb
+++ b/jupyter/chap16.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "nearby-contemporary",
+   "id": "involved-hampshire",
    "metadata": {},
    "source": [
     "# Chapter 16"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "boxed-certificate",
+   "id": "senior-paintball",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -26,8 +26,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
-   "id": "veterinary-timer",
+   "execution_count": 1,
+   "id": "surprising-hypothesis",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "coral-content",
+   "id": "capable-programming",
    "metadata": {},
    "source": [
     "## Code from previous notebooks"
@@ -58,8 +58,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
-   "id": "romantic-edgar",
+   "execution_count": 2,
+   "id": "straight-milan",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,33 +71,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 254,
-   "id": "unlikely-protest",
+   "execution_count": 3,
+   "id": "enormous-economy",
    "metadata": {},
    "outputs": [],
    "source": [
+    "from modsim import linrange\n",
     "from modsim import TimeSeries\n",
-    "from numpy import arange\n",
     "\n",
     "def run_simulation(system, change_func):\n",
-    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
+    "    t_array = linrange(system.t_0, system.t_end, system.dt)\n",
+    "    n = len(t_array)\n",
     "    \n",
-    "    results = TimeSeries(quantity='Temperature')\n",
-    "    results[t_0] = system.T_init\n",
+    "    series = TimeSeries(index=t_array)\n",
+    "    series.iloc[0] = system.T_init\n",
     "    \n",
-    "    for t in arange(t_0, t_end, dt):\n",
-    "        T = results[t]\n",
-    "        results[t+dt] = T + change_func(T, t, system)\n",
+    "    for i in range(n-1):\n",
+    "        t = t_array[i]\n",
+    "        T = series.iloc[i]\n",
+    "        series.iloc[i+1] = T + change_func(T, t, system)\n",
     "    \n",
-    "    system.t_end = results.index[-1]\n",
-    "    system.T_final = results[system.t_end]\n",
-    "    return results"
+    "    system.t_end = t_array[-1]\n",
+    "    system.T_final = series.iloc[-1]\n",
+    "    return series"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 255,
-   "id": "rubber-norway",
+   "execution_count": 4,
+   "id": "defined-expert",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,371 +113,24 @@
     "                  t_end=t_end,\n",
     "                  T_env=22,\n",
     "                  t_0=0,\n",
-    "                  dt=0.1)"
+    "                  dt=1)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "tamil-mobile",
+   "id": "global-shooting",
    "metadata": {},
    "source": [
     "In the previous chapter we wrote a simulation of a cooling cup of\n",
     "coffee. Given the initial temperature of the coffee, the temperature of the atmosphere, and the rate parameter, `r`, we can predict how the\n",
     "temperature of the coffee will change over time.\n",
     "\n",
-    "In general, we don't know the value of `r`, but we can use measurements to estimate it. Given an initial temperature, a final temperature, and the time in between, we can find `r` by trial and error.\n",
-    "\n",
-    "In this chapter, we'll see a better way to find `r`, using a **bisection search**.\n",
-    "\n",
-    "And then we'll get back to solving the coffee cooling problem."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "requested-pantyhose",
-   "metadata": {},
-   "source": [
-    "## Finding roots\n",
-    "\n",
-    "The ModSim library provides a method called `root_bisect` that finds the roots of non-linear equations. As a simple example, suppose you want to find the roots of the polynomial \n",
-    "\n",
-    "$$f(x) = (x - 1)(x - 2)(x - 3)$$ \n",
-    "\n",
-    "where **root** means a value of $x$ that makes $f(x)=0$. Because of the way I wrote the polynomial, we can see that if $x=1$, the first factor is 0; if $x=2$, the second factor is 0; and if $x=3$, the third factor is 0, so those are the roots.\n",
-    "\n",
-    "I'll use this example to demonstrate `root_bisect`. First, we have to\n",
-    "write a function that evaluates $f$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "id": "three-reporter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def func(x):\n",
-    "    return (x-1) * (x-2) * (x-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sharing-transcript",
-   "metadata": {},
-   "source": [
-    "Now we call `root_bisect` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "id": "stable-costa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "      converged: True\n",
-       "           flag: 'converged'\n",
-       " function_calls: 3\n",
-       "     iterations: 2\n",
-       "           root: 2.0"
-      ]
-     },
-     "execution_count": 257,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from scipy.optimize import root_scalar\n",
-    "\n",
-    "res = root_scalar(func, bracket=[1.5, 2.5])\n",
-    "res"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "secondary-sapphire",
-   "metadata": {},
-   "source": [
-    "The first argument is the function whose roots we want. The second\n",
-    "argument is an interval that contains a root. The result is an object\n",
-    "that contains several variables, including `root`, which stores the root\n",
-    "that was found."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "nonprofit-jordan",
-   "metadata": {},
-   "source": [
-    "If we provide a different interval, we find a different root."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "id": "earned-norfolk",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.0"
-      ]
-     },
-     "execution_count": 258,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res = root_scalar(func, bracket=[2.5, 3.5])\n",
-    "\n",
-    "res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "assisted-oregon",
-   "metadata": {},
-   "source": [
-    "If the interval doesn't contain a root, you'll get a `ValueError`:\n",
-    "\n",
-    "```\n",
-    "res = root_scalar(func, bracket=[4, 5])\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "selective-sector",
-   "metadata": {},
-   "source": [
-    "## Estimating `r`\n",
-    "\n",
-    "So how can we use `root_scalar` to estimate `r`?\n",
-    "\n",
-    "What we want is the value of `r` that yields a final temperature of\n",
-    "70 °C. To work with `root_scalar`, we need a function that takes `r` as a parameter and returns the difference between the final temperature and the goal:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 259,
-   "id": "empirical-leisure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func(r, system):\n",
-    "    system.r = r\n",
-    "    results = run_simulation(system, change_func)\n",
-    "    return system.T_final - 70"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "green-manitoba",
-   "metadata": {},
-   "source": [
-    "I call a function like this an \"error function\\\" because it returns the\n",
-    "difference between what we got and what we wanted, that is, the error.\n",
-    "When we find the right value of `r`, this error will be 0.\n",
-    "\n",
-    "We can test `error_func` like this, using the initial guess `r=0.01`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 260,
-   "id": "cubic-shopping",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "0.6000000000000001",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m/home/downey/anaconda3/envs/ModSimPy/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 0.6000000000000001",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-260-038f0cf9b128>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcoffee\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_system\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mT_init\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m90\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvolume\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_end\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0merror_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoffee\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-259-97c7981df9e1>\u001b[0m in \u001b[0;36merror_func\u001b[0;34m(r, system)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0merror_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msystem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0msystem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_simulation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchange_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0msystem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT_final\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m70\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-254-7a85a112ecb2>\u001b[0m in \u001b[0;36mrun_simulation\u001b[0;34m(system, change_func)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_end\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m         \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mT\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mchange_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msystem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/downey/anaconda3/envs/ModSimPy/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    822\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    823\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 824\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    825\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    826\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/downey/anaconda3/envs/ModSimPy/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m    930\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 932\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    933\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    934\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/downey/anaconda3/envs/ModSimPy/lib/python3.7/site-packages/pandas/core/indexes/numeric.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    393\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mnan_idxs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 395\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    396\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    397\u001b[0m     \u001b[0;31m# ----------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/downey/anaconda3/envs/ModSimPy/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 0.6000000000000001"
-     ]
-    }
-   ],
-   "source": [
-    "coffee = make_system(T_init=90, volume=300, r=0.01, t_end=30)\n",
-    "error_func(0.01, coffee)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "floating-trace",
-   "metadata": {},
-   "source": [
-    "The result is an error of 2.3 °C, because the final temperature with\n",
-    "this value of `r` is too high."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "id": "conscious-closer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-10.907066281994297"
-      ]
-     },
-     "execution_count": 165,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "error_func(0.02, coffee)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "compliant-brake",
-   "metadata": {},
-   "source": [
-    "With `r=0.02`, the error is  about -11°C, which means that the final temperature is too low. So we know that the correct value must be in between.\n",
-    "\n",
-    "Now we can call `root_scalar` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "id": "global-directory",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = root_scalar(error_func, coffee, bracket=[0.01, 0.02])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "referenced-virgin",
-   "metadata": {},
-   "source": [
-    "The first argument is the error function.\n",
-    "The second argument is the `System` object, which `root_scalar` will pass as an argument to `error_func`.\n",
-    "The third argument is an interval that brackets the root.\n",
-    "\n",
-    "Here are the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "id": "swedish-separate",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "      converged: True\n",
-       "           flag: 'converged'\n",
-       " function_calls: 7\n",
-       "     iterations: 6\n",
-       "           root: 0.011543084584004043"
-      ]
-     },
-     "execution_count": 167,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "id": "complimentary-remainder",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.011543084584004043"
-      ]
-     },
-     "execution_count": 170,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r_coffee = res.root\n",
-    "r_coffee"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "liable-natural",
-   "metadata": {},
-   "source": [
-    "In this example, `r_coffee` turns out to be about `0.0115`, in units of min$^{-1}$ (inverse minutes).\n",
-    "\n",
-    "We can confirm that this value is correct by setting `r` to the root we found and running the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 171,
-   "id": "threaded-neutral",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "69.99999999996255"
-      ]
-     },
-     "execution_count": 171,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee.r = res.root\n",
-    "results = run_simulation(coffee, change_func)\n",
-    "coffee.T_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "smaller-experience",
-   "metadata": {},
-   "source": [
-    "The result is very close to 70 C."
+    "..."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "obvious-integer",
+   "id": "ceramic-calibration",
    "metadata": {},
    "source": [
     "## Mixing liquids\n",
@@ -516,8 +171,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
-   "id": "going-error",
+   "execution_count": 5,
+   "id": "orange-beaver",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -537,7 +192,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "quick-frederick",
+   "id": "quiet-payroll",
    "metadata": {},
    "source": [
     "The first two lines extract volume and temperature from the two `System` objects. Then the following two lines compute the volume and temperature of the mixture. Finally, `mix` makes a new `System` object and returns it.\n",
@@ -554,28 +209,29 @@
   },
   {
    "cell_type": "markdown",
-   "id": "composite-antibody",
+   "id": "talented-appendix",
    "metadata": {},
    "source": [
     "## Mix first or last?\n",
     "\n",
-    "First I'll create objects to represent the coffee and cream.\n",
-    "For `r_coffee`, I'll use the value we just computed."
+    "First I'll create objects to represent the coffee and milk.\n",
+    "For `r_coffee` and `r_milk`, I'll use the values we computed in the previous chapter."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
-   "id": "latin-herald",
+   "execution_count": 6,
+   "id": "breathing-pizza",
    "metadata": {},
    "outputs": [],
    "source": [
+    "r_coffee = 0.0115\n",
     "coffee = make_system(T_init=90, volume=300, r=r_coffee, t_end=30)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "trying-perception",
+   "id": "miniature-doctrine",
    "metadata": {},
    "source": [
     "For `r_milk`, I'll use the value I estimated in the exercise from the previous chapter."
@@ -583,8 +239,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
-   "id": "proud-opportunity",
+   "execution_count": 7,
+   "id": "faced-start",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -594,7 +250,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "changing-newport",
+   "id": "interior-alpha",
    "metadata": {},
    "source": [
     "Now we can mix them and simulate 30 minutes:"
@@ -602,17 +258,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
-   "id": "powerful-cradle",
+   "execution_count": 8,
+   "id": "stock-release",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "61.428571428540685"
+       "61.48016207445017"
       ]
      },
-     "execution_count": 237,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -626,10 +282,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ordered-maple",
+   "id": "committed-preference",
    "metadata": {},
    "source": [
-    "The final temperature is 61.4 °C which is still warm enough to be\n",
+    "The final temperature is 61.5 °C which is still warm enough to be\n",
     "enjoyable. Would we do any better if we added the milk last?\n",
     "\n",
     "I'll simulate the coffee and milk separately, and then mix them:"
@@ -637,23 +293,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
-   "id": "painted-politics",
+   "execution_count": 9,
+   "id": "integral-character",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "63.10928927119819"
+       "62.91117032872072"
       ]
      },
-     "execution_count": 152,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "coffee_results = run_simulation(coffee, change_func)\n",
+    "run_simulation(coffee, change_func)\n",
     "run_simulation(milk, change_func)\n",
     "mix_last = mix(coffee, milk)\n",
     "mix_last.T_final"
@@ -661,17 +317,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "executive-transcription",
+   "id": "trying-jacob",
    "metadata": {},
    "source": [
-    "After mixing, the temperature is 63.1 °C, so it looks like adding the\n",
-    "milk at the end is better by about 1.7 °C. But is that the best we can\n",
-    "do?"
+    "After mixing, the temperature is 62.9 °C, so it looks like adding the\n",
+    "milk at the end is better. \n",
+    "But is that the best we can do?"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "appointed-reach",
+   "id": "enclosed-shore",
    "metadata": {},
    "source": [
     "## Optimization\n",
@@ -681,8 +337,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
-   "id": "shared-disco",
+   "execution_count": 10,
+   "id": "accepting-consumer",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -702,37 +358,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "pleasant-webster",
-   "metadata": {},
-   "source": [
-    "When `t_add=0`, we add the milk immediately; when `t_add=30`, we add it\n",
-    "at the end. Now we can sweep the range of values in between:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "id": "authentic-barrier",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee = make_system(T_init=90, volume=300, r=r_coffee, t_end=30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 245,
-   "id": "tutorial-momentum",
+   "id": "critical-pound",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "milk = make_system(T_init=5, volume=50, r=0.133, t_end=15)"
+    "When `t_add` is`0`, we add the milk immediately; when `t_add` is `30`, we add it at the end. Now we can sweep the range of values in between:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 246,
-   "id": "split-dance",
+   "execution_count": 11,
+   "id": "isolated-georgia",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -746,7 +381,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "measured-consideration",
+   "id": "latin-fireplace",
    "metadata": {},
    "source": [
     "Here's what the results look like:"
@@ -754,13 +389,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 247,
-   "id": "impaired-partnership",
+   "execution_count": 12,
+   "id": "possible-prevention",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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W4L3fuvsbsTsKqWn27d/HXxf/lefSn6NVo1Y8deZTDO4wOOyyRKSYWLegHgPecvdRZpYAJAKzgN+4e76ZPQT8Brit6EbuvhLoD2BmdYH1wPQiq/zF3f8U49qlBvps62eM+3Acq79dzQ+7/5BfDfyVWk0icSpmAWVmTYEhwBgAd88l0mqaWWS1+cCocnY1HPjS3dfGoEypJfbt38eTi59kcvpkWie25ukzn+bUDqeGXZaIlCGWQy53JXIaLs3MPjWziWZWfGyYq4A3y9nPpcALxZbdaGafmdkkM2tRSfVKDbVk6xIu+dclpC1L46JuF/GPC/6hcBKpBmIZUPWAAcB4dz8e2AXcXvimmd0B5ANTS9tBcFrwAuDlIovHA0cTOQW4EXiklG2vNbOFZrZw69atJa0iNdze/L38eeGfufLNK9mTv4enz3yae069R6f0RKqJWF6DygKy3H1B8PoVgoAys9HA94Hh7u5l7OMc4BN331y4oOhzM3sGeL2kDd19AjABYODAgWV9htRAi7csZtyH41iTvYZRx4ziVyf8iiYJTcIuS0QqIGYB5e6bzCzTzHoEnR6GAxlmdjaRThFnuPvucnbzE4qd3jOzdu6+MXh5EbCssmuX6mtv/l6eXPwkUzKm0CaxDRPOmsAp7U8JuywROQyx7sX3c2BqcKpuNTAW+BhoAMwyM4D57n69mbUHJrr7uQBmlgicBVxXbJ8Pm1l/It3W15TwvtRSRVtNPzrmR/xy4C81JYZINRbTgHL3xcDAYou7lbLuBuDcIq93Ay1LWO+KSixRaoC9+Xt54tMnmJIxhXaN2/HMiGc4ud3JYZclIkeozIAys1OAy4HTgXbAHiKn1P4NPO/u38a8QpEyfLrlU+768C7WZK/hxz1+zM0n3KxWk0gNUWpAmdmbwAbgNeD3wBagIXAMMAx4zcz+7O4zqqJQkaL25O/h/z79P57PeJ52jdsxccRETmp3UthliUglKqsFdYW7byu2bCfwSfB4xMxSYlaZSCk+2fwJd/33LtZmr1WrSaQGKyugmgc98D4sutDMTgc2uPuXJQSYSMzsyd/D4588ztTlU2nfpD3PjniWQe0GhV2WiMRIWQH1KPDbEpbvCd47Pwb1iJRow84NXDfrOtZkr+HSHpdy8wk3k1g/MeyyRCSGygqozu7+WfGF7r7QzDrHriSRg2XlZHH121eTk5eja00itUhZAdWwjPcaVXYhIiXJzM7kqplXsTtvNxNHTKR3y95hlyQiVaSssfg+NrNrii80s6uBRbErSSRibfZaxr49lr35e3l25LMKJ5FapqwW1C+A6WZ2Gd8F0kAggcgQQyIx89W3X/Gzt39GXkEeE0dMpEdyj7BLEpEqVmpABYOynmpmw4jMiAvwb3d/t0oqk1pr9TeruXrm1RR4Ac+OfJbuLbqHXZKIhKCsG3WbuPtOd38PeK+sdWJWndQ6q75exc9m/gwzI21kGl2bdw27JBEJSVnXoF4zs0fMbEjRiQbNrKuZXW1mbwNnx75EqS0+//pzrp55NXWsDpNGTlI4idRyZZ3iG25m5xIZLXywmSUDecBKImPxjXb3TVVTptR0K3es5Gczf0ZC3QQmjZzEUU2PCrskEQlZmYPFuvsbwBtVVIvUUsu3L+eaWdfQqF4jJo2YRGrT1LBLEpE4EMsp30XKlb4tnatnXk1ivUQmjVQ4ich3FFASmqVbl3LNzGtomtCUtLPTSE1SOInIdxRQEoolW5dw7axradagGWkj0+jQpEPYJYlInIkqoMzsNDMbGzxvZWZdYluW1GSfbvmU62ZdR3LDZNLOTqNdk3ZhlyQicajcgDKzu4HbgN8Ei+oDz8eyKKm5Fm1exHWzrqNVo1ZMGjmJto3bhl2SiMSpaFpQFwEXALsA3H0DkBTLoqRm+njTx/zPO/9D28ZtmTRyEm0atwm7JBGJY9EEVK67O+AARW/aFYnW/I3z+X/v/D/aN27PpJGTaJXYKuySRCTORRNQfzezp4nMsHsN8A7wTGzLkprkv+v/y42zb6RjUkeeHfksKY1Swi5JRKqBMm/UNTMDXgJ6AtlAD+Aud59VBbVJDTB3/VxuevcmujTrwjMjnqFFwxZhlyQi1UR5I0m4mf3T3U8AKhxKZtYcmEhkNHQHrgIuJjJdfC7wJTDW3b8pYds1QA6wH8h394HB8mQiodkZWAP8yN2/rmhtEnsfZH3AL977Bd2ad2PCWRNo3rB52CWJSDUSzSm++WZ24mHu/zHgLXfvCRwHLCcSdH3dvR/wOd/1DizJMHfvXxhOgduB2e7eHZgdvJY4MydzDje9dxPdW3TnmRHPKJxEpMKiCahhwDwz+9LMPjOzpWb2WXkbmVlTYAjwLIC757r7N+4+093zg9XmAx0rWPOFwOTg+WTgBxXcXmJs9rrZ3DznZnol9+KZEc/QrEGzsEsSkWqozFN8gXMOc99dga1AmpkdR2RW3pvcfVeRda4icrquJA7MNDMHnnb3CcHyNu6+EcDdN5pZ65I2NrNrgWsBOnXqdJiHIBU1a+0sfv3+r+md0punznyKpATdkSAihyeaFpSX8ihPPWAAMN7djydyH9WB03FmdgeQD0wtZfvB7j6ASEDeYGZDovjM74p2n+DuA919YKtW6tJcFd5a8xa3vn8rfVP68vSZTyucROSIRNOC+jeRQDKgIdCFyJxQfcrZLgvIcvcFwetXCALKzEYD3weGB/dYHSK4IRh332Jm04FBwAfAZjNrF7Se2gFbojgGibE3Vr/Bb+b+hv6t+vPXM/9K4/q6XU5Ejky5LSh3P9bd+wU/uxMJirlRbLcJyDSzHsGi4UCGmZ1NZOikC9x9d0nbmlljM0sqfA6MAJYFb88ARgfPRwOvlVeLxNa/vvwXv5n7Gwa0HsD4M8crnESkUkTTgjqIu39SgV59PwemmlkCsBoYC3wMNABmRW6zYr67X29m7YGJ7n4u0AaYHrxfD5jm7m8F+3yQyM3DVwPrgEsqegxSeV5b9RrjPhzHoLaDePx7j5NYPzHskkSkhig3oMzsl0Ve1iFyXWlrNDt398XAwGKLu5Wy7gbg3OD5aiLd0ktabzuR1piEbPoX07n7v3dzcruTeex7j9GoXqOwSxKRGiSaFlTRK935RK5JvRqbcqS6ePnzl7l33r0Mbj+YR4c9SsN6DcMuSURqmGgCKsPdXy66wMwuAV4uZX2p4V5a8RL3L7if0zuczl+G/YUGdRuEXZKI1EDRdDMvaaSHskZ/kBps2vJp3L/gfoZ2HMqjwx5VOIlIzJTagjKzc4hcE+pgZo8XeaspkVN9Usv8LeNvPPzxw3wv9Xv86Yw/Ub9u/bBLEpEarKxTfBuAhUQmK1xUZHkOcHMsi5L483zG8zz88cOcddRZPDTkIerXUTiJSGyVGlDuvgRYYmbT3D2vCmuSOLNk6xL+uPCPDO80XOEkIlUmmk4Snc3sD0BvIiNJAODuXWNWlcSNPfl7uHPunbRJbMP9g+9XOIlIlYmmk0QaMJ7IdadhwBTgb7EsSuLHo4seZU32Gu4ffD9NEpqEXY6I1CLRBFQjd58NmLuvdfd7gO/FtiyJB/M3zmfaimlc3utyBrUbFHY5IlLLRHOKb6+Z1QG+MLMbgfVAiVNcSM2Rk5vDuA/H0blpZ24acFPY5YhILRRNC+oXQCLwv8AJwOV8N1ir1FAPfvQgW3dv5YHTHtAoESISijJbUGZWF/iRu98K7CQy2KvUcO+ue5cZX87g2n7XcmyrY8MuR0RqqTJbUO6+HzjBgmHFpebbsXcHv5v3O3ol9+L6fteHXY6I1GLRXIP6FHjNzF4mMisuAO7+j5hVJaFwd+6bdx85uTlMHDFRI0WISKiiCahkYDsH99xzQAFVw7y++nXeWfcOvzzhl3Rv0T3sckSklis3oNxd151qgU27NvGHBX9gQOsBXNn7yrDLEREpvxefmR1jZrPNbFnwup+Z3Rn70qSquDt3fXgX+Z7P/YPvp26dumGXJCISVTfzZ4hMr5EH4O6fAZfGsiipWi+tfIl5G+dxy8BbSG2aGnY5IiJAdAGV6O4fFVum6TZqiHXZ6/jzoj8zuP1gLjnmkrDLERE5IJqA2mZmRxPpGIGZjQI2xrQqqRL7C/bz27m/pV6devzu1N+huwlEJJ5E04vvBmAC0NPM1gNfAZfFtCqpEmnpaSzZuoQHT3+QNo3bhF2OiMhBounFtxo408waA3XcPSf2ZUmsrdyxkicXP8lZR53FuV3ODbscEZFDRNOLr2Uw5ft/gDlm9piZtYx9aRIrefvzuGPuHTRLaMa4k8fp1J6IxKVorkG9CGwFfgiMCp6/FM3Ozay5mb1iZivMbLmZnWJmfwxef2Zm082seQnbpZrZe8E26WZ2U5H37jGz9Wa2OHjon/8VNH7JeFZ+vZJ7Tr2HFg1bhF2OiEiJogmoZHe/z92/Ch73A82j3P9jwFvu3hM4DlgOzAL6uns/4HMiXdiLywd+5e69gJOBG8ysd5H3/+Lu/YPHG1HWIsDiLYt5dtmzXNTtIoamDg27HBGRUkUTUO+Z2aVmVid4/Aj4d3kbmVlTYAjwLIC757r7N+4+090Lu6nPBzoW39bdN7r7J8HzHCLB1iG6Q5LS7M7bzZ0f3knbxLb8+sRfh12OiEiZogmo64BpQG7weBH4pZnlmFl2Gdt1JXI6MM3MPjWziUFHi6KuAt4s68PNrDNwPLCgyOIbg1OEk8ysxHNUZnatmS00s4Vbt24t6yNqjUc/eZS12Wu5b/B9mr5dROJeuQHl7knuXsfd6wWPOsGyJHdvWsam9YABwHh3P57ISOi3F75pZncQOZU3tbQdmFkT4FXgF+5eGIbjgaOB/kTux3qklLonuPtAdx/YqlWr8g6zxpu/cT4vrHhB07eLSLURzX1QmFk/oHPR9aOYbiMLyHL3wpbPKwQBZWajge8Dw93dS/nM+kTCaWrRz3L3zUXWeQZ4PZpjqM2yc7O5c+6ddGnWRdO3i0i1UW5AmdkkoB+QDhQEi8udbsPdN5lZppn1cPeVwHAgw8zOBm4DznD33aV8phG5drXc3f9c7L127l44ksVFwLLyjqG2e+ijh9i2ZxvPD3te07eLSLURTQvqZHfvXf5qJfo5MNXMEoDVRKaM/xhoAMwK7r+Z7+7Xm1l7YKK7nwsMBq4AlprZ4mBfvw167D1sZv2JhOQaItfIpBSz181mxpczuK7fdfRN6Rt2OSIiUYsmoOaZWW93z6jozt19MTCw2OJupay7ATg3eD4XKPHuUXe/oqJ11Fbb92zn3nn30iu5F9f1U46LSPUSTUBNJhJSm4B9RILDg/uYJE65O/fNv4+duTt5dsSzmr5dRKqdaAJqEsHpNr67BiVx7vXVrzN73Wx+dcKv6NaixEariEhciyag1rn7jJhXIpVm065NPLDgAQa0HsAVvXVGVESqp2gCaoWZTQP+ReQUHxBVN3MJQYEXMO7Dcez3/dx/mqZvF5HqK5qAakQkmEYUWVZuN3MJx0srX2L+xvmMO3kcqUmavl1Eqq9o5oMaWxWFyJFbm72WPy/8M4M7aPp2Ean+opkP6hgzm21my4LX/czsztiXJhWRX5DPHXPvIKFuAveeeq/meBKRai+awWKfITIlRh6Au38GXBrLoqTinkt/jiVbl3DHSXfQOrF12OWIiByxaAIq0d0/KrYsv8Q1JRSF07ePOGoE53Q5J+xyREQqRTQBtc3MjibSMQIzG0VkFHGJA7n7c/nt3N/SLKEZd558p07tiUiNEU0vvhuACUBPM1sPfAVcFtOqJGrjl4zn868/54nvPaHp20WkRommF99q4MxgssE6wQy3EgcWb1nMpGWTuLj7xZyRekbY5YiIVKqo5oMCcPddsSxEKmZ33m7umHsHbRPbcuvAW8MuR0Sk0kUdUBJf/rLoL6zLWcekkZM0fbuI1EjRdJKQODNvwzxeXPkil/e6nBPbnhh2OSIiMVFqC8rMLi5rQ43FF47s3GzGfThO07eLSI1X1im+88t4T2PxheTBBQ+ybc82pg6bqunbRaRGKzWgNAZf/Jm9djb/Wv0vrj/uevqk9Am7HBGRmIqqk4SZnQf0AQ78k93d741VUXKo7Xu2c+/8yPTt1/a7NuxyRERirtyAMrOngERgGDARGAUUH/pIYsjduXfevd9N315H07eLSM0XTS++U939SuBrd/8dcAqgiYaq0OurX+fdzHf53wH/q+nbRaTWiCag9gQ/d5tZeyKjmneJXUlSVH5BPk8ufpK+Lftyea/Lwy5HRKTKRBNQr5tZc+CPwCfAGuDFGNYkRcxaO4v1O9dzTb9rNH27iNQq5QaUu9/n7t+4+6vAUUBPdx8Xzc7NrLmZvWJmK8xsuZmdYmZ/DF5/ZmbTg/AraduzzWylma0ys9uLLE82s1lm9kXws8aOkOrupC1Lo3PTzgxNHRp2OSIiVSqqkSTM7FQz+ynwY+BCM7syyv0/Brzl7j2B44DlwCygr7v3Az4nMhli8c+rCzwJnAP0Bn5iZr2Dt28HZrt7d2B28LpGmr9xPst3LGds37HUMQ36ISK1SzS9+P4GHA0sBvYHix2YUs52TYEhwBgAd88FcoGZRVabT6RXYHGDgFXBSOqY2YvAhUBG8HNosN5kYA5wW3nHUR1NWjaJVo1a8f2u3w+7FBGRKhfNfVADgd7u7hXcd1dgK5BmZscBi4Cbio2KfhXwUgnbdgAyi7zOAk4Knrdx940A7r7RzEqc39zMrgWuBejUqVMFSw9fxvYM5m+cz80n3ExC3YSwyxERqXLRnDdaBrQ9jH3XAwYA4939eGAXRU7HmdkdRKaOn1rCtiVNC1uhgHT3Ce4+0N0HtmrVqiKbxoXnlj1H4/qNueSYS8IuRUQkFNG0oFKADDP7CNhXuNDdLyhnuywgy90XBK9fIQgoMxsNfB8YXkrLLIuD77XqCGwInm82s3ZB66kdsCWKY6hWsnKyeHvt24zuPZqkhKSwyxERCUU0AXXP4ezY3TeZWaaZ9XD3lcBwIkF3NpFrRme4++5SNv8Y6G5mXYD1wKXAT4P3ZgCjgQeDn68dTn3xbErGFOpYHS7rdVnYpYiIhCaaKd/fP4L9/xyYamYJwGpgLJHwaQDMMjOA+e5+fXAT8ER3P9fd883sRuBtoC4wyd3Tg30+CPzdzK4G1gE16hzYjr07mP7FdM7vej5tGrcJuxwRkdCUNR/UXHc/zcxyOPj6jwHu7k3L27m7LybSyaKoEsfqcfcNwLlFXr8BvFHCetuJtMZqpBdXvMje/XsZ02dM2KWIiISqrBbUZQDurosgVWR33m5eWPECQ1OH0rV517DLEREJVVm9+KYXPjGzV6ugllrvn6v+yTf7vuGqvleFXYqISOjKCqiiXb31z/kYyy/IZ0rGFPq36s/xrY8PuxwRkdCVFVBeynOJgZlrZrJ+53q1nkREAmVdgzrOzLKJtKQaBc+hAp0kJDruTlp6Gl2adeGM1DPCLkdEJC6UGlDurrkdqsi8jfNYsWMF9556rwaFFREJ6K9hHJi0bBKtG7XmvK7nhV2KiEjcUECFLH17Ogs2LuDy3pdrUFgRkSIUUCF7btlzNKnfhFHHlDTriIhI7aWAClFmTiYz187kkh6XaFBYEZFiFFAhmpIeGRT28l6Xh12KiEjcUUCFZMfeHfxz1T85v+v5tE4scc5FEZFaTQEVkhdWvBAZFLbvmLBLERGJSwqoEBQOCjssdRhdm2kUKRGRkiigQjB91XS+3fethjUSESmDAqqK5RXkMSV9Cse3Pp7+rfuHXY6ISNxSQFWxmWtmsmHXBrWeRETKoYCqQu5O2rI0ujbrypCOQ8IuR0QkrimgqtC8DfNY+fVKxvQZo0FhRUTKob+SVWhSugaFFRGJlgKqimhQWBGRilFAVZG0ZWk0qd+ES465JOxSRESqBQVUFcjMzmTW2ln8qMePaJLQJOxyRESqhZgGlJk1N7NXzGyFmS03s1PM7BIzSzezAjMbWMp2PcxscZFHtpn9InjvHjNbX+S9c2N5DJVhcsZk6lpdLut1WdiliIhUG6VO+V5JHgPecvdRZpYAJALfABcDT5e2kbuvBPoDmFldYD0wvcgqf3H3P8Wo5kq1fc/2yKCwR2tQWBGRiohZQJlZU2AIMAbA3XOBXCIBhZlFu6vhwJfuvrbSi6wCL6x4gdz9uYzpMybsUkREqpVYnuLrCmwF0szsUzObaGaND2M/lwIvFFt2o5l9ZmaTzKxFSRuZ2bVmttDMFm7duvUwPvbIFR0UtkuzLqHUICJSXcUyoOoBA4Dx7n48sAu4vSI7CE4LXgC8XGTxeOBoIqcANwKPlLStu09w94HuPrBVq1YVr74STF81nezcbMb2HRvK54uIVGexDKgsIMvdFwSvXyESWBVxDvCJu28uXODum919v7sXAM8Agyql2kqWV5DH5PTJDGg9QIPCiogchpgFlLtvAjLNrEewaDiQUcHd/IRip/fMrF2RlxcByw67yBh6e83bbNy1Ua0nEZHDFOv7oH4OTDWzz4icknvAzC4ysyzgFODfZvY2gJm1N7M3Cjc0s0TgLOAfxfb5sJktDfY5DLg5xsdQYYWDwh7d7GgNCisicphi2s3c3RcDxe91ms7BXcYL190AnFvk9W6gZQnrXVG5VVa+/274L59//Tn3Db5Pg8KKiBwm/fWMgbRlaZFBYbtoUFgRkcOlgKpk6dvSWbBpAVf0voL6deuHXY6ISLWlgKpkk5ZNIql+EqOOGRV2KSIi1ZoCqhKty17HO+ve0aCwIiKVQAFViaZkTNGgsCIilUQBVUkKB4W94OgLaJUYzsgVIiI1iQKqkkxbMY3c/bmM7jM67FJERGoEBVQl2J23mxdXvMj3On1Pg8KKiFQSBVQl+McX/9CgsCIilUwBdYTyCvKYkjGFAa0HcFyr48IuR0SkxlBAHaG3vnqLjbs2clXfq8IuRUSkRlFAHQF3Jy09Mijs6R1PD7scEZEaRQF1BD7c8CFffP0FY/uO1aCwIiKVTH9Vj0DasjRaJ7bm3C7nlr+yiIhUiALqMC3btoyPNn3Elb2v1KCwIiIxoIA6TIWDwv6w+w/DLkVEpEZSQB2GddnreGftO/y45481KKyISIwooA7D5PTJ1KtTT4PCiojEkAKqgrbt2XZgUNiURilhlyMiUmMpoCpo2vJp5BXkaVBYEZEYU0BVwO683by4UoPCiohUBQVUBbz6xavk5OZoWCMRkSoQ04Ays+Zm9oqZrTCz5WZ2ipldYmbpZlZgZgPL2HaNmS01s8VmtrDI8mQzm2VmXwQ/W8TyGAoVDgp7QpsT6NeqX1V8pIhIrRbrFtRjwFvu3hM4DlgOLAMuBj6IYvth7t7f3YsG2e3AbHfvDswOXsfcW1+9xaZdm9R6EhGpIvVitWMzawoMAcYAuHsukAt8E7x/uLu+EBgaPJ8MzAFuO9ydRcPdmbRsEt2ad+O0DqfF8qNERCQQyxZUV2ArkGZmn5rZRDNrXIHtHZhpZovM7Noiy9u4+0aA4Gfryiu5ZHPXz2XVN6s0KKyISBWK5V/besAAYLy7Hw/somKn4wa7+wDgHOAGMxtSkQ83s2vNbKGZLdy6dWtFNj1EWnoabRLbcE7nc45oPyIiEr1YBlQWkOXuC4LXrxAJrKi4+4bg5xZgOjAoeGuzmbUDCH5uKWX7Ce4+0N0HtmrV6jAPAZZuXcrHmz7mit5XaFBYEZEqFLOAcvdNQKaZ9QgWDQcyotnWzBqbWVLhc2AEkc4VADOAwrtkRwOvVVrRJVibs5Z2jdsx6phRsfwYEREpxtw9djs36w9MBBKA1cBYIh0c/g9oRaTDxGJ3H2lm7YGJ7n6umXUl0mqCyKnCae7++2CfLYG/A52AdcAl7r6jrDoGDhzoCxcuLGuVMu0v2E/dOnUPe3sRESmdmS0q1ls7sjyWARUvjjSgREQkdkoLKHVJExGRuKSAEhGRuKSAEhGRuKSAEhGRuKSAEhGRuKSAEhGRuKSAEhGRuKSAEhGRuFQrbtQ1s63A2iPYRQqwrZLKiTc6tuqnph4X6Niqo8o4rqPc/ZBBU2tFQB0pM1tY0l3ONYGOrfqpqccFOrbqKJbHpVN8IiISlxRQIiISlxRQ0ZkQdgExpGOrfmrqcYGOrTqK2XHpGpSIiMQltaBERCQuKaBERCQuKaDKYWZnm9lKM1tlZreHXU9lMrM1ZrbUzBabWbWd0dHMJpnZFjNbVmRZspnNMrMvgp8twqzxcJVybPeY2frge1tsZueGWePhMLNUM3vPzJabWbqZ3RQsr/bfWxnHVhO+t4Zm9pGZLQmO7XfB8ph8b7oGVQYzqwt8DpwFZAEfAz9x94xQC6skZrYGGOju1frmQTMbAuwEprh732DZw8AOd38w+IdFC3e/Lcw6D0cpx3YPsNPd/xRmbUfCzNoB7dz9EzNLAhYBPwDGUM2/tzKO7UdU/+/NgMbuvtPM6gNzgZuAi4nB96YWVNkGAavcfbW75wIvAheGXJMU4+4fADuKLb4QmBw8n0zkD0S1U8qxVXvuvtHdPwme5wDLgQ7UgO+tjGOr9jxiZ/CyfvBwYvS9KaDK1gHILPI6ixryH1rAgZlmtsjMrg27mErWxt03QuQPBtA65Hoq241m9llwCrDanQYrysw6A8cDC6hh31uxY4Ma8L2ZWV0zWwxsAWa5e8y+NwVU2ayEZTXpnOhgdx8AnAPcEJxOkvg3Hjga6A9sBB4JtZojYGZNgFeBX7h7dtj1VKYSjq1GfG/uvt/d+wMdgUFm1jdWn6WAKlsWkFrkdUdgQ0i1VDp33xD83AJMJ3JKs6bYHFwLKLwmsCXkeiqNu28O/kgUAM9QTb+34BrGq8BUd/9HsLhGfG8lHVtN+d4Kufs3wBzgbGL0vSmgyvYx0N3MuphZAnApMCPkmiqFmTUOLuBiZo2BEcCysreqVmYAo4Pno4HXQqylUhX+IQhcRDX83oKL7c8Cy939z0XeqvbfW2nHVkO+t1Zm1jx43gg4E1hBjL439eIrR9AV9FGgLjDJ3X8fbkWVw8y6Emk1AdQDplXXYzOzF4ChRIb93wzcDfwT+DvQCVgHXOLu1a6zQSnHNpTIaSIH1gDXFZ7/ry7M7DTgP8BSoCBY/Fsi12qq9fdWxrH9hOr/vfUj0gmiLpEGzt/d/V4za0kMvjcFlIiIxCWd4hMRkbikgBIRkbikgBIRkbikgBIRkbikgBIRkbikgJK4ZmYti4z+vKnIaNA7zeyvYdcHYGa/MLPEIq/fKHKvyM5SNyx/vxdYOSPom9m9Znbm4X5GsX2Zmb1rZk0rsE25NZaz/UG/uwpue6OZjT3cz5b4p27mUm3E6yjeZY0Kb2Y73b1J1VdVcWZ2HnCmu99chZ+5hsMcUT8Itg/d/fhKL0ziglpQUi2Z2VAzez14fo+ZTTazmRaZ4+piM3vYInNdvRUMO4OZnWBm7weD475d7M7+wv0+Z2ajirzeWeTz5pjZK2a2wsymBi2O/wXaA++Z2XvBumvMLKWM2jsH+5hoZsuCfZ1pZh9aZD6dQcF6Y8zsieD5a2Z2ZfD8OjObWrze4HN/Z2afBMfeM1jeyiJz9HxiZk+b2dpS6ruMYASAw6zxOTN73Mz+a2ari9R14LsKXj8RbFfS726Emc0Lan3ZIuPZYWYPmlmGRQZa/ROAu+8G1hTWIjWPAkpqiqOB84gM+/888J67HwvsAc4LQur/gFHufgIwCajoyBnHA78AegNdiQy2+ziR8RmHufuwCuyrG/AY0A/oCfwUOA24hcioA8VdC9xlZqcDvwJ+Xsp+twUDAI8P9gWR0SfeDZZPJ3K3f0kGE5m76HBrBGgXrPN94MFS1gGg+O8uCM07ibTiBgALgV+aWTKRoYH6uHs/4P4iu1kInF7W50j1VS/sAkQqyZvunmdmS4kMw/JWsHwp0BnoAfQFZpkZwToVHWbmI3fPArDIdAOdiUzYdji+cvelwb7Sgdnu7kH9nYuv7O6bzewu4D3gojKGkSkcdHURkUnkIBIYFwX7ecvMvi5l2+Rg/qLDqjHwz2Aw1Awza1PKOqU5mUj4fxh8RwnAPCAb2AtMNLN/A68X2WYLkfCUGkgBJTXFPgB3LzCzPP/u4moBkf/ODUh391PK2U8+wZkFi/yVTCj+GYH9HNn/P0X3VVDkdWG9JTkW2E7ktFh5+y1aX0nTxpQk38zqBAFzuDUW3abwcw/8TgMNS9nWiMwv9JND3oicxhtOZMDmG4HvFdnXnlL2J9WcTvFJbbESaGVmp0BkOgQz61PCemuAE4LnFxKZMbQ8OUBSZRRZmuAP9DlETjPeYmZdKrD5XCLTjWNmI4DSJspbSeTUZWVbC/Q2swZm1oxI0BQq+rubDww2s25BrYlmdkxwHaqZu79B5BRr/yLbH0M1HBVcoqOAklrB3XOBUcBDZrYEWAycWsKqzwBnmNlHwEnArih2PwF4s/BCf2UzswZBXVcFc3j9CpgUtPCi8TtghJl9QiTkNhIJhuL+TWSk9Erl7plERrr+DJgKfFrk7QO/O3ffCowBXjCzz4gEVk8iAfZ6sOx9oGgvw8HAO5Vds8QHdTMXqeGCgNvv7vlBC3J8MCNq8fXaAVPc/ayqrvFwmNnxwC/d/Yqwa5HY0DUokZqvE/B3M6sD5ALXlLSSu280s2fMrGk1mX49BRgXdhESO2pBiYhIXNI1KBERiUsKKBERiUsKKBERiUsKKBERiUsKKBERiUv/HzJOPMQtb0bOAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -774,7 +409,7 @@
    "source": [
     "from modsim import decorate\n",
     "\n",
-    "sweep.plot(label='mixture')\n",
+    "sweep.plot(label='mixture', color='C2')\n",
     "\n",
     "decorate(xlabel='Time until mixing (minutes)',\n",
     "         ylabel='Final emperature (C)')"
@@ -782,20 +417,24 @@
   },
   {
    "cell_type": "markdown",
-   "id": "conservative-phrase",
+   "id": "light-poland",
    "metadata": {},
    "source": [
-    "Note that this is a parameter sweep, not a time series. The x-axis is the time when we add the milk, not the index of a `TimeSeries`.\n",
+    "Note that this is a parameter sweep, not a time series.\n",
     "\n",
     "The final temperature is maximized when `t_add=30`, so adding the milk\n",
     "at the end is optimal.\n",
     "\n",
-    "As an exercise you will have a chance to explore this solution and try some variations. For example, suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office. In that case is it better to add the milk at the coffee shop, or wait until I get to the office?"
+    "As an exercise you will have a chance to explore a variation of the coffee cooling problem:\n",
+    "\n",
+    "> Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office. In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
+    "\n",
+    "But first, let's do some analysis."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "accomplished-tulsa",
+   "id": "industrial-circle",
    "metadata": {},
    "source": [
     "## Analysis\n",
@@ -817,27 +456,21 @@
   },
   {
    "cell_type": "markdown",
-   "id": "opponent-trace",
+   "id": "molecular-happiness",
    "metadata": {},
    "source": [
     "Now we can use the observed data to estimate the parameter $r$. If we\n",
     "observe the that temperature at $t_{end}$ is $T_{final}$, we can plug these values into the particular solution and solve for $r$. The result is:\n",
     "\n",
-    "$$r = \\frac{1}{t_{end}} \\log{\\left (\\frac{T_{init} - T_{env}}{T_{final} - T_{env}} \\right )}$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "electoral-archives",
-   "metadata": {},
-   "source": [
-    "The following function takes a `System` object with a simulated `T_final` and computes `r`:"
+    "$$r = \\frac{1}{t_{end}} \\log{\\left (\\frac{T_{init} - T_{env}}{T_{final} - T_{env}} \\right )}$$\n",
+    "\n",
+    "The following function takes a `System` object and computes `r`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
-   "id": "little-bahrain",
+   "execution_count": 13,
+   "id": "dressed-supply",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -855,16 +488,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "superior-harmony",
+   "id": "young-shift",
    "metadata": {},
    "source": [
-    "Can can use this function to compute `r` for the coffee, given the parameters of the problem."
+    "We can use this function to compute `r` for the coffee, given the parameters of the problem."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
-   "id": "executive-queensland",
+   "execution_count": 14,
+   "id": "bacterial-tonight",
    "metadata": {},
    "outputs": [
     {
@@ -873,52 +506,24 @@
        "0.01161022314227386"
       ]
      },
-     "execution_count": 198,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "coffee = make_system(T_init=90, volume=300, r=0, t_end=30)\n",
-    "coffee.T_final = 70\n",
-    "r_coffee2 = compute_r(coffee)\n",
+    "coffee2 = make_system(T_init=90, volume=300, r=0, t_end=30)\n",
+    "coffee2.T_final = 70\n",
+    "r_coffee2 = compute_r(coffee2)\n",
     "r_coffee2"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "arctic-retention",
-   "metadata": {},
-   "source": [
-    "Notice that this value is not the same as `r_coffee` in the simulations.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "id": "defined-wound",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.011543084584004043"
-      ]
-     },
-     "execution_count": 199,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r_coffee"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "passive-impression",
+   "id": "removable-authority",
    "metadata": {},
    "source": [
+    "This value is close to the value of `r` we computed in the previous chapter, `0.115`, but not exactly the same.\n",
     "That's because the simulations use discrete time steps, and the analysis uses continuous time.\n",
     "\n",
     "Nevertheless, the results of any analysis are consistent with the simulation.\n",
@@ -927,8 +532,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
-   "id": "mediterranean-arbitration",
+   "execution_count": 15,
+   "id": "civic-trail",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -937,44 +542,31 @@
     "\n",
     "def run_analysis(system):\n",
     "    T_env, T_init, r = system.T_env, system.T_init, system.r\n",
-    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
     "    \n",
-    "    t_array = arange(t_0, t_end+dt, dt)    \n",
+    "    t_array = linrange(system.t_0, system.t_end, system.dt)    \n",
     "    T_array = T_env + (T_init - T_env) * exp(-r * t_array)\n",
     "    \n",
     "    system.T_final = T_array[-1]\n",
-    "    return Series(T_array, index=t_array)"
+    "    return TimeSeries(T_array, index=t_array)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "efficient-sheffield",
+   "id": "eleven-faculty",
    "metadata": {},
    "source": [
-    "The first two lines unpack the system variables.\n",
-    "`t_array` is a NumPy array of time stamps.\n",
-    "`T_array` is and array of the corresponding temperatures.\n",
-    "The result is a Pandas `Series`.\n",
+    "The first line unpacks the system variables.\n",
+    "The next two lines compute `t_array`, which is a NumPy array of time stamps, and `T_array`, which is an array of the corresponding temperatures.\n",
+    "\n",
+    "The last two lines store the final temperature in the `System` object and return the results in a `TimeSeries`.\n",
     "\n",
     "We can run it like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
-   "id": "steady-customs",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee2 = make_system(T_init=90, r=r_coffee2, \n",
-    "                      volume=300, t_end=30)\n",
-    "results2 = run_analysis(coffee2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "id": "suspected-birthday",
+   "execution_count": 16,
+   "id": "terminal-carry",
    "metadata": {},
    "outputs": [
     {
@@ -983,18 +575,20 @@
        "70.0"
       ]
      },
-     "execution_count": 206,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "coffee2.r = r_coffee2\n",
+    "results2 = run_analysis(coffee2)\n",
     "coffee2.T_final"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "alpha-casino",
+   "id": "approved-subject",
    "metadata": {},
    "source": [
     "The final temperature is 70 °C, as it should be. In fact, the results\n",
@@ -1003,19 +597,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "laden-lloyd",
+   "execution_count": 17,
+   "id": "grand-silence",
    "metadata": {},
    "outputs": [],
    "source": [
-    "coffee = make_system(T_init=90, volume=300, r=r_coffee, t_end=30)\n",
+    "coffee.r = 0.011543\n",
     "results = run_simulation(coffee, change_func)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
-   "id": "mighty-imperial",
+   "execution_count": 18,
+   "id": "monetary-sensitivity",
    "metadata": {},
    "outputs": [
     {
@@ -1024,7 +618,7 @@
        "True"
       ]
      },
-     "execution_count": 209,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1037,7 +631,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "common-profile",
+   "id": "polish-focus",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -1045,134 +639,25 @@
   },
   {
    "cell_type": "markdown",
-   "id": "greatest-warrant",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Use `root_scalar` to estimate `r_milk`, given that it starts at 5 °C and reaches 20 °C after 15 minutes.  \n",
-    "\n",
-    "1. Write an error function that takes a possible value of `r`, runs a simulation, and returns the difference between `T_final` and 20 °C.\n",
-    "\n",
-    "2. Try your error function and find two values of `r` that bracket a root.\n",
-    "\n",
-    "3. Use `root_scalar` to compute `r_milk`.\n",
-    "\n",
-    "4. Run the simulation with the computed value and confirm that `T_final` is 20 °C."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 217,
-   "id": "powered-deficit",
+   "id": "respected-inclusion",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Solution\n",
-    "\n",
-    "def error_func2(r):\n",
-    "    system = make_system(T_init=5, volume=50, r=r, t_end=15)\n",
-    "    run_simulation(system, change_func)\n",
-    "    return system.T_final - 20"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 218,
-   "id": "after-novelty",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-1.500149245609034"
-      ]
-     },
-     "execution_count": 218,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "error_func2(r=0.1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 219,
-   "id": "universal-steel",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.4018656744898585"
-      ]
-     },
-     "execution_count": 219,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "error_func2(r=0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 220,
-   "id": "frequent-albuquerque",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "      converged: True\n",
-       "           flag: 'converged'\n",
-       " function_calls: 9\n",
-       "     iterations: 8\n",
-       "           root: 0.13296078935466452"
-      ]
-     },
-     "execution_count": 220,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "res = root_scalar(error_func2, bracket=[0.1, 0.2])\n",
-    "res"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 221,
-   "id": "future-pregnancy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "r_milk = res.root"
+    "**Exercise:** Use `compute_r` to compute `r_milk` according to the analytic solution.  Run the analysis with this value of `r_milk` and confirm that the results are consistent with the simulation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
-   "id": "arctic-wireless",
+   "execution_count": 19,
+   "id": "headed-circle",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "19.999999999999996"
+       "0.14267107756641806"
       ]
      },
-     "execution_count": 224,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1180,85 +665,54 @@
    "source": [
     "# Solution\n",
     "\n",
-    "milk = make_system(T_init=5, volume=50, r=r_milk, t_end=15)\n",
-    "results = run_simulation(milk, change_func)\n",
-    "milk.T_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "competitive-kingdom",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Use `compute_r` to compute `r_milk` according to the analytic solution.  Run the analysis with this value of `r_milk` and confirm that the results are consistent with the simulation."
+    "milk2 = make_system(T_init=5, volume=50, r=0, t_end=15)\n",
+    "milk2.T_final = 20\n",
+    "r_milk2 = compute_r(milk2)\n",
+    "r_milk2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
-   "id": "twenty-andrew",
+   "execution_count": 20,
+   "id": "enormous-consolidation",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.14267107756641806"
+       "20.0"
       ]
      },
-     "execution_count": 229,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "milk = make_system(T_init=5, volume=50, r=0, t_end=15)\n",
-    "milk.T_final = 20\n",
-    "r_milk2 = compute_r(milk)\n",
-    "r_milk2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 230,
-   "id": "automated-premium",
-   "metadata": {},
-   "outputs": [],
    "source": [
     "# Solution\n",
     "\n",
     "milk2 = make_system(T_init=5, volume=50, r=r_milk2, t_end=15)\n",
-    "results2 = run_analysis(milk2)"
+    "results2 = run_analysis(milk2)\n",
+    "milk2.T_final"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
-   "id": "naughty-communication",
+   "execution_count": 23,
+   "id": "social-stamp",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20.0"
-      ]
-     },
-     "execution_count": 231,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Solution\n",
     "\n",
-    "milk2.T_final"
+    "milk = make_system(T_init=5, volume=50, r=0.13296, t_end=15)\n",
+    "results = run_simulation(milk, change_func)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
-   "id": "architectural-hierarchy",
+   "execution_count": 24,
+   "id": "exclusive-puzzle",
    "metadata": {},
    "outputs": [
     {
@@ -1267,7 +721,7 @@
        "True"
       ]
      },
-     "execution_count": 232,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1282,7 +736,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "regulated-liberty",
+   "id": "chinese-explanation",
    "metadata": {},
    "source": [
     "**Exercise:**  Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
@@ -1292,8 +746,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "id": "significant-corps",
+   "execution_count": null,
+   "id": "terminal-billy",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1309,7 +763,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "vocal-illinois",
+   "id": "nuclear-going",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap17.ipynb b/jupyter/chap17.ipynb
new file mode 100644
index 00000000..aca865e8
--- /dev/null
+++ b/jupyter/chap17.ipynb
@@ -0,0 +1,665 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "interracial-guitar",
+   "metadata": {},
+   "source": [
+    "# Chapter 17"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "restricted-thumb",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "premier-coach",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tamil-helping",
+   "metadata": {},
+   "source": [
+    "**Pharmacokinetics** is the study of how drugs and other substances move around the body, react, and are eliminated. In this chapter, we\n",
+    "implement one of the most widely used pharmacokinetic models: the\n",
+    "so-called **minimal model** of glucose and insulin in the blood stream.\n",
+    "\n",
+    "My presentation in this chapter follows Bergman (2005) \"Minimal Model\"\n",
+    "(abstract at <http://modsimpy.com/bergman>, PDF at\n",
+    "<http://modsimpy.com/minmod>)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stable-lemon",
+   "metadata": {},
+   "source": [
+    "## The glucose-insulin system\n",
+    "\n",
+    "**Glucose** is a form of sugar that circulates in the blood of animals; it is used as fuel for muscles, the brain, and other organs. The concentration of blood sugar is controlled by the hormone system, and especially by **insulin**, which is produced by the pancreas and has the effect of reducing blood sugar.\n",
+    "\n",
+    "In people with normal pancreatic function, the hormone system maintains **homeostasis**; that is, it keeps the concentration of blood sugar in a range that is neither too high or too low.\n",
+    "\n",
+    "But if the pancreas does not produce enough insulin, or if the cells\n",
+    "that should respond to insulin become insensitive, blood sugar can\n",
+    "become elevated, a condition called **hyperglycemia**. Long term, severe hyperglycemia is the defining symptom of **diabetes mellitus**, a serious disease that affects almost 10% of the population in the U.S. (see <http://modsimpy.com/cdc>)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sharp-yukon",
+   "metadata": {},
+   "source": [
+    "One of the most-used tests for hyperglycemia and diabetes is the\n",
+    "frequently sampled intravenous glucose tolerance test (FSIGT), in which glucose is injected into the blood stream of a fasting subject (someone who has not eaten recently); then blood samples are collected at intervals of 2--10 minutes for 3 hours. The samples are analyzed to\n",
+    "measure the concentrations of glucose and insulin.\n",
+    "\n",
+    "By analyzing these measurements, we can estimate several parameters of\n",
+    "the subject's response; the most important is a parameter denoted $S_I$, which quantifies the effect of insulin on the rate of reduction in blood sugar."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "round-roommate",
+   "metadata": {},
+   "source": [
+    "## The glucose minimal model\n",
+    "\n",
+    "The \"minimal model\", as proposed by Bergman, Ider, Bowden, and\n",
+    "Cobelli, consists of two parts: the glucose model and the insulin model. I will present an implementation of the glucose model; as a case study, you will have the chance to implement the insulin model.\n",
+    "\n",
+    "The original model was developed in the 1970s; since then, many\n",
+    "variations and extensions have been proposed. Bergman's comments on the development of the model provide insight into their process:\n",
+    "\n",
+    "> We applied the principle of Occam's Razor, i.e. by asking what was the simplest model based upon known physiology that could account for the insulin-glucose relationship revealed in the data. Such a model must be simple enough to account totally for the measured glucose (given the insulin input), yet it must be possible, using mathematical techniques, to estimate all the characteristic parameters of the model from a single data set (thus avoiding unverifiable assumptions)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "accompanied-battery",
+   "metadata": {},
+   "source": [
+    "The most useful models are the ones that achieve this balance: including enough realism to capture the essential features of the system without so much complexity that they are impractical. In this example, the practical limit is the ability to estimate the parameters of the model using data, and to interpret the parameters meaningfully.\n",
+    "\n",
+    "Bergman discusses the features he and his colleagues thought were\n",
+    "essential:\n",
+    "\n",
+    "> (1) Glucose, once elevated by injection, returns to basal level due to two effects: the effect of glucose itself to normalize its own concentration \\[...\\] as well as the catalytic effect of insulin to allow glucose to self-normalize (2) Also, we discovered that the effect of insulin on net glucose disappearance must be sluggish --- that is, that insulin acts slowly because insulin must first move from plasma to a remote compartment \\[...\\] to exert its action on glucose disposal.\n",
+    "\n",
+    "To paraphrase the second point, the effect of insulin on glucose\n",
+    "disposal, as seen in the data, happens more slowly than we would expect if it depended primarily on the the concentration of insulin in the blood. Bergman's group hypothesized that insulin must move relatively slowly from the blood to a \"remote compartment\" where it has its effect."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bottom-extent",
+   "metadata": {},
+   "source": [
+    "At the time, the remote compartment was a modeling abstraction that\n",
+    "might, or might not, represent something physical. Later, according to\n",
+    "Bergman, it was \"shown to be interstitial fluid\", that is, the fluid\n",
+    "that surrounds tissue cells. In the history of mathematical modeling, it is common for hypothetical entities, added to models to achieve\n",
+    "particular effects, to be found later to correspond to physical\n",
+    "entities.\n",
+    "\n",
+    "The glucose model consists of two differential equations:\n",
+    "\n",
+    "$$\\frac{dG}{dt} = -k_1 \\left[ G(t) - G_b \\right] - X(t) G(t)$$\n",
+    "\n",
+    "$$\\frac{dX}{dt} = k_3 \\left[I(t) - I_b \\right] - k_2 X(t)$$ \n",
+    "\n",
+    "where\n",
+    "\n",
+    "-   $G$ is the concentration of blood glucose as a function of time and $dG/dt$ is its rate of change.\n",
+    "\n",
+    "-   $X$ is the concentration of insulin in the tissue fluid as a\n",
+    "    function of time, and $dX/dt$ is its rate of change.\n",
+    "\n",
+    "-   $I$ is the concentration of insulin in the blood as a function of\n",
+    "    time, which is taken as an input into the model, based on\n",
+    "    measurements.\n",
+    "\n",
+    "-   $G_b$ is the basal concentration of blood glucose and $I_b$ is the\n",
+    "    basal concentration of blood insulin, that is, the concentrations at equilibrium. Both are constants estimated from measurements at the\n",
+    "    beginning or end of the test.\n",
+    "\n",
+    "-   $k_1$, $k_2$, and $k_3$ are positive-valued parameters that control the rates of appearance and disappearance for glucose and insulin."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dutch-retailer",
+   "metadata": {},
+   "source": [
+    "We can interpret the terms in the equations one by one:\n",
+    "\n",
+    "-   $-k_1 \\left[ G(t) - G_b \\right]$ is the rate of glucose\n",
+    "    disappearance due to the effect of glucose itself. When $G(t)$ is\n",
+    "    above basal level, $G_b$, this term is negative; when $G(t)$ is\n",
+    "    below basal level this term is positive. So in the absence of\n",
+    "    insulin, this term tends to restore blood glucose to basal level.\n",
+    "\n",
+    "-   $-X(t) G(t)$ models the interaction of glucose and insulin in tissue\n",
+    "    fluid, so the rate increases as either $X$ or $G$ increases. This\n",
+    "    term does not require a rate parameter because the units of $X$ are\n",
+    "    unspecified; we can consider $X$ to be in whatever units make the\n",
+    "    parameter of this term 1.\n",
+    "\n",
+    "-   $k_3 \\left[ I(t) - I_b \\right]$ is the rate at which insulin\n",
+    "    diffuses between blood and tissue fluid. When $I(t)$ is above basal\n",
+    "    level, insulin diffuses from the blood into the tissue fluid. When\n",
+    "    $I(t)$ is below basal level, insulin diffuses from tissue to the\n",
+    "    blood.\n",
+    "\n",
+    "-   $-k_2 X(t)$ is the rate of insulin disappearance in tissue fluid as\n",
+    "    it is consumed or broken down."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "regional-receptor",
+   "metadata": {},
+   "source": [
+    "The initial state of the model is $X(0) = I_b$ and $G(0) = G_0$, where\n",
+    "$G_0$ is a constant that represents the concentration of blood sugar\n",
+    "immediately after the injection. In theory we could estimate $G_0$ based on measurements, but in practice it takes time for the injected glucose to spread through the blood volume. Since $G_0$ is not measurable, it is treated as a **free parameter** of the model, which means that we are free to choose it to fit the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exact-heating",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "To develop and test the model, we'll use data from Pacini and Bergman, \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 23: 113-122, (1986)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "separated-strip",
+   "metadata": {},
+   "source": [
+    "The following cell reads the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "looking-needle",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'glucose_insulin.csv'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/glucose_insulin.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dressed-regard",
+   "metadata": {},
+   "source": [
+    "We can use Pandas to read the data file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "naval-geology",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>glucose</th>\n",
+       "      <th>insulin</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>92</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>350</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>287</td>\n",
+       "      <td>130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>251</td>\n",
+       "      <td>85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>240</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      glucose  insulin\n",
+       "time                  \n",
+       "0          92       11\n",
+       "2         350       26\n",
+       "4         287      130\n",
+       "6         251       85\n",
+       "8         240       51"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pandas import read_csv\n",
+    "\n",
+    "data = read_csv('glucose_insulin.csv', index_col='time')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "close-payday",
+   "metadata": {},
+   "source": [
+    "`data` has two columns: `glucose` is the concentration of blood glucose in mg/dL; `insulin` is concentration of insulin in the blood in μU/mL (a medical \"unit\", denoted U, is an amount defined by convention in context). The index is time in minutes.\n",
+    "\n",
+    "This dataset represents glucose and insulin concentrations over\n",
+    "182 min for a subject with normal insulin production and sensitivity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "burning-valuable",
+   "metadata": {},
+   "source": [
+    "## Interpolation\n",
+    "\n",
+    "Before we are ready to implement the model, there's one problem we have to solve. In the differential equations, $I$ is a function that can be evaluated at any time, $t$. But in the `DataFrame`, we only have measurements at discrete times. This is a job for interpolation!\n",
+    "\n",
+    "The ModSim library provides a function named `interpolate`, which is a\n",
+    "wrapper for the SciPy function `interp1d`. It takes any kind of `Series` as a parameter, including `TimeSeries` and `SweepSeries`, and returns a function. That's right, I said it returns a *function*.\n",
+    "\n",
+    "So we can call `interpolate` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "terminal-teaching",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import interpolate\n",
+    "\n",
+    "I = interpolate(data.insulin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "banner-shakespeare",
+   "metadata": {},
+   "source": [
+    "Then we can call the new function, `I`, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "intense-thursday",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(31.66666667)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "I(18)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "severe-desire",
+   "metadata": {},
+   "source": [
+    "The result is 31.66, which is a linear interpolation between the actual measurements at `t=16` and `t=19`. \n",
+    "\n",
+    "We can also pass an array as an argument to `I`.  Here's an array of equally-spaced values from `t_0` to `t_end`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "least-pattern",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import linrange\n",
+    "\n",
+    "t_0 = data.index[0]\n",
+    "t_end = data.index[-1]\n",
+    "t_array = linrange(t_0, t_end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "empirical-tackle",
+   "metadata": {},
+   "source": [
+    "And here are the corresponding values of `I`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "mounted-venice",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "I_array = I(t_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "environmental-variance",
+   "metadata": {},
+   "source": [
+    "Here's what the interpolated values look like along with the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "completed-registrar",
+   "metadata": {},
+   "source": [
+    "`interpolate` can take additional arguments, which it passes along to\n",
+    "`interp1d`. You can read about these options at\n",
+    "<http://modsimpy.com/interp>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "engaging-watershed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.pyplot import plot\n",
+    "from modsim import decorate\n",
+    "\n",
+    "data.insulin.plot(style='o', color='C2', label='insulin data')\n",
+    "plot(t_array, I_array, color='C2', label='interpolated')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration ($\\mu$U/mL)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adopted-locking",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In the next chapter, we will use interpolated data to run the simulation\n",
+    "of the glucose-insulin system.\n",
+    "\n",
+    "\n",
+    "\n",
+    "[^1]: Bergman RN, Ider YZ, Bowden CR, Cobelli C., \"Quantitative\n",
+    "    estimation of insulin sensitivity\\\", Am J Physiol. 1979\n",
+    "    Jun;236(6):E667-77. Abstract at <http://modsimpy.com/insulin>.\n",
+    "\n",
+    "[^2]: \"MINMOD: A computer program to calculate insulin sensitivity and\n",
+    "    pancreatic responsivity from the frequently sampled intravenous\n",
+    "    glucose tolerance test\\\", *Computer Methods and Programs in\n",
+    "    Biomedicine* 23: 113-122, 1986."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "floppy-store",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hungarian-newman",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Read the documentation of `scipy.interpolate.interp1d` at <https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html>.  Pass a keyword argument to `interpolate` to specify one of the other kinds of interpolation, and run the code again to see what it looks like. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "endless-network",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "I = interpolate(data.insulin, kind='cubic')\n",
+    "I_array = I(t_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "entire-concern",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.insulin.plot(style='o', color='C2', label='insulin data')\n",
+    "plot(t_array, I_array, color='C2', label='interpolated')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration ($\\mu$U/mL)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "furnished-recognition",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Interpolate the glucose data and generate a plot, similar to the previous one, that shows the data points and the interpolated curve evaluated at the time values in `ts`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "representative-acquisition",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "G = interpolate(data.glucose)\n",
+    "G_array = G(t_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "rocky-sydney",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.glucose.plot(style='o', color='C0', label='gluciose data')\n",
+    "plot(t_array, G_array, color='C0', label='interpolated')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration (mg/dL)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opened-scheduling",
+   "metadata": {},
+   "source": [
+    "### Under the hood"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "still-tolerance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%psource interpolate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "essential-fishing",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/chap18.ipynb b/jupyter/chap18.ipynb
new file mode 100644
index 00000000..5cb0c53a
--- /dev/null
+++ b/jupyter/chap18.ipynb
@@ -0,0 +1,869 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "electric-netherlands",
+   "metadata": {},
+   "source": [
+    "# Chapter 18"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tribal-blame",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "hungry-lucas",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "original-photographer",
+   "metadata": {},
+   "source": [
+    "### Code from the previous chapter\n",
+    "\n",
+    "Read the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fewer-weather",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'glucose_insulin.csv'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/glucose_insulin.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "computational-border",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_csv\n",
+    "\n",
+    "data = read_csv(filename, index_col='time');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unique-domestic",
+   "metadata": {},
+   "source": [
+    "Interpolate the insulin data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "oriented-translation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import interpolate\n",
+    "\n",
+    "I = interpolate(data.insulin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "horizontal-bridges",
+   "metadata": {},
+   "source": [
+    "In this chapter, we implement the glucose minimal model described in the previous chapter. We'll start with `run_simulation`, which solves\n",
+    "differential equations using discrete time steps. This method works well enough for many applications, but it is not very accurate. In this chapter we explore a better option: using an **ODE solver**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stunning-giving",
+   "metadata": {},
+   "source": [
+    "## Implementation\n",
+    "\n",
+    "To get started, let's assume that the parameters of the model are known.\n",
+    "We'll implement the model and use it to generate time series for `G` and `X`. Then we'll see how to find the parameters that generate the series that best fits the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "polished-burner",
+   "metadata": {},
+   "source": [
+    "We can pass `params` and `data` to `make_system`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "substantial-literacy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import State, System\n",
+    "\n",
+    "def make_system(params, data):\n",
+    "    G0, k1, k2, k3 = params\n",
+    "    \n",
+    "    Gb = data.glucose[0]\n",
+    "    Ib = data.insulin[0]\n",
+    "    I = interpolate(data.insulin)\n",
+    "    \n",
+    "    t_0 = data.index[0]\n",
+    "    t_end = data.index[-1]\n",
+    "    \n",
+    "    init = State(G=G0, X=0)\n",
+    "    \n",
+    "    return System(params=params, init=init, \n",
+    "                  Gb=Gb, Ib=Ib, I=I,\n",
+    "                  t_0=t_0, t_end=t_end, dt=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "metallic-motorcycle",
+   "metadata": {},
+   "source": [
+    "`make_system` uses the measurements at `t=0` as the basal levels, `Gb`\n",
+    "and `Ib`. It gets `t_0` and `t_end` from the data. And it uses the\n",
+    "parameter `G0` as the initial value for `G`. Then it packs everything\n",
+    "into a `System` object."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "confident-performance",
+   "metadata": {},
+   "source": [
+    "Taking advantage of estimates from prior work, we'll start with these\n",
+    "values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "under-rhythm",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#        G0,  k1,   k2,   k3\n",
+    "params = 290, 0.03, 0.02, 1e-05\n",
+    "system = make_system(params, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stupid-retro",
+   "metadata": {},
+   "source": [
+    "Here's the update function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "roman-archive",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func(state, t, system):\n",
+    "    G, X = state\n",
+    "    G0, k1, k2, k3 = system.params \n",
+    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
+    "    dt = system.dt\n",
+    "        \n",
+    "    dGdt = -k1 * (G - Gb) - X*G\n",
+    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
+    "    \n",
+    "    G += dGdt * dt\n",
+    "    X += dXdt * dt\n",
+    "\n",
+    "    return State(G=G, X=X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "basic-subdivision",
+   "metadata": {},
+   "source": [
+    "As usual, the update function takes a `State` object, a time, and a\n",
+    "`System` object as parameters. The first line uses multiple assignment\n",
+    "to extract the current values of `G` and `X`.\n",
+    "\n",
+    "The following lines unpack the parameters we need from the `System`\n",
+    "object.\n",
+    "\n",
+    "Computing the derivatives `dGdt` and `dXdt` is straightforward; we just\n",
+    "translate the equations from math notation to Python.\n",
+    "\n",
+    "Then, to perform the update, we multiply each derivative by the discrete\n",
+    "time step `dt`, which is 2 min in this example. The return value is a\n",
+    "`State` object with the new values of `G` and `X`.\n",
+    "\n",
+    "Before running the simulation, it is a good idea to run the update\n",
+    "function with the initial conditions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "sapphire-shannon",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "G    278.12\n",
+       "X      0.00\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "update_func(system.init, system.t_0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "strange-citation",
+   "metadata": {},
+   "source": [
+    "If it runs without errors and there is nothing obviously wrong with the\n",
+    "results, we are ready to run the simulation. We'll use this version of\n",
+    "`run_simulation`, which is very similar to previous versions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "willing-masters",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import linrange, TimeFrame\n",
+    "\n",
+    "def run_simulation(system, update_func):\n",
+    "    init = system.init\n",
+    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
+    "    \n",
+    "    t_array = linrange(system.t_0, system.t_end, system.dt)\n",
+    "    n = len(t_array)\n",
+    "    \n",
+    "    frame = TimeFrame(index=t_array, columns=init.index)\n",
+    "    frame.iloc[0] = system.init\n",
+    "    \n",
+    "    for i in range(n-1):\n",
+    "        t = t_array[i]\n",
+    "        state = frame.iloc[i]\n",
+    "        frame.iloc[i+1] = update_func(state, t, system)\n",
+    "    \n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "musical-loading",
+   "metadata": {},
+   "source": [
+    "We can run it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "international-germany",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>G</th>\n",
+       "      <th>X</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.0</th>\n",
+       "      <td>290</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>278.12</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.0</th>\n",
+       "      <td>266.9528</td>\n",
+       "      <td>0.0003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.0</th>\n",
+       "      <td>256.29546</td>\n",
+       "      <td>0.002668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.0</th>\n",
+       "      <td>245.07014</td>\n",
+       "      <td>0.004041</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             G         X\n",
+       "0.0        290         0\n",
+       "2.0     278.12       0.0\n",
+       "4.0   266.9528    0.0003\n",
+       "6.0  256.29546  0.002668\n",
+       "8.0  245.07014  0.004041"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = run_simulation(system, update_func)\n",
+    "results.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "specialized-thomson",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "results.G.plot(style='-', label='simulation')\n",
+    "data.glucose.plot(style='o', color='C0', label='glucose data')\n",
+    "decorate(ylabel='Concentration (mg/dL)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "authentic-series",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results.X.plot(color='C1', label='remote insulin')\n",
+    "\n",
+    "decorate(xlabel='Time (min)', \n",
+    "         ylabel='Concentration (arbitrary units)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "diagnostic-service",
+   "metadata": {},
+   "source": [
+    " shows the results. The top plot shows\n",
+    "simulated glucose levels from the model along with the measured data.\n",
+    "The bottom plot shows simulated insulin levels in tissue fluid, which is in unspecified units, and not to be confused with measured insulin\n",
+    "levels in the blood.\n",
+    "\n",
+    "With the parameters I chose, the model fits the data well, except for\n",
+    "the first few data points, where we don't expect the model to be\n",
+    "accurate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "valid-evanescence",
+   "metadata": {},
+   "source": [
+    "## Solving differential equations\n",
+    "\n",
+    "So far we have solved differential equations by rewriting them as\n",
+    "difference equations. In the current example, the differential equations are: \n",
+    "\n",
+    "$$\\frac{dG}{dt} = -k_1 \\left[ G(t) - G_b \\right] - X(t) G(t)$$\n",
+    "\n",
+    "$$\\frac{dX}{dt} = k_3 \\left[I(t) - I_b \\right] - k_2 X(t)$$ \n",
+    "\n",
+    "If we multiply both sides by $dt$, we have:\n",
+    "\n",
+    "$$dG = \\left[ -k_1 \\left[ G(t) - G_b \\right] - X(t) G(t) \\right] dt$$\n",
+    "\n",
+    "$$dX = \\left[ k_3 \\left[I(t) - I_b \\right] - k_2 X(t) \\right] dt$$ \n",
+    "\n",
+    "When $dt$ is very small, or more precisely **infinitesimal**, this equation is exact. But in our simulations, $dt$ is 2 min, which is not very small. In effect, the simulations assume that the derivatives $dG/dt$ and $dX/dt$ are constant during each 2 min time step."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "underlying-grave",
+   "metadata": {},
+   "source": [
+    "This method, evaluating derivatives at discrete time steps and assuming that they are constant in between, is called **Euler's method** (see <http://modsimpy.com/euler>).\n",
+    "\n",
+    "Euler's method is good enough for some simple problems, but it is not\n",
+    "very accurate. Other methods are more accurate, but many of them are\n",
+    "substantially more complicated.\n",
+    "\n",
+    "One of the best simple methods is called **Ralston's method**. The\n",
+    "ModSim library provides a function called `run_ode_solver` that\n",
+    "implements it.\n",
+    "\n",
+    "The \"ODE\" in `run_ode_solver` stands for \"ordinary differential\n",
+    "equation\". The equations we are solving are \"ordinary\" because all the\n",
+    "derivatives are with respect to the same variable; in other words, there are no partial derivatives.\n",
+    "\n",
+    "To use `run_ode_solver`, we have to provide a \"slope function\", like\n",
+    "this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "expected-collapse",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    G, X = state\n",
+    "    G0, k1, k2, k3 = system.params \n",
+    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
+    "        \n",
+    "    dGdt = -k1 * (G - Gb) - X*G\n",
+    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
+    "    \n",
+    "    return dGdt, dXdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "modified-surname",
+   "metadata": {},
+   "source": [
+    "`slope_func` is similar to `update_func`; in fact, it takes the same\n",
+    "parameters in the same order. But `slope_func` is simpler, because all\n",
+    "we have to do is compute the derivatives, that is, the slopes. We don't\n",
+    "have to do the updates; `run_ode_solver` does them for us.\n",
+    "\n",
+    "Now we can call `run_ode_solver` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "frequent-cartoon",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import run_solve_ivp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ongoing-constraint",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "  message: 'The solver successfully reached the end of the integration interval.'\n",
+       "     nfev: 86\n",
+       "     njev: 0\n",
+       "      nlu: 0\n",
+       "      sol: None\n",
+       "   status: 0\n",
+       "  success: True\n",
+       " t_events: None\n",
+       " y_events: None"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results2, details = run_solve_ivp(system, slope_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "removed-bubble",
+   "metadata": {},
+   "source": [
+    "`run_ode_solver` is similar to `run_simulation`: it takes a `System`\n",
+    "object and a slope function as parameters. It returns two values: a\n",
+    "`TimeFrame` with the solution and a `ModSimSeries` with additional\n",
+    "information.\n",
+    "\n",
+    "A `ModSimSeries` is like a `System` or `State` object; it contains a set\n",
+    "of variables and their values. The `ModSimSeries` from `run_ode_solver`,\n",
+    "which we assign to `details`, contains information about how the solver\n",
+    "ran, including a success code and diagnostic message.\n",
+    "\n",
+    "The `TimeFrame`, which we assign to `results`, has one row for each time\n",
+    "step and one column for each state variable. In this example, the rows\n",
+    "are time from 0 to 182 minutes; the columns are the state variables, `G`\n",
+    "and `X`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "important-crawford",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "results2.G.plot(style='-', label='simulation')\n",
+    "data.glucose.plot(style='o', color='C0', label='glucose data')\n",
+    "decorate(ylabel='Concentration (mg/dL)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "virtual-standard",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results2.X.plot(color='C1', label='remote insulin')\n",
+    "\n",
+    "decorate(xlabel='Time (min)', \n",
+    "         ylabel='Concentration (arbitrary units)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "corrected-indonesia",
+   "metadata": {},
+   "source": [
+    "shows the results from `run_simulation` and\n",
+    "`run_ode_solver`. The difference between them is barely visible.\n",
+    "\n",
+    "We can compute the percentage differences like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "disciplinary-washington",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.004442701427073649"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diff = results.G - results2.G\n",
+    "percent_diff = diff / results2.G * 100\n",
+    "percent_diff.abs().max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "electronic-navigation",
+   "metadata": {},
+   "source": [
+    "The largest percentage difference is less than 2%, which is small enough that it probably doesn't matter in practice. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "potential-dispute",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "You might be interested in this article about [people making a DIY artificial pancreas](https://www.bloomberg.com/news/features/2018-08-08/the-250-biohack-that-s-revolutionizing-life-with-diabetes)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "north-suspension",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bright-damage",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Our solution to the differential equations is only approximate because we used a finite step size, `dt=2` minutes.\n",
+    "\n",
+    "If we make the step size smaller, we expect the solution to be more accurate.  Run the simulation with `dt=1` and compare the results.  What is the largest relative error between the two solutions?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "charitable-craft",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "  message: 'The solver successfully reached the end of the integration interval.'\n",
+       "     nfev: 86\n",
+       "     njev: 0\n",
+       "      nlu: 0\n",
+       "      sol: None\n",
+       "   status: 0\n",
+       "  success: True\n",
+       " t_events: None\n",
+       " y_events: None"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "system.dt = 1\n",
+    "results3, details = run_simulation(system, slope_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "encouraging-outline",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results2.G.plot(style='C2--', label='run_ode_solver (dt=2)')\n",
+    "results3.G.plot(style='C3:', label='run_ode_solver (dt=1)')\n",
+    "\n",
+    "decorate(xlabel='Time (m)', ylabel='mg/dL')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "aware-patrick",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "diff = (results2.G - results3.G).dropna()\n",
+    "percent_diff = diff / results2.G * 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "fiscal-baker",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "max(abs(percent_diff))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "turkish-principle",
+   "metadata": {},
+   "source": [
+    "## Under the hood\n",
+    "\n",
+    "Here's the source code for `run_solve_ivp` if you'd like to know how it works.\n",
+    "\n",
+    "[RK45](https://en.wikipedia.org/wiki/List_of_Runge–Kutta_methods)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hispanic-summer",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "distinct-advance",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/modsim.py b/jupyter/modsim.py
new file mode 100644
index 00000000..3512ea30
--- /dev/null
+++ b/jupyter/modsim.py
@@ -0,0 +1,1153 @@
+"""
+Code from Modeling and Simulation in Python.
+
+Copyright 2020 Allen Downey
+
+MIT License: https://opensource.org/licenses/MIT
+"""
+
+import logging
+
+logger = logging.getLogger(name="modsim.py")
+
+# make sure we have Python 3.6 or better
+import sys
+
+if sys.version_info < (3, 6):
+    logger.warning("modsim.py depends on Python 3.6 features.")
+
+import inspect
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import scipy
+
+from scipy.interpolate import interp1d
+from scipy.interpolate import InterpolatedUnivariateSpline
+
+from scipy.integrate import odeint
+from scipy.integrate import solve_ivp
+
+from types import SimpleNamespace
+
+
+import pint
+
+units = pint.UnitRegistry()
+Quantity = units.Quantity
+
+
+def flip(p=0.5):
+    """Flips a coin with the given probability.
+
+    p: float 0-1
+
+    returns: boolean (True or False)
+    """
+    return np.random.random() < p
+
+
+def cart2pol(x, y, z=None):
+    """Convert Cartesian coordinates to polar.
+
+    x: number or sequence
+    y: number or sequence
+    z: number or sequence (optional)
+
+    returns: theta, rho OR theta, rho, z
+    """
+    x = np.asarray(x)
+    y = np.asarray(y)
+
+    rho = np.hypot(x, y)
+    theta = np.arctan2(y, x)
+
+    if z is None:
+        return theta, rho
+    else:
+        return theta, rho, z
+
+
+def pol2cart(theta, rho, z=None):
+    """Convert polar coordinates to Cartesian.
+
+    theta: number or sequence in radians
+    rho: number or sequence
+    z: number or sequence (optional)
+
+    returns: x, y OR x, y, z
+    """
+    x = rho * np.cos(theta)
+    y = rho * np.sin(theta)
+
+    if z is None:
+        return x, y
+    else:
+        return x, y, z
+
+from numpy import linspace
+
+def linrange(start, stop, step=1, **options):
+    """Make an array of equally spaced values.
+
+    start: first value
+    stop: last value (might be approximate)
+    step: difference between elements (should be consistent)
+
+    returns: NumPy array
+    """
+    n = int(round((stop-start) / step))
+    return linspace(start, stop, n+1, **options)
+
+
+def leastsq(error_func, x0, *args, **options):
+    """Find the parameters that yield the best fit for the data.
+
+    `x0` can be a sequence, array, Series, or Params
+
+    Positional arguments are passed along to `error_func`.
+
+    Keyword arguments are passed to `scipy.optimize.leastsq`
+
+    error_func: function that computes a sequence of errors
+    x0: initial guess for the best parameters
+    args: passed to error_func
+    options: passed to leastsq
+
+    :returns: Params object with best_params and ModSimSeries with details
+    """
+    # override `full_output` so we get a message if something goes wrong
+    options["full_output"] = True
+
+    # run leastsq
+    t = scipy.optimize.leastsq(error_func, x0=x0, args=args, **options)
+    best_params, cov_x, infodict, mesg, ier = t
+
+    # pack the results into a ModSimSeries object
+    details = ModSimSeries(infodict)
+    details.set(cov_x=cov_x, mesg=mesg, ier=ier)
+
+    # if we got a Params object, we should return a Params object
+    if isinstance(x0, Params):
+        best_params = Params(Series(best_params, x0.index))
+
+    # return the best parameters and details
+    return best_params, details
+
+
+def minimize_scalar(min_func, bounds, *args, **options):
+    """Finds the input value that minimizes `min_func`.
+
+    Wrapper for
+    https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
+
+    min_func: computes the function to be minimized
+    bounds: sequence of two values, lower and upper bounds of the range to be searched
+    args: any additional positional arguments are passed to min_func
+    options: any keyword arguments are passed as options to minimize_scalar
+
+    returns: ModSimSeries object
+    """
+    try:
+        min_func(bounds[0], *args)
+    except Exception as e:
+        msg = """Before running scipy.integrate.minimize_scalar, I tried
+                 running the slope function you provided with the
+                 initial conditions in system and t=0, and I got
+                 the following error:"""
+        logger.error(msg)
+        raise (e)
+
+    underride(options, xatol=1e-3)
+
+    res = scipy.optimize.minimize_scalar(
+        min_func,
+        bracket=bounds,
+        bounds=bounds,
+        args=args,
+        method="bounded",
+        options=options,
+    )
+
+    if not res.success:
+        msg = (
+            """scipy.optimize.minimize_scalar did not succeed.
+                 The message it returned is %s"""
+            % res.message
+        )
+        raise Exception(msg)
+
+    return ModSimSeries(res)
+
+
+def maximize_scalar(max_func, bounds, *args, **options):
+    """Finds the input value that maximizes `max_func`.
+
+    Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
+
+    min_func: computes the function to be maximized
+    bounds: sequence of two values, lower and upper bounds of the
+            range to be searched
+    args: any additional positional arguments are passed to max_func
+    options: any keyword arguments are passed as options to minimize_scalar
+
+    returns: ModSimSeries object
+    """
+
+    def min_func(*args):
+        return -max_func(*args)
+
+    res = minimize_scalar(min_func, bounds, *args, **options)
+
+    # we have to negate the function value before returning res
+    res.fun = -res.fun
+    return res
+
+
+def minimize_golden(min_func, bracket, *args, **options):
+    """Find the minimum of a function by golden section search.
+
+    Based on
+    https://en.wikipedia.org/wiki/Golden-section_search#Iterative_algorithm
+
+    :param min_func: function to be minimized
+    :param bracket: interval containing a minimum
+    :param args: arguments passes to min_func
+    :param options: rtol and maxiter
+
+    :return: ModSimSeries
+    """
+    maxiter = options.get("maxiter", 100)
+    rtol = options.get("rtol", 1e-3)
+
+    def success(**kwargs):
+        return ModSimSeries(dict(success=True, **kwargs))
+
+    def failure(**kwargs):
+        return ModSimSeries(dict(success=False, **kwargs))
+
+    a, b = bracket
+    ya = min_func(a, *args)
+    yb = min_func(b, *args)
+
+    phi = 2 / (np.sqrt(5) - 1)
+    h = b - a
+    c = b - h / phi
+    yc = min_func(c, *args)
+
+    d = a + h / phi
+    yd = min_func(d, *args)
+
+    if yc > ya or yc > yb:
+        return failure(message="The bracket is not well-formed.")
+
+    for i in range(maxiter):
+
+        # check for convergence
+        if abs(h / c) < rtol:
+            return success(x=c, fun=yc)
+
+        if yc < yd:
+            b, yb = d, yd
+            d, yd = c, yc
+            h = b - a
+            c = b - h / phi
+            yc = min_func(c, *args)
+        else:
+            a, ya = c, yc
+            c, yc = d, yd
+            h = b - a
+            d = a + h / phi
+            yd = min_func(d, *args)
+
+    # if we exited the loop, too many iterations
+    return failure(root=c, message="maximum iterations = %d exceeded" % maxiter)
+
+
+def maximize_golden(max_func, bracket, *args, **options):
+    """Find the maximum of a function by golden section search.
+
+    :param min_func: function to be maximized
+    :param bracket: interval containing a maximum
+    :param args: arguments passes to min_func
+    :param options: rtol and maxiter
+
+    :return: ModSimSeries
+    """
+
+    def min_func(*args):
+        return -max_func(*args)
+
+    res = minimize_golden(min_func, bracket, *args, **options)
+
+    # we have to negate the function value before returning res
+    res.fun = -res.fun
+    return res
+
+
+def minimize_powell(min_func, x0, *args, **options):
+    """Finds the input value that minimizes `min_func`.
+    Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
+    min_func: computes the function to be minimized
+    x0: initial guess
+    args: any additional positional arguments are passed to min_func
+    options: any keyword arguments are passed as options to minimize_scalar
+    returns: ModSimSeries object
+    """
+    underride(options, tol=1e-3)
+
+    res = scipy.optimize.minimize(min_func, x0, *args, **options)
+
+    return ModSimSeries(res)
+
+
+# make aliases for minimize and maximize
+minimize = minimize_golden
+maximize = maximize_golden
+
+
+def run_odeint(system, slope_func, **options):
+    """Integrates an ordinary differential equation.
+
+    `system` should contain system parameters and `ts`, which
+    is an array or Series that specifies the time when the
+    solution will be computed.
+
+    system: System object
+    slope_func: function that computes slopes
+
+    returns: TimeFrame
+    """
+    # make sure `system` contains `ts`
+    if not hasattr(system, "ts"):
+        msg = """It looks like `system` does not contain `ts`
+                 as a system variable.  `ts` should be an array
+                 or Series that specifies the times when the
+                 solution will be computed:"""
+        raise ValueError(msg)
+
+    # make sure `system` contains `init`
+    if not hasattr(system, "init"):
+        msg = """It looks like `system` does not contain `init`
+                 as a system variable.  `init` should be a State
+                 object that specifies the initial condition:"""
+        raise ValueError(msg)
+
+    # try running the slope function with the initial conditions
+    try:
+        slope_func(system.init, system.ts[0], system)
+    except Exception as e:
+        msg = """Before running scipy.integrate.odeint, I tried
+                 running the slope function you provided with the
+                 initial conditions in system and t=0, and I got
+                 the following error:"""
+        logger.error(msg)
+        raise (e)
+
+    # when odeint calls slope_func, it should pass `system` as
+    # the third argument.  To make that work, we have to make a
+    # tuple with a single element and pass the tuple to odeint as `args`
+    args = (system,)
+
+    # now we're ready to run `odeint` with `init` and `ts` from `system`
+    array = odeint(slope_func, list(system.init), system.ts, args, **options)
+
+    # the return value from odeint is an array, so let's pack it into
+    # a TimeFrame with appropriate columns and index
+    frame = TimeFrame(
+        array, columns=system.init.index, index=system.ts, dtype=np.float64
+    )
+    return frame
+
+
+from scipy.integrate import solve_ivp
+
+def run_solve_ivp(system, slope_func, **options):
+    """Computes a numerical solution to a differential equation.
+
+    `system` must contain `init` with initial conditions,
+    `t_0` with the start time, and `t_end` with the end time.
+
+    It can contain any other parameters required by the slope function.
+
+    `options` can be any legal options of `scipy.integrate.solve_ivp`
+
+    system: System object
+    slope_func: function that computes slopes
+
+    returns: TimeFrame
+    """
+    # make sure `system` contains `init`
+    if not hasattr(system, "init"):
+        msg = """It looks like `system` does not contain `init`
+                 as a system variable.  `init` should be a State
+                 object that specifies the initial condition:"""
+        raise ValueError(msg)
+
+    # make sure `system` contains `t_end`
+    if not hasattr(system, "t_end"):
+        msg = """It looks like `system` does not contain `t_end`
+                 as a system variable.  `t_end` should be the
+                 final time:"""
+        raise ValueError(msg)
+
+    # the default value for t_0 is 0
+    t_0 = getattr(system, "t_0", 0)
+
+    # try running the slope function with the initial conditions
+    try:
+        slope_func(t_0, system.init, system)
+    except Exception as e:
+        msg = """Before running scipy.integrate.solve_ivp, I tried
+                 running the slope function you provided with the
+                 initial conditions in `system` and `t=t_0` and I got
+                 the following error:"""
+        logger.error(msg)
+        raise (e)
+
+    # TODO: make dense solution the default
+
+    # run the solver
+    bunch = solve_ivp(slope_func, [t_0, system.t_end], system.init,
+                      args=[system], **options)
+
+    # separate the results from the details
+    y = bunch.pop("y")
+    t = bunch.pop("t")
+
+    # pack the results into a TimeFrame
+    results = TimeFrame(y.T, index=t, columns=system.init.index)
+    return results, bunch
+
+
+def check_system(system, slope_func):
+    """Make sure the system object has the fields we need for run_ode_solver.
+
+    :param system:
+    :param slope_func:
+    :return:
+    """
+    # make sure `system` contains `init`
+    if not hasattr(system, "init"):
+        msg = """It looks like `system` does not contain `init`
+                 as a system variable.  `init` should be a State
+                 object that specifies the initial condition:"""
+        raise ValueError(msg)
+
+    # make sure `system` contains `t_end`
+    if not hasattr(system, "t_end"):
+        msg = """It looks like `system` does not contain `t_end`
+                 as a system variable.  `t_end` should be the
+                 final time:"""
+        raise ValueError(msg)
+
+    # the default value for t_0 is 0
+    t_0 = getattr(system, "t_0", 0)
+
+    # get the initial conditions
+    init = system.init
+
+    # get t_end
+    t_end = system.t_end
+
+    # if dt is not specified, take 100 steps
+    try:
+        dt = system.dt
+    except KeyError:
+        dt = t_end / 100
+
+    return init, t_0, t_end, dt
+
+
+def run_euler(system, slope_func, **options):
+    """Computes a numerical solution to a differential equation.
+
+    `system` must contain `init` with initial conditions,
+    `t_end` with the end time, and `dt` with the time step.
+
+    `system` may contain `t_0` to override the default, 0
+
+    It can contain any other parameters required by the slope function.
+
+    `options` can be ...
+
+    system: System object
+    slope_func: function that computes slopes
+
+    returns: TimeFrame
+    """
+    # the default message if nothing changes
+    msg = "The solver successfully reached the end of the integration interval."
+
+    # get parameters from system
+    init, t_0, t_end, dt = check_system(system, slope_func)
+
+    # make the TimeFrame
+    frame = TimeFrame(columns=init.index)
+    frame.row[t_0] = init
+    ts = linrange(t_0, t_end, dt) * get_units(t_end)
+
+    # run the solver
+    for t1 in ts:
+        y1 = frame.row[t1]
+        slopes = slope_func(y1, t1, system)
+        y2 = [y + slope * dt for y, slope in zip(y1, slopes)]
+        t2 = t1 + dt
+        frame.row[t2] = y2
+
+    details = ModSimSeries(dict(message="Success"))
+    return frame, details
+
+
+def run_ralston(system, slope_func, **options):
+    """Computes a numerical solution to a differential equation.
+
+    `system` must contain `init` with initial conditions,
+     and `t_end` with the end time.
+
+     `system` may contain `t_0` to override the default, 0
+
+    It can contain any other parameters required by the slope function.
+
+    `options` can be ...
+
+    system: System object
+    slope_func: function that computes slopes
+
+    returns: TimeFrame
+    """
+    # the default message if nothing changes
+    msg = "The solver successfully reached the end of the integration interval."
+
+    # get parameters from system
+    init, t_0, t_end, dt = check_system(system, slope_func)
+
+    # make the TimeFrame
+    frame = TimeFrame(columns=init.index)
+    frame.row[t_0] = init
+    ts = linrange(t_0, t_end, dt) * get_units(t_end)
+
+    event_func = options.get("events", None)
+    z1 = np.nan
+
+    def project(y1, t1, slopes, dt):
+        t2 = t1 + dt
+        y2 = [y + slope * dt for y, slope in zip(y1, slopes)]
+        return y2, t2
+
+    # run the solver
+    for t1 in ts:
+        y1 = frame.row[t1]
+
+        # evaluate the slopes at the start of the time step
+        slopes1 = slope_func(y1, t1, system)
+
+        # evaluate the slopes at the two-thirds point
+        y_mid, t_mid = project(y1, t1, slopes1, 2 * dt / 3)
+        slopes2 = slope_func(y_mid, t_mid, system)
+
+        # compute the weighted sum of the slopes
+        slopes = [(k1 + 3 * k2) / 4 for k1, k2 in zip(slopes1, slopes2)]
+
+        # compute the next time stamp
+        y2, t2 = project(y1, t1, slopes, dt)
+
+        # check for a terminating event
+        if event_func:
+            z2 = event_func(y2, t2, system)
+            if z1 * z2 < 0:
+                scale = magnitude(z1 / (z1 - z2))
+                y2, t2 = project(y1, t1, slopes, scale * dt)
+                frame.row[t2] = y2
+                msg = "A termination event occurred."
+                break
+            else:
+                z1 = z2
+
+        # store the results
+        frame.row[t2] = y2
+
+    details = ModSimSeries(dict(success=True, message=msg))
+    return frame, details
+
+
+run_ode_solver = run_ralston
+
+# TODO: Implement leapfrog
+
+
+def fsolve(func, x0, *args, **options):
+    """Return the roots of the (non-linear) equations
+    defined by func(x) = 0 given a starting estimate.
+
+    Uses scipy.optimize.fsolve, with extra error-checking.
+
+    func: function to find the roots of
+    x0: scalar or array, initial guess
+    args: additional positional arguments are passed along to fsolve,
+          which passes them along to func
+
+    returns: solution as an array
+    """
+    # make sure we can run the given function with x0
+    try:
+        func(x0, *args)
+    except Exception as e:
+        msg = """Before running scipy.optimize.fsolve, I tried
+                 running the error function you provided with the x0
+                 you provided, and I got the following error:"""
+        logger.error(msg)
+        raise (e)
+
+    # make the tolerance more forgiving than the default
+    underride(options, xtol=1e-6)
+
+    # run fsolve
+    result = scipy.optimize.fsolve(func, x0, args=args, **options)
+
+    return result
+
+
+
+def crossings(series, value):
+    """Find the labels where the series passes through value.
+
+    The labels in series must be increasing numerical values.
+
+    series: Series
+    value: number
+
+    returns: sequence of labels
+    """
+    values = magnitudes(series - value)
+    interp = InterpolatedUnivariateSpline(series.index, values)
+    return interp.roots()
+
+
+def has_nan(a):
+    """Checks whether the an array contains any NaNs.
+
+    :param a: NumPy array or Pandas Series
+    :return: boolean
+    """
+    return np.any(np.isnan(a))
+
+
+def is_strictly_increasing(a):
+    """Checks whether the elements of an array are strictly increasing.
+
+    :param a: NumPy array or Pandas Series
+    :return: boolean
+    """
+    return np.all(np.diff(a) > 0)
+
+
+def interpolate(series, **options):
+    """Creates an interpolation function.
+
+    series: Series object
+    options: any legal options to scipy.interpolate.interp1d
+
+    returns: function that maps from the index to the values
+    """
+    if has_nan(series.index):
+        msg = """The Series you passed to interpolate contains
+                 NaN values in the index, which would result in
+                 undefined behavior.  So I'm putting a stop to that."""
+        raise ValueError(msg)
+
+    if not is_strictly_increasing(series.index):
+        msg = """The Series you passed to interpolate has an index
+                 that is not strictly increasing, which would result in
+                 undefined behavior.  So I'm putting a stop to that."""
+        raise ValueError(msg)
+
+    # make the interpolate function extrapolate past the ends of
+    # the range, unless `options` already specifies a value for `fill_value`
+    underride(options, fill_value="extrapolate")
+
+    # call interp1d, which returns a new function object
+    x = series.index
+    y = series.values
+    interp_func = interp1d(x, y, **options)
+    return interp_func
+
+
+def interpolate_inverse(series, **options):
+    """Interpolate the inverse function of a Series.
+
+    series: Series object, represents a mapping from `a` to `b`
+    options: any legal options to scipy.interpolate.interp1d
+
+    returns: interpolation object, can be used as a function
+             from `b` to `a`
+    """
+    inverse = Series(series.index, index=series.values)
+    interp_func = interpolate(inverse, **options)
+    return interp_func
+
+
+def gradient(series, **options):
+    """Computes the numerical derivative of a series.
+
+    If the elements of series have units, they are dropped.
+
+    series: Series object
+    options: any legal options to np.gradient
+
+    returns: Series, same subclass as series
+    """
+    x = magnitudes(series.index)
+    y = magnitudes(series.values)
+    # units = get_units(series)
+
+    a = np.gradient(y, x, **options)
+    return series.__class__(a, series.index)
+
+
+def source_code(obj):
+    """Prints the source code for a given object.
+
+    obj: function or method object
+    """
+    print(inspect.getsource(obj))
+
+
+def underride(d, **options):
+    """Add key-value pairs to d only if key is not in d.
+
+    If d is None, create a new dictionary.
+
+    d: dictionary
+    options: keyword args to add to d
+    """
+    if d is None:
+        d = {}
+
+    for key, val in options.items():
+        d.setdefault(key, val)
+
+    return d
+
+
+
+def contour(df, **options):
+    """Makes a contour plot from a DataFrame.
+
+    Wrapper for plt.contour
+    https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.contour.html
+
+    Note: columns and index must be numerical
+
+    df: DataFrame
+    options: passed to plt.contour
+    """
+    fontsize = options.pop("fontsize", 12)
+    underride(options, cmap="viridis")
+    x = df.columns
+    y = df.index
+    X, Y = np.meshgrid(x, y)
+    cs = plt.contour(X, Y, df, **options)
+    plt.clabel(cs, inline=1, fontsize=fontsize)
+
+
+def savefig(filename, **options):
+    """Save the current figure.
+
+    Keyword arguments are passed along to plt.savefig
+
+    https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html
+
+    filename: string
+    """
+    print("Saving figure to file", filename)
+    plt.savefig(filename, **options)
+
+
+def decorate(**options):
+    """Decorate the current axes.
+
+    Call decorate with keyword arguments like
+    decorate(title='Title',
+             xlabel='x',
+             ylabel='y')
+
+    The keyword arguments can be any of the axis properties
+    https://matplotlib.org/api/axes_api.html
+    """
+    ax = plt.gca()
+    ax.set(**options)
+
+    handles, labels = ax.get_legend_handles_labels()
+    if handles:
+        ax.legend(handles, labels)
+
+    plt.tight_layout()
+
+
+def remove_from_legend(bad_labels):
+    """Removes some labels from the legend.
+
+    bad_labels: sequence of strings
+    """
+    ax = plt.gca()
+    handles, labels = ax.get_legend_handles_labels()
+    handle_list, label_list = [], []
+    for handle, label in zip(handles, labels):
+        if label not in bad_labels:
+            handle_list.append(handle)
+            label_list.append(label)
+    ax.legend(handle_list, label_list)
+
+
+def subplot(nrows, ncols, plot_number, **options):
+    figsize = {(2, 1): (8, 8), (3, 1): (8, 10)}
+    key = nrows, ncols
+    default = (8, 5.5)
+    width, height = figsize.get(key, default)
+
+    plt.subplot(nrows, ncols, plot_number, **options)
+    fig = plt.gcf()
+    fig.set_figwidth(width)
+    fig.set_figheight(height)
+
+
+def TimeSeries(*args, **kwargs):
+    """
+    """
+    if args or kwargs:
+        series = pd.Series(*args, **kwargs)
+    else:
+        series = pd.Series([], dtype=np.float64)
+
+    series.index.name = 'Time'
+    if 'name' not in kwargs:
+        series.name = 'Quantity'
+    return series
+
+
+def SweepSeries(*args, **kwargs):
+    """
+    """
+    if args or kwargs:
+        series = pd.Series(*args, **kwargs)
+    else:
+        series = pd.Series([], dtype=np.float64)
+
+    series.index.name = 'Parameter'
+    if 'name' not in kwargs:
+        series.name = 'Metric'
+    return series
+
+
+def show(series):
+    """
+    """
+    if not isinstance(series, pd.Series):
+        return series
+    df = pd.DataFrame(series)
+    return df
+
+
+def plot(x, y=None, **options):
+    """
+    """
+    if y is not None:
+        plt.plot(x, y, **options)
+    else:
+        # x better be a Series
+        x.plot(**options)
+
+
+def State(**kwargs):
+    """Contains state variables and their values."""
+    return pd.Series(kwargs)
+
+
+class System(SimpleNamespace):
+    """Contains system parameters and their values.
+
+    Takes keyword arguments and stores them as attributes.
+    """
+    pass
+
+
+def TimeFrame(*args, **kwargs):
+    """DataFrame that maps from time to State.
+    """
+    return pd.DataFrame(*args, **kwargs)
+
+
+def SweepFrame(*args, **kwargs):
+    """DataFrame that maps from parameter value to SweepSeries.
+    """
+    return pd.DataFrame(*args, **kwargs)
+
+
+def Vector(*args, units=None):
+    """Make a ModSimVector.
+
+    args: can be a single argument or sequence
+    units: Pint Unit object or Quantity
+
+    If there's only one argument, it should be a sequence.
+
+    Otherwise, the arguments are treated as coordinates.
+
+    returns: ModSimVector
+    """
+    if len(args) == 1:
+        args = args[0]
+
+        # if it's a series, pull out the values
+        if isinstance(args, Series):
+            args = args.values
+
+    # see if any of the arguments have units; if so, save the first one
+    for elt in args:
+        found_units = getattr(elt, "units", None)
+        if found_units:
+            break
+
+    if found_units:
+        # if there are units, remove them
+        args = [float(magnitude(elt)) for elt in args]
+    else:
+        # otherwise, just ensure that all elements are floats (to avoid overflow issues in numpy)
+        args = [float(elt) for elt in args]
+
+    # if the units keyword is provided, it overrides the units in args
+    if units is not None:
+        found_units = units
+
+    return ModSimVector(args, found_units)
+
+
+## Vector functions (should work with any sequence)
+
+
+def vector_mag(v):
+    """Vector magnitude with units.
+
+    returns: number or Quantity
+    """
+    a = magnitude(v)
+    units = get_first_unit(v)
+    return np.sqrt(np.dot(a, a)) * units
+
+
+def vector_mag2(v):
+    """Vector magnitude squared with units.
+
+    returns: number of Quantity
+    """
+    a = magnitude(v)
+    units = get_first_unit(v)
+    return np.dot(a, a) * units * units
+
+
+def vector_angle(v):
+    """Angle between v and the positive x axis.
+
+    Only works with 2-D vectors.
+
+    returns: number in radians
+    """
+    assert len(v) == 2
+    x, y = v
+    return np.arctan2(y, x)
+
+
+def vector_polar(v):
+    """Vector magnitude and angle.
+
+    returns: (number or quantity, number in radians)
+    """
+    return vector_mag(v), vector_angle(v)
+
+
+def vector_hat(v):
+    """Unit vector in the direction of v.
+
+    The result should have no units.
+
+    returns: Vector or array
+    """
+    # get the size of the vector
+    mag = vector_mag(v)
+
+    # check if the magnitude of the Quantity is 0
+    if magnitude(mag) == 0:
+        if isinstance(v, ModSimVector):
+            return Vector(magnitude(v))
+        else:
+            return magnitude(np.asarray(v))
+    else:
+        return v / mag
+
+
+def vector_perp(v):
+    """Perpendicular Vector (rotated left).
+
+    Only works with 2-D Vectors.
+
+    returns: Vector
+    """
+    assert len(v) == 2
+    x, y = v
+    return Vector(-y, x)
+
+
+def vector_dot(v, w):
+    """Dot product of v and w.
+
+    returns: number or Quantity
+    """
+    a1 = magnitude(v)
+    a2 = magnitude(w)
+    return np.dot(a1, a2) * get_first_unit(v) * get_first_unit(w)
+
+
+def vector_cross(v, w):
+    """Cross product of v and w.
+
+    returns: number or Quantity for 2-D, Vector for 3-D
+    """
+    a1 = magnitude(v)
+    a2 = magnitude(w)
+    res = np.cross(a1, a2)
+
+    if len(v) == 3 and (isinstance(v, ModSimVector) or isinstance(w, ModSimVector)):
+        return ModSimVector(res, get_first_unit(v) * get_first_unit(w))
+    else:
+        return res * get_first_unit(v) * get_first_unit(w)
+
+
+def vector_proj(v, w):
+    """Projection of v onto w.
+
+    Results has the units of v, but that might not make sense unless
+    v and w have the same units.
+
+    returns: array or Vector with direction of w and units of v.
+    """
+    w_hat = vector_hat(w)
+    return vector_dot(v, w_hat) * w_hat
+
+
+def scalar_proj(v, w):
+    """Returns the scalar projection of v onto w.
+
+    Which is the magnitude of the projection of v onto w.
+
+    Results has the units of v, but that might not make sense unless
+    v and w have the same units.
+
+    returns: scalar with units of v.
+    """
+    return vector_dot(v, vector_hat(w))
+
+
+def vector_dist(v, w):
+    """Euclidean distance from v to w, with units."""
+    if isinstance(v, list):
+        v = np.asarray(v)
+    return vector_mag(v - w)
+
+
+def vector_diff_angle(v, w):
+    """Angular difference between two vectors, in radians.
+    """
+    if len(v) == 2:
+        return vector_angle(v) - vector_angle(w)
+    else:
+        # TODO: see http://www.euclideanspace.com/maths/algebra/
+        # vectors/angleBetween/
+        raise NotImplementedError()
+
+
+class ModSimVector(Quantity):
+    """Represented as a Pint Quantity with a NumPy array
+
+    x, y, z, mag, mag2, and angle are accessible as attributes.
+    """
+
+    @property
+    def x(self):
+        """Returns the x component with units."""
+        return self[0]
+
+    @property
+    def y(self):
+        """Returns the y component with units."""
+        return self[1]
+
+    @property
+    def z(self):
+        """Returns the z component with units."""
+        return self[2]
+
+    @property
+    def mag(self):
+        """Returns the magnitude with units."""
+        return vector_mag(self)
+
+    @property
+    def mag2(self):
+        """Returns the magnitude squared with units."""
+        return vector_mag2(self)
+
+    @property
+    def angle(self):
+        """Returns the angle between self and the positive x axis."""
+        return vector_angle(self)
+
+    # make the vector functions available as methods
+    polar = vector_polar
+    hat = vector_hat
+    perp = vector_perp
+    dot = vector_dot
+    cross = vector_cross
+    proj = vector_proj
+    comp = scalar_proj
+    dist = vector_dist
+    diff_angle = vector_diff_angle
+
+
+def plot_segment(A, B, **options):
+    """Plots a line segment between two Vectors.
+
+    For 3-D vectors, the z axis is ignored.
+
+    Additional options are passed along to plot().
+
+    A: Vector
+    B: Vector
+    """
+    xs = A.x, B.x
+    ys = A.y, B.y
+    plot(xs, ys, **options)
+
+from time import sleep
+from IPython.display import clear_output
+
+def animate(results, draw_func, interval=None):
+    """Animate results from a simulation.
+
+    results: TimeFrame
+    draw_func: function that draws state
+    interval: time between frames in seconds
+    """
+    plt.figure()
+    try:
+        for t, state in results.iterrows():
+            draw_func(state, t)
+            plt.show()
+            if interval:
+                sleep(interval)
+            clear_output(wait=True)
+        draw_func(state, t)
+        plt.show()
+    except KeyboardInterrupt:
+        pass

From 3213d25cbb2fdb0df32210f55471b92c2eae9eb4 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 2 Feb 2021 11:05:47 -0500
Subject: [PATCH 024/144] Adding figure

---
 figs/baseball_drag.png | Bin 0 -> 250310 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 figs/baseball_drag.png

diff --git a/figs/baseball_drag.png b/figs/baseball_drag.png
new file mode 100644
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From 43c844287133fa9e21943b711c66c7760bc659e5 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 2 Feb 2021 11:12:55 -0500
Subject: [PATCH 025/144] Revising chapters

---
 jupyter/chap20.ipynb | 1108 ++++++++++++++++++++++++
 jupyter/chap21.ipynb | 1087 +++++++++++++++++++++++
 jupyter/chap22.ipynb | 1961 ++++++++++++++++++++++++++++++++++++++++++
 jupyter/modsim.py    |  173 ++--
 4 files changed, 4236 insertions(+), 93 deletions(-)
 create mode 100644 jupyter/chap20.ipynb
 create mode 100644 jupyter/chap21.ipynb
 create mode 100644 jupyter/chap22.ipynb

diff --git a/jupyter/chap20.ipynb b/jupyter/chap20.ipynb
new file mode 100644
index 00000000..a2cc521d
--- /dev/null
+++ b/jupyter/chap20.ipynb
@@ -0,0 +1,1108 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "funded-utilization",
+   "metadata": {},
+   "source": [
+    "# Chapter 20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "furnished-portsmouth",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "pending-adams",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "embedded-gentleman",
+   "metadata": {},
+   "source": [
+    "So far the differential equations we've worked with have been **first\n",
+    "order**, which means they involve only first derivatives. In this\n",
+    "chapter, we turn our attention to second order ODEs, which can involve\n",
+    "both first and second derivatives.\n",
+    "\n",
+    "We'll revisit the falling penny example from\n",
+    "Chapter xxx, and use `run_solve_ivp` to find the position and velocity of the penny as it falls, with and without air resistance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "isolated-louis",
+   "metadata": {},
+   "source": [
+    "## Newton's second law of motion\n",
+    "\n",
+    "First order ODEs can be written \n",
+    "\n",
+    "$$\\frac{dy}{dx} = G(x, y)$$ \n",
+    "\n",
+    "where $G$ is some function of $x$ and $y$ (see <http://modsimpy.com/ode>). Second order ODEs can be written \n",
+    "\n",
+    "$$\\frac{d^2y}{dx^2} = H(x, y, \\frac{dy}{dt})$$\n",
+    "\n",
+    "where $H$ is a function of $x$, $y$, and $dy/dx$.\n",
+    "\n",
+    "In this chapter, we will work with one of the most famous and useful\n",
+    "second order ODEs, Newton's second law of motion: \n",
+    "\n",
+    "$$F = m a$$ \n",
+    "\n",
+    "where $F$ is a force or the total of a set of forces, $m$ is the mass of a moving object, and $a$ is its acceleration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "drawn-symphony",
+   "metadata": {},
+   "source": [
+    "Newton's law might not look like a differential equation, until we\n",
+    "realize that acceleration, $a$, is the second derivative of position,\n",
+    "$y$, with respect to time, $t$. With the substitution\n",
+    "\n",
+    "$$a = \\frac{d^2y}{dt^2}$$ \n",
+    "\n",
+    "Newton's law can be written\n",
+    "\n",
+    "$$\\frac{d^2y}{dt^2} = F / m$$ \n",
+    "\n",
+    "And that's definitely a second order ODE.\n",
+    "In general, $F$ can be a function of time, position, and velocity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "swiss-vietnam",
+   "metadata": {},
+   "source": [
+    "Of course, this \"law\" is really a model in the sense that it is a\n",
+    "simplification of the real world. Although it is often approximately\n",
+    "true:\n",
+    "\n",
+    "-   It only applies if $m$ is constant. If mass depends on time,\n",
+    "    position, or velocity, we have to use a more general form of\n",
+    "    Newton's law (see <http://modsimpy.com/varmass>).\n",
+    "\n",
+    "-   It is not a good model for very small things, which are better\n",
+    "    described by another model, quantum mechanics.\n",
+    "\n",
+    "-   And it is not a good model for things moving very fast, which are\n",
+    "    better described by yet another model, relativistic mechanics.\n",
+    "\n",
+    "However, for medium-sized things with constant mass, moving at\n",
+    "medium-sized speeds, Newton's model is extremely useful. If we can\n",
+    "quantify the forces that act on such an object, we can predict how it\n",
+    "will move."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "coordinate-three",
+   "metadata": {},
+   "source": [
+    "## Dropping pennies\n",
+    "\n",
+    "As a first example, let's get back to the penny falling from the Empire State Building, which we considered in\n",
+    "Chapter xxx. We will implement two models of this system: first without air resistance, then with.\n",
+    "\n",
+    "Given that the Empire State Building is 381 m high, and assuming that\n",
+    "the penny is dropped from a standstill, the initial conditions are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "compatible-increase",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import State\n",
+    "\n",
+    "init = State(y=381, v=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "intellectual-radiation",
+   "metadata": {},
+   "source": [
+    "where `y` is height above the sidewalk and `v` is velocity. \n",
+    "\n",
+    "The units `m` and `s` are from the `units` object provided by Pint:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "wicked-thomas",
+   "metadata": {},
+   "source": [
+    "The only system parameter is the acceleration of gravity:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "reverse-authorization",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g = 9.8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "developing-newfoundland",
+   "metadata": {},
+   "source": [
+    "In addition, we'll specify the duration of the simulation and the step\n",
+    "size:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "square-toolbox",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_end = 10\n",
+    "dt = 0.1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "precise-tobago",
+   "metadata": {},
+   "source": [
+    "With these parameters, the number of time steps is 100, which is good\n",
+    "enough for many problems. Once we have a solution, we will increase the\n",
+    "number of steps and see what effect it has on the results.\n",
+    "\n",
+    "We need a `System` object to store the parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "built-piece",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import System\n",
+    "\n",
+    "system = System(init=init, g=g, t_end=t_end, dt=dt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "heavy-boards",
+   "metadata": {},
+   "source": [
+    "Now we need a slope function, and here's where things get tricky. As we have seen, `run_solve_ivp` can solve systems of first order ODEs, but Newton's law is a second order ODE. However, if we recognize that\n",
+    "\n",
+    "1.  Velocity, $v$, is the derivative of position, $dy/dt$, and\n",
+    "\n",
+    "2.  Acceleration, $a$, is the derivative of velocity, $dv/dt$,\n",
+    "\n",
+    "we can rewrite Newton's law as a system of first order ODEs:\n",
+    "\n",
+    "$$\\frac{dy}{dt} = v$$ \n",
+    "\n",
+    "$$\\frac{dv}{dt} = a$$ \n",
+    "\n",
+    "And we can translate those\n",
+    "equations into a slope function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "occupied-mercury",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    y, v = state\n",
+    "\n",
+    "    dydt = v\n",
+    "    dvdt = -system.g\n",
+    "    \n",
+    "    return dydt, dvdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opening-adolescent",
+   "metadata": {},
+   "source": [
+    "The first parameter, `state`, contains the position and velocity of the\n",
+    "penny. The last parameter, `system`, contains the system parameter `g`,\n",
+    "which is the magnitude of acceleration due to gravity.\n",
+    "\n",
+    "The second parameter, `t`, is time. It is not used in this slope\n",
+    "function because none of the factors of the model are time dependent. I include it anyway because this function will be called by `run_solve_ivp`, which always provides the same arguments,\n",
+    "whether they are needed or not.\n",
+    "\n",
+    "The rest of the function is a straightforward translation of the\n",
+    "differential equations, with the substitution $a = -g$, which indicates that acceleration due to gravity is in the direction of decreasing $y$. `slope_func` returns a sequence containing the two derivatives.\n",
+    "\n",
+    "Before calling `run_solve_ivp`, it is a good idea to test the slope\n",
+    "function with the initial conditions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "positive-feeling",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "-9.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "dydt, dvdt = slope_func(0, system.init, system)\n",
+    "print(dydt)\n",
+    "print(dvdt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "false-charlotte",
+   "metadata": {},
+   "source": [
+    "The result is 0 m/s for velocity and 9.8 m/s$^2$ for acceleration. Now we call `run_solve_ivp` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "lovely-management",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "  message: 'The solver successfully reached the end of the integration interval.'\n",
+       "     nfev: 38\n",
+       "     njev: 0\n",
+       "      nlu: 0\n",
+       "      sol: None\n",
+       "   status: 0\n",
+       "  success: True\n",
+       " t_events: None\n",
+       " y_events: None"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import run_solve_ivp\n",
+    "\n",
+    "results, details = run_solve_ivp(system, slope_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "assisted-swimming",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y</th>\n",
+       "      <th>v</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.0</th>\n",
+       "      <td>381.000</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.2</th>\n",
+       "      <td>380.804</td>\n",
+       "      <td>-1.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.4</th>\n",
+       "      <td>380.216</td>\n",
+       "      <td>-3.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.6</th>\n",
+       "      <td>379.236</td>\n",
+       "      <td>-5.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.8</th>\n",
+       "      <td>377.864</td>\n",
+       "      <td>-7.84</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           y     v\n",
+       "0.0  381.000  0.00\n",
+       "0.2  380.804 -1.96\n",
+       "0.4  380.216 -3.92\n",
+       "0.6  379.236 -5.88\n",
+       "0.8  377.864 -7.84"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "solved-chambers",
+   "metadata": {},
+   "source": [
+    "`results` is a `TimeFrame` with two columns: `y` contains the height of\n",
+    "the penny; `v` contains its velocity.\n",
+    "\n",
+    "We can plot the results like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "authorized-barrier",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "results.y.plot()\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Position (m)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "differential-airfare",
+   "metadata": {},
+   "source": [
+    "Since acceleration is constant, velocity increases linearly and position decreases quadratically; as a result, the height curve is a parabola.\n",
+    "\n",
+    "The last value of `results.y` is negative, which means we ran the simulation too long. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "protected-fiber",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-108.99999999999869"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t_end = results.index[-1]\n",
+    "results.y[t_end]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "metallic-tamil",
+   "metadata": {},
+   "source": [
+    "One way to solve this problem is to use the results to\n",
+    "estimate the time when the penny hits the sidewalk.\n",
+    "\n",
+    "The ModSim library provides `crossings`, which takes a `TimeSeries` and a value, and returns a sequence of times when the series passes through the value. We can find the time when the height of the penny is `0` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "japanese-clear",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([8.81788535])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import crossings\n",
+    "\n",
+    "t_crossings = crossings(results.y, 0)\n",
+    "t_crossings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "demonstrated-emission",
+   "metadata": {},
+   "source": [
+    "The result is an array with a single value, 8.818 s. Now, we could run\n",
+    "the simulation again with `t_end = 8.818`, but there's a better way."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "blind-dominant",
+   "metadata": {},
+   "source": [
+    "## Events\n",
+    "\n",
+    "As an option, `run_solve_ivp` can take an **event function**, which\n",
+    "detects an \"event\", like the penny hitting the sidewalk, and ends the\n",
+    "simulation.\n",
+    "\n",
+    "Event functions take the same parameters as slope functions, `state`,\n",
+    "`t`, and `system`. They should return a value that passes through `0`\n",
+    "when the event occurs. Here's an event function that detects the penny\n",
+    "hitting the sidewalk:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "comfortable-simple",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(t, state, system):\n",
+    "    y, v = state\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "closing-vehicle",
+   "metadata": {},
+   "source": [
+    "The return value is the height of the penny, `y`, which passes through\n",
+    "`0` when the penny hits the sidewalk.\n",
+    "\n",
+    "We pass the event function to `run_solve_ivp` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "exotic-shareware",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "  message: 'A termination event occurred.'\n",
+       "     nfev: 38\n",
+       "     njev: 0\n",
+       "      nlu: 0\n",
+       "      sol: None\n",
+       "   status: 1\n",
+       "  success: True\n",
+       " t_events: [array([8.81788535])]\n",
+       " y_events: [array([[ 5.68434189e-14, -8.64152764e+01]])]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results, details = run_solve_ivp(system, slope_func,\n",
+    "                                 events=event_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "recreational-blair",
+   "metadata": {},
+   "source": [
+    "Then we can get the flight time and final velocity like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "appropriate-roberts",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8.8"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t_end = results.index[-1]\n",
+    "t_end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "orange-retro",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.5440000000007217\n",
+      "-86.24000000000001\n"
+     ]
+    }
+   ],
+   "source": [
+    "y, v = results.iloc[-1]\n",
+    "print(y)\n",
+    "print(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cleared-jamaica",
+   "metadata": {},
+   "source": [
+    "If there were no air resistance, the penny would hit the sidewalk (or someone's head) at more than 300 km/h.\n",
+    "\n",
+    "So it's a good thing there is air resistance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "induced-albert",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "But air resistance...\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "straight-johns",
+   "metadata": {},
+   "source": [
+    "### Exercises\n",
+    "\n",
+    "**Exercise:** Here's a question from the web site [Ask an Astronomer](http://curious.astro.cornell.edu/about-us/39-our-solar-system/the-earth/other-catastrophes/57-how-long-would-it-take-the-earth-to-fall-into-the-sun-intermediate):\n",
+    "\n",
+    "\"If the Earth suddenly stopped orbiting the Sun, I know eventually it would be pulled in by the Sun's gravity and hit it. How long would it take the Earth to hit the Sun? I imagine it would go slowly at first and then pick up speed.\"\n",
+    "\n",
+    "Use `run_solve_ivp` to answer this question.\n",
+    "\n",
+    "Here are some suggestions about how to proceed:\n",
+    "\n",
+    "1.  Look up the Law of Universal Gravitation and any constants you need.  I suggest you work entirely in SI units: meters, kilograms, and Newtons.\n",
+    "\n",
+    "2.  When the distance between the Earth and the Sun gets small, this system behaves badly, so you should use an event function to stop when the surface of Earth reaches the surface of the Sun.\n",
+    "\n",
+    "3. Express your answer in days, and plot the results as millions of kilometers versus days.\n",
+    "\n",
+    "If you read the reply by Dave Rothstein, you will see other ways to solve the problem, and a good discussion of the modeling decisions behind them.\n",
+    "\n",
+    "You might also be interested to know that [it's actually not that easy to get to the Sun](https://www.theatlantic.com/science/archive/2018/08/parker-solar-probe-launch-nasa/567197/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "forbidden-distributor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "r_0 = 150e9   # 150 million km in m\n",
+    "v_0 = 0\n",
+    "init = State(r=r_0,\n",
+    "             v=v_0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "former-taxation",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "702370000.0"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "radius_earth = 6.37e6  # meters\n",
+    "radius_sun = 696e6     # meters\n",
+    "r_final = radius_sun + radius_earth\n",
+    "r_final"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "biological-creek",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "213.56265216338966"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r_0 / r_final"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "oriental-riverside",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_end = 1e7  # seconds\n",
+    "\n",
+    "system = System(init=init,\n",
+    "                G=6.674e-11,   # N m^2 / kg^2\n",
+    "                m1=1.989e30,   # kg\n",
+    "                m2=5.972e24,   # kg\n",
+    "                r_final=radius_sun + radius_earth,\n",
+    "                t_end=t_end)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "radio-reproduction",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def universal_gravitation(state, system):\n",
+    "    \"\"\"Computes gravitational force.\n",
+    "    \n",
+    "    state: State object with distance r\n",
+    "    system: System object with m1, m2, and G\n",
+    "    \"\"\"\n",
+    "    r, v = state\n",
+    "    G, m1, m2 = system.G, system.m1, system.m2\n",
+    "    \n",
+    "    force = G * m1 * m2 / r**2\n",
+    "    return force"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "heavy-cologne",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.5233701151999997e+22"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "universal_gravitation(init, system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "little-electric",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def slope_func(t, state, system):\n",
+    "    \"\"\"Compute derivatives of the state.\n",
+    "    \n",
+    "    state: position, velocity\n",
+    "    t: time\n",
+    "    system: System object containing `m2`\n",
+    "    \n",
+    "    returns: derivatives of y and v\n",
+    "    \"\"\"\n",
+    "    y, v = state\n",
+    "    m2 = system.m2    \n",
+    "\n",
+    "    force = universal_gravitation(state, system)\n",
+    "    dydt = v\n",
+    "    dvdt = -force / m2\n",
+    "    \n",
+    "    return dydt, dvdt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "continental-details",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, -0.005899815999999999)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "slope_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "suitable-traveler",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def event_func(t, state, system):\n",
+    "    r, v = state\n",
+    "    return r - system.r_final"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "upper-victory",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "149297630000.0"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "event_func(0, init, system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "transparent-treat",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "  message: 'A termination event occurred.'\n",
+       "     nfev: 188\n",
+       "     njev: 0\n",
+       "      nlu: 0\n",
+       "      sol: None\n",
+       "   status: 1\n",
+       "  success: True\n",
+       " t_events: [array([5599828.17941854])]\n",
+       " y_events: [array([[ 7.02370000e+08, -6.13362867e+05]])]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results, details = run_solve_ivp(system, slope_func, \n",
+    "                                 events=event_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "brutal-woman",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5400000.0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "t_event = results.index[-1]\n",
+    "t_event"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "gentle-burst",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "from modsim import units\n",
+    "\n",
+    "seconds = t_event * units.second\n",
+    "days = seconds.to(units.day)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "comfortable-galaxy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results.index /= 60 * 60 * 24"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "satisfactory-latitude",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results.r /= 1e9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "significant-rebound",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results.r.plot(label='r')\n",
+    "\n",
+    "decorate(xlabel='Time (day)',\n",
+    "         ylabel='Distance from sun (million km)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "right-colleague",
+   "metadata": {},
+   "source": [
+    "## Under the hood\n",
+    "\n",
+    "`solve_ivp`\n",
+    "\n",
+    "Here is the source code for `crossings` so you can see what's happening under the hood:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "chief-error",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%psource crossings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "comparable-company",
+   "metadata": {},
+   "source": [
+    "The [documentation of InterpolatedUnivariateSpline is here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.InterpolatedUnivariateSpline.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "listed-shelter",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/chap21.ipynb b/jupyter/chap21.ipynb
new file mode 100644
index 00000000..5601492d
--- /dev/null
+++ b/jupyter/chap21.ipynb
@@ -0,0 +1,1087 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "excited-advance",
+   "metadata": {},
+   "source": [
+    "# Chapter 21"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unauthorized-constitutional",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "medieval-cheese",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "running-field",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we simulated a penny falling in a vacuum, that\n",
+    "is, without air resistance. But the computational framework we used is\n",
+    "very general; it is easy to add additional forces, including drag.\n",
+    "\n",
+    "In this chapter, I present a model of drag force and add it to the\n",
+    "simulation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "refined-yield",
+   "metadata": {},
+   "source": [
+    "## Drag force\n",
+    "\n",
+    "As an object moves through a fluid, like air, the object applies force\n",
+    "to the air and, in accordance with Newton's third law of motion, the air applies an equal and opposite force to the object (see\n",
+    "<http://modsimpy.com/newton>).\n",
+    "\n",
+    "The direction of this **drag force** is opposite the direction of\n",
+    "travel, and its magnitude is given by the drag equation (see\n",
+    "<http://modsimpy.com/drageq>): \n",
+    "\n",
+    "$$F_d = \\frac{1}{2}~\\rho~v^2~C_d~A$$\n",
+    "\n",
+    "where\n",
+    "\n",
+    "-   $F_d$ is force due to drag, in newtons (N).\n",
+    "\n",
+    "-   $\\rho$ is the density of the fluid in kg/m^3^.\n",
+    "\n",
+    "-   $v$ is the magnitude of velocity in m/s.\n",
+    "\n",
+    "-   $A$ is the **reference area** of the object, in m^2^. In this\n",
+    "    context, the reference area is the projected frontal area, that is, the visible area of the object as seen from a point on its line of\n",
+    "    travel (and far away).\n",
+    "\n",
+    "-   $C_d$ is the **drag coefficient**, a dimensionless quantity that\n",
+    "    depends on the shape of the object (including length but not frontal area), its surface properties, and how it interacts with the fluid."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "domestic-studio",
+   "metadata": {},
+   "source": [
+    "For objects moving at moderate speeds through air, typical drag\n",
+    "coefficients are between 0.1 and 1.0, with blunt objects at the high end of the range and streamlined objects at the low end (see\n",
+    "<http://modsimpy.com/dragco>).\n",
+    "\n",
+    "For simple geometric objects we can sometimes guess the drag coefficient with reasonable accuracy; for more complex objects we usually have to take measurements and estimate $C_d$ from data.\n",
+    "\n",
+    "Of course, the drag equation is itself a model, based on the assumption that $C_d$ does not depend on the other terms in the equation: density, velocity, and area. For objects moving in air at moderate speeds (below 45 mph or 20 m/s), this model might be good enough, but we should remember to revisit this assumption.\n",
+    "\n",
+    "For the falling penny, we can use measurements to estimate $C_d$. In\n",
+    "particular, we can measure **terminal velocity**, $v_{term}$, which is\n",
+    "the speed where drag force equals force due to gravity:\n",
+    "\n",
+    "$$\\frac{1}{2}~\\rho~v_{term}^2~C_d~A = m g$$ \n",
+    "\n",
+    "where $m$ is the mass of the object and $g$ is acceleration due to gravity. Solving this equation for\n",
+    "$C_d$ yields: \n",
+    "\n",
+    "$$C_d = \\frac{2~m g}{\\rho~v_{term}^2~A}$$ \n",
+    "\n",
+    "According to *Mythbusters*, the terminal velocity of a penny is between 35 and 65 mph (see <http://modsimpy.com/mythbust>). Using the low end of their range, 40 mph or about 18 m/s, the estimated value of $C_d$ is 0.44, which is close to the drag coefficient of a smooth sphere."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afraid-expression",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exempt-mustang",
+   "metadata": {},
+   "source": [
+    "Now we are ready to add air resistance to the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "human-cloud",
+   "metadata": {},
+   "source": [
+    "## Implementation\n",
+    "\n",
+    "As the number of system parameters increases, and as we need to do more work to compute them, we will find it useful to define a `Params` object to contain the quantities we need to make a `System` object. `Params` objects are similar to `System` and `State` objects; in fact, all three have the same capabilities. I have given them different names to document the different roles they play.\n",
+    "\n",
+    "Here's the `Params` object for the falling penny:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "scheduled-element",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import pi\n",
+    "from modsim import State\n",
+    "from modsim import System\n",
+    "\n",
+    "def make_system(mass, diameter, rho, v_init, v_term):\n",
+    "    area = pi * (diameter/2)**2\n",
+    "\n",
+    "    g = 9.8   # m/s**2\n",
+    "    C_d = 2 * mass * g / (rho * area * v_term**2)\n",
+    "\n",
+    "    height = 381 # m\n",
+    "    init = State(y=height, v=v_init)\n",
+    "    t_end = 30  # s\n",
+    "\n",
+    "    return System(init=init,\n",
+    "                    rho=rho,\n",
+    "                    C_d=C_d,\n",
+    "                    g=g,\n",
+    "                    area=area,\n",
+    "                    mass=mass,\n",
+    "                    t_end=t_end\n",
+    "                   )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "circular-techno",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mass = 2.5e-3   # kg\n",
+    "diameter = 19e-3   # m\n",
+    "rho = 1.2   # kg/m**3\n",
+    "v_init = 0   # m / s\n",
+    "v_term = 18   # m / s\n",
+    "\n",
+    "system = make_system(mass, diameter, rho, v_init, v_term)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "instrumental-gross",
+   "metadata": {},
+   "source": [
+    "The mass and diameter are from <http://modsimpy.com/penny>. The density\n",
+    "of air depends on temperature, barometric pressure (which depends on\n",
+    "altitude), humidity, and composition (<http://modsimpy.com/density>). I\n",
+    "chose a value that might be typical in Boston, Massachusetts at 20 °C.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "primary-advocate",
+   "metadata": {},
+   "source": [
+    "Now here's a version of the slope function that includes drag:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "noble-stick",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    y, v = state\n",
+    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
+    "    mass, g = system.mass, system.g\n",
+    "    \n",
+    "    f_drag = rho * v**2 * C_d * area / 2\n",
+    "    a_drag = f_drag / mass\n",
+    "    \n",
+    "    dydt = v\n",
+    "    dvdt = -g + a_drag\n",
+    "    \n",
+    "    return dydt, dvdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "amended-summit",
+   "metadata": {},
+   "source": [
+    "`f_drag` is force due to drag, based on the drag equation. `a_drag` is\n",
+    "acceleration due to drag, based on Newton's second law.\n",
+    "\n",
+    "To compute total acceleration, we add accelerations due to gravity and\n",
+    "drag. `g` is negated because it is in the direction of decreasing `y`,\n",
+    "and `a_drag` is positive because it is in the direction of increasing\n",
+    "`y`. In the next chapter we will use `Vector` objects to keep track of\n",
+    "the direction of forces and add them up in a less error-prone way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "collectible-austria",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, -9.8)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "slope_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "psychological-style",
+   "metadata": {},
+   "source": [
+    "To stop the simulation when the penny hits the sidewalk, we'll use the\n",
+    "event function from Section xxx:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "practical-nowhere",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(t, state, system):\n",
+    "    y, v = state\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "seventh-biology",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "381"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "event_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "executive-there",
+   "metadata": {},
+   "source": [
+    "Now we can run the simulation like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "liberal-dictionary",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A termination event occurred.'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import run_solve_ivp\n",
+    "\n",
+    "results, details = run_solve_ivp(system, slope_func,\n",
+    "                                  events=event_func)\n",
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "mysterious-whale",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y</th>\n",
+       "      <th>v</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.000000</th>\n",
+       "      <td>381.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.448789</th>\n",
+       "      <td>380.022075</td>\n",
+       "      <td>-4.312820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.897579</th>\n",
+       "      <td>377.200545</td>\n",
+       "      <td>-8.156877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.346368</th>\n",
+       "      <td>372.817221</td>\n",
+       "      <td>-11.248931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.795157</th>\n",
+       "      <td>367.230596</td>\n",
+       "      <td>-13.539505</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   y          v\n",
+       "0.000000  381.000000   0.000000\n",
+       "0.448789  380.022075  -4.312820\n",
+       "0.897579  377.200545  -8.156877\n",
+       "1.346368  372.817221 -11.248931\n",
+       "1.795157  367.230596 -13.539505"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dimensional-longitude",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y</th>\n",
+       "      <th>v</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>20.644308</th>\n",
+       "      <td>3.229229e+01</td>\n",
+       "      <td>-17.988889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21.093097</th>\n",
+       "      <td>2.421654e+01</td>\n",
+       "      <td>-17.991564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21.541886</th>\n",
+       "      <td>1.614743e+01</td>\n",
+       "      <td>-18.001510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21.990676</th>\n",
+       "      <td>8.076745e+00</td>\n",
+       "      <td>-18.009752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.439465</th>\n",
+       "      <td>7.105427e-15</td>\n",
+       "      <td>-18.011383</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      y          v\n",
+       "20.644308  3.229229e+01 -17.988889\n",
+       "21.093097  2.421654e+01 -17.991564\n",
+       "21.541886  1.614743e+01 -18.001510\n",
+       "21.990676  8.076745e+00 -18.009752\n",
+       "22.439465  7.105427e-15 -18.011383"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "emerging-domestic",
+   "metadata": {},
+   "source": [
+    "The final height is close to 0, as expected.\n",
+    "\n",
+    "Interestingly, the final velocity is not exactly terminal velocity, which suggests that there are some numerical errors.\n",
+    "\n",
+    "We can get the flight time from `results`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cleared-willow",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22.43946505804431"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t_sidewalk = results.index[-1]\n",
+    "t_sidewalk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "rising-nutrition",
+   "metadata": {},
+   "source": [
+    "Here's the plot of position as a function of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "touched-calculation",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "def plot_position(results):\n",
+    "    results.y.plot()\n",
+    "        \n",
+    "    decorate(xlabel='Time (s)',\n",
+    "         ylabel='Position (m)')\n",
+    "    \n",
+    "plot_position(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prospective-favor",
+   "metadata": {},
+   "source": [
+    "And velocity as a function of time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "coordinate-albert",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_velocity(results):\n",
+    "\n",
+    "    results.v.plot(color='C1', label='v')\n",
+    "        \n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Velocity (m/s)')\n",
+    "    \n",
+    "plot_velocity(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "familiar-antarctica",
+   "metadata": {},
+   "source": [
+    "From an initial velocity of 0, the penny accelerates downward until it reaches terminal velocity; after that, velocity is constant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "recovered-reverse",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "portable-office",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Run the simulation with an initial velocity, downward, that exceeds the penny's terminal velocity.  \n",
+    "\n",
+    "What do you expect to happen?  Plot velocity and position as a function of time, and see if they are consistent with your prediction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "powerful-springfield",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "v_init = -30\n",
+    "system2 = make_system(mass, diameter, rho, v_init, v_term)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "liberal-fighter",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A termination event occurred.'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results2, details2 = run_solve_ivp(system2, slope_func, \n",
+    "                                 events=event_func)\n",
+    "details2.message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "informed-representation",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.635183673114202"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t_sidewalk = results2.index[-1]\n",
+    "t_sidewalk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "governmental-arena",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FR79Ywp4Dh4IuTUQigAJKAmVm9GxSifED2nL9BVUZNm0t7QdPZNSCjWqZJJLHKaAkIhQvFMdjPRryed+WlC8ez30fzOOGN2by47a9QZcmIgFRQElEOa9SCT67uyWP9WjIwtRddBk6iae+WcH+g2qZJJLXhC2gzKyymX1vZsvMbImZ3R8af8TMNpjZ/NDtkgzrDDKzVWa2wsw6h6s2iWyxMcaNF1blu4Ft6XbeOTz//So6DpnI+GVbgi5NRHKQhWs/v5lVACq4+1wzKwrMAXoAVwN73f2p45ZPBD4AmgPnAOOAOu5+0j+dk5KSPCUlJSz1S+SYsXoHD41czMqte+mYWI6HuyVSqWShoMsSkWxiZnPcPen48bBtQbn7JnefG3q8B1gGVDzFKt2BD909zd3XAKtIDyvJ4y6sUZov72vNX7rUY8rK7XR4eiIvTljFwcNqmSSSm+XIMSgzqwY0BmaGhu4xs4Vm9qaZlQyNVQTWZ1gtlRMEmpn1MbMUM0vZtk1dsvOK/PliuKttTcYNTCa5TgJPjllB12cmMe3H7UGXJiJhEvaAMrMiwCdAP3ffDbwE1AQaAZuAwccWPcHqv9v/6O6vunuSuyclJCSEp2iJWBVLFOSVG5N486YkDh45ynWvzaTfh/PYuked0kVym7AGlJnFkR5O77n7pwDuvsXdj7j7UeA1/rsbLxWonGH1SsDGcNYn0atdvXKM7Z/Mfe1q8dWizbR/aiLDp63liFomieQa4ZzFZ8AbwDJ3fzrDeIUMi10BLA49HgX0MrMCZlYdqA3MCld9Ev3i42IZ0KkuY/q15vzKJXh41BK6vzCF+et3Bl2aiGSDcG5BtQRuBNodN6X8STNbZGYLgYuB/gDuvgQYASwFxgB9TzWDT+SYGglFeOfW5jx/XWO27k7jihen8r+fLWLnPnVKF4lmYZtmnhM0zVyOt+fAIYaOW8mwaWspXjCOQV3rcWXTSqRv0ItIJDrZNPNMBZSZJQGtST8/aT/pu+XGuXugV51TQMnJLN24m7+OXMTcn3bSrFpJHuvRkHrliwVdloicwFmdB2VmN5nZXGAQUBBYAWwFWgFjzWy4mVUJR8EiWZF4TjE+vrMF//rDuazaupdLn53C418uZW/a4aBLE5FMynea1wsDLd19/4leNLNGpE9m+Cmb6xLJspgY45pmVeiUWJ4nv1nOa5PX8MWCTfytWyJdG5bXbj+RCKdjUJJnzFn3Cw+NXMzSTbtpUyeBv1/egGplCgddlkiel9VjUNWBe4FqZNjqcvfLs7HGM6aAkjN1+MhR3pmxjsHf/sDBI0e5K7kmd7WtSXxcbNClieRZWQ2oBaSf07QI+E8DNHefmJ1FnikFlJytrbsP8I8vlzFqwUaqli7Eo5c3oG3dskGXJZInZTWgZrr7BWGpLAsUUJJVU1dt56HPF7N62690bViehy5L5JwSBYMuSyRPyWpAXUf6ZIhvgbRj48e6lQdFASXZIe3wEV6fvIZnx68kNsbo16E2N7esTlysrucpkhOyGlBPkN4V4kf+u4vP3b1dtlZ5hhRQkp3W/7yPR0YtYfzyrdQpV4R/9DiX5tVLBV2WSK6X1YBaDpzn7hHVO0YBJeEwdukWHhm1hA079/OHJpUYdEk9yhQpEHRZIrlWVi9YuAAoka0ViUSojonlGDugDXe3rcmoBRto99QE3p2xTp3SRXJYZregJgDnAbP57TEoTTOXXG3V1j08NHIJ01fv4PzKJfhH94acW6l40GWJ5CpZ3cWXfKJxTTOXvMDdGbVgI4+NXsbPv6Zx44VVGdCpLsULxgVdmkiucFYBZWbmp0mwzCwTLgooyUm79h9iyNgfeHv6WkoVLsBfL61P90bnqGWSSBad7TGo783s3uMbwppZfjNrZ2bDgd7ZWahIpCpeMI5HLm/AqHtaUbFkQfp9NJ9rX5vBqq17gi5NJFc6XUB1AY4AH5jZRjNbamargZXAtcAQdx8W5hpFIkrDisX57K4WPH5FQ5Zt2kOXoZP515jl7DuoTuki2SnTzWLNLA4oA+x3953hLCqztItPgrZjbxr//Ho5/56TSsUSBflbt0Q6JZbTbj+RM5DVaea4+yF33xQp4SQSCUoXKcD/XXU+/77zIooUyMcd78zh1uEp/LRjX9CliUQ99XIRyQbNqpVi9H2t+Oul9Zm5egcdh0zkufErSTt8JOjSRKJW2ALKzCqb2fdmtszMlpjZ/aHxUmY21sxWhu5LZlhnkJmtMrMVZtY5XLWJhENcbAy3ta7BuIHJdKhfjsFjf6Dr0MlMWbk96NJEolI4t6AOAwPdvT5wIdDXzBKBB4Dx7l4bGB96Tui1XkAD0idnvGhmukiPRJ0KxQvywvVNGH5Lc466c8MbM7nn/bls2X0g6NJEokqmAsrMeoa2eHaZ2W4z22Nmu0+1Tuh41dzQ4z3AMqAi0B0YHlpsONAj9Lg78KG7p7n7GmAV0PyM/4tEIkRynQTG9GtD/w51+HbpFtoPnsibU9Zw+MjR068sIpnegnoSuNzdi7t7MXcv6u7FMvshZlYNaAzMBMq5+yZIDzHg2FXiKgLrM6yWGho7/r36mFmKmaVs27YtsyWIBCI+Lpb7O9RmbP82NK1akr+PXkq356cyZ93PQZcmEvEyG1Bb3H3Z2XyAmRUBPgH6ufuptrpONC/3d3Pg3f1Vd09y96SEhISzKUkkx1UtXZhhNzfj5RuasHPfQf7w0nT+8vFCfv41oi4QIBJR8mVyuRQz+wgYyW+bxX56qpVC5059AryXYdktZlbB3TeZWQVga2g8FaicYfVKwMZM1icS8cyMLg0r0Lp2As+OX8kbU9bwzdLNPNClHlcnVSYmRudOiWSU2S2oYsA+oBPQLXS77FQrWPqZim8Ay9z96QwvjeK/7ZF6A59nGO9lZgXMrDrpV/Cdlcn6RKJG4QL5GHRJfb68rzV1yhblgU8X8YeXp7Fk466gSxOJKJnuJHHGb2zWCpgMLOK/V+H9X9KPQ40AqgA/AVe5+8+hdR4EbiF9BmA/d//6VJ+hThIS7dydT+du4P99tYxf9h2kd4tqDOhYh6Lx6pQueUdWL7dRCXgOaEn6caEpwP3unprdhZ4JBZTkFrv2HeKpb1fw7sx1JBQpwF8vS6TbeRXUMknyhKy2OnqL9F1w55A+s+6L0JiIZIPiheJ4rEdDRt7dknLF4rnvg3nc8MZMfty2N+jSRAKT2YBKcPe33P1w6DYM0BQ6kWx2fuUSjOzbksd6NGRh6i66DJ3EU9+sYP9BtUySvCezAbXdzG4ws9jQ7QZgRzgLE8mrYmOMGy+syncD29LtvHN4/vtVdBwykfHLtgRdmkiOymxA3QJcDWwGNgFXhsZEJEwSihbg6Wsa8WGfCykYF8utw1O4/e0UUn9Rp3TJG8I2iy8naJKE5BWHjhzlzSlrGDpuJY5zb7va3N66Bvnz6YIEEv3Oahafmf3Z3Z80s+c4cVeH+7K3zDOjgJK8ZsPO/Tz2xVLGLNlMzYTCPNa9IS1qlQm6LJEsOdtZfMfaG6UAc05wE5EcVLFEQV6+sSlv3dSMQ0ec616fSb8P57F1jzqlS+5zylZH7v5F6OE+d/93xtfM7KqwVSUip3RxvbJcVLM0L074kZcn/Mj4ZVv5U+e63HBhVWLVMklyiczuwB6UyTERySHxcbEM6FiHb/q3oVGVEjw8agmXPz+FeT/9EnRpItnilFtQZtYVuASoaGbPZnipGOntiEQkYNXLFObtW5rz1aLN/H30Enq+NI1ezarwly51KVEof9DliZy103Uz30j68afL+e0xpz1A/3AVJSJnxsy49LwKJNdNYMjYHxg2bS3fLNnMoK71+EOTSuqULlEps7348rl7xG0xaRafyIkt3bibhz5fzJx1v9CsWkke69GQeuUzfY1RkRx1VrP4zGxE6OE8M1uY4bbIzBaGpVIRybLEc4rx7zsu4sk/nMeqrXu59NkpPP7lUvamRdzfmSIndbrzoI5dWLDqiV5393VhqywTtAUlcnq//HqQJ79Zzgez1lO+WDx/65ZI14bl1SldIsZZbUG5+6bQw+3A+lAgFQDOR1e7FYkKJQvn54me5/Hp3S0oVTg/d783l95vzWbt9l+DLk3klDI7zXwSEG9mFYHxwM3AsHAVJSLZr0mVkoy6pyUPd0tk7rpf6DR0EkPH/cCBQ+qULpEpswFl7r4P6Ak85+5XAInhK0tEwiFfbAw3t6zOdwOT6dygPEPHraTz0ElM/GFb0KWJ/E6mA8rMLgKuB74MjZ1uirqIRKiyxeJ57trGvHvrBcSa0fvNWdz93hw271LLJIkcmQ2ofqR3jvjM3ZeYWQ3g+1OtYGZvmtlWM1ucYewRM9tgZvNDt0syvDbIzFaZ2Qoz63wW/y0icoZa1S7D1/1a86dOdRi/bCvtB0/g9cmrOXTkaNCliZzZ5TbMrCjg7n7a61CbWRtgL/C2uzcMjT0C7HX3p45bNhH4AGhO+mXlxwF13P2UO8c1i08k+6z/eR8Pj1rCd8u3Uq98UR7r0ZBm1UoFXZbkAWfbzfzYyuea2TxgMbDUzOaYWYNTrePuk4CfM1lfd+BDd09z9zXAKtLDSkRySOVShXijdxKv3tiUPQcOc9XL0/mffy9gx960oEuTPCqzu/heAQa4e1V3rwIMBF47y8+8J3Sy75tmVjI0VhFYn2GZ1NDY75hZHzNLMbOUbdt0YFckO5kZnRqUZ+yANtzVtiafzdtAu8ETeW/mOo4ejd6Lm0p0ymxAFXb3/xxzcvcJQOGz+LyXgJpAI9IvHT84NH6iMwZP+H+Du7/q7knunpSQkHAWJYjI6RTKn4+/dKnH1/e3pn6Fojz42WKueGkaizfsCro0yUMyG1CrzewhM6sWuv0VWHOmH+buW9z9iLsfJX0L7NhuvFSgcoZFK6ETgUUCV7tcUT64/UKGXHM+G37Zx+XPT+GRUUvYfeBQ0KVJHpDZgLoFSAA+Dd3KkH6y7hkxswoZnl5B+jEtgFFALzMrYGbVgdrArDN9fxHJfmbGFY0rMX5gW264sCrDp6+l3VMT+Xz+Bs5kkpXImTpdL7544E6gFrAIeNPdM/Wnk5l9ALQlPcy2AA+HnjcifffdWuCOY+2UzOxB0oPwMNDP3b8+3WdoFp9IzluUuosHRy5iYeouLqpRmsd6NKRW2SJBlyVR7GSz+E4XUB8Bh4DJQFdgrbv3C1eRZ0oBJRKMI0edD2b9xJNjlrP/0BFub12De9vVpmD+2KBLkyh0tgG1yN3PDT3OB8xy9ybhK/PMKKBEgrV9bxpPfLWcT+amUrFEQR65vAEdE8sFXZZEmbM9D+o/u/Mi8YKFIhKsMkUKMPjq8/moz4UULhDL7W+ncNvw2az/eV/QpUkucLotqCPAsZ78BhQE9oUeu7sHeolObUGJRI5DR44ybOpahoz7gSNHnXvb1eL2NjUokE+7/eTUzvZ6ULHuXix0K+ru+TI81vWjReQ/4mJjuL1NDcYPTKZ9/bI89e0PdB06mSkrtwddmkSpzE4zFxHJlArFC/Li9U0ZfktzjrpzwxszufeDeWzZrU7pcmYUUCISFsl1EhjTrw39O9ThmyWbaT94Im9MWcNhdUqXTFJAiUjYxMfFcn+H2ozt34amVUvy2OilXPbcFOasy2wfacnLFFAiEnZVSxdm2M3NePmGJuzaf4g/vDSdv3y8kJ9/PRh0aRLBFFAikiPMjC4NKzBuQDJ3JNfgk7mptBs8gQ9m/aRO6XJCCigRyVGFC+RjUNf6fHV/a+qUK8qgTxfRU53S5QQUUCISiDrlivJRnwt5+urzSVWndDkBBZSIBMbM6Nnk953SR85Tp3RRQIlIBCheMI6/d2/IqL6tqFginn4fzefa12awauueoEuTACmgRCRinFupOJ/e3ZLHr2jIsk176PrMZP41Zjn7DqoVaF6kgBKRiBIbY1x/QVXGD0yme6OKvDThRzo+PYlvlmzWbr88RgElIhGpTJECPHXV+fz7zosoUiAfd7wzh1uHp/DTDnVKzysUUCIS0ZpVK8Xo+1rx4CX1mbl6Bx2HTOS58StJO3wk6NIkzBRQIhLxjnVKHzcwmQ71yzF4rDql5wUKKBGJGhWKF+SF65v8plP6Pe/PVaf0XCpsAWVmb5rZVjNbnGGslJmNNbOVofuSGV4bZGarzGyFmXUOV10iEv0ydkr/dukWdUrPpcK5BTUM6HLc2APAeHevDYwPPcfMEoFeQIPQOi+amS7DKSInlbFTelK19E7p3Z6fypx1vwRdmmSTsAWUu08Cju+p3x0YHno8HOiRYfxDd09z9zXAKqB5uGoTkdyjaunCvHVTeqf0nfsO8oeXpvHAJwv5RZ3So15OH4Mq5+6bAEL3ZUPjFYH1GZZLDY39jpn1MbMUM0vZtm1bWIsVkejwm07pbWrw8Zz0TukfzVan9GgWKZMk7ARjJ/xVufur7p7k7kkJCQlhLktEoknhAvkYdEl9vryvNbXKFuEvnyziypensXTj7qBLk7OQ0wG1xcwqAITut4bGU4HKGZarBGzM4dpEJJeoW74oI+64iKeuOp+1O/bR7fkp/P2LpexRp/SoktMBNQroHXrcG/g8w3gvMytgZtWB2sCsHK5NRHIRM+PKppX4bmAyvZpV5q1pa+jw9ERGL9yolklRIpzTzD8ApgN1zSzVzG4F/gl0NLOVQMfQc9x9CTACWAqMAfq6u04TF5EsK1EoP49fcS6f3tWCMkUKcM/78/jjm7NYs/3XoEuT07Bo/ksiKSnJU1JSgi5DRKLEkaPOuzPW8dQ3K0g7fJQ729bk7rY1iY/TWS1BMrM57p50/HikTJIQEQm72Bijd4tqjB+YTNdzy/Ps+JV0GjKJ71dsPf3KkuMUUCKS55QtFs8zvRrz/m0XkC/WuPmt2dz5zhw27twfdGmSgQJKRPKsFrXKMOb+NvxP57pM+GErHZ6eyCsTf+SQWiZFBAWUiORp+fPF0PfiWoztn0yLmmV44uvlXPrsZGau3hF0aXmeAkpEBKhcqhCv907itT8m8WvaEa55dQYDRsxn+960oEvLsxRQIiIZdEwsx7gByfS9uCZfLNhIu6cm8M6MdRxRy6Qcp4ASETlOwfyx/E/nenx9fxsaVizOQyMXc8WLU1mYujPo0vIUBZSIyEnUKluE9267gGd6NWLTrgN0f2EqD41czK79apmUExRQIiKnYGZ0b1SR8QOT6X1RNd6buY72gyfw6dxUtUwKMwWUiEgmFIuP45HLGzDqnlZUKlmIASMWcM2rM/hhy56gS8u1FFAiImegYcXifHpXC57oeS4rNu/hkmcm88RXy/g17XDQpeU6CigRkTMUE2Nc27wK3w1MpmeTirwyaTUdnp7ImMWbtNsvGymgRETOUukiBXjyyvP55K6LKF4wjjvfncvNw2azboc6pWcHBZSISBY1rVqK0fe24qHLEpm95mc6DpnE0HE/cOCQrhqUFQooEZFskC82hltbVee7P7Wlc4PyDB23ks5DJzFBndLPmgJKRCQblSsWz3PXNubdWy8g1oyb3prN3e/NYdMudUo/UwooEZEwaFW7DF/3a82fOtVh/LKttB88kdcmrVan9DOggBIRCZMC+WK5p11txg1I5sIapXn8q2Vc9uwUZq35OejSokIgAWVma81skZnNN7OU0FgpMxtrZitD9yWDqE1EJLtVLlWIN3on8eqNTdmbdpirX5nOwBEL1Cn9NILcgrrY3RtluA79A8B4d68NjA89FxHJFcyMTg3KM3ZAG+5qW5NRCzbQ7qkJvKtO6ScVSbv4ugPDQ4+HAz2CK0VEJDwK5c/HX7rU4+v7W9PgnOL8deRier40jUWpu4IuLeIEFVAOfGtmc8ysT2isnLtvAgjdlw2oNhGRsKtVtijv334BQ69pxIZf9tP9hSk8/Lk6pWeUL6DPbenuG82sLDDWzJZndsVQoPUBqFKlSrjqExEJOzOjR+OKXFyvLEPG/sDb09fy5aLN/PXS+nRvdA5mFnSJgQpkC8rdN4butwKfAc2BLWZWASB0f8Kz29z9VXdPcvekhISEnCpZRCRsihdM75T+ed9WVCwRT7+P5nPtazNYtTVvd0rP8YAys8JmVvTYY6ATsBgYBfQOLdYb+DynaxMRCdK5lYrz6d0tefyKhizduJuuz0zmX2OWs+9g3uyUbjndedfMapC+1QTpuxjfd/fHzaw0MAKoAvwEXOXupzxZICkpyVNSUsJar4hIELbvTeOfXy/n4zmpVCxRkIe7JdKpQfmgywoLM5uTYUb3f8ejuTW8AkpEcrtZa37moZGLWbFlDx3ql+Xhbg2oXKpQ0GVlq5MFVCRNMxcRkeM0r16K0fe14sFL6jPtxx10HDKRF75fRdrh3N8pXQElIhLh4mJjuL1NDcYPTKZdvbL83zcr6PrMZKau2h50aWGlgBIRiRIVihfkxeub8tbNzTh8xLn+9Znc98E8tu4+EHRpYaGAEhGJMhfXLcu3/dtwf/vajFm8mfaDJ/LW1DUczmWd0hVQIiJRKD4ulv4d6/BN/zY0qlKCR79YSvcXpjLvp1+CLi3bKKBERKJY9TKFefuW5rxwXRO2702j50vTGPTpInbuOxh0aVmmgBIRiXJmxqXnVWD8wLbc2rI6I1LW027wREakrOdoFHdKV0CJiOQSRQrk46+XJTL63lbUKFOYP3+8kKtfmc6yTbuDLu2sKKBERHKZ+hWKMeKOi/i/K89j9fZfuey5Kfxj9FL2pkVXyyQFlIhILhQTY1yVVJnvBiZzdVJl3pi6hvaDJ/Dlwk1ESwchBZSISC5WolB+nuh5Lp/e1YIyRQrQ9/25/PHNWazetjfo0k5LASUikgc0rlKSUfe04tHLGzD/p510GTqZp79dwYFDkdsySQElIpJHxMYYvVtUY/yfkrnk3PI8+90qOg6ZyPfLT3j5vcApoERE8piyReMZ2qsx799+AfljY7h52Gz6vJ3Chp37gy7tNxRQIiJ5VIuaZfj6/jb8uUtdJq/cTofBE3lpwo8cPBwZLZMUUCIieVj+fDHc3bYWYwe0oXXtMvxrzHIueXYy03/cEXRpCigREYFKJQvx6h+TeKN3EgcOHeHa12bQ/6P5bNuTFlhNCigREfmP9vXLMbZ/Mve2q8WXCzfRbvAE3p6+liMBtExSQImIyG8UzB/LwE51GdOvNedXKsHfPl9CjxemMn/9zhytI+ICysy6mNkKM1tlZg8EXY+ISF5VI6EI79zanOeubcyW3Qe44sWpPPjZInbtO5Qjnx9RAWVmscALQFcgEbjWzBKDrUpEJO8yM7qdfw7jByZzc4vqfDDrJ9oNnsDHc1LD3jIpogIKaA6scvfV7n4Q+BDoHnBNIiJ5XtH4OP7WLZEv7m1F1dKF+NO/F3DNKzNYsXlP2D4z0gKqIrA+w/PU0Nh/mFkfM0sxs5Rt27blaHEiInldg3OK8/GdLfjXH87lh617+GLBxrB9Vr6wvfPZsROM/WYb0t1fBV4FSEpKio6WvCIiuUhMjHFNsyp0SixPfFxs2D4n0gIqFaic4XklIHzxLCIiZ61k4fxhff9I28U3G6htZtXNLD/QCxgVcE0iIhKAiNqCcvfDZnYP8A0QC7zp7ksCLktERAIQUQEF4O5fAV8FXYeIiAQr0nbxiYiIAAooERGJUAooERGJSAooERGJSAooERGJSBbuZn/hZGbbgHVZfJsywPZsKCev0/eYdfoOs4e+x6zL6e+wqrsnHD8Y1QGVHcwsxd2Tgq4j2ul7zDp9h9lD32PWRcp3qF18IiISkRRQIiISkRRQoc7okmX6HrNO32H20PeYdRHxHeb5Y1AiIhKZtAUlIiIRSQElIiIRKU8HlJl1MbMVZrbKzB4Iup5oZGZrzWyRmc03s5Sg64kWZvammW01s8UZxkqZ2VgzWxm6LxlkjdHgJN/jI2a2IfSbnG9mlwRZY6Qzs8pm9r2ZLTOzJWZ2f2g88N9jng0oM4sFXgC6AonAtWaWGGxVUetid28UCedNRJFhQJfjxh4Axrt7bWB86Lmc2jB+/z0CDAn9JhuFLuEjJ3cYGOju9YELgb6hfwsD/z3m2YACmgOr3H21ux8EPgS6B1yT5BHuPgn4+bjh7sDw0OPhQI+crCkaneR7lDPg7pvcfW7o8R5gGVCRCPg95uWAqgisz/A8NTQmZ8aBb81sjpn1CbqYKFfO3TdB+j8aQNmA64lm95jZwtAuQO0qzSQzqwY0BmYSAb/HvBxQdoIxzbk/cy3dvQnpu0r7mlmboAuSPO8loCbQCNgEDA60mihhZkWAT4B+7r476HogbwdUKlA5w/NKwMaAaola7r4xdL8V+Iz0XadydraYWQWA0P3WgOuJSu6+xd2PuPtR4DX0mzwtM4sjPZzec/dPQ8OB/x7zckDNBmqbWXUzyw/0AkYFXFNUMbPCZlb02GOgE7D41GvJKYwCeoce9wY+D7CWqHXsH9WQK9Bv8pTMzIA3gGXu/nSGlwL/PebpThKh6adDgVjgTXd/PNiKoouZ1SB9qwkgH/C+vsPMMbMPgLakX9ZgC/AwMBIYAVQBfgKucndNADiFk3yPbUnfvefAWuCOY8dS5PfMrBUwGVgEHA0N/y/px6EC/T3m6YASEZHIlZd38YmISARTQImISERSQImISERSQImISERSQImISERSQIlkIzMrnaGL9uYMXbX3mtmLYfrMfmb2x1O8fpmZPRqOzxYJJ00zFwkTM3sE2OvuT4XxM/IBc4Em7n74JMtYaJmW7r4vXLWIZDdtQYnkADNra2ajQ48fMbPhZvZt6HpaPc3sydB1tcaE2s5gZk3NbGKoEe83x3VIOKYdMPdYOJnZfWa2NNQo9UMAT/8rdAJwWY78x4pkEwWUSDBqApeSfkmDd4Hv3f1cYD9waSikngOudPemwJvAibp0tATmZHj+ANDY3c8D7swwngK0zvb/CpEwyhd0ASJ51NfufsjMFpHeamtMaHwRUA2oCzQExqbvoSOW9M7cx6tA+vV7jlkIvGdmI0lvnXTMVuCc7CtfJPwUUCLBSANw96Nmdsj/ezD4KOn/XxqwxN0vOs377AfiMzy/FGgDXA48ZGYNQrv/4kPLikQN7eITiUwrgAQzuwjSL4dgZg1OsNwyoFZomRigsrt/D/wZKAEUCS1XB3X1liijgBKJQO5+ELgS+JeZLQDmAy1OsOjXpG8xQfpuwHdDuw3nAUPcfWfotYuBL8NZs0h20zRzkShnZp8Bf3b3lSd5vRzpl0Jpn7OViWSNAkokyplZXaCcu086yevNgEPuPj9HCxPJIgWUiIhEJB2DEhGRiKSAEhGRiKSAEhGRiKSAEhGRiKSAEhGRiPT/AdoZz1lvEI3YAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_position(results2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "encouraging-aside",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "plot_velocity(results2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "educational-rainbow",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose we drop a quarter from the Empire State Building and find that its flight time is 19.1 seconds.  Use this measurement to estimate terminal velocity and coefficient of drag.\n",
+    "\n",
+    "You can get the relevant dimensions of a quarter from https://en.wikipedia.org/wiki/Quarter_(United_States_coin).\n",
+    "\n",
+    "1. Create a `Params` object with the system parameters.  \n",
+    "\n",
+    "2. Use `make_system` to create a `System` object.  We don't know `v_term`, so we'll start with the inital guess `v_term = 18 * m / s`.\n",
+    "\n",
+    "3. Call `run_solve_ivp` to simulate the system.  How does the flight time of the simulation compare to the measurement?\n",
+    "\n",
+    "4. Try a few different values of `t_term` and see if you can get the simulated flight time close to 19.1 seconds.\n",
+    "\n",
+    "5. Optionally, write an error function and use `root_scalar` to improve your estimate.\n",
+    "\n",
+    "6. Use your best estimate of `v_term` to compute `C_d`.\n",
+    "\n",
+    "Note: I fabricated the observed flight time, so don't take the results of this exercise too seriously."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "advisory-tunnel",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "mass = 5.67e-3 # kg\n",
+    "diameter = 24.26e-3 # m\n",
+    "v_term = 18 # m / s\n",
+    "flight_time = 19.1 # s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "underlying-sperm",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "namespace(init=y    381\n",
+       "               v      0\n",
+       "               dtype: int64,\n",
+       "          rho=1.2,\n",
+       "          C_d=0.6183600157463346,\n",
+       "          g=9.8,\n",
+       "          area=0.000462244204111976,\n",
+       "          mass=0.00567,\n",
+       "          t_end=30)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "v_init = 0\n",
+    "system3 = make_system(mass, diameter, rho, v_init, v_term)\n",
+    "system3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "standard-allah",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A termination event occurred.'"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# Run the simulation\n",
+    "\n",
+    "results3, details3 = run_solve_ivp(system3, slope_func, \n",
+    "                                   events=event_func)\n",
+    "details3.message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "atlantic-southeast",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22.43946505804431"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# And get the flight time\n",
+    "\n",
+    "t_sidewalk = results3.index[-1]\n",
+    "t_sidewalk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "sorted-leisure",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# The flight time is a little long, \n",
+    "# so we could increase `v_term` and try again.\n",
+    "\n",
+    "# Or we could write an error function\n",
+    "\n",
+    "def error_func(guess):\n",
+    "    \"\"\"Final height as a function of C_d.\n",
+    "    \n",
+    "    guess: guess at v_term\n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: height in m\n",
+    "    \"\"\"\n",
+    "    print(guess)\n",
+    "    v_term = guess\n",
+    "    system = make_system(mass, diameter, rho, v_init, v_term)\n",
+    "    results, details = run_solve_ivp(system, slope_func, \n",
+    "                                     events=event_func)\n",
+    "    t_sidewalk = results.index[-1]\n",
+    "    error = t_sidewalk - flight_time\n",
+    "    return error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "perfect-suffering",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3.3394650580443077"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# We can test the error function like this\n",
+    "v_guess1 = 18\n",
+    "error_func(v_guess1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "juvenile-provincial",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "-0.22717020844738656"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "v_guess2 = 22\n",
+    "error_func(v_guess2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "incorporate-jungle",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18.0\n",
+      "22.0\n",
+      "21.745227429805748\n",
+      "21.688258716910187\n",
+      "21.688401491107182\n",
+      "21.688401102071705\n",
+      "21.68840110206904\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# Now we can use `root_scalar` to find the value of \n",
+    "# `v_term` that yields the measured flight time.\n",
+    "\n",
+    "from scipy.optimize import root_scalar\n",
+    "\n",
+    "res = root_scalar(error_func, bracket=[v_guess1, v_guess2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "conceptual-questionnaire",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21.68840110206904"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "v_term = res.root\n",
+    "v_term"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "designed-variation",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4259232394493775"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# Plugging in the estimated value, \n",
+    "# we can use `make_system` to compute `C_d`\n",
+    "\n",
+    "system4 = make_system(mass, diameter, rho, v_init, v_term)\n",
+    "system4.C_d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "meaning-instruction",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/chap22.ipynb b/jupyter/chap22.ipynb
new file mode 100644
index 00000000..3e157a17
--- /dev/null
+++ b/jupyter/chap22.ipynb
@@ -0,0 +1,1961 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "collaborative-people",
+   "metadata": {},
+   "source": [
+    "# Chapter 22"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "connected-covering",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "widespread-detective",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "regional-block",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we modeled objects moving in one dimension, with and without drag. Now let's move on to two dimensions, and baseball!\n",
+    "\n",
+    "In this chapter we model the flight of a baseball including the effect\n",
+    "of air resistance. In the next chapter we use this model to solve an\n",
+    "optimization problem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "looking-sampling",
+   "metadata": {},
+   "source": [
+    "## Baseball\n",
+    "\n",
+    "To model the flight of a baseball, we have to make some modeling\n",
+    "decisions. To get started, we ignore any spin that might be on the ball, and the resulting Magnus force (see <http://modsimpy.com/magnus>). Under this assumption, the ball travels in a vertical plane, so we'll run simulations in two dimensions, rather than three.\n",
+    "\n",
+    "Air resistance has a substantial effect on most projectiles in air, so\n",
+    "we will include a drag force.\n",
+    "\n",
+    "To model air resistance, we'll need the mass, frontal area, and drag\n",
+    "coefficient of a baseball. Mass and diameter are easy to find (see\n",
+    "<http://modsimpy.com/baseball>). Drag coefficient is only a little\n",
+    "harder; according to *The Physics of Baseball*[^1], the drag coefficient of a baseball is approximately 0.33 (with no units).\n",
+    "\n",
+    "However, this value *does* depend on velocity. At low velocities it\n",
+    "might be as high as 0.5, and at high velocities as low as 0.28.\n",
+    "Furthermore, the transition between these regimes typically happens\n",
+    "exactly in the range of velocities we are interested in, between 20 m/s and 40 m/s.\n",
+    "\n",
+    "Nevertheless, we'll start with a simple model where the drag coefficient does not depend on velocity; as an exercise at the end of the chapter, you will have a chance to implement a more detailed model and see what effect is has on the results.\n",
+    "\n",
+    "But first we need a new computational tool, the `Vector` object."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "worthy-wheel",
+   "metadata": {},
+   "source": [
+    "## Vectors\n",
+    "\n",
+    "Now that we are working in two dimensions, we will find it useful to\n",
+    "work with **vector quantities**, that is, quantities that represent both a magnitude and a direction. We will use vectors to represent positions, velocities, accelerations, and forces in two and three dimensions.\n",
+    "\n",
+    "The ModSim library provides a `Vector` object that represents a vector\n",
+    "quantity. A `Vector` object is a like a NumPy array; it contains\n",
+    "elements that represent the **components** of the vector. For example,\n",
+    "in a `Vector` that represents a position in space, the components are\n",
+    "the $x$ and $y$ coordinates (and a $z$ coordinate in 3-D). A `Vector`\n",
+    "object can also have units, like the quantities we've seen in previous\n",
+    "chapters.\n",
+    "\n",
+    "You can create a `Vector` by specifying its components. The following\n",
+    "`Vector` represents a point 3 m to the right (or east) and 4 m up (or\n",
+    "north) from an implicit origin:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "smaller-hudson",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "x    3\n",
+       "y    4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import Vector\n",
+    "\n",
+    "A = Vector(3, 4)\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "suited-helena",
+   "metadata": {},
+   "source": [
+    "You can access the components of a `Vector` by name using the dot\n",
+    "operator: for example, `A.x` or `A.y`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "prompt-vault",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 4)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A.x, A.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "continuous-convert",
+   "metadata": {},
+   "source": [
+    "You can also access them by index using brackets: for example, `A[0]` or `A[1]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "strange-credit",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 4)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A[0], A[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "actual-blond",
+   "metadata": {},
+   "source": [
+    "`Vector` objects support most mathematical operations, including\n",
+    "addition and subtraction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "integrated-inspector",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "x    1\n",
+       "y    2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B = Vector(1, 2)\n",
+    "B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "advance-integral",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "x    4\n",
+       "y    6\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A + B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "respected-acrobat",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "x    2\n",
+       "y    2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A - B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "incorporate-longer",
+   "metadata": {},
+   "source": [
+    "For the definition and graphical interpretation of these operations, see <http://modsimpy.com/vecops>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reliable-soundtrack",
+   "metadata": {},
+   "source": [
+    "We can think of a `Vector` as a point defined by its coordinates `x` and `y`.\n",
+    "Equivalently, we can think of it as a point defined by a magnitude and angle.\n",
+    "\n",
+    "**Magnitude** is the length of the vector: if the `Vector` represents a position, magnitude is the distance from the origin; if it represents a velocity, magnitude is speed, that is, how fast the object is moving, regardless of direction.\n",
+    "\n",
+    "The **angle** of a `Vector` is its direction, expressed as the angle in radians from the positive $x$ axis. In the Cartesian plane, the angle 0 rad is due east, and the angle $\\pi$ rad is due west.\n",
+    "\n",
+    "ModSim provides functions to compute the magnitude and angle of a `Vector`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "affected-programmer",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5.0, 0.9272952180016122)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import vector_mag, vector_angle\n",
+    "\n",
+    "vector_mag(A), vector_angle(A)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "great-advice",
+   "metadata": {},
+   "source": [
+    "The result from `vector_angle` is in radians, and most Python functions expect angles in radians. \n",
+    "But many people think more naturally in degrees. \n",
+    "It can be awkward, and error-prone, to use both units in the same program.\n",
+    "Fortunately, NumPy provides a function to convert degrees to radians:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cellular-community",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7853981633974483"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from numpy import deg2rad\n",
+    "\n",
+    "angle = 45\n",
+    "theta = deg2rad(angle)\n",
+    "theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "congressional-panel",
+   "metadata": {},
+   "source": [
+    "And radians to degrees:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "informed-statistics",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "45.0"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from numpy import rad2deg\n",
+    "\n",
+    "rad2deg(theta)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "responsible-gentleman",
+   "metadata": {},
+   "source": [
+    "Following convention, I'll use `angle` for a value in degrees and `theta` for a value in radians.\n",
+    "\n",
+    "If you are given an angle and velocity, you can make a `Vector` using\n",
+    "`pol2cart`, which converts from polar to Cartesian coordinates. To\n",
+    "demonstrate, I'll extract the angle and magnitude of `A`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "exempt-douglas",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mag = vector_mag(A)\n",
+    "angle = vector_angle(A)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "enhanced-toner",
+   "metadata": {},
+   "source": [
+    "And then make a new `Vector` with the same components:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "monetary-firmware",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "x    3.0\n",
+       "y    4.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import pol2cart\n",
+    "\n",
+    "x, y = pol2cart(angle, mag)\n",
+    "Vector(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lucky-plastic",
+   "metadata": {},
+   "source": [
+    "Another way to represent the direction of `A` is a **unit vector**,\n",
+    "which is a vector with magnitude 1 that points in the same direction as\n",
+    "`A`. You can compute a unit vector by dividing a vector by its\n",
+    "magnitude:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "explicit-piano",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "x    0.6\n",
+       "y    0.8\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A / vector_mag(A)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "respected-oliver",
+   "metadata": {},
+   "source": [
+    "We can do the same thing using the `hat` function, so named because unit\n",
+    "vectors are conventionally decorated with a hat, like this: $\\hat{A}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "relative-republic",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "x    0.6\n",
+       "y    0.8\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import vector_hat\n",
+    "\n",
+    "vector_hat(A)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "novel-somewhere",
+   "metadata": {},
+   "source": [
+    "Now let's get back to the game."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "similar-local",
+   "metadata": {},
+   "source": [
+    "## Simulating baseball flight\n",
+    "\n",
+    "Let's simulate the flight of a baseball that is batted from home plate\n",
+    "at an angle of 45 ° and initial speed 40 m/s. Using the center of home\n",
+    "plate as the origin, the x-axis is parallel to the ground; the y-axis is vertical. The initial height is about 1 m.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ancient-thunder",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import pi\n",
+    "\n",
+    "mass = 145e-3 # kg \n",
+    "diameter = 73e-3 # m \n",
+    "area = pi * (diameter/2)**2\n",
+    "g = 9.8 # m/s**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "still-ownership",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rho = 1.2 # kg/m**3\n",
+    "C_d = 0.33\n",
+    "t_end = 10 # s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "detected-return",
+   "metadata": {},
+   "source": [
+    "The mass, diameter, and drag coefficient of the baseball are from the\n",
+    "sources in Section xxx. The acceleration of gravity, `g`, is a well-known quantity.\n",
+    "\n",
+    "The density of air, `rho`, is based on a temperature of 20 °C at sea level (see <http://modsimpy.com/tempress>). I chose the\n",
+    "value of `t_end` to run the simulation long enough for the ball to land on the ground.\n",
+    "\n",
+    "The following function makes a `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "bored-billy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import State, System\n",
+    "from numpy import deg2rad\n",
+    "\n",
+    "def make_system(angle, velocity, C_d, rho):\n",
+    "    \n",
+    "    # convert angle to degrees\n",
+    "    theta = deg2rad(angle)\n",
+    "    \n",
+    "    # compute x and y components of velocity\n",
+    "    vx, vy = pol2cart(theta, velocity)\n",
+    "    \n",
+    "    # make the initial state\n",
+    "    init = State(x=0, y=1, vx=vx, vy=vy)\n",
+    "        \n",
+    "    return System(init=init, \n",
+    "                  area=area,\n",
+    "                  g=g,\n",
+    "                  mass=mass,\n",
+    "                  rho=rho,\n",
+    "                  C_d=C_d,\n",
+    "                  t_end=t_end)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "generic-budapest",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = 0          # m\n",
+    "y = 1          # m\n",
+    "angle = 45     # degree\n",
+    "velocity = 40  # m / s\n",
+    "\n",
+    "system = make_system(angle, velocity, C_d, rho)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "occasional-given",
+   "metadata": {},
+   "source": [
+    "`make_system` uses `np.deg2rad` to convert `angle` to radians and\n",
+    "`pol2cart` to compute the $x$ and $y$ components of the initial\n",
+    "velocity.\n",
+    "\n",
+    "Next we need a function to compute drag force:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "legal-terminal",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drag_force(V, system):\n",
+    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
+    "    \n",
+    "    mag = -rho * vector_mag(V)**2 * C_d * area / 2\n",
+    "    direction = vector_hat(V)\n",
+    "    f_drag = direction * mag\n",
+    "    return f_drag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "attractive-genome",
+   "metadata": {},
+   "source": [
+    "This function takes `V` as a `Vector` and returns `f_drag` as a\n",
+    "`Vector`. It uses the drag equation to compute the magnitude of the drag\n",
+    "force, and the `hat` function to compute the direction. `-V.hat()`\n",
+    "computes a unit vector pointing in the opposite direction of `V`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "functional-thong",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "x   -0.117197\n",
+       "y   -0.117197\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import ModSimVector\n",
+    "\n",
+    "V_test = Vector(10, 10)\n",
+    "drag_force(V_test, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "absent-vector",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Now we're ready for a slope function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "suitable-salem",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    x, y, vx, vy = state\n",
+    "    mass, g = system.mass, system.g\n",
+    "    \n",
+    "    V = Vector(vx, vy)\n",
+    "    a_drag = drag_force(V, system) / mass\n",
+    "    a_grav = Vector(0, -g)\n",
+    "    \n",
+    "    A = a_grav + a_drag\n",
+    "    \n",
+    "    return V.x, V.y, A.x, A.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "crude-parcel",
+   "metadata": {},
+   "source": [
+    "As usual, the parameters of the slope function are a `State` object,\n",
+    "time, and a `System` object. In this example, we don't use `t`, but we\n",
+    "can't leave it out because when `run_solve_ivp` calls the slope\n",
+    "function, it always provides the same arguments, whether they are needed\n",
+    "or not.\n",
+    "\n",
+    "The `State` object contains two state variables, `R` and `V`, that\n",
+    "represent position and velocity as `Vector` objects.\n",
+    "\n",
+    "The return values from the slope function are the derivatives of these\n",
+    "quantities. The derivative of position is velocity, so the first return\n",
+    "value is `V`, which we extracted from the `State` object. The derivative\n",
+    "of velocity is acceleration, and that's what we have to compute.\n",
+    "\n",
+    "The total acceleration of the baseball is the sum of accelerations due\n",
+    "to gravity and drag. These quantities have both magnitude and direction,\n",
+    "so they are represented by `Vector` objects.\n",
+    "\n",
+    "We already saw how `a_drag` is computed. `a_grav` is a `Vector` with\n",
+    "magnitude `g` pointed in the negative `y` direction.\n",
+    "\n",
+    "Using vectors to represent forces and accelerations makes the code\n",
+    "concise, readable, and less error-prone. In particular, when we add\n",
+    "`a_grav` and `a_drag`, the directions are likely to be correct, because\n",
+    "they are encoded in the `Vector` objects. And the units are certain to\n",
+    "be correct, because otherwise Pint would report an error.\n",
+    "\n",
+    "As always, we can test the slope function by running it with the initial\n",
+    "conditions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "closing-simon",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(28.284271247461902, 28.2842712474619, -6.466030881564545, -16.266030881564546)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "slope_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "regulated-railway",
+   "metadata": {},
+   "source": [
+    "We can use an event function to stop the simulation when the ball hits\n",
+    "the ground."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "brief-level",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(t, state, system):\n",
+    "    x, y, vx, vy = state\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "victorian-sharing",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "event_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unlikely-dressing",
+   "metadata": {},
+   "source": [
+    "The event function takes the same parameters as the slope function, and\n",
+    "returns the y coordinate of `R`. When the y coordinate passes through 0,\n",
+    "the simulation stops.\n",
+    "\n",
+    "Now we're ready to run the simulation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "special-background",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A termination event occurred.'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import run_solve_ivp\n",
+    "\n",
+    "results, details = run_solve_ivp(system, slope_func,\n",
+    "                                 events=event_func)\n",
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "thick-ozone",
+   "metadata": {},
+   "source": [
+    "`results` is a `TimeFrame` with one column for each of the state variables, `x`, `y`, `vx`, and `vy`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "complex-reviewer",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>vx</th>\n",
+       "      <th>vy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4.604496</th>\n",
+       "      <td>93.473971</td>\n",
+       "      <td>8.296642e+00</td>\n",
+       "      <td>15.013486</td>\n",
+       "      <td>-19.334826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.704594</th>\n",
+       "      <td>94.966756</td>\n",
+       "      <td>6.325368e+00</td>\n",
+       "      <td>14.802553</td>\n",
+       "      <td>-20.038175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.804692</th>\n",
+       "      <td>96.438515</td>\n",
+       "      <td>4.284486e+00</td>\n",
+       "      <td>14.590855</td>\n",
+       "      <td>-20.726780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.904789</th>\n",
+       "      <td>97.889087</td>\n",
+       "      <td>2.175515e+00</td>\n",
+       "      <td>14.378566</td>\n",
+       "      <td>-21.400392</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.004887</th>\n",
+       "      <td>99.318296</td>\n",
+       "      <td>-7.105427e-15</td>\n",
+       "      <td>14.165894</td>\n",
+       "      <td>-22.058763</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  x             y         vx         vy\n",
+       "4.604496  93.473971  8.296642e+00  15.013486 -19.334826\n",
+       "4.704594  94.966756  6.325368e+00  14.802553 -20.038175\n",
+       "4.804692  96.438515  4.284486e+00  14.590855 -20.726780\n",
+       "4.904789  97.889087  2.175515e+00  14.378566 -21.400392\n",
+       "5.004887  99.318296 -7.105427e-15  14.165894 -22.058763"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dominant-weekly",
+   "metadata": {},
+   "source": [
+    "We can get the flight time like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "medieval-calvin",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.004887034868351"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flight_time = results.index[-1]\n",
+    "flight_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "convenient-heading",
+   "metadata": {},
+   "source": [
+    "And the final `x` coordinate like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "gorgeous-survey",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "99.31829628352206"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x, y, vx, vy = results.iloc[-1]\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "sensitive-century",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>vx</th>\n",
+       "      <th>vy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4.604496</th>\n",
+       "      <td>93.473971</td>\n",
+       "      <td>8.296642e+00</td>\n",
+       "      <td>15.013486</td>\n",
+       "      <td>-19.334826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.704594</th>\n",
+       "      <td>94.966756</td>\n",
+       "      <td>6.325368e+00</td>\n",
+       "      <td>14.802553</td>\n",
+       "      <td>-20.038175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.804692</th>\n",
+       "      <td>96.438515</td>\n",
+       "      <td>4.284486e+00</td>\n",
+       "      <td>14.590855</td>\n",
+       "      <td>-20.726780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.904789</th>\n",
+       "      <td>97.889087</td>\n",
+       "      <td>2.175515e+00</td>\n",
+       "      <td>14.378566</td>\n",
+       "      <td>-21.400392</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.004887</th>\n",
+       "      <td>99.318296</td>\n",
+       "      <td>-7.105427e-15</td>\n",
+       "      <td>14.165894</td>\n",
+       "      <td>-22.058763</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  x             y         vx         vy\n",
+       "4.604496  93.473971  8.296642e+00  15.013486 -19.334826\n",
+       "4.704594  94.966756  6.325368e+00  14.802553 -20.038175\n",
+       "4.804692  96.438515  4.284486e+00  14.590855 -20.726780\n",
+       "4.904789  97.889087  2.175515e+00  14.378566 -21.400392\n",
+       "5.004887  99.318296 -7.105427e-15  14.165894 -22.058763"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "continuous-quick",
+   "metadata": {},
+   "source": [
+    "## Trajectories\n",
+    "\n",
+    "We can plot the $x$ and $y$ components of position like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "spare-burst",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "results.x.plot(color='C4')\n",
+    "results.y.plot(color='C2', style='--')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Position (m)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "horizontal-bench",
+   "metadata": {},
+   "source": [
+    "As expected, the $x$\n",
+    "component increases as the ball moves away from home plate. The $y$\n",
+    "position climbs initially and then descends, falling to 0 m near 5.0 s.\n",
+    "\n",
+    "Another way to view the same data is to plot the $x$ component on the\n",
+    "x-axis and the $y$ component on the y-axis, so the plotted line follows the trajectory of the ball through the plane:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "dated-browse",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.pyplot import plot\n",
+    "\n",
+    "def plot_trajectory(results):\n",
+    "    plot(results.x, results.y, label='trajectory')\n",
+    "\n",
+    "    decorate(xlabel='x position (m)',\n",
+    "             ylabel='y position (m)')\n",
+    "\n",
+    "plot_trajectory(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "certified-synthetic",
+   "metadata": {},
+   "source": [
+    "This way of visualizing the results is called a **trajectory plot** (see <http://modsimpy.com/trajec>).\n",
+    "\n",
+    "A trajectory plot can be easier to interpret than a time series plot,\n",
+    "because it shows what the motion of the projectile would look like (at\n",
+    "least from one point of view). Both plots can be useful, but don't get\n",
+    "them mixed up! If you are looking at a time series plot and interpreting it as a trajectory, you will be very confused.\n",
+    "\n",
+    "\n",
+    "[^1]: Adair, *The Physics of Baseball*, Third Edition, Perennial, 2002."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "surprised-bandwidth",
+   "metadata": {},
+   "source": [
+    "## Animation\n",
+    "\n",
+    "One of the best ways to visualize the results of a physical model is animation.  If there are problems with the model, animation can make them apparent.\n",
+    "\n",
+    "The ModSimPy library provides `animate`, which takes as parameters a `TimeSeries` and a draw function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hollow-maintenance",
+   "metadata": {},
+   "source": [
+    "The draw function should take as parameters a `State` object and the time.  It should draw a single frame of the animation.\n",
+    "\n",
+    "Inside the draw function, you almost always have to call `set_xlim` and `set_ylim`.  Otherwise `matplotlib` auto-scales the axes, which is usually not what you want."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "worth-leeds",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xs = results.x\n",
+    "ys = results.y\n",
+    "\n",
+    "def draw_func(t, state):\n",
+    "    plot(state.x, state.y, 'bo')\n",
+    "    decorate(xlabel='x position (m)',\n",
+    "             ylabel='y position (m)',\n",
+    "             xlim=(xs.min(), xs.max()),\n",
+    "             ylim=(ys.min(), ys.max()),\n",
+    "            )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "general-peace",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import animate\n",
+    "\n",
+    "animate(results, draw_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tested-friendly",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "strategic-reduction",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Run the simulation with and without air resistance.  How wrong would we be if we ignored drag?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "caroline-theta",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hint\n",
+    "\n",
+    "system2 = make_system(angle, velocity, 0, rho)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "perfect-property",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A termination event occurred.'"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results2, details2 = run_solve_ivp(system2, slope_func, \n",
+    "                                   events=event_func)\n",
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "demographic-destination",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "plot_trajectory(results)\n",
+    "plot_trajectory(results2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "understood-colombia",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "99.31829628352206"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "x_dist = results.iloc[-1].x\n",
+    "x_dist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "mediterranean-equivalent",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "164.25925502413202"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "x_dist_no_drag = results2.iloc[-1].x\n",
+    "x_dist_no_drag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "amateur-cowboy",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / meter$^3$.  How much farther would a ball hit with the same velocity and launch angle travel?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "surprised-polls",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hint\n",
+    "\n",
+    "system3 = make_system(angle, velocity, C_d, 1.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "welsh-match",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105.78838005859807"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results3, details3 = run_solve_ivp(system3, slope_func, \n",
+    "                                   events=event_func)\n",
+    "\n",
+    "x_dist3 = results3.iloc[-1].x\n",
+    "x_dist3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "suspended-advantage",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.470083775076006"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "x_dist3 - x_dist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adjacent-mediterranean",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The model so far is based on the assumption that coefficient of drag does not depend on velocity, but in reality it does.  The following figure, from Adair, [*The Physics of Baseball*](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC), shows coefficient of drag as a function of velocity.\n",
+    "\n",
+    "<img src=\"https://github.com/AllenDowney/ModSimPy/raw/master/figs/baseball_drag.png\" width=\"400\">\n",
+    "\n",
+    "\n",
+    "I used [an online graph digitizer](https://automeris.io/WebPlotDigitizer/) to extract the data and save it in a CSV file.  Here's how we can read it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "crazy-sense",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'baseball_drag.csv'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/baseball_drag.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "smaller-armenia",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Velocity in mph</th>\n",
+       "      <th>Drag coefficient</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Velocity in meters per second</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.026146</th>\n",
+       "      <td>0.058486</td>\n",
+       "      <td>0.49965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.871509</th>\n",
+       "      <td>19.845000</td>\n",
+       "      <td>0.49878</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17.647351</th>\n",
+       "      <td>39.476000</td>\n",
+       "      <td>0.49704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.432914</th>\n",
+       "      <td>50.181000</td>\n",
+       "      <td>0.48225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26.882303</th>\n",
+       "      <td>60.134000</td>\n",
+       "      <td>0.45004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30.636992</th>\n",
+       "      <td>68.533000</td>\n",
+       "      <td>0.40914</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32.977694</th>\n",
+       "      <td>73.769000</td>\n",
+       "      <td>0.38042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34.604472</th>\n",
+       "      <td>77.408000</td>\n",
+       "      <td>0.36562</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37.497268</th>\n",
+       "      <td>83.879000</td>\n",
+       "      <td>0.34822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40.460249</th>\n",
+       "      <td>90.507000</td>\n",
+       "      <td>0.33081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43.492522</th>\n",
+       "      <td>97.290000</td>\n",
+       "      <td>0.31427</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46.733562</th>\n",
+       "      <td>104.540000</td>\n",
+       "      <td>0.30035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50.886563</th>\n",
+       "      <td>113.830000</td>\n",
+       "      <td>0.28816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54.047136</th>\n",
+       "      <td>120.900000</td>\n",
+       "      <td>0.28381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56.926074</th>\n",
+       "      <td>127.340000</td>\n",
+       "      <td>0.28033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60.086646</th>\n",
+       "      <td>134.410000</td>\n",
+       "      <td>0.28207</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               Velocity in mph  Drag coefficient\n",
+       "Velocity in meters per second                                   \n",
+       "0.026146                              0.058486           0.49965\n",
+       "8.871509                             19.845000           0.49878\n",
+       "17.647351                            39.476000           0.49704\n",
+       "22.432914                            50.181000           0.48225\n",
+       "26.882303                            60.134000           0.45004\n",
+       "30.636992                            68.533000           0.40914\n",
+       "32.977694                            73.769000           0.38042\n",
+       "34.604472                            77.408000           0.36562\n",
+       "37.497268                            83.879000           0.34822\n",
+       "40.460249                            90.507000           0.33081\n",
+       "43.492522                            97.290000           0.31427\n",
+       "46.733562                           104.540000           0.30035\n",
+       "50.886563                           113.830000           0.28816\n",
+       "54.047136                           120.900000           0.28381\n",
+       "56.926074                           127.340000           0.28033\n",
+       "60.086646                           134.410000           0.28207"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pandas import read_csv\n",
+    "from modsim import Quantity, units\n",
+    "\n",
+    "baseball_drag = read_csv(filename)\n",
+    "mph = Quantity(baseball_drag['Velocity in mph'], units.mph)\n",
+    "mps = mph.to(units.meter / units.second)\n",
+    "baseball_drag.index = mps.magnitude\n",
+    "baseball_drag.index.name = 'Velocity in meters per second'\n",
+    "baseball_drag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "artistic-helping",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import interpolate, linspace\n",
+    "\n",
+    "drag_interp = interpolate(baseball_drag['Drag coefficient'])\n",
+    "vs = linspace(0, 60)\n",
+    "cds = drag_interp(vs)\n",
+    "plot(vs, cds)\n",
+    "decorate(xlabel='Velocity (m/s)', ylabel='C_d')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reliable-bottom",
+   "metadata": {},
+   "source": [
+    "Modify the model to include the dependence of `C_d` on velocity, and see how much it affects the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "empty-palace",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def drag_force(V, system):\n",
+    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
+    "    \n",
+    "    v: velocity\n",
+    "    system: System object with rho, C_d, area\n",
+    "    \n",
+    "    returns: Vector drag force\n",
+    "    \"\"\"\n",
+    "    rho, area = system.rho, system.area\n",
+    "    \n",
+    "    C_d = drag_interp(vector_mag(V))\n",
+    "    mag = -rho * vector_mag(V)**2 * C_d * area / 2\n",
+    "    direction = vector_hat(V)\n",
+    "    f_drag = direction * mag\n",
+    "    return f_drag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "satellite-delay",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "system4 = make_system(angle, velocity, C_d, rho)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "tired-sleeve",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "x   -1.023081\n",
+       "y   -1.023081\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "V = Vector(30, 30)\n",
+    "f_drag = drag_force(V, system4)\n",
+    "f_drag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "ambient-castle",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(28.284271247461902, 28.2842712474619, -6.534891184395127, -16.334891184395126)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "slope_func(0, system4.init, system4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "plain-retail",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A termination event occurred.'"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results4, details4 = run_solve_ivp(system4, slope_func, \n",
+    "                                   events=event_func)\n",
+    "details4.message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "black-amino",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>vx</th>\n",
+       "      <th>vy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4.525854</th>\n",
+       "      <td>85.443182</td>\n",
+       "      <td>7.746401e+00</td>\n",
+       "      <td>12.556069</td>\n",
+       "      <td>-18.457129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.624243</th>\n",
+       "      <td>86.667639</td>\n",
+       "      <td>5.898175e+00</td>\n",
+       "      <td>12.324797</td>\n",
+       "      <td>-19.075086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.722631</th>\n",
+       "      <td>87.869695</td>\n",
+       "      <td>3.989943e+00</td>\n",
+       "      <td>12.094833</td>\n",
+       "      <td>-19.677696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.821019</th>\n",
+       "      <td>89.049363</td>\n",
+       "      <td>2.023337e+00</td>\n",
+       "      <td>11.866567</td>\n",
+       "      <td>-20.265039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.919407</th>\n",
+       "      <td>90.206647</td>\n",
+       "      <td>1.065814e-14</td>\n",
+       "      <td>11.640440</td>\n",
+       "      <td>-20.837214</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  x             y         vx         vy\n",
+       "4.525854  85.443182  7.746401e+00  12.556069 -18.457129\n",
+       "4.624243  86.667639  5.898175e+00  12.324797 -19.075086\n",
+       "4.722631  87.869695  3.989943e+00  12.094833 -19.677696\n",
+       "4.821019  89.049363  2.023337e+00  11.866567 -20.265039\n",
+       "4.919407  90.206647  1.065814e-14  11.640440 -20.837214"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results4.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "uniform-determination",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "x_dist4 = results4.iloc[-1].x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "earned-cliff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-9.111649717288998"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "x_dist4 - x_dist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "spiritual-shareware",
+   "metadata": {},
+   "source": [
+    "### Under the hood\n",
+    "\n",
+    "`Vector` "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/modsim.py b/jupyter/modsim.py
index 3512ea30..164cd36a 100644
--- a/jupyter/modsim.py
+++ b/jupyter/modsim.py
@@ -361,8 +361,6 @@ def run_odeint(system, slope_func, **options):
     return frame
 
 
-from scipy.integrate import solve_ivp
-
 def run_solve_ivp(system, slope_func, **options):
     """Computes a numerical solution to a differential equation.
 
@@ -406,7 +404,33 @@ def run_solve_ivp(system, slope_func, **options):
         logger.error(msg)
         raise (e)
 
-    # TODO: make dense solution the default
+    # get the list of event functions
+    events = options.get('events', [])
+
+    # if there's only one event function, put it in a list
+    try:
+        iter(events)
+    except TypeError:
+        events = [events]
+
+    for event_func in events:
+        # make events terminal unless otherwise specified
+        if not hasattr(event_func, 'terminal'):
+            event_func.terminal = True
+
+        # test the event function with the initial conditions
+        try:
+            event_func(t_0, system.init, system)
+        except Exception as e:
+            msg = """Before running scipy.integrate.solve_ivp, I tried
+                     running the event function you provided with the
+                     initial conditions in `system` and `t=t_0` and I got
+                     the following error:"""
+            logger.error(msg)
+            raise (e)
+
+    # get dense output unless otherwise specified
+    underride(options, dense_output=True)
 
     # run the solver
     bunch = solve_ivp(slope_func, [t_0, system.t_end], system.init,
@@ -416,8 +440,25 @@ def run_solve_ivp(system, slope_func, **options):
     y = bunch.pop("y")
     t = bunch.pop("t")
 
-    # pack the results into a TimeFrame
-    results = TimeFrame(y.T, index=t, columns=system.init.index)
+    # get the column names from `init`, if possible
+    if hasattr(system.init, 'index'):
+        columns = system.init.index
+    else:
+        columns = range(len(system.init))
+
+    # evaluate the results at 51 equally-spaced points
+    if options.get('dense_output', False):
+        t_end = t[-1]
+        t_array = linspace(t_0, t_end, 51)
+        y_array = bunch.sol(t_array)
+
+        # pack the results into a TimeFrame
+        results = TimeFrame(y_array.T, index=t_array,
+                        columns=columns)
+    else:
+        results = TimeFrame(y.T, index=t,
+                        columns=columns)
+
     return results, bunch
 
 
@@ -620,7 +661,7 @@ def crossings(series, value):
 
     returns: sequence of labels
     """
-    values = magnitudes(series - value)
+    values = series.values - value
     interp = InterpolatedUnivariateSpline(series.index, values)
     return interp.roots()
 
@@ -860,9 +901,9 @@ def plot(x, y=None, **options):
         x.plot(**options)
 
 
-def State(**kwargs):
-    """Contains state variables and their values."""
-    return pd.Series(kwargs)
+def State(**variables):
+    """Contains the values of state variables."""
+    return pd.Series(variables)
 
 
 class System(SimpleNamespace):
@@ -885,66 +926,16 @@ def SweepFrame(*args, **kwargs):
     return pd.DataFrame(*args, **kwargs)
 
 
-def Vector(*args, units=None):
-    """Make a ModSimVector.
-
-    args: can be a single argument or sequence
-    units: Pint Unit object or Quantity
-
-    If there's only one argument, it should be a sequence.
-
-    Otherwise, the arguments are treated as coordinates.
-
-    returns: ModSimVector
-    """
-    if len(args) == 1:
-        args = args[0]
-
-        # if it's a series, pull out the values
-        if isinstance(args, Series):
-            args = args.values
-
-    # see if any of the arguments have units; if so, save the first one
-    for elt in args:
-        found_units = getattr(elt, "units", None)
-        if found_units:
-            break
-
-    if found_units:
-        # if there are units, remove them
-        args = [float(magnitude(elt)) for elt in args]
-    else:
-        # otherwise, just ensure that all elements are floats (to avoid overflow issues in numpy)
-        args = [float(elt) for elt in args]
-
-    # if the units keyword is provided, it overrides the units in args
-    if units is not None:
-        found_units = units
-
-    return ModSimVector(args, found_units)
-
-
 ## Vector functions (should work with any sequence)
 
-
 def vector_mag(v):
-    """Vector magnitude with units.
-
-    returns: number or Quantity
-    """
-    a = magnitude(v)
-    units = get_first_unit(v)
-    return np.sqrt(np.dot(a, a)) * units
+    """Vector magnitude."""
+    return np.sqrt(np.dot(v, v))
 
 
 def vector_mag2(v):
-    """Vector magnitude squared with units.
-
-    returns: number of Quantity
-    """
-    a = magnitude(v)
-    units = get_first_unit(v)
-    return np.dot(a, a) * units * units
+    """Vector magnitude squared."""
+    return np.dot(v, v)
 
 
 def vector_angle(v):
@@ -952,7 +943,7 @@ def vector_angle(v):
 
     Only works with 2-D vectors.
 
-    returns: number in radians
+    returns: angle in radians
     """
     assert len(v) == 2
     x, y = v
@@ -962,7 +953,7 @@ def vector_angle(v):
 def vector_polar(v):
     """Vector magnitude and angle.
 
-    returns: (number or quantity, number in radians)
+    returns: (number, angle in radians)
     """
     return vector_mag(v), vector_angle(v)
 
@@ -970,19 +961,12 @@ def vector_polar(v):
 def vector_hat(v):
     """Unit vector in the direction of v.
 
-    The result should have no units.
-
     returns: Vector or array
     """
-    # get the size of the vector
-    mag = vector_mag(v)
-
     # check if the magnitude of the Quantity is 0
-    if magnitude(mag) == 0:
-        if isinstance(v, ModSimVector):
-            return Vector(magnitude(v))
-        else:
-            return magnitude(np.asarray(v))
+    mag = vector_mag(v)
+    if mag == 0:
+        return v
     else:
         return v / mag
 
@@ -1004,9 +988,7 @@ def vector_dot(v, w):
 
     returns: number or Quantity
     """
-    a1 = magnitude(v)
-    a2 = magnitude(w)
-    return np.dot(a1, a2) * get_first_unit(v) * get_first_unit(w)
+    return np.dot(v, w)
 
 
 def vector_cross(v, w):
@@ -1014,22 +996,17 @@ def vector_cross(v, w):
 
     returns: number or Quantity for 2-D, Vector for 3-D
     """
-    a1 = magnitude(v)
-    a2 = magnitude(w)
-    res = np.cross(a1, a2)
+    res = np.cross(v, w)
 
-    if len(v) == 3 and (isinstance(v, ModSimVector) or isinstance(w, ModSimVector)):
-        return ModSimVector(res, get_first_unit(v) * get_first_unit(w))
+    if len(v) == 3:
+        return Vector(*res)
     else:
-        return res * get_first_unit(v) * get_first_unit(w)
+        return res
 
 
 def vector_proj(v, w):
     """Projection of v onto w.
 
-    Results has the units of v, but that might not make sense unless
-    v and w have the same units.
-
     returns: array or Vector with direction of w and units of v.
     """
     w_hat = vector_hat(w)
@@ -1041,9 +1018,6 @@ def scalar_proj(v, w):
 
     Which is the magnitude of the projection of v onto w.
 
-    Results has the units of v, but that might not make sense unless
-    v and w have the same units.
-
     returns: scalar with units of v.
     """
     return vector_dot(v, vector_hat(w))
@@ -1068,7 +1042,7 @@ def vector_diff_angle(v, w):
 
 
 class ModSimVector(Quantity):
-    """Represented as a Pint Quantity with a NumPy array
+    """Represented as an array of 2 or 3 elements.
 
     x, y, z, mag, mag2, and angle are accessible as attributes.
     """
@@ -1115,6 +1089,19 @@ def angle(self):
     diff_angle = vector_diff_angle
 
 
+def Vector(x, y, z=None, **options):
+    """
+    """
+    if z is None:
+        return pd.Series(dict(x=x, y=y), **options)
+    else:
+        return pd.Series(dict(x=x, y=y, z=z), **options)
+
+mag = vector_mag
+angle = vector_angle
+hat = vector_hat
+
+
 def plot_segment(A, B, **options):
     """Plots a line segment between two Vectors.
 
@@ -1142,12 +1129,12 @@ def animate(results, draw_func, interval=None):
     plt.figure()
     try:
         for t, state in results.iterrows():
-            draw_func(state, t)
+            draw_func(t, state)
             plt.show()
             if interval:
                 sleep(interval)
             clear_output(wait=True)
-        draw_func(state, t)
+        draw_func(t, state)
         plt.show()
     except KeyboardInterrupt:
         pass

From 239292e14937e43ec01da6a744287853660c7433 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Wed, 3 Feb 2021 14:00:54 -0500
Subject: [PATCH 026/144] Revising chapters

---
 jupyter/chap21.ipynb |  520 ++++++++--------
 jupyter/chap22.ipynb |  701 +++++++++++----------
 jupyter/chap23.ipynb |  877 ++++++++++++++++++++++++++
 jupyter/chap24.ipynb | 1396 ++++++++++++++++++++++++++++++++++++++++++
 jupyter/modsim.py    |  115 ++--
 5 files changed, 2976 insertions(+), 633 deletions(-)
 create mode 100644 jupyter/chap23.ipynb
 create mode 100644 jupyter/chap24.ipynb

diff --git a/jupyter/chap21.ipynb b/jupyter/chap21.ipynb
index 5601492d..35762a67 100644
--- a/jupyter/chap21.ipynb
+++ b/jupyter/chap21.ipynb
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "running-field",
+   "id": "superb-february",
    "metadata": {},
    "source": [
     "In the previous chapter we simulated a penny falling in a vacuum, that\n",
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "refined-yield",
+   "id": "interesting-freeware",
    "metadata": {},
    "source": [
     "## Drag force\n",
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "domestic-studio",
+   "id": "unable-scheduling",
    "metadata": {},
    "source": [
     "For objects moving at moderate speeds through air, typical drag\n",
@@ -118,41 +118,63 @@
     "\n",
     "$$C_d = \\frac{2~m g}{\\rho~v_{term}^2~A}$$ \n",
     "\n",
-    "According to *Mythbusters*, the terminal velocity of a penny is between 35 and 65 mph (see <http://modsimpy.com/mythbust>). Using the low end of their range, 40 mph or about 18 m/s, the estimated value of $C_d$ is 0.44, which is close to the drag coefficient of a smooth sphere."
+    "According to *Mythbusters*, the terminal velocity of a penny is between 35 and 65 mph (see <http://modsimpy.com/mythbust>). Using the low end of their range, 40 mph or about 18 m/s, the estimated value of $C_d$ is 0.44, which is close to the drag coefficient of a smooth sphere.\n",
+    "\n",
+    "Now we are ready to add air resistance to the model."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "afraid-expression",
+   "cell_type": "markdown",
+   "id": "human-cloud",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## The Params Object\n",
+    "\n",
+    "As the number of system parameters increases, and as we need to do more work to compute them, we will find it useful to define a `Params` object to contain the quantities we need to make a `System` object. `Params` objects are similar to `System` objects, and we initialize them the same way.\n",
+    "\n",
+    "Here's the `Params` object for the falling penny:"
+   ]
   },
   {
-   "cell_type": "markdown",
-   "id": "exempt-mustang",
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "established-knitting",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Now we are ready to add air resistance to the model."
+    "from modsim import Params\n",
+    "\n",
+    "params = Params(\n",
+    "    mass = 0.0025,      # kg\n",
+    "    diameter = 0.019,   # m\n",
+    "    rho = 1.2,     # kg/m**3\n",
+    "    g = 9.8,       # m/s**2\n",
+    "    v_init = 0,    # m / s\n",
+    "    v_term = 18,   # m / s\n",
+    "    height = 381,  # m\n",
+    "    t_end = 30,    # s\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "human-cloud",
+   "id": "instrumental-gross",
    "metadata": {},
    "source": [
-    "## Implementation\n",
+    "The mass and diameter are from <http://modsimpy.com/penny>. The density\n",
+    "of air depends on temperature, barometric pressure (which depends on\n",
+    "altitude), humidity, and composition (<http://modsimpy.com/density>). I\n",
+    "chose a value that might be typical in Boston, Massachusetts at 20 °C.\n",
     "\n",
-    "As the number of system parameters increases, and as we need to do more work to compute them, we will find it useful to define a `Params` object to contain the quantities we need to make a `System` object. `Params` objects are similar to `System` and `State` objects; in fact, all three have the same capabilities. I have given them different names to document the different roles they play.\n",
     "\n",
-    "Here's the `Params` object for the falling penny:"
+    "Here's a version of `make_system` that takes the `Params` object and computes the inital state, `init`, the area, and the coefficient of drag.\n",
+    "Then it returns a `System` object with the quantities we'll need for the simulation. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "scheduled-element",
+   "execution_count": 3,
+   "id": "published-jesus",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,51 +182,134 @@
     "from modsim import State\n",
     "from modsim import System\n",
     "\n",
-    "def make_system(mass, diameter, rho, v_init, v_term):\n",
-    "    area = pi * (diameter/2)**2\n",
+    "def make_system(params):\n",
+    "    init = State(y=params.height, v=params.v_init)\n",
     "\n",
-    "    g = 9.8   # m/s**2\n",
-    "    C_d = 2 * mass * g / (rho * area * v_term**2)\n",
+    "    area = pi * (params.diameter/2)**2\n",
     "\n",
-    "    height = 381 # m\n",
-    "    init = State(y=height, v=v_init)\n",
-    "    t_end = 30  # s\n",
+    "    C_d = (2 * params.mass * params.g / \n",
+    "           (params.rho * area * params.v_term**2))\n",
     "\n",
     "    return System(init=init,\n",
-    "                    rho=rho,\n",
-    "                    C_d=C_d,\n",
-    "                    g=g,\n",
-    "                    area=area,\n",
-    "                    mass=mass,\n",
-    "                    t_end=t_end\n",
-    "                   )"
+    "                  area=area,\n",
+    "                  C_d=C_d,\n",
+    "                  mass=params.mass,\n",
+    "                  rho=params.rho,\n",
+    "                  g=params.g,\n",
+    "                  t_end=params.t_end\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "personalized-kruger",
+   "metadata": {},
+   "source": [
+    "And here's how we call it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "circular-techno",
+   "execution_count": 4,
+   "id": "executive-protection",
    "metadata": {},
    "outputs": [],
    "source": [
-    "mass = 2.5e-3   # kg\n",
-    "diameter = 19e-3   # m\n",
-    "rho = 1.2   # kg/m**3\n",
-    "v_init = 0   # m / s\n",
-    "v_term = 18   # m / s\n",
+    "system = make_system(params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "portable-carbon",
+   "metadata": {},
+   "source": [
+    "Based on the mass and diameter of the penny, the density of air, and acceleration due to gravity, and the observed terminal velocity, we estimate that the coefficient of drag is about 0.44."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "quick-cedar",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4445009981135434"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.C_d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "inner-telescope",
+   "metadata": {},
+   "source": [
+    "It might not be obvious why it is useful to create a `Params` object just to create a `System` object.\n",
+    "In fact, if we only run one simulation, it might not be useful.  But it helps when we want to change or sweep the parameters.\n",
     "\n",
-    "system = make_system(mass, diameter, rho, v_init, v_term)"
+    "For example, suppose we learn that the terminal velocity of a penny is actually closer to 20 m/s.\n",
+    "We can make a `Params` object with the new value, and a corresponding `System` object, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "great-crime",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params2 = params.set(v_term=20)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "instrumental-gross",
+   "id": "outstanding-truth",
    "metadata": {},
    "source": [
-    "The mass and diameter are from <http://modsimpy.com/penny>. The density\n",
-    "of air depends on temperature, barometric pressure (which depends on\n",
-    "altitude), humidity, and composition (<http://modsimpy.com/density>). I\n",
-    "chose a value that might be typical in Boston, Massachusetts at 20 °C.\n"
+    "The result from `set` is a new `Params` object that is identical to the original except for the given value of `v_term`. \n",
+    "\n",
+    "If we pass `params2` to `make_system`, we see that it computes a different value of `C_d`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "independent-trace",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3600458084719701"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system2 = make_system(params2)\n",
+    "system2.C_d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "charged-explorer",
+   "metadata": {},
+   "source": [
+    "If the terminal velocity of the penny is 20 m/s, rather than 18 m/s, that implies that the coefficient of drag is 0.36, rather than 0.44.\n",
+    "And that makes sense, since lower drag implies faster terminal velocity.\n",
+    "\n",
+    "Using `Params` objects to make `System` objects helps make sure that relationships like this are consistent.  And since we are always making new objects, rather than modifying existing objects, we are less likely to make a mistake."
    ]
   },
   {
@@ -212,12 +317,14 @@
    "id": "primary-advocate",
    "metadata": {},
    "source": [
-    "Now here's a version of the slope function that includes drag:"
+    "## Simulation\n",
+    "\n",
+    "Now let's get to the simulation.  Here's a version of the slope function that includes drag:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "id": "noble-stick",
    "metadata": {},
    "outputs": [],
@@ -238,7 +345,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "amended-summit",
+   "id": "baking-class",
    "metadata": {},
    "source": [
     "`f_drag` is force due to drag, based on the drag equation. `a_drag` is\n",
@@ -253,8 +360,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "collectible-austria",
+   "execution_count": 9,
+   "id": "velvet-tunisia",
    "metadata": {},
    "outputs": [
     {
@@ -263,7 +370,7 @@
        "(0, -9.8)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -283,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "id": "practical-nowhere",
    "metadata": {},
    "outputs": [],
@@ -295,8 +402,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "seventh-biology",
+   "execution_count": 11,
+   "id": "animated-clause",
    "metadata": {},
    "outputs": [
     {
@@ -305,7 +412,7 @@
        "381"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -324,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "id": "liberal-dictionary",
    "metadata": {},
    "outputs": [
@@ -334,7 +441,7 @@
        "'A termination event occurred.'"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -343,93 +450,22 @@
     "from modsim import run_solve_ivp\n",
     "\n",
     "results, details = run_solve_ivp(system, slope_func,\n",
-    "                                  events=event_func)\n",
+    "                                 events=event_func)\n",
     "details.message"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "mysterious-whale",
+   "cell_type": "markdown",
+   "id": "incident-paradise",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>y</th>\n",
-       "      <th>v</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.000000</th>\n",
-       "      <td>381.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.448789</th>\n",
-       "      <td>380.022075</td>\n",
-       "      <td>-4.312820</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.897579</th>\n",
-       "      <td>377.200545</td>\n",
-       "      <td>-8.156877</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.346368</th>\n",
-       "      <td>372.817221</td>\n",
-       "      <td>-11.248931</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.795157</th>\n",
-       "      <td>367.230596</td>\n",
-       "      <td>-13.539505</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   y          v\n",
-       "0.000000  381.000000   0.000000\n",
-       "0.448789  380.022075  -4.312820\n",
-       "0.897579  377.200545  -8.156877\n",
-       "1.346368  372.817221 -11.248931\n",
-       "1.795157  367.230596 -13.539505"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "results.head()"
+    "Here are the last few time steps:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "dimensional-longitude",
+   "execution_count": 13,
+   "id": "coastal-anthropology",
    "metadata": {},
    "outputs": [
     {
@@ -496,7 +532,7 @@
        "22.439465  7.105427e-15 -18.011383"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -507,7 +543,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "emerging-domestic",
+   "id": "suburban-martial",
    "metadata": {},
    "source": [
     "The final height is close to 0, as expected.\n",
@@ -519,8 +555,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "cleared-willow",
+   "execution_count": 14,
+   "id": "interim-underground",
    "metadata": {},
    "outputs": [
     {
@@ -529,7 +565,7 @@
        "22.43946505804431"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -541,7 +577,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "rising-nutrition",
+   "id": "institutional-colors",
    "metadata": {},
    "source": [
     "Here's the plot of position as a function of time."
@@ -549,8 +585,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "touched-calculation",
+   "execution_count": 15,
+   "id": "small-franchise",
    "metadata": {},
    "outputs": [
     {
@@ -580,7 +616,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "prospective-favor",
+   "id": "plain-phone",
    "metadata": {},
    "source": [
     "And velocity as a function of time:"
@@ -588,8 +624,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "coordinate-albert",
+   "execution_count": 16,
+   "id": "analyzed-criticism",
    "metadata": {},
    "outputs": [
     {
@@ -618,7 +654,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "familiar-antarctica",
+   "id": "careful-causing",
    "metadata": {},
    "source": [
     "From an initial velocity of 0, the penny accelerates downward until it reaches terminal velocity; after that, velocity is constant."
@@ -626,7 +662,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "recovered-reverse",
+   "id": "inside-confidence",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "planned-endorsement",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -634,31 +678,33 @@
   },
   {
    "cell_type": "markdown",
-   "id": "portable-office",
+   "id": "respective-address",
    "metadata": {},
    "source": [
-    "**Exercise:** Run the simulation with an initial velocity, downward, that exceeds the penny's terminal velocity.  \n",
+    "**Exercise:** Run the simulation with a downward initial velocity that exceeds the penny's terminal velocity.\n",
+    "\n",
+    "What do you expect to happen?  Plot velocity and position as a function of time, and see if they are consistent with your prediction.\n",
     "\n",
-    "What do you expect to happen?  Plot velocity and position as a function of time, and see if they are consistent with your prediction."
+    "Hint: Use `params.set` to make a new `Params` object with a different initial velocity."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "powerful-springfield",
+   "execution_count": 17,
+   "id": "inclusive-twenty",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Solution\n",
     "\n",
-    "v_init = -30\n",
-    "system2 = make_system(mass, diameter, rho, v_init, v_term)"
+    "params = params.set(v_init=-30)\n",
+    "system2 = make_system(params)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "liberal-fighter",
+   "execution_count": 18,
+   "id": "vertical-judge",
    "metadata": {},
    "outputs": [
     {
@@ -667,7 +713,7 @@
        "'A termination event occurred.'"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -682,8 +728,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "informed-representation",
+   "execution_count": 19,
+   "id": "greenhouse-madagascar",
    "metadata": {},
    "outputs": [
     {
@@ -692,7 +738,7 @@
        "20.635183673114202"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -704,8 +750,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "governmental-arena",
+   "execution_count": 20,
+   "id": "sudden-details",
    "metadata": {},
    "outputs": [
     {
@@ -727,8 +773,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "encouraging-aside",
+   "execution_count": 21,
+   "id": "opening-jurisdiction",
    "metadata": {
     "scrolled": false
    },
@@ -754,20 +800,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "educational-rainbow",
+   "id": "smaller-millennium",
    "metadata": {},
    "source": [
     "**Exercise:** Suppose we drop a quarter from the Empire State Building and find that its flight time is 19.1 seconds.  Use this measurement to estimate terminal velocity and coefficient of drag.\n",
     "\n",
     "You can get the relevant dimensions of a quarter from https://en.wikipedia.org/wiki/Quarter_(United_States_coin).\n",
     "\n",
-    "1. Create a `Params` object with the system parameters.  \n",
+    "1. Create a `Params` object with new values of `mass` and `diameter`. We don't know `v_term`, so we'll start with the initial guess 18 m/s.\n",
     "\n",
-    "2. Use `make_system` to create a `System` object.  We don't know `v_term`, so we'll start with the inital guess `v_term = 18 * m / s`.\n",
+    "2. Use `make_system` to create a `System` object.  \n",
     "\n",
     "3. Call `run_solve_ivp` to simulate the system.  How does the flight time of the simulation compare to the measurement?\n",
     "\n",
-    "4. Try a few different values of `t_term` and see if you can get the simulated flight time close to 19.1 seconds.\n",
+    "4. Try a few different values of `v_term` and see if you can get the simulated flight time close to 19.1 seconds.\n",
     "\n",
     "5. Optionally, write an error function and use `root_scalar` to improve your estimate.\n",
     "\n",
@@ -778,56 +824,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "advisory-tunnel",
+   "execution_count": 22,
+   "id": "compact-bunny",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Solution\n",
     "\n",
-    "mass = 5.67e-3 # kg\n",
-    "diameter = 24.26e-3 # m\n",
-    "v_term = 18 # m / s\n",
-    "flight_time = 19.1 # s"
+    "params_quarter = params.set(\n",
+    "    mass = 0.0057,      # kg\n",
+    "    diameter = 0.024,   # m\n",
+    "    flight_time = 19.1,   # s\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "underlying-sperm",
+   "execution_count": 23,
+   "id": "shared-contrary",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "namespace(init=y    381\n",
-       "               v      0\n",
-       "               dtype: int64,\n",
-       "          rho=1.2,\n",
-       "          C_d=0.6183600157463346,\n",
-       "          g=9.8,\n",
-       "          area=0.000462244204111976,\n",
-       "          mass=0.00567,\n",
-       "          t_end=30)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Solution\n",
     "\n",
-    "v_init = 0\n",
-    "system3 = make_system(mass, diameter, rho, v_init, v_term)\n",
-    "system3"
+    "system3 = make_system(params_quarter)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "standard-allah",
+   "execution_count": 24,
+   "id": "portable-account",
    "metadata": {},
    "outputs": [
     {
@@ -836,7 +862,7 @@
        "'A termination event occurred.'"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -853,17 +879,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "atlantic-southeast",
+   "execution_count": 25,
+   "id": "arabic-shareware",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "22.43946505804431"
+       "20.63518367311417"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -879,8 +905,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "sorted-leisure",
+   "execution_count": 26,
+   "id": "valued-literature",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -891,7 +917,7 @@
     "\n",
     "# Or we could write an error function\n",
     "\n",
-    "def error_func(guess):\n",
+    "def error_func(guess, params):\n",
     "    \"\"\"Final height as a function of C_d.\n",
     "    \n",
     "    guess: guess at v_term\n",
@@ -900,19 +926,19 @@
     "    returns: height in m\n",
     "    \"\"\"\n",
     "    print(guess)\n",
-    "    v_term = guess\n",
-    "    system = make_system(mass, diameter, rho, v_init, v_term)\n",
+    "    params = params.set(v_term=guess)\n",
+    "    system = make_system(params)\n",
     "    results, details = run_solve_ivp(system, slope_func, \n",
     "                                     events=event_func)\n",
     "    t_sidewalk = results.index[-1]\n",
-    "    error = t_sidewalk - flight_time\n",
+    "    error = t_sidewalk - params.flight_time\n",
     "    return error"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "perfect-suffering",
+   "execution_count": 27,
+   "id": "sufficient-retail",
    "metadata": {},
    "outputs": [
     {
@@ -925,10 +951,10 @@
     {
      "data": {
       "text/plain": [
-       "3.3394650580443077"
+       "1.5351836731141688"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -938,13 +964,13 @@
     "\n",
     "# We can test the error function like this\n",
     "v_guess1 = 18\n",
-    "error_func(v_guess1)"
+    "error_func(v_guess1, params_quarter)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "juvenile-provincial",
+   "execution_count": 28,
+   "id": "comparable-lounge",
    "metadata": {},
    "outputs": [
     {
@@ -957,10 +983,10 @@
     {
      "data": {
       "text/plain": [
-       "-0.22717020844738656"
+       "-2.1591256962719996"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -969,13 +995,13 @@
     "# Solution\n",
     "\n",
     "v_guess2 = 22\n",
-    "error_func(v_guess2)"
+    "error_func(v_guess2, params_quarter)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "incorporate-jungle",
+   "execution_count": 29,
+   "id": "ready-people",
    "metadata": {},
    "outputs": [
     {
@@ -984,11 +1010,12 @@
      "text": [
       "18.0\n",
       "22.0\n",
-      "21.745227429805748\n",
-      "21.688258716910187\n",
-      "21.688401491107182\n",
-      "21.688401102071705\n",
-      "21.68840110206904\n"
+      "19.662214524680426\n",
+      "19.475274945870567\n",
+      "19.459673874997012\n",
+      "19.459697194820343\n",
+      "19.459697175160358\n",
+      "19.45969717515935\n"
      ]
     }
    ],
@@ -1000,22 +1027,23 @@
     "\n",
     "from scipy.optimize import root_scalar\n",
     "\n",
-    "res = root_scalar(error_func, bracket=[v_guess1, v_guess2])"
+    "res = root_scalar(error_func, params_quarter,\n",
+    "                  bracket=[v_guess1, v_guess2])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "conceptual-questionnaire",
+   "execution_count": 30,
+   "id": "miniature-remark",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "21.68840110206904"
+       "19.459697175160358"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1029,17 +1057,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "id": "designed-variation",
+   "execution_count": 31,
+   "id": "frozen-termination",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.4259232394493775"
+       "0.5434569672577239"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1050,17 +1078,21 @@
     "# Plugging in the estimated value, \n",
     "# we can use `make_system` to compute `C_d`\n",
     "\n",
-    "system4 = make_system(mass, diameter, rho, v_init, v_term)\n",
+    "system4 = make_system(params_quarter.set(v_term=res.root))\n",
     "system4.C_d"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "meaning-instruction",
+   "cell_type": "markdown",
+   "id": "colored-oxygen",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## Under the Hood\n",
+    "\n",
+    "`Params` is a `SimpleNamespace`, like `System`.\n",
+    "\n",
+    "`Params` and `System` are actually the same; I have given them different names to document the different roles they play."
+   ]
   }
  ],
  "metadata": {
diff --git a/jupyter/chap22.ipynb b/jupyter/chap22.ipynb
index 3e157a17..e75cf06e 100644
--- a/jupyter/chap22.ipynb
+++ b/jupyter/chap22.ipynb
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "regional-block",
+   "id": "worst-london",
    "metadata": {},
    "source": [
     "In the previous chapter we modeled objects moving in one dimension, with and without drag. Now let's move on to two dimensions, and baseball!\n",
@@ -62,13 +62,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "looking-sampling",
+   "id": "contained-bones",
    "metadata": {},
    "source": [
     "## Baseball\n",
     "\n",
-    "To model the flight of a baseball, we have to make some modeling\n",
-    "decisions. To get started, we ignore any spin that might be on the ball, and the resulting Magnus force (see <http://modsimpy.com/magnus>). Under this assumption, the ball travels in a vertical plane, so we'll run simulations in two dimensions, rather than three.\n",
+    "To model the flight of a baseball, we have to make some\n",
+    "decisions. To get started, we'll ignore any spin that might be on the ball, and the resulting Magnus force (see <http://modsimpy.com/magnus>). Under this assumption, the ball travels in a vertical plane, so we'll run simulations in two dimensions, rather than three.\n",
     "\n",
     "Air resistance has a substantial effect on most projectiles in air, so\n",
     "we will include a drag force.\n",
@@ -76,7 +76,7 @@
     "To model air resistance, we'll need the mass, frontal area, and drag\n",
     "coefficient of a baseball. Mass and diameter are easy to find (see\n",
     "<http://modsimpy.com/baseball>). Drag coefficient is only a little\n",
-    "harder; according to *The Physics of Baseball*[^1], the drag coefficient of a baseball is approximately 0.33 (with no units).\n",
+    "harder; according to *[The Physics of Baseball](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC)*, the drag coefficient of a baseball is approximately 0.33 (with no units).\n",
     "\n",
     "However, this value *does* depend on velocity. At low velocities it\n",
     "might be as high as 0.5, and at high velocities as low as 0.28.\n",
@@ -95,26 +95,20 @@
    "source": [
     "## Vectors\n",
     "\n",
-    "Now that we are working in two dimensions, we will find it useful to\n",
+    "Now that we are working in two dimensions, it will be useful to\n",
     "work with **vector quantities**, that is, quantities that represent both a magnitude and a direction. We will use vectors to represent positions, velocities, accelerations, and forces in two and three dimensions.\n",
     "\n",
-    "The ModSim library provides a `Vector` object that represents a vector\n",
-    "quantity. A `Vector` object is a like a NumPy array; it contains\n",
-    "elements that represent the **components** of the vector. For example,\n",
-    "in a `Vector` that represents a position in space, the components are\n",
-    "the $x$ and $y$ coordinates (and a $z$ coordinate in 3-D). A `Vector`\n",
-    "object can also have units, like the quantities we've seen in previous\n",
-    "chapters.\n",
+    "ModSim provides a function called `Vector` the creates a Pandas `Series` that contains the **components** of the vector.\n",
+    "In a `Vector` that represents a position in space, the components are the $x$ and $y$ coordinates in 2-D, plus a $z$ coordinate if the `Vector` is in 3-D.\n",
     "\n",
     "You can create a `Vector` by specifying its components. The following\n",
-    "`Vector` represents a point 3 m to the right (or east) and 4 m up (or\n",
-    "north) from an implicit origin:"
+    "`Vector` represents a point 3 units to the right (or east) and 4 units up (or north) from an implicit origin:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "smaller-hudson",
+   "id": "current-wheel",
    "metadata": {},
    "outputs": [
     {
@@ -139,17 +133,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "suited-helena",
+   "id": "retired-strand",
    "metadata": {},
    "source": [
     "You can access the components of a `Vector` by name using the dot\n",
-    "operator: for example, `A.x` or `A.y`. "
+    "operator, like this:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "prompt-vault",
+   "id": "quarterly-startup",
    "metadata": {},
    "outputs": [
     {
@@ -169,16 +163,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "continuous-convert",
+   "id": "welcome-violin",
    "metadata": {},
    "source": [
-    "You can also access them by index using brackets: for example, `A[0]` or `A[1]`."
+    "You can also access them by index using brackets, like this:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "strange-credit",
+   "id": "occupied-macedonia",
    "metadata": {},
    "outputs": [
     {
@@ -198,7 +192,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "actual-blond",
+   "id": "collective-anthony",
    "metadata": {},
    "source": [
     "`Vector` objects support most mathematical operations, including\n",
@@ -208,7 +202,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "integrated-inspector",
+   "id": "authentic-brazil",
    "metadata": {},
    "outputs": [
     {
@@ -232,7 +226,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "advance-integral",
+   "id": "hairy-bahamas",
    "metadata": {},
    "outputs": [
     {
@@ -255,7 +249,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "respected-acrobat",
+   "id": "assumed-yukon",
    "metadata": {},
    "outputs": [
     {
@@ -277,7 +271,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "incorporate-longer",
+   "id": "criminal-advertiser",
    "metadata": {},
    "source": [
     "For the definition and graphical interpretation of these operations, see <http://modsimpy.com/vecops>."
@@ -285,23 +279,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "reliable-soundtrack",
+   "id": "exact-texture",
    "metadata": {},
    "source": [
-    "We can think of a `Vector` as a point defined by its coordinates `x` and `y`.\n",
-    "Equivalently, we can think of it as a point defined by a magnitude and angle.\n",
+    "We can specify a `Vector` with coordinates `x` and `y`, as in the previous examples.\n",
+    "Equivalently, we can specify a `Vector` with a magnitude and angle.\n",
     "\n",
     "**Magnitude** is the length of the vector: if the `Vector` represents a position, magnitude is the distance from the origin; if it represents a velocity, magnitude is speed, that is, how fast the object is moving, regardless of direction.\n",
     "\n",
-    "The **angle** of a `Vector` is its direction, expressed as the angle in radians from the positive $x$ axis. In the Cartesian plane, the angle 0 rad is due east, and the angle $\\pi$ rad is due west.\n",
+    "The **angle** of a `Vector` is its direction, expressed as an angle in radians from the positive $x$ axis. In the Cartesian plane, the angle 0 rad is due east, and the angle $\\pi$ rad is due west.\n",
     "\n",
-    "ModSim provides functions to compute the magnitude and angle of a `Vector`."
+    "ModSim provides functions to compute the magnitude and angle of a `Vector`.  For example, here are the magnitude and angle of `A`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "affected-programmer",
+   "id": "altered-march",
    "metadata": {},
    "outputs": [
     {
@@ -318,7 +312,9 @@
    "source": [
     "from modsim import vector_mag, vector_angle\n",
     "\n",
-    "vector_mag(A), vector_angle(A)"
+    "mag = vector_mag(A)\n",
+    "theta = vector_angle(A)\n",
+    "mag, theta"
    ]
   },
   {
@@ -326,22 +322,23 @@
    "id": "great-advice",
    "metadata": {},
    "source": [
-    "The result from `vector_angle` is in radians, and most Python functions expect angles in radians. \n",
+    "The magnitude is 5 because the length of `A` is the hypotenuse of a 3-4-5 triangle.\n",
+    "\n",
+    "The result from `vector_angle` is in radians, and most Python functions, like `sin` and `cos`, work with radians. \n",
     "But many people think more naturally in degrees. \n",
-    "It can be awkward, and error-prone, to use both units in the same program.\n",
-    "Fortunately, NumPy provides a function to convert degrees to radians:"
+    "Fortunately, NumPy provides a function to convert radians to degrees:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "cellular-community",
+   "id": "departmental-senator",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.7853981633974483"
+       "53.13010235415598"
       ]
      },
      "execution_count": 9,
@@ -350,31 +347,30 @@
     }
    ],
    "source": [
-    "from numpy import deg2rad\n",
+    "from numpy import rad2deg\n",
     "\n",
-    "angle = 45\n",
-    "theta = deg2rad(angle)\n",
-    "theta"
+    "angle = rad2deg(theta)\n",
+    "angle"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "congressional-panel",
+   "id": "younger-mortality",
    "metadata": {},
    "source": [
-    "And radians to degrees:"
+    "And a function to convert degrees to radians:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "informed-statistics",
+   "id": "cellular-community",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "45.0"
+       "0.9272952180016122"
       ]
      },
      "execution_count": 10,
@@ -383,9 +379,10 @@
     }
    ],
    "source": [
-    "from numpy import rad2deg\n",
+    "from numpy import deg2rad\n",
     "\n",
-    "rad2deg(theta)"
+    "theta = deg2rad(angle)\n",
+    "theta"
    ]
   },
   {
@@ -396,32 +393,12 @@
     "Following convention, I'll use `angle` for a value in degrees and `theta` for a value in radians.\n",
     "\n",
     "If you are given an angle and velocity, you can make a `Vector` using\n",
-    "`pol2cart`, which converts from polar to Cartesian coordinates. To\n",
-    "demonstrate, I'll extract the angle and magnitude of `A`:"
+    "`pol2cart`, which converts from polar to Cartesian coordinates. For example, here's a new `Vector` with the same angle and magnitude of `A`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "exempt-douglas",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mag = vector_mag(A)\n",
-    "angle = vector_angle(A)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "enhanced-toner",
-   "metadata": {},
-   "source": [
-    "And then make a new `Vector` with the same components:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
    "id": "monetary-firmware",
    "metadata": {},
    "outputs": [
@@ -433,7 +410,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -441,7 +418,7 @@
    "source": [
     "from modsim import pol2cart\n",
     "\n",
-    "x, y = pol2cart(angle, mag)\n",
+    "x, y = pol2cart(theta, mag)\n",
     "Vector(x, y)"
    ]
   },
@@ -458,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "explicit-piano",
    "metadata": {},
    "outputs": [
@@ -470,7 +447,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -484,13 +461,12 @@
    "id": "respected-oliver",
    "metadata": {},
    "source": [
-    "We can do the same thing using the `hat` function, so named because unit\n",
-    "vectors are conventionally decorated with a hat, like this: $\\hat{A}$."
+    "We can do the same thing using the `vector_hat` function, so named because unit vectors are conventionally decorated with a hat, like this: $\\hat{A}$."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "relative-republic",
    "metadata": {},
    "outputs": [
@@ -502,7 +478,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -515,7 +491,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "novel-somewhere",
+   "id": "italian-payment",
    "metadata": {},
    "source": [
     "Now let's get back to the game."
@@ -529,35 +505,48 @@
     "## Simulating baseball flight\n",
     "\n",
     "Let's simulate the flight of a baseball that is batted from home plate\n",
-    "at an angle of 45 ° and initial speed 40 m/s. Using the center of home\n",
-    "plate as the origin, the x-axis is parallel to the ground; the y-axis is vertical. The initial height is about 1 m.\n"
+    "at an angle of 45° and initial speed 40 m/s. We'll use the center of home plate as the origin, a horizontal x-axis (parallel to the ground), and vertical y-axis (perpendicular to the ground). The initial height is about 1 m."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "ancient-thunder",
+   "execution_count": 14,
+   "id": "attempted-narrow",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from numpy import pi\n",
+    "from modsim import Params\n",
+    "\n",
+    "params = Params(\n",
+    "    x = 0,          # m\n",
+    "    y = 1,          # m\n",
+    "    angle = 45,     # degree\n",
+    "    velocity = 40,  # m / s\n",
     "\n",
-    "mass = 145e-3 # kg \n",
-    "diameter = 73e-3 # m \n",
-    "area = pi * (diameter/2)**2\n",
-    "g = 9.8 # m/s**2"
+    "    mass = 145e-3,    # kg \n",
+    "    diameter = 73e-3, # m \n",
+    "    C_d = 0.33,     # dimensionless\n",
+    "\n",
+    "    rho = 1.2,      # kg/m**3\n",
+    "    g = 9.8,        # m/s**2\n",
+    ")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "still-ownership",
+   "cell_type": "markdown",
+   "id": "thousand-arlington",
+   "metadata": {},
+   "source": [
+    "I got the mass and diameter of the baseball from [Wikipedia](https://en.wikipedia.org/wiki/Baseball_(ball)) and the coefficient of drag is from *[The Physics of Baseball](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC)*:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prospective-philadelphia",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "rho = 1.2 # kg/m**3\n",
-    "C_d = 0.33\n",
-    "t_end = 10 # s"
+    "The density of air, `rho`, is based on a temperature of 20 °C at sea level (see <http://modsimpy.com/tempress>). \n",
+    "And we'll need the acceleration of gravity, `g`."
    ]
   },
   {
@@ -565,58 +554,102 @@
    "id": "detected-return",
    "metadata": {},
    "source": [
-    "The mass, diameter, and drag coefficient of the baseball are from the\n",
-    "sources in Section xxx. The acceleration of gravity, `g`, is a well-known quantity.\n",
-    "\n",
-    "The density of air, `rho`, is based on a temperature of 20 °C at sea level (see <http://modsimpy.com/tempress>). I chose the\n",
-    "value of `t_end` to run the simulation long enough for the ball to land on the ground.\n",
-    "\n",
-    "The following function makes a `System` object."
+    "The following function uses these quantities to make a `System` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "id": "bored-billy",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from modsim import State, System\n",
-    "from numpy import deg2rad\n",
+    "from modsim import State, System, pol2cart\n",
+    "from numpy import pi, deg2rad\n",
     "\n",
-    "def make_system(angle, velocity, C_d, rho):\n",
+    "def make_system(params):\n",
     "    \n",
     "    # convert angle to degrees\n",
-    "    theta = deg2rad(angle)\n",
+    "    theta = deg2rad(params.angle)\n",
     "    \n",
     "    # compute x and y components of velocity\n",
-    "    vx, vy = pol2cart(theta, velocity)\n",
+    "    vx, vy = pol2cart(theta, params.velocity)\n",
     "    \n",
     "    # make the initial state\n",
-    "    init = State(x=0, y=1, vx=vx, vy=vy)\n",
-    "        \n",
-    "    return System(init=init, \n",
-    "                  area=area,\n",
-    "                  g=g,\n",
-    "                  mass=mass,\n",
-    "                  rho=rho,\n",
-    "                  C_d=C_d,\n",
-    "                  t_end=t_end)"
+    "    init = State(x=params.x, y=params.y, vx=vx, vy=vy)\n",
+    "    \n",
+    "    # compute the frontal area\n",
+    "    area = pi * (params.diameter/2)**2\n",
+    "\n",
+    "    return System(init = init,\n",
+    "                  mass = params.mass,\n",
+    "                  area = area,\n",
+    "                  C_d = params.C_d,\n",
+    "                  rho = params.rho,\n",
+    "                  g = params.g,\n",
+    "                  t_end=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "combined-heritage",
+   "metadata": {},
+   "source": [
+    "`make_system` uses `deg2rad` to convert `angle` to radians and\n",
+    "`pol2cart` to compute the $x$ and $y$ components of the initial\n",
+    "velocity.\n",
+    "\n",
+    "`init` is a `State` object with four state variables:\n",
+    "\n",
+    "* `x` and `y` are the components of position.\n",
+    "\n",
+    "* `vx` and `vy` are the components of velocity.\n",
+    "\n",
+    "The `System` object also contains `t_end`, which is 10 seconds, long enough for the ball to land on the ground.\n",
+    "Here's the `System` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "generic-budapest",
+   "execution_count": 17,
+   "id": "confirmed-knife",
    "metadata": {},
    "outputs": [],
    "source": [
-    "x = 0          # m\n",
-    "y = 1          # m\n",
-    "angle = 45     # degree\n",
-    "velocity = 40  # m / s\n",
-    "\n",
-    "system = make_system(angle, velocity, C_d, rho)"
+    "system = make_system(params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tired-abortion",
+   "metadata": {},
+   "source": [
+    "And here's the initial `State`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "dirty-retailer",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "x      0.000000\n",
+       "y      1.000000\n",
+       "vx    28.284271\n",
+       "vy    28.284271\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.init"
    ]
   },
   {
@@ -624,10 +657,6 @@
    "id": "occasional-given",
    "metadata": {},
    "source": [
-    "`make_system` uses `np.deg2rad` to convert `angle` to radians and\n",
-    "`pol2cart` to compute the $x$ and $y$ components of the initial\n",
-    "velocity.\n",
-    "\n",
     "Next we need a function to compute drag force:"
    ]
   },
@@ -641,34 +670,41 @@
     "def drag_force(V, system):\n",
     "    rho, C_d, area = system.rho, system.C_d, system.area\n",
     "    \n",
-    "    mag = -rho * vector_mag(V)**2 * C_d * area / 2\n",
-    "    direction = vector_hat(V)\n",
-    "    f_drag = direction * mag\n",
+    "    mag = rho * vector_mag(V)**2 * C_d * area / 2\n",
+    "    direction = -vector_hat(V)\n",
+    "    f_drag = mag * direction\n",
     "    return f_drag"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "attractive-genome",
+   "id": "spatial-cooperation",
    "metadata": {},
    "source": [
     "This function takes `V` as a `Vector` and returns `f_drag` as a\n",
-    "`Vector`. It uses the drag equation to compute the magnitude of the drag\n",
-    "force, and the `hat` function to compute the direction. `-V.hat()`\n",
-    "computes a unit vector pointing in the opposite direction of `V`."
+    "`Vector`. \n",
+    "\n",
+    "* It uses `vector_mag` to compute the magnitude of `V`, \n",
+    "and the drag equation to compute the magnitude of the drag force, `mag`.\n",
+    "\n",
+    "* Then it uses `vector_hat` to compute `direction`, which is a unit vector in the opposite direction of `V`.\n",
+    "\n",
+    "* Finally, it computes the drag force vector by multiplying `mag` and `direction`.\n",
+    "\n",
+    "We can test it like this:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "functional-thong",
+   "id": "equipped-dragon",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "x   -0.117197\n",
-       "y   -0.117197\n",
+       "x   -0.937574\n",
+       "y   -0.937574\n",
        "dtype: float64"
       ]
      },
@@ -680,7 +716,8 @@
    "source": [
     "from modsim import ModSimVector\n",
     "\n",
-    "V_test = Vector(10, 10)\n",
+    "vx, vy = system.init.vx, system.init.vy\n",
+    "V_test = Vector(vx, vy)\n",
     "drag_force(V_test, system)"
    ]
   },
@@ -689,6 +726,7 @@
    "id": "absent-vector",
    "metadata": {},
    "source": [
+    "The result is a `Vector` that represents the drag force on the baseball, in Newtons, under the initial conditions.\n",
     "\n",
     "Now we're ready for a slope function:"
    ]
@@ -715,38 +753,40 @@
   },
   {
    "cell_type": "markdown",
-   "id": "crude-parcel",
+   "id": "pointed-graph",
    "metadata": {},
    "source": [
-    "As usual, the parameters of the slope function are a `State` object,\n",
-    "time, and a `System` object. In this example, we don't use `t`, but we\n",
-    "can't leave it out because when `run_solve_ivp` calls the slope\n",
-    "function, it always provides the same arguments, whether they are needed\n",
-    "or not.\n",
+    "As usual, the parameters of the slope function are a time, a `State` object, and a `System` object. \n",
+    "In this example, we don't use `t`, but we can't leave it out because when `run_solve_ivp` calls the slope function, it always provides the same arguments, whether they are needed or not.\n",
     "\n",
-    "The `State` object contains two state variables, `R` and `V`, that\n",
-    "represent position and velocity as `Vector` objects.\n",
+    "`slope_func` unpacks the `State` object into variables `x`, `y`, `vx`, and `vy`.\n",
+    "Then it packs `vx` and `vy` into a `Vector`, which it uses to compute drag force and acceleration due to drag, `a_drag`.\n",
     "\n",
-    "The return values from the slope function are the derivatives of these\n",
-    "quantities. The derivative of position is velocity, so the first return\n",
-    "value is `V`, which we extracted from the `State` object. The derivative\n",
-    "of velocity is acceleration, and that's what we have to compute.\n",
+    "To represent acceleration due to gravity, it makes a `Vector` with magnitude `g` in the negative $y$ direction.\n",
     "\n",
-    "The total acceleration of the baseball is the sum of accelerations due\n",
-    "to gravity and drag. These quantities have both magnitude and direction,\n",
-    "so they are represented by `Vector` objects.\n",
+    "The total acceleration of the baseball, `A`, is the sum of accelerations due to gravity and drag."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "proved-mother",
+   "metadata": {},
+   "source": [
+    "The return value is a sequence that contains:\n",
     "\n",
-    "We already saw how `a_drag` is computed. `a_grav` is a `Vector` with\n",
-    "magnitude `g` pointed in the negative `y` direction.\n",
+    "* The components of velocity, `V.x` and `V.y`.\n",
     "\n",
-    "Using vectors to represent forces and accelerations makes the code\n",
-    "concise, readable, and less error-prone. In particular, when we add\n",
-    "`a_grav` and `a_drag`, the directions are likely to be correct, because\n",
-    "they are encoded in the `Vector` objects. And the units are certain to\n",
-    "be correct, because otherwise Pint would report an error.\n",
+    "* The components of acceleration, `A.x` and `A.y`.\n",
     "\n",
-    "As always, we can test the slope function by running it with the initial\n",
-    "conditions:"
+    "Together, these components represent the slope of the state variables, because `V` is the derivative of position and `A` is the derivative of velocity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "crude-parcel",
+   "metadata": {},
+   "source": [
+    "As always, we can test the slope function by running it with the initial conditions:"
    ]
   },
   {
@@ -775,8 +815,11 @@
    "id": "regulated-railway",
    "metadata": {},
    "source": [
-    "We can use an event function to stop the simulation when the ball hits\n",
-    "the ground."
+    "Using vectors to represent forces and accelerations makes the code\n",
+    "concise, readable, and less error-prone. In particular, when we add\n",
+    "`a_grav` and `a_drag`, the directions are likely to be correct, because they are encoded in the `Vector` objects.\n",
+    "\n",
+    "We're almost ready to run the simulation.  The last thing we need is an event function that stops when the ball hits the ground."
    ]
   },
   {
@@ -791,10 +834,20 @@
     "    return y"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "pressing-oracle",
+   "metadata": {},
+   "source": [
+    "The event function takes the same parameters as the slope function, and returns the $y$ coordinate of position. When the $y$ coordinate passes through 0, the simulation stops.\n",
+    "\n",
+    "As we did with `slope_func`, we can test `event_func` with the initial conditions."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "victorian-sharing",
+   "id": "necessary-registrar",
    "metadata": {},
    "outputs": [
     {
@@ -817,10 +870,6 @@
    "id": "unlikely-dressing",
    "metadata": {},
    "source": [
-    "The event function takes the same parameters as the slope function, and\n",
-    "returns the y coordinate of `R`. When the y coordinate passes through 0,\n",
-    "the simulation stops.\n",
-    "\n",
     "Now we're ready to run the simulation:"
    ]
   },
@@ -851,16 +900,18 @@
   },
   {
    "cell_type": "markdown",
-   "id": "thick-ozone",
+   "id": "solid-tribune",
    "metadata": {},
    "source": [
-    "`results` is a `TimeFrame` with one column for each of the state variables, `x`, `y`, `vx`, and `vy`."
+    "`details` contains information about the simulation, including a message that indicates that a \"termination event\" occurred; that is, the simulated ball reached the ground.\n",
+    "\n",
+    "`results` is a `TimeFrame` with one column for each of the state variables:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "complex-reviewer",
+   "id": "alternative-history",
    "metadata": {},
    "outputs": [
     {
@@ -983,7 +1034,7 @@
    "id": "convenient-heading",
    "metadata": {},
    "source": [
-    "And the final `x` coordinate like this:"
+    "And the final state like this:"
    ]
   },
   {
@@ -995,7 +1046,11 @@
     {
      "data": {
       "text/plain": [
-       "99.31829628352206"
+       "x     9.931830e+01\n",
+       "y    -7.105427e-15\n",
+       "vx    1.416589e+01\n",
+       "vy   -2.205876e+01\n",
+       "Name: 5.004887034868351, dtype: float64"
       ]
      },
      "execution_count": 28,
@@ -1004,90 +1059,28 @@
     }
    ],
    "source": [
-    "x, y, vx, vy = results.iloc[-1]\n",
-    "x"
+    "final_state = results.iloc[-1]\n",
+    "final_state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "alternate-present",
+   "metadata": {},
+   "source": [
+    "The final value of `y` is close to 0, as it should be.  The final value of `x` tells us how far the ball flew, in meters."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "sensitive-century",
+   "id": "conservative-collins",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "      <th>vx</th>\n",
-       "      <th>vy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4.604496</th>\n",
-       "      <td>93.473971</td>\n",
-       "      <td>8.296642e+00</td>\n",
-       "      <td>15.013486</td>\n",
-       "      <td>-19.334826</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.704594</th>\n",
-       "      <td>94.966756</td>\n",
-       "      <td>6.325368e+00</td>\n",
-       "      <td>14.802553</td>\n",
-       "      <td>-20.038175</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.804692</th>\n",
-       "      <td>96.438515</td>\n",
-       "      <td>4.284486e+00</td>\n",
-       "      <td>14.590855</td>\n",
-       "      <td>-20.726780</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.904789</th>\n",
-       "      <td>97.889087</td>\n",
-       "      <td>2.175515e+00</td>\n",
-       "      <td>14.378566</td>\n",
-       "      <td>-21.400392</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5.004887</th>\n",
-       "      <td>99.318296</td>\n",
-       "      <td>-7.105427e-15</td>\n",
-       "      <td>14.165894</td>\n",
-       "      <td>-22.058763</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                  x             y         vx         vy\n",
-       "4.604496  93.473971  8.296642e+00  15.013486 -19.334826\n",
-       "4.704594  94.966756  6.325368e+00  14.802553 -20.038175\n",
-       "4.804692  96.438515  4.284486e+00  14.590855 -20.726780\n",
-       "4.904789  97.889087  2.175515e+00  14.378566 -21.400392\n",
-       "5.004887  99.318296 -7.105427e-15  14.165894 -22.058763"
+       "99.31829628352206"
       ]
      },
      "execution_count": 29,
@@ -1096,7 +1089,68 @@
     }
    ],
    "source": [
-    "results.tail()"
+    "final_state.x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "resident-fountain",
+   "metadata": {},
+   "source": [
+    "We can also get the final velocity, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "superior-capture",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "x    14.165894\n",
+       "y   -22.058763\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_V = Vector(final_state.vx, final_state.vy)\n",
+    "final_V"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "amended-antenna",
+   "metadata": {},
+   "source": [
+    "The speed of the ball on impact is about 26 m/s, which is substantially slower than the initial velocity, 40 m/s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "fresh-damages",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26.215674453237636"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vector_mag(final_V)"
    ]
   },
   {
@@ -1106,12 +1160,12 @@
    "source": [
     "## Trajectories\n",
     "\n",
-    "We can plot the $x$ and $y$ components of position like this:"
+    "To visualize the results, we can plot the $x$ and $y$ components of position like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 32,
    "id": "spare-burst",
    "metadata": {},
    "outputs": [
@@ -1143,17 +1197,15 @@
    "id": "horizontal-bench",
    "metadata": {},
    "source": [
-    "As expected, the $x$\n",
-    "component increases as the ball moves away from home plate. The $y$\n",
-    "position climbs initially and then descends, falling to 0 m near 5.0 s.\n",
+    "As expected, the $x$ component increases as the ball moves away from home plate. The $y$ position climbs initially and then descends, falling to 0 m near 5.0 s.\n",
     "\n",
-    "Another way to view the same data is to plot the $x$ component on the\n",
+    "Another way to view the results is to plot the $x$ component on the\n",
     "x-axis and the $y$ component on the y-axis, so the plotted line follows the trajectory of the ball through the plane:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 33,
    "id": "dated-browse",
    "metadata": {},
    "outputs": [
@@ -1192,15 +1244,12 @@
     "A trajectory plot can be easier to interpret than a time series plot,\n",
     "because it shows what the motion of the projectile would look like (at\n",
     "least from one point of view). Both plots can be useful, but don't get\n",
-    "them mixed up! If you are looking at a time series plot and interpreting it as a trajectory, you will be very confused.\n",
-    "\n",
-    "\n",
-    "[^1]: Adair, *The Physics of Baseball*, Third Edition, Perennial, 2002."
+    "them mixed up! If you are looking at a time series plot and interpreting it as a trajectory, you will be very confused."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "surprised-bandwidth",
+   "id": "healthy-pulse",
    "metadata": {},
    "source": [
     "## Animation\n",
@@ -1212,7 +1261,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hollow-maintenance",
+   "id": "honest-simple",
    "metadata": {},
    "source": [
     "The draw function should take as parameters a `State` object and the time.  It should draw a single frame of the animation.\n",
@@ -1222,8 +1271,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "worth-leeds",
+   "execution_count": 34,
+   "id": "large-gates",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1241,8 +1290,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "id": "general-peace",
+   "execution_count": 35,
+   "id": "looking-community",
    "metadata": {},
    "outputs": [
     {
@@ -1266,7 +1315,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "tested-friendly",
+   "id": "final-terminal",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -1274,7 +1323,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "strategic-reduction",
+   "id": "romance-serum",
    "metadata": {},
    "source": [
     "**Exercise:** Run the simulation with and without air resistance.  How wrong would we be if we ignored drag?"
@@ -1282,20 +1331,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "caroline-theta",
+   "execution_count": 37,
+   "id": "excessive-challenge",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Hint\n",
     "\n",
-    "system2 = make_system(angle, velocity, 0, rho)"
+    "system2 = make_system(params.set(C_d=0))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "id": "perfect-property",
+   "execution_count": 38,
+   "id": "recognized-format",
    "metadata": {},
    "outputs": [
     {
@@ -1304,7 +1353,7 @@
        "'A termination event occurred.'"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1319,8 +1368,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "demographic-destination",
+   "execution_count": 39,
+   "id": "sitting-combine",
    "metadata": {},
    "outputs": [
     {
@@ -1345,8 +1394,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "understood-colombia",
+   "execution_count": 44,
+   "id": "developing-capitol",
    "metadata": {},
    "outputs": [
     {
@@ -1355,7 +1404,7 @@
        "99.31829628352206"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1369,8 +1418,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "mediterranean-equivalent",
+   "execution_count": 45,
+   "id": "elder-variance",
    "metadata": {},
    "outputs": [
     {
@@ -1379,7 +1428,7 @@
        "164.25925502413202"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1393,7 +1442,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "amateur-cowboy",
+   "id": "bulgarian-nowhere",
    "metadata": {},
    "source": [
     "**Exercise:** The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / meter$^3$.  How much farther would a ball hit with the same velocity and launch angle travel?"
@@ -1401,20 +1450,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "id": "surprised-polls",
+   "execution_count": 46,
+   "id": "conventional-chest",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Hint\n",
     "\n",
-    "system3 = make_system(angle, velocity, C_d, 1.0)"
+    "system3 = make_system(params.set(rho=1.0))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "welsh-match",
+   "execution_count": 47,
+   "id": "south-profession",
    "metadata": {},
    "outputs": [
     {
@@ -1423,7 +1472,7 @@
        "105.78838005859807"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1440,8 +1489,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "id": "suspended-advantage",
+   "execution_count": 48,
+   "id": "removable-running",
    "metadata": {},
    "outputs": [
     {
@@ -1450,7 +1499,7 @@
        "6.470083775076006"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1463,7 +1512,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adjacent-mediterranean",
+   "id": "enormous-auditor",
    "metadata": {},
    "source": [
     "**Exercise:** The model so far is based on the assumption that coefficient of drag does not depend on velocity, but in reality it does.  The following figure, from Adair, [*The Physics of Baseball*](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC), shows coefficient of drag as a function of velocity.\n",
@@ -1476,8 +1525,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "id": "crazy-sense",
+   "execution_count": 49,
+   "id": "artistic-baghdad",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1491,8 +1540,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "id": "smaller-armenia",
+   "execution_count": 50,
+   "id": "general-ethiopia",
    "metadata": {},
    "outputs": [
     {
@@ -1631,7 +1680,7 @@
        "60.086646                           134.410000           0.28207"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1650,8 +1699,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "id": "artistic-helping",
+   "execution_count": 51,
+   "id": "pregnant-small",
    "metadata": {},
    "outputs": [
     {
@@ -1679,7 +1728,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "reliable-bottom",
+   "id": "promotional-background",
    "metadata": {},
    "source": [
     "Modify the model to include the dependence of `C_d` on velocity, and see how much it affects the results."
@@ -1687,8 +1736,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "id": "empty-palace",
+   "execution_count": 52,
+   "id": "sporting-gazette",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1713,20 +1762,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "id": "satellite-delay",
+   "execution_count": 53,
+   "id": "scheduled-cutting",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Solution\n",
     "\n",
-    "system4 = make_system(angle, velocity, C_d, rho)"
+    "system4 = make_system(params)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "id": "tired-sleeve",
+   "execution_count": 54,
+   "id": "streaming-lebanon",
    "metadata": {},
    "outputs": [
     {
@@ -1737,7 +1786,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1752,8 +1801,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "id": "ambient-castle",
+   "execution_count": 55,
+   "id": "animated-prevention",
    "metadata": {},
    "outputs": [
     {
@@ -1762,7 +1811,7 @@
        "(28.284271247461902, 28.2842712474619, -6.534891184395127, -16.334891184395126)"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1775,8 +1824,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "id": "plain-retail",
+   "execution_count": 56,
+   "id": "practical-reservation",
    "metadata": {},
    "outputs": [
     {
@@ -1785,7 +1834,7 @@
        "'A termination event occurred.'"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1800,8 +1849,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "id": "black-amino",
+   "execution_count": 57,
+   "id": "supposed-school",
    "metadata": {},
    "outputs": [
     {
@@ -1880,7 +1929,7 @@
        "4.919407  90.206647  1.065814e-14  11.640440 -20.837214"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1893,8 +1942,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "id": "uniform-determination",
+   "execution_count": 58,
+   "id": "recognized-blend",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1905,8 +1954,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "id": "earned-cliff",
+   "execution_count": 59,
+   "id": "sensitive-butterfly",
    "metadata": {},
    "outputs": [
     {
@@ -1915,7 +1964,7 @@
        "-9.111649717288998"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1928,7 +1977,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "spiritual-shareware",
+   "id": "casual-satisfaction",
    "metadata": {},
    "source": [
     "### Under the hood\n",
diff --git a/jupyter/chap23.ipynb b/jupyter/chap23.ipynb
new file mode 100644
index 00000000..e48d1c72
--- /dev/null
+++ b/jupyter/chap23.ipynb
@@ -0,0 +1,877 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "foreign-pepper",
+   "metadata": {},
+   "source": [
+    "# Chapter 23"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "competitive-backing",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "charming-location",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "seeing-contractor",
+   "metadata": {},
+   "source": [
+    "### Code from the previous chapter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "quantitative-montana",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import Params\n",
+    "\n",
+    "params = Params(\n",
+    "    x = 0,          # m\n",
+    "    y = 1,          # m\n",
+    "    angle = 45,     # degree\n",
+    "    velocity = 40,  # m / s\n",
+    "\n",
+    "    mass = 145e-3,    # kg \n",
+    "    diameter = 73e-3, # m \n",
+    "    C_d = 0.33,     # dimensionless\n",
+    "\n",
+    "    rho = 1.2,      # kg/m**3\n",
+    "    g = 9.8,        # m/s**2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "preliminary-industry",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import State, System, pol2cart\n",
+    "from numpy import pi, deg2rad\n",
+    "\n",
+    "def make_system(params):\n",
+    "    \n",
+    "    # convert angle to degrees\n",
+    "    theta = deg2rad(params.angle)\n",
+    "    \n",
+    "    # compute x and y components of velocity\n",
+    "    vx, vy = pol2cart(theta, params.velocity)\n",
+    "    \n",
+    "    # make the initial state\n",
+    "    init = State(x=params.x, y=params.y, vx=vx, vy=vy)\n",
+    "    \n",
+    "    # compute the frontal area\n",
+    "    area = pi * (params.diameter/2)**2\n",
+    "\n",
+    "    return System(init = init,\n",
+    "                  mass = params.mass,\n",
+    "                  area = area,\n",
+    "                  C_d = params.C_d,\n",
+    "                  rho = params.rho,\n",
+    "                  g = params.g,\n",
+    "                  t_end=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "absolute-percentage",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import vector_mag, vector_hat\n",
+    "\n",
+    "def drag_force(V, system):\n",
+    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
+    "    \n",
+    "    mag = rho * vector_mag(V)**2 * C_d * area / 2\n",
+    "    direction = -vector_hat(V)\n",
+    "    f_drag = mag * direction\n",
+    "    return f_drag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "common-batman",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import Vector\n",
+    "\n",
+    "def slope_func(t, state, system):\n",
+    "    x, y, vx, vy = state\n",
+    "    mass, g = system.mass, system.g\n",
+    "    \n",
+    "    V = Vector(vx, vy)\n",
+    "    a_drag = drag_force(V, system) / mass\n",
+    "    a_grav = Vector(0, -g)\n",
+    "    \n",
+    "    A = a_grav + a_drag\n",
+    "    \n",
+    "    return V.x, V.y, A.x, A.y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "italic-amino",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(t, state, system):\n",
+    "    x, y, vx, vy = state\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "angry-pledge",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we developed a model of the flight of a\n",
+    "baseball, including gravity and a simple version of drag, but neglecting spin, Magnus force, and the dependence of the coefficient of drag on velocity.\n",
+    "\n",
+    "In this chapter we apply that model to an optimization problem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "decent-birth",
+   "metadata": {},
+   "source": [
+    "## The Manny Ramirez problem\n",
+    "\n",
+    "Manny Ramirez is a former member of the Boston Red Sox (an American\n",
+    "baseball team) who was notorious for his relaxed attitude and taste for practical jokes. Our objective in this chapter is to solve the following Manny-inspired problem:\n",
+    "\n",
+    "> What is the minimum effort required to hit a home run in Fenway Park?\n",
+    "\n",
+    "Fenway Park is a baseball stadium in Boston, Massachusetts. One of its\n",
+    "most famous features is the \"Green Monster\", which is a wall in left\n",
+    "field that is unusually close to home plate, only 310 feet away. To\n",
+    "compensate for the short distance, the wall is unusually high, at 37\n",
+    "feet (see <http://modsimpy.com/wally>)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exposed-diving",
+   "metadata": {},
+   "source": [
+    "We want to find the minimum velocity at which a ball can leave home\n",
+    "plate and still go over the Green Monster. We'll proceed in the\n",
+    "following steps:\n",
+    "\n",
+    "1.  For a given velocity, we'll find the optimal **launch angle**, that is, the angle the ball should leave home plate to maximize its height when it reaches the wall.\n",
+    "\n",
+    "2.  Then we'll find the minimal velocity that clears the wall, given\n",
+    "    that it has the optimal launch angle."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "received-diamond",
+   "metadata": {},
+   "source": [
+    "## Finding the range\n",
+    "\n",
+    "Suppose we want to find the launch angle that maximizes **range**, that is, the distance the ball travels in the air before landing. We'll use a function in the ModSim library, `maximize`, which takes a function and finds its maximum.\n",
+    "\n",
+    "The function we pass to `maximize` should take launch angle in degrees, simulate the flight of a ball launched at that angle, and return the distance the ball travels along the $x$ axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "forty-knitting",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import run_solve_ivp\n",
+    "\n",
+    "def range_func(angle, params):\n",
+    "    params = params.set(angle=angle)\n",
+    "    system = make_system(params)\n",
+    "    results, details = run_solve_ivp(system, slope_func,\n",
+    "                                     events=event_func)\n",
+    "    x_dist = results.iloc[-1].x\n",
+    "    print(angle, x_dist)\n",
+    "    return x_dist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exact-cigarette",
+   "metadata": {},
+   "source": [
+    "`range_func` makes a new `System` object with the given value of\n",
+    "`angle`. Then it calls `run_solve_ivp` and\n",
+    "returns the final value of `x` from the results.\n",
+    "\n",
+    "We can call `range_func` directly like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "senior-counter",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "45 99.31829628352206\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "99.31829628352206"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "range_func(45, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "paperback-passing",
+   "metadata": {},
+   "source": [
+    "And we can sweep a sequence of angles like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "biological-evans",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20.0 78.09741067882733\n",
+      "23.0 84.11542610650983\n",
+      "26.0 89.1319241236397\n",
+      "29.0 93.17466724082831\n",
+      "32.0 96.27134737391347\n",
+      "35.0 98.44725560274013\n",
+      "38.0 99.72474586365286\n",
+      "41.0 100.12347299697637\n",
+      "44.0 99.66100302635327\n",
+      "47.0 98.35354763684836\n",
+      "50.0 96.21673516565127\n",
+      "53.0 93.26623796736264\n",
+      "56.0 89.51751617672979\n",
+      "59.0 84.98724481958084\n",
+      "62.0 79.69434524339994\n",
+      "65.0 73.66129770133254\n",
+      "68.0 66.91470886279747\n",
+      "71.0 59.48269492939761\n",
+      "74.0 51.40287109940439\n",
+      "77.0 42.72047925533449\n",
+      "80.0 33.48437980813933\n"
+     ]
+    }
+   ],
+   "source": [
+    "from modsim import linspace, SweepSeries\n",
+    "\n",
+    "angles = linspace(20, 80, 21)\n",
+    "sweep = SweepSeries()\n",
+    "\n",
+    "for angle in angles:\n",
+    "    x_dist = range_func(angle, params)\n",
+    "    sweep[angle] = x_dist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "premium-contribution",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "experienced-providence",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "sweep.plot()\n",
+    "\n",
+    "decorate(xlabel='Launch angle (degree)',\n",
+    "         ylabel='Range (meter)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "conscious-blade",
+   "metadata": {},
+   "source": [
+    "It looks like the optimal angle is near 40°.\n",
+    "\n",
+    "We can find the optimal angle more precisely and more efficiently using `maximize_scalar`, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "vocal-nerve",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 17.468795355645703\n",
+      "34.37694101250946 98.06996498016372\n",
+      "55.62305898749054 90.03182421721925\n",
+      "21.246117974981075 80.72039493627989\n",
+      "41.173855871043976 100.1200188931698\n",
+      "40.980120907837964 100.12368377099199\n",
+      "40.88213731907024 100.12417004431842\n",
+      "40.879254201241096 100.12417043309691\n",
+      "40.87958814091176 100.12417042882092\n",
+      "40.878920261570435 100.12417042669497\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Solution found.'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import maximize_scalar\n",
+    "\n",
+    "res = maximize_scalar(range_func, [0, 90], params)\n",
+    "res.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "violent-explanation",
+   "metadata": {},
+   "source": [
+    "The first parameter is the function we want to maximize. The second is\n",
+    "the range of values we want to search; in this case, it's the range of\n",
+    "angles from 0° to 90°. \n",
+    "\n",
+    "The return value from `maximize` is an object that contains the\n",
+    "results, including `x`, which is the angle that yielded the highest\n",
+    "range, and `fun`, which is the value of `range_func` when it's evaluated at `x`, that is, range when the baseball is launched at the optimal angle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "figured-uniform",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(40.879254201241096, 100.12417043309691)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res.x, res.fun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "shaped-southeast",
+   "metadata": {},
+   "source": [
+    "For these parameters, the optimal angle is about 41°, which yields a\n",
+    "range of 100 m.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "temporal-extension",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "\n",
+    "\n",
+    "If you enjoy this exercise, you might be interested in this paper: \"How to hit home runs: Optimum baseball bat swing parameters for maximum range trajectories\", by Sawicki, Hubbard, and Stronge, at\n",
+    "<http://modsimpy.com/runs>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "processed-constitution",
+   "metadata": {},
+   "source": [
+    "## Exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "handmade-rhythm",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Let's finish off the Manny Ramirez problem:\n",
+    "\n",
+    "> What is the minimum effort required to hit a home run in Fenway Park?\n",
+    "\n",
+    "Although the problem asks for a minimum, it is not an optimization problem.  Rather, we want to solve for the initial velocity that just barely gets the ball to the top of the wall, given that it is launched at the optimal angle.\n",
+    "\n",
+    "And we have to be careful about what we mean by \"optimal\".  For this problem, we don't want the longest range, we want the maximum height at the point where it reaches the wall.\n",
+    "\n",
+    "If you are ready to solve the problem on your own, go ahead.  Otherwise I will walk you through the process with an outline and some starter code.\n",
+    "\n",
+    "As a first step, write an `event_func` that stops the simulation when the ball reaches the wall at 310 feet (94.5 m).\n",
+    "Test your function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "studied-association",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def event_func(t, state, system):\n",
+    "    x, y, vx, vy = state\n",
+    "    return x - 94.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "developmental-alabama",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-94.5"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "system = make_system(params)\n",
+    "event_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "novel-appointment",
+   "metadata": {},
+   "source": [
+    "Next, write a function called `height_func` that takes a launch angle, simulates the flight of a baseball, and returns the height of the baseball when it reaches the wall.\n",
+    "Test your function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ignored-decrease",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def height_func(angle, params):\n",
+    "    params = params.set(angle=angle)\n",
+    "    system = make_system(params)\n",
+    "\n",
+    "    results, details = run_solve_ivp(system, slope_func, \n",
+    "                                     events=event_func)\n",
+    "    height = results.iloc[-1].y\n",
+    "    return height"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "western-communist",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.647999317446708"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "height_func(40, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "located-lawsuit",
+   "metadata": {},
+   "source": [
+    "Now use `maximize_scalar` to find the optimal angle.  Is it higher or lower than the angle that maximizes range?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "duplicate-madison",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Solution found.'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "bounds = [0, 90]\n",
+    "res = maximize_scalar(height_func, bounds, params)\n",
+    "res.message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "legislative-prospect",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(43.10025059565446, 7.202365708497986)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "res.x, res.fun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "interpreted-telephone",
+   "metadata": {},
+   "source": [
+    "Even though we are finding the \"minimum\" velocity, we are not really solving a minimization problem.  Rather, we want to find the velocity that makes the height at the wall exactly 37 feet (11.3 m), given that it's launched at the optimal angle.  And that's a job for `root_scalar`.\n",
+    "\n",
+    "Write an error function that takes a velocity and a `System` object as parameters.  It should use `maximize_scalar` to find the highest possible height of the ball at the wall, for the given velocity.  Then it should return the difference between that optimal height and 11.3 meters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "egyptian-shadow",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def error_func(velocity, params):\n",
+    "    print(velocity)\n",
+    "    params = params.set(velocity=velocity)\n",
+    "    bounds = [0, 90]\n",
+    "    res = maximize_scalar(height_func, bounds, params)\n",
+    "    return res.fun - 11.3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "documentary-guidance",
+   "metadata": {},
+   "source": [
+    "Test your error function before you call `root_scalar`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "sustainable-supply",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "40\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "-4.097634291502015"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "error_func(40, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reserved-shelter",
+   "metadata": {},
+   "source": [
+    "Then use `root_scalar` to find the answer to the problem, the minimum velocity that gets the ball out of the park."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "certified-webster",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30.0\n",
+      "50.0\n",
+      "42.96216812977292\n",
+      "41.04044041129342\n",
+      "41.04880372505707\n",
+      "41.048542232137436\n",
+      "41.04854219528224\n",
+      "41.048542195283254\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "from scipy.optimize import root_scalar\n",
+    "\n",
+    "bracket = [30, 50]\n",
+    "res = root_scalar(error_func, params, bracket=bracket)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "silver-bernard",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "      converged: True\n",
+       "           flag: 'converged'\n",
+       " function_calls: 8\n",
+       "     iterations: 7\n",
+       "           root: 41.04854219528224"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "absent-encoding",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "41.04854219528224"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "min_velocity = res.root\n",
+    "min_velocity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "approved-fiction",
+   "metadata": {},
+   "source": [
+    "And just to check, run `error_func` with the value you found."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "thick-jungle",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "41.04854219528224\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "-3.907985046680551e-14"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "error_func(min_velocity, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "simple-steps",
+   "metadata": {},
+   "source": [
+    "## Under the Hood\n",
+    "\n",
+    "`maximize_scalar` uses a golden section search, which you can read about at <http://modsimpy.com/minimize>)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/chap24.ipynb b/jupyter/chap24.ipynb
new file mode 100644
index 00000000..ed8daff0
--- /dev/null
+++ b/jupyter/chap24.ipynb
@@ -0,0 +1,1396 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "august-parks",
+   "metadata": {},
+   "source": [
+    "# Chapter 24"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "suspended-puppy",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cutting-ultimate",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hispanic-pressure",
+   "metadata": {},
+   "source": [
+    "In this chapter we model systems that involve rotating objects. In\n",
+    "general, rotation is complicated: in three dimensions, objects can\n",
+    "rotate around three axes; objects are often easier to spin around some\n",
+    "axes than others; and they may be stable when spinning around some axes but not others.\n",
+    "\n",
+    "If the configuration of an object changes over time, it might become\n",
+    "easier or harder to spin, which explains the surprising dynamics of\n",
+    "gymnasts, divers, ice skaters, etc.\n",
+    "\n",
+    "And when you apply a twisting force to a rotating object, the effect is often contrary to intuition. For an example, see this video on\n",
+    "gyroscopic precession <http://modsimpy.com/precess>.\n",
+    "\n",
+    "In this chapter, we will not take on the physics of rotation in all its glory. Rather, we will focus on simple scenarios where all rotation and all twisting forces are around a single axis. In that case, we can treat some vector quantities as if they were scalars (in the same way that we sometimes treat velocity as a scalar with an implicit direction).\n",
+    "\n",
+    "This approach makes it possible to simulate and analyze many interesting systems, but you will also encounter systems that would be better approached with the more general toolkit."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "rural-secret",
+   "metadata": {},
+   "source": [
+    "The fundamental ideas in this chapter and the next are **angular\n",
+    "velocity**, **angular acceleration**, **torque**, and **moment of\n",
+    "inertia**. If you are not already familiar with these concepts, I will\n",
+    "define them as we go along, and I will point to additional reading.\n",
+    "\n",
+    "At the end of the next chapter, you will use these tools to simulate the behavior of a yo-yo (see <http://modsimpy.com/yoyo>). But we'll work our way up to it gradually, starting with toilet paper."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "protecting-photography",
+   "metadata": {},
+   "source": [
+    "## The physics of toilet paper\n",
+    "\n",
+    "As a simple example of a system with rotation, we'll simulate the\n",
+    "manufacture of a roll of toilet paper. Starting with a cardboard tube at the center, we will roll up 47 m of paper, the typical length of a roll of toilet paper in the U.S. (see <http://modsimpy.com/paper>).\n",
+    "\n",
+    "![Diagram of a roll of toilet paper, showing change in paper length as a result of a small rotation, $d\\theta$.](figs/paper_roll.pdf){height=\"2.5in\"}\n",
+    "\n",
+    "This figure shows a diagram of the system: $r$ represents\n",
+    "the radius of the roll at a point in time. Initially, $r$ is the radius of the cardboard core, $R_{min}$. When the roll is complete, $r$ is $R_{max}$.\n",
+    "\n",
+    "I'll use $\\theta$ to represent the total rotation of the roll in\n",
+    "radians. In the diagram, $d\\theta$ represents a small increase in\n",
+    "$\\theta$, which corresponds to a distance along the circumference of the roll of $r~d\\theta$.\n",
+    "\n",
+    "Finally, I'll use $y$ to represent the total length of paper that's been rolled. Initially, $\\theta=0$ and $y=0$. For each small increase in $\\theta$, there is a corresponding increase in $y$: \n",
+    "\n",
+    "$$dy = r~d\\theta$$\n",
+    "\n",
+    "If we divide both sides by a small increase in time, $dt$, we get a\n",
+    "differential equation for $y$ as a function of time.\n",
+    "\n",
+    "$$\\frac{dy}{dt} = r \\frac{d\\theta}{dt}$$ \n",
+    "\n",
+    "As we roll up the paper, $r$ increases, too. Assuming that $r$ increases by a fixed amount per revolution, we can write \n",
+    "\n",
+    "$$dr = k~d\\theta$$ \n",
+    "\n",
+    "Where $k$ is an unknown constant we'll have to figure out. Again, we can divide both sides by $dt$ to get a differential equation in time:\n",
+    "\n",
+    "$$\\frac{dr}{dt} = k \\frac{d\\theta}{dt}$$ \n",
+    "\n",
+    "Finally, let's assume that $\\theta$ increases at a constant rate of 10 rad/s (about 95 revolutions per minute): \n",
+    "\n",
+    "$$\\frac{d\\theta}{dt} = 10$$ \n",
+    "\n",
+    "This rate of change is called an **angular velocity**. Now we have a system of three differential equations we can use to simulate the system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prostate-smell",
+   "metadata": {},
+   "source": [
+    "## Implementation\n",
+    "\n",
+    "At this point we have a pretty standard process for writing simulations like this.\n",
+    "\n",
+    "First we'll create a `Params` object with the parameters of the system:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "honey-translator",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import Params\n",
+    "\n",
+    "params = Params(Rmin = 0.02,  # m \n",
+    "                Rmax = 0.055, # m \n",
+    "                L = 47,       # m \n",
+    "                omega = 10,   # radian / s \n",
+    "               )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "caring-unemployment",
+   "metadata": {},
+   "source": [
+    "`Rmin` and `Rmax` are the initial and final values for the radius, `r`.\n",
+    "`L` is the total length of the paper. `t_end` is the length of the\n",
+    "simulation in time, and `dt` is the time step for the ODE solver.\n",
+    "\n",
+    "We use the `Params` object to make a `System` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "fresh-domestic",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import State, System\n",
+    "\n",
+    "def make_system(params):\n",
+    "    init = State(theta = 0,   # radian\n",
+    "                 y = 0,       # m,\n",
+    "                 r = params.Rmin\n",
+    "                )\n",
+    "    \n",
+    "    k = estimate_k(params)\n",
+    "\n",
+    "    return System(init=init, \n",
+    "                  k=k,\n",
+    "                  omega = params.omega,\n",
+    "                  Rmin = params.Rmin,\n",
+    "                  Rmax = params.Rmax,\n",
+    "                  L = params.L,\n",
+    "                  t_end = 130,\n",
+    "                  num = 2000,\n",
+    "                 )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "strong-harrison",
+   "metadata": {},
+   "source": [
+    "The initial state contains three variables, `theta`, `y`, and `r`.\n",
+    "\n",
+    "`estimate_k` computes the parameter, `k`, that relates `theta` and `r`.\n",
+    "Here's how it works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "common-sender",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import pi\n",
+    "\n",
+    "def estimate_k(params):\n",
+    "    Rmin, Rmax, L = params.Rmin, params.Rmax, params.L\n",
+    "    \n",
+    "    Ravg = (Rmax + Rmin) / 2\n",
+    "    Cavg = 2 * pi * Ravg\n",
+    "    revs = L / Cavg\n",
+    "    rads = 2 * pi * revs\n",
+    "    k = (Rmax - Rmin) / rads\n",
+    "    return k"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "compact-atlas",
+   "metadata": {},
+   "source": [
+    "`Ravg` is the average radius, half way between `Rmin` and `Rmax`, so\n",
+    "`Cavg` is the circumference of the roll when `r` is `Ravg`.\n",
+    "\n",
+    "`revs` is the total number of revolutions it would take to roll up\n",
+    "length `L` if `r` were constant at `Ravg`. And `rads` is just `revs`\n",
+    "converted to radians.\n",
+    "\n",
+    "Finally, `k` is the change in `r` for each radian of revolution. For\n",
+    "these parameters, `k` is about `2.8e-5` m/rad."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "environmental-nitrogen",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "theta    0.00\n",
+       "y        0.00\n",
+       "r        0.02\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = make_system(params)\n",
+    "system.init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "improving-acrobat",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.7925531914893616e-05"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.k"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beginning-artwork",
+   "metadata": {},
+   "source": [
+    "Now we can use the differential equations from the previous section to\n",
+    "write a slope function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "signed-eight",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    theta, y, r = state\n",
+    "    k, omega = system.k, system.omega\n",
+    "    \n",
+    "    dydt = r * omega\n",
+    "    drdt = k * omega\n",
+    "    \n",
+    "    return omega, dydt, drdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "celtic-civilian",
+   "metadata": {},
+   "source": [
+    "As usual, the slope function takes a `State` object, a time, and a\n",
+    "`System` object. The `State` object contains hypothetical values of\n",
+    "`theta`, `y`, and `r` at time `t`. The job of the slope function is to\n",
+    "compute the time derivatives of these values. The derivative of `theta` is angular velocity, which is often denoted `omega`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tracked-delay",
+   "metadata": {},
+   "source": [
+    "And as usual, we should test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "employed-palestine",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 0.2, 0.0002792553191489362)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "slope_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "russian-rochester",
+   "metadata": {},
+   "source": [
+    "We'd like to stop the simulation when the length of paper on the roll is `L`. We can do that with an event function that passes through 0 when `y` equals `L`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "strong-custody",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(t, state, system):\n",
+    "    theta, y, r = state\n",
+    "    return y - system.L"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "affecting-pathology",
+   "metadata": {},
+   "source": [
+    "Now we can run the simulation like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "stable-lying",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A termination event occurred.'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import run_solve_ivp\n",
+    "\n",
+    "results, details = run_solve_ivp(system, slope_func,\n",
+    "                                  events=event_func)\n",
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "separated-colleague",
+   "metadata": {},
+   "source": [
+    "### Plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "satisfied-camel",
+   "metadata": {},
+   "source": [
+    "Plotting `theta`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "excited-marshall",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "def plot_theta(results):\n",
+    "    results.theta.plot(color='C0', label='theta')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Angle (rad)')\n",
+    "    \n",
+    "plot_theta(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "legislative-sixth",
+   "metadata": {},
+   "source": [
+    "Plotting `y`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "plastic-operations",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_y(results):\n",
+    "    results.y.plot(color='C1', label='y')\n",
+    "\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Length (m)')\n",
+    "    \n",
+    "plot_y(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "enclosed-stand",
+   "metadata": {},
+   "source": [
+    "Plotting `r`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "pleased-edward",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_r(results):\n",
+    "    results.r.plot(color='C2', label='r')\n",
+    "\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Radius (m)')\n",
+    "    \n",
+    "plot_r(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "satellite-evans",
+   "metadata": {},
+   "source": [
+    " `theta` grows linearly over time, as we should expect. As a result, `r` also grows linearly. But since the derivative of `y` depends on `r`, and `r` is increasing, `y` grows with increasing slope.\n",
+    "\n",
+    "Because this system is so simple, it is almost silly to simulate it. As we'll see in the next section, it is easy enough to solve the\n",
+    "differential equations analytically. But it is often useful to start\n",
+    "with a simple simulation as a way of exploring and checking assumptions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "quantitative-student",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>theta</th>\n",
+       "      <th>y</th>\n",
+       "      <th>r</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>125.082541</th>\n",
+       "      <td>1250.825413</td>\n",
+       "      <td>46.862152</td>\n",
+       "      <td>0.054930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125.145239</th>\n",
+       "      <td>1251.452393</td>\n",
+       "      <td>46.896598</td>\n",
+       "      <td>0.054947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125.207937</th>\n",
+       "      <td>1252.079373</td>\n",
+       "      <td>46.931054</td>\n",
+       "      <td>0.054965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125.270635</th>\n",
+       "      <td>1252.706353</td>\n",
+       "      <td>46.965522</td>\n",
+       "      <td>0.054982</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125.333333</th>\n",
+       "      <td>1253.333333</td>\n",
+       "      <td>47.000000</td>\n",
+       "      <td>0.055000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  theta          y         r\n",
+       "125.082541  1250.825413  46.862152  0.054930\n",
+       "125.145239  1251.452393  46.896598  0.054947\n",
+       "125.207937  1252.079373  46.931054  0.054965\n",
+       "125.270635  1252.706353  46.965522  0.054982\n",
+       "125.333333  1253.333333  47.000000  0.055000"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "potential-strength",
+   "metadata": {},
+   "source": [
+    "The elapsed time is about 2 minutes, which is plausible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "systematic-differential",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.08888888888889"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t_final = results.index[-1]\n",
+    "t_final / 60"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "incident-delight",
+   "metadata": {},
+   "source": [
+    "The final value of `y` is 47 meters, as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "suffering-directory",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(47.0, 47)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_state = results.iloc[-1] \n",
+    "final_state.y, params.L"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "nasty-triple",
+   "metadata": {},
+   "source": [
+    "The final value of radius is `Rmax`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "cutting-proceeding",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.05500000000000002, 0.055)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_state.r, params.Rmax"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "charitable-mystery",
+   "metadata": {},
+   "source": [
+    "And the total number of rotations is close to 200, which seems plausible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "removable-brother",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "199.47419534184226"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "radians = final_state.theta\n",
+    "rotations = radians / 2 / pi\n",
+    "rotations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "continued-angel",
+   "metadata": {},
+   "source": [
+    "## Animation\n",
+    "\n",
+    "Here's a draw function that animates the results using `matplotlib` patches."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "confused-preview",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.patches import Circle, Arrow\n",
+    "from matplotlib.pyplot import gca, axis\n",
+    "from modsim import pol2cart\n",
+    "\n",
+    "def draw_func(t, state):\n",
+    "    # get radius in mm\n",
+    "    theta, y, r = state\n",
+    "    radius = r\n",
+    "    \n",
+    "    # draw the paper\n",
+    "    circle = Circle([0, 0], radius)\n",
+    "    gca().add_patch(circle)\n",
+    "    \n",
+    "    # draw the core\n",
+    "    core = Circle([0, 0], params.Rmin, color='0.9')\n",
+    "    gca().add_patch(core)\n",
+    "    \n",
+    "    # draw the perimeter\n",
+    "    core = Circle([0, 0], params.Rmax, \n",
+    "                  color='0.7', fill=False)\n",
+    "    gca().add_patch(core)\n",
+    "    \n",
+    "    # draw an arrow to show rotation\n",
+    "    dx, dy = pol2cart(theta, radius)\n",
+    "    arrow = Arrow(0, 0, dx, dy, color='C1', width=0.01)\n",
+    "    gca().add_patch(arrow)\n",
+    "\n",
+    "    # make the aspect ratio 1\n",
+    "    decorate(xlim=[-0.1, 0.1],\n",
+    "             ylim=[-0.07, 0.07])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "naughty-tenant",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "draw_func(0, system.init)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "distant-nancy",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "draw_func(t_final, final_state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "identical-expense",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import animate\n",
+    "\n",
+    "# animate(results, draw_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "contained-taiwan",
+   "metadata": {},
+   "source": [
+    "Now that we have a working simulation, it is also useful to do some analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "electric-longer",
+   "metadata": {},
+   "source": [
+    "## Analysis\n",
+    "\n",
+    "The differential equations in Section xx are simple enough that we can just solve them. Since angular velocity is constant: \n",
+    "\n",
+    "$$\\frac{d\\theta}{dt} = \\omega$$ \n",
+    "\n",
+    "We can find $\\theta$ as a function of time by integrating both sides:\n",
+    "\n",
+    "$$\\theta(t) = \\omega t + C_1$$ \n",
+    "\n",
+    "With the initial condition $\\theta(0)=0$, we find $C_1=0$. Similarly,\n",
+    "\n",
+    "$$\\frac{dr}{dt} = k \\omega$$ \n",
+    "\n",
+    "So\n",
+    "\n",
+    "$$r(t) = k \\omega t + C_2$$ \n",
+    "\n",
+    "With the initial condition $r(0)=R_{min}$, we find $C_2=R_{min}$. Then we can plug the solution for $r$ into the equation for $y$: \n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\frac{dy}{dt} & = r \\omega                      \\\\\n",
+    "              & = \\left[ k \\omega t + R_{min} \\right] \\omega \\nonumber\\end{aligned}$$\n",
+    "              \n",
+    "Integrating both sides yields:\n",
+    "\n",
+    "$$y(t) = \\left[ k \\omega t^2 / 2 + R_{min} t \\right] \\omega + C_3$$ \n",
+    "\n",
+    "So $y$ is a parabola, as you might have guessed. With initial condition $y(0)=0$, we find $C_3=0$.\n",
+    "\n",
+    "We can also use these equations to find the relationship between $y$ and $r$, independent of time, which we can use to compute $k$. Using a move we saw in Section xxx, I'll divide Equations 1 and\n",
+    "2, yielding\n",
+    "\n",
+    "$$\\frac{dr}{dy} = \\frac{k}{r}$$ \n",
+    "\n",
+    "Separating variables yields\n",
+    "\n",
+    "$$r~dr = k~dy$$ \n",
+    "\n",
+    "Integrating both sides yields \n",
+    "\n",
+    "$$r^2 / 2 = k y + C$$ \n",
+    "\n",
+    "When $y=0$, $r=R_{min}$, so $$C = \\frac{1}{2} R_{min}^2$$ Solving for $y$, we have \n",
+    "\n",
+    "$$y = \\frac{1}{2k} (r^2 - R_{min}^2)                 \\label{eqn3}$$\n",
+    "\n",
+    "When $y=L$, $r=R_{max}$; substituting in those values yields\n",
+    "\n",
+    "$$L = \\frac{1}{2k} (R_{max}^2 - R_{min}^2)$$ \n",
+    "\n",
+    "Solving for $k$ yields\n",
+    "\n",
+    "$$k =  \\frac{1}{2L} (R_{max}^2 - R_{min}^2)           \\label{eqn4}$$\n",
+    "\n",
+    "Plugging in the values of the parameters yields `2.8e-5` m/rad, the same as the \"estimate\" we computed in Section xxx. In this case the estimate turns out to be exact."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "welcome-organization",
+   "metadata": {},
+   "source": [
+    "We can also see the relationship between `y` and `r`, which I derive analytically in the book."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "foreign-needle",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hindu-civilization",
+   "metadata": {},
+   "source": [
+    "### Rolling paper\n",
+    "\n",
+    "We'll start by loading the units we need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "active-absorption",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'UNITS' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-22-2bc8c7513e21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mradian\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUNITS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mradian\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUNITS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUNITS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msecond\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'UNITS' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "radian = UNITS.radian\n",
+    "m = UNITS.meter\n",
+    "s = UNITS.second"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fancy-floating",
+   "metadata": {},
+   "source": [
+    "And creating a `Params` object with the system parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "british-method",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = Params(Rmin = 0.02 * m,\n",
+    "                Rmax = 0.055 * m,\n",
+    "                L = 47 * m,\n",
+    "                omega = 10 * radian / s,\n",
+    "                t_end = 130 * s,\n",
+    "                dt = 1*s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "governing-paraguay",
+   "metadata": {},
+   "source": [
+    "The following function estimates the parameter `k`, which is the increase in the radius of the roll for each radian of rotation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "spread-fabric",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def estimate_k(params):\n",
+    "    \"\"\"Estimates the parameter `k`.\n",
+    "    \n",
+    "    params: Params with Rmin, Rmax, and L\n",
+    "    \n",
+    "    returns: k in meters per radian\n",
+    "    \"\"\"\n",
+    "    Rmin, Rmax, L = params.Rmin, params.Rmax, params.L\n",
+    "    \n",
+    "    Ravg = (Rmax + Rmin) / 2\n",
+    "    Cavg = 2 * pi * Ravg\n",
+    "    revs = L / Cavg\n",
+    "    rads = 2 * pi * revs\n",
+    "    k = (Rmax - Rmin) / rads\n",
+    "    return k"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "plain-chambers",
+   "metadata": {},
+   "source": [
+    "As usual, `make_system` takes a `Params` object and returns a `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "tutorial-direction",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(params):\n",
+    "    \"\"\"Make a system object.\n",
+    "    \n",
+    "    params: Params with Rmin, Rmax, and L\n",
+    "    \n",
+    "    returns: System with init, k, and ts\n",
+    "    \"\"\"\n",
+    "    init = State(theta = 0 * radian,\n",
+    "                 y = 0 * m,\n",
+    "                 r = params.Rmin)\n",
+    "    \n",
+    "    k = estimate_k(params)\n",
+    "\n",
+    "    return System(params, init=init, k=k)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "separate-flexibility",
+   "metadata": {},
+   "source": [
+    "Testing `make_system`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "latter-retention",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aggressive-bikini",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "processed-archive",
+   "metadata": {},
+   "source": [
+    "Now we can write a slope function based on the differential equations\n",
+    "\n",
+    "$\\omega = \\frac{d\\theta}{dt} = 10$\n",
+    "\n",
+    "$\\frac{dy}{dt} = r \\frac{d\\theta}{dt}$\n",
+    "\n",
+    "$\\frac{dr}{dt} = k \\frac{d\\theta}{dt}$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "unable-track",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Computes the derivatives of the state variables.\n",
+    "    \n",
+    "    state: State object with theta, y, r\n",
+    "    t: time\n",
+    "    system: System object with r, k\n",
+    "    \n",
+    "    returns: sequence of derivatives\n",
+    "    \"\"\"\n",
+    "    theta, y, r = state\n",
+    "    k, omega = system.k, system.omega\n",
+    "    \n",
+    "    dydt = r * omega\n",
+    "    drdt = k * omega\n",
+    "    \n",
+    "    return omega, dydt, drdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "behind-aaron",
+   "metadata": {},
+   "source": [
+    "Testing `slope_func`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "middle-paint",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(system.init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "better-filing",
+   "metadata": {},
+   "source": [
+    "We'll use an event function to stop when `y=L`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "numerous-legislature",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(state, t, system):\n",
+    "    \"\"\"Detects when we've rolled length `L`.\n",
+    "    \n",
+    "    state: State object with theta, y, r\n",
+    "    t: time\n",
+    "    system: System object with L\n",
+    "    \n",
+    "    returns: difference between `y` and `L`\n",
+    "    \"\"\"\n",
+    "    theta, y, r = state\n",
+    "    \n",
+    "    return y - system.L"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "broad-place",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_func(system.init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "polished-phrase",
+   "metadata": {},
+   "source": [
+    "Now we can run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "monetary-charlotte",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_solve_ivp(system, slope_func, events=event_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "religious-conditions",
+   "metadata": {},
+   "source": [
+    "And look at the results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lucky-advance",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Run the simulation again with a smaller step size to smooth out the animation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "noted-amount",
+   "metadata": {},
+   "source": [
+    "### Exercises\n",
+    "\n",
+    "**Exercise:** Since we keep `omega` constant, the linear velocity of the paper increases with radius.  Use `gradient` to estimate the derivative of `results.y`.  What is the peak linear velocity?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "given-panic",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "dydt = gradient(results.y);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "foreign-reservation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(dydt, label='dydt')\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Linear velocity (m/s)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "proof-abraham",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "linear_velocity = get_last_value(dydt) * m/s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "advisory-integer",
+   "metadata": {},
+   "source": [
+    "Now suppose the peak velocity is the limit; that is, we can't move the paper any faster than that.\n",
+    "\n",
+    "Nevertheless, we might be able to speed up the process by keeping the linear velocity at the maximum all the time.\n",
+    "\n",
+    "Write a slope function that keeps the linear velocity, `dydt`, constant, and computes the angular velocity, `omega`, accordingly.\n",
+    "\n",
+    "Run the simulation and see how much faster we could finish rolling the paper."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "historic-barrier",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Computes the derivatives of the state variables.\n",
+    "    \n",
+    "    state: State object with theta, y, r\n",
+    "    t: time\n",
+    "    system: System object with r, k\n",
+    "    \n",
+    "    returns: sequence of derivatives\n",
+    "    \"\"\"\n",
+    "    theta, y, r = state\n",
+    "    k, omega = system.k, system.omega\n",
+    "    \n",
+    "    dydt = linear_velocity\n",
+    "    omega = dydt / r\n",
+    "    drdt = k * omega\n",
+    "    \n",
+    "    return omega, dydt, drdt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "false-identity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "slope_func(system.init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "physical-novelty",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "results, details = run_solve_ivp(system, slope_func, events=event_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "peaceful-doctor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "t_final = get_last_label(results) * s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "limiting-license",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "plot_three(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "least-analysis",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/modsim.py b/jupyter/modsim.py
index 164cd36a..894cabe3 100644
--- a/jupyter/modsim.py
+++ b/jupyter/modsim.py
@@ -30,12 +30,12 @@
 from scipy.integrate import solve_ivp
 
 from types import SimpleNamespace
+from copy import copy
 
+#import pint
 
-import pint
-
-units = pint.UnitRegistry()
-Quantity = units.Quantity
+#units = pint.UnitRegistry()
+#Quantity = units.Quantity
 
 
 def flip(p=0.5):
@@ -153,9 +153,8 @@ def minimize_scalar(min_func, bounds, *args, **options):
         min_func(bounds[0], *args)
     except Exception as e:
         msg = """Before running scipy.integrate.minimize_scalar, I tried
-                 running the slope function you provided with the
-                 initial conditions in system and t=0, and I got
-                 the following error:"""
+                 running the function you provided with the
+                 lower bound, and I got the following error:"""
         logger.error(msg)
         raise (e)
 
@@ -178,7 +177,7 @@ def minimize_scalar(min_func, bounds, *args, **options):
         )
         raise Exception(msg)
 
-    return ModSimSeries(res)
+    return res
 
 
 def maximize_scalar(max_func, bounds, *args, **options):
@@ -194,7 +193,6 @@ def maximize_scalar(max_func, bounds, *args, **options):
 
     returns: ModSimSeries object
     """
-
     def min_func(*args):
         return -max_func(*args)
 
@@ -446,10 +444,14 @@ def run_solve_ivp(system, slope_func, **options):
     else:
         columns = range(len(system.init))
 
-    # evaluate the results at 51 equally-spaced points
+    # evaluate the results at equally-spaced points
     if options.get('dense_output', False):
-        t_end = t[-1]
-        t_array = linspace(t_0, t_end, 51)
+        try:
+            num = system.num
+        except AttributeError:
+            num = 51
+        t_final = t[-1]
+        t_array = linspace(t_0, t_final, num)
         y_array = bunch.sol(t_array)
 
         # pack the results into a TimeFrame
@@ -495,7 +497,7 @@ def check_system(system, slope_func):
     # if dt is not specified, take 100 steps
     try:
         dt = system.dt
-    except KeyError:
+    except AttributeError:
         dt = t_end / 100
 
     return init, t_0, t_end, dt
@@ -650,7 +652,6 @@ def fsolve(func, x0, *args, **options):
     return result
 
 
-
 def crossings(series, value):
     """Find the labels where the series passes through value.
 
@@ -906,7 +907,43 @@ def State(**variables):
     return pd.Series(variables)
 
 
-class System(SimpleNamespace):
+class SettableNamespace(SimpleNamespace):
+    """Contains a collection of parameters.
+
+    Used to make a System object.
+
+    Takes keyword arguments and stores them as attributes.
+    """
+    def get(self, name, default=None):
+        """Look up a variable.
+
+        name: string varname
+        default: value returned if `name` is not present
+        """
+        try:
+            return self.__getattribute__(name, default)
+        except AttributeError:
+            return default
+
+    def set(self, **variables):
+        """Make a copy and update the given variables.
+
+        returns: Params
+        """
+        new = copy(self)
+        new.__dict__.update(variables)
+        return new
+
+
+class System(SettableNamespace):
+    """Contains system parameters and their values.
+
+    Takes keyword arguments and stores them as attributes.
+    """
+    pass
+
+
+class Params(SettableNamespace):
     """Contains system parameters and their values.
 
     Takes keyword arguments and stores them as attributes.
@@ -1041,54 +1078,6 @@ def vector_diff_angle(v, w):
         raise NotImplementedError()
 
 
-class ModSimVector(Quantity):
-    """Represented as an array of 2 or 3 elements.
-
-    x, y, z, mag, mag2, and angle are accessible as attributes.
-    """
-
-    @property
-    def x(self):
-        """Returns the x component with units."""
-        return self[0]
-
-    @property
-    def y(self):
-        """Returns the y component with units."""
-        return self[1]
-
-    @property
-    def z(self):
-        """Returns the z component with units."""
-        return self[2]
-
-    @property
-    def mag(self):
-        """Returns the magnitude with units."""
-        return vector_mag(self)
-
-    @property
-    def mag2(self):
-        """Returns the magnitude squared with units."""
-        return vector_mag2(self)
-
-    @property
-    def angle(self):
-        """Returns the angle between self and the positive x axis."""
-        return vector_angle(self)
-
-    # make the vector functions available as methods
-    polar = vector_polar
-    hat = vector_hat
-    perp = vector_perp
-    dot = vector_dot
-    cross = vector_cross
-    proj = vector_proj
-    comp = scalar_proj
-    dist = vector_dist
-    diff_angle = vector_diff_angle
-
-
 def Vector(x, y, z=None, **options):
     """
     """

From 864b94cc1f8c28150b872da1ae01cda43f480d6e Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 4 Feb 2021 12:05:08 -0500
Subject: [PATCH 027/144] Revising chapters

---
 jupyter/chap22.ipynb |  448 +++++++------
 jupyter/chap24.ipynb | 1041 +++++++++++++------------------
 jupyter/chap25.ipynb | 1421 ++++++++++++++++++++++++++++++++++++++++++
 jupyter/modsim.py    |  240 +++----
 4 files changed, 2160 insertions(+), 990 deletions(-)
 create mode 100644 jupyter/chap25.ipynb

diff --git a/jupyter/chap22.ipynb b/jupyter/chap22.ipynb
index e75cf06e..ea1b146d 100644
--- a/jupyter/chap22.ipynb
+++ b/jupyter/chap22.ipynb
@@ -26,8 +26,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "widespread-detective",
+   "execution_count": null,
+   "id": "blank-mapping",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -40,17 +40,32 @@
     "try:\n",
     "    import pint\n",
     "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "widespread-detective",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename)\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    urlretrieve(url+filename, filename)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "worst-london",
+   "id": "alert-banks",
    "metadata": {},
    "source": [
     "In the previous chapter we modeled objects moving in one dimension, with and without drag. Now let's move on to two dimensions, and baseball!\n",
@@ -62,7 +77,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "contained-bones",
+   "id": "functional-abortion",
    "metadata": {},
    "source": [
     "## Baseball\n",
@@ -108,7 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "current-wheel",
+   "id": "incorrect-cigarette",
    "metadata": {},
    "outputs": [
     {
@@ -133,7 +148,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "retired-strand",
+   "id": "digital-sunrise",
    "metadata": {},
    "source": [
     "You can access the components of a `Vector` by name using the dot\n",
@@ -143,7 +158,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "quarterly-startup",
+   "id": "bridal-jamaica",
    "metadata": {},
    "outputs": [
     {
@@ -163,7 +178,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "welcome-violin",
+   "id": "under-attribute",
    "metadata": {},
    "source": [
     "You can also access them by index using brackets, like this:"
@@ -172,7 +187,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "occupied-macedonia",
+   "id": "incorrect-postage",
    "metadata": {},
    "outputs": [
     {
@@ -192,7 +207,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "collective-anthony",
+   "id": "solved-bathroom",
    "metadata": {},
    "source": [
     "`Vector` objects support most mathematical operations, including\n",
@@ -202,7 +217,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "authentic-brazil",
+   "id": "upset-morning",
    "metadata": {},
    "outputs": [
     {
@@ -226,7 +241,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "hairy-bahamas",
+   "id": "every-affiliation",
    "metadata": {},
    "outputs": [
     {
@@ -249,7 +264,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "assumed-yukon",
+   "id": "ideal-sperm",
    "metadata": {},
    "outputs": [
     {
@@ -271,7 +286,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "criminal-advertiser",
+   "id": "tender-translator",
    "metadata": {},
    "source": [
     "For the definition and graphical interpretation of these operations, see <http://modsimpy.com/vecops>."
@@ -279,7 +294,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "exact-texture",
+   "id": "fewer-adolescent",
    "metadata": {},
    "source": [
     "We can specify a `Vector` with coordinates `x` and `y`, as in the previous examples.\n",
@@ -295,7 +310,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "altered-march",
+   "id": "chinese-assembly",
    "metadata": {},
    "outputs": [
     {
@@ -332,7 +347,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "departmental-senator",
+   "id": "awful-defense",
    "metadata": {},
    "outputs": [
     {
@@ -355,7 +370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "younger-mortality",
+   "id": "excited-catch",
    "metadata": {},
    "source": [
     "And a function to convert degrees to radians:"
@@ -491,7 +506,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "italian-payment",
+   "id": "boring-river",
    "metadata": {},
    "source": [
     "Now let's get back to the game."
@@ -511,7 +526,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "attempted-narrow",
+   "id": "positive-current",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -529,12 +544,13 @@
     "\n",
     "    rho = 1.2,      # kg/m**3\n",
     "    g = 9.8,        # m/s**2\n",
+    "    t_end = 10,     # s\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "thousand-arlington",
+   "id": "bacterial-favorite",
    "metadata": {},
    "source": [
     "I got the mass and diameter of the baseball from [Wikipedia](https://en.wikipedia.org/wiki/Baseball_(ball)) and the coefficient of drag is from *[The Physics of Baseball](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC)*:"
@@ -542,7 +558,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "prospective-philadelphia",
+   "id": "unknown-extreme",
    "metadata": {},
    "source": [
     "The density of air, `rho`, is based on a temperature of 20 °C at sea level (see <http://modsimpy.com/tempress>). \n",
@@ -581,18 +597,15 @@
     "    # compute the frontal area\n",
     "    area = pi * (params.diameter/2)**2\n",
     "\n",
-    "    return System(init = init,\n",
-    "                  mass = params.mass,\n",
+    "    return System(params,\n",
+    "                  init = init,\n",
     "                  area = area,\n",
-    "                  C_d = params.C_d,\n",
-    "                  rho = params.rho,\n",
-    "                  g = params.g,\n",
-    "                  t_end=10)"
+    "                 )"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "combined-heritage",
+   "id": "considered-conservation",
    "metadata": {},
    "source": [
     "`make_system` uses `deg2rad` to convert `angle` to radians and\n",
@@ -611,8 +624,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "confirmed-knife",
+   "execution_count": 16,
+   "id": "greatest-soviet",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -621,7 +634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "tired-abortion",
+   "id": "metropolitan-mainstream",
    "metadata": {},
    "source": [
     "And here's the initial `State`:"
@@ -629,8 +642,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "dirty-retailer",
+   "execution_count": 17,
+   "id": "studied-congo",
    "metadata": {},
    "outputs": [
     {
@@ -643,7 +656,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -662,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "legal-terminal",
    "metadata": {},
    "outputs": [],
@@ -678,7 +691,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "spatial-cooperation",
+   "id": "informational-intersection",
    "metadata": {},
    "source": [
     "This function takes `V` as a `Vector` and returns `f_drag` as a\n",
@@ -696,8 +709,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "equipped-dragon",
+   "execution_count": 19,
+   "id": "detailed-league",
    "metadata": {},
    "outputs": [
     {
@@ -708,13 +721,13 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from modsim import ModSimVector\n",
+    "from modsim import Vector\n",
     "\n",
     "vx, vy = system.init.vx, system.init.vy\n",
     "V_test = Vector(vx, vy)\n",
@@ -733,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "suitable-salem",
    "metadata": {},
    "outputs": [],
@@ -744,7 +757,7 @@
     "    \n",
     "    V = Vector(vx, vy)\n",
     "    a_drag = drag_force(V, system) / mass\n",
-    "    a_grav = Vector(0, -g)\n",
+    "    a_grav = g * Vector(0, -1)\n",
     "    \n",
     "    A = a_grav + a_drag\n",
     "    \n",
@@ -753,7 +766,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "pointed-graph",
+   "id": "alpha-tiger",
    "metadata": {},
    "source": [
     "As usual, the parameters of the slope function are a time, a `State` object, and a `System` object. \n",
@@ -769,7 +782,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "proved-mother",
+   "id": "wired-judgment",
    "metadata": {},
    "source": [
     "The return value is a sequence that contains:\n",
@@ -791,7 +804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "closing-simon",
    "metadata": {},
    "outputs": [
@@ -801,7 +814,7 @@
        "(28.284271247461902, 28.2842712474619, -6.466030881564545, -16.266030881564546)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -824,7 +837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "brief-level",
    "metadata": {},
    "outputs": [],
@@ -836,7 +849,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "pressing-oracle",
+   "id": "martial-strengthening",
    "metadata": {},
    "source": [
     "The event function takes the same parameters as the slope function, and returns the $y$ coordinate of position. When the $y$ coordinate passes through 0, the simulation stops.\n",
@@ -846,8 +859,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "necessary-registrar",
+   "execution_count": 23,
+   "id": "pointed-bathroom",
    "metadata": {},
    "outputs": [
     {
@@ -856,7 +869,7 @@
        "1.0"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -875,7 +888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "special-background",
    "metadata": {},
    "outputs": [
@@ -885,7 +898,7 @@
        "'A termination event occurred.'"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -900,7 +913,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "solid-tribune",
+   "id": "golden-november",
    "metadata": {},
    "source": [
     "`details` contains information about the simulation, including a message that indicates that a \"termination event\" occurred; that is, the simulated ball reached the ground.\n",
@@ -910,8 +923,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "alternative-history",
+   "execution_count": 25,
+   "id": "opening-lecture",
    "metadata": {},
    "outputs": [
     {
@@ -990,7 +1003,7 @@
        "5.004887  99.318296 -7.105427e-15  14.165894 -22.058763"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1009,7 +1022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "medieval-calvin",
    "metadata": {},
    "outputs": [
@@ -1019,7 +1032,7 @@
        "5.004887034868351"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1039,7 +1052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "gorgeous-survey",
    "metadata": {},
    "outputs": [
@@ -1053,7 +1066,7 @@
        "Name: 5.004887034868351, dtype: float64"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1065,7 +1078,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "alternate-present",
+   "id": "invalid-japan",
    "metadata": {},
    "source": [
     "The final value of `y` is close to 0, as it should be.  The final value of `x` tells us how far the ball flew, in meters."
@@ -1073,8 +1086,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "id": "conservative-collins",
+   "execution_count": 28,
+   "id": "agreed-sugar",
    "metadata": {},
    "outputs": [
     {
@@ -1083,18 +1096,19 @@
        "99.31829628352206"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "final_state.x"
+    "x_dist = final_state.x\n",
+    "x_dist"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "resident-fountain",
+   "id": "recreational-template",
    "metadata": {},
    "source": [
     "We can also get the final velocity, like this:"
@@ -1102,8 +1116,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "id": "superior-capture",
+   "execution_count": 29,
+   "id": "instrumental-nurse",
    "metadata": {},
    "outputs": [
     {
@@ -1114,7 +1128,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1126,7 +1140,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "amended-antenna",
+   "id": "economic-chrome",
    "metadata": {},
    "source": [
     "The speed of the ball on impact is about 26 m/s, which is substantially slower than the initial velocity, 40 m/s."
@@ -1134,8 +1148,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "fresh-damages",
+   "execution_count": 30,
+   "id": "noted-triumph",
    "metadata": {},
    "outputs": [
     {
@@ -1144,7 +1158,7 @@
        "26.215674453237636"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1165,7 +1179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "id": "spare-burst",
    "metadata": {},
    "outputs": [
@@ -1205,7 +1219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "id": "dated-browse",
    "metadata": {},
    "outputs": [
@@ -1249,7 +1263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "healthy-pulse",
+   "id": "precise-theology",
    "metadata": {},
    "source": [
     "## Animation\n",
@@ -1261,7 +1275,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "honest-simple",
+   "id": "elegant-century",
    "metadata": {},
    "source": [
     "The draw function should take as parameters a `State` object and the time.  It should draw a single frame of the animation.\n",
@@ -1271,8 +1285,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "large-gates",
+   "execution_count": 33,
+   "id": "sensitive-debate",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1290,32 +1304,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "id": "looking-community",
+   "execution_count": 34,
+   "id": "civic-federation",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from modsim import animate\n",
     "\n",
-    "animate(results, draw_func)"
+    "# animate(results, draw_func)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "final-terminal",
+   "id": "fatal-effort",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -1323,7 +1324,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "romance-serum",
+   "id": "experimental-cooper",
    "metadata": {},
    "source": [
     "**Exercise:** Run the simulation with and without air resistance.  How wrong would we be if we ignored drag?"
@@ -1331,8 +1332,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "excessive-challenge",
+   "execution_count": 35,
+   "id": "negative-dictionary",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1343,8 +1344,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "recognized-format",
+   "execution_count": 36,
+   "id": "green-folder",
    "metadata": {},
    "outputs": [
     {
@@ -1353,7 +1354,7 @@
        "'A termination event occurred.'"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1368,8 +1369,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "id": "sitting-combine",
+   "execution_count": 37,
+   "id": "lucky-degree",
    "metadata": {},
    "outputs": [
     {
@@ -1394,17 +1395,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "id": "developing-capitol",
+   "execution_count": 38,
+   "id": "floating-membrane",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "99.31829628352206"
+       "164.25925502413202"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1412,23 +1413,23 @@
    "source": [
     "# Solution\n",
     "\n",
-    "x_dist = results.iloc[-1].x\n",
-    "x_dist"
+    "x_dist2 = results2.iloc[-1].x\n",
+    "x_dist2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "id": "elder-variance",
+   "execution_count": 39,
+   "id": "sorted-century",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "164.25925502413202"
+       "64.94095874060996"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1436,13 +1437,12 @@
    "source": [
     "# Solution\n",
     "\n",
-    "x_dist_no_drag = results2.iloc[-1].x\n",
-    "x_dist_no_drag"
+    "x_dist2 - x_dist"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bulgarian-nowhere",
+   "id": "about-martial",
    "metadata": {},
    "source": [
     "**Exercise:** The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / meter$^3$.  How much farther would a ball hit with the same velocity and launch angle travel?"
@@ -1450,8 +1450,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "id": "conventional-chest",
+   "execution_count": 40,
+   "id": "fifty-burning",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1462,8 +1462,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "id": "south-profession",
+   "execution_count": 41,
+   "id": "adjustable-capital",
    "metadata": {},
    "outputs": [
     {
@@ -1472,7 +1472,7 @@
        "105.78838005859807"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1489,8 +1489,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "id": "removable-running",
+   "execution_count": 42,
+   "id": "golden-exhibition",
    "metadata": {},
    "outputs": [
     {
@@ -1499,7 +1499,7 @@
        "6.470083775076006"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1512,7 +1512,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "enormous-auditor",
+   "id": "christian-scotland",
    "metadata": {},
    "source": [
     "**Exercise:** The model so far is based on the assumption that coefficient of drag does not depend on velocity, but in reality it does.  The following figure, from Adair, [*The Physics of Baseball*](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC), shows coefficient of drag as a function of velocity.\n",
@@ -1525,8 +1525,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "id": "artistic-baghdad",
+   "execution_count": 43,
+   "id": "guided-alarm",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1540,8 +1540,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "id": "general-ethiopia",
+   "execution_count": 44,
+   "id": "listed-austria",
    "metadata": {},
    "outputs": [
     {
@@ -1600,61 +1600,6 @@
        "      <td>60.134000</td>\n",
        "      <td>0.45004</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30.636992</th>\n",
-       "      <td>68.533000</td>\n",
-       "      <td>0.40914</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32.977694</th>\n",
-       "      <td>73.769000</td>\n",
-       "      <td>0.38042</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34.604472</th>\n",
-       "      <td>77.408000</td>\n",
-       "      <td>0.36562</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37.497268</th>\n",
-       "      <td>83.879000</td>\n",
-       "      <td>0.34822</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40.460249</th>\n",
-       "      <td>90.507000</td>\n",
-       "      <td>0.33081</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43.492522</th>\n",
-       "      <td>97.290000</td>\n",
-       "      <td>0.31427</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46.733562</th>\n",
-       "      <td>104.540000</td>\n",
-       "      <td>0.30035</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50.886563</th>\n",
-       "      <td>113.830000</td>\n",
-       "      <td>0.28816</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54.047136</th>\n",
-       "      <td>120.900000</td>\n",
-       "      <td>0.28381</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56.926074</th>\n",
-       "      <td>127.340000</td>\n",
-       "      <td>0.28033</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60.086646</th>\n",
-       "      <td>134.410000</td>\n",
-       "      <td>0.28207</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
@@ -1666,21 +1611,10 @@
        "8.871509                             19.845000           0.49878\n",
        "17.647351                            39.476000           0.49704\n",
        "22.432914                            50.181000           0.48225\n",
-       "26.882303                            60.134000           0.45004\n",
-       "30.636992                            68.533000           0.40914\n",
-       "32.977694                            73.769000           0.38042\n",
-       "34.604472                            77.408000           0.36562\n",
-       "37.497268                            83.879000           0.34822\n",
-       "40.460249                            90.507000           0.33081\n",
-       "43.492522                            97.290000           0.31427\n",
-       "46.733562                           104.540000           0.30035\n",
-       "50.886563                           113.830000           0.28816\n",
-       "54.047136                           120.900000           0.28381\n",
-       "56.926074                           127.340000           0.28033\n",
-       "60.086646                           134.410000           0.28207"
+       "26.882303                            60.134000           0.45004"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1694,13 +1628,13 @@
     "mps = mph.to(units.meter / units.second)\n",
     "baseball_drag.index = mps.magnitude\n",
     "baseball_drag.index.name = 'Velocity in meters per second'\n",
-    "baseball_drag"
+    "baseball_drag.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "id": "pregnant-small",
+   "execution_count": 45,
+   "id": "american-reverse",
    "metadata": {},
    "outputs": [
     {
@@ -1728,7 +1662,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "promotional-background",
+   "id": "legislative-jamaica",
    "metadata": {},
    "source": [
     "Modify the model to include the dependence of `C_d` on velocity, and see how much it affects the results."
@@ -1736,14 +1670,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "id": "sporting-gazette",
+   "execution_count": 46,
+   "id": "dominant-passing",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Solution\n",
     "\n",
-    "def drag_force(V, system):\n",
+    "def drag_force2(V, system):\n",
     "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
     "    \n",
     "    v: velocity\n",
@@ -1762,8 +1696,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "id": "scheduled-cutting",
+   "execution_count": 47,
+   "id": "double-carol",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def slope_func2(t, state, system):\n",
+    "    x, y, vx, vy = state\n",
+    "    mass, g = system.mass, system.g\n",
+    "    \n",
+    "    V = Vector(vx, vy)\n",
+    "    a_drag = drag_force2(V, system) / mass\n",
+    "    a_grav = g * Vector(0, -1)\n",
+    "    \n",
+    "    A = a_grav + a_drag\n",
+    "    \n",
+    "    return V.x, V.y, A.x, A.y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "antique-gathering",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1774,19 +1730,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "id": "streaming-lebanon",
+   "execution_count": 49,
+   "id": "undefined-moral",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "x   -1.023081\n",
-       "y   -1.023081\n",
+       "x   -1.054771\n",
+       "y   -1.054771\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1801,17 +1757,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "id": "animated-prevention",
+   "execution_count": 50,
+   "id": "atomic-socket",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(28.284271247461902, 28.2842712474619, -6.534891184395127, -16.334891184395126)"
+       "(28.284271247461902, 28.2842712474619, -6.466030881564545, -16.266030881564546)"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1824,8 +1780,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "id": "practical-reservation",
+   "execution_count": 51,
+   "id": "abandoned-amount",
    "metadata": {},
    "outputs": [
     {
@@ -1834,7 +1790,7 @@
        "'A termination event occurred.'"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1842,15 +1798,15 @@
    "source": [
     "# Solution\n",
     "\n",
-    "results4, details4 = run_solve_ivp(system4, slope_func, \n",
+    "results4, details4 = run_solve_ivp(system4, slope_func2, \n",
     "                                   events=event_func)\n",
     "details4.message"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "id": "supposed-school",
+   "execution_count": 52,
+   "id": "adjusted-coating",
    "metadata": {},
    "outputs": [
     {
@@ -1929,7 +1885,7 @@
        "4.919407  90.206647  1.065814e-14  11.640440 -20.837214"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1942,20 +1898,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "id": "recognized-blend",
+   "execution_count": 53,
+   "id": "excellent-dressing",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "90.20664656623306"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Solution\n",
     "\n",
-    "x_dist4 = results4.iloc[-1].x"
+    "x_dist4 = results4.iloc[-1].x\n",
+    "x_dist4"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "id": "sensitive-butterfly",
+   "execution_count": 54,
+   "id": "equal-component",
    "metadata": {},
    "outputs": [
     {
@@ -1964,7 +1932,7 @@
        "-9.111649717288998"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1977,7 +1945,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "casual-satisfaction",
+   "id": "italian-coordinate",
    "metadata": {},
    "source": [
     "### Under the hood\n",
diff --git a/jupyter/chap24.ipynb b/jupyter/chap24.ipynb
index ed8daff0..b9fe4d9c 100644
--- a/jupyter/chap24.ipynb
+++ b/jupyter/chap24.ipynb
@@ -45,12 +45,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hispanic-pressure",
+   "id": "composed-carol",
    "metadata": {},
    "source": [
-    "In this chapter we model systems that involve rotating objects. In\n",
-    "general, rotation is complicated: in three dimensions, objects can\n",
-    "rotate around three axes; objects are often easier to spin around some\n",
+    "In this chapter we model systems that involve rotating objects. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "documentary-network",
+   "metadata": {},
+   "source": [
+    "## Rotation\n",
+    "\n",
+    "Rotation is complicated: in three dimensions, objects can rotate around three axes; objects are often easier to spin around some\n",
     "axes than others; and they may be stable when spinning around some axes but not others.\n",
     "\n",
     "If the configuration of an object changes over time, it might become\n",
@@ -67,7 +75,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "rural-secret",
+   "id": "political-acrylic",
    "metadata": {},
    "source": [
     "The fundamental ideas in this chapter and the next are **angular\n",
@@ -75,18 +83,18 @@
     "inertia**. If you are not already familiar with these concepts, I will\n",
     "define them as we go along, and I will point to additional reading.\n",
     "\n",
-    "At the end of the next chapter, you will use these tools to simulate the behavior of a yo-yo (see <http://modsimpy.com/yoyo>). But we'll work our way up to it gradually, starting with toilet paper."
+    "As a case study, you can use these tools to simulate the behavior of a yo-yo (see <http://modsimpy.com/yoyo>). But we'll work our way up to it gradually, starting with toilet paper."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "protecting-photography",
+   "id": "integral-welsh",
    "metadata": {},
    "source": [
     "## The physics of toilet paper\n",
     "\n",
     "As a simple example of a system with rotation, we'll simulate the\n",
-    "manufacture of a roll of toilet paper. Starting with a cardboard tube at the center, we will roll up 47 m of paper, the typical length of a roll of toilet paper in the U.S. (see <http://modsimpy.com/paper>).\n",
+    "manufacture of a roll of toilet paper, as shown in this video <https://youtu.be/Z74OfpUbeac?t=231>. Starting with a cardboard tube at the center, we will roll up 47 m of paper, the typical length of a roll of toilet paper in the U.S. (see <http://modsimpy.com/paper>).\n",
     "\n",
     "![Diagram of a roll of toilet paper, showing change in paper length as a result of a small rotation, $d\\theta$.](figs/paper_roll.pdf){height=\"2.5in\"}\n",
     "\n",
@@ -95,8 +103,14 @@
     "\n",
     "I'll use $\\theta$ to represent the total rotation of the roll in\n",
     "radians. In the diagram, $d\\theta$ represents a small increase in\n",
-    "$\\theta$, which corresponds to a distance along the circumference of the roll of $r~d\\theta$.\n",
-    "\n",
+    "$\\theta$, which corresponds to a distance along the circumference of the roll of $r~d\\theta$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "political-prerequisite",
+   "metadata": {},
+   "source": [
     "Finally, I'll use $y$ to represent the total length of paper that's been rolled. Initially, $\\theta=0$ and $y=0$. For each small increase in $\\theta$, there is a corresponding increase in $y$: \n",
     "\n",
     "$$dy = r~d\\theta$$\n",
@@ -114,9 +128,9 @@
     "\n",
     "$$\\frac{dr}{dt} = k \\frac{d\\theta}{dt}$$ \n",
     "\n",
-    "Finally, let's assume that $\\theta$ increases at a constant rate of 10 rad/s (about 95 revolutions per minute): \n",
+    "Finally, let's assume that $\\theta$ increases at a constant rate of $\\omega = 300$ rad/s (about 2900 revolutions per minute): \n",
     "\n",
-    "$$\\frac{d\\theta}{dt} = 10$$ \n",
+    "$$\\frac{d\\theta}{dt} = \\omega$$ \n",
     "\n",
     "This rate of change is called an **angular velocity**. Now we have a system of three differential equations we can use to simulate the system."
    ]
@@ -135,17 +149,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 101,
+   "id": "artificial-cotton",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import units\n",
+    "\n",
+    "m = units.meter\n",
+    "rad = units.radian\n",
+    "s = units.second"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
    "id": "honey-translator",
    "metadata": {},
    "outputs": [],
    "source": [
     "from modsim import Params\n",
     "\n",
-    "params = Params(Rmin = 0.02,  # m \n",
-    "                Rmax = 0.055, # m \n",
-    "                L = 47,       # m \n",
-    "                omega = 10,   # radian / s \n",
+    "params = Params(Rmin = 0.02 * m,\n",
+    "                Rmax = 0.055 * m,\n",
+    "                L = 47 * m,\n",
+    "                theta_0 = 0 * rad,\n",
+    "                y_0 = 0 * m,\n",
+    "                omega = 300 * rad / s,\n",
+    "                t_end = 130 * s\n",
     "               )"
    ]
   },
@@ -163,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 48,
    "id": "fresh-domestic",
    "metadata": {},
    "outputs": [],
@@ -171,21 +202,16 @@
     "from modsim import State, System\n",
     "\n",
     "def make_system(params):\n",
-    "    init = State(theta = 0,   # radian\n",
-    "                 y = 0,       # m,\n",
+    "    init = State(theta = params.theta_0,\n",
+    "                 y = params.y_0,\n",
     "                 r = params.Rmin\n",
     "                )\n",
     "    \n",
     "    k = estimate_k(params)\n",
     "\n",
-    "    return System(init=init, \n",
+    "    return System(params,\n",
+    "                  init=init, \n",
     "                  k=k,\n",
-    "                  omega = params.omega,\n",
-    "                  Rmin = params.Rmin,\n",
-    "                  Rmax = params.Rmax,\n",
-    "                  L = params.L,\n",
-    "                  t_end = 130,\n",
-    "                  num = 2000,\n",
     "                 )"
    ]
   },
@@ -202,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 49,
    "id": "common-sender",
    "metadata": {},
    "outputs": [],
@@ -222,7 +248,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "compact-atlas",
+   "id": "seasonal-cherry",
    "metadata": {},
    "source": [
     "`Ravg` is the average radius, half way between `Rmin` and `Rmax`, so\n",
@@ -238,20 +264,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "environmental-nitrogen",
+   "execution_count": 50,
+   "id": "fitting-might",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "theta    0.00\n",
-       "y        0.00\n",
-       "r        0.02\n",
-       "dtype: float64"
+       "theta      0 radian\n",
+       "y           0 meter\n",
+       "r        0.02 meter\n",
+       "dtype: object"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -263,17 +289,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "improving-acrobat",
+   "execution_count": 51,
+   "id": "backed-vehicle",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "2.7925531914893616×10<sup>-5</sup> meter"
+      ],
+      "text/latex": [
+       "$2.7925531914893616\\times 10^{-5}\\ \\mathrm{meter}$"
+      ],
       "text/plain": [
-       "2.7925531914893616e-05"
+       "2.7925531914893616e-05 <Unit('meter')>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -293,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 52,
    "id": "signed-eight",
    "metadata": {},
    "outputs": [],
@@ -310,7 +342,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "celtic-civilian",
+   "id": "maritime-funds",
    "metadata": {},
    "source": [
     "As usual, the slope function takes a `State` object, a time, and a\n",
@@ -321,25 +353,27 @@
   },
   {
    "cell_type": "markdown",
-   "id": "tracked-delay",
+   "id": "trained-witness",
    "metadata": {},
    "source": [
-    "And as usual, we should test the slope function with the initial conditions."
+    "And as usual, we'll test the slope function with the initial conditions."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "employed-palestine",
+   "execution_count": 53,
+   "id": "exterior-water",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(10, 0.2, 0.0002792553191489362)"
+       "(300.0 <Unit('radian / second')>,\n",
+       " 6.0 <Unit('meter * radian / second')>,\n",
+       " 0.008377659574468085 <Unit('meter * radian / second')>)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -358,7 +392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 54,
    "id": "strong-custody",
    "metadata": {},
    "outputs": [],
@@ -368,175 +402,78 @@
     "    return y - system.L"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "affecting-pathology",
-   "metadata": {},
-   "source": [
-    "Now we can run the simulation like this:"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "stable-lying",
+   "execution_count": 55,
+   "id": "moved-present",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "-47 meter"
+      ],
+      "text/latex": [
+       "$-47\\ \\mathrm{meter}$"
+      ],
       "text/plain": [
-       "'A termination event occurred.'"
+       "-47 <Unit('meter')>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from modsim import run_solve_ivp\n",
-    "\n",
-    "results, details = run_solve_ivp(system, slope_func,\n",
-    "                                  events=event_func)\n",
-    "details.message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "separated-colleague",
-   "metadata": {},
-   "source": [
-    "### Plotting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "satisfied-camel",
-   "metadata": {},
-   "source": [
-    "Plotting `theta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "excited-marshall",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "def plot_theta(results):\n",
-    "    results.theta.plot(color='C0', label='theta')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angle (rad)')\n",
-    "    \n",
-    "plot_theta(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "legislative-sixth",
-   "metadata": {},
-   "source": [
-    "Plotting `y`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "plastic-operations",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_y(results):\n",
-    "    results.y.plot(color='C1', label='y')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Length (m)')\n",
-    "    \n",
-    "plot_y(results)"
+    "event_func(0, system.init, system)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "enclosed-stand",
+   "id": "affecting-pathology",
    "metadata": {},
    "source": [
-    "Plotting `r`"
+    "Now we can run the simulation like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "pleased-edward",
+   "execution_count": 56,
+   "id": "stable-lying",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "'A termination event occurred.'"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "def plot_r(results):\n",
-    "    results.r.plot(color='C2', label='r')\n",
+    "from modsim import run_solve_ivp\n",
     "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Radius (m)')\n",
-    "    \n",
-    "plot_r(results)"
+    "results, details = run_solve_ivp(system, slope_func,\n",
+    "                                  events=event_func)\n",
+    "details.message"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "satellite-evans",
+   "id": "experienced-pathology",
    "metadata": {},
    "source": [
-    " `theta` grows linearly over time, as we should expect. As a result, `r` also grows linearly. But since the derivative of `y` depends on `r`, and `r` is increasing, `y` grows with increasing slope.\n",
-    "\n",
-    "Because this system is so simple, it is almost silly to simulate it. As we'll see in the next section, it is easy enough to solve the\n",
-    "differential equations analytically. But it is often useful to start\n",
-    "with a simple simulation as a way of exploring and checking assumptions."
+    "Here are the last few time steps."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "quantitative-student",
+   "execution_count": 64,
+   "id": "careful-bahrain",
    "metadata": {},
    "outputs": [
     {
@@ -567,49 +504,49 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>125.082541</th>\n",
-       "      <td>1250.825413</td>\n",
-       "      <td>46.862152</td>\n",
-       "      <td>0.054930</td>\n",
+       "      <th>3.843556</th>\n",
+       "      <td>1153.066667</td>\n",
+       "      <td>41.625707</td>\n",
+       "      <td>0.0522</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>125.145239</th>\n",
-       "      <td>1251.452393</td>\n",
-       "      <td>46.896598</td>\n",
-       "      <td>0.054947</td>\n",
+       "      <th>3.927111</th>\n",
+       "      <td>1178.133333</td>\n",
+       "      <td>42.942960</td>\n",
+       "      <td>0.0529</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>125.207937</th>\n",
-       "      <td>1252.079373</td>\n",
-       "      <td>46.931054</td>\n",
-       "      <td>0.054965</td>\n",
+       "      <th>4.010667</th>\n",
+       "      <td>1203.200000</td>\n",
+       "      <td>44.277760</td>\n",
+       "      <td>0.0536</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>125.270635</th>\n",
-       "      <td>1252.706353</td>\n",
-       "      <td>46.965522</td>\n",
-       "      <td>0.054982</td>\n",
+       "      <th>4.094222</th>\n",
+       "      <td>1228.266667</td>\n",
+       "      <td>45.630107</td>\n",
+       "      <td>0.0543</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>125.333333</th>\n",
+       "      <th>4.177778</th>\n",
        "      <td>1253.333333</td>\n",
        "      <td>47.000000</td>\n",
-       "      <td>0.055000</td>\n",
+       "      <td>0.0550</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                  theta          y         r\n",
-       "125.082541  1250.825413  46.862152  0.054930\n",
-       "125.145239  1251.452393  46.896598  0.054947\n",
-       "125.207937  1252.079373  46.931054  0.054965\n",
-       "125.270635  1252.706353  46.965522  0.054982\n",
-       "125.333333  1253.333333  47.000000  0.055000"
+       "                theta          y       r\n",
+       "3.843556  1153.066667  41.625707  0.0522\n",
+       "3.927111  1178.133333  42.942960  0.0529\n",
+       "4.010667  1203.200000  44.277760  0.0536\n",
+       "4.094222  1228.266667  45.630107  0.0543\n",
+       "4.177778  1253.333333  47.000000  0.0550"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -620,37 +557,36 @@
   },
   {
    "cell_type": "markdown",
-   "id": "potential-strength",
+   "id": "underlying-variance",
    "metadata": {},
    "source": [
-    "The elapsed time is about 2 minutes, which is plausible."
+    "At $\\omega = 300$ rad/s, the time it takes to complete one roll is about 4.2 seconds, which is consistent with what we see in the video."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "systematic-differential",
+   "execution_count": 65,
+   "id": "individual-patrick",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2.08888888888889"
+       "4.177777777777778"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "t_final = results.index[-1]\n",
-    "t_final / 60"
+    "results.index[-1]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "incident-delight",
+   "id": "informative-porter",
    "metadata": {},
    "source": [
     "The final value of `y` is 47 meters, as expected."
@@ -658,29 +594,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "id": "suffering-directory",
+   "execution_count": 19,
+   "id": "indian-skirt",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(47.0, 47)"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "47.0 47 meter\n"
+     ]
     }
    ],
    "source": [
     "final_state = results.iloc[-1] \n",
-    "final_state.y, params.L"
+    "print(final_state.y, params.L)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "nasty-triple",
+   "id": "running-tutorial",
    "metadata": {},
    "source": [
     "The final value of radius is `Rmax`."
@@ -688,28 +621,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "cutting-proceeding",
+   "execution_count": 20,
+   "id": "higher-conflict",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(0.05500000000000002, 0.055)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.05500000000000002 0.055 meter\n"
+     ]
     }
    ],
    "source": [
-    "final_state.r, params.Rmax"
+    "print(final_state.r, params.Rmax)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "charitable-mystery",
+   "id": "tamil-referral",
    "metadata": {},
    "source": [
     "And the total number of rotations is close to 200, which seems plausible."
@@ -717,8 +647,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "id": "removable-brother",
+   "execution_count": 21,
+   "id": "employed-ordinary",
    "metadata": {},
    "outputs": [
     {
@@ -727,7 +657,7 @@
        "199.47419534184226"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -740,62 +670,31 @@
   },
   {
    "cell_type": "markdown",
-   "id": "continued-angel",
+   "id": "otherwise-mississippi",
    "metadata": {},
    "source": [
-    "## Animation\n",
-    "\n",
-    "Here's a draw function that animates the results using `matplotlib` patches."
+    "As an exercise, we'll see how fast the paper is moving.  But first, let's take a closer look at the results."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "confused-preview",
+   "cell_type": "markdown",
+   "id": "lesser-consumer",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from matplotlib.patches import Circle, Arrow\n",
-    "from matplotlib.pyplot import gca, axis\n",
-    "from modsim import pol2cart\n",
+    "## Plotting\n",
     "\n",
-    "def draw_func(t, state):\n",
-    "    # get radius in mm\n",
-    "    theta, y, r = state\n",
-    "    radius = r\n",
-    "    \n",
-    "    # draw the paper\n",
-    "    circle = Circle([0, 0], radius)\n",
-    "    gca().add_patch(circle)\n",
-    "    \n",
-    "    # draw the core\n",
-    "    core = Circle([0, 0], params.Rmin, color='0.9')\n",
-    "    gca().add_patch(core)\n",
-    "    \n",
-    "    # draw the perimeter\n",
-    "    core = Circle([0, 0], params.Rmax, \n",
-    "                  color='0.7', fill=False)\n",
-    "    gca().add_patch(core)\n",
-    "    \n",
-    "    # draw an arrow to show rotation\n",
-    "    dx, dy = pol2cart(theta, radius)\n",
-    "    arrow = Arrow(0, 0, dx, dy, color='C1', width=0.01)\n",
-    "    gca().add_patch(arrow)\n",
-    "\n",
-    "    # make the aspect ratio 1\n",
-    "    decorate(xlim=[-0.1, 0.1],\n",
-    "             ylim=[-0.07, 0.07])"
+    "Here's what `theta` looks like over time."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "id": "naughty-tenant",
+   "execution_count": 66,
+   "id": "included-schema",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -807,18 +706,35 @@
     }
    ],
    "source": [
-    "draw_func(0, system.init)"
+    "from modsim import decorate\n",
+    "\n",
+    "def plot_theta(results):\n",
+    "    results.theta.plot(color='C0', label='theta')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Angle (rad)')\n",
+    "    \n",
+    "plot_theta(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "regulation-runner",
+   "metadata": {},
+   "source": [
+    "`theta` grows linearly, as we should expect with constant angular velocity.\n",
+    "\n",
+    "Here's what `r` looks like over time."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "id": "distant-nancy",
+   "execution_count": 67,
+   "id": "persistent-siemens",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -830,20 +746,34 @@
     }
    ],
    "source": [
-    "draw_func(t_final, final_state)"
+    "def plot_r(results):\n",
+    "    results.r.plot(color='C2', label='r')\n",
+    "\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Radius (m)')\n",
+    "    \n",
+    "plot_r(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dedicated-tuning",
+   "metadata": {},
+   "source": [
+    "`r` also increases linearly.\n",
+    "\n",
+    "But since the derivative of `y` depends on `r`, and `r` is increasing, `y` grows with increasing slope."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "id": "identical-expense",
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 68,
+   "id": "contrary-typing",
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -855,22 +785,29 @@
     }
    ],
    "source": [
-    "from modsim import animate\n",
+    "def plot_y(results):\n",
+    "    results.y.plot(color='C1', label='y')\n",
     "\n",
-    "# animate(results, draw_func)"
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Length (m)')\n",
+    "    \n",
+    "plot_y(results)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "contained-taiwan",
+   "id": "furnished-executive",
    "metadata": {},
    "source": [
-    "Now that we have a working simulation, it is also useful to do some analysis."
+    "Because this system is so simple, it is almost silly to simulate it; as we'll see in the next section, it is easy enough to solve the\n",
+    "differential equations analytically. \n",
+    "\n",
+    "However, it is often useful to start with simulation as a way of exploring and checking assumptions."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "electric-longer",
+   "id": "better-asbestos",
    "metadata": {},
    "source": [
     "## Analysis\n",
@@ -881,17 +818,17 @@
     "\n",
     "We can find $\\theta$ as a function of time by integrating both sides:\n",
     "\n",
-    "$$\\theta(t) = \\omega t + C_1$$ \n",
+    "$$\\theta(t) = \\omega t$$ \n",
     "\n",
-    "With the initial condition $\\theta(0)=0$, we find $C_1=0$. Similarly,\n",
+    "Similarly, we can solve this equation\n",
     "\n",
     "$$\\frac{dr}{dt} = k \\omega$$ \n",
     "\n",
-    "So\n",
+    "to find\n",
     "\n",
-    "$$r(t) = k \\omega t + C_2$$ \n",
+    "$$r(t) = k \\omega t + R_{min}$$ \n",
     "\n",
-    "With the initial condition $r(0)=R_{min}$, we find $C_2=R_{min}$. Then we can plug the solution for $r$ into the equation for $y$: \n",
+    "Then we can plug the solution for $r$ into the equation for $y$: \n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{dy}{dt} & = r \\omega                      \\\\\n",
@@ -899,10 +836,16 @@
     "              \n",
     "Integrating both sides yields:\n",
     "\n",
-    "$$y(t) = \\left[ k \\omega t^2 / 2 + R_{min} t \\right] \\omega + C_3$$ \n",
-    "\n",
-    "So $y$ is a parabola, as you might have guessed. With initial condition $y(0)=0$, we find $C_3=0$.\n",
+    "$$y(t) = \\left[ k \\omega t^2 / 2 + R_{min} t \\right] \\omega$$ \n",
     "\n",
+    "So $y$ is a parabola, as you might have guessed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "meaningful-kazakhstan",
+   "metadata": {},
+   "source": [
     "We can also use these equations to find the relationship between $y$ and $r$, independent of time, which we can use to compute $k$. Using a move we saw in Section xxx, I'll divide Equations 1 and\n",
     "2, yielding\n",
     "\n",
@@ -916,7 +859,11 @@
     "\n",
     "$$r^2 / 2 = k y + C$$ \n",
     "\n",
-    "When $y=0$, $r=R_{min}$, so $$C = \\frac{1}{2} R_{min}^2$$ Solving for $y$, we have \n",
+    "When $y=0$, $r=R_{min}$, so \n",
+    "\n",
+    "$$C = \\frac{1}{2} R_{min}^2$$ \n",
+    "\n",
+    "Solving for $y$, we have \n",
     "\n",
     "$$y = \\frac{1}{2k} (r^2 - R_{min}^2)                 \\label{eqn3}$$\n",
     "\n",
@@ -928,195 +875,149 @@
     "\n",
     "$$k =  \\frac{1}{2L} (R_{max}^2 - R_{min}^2)           \\label{eqn4}$$\n",
     "\n",
-    "Plugging in the values of the parameters yields `2.8e-5` m/rad, the same as the \"estimate\" we computed in Section xxx. In this case the estimate turns out to be exact."
+    "Plugging in the values of the parameters yields `2.8e-5` m/rad, the same as the \"estimate\" we computed in Section xxx. "
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "welcome-organization",
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "acknowledged-register",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.7925531914893616e-05 meter 2.7925531914893616e-05 meter\n"
+     ]
+    }
+   ],
    "source": [
-    "We can also see the relationship between `y` and `r`, which I derive analytically in the book."
+    "k = (params.Rmax**2 - params.Rmin**2) / (2 * params.L)\n",
+    "print(k, system.k)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "foreign-needle",
+   "id": "retired-skill",
    "metadata": {},
    "source": [
-    "## Summary"
+    "In this case the estimate turns out to be exact."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "hindu-civilization",
+   "id": "foreign-needle",
    "metadata": {},
    "source": [
-    "### Rolling paper\n",
-    "\n",
-    "We'll start by loading the units we need."
+    "## Summary"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "active-absorption",
+   "cell_type": "markdown",
+   "id": "thick-luther",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'UNITS' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-22-2bc8c7513e21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mradian\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUNITS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mradian\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUNITS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUNITS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msecond\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'UNITS' is not defined"
-     ]
-    }
-   ],
    "source": [
-    "radian = UNITS.radian\n",
-    "m = UNITS.meter\n",
-    "s = UNITS.second"
+    "### Exercises\n",
+    "\n",
+    "**Exercise:** Since we keep `omega` constant, the linear velocity of the paper increases with radius.  We can use `gradient` to estimate the derivative of `results.y`."
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "fancy-floating",
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "cardiac-hospital",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "And creating a `Params` object with the system parameters"
+    "# Solution\n",
+    "\n",
+    "from modsim import gradient\n",
+    "\n",
+    "dydt = gradient(results.y);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "british-method",
+   "execution_count": 90,
+   "id": "welsh-charleston",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(Rmin = 0.02 * m,\n",
-    "                Rmax = 0.055 * m,\n",
-    "                L = 47 * m,\n",
-    "                omega = 10 * radian / s,\n",
-    "                t_end = 130 * s,\n",
-    "                dt = 1*s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "governing-paraguay",
-   "metadata": {},
-   "source": [
-    "The following function estimates the parameter `k`, which is the increase in the radius of the roll for each radian of rotation. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "spread-fabric",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def estimate_k(params):\n",
-    "    \"\"\"Estimates the parameter `k`.\n",
-    "    \n",
-    "    params: Params with Rmin, Rmax, and L\n",
-    "    \n",
-    "    returns: k in meters per radian\n",
-    "    \"\"\"\n",
-    "    Rmin, Rmax, L = params.Rmin, params.Rmax, params.L\n",
-    "    \n",
-    "    Ravg = (Rmax + Rmin) / 2\n",
-    "    Cavg = 2 * pi * Ravg\n",
-    "    revs = L / Cavg\n",
-    "    rads = 2 * pi * revs\n",
-    "    k = (Rmax - Rmin) / rads\n",
-    "    return k"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "plain-chambers",
-   "metadata": {},
-   "source": [
-    "As usual, `make_system` takes a `Params` object and returns a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "tutorial-direction",
-   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params with Rmin, Rmax, and L\n",
-    "    \n",
-    "    returns: System with init, k, and ts\n",
-    "    \"\"\"\n",
-    "    init = State(theta = 0 * radian,\n",
-    "                 y = 0 * m,\n",
-    "                 r = params.Rmin)\n",
-    "    \n",
-    "    k = estimate_k(params)\n",
+    "# Solution\n",
     "\n",
-    "    return System(params, init=init, k=k)"
+    "dydt.plot(label='dydt')\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Linear velocity (m/s)')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "separate-flexibility",
+   "id": "becoming-birthday",
    "metadata": {},
    "source": [
-    "Testing `make_system`"
+    "With constant angular velocity, linear velocity is increasing, reaching its maximum at the end."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "latter-retention",
+   "execution_count": 92,
+   "id": "informational-washer",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aggressive-bikini",
-   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16.394999999999992"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "system.init"
+    "max_linear_velocity = dydt.iloc[-1]\n",
+    "max_linear_velocity"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "processed-archive",
+   "id": "bizarre-variation",
    "metadata": {},
    "source": [
-    "Now we can write a slope function based on the differential equations\n",
+    "Now suppose this peak velocity is the limiting factor; that is, we can't move the paper any faster than that.\n",
     "\n",
-    "$\\omega = \\frac{d\\theta}{dt} = 10$\n",
+    "Nevertheless, we might be able to speed up the process by keeping the linear velocity at the maximum all the time.\n",
     "\n",
-    "$\\frac{dy}{dt} = r \\frac{d\\theta}{dt}$\n",
+    "Write a slope function that keeps the linear velocity, `dydt`, constant, and computes the angular velocity, `omega`, accordingly.\n",
     "\n",
-    "$\\frac{dr}{dt} = k \\frac{d\\theta}{dt}$\n"
+    "Run the simulation and see how much faster we could finish rolling the paper."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "unable-track",
+   "execution_count": 93,
+   "id": "excellent-japanese",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def slope_func(state, t, system):\n",
+    "# Solution\n",
+    "\n",
+    "def slope_func(t, state, system):\n",
     "    \"\"\"Computes the derivatives of the state variables.\n",
     "    \n",
     "    state: State object with theta, y, r\n",
@@ -1128,245 +1029,167 @@
     "    theta, y, r = state\n",
     "    k, omega = system.k, system.omega\n",
     "    \n",
-    "    dydt = r * omega\n",
+    "    dydt = system.linear_velocity\n",
+    "    omega = dydt / r\n",
     "    drdt = k * omega\n",
     "    \n",
     "    return omega, dydt, drdt"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "behind-aaron",
-   "metadata": {},
-   "source": [
-    "Testing `slope_func`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "middle-paint",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "better-filing",
-   "metadata": {},
-   "source": [
-    "We'll use an event function to stop when `y=L`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "numerous-legislature",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Detects when we've rolled length `L`.\n",
-    "    \n",
-    "    state: State object with theta, y, r\n",
-    "    t: time\n",
-    "    system: System object with L\n",
-    "    \n",
-    "    returns: difference between `y` and `L`\n",
-    "    \"\"\"\n",
-    "    theta, y, r = state\n",
-    "    \n",
-    "    return y - system.L"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "broad-place",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "event_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "polished-phrase",
-   "metadata": {},
-   "source": [
-    "Now we can run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "monetary-charlotte",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_solve_ivp(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "religious-conditions",
-   "metadata": {},
-   "source": [
-    "And look at the results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "lucky-advance",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Run the simulation again with a smaller step size to smooth out the animation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "noted-amount",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Since we keep `omega` constant, the linear velocity of the paper increases with radius.  Use `gradient` to estimate the derivative of `results.y`.  What is the peak linear velocity?"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "given-panic",
+   "execution_count": 94,
+   "id": "steady-member",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "dydt = gradient(results.y);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "foreign-reservation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(dydt, label='dydt')\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Linear velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "proof-abraham",
-   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(819.7499999999997 <Unit('1 / meter')>,\n",
+       " 16.394999999999992,\n",
+       " 0.02289195478723403 <Unit('dimensionless')>)"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Solution\n",
     "\n",
-    "linear_velocity = get_last_value(dydt) * m/s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "advisory-integer",
-   "metadata": {},
-   "source": [
-    "Now suppose the peak velocity is the limit; that is, we can't move the paper any faster than that.\n",
-    "\n",
-    "Nevertheless, we might be able to speed up the process by keeping the linear velocity at the maximum all the time.\n",
-    "\n",
-    "Write a slope function that keeps the linear velocity, `dydt`, constant, and computes the angular velocity, `omega`, accordingly.\n",
-    "\n",
-    "Run the simulation and see how much faster we could finish rolling the paper."
+    "system.linear_velocity = max_linear_velocity\n",
+    "slope_func(0, system.init, system)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "historic-barrier",
+   "execution_count": 95,
+   "id": "applied-sacramento",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A termination event occurred.'"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Solution\n",
     "\n",
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object with theta, y, r\n",
-    "    t: time\n",
-    "    system: System object with r, k\n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    theta, y, r = state\n",
-    "    k, omega = system.k, system.omega\n",
-    "    \n",
-    "    dydt = linear_velocity\n",
-    "    omega = dydt / r\n",
-    "    drdt = k * omega\n",
-    "    \n",
-    "    return omega, dydt, drdt"
+    "results, details = run_solve_ivp(system, slope_func, \n",
+    "                                 events=event_func)\n",
+    "details.message"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "false-identity",
+   "execution_count": 96,
+   "id": "adult-chuck",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.8667276608722188"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Solution\n",
     "\n",
-    "slope_func(system.init, 0, system)"
+    "t_final = results.index[-1]\n",
+    "t_final"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "physical-novelty",
+   "execution_count": 97,
+   "id": "conditional-cliff",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Solution\n",
     "\n",
-    "results, details = run_solve_ivp(system, slope_func, events=event_func)\n",
-    "details"
+    "plot_theta(results)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "peaceful-doctor",
+   "execution_count": 98,
+   "id": "arranged-queensland",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Solution\n",
     "\n",
-    "t_final = get_last_label(results) * s"
+    "plot_r(results)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "limiting-license",
+   "execution_count": 99,
+   "id": "similar-variance",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Solution\n",
     "\n",
-    "plot_three(results)"
+    "plot_y(results)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "least-analysis",
+   "id": "lovely-orange",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/jupyter/chap25.ipynb b/jupyter/chap25.ipynb
new file mode 100644
index 00000000..93578711
--- /dev/null
+++ b/jupyter/chap25.ipynb
@@ -0,0 +1,1421 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "unauthorized-winter",
+   "metadata": {},
+   "source": [
+    "# Chapter 25"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "complete-innocent",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "practical-roman",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "    \n",
+    "try:\n",
+    "    import modsim\n",
+    "except ImportError:\n",
+    "    !pip install modsimpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "desirable-jason",
+   "metadata": {},
+   "source": [
+    "Intro"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "earned-motorcycle",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we modeled a scenario with constant angular\n",
+    "velocity. In this chapter we make it more complex; we'll model a teapot, on a turntable, revolving with constant angular acceleration and deceleration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "together-tobacco",
+   "metadata": {},
+   "source": [
+    "## Angular acceleration\n",
+    "\n",
+    "Just as linear acceleration is the derivative of velocity, **angular\n",
+    "acceleration** is the derivative of angular velocity. And just as linear acceleration is caused by force, angular acceleration is caused by the rotational version of force, **torque**. If you are not familiar with torque, you can read about it at <http://modsimpy.com/torque>.\n",
+    "\n",
+    "In general, torque is a vector quantity, defined as the **cross\n",
+    "product** of $\\vec{r}$ and $\\vec{F}$, where $\\vec{r}$ is the **lever\n",
+    "arm**, a vector from the point of rotation to the point where the force is applied, and $\\vec{F}$ is the vector that represents the magnitude and direction of the force."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "promotional-trigger",
+   "metadata": {},
+   "source": [
+    "However, for the problems in this chapter, we only need the *magnitude* of torque; we don't care about the direction. In that case, we can compute \n",
+    "\n",
+    "$$\\tau = r F \\sin \\theta$$ \n",
+    "\n",
+    "where $\\tau$ is torque, $r$ is the length of the lever arm, $F$ is the magnitude of force, and $\\theta$ is the angle between $\\vec{r}$ and $\\vec{F}$.\n",
+    "\n",
+    "Since torque is the product of a length and a force, it is expressed in newton meters (Nm)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "infrared-summit",
+   "metadata": {},
+   "source": [
+    "## Moment of inertia\n",
+    "\n",
+    "In the same way that linear acceleration is related to force by Newton's second law of motion, $F=ma$, angular acceleration is related to torque by another form of Newton's law: \n",
+    "\n",
+    "$$\\tau = I \\alpha$$ \n",
+    "\n",
+    "where $\\alpha$ is angular acceleration and $I$ is **moment of inertia**. Just as mass is what makes it hard to accelerate an object, moment of inertia is what makes it hard to spin an object.\n",
+    "(That might sound like a dumb way to describe mass, but might actually be the most fundamental definition.)\n",
+    "\n",
+    "In the most general case, a 3-D object rotating around an arbitrary\n",
+    "axis, moment of inertia is a tensor, which is a function that takes a\n",
+    "vector as a parameter and returns a vector as a result.\n",
+    "\n",
+    "Fortunately, in a system where all rotation and torque happens around a single axis, we don't have to deal with the most general case. We can treat moment of inertia as a scalar quantity.\n",
+    "\n",
+    "For a small object with mass $m$, rotating around a point at distance\n",
+    "$r$, the moment of inertia is $I = m r^2$. For more complex objects, we can compute $I$ by dividing the object into small masses, computing\n",
+    "moments of inertia for each mass, and adding them up.\n",
+    "\n",
+    "However, for most simple shapes, people have already done the\n",
+    "calculations; you can just look up the answers. For example, see\n",
+    "<http://modsimpy.com/moment>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "julian-klein",
+   "metadata": {},
+   "source": [
+    "## Teapots and turntables\n",
+    "\n",
+    "Tables in Chinese restaurants often have a rotating tray or turntable\n",
+    "that makes it easy for customers to share dishes. These turntables are\n",
+    "supported by low-friction bearings that allow them to turn easily and\n",
+    "glide. However, they can be heavy, especially when they are loaded with food, so they have a high moment of inertia.\n",
+    "\n",
+    "Suppose I am sitting at a table with a pot of tea on the turntable\n",
+    "directly in front of me, and the person sitting directly opposite asks\n",
+    "me to pass the tea. I push on the edge of the turntable with 1 N of\n",
+    "force until it has turned 0.5 rad, then let go. The turntable glides\n",
+    "until it comes to a stop 1.5 rad from the starting position. How much\n",
+    "force should I apply for a second push so the teapot glides to a stop\n",
+    "directly opposite me?\n",
+    "\n",
+    "We'll answer this question in these steps:\n",
+    "\n",
+    "1.  I'll use the results from the first push to estimate the coefficient of friction for the turntable.\n",
+    "\n",
+    "2.  As an exercise, you'll use that coefficient of friction to estimate the force needed to rotate the turntable through the remaining angle.\n",
+    "\n",
+    "Our simulation will use the following parameters:\n",
+    "\n",
+    "1.  The radius of the turntable is 0.5 m, and its weight is 7 kg.\n",
+    "\n",
+    "2.  The teapot weights 0.3 kg, and it sits 0.4 m from the center of the turntable.\n",
+    "\n",
+    "![Diagram of a turntable with a\n",
+    "teapot.](figs/teapot.pdf){height=\"2.5in\"}\n",
+    "\n",
+    "This figure shows the scenario, where $F$ is the force I apply to the turntable at the perimeter, perpendicular to the moment arm, $r$, and $\\tau$ is the resulting torque. The blue circle near the bottom is the teapot.\n",
+    "\n",
+    "Here's a `Params` object with the parameters of the scenario:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cathedral-lithuania",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pint\n",
+    "\n",
+    "units = pint.UnitRegistry()\n",
+    "rad = units.radian\n",
+    "m = units.meter\n",
+    "s = units.second\n",
+    "kg = units.kilogram\n",
+    "N = units.newton"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "enclosed-happiness",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import Params\n",
+    "from numpy import pi\n",
+    "\n",
+    "params = Params(radius_disk=0.5,     # m\n",
+    "                mass_disk=7,         # kg\n",
+    "                radius_pot=0.4,      # m\n",
+    "                mass_pot=0.3,        # kg\n",
+    "                force=1,             # N\n",
+    "                torque_friction=0.2, # N*m\n",
+    "                theta_0=0,           # radian\n",
+    "                theta_push=0.5,      # radian\n",
+    "                theta_test=1.5,      # radian\n",
+    "                theta_target=pi,     # radian\n",
+    "                t_end=20             # s\n",
+    "               )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "robust-adrian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import Params\n",
+    "from numpy import pi\n",
+    "\n",
+    "params = Params(radius_disk = 0.5*m,\n",
+    "                mass_disk = 7*kg,\n",
+    "                radius_pot = 0.4*m,\n",
+    "                mass_pot = 0.3*kg,\n",
+    "                force = 1*N,\n",
+    "                torque_friction = 0.2*N*m,\n",
+    "                theta_0 = 0*rad,\n",
+    "                omega_0 = 0*rad/s,\n",
+    "                theta_push = 0.5*rad,\n",
+    "                theta_test = 1.5*rad,\n",
+    "                theta_target = pi*rad,\n",
+    "                t_end = 20*s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "second-leather",
+   "metadata": {},
+   "source": [
+    "`make_system` creates the initial state, `init`, and computes the total\n",
+    "moment of inertia for the turntable and the teapot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "acoustic-furniture",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import State, System\n",
+    "\n",
+    "def make_system(params):\n",
+    "    \"\"\"Make a system object.\n",
+    "    \n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    mass_disk, mass_pot = params.mass_disk, params.mass_pot\n",
+    "    radius_disk, radius_pot = params.radius_disk, params.radius_pot\n",
+    "    \n",
+    "    init = State(theta=params.theta_0, omega=params.omega_0)\n",
+    "    \n",
+    "    I_disk = mass_disk * radius_disk**2 / 2\n",
+    "    I_pot = mass_pot * radius_pot**2\n",
+    "    \n",
+    "    return System(params, \n",
+    "                  init=init, \n",
+    "                  I=I_disk+I_pot,\n",
+    "                  )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "convenient-control",
+   "metadata": {},
+   "source": [
+    "In the initial state, `theta` represents the angle of the table in rad;\n",
+    "`omega` represents the angular velocity in rad/s.\n",
+    "\n",
+    "`I_disk` is the moment of inertia of the turntable, which is based on\n",
+    "the moment of inertia for a horizontal disk revolving around a vertical axis through its center: \n",
+    "\n",
+    "$$I_{disk} = m r^2 / 2$$ \n",
+    "\n",
+    "`I_pot` is the moment\n",
+    "of inertia of the teapot, which I treat as a point mass with:\n",
+    "\n",
+    "$$I_{point} = m r^2$$ \n",
+    "\n",
+    "In SI units, moment of inertia is expressed in kg m$^2$.\n",
+    "\n",
+    "Now we can make a `System` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "objective-station",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system1 = make_system(params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "crucial-recognition",
+   "metadata": {},
+   "source": [
+    "Here's a slope that takes the current state, which contains angle and\n",
+    "angular velocity, and returns the derivatives, angular velocity and\n",
+    "angular acceleration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ranking-local",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    \"\"\"Computes the derivatives of the state variables.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time\n",
+    "    system: System object \n",
+    "    \n",
+    "    returns: sequence of derivatives\n",
+    "    \"\"\"\n",
+    "    theta, omega = state\n",
+    "    radius_disk, force = system.radius_disk, system.force\n",
+    "    torque_friction, I = system.torque_friction, system.I\n",
+    "    \n",
+    "    torque = radius_disk * force - torque_friction\n",
+    "    alpha = torque / I\n",
+    "    \n",
+    "    return omega, alpha "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exposed-court",
+   "metadata": {},
+   "source": [
+    "In this scenario, the force I apply to the turntable is always\n",
+    "perpendicular to the lever arm, so $\\sin \\theta = 1$ and the torque due\n",
+    "to force is $\\tau = r F$.\n",
+    "\n",
+    "`torque_friction` represents the torque due to friction. Because the\n",
+    "turntable is rotating in the direction of positive `theta`, friction\n",
+    "acts in the direction of negative `theta`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "saved-purple",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0 <Unit('radian / second')>,\n",
+       " 0.32502708559046584 <Unit('newton / kilogram / meter')>)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "slope_func(0, system1.init, system1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "decent-microwave",
+   "metadata": {},
+   "source": [
+    "We are almost ready to run the simulation, but first there's a problem we have to address.\n",
+    "\n",
+    "When I stop pushing on the turntable, the angular acceleration changes\n",
+    "abruptly. We could implement the slope function with an `if` statement\n",
+    "that checks the value of `theta` and sets `force` accordingly. And for a coarse model like this one, that might be fine. But we will get more\n",
+    "accurate results if we simulate the system in two phases:\n",
+    "\n",
+    "1.  During the first phase, force is constant, and we run until `theta` is 0.5 radians.\n",
+    "\n",
+    "2.  During the second phase, force is 0, and we run until `omega` is 0.\n",
+    "\n",
+    "Then we can combine the results of the two phases into a single\n",
+    "`TimeFrame`.\n",
+    "\n",
+    "Here's the event function I'll use for Phase 1; it stops the simulation when `theta` reaches `theta_end`, which is when I stop pushing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "black-wichita",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func1(t, state, system):\n",
+    "    \"\"\"Stops when theta reaches theta_push.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time\n",
+    "    system: System object \n",
+    "    \n",
+    "    returns: difference from target\n",
+    "    \"\"\"\n",
+    "    theta, omega = state\n",
+    "    return theta - system.theta_push"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "separate-college",
+   "metadata": {},
+   "source": [
+    "As usual, we'll test the event function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ranking-google",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-0.5 radian"
+      ],
+      "text/latex": [
+       "$-0.5\\ \\mathrm{radian}$"
+      ],
+      "text/plain": [
+       "-0.5 <Unit('radian')>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "event_func1(0, system1.init, system1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reduced-sharp",
+   "metadata": {},
+   "source": [
+    "Now we can run the first phase of the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "meaning-philosophy",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A termination event occurred.'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from modsim import run_solve_ivp\n",
+    "\n",
+    "results1, details1 = run_solve_ivp(system1, slope_func,\n",
+    "                                   events=event_func1)\n",
+    "details1.message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "focused-invention",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>theta</th>\n",
+       "      <th>omega</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1.613720</th>\n",
+       "      <td>0.4232</td>\n",
+       "      <td>0.524503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.648800</th>\n",
+       "      <td>0.4418</td>\n",
+       "      <td>0.535905</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.683881</th>\n",
+       "      <td>0.4608</td>\n",
+       "      <td>0.547307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.718962</th>\n",
+       "      <td>0.4802</td>\n",
+       "      <td>0.558709</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.754043</th>\n",
+       "      <td>0.5000</td>\n",
+       "      <td>0.570111</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           theta     omega\n",
+       "1.613720  0.4232  0.524503\n",
+       "1.648800  0.4418  0.535905\n",
+       "1.683881  0.4608  0.547307\n",
+       "1.718962  0.4802  0.558709\n",
+       "1.754043  0.5000  0.570111"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "willing-receipt",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "Before we run the second phase, we have to extract the final time and\n",
+    "state of the first phase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "russian-experience",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_0 = results1.index[-1]\n",
+    "init2 = results1.iloc[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aware-generator",
+   "metadata": {},
+   "source": [
+    "Now we can make a `System` object for Phase 2, with the initial state\n",
+    "from Phase 1, and with `force=0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "chinese-cover",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system2 = System(system1, t_0=t_0, init=init2, force=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "actual-monaco",
+   "metadata": {},
+   "source": [
+    "For the second phase, we need an event function that stops when the\n",
+    "turntable stops; that is, when angular velocity is 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "recovered-section",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func2(t, state, system):\n",
+    "    \"\"\"Stops when omega is 0.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time\n",
+    "    system: System object \n",
+    "    \n",
+    "    returns: omega\n",
+    "    \"\"\"\n",
+    "    theta, omega = state\n",
+    "    return omega"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "corporate-taste",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5701114676889649"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "event_func2(system2.t_0, system2.init, system2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "administrative-major",
+   "metadata": {},
+   "source": [
+    "Now we can run the second phase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "destroyed-adrian",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A termination event occurred.'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results2, details2 = run_solve_ivp(system2, slope_func,\n",
+    "                                   events=event_func2)\n",
+    "details2.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hindu-requirement",
+   "metadata": {},
+   "source": [
+    "Pandas provides `combine_first`, which combines `results1` and\n",
+    "`results2`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "frank-scene",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = results1.combine_first(results2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "metropolitan-roommate",
+   "metadata": {},
+   "source": [
+    "Now we can plot `theta` for both phases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "distributed-humanitarian",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import decorate\n",
+    "\n",
+    "def plot_theta(results):\n",
+    "    results.theta.plot(label='theta')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Angle (rad)')\n",
+    "    \n",
+    "plot_theta(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "excess-confidence",
+   "metadata": {},
+   "source": [
+    "And `omega`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "proved-surfing",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_omega(results):\n",
+    "    results.omega.plot(label='omega', color='C1')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Angular velocity (rad/s)')\n",
+    "    \n",
+    "plot_omega(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "handy-frontier",
+   "metadata": {},
+   "source": [
+    "Angular velocity, `omega`, increases linearly while I am pushing, and decreases linearly after I let go. The angle, `theta`, is the integral of angular velocity, so it forms a parabola during each phase.\n",
+    "\n",
+    "In the next section, we'll use this simulation to estimate the torque\n",
+    "due to friction."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "careful-minneapolis",
+   "metadata": {},
+   "source": [
+    "## Estimating friction\n",
+    "\n",
+    "Let's take the code from the previous section and wrap it in a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "constant-wallace",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_two_phases(force, torque_friction, params):\n",
+    "    \"\"\"Run both phases.\n",
+    "    \n",
+    "    force: force applied to the turntable\n",
+    "    torque_friction: friction due to torque\n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: TimeFrame of simulation results\n",
+    "    \"\"\"\n",
+    "    # put the specified parameters into the Params object\n",
+    "    params = Params(params, \n",
+    "                    force=force, \n",
+    "                    torque_friction=torque_friction)\n",
+    "\n",
+    "    # run phase 1\n",
+    "    system1 = make_system(params)\n",
+    "    results1, details1 = run_solve_ivp(system1, slope_func, \n",
+    "                                       events=event_func1)\n",
+    "\n",
+    "    # get the final state from phase 1\n",
+    "    t_0 = results1.index[-1]\n",
+    "    init2 = results1.iloc[-1]\n",
+    "    \n",
+    "    # run phase 2\n",
+    "    system2 = System(system1, t_0=t_0, init=init2, force=0)\n",
+    "    results2, details2 = run_solve_ivp(system2, slope_func, \n",
+    "                                       events=event_func2)\n",
+    "    \n",
+    "    # combine and return the results\n",
+    "    results = results1.combine_first(results2)\n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "positive-possible",
+   "metadata": {},
+   "source": [
+    "Let's test it with the same parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "played-ranking",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>theta</th>\n",
+       "      <th>omega</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4.174622</th>\n",
+       "      <td>1.2452</td>\n",
+       "      <td>4.560892e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.227244</th>\n",
+       "      <td>1.2473</td>\n",
+       "      <td>3.420669e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.279865</th>\n",
+       "      <td>1.2488</td>\n",
+       "      <td>2.280446e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.332486</th>\n",
+       "      <td>1.2497</td>\n",
+       "      <td>1.140223e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.385107</th>\n",
+       "      <td>1.2500</td>\n",
+       "      <td>2.775558e-16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           theta         omega\n",
+       "4.174622  1.2452  4.560892e-02\n",
+       "4.227244  1.2473  3.420669e-02\n",
+       "4.279865  1.2488  2.280446e-02\n",
+       "4.332486  1.2497  1.140223e-02\n",
+       "4.385107  1.2500  2.775558e-16"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "force = 1\n",
+    "torque_friction = 0.2\n",
+    "results = run_two_phases(params.force, params.torque_friction, params)\n",
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "damaged-concert",
+   "metadata": {},
+   "source": [
+    "And check the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "bulgarian-halloween",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.2499999999999993"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta_final = results.iloc[-1].theta\n",
+    "theta_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "electronic-latin",
+   "metadata": {},
+   "source": [
+    "We can use `run_two_phases` to write an error function we can use, with\n",
+    "`root_bisect`, to find the torque due to friction that yields the\n",
+    "observed results from the first push, a total rotation of 1.5 rad."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "skilled-diving",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def error_func1(torque_friction, params):\n",
+    "    \"\"\"Error function for root_scalar.\n",
+    "    \n",
+    "    torque_friction: hypothetical value\n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: offset from target value\n",
+    "    \"\"\"\n",
+    "    force = 1\n",
+    "    results = run_two_phases(force, torque_friction, params)\n",
+    "    theta_final = results.iloc[-1].theta\n",
+    "    print(torque_friction, theta_final)\n",
+    "    return theta_final - params.theta_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "generous-parcel",
+   "metadata": {},
+   "source": [
+    "Testing the error function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "united-ranch",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.1 2.5000000000000018\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "1.0000000000000018 radian"
+      ],
+      "text/latex": [
+       "$1.0000000000000018\\ \\mathrm{radian}$"
+      ],
+      "text/plain": [
+       "1.0000000000000018 <Unit('radian')>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guess1 = 0.1\n",
+    "error_func1(guess1, params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "rotary-brazilian",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.2 1.2499999999999993\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "-0.25000000000000067 radian"
+      ],
+      "text/latex": [
+       "$-0.25000000000000067\\ \\mathrm{radian}$"
+      ],
+      "text/plain": [
+       "-0.25000000000000067 <Unit('radian')>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guess2 = 0.2\n",
+    "error_func1(guess2, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "comparable-physiology",
+   "metadata": {},
+   "source": [
+    "And running `root_scalar`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "hungarian-cattle",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.1 2.5000000000000018\n",
+      "0.2 1.2499999999999993\n",
+      "0.18 1.388888888888888\n",
+      "0.16559999999999983 1.5096618357487925\n",
+      "0.16675199999999987 1.4992323930147764\n",
+      "0.16666721279999988 1.4999950848161059\n",
+      "0.16666666666487695 1.5000000000161076\n",
+      "0.16666666666666669 1.5000000000000002\n",
+      "0.16666666666766675 1.4999999999909974\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.optimize import root_scalar\n",
+    "\n",
+    "res = root_scalar(error_func1, params, bracket=[guess1, guess2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abandoned-remedy",
+   "metadata": {},
+   "source": [
+    "The result is 0.166 Nm, a little less than the initial guess."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "knowing-sleeve",
+   "metadata": {},
+   "source": [
+    "## Animation\n",
+    "\n",
+    "\n",
+    "Here's a draw function we can use to animate the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "funky-affect",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.patches import Circle, Arrow\n",
+    "from matplotlib.pyplot import gca, axis\n",
+    "from modsim import pol2cart\n",
+    "\n",
+    "def draw_func(t, state, params):\n",
+    "    theta, omega = state\n",
+    "    \n",
+    "    # draw a circle for the table\n",
+    "    circle1 = Circle([0, 0], params.radius_disk)\n",
+    "    gca().add_patch(circle1)\n",
+    "    \n",
+    "    # draw a circle for the teapot\n",
+    "    center = pol2cart(theta, params.radius_pot)\n",
+    "    circle2 = Circle(center, 0.05, color='C1')\n",
+    "    gca().add_patch(circle2)\n",
+    "\n",
+    "    axis('equal')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "gentle-transmission",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modsim import remove_units\n",
+    "\n",
+    "params_no_unit = remove_units(params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "illegal-remainder",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "state = results.iloc[0]\n",
+    "draw_func(0, state, params_no_unit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "toxic-shark",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from modsim import animate\n",
+    "\n",
+    "animate(results, draw_func, params_no_unit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "horizontal-shade",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "professional-modem",
+   "metadata": {},
+   "source": [
+    "Now that we know the torque due to friction, we can compute the force\n",
+    "needed to rotate the turntable through the remaining angle, that is,\n",
+    "from 1.5 rad to 3.14 rad.\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "practical-cabinet",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Exercises\n",
+    "\n",
+    "Now finish off the example by estimating the force that delivers the teapot to the desired position.\n",
+    "\n",
+    "Write an error function that takes `force` and `params` and returns the offset from the desired angle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "apparent-lancaster",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "def error_func2(force, params):\n",
+    "    \"\"\"Error function for root_scalar.\n",
+    "    \n",
+    "    force: hypothetical value\n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: offset from target value\n",
+    "    \"\"\"\n",
+    "    results = run_two_phases(force, params.torque_friction, params)\n",
+    "    theta_final = results.iloc[-1].theta\n",
+    "    print(force, theta_final)\n",
+    "    remaining_angle = params.theta_target - params.theta_test\n",
+    "    return theta_final - remaining_angle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "persistent-rings",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0 1.2499999999999993\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "-0.3915926535897938 radian"
+      ],
+      "text/latex": [
+       "$-0.3915926535897938\\ \\mathrm{radian}$"
+      ],
+      "text/plain": [
+       "-0.3915926535897938 <Unit('radian')>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "guess1 = 1.0\n",
+    "params2 = params.set(torque_friction=torque_friction)\n",
+    "error_func2(guess1, params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "previous-pittsburgh",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0 2.50000000000001\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "0.8584073464102171 radian"
+      ],
+      "text/latex": [
+       "$0.8584073464102171\\ \\mathrm{radian}$"
+      ],
+      "text/plain": [
+       "0.8584073464102171 <Unit('radian')>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "guess2 = 2.0\n",
+    "error_func2(guess2, params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "governing-component",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0 1.2499999999999993\n",
+      "2.0 2.50000000000001\n",
+      "1.3132741228718323 1.64159265358979\n",
+      "1.3132741228728328 1.6415926535910412\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "res = root_scalar(error_func2, params, bracket=[guess1, guess2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "statistical-behalf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.14159265358979"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "force = res.root\n",
+    "results = run_two_phases(force, params.torque_friction, params)\n",
+    "theta_final = results.iloc[-1].theta\n",
+    "theta_final + 1.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "unauthorized-equity",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/modsim.py b/jupyter/modsim.py
index 894cabe3..b59bd97c 100644
--- a/jupyter/modsim.py
+++ b/jupyter/modsim.py
@@ -32,10 +32,10 @@
 from types import SimpleNamespace
 from copy import copy
 
-#import pint
+import pint
 
-#units = pint.UnitRegistry()
-#Quantity = units.Quantity
+units = pint.UnitRegistry()
+Quantity = units.Quantity
 
 
 def flip(p=0.5):
@@ -305,60 +305,6 @@ def minimize_powell(min_func, x0, *args, **options):
 maximize = maximize_golden
 
 
-def run_odeint(system, slope_func, **options):
-    """Integrates an ordinary differential equation.
-
-    `system` should contain system parameters and `ts`, which
-    is an array or Series that specifies the time when the
-    solution will be computed.
-
-    system: System object
-    slope_func: function that computes slopes
-
-    returns: TimeFrame
-    """
-    # make sure `system` contains `ts`
-    if not hasattr(system, "ts"):
-        msg = """It looks like `system` does not contain `ts`
-                 as a system variable.  `ts` should be an array
-                 or Series that specifies the times when the
-                 solution will be computed:"""
-        raise ValueError(msg)
-
-    # make sure `system` contains `init`
-    if not hasattr(system, "init"):
-        msg = """It looks like `system` does not contain `init`
-                 as a system variable.  `init` should be a State
-                 object that specifies the initial condition:"""
-        raise ValueError(msg)
-
-    # try running the slope function with the initial conditions
-    try:
-        slope_func(system.init, system.ts[0], system)
-    except Exception as e:
-        msg = """Before running scipy.integrate.odeint, I tried
-                 running the slope function you provided with the
-                 initial conditions in system and t=0, and I got
-                 the following error:"""
-        logger.error(msg)
-        raise (e)
-
-    # when odeint calls slope_func, it should pass `system` as
-    # the third argument.  To make that work, we have to make a
-    # tuple with a single element and pass the tuple to odeint as `args`
-    args = (system,)
-
-    # now we're ready to run `odeint` with `init` and `ts` from `system`
-    array = odeint(slope_func, list(system.init), system.ts, args, **options)
-
-    # the return value from odeint is an array, so let's pack it into
-    # a TimeFrame with appropriate columns and index
-    frame = TimeFrame(
-        array, columns=system.init.index, index=system.ts, dtype=np.float64
-    )
-    return frame
-
-
 def run_solve_ivp(system, slope_func, **options):
     """Computes a numerical solution to a differential equation.
 
@@ -374,6 +320,8 @@ def run_solve_ivp(system, slope_func, **options):
 
     returns: TimeFrame
     """
+    system = remove_units(system)
+
     # make sure `system` contains `init`
     if not hasattr(system, "init"):
         msg = """It looks like `system` does not contain `init`
@@ -740,9 +688,8 @@ def gradient(series, **options):
 
     returns: Series, same subclass as series
     """
-    x = magnitudes(series.index)
-    y = magnitudes(series.values)
-    # units = get_units(series)
+    x = series.index
+    y = series.values
 
     a = np.gradient(y, x, **options)
     return series.__class__(a, series.index)
@@ -773,7 +720,6 @@ def underride(d, **options):
     return d
 
 
-
 def contour(df, **options):
     """Makes a contour plot from a DataFrame.
 
@@ -843,70 +789,6 @@ def remove_from_legend(bad_labels):
     ax.legend(handle_list, label_list)
 
 
-def subplot(nrows, ncols, plot_number, **options):
-    figsize = {(2, 1): (8, 8), (3, 1): (8, 10)}
-    key = nrows, ncols
-    default = (8, 5.5)
-    width, height = figsize.get(key, default)
-
-    plt.subplot(nrows, ncols, plot_number, **options)
-    fig = plt.gcf()
-    fig.set_figwidth(width)
-    fig.set_figheight(height)
-
-
-def TimeSeries(*args, **kwargs):
-    """
-    """
-    if args or kwargs:
-        series = pd.Series(*args, **kwargs)
-    else:
-        series = pd.Series([], dtype=np.float64)
-
-    series.index.name = 'Time'
-    if 'name' not in kwargs:
-        series.name = 'Quantity'
-    return series
-
-
-def SweepSeries(*args, **kwargs):
-    """
-    """
-    if args or kwargs:
-        series = pd.Series(*args, **kwargs)
-    else:
-        series = pd.Series([], dtype=np.float64)
-
-    series.index.name = 'Parameter'
-    if 'name' not in kwargs:
-        series.name = 'Metric'
-    return series
-
-
-def show(series):
-    """
-    """
-    if not isinstance(series, pd.Series):
-        return series
-    df = pd.DataFrame(series)
-    return df
-
-
-def plot(x, y=None, **options):
-    """
-    """
-    if y is not None:
-        plt.plot(x, y, **options)
-    else:
-        # x better be a Series
-        x.plot(**options)
-
-
-def State(**variables):
-    """Contains the values of state variables."""
-    return pd.Series(variables)
-
-
 class SettableNamespace(SimpleNamespace):
     """Contains a collection of parameters.
 
@@ -914,6 +796,12 @@ class SettableNamespace(SimpleNamespace):
 
     Takes keyword arguments and stores them as attributes.
     """
+    def __init__(self, namespace=None, **kwargs):
+        super().__init__()
+        if namespace:
+            self.__dict__.update(namespace.__dict__)
+        self.__dict__.update(kwargs)
+
     def get(self, name, default=None):
         """Look up a variable.
 
@@ -935,6 +823,46 @@ def set(self, **variables):
         return new
 
 
+def magnitude(x):
+    """Returns the magnitude of a Quantity or number.
+
+    x: Quantity or number
+
+    returns: number
+    """
+    return x.magnitude if hasattr(x, 'magnitude') else x
+
+
+def remove_units(namespace):
+    """Removes units from the values in a Namespace.
+
+    Only removes units from top-level values;
+    does not traverse nested values.
+
+    returns: new Namespace object
+    """
+    res = copy(namespace)
+    for label, value in res.__dict__.items():
+        if isinstance(value, pd.Series):
+            value = remove_units_series(value)
+        res.__dict__[label] = magnitude(value)
+    return res
+
+
+def remove_units_series(series):
+    """Removes units from the values in a Series.
+
+    Only removes units from top-level values;
+    does not traverse nested values.
+
+    returns: new Series object
+    """
+    res = copy(series)
+    for label, value in res.iteritems():
+        res[label] = magnitude(value)
+    return res
+
+
 class System(SettableNamespace):
     """Contains system parameters and their values.
 
@@ -951,6 +879,39 @@ class Params(SettableNamespace):
     pass
 
 
+def State(**variables):
+    """Contains the values of state variables."""
+    return pd.Series(variables)
+
+
+def TimeSeries(*args, **kwargs):
+    """
+    """
+    if args or kwargs:
+        series = pd.Series(*args, **kwargs)
+    else:
+        series = pd.Series([], dtype=np.float64)
+
+    series.index.name = 'Time'
+    if 'name' not in kwargs:
+        series.name = 'Quantity'
+    return series
+
+
+def SweepSeries(*args, **kwargs):
+    """
+    """
+    if args or kwargs:
+        series = pd.Series(*args, **kwargs)
+    else:
+        series = pd.Series([], dtype=np.float64)
+
+    series.index.name = 'Parameter'
+    if 'name' not in kwargs:
+        series.name = 'Metric'
+    return series
+
+
 def TimeFrame(*args, **kwargs):
     """DataFrame that maps from time to State.
     """
@@ -963,6 +924,15 @@ def SweepFrame(*args, **kwargs):
     return pd.DataFrame(*args, **kwargs)
 
 
+def Vector(x, y, z=None, **options):
+    """
+    """
+    if z is None:
+        return pd.Series(dict(x=x, y=y), **options)
+    else:
+        return pd.Series(dict(x=x, y=y, z=z), **options)
+
+
 ## Vector functions (should work with any sequence)
 
 def vector_mag(v):
@@ -1078,19 +1048,6 @@ def vector_diff_angle(v, w):
         raise NotImplementedError()
 
 
-def Vector(x, y, z=None, **options):
-    """
-    """
-    if z is None:
-        return pd.Series(dict(x=x, y=y), **options)
-    else:
-        return pd.Series(dict(x=x, y=y, z=z), **options)
-
-mag = vector_mag
-angle = vector_angle
-hat = vector_hat
-
-
 def plot_segment(A, B, **options):
     """Plots a line segment between two Vectors.
 
@@ -1105,10 +1062,11 @@ def plot_segment(A, B, **options):
     ys = A.y, B.y
     plot(xs, ys, **options)
 
+
 from time import sleep
 from IPython.display import clear_output
 
-def animate(results, draw_func, interval=None):
+def animate(results, draw_func, *args, interval=None):
     """Animate results from a simulation.
 
     results: TimeFrame
@@ -1118,12 +1076,12 @@ def animate(results, draw_func, interval=None):
     plt.figure()
     try:
         for t, state in results.iterrows():
-            draw_func(t, state)
+            draw_func(t, state, *args)
             plt.show()
             if interval:
                 sleep(interval)
             clear_output(wait=True)
-        draw_func(t, state)
+        draw_func(t, state, *args)
         plt.show()
     except KeyboardInterrupt:
         pass

From 1fa8108b5731986daeb895445ef514f91eb05f6b Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 5 Feb 2021 16:23:42 -0500
Subject: [PATCH 028/144] Adding queue

---
 book/figs/queue.png | Bin 0 -> 9665 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 book/figs/queue.png

diff --git a/book/figs/queue.png b/book/figs/queue.png
new file mode 100644
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From 2e049b64202067a37334e1211a723ec5adbddfd3 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sun, 7 Feb 2021 13:54:44 -0500
Subject: [PATCH 029/144] Adding minmod paper

---
 papers/Bostonetal2003MinModMillenium.pdf | Bin 0 -> 1819085 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 papers/Bostonetal2003MinModMillenium.pdf

diff --git a/papers/Bostonetal2003MinModMillenium.pdf b/papers/Bostonetal2003MinModMillenium.pdf
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From 6cac03b114a0bf0893aa160048ba6dae460f5f86 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sun, 7 Feb 2021 16:52:41 -0500
Subject: [PATCH 030/144] Adding paper

---
 papers/pacini1986.pdf | Bin 0 -> 708903 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 papers/pacini1986.pdf

diff --git a/papers/pacini1986.pdf b/papers/pacini1986.pdf
new file mode 100644
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From ba9e01ddfd6c362c4bf9cdff2e70cc50c4dc9045 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 12 Feb 2021 16:23:44 -0500
Subject: [PATCH 031/144] Removing jupyter directory

---
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 jupyter/chap02.ipynb | 1694 ------------------------------------
 jupyter/chap03.ipynb | 1075 -----------------------
 jupyter/chap04.ipynb | 1208 --------------------------
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 jupyter/chap06.ipynb |  785 -----------------
 jupyter/chap07.ipynb |  915 -------------------
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 jupyter/chap12.ipynb | 1037 ----------------------
 jupyter/chap13.ipynb |  739 ----------------
 jupyter/chap14.ipynb |  921 --------------------
 jupyter/chap15.ipynb | 1038 ----------------------
 jupyter/chap16.ipynb |  793 -----------------
 jupyter/chap17.ipynb |  665 --------------
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 jupyter/chap22.ipynb | 1978 ------------------------------------------
 jupyter/chap23.ipynb |  877 -------------------
 jupyter/chap24.ipynb | 1219 --------------------------
 jupyter/chap25.ipynb | 1421 ------------------------------
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diff --git a/jupyter/chap01.ipynb b/jupyter/chap01.ipynb
deleted file mode 100644
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "uniform-persian",
-   "metadata": {},
-   "source": [
-    "# Chapter 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "biblical-enhancement",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "confirmed-budapest",
-   "metadata": {},
-   "source": [
-    "## Jupyter\n",
-    "\n",
-    "Welcome to *Modeling and Simulation in Python*, and welcome to Jupyter.\n",
-    "\n",
-    "This is a Jupyter notebook, which is a development environment where you can write and run Python code.  Each notebook is divided into cells.  Each cell contains either text (like this cell) or Python code."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "danish-scope",
-   "metadata": {},
-   "source": [
-    "To run a cell, hold down SHIFT and press ENTER.  \n",
-    "\n",
-    "* If you run a text cell, Jupyter formats the text and displays the result.\n",
-    "\n",
-    "* If you run a code cell, Jupyter runs the Python code in the cell and displays the result, if any.\n",
-    "\n",
-    "If you run the following cell, it checks whether the libraries you need are installed.  If so, the cell produces no output.  If not, you'll see updates from the installer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "asian-america",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim as ms\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "copyrighted-desperate",
-   "metadata": {},
-   "source": [
-    "If the previous cell runs without producing any error messages, you are all set.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hourly-financing",
-   "metadata": {},
-   "source": [
-    "## Modeling\n",
-    "\n",
-    "This book is about modeling and simulation of physical systems. The\n",
-    "following diagram shows what I mean by \"modeling\":\n",
-    "\n",
-    "<img src =\"https://github.com/AllenDowney/ModSimPy/raw/master/book/figs/modeling_framework.png\" width=\"400\">"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "structured-receiver",
-   "metadata": {},
-   "source": [
-    "Starting in the lower left, the **system** is something in the real\n",
-    "world we are interested in. \n",
-    "To model the system, we have to decide which elements of the real world to include and which we can leave out.\n",
-    "This process is called **abstraction**.\n",
-    "\n",
-    "The result of abstraction is a **model**, which is a description of the system that includes only the features we think are essential. A model\n",
-    "can be represented in the form of diagrams and equations, which can be\n",
-    "used for mathematical **analysis**. It can also be implemented in the\n",
-    "form of a computer program, which can run **simulations**.\n",
-    "\n",
-    "The result of analysis and simulation might be a **prediction** about\n",
-    "what the system will do, an **explanation** of why it behaves the way it\n",
-    "does, or a **design** intended to achieve a purpose.\n",
-    "\n",
-    "We can **validate** predictions and test designs by taking\n",
-    "**measurements** from the real world and comparing the **data** we get\n",
-    "with the results from analysis and simulation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "center-accommodation",
-   "metadata": {},
-   "source": [
-    "For any physical system, there are many possible models, each one\n",
-    "including and excluding different features, or including different\n",
-    "levels of detail. The goal of the modeling process is to find the model\n",
-    "best suited to its purpose (prediction, explanation, or design).\n",
-    "\n",
-    "Sometimes the best model is the most detailed. If we include more\n",
-    "features, the model is more realistic, and we expect its predictions to\n",
-    "be more accurate.\n",
-    "\n",
-    "But often a simpler model is better. If we include only the essential\n",
-    "features and leave out the rest, we get models that are easier to work\n",
-    "with, and the explanations they provide can be clearer and more\n",
-    "compelling."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "confirmed-highlight",
-   "metadata": {},
-   "source": [
-    "As an example, suppose someone asks you why the orbit of the Earth is\n",
-    "elliptical. If you model the Earth and Sun as point masses (ignoring\n",
-    "their actual size), compute the gravitational force between them using\n",
-    "Newton's law of universal gravitation, and compute the resulting orbit\n",
-    "using Newton's laws of motion, you can show that the result is an\n",
-    "ellipse.\n",
-    "\n",
-    "Of course, the actual orbit of Earth is not a perfect ellipse, because\n",
-    "of the gravitational forces of the Moon, Jupiter, and other objects in\n",
-    "the solar system, and because Newton's laws of motion are only\n",
-    "approximately true (they don't take into account relativistic effects).\n",
-    "\n",
-    "But adding these features to the model would not improve the\n",
-    "explanation; more detail would only be a distraction from the\n",
-    "fundamental cause. However, if the goal is to predict the position of\n",
-    "the Earth with great precision, including more details might be\n",
-    "necessary."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "stretch-geneva",
-   "metadata": {},
-   "source": [
-    "Choosing the best model depends on what the model is for. It is usually\n",
-    "a good idea to start with a simple model, even if it is likely to be too\n",
-    "simple, and test whether it is good enough for its purpose. Then you can\n",
-    "add features gradually, starting with the ones you expect to be most\n",
-    "essential. This process is called **iterative modeling**.\n",
-    "\n",
-    "Comparing results of successive models provides a form of **internal\n",
-    "validation**, so you can catch conceptual, mathematical, and software\n",
-    "errors. And by adding and removing features, you can tell which ones\n",
-    "have the biggest effect on the results, and which can be ignored.\n",
-    "\n",
-    "Comparing results to data from the real world provides **external\n",
-    "validation**, which is generally the strongest test."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "criminal-lunch",
-   "metadata": {},
-   "source": [
-    "## The falling penny myth\n",
-    "\n",
-    "Let's see an example of how models are used. You might have heard that a\n",
-    "penny dropped from the top of the Empire State Building would be going\n",
-    "so fast when it hit the pavement that it would be embedded in the\n",
-    "concrete; or if it hit a person, it would break their skull.\n",
-    "\n",
-    "We can test this myth by making and analyzing a model. To get started,\n",
-    "we'll assume that the effect of air resistance is small. This will turn out to be a bad assumption, but bear with me.\n",
-    "If air resistance is negligible, the primary force acting on the penny\n",
-    "is gravity, which causes the penny to accelerate downward.\n",
-    "\n",
-    "If the initial velocity is 0, the velocity after $t$ seconds is \n",
-    "\n",
-    "$$v = a t$$\n",
-    "\n",
-    "and the distance the penny has dropped is \n",
-    "\n",
-    "$$x = a t^2 / 2$$ \n",
-    "\n",
-    "To find the time until the penny reaches the sidewalk, we can solve for $t$:\n",
-    "\n",
-    "$$t = \\sqrt{ 2 x / a}$$ \n",
-    "\n",
-    "Plugging in the acceleration of gravity, $a = 9.8$ m/s$^2$, and the height of the Empire State Building, $x = 381$ m, we get $t = 8.8$ s. \n",
-    "\n",
-    "Then computing $v = a t$ we get a velocity on impact of $86$ m/s, which is about 190 miles per hour. That sounds like it could hurt."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "documentary-diagnosis",
-   "metadata": {},
-   "source": [
-    "Of course, these results are not exact because the model is based on\n",
-    "simplifications. For example, we assume that gravity is constant. In\n",
-    "fact, the force of gravity is different on different parts of the globe, and it gets weaker as you move away from the surface. But these differences are small, so ignoring them is probably a good choice for this scenario.\n",
-    "\n",
-    "On the other hand, ignoring air resistance is not a good choice. Once\n",
-    "the penny gets to about 29 m/s, the upward force of air resistance\n",
-    "equals the downward force of gravity, so the penny stops accelerating.\n",
-    "This is the **terminal velocity** of the penny in air.\n",
-    "\n",
-    "Once the penny reaches terminal velocity, it doesn't matter how much farther it falls; it hits the sidewalk at about 29 m/s.\n",
-    "That's much less than 86 m/s, as the simple model predicts."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "recent-appliance",
-   "metadata": {},
-   "source": [
-    "The statistician George Box famously said \"All models are wrong, but\n",
-    "some are useful.\" He was talking about statistical models, but his wise words apply to all kinds of models. Our first model, which ignores air resistance, is very wrong, and probably not useful. The second model, which takes air resistance into account, is still wrong, but it's better, and it's good enough to refute the myth.\n",
-    "\n",
-    "The television show *Mythbusters* has tested the myth of the falling\n",
-    "penny more carefully; you can view the results at\n",
-    "<http://modsimpy.com/myth>. Their work is based on a mathematical model of motion, measurements to determine the force of air resistance on a penny, and a physical model of a human head."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "brief-zoning",
-   "metadata": {},
-   "source": [
-    "## Computation\n",
-    "\n",
-    "Let me show you how I computed the results from the\n",
-    "previous section using Python.\n",
-    "First we'll create a variable to represent acceleration due to gravity in meters per second squared (m/s^2)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "eleven-marine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = 9.8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "upset-myanmar",
-   "metadata": {},
-   "source": [
-    "A **variable** is a name that corresponds to a value. In this example, the name is `a` and the value is the number `9.8`.\n",
-    "\n",
-    "Suppose we let the penny drop for $3.4$ seconds (s).  I'll create a variable to represent this time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "following-launch",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = 3.4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "greek-heritage",
-   "metadata": {},
-   "source": [
-    "Now we can compute the velocity of the penny after `t` seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "mature-duration",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v = a * t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "qualified-diabetes",
-   "metadata": {},
-   "source": [
-    "Python uses the symbol `*` for multiplication.  The other arithmetic operators are `+` for addition, `-` for subtraction, `/` for division, and `**` for exponentiation.\n",
-    "\n",
-    "When you assign a value to a variable, Jupyter does not show the result automatically, but you can display the value of a variable like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "considered-inclusion",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "33.32"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "northern-saturday",
-   "metadata": {},
-   "source": [
-    "After $3.4$ s, the velocity of the penny is about $33$ m/s (ignoring air resistance).  Now let's see how far it would travel during that time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "valued-electricity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "56.644"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = a * t**2 / 2\n",
-    "x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "yellow-business",
-   "metadata": {},
-   "source": [
-    "It would travel about $56$ m.  Now, going in the other direction, let's compute the time it takes to fall 381 m, the height of the Empire State Building."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "closed-month",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h = 381"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fuzzy-lease",
-   "metadata": {},
-   "source": [
-    "For this computation, we need the square root function, which is provided by a library called NumPy.\n",
-    "Before we can use it, we have to import it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "nuclear-clone",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import sqrt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unlimited-swiss",
-   "metadata": {},
-   "source": [
-    "Now we can use it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "quarterly-nightmare",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8.817885349720552"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t = sqrt(2 * h / a)\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "velvet-oklahoma",
-   "metadata": {},
-   "source": [
-    "With no air resistance, it would take about $8.8$ s for the penny to reach the sidewalk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "quality-external",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "86.41527642726142"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v = a * t\n",
-    "v"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "human-phase",
-   "metadata": {},
-   "source": [
-    "And its velocity on impact would be about $86$ m/s.\n",
-    "\n",
-    "Python displays results with about 16 digits, which gives the impression that they are very precise, but don't be fooled.\n",
-    "The numbers we get out are only as good as the numbers we put in.\n",
-    "\n",
-    "For example, the \"roof height\" of the Empire State Building is $380$ m [according to Wikipedia](https://en.wikipedia.org/wiki/Empire_State_Building).\n",
-    "I chose $h=381$ m for this example on the assumption that the observation deck is on the roof and you drop the penny from a 1 meter railing.\n",
-    "But that's probably not right, so we should treat this value as an approximation where only the first two digits are likely to be right.\n",
-    "\n",
-    "If the precision of the inputs is two digits, the precision of the outputs is two digits, *at most*.\n",
-    "That's why, if the output is `86.41527642726142`, I report it as \"about 86\"."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "consecutive-sleeve",
-   "metadata": {},
-   "source": [
-    "## Computation with units\n",
-    "\n",
-    "The computation in the previous section uses numbers without units.  \n",
-    "When we set `h=381`, we left out the meters, and when we set `a=9.8`, we left out the meters per second squared.\n",
-    "And, when we got the result `v=86`, we added back the meters per second.\n",
-    "\n",
-    "Leaving units of out computation is common practice, but it tends to cause errors, including the very expensive failure of the [Mars Climate Orbiter](https://en.wikipedia.org/wiki/Mars_Climate_Orbiter) in 1999.\n",
-    "When possible, it is better to include units in the computation.\n",
-    "\n",
-    "To represent units, we'll use a Python library called [Pint](https://pint.readthedocs.io/en/latest/).\n",
-    "The ModSim library initializes Pint and provides an object called `units` that contains variables representing pretty much every unit you've ever heard of.\n",
-    "\n",
-    "We can import it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "based-belief",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import units"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unlike-opera",
-   "metadata": {},
-   "source": [
-    "Now we can create variables named `meter` and `second`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "russian-popularity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "meter"
-      ],
-      "text/latex": [
-       "$\\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "<Unit('meter')>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "meter = units.meter\n",
-    "meter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "endless-paint",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "second"
-      ],
-      "text/latex": [
-       "$\\mathrm{second}$"
-      ],
-      "text/plain": [
-       "<Unit('second')>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "second = units.second\n",
-    "second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "spiritual-scenario",
-   "metadata": {},
-   "source": [
-    "To find out what other units are defined, type `units.` (including the period) in the next cell and then press TAB.  You should see a pop-up menu with a list of units."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "everyday-location",
-   "metadata": {},
-   "source": [
-    "We can use `meter` and `second` to create a variable named `a` and give it the value of acceleration due to gravity. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "earlier-bandwidth",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "9.8 meter/second<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$9.8\\ \\frac{\\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "9.8 <Unit('meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = 9.8 * meter / second**2\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "generic-bowling",
-   "metadata": {},
-   "source": [
-    "The result is a **quantity** with two parts, called `magnitude` and `units`, which we can access like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "missing-privilege",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9.8"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.magnitude"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "fourth-swedish",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "meter/second<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$\\frac{\\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "<Unit('meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.units"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "realistic-techno",
-   "metadata": {},
-   "source": [
-    "Now we can create a quantity that represents $3.4$ s."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "apart-france",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "3.4 second"
-      ],
-      "text/latex": [
-       "$3.4\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "3.4 <Unit('second')>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t = 3.4 * second\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "severe-share",
-   "metadata": {},
-   "source": [
-    "And use it to compute the distance a penny would fall after `t` seconds with constant acceleration `a`.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "alien-scout",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "56.644 meter"
-      ],
-      "text/latex": [
-       "$56.644\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "56.644 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a * t**2 / 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "wicked-indianapolis",
-   "metadata": {},
-   "source": [
-    "Notice that the units of the result are correct.\n",
-    "If we create a quantity to represent the height of the Empire State Building:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "confidential-costs",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h = 381 * meter"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hollow-programmer",
-   "metadata": {},
-   "source": [
-    "We can use it to compute the time to reach the sidewalk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "studied-opera",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "8.817885349720552 second"
-      ],
-      "text/latex": [
-       "$8.817885349720552\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "8.817885349720552 <Unit('second')>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t = sqrt(2 * h / a)\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "seasonal-laser",
-   "metadata": {},
-   "source": [
-    "And the velocity of the penny on impact:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "exterior-greek",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "86.41527642726142 meter/second"
-      ],
-      "text/latex": [
-       "$86.41527642726142\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "86.41527642726142 <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v = a * t\n",
-    "v"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "superior-biography",
-   "metadata": {},
-   "source": [
-    "As in the previous section, the result is about $86$, but now it has the correct units, m/s.\n",
-    "\n",
-    "With Pint quantities, we can convert from one set of units to another like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "antique-landing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mile = units.mile\n",
-    "hour = units.hour"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "included-failure",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "193.30546802805438 mile/hour"
-      ],
-      "text/latex": [
-       "$193.30546802805438\\ \\frac{\\mathrm{mile}}{\\mathrm{hour}}$"
-      ],
-      "text/plain": [
-       "193.30546802805438 <Unit('mile / hour')>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v.to(mile/hour)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "progressive-charleston",
-   "metadata": {},
-   "source": [
-    "If you are more familiar with miles per hour, this result might be easier to interpret.\n",
-    "And it might give you a sense that this model is not realistic."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "continuing-democrat",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "This chapter introduces a modeling framework that consists of three steps:\n",
-    "\n",
-    "* Abstraction is the process of defining a model, deciding which elements of the real world to include and which can be left out.\n",
-    "\n",
-    "* Analysis and simulation are ways to use a model to generate predictions, explain why things behave as they to, and design things that behave as we want.\n",
-    "\n",
-    "* Validation is how we test whether the model is right, often by comparing predictions with measurements from the real world.\n",
-    "\n",
-    "As a first example, we modeled a penny dropped from the Empire State building, including gravity but ignoring air resistance.\n",
-    "In the exercises, you'll have a chance to try a better model, including air resistance.\n",
-    "\n",
-    "This chapter also presents Pint, a library for doing computation with units, which is convenient for converting between different units and important for avoiding catastrophic errors."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "thousand-equation",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "handmade-zoning",
-   "metadata": {},
-   "source": [
-    "**Exercise** In mathematical notation, we can write an equation like $v = a t$ and it's understood that we are multiplying $a$ and $t$.\n",
-    "But that doesn't work in Python.  If you put two variables side-by-side, like this:\n",
-    "\n",
-    "```\n",
-    "v = a t\n",
-    "```\n",
-    "\n",
-    "you'll get a **syntax error**, which means that something is wrong with the punctuation of the program.\n",
-    "\n",
-    "Try it out in the next cell so you see what the error message looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "postal-marking",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "33.32 meter/second"
-      ],
-      "text/latex": [
-       "$33.32\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "33.32 <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = 9.8 * meter / second**2\n",
-    "t = 3.4 * second\n",
-    "\n",
-    "v = a * t\n",
-    "v"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "pressing-sugar",
-   "metadata": {},
-   "source": [
-    "**Exercise:** In this chapter we used the `sqrt` function from the NumPy library.  NumPy also provides a variable named `pi` that contains an approximation of the mathematical constant $\\pi$.\n",
-    "We can import it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "legal-observer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.141592653589793"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from numpy import pi\n",
-    "pi"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aggregate-mambo",
-   "metadata": {},
-   "source": [
-    "NumPy provides other functions we'll use, including `log`, `exp`, `sin`, and `cos`.\n",
-    "Import `sin` and `cos` from NumPy and compute\n",
-    "\n",
-    "$$sin^2 \\pi/4 + cos^2 \\pi/4$$\n",
-    "\n",
-    "Note: A mathematical identity tells us that the answer should be $1$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "pressing-belgium",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.0"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "from numpy import sin, cos\n",
-    "\n",
-    "sin(pi/4)**2 + cos(pi/4)**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unlimited-delivery",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose you bring a 10 foot pole to the top of the Empire State Building and use it to drop the penny from `h` plus 10 feet.\n",
-    "\n",
-    "Define a variable named `foot` that contains the unit `foot` provided by `UNITS`.  Define a variable named `pole_height` and give it the value 10 feet.\n",
-    "\n",
-    "What happens if you add `h`, which is in units of meters, to `pole_height`, which is in units of feet?  What happens if you write the addition the other way around?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "inner-equivalent",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h = 381 * meter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "nuclear-thirty",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "384.048 meter"
-      ],
-      "text/latex": [
-       "$384.048\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "384.048 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "foot = units.foot\n",
-    "pole_height = 10 * foot\n",
-    "\n",
-    "h + pole_height"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "available-steering",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "1260.0 foot"
-      ],
-      "text/latex": [
-       "$1260.0\\ \\mathrm{foot}$"
-      ],
-      "text/plain": [
-       "1260.0 <Unit('foot')>"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "pole_height + h"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aggressive-climate",
-   "metadata": {},
-   "source": [
-    "**Exercise**: Why would it be nonsensical to add `a` and `t`? What happens if you try?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "documentary-doctrine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = 9.8 * meter / second**2\n",
-    "t = 3.4 * second"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "primary-partner",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "steady-chancellor",
-   "metadata": {},
-   "source": [
-    "In this example, you should get a `DimensionalityError`, which is defined by Pint to indicate that you have violated a rules of dimensional analysis: you cannot add quantities with different dimensions.\n",
-    "\n",
-    "The error messages you get from Python are big and scary, but if you read them carefully, they contain a lot of useful information.\n",
-    "\n",
-    "1.  Start from the bottom and read up.\n",
-    "2.  The last line usually tells you what type of error happened, and sometimes additional information.\n",
-    "3.  The previous lines are a \"traceback\" of what was happening when the error occurred.  The first section of the traceback shows the code you wrote.  The following sections are often from Python libraries.\n",
-    "\n",
-    "Before you go on, you might want to delete the erroneous code so the notebook can run without errors."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "valid-empire",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose instead of dropping the penny, you throw it downward at its terminal velocity, $29$ m/s.  How long would it take to fall $381$ m?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "greenhouse-reason",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "13.137931034482758 second"
-      ],
-      "text/latex": [
-       "$13.137931034482758\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "13.137931034482758 <Unit('second')>"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "h = 381 * meter\n",
-    "v = 29 * meter / second\n",
-    "\n",
-    "t = h / v\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "asian-murray",
-   "metadata": {},
-   "source": [
-    "**Exercise:** So far we have considered two models of a falling penny:\n",
-    "\n",
-    "* If we ignore air resistance, the penny falls with constant acceleration, and we can compute the time to reach the sidewalk and the velocity of the penny when it gets there.\n",
-    "\n",
-    "* If we take air resistance into account, and drop the penny at its terminal velocity, it falls with constant velocity. \n",
-    "\n",
-    "Now let's consider a third model that includes elements of the first two: let's assume that the acceleration of the penny is `a` until the penny reaches $29$ m/s, and then $0$ m/s afterwards.  What is the total time for the penny to fall $381$ m?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "detailed-minnesota",
-   "metadata": {},
-   "source": [
-    "You can break this question into three parts:\n",
-    "\n",
-    "1. How long would the penny take to reach $29$ m/s with constant acceleration `a`.\n",
-    "2. How far would it fall during that time?\n",
-    "3. How long would it take to fall the remaining distance with constant velocity $29$ m/s?\n",
-    "\n",
-    "Suggestion: Assign each intermediate result to a variable with a meaningful name.  And assign units to all quantities!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "secure-crowd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = 9.8 * meter / second**2\n",
-    "h = 381 * meter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "thirty-minneapolis",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "v_terminal = 29 * meter / second "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "premier-seeking",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time to reach terminal velocity 2.9591836734693877 second\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "t1 = v_terminal / a\n",
-    "print('Time to reach terminal velocity', t1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "brave-laundry",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Height fallen in t1 42.90816326530612 meter\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "h1 = a * t1**2 / 2\n",
-    "print('Height fallen in t1', h1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "adjusted-consultation",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time to fall remaining distance 11.658339197748065 second\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "t2 = (h - h1) / v_terminal\n",
-    "print('Time to fall remaining distance', t2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "understanding-consortium",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total falling time 14.617522871217453 second\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "t_total = t1 + t2\n",
-    "print('Total falling time', t_total)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "composed-tunnel",
-   "metadata": {},
-   "source": [
-    "**Exercise:** When I was in high school, the pitcher on the baseball team claimed that, when he threw a fastball, he was throwing the ball down; that is, the ball was leaving his hand at a downward angle.\n",
-    "I was skeptical; watching from the side, I thought the ball was leaving his hand at an upward angle.\n",
-    "\n",
-    "Can you think of a simple model you could use to settle the argument?  What factors would you include and what could you ignore?  What quantities would you have to look up or estimate?\n",
-    "\n",
-    "I suggest you convert all quantities to [SI units](https://en.wikipedia.org/wiki/International_System_of_Units): meters and seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "about-complex",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# I suggest the following model: \n",
-    "\n",
-    "# 1. Let's ignore the motion of the ball toward home plate and\n",
-    "#    think about how far the ball would drop while it's in flight.\n",
-    "\n",
-    "# 2. Let's ignore air resistance.  Since we are only thinking \n",
-    "#    about the relatively slow motion in the vertical direction, \n",
-    "#    this is probably a good assumption.\n",
-    "\n",
-    "# 3. Let's also ignore the effect of spin.  This is probably a\n",
-    "#    less good assumption.\n",
-    "\n",
-    "# The distance from the pitcher's mound to home plate is about 60\n",
-    "# feet, but the point where the ball is released is a bit closer.\n",
-    "\n",
-    "# An average pitcher in high school might be able to throw a ball\n",
-    "# at 80 mph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "unique-owner",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "v = (80 * mile / hour).to(meter/second)\n",
-    "x = (60 * foot).to(meter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "minus-batman",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.5113636363636364 second"
-      ],
-      "text/latex": [
-       "$0.5113636363636364\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "0.5113636363636364 <Unit('second')>"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "t = x / v\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "exact-vegetable",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "1.2813145661157026 meter"
-      ],
-      "text/latex": [
-       "$1.2813145661157026\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "1.2813145661157026 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "a = 9.8 * meter / second**2\n",
-    "h = a * t**2 / 2\n",
-    "h"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "neutral-lightning",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# In the time it takes the ball to reach home plate, it drops\n",
-    "# about 1.3 m.  If the release point is at 2 m, which is plausible,\n",
-    "# it would cross the plate at 0.7 m, which is in the strike zone.\n",
-    "\n",
-    "# So I could be wrong -- it is plausible that the ball leaves\n",
-    "# the pitcher's hand at a downward angle, at least for some pitches."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "jewish-secret",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose I run a 10K race in 44:52.  What is my average page in minutes per mile?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "virgin-cambodia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mile = units.mile\n",
-    "kilometer = units.kilometer\n",
-    "minute = units.minute"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "right-intention",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "2692.0 second"
-      ],
-      "text/latex": [
-       "$2692.0\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "2692.0 <Unit('second')>"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "t = 52 * second + 44 * minute\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "mineral-sally",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.003714710252600297 kilometer/second"
-      ],
-      "text/latex": [
-       "$0.003714710252600297\\ \\frac{\\mathrm{kilometer}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "0.003714710252600297 <Unit('kilometer / second')>"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "v = 10 * kilometer / t\n",
-    "v"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "preceding-cricket",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "7.220590079999998 minute/mile"
-      ],
-      "text/latex": [
-       "$7.220590079999998\\ \\frac{\\mathrm{minute}}{\\mathrm{mile}}$"
-      ],
-      "text/plain": [
-       "7.220590079999998 <Unit('minute / mile')>"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "pace = (1 / v).to(minute / mile)\n",
-    "pace"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "effective-rendering",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "7.0 minute/mile"
-      ],
-      "text/latex": [
-       "$7.0\\ \\frac{\\mathrm{minute}}{\\mathrm{mile}}$"
-      ],
-      "text/plain": [
-       "7.0 <Unit('minute / mile')>"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# To convert to minutes and seconds, we can use np.round.\n",
-    "# But we haven't covered that yet.\n",
-    "\n",
-    "from numpy import round\n",
-    "\n",
-    "round(pace)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "printable-reply",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "13.235404799999895 second/mile"
-      ],
-      "text/latex": [
-       "$13.235404799999895\\ \\frac{\\mathrm{second}}{\\mathrm{mile}}$"
-      ],
-      "text/plain": [
-       "13.235404799999895 <Unit('second / mile')>"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "remainder = pace - round(pace)\n",
-    "remainder.to(second/mile)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "environmental-wallet",
-   "metadata": {},
-   "source": [
-    "## Jupyter"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "quality-probe",
-   "metadata": {},
-   "source": [
-    "### Adding and removing cells\n",
-    "\n",
-    "You can add and remove cells from a notebook using the buttons in the toolbar and the items in the menu, both of which you should see at the top of this notebook.\n",
-    "\n",
-    "Try the following exercises:\n",
-    "\n",
-    "1.  From the Insert menu select \"Insert cell below\" to add a cell below this one.  By default, you get a code cell, as you can see in the pulldown menu that says \"Code\".\n",
-    "\n",
-    "2.  In the new cell, add a print statement like `print('Hello')`, and run it.\n",
-    "\n",
-    "3.  Add another cell, select the new cell, and then click on the pulldown menu that says \"Code\" and select \"Markdown\".  This makes the new cell a text cell.\n",
-    "\n",
-    "4.  In the new cell, type some text, and then run it.\n",
-    "\n",
-    "5.  Use the arrow buttons in the toolbar to move cells up and down.\n",
-    "\n",
-    "6.  Use the cut, copy, and paste buttons to delete, add, and move cells.\n",
-    "\n",
-    "7.  As you make changes, Jupyter saves your notebook automatically, but if you want to make sure, you can press the save button, which looks like a floppy disk from the 1990s.\n",
-    "\n",
-    "8.  Finally, when you are done with a notebook, select \"Close and Halt\" from the File menu."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "suffering-intro",
-   "metadata": {},
-   "source": [
-    "### Restart and run all\n",
-    "\n",
-    "When you change the contents of a cell, you have to run it again for those changes to have an effect.  If you forget to do that, the results can be confusing, because the code you are looking at is not the code you ran.\n",
-    "\n",
-    "If you ever lose track of which cells have run, and in what order, you should go to the Kernel menu and select \"Restart & Run All\".  Restarting the kernel means that all of your variables get deleted, and running all the cells means all of your code will run again, in the right order.\n",
-    "\n",
-    "**Exercise:**  Select \"Restart & Run All\" now and confirm that it does what you want."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "weighted-quebec",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap02.ipynb b/jupyter/chap02.ipynb
deleted file mode 100644
index 3c23b290..00000000
--- a/jupyter/chap02.ipynb
+++ /dev/null
@@ -1,1694 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "victorian-latitude",
-   "metadata": {},
-   "source": [
-    "# Chapter 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cathedral-glasgow",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "burning-chorus",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "    \n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "above-denial",
-   "metadata": {},
-   "source": [
-    "# Bike share\n",
-    "\n",
-    "This chapter presents a simple model of a bike share system and\n",
-    "demonstrates the features of Python we'll use to develop simulations of\n",
-    "real-world systems.\n",
-    "\n",
-    "Along the way, we'll make decisions about how to model the system. In\n",
-    "the next chapter we'll review these decisions and gradually improve the\n",
-    "model.\n",
-    "\n",
-    "## Modeling\n",
-    "\n",
-    "Imagine a bike share system for students traveling between Olin College\n",
-    "and Wellesley College, which are about 3 miles apart in eastern\n",
-    "Massachusetts.\n",
-    "\n",
-    "Suppose the system contains 12 bikes and two bike racks, one at Olin and\n",
-    "one at Wellesley, each with the capacity to hold 12 bikes.\n",
-    "\n",
-    "As students arrive, check out a bike, and ride to the other campus, the\n",
-    "number of bikes in each location changes. In the simulation, we'll need\n",
-    "to keep track of where the bikes are. To do that, we'll use a function called `State`, which is defined in the ModSim library.\n",
-    "We can import it like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "wrapped-workplace",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import State"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "accredited-evanescence",
-   "metadata": {},
-   "source": [
-    "Now we can call it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "incorrect-comparison",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "living-wayne",
-   "metadata": {},
-   "source": [
-    "The expressions in parentheses are **keyword arguments**.\n",
-    "They create two variables, `olin` and `wellesley`, and give them values.\n",
-    "\n",
-    "Then we call the `State` function.\n",
-    "The result is a `State` object, which is a collection of **state variables**.\n",
-    "\n",
-    "In this example, the state variables represent the number of\n",
-    "bikes at each location. The initial values are 10 and 2, indicating that there are 10 bikes at Olin and 2 at Wellesley. \n",
-    "\n",
-    "The `State` object is assigned to a new variable named `bikeshare`.\n",
-    "We can read the variables inside a `State` object using the **dot\n",
-    "operator**, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "brief-diversity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare.olin"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "intermediate-midwest",
-   "metadata": {},
-   "source": [
-    "And this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "designed-brazilian",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare.wellesley"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "phantom-oklahoma",
-   "metadata": {},
-   "source": [
-    "Or, to display the state variables and their values, you can just type the name of the object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "impaired-potter",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>State</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "namespace(olin=10, wellesley=2)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fleet-beijing",
-   "metadata": {},
-   "source": [
-    "These values make up the **state** of the system.\n",
-    "We can update the state by assigning new values to the variables. \n",
-    "For example, if a student moves a bike from Olin to Wellesley, we can figure out the new values and assign them:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "floppy-trainer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare.olin = 9\n",
-    "bikeshare.wellesley = 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "natural-gossip",
-   "metadata": {},
-   "source": [
-    "Or we can use **update operators**, `-=` and `+=`, to subtract 1 from\n",
-    "`olin` and add 1 to `wellesley`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "hungarian-bride",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare.olin -= 1\n",
-    "bikeshare.wellesley += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "radical-mills",
-   "metadata": {},
-   "source": [
-    "The result is the same either way."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "controversial-opportunity",
-   "metadata": {},
-   "source": [
-    "## Defining functions\n",
-    "\n",
-    "So far we have used functions defined in NumPy and ModSim. Now we're going to define our own function.\n",
-    "\n",
-    "When you are developing code in Jupyter, it is often efficient to write a few lines of code, test them to confirm they do what you intend, and then use them to define a new function. For example, these lines move a bike from Olin to Wellesley:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "vertical-drawing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare.olin -= 1\n",
-    "bikeshare.wellesley += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "approximate-rolling",
-   "metadata": {},
-   "source": [
-    "Rather than repeat them every time a bike moves, we can define a new\n",
-    "function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "significant-nutrition",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_wellesley():\n",
-    "    bikeshare.olin -= 1\n",
-    "    bikeshare.wellesley += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "generous-tracker",
-   "metadata": {},
-   "source": [
-    "`def` is a special word in Python that indicates we are defining a new\n",
-    "function. The name of the function is `bike_to_wellesley`. The empty\n",
-    "parentheses indicate that this function requires no additional\n",
-    "information when it runs. The colon indicates the beginning of an\n",
-    "indented **code block**.\n",
-    "\n",
-    "The next two lines are the **body** of the function. They have to be\n",
-    "indented; by convention, the indentation is 4 spaces.\n",
-    "\n",
-    "When you define a function, it has no immediate effect. The body of the\n",
-    "function doesn't run until you **call** the function. Here's how to call\n",
-    "this function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "moving-jurisdiction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bike_to_wellesley()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "meaningful-christmas",
-   "metadata": {},
-   "source": [
-    "When you call the function, it runs the statements in the body, which\n",
-    "update the variables of the `bikeshare` object; you can check by\n",
-    "displaying the new state."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "proper-symposium",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>State</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "namespace(olin=6, wellesley=6)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eleven-brook",
-   "metadata": {},
-   "source": [
-    "When you call a function, you have to include the parentheses. If you\n",
-    "leave them out, you get this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "identical-yacht",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.bike_to_wellesley()>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bike_to_wellesley"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "premier-youth",
-   "metadata": {},
-   "source": [
-    "This result indicates that `bike_to_wellesley` is a function. You don't\n",
-    "have to know what `__main__` means, but if you see something like this,\n",
-    "it probably means that you looked up a function but you didn't actually\n",
-    "call it. So don't forget the parentheses."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "brazilian-medicare",
-   "metadata": {},
-   "source": [
-    "## Print statements\n",
-    "\n",
-    "As you write more complicated programs, it is easy to lose track of what\n",
-    "is going on. One of the most useful tools for debugging is the **print\n",
-    "statement**, which displays text in the Jupyter notebook.\n",
-    "\n",
-    "Normally when Jupyter runs the code in a cell, it displays the value of\n",
-    "the last line of code. For example, if you run:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "heavy-patrol",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare.olin\n",
-    "bikeshare.wellesley"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ancient-projection",
-   "metadata": {},
-   "source": [
-    "Jupyter runs both lines, but it only displays the value of the\n",
-    "second. If you want to display more than one value, you can use\n",
-    "print statements:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "french-preference",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6\n",
-      "6\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(bikeshare.olin)\n",
-    "print(bikeshare.wellesley)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "original-hollywood",
-   "metadata": {},
-   "source": [
-    "When you call the `print` function, you can put a variable name in\n",
-    "parentheses, as in the previous example, or you can provide a sequence\n",
-    "of variables separated by commas, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "alternative-keyboard",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6 6\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(bikeshare.olin, bikeshare.wellesley)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "described-produce",
-   "metadata": {},
-   "source": [
-    "Python looks up the values of the variables and displays them; in this\n",
-    "example, it displays two values on the same line, with a space between\n",
-    "them.\n",
-    "\n",
-    "Print statements are useful for debugging functions. For example, we can\n",
-    "add a print statement to `move_bike`, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "robust-holly",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_wellesley():\n",
-    "    print('Moving a bike to Wellesley')\n",
-    "    bikeshare.olin -= 1\n",
-    "    bikeshare.wellesley += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "vital-lender",
-   "metadata": {},
-   "source": [
-    "Each time we call this version of the function, it displays a message,\n",
-    "which can help us keep track of what the program is doing.\n",
-    "The message in this example is a **string**, which is a sequence of\n",
-    "letters and other symbols in quotes.\n",
-    "\n",
-    "Just like `bike_to_wellesley`, we can define a function that moves a\n",
-    "bike from Wellesley to Olin:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "fifteen-atmosphere",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_olin():\n",
-    "    print('Moving a bike to Olin')\n",
-    "    bikeshare.wellesley -= 1\n",
-    "    bikeshare.olin += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "requested-glasgow",
-   "metadata": {},
-   "source": [
-    "And call it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "matched-narrow",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Moving a bike to Olin\n"
-     ]
-    }
-   ],
-   "source": [
-    "bike_to_olin()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sitting-semiconductor",
-   "metadata": {},
-   "source": [
-    "One benefit of defining functions is that you avoid repeating chunks of\n",
-    "code, which makes programs smaller. Another benefit is that the name you\n",
-    "give the function documents what it does, which makes programs more\n",
-    "readable."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "enhanced-maintenance",
-   "metadata": {},
-   "source": [
-    "## If statements\n",
-    "\n",
-    "The ModSim library provides a function called `flip`, which we can import like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "active-graduate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import flip"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adopted-train",
-   "metadata": {},
-   "source": [
-    "When you call it, you provide a probability between 0 and 1, like `0.7`, as an example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "illegal-metropolitan",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flip(0.7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "appropriate-funds",
-   "metadata": {},
-   "source": [
-    "The result is one of two values: `True` with probability 0.7 or `False`\n",
-    "with probability 0.3. If you run `flip` like this 100 times, you should\n",
-    "get `True` about 70 times and `False` about 30 times. But the results\n",
-    "are random, so they might differ from these expectations.\n",
-    "\n",
-    "`True` and `False` are special values defined by Python. Note that they\n",
-    "are not strings. There is a difference between `True`, which is a\n",
-    "special value, and `'True'`, which is a string.\n",
-    "\n",
-    "`True` and `False` are called **boolean** values because they are\n",
-    "related to Boolean algebra (<https://modsimpy.com/boolean>).\n",
-    "\n",
-    "We can use boolean values to control the behavior of the program, using\n",
-    "an **if statement**:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "excessive-murder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if flip(0.5):\n",
-    "    print('heads')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "seventh-profile",
-   "metadata": {},
-   "source": [
-    "If the result from `flip` is `True`, the program displays the string\n",
-    "`'heads'`. Otherwise it does nothing.\n",
-    "\n",
-    "The syntax for `if` statements is similar to the syntax for\n",
-    "function definitions: the first line has to end with a colon, and the\n",
-    "lines inside the `if` statement have to be indented.\n",
-    "\n",
-    "Optionally, you can add an **else clause** to indicate what should\n",
-    "happen if the result is `False`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "fundamental-nursing",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tails\n"
-     ]
-    }
-   ],
-   "source": [
-    "if flip(0.5):\n",
-    "    print('heads')\n",
-    "else:\n",
-    "    print('tails')    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "recovered-chemical",
-   "metadata": {},
-   "source": [
-    "Now we can use `flip` to simulate the arrival of students who want to\n",
-    "borrow a bike. Suppose students arrive at the Olin station every 2\n",
-    "minutes, on average. In that case, the chance of an arrival during any\n",
-    "one-minute period is 50%, and we can simulate it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "twenty-health",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Moving a bike to Wellesley\n"
-     ]
-    }
-   ],
-   "source": [
-    "if flip(0.5):\n",
-    "    bike_to_wellesley()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "difficult-construction",
-   "metadata": {},
-   "source": [
-    "If students arrive at the Wellesley station every 3 minutes, on average,\n",
-    "the chance of an arrival during any one-minute period is 33%, and we can\n",
-    "simulate it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "played-character",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Moving a bike to Olin\n"
-     ]
-    }
-   ],
-   "source": [
-    "if flip(0.33):\n",
-    "    bike_to_olin()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "standard-party",
-   "metadata": {},
-   "source": [
-    "We can combine these snippets into a function that simulates a **time\n",
-    "step**, which is an interval of time, in this case one minute:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "ecological-colon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step():\n",
-    "    if flip(0.5):\n",
-    "        bike_to_wellesley()\n",
-    "    \n",
-    "    if flip(0.33):\n",
-    "        bike_to_olin()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "amateur-exposure",
-   "metadata": {},
-   "source": [
-    "Then we can simulate a time step like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "mediterranean-german",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "step()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sought-mobile",
-   "metadata": {},
-   "source": [
-    "Even though there are no values in parentheses, we have to include them."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "organic-proportion",
-   "metadata": {},
-   "source": [
-    "## Parameters\n",
-    "\n",
-    "The previous version of `step` is fine if the arrival probabilities\n",
-    "never change, but in reality, these probabilities vary over time.\n",
-    "\n",
-    "So instead of putting the constant values 0.5 and 0.33 in `step` we can replace them with **parameters**. Parameters are variables whose values are set when a function is called.\n",
-    "\n",
-    "Here's a version of `step` that takes two parameters, `p1` and `p2`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "hollywood-shopping",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step(p1, p2):\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley()\n",
-    "    \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "encouraging-arkansas",
-   "metadata": {},
-   "source": [
-    "The values of `p1` and `p2` are not set inside this function; instead,\n",
-    "they are provided when the function is called, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "buried-alert",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n"
-     ]
-    }
-   ],
-   "source": [
-    "step(0.5, 0.33)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aggregate-dynamics",
-   "metadata": {},
-   "source": [
-    "The values you provide when you call the function are called\n",
-    "**arguments**. The arguments, `0.5` and `0.33` in this example, get\n",
-    "assigned to the parameters, `p1` and `p2`, in order. So running this\n",
-    "function has the same effect as:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "recognized-denmark",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Moving a bike to Wellesley\n"
-     ]
-    }
-   ],
-   "source": [
-    "p1 = 0.5\n",
-    "p2 = 0.33\n",
-    "\n",
-    "if flip(p1):\n",
-    "    bike_to_wellesley()\n",
-    "    \n",
-    "if flip(p2):\n",
-    "    bike_to_olin()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "raised-museum",
-   "metadata": {},
-   "source": [
-    "The advantage of using parameters is that you can call the same function many times, providing different arguments each time.\n",
-    "\n",
-    "Adding parameters to a function is called **generalization**, because it makes the function more general, that is, less specialized."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "scenic-african",
-   "metadata": {},
-   "source": [
-    "## For loops\n",
-    "\n",
-    "At some point you will get sick of running cells over and over.\n",
-    "Fortunately, there is an easy way to repeat a chunk of code, the **for\n",
-    "loop**. Here's an example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "polish-river",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "Moving a bike to Wellesley\n",
-      "1\n",
-      "Moving a bike to Wellesley\n",
-      "2\n",
-      "Moving a bike to Wellesley\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in range(3):\n",
-    "    print(i)\n",
-    "    bike_to_wellesley()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "compatible-conspiracy",
-   "metadata": {},
-   "source": [
-    "The syntax here should look familiar; the first line ends with a\n",
-    "colon, and the lines inside the `for` loop are indented. The other\n",
-    "elements of the loop are:\n",
-    "\n",
-    "-   The words `for` and `in` are special words we have to use in a for\n",
-    "    loop.\n",
-    "\n",
-    "-   `range` is a Python function we're using here to control the number of times the loop runs.\n",
-    "\n",
-    "-   `i` is a **loop variable** that gets created when the for loop runs.\n",
-    "\n",
-    "When this loop runs, it runs the statements inside the loop three times.  The first time, the value of `i` is `0`; the second time, it is `1`; the third time, it is `2`.\n",
-    "\n",
-    "Each time through the loop, it prints the value of `i` and moves one bike Olin to Wellesley."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "breeding-groove",
-   "metadata": {},
-   "source": [
-    "## TimeSeries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "flexible-projection",
-   "metadata": {},
-   "source": [
-    "When we run a simulation, we often want to save the results for later analysis. The ModSim library provides a `TimeSeries` object for this purpose. A `TimeSeries` contains a sequence of time stamps and a\n",
-    "corresponding sequence of quantities.\n",
-    "\n",
-    "In this example, the time stamps are integers representing minutes, and the quantities are the number of bikes at one location.\n",
-    "\n",
-    "Since we have moved a number of bikes around, let's start again with a new `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "every-consultation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cross-sharp",
-   "metadata": {},
-   "source": [
-    "We can create a new, empty `TimeSeries` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "changing-planet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import TimeSeries\n",
-    "\n",
-    "results = TimeSeries()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "attractive-revision",
-   "metadata": {},
-   "source": [
-    "And we can add a quantity like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "aquatic-richardson",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results[0] = bikeshare.olin"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "searching-funeral",
-   "metadata": {},
-   "source": [
-    "The number in brackets is the time stamp, also called a **label**.\n",
-    "\n",
-    "We can use a `TimeSeries` inside a for loop to store the results of the simulation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "english-titanium",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n",
-      "1\n",
-      "Moving a bike to Olin\n",
-      "2\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in range(3):\n",
-    "    print(i)\n",
-    "    step(0.6, 0.6)\n",
-    "    results[i+1] = bikeshare.olin"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "prospective-joining",
-   "metadata": {},
-   "source": [
-    "Each time through the loop, we print the value of `i` and call `step`, which updates `bikeshare`.\n",
-    "Then we store the number of bikes at Olin in `results`. \n",
-    "We use the loop variable, `i`, to compute the time stamp, `i+1`.\n",
-    "\n",
-    "The first time through the loop, the value of `i` is `0`, so the time stamp is `1`.\n",
-    "The last time, the value of `i` is `2`, so the time stamp is `3`.\n",
-    "\n",
-    "When the loop exits, `results` contains 4 time stamps, from 0 through\n",
-    "3, and the number of bikes at Olin at the end of each time step.\n",
-    "\n",
-    "We can display the `TimeSeries` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "indonesian-singing",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Time\n",
-       "0    10\n",
-       "1    10\n",
-       "2    11\n",
-       "3    11\n",
-       "Name: Quantity, dtype: int64"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "small-encoding",
-   "metadata": {},
-   "source": [
-    "The left column is the time stamps; the right column is the quantities (which might be negative, depending on the state of the system).\n",
-    "\n",
-    "At the bottom, `dtype` is the type of the data in the `TimeSeries`; you can ignore this for now.\n",
-    "\n",
-    "ModSim provides a function called `show` that displays the `TimeSeries` as a table:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "verbal-bikini",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Quantity</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Time</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Quantity\n",
-       "Time          \n",
-       "0           10\n",
-       "1           10\n",
-       "2           11\n",
-       "3           11"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import show\n",
-    "\n",
-    "show(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "visible-montgomery",
-   "metadata": {},
-   "source": [
-    "You don't have to use `show`, but I think it looks better."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "following-contrary",
-   "metadata": {},
-   "source": [
-    "## Plotting\n",
-    "\n",
-    "`results` provides a function called `plot` we can use to plot\n",
-    "the results, and the ModSim library provides `decorate`, which we can use to label the axes and give the figure a title:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "saved-hands",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "results.plot()\n",
-    "\n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Time step (min)', \n",
-    "         ylabel='Number of bikes')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "limited-interstate",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "This chapter introduces the tools we need to run simulations, record the results, and plot them.\n",
-    "\n",
-    "We used a `State` object to represent the state of the system.\n",
-    "Then we used the `flip` function and an `if` statement to simulate a single time step.\n",
-    "\n",
-    "We used `for` loop to simulate a series of steps, and a `TimeSeries` to record the results.\n",
-    "\n",
-    "Finally, we used `plot` and `decorate` to plot the results.\n",
-    "\n",
-    "In the next chapter, we will extend this simulation to make it a little more realistic."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fallen-surprise",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "capital-internship",
-   "metadata": {},
-   "source": [
-    "**Exercise:** What happens if you spell the name of a state variable wrong?  Edit the following cell, change the spelling of `wellesley`, and run it.\n",
-    "\n",
-    "The error message uses the word \"attribute\", which is another name for what we are calling a state variable. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "helpful-zambia",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)\n",
-    "\n",
-    "bikeshare.wellesley"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dirty-multiple",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Make a `State` object with a third state variable, called `babson`, with initial value 0, and display the state of the system."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "beneficial-mainland",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>State</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>babson</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "namespace(olin=10, wellesley=2, babson=0)"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "bikeshare = State(olin=10, wellesley=2, babson=0)\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "christian-madrid",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Wrap the code in the chapter in a function named `run_simulation` that takes three parameters, named `p1`, `p2`, and `num_steps`.\n",
-    "\n",
-    "It should:\n",
-    "\n",
-    "1. Create a `TimeSeries` object to hold the results.\n",
-    "\n",
-    "2. Use a for loop to run `step` the number of times specified by `num_steps`, passing along the specified values of `p1` and `p2`.\n",
-    "\n",
-    "3. After each step, it should save the number of bikes at Olin in the `TimeSeries`.\n",
-    "\n",
-    "4. After the for loop, it should plot the results and\n",
-    "\n",
-    "5. Decorate the axes.\n",
-    "\n",
-    "To test your function:\n",
-    "\n",
-    "1. Create a `State` object with the initial state of the system.\n",
-    "\n",
-    "2. Call `run_simulation` with appropriate parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "former-frost",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def run_simulation(p1, p2, num_steps):\n",
-    "    results = TimeSeries()\n",
-    "    results[0] = bikeshare.olin\n",
-    "    \n",
-    "    for i in range(num_steps):\n",
-    "        step(p1, p2)\n",
-    "        results[i+1] = bikeshare.olin\n",
-    "        \n",
-    "    results.plot()\n",
-    "    decorate(title='Olin-Wellesley Bikeshare',\n",
-    "             xlabel='Time step (min)', \n",
-    "             ylabel='Number of bikes')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "spare-honduras",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n",
-      "Moving a bike to Olin\n",
-      "Moving a bike to Olin\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n",
-      "Moving a bike to Olin\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n",
-      "Moving a bike to Wellesley\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "bikeshare = State(olin=10, wellesley=2)\n",
-    "run_simulation(0.3, 0.2, 60)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "instructional-finnish",
-   "metadata": {},
-   "source": [
-    "## Opening the hood\n",
-    "\n",
-    "This section contains additional information about the functions we've used an pointers to their documentation.\n",
-    "\n",
-    "You don't need to know anything in these sections, so if you are already feeling overwhelmed, you might want to skip them.  But if you are curious, read on."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "digital-stretch",
-   "metadata": {},
-   "source": [
-    "The `State` object defined in the ModSim library, is based on the `SimpleNamespace` object defined in a standard Python library called `types`; the documentation is at <https://docs.python.org/3.7/library/types.html#types.SimpleNamespace>."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "declared-collar",
-   "metadata": {},
-   "source": [
-    "The `TimeSeries` object is based on the `Series` object defined by a library called Pandas.\n",
-    "The documentation is at <https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html>.\n",
-    "\n",
-    "`show` works by creating another Pandas object, called a `DataFrame`, which can be displayed as a table.\n",
-    "We'll use `DataFrame` objects in future chapters."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "comfortable-pillow",
-   "metadata": {},
-   "source": [
-    "`Series` objects provide their own `plot` function, but the syntax is different from the other functions we've used, so ModSim provides a function called `plot` just to make it consistent.\n",
-    "\n",
-    "`decorate` is based on Matplotlib, which is most widely-used plotting function for Python.  Matplotlib provides separate functions for `title`, `xlabel`, and `ylabel`.\n",
-    "`decorate` makes them a little easier to use."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "banner-policy",
-   "metadata": {},
-   "source": [
-    "The `flip` function uses NumPy's `random` function to generate a random number between 0 and 1, then returns `True` or `False` with the given probability.\n",
-    "\n",
-    "You can get the source code for `flip` by running the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "agricultural-midwest",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "def flip(p=0.5):\n",
-      "    \"\"\"Flips a coin with the given probability.\n",
-      "\n",
-      "    p: float 0-1\n",
-      "\n",
-      "    returns: boolean (True or False)\n",
-      "    \"\"\"\n",
-      "    return np.random.random() < p\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from modsim import source_code\n",
-    "\n",
-    "source_code(flip)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "running-membership",
-   "metadata": {},
-   "source": [
-    "You might not understand everything in this function yet, but you will."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "junior-lindsay",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap03.ipynb b/jupyter/chap03.ipynb
deleted file mode 100644
index 3854e10c..00000000
--- a/jupyter/chap03.ipynb
+++ /dev/null
@@ -1,1075 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "signal-format",
-   "metadata": {},
-   "source": [
-    "# Chapter 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "welcome-gentleman",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "backed-christianity",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "coral-steering",
-   "metadata": {},
-   "source": [
-    "To paraphrase two Georges, \"All models are wrong, but some models are\n",
-    "more wrong than others.\" In this chapter, I demonstrate the process we\n",
-    "use to make models less wrong.\n",
-    "\n",
-    "As an example, we'll review the bikeshare model from the previous\n",
-    "chapter, consider its strengths and weaknesses, and gradually improve\n",
-    "it. We'll also see ways to use the model to understand the behavior of\n",
-    "the system and evaluate designs intended to make it work better."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "twelve-length",
-   "metadata": {},
-   "source": [
-    "## Iterative modeling\n",
-    "\n",
-    "The model we have so far is simple, but it is based on unrealistic\n",
-    "assumptions. Before you go on, take a minute to review the model from\n",
-    "the previous chapter. What assumptions is it based on? Make a list of\n",
-    "ways this model might be unrealistic; that is, what are the differences between the model and the real world?\n",
-    "\n",
-    "Here are some of the differences on my list:\n",
-    "\n",
-    "-   In the model, a student is equally likely to arrive during any\n",
-    "    one-minute period. In reality, this probability varies depending on time of day, day of the week, etc.\n",
-    "\n",
-    "-   The model does not account for travel time from one bike station to another.\n",
-    "\n",
-    "-   The model does not check whether a bike is available, so it's\n",
-    "    possible for the number of bikes to be negative (as you might have\n",
-    "    noticed in some of your simulations)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sweet-heater",
-   "metadata": {},
-   "source": [
-    "Some of these modeling decisions are better than others. For example,\n",
-    "the first assumption might be reasonable if we simulate the system for a short period of time, like one hour.\n",
-    "\n",
-    "The second assumption is not very realistic, but it might not affect the results very much, depending on what we use the model for.\n",
-    "\n",
-    "On the other hand, the third assumption seems problematic, and it is\n",
-    "relatively easy to fix. In this chapter, we will.\n",
-    "\n",
-    "This process, starting with a simple model, identifying the most\n",
-    "important problems, and making gradual improvements, is called\n",
-    "**iterative modeling**.\n",
-    "\n",
-    "For any physical system, there are many possible models, based on\n",
-    "different assumptions and simplifications. It often takes several\n",
-    "iterations to develop a model that is good enough for the intended\n",
-    "purpose, but no more complicated than necessary."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "female-salem",
-   "metadata": {},
-   "source": [
-    "## More than one State object\n",
-    "\n",
-    "Before we go on, I want to make a few changes to the code from the\n",
-    "previous chapter. First I'll generalize the functions we wrote so they\n",
-    "take a `State` object as a parameter. Then, I'll make the code more\n",
-    "readable by adding documentation.\n",
-    "\n",
-    "Here is one of the functions from the previous chapter, `bike_to_wellesley`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "embedded-heavy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_wellesley():\n",
-    "    bikeshare.olin -= 1\n",
-    "    bikeshare.wellesley += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "artificial-remains",
-   "metadata": {},
-   "source": [
-    "When this function is called, it modifies `bikeshare`. As long as there\n",
-    "is only one `State` object, that's fine, but what if there is more than\n",
-    "one bike share system in the world? Or what if we want to run more than\n",
-    "one simulation?\n",
-    "\n",
-    "This function would be more flexible if it took a `State` object as a\n",
-    "parameter. Here's what that looks like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "unusual-advancement",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_wellesley(state):\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "serial-mortality",
-   "metadata": {},
-   "source": [
-    "The name of the parameter is `state` rather than `bikeshare` as a\n",
-    "reminder that the value of `state` could be any `State` object, not just\n",
-    "`bikeshare`.\n",
-    "\n",
-    "This version of `bike_to_wellesley` requires a `State` object as a\n",
-    "parameter, so we have to provide one when we call it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "packed-hungarian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import State\n",
-    "\n",
-    "bikeshare = State(olin=10, wellesley=2)\n",
-    "bike_to_wellesley(bikeshare)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fewer-rhythm",
-   "metadata": {},
-   "source": [
-    "Again, the argument we provide gets assigned to the parameter, so this\n",
-    "function call has the same effect as:\n",
-    "\n",
-    "```\n",
-    "state = bikeshare \n",
-    "state.olin -= 1 \n",
-    "state.wellesley += 1\n",
-    "```\n",
-    "\n",
-    "Now we can create as many `State` objects as we want:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "right-assessment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare1 = State(olin=10, wellesley=2)\n",
-    "bikeshare2 = State(olin=2, wellesley=10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "occupied-navigator",
-   "metadata": {},
-   "source": [
-    "And update them independently:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "cleared-advocacy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bike_to_wellesley(bikeshare1)\n",
-    "bike_to_wellesley(bikeshare2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "brown-ferry",
-   "metadata": {},
-   "source": [
-    "Changes in `bikeshare1` do not affect `bikeshare2`, and vice versa. So\n",
-    "we can simulate different bike share systems, or run multiple\n",
-    "simulations of the same system."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "magnetic-packing",
-   "metadata": {},
-   "source": [
-    "## Documentation\n",
-    "\n",
-    "Another problem with the code we have so far is that it contains no\n",
-    "**documentation**. Documentation is text we add to a program to help\n",
-    "other programmers read and understand it. It has no effect on the\n",
-    "program when it runs.\n",
-    "\n",
-    "There are two forms of documentation, **docstrings** and **comments**:\n",
-    "\n",
-    "* A docstring is a string in triple-quotes that appears at the beginning of a function.\n",
-    "\n",
-    "* A comment is a line of text that begins with a hash symbol, `#`.\n",
-    "\n",
-    "Here's a version of `bike_to_olin` with a docstring and a comment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "moral-parallel",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    # We decrease one state variable and increase the\n",
-    "    # other, so the total number of bikes is unchanged.\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "therapeutic-utility",
-   "metadata": {},
-   "source": [
-    "Docstrings follow a conventional format:\n",
-    "\n",
-    "-   The first line is a single sentence that describes what the function does.\n",
-    "\n",
-    "-   The following lines explain what the parameters are.\n",
-    "\n",
-    "A function's docstring should include the information someone needs to\n",
-    "know to *use* the function; it should not include details about how the function works.\n",
-    "\n",
-    "Comments provide details about how the function works, especially if there is something that would not be obvious to someone reading the program."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "american-clear",
-   "metadata": {},
-   "source": [
-    "## Negative bikes\n",
-    "\n",
-    "The changes we've made so far improve the quality of the code, but we\n",
-    "haven't done anything to improve the quality of the model yet. Let's do that now.\n",
-    "\n",
-    "Currently the simulation does not check whether a bike is available when a customer arrives, so the number of bikes at a location can be\n",
-    "negative. That's not very realistic. Here's a version of\n",
-    "`bike_to_olin` that fixes the problem:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "divine-leisure",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_olin(state):\n",
-    "    if state.wellesley == 0:\n",
-    "        return\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "decimal-denver",
-   "metadata": {},
-   "source": [
-    "The first line checks whether the number of bikes at Wellesley is zero. If so, it uses a **return statement**, which causes the function to end immediately, without running the rest of the statements. So if there are no bikes at Wellesley, we \"return\" from `bike_to_olin` without changing the state.\n",
-    "\n",
-    "We can test it by initializing the state with no bikes at Wellesley And calling `bike_to_olin`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "choice-cooking",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=12, wellesley=0)\n",
-    "bike_to_olin(bikeshare)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "persistent-denmark",
-   "metadata": {},
-   "source": [
-    "The state of the system should be unchanged."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "twelve-moderator",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "namespace(olin=12, wellesley=0)"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "criminal-general",
-   "metadata": {},
-   "source": [
-    "No more negative bikes (at least at Wellesley)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "gorgeous-found",
-   "metadata": {},
-   "source": [
-    "## Comparison operators\n",
-    "\n",
-    "The updated version of `bike_to_olin` uses the equals operator, `==`, which compares two values and returns `True` if they are equal and `False` otherwise.\n",
-    "\n",
-    "It is easy to confuse the equals operators with the assignment operator, `=`, which assigns a value to a variable. For example, the following statement creates a variable, `x`, if it doesn't already exist, and gives it the value `5`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "level-burns",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "weighted-monster",
-   "metadata": {},
-   "source": [
-    "On the other hand, the following statement checks whether `x` is `5` and\n",
-    "returns `True` or `False`. It does not create `x` or change its value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "civic-remains",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x == 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "forward-perth",
-   "metadata": {},
-   "source": [
-    "You can use the equals operator in an `if` statement, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "affecting-naples",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "yes, x is 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "if x == 5:\n",
-    "    print('yes, x is 5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "consolidated-anatomy",
-   "metadata": {},
-   "source": [
-    "If you make a mistake and use `=` in an `if` statement, like this:\n",
-    "\n",
-    "```\n",
-    "if x = 5:\n",
-    "    print('yes, x is 5')\n",
-    "```\n",
-    "\n",
-    "That's a **syntax error**, which means that the structure of the program is invalid. Python will print an error message and the program won't run."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "twelve-defensive",
-   "metadata": {},
-   "source": [
-    "The equals operator is one of the **comparison operators**. The others\n",
-    "are:\n",
-    "\n",
-    "| Operation             \t| Symbol \t|\n",
-    "|-----------------------\t|--------\t|\n",
-    "| Less than             \t| `<`      \t|\n",
-    "| Greater than          \t| `>`      \t|\n",
-    "| Less than or equal    \t| `<=`     \t|\n",
-    "| Greater than or equal \t| `>=`     \t|\n",
-    "| Equal                 \t| `==`     \t|\n",
-    "| Not equal             \t| `!=`     \t|"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "center-sequence",
-   "metadata": {},
-   "source": [
-    "## Metrics\n",
-    "\n",
-    "Getting back to the bike share system, at this point we have the ability to simulate the behavior of the system. Since the arrival of customers is random, the state of the system is different each time we run a\n",
-    "simulation. Models like this are called random or **stochastic**; models\n",
-    "that do the same thing every time they run are **deterministic**.\n",
-    "\n",
-    "Suppose we want to use our model to predict how well the bike share\n",
-    "system will work, or to design a system that works better. First, we\n",
-    "have to decide what we mean by \"how well\" and \"better\".\n",
-    "\n",
-    "From the customer's point of view, we might like to know the probability of finding an available bike. From the system-owner's point of view, we\n",
-    "might want to minimize the number of customers who don't get a bike when\n",
-    "they want one, or maximize the number of bikes in use. Statistics like\n",
-    "these that quantify how well the system works are called **metrics**.\n",
-    "\n",
-    "As a simple example, let's measure the number of unhappy customers.\n",
-    "Here's a version of `bike_to_olin` that keeps track of the number of\n",
-    "customers who arrive at a station with no bikes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "arbitrary-ferry",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_olin(state):\n",
-    "    if state.wellesley == 0:\n",
-    "        state.wellesley_empty += 1\n",
-    "        return\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "severe-contact",
-   "metadata": {},
-   "source": [
-    "If a customer arrives at the Wellesley station and finds no bike\n",
-    "available, `bike_to_olin` updates `wellesley_empty` which counts the\n",
-    "number of unhappy customers.\n",
-    "\n",
-    "This function only works if we initialize `wellesley_empty` when we\n",
-    "create the `State` object, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "cardiovascular-montgomery",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=12, wellesley=0, \n",
-    "                  wellesley_empty=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "computational-prior",
-   "metadata": {},
-   "source": [
-    "We can test it by calling `bike_to_olin`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "cosmetic-above",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "bike_to_olin(bikeshare)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "pleased-gasoline",
-   "metadata": {},
-   "source": [
-    "There should be 12 bikes at Olin, no bikes at Wellesley, and one unhappy customer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "bulgarian-palestine",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley_empty</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "namespace(olin=12, wellesley=0, wellesley_empty=1)"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "revised-associate",
-   "metadata": {},
-   "source": [
-    "Looks good!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "native-kidney",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "In this chapter, we wrote several versions of `bike_to_olin`:\n",
-    "\n",
-    "* We added a parameter, `state`, so we can work with more than one `State` object.\n",
-    "\n",
-    "* We added a docstring that explains how to use the function and a comment that explains how it works.\n",
-    "\n",
-    "* We used a conditional operator, `==`, to check whether a bike is available, in order to avoid negative bikes.\n",
-    "\n",
-    "* We added a state variable, `wellesley_empty`, to count the number of unhappy customers, which is a metric we'll use to quantify how well the system works.\n",
-    "\n",
-    "In the exercises, you'll update `bike_to_wellesley` the same way and test it by running a simulation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "impaired-cyprus",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "careful-hacker",
-   "metadata": {},
-   "source": [
-    "Here's the code we have so far, with docstrings, all in one place."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "wrong-internet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import TimeSeries, decorate\n",
-    "\n",
-    "def run_simulation(state, p1, p2, num_steps):\n",
-    "    \"\"\"Simulate the given number of time steps.\n",
-    "    \n",
-    "    state: State object\n",
-    "    p1: probability of an Olin->Wellesley customer arrival\n",
-    "    p2: probability of a Wellesley->Olin customer arrival\n",
-    "    num_steps: number of time steps\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[0] = state.olin\n",
-    "    \n",
-    "    for i in range(num_steps):\n",
-    "        step(state, p1, p2)\n",
-    "        results[i+1] = state.olin\n",
-    "        \n",
-    "    results.plot(label='Olin')\n",
-    "    decorate(title='Olin-Wellesley Bikeshare',\n",
-    "             xlabel='Time step (min)', \n",
-    "             ylabel='Number of bikes')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "instrumental-copyright",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import flip\n",
-    "\n",
-    "def step(state, p1, p2):\n",
-    "    \"\"\"Simulate one time step.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    p1: probability of an Olin->Wellesley ride\n",
-    "    p2: probability of a Wellesley->Olin ride\n",
-    "    \"\"\"\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley(state)\n",
-    "    \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin(state)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "improved-renaissance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.wellesley == 0:\n",
-    "        state.wellesley_empty += 1\n",
-    "        return\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "unavailable-maker",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bigger-rapid",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Modify `bike_to_wellesley` so it checks whether a bike is available at Olin.  If not, it should add one to `olin_empty`.\n",
-    "\n",
-    "To test it, create a `State` that initializes `olin` and `olin_empty` to `0`, run `bike_to_wellesley`, and check the result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "phantom-carter",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.olin == 0:\n",
-    "        state.olin_empty += 1\n",
-    "        return\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "adopted-contrary",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "bikeshare = State(olin=0, wellesley=12,\n",
-    "                  olin_empty=0, wellesley_empty=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "comparable-natural",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "bike_to_wellesley(bikeshare)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "attractive-amendment",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>olin_empty</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley_empty</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "namespace(olin=0, wellesley=12, olin_empty=1, wellesley_empty=0)"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "possible-initial",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Now run the simulation a few times and confirm that the number of bikes is never negative.  What's the final state of the system?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "immune-shock",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "bikeshare = State(olin=10, wellesley=2,\n",
-    "                  olin_empty=0, wellesley_empty=0)\n",
-    "\n",
-    "run_simulation(bikeshare, 0.3, 0.2, 60)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "sporting-collectible",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>olin_empty</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley_empty</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "namespace(olin=0, wellesley=12, olin_empty=2, wellesley_empty=0)"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "owned-fitting",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap04.ipynb b/jupyter/chap04.ipynb
deleted file mode 100644
index 45528b03..00000000
--- a/jupyter/chap04.ipynb
+++ /dev/null
@@ -1,1208 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "existing-guidance",
-   "metadata": {},
-   "source": [
-    "# Chapter 4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hawaiian-detective",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "informed-defendant",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "caring-gnome",
-   "metadata": {},
-   "source": [
-    "Here the code from previous chapters we'll reuse."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "stylish-raising",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import flip\n",
-    "\n",
-    "def step(state, p1, p2):\n",
-    "    \"\"\"Simulate one time step.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    p1: probability of an Olin->Wellesley ride\n",
-    "    p2: probability of a Wellesley->Olin ride\n",
-    "    \"\"\"\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley(state)\n",
-    "    \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin(state)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "wound-bottle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.wellesley == 0:\n",
-    "        state.wellesley_empty += 1\n",
-    "        return\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "prescribed-affect",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.olin == 0:\n",
-    "        state.olin_empty += 1\n",
-    "        return\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "atlantic-collectible",
-   "metadata": {},
-   "source": [
-    "In the previous chapter we defined metrics that quantify the performance of bike sharing this system. In this chapter we see how those metrics depend on the parameters of the system, like the Customer rate of customers at bike stations.\n",
-    "\n",
-    "We also discuss a program development strategy, called incremental\n",
-    "development, that might help you write programs faster and spend less\n",
-    "time debugging."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "strategic-newspaper",
-   "metadata": {},
-   "source": [
-    "## Functions that return values\n",
-    "\n",
-    "We have seen several functions that return values; for example, when you run `sqrt`, it returns a number you can assign to a variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "imposed-pregnancy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.4142135623730951"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from numpy import sqrt\n",
-    "\n",
-    "root_2 = sqrt(2)\n",
-    "root_2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unsigned-recipe",
-   "metadata": {},
-   "source": [
-    "When you run `State`, it returns a new `State` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "accessible-wallace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "namespace(olin=10, wellesley=2)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import State\n",
-    "\n",
-    "bikeshare = State(olin=10, wellesley=2)\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "missing-pendant",
-   "metadata": {},
-   "source": [
-    "Not all functions have return values. For example, when you run `step`,\n",
-    "it updates a `State` object, but it doesn't return a value.\n",
-    "\n",
-    "To write functions that return values, we can use a `return` statement, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "minimal-supervisor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def add_five(x):\n",
-    "    return x + 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sized-intensity",
-   "metadata": {},
-   "source": [
-    "`add_five` takes a parameter, `x`, which could be any number. It\n",
-    "computes `x + 5` and returns the result. So if we run it like this, the\n",
-    "result is `8`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "warming-program",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "add_five(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "rental-representation",
-   "metadata": {},
-   "source": [
-    "As a more useful example, here's a version of `run_simulation` that\n",
-    "creates a `State` object, runs a simulation, and then returns the\n",
-    "`State` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "sitting-cleveland",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import TimeSeries\n",
-    "\n",
-    "def run_simulation(p1, p2, num_steps):\n",
-    "    state = State(olin=10, wellesley=2,\n",
-    "                  olin_empty=0, wellesley_empty=0)\n",
-    "    \n",
-    "    for i in range(num_steps):\n",
-    "        step(state, p1, p2)\n",
-    "        \n",
-    "    return state"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "minimal-ability",
-   "metadata": {},
-   "source": [
-    "We can call `run_simulation` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "difficult-shepherd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "final_state = run_simulation(0.3, 0.2, 60)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "charming-wheel",
-   "metadata": {},
-   "source": [
-    "The result is a `State` object that represents the final state of the system, including the metrics we'll use to evaluate the performance of the system:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "tough-sweet",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(final_state.olin_empty, \n",
-    "      final_state.wellesley_empty)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aggregate-lightweight",
-   "metadata": {},
-   "source": [
-    "The simulation we just ran starts with `olin=10` and `wellesley=2`, and uses the values `p1=0.3`, `p2=0.2`, and `num_steps=60`. \n",
-    "These five values are **parameters of the model**, which are quantities that determine the behavior of the system.\n",
-    "\n",
-    "It is easy to get the parameters of a model confused with the parameters of a function. \n",
-    "In fact, it is common for the parameters of the model to appear as parameters in functions. \n",
-    "\n",
-    "For example, the previous version of `run_simulation` takes `p1`, `p2`, and `num_steps` as parameters.\n",
-    "So we can call `run_simulation` with different parameters and see how\n",
-    "the metrics, like the number of unhappy customers, depend on the\n",
-    "parameters. But before we do that, we need a new version of a `for` loop."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "valuable-aircraft",
-   "metadata": {},
-   "source": [
-    "## Loops and arrays\n",
-    "\n",
-    "In `run_simulation`, we use this `for` loop:\n",
-    "\n",
-    "```\n",
-    "    for i in range(num_steps):\n",
-    "        step(state, p1, p2)\n",
-    "```\n",
-    "\n",
-    "In this example, `range` creates a sequence of numbers from 0 to `num_steps` (including `0` but not `num_steps`). \n",
-    "Each time through the loop, the next number in the sequence gets assigned to the loop variable, `i`.\n",
-    "\n",
-    "But `range` only works with integers; to get a sequence of non-integer\n",
-    "values, we can use `linspace`, which is defined NumPy:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "bound-juice",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.  , 0.25, 0.5 , 0.75, 1.  ])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from numpy import linspace\n",
-    "\n",
-    "p1_array = linspace(0, 1, 5)\n",
-    "p1_array"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ordered-colleague",
-   "metadata": {},
-   "source": [
-    "The arguments indicate where the sequence should start and stop, and how\n",
-    "many elements it should contain. In this example, the sequence contains\n",
-    "`5` equally-spaced numbers, starting at `0` and ending at `1`.\n",
-    "\n",
-    "The result is a NumPy **array**, which is a new kind of object we have\n",
-    "not seen before. An array is a container for a sequence of numbers.\n",
-    "\n",
-    "We can use an array in a `for` loop like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "commercial-methodology",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0\n",
-      "0.25\n",
-      "0.5\n",
-      "0.75\n",
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "for p1 in p1_array:\n",
-    "    print(p1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "finnish-budapest",
-   "metadata": {},
-   "source": [
-    "When this loop runs, it\n",
-    "\n",
-    "1.  Gets the first value from the array and assigns it to `p1`.\n",
-    "\n",
-    "2.  Runs the body of the loop, which prints `p1`.\n",
-    "\n",
-    "3.  Gets the next value from the array and assigns it to `p1`.\n",
-    "\n",
-    "4.  Runs the body of the loop, which prints `p1`.\n",
-    "\n",
-    "And so on, until it gets to the end of the array. This will come in handy in the next section."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "crazy-belize",
-   "metadata": {},
-   "source": [
-    "## Sweeping parameters\n",
-    "\n",
-    "If we know the actual values of parameters like `p1` and `p2`, we can\n",
-    "use them to make specific predictions, like how many bikes will be at\n",
-    "Olin after one hour.\n",
-    "\n",
-    "But prediction is not the only goal; models like this are also used to\n",
-    "explain why systems behave as they do and to evaluate alternative\n",
-    "designs. For example, if we observe the system and notice that we often run out of bikes at a particular time, we could use the model to figure out why that happens. And if we are considering adding more bikes, or another station, we could evaluate the effect of various \"what if\" scenarios.\n",
-    "\n",
-    "As an example, suppose we have enough data to estimate that `p2` is\n",
-    "about `0.2`, but we don't have any information about `p1`. We could run simulations with a range of values for `p1` and see how the results vary. This process is called **sweeping** a parameter, in the sense that the value of the parameter \"sweeps\" through a range of possible values.\n",
-    "\n",
-    "Now that we know about loops and arrays, we can use them like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "working-chair",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0 0\n",
-      "0.12 0\n",
-      "0.24 0\n",
-      "0.36 0\n",
-      "0.48 7\n",
-      "0.6 7\n"
-     ]
-    }
-   ],
-   "source": [
-    "p1_array = linspace(0, 0.6, 6)\n",
-    "p2 = 0.2\n",
-    "num_steps = 60\n",
-    "\n",
-    "for p1 in p1_array:\n",
-    "    final_state = run_simulation(p1, p2, num_steps)\n",
-    "    print(p1, final_state.olin_empty)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "chicken-mainstream",
-   "metadata": {},
-   "source": [
-    "Each time through the loop, we run a simulation with a different value\n",
-    "of `p1` and the same value of `p2`, `0.2`. Then we print `p1` and the\n",
-    "number of unhappy customers at Olin.\n",
-    "\n",
-    "To save and plot the results, we can use a `SweepSeries` object, which\n",
-    "is similar to a `TimeSeries`; the difference is that the labels in a\n",
-    "`SweepSeries` are parameter values rather than time values.\n",
-    "\n",
-    "We can create an empty `SweepSeries` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "instrumental-session",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import SweepSeries\n",
-    "\n",
-    "sweep = SweepSeries()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "listed-orleans",
-   "metadata": {},
-   "source": [
-    "And add values like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "hollywood-technical",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for p1 in p1_array:\n",
-    "    final_state = run_simulation(p1, p2, num_steps)\n",
-    "    sweep[p1] = final_state.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "instructional-showcase",
-   "metadata": {},
-   "source": [
-    "The result is a `SweepSeries` that maps from each value of `p1` to the\n",
-    "resulting number of unhappy customers. Then we can plot the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "hollywood-spirit",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "sweep.plot(label='Olin')\n",
-    "\n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Customer rate at Olin (p1 in customers/min)', \n",
-    "         ylabel='Number of unhappy customers')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "intense-start",
-   "metadata": {},
-   "source": [
-    "NumPy provides functions that compute a variety of summary statistics, like `mean`, `median`, and `std` (which computes standard deviation).\n",
-    "\n",
-    "We can use `mean` to compute the average of the values in a series, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "contemporary-enough",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.166666666666667"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from numpy import mean\n",
-    "\n",
-    "mean(sweep)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "owned-senior",
-   "metadata": {},
-   "source": [
-    "In this example, computing the mean might not be useful, but in the exercises below, it will be."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "korean-christianity",
-   "metadata": {},
-   "source": [
-    "## Incremental development\n",
-    "\n",
-    "When you start writing programs that are more than a few lines, you\n",
-    "might find yourself spending more and more time debugging. The more code you write before you start debugging, the harder it is to find the problem.\n",
-    "\n",
-    "**Incremental development** is a way of programming that tries to\n",
-    "minimize the pain of debugging. The fundamental steps are:\n",
-    "\n",
-    "1.  Always start with a working program. If you have an example from a\n",
-    "    book, or a program you wrote that is similar to what you are working\n",
-    "    on, start with that. Otherwise, start with something you *know* is\n",
-    "    correct, like `x=5`. Run the program and confirm that it does what\n",
-    "    you expect.\n",
-    "\n",
-    "2.  Make one small, testable change at a time. A \"testable\" change is\n",
-    "    one that displays something or has some other effect you can check.\n",
-    "    Ideally, you should know what the correct answer is, or be able to\n",
-    "    check it by performing another computation.\n",
-    "\n",
-    "3.  Run the program and see if the change worked. If so, go back to\n",
-    "    Step 2. If not, you will have to do some debugging, but if the\n",
-    "    change you made was small, it shouldn't take long to find the\n",
-    "    problem.\n",
-    "\n",
-    "When this process works, your changes usually work the first time, or if they don't, the problem is obvious. In practice, there are two problems with incremental development:\n",
-    "\n",
-    "-   Sometimes you have to write extra code to generate visible output\n",
-    "    that you can check. This extra code is called **scaffolding**\n",
-    "    because you use it to build the program and then remove it when you\n",
-    "    are done. That might seem like a waste, but time you spend on\n",
-    "    scaffolding is almost always time you save on debugging.\n",
-    "\n",
-    "-   When you are getting started, it might not be obvious how to choose\n",
-    "    the steps that get from `x=5` to the program you are trying to\n",
-    "    write. You will see more examples of this process as we go along,\n",
-    "    and you will get better with experience.\n",
-    "\n",
-    "If you find yourself writing more than a few lines of code before you\n",
-    "start testing, and you are spending a lot of time debugging, try\n",
-    "incremental development."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "nominated-assault",
-   "metadata": {},
-   "source": [
-    "## Summary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "appreciated-preview",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "primary-quest",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write a function called `make_state` that creates a `State` object with the state variables `olin=10` and `wellesley=2`, and then returns the new `State` object.\n",
-    "\n",
-    "Write a line of code that calls `make_state` and assigns the result to a variable named `init`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "reflected-freedom",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def make_state():\n",
-    "    state = State(olin=10, wellesley=2)\n",
-    "    return state"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "north-formation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "init = make_state()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "robust-blair",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Read the documentation of `linspace` at <>.\n",
-    "Then use it to make an array of 101 equally spaced points between 0 and 1 (including both)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "collected-butter",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.  , 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ,\n",
-       "       0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 , 0.21,\n",
-       "       0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 , 0.31, 0.32,\n",
-       "       0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 , 0.41, 0.42, 0.43,\n",
-       "       0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 , 0.51, 0.52, 0.53, 0.54,\n",
-       "       0.55, 0.56, 0.57, 0.58, 0.59, 0.6 , 0.61, 0.62, 0.63, 0.64, 0.65,\n",
-       "       0.66, 0.67, 0.68, 0.69, 0.7 , 0.71, 0.72, 0.73, 0.74, 0.75, 0.76,\n",
-       "       0.77, 0.78, 0.79, 0.8 , 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87,\n",
-       "       0.88, 0.89, 0.9 , 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98,\n",
-       "       0.99, 1.  ])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "p1_array = linspace(0, 1, 101)\n",
-    "p1_array"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fleet-debut",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Wrap the code from this chapter in a function named `sweep_p1` that takes an array called `p1_array` as a parameter.  It should create a new `SweepSeries`, run a simulation for each value of `p1` in `p1_array`, store the results in the `SweepSeries`, and return the `SweepSeries`.\n",
-    "\n",
-    "Use your function to plot the number of unhappy customers at Olin as a function of `p1`.  Label the axes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "authorized-sarah",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def sweep_p1(p1_array):\n",
-    "    p2 = 0.2\n",
-    "    num_steps = 60\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for p1 in p1_array:\n",
-    "        state = run_simulation(p1, p2, num_steps)\n",
-    "        sweep[p1] = state.olin_empty\n",
-    "        \n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "romance-wisdom",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "p1_array = linspace(0, 1, 101)\n",
-    "sweep = sweep_p1(p1_array)\n",
-    "sweep.plot(label='Olin')\n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Customer rate at Olin (p1 in customers/min)', \n",
-    "         ylabel='Number of unhappy customers')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "developmental-broad",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write a function called `sweep_p2` that runs simulations with `p1=0.5` and a range of values for `p2`.  It should store the results in a `SweepSeries` and return the `SweepSeries`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "norman-banana",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def sweep_p2(p2_array):\n",
-    "    p1 = 0.5\n",
-    "    num_steps = 60\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for p2 in p2_array:\n",
-    "        state = run_simulation(p1, p2, num_steps)\n",
-    "        sweep[p2] = state.olin_empty\n",
-    "        \n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "mexican-robert",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "p2_array = linspace(0, 1, 101)\n",
-    "sweep = sweep_p2(p2_array)\n",
-    "sweep.plot(label='Olin')\n",
-    "\n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Customer rate at Wellesley (p2 in customers/min)', \n",
-    "         ylabel='Number of unhappy customers')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "separate-mention",
-   "metadata": {},
-   "source": [
-    "## Optional Exercises\n",
-    "\n",
-    "The following two exercises are a little more challenging.  If you are comfortable with what you have learned so far, you should give them a try.  If you feel like you have your hands full, you might want to skip them for now."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bearing-orbit",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Because our simulations are random, the results vary from one run to another, and the results of a parameter sweep tend to be noisy.  We can get a clearer picture of the relationship between a parameter and a metric by running multiple simulations with the same parameter and taking the average of the results.\n",
-    "\n",
-    "Write a function called `run_multiple_simulations` that takes as parameters `p1`, `p2`, `num_steps`, and `num_runs`.\n",
-    "\n",
-    "`num_runs` specifies how many times it should call `run_simulation`.\n",
-    "\n",
-    "After each run, it should store the total number of unhappy customers (at Olin or Wellesley) in a `TimeSeries`.\n",
-    "At the end, it should return the `TimeSeries`.\n",
-    "\n",
-    "Test your function with parameters\n",
-    "\n",
-    "```\n",
-    "p1 = 0.3\n",
-    "p2 = 0.3\n",
-    "num_steps = 60\n",
-    "num_runs = 10\n",
-    "```\n",
-    "\n",
-    "Display the resulting `TimeSeries` and use the `mean` function from NumPy to compute the average number of unhappy customers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "accredited-salmon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def run_multiple_simulations(p1, p2, num_steps, num_runs):\n",
-    "    totals = TimeSeries()\n",
-    "    \n",
-    "    for i in range(num_runs):\n",
-    "        state = run_simulation(p1, p2, num_steps)\n",
-    "        totals[i] = state.olin_empty + state.wellesley_empty\n",
-    "        \n",
-    "    return totals"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "visible-allowance",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Quantity</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Time</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Quantity\n",
-       "Time          \n",
-       "0            7\n",
-       "1            3\n",
-       "2            1\n",
-       "3            3\n",
-       "4            4\n",
-       "5            5\n",
-       "6            2\n",
-       "7            3\n",
-       "8            0\n",
-       "9            0"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "from modsim import show\n",
-    "\n",
-    "p1 = 0.3\n",
-    "p2 = 0.3\n",
-    "num_steps = 60\n",
-    "num_runs = 10\n",
-    "totals = run_multiple_simulations(p1, p2, num_steps, num_runs)\n",
-    "show(totals)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "spatial-fundamentals",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.8"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "mean(totals)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "structural-expense",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Continuting the previous exercise, use `run_multiple_simulations` to run simulations with a range of values for `p1` and\n",
-    "\n",
-    "```\n",
-    "p2 = 0.3\n",
-    "num_steps = 60\n",
-    "num_runs = 20\n",
-    "```\n",
-    "\n",
-    "Store the results in a `SweepSeries`, then plot the average number of unhappy customers as a function of `p1`.  Label the axes.\n",
-    "\n",
-    "What value of `p1` minimizes the average number of unhappy customers?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "reverse-emphasis",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "p1_array = linspace(0, 1, 20)\n",
-    "p2 = 0.3\n",
-    "num_steps = 60\n",
-    "num_runs = 20\n",
-    "\n",
-    "sweep = SweepSeries()\n",
-    "for p1 in p1_array:\n",
-    "    totals = run_multiple_simulations(p1, p2, num_steps, num_runs)\n",
-    "    sweep[p1] = mean(totals)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "broad-latitude",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "sweep.plot(label='total', color='green')\n",
-    "    \n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Customer rate at Olin (p1 in customers/min)', \n",
-    "         ylabel='Average total unhappy customers')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "powerful-planner",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap05.ipynb b/jupyter/chap05.ipynb
deleted file mode 100644
index 39cea07e..00000000
--- a/jupyter/chap05.ipynb
+++ /dev/null
@@ -1,1455 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "wicked-mozambique",
-   "metadata": {},
-   "source": [
-    "# Chapter 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "inclusive-theater",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "legislative-rating",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "therapeutic-genealogy",
-   "metadata": {},
-   "source": [
-    "In this chapter..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "informal-contractor",
-   "metadata": {},
-   "source": [
-    "# World population\n",
-    "\n",
-    "In 1968 Paul Erlich published *The Population Bomb*, in which he\n",
-    "predicted that world population would grow quickly during the 1970s,\n",
-    "that agricultural production could not keep up, and that mass starvation\n",
-    "in the next two decades was inevitable (see\n",
-    "<https://modsimpy.com/popbomb>). As someone who grew up during those\n",
-    "decades, I am happy to report that those predictions were wrong.\n",
-    "\n",
-    "But world population growth is still a topic of concern, and it is an\n",
-    "open question how many people the earth can sustain while maintaining\n",
-    "and improving our quality of life.\n",
-    "\n",
-    "In this chapter and the next, we use tools from the previous chapters to\n",
-    "explain world population growth since 1950 and generate predictions for\n",
-    "the next 50-100 years.\n",
-    "\n",
-    "For background on world population growth, watch this video from the\n",
-    "American Museum of Natural History <https://modsimpy.com/human>."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dying-browse",
-   "metadata": {},
-   "source": [
-    "## World Population Data\n",
-    "\n",
-    "The Wikipedia article on world population contains tables with estimates of world population from prehistory to the present, and projections for the future (<https://modsimpy.com/worldpop>)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "continuing-cancellation",
-   "metadata": {},
-   "source": [
-    "The following cell downloads a copy of https://en.wikipedia.org/wiki/World_population_estimates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "convenient-stroke",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates.html'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "acknowledged-bracelet",
-   "metadata": {},
-   "source": [
-    "To read this data, we will use Pandas, which provides functions for\n",
-    "working with data. The function we'll use is `read_html`, which can read a web page and extract data from any tables it contains. Before we can use it, we have to import it. You have already seen this import\n",
-    "statement:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "israeli-finding",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas import read_html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "regulation-parade",
-   "metadata": {},
-   "source": [
-    "Now we can use it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "instant-beverage",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = 'World_population_estimates.html'\n",
-    "tables = read_html(filename,\n",
-    "                   header=0, \n",
-    "                   index_col=0,\n",
-    "                   decimal='M')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dried-immunology",
-   "metadata": {},
-   "source": [
-    "The arguments are:\n",
-    "\n",
-    "-   `filename`: The name of the file (including the directory it's in)\n",
-    "    as a string. This argument can also be a URL starting with `http`.\n",
-    "\n",
-    "-   `header`: Indicates which row of each table should be considered the\n",
-    "    header, that is, the set of labels that identify the columns. In\n",
-    "    this case it is the first row (numbered 0).\n",
-    "\n",
-    "-   `index_col`: Indicates which column of each table should be\n",
-    "    considered the **index**, that is, the set of labels that identify\n",
-    "    the rows. In this case it is the first column, which contains the\n",
-    "    years.\n",
-    "\n",
-    "-   `decimal`: Normally this argument is used to indicate which\n",
-    "    character should be considered a decimal point, because some\n",
-    "    conventions use a period and some use a comma. In this case I am\n",
-    "    abusing the feature by treating `M` as a decimal point, which allows\n",
-    "    some of the estimates, which are expressed in millions, to be read\n",
-    "    as numbers.\n",
-    "\n",
-    "The result, which is assigned to `tables`, is a sequence that contains\n",
-    "one `DataFrame` for each table. A `DataFrame` is an object, defined by\n",
-    "Pandas, that represents tabular data.\n",
-    "\n",
-    "To select a `DataFrame` from `tables`, we can use the bracket operator\n",
-    "like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "driving-wrapping",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table2 = tables[2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "simplified-convert",
-   "metadata": {},
-   "source": [
-    "This line selects the third table (numbered 2), which contains\n",
-    "population estimates from 1950 to 2016.\n",
-    "\n",
-    "We can use `head` to display the first few lines of the table."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "running-alcohol",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>United States Census Bureau (2017)[28]</th>\n",
-       "      <th>Population Reference Bureau (1973–2016)[15]</th>\n",
-       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
-       "      <th>Maddison (2008)[17]</th>\n",
-       "      <th>HYDE (2007)[24]</th>\n",
-       "      <th>Tanton (1994)[18]</th>\n",
-       "      <th>Biraben (1980)[19]</th>\n",
-       "      <th>McEvedy &amp; Jones (1978)[20]</th>\n",
-       "      <th>Thomlinson (1975)[21]</th>\n",
-       "      <th>Durand (1974)[22]</th>\n",
-       "      <th>Clark (1967)[23]</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1950</th>\n",
-       "      <td>2557628654</td>\n",
-       "      <td>2.516000e+09</td>\n",
-       "      <td>2.525149e+09</td>\n",
-       "      <td>2.544000e+09</td>\n",
-       "      <td>2.527960e+09</td>\n",
-       "      <td>2.400000e+09</td>\n",
-       "      <td>2.527000e+09</td>\n",
-       "      <td>2.500000e+09</td>\n",
-       "      <td>2.400000e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.486000e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1951</th>\n",
-       "      <td>2594939877</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.572851e+09</td>\n",
-       "      <td>2.571663e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1952</th>\n",
-       "      <td>2636772306</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.619292e+09</td>\n",
-       "      <td>2.617949e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1953</th>\n",
-       "      <td>2682053389</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.665865e+09</td>\n",
-       "      <td>2.665959e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1954</th>\n",
-       "      <td>2730228104</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.713172e+09</td>\n",
-       "      <td>2.716927e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      United States Census Bureau (2017)[28]  \\\n",
-       "Year                                           \n",
-       "1950                              2557628654   \n",
-       "1951                              2594939877   \n",
-       "1952                              2636772306   \n",
-       "1953                              2682053389   \n",
-       "1954                              2730228104   \n",
-       "\n",
-       "      Population Reference Bureau (1973–2016)[15]  \\\n",
-       "Year                                                \n",
-       "1950                                 2.516000e+09   \n",
-       "1951                                          NaN   \n",
-       "1952                                          NaN   \n",
-       "1953                                          NaN   \n",
-       "1954                                          NaN   \n",
-       "\n",
-       "      United Nations Department of Economic and Social Affairs (2015)[16]  \\\n",
-       "Year                                                                        \n",
-       "1950                                       2.525149e+09                     \n",
-       "1951                                       2.572851e+09                     \n",
-       "1952                                       2.619292e+09                     \n",
-       "1953                                       2.665865e+09                     \n",
-       "1954                                       2.713172e+09                     \n",
-       "\n",
-       "      Maddison (2008)[17]  HYDE (2007)[24]  Tanton (1994)[18]  \\\n",
-       "Year                                                            \n",
-       "1950         2.544000e+09     2.527960e+09       2.400000e+09   \n",
-       "1951         2.571663e+09              NaN                NaN   \n",
-       "1952         2.617949e+09              NaN                NaN   \n",
-       "1953         2.665959e+09              NaN                NaN   \n",
-       "1954         2.716927e+09              NaN                NaN   \n",
-       "\n",
-       "      Biraben (1980)[19]  McEvedy & Jones (1978)[20]  Thomlinson (1975)[21]  \\\n",
-       "Year                                                                          \n",
-       "1950        2.527000e+09                2.500000e+09           2.400000e+09   \n",
-       "1951                 NaN                         NaN                    NaN   \n",
-       "1952                 NaN                         NaN                    NaN   \n",
-       "1953                 NaN                         NaN                    NaN   \n",
-       "1954                 NaN                         NaN                    NaN   \n",
-       "\n",
-       "     Durand (1974)[22]  Clark (1967)[23]  \n",
-       "Year                                      \n",
-       "1950               NaN      2.486000e+09  \n",
-       "1951               NaN               NaN  \n",
-       "1952               NaN               NaN  \n",
-       "1953               NaN               NaN  \n",
-       "1954               NaN               NaN  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table2.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "valid-editing",
-   "metadata": {},
-   "source": [
-    "The first column, which is labeled `Year`, is special.  It is the **index** for this `DataFrame`, which means it contains the labels for the rows.\n",
-    "\n",
-    "Some of the values use scientific notation; for example, `2.544000e+09` is shorthand for $2.544 \\cdot 10^9$ or 2.544 billion.\n",
-    "\n",
-    "`NaN` is a special value that indicates missing data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "plastic-senate",
-   "metadata": {},
-   "source": [
-    "The column labels are long strings, which makes them hard to work with.\n",
-    "We can replace them with shorter strings like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "engaging-regular",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "communist-crowd",
-   "metadata": {},
-   "source": [
-    "Now we can select a column from the `DataFrame` using the dot operator,\n",
-    "like selecting a state variable from a `State` object.\n",
-    "\n",
-    "Here are the estimates from the United States Census Bureau:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "amended-negative",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census = table2.census / 1e9"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "absent-heart",
-   "metadata": {},
-   "source": [
-    "The result is a Pandas `Series`, which is similar to the `TimeSeries` and `SweepSeries` objects we've been using.\n",
-    "\n",
-    "The number `1e9` is a shorter way to write `1000000000` or one billion.\n",
-    "When we divide a `Series` by a number, it divides all of the elements of the `Series`.\n",
-    "From here on, we'll express population estimates in terms of billions.\n",
-    "\n",
-    "We can use `tail` to see the last few elements of the `Series`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "graduate-specialist",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "2012    7.013871\n",
-       "2013    7.092128\n",
-       "2014    7.169968\n",
-       "2015    7.247893\n",
-       "2016    7.325997\n",
-       "Name: census, dtype: float64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "census.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "brilliant-brook",
-   "metadata": {},
-   "source": [
-    "The left column is the **index** of the `Series`; in this example it contains the dates.\n",
-    "The right column contains the **values**, which are population estimates.\n",
-    "In 2016 the world population was about 7.3 billion.\n",
-    "\n",
-    "Here are the estimates from the United Nations\n",
-    "Department of Economic and Social Affairs (U.N. DESA):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "linear-admission",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "2012    7.080072\n",
-       "2013    7.162119\n",
-       "2014    7.243784\n",
-       "2015    7.349472\n",
-       "2016         NaN\n",
-       "Name: un, dtype: float64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "un.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "mental-sussex",
-   "metadata": {},
-   "source": [
-    "The most recent estimate we have from the U.N. is for 2015, so the value for 2016 is `NaN`.\n",
-    "\n",
-    "Now we can plot the estimates like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "ordered-garage",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "def plot_estimates():\n",
-    "    census.plot(style=':', label='US Census')\n",
-    "    un.plot(style='--', label='UN DESA')\n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)') "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "invisible-mouse",
-   "metadata": {},
-   "source": [
-    "The keyword argument `style=':'` specifies a dotted line; `style='--'` specifies a dashed line.\n",
-    "The `label` argument provides the string that appears in the legend.\n",
-    "\n",
-    "And here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "periodic-weekend",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_estimates()\n",
-    "decorate(title='World Population Estimates')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "labeled-magic",
-   "metadata": {},
-   "source": [
-    "The lines overlap almost completely, but the most recent estimates diverge slightly.\n",
-    "In the next section, we'll quantify these differences."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "tender-summer",
-   "metadata": {},
-   "source": [
-    "## Absolute and relative errors\n",
-    "\n",
-    "Estimates of world population from the U.S. Census and the U.N. DESA differ slightly.\n",
-    "One way to characterize this difference is **absolute error**, which is the absolute value of the difference between the two.\n",
-    "\n",
-    "To compute absolute value, we can import `abs` from NumPy:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "secondary-player",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import abs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "caring-garlic",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "2012    0.066201\n",
-       "2013    0.069991\n",
-       "2014    0.073816\n",
-       "2015    0.101579\n",
-       "2016         NaN\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abs_error = abs(un - census)\n",
-    "abs_error.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bridal-extraction",
-   "metadata": {},
-   "source": [
-    "When you subtract two `Series` objects, the result is a new `Series`.\n",
-    "Because one of the estimates from 2016 is `NaN`, the result is `NaN`.\n",
-    "\n",
-    "To summarize the results, we can compute the **mean absolute error**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "corresponding-emerald",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.029034508242424265"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from numpy import mean\n",
-    "\n",
-    "mean(abs_error)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "identical-archive",
-   "metadata": {},
-   "source": [
-    "On average, the estimates differ by about 0.029 billion.\n",
-    "But we can also use `max` to compute the maximum absolute error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "headed-witch",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.10157921199999986"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from numpy import max\n",
-    "\n",
-    "max(abs_error)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "composite-partnership",
-   "metadata": {},
-   "source": [
-    "In the worst case, they differ by about 0.1 billion.\n",
-    "\n",
-    "Now 0.1 billion is a lot of people, so that might sound like a serious discrepancy.\n",
-    "But counting everyone is the world is hard, and we should not expect the estimates to be exact.\n",
-    "\n",
-    "Another way to quantify the magnitude of the difference is **relative error**, which is the size of the error divided by the estimates themselves."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "advisory-complex",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "2012    0.943860\n",
-       "2013    0.986888\n",
-       "2014    1.029514\n",
-       "2015    1.401500\n",
-       "2016         NaN\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rel_error = abs_error / census * 100\n",
-    "rel_error.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "greater-register",
-   "metadata": {},
-   "source": [
-    "I multiplied by 100 so we can interpret the results as a percentage.  In 2015, the difference between the estimates is about 1.4%, and that happens to be the maximum.\n",
-    "\n",
-    "Again, we can summarize the results by computing the mean."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "frank-owner",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.5946585816022846"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mean(rel_error)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "peaceful-fleece",
-   "metadata": {},
-   "source": [
-    "The mean relative error is about 0.6%.\n",
-    "So that's not bad.\n",
-    "\n",
-    "You might wonder why I divided by `census` rather than `un`.\n",
-    "In general, if you think one estimate is better than the other, you put the better one in the denominator.\n",
-    "In this case, I don't know which is better, so I put the smaller one in the denominator, which makes the computed errors a little bigger."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "vietnamese-excuse",
-   "metadata": {},
-   "source": [
-    "## Modeling Population Growth\n",
-    "\n",
-    "Suppose we want to predict world population growth over the next 50 or\n",
-    "100 years. We can do that by developing a model that describes how\n",
-    "populations grow, fitting the model to the data we have so far, and then using the model to generate predictions.\n",
-    "\n",
-    "In the next few sections I demonstrate this process starting with simple models and gradually improving them.\n",
-    "\n",
-    "Although there is some curvature in the plotted estimates, it looks like world population growth has been close to linear since 1960 or so. So we'll start with a model that has constant growth.\n",
-    "\n",
-    "To fit the model to the data, we'll compute the average annual growth\n",
-    "from 1950 to 2016. Since the UN and Census data are so close, we'll use the Census data.\n",
-    "\n",
-    "We can select a value from a `Series` using the bracket operator:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "undefined-sauce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.557628654"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "census[1950]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "precise-correlation",
-   "metadata": {},
-   "source": [
-    "So we can get the total growth during the interval like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "postal-debate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "total_growth = census[2016] - census[1950]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dutch-sample",
-   "metadata": {},
-   "source": [
-    "The numbers in brackets are called **labels**, because they label the\n",
-    "rows of the `Series`.\n",
-    "\n",
-    "In this example, the labels 2016 and 1950 are part of the data, so it\n",
-    "would be better not to make them part of the program. \n",
-    "Putting values like these in the program is called **hard coding**; it is considered bad practice because if the data change in the future, we have to change the program (see <https://modsimpy.com/hardcode>).\n",
-    "\n",
-    "It would be better to get the labels from the `Series`.\n",
-    "We can do that by selecting the index from `census` and then selecting the first element."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "functional-trick",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1950"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_0 = census.index[0]\n",
-    "t_0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "empty-siemens",
-   "metadata": {},
-   "source": [
-    "So `t_0` is the label of the first element, which is 1950.\n",
-    "We can get the label of the last element like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "acoustic-south",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2016"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_end = census.index[-1]\n",
-    "t_end"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "express-metallic",
-   "metadata": {},
-   "source": [
-    "The value `-1` indicates the last element; `-2` indicates the second to last element, and so on.\n",
-    "\n",
-    "The difference between `t_0` and `t_end` is the elapsed time between them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "planned-remains",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "66"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "elapsed_time = t_end - t_0\n",
-    "elapsed_time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bridal-royal",
-   "metadata": {},
-   "source": [
-    "Now we can use `t_0` and `t_end` to select the population at the beginning and end of the interval."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "quarterly-aggregate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_0 = census[t_0]\n",
-    "p_end = census[t_end]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "driven-castle",
-   "metadata": {},
-   "source": [
-    "And compute the total growth during the interval."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "prescription-optics",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.768368055"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "total_growth = p_end - p_0\n",
-    "total_growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "accepted-auditor",
-   "metadata": {},
-   "source": [
-    "Finally, we can compute average annual growth."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "convertible-patch",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.07224800083333333"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "annual_growth = total_growth / elapsed_time\n",
-    "annual_growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "japanese-merit",
-   "metadata": {},
-   "source": [
-    "From 1950 to 2016, world population grew by about 0.07 billion people per year, on average.\n",
-    "The next step is to use this estimate to simulate population growth."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "public-verification",
-   "metadata": {},
-   "source": [
-    "## Simulation\n",
-    "\n",
-    "Our simulation will start with the observed population in 1950, `p_0`,\n",
-    "and add `annual_growth` each year. To store the results, we'll use a\n",
-    "`TimeSeries` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "duplicate-leave",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import TimeSeries\n",
-    "\n",
-    "results = TimeSeries(time='Year', quantity='Population')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "expired-salmon",
-   "metadata": {},
-   "source": [
-    "In this example, I use the keyword arguments `time` and `quantity` to give names to the index and the values.\n",
-    "These names don't affect the computation, but they appear when we display or plot the `TimeSeries`.\n",
-    "\n",
-    "We can set the first value in the new `TimeSeries` like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "convenient-thong",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results[t_0] = p_0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "constant-casino",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like so far."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "israeli-surveillance",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Population</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1950</th>\n",
-       "      <td>2.557629</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Population\n",
-       "Year            \n",
-       "1950    2.557629"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import show\n",
-    "\n",
-    "show(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "stretch-snapshot",
-   "metadata": {},
-   "source": [
-    "Now we set the rest of the values by simulating annual growth:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "attended-morris",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for t in range(t_0, t_end):\n",
-    "    results[t+1] = results[t] + annual_growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "signed-colleague",
-   "metadata": {},
-   "source": [
-    "The values of `t` go from from `t_0` to `t_end`, including the first but not the last.\n",
-    "\n",
-    "Inside the loop, we compute the population for the next year by adding the population for the current year and `annual_growth`. \n",
-    "\n",
-    "The last time through the loop, the value of `t` is 2015, so the last label in `results` is 2016.\n",
-    "\n",
-    "Here's what the results look like, compared to the estimates."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "wrong-brooks",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results.plot(color='gray', label='model')\n",
-    "plot_estimates()\n",
-    "decorate(title='Constant Growth Model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "automated-albany",
-   "metadata": {},
-   "source": [
-    "The keyword argument `color='gray'` specifies the color of the line.  For details on color specification see <https://modsimpy.com/color>.\n",
-    "\n",
-    "From 1950 to 1990, the model does not fit the data particularly well, but after that, it's pretty good."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unnecessary-million",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "This chapter is a first step toward modeling changes in world population growth during the last 70 years.\n",
-    "\n",
-    "We used Pandas to read data from a web page and store the results in a `DataFrame`.\n",
-    "From the `DataFrame` we selected two `Series` objects and used them to compute absolute and relative errors.\n",
-    "\n",
-    "Then we computed average population growth and used it to build a simple model with constant annual growth.\n",
-    "The model fits recent data pretty well; nevertheless, there are two reasons we should be skeptical:\n",
-    "\n",
-    "* There is no obvious mechanism that could cause population growth to be constant from year to year. Changes in population are determined by the fraction of people who die and the fraction of people who give birth, so we expect them to depend on the current population.\n",
-    "\n",
-    "* According to this model, world population would keep growing at the same rate forever, and that does not seem reasonable.\n",
-    "\n",
-    "In the next chapter we'll consider other models that might fit the data better and make more credible predictions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "advanced-ivory",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hearing-today",
-   "metadata": {},
-   "source": [
-    "Here's the code from this chapter all in one place."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "terminal-reynolds",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = census.index[0]\n",
-    "t_end = census.index[-1]\n",
-    "elapsed_time = t_end - t_0\n",
-    "\n",
-    "p_0 = census[t_0]\n",
-    "p_end = census[t_end]\n",
-    "\n",
-    "total_growth = p_end - p_0\n",
-    "annual_growth = total_growth / elapsed_time\n",
-    "\n",
-    "results = TimeSeries(time='Year', quantity='Population')\n",
-    "results[t_0] = p_0\n",
-    "\n",
-    "for t in range(t_0, t_end):\n",
-    "    results[t+1] = results[t] + annual_growth"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "organizational-memphis",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results.plot(color='gray', label='model')\n",
-    "plot_estimates()\n",
-    "decorate(title='Constant Growth Model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ecological-welsh",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Try fitting the model using data from 1970 to the present, and see if that does a better job.\n",
-    "\n",
-    "Suggestions: \n",
-    "\n",
-    "1. Define `t_1` to be 1970 and `p_1` to be the population in 1970.  Use `t_1` and `p_1` to compute annual growth, but use `t_0` and `p_0` to run the simulation. \n",
-    "\n",
-    "2. You might want to add a constant to the starting value to match the data better."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "rising-anger",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.07854997754347826"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "t_0 = census.index[0]\n",
-    "t_1 = 1970\n",
-    "t_end = census.index[-1]\n",
-    "elapsed_time = t_end - t_1\n",
-    "\n",
-    "p_0 = census[t_0]\n",
-    "p_1 = census[t_1]\n",
-    "p_end = census[t_end]\n",
-    "\n",
-    "total_growth = p_end - p_1\n",
-    "annual_growth = total_growth / elapsed_time\n",
-    "annual_growth"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "false-handbook",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results = TimeSeries(time='Year', quantity='Population')\n",
-    "results[t_0] = p_0 - 0.45\n",
-    "\n",
-    "for t in range(t_0, t_end):\n",
-    "    results[t+1] = results[t] + annual_growth"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "political-loading",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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BodMFxMfH0717dxwOh3djvgSPJygRqQIMA/5wkfV3AncC56ZhK6WUKoWjW6zhvP6PQas4iH2CeduPc9f763m8xxFO71pFYV4e1/SIZuDAgYSEhHg7YreUxYm61wAbjDHHL7TSGDPFGBNtjIkOCwsrg3BKZ//+/XTq1Om8ZU899RR///vfAVi9ejU9evQgMjKS9u3b89RTT11wP2vXrqV///60bdv23MVns7KyPB2+UqoiyzoJcx6BKQMgdQ9nTmew7UgGBmjESeJqnuDY5qU0bdqUe+65h2uvvbbcJCcomyG+cVTg4b2JEycya9YsunTpQmFhIbt27frFNsePH2fMmDF8+umn9OrVC2MMX3zxBWfOnClXfyxKKR+y+VOY+wfIyYDud8LAPzDh/W2czlrHhNCDHDiQTNewMOLjb6JVq1bejvayeLQHJSIhwGDgS0++jjelpKTQsGFDABwOBx06dPjFNm+++SYTJ06kV69egHV9vtGjR1O/fn0yMzO57bbb6NatG1FRUXz99dcAfPjhh4wcOZIhQ4bQunVrHn/8cQAKCwuZNGkSnTp1onPnzvzzn/8EYODAgRSd5Jyamkp4eDgA27ZtO1fWIyIigj179nj0/VBKlZH8LEy9DiyP+5K8wS9wpsDBoJqpdDm7jtTUE1x77bXcfffd5TY5gYd7UMaYLKDuldzn2HdWMfrqJoyJbkp+oZOb313Djd2b8puoJmTnFTLpg7Xc3LM513dpxOmcfO6Yuo5b+4QzpFNDTmbmcc+09dzRryVxHeqTciaHetWDflU8Dz/8MG3btmXgwIEMGTKEiRMnEhR0/j63bt3KxIkTL/j8v/71r8TExPD++++Tnp5O9+7diYuLA2DTpk1s3LiRwMBA2rZtywMPPEBKSgqHDx9m69atAKSnp5cY39tvv83kyZMZP348eXl5WndKqfLq9BFIfAKa94bo26DrJFbXGsbN767hzuTFsH8NhYWFXNOnB/379//F51B5pBeLvYSLTb8sWv7EE0+wbt064uPj+eSTT0p9xfX58+fz4osvEhkZycCBA8nJyeHAgQOAdVHZmjVrEhQURIcOHUhOTqZly5YkJSXxwAMPMHfuXGrUqFHi/nv16sXzzz/PSy+9RHJyMsHBwaWKTynlZQW5sOwVeD0atn/D/iMpzNlyBCNC1awjDKt1mLzdy2nVqhX33Xcf8fHxFSI5QTm81NHMu3qdux/g8DvvcXAVx3mPawQFnPe4TtUq5z12p/dUt27dc1chL3Ly5ElatGhx7vFVV13FPffcwx133EFYWBhpaWnUrfu/jmPHjh1Zv349w4cP/8X+i45HtW3b9rzla9asOa/Mh8PhoKCggNq1a7N582bmzZvHm2++yaxZs3j//ffx9/fH6bRKMefk5Jx73k033USPHj347rvvSEhI4N133yUmJuaS7VZK+YCkpfDtZDj1M7QdCgl/5cmvTnB41w6OrvkvR44cpmODBiQkTDw3rF+RaA/qEqpVq0bDhg1ZuHAhYCWnuXPn0rdvXwC+++67c6Xc9+zZg8PhoFatWuft4/7772fq1KmsWbPm3LJp06Zx7NgxEhISeP3118/tY+PGjSXGk5qaitPpZNSoUTz77LNs2LABgPDwcNavXw/A559/fm77pKQkWrZsyYMPPsiwYcPOlaNXSpUDzgLycfBmk7+RPvxD0v1qMyD4IL0yV3D6dAbDhg3jjjvuqJDJCcphD8obPvroI+677z4effRRwCqjcdVVVwHw8ccf8/DDDxMSEoK/vz/Tp0//xclv9evX59NPP+Wxxx4jJSUFPz8/+vfvz8iRI/nLX/7CQw89REREBMYYwsPDmTNnzkVjOXz4MLfeeuu53lJR0cPHHnuMG264gY8//vi8HtLMmTOZNm0aAQEBNGjQgCeeeOKKvjdKqSsoJwOWvmxdASLmz9AqlqQxC5jyn7WY75aSsXstADED+tGnT58yrc3kDVpuQ+l7rJS3OZ2waRosfAYyU9nVdAz/bfoYk+Nas2nTJuYvWkJu5hk6d+587th0RaLlNpRSyhcd+wm+vh+OboKmPWD8Z0xd48/ePUcJTlrC8ePHaNKkCQk33uC1woHeoglKKaW8yVGFgsw03g/7I7HX30stRx4dshOpcmIXObVqMmrUqDIrHOhrNEEppVRZys+Gla/Dqf0w4i0Ia8up29fwwZurODV/OXk/r8Pf35/YWN8pHOgtmqCUUqosGAPbvrSKB2Yc5ECDwbwxaz0vjIrk513bGObYQM6+bKKifK9woLdoglJKKU87mQRf3QcHVkL9zvCbt/lufwP2bjnI62+9Q8bJE7Ro0YL4+HifLBzoLZqglFLKU4pqNAXWoOD0MabWfoh2sffSOkSoenA+ESf34ahbhxtvvJE2bdpUyuNMJdETdS/hUuU2Jk2aROPGjcnNzQXOv1BrcQ6Hg8jISDp27EiXLl145ZVXzp3PtGTJEmrWrElkZOS524IFCwDren0dO3YkIiKCyMjI8074LSgoIDQ0lD/84YLltpRS3pCfA8v+AR8NwzgLoWooBfeu5QsTQ+KKH3n77bc5euQwCQnx3HvvvT5d1dabtAd1BTgcDt5//33uueeeErcLDg5m06ZNgHUV9JtuuomMjAyefvppAPr16/eLk3RXrVrFnDlz2LBhA4GBgaSmppKXl3du/fz582nbti2zZs3i+eef1z9ypbzJGNg22z7OdIBD9WP409uLmfLbAWxY9yP9sleRfzqP6OjyVTjQW7QHdQU89NBD/POf/6SgoMDt59SrV48pU6bwxhtvUNLJ0kePHiU0NPTcdflCQ0Np1KjRufUzZsxg8uTJNGvWjNWrV19+I5RSv07GYXg/AT6/FRNUHW75ho293yTX6eDVt94hMTGR5s2blcvCgd5S/npQHwz95bKOI6D7HZCXBdPH/HJ95E0QNR4y02DWLeevu/W7Xx1Ss2bN6Nu3Lx9//DHXX3+9289r2bIlTqeTlJQUAJYtW0ZkZOS59V988QXx8fE888wztGnThri4OMaOHcuAAQMAyM7OZuHChbzzzjukp6czY8aMczWnlFJlpLAAHP7WMJ7TMKX6g4RefRv9goNIXTqP9mnJVA8LY9T48eW6NpM3aA/qEi5VbqPIH//4R/72t7+dO6bkLtfeU79+/di0adO521VXXUW1atVYv349U6ZMISwsjLFjx/Lhhx8CMGfOHAYNGkRISAijRo1i9uzZWu9JqbKSexYW/RXe6kFu9hnwD8Rx+zw21LmObT/9xJQpU0hNPcHQoUPLfeFAbyl/PaiSejxVQkpeX7VuqXtM7pTbAGjVqhWRkZHMmjXL7X0nJSXhcDioV68eO3bsuOh2DoeDgQMHMnDgQDp37szUqVOZNGkSM2bMYMWKFecmZaSlpbF48eJzBQ+VUh7gLIRN02HRc3D2OPsbXsO9ry5k+gMJbFm/lpZHV+B0OunRuzf9+vWrMLWZvKH8Jagy5lpuIzY29ly5jcmTJ/9i2z/96U8MHXqBIcgLOHHiBHfffTf3339/iRMbdu3ahZ+fH61btwasKrvNmzfn9OnTLF++nIMHD547PvXBBx8wY8YMTVBKeUpmKnw0HI5vxdm4G35jp3PS2YqG8zbz73emkH82nfbt2xMXF0edOnW8HW25pwnKDSWV23DVsWNHunbteq5GU3HZ2dlERkaSn5+Pv78/EyZM4JFHHjm3vvgxqD//+c+0aNGCBx54gPT0dPz9/WnVqhVTpkzhyy+/JCYm5ryihsOHD+fxxx8nNzf3vOVKqV8pOx2Ca0FIXfLDOvB8+jVUbTqasVRlQ+IXNDt6mNCGDUkYPYLmzZt7O9oKQ8ttKH2PlbqYsymw+HnY+gWpk5YR2jAcgOe+2Qwn9lF46CeqVatGbGwsXbp00dM8LpOW21BKKXflZcHqN2H5q1CQw84mNzDxrR/59P7aJG1dB1tWISL079+/UhQO9JYSE5SI9AJuBvoBDYFsYCvwHTDNGJPh8QiVUqos5Z6Ft3pCxkFyW11L4JBnqerXkP5z1jFj6nvkZ58hIiKCmJiYClc40NdcNEGJyPfAEeBr4K9AChAEtAEGAV+LyCvGmG88HaQxRrvOHuJLQ7xKedWJXRDWFgKrURA1kfuXBxLk34/JZxzMm/cx1Y4fp0HTpiQkjKVx48bejrZSKKkHNcEYk1ps2Vlgg337h4iEeiwyW1BQEGlpadStW1eT1BVmjCEtLU2nwarKLWUHJD6B2ZPIjmFz6NC1L/4Df0dvv52k7dvCRx/toGbNyl040FsumqAukJwua5tfq0mTJhw6dIgTJ054+qUqpaCgoEpXRlopAM4csyZAbPwYqlRnQ5uHuWnWCabXSiF19waSf/wRf39/YmJi6NWrF/7+esi+rF3yHReRkcBLQD1A7JsxxtTwcGwABAQE/OKkWKWU+lXyc+DtvpjsdNI73Urta/5MK6pyu/9qFnz2Ibk5WjjQF7jzleBl4HpjzMUvdaCUUr6usAB2fw/troOAIAqHvMy4b7OpcqoVTxxKJTFxBtmpqVo40Ie4k6COa3JSSpVbxsDuebDgSTixk8U932dgwkgcnUdye/4+kjavZsaMvdSpo4UDfY07CWqdiMwEvgJyixYaY770VFBKKXVFHN4AiU/A/mVQ5yrWdHuVW5cEMrXZEc4kbTxXZy0hIYFu3brhcDi8HbFy4U6CqgFkAfEuywygCUop5bsK82HmzeTnZrM/+klaX/MAkYXwhHMlq775mLy8PLp168aAAQO0NpOPumSCMsbcerk7F5FawLtAJ6ykdpsxZtXl7k8ppUqUdRJ+fBf6TAb/QJw3TOfGz1NwHK7BU7v2kJiYSHp6Om3atGHw4MGEhnr8TBn1K7gzi68J8DrQByvJLAcmG2MOubH/fwFzjTGjRaQKoF9TlFJXXkEurJ0CP/wdk5PBzCP1GDF6AkFNovh9ws9sWrmEzz47QL169ZgwYQItW7b0dsTKDe4M8X0AfAIUlaq92V42uKQniUgNoD8wCcAYkwfkXW6gSin1C04nbP0CFj0D6QegVRyb2z7Cn2dnUKfDIbJ+3sjmzZsJCQnhuuuuIyoqCj8/rdNaXriToMKMMR+4PP5QRB5y43ktgRPAByLSBViP1fPKdN1IRO4E7gSrdLpSSpWGWfU66c4Q1kdPIe66sXTMz+fvJ5ez7rvpOJ1OemvhwHLLnQSVKiI3AzPsx+OANDf33RV4wBizRkT+Bfwe+IvrRsaYKcAUsMptuBu4UqqSOrELlr4E1/4dQuogN83i/748QPZxQ9jmzSxatIjTp0/ToUMH4uLiqF27trcjVpfJnb7ubcANwDHgKDDaXnYph4BDxpg19uPPsRKWUkqV3tkTMOcReKsXzt3zefXjzzlxJheqN+DhPmH0K9zCV199RdWqVZk0aRJjxozR5FTOuTOL7wAwrLQ7NsYcE5GDItLWGLMLiAW2X0aMSqnKzBhY/gos+ycUZEO333Kg0318PHU37fYd5ezedWzbto3q1aszfPhwLRxYgZRUbuNxY8zLIvI61uy98xhjHnRj/w8A0+0ZfEnAZU9ZV0pVUiJwZCNJNaKZ2+Bu7r12CA1zc3mux27WzZmmhQMrsJJ6UEWXN1pXwjYlMsZsAn5RxlcppUqUvMq6NNGwNyCsDYx6j4+/38vxjBx+XLeOpUuWkJmZSUREBLGxsdSoUSbXrlZlrKRyG9/aP6eWXThKqUrt1H5IfBK2f0VB1YY8+8kCxo1tQLsGNbipfRALFyzjv98dp2nTpowbN04LB1ZwJQ3xfcsFhvaKGGNKfVxKKaUuavELsPyfGD8HMvCPnI26i9Xvb6HboRNsXPwdu3fvplatWowePZoOHTrocaZKoKQhvr+XWRRKqcrJGOsYE0B+JnvC4vhPlQm8NGAIgTk5PNQuk3Xff4K/vz+xsbH07NlTCwdWIiUN8S0ty0CUUpXMgTXw/eOYuKeQqwbB4GdZtmI/mcknWb5qDauW/0BOTg5RUVEMGjRICwdWQiUN8f1EyUN8ER6JSClVsZ0+Yh1n+mkWhdUa8tKczfRK6MjAtmH0Ccsja9MqFu1No0WLFiQkJFC/fn1vR6y8pKS+8nVlFoVSqnJYMwUWPIVxFiD9HqOw12Q2fbSN8BNpTPtxHklJSdStW5dx48bRunVrPc5UyZU0xJdcloEopSqwomNNIhyu24vn8sfx2sCR5Odkc3OjVDYtTiQwMJAhQ4YQHR2thQMVUPIQ33JjTF8ROYM11CeuP40xeuKBUqpkaftg7h+g/XWYqAlIt9+yo9owMlftJ3HJcjavXUFBQQHdu3dnwIABBAcHezti5UNK6kH1tX9WL7twlFIVQl4mLPsHrHwd46jCRyfbI7nJTOjZnAbOE3Q9s5ofl2vhQFUyt+ZrikhXoC92wUJjzEaPRqWUKr/2LoBvJsPpQxBxI8Q9xYqvDhORkcGHH37IgQNaOFC5x52Kuk9gFSv80l70oYh8Zox5zqORKaXKJ2M461eNJ6q+yJNDfosUZDOk2kF+WrOFnKpVtXCgcps7PahxQJQxJgdARF4ENgCaoJRSkJ8Ny18FPwem/++Q1oNJHn01yd/sYN6SZezZuApjDH369KFfv34EBgZ6O2JVTriToPYDQUCO/TgQ2OepgJRS5cieBfDfR+HUftbVvpbEszv4/bXtKUg7SJ+cNez48YwWDlSXraRZfEVlNnKBbSKSaD8eDCwvm/CUUj7pzDGY+3vYNhtC28DEb5m9qTbm7Fneffddjhw5QqNGjRg9ejTNmjXzdrSqnCqpB1VUZmM9MNtl+RKPRaOUKh8yU3Huns+nVW+h95inqVlF6JK/gB07t3OmenVGjBhBRESEnmirfpWSpplrmQ2l1P8c3Qy758OA30GDTqTdsZH3Pt7BqR/WcWrXWvz8/BgwYAC9e/fWwoHqirhUuY0pwFxjTH6xdS2BScB+Y8z7Ho1QKeVdeVmw5HlY9RY5VWrxt+O9+NPo3hw4eIghznWkbs+kS5cuxMTEaOFAdUWVNMR3B/AI8KqInAROYE2WaAHsBd4wxnzt+RCVUl6zdyHMeRjSk6HrRKYFTWL1tnRe//cU0lOP06xZMxISbqJRo0bejlRVQCUN8R0DHgceF5FwoCGQDew2xmSVTXhKKa/JycB8Nok0apISP4N6bboTND+Rbhm7oXYtxowZQ/v27fU4k/IYt64kYYzZjzXdXClVkRkD+xZBy0EQVJPccV8y9tNUIrcaai38N/7+/gweHEePHj20cKDyOP0LU0pZTh+F7x6FXd8xo9nTjJpwP5uOFRLDTxQeyyGqa1cGDRpE1apVvR2pqiQ0QSlV2RkDmz6BeX+Agly2dnyMv21rzZ433sOcPkbLli2Jj4/XwoGqzGmCUqqy+/ZB2PARp0KjKRz8POtX72IIG6kTUIt4LRyovMidi8X2AZ4CmtvbF9WD0ssQK1VeGQPOQnD4YzqOZMrOYGam92XAp/MIDg5i+DWDtXCg8jp3elDvAQ9jXVGi0LPhKKU87lQy5tsH2e3flgbDn+GnowEcyK9HP+cWevTQwoHKd7iToDKMMd97PBKllGc5nbDuPUh8EifwQVYrju3/lBb5yXRq04b4+Hjq1q3r7SiVOsedBLVYRP6GVQ8qt2ihMWaDx6JSSl1Z6Qdwzr4Xv+Rl5DTuzdcmloxDQo/aOSQkaOFA5ZvcSVA97J/RLssMEHPlw1FKeUTOabIPbeb5/NspOFyHsGpwx4hBREZGauFA5bMumaCMMYPKIhCl1BWWfoDszV+R3vk2du1MZYm5k9SAGtzQrxN9+/bVwoHK57kzi68m8CTQ3160FHjGGJPhxnP3A2ewJlcUGGOiS36GUupXMwbWf4CZ/xeceQU8vMif9n4n6NSxIw/FxVGrVi1vR6iUW9wZ4nsf2ArcYD+eAHwAjHTzNQYZY1IvIzalVGmdSiZ/9v0EHPiBI1Va8Y/C4XRuEMItw26jadOm3o5OqVJxJ0FdZYwZ5fL4aRHZ5KF4lFKXqyCPnCmDKcjKYJYZSkpQNJOui6Nz5856oq0ql9xJUNki0tcYsxzOnbib7eb+DTBfRAzwjjFmSvENRORO4E5AS0MrdTkyDnParyZrVq/iaG5/Fjo7MqJPJPfH9iUgIMDb0Sl12dxJUPcAU+1jUQKcxCpW6I4+xpgjIlIPSBSRncaYH1w3sJPWFIDo6GjjduRKVXbOQpxr3iF//lO8VXAD2Y4wukZdzxsxMVSvXt3b0Sn1q7kzi28T0EVEatiPT7u7c2PMEftniojMBroDP5T8LKXUpTiPbSfv8zsISt3KDtOGgyGdeWz8UFo0a+Lt0JS6Ykoq+X6zMWaaiDxSbDkAxphXStqxiFQF/IwxZ+z78cAzvz5kpSq3lHn/oPaqv5JvAllSbTTNrn2ENzp00ONMqsIpqQdVVPTlQmMF7gzF1Qdm2/80/sAnxpi5pQtPKVUk8+xZfli2jLS122ng7Mqe9vdzx5jrtHCgqrBKKvn+jn13gTFmhes6e6JEiYwxSUCXXxeeUqow8xTb37+HtSl+7PfvQI+rf0OvQYOI08KBqoJz56vX60BXN5Yppa4g43RyZOG/qbnyBTo6z7LVMYTR4yfSpZWez6Qqh5KOQfUCegNhxY5D1QC0SIxSHnRsz0YOzZhMtHMzJxwNSYt/g7E9hul181SlUlIPqgpQzd7G9TjUaWC0J4NSqrI6c+YMixcv5tCGRCaY3XxdZxLX3fsyYQF63TxV+ZR0DGopsFREPjTGJJdhTEpVOgUFBcyf9TbHdqxgt38XBvYciqPnEwyvFebt0JTyGneOQWXZ9aA6AkFFC40xWm5DqV/JGMPO9csx8//MtXmbOOIIpePwP9IjsrO3Q1PK69xJUNOBmcB1wN3AROCEJ4NSqjI4fDCZuVNf5Ib82YRIHumdb6fh9c/SqIrOzlMK3EtQdY0x74nIZJdhv6WeDkypiio9PZ1Fixaxb8sa7mE2h4Lb0PrWd6hVv723Q1PKp7iToPLtn0dFZChwBNDrqShVSnl5ecyZ8zWpG79mhyOS6/rFE9DpdtrVaw16FQilfsGdBPWcfaHYR7HOf6oBPOzRqJSqQIwxbN7wIxnzX+La3B/wczjZMHA8PQfGejs0pXyaOxeLnWPfzQC0/LtSpZCcnMzsT95mZM7nREoK2U0HEDD8FXqGtvJ2aEr5vJJO1H2dEq65Z4x50CMRKVUBnDx5ksTEBezesZU7mUWhfyA5o2YR3CHB26EpVW6U1INaV2ZRKFVB5OTkMPf7/5K24XM2OKK5YVAsddqNJ6BuC/Cv4u3wlCpXSjpRd2pZBqJUeeZ0Olm3dg0Zi/7F4LwlBDtyaH91HL0HDPB2aEqVW5c8BiUii7nAUJ+eqKuUZd++fcz/7D8Mzvqa7n5HyG3UHb/r/0HvhhHeDk2pcs2dWXyPudwPAkYBBZ4JR6nyIyUlhXnzE0nau4fxfvOo6ijk5ND3qHP1KJ02rtQV4M4svvXFFq3QE3VVZZaVlcXiuV9xduMsVjr6MSE+nvD2t+OoFoYEBF16B0opt7gzxFfH5aEfcDXQwGMRKeWjCgsLWbTkBwJWvUpcwUr8HQU07RRD7969vR2aUhWSO0N867GOQQnW0N7PwO2eDEopX2KMYdeuXSz+7C2GFcyhsaSRGx6DY+hL9A5r4+3wlKqw3Bnia1EWgSjli7YnHWTp4sWkHPqZYQHbwb82WSPfJaStzhFSytPcGeILAu4F+mL1pJYD/zbG5Hg4NqW85uzZsyz/+kNCds5kt18C44ZeQ0Tnh3AEVgetaqtUmXBniO8j4AzWdfgAxgEfA2M8FZRS3nImK4fZX39N1J7XSHBuJTugKi0GhtO0e3dvh6ZUpeNOgmprjOni8nixiGz2VEBKeYMxhm3btrHzi2cZ61yAww9yut5JyOA/EhJc29vhKVUpuZOgNopIT2PMagAR6QGs8GxYSpWNgkInHy3YwPF928g7sZ/YYMOxWkNoPvYl/GtqVRmlvMmdBNUDuEVEDtiPmwE7ROQnwBhj9HR5VS5lnEpjx6xnGHrkaz6QkVw/7GYiu/wZP4fD26EppXAvQQ3xeBRKlaFF246wfd673Hj6A3pykhPV2vDwqNEEtuzq7dCUUi7cmWaeLCJdgH72omXGGD0GpcoVY4xVOHDzZkK+eYj7zSbSqzTkbMJ/COs6Ri9NpJQPcmea+WTgDuBLe9E0EZlijHm9hKcp5TOOn87hmXdnUp18gs8cplftLpxo9xvCYu8HhzuDCEopb3Dnv/N2oIcxJhNARF4CVvG/aedK+aQzOfkUHN/N2S8f47WMVXzpfz3tRj5Op06dEO0xKeXz3ElQAhS6PC60lynls174fAWNtr7NTfyX6hiONhvOiNEvE1CzvrdDU0q5yZ0E9QGwRkRmYyWm4cB77r6AiDiwqvMeNsZcd1lRKuWGn1MzaVgjkJ82b2TYtodob/ZwsE4v6o76B02adPB2eEqpUnJnksQrIrIE61JHALcaYzaW4jUmAzuAGqUPTyn37D6WwZuvv4yjeiNq5qbSteFw6vXqRfPOsd4OTSl1mUpzhFgAJ6UY3hORJsBQ4K/AI6ULTamSZWTls+v4GdpkrKDu93/kXwEHWGoGU3/sc7Rt21aPMylVzrkzi+8JrOvufYGVnD4Qkc+MMc+5sf9XgceB6iXs/07gToBmzZq5sUulLK9Mn03MobeoJRvJoAZ7Oj1Gn+H/h39AFW+HppS6AtzpQY0DooquXi4iLwIbgBITlIhcB6QYY9aLyMCLbWeMmQJMAYiOjjbuha0qo0Kn4csNh4hpG8rurZsYeeRV2shedjQZR7Mxf6V1zbreDlEpdQW5k6D2A0FAUXmNQGCfG8/rAwwTkWvt59cQkWnGmJsvJ1Clkg4d5ehXf+Efwe0ILMilc/h4GscMpn1TLRqoVEXkToLKBbaJSCJWPajBwHIReQ3AGPPghZ5kjPkD8AcAuwf1mCYnVVqbD6az9WAa12V/Q5MV/+BB/zOsqHIN9cc+R6tWrbwdnlLKg9xJULPtW5ElnglFqV/6cd50Yg6+QU05wkG/ppzp/Ty94m7GT4sGKlXhuTPNfOqvfRFjzBI0sSk3ZGTl89qiPYy9uhHH9myh7eFvqC45bGz3OO2HP0LT4GBvh6iUKiN6ITLlU/JT93H12keYs7EzZ5zV6NhmPB3jEogK0ytAKFXZaIJSXjd361E27krifjOD2ls/Ic7Pj2rVGtLsN/cRHh7u7fCUUl6iCUp5nd+697h//5tUJZutAZHIoD/Rt2esHmdSqpK7aIISkW+xZu1dkDFmmEciUhVe2tlcnvhqMzde3Yj8o7vJP7CZo9KQU10fJCrhJqpU0RNtlVIl96D+bv8cCTQAptmPx2GdG6VUqRhjEKDqgcU8su93rEjqxs+mOZ073Uj9uDja1Kzp7RCVUj7kognKGLMUQESeNcb0d1n1rYj84PHIVIXy6doDbFm7mL/4f0zwsR+pRU0a1K1FzIjbadKkibfDU0r5IHeOQYWJSEtjTBKAiLQAwjwblqoICp3WCLHDT+i445/cmPo+ZwlhcfA11Lvm/xjcOVIv6KqUuih3EtTDwBIRSbIfhwN3eSwiVSGknMnh/inzGdOtOTWyj3Fi/2kyHH3w6zuZvn1jCAgI8HaISikf586JunNFpDXQzl600xiT69mwVHmVkZVPTUcOoT++xtSzr/HD4h6s5mqiooYTGRNDtWrVvB2iUqqcKGkW38iLrLpKRDDGfOmhmFQ59a9528hf8x4PBXyJf+4pkmhNVuO+3HX9LTRo0MDb4SmlypmSelDXl7DOAJqgFAdPZlErJIDqQQGMOvJ3mji/JCm3KetqjCXi2lsZoYUDlVKXqaRZfLeKiB8w2hgzqwxjUuXEsfRsnn3lFbpd3Y1GVQpIPlCDugE30GzQrYzq0QOHw+HtEJVS5ViJx6CMMU4RuR/QBKUASD2by5ZD6cQE7aH+gqeZ4r+WlT91ZwH9iO6WwMCBAwkJCfF2mEqpCsCdWXyJIvIYMBPILFpojDnpsaiUz5o++2u67n0TZBOZUp0lxHG65XDuSbiGsDA9+0ApdeW4k6Bus3/e57LMAC2vfDjK12Rk5fPvpfsY36MZTeuEMM6xmOp+u0k0/UgKHUxswlAtHKiU8gh3ppm3KItAlG/KO7adLqseZ2veJDYQws7dtQgKuY/egxK44+qr9YKuSimPuWSCEpEA4B6g6HJHS4B3jDH5HoxLedG/l+wj+/BWHgn8htCtXzDYP5C5W75mCx3p0XsQ/fv3JygoyNthKqUqOHeG+P4NBABv2Y8n2Mt+66mgVNnLziskuIo1667nzhfocuxzCh1BbKjSm8V5EYR36Mp9cXHUqVPHy5EqpSoLdxJUN2NMF5fHi0Rks6cCUmVvffJJXvzgM34/cSRXtwijeXhrNmfGknimNbXqt+SG+HgtHKiUKnPuJKhCEbnKGLMPQERaAoWeDUt5WkZ2PmlncmiZuYkuP/yDz1jM/j2Gz9eHsm1bDtWq9SZ+RCxdunTRE22VUl7hToL6HbDYvlisAM2BWz0alfIo43TyzzdeZVze55C/E0dIKHvDxzN7zWHy/NLo378/ffr00cKBSimvKulafA8BK4ClQGugLVaC0ovFlkNZeQV8teEwN3Zvhh/wO/9PCZAcDrSbzJdJwWQk59G5c2diY2OpqYUDlVI+oKQeVBPgX1hXMd8CrMRKWAcBTVDlSdZJkue8So+ts/ix1jx6tGvOydh/MXflFo5sPUGTJvUYPS5BCwcqpXxKSdfiewxARKoA0UBvrJN2/yMi6caYDmUTorochU7DN4uWEnVkJuEHv6J9fhanmw2gTmA6n366il27dlGzZk1GjRpFx44d9TiTUsrnuHMMKhioAdS0b0eAnzwZlPr1/E4l8ZvlwynAHyJvJPfq37Jqewprp32Dv78/MTEx9OzZUwsHKqV8VknHoKYAHYEzwBqsIb5XjDGnyig2VRp5meyc/y4bt+9kxENvEFz3KrISXiGwwzWs3XWIJTMSyc7OJioqihgtHKiUKgdK6kE1AwKBPcBh4BCQXgYxqVI4sGcL9XZOI2jrp7TLzYCA9qSezqJpaHUOh/Zn/rTZpKamEh4eTkJCghYOVEqVGyUdgxoi1oGJjljHnx4FOonISWCVMebJMopRXUTGsik0W/g7CsUBHUdA9ztp26Q7J1JTmTbta/bt20edOnUYO3YsbbVwoFKqnLlUPSgDbBWRdCDDvl0HdAc0QZW1syeYN+1vHKoewe3jb6Zmxzi2H7qPegPuIrRRczIzM1n83/+yYcMGAgMDiY+Pp3v37lo4UClVLpV0DOpBrJ5THyAfa4r5KuB93JgkISJBwA9Yw4T+wOfa6yq9L9cf5MS2xdwVsgS2f0OCM5/lfrcBN0OdlnQY9zwFBQWsXLmSH374gby8PKKjo7VwoFKq3CupBxUOfA48bIw5ehn7zgVijDFn7SuiLxeR740xqy9jX5XGzmOnmb3xMI8ntMPhJ0Qv/y3NTq3GBNZAuv0Wom+jb1gbAIwx7NixgwULFnDq1Clat27N4MGDtXCgUqpCKOkY1CO/Zsf28OBZ+2GAfTO/Zp8VkTGGTQfTuapeNWoE+pO2YxlNV33Ivi7v0KZRHZr2Gw/cjHQaBVX+1yM6cuQI8+fPJzk5mbCwMG6++Wauuuoq7zVEKaWuMHfOg7psIuIA1gOtgDeNMWsusM2dwJ0AzZo182Q4PsUYg4iw4+gZJr01n/ei9hGd9i19UrbTO6ga4ncYqIN0veW85505c4aFCxeyefNmQkJCGDp0KF27dtXCgUqpCkesjo6HX0SkFjAbeMAYs/Vi20VHR5t169Z5PB5vKnQaHpixgZah1XgsoS3m+Hac7wzA4cyDRlFw9a3QaRQEnn+eUn5+PitXrmTFihU4nU569OhBv379tHCgUqrcE5H1xpjo4ss92oMqYoxJF5ElwBDgogmqojqZmcfGA6eIbV8fx9mjXH/6U2o5Q4CnkbB2OPo8aE0Tb9D5F881xvDTTz+xcOFCTp8+Tfv27Rk8eDC1a9cu83YopVRZ8liCEpEwIN9OTsFAHPCSp17P1xQN4QG8mbiNlPVf0b/1VgJ+XsQ1xgl1hlkb+vlB7F8uuI+DBw8yb948Dh8+TMOGDRk5ciTNmzcvqyYopZRXebIH1RCYah+H8gNmGWPmePD1fMbWwxk8MnMjb9zUlTYNavCw8wOqOaZCamPo+whE3gR1Lz6hIT09nQULFrBt2zaqV6/O8OHDtXCgUqrS8ViCMsZsAaI8tX9fYoxhffIpggIcdKqRTYvdn/CfrKlkHn4DGvSjWp+7IWIYtBwEfhc/aTY3N5dly5axevVqREQLByqlKrUyOQZVUeUXOglw+FGQl8Psj/7FTcGrIOtHqhonVRtHQ6j99tbvYN0uwul0snHjRhYvXkxmZiYRERHExsZSo0aNMmqJUkr5Hk1Ql+mF77ayd+9u3ps8kgAxPOP4D+KoBX0esobwQlu7tZ+ff/6ZefPmcfz4cZo2bcq4ceNo3LixR2NXSqnyQBOUm5JOnGXm2gM81jmbgO1f8NCWWZz1q05+wXACqoTguGsp1LnKmvTghrS0NBITE88VDhw9ejQdOnTQ40xKKWXTBFWCw+nZVKviT82QALI3zOTGtS8T8OMxcFQhuNVggiNuAD87objZY8rOzmbp0qX8+OOP5woH9urVC39//VUopZQr/VQspmh6+NH9O5n2n3/SfNAkbozrRbsGNTDhbaDLn6D99RBcuvOQCgsLWb9+PUuWLNHCgUop5QZNUDZjDL//cC4xhStIMCtpeHg9/xcAacG9gF44uoyBLmMua7979uwhMTGR1NRUWrRoQXx8vBYOVEqpS6jUCerbzUfYcegkjw/thORk8HzyeBwUQsMuEPc0dPwNdWtf/omxKSkpzJ8//1zhwBtvvJE2bdrocSallHJDpUpQyWmZJG4/zu1dgpEd3xK5fDqNsiF/yGICgmvh+M1b0DgaQlv9qtfJzMxk8eLF5woHJiQk0K1bNy0cqJRSpVChE1R2XiGrk9KIDq9N9aAADq2YSed178DCXYChcVh7mnYb/b+JDl1u/FWvV1BQwJo1a1i2bBl5eXl069aNAQMGaOFApZS6DBUqQRU6DVsOpVO/RhCNagWz++ckFn78Jjkj7+Oaq1sTXes0jjCDdP49dBiBX712V+R1L1Q4MD4+ntDQ0Cuyf6WUqowqVIJKz8pj4luJvNL5II2cy4lIWkqXgELyqgwEWhPY9wHo/9AVfc0jR44wb948Dhw4QL169bRwoFJKXSEVKkHVLUxlU8i9+O3Jh9rhSN+HoNMoqtSzLzNUwnXwSksLByqllGdVqARFzcb4DfoDtBgIjbuCB2bLFS8c2Lt3by0cqJRSHlCxEhRAv0c9stvihQM7dOhAXFycFg5USikPqXgJygMOHDjAvHnzOHLkiBYOVEqpMqIJqgTFCweOGDGCiIgIPdFWKaXKgCaoCyheOHDAgAH07t1bCwcqpVQZ0gTlQgsHKqWU79AEZUtKSmLevHmkpKRo4UCllPIBlT5BpaamkpiYyO7du6lVq5YWDlRKKR9RaRNU8cKBsbGx9OzZUwsHKqWUj6h0n8aFhYWsW7eOpUuXkpOTQ1RUFIMGDdLCgUop5WMqTYIqKhw4f/580tLSaNGiBQkJCdSvX9/boSmllLqASpGgjh8/zvz580lKSqJu3bqMGzeO1q1b63EmpZTyYRU6QRUvHDhkyBCio6O1cKBSSpUDFTJBFRQUsHr1apYtW0ZBQQHdunVj4MCBBAcHezs0pZRSbqpQCcoYw/bt21mwYAHp6em0adOGwYMHa+FApZQqhypMgjLGMH36dPbt20e9evWYMGECLVu29HZYSimlLlOFSVAiQqtWrWjfvj1RUVFaOFAppco5jyUoEWkKfAQ0AJzAFGPMvzz1egA9e/b05O6VUkqVIU/2oAqAR40xG0SkOrBeRBKNMds9+JpKKaUqCI+NgxljjhpjNtj3zwA7AL36qlJKKbeUyYEaEQkHooA1F1h3p4isE5F1J06cKItwlFJKlQMeT1AiUg34AnjIGHO6+HpjzBRjTLQxJjosLMzT4SillConPJqgRCQAKzlNN8Z86cnXUkopVbF4LEGJdaG794AdxphXPPU6SimlKiZP9qD6ABOAGBHZZN+u9eDrKaWUqkA8Ns3cGLMc0MuFK6WUuixijPF2DOeIyAkg+VfuJhRIvQLh+BJtk++raO0BbVN5URHa1NwY84tZcj6VoK4EEVlnjIn2dhxXkrbJ91W09oC2qbyoiG0qohesU0op5ZM0QSmllPJJFTFBTfF2AB6gbfJ9Fa09oG0qLypim4AKeAxKKaVUxVARe1BKKaUqAE1QSimlfJLPJygReV9EUkRkq8uyLiKySkR+EpFvRaSGvTxcRLJdrlzxtstzrra33ysir9mXYvKK0rTJXhdhr9tmrw+yl5fLNonIeJff0SYRcYpIpL2uvLYpQESm2st3iMgfXJ7jE20qZXuqiMgH9vLNIjLQ5Tk+0R47lqYisth+z7eJyGR7eR0RSRSRPfbP2i7P+YMd+y4RSXBZ7hPtKm2bRKSuvf1ZEXmj2L58ok2XzRjj0zegP9AV2Oqy7EdggH3/NuBZ+36463bF9rMW6IV1dYvvgWvKSZv8gS1AF/txXcBRnttU7HmdgaQK8Hu6CfjUvh8C7AfCfalNpWzPfcAH9v16wHrAz5faY8fSEOhq368O7AY6AC8Dv7eX/x54yb7fAdgMBAItgH2+9v90GW2qCvQF7gbeKLYvn2jT5d58vgdljPkBOFlscVvgB/t+IjCqpH2ISEOghjFmlbF+ax8BI65wqG4rZZvigS3GmM32c9OMMYXlvE2uxgEzoNz/ngxQVUT8gWAgDzjtS20qZXs6AAvt56UA6UC0L7XHju1ihVGHA1PtzabyvxiHY32RyDXG/AzsBbr7UrtK2yZjTKaxLi2X47ofX2rT5fL5BHURW4Fh9v0xQFOXdS1EZKOILBWRfvayxsAhl20O4XvVfS/WpjaAEZF5IrJBRB63l5fnNrkai52gKN9t+hzIBI4CB4C/G2NO4vttulh7NgPDRcRfRFoAV9vrfLY9cn5h1PrGmKNgfeBj9QLBivWgy9OK4vfJdrnZpovxyTaVRnlNULcB94nIeqwucJ69/CjQzBgTBTwCfGKPqV9o3NXX5tdfrE3+WN338fbP34hILOW7TQCISA8gyxhTdEykPLepO1AINMIaOnpURFri+226WHvex/pAWwe8CqwECvDR9sglCqO6bnqBZaaE5V5TijZddBcXWOb131VpeOxq5p5kjNmJNfSFiLQBhtrLc4Fc+/56EdmH1QM5BDRx2UUT4EhZxnwpF2sTVuxLjTGp9rr/Yh1HmEb5bVORG/lf7wnK9+/pJmCuMSYfSBGRFUA0sAwfblMJ/0sFwMNF24nISmAPcAofa49cuDDqcRFpaIw5ag91pdjLD3F+T74ofp/62ytlmy7Gp9p0OcplD0pE6tk//YA/A2/bj8NExGHfbwm0xjoAfxQ4IyI97VkstwBfeyX4i7hYm4B5QISIhNjHNwYA28t5m4qWjQE+LVpWztt0AKv2mYhIVaAnsNPX21TC/1KI3Q5EZDBQYIzxub87O4YLFUb9Bpho35/I/2L8BrhRRALtocvWwFpfatdltOmCfKlNl83bszQudcP6hn0UyMf6RnA7MBlrZstu4EX+d0WMUcA2rPHzDcD1LvuJxhpv3we8UfQcX2+Tvf3Ndru2Ai9XkDYNBFZfYD/lsk1ANeAz+/e0Hfidr7WplO0JB3ZhHaBfgFUOwafaY8fSF2vYaguwyb5dizXbdSFWr28hUMflOX+yY9+Fy6w2X2nXZbZpP9YEmLP277aDL7Xpcm96qSOllFI+qVwO8SmllKr4NEEppZTySZqglFJK+SRNUEoppXySJiillFI+SROUUh5knxe1XESucVl2g4jM9WZcSpUHOs1cKQ8TkU5Y50hFAQ6s81qGGGP2Xca+HMaYwisboVK+SROUUmVARF7GuphsVftnc6xSI/7AU8aYr+0Lg35sbwNwvzFmpVi1mJ7EOsk20hjToWyjV8o7NEEpVQbsywZtwLoY6xxgmzFmmojUwqrZE4V19QCnMSZHRFoDM4wx0XaC+g7oZKwSEUpVCuXyYrFKlTfGmEwRmYl1KZobgOtF5DF7dRDQDOtCnm+IVV24EOtCx0XWanJSlY0mKKXKjtO+CTDKGLPLdaWIPAUcB7pgTWByLUCXWUYxKuUzdBafUmVvHvCAfYVpRCTKXl4TOGqMcQITsCZUKFVpaYJSquw9CwQAW0Rkq/0Y4C1gooisxhre016TqtR0koRSSimfpD0opZRSPkkTlFJKKZ+kCUoppZRP0gSllFLKJ2mCUkop5ZM0QSmllPJJmqCUUkr5pP8HDQO+gWBQRYQAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results.plot(color='gray', label='model')\n",
-    "plot_estimates()\n",
-    "decorate(title='Constant Growth Model, t1=1970')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "improved-sigma",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap06.ipynb b/jupyter/chap06.ipynb
deleted file mode 100644
index 26cdf959..00000000
--- a/jupyter/chap06.ipynb
+++ /dev/null
@@ -1,785 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "creative-motel",
-   "metadata": {},
-   "source": [
-    "# Chapter 6"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "parental-symposium",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "compound-church",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "quality-spectrum",
-   "metadata": {},
-   "source": [
-    "In the previous chapter we simulated a model of world population with\n",
-    "constant growth. In this chapter we see if we can make a better model\n",
-    "with growth proportional to the population.\n",
-    "\n",
-    "But first, we'll improve the code from the previous chapter by\n",
-    "encapsulating it in a function and using `System` objects."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ranking-tamil",
-   "metadata": {},
-   "source": [
-    "Here's the code that reads the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "annoying-herald",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates.html'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "shared-offense",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas import read_html\n",
-    "\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "table2 = tables[2]\n",
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "therapeutic-merchant",
-   "metadata": {},
-   "source": [
-    "## System objects\n",
-    "\n",
-    "Like a `State` object, a `System` object contains variables and their\n",
-    "values. The difference is:\n",
-    "\n",
-    "-   `State` objects contain state variables that get updated in the course of a simulation.\n",
-    "\n",
-    "-   `System` objects contain **system parameters**, which usually don't get updated over the course of a simulation.\n",
-    "\n",
-    "For example, in the bike share model, state variables include the number of bikes at each location, which get updated whenever a customer moves a bike. System parameters include the number of locations, total number of bikes, and arrival rates at each location.\n",
-    "\n",
-    "In the population model, the only state variable is the population.\n",
-    "System parameters include the annual growth rate, the initial time and\n",
-    "population, and the end time.\n",
-    "\n",
-    "Suppose we have the following variables, as computed in the previous\n",
-    "chapter (assuming `table2` is the `DataFrame` we read from the file):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "numerous-university",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "census = table2.census / 1e9\n",
-    "\n",
-    "t_0 = census.index[0]\n",
-    "t_end = census.index[-1]\n",
-    "elapsed_time = t_end - t_0\n",
-    "\n",
-    "p_0 = census[t_0]\n",
-    "p_end = census[t_end]\n",
-    "\n",
-    "total_growth = p_end - p_0\n",
-    "annual_growth = total_growth / elapsed_time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "starting-cooling",
-   "metadata": {},
-   "source": [
-    "Some of these are parameters we need to simulate the system; others are temporary values we can discard. \n",
-    "To distinguish between them, we'll put the parameters we need into a `System` object like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "colonial-domestic",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import System\n",
-    "\n",
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                annual_growth=annual_growth)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fleet-beaver",
-   "metadata": {},
-   "source": [
-    "`t0` and `t_end` are the first and last years; `p_0` is the initial\n",
-    "population, and `annual_growth` is the estimated annual growth.\n",
-    "\n",
-    "Here's what `system` looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "floral-routine",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>System</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>1950.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>2016.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p_0</th>\n",
-       "      <td>2.557629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>annual_growth</th>\n",
-       "      <td>0.072248</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "namespace(t_0=1950,\n",
-       "          t_end=2016,\n",
-       "          p_0=2.557628654,\n",
-       "          annual_growth=0.07224800083333333)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "combined-banking",
-   "metadata": {},
-   "source": [
-    "Next we'll wrap the code from the previous chapter in a function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "pacific-challenge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import TimeSeries\n",
-    "\n",
-    "def run_simulation1(system):\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in range(system.t_0, system.t_end):\n",
-    "        results[t+1] = results[t] + system.annual_growth\n",
-    "    \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "technological-medicine",
-   "metadata": {},
-   "source": [
-    "`run_simulation1` takes a `System` object and uses the parameters in it to determine `t_0`, `t_end`, and `annual_growth`.\n",
-    "\n",
-    "Inside the loop, it stores the results in a `TimeSeries` which it returns at the end.\n",
-    "\n",
-    "The following function plots the results along with the estimates\n",
-    "`census` and `un`:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "grateful-terror",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "Here's how we call it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "electoral-breach",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results1 = run_simulation1(system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ordinary-sound",
-   "metadata": {},
-   "source": [
-    "Here's the function we used in the previous chapter to plot the estimates."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "peripheral-cassette",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "def plot_estimates():\n",
-    "    census.plot(style=':', label='US Census')\n",
-    "    un.plot(style='--', label='UN DESA')\n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)') "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "warming-audience",
-   "metadata": {},
-   "source": [
-    "And here are the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "capable-diana",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results1.plot(label='model', color='gray')\n",
-    "plot_estimates()\n",
-    "decorate(title='Constant Growth Model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "exposed-witness",
-   "metadata": {},
-   "source": [
-    "It might not be obvious that using functions and `System` objects is a\n",
-    "big improvement, and for a simple model that we run only once, maybe\n",
-    "it's not. But as we work with more complex models, and when we run many simulations with different parameters, we'll see that the organization of the code makes a big difference.\n",
-    "\n",
-    "Now let's see if we can improve the model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "geographic-hormone",
-   "metadata": {},
-   "source": [
-    "## Proportional growth model\n",
-    "\n",
-    "The biggest problem with the constant growth model is that it doesn't\n",
-    "make any sense. It is hard to imagine how people all over the world\n",
-    "could conspire to keep population growth constant from year to year.\n",
-    "\n",
-    "On the other hand, if some fraction of the population dies each year,\n",
-    "and some fraction gives birth, we can compute the net change in the\n",
-    "population like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "laughing-wesley",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation2(system):\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in range(system.t_0, system.t_end):\n",
-    "        births = system.birth_rate * results[t]\n",
-    "        deaths = system.death_rate * results[t]\n",
-    "        results[t+1] = results[t] + births - deaths\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "educated-portugal",
-   "metadata": {},
-   "source": [
-    "Now we can choose the values of `birth_rate` and `death_rate` that best fit the data. \n",
-    "\n",
-    "For the death rate, I'll use 7.7 deaths per 1000 people, which was roughly the global death rate in 2020 (see <https://www.indexmundi.com/world/death_rate.html>).\n",
-    "I chose the birth rate by hand to fit the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "wired-brief",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.death_rate = 7.7 / 1000\n",
-    "system.birth_rate = 25 / 1000"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sufficient-contest",
-   "metadata": {},
-   "source": [
-    "Then I ran the simulation and plotted the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "looking-trace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results2 = run_simulation2(system)\n",
-    "results2.plot(label='model', color='gray')\n",
-    "plot_estimates()\n",
-    "decorate(title='Proportional Growth Model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "suited-costs",
-   "metadata": {},
-   "source": [
-    "The proportional model fits\n",
-    "the data well from 1950 to 1965, but not so well after that. Overall,\n",
-    "the **quality of fit** is not as good as the constant growth model,\n",
-    "which is surprising, because it seems like the proportional model is\n",
-    "more realistic.\n",
-    "\n",
-    "In the next chapter we'll try one more time to find a model that makes\n",
-    "sense and fits the data. But first, I want to make a few more\n",
-    "improvements to the code."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "appropriate-checkout",
-   "metadata": {},
-   "source": [
-    "## Factoring out the update function\n",
-    "\n",
-    "`run_simulation1` and `run_simulation2` are nearly identical except for the body of the `for` loop, where we compute the population for the next year.\n",
-    "\n",
-    "Rather than repeat identical code, we can separate the things that\n",
-    "change from the things that don't. First, I'll pull out the update code from `run_simulation2` and make it a function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "handmade-permit",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def growth_func1(pop, t, system):\n",
-    "    births = system.birth_rate * pop\n",
-    "    deaths = system.death_rate * pop\n",
-    "    return births - deaths"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fabulous-bankruptcy",
-   "metadata": {},
-   "source": [
-    "This function takes as arguments the current population, current year,\n",
-    "and a `System` object; it returns the net population growth during the current year.\n",
-    "\n",
-    "This update function does not use `t`, so we could leave it out. But we will see other functions that need it, and it is convenient if they all take the same parameters, used or not.\n",
-    "\n",
-    "Now we can write a function that runs any model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "civilian-accused",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, growth_func):\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in range(system.t_0, system.t_end):\n",
-    "        growth = growth_func(results[t], t, system)\n",
-    "        results[t+1] = results[t] + growth\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "failing-assist",
-   "metadata": {},
-   "source": [
-    "This function demonstrates a feature we have not seen before: it takes a\n",
-    "function as a parameter! When we call `run_simulation`, the second\n",
-    "parameter is a function, like `growth_func1`, that computes the\n",
-    "population for the next year.\n",
-    "\n",
-    "Here's how we call it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "wicked-seeking",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, growth_func1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "simple-camel",
-   "metadata": {},
-   "source": [
-    "Passing a function as an argument is the same as passing any other\n",
-    "value. The argument, which is `growth_func1` in this example, gets\n",
-    "assigned to the parameter, which is called `growth_func`. Inside\n",
-    "`run_simulation`, we can run `growth_func` just like any other function.\n",
-    "\n",
-    "Each time through the loop, `run_simulation` calls `growth_func1` to compute net growth, and uses it to compute the population during the next year."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "spectacular-paradise",
-   "metadata": {},
-   "source": [
-    "## Combining birth and death\n",
-    "\n",
-    "We can simplify the code slightly by combining births and deaths to compute the net growth rate. \n",
-    "Instead of two parameters, `birth_rate` and `death_rate`, we can write the update function in terms of a single parameter that represents the difference:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "impressive-model",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.alpha = system.birth_rate - system.death_rate"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "modern-uncertainty",
-   "metadata": {},
-   "source": [
-    "The name of this parameter, `alpha`, is the conventional name for a\n",
-    "proportional growth rate.\n",
-    "\n",
-    "Here's the modified version of `growth_func1`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "familiar-helena",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def growth_func2(pop, t, system):\n",
-    "    return system.alpha * pop"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "understanding-typing",
-   "metadata": {},
-   "source": [
-    "And here's how we run it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "independent-effectiveness",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, growth_func2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "everyday-delicious",
-   "metadata": {},
-   "source": [
-    "The results are the same as the previous versions, but now the code is organized in a way that makes it easy to explore other models."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "attractive-steps",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "In this chapter, we wrapped the code from the previous chapter in functions and used a `System` object to store the parameters of the system.\n",
-    "\n",
-    "We explored a new model of population growth, where the number of births and deaths is proportional to the current population.  This model seems more realistic, but it turns out not to fit the data particularly well.\n",
-    "\n",
-    "In the next chapter, we'll try one more model, which is based on the assumption that the population can't keep growing forever.\n",
-    "But first, you might want to work on some exercises."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "looking-douglas",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "compliant-preserve",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Maybe the reason the proportional model doesn't work very well is that the growth rate, `alpha`, is changing over time.  So let's try a model with different growth rates before and after 1980 (as an arbitrary choice).\n",
-    "\n",
-    "Write an update function that takes `pop`, `t`, and `system` as parameters.  The system object, `system`, should contain two parameters: the growth rate before 1980, `alpha1`, and the growth rate after 1980, `alpha2`.  It should use `t` to determine which growth rate to use.\n",
-    "\n",
-    "Test your function by calling it directly, then pass it to `run_simulation`.  Plot the results.  Adjust the parameters `alpha1` and `alpha2` to fit the data as well as you can."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "minus-recommendation",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def growth_func3(pop, t, system):\n",
-    "    \"\"\"Compute the population next year.\n",
-    "    \n",
-    "    pop: current population\n",
-    "    t: current year\n",
-    "    system: system object containing parameters of the model\n",
-    "    \n",
-    "    returns: population next year\n",
-    "    \"\"\"\n",
-    "    if t < 1980:\n",
-    "        return system.alpha1 * pop\n",
-    "    else:\n",
-    "        return system.alpha2 * pop"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "headed-amsterdam",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system.alpha1 = 19 / 1000\n",
-    "system.alpha2 = 15 / 1000\n",
-    "\n",
-    "results3 = run_simulation(system, growth_func3)\n",
-    "results3.plot(label='model', color='gray')\n",
-    "plot_estimates()\n",
-    "decorate(title='Proportional growth, parameter changes over time')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "preliminary-carnival",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Using two parameters, we can make the model fit the data better.\n",
-    "# But it still seems like the shape of the function is not right."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "median-policy",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap07.ipynb b/jupyter/chap07.ipynb
deleted file mode 100644
index e331b338..00000000
--- a/jupyter/chap07.ipynb
+++ /dev/null
@@ -1,915 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "regular-director",
-   "metadata": {},
-   "source": [
-    "# Chapter 7"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "disciplinary-occasion",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "similar-wellington",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "indie-scoop",
-   "metadata": {},
-   "source": [
-    "In the previous chapter we developed a population model where net growth during each time step is proportional to the current population. This model seems more realistic than the constant growth model, but it does not fit the data as well.\n",
-    "\n",
-    "There are a few things we could try to improve the model:\n",
-    "\n",
-    "-   Maybe net growth depends on the current population, but the\n",
-    "    relationship is quadratic, not linear.\n",
-    "\n",
-    "-   Maybe the net growth rate varies over time.\n",
-    "\n",
-    "In this chapter, we'll explore the first option.\n",
-    "In the exercises, you will have a chance to try the second. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "qualified-houston",
-   "metadata": {},
-   "source": [
-    "Here's the code that reads the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "major-arkansas",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates.html'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "intense-columbus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas import read_html\n",
-    "\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "table2 = tables[2]\n",
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "wound-necklace",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "census = table2.census / 1e9"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "intended-matrix",
-   "metadata": {},
-   "source": [
-    "And here are the functions from the previous chapter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "former-accuracy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import TimeSeries\n",
-    "\n",
-    "def run_simulation(system, growth_func):\n",
-    "    \"\"\"Simulate the system using any update function.\n",
-    "    \n",
-    "    system: System object\n",
-    "    growth_func: function that computes the population next year\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in range(system.t_0, system.t_end):\n",
-    "        growth = growth_func(results[t], t, system)\n",
-    "        results[t+1] = results[t] + growth\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "duplicate-insert",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "def plot_estimates():\n",
-    "    census.plot(style=':', label='US Census')\n",
-    "    un.plot(style='--', label='UN DESA')\n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "champion-retreat",
-   "metadata": {},
-   "source": [
-    "## Quadratic growth\n",
-    "\n",
-    "It makes sense that net growth should depend on the current population, but maybe it's not a linear relationship, like this:\n",
-    "\n",
-    "```\n",
-    "net_growth = system.alpha * pop\n",
-    "```\n",
-    "\n",
-    "Maybe it's a quadratic relationship, like this:\n",
-    "\n",
-    "```\n",
-    "net_growth = system.alpha * pop + system.beta * pop**2\n",
-    "```\n",
-    "\n",
-    "We can test that conjecture with a new update function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "sized-hawaiian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def growth_func_quad(pop, t, system):\n",
-    "    return system.alpha * pop + system.beta * pop**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "amateur-strand",
-   "metadata": {},
-   "source": [
-    "Here's the `System` object we'll use, initialized with `t_0`, `p_0`, and `t_end`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "interstate-canal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import System\n",
-    "\n",
-    "t_0 = census.index[0]\n",
-    "p_0 = census[t_0]\n",
-    "t_end = census.index[-1]\n",
-    "\n",
-    "system = System(t_0=t_0,\n",
-    "                p_0=p_0,\n",
-    "                t_end=t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "acting-transportation",
-   "metadata": {},
-   "source": [
-    "Now we have to add the parameters `alpha` and `beta` .\n",
-    "I chose the following values by trial and error; we'll see better ways to do it later."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "banned-dallas",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.alpha = 25 / 1000\n",
-    "system.beta = -1.8 / 1000"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "supreme-wellington",
-   "metadata": {},
-   "source": [
-    "And here's how we run it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "positive-scheme",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, growth_func_quad)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "satisfied-density",
-   "metadata": {},
-   "source": [
-    "And here are the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "flush-fellowship",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results.plot(color='gray', label='model')\n",
-    "plot_estimates()\n",
-    "decorate(title='Quadratic Growth Model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "organized-combining",
-   "metadata": {},
-   "source": [
-    "The model fits the data well over the whole range, with just a bit of space between them in the 1960s.\n",
-    "\n",
-    "Of course, we should expect the quadratic model to fit better than the\n",
-    "constant and proportional models because it has two parameters we can\n",
-    "choose, where the other models have only one. In general, the more\n",
-    "parameters you have to play with, the better you should expect the model\n",
-    "to fit.\n",
-    "\n",
-    "But fitting the data is not the only reason to think the quadratic model\n",
-    "might be a good choice. It also makes sense; that is, there is a\n",
-    "legitimate reason to expect the relationship between growth and\n",
-    "population to have this form.\n",
-    "\n",
-    "To understand it, let's look at net growth as a function of population."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "powerful-century",
-   "metadata": {},
-   "source": [
-    "## Net growth\n",
-    "\n",
-    "Let's plot the relationship between growth and population in the quadratic model.\n",
-    "\n",
-    "I'll use `linspace` to make an array of 101 populations from 0 to 15 billion."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "excited-catch",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import linspace\n",
-    "\n",
-    "pop_array = linspace(0, 15, 101)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "tracked-guide",
-   "metadata": {},
-   "source": [
-    "Now I'll use the quadratic model to compute net growth for each population."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "classical-badge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "growth_array = (system.alpha * pop_array + \n",
-    "                system.beta * pop_array**2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "quantitative-differential",
-   "metadata": {},
-   "source": [
-    "To plot the growth rate versus population, we can import the `plot` function from Matplotlib:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "interim-consent",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib.pyplot import plot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "published-average",
-   "metadata": {},
-   "source": [
-    "And use it like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "received-crossing",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(pop_array, growth_array, label='growth')\n",
-    "\n",
-    "decorate(xlabel='Population (billions)',\n",
-    "         ylabel='Net growth (billions)',\n",
-    "         title='Growth vs. Population')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "plastic-wisdom",
-   "metadata": {},
-   "source": [
-    "Note that the x-axis is not time, as in the previous figures, but population. We can divide this curve into four regimes of behavior:\n",
-    "\n",
-    "-   When the population is less than 3-4 billion, net growth is\n",
-    "    proportional to population, as in the proportional model. In this\n",
-    "    regime, the population grows slowly because the population is small.\n",
-    "\n",
-    "-   Between 4 billion and 10 billion, the population grows quickly\n",
-    "    because there are a lot of people.\n",
-    "\n",
-    "-   Above 10 billion, population grows more slowly; this behavior models\n",
-    "    the effect of resource limitations that decrease birth rates or\n",
-    "    increase death rates.\n",
-    "\n",
-    "-   Above 14 billion, resources are so limited that the death rate\n",
-    "    exceeds the birth rate and net growth becomes negative.\n",
-    "\n",
-    "Just below 14 billion, there is a point where net growth is 0, which\n",
-    "means that the population does not change. At this point, the birth and death rates are equal, so the population is in **equilibrium**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "relevant-detection",
-   "metadata": {},
-   "source": [
-    "## Equilibrium\n",
-    "\n",
-    "To find the equilibrium point, we can find the roots, or zeros, of this equation: \n",
-    "\n",
-    "$$\\Delta p = \\alpha p + \\beta p^2$$ \n",
-    "\n",
-    "where $\\Delta p$ is net\n",
-    "population growth, $p$ is current population, and $\\alpha$ and $\\beta$\n",
-    "are the parameters of the model. We can rewrite the right hand side like\n",
-    "this: \n",
-    "\n",
-    "$$\\Delta p = p (\\alpha + \\beta p)$$ \n",
-    "\n",
-    "which is $0$ when $p=0$ or $p=-\\alpha/\\beta$.\n",
-    "We can use the parameters of the system to compute the second equilibrium point:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "lightweight-entrepreneur",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13.88888888888889"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "-system.alpha / system.beta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "stuck-flood",
-   "metadata": {},
-   "source": [
-    "With these parameters, net growth is 0 when the population is about 13.9 billion.\n",
-    "\n",
-    "In the context of population modeling, the quadratic model is more\n",
-    "conventionally written like this: \n",
-    "\n",
-    "$$\\Delta p = r p (1 - p / K)$$ \n",
-    "\n",
-    "This is the same model; it's just a different way to **parameterize** it. Given $\\alpha$ and $\\beta$, we can compute $r=\\alpha$ and $K=-\\alpha/\\beta$.\n",
-    "\n",
-    "In this version, it is easier to interpret the parameters: $r$ is the\n",
-    "maximum growth rate, observed when $p$ is small, and $K$ is the\n",
-    "equilibrium point. $K$ is also called the **carrying capacity**, since\n",
-    "it indicates the maximum population the environment can sustain."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "derived-oliver",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "In this chapter we implemented a quadratic growth model where net growth depends on the current population and the population squared.\n",
-    "\n",
-    "This model fits the data well, and we saw one reason why: it is based on the assumption that there is a limit to the number of people the Earth can support.\n",
-    "\n",
-    "In the next chapter we'll use the models we have developed to generate\n",
-    "predictions.\n",
-    "But first, I want to warn you about a few things that can go wrong when you write functions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bibliographic-handy",
-   "metadata": {},
-   "source": [
-    "## Dysfunctions\n",
-    "\n",
-    "When people learn about functions, there are a few things they often\n",
-    "find confusing. In this section I present and explain some common\n",
-    "problems.\n",
-    "\n",
-    "As an example, suppose you want a function that takes a\n",
-    "`System` object, with variables `alpha` and `beta`, and computes the\n",
-    "carrying capacity, `-alpha/beta`. \n",
-    "Here's a good solution:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "lucky-insurance",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "13.88888888888889\n"
-     ]
-    }
-   ],
-   "source": [
-    "def carrying_capacity(system):\n",
-    "    K = -system.alpha / system.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "pop = carrying_capacity(sys1)\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "accredited-fitting",
-   "metadata": {},
-   "source": [
-    "Now let's see all the ways that can go wrong."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "transparent-cotton",
-   "metadata": {},
-   "source": [
-    "Dysfunction \\#1: Not using parameters. In the following version, the\n",
-    "function doesn't take any parameters; when `sys1` appears inside the\n",
-    "function, it refers to the object we create outside the function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "aggregate-baker",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "13.88888888888889\n"
-     ]
-    }
-   ],
-   "source": [
-    "def carrying_capacity():\n",
-    "    K = -sys1.alpha / sys1.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "pop = carrying_capacity()\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "otherwise-belarus",
-   "metadata": {},
-   "source": [
-    "This version actually works, but it is not as versatile as it could be.\n",
-    "If there are several `System` objects, this function can only work with one of them, and only if it is named `sys1`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "naughty-crazy",
-   "metadata": {},
-   "source": [
-    "Dysfunction \\#2: Clobbering the parameters. When people first learn\n",
-    "about parameters, they often write functions like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "outdoor-petroleum",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "13.88888888888889\n"
-     ]
-    }
-   ],
-   "source": [
-    "# WRONG\n",
-    "def carrying_capacity(system):\n",
-    "    system = System(alpha=0.025, beta=-0.0018)\n",
-    "    K = -system.alpha / system.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys1 = System(alpha=0.03, beta=-0.002)\n",
-    "pop = carrying_capacity(sys1)\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "afraid-street",
-   "metadata": {},
-   "source": [
-    "In this example, we have a `System` object named `sys1` that gets passed\n",
-    "as an argument to `carrying_capacity`. But when the function runs, it\n",
-    "ignores the argument and immediately replaces it with a new `System`\n",
-    "object. As a result, this function always returns the same value, no\n",
-    "matter what argument is passed.\n",
-    "\n",
-    "When you write a function, you generally don't know what the values of\n",
-    "the parameters will be. Your job is to write a function that works for\n",
-    "any valid values. If you assign your own values to the parameters, you\n",
-    "defeat the whole purpose of functions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "expired-detail",
-   "metadata": {},
-   "source": [
-    "Dysfunction \\#3: No return value. Here's a version that computes the\n",
-    "value of `K` but doesn't return it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "increasing-database",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "# WRONG\n",
-    "def carrying_capacity(system):\n",
-    "    K = -system.alpha / system.beta\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "pop = carrying_capacity(sys1)\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "brutal-chile",
-   "metadata": {},
-   "source": [
-    "A function that doesn't have a return statement actually returns a special value called `None`, so in this example the value of `pop` is `None`. If you are debugging a program and find that the value of a variable is `None` when it shouldn't be, a function without a return statement is a likely cause."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "meaning-thriller",
-   "metadata": {},
-   "source": [
-    "Dysfunction \\#4: Ignoring the return value. Finally, here's a version\n",
-    "where the function is correct, but the way it's used is not.\n",
-    "\n",
-    "```\n",
-    "def carrying_capacity(system):\n",
-    "    K = -system.alpha / system.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "carrying_capacity(sys1)\n",
-    "print(K)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "opposed-vessel",
-   "metadata": {},
-   "source": [
-    "In this example, `carrying_capacity` runs and returns `K`, but the\n",
-    "return value doesn't get displayed or assigned to a variable.\n",
-    "If we try to print `K`, we get a `NameError`, because `K` only exists inside the function.\n",
-    "\n",
-    "When you call a function that returns a value, you should do something\n",
-    "with the result."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "quarterly-ethiopia",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "painted-samuel",
-   "metadata": {},
-   "source": [
-    "**Exercise:** In a previous section, we saw a different way to parameterize the quadratic model:\n",
-    "\n",
-    "$$ \\Delta p = r p (1 - p / K) $$\n",
-    "\n",
-    "where $r=\\alpha$ and $K=-\\alpha/\\beta$.  \n",
-    "\n",
-    "Write a version of `growth_func` that implements this version of the model.  Test it by computing the values of `r` and `K` that correspond to `alpha=0.025, beta=-0.0018`, and confirm that you get the same results. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "unauthorized-settle",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.025, 13.88888888888889)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system.r = system.alpha\n",
-    "system.K = -system.alpha/system.beta\n",
-    "\n",
-    "system.r, system.K"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "primary-roman",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def growth_func_quad2(pop, t, system):\n",
-    "    return system.r * pop * (1 - pop / system.K)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "ordinary-solid",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results2 = run_simulation(system, growth_func_quad2)\n",
-    "results2.plot(color='gray', label='model')\n",
-    "plot_estimates()\n",
-    "decorate(title='Quadratic Growth Model, alternate parameters')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ignored-architect",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  What happens if we start with an initial population above the carrying capacity, like 20 billion?  Run the model with initial populations between 1 and 20 billion, and plot the results on the same axes.\n",
-    "\n",
-    "Hint: If there are too many labels in the legend, you can plot results like this:\n",
-    "\n",
-    "```\n",
-    "    results.plot(label='_nolegend')\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "intermediate-kazakhstan",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "p0_array = linspace(1, 25, 11)\n",
-    "\n",
-    "for p_0 in p0_array:\n",
-    "    system.p_0 = p_0\n",
-    "    results3 = run_simulation(system, growth_func_quad)\n",
-    "    results3.plot(label='_nolegend')\n",
-    "\n",
-    "decorate(xlabel='Year',\n",
-    "         ylabel='Population (billions)',\n",
-    "         title='Projections with hypothetical starting populations')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "positive-enforcement",
-   "metadata": {},
-   "source": [
-    "## Under the hood\n",
-    "\n",
-    "Explain the difference between the Pandas and Matplotlib `plot` functions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "described-funds",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap08.ipynb b/jupyter/chap08.ipynb
deleted file mode 100644
index 92c864d6..00000000
--- a/jupyter/chap08.ipynb
+++ /dev/null
@@ -1,1037 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "external-reward",
-   "metadata": {},
-   "source": [
-    "# Chapter 8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "compound-announcement",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "tropical-medicine",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "chicken-emphasis",
-   "metadata": {},
-   "source": [
-    "In the previous chapter we developed a quadratic model of world\n",
-    "population growth from 1950 to 2016. It is a simple model, but it fits\n",
-    "the data well and the mechanisms it's based on are plausible.\n",
-    "\n",
-    "In this chapter we'll use the quadratic model to generate projections of future growth, and compare our results to projections from actual\n",
-    "demographers."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "global-international",
-   "metadata": {},
-   "source": [
-    "Here's the code that downloads the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "necessary-factor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates.html'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "designed-entrance",
-   "metadata": {},
-   "source": [
-    "And here's the code that reads `table2`, which contains world populations estimates from the U.S. Census and U.N. DESA, among other organizations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "changed-desktop",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas import read_html\n",
-    "\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "metallic-inventory",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table2 = tables[2]\n",
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "further-armstrong",
-   "metadata": {},
-   "source": [
-    "## Generating Projections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "concrete-lightning",
-   "metadata": {},
-   "source": [
-    "Now let's run the quadratic model, extending the results until 2100, and see how our projections compare to the professionals'.\n",
-    "\n",
-    "Here's the code we'll need from the previous chapter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "extraordinary-shade",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import TimeSeries\n",
-    "\n",
-    "def run_simulation(system, growth_func):\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in range(system.t_0, system.t_end):\n",
-    "        growth = growth_func(results[t], t, system)\n",
-    "        results[t+1] = results[t] + growth\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "indirect-russia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def growth_func_quad(pop, t, system):\n",
-    "    return system.alpha * pop + system.beta * pop**2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "collaborative-schedule",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census = table2.census / 1e9\n",
-    "un = table2.un / 1e9"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "comfortable-compression",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import System\n",
-    "\n",
-    "t_0 = census.index[0]\n",
-    "p_0 = census[t_0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "working-cleveland",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = System(t_0=t_0,\n",
-    "                p_0=p_0,\n",
-    "                t_end=2100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "sophisticated-adult",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.alpha = 25 / 1000\n",
-    "system.beta = -1.8 / 1000"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "broken-windsor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, growth_func_quad)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "latest-function",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Quantity</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Time</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2096</th>\n",
-       "      <td>12.462519</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2097</th>\n",
-       "      <td>12.494516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2098</th>\n",
-       "      <td>12.525875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2099</th>\n",
-       "      <td>12.556607</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2100</th>\n",
-       "      <td>12.586719</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Quantity\n",
-       "Time           \n",
-       "2096  12.462519\n",
-       "2097  12.494516\n",
-       "2098  12.525875\n",
-       "2099  12.556607\n",
-       "2100  12.586719"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import show\n",
-    "\n",
-    "show(results.tail())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "outer-ensemble",
-   "metadata": {},
-   "source": [
-    "And here are the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "portable-pottery",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "results.plot(color='gray', label='model')\n",
-    "decorate(xlabel='Year', \n",
-    "         ylabel='World population (billion)',\n",
-    "         title='Quadratic Model Projection')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "macro-carroll",
-   "metadata": {},
-   "source": [
-    "According to the model, population growth will slow gradually after 2020, approaching 12.5 billion by 2100.\n",
-    "\n",
-    "I am using the word \"projection\" deliberately, rather than\n",
-    "\"prediction\", with the following distinction: \"prediction\" implies\n",
-    "something like \"this is what we should reasonably expect to happen, at\n",
-    "least approximately\"; \"projection\" implies something like \"if this\n",
-    "model is actually a good description of what is happening in this\n",
-    "system, and if nothing in the future causes the parameters of the model to change, this is what would happen.\"\n",
-    "\n",
-    "Using \"projection\" leaves open the possibility that there are important things in the real world that are not captured in the model. It also suggests that, even if the model is good, the parameters we estimate based on the past might be different in the future.\n",
-    "\n",
-    "The quadratic model we've been working with is based on the assumption\n",
-    "that population growth is limited by the availability of resources; in\n",
-    "that scenario, as the population approaches carrying capacity, birth\n",
-    "rates fall and death rates rise because resources become scarce.\n",
-    "\n",
-    "If that assumption is valid, we might be able to use actual population\n",
-    "growth to estimate carrying capacity, especially if we observe the\n",
-    "transition into the regime where the growth rate starts to fall.\n",
-    "\n",
-    "But in the case of world population growth, those conditions don't\n",
-    "apply. Over the last 50 years, the net growth rate has leveled off, but not yet started to fall, so we don't have enough data to make a credible estimate of carrying capacity. And resource limitations are probably *not* the primary reason growth has slowed. As evidence, consider:\n",
-    "\n",
-    "-   First, the death rate is not increasing; rather, it has declined\n",
-    "    from 1.9% in 1950 to 0.8% now (see <http://modsimpy.com/mortality>).\n",
-    "    So the decrease in net growth is due entirely to declining birth\n",
-    "    rates.\n",
-    "\n",
-    "-   Second, the relationship between resources and birth rate is the\n",
-    "    opposite of what the model assumes; as nations develop and people\n",
-    "    become more wealthy, birth rates tend to fall.\n",
-    "\n",
-    "We should not take too seriously the idea that this model can estimate\n",
-    "carrying capacity. But the predictions of a model can be credible even\n",
-    "if the assumptions of the model are not strictly true. For example,\n",
-    "population growth might behave *as if* it is resource limited, even if\n",
-    "the actual mechanism is something else.\n",
-    "\n",
-    "In fact, demographers who study population growth often use models\n",
-    "similar to ours. In the next section, we'll compare our projections to\n",
-    "theirs."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "small-seminar",
-   "metadata": {},
-   "source": [
-    "## Projections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "greater-illness",
-   "metadata": {},
-   "source": [
-    "From the same page where we got the past population estimates, we'll read `table3`, which contains predictions for population growth over the next 50-100 years, generated by the U.S. Census, U.N. DESA, and the Population Reference Bureau."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "precious-contribution",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>United States Census Bureau (2015)[28]</th>\n",
-       "      <th>Population Reference Bureau (1973-2015)[15]</th>\n",
-       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2016</th>\n",
-       "      <td>7.334772e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7.432663e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017</th>\n",
-       "      <td>7.412779e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018</th>\n",
-       "      <td>7.490428e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019</th>\n",
-       "      <td>7.567403e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020</th>\n",
-       "      <td>7.643402e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7.758157e+09</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      United States Census Bureau (2015)[28]  \\\n",
-       "Year                                           \n",
-       "2016                            7.334772e+09   \n",
-       "2017                            7.412779e+09   \n",
-       "2018                            7.490428e+09   \n",
-       "2019                            7.567403e+09   \n",
-       "2020                            7.643402e+09   \n",
-       "\n",
-       "      Population Reference Bureau (1973-2015)[15]  \\\n",
-       "Year                                                \n",
-       "2016                                          NaN   \n",
-       "2017                                          NaN   \n",
-       "2018                                          NaN   \n",
-       "2019                                          NaN   \n",
-       "2020                                          NaN   \n",
-       "\n",
-       "      United Nations Department of Economic and Social Affairs (2015)[16]  \n",
-       "Year                                                                       \n",
-       "2016                                       7.432663e+09                    \n",
-       "2017                                                NaN                    \n",
-       "2018                                                NaN                    \n",
-       "2019                                                NaN                    \n",
-       "2020                                       7.758157e+09                    "
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table3 = tables[3]\n",
-    "table3.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "coated-smoke",
-   "metadata": {},
-   "source": [
-    "Some values are `NaN`, which indicates missing data, because some organizations did not publish projections for some years.\n",
-    "\n",
-    "The column names are long strings; for convenience, I'll replace them with abbreviations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "headed-tuner",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table3.columns = ['census', 'prb', 'un']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ceramic-scroll",
-   "metadata": {},
-   "source": [
-    "The following function plots projections from the U.N. DESA and U.S. Census.  It uses `dropna` to remove the `NaN` values from each series before plotting it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "paperback-delay",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_projections(table):\n",
-    "    \"\"\"Plot world population projections.\n",
-    "    \n",
-    "    table: DataFrame with columns 'un' and 'census'\n",
-    "    \"\"\"\n",
-    "    census_proj = table.census.dropna() / 1e9\n",
-    "    un_proj = table.un.dropna() / 1e9\n",
-    "    \n",
-    "    census_proj.plot(style=':', label='US Census')\n",
-    "    un_proj.plot(style='--', label='UN DESA')\n",
-    "    \n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "prostate-matrix",
-   "metadata": {},
-   "source": [
-    "Here are their projections compared to the results of the quadratic model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "billion-dynamics",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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3AfAX4D8OZWuBl40xRSLyW+Bl4GcujEEppaqF7OxsDh48yIEDB66dU2rTpg1jxowhMjKS+vXrezjCyueyBGWM+UpEQkuVrXF4uh2Y7KrjK6VUVZefn8+hQ4fYv38/J06cwBhDixYtGDFiBFFRUVWi+e5OeLKTxBPApzdbKCJPAU+B1Z6qlFI1gc1m48SJE+zZs4fDhw9TVFREYGAggwYNokuXLjRt2tTTIbqNRxKUiPwCKAI+vtk6xpg5wBywOkm4KTSllPKIjIwM9u7dy969e7l8+TJ16tShW7dudO/enVatWnnFhbPu5vYEJSLTsDpPDDdVoQuhUkq5SF5eHgcPHmTv3r2cPn0aESEiIoLY2Fg6depErVo1+0ogt16lJSKjsTpFjDfGXHXnsSvbiRMn6NKly3Vls2fPvjY00fTp02nVqhX5+fmAdSFtaGhomfvy9fWle/fuREVF0a1bN37/+99fu55p48aNNGrUiO7du1+7rVu3DrDG64uKiiI6Opru3buzY8eOa/ssKioiODiYl19+ubJfulLqDhhjOH36NEuWLOF3v/sdy5cvJzc3lxEjRjBz5kwee+wxoqKianxyAtd2M58PDAWCRSQFeA2r154/sNZeXd1ujPmhq2LwNF9fX+bOncvTTz9d7np169Zlz549gDUK+qOPPsqlS5d4/fXXARg0aNANF+lu27aN5cuXs2vXLvz9/UlPT79u4rA1a9bQqVMnPvvsM956660a2TyglDfJy8tj3759JCYmkpaWRu3atYmOjqZnz56EhIToZ7QMruzF90gZxf921fG80fPPP88f/vAHnnzySae3adasGXPmzKF3795lTnBY4uzZswQHB18bly84OPi65fPnz+e5557j73//O9u3b782pYdSyn2MMaSmppKQkMDBgwcpLCykZcuWjBs3ji5dulw3rqa6UfWpQ74/9sayqInQ50kouAofT7lxefdHocdjcCUDPpt6/bIZK+44pLZt2zJw4EA++ugj7rvvPqe3Cw8Px2azkZaWBsDmzZvp3r37teULFy4kNjaWX/3qV3Ts2JERI0bw0EMPMWTIEAByc3NZv349//znP8nKymL+/PmaoJRyo/z8fPbv309iYiLnzp3Dz8+Prl270qtXL0JCQjwdXpVRfRKUm91quo0SP//5zxk/fjxjx5aRQMvh2H+krCY+gMTERDZv3kxcXBwPPfQQv/nNb5g+fTrLly/nnnvuoV69ejzwwAP8+te/5g9/+MMNYwEqpSpXZmYmO3fuZM+ePeTn59OiRQvGjh1L165dtbZUAdUnQZVX46ldr/zl9YNuu8bkzHQbAO3bt6d79+589tlnTu/7+PHj+Pr60qxZMw4fPnzT9Xx9fRk6dChDhw6la9eufPjhh0yfPp358+ezdevWa50yMjIyiIuLuzbhoVKq8hhjOH78ODt37uTIkSP4+PgQFRVFnz59amz38MpSfRKUm91qug1Hv/jFL5yuQV24cIEf/vCH/OQnPyn3HzspKQkfH59rEw3u2bOHdu3acfnyZbZs2cLp06ev/WJ7//33mT9/viYopSpRQUEB+/btY8eOHaSnp1O/fn0GDx5MTExMtRkLz9M0Qd2B8qbbcBQVFUXPnj3ZtWtXmfvJzc2le/fuFBYWUqtWLb73ve/xwgsvXFte+hzUK6+8QlhYGM888wxZWVnUqlWL9u3bM2fOHBYtWsSwYcOua06YMGECL774Ivn5+drMoNQdysrKYufOnezatYv8/HxatmzJxIkTtWu4C+h0G+qm9D1W6jtnz54lPj6egwcPAhAZGUnfvn1p3bq1NuPdIbdPt6GUUlVdyfml+Ph4jh8/Tu3atenXrx99+/alUaNGng6v2tMEpZRSpRQXF3Pw4EHi4+M5f/48AQEBDB8+nJiYGOrUqePp8GoMTVBKKWVXUFDArl272L59O5cuXSI4OJjx48fTtWtXPb9UmjFgKwZf170v+o4rpWq8vLw8du7cyfbt28nNzaVt27aMGTOGDh061NzzS8bAlXS4dAqy7LfIidCkHRxaBot/CENfgrufdVkImqCUUjVWbm4u27dvZ8eOHeTn59OhQwcGDx5M69atPR2ae9hscOk0ZByF9GMQNgiaR8GpHfCfCVCUe/36TcKsBBUYDj2nQkh3l4anCUopVeNcuXKFbdu28fXXX1NQUEDnzp0ZPHgwLVu29HRorpGbBRnHoE5jCG4Pl1Nh3mTITIaivO/WG/W2laAat4He34fGbaFRG+u+cRuoY+8Y0qIL3Psbl4etCcpLhIaGkpCQcMOgr7e7jlLq5rKzs4mPjychIYGioiK6dOnCoEGDaNasmadDqxzGgIh1bmjtq3D+AJw/BFescT3p9yMY/TbUC7KSTsQ9ENwBgjpY9/Xts/U2DIFRb3ruddhpglJKVXvZ2dls2bKFxMREbDYb0dHRDBw4sGr/2Lt4As4dgLRD3yWiFl1hyvvg4wvfrLBqPB1GQnBHKwG16GptW8sfHv3Eo+E7QxPUHThx4gSjR49m4MCBbN++nW7dujFjxgxee+010tLS+Pjjj2nfvj1PPPEEx48fp169esyZM4fo6GgyMjJ45JFHuHDhAn369LlucNh58+bx5z//mYKCAvr27cvf/vY3HehVqQq4evUqW7duZefOndhsNrp168agQYNo0qSJp0Nzns1mNcWl7obci9D3B1b559OtMgSahFpNc+0GfLfds7ut2lQVVi0S1KpVqzh37lyl7rNFixaMHj36lusdO3aMzz///NocTv/973/ZsmULy5Yt46233qJNmzb06NGDJUuWsGHDBqZOncqePXt4/fXXGThwIK+++iorVqxgzpw5gDV6w6effsrWrVvx8/PjRz/6ER9//DFTp069RSRKqRJ5eXls27aN7du3U1BQQHR0NEOGDCEwMNDToZWvpIkOYPc82PsJpO6BgmyrrH4z6POUtU7sm1CrDjTrDLXr37ivKp6coJokKE8KCwuja1er2hwVFcXw4cMREbp27cqJEyc4efIkCxcuBGDYsGFkZGRw6dIlvvrqKxYtWgTA2LFjr/2iW79+PYmJifTu3RuwehlVm/ZxpVysoKCAnTt3snXrVvLy8oiMjGTo0KE0bdrU06GVLTcLUr6GU9vgzC44uxee2wt1GkL2OSi8Ct0egpAe1i2403eJJ/Ruj4buDtUiQTlT03EVx8FXfXx8rj338fGhqKiozIv7Sq6rKOv6CmMM06ZN4+2333ZRxEpVP0VFRSQkJLBlyxauXLlChw4duOeee7yvV96lM1by8W8Aez+FxT8ADIiv1UQXOR4Kc611Bs+ybjVYtUhQ3mzw4MF8/PHH/PKXv2Tjxo0EBwfTsGHDa+WvvPIKK1euvDa31PDhw5kwYQIzZ86kWbNmZGZmkp2dTbt27Tz8SpTyPjabjX379hEXF8fly5cJCwvjnnvuoU2bNp4OzTp3lJ5k1Y5Obbfus07BA/+GrpOhVS8Y+jK07QetY8pupqvhNEG52OzZs5kxYwbR0dHUq1ePDz/8ELCm5njkkUfo2bMnQ4YMoW3btoA1QvIbb7xBbGwsNpsNPz8//vrXv2qCUsqBMYbk5GTWrVvH+fPnCQkJYeLEiTdMGOrmoCDzONiKoGknuJwCf+tnLavfDNr1t7p5t7YP2h3cHob+zHPxVgE63Ya6KX2PlTc6e/Ysa9eu5dtvv6VJkyYMHz6cyMhIzwxJdDUTjm+E43HWfdYpiJpkdfU2BvYvgFY9rZEXqkGnBVfR6TaUUlVaVlYWGzZsYP/+/dStW5fRo0cTExPj3kswCvOsYYFKrif6YBykHQT/hhA2GAY8CxHDrGUiED3FfbFVQ+UmKBHpDzwODAJaArnAAWAFMM8Yc8nlESqlarTc3Fw2b97Mzp07EREGDhzI3Xff7b5pL7JOQdIqOLIKTm4FHz/42bfg6wexv7aSU0gPl47qXVPd9B0VkZVAKrAUeBNIA+oAHYF7gKUi8ntjzDJ3BFoWY0zNHWnYxapC06+q3oqLi0lISGDjxo3k5eXRvXt37rnnHho2bOjaA9ts1r2PD8S/B2tesZ4HRkCvGdbwQCXaD3dtLDVceSn/e8aY9FJlOcAu++13IuKxcULq1KlDRkYGQUFBmqQqmTGGjIwMnZhNecyxY8dYvXo16enphIeHExsbS/PmzV13wPwc6zxS0io4uhoe+BeED7VusW9Ax9HWUEHKrW6aoMpIThVax1Vat25NSkoKFy5c8FQI1VqdOnVqzpQDymukp6ezZs0ajh49SmBgIA8//DAdO3Z03Y/QnAvWtUgnNkNxAfg3gg4jrGY7sM41lZxvUm53y0ZTEZkE/BZoBoj9ZowxLq5nl8/Pz8+zXUqVUpUmLy+PTZs2sXPnTvz8/Bg5ciR9+vSp/Flss89Zk+35+FrTSdQLhPzL1vBBHUdb1yT5+lXuMVWFOfPXfwe4zxhz2NXBKKVqFpvNxq5du4iLi+Pq1av06NGDYcOGERAQUHkHKUlKh5bAyXjAQMRwK0H5+ML/rKu8Y6lK5UyCOq/JSSlV2U6ePMnKlSs5f/487dq1Y9SoUZU3NFHOBagfbHX1Xvsq7PsUmna2piiPnGgNsKq8njMJKkFEPgWWAPklhcaYRa4KSilVfeXk5LBu3Tr27t1Lo0aNmDx5cuVcaJubBQcXw/7PrZrS0/HQPBIGzYKBL2hSqoKcSVANgatArEOZATRBKaWcZrPZ+Prrr4mLi6OwsJCBAwcyaNAgateufWc7vnTGqiV9s9yavjy4Ewz5GdS1z/nUtOOdB6884pYJyhgzoyI7FpG5wDggzRjTxV4WCHwKhAIngAeNMRcrsn+lVNVx+vRpvvzyS86dO0d4eDhjxowhKCio4jtMP2oNM9S2rzXy96lt0ON70P1R66JZvfSkWrjlWHwi0hp4D7gbq+a0BXjOGJNyi+0GY1039R+HBPUOkGmM+Y2IvAQ0McbccrTEssbiU0p5vytXrrBu3Tr27NlDw4YNGTVqFHfddVfFmvPyLllNeLs/hpSd0LI7/GCTtcxmsy6sVVXSnYzF9z7wX6BkUKnH7WUjy9vIGPOViISWKp4ADLU//hDYCOhwvkpVMzabjcTERDZs2EBBQQF33303gwcPrnhz3ubfw6bfWk14TTvDyF9B9EPfLdfkVC05k6CaGmPed3j+gYg8X8HjNTfGnAUwxpwVkZtOFSsiTwFPAdemolBKeb9z586xfPlyzpw5Q1hYGPfee+/tz2ibd9ma7rzrZOtapcAw6PG4vQmvpzbh1RDOJKh0EXkcmG9//giQ4bqQLMaYOcAcsJr4XH08pdSdKSwsZOPGjWzbto26desyadIkunTpcnvNeelHYecc2DMfCrKhdj0rMUXdb91UjeJMgnoC+AvwB6xzUPH2soo4LyIt7bWnllgD0Cqlqrhjx46xYsUKsrKy6NGjByNHjqRu3brO76CoAOY/DMnrwbe2NadS36esWWdVjeVML75TwPhKOt4yYBrwG/v90krar1LKA3Jycli9ejUHDhwgKCiIadOmERoa6tzGVzOt65XuGge1akODljDsFeg5HQJus0lQVUvlTbfxojHmHRF5D6vmdB1jzLPl7VhE5mN1iAgWkRTgNazE9JmIfB84xXcdL5RSVYgxht27d7N27VoKCwsZMmQIAwcOdG7svAtJsO2vsO8za4DWn34DAc1g4l9dH7iqUsr7byoZ3qhC/buNMY/cZJFOoKJUFZaens7y5cs5efIk7dq1Y9y4cQQHOzHzTua31txK36yAWv4Q/aA1SGvATftKqRquvOk2vrDff+i+cJRS3spmsxEfH8/GjRvx8/Pjvvvuo0ePHuV3gjDGun6pbmMrKZ3eCYNnQd8fWmPlKVWO8pr4vqCMpr0SxpjKOi+llPJy58+fZ+nSpZw9e5bOnTszduzY8kcct9kgaYV1/VLt+jB9OTQMgRcO6XQWymnlNfG967YolFJeqbi4mM2bN7N582bq1q1764FdiwutwVq3/BHSk6BJGPScatWkRDQ5qdtSXhPfJncGopTyLqmpqSxdupS0tDSio6MZNWoU9erVK3+jxA/gy1nQvAs88G9ragvfSp50UNUY5TXx7af8Jr5ol0SklPIoxwtuAwICeOSRR+jY8SYjghflQ8L70Ki11V2828PQuC10iL3paA/FNutrxddHR4NQ5Svvp804t0WhlPIKp0+fZunSpWRkZNCzZ09GjhxJnTp1blyxuBD2fAyb/hcup1ijPdw1DvwbQMdR5R5j4a4UGtf1IzaqhYtehaouymviO+nOQJRSnlNUVERcXBzx8fE0btyY733ve4SHh5e9ctJKWPUSXDwBrXvDxL9B+JCb7jstO49n/rubqf1DGRvdktFdWnDkXLZrXoiqVspr4ttijBkoItlYTX3ieG+MaeimGJVSLpSamsqSJUu4cOECvXr1YuTIkfj7+1+/ks0GtiJrxIeCK1ZN6dHPbtqUd+R8NunZ+QxoH0xQfX98RDD2MwYN6/gRExrojpemqrhbzgflDXQ+KKUqn2MPvfr16zN+/Hjat29//UrGwJHVEPeGNT7eoBesZAU3THFhjLnWu++Bv8dzJb+IVc8PdsdLUVXcncwHhYj0BAZin7DQGLO7kuNTSrlRWloaS5Ys4ezZs0RHRzN69OgbB3dNjoMNb8CZBKu7eKC9ya+MuZeW70vlD2uPsPyZQdSt7cvbk7oSHOB/w3pK3Y5bJigReRVrzLxF9qIPRORzY8wbLo1MKVXpbDYb27ZtIy4uDn9/fx588EHuuuuuG1dc+yps/RM0bAX3/Qm6P3bdNUzGGBJOXiQsuD7BAf40DfAnNKg+WbkF1K1dl47NG7jxVanqypkp3w8DPYwxefbndYFdxpgy/qtdQ5v4lLpzmZmZLFmyhNOnT9O5c2fGjRtH/fr1v1vh8lkrCdUPhpREOLUNev8P+N3Yi+905lUGvRPHT0d25JnhHdz4KlR1dCdNfCeAOkCe/bk/kFx5oSmlXMkYw9dff826devw8fHh/vvvp2vXrt+NBpGfA/F/hvj3rGnU7/sjtO5l3Rz8a/NxLucW8kJsJ9oE1uPf02LoHxHk/hekaozyevGVTLORDxwUkbX25yOBLe4JTyl1Jy5fvszSpUs5fvw4ERERjB8/noYN7R1wi4tgzzyIewtyzludIO5+7rrtM68UEFi/NgDJF3K4kF1wrTPE8Luau/vlqBqmvBpUSZtaIrDYoXyjy6JRSlWagwcPsnz5coqLixk7diy9evW6fgy9Db+GrX+ENn3hoY+hTe/rtl++L5UXPt3L6pmDCQuuz68ndKGW740dJJRylfIu1NVpNpSqgvLz81m5ciV79+4lJCSESZMmERRkb4o7tx98/aFpR+v8UquecNf4a9cyfX0ik4Z1/OjUogF9w4KYfncoAf7W14QmJ+Vut5puYw6wyhhTWGpZODAdOGGMmevSCJVSTjt16hSLFy/m0qVLDB48mMGDB+Pr6ws5F2D967B7Htx1Hzz0ETRuY93scguK+Z8PExjeuRm/f6g7TRv48/MxbusLpdQNymviexJ4AfijiGQCF7A6S4QBx4C/GGOWuj5EpdStFBcXs2nTJrZs2UKjRo2YPn06bdu2tc4zbf87xL0NhVeg/4+tCQPtthxNZ82hc/xqQhfq1vblgxm96dxCB4lR3qG8Jr5zwIvAiyISCrQEcoEjxpir7glPKXUrGRkZLF68mDNnztCtWzfuvffe74Yq2vF3a5r1iGEw+rfQtCPGGLB3dDialk1cUhoZOfkEBfjTo20Tz74YpRzoUEdKVVHGGHbt2sXq1avx9fVl3LhxREVFQdZpuJoOIT2sLuTffgWd7gURzmTl8uSHCTw/ogOxUS0oKLIhAn56fkl50B0NdaSU8i5Xrlzhiy++ICkpibCwMCZOnEjDurVh0zvWNOvB7eEHm8E/ANPpXtJzCmjawJ/mDfxp2sCfWr5Wp4jatTQxKe+lCUqpKiY5OZklS5aQm5tLbGws/fr2RY6shFUvQ9ZJiJwAsW9c65n30sL97DyRydqZg6nl68OHT/Tx8CtQyjmaoJSqIoqLi1m/fj3btm2jadOmPPbYY7Ro0QKSVsEnj0LTzjB1KYQP5cCZS3QIKMa/li9jo1vStXUjT4ev1G1zZrDYu4HZQDv7+iXzQd1kNjOlVGXLzMxk4cKFpKam0qtXL0YNG4Jf1jGgBXQYCRP/AV0ng68fh1IvM+69LfxqQhRT+4cyuGNTT4evVIU4U4P6NzATa0SJYteGo5Qqbd++faxYsQIfHx+mTJlCZJ00+NdgyL0Iz+8H/wZciJhE0vFLDOwQzF0tG/DbB7oypmtLT4eu1B1xJkFdMsasdHkkSqnrOI4I0bZtWyaNHkqjbb+BfZ9YczNN+cCa2RZ4Zcl+Ek5cJP7lYfjX8uWh3m09G7xSlcCZBBUnIv+LNR9UfkmhMWaXy6JSqoZLTU1l4cKFXLx4kSFDhjC4Ryd8/tHf6jY++EWKB77Aon3pjGheQJP6tXnp3rswxuBfy9fToStVaZxJUH3t94591A0wrPLDUapmM8awbds21q9fT0BAAFMfup/QTl2thf1+bA1T1Kwz36bl8LOF+3jp3s48NTiCsOD65e9YqSrolgnKGHOPOwJRqqbLyclh6dKlHDt2jM4dO3Bf4HHqLRwNT26App041eXH7Pg2gynNoH2zABb/6G6itXeeqsac6cXXCHgNGGwv2gT8yhhzyZWBKVWTJCcns3jxYvLy8hjTpwMxR95FkpKh64NQNxCAf36VzBd7UxndpQUN6vjRrU1jzwatlIs5cxn5XCAbeNB+uwy8fycHFZGZInJQRA6IyHwRuXFOaaVqgOLiYtauXcu8efOoV68eT3bMpPeOpxFsmMcXszRiNt/m1QNgVmwn1r4whAZ1/DwctVLu4UyCijDGvGaMOW6/vQ5U+BooEWkFPAvEGGO6AL7AwxXdn1JV1cWLF5k7dy7x8fH06tWLJ598kuZBTWDgTPjRNjJb3M0vFh/go20nAWhSvzbNG+pvOVVzONNJIldEBhpjtsC1C3dzK+G4dUWkEKgHpN7h/pSqUg4fPszSpUsBw5TQS0RGBoCfH9l3v8TKA+d40K8uQX6w4On+dGjWwNPhKuURziSop4EP7eeiBMjEmqywQowxZ0TkXeAUVqJbY4xZU9H9KVWVlDTp7dixg5DAACbnf0KTE8egYwSED+XTr0/z5peH6d6mMR2bN9C5mVSN5kwvvj1ANxFpaH9++U4OKCJNgAlYEx9mAZ+LyOPGmHml1nsKeAqwJl5TqorLysri888/JzU1lT5NcxmZ9idqBbfn7L1fkNmkK1HA1P6h9A0LomNzrTUpVd6U748bY+aJyAulygEwxvy+gsccAXxrjLlg398iYABwXYIyxszBmnKemJgY75+0SqlyfPPNNyxduhRjDA/2aspdib+Au5/FNuQlHn9vBw3rHmDxj+6mdi0fHdhVKbvyalAlV/6V9VPuThLGKaCfiNTDauIbDuhshKpaKi4uZt26dWzfvp2WQQ2Z8th0mjRqxLF2PQmP6ouPj/C7B7vTQjs/KHWD8qZ8/6f94TpjzFbHZfaOEhVijNkhIguAXUARsBt7TUmp6iQrK4sFCxZw5swZevsdJTZnG7XqP8XeM5eZ+N8M3r7/NA/3aUt3vZ5JqTI500niPaCnE2VOM8a8hnXxr1LVUlJSEkuWLMYU5jHFLCeyAVwa9R8a1a5PdGvD6+OjGNctxNNhKuXVyjsH1R/r3FDTUuehGmJdu6SUKuW6Jr1al5hctIDAPg/xt1pTmfvZOdbOtAZ3ndo/1NOhKuX1yqtB1QYC7Os4noe6DEx2ZVBKVUXXNen17k2sbEXC/w86jWJo6mVyqU09f/1tp5SzyjsHtQnYJCIfGGNOujEmpaqcpKQklixejK3wKpNH3k3nfrE8/2lLQpLr8HIniAxpSGSIXtOk1O1w5hzUVft8UFHAta5GxhidbkPVeMXFxaxfv55t27bRQjKYIisJbNgdfITAen40rKvj5ilVUc4kqI+BT4FxwA+BacAFVwalVFVw6dIlFnz2KSmpZ4kxexgenM57jd5lSosRhAKvT+ji6RCVqtKcGSw2yBjzb6DQGLPJGPME0M/FcSnl1Y4cOcI//vEP0s6fZbJZwdgBXbj48Bd88m09dp266OnwlKoWnKlBFdrvz4rIWKyBXVu7LiSlvFdxcTEbNmwgPj6eFi1acO+YMRw52J2o2Am0BDa9eA8B/s58rJRSt+LMJ+kN+0CxP8W6/qkhMNOlUSnlhS5dusSCBQtISUkhJiaGUaNG8cG2U7y1qRaremTTsXkDTU5KVSJnBotdbn94CdDp31WNdPToURYvXkxxcTEPPPAAXbpY55emDQilX7gO7qqUK5R3oe57lDPmnjHmWZdEpJQXKS4uJi4ujq1bt9K8eXOmTJlCUFDQteV+vj50aaWDuyrlCuXVoHQAV1WjXbp0iYULF3L69Gl69erF6NGjqVVLm/CUcpfyLtT90J2BKOVNHJv0Jk2aRNeuXT0dklI1zi1/DopIHGU09emFuqo6stlsbNiw4aZNekop93GmvWKWw+M6wANY02QoVa1cvnyZhQsXcurUKXr27Mno0aPx89ORIJTyFGd68SWWKtoqIptcFI9SHnHs2DEWL15MUVGRNukp5SWcaeILdHjqA/QCWrgsIqXcyGazERcXx5YtW2jWrBlTpkwhODjY02EppXCuiS8R6xyUYDXtfQt835VBKeUO2qSnlHdzpokvzB2BKOVOJU16hYWF3H///URHR3s6JKVUKc408dUBfgQMxKpJbQH+bozJc3FsSlU6bdJTqupwponvP0A21jh8AI8AHwFTXBWUUq6QnZ3NwoULOXnyJD169ODee+/VJj2lvJgzCaqTMaabw/M4EdnrqoCUcoXk5GQWLVpEYWEhEydOpFu3brfeSCnlUc4kqN0i0s8Ysx1ARPoCW10bllKVw2azsXHjRjZv3kyzZs2YPHkyTZs29XRYSiknOJOg+gJTReSU/Xlb4LCI7AeMMUbPLiuv5Nik1717d8aMGaNNekpVIc4kqNEuj0KpSubYS0+b9JSqmpzpZn5SRLoBg+xFm40xeg5KeSXtpadU9eFMN/PngCeBRfaieSIyxxjzXjmbKeV2jhfeai89pao+Z5r4vg/0NcZcARCR3wLb+K7buVIep9NjKFX9OJOgBCh2eF5sL1PK40rPeDt58mRt0lOqmnAmQb0P7BCRxViJaQLwb5dGpZQTSs94O2rUKG3SU6oacaaTxO9FZCPWUEcAM4wxu10alVK3cOTIEZYsWUJxcTEPPPAAXbp08XRISqlK5kwNqoQANrR5T3lQcXExGzZsID4+Xme8Vaqac6YX36tY4+4txEpO74vI58aYNyp6UBFpDPwL6II1AO0TxphtFd2fqhkuXbrEggULSElJISYmhlGjRlGr1u38xlJKVSXOfLofAXqUjF4uIr8BdgEVTlDAn4BVxpjJIlIbqHcH+1I1QFJSEkuWLMFmszF58mSioqI8HZJSysWcSVAngDpAyfQa/kByRQ8oIg2BwcB0AGNMAVBQ0f2p6q24uJh169axfft2WrZsyeTJkwkMDLz1hkqpKs+ZBJUPHBSRtVjNcSOBLSLyZwBjzLO3ecxw4AJWU2E3rBl7nyu5zqqEiDwFPAXQtm3b2zyEqg6ysrJYsGABZ86coXfv3sTGxmqTnlI1iBhjyl9BZFp5y40xH97WAUVigO3A3caYHSLyJ+CyMeaXN9smJibGJCQk3M5hVBV36NAhvvjiC4wxjB8/nsjISE+HpJRyERFJNMbElC53ppv5bSUgJ6QAKcaYHfbnC4CXKvkYqooqLCxk9erVJCYmEhISwgMPPKBNekrVUG5vLzHGnBOR0yLSyRiTBAwHDrk7DuV90tLSWLBgARcuXGDAgAEMGzYMX19fT4ellPIQTzXoPwN8bO/BdxyY4aE4lBcwxpCYmMjq1avx9/fn8ccfJyIiwtNhKaU8zCMJyhizB7ihvVHVPLm5uXzxxRccPnyYiIgIJk6cSEBAgKfDUkp5gZsmKBH5AqvXXpmMMeNdEpGqMU6dOsXChQvJyclhxIgRDBgwABEdqEQpZSmvBvWu/X4S0AKYZ3/+CNa1UUpViM1mY/PmzWzatInGjRvzxBNP0KpVK0+HpZTyMjdNUMaYTQAi8mtjzGCHRV+IyFcuj0xVS5cvX2bx4sWcOHGCrl27MnbsWPz9/T0dllLKCzlzDqqpiIQbY44DiEgY0NS1YanqKCkpiaVLl1JUVMSECRPo1q2bNukppW7KmQQ1E9goIsftz0OBH7gsIlXtFBUVsWbNGr7++mtatGjB5MmTdQRypdQtOXOh7ioR6QB0thd9Y4zJd21YqrpIS0tj0aJFnD9/nr59+zJixAgdrkgp5ZTyevFNusmiCBHBGLPIRTGpasAYw86dO1m7di3+/v488sgjdOzY0dNhKaWqkPJ+yt5XzjIDaIJSZcrOzmbp0qUkJyfToUMHxo8fr9c2KaVuW3m9+GaIiA8w2RjzmRtjUlXYN998wxdffEFBQQFjxowhJiZGO0IopSqk3JMBxhibiPwE0ASlylVQUMDq1avZtWsXLVq0YNKkSTRtqp09lVIV58zZ6rUiMgv4FLg2Z5MxJtNlUakq5cyZMyxatIjMzEwd5FUpVWmcSVBP2O9/7FBmsCYeVDWYzWZjy5YtbNq0iYCAAKZOnUpYWJinw1JKVRPOdDPXbxx1g6ysLBYvXsypU6eIiopi7Nix1K1b19NhKaWqkVsmKBHxA54GSoY72gj80xhT6MK4lJcyxrB3715WrVqFMYaJEycSHR2tHSGUUpXOmSa+vwN+wN/sz79nL/sfVwWlvFNOTg7Lly8nKSmJNm3acP/999OkSRNPh6WUqqacSVC9jTHdHJ5vEJG9rgpIeadDhw6xYsUK8vPzGTlyJP369cPHx8fTYSmlqjFnElSxiEQYY5IBRCQcKHZtWMpb5ObmsmrVKvbt20fLli2ZOHEizZo183RYSqkawJkE9f+AOPtgsQK0Q6dorxGOHTvGsmXLyMnJYciQIQwaNEi7jyul3Ka8sfieB7YCm4AOQCesBKWDxVZzBQUFrFmzhsTERIKDg3n44YcJCQnxdFhKqRqmvBpUa+BPWKOY7wPisRLWaUATVDV16tQplixZwsWLF+nXrx/Dhg3Dz8/P02EppWqg8sbimwUgIrWBGGAA1kW7/yciWcaYSPeEqNyhqKiIuLg44uPjady4MdOnT6ddu3aeDkspVYM5cw6qLtAQaGS/pQL7XRmUcq+UlBSWLVvGhQsX6NmzJ7GxsToNu1LK48o7BzUHiAKygR1YTXy/N8ZcdFNsysUKCwuJi4tj+/btNGjQgMcee4z27dt7OiyllALKr0G1BfyBo8AZIAXIckNMyg1OnjzJsmXLyMzMpFevXowcOVJrTUopr1LeOajRYo1fE4V1/umnQBcRyQS2GWNec1OMqhIVFBSwfv16du7cSePGjXWAV6WU17rVfFAGOCAiWcAl+20c0AfQBFXFfPvttyxbtoysrCz69OnD8OHDqV27tqfDUkqpMpV3DupZrJrT3UAhVhfzbcBctJNElZKfn8+aNWvYtWsXgYGBzJgxg7Zt23o6LKWUKld5NahQYAEw0xhz1j3hqMqWlJTEl19+SXZ2NgMGDGDo0KF6XZNSqkoo7xzUC+4MRFWu7OxsVq5cyeHDh2nevDkPPvggrVq18nRYSinlNGeug1JViDGGhIQE1q9fT3FxMcOHD6d///46hp5SqsrRBFWNpKWlsXz5ck6fPk14eDhjx44lMDDQ02EppVSFeCxBiYgvkACcMcaM81Qc1UFRURFfffUVW7duxd/fX2e5VUpVC56sQT0HHMYaRklV0IkTJ1i+fDkZGRlER0cTGxtL/fr1PR2WUkrdMY8kKBFpDYwF3gS0M0YF5OTksHbtWvbt20eTJk14/PHHiYiI8HRYSilVaTxVg/oj8CLQ4GYriMhTwFOAXrPjwGazkZCQwIYNGygqKmLQoEEMGjRIu44rpaodtycoERkHpBljEkVk6M3WM8bMAeYAxMTEGPdE591SUlL48ssvOXv2LOHh4YwZM4agoCBPh6WUUi7hiRrU3cB4ERkD1AEaisg8Y8zjHoilSrh69Srr169n165dNGjQgMmTJxMZGamdIJRS1ZrbE5Qx5mXgZQB7DWqWJqeyGWPYvXs369atIy8vj379+jF06FAddVwpVSPodVBeKjU1lZUrV5KSkkLbtm0ZM2YMzZs393RYSinlNh5NUMaYjcBGT8bgbXJycli/fj179uyhfv36TJgwgW7dumlznlKqxtEalJcoLi5mx44dbNq0iaKiIgYMGMDgwYO1OU8pVWNpgvIwYwxHjx5l9erVZGZm0rFjR2JjY7V3nlKqxtME5UHp6emsWrWK5ORkgoODeeyxx2jfvr2nw1JKKa+gCcoDcnNz+eqrr9i5cyd+fn6MGjWK3r1764jjSinlQBOUGxUVFbFz5042b95MXl4ePXv2ZNiwYTp2nlJKlUETlBsYYzh48CDr168nKyuLiIgIRo4cqd3GlVKqHJqgXOzkyZOsWbOG1NRUmjdvroO6KqWUkzRBuUh6ejrr1q0jKSmJBg0aMGHCBKKjo/Hx8fF0aEopVSVogqpkOTk5bNq0icTERPz8/Bg2bBj9+vXT0caVUuo2aYKqJLm5uWzdupUdO3ZQXFxMTEwMQ4YM0Q4QSilVQZqg7lBBQQHbt28nPj6e/Px8unbtytChQwkMDPR0aEopVaVpgqqgoqIiEhIS2Lx5M1evXqVTp07cc8892jNPKaUqiSao22Sz2dizZw+bNm3i8uXLhIWFMWzYMFq3bu3p0JRSqlrRBOUkm83GgQMH2LRpE5mZmbRq1YoJEyYQHh7u6dCUUqpa0gR1CzabjX379rF582YyMzNp3rw5Dz/8MB07dtQpMJRSyoU0Qd1EcXHxtcR08eJFWrRowYMPPkjnzp01MSmllBtogiqluLiYPXv2sGXLFrKysmjZsqXWmJRSygM0QdkVFRVdS0yXLl2iVatWjBkzhvbt22tiUkopD6jxCSo/P5+EhAS2b99OTk4OrVu3Zty4cURERGhiUkopD6qxCSonJ4ft27eTkJBAfn4+4eHh3H///YSFhWliUkopL1DjElRGRgbx8fHs3bsXm81GZGQkAwYMICQkxNOhKaWUclBjElRqaipbt27l0KFD+Pr60r17dwYMGKBDEimllJeqEQlq8eLF7Nu3D39/fwYOHEjfvn0JCAjwdFhKKaXKUSMSVGhoKM2bN6dXr174+/t7OhyllFJOqBEJqkePHp4OQSml1G3S6V2VUkp5JU1QSimlvJImKKWUUl5JE5RSSimvpAlKKaWUV9IEpZRSyitpglJKKeWVNEEppZTySmKM8XQMtyQiF4CTno6jHMFAuqeDqICqGHdVjBk0bneqijFD1Yy7smJuZ4xpWrqwSiQobyciCcaYGE/HcbuqYtxVMWbQuN2pKsYMVTNuV8esTXxKKaW8kiYopZRSXkkTVOWY4+kAKqgqxl0VYwaN252qYsxQNeN2acx6DkoppZRX0hqUUkopr6QJSimllFfSBFUGEZkrImkicsChrJuIbBOR/SLyhYg0dFgWbV920L68jr28l/35MRH5s4iIt8QtIn4i8qG9/LCIvOywjdviFpE2IhJnj+GgiDxnLw8UkbUictR+38Rhm5ftsSWJyKiqELeIjBSRRHt8iSIyrCrE7bBdWxHJEZFZ7o67gv8jHv9MVuB/xOOfyXJinmJ/bhORmFLbuO7zaIzRW6kbMBjoCRxwKPsaGGJ//ATwa/vjWsA+oJv9eRDga3+8E+gPCLASuNeL4n4U+MT+uB5wAgh1d9xAS6Cn/XED4AgQCbwDvGQvfwn4rf1xJLAX8AfCgGRPvN8ViLsHEGJ/3AU447Avr43bYbuFwOfALHfHXYH32is+kxWI2+OfyXJivgvoBGwEYhzWd+nn0SV/mOpwA0K5/ov+Mt91KmkDHLI/HgPMu8kf+huH548A//SiuB8BvrB/mIPs/4iBnorb4XhLgZFAEtDS4b1Msj9+GXjZYf3V9g+BV8ddal0BMuwfaq+PG5gI/C8wG3uC8mTcTvyPeNVn8jbi9rrPZEnMDs83cn2CcunnUZv4nHcAGG9/PAXryx6gI2BEZLWI7BKRF+3lrYAUh+1T7GXudrO4FwBXgLPAKeBdY0wmHoxbREKxaho7gObGmLMA9vtm9tVaAafLiM/b43b0ALDbGJOPl8ctIvWBnwGvl9rcI3E7+V573WfSybi96jNZKuabcennsdbtblCDPQH8WUReBZYBBfbyWsBAoDdwFVgvIolYNZfSPNGn/2Zx9wGKgRCgCbBZRNZh/bovzeVxi0gAVjPS88aYy+U0V98sPm+Pu2T9KOC3QGxJURmreVPcrwN/MMbklFrH7XHfRsxe9Zm8jbi95jNZOubyVi2jrNI+j5qgnGSM+Qb7l4qIdATG2helAJuMMen2ZV9inQeaB7R22EVrINVtAduVE/ejwCpjTCGQJiJbgRhgM26OW0T8sD4MHxtjFtmLz4tIS2PMWRFpCaTZy1P4rhboGF+Kl8eNiLQGFgNTjTHJ9mJvj7svMFlE3gEaAzYRybNv77a4K/A/4hWfyduM2ys+kzeJ+WZc+nnUJj4niUhJk4cP8ArwD/ui1UC0iNQTkVrAEKzzPGeBbBHpZ++9MhWrPddb4j4FDBNLfaAfVpuxW+O2H+PfwGFjzO8dFi0DptkfT3OIYRnwsIj4i0gY0AHY6e1xi0hjYAVWe/3WkpW9PW5jzCBjTKgxJhT4I/CWMeYv7oy7Av8jXvGZrEDcHv9MlhPzzbj28+iOE21V7QbMx2oHLsT6JfB94Dmsk5ZHgN9g73hgX/9x4CDW+Z53HMpj7GXJwF8ct/F03EAAVq+sg8Ah4P95Im6sphiD1etqj/02Busk8XrgqP0+0GGbX9hjS8KhZ5A3x4314+CKw7p7gGbeHnepbWdzfS8+t8Rdwf8Rj38mK/A/4vHPZDkx34/1nZIPnAdWu+PzqEMdKaWU8kraxKeUUsoraYJSSinllTRBKaWU8kqaoJRSSnklTVBKKaW8kiYopVxMRIJEZI/9dk5Eztgf54jI3zwdn1LeSruZK+VGIjIbyDHGvOvpWJTydlqDUspDRGSoiCy3P54t1lxAa0TkhIhMEpF3xJpPZ5V9+JmSOXY2iTWn1Gr7UDlKVUuaoJTyHhFYYyVOwBo3Ls4Y0xXIBcbak9R7wGRjTC9gLvCmp4JVytV0sFilvMdKY0yhiOwHfIFV9vL9WPN8dcKa7HCtfURsX6yhrZSqljRBKeU98gGMMTYRKTTfnSC2YX1WBThojOnvqQCVcidt4lOq6kgCmopIf7CmRbDPL6VUtaQJSqkqwhhTAEwGfisie7FGmh7g0aCUciHtZq6UUsoraQ1KKaWUV9IEpZRSyitpglJKKeWVNEEppZTySpqglFJKeSVNUEoppbySJiillFJe6f8DiGmH3voZZBsAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_projections(table3)\n",
-    "results.plot(color='gray', label='model')\n",
-    "decorate(title='Quadratic Model Projection')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "integrated-there",
-   "metadata": {},
-   "source": [
-    "The U.N. DESA expects the world population to reach 11 billion around 2100, and then level off.\n",
-    "Projections by U.S. Census are a little lower, and they only go until 2050.\n",
-    "\n",
-    "Real demographers expect world population to grow more slowly than our model projects, probably because their models are broken down by region and country, where conditions are different, and they take into account expected economic development.\n",
-    "\n",
-    "Nevertheless, their projections are qualitatively similar to ours, and\n",
-    "theirs differ from each other almost as much as they differ from ours.\n",
-    "So the results from this model, simple as it is, are not entirely unreasonable.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "serial-binary",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "\n",
-    "You might be interested in this [video by Hans Rosling about the demographic changes we expect in this century](https://www.youtube.com/watch?v=ezVk1ahRF78)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "authorized-suggestion",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:** The net growth rate of world population has been declining for several decades.  That observation suggests one more way to generate more realistic projections, by extrapolating observed changes in growth rate.\n",
-    "\n",
-    "To compute past growth rates, we'll use a function called `diff`, which computes the difference between successive elements in a `Series`.  For example, here are the changes from one year to the next in `census`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "handmade-funeral",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "1950         NaN\n",
-       "1951    0.037311\n",
-       "1952    0.041832\n",
-       "1953    0.045281\n",
-       "1954    0.048175\n",
-       "Name: census, dtype: float64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diff = census.diff()\n",
-    "diff.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "discrete-scanner",
-   "metadata": {},
-   "source": [
-    "The first element is `NaN` because we don't have the data for 1945, so we can't compute the first difference.\n",
-    "\n",
-    "If we divide these differences by the populations, the result is an estimate of the growth rate during each year: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "objective-accused",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "1950         NaN\n",
-       "1951    0.014378\n",
-       "1952    0.015865\n",
-       "1953    0.016883\n",
-       "1954    0.017645\n",
-       "Name: census, dtype: float64"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alpha = census.diff() / census\n",
-    "alpha.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "weighted-trouble",
-   "metadata": {},
-   "source": [
-    "The following function computes and plots the growth rates for the `census` and `un` estimates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "unique-matrix",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_alpha():\n",
-    "    alpha_census = census.diff() / census\n",
-    "    alpha_census.plot(style='.', label='US Census')\n",
-    "\n",
-    "    alpha_un = un.diff() / un\n",
-    "    alpha_un.plot(style='.', label='UN DESA')\n",
-    "\n",
-    "    decorate(xlabel='Year', label='Net growth rate')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "flexible-amateur",
-   "metadata": {},
-   "source": [
-    "And here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "pressing-proceeding",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_alpha()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cross-reducing",
-   "metadata": {},
-   "source": [
-    "Other than a bump around 1990, net growth rate has been declining roughly linearly since 1970.\n",
-    "\n",
-    "We can model the decline by fitting a line to this data and extrapolating into the future.\n",
-    "\n",
-    "Here's a function that takes a time stamp and computes a line that roughly fits the growth rates since 1970."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "mexican-denver",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def alpha_func(t):\n",
-    "    intercept = 0.02\n",
-    "    slope = -0.00021\n",
-    "    return intercept + slope * (t - 1970)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "handy-dubai",
-   "metadata": {},
-   "source": [
-    "To see what it looks like, I'll create an array of time stamps from 1960 to 2020 and use `alpha_func` to compute the corresponding growth rates."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "addressed-worker",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import linspace\n",
-    "\n",
-    "t_array = linspace(1960, 2020, 5)\n",
-    "alpha_array = alpha_func(t_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "jewish-operations",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like, compared to the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "cathedral-shakespeare",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f968672d910>]"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from matplotlib.pyplot import plot\n",
-    "\n",
-    "plot_alpha()\n",
-    "plot(t_array, alpha_array, color='gray')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "expensive-viewer",
-   "metadata": {},
-   "source": [
-    "If you don't like the `slope` and `intercept` I chose, feel free to adjust them.\n",
-    "\n",
-    "Now, as an exercise, you can use this function to make a projection of world population until 2100.\n",
-    "\n",
-    "1. Create a `System` object that includes `alpha_func` as a system variable.\n",
-    "\n",
-    "2. Define a growth function that uses `alpha_func` to compute the net growth rate at the given time `t`.\n",
-    "\n",
-    "3. Run a simulation from 1960 to 2100 with your update function, and plot the results.\n",
-    "\n",
-    "4. Compare your projections with those from the US Census and UN."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "common-april",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "t_0 = 1960\n",
-    "t_end = 2100\n",
-    "p_0 = census[t_0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "ignored-chain",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                alpha_func=alpha_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "color-accountability",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def growth_func_alpha(pop, t, system):\n",
-    "    return system.alpha_func(t) * pop"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "numeric-raise",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.06725033332680001"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "growth_func_alpha(p_0, t_0, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "included-vehicle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results2 = run_simulation(system, growth_func_alpha);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "brown-rating",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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9635cXBMqq2pzDmrO0zdn2vb19rrlfqCf9y336wT43nK/fi2/W+47U3sKDQ3l0qVLtyy7ePHiLRe2RkdH88wzz/Cd73yHBg0acOHCBUJDQwvXx8bGsmPHDkaPHn3b/gvOR7Vt2/aW5Vu2bMHf37/wvre3N7m5udSrV4+vv/6azz//nLfeeou5c+cyY8YMfHx8yM+33qyO1zU99thj9OzZk2XLljF8+HDeeecdBg0adMdyK6XcJPcGpOyDM4lWIjqzCxp3gtFvgW8gnN4BIc2h3Sho0sX6q2OdZsAvCLpOdmv4ZeGxvfhE5CkR2S4i21NTU90dzm1q165N48aNWbNmDWAlpxUrVtC3b18Ali1bRsFsxYcPH8bb25u6deveso8f/OAHvP/++2zZsqVw2UcffcS5c+cYPnw4//znPwv3sWvXrlLjSUtLIz8/n4cffpjf/va37Ny5E7CmKtmxYwcA8+fPL9w+KSmJqKgonnvuOR588EF2795djmdDKVXhMi9ZSafAO4Ng+kBY+gLsXwxBodCoo7VOxDpH9Ph8GPQLaHf/zeRUhXlsDcoYMx2YDhAfH++R89J/8MEHfP/73+dHP/oRAL/61a+Ijo4G4MMPP+TFF18kKCgIHx8fPv74Y7y9vW95fKNGjZg9ezYvvfQSKSkpeHl50b9/f8aOHcsvf/lLXnjhBTp16oQxhsjISJYuXVpiLKdPn2bq1KmFtaXf//73ALz00ktMmDCBDz/88JYa0pw5c/joo4/w9fUlPDycV199tUKfG6XUXco4DSc2wslN1jVGKfshoC68fAy8vKDvD63tmnSxziFVg1MldyIFv9BdsnPrHNRSY0yHIssTgJeMMU7NQhgfH2+KTlh44MAB2rdvX0GRquLoc6yUi+Tnw/m9ViLqOhl8A2DVq7Dx7+BfB5p1h4heEHEPtOgNXt533mcVJiI7jDG39ex2WQ1KRGYBA4EwEUkGfgVcBP4JNACWiUiiMWa4q2JQSimPce0CHFoGR9fCsXVw/YK1vHFnqyND/DToOB4axlT7hOQsV/bim1jCqoWuOqZSSnmMrMtw/Euo1xIaxVgXwS55FmqHW926o++FFn2gbnNr+3qRbg3XE3nsOSillKpS8vMheatVQ0paC8nbweRBrx9YF7c2i4dnNkHD9jXi/FFF0ASllFJldf2iNQpD067W/VkTISvd6sjQ90WrltSsu7XO29eqSSmnaYJSSqm7cTEJDi23/k58BSHN4PmvrZ52k+ZBaDQE1nN3lNWCJiillCpNfr7VJCdys6cdWJ0Z+r4Ibe+/uW2zaj3EaKXz2At1Pd2dptuYMmUKTZs2JTs7G7h1oNaivL29iYuLIzY2ls6dO/Pmm28WXs+UkJBASEgIcXFxhX+rV68GrPH6YmNj6dSpE3Fxcbdc8Jubm0tYWBg/+9nPKrroSlV/OVnwzefw6fPW1BKp9mwErYfD8N/Dc4nwvU0w+JfQrJueU3IRrUG5kLe3NzNmzOCZZ54pdbvAwEASExMBaxT0xx57jIyMDH79618D0K9fv9su0t20aRNLly5l586d+Pv7k5aWxo0bNwrXr1y5krZt2zJ37lzeeOONajH+oVIud/kMrPkNHFwG2ZfBrza0GnJzbLvIPtafqhRag3KhF154gb/+9a/k5uY6/ZiGDRsyffp0/vWvf1HaRdRnz54lLCyscFy+sLAwmjRpUrh+1qxZPP/880RERLB5s+tnvlSqSsrPg2PrISnBuu9XC45+ATEPwqQF8HISTHhfOze4SfWpQf1v5O3LYsdAj+9YI/1+PP729XGPQZdJ1gV0c4sMpDh1WblDioiIoG/fvnz44Yc88MADTj8uKiqK/Px8UlJSAPjyyy+Ji4srXL9gwQKGDRvGb37zG9q0acOQIUN45JFHGDBgAACZmZmsWbOGt99+m/T0dGbNmlU455RSNZ4xkLwN9i6AfQvh6nlo0ReiBkJACPzwgF4o6yG0BlVGd5puo8DPf/5z/vznPxeeU3KWY+2pX79+JCYmFv5FR0dTu3ZtduzYwfTp02nQoAGPPPII7733HgBLly7l3nvvJSgoiIcffpiFCxfqfE9KFVjwJLw7FLb/D5r3gPHvWb3vCmhy8hjVpwZVWo3HL6j09bVC77rG5Mx0GwCtWrUiLi6OuXPnOr3vpKQkvL29adiwIQcOHChxO29vbwYOHMjAgQPp2LEj77//PlOmTGHWrFls3LixsFPGhQsXWLt2beGEh0rVGFfOwdezYfccmLwEajeAzhOt80rtRkJAHXdHqEpRfRJUJXOcbmPw4MGF0208//zzt237i1/8gpEji2mCLEZqairf/e53+cEPflBqx4ZDhw7h5eVVONFgYmIiLVq04PLly2zYsIFTp04Vnp/63//+x6xZszRBqZohL8fqgbfrIzi80hrNofk91th3tRtAa/0cVBWaoMqhtOk2HMXGxtK1a9fCOZqKyszMJC4ujpycHHx8fPjWt77FD3/4w8L1Rc9BvfLKK7Rs2ZJnn32W9PR0fHx8aNWqFdOnT+eTTz5h0KBBt0xqOHr0aF5++WWys7NvWa5UtZKTaU3cd/k0zJlkjXnX5zmImwRhOmN0VeTS6TYqik634R76HCuPl5VhdXbY9RHUagiPzbaWn9wCTbuBt/4GrwoqfboNpZRymZNbYPu71syyuVnWqA7R995cH9HTfbGpCqMJSilVNdy4Bt7+Vq3o2Do4tMJqvuvyuDU4q16MXu1oglJKebZLJ2Dbf2HnB/DA3yH2Ibjne9D7Weuck6q2XDmj7gxgFJBSMOW7iNQH5gCRwHFggjHmUkn7UErVUMbA8Q2w5T9w6DNAoP0DUD/KWu9f263hqcrhygt13wNGFFn2U2CNMaY1sMa+r5RSloIL2o2BpS9Y01n0eQFe2G0NOdS4szujU5XMlVO+rxeRyCKLRwMD7dvvAwnAT1wVg1Kqikg/BdvegQNL4LsbrDHxHp0JdSO0Ga8Gq+yhjhoZY84C2P8blrShiDwlIttFZHtqamqlBegukZGRpKWllXsbpaqU1EPwyVPw987w1T+gUazVdRygQVtNTjWcx3aSMMZMB6aDdR2Um8NRSlW01G/grZ5WErrnGej5tFVjUspW2TWo8yLSGMD+n1LJx69Qx48fp127djz55JN06NCBSZMmsXr1avr06UPr1q3ZunUrFy9eZMyYMXTq1Il77rmH3bt3A9b4eMOGDaNLly48/fTTtwwO+9FHH9GjRw/i4uJ4+umndaBXVX2c3gE73rNuN2gDo96EF/bC8Nc1OanbVHYNagnwBPAH+//iitjpihUrOHfuXEXsqlB4eDgjRhTt43G7I0eOMG/ePKZPn0737t2ZOXMmGzZsYMmSJbzxxhs0b96cLl26sGjRIr744gsmT55MYmIiv/71r+nbty+vvvoqy5YtY/r06YA1esOcOXPYuHEjvr6+fO973+Pjjz9m8uTJd4hEKQ92cjOs+xMcXWMNQdTpUfANgPhp7o5MeTBXdjOfhdUhIkxEkoFfYSWmuSLybeAkUMwkTVVLy5Yt6dixI2CNuTd48GBEhI4dO3L8+HFOnDjBggULABg0aBAXLlwgIyOD9evX88knnwAwcuRI6tWrB8CaNWvYsWMH3bt3B6xx+ho2LPFUnVKe7fx+WP4yHP8SgsJgyGvQ/UkrOSl1B67sxTexhFWDK/pYztR0XMVx8FUvL6/C+15eXuTm5uLjc/tTXDBKeXGjlRtjeOKJJ/j973/vooiVcjFjIPuKNZWFjz9cTILhb0C3KVbvvAqQm5vL5cuXycjI4MqVK1y/fp3MzMzC/9nZ2eTk5JCbm1v4v6AZXUQKP3u+vr74+fnd8hcYGEitWrUK/2rXrk1ISAiBgYGlzjCgKp7HdpKoLvr378/HH3/ML3/5SxISEggLC6NOnTqFy1955RWWL19eOLfU4MGDGT16NC+++CINGzbk4sWLXLlyhRYtWri5JEo54cRXsOpVa+DWiTMhNBpe2FOmSQBzc3O5cOECaWlppKWlceHChcIWiGvXrhX7mMDAQIKCgvD398fHx4eAgABq166Nr69vYXIpSFTGGHJzc7lx4waZmZlkZGSQnZ3N9evXyc3NvW3ffn5+1K1bl3r16lG3bl0aNGhAgwYNaNiwIQEBWiN0BU1QLvbaa68xdepUOnXqRFBQEO+//z5gTc0xceJEunbtyoABA4iIsE4Qx8TE8Lvf/Y5hw4aRn5+Pr68vb731liYo5dlSDsDq1+CbFRDcGLp86+Y6J5JTdnY2586d48yZM5w7d46zZ8+SlpZ2S+ehunXrUr9+fRo1akRISEjhX3BwMEFBQQQEBODlVf5+X8YYbty4wbVr17h27RpXr14lIyODS5cukZ6ezqVLl0hKSiInJ6fwMcHBwTRs2JAmTZrQpEkTmjZtSnBwcLljqel0ug1VIn2OlVP2LYT508CvNvR9EXp+15rFuhQZGRmcPHmy8C8l5WaH3uDgYBo3bkx4eDgNGjQgLCyM0NBQfH19XV0SpxljyMjIICUlhdTUVFJTUzl37hwpKSmFSTU4OJimTZvSokULIiMjadSokTYRlkCn21BKVZzMdLiaYnUVjxoIvZ+DPs9DUP3iN8/MJCkpiaNHj3Ls2DHS09MBq9msefPmxMTE0KRJExo3bkzt2p4/zp6IULduXerWrUubNm0Kl+fk5HDu3DlOnz7NmTNnOHXqFAcPHgQgICCgMFm1atWK0NBQTVh3UGqCEpFewONAP6AxkAnsBZYBHxljMlweoVLKc+RkWSOLr/8L1GsBT62DwHow9Ne3bGaM4dy5cxw6dIgjR45w5swZjDH4+/vTsmVLevbsSUREBOHh4RXSLOcpfH19ad68Oc2bNy9clpGRUdij98SJExw6dIjPP/+cevXq0apVK1q1akXLli09qoboKUpMUCKyHDiDda3S61gX1QYAbYB7gcUi8qYxZkllBFocY4z+AnGRqtD0qypRfh7sngtrX4eMUxA92EpKDp+//Px8Tpw4wcGDBzl06BAZGRmICE2bNqV///5ER0fTtGnTapWQnBESEkLnzp3p3Nka6PbSpUscOXKEI0eOkJiYyLZt2/D19aVVq1a0b9+eNm3a3NI7uCYr8RyUiIQZY0od+M2ZbSpCceegjh07RnBwsFaTXcAYw4ULF7hy5QotW7Z0dzjKE3w9BxY+BY3jrMQUNRCw3isnT55kz5497N+/n8zMTHx8fIiOjqZdu3a0adOGoKDSz0fVZLm5uYVJ/eDBg1y9ehVvb2+ioqKIjY2lffv2+Pn5uTtMlyvpHFSV7SSRk5NDcnIyWVlZboqqegsICKBZs2ba7FCTXTpuTRYYNQDycuDQcmg3Cry8OHfuHHv27GHv3r1cvnwZX19f2rZtS0xMDNHR0TXiS7WiGWM4deoUBw4c4MCBA2RkZODr60u7du3o1KkTUVFR1bb2WeYEJSJjgT9ijTwu9p8xxtRxRaDFKS5BKaVcJCcTNvwNNv7N6jL+7A7w8iYrK4s9e/awa9cuzp49i5eXF61ataJDhw60bduWPLz5yYLd9IoOZWIPHVevPAqS1e7du9m3bx9ZWVnUqlWLTp060a1bN0JDQ90dYoUqTy++PwEPGGMOVHxYSimPYQwcXAqf/xzST0KHhzFDfsOJk6fYuXMnBw4cIDc3l/DwcO677z46dOiAf0Agx9Ku4ufnZzUNX8vmatbtF7mquyMiREREEBERwYgRIzh8+DC7d+9my5YtbNq0icjISLp27Ur79u2LHa2munCmBrXRGNOnkuIpltaglKoEJzfDjOHQMIYbQ95g9+Vgtm3bRkpKCv7+/nTs2JGuXbvSuHHjwof8dMFuVu4/z1c/HUSAr7d2XHKxK1eukJiYyK5du7h06RKBgYF06dKFHj16EBIS4u7wyqw8TXx/B8KBRUB2wXJjzCcVHGOJNEEp5SLZVyB5G0QPAmO4uH0BW9Nqkfj112RnZxMeHk6PHj3o0KEDvr6+pF+/wXtfHWdyr0jq1/JjT3IGp9OvMzQmHG8vTUyVxRjDsWPH2L59e+F1Vu3bt6dnz540b968yv1IKE8TXx3gOjDMYZkBKi1BKaUqmDGwZx6s/CVkZZD86Bd8tXMvBw4cwMvLi5iYGHr06EGzZs0QkcLLDlKuZPOPNYeJDK3FmC5N6dgshI7Nqu4v96pKRIiKiiIqKoqMjAy2bt3Kzp072b9/P40bN6ZXr17ExsZW+U4VVbYXn1KqjM7tgc9+jDm5iW/qD+Erv4GcPH8Rf39/4uPj6dmzZ+E4csYYfrl4L/4+3vxyVAwAZ9IzaVJXp2L3NDdu3Cg8T5WWlkbdunXp3bs3cXFxHt8bt8w1KBFpBvwT6INVc9oAPG+MSa7wKJVSrnX5LGb6IPb7dmB9nZ+QcimHkJA8hg8fTpcuXQovEE27mk1YbX9EBG8RHFvvNDl5Jj8/P+Lj4+nWrRuHDh1iw4YNfPbZZ6xbt46ePXvSo0ePKncBsDPnoFYBM4EP7UWPA5OMMUPLfFCR54HvYHVZ/68x5m+lba81KKXKKfUQJqwN+/fvZ93KT0m9nE1YWBj9+vUjNjYWb++bI44v2nWaH8//mpUvDqBlWMXM36QqnzGGEydOsHHjRo4cOUJgYCC9e/emR48eHnedWnk6SSQaY+LutOwuAukAzAZ6ADeAFcAzxpjDJT1GE5RSZXQtjfwVP2f/nl2sr/coqelXCQsLY8CAAcTExBSeozieZs2vFBlWi9Qr2byzIYmn+0dTv5ZnfZGpsjlz5gwJCQkcPnyYoKAg+vTpQ/fu3T2m6a88CWo18B4wy140EZhqjCnTzLgiMh4Ybox50r7/SyDbGPOnkh6jCUqpu2QMJnEW+5b/l3U5nUijPg3CQuk/YOAtiQkgOzePXr//gl5Robw1qasbg1aulpycTEJCAkePHqVWrVr07duX+Ph4t19LVZ4EFQH8C+iFdQ7qK6xzUCfKGEh7rAFoe2GNjr4G2G6MebbIdk8BTwFERER0O3GiTIdTquYxhmMzvsOqU76clXAa1KvDgMHDiImJKex+nHolm8/2nOWJ3pEArP8mlXaNg2kYrDPD1gQnT54kISGhcEzT/v3707VrV7f1+vOosfhE5NvA94GrwH4g0xjzYknbaw1KKSfk5XI+NY3Va9Zw5MgR6gR4M2j4KDp17nzbdTHvfJnE75cfZNWL/Ylq4PnzLynXOH78OF988QWnTp2iQYMGDB06lFatWlX6dVR3naBE5GVjzJ9E5J9YNadbGGOeq6DA3gCSjTH/LmkbTVBKlS7j0EYSFr1PYlZTAgIC6NevHz169ChsurmRm88Hm47TLrwOfVuHkZWTx9mMLO0EoTDGcPDgQVavXs3Fixdp2bIlw4YNIzw8vNJiKEs384Kx9yo8M4hIQ2NMit18OBaruU8pdZeyr1ziy9l/ZcsZgyGcXm3D6Td6MoGBt3cF/2DTCQa1a0jf1mEE+HprclKAddFvwTxU27dvZ926dbz99tvExcVx7733UqdOpY0Lfntsbmri+xIIBXKAHxpj1pS2vdaglLqVMYav1y5k9YZtXDMBdKp/g3vHP03d8JujiO84cYkPNh3n/43vjI+3Fxev3dBeeeqOMjMz+fLLL9m6dSsiQu/evenbt69Le/zddQ1KRD6lmKa9AsaYB8sajDGmX1kfq1RNd+bMGZYvX05ycjJNva8zcXhfmnYfedt2qVey2HbsIsmXMokMq6XJSTklMDCQYcOG0b17d9asWcP69ev5+uuvGTFiBG3btq3U81OlnYMaUNoDjTHrXBJRMbQGpRRcv36dNUvmsPPQSWrVqsXgwYOJi22H+FnNeTdy8/njioO0aVSbR7pHYIwhOzefAF/vO+xZqZIdP36czz77jNTUVFq3bs2IESOoX79+hR7jrmtQlZmAlFIly8/PZ/vWzaxdvYrsvHzu8U9iwHf/RUDtWwdp9fUW9p3JwNfb6iosIpqcVLlFRkby9NNPs3XrVhISEvj3v/9Nnz59XN7sB6XXoPZQehNfJ1cFVZTWoFRNdebMGT79ZC7nLmTQ0pzgvna1aTD6NxBYF4CvT6Xz588P8e/Hu1InwJecvPzCBKVURbty5QqrVq1iz5491K1bt7DZr7zK0otvVLmPqpQqkxs3bvDFF1+wdetWapmrjPPfScyYHyHt7rtlOy8RTly8xqmL14ltEqLJSblUcHAwY8eOpWvXrnz22WfMnj2bxx57jNatW7vkeDrdhlIe5ptvvuGzpZ+SceUq8fHxDG5yjYC2QyCoPsYY3l6fxI3cfJ4bbH0p5Obl46OJSVWyvLw89uzZQ+diLgS/W2XpxbfBGNNXRK5gNfWJ439jjPs6xytVDV25coUVy5ez/8ABGnCBaSOG0rznrb3zRITD56+SlZtXOL26JiflDt7e3sTFxbn0GKV1kuhr/w92aQRK1XDGGHbu3MmqlZ+TeyObQeYrerdvjHcH6/r11CvZ/GH5QV4Y0prm9YP4/diO+PloUlLVn1ND2IpIV6Av9oSFxphdLo1KqRoiPT2dJUuWcOzYMVrKaUb6biR01K+g43iwm01y8vL54uB5BrRtQPP6QZqcVI3hzIy6rwLjgU/sRe+JyDxjzO9cGplS1Zgxhh07drBq1SoARsWG0DV9I/LwMqgXyYGzl1m9/zzPDm5Nk7qBbPzpIIL83DslglKVzZl3/ESgizEmC0BE/gDsBDRBKVUGjrWmqEZ1eODRqdQNCYH8H4C39ZFcsfccH285wWM9Iwit7a/JSdVIzrzrjwMBQJZ93x846qqAlKqubqk15d1gJGvolp+H1HkeRPjqeDrB/r50bBbC9+6NZkrvSOrp8ESqBiutF1/BNBvZwD4RWWXfHwpsqJzwlKoeMjIyWLJkCUlJSbT0z+DB3LnU7TAMRr4JXl5k5+bxo7lf06FpCP+dHI+/jzf+PjoKhKrZSqtBFVx4tANY6LA8wWXRKFUN7d27l2XLlpGXl8tIn410y9uHjPkLdH6ULccu0j3S4O/jzXtTe9AiNMjd4SrlMUrrZv5+ZQaiVHWTlZXF8uXL2b17N82aNeOhhx6i/r5AiPk/CI3mqyNpPPbOFv76SGce6tKMtuF6RYdSju403cZ0YIUxJqfIuihgCnDcGDPDpREqVQWdOHGChQsXcvnyZQYGHaHffQ/hVb8+9PuhNS8T0Cs6lL+M78zIjk3cHa5SHqm0Jr7vAD8E/iYiF4FUrM4SLYEjwL+MMYtdH6JSVUdeXh5r165l48aN1A/yZhpzaeaVB7mZAPx99WE+3nKCz1/oT71afozr1szNESvluUpr4jsHvAy8LCKRQGMgE/jGGHO9PAcVkReBJ7E6XewBphZ0Y1eqqkpLS2PBggWcO3eOrvWuMvzi//BrNRDGTscE1kOAITENycvPp5a/dhtX6k6c+pQYY45jdTcvNxFpCjwHxBhjMkVkLvAo8F5F7F+pymaMITExkeXLl+Pr68sjnYJo9/Vf4d5fkN/3h/xm2UFq+6fy0vC2xDYJIbZJyJ13qpRyLkG56LiBIpIDBAFn3BSHUuWSnZ3N0qVL2bt3Ly0jmvHQuAkEBwVA/ABo3gMvIPNGHj5eUji4q1LKOZWeoIwxp0XkL8BJrCbDlcaYlUW3E5GngKcAIiIiKjdIpZxw+vRpFixYQHp6OoOaZtPn4pt4eT9EntTmv0mhjKx1vXBwVy8vTUxK3a1KH3VSROoBo7E6WzQBaonI40W3M8ZMN8bEG2PiGzRoUNlhKlUiYwxfffUVM2bMID/3BlPq76Bf8lt4dRgD/sGkXMnirS+OsDjxNIAmJ6XKyJnBYvsArwEt7O0L5oOKKuMxhwDHjDGp9v4/AXoDH5Vxf0pVmmvXrrFo0SKOHDlCu2b1eTDlHwRmZ8EjH7E7uB+dvH1pHOLLZ8/3o1m9QHeHq1SV5kwN6l3gTazpNroD8fb/sjoJ3CMiQWI1yA8GDpRjf0pVihMnTvCf//yHY8eOcf/99zPBdy2BdRvBUwl8lhvPg//ayLpvUgFoXj9IzzcpVU7OnIPKMMYsr6gDGmO2iMh8rBHRc4FdWBcEK+WRjDFs2rSJ1atXU69uHSY99BDhUbEQ+w553gF4+wcxtG4+vxkdS+/oUHeHq1S1IcaY0jewptfwxpoPKrtguTFmp2tDuyk+Pt5s3779zhsqVcGysrJYvHgxBw8eJCayMQ+m/gP/hq1g8iLm70jm/a+OM/fpXgT66cCuSpWViOwwxsQXXe5MDaqn/d/xwQYYVBGBKeWpzp49y7x588jIyGB4hzB67nsFqdMIhv0WgIbB/jQI9ic7N08TlFIucMcEZYy5tzICUcpTGGPYuXMny5cvp1ZQEFPaZdJ8z88hsh/7+v6Do+cDeDAc+rdpQL/WYXquSSkXcaYXXwjwK6C/vWgd8BtjTIYrA1PKHW7cuMFnn33G119/TXR0NA/dN4haH4+Ent+FYb/jzQ8TOXbhGvd1CMfX20uTk1Iu5EwT3wxgLzDBvv8t4H/AWFcFpZQ7pKWlMW/ePFJSUhjQvSP9h9yHl18gVyavxgTUoY63L38e3xkvAV/vSr+EUKkax5kEFW2Medjh/q9FJNFF8SjlFvv27WPJkiX4+PjweP/WRG96FryncGPwbxk7Yy9tGgXz1qSu1Ncp2JWqNM4kqEwR6WuM2QCFF+5mujYspSpHfn4+a9as4auvvqJZ06aMa5ZGyLpnoElX6PV9/Hy8mNa3JVFhtdwdqlI1jjMJ6hngfftclAAXsSYrVKpKu379OgsWLCApKYn4Lp0ZcX0+3ps/Ja/jo7zu9RQjLwXQrQ5M7KFjQSrlDs704ksEOotIHfv+ZVcHpZSrnT17lrlz53LlyhUefPBBujSvDe88D8N/z9XOT/LFWxtpWP8S3VrUd3eoStVYpU35/rgx5iMR+WGR5QAYY950cWxKucTu3bv59NNPCQwMZOrUqTRt2hSAPePWExMVQYiXsOy5fjqpoFJuVtonsKDRPbiYdaUPP6GUB8rLy2PVqlVs2bKFFi1aMG7cOGrXrg3A3tMZPPjuXl4dlc/UPi01OSnlAUqb8v1t++ZqY8xGx3V2Rwmlqoxr164xb948Tpw4Qc+ePRk6dCje3jdHf4htUoc3HurI6LgmboxSKeXImZ+J/wS6OrFMKY90+vRp5s6dy/Xr13nooYfo1KnTbduIiHaGUMrDlHYOqhfWPE0NipyHqoM1eKxSHi8xMZGlS5dSu3Ztpk2bRuPGjd0dklLKSaXVoPyA2vY2juehLgPjXBmUUuWVn5/PqlWr2Lx5My1btmTcuHEEBQW5Oyyl1F0o7RzUOmCdiLxnjDlRiTEpVS5ZWVnMnz+fo0eP0qNHD4YNG3bL+SalVNXgzDmo6yLyZyAWCChYaIwp03QbItIWmOOwKAp41Rjzt7LsTylHaWlpzJ49m0uXLjFq1Ci6devm7pCUUmXkTIL6GCuhjAK+CzwBpJb1gMaYQ0AcgIh4A6eBhWXdn1IFjhw5wvz58/H29mby5Mm0aNHC3SEppcrBmQQVaox5V0Sed2j2W1dBxx8MHNUmRFUexhg2b97MqlWraNiwIY8++ih169Z1d1hKqXJyJkHl2P/PishI4AzQrIKO/ygwq7gVIvIU8BRARIR2/1XFy83NZdmyZSQmJtK+fXvGjBmDn5+OOK5UdSDGlD4ohIiMAr4EmmNd/1QH+LUxZkm5Dizih5XsYo0x50vbNj4+3mzfvr08h1PV0NWrV5kzZw7JyckMGDCAAQMG6ASCSlVBIrLDGBNfdLkzg8UutW9mABU5/ft9wM47JSelinPmzBnmzJlDZmYm48ePJyYmxt0hKaUqWGkX6v6TUsbcM8Y8V85jT6SE5j2lSrN3714WL15MrVq1mDZtGuHh4e4OSSnlAqXVoFzWpiYiQcBQ4GlXHUNVP8YY1q5dy5dffknz5s155JFHqFVLJxJUqroq7ULd9111UGPMdSDUVftX1U9OTg6LFi1i//79xMXFMWrUKL34Vqlq7o7noERkLcU09ZX1Ql2l7tbVq1eZPXs2p0+fZujQofTq1Us7QyhVAzjTzfwlh9sBwMNArmvCUepW58+fZ9asWVy/fp1HHnmEdu3auTskpVQlcaYX344iizZW4IW6SpXom2++YcGCBfj7+zN16lQdiVypGsaZJr76Dne9gG6AdptSLmOMYcuWLaxcuZLw8HAeffRR6tSp4+6wlFKVzJkmvh1Y56AEq2nvGPBtVwalaq78/HyWL1/O9u3badeuHQ899JCODKFUDeVME1/LyghEKcdpMnr37s2QIUO0M4RSNZgzTXwBwPeAvlg1qQ3A/xljslwcm6pBLl26xKxZs7hw4QIPPPAAXbt2dXdISik3c6aJ7wPgCtY4fGCNAPEhMN5VQama5eTJk8yZM4f8/Hwef/xxWrbUSrtSyrkE1dYY09nh/loR+dpVAamaZffu3SxZsoSQkBAee+wxQkP1+m2llMWZBLVLRO4xxmwGEJGewEbXhqWqO2MMCQkJrF+/nhYtWjBhwgSCgoLcHZZSyoM4k6B6ApNF5KR9PwI4ICJ7AGOM6eSy6FS1lJOTw5IlS9i7d68OW6SUKpEzCWqEy6NQNYbjHE6DBw+mT58+2lNPKVUsZ7qZnxCRzkA/e9GXxhg9B6XuWkpKCjNnzuTatWtMmDCB9u3buzskpZQH87rTBiLyPPAx0ND++0hEnnV1YKp6OXLkCO+++y55eXlMnTpVk5NS6o6caeL7NtDTGHMNQET+CGziZrdzpUq1detWVqxYQaNGjZg4caIOW6SUcoozCUqAPIf7efayMhORusA7QAesi3+nGWM2lWefyvPk5+ezYsUKtm3bRtu2bRk7dqwOW6SUcpozCep/wBYRWYiVmEYD75bzuH8HVhhjxomIH6D9i6uZ7Oxs5s+fz5EjR+jVqxdDhgzBy+uOLcpKKVXImU4Sb4pIAtZQRwBTjTG7ynpAEakD9Aem2Pu/Adwo6/6U50lPT2fmzJlcuHCBUaNG0a1bN3eHpJSqgpypQRUQIJ9yNu8BUUAq8D+7d+AO4PmCc1yqaktOTmb27Nnk5uYyadIkoqKi3B2SUqqKcqYX36vA+0A9IAwrsbxSjmP6AF2xBpztAlwDflrMcZ8Ske0isj01NbUch1OVZe/evbz33nv4+fnx5JNPanJSSpWLGGNK30DkANClYPRyEQkEdhpjytRPWETCgc3GmEj7fj/gp8aYkSU9Jj4+3mzfvr0sh1OVwBjD+vXrSUhIICIigkceeUSHLVJKOU1Edhhj4osud6aJ7zgQABRMr+EPHC1rIMaYcyJySkTaGmMOAYOB/WXdn3Kv3NxclixZwp49e+jcuTOjRo3Cx+duWo6VUqp4znyTZAP7RGQVVpfwocAGEfkHgDHmuTIc91ngY7sHXxIwtQz7UG527do15syZw6lTpxg0aBB9+/bVYYuUUhXGmQS10P4rkFDegxpjEoHbqnOq6khNTWXmzJlcvXqV8ePHExMT4+6QlFLVjDPdzN+vjEBU1XH06FHmzZuHr68vU6ZMoWnTpu4OSSlVDenJAnVXtm3bxvLly2nYsCETJ04kJCTE3SEppaopTVDKKfn5+axcuZItW7bQunVrHn74Yfz9/d0dllKqGtMEpe4oOzubBQsWcPjwYe655x6GDh2qwxYppVyuxAQlIp9i9dorljHmQZdEpDxKeno6s2bNIjU1lZEjRxIfr31blFKVo7Qa1F/s/2OBcOAj+/5ErGujVDVXdNii6Ohod4eklKpBSkxQxph1ACLyW2NMf4dVn4rIepdHptxqz549LF68mDp16jBlyhTCwsLcHZJSqoZx5hxUAxGJMsYkAYhIS6CBa8NS7mKMISEhgfXr19OiRQsmTJigwxYppdzCmQT1IpAgIkn2/UjgaZdFpNwmJyeHxYsXs2/fPuLi4hg1ahTe3t7uDkspVUM5c6HuChFpDbSzFx00xmS7NixV2a5evcrs2bM5ffo0Q4YMoXfv3jpskVLKrUrrxTe2hFXRIoIx5hMXxaQq2fnz55k5cyaZmZlMmDCB9u3LNFC9UkpVqNJqUA+Uss4AmqCqgUOHDrFgwQICAgKYOnUqjRs3dndISikFlN6Lb6qIeAHjjDFzKzEmVQmMMWzevJmVK1fSuHFjJk6cSHBwsLvDUkqpQqWegzLG5IvIDwBNUNVIXl4ey5YtY9euXcTExDBmzBh8fX3dHZZSSt3CmV58q0TkJWAO1vTsABhjLrosKuUymZmZzJ07l+PHj9OvXz/uvfde7QyhlPJIziSoafb/7zssM0BUxYejXOnChQvMnDmTjIwMxowZQ+fOnd0dklJKlciZbuYtK/qgInIcuALkAbnFzUWvKtaxY8eYO3cuXl5eTJ48mYiICHeHpJRSpbpjghIRX+AZoGC4owTgbWNMTjmPfa8xJq2c+1BO2LlzJ8uWLSM0NJSJEydSr149d4eklFJ35EwT3/8BvsC/7fvfspc96aqgVMVwnMMpOjqacePGERAQ4O6wlFLKKc4kqO7GGMeTFV+IyNflPK4BVoqIwaqNTS+6gYg8BTwFaHNUGWRmZjJ//nySkpLo2bMnw4YN0zmclFJVijMJKk9Eoo0xRwFEJArr3FF59DHGnBGRhli9BA8aY24ZId1OWtMB4uPjS5yXSt0uLS2NWbNmkZ6ezoMPPkiXLl3cHZJSSt01ZxLUj4G19mCxArQAppbnoMaYM/b/FBFZCPQAdAqPCnDkyBHmz5+Pt7c3TzzxhNY+lVJVVmlj8b0AbATWAa2BtlgJqlyDxYpILcDLGHPFvj0M+E1Z96csBSNDrFq1ioYNGzJx4kRCQkLcHZZSSpVZaTWoZsDfsUYx3w18hZWwTgHlGc28EbDQvjjUB5hpjFlRjv3VeLm5uSxbtozExETat2/PmDFj8PPzc3dYSilVLqWNxfcSgIj4AfFAb6yLdv8rIunGmJiyHNCe+FCvEK0gV69eZc6cOSQnJzNgwAAGDBigI0MopaoFZ85BBQJ1gBD77wywx5VBKeecPXuW2bNnk5mZyfjx44mJKdNvBqWU8kilnYOaDsRijfiwBauJ701jzKVKik2VYt++fSxatIigoCCmTZtGeHi4u0NSSqkKVVoNKgLwBw4Dp4FkIL0SYlKlMMaQkJDA+vXrad68ORMmTKB27druDksppSpcaeegRoh1MiMW6/zTj4AOInIR2GSM+VUlxahs2dnZLFy4kEOHDhEXF8fIkSPx8XGmlVYppaqeO80HZYC9IpIOZNh/o7CuW9IEVYnS0tKYPXs2Fy9eZMSIEfTo0UM7QyilqrXSzkE9h1Vz6gPkYHUx3wTMQDtJVKpDhw7xySef4OPjw+TJk4mMjHR3SEop5XKl1aAigfnAi8aYs5UTjnJkjGHdunWsW7eOJk2aMGHCBL34VilVY5R2DuqHlRmIulVWVhYLFy7km2++0fNNSqkaSb/xPFBqaipz5szh0qVL3HfffXTv3l3PNymlahxNUB7m4MGDLFy4EF9fXyZPnkyLFi3cHZJSSrmFJigP4Xh9k55vUkopTVAeISsri08++YTDhw/r+SallLLpt6CbpaamMnv2bNLT07n//vuJj4/X801KKYUmKLfas2cPn376KX5+fnq+SSmlitAE5QZ5eXl8/vnnbNu2jebNmzNu3Djq1Knj7rCUUsqjuC1BiYg3sB04bYwZ5a44KltGRgbz588nOTmZe+65hyFDhuDt7e3usJRSyuO4swb1PHAAa66pGiEpKYkFCxaQm5vLuHHjiI2NdXdISinlsdySoESkGTASeB2o9iNWGGP48ssvWbt2LQ0aNGDChAmEhYW5OyyllPJo7qpB/Q14GQguaQMReQp4CiAiIqJyonKBzMxMFi5cyOHDh+nYsSOjRo3Cz8/P3WEppZTHq/QEJSKjgBRjzA4RGVjSdsaY6cB0gPj4eFM50VWsM2fOMG/ePC5fvqxdyJVS6i65owbVB3hQRO4HAoA6IvKRMeZxN8TiEsYYduzYwYoVK6hVqxZTp06lWbNm7g5LKaWqlEpPUMaYnwE/A7BrUC9Vp+SUlZXF0qVL2bdvH9HR0YwdO5agoCB3h6WUUlWOXgdVgc6cOcP8+fNJT09n8ODB9OnTR5v0lFKqjNyaoIwxCUCCO2OoCMYYtm7dyqpVq6hVqxZTpkyp0h07lFLKE2gNqpyysrJYsmQJBw4coE2bNowePVqb9JRSqgJogiqH06dPM3/+fC5fvszQoUPp1auXNukppVQF0QRVBsYYNm/ezOrVqwkODtZeekop5QKaoO7S9evXWbJkCYcOHaJdu3Y8+OCDBAYGujsspZSqdjRB3YWkpCQWLlxIZmYmI0aMoEePHtqkp5RSLqIJygl5eXmsWbOGTZs2ERYWxqRJkwgPD3d3WEopVa1pgrqDtLQ0FixYwLlz5+jWrRvDhw/H19fX3WEppVS1pwmqBMYYdu7cyYoVK/D19eWRRx6hXbt27g5LKaVqDE1Qxbh+/TqffvopBw8eJCoqijFjxhAcXOLA60oppVxAE1QRSUlJLFq0iGvXrum1TUop5UaaoGy5ubmsXbuWr776itDQUCZOnEjjxo3dHZZSStVYmqCAc+fOsXDhQlJSUujatSvDhw/XSQWVUsrNanSCys/PZ8OGDaxbt46goCAmTpxImzZt3B2WUkopanCCSktLY9GiRZw+fZrY2Fjuv/9+HeRVKaU8SI1LUAVTY6xevRpfX18efvhhOnTo4O6wlFJKFVGjElR6ejqLFy/m+PHjtG7dmgceeEC7jyullIeq9AQlIgHAesDfPv58Y8yvXHlMYwyJiYmsWLECgAceeIAuXbpo93GllPJg7qhBZQODjDFXRcQX2CAiy40xm111wEWLFrF7925atGjB6NGjqVevnqsOpZRSqoJUeoIyxhjgqn3X1/4zrjxmVFQU4eHh3HPPPVprUkqpKsIt56BExBvYAbQC3jLGbClmm6eApwAiIiLKdbzOnTuX6/FKKaUqn5c7DmqMyTPGxAHNgB4icls3OmPMdGNMvDEmvkGDBpUeo1JKKfdyS4IqYIxJBxKAEe6MQymllOep9AQlIg1EpK59OxAYAhys7DiUUkp5Nnecg2oMvG+fh/IC5hpjlrohDqWUUh7MHb34dgNdKvu4Simlqha3noNSSimlSqIJSimllEfSBKWUUsojiTWwg2cTkVTgRDl3EwakVUA47qRl8AxaBs+gZXC/ioq/hTHmtgteq0SCqggist0YE+/uOMpDy+AZtAyeQcvgfq6OX5v4lFJKeSRNUEoppTxSTUpQ090dQAXQMngGLYNn0DK4n0vjrzHnoJRSSlUtNakGpZRSqgrRBKWUUsojVdkEJSIzRCRFRPY6LOssIptEZI+IfCoidRzWdbLX7bPXB9jLu9n3j4jIP6QSp9y9mzKIiK+IvG8vPyAiP3N4jDvL0FxE1tox7ROR5+3l9UVklYgctv/Xc3jMz+xYD4nIcHeX427LICJDRWSHHesOERlU1crg8LgIEbkqIi9VxTJ42ue6DO8lj/pclxL/ePt+vojEF3mM6z7Pxpgq+Qf0B7oCex2WbQMG2LenAb+1b/sAu4HO9v1QwNu+vRXoBQiwHLjPQ8vwGDDbvh0EHAciPaAMjYGu9u1g4BsgBvgT8FN7+U+BP9q3Y4CvAX+gJXDU3a9FGcrQBWhi3+4AnHbYV5Uog8PjFgDzgJeqWhk88XNdhjJ41Oe6lPjbA22x5u+Ld9jepZ9nl79gLn4yI7n1y/0yNzt+NAf227fvBz4q4cU46HB/IvC2h5ZhIvCp/aEMtd849T2hDEXKsxgYChwCGjs8z4fs2z8Dfuaw/ef2m9hjynGnMhTZVoAL9ge0SpUBGAP8GXgNO0FVpTJ48uf6Lsrg0Z/rgvgd7idwa4Jy6ee5yjbxlWAv8KB9ezzWFzxAG8CIyOcislNEXraXNwWSHR6fbC9zp5LKMB+4BpwFTgJ/McZcxIPKICKRWLWLLUAjY8xZAPt/Q3uzpsAph4cVxOsR5XCyDI4eBnYZY7KpQmUQkVrAT4BfF3l4lSkDHv65drIMHvu5LhJ/SVz6eXbHhIWuNA34h4i8CiwBbtjLfYC+QHfgOrBGRHZg1VaKcne/+5LK0APIA5oA9YAvRWQ11i/4oiq9DCJSG6u56AVjzOVSmptLitft5biLMhRsHwv8ERhWsKiYzTy1DL8G/mqMuVpkm6pUBo/9XN9FGTzyc100/tI2LWZZhX2eq1WCMsYcxP6yEJE2wEh7VTKwzhiTZq/7DOvcz0dAM4ddNAPOVFrAxSilDI8BK4wxOUCKiGwE4oEvcXMZRMQX6838sTHmE3vxeRFpbIw5KyKNgRR7eTI3a4VwM95k3FiOuywDItIMWAhMNsYctRdXpTL0BMaJyJ+AukC+iGTZj68qZfDIz/VdlsHjPtclxF8Sl36eq1UTn4gUNF94Aa8A/7FXfQ50EpEgEfEBBmCd2zkLXBGRe+weJpOx2lzdppQynAQGiaUWcA9WG69by2Af813ggDHmTYdVS4An7NtPOMS0BHhURPxFpCXQGtjqznLcbRlEpC6wDKvtfWPBxlWpDMaYfsaYSGNMJPA34A1jzL+qUhnwwM91GcrgUZ/rUuIviWs/z5V90q0CT97Nwmq3zcHK1t8Gnsc6yfgN8Afszgb29o8D+7DO8fzJYXm8vewo8C/Hx3hSGYDaWL2t9gH7gR97SBn6YlXddwOJ9t/9WCd81wCH7f/1HR7zCzvWQzj07HFXOe62DFg/HK45bJsINKxKZSjy2Ne4tRdflSmDp32uy/Be8qjPdSnxP4T1HZUNnAc+d3iMyz7POtSRUkopj1StmviUUkpVH5qglFJKeSRNUEoppTySJiillFIeSROUUkopj6QJSikXE5FQEUm0/86JyGn79lUR+be741PKU2k3c6UqkYi8Blw1xvzF3bEo5em0BqWUm4jIQBFZat9+Tax5gVaKyHERGSsifxJrPp0V9vAzBXPsrBNrHqrP7WFzlKqWNEEp5TmiscZeHI01ntxaY0xHIBMYaSepfwLjjDHdgBnA6+4KVilXq1aDxSpVxS03xuSIyB7AG1hhL9+DNW9YW6wJElfZo2N7Yw2VpVS1pAlKKc+RDWCMyReRHHPzBHE+1mdVgH3GmF7uClCpyqRNfEpVHYeABiLSC6xpEew5qZSqljRBKVVFGGNuAOOAP4rI11gjTfd2a1BKuZB2M1dKKeWRtAallFLKI2mCUkop5ZE0QSmllPJImqCUUkp5JE1QSimlPJImKKWUUh5JE5RSSimP9P8BUsWYZ+r5wgIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_projections(table3)\n",
-    "results2.plot(color='gray', label='model')\n",
-    "decorate(title='Proportional model, linearly decreasing rate')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "laughing-cylinder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# If the net growth rate continues to decrease linearly,\n",
-    "# world population will peak around 2065 at about 9.8 billion,\n",
-    "# and then start to decline."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "personalized-parking",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2100, 12.58671867833247)"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results.idxmax(), results.max()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "simple-version",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap09.ipynb b/jupyter/chap09.ipynb
deleted file mode 100644
index 87d7b420..00000000
--- a/jupyter/chap09.ipynb
+++ /dev/null
@@ -1,1378 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "asian-customer",
-   "metadata": {},
-   "source": [
-    "# Chapter 9"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "gentle-purchase",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "appropriate-burns",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "biblical-murder",
-   "metadata": {},
-   "source": [
-    "In this chapter we express the models from previous chapters as\n",
-    "difference equations and differential equations, solve the equations,\n",
-    "and derive the functional forms of the solutions. We also discuss the\n",
-    "complementary roles of mathematical analysis and simulation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hybrid-retrieval",
-   "metadata": {},
-   "source": [
-    "## Recurrence relations\n",
-    "\n",
-    "The population models in the previous chapter and this one are simple\n",
-    "enough that we didn't really need to run simulations. We could have\n",
-    "solved them mathematically. For example, we wrote the constant growth\n",
-    "model like this:\n",
-    "\n",
-    "```\n",
-    "results[t+1] = results[t] + annual_growth\n",
-    "```\n",
-    "\n",
-    "In mathematical notation, we would write the same model like this:\n",
-    "\n",
-    "$$x_{n+1} = x_n + c$$ \n",
-    "\n",
-    "where $x_n$ is the population during year $n$,\n",
-    "$x_0$ is a given initial population, and $c$ is constant annual growth.\n",
-    "This way of representing the model is a **recurrence relation**; see\n",
-    "<http://modsimpy.com/recur>.\n",
-    "\n",
-    "Sometimes it is possible to solve a recurrence relation by writing an\n",
-    "equation that computes $x_n$, for a given value of $n$, directly; that\n",
-    "is, without computing the intervening values from $x_1$ through\n",
-    "$x_{n-1}$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "specified-mexican",
-   "metadata": {},
-   "source": [
-    "In the case of constant growth we can see that $x_1 = x_0 + c$, and\n",
-    "$x_2 = x_1 + c$. Combining these, we get $x_2 = x_0 + 2c$, then\n",
-    "$x_3 = x_0 + 3c$, and it is not hard to conclude that in general\n",
-    "\n",
-    "$$x_n = x_0 + nc$$ \n",
-    "\n",
-    "So if we want to know $x_{100}$ and we don't care\n",
-    "about the other values, we can compute it with one multiplication and\n",
-    "one addition."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "motivated-consumer",
-   "metadata": {},
-   "source": [
-    "We can also write the proportional model as a recurrence relation:\n",
-    "\n",
-    "$$x_{n+1} = x_n + \\alpha x_n$$ \n",
-    "\n",
-    "Or more conventionally as:\n",
-    "\n",
-    "$$x_{n+1} = x_n (1 + \\alpha)$$ \n",
-    "\n",
-    "Now we can see that\n",
-    "\n",
-    "$x_1 = x_0 (1 + \\alpha)$, and $x_2 = x_0 (1 + \\alpha)^2$, and in general\n",
-    "\n",
-    "$$x_n = x_0 (1 + \\alpha)^n$$ \n",
-    "\n",
-    "This result is a **geometric progression**;\n",
-    "see <http://modsimpy.com/geom>. When $\\alpha$ is positive, the factor\n",
-    "$1+\\alpha$ is greater than 1, so the elements of the sequence grow\n",
-    "without bound.\n",
-    "\n",
-    "Finally, we can write the quadratic model like this:\n",
-    "\n",
-    "$$x_{n+1} = x_n + \\alpha x_n + \\beta x_n^2$$ \n",
-    "\n",
-    "or with the more\n",
-    "conventional parameterization like this:\n",
-    "\n",
-    "$$x_{n+1} = x_n + r x_n (1 - x_n / K)$$ \n",
-    "\n",
-    "There is no analytic solution to\n",
-    "this equation, but we can approximate it with a differential equation\n",
-    "and solve that, which is what we'll do in the next section."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "decreased-location",
-   "metadata": {},
-   "source": [
-    "## Differential equations\n",
-    "\n",
-    "Starting again with the constant growth model \n",
-    "\n",
-    "$$x_{n+1} = x_n + c$$ \n",
-    "\n",
-    "If we define $\\Delta x$ to be the change in $x$ from one time step to the next, we can write: \n",
-    "\n",
-    "$$\\Delta x = x_{n+1} - x_n = c$$ \n",
-    "\n",
-    "If we define\n",
-    "$\\Delta t$ to be the time step, which is one year in the example, we can\n",
-    "write the rate of change per unit of time like this:\n",
-    "\n",
-    "$$\\frac{\\Delta x}{\\Delta t} = c$$ \n",
-    "\n",
-    "This model is **discrete**, which\n",
-    "means it is only defined at integer values of $n$ and not in between.\n",
-    "But in reality, people are born and die all the time, not once a year,\n",
-    "so a **continuous** model might be more realistic."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "covered-defense",
-   "metadata": {},
-   "source": [
-    "We can make this model continuous by writing the rate of change in the\n",
-    "form of a derivative: \n",
-    "\n",
-    "$$\\frac{dx}{dt} = c$$ \n",
-    "\n",
-    "This way of representing the model is a **differential equation**; see <http://modsimpy.com/diffeq>.\n",
-    "\n",
-    "We can solve this differential equation if we multiply both sides by\n",
-    "$dt$: \n",
-    "\n",
-    "$$dx = c dt$$ \n",
-    "\n",
-    "And then integrate both sides: \n",
-    "\n",
-    "$$x(t) = c t + x_0$$\n",
-    "\n",
-    "Similarly, we can write the proportional growth model like this:\n",
-    "\n",
-    "$$\\frac{\\Delta x}{\\Delta t} = \\alpha x$$ \n",
-    "\n",
-    "And as a differential equation\n",
-    "like this: \n",
-    "\n",
-    "$$\\frac{dx}{dt} = \\alpha x$$ \n",
-    "\n",
-    "If we multiply both sides by\n",
-    "$dt$ and divide by $x$, we get \n",
-    "\n",
-    "$$\\frac{1}{x}~dx = \\alpha~dt$$ \n",
-    "\n",
-    "Now we\n",
-    "integrate both sides, yielding: \n",
-    "\n",
-    "$$\\ln x = \\alpha t + K$$ \n",
-    "\n",
-    "where $\\ln$ is the natural logarithm and $K$ is the constant of integration."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "underlying-visitor",
-   "metadata": {},
-   "source": [
-    "Exponentiating both sides, we have\n",
-    "\n",
-    "$$\\exp(\\ln(x)) = \\exp(\\alpha t + K)$$ \n",
-    "\n",
-    "The exponential function can be written $\\exp(x)$ or $e^x$. In this book I use the first form because it resembles the Python code.\n",
-    "\n",
-    "We can rewrite the previous equation as\n",
-    "\n",
-    "$$x = \\exp(\\alpha t) \\exp(K)$$ \n",
-    "\n",
-    "Since $K$ is an arbitrary constant,\n",
-    "$\\exp(K)$ is also an arbitrary constant, so we can write\n",
-    "\n",
-    "$$x = C \\exp(\\alpha t)$$ \n",
-    "\n",
-    "where $C = \\exp(K)$. There are many solutions\n",
-    "to this differential equation, with different values of $C$. The\n",
-    "particular solution we want is the one that has the value $x_0$ when\n",
-    "$t=0$.\n",
-    "\n",
-    "When $t=0$, $x(t) = C$, so $C = x_0$ and the solution we want is\n",
-    "$$x(t) = x_0 \\exp(\\alpha t)$$ If you would like to see this derivation\n",
-    "done more carefully, you might like this video:\n",
-    "<http://modsimpy.com/khan1>."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "czech-scratch",
-   "metadata": {},
-   "source": [
-    "## Analysis and simulation\n",
-    "\n",
-    "Once you have designed a model, there are generally two ways to proceed: simulation and analysis. Simulation often comes in the form of a computer program that models changes in a system over time, like births and deaths, or bikes moving from place to place. Analysis often comes in the form of algebra; that is, symbolic manipulation using mathematical notation.\n",
-    "\n",
-    "Analysis and simulation have different capabilities and limitations.\n",
-    "Simulation is generally more versatile; it is easy to add and remove\n",
-    "parts of a program and test many versions of a model, as we have done in the previous examples.\n",
-    "\n",
-    "But there are several things we can do with analysis that are harder or impossible with simulations:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "embedded-miniature",
-   "metadata": {},
-   "source": [
-    "-   With analysis we can sometimes compute, exactly and efficiently, a\n",
-    "    value that we could only approximate, less efficiently, with\n",
-    "    simulation. For example, in the quadratic model we plotted growth rate versus population and saw net crossed through zero when the population is\n",
-    "    near 14 billion.  We could estimate the crossing point using a\n",
-    "    numerical search algorithm (more about that later). But with the\n",
-    "    analysis in Section xxx, we get the general result that\n",
-    "    $K=-\\alpha/\\beta$.\n",
-    "\n",
-    "-   Analysis often provides \"computational shortcuts\", that is, the\n",
-    "    ability to jump forward in time to compute the state of a system\n",
-    "    many time steps in the future without computing the intervening\n",
-    "    states.\n",
-    "\n",
-    "-   We can use analysis to state and prove generalizations about models;\n",
-    "    for example, we might prove that certain results will always or\n",
-    "    never occur. With simulations, we can show examples and sometimes\n",
-    "    find counterexamples, but it is hard to write proofs.\n",
-    "\n",
-    "-   Analysis can provide insight into models and the systems they\n",
-    "    describe; for example, sometimes we can identify regimes of\n",
-    "    qualitatively different behavior and key parameters that control\n",
-    "    those behaviors."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "impressive-planning",
-   "metadata": {},
-   "source": [
-    "When people see what analysis can do, they sometimes get drunk with\n",
-    "power, and imagine that it gives them a special ability to see past the veil of the material world and discern the laws of mathematics that govern the universe. When they analyze a model of a physical system, they talk about \"the math behind it\" as if our world is the mere shadow of a world of ideal mathematical entities (I am not making this up; see <http://modsimpy.com/plato>.).\n",
-    "\n",
-    "This is, of course, nonsense. Mathematical notation is a language\n",
-    "designed by humans for a purpose, specifically to facilitate symbolic\n",
-    "manipulations like algebra. Similarly, programming languages are\n",
-    "designed for a purpose, specifically to represent computational ideas\n",
-    "and run programs.\n",
-    "\n",
-    "Each of these languages is good for the purposes it was designed for and less good for other purposes. But they are often complementary, and one of the goals of this book is to show how they can be used together."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "graduate-conducting",
-   "metadata": {},
-   "source": [
-    "## Analysis with WolframAlpha\n",
-    "\n",
-    "Until recently, most analysis was done by rubbing graphite on wood\n",
-    "pulp, a process that is laborious and error-prone. A useful\n",
-    "alternative is symbolic computation. If you have used services like\n",
-    "WolframAlpha, you have used symbolic computation.\n",
-    "\n",
-    "For example, if you go to <https://www.wolframalpha.com/> and type\n",
-    "\n",
-    "```\n",
-    "df(t) / dt = alpha f(t)\n",
-    "```\n",
-    "\n",
-    "WolframAlpha infers that `f(t)` is a function of `t` and `alpha` is a\n",
-    "parameter; it classifies the query as a \"first-order linear ordinary\n",
-    "differential equation\\\", and reports the general solution:\n",
-    "\n",
-    "$$f(t) = c_1 \\exp(\\alpha t)$$ \n",
-    "\n",
-    "If you add a second equation to specify\n",
-    "the initial condition:\n",
-    "\n",
-    "```\n",
-    "df(t) / dt = alpha f(t),  f(0) = p_0\n",
-    "```\n",
-    "\n",
-    "WolframAlpha reports the particular solution:\n",
-    "\n",
-    "$$f(t) = p_0 \\exp(\\alpha t)$$\n",
-    "\n",
-    "WolframAlpha is based on Mathematica, a powerful programming language\n",
-    "designed specifically for symbolic computation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "beginning-tamil",
-   "metadata": {},
-   "source": [
-    "## Analysis with SymPy\n",
-    "\n",
-    "Python has a library called SymPy that provides symbolic computation\n",
-    "tools similar to Mathematica. They are not as easy to use as\n",
-    "WolframAlpha, but they have some other advantages.\n",
-    "\n",
-    "Before we can use SymPy, we have to import it:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "civic-payday",
-   "metadata": {},
-   "source": [
-    "SymPy defines a `Symbol` object that represents symbolic variable names,\n",
-    "functions, and other mathematical entities.\n",
-    "\n",
-    "The `symbols` function takes a string and returns `Symbol` objects. So\n",
-    "if we run this assignment:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "flush-balance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sympy import symbols\n",
-    "\n",
-    "t = symbols('t')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "faced-praise",
-   "metadata": {},
-   "source": [
-    "Python understands that `t` is a symbol, not a numerical value. If we\n",
-    "now run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ancient-anderson",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle t + 1$"
-      ],
-      "text/plain": [
-       "t + 1"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expr = t + 1\n",
-    "expr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "industrial-confidence",
-   "metadata": {},
-   "source": [
-    "Python doesn't try to perform numerical addition; rather, it creates a\n",
-    "new `Symbol` that represents the sum of `t` and `1`. We can evaluate the\n",
-    "sum using `subs`, which substitutes a value for a symbol. This example\n",
-    "substitutes 2 for `t`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "familiar-oakland",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle 3$"
-      ],
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expr.subs(t, 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "progressive-celebration",
-   "metadata": {},
-   "source": [
-    "Functions in SymPy are represented by a special kind of `Symbol`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "incomplete-sleep",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "f"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sympy import Function\n",
-    "\n",
-    "f = Function('f')\n",
-    "f"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "legislative-dispatch",
-   "metadata": {},
-   "source": [
-    "Now if we write `f(t)`, we get an object that represents the evaluation of a function, $f$, at a value, $t$. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "inside-sally",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle f{\\left(t \\right)}$"
-      ],
-      "text/plain": [
-       "f(t)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f(t)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "interior-index",
-   "metadata": {},
-   "source": [
-    "But again SymPy doesn't actually\n",
-    "try to evaluate it."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "indian-trout",
-   "metadata": {},
-   "source": [
-    "## Differential equations in SymPy\n",
-    "\n",
-    "SymPy provides a function, `diff`, that can differentiate a function. We can apply it to `f(t)` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "requested-program",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)}$"
-      ],
-      "text/plain": [
-       "Derivative(f(t), t)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sympy import diff\n",
-    "\n",
-    "dfdt = diff(f(t), t)\n",
-    "dfdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "treated-commissioner",
-   "metadata": {},
-   "source": [
-    "The result is a `Symbol` that represents the derivative of `f` with\n",
-    "respect to `t`. But again, SymPy doesn't try to compute the derivative\n",
-    "yet.\n",
-    "\n",
-    "To represent a differential equation, we use `Eq`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "incomplete-parker",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)} = \\alpha f{\\left(t \\right)}$"
-      ],
-      "text/plain": [
-       "Eq(Derivative(f(t), t), alpha*f(t))"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sympy import Eq\n",
-    "\n",
-    "alpha = symbols('alpha')\n",
-    "eq1 = Eq(dfdt, alpha*f(t))\n",
-    "eq1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "concerned-asthma",
-   "metadata": {},
-   "source": [
-    "The result is an object that represents an equation.  Now\n",
-    "we can use `dsolve` to solve this differential equation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "described-overhead",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle f{\\left(t \\right)} = C_{1} e^{\\alpha t}$"
-      ],
-      "text/plain": [
-       "Eq(f(t), C1*exp(alpha*t))"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sympy import dsolve\n",
-    "\n",
-    "solution_eq = dsolve(eq1)\n",
-    "solution_eq"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "inside-medicine",
-   "metadata": {},
-   "source": [
-    "The result is the **general\n",
-    "solution**, which still contains an unspecified constant, $C_1$. To get the **particular solution** where $f(0) = p_0$, we substitute `p_0` for `C1`. First, we have to create two more symbols:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "material-voice",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "C1, p_0 = symbols('C1 p_0')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "serious-license",
-   "metadata": {},
-   "source": [
-    "Now we can perform the substitution:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "supposed-domestic",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle f{\\left(t \\right)} = p_{0} e^{\\alpha t}$"
-      ],
-      "text/plain": [
-       "Eq(f(t), p_0*exp(alpha*t))"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "particular = solution_eq.subs(C1, p_0)\n",
-    "particular"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "modern-raleigh",
-   "metadata": {},
-   "source": [
-    "The result is called the **exponential growth curve**; see\n",
-    "<http://modsimpy.com/expo>."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "negative-chance",
-   "metadata": {},
-   "source": [
-    "## Solving the quadratic growth model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "arabic-victorian",
-   "metadata": {},
-   "source": [
-    "We'll use the (r, K) parameterization, so we'll need two more symbols:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "political-chinese",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r, K = symbols('r K')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "virtual-temperature",
-   "metadata": {},
-   "source": [
-    "Now we can write the differential equation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "lined-queen",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)} = r \\left(1 - \\frac{f{\\left(t \\right)}}{K}\\right) f{\\left(t \\right)}$"
-      ],
-      "text/plain": [
-       "Eq(Derivative(f(t), t), r*(1 - f(t)/K)*f(t))"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eq2 = Eq(diff(f(t), t), r * f(t) * (1 - f(t)/K))\n",
-    "eq2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "relative-zoning",
-   "metadata": {},
-   "source": [
-    "And solve it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "printable-typing",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle f{\\left(t \\right)} = \\frac{K e^{C_{1} K + r t}}{e^{C_{1} K + r t} - 1}$"
-      ],
-      "text/plain": [
-       "Eq(f(t), K*exp(C1*K + r*t)/(exp(C1*K + r*t) - 1))"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "solution_eq = dsolve(eq2)\n",
-    "solution_eq"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "convertible-simple",
-   "metadata": {},
-   "source": [
-    "The result, `solution_eq`, contains `rhs`, which is the right-hand side of the solution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "final-treatment",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\frac{K e^{C_{1} K + r t}}{e^{C_{1} K + r t} - 1}$"
-      ],
-      "text/plain": [
-       "K*exp(C1*K + r*t)/(exp(C1*K + r*t) - 1)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "general = solution_eq.rhs\n",
-    "general"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "phantom-melbourne",
-   "metadata": {},
-   "source": [
-    "We can evaluate the right-hand side at $t=0$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "freelance-admission",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\frac{K e^{C_{1} K}}{e^{C_{1} K} - 1}$"
-      ],
-      "text/plain": [
-       "K*exp(C1*K)/(exp(C1*K) - 1)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "at_0 = general.subs(t, 0)\n",
-    "at_0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "precise-radar",
-   "metadata": {},
-   "source": [
-    "Now we want to find the value of `C1` that makes `f(0) = p_0`.\n",
-    "\n",
-    "So we'll create the equation `at_0 = p_0` and solve for `C1`.  Because this is just an algebraic identity, not a differential equation, we use `solve`, not `dsolve`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "orange-glasgow",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sympy import solve\n",
-    "\n",
-    "solutions = solve(Eq(at_0, p_0), C1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "periodic-bikini",
-   "metadata": {},
-   "source": [
-    "The result from `solve` is a list of solutions.  In this case, [we have reason to expect only one solution](https://en.wikipedia.org/wiki/Picard%E2%80%93Lindel%C3%B6f_theorem), but we still get a list, so we have to use the bracket operator, `[0]`, to select the first one."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "expensive-palace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(list, 1)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(solutions), len(solutions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "worthy-sleeve",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\frac{\\log{\\left(- \\frac{p_{0}}{K - p_{0}} \\right)}}{K}$"
-      ],
-      "text/plain": [
-       "log(-p_0/(K - p_0))/K"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "value_of_C1 = solutions[0]\n",
-    "value_of_C1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "lightweight-pickup",
-   "metadata": {},
-   "source": [
-    "Now in the general solution, we want to replace `C1` with the value of `C1` we just figured out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "aggressive-queue",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle - \\frac{K p_{0} e^{r t}}{\\left(K - p_{0}\\right) \\left(- \\frac{p_{0} e^{r t}}{K - p_{0}} - 1\\right)}$"
-      ],
-      "text/plain": [
-       "-K*p_0*exp(r*t)/((K - p_0)*(-p_0*exp(r*t)/(K - p_0) - 1))"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "particular = general.subs(C1, value_of_C1)\n",
-    "particular"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "electric-albania",
-   "metadata": {},
-   "source": [
-    "The result is complicated, but SymPy provides a function that tries to simplify it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "funded-tolerance",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\frac{K p_{0} e^{r t}}{K + p_{0} e^{r t} - p_{0}}$"
-      ],
-      "text/plain": [
-       "K*p_0*exp(r*t)/(K + p_0*exp(r*t) - p_0)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "particular.simplify()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "scenic-preparation",
-   "metadata": {},
-   "source": [
-    "This function is called the **logistic growth curve**; see\n",
-    "<http://modsimpy.com/logistic>. In the context of growth models, the\n",
-    "logistic function is often written like this:\n",
-    "\n",
-    "$$f(t) = \\frac{K}{1 + A \\exp(-rt)}$$ \n",
-    "\n",
-    "where $A = (K - p_0) / p_0$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "burning-sherman",
-   "metadata": {},
-   "source": [
-    "We can use SymPy to confirm that these two forms are equivalent.  First we represent the alternative version of the logistic function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "institutional-finish",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\frac{K - p_{0}}{p_{0}}$"
-      ],
-      "text/plain": [
-       "(K - p_0)/p_0"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A = (K - p_0) / p_0\n",
-    "A"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "noble-auditor",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\frac{K}{1 + \\frac{\\left(K - p_{0}\\right) e^{- r t}}{p_{0}}}$"
-      ],
-      "text/plain": [
-       "K/(1 + (K - p_0)*exp(-r*t)/p_0)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sympy import exp\n",
-    "\n",
-    "logistic = K / (1 + A * exp(-r*t))\n",
-    "logistic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "brave-conditions",
-   "metadata": {},
-   "source": [
-    "To see whether two expressions are equivalent, we can check whether their difference simplifies to 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "industrial-administrator",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle 0$"
-      ],
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(particular - logistic).simplify()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "therapeutic-topic",
-   "metadata": {},
-   "source": [
-    "This test only works one way: if SymPy says the difference reduces to 0, the expressions are definitely equivalent (and not just numerically close).\n",
-    "\n",
-    "But if SymPy can't find a way to simplify the result to 0, that doesn't necessarily mean there isn't one.  Testing whether two expressions are equivalent is a surprisingly hard problem; in fact, there is no algorithm that can solve it in general."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cutting-access",
-   "metadata": {},
-   "source": [
-    "If you would like to see this differential equation solved by hand, you might like this video: <http://modsimpy.com/khan2>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "outstanding-launch",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "In this chapter we wrote the growth models from the previous chapters in terms of difference and differential equations.\n",
-    "\n",
-    "What I called the constant growth model is more commonly called **linear growth** because the solution is a line.  If we model time as continuous, the solution is\n",
-    "\n",
-    "$$f(t) = p_0 + \\alpha t$$\n",
-    "\n",
-    "where $\\alpha$ is the growth rate.\n",
-    "\n",
-    "Similarly, the proportional growth model is usually called **exponential growth** because the solution is exponential:\n",
-    "\n",
-    "$$f(t) = p_0 \\exp{\\alpha t}$$\n",
-    "\n",
-    "Finally, the quadratic growth model is called **logistic growth** because the solution is the logistic function:\n",
-    "\n",
-    "$$f(t) = \\frac{K}{1 + A \\exp(-rt)}$$ \n",
-    "\n",
-    "where $A = (K - p_0) / p_0$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adjacent-tracy",
-   "metadata": {},
-   "source": [
-    "I avoided these terms until now because they are based on results we had not derived yet."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "offshore-passage",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ideal-ecology",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Solve the quadratic growth equation using the alternative parameterization\n",
-    "\n",
-    "$\\frac{df(t)}{dt} = \\alpha f(t) + \\beta f^2(t) $"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "internal-knock",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "alpha, beta = symbols('alpha beta')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "smooth-sunglasses",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)} = \\alpha f{\\left(t \\right)} + \\beta f^{2}{\\left(t \\right)}$"
-      ],
-      "text/plain": [
-       "Eq(Derivative(f(t), t), alpha*f(t) + beta*f(t)**2)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "eq3 = Eq(diff(f(t), t), alpha*f(t) + beta*f(t)**2)\n",
-    "eq3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "fundamental-arlington",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle f{\\left(t \\right)} = \\frac{\\alpha e^{\\alpha \\left(C_{1} + t\\right)}}{\\beta \\left(1 - e^{\\alpha \\left(C_{1} + t\\right)}\\right)}$"
-      ],
-      "text/plain": [
-       "Eq(f(t), alpha*exp(alpha*(C1 + t))/(beta*(1 - exp(alpha*(C1 + t)))))"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "solution_eq = dsolve(eq3)\n",
-    "solution_eq"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "desirable-alpha",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\frac{\\alpha e^{\\alpha \\left(C_{1} + t\\right)}}{\\beta \\left(1 - e^{\\alpha \\left(C_{1} + t\\right)}\\right)}$"
-      ],
-      "text/plain": [
-       "alpha*exp(alpha*(C1 + t))/(beta*(1 - exp(alpha*(C1 + t))))"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "general = solution_eq.rhs\n",
-    "general"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "coupled-grounds",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "at_0 = general.subs(t, 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "looking-ground",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\frac{\\log{\\left(\\frac{\\beta p_{0}}{\\alpha + \\beta p_{0}} \\right)}}{\\alpha}$"
-      ],
-      "text/plain": [
-       "log(beta*p_0/(alpha + beta*p_0))/alpha"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "solutions = solve(Eq(at_0, p_0), C1)\n",
-    "value_of_C1 = solutions[0]\n",
-    "value_of_C1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "anticipated-paragraph",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\frac{\\alpha p_{0} e^{\\alpha t}}{\\alpha - \\beta p_{0} e^{\\alpha t} + \\beta p_{0}}$"
-      ],
-      "text/plain": [
-       "alpha*p_0*exp(alpha*t)/(alpha - beta*p_0*exp(alpha*t) + beta*p_0)"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "particular = general.subs(C1, value_of_C1)\n",
-    "particular.simplify()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "residential-spanking",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Use [WolframAlpha](https://www.wolframalpha.com/) to solve the quadratic growth model, using either or both forms of parameterization:\n",
-    "\n",
-    "    df(t) / dt = alpha f(t) + beta f(t)^2\n",
-    "\n",
-    "or\n",
-    "\n",
-    "    df(t) / dt = r f(t) (1 - f(t)/K)\n",
-    "\n",
-    "Find the general solution and also the particular solution where `f(0) = p_0`."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap10.ipynb b/jupyter/chap10.ipynb
deleted file mode 100644
index e404b395..00000000
--- a/jupyter/chap10.ipynb
+++ /dev/null
@@ -1,656 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "dense-storm",
-   "metadata": {},
-   "source": [
-    "# Chapter 10"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "confused-bargain",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "inner-scott",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "thirty-medication",
-   "metadata": {},
-   "source": [
-    "# Case studies\n",
-    "\n",
-    "This chapter presents case studies where you can practice the tools we have learned so far."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "industrial-mercy",
-   "metadata": {},
-   "source": [
-    "## Prehistoric World Population\n",
-    "\n",
-    "On the Wikipedia page about world population estimates, the first table contains estimates for prehistoric populations.  The following cells process this table and plot some of the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "usual-penguin",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates.html'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "thick-lincoln",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from pandas import read_html\n",
-    "\n",
-    "filename = 'World_population_estimates.html'\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "len(tables)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "disciplinary-chosen",
-   "metadata": {},
-   "source": [
-    "Select `tables[1]`, which is the second table on the page."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "conscious-orange",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Population Reference Bureau (1973–2016)[15]</th>\n",
-       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
-       "      <th>Maddison (2008)[17]</th>\n",
-       "      <th>HYDE (2010)[citation needed]</th>\n",
-       "      <th>Tanton (1994)[18]</th>\n",
-       "      <th>Biraben (1980)[19]</th>\n",
-       "      <th>McEvedy &amp; Jones (1978)[20]</th>\n",
-       "      <th>Thomlinson (1975)[21]</th>\n",
-       "      <th>Durand (1974)[22]</th>\n",
-       "      <th>Clark (1967)[23]</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>-10000</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2M[24]</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1–10M</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-9000</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-8000</th>\n",
-       "      <td>5.</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5–10M</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-7000</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8.</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-6000</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>11.</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Population Reference Bureau (1973–2016)[15]  \\\n",
-       "Year                                                 \n",
-       "-10000                                         NaN   \n",
-       "-9000                                          NaN   \n",
-       "-8000                                           5.   \n",
-       "-7000                                          NaN   \n",
-       "-6000                                          NaN   \n",
-       "\n",
-       "        United Nations Department of Economic and Social Affairs (2015)[16]  \\\n",
-       "Year                                                                          \n",
-       "-10000                                                NaN                     \n",
-       "-9000                                                 NaN                     \n",
-       "-8000                                                 NaN                     \n",
-       "-7000                                                 NaN                     \n",
-       "-6000                                                 NaN                     \n",
-       "\n",
-       "       Maddison (2008)[17] HYDE (2010)[citation needed]  Tanton (1994)[18]  \\\n",
-       "Year                                                                         \n",
-       "-10000                 NaN                       2M[24]                NaN   \n",
-       "-9000                  NaN                           4.                NaN   \n",
-       "-8000                  NaN                           5.                NaN   \n",
-       "-7000                  NaN                           8.                NaN   \n",
-       "-6000                  NaN                          11.                NaN   \n",
-       "\n",
-       "        Biraben (1980)[19]  McEvedy & Jones (1978)[20] Thomlinson (1975)[21]  \\\n",
-       "Year                                                                           \n",
-       "-10000                 NaN                         4.0                 1–10M   \n",
-       "-9000                  NaN                         NaN                   NaN   \n",
-       "-8000                  NaN                         NaN                   NaN   \n",
-       "-7000                  NaN                         NaN                   NaN   \n",
-       "-6000                  NaN                         NaN                   NaN   \n",
-       "\n",
-       "       Durand (1974)[22] Clark (1967)[23]  \n",
-       "Year                                       \n",
-       "-10000               NaN              NaN  \n",
-       "-9000                NaN              NaN  \n",
-       "-8000              5–10M              NaN  \n",
-       "-7000                NaN              NaN  \n",
-       "-6000                NaN              NaN  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table1 = tables[1]\n",
-    "table1.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hungry-monster",
-   "metadata": {},
-   "source": [
-    "Not all researchers provide estimates for the same dates.\n",
-    "\n",
-    "Again, we'll replace the long column names with more convenient abbreviations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "thick-blanket",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table1.columns = ['PRB', 'UN', 'Maddison', 'HYDE', 'Tanton', \n",
-    "                  'Biraben', 'McEvedy & Jones', 'Thomlinson', 'Durand', 'Clark']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "substantial-implement",
-   "metadata": {},
-   "source": [
-    "Some of the estimates are in a form Pandas doesn't recognize as numbers, but we can coerce them to be numeric."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "ranking-prescription",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in table1.columns:\n",
-    "    table1[col] = pd.to_numeric(table1[col], errors='coerce')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "amazing-difference",
-   "metadata": {},
-   "source": [
-    "Here are the results.  Notice that we are working in millions now, not billions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "metallic-offense",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "table1.plot()\n",
-    "decorate(xlim=[-10000, 2000], xlabel='Year', \n",
-    "         ylabel='World population (millions)',\n",
-    "         title='Prehistoric population estimates')\n",
-    "plt.legend(fontsize='small');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "human-conservation",
-   "metadata": {},
-   "source": [
-    "We can use `xlim` to zoom in on everything after Year 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "solar-action",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "table1.plot()\n",
-    "decorate(xlim=[0, 2000], xlabel='Year', \n",
-    "         ylabel='World population (millions)',\n",
-    "         title='CE population estimates')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "wanted-fantasy",
-   "metadata": {},
-   "source": [
-    "See if you can find a model that fits these data well from Year 0 to 1950.\n",
-    "\n",
-    "How well does your best model predict actual population growth from 1950 to the present?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "together-jackson",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# The function I found that best matches the data has the form\n",
-    "# a + b / (c - x)\n",
-    "\n",
-    "# This function is hard to explain physically; that is, it doesn't\n",
-    "# correspond to a growth model that makes sense in terms of human \n",
-    "# behavior.\n",
-    "\n",
-    "# And it implies that the population goes to infinity in 2040.\n",
-    "\n",
-    "from numpy import linspace\n",
-    "from matplotlib.pyplot import plot\n",
-    "\n",
-    "xs = linspace(100, 1950)\n",
-    "ys = 110 + 200000 / (2040 - xs)\n",
-    "table1.plot()\n",
-    "plot(xs, ys, color='gray', alpha=0.5, label='model')\n",
-    "\n",
-    "decorate(xlim=[0, 2000], xlabel='Year', \n",
-    "         ylabel='World population (millions)',\n",
-    "         title='CE population estimates')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "simple-verse",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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GA/+3fUIUEZHuZt++faxZs4YZM2YwceLEJvc77Y26YVL6RcTnvegalIiItEJNTQ3Lly+nX79+XHHFFc3uG80ovk+a2Q4zKzezCjM7bGYV7RatiIh0G2vWrGH//v0sXLiQvn37NrtvNFMd/QxYFD4JV0REpFX279/P6tWrmT59OpMmTTrt/tFcg9qv5CQiIm1RN2qvb9++p+3aqxNNC2q9mf0RWE4wWAIAd1/WqihFRKTbefXVVykqKuLGG2+kX79+UR0TTYJKAY4Bl0Wsc6DVCcrMvgp8NjzPe8Bd7l7Z2vOJiEji+uCDD1i7di1z5sxh8uTJUR8XzSi+u9oUWQNmNhr4EjDF3Y+b2ZPAzcCD7VmOiIjEX3l5OcuXL2fEiBFRd+3ViWYUX4aZPWNmxWa238yeNrOMVkcbSAL6mlkS0A8obOP5REQkwdTU1LB06VJqamq44YYbSEqK6hGE9aIZJPEA8BzBAwtHA8+H61rF3T8Efg7sBYqAcnf/S8P9zOxeM1tvZusPHDjQ2uJERCROVqxYQUFBAddeey1Dhgxp8fHRJKh0d3/A3avD14NAeotLCoVP570WGEuQ9Pqb2W0N93P3xe6e4+456emtLk5EROJg27ZtvPnmm5x99tlMmTKlVeeIJkEdDB+z0TN83QaUtKq0wCXAHnc/4O5VBIMtPtaG84mISAI5dOgQzz77LKNGjeLSS5t8tOBpRZOg7gZuBPYRdMldH65rrb3AOWbWz8wMWADoPisRkS6gurqap556CjPj+uuvb/F1p0jRjOLbC1zT6hI+er63zGwp8A5QDWwEFrfX+UVEJH7+8pe/UFRUxE033URqamqbztVkgjKzb7r7z8zs1wT3K53C3b/U2kLd/T7gvtYeLyIiiWfr1q28/fbbnHvuuVFNZXQ6zbWg6rrd1re5FBER6dJKSkp47rnnyMjIYMGCBe1yziYTlLs/H74/1C4liYhIl1RVVcVTTz1Fz549uf766+nZs2e7nLe5Lr7naaRrr467t9t1KRER6ZzcnRdeeIH9+/fz6U9/mkGDBrXbuZvr4vt5u5UiIiJd0po1a9i8eTPz589n/Pjx7Xru5rr4VrVrSSIi0qVs2bKFv/71r0yfPp358+e3+/mb6+J7j+a7+Ga0ezQiItIp5Ofns3z5crKysrjmmmsIbmttX8118V3d7qWJiEind+jQIZ544glSUlK46aab2nQzbnOa6+LLi0mJIiLSaVVWVvLYY49RW1vLrbfeGvXDB1ujyamOzGxN+H7YzCoavscsIhERSUg1NTU8+eSTlJaWctNNN5GWlhbT8pprQZ0fvg+MaQQiIpLw6oaT79mzh2uvvZbs7OyYlxlVx6GZnQWcTzBoYo27b4xpVCIiklDWrl3Lxo0bueCCC5g1a1aHlBnNE3W/AzwEpAFDgQfN7N9iHZiIiCSG999/n5UrVzJ16lQ+/vGPd1i50bSgbgFmu3slgJn9hGAm8h/EMjAREYm/goICnnnmGTIyMrjuuutiMpy8KdE8DyoX6BPxuTewKybRiIhIwjh48CCPP/44AwYM4Oabb47ZcPKmNHejbt1jNk4AW83slfDzpcCajglPRETiobS0lIcffhgz47bbbqN///4dHkNz6bDuMRsbgGci1r8Ws2hERCTuysvLefjhh6murubOO++M+XDypjQ3zDwmj9kws4nAHyNWjQO+4+6/jEV5IiISvcOHD/Pwww9TWVnJHXfcwbBhw+IWS3M36j5vZovMLLmRbePM7HtmdndLC3T37e4+y91nAXOAY5zaQhMRkTg4evQojzzyCIcPH+bWW29l5MiRcY2nuS6+zwFfA35pZqXAAYLBEmOBncBv3P3ZNpa/ANilaZVEROLr+PHjLFmyhEOHDnHrrbeSmZkZ75Ca7eLbB3wT+KaZZQMjgePAB+5+rJ3Kvxl4vLENZnYvcC9AVlZWOxUnIiINnThxgkcffZQDBw5w8803d8gsEdEw9yafqBHbgs16AYXAVHff39y+OTk5vn79+uZ2ERGRVqiqqmLJkiXk5+dz4403MmnSpA6Pwcw2uHtOw/XR3AcVK1cC75wuOYmISGxUV1fzxBNPsHfvXj75yU/GJTk1J54J6haa6N4TEZHYqqmp4amnnmL37t1cc801TJs2Ld4hfURcEpSZ9SO44XdZPMoXEenOqqqqeOKJJ/jggw+46qqrmD17drxDatRp560ws/OA+4Ex4f4GuLuPa22h4SCL+Nz5JSLSjdU9cDA/P59FixZx1llnxTukJkUzsdLvga8SzChRE9twREQkVo4cOcKjjz5KcXEx119/PVOnTo13SM2KJkGVu/tLMY9ERERipm76ooqKCm655RbOPPPMeId0WtEkqFfN7P8SXC86UbfS3d+JWVQiItJuDh48yCOPPMKJEye4/fbbO829pdEkqHnhe+QYdQcubv9wRESkPRUVFbFkyRLMjDvvvJMRI0bEO6SonTZBuXvHPT5RRETaTV5eHo8//jh9+vTh9ttvj9us5K0VzSi+QcB9wIXhqlXA99y9PJaBiYhI6+3YsYMnn3ySwYMHc/vtt5OSkhLvkFosmvug/gAcBm4MXxXAA7EMSkREWu/dd9/liSeeID09nbvuuqtTJieI7hrUGe7+qYjP3zWzTTGKR0REWsndefXVV/nb3/5GdnY2N998M7179453WK0WTYI6bmbnu/saqL9x93hswxIRkZaoqqpi+fLlvP/++8yePZuFCxfSs2fPeIfVJtEkqH8GHgqvRRlQCtwZy6BERCR6hw8f5oknnqCwsJDLLruMc845BzOLd1htFs0ovk3ATDNLCT9XxDooERGJTlFREY8//jgnTpzg5ptvZuLEifEOqd00maDM7DZ3X2JmX2uwHgB3/0WMYxMRkWZs27aNZ555hn79+nH33XczfPjweIfUrpprQfUP3wc2si0+TzkUERHcnbVr17Jy5UoyMjK46aabGDBgQLzDanfNPfL9t+HiCndfG7ktHCghIiIdrLq6mj/96U9s3ryZadOmce2115KUFM1wgs4nmlr9Gmg4H3tj60REJIYqKipYunQp+fn5XHTRRVx44YVdYjBEU5q7BnUu8DEgvcF1qBSgc49dFBHpZHbt2sWyZcuoqqrqFI/KaA/NtaB6AQPCfSKvQ1UA17elUDMbDPwOmEZwPetud3+jLecUEemKamtrWb16NatWrSI9PZ0bb7yRoUOHxjusDtHcNahVwCoze9Dd89q53F8BL7v79WbWC+jXzucXEen0jh49yrJly9i9ezczZsxg4cKF9OrVK95hdZhorkEdC58HNRXoU7fS3Vv1uI3wfqoLCW/2dfeTwMnWnEtEpKvau3cvS5cu5dixY1x99dWcddZZXfp6U2OimSz2UeDvwFjgu0Au8HYbyhwHHAAeMLONZvY7M+vfcCczu9fM1pvZ+gMHDrShOBGRzsPdef3113nwwQdJSkrinnvuYc6cOd0uOUF0CSrN3X8PVLn7Kne/GzinDWUmEYwA/C93nw0cBf614U7uvtjdc9w9Jz09vQ3FiYh0DpWVlTz55JO88sorTJw4kXvvvZeRI0fGO6y4iaaLryp8LzKzhUAhkNGGMguAAnd/K/y8lEYSlIhId1JQUMCyZcsoLy/n8ssvZ968ed2y1RQpmgT1g3Ci2K8T3P+UAny1tQW6+z4zyzezie6+HVgAvN/a84mIdGY1NTWsWrWKNWvWkJKSwp133klmZma8w0oI0UwW+6dwsRxor8e//wvwaDiCbzdwVzudV0Sk0zhw4ADPPPMMRUVFzJo1iyuuuKJTP7+pvTV3o+6vaWbOPXf/UmsLDWdIz2nt8SIinZm789Zbb7FixQp69+7NjTfeyOTJk+MdVsJprgW1vsOiEBHpJsrLy3n22WfZs2cPEyZMYNGiRV1yotf20NyNug91ZCAiIl2Zu/Pee+/x4osvUltby6JFi5g9e3a3HwjRnNNegzKzV2mkq6+1N+qKiHQ3x44d48UXX2Tr1q1kZmZy3XXXMWTIkHiHlfCiGcX3jYjlPsCngOrYhCMi0nXUtZr+/Oc/U1lZycUXX8x5551Hjx7R3IIq0Yzi29Bg1VozWxWjeEREuoRDhw7xwgsvsGvXLkaPHs2iRYu63BNvYy2aLr7IdmgPYA4wImYRiYh0YrW1tbz55pu8+uqr9OjRgyuvvJKcnBy1mlohmi6+DQTXoIyga28PcE8sgxIR6YwKCwt5/vnn2bdvHxMnTuTKK69k0KBB8Q6r04qmi29sRwQiItJZnTx5kldffZW33nqL/v37c8MNNzB58mSN0GujaLr4+gBfAM4naEmtIZjotTLGsYmIJLwPPviAF198kfLycubMmcMll1xCnz59Tn+gnFY0XXwPA4cJ5uEDuAV4BLghVkGJiCS6kpIS/vznP7Njxw6GDh3KXXfdRVZWVrzD6lKiSVAT3X1mxOdXzWxzrAISEUlklZWVrF69mrfeeoukpCQuvfRS5s2bR8+ePeMdWpcTTYLaaGbnuPubAGY2D1gb27BERBKLu7Np0yZWrlzJ0aNHmTVrFgsWLNA0RTEUTYKaB3zGzPaGn7OAbWb2HuDuPiNm0YmIJID8/HxeeuklioqKyMjI4NOf/jSjRo2Kd1hdXjQJ6oqYRyEikoAqKipYsWIF7733HgMHDuQTn/gE06dP1+i8DhLNMPM8M5sJXBCu+pu76xqUiHRZJ06cYO3atbz55pvU1tZy/vnnc8EFF9CrV694h9atRDPM/MvA54Bl4aolZrbY3X/dzGEiIp1OdXU1b7/9Nn/72984fvw4U6dOZcGCBaSmpsY7tG4pmi6+e4B57n4UwMx+CrzBP4adt5iZ5RIMXa8Bqt1dDy8Ukbipra3l3Xff5bXXXqO8vJxx48axYMECXWeKs2gSlBEkkjo14bq2+ri7H2yH84iItIq7s2PHDlauXElxcTEjR47kmmuuYdy4cfEOTYguQT0AvGVmzxAkpmuB38c0KhGRGMvPz2fFihXs3buXIUOGcP311zNlyhQNgEgg0QyS+IWZvUYw1RHAXe6+sY3lOvAXM3Pgt+6+uOEOZnYvcC+gu7NFpN0UFBSwevVqduzYwYABA1i4cCGzZ8/WjbYJKJoWVB0Dammf7r3z3L3QzIYBr5jZ3919deQOYdJaDJCTk/ORJ/qKiLREfn4+q1atYteuXfTt25eLL76YefPmaWReAotmFN93CObde5ogOT1gZk+5+w9aW6i7F4bvxWHX4dnA6uaPEhFpuby8PFavXs3u3bvp168fl1xyCXPnzlVi6gSiaUHdAsyum73czH4CvAO0KkGZWX+gh7sfDpcvA77XmnOJiDQlNzeXVatWkZubS//+/bn00kvJyclRYupEoklQuUAfoO7xGr2BXW0oczjwTHghMgl4zN1fbsP5RESAYFTenj17WL16NXl5eQwYMIDLL7+cOXPmkJycHO/wpIWiSVAngK1m9grB4IZLgTVm9u8A7v6llhTo7ruBmafdUUQkSrW1tWzdupXXX3+dffv2MXDgQK644grOOussJaZOLJoE9Uz4qvNabEIREWmZkydPsnHjRt544w3Ky8tJS0tj0aJFzJgxg6SklowBk0QUzTDzhzoiEBGRaB05coR169bx9ttvU1lZSVZWFldeeSUTJkzQfUxdiP7EEJFOo6SkhNdff53NmzdTU1PDpEmT+NjHPkZmZma8Q5MYUIISkYTm7uzcuZN169axc+dOevbsyaxZszj33HNJS0uLd3gSQ0pQIpKQTpw4waZNm1i3bh2lpaUMGDCA+fPnk5OTo6fYdhNNJigze55g1F6j3P2amEQkIt3awYMHWbduHZs3b+bkyZNkZGRw0UUXMWXKFE1H1M0014L6efj+SWAEsCT8fAvBvVEiIu2itraWHTt2sG7dOnbv3k3Pnj2ZNm0aZ599th550Y01maDcfRWAmX3f3S+M2PS8mWlaIhFps/LycjZu3MjGjRupqKhg4MCBfPzjH2fOnDn0798/3uFJnEVzDSrdzMaFN9hiZmOB9NiGJSJdVV1racOGDezcuRN354wzzuDyyy9n4sSJ6saTetEkqK8Cr5nZ7vBzNvD5mEUkIl1SWVkZ77zzDps2beLw4cMMGDCA888/n9mzZ+uR6tKoaG7UfdnMxgOTwlV/d/cTsQ1LRLqC6upqtm/fzsaNG9m1K5jCc/z48Vx11VVMmDCBHj16xDlCSWTNjeL7ZBObzjAz3H1ZjGISkU7M3fnwww/ZtGkTW7dupbKykpSUFObPn8/s2bMZNGhQvEOUTqK5FtSiZrY5oAQlIvXKy8vZvHkz7777LiUlJSQlJTF58mRmzpzJ2LFj1VqSFmtuFN9dZtYDuN7dn+zAmESkkzh58iTbtm1j8+bN7NmzB4AxY8Zw3nnnMWXKFHr37h3nCKUza/YalLvXmtn/AJSgRASAmpoadu7cyZYtW9i+fTtVVVWkpqYyf/58Zs6cqQEP0m6iGcX3ipl9A/gjcLRupbuXxiwqEUkotbW15ObmsmXLFrZt20ZlZSV9+/Zl+vTpzJw5k8zMTM0iLu0umgR1d/j+xYh1DoxrS8Fm1hNYD3zo7le35Vwi0v7cnYKCArZs2cLWrVs5evQovXr1YtKkSUybNo1x48bpniWJqWiGmY+NUdlfBrYBKTE6v4i0UN0IvPfff59t27ZRVlZGz549mTBhAtOmTWP8+PF6Qq10mNMmKDNLBv4ZqJvu6DXgt+5e1dpCzSwDWAj8EPhaa88jIm1XW1tLfn5+fVI6fPgwPXr0YNy4cVx00UVMmjRJgx0kLqLp4vsvIBn4z/Dz7eG6z7ah3F8C3wQGNrWDmd0L3AuQlZXVhqJEpKG6a0rvv/8+f//73zl69Cg9e/bkzDPPZPLkyUycOJE+ffrEO0zp5qJJUHPdfWbE57+a2ebWFmhmVwPF7r7BzC5qaj93XwwsBsjJyWnysR8iEp2TJ0+ya9cuPvjgA7Zv387x48dJTk5m/PjxTJkyhfHjx9OrV694hylSL5oEVWNmZ7j7LgAzGwfUtKHM84BrzOwqoA+QYmZL3P22NpxTRBpx+PDh+oS0e/duampq6NOnD+PHj2fy5MmceeaZuqYkCSuaBPU/gVfDyWINGAPc1doC3f1bwLcAwhbUN5ScRNqHu1NcXMz27dvZvn07hYWFAAwePJicnBwmTpxIVlaWRt9Jp9DcXHxfAdYCq4DxwESCBKXJYkUSyMmTJ8nNzWXHjh3s3LmTsrIyAEaPHs3FF1/MxIkTSU9P131K0uk014LKAH5FMIv5u8DrBAkrH2iXBOXurxGMChSRFigpKWHnzp3s2LGD3NxcampqSE5OZty4cZx//vlMmDCBgQObHIMk0ik0NxffNwDMrBeQA3yM4Kbd/zazMnef0jEhikhVVRV5eXn1raTS0mAil7S0NObOncv48ePJysoiKSmaXnuRziGaf819CW6mHRS+CoH3YhmUSHfn7uzbt4/du3eza9cu9u7dS01NDUlJSYwdO5Z58+Yxfvx4zXsnXVpz16AWA1OBw8BbBF18v3D3Qx0Um0i3UlFRUZ+Qdu/ezbFjxwAYNmwYc+fOZdy4cWRnZ2vUnXQbzbWgsoDewA7gQ6AAKOuAmES6hePHj5OXl8eePXvYs2cPBw4cAKB///6ceeaZjBs3jnHjxulaknRbzV2DusKCYT9TCa4/fR2YZmalwBvufl8HxSjSJZw8eZK8vDxyc3PZs2cPRUVFACQlJZGVlcWsWbM444wzGDZsmEbciXD650E5sMXMyoDy8HU1cDagBCXSjKqqKvLz88nNzSU3N5cPP/yQ2tpaevbsSUZGBvPnz2fs2LGMHj1agxtEGtHcNagvEbSczgOqCIaYvwH8AQ2SEPmIyspK8vPzycvLIy8vj8LCQmprazEzRo0axcc+9jHGjh1LZmamriOJRKG5P9uygaXAV929qGPCEek8jh49yt69e+sT0v79+3F3evTowejRozn33HMZM2YMWVlZmg1cpBWauwalx2CIhNydkpIS8vPz2bt3L/n5+ZSUlADBNaSMjAwuvPBCxowZQ0ZGhlpIIu1AHd8ijaiurqawsLA+GeXn53P8+HEA+vbtS2ZmJrNmzWLMmDGMGjVKc9uJxIASlHR77k55eTkFBQUUFBTw4YcfUlRURE1NMGl/WloaEydOJDMzk6ysLNLS0jTKTqQDKEFJt3Py5EkKCwtPSUhHjhwBgu66UaNGMW/ePDIzM8nMzKR///5xjlike1KCki6tpqaG4uJiPvzwQwoLCyksLKS4uJjgDoqgdTRu3DgyMjLIyMhg2LBh6q4TSRBKUNJl1A1kiExG+/bto7q6GgiuHY0aNYoJEyaQmZnJ6NGj6devX5yjFpGmKEFJp1RbW0tpaSmFhYUUFRVRVFTEvn37OHEieBJMcnIyI0eOJCcnh9GjRzNq1ChSU1N17UikE+nwBGVmfYDVBPP8JQFLNW2SNKe2tpaDBw9SVFRU3yoqKiqiqqoKCK4bDR8+nOnTpzNq1ChGjx7N0KFD6dGjR5wjF5G2iEcL6gRwsbsfMbNkYI2ZveTub8YhFkkwJ0+eZP/+/ezbt6/+VVxcXN9Nl5yczIgRI5g9ezYjR45k5MiRpKenKxmJdEEdnqDC+f2OhB+Tw5d3dBwSX+7O4cOHP5KM6h7EB8E1oxEjRjB37lxGjBjByJEjSUtLUzIS6Sbicg3KzHoCG4Azgf9w97fiEYd0jJMnT3LgwAH2799/yquysrJ+n8GDBzNy5EhmzJjBiBEjGDFiBCkpKbpmJNKNxSVBuXsNMMvMBgPPmNk0d98SuY+Z3QvcC5CVldXxQUqL1dbWUlJSQnFxMQcOHKC4uJj9+/ef0irq1asXw4YNY+rUqQwbNozhw4czfPhw+vTpE8fIRSQRxXUUn7uXmdlrwBXAlgbbFgOLAXJyctQFmEDcnbKyMoqLi09JRgcPHqyffcHMSE1NZfjw4cyYMaM+EQ0ePFitIhGJSjxG8aUDVWFy6gtcAvy0o+OQ06utreXQoUMcOHCAAwcOcPDgwfrlukELAIMGDWLYsGH1D9sbNmwYQ4cO1YSpItIm8WhBjQQeCq9D9QCedPc/xSEOCVVVVVFaWsrBgwfrX3UJqa5FBJCSkkJ6ejo5OTkMHTqU4cOHk56erkdJiEhMxGMU37vA7I4ut7tzd44dO3ZKEqp7lZWVnbLv4MGDSU9P54wzziA9PZ309HSGDh2qRCQiHUozSXQxVVVVlJSUNPqKHDWXlJTE0KFDycjIYObMmQwdOpShQ4eSlpamrjkRSQhKUJ1QdXU1ZWVllJSUUFpaesp7RUXFKfumpKSQlpbGtGnTSEtLq09EgwYN0mAFEUloSlAJqrq6mkOHDlFaWvqRV3l5ef1s3BDc0DpkyBCys7NJS0s75aXWkIh0VkpQcVRZWXlKEjp06FD954Ytod69e5OWlkZGRgYzZswgLS2NIUOGkJaWRt++feNUAxGR2FGCiqHa2lrKy8vrE8+hQ4coKyurX657hHid/v37k5qaSnZ2NqmpqQwZMoQhQ4aQmppKv3791CUnIt2KElQbuDtHjhypTzplZWUfeUV2xfXo0YPBgweTmprKyJEjSU1NrU9EqampGiUnIhJBCaoZdROalpeXU1ZWdsp7XUKKvE8I/tEKGj16NFOnTq1PQqmpqaSkpGiiUxGRKHXrBFVdXU1FRQXl5eX1r8hEVFFR8ZEE1LdvXwYPHsywYcOYMGECqampDB48uP6lQQkiIu2jyyYod+fo0aOUl5efkoQil48cOfKR4wYMGMDgwYMZNWoUkydPrk88gwYNYvDgwfTq1SsOtRER6X46ZYJyd44fP05FRUV9wmlsuWHrJykpiUGDBjFo0CDGjx9fv1z3SklJISmpU34lIiJdTqf4bVxRUcGyZcvqE09jyadHjx4MHDiQQYMGMXr0aCZPnlyfdOreNRJORKTz6BQJ6ujRo+Tn55OSklKffFJSUk559e/fXwMQRES6kE6RoEaOHMmXv/zleIchIiIdSE0OERFJSEpQIiKSkJSgREQkIXV4gjKzTDN71cy2mdlWM9PFJRER+Yh4DJKoBr7u7u+Y2UBgg5m94u7vxyEWERFJUB3egnL3Ind/J1w+DGwDRnd0HCIiktjieg3KzLKB2cBb8YxDREQST9wSlJkNAJ4GvuLuFY1sv9fM1pvZ+gMHDnR8gCIiEldxSVBmlkyQnB5192WN7ePui909x91z0tPTOzZAERGJu3iM4jPg98A2d/9FR5cvIiKdQzxaUOcBtwMXm9mm8HVVHOIQEZEE1uHDzN19DaApxUVEpFmaSUJERBKSEpSIiCQkc/d4x3BaZnYY2B7vOGJsKHAw3kHEmOrYNaiOXUMi1XGMu39kuHaneB4UsN3dc+IdRCyZ2XrVsfNTHbsG1TExqItPREQSkhKUiIgkpM6SoBbHO4AOoDp2Dapj16A6JoBOMUhCRES6n87SghIRkW5GCUpERBJSvGYz/4OZFZvZloh1M83sDTN7z8yeN7OUcH22mR2PmLfv/0UcMyfcf6eZ/Xs4EW1CaEkdw20zwm1bw+19wvVdoo5mdmvEz3CTmdWa2axwW8LWEVpcz2Qzeyhcv83MvhVxTMLWs4V17GVmD4TrN5vZRRHHJGQdzSzTzF4NfyZbzezL4fohZvaKme0I31MjjvlWWI/tZnZ5xPouUUczSwv3P2Jmv2lwrsSoo7t3+Au4EDgL2BKx7m1gfrh8N/D9cDk7cr8G51kHnEswt99LwJXxqE871DEJeBeYGX5OA3p2pTo2OG46sLsz/Bxb8bP8NPBEuNwPyAWyE72eLazjF4EHwuVhwAagRyLXERgJnBUuDwQ+AKYAPwP+NVz/r8BPw+UpwGagNzAW2JXo/ydbUcf+wPnAPwG/aXCuhKhjXFpQ7r4aKG2weiKwOlx+BfhUc+cws5FAiru/4cE3+jBwXTuH2motrONlwLvuvjk8tsTda7pYHSPdAjwOif9zhBbX04H+ZpYE9AVOAhWJXs8W1nEKsDI8rhgoA3ISuY7uXuTu74TLh4FtwGjgWuChcLeH+Ee81xL8oXHC3fcAO4Gzu1Id3f2oB5N3V0aeJ5HqmEjXoLYA14TLNwCZEdvGmtlGM1tlZheE60YDBRH7FITrEllTdZwAuJn92czeMbNvhuu7Uh0j3USYoOicdYSm67kUOAoUAXuBn7t7KZ2znk3VcTNwrZklmdlYYE64rVPU0cyygdnAW8Bwdy+C4Bc8QYsQgrjzIw6rq0tXqmNTEqaOiZSg7ga+aGYbCJqnJ8P1RUCWu88GvgY8FvaFN9Ynmuhj5puqYxJBU/vW8P0TZraArlVHAMxsHnDM3euudXTGOkLT9TwbqAFGEXQNfd3MxtE569lUHf9A8EtrPfBL4HWgmk5QRzMbQPA076+4e0VzuzayzptZnzBaUMcmT9HIurjUMWHm4nP3vxN0dWFmE4CF4foTwIlweYOZ7SJocRQAGRGnyAAKOzLmlmqqjgR1WeXuB8NtLxJcD1hC16ljnZv5R+sJOuHPEZqt56eBl929Cig2s7VADvA3Olk9m/k/WQ18tW4/M3sd2AEcIoHraGbJBL+4H3X3ZeHq/WY20t2Lwq6t4nB9Aae2/uvqktD/XltYx6YkTB0TpgVlZsPC9x7AvwH/L/ycbmY9w+VxwHiCC+xFwGEzOyccYfIZ4Nm4BB+lpuoI/BmYYWb9wmsX84H3u1gd69bdADxRt64z1hGaredegqdFm5n1B84B/t4Z69nM/8l+Yd0ws0uBandP6H+vYTy/B7a5+y8iNj0H3BEu38E/4n0OuNnMeofdmOOBdV2sjo1KqDrGY2QGwV/QRUAVQba+B/gywaiTD4Cf8I9ZLj4FbCXo934HWBRxnhyCfvJdwG/qjkmEV0vqGO5/W1jPLcDPumgdLwLebOQ8CVvHVvx7HQA8Ff4s3wf+Z2eoZwvrmE3w+JttwAqCRyUkdB0Jus6dYLTspvB1FcGI2ZUELcCVwJCIY/53WI/tRIxi62J1zCUYHHMk/LlPSaQ6aqojERFJSAnTxSciIhJJCUpERBKSEpSIiCQkJSgREUlISlAiIpKQlKBEOkh4b9QaM7syYt2NZvZyPOMSSVQaZi7SgcxsGsF9UrOBngT3qlzh7rtaca6e7l7TvhGKJA4lKJEOZmY/I5hQtn/4PobgESRJwP3u/mw42ecj4T4A/8PdX7fg2Uv3EdxUO8vdp3Rs9CIdRwlKpIOF0wS9QzD56p+Are6+xMwGEzyHZzbBjAC17l5pZuOBx909J0xQLwDTPHgMhEiXlTCTxYp0F+5+1Mz+SDC9zI3AIjP7Rri5D5BFMDnnbyx46nANwQTJddYpOUl3oAQlEh+14cuAT7n79siNZnY/sB+YSTCYKfKhckc7KEaRuNIoPpH4+jPwL+Gs0ZjZ7HD9IKDI3WuB2wkGVIh0K0pQIvH1fSAZeNfMtoSfAf4TuMPM3iTo3lOrSbodDZIQEZGEpBaUiIgkJCUoERFJSEpQIiKSkJSgREQkISlBiYhIQlKCEhGRhKQEJSIiCen/B7ItKS9fJ1QOAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# And it doesn't do a particularly good job of predicting\n",
-    "# actual growth from 1940 to the present.\n",
-    "\n",
-    "# TODO: load these series from table2\n",
-    "#plot(census, ':', label='US Census')\n",
-    "#plot(un, '--', label='UN DESA')\n",
-    "\n",
-    "xs = linspace(1940, 2020)\n",
-    "ys = 110 + 200000 / (2040 - xs)\n",
-    "plot(xs, ys/1000, color='gray', label='model')\n",
-    "\n",
-    "decorate(xlim=[1950, 2016], xlabel='Year', \n",
-    "         ylabel='World population (billions)',\n",
-    "         title='Prehistoric population estimates')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ruled-spain",
-   "metadata": {},
-   "source": [
-    "## One queue or two?\n",
-    "\n",
-    "This case study is related to **queueing theory**, which is the study of systems that involve waiting in lines, also known as \"queues\".\n",
-    "\n",
-    "Suppose you are designing the checkout area for a new store. There is\n",
-    "enough room in the store for two checkout counters and a waiting area\n",
-    "for customers. You can make two lines, one for each counter, or one line that feeds both counters.\n",
-    "\n",
-    "In theory, you might expect a single line to be better, but it has some practical drawbacks: in order to maintain a single line, you might have to install barriers, and customers might be put off by what seems to be a longer line, even if it moves faster.\n",
-    "\n",
-    "So you'd like to check whether the single line is really better and by\n",
-    "how much. Simulation can help answer this question.\n",
-    "\n",
-    "This figure shows the three scenarios we'll consider:\n",
-    "\n",
-    "![One queue, one server (left), one queue, two servers (middle), two\n",
-    "queues, two servers (right).](figs/queue.pdf){width=\"4.5in\"}\n",
-    "\n",
-    "As we did in the bike share model, we'll assume that a customer is equally likely to arrive during any time step. I'll denote this probability using the Greek letter lambda, $\\lambda$, or the variable name `lam`. The value of $\\lambda$ probably varies from day to day, so we'll have to consider a range of possibilities.\n",
-    "\n",
-    "Based on data from other stores, you know that it takes 5 minutes for a customer to check out, on average. But checkout times are variable: most customers take less than 5 minutes, but some take substantially more. A simple way to model this variability is to assume that when a customer is checking out, they always have the same probability of finishing during the next time step, regardless of how long they have been checking out. I'll denote this probability using the Greek letter mu, $\\mu$, or the variable name `mu`.\n",
-    "\n",
-    "If we choose $\\mu=1/5$ per minute, the average time for each checkout\n",
-    "will be 5 minutes, which is consistent with the data. Most people takes less than 5 minutes, but a few take substantially longer, which is probably not a bad model of the distribution in real stores.\n",
-    "\n",
-    "Now we're ready to get started. In the repository for this book, you'll find a notebook called `queue.ipynb` that contains some code to get you started and instructions.\n",
-    "\n",
-    "As always, you should practice incremental development: write no more\n",
-    "than one or two lines of code a time, and test as you go!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "forward-point",
-   "metadata": {},
-   "source": [
-    "## Predicting salmon populations\n",
-    "\n",
-    "Each year the U.S. Atlantic Salmon Assessment Committee reports\n",
-    "estimates of salmon populations in oceans and rivers in the northeastern United States. The reports are useful for monitoring changes in these populations, but they generally do not include predictions.\n",
-    "\n",
-    "The goal of this case study is to model year-to-year changes in\n",
-    "population, evaluate how predictable these changes are, and estimate the probability that a particular population will increase or decrease in the next 10 years.\n",
-    "\n",
-    "As an example, I use data from page 18 of the 2017 report, which\n",
-    "provides population estimates for the Narraguagus and Sheepscot Rivers\n",
-    "in Maine.\n",
-    "\n",
-    "In the repository for this book, you'll find a notebook called\n",
-    "`salmon.ipynb` that contains some code to get you started and\n",
-    "instructions.\n",
-    "\n",
-    "You should take my instructions as suggestions; if you want to try\n",
-    "something different, please do!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "limiting-moore",
-   "metadata": {},
-   "source": [
-    "## Tree growth\n",
-    "\n",
-    "This case study is based on \"Height-Age Curves for Planted Stands of\n",
-    "Douglas Fir, with Adjustments for Density\", a working paper by\n",
-    "Flewelling, Collier, Gonyea, Marshall, and Turnblom.\n",
-    "\n",
-    "It provides \"site index curves\", which are curves that show the\n",
-    "expected height of the tallest tree in a stand of Douglas firs as a\n",
-    "function of age, for a stand where the trees are the same age.\n",
-    "\n",
-    "Depending on the quality of the site, the trees might grow more quickly or slowing. So each curve is identified by a \"site index\\\" that indicates the quality of the site.\n",
-    "\n",
-    "The following figure shows site curves for three different site\n",
-    "indices. \n",
-    "\n",
-    "![Site index curves for tree\n",
-    "growth.](figs/trees-fig01.pdf){height=\"3in\"}\n",
-    "\n",
-    "The goal of this case study is to explain the shape of these\n",
-    "curves, that is, why trees grow the way they do.\n",
-    "\n",
-    "As a starting place, let's assume that the ability of the tree to gain\n",
-    "mass is limited by the area it exposes to sunlight, and that the growth rate (in mass) is proportional to that area. In that case we can write:\n",
-    "\n",
-    "$$m_{n+1} = m_n + \\alpha A$$\n",
-    "\n",
-    "where $m_n$ is the mass of the at time step $n$, $A$ is the area exposed to sunlight, and $\\alpha$ is an unknown growth parameter.\n",
-    "\n",
-    "To get from $m$ to $A$, I'll make the additional assumption that mass is proportional to height raised to an unknown power: $m = \\beta h^D$ where $h$ is height, $\\beta$ is an unknown constant of proportionality, and $D$ is the dimension that relates height and mass. I start by assuming $D=3$, but then revisit that assumption.\n",
-    "\n",
-    "Finally, we'll assume that area is proportional to height squared:\n",
-    "\n",
-    "$$A = \\gamma h^2$$\n",
-    "\n",
-    "I specify height in feet, and choose units for mass and area so that\n",
-    "$\\beta=1$ and $\\gamma=1$. Putting all that together, we can write a\n",
-    "difference equation for height:\n",
-    "\n",
-    "$$h_{n+1}^D = h_n^D + \\alpha h_n^2$$\n",
-    "\n",
-    "With $D=3$, the solution to this equation is close to a straight line,\n",
-    "which is not a bad model for the growth curves. But the model implies\n",
-    "that trees can grow forever, and we know that's not true. As trees get\n",
-    "taller, it gets harder for them to move water and nutrients against the force of gravity, and their growth slows.\n",
-    "\n",
-    "We can model this effect by adding a factor to the model similar to what we saw in the logistic model of population growth. Instead of assuming:\n",
-    "\n",
-    "$$m_{n+1} = m_n + \\alpha A$$\n",
-    "\n",
-    "Let's assume\n",
-    "\n",
-    "$$m_{n+1} = m_n + \\alpha A (1 - h / K)$$\n",
-    "\n",
-    "where $K$ is similar to the carrying capacity of the logistic model. As $h$ approaches $K$, the factor $(1 - h/K)$ goes to 0, causing growth to level off.\n",
-    "\n",
-    "In the repository for this book, you'll find a notebook called\n",
-    "`trees.ipynb` that implements both models and uses them to fit the data.\n",
-    "\n",
-    "There are no exercises in this case study; it is mostly meant as an\n",
-    "example of what you can do with the tools we have so far, and a preview of what we will be able to do with the tools in the next few chapters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "australian-gregory",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap11.ipynb b/jupyter/chap11.ipynb
deleted file mode 100644
index 040c405c..00000000
--- a/jupyter/chap11.ipynb
+++ /dev/null
@@ -1,918 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "pressing-munich",
-   "metadata": {},
-   "source": [
-    "# Chapter 11"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "detected-creation",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "breathing-commonwealth",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "persistent-carbon",
-   "metadata": {},
-   "source": [
-    "In this chapter, we develop a model of an epidemic as it spreads in a\n",
-    "susceptible population, and use it to evaluate the effectiveness of\n",
-    "possible interventions.\n",
-    "\n",
-    "My presentation of the model in the next few chapters is based on an excellent article by David Smith and Lang Moore, [^1]: Smith and Moore, \"The SIR Model for Spread of Disease,\" Journal of Online Mathematics and its Applications, December 2001, available at <http://modsimpy.com/sir>."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "working-patent",
-   "metadata": {},
-   "source": [
-    "## The Freshman Plague\n",
-    "\n",
-    "Every year at Olin College, about 90 new students come to campus from\n",
-    "around the country and the world. Most of them arrive healthy and happy, but usually at least one brings with them some kind of infectious disease. A few weeks later, predictably, some fraction of the incoming class comes down with what we call \"The Freshman Plague\".\n",
-    "\n",
-    "In this chapter we introduce a well-known model of infectious disease,\n",
-    "the Kermack-McKendrick model, and use it to explain the progression of\n",
-    "the disease over the course of the semester, predict the effect of\n",
-    "possible interventions (like immunization) and design the most effective intervention campaign.\n",
-    "\n",
-    "So far we have done our own modeling; that is, we've chosen physical\n",
-    "systems, identified factors that seem important, and made decisions\n",
-    "about how to represent them. In this chapter we start with an existing\n",
-    "model and reverse-engineer it. Along the way, we consider the modeling\n",
-    "decisions that went into it and identify its capabilities and\n",
-    "limitations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "distant-expense",
-   "metadata": {},
-   "source": [
-    "## The SIR model\n",
-    "\n",
-    "The Kermack-McKendrick model is a simple version of an **SIR model**,\n",
-    "so-named because it considers three categories of people:\n",
-    "\n",
-    "-   **S**: People who are \"susceptible\\\", that is, capable of\n",
-    "    contracting the disease if they come into contact with someone who\n",
-    "    is infected.\n",
-    "\n",
-    "-   **I**: People who are \"infectious\\\", that is, capable of passing\n",
-    "    along the disease if they come into contact with someone\n",
-    "    susceptible.\n",
-    "\n",
-    "-   **R**: People who are \"recovered\\\". In the basic version of the\n",
-    "    model, people who have recovered are considered to be immune to\n",
-    "    reinfection. That is a reasonable model for some diseases, but not\n",
-    "    for others, so it should be on the list of assumptions to reconsider\n",
-    "    later."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "reasonable-kitchen",
-   "metadata": {},
-   "source": [
-    "Let's think about how the number of people in each category changes over time. Suppose we know that people with the disease are infectious for a period of 4 days, on average. If 100 people are infectious at a\n",
-    "particular point in time, and we ignore the particular time each one\n",
-    "became infected, we expect about 1 out of 4 to recover on any particular day.\n",
-    "\n",
-    "Putting that a different way, if the time between recoveries is 4 days, the recovery rate is about 0.25 recoveries per day, which we'll denote with the Greek letter gamma, $\\gamma$, or the variable name `gamma`.\n",
-    "\n",
-    "If the total number of people in the population is $N$, and the fraction currently infectious is $i$, the total number of recoveries we expect per day is $\\gamma i N$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "important-yugoslavia",
-   "metadata": {},
-   "source": [
-    "Now let's think about the number of new infections. Suppose we know that each susceptible person comes into contact with 1 person every 3 days, on average, in a way that would cause them to become infected if the other person is infected. We'll denote this contact rate with the Greek letter beta, $\\beta$.\n",
-    "\n",
-    "It's probably not reasonable to assume that we know $\\beta$ ahead of\n",
-    "time, but later we'll see how to estimate it based on data from previous outbreaks.\n",
-    "\n",
-    "If $s$ is the fraction of the population that's susceptible, $s N$ is\n",
-    "the number of susceptible people, $\\beta s N$ is the number of contacts per day, and $\\beta s i N$ is the number of those contacts where the other person is infectious."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "virgin-cambodia",
-   "metadata": {},
-   "source": [
-    "In summary:\n",
-    "\n",
-    "-   The number of recoveries we expect per day is $\\gamma i N$; dividing by $N$ yields the fraction of the population that recovers in a day, which is $\\gamma i$.\n",
-    "\n",
-    "-   The number of new infections we expect per day is $\\beta s i N$;\n",
-    "    dividing by $N$ yields the fraction of the population that gets\n",
-    "    infected in a day, which is $\\beta s i$.\n",
-    "\n",
-    "This model assumes that the population is closed; that is, no one\n",
-    "arrives or departs, so the size of the population, $N$, is constant."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fourth-celtic",
-   "metadata": {},
-   "source": [
-    "## The SIR equations\n",
-    "\n",
-    "If we treat time as a continuous quantity, we can write differential\n",
-    "equations that describe the rates of change for $s$, $i$, and $r$ (where $r$ is the fraction of the population that has recovered):\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "\\frac{ds}{dt} &= -\\beta s i \\\\\n",
-    "\\frac{di}{dt} &= \\beta s i - \\gamma i\\\\\n",
-    "\\frac{dr}{dt} &= \\gamma i\\end{aligned}$$ \n",
-    "\n",
-    "To avoid cluttering the equations, I leave it implied that $s$ is a function of time, $s(t)$, and likewise for $i$ and $r$.\n",
-    "\n",
-    "SIR models are examples of **compartment models**, so-called because\n",
-    "they divide the world into discrete categories, or compartments, and\n",
-    "describe transitions from one compartment to another. Compartments are\n",
-    "also called **stocks** and transitions between them are called\n",
-    "**flows**.\n",
-    "\n",
-    "In this example, there are three stocks---susceptible, infectious, and\n",
-    "recovered---and two flows---new infections and recoveries. Compartment\n",
-    "models are often represented visually using stock and flow diagrams (see <http://modsimpy.com/stock>).\n",
-    "\n",
-    "The following figure shows the stock and flow diagram for an SIR\n",
-    "model.\n",
-    "\n",
-    "![Stock and flow diagram for an SIR\n",
-    "model.](figs/stock_flow1.pdf){width=\"4in\"}\n",
-    "\n",
-    "Stocks are represented by rectangles, flows by arrows. The widget in the middle of the arrows represents a valve that controls the rate of flow; the diagram shows the parameters that control the valves."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "martial-details",
-   "metadata": {},
-   "source": [
-    "## Implementation\n",
-    "\n",
-    "For a given physical system, there are many possible models, and for a\n",
-    "given model, there are many ways to represent it. For example, we can\n",
-    "represent an SIR model as a stock-and-flow diagram, as a set of\n",
-    "differential equations, or as a Python program. The process of\n",
-    "representing a model in these forms is called **implementation**. In\n",
-    "this section, we implement the SIR model in Python.\n",
-    "\n",
-    "I'll represent the initial state of the system using a `State` object\n",
-    "with state variables `S`, `I`, and `R`; they represent the fraction of\n",
-    "the population in each compartment.\n",
-    "\n",
-    "We can initialize the `State` object with the *number* of people in each compartment, assuming there is one infected student in a class of 90:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "criminal-change",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "S    89\n",
-       "I     1\n",
-       "R     0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init = State(S=89, I=1, R=0)\n",
-    "init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "chronic-jonathan",
-   "metadata": {},
-   "source": [
-    "And then convert the numbers to fractions by dividing by the total:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "pediatric-ratio",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "S    0.988889\n",
-       "I    0.011111\n",
-       "R    0.000000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from numpy import sum\n",
-    "\n",
-    "init /= sum(init)\n",
-    "init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ordinary-scottish",
-   "metadata": {},
-   "source": [
-    "For now, let's assume we know the time between contacts and time between\n",
-    "recoveries:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "little-stylus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tc = 3             # time between contacts in days \n",
-    "tr = 4             # recovery time in days"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "covered-avenue",
-   "metadata": {},
-   "source": [
-    "We can use them to compute the parameters of the model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "veterinary-aerospace",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "lightweight-delta",
-   "metadata": {},
-   "source": [
-    "Now we need a `System` object to store the parameters and initial\n",
-    "conditions. The following function takes the system parameters and returns a new `System` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "nasty-sherman",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#export\n",
-    "def make_system(beta, gamma):\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "victorian-blogger",
-   "metadata": {},
-   "source": [
-    "The default value for `t_end` is 14 weeks, about the length of a\n",
-    "semester.\n",
-    "\n",
-    "Here's what the `System` object looks like. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "supported-shadow",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "namespace(init=S    0.988889\n",
-       "               I    0.011111\n",
-       "               R    0.000000\n",
-       "               dtype: float64,\n",
-       "          t0=0,\n",
-       "          t_end=98,\n",
-       "          beta=0.3333333333333333,\n",
-       "          gamma=0.25)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(beta, gamma)\n",
-    "system"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alone-desire",
-   "metadata": {},
-   "source": [
-    "## The update function\n",
-    "\n",
-    "At any point in time, the state of the system is represented by a\n",
-    "`State` object with three variables, `S`, `I` and `R`. So I'll define an\n",
-    "update function that takes as parameters a `State` object, the current\n",
-    "time, and a `System` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "wound-wayne",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#export\n",
-    "def update_func(state, t, system):\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = system.beta * i * s    \n",
-    "    recovered = system.gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aboriginal-malpractice",
-   "metadata": {},
-   "source": [
-    "The first line uses a feature we have not seen before, **multiple\n",
-    "assignment**. The value on the right side is a `State` object that\n",
-    "contains three values. The left side is a sequence of three variable\n",
-    "names. The assignment does just what we want: it assigns the three\n",
-    "values from the `State` object to the three variables, in order.\n",
-    "\n",
-    "The variables `s`, `i` and `r`, are lowercase to distinguish them\n",
-    "from the state variables, `S`, `I` and `R`.\n",
-    "\n",
-    "The update function computes `infected` and `recovered` as a fraction of the population, then updates `s`, `i` and `r`. The return value is a `State` that contains the updated values.\n",
-    "\n",
-    "When we call `update_func` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "assigned-feelings",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "S    0.985226\n",
-       "I    0.011996\n",
-       "R    0.002778\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "state = update_func(init, 0, system)\n",
-    "state"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "standard-search",
-   "metadata": {},
-   "source": [
-    "You might notice that this version of `update_func` does not use one of its parameters, `t`. I include it anyway because update functions\n",
-    "sometimes depend on time, and it is convenient if they all take the same parameters, whether they need them or not."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "informational-cisco",
-   "metadata": {},
-   "source": [
-    "## Running the simulation\n",
-    "\n",
-    "Now we can simulate the model over a sequence of time steps:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "occasional-pottery",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#export\n",
-    "from numpy import arange\n",
-    "\n",
-    "def run_simulation(system, update_func):\n",
-    "    state = system.init\n",
-    "\n",
-    "    for t in arange(system.t0, system.t_end):\n",
-    "        state = update_func(state, t, system)\n",
-    "\n",
-    "    return state"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fifteen-metallic",
-   "metadata": {},
-   "source": [
-    "The parameters of `run_simulation` are the `System` object and the\n",
-    "update function. The `System` object contains the parameters, initial\n",
-    "conditions, and values of `t0` and `t_end`.\n",
-    "\n",
-    "We can call `run_simulation` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "differential-difference",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(beta, gamma)\n",
-    "final_state = run_simulation(system, update_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "incredible-marsh",
-   "metadata": {},
-   "source": [
-    "The result is the final state of the system:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "weekly-acrobat",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "S    0.520568\n",
-       "I    0.000666\n",
-       "R    0.478766\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "final_state"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "behind-removal",
-   "metadata": {},
-   "source": [
-    "This result indicates that after 14 weeks (98 days), about 52% of the\n",
-    "population is still susceptible, which means they were never infected,\n",
-    "less than 1% are actively infected, and 48% have recovered, which means they were infected at some point."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "serial-genius",
-   "metadata": {},
-   "source": [
-    "## Collecting the results\n",
-    "\n",
-    "The previous version of `run_simulation` only returns the final state,\n",
-    "but we might want to see how the state changes over time. We'll consider two ways to do that: first, using three `TimeSeries` objects, then using a new object called a `TimeFrame`.\n",
-    "\n",
-    "Here's the first version:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "advanced-recommendation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import TimeSeries\n",
-    "\n",
-    "def run_simulation(system, update_func):\n",
-    "    S = TimeSeries()\n",
-    "    I = TimeSeries()\n",
-    "    R = TimeSeries()\n",
-    "\n",
-    "    state = system.init\n",
-    "    t0 = system.t0\n",
-    "    S[t0], I[t0], R[t0] = state\n",
-    "    \n",
-    "    for t in arange(system.t0, system.t_end):\n",
-    "        state = update_func(state, t, system)\n",
-    "        S[t+1], I[t+1], R[t+1] = state\n",
-    "    \n",
-    "    return S, I, R"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "behind-breathing",
-   "metadata": {},
-   "source": [
-    "First, we create `TimeSeries` objects to store the results. Notice that\n",
-    "the variables `S`, `I`, and `R` are `TimeSeries` objects now.\n",
-    "\n",
-    "Next we initialize `state`, `t0`, and the first elements of `S`, `I` and\n",
-    "`R`.\n",
-    "\n",
-    "Inside the loop, we use `update_func` to compute the state of the system\n",
-    "at the next time step, then use multiple assignment to unpack the\n",
-    "elements of `state`, assigning each to the corresponding `TimeSeries`.\n",
-    "\n",
-    "At the end of the function, we return the values `S`, `I`, and `R`. This\n",
-    "is the first example we have seen where a function returns more than one\n",
-    "value.\n",
-    "\n",
-    "Now we can run the function like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "subtle-zambia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(beta, gamma)\n",
-    "S, I, R = run_simulation(system, update_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "smoking-toddler",
-   "metadata": {},
-   "source": [
-    "We'll use the following function to plot the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "welcome-tractor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#export\n",
-    "from modsim import decorate\n",
-    "\n",
-    "def plot_results(S, I, R):\n",
-    "    S.plot(style='--', label='Susceptible')\n",
-    "    I.plot(style='-', label='Infected')\n",
-    "    R.plot(style=':', label='Resistant')\n",
-    "    decorate(xlabel='Time (days)',\n",
-    "             ylabel='Fraction of population')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "important-command",
-   "metadata": {},
-   "source": [
-    "And run it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "agricultural-joining",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_results(S, I, R)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "civic-pharmacy",
-   "metadata": {},
-   "source": [
-    "Notice that it takes about three weeks (21 days) for the outbreak to get going, and about six weeks (42 days) before it peaks. The fraction of the population that's infected is never very high, but it adds up. In total, almost half the population gets sick."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unexpected-empire",
-   "metadata": {},
-   "source": [
-    "## Now with a TimeFrame\n",
-    "\n",
-    "If the number of state variables is small, storing them as separate\n",
-    "`TimeSeries` objects might not be so bad. But a better alternative is to use a `TimeFrame`, which is another object defined in the ModSim\n",
-    "library.\n",
-    "\n",
-    "A `TimeFrame` is a kind of a `DataFrame`, which we used in.\n",
-    "\n",
-    "Here's a more concise version of `run_simulation` using a `TimeFrame`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "native-central",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#export\n",
-    "from modsim import TimeFrame\n",
-    "\n",
-    "def run_simulation(system, update_func):\n",
-    "    frame = TimeFrame(columns=system.init.index)\n",
-    "    frame.loc[system.t0] = system.init\n",
-    "    \n",
-    "    for t in arange(system.t0, system.t_end):\n",
-    "        frame.loc[t+1] = update_func(frame.loc[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "beginning-secret",
-   "metadata": {},
-   "source": [
-    "The first line creates an empty `TimeFrame` with one column for each\n",
-    "state variable. Then, before the loop starts, we store the initial\n",
-    "conditions in the `TimeFrame` at `t0`. Based on the way we've been using\n",
-    "`TimeSeries` objects, it is tempting to write:\n",
-    "\n",
-    "```\n",
-    "frame[system.t0] = system.init\n",
-    "```\n",
-    "\n",
-    "But when you use the bracket operator with a `TimeFrame` or `DataFrame`, it selects a column, not a row. \n",
-    "\n",
-    "To select a row, we have to use `loc`, like this:\n",
-    "\n",
-    "```\n",
-    "frame.loc[system.t0] = system.init\n",
-    "```\n",
-    "\n",
-    "Since the value on the right side is a `State`, the assignment matches\n",
-    "up the index of the `State` with the columns of the `TimeFrame`; that\n",
-    "is, it assigns the `S` value from `system.init` to the `S` column of\n",
-    "`frame`, and likewise with `I` and `R`.\n",
-    "\n",
-    "We use the same feature to write the loop more concisely, assigning the `State` we get from `update_func` directly to the next row of\n",
-    "`frame`.\n",
-    "\n",
-    "Finally, we return `frame`. We can call this version of `run_simulation` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "quick-fifty",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "global-complement",
-   "metadata": {},
-   "source": [
-    "And plot the results like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "listed-enough",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_results(results.S, results.I, results.R)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "welsh-distinction",
-   "metadata": {},
-   "source": [
-    "As with a `DataFrame`, we can use the dot operator to select columns\n",
-    "from a `TimeFrame`.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "neutral-replacement",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "induced-tunnel",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "floral-remove",
-   "metadata": {},
-   "source": [
-    "**Exercise**  Suppose the time between contacts is 4 days and the recovery time is 5 days.  After 14 weeks, how many students, total, have been infected?\n",
-    "\n",
-    "Hint: what is the change in `S` between the beginning and the end of the simulation?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "recovered-freeze",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.3787177442414792"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "tc = 4      # time between contacts in days \n",
-    "tr = 5      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)\n",
-    "s_0 = system.init.S\n",
-    "\n",
-    "final = run_simulation(system, update_func)\n",
-    "s_end = final.S[system.t_end]\n",
-    "\n",
-    "s_0 - s_end"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "interpreted-colony",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "given-canberra",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap12.ipynb b/jupyter/chap12.ipynb
deleted file mode 100644
index c515d65c..00000000
--- a/jupyter/chap12.ipynb
+++ /dev/null
@@ -1,1037 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "combined-semiconductor",
-   "metadata": {},
-   "source": [
-    "# Chapter 12"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "excellent-orchestra",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "absolute-density",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "athletic-diameter",
-   "metadata": {},
-   "source": [
-    "### Code\n",
-    "\n",
-    "Here's the code from the previous notebook that we'll need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "expensive-venice",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import State, System\n",
-    "\n",
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "satellite-fleet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State with variables S, I, R\n",
-    "    t: time step\n",
-    "    system: System with beta and gamma\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = system.beta * i * s    \n",
-    "    recovered = system.gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "affiliated-metabolism",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import arange\n",
-    "from modsim import TimeFrame\n",
-    "\n",
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    frame = TimeFrame(columns=system.init.index)\n",
-    "    frame.loc[system.t0] = system.init\n",
-    "    \n",
-    "    for t in arange(system.t0, system.t_end):\n",
-    "        frame.loc[t+1] = update_func(frame.loc[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "identical-steam",
-   "metadata": {},
-   "source": [
-    "In the previous chapter I presented the SIR model of infectious disease and used it to model the Freshman Plague at Olin. In this chapter we'll consider metrics intended to quantify the effects of the disease and interventions intended to reduce those effects."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "complex-renewal",
-   "metadata": {},
-   "source": [
-    "## Immunization\n",
-    "\n",
-    "Models like this are useful for testing \"what if?\" scenarios. As an\n",
-    "example, we'll consider the effect of immunization.\n",
-    "\n",
-    "Suppose there is a vaccine that causes a student to become immune to the Freshman Plague without being infected. How might you modify the model to capture this effect?\n",
-    "\n",
-    "One option is to treat immunization as a shortcut from susceptible to\n",
-    "recovered without going through infectious. We can implement this\n",
-    "feature like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "recent-cooper",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def add_immunization(system, fraction):\n",
-    "    system.init.S -= fraction\n",
-    "    system.init.R += fraction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "arranged-screening",
-   "metadata": {},
-   "source": [
-    "`add_immunization` moves the given fraction of the population from `S`\n",
-    "to `R`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "found-learning",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tc = 3             # time between contacts in days \n",
-    "tr = 4             # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)\n",
-    "results = run_simulation(system, update_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unsigned-joseph",
-   "metadata": {},
-   "source": [
-    "If we assume that 10% of students are vaccinated at the\n",
-    "beginning of the semester, and the vaccine is 100% effective, we can\n",
-    "simulate the effect like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "funny-copper",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system2 = make_system(beta, gamma)\n",
-    "add_immunization(system2, 0.1)\n",
-    "results2 = run_simulation(system2, update_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bronze-techno",
-   "metadata": {},
-   "source": [
-    "The following figure shows `S` as a function of time, with and\n",
-    "without immunization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "divided-biotechnology",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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FnV+sYOmuxEvvpFQZVtYeKaiSczl/9pqgnKxWRCAzR1xFjbAA7p+0mkXbjzk7JKUcws/PjxMnTmiSckPGGE6cOIGfn1+x9tNOEi4iKSWDe75cxfajpxl3Zyuua1zZ2SEpVaIyMzOJj4+/5Ls2qnzy8/OjevXqeHt7X7C8qE4SmqBcSHJqJvdOXEXcoWQ+HtSSG5tWcXZISinlUNqLr4wI9fdm6tB2NK9RgUdmrOfnTUecHZJSSjmNJigXE+LnzeQh7WhVswKPf7OeBXE6lLxSyj1pgnJBQb5eTLyvLU2qhTLy63XacUIp5ZY0QbmoYFtLqn7lYB6ctpYlO7ULulLKvWiCcmGh/t5MHdKe2IqBPDBlDWv3n3J2SEopVWo0Qbm4sEAfpgxtR1SIL0O+Ws2Oo1oWSSnlHjRBlQFRwX5MG9oeP28P7vlyJQdOlF6Jf6WUchZNUGVEjfAApgxpT3pWDvdMXMmxM/qyo1KqfNMEVYbUrxzMpPvbcux0OkO/WsO59ItHzVRKqfLCoQlKRG4QkR0isktEni9gfZiIzBaRTSKySkSaODKe8qBVzTDG3dWSuMPJPDx9HZnZOc4OSSmlHMJhCUpEPIFxwI1AI2CQiDTKt9mLwAZjTDPgXuAjR8VTnnRvUIk3+jblj7+P89LszVp8UylVLjmyBdUO2GWM2WOMyQC+Afrk26YR8BuAMWY7EC0ilRwYU7kxqF1NHuteh+/WxPPRbzudHY5SSpU4RyaoasDBPPPxtmV5bQT6AYhIO6AWUD3/gURkuIisEZE1x48fd1C4Zc+TPevRv3V1PvzfTuasP+TscJRSqkQ5MkFJAcvy34t6CwgTkQ3Ao8B64KIn/8aYz40xbYwxbSIjI0s80LJKRHizb1M6xIbz7MxNrN530tkhKaVUiXFkgooHauSZrw4czruBMea0MeZ+Y0wLrGdQkcBeB8ZU7vh4eTD+7tZUC/Pnwalr9R0ppVS54cgEtRqoKyIxIuIDDATm5t1ARCrY1gEMA/40xpx2YEyQcQ5ysh16itJWIcCHife1JccY7v9qFcmpmc4OSSmlrpjDEpQxJgt4BPgV2AZ8Z4yJE5ERIjLCtllDIE5EtmP19nvcUfHkWvQmfNwKln8CaY7NhaUppmIg4+9uzYGTKTw2Yz3ZOdqzTylVtrnfiLo75sOSD+HgCvAJgpZ3Q/sREB5TYjE609crD/Di7M08eE0sL9zU0NnhKKVUkYoaUdertINxuvo3WtOhdbByPKz+ElZ9Dg17wVWPQfUCr1OZcWf7mmw7cprP/txD/crB9Gt1UadIpZQqE9y31FG1VtDvc3hiM3R6HHYvhgk9YOKNsHMhlLGWZV6v9GpEh9hwnp+1mQ0Hk5wdjlJKXRb3TVDnhVSBa0fDU3Fw/f9B0gGY3h/Gd4bNM8tkhwpvTw8+uas1UcG+jJi6luNn0p0dklJKFZsmqPN8g6Hjw/DYeujzCWSnww9DYVw72DADsstWYdbwQB8+u6c1p1IyeORrrdmnlCp7NEHl5+UDLe+Ch1fCgCng5Q9zRsDY1rB+WplKVI2rhvLWbU1Zufckb83f7uxwlFKqWDRBFcbDAxr1gRF/wcAZ4B8GP46EcW1h4zdl5tZf35bVue+qaL5cspe5Gw9fegellHIRmqAuRQQa3AQPLIJB34BPIMx+ED7pAHFzykRnihdvakjb6DCem7lJh4xXSpUZmqDsJWJ1Tx/+JwyYCuIB3w+Gz7vCrt9cOlH5eHkw7s5WBPp68dD0tZzVgQ6VUmXAJROUiHQSkYUi8reI7BGRvSKypzSCc0keHtCoNzy0DG4dDyknYVo/mNIbDq93dnSFigrx4+NBLdmXeI4XZukYUkop12dPC+pL4H3gaqAt0Mb26d48PKHFIHh0DdzwNiTEWa2pH4bBqf3Ojq5AHWtH8PR19flp42GmrnDNGJVS6jx7ElSyMWa+MeaYMebE+cnhkZUVXr7QYYTVPb3z07DtJxjbBha+4pK1/h7qUpvuDaJ4/b9b9SVepZRLsydBLRKRd0Sko4i0Oj85PLKyxi8UerwCj66DJv1h6UcwpiWsmehSXdM9PIT3BzQnKtiPkdPXkZyilc+VUq7pksViRWRRAYuNMaa7Y0Iq2hUXiy0th9fDLy/CgWVQqQnc8BbEdHZ2VLnWHTjFgPHLubZhJT69uxUiBY0vqZRSjlVUsdhLtqCMMd0KmJySnMqUqi3h/nlw+2TrVt/kW+C7wVYpJRfQqmYYz95Qn1/ijurzKKWUS7KnF1+oiLwvImts03siEloawZV5ItD4VnhkFXR9Ef7+Bca2gz/fgSzn18cbdnUs3epH8u//bmPLoWRnh6OUUhew5xnUROAMMMA2nQYmOTKocsfbH7o+B4+shro94fd/wycdrfennMjDQ3hvQAvCA3145Ot1+n6UUsql2JOgahtjXjXG7LFNrwGxjg6sXKpQE+6YCnf9ABjr/anvBsPpI04LKTzQhzGDWnLgZAqv/LjFaXEopVR+9iSoVBG5+vyMiHQCUh0Xkhuoey08vAK6vWSN8Du2Laz8zGn1/drFhPNo97rMWneIOesPOSUGpZTKz54E9RAwTkT2ich+YCwwwrFhuQEvX+jyLDy8HGq0hfnPWgMmHtnklHAe7V6HNrXCeHnOFvafOOeUGJRSKi97evFtMMY0B5oBTY0xLY0xGx0fmpuIqA13z4LbvoTkeKsaxcJXIbN0G6lenh58OLAFHgKPfbNBx49SSjldoQlKRO62fT4lIk8Bw4BheeZVSRGBpv1h5CqrfNLSD61OFHv/LNUwqocF8NZtzdh4MIn3F/5dqudWSqn8impBBdo+gwuYghwcl3sKCIc+42DwT1bSmtwLfnoc0kqvC/hNTaswsG0Nxv+xmxV7tKKVUsp57Kkk0ckYs/RSy0pLmakkcaUyUmDxm7B8HARVhl4fQb3rSuXU59KzuHnMX2Rk5TD/8WsIDfAulfMqpdzPFVWSAD62c5kqST4BcN2/Yej/rDp/X98Ocx6G1CSHnzrQ14uPBrbk2Jl0XpqjQ3MopZzDq7AVItIRuAqIzPfMKQTwdHRgyqZ6a3jwD/jjbVjyIexeBL3HWC/8OlDzGhV4smc93vl1B90bRNGvVXWHnk8ppfIrqgXlg/WsyYsLnz+dBvo7PjSVy8vXqpQ+7H/gFwLT+8PcRyHdscO3j+hSm3bR4bzyYxwHT6Y49FxKKZWfPc+gahljXKaaqNs8gypMVjosehOWjYHQ6nDrpxB99aX3u0wHT6Zw40d/0ahKCDOGd8DTQ6ueK6VKzpU+g0qxjQc1T0R+Pz+VcIzKXl6+0PM1uH8+iCd8dQv8+hJkpjnkdDXCA3itd2NW7TvJ53/uccg5lFKqIPYkqOnAdiAGeA3YB6x2YEzKHjU7wIgl0GYILB8LX3SDo5sdcqp+rapxU9PKvL9wB3GHteq5Uqp02JOgIowxXwKZxpg/jDFDgA4OjkvZwzcIbnkf7poJKSfgi+7WSL4lXNNPRHjj1qaEBfjw5LcbSMt0Ts1ApZR7sSdBnR8T/IiI3CwiLQHt0uVK6vaEh5ZDveth4SswpY9VNqkEhQX68M7tzfk74Szv/LqjRI+tlFIFsSdB/ds2QOHTwChgAvCkQ6NSxRcYAQOmQu+xcGgdfNoJ4maX6Cm61Ivk3o61+HLJXpbtTizRYyulVH6X7MXnaty+F589TuyGWQ/AobXQ4i648T/W7cASkJKRxc1jlpCRlcMvT3Qm2E+rTCilLl9RvfgKTVAi8jFQaPYyxjxWMuEVjyYoO2VnWi/3/vkuhMdC/y+hassSOfS6A6fo/+ky+rWqzru3Ny+RYyql3NPldjNfA6wtYlKuzNMbur8M9/0XstJgQk9YOgZyrnwYjVY1w3i4ax1mro3n17ijJRCsUkpdTG/xuYOUk1blie3/hdo9oO94CIq6okNmZOVw67ilJJxO49cnr6FikG8JBauUcidX9KKuiCzK+4JucV7UFZEbRGSHiOwSkecLWB8qIj+JyEYRiROR++05riqmgHC4Yxrc/D7sW2J1oNi96IoO6ePlwQd3tOBMWhYvzdaCskqpkmdPL75RwDO26V/ABqzbf0USEU9gHHAj0AgYJCKN8m02EthqG7G3K/CeiPjYG7wqBhFoOxSGLwL/MJjaF/73GmRnXfYh61cO5qnr6vFrXAJzNhwqwWCVUsq+Id/X5pmWGmOeAtrbcex2wC5jzB5jTAbwDdAn/+GBYBERrMK0J4HL/42pLq1SYxi+GFrdA0veh69uvqJ3ph7oHEvrWmG88mMcR5JLd5h6pVT5Zs8tvvA8U0URuR6obMexqwEH88zH25blNRZoCBwGNgOPG2MueoovIsNFZI2IrDl+/Lgdp1ZF8gmA3h9DvwmQsAXGXw075l/WoTw9hPdub05WtuHZmZv0Vp9SqsTYc4tvLf/06FuO9cLuUDv2K6jsdf7fXtdj3TKsCrQAxopIyEU7GfO5MaaNMaZNZGSkHadWdml2Ozz4p1UVfcZAq+hsdual98snumIgL97UgL92JjJt5QEHBKqUckf23OKLMcbE2j7rGmOuM8YssePY8UCNPPPVsVpKed0PzDKWXcBeoIG9wasSEFHbGrW37QNW0dlJN0JS8ZPM3R1q0bluRf5v3jYOnNCxo5RSV86eW3x+IvKUiMwSkR9E5EkR8bPj2KuBuiISY+v4MBCYm2+bA0AP23kqAfUBHdOhtHn7wc3vwu1fwbHtML5zsW/5iQhv39YMTxFGfb+RnBy91aeUujL23OKbAjQGPuafZ0ZTL7WTMSYLeAT4FdgGfGeMiRORESIywrbZ68BVIrIZ+A14zhijRd6cpXFfa3j5CjWtW34L/lWsW35VK/jzSq9GrNp3kolL9zowUKWUO7BnRN2Ntm7gRS4rLfqibinITINfX4Q1X0KN9tB/EoTm799SMGMMD0xZw587E5n32NXUiQp2cLBKqbLsSkfUXS8iueM/iUh7YGlJBadckLefNc7UbV9CQhx81hl2/WbXriLCm/2aEuDjydPfbSQr+8pLKyml3JM9Cao9sExE9onIPqyefF1EZLOIbHJodMq5mva33pkKqgTTboNFb9o1GGJUsB+v92nCxvhkPtNh4pVSl8nLjm1ucHgUynVVrAvDfoOfn7aqox9YYbWsgoru7t+reVV+iTvKh//7m271o2hU9aK3B5RSqkj2dDPfD1QAetmmCsaY/ecnB8enXIFPANz6iTUY4sGV1i2//csvudvrfZoQ6u/N099vJCNLb/UppYrHnm7mjwPTgSjbNE1EHnV0YMrFiFjlkYYuBC8/q0TSso+hiE424YE+vNm3KduOnGbs7ztLMVilVHlgzzOooUB7Y8wrxphXgA7AA44NS7msKs2srugNboIFL8O3d0NqUqGbX9e4Mv1aVWPc4t1sPFj4dkoplZ89CUqAvE/Gsym4jJFyF36hMGAqXP8m/P0LfN4VjhTeX+bVXo2JDPLl6e83kpZ56U4WSikF9iWoScBKERktIq8BK4AvHRuWcnki0HEk3PezbcTea2HdlAI3DfX35u3+zdh17CzvL/y7lANVSpVV9nSSeB+rZt5J4ARwvzHmQwfHpcqKmh3gwb+sz7mPwpyRkHnxsBtd6kVyZ/uafPHXHlbvO+mEQJVSZY09LajzBKsaud7eUxcKioR7ZsM1z8CGaTChJ5zYfdFmL97UkOph/oz6fiMpGTrsl1KqaPb04nsFmAyEARWBSSLysqMDU2WMhyd0fxnumgmn463nUtt+umCTIF8v3unfnAMnU/i/edudE6dSqsywpwU1CGhrjBltjHkVqxffXY4NS5VZdXtaY0xF1LF6+C14+YKCsx1iIxjSKYapK/azZKfWBVZKFc6eBLUPyDu8hi9w8f0bpc6rUBOG/GKNMbXsY5jcC04fyV39zPX1qRMVxDMzN5KcWvwBEpVS7sGeBJUOxInIVyIyCdgCnBWRMSIyxrHhqTLLy9caY6rfBDiy0ao+sWcxAH7enrw/oDnHzqTz2k9xzo1TKeWy7ElQs4EXgUXAYuAlYD7WEPBrHRaZKh+a3Q4PLAL/cJjaF/54B3JyaFa9AiO71WHWukP8GnfU2VEqpVzQJceDcjU6HlQZlX4W/vsEbP4e6vSEfp+T4VOBvp8s5WhyGr8+eQ0Vg3ydHaVSqpRd6XhQSl053yDo9wXc/D7s/QPGd8bnyFo+uKMFZ9KzeGHWZsraf5aUUo6lCUqVHhFoOxSGLrC6pU+6gXp7p/FMz3os3JrAzLXxzo5QKeVCCk1QIjLV9vl46YWj3ELVllbB2brXwy/PM+zoaLrW8uW1n7Zy8GSKs6NTSrmIolpQrUWkFjBERMJEJDzvVFoBqnLKPwwGToeeryPbf2ZC2tM0ZA+jvt9ITo7e6lNKFZ2gxgO/AA34p8fe+Ul7KagrJwKdHoP75+GVk8E3nv+i7oFv+fIvHSZeKVVEgjLGjDHGNAQmGmNijTExeabYUoxRlXc1O8CIJXjEduHf3pOo+ttI/j5wyNlRKaWczJ5q5g+JSHMRecQ2NSuNwJSbCYxA7vyec51f4nqPlQR+1YP0g+udHZVSyonsKRb7GBcO+T5dh3xXDuHhQWCPZ9nYYxoe2el4TuwJqycUOay8Uqr8sqeb+TB0yHdVilp3vpmJTabwV1Yj+Plp+H5wkcPKK6XKJx3yXbmkJ/t05PWQ0Yz1vBez/Werll+8VtZSyp0Ud8j30eiQ76oUBPh48cHAVnyYeiPvVfvIqjIx8TpYOgZycpwdnlKqFBR3yPdT6JDvqpQ0r1GBJ3vWY+zOMH7q8C3UvxEW/gum94ezx5wdnlLKwewqdWSMWWfrdv6RMUa7VqlSM6JLbdrFhPPC/IPs7zHequW3fyl82gl2/+7s8JRSDqS1+JRL8/QQPrijBR4ewhPfbSSr1f3W8B0BtuE7FvwLsjKcHaZSygE0QSmXV62CP2/2bcr6A0mM+W0nVGpkJak2Q2DZGPiyJyTucnaYSqkSpglKlQm9mlfltlbVGbtoFyv2nACfALjlA7hjOiTtt3r5rZui70wpVY7Y86JuPxHZKSLJInJaRM6IyOnSCE6pvF7r05haEYE8+e0GTp2z3dZreAs8tAyqtYa5j8J390DKSecGqpQqEfa0oP4D9DbGhBpjQowxwcaYEEcHplR+Qb5ejBnYksSz6Tz3w6Z/BjgMqQr3zoWe/w92/AKfdNQOFEqVA/YkqARjzDaHR6KUHZpWD+W5GxqwYGsC01Ye+GeFhwd0ehwe+A38Qq0OFPOfg8xU5wWrlLoi9iSoNSLyrYgMst3u6yci/RwemVKFGNIphq71I3n9v1vZdiTf3eYqza3BENs/BCvHw2fXwGF9M0KpssieBBUCpADXAb1s0y2ODEqponh4CO/d3pwK/t6M/Hod59KzLtzA2x9ufAvumQPpZ2HCtbD4bcjOdEq8SqnLI8aBvZ5E5AbgI8ATmGCMeSvf+meAu2yzXkBDINIYU+hT7jZt2pg1a3S8RAUr9pzgzi9W0KdFNd4f0ByRAkpEpp6Cec/A5u+toeZvHQ9RDUo/WKVUgURkrTGmTUHr7OnFV11EZovIMRFJEJEfRKS6Hft5AuOAG4FGwCARaZR3G2PMO8aYFsaYFsALwB9FJSel8uoQG8ET19Zj9vpDfL8mvuCN/MPgtglw+2Q4td+65bfsY8jJLnh7pZTLsLdY7FygKlAN+Mm27FLaAbuMMXuMMRnAN0CfIrYfBMyw47hK5RrZrQ5X1Y7glblb2HH0TOEbNr4VRq6EOtfCgpdh0o36cq9SLs6eBBVpjJlkjMmyTV8BkXbsVw04mGc+3rbsIiISANwA/FDI+uEiskZE1hw/ftyOUyt34ekhfDiwBUG+3jw0fS1n8z+PyisoCgZOh76fw/EdML4TLBurrSmlXJQ9CSpRRO4WEU/bdDdwwo79ChozqrAHXr2ApYXd3jPGfG6MaWOMaRMZaU9uVO4kKtiPjwe1ZF/iOV6YtZkin6uKQPM7rNZUbDdY8BJMvB6ObS+9gJVSdrEnQQ0BBgBHgSNAf9uyS4kHauSZrw4cLmTbgejtPXUFOtaOYNT19flp42Gmrth/6R2CK8OgGdBvApzYbZVK+uMd7emnlAtxWC8+EfEC/gZ6AIeA1cCdxpi4fNuFAnuBGsaYc5c6rvbiU4XJyTE8MGUNf+48zncPdqRlzTD7djx7HOY/C3GzoFIT6DUGqrd2bLBKKeAye/GJyLO2z49FZEz+6VInNcZkAY8AvwLbgO+MMXEiMkJERuTZtC+wwJ7kpFRRPDyE9wY0p1KIHyOnr+PE2XT7dgyKhNsnwcCvrTp+E3rA/OchvYhOF0ophyu0BSUivYwxP4nI4ILWG2MmOzSyQmgLSl3K5vhkbhu/jLbRYUy+vx1ensUo2p92Gn77f7B6AoRUg5vegQY3OS5YpdzcZbWgjDE/2b6mGGMm552wKkso5ZKaVg/ljVubsHTXCd75dUfxdvYLgZvfhaELrO/fDIIZd0LSwUvvq5QqUfb81/IFO5cp5TJub1ODezrU4rM/9/DzpiPFP0CNdvDgn1aF9D2LYFw7WPqRdqJQqhR5FbZCRG4EbgKq5XvmFAIU8bKJUq7hX7c0Iu5wMs/M3EidqCDqVw4u3gE8va0K6Y37wrxnYeErsOFr67ZfzDWOCVoplauoFtRhYA2QBqzNM80Frnd8aEpdGR8vDz69uzWBvl48MGXNP4McFleFmnDnNzDoG2v4jsm9YOYQSD5UsgErpS5wyW7mIhICnDPGZNvmPQFfY4xTnkNpJwlVXOsOnGLgZytoEx3G5CHt8C5Op4n8MlNhyYew5APw8ITOT0PHR8Dbr8TiVcqdXFGxWGAB4J9n3h/4X0kEplRpaFUzjDf7NWXZ7hO88fMVjr3p7Q/dXrAqUdTuDr+/Dp+0h23/BQeODKCUO7InQfkZY86en7F9D3BcSEqVvP6tqzPs6hi+WraPb1YduPQOlxIeY9X1u2cOePnBt3dZt/6ObLzyYyulAPsS1DkRaXV+RkRaAzqOtipznr+xAdfUi+TlOVtYvtuecpJ2qN0NRiyFm96FhDj4rAvMGQmnC6vqpZSylz3PoNpiDZVx/l9cFeAOY8xaB8dWIH0Gpa5Ecmomt326jONn0pn98FXERgaV3MFTk+DPd2DV5yCe0HGk1QvQL6TkzqFUOVPUMyi7avGJiDdQH6tC+XZjjNNeBtEEpa7UwZMp3DpuKSH+3sx66CrCAn1K9gSn9sFvr8OWmRAQAV2eg9b3gZdvyZ5HqXLgSjtJgJWcGgEtsUbGvbekglOqtNUID+Dze1tzKCmVB6etJSMrp2RPEBYN/b+EBxZBVCOrEO3HbWDDDB17SqlisGfI91eBj21TN+A/QG8Hx6WUQ7WuFc47/Zuxau9Jnp25segxpC5XtVYw+Ce4exYEhMGcEfBpJ9g6V3v8KWUHe1pQ/bGGzDhqjLkfaA7ovQpV5vVpUY1nrq/PnA2Hi1+zz14iUKcHPLAY+k+CnCz47h747BrY8YsmKqWKYE+CSjXG5ABZtpd2jwGxjg1LqdLxcNfa3Nm+Jp8s3m3fQIeXy8MDmvSDh1fAreMh/TTMuAO+6K6JSqlC2JOg1ohIBeALrFJH64BVjgxKqdIiIvy/3o25tmEUr/64hQVxRx17Qk8vaDEIHlljDYyYcsJKVJ9dA9t+gpwSfh6mVBlWZC8+ERGgujHmoG0+GggxxmwqnfAupr34lCOkZGQx6IuVbD9ymilD2tE+NqJ0TpydCZu+s7qnn9oLkQ3g6qegyW1WMlOqnLvsXnzGyl5z8szvc2ZyUspRAny8mHRfW6qF+TNs8hriDieXzok9vaHlXVaLqt8EEA+YPRw+bgWrvoAMHXpNuS97bvGtsL2sq1S5Fh7ow7Sh7Qn282LwxFXsTTxXeif39IJmt1tVKQbOgKAomDcKPmgMi96Ec4mlF4tSLsKeShJbsd6D2gecw3pZ1xhjmjk8ugLoLT7laLuOnWXAZ8sJ8PFk5oirqBzqhErlxsCBFdYgiX/Pt+r9NRsAHR6GqIalH49SDnJZlSREpKYx5oCI1CpovTHGgV2eCqcJSpWGTfFJ3PnFSqJCfPl2eEcig534ZsXxHbDiE9j4DWSlQWw3aD8C6va0hvxQqgy73AS1zhjTyvb9B2PMbQ6M0W6aoFRpWb3vJPd+uYpaEQHMeKBDyZdEKq5zJ2DtJFg9Ac4csSpWtB0GLe8G/zDnxqbUZbrcThKS57u+96TcTtvocCYMbsOexHPcM3ElyalOK0FpCYyAa0bBE5utl36Dq8KCl+G9BjDnYYhfo+9TqXKlqARlCvmulNvoVKcin93dmh1Hz3DvxFXOT1Jg9fxr0g+GzIcRS6DFnbD1R5jQA8Z3tnr/pZ5ydpRKXbGibvFl80+nCH/gfH/X850knDKGgN7iU86wIO4oI79eR8MqIUwd0p7QAG9nh3Sh9DOw+XtYMwmObrI6VTTsDa3ugVpXW5UslHJBVzzchivRBKWc5X9bE3h4+jrqVQ5i2tD2VAhw8jOpwhzeAOunwqbvIT0ZQmtC8zug+SCIqO3s6JS6gCYopUrIou3HeHDqWupEBTF1aDsigly4bnJmKmz/GTbOgN2/g8mB6m2h6QDrFmFgRWdHqJQmKKVK0uIdVpKqHubP9GEdnPOeVHGdPgKbv7NaVQmbrRF/a3eDxv2gwc3gX8HZESo3pQlKqRK2Ys8Jhk1eQ4UAb6YPa0+tiEBnh2S/hK1WstryAyQdAE8fqHMtNOoD9W7QZKVKlSYopRxgU3wSgyeuwsvTg2lD21O/crCzQyoeY+DQOoibBXGz4fQh8PCG2C7QsBfUuxGCKzk7SlXOaYJSykF2Jpzh7i9XkpqRzYTBbWkXE+7skC5PTg4cXmd1V982F07tA8R6ZtXgJqtlFdnAGoBRqRKkCUopB4o/lcLgias4eCqVD+9owU1Nqzg7pCtjDBzbanWw2P5fOLLRWl6hlpWo6l4H0Z3A29+5capyQROUUg6WlJLB0MlrWHfgFK/c0oj7O8U4O6SSk3wIdi6Av3+FPYshK9V6zyr6aqjdw+psoa0rdZk0QSlVCtIys3lsxnoWbE3gvquiefnmhnh5lrMXZDNTYf9S2PUb7FwIJ3Zay4OrQGxXiOkCMddAaDWnhqnKDk1QSpWS7BzDm/O28eWSvXStH8nHg1oS7OdiVSdKUtIBq1W1e5H1mXrSWh5e22phRV8NtTppwlKF0gSlVCn7euUBXvlxC7Ujg5gwuA01wgOcHZLj5eRYz672/gl7/4D9y61KFmA9v6rZEWp2sKaK9bX8kgI0QSnlFEt3JfLQtLV4egjj7mzFVXXcrHJDTjYkbIH9y6zbggdWwLnj1jq/UKjWBmq0s3oKVmulQ4a4KU1QSjnJ3sRzDJ+yht3Hz/LiTQ0ZenUM4q6dCYyBk3vgwHI4uMoaHuTYVnIHSwivDdVaQ9WWULUFVG4GvkHOjFiVAqclKBG5AfgI8AQmGGPeKmCbrsCHgDeQaIzpUtQxNUGpsuZsehajvtvIL3FH6d28Kv/XrymBvl7ODss1pJ223r86tA4OrbWmM0dsKwUq1rUSVeWmUKWZ9V1rCJYrTklQIuIJ/A30BOKB1cAgY8zWPNtUAJYBN9iGl48yxhwr6riaoFRZZIzhk8W7eW/BDmIqBvLJXa3LXuWJ0nLmqFWR/cgG6x2so5sh+eA/64MqQaXGENXINjWEyPrgU4bKTalczkpQHYHRxpjrbfMvABhj/i/PNg8DVY0xL9t7XE1QqixbtiuRx77ZwNn0TF7v04Tb29RwdkhlQ8pJK1ElxFnPtRK2wLHtkJ1u20CgQk0rUUXWh4r1IKKu9RkY4dTQVdGKSlCOvM9QDcjz3x7igfb5tqkHeIvIYiAY+MgYMyX/gURkODAcoGbNmg4JVqnScFWdisx7/Goen7GBZ2ZuYumuRP7frU0IKc9d0UtCQLhVIzA2zxOAnGw4uReOb4Nj2+D4djj+N+z5I0/iwup8EV4bIupY42GFx0J4jPWpHTNcmiMTVEFPgvM317yA1kAPrFF7l4vICmPM3xfsZMznwOdgtaAcEKtSpSYq2I9pw9oz9vddjPl9J2v2n+LDO1rQJrqM1vFzFg9PqFjHmhr2+md5TjYk7YfEXdaLxIk74eRu2PcXbPrmwmP4VYCwaAirZXWFP/9ZoSaE1gAfN3g9wIU5MkHFA3nvX1QHDhewTaIx5hxwTkT+BJpjPbtSqtzy9BAev7YuV9etyJPfbmDAZ8sZ2a0Oj3avi4+Xvh90RTw8ba2kWOC6C9dlpFiFcE/ugVN7bZ/7rVuHO+ZDdsaF2wdEQEg1K1mFVrO+h1SDkKoQUsWqoKE1CR3Gkc+gvLASTQ/gEFYniTuNMXF5tmkIjAWuB3yAVcBAY8yWwo6rz6BUeXM2PYvRc+OYuTaehlVCePf2ZjSuGurssNxPTg6cTbCqYyQdsFphpw9BcjwkHYTTh/958Tgv/zArUQVXhqDK1hAlQZUhKMqaAqMgKNJqrbnrKwZFcGY385uwupB7AhONMW+IyAgAY8x42zbPAPcDOVhd0T8s6piaoFR5tXBrAi/O3sypcxmM7FaHkd3qaGvK1aSfsRLV6UPWKMVnDlufZxOs7vFnEuDsUcjJunhfD28IjLQ6bQRGQkBFq8t8QLj1PSDC+u4fbvsMAy/f0v8ZS5m+qKtUGZGUksHouXHM2XCYepWCeLNvU302Vdbk5EBaki1pHYVziXDuGJw9ZlXSOJcIKYnW95STkHG28GN5B1qJyr+C1QLzr/DPd78KVkUOvxDwDbn40zcEPF3/fTtNUEqVMb9tS+CVH+M4lJTKoHY1ef7GBoT6a0+/cikzzSqym3LCSlgpJ6z51FOQmmQtSz1lJb3UJOsz7TRknrv0sb38wTf4n8knyKrO4RNkvTfmG2x9+gSCd4BteYCVGH0CrGXeARd+9/It0VuVzupmrpS6TD0aVqJDbAQfLPybiUv3snDrUZ69oQH9W1XHw0OfY5Qr3n7gXdXqeFEcWRmQlgzpp/N9nrGmtNPWsoyz/yxLP2vdnkw/C5kptk87Et0FxOoY4uVnvXM25Jdi7l+MM2kLSinXtuVQMq/OjWPt/lO0qFGB13o3pnmNCs4OS5UXOTlWsspMgYxz1nT+e2aK1fMxK9UaCyzjHGSl2bZPtW4/dre7zkKB9BafUmWcMYbZ6w/xf/O3c/xMOn1bVmPU9fWpVkG7OKuyragEpV2ElCoDRIR+rarz+9NdGNGlNj9vPkK3dxfz1vztnE7LdHZ4SjmEtqCUKoPiT6Xw3oK/mb3+EKH+3jzYJZb7roomwEcfK6uyRW/xKVVObTmUzHsLdrBox3EqBvnwUNc63NW+Jn7ens4OTSm7aIJSqpxbu/8k7/76N8v3nKBikC8PdI7hrg61CNJxp5SL0wSllJtYsecE4xbt4q+diVQI8GZwx2ju6ViLikHlvyKBKps0QSnlZtYfOMW4Rbv437Zj+Hh5cFur6gy9OoY6UTqEunItmqCUclO7jp3lyyV7+GHdITKycuhctyKDO0bTrUEUnvrCr3IBmqCUcnOJZ9P5euUBpq/cT8LpdKqH+XNn+5r0b1WdqBA/Z4en3JgmKKUUAJnZOSzcmsDkZftYufcknh5CjwZRDGxXg2vqRuLlqa9GqtKltfiUUgB4e3pwU9Mq3NS0CruPn+W71QeZuTaeBVsTqBjkQ+/m1ejXqhqNq4YgOnaRcjJtQSnl5jKycli04xiz1x3i9+3HyMjOoXZkILc0q0qv5lWoExXs7BBVOaa3+JRSdklKyeDnzUf4aeNhVu49iTFQv1Iw1zepzPWNK9GoirasVMnSBKWUKraE02nM23yE+ZuPsnq/laxqhPtzbcNKdG8QRbuYcHy9tGKFujKaoJRSVyTxbDr/25rAgq0JLN2VSHpWDoE+nnSqU5HO9SK5pm5FakUEOjtMVQZpglJKlZjUjGyW7U7k9+3HWLzjOIeSUgGoGR7AVbUj6Fg7go6xEdp9XdlFE5RSyiGMMew7kcJfO4/z59+JrNx7gjNpWQDEVgykbXQ4baLDaBcTTs3wAH1+pS6iCUopVSqycwxbD59m+Z5EVu09yep9p0hOtcarigj0oWXNCrSsGUbz6hVoWi2U0ABvJ0esnE0TlFLKKXJyDLuOn2X1vpOsP5DE+gOn2H38XO76WhEBNK0WSuOqoTSqGkLjqiFa2NbNaIJSSrmMpJQMNh9KZlN8Mltsn+efYwFUDPKlQeVg6lUKpkHlYGpHBVEnKohQf21tlUdaSUIp5TIqBPjQuW4knetG5i5LTslk65HTxB1OZsfRM+xIOMPXq/aTlpmTu01UsC+1I4OIiQwktmIgMRUDqRURSI1wf+3uXk5pglJKOV1ogLfV+692RO6y7BzDwZMp7Dp2ll3Hz7Iz4Sx7E88yb/MRklIyc7cTgaqh/tQI96d6WAA1wgKoHuZPtTB/qlXwp3KoH95aY7BM0gSllHJJnh5CdMVAoisGci2VLlh36lwGexLPceDkOfafSGH/iRQOnrR6EyacTr9gWxGr9VU51J8qIX5UDvWjUogflUJ8qRTiR1SwL5HBvoT6e2svQxejCUopVeaEBfrQOtCH1rXCLlqXlpnN4aRUDielcSgphUOnUjmSnMbR02nsOn6WJbsSOZueddF+3p5CxSBfIoJ8iAg8/+lDeKAv4YHehAX4EBboQ1iANxUCfAj199aWmYNpglJKlSt+3p7ERgYRG1n46MHn0rM4diado8lpHD+bzvEz6Rw7k8aJsxmcOJtO4tkMdiac4cS5DNKzcgo9TpCvF6H+3oT4exPq70WIn/U92M+LYD9vQvy8CPbzIsjXmyA/L4J8PQn09SLQx8v69PXEx9NDW26F0ASllHI7gb5exPh6EVPx0uWZUjKyOHE2g6SUTE6lZHAqxfqenJpJUkomSakZnE7N4nRqJgdOppCcmsmZtKwCW2kF8fIQAnw8CfDxIsDHE38fTwJ8PPHz9sTf+5/v5yd/b0/8vD3w9fLAz9sTX28PfL088fWyPn28rHU+5yfPCz+9PT3w9pQykRQ1QSmlVBECfLwICPeiRnjx9svOMZxNz+JcupWsziet8/Pn0rNIycjO/UzJsD5TM7JJycjmTFoWx8+kk5ppzadlZpOemUNGduEtuuLw9hRbsvLI/e51fpmH9d3L0wNvD8HTw1pufVrzXp4eVAnx4+VbGpVIPAXRBKWUUg7g6SGE+nuX+PtbWdlWkkrLzCEtM5uMrBzSs3JIz8omLTOHjKwcMrL/SWbpWTlkZlvLz39mZOWQmWPIzMohK8eQnpVDVrb1PSPb9j3bkJljcpenZGSRlWPIzjFkZRuycnI4nZp56YCvgCYopZQqQ7w8PfDy9CDAx9mROJ52QVFKKeWSNEEppZRySZqglFJKuSRNUEoppVySQxOUiNwgIjtEZJeIPF/A+q4ikiwiG2zTK46MRymlVNnhsF58IuIJjAN6AvHAahGZa4zZmm/Tv4wxtzgqDqWUUmWTI1tQ7YBdxpg9xpgM4BugjwPPp5RSqhxxZIKqBhzMMx9vW5ZfRxHZKCLzRaRxQQcSkeEiskZE1hw/ftwRsSqllHIxjkxQBRV6yj987zqgljGmOfAxMKegAxljPjfGtDHGtImMjCxoE6WUUuWMIytJxAM18sxXBw7n3cAYczrP93ki8omIVDTGJBZ20LVr1yaKyP4rjK0iUOg53JRek4vpNSmYXpeL6TW5mL3XpFZhKxyZoFYDdUUkBjgEDATuzLuBiFQGEowxRkTaYbXoThR1UGPMFTehRGSNMabNlR6nPNFrcjG9JgXT63IxvSYXK4lr4rAEZYzJEpFHgF8BT2CiMSZOREbY1o8H+gMPiUgWkAoMNMbkvw2olFLKDTm0WKwxZh4wL9+y8Xm+jwXGOjIGpZRSZZO7VpL43NkBuCC9JhfTa1IwvS4X02tysSu+JqJ31JRSSrkid21BKaWUcnGaoJRSSrkkt0tQlypg6w5EpIaILBKRbSISJyKP25aHi8hCEdlp+wxzdqylTUQ8RWS9iPzXNu/W10REKojITBHZbvv70lGviTxp+3ezRURmiIifO14TEZkoIsdEZEueZYVeBxF5wfZ7d4eIXG/POdwqQeUpYHsj0AgYJCKNnBuVU2QBTxtjGgIdgJG26/A88Jsxpi7wm23e3TwObMsz7+7X5CPgF2NMA6A51rVx22siItWAx4A2xpgmWK/QDMQ9r8lXwA35lhV4HWy/XwYCjW37fGL7fVwkt0pQaAFbAIwxR4wx62zfz2D90qmGdS0m2zabDNzqlACdRESqAzcDE/IsdttrIiIhwDXAlwDGmAxjTBJufE1svAB/EfECArAq5LjdNTHG/AmczLe4sOvQB/jGGJNujNkL7ML6fVwkd0tQ9hawdRsiEg20BFYClYwxR8BKYkCUE0Nzhg+BZ4GcPMvc+ZrEAseBSbbbnhNEJBA3vibGmEPAu8AB4AiQbIxZgBtfk3wKuw6X9bvX3RKUPQVs3YaIBAE/AE/krYvojkTkFuCYMWats2NxIV5AK+BTY0xL4BzuceuqULZnKn2AGKAqECgidzs3qjLhsn73uluCumQBW3chIt5YyWm6MWaWbXGCiFSxra8CHHNWfE7QCegtIvuwbv12F5FpuPc1iQfijTErbfMzsRKWO1+Ta4G9xpjjxphMYBZwFe59TfIq7Dpc1u9ed0tQuQVsRcQH66HdXCfHVOpERLCeK2wzxryfZ9VcYLDt+2Dgx9KOzVmMMS8YY6obY6Kx/l78boy5G/e+JkeBgyJS37aoB7AVN74mWLf2OohIgO3fUQ+sZ7jufE3yKuw6zAUGioivrYB4XWDVpQ7mdpUkROQmrGcN5wvYvuHciEqfiFwN/AVs5p/nLS9iPYf6DqiJ9Q/xdmNM/oeg5Z6IdAVGGWNuEZEI3PiaiEgLrE4jPsAe4H6s/9i68zV5DbgDqzfsemAYEISbXRMRmQF0xRpWIwF4FWtMvwKvg4i8BAzBum5PGGPmX/Ic7paglFJKlQ3udotPKaVUGaEJSimllEvSBKWUUsolaYJSSinlkjRBKaWUckmaoJS6BBGJEJENtumoiByyfT8rIp846JxPiMi9BSyPzls9ugTO4yMif9rqyinlUvQvpVKXYIw5AbQAEJHRwFljzLuOOp8tWQzBqtrgUMaYDBH5Deu9numOPp9SxaEtKKUuk4h0zTNu1GgRmSwiC0Rkn4j0E5H/iMhmEfnFVloKEWktIn+IyFoR+fV8WZh8ugPrjDFZefbZKCLLgZF5zh8tIn+JyDrbdJVt+VQR6ZNnu+ki0ltEGovIKlvrb5OI1LVtMge4yxHXSKkroQlKqZJTG2u4jj7ANGCRMaYpkArcbEtSHwP9jTGtgYlAQZVMOgF5i9ZOAh4zxnTMt90xoKcxphVWC2iMbfkErIoPiEgoVq24ecAI4CNjTAugDVZ9NIAtQNvL/JmVchi9xadUyZlvjMkUkc1YpbR+sS3fDEQD9YEmwEKrjBueWEM25FcF26CJtgRTwRjzh23dVKwBNwG8gbG2ckTZQD0AY8wfIjJORKKAfsAPxpgsWwvsJdu4V7OMMTtt22eLSIaIBNvGB1PKJWiCUqrkpAMYY3JEJNP8U0csB+vfmgBxBbSE8ksF/GzfhcKHJXgSqwZac6y7IWl51k3Fum03EOt5FsaYr0VkJVYr71cRGWaM+d22vW++/ZVyOr3Fp1Tp2QFEikhHsIY8EZHGBWy3DagDYBvBNtlW4BcufFYUChwxxuQA92C1yM77CnjCdow42/ligT3GmDFY1aWb2ZZHAOeHj1DKZWiCUqqUGGMygP7A2yKyEdiA9Xwov/lYQ62fdz8wznaLLjXP8k+AwSKyAuv23rk850rASnST8mx/B7BFRDYADYAptuXdsJ5RKeVStJq5Ui5IRGYDz55/TnQZ+wdgPftqZYxJvsS2s4AXjDE7LudcSjmKtqCUck3PY3WWKDYRuRbYDnxsR3LyAeZoclKuSFtQSimlXJK2oJRSSrkkTVBKKaVckiYopZRSLkkTlFJKKZekCUoppZRL+v9+KVXsHIIvvgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results.S.plot(label='No immunization')\n",
-    "results2.S.plot(label='10% immunization')\n",
-    "\n",
-    "decorate(xlabel='Time (days)',\n",
-    "         ylabel='Fraction of population')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "postal-cemetery",
-   "metadata": {},
-   "source": [
-    "## Metrics\n",
-    "\n",
-    "When we plot a time series, we get a view of everything that happened\n",
-    "when the model ran, but often we want to boil it down to a few numbers\n",
-    "that summarize the outcome. These summary statistics are called\n",
-    "**metrics**, as we saw in Section xxx.\n",
-    "\n",
-    "In the SIR model, we might want to know the time until the peak of the\n",
-    "outbreak, the number of people who are sick at the peak, the number of\n",
-    "students who will still be sick at the end of the semester, or the total number of students who get sick at any point.\n",
-    "\n",
-    "As an example, I will focus on the last one --- the total number of sick students --- and we will consider interventions intended to minimize it.\n",
-    "\n",
-    "When a person gets infected, they move from `S` to `I`, so we can get\n",
-    "the total number of infections by computing the difference in `S` at the beginning and the end:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "synthetic-element",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_total_infected(results, system):\n",
-    "    s_0 = results.S[system.t0]\n",
-    "    s_end = results.S[system.t_end]\n",
-    "    return s_0 - s_end"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "recovered-picnic",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.468320811028781"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "calc_total_infected(results, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "american-transfer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.30650802853979753"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "calc_total_infected(results2, system2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adverse-trance",
-   "metadata": {},
-   "source": [
-    "Without immunization, almost 47% of the population gets infected at some point. With 10% immunization, only 31% get infected. That's pretty good."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eight-maximum",
-   "metadata": {},
-   "source": [
-    "## Sweeping Immunization\n",
-    "\n",
-    "Now let's see what happens if we administer more vaccines. This\n",
-    "following function sweeps a range of immunization rates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "progressive-architect",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_immunity(immunize_array):\n",
-    "    sweep = SweepSeries()\n",
-    "\n",
-    "    for fraction in immunize_array:\n",
-    "        sir = make_system(beta, gamma)\n",
-    "        add_immunization(sir, fraction)\n",
-    "        results = run_simulation(sir, update_func)\n",
-    "        sweep[fraction] = calc_total_infected(results, sir)\n",
-    "\n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "indie-seeker",
-   "metadata": {},
-   "source": [
-    "The parameter of `sweep_immunity` is an array of immunization rates. The\n",
-    "result is a `SweepSeries` object that maps from each immunization rate\n",
-    "to the resulting fraction of students ever infected.\n",
-    "\n",
-    "The following figure shows a plot of the `SweepSeries`. Notice that\n",
-    "the x-axis is the immunization rate, not time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "measured-pavilion",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "immunize_array = linspace(0, 1, 21)\n",
-    "infected_sweep = sweep_immunity(immunize_array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "interior-humanitarian",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "infected_sweep.plot()\n",
-    "\n",
-    "decorate(xlabel='Fraction immunized',\n",
-    "         ylabel='Total fraction infected',\n",
-    "         title='Fraction infected vs. immunization rate')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "turkish-mumbai",
-   "metadata": {},
-   "source": [
-    "As the immunization rate increases, the number of infections drops\n",
-    "steeply. If 40% of the students are immunized, fewer than 4% get sick.\n",
-    "That's because immunization has two effects: it protects the people who get immunized (of course) but it also protects the rest of the\n",
-    "population.\n",
-    "\n",
-    "Reducing the number of \"susceptibles\" and increasing the number of\n",
-    "\"resistants\" makes it harder for the disease to spread, because some\n",
-    "fraction of contacts are wasted on people who cannot be infected. This\n",
-    "phenomenon is called **herd immunity**, and it is an important element\n",
-    "of public health (see <http://modsimpy.com/herd>)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "french-spouse",
-   "metadata": {},
-   "source": [
-    "The steepness of the curve is a blessing and a curse. It's a blessing\n",
-    "because it means we don't have to immunize everyone, and vaccines can\n",
-    "protect the \"herd\" even if they are not 100% effective.\n",
-    "\n",
-    "But it's a curse because a small decrease in immunization can cause a\n",
-    "big increase in infections. In this example, if we drop from 80%\n",
-    "immunization to 60%, that might not be too bad. But if we drop from 40% to 20%, that would trigger a major outbreak, affecting more than 15% of the population. For a serious disease like measles, just to name one, that would be a public health catastrophe.\n",
-    "\n",
-    "One use of models like this is to demonstrate phenomena like herd\n",
-    "immunity and to predict the effect of interventions like vaccination.\n",
-    "Another use is to evaluate alternatives and guide decision making. We'll see an example in the next section."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "changed-capability",
-   "metadata": {},
-   "source": [
-    "## Hand washing\n",
-    "\n",
-    "Suppose you are the Dean of Student Life, and you have a budget of just \\$1200 to combat the Freshman Plague. You have two options for spending this money:\n",
-    "\n",
-    "1.  You can pay for vaccinations, at a rate of \\$100 per dose.\n",
-    "\n",
-    "2.  You can spend money on a campaign to remind students to wash hands\n",
-    "    frequently.\n",
-    "\n",
-    "We have already seen how we can model the effect of vaccination. Now\n",
-    "let's think about the hand-washing campaign. We'll have to answer two\n",
-    "questions:\n",
-    "\n",
-    "1.  How should we incorporate the effect of hand washing in the model?\n",
-    "\n",
-    "2.  How should we quantify the effect of the money we spend on a\n",
-    "    hand-washing campaign?\n",
-    "\n",
-    "For the sake of simplicity, let's assume that we have data from a\n",
-    "similar campaign at another school showing that a well-funded campaign\n",
-    "can change student behavior enough to reduce the infection rate by 20%.\n",
-    "\n",
-    "In terms of the model, hand washing has the effect of reducing `beta`.\n",
-    "That's not the only way we could incorporate the effect, but it seems\n",
-    "reasonable and it's easy to implement."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alpine-guest",
-   "metadata": {},
-   "source": [
-    "Now we have to model the relationship between the money we spend and the\n",
-    "effectiveness of the campaign. Again, let's suppose we have data from\n",
-    "another school that suggests:\n",
-    "\n",
-    "-   If we spend \\$500 on posters, materials, and staff time, we can\n",
-    "    change student behavior in a way that decreases the effective value of `beta` by 10%.\n",
-    "\n",
-    "-   If we spend \\$1000, the total decrease in `beta` is almost 20%.\n",
-    "\n",
-    "-   Above \\$1000, additional spending has little additional benefit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "closed-feature",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "agricultural-trinidad",
-   "metadata": {},
-   "source": [
-    "### Logistic function"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cheap-structure",
-   "metadata": {},
-   "source": [
-    "To model the effect of a hand-washing campaign, I'll use a [generalized logistic function](https://en.wikipedia.org/wiki/Generalised_logistic_function) (GLF), which is a convenient function for modeling curves that have a generally sigmoid shape.  The parameters of the GLF correspond to various features of the curve in a way that makes it easy to find a function that has the shape you want, based on data or background information about the scenario."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "blond-armstrong",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import exp\n",
-    "\n",
-    "def logistic(x, A=0, B=1, C=1, M=0, K=1, Q=1, nu=1):\n",
-    "    \"\"\"Computes the generalize logistic function.\n",
-    "    \n",
-    "    A: controls the lower bound\n",
-    "    B: controls the steepness of the transition \n",
-    "    C: not all that useful, AFAIK\n",
-    "    M: controls the location of the transition\n",
-    "    K: controls the upper bound\n",
-    "    Q: shift the transition left or right\n",
-    "    nu: affects the symmetry of the transition\n",
-    "    \n",
-    "    returns: float or array\n",
-    "    \"\"\"\n",
-    "    exponent = -B * (x - M)\n",
-    "    denom = C + Q * exp(exponent)\n",
-    "    return A + (K-A) / denom ** (1/nu)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "seven-budget",
-   "metadata": {},
-   "source": [
-    "The following array represents the range of possible spending."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "disturbed-amount",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spending = linspace(0, 1200, 21)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "supreme-nutrition",
-   "metadata": {},
-   "source": [
-    "`compute_factor` computes the reduction in `beta` for a given level of campaign spending.\n",
-    "\n",
-    "`M` is chosen so the transition happens around \\$500.\n",
-    "\n",
-    "`K` is the maximum reduction in `beta`, 20%.\n",
-    "\n",
-    "`B` is chosen by trial and error to yield a curve that seems feasible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "otherwise-answer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_factor(spending):\n",
-    "    \"\"\"Reduction factor as a function of spending.\n",
-    "    \n",
-    "    spending: dollars from 0 to 1200\n",
-    "    \n",
-    "    returns: fractional reduction in beta\n",
-    "    \"\"\"\n",
-    "    return logistic(spending, M=500, K=0.2, B=0.01)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "expected-venture",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "seventh-strike",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "percent_reduction = compute_factor(spending) * 100\n",
-    "\n",
-    "plot(spending, percent_reduction)\n",
-    "\n",
-    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
-    "         ylabel='Percent reduction in infection rate',\n",
-    "         title='Effect of hand washing on infection rate')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "legal-michigan",
-   "metadata": {},
-   "source": [
-    "The result is the following function, which\n",
-    "takes spending as a parameter and returns `factor`, which is the factor\n",
-    "by which `beta` is reduced:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "obvious-congress",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_factor(spending):\n",
-    "    return logistic(spending, M=500, K=0.2, B=0.01)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sunset-investing",
-   "metadata": {},
-   "source": [
-    "I use `compute_factor` to write `add_hand_washing`, which takes a\n",
-    "`System` object and a budget, and modifies `system.beta` to model the\n",
-    "effect of hand washing:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "polish-multiple",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def add_hand_washing(system, spending):\n",
-    "    factor = compute_factor(spending)\n",
-    "    system.beta *= (1 - factor)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "worthy-fellowship",
-   "metadata": {},
-   "source": [
-    "Now we can sweep a range of values for `spending` and use the simulation\n",
-    "to compute the effect:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "statistical-garden",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_hand_washing(spending_array):\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for spending in spending_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        add_hand_washing(system, spending)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[spending] = calc_total_infected(results, system)\n",
-    "        \n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "clinical-surge",
-   "metadata": {},
-   "source": [
-    "Here's how we run it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "joined-graduation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import linspace\n",
-    "\n",
-    "spending_array = linspace(0, 1200, 20)\n",
-    "infected_sweep2 = sweep_hand_washing(spending_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "designing-smile",
-   "metadata": {},
-   "source": [
-    "The following figure shows the result. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "cheap-decision",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "infected_sweep2.plot()\n",
-    "\n",
-    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
-    "         ylabel='Total fraction infected',\n",
-    "         title='Effect of hand washing on total infections')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "selective-right",
-   "metadata": {},
-   "source": [
-    "Below \\$200, the campaign has little effect. \n",
-    "\n",
-    "At \\$800 it has a substantial effect, reducing total infections from more than 45% to about 20%. \n",
-    "\n",
-    "Above \\$800, the additional benefit is small."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "lucky-boulder",
-   "metadata": {},
-   "source": [
-    "## Optimization\n",
-    "\n",
-    "Let's put it all together. With a fixed budget of \\$1200, we have to\n",
-    "decide how many doses of vaccine to buy and how much to spend on the\n",
-    "hand-washing campaign.\n",
-    "\n",
-    "Here are the parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "surrounded-copying",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_students = 90\n",
-    "budget = 1200\n",
-    "price_per_dose = 100\n",
-    "max_doses = int(budget / price_per_dose)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "expired-conditioning",
-   "metadata": {},
-   "source": [
-    "The fraction `budget/price_per_dose` might not be an integer. `int` is a\n",
-    "built-in function that converts numbers to integers, rounding down.\n",
-    "\n",
-    "We'll sweep the range of possible doses:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "shaped-utility",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dose_array = arange(max_doses+1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "occupational-reply",
-   "metadata": {},
-   "source": [
-    "In this example we call `linrange` with only one argument; it returns a\n",
-    "NumPy array with the integers from 0 to `max_doses`. With the argument\n",
-    "`endpoint=True`, the result includes both endpoints.\n",
-    "\n",
-    "Then we run the simulation for each element of `dose_array`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "recognized-release",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_doses(dose_array):\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for doses in dose_array:\n",
-    "        fraction = doses / num_students\n",
-    "        spending = budget - doses * price_per_dose\n",
-    "        \n",
-    "        system = make_system(beta, gamma)\n",
-    "        add_immunization(system, fraction)\n",
-    "        add_hand_washing(system, spending)\n",
-    "        \n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[doses] = calc_total_infected(results, system)\n",
-    "\n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cardiac-mitchell",
-   "metadata": {},
-   "source": [
-    "For each number of doses, we compute the fraction of students we can\n",
-    "immunize, `fraction` and the remaining budget we can spend on the\n",
-    "campaign, `spending`. Then we run the simulation with those quantities\n",
-    "and store the number of infections.\n",
-    "\n",
-    "The following figure shows the result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "worth-mounting",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "infected_sweep3 = sweep_doses(dose_array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "adjusted-highlight",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "infected_sweep3.plot()\n",
-    "\n",
-    "decorate(xlabel='Doses of vaccine',\n",
-    "         ylabel='Total fraction infected',\n",
-    "         title='Total infections vs. doses')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dynamic-easter",
-   "metadata": {},
-   "source": [
-    "If we buy no doses of vaccine and spend the entire budget on the campaign, the fraction infected is around 19%. At 4 doses, we have \\$800 left for the campaign, and this is the optimal point that minimizes the number of students who get sick.\n",
-    "\n",
-    "As we increase the number of doses, we have to cut campaign spending,\n",
-    "which turns out to make things worse. But interestingly, when we get\n",
-    "above 10 doses, the effect of herd immunity starts to kick in, and the\n",
-    "number of sick students goes down again."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "amino-excess",
-   "metadata": {},
-   "source": [
-    "## Summary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "timely-bottom",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Suppose the price of the vaccine drops to $50 per dose.  How does that affect the optimal allocation of the spending?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "drawn-hindu",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose we have the option to quarantine infected students.  For example, a student who feels ill might be moved to an infirmary, or a private dorm room, until they are no longer infectious.\n",
-    "\n",
-    "How might you incorporate the effect of quarantine in the SIR model?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "impressive-librarian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "\"\"\"There is no unique best answer to this question,\n",
-    "but one simple option is to model quarantine as an\n",
-    "effective reduction in gamma, on the assumption that\n",
-    "quarantine reduces the number of infectious contacts\n",
-    "per infected student.\n",
-    "\n",
-    "Another option would be to add a fourth compartment\n",
-    "to the model to track the fraction of the population\n",
-    "in quarantine at each point in time.  This approach\n",
-    "would be more complex, and it is not obvious that it\n",
-    "is substantially better.\n",
-    "\n",
-    "The following function could be used, like \n",
-    "add_immunization and add_hand_washing, to adjust the\n",
-    "parameters in order to model various interventions.\n",
-    "\n",
-    "In this example, `high` is the highest duration of\n",
-    "the infection period, with no quarantine.  `low` is\n",
-    "the lowest duration, on the assumption that it takes\n",
-    "some time to identify infectious students.\n",
-    "\n",
-    "`fraction` is the fraction of infected students who \n",
-    "are quarantined as soon as they are identified.\n",
-    "\"\"\"\n",
-    "\n",
-    "def add_quarantine(system, fraction):\n",
-    "    \"\"\"Model the effect of quarantine by adjusting gamma.\n",
-    "    \n",
-    "    system: System object\n",
-    "    fraction: fraction of students quarantined\n",
-    "    \"\"\"\n",
-    "    # `low` represents the number of days a student \n",
-    "    # is infectious if quarantined.\n",
-    "    # `high` is the number of days they are infectious\n",
-    "    # if not quarantined\n",
-    "    low = 1\n",
-    "    high = 4\n",
-    "    tr = high - fraction * (high-low)\n",
-    "    system.gamma = 1 / tr"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap13.ipynb b/jupyter/chap13.ipynb
deleted file mode 100644
index 02d03388..00000000
--- a/jupyter/chap13.ipynb
+++ /dev/null
@@ -1,739 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "artistic-wells",
-   "metadata": {},
-   "source": [
-    "# Chapter 13"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ceramic-rendering",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "upper-basic",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "extraordinary-router",
-   "metadata": {},
-   "source": [
-    "### Code from previous chapters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "spiritual-product",
-   "metadata": {},
-   "source": [
-    "`make_system`, `plot_results`, and `calc_total_infected` are unchanged."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "entitled-playlist",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import State, System\n",
-    "\n",
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "monthly-width",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State with variables S, I, R\n",
-    "    t: time step\n",
-    "    system: System with beta and gamma\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = system.beta * i * s    \n",
-    "    recovered = system.gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "antique-timer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import arange\n",
-    "from modsim import TimeFrame\n",
-    "\n",
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    frame = TimeFrame(columns=system.init.index)\n",
-    "    frame.loc[system.t0] = system.init\n",
-    "    \n",
-    "    for t in arange(system.t0, system.t_end):\n",
-    "        frame.loc[t+1] = update_func(frame.loc[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "swiss-system",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_total_infected(results, system):\n",
-    "    s_0 = results.S[system.t0]\n",
-    "    s_end = results.S[system.t_end]\n",
-    "    return s_0 - s_end"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "electoral-montgomery",
-   "metadata": {},
-   "source": [
-    "In the previous chapters I presented an SIR model of infectious disease, specifically the Kermack-McKendrick model. We extended the model to include vaccination and the effect of a hand-washing campaign, and used the extended model to allocate a limited budget optimally, that is, to minimize the number of infections.\n",
-    "\n",
-    "But we assumed that the parameters of the model, contact rate and\n",
-    "recovery rate, were known. In this chapter, we explore the behavior of\n",
-    "the model as we vary these parameters, use analysis to understand these relationships better, and propose a method for using data to estimate parameters."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "nervous-spread",
-   "metadata": {},
-   "source": [
-    "## Sweeping beta\n",
-    "\n",
-    "Recall that $\\beta$ is the contact rate, which captures both the\n",
-    "frequency of interaction between people and the fraction of those\n",
-    "interactions that result in a new infection. If $N$ is the size of the\n",
-    "population and $s$ is the fraction that's susceptible, $s N$ is the\n",
-    "number of susceptibles, $\\beta s N$ is the number of contacts per day\n",
-    "between susceptibles and other people, and $\\beta s i N$ is the number\n",
-    "of those contacts where the other person is infectious.\n",
-    "\n",
-    "As $\\beta$ increases, we expect the total number of infections to\n",
-    "increase. To quantify that relationship, I'll create a range of values\n",
-    "for $\\beta$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "amazing-operations",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import linspace\n",
-    "\n",
-    "beta_array = linspace(0.1, 1.1, 11)\n",
-    "gamma = 0.25"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adopted-selling",
-   "metadata": {},
-   "source": [
-    "Then run the simulation for each value and print the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "brief-minutes",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.1 0.0072309016649785285\n",
-      "0.2 0.038410532615067994\n",
-      "0.30000000000000004 0.33703425948982\n",
-      "0.4 0.6502429153895082\n",
-      "0.5 0.8045061124629623\n",
-      "0.6 0.8862866308018508\n",
-      "0.7000000000000001 0.9316695082755875\n",
-      "0.8 0.9574278300784942\n",
-      "0.9 0.9720993156325133\n",
-      "1.0 0.9803437149675784\n",
-      "1.1 0.9848347293510136\n"
-     ]
-    }
-   ],
-   "source": [
-    "for beta in beta_array:\n",
-    "    system = make_system(beta, gamma)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    print(beta, calc_total_infected(results, system))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "understanding-teach",
-   "metadata": {},
-   "source": [
-    "We can wrap that code in a function and store the results in a\n",
-    "`SweepSeries` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "single-burke",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_beta(beta_array, gamma):\n",
-    "    sweep = SweepSeries()\n",
-    "    for beta in beta_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[beta] = calc_total_infected(results, system)\n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "wired-wrist",
-   "metadata": {},
-   "source": [
-    "Now we can run `sweep_beta` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "collected-practice",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "infected_sweep = sweep_beta(beta_array, gamma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "exempt-binary",
-   "metadata": {},
-   "source": [
-    "And plot the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "competitive-amount",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'gamma = 0.25'"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "label = f'gamma = {gamma}'\n",
-    "label"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "imported-huntington",
-   "metadata": {},
-   "source": [
-    "The first line uses string operations to assemble a label for the\n",
-    "plotted line:\n",
-    "\n",
-    "-   When the `+` operator is applied to strings, it joins them\n",
-    "    end-to-end, which is called **concatenation**.\n",
-    "\n",
-    "-   The function `str` converts any type of object to a String\n",
-    "    representation. In this case, `gamma` is a number, so we have to\n",
-    "    convert it to a string before trying to concatenate it.\n",
-    "\n",
-    "If the value of `gamma` is `0.25`, the value of `label` is the string\n",
-    "`'gamma = 0.25'`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "certain-second",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "infected_sweep.plot(label=label)\n",
-    "\n",
-    "decorate(xlabel='Contact rate (beta)',\n",
-    "         ylabel='Fraction infected')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "affiliated-honduras",
-   "metadata": {},
-   "source": [
-    "Remember that this figure\n",
-    "is a parameter sweep, not a time series, so the x-axis is the parameter\n",
-    "`beta`, not time.\n",
-    "\n",
-    "When `beta` is small, the contact rate is low and the outbreak never\n",
-    "really takes off; the total number of infected students is near zero. As\n",
-    "`beta` increases, it reaches a threshold near 0.3 where the fraction of\n",
-    "infected students increases quickly. When `beta` exceeds 0.5, more than\n",
-    "80% of the population gets sick."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "worst-alexandria",
-   "metadata": {},
-   "source": [
-    "## Sweeping gamma\n",
-    "\n",
-    "Let's see what that looks like for a few different values of `gamma`.\n",
-    "Again, we'll use `linspace` to make an array of values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "advised-venice",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gamma_array = linspace(0.1, 0.7, 4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sensitive-folder",
-   "metadata": {},
-   "source": [
-    "And run `sweep_beta` for each value of `gamma`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "determined-consent",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for gamma in gamma_array:\n",
-    "    infected_sweep = sweep_beta(beta_array, gamma)\n",
-    "    label = 'gamma = ' + str(gamma)\n",
-    "    plot(infected_sweep, label=label)\n",
-    "    \n",
-    "decorate()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "tutorial-venture",
-   "metadata": {},
-   "source": [
-    "The following figure shows the results. When `gamma` is low, the\n",
-    "recovery rate is low, which means people are infectious longer. In that\n",
-    "case, even a low contact rate (`beta`) results in an epidemic.\n",
-    "\n",
-    "When `gamma` is high, `beta` has to be even higher to get things going."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "rising-flour",
-   "metadata": {},
-   "source": [
-    "## SweepFrame\n",
-    "\n",
-    "In the previous section, we swept a range of values for `gamma`, and for\n",
-    "each value, we swept a range of values for `beta`. This process is a\n",
-    "**two-dimensional sweep**.\n",
-    "\n",
-    "If we want to store the results, rather than plot them, we can use a\n",
-    "`SweepFrame`, which is a kind of `DataFrame` where the rows sweep one\n",
-    "parameter, the columns sweep another parameter, and the values contain\n",
-    "metrics from a simulation.\n",
-    "\n",
-    "This function shows how it works:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "aquatic-federal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_parameters(beta_array, gamma_array):\n",
-    "    frame = SweepFrame(columns=gamma_array)\n",
-    "    for gamma in gamma_array:\n",
-    "        frame[gamma] = sweep_beta(beta_array, gamma)\n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "level-distinction",
-   "metadata": {},
-   "source": [
-    "`sweep_parameters` takes as parameters an array of values for `beta` and\n",
-    "an array of values for `gamma`.\n",
-    "\n",
-    "It creates a `SweepFrame` to store the results, with one column for each\n",
-    "value of `gamma` and one row for each value of `beta`.\n",
-    "\n",
-    "Each time through the loop, we run `sweep_beta`. The result is a\n",
-    "`SweepSeries` object with one element for each value of `beta`. The\n",
-    "assignment inside the loop stores the `SweepSeries` as a new column in\n",
-    "the `SweepFrame`, corresponding to the current value of `gamma`.\n",
-    "\n",
-    "At the end, the `SweepFrame` stores the fraction of students infected\n",
-    "for each pair of parameters, `beta` and `gamma`.\n",
-    "\n",
-    "We can run `sweep_parameters` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "julian-liberia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frame = sweep_parameters(beta_array, gamma_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sharing-contemporary",
-   "metadata": {},
-   "source": [
-    "With the results in a `SweepFrame`, we can plot each column like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "superior-redhead",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for gamma in gamma_array:\n",
-    "    label = f'gamma = {gamma}'\n",
-    "    plot(frame[gamma], label=label)\n",
-    "\n",
-    "decorate(xlabel='Contact rate (beta)',\n",
-    "         ylabel='Fraction infected')\n",
-    "\n",
-    "plt.legend(bbox_to_anchor=(1.02, 1.02))\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "english-photographer",
-   "metadata": {},
-   "source": [
-    "Alternatively, we can plot each row like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "regular-drama",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for beta in beta_array:\n",
-    "    label = f'beta = {beta}'\n",
-    "    plot(frame.loc[beta], label=label)\n",
-    "    \n",
-    "decorate(xlabel='Recovery rate (gamma)',\n",
-    "         ylabel='Fraction infected')\n",
-    "\n",
-    "plt.legend(bbox_to_anchor=(1.02, 1.02))\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "selective-evaluation",
-   "metadata": {},
-   "source": [
-    "This example demonstrates one use of a `SweepFrame`: we can run the analysis once, save the results, and then generate different visualizations.\n",
-    "\n",
-    "Another way to visualize the results of a two-dimensional sweep is a\n",
-    "**contour plot**, which shows the parameters on the axes and contour\n",
-    "lines, that is, lines of constant value. In this example, the value is\n",
-    "the fraction of students infected.\n",
-    "\n",
-    "The ModSim library provides `contour`, which takes a `SweepFrame` as a\n",
-    "parameter:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "logical-method",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import contour\n",
-    "\n",
-    "contour(frame)\n",
-    "\n",
-    "decorate(xlabel='Recovery rate (gamma)',\n",
-    "         ylabel='Contact rate (beta)',\n",
-    "         title='Fraction infected, contour plot')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "corresponding-headline",
-   "metadata": {},
-   "source": [
-    "Infection rates are lowest in the lower right, where the contact rate is and the recovery rate is high. They increase as we move to the upper left, where the contact rate is high and the recovery rate is low.\n",
-    "\n",
-    "This figure suggests that there might be a relationship between `beta`\n",
-    "and `gamma` that determines the outcome of the model. In fact, there is.\n",
-    "In the next chapter we'll explore it by running simulations, then derive it by analysis.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "located-double",
-   "metadata": {},
-   "source": [
-    "## Summary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "flying-tracker",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "stopped-breathing",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Suppose the infectious period for the Freshman Plague is known to be 2 days on average, and suppose during one particularly bad year, 40% of the class is infected at some point.  Estimate the time between contacts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "talented-crack",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Sweep beta with fixed gamma\n",
-    "gamma = 1/2\n",
-    "infected_sweep = sweep_beta(beta_array, gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "pressed-isaac",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Interpolating by eye, we can see that the infection rate passes through 0.4\n",
-    "# when beta is between 0.6 and 0.7\n",
-    "# We can use the `crossings` function to interpolate more precisely\n",
-    "# (although we don't know about it yet :)\n",
-    "beta_estimate = crossings(infected_sweep, 0.4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "equal-crazy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Time between contacts is 1/beta\n",
-    "time_between_contacts = 1/beta_estimate"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap14.ipynb b/jupyter/chap14.ipynb
deleted file mode 100644
index 81ead454..00000000
--- a/jupyter/chap14.ipynb
+++ /dev/null
@@ -1,921 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "happy-minutes",
-   "metadata": {},
-   "source": [
-    "# Chapter 14"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "impressed-headline",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "conditional-complaint",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "therapeutic-scanning",
-   "metadata": {},
-   "source": [
-    "### Code from previous chapters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "level-struggle",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import State, System\n",
-    "\n",
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "offshore-remainder",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State with variables S, I, R\n",
-    "    t: time step\n",
-    "    system: System with beta and gamma\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = system.beta * i * s    \n",
-    "    recovered = system.gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "ordinary-elephant",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import arange\n",
-    "from modsim import TimeFrame\n",
-    "\n",
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    frame = TimeFrame(columns=system.init.index)\n",
-    "    frame.loc[system.t0] = system.init\n",
-    "    \n",
-    "    for t in arange(system.t0, system.t_end):\n",
-    "        frame.loc[t+1] = update_func(frame.loc[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "fiscal-testament",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_total_infected(results, system):\n",
-    "    s_0 = results.S[system.t0]\n",
-    "    s_end = results.S[system.t_end]\n",
-    "    return s_0 - s_end"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "engaging-consequence",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import SweepSeries\n",
-    "\n",
-    "def sweep_beta(beta_array, gamma):\n",
-    "    \"\"\"Sweep a range of values for beta.\n",
-    "    \n",
-    "    beta_array: array of beta values\n",
-    "    gamma: recovery rate\n",
-    "    \n",
-    "    returns: SweepSeries that maps from beta to total infected\n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    for beta in beta_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[beta] = calc_total_infected(results, system)\n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "proud-ability",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import SweepFrame\n",
-    "\n",
-    "def sweep_parameters(beta_array, gamma_array):\n",
-    "    \"\"\"Sweep a range of values for beta and gamma.\n",
-    "    \n",
-    "    beta_array: array of infection rates\n",
-    "    gamma_array: array of recovery rates\n",
-    "    \n",
-    "    returns: SweepFrame with one row for each beta\n",
-    "             and one column for each gamma\n",
-    "    \"\"\"\n",
-    "    frame = SweepFrame(columns=gamma_array)\n",
-    "    for gamma in gamma_array:\n",
-    "        frame[gamma] = sweep_beta(beta_array, gamma)\n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "flying-chosen",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0.2</th>\n",
-       "      <th>0.4</th>\n",
-       "      <th>0.6</th>\n",
-       "      <th>0.8</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Parameter</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.1</th>\n",
-       "      <td>0.010756</td>\n",
-       "      <td>0.003642</td>\n",
-       "      <td>0.002191</td>\n",
-       "      <td>0.001567</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.2</th>\n",
-       "      <td>0.118984</td>\n",
-       "      <td>0.010763</td>\n",
-       "      <td>0.005447</td>\n",
-       "      <td>0.003644</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.3</th>\n",
-       "      <td>0.589095</td>\n",
-       "      <td>0.030185</td>\n",
-       "      <td>0.010771</td>\n",
-       "      <td>0.006526</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.4</th>\n",
-       "      <td>0.801339</td>\n",
-       "      <td>0.131563</td>\n",
-       "      <td>0.020917</td>\n",
-       "      <td>0.010780</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.5</th>\n",
-       "      <td>0.896577</td>\n",
-       "      <td>0.396409</td>\n",
-       "      <td>0.046140</td>\n",
-       "      <td>0.017640</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                0.2       0.4       0.6       0.8\n",
-       "Parameter                                        \n",
-       "0.1        0.010756  0.003642  0.002191  0.001567\n",
-       "0.2        0.118984  0.010763  0.005447  0.003644\n",
-       "0.3        0.589095  0.030185  0.010771  0.006526\n",
-       "0.4        0.801339  0.131563  0.020917  0.010780\n",
-       "0.5        0.896577  0.396409  0.046140  0.017640"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "beta_array = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 , 1.1]\n",
-    "gamma_array = [0.2, 0.4, 0.6, 0.8]\n",
-    "frame = sweep_parameters(beta_array, gamma_array)\n",
-    "frame.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "disturbed-joshua",
-   "metadata": {},
-   "source": [
-    "In the previous chapters we used simulation to predict the effect of an infectious disease in a susceptible population and to design\n",
-    "interventions that would minimize the effect.\n",
-    "\n",
-    "In this chapter we use analysis to investigate the relationship between the parameters, `beta` and `gamma`, and the outcome of the simulation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "respiratory-twelve",
-   "metadata": {},
-   "source": [
-    "## Nondimensionalization\n",
-    "\n",
-    "The figures in\n",
-    "Section [\\[sweepframe\\]](#sweepframe){reference-type=\"ref\"\n",
-    "reference=\"sweepframe\"} suggest that there is a relationship between the parameters of the SIR model, `beta` and `gamma`, that determines the outcome of the simulation, the fraction of students infected. Let's think what that relationship might be.\n",
-    "\n",
-    "-   When `beta` exceeds `gamma`, that means there are more contacts\n",
-    "    (that is, potential infections) than recoveries during each day (or other unit of time). The difference between `beta` and `gamma` might be called the \"excess contact rate\\\", in units of contacts per day.\n",
-    "\n",
-    "-   As an alternative, we might consider the ratio `beta/gamma`, which\n",
-    "    is the number of contacts per recovery. Because the numerator and\n",
-    "    denominator are in the same units, this ratio is **dimensionless**, which means it has no units.\n",
-    "\n",
-    "Describing physical systems using dimensionless parameters is often a\n",
-    "useful move in the modeling and simulation game. It is so useful, in\n",
-    "fact, that it has a name: **nondimensionalization** (see\n",
-    "<http://modsimpy.com/nondim>).\n",
-    "\n",
-    "So we'll try the second option first."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "caroline-compiler",
-   "metadata": {},
-   "source": [
-    "## Exploring the results\n",
-    "\n",
-    "Suppose we have a `SweepFrame` with one row for each value of `beta` and one column for each value of `gamma`. Each element in the `SweepFrame` is the fraction of students infected in a simulation with a given pair of parameters.\n",
-    "\n",
-    "We can print the values in the `SweepFrame` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "satisfied-japan",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.1 0.2 0.010756340768063644\n",
-      "0.2 0.2 0.11898421353185373\n",
-      "0.3 0.2 0.5890954199973404\n",
-      "0.4 0.2 0.8013385277185551\n",
-      "0.5 0.2 0.8965769637207062\n",
-      "0.6 0.2 0.942929291399791\n",
-      "0.7 0.2 0.966299311298026\n",
-      "0.8 0.2 0.9781518959989762\n",
-      "0.9 0.2 0.9840568957948106\n",
-      "1.0 0.2 0.9868823507202488\n",
-      "1.1 0.2 0.988148177093735\n",
-      "0.1 0.4 0.0036416926514175607\n",
-      "0.2 0.4 0.010763463373360094\n",
-      "0.3 0.4 0.030184952469116566\n",
-      "0.4 0.4 0.131562924303259\n",
-      "0.5 0.4 0.3964094037932606\n",
-      "0.6 0.4 0.5979016626615987\n",
-      "0.7 0.4 0.7284704154876106\n",
-      "0.8 0.4 0.8144604459153759\n",
-      "0.9 0.4 0.8722697237137128\n",
-      "1.0 0.4 0.9116692168795855\n",
-      "1.1 0.4 0.9386802509510287\n",
-      "0.1 0.6 0.002190722188881611\n",
-      "0.2 0.6 0.005446688837466351\n",
-      "0.3 0.6 0.010771139974975585\n",
-      "0.4 0.6 0.020916599304195316\n",
-      "0.5 0.6 0.04614035896610047\n",
-      "0.6 0.6 0.13288938996079536\n",
-      "0.7 0.6 0.3118432512847451\n",
-      "0.8 0.6 0.47832565854255393\n",
-      "0.9 0.6 0.605687582114665\n",
-      "1.0 0.6 0.7014254793376209\n",
-      "1.1 0.6 0.7738176405451065\n",
-      "0.1 0.8 0.0015665254038139675\n",
-      "0.2 0.8 0.003643953969662994\n",
-      "0.3 0.8 0.006526163529085194\n",
-      "0.4 0.8 0.010779807499500693\n",
-      "0.5 0.8 0.017639902596349066\n",
-      "0.6 0.8 0.030291868201986594\n",
-      "0.7 0.8 0.05882382948158804\n",
-      "0.8 0.8 0.13358889291095588\n",
-      "0.9 0.8 0.2668895539427739\n",
-      "1.0 0.8 0.40375121210421994\n",
-      "1.1 0.8 0.519583469821867\n"
-     ]
-    }
-   ],
-   "source": [
-    "for gamma in frame.columns:\n",
-    "    column = frame[gamma]\n",
-    "    for beta in column.index:\n",
-    "        frac_infected = column[beta]\n",
-    "        print(beta, gamma, frac_infected)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "automotive-solution",
-   "metadata": {},
-   "source": [
-    "This is the first example we've seen with one `for` loop inside another:\n",
-    "\n",
-    "-   Each time the outer loop runs, it selects a value of `gamma` from\n",
-    "    the columns of the `DataFrame` and extracts the corresponding\n",
-    "    column.\n",
-    "\n",
-    "-   Each time the inner loop runs, it selects a value of `beta` from the\n",
-    "    column and selects the corresponding element, which is the fraction\n",
-    "    of students infected.\n",
-    "\n",
-    "In the example from the previous chapter, `frame` has 4 columns, one for\n",
-    "each value of `gamma`, and 11 rows, one for each value of `beta`. So\n",
-    "these loops print 44 lines, one for each pair of parameters.\n",
-    "\n",
-    "The following function encapulates the previous loop and plots the\n",
-    "fraction infected as a function of the ratio `beta/gamma`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "baking-tactics",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib.pyplot import plot\n",
-    "\n",
-    "def plot_sweep_frame(frame):\n",
-    "    for gamma in frame.columns:\n",
-    "        series = frame[gamma]\n",
-    "        for beta in series.index:\n",
-    "            frac_infected = series[beta]\n",
-    "            plot(beta/gamma, frac_infected, 'o', \n",
-    "                 color='C1', alpha=0.4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "dental-outreach",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "plot_sweep_frame(frame)\n",
-    "\n",
-    "decorate(xlabel='Contact number (beta/gamma)',\n",
-    "         ylabel='Fraction infected')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "systematic-gossip",
-   "metadata": {},
-   "source": [
-    "The results fall on a single curve, at least approximately. That means that we can predict the fraction of students who will be infected based on a single parameter, the ratio `beta/gamma`. We don't need to know the values of `beta` and `gamma` separately."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "theoretical-arrow",
-   "metadata": {},
-   "source": [
-    "## Contact number\n",
-    "\n",
-    "From Section xxx, recall that the number of new infections in a\n",
-    "given day is $\\beta s i N$, and the number of recoveries is\n",
-    "$\\gamma i N$. If we divide these quantities, the result is\n",
-    "$\\beta s / \\gamma$, which is the number of new infections per recovery\n",
-    "(as a fraction of the population).\n",
-    "\n",
-    "When a new disease is introduced to a susceptible population, $s$ is\n",
-    "approximately 1, so the number of people infected by each sick person is $\\beta / \\gamma$. This ratio is called the \"contact number\\\" or \"basic reproduction number\\\" (see <http://modsimpy.com/contact>). By convention it is usually denoted $R_0$, but in the context of an SIR model, this notation is confusing, so we'll use $c$ instead."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "intimate-romance",
-   "metadata": {},
-   "source": [
-    "The results in the previous section suggest that there is a relationship between $c$ and the total number of infections. We can derive this relationship by analyzing the differential equations from\n",
-    "Section xxx:\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "\\frac{ds}{dt} &= -\\beta s i \\\\\n",
-    "\\frac{di}{dt} &= \\beta s i - \\gamma i\\\\\n",
-    "\\frac{dr}{dt} &= \\gamma i\\end{aligned}$$ \n",
-    "\n",
-    "In the same way we divided the\n",
-    "contact rate by the infection rate to get the dimensionless quantity\n",
-    "$c$, now we'll divide $di/dt$ by $ds/dt$ to get a ratio of rates:\n",
-    "\n",
-    "$$\\frac{di}{ds} = -1 + \\frac{1}{cs}$$ \n",
-    "\n",
-    "Dividing one differential equation by another is not an obvious move, but in this case it is useful because it gives us a relationship between $i$, $s$ and $c$ that does not depend on time. From that relationship, we can derive an equation that relates $c$ to the final value of $s$. In theory, this equation makes it possible to infer $c$ by observing the course of an epidemic."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "revolutionary-motorcycle",
-   "metadata": {},
-   "source": [
-    "Here's how the derivation goes. We multiply both sides of the previous\n",
-    "equation by $ds$: \n",
-    "\n",
-    "$$di = \\left( -1 + \\frac{1}{cs} \\right) ds$$ \n",
-    "\n",
-    "And then integrate both sides: \n",
-    "\n",
-    "$$i = -s + \\frac{1}{c} \\log s + q$$ \n",
-    "\n",
-    "where $q$ is a constant of integration. Rearranging terms yields:\n",
-    "\n",
-    "$$q = i + s - \\frac{1}{c} \\log s$$ \n",
-    "\n",
-    "Now let's see if we can figure out what $q$ is. At the beginning of an epidemic, if the fraction infected is small and nearly everyone is susceptible, we can use the approximations $i(0) = 0$ and $s(0) = 1$ to compute $q$:\n",
-    "\n",
-    "$$q = 0 + 1 + \\frac{1}{c} \\log 1$$ \n",
-    "\n",
-    "Since $\\log 1 = 0$, we get $q = 1$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "spread-convert",
-   "metadata": {},
-   "source": [
-    "Now, at the end of the epidemic, let's assume that $i(\\infty) = 0$, and $s(\\infty)$ is an unknown quantity, $s_{\\infty}$. Now we have:\n",
-    "\n",
-    "$$q = 1 = 0 + s_{\\infty}- \\frac{1}{c} \\log s_{\\infty}$$ \n",
-    "\n",
-    "Solving for $c$, we get $$c = \\frac{\\log s_{\\infty}}{s_{\\infty}- 1}$$ By relating $c$ and $s_{\\infty}$, this equation makes it possible to estimate $c$ based on data, and possibly predict the behavior of future epidemics."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "intended-worker",
-   "metadata": {},
-   "source": [
-    "## Analysis and simulation\n",
-    "\n",
-    "Let's compare this analytic result to the results from simulation. I'll create an array of values for $s_{\\infty}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "previous-schedule",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import linspace\n",
-    "\n",
-    "s_inf_array = linspace(0.0001, 0.999, 31)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "positive-float",
-   "metadata": {},
-   "source": [
-    "And compute the corresponding values of $c$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "valuable-landscape",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import log\n",
-    "\n",
-    "c_array = log(s_inf_array) / (s_inf_array - 1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "premium-carry",
-   "metadata": {},
-   "source": [
-    "To get the total infected, we compute the difference between $s(0)$ and\n",
-    "$s(\\infty)$, then store the results in a `Series`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "composite-cabinet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frac_infected = 1 - s_inf_array"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bottom-print",
-   "metadata": {},
-   "source": [
-    "Recall from Section [\\[dataframe\\]](#dataframe){reference-type=\"ref\"\n",
-    "reference=\"dataframe\"} that a `Series` object contains an index and a\n",
-    "corresponding sequence of values. In this case, the index is `c_array`\n",
-    "and the values are from `frac_infected`.\n",
-    "\n",
-    "Now we can plot the results:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "active-street",
-   "metadata": {},
-   "source": [
-    " compares the analytic results from this\n",
-    "section with the simulation results from\n",
-    "Section [\\[nondim\\]](#nondim){reference-type=\"ref\" reference=\"nondim\"}."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "integral-reading",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_sweep_frame(frame)\n",
-    "plot(c_array, frac_infected, label='analysis')\n",
-    "\n",
-    "decorate(xlabel='Contact number (c)',\n",
-    "         ylabel='Fraction infected')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "peaceful-interest",
-   "metadata": {},
-   "source": [
-    "When the contact number exceeds 1, analysis and simulation agree. When\n",
-    "the contact number is less than 1, they do not: analysis indicates there should be no infections; in the simulations there are a small number of infections.\n",
-    "\n",
-    "The reason for the discrepancy is that the simulation divides time into a discrete series of days, whereas the analysis treats time as a\n",
-    "continuous quantity. In other words, the two methods are actually based on different models. So which model is better?\n",
-    "\n",
-    "Probably neither. When the contact number is small, the early progress\n",
-    "of the epidemic depends on details of the scenario. If we are lucky, the original infected person, \"patient zero\", infects no one and there is no epidemic. If we are unlucky, patient zero might have a large number of close friends, or might work in the dining hall (and fail to observe safe food handling procedures).\n",
-    "\n",
-    "For contact numbers near or less than 1, we might need a more detailed\n",
-    "model. But for higher contact numbers the SIR model might be good\n",
-    "enough."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "beginning-capital",
-   "metadata": {},
-   "source": [
-    "## Estimating contact number\n",
-    "\n",
-    "Figure xxx shows that if we know the contact number, we can compute the fraction infected. But we can also read the figure the other way; that is, at the end of an epidemic, if we can estimate the fraction of the population that was ever infected, we can use it to estimate the contact number.\n",
-    "\n",
-    "Well, in theory we can. In practice, it might not work very well,\n",
-    "because of the shape of the curve. When the contact number is near 2,\n",
-    "the curve is quite steep, which means that small changes in $c$ yield\n",
-    "big changes in the number of infections. If we observe that the total\n",
-    "fraction infected is anywhere from 20% to 80%, we would conclude that\n",
-    "$c$ is near 2.\n",
-    "\n",
-    "On the other hand, for larger contact numbers, nearly the entire\n",
-    "population is infected, so the curve is nearly flat. In that case we\n",
-    "would not be able to estimate $c$ precisely, because any value greater\n",
-    "than 3 would yield effectively the same results. Fortunately, this is\n",
-    "unlikely to happen in the real world; very few epidemics affect anything close to 90% of the population.\n",
-    "\n",
-    "So the SIR model has limitations; nevertheless, it provides insight into the behavior of infectious disease, especially the phenomenon of herd immunity. As we saw in Chapter xxx, if we know the parameters of the model, we can use it to evaluate possible interventions. And as we saw in this chapter, we might be able to use data from earlier outbreaks to estimate the parameters.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "olive-niger",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "upper-interim",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  If we didn't know about contact numbers, we might have explored other possibilities, like the difference between `beta` and `gamma`, rather than their ratio.\n",
-    "\n",
-    "Write a version of `plot_sweep_frame`, called `plot_sweep_frame_difference`, that plots the fraction infected versus the difference `beta-gamma`.\n",
-    "\n",
-    "What do the results look like, and what does that imply? "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "talented-discovery",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def plot_sweep_frame_difference(frame):\n",
-    "    for gamma in frame.columns:\n",
-    "        column = frame[gamma]\n",
-    "        for beta in column.index:\n",
-    "            frac_infected = column[beta]\n",
-    "            plot(beta - gamma, frac_infected, 'ro', \n",
-    "                 color='C1', alpha=0.4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "bright-label",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_sweep_frame_difference(frame)\n",
-    "\n",
-    "decorate(xlabel='Excess infection rate (infections-recoveries per day)',\n",
-    "         ylabel='Fraction infected')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "suspended-louisiana",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# The results don't fall on a line, which means that if we \n",
-    "# know the difference between `beta` and `gamma`, \n",
-    "# but not their ratio, that's not enough to predict \n",
-    "# the fraction infected."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "understanding-fault",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose you run a survey at the end of the semester and find that 26% of students had the Freshman Plague at some point.\n",
-    "\n",
-    "What is your best estimate of `c`?\n",
-    "\n",
-    "Hint: if you print `frac_infected_series`, you can read off the answer. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "another-timing",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9.211261    0.999900\n",
-       "3.516747    0.966603\n",
-       "2.901137    0.933307\n",
-       "2.558511    0.900010\n",
-       "2.325167    0.866713\n",
-       "2.150496    0.833417\n",
-       "2.012246    0.800120\n",
-       "1.898689    0.766823\n",
-       "1.802908    0.733527\n",
-       "1.720492    0.700230\n",
-       "1.648460    0.666933\n",
-       "1.584709    0.633637\n",
-       "1.527703    0.600340\n",
-       "1.476285    0.567043\n",
-       "1.429566    0.533747\n",
-       "1.386847    0.500450\n",
-       "1.347569    0.467153\n",
-       "1.311281    0.433857\n",
-       "1.277610    0.400560\n",
-       "1.246247    0.367263\n",
-       "1.216935    0.333967\n",
-       "1.189452    0.300670\n",
-       "1.163613    0.267373\n",
-       "1.139256    0.234077\n",
-       "1.116242    0.200780\n",
-       "1.094449    0.167483\n",
-       "1.073772    0.134187\n",
-       "1.054117    0.100890\n",
-       "1.035401    0.067593\n",
-       "1.017551    0.034297\n",
-       "1.000500    0.001000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "from pandas import Series\n",
-    "\n",
-    "Series(frac_infected, index=c_array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "gross-terry",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# It looks like the fraction infected is 0.26 when the contact \n",
-    "# number is about 1.16"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "stopped-zealand",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap15.ipynb b/jupyter/chap15.ipynb
deleted file mode 100644
index cd848e13..00000000
--- a/jupyter/chap15.ipynb
+++ /dev/null
@@ -1,1038 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "located-subscription",
-   "metadata": {},
-   "source": [
-    "# Chapter 15"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "american-article",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "adjusted-motivation",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "italic-murder",
-   "metadata": {},
-   "source": [
-    "So far the systems we have studied have been physical in the sense that they exist in the world, but they have not been physics, in the sense of what physics classes are usually about. In the next few chapters, we'll do some physics, starting with **thermal systems**, that is, systems where the temperature of objects changes as heat transfers from one to another."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "honey-rolling",
-   "metadata": {},
-   "source": [
-    "## The coffee cooling problem\n",
-    "\n",
-    "The coffee cooling problem was discussed by Jearl Walker in \n",
-    "\"The Amateur Scientist\", *Scientific American*, Volume 237, Issue 5, November 1977. Since then it has become a standard example of modeling and simulation.\n",
-    "\n",
-    "Here is my version of the problem:\n",
-    "\n",
-    "> Suppose I stop on the way to work to pick up a cup of coffee, which I take with milk. Assuming that I want the coffee to be as hot as possible when I arrive at work, should I add the milk at the coffee shop, wait until I get to work, or add the milk at some point in between?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "appreciated-ukraine",
-   "metadata": {},
-   "source": [
-    "To help answer this question, I made a trial run with the milk and\n",
-    "coffee in separate containers and took some measurements (not really):\n",
-    "\n",
-    "-   When served, the temperature of the coffee is 90 °C. The volume is\n",
-    "    300 mL.\n",
-    "\n",
-    "-   The milk is at an initial temperature of 5 °C, and I take about\n",
-    "    50 mL.\n",
-    "\n",
-    "-   The ambient temperature in my car is 22 °C.\n",
-    "\n",
-    "-   The coffee is served in a well insulated cup. When I arrive at work after 30 minutes, the temperature of the coffee has fallen to 70 °C.\n",
-    "\n",
-    "-   The milk container is not as well insulated. After 15 minutes, it\n",
-    "    warms up to 20 °C, nearly the ambient temperature.\n",
-    "\n",
-    "To use this data and answer the question, we have to know something\n",
-    "about temperature and heat, and we have to make some modeling decisions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "juvenile-republican",
-   "metadata": {},
-   "source": [
-    "## Temperature and heat\n",
-    "\n",
-    "To understand how coffee cools (and milk warms), we need a model of\n",
-    "temperature and heat. **Temperature** is a property of an object or a\n",
-    "system; in SI units it is measured in degrees Celsius (°C). Temperature quantifies how hot or cold the object is, which is related to the average velocity of the particles that make up the object.\n",
-    "\n",
-    "When particles in a hot object contact particles in a cold object, the\n",
-    "hot object gets cooler and the cold object gets warmer as energy is\n",
-    "transferred from one to the other. The transferred energy is called\n",
-    "**heat**; in SI units it is measured in joules (J).\n",
-    "\n",
-    "Heat is related to temperature by the following equation (see\n",
-    "<http://modsimpy.com/thermass>): \n",
-    "\n",
-    "$$Q = C~\\Delta T$$ \n",
-    "\n",
-    "where $Q$ is the amount of heat transferred to an object, $\\Delta T$ is resulting change in temperature, and $C$ is the **thermal mass** of the object, which quantifies how much energy it takes to heat or cool it. In SI units, thermal mass is measured in joules per degree Celsius (J/°C)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "purple-edward",
-   "metadata": {},
-   "source": [
-    "For objects made primarily from one material, thermal mass can be\n",
-    "computed like this: \n",
-    "\n",
-    "$$C = m c_p$$ \n",
-    "\n",
-    "where $m$ is the mass of the object and $c_p$ is the **specific heat capacity** of the material (see <http://modsimpy.com/specheat>).\n",
-    "\n",
-    "We can use these equations to estimate the thermal mass of a cup of\n",
-    "coffee. The specific heat capacity of coffee is probably close to that\n",
-    "of water, which is 4.2 J/g/°C. Assuming that the density of coffee is\n",
-    "close to that of water, which is 1 g/mL, the mass of 300 mL of coffee is 300 g, and the thermal mass is 1260 J/°C.\n",
-    "\n",
-    "So when a cup of coffee cools from 90 °C to 70 °C, the change in\n",
-    "temperature, $\\Delta T$ is 20 °C, which means that 25 200 J of heat\n",
-    "energy was transferred from the coffee to the surrounding environment\n",
-    "(the cup holder and air in my car).\n",
-    "\n",
-    "To give you a sense of how much energy that is, if you were able to\n",
-    "harness all of that heat to do work (which you cannot), you could\n",
-    "use it to lift a cup of coffee from sea level to 8571 m, just shy of the height of Mount Everest, 8848 m.\n",
-    "\n",
-    "Assuming that the cup has less mass than the coffee, and is made from a material with lower specific heat, we can ignore the thermal mass of the cup. For a cup with substantial thermal mass, like a ceramic mug, we might consider a model that computes the temperature of coffee and cup separately."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "running-metropolitan",
-   "metadata": {},
-   "source": [
-    "## Heat transfer\n",
-    "\n",
-    "In a situation like the coffee cooling problem, there are three ways\n",
-    "heat transfers from one object to another (see <http://modsimpy.com/transfer>):\n",
-    "\n",
-    "-   Conduction: When objects at different temperatures come into\n",
-    "    contact, the faster-moving particles of the higher-temperature\n",
-    "    object transfer kinetic energy to the slower-moving particles of the lower-temperature object.\n",
-    "\n",
-    "-   Convection: When particles in a gas or liquid flow from place to\n",
-    "    place, they carry heat energy with them. Fluid flows can be caused\n",
-    "    by external action, like stirring, or by internal differences in\n",
-    "    temperature. For example, you might have heard that hot air rises,\n",
-    "    which is a form of \"natural convection\\\".\n",
-    "\n",
-    "-   Radiation: As the particles in an object move due to thermal energy,\n",
-    "    they emit electromagnetic radiation. The energy carried by this\n",
-    "    radiation depends on the object's temperature and surface properties\n",
-    "    (see <http://modsimpy.com/thermrad>)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "convertible-alabama",
-   "metadata": {},
-   "source": [
-    "For objects like coffee in a car, the effect of radiation is much\n",
-    "smaller than the effects of conduction and convection, so we will ignore it.\n",
-    "\n",
-    "Convection can be a complex topic, since it often depends on details of fluid flow in three dimensions. But for this problem we will be able to get away with a simple model called \"Newton's law of cooling\"."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "expected-implementation",
-   "metadata": {},
-   "source": [
-    "## Newton's law of cooling\n",
-    "\n",
-    "Newton's law of cooling asserts that the temperature rate of change for an object is proportional to the difference in temperature between the\n",
-    "object and the surrounding environment:\n",
-    "\n",
-    "$$\\frac{dT}{dt} = -r (T - T_{env})$$ \n",
-    "\n",
-    "where $T$, the temperature of the object, is a function of time, $t$; $T_{env}$ is the temperature of the environment, and $r$ is a constant that characterizes how quickly heat is transferred between the system and the environment."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "educated-accommodation",
-   "metadata": {},
-   "source": [
-    "Newton's so-called \"law \" is really a model: it is a good approximation in some conditions and less good in others.\n",
-    "\n",
-    "For example, if the primary mechanism of heat transfer is conduction,\n",
-    "Newton's law is \"true\", which is to say that $r$ is constant over a\n",
-    "wide range of temperatures. And sometimes we can estimate $r$ based on\n",
-    "the material properties and shape of the object.\n",
-    "\n",
-    "When convection contributes a non-negligible fraction of heat transfer, $r$ depends on temperature, but Newton's law is often accurate enough, at least over a narrow range of temperatures. In this case $r$ usually has to be estimated experimentally, since it depends on details of surface shape, air flow, evaporation, etc.\n",
-    "\n",
-    "When radiation makes up a substantial part of heat transfer, Newton's\n",
-    "law is not a good model at all. This is the case for objects in space or in a vacuum, and for objects at high temperatures (more than a few\n",
-    "hundred degrees Celsius, say).\n",
-    "\n",
-    "However, for a situation like the coffee cooling problem, we expect\n",
-    "Newton's model to be quite good."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "determined-seventh",
-   "metadata": {},
-   "source": [
-    "## Implementation\n",
-    "\n",
-    "To get started, let's forget about the milk temporarily and focus on the coffee. \n",
-    "\n",
-    "Here's a function that takes the parameters of the system and makes a `System` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "geological-graph",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import System\n",
-    "\n",
-    "def make_system(T_init, volume, r, t_end):\n",
-    "    return System(T_init=T_init,\n",
-    "                  T_final=T_init,\n",
-    "                  volume=volume,\n",
-    "                  r=r,\n",
-    "                  t_end=t_end,\n",
-    "                  T_env=22,\n",
-    "                  t_0=0,\n",
-    "                  dt=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "further-allen",
-   "metadata": {},
-   "source": [
-    "The values of `T_init`, `volume`, and `t_end` come from the statement of the problem:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "eastern-alignment",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee = make_system(T_init=90, volume=300, r=0.01, t_end=30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "corrected-cross",
-   "metadata": {},
-   "source": [
-    "I chose the value of `r` arbitrarily for now; we will figure out how to estimate it soon.\n",
-    "\n",
-    "In addition, `make_system` sets the temperature of the environment, `T_env`, and the time step, `dt`, which we will use use to simulate the cooling process.\n",
-    "\n",
-    "Strictly speaking, Newton's law is a differential equation, but over a short period of time we can approximate it with a difference equation:\n",
-    "\n",
-    "$$\\Delta T = -r (T - T_{env}) dt$$ \n",
-    "\n",
-    "where $dt$ is the time step and $\\Delta T$ is the change in temperature during that time step.\n",
-    "\n",
-    "Note: I use $\\Delta T$ to denote a change in temperature over time, but in the context of heat transfer, you might also see $\\Delta T$ used to denote the difference in temperature between an object and its\n",
-    "environment, $T - T_{env}$. To minimize confusion, I avoid this second\n",
-    "use.\n",
-    "\n",
-    "The following function takes the current temperature, `T`, the current time `t`, and a `System` object, and computes the change in temperature during a time step:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "vulnerable-sarah",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def change_func(T, t, system):\n",
-    "    r, T_env, dt = system.r, system.T_env, system.dt    \n",
-    "    return -r * (T - T_env) * dt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unknown-programming",
-   "metadata": {},
-   "source": [
-    "We can test it with the initial temperature of the coffee, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "killing-tragedy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.68"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "change_func(coffee.T_init, 0, coffee)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "geographic-female",
-   "metadata": {},
-   "source": [
-    "With `dt=1` minute, the temperature drops by about 0.7 °C/min, at least for this value of `r`.\n",
-    "\n",
-    "Now here's a version of `run_simulation` that simulates a series of time steps from `t_0` to `t_end`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "basic-extra",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import linrange\n",
-    "from modsim import TimeSeries\n",
-    "\n",
-    "def run_simulation(system, change_func):\n",
-    "    t_array = linrange(system.t_0, system.t_end, system.dt)\n",
-    "    n = len(t_array)\n",
-    "    \n",
-    "    series = TimeSeries(index=t_array)\n",
-    "    series.iloc[0] = system.T_init\n",
-    "    \n",
-    "    for i in range(n-1):\n",
-    "        t = t_array[i]\n",
-    "        T = series.iloc[i]\n",
-    "        series.iloc[i+1] = T + change_func(T, t, system)\n",
-    "    \n",
-    "    system.t_end = t_array[-1]\n",
-    "    system.T_final = series.iloc[-1]\n",
-    "    return series"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "voluntary-velvet",
-   "metadata": {},
-   "source": [
-    "This function is similar to previous versions of `run_simulation`.\n",
-    "\n",
-    "One difference is that it uses `linrange` to make an array of values\n",
-    "from `t_0` to `t_end` with time step `dt`.\n",
-    "\n",
-    "We can run it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "after-championship",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(coffee, change_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "consecutive-party",
-   "metadata": {},
-   "source": [
-    "The result is a `TimeSeries` with one row per time step. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "proof-guard",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Time\n",
-       "0.0    90.000000\n",
-       "1.0    89.320000\n",
-       "2.0    88.646800\n",
-       "3.0    87.980332\n",
-       "4.0    87.320529\n",
-       "Name: Quantity, dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "limiting-legislation",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "expected-company",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "results.plot(label='coffee')\n",
-    "\n",
-    "decorate(xlabel='Time (minute)',\n",
-    "         ylabel='Temperature (C)',\n",
-    "         title='Coffee Cooling')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alien-score",
-   "metadata": {},
-   "source": [
-    "The temperature after 30 minutes is 72.3 °C, which is a little higher than what's stated in the problem, 70 °C. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "protecting-picture",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "72.2996253904031"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee.T_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "tight-operation",
-   "metadata": {},
-   "source": [
-    "By trial and error, we could find the value of `r` where the final temperature is precisely 70 °C.\n",
-    "But it is more efficient to use a root-finding algorithm."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "equipped-blackberry",
-   "metadata": {},
-   "source": [
-    "## Finding roots\n",
-    "\n",
-    "The SciPy library provides a function called `root_scalar` that finds the roots of non-linear equations. As a simple example, suppose you want to find the roots of the polynomial \n",
-    "\n",
-    "$$f(x) = (x - 1)(x - 2)(x - 3)$$ \n",
-    "\n",
-    "A **root** is a value of $x$ that makes $f(x)=0$. Because of the way I wrote the polynomial, we can see that if $x=1$, the first factor is 0; if $x=2$, the second factor is 0; and if $x=3$, the third factor is 0, so those are the roots.\n",
-    "\n",
-    "I'll use this example to demonstrate `root_scalar`. First, we have to\n",
-    "write a function that evaluates $f$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "round-budapest",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def func(x):\n",
-    "    return (x-1) * (x-2) * (x-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "injured-limitation",
-   "metadata": {},
-   "source": [
-    "Now we call `root_scalar` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "regulated-cloud",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "      converged: True\n",
-       "           flag: 'converged'\n",
-       " function_calls: 3\n",
-       "     iterations: 2\n",
-       "           root: 2.0"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from scipy.optimize import root_scalar\n",
-    "\n",
-    "res = root_scalar(func, bracket=[1.5, 2.5])\n",
-    "res"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "regulated-weekend",
-   "metadata": {},
-   "source": [
-    "The first argument is the function whose roots we want. The second\n",
-    "argument is an interval that contains or \"brackets\" a root. The result is an object that contains several variables, including `root`, which stores the root that was found."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "advised-climate",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "changing-winter",
-   "metadata": {},
-   "source": [
-    "If we provide a different interval, we find a different root."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "authentic-hunter",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.0"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res = root_scalar(func, bracket=[2.5, 3.5])\n",
-    "res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bound-freight",
-   "metadata": {},
-   "source": [
-    "If the interval doesn't contain a root, you'll get a `ValueError`:\n",
-    "\n",
-    "```\n",
-    "res = root_scalar(func, bracket=[4, 5])\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "caroline-divide",
-   "metadata": {},
-   "source": [
-    "Now we can use `root_scalar` to estimate `r`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "imperial-engineering",
-   "metadata": {},
-   "source": [
-    "## Estimating `r`\n",
-    "\n",
-    "What we want is the value of `r` that yields a final temperature of\n",
-    "70 °C. To use `root_scalar`, we need a function that takes `r` as a parameter and returns the difference between the final temperature and the goal:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "quality-colors",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func(r, system):\n",
-    "    system.r = r\n",
-    "    results = run_simulation(system, change_func)\n",
-    "    return system.T_final - 70"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "random-loading",
-   "metadata": {},
-   "source": [
-    "This is called an \"error function\" because it returns the\n",
-    "difference between what we got and what we wanted, that is, the error.\n",
-    "With the right value of `r`, the error is 0.\n",
-    "\n",
-    "We can test `error_func` like this, using the initial guess `r=0.01`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "unknown-pharmacy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.2996253904030937"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee = make_system(T_init=90, volume=300, r=0.01, t_end=30)\n",
-    "error_func(0.01, coffee)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "moderate-monkey",
-   "metadata": {},
-   "source": [
-    "The result is an error of 2.3 °C, because the final temperature with\n",
-    "this value of `r` is too high."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "fossil-israel",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-10.907066281994297"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "error_func(0.02, coffee)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "south-fabric",
-   "metadata": {},
-   "source": [
-    "With `r=0.02`, the error is  about -11°C, which means that the final temperature is too low. So we know that the correct value must be in between.\n",
-    "\n",
-    "So we can call `root_scalar` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "backed-while",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = root_scalar(error_func, coffee, bracket=[0.01, 0.02])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "exotic-development",
-   "metadata": {},
-   "source": [
-    "The first argument is the error function.\n",
-    "The second argument is the `System` object, which `root_scalar` passes as an argument to `error_func`.\n",
-    "The third argument is an interval that brackets the root.\n",
-    "\n",
-    "Here are the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "crazy-panic",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "      converged: True\n",
-       "           flag: 'converged'\n",
-       " function_calls: 7\n",
-       "     iterations: 6\n",
-       "           root: 0.011543084584004043"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "early-twelve",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.011543084584004043"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r_coffee = res.root\n",
-    "r_coffee"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "greenhouse-designer",
-   "metadata": {},
-   "source": [
-    "In this example, `r_coffee` turns out to be about `0.0115`, in units of min$^{-1}$ (inverse minutes).\n",
-    "\n",
-    "We can confirm that this value is correct by setting `r` to the root we found and running the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "foster-lodging",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "69.99999999996255"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee.r = res.root\n",
-    "run_simulation(coffee, change_func)\n",
-    "coffee.T_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sweet-horizontal",
-   "metadata": {},
-   "source": [
-    "The final temperature is very close to 70 °C."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "imposed-complement",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:**  Simulate the temperature of 50 mL of milk with a starting temperature of 5 °C, in a vessel with `r=0.1`, for 15 minutes, and plot the results.\n",
-    "\n",
-    "By trial and error, find a value for `r` that makes the final temperature close to 20 °C."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "radical-accident",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18.499850754390966"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "milk = make_system(T_init=5, t_end=15, r=0.1, volume=50)\n",
-    "results_milk = run_simulation(milk, change_func)\n",
-    "milk.T_final"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "extraordinary-andrew",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results_milk.plot(color='C1', label='milk')\n",
-    "\n",
-    "decorate(xlabel='Time (minutes)',\n",
-    "         ylabel='Temperature (C)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hispanic-ghana",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write an error function that simulates the temperature of the milk and returns the difference between the final temperature and 20 °C.  Use it to estimate the value of `r` for the milk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "naughty-organ",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def error_func2(r, system):\n",
-    "    system.r = r\n",
-    "    results = run_simulation(system, change_func)\n",
-    "    return system.T_final - 20"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "separate-amateur",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "      converged: True\n",
-       "           flag: 'converged'\n",
-       " function_calls: 9\n",
-       "     iterations: 8\n",
-       "           root: 0.13296078935466452"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "root_scalar(error_func2, milk, bracket=[0.1, 0.2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "cloudy-reminder",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19.999999999999996"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "run_simulation(milk, change_func)\n",
-    "milk.T_final"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "present-french",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "mighty-transport",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap16.ipynb b/jupyter/chap16.ipynb
deleted file mode 100644
index d704de50..00000000
--- a/jupyter/chap16.ipynb
+++ /dev/null
@@ -1,793 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "involved-hampshire",
-   "metadata": {},
-   "source": [
-    "# Chapter 16"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "senior-paintball",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "surprising-hypothesis",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "capable-programming",
-   "metadata": {},
-   "source": [
-    "## Code from previous notebooks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "straight-milan",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def change_func(T, t, system):\n",
-    "    r, T_env, dt = system.r, system.T_env, system.dt\n",
-    "    \n",
-    "    return -r * (T - T_env) * dt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "enormous-economy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import linrange\n",
-    "from modsim import TimeSeries\n",
-    "\n",
-    "def run_simulation(system, change_func):\n",
-    "    t_array = linrange(system.t_0, system.t_end, system.dt)\n",
-    "    n = len(t_array)\n",
-    "    \n",
-    "    series = TimeSeries(index=t_array)\n",
-    "    series.iloc[0] = system.T_init\n",
-    "    \n",
-    "    for i in range(n-1):\n",
-    "        t = t_array[i]\n",
-    "        T = series.iloc[i]\n",
-    "        series.iloc[i+1] = T + change_func(T, t, system)\n",
-    "    \n",
-    "    system.t_end = t_array[-1]\n",
-    "    system.T_final = series.iloc[-1]\n",
-    "    return series"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "defined-expert",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import System\n",
-    "\n",
-    "def make_system(T_init, volume, r, t_end):\n",
-    "    return System(T_init=T_init,\n",
-    "                  T_final=T_init,\n",
-    "                  volume=volume,\n",
-    "                  r=r,\n",
-    "                  t_end=t_end,\n",
-    "                  T_env=22,\n",
-    "                  t_0=0,\n",
-    "                  dt=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "global-shooting",
-   "metadata": {},
-   "source": [
-    "In the previous chapter we wrote a simulation of a cooling cup of\n",
-    "coffee. Given the initial temperature of the coffee, the temperature of the atmosphere, and the rate parameter, `r`, we can predict how the\n",
-    "temperature of the coffee will change over time.\n",
-    "\n",
-    "..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ceramic-calibration",
-   "metadata": {},
-   "source": [
-    "## Mixing liquids\n",
-    "\n",
-    "When we mix two liquids, the temperature of the mixture depends on the\n",
-    "temperatures of the ingredients, but it might not be obvious how to\n",
-    "compute it.\n",
-    "\n",
-    "Assuming there are no chemical reactions that either produce or consume heat, the total thermal energy of the system is the same before and after mixing; in other words, thermal energy is **conserved**.\n",
-    "\n",
-    "If the temperature of the first liquid is $T_1$, the temperature of the second liquid is $T_2$, and the final temperature of the mixture is $T$, the heat transfer into the first liquid is $C_1 (T - T_1)$ and the heat transfer into the second liquid is $C_2 (T - T_2)$, where $C_1$ and $C_2$ are the thermal masses of the liquids.\n",
-    "\n",
-    "In order to conserve energy, these heat transfers must add up to 0:\n",
-    "\n",
-    "$$C_1 (T - T_1) + C_2 (T - T_2) = 0$$ \n",
-    "\n",
-    "We can solve this equation for T:\n",
-    "\n",
-    "$$T = \\frac{C_1 T_1 + C_2 T_2}{C_1 + C_2}$$ \n",
-    "\n",
-    "For the coffee cooling problem, we have the volume of each liquid; if we also know the density, $\\rho$, and the specific heat capacity, $c_p$, we can compute thermal mass: \n",
-    "\n",
-    "$$C = \\rho V c_p$$ \n",
-    "\n",
-    "If the liquids have the same density and heat capacity, they drop out of the equation, and we can write:\n",
-    "\n",
-    "$$T = \\frac{V_1 T_1 + V_2 T_2}{V_1 + V_2}$$ \n",
-    "\n",
-    "where $V_1$ and $V_2$ are the volumes of the liquids.\n",
-    "\n",
-    "As an approximation, I'll assume that milk and coffee have the same\n",
-    "density and specific heat. As an exercise, you can look up these\n",
-    "quantities and see how good this assumption is.\n",
-    "\n",
-    "The following function takes two `System` objects that represent the\n",
-    "coffee and milk, and creates a new `System` to represent the mixture:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "orange-beaver",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mix(system1, system2):\n",
-    "    \n",
-    "    V1, V2 = system1.volume, system2.volume\n",
-    "    T1, T2 = system1.T_final, system2.T_final\n",
-    "    \n",
-    "    V_mix = V1 + V2\n",
-    "    T_mix = (V1 * T1 + V2 * T2) / V_mix\n",
-    "    \n",
-    "    return make_system(T_init=T_mix,\n",
-    "                       volume=V_mix,\n",
-    "                       r=system1.r,\n",
-    "                       t_end=30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "quiet-payroll",
-   "metadata": {},
-   "source": [
-    "The first two lines extract volume and temperature from the two `System` objects. Then the following two lines compute the volume and temperature of the mixture. Finally, `mix` makes a new `System` object and returns it.\n",
-    "\n",
-    "This function uses the value of `r` from `system1` as the value of `r`\n",
-    "for the mixture. If `system1` represents the coffee, and we are adding\n",
-    "the milk to the coffee, this is probably a reasonable choice. On the\n",
-    "other hand, when we increase the amount of liquid in the coffee cup,\n",
-    "that might change `r`. So this is an assumption we might want to\n",
-    "revisit.\n",
-    "\n",
-    "Now we have everything we need to solve the problem."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "talented-appendix",
-   "metadata": {},
-   "source": [
-    "## Mix first or last?\n",
-    "\n",
-    "First I'll create objects to represent the coffee and milk.\n",
-    "For `r_coffee` and `r_milk`, I'll use the values we computed in the previous chapter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "breathing-pizza",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r_coffee = 0.0115\n",
-    "coffee = make_system(T_init=90, volume=300, r=r_coffee, t_end=30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "miniature-doctrine",
-   "metadata": {},
-   "source": [
-    "For `r_milk`, I'll use the value I estimated in the exercise from the previous chapter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "faced-start",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r_milk = 0.133\n",
-    "milk = make_system(T_init=5, volume=50, r=r_milk, t_end=15)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "interior-alpha",
-   "metadata": {},
-   "source": [
-    "Now we can mix them and simulate 30 minutes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "stock-release",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "61.48016207445017"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mix_first = mix(coffee, milk)\n",
-    "run_simulation(mix_first, change_func)\n",
-    "\n",
-    "mix_first.T_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "committed-preference",
-   "metadata": {},
-   "source": [
-    "The final temperature is 61.5 °C which is still warm enough to be\n",
-    "enjoyable. Would we do any better if we added the milk last?\n",
-    "\n",
-    "I'll simulate the coffee and milk separately, and then mix them:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "integral-character",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "62.91117032872072"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run_simulation(coffee, change_func)\n",
-    "run_simulation(milk, change_func)\n",
-    "mix_last = mix(coffee, milk)\n",
-    "mix_last.T_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "trying-jacob",
-   "metadata": {},
-   "source": [
-    "After mixing, the temperature is 62.9 °C, so it looks like adding the\n",
-    "milk at the end is better. \n",
-    "But is that the best we can do?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "enclosed-shore",
-   "metadata": {},
-   "source": [
-    "## Optimization\n",
-    "\n",
-    "Adding the milk after 30 minutes is better than adding it immediately, but maybe there's something in between that's even better. To find out, I'll use the following function, which takes the time to add the milk, `t_add`, as a parameter:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "accepting-consumer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_and_mix(t_add, t_total):\n",
-    "    coffee.t_end = t_add\n",
-    "    coffee_results = run_simulation(coffee, change_func)\n",
-    "    \n",
-    "    milk.t_end = t_add\n",
-    "    milk_results = run_simulation(milk, change_func)\n",
-    "    \n",
-    "    mixture = mix(coffee, milk)\n",
-    "    mixture.t_end = t_total - t_add\n",
-    "    results = run_simulation(mixture, change_func)\n",
-    "\n",
-    "    return mixture.T_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "critical-pound",
-   "metadata": {},
-   "source": [
-    "When `t_add` is`0`, we add the milk immediately; when `t_add` is `30`, we add it at the end. Now we can sweep the range of values in between:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "isolated-georgia",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import SweepSeries\n",
-    "from numpy import linspace\n",
-    "\n",
-    "sweep = SweepSeries()\n",
-    "for t_add in linspace(0, 30, 11):\n",
-    "    sweep[t_add] = run_and_mix(t_add, 30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "latin-fireplace",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "possible-prevention",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "sweep.plot(label='mixture', color='C2')\n",
-    "\n",
-    "decorate(xlabel='Time until mixing (minutes)',\n",
-    "         ylabel='Final emperature (C)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "light-poland",
-   "metadata": {},
-   "source": [
-    "Note that this is a parameter sweep, not a time series.\n",
-    "\n",
-    "The final temperature is maximized when `t_add=30`, so adding the milk\n",
-    "at the end is optimal.\n",
-    "\n",
-    "As an exercise you will have a chance to explore a variation of the coffee cooling problem:\n",
-    "\n",
-    "> Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office. In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
-    "\n",
-    "But first, let's do some analysis."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "industrial-circle",
-   "metadata": {},
-   "source": [
-    "## Analysis\n",
-    "\n",
-    "Simulating Newton's law of cooling is almost silly, because we can solve the differential equation analytically. If\n",
-    "\n",
-    "$$\\frac{dT}{dt} = -r (T - T_{env})$$ \n",
-    "\n",
-    "the general solution is\n",
-    "\n",
-    "$$T{\\left (t \\right )} = C \\exp(-r t) + T_{env}$$ \n",
-    "\n",
-    "and the particular solution where $T(0) = T_{init}$ is\n",
-    "\n",
-    "$$T_{env} + \\left(- T_{env} + T_{init}\\right) \\exp(-r t)$$ \n",
-    "\n",
-    "If you would like to see this solution done by hand, you can watch this video: <http://modsimpy.com/khan3>."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "molecular-happiness",
-   "metadata": {},
-   "source": [
-    "Now we can use the observed data to estimate the parameter $r$. If we\n",
-    "observe the that temperature at $t_{end}$ is $T_{final}$, we can plug these values into the particular solution and solve for $r$. The result is:\n",
-    "\n",
-    "$$r = \\frac{1}{t_{end}} \\log{\\left (\\frac{T_{init} - T_{env}}{T_{final} - T_{env}} \\right )}$$\n",
-    "\n",
-    "The following function takes a `System` object and computes `r`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "dressed-supply",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import log\n",
-    "\n",
-    "def compute_r(system):\n",
-    "    t_end = system.t_end\n",
-    "    T_init = system.T_init\n",
-    "    T_final = system.T_final\n",
-    "    T_env = system.T_env\n",
-    "    \n",
-    "    r = log((T_init - T_env) / (T_final - T_env)) / t_end\n",
-    "    return r"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "young-shift",
-   "metadata": {},
-   "source": [
-    "We can use this function to compute `r` for the coffee, given the parameters of the problem."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "bacterial-tonight",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.01161022314227386"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee2 = make_system(T_init=90, volume=300, r=0, t_end=30)\n",
-    "coffee2.T_final = 70\n",
-    "r_coffee2 = compute_r(coffee2)\n",
-    "r_coffee2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "removable-authority",
-   "metadata": {},
-   "source": [
-    "This value is close to the value of `r` we computed in the previous chapter, `0.115`, but not exactly the same.\n",
-    "That's because the simulations use discrete time steps, and the analysis uses continuous time.\n",
-    "\n",
-    "Nevertheless, the results of any analysis are consistent with the simulation.\n",
-    "To check, we'll use the following function, which takes a `System` object and uses the analytic result to compute a time series:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "civic-trail",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import exp\n",
-    "from pandas import Series\n",
-    "\n",
-    "def run_analysis(system):\n",
-    "    T_env, T_init, r = system.T_env, system.T_init, system.r\n",
-    "    \n",
-    "    t_array = linrange(system.t_0, system.t_end, system.dt)    \n",
-    "    T_array = T_env + (T_init - T_env) * exp(-r * t_array)\n",
-    "    \n",
-    "    system.T_final = T_array[-1]\n",
-    "    return TimeSeries(T_array, index=t_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eleven-faculty",
-   "metadata": {},
-   "source": [
-    "The first line unpacks the system variables.\n",
-    "The next two lines compute `t_array`, which is a NumPy array of time stamps, and `T_array`, which is an array of the corresponding temperatures.\n",
-    "\n",
-    "The last two lines store the final temperature in the `System` object and return the results in a `TimeSeries`.\n",
-    "\n",
-    "We can run it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "terminal-carry",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "70.0"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee2.r = r_coffee2\n",
-    "results2 = run_analysis(coffee2)\n",
-    "coffee2.T_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "approved-subject",
-   "metadata": {},
-   "source": [
-    "The final temperature is 70 °C, as it should be. In fact, the results\n",
-    "are identical to what we got by simulation, with a small difference due to rounding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "grand-silence",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee.r = 0.011543\n",
-    "results = run_simulation(coffee, change_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "monetary-sensitivity",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from numpy import allclose\n",
-    "\n",
-    "allclose(results, results2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "polish-focus",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "respected-inclusion",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Use `compute_r` to compute `r_milk` according to the analytic solution.  Run the analysis with this value of `r_milk` and confirm that the results are consistent with the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "headed-circle",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.14267107756641806"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "milk2 = make_system(T_init=5, volume=50, r=0, t_end=15)\n",
-    "milk2.T_final = 20\n",
-    "r_milk2 = compute_r(milk2)\n",
-    "r_milk2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "enormous-consolidation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20.0"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "milk2 = make_system(T_init=5, volume=50, r=r_milk2, t_end=15)\n",
-    "results2 = run_analysis(milk2)\n",
-    "milk2.T_final"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "social-stamp",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "milk = make_system(T_init=5, volume=50, r=0.13296, t_end=15)\n",
-    "results = run_simulation(milk, change_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "exclusive-puzzle",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "from numpy import allclose\n",
-    "\n",
-    "allclose(results, results2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "chinese-explanation",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
-    "\n",
-    "Hint: Think about the simplest way to represent the behavior of a refrigerator in this model.  The change you make to test this variation of the problem should be very small!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "terminal-billy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# A refrigerator keeps the milk at a constant temperature,\n",
-    "# so it is like a container with r = 0.\n",
-    "\n",
-    "# With T_init=5 and r_milk = 0, \n",
-    "# it is best to add the milk at the beginning."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "nuclear-going",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap17.ipynb b/jupyter/chap17.ipynb
deleted file mode 100644
index aca865e8..00000000
--- a/jupyter/chap17.ipynb
+++ /dev/null
@@ -1,665 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "interracial-guitar",
-   "metadata": {},
-   "source": [
-    "# Chapter 17"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "restricted-thumb",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "premier-coach",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "tamil-helping",
-   "metadata": {},
-   "source": [
-    "**Pharmacokinetics** is the study of how drugs and other substances move around the body, react, and are eliminated. In this chapter, we\n",
-    "implement one of the most widely used pharmacokinetic models: the\n",
-    "so-called **minimal model** of glucose and insulin in the blood stream.\n",
-    "\n",
-    "My presentation in this chapter follows Bergman (2005) \"Minimal Model\"\n",
-    "(abstract at <http://modsimpy.com/bergman>, PDF at\n",
-    "<http://modsimpy.com/minmod>)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "stable-lemon",
-   "metadata": {},
-   "source": [
-    "## The glucose-insulin system\n",
-    "\n",
-    "**Glucose** is a form of sugar that circulates in the blood of animals; it is used as fuel for muscles, the brain, and other organs. The concentration of blood sugar is controlled by the hormone system, and especially by **insulin**, which is produced by the pancreas and has the effect of reducing blood sugar.\n",
-    "\n",
-    "In people with normal pancreatic function, the hormone system maintains **homeostasis**; that is, it keeps the concentration of blood sugar in a range that is neither too high or too low.\n",
-    "\n",
-    "But if the pancreas does not produce enough insulin, or if the cells\n",
-    "that should respond to insulin become insensitive, blood sugar can\n",
-    "become elevated, a condition called **hyperglycemia**. Long term, severe hyperglycemia is the defining symptom of **diabetes mellitus**, a serious disease that affects almost 10% of the population in the U.S. (see <http://modsimpy.com/cdc>)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sharp-yukon",
-   "metadata": {},
-   "source": [
-    "One of the most-used tests for hyperglycemia and diabetes is the\n",
-    "frequently sampled intravenous glucose tolerance test (FSIGT), in which glucose is injected into the blood stream of a fasting subject (someone who has not eaten recently); then blood samples are collected at intervals of 2--10 minutes for 3 hours. The samples are analyzed to\n",
-    "measure the concentrations of glucose and insulin.\n",
-    "\n",
-    "By analyzing these measurements, we can estimate several parameters of\n",
-    "the subject's response; the most important is a parameter denoted $S_I$, which quantifies the effect of insulin on the rate of reduction in blood sugar."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "round-roommate",
-   "metadata": {},
-   "source": [
-    "## The glucose minimal model\n",
-    "\n",
-    "The \"minimal model\", as proposed by Bergman, Ider, Bowden, and\n",
-    "Cobelli, consists of two parts: the glucose model and the insulin model. I will present an implementation of the glucose model; as a case study, you will have the chance to implement the insulin model.\n",
-    "\n",
-    "The original model was developed in the 1970s; since then, many\n",
-    "variations and extensions have been proposed. Bergman's comments on the development of the model provide insight into their process:\n",
-    "\n",
-    "> We applied the principle of Occam's Razor, i.e. by asking what was the simplest model based upon known physiology that could account for the insulin-glucose relationship revealed in the data. Such a model must be simple enough to account totally for the measured glucose (given the insulin input), yet it must be possible, using mathematical techniques, to estimate all the characteristic parameters of the model from a single data set (thus avoiding unverifiable assumptions)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "accompanied-battery",
-   "metadata": {},
-   "source": [
-    "The most useful models are the ones that achieve this balance: including enough realism to capture the essential features of the system without so much complexity that they are impractical. In this example, the practical limit is the ability to estimate the parameters of the model using data, and to interpret the parameters meaningfully.\n",
-    "\n",
-    "Bergman discusses the features he and his colleagues thought were\n",
-    "essential:\n",
-    "\n",
-    "> (1) Glucose, once elevated by injection, returns to basal level due to two effects: the effect of glucose itself to normalize its own concentration \\[...\\] as well as the catalytic effect of insulin to allow glucose to self-normalize (2) Also, we discovered that the effect of insulin on net glucose disappearance must be sluggish --- that is, that insulin acts slowly because insulin must first move from plasma to a remote compartment \\[...\\] to exert its action on glucose disposal.\n",
-    "\n",
-    "To paraphrase the second point, the effect of insulin on glucose\n",
-    "disposal, as seen in the data, happens more slowly than we would expect if it depended primarily on the the concentration of insulin in the blood. Bergman's group hypothesized that insulin must move relatively slowly from the blood to a \"remote compartment\" where it has its effect."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bottom-extent",
-   "metadata": {},
-   "source": [
-    "At the time, the remote compartment was a modeling abstraction that\n",
-    "might, or might not, represent something physical. Later, according to\n",
-    "Bergman, it was \"shown to be interstitial fluid\", that is, the fluid\n",
-    "that surrounds tissue cells. In the history of mathematical modeling, it is common for hypothetical entities, added to models to achieve\n",
-    "particular effects, to be found later to correspond to physical\n",
-    "entities.\n",
-    "\n",
-    "The glucose model consists of two differential equations:\n",
-    "\n",
-    "$$\\frac{dG}{dt} = -k_1 \\left[ G(t) - G_b \\right] - X(t) G(t)$$\n",
-    "\n",
-    "$$\\frac{dX}{dt} = k_3 \\left[I(t) - I_b \\right] - k_2 X(t)$$ \n",
-    "\n",
-    "where\n",
-    "\n",
-    "-   $G$ is the concentration of blood glucose as a function of time and $dG/dt$ is its rate of change.\n",
-    "\n",
-    "-   $X$ is the concentration of insulin in the tissue fluid as a\n",
-    "    function of time, and $dX/dt$ is its rate of change.\n",
-    "\n",
-    "-   $I$ is the concentration of insulin in the blood as a function of\n",
-    "    time, which is taken as an input into the model, based on\n",
-    "    measurements.\n",
-    "\n",
-    "-   $G_b$ is the basal concentration of blood glucose and $I_b$ is the\n",
-    "    basal concentration of blood insulin, that is, the concentrations at equilibrium. Both are constants estimated from measurements at the\n",
-    "    beginning or end of the test.\n",
-    "\n",
-    "-   $k_1$, $k_2$, and $k_3$ are positive-valued parameters that control the rates of appearance and disappearance for glucose and insulin."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dutch-retailer",
-   "metadata": {},
-   "source": [
-    "We can interpret the terms in the equations one by one:\n",
-    "\n",
-    "-   $-k_1 \\left[ G(t) - G_b \\right]$ is the rate of glucose\n",
-    "    disappearance due to the effect of glucose itself. When $G(t)$ is\n",
-    "    above basal level, $G_b$, this term is negative; when $G(t)$ is\n",
-    "    below basal level this term is positive. So in the absence of\n",
-    "    insulin, this term tends to restore blood glucose to basal level.\n",
-    "\n",
-    "-   $-X(t) G(t)$ models the interaction of glucose and insulin in tissue\n",
-    "    fluid, so the rate increases as either $X$ or $G$ increases. This\n",
-    "    term does not require a rate parameter because the units of $X$ are\n",
-    "    unspecified; we can consider $X$ to be in whatever units make the\n",
-    "    parameter of this term 1.\n",
-    "\n",
-    "-   $k_3 \\left[ I(t) - I_b \\right]$ is the rate at which insulin\n",
-    "    diffuses between blood and tissue fluid. When $I(t)$ is above basal\n",
-    "    level, insulin diffuses from the blood into the tissue fluid. When\n",
-    "    $I(t)$ is below basal level, insulin diffuses from tissue to the\n",
-    "    blood.\n",
-    "\n",
-    "-   $-k_2 X(t)$ is the rate of insulin disappearance in tissue fluid as\n",
-    "    it is consumed or broken down."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "regional-receptor",
-   "metadata": {},
-   "source": [
-    "The initial state of the model is $X(0) = I_b$ and $G(0) = G_0$, where\n",
-    "$G_0$ is a constant that represents the concentration of blood sugar\n",
-    "immediately after the injection. In theory we could estimate $G_0$ based on measurements, but in practice it takes time for the injected glucose to spread through the blood volume. Since $G_0$ is not measurable, it is treated as a **free parameter** of the model, which means that we are free to choose it to fit the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "exact-heating",
-   "metadata": {},
-   "source": [
-    "## Data\n",
-    "\n",
-    "To develop and test the model, we'll use data from Pacini and Bergman, \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 23: 113-122, (1986)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "separated-strip",
-   "metadata": {},
-   "source": [
-    "The following cell reads the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "looking-needle",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "filename = 'glucose_insulin.csv'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/glucose_insulin.csv"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dressed-regard",
-   "metadata": {},
-   "source": [
-    "We can use Pandas to read the data file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "naval-geology",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>glucose</th>\n",
-       "      <th>insulin</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>time</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>92</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>350</td>\n",
-       "      <td>26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>287</td>\n",
-       "      <td>130</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>251</td>\n",
-       "      <td>85</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>240</td>\n",
-       "      <td>51</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      glucose  insulin\n",
-       "time                  \n",
-       "0          92       11\n",
-       "2         350       26\n",
-       "4         287      130\n",
-       "6         251       85\n",
-       "8         240       51"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from pandas import read_csv\n",
-    "\n",
-    "data = read_csv('glucose_insulin.csv', index_col='time')\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "close-payday",
-   "metadata": {},
-   "source": [
-    "`data` has two columns: `glucose` is the concentration of blood glucose in mg/dL; `insulin` is concentration of insulin in the blood in μU/mL (a medical \"unit\", denoted U, is an amount defined by convention in context). The index is time in minutes.\n",
-    "\n",
-    "This dataset represents glucose and insulin concentrations over\n",
-    "182 min for a subject with normal insulin production and sensitivity."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "burning-valuable",
-   "metadata": {},
-   "source": [
-    "## Interpolation\n",
-    "\n",
-    "Before we are ready to implement the model, there's one problem we have to solve. In the differential equations, $I$ is a function that can be evaluated at any time, $t$. But in the `DataFrame`, we only have measurements at discrete times. This is a job for interpolation!\n",
-    "\n",
-    "The ModSim library provides a function named `interpolate`, which is a\n",
-    "wrapper for the SciPy function `interp1d`. It takes any kind of `Series` as a parameter, including `TimeSeries` and `SweepSeries`, and returns a function. That's right, I said it returns a *function*.\n",
-    "\n",
-    "So we can call `interpolate` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "terminal-teaching",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import interpolate\n",
-    "\n",
-    "I = interpolate(data.insulin)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "banner-shakespeare",
-   "metadata": {},
-   "source": [
-    "Then we can call the new function, `I`, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "intense-thursday",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(31.66666667)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "I(18)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "severe-desire",
-   "metadata": {},
-   "source": [
-    "The result is 31.66, which is a linear interpolation between the actual measurements at `t=16` and `t=19`. \n",
-    "\n",
-    "We can also pass an array as an argument to `I`.  Here's an array of equally-spaced values from `t_0` to `t_end`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "least-pattern",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import linrange\n",
-    "\n",
-    "t_0 = data.index[0]\n",
-    "t_end = data.index[-1]\n",
-    "t_array = linrange(t_0, t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "empirical-tackle",
-   "metadata": {},
-   "source": [
-    "And here are the corresponding values of `I`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "mounted-venice",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "I_array = I(t_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "environmental-variance",
-   "metadata": {},
-   "source": [
-    "Here's what the interpolated values look like along with the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "completed-registrar",
-   "metadata": {},
-   "source": [
-    "`interpolate` can take additional arguments, which it passes along to\n",
-    "`interp1d`. You can read about these options at\n",
-    "<http://modsimpy.com/interp>."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "engaging-watershed",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from matplotlib.pyplot import plot\n",
-    "from modsim import decorate\n",
-    "\n",
-    "data.insulin.plot(style='o', color='C2', label='insulin data')\n",
-    "plot(t_array, I_array, color='C2', label='interpolated')\n",
-    "\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adopted-locking",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "In the next chapter, we will use interpolated data to run the simulation\n",
-    "of the glucose-insulin system.\n",
-    "\n",
-    "\n",
-    "\n",
-    "[^1]: Bergman RN, Ider YZ, Bowden CR, Cobelli C., \"Quantitative\n",
-    "    estimation of insulin sensitivity\\\", Am J Physiol. 1979\n",
-    "    Jun;236(6):E667-77. Abstract at <http://modsimpy.com/insulin>.\n",
-    "\n",
-    "[^2]: \"MINMOD: A computer program to calculate insulin sensitivity and\n",
-    "    pancreatic responsivity from the frequently sampled intravenous\n",
-    "    glucose tolerance test\\\", *Computer Methods and Programs in\n",
-    "    Biomedicine* 23: 113-122, 1986."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "floppy-store",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hungarian-newman",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Read the documentation of `scipy.interpolate.interp1d` at <https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html>.  Pass a keyword argument to `interpolate` to specify one of the other kinds of interpolation, and run the code again to see what it looks like. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "endless-network",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "I = interpolate(data.insulin, kind='cubic')\n",
-    "I_array = I(t_array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "entire-concern",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data.insulin.plot(style='o', color='C2', label='insulin data')\n",
-    "plot(t_array, I_array, color='C2', label='interpolated')\n",
-    "\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "furnished-recognition",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Interpolate the glucose data and generate a plot, similar to the previous one, that shows the data points and the interpolated curve evaluated at the time values in `ts`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "representative-acquisition",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "G = interpolate(data.glucose)\n",
-    "G_array = G(t_array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "rocky-sydney",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data.glucose.plot(style='o', color='C0', label='gluciose data')\n",
-    "plot(t_array, G_array, color='C0', label='interpolated')\n",
-    "\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration (mg/dL)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "opened-scheduling",
-   "metadata": {},
-   "source": [
-    "### Under the hood"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "still-tolerance",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%psource interpolate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "essential-fishing",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap18.ipynb b/jupyter/chap18.ipynb
deleted file mode 100644
index 5cb0c53a..00000000
--- a/jupyter/chap18.ipynb
+++ /dev/null
@@ -1,869 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "electric-netherlands",
-   "metadata": {},
-   "source": [
-    "# Chapter 18"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "tribal-blame",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "hungry-lucas",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "original-photographer",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter\n",
-    "\n",
-    "Read the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "fewer-weather",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "filename = 'glucose_insulin.csv'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/glucose_insulin.csv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "computational-border",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas import read_csv\n",
-    "\n",
-    "data = read_csv(filename, index_col='time');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unique-domestic",
-   "metadata": {},
-   "source": [
-    "Interpolate the insulin data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "oriented-translation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import interpolate\n",
-    "\n",
-    "I = interpolate(data.insulin)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "horizontal-bridges",
-   "metadata": {},
-   "source": [
-    "In this chapter, we implement the glucose minimal model described in the previous chapter. We'll start with `run_simulation`, which solves\n",
-    "differential equations using discrete time steps. This method works well enough for many applications, but it is not very accurate. In this chapter we explore a better option: using an **ODE solver**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "stunning-giving",
-   "metadata": {},
-   "source": [
-    "## Implementation\n",
-    "\n",
-    "To get started, let's assume that the parameters of the model are known.\n",
-    "We'll implement the model and use it to generate time series for `G` and `X`. Then we'll see how to find the parameters that generate the series that best fits the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "polished-burner",
-   "metadata": {},
-   "source": [
-    "We can pass `params` and `data` to `make_system`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "substantial-literacy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import State, System\n",
-    "\n",
-    "def make_system(params, data):\n",
-    "    G0, k1, k2, k3 = params\n",
-    "    \n",
-    "    Gb = data.glucose[0]\n",
-    "    Ib = data.insulin[0]\n",
-    "    I = interpolate(data.insulin)\n",
-    "    \n",
-    "    t_0 = data.index[0]\n",
-    "    t_end = data.index[-1]\n",
-    "    \n",
-    "    init = State(G=G0, X=0)\n",
-    "    \n",
-    "    return System(params=params, init=init, \n",
-    "                  Gb=Gb, Ib=Ib, I=I,\n",
-    "                  t_0=t_0, t_end=t_end, dt=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "metallic-motorcycle",
-   "metadata": {},
-   "source": [
-    "`make_system` uses the measurements at `t=0` as the basal levels, `Gb`\n",
-    "and `Ib`. It gets `t_0` and `t_end` from the data. And it uses the\n",
-    "parameter `G0` as the initial value for `G`. Then it packs everything\n",
-    "into a `System` object."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "confident-performance",
-   "metadata": {},
-   "source": [
-    "Taking advantage of estimates from prior work, we'll start with these\n",
-    "values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "under-rhythm",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#        G0,  k1,   k2,   k3\n",
-    "params = 290, 0.03, 0.02, 1e-05\n",
-    "system = make_system(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "stupid-retro",
-   "metadata": {},
-   "source": [
-    "Here's the update function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "roman-archive",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    G, X = state\n",
-    "    G0, k1, k2, k3 = system.params \n",
-    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
-    "    dt = system.dt\n",
-    "        \n",
-    "    dGdt = -k1 * (G - Gb) - X*G\n",
-    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
-    "    \n",
-    "    G += dGdt * dt\n",
-    "    X += dXdt * dt\n",
-    "\n",
-    "    return State(G=G, X=X)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "basic-subdivision",
-   "metadata": {},
-   "source": [
-    "As usual, the update function takes a `State` object, a time, and a\n",
-    "`System` object as parameters. The first line uses multiple assignment\n",
-    "to extract the current values of `G` and `X`.\n",
-    "\n",
-    "The following lines unpack the parameters we need from the `System`\n",
-    "object.\n",
-    "\n",
-    "Computing the derivatives `dGdt` and `dXdt` is straightforward; we just\n",
-    "translate the equations from math notation to Python.\n",
-    "\n",
-    "Then, to perform the update, we multiply each derivative by the discrete\n",
-    "time step `dt`, which is 2 min in this example. The return value is a\n",
-    "`State` object with the new values of `G` and `X`.\n",
-    "\n",
-    "Before running the simulation, it is a good idea to run the update\n",
-    "function with the initial conditions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "sapphire-shannon",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "G    278.12\n",
-       "X      0.00\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "update_func(system.init, system.t_0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "strange-citation",
-   "metadata": {},
-   "source": [
-    "If it runs without errors and there is nothing obviously wrong with the\n",
-    "results, we are ready to run the simulation. We'll use this version of\n",
-    "`run_simulation`, which is very similar to previous versions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "willing-masters",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import linrange, TimeFrame\n",
-    "\n",
-    "def run_simulation(system, update_func):\n",
-    "    init = system.init\n",
-    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
-    "    \n",
-    "    t_array = linrange(system.t_0, system.t_end, system.dt)\n",
-    "    n = len(t_array)\n",
-    "    \n",
-    "    frame = TimeFrame(index=t_array, columns=init.index)\n",
-    "    frame.iloc[0] = system.init\n",
-    "    \n",
-    "    for i in range(n-1):\n",
-    "        t = t_array[i]\n",
-    "        state = frame.iloc[i]\n",
-    "        frame.iloc[i+1] = update_func(state, t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "musical-loading",
-   "metadata": {},
-   "source": [
-    "We can run it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "international-germany",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>G</th>\n",
-       "      <th>X</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.0</th>\n",
-       "      <td>290</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.0</th>\n",
-       "      <td>278.12</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.0</th>\n",
-       "      <td>266.9528</td>\n",
-       "      <td>0.0003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.0</th>\n",
-       "      <td>256.29546</td>\n",
-       "      <td>0.002668</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.0</th>\n",
-       "      <td>245.07014</td>\n",
-       "      <td>0.004041</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             G         X\n",
-       "0.0        290         0\n",
-       "2.0     278.12       0.0\n",
-       "4.0   266.9528    0.0003\n",
-       "6.0  256.29546  0.002668\n",
-       "8.0  245.07014  0.004041"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = run_simulation(system, update_func)\n",
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "specialized-thomson",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "results.G.plot(style='-', label='simulation')\n",
-    "data.glucose.plot(style='o', color='C0', label='glucose data')\n",
-    "decorate(ylabel='Concentration (mg/dL)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "authentic-series",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results.X.plot(color='C1', label='remote insulin')\n",
-    "\n",
-    "decorate(xlabel='Time (min)', \n",
-    "         ylabel='Concentration (arbitrary units)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "diagnostic-service",
-   "metadata": {},
-   "source": [
-    " shows the results. The top plot shows\n",
-    "simulated glucose levels from the model along with the measured data.\n",
-    "The bottom plot shows simulated insulin levels in tissue fluid, which is in unspecified units, and not to be confused with measured insulin\n",
-    "levels in the blood.\n",
-    "\n",
-    "With the parameters I chose, the model fits the data well, except for\n",
-    "the first few data points, where we don't expect the model to be\n",
-    "accurate."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "valid-evanescence",
-   "metadata": {},
-   "source": [
-    "## Solving differential equations\n",
-    "\n",
-    "So far we have solved differential equations by rewriting them as\n",
-    "difference equations. In the current example, the differential equations are: \n",
-    "\n",
-    "$$\\frac{dG}{dt} = -k_1 \\left[ G(t) - G_b \\right] - X(t) G(t)$$\n",
-    "\n",
-    "$$\\frac{dX}{dt} = k_3 \\left[I(t) - I_b \\right] - k_2 X(t)$$ \n",
-    "\n",
-    "If we multiply both sides by $dt$, we have:\n",
-    "\n",
-    "$$dG = \\left[ -k_1 \\left[ G(t) - G_b \\right] - X(t) G(t) \\right] dt$$\n",
-    "\n",
-    "$$dX = \\left[ k_3 \\left[I(t) - I_b \\right] - k_2 X(t) \\right] dt$$ \n",
-    "\n",
-    "When $dt$ is very small, or more precisely **infinitesimal**, this equation is exact. But in our simulations, $dt$ is 2 min, which is not very small. In effect, the simulations assume that the derivatives $dG/dt$ and $dX/dt$ are constant during each 2 min time step."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "underlying-grave",
-   "metadata": {},
-   "source": [
-    "This method, evaluating derivatives at discrete time steps and assuming that they are constant in between, is called **Euler's method** (see <http://modsimpy.com/euler>).\n",
-    "\n",
-    "Euler's method is good enough for some simple problems, but it is not\n",
-    "very accurate. Other methods are more accurate, but many of them are\n",
-    "substantially more complicated.\n",
-    "\n",
-    "One of the best simple methods is called **Ralston's method**. The\n",
-    "ModSim library provides a function called `run_ode_solver` that\n",
-    "implements it.\n",
-    "\n",
-    "The \"ODE\" in `run_ode_solver` stands for \"ordinary differential\n",
-    "equation\". The equations we are solving are \"ordinary\" because all the\n",
-    "derivatives are with respect to the same variable; in other words, there are no partial derivatives.\n",
-    "\n",
-    "To use `run_ode_solver`, we have to provide a \"slope function\", like\n",
-    "this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "expected-collapse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(t, state, system):\n",
-    "    G, X = state\n",
-    "    G0, k1, k2, k3 = system.params \n",
-    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
-    "        \n",
-    "    dGdt = -k1 * (G - Gb) - X*G\n",
-    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
-    "    \n",
-    "    return dGdt, dXdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "modified-surname",
-   "metadata": {},
-   "source": [
-    "`slope_func` is similar to `update_func`; in fact, it takes the same\n",
-    "parameters in the same order. But `slope_func` is simpler, because all\n",
-    "we have to do is compute the derivatives, that is, the slopes. We don't\n",
-    "have to do the updates; `run_ode_solver` does them for us.\n",
-    "\n",
-    "Now we can call `run_ode_solver` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "frequent-cartoon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import run_solve_ivp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "ongoing-constraint",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "  message: 'The solver successfully reached the end of the integration interval.'\n",
-       "     nfev: 86\n",
-       "     njev: 0\n",
-       "      nlu: 0\n",
-       "      sol: None\n",
-       "   status: 0\n",
-       "  success: True\n",
-       " t_events: None\n",
-       " y_events: None"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results2, details = run_solve_ivp(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "removed-bubble",
-   "metadata": {},
-   "source": [
-    "`run_ode_solver` is similar to `run_simulation`: it takes a `System`\n",
-    "object and a slope function as parameters. It returns two values: a\n",
-    "`TimeFrame` with the solution and a `ModSimSeries` with additional\n",
-    "information.\n",
-    "\n",
-    "A `ModSimSeries` is like a `System` or `State` object; it contains a set\n",
-    "of variables and their values. The `ModSimSeries` from `run_ode_solver`,\n",
-    "which we assign to `details`, contains information about how the solver\n",
-    "ran, including a success code and diagnostic message.\n",
-    "\n",
-    "The `TimeFrame`, which we assign to `results`, has one row for each time\n",
-    "step and one column for each state variable. In this example, the rows\n",
-    "are time from 0 to 182 minutes; the columns are the state variables, `G`\n",
-    "and `X`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "important-crawford",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "results2.G.plot(style='-', label='simulation')\n",
-    "data.glucose.plot(style='o', color='C0', label='glucose data')\n",
-    "decorate(ylabel='Concentration (mg/dL)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "virtual-standard",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results2.X.plot(color='C1', label='remote insulin')\n",
-    "\n",
-    "decorate(xlabel='Time (min)', \n",
-    "         ylabel='Concentration (arbitrary units)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "corrected-indonesia",
-   "metadata": {},
-   "source": [
-    "shows the results from `run_simulation` and\n",
-    "`run_ode_solver`. The difference between them is barely visible.\n",
-    "\n",
-    "We can compute the percentage differences like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "disciplinary-washington",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.004442701427073649"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diff = results.G - results2.G\n",
-    "percent_diff = diff / results2.G * 100\n",
-    "percent_diff.abs().max()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "electronic-navigation",
-   "metadata": {},
-   "source": [
-    "The largest percentage difference is less than 2%, which is small enough that it probably doesn't matter in practice. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "potential-dispute",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "You might be interested in this article about [people making a DIY artificial pancreas](https://www.bloomberg.com/news/features/2018-08-08/the-250-biohack-that-s-revolutionizing-life-with-diabetes)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "north-suspension",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bright-damage",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Our solution to the differential equations is only approximate because we used a finite step size, `dt=2` minutes.\n",
-    "\n",
-    "If we make the step size smaller, we expect the solution to be more accurate.  Run the simulation with `dt=1` and compare the results.  What is the largest relative error between the two solutions?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "charitable-craft",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "  message: 'The solver successfully reached the end of the integration interval.'\n",
-       "     nfev: 86\n",
-       "     njev: 0\n",
-       "      nlu: 0\n",
-       "      sol: None\n",
-       "   status: 0\n",
-       "  success: True\n",
-       " t_events: None\n",
-       " y_events: None"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system.dt = 1\n",
-    "results3, details = run_simulation(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "encouraging-outline",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results2.G.plot(style='C2--', label='run_ode_solver (dt=2)')\n",
-    "results3.G.plot(style='C3:', label='run_ode_solver (dt=1)')\n",
-    "\n",
-    "decorate(xlabel='Time (m)', ylabel='mg/dL')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "aware-patrick",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "diff = (results2.G - results3.G).dropna()\n",
-    "percent_diff = diff / results2.G * 100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "fiscal-baker",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.0"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "max(abs(percent_diff))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "turkish-principle",
-   "metadata": {},
-   "source": [
-    "## Under the hood\n",
-    "\n",
-    "Here's the source code for `run_solve_ivp` if you'd like to know how it works.\n",
-    "\n",
-    "[RK45](https://en.wikipedia.org/wiki/List_of_Runge–Kutta_methods)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hispanic-summer",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "distinct-advance",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap20.ipynb b/jupyter/chap20.ipynb
deleted file mode 100644
index a2cc521d..00000000
--- a/jupyter/chap20.ipynb
+++ /dev/null
@@ -1,1108 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "funded-utilization",
-   "metadata": {},
-   "source": [
-    "# Chapter 20"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "furnished-portsmouth",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "pending-adams",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "embedded-gentleman",
-   "metadata": {},
-   "source": [
-    "So far the differential equations we've worked with have been **first\n",
-    "order**, which means they involve only first derivatives. In this\n",
-    "chapter, we turn our attention to second order ODEs, which can involve\n",
-    "both first and second derivatives.\n",
-    "\n",
-    "We'll revisit the falling penny example from\n",
-    "Chapter xxx, and use `run_solve_ivp` to find the position and velocity of the penny as it falls, with and without air resistance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "isolated-louis",
-   "metadata": {},
-   "source": [
-    "## Newton's second law of motion\n",
-    "\n",
-    "First order ODEs can be written \n",
-    "\n",
-    "$$\\frac{dy}{dx} = G(x, y)$$ \n",
-    "\n",
-    "where $G$ is some function of $x$ and $y$ (see <http://modsimpy.com/ode>). Second order ODEs can be written \n",
-    "\n",
-    "$$\\frac{d^2y}{dx^2} = H(x, y, \\frac{dy}{dt})$$\n",
-    "\n",
-    "where $H$ is a function of $x$, $y$, and $dy/dx$.\n",
-    "\n",
-    "In this chapter, we will work with one of the most famous and useful\n",
-    "second order ODEs, Newton's second law of motion: \n",
-    "\n",
-    "$$F = m a$$ \n",
-    "\n",
-    "where $F$ is a force or the total of a set of forces, $m$ is the mass of a moving object, and $a$ is its acceleration."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "drawn-symphony",
-   "metadata": {},
-   "source": [
-    "Newton's law might not look like a differential equation, until we\n",
-    "realize that acceleration, $a$, is the second derivative of position,\n",
-    "$y$, with respect to time, $t$. With the substitution\n",
-    "\n",
-    "$$a = \\frac{d^2y}{dt^2}$$ \n",
-    "\n",
-    "Newton's law can be written\n",
-    "\n",
-    "$$\\frac{d^2y}{dt^2} = F / m$$ \n",
-    "\n",
-    "And that's definitely a second order ODE.\n",
-    "In general, $F$ can be a function of time, position, and velocity."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "swiss-vietnam",
-   "metadata": {},
-   "source": [
-    "Of course, this \"law\" is really a model in the sense that it is a\n",
-    "simplification of the real world. Although it is often approximately\n",
-    "true:\n",
-    "\n",
-    "-   It only applies if $m$ is constant. If mass depends on time,\n",
-    "    position, or velocity, we have to use a more general form of\n",
-    "    Newton's law (see <http://modsimpy.com/varmass>).\n",
-    "\n",
-    "-   It is not a good model for very small things, which are better\n",
-    "    described by another model, quantum mechanics.\n",
-    "\n",
-    "-   And it is not a good model for things moving very fast, which are\n",
-    "    better described by yet another model, relativistic mechanics.\n",
-    "\n",
-    "However, for medium-sized things with constant mass, moving at\n",
-    "medium-sized speeds, Newton's model is extremely useful. If we can\n",
-    "quantify the forces that act on such an object, we can predict how it\n",
-    "will move."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "coordinate-three",
-   "metadata": {},
-   "source": [
-    "## Dropping pennies\n",
-    "\n",
-    "As a first example, let's get back to the penny falling from the Empire State Building, which we considered in\n",
-    "Chapter xxx. We will implement two models of this system: first without air resistance, then with.\n",
-    "\n",
-    "Given that the Empire State Building is 381 m high, and assuming that\n",
-    "the penny is dropped from a standstill, the initial conditions are:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "compatible-increase",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import State\n",
-    "\n",
-    "init = State(y=381, v=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "intellectual-radiation",
-   "metadata": {},
-   "source": [
-    "where `y` is height above the sidewalk and `v` is velocity. \n",
-    "\n",
-    "The units `m` and `s` are from the `units` object provided by Pint:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "wicked-thomas",
-   "metadata": {},
-   "source": [
-    "The only system parameter is the acceleration of gravity:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "reverse-authorization",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g = 9.8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "developing-newfoundland",
-   "metadata": {},
-   "source": [
-    "In addition, we'll specify the duration of the simulation and the step\n",
-    "size:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "square-toolbox",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_end = 10\n",
-    "dt = 0.1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "precise-tobago",
-   "metadata": {},
-   "source": [
-    "With these parameters, the number of time steps is 100, which is good\n",
-    "enough for many problems. Once we have a solution, we will increase the\n",
-    "number of steps and see what effect it has on the results.\n",
-    "\n",
-    "We need a `System` object to store the parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "built-piece",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import System\n",
-    "\n",
-    "system = System(init=init, g=g, t_end=t_end, dt=dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "heavy-boards",
-   "metadata": {},
-   "source": [
-    "Now we need a slope function, and here's where things get tricky. As we have seen, `run_solve_ivp` can solve systems of first order ODEs, but Newton's law is a second order ODE. However, if we recognize that\n",
-    "\n",
-    "1.  Velocity, $v$, is the derivative of position, $dy/dt$, and\n",
-    "\n",
-    "2.  Acceleration, $a$, is the derivative of velocity, $dv/dt$,\n",
-    "\n",
-    "we can rewrite Newton's law as a system of first order ODEs:\n",
-    "\n",
-    "$$\\frac{dy}{dt} = v$$ \n",
-    "\n",
-    "$$\\frac{dv}{dt} = a$$ \n",
-    "\n",
-    "And we can translate those\n",
-    "equations into a slope function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "occupied-mercury",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(t, state, system):\n",
-    "    y, v = state\n",
-    "\n",
-    "    dydt = v\n",
-    "    dvdt = -system.g\n",
-    "    \n",
-    "    return dydt, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "opening-adolescent",
-   "metadata": {},
-   "source": [
-    "The first parameter, `state`, contains the position and velocity of the\n",
-    "penny. The last parameter, `system`, contains the system parameter `g`,\n",
-    "which is the magnitude of acceleration due to gravity.\n",
-    "\n",
-    "The second parameter, `t`, is time. It is not used in this slope\n",
-    "function because none of the factors of the model are time dependent. I include it anyway because this function will be called by `run_solve_ivp`, which always provides the same arguments,\n",
-    "whether they are needed or not.\n",
-    "\n",
-    "The rest of the function is a straightforward translation of the\n",
-    "differential equations, with the substitution $a = -g$, which indicates that acceleration due to gravity is in the direction of decreasing $y$. `slope_func` returns a sequence containing the two derivatives.\n",
-    "\n",
-    "Before calling `run_solve_ivp`, it is a good idea to test the slope\n",
-    "function with the initial conditions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "positive-feeling",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "-9.8\n"
-     ]
-    }
-   ],
-   "source": [
-    "dydt, dvdt = slope_func(0, system.init, system)\n",
-    "print(dydt)\n",
-    "print(dvdt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "false-charlotte",
-   "metadata": {},
-   "source": [
-    "The result is 0 m/s for velocity and 9.8 m/s$^2$ for acceleration. Now we call `run_solve_ivp` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "lovely-management",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "  message: 'The solver successfully reached the end of the integration interval.'\n",
-       "     nfev: 38\n",
-       "     njev: 0\n",
-       "      nlu: 0\n",
-       "      sol: None\n",
-       "   status: 0\n",
-       "  success: True\n",
-       " t_events: None\n",
-       " y_events: None"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import run_solve_ivp\n",
-    "\n",
-    "results, details = run_solve_ivp(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "assisted-swimming",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>y</th>\n",
-       "      <th>v</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.0</th>\n",
-       "      <td>381.000</td>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.2</th>\n",
-       "      <td>380.804</td>\n",
-       "      <td>-1.96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.4</th>\n",
-       "      <td>380.216</td>\n",
-       "      <td>-3.92</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.6</th>\n",
-       "      <td>379.236</td>\n",
-       "      <td>-5.88</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.8</th>\n",
-       "      <td>377.864</td>\n",
-       "      <td>-7.84</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           y     v\n",
-       "0.0  381.000  0.00\n",
-       "0.2  380.804 -1.96\n",
-       "0.4  380.216 -3.92\n",
-       "0.6  379.236 -5.88\n",
-       "0.8  377.864 -7.84"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "solved-chambers",
-   "metadata": {},
-   "source": [
-    "`results` is a `TimeFrame` with two columns: `y` contains the height of\n",
-    "the penny; `v` contains its velocity.\n",
-    "\n",
-    "We can plot the results like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "authorized-barrier",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "results.y.plot()\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Position (m)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "differential-airfare",
-   "metadata": {},
-   "source": [
-    "Since acceleration is constant, velocity increases linearly and position decreases quadratically; as a result, the height curve is a parabola.\n",
-    "\n",
-    "The last value of `results.y` is negative, which means we ran the simulation too long. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "protected-fiber",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-108.99999999999869"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_end = results.index[-1]\n",
-    "results.y[t_end]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "metallic-tamil",
-   "metadata": {},
-   "source": [
-    "One way to solve this problem is to use the results to\n",
-    "estimate the time when the penny hits the sidewalk.\n",
-    "\n",
-    "The ModSim library provides `crossings`, which takes a `TimeSeries` and a value, and returns a sequence of times when the series passes through the value. We can find the time when the height of the penny is `0` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "japanese-clear",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([8.81788535])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import crossings\n",
-    "\n",
-    "t_crossings = crossings(results.y, 0)\n",
-    "t_crossings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "demonstrated-emission",
-   "metadata": {},
-   "source": [
-    "The result is an array with a single value, 8.818 s. Now, we could run\n",
-    "the simulation again with `t_end = 8.818`, but there's a better way."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "blind-dominant",
-   "metadata": {},
-   "source": [
-    "## Events\n",
-    "\n",
-    "As an option, `run_solve_ivp` can take an **event function**, which\n",
-    "detects an \"event\", like the penny hitting the sidewalk, and ends the\n",
-    "simulation.\n",
-    "\n",
-    "Event functions take the same parameters as slope functions, `state`,\n",
-    "`t`, and `system`. They should return a value that passes through `0`\n",
-    "when the event occurs. Here's an event function that detects the penny\n",
-    "hitting the sidewalk:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "comfortable-simple",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(t, state, system):\n",
-    "    y, v = state\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "closing-vehicle",
-   "metadata": {},
-   "source": [
-    "The return value is the height of the penny, `y`, which passes through\n",
-    "`0` when the penny hits the sidewalk.\n",
-    "\n",
-    "We pass the event function to `run_solve_ivp` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "exotic-shareware",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "  message: 'A termination event occurred.'\n",
-       "     nfev: 38\n",
-       "     njev: 0\n",
-       "      nlu: 0\n",
-       "      sol: None\n",
-       "   status: 1\n",
-       "  success: True\n",
-       " t_events: [array([8.81788535])]\n",
-       " y_events: [array([[ 5.68434189e-14, -8.64152764e+01]])]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_solve_ivp(system, slope_func,\n",
-    "                                 events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "recreational-blair",
-   "metadata": {},
-   "source": [
-    "Then we can get the flight time and final velocity like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "appropriate-roberts",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8.8"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_end = results.index[-1]\n",
-    "t_end"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "orange-retro",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.5440000000007217\n",
-      "-86.24000000000001\n"
-     ]
-    }
-   ],
-   "source": [
-    "y, v = results.iloc[-1]\n",
-    "print(y)\n",
-    "print(v)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cleared-jamaica",
-   "metadata": {},
-   "source": [
-    "If there were no air resistance, the penny would hit the sidewalk (or someone's head) at more than 300 km/h.\n",
-    "\n",
-    "So it's a good thing there is air resistance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "induced-albert",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "But air resistance...\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "straight-johns",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Here's a question from the web site [Ask an Astronomer](http://curious.astro.cornell.edu/about-us/39-our-solar-system/the-earth/other-catastrophes/57-how-long-would-it-take-the-earth-to-fall-into-the-sun-intermediate):\n",
-    "\n",
-    "\"If the Earth suddenly stopped orbiting the Sun, I know eventually it would be pulled in by the Sun's gravity and hit it. How long would it take the Earth to hit the Sun? I imagine it would go slowly at first and then pick up speed.\"\n",
-    "\n",
-    "Use `run_solve_ivp` to answer this question.\n",
-    "\n",
-    "Here are some suggestions about how to proceed:\n",
-    "\n",
-    "1.  Look up the Law of Universal Gravitation and any constants you need.  I suggest you work entirely in SI units: meters, kilograms, and Newtons.\n",
-    "\n",
-    "2.  When the distance between the Earth and the Sun gets small, this system behaves badly, so you should use an event function to stop when the surface of Earth reaches the surface of the Sun.\n",
-    "\n",
-    "3. Express your answer in days, and plot the results as millions of kilometers versus days.\n",
-    "\n",
-    "If you read the reply by Dave Rothstein, you will see other ways to solve the problem, and a good discussion of the modeling decisions behind them.\n",
-    "\n",
-    "You might also be interested to know that [it's actually not that easy to get to the Sun](https://www.theatlantic.com/science/archive/2018/08/parker-solar-probe-launch-nasa/567197/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "forbidden-distributor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "r_0 = 150e9   # 150 million km in m\n",
-    "v_0 = 0\n",
-    "init = State(r=r_0,\n",
-    "             v=v_0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "former-taxation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "702370000.0"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "radius_earth = 6.37e6  # meters\n",
-    "radius_sun = 696e6     # meters\n",
-    "r_final = radius_sun + radius_earth\n",
-    "r_final"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "biological-creek",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "213.56265216338966"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r_0 / r_final"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "oriental-riverside",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_end = 1e7  # seconds\n",
-    "\n",
-    "system = System(init=init,\n",
-    "                G=6.674e-11,   # N m^2 / kg^2\n",
-    "                m1=1.989e30,   # kg\n",
-    "                m2=5.972e24,   # kg\n",
-    "                r_final=radius_sun + radius_earth,\n",
-    "                t_end=t_end)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "radio-reproduction",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def universal_gravitation(state, system):\n",
-    "    \"\"\"Computes gravitational force.\n",
-    "    \n",
-    "    state: State object with distance r\n",
-    "    system: System object with m1, m2, and G\n",
-    "    \"\"\"\n",
-    "    r, v = state\n",
-    "    G, m1, m2 = system.G, system.m1, system.m2\n",
-    "    \n",
-    "    force = G * m1 * m2 / r**2\n",
-    "    return force"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "heavy-cologne",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.5233701151999997e+22"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "universal_gravitation(init, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "little-electric",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def slope_func(t, state, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object containing `m2`\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    m2 = system.m2    \n",
-    "\n",
-    "    force = universal_gravitation(state, system)\n",
-    "    dydt = v\n",
-    "    dvdt = -force / m2\n",
-    "    \n",
-    "    return dydt, dvdt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "continental-details",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0, -0.005899815999999999)"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "slope_func(0, system.init, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "suitable-traveler",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def event_func(t, state, system):\n",
-    "    r, v = state\n",
-    "    return r - system.r_final"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "upper-victory",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "149297630000.0"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "event_func(0, init, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "transparent-treat",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "  message: 'A termination event occurred.'\n",
-       "     nfev: 188\n",
-       "     njev: 0\n",
-       "      nlu: 0\n",
-       "      sol: None\n",
-       "   status: 1\n",
-       "  success: True\n",
-       " t_events: [array([5599828.17941854])]\n",
-       " y_events: [array([[ 7.02370000e+08, -6.13362867e+05]])]"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results, details = run_solve_ivp(system, slope_func, \n",
-    "                                 events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "brutal-woman",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5400000.0"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "t_event = results.index[-1]\n",
-    "t_event"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "gentle-burst",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "from modsim import units\n",
-    "\n",
-    "seconds = t_event * units.second\n",
-    "days = seconds.to(units.day)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "comfortable-galaxy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results.index /= 60 * 60 * 24"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "satisfactory-latitude",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results.r /= 1e9"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "significant-rebound",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results.r.plot(label='r')\n",
-    "\n",
-    "decorate(xlabel='Time (day)',\n",
-    "         ylabel='Distance from sun (million km)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "right-colleague",
-   "metadata": {},
-   "source": [
-    "## Under the hood\n",
-    "\n",
-    "`solve_ivp`\n",
-    "\n",
-    "Here is the source code for `crossings` so you can see what's happening under the hood:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "chief-error",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%psource crossings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "comparable-company",
-   "metadata": {},
-   "source": [
-    "The [documentation of InterpolatedUnivariateSpline is here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.InterpolatedUnivariateSpline.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "listed-shelter",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap21.ipynb b/jupyter/chap21.ipynb
deleted file mode 100644
index 35762a67..00000000
--- a/jupyter/chap21.ipynb
+++ /dev/null
@@ -1,1119 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "excited-advance",
-   "metadata": {},
-   "source": [
-    "# Chapter 21"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unauthorized-constitutional",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "medieval-cheese",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "superb-february",
-   "metadata": {},
-   "source": [
-    "In the previous chapter we simulated a penny falling in a vacuum, that\n",
-    "is, without air resistance. But the computational framework we used is\n",
-    "very general; it is easy to add additional forces, including drag.\n",
-    "\n",
-    "In this chapter, I present a model of drag force and add it to the\n",
-    "simulation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "interesting-freeware",
-   "metadata": {},
-   "source": [
-    "## Drag force\n",
-    "\n",
-    "As an object moves through a fluid, like air, the object applies force\n",
-    "to the air and, in accordance with Newton's third law of motion, the air applies an equal and opposite force to the object (see\n",
-    "<http://modsimpy.com/newton>).\n",
-    "\n",
-    "The direction of this **drag force** is opposite the direction of\n",
-    "travel, and its magnitude is given by the drag equation (see\n",
-    "<http://modsimpy.com/drageq>): \n",
-    "\n",
-    "$$F_d = \\frac{1}{2}~\\rho~v^2~C_d~A$$\n",
-    "\n",
-    "where\n",
-    "\n",
-    "-   $F_d$ is force due to drag, in newtons (N).\n",
-    "\n",
-    "-   $\\rho$ is the density of the fluid in kg/m^3^.\n",
-    "\n",
-    "-   $v$ is the magnitude of velocity in m/s.\n",
-    "\n",
-    "-   $A$ is the **reference area** of the object, in m^2^. In this\n",
-    "    context, the reference area is the projected frontal area, that is, the visible area of the object as seen from a point on its line of\n",
-    "    travel (and far away).\n",
-    "\n",
-    "-   $C_d$ is the **drag coefficient**, a dimensionless quantity that\n",
-    "    depends on the shape of the object (including length but not frontal area), its surface properties, and how it interacts with the fluid."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unable-scheduling",
-   "metadata": {},
-   "source": [
-    "For objects moving at moderate speeds through air, typical drag\n",
-    "coefficients are between 0.1 and 1.0, with blunt objects at the high end of the range and streamlined objects at the low end (see\n",
-    "<http://modsimpy.com/dragco>).\n",
-    "\n",
-    "For simple geometric objects we can sometimes guess the drag coefficient with reasonable accuracy; for more complex objects we usually have to take measurements and estimate $C_d$ from data.\n",
-    "\n",
-    "Of course, the drag equation is itself a model, based on the assumption that $C_d$ does not depend on the other terms in the equation: density, velocity, and area. For objects moving in air at moderate speeds (below 45 mph or 20 m/s), this model might be good enough, but we should remember to revisit this assumption.\n",
-    "\n",
-    "For the falling penny, we can use measurements to estimate $C_d$. In\n",
-    "particular, we can measure **terminal velocity**, $v_{term}$, which is\n",
-    "the speed where drag force equals force due to gravity:\n",
-    "\n",
-    "$$\\frac{1}{2}~\\rho~v_{term}^2~C_d~A = m g$$ \n",
-    "\n",
-    "where $m$ is the mass of the object and $g$ is acceleration due to gravity. Solving this equation for\n",
-    "$C_d$ yields: \n",
-    "\n",
-    "$$C_d = \\frac{2~m g}{\\rho~v_{term}^2~A}$$ \n",
-    "\n",
-    "According to *Mythbusters*, the terminal velocity of a penny is between 35 and 65 mph (see <http://modsimpy.com/mythbust>). Using the low end of their range, 40 mph or about 18 m/s, the estimated value of $C_d$ is 0.44, which is close to the drag coefficient of a smooth sphere.\n",
-    "\n",
-    "Now we are ready to add air resistance to the model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "human-cloud",
-   "metadata": {},
-   "source": [
-    "## The Params Object\n",
-    "\n",
-    "As the number of system parameters increases, and as we need to do more work to compute them, we will find it useful to define a `Params` object to contain the quantities we need to make a `System` object. `Params` objects are similar to `System` objects, and we initialize them the same way.\n",
-    "\n",
-    "Here's the `Params` object for the falling penny:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "established-knitting",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import Params\n",
-    "\n",
-    "params = Params(\n",
-    "    mass = 0.0025,      # kg\n",
-    "    diameter = 0.019,   # m\n",
-    "    rho = 1.2,     # kg/m**3\n",
-    "    g = 9.8,       # m/s**2\n",
-    "    v_init = 0,    # m / s\n",
-    "    v_term = 18,   # m / s\n",
-    "    height = 381,  # m\n",
-    "    t_end = 30,    # s\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "instrumental-gross",
-   "metadata": {},
-   "source": [
-    "The mass and diameter are from <http://modsimpy.com/penny>. The density\n",
-    "of air depends on temperature, barometric pressure (which depends on\n",
-    "altitude), humidity, and composition (<http://modsimpy.com/density>). I\n",
-    "chose a value that might be typical in Boston, Massachusetts at 20 °C.\n",
-    "\n",
-    "\n",
-    "Here's a version of `make_system` that takes the `Params` object and computes the inital state, `init`, the area, and the coefficient of drag.\n",
-    "Then it returns a `System` object with the quantities we'll need for the simulation. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "published-jesus",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import pi\n",
-    "from modsim import State\n",
-    "from modsim import System\n",
-    "\n",
-    "def make_system(params):\n",
-    "    init = State(y=params.height, v=params.v_init)\n",
-    "\n",
-    "    area = pi * (params.diameter/2)**2\n",
-    "\n",
-    "    C_d = (2 * params.mass * params.g / \n",
-    "           (params.rho * area * params.v_term**2))\n",
-    "\n",
-    "    return System(init=init,\n",
-    "                  area=area,\n",
-    "                  C_d=C_d,\n",
-    "                  mass=params.mass,\n",
-    "                  rho=params.rho,\n",
-    "                  g=params.g,\n",
-    "                  t_end=params.t_end\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "personalized-kruger",
-   "metadata": {},
-   "source": [
-    "And here's how we call it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "executive-protection",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "portable-carbon",
-   "metadata": {},
-   "source": [
-    "Based on the mass and diameter of the penny, the density of air, and acceleration due to gravity, and the observed terminal velocity, we estimate that the coefficient of drag is about 0.44."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "quick-cedar",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.4445009981135434"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.C_d"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "inner-telescope",
-   "metadata": {},
-   "source": [
-    "It might not be obvious why it is useful to create a `Params` object just to create a `System` object.\n",
-    "In fact, if we only run one simulation, it might not be useful.  But it helps when we want to change or sweep the parameters.\n",
-    "\n",
-    "For example, suppose we learn that the terminal velocity of a penny is actually closer to 20 m/s.\n",
-    "We can make a `Params` object with the new value, and a corresponding `System` object, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "great-crime",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params2 = params.set(v_term=20)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "outstanding-truth",
-   "metadata": {},
-   "source": [
-    "The result from `set` is a new `Params` object that is identical to the original except for the given value of `v_term`. \n",
-    "\n",
-    "If we pass `params2` to `make_system`, we see that it computes a different value of `C_d`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "independent-trace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.3600458084719701"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system2 = make_system(params2)\n",
-    "system2.C_d"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "charged-explorer",
-   "metadata": {},
-   "source": [
-    "If the terminal velocity of the penny is 20 m/s, rather than 18 m/s, that implies that the coefficient of drag is 0.36, rather than 0.44.\n",
-    "And that makes sense, since lower drag implies faster terminal velocity.\n",
-    "\n",
-    "Using `Params` objects to make `System` objects helps make sure that relationships like this are consistent.  And since we are always making new objects, rather than modifying existing objects, we are less likely to make a mistake."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "primary-advocate",
-   "metadata": {},
-   "source": [
-    "## Simulation\n",
-    "\n",
-    "Now let's get to the simulation.  Here's a version of the slope function that includes drag:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "noble-stick",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(t, state, system):\n",
-    "    y, v = state\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    mass, g = system.mass, system.g\n",
-    "    \n",
-    "    f_drag = rho * v**2 * C_d * area / 2\n",
-    "    a_drag = f_drag / mass\n",
-    "    \n",
-    "    dydt = v\n",
-    "    dvdt = -g + a_drag\n",
-    "    \n",
-    "    return dydt, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "baking-class",
-   "metadata": {},
-   "source": [
-    "`f_drag` is force due to drag, based on the drag equation. `a_drag` is\n",
-    "acceleration due to drag, based on Newton's second law.\n",
-    "\n",
-    "To compute total acceleration, we add accelerations due to gravity and\n",
-    "drag. `g` is negated because it is in the direction of decreasing `y`,\n",
-    "and `a_drag` is positive because it is in the direction of increasing\n",
-    "`y`. In the next chapter we will use `Vector` objects to keep track of\n",
-    "the direction of forces and add them up in a less error-prone way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "velvet-tunisia",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, -9.8)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(0, system.init, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "psychological-style",
-   "metadata": {},
-   "source": [
-    "To stop the simulation when the penny hits the sidewalk, we'll use the\n",
-    "event function from Section xxx:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "practical-nowhere",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(t, state, system):\n",
-    "    y, v = state\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "animated-clause",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "381"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "event_func(0, system.init, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "executive-there",
-   "metadata": {},
-   "source": [
-    "Now we can run the simulation like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "liberal-dictionary",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A termination event occurred.'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import run_solve_ivp\n",
-    "\n",
-    "results, details = run_solve_ivp(system, slope_func,\n",
-    "                                 events=event_func)\n",
-    "details.message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "incident-paradise",
-   "metadata": {},
-   "source": [
-    "Here are the last few time steps:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "coastal-anthropology",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>y</th>\n",
-       "      <th>v</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>20.644308</th>\n",
-       "      <td>3.229229e+01</td>\n",
-       "      <td>-17.988889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21.093097</th>\n",
-       "      <td>2.421654e+01</td>\n",
-       "      <td>-17.991564</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21.541886</th>\n",
-       "      <td>1.614743e+01</td>\n",
-       "      <td>-18.001510</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21.990676</th>\n",
-       "      <td>8.076745e+00</td>\n",
-       "      <td>-18.009752</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22.439465</th>\n",
-       "      <td>7.105427e-15</td>\n",
-       "      <td>-18.011383</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      y          v\n",
-       "20.644308  3.229229e+01 -17.988889\n",
-       "21.093097  2.421654e+01 -17.991564\n",
-       "21.541886  1.614743e+01 -18.001510\n",
-       "21.990676  8.076745e+00 -18.009752\n",
-       "22.439465  7.105427e-15 -18.011383"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "suburban-martial",
-   "metadata": {},
-   "source": [
-    "The final height is close to 0, as expected.\n",
-    "\n",
-    "Interestingly, the final velocity is not exactly terminal velocity, which suggests that there are some numerical errors.\n",
-    "\n",
-    "We can get the flight time from `results`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "interim-underground",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22.43946505804431"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_sidewalk = results.index[-1]\n",
-    "t_sidewalk"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "institutional-colors",
-   "metadata": {},
-   "source": [
-    "Here's the plot of position as a function of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "small-franchise",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "def plot_position(results):\n",
-    "    results.y.plot()\n",
-    "        \n",
-    "    decorate(xlabel='Time (s)',\n",
-    "         ylabel='Position (m)')\n",
-    "    \n",
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "plain-phone",
-   "metadata": {},
-   "source": [
-    "And velocity as a function of time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "analyzed-criticism",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_velocity(results):\n",
-    "\n",
-    "    results.v.plot(color='C1', label='v')\n",
-    "        \n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')\n",
-    "    \n",
-    "plot_velocity(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "careful-causing",
-   "metadata": {},
-   "source": [
-    "From an initial velocity of 0, the penny accelerates downward until it reaches terminal velocity; after that, velocity is constant."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "inside-confidence",
-   "metadata": {},
-   "source": [
-    "## Summary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "planned-endorsement",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "respective-address",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Run the simulation with a downward initial velocity that exceeds the penny's terminal velocity.\n",
-    "\n",
-    "What do you expect to happen?  Plot velocity and position as a function of time, and see if they are consistent with your prediction.\n",
-    "\n",
-    "Hint: Use `params.set` to make a new `Params` object with a different initial velocity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "inclusive-twenty",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "params = params.set(v_init=-30)\n",
-    "system2 = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "vertical-judge",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A termination event occurred.'"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results2, details2 = run_solve_ivp(system2, slope_func, \n",
-    "                                 events=event_func)\n",
-    "details2.message"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "greenhouse-madagascar",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20.635183673114202"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_sidewalk = results2.index[-1]\n",
-    "t_sidewalk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "sudden-details",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_position(results2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "opening-jurisdiction",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_velocity(results2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "smaller-millennium",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose we drop a quarter from the Empire State Building and find that its flight time is 19.1 seconds.  Use this measurement to estimate terminal velocity and coefficient of drag.\n",
-    "\n",
-    "You can get the relevant dimensions of a quarter from https://en.wikipedia.org/wiki/Quarter_(United_States_coin).\n",
-    "\n",
-    "1. Create a `Params` object with new values of `mass` and `diameter`. We don't know `v_term`, so we'll start with the initial guess 18 m/s.\n",
-    "\n",
-    "2. Use `make_system` to create a `System` object.  \n",
-    "\n",
-    "3. Call `run_solve_ivp` to simulate the system.  How does the flight time of the simulation compare to the measurement?\n",
-    "\n",
-    "4. Try a few different values of `v_term` and see if you can get the simulated flight time close to 19.1 seconds.\n",
-    "\n",
-    "5. Optionally, write an error function and use `root_scalar` to improve your estimate.\n",
-    "\n",
-    "6. Use your best estimate of `v_term` to compute `C_d`.\n",
-    "\n",
-    "Note: I fabricated the observed flight time, so don't take the results of this exercise too seriously."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "compact-bunny",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "params_quarter = params.set(\n",
-    "    mass = 0.0057,      # kg\n",
-    "    diameter = 0.024,   # m\n",
-    "    flight_time = 19.1,   # s\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "shared-contrary",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system3 = make_system(params_quarter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "portable-account",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A termination event occurred.'"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Run the simulation\n",
-    "\n",
-    "results3, details3 = run_solve_ivp(system3, slope_func, \n",
-    "                                   events=event_func)\n",
-    "details3.message"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "arabic-shareware",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20.63518367311417"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# And get the flight time\n",
-    "\n",
-    "t_sidewalk = results3.index[-1]\n",
-    "t_sidewalk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "valued-literature",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# The flight time is a little long, \n",
-    "# so we could increase `v_term` and try again.\n",
-    "\n",
-    "# Or we could write an error function\n",
-    "\n",
-    "def error_func(guess, params):\n",
-    "    \"\"\"Final height as a function of C_d.\n",
-    "    \n",
-    "    guess: guess at v_term\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: height in m\n",
-    "    \"\"\"\n",
-    "    print(guess)\n",
-    "    params = params.set(v_term=guess)\n",
-    "    system = make_system(params)\n",
-    "    results, details = run_solve_ivp(system, slope_func, \n",
-    "                                     events=event_func)\n",
-    "    t_sidewalk = results.index[-1]\n",
-    "    error = t_sidewalk - params.flight_time\n",
-    "    return error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "sufficient-retail",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "18\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "1.5351836731141688"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# We can test the error function like this\n",
-    "v_guess1 = 18\n",
-    "error_func(v_guess1, params_quarter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "comparable-lounge",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "22\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "-2.1591256962719996"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "v_guess2 = 22\n",
-    "error_func(v_guess2, params_quarter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "ready-people",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "18.0\n",
-      "22.0\n",
-      "19.662214524680426\n",
-      "19.475274945870567\n",
-      "19.459673874997012\n",
-      "19.459697194820343\n",
-      "19.459697175160358\n",
-      "19.45969717515935\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Now we can use `root_scalar` to find the value of \n",
-    "# `v_term` that yields the measured flight time.\n",
-    "\n",
-    "from scipy.optimize import root_scalar\n",
-    "\n",
-    "res = root_scalar(error_func, params_quarter,\n",
-    "                  bracket=[v_guess1, v_guess2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "miniature-remark",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19.459697175160358"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "v_term = res.root\n",
-    "v_term"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "frozen-termination",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.5434569672577239"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Plugging in the estimated value, \n",
-    "# we can use `make_system` to compute `C_d`\n",
-    "\n",
-    "system4 = make_system(params_quarter.set(v_term=res.root))\n",
-    "system4.C_d"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "colored-oxygen",
-   "metadata": {},
-   "source": [
-    "## Under the Hood\n",
-    "\n",
-    "`Params` is a `SimpleNamespace`, like `System`.\n",
-    "\n",
-    "`Params` and `System` are actually the same; I have given them different names to document the different roles they play."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap22.ipynb b/jupyter/chap22.ipynb
deleted file mode 100644
index ea1b146d..00000000
--- a/jupyter/chap22.ipynb
+++ /dev/null
@@ -1,1978 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "collaborative-people",
-   "metadata": {},
-   "source": [
-    "# Chapter 22"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "connected-covering",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "blank-mapping",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "widespread-detective",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from os.path import exists\n",
-    "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename)\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    urlretrieve(url+filename, filename)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alert-banks",
-   "metadata": {},
-   "source": [
-    "In the previous chapter we modeled objects moving in one dimension, with and without drag. Now let's move on to two dimensions, and baseball!\n",
-    "\n",
-    "In this chapter we model the flight of a baseball including the effect\n",
-    "of air resistance. In the next chapter we use this model to solve an\n",
-    "optimization problem."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "functional-abortion",
-   "metadata": {},
-   "source": [
-    "## Baseball\n",
-    "\n",
-    "To model the flight of a baseball, we have to make some\n",
-    "decisions. To get started, we'll ignore any spin that might be on the ball, and the resulting Magnus force (see <http://modsimpy.com/magnus>). Under this assumption, the ball travels in a vertical plane, so we'll run simulations in two dimensions, rather than three.\n",
-    "\n",
-    "Air resistance has a substantial effect on most projectiles in air, so\n",
-    "we will include a drag force.\n",
-    "\n",
-    "To model air resistance, we'll need the mass, frontal area, and drag\n",
-    "coefficient of a baseball. Mass and diameter are easy to find (see\n",
-    "<http://modsimpy.com/baseball>). Drag coefficient is only a little\n",
-    "harder; according to *[The Physics of Baseball](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC)*, the drag coefficient of a baseball is approximately 0.33 (with no units).\n",
-    "\n",
-    "However, this value *does* depend on velocity. At low velocities it\n",
-    "might be as high as 0.5, and at high velocities as low as 0.28.\n",
-    "Furthermore, the transition between these regimes typically happens\n",
-    "exactly in the range of velocities we are interested in, between 20 m/s and 40 m/s.\n",
-    "\n",
-    "Nevertheless, we'll start with a simple model where the drag coefficient does not depend on velocity; as an exercise at the end of the chapter, you will have a chance to implement a more detailed model and see what effect is has on the results.\n",
-    "\n",
-    "But first we need a new computational tool, the `Vector` object."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "worthy-wheel",
-   "metadata": {},
-   "source": [
-    "## Vectors\n",
-    "\n",
-    "Now that we are working in two dimensions, it will be useful to\n",
-    "work with **vector quantities**, that is, quantities that represent both a magnitude and a direction. We will use vectors to represent positions, velocities, accelerations, and forces in two and three dimensions.\n",
-    "\n",
-    "ModSim provides a function called `Vector` the creates a Pandas `Series` that contains the **components** of the vector.\n",
-    "In a `Vector` that represents a position in space, the components are the $x$ and $y$ coordinates in 2-D, plus a $z$ coordinate if the `Vector` is in 3-D.\n",
-    "\n",
-    "You can create a `Vector` by specifying its components. The following\n",
-    "`Vector` represents a point 3 units to the right (or east) and 4 units up (or north) from an implicit origin:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "incorrect-cigarette",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "x    3\n",
-       "y    4\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import Vector\n",
-    "\n",
-    "A = Vector(3, 4)\n",
-    "A"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "digital-sunrise",
-   "metadata": {},
-   "source": [
-    "You can access the components of a `Vector` by name using the dot\n",
-    "operator, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "bridal-jamaica",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 4)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A.x, A.y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "under-attribute",
-   "metadata": {},
-   "source": [
-    "You can also access them by index using brackets, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "incorrect-postage",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 4)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A[0], A[1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "solved-bathroom",
-   "metadata": {},
-   "source": [
-    "`Vector` objects support most mathematical operations, including\n",
-    "addition and subtraction:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "upset-morning",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "x    1\n",
-       "y    2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "B = Vector(1, 2)\n",
-    "B"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "every-affiliation",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "x    4\n",
-       "y    6\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A + B"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "ideal-sperm",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "x    2\n",
-       "y    2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A - B"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "tender-translator",
-   "metadata": {},
-   "source": [
-    "For the definition and graphical interpretation of these operations, see <http://modsimpy.com/vecops>."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fewer-adolescent",
-   "metadata": {},
-   "source": [
-    "We can specify a `Vector` with coordinates `x` and `y`, as in the previous examples.\n",
-    "Equivalently, we can specify a `Vector` with a magnitude and angle.\n",
-    "\n",
-    "**Magnitude** is the length of the vector: if the `Vector` represents a position, magnitude is the distance from the origin; if it represents a velocity, magnitude is speed, that is, how fast the object is moving, regardless of direction.\n",
-    "\n",
-    "The **angle** of a `Vector` is its direction, expressed as an angle in radians from the positive $x$ axis. In the Cartesian plane, the angle 0 rad is due east, and the angle $\\pi$ rad is due west.\n",
-    "\n",
-    "ModSim provides functions to compute the magnitude and angle of a `Vector`.  For example, here are the magnitude and angle of `A`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "chinese-assembly",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5.0, 0.9272952180016122)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import vector_mag, vector_angle\n",
-    "\n",
-    "mag = vector_mag(A)\n",
-    "theta = vector_angle(A)\n",
-    "mag, theta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "great-advice",
-   "metadata": {},
-   "source": [
-    "The magnitude is 5 because the length of `A` is the hypotenuse of a 3-4-5 triangle.\n",
-    "\n",
-    "The result from `vector_angle` is in radians, and most Python functions, like `sin` and `cos`, work with radians. \n",
-    "But many people think more naturally in degrees. \n",
-    "Fortunately, NumPy provides a function to convert radians to degrees:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "awful-defense",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "53.13010235415598"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from numpy import rad2deg\n",
-    "\n",
-    "angle = rad2deg(theta)\n",
-    "angle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "excited-catch",
-   "metadata": {},
-   "source": [
-    "And a function to convert degrees to radians:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "cellular-community",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.9272952180016122"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from numpy import deg2rad\n",
-    "\n",
-    "theta = deg2rad(angle)\n",
-    "theta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "responsible-gentleman",
-   "metadata": {},
-   "source": [
-    "Following convention, I'll use `angle` for a value in degrees and `theta` for a value in radians.\n",
-    "\n",
-    "If you are given an angle and velocity, you can make a `Vector` using\n",
-    "`pol2cart`, which converts from polar to Cartesian coordinates. For example, here's a new `Vector` with the same angle and magnitude of `A`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "monetary-firmware",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "x    3.0\n",
-       "y    4.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import pol2cart\n",
-    "\n",
-    "x, y = pol2cart(theta, mag)\n",
-    "Vector(x, y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "lucky-plastic",
-   "metadata": {},
-   "source": [
-    "Another way to represent the direction of `A` is a **unit vector**,\n",
-    "which is a vector with magnitude 1 that points in the same direction as\n",
-    "`A`. You can compute a unit vector by dividing a vector by its\n",
-    "magnitude:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "explicit-piano",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "x    0.6\n",
-       "y    0.8\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A / vector_mag(A)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "respected-oliver",
-   "metadata": {},
-   "source": [
-    "We can do the same thing using the `vector_hat` function, so named because unit vectors are conventionally decorated with a hat, like this: $\\hat{A}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "relative-republic",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "x    0.6\n",
-       "y    0.8\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import vector_hat\n",
-    "\n",
-    "vector_hat(A)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "boring-river",
-   "metadata": {},
-   "source": [
-    "Now let's get back to the game."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "similar-local",
-   "metadata": {},
-   "source": [
-    "## Simulating baseball flight\n",
-    "\n",
-    "Let's simulate the flight of a baseball that is batted from home plate\n",
-    "at an angle of 45° and initial speed 40 m/s. We'll use the center of home plate as the origin, a horizontal x-axis (parallel to the ground), and vertical y-axis (perpendicular to the ground). The initial height is about 1 m."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "positive-current",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import Params\n",
-    "\n",
-    "params = Params(\n",
-    "    x = 0,          # m\n",
-    "    y = 1,          # m\n",
-    "    angle = 45,     # degree\n",
-    "    velocity = 40,  # m / s\n",
-    "\n",
-    "    mass = 145e-3,    # kg \n",
-    "    diameter = 73e-3, # m \n",
-    "    C_d = 0.33,     # dimensionless\n",
-    "\n",
-    "    rho = 1.2,      # kg/m**3\n",
-    "    g = 9.8,        # m/s**2\n",
-    "    t_end = 10,     # s\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bacterial-favorite",
-   "metadata": {},
-   "source": [
-    "I got the mass and diameter of the baseball from [Wikipedia](https://en.wikipedia.org/wiki/Baseball_(ball)) and the coefficient of drag is from *[The Physics of Baseball](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC)*:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unknown-extreme",
-   "metadata": {},
-   "source": [
-    "The density of air, `rho`, is based on a temperature of 20 °C at sea level (see <http://modsimpy.com/tempress>). \n",
-    "And we'll need the acceleration of gravity, `g`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "detected-return",
-   "metadata": {},
-   "source": [
-    "The following function uses these quantities to make a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "bored-billy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import State, System, pol2cart\n",
-    "from numpy import pi, deg2rad\n",
-    "\n",
-    "def make_system(params):\n",
-    "    \n",
-    "    # convert angle to degrees\n",
-    "    theta = deg2rad(params.angle)\n",
-    "    \n",
-    "    # compute x and y components of velocity\n",
-    "    vx, vy = pol2cart(theta, params.velocity)\n",
-    "    \n",
-    "    # make the initial state\n",
-    "    init = State(x=params.x, y=params.y, vx=vx, vy=vy)\n",
-    "    \n",
-    "    # compute the frontal area\n",
-    "    area = pi * (params.diameter/2)**2\n",
-    "\n",
-    "    return System(params,\n",
-    "                  init = init,\n",
-    "                  area = area,\n",
-    "                 )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "considered-conservation",
-   "metadata": {},
-   "source": [
-    "`make_system` uses `deg2rad` to convert `angle` to radians and\n",
-    "`pol2cart` to compute the $x$ and $y$ components of the initial\n",
-    "velocity.\n",
-    "\n",
-    "`init` is a `State` object with four state variables:\n",
-    "\n",
-    "* `x` and `y` are the components of position.\n",
-    "\n",
-    "* `vx` and `vy` are the components of velocity.\n",
-    "\n",
-    "The `System` object also contains `t_end`, which is 10 seconds, long enough for the ball to land on the ground.\n",
-    "Here's the `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "greatest-soviet",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "metropolitan-mainstream",
-   "metadata": {},
-   "source": [
-    "And here's the initial `State`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "studied-congo",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "x      0.000000\n",
-       "y      1.000000\n",
-       "vx    28.284271\n",
-       "vy    28.284271\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "occasional-given",
-   "metadata": {},
-   "source": [
-    "Next we need a function to compute drag force:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "legal-terminal",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(V, system):\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    mag = rho * vector_mag(V)**2 * C_d * area / 2\n",
-    "    direction = -vector_hat(V)\n",
-    "    f_drag = mag * direction\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "informational-intersection",
-   "metadata": {},
-   "source": [
-    "This function takes `V` as a `Vector` and returns `f_drag` as a\n",
-    "`Vector`. \n",
-    "\n",
-    "* It uses `vector_mag` to compute the magnitude of `V`, \n",
-    "and the drag equation to compute the magnitude of the drag force, `mag`.\n",
-    "\n",
-    "* Then it uses `vector_hat` to compute `direction`, which is a unit vector in the opposite direction of `V`.\n",
-    "\n",
-    "* Finally, it computes the drag force vector by multiplying `mag` and `direction`.\n",
-    "\n",
-    "We can test it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "detailed-league",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "x   -0.937574\n",
-       "y   -0.937574\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import Vector\n",
-    "\n",
-    "vx, vy = system.init.vx, system.init.vy\n",
-    "V_test = Vector(vx, vy)\n",
-    "drag_force(V_test, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "absent-vector",
-   "metadata": {},
-   "source": [
-    "The result is a `Vector` that represents the drag force on the baseball, in Newtons, under the initial conditions.\n",
-    "\n",
-    "Now we're ready for a slope function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "suitable-salem",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(t, state, system):\n",
-    "    x, y, vx, vy = state\n",
-    "    mass, g = system.mass, system.g\n",
-    "    \n",
-    "    V = Vector(vx, vy)\n",
-    "    a_drag = drag_force(V, system) / mass\n",
-    "    a_grav = g * Vector(0, -1)\n",
-    "    \n",
-    "    A = a_grav + a_drag\n",
-    "    \n",
-    "    return V.x, V.y, A.x, A.y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alpha-tiger",
-   "metadata": {},
-   "source": [
-    "As usual, the parameters of the slope function are a time, a `State` object, and a `System` object. \n",
-    "In this example, we don't use `t`, but we can't leave it out because when `run_solve_ivp` calls the slope function, it always provides the same arguments, whether they are needed or not.\n",
-    "\n",
-    "`slope_func` unpacks the `State` object into variables `x`, `y`, `vx`, and `vy`.\n",
-    "Then it packs `vx` and `vy` into a `Vector`, which it uses to compute drag force and acceleration due to drag, `a_drag`.\n",
-    "\n",
-    "To represent acceleration due to gravity, it makes a `Vector` with magnitude `g` in the negative $y$ direction.\n",
-    "\n",
-    "The total acceleration of the baseball, `A`, is the sum of accelerations due to gravity and drag."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "wired-judgment",
-   "metadata": {},
-   "source": [
-    "The return value is a sequence that contains:\n",
-    "\n",
-    "* The components of velocity, `V.x` and `V.y`.\n",
-    "\n",
-    "* The components of acceleration, `A.x` and `A.y`.\n",
-    "\n",
-    "Together, these components represent the slope of the state variables, because `V` is the derivative of position and `A` is the derivative of velocity."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "crude-parcel",
-   "metadata": {},
-   "source": [
-    "As always, we can test the slope function by running it with the initial conditions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "closing-simon",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(28.284271247461902, 28.2842712474619, -6.466030881564545, -16.266030881564546)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(0, system.init, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "regulated-railway",
-   "metadata": {},
-   "source": [
-    "Using vectors to represent forces and accelerations makes the code\n",
-    "concise, readable, and less error-prone. In particular, when we add\n",
-    "`a_grav` and `a_drag`, the directions are likely to be correct, because they are encoded in the `Vector` objects.\n",
-    "\n",
-    "We're almost ready to run the simulation.  The last thing we need is an event function that stops when the ball hits the ground."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "brief-level",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(t, state, system):\n",
-    "    x, y, vx, vy = state\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "martial-strengthening",
-   "metadata": {},
-   "source": [
-    "The event function takes the same parameters as the slope function, and returns the $y$ coordinate of position. When the $y$ coordinate passes through 0, the simulation stops.\n",
-    "\n",
-    "As we did with `slope_func`, we can test `event_func` with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "pointed-bathroom",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.0"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "event_func(0, system.init, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "unlikely-dressing",
-   "metadata": {},
-   "source": [
-    "Now we're ready to run the simulation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "special-background",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A termination event occurred.'"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import run_solve_ivp\n",
-    "\n",
-    "results, details = run_solve_ivp(system, slope_func,\n",
-    "                                 events=event_func)\n",
-    "details.message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "golden-november",
-   "metadata": {},
-   "source": [
-    "`details` contains information about the simulation, including a message that indicates that a \"termination event\" occurred; that is, the simulated ball reached the ground.\n",
-    "\n",
-    "`results` is a `TimeFrame` with one column for each of the state variables:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "opening-lecture",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "      <th>vx</th>\n",
-       "      <th>vy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4.604496</th>\n",
-       "      <td>93.473971</td>\n",
-       "      <td>8.296642e+00</td>\n",
-       "      <td>15.013486</td>\n",
-       "      <td>-19.334826</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.704594</th>\n",
-       "      <td>94.966756</td>\n",
-       "      <td>6.325368e+00</td>\n",
-       "      <td>14.802553</td>\n",
-       "      <td>-20.038175</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.804692</th>\n",
-       "      <td>96.438515</td>\n",
-       "      <td>4.284486e+00</td>\n",
-       "      <td>14.590855</td>\n",
-       "      <td>-20.726780</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.904789</th>\n",
-       "      <td>97.889087</td>\n",
-       "      <td>2.175515e+00</td>\n",
-       "      <td>14.378566</td>\n",
-       "      <td>-21.400392</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5.004887</th>\n",
-       "      <td>99.318296</td>\n",
-       "      <td>-7.105427e-15</td>\n",
-       "      <td>14.165894</td>\n",
-       "      <td>-22.058763</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  x             y         vx         vy\n",
-       "4.604496  93.473971  8.296642e+00  15.013486 -19.334826\n",
-       "4.704594  94.966756  6.325368e+00  14.802553 -20.038175\n",
-       "4.804692  96.438515  4.284486e+00  14.590855 -20.726780\n",
-       "4.904789  97.889087  2.175515e+00  14.378566 -21.400392\n",
-       "5.004887  99.318296 -7.105427e-15  14.165894 -22.058763"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dominant-weekly",
-   "metadata": {},
-   "source": [
-    "We can get the flight time like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "medieval-calvin",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5.004887034868351"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flight_time = results.index[-1]\n",
-    "flight_time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "convenient-heading",
-   "metadata": {},
-   "source": [
-    "And the final state like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "gorgeous-survey",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "x     9.931830e+01\n",
-       "y    -7.105427e-15\n",
-       "vx    1.416589e+01\n",
-       "vy   -2.205876e+01\n",
-       "Name: 5.004887034868351, dtype: float64"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "final_state = results.iloc[-1]\n",
-    "final_state"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "invalid-japan",
-   "metadata": {},
-   "source": [
-    "The final value of `y` is close to 0, as it should be.  The final value of `x` tells us how far the ball flew, in meters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "agreed-sugar",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "99.31829628352206"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x_dist = final_state.x\n",
-    "x_dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "recreational-template",
-   "metadata": {},
-   "source": [
-    "We can also get the final velocity, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "instrumental-nurse",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "x    14.165894\n",
-       "y   -22.058763\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "final_V = Vector(final_state.vx, final_state.vy)\n",
-    "final_V"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "economic-chrome",
-   "metadata": {},
-   "source": [
-    "The speed of the ball on impact is about 26 m/s, which is substantially slower than the initial velocity, 40 m/s."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "noted-triumph",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "26.215674453237636"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vector_mag(final_V)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "continuous-quick",
-   "metadata": {},
-   "source": [
-    "## Trajectories\n",
-    "\n",
-    "To visualize the results, we can plot the $x$ and $y$ components of position like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "spare-burst",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "results.x.plot(color='C4')\n",
-    "results.y.plot(color='C2', style='--')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Position (m)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "horizontal-bench",
-   "metadata": {},
-   "source": [
-    "As expected, the $x$ component increases as the ball moves away from home plate. The $y$ position climbs initially and then descends, falling to 0 m near 5.0 s.\n",
-    "\n",
-    "Another way to view the results is to plot the $x$ component on the\n",
-    "x-axis and the $y$ component on the y-axis, so the plotted line follows the trajectory of the ball through the plane:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "dated-browse",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from matplotlib.pyplot import plot\n",
-    "\n",
-    "def plot_trajectory(results):\n",
-    "    plot(results.x, results.y, label='trajectory')\n",
-    "\n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)')\n",
-    "\n",
-    "plot_trajectory(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "certified-synthetic",
-   "metadata": {},
-   "source": [
-    "This way of visualizing the results is called a **trajectory plot** (see <http://modsimpy.com/trajec>).\n",
-    "\n",
-    "A trajectory plot can be easier to interpret than a time series plot,\n",
-    "because it shows what the motion of the projectile would look like (at\n",
-    "least from one point of view). Both plots can be useful, but don't get\n",
-    "them mixed up! If you are looking at a time series plot and interpreting it as a trajectory, you will be very confused."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "precise-theology",
-   "metadata": {},
-   "source": [
-    "## Animation\n",
-    "\n",
-    "One of the best ways to visualize the results of a physical model is animation.  If there are problems with the model, animation can make them apparent.\n",
-    "\n",
-    "The ModSimPy library provides `animate`, which takes as parameters a `TimeSeries` and a draw function."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "elegant-century",
-   "metadata": {},
-   "source": [
-    "The draw function should take as parameters a `State` object and the time.  It should draw a single frame of the animation.\n",
-    "\n",
-    "Inside the draw function, you almost always have to call `set_xlim` and `set_ylim`.  Otherwise `matplotlib` auto-scales the axes, which is usually not what you want."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "sensitive-debate",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xs = results.x\n",
-    "ys = results.y\n",
-    "\n",
-    "def draw_func(t, state):\n",
-    "    plot(state.x, state.y, 'bo')\n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)',\n",
-    "             xlim=(xs.min(), xs.max()),\n",
-    "             ylim=(ys.min(), ys.max()),\n",
-    "            )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "civic-federation",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import animate\n",
-    "\n",
-    "# animate(results, draw_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fatal-effort",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "experimental-cooper",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Run the simulation with and without air resistance.  How wrong would we be if we ignored drag?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "negative-dictionary",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Hint\n",
-    "\n",
-    "system2 = make_system(params.set(C_d=0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "green-folder",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A termination event occurred.'"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results2, details2 = run_solve_ivp(system2, slope_func, \n",
-    "                                   events=event_func)\n",
-    "details.message"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "lucky-degree",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_trajectory(results)\n",
-    "plot_trajectory(results2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "floating-membrane",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "164.25925502413202"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "x_dist2 = results2.iloc[-1].x\n",
-    "x_dist2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "sorted-century",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "64.94095874060996"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "x_dist2 - x_dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "about-martial",
-   "metadata": {},
-   "source": [
-    "**Exercise:** The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / meter$^3$.  How much farther would a ball hit with the same velocity and launch angle travel?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "fifty-burning",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Hint\n",
-    "\n",
-    "system3 = make_system(params.set(rho=1.0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "adjustable-capital",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "105.78838005859807"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results3, details3 = run_solve_ivp(system3, slope_func, \n",
-    "                                   events=event_func)\n",
-    "\n",
-    "x_dist3 = results3.iloc[-1].x\n",
-    "x_dist3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "golden-exhibition",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6.470083775076006"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "x_dist3 - x_dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "christian-scotland",
-   "metadata": {},
-   "source": [
-    "**Exercise:** The model so far is based on the assumption that coefficient of drag does not depend on velocity, but in reality it does.  The following figure, from Adair, [*The Physics of Baseball*](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC), shows coefficient of drag as a function of velocity.\n",
-    "\n",
-    "<img src=\"https://github.com/AllenDowney/ModSimPy/raw/master/figs/baseball_drag.png\" width=\"400\">\n",
-    "\n",
-    "\n",
-    "I used [an online graph digitizer](https://automeris.io/WebPlotDigitizer/) to extract the data and save it in a CSV file.  Here's how we can read it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "guided-alarm",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "filename = 'baseball_drag.csv'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/baseball_drag.csv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "listed-austria",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Velocity in mph</th>\n",
-       "      <th>Drag coefficient</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Velocity in meters per second</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.026146</th>\n",
-       "      <td>0.058486</td>\n",
-       "      <td>0.49965</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.871509</th>\n",
-       "      <td>19.845000</td>\n",
-       "      <td>0.49878</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17.647351</th>\n",
-       "      <td>39.476000</td>\n",
-       "      <td>0.49704</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22.432914</th>\n",
-       "      <td>50.181000</td>\n",
-       "      <td>0.48225</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26.882303</th>\n",
-       "      <td>60.134000</td>\n",
-       "      <td>0.45004</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               Velocity in mph  Drag coefficient\n",
-       "Velocity in meters per second                                   \n",
-       "0.026146                              0.058486           0.49965\n",
-       "8.871509                             19.845000           0.49878\n",
-       "17.647351                            39.476000           0.49704\n",
-       "22.432914                            50.181000           0.48225\n",
-       "26.882303                            60.134000           0.45004"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from pandas import read_csv\n",
-    "from modsim import Quantity, units\n",
-    "\n",
-    "baseball_drag = read_csv(filename)\n",
-    "mph = Quantity(baseball_drag['Velocity in mph'], units.mph)\n",
-    "mps = mph.to(units.meter / units.second)\n",
-    "baseball_drag.index = mps.magnitude\n",
-    "baseball_drag.index.name = 'Velocity in meters per second'\n",
-    "baseball_drag.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "american-reverse",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import interpolate, linspace\n",
-    "\n",
-    "drag_interp = interpolate(baseball_drag['Drag coefficient'])\n",
-    "vs = linspace(0, 60)\n",
-    "cds = drag_interp(vs)\n",
-    "plot(vs, cds)\n",
-    "decorate(xlabel='Velocity (m/s)', ylabel='C_d')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "legislative-jamaica",
-   "metadata": {},
-   "source": [
-    "Modify the model to include the dependence of `C_d` on velocity, and see how much it affects the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "dominant-passing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def drag_force2(V, system):\n",
-    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
-    "    \n",
-    "    v: velocity\n",
-    "    system: System object with rho, C_d, area\n",
-    "    \n",
-    "    returns: Vector drag force\n",
-    "    \"\"\"\n",
-    "    rho, area = system.rho, system.area\n",
-    "    \n",
-    "    C_d = drag_interp(vector_mag(V))\n",
-    "    mag = -rho * vector_mag(V)**2 * C_d * area / 2\n",
-    "    direction = vector_hat(V)\n",
-    "    f_drag = direction * mag\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "double-carol",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def slope_func2(t, state, system):\n",
-    "    x, y, vx, vy = state\n",
-    "    mass, g = system.mass, system.g\n",
-    "    \n",
-    "    V = Vector(vx, vy)\n",
-    "    a_drag = drag_force2(V, system) / mass\n",
-    "    a_grav = g * Vector(0, -1)\n",
-    "    \n",
-    "    A = a_grav + a_drag\n",
-    "    \n",
-    "    return V.x, V.y, A.x, A.y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "antique-gathering",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system4 = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "undefined-moral",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "x   -1.054771\n",
-       "y   -1.054771\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "V = Vector(30, 30)\n",
-    "f_drag = drag_force(V, system4)\n",
-    "f_drag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "atomic-socket",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(28.284271247461902, 28.2842712474619, -6.466030881564545, -16.266030881564546)"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "slope_func(0, system4.init, system4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "abandoned-amount",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A termination event occurred.'"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results4, details4 = run_solve_ivp(system4, slope_func2, \n",
-    "                                   events=event_func)\n",
-    "details4.message"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "adjusted-coating",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "      <th>vx</th>\n",
-       "      <th>vy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4.525854</th>\n",
-       "      <td>85.443182</td>\n",
-       "      <td>7.746401e+00</td>\n",
-       "      <td>12.556069</td>\n",
-       "      <td>-18.457129</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.624243</th>\n",
-       "      <td>86.667639</td>\n",
-       "      <td>5.898175e+00</td>\n",
-       "      <td>12.324797</td>\n",
-       "      <td>-19.075086</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.722631</th>\n",
-       "      <td>87.869695</td>\n",
-       "      <td>3.989943e+00</td>\n",
-       "      <td>12.094833</td>\n",
-       "      <td>-19.677696</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.821019</th>\n",
-       "      <td>89.049363</td>\n",
-       "      <td>2.023337e+00</td>\n",
-       "      <td>11.866567</td>\n",
-       "      <td>-20.265039</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.919407</th>\n",
-       "      <td>90.206647</td>\n",
-       "      <td>1.065814e-14</td>\n",
-       "      <td>11.640440</td>\n",
-       "      <td>-20.837214</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  x             y         vx         vy\n",
-       "4.525854  85.443182  7.746401e+00  12.556069 -18.457129\n",
-       "4.624243  86.667639  5.898175e+00  12.324797 -19.075086\n",
-       "4.722631  87.869695  3.989943e+00  12.094833 -19.677696\n",
-       "4.821019  89.049363  2.023337e+00  11.866567 -20.265039\n",
-       "4.919407  90.206647  1.065814e-14  11.640440 -20.837214"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results4.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "excellent-dressing",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "90.20664656623306"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "x_dist4 = results4.iloc[-1].x\n",
-    "x_dist4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "equal-component",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-9.111649717288998"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "x_dist4 - x_dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "italian-coordinate",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "`Vector` "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap23.ipynb b/jupyter/chap23.ipynb
deleted file mode 100644
index e48d1c72..00000000
--- a/jupyter/chap23.ipynb
+++ /dev/null
@@ -1,877 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "foreign-pepper",
-   "metadata": {},
-   "source": [
-    "# Chapter 23"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "competitive-backing",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "charming-location",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "seeing-contractor",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "quantitative-montana",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import Params\n",
-    "\n",
-    "params = Params(\n",
-    "    x = 0,          # m\n",
-    "    y = 1,          # m\n",
-    "    angle = 45,     # degree\n",
-    "    velocity = 40,  # m / s\n",
-    "\n",
-    "    mass = 145e-3,    # kg \n",
-    "    diameter = 73e-3, # m \n",
-    "    C_d = 0.33,     # dimensionless\n",
-    "\n",
-    "    rho = 1.2,      # kg/m**3\n",
-    "    g = 9.8,        # m/s**2\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "preliminary-industry",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import State, System, pol2cart\n",
-    "from numpy import pi, deg2rad\n",
-    "\n",
-    "def make_system(params):\n",
-    "    \n",
-    "    # convert angle to degrees\n",
-    "    theta = deg2rad(params.angle)\n",
-    "    \n",
-    "    # compute x and y components of velocity\n",
-    "    vx, vy = pol2cart(theta, params.velocity)\n",
-    "    \n",
-    "    # make the initial state\n",
-    "    init = State(x=params.x, y=params.y, vx=vx, vy=vy)\n",
-    "    \n",
-    "    # compute the frontal area\n",
-    "    area = pi * (params.diameter/2)**2\n",
-    "\n",
-    "    return System(init = init,\n",
-    "                  mass = params.mass,\n",
-    "                  area = area,\n",
-    "                  C_d = params.C_d,\n",
-    "                  rho = params.rho,\n",
-    "                  g = params.g,\n",
-    "                  t_end=10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "absolute-percentage",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import vector_mag, vector_hat\n",
-    "\n",
-    "def drag_force(V, system):\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    mag = rho * vector_mag(V)**2 * C_d * area / 2\n",
-    "    direction = -vector_hat(V)\n",
-    "    f_drag = mag * direction\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "common-batman",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import Vector\n",
-    "\n",
-    "def slope_func(t, state, system):\n",
-    "    x, y, vx, vy = state\n",
-    "    mass, g = system.mass, system.g\n",
-    "    \n",
-    "    V = Vector(vx, vy)\n",
-    "    a_drag = drag_force(V, system) / mass\n",
-    "    a_grav = Vector(0, -g)\n",
-    "    \n",
-    "    A = a_grav + a_drag\n",
-    "    \n",
-    "    return V.x, V.y, A.x, A.y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "italic-amino",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(t, state, system):\n",
-    "    x, y, vx, vy = state\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "angry-pledge",
-   "metadata": {},
-   "source": [
-    "In the previous chapter we developed a model of the flight of a\n",
-    "baseball, including gravity and a simple version of drag, but neglecting spin, Magnus force, and the dependence of the coefficient of drag on velocity.\n",
-    "\n",
-    "In this chapter we apply that model to an optimization problem."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "decent-birth",
-   "metadata": {},
-   "source": [
-    "## The Manny Ramirez problem\n",
-    "\n",
-    "Manny Ramirez is a former member of the Boston Red Sox (an American\n",
-    "baseball team) who was notorious for his relaxed attitude and taste for practical jokes. Our objective in this chapter is to solve the following Manny-inspired problem:\n",
-    "\n",
-    "> What is the minimum effort required to hit a home run in Fenway Park?\n",
-    "\n",
-    "Fenway Park is a baseball stadium in Boston, Massachusetts. One of its\n",
-    "most famous features is the \"Green Monster\", which is a wall in left\n",
-    "field that is unusually close to home plate, only 310 feet away. To\n",
-    "compensate for the short distance, the wall is unusually high, at 37\n",
-    "feet (see <http://modsimpy.com/wally>)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "exposed-diving",
-   "metadata": {},
-   "source": [
-    "We want to find the minimum velocity at which a ball can leave home\n",
-    "plate and still go over the Green Monster. We'll proceed in the\n",
-    "following steps:\n",
-    "\n",
-    "1.  For a given velocity, we'll find the optimal **launch angle**, that is, the angle the ball should leave home plate to maximize its height when it reaches the wall.\n",
-    "\n",
-    "2.  Then we'll find the minimal velocity that clears the wall, given\n",
-    "    that it has the optimal launch angle."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "received-diamond",
-   "metadata": {},
-   "source": [
-    "## Finding the range\n",
-    "\n",
-    "Suppose we want to find the launch angle that maximizes **range**, that is, the distance the ball travels in the air before landing. We'll use a function in the ModSim library, `maximize`, which takes a function and finds its maximum.\n",
-    "\n",
-    "The function we pass to `maximize` should take launch angle in degrees, simulate the flight of a ball launched at that angle, and return the distance the ball travels along the $x$ axis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "forty-knitting",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import run_solve_ivp\n",
-    "\n",
-    "def range_func(angle, params):\n",
-    "    params = params.set(angle=angle)\n",
-    "    system = make_system(params)\n",
-    "    results, details = run_solve_ivp(system, slope_func,\n",
-    "                                     events=event_func)\n",
-    "    x_dist = results.iloc[-1].x\n",
-    "    print(angle, x_dist)\n",
-    "    return x_dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "exact-cigarette",
-   "metadata": {},
-   "source": [
-    "`range_func` makes a new `System` object with the given value of\n",
-    "`angle`. Then it calls `run_solve_ivp` and\n",
-    "returns the final value of `x` from the results.\n",
-    "\n",
-    "We can call `range_func` directly like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "senior-counter",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "45 99.31829628352206\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "99.31829628352206"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "range_func(45, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "paperback-passing",
-   "metadata": {},
-   "source": [
-    "And we can sweep a sequence of angles like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "biological-evans",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "20.0 78.09741067882733\n",
-      "23.0 84.11542610650983\n",
-      "26.0 89.1319241236397\n",
-      "29.0 93.17466724082831\n",
-      "32.0 96.27134737391347\n",
-      "35.0 98.44725560274013\n",
-      "38.0 99.72474586365286\n",
-      "41.0 100.12347299697637\n",
-      "44.0 99.66100302635327\n",
-      "47.0 98.35354763684836\n",
-      "50.0 96.21673516565127\n",
-      "53.0 93.26623796736264\n",
-      "56.0 89.51751617672979\n",
-      "59.0 84.98724481958084\n",
-      "62.0 79.69434524339994\n",
-      "65.0 73.66129770133254\n",
-      "68.0 66.91470886279747\n",
-      "71.0 59.48269492939761\n",
-      "74.0 51.40287109940439\n",
-      "77.0 42.72047925533449\n",
-      "80.0 33.48437980813933\n"
-     ]
-    }
-   ],
-   "source": [
-    "from modsim import linspace, SweepSeries\n",
-    "\n",
-    "angles = linspace(20, 80, 21)\n",
-    "sweep = SweepSeries()\n",
-    "\n",
-    "for angle in angles:\n",
-    "    x_dist = range_func(angle, params)\n",
-    "    sweep[angle] = x_dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "premium-contribution",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "experienced-providence",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "sweep.plot()\n",
-    "\n",
-    "decorate(xlabel='Launch angle (degree)',\n",
-    "         ylabel='Range (meter)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "conscious-blade",
-   "metadata": {},
-   "source": [
-    "It looks like the optimal angle is near 40°.\n",
-    "\n",
-    "We can find the optimal angle more precisely and more efficiently using `maximize_scalar`, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "vocal-nerve",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 17.468795355645703\n",
-      "34.37694101250946 98.06996498016372\n",
-      "55.62305898749054 90.03182421721925\n",
-      "21.246117974981075 80.72039493627989\n",
-      "41.173855871043976 100.1200188931698\n",
-      "40.980120907837964 100.12368377099199\n",
-      "40.88213731907024 100.12417004431842\n",
-      "40.879254201241096 100.12417043309691\n",
-      "40.87958814091176 100.12417042882092\n",
-      "40.878920261570435 100.12417042669497\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Solution found.'"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import maximize_scalar\n",
-    "\n",
-    "res = maximize_scalar(range_func, [0, 90], params)\n",
-    "res.message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "violent-explanation",
-   "metadata": {},
-   "source": [
-    "The first parameter is the function we want to maximize. The second is\n",
-    "the range of values we want to search; in this case, it's the range of\n",
-    "angles from 0° to 90°. \n",
-    "\n",
-    "The return value from `maximize` is an object that contains the\n",
-    "results, including `x`, which is the angle that yielded the highest\n",
-    "range, and `fun`, which is the value of `range_func` when it's evaluated at `x`, that is, range when the baseball is launched at the optimal angle."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "figured-uniform",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(40.879254201241096, 100.12417043309691)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res.x, res.fun"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "shaped-southeast",
-   "metadata": {},
-   "source": [
-    "For these parameters, the optimal angle is about 41°, which yields a\n",
-    "range of 100 m.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "temporal-extension",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "\n",
-    "\n",
-    "If you enjoy this exercise, you might be interested in this paper: \"How to hit home runs: Optimum baseball bat swing parameters for maximum range trajectories\", by Sawicki, Hubbard, and Stronge, at\n",
-    "<http://modsimpy.com/runs>."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "processed-constitution",
-   "metadata": {},
-   "source": [
-    "## Exercise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "handmade-rhythm",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Let's finish off the Manny Ramirez problem:\n",
-    "\n",
-    "> What is the minimum effort required to hit a home run in Fenway Park?\n",
-    "\n",
-    "Although the problem asks for a minimum, it is not an optimization problem.  Rather, we want to solve for the initial velocity that just barely gets the ball to the top of the wall, given that it is launched at the optimal angle.\n",
-    "\n",
-    "And we have to be careful about what we mean by \"optimal\".  For this problem, we don't want the longest range, we want the maximum height at the point where it reaches the wall.\n",
-    "\n",
-    "If you are ready to solve the problem on your own, go ahead.  Otherwise I will walk you through the process with an outline and some starter code.\n",
-    "\n",
-    "As a first step, write an `event_func` that stops the simulation when the ball reaches the wall at 310 feet (94.5 m).\n",
-    "Test your function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "studied-association",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def event_func(t, state, system):\n",
-    "    x, y, vx, vy = state\n",
-    "    return x - 94.5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "developmental-alabama",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-94.5"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system = make_system(params)\n",
-    "event_func(0, system.init, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "novel-appointment",
-   "metadata": {},
-   "source": [
-    "Next, write a function called `height_func` that takes a launch angle, simulates the flight of a baseball, and returns the height of the baseball when it reaches the wall.\n",
-    "Test your function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "ignored-decrease",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def height_func(angle, params):\n",
-    "    params = params.set(angle=angle)\n",
-    "    system = make_system(params)\n",
-    "\n",
-    "    results, details = run_solve_ivp(system, slope_func, \n",
-    "                                     events=event_func)\n",
-    "    height = results.iloc[-1].y\n",
-    "    return height"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "western-communist",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6.647999317446708"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "height_func(40, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "located-lawsuit",
-   "metadata": {},
-   "source": [
-    "Now use `maximize_scalar` to find the optimal angle.  Is it higher or lower than the angle that maximizes range?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "duplicate-madison",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Solution found.'"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "bounds = [0, 90]\n",
-    "res = maximize_scalar(height_func, bounds, params)\n",
-    "res.message"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "legislative-prospect",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(43.10025059565446, 7.202365708497986)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "res.x, res.fun"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "interpreted-telephone",
-   "metadata": {},
-   "source": [
-    "Even though we are finding the \"minimum\" velocity, we are not really solving a minimization problem.  Rather, we want to find the velocity that makes the height at the wall exactly 37 feet (11.3 m), given that it's launched at the optimal angle.  And that's a job for `root_scalar`.\n",
-    "\n",
-    "Write an error function that takes a velocity and a `System` object as parameters.  It should use `maximize_scalar` to find the highest possible height of the ball at the wall, for the given velocity.  Then it should return the difference between that optimal height and 11.3 meters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "egyptian-shadow",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def error_func(velocity, params):\n",
-    "    print(velocity)\n",
-    "    params = params.set(velocity=velocity)\n",
-    "    bounds = [0, 90]\n",
-    "    res = maximize_scalar(height_func, bounds, params)\n",
-    "    return res.fun - 11.3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "documentary-guidance",
-   "metadata": {},
-   "source": [
-    "Test your error function before you call `root_scalar`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "sustainable-supply",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "40\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "-4.097634291502015"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "error_func(40, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "reserved-shelter",
-   "metadata": {},
-   "source": [
-    "Then use `root_scalar` to find the answer to the problem, the minimum velocity that gets the ball out of the park."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "certified-webster",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "30.0\n",
-      "50.0\n",
-      "42.96216812977292\n",
-      "41.04044041129342\n",
-      "41.04880372505707\n",
-      "41.048542232137436\n",
-      "41.04854219528224\n",
-      "41.048542195283254\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "from scipy.optimize import root_scalar\n",
-    "\n",
-    "bracket = [30, 50]\n",
-    "res = root_scalar(error_func, params, bracket=bracket)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "silver-bernard",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "      converged: True\n",
-       "           flag: 'converged'\n",
-       " function_calls: 8\n",
-       "     iterations: 7\n",
-       "           root: 41.04854219528224"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "res"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "absent-encoding",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "41.04854219528224"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "min_velocity = res.root\n",
-    "min_velocity"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "approved-fiction",
-   "metadata": {},
-   "source": [
-    "And just to check, run `error_func` with the value you found."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "thick-jungle",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "41.04854219528224\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "-3.907985046680551e-14"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "error_func(min_velocity, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "simple-steps",
-   "metadata": {},
-   "source": [
-    "## Under the Hood\n",
-    "\n",
-    "`maximize_scalar` uses a golden section search, which you can read about at <http://modsimpy.com/minimize>)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap24.ipynb b/jupyter/chap24.ipynb
deleted file mode 100644
index b9fe4d9c..00000000
--- a/jupyter/chap24.ipynb
+++ /dev/null
@@ -1,1219 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "august-parks",
-   "metadata": {},
-   "source": [
-    "# Chapter 24"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "suspended-puppy",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cutting-ultimate",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "composed-carol",
-   "metadata": {},
-   "source": [
-    "In this chapter we model systems that involve rotating objects. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "documentary-network",
-   "metadata": {},
-   "source": [
-    "## Rotation\n",
-    "\n",
-    "Rotation is complicated: in three dimensions, objects can rotate around three axes; objects are often easier to spin around some\n",
-    "axes than others; and they may be stable when spinning around some axes but not others.\n",
-    "\n",
-    "If the configuration of an object changes over time, it might become\n",
-    "easier or harder to spin, which explains the surprising dynamics of\n",
-    "gymnasts, divers, ice skaters, etc.\n",
-    "\n",
-    "And when you apply a twisting force to a rotating object, the effect is often contrary to intuition. For an example, see this video on\n",
-    "gyroscopic precession <http://modsimpy.com/precess>.\n",
-    "\n",
-    "In this chapter, we will not take on the physics of rotation in all its glory. Rather, we will focus on simple scenarios where all rotation and all twisting forces are around a single axis. In that case, we can treat some vector quantities as if they were scalars (in the same way that we sometimes treat velocity as a scalar with an implicit direction).\n",
-    "\n",
-    "This approach makes it possible to simulate and analyze many interesting systems, but you will also encounter systems that would be better approached with the more general toolkit."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "political-acrylic",
-   "metadata": {},
-   "source": [
-    "The fundamental ideas in this chapter and the next are **angular\n",
-    "velocity**, **angular acceleration**, **torque**, and **moment of\n",
-    "inertia**. If you are not already familiar with these concepts, I will\n",
-    "define them as we go along, and I will point to additional reading.\n",
-    "\n",
-    "As a case study, you can use these tools to simulate the behavior of a yo-yo (see <http://modsimpy.com/yoyo>). But we'll work our way up to it gradually, starting with toilet paper."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "integral-welsh",
-   "metadata": {},
-   "source": [
-    "## The physics of toilet paper\n",
-    "\n",
-    "As a simple example of a system with rotation, we'll simulate the\n",
-    "manufacture of a roll of toilet paper, as shown in this video <https://youtu.be/Z74OfpUbeac?t=231>. Starting with a cardboard tube at the center, we will roll up 47 m of paper, the typical length of a roll of toilet paper in the U.S. (see <http://modsimpy.com/paper>).\n",
-    "\n",
-    "![Diagram of a roll of toilet paper, showing change in paper length as a result of a small rotation, $d\\theta$.](figs/paper_roll.pdf){height=\"2.5in\"}\n",
-    "\n",
-    "This figure shows a diagram of the system: $r$ represents\n",
-    "the radius of the roll at a point in time. Initially, $r$ is the radius of the cardboard core, $R_{min}$. When the roll is complete, $r$ is $R_{max}$.\n",
-    "\n",
-    "I'll use $\\theta$ to represent the total rotation of the roll in\n",
-    "radians. In the diagram, $d\\theta$ represents a small increase in\n",
-    "$\\theta$, which corresponds to a distance along the circumference of the roll of $r~d\\theta$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "political-prerequisite",
-   "metadata": {},
-   "source": [
-    "Finally, I'll use $y$ to represent the total length of paper that's been rolled. Initially, $\\theta=0$ and $y=0$. For each small increase in $\\theta$, there is a corresponding increase in $y$: \n",
-    "\n",
-    "$$dy = r~d\\theta$$\n",
-    "\n",
-    "If we divide both sides by a small increase in time, $dt$, we get a\n",
-    "differential equation for $y$ as a function of time.\n",
-    "\n",
-    "$$\\frac{dy}{dt} = r \\frac{d\\theta}{dt}$$ \n",
-    "\n",
-    "As we roll up the paper, $r$ increases, too. Assuming that $r$ increases by a fixed amount per revolution, we can write \n",
-    "\n",
-    "$$dr = k~d\\theta$$ \n",
-    "\n",
-    "Where $k$ is an unknown constant we'll have to figure out. Again, we can divide both sides by $dt$ to get a differential equation in time:\n",
-    "\n",
-    "$$\\frac{dr}{dt} = k \\frac{d\\theta}{dt}$$ \n",
-    "\n",
-    "Finally, let's assume that $\\theta$ increases at a constant rate of $\\omega = 300$ rad/s (about 2900 revolutions per minute): \n",
-    "\n",
-    "$$\\frac{d\\theta}{dt} = \\omega$$ \n",
-    "\n",
-    "This rate of change is called an **angular velocity**. Now we have a system of three differential equations we can use to simulate the system."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "prostate-smell",
-   "metadata": {},
-   "source": [
-    "## Implementation\n",
-    "\n",
-    "At this point we have a pretty standard process for writing simulations like this.\n",
-    "\n",
-    "First we'll create a `Params` object with the parameters of the system:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "artificial-cotton",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import units\n",
-    "\n",
-    "m = units.meter\n",
-    "rad = units.radian\n",
-    "s = units.second"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "honey-translator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import Params\n",
-    "\n",
-    "params = Params(Rmin = 0.02 * m,\n",
-    "                Rmax = 0.055 * m,\n",
-    "                L = 47 * m,\n",
-    "                theta_0 = 0 * rad,\n",
-    "                y_0 = 0 * m,\n",
-    "                omega = 300 * rad / s,\n",
-    "                t_end = 130 * s\n",
-    "               )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "caring-unemployment",
-   "metadata": {},
-   "source": [
-    "`Rmin` and `Rmax` are the initial and final values for the radius, `r`.\n",
-    "`L` is the total length of the paper. `t_end` is the length of the\n",
-    "simulation in time, and `dt` is the time step for the ODE solver.\n",
-    "\n",
-    "We use the `Params` object to make a `System` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "fresh-domestic",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import State, System\n",
-    "\n",
-    "def make_system(params):\n",
-    "    init = State(theta = params.theta_0,\n",
-    "                 y = params.y_0,\n",
-    "                 r = params.Rmin\n",
-    "                )\n",
-    "    \n",
-    "    k = estimate_k(params)\n",
-    "\n",
-    "    return System(params,\n",
-    "                  init=init, \n",
-    "                  k=k,\n",
-    "                 )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "strong-harrison",
-   "metadata": {},
-   "source": [
-    "The initial state contains three variables, `theta`, `y`, and `r`.\n",
-    "\n",
-    "`estimate_k` computes the parameter, `k`, that relates `theta` and `r`.\n",
-    "Here's how it works:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "common-sender",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import pi\n",
-    "\n",
-    "def estimate_k(params):\n",
-    "    Rmin, Rmax, L = params.Rmin, params.Rmax, params.L\n",
-    "    \n",
-    "    Ravg = (Rmax + Rmin) / 2\n",
-    "    Cavg = 2 * pi * Ravg\n",
-    "    revs = L / Cavg\n",
-    "    rads = 2 * pi * revs\n",
-    "    k = (Rmax - Rmin) / rads\n",
-    "    return k"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "seasonal-cherry",
-   "metadata": {},
-   "source": [
-    "`Ravg` is the average radius, half way between `Rmin` and `Rmax`, so\n",
-    "`Cavg` is the circumference of the roll when `r` is `Ravg`.\n",
-    "\n",
-    "`revs` is the total number of revolutions it would take to roll up\n",
-    "length `L` if `r` were constant at `Ravg`. And `rads` is just `revs`\n",
-    "converted to radians.\n",
-    "\n",
-    "Finally, `k` is the change in `r` for each radian of revolution. For\n",
-    "these parameters, `k` is about `2.8e-5` m/rad."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "fitting-might",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "theta      0 radian\n",
-       "y           0 meter\n",
-       "r        0.02 meter\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params)\n",
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "backed-vehicle",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "2.7925531914893616×10<sup>-5</sup> meter"
-      ],
-      "text/latex": [
-       "$2.7925531914893616\\times 10^{-5}\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "2.7925531914893616e-05 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.k"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "beginning-artwork",
-   "metadata": {},
-   "source": [
-    "Now we can use the differential equations from the previous section to\n",
-    "write a slope function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "signed-eight",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(t, state, system):\n",
-    "    theta, y, r = state\n",
-    "    k, omega = system.k, system.omega\n",
-    "    \n",
-    "    dydt = r * omega\n",
-    "    drdt = k * omega\n",
-    "    \n",
-    "    return omega, dydt, drdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "maritime-funds",
-   "metadata": {},
-   "source": [
-    "As usual, the slope function takes a `State` object, a time, and a\n",
-    "`System` object. The `State` object contains hypothetical values of\n",
-    "`theta`, `y`, and `r` at time `t`. The job of the slope function is to\n",
-    "compute the time derivatives of these values. The derivative of `theta` is angular velocity, which is often denoted `omega`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "trained-witness",
-   "metadata": {},
-   "source": [
-    "And as usual, we'll test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "exterior-water",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(300.0 <Unit('radian / second')>,\n",
-       " 6.0 <Unit('meter * radian / second')>,\n",
-       " 0.008377659574468085 <Unit('meter * radian / second')>)"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(0, system.init, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "russian-rochester",
-   "metadata": {},
-   "source": [
-    "We'd like to stop the simulation when the length of paper on the roll is `L`. We can do that with an event function that passes through 0 when `y` equals `L`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "strong-custody",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(t, state, system):\n",
-    "    theta, y, r = state\n",
-    "    return y - system.L"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "moved-present",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-47 meter"
-      ],
-      "text/latex": [
-       "$-47\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "-47 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "event_func(0, system.init, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "affecting-pathology",
-   "metadata": {},
-   "source": [
-    "Now we can run the simulation like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "stable-lying",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A termination event occurred.'"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import run_solve_ivp\n",
-    "\n",
-    "results, details = run_solve_ivp(system, slope_func,\n",
-    "                                  events=event_func)\n",
-    "details.message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "experienced-pathology",
-   "metadata": {},
-   "source": [
-    "Here are the last few time steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "careful-bahrain",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>theta</th>\n",
-       "      <th>y</th>\n",
-       "      <th>r</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3.843556</th>\n",
-       "      <td>1153.066667</td>\n",
-       "      <td>41.625707</td>\n",
-       "      <td>0.0522</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.927111</th>\n",
-       "      <td>1178.133333</td>\n",
-       "      <td>42.942960</td>\n",
-       "      <td>0.0529</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.010667</th>\n",
-       "      <td>1203.200000</td>\n",
-       "      <td>44.277760</td>\n",
-       "      <td>0.0536</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.094222</th>\n",
-       "      <td>1228.266667</td>\n",
-       "      <td>45.630107</td>\n",
-       "      <td>0.0543</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.177778</th>\n",
-       "      <td>1253.333333</td>\n",
-       "      <td>47.000000</td>\n",
-       "      <td>0.0550</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                theta          y       r\n",
-       "3.843556  1153.066667  41.625707  0.0522\n",
-       "3.927111  1178.133333  42.942960  0.0529\n",
-       "4.010667  1203.200000  44.277760  0.0536\n",
-       "4.094222  1228.266667  45.630107  0.0543\n",
-       "4.177778  1253.333333  47.000000  0.0550"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "underlying-variance",
-   "metadata": {},
-   "source": [
-    "At $\\omega = 300$ rad/s, the time it takes to complete one roll is about 4.2 seconds, which is consistent with what we see in the video."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "individual-patrick",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.177777777777778"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.index[-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "informative-porter",
-   "metadata": {},
-   "source": [
-    "The final value of `y` is 47 meters, as expected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "indian-skirt",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "47.0 47 meter\n"
-     ]
-    }
-   ],
-   "source": [
-    "final_state = results.iloc[-1] \n",
-    "print(final_state.y, params.L)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "running-tutorial",
-   "metadata": {},
-   "source": [
-    "The final value of radius is `Rmax`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "higher-conflict",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.05500000000000002 0.055 meter\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(final_state.r, params.Rmax)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "tamil-referral",
-   "metadata": {},
-   "source": [
-    "And the total number of rotations is close to 200, which seems plausible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "employed-ordinary",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "199.47419534184226"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "radians = final_state.theta\n",
-    "rotations = radians / 2 / pi\n",
-    "rotations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "otherwise-mississippi",
-   "metadata": {},
-   "source": [
-    "As an exercise, we'll see how fast the paper is moving.  But first, let's take a closer look at the results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "lesser-consumer",
-   "metadata": {},
-   "source": [
-    "## Plotting\n",
-    "\n",
-    "Here's what `theta` looks like over time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "included-schema",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "def plot_theta(results):\n",
-    "    results.theta.plot(color='C0', label='theta')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angle (rad)')\n",
-    "    \n",
-    "plot_theta(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "regulation-runner",
-   "metadata": {},
-   "source": [
-    "`theta` grows linearly, as we should expect with constant angular velocity.\n",
-    "\n",
-    "Here's what `r` looks like over time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "persistent-siemens",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_r(results):\n",
-    "    results.r.plot(color='C2', label='r')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Radius (m)')\n",
-    "    \n",
-    "plot_r(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dedicated-tuning",
-   "metadata": {},
-   "source": [
-    "`r` also increases linearly.\n",
-    "\n",
-    "But since the derivative of `y` depends on `r`, and `r` is increasing, `y` grows with increasing slope."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "contrary-typing",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_y(results):\n",
-    "    results.y.plot(color='C1', label='y')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Length (m)')\n",
-    "    \n",
-    "plot_y(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "furnished-executive",
-   "metadata": {},
-   "source": [
-    "Because this system is so simple, it is almost silly to simulate it; as we'll see in the next section, it is easy enough to solve the\n",
-    "differential equations analytically. \n",
-    "\n",
-    "However, it is often useful to start with simulation as a way of exploring and checking assumptions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "better-asbestos",
-   "metadata": {},
-   "source": [
-    "## Analysis\n",
-    "\n",
-    "The differential equations in Section xx are simple enough that we can just solve them. Since angular velocity is constant: \n",
-    "\n",
-    "$$\\frac{d\\theta}{dt} = \\omega$$ \n",
-    "\n",
-    "We can find $\\theta$ as a function of time by integrating both sides:\n",
-    "\n",
-    "$$\\theta(t) = \\omega t$$ \n",
-    "\n",
-    "Similarly, we can solve this equation\n",
-    "\n",
-    "$$\\frac{dr}{dt} = k \\omega$$ \n",
-    "\n",
-    "to find\n",
-    "\n",
-    "$$r(t) = k \\omega t + R_{min}$$ \n",
-    "\n",
-    "Then we can plug the solution for $r$ into the equation for $y$: \n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "\\frac{dy}{dt} & = r \\omega                      \\\\\n",
-    "              & = \\left[ k \\omega t + R_{min} \\right] \\omega \\nonumber\\end{aligned}$$\n",
-    "              \n",
-    "Integrating both sides yields:\n",
-    "\n",
-    "$$y(t) = \\left[ k \\omega t^2 / 2 + R_{min} t \\right] \\omega$$ \n",
-    "\n",
-    "So $y$ is a parabola, as you might have guessed."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "meaningful-kazakhstan",
-   "metadata": {},
-   "source": [
-    "We can also use these equations to find the relationship between $y$ and $r$, independent of time, which we can use to compute $k$. Using a move we saw in Section xxx, I'll divide Equations 1 and\n",
-    "2, yielding\n",
-    "\n",
-    "$$\\frac{dr}{dy} = \\frac{k}{r}$$ \n",
-    "\n",
-    "Separating variables yields\n",
-    "\n",
-    "$$r~dr = k~dy$$ \n",
-    "\n",
-    "Integrating both sides yields \n",
-    "\n",
-    "$$r^2 / 2 = k y + C$$ \n",
-    "\n",
-    "When $y=0$, $r=R_{min}$, so \n",
-    "\n",
-    "$$C = \\frac{1}{2} R_{min}^2$$ \n",
-    "\n",
-    "Solving for $y$, we have \n",
-    "\n",
-    "$$y = \\frac{1}{2k} (r^2 - R_{min}^2)                 \\label{eqn3}$$\n",
-    "\n",
-    "When $y=L$, $r=R_{max}$; substituting in those values yields\n",
-    "\n",
-    "$$L = \\frac{1}{2k} (R_{max}^2 - R_{min}^2)$$ \n",
-    "\n",
-    "Solving for $k$ yields\n",
-    "\n",
-    "$$k =  \\frac{1}{2L} (R_{max}^2 - R_{min}^2)           \\label{eqn4}$$\n",
-    "\n",
-    "Plugging in the values of the parameters yields `2.8e-5` m/rad, the same as the \"estimate\" we computed in Section xxx. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "acknowledged-register",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.7925531914893616e-05 meter 2.7925531914893616e-05 meter\n"
-     ]
-    }
-   ],
-   "source": [
-    "k = (params.Rmax**2 - params.Rmin**2) / (2 * params.L)\n",
-    "print(k, system.k)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "retired-skill",
-   "metadata": {},
-   "source": [
-    "In this case the estimate turns out to be exact."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "foreign-needle",
-   "metadata": {},
-   "source": [
-    "## Summary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "thick-luther",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Since we keep `omega` constant, the linear velocity of the paper increases with radius.  We can use `gradient` to estimate the derivative of `results.y`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "cardiac-hospital",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "from modsim import gradient\n",
-    "\n",
-    "dydt = gradient(results.y);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "welsh-charleston",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "dydt.plot(label='dydt')\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Linear velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "becoming-birthday",
-   "metadata": {},
-   "source": [
-    "With constant angular velocity, linear velocity is increasing, reaching its maximum at the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "informational-washer",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16.394999999999992"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max_linear_velocity = dydt.iloc[-1]\n",
-    "max_linear_velocity"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bizarre-variation",
-   "metadata": {},
-   "source": [
-    "Now suppose this peak velocity is the limiting factor; that is, we can't move the paper any faster than that.\n",
-    "\n",
-    "Nevertheless, we might be able to speed up the process by keeping the linear velocity at the maximum all the time.\n",
-    "\n",
-    "Write a slope function that keeps the linear velocity, `dydt`, constant, and computes the angular velocity, `omega`, accordingly.\n",
-    "\n",
-    "Run the simulation and see how much faster we could finish rolling the paper."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "excellent-japanese",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def slope_func(t, state, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object with theta, y, r\n",
-    "    t: time\n",
-    "    system: System object with r, k\n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    theta, y, r = state\n",
-    "    k, omega = system.k, system.omega\n",
-    "    \n",
-    "    dydt = system.linear_velocity\n",
-    "    omega = dydt / r\n",
-    "    drdt = k * omega\n",
-    "    \n",
-    "    return omega, dydt, drdt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "steady-member",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(819.7499999999997 <Unit('1 / meter')>,\n",
-       " 16.394999999999992,\n",
-       " 0.02289195478723403 <Unit('dimensionless')>)"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system.linear_velocity = max_linear_velocity\n",
-    "slope_func(0, system.init, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "applied-sacramento",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A termination event occurred.'"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results, details = run_solve_ivp(system, slope_func, \n",
-    "                                 events=event_func)\n",
-    "details.message"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "adult-chuck",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.8667276608722188"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "t_final = results.index[-1]\n",
-    "t_final"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "conditional-cliff",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_theta(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "arranged-queensland",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_r(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "similar-variance",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_y(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "lovely-orange",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/jupyter/chap25.ipynb b/jupyter/chap25.ipynb
deleted file mode 100644
index 93578711..00000000
--- a/jupyter/chap25.ipynb
+++ /dev/null
@@ -1,1421 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "unauthorized-winter",
-   "metadata": {},
-   "source": [
-    "# Chapter 25"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "complete-innocent",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "practical-roman",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "    \n",
-    "try:\n",
-    "    import modsim\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "desirable-jason",
-   "metadata": {},
-   "source": [
-    "Intro"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "earned-motorcycle",
-   "metadata": {},
-   "source": [
-    "In the previous chapter we modeled a scenario with constant angular\n",
-    "velocity. In this chapter we make it more complex; we'll model a teapot, on a turntable, revolving with constant angular acceleration and deceleration."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "together-tobacco",
-   "metadata": {},
-   "source": [
-    "## Angular acceleration\n",
-    "\n",
-    "Just as linear acceleration is the derivative of velocity, **angular\n",
-    "acceleration** is the derivative of angular velocity. And just as linear acceleration is caused by force, angular acceleration is caused by the rotational version of force, **torque**. If you are not familiar with torque, you can read about it at <http://modsimpy.com/torque>.\n",
-    "\n",
-    "In general, torque is a vector quantity, defined as the **cross\n",
-    "product** of $\\vec{r}$ and $\\vec{F}$, where $\\vec{r}$ is the **lever\n",
-    "arm**, a vector from the point of rotation to the point where the force is applied, and $\\vec{F}$ is the vector that represents the magnitude and direction of the force."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "promotional-trigger",
-   "metadata": {},
-   "source": [
-    "However, for the problems in this chapter, we only need the *magnitude* of torque; we don't care about the direction. In that case, we can compute \n",
-    "\n",
-    "$$\\tau = r F \\sin \\theta$$ \n",
-    "\n",
-    "where $\\tau$ is torque, $r$ is the length of the lever arm, $F$ is the magnitude of force, and $\\theta$ is the angle between $\\vec{r}$ and $\\vec{F}$.\n",
-    "\n",
-    "Since torque is the product of a length and a force, it is expressed in newton meters (Nm)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "infrared-summit",
-   "metadata": {},
-   "source": [
-    "## Moment of inertia\n",
-    "\n",
-    "In the same way that linear acceleration is related to force by Newton's second law of motion, $F=ma$, angular acceleration is related to torque by another form of Newton's law: \n",
-    "\n",
-    "$$\\tau = I \\alpha$$ \n",
-    "\n",
-    "where $\\alpha$ is angular acceleration and $I$ is **moment of inertia**. Just as mass is what makes it hard to accelerate an object, moment of inertia is what makes it hard to spin an object.\n",
-    "(That might sound like a dumb way to describe mass, but might actually be the most fundamental definition.)\n",
-    "\n",
-    "In the most general case, a 3-D object rotating around an arbitrary\n",
-    "axis, moment of inertia is a tensor, which is a function that takes a\n",
-    "vector as a parameter and returns a vector as a result.\n",
-    "\n",
-    "Fortunately, in a system where all rotation and torque happens around a single axis, we don't have to deal with the most general case. We can treat moment of inertia as a scalar quantity.\n",
-    "\n",
-    "For a small object with mass $m$, rotating around a point at distance\n",
-    "$r$, the moment of inertia is $I = m r^2$. For more complex objects, we can compute $I$ by dividing the object into small masses, computing\n",
-    "moments of inertia for each mass, and adding them up.\n",
-    "\n",
-    "However, for most simple shapes, people have already done the\n",
-    "calculations; you can just look up the answers. For example, see\n",
-    "<http://modsimpy.com/moment>."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "julian-klein",
-   "metadata": {},
-   "source": [
-    "## Teapots and turntables\n",
-    "\n",
-    "Tables in Chinese restaurants often have a rotating tray or turntable\n",
-    "that makes it easy for customers to share dishes. These turntables are\n",
-    "supported by low-friction bearings that allow them to turn easily and\n",
-    "glide. However, they can be heavy, especially when they are loaded with food, so they have a high moment of inertia.\n",
-    "\n",
-    "Suppose I am sitting at a table with a pot of tea on the turntable\n",
-    "directly in front of me, and the person sitting directly opposite asks\n",
-    "me to pass the tea. I push on the edge of the turntable with 1 N of\n",
-    "force until it has turned 0.5 rad, then let go. The turntable glides\n",
-    "until it comes to a stop 1.5 rad from the starting position. How much\n",
-    "force should I apply for a second push so the teapot glides to a stop\n",
-    "directly opposite me?\n",
-    "\n",
-    "We'll answer this question in these steps:\n",
-    "\n",
-    "1.  I'll use the results from the first push to estimate the coefficient of friction for the turntable.\n",
-    "\n",
-    "2.  As an exercise, you'll use that coefficient of friction to estimate the force needed to rotate the turntable through the remaining angle.\n",
-    "\n",
-    "Our simulation will use the following parameters:\n",
-    "\n",
-    "1.  The radius of the turntable is 0.5 m, and its weight is 7 kg.\n",
-    "\n",
-    "2.  The teapot weights 0.3 kg, and it sits 0.4 m from the center of the turntable.\n",
-    "\n",
-    "![Diagram of a turntable with a\n",
-    "teapot.](figs/teapot.pdf){height=\"2.5in\"}\n",
-    "\n",
-    "This figure shows the scenario, where $F$ is the force I apply to the turntable at the perimeter, perpendicular to the moment arm, $r$, and $\\tau$ is the resulting torque. The blue circle near the bottom is the teapot.\n",
-    "\n",
-    "Here's a `Params` object with the parameters of the scenario:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "cathedral-lithuania",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pint\n",
-    "\n",
-    "units = pint.UnitRegistry()\n",
-    "rad = units.radian\n",
-    "m = units.meter\n",
-    "s = units.second\n",
-    "kg = units.kilogram\n",
-    "N = units.newton"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "enclosed-happiness",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import Params\n",
-    "from numpy import pi\n",
-    "\n",
-    "params = Params(radius_disk=0.5,     # m\n",
-    "                mass_disk=7,         # kg\n",
-    "                radius_pot=0.4,      # m\n",
-    "                mass_pot=0.3,        # kg\n",
-    "                force=1,             # N\n",
-    "                torque_friction=0.2, # N*m\n",
-    "                theta_0=0,           # radian\n",
-    "                theta_push=0.5,      # radian\n",
-    "                theta_test=1.5,      # radian\n",
-    "                theta_target=pi,     # radian\n",
-    "                t_end=20             # s\n",
-    "               )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "robust-adrian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import Params\n",
-    "from numpy import pi\n",
-    "\n",
-    "params = Params(radius_disk = 0.5*m,\n",
-    "                mass_disk = 7*kg,\n",
-    "                radius_pot = 0.4*m,\n",
-    "                mass_pot = 0.3*kg,\n",
-    "                force = 1*N,\n",
-    "                torque_friction = 0.2*N*m,\n",
-    "                theta_0 = 0*rad,\n",
-    "                omega_0 = 0*rad/s,\n",
-    "                theta_push = 0.5*rad,\n",
-    "                theta_test = 1.5*rad,\n",
-    "                theta_target = pi*rad,\n",
-    "                t_end = 20*s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "second-leather",
-   "metadata": {},
-   "source": [
-    "`make_system` creates the initial state, `init`, and computes the total\n",
-    "moment of inertia for the turntable and the teapot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "acoustic-furniture",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import State, System\n",
-    "\n",
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    mass_disk, mass_pot = params.mass_disk, params.mass_pot\n",
-    "    radius_disk, radius_pot = params.radius_disk, params.radius_pot\n",
-    "    \n",
-    "    init = State(theta=params.theta_0, omega=params.omega_0)\n",
-    "    \n",
-    "    I_disk = mass_disk * radius_disk**2 / 2\n",
-    "    I_pot = mass_pot * radius_pot**2\n",
-    "    \n",
-    "    return System(params, \n",
-    "                  init=init, \n",
-    "                  I=I_disk+I_pot,\n",
-    "                  )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "convenient-control",
-   "metadata": {},
-   "source": [
-    "In the initial state, `theta` represents the angle of the table in rad;\n",
-    "`omega` represents the angular velocity in rad/s.\n",
-    "\n",
-    "`I_disk` is the moment of inertia of the turntable, which is based on\n",
-    "the moment of inertia for a horizontal disk revolving around a vertical axis through its center: \n",
-    "\n",
-    "$$I_{disk} = m r^2 / 2$$ \n",
-    "\n",
-    "`I_pot` is the moment\n",
-    "of inertia of the teapot, which I treat as a point mass with:\n",
-    "\n",
-    "$$I_{point} = m r^2$$ \n",
-    "\n",
-    "In SI units, moment of inertia is expressed in kg m$^2$.\n",
-    "\n",
-    "Now we can make a `System` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "objective-station",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system1 = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "crucial-recognition",
-   "metadata": {},
-   "source": [
-    "Here's a slope that takes the current state, which contains angle and\n",
-    "angular velocity, and returns the derivatives, angular velocity and\n",
-    "angular acceleration:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "ranking-local",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(t, state, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    theta, omega = state\n",
-    "    radius_disk, force = system.radius_disk, system.force\n",
-    "    torque_friction, I = system.torque_friction, system.I\n",
-    "    \n",
-    "    torque = radius_disk * force - torque_friction\n",
-    "    alpha = torque / I\n",
-    "    \n",
-    "    return omega, alpha "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "exposed-court",
-   "metadata": {},
-   "source": [
-    "In this scenario, the force I apply to the turntable is always\n",
-    "perpendicular to the lever arm, so $\\sin \\theta = 1$ and the torque due\n",
-    "to force is $\\tau = r F$.\n",
-    "\n",
-    "`torque_friction` represents the torque due to friction. Because the\n",
-    "turntable is rotating in the direction of positive `theta`, friction\n",
-    "acts in the direction of negative `theta`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "saved-purple",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0 <Unit('radian / second')>,\n",
-       " 0.32502708559046584 <Unit('newton / kilogram / meter')>)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(0, system1.init, system1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "decent-microwave",
-   "metadata": {},
-   "source": [
-    "We are almost ready to run the simulation, but first there's a problem we have to address.\n",
-    "\n",
-    "When I stop pushing on the turntable, the angular acceleration changes\n",
-    "abruptly. We could implement the slope function with an `if` statement\n",
-    "that checks the value of `theta` and sets `force` accordingly. And for a coarse model like this one, that might be fine. But we will get more\n",
-    "accurate results if we simulate the system in two phases:\n",
-    "\n",
-    "1.  During the first phase, force is constant, and we run until `theta` is 0.5 radians.\n",
-    "\n",
-    "2.  During the second phase, force is 0, and we run until `omega` is 0.\n",
-    "\n",
-    "Then we can combine the results of the two phases into a single\n",
-    "`TimeFrame`.\n",
-    "\n",
-    "Here's the event function I'll use for Phase 1; it stops the simulation when `theta` reaches `theta_end`, which is when I stop pushing:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "black-wichita",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func1(t, state, system):\n",
-    "    \"\"\"Stops when theta reaches theta_push.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: difference from target\n",
-    "    \"\"\"\n",
-    "    theta, omega = state\n",
-    "    return theta - system.theta_push"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "separate-college",
-   "metadata": {},
-   "source": [
-    "As usual, we'll test the event function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "ranking-google",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-0.5 radian"
-      ],
-      "text/latex": [
-       "$-0.5\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "-0.5 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "event_func1(0, system1.init, system1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "reduced-sharp",
-   "metadata": {},
-   "source": [
-    "Now we can run the first phase of the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "meaning-philosophy",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A termination event occurred.'"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from modsim import run_solve_ivp\n",
-    "\n",
-    "results1, details1 = run_solve_ivp(system1, slope_func,\n",
-    "                                   events=event_func1)\n",
-    "details1.message"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "focused-invention",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>theta</th>\n",
-       "      <th>omega</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1.613720</th>\n",
-       "      <td>0.4232</td>\n",
-       "      <td>0.524503</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.648800</th>\n",
-       "      <td>0.4418</td>\n",
-       "      <td>0.535905</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.683881</th>\n",
-       "      <td>0.4608</td>\n",
-       "      <td>0.547307</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.718962</th>\n",
-       "      <td>0.4802</td>\n",
-       "      <td>0.558709</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.754043</th>\n",
-       "      <td>0.5000</td>\n",
-       "      <td>0.570111</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           theta     omega\n",
-       "1.613720  0.4232  0.524503\n",
-       "1.648800  0.4418  0.535905\n",
-       "1.683881  0.4608  0.547307\n",
-       "1.718962  0.4802  0.558709\n",
-       "1.754043  0.5000  0.570111"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results1.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "willing-receipt",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "Before we run the second phase, we have to extract the final time and\n",
-    "state of the first phase."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "russian-experience",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = results1.index[-1]\n",
-    "init2 = results1.iloc[-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aware-generator",
-   "metadata": {},
-   "source": [
-    "Now we can make a `System` object for Phase 2, with the initial state\n",
-    "from Phase 1, and with `force=0`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "chinese-cover",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system2 = System(system1, t_0=t_0, init=init2, force=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "actual-monaco",
-   "metadata": {},
-   "source": [
-    "For the second phase, we need an event function that stops when the\n",
-    "turntable stops; that is, when angular velocity is 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "recovered-section",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func2(t, state, system):\n",
-    "    \"\"\"Stops when omega is 0.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: omega\n",
-    "    \"\"\"\n",
-    "    theta, omega = state\n",
-    "    return omega"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "corporate-taste",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.5701114676889649"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "event_func2(system2.t_0, system2.init, system2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "administrative-major",
-   "metadata": {},
-   "source": [
-    "Now we can run the second phase."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "destroyed-adrian",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A termination event occurred.'"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results2, details2 = run_solve_ivp(system2, slope_func,\n",
-    "                                   events=event_func2)\n",
-    "details2.message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "hindu-requirement",
-   "metadata": {},
-   "source": [
-    "Pandas provides `combine_first`, which combines `results1` and\n",
-    "`results2`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "frank-scene",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = results1.combine_first(results2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "metropolitan-roommate",
-   "metadata": {},
-   "source": [
-    "Now we can plot `theta` for both phases."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "distributed-humanitarian",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import decorate\n",
-    "\n",
-    "def plot_theta(results):\n",
-    "    results.theta.plot(label='theta')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angle (rad)')\n",
-    "    \n",
-    "plot_theta(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "excess-confidence",
-   "metadata": {},
-   "source": [
-    "And `omega`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "proved-surfing",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_omega(results):\n",
-    "    results.omega.plot(label='omega', color='C1')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angular velocity (rad/s)')\n",
-    "    \n",
-    "plot_omega(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "handy-frontier",
-   "metadata": {},
-   "source": [
-    "Angular velocity, `omega`, increases linearly while I am pushing, and decreases linearly after I let go. The angle, `theta`, is the integral of angular velocity, so it forms a parabola during each phase.\n",
-    "\n",
-    "In the next section, we'll use this simulation to estimate the torque\n",
-    "due to friction."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "careful-minneapolis",
-   "metadata": {},
-   "source": [
-    "## Estimating friction\n",
-    "\n",
-    "Let's take the code from the previous section and wrap it in a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "constant-wallace",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_two_phases(force, torque_friction, params):\n",
-    "    \"\"\"Run both phases.\n",
-    "    \n",
-    "    force: force applied to the turntable\n",
-    "    torque_friction: friction due to torque\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: TimeFrame of simulation results\n",
-    "    \"\"\"\n",
-    "    # put the specified parameters into the Params object\n",
-    "    params = Params(params, \n",
-    "                    force=force, \n",
-    "                    torque_friction=torque_friction)\n",
-    "\n",
-    "    # run phase 1\n",
-    "    system1 = make_system(params)\n",
-    "    results1, details1 = run_solve_ivp(system1, slope_func, \n",
-    "                                       events=event_func1)\n",
-    "\n",
-    "    # get the final state from phase 1\n",
-    "    t_0 = results1.index[-1]\n",
-    "    init2 = results1.iloc[-1]\n",
-    "    \n",
-    "    # run phase 2\n",
-    "    system2 = System(system1, t_0=t_0, init=init2, force=0)\n",
-    "    results2, details2 = run_solve_ivp(system2, slope_func, \n",
-    "                                       events=event_func2)\n",
-    "    \n",
-    "    # combine and return the results\n",
-    "    results = results1.combine_first(results2)\n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "positive-possible",
-   "metadata": {},
-   "source": [
-    "Let's test it with the same parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "played-ranking",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>theta</th>\n",
-       "      <th>omega</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4.174622</th>\n",
-       "      <td>1.2452</td>\n",
-       "      <td>4.560892e-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.227244</th>\n",
-       "      <td>1.2473</td>\n",
-       "      <td>3.420669e-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.279865</th>\n",
-       "      <td>1.2488</td>\n",
-       "      <td>2.280446e-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.332486</th>\n",
-       "      <td>1.2497</td>\n",
-       "      <td>1.140223e-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.385107</th>\n",
-       "      <td>1.2500</td>\n",
-       "      <td>2.775558e-16</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           theta         omega\n",
-       "4.174622  1.2452  4.560892e-02\n",
-       "4.227244  1.2473  3.420669e-02\n",
-       "4.279865  1.2488  2.280446e-02\n",
-       "4.332486  1.2497  1.140223e-02\n",
-       "4.385107  1.2500  2.775558e-16"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "force = 1\n",
-    "torque_friction = 0.2\n",
-    "results = run_two_phases(params.force, params.torque_friction, params)\n",
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "damaged-concert",
-   "metadata": {},
-   "source": [
-    "And check the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "bulgarian-halloween",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.2499999999999993"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "theta_final = results.iloc[-1].theta\n",
-    "theta_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "electronic-latin",
-   "metadata": {},
-   "source": [
-    "We can use `run_two_phases` to write an error function we can use, with\n",
-    "`root_bisect`, to find the torque due to friction that yields the\n",
-    "observed results from the first push, a total rotation of 1.5 rad."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "skilled-diving",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func1(torque_friction, params):\n",
-    "    \"\"\"Error function for root_scalar.\n",
-    "    \n",
-    "    torque_friction: hypothetical value\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: offset from target value\n",
-    "    \"\"\"\n",
-    "    force = 1\n",
-    "    results = run_two_phases(force, torque_friction, params)\n",
-    "    theta_final = results.iloc[-1].theta\n",
-    "    print(torque_friction, theta_final)\n",
-    "    return theta_final - params.theta_test"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "generous-parcel",
-   "metadata": {},
-   "source": [
-    "Testing the error function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "united-ranch",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.1 2.5000000000000018\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "1.0000000000000018 radian"
-      ],
-      "text/latex": [
-       "$1.0000000000000018\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "1.0000000000000018 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "guess1 = 0.1\n",
-    "error_func1(guess1, params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "rotary-brazilian",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.2 1.2499999999999993\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "-0.25000000000000067 radian"
-      ],
-      "text/latex": [
-       "$-0.25000000000000067\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "-0.25000000000000067 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "guess2 = 0.2\n",
-    "error_func1(guess2, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "comparable-physiology",
-   "metadata": {},
-   "source": [
-    "And running `root_scalar`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "hungarian-cattle",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.1 2.5000000000000018\n",
-      "0.2 1.2499999999999993\n",
-      "0.18 1.388888888888888\n",
-      "0.16559999999999983 1.5096618357487925\n",
-      "0.16675199999999987 1.4992323930147764\n",
-      "0.16666721279999988 1.4999950848161059\n",
-      "0.16666666666487695 1.5000000000161076\n",
-      "0.16666666666666669 1.5000000000000002\n",
-      "0.16666666666766675 1.4999999999909974\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.optimize import root_scalar\n",
-    "\n",
-    "res = root_scalar(error_func1, params, bracket=[guess1, guess2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "abandoned-remedy",
-   "metadata": {},
-   "source": [
-    "The result is 0.166 Nm, a little less than the initial guess."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "knowing-sleeve",
-   "metadata": {},
-   "source": [
-    "## Animation\n",
-    "\n",
-    "\n",
-    "Here's a draw function we can use to animate the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "funky-affect",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib.patches import Circle, Arrow\n",
-    "from matplotlib.pyplot import gca, axis\n",
-    "from modsim import pol2cart\n",
-    "\n",
-    "def draw_func(t, state, params):\n",
-    "    theta, omega = state\n",
-    "    \n",
-    "    # draw a circle for the table\n",
-    "    circle1 = Circle([0, 0], params.radius_disk)\n",
-    "    gca().add_patch(circle1)\n",
-    "    \n",
-    "    # draw a circle for the teapot\n",
-    "    center = pol2cart(theta, params.radius_pot)\n",
-    "    circle2 = Circle(center, 0.05, color='C1')\n",
-    "    gca().add_patch(circle2)\n",
-    "\n",
-    "    axis('equal')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "gentle-transmission",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import remove_units\n",
-    "\n",
-    "params_no_unit = remove_units(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "illegal-remainder",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "state = results.iloc[0]\n",
-    "draw_func(0, state, params_no_unit)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "toxic-shark",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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P3z8q/ABar8KXkNM+0D8q/ADSCF/CTvtA/6jwA6a5rYNttZq/lHDT+1j9o8IPmI37tKGLHGvVG7f9ocIPGP0pKxKhN27jp8IPGL1ZJRKhfSF+KvyA2Vzd4HUEEV/YpH0hbir8gKmub/Y6gogv7Ne+EDcVfoC0tndy+Fir1zFEfKG2UYUfLxV+gNS924LTAToiADS3deqKb3FS4QdITYNGNCJd1WqfiIsKP0BqG1q8jiDiK7WN2ifiocIPEM1ZihxPf/XGR4UfIBrhixxPI/z4qPADRKMZkeNpn4iPCj9ANJoROZ72ifio8APkiA5BEzlO/THtE/FQ4QdIe0en1xFEfKVN+0RcklL4ZrbQzLaY2dtmdkcPj5uZ/SD6+DozOyMZ6w0bnRZZ5HjaJ+KTcOGbWSbwY+AyYAZwtZnN6LbYZcDk6NfNwE8TXW8YaTQjcrw2FX5ckjHCPwt42zm33TnXCjwCLO62zGLg5y5iFTDUzEYnYd2hotGMyPE6OjUIikcyCr8M2NPl56roffEuA4CZ3WxmlWZWWVdXl4R4IpKudG6p+CSj8K2H+7r/M8SyTORO55Y65yqccxXFxcUJh0snmRk9vYwi4ZWVqeNO4pGMV6sKGNfl57HAvn4sI33IytDGLdJVlgZBcUlGg6wBJpvZRDPLAa4ClndbZjmwJHq0zjyg3jlXnYR1h4pG+CLH0z4Rn6xEn8A5125mtwLPApnA/c65jWZ2S/Txe4AVwCLgbeAYcGOi6w2j7Ext3CJdaZ+IT8KFD+CcW0Gk1Lved0+X2w74YjLWFWYFedleRxDxlYJc7RPx0KRwgJQU5HodQcRXSgq1T8RDhR8gJYV5XkcQ8ZVS7RNxUeEHiEb4Iscr1j4RFxV+gGg0I3I87RPxUeEHiOYrRY6nv3rjo8IPkNICjWZEutIIPz4q/ADRCF/kv+VkZjAsX4dlxkOFHyB52ZkU5iXloxMigVdckIuZPngVDxV+wIwq0p+wIqC/ePtDhR8wU0cVeh1BxBemaV+Imwo/YE4r00YuAnBaWZHXEQJHhR8wM7WRiwAq/P5Q4QfMzLIi9D6VhF1OZgZTRxV4HSNwVPgBU5iXzYTh+V7HEPHUlFFDyMlSfcVLr1gAaVpHwk7TOf2jwg+gWWO1sUu4nVY21OsIgaTCDyCN8CXsNMLvHxV+AOmNWwkzvWHbfyr8ACrMy2ZqqTZ4CadZY4v0hm0/6VULqPnTS7yOIOKJ+dNLvY4QWCr8gFqgjV5C6tIZGuz0lwo/oOaMG6rLu0noTBw5mFNLNJ3ZXyr8gDIz5k/TSEfCRdt8YlT4AaZpHQmbBTO0zSdChR9g508eSV62/gklHIbmZ1MxYZjXMQJNbRFgedmZnH/qSK9jiKTExVOKycpUZSVCr17AaVpHwkLTOYlT4QfcghmlZGfqY7eS3gZlZ3LRlGKvYwSeCj/gRg7J5UMzRnkdQ2RAfWz2GArysr2OEXgq/DRw3bwJXkcQGVDXn6NtPBkSKnwzG25mz5vZtuj3E95CN7NxZvaimW02s41m9uVE1iknOueUEUwuGeJ1DJEBMWfcUJ0hNkkSHeHfAax0zk0GVkZ/7q4d+Afn3HRgHvBFM5uR4HqlG43yJV1dr207aRIt/MXAg9HbDwIf776Ac67aOfd69HYjsBkoS3C90s0nzyhjcE6m1zFEkmpYfjaXzx7tdYy0kWjhlzrnqiFS7MBJP/dsZuXA6cDqBNcr3RTkZbP4dP0elfRyZcU4crM0kEmWPgvfzF4wsw09fC2OZ0VmNgR4HLjNOddwkuVuNrNKM6usq6uLZxWhpz99JZ1kGFx7trbpZMrqawHn3ILeHjOzGjMb7ZyrNrPRQG0vy2UTKftfOuee6GN9S4GlABUVFa6vfPLfpo8upGLCMCp3HfY6ikjCLpxSzPgR+V7HSCuJTuksB26I3r4BeKr7AmZmwH3AZufcdxJcn/ThpvMneh1BJCm0LSdfooX/LeBSM9sGXBr9GTMbY2YrosucB1wPXGJmb0S/FiW4XunFwpmjmDVWh7BJsJ0zaQQXTNYna5Otzymdk3HOHQTm93D/PmBR9PafAX32P0XMjNsXTuPaZXpfXILr9sumeR0hLemTtmnovFNH6iyaElgLPzCKOeOGeh0jLanw09TtC6dh+rtKAiYzw/jKh6d6HSNtqfDT1Glji1h0mj6wIsHyqbljOVWnCRkwKvw09pUPTSUrQ8N8CYbcrAxuWzDF6xhpTYWfxiaOHMyVZ47zOoZITD5zbjmjivK8jpHWVPhp7rb5kxmUrY+mi78V5mXxhYtP9TpG2lPhp7mSwjy9CSa+d9dHZlCUrwucDDQVfgjceG45Z5afcKkCEV+4eGqxph5TRIUfAhkZxr9fMZu8bP1zi78U5GXxzU+e5nWM0FADhET5yMH844f16UXxl69/ZAajiwZ5HSM0VPghoqkd8RNN5aSeCj9E3pva0VE74rWCvCy+9clZXscIHRV+yJSPHKyjdsRzX//IDB1z7wEVfgjdeG45Z5UP9zqGhJSmcryjwg+hjAzjB1efTnFBrtdRJGTGDhvEd66c43WM0FLhh9Soojx+dv1ccrK0CUhq5Odkcu+SCoYPzvE6Smhpbw+xM8YP4/9+fKbXMSQEzOA7V85m+uhCr6OEmgo/5D5VMU7XDpUB97eXTGbhTJ2u22sqfOHORdO5cIquHyoD47KZo7htwWSvYwgqfCFylaEfXn06k0YO9jqKpJnpowv59pWzMV1+zRdU+AJA0aBsli6poCAvoevai7xvxOAc7l0yl/wcbVN+ocKX951aMoQfXXMGOZnaLCQxedkZ/PS6uYwdlu91FOlCe7Yc56Ipxfzg6tN1aUTpt5ysDJZeX8FZE/XhPr9R4csJFs4cxXc+PQd1vsQrO9P4yTVn6CAAn1LhS48+NnsMd18xG73XJrHKzDC+f9XpLJhR6nUU6YUKX3p1xdyx3P03szTSlz5lZ0aO9Fp0mo619zMVvpzUpyrG8d1Pz9GcvvQqJyuDn147V2UfACp86dPiOWX86JrTdfSOnCAvO4N7l1RoGicgtAdLTBbOHM3SJXMpyNUx1RIxND+bB248i4v0Bm1gqPAlZhdPLeG3XzyX8hE6tjrsppQOYfkXz2fepBFeR5E4qPAlLqeWFPDUF8/ngskjvY4iHlkwvZQnvnAe4/WLP3ASKnwzG25mz5vZtuj3Xq+QbWaZZvZXM3smkXWK94qif8rfeF6511EkxW794Kncu2QuQzS1F0iJjvDvAFY65yYDK6M/9+bLwOYE1yc+kZlh/MtHP8DdV8zSm7khMCg7kx9efTpf+fBUnQgtwBLdUxcDD0ZvPwh8vKeFzGws8BFgWYLrE5+5smIcv7r5bEYO0eUS09WYojweveUcPjp7jNdRJEGJFn6pc64aIPq9pJflvgf8E9DZ1xOa2c1mVmlmlXV1dQnGk1SYO2E4y289jzPGD/U6iiTZOZNG8NSt5zOzrMjrKJIEfRa+mb1gZht6+FocywrM7HKg1jm3NpblnXNLnXMVzrmK4mId7hUUY4YO4rFbzuXORdPI1XVyAy8/J5P/vfgDPPy5s3Wx+zTS5zsvzrkFvT1mZjVmNto5V21mo4HaHhY7D/iYmS0C8oBCM3vIOXddv1OLL2VkGDdfeArzp5fyj4++yeu7j3gdSfph3qTh/PsVsxk3XEfhpJtEh2LLgRuit28Anuq+gHPuq865sc65cuAq4A8q+/R2SvEQjfYD6L1R/a8+N09ln6YS3Ru/BVxqZtuAS6M/Y2ZjzGxFouEkuN4b7a/48gWa2w+AeZOG8+xtF7LknHIdhZPGzDnndYZeVVRUuMrKSq9jSII6Ox3L/rydbz+3lZb2Pt+3lxTKz8nkjsumcf28CSr6NGFma51zFT09pk9PyIB7b7R/+awxfPf5rTzx1710dPp3oBEGWRnGVWeN42/nT6akIM/rOJIiGuFLym2raeTfn93Cc5tqvI4SOmZw+awx/MOlUygfOdjrODIANMIXX5lcWsDSJRW8vvsw//b7t1i945DXkULhgskjuX3hNB1TH2IqfPHMGeOH8evPn8OLW2q5+7+2sLm6wetIaWn2uKHcvnAq556iE96FnQpfPPfBqSVcPKWY5W/uY9mfdrB+b73XkdLCGeOHcvOFk1g4U1eikggVvviCmbF4ThmL55Txxp4jPLRqF0+/uU9H9cQpPyeTxXPGcN28CXxgjKZu5HgqfPGdOeOGMmfcUO76yHQeraziodW72HXwmNexfO2U4sFcN28CfzN3LIV52V7HEZ9S4YtvDc3P4XMXTuKzF0zk5W0H+MWru3hxS60O6YzKyjA+9IFSrjt7Aueeqvl56ZsKX3zPzLhoSjEXTSmmur6JZzfs54XNtazecZC2jnCVf05WBueeMoL500v58IxSSgp1DL3ETsfhS2A1Nrfx0tY6XthUw4tb6qhvavM60oAYPjiHD04t4dIZJVwwuZjButqUnISOw5e0VJCXzeWzxnD5rDG0d3SyZudhVm6u4YXNNewM+Jz/KcWDWTC9lAUzSpk7fhgZGTrtgSROI3xJS7WNzWzYW8+6qno27K1n/d56ahpavI7VozFFecwsK+K0siJmjo181xXEpL80wpfQKSnI45JpeVwyrfT9+977JbC+qoH1e4+wubqR2sbmlL0PkJOZQWlRLtNHFb5f7rPKihihcpcUUeFLaPT0S8A5x6GjrdQ2tlDb2EJNQzN10e+1DS3UNDbT0NRGR6ejrcPR0elojx4llJVhZGYY2ZmR70WDsiktzKOkIJeS6PfSwjxKCnMpLchj2OAcr/7XRQAVvoScmTFiSC4jhuQyXR9IlTSnyxGJiISECl9EJCRU+CIiIaHCFxEJCRW+iEhIqPBFREJChS8iEhIqfBGRkFDhi4iEhK9PnmZmdcCuPhYbCRxIQZz+8HM28Hc+P2cD5UuEn7OBv/PFkm2Cc664pwd8XfixMLPK3s4M5zU/ZwN/5/NzNlC+RPg5G/g7X6LZNKUjIhISKnwRkZBIh8Jf6nWAk/BzNvB3Pj9nA+VLhJ+zgb/zJZQt8HP4IiISm3QY4YuISAxU+CIiIRG4wjez4Wb2vJlti34f1styf2dmG81sg5n9yszyfJRtqJk9ZmZvmdlmMztnoLPFky+6bKaZ/dXMnvFLNjMbZ2YvRl+zjWb25RTkWmhmW8zsbTO7o4fHzcx+EH18nZmdMdCZ4sh2bTTTOjN7xcxmpypbLPm6LHemmXWY2RV+ymZmF5vZG9Ft7aVUZYsln5kVmdnTZvZmNN+NMT2xcy5QX8DdwB3R23cA/9bDMmXADmBQ9OffAJ/xQ7boYw8Cn43ezgGG+uW167Ls3wMPA8/4JRswGjgjersA2ArMGMBMmcA7wKTov9Ob3dcHLAJ+DxgwD1idotcrlmznAsOity9LVbZY83VZ7g/ACuAKv2QDhgKbgPHRn0v89NoBd763jwDFwCEgp6/nDtwIH1hMpDCJfv94L8tlAYPMLAvIB/YNfLS+s5lZIXAhcB+Ac67VOXckBdliygdgZmOBjwDLUhMLiCGbc67aOfd69HYjsJnIL/eBchbwtnNuu3OuFXgkmrOrxcDPXcQqYKiZpeLquH1mc8694pw7HP1xFTA2Bblizhf1JeBxoNZn2a4BnnDO7QZwzvktnwMKzMyAIUQKv72vJw5i4Zc656ohUgBASfcFnHN7gf8AdgPVQL1z7jk/ZCPyW7sO+M/olMkyMxucgmyx5gP4HvBPQGeKckHs2QAws3LgdGD1AGYqA/Z0+bmKE3/BxLLMQIh3vTcR+UskVfrMZ2ZlwCeAe1KYC2J77aYAw8zsj2a21syWpCxdbPl+BEwnMpBdD3zZOdfn/pqVrITJZGYvAKN6eOhrMf73w4j8RpwIHAEeNbPrnHMPeZ2NyGt+BvAl59xqM/s+kSmMryeaLRn5zOxyoNY5t9bMLk5Gpi7Pnehr997zDCEyKrzNOdeQjGy9raqH+7ofxxzLMgMh5vWa2QeJFP75A5qo22p7uK97vu8BtzvnOiID1ZSJJVsWMBeYDwwCXjWzVc65rQMdjtjyfRh4A7gEOAV43sz+1Nf+4MvCd84t6O0xM6sxs9HOueron849/am1ANjhnKuL/jdPEJnPTLjwk5CtCqhyzr03Mn2MSOEnRRLynQd8zMwWAXlAoZk95Jy7zgfZMLNsImX/S+fcE4lm6kMVMK7Lz2M5cWowlmUGQkzrNbNZRKbmLnPOHUxBrvfEkq8CeCRa9iOBRWbW7px70gfZqoADzrmjwFEzexmYTeR9o4EWS74bgW+5yCT+22a2A5gGvHayJw7ilM5y4Ibo7RuAp3pYZjcwz8zyo3Nc84nM93qezTm3H9hjZlOjd80n8uZQKsSS76vOubHOuXLgKuAPySj7ZGSL/lveB2x2zn0nBZnWAJPNbKKZ5RB5PZZ3W2Y5sCR6tM48ItOH1X7IZmbjgSeA61M0Mo0rn3NuonOuPLqtPQZ8IQVlH1M2ItvfBWaWZWb5wNmkpkNizbebSHdgZqXAVGB7n8+cqneek/UFjABWAtui34dH7x8DrOiy3DeAt4ANwC+AXB9lmwNUAuuAJ4keSeGXfF2Wv5jUHaXTZzYiUxIu+rq9Ef1aNMC5FhEZ1b0DfC163y3ALdHbBvw4+vh6oCIVr1eM2ZYBh7u8VpWpyhZLvm7LPkCKjtKJNRvwj0QGYxuITB/65rWL7hfPRbe5DcB1sTyvTq0gIhISQZzSERGRflDhi4iEhApfRCQkVPgiIiGhwhcRCQkVvohISKjwRURC4v8DdiUfMvk2SWgAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from modsim import animate\n",
-    "\n",
-    "animate(results, draw_func, params_no_unit)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "horizontal-shade",
-   "metadata": {},
-   "source": [
-    "## Summary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "professional-modem",
-   "metadata": {},
-   "source": [
-    "Now that we know the torque due to friction, we can compute the force\n",
-    "needed to rotate the turntable through the remaining angle, that is,\n",
-    "from 1.5 rad to 3.14 rad.\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "practical-cabinet",
-   "metadata": {},
-   "source": [
-    "\n",
-    "### Exercises\n",
-    "\n",
-    "Now finish off the example by estimating the force that delivers the teapot to the desired position.\n",
-    "\n",
-    "Write an error function that takes `force` and `params` and returns the offset from the desired angle."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "apparent-lancaster",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def error_func2(force, params):\n",
-    "    \"\"\"Error function for root_scalar.\n",
-    "    \n",
-    "    force: hypothetical value\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: offset from target value\n",
-    "    \"\"\"\n",
-    "    results = run_two_phases(force, params.torque_friction, params)\n",
-    "    theta_final = results.iloc[-1].theta\n",
-    "    print(force, theta_final)\n",
-    "    remaining_angle = params.theta_target - params.theta_test\n",
-    "    return theta_final - remaining_angle"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "persistent-rings",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0 1.2499999999999993\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "-0.3915926535897938 radian"
-      ],
-      "text/latex": [
-       "$-0.3915926535897938\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "-0.3915926535897938 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "guess1 = 1.0\n",
-    "params2 = params.set(torque_friction=torque_friction)\n",
-    "error_func2(guess1, params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "previous-pittsburgh",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.0 2.50000000000001\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "0.8584073464102171 radian"
-      ],
-      "text/latex": [
-       "$0.8584073464102171\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "0.8584073464102171 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "guess2 = 2.0\n",
-    "error_func2(guess2, params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "governing-component",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0 1.2499999999999993\n",
-      "2.0 2.50000000000001\n",
-      "1.3132741228718323 1.64159265358979\n",
-      "1.3132741228728328 1.6415926535910412\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "res = root_scalar(error_func2, params, bracket=[guess1, guess2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "statistical-behalf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.14159265358979"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "force = res.root\n",
-    "results = run_two_phases(force, params.torque_friction, params)\n",
-    "theta_final = results.iloc[-1].theta\n",
-    "theta_final + 1.5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "unauthorized-equity",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 1b7804da84381938d7bdbe5954053afa6c5213fd Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 12 Feb 2021 16:31:08 -0500
Subject: [PATCH 032/144] Updating chapters

---
 chapters/chap01.ipynb | 1254 +++++++++++++++++++++++++++++++++++++++++
 chapters/chap02.ipynb | 1204 +++++++++++++++++++++++++++++++++++++++
 chapters/chap03.ipynb |  834 +++++++++++++++++++++++++++
 chapters/chap04.ipynb |  853 ++++++++++++++++++++++++++++
 chapters/chap05.ipynb | 1000 ++++++++++++++++++++++++++++++++
 chapters/chap06.ipynb |  698 +++++++++++++++++++++++
 chapters/chap07.ipynb |  811 ++++++++++++++++++++++++++
 chapters/chap08.ipynb |  740 ++++++++++++++++++++++++
 chapters/chap09.ipynb | 1054 ++++++++++++++++++++++++++++++++++
 chapters/chap10.ipynb |  420 ++++++++++++++
 chapters/chap11.ipynb |  821 +++++++++++++++++++++++++++
 chapters/chap12.ipynb |  852 ++++++++++++++++++++++++++++
 chapters/chap13.ipynb |  566 +++++++++++++++++++
 chapters/chap14.ipynb |  570 +++++++++++++++++++
 chapters/chap15.ipynb |  830 +++++++++++++++++++++++++++
 chapters/chap16.ipynb |  642 +++++++++++++++++++++
 chapters/chap17.ipynb |  547 ++++++++++++++++++
 chapters/chap18.ipynb |  783 +++++++++++++++++++++++++
 chapters/chap19.ipynb |  288 ++++++++++
 chapters/chap20.ipynb |  794 ++++++++++++++++++++++++++
 chapters/chap21.ipynb |  759 +++++++++++++++++++++++++
 chapters/chap22.ipynb | 1248 ++++++++++++++++++++++++++++++++++++++++
 chapters/chap23.ipynb |  557 ++++++++++++++++++
 chapters/chap24.ipynb |  901 +++++++++++++++++++++++++++++
 chapters/chap25.ipynb | 1076 +++++++++++++++++++++++++++++++++++
 chapters/chap26.ipynb |  389 +++++++++++++
 26 files changed, 20491 insertions(+)
 create mode 100644 chapters/chap01.ipynb
 create mode 100644 chapters/chap02.ipynb
 create mode 100644 chapters/chap03.ipynb
 create mode 100644 chapters/chap04.ipynb
 create mode 100644 chapters/chap05.ipynb
 create mode 100644 chapters/chap06.ipynb
 create mode 100644 chapters/chap07.ipynb
 create mode 100644 chapters/chap08.ipynb
 create mode 100644 chapters/chap09.ipynb
 create mode 100644 chapters/chap10.ipynb
 create mode 100644 chapters/chap11.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "uniform-persian",
+   "metadata": {},
+   "source": [
+    "# Chapter 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "biblical-enhancement",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "confirmed-budapest",
+   "metadata": {},
+   "source": [
+    "## Jupyter\n",
+    "\n",
+    "Welcome to *Modeling and Simulation in Python*, and welcome to Jupyter.\n",
+    "\n",
+    "This is a Jupyter notebook, which is a development environment where you can write and run Python code.  Each notebook is divided into cells.  Each cell contains either text (like this cell) or Python code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "danish-scope",
+   "metadata": {},
+   "source": [
+    "To run a cell, hold down SHIFT and press ENTER.  \n",
+    "\n",
+    "* If you run a text cell, Jupyter formats the text and displays the result.\n",
+    "\n",
+    "* If you run a code cell, Jupyter runs the Python code in the cell and displays the result, if any.\n",
+    "\n",
+    "If you run the following cell, it checks whether the libraries you need are installed.  If so, the cell produces no output.  If not, you'll see updates from the installer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "asian-america",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# check if the libraries we need are installed\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint\n",
+    "    \n",
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "copyrighted-desperate",
+   "metadata": {},
+   "source": [
+    "If the previous cell runs without producing any error messages, you are all set.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hourly-financing",
+   "metadata": {},
+   "source": [
+    "## Modeling\n",
+    "\n",
+    "This book is about modeling and simulation of physical systems. The\n",
+    "following diagram shows what I mean by \"modeling\":\n",
+    "\n",
+    "<img src =\"https://github.com/AllenDowney/ModSimPy/raw/master/book/figs/modeling_framework.png\" width=\"400\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "structured-receiver",
+   "metadata": {},
+   "source": [
+    "Starting in the lower left, the **system** is something in the real\n",
+    "world we are interested in. \n",
+    "To model the system, we have to decide which elements of the real world to include and which we can leave out.\n",
+    "This process is called **abstraction**.\n",
+    "\n",
+    "The result of abstraction is a **model**, which is a description of the system that includes only the features we think are essential. A model\n",
+    "can be represented in the form of diagrams and equations, which can be\n",
+    "used for mathematical **analysis**. It can also be implemented in the\n",
+    "form of a computer program, which can run **simulations**.\n",
+    "\n",
+    "The result of analysis and simulation might be a **prediction** about\n",
+    "what the system will do, an **explanation** of why it behaves the way it\n",
+    "does, or a **design** intended to achieve a purpose.\n",
+    "\n",
+    "We can **validate** predictions and test designs by taking\n",
+    "**measurements** from the real world and comparing the **data** we get\n",
+    "with the results from analysis and simulation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "center-accommodation",
+   "metadata": {},
+   "source": [
+    "For any physical system, there are many possible models, each one\n",
+    "including and excluding different features, or including different\n",
+    "levels of detail. The goal of the modeling process is to find the model\n",
+    "best suited to its purpose (prediction, explanation, or design).\n",
+    "\n",
+    "Sometimes the best model is the most detailed. If we include more\n",
+    "features, the model is more realistic, and we expect its predictions to\n",
+    "be more accurate.\n",
+    "\n",
+    "But often a simpler model is better. If we include only the essential\n",
+    "features and leave out the rest, we get models that are easier to work\n",
+    "with, and the explanations they provide can be clearer and more\n",
+    "compelling."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "confirmed-highlight",
+   "metadata": {},
+   "source": [
+    "As an example, suppose someone asks you why the orbit of the Earth is\n",
+    "elliptical. If you model the Earth and Sun as point masses (ignoring\n",
+    "their actual size), compute the gravitational force between them using\n",
+    "Newton's law of universal gravitation, and compute the resulting orbit\n",
+    "using Newton's laws of motion, you can show that the result is an\n",
+    "ellipse.\n",
+    "\n",
+    "Of course, the actual orbit of Earth is not a perfect ellipse, because\n",
+    "of the gravitational forces of the Moon, Jupiter, and other objects in\n",
+    "the solar system, and because Newton's laws of motion are only\n",
+    "approximately true (they don't take into account relativistic effects).\n",
+    "\n",
+    "But adding these features to the model would not improve the\n",
+    "explanation; more detail would only be a distraction from the\n",
+    "fundamental cause. However, if the goal is to predict the position of\n",
+    "the Earth with great precision, including more details might be\n",
+    "necessary."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stretch-geneva",
+   "metadata": {},
+   "source": [
+    "Choosing the best model depends on what the model is for. It is usually\n",
+    "a good idea to start with a simple model, even if it is likely to be too\n",
+    "simple, and test whether it is good enough for its purpose. Then you can\n",
+    "add features gradually, starting with the ones you expect to be most\n",
+    "essential. This process is called **iterative modeling**.\n",
+    "\n",
+    "Comparing results of successive models provides a form of **internal\n",
+    "validation**, so you can catch conceptual, mathematical, and software\n",
+    "errors. And by adding and removing features, you can tell which ones\n",
+    "have the biggest effect on the results, and which can be ignored.\n",
+    "\n",
+    "Comparing results to data from the real world provides **external\n",
+    "validation**, which is generally the strongest test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "criminal-lunch",
+   "metadata": {},
+   "source": [
+    "## The falling penny myth\n",
+    "\n",
+    "Let's see an example of how models are used. You might have heard that a\n",
+    "penny dropped from the top of the Empire State Building would be going\n",
+    "so fast when it hit the pavement that it would be embedded in the\n",
+    "concrete; or if it hit a person, it would break their skull.\n",
+    "\n",
+    "We can test this myth by making and analyzing a model. To get started,\n",
+    "we'll assume that the effect of air resistance is small. This will turn out to be a bad assumption, but bear with me.\n",
+    "If air resistance is negligible, the primary force acting on the penny\n",
+    "is gravity, which causes the penny to accelerate downward.\n",
+    "\n",
+    "If the initial velocity is 0, the velocity after $t$ seconds is \n",
+    "\n",
+    "$$v = a t$$\n",
+    "\n",
+    "and the distance the penny has dropped is \n",
+    "\n",
+    "$$x = a t^2 / 2$$ \n",
+    "\n",
+    "To find the time until the penny reaches the sidewalk, we can solve for $t$:\n",
+    "\n",
+    "$$t = \\sqrt{ 2 x / a}$$ \n",
+    "\n",
+    "Plugging in the acceleration of gravity, $a = 9.8$ m/s$^2$, and the height of the Empire State Building, $x = 381$ m, we get $t = 8.8$ s. \n",
+    "\n",
+    "Then computing $v = a t$ we get a velocity on impact of $86$ m/s, which is about 190 miles per hour. That sounds like it could hurt."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "documentary-diagnosis",
+   "metadata": {},
+   "source": [
+    "Of course, these results are not exact because the model is based on\n",
+    "simplifications. For example, we assume that gravity is constant. In\n",
+    "fact, the force of gravity is different on different parts of the globe, and it gets weaker as you move away from the surface. But these differences are small, so ignoring them is probably a good choice for this scenario.\n",
+    "\n",
+    "On the other hand, ignoring air resistance is not a good choice. Once\n",
+    "the penny gets to about 29 m/s, the upward force of air resistance\n",
+    "equals the downward force of gravity, so the penny stops accelerating.\n",
+    "This is the **terminal velocity** of the penny in air.\n",
+    "\n",
+    "Once the penny reaches terminal velocity, it doesn't matter how much farther it falls; it hits the sidewalk at about 29 m/s.\n",
+    "That's much less than 86 m/s, as the simple model predicts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "recent-appliance",
+   "metadata": {},
+   "source": [
+    "The statistician George Box famously said \"All models are wrong, but\n",
+    "some are useful.\" He was talking about statistical models, but his wise words apply to all kinds of models. Our first model, which ignores air resistance, is very wrong, and probably not useful. The second model, which takes air resistance into account, is still wrong, but it's better, and it's good enough to refute the myth.\n",
+    "\n",
+    "The television show *Mythbusters* has tested the myth of the falling\n",
+    "penny more carefully; you can view the results at\n",
+    "<http://modsimpy.com/myth>. Their work is based on a mathematical model of motion, measurements to determine the force of air resistance on a penny, and a physical model of a human head."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "brief-zoning",
+   "metadata": {},
+   "source": [
+    "## Computation\n",
+    "\n",
+    "Let me show you how I computed the results from the\n",
+    "previous section using Python.\n",
+    "First we'll create a variable to represent acceleration due to gravity in meters per second squared (m/s^2)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "eleven-marine",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 9.8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "upset-myanmar",
+   "metadata": {},
+   "source": [
+    "A **variable** is a name that corresponds to a value. In this example, the name is `a` and the value is the number `9.8`.\n",
+    "\n",
+    "Suppose we let the penny drop for $3.4$ seconds (s).  I'll create a variable to represent this time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "following-launch",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = 3.4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "greek-heritage",
+   "metadata": {},
+   "source": [
+    "Now we can compute the velocity of the penny after `t` seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "mature-duration",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = a * t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "qualified-diabetes",
+   "metadata": {},
+   "source": [
+    "Python uses the symbol `*` for multiplication.  The other arithmetic operators are `+` for addition, `-` for subtraction, `/` for division, and `**` for exponentiation.\n",
+    "\n",
+    "When you assign a value to a variable, Jupyter does not show the result automatically, but you can display the value of a variable like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "considered-inclusion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "northern-saturday",
+   "metadata": {},
+   "source": [
+    "After $3.4$ s, the velocity of the penny is about $33$ m/s (ignoring air resistance).  Now let's see how far it would travel during that time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "valued-electricity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = a * t**2 / 2\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "yellow-business",
+   "metadata": {},
+   "source": [
+    "It would travel about $56$ m.  Now, going in the other direction, let's compute the time it takes to fall 381 m, the height of the Empire State Building."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "closed-month",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = 381"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fuzzy-lease",
+   "metadata": {},
+   "source": [
+    "For this computation, we need the square root function, which is provided by a library called NumPy.\n",
+    "Before we can use it, we have to import it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "nuclear-clone",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import sqrt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unlimited-swiss",
+   "metadata": {},
+   "source": [
+    "Now we can use it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "quarterly-nightmare",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = sqrt(2 * h / a)\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "velvet-oklahoma",
+   "metadata": {},
+   "source": [
+    "With no air resistance, it would take about $8.8$ s for the penny to reach the sidewalk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "quality-external",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = a * t\n",
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "human-phase",
+   "metadata": {},
+   "source": [
+    "And its velocity on impact would be about $86$ m/s.\n",
+    "\n",
+    "Python displays results with about 16 digits, which gives the impression that they are very precise, but don't be fooled.\n",
+    "The numbers we get out are only as good as the numbers we put in.\n",
+    "\n",
+    "For example, the \"roof height\" of the Empire State Building is $380$ m [according to Wikipedia](https://en.wikipedia.org/wiki/Empire_State_Building).\n",
+    "I chose $h=381$ m for this example on the assumption that the observation deck is on the roof and you drop the penny from a 1 meter railing.\n",
+    "But that's probably not right, so we should treat this value as an approximation where only the first two digits are likely to be right.\n",
+    "\n",
+    "If the precision of the inputs is two digits, the precision of the outputs is two digits, *at most*.\n",
+    "That's why, if the output is `86.41527642726142`, I report it as \"about 86\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "consecutive-sleeve",
+   "metadata": {},
+   "source": [
+    "## Computation with units\n",
+    "\n",
+    "The computation in the previous section uses numbers without units.  \n",
+    "When we set `h=381`, we left out the meters, and when we set `a=9.8`, we left out the meters per second squared.\n",
+    "And, when we got the result `v=86`, we added back the meters per second.\n",
+    "\n",
+    "Leaving units of out computation is common practice, but it tends to cause errors, including the very expensive failure of the [Mars Climate Orbiter](https://en.wikipedia.org/wiki/Mars_Climate_Orbiter) in 1999.\n",
+    "When possible, it is better to include units in the computation.\n",
+    "\n",
+    "To represent units, we'll use a Python library called [Pint](https://pint.readthedocs.io/en/latest/).\n",
+    "The ModSim library initializes Pint and provides an object called `units` that contains variables representing pretty much every unit you've ever heard of.\n",
+    "\n",
+    "We can import it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "based-belief",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unlike-opera",
+   "metadata": {},
+   "source": [
+    "Now we can create variables named `meter` and `second`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "russian-popularity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meter = units.meter\n",
+    "meter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "endless-paint",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "second = units.second\n",
+    "second"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "spiritual-scenario",
+   "metadata": {},
+   "source": [
+    "To find out what other units are defined, type `units.` (including the period) in the next cell and then press TAB.  You should see a pop-up menu with a list of units."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "everyday-location",
+   "metadata": {},
+   "source": [
+    "We can use `meter` and `second` to create a variable named `a` and give it the value of acceleration due to gravity. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "earlier-bandwidth",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 9.8 * meter / second**2\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "generic-bowling",
+   "metadata": {},
+   "source": [
+    "The result is a **quantity** with two parts, called `magnitude` and `units`, which we can access like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "missing-privilege",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a.magnitude"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "fourth-swedish",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a.units"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "realistic-techno",
+   "metadata": {},
+   "source": [
+    "Now we can create a quantity that represents $3.4$ s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "apart-france",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = 3.4 * second\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "severe-share",
+   "metadata": {},
+   "source": [
+    "And use it to compute the distance a penny would fall after `t` seconds with constant acceleration `a`.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "alien-scout",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a * t**2 / 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "wicked-indianapolis",
+   "metadata": {},
+   "source": [
+    "Notice that the units of the result are correct.\n",
+    "If we create a quantity to represent the height of the Empire State Building:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "confidential-costs",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = 381 * meter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hollow-programmer",
+   "metadata": {},
+   "source": [
+    "We can use it to compute the time to reach the sidewalk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "studied-opera",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = sqrt(2 * h / a)\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "seasonal-laser",
+   "metadata": {},
+   "source": [
+    "And the velocity of the penny on impact:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "exterior-greek",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = a * t\n",
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "superior-biography",
+   "metadata": {},
+   "source": [
+    "As in the previous section, the result is about $86$, but now it has the correct units, m/s.\n",
+    "\n",
+    "With Pint quantities, we can convert from one set of units to another like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "antique-landing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mile = units.mile\n",
+    "hour = units.hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "included-failure",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v.to(mile/hour)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "progressive-charleston",
+   "metadata": {},
+   "source": [
+    "If you are more familiar with miles per hour, this result might be easier to interpret.\n",
+    "And it might give you a sense that this model is not realistic."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "continuing-democrat",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This chapter introduces a modeling framework that consists of three steps:\n",
+    "\n",
+    "* Abstraction is the process of defining a model, deciding which elements of the real world to include and which can be left out.\n",
+    "\n",
+    "* Analysis and simulation are ways to use a model to generate predictions, explain why things behave as they to, and design things that behave as we want.\n",
+    "\n",
+    "* Validation is how we test whether the model is right, often by comparing predictions with measurements from the real world.\n",
+    "\n",
+    "As a first example, we modeled a penny dropped from the Empire State building, including gravity but ignoring air resistance.\n",
+    "In the exercises, you'll have a chance to try a better model, including air resistance.\n",
+    "\n",
+    "This chapter also presents Pint, a library for doing computation with units, which is convenient for converting between different units and important for avoiding catastrophic errors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "thousand-equation",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "handmade-zoning",
+   "metadata": {},
+   "source": [
+    "**Exercise** In mathematical notation, we can write an equation like $v = a t$ and it's understood that we are multiplying $a$ and $t$.\n",
+    "But that doesn't work in Python.  If you put two variables side-by-side, like this:\n",
+    "\n",
+    "```\n",
+    "v = a t\n",
+    "```\n",
+    "\n",
+    "you'll get a **syntax error**, which means that something is wrong with the punctuation of the program.\n",
+    "\n",
+    "Try it out in the next cell so you see what the error message looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "postal-marking",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 9.8 * meter / second**2\n",
+    "t = 3.4 * second\n",
+    "\n",
+    "v = a * t\n",
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pressing-sugar",
+   "metadata": {},
+   "source": [
+    "**Exercise:** In this chapter we used the `sqrt` function from the NumPy library.  NumPy also provides a variable named `pi` that contains an approximation of the mathematical constant $\\pi$.\n",
+    "We can import it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "legal-observer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import pi\n",
+    "pi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aggregate-mambo",
+   "metadata": {},
+   "source": [
+    "NumPy provides other functions we'll use, including `log`, `exp`, `sin`, and `cos`.\n",
+    "Import `sin` and `cos` from NumPy and compute\n",
+    "\n",
+    "$$sin^2 \\pi/4 + cos^2 \\pi/4$$\n",
+    "\n",
+    "Note: A mathematical identity tells us that the answer should be $1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "pressing-belgium",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unlimited-delivery",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose you bring a 10 foot pole to the top of the Empire State Building and use it to drop the penny from `h` plus 10 feet.\n",
+    "\n",
+    "Define a variable named `foot` that contains the unit `foot` provided by `UNITS`.  Define a variable named `pole_height` and give it the value 10 feet.\n",
+    "\n",
+    "What happens if you add `h`, which is in units of meters, to `pole_height`, which is in units of feet?  What happens if you write the addition the other way around?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "inner-equivalent",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = 381 * meter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "nuclear-thirty",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "available-steering",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aggressive-climate",
+   "metadata": {},
+   "source": [
+    "**Exercise**: Why would it be nonsensical to add `a` and `t`? What happens if you try?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "documentary-doctrine",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 9.8 * meter / second**2\n",
+    "t = 3.4 * second"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "primary-partner",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "steady-chancellor",
+   "metadata": {},
+   "source": [
+    "In this example, you should get a `DimensionalityError`, which is defined by Pint to indicate that you have violated a rules of dimensional analysis: you cannot add quantities with different dimensions.\n",
+    "\n",
+    "The error messages you get from Python are big and scary, but if you read them carefully, they contain a lot of useful information.\n",
+    "\n",
+    "1.  Start from the bottom and read up.\n",
+    "2.  The last line usually tells you what type of error happened, and sometimes additional information.\n",
+    "3.  The previous lines are a \"traceback\" of what was happening when the error occurred.  The first section of the traceback shows the code you wrote.  The following sections are often from Python libraries.\n",
+    "\n",
+    "Before you go on, you might want to delete the erroneous code so the notebook can run without errors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "valid-empire",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose instead of dropping the penny, you throw it downward at its terminal velocity, $29$ m/s.  How long would it take to fall $381$ m?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "greenhouse-reason",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "asian-murray",
+   "metadata": {},
+   "source": [
+    "**Exercise:** So far we have considered two models of a falling penny:\n",
+    "\n",
+    "* If we ignore air resistance, the penny falls with constant acceleration, and we can compute the time to reach the sidewalk and the velocity of the penny when it gets there.\n",
+    "\n",
+    "* If we take air resistance into account, and drop the penny at its terminal velocity, it falls with constant velocity. \n",
+    "\n",
+    "Now let's consider a third model that includes elements of the first two: let's assume that the acceleration of the penny is `a` until the penny reaches $29$ m/s, and then $0$ m/s afterwards.  What is the total time for the penny to fall $381$ m?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "detailed-minnesota",
+   "metadata": {},
+   "source": [
+    "You can break this question into three parts:\n",
+    "\n",
+    "1. How long would the penny take to reach $29$ m/s with constant acceleration `a`.\n",
+    "2. How far would it fall during that time?\n",
+    "3. How long would it take to fall the remaining distance with constant velocity $29$ m/s?\n",
+    "\n",
+    "Suggestion: Assign each intermediate result to a variable with a meaningful name.  And assign units to all quantities!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "secure-crowd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 9.8 * meter / second**2\n",
+    "h = 381 * meter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "thirty-minneapolis",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "premier-seeking",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "brave-laundry",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "adjusted-consultation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "understanding-consortium",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "composed-tunnel",
+   "metadata": {},
+   "source": [
+    "**Exercise:** When I was in high school, the pitcher on the baseball team claimed that, when he threw a fastball, he was throwing the ball down; that is, the ball was leaving his hand at a downward angle.\n",
+    "I was skeptical; watching from the side, I thought the ball was leaving his hand at an upward angle.\n",
+    "\n",
+    "Can you think of a simple model you could use to settle the argument?  What factors would you include and what could you ignore?  What quantities would you have to look up or estimate?\n",
+    "\n",
+    "I suggest you convert all quantities to [SI units](https://en.wikipedia.org/wiki/International_System_of_Units): meters and seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "about-complex",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "unique-owner",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "minus-batman",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "exact-vegetable",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "neutral-lightning",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "jewish-secret",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose I run a 10K race in 44:52.  What is my average page in minutes per mile?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "virgin-cambodia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mile = units.mile\n",
+    "kilometer = units.kilometer\n",
+    "minute = units.minute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "right-intention",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "mineral-sally",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "preceding-cricket",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "effective-rendering",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "printable-reply",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "environmental-wallet",
+   "metadata": {},
+   "source": [
+    "## More Jupyter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "quality-probe",
+   "metadata": {},
+   "source": [
+    "You can add and remove cells from a notebook using the buttons in the toolbar and the items in the menu, both of which you should see at the top of this notebook.\n",
+    "\n",
+    "Try the following exercises:\n",
+    "\n",
+    "1.  From the Insert menu select \"Insert cell below\" to add a cell below this one.  By default, you get a code cell, as you can see in the pulldown menu that says \"Code\".\n",
+    "\n",
+    "2.  In the new cell, add a print statement like `print('Hello')`, and run it.\n",
+    "\n",
+    "3.  Add another cell, select the new cell, and then click on the pulldown menu that says \"Code\" and select \"Markdown\".  This makes the new cell a text cell.\n",
+    "\n",
+    "4.  In the new cell, type some text, and then run it.\n",
+    "\n",
+    "5.  Use the arrow buttons in the toolbar to move cells up and down.\n",
+    "\n",
+    "6.  Use the cut, copy, and paste buttons to delete, add, and move cells.\n",
+    "\n",
+    "7.  As you make changes, Jupyter saves your notebook automatically, but if you want to make sure, you can press the save button, which looks like a floppy disk from the 1990s.\n",
+    "\n",
+    "8.  Finally, when you are done with a notebook, select \"Close and Halt\" from the File menu."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "successful-kentucky",
+   "metadata": {},
+   "source": [
+    "When you change the contents of a cell, you have to run it again for those changes to have an effect.  If you forget to do that, the results can be confusing, because the code you are looking at is not the code you ran.\n",
+    "\n",
+    "If you ever lose track of which cells have run, and in what order, you should go to the Kernel menu and select \"Restart & Run All\".  Restarting the kernel means that all of your variables get deleted, and running all the cells means all of your code will run again, in the right order."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "suffering-intro",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Select \"Restart & Run All\" now and confirm that it does what you want."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "weighted-quebec",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap02.ipynb b/chapters/chap02.ipynb
new file mode 100644
index 00000000..f9bdf584
--- /dev/null
+++ b/chapters/chap02.ipynb
@@ -0,0 +1,1204 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "victorian-latitude",
+   "metadata": {},
+   "source": [
+    "# Chapter 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cathedral-glasgow",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "electronic-radius",
+   "metadata": {},
+   "source": [
+    "This chapter presents a simple model of a bike share system and\n",
+    "demonstrates the features of Python we'll use to develop simulations of real-world systems.\n",
+    "\n",
+    "Along the way, we'll make decisions about how to model the system. In\n",
+    "the next chapter we'll review these decisions and gradually improve the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "above-denial",
+   "metadata": {},
+   "source": [
+    "## Modeling a Bike Share System\n",
+    "\n",
+    "Imagine a bike share system for students traveling between Olin College and Wellesley College, which are about 3 miles apart in eastern Massachusetts.\n",
+    "\n",
+    "Suppose the system contains 12 bikes and two bike racks, one at Olin and one at Wellesley, each with the capacity to hold 12 bikes.\n",
+    "\n",
+    "As students arrive, check out a bike, and ride to the other campus, the number of bikes in each location changes. In the simulation, we'll need to keep track of where the bikes are. To do that, we'll use a function called `State`, which is defined in the ModSim library."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "incorrect-comparison",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=10, wellesley=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "living-wayne",
+   "metadata": {},
+   "source": [
+    "The expressions in parentheses are **keyword arguments**.\n",
+    "They create two variables, `olin` and `wellesley`, and give them values.\n",
+    "\n",
+    "Then we call the `State` function.\n",
+    "The result is a `State` object, which is a collection of **state variables**.\n",
+    "\n",
+    "In this example, the state variables represent the number of\n",
+    "bikes at each location. The initial values are 10 and 2, indicating that there are 10 bikes at Olin and 2 at Wellesley. \n",
+    "\n",
+    "The `State` object is assigned to a new variable named `bikeshare`.\n",
+    "We can read the variables inside a `State` object using the **dot\n",
+    "operator**, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "brief-diversity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare.olin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "intermediate-midwest",
+   "metadata": {},
+   "source": [
+    "And this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "designed-brazilian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare.wellesley"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "phantom-oklahoma",
+   "metadata": {},
+   "source": [
+    "Or, to display the state variables and their values, you can just type the name of the object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "impaired-potter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fleet-beijing",
+   "metadata": {},
+   "source": [
+    "These values make up the **state** of the system.\n",
+    "\n",
+    "The ModSim library provides a function called `show` that displays a `State` object as a table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "basic-fabric",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "show(bikeshare)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "delayed-ocean",
+   "metadata": {},
+   "source": [
+    "You don't have to use `show`, but I think it looks better.\n",
+    "\n",
+    "We can update the state by assigning new values to the variables. \n",
+    "For example, if a student moves a bike from Olin to Wellesley, we can figure out the new values and assign them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "floppy-trainer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare.olin = 9\n",
+    "bikeshare.wellesley = 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "natural-gossip",
+   "metadata": {},
+   "source": [
+    "Or we can use **update operators**, `-=` and `+=`, to subtract 1 from\n",
+    "`olin` and add 1 to `wellesley`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "hungarian-bride",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare.olin -= 1\n",
+    "bikeshare.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "radical-mills",
+   "metadata": {},
+   "source": [
+    "The result is the same either way."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "controversial-opportunity",
+   "metadata": {},
+   "source": [
+    "## Defining functions\n",
+    "\n",
+    "So far we have used functions defined in NumPy and ModSim. Now we're going to define our own function.\n",
+    "\n",
+    "When you are developing code in Jupyter, it is often efficient to write a few lines of code, test them to confirm they do what you intend, and then use them to define a new function. For example, these lines move a bike from Olin to Wellesley:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "vertical-drawing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare.olin -= 1\n",
+    "bikeshare.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "approximate-rolling",
+   "metadata": {},
+   "source": [
+    "Rather than repeat them every time a bike moves, we can define a new\n",
+    "function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "significant-nutrition",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley():\n",
+    "    bikeshare.olin -= 1\n",
+    "    bikeshare.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "generous-tracker",
+   "metadata": {},
+   "source": [
+    "`def` is a special word in Python that indicates we are defining a new\n",
+    "function. The name of the function is `bike_to_wellesley`. The empty\n",
+    "parentheses indicate that this function requires no additional\n",
+    "information when it runs. The colon indicates the beginning of an\n",
+    "indented **code block**.\n",
+    "\n",
+    "The next two lines are the **body** of the function. They have to be\n",
+    "indented; by convention, the indentation is 4 spaces.\n",
+    "\n",
+    "When you define a function, it has no immediate effect. The body of the\n",
+    "function doesn't run until you **call** the function. Here's how to call\n",
+    "this function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "moving-jurisdiction",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bike_to_wellesley()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "meaningful-christmas",
+   "metadata": {},
+   "source": [
+    "When you call the function, it runs the statements in the body, which\n",
+    "update the variables of the `bikeshare` object; you can check by\n",
+    "displaying the new state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "proper-symposium",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eleven-brook",
+   "metadata": {},
+   "source": [
+    "When you call a function, you have to include the parentheses. If you\n",
+    "leave them out, you get this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "identical-yacht",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bike_to_wellesley"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "premier-youth",
+   "metadata": {},
+   "source": [
+    "This result indicates that `bike_to_wellesley` is a function. You don't\n",
+    "have to know what `__main__` means, but if you see something like this,\n",
+    "it probably means that you looked up a function but you didn't actually\n",
+    "call it. So don't forget the parentheses."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "brazilian-medicare",
+   "metadata": {},
+   "source": [
+    "## Print statements\n",
+    "\n",
+    "As you write more complicated programs, it is easy to lose track of what\n",
+    "is going on. One of the most useful tools for debugging is the **print\n",
+    "statement**, which displays text in the Jupyter notebook.\n",
+    "\n",
+    "Normally when Jupyter runs the code in a cell, it displays the value of\n",
+    "the last line of code. For example, if you run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "heavy-patrol",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare.olin\n",
+    "bikeshare.wellesley"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ancient-projection",
+   "metadata": {},
+   "source": [
+    "Jupyter runs both lines, but it only displays the value of the\n",
+    "second. If you want to display more than one value, you can use\n",
+    "print statements:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "french-preference",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(bikeshare.olin)\n",
+    "print(bikeshare.wellesley)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "original-hollywood",
+   "metadata": {},
+   "source": [
+    "When you call the `print` function, you can put a variable name in\n",
+    "parentheses, as in the previous example, or you can provide a sequence\n",
+    "of variables separated by commas, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "alternative-keyboard",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(bikeshare.olin, bikeshare.wellesley)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "described-produce",
+   "metadata": {},
+   "source": [
+    "Python looks up the values of the variables and displays them; in this\n",
+    "example, it displays two values on the same line, with a space between\n",
+    "them.\n",
+    "\n",
+    "Print statements are useful for debugging functions. For example, we can\n",
+    "add a print statement to `move_bike`, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "robust-holly",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley():\n",
+    "    print('Moving a bike to Wellesley')\n",
+    "    bikeshare.olin -= 1\n",
+    "    bikeshare.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "vital-lender",
+   "metadata": {},
+   "source": [
+    "Each time we call this version of the function, it displays a message,\n",
+    "which can help us keep track of what the program is doing.\n",
+    "The message in this example is a **string**, which is a sequence of\n",
+    "letters and other symbols in quotes.\n",
+    "\n",
+    "Just like `bike_to_wellesley`, we can define a function that moves a\n",
+    "bike from Wellesley to Olin:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "fifteen-atmosphere",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_olin():\n",
+    "    print('Moving a bike to Olin')\n",
+    "    bikeshare.wellesley -= 1\n",
+    "    bikeshare.olin += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "requested-glasgow",
+   "metadata": {},
+   "source": [
+    "And call it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "matched-narrow",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bike_to_olin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sitting-semiconductor",
+   "metadata": {},
+   "source": [
+    "One benefit of defining functions is that you avoid repeating chunks of\n",
+    "code, which makes programs smaller. Another benefit is that the name you\n",
+    "give the function documents what it does, which makes programs more\n",
+    "readable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "enhanced-maintenance",
+   "metadata": {},
+   "source": [
+    "## If statements\n",
+    "\n",
+    "The ModSim library provides a function called `flip` that generates random \"coin tosses\".\n",
+    "When you call it, you provide a probability between 0 and 1, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "illegal-metropolitan",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flip(0.7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "appropriate-funds",
+   "metadata": {},
+   "source": [
+    "The result is one of two values: `True` with probability 0.7 (in this example) or `False`\n",
+    "with probability 0.3. If you run `flip` like this 100 times, you should\n",
+    "get `True` about 70 times and `False` about 30 times. But the results\n",
+    "are random, so they might differ from these expectations.\n",
+    "\n",
+    "`True` and `False` are special values defined by Python. Note that they\n",
+    "are not strings. There is a difference between `True`, which is a\n",
+    "special value, and `'True'`, which is a string.\n",
+    "\n",
+    "`True` and `False` are called **boolean** values because they are\n",
+    "related to Boolean algebra (<https://modsimpy.com/boolean>).\n",
+    "\n",
+    "We can use boolean values to control the behavior of the program, using\n",
+    "an **if statement**:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "excessive-murder",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if flip(0.5):\n",
+    "    print('heads')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "seventh-profile",
+   "metadata": {},
+   "source": [
+    "If the result from `flip` is `True`, the program displays the string\n",
+    "`'heads'`. Otherwise it does nothing.\n",
+    "\n",
+    "The syntax for `if` statements is similar to the syntax for\n",
+    "function definitions: the first line has to end with a colon, and the\n",
+    "lines inside the `if` statement have to be indented.\n",
+    "\n",
+    "Optionally, you can add an **else clause** to indicate what should\n",
+    "happen if the result is `False`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "fundamental-nursing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if flip(0.5):\n",
+    "    print('heads')\n",
+    "else:\n",
+    "    print('tails')    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "recovered-chemical",
+   "metadata": {},
+   "source": [
+    "Now we can use `flip` to simulate the arrival of students who want to\n",
+    "borrow a bike. Suppose students arrive at the Olin station every 2\n",
+    "minutes, on average. In that case, the chance of an arrival during any\n",
+    "one-minute period is 50%, and we can simulate it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "twenty-health",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if flip(0.5):\n",
+    "    bike_to_wellesley()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "difficult-construction",
+   "metadata": {},
+   "source": [
+    "If students arrive at the Wellesley station every 3 minutes, on average,\n",
+    "the chance of an arrival during any one-minute period is 33%, and we can\n",
+    "simulate it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "played-character",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if flip(0.33):\n",
+    "    bike_to_olin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "standard-party",
+   "metadata": {},
+   "source": [
+    "We can combine these snippets into a function that simulates a **time\n",
+    "step**, which is an interval of time, in this case one minute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "ecological-colon",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def step():\n",
+    "    if flip(0.5):\n",
+    "        bike_to_wellesley()\n",
+    "    \n",
+    "    if flip(0.33):\n",
+    "        bike_to_olin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "amateur-exposure",
+   "metadata": {},
+   "source": [
+    "Then we can simulate a time step like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "mediterranean-german",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sought-mobile",
+   "metadata": {},
+   "source": [
+    "Even though there are no values in parentheses, we have to include them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "organic-proportion",
+   "metadata": {},
+   "source": [
+    "## Parameters\n",
+    "\n",
+    "The previous version of `step` is fine if the arrival probabilities\n",
+    "never change, but in reality, these probabilities vary over time.\n",
+    "\n",
+    "So instead of putting the constant values 0.5 and 0.33 in `step` we can replace them with **parameters**. Parameters are variables whose values are set when a function is called.\n",
+    "\n",
+    "Here's a version of `step` that takes two parameters, `p1` and `p2`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "hollywood-shopping",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def step(p1, p2):\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley()\n",
+    "    \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "encouraging-arkansas",
+   "metadata": {},
+   "source": [
+    "The values of `p1` and `p2` are not set inside this function; instead,\n",
+    "they are provided when the function is called, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "buried-alert",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step(0.5, 0.33)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aggregate-dynamics",
+   "metadata": {},
+   "source": [
+    "The values you provide when you call the function are called\n",
+    "**arguments**. The arguments, `0.5` and `0.33` in this example, get\n",
+    "assigned to the parameters, `p1` and `p2`, in order. So running this\n",
+    "function has the same effect as:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "recognized-denmark",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p1 = 0.5\n",
+    "p2 = 0.33\n",
+    "\n",
+    "if flip(p1):\n",
+    "    bike_to_wellesley()\n",
+    "    \n",
+    "if flip(p2):\n",
+    "    bike_to_olin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "raised-museum",
+   "metadata": {},
+   "source": [
+    "The advantage of using parameters is that you can call the same function many times, providing different arguments each time.\n",
+    "\n",
+    "Adding parameters to a function is called **generalization**, because it makes the function more general, that is, less specialized."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "scenic-african",
+   "metadata": {},
+   "source": [
+    "## For loops\n",
+    "\n",
+    "At some point you will get sick of running cells over and over.\n",
+    "Fortunately, there is an easy way to repeat a chunk of code, the **for\n",
+    "loop**. Here's an example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "polish-river",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(3):\n",
+    "    print(i)\n",
+    "    bike_to_wellesley()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "compatible-conspiracy",
+   "metadata": {},
+   "source": [
+    "The syntax here should look familiar; the first line ends with a\n",
+    "colon, and the lines inside the `for` loop are indented. The other\n",
+    "elements of the loop are:\n",
+    "\n",
+    "-   The words `for` and `in` are special words we have to use in a for\n",
+    "    loop.\n",
+    "\n",
+    "-   `range` is a Python function we're using here to control the number of times the loop runs.\n",
+    "\n",
+    "-   `i` is a **loop variable** that gets created when the for loop runs.\n",
+    "\n",
+    "When this loop runs, it runs the statements inside the loop three times.  The first time, the value of `i` is `0`; the second time, it is `1`; the third time, it is `2`.\n",
+    "\n",
+    "Each time through the loop, it prints the value of `i` and moves one bike Olin to Wellesley."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "breeding-groove",
+   "metadata": {},
+   "source": [
+    "## TimeSeries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "flexible-projection",
+   "metadata": {},
+   "source": [
+    "When we run a simulation, we often want to save the results for later analysis. The ModSim library provides a `TimeSeries` object for this purpose. A `TimeSeries` contains a sequence of time stamps and a\n",
+    "corresponding sequence of quantities.\n",
+    "\n",
+    "In this example, the time stamps are integers representing minutes, and the quantities are the number of bikes at one location.\n",
+    "\n",
+    "Since we have moved a number of bikes around, let's start again with a new `State` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "every-consultation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=10, wellesley=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cross-sharp",
+   "metadata": {},
+   "source": [
+    "We can create a new, empty `TimeSeries` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "changing-planet",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = TimeSeries()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "attractive-revision",
+   "metadata": {},
+   "source": [
+    "And we can add a quantity like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "aquatic-richardson",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results[0] = bikeshare.olin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "searching-funeral",
+   "metadata": {},
+   "source": [
+    "The number in brackets is the time stamp, also called a **label**.\n",
+    "\n",
+    "We can use a `TimeSeries` inside a for loop to store the results of the simulation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "english-titanium",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(3):\n",
+    "    print(i)\n",
+    "    step(0.6, 0.6)\n",
+    "    results[i+1] = bikeshare.olin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prospective-joining",
+   "metadata": {},
+   "source": [
+    "Each time through the loop, we print the value of `i` and call `step`, which updates `bikeshare`.\n",
+    "Then we store the number of bikes at Olin in `results`. \n",
+    "We use the loop variable, `i`, to compute the time stamp, `i+1`.\n",
+    "\n",
+    "The first time through the loop, the value of `i` is `0`, so the time stamp is `1`.\n",
+    "The last time, the value of `i` is `2`, so the time stamp is `3`.\n",
+    "\n",
+    "When the loop exits, `results` contains 4 time stamps, from 0 through\n",
+    "3, and the number of bikes at Olin at the end of each time step.\n",
+    "\n",
+    "We can display the `TimeSeries` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "indonesian-singing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "small-encoding",
+   "metadata": {},
+   "source": [
+    "The left column is the time stamps; the right column is the quantities (which might be negative, depending on the state of the system).\n",
+    "\n",
+    "At the bottom, `dtype` is the type of the data in the `TimeSeries`; you can ignore this for now.\n",
+    "\n",
+    "The `show` function displays a `TimeSeries` as a table:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "verbal-bikini",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "show(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "following-contrary",
+   "metadata": {},
+   "source": [
+    "## Plotting\n",
+    "\n",
+    "`results` provides a function called `plot` we can use to plot\n",
+    "the results, and the ModSim library provides `decorate`, which we can use to label the axes and give the figure a title:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "saved-hands",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.plot()\n",
+    "\n",
+    "decorate(title='Olin-Wellesley Bikeshare',\n",
+    "         xlabel='Time step (min)', \n",
+    "         ylabel='Number of bikes')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "limited-interstate",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This chapter introduces the tools we need to run simulations, record the results, and plot them.\n",
+    "\n",
+    "We used a `State` object to represent the state of the system.\n",
+    "Then we used the `flip` function and an `if` statement to simulate a single time step.\n",
+    "We used `for` loop to simulate a series of steps, and a `TimeSeries` to record the results.\n",
+    "Finally, we used `plot` and `decorate` to plot the results.\n",
+    "\n",
+    "In the next chapter, we will extend this simulation to make it a little more realistic."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fallen-surprise",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "capital-internship",
+   "metadata": {},
+   "source": [
+    "**Exercise:** What happens if you spell the name of a state variable wrong?  Edit the following cell, change the spelling of `wellesley`, and run it.\n",
+    "\n",
+    "The error message uses the word \"attribute\", which is another name for what we are calling a state variable. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "helpful-zambia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=10, wellesley=2)\n",
+    "\n",
+    "bikeshare.wellesley"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dirty-multiple",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Make a `State` object with a third state variable, called `babson`, with initial value 0, and display the state of the system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "beneficial-mainland",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "christian-madrid",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Wrap the code in the chapter in a function named `run_simulation` that takes three parameters, named `p1`, `p2`, and `num_steps`.\n",
+    "\n",
+    "It should:\n",
+    "\n",
+    "1. Create a `TimeSeries` object to hold the results.\n",
+    "\n",
+    "2. Use a for loop to run `step` the number of times specified by `num_steps`, passing along the specified values of `p1` and `p2`.\n",
+    "\n",
+    "3. After each step, it should save the number of bikes at Olin in the `TimeSeries`.\n",
+    "\n",
+    "4. After the for loop, it should plot the results and\n",
+    "\n",
+    "5. Decorate the axes.\n",
+    "\n",
+    "To test your function:\n",
+    "\n",
+    "1. Create a `State` object with the initial state of the system.\n",
+    "\n",
+    "2. Call `run_simulation` with appropriate parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "former-frost",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "spare-honduras",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "instructional-finnish",
+   "metadata": {},
+   "source": [
+    "## Under the Hood\n",
+    "\n",
+    "This section contains additional information about the functions we've used and pointers to their documentation.\n",
+    "\n",
+    "You don't need to know anything in these sections, so if you are already feeling overwhelmed, you might want to skip them.  But if you are curious, read on."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "quick-citizen",
+   "metadata": {},
+   "source": [
+    "`State` and `TimeSeries` objects are based on the `Series` object defined by a the Pandas library.\n",
+    "The documentation is at <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html>.\n",
+    "\n",
+    "`show` works by creating another Pandas object, called a `DataFrame`, which can be displayed as a table.\n",
+    "We'll use `DataFrame` objects in future chapters.\n",
+    "\n",
+    "`Series` objects provide their own `plot` function which is why we call it like this:\n",
+    "\n",
+    "```\n",
+    "results.plot()\n",
+    "```\n",
+    "\n",
+    "Instead of like this:\n",
+    "\n",
+    "```\n",
+    "plot(results)\n",
+    "```\n",
+    "\n",
+    "You can read the documentation of `Series.plot` at <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.html>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "digital-stretch",
+   "metadata": {},
+   "source": [
+    "`decorate` is based on Matplotlib, which is a widely-used plotting library for Python.  Matplotlib provides separate functions for `title`, `xlabel`, and `ylabel`.\n",
+    "`decorate` makes them a little easier to use.\n",
+    "For the list of keyword arguments you can pass to `decorate`, see <https://matplotlib.org/3.2.2/api/axes_api.html?highlight=axes#module-matplotlib.axes>.\n",
+    "\n",
+    "The `flip` function uses NumPy's `random` function to generate a random number between 0 and 1, then returns `True` or `False` with the given probability.\n",
+    "\n",
+    "You can get the source code for `flip` (or any other function) by running the following cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "agricultural-midwest",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_code(flip)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "running-membership",
+   "metadata": {},
+   "source": [
+    "You might not understand everything in this function yet, but you will."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "junior-lindsay",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap03.ipynb b/chapters/chap03.ipynb
new file mode 100644
index 00000000..f905d62f
--- /dev/null
+++ b/chapters/chap03.ipynb
@@ -0,0 +1,834 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "signal-format",
+   "metadata": {},
+   "source": [
+    "# Chapter 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "welcome-gentleman",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "coral-steering",
+   "metadata": {},
+   "source": [
+    "To paraphrase two Georges, \"All models are wrong, but some models are\n",
+    "more wrong than others.\" In this chapter, I demonstrate the process we\n",
+    "use to make models less wrong.\n",
+    "\n",
+    "As an example, we'll review the bikeshare model from the previous\n",
+    "chapter, consider its strengths and weaknesses, and gradually improve\n",
+    "it. We'll also see ways to use the model to understand the behavior of\n",
+    "the system and evaluate designs intended to make it work better."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "twelve-length",
+   "metadata": {},
+   "source": [
+    "## Iterative modeling\n",
+    "\n",
+    "The model we have so far is simple, but it is based on unrealistic\n",
+    "assumptions. Before you go on, take a minute to review the model from\n",
+    "the previous chapter. What assumptions is it based on? Make a list of\n",
+    "ways this model might be unrealistic; that is, what are the differences between the model and the real world?\n",
+    "\n",
+    "Here are some of the differences on my list:\n",
+    "\n",
+    "-   In the model, a student is equally likely to arrive during any\n",
+    "    one-minute period. In reality, this probability varies depending on time of day, day of the week, etc.\n",
+    "\n",
+    "-   The model does not account for travel time from one bike station to another.\n",
+    "\n",
+    "-   The model does not check whether a bike is available, so it's\n",
+    "    possible for the number of bikes to be negative (as you might have\n",
+    "    noticed in some of your simulations)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sweet-heater",
+   "metadata": {},
+   "source": [
+    "Some of these modeling decisions are better than others. For example,\n",
+    "the first assumption might be reasonable if we simulate the system for a short period of time, like one hour.\n",
+    "\n",
+    "The second assumption is not very realistic, but it might not affect the results very much, depending on what we use the model for.\n",
+    "\n",
+    "On the other hand, the third assumption seems problematic, and it is\n",
+    "relatively easy to fix. In this chapter, we will.\n",
+    "\n",
+    "This process, starting with a simple model, identifying the most\n",
+    "important problems, and making gradual improvements, is called\n",
+    "**iterative modeling**.\n",
+    "\n",
+    "For any physical system, there are many possible models, based on\n",
+    "different assumptions and simplifications. It often takes several\n",
+    "iterations to develop a model that is good enough for the intended\n",
+    "purpose, but no more complicated than necessary."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "female-salem",
+   "metadata": {},
+   "source": [
+    "## More than one State object\n",
+    "\n",
+    "Before we go on, I want to make a few changes to the code from the\n",
+    "previous chapter. First I'll generalize the functions we wrote so they\n",
+    "take a `State` object as a parameter. Then, I'll make the code more\n",
+    "readable by adding documentation.\n",
+    "\n",
+    "Here is one of the functions from the previous chapter, `bike_to_wellesley`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "embedded-heavy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley():\n",
+    "    bikeshare.olin -= 1\n",
+    "    bikeshare.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "artificial-remains",
+   "metadata": {},
+   "source": [
+    "When this function is called, it modifies `bikeshare`. As long as there\n",
+    "is only one `State` object, that's fine, but what if there is more than\n",
+    "one bike share system in the world? Or what if we want to run more than\n",
+    "one simulation?\n",
+    "\n",
+    "This function would be more flexible if it took a `State` object as a\n",
+    "parameter. Here's what that looks like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "unusual-advancement",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley(state):\n",
+    "    state.olin -= 1\n",
+    "    state.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "serial-mortality",
+   "metadata": {},
+   "source": [
+    "The name of the parameter is `state` rather than `bikeshare` as a\n",
+    "reminder that the value of `state` could be any `State` object, not just\n",
+    "`bikeshare`.\n",
+    "\n",
+    "This version of `bike_to_wellesley` requires a `State` object as a\n",
+    "parameter, so we have to provide one when we call it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "packed-hungarian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "bikeshare = State(olin=10, wellesley=2)\n",
+    "bike_to_wellesley(bikeshare)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fewer-rhythm",
+   "metadata": {},
+   "source": [
+    "Again, the argument we provide gets assigned to the parameter, so this\n",
+    "function call has the same effect as:\n",
+    "\n",
+    "```\n",
+    "state = bikeshare \n",
+    "state.olin -= 1 \n",
+    "state.wellesley += 1\n",
+    "```\n",
+    "\n",
+    "Now we can create as many `State` objects as we want:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "right-assessment",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare1 = State(olin=10, wellesley=2)\n",
+    "bikeshare2 = State(olin=2, wellesley=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "occupied-navigator",
+   "metadata": {},
+   "source": [
+    "And update them independently:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "cleared-advocacy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bike_to_wellesley(bikeshare1)\n",
+    "bike_to_wellesley(bikeshare2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "brown-ferry",
+   "metadata": {},
+   "source": [
+    "Changes in `bikeshare1` do not affect `bikeshare2`, and vice versa. So\n",
+    "we can simulate different bike share systems, or run multiple\n",
+    "simulations of the same system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "magnetic-packing",
+   "metadata": {},
+   "source": [
+    "## Documentation\n",
+    "\n",
+    "Another problem with the code we have so far is that it contains no\n",
+    "**documentation**. Documentation is text we add to a program to help\n",
+    "other programmers read and understand it. It has no effect on the\n",
+    "program when it runs.\n",
+    "\n",
+    "There are two forms of documentation, **docstrings** and **comments**:\n",
+    "\n",
+    "* A docstring is a string in triple-quotes that appears at the beginning of a function.\n",
+    "\n",
+    "* A comment is a line of text that begins with a hash symbol, `#`.\n",
+    "\n",
+    "Here's a version of `bike_to_olin` with a docstring and a comment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "moral-parallel",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_olin(state):\n",
+    "    \"\"\"Move one bike from Wellesley to Olin.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    # We decrease one state variable and increase the\n",
+    "    # other, so the total number of bikes is unchanged.\n",
+    "    state.wellesley -= 1\n",
+    "    state.olin += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "therapeutic-utility",
+   "metadata": {},
+   "source": [
+    "Docstrings follow a conventional format:\n",
+    "\n",
+    "-   The first line is a single sentence that describes what the function does.\n",
+    "\n",
+    "-   The following lines explain what the parameters are.\n",
+    "\n",
+    "A function's docstring should include the information someone needs to\n",
+    "know to *use* the function; it should not include details about how the function works.\n",
+    "\n",
+    "Comments provide details about how the function works, especially if there is something that would not be obvious to someone reading the program."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "american-clear",
+   "metadata": {},
+   "source": [
+    "## Negative bikes\n",
+    "\n",
+    "The changes we've made so far improve the quality of the code, but we\n",
+    "haven't done anything to improve the quality of the model yet. Let's do that now.\n",
+    "\n",
+    "Currently the simulation does not check whether a bike is available when a customer arrives, so the number of bikes at a location can be\n",
+    "negative. That's not very realistic. Here's a version of\n",
+    "`bike_to_olin` that fixes the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "divine-leisure",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_olin(state):\n",
+    "    if state.wellesley == 0:\n",
+    "        return\n",
+    "    state.wellesley -= 1\n",
+    "    state.olin += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "decimal-denver",
+   "metadata": {},
+   "source": [
+    "The first line checks whether the number of bikes at Wellesley is zero. If so, it uses a **return statement**, which causes the function to end immediately, without running the rest of the statements. So if there are no bikes at Wellesley, we \"return\" from `bike_to_olin` without changing the state.\n",
+    "\n",
+    "We can test it by initializing the state with no bikes at Wellesley And calling `bike_to_olin`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "choice-cooking",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=12, wellesley=0)\n",
+    "bike_to_olin(bikeshare)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "persistent-denmark",
+   "metadata": {},
+   "source": [
+    "The state of the system should be unchanged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "twelve-moderator",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "criminal-general",
+   "metadata": {},
+   "source": [
+    "No more negative bikes (at least at Wellesley)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "gorgeous-found",
+   "metadata": {},
+   "source": [
+    "## Comparison operators\n",
+    "\n",
+    "The updated version of `bike_to_olin` uses the equals operator, `==`, which compares two values and returns `True` if they are equal and `False` otherwise.\n",
+    "\n",
+    "It is easy to confuse the equals operators with the assignment operator, `=`, which assigns a value to a variable. For example, the following statement creates a variable, `x`, if it doesn't already exist, and gives it the value `5`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "level-burns",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "weighted-monster",
+   "metadata": {},
+   "source": [
+    "On the other hand, the following statement checks whether `x` is `5` and\n",
+    "returns `True` or `False`. It does not create `x` or change its value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "civic-remains",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x == 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "forward-perth",
+   "metadata": {},
+   "source": [
+    "You can use the equals operator in an `if` statement, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "affecting-naples",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if x == 5:\n",
+    "    print('yes, x is 5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "consolidated-anatomy",
+   "metadata": {},
+   "source": [
+    "If you make a mistake and use `=` in an `if` statement, like this:\n",
+    "\n",
+    "```\n",
+    "if x = 5:\n",
+    "    print('yes, x is 5')\n",
+    "```\n",
+    "\n",
+    "That's a **syntax error**, which means that the structure of the program is invalid. Python will print an error message and the program won't run."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "twelve-defensive",
+   "metadata": {},
+   "source": [
+    "The equals operator is one of the **comparison operators**. The others\n",
+    "are:\n",
+    "\n",
+    "| Operation             \t| Symbol \t|\n",
+    "|-----------------------\t|--------\t|\n",
+    "| Less than             \t| `<`      \t|\n",
+    "| Greater than          \t| `>`      \t|\n",
+    "| Less than or equal    \t| `<=`     \t|\n",
+    "| Greater than or equal \t| `>=`     \t|\n",
+    "| Equal                 \t| `==`     \t|\n",
+    "| Not equal             \t| `!=`     \t|"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "center-sequence",
+   "metadata": {},
+   "source": [
+    "## Metrics\n",
+    "\n",
+    "Getting back to the bike share system, at this point we have the ability to simulate the behavior of the system. Since the arrival of customers is random, the state of the system is different each time we run a\n",
+    "simulation. Models like this are called random or **stochastic**; models\n",
+    "that do the same thing every time they run are **deterministic**.\n",
+    "\n",
+    "Suppose we want to use our model to predict how well the bike share\n",
+    "system will work, or to design a system that works better. First, we\n",
+    "have to decide what we mean by \"how well\" and \"better\".\n",
+    "\n",
+    "From the customer's point of view, we might like to know the probability of finding an available bike. From the system-owner's point of view, we\n",
+    "might want to minimize the number of customers who don't get a bike when\n",
+    "they want one, or maximize the number of bikes in use. Statistics like\n",
+    "these that quantify how well the system works are called **metrics**.\n",
+    "\n",
+    "As a simple example, let's measure the number of unhappy customers.\n",
+    "Here's a version of `bike_to_olin` that keeps track of the number of\n",
+    "customers who arrive at a station with no bikes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "arbitrary-ferry",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_olin(state):\n",
+    "    if state.wellesley == 0:\n",
+    "        state.wellesley_empty += 1\n",
+    "        return\n",
+    "    state.wellesley -= 1\n",
+    "    state.olin += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "severe-contact",
+   "metadata": {},
+   "source": [
+    "If a customer arrives at the Wellesley station and finds no bike\n",
+    "available, `bike_to_olin` updates `wellesley_empty` which counts the\n",
+    "number of unhappy customers.\n",
+    "\n",
+    "This function only works if we initialize `wellesley_empty` when we\n",
+    "create the `State` object, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "cardiovascular-montgomery",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = State(olin=12, wellesley=0, \n",
+    "                  wellesley_empty=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "computational-prior",
+   "metadata": {},
+   "source": [
+    "We can test it by calling `bike_to_olin`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "cosmetic-above",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "bike_to_olin(bikeshare)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pleased-gasoline",
+   "metadata": {},
+   "source": [
+    "There should be 12 bikes at Olin, no bikes at Wellesley, and one unhappy customer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "bulgarian-palestine",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "revised-associate",
+   "metadata": {},
+   "source": [
+    "Looks good!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "native-kidney",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this chapter, we wrote several versions of `bike_to_olin`:\n",
+    "\n",
+    "* We added a parameter, `state`, so we can work with more than one `State` object.\n",
+    "\n",
+    "* We added a docstring that explains how to use the function and a comment that explains how it works.\n",
+    "\n",
+    "* We used a conditional operator, `==`, to check whether a bike is available, in order to avoid negative bikes.\n",
+    "\n",
+    "* We added a state variable, `wellesley_empty`, to count the number of unhappy customers, which is a metric we'll use to quantify how well the system works.\n",
+    "\n",
+    "In the exercises, you'll update `bike_to_wellesley` the same way and test it by running a simulation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "impaired-cyprus",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "careful-hacker",
+   "metadata": {},
+   "source": [
+    "Here's the code we have so far, with docstrings, all in one place."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "wrong-internet",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def run_simulation(state, p1, p2, num_steps):\n",
+    "    \"\"\"Simulate the given number of time steps.\n",
+    "    \n",
+    "    state: State object\n",
+    "    p1: probability of an Olin->Wellesley customer arrival\n",
+    "    p2: probability of a Wellesley->Olin customer arrival\n",
+    "    num_steps: number of time steps\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[0] = state.olin\n",
+    "    \n",
+    "    for i in range(num_steps):\n",
+    "        step(state, p1, p2)\n",
+    "        results[i+1] = state.olin\n",
+    "        \n",
+    "    results.plot(label='Olin')\n",
+    "    decorate(title='Olin-Wellesley Bikeshare',\n",
+    "             xlabel='Time step (min)', \n",
+    "             ylabel='Number of bikes')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "instrumental-copyright",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def step(state, p1, p2):\n",
+    "    \"\"\"Simulate one time step.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    p1: probability of an Olin->Wellesley ride\n",
+    "    p2: probability of a Wellesley->Olin ride\n",
+    "    \"\"\"\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley(state)\n",
+    "    \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin(state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "improved-renaissance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_olin(state):\n",
+    "    \"\"\"Move one bike from Wellesley to Olin.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    if state.wellesley == 0:\n",
+    "        state.wellesley_empty += 1\n",
+    "        return\n",
+    "    state.wellesley -= 1\n",
+    "    state.olin += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "unavailable-maker",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley(state):\n",
+    "    \"\"\"Move one bike from Olin to Wellesley.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    state.olin -= 1\n",
+    "    state.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bigger-rapid",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Modify `bike_to_wellesley` so it checks whether a bike is available at Olin.  If not, it should add one to `olin_empty`.\n",
+    "\n",
+    "To test it, create a `State` that initializes `olin` and `olin_empty` to `0`, run `bike_to_wellesley`, and check the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "phantom-carter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "adopted-contrary",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "comparable-natural",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "attractive-amendment",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "possible-initial",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Now run the simulation a few times and confirm that the number of bikes is never negative.  What's the final state of the system?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "immune-shock",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "sporting-collectible",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap04.ipynb b/chapters/chap04.ipynb
new file mode 100644
index 00000000..03a177b0
--- /dev/null
+++ b/chapters/chap04.ipynb
@@ -0,0 +1,853 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "existing-guidance",
+   "metadata": {},
+   "source": [
+    "# Chapter 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hawaiian-detective",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "caring-gnome",
+   "metadata": {},
+   "source": [
+    "Here the code from previous chapters we'll reuse."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "stylish-raising",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def step(state, p1, p2):\n",
+    "    \"\"\"Simulate one time step.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    p1: probability of an Olin->Wellesley ride\n",
+    "    p2: probability of a Wellesley->Olin ride\n",
+    "    \"\"\"\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley(state)\n",
+    "    \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin(state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "wound-bottle",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_olin(state):\n",
+    "    \"\"\"Move one bike from Wellesley to Olin.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    if state.wellesley == 0:\n",
+    "        state.wellesley_empty += 1\n",
+    "        return\n",
+    "    state.wellesley -= 1\n",
+    "    state.olin += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "prescribed-affect",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley(state):\n",
+    "    \"\"\"Move one bike from Olin to Wellesley.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    \"\"\"\n",
+    "    if state.olin == 0:\n",
+    "        state.olin_empty += 1\n",
+    "        return\n",
+    "    state.olin -= 1\n",
+    "    state.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "atlantic-collectible",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we defined metrics that quantify the performance of bike sharing this system. In this chapter we see how those metrics depend on the parameters of the system, like the Customer rate of customers at bike stations.\n",
+    "\n",
+    "We also discuss a program development strategy, called incremental\n",
+    "development, that might help you write programs faster and spend less\n",
+    "time debugging."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "strategic-newspaper",
+   "metadata": {},
+   "source": [
+    "## Functions that return values\n",
+    "\n",
+    "We have seen several functions that return values; for example, when you run `sqrt`, it returns a number you can assign to a variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "imposed-pregnancy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import sqrt\n",
+    "\n",
+    "root_2 = sqrt(2)\n",
+    "root_2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unsigned-recipe",
+   "metadata": {},
+   "source": [
+    "When you run `State`, it returns a new `State` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "accessible-wallace",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "bikeshare = State(olin=10, wellesley=2)\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "missing-pendant",
+   "metadata": {},
+   "source": [
+    "Not all functions have return values. For example, when you run `step`,\n",
+    "it updates a `State` object, but it doesn't return a value.\n",
+    "\n",
+    "To write functions that return values, we can use a `return` statement, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "minimal-supervisor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_five(x):\n",
+    "    return x + 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sized-intensity",
+   "metadata": {},
+   "source": [
+    "`add_five` takes a parameter, `x`, which could be any number. It\n",
+    "computes `x + 5` and returns the result. So if we run it like this, the\n",
+    "result is `8`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "warming-program",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "add_five(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "rental-representation",
+   "metadata": {},
+   "source": [
+    "As a more useful example, here's a version of `run_simulation` that\n",
+    "creates a `State` object, runs a simulation, and then returns the\n",
+    "`State` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "sitting-cleveland",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def run_simulation(p1, p2, num_steps):\n",
+    "    state = State(olin=10, wellesley=2,\n",
+    "                  olin_empty=0, wellesley_empty=0)\n",
+    "    \n",
+    "    for i in range(num_steps):\n",
+    "        step(state, p1, p2)\n",
+    "        \n",
+    "    return state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "minimal-ability",
+   "metadata": {},
+   "source": [
+    "We can call `run_simulation` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "difficult-shepherd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_state = run_simulation(0.3, 0.2, 60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "charming-wheel",
+   "metadata": {},
+   "source": [
+    "The result is a `State` object that represents the final state of the system, including the metrics we'll use to evaluate the performance of the system:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "tough-sweet",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(final_state.olin_empty, \n",
+    "      final_state.wellesley_empty)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aggregate-lightweight",
+   "metadata": {},
+   "source": [
+    "The simulation we just ran starts with `olin=10` and `wellesley=2`, and uses the values `p1=0.3`, `p2=0.2`, and `num_steps=60`. \n",
+    "These five values are **parameters of the model**, which are quantities that determine the behavior of the system.\n",
+    "\n",
+    "It is easy to get the parameters of a model confused with the parameters of a function. \n",
+    "In fact, it is common for the parameters of the model to appear as parameters in functions. \n",
+    "\n",
+    "For example, the previous version of `run_simulation` takes `p1`, `p2`, and `num_steps` as parameters.\n",
+    "So we can call `run_simulation` with different parameters and see how\n",
+    "the metrics, like the number of unhappy customers, depend on the\n",
+    "parameters. But before we do that, we need a new version of a `for` loop."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "valuable-aircraft",
+   "metadata": {},
+   "source": [
+    "## Loops and arrays\n",
+    "\n",
+    "In `run_simulation`, we use this `for` loop:\n",
+    "\n",
+    "```\n",
+    "    for i in range(num_steps):\n",
+    "        step(state, p1, p2)\n",
+    "```\n",
+    "\n",
+    "In this example, `range` creates a sequence of numbers from 0 to `num_steps` (including `0` but not `num_steps`). \n",
+    "Each time through the loop, the next number in the sequence gets assigned to the loop variable, `i`.\n",
+    "\n",
+    "But `range` only works with integers; to get a sequence of non-integer\n",
+    "values, we can use `linspace`, which is defined NumPy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bound-juice",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import linspace\n",
+    "\n",
+    "p1_array = linspace(0, 1, 5)\n",
+    "p1_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ordered-colleague",
+   "metadata": {},
+   "source": [
+    "The arguments indicate where the sequence should start and stop, and how\n",
+    "many elements it should contain. In this example, the sequence contains\n",
+    "`5` equally-spaced numbers, starting at `0` and ending at `1`.\n",
+    "\n",
+    "The result is a NumPy **array**, which is a new kind of object we have\n",
+    "not seen before. An array is a container for a sequence of numbers.\n",
+    "\n",
+    "We can use an array in a `for` loop like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "commercial-methodology",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for p1 in p1_array:\n",
+    "    print(p1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "finnish-budapest",
+   "metadata": {},
+   "source": [
+    "When this loop runs, it\n",
+    "\n",
+    "1.  Gets the first value from the array and assigns it to `p1`.\n",
+    "\n",
+    "2.  Runs the body of the loop, which prints `p1`.\n",
+    "\n",
+    "3.  Gets the next value from the array and assigns it to `p1`.\n",
+    "\n",
+    "4.  Runs the body of the loop, which prints `p1`.\n",
+    "\n",
+    "And so on, until it gets to the end of the array. This will come in handy in the next section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "crazy-belize",
+   "metadata": {},
+   "source": [
+    "## Sweeping parameters\n",
+    "\n",
+    "If we know the actual values of parameters like `p1` and `p2`, we can\n",
+    "use them to make specific predictions, like how many bikes will be at\n",
+    "Olin after one hour.\n",
+    "\n",
+    "But prediction is not the only goal; models like this are also used to\n",
+    "explain why systems behave as they do and to evaluate alternative\n",
+    "designs. For example, if we observe the system and notice that we often run out of bikes at a particular time, we could use the model to figure out why that happens. And if we are considering adding more bikes, or another station, we could evaluate the effect of various \"what if\" scenarios.\n",
+    "\n",
+    "As an example, suppose we have enough data to estimate that `p2` is\n",
+    "about `0.2`, but we don't have any information about `p1`. We could run simulations with a range of values for `p1` and see how the results vary. This process is called **sweeping** a parameter, in the sense that the value of the parameter \"sweeps\" through a range of possible values.\n",
+    "\n",
+    "Now that we know about loops and arrays, we can use them like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "working-chair",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p1_array = linspace(0, 0.6, 6)\n",
+    "p2 = 0.2\n",
+    "num_steps = 60\n",
+    "\n",
+    "for p1 in p1_array:\n",
+    "    final_state = run_simulation(p1, p2, num_steps)\n",
+    "    print(p1, final_state.olin_empty)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "chicken-mainstream",
+   "metadata": {},
+   "source": [
+    "Each time through the loop, we run a simulation with a different value\n",
+    "of `p1` and the same value of `p2`, `0.2`. Then we print `p1` and the\n",
+    "number of unhappy customers at Olin.\n",
+    "\n",
+    "To save and plot the results, we can use a `SweepSeries` object, which\n",
+    "is similar to a `TimeSeries`; the difference is that the labels in a\n",
+    "`SweepSeries` are parameter values rather than time values.\n",
+    "\n",
+    "We can create an empty `SweepSeries` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "instrumental-session",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "sweep = SweepSeries()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "listed-orleans",
+   "metadata": {},
+   "source": [
+    "And add values like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "hollywood-technical",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for p1 in p1_array:\n",
+    "    final_state = run_simulation(p1, p2, num_steps)\n",
+    "    sweep[p1] = final_state.olin_empty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "instructional-showcase",
+   "metadata": {},
+   "source": [
+    "The result is a `SweepSeries` that maps from each value of `p1` to the\n",
+    "resulting number of unhappy customers. Then we can plot the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "hollywood-spirit",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "sweep.plot(label='Olin')\n",
+    "\n",
+    "decorate(title='Olin-Wellesley Bikeshare',\n",
+    "         xlabel='Customer rate at Olin (p1 in customers/min)', \n",
+    "         ylabel='Number of unhappy customers')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "intense-start",
+   "metadata": {},
+   "source": [
+    "NumPy provides functions that compute a variety of summary statistics, like `mean`, `median`, and `std` (which computes standard deviation).\n",
+    "\n",
+    "We can use `mean` to compute the average of the values in a series, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "contemporary-enough",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import mean\n",
+    "\n",
+    "mean(sweep)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "owned-senior",
+   "metadata": {},
+   "source": [
+    "In this example, computing the mean might not be useful, but in the exercises below, it will be."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "korean-christianity",
+   "metadata": {},
+   "source": [
+    "## Incremental development\n",
+    "\n",
+    "When you start writing programs that are more than a few lines, you\n",
+    "might find yourself spending more and more time debugging. The more code you write before you start debugging, the harder it is to find the problem.\n",
+    "\n",
+    "**Incremental development** is a way of programming that tries to\n",
+    "minimize the pain of debugging. The fundamental steps are:\n",
+    "\n",
+    "1.  Always start with a working program. If you have an example from a\n",
+    "    book, or a program you wrote that is similar to what you are working\n",
+    "    on, start with that. Otherwise, start with something you *know* is\n",
+    "    correct, like `x=5`. Run the program and confirm that it does what\n",
+    "    you expect.\n",
+    "\n",
+    "2.  Make one small, testable change at a time. A \"testable\" change is\n",
+    "    one that displays something or has some other effect you can check.\n",
+    "    Ideally, you should know what the correct answer is, or be able to\n",
+    "    check it by performing another computation.\n",
+    "\n",
+    "3.  Run the program and see if the change worked. If so, go back to\n",
+    "    Step 2. If not, you will have to do some debugging, but if the\n",
+    "    change you made was small, it shouldn't take long to find the\n",
+    "    problem.\n",
+    "\n",
+    "When this process works, your changes usually work the first time, or if they don't, the problem is obvious. In practice, there are two problems with incremental development:\n",
+    "\n",
+    "-   Sometimes you have to write extra code to generate visible output\n",
+    "    that you can check. This extra code is called **scaffolding**\n",
+    "    because you use it to build the program and then remove it when you\n",
+    "    are done. That might seem like a waste, but time you spend on\n",
+    "    scaffolding is almost always time you save on debugging.\n",
+    "\n",
+    "-   When you are getting started, it might not be obvious how to choose\n",
+    "    the steps that get from `x=5` to the program you are trying to\n",
+    "    write. You will see more examples of this process as we go along,\n",
+    "    and you will get better with experience.\n",
+    "\n",
+    "If you find yourself writing more than a few lines of code before you\n",
+    "start testing, and you are spending a lot of time debugging, try\n",
+    "incremental development."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "nominated-assault",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "appreciated-preview",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "primary-quest",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a function called `make_state` that creates a `State` object with the state variables `olin=10` and `wellesley=2`, and then returns the new `State` object.\n",
+    "\n",
+    "Write a line of code that calls `make_state` and assigns the result to a variable named `init`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "reflected-freedom",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "north-formation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "robust-blair",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Read the documentation of `linspace` at <>.\n",
+    "Then use it to make an array of 101 equally spaced points between 0 and 1 (including both)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "collected-butter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fleet-debut",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Wrap the code from this chapter in a function named `sweep_p1` that takes an array called `p1_array` as a parameter.  It should create a new `SweepSeries`, run a simulation for each value of `p1` in `p1_array`, store the results in the `SweepSeries`, and return the `SweepSeries`.\n",
+    "\n",
+    "Use your function to plot the number of unhappy customers at Olin as a function of `p1`.  Label the axes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "authorized-sarah",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "romance-wisdom",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "developmental-broad",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a function called `sweep_p2` that runs simulations with `p1=0.5` and a range of values for `p2`.  It should store the results in a `SweepSeries` and return the `SweepSeries`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "norman-banana",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "mexican-robert",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "separate-mention",
+   "metadata": {},
+   "source": [
+    "## Optional Exercises\n",
+    "\n",
+    "The following two exercises are a little more challenging.  If you are comfortable with what you have learned so far, you should give them a try.  If you feel like you have your hands full, you might want to skip them for now."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bearing-orbit",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Because our simulations are random, the results vary from one run to another, and the results of a parameter sweep tend to be noisy.  We can get a clearer picture of the relationship between a parameter and a metric by running multiple simulations with the same parameter and taking the average of the results.\n",
+    "\n",
+    "Write a function called `run_multiple_simulations` that takes as parameters `p1`, `p2`, `num_steps`, and `num_runs`.\n",
+    "\n",
+    "`num_runs` specifies how many times it should call `run_simulation`.\n",
+    "\n",
+    "After each run, it should store the total number of unhappy customers (at Olin or Wellesley) in a `TimeSeries`.\n",
+    "At the end, it should return the `TimeSeries`.\n",
+    "\n",
+    "Test your function with parameters\n",
+    "\n",
+    "```\n",
+    "p1 = 0.3\n",
+    "p2 = 0.3\n",
+    "num_steps = 60\n",
+    "num_runs = 10\n",
+    "```\n",
+    "\n",
+    "Display the resulting `TimeSeries` and use the `mean` function from NumPy to compute the average number of unhappy customers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "accredited-salmon",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "visible-allowance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "spatial-fundamentals",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "structural-expense",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Continuting the previous exercise, use `run_multiple_simulations` to run simulations with a range of values for `p1` and\n",
+    "\n",
+    "```\n",
+    "p2 = 0.3\n",
+    "num_steps = 60\n",
+    "num_runs = 20\n",
+    "```\n",
+    "\n",
+    "Store the results in a `SweepSeries`, then plot the average number of unhappy customers as a function of `p1`.  Label the axes.\n",
+    "\n",
+    "What value of `p1` minimizes the average number of unhappy customers?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "reverse-emphasis",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "broad-latitude",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "powerful-planner",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap05.ipynb b/chapters/chap05.ipynb
new file mode 100644
index 00000000..99dd47af
--- /dev/null
+++ b/chapters/chap05.ipynb
@@ -0,0 +1,1000 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "wicked-mozambique",
+   "metadata": {},
+   "source": [
+    "# Chapter 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "inclusive-theater",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "therapeutic-genealogy",
+   "metadata": {},
+   "source": [
+    "In this chapter..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "informal-contractor",
+   "metadata": {},
+   "source": [
+    "# World population\n",
+    "\n",
+    "In 1968 Paul Erlich published *The Population Bomb*, in which he\n",
+    "predicted that world population would grow quickly during the 1970s,\n",
+    "that agricultural production could not keep up, and that mass starvation\n",
+    "in the next two decades was inevitable (see\n",
+    "<https://modsimpy.com/popbomb>). As someone who grew up during those\n",
+    "decades, I am happy to report that those predictions were wrong.\n",
+    "\n",
+    "But world population growth is still a topic of concern, and it is an\n",
+    "open question how many people the earth can sustain while maintaining\n",
+    "and improving our quality of life.\n",
+    "\n",
+    "In this chapter and the next, we use tools from the previous chapters to\n",
+    "explain world population growth since 1950 and generate predictions for\n",
+    "the next 50-100 years.\n",
+    "\n",
+    "For background on world population growth, watch this video from the\n",
+    "American Museum of Natural History <https://modsimpy.com/human>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dying-browse",
+   "metadata": {},
+   "source": [
+    "## World Population Data\n",
+    "\n",
+    "The Wikipedia article on world population contains tables with estimates of world population from prehistory to the present, and projections for the future (<https://modsimpy.com/worldpop>)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "continuing-cancellation",
+   "metadata": {},
+   "source": [
+    "The following cell downloads a copy of https://en.wikipedia.org/wiki/World_population_estimates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "convenient-stroke",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates.html'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acknowledged-bracelet",
+   "metadata": {},
+   "source": [
+    "To read this data, we will use Pandas, which provides functions for\n",
+    "working with data. The function we'll use is `read_html`, which can read a web page and extract data from any tables it contains. Before we can use it, we have to import it. You have already seen this import\n",
+    "statement:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "israeli-finding",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "regulation-parade",
+   "metadata": {},
+   "source": [
+    "Now we can use it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "instant-beverage",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'World_population_estimates.html'\n",
+    "tables = read_html(filename,\n",
+    "                   header=0, \n",
+    "                   index_col=0,\n",
+    "                   decimal='M')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dried-immunology",
+   "metadata": {},
+   "source": [
+    "The arguments are:\n",
+    "\n",
+    "-   `filename`: The name of the file (including the directory it's in)\n",
+    "    as a string. This argument can also be a URL starting with `http`.\n",
+    "\n",
+    "-   `header`: Indicates which row of each table should be considered the\n",
+    "    header, that is, the set of labels that identify the columns. In\n",
+    "    this case it is the first row (numbered 0).\n",
+    "\n",
+    "-   `index_col`: Indicates which column of each table should be\n",
+    "    considered the **index**, that is, the set of labels that identify\n",
+    "    the rows. In this case it is the first column, which contains the\n",
+    "    years.\n",
+    "\n",
+    "-   `decimal`: Normally this argument is used to indicate which\n",
+    "    character should be considered a decimal point, because some\n",
+    "    conventions use a period and some use a comma. In this case I am\n",
+    "    abusing the feature by treating `M` as a decimal point, which allows\n",
+    "    some of the estimates, which are expressed in millions, to be read\n",
+    "    as numbers.\n",
+    "\n",
+    "The result, which is assigned to `tables`, is a sequence that contains\n",
+    "one `DataFrame` for each table. A `DataFrame` is an object, defined by\n",
+    "Pandas, that represents tabular data.\n",
+    "\n",
+    "To select a `DataFrame` from `tables`, we can use the bracket operator\n",
+    "like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "driving-wrapping",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table2 = tables[2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "simplified-convert",
+   "metadata": {},
+   "source": [
+    "This line selects the third table (numbered 2), which contains\n",
+    "population estimates from 1950 to 2016.\n",
+    "\n",
+    "We can use `head` to display the first few lines of the table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "running-alcohol",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table2.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "valid-editing",
+   "metadata": {},
+   "source": [
+    "The first column, which is labeled `Year`, is special.  It is the **index** for this `DataFrame`, which means it contains the labels for the rows.\n",
+    "\n",
+    "Some of the values use scientific notation; for example, `2.544000e+09` is shorthand for $2.544 \\cdot 10^9$ or 2.544 billion.\n",
+    "\n",
+    "`NaN` is a special value that indicates missing data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-senate",
+   "metadata": {},
+   "source": [
+    "The column labels are long strings, which makes them hard to work with.\n",
+    "We can replace them with shorter strings like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "engaging-regular",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "communist-crowd",
+   "metadata": {},
+   "source": [
+    "Now we can select a column from the `DataFrame` using the dot operator,\n",
+    "like selecting a state variable from a `State` object.\n",
+    "\n",
+    "Here are the estimates from the United States Census Bureau:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "amended-negative",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census = table2.census / 1e9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "absent-heart",
+   "metadata": {},
+   "source": [
+    "The result is a Pandas `Series`, which is similar to the `TimeSeries` and `SweepSeries` objects we've been using.\n",
+    "\n",
+    "The number `1e9` is a shorter way to write `1000000000` or one billion.\n",
+    "When we divide a `Series` by a number, it divides all of the elements of the `Series`.\n",
+    "From here on, we'll express population estimates in terms of billions.\n",
+    "\n",
+    "We can use `tail` to see the last few elements of the `Series`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "graduate-specialist",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "brilliant-brook",
+   "metadata": {},
+   "source": [
+    "The left column is the **index** of the `Series`; in this example it contains the dates.\n",
+    "The right column contains the **values**, which are population estimates.\n",
+    "In 2016 the world population was about 7.3 billion.\n",
+    "\n",
+    "Here are the estimates from the United Nations\n",
+    "Department of Economic and Social Affairs (U.N. DESA):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "linear-admission",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "un = table2.un / 1e9\n",
+    "un.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mental-sussex",
+   "metadata": {},
+   "source": [
+    "The most recent estimate we have from the U.N. is for 2015, so the value for 2016 is `NaN`.\n",
+    "\n",
+    "Now we can plot the estimates like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ordered-garage",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def plot_estimates():\n",
+    "    census.plot(style=':', label='US Census')\n",
+    "    un.plot(style='--', label='UN DESA')\n",
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)') "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "invisible-mouse",
+   "metadata": {},
+   "source": [
+    "The keyword argument `style=':'` specifies a dotted line; `style='--'` specifies a dashed line.\n",
+    "The `label` argument provides the string that appears in the legend.\n",
+    "\n",
+    "And here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "periodic-weekend",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_estimates()\n",
+    "decorate(title='World Population Estimates')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "labeled-magic",
+   "metadata": {},
+   "source": [
+    "The lines overlap almost completely, but the most recent estimates diverge slightly.\n",
+    "In the next section, we'll quantify these differences."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tender-summer",
+   "metadata": {},
+   "source": [
+    "## Absolute and relative errors\n",
+    "\n",
+    "Estimates of world population from the U.S. Census and the U.N. DESA differ slightly.\n",
+    "One way to characterize this difference is **absolute error**, which is the absolute value of the difference between the two.\n",
+    "\n",
+    "To compute absolute value, we can import `abs` from NumPy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "secondary-player",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import abs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "caring-garlic",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "abs_error = abs(un - census)\n",
+    "abs_error.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bridal-extraction",
+   "metadata": {},
+   "source": [
+    "When you subtract two `Series` objects, the result is a new `Series`.\n",
+    "Because one of the estimates from 2016 is `NaN`, the result is `NaN`.\n",
+    "\n",
+    "To summarize the results, we can compute the **mean absolute error**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "corresponding-emerald",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import mean\n",
+    "\n",
+    "mean(abs_error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "identical-archive",
+   "metadata": {},
+   "source": [
+    "On average, the estimates differ by about 0.029 billion.\n",
+    "But we can also use `max` to compute the maximum absolute error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "headed-witch",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from numpy import max\n",
+    "\n",
+    "max(abs_error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "composite-partnership",
+   "metadata": {},
+   "source": [
+    "In the worst case, they differ by about 0.1 billion.\n",
+    "\n",
+    "Now 0.1 billion is a lot of people, so that might sound like a serious discrepancy.\n",
+    "But counting everyone is the world is hard, and we should not expect the estimates to be exact.\n",
+    "\n",
+    "Another way to quantify the magnitude of the difference is **relative error**, which is the size of the error divided by the estimates themselves."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "advisory-complex",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "rel_error = abs_error / census * 100\n",
+    "rel_error.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "greater-register",
+   "metadata": {},
+   "source": [
+    "I multiplied by 100 so we can interpret the results as a percentage.  In 2015, the difference between the estimates is about 1.4%, and that happens to be the maximum.\n",
+    "\n",
+    "Again, we can summarize the results by computing the mean."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "frank-owner",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean(rel_error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "peaceful-fleece",
+   "metadata": {},
+   "source": [
+    "The mean relative error is about 0.6%.\n",
+    "So that's not bad.\n",
+    "\n",
+    "You might wonder why I divided by `census` rather than `un`.\n",
+    "In general, if you think one estimate is better than the other, you put the better one in the denominator.\n",
+    "In this case, I don't know which is better, so I put the smaller one in the denominator, which makes the computed errors a little bigger."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "vietnamese-excuse",
+   "metadata": {},
+   "source": [
+    "## Modeling Population Growth\n",
+    "\n",
+    "Suppose we want to predict world population growth over the next 50 or\n",
+    "100 years. We can do that by developing a model that describes how\n",
+    "populations grow, fitting the model to the data we have so far, and then using the model to generate predictions.\n",
+    "\n",
+    "In the next few sections I demonstrate this process starting with simple models and gradually improving them.\n",
+    "\n",
+    "Although there is some curvature in the plotted estimates, it looks like world population growth has been close to linear since 1960 or so. So we'll start with a model that has constant growth.\n",
+    "\n",
+    "To fit the model to the data, we'll compute the average annual growth\n",
+    "from 1950 to 2016. Since the UN and Census data are so close, we'll use the Census data.\n",
+    "\n",
+    "We can select a value from a `Series` using the bracket operator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "undefined-sauce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census[1950]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "precise-correlation",
+   "metadata": {},
+   "source": [
+    "So we can get the total growth during the interval like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "postal-debate",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_growth = census[2016] - census[1950]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dutch-sample",
+   "metadata": {},
+   "source": [
+    "The numbers in brackets are called **labels**, because they label the\n",
+    "rows of the `Series`.\n",
+    "\n",
+    "In this example, the labels 2016 and 1950 are part of the data, so it\n",
+    "would be better not to make them part of the program. \n",
+    "Putting values like these in the program is called **hard coding**; it is considered bad practice because if the data change in the future, we have to change the program (see <https://modsimpy.com/hardcode>).\n",
+    "\n",
+    "It would be better to get the labels from the `Series`.\n",
+    "We can do that by selecting the index from `census` and then selecting the first element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "functional-trick",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_0 = census.index[0]\n",
+    "t_0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "empty-siemens",
+   "metadata": {},
+   "source": [
+    "So `t_0` is the label of the first element, which is 1950.\n",
+    "We can get the label of the last element like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "acoustic-south",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_end = census.index[-1]\n",
+    "t_end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "express-metallic",
+   "metadata": {},
+   "source": [
+    "The value `-1` indicates the last element; `-2` indicates the second to last element, and so on.\n",
+    "\n",
+    "The difference between `t_0` and `t_end` is the elapsed time between them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "planned-remains",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "elapsed_time = t_end - t_0\n",
+    "elapsed_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bridal-royal",
+   "metadata": {},
+   "source": [
+    "Now we can use `t_0` and `t_end` to select the population at the beginning and end of the interval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "quarterly-aggregate",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p_0 = census[t_0]\n",
+    "p_end = census[t_end]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "driven-castle",
+   "metadata": {},
+   "source": [
+    "And compute the total growth during the interval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "prescription-optics",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_growth = p_end - p_0\n",
+    "total_growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "accepted-auditor",
+   "metadata": {},
+   "source": [
+    "Finally, we can compute average annual growth."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "convertible-patch",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "annual_growth = total_growth / elapsed_time\n",
+    "annual_growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "japanese-merit",
+   "metadata": {},
+   "source": [
+    "From 1950 to 2016, world population grew by about 0.07 billion people per year, on average.\n",
+    "The next step is to use this estimate to simulate population growth."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "public-verification",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "Our simulation will start with the observed population in 1950, `p_0`,\n",
+    "and add `annual_growth` each year. To store the results, we'll use a\n",
+    "`TimeSeries` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "duplicate-leave",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "results = TimeSeries(time='Year', quantity='Population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "expired-salmon",
+   "metadata": {},
+   "source": [
+    "In this example, I use the keyword arguments `time` and `quantity` to give names to the index and the values.\n",
+    "These names don't affect the computation, but they appear when we display or plot the `TimeSeries`.\n",
+    "\n",
+    "We can set the first value in the new `TimeSeries` like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "convenient-thong",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results[t_0] = p_0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "constant-casino",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like so far."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "israeli-surveillance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "show(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stretch-snapshot",
+   "metadata": {},
+   "source": [
+    "Now we set the rest of the values by simulating annual growth:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "attended-morris",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for t in range(t_0, t_end):\n",
+    "    results[t+1] = results[t] + annual_growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "signed-colleague",
+   "metadata": {},
+   "source": [
+    "The values of `t` go from from `t_0` to `t_end`, including the first but not the last.\n",
+    "\n",
+    "Inside the loop, we compute the population for the next year by adding the population for the current year and `annual_growth`. \n",
+    "\n",
+    "The last time through the loop, the value of `t` is 2015, so the last label in `results` is 2016.\n",
+    "\n",
+    "Here's what the results look like, compared to the estimates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "wrong-brooks",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.plot(color='gray', label='model')\n",
+    "plot_estimates()\n",
+    "decorate(title='Constant Growth Model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "automated-albany",
+   "metadata": {},
+   "source": [
+    "The keyword argument `color='gray'` specifies the color of the line.  For details on color specification see <https://modsimpy.com/color>.\n",
+    "\n",
+    "From 1950 to 1990, the model does not fit the data particularly well, but after that, it's pretty good."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unnecessary-million",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This chapter is a first step toward modeling changes in world population growth during the last 70 years.\n",
+    "\n",
+    "We used Pandas to read data from a web page and store the results in a `DataFrame`.\n",
+    "From the `DataFrame` we selected two `Series` objects and used them to compute absolute and relative errors.\n",
+    "\n",
+    "Then we computed average population growth and used it to build a simple model with constant annual growth.\n",
+    "The model fits recent data pretty well; nevertheless, there are two reasons we should be skeptical:\n",
+    "\n",
+    "* There is no obvious mechanism that could cause population growth to be constant from year to year. Changes in population are determined by the fraction of people who die and the fraction of people who give birth, so we expect them to depend on the current population.\n",
+    "\n",
+    "* According to this model, world population would keep growing at the same rate forever, and that does not seem reasonable.\n",
+    "\n",
+    "In the next chapter we'll consider other models that might fit the data better and make more credible predictions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "advanced-ivory",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hearing-today",
+   "metadata": {},
+   "source": [
+    "Here's the code from this chapter all in one place."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "terminal-reynolds",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_0 = census.index[0]\n",
+    "t_end = census.index[-1]\n",
+    "elapsed_time = t_end - t_0\n",
+    "\n",
+    "p_0 = census[t_0]\n",
+    "p_end = census[t_end]\n",
+    "\n",
+    "total_growth = p_end - p_0\n",
+    "annual_growth = total_growth / elapsed_time\n",
+    "\n",
+    "results = TimeSeries(time='Year', quantity='Population')\n",
+    "results[t_0] = p_0\n",
+    "\n",
+    "for t in range(t_0, t_end):\n",
+    "    results[t+1] = results[t] + annual_growth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "organizational-memphis",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.plot(color='gray', label='model')\n",
+    "plot_estimates()\n",
+    "decorate(title='Constant Growth Model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecological-welsh",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Try fitting the model using data from 1970 to the present, and see if that does a better job.\n",
+    "\n",
+    "Suggestions: \n",
+    "\n",
+    "1. Define `t_1` to be 1970 and `p_1` to be the population in 1970.  Use `t_1` and `p_1` to compute annual growth, but use `t_0` and `p_0` to run the simulation. \n",
+    "\n",
+    "2. You might want to add a constant to the starting value to match the data better."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "rising-anger",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "false-handbook",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "political-loading",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "improved-sigma",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap06.ipynb b/chapters/chap06.ipynb
new file mode 100644
index 00000000..d64d790c
--- /dev/null
+++ b/chapters/chap06.ipynb
@@ -0,0 +1,698 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "creative-motel",
+   "metadata": {},
+   "source": [
+    "# Chapter 6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "parental-symposium",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "quality-spectrum",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we simulated a model of world population with\n",
+    "constant growth. In this chapter we see if we can make a better model\n",
+    "with growth proportional to the population.\n",
+    "\n",
+    "But first, we'll improve the code from the previous chapter by\n",
+    "encapsulating it in a function and using `System` objects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ranking-tamil",
+   "metadata": {},
+   "source": [
+    "Here's the code that reads the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "annoying-herald",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates.html'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "shared-offense",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_html\n",
+    "\n",
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
+    "table2 = tables[2]\n",
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "therapeutic-merchant",
+   "metadata": {},
+   "source": [
+    "## System objects\n",
+    "\n",
+    "Like a `State` object, a `System` object contains variables and their\n",
+    "values. The difference is:\n",
+    "\n",
+    "-   `State` objects contain state variables that get updated in the course of a simulation.\n",
+    "\n",
+    "-   `System` objects contain **system parameters**, which usually don't get updated over the course of a simulation.\n",
+    "\n",
+    "For example, in the bike share model, state variables include the number of bikes at each location, which get updated whenever a customer moves a bike. System parameters include the number of locations, total number of bikes, and arrival rates at each location.\n",
+    "\n",
+    "In the population model, the only state variable is the population.\n",
+    "System parameters include the annual growth rate, the initial time and\n",
+    "population, and the end time.\n",
+    "\n",
+    "Suppose we have the following variables, as computed in the previous\n",
+    "chapter (assuming `table2` is the `DataFrame` we read from the file):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "numerous-university",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "un = table2.un / 1e9\n",
+    "census = table2.census / 1e9\n",
+    "\n",
+    "t_0 = census.index[0]\n",
+    "t_end = census.index[-1]\n",
+    "elapsed_time = t_end - t_0\n",
+    "\n",
+    "p_0 = census[t_0]\n",
+    "p_end = census[t_end]\n",
+    "\n",
+    "total_growth = p_end - p_0\n",
+    "annual_growth = total_growth / elapsed_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "starting-cooling",
+   "metadata": {},
+   "source": [
+    "Some of these are parameters we need to simulate the system; others are temporary values we can discard. \n",
+    "To distinguish between them, we'll put the parameters we need into a `System` object like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "colonial-domestic",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "system = System(t_0=t_0, \n",
+    "                t_end=t_end,\n",
+    "                p_0=p_0,\n",
+    "                annual_growth=annual_growth)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fleet-beaver",
+   "metadata": {},
+   "source": [
+    "`t0` and `t_end` are the first and last years; `p_0` is the initial\n",
+    "population, and `annual_growth` is the estimated annual growth.\n",
+    "\n",
+    "Here's what `system` looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "floral-routine",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "combined-banking",
+   "metadata": {},
+   "source": [
+    "Next we'll wrap the code from the previous chapter in a function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "pacific-challenge",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def run_simulation1(system):\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.p_0\n",
+    "    \n",
+    "    for t in range(system.t_0, system.t_end):\n",
+    "        results[t+1] = results[t] + system.annual_growth\n",
+    "    \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "technological-medicine",
+   "metadata": {},
+   "source": [
+    "`run_simulation1` takes a `System` object and uses the parameters in it to determine `t_0`, `t_end`, and `annual_growth`.\n",
+    "\n",
+    "Inside the loop, it stores the results in a `TimeSeries` which it returns at the end.\n",
+    "\n",
+    "The following function plots the results along with the estimates\n",
+    "`census` and `un`:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "grateful-terror",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "Here's how we call it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "electoral-breach",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results1 = run_simulation1(system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ordinary-sound",
+   "metadata": {},
+   "source": [
+    "Here's the function we used in the previous chapter to plot the estimates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "peripheral-cassette",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def plot_estimates():\n",
+    "    census.plot(style=':', label='US Census')\n",
+    "    un.plot(style='--', label='UN DESA')\n",
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)') "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "warming-audience",
+   "metadata": {},
+   "source": [
+    "And here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "capable-diana",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results1.plot(label='model', color='gray')\n",
+    "plot_estimates()\n",
+    "decorate(title='Constant Growth Model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exposed-witness",
+   "metadata": {},
+   "source": [
+    "It might not be obvious that using functions and `System` objects is a\n",
+    "big improvement, and for a simple model that we run only once, maybe\n",
+    "it's not. But as we work with more complex models, and when we run many simulations with different parameters, we'll see that the organization of the code makes a big difference.\n",
+    "\n",
+    "Now let's see if we can improve the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "geographic-hormone",
+   "metadata": {},
+   "source": [
+    "## Proportional growth model\n",
+    "\n",
+    "The biggest problem with the constant growth model is that it doesn't\n",
+    "make any sense. It is hard to imagine how people all over the world\n",
+    "could conspire to keep population growth constant from year to year.\n",
+    "\n",
+    "On the other hand, if some fraction of the population dies each year,\n",
+    "and some fraction gives birth, we can compute the net change in the\n",
+    "population like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "laughing-wesley",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation2(system):\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.p_0\n",
+    "    \n",
+    "    for t in range(system.t_0, system.t_end):\n",
+    "        births = system.birth_rate * results[t]\n",
+    "        deaths = system.death_rate * results[t]\n",
+    "        results[t+1] = results[t] + births - deaths\n",
+    "        \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "educated-portugal",
+   "metadata": {},
+   "source": [
+    "Now we can choose the values of `birth_rate` and `death_rate` that best fit the data. \n",
+    "\n",
+    "For the death rate, I'll use 7.7 deaths per 1000 people, which was roughly the global death rate in 2020 (see <https://www.indexmundi.com/world/death_rate.html>).\n",
+    "I chose the birth rate by hand to fit the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "wired-brief",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.death_rate = 7.7 / 1000\n",
+    "system.birth_rate = 25 / 1000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sufficient-contest",
+   "metadata": {},
+   "source": [
+    "Then I ran the simulation and plotted the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "looking-trace",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results2 = run_simulation2(system)\n",
+    "results2.plot(label='model', color='gray')\n",
+    "plot_estimates()\n",
+    "decorate(title='Proportional Growth Model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "suited-costs",
+   "metadata": {},
+   "source": [
+    "The proportional model fits\n",
+    "the data well from 1950 to 1965, but not so well after that. Overall,\n",
+    "the **quality of fit** is not as good as the constant growth model,\n",
+    "which is surprising, because it seems like the proportional model is\n",
+    "more realistic.\n",
+    "\n",
+    "In the next chapter we'll try one more time to find a model that makes\n",
+    "sense and fits the data. But first, I want to make a few more\n",
+    "improvements to the code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "appropriate-checkout",
+   "metadata": {},
+   "source": [
+    "## Factoring out the update function\n",
+    "\n",
+    "`run_simulation1` and `run_simulation2` are nearly identical except for the body of the `for` loop, where we compute the population for the next year.\n",
+    "\n",
+    "Rather than repeat identical code, we can separate the things that\n",
+    "change from the things that don't. First, I'll pull out the update code from `run_simulation2` and make it a function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "handmade-permit",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def growth_func1(pop, t, system):\n",
+    "    births = system.birth_rate * pop\n",
+    "    deaths = system.death_rate * pop\n",
+    "    return births - deaths"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fabulous-bankruptcy",
+   "metadata": {},
+   "source": [
+    "This function takes as arguments the current population, current year,\n",
+    "and a `System` object; it returns the net population growth during the current year.\n",
+    "\n",
+    "This update function does not use `t`, so we could leave it out. But we will see other functions that need it, and it is convenient if they all take the same parameters, used or not.\n",
+    "\n",
+    "Now we can write a function that runs any model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "civilian-accused",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, growth_func):\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.p_0\n",
+    "    \n",
+    "    for t in range(system.t_0, system.t_end):\n",
+    "        growth = growth_func(results[t], t, system)\n",
+    "        results[t+1] = results[t] + growth\n",
+    "        \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "failing-assist",
+   "metadata": {},
+   "source": [
+    "This function demonstrates a feature we have not seen before: it takes a\n",
+    "function as a parameter! When we call `run_simulation`, the second\n",
+    "parameter is a function, like `growth_func1`, that computes the\n",
+    "population for the next year.\n",
+    "\n",
+    "Here's how we call it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "wicked-seeking",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, growth_func1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "simple-camel",
+   "metadata": {},
+   "source": [
+    "Passing a function as an argument is the same as passing any other\n",
+    "value. The argument, which is `growth_func1` in this example, gets\n",
+    "assigned to the parameter, which is called `growth_func`. Inside\n",
+    "`run_simulation`, we can run `growth_func` just like any other function.\n",
+    "\n",
+    "Each time through the loop, `run_simulation` calls `growth_func1` to compute net growth, and uses it to compute the population during the next year."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "spectacular-paradise",
+   "metadata": {},
+   "source": [
+    "## Combining birth and death\n",
+    "\n",
+    "We can simplify the code slightly by combining births and deaths to compute the net growth rate. \n",
+    "Instead of two parameters, `birth_rate` and `death_rate`, we can write the update function in terms of a single parameter that represents the difference:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "impressive-model",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.alpha = system.birth_rate - system.death_rate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "modern-uncertainty",
+   "metadata": {},
+   "source": [
+    "The name of this parameter, `alpha`, is the conventional name for a\n",
+    "proportional growth rate.\n",
+    "\n",
+    "Here's the modified version of `growth_func1`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "familiar-helena",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def growth_func2(pop, t, system):\n",
+    "    return system.alpha * pop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "understanding-typing",
+   "metadata": {},
+   "source": [
+    "And here's how we run it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "independent-effectiveness",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, growth_func2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "everyday-delicious",
+   "metadata": {},
+   "source": [
+    "The results are the same as the previous versions, but now the code is organized in a way that makes it easy to explore other models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "attractive-steps",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this chapter, we wrapped the code from the previous chapter in functions and used a `System` object to store the parameters of the system.\n",
+    "\n",
+    "We explored a new model of population growth, where the number of births and deaths is proportional to the current population.  This model seems more realistic, but it turns out not to fit the data particularly well.\n",
+    "\n",
+    "In the next chapter, we'll try one more model, which is based on the assumption that the population can't keep growing forever.\n",
+    "But first, you might want to work on some exercises."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "looking-douglas",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "compliant-preserve",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Maybe the reason the proportional model doesn't work very well is that the growth rate, `alpha`, is changing over time.  So let's try a model with different growth rates before and after 1980 (as an arbitrary choice).\n",
+    "\n",
+    "Write an update function that takes `pop`, `t`, and `system` as parameters.  The system object, `system`, should contain two parameters: the growth rate before 1980, `alpha1`, and the growth rate after 1980, `alpha2`.  It should use `t` to determine which growth rate to use.\n",
+    "\n",
+    "Test your function by calling it directly, then pass it to `run_simulation`.  Plot the results.  Adjust the parameters `alpha1` and `alpha2` to fit the data as well as you can."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "minus-recommendation",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "headed-amsterdam",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "preliminary-carnival",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adaptive-tiger",
+   "metadata": {},
+   "source": [
+    "## Under the Hood\n",
+    "\n",
+    "The `System` object defined in the ModSim library, is based on the `SimpleNamespace` object defined in a standard Python library called `types`; the documentation is at <https://docs.python.org/3.7/library/types.html#types.SimpleNamespace>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "injured-mailman",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap07.ipynb b/chapters/chap07.ipynb
new file mode 100644
index 00000000..7bd8ec6e
--- /dev/null
+++ b/chapters/chap07.ipynb
@@ -0,0 +1,811 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "regular-director",
+   "metadata": {},
+   "source": [
+    "# Chapter 7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "disciplinary-occasion",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "indie-scoop",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we developed a population model where net growth during each time step is proportional to the current population. This model seems more realistic than the constant growth model, but it does not fit the data as well.\n",
+    "\n",
+    "There are a few things we could try to improve the model:\n",
+    "\n",
+    "-   Maybe net growth depends on the current population, but the\n",
+    "    relationship is quadratic, not linear.\n",
+    "\n",
+    "-   Maybe the net growth rate varies over time.\n",
+    "\n",
+    "In this chapter, we'll explore the first option.\n",
+    "In the exercises, you will have a chance to try the second. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "qualified-houston",
+   "metadata": {},
+   "source": [
+    "Here's the code that reads the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "major-arkansas",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates.html'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "intense-columbus",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_html\n",
+    "\n",
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
+    "table2 = tables[2]\n",
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "wound-necklace",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "un = table2.un / 1e9\n",
+    "census = table2.census / 1e9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "intended-matrix",
+   "metadata": {},
+   "source": [
+    "And here are the functions from the previous chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "former-accuracy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def run_simulation(system, growth_func):\n",
+    "    \"\"\"Simulate the system using any update function.\n",
+    "    \n",
+    "    system: System object\n",
+    "    growth_func: function that computes the population next year\n",
+    "    \n",
+    "    returns: TimeSeries\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.p_0\n",
+    "    \n",
+    "    for t in range(system.t_0, system.t_end):\n",
+    "        growth = growth_func(results[t], t, system)\n",
+    "        results[t+1] = results[t] + growth\n",
+    "        \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "duplicate-insert",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def plot_estimates():\n",
+    "    census.plot(style=':', label='US Census')\n",
+    "    un.plot(style='--', label='UN DESA')\n",
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "champion-retreat",
+   "metadata": {},
+   "source": [
+    "## Quadratic growth\n",
+    "\n",
+    "It makes sense that net growth should depend on the current population, but maybe it's not a linear relationship, like this:\n",
+    "\n",
+    "```\n",
+    "net_growth = system.alpha * pop\n",
+    "```\n",
+    "\n",
+    "Maybe it's a quadratic relationship, like this:\n",
+    "\n",
+    "```\n",
+    "net_growth = system.alpha * pop + system.beta * pop**2\n",
+    "```\n",
+    "\n",
+    "We can test that conjecture with a new update function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "sized-hawaiian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def growth_func_quad(pop, t, system):\n",
+    "    return system.alpha * pop + system.beta * pop**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "amateur-strand",
+   "metadata": {},
+   "source": [
+    "Here's the `System` object we'll use, initialized with `t_0`, `p_0`, and `t_end`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "interstate-canal",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "t_0 = census.index[0]\n",
+    "p_0 = census[t_0]\n",
+    "t_end = census.index[-1]\n",
+    "\n",
+    "system = System(t_0=t_0,\n",
+    "                p_0=p_0,\n",
+    "                t_end=t_end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acting-transportation",
+   "metadata": {},
+   "source": [
+    "Now we have to add the parameters `alpha` and `beta` .\n",
+    "I chose the following values by trial and error; we'll see better ways to do it later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "banned-dallas",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.alpha = 25 / 1000\n",
+    "system.beta = -1.8 / 1000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "supreme-wellington",
+   "metadata": {},
+   "source": [
+    "And here's how we run it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "positive-scheme",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, growth_func_quad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "satisfied-density",
+   "metadata": {},
+   "source": [
+    "And here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "flush-fellowship",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.plot(color='gray', label='model')\n",
+    "plot_estimates()\n",
+    "decorate(title='Quadratic Growth Model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "organized-combining",
+   "metadata": {},
+   "source": [
+    "The model fits the data well over the whole range, with just a bit of space between them in the 1960s.\n",
+    "\n",
+    "Of course, we should expect the quadratic model to fit better than the\n",
+    "constant and proportional models because it has two parameters we can\n",
+    "choose, where the other models have only one. In general, the more\n",
+    "parameters you have to play with, the better you should expect the model\n",
+    "to fit.\n",
+    "\n",
+    "But fitting the data is not the only reason to think the quadratic model\n",
+    "might be a good choice. It also makes sense; that is, there is a\n",
+    "legitimate reason to expect the relationship between growth and\n",
+    "population to have this form.\n",
+    "\n",
+    "To understand it, let's look at net growth as a function of population."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "powerful-century",
+   "metadata": {},
+   "source": [
+    "## Net growth\n",
+    "\n",
+    "Let's plot the relationship between growth and population in the quadratic model.\n",
+    "\n",
+    "I'll use `linspace` to make an array of 101 populations from 0 to 15 billion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "excited-catch",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import linspace\n",
+    "\n",
+    "pop_array = linspace(0, 15, 101)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tracked-guide",
+   "metadata": {},
+   "source": [
+    "Now I'll use the quadratic model to compute net growth for each population."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "classical-badge",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "growth_array = (system.alpha * pop_array + \n",
+    "                system.beta * pop_array**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "quantitative-differential",
+   "metadata": {},
+   "source": [
+    "To plot the growth rate versus population, we can import the `plot` function from Matplotlib:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "interim-consent",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.pyplot import plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "published-average",
+   "metadata": {},
+   "source": [
+    "And use it like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "received-crossing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(pop_array, growth_array, label='growth')\n",
+    "\n",
+    "decorate(xlabel='Population (billions)',\n",
+    "         ylabel='Net growth (billions)',\n",
+    "         title='Growth vs. Population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-wisdom",
+   "metadata": {},
+   "source": [
+    "Note that the x-axis is not time, as in the previous figures, but population. We can divide this curve into four regimes of behavior:\n",
+    "\n",
+    "-   When the population is less than 3-4 billion, net growth is\n",
+    "    proportional to population, as in the proportional model. In this\n",
+    "    regime, the population grows slowly because the population is small.\n",
+    "\n",
+    "-   Between 4 billion and 10 billion, the population grows quickly\n",
+    "    because there are a lot of people.\n",
+    "\n",
+    "-   Above 10 billion, population grows more slowly; this behavior models\n",
+    "    the effect of resource limitations that decrease birth rates or\n",
+    "    increase death rates.\n",
+    "\n",
+    "-   Above 14 billion, resources are so limited that the death rate\n",
+    "    exceeds the birth rate and net growth becomes negative.\n",
+    "\n",
+    "Just below 14 billion, there is a point where net growth is 0, which\n",
+    "means that the population does not change. At this point, the birth and death rates are equal, so the population is in **equilibrium**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "relevant-detection",
+   "metadata": {},
+   "source": [
+    "## Equilibrium\n",
+    "\n",
+    "To find the equilibrium point, we can find the roots, or zeros, of this equation: \n",
+    "\n",
+    "$$\\Delta p = \\alpha p + \\beta p^2$$ \n",
+    "\n",
+    "where $\\Delta p$ is net\n",
+    "population growth, $p$ is current population, and $\\alpha$ and $\\beta$\n",
+    "are the parameters of the model. We can rewrite the right hand side like\n",
+    "this: \n",
+    "\n",
+    "$$\\Delta p = p (\\alpha + \\beta p)$$ \n",
+    "\n",
+    "which is $0$ when $p=0$ or $p=-\\alpha/\\beta$.\n",
+    "We can use the parameters of the system to compute the second equilibrium point:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "lightweight-entrepreneur",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "-system.alpha / system.beta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stuck-flood",
+   "metadata": {},
+   "source": [
+    "With these parameters, net growth is 0 when the population is about 13.9 billion.\n",
+    "\n",
+    "In the context of population modeling, the quadratic model is more\n",
+    "conventionally written like this: \n",
+    "\n",
+    "$$\\Delta p = r p (1 - p / K)$$ \n",
+    "\n",
+    "This is the same model; it's just a different way to **parameterize** it. Given $\\alpha$ and $\\beta$, we can compute $r=\\alpha$ and $K=-\\alpha/\\beta$.\n",
+    "\n",
+    "In this version, it is easier to interpret the parameters: $r$ is the\n",
+    "maximum growth rate, observed when $p$ is small, and $K$ is the\n",
+    "equilibrium point. $K$ is also called the **carrying capacity**, since\n",
+    "it indicates the maximum population the environment can sustain."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "derived-oliver",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this chapter we implemented a quadratic growth model where net growth depends on the current population and the population squared.\n",
+    "\n",
+    "This model fits the data well, and we saw one reason why: it is based on the assumption that there is a limit to the number of people the Earth can support.\n",
+    "\n",
+    "In the next chapter we'll use the models we have developed to generate\n",
+    "predictions.\n",
+    "But first, I want to warn you about a few things that can go wrong when you write functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bibliographic-handy",
+   "metadata": {},
+   "source": [
+    "## Dysfunctions\n",
+    "\n",
+    "When people learn about functions, there are a few things they often\n",
+    "find confusing. In this section I present and explain some common\n",
+    "problems.\n",
+    "\n",
+    "As an example, suppose you want a function that takes a\n",
+    "`System` object, with variables `alpha` and `beta`, and computes the\n",
+    "carrying capacity, `-alpha/beta`. \n",
+    "Here's a good solution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "lucky-insurance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def carrying_capacity(system):\n",
+    "    K = -system.alpha / system.beta\n",
+    "    return K\n",
+    "    \n",
+    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
+    "pop = carrying_capacity(sys1)\n",
+    "print(pop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "accredited-fitting",
+   "metadata": {},
+   "source": [
+    "Now let's see all the ways that can go wrong."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "transparent-cotton",
+   "metadata": {},
+   "source": [
+    "Dysfunction \\#1: Not using parameters. In the following version, the\n",
+    "function doesn't take any parameters; when `sys1` appears inside the\n",
+    "function, it refers to the object we create outside the function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "aggregate-baker",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def carrying_capacity():\n",
+    "    K = -sys1.alpha / sys1.beta\n",
+    "    return K\n",
+    "    \n",
+    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
+    "pop = carrying_capacity()\n",
+    "print(pop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "otherwise-belarus",
+   "metadata": {},
+   "source": [
+    "This version actually works, but it is not as versatile as it could be.\n",
+    "If there are several `System` objects, this function can only work with one of them, and only if it is named `sys1`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "naughty-crazy",
+   "metadata": {},
+   "source": [
+    "Dysfunction \\#2: Clobbering the parameters. When people first learn\n",
+    "about parameters, they often write functions like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "outdoor-petroleum",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# WRONG\n",
+    "def carrying_capacity(system):\n",
+    "    system = System(alpha=0.025, beta=-0.0018)\n",
+    "    K = -system.alpha / system.beta\n",
+    "    return K\n",
+    "    \n",
+    "sys1 = System(alpha=0.03, beta=-0.002)\n",
+    "pop = carrying_capacity(sys1)\n",
+    "print(pop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afraid-street",
+   "metadata": {},
+   "source": [
+    "In this example, we have a `System` object named `sys1` that gets passed\n",
+    "as an argument to `carrying_capacity`. But when the function runs, it\n",
+    "ignores the argument and immediately replaces it with a new `System`\n",
+    "object. As a result, this function always returns the same value, no\n",
+    "matter what argument is passed.\n",
+    "\n",
+    "When you write a function, you generally don't know what the values of\n",
+    "the parameters will be. Your job is to write a function that works for\n",
+    "any valid values. If you assign your own values to the parameters, you\n",
+    "defeat the whole purpose of functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "expired-detail",
+   "metadata": {},
+   "source": [
+    "Dysfunction \\#3: No return value. Here's a version that computes the\n",
+    "value of `K` but doesn't return it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "increasing-database",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# WRONG\n",
+    "def carrying_capacity(system):\n",
+    "    K = -system.alpha / system.beta\n",
+    "    \n",
+    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
+    "pop = carrying_capacity(sys1)\n",
+    "print(pop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "brutal-chile",
+   "metadata": {},
+   "source": [
+    "A function that doesn't have a return statement actually returns a special value called `None`, so in this example the value of `pop` is `None`. If you are debugging a program and find that the value of a variable is `None` when it shouldn't be, a function without a return statement is a likely cause."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "meaning-thriller",
+   "metadata": {},
+   "source": [
+    "Dysfunction \\#4: Ignoring the return value. Finally, here's a version\n",
+    "where the function is correct, but the way it's used is not.\n",
+    "\n",
+    "```\n",
+    "def carrying_capacity(system):\n",
+    "    K = -system.alpha / system.beta\n",
+    "    return K\n",
+    "    \n",
+    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
+    "carrying_capacity(sys1)\n",
+    "print(K)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opposed-vessel",
+   "metadata": {},
+   "source": [
+    "In this example, `carrying_capacity` runs and returns `K`, but the\n",
+    "return value doesn't get displayed or assigned to a variable.\n",
+    "If we try to print `K`, we get a `NameError`, because `K` only exists inside the function.\n",
+    "\n",
+    "When you call a function that returns a value, you should do something\n",
+    "with the result."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "quarterly-ethiopia",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "painted-samuel",
+   "metadata": {},
+   "source": [
+    "**Exercise:** In a previous section, we saw a different way to parameterize the quadratic model:\n",
+    "\n",
+    "$$ \\Delta p = r p (1 - p / K) $$\n",
+    "\n",
+    "where $r=\\alpha$ and $K=-\\alpha/\\beta$.  \n",
+    "\n",
+    "Write a version of `growth_func` that implements this version of the model.  Test it by computing the values of `r` and `K` that correspond to `alpha=0.025, beta=-0.0018`, and confirm that you get the same results. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "unauthorized-settle",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "primary-roman",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ordinary-solid",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ignored-architect",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  What happens if we start with an initial population above the carrying capacity, like 20 billion?  Run the model with initial populations between 1 and 20 billion, and plot the results on the same axes.\n",
+    "\n",
+    "Hint: If there are too many labels in the legend, you can plot results like this:\n",
+    "\n",
+    "```\n",
+    "    results.plot(label='_nolegend')\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "intermediate-kazakhstan",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "positive-enforcement",
+   "metadata": {},
+   "source": [
+    "## Under the hood\n",
+    "\n",
+    "Explain the difference between the Pandas and Matplotlib `plot` functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "described-funds",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap08.ipynb b/chapters/chap08.ipynb
new file mode 100644
index 00000000..7d794270
--- /dev/null
+++ b/chapters/chap08.ipynb
@@ -0,0 +1,740 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "external-reward",
+   "metadata": {},
+   "source": [
+    "# Chapter 8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "compound-announcement",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "chicken-emphasis",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we developed a quadratic model of world\n",
+    "population growth from 1950 to 2016. It is a simple model, but it fits\n",
+    "the data well and the mechanisms it's based on are plausible.\n",
+    "\n",
+    "In this chapter we'll use the quadratic model to generate projections of future growth, and compare our results to projections from actual\n",
+    "demographers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "global-international",
+   "metadata": {},
+   "source": [
+    "Here's the code that downloads the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "necessary-factor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates.html'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "designed-entrance",
+   "metadata": {},
+   "source": [
+    "And here's the code that reads `table2`, which contains world populations estimates from the U.S. Census and U.N. DESA, among other organizations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "changed-desktop",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_html\n",
+    "\n",
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "metallic-inventory",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table2 = tables[2]\n",
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "further-armstrong",
+   "metadata": {},
+   "source": [
+    "## Generating Projections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "concrete-lightning",
+   "metadata": {},
+   "source": [
+    "Now let's run the quadratic model, extending the results until 2100, and see how our projections compare to the professionals'.\n",
+    "\n",
+    "Here's the code we'll need from the previous chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "extraordinary-shade",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def run_simulation(system, growth_func):\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.p_0\n",
+    "    \n",
+    "    for t in range(system.t_0, system.t_end):\n",
+    "        growth = growth_func(results[t], t, system)\n",
+    "        results[t+1] = results[t] + growth\n",
+    "        \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "indirect-russia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def growth_func_quad(pop, t, system):\n",
+    "    return system.alpha * pop + system.beta * pop**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "collaborative-schedule",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "census = table2.census / 1e9\n",
+    "un = table2.un / 1e9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "comfortable-compression",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "t_0 = census.index[0]\n",
+    "p_0 = census[t_0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "working-cleveland",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = System(t_0=t_0,\n",
+    "                p_0=p_0,\n",
+    "                t_end=2100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "sophisticated-adult",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.alpha = 25 / 1000\n",
+    "system.beta = -1.8 / 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "broken-windsor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, growth_func_quad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "latest-function",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "show(results.tail())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "outer-ensemble",
+   "metadata": {},
+   "source": [
+    "And here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "portable-pottery",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "results.plot(color='gray', label='model')\n",
+    "decorate(xlabel='Year', \n",
+    "         ylabel='World population (billion)',\n",
+    "         title='Quadratic Model Projection')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "macro-carroll",
+   "metadata": {},
+   "source": [
+    "According to the model, population growth will slow gradually after 2020, approaching 12.5 billion by 2100.\n",
+    "\n",
+    "I am using the word \"projection\" deliberately, rather than\n",
+    "\"prediction\", with the following distinction: \"prediction\" implies\n",
+    "something like \"this is what we should reasonably expect to happen, at\n",
+    "least approximately\"; \"projection\" implies something like \"if this\n",
+    "model is actually a good description of what is happening in this\n",
+    "system, and if nothing in the future causes the parameters of the model to change, this is what would happen.\"\n",
+    "\n",
+    "Using \"projection\" leaves open the possibility that there are important things in the real world that are not captured in the model. It also suggests that, even if the model is good, the parameters we estimate based on the past might be different in the future.\n",
+    "\n",
+    "The quadratic model we've been working with is based on the assumption\n",
+    "that population growth is limited by the availability of resources; in\n",
+    "that scenario, as the population approaches carrying capacity, birth\n",
+    "rates fall and death rates rise because resources become scarce.\n",
+    "\n",
+    "If that assumption is valid, we might be able to use actual population\n",
+    "growth to estimate carrying capacity, especially if we observe the\n",
+    "transition into the regime where the growth rate starts to fall.\n",
+    "\n",
+    "But in the case of world population growth, those conditions don't\n",
+    "apply. Over the last 50 years, the net growth rate has leveled off, but not yet started to fall, so we don't have enough data to make a credible estimate of carrying capacity. And resource limitations are probably *not* the primary reason growth has slowed. As evidence, consider:\n",
+    "\n",
+    "-   First, the death rate is not increasing; rather, it has declined\n",
+    "    from 1.9% in 1950 to 0.8% now (see <http://modsimpy.com/mortality>).\n",
+    "    So the decrease in net growth is due entirely to declining birth\n",
+    "    rates.\n",
+    "\n",
+    "-   Second, the relationship between resources and birth rate is the\n",
+    "    opposite of what the model assumes; as nations develop and people\n",
+    "    become more wealthy, birth rates tend to fall.\n",
+    "\n",
+    "We should not take too seriously the idea that this model can estimate\n",
+    "carrying capacity. But the predictions of a model can be credible even\n",
+    "if the assumptions of the model are not strictly true. For example,\n",
+    "population growth might behave *as if* it is resource limited, even if\n",
+    "the actual mechanism is something else.\n",
+    "\n",
+    "In fact, demographers who study population growth often use models\n",
+    "similar to ours. In the next section, we'll compare our projections to\n",
+    "theirs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "small-seminar",
+   "metadata": {},
+   "source": [
+    "## Projections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "greater-illness",
+   "metadata": {},
+   "source": [
+    "From the same page where we got the past population estimates, we'll read `table3`, which contains predictions for population growth over the next 50-100 years, generated by the U.S. Census, U.N. DESA, and the Population Reference Bureau."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "precious-contribution",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table3 = tables[3]\n",
+    "table3.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "coated-smoke",
+   "metadata": {},
+   "source": [
+    "Some values are `NaN`, which indicates missing data, because some organizations did not publish projections for some years.\n",
+    "\n",
+    "The column names are long strings; for convenience, I'll replace them with abbreviations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "headed-tuner",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table3.columns = ['census', 'prb', 'un']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ceramic-scroll",
+   "metadata": {},
+   "source": [
+    "The following function plots projections from the U.N. DESA and U.S. Census.  It uses `dropna` to remove the `NaN` values from each series before plotting it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "paperback-delay",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_projections(table):\n",
+    "    \"\"\"Plot world population projections.\n",
+    "    \n",
+    "    table: DataFrame with columns 'un' and 'census'\n",
+    "    \"\"\"\n",
+    "    census_proj = table.census.dropna() / 1e9\n",
+    "    un_proj = table.un.dropna() / 1e9\n",
+    "    \n",
+    "    census_proj.plot(style=':', label='US Census')\n",
+    "    un_proj.plot(style='--', label='UN DESA')\n",
+    "    \n",
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prostate-matrix",
+   "metadata": {},
+   "source": [
+    "Here are their projections compared to the results of the quadratic model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "billion-dynamics",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_projections(table3)\n",
+    "results.plot(color='gray', label='model')\n",
+    "decorate(title='Quadratic Model Projection')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "integrated-there",
+   "metadata": {},
+   "source": [
+    "The U.N. DESA expects the world population to reach 11 billion around 2100, and then level off.\n",
+    "Projections by U.S. Census are a little lower, and they only go until 2050.\n",
+    "\n",
+    "Real demographers expect world population to grow more slowly than our model projects, probably because their models are broken down by region and country, where conditions are different, and they take into account expected economic development.\n",
+    "\n",
+    "Nevertheless, their projections are qualitatively similar to ours, and\n",
+    "theirs differ from each other almost as much as they differ from ours.\n",
+    "So the results from this model, simple as it is, are not entirely unreasonable.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "serial-binary",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "\n",
+    "You might be interested in this [video by Hans Rosling about the demographic changes we expect in this century](https://www.youtube.com/watch?v=ezVk1ahRF78)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "authorized-suggestion",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "**Exercise:** The net growth rate of world population has been declining for several decades.  That observation suggests one more way to generate more realistic projections, by extrapolating observed changes in growth rate.\n",
+    "\n",
+    "To compute past growth rates, we'll use a function called `diff`, which computes the difference between successive elements in a `Series`.  For example, here are the changes from one year to the next in `census`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "handmade-funeral",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diff = census.diff()\n",
+    "diff.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "discrete-scanner",
+   "metadata": {},
+   "source": [
+    "The first element is `NaN` because we don't have the data for 1945, so we can't compute the first difference.\n",
+    "\n",
+    "If we divide these differences by the populations, the result is an estimate of the growth rate during each year: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "objective-accused",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha = census.diff() / census\n",
+    "alpha.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "weighted-trouble",
+   "metadata": {},
+   "source": [
+    "The following function computes and plots the growth rates for the `census` and `un` estimates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "unique-matrix",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_alpha():\n",
+    "    alpha_census = census.diff() / census\n",
+    "    alpha_census.plot(style='.', label='US Census')\n",
+    "\n",
+    "    alpha_un = un.diff() / un\n",
+    "    alpha_un.plot(style='.', label='UN DESA')\n",
+    "\n",
+    "    decorate(xlabel='Year', label='Net growth rate')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "flexible-amateur",
+   "metadata": {},
+   "source": [
+    "And here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "pressing-proceeding",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_alpha()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cross-reducing",
+   "metadata": {},
+   "source": [
+    "Other than a bump around 1990, net growth rate has been declining roughly linearly since 1970.\n",
+    "\n",
+    "We can model the decline by fitting a line to this data and extrapolating into the future.\n",
+    "\n",
+    "Here's a function that takes a time stamp and computes a line that roughly fits the growth rates since 1970."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "mexican-denver",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def alpha_func(t):\n",
+    "    intercept = 0.02\n",
+    "    slope = -0.00021\n",
+    "    return intercept + slope * (t - 1970)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "handy-dubai",
+   "metadata": {},
+   "source": [
+    "To see what it looks like, I'll create an array of time stamps from 1960 to 2020 and use `alpha_func` to compute the corresponding growth rates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "addressed-worker",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import linspace\n",
+    "\n",
+    "t_array = linspace(1960, 2020, 5)\n",
+    "alpha_array = alpha_func(t_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "jewish-operations",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like, compared to the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "cathedral-shakespeare",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.pyplot import plot\n",
+    "\n",
+    "plot_alpha()\n",
+    "plot(t_array, alpha_array, color='gray')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "expensive-viewer",
+   "metadata": {},
+   "source": [
+    "If you don't like the `slope` and `intercept` I chose, feel free to adjust them.\n",
+    "\n",
+    "Now, as an exercise, you can use this function to make a projection of world population until 2100.\n",
+    "\n",
+    "1. Create a `System` object that includes `alpha_func` as a system variable.\n",
+    "\n",
+    "2. Define a growth function that uses `alpha_func` to compute the net growth rate at the given time `t`.\n",
+    "\n",
+    "3. Run a simulation from 1960 to 2100 with your update function, and plot the results.\n",
+    "\n",
+    "4. Compare your projections with those from the US Census and UN."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "common-april",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "ignored-chain",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "color-accountability",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "numeric-raise",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "included-vehicle",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "brown-rating",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "laughing-cylinder",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "personalized-parking",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "simple-version",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap09.ipynb b/chapters/chap09.ipynb
new file mode 100644
index 00000000..9aa9f8e7
--- /dev/null
+++ b/chapters/chap09.ipynb
@@ -0,0 +1,1054 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "asian-customer",
+   "metadata": {},
+   "source": [
+    "# Chapter 9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "gentle-purchase",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "biblical-murder",
+   "metadata": {},
+   "source": [
+    "In this chapter we express the models from previous chapters as\n",
+    "difference equations and differential equations, solve the equations,\n",
+    "and derive the functional forms of the solutions. We also discuss the\n",
+    "complementary roles of mathematical analysis and simulation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hybrid-retrieval",
+   "metadata": {},
+   "source": [
+    "## Recurrence relations\n",
+    "\n",
+    "The population models in the previous chapter and this one are simple\n",
+    "enough that we didn't really need to run simulations. We could have\n",
+    "solved them mathematically. For example, we wrote the constant growth\n",
+    "model like this:\n",
+    "\n",
+    "```\n",
+    "results[t+1] = results[t] + annual_growth\n",
+    "```\n",
+    "\n",
+    "In mathematical notation, we would write the same model like this:\n",
+    "\n",
+    "$$x_{n+1} = x_n + c$$ \n",
+    "\n",
+    "where $x_n$ is the population during year $n$,\n",
+    "$x_0$ is a given initial population, and $c$ is constant annual growth.\n",
+    "This way of representing the model is a **recurrence relation**; see\n",
+    "<http://modsimpy.com/recur>.\n",
+    "\n",
+    "Sometimes it is possible to solve a recurrence relation by writing an\n",
+    "equation that computes $x_n$, for a given value of $n$, directly; that\n",
+    "is, without computing the intervening values from $x_1$ through\n",
+    "$x_{n-1}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "specified-mexican",
+   "metadata": {},
+   "source": [
+    "In the case of constant growth we can see that $x_1 = x_0 + c$, and\n",
+    "$x_2 = x_1 + c$. Combining these, we get $x_2 = x_0 + 2c$, then\n",
+    "$x_3 = x_0 + 3c$, and it is not hard to conclude that in general\n",
+    "\n",
+    "$$x_n = x_0 + nc$$ \n",
+    "\n",
+    "So if we want to know $x_{100}$ and we don't care\n",
+    "about the other values, we can compute it with one multiplication and\n",
+    "one addition."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "motivated-consumer",
+   "metadata": {},
+   "source": [
+    "We can also write the proportional model as a recurrence relation:\n",
+    "\n",
+    "$$x_{n+1} = x_n + \\alpha x_n$$ \n",
+    "\n",
+    "Or more conventionally as:\n",
+    "\n",
+    "$$x_{n+1} = x_n (1 + \\alpha)$$ \n",
+    "\n",
+    "Now we can see that\n",
+    "\n",
+    "$x_1 = x_0 (1 + \\alpha)$, and $x_2 = x_0 (1 + \\alpha)^2$, and in general\n",
+    "\n",
+    "$$x_n = x_0 (1 + \\alpha)^n$$ \n",
+    "\n",
+    "This result is a **geometric progression**;\n",
+    "see <http://modsimpy.com/geom>. When $\\alpha$ is positive, the factor\n",
+    "$1+\\alpha$ is greater than 1, so the elements of the sequence grow\n",
+    "without bound.\n",
+    "\n",
+    "Finally, we can write the quadratic model like this:\n",
+    "\n",
+    "$$x_{n+1} = x_n + \\alpha x_n + \\beta x_n^2$$ \n",
+    "\n",
+    "or with the more\n",
+    "conventional parameterization like this:\n",
+    "\n",
+    "$$x_{n+1} = x_n + r x_n (1 - x_n / K)$$ \n",
+    "\n",
+    "There is no analytic solution to\n",
+    "this equation, but we can approximate it with a differential equation\n",
+    "and solve that, which is what we'll do in the next section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "decreased-location",
+   "metadata": {},
+   "source": [
+    "## Differential equations\n",
+    "\n",
+    "Starting again with the constant growth model \n",
+    "\n",
+    "$$x_{n+1} = x_n + c$$ \n",
+    "\n",
+    "If we define $\\Delta x$ to be the change in $x$ from one time step to the next, we can write: \n",
+    "\n",
+    "$$\\Delta x = x_{n+1} - x_n = c$$ \n",
+    "\n",
+    "If we define\n",
+    "$\\Delta t$ to be the time step, which is one year in the example, we can\n",
+    "write the rate of change per unit of time like this:\n",
+    "\n",
+    "$$\\frac{\\Delta x}{\\Delta t} = c$$ \n",
+    "\n",
+    "This model is **discrete**, which\n",
+    "means it is only defined at integer values of $n$ and not in between.\n",
+    "But in reality, people are born and die all the time, not once a year,\n",
+    "so a **continuous** model might be more realistic."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "covered-defense",
+   "metadata": {},
+   "source": [
+    "We can make this model continuous by writing the rate of change in the\n",
+    "form of a derivative: \n",
+    "\n",
+    "$$\\frac{dx}{dt} = c$$ \n",
+    "\n",
+    "This way of representing the model is a **differential equation**; see <http://modsimpy.com/diffeq>.\n",
+    "\n",
+    "We can solve this differential equation if we multiply both sides by\n",
+    "$dt$: \n",
+    "\n",
+    "$$dx = c dt$$ \n",
+    "\n",
+    "And then integrate both sides: \n",
+    "\n",
+    "$$x(t) = c t + x_0$$\n",
+    "\n",
+    "Similarly, we can write the proportional growth model like this:\n",
+    "\n",
+    "$$\\frac{\\Delta x}{\\Delta t} = \\alpha x$$ \n",
+    "\n",
+    "And as a differential equation\n",
+    "like this: \n",
+    "\n",
+    "$$\\frac{dx}{dt} = \\alpha x$$ \n",
+    "\n",
+    "If we multiply both sides by\n",
+    "$dt$ and divide by $x$, we get \n",
+    "\n",
+    "$$\\frac{1}{x}~dx = \\alpha~dt$$ \n",
+    "\n",
+    "Now we\n",
+    "integrate both sides, yielding: \n",
+    "\n",
+    "$$\\ln x = \\alpha t + K$$ \n",
+    "\n",
+    "where $\\ln$ is the natural logarithm and $K$ is the constant of integration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "underlying-visitor",
+   "metadata": {},
+   "source": [
+    "Exponentiating both sides, we have\n",
+    "\n",
+    "$$\\exp(\\ln(x)) = \\exp(\\alpha t + K)$$ \n",
+    "\n",
+    "The exponential function can be written $\\exp(x)$ or $e^x$. In this book I use the first form because it resembles the Python code.\n",
+    "\n",
+    "We can rewrite the previous equation as\n",
+    "\n",
+    "$$x = \\exp(\\alpha t) \\exp(K)$$ \n",
+    "\n",
+    "Since $K$ is an arbitrary constant,\n",
+    "$\\exp(K)$ is also an arbitrary constant, so we can write\n",
+    "\n",
+    "$$x = C \\exp(\\alpha t)$$ \n",
+    "\n",
+    "where $C = \\exp(K)$. There are many solutions\n",
+    "to this differential equation, with different values of $C$. The\n",
+    "particular solution we want is the one that has the value $x_0$ when\n",
+    "$t=0$.\n",
+    "\n",
+    "When $t=0$, $x(t) = C$, so $C = x_0$ and the solution we want is\n",
+    "$$x(t) = x_0 \\exp(\\alpha t)$$ If you would like to see this derivation\n",
+    "done more carefully, you might like this video:\n",
+    "<http://modsimpy.com/khan1>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "czech-scratch",
+   "metadata": {},
+   "source": [
+    "## Analysis and simulation\n",
+    "\n",
+    "Once you have designed a model, there are generally two ways to proceed: simulation and analysis. Simulation often comes in the form of a computer program that models changes in a system over time, like births and deaths, or bikes moving from place to place. Analysis often comes in the form of algebra; that is, symbolic manipulation using mathematical notation.\n",
+    "\n",
+    "Analysis and simulation have different capabilities and limitations.\n",
+    "Simulation is generally more versatile; it is easy to add and remove\n",
+    "parts of a program and test many versions of a model, as we have done in the previous examples.\n",
+    "\n",
+    "But there are several things we can do with analysis that are harder or impossible with simulations:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "embedded-miniature",
+   "metadata": {},
+   "source": [
+    "-   With analysis we can sometimes compute, exactly and efficiently, a\n",
+    "    value that we could only approximate, less efficiently, with\n",
+    "    simulation. For example, in the quadratic model we plotted growth rate versus population and saw net crossed through zero when the population is\n",
+    "    near 14 billion.  We could estimate the crossing point using a\n",
+    "    numerical search algorithm (more about that later). But with the\n",
+    "    analysis in Section xxx, we get the general result that\n",
+    "    $K=-\\alpha/\\beta$.\n",
+    "\n",
+    "-   Analysis often provides \"computational shortcuts\", that is, the\n",
+    "    ability to jump forward in time to compute the state of a system\n",
+    "    many time steps in the future without computing the intervening\n",
+    "    states.\n",
+    "\n",
+    "-   We can use analysis to state and prove generalizations about models;\n",
+    "    for example, we might prove that certain results will always or\n",
+    "    never occur. With simulations, we can show examples and sometimes\n",
+    "    find counterexamples, but it is hard to write proofs.\n",
+    "\n",
+    "-   Analysis can provide insight into models and the systems they\n",
+    "    describe; for example, sometimes we can identify regimes of\n",
+    "    qualitatively different behavior and key parameters that control\n",
+    "    those behaviors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "impressive-planning",
+   "metadata": {},
+   "source": [
+    "When people see what analysis can do, they sometimes get drunk with\n",
+    "power, and imagine that it gives them a special ability to see past the veil of the material world and discern the laws of mathematics that govern the universe. When they analyze a model of a physical system, they talk about \"the math behind it\" as if our world is the mere shadow of a world of ideal mathematical entities (I am not making this up; see <http://modsimpy.com/plato>.).\n",
+    "\n",
+    "This is, of course, nonsense. Mathematical notation is a language\n",
+    "designed by humans for a purpose, specifically to facilitate symbolic\n",
+    "manipulations like algebra. Similarly, programming languages are\n",
+    "designed for a purpose, specifically to represent computational ideas\n",
+    "and run programs.\n",
+    "\n",
+    "Each of these languages is good for the purposes it was designed for and less good for other purposes. But they are often complementary, and one of the goals of this book is to show how they can be used together."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "graduate-conducting",
+   "metadata": {},
+   "source": [
+    "## Analysis with WolframAlpha\n",
+    "\n",
+    "Until recently, most analysis was done by rubbing graphite on wood\n",
+    "pulp, a process that is laborious and error-prone. A useful\n",
+    "alternative is symbolic computation. If you have used services like\n",
+    "WolframAlpha, you have used symbolic computation.\n",
+    "\n",
+    "For example, if you go to <https://www.wolframalpha.com/> and type\n",
+    "\n",
+    "```\n",
+    "df(t) / dt = alpha f(t)\n",
+    "```\n",
+    "\n",
+    "WolframAlpha infers that `f(t)` is a function of `t` and `alpha` is a\n",
+    "parameter; it classifies the query as a \"first-order linear ordinary\n",
+    "differential equation\\\", and reports the general solution:\n",
+    "\n",
+    "$$f(t) = c_1 \\exp(\\alpha t)$$ \n",
+    "\n",
+    "If you add a second equation to specify\n",
+    "the initial condition:\n",
+    "\n",
+    "```\n",
+    "df(t) / dt = alpha f(t),  f(0) = p_0\n",
+    "```\n",
+    "\n",
+    "WolframAlpha reports the particular solution:\n",
+    "\n",
+    "$$f(t) = p_0 \\exp(\\alpha t)$$\n",
+    "\n",
+    "WolframAlpha is based on Mathematica, a powerful programming language\n",
+    "designed specifically for symbolic computation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beginning-tamil",
+   "metadata": {},
+   "source": [
+    "## Analysis with SymPy\n",
+    "\n",
+    "Python has a library called SymPy that provides symbolic computation\n",
+    "tools similar to Mathematica. They are not as easy to use as\n",
+    "WolframAlpha, but they have some other advantages.\n",
+    "\n",
+    "Before we can use SymPy, we have to import it:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "civic-payday",
+   "metadata": {},
+   "source": [
+    "SymPy defines a `Symbol` object that represents symbolic variable names,\n",
+    "functions, and other mathematical entities.\n",
+    "\n",
+    "The `symbols` function takes a string and returns `Symbol` objects. So\n",
+    "if we run this assignment:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "flush-balance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sympy import symbols\n",
+    "\n",
+    "t = symbols('t')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faced-praise",
+   "metadata": {},
+   "source": [
+    "Python understands that `t` is a symbol, not a numerical value. If we\n",
+    "now run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ancient-anderson",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "expr = t + 1\n",
+    "expr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "industrial-confidence",
+   "metadata": {},
+   "source": [
+    "Python doesn't try to perform numerical addition; rather, it creates a\n",
+    "new `Symbol` that represents the sum of `t` and `1`. We can evaluate the\n",
+    "sum using `subs`, which substitutes a value for a symbol. This example\n",
+    "substitutes 2 for `t`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "familiar-oakland",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "expr.subs(t, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "progressive-celebration",
+   "metadata": {},
+   "source": [
+    "Functions in SymPy are represented by a special kind of `Symbol`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "incomplete-sleep",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sympy import Function\n",
+    "\n",
+    "f = Function('f')\n",
+    "f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "legislative-dispatch",
+   "metadata": {},
+   "source": [
+    "Now if we write `f(t)`, we get an object that represents the evaluation of a function, $f$, at a value, $t$. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "inside-sally",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f(t)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "interior-index",
+   "metadata": {},
+   "source": [
+    "But again SymPy doesn't actually\n",
+    "try to evaluate it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "indian-trout",
+   "metadata": {},
+   "source": [
+    "## Differential equations in SymPy\n",
+    "\n",
+    "SymPy provides a function, `diff`, that can differentiate a function. We can apply it to `f(t)` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "requested-program",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sympy import diff\n",
+    "\n",
+    "dfdt = diff(f(t), t)\n",
+    "dfdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "treated-commissioner",
+   "metadata": {},
+   "source": [
+    "The result is a `Symbol` that represents the derivative of `f` with\n",
+    "respect to `t`. But again, SymPy doesn't try to compute the derivative\n",
+    "yet.\n",
+    "\n",
+    "To represent a differential equation, we use `Eq`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "incomplete-parker",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sympy import Eq\n",
+    "\n",
+    "alpha = symbols('alpha')\n",
+    "eq1 = Eq(dfdt, alpha*f(t))\n",
+    "eq1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "concerned-asthma",
+   "metadata": {},
+   "source": [
+    "The result is an object that represents an equation.  Now\n",
+    "we can use `dsolve` to solve this differential equation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "described-overhead",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sympy import dsolve\n",
+    "\n",
+    "solution_eq = dsolve(eq1)\n",
+    "solution_eq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "inside-medicine",
+   "metadata": {},
+   "source": [
+    "The result is the **general\n",
+    "solution**, which still contains an unspecified constant, $C_1$. To get the **particular solution** where $f(0) = p_0$, we substitute `p_0` for `C1`. First, we have to create two more symbols:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "material-voice",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C1, p_0 = symbols('C1 p_0')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "serious-license",
+   "metadata": {},
+   "source": [
+    "Now we can perform the substitution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "supposed-domestic",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "particular = solution_eq.subs(C1, p_0)\n",
+    "particular"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "modern-raleigh",
+   "metadata": {},
+   "source": [
+    "The result is called the **exponential growth curve**; see\n",
+    "<http://modsimpy.com/expo>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "negative-chance",
+   "metadata": {},
+   "source": [
+    "## Solving the quadratic growth model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "arabic-victorian",
+   "metadata": {},
+   "source": [
+    "We'll use the (r, K) parameterization, so we'll need two more symbols:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "political-chinese",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r, K = symbols('r K')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "virtual-temperature",
+   "metadata": {},
+   "source": [
+    "Now we can write the differential equation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "lined-queen",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eq2 = Eq(diff(f(t), t), r * f(t) * (1 - f(t)/K))\n",
+    "eq2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "relative-zoning",
+   "metadata": {},
+   "source": [
+    "And solve it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "printable-typing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "solution_eq = dsolve(eq2)\n",
+    "solution_eq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "convertible-simple",
+   "metadata": {},
+   "source": [
+    "The result, `solution_eq`, contains `rhs`, which is the right-hand side of the solution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "final-treatment",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "general = solution_eq.rhs\n",
+    "general"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "phantom-melbourne",
+   "metadata": {},
+   "source": [
+    "We can evaluate the right-hand side at $t=0$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "freelance-admission",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "at_0 = general.subs(t, 0)\n",
+    "at_0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "precise-radar",
+   "metadata": {},
+   "source": [
+    "Now we want to find the value of `C1` that makes `f(0) = p_0`.\n",
+    "\n",
+    "So we'll create the equation `at_0 = p_0` and solve for `C1`.  Because this is just an algebraic identity, not a differential equation, we use `solve`, not `dsolve`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "orange-glasgow",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sympy import solve\n",
+    "\n",
+    "solutions = solve(Eq(at_0, p_0), C1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "periodic-bikini",
+   "metadata": {},
+   "source": [
+    "The result from `solve` is a list of solutions.  In this case, [we have reason to expect only one solution](https://en.wikipedia.org/wiki/Picard%E2%80%93Lindel%C3%B6f_theorem), but we still get a list, so we have to use the bracket operator, `[0]`, to select the first one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "expensive-palace",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(solutions), len(solutions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "worthy-sleeve",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "value_of_C1 = solutions[0]\n",
+    "value_of_C1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lightweight-pickup",
+   "metadata": {},
+   "source": [
+    "Now in the general solution, we want to replace `C1` with the value of `C1` we just figured out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "aggressive-queue",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "particular = general.subs(C1, value_of_C1)\n",
+    "particular"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "electric-albania",
+   "metadata": {},
+   "source": [
+    "The result is complicated, but SymPy provides a function that tries to simplify it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "funded-tolerance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "particular.simplify()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "scenic-preparation",
+   "metadata": {},
+   "source": [
+    "This function is called the **logistic growth curve**; see\n",
+    "<http://modsimpy.com/logistic>. In the context of growth models, the\n",
+    "logistic function is often written like this:\n",
+    "\n",
+    "$$f(t) = \\frac{K}{1 + A \\exp(-rt)}$$ \n",
+    "\n",
+    "where $A = (K - p_0) / p_0$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "burning-sherman",
+   "metadata": {},
+   "source": [
+    "We can use SymPy to confirm that these two forms are equivalent.  First we represent the alternative version of the logistic function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "institutional-finish",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = (K - p_0) / p_0\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "noble-auditor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sympy import exp\n",
+    "\n",
+    "logistic = K / (1 + A * exp(-r*t))\n",
+    "logistic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "brave-conditions",
+   "metadata": {},
+   "source": [
+    "To see whether two expressions are equivalent, we can check whether their difference simplifies to 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "industrial-administrator",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "(particular - logistic).simplify()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "therapeutic-topic",
+   "metadata": {},
+   "source": [
+    "This test only works one way: if SymPy says the difference reduces to 0, the expressions are definitely equivalent (and not just numerically close).\n",
+    "\n",
+    "But if SymPy can't find a way to simplify the result to 0, that doesn't necessarily mean there isn't one.  Testing whether two expressions are equivalent is a surprisingly hard problem; in fact, there is no algorithm that can solve it in general."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cutting-access",
+   "metadata": {},
+   "source": [
+    "If you would like to see this differential equation solved by hand, you might like this video: <http://modsimpy.com/khan2>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "outstanding-launch",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this chapter we wrote the growth models from the previous chapters in terms of difference and differential equations.\n",
+    "\n",
+    "What I called the constant growth model is more commonly called **linear growth** because the solution is a line.  If we model time as continuous, the solution is\n",
+    "\n",
+    "$$f(t) = p_0 + \\alpha t$$\n",
+    "\n",
+    "where $\\alpha$ is the growth rate.\n",
+    "\n",
+    "Similarly, the proportional growth model is usually called **exponential growth** because the solution is exponential:\n",
+    "\n",
+    "$$f(t) = p_0 \\exp{\\alpha t}$$\n",
+    "\n",
+    "Finally, the quadratic growth model is called **logistic growth** because the solution is the logistic function:\n",
+    "\n",
+    "$$f(t) = \\frac{K}{1 + A \\exp(-rt)}$$ \n",
+    "\n",
+    "where $A = (K - p_0) / p_0$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adjacent-tracy",
+   "metadata": {},
+   "source": [
+    "I avoided these terms until now because they are based on results we had not derived yet."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "offshore-passage",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ideal-ecology",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Solve the quadratic growth equation using the alternative parameterization\n",
+    "\n",
+    "$\\frac{df(t)}{dt} = \\alpha f(t) + \\beta f^2(t) $"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "internal-knock",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "smooth-sunglasses",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "fundamental-arlington",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "desirable-alpha",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "coupled-grounds",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "looking-ground",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "anticipated-paragraph",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "residential-spanking",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Use [WolframAlpha](https://www.wolframalpha.com/) to solve the quadratic growth model, using either or both forms of parameterization:\n",
+    "\n",
+    "    df(t) / dt = alpha f(t) + beta f(t)^2\n",
+    "\n",
+    "or\n",
+    "\n",
+    "    df(t) / dt = r f(t) (1 - f(t)/K)\n",
+    "\n",
+    "Find the general solution and also the particular solution where `f(0) = p_0`."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap10.ipynb b/chapters/chap10.ipynb
new file mode 100644
index 00000000..5ef4f184
--- /dev/null
+++ b/chapters/chap10.ipynb
@@ -0,0 +1,420 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dense-storm",
+   "metadata": {},
+   "source": [
+    "# Chapter 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "confused-bargain",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "thirty-medication",
+   "metadata": {},
+   "source": [
+    "# Case studies\n",
+    "\n",
+    "This chapter presents case studies where you can practice the tools we have learned so far."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "industrial-mercy",
+   "metadata": {},
+   "source": [
+    "## Prehistoric World Population\n",
+    "\n",
+    "On the Wikipedia page about world population estimates, the first table contains estimates for prehistoric populations.  The following cells process this table and plot some of the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "usual-penguin",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'World_population_estimates.html'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "thick-lincoln",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_html\n",
+    "\n",
+    "filename = 'World_population_estimates.html'\n",
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
+    "len(tables)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "disciplinary-chosen",
+   "metadata": {},
+   "source": [
+    "Select `tables[1]`, which is the second table on the page."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "conscious-orange",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table1 = tables[1]\n",
+    "table1.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hungry-monster",
+   "metadata": {},
+   "source": [
+    "Not all researchers provide estimates for the same dates.\n",
+    "\n",
+    "Again, we'll replace the long column names with more convenient abbreviations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "thick-blanket",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table1.columns = ['PRB', 'UN', 'Maddison', 'HYDE', 'Tanton', \n",
+    "                  'Biraben', 'McEvedy & Jones', 'Thomlinson', 'Durand', 'Clark']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "substantial-implement",
+   "metadata": {},
+   "source": [
+    "Some of the estimates are in a form Pandas doesn't recognize as numbers, but we can coerce them to be numeric."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ranking-prescription",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in table1.columns:\n",
+    "    table1[col] = pd.to_numeric(table1[col], errors='coerce')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "amazing-difference",
+   "metadata": {},
+   "source": [
+    "Here are the results.  Notice that we are working in millions now, not billions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "metallic-offense",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "table1.plot()\n",
+    "decorate(xlim=[-10000, 2000], xlabel='Year', \n",
+    "         ylabel='World population (millions)',\n",
+    "         title='Prehistoric population estimates')\n",
+    "plt.legend(fontsize='small');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "human-conservation",
+   "metadata": {},
+   "source": [
+    "We can use `xlim` to zoom in on everything after Year 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "solar-action",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table1.plot()\n",
+    "decorate(xlim=[0, 2000], xlabel='Year', \n",
+    "         ylabel='World population (millions)',\n",
+    "         title='CE population estimates')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "wanted-fantasy",
+   "metadata": {},
+   "source": [
+    "See if you can find a model that fits these data well from Year 0 to 1950.\n",
+    "\n",
+    "How well does your best model predict actual population growth from 1950 to the present?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "together-jackson",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "simple-verse",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ruled-spain",
+   "metadata": {},
+   "source": [
+    "## One queue or two?\n",
+    "\n",
+    "This case study is related to **queueing theory**, which is the study of systems that involve waiting in lines, also known as \"queues\".\n",
+    "\n",
+    "Suppose you are designing the checkout area for a new store. There is\n",
+    "enough room in the store for two checkout counters and a waiting area\n",
+    "for customers. You can make two lines, one for each counter, or one line that feeds both counters.\n",
+    "\n",
+    "In theory, you might expect a single line to be better, but it has some practical drawbacks: in order to maintain a single line, you might have to install barriers, and customers might be put off by what seems to be a longer line, even if it moves faster.\n",
+    "\n",
+    "So you'd like to check whether the single line is really better and by\n",
+    "how much. Simulation can help answer this question.\n",
+    "\n",
+    "This figure shows the three scenarios we'll consider:\n",
+    "\n",
+    "![One queue, one server (left), one queue, two servers (middle), two\n",
+    "queues, two servers (right).](figs/queue.pdf){width=\"4.5in\"}\n",
+    "\n",
+    "As we did in the bike share model, we'll assume that a customer is equally likely to arrive during any time step. I'll denote this probability using the Greek letter lambda, $\\lambda$, or the variable name `lam`. The value of $\\lambda$ probably varies from day to day, so we'll have to consider a range of possibilities.\n",
+    "\n",
+    "Based on data from other stores, you know that it takes 5 minutes for a customer to check out, on average. But checkout times are variable: most customers take less than 5 minutes, but some take substantially more. A simple way to model this variability is to assume that when a customer is checking out, they always have the same probability of finishing during the next time step, regardless of how long they have been checking out. I'll denote this probability using the Greek letter mu, $\\mu$, or the variable name `mu`.\n",
+    "\n",
+    "If we choose $\\mu=1/5$ per minute, the average time for each checkout\n",
+    "will be 5 minutes, which is consistent with the data. Most people takes less than 5 minutes, but a few take substantially longer, which is probably not a bad model of the distribution in real stores.\n",
+    "\n",
+    "Now we're ready to get started. In the repository for this book, you'll find a notebook called `queue.ipynb` that contains some code to get you started and instructions.\n",
+    "\n",
+    "As always, you should practice incremental development: write no more\n",
+    "than one or two lines of code a time, and test as you go!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "forward-point",
+   "metadata": {},
+   "source": [
+    "## Predicting salmon populations\n",
+    "\n",
+    "Each year the U.S. Atlantic Salmon Assessment Committee reports\n",
+    "estimates of salmon populations in oceans and rivers in the northeastern United States. The reports are useful for monitoring changes in these populations, but they generally do not include predictions.\n",
+    "\n",
+    "The goal of this case study is to model year-to-year changes in\n",
+    "population, evaluate how predictable these changes are, and estimate the probability that a particular population will increase or decrease in the next 10 years.\n",
+    "\n",
+    "As an example, I use data from page 18 of the 2017 report, which\n",
+    "provides population estimates for the Narraguagus and Sheepscot Rivers\n",
+    "in Maine.\n",
+    "\n",
+    "In the repository for this book, you'll find a notebook called\n",
+    "`salmon.ipynb` that contains some code to get you started and\n",
+    "instructions.\n",
+    "\n",
+    "You should take my instructions as suggestions; if you want to try\n",
+    "something different, please do!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "limiting-moore",
+   "metadata": {},
+   "source": [
+    "## Tree growth\n",
+    "\n",
+    "This case study is based on \"Height-Age Curves for Planted Stands of\n",
+    "Douglas Fir, with Adjustments for Density\", a working paper by\n",
+    "Flewelling, Collier, Gonyea, Marshall, and Turnblom.\n",
+    "\n",
+    "It provides \"site index curves\", which are curves that show the\n",
+    "expected height of the tallest tree in a stand of Douglas firs as a\n",
+    "function of age, for a stand where the trees are the same age.\n",
+    "\n",
+    "Depending on the quality of the site, the trees might grow more quickly or slowing. So each curve is identified by a \"site index\\\" that indicates the quality of the site.\n",
+    "\n",
+    "The following figure shows site curves for three different site\n",
+    "indices. \n",
+    "\n",
+    "![Site index curves for tree\n",
+    "growth.](figs/trees-fig01.pdf){height=\"3in\"}\n",
+    "\n",
+    "The goal of this case study is to explain the shape of these\n",
+    "curves, that is, why trees grow the way they do.\n",
+    "\n",
+    "As a starting place, let's assume that the ability of the tree to gain\n",
+    "mass is limited by the area it exposes to sunlight, and that the growth rate (in mass) is proportional to that area. In that case we can write:\n",
+    "\n",
+    "$$m_{n+1} = m_n + \\alpha A$$\n",
+    "\n",
+    "where $m_n$ is the mass of the at time step $n$, $A$ is the area exposed to sunlight, and $\\alpha$ is an unknown growth parameter.\n",
+    "\n",
+    "To get from $m$ to $A$, I'll make the additional assumption that mass is proportional to height raised to an unknown power: $m = \\beta h^D$ where $h$ is height, $\\beta$ is an unknown constant of proportionality, and $D$ is the dimension that relates height and mass. I start by assuming $D=3$, but then revisit that assumption.\n",
+    "\n",
+    "Finally, we'll assume that area is proportional to height squared:\n",
+    "\n",
+    "$$A = \\gamma h^2$$\n",
+    "\n",
+    "I specify height in feet, and choose units for mass and area so that\n",
+    "$\\beta=1$ and $\\gamma=1$. Putting all that together, we can write a\n",
+    "difference equation for height:\n",
+    "\n",
+    "$$h_{n+1}^D = h_n^D + \\alpha h_n^2$$\n",
+    "\n",
+    "With $D=3$, the solution to this equation is close to a straight line,\n",
+    "which is not a bad model for the growth curves. But the model implies\n",
+    "that trees can grow forever, and we know that's not true. As trees get\n",
+    "taller, it gets harder for them to move water and nutrients against the force of gravity, and their growth slows.\n",
+    "\n",
+    "We can model this effect by adding a factor to the model similar to what we saw in the logistic model of population growth. Instead of assuming:\n",
+    "\n",
+    "$$m_{n+1} = m_n + \\alpha A$$\n",
+    "\n",
+    "Let's assume\n",
+    "\n",
+    "$$m_{n+1} = m_n + \\alpha A (1 - h / K)$$\n",
+    "\n",
+    "where $K$ is similar to the carrying capacity of the logistic model. As $h$ approaches $K$, the factor $(1 - h/K)$ goes to 0, causing growth to level off.\n",
+    "\n",
+    "In the repository for this book, you'll find a notebook called\n",
+    "`trees.ipynb` that implements both models and uses them to fit the data.\n",
+    "\n",
+    "There are no exercises in this case study; it is mostly meant as an\n",
+    "example of what you can do with the tools we have so far, and a preview of what we will be able to do with the tools in the next few chapters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "australian-gregory",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap11.ipynb b/chapters/chap11.ipynb
new file mode 100644
index 00000000..6cd6a696
--- /dev/null
+++ b/chapters/chap11.ipynb
@@ -0,0 +1,821 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "pressing-munich",
+   "metadata": {},
+   "source": [
+    "# Chapter 11"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "detected-creation",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "persistent-carbon",
+   "metadata": {},
+   "source": [
+    "In this chapter, we develop a model of an epidemic as it spreads in a\n",
+    "susceptible population, and use it to evaluate the effectiveness of\n",
+    "possible interventions.\n",
+    "\n",
+    "My presentation of the model in the next few chapters is based on an excellent article by David Smith and Lang Moore, [^1]: Smith and Moore, \"The SIR Model for Spread of Disease,\" Journal of Online Mathematics and its Applications, December 2001, available at <http://modsimpy.com/sir>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "working-patent",
+   "metadata": {},
+   "source": [
+    "## The Freshman Plague\n",
+    "\n",
+    "Every year at Olin College, about 90 new students come to campus from\n",
+    "around the country and the world. Most of them arrive healthy and happy, but usually at least one brings with them some kind of infectious disease. A few weeks later, predictably, some fraction of the incoming class comes down with what we call \"The Freshman Plague\".\n",
+    "\n",
+    "In this chapter we introduce a well-known model of infectious disease,\n",
+    "the Kermack-McKendrick model, and use it to explain the progression of\n",
+    "the disease over the course of the semester, predict the effect of\n",
+    "possible interventions (like immunization) and design the most effective intervention campaign.\n",
+    "\n",
+    "So far we have done our own modeling; that is, we've chosen physical\n",
+    "systems, identified factors that seem important, and made decisions\n",
+    "about how to represent them. In this chapter we start with an existing\n",
+    "model and reverse-engineer it. Along the way, we consider the modeling\n",
+    "decisions that went into it and identify its capabilities and\n",
+    "limitations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "distant-expense",
+   "metadata": {},
+   "source": [
+    "## The SIR model\n",
+    "\n",
+    "The Kermack-McKendrick model is a simple version of an **SIR model**,\n",
+    "so-named because it considers three categories of people:\n",
+    "\n",
+    "-   **S**: People who are \"susceptible\\\", that is, capable of\n",
+    "    contracting the disease if they come into contact with someone who\n",
+    "    is infected.\n",
+    "\n",
+    "-   **I**: People who are \"infectious\\\", that is, capable of passing\n",
+    "    along the disease if they come into contact with someone\n",
+    "    susceptible.\n",
+    "\n",
+    "-   **R**: People who are \"recovered\\\". In the basic version of the\n",
+    "    model, people who have recovered are considered to be immune to\n",
+    "    reinfection. That is a reasonable model for some diseases, but not\n",
+    "    for others, so it should be on the list of assumptions to reconsider\n",
+    "    later."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reasonable-kitchen",
+   "metadata": {},
+   "source": [
+    "Let's think about how the number of people in each category changes over time. Suppose we know that people with the disease are infectious for a period of 4 days, on average. If 100 people are infectious at a\n",
+    "particular point in time, and we ignore the particular time each one\n",
+    "became infected, we expect about 1 out of 4 to recover on any particular day.\n",
+    "\n",
+    "Putting that a different way, if the time between recoveries is 4 days, the recovery rate is about 0.25 recoveries per day, which we'll denote with the Greek letter gamma, $\\gamma$, or the variable name `gamma`.\n",
+    "\n",
+    "If the total number of people in the population is $N$, and the fraction currently infectious is $i$, the total number of recoveries we expect per day is $\\gamma i N$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "important-yugoslavia",
+   "metadata": {},
+   "source": [
+    "Now let's think about the number of new infections. Suppose we know that each susceptible person comes into contact with 1 person every 3 days, on average, in a way that would cause them to become infected if the other person is infected. We'll denote this contact rate with the Greek letter beta, $\\beta$.\n",
+    "\n",
+    "It's probably not reasonable to assume that we know $\\beta$ ahead of\n",
+    "time, but later we'll see how to estimate it based on data from previous outbreaks.\n",
+    "\n",
+    "If $s$ is the fraction of the population that's susceptible, $s N$ is\n",
+    "the number of susceptible people, $\\beta s N$ is the number of contacts per day, and $\\beta s i N$ is the number of those contacts where the other person is infectious."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "virgin-cambodia",
+   "metadata": {},
+   "source": [
+    "In summary:\n",
+    "\n",
+    "-   The number of recoveries we expect per day is $\\gamma i N$; dividing by $N$ yields the fraction of the population that recovers in a day, which is $\\gamma i$.\n",
+    "\n",
+    "-   The number of new infections we expect per day is $\\beta s i N$;\n",
+    "    dividing by $N$ yields the fraction of the population that gets\n",
+    "    infected in a day, which is $\\beta s i$.\n",
+    "\n",
+    "This model assumes that the population is closed; that is, no one\n",
+    "arrives or departs, so the size of the population, $N$, is constant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fourth-celtic",
+   "metadata": {},
+   "source": [
+    "## The SIR equations\n",
+    "\n",
+    "If we treat time as a continuous quantity, we can write differential\n",
+    "equations that describe the rates of change for $s$, $i$, and $r$ (where $r$ is the fraction of the population that has recovered):\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\frac{ds}{dt} &= -\\beta s i \\\\\n",
+    "\\frac{di}{dt} &= \\beta s i - \\gamma i\\\\\n",
+    "\\frac{dr}{dt} &= \\gamma i\\end{aligned}$$ \n",
+    "\n",
+    "To avoid cluttering the equations, I leave it implied that $s$ is a function of time, $s(t)$, and likewise for $i$ and $r$.\n",
+    "\n",
+    "SIR models are examples of **compartment models**, so-called because\n",
+    "they divide the world into discrete categories, or compartments, and\n",
+    "describe transitions from one compartment to another. Compartments are\n",
+    "also called **stocks** and transitions between them are called\n",
+    "**flows**.\n",
+    "\n",
+    "In this example, there are three stocks---susceptible, infectious, and\n",
+    "recovered---and two flows---new infections and recoveries. Compartment\n",
+    "models are often represented visually using stock and flow diagrams (see <http://modsimpy.com/stock>).\n",
+    "\n",
+    "The following figure shows the stock and flow diagram for an SIR\n",
+    "model.\n",
+    "\n",
+    "![Stock and flow diagram for an SIR\n",
+    "model.](figs/stock_flow1.pdf){width=\"4in\"}\n",
+    "\n",
+    "Stocks are represented by rectangles, flows by arrows. The widget in the middle of the arrows represents a valve that controls the rate of flow; the diagram shows the parameters that control the valves."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "martial-details",
+   "metadata": {},
+   "source": [
+    "## Implementation\n",
+    "\n",
+    "For a given physical system, there are many possible models, and for a\n",
+    "given model, there are many ways to represent it. For example, we can\n",
+    "represent an SIR model as a stock-and-flow diagram, as a set of\n",
+    "differential equations, or as a Python program. The process of\n",
+    "representing a model in these forms is called **implementation**. In\n",
+    "this section, we implement the SIR model in Python.\n",
+    "\n",
+    "I'll represent the initial state of the system using a `State` object\n",
+    "with state variables `S`, `I`, and `R`; they represent the fraction of\n",
+    "the population in each compartment.\n",
+    "\n",
+    "We can initialize the `State` object with the *number* of people in each compartment, assuming there is one infected student in a class of 90:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "criminal-change",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init = State(S=89, I=1, R=0)\n",
+    "init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "chronic-jonathan",
+   "metadata": {},
+   "source": [
+    "And then convert the numbers to fractions by dividing by the total:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "pediatric-ratio",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import sum\n",
+    "\n",
+    "init /= sum(init)\n",
+    "init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ordinary-scottish",
+   "metadata": {},
+   "source": [
+    "For now, let's assume we know the time between contacts and time between\n",
+    "recoveries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "little-stylus",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc = 3             # time between contacts in days \n",
+    "tr = 4             # recovery time in days"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "covered-avenue",
+   "metadata": {},
+   "source": [
+    "We can use them to compute the parameters of the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "veterinary-aerospace",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lightweight-delta",
+   "metadata": {},
+   "source": [
+    "Now we need a `System` object to store the parameters and initial\n",
+    "conditions. The following function takes the system parameters and returns a new `System` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "nasty-sherman",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(beta, gamma):\n",
+    "    init = State(S=89, I=1, R=0)\n",
+    "    init /= sum(init)\n",
+    "\n",
+    "    t0 = 0\n",
+    "    t_end = 7 * 14\n",
+    "\n",
+    "    return System(init=init, t0=t0, t_end=t_end,\n",
+    "                  beta=beta, gamma=gamma)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "victorian-blogger",
+   "metadata": {},
+   "source": [
+    "The default value for `t_end` is 14 weeks, about the length of a\n",
+    "semester.\n",
+    "\n",
+    "Here's what the `System` object looks like. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "supported-shadow",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "system = make_system(beta, gamma)\n",
+    "system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "alone-desire",
+   "metadata": {},
+   "source": [
+    "## The update function\n",
+    "\n",
+    "At any point in time, the state of the system is represented by a\n",
+    "`State` object with three variables, `S`, `I` and `R`. So I'll define an\n",
+    "update function that takes as parameters a `State` object, the current\n",
+    "time, and a `System` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "wound-wayne",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def update_func(state, t, system):\n",
+    "    s, i, r = state\n",
+    "\n",
+    "    infected = system.beta * i * s    \n",
+    "    recovered = system.gamma * i\n",
+    "    \n",
+    "    s -= infected\n",
+    "    i += infected - recovered\n",
+    "    r += recovered\n",
+    "    \n",
+    "    return State(S=s, I=i, R=r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aboriginal-malpractice",
+   "metadata": {},
+   "source": [
+    "The first line uses a feature we have not seen before, **multiple\n",
+    "assignment**. The value on the right side is a `State` object that\n",
+    "contains three values. The left side is a sequence of three variable\n",
+    "names. The assignment does just what we want: it assigns the three\n",
+    "values from the `State` object to the three variables, in order.\n",
+    "\n",
+    "The variables `s`, `i` and `r`, are lowercase to distinguish them\n",
+    "from the state variables, `S`, `I` and `R`.\n",
+    "\n",
+    "The update function computes `infected` and `recovered` as a fraction of the population, then updates `s`, `i` and `r`. The return value is a `State` that contains the updated values.\n",
+    "\n",
+    "When we call `update_func` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "assigned-feelings",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state = update_func(init, 0, system)\n",
+    "state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "standard-search",
+   "metadata": {},
+   "source": [
+    "You might notice that this version of `update_func` does not use one of its parameters, `t`. I include it anyway because update functions\n",
+    "sometimes depend on time, and it is convenient if they all take the same parameters, whether they need them or not."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "informational-cisco",
+   "metadata": {},
+   "source": [
+    "## Running the simulation\n",
+    "\n",
+    "Now we can simulate the model over a sequence of time steps:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "occasional-pottery",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from numpy import arange\n",
+    "\n",
+    "def run_simulation(system, update_func):\n",
+    "    state = system.init\n",
+    "\n",
+    "    for t in arange(system.t0, system.t_end):\n",
+    "        state = update_func(state, t, system)\n",
+    "\n",
+    "    return state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fifteen-metallic",
+   "metadata": {},
+   "source": [
+    "The parameters of `run_simulation` are the `System` object and the\n",
+    "update function. The `System` object contains the parameters, initial\n",
+    "conditions, and values of `t0` and `t_end`.\n",
+    "\n",
+    "We can call `run_simulation` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "differential-difference",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(beta, gamma)\n",
+    "final_state = run_simulation(system, update_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "incredible-marsh",
+   "metadata": {},
+   "source": [
+    "The result is the final state of the system:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "weekly-acrobat",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "behind-removal",
+   "metadata": {},
+   "source": [
+    "This result indicates that after 14 weeks (98 days), about 52% of the\n",
+    "population is still susceptible, which means they were never infected,\n",
+    "less than 1% are actively infected, and 48% have recovered, which means they were infected at some point."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "serial-genius",
+   "metadata": {},
+   "source": [
+    "## Collecting the results\n",
+    "\n",
+    "The previous version of `run_simulation` only returns the final state,\n",
+    "but we might want to see how the state changes over time. We'll consider two ways to do that: first, using three `TimeSeries` objects, then using a new object called a `TimeFrame`.\n",
+    "\n",
+    "Here's the first version:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "advanced-recommendation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def run_simulation(system, update_func):\n",
+    "    S = TimeSeries()\n",
+    "    I = TimeSeries()\n",
+    "    R = TimeSeries()\n",
+    "\n",
+    "    state = system.init\n",
+    "    t0 = system.t0\n",
+    "    S[t0], I[t0], R[t0] = state\n",
+    "    \n",
+    "    for t in arange(system.t0, system.t_end):\n",
+    "        state = update_func(state, t, system)\n",
+    "        S[t+1], I[t+1], R[t+1] = state\n",
+    "    \n",
+    "    return S, I, R"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "behind-breathing",
+   "metadata": {},
+   "source": [
+    "First, we create `TimeSeries` objects to store the results. Notice that\n",
+    "the variables `S`, `I`, and `R` are `TimeSeries` objects now.\n",
+    "\n",
+    "Next we initialize `state`, `t0`, and the first elements of `S`, `I` and\n",
+    "`R`.\n",
+    "\n",
+    "Inside the loop, we use `update_func` to compute the state of the system\n",
+    "at the next time step, then use multiple assignment to unpack the\n",
+    "elements of `state`, assigning each to the corresponding `TimeSeries`.\n",
+    "\n",
+    "At the end of the function, we return the values `S`, `I`, and `R`. This\n",
+    "is the first example we have seen where a function returns more than one\n",
+    "value.\n",
+    "\n",
+    "Now we can run the function like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "subtle-zambia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(beta, gamma)\n",
+    "S, I, R = run_simulation(system, update_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "smoking-toddler",
+   "metadata": {},
+   "source": [
+    "We'll use the following function to plot the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "welcome-tractor",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def plot_results(S, I, R):\n",
+    "    S.plot(style='--', label='Susceptible')\n",
+    "    I.plot(style='-', label='Infected')\n",
+    "    R.plot(style=':', label='Resistant')\n",
+    "    decorate(xlabel='Time (days)',\n",
+    "             ylabel='Fraction of population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "important-command",
+   "metadata": {},
+   "source": [
+    "And run it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "agricultural-joining",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_results(S, I, R)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "civic-pharmacy",
+   "metadata": {},
+   "source": [
+    "Notice that it takes about three weeks (21 days) for the outbreak to get going, and about six weeks (42 days) before it peaks. The fraction of the population that's infected is never very high, but it adds up. In total, almost half the population gets sick."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unexpected-empire",
+   "metadata": {},
+   "source": [
+    "## Now with a TimeFrame\n",
+    "\n",
+    "If the number of state variables is small, storing them as separate\n",
+    "`TimeSeries` objects might not be so bad. But a better alternative is to use a `TimeFrame`, which is another object defined in the ModSim\n",
+    "library.\n",
+    "\n",
+    "A `TimeFrame` is a kind of a `DataFrame`, which we used in.\n",
+    "\n",
+    "Here's a more concise version of `run_simulation` using a `TimeFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "native-central",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    frame = TimeFrame(columns=system.init.index)\n",
+    "    frame.loc[system.t0] = system.init\n",
+    "    \n",
+    "    for t in arange(system.t0, system.t_end):\n",
+    "        frame.loc[t+1] = update_func(frame.loc[t], t, system)\n",
+    "    \n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beginning-secret",
+   "metadata": {},
+   "source": [
+    "The first line creates an empty `TimeFrame` with one column for each\n",
+    "state variable. Then, before the loop starts, we store the initial\n",
+    "conditions in the `TimeFrame` at `t0`. Based on the way we've been using\n",
+    "`TimeSeries` objects, it is tempting to write:\n",
+    "\n",
+    "```\n",
+    "frame[system.t0] = system.init\n",
+    "```\n",
+    "\n",
+    "But when you use the bracket operator with a `TimeFrame` or `DataFrame`, it selects a column, not a row. \n",
+    "\n",
+    "To select a row, we have to use `loc`, like this:\n",
+    "\n",
+    "```\n",
+    "frame.loc[system.t0] = system.init\n",
+    "```\n",
+    "\n",
+    "Since the value on the right side is a `State`, the assignment matches\n",
+    "up the index of the `State` with the columns of the `TimeFrame`; that\n",
+    "is, it assigns the `S` value from `system.init` to the `S` column of\n",
+    "`frame`, and likewise with `I` and `R`.\n",
+    "\n",
+    "We use the same feature to write the loop more concisely, assigning the `State` we get from `update_func` directly to the next row of\n",
+    "`frame`.\n",
+    "\n",
+    "Finally, we return `frame`. We can call this version of `run_simulation` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "quick-fifty",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, update_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "global-complement",
+   "metadata": {},
+   "source": [
+    "And plot the results like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "listed-enough",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_results(results.S, results.I, results.R)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "welsh-distinction",
+   "metadata": {},
+   "source": [
+    "As with a `DataFrame`, we can use the dot operator to select columns\n",
+    "from a `TimeFrame`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "neutral-replacement",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "induced-tunnel",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "floral-remove",
+   "metadata": {},
+   "source": [
+    "**Exercise**  Suppose the time between contacts is 4 days and the recovery time is 5 days.  After 14 weeks, how many students, total, have been infected?\n",
+    "\n",
+    "Hint: what is the change in `S` between the beginning and the end of the simulation?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "recovered-freeze",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "interpreted-colony",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "given-canberra",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap12.ipynb b/chapters/chap12.ipynb
new file mode 100644
index 00000000..a693d9d7
--- /dev/null
+++ b/chapters/chap12.ipynb
@@ -0,0 +1,852 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "combined-semiconductor",
+   "metadata": {},
+   "source": [
+    "# Chapter 12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "excellent-orchestra",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "growing-sperm",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import code from previous notebooks\n",
+    "\n",
+    "from chap11 import make_system\n",
+    "from chap11 import update_func\n",
+    "from chap11 import run_simulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "identical-steam",
+   "metadata": {},
+   "source": [
+    "In the previous chapter I presented the SIR model of infectious disease and used it to model the Freshman Plague at Olin. In this chapter we'll consider metrics intended to quantify the effects of the disease and interventions intended to reduce those effects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "complex-renewal",
+   "metadata": {},
+   "source": [
+    "## Immunization\n",
+    "\n",
+    "Models like this are useful for testing \"what if?\" scenarios. As an\n",
+    "example, we'll consider the effect of immunization.\n",
+    "\n",
+    "Suppose there is a vaccine that causes a student to become immune to the Freshman Plague without being infected. How might you modify the model to capture this effect?\n",
+    "\n",
+    "One option is to treat immunization as a shortcut from susceptible to\n",
+    "recovered without going through infectious. We can implement this\n",
+    "feature like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "recent-cooper",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_immunization(system, fraction):\n",
+    "    system.init.S -= fraction\n",
+    "    system.init.R += fraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "arranged-screening",
+   "metadata": {},
+   "source": [
+    "`add_immunization` moves the given fraction of the population from `S`\n",
+    "to `R`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "found-learning",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc = 3             # time between contacts in days \n",
+    "tr = 4             # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "system = make_system(beta, gamma)\n",
+    "results = run_simulation(system, update_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unsigned-joseph",
+   "metadata": {},
+   "source": [
+    "If we assume that 10% of students are vaccinated at the\n",
+    "beginning of the semester, and the vaccine is 100% effective, we can\n",
+    "simulate the effect like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "funny-copper",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system2 = make_system(beta, gamma)\n",
+    "add_immunization(system2, 0.1)\n",
+    "results2 = run_simulation(system2, update_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bronze-techno",
+   "metadata": {},
+   "source": [
+    "The following figure shows `S` as a function of time, with and\n",
+    "without immunization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "divided-biotechnology",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.S.plot(label='No immunization')\n",
+    "results2.S.plot(label='10% immunization')\n",
+    "\n",
+    "decorate(xlabel='Time (days)',\n",
+    "         ylabel='Fraction of population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "postal-cemetery",
+   "metadata": {},
+   "source": [
+    "## Metrics\n",
+    "\n",
+    "When we plot a time series, we get a view of everything that happened\n",
+    "when the model ran, but often we want to boil it down to a few numbers\n",
+    "that summarize the outcome. These summary statistics are called\n",
+    "**metrics**, as we saw in Section xxx.\n",
+    "\n",
+    "In the SIR model, we might want to know the time until the peak of the\n",
+    "outbreak, the number of people who are sick at the peak, the number of\n",
+    "students who will still be sick at the end of the semester, or the total number of students who get sick at any point.\n",
+    "\n",
+    "As an example, I will focus on the last one --- the total number of sick students --- and we will consider interventions intended to minimize it.\n",
+    "\n",
+    "When a person gets infected, they move from `S` to `I`, so we can get\n",
+    "the total number of infections by computing the difference in `S` at the beginning and the end:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "synthetic-element",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def calc_total_infected(results, system):\n",
+    "    s_0 = results.S[system.t0]\n",
+    "    s_end = results.S[system.t_end]\n",
+    "    return s_0 - s_end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "recovered-picnic",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calc_total_infected(results, system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "american-transfer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calc_total_infected(results2, system2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adverse-trance",
+   "metadata": {},
+   "source": [
+    "Without immunization, almost 47% of the population gets infected at some point. With 10% immunization, only 31% get infected. That's pretty good."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eight-maximum",
+   "metadata": {},
+   "source": [
+    "## Sweeping Immunization\n",
+    "\n",
+    "Now let's see what happens if we administer more vaccines. This\n",
+    "following function sweeps a range of immunization rates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "progressive-architect",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_immunity(immunize_array):\n",
+    "    sweep = SweepSeries()\n",
+    "\n",
+    "    for fraction in immunize_array:\n",
+    "        sir = make_system(beta, gamma)\n",
+    "        add_immunization(sir, fraction)\n",
+    "        results = run_simulation(sir, update_func)\n",
+    "        sweep[fraction] = calc_total_infected(results, sir)\n",
+    "\n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "indie-seeker",
+   "metadata": {},
+   "source": [
+    "The parameter of `sweep_immunity` is an array of immunization rates. The\n",
+    "result is a `SweepSeries` object that maps from each immunization rate\n",
+    "to the resulting fraction of students ever infected.\n",
+    "\n",
+    "The following figure shows a plot of the `SweepSeries`. Notice that\n",
+    "the x-axis is the immunization rate, not time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "measured-pavilion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "immunize_array = linspace(0, 1, 21)\n",
+    "infected_sweep = sweep_immunity(immunize_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "interior-humanitarian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infected_sweep.plot()\n",
+    "\n",
+    "decorate(xlabel='Fraction immunized',\n",
+    "         ylabel='Total fraction infected',\n",
+    "         title='Fraction infected vs. immunization rate')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "turkish-mumbai",
+   "metadata": {},
+   "source": [
+    "As the immunization rate increases, the number of infections drops\n",
+    "steeply. If 40% of the students are immunized, fewer than 4% get sick.\n",
+    "That's because immunization has two effects: it protects the people who get immunized (of course) but it also protects the rest of the\n",
+    "population.\n",
+    "\n",
+    "Reducing the number of \"susceptibles\" and increasing the number of\n",
+    "\"resistants\" makes it harder for the disease to spread, because some\n",
+    "fraction of contacts are wasted on people who cannot be infected. This\n",
+    "phenomenon is called **herd immunity**, and it is an important element\n",
+    "of public health (see <http://modsimpy.com/herd>)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "french-spouse",
+   "metadata": {},
+   "source": [
+    "The steepness of the curve is a blessing and a curse. It's a blessing\n",
+    "because it means we don't have to immunize everyone, and vaccines can\n",
+    "protect the \"herd\" even if they are not 100% effective.\n",
+    "\n",
+    "But it's a curse because a small decrease in immunization can cause a\n",
+    "big increase in infections. In this example, if we drop from 80%\n",
+    "immunization to 60%, that might not be too bad. But if we drop from 40% to 20%, that would trigger a major outbreak, affecting more than 15% of the population. For a serious disease like measles, just to name one, that would be a public health catastrophe.\n",
+    "\n",
+    "One use of models like this is to demonstrate phenomena like herd\n",
+    "immunity and to predict the effect of interventions like vaccination.\n",
+    "Another use is to evaluate alternatives and guide decision making. We'll see an example in the next section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "changed-capability",
+   "metadata": {},
+   "source": [
+    "## Hand washing\n",
+    "\n",
+    "Suppose you are the Dean of Student Life, and you have a budget of just \\$1200 to combat the Freshman Plague. You have two options for spending this money:\n",
+    "\n",
+    "1.  You can pay for vaccinations, at a rate of \\$100 per dose.\n",
+    "\n",
+    "2.  You can spend money on a campaign to remind students to wash hands\n",
+    "    frequently.\n",
+    "\n",
+    "We have already seen how we can model the effect of vaccination. Now\n",
+    "let's think about the hand-washing campaign. We'll have to answer two\n",
+    "questions:\n",
+    "\n",
+    "1.  How should we incorporate the effect of hand washing in the model?\n",
+    "\n",
+    "2.  How should we quantify the effect of the money we spend on a\n",
+    "    hand-washing campaign?\n",
+    "\n",
+    "For the sake of simplicity, let's assume that we have data from a\n",
+    "similar campaign at another school showing that a well-funded campaign\n",
+    "can change student behavior enough to reduce the infection rate by 20%.\n",
+    "\n",
+    "In terms of the model, hand washing has the effect of reducing `beta`.\n",
+    "That's not the only way we could incorporate the effect, but it seems\n",
+    "reasonable and it's easy to implement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "alpine-guest",
+   "metadata": {},
+   "source": [
+    "Now we have to model the relationship between the money we spend and the\n",
+    "effectiveness of the campaign. Again, let's suppose we have data from\n",
+    "another school that suggests:\n",
+    "\n",
+    "-   If we spend \\$500 on posters, materials, and staff time, we can\n",
+    "    change student behavior in a way that decreases the effective value of `beta` by 10%.\n",
+    "\n",
+    "-   If we spend \\$1000, the total decrease in `beta` is almost 20%.\n",
+    "\n",
+    "-   Above \\$1000, additional spending has little additional benefit."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "agricultural-trinidad",
+   "metadata": {},
+   "source": [
+    "## Logistic function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cheap-structure",
+   "metadata": {},
+   "source": [
+    "To model the effect of a hand-washing campaign, I'll use a [generalized logistic function](https://en.wikipedia.org/wiki/Generalised_logistic_function) (GLF), which is a convenient function for modeling curves that have a generally sigmoid shape.  The parameters of the GLF correspond to various features of the curve in a way that makes it easy to find a function that has the shape you want, based on data or background information about the scenario."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "blond-armstrong",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import exp\n",
+    "\n",
+    "def logistic(x, A=0, B=1, C=1, M=0, K=1, Q=1, nu=1):\n",
+    "    \"\"\"Computes the generalize logistic function.\n",
+    "    \n",
+    "    A: controls the lower bound\n",
+    "    B: controls the steepness of the transition \n",
+    "    C: not all that useful, AFAIK\n",
+    "    M: controls the location of the transition\n",
+    "    K: controls the upper bound\n",
+    "    Q: shift the transition left or right\n",
+    "    nu: affects the symmetry of the transition\n",
+    "    \n",
+    "    returns: float or array\n",
+    "    \"\"\"\n",
+    "    exponent = -B * (x - M)\n",
+    "    denom = C + Q * exp(exponent)\n",
+    "    return A + (K-A) / denom ** (1/nu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "seven-budget",
+   "metadata": {},
+   "source": [
+    "The following array represents the range of possible spending."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "disturbed-amount",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spending = linspace(0, 1200, 21)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "supreme-nutrition",
+   "metadata": {},
+   "source": [
+    "`compute_factor` computes the reduction in `beta` for a given level of campaign spending.\n",
+    "\n",
+    "`M` is chosen so the transition happens around \\$500.\n",
+    "\n",
+    "`K` is the maximum reduction in `beta`, 20%.\n",
+    "\n",
+    "`B` is chosen by trial and error to yield a curve that seems feasible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "otherwise-answer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_factor(spending):\n",
+    "    \"\"\"Reduction factor as a function of spending.\n",
+    "    \n",
+    "    spending: dollars from 0 to 1200\n",
+    "    \n",
+    "    returns: fractional reduction in beta\n",
+    "    \"\"\"\n",
+    "    return logistic(spending, M=500, K=0.2, B=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "expected-venture",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "seventh-strike",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "percent_reduction = compute_factor(spending) * 100\n",
+    "\n",
+    "make_series(spending, percent_reduction).plot()\n",
+    "\n",
+    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
+    "         ylabel='Percent reduction in infection rate',\n",
+    "         title='Effect of hand washing on infection rate')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "legal-michigan",
+   "metadata": {},
+   "source": [
+    "The result is the following function, which\n",
+    "takes spending as a parameter and returns `factor`, which is the factor\n",
+    "by which `beta` is reduced:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "obvious-congress",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_factor(spending):\n",
+    "    return logistic(spending, M=500, K=0.2, B=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sunset-investing",
+   "metadata": {},
+   "source": [
+    "I use `compute_factor` to write `add_hand_washing`, which takes a\n",
+    "`System` object and a budget, and modifies `system.beta` to model the\n",
+    "effect of hand washing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "polish-multiple",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_hand_washing(system, spending):\n",
+    "    factor = compute_factor(spending)\n",
+    "    system.beta *= (1 - factor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "worthy-fellowship",
+   "metadata": {},
+   "source": [
+    "Now we can sweep a range of values for `spending` and use the simulation\n",
+    "to compute the effect:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "statistical-garden",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_hand_washing(spending_array):\n",
+    "    sweep = SweepSeries()\n",
+    "    \n",
+    "    for spending in spending_array:\n",
+    "        system = make_system(beta, gamma)\n",
+    "        add_hand_washing(system, spending)\n",
+    "        results = run_simulation(system, update_func)\n",
+    "        sweep[spending] = calc_total_infected(results, system)\n",
+    "        \n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "clinical-surge",
+   "metadata": {},
+   "source": [
+    "Here's how we run it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "joined-graduation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import linspace\n",
+    "\n",
+    "spending_array = linspace(0, 1200, 20)\n",
+    "infected_sweep2 = sweep_hand_washing(spending_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "designing-smile",
+   "metadata": {},
+   "source": [
+    "The following figure shows the result. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "cheap-decision",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infected_sweep2.plot()\n",
+    "\n",
+    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
+    "         ylabel='Total fraction infected',\n",
+    "         title='Effect of hand washing on total infections')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "selective-right",
+   "metadata": {},
+   "source": [
+    "Below \\$200, the campaign has little effect. \n",
+    "\n",
+    "At \\$800 it has a substantial effect, reducing total infections from more than 45% to about 20%. \n",
+    "\n",
+    "Above \\$800, the additional benefit is small."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lucky-boulder",
+   "metadata": {},
+   "source": [
+    "## Optimization\n",
+    "\n",
+    "Let's put it all together. With a fixed budget of \\$1200, we have to\n",
+    "decide how many doses of vaccine to buy and how much to spend on the\n",
+    "hand-washing campaign.\n",
+    "\n",
+    "Here are the parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "surrounded-copying",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_students = 90\n",
+    "budget = 1200\n",
+    "price_per_dose = 100\n",
+    "max_doses = int(budget / price_per_dose)\n",
+    "max_doses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "expired-conditioning",
+   "metadata": {},
+   "source": [
+    "The fraction `budget/price_per_dose` might not be an integer. `int` is a\n",
+    "built-in function that converts numbers to integers, rounding down.\n",
+    "\n",
+    "We'll sweep the range of possible doses:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "shaped-utility",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dose_array = linrange(max_doses)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "occupational-reply",
+   "metadata": {},
+   "source": [
+    "In this example we call `linrange` with only one argument; it returns a NumPy array with the integers from 0 to `max_doses`, including both.\n",
+    "\n",
+    "Then we run the simulation for each element of `dose_array`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "recognized-release",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_doses(dose_array):\n",
+    "    sweep = SweepSeries()\n",
+    "    \n",
+    "    for doses in dose_array:\n",
+    "        fraction = doses / num_students\n",
+    "        spending = budget - doses * price_per_dose\n",
+    "        \n",
+    "        system = make_system(beta, gamma)\n",
+    "        add_immunization(system, fraction)\n",
+    "        add_hand_washing(system, spending)\n",
+    "        \n",
+    "        results = run_simulation(system, update_func)\n",
+    "        sweep[doses] = calc_total_infected(results, system)\n",
+    "\n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cardiac-mitchell",
+   "metadata": {},
+   "source": [
+    "For each number of doses, we compute the fraction of students we can\n",
+    "immunize, `fraction` and the remaining budget we can spend on the\n",
+    "campaign, `spending`. Then we run the simulation with those quantities\n",
+    "and store the number of infections.\n",
+    "\n",
+    "The following figure shows the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "worth-mounting",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infected_sweep3 = sweep_doses(dose_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "adjusted-highlight",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infected_sweep3.plot()\n",
+    "\n",
+    "decorate(xlabel='Doses of vaccine',\n",
+    "         ylabel='Total fraction infected',\n",
+    "         title='Total infections vs. doses')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dynamic-easter",
+   "metadata": {},
+   "source": [
+    "If we buy no doses of vaccine and spend the entire budget on the campaign, the fraction infected is around 19%. At 4 doses, we have \\$800 left for the campaign, and this is the optimal point that minimizes the number of students who get sick.\n",
+    "\n",
+    "As we increase the number of doses, we have to cut campaign spending,\n",
+    "which turns out to make things worse. But interestingly, when we get\n",
+    "above 10 doses, the effect of herd immunity starts to kick in, and the\n",
+    "number of sick students goes down again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "amino-excess",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "timely-bottom",
+   "metadata": {},
+   "source": [
+    "### Exercises\n",
+    "\n",
+    "**Exercise:** Suppose the price of the vaccine drops to $50 per dose.  How does that affect the optimal allocation of the spending?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "drawn-hindu",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose we have the option to quarantine infected students.  For example, a student who feels ill might be moved to an infirmary, or a private dorm room, until they are no longer infectious.\n",
+    "\n",
+    "How might you incorporate the effect of quarantine in the SIR model?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "impressive-librarian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap13.ipynb b/chapters/chap13.ipynb
new file mode 100644
index 00000000..f2ef27f3
--- /dev/null
+++ b/chapters/chap13.ipynb
@@ -0,0 +1,566 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "artistic-wells",
+   "metadata": {},
+   "source": [
+    "# Chapter 13"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ceramic-rendering",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "apparent-horizon",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import code from previous notebooks\n",
+    "\n",
+    "from chap11 import make_system\n",
+    "from chap11 import update_func\n",
+    "from chap11 import run_simulation\n",
+    "from chap12 import calc_total_infected"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "electoral-montgomery",
+   "metadata": {},
+   "source": [
+    "In the previous chapters I presented an SIR model of infectious disease, specifically the Kermack-McKendrick model. We extended the model to include vaccination and the effect of a hand-washing campaign, and used the extended model to allocate a limited budget optimally, that is, to minimize the number of infections.\n",
+    "\n",
+    "But we assumed that the parameters of the model, contact rate and\n",
+    "recovery rate, were known. In this chapter, we explore the behavior of\n",
+    "the model as we vary these parameters, use analysis to understand these relationships better, and propose a method for using data to estimate parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "nervous-spread",
+   "metadata": {},
+   "source": [
+    "## Sweeping beta\n",
+    "\n",
+    "Recall that $\\beta$ is the contact rate, which captures both the\n",
+    "frequency of interaction between people and the fraction of those\n",
+    "interactions that result in a new infection. If $N$ is the size of the\n",
+    "population and $s$ is the fraction that's susceptible, $s N$ is the\n",
+    "number of susceptibles, $\\beta s N$ is the number of contacts per day\n",
+    "between susceptibles and other people, and $\\beta s i N$ is the number\n",
+    "of those contacts where the other person is infectious.\n",
+    "\n",
+    "As $\\beta$ increases, we expect the total number of infections to\n",
+    "increase. To quantify that relationship, I'll create a range of values\n",
+    "for $\\beta$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "amazing-operations",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beta_array = linspace(0.1, 1.1, 11)\n",
+    "gamma = 0.25"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adopted-selling",
+   "metadata": {},
+   "source": [
+    "Then run the simulation for each value and print the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "brief-minutes",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for beta in beta_array:\n",
+    "    system = make_system(beta, gamma)\n",
+    "    results = run_simulation(system, update_func)\n",
+    "    print(beta, calc_total_infected(results, system))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "understanding-teach",
+   "metadata": {},
+   "source": [
+    "We can wrap that code in a function and store the results in a\n",
+    "`SweepSeries` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "single-burke",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def sweep_beta(beta_array, gamma):\n",
+    "    sweep = SweepSeries()\n",
+    "    for beta in beta_array:\n",
+    "        system = make_system(beta, gamma)\n",
+    "        results = run_simulation(system, update_func)\n",
+    "        sweep[beta] = calc_total_infected(results, system)\n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "wired-wrist",
+   "metadata": {},
+   "source": [
+    "Now we can run `sweep_beta` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "collected-practice",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infected_sweep = sweep_beta(beta_array, gamma)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exempt-binary",
+   "metadata": {},
+   "source": [
+    "And plot the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "competitive-amount",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "label = f'gamma = {gamma}'\n",
+    "label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "imported-huntington",
+   "metadata": {},
+   "source": [
+    "The first line uses a formatted string literal, also called an **f-string** to assemble a label for the plotted line:\n",
+    "\n",
+    "-   An f-string starts with the letter \"f\" followed by a string in single or double quote. \n",
+    "\n",
+    "-   The string can contain any number of format specifiers in squiggly brackets, `{}`.\n",
+    "\n",
+    "-   When a variable name appears in a format specifier, it is replaced with the value of the variable.\n",
+    "\n",
+    "In this example, if the value of `gamma` is `0.25`, the value of `label` is `'gamma = 0.25'`.\n",
+    "\n",
+    "You can read more about f-strings at <https://docs.python.org/3/tutorial/inputoutput.html#tut-f-strings>.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "certain-second",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infected_sweep.plot(label=label)\n",
+    "\n",
+    "decorate(xlabel='Contact rate (beta)',\n",
+    "         ylabel='Fraction infected')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "affiliated-honduras",
+   "metadata": {},
+   "source": [
+    "Remember that this figure\n",
+    "is a parameter sweep, not a time series, so the x-axis is the parameter\n",
+    "`beta`, not time.\n",
+    "\n",
+    "When `beta` is small, the contact rate is low and the outbreak never\n",
+    "really takes off; the total number of infected students is near zero. As\n",
+    "`beta` increases, it reaches a threshold near 0.3 where the fraction of\n",
+    "infected students increases quickly. When `beta` exceeds 0.5, more than\n",
+    "80% of the population gets sick."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "worst-alexandria",
+   "metadata": {},
+   "source": [
+    "## Sweeping gamma\n",
+    "\n",
+    "Let's see what that looks like for a few different values of `gamma`.\n",
+    "Again, we'll use `linspace` to make an array of values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "advised-venice",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamma_array = linspace(0.1, 0.7, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sensitive-folder",
+   "metadata": {},
+   "source": [
+    "And run `sweep_beta` for each value of `gamma`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "determined-consent",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for gamma in gamma_array:\n",
+    "    infected_sweep = sweep_beta(beta_array, gamma)\n",
+    "    label = 'gamma = ' + str(gamma)\n",
+    "    infected_sweep.plot(label=label)\n",
+    "    \n",
+    "decorate()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tutorial-venture",
+   "metadata": {},
+   "source": [
+    "When `gamma` is low, the recovery rate is low, which means people are infectious longer.\n",
+    "In that case, even a low contact rate (`beta`) results in an epidemic.\n",
+    "\n",
+    "When `gamma` is high, `beta` has to be even higher to get things going."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "rising-flour",
+   "metadata": {},
+   "source": [
+    "## SweepFrame\n",
+    "\n",
+    "In the previous section, we swept a range of values for `gamma`, and for\n",
+    "each value, we swept a range of values for `beta`. This process is a\n",
+    "**two-dimensional sweep**.\n",
+    "\n",
+    "If we want to store the results, rather than plot them, we can use a\n",
+    "`SweepFrame`, which is a kind of `DataFrame` where the rows sweep one\n",
+    "parameter, the columns sweep another parameter, and the values contain\n",
+    "metrics from a simulation.\n",
+    "\n",
+    "This function shows how it works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "aquatic-federal",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def sweep_parameters(beta_array, gamma_array):\n",
+    "    frame = SweepFrame(columns=gamma_array)\n",
+    "    for gamma in gamma_array:\n",
+    "        frame[gamma] = sweep_beta(beta_array, gamma)\n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "level-distinction",
+   "metadata": {},
+   "source": [
+    "`sweep_parameters` takes as parameters an array of values for `beta` and\n",
+    "an array of values for `gamma`.\n",
+    "\n",
+    "It creates a `SweepFrame` to store the results, with one column for each\n",
+    "value of `gamma` and one row for each value of `beta`.\n",
+    "\n",
+    "Each time through the loop, we run `sweep_beta`. The result is a\n",
+    "`SweepSeries` object with one element for each value of `beta`. The\n",
+    "assignment inside the loop stores the `SweepSeries` as a new column in\n",
+    "the `SweepFrame`, corresponding to the current value of `gamma`.\n",
+    "\n",
+    "At the end, the `SweepFrame` stores the fraction of students infected\n",
+    "for each pair of parameters, `beta` and `gamma`.\n",
+    "\n",
+    "We can run `sweep_parameters` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "julian-liberia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frame = sweep_parameters(beta_array, gamma_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sharing-contemporary",
+   "metadata": {},
+   "source": [
+    "With the results in a `SweepFrame`, we can plot each column like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "superior-redhead",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for gamma in gamma_array:\n",
+    "    label = f'gamma = {gamma}'\n",
+    "    frame[gamma].plot(label=label)\n",
+    "\n",
+    "decorate(xlabel='Contact rate (beta)',\n",
+    "         ylabel='Fraction infected')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "english-photographer",
+   "metadata": {},
+   "source": [
+    "Alternatively, we can plot each row like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "regular-drama",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for beta in [0.2, 0.5, 0.8, 1.1]:\n",
+    "    label = f'beta = {beta}'\n",
+    "    frame.loc[beta].plot(label=label)\n",
+    "    \n",
+    "decorate(xlabel='Recovery rate (gamma)',\n",
+    "         ylabel='Fraction infected')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "selective-evaluation",
+   "metadata": {},
+   "source": [
+    "This example demonstrates one use of a `SweepFrame`: we can run the analysis once, save the results, and then generate different visualizations.\n",
+    "\n",
+    "Another way to visualize the results of a two-dimensional sweep is a\n",
+    "**contour plot**, which shows the parameters on the axes and contour\n",
+    "lines, that is, lines of constant value. In this example, the value is\n",
+    "the fraction of students infected.\n",
+    "\n",
+    "The ModSim library provides `contour`, which takes a `SweepFrame` as a\n",
+    "parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "logical-method",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "contour(frame)\n",
+    "\n",
+    "decorate(xlabel='Recovery rate (gamma)',\n",
+    "         ylabel='Contact rate (beta)',\n",
+    "         title='Fraction infected, contour plot')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "corresponding-headline",
+   "metadata": {},
+   "source": [
+    "Infection rates are lowest in the lower right, where the contact rate is and the recovery rate is high. They increase as we move to the upper left, where the contact rate is high and the recovery rate is low.\n",
+    "\n",
+    "This figure suggests that there might be a relationship between `beta`\n",
+    "and `gamma` that determines the outcome of the model. In fact, there is.\n",
+    "In the next chapter we'll explore it by running simulations, then derive it by analysis.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "located-double",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "flying-tracker",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stopped-breathing",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Suppose the infectious period for the Freshman Plague is known to be 2 days on average, and suppose during one particularly bad year, 40% of the class is infected at some point.  Estimate the time between contacts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "talented-crack",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "pressed-isaac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "equal-crazy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "desirable-crystal",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap14.ipynb b/chapters/chap14.ipynb
new file mode 100644
index 00000000..5a8f9484
--- /dev/null
+++ b/chapters/chap14.ipynb
@@ -0,0 +1,570 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "happy-minutes",
+   "metadata": {},
+   "source": [
+    "# Chapter 14"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "impressed-headline",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "level-struggle",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import code from previous notebooks\n",
+    "\n",
+    "from chap11 import make_system\n",
+    "from chap11 import update_func\n",
+    "from chap11 import run_simulation\n",
+    "\n",
+    "from chap12 import calc_total_infected\n",
+    "\n",
+    "from chap13 import sweep_beta\n",
+    "from chap13 import sweep_parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "disturbed-joshua",
+   "metadata": {},
+   "source": [
+    "In the previous chapters we used simulation to predict the effect of an infectious disease in a susceptible population and to design\n",
+    "interventions that would minimize the effect.\n",
+    "\n",
+    "In this chapter we use analysis to investigate the relationship between the parameters, `beta` and `gamma`, and the outcome of the simulation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "respiratory-twelve",
+   "metadata": {},
+   "source": [
+    "## Nondimensionalization\n",
+    "\n",
+    "The figures in\n",
+    "Section [\\[sweepframe\\]](#sweepframe){reference-type=\"ref\"\n",
+    "reference=\"sweepframe\"} suggest that there is a relationship between the parameters of the SIR model, `beta` and `gamma`, that determines the outcome of the simulation, the fraction of students infected. Let's think what that relationship might be.\n",
+    "\n",
+    "-   When `beta` exceeds `gamma`, that means there are more contacts\n",
+    "    (that is, potential infections) than recoveries during each day (or other unit of time). The difference between `beta` and `gamma` might be called the \"excess contact rate\\\", in units of contacts per day.\n",
+    "\n",
+    "-   As an alternative, we might consider the ratio `beta/gamma`, which\n",
+    "    is the number of contacts per recovery. Because the numerator and\n",
+    "    denominator are in the same units, this ratio is **dimensionless**, which means it has no units.\n",
+    "\n",
+    "Describing physical systems using dimensionless parameters is often a\n",
+    "useful move in the modeling and simulation game. It is so useful, in\n",
+    "fact, that it has a name: **nondimensionalization** (see\n",
+    "<http://modsimpy.com/nondim>).\n",
+    "\n",
+    "So we'll try the second option first."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "caroline-compiler",
+   "metadata": {},
+   "source": [
+    "## Exploring the results\n",
+    "\n",
+    "Suppose we have a `SweepFrame` with one row for each value of `beta` and one column for each value of `gamma`. Each element in the `SweepFrame` is the fraction of students infected in a simulation with a given pair of parameters.\n",
+    "\n",
+    "We can print the values in the `SweepFrame` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "flying-chosen",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beta_array = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 , 1.1]\n",
+    "gamma_array = [0.2, 0.4, 0.6, 0.8]\n",
+    "frame = sweep_parameters(beta_array, gamma_array)\n",
+    "frame.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "satisfied-japan",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for gamma in frame.columns:\n",
+    "    column = frame[gamma]\n",
+    "    for beta in column.index:\n",
+    "        frac_infected = column[beta]\n",
+    "        print(beta, gamma, frac_infected)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "automotive-solution",
+   "metadata": {},
+   "source": [
+    "This is the first example we've seen with one `for` loop inside another:\n",
+    "\n",
+    "-   Each time the outer loop runs, it selects a value of `gamma` from\n",
+    "    the columns of the `DataFrame` and extracts the corresponding\n",
+    "    column.\n",
+    "\n",
+    "-   Each time the inner loop runs, it selects a value of `beta` from the\n",
+    "    column and selects the corresponding element, which is the fraction\n",
+    "    of students infected.\n",
+    "\n",
+    "In the example from the previous chapter, `frame` has 4 columns, one for\n",
+    "each value of `gamma`, and 11 rows, one for each value of `beta`. So\n",
+    "these loops print 44 lines, one for each pair of parameters.\n",
+    "\n",
+    "The following function encapulates the previous loop and plots the\n",
+    "fraction infected as a function of the ratio `beta/gamma`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "baking-tactics",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.pyplot import plot\n",
+    "\n",
+    "def plot_sweep_frame(frame):\n",
+    "    for gamma in frame.columns:\n",
+    "        series = frame[gamma]\n",
+    "        for beta in series.index:\n",
+    "            frac_infected = series[beta]\n",
+    "            plot(beta/gamma, frac_infected, 'o', \n",
+    "                 color='C1', alpha=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dental-outreach",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_sweep_frame(frame)\n",
+    "\n",
+    "decorate(xlabel='Contact number (beta/gamma)',\n",
+    "         ylabel='Fraction infected')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "systematic-gossip",
+   "metadata": {},
+   "source": [
+    "The results fall on a single curve, at least approximately. That means that we can predict the fraction of students who will be infected based on a single parameter, the ratio `beta/gamma`. We don't need to know the values of `beta` and `gamma` separately."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "theoretical-arrow",
+   "metadata": {},
+   "source": [
+    "## Contact number\n",
+    "\n",
+    "From Section xxx, recall that the number of new infections in a\n",
+    "given day is $\\beta s i N$, and the number of recoveries is\n",
+    "$\\gamma i N$. If we divide these quantities, the result is\n",
+    "$\\beta s / \\gamma$, which is the number of new infections per recovery\n",
+    "(as a fraction of the population).\n",
+    "\n",
+    "When a new disease is introduced to a susceptible population, $s$ is\n",
+    "approximately 1, so the number of people infected by each sick person is $\\beta / \\gamma$. This ratio is called the \"contact number\\\" or \"basic reproduction number\\\" (see <http://modsimpy.com/contact>). By convention it is usually denoted $R_0$, but in the context of an SIR model, this notation is confusing, so we'll use $c$ instead."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "intimate-romance",
+   "metadata": {},
+   "source": [
+    "The results in the previous section suggest that there is a relationship between $c$ and the total number of infections. We can derive this relationship by analyzing the differential equations from\n",
+    "Section xxx:\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\frac{ds}{dt} &= -\\beta s i \\\\\n",
+    "\\frac{di}{dt} &= \\beta s i - \\gamma i\\\\\n",
+    "\\frac{dr}{dt} &= \\gamma i\\end{aligned}$$ \n",
+    "\n",
+    "In the same way we divided the\n",
+    "contact rate by the infection rate to get the dimensionless quantity\n",
+    "$c$, now we'll divide $di/dt$ by $ds/dt$ to get a ratio of rates:\n",
+    "\n",
+    "$$\\frac{di}{ds} = -1 + \\frac{1}{cs}$$ \n",
+    "\n",
+    "Dividing one differential equation by another is not an obvious move, but in this case it is useful because it gives us a relationship between $i$, $s$ and $c$ that does not depend on time. From that relationship, we can derive an equation that relates $c$ to the final value of $s$. In theory, this equation makes it possible to infer $c$ by observing the course of an epidemic."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "revolutionary-motorcycle",
+   "metadata": {},
+   "source": [
+    "Here's how the derivation goes. We multiply both sides of the previous\n",
+    "equation by $ds$: \n",
+    "\n",
+    "$$di = \\left( -1 + \\frac{1}{cs} \\right) ds$$ \n",
+    "\n",
+    "And then integrate both sides: \n",
+    "\n",
+    "$$i = -s + \\frac{1}{c} \\log s + q$$ \n",
+    "\n",
+    "where $q$ is a constant of integration. Rearranging terms yields:\n",
+    "\n",
+    "$$q = i + s - \\frac{1}{c} \\log s$$ \n",
+    "\n",
+    "Now let's see if we can figure out what $q$ is. At the beginning of an epidemic, if the fraction infected is small and nearly everyone is susceptible, we can use the approximations $i(0) = 0$ and $s(0) = 1$ to compute $q$:\n",
+    "\n",
+    "$$q = 0 + 1 + \\frac{1}{c} \\log 1$$ \n",
+    "\n",
+    "Since $\\log 1 = 0$, we get $q = 1$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "spread-convert",
+   "metadata": {},
+   "source": [
+    "Now, at the end of the epidemic, let's assume that $i(\\infty) = 0$, and $s(\\infty)$ is an unknown quantity, $s_{\\infty}$. Now we have:\n",
+    "\n",
+    "$$q = 1 = 0 + s_{\\infty}- \\frac{1}{c} \\log s_{\\infty}$$ \n",
+    "\n",
+    "Solving for $c$, we get $$c = \\frac{\\log s_{\\infty}}{s_{\\infty}- 1}$$ By relating $c$ and $s_{\\infty}$, this equation makes it possible to estimate $c$ based on data, and possibly predict the behavior of future epidemics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "intended-worker",
+   "metadata": {},
+   "source": [
+    "## Analysis and simulation\n",
+    "\n",
+    "Let's compare this analytic result to the results from simulation. I'll create an array of values for $s_{\\infty}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "previous-schedule",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import linspace\n",
+    "\n",
+    "s_inf_array = linspace(0.0001, 0.999, 31)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "positive-float",
+   "metadata": {},
+   "source": [
+    "And compute the corresponding values of $c$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "valuable-landscape",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import log\n",
+    "\n",
+    "c_array = log(s_inf_array) / (s_inf_array - 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "premium-carry",
+   "metadata": {},
+   "source": [
+    "To get the total infected, we compute the difference between $s(0)$ and\n",
+    "$s(\\infty)$, then store the results in a `Series`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "composite-cabinet",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frac_infected = 1 - s_inf_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "satisfactory-blink",
+   "metadata": {},
+   "source": [
+    "We can use `make_series` to put `c_array`\n",
+    "and `frac_infected` in a Pandas `Series`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "appropriate-geology",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frac_infected_series = make_series(c_array, frac_infected)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bottom-print",
+   "metadata": {},
+   "source": [
+    "Now we can plot the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "integral-reading",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_sweep_frame(frame)\n",
+    "frac_infected_series.plot(label='analysis')\n",
+    "\n",
+    "decorate(xlabel='Contact number (c)',\n",
+    "         ylabel='Fraction infected')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "peaceful-interest",
+   "metadata": {},
+   "source": [
+    "When the contact number exceeds 1, analysis and simulation agree. When\n",
+    "the contact number is less than 1, they do not: analysis indicates there should be no infections; in the simulations there are a small number of infections.\n",
+    "\n",
+    "The reason for the discrepancy is that the simulation divides time into a discrete series of days, whereas the analysis treats time as a\n",
+    "continuous quantity. In other words, the two methods are actually based on different models. So which model is better?\n",
+    "\n",
+    "Probably neither. When the contact number is small, the early progress\n",
+    "of the epidemic depends on details of the scenario. If we are lucky, the original infected person, \"patient zero\", infects no one and there is no epidemic. If we are unlucky, patient zero might have a large number of close friends, or might work in the dining hall (and fail to observe safe food handling procedures).\n",
+    "\n",
+    "For contact numbers near or less than 1, we might need a more detailed\n",
+    "model. But for higher contact numbers the SIR model might be good\n",
+    "enough."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beginning-capital",
+   "metadata": {},
+   "source": [
+    "## Estimating contact number\n",
+    "\n",
+    "Figure xxx shows that if we know the contact number, we can compute the fraction infected. But we can also read the figure the other way; that is, at the end of an epidemic, if we can estimate the fraction of the population that was ever infected, we can use it to estimate the contact number.\n",
+    "\n",
+    "Well, in theory we can. In practice, it might not work very well,\n",
+    "because of the shape of the curve. When the contact number is near 2,\n",
+    "the curve is quite steep, which means that small changes in $c$ yield\n",
+    "big changes in the number of infections. If we observe that the total\n",
+    "fraction infected is anywhere from 20% to 80%, we would conclude that\n",
+    "$c$ is near 2.\n",
+    "\n",
+    "On the other hand, for larger contact numbers, nearly the entire\n",
+    "population is infected, so the curve is nearly flat. In that case we\n",
+    "would not be able to estimate $c$ precisely, because any value greater\n",
+    "than 3 would yield effectively the same results. Fortunately, this is\n",
+    "unlikely to happen in the real world; very few epidemics affect anything close to 90% of the population.\n",
+    "\n",
+    "So the SIR model has limitations; nevertheless, it provides insight into the behavior of infectious disease, especially the phenomenon of herd immunity. As we saw in Chapter xxx, if we know the parameters of the model, we can use it to evaluate possible interventions. And as we saw in this chapter, we might be able to use data from earlier outbreaks to estimate the parameters.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "olive-niger",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "upper-interim",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  If we didn't know about contact numbers, we might have explored other possibilities, like the difference between `beta` and `gamma`, rather than their ratio.\n",
+    "\n",
+    "Write a version of `plot_sweep_frame`, called `plot_sweep_frame_difference`, that plots the fraction infected versus the difference `beta-gamma`.\n",
+    "\n",
+    "What do the results look like, and what does that imply? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "talented-discovery",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "bright-label",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "suspended-louisiana",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "understanding-fault",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose you run a survey at the end of the semester and find that 26% of students had the Freshman Plague at some point.\n",
+    "\n",
+    "What is your best estimate of `c`?\n",
+    "\n",
+    "Hint: if you print `frac_infected_series`, you can read off the answer. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "another-timing",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "gross-terry",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "found-surgery",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap15.ipynb b/chapters/chap15.ipynb
new file mode 100644
index 00000000..29ba9961
--- /dev/null
+++ b/chapters/chap15.ipynb
@@ -0,0 +1,830 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "located-subscription",
+   "metadata": {},
+   "source": [
+    "# Chapter 15"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "american-article",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "italic-murder",
+   "metadata": {},
+   "source": [
+    "So far the systems we have studied have been physical in the sense that they exist in the world, but they have not been physics, in the sense of what physics classes are usually about. In the next few chapters, we'll do some physics, starting with **thermal systems**, that is, systems where the temperature of objects changes as heat transfers from one to another."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adolescent-phone",
+   "metadata": {},
+   "source": [
+    "## The coffee cooling problem\n",
+    "\n",
+    "The coffee cooling problem was discussed by Jearl Walker in \n",
+    "\"The Amateur Scientist\", *Scientific American*, Volume 237, Issue 5, November 1977. Since then it has become a standard example of modeling and simulation.\n",
+    "\n",
+    "Here is my version of the problem:\n",
+    "\n",
+    "> Suppose I stop on the way to work to pick up a cup of coffee, which I take with milk. Assuming that I want the coffee to be as hot as possible when I arrive at work, should I add the milk at the coffee shop, wait until I get to work, or add the milk at some point in between?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "appreciated-ukraine",
+   "metadata": {},
+   "source": [
+    "To help answer this question, I made a trial run with the milk and\n",
+    "coffee in separate containers and took some measurements (not really):\n",
+    "\n",
+    "-   When served, the temperature of the coffee is 90 °C. The volume is\n",
+    "    300 mL.\n",
+    "\n",
+    "-   The milk is at an initial temperature of 5 °C, and I take about\n",
+    "    50 mL.\n",
+    "\n",
+    "-   The ambient temperature in my car is 22 °C.\n",
+    "\n",
+    "-   The coffee is served in a well insulated cup. When I arrive at work after 30 minutes, the temperature of the coffee has fallen to 70 °C.\n",
+    "\n",
+    "-   The milk container is not as well insulated. After 15 minutes, it\n",
+    "    warms up to 20 °C, nearly the ambient temperature.\n",
+    "\n",
+    "To use this data and answer the question, we have to know something\n",
+    "about temperature and heat, and we have to make some modeling decisions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "arranged-ridge",
+   "metadata": {},
+   "source": [
+    "## Temperature and heat\n",
+    "\n",
+    "To understand how coffee cools (and milk warms), we need a model of\n",
+    "temperature and heat. **Temperature** is a property of an object or a\n",
+    "system; in SI units it is measured in degrees Celsius (°C). Temperature quantifies how hot or cold the object is, which is related to the average velocity of the particles that make up the object.\n",
+    "\n",
+    "When particles in a hot object contact particles in a cold object, the\n",
+    "hot object gets cooler and the cold object gets warmer as energy is\n",
+    "transferred from one to the other. The transferred energy is called\n",
+    "**heat**; in SI units it is measured in joules (J).\n",
+    "\n",
+    "Heat is related to temperature by the following equation (see\n",
+    "<http://modsimpy.com/thermass>): \n",
+    "\n",
+    "$$Q = C~\\Delta T$$ \n",
+    "\n",
+    "where $Q$ is the amount of heat transferred to an object, $\\Delta T$ is resulting change in temperature, and $C$ is the **thermal mass** of the object, which quantifies how much energy it takes to heat or cool it. In SI units, thermal mass is measured in joules per degree Celsius (J/°C)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "purple-edward",
+   "metadata": {},
+   "source": [
+    "For objects made primarily from one material, thermal mass can be\n",
+    "computed like this: \n",
+    "\n",
+    "$$C = m c_p$$ \n",
+    "\n",
+    "where $m$ is the mass of the object and $c_p$ is the **specific heat capacity** of the material (see <http://modsimpy.com/specheat>).\n",
+    "\n",
+    "We can use these equations to estimate the thermal mass of a cup of\n",
+    "coffee. The specific heat capacity of coffee is probably close to that\n",
+    "of water, which is 4.2 J/g/°C. Assuming that the density of coffee is\n",
+    "close to that of water, which is 1 g/mL, the mass of 300 mL of coffee is 300 g, and the thermal mass is 1260 J/°C.\n",
+    "\n",
+    "So when a cup of coffee cools from 90 °C to 70 °C, the change in\n",
+    "temperature, $\\Delta T$ is 20 °C, which means that 25 200 J of heat\n",
+    "energy was transferred from the coffee to the surrounding environment\n",
+    "(the cup holder and air in my car).\n",
+    "\n",
+    "To give you a sense of how much energy that is, if you were able to\n",
+    "harness all of that heat to do work (which you cannot), you could\n",
+    "use it to lift a cup of coffee from sea level to 8571 m, just shy of the height of Mount Everest, 8848 m.\n",
+    "\n",
+    "Assuming that the cup has less mass than the coffee, and is made from a material with lower specific heat, we can ignore the thermal mass of the cup. For a cup with substantial thermal mass, like a ceramic mug, we might consider a model that computes the temperature of coffee and cup separately."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "other-finnish",
+   "metadata": {},
+   "source": [
+    "## Heat transfer\n",
+    "\n",
+    "In a situation like the coffee cooling problem, there are three ways\n",
+    "heat transfers from one object to another (see <http://modsimpy.com/transfer>):\n",
+    "\n",
+    "-   Conduction: When objects at different temperatures come into\n",
+    "    contact, the faster-moving particles of the higher-temperature\n",
+    "    object transfer kinetic energy to the slower-moving particles of the lower-temperature object.\n",
+    "\n",
+    "-   Convection: When particles in a gas or liquid flow from place to\n",
+    "    place, they carry heat energy with them. Fluid flows can be caused\n",
+    "    by external action, like stirring, or by internal differences in\n",
+    "    temperature. For example, you might have heard that hot air rises,\n",
+    "    which is a form of \"natural convection\\\".\n",
+    "\n",
+    "-   Radiation: As the particles in an object move due to thermal energy,\n",
+    "    they emit electromagnetic radiation. The energy carried by this\n",
+    "    radiation depends on the object's temperature and surface properties\n",
+    "    (see <http://modsimpy.com/thermrad>)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "friendly-nurse",
+   "metadata": {},
+   "source": [
+    "For objects like coffee in a car, the effect of radiation is much\n",
+    "smaller than the effects of conduction and convection, so we will ignore it.\n",
+    "\n",
+    "Convection can be a complex topic, since it often depends on details of fluid flow in three dimensions. But for this problem we will be able to get away with a simple model called \"Newton's law of cooling\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "heavy-forward",
+   "metadata": {},
+   "source": [
+    "## Newton's law of cooling\n",
+    "\n",
+    "Newton's law of cooling asserts that the temperature rate of change for an object is proportional to the difference in temperature between the\n",
+    "object and the surrounding environment:\n",
+    "\n",
+    "$$\\frac{dT}{dt} = -r (T - T_{env})$$ \n",
+    "\n",
+    "where $T$, the temperature of the object, is a function of time, $t$; $T_{env}$ is the temperature of the environment, and $r$ is a constant that characterizes how quickly heat is transferred between the system and the environment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "educated-accommodation",
+   "metadata": {},
+   "source": [
+    "Newton's so-called \"law \" is really a model: it is a good approximation in some conditions and less good in others.\n",
+    "\n",
+    "For example, if the primary mechanism of heat transfer is conduction,\n",
+    "Newton's law is \"true\", which is to say that $r$ is constant over a\n",
+    "wide range of temperatures. And sometimes we can estimate $r$ based on\n",
+    "the material properties and shape of the object.\n",
+    "\n",
+    "When convection contributes a non-negligible fraction of heat transfer, $r$ depends on temperature, but Newton's law is often accurate enough, at least over a narrow range of temperatures. In this case $r$ usually has to be estimated experimentally, since it depends on details of surface shape, air flow, evaporation, etc.\n",
+    "\n",
+    "When radiation makes up a substantial part of heat transfer, Newton's\n",
+    "law is not a good model at all. This is the case for objects in space or in a vacuum, and for objects at high temperatures (more than a few\n",
+    "hundred degrees Celsius, say).\n",
+    "\n",
+    "However, for a situation like the coffee cooling problem, we expect\n",
+    "Newton's model to be quite good."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "determined-seventh",
+   "metadata": {},
+   "source": [
+    "## Implementation\n",
+    "\n",
+    "To get started, let's forget about the milk temporarily and focus on the coffee. \n",
+    "\n",
+    "Here's a function that takes the parameters of the system and makes a `System` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "geological-graph",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(T_init, volume, r, t_end):\n",
+    "    return System(T_init=T_init,\n",
+    "                  T_final=T_init,\n",
+    "                  volume=volume,\n",
+    "                  r=r,\n",
+    "                  t_end=t_end,\n",
+    "                  T_env=22,\n",
+    "                  t_0=0,\n",
+    "                  dt=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cardiac-lesson",
+   "metadata": {},
+   "source": [
+    "The values of `T_init`, `volume`, and `t_end` come from the statement of the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "eastern-alignment",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee = make_system(T_init=90, volume=300, r=0.01, t_end=30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "horizontal-background",
+   "metadata": {},
+   "source": [
+    "I chose the value of `r` arbitrarily for now; we will figure out how to estimate it soon.\n",
+    "\n",
+    "In addition, `make_system` sets the temperature of the environment, `T_env`, and the time step, `dt`, which we will use use to simulate the cooling process.\n",
+    "\n",
+    "Strictly speaking, Newton's law is a differential equation, but over a short period of time we can approximate it with a difference equation:\n",
+    "\n",
+    "$$\\Delta T = -r (T - T_{env}) dt$$ \n",
+    "\n",
+    "where $dt$ is the time step and $\\Delta T$ is the change in temperature during that time step.\n",
+    "\n",
+    "Note: I use $\\Delta T$ to denote a change in temperature over time, but in the context of heat transfer, you might also see $\\Delta T$ used to denote the difference in temperature between an object and its\n",
+    "environment, $T - T_{env}$. To minimize confusion, I avoid this second\n",
+    "use.\n",
+    "\n",
+    "The following function takes the current temperature, `T`, the current time `t`, and a `System` object, and computes the change in temperature during a time step:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "vulnerable-sarah",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def change_func(T, t, system):\n",
+    "    r, T_env, dt = system.r, system.T_env, system.dt    \n",
+    "    return -r * (T - T_env) * dt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unknown-programming",
+   "metadata": {},
+   "source": [
+    "We can test it with the initial temperature of the coffee, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "killing-tragedy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "change_func(coffee.T_init, 0, coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "geographic-female",
+   "metadata": {},
+   "source": [
+    "With `dt=1` minute, the temperature drops by about 0.7 °C/min, at least for this value of `r`.\n",
+    "\n",
+    "Now here's a version of `run_simulation` that simulates a series of time steps from `t_0` to `t_end`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "wireless-handy",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, change_func):\n",
+    "    t_array = linrange(system.t_0, system.t_end, system.dt)\n",
+    "    n = len(t_array)\n",
+    "    \n",
+    "    series = TimeSeries(index=t_array)\n",
+    "    series.iloc[0] = system.T_init\n",
+    "    \n",
+    "    for i in range(n-1):\n",
+    "        t = t_array[i]\n",
+    "        T = series.iloc[i]\n",
+    "        series.iloc[i+1] = T + change_func(T, t, system)\n",
+    "    \n",
+    "    system.t_end = t_array[-1]\n",
+    "    system.T_final = series.iloc[-1]\n",
+    "    return series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "voluntary-velvet",
+   "metadata": {},
+   "source": [
+    "This function is similar to previous versions of `run_simulation`.\n",
+    "\n",
+    "One difference is that it uses `linrange` to make an array of values\n",
+    "from `t_0` to `t_end` with time step `dt`.\n",
+    "\n",
+    "We can run it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "after-championship",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(coffee, change_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "consecutive-party",
+   "metadata": {},
+   "source": [
+    "The result is a `TimeSeries` with one row per time step. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "proof-guard",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "limiting-legislation",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "expected-company",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "results.plot(label='coffee')\n",
+    "\n",
+    "decorate(xlabel='Time (minute)',\n",
+    "         ylabel='Temperature (C)',\n",
+    "         title='Coffee Cooling')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "incomplete-demonstration",
+   "metadata": {},
+   "source": [
+    "The temperature after 30 minutes is 72.3 °C, which is a little higher than what's stated in the problem, 70 °C. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "protecting-picture",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee.T_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tight-operation",
+   "metadata": {},
+   "source": [
+    "By trial and error, we could find the value of `r` where the final temperature is precisely 70 °C.\n",
+    "But it is more efficient to use a root-finding algorithm."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "equipped-blackberry",
+   "metadata": {},
+   "source": [
+    "## Finding roots\n",
+    "\n",
+    "ModSim provides a function called `root_scalar` that finds the roots of non-linear equations. As a simple example, suppose you want to find the roots of the polynomial \n",
+    "\n",
+    "$$f(x) = (x - 1)(x - 2)(x - 3)$$ \n",
+    "\n",
+    "A **root** is a value of $x$ that makes $f(x)=0$. Because of the way I wrote the polynomial, we can see that if $x=1$, the first factor is 0; if $x=2$, the second factor is 0; and if $x=3$, the third factor is 0, so those are the roots.\n",
+    "\n",
+    "I'll use this example to demonstrate `root_scalar`. First, we have to\n",
+    "write a function that evaluates $f$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "round-budapest",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def func(x):\n",
+    "    return (x-1) * (x-2) * (x-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "injured-limitation",
+   "metadata": {},
+   "source": [
+    "Now we call `root_scalar` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "regulated-cloud",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = root_scalar(func, bracket=[1.5, 2.5])\n",
+    "res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "regulated-weekend",
+   "metadata": {},
+   "source": [
+    "The first argument is the function whose roots we want. The second\n",
+    "argument is an interval that contains or \"brackets\" a root. The result is an object that contains several variables, including `root`, which stores the root that was found."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "graphic-preliminary",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res.root"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "changing-winter",
+   "metadata": {},
+   "source": [
+    "If we provide a different interval, we find a different root."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "authentic-hunter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = root_scalar(func, bracket=[2.5, 3.5])\n",
+    "res.root"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bound-freight",
+   "metadata": {},
+   "source": [
+    "If the interval doesn't contain a root, you'll get a `ValueError` and a message like \"f(a) and f(b) must have different signs\".\n",
+    "\n",
+    "```\n",
+    "res = root_scalar(func, bracket=[4, 5])\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "impossible-spanish",
+   "metadata": {},
+   "source": [
+    "Now we can use `root_scalar` to estimate `r`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "imperial-engineering",
+   "metadata": {},
+   "source": [
+    "## Estimating `r`\n",
+    "\n",
+    "What we want is the value of `r` that yields a final temperature of\n",
+    "70 °C. To use `root_scalar`, we need a function that takes `r` as a parameter and returns the difference between the final temperature and the goal:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "quality-colors",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def error_func(r, system):\n",
+    "    system.r = r\n",
+    "    results = run_simulation(system, change_func)\n",
+    "    return system.T_final - 70"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "random-loading",
+   "metadata": {},
+   "source": [
+    "This is called an \"error function\" because it returns the\n",
+    "difference between what we got and what we wanted, that is, the error.\n",
+    "With the right value of `r`, the error is 0.\n",
+    "\n",
+    "We can test `error_func` like this, using the initial guess `r=0.01`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "unknown-pharmacy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee = make_system(T_init=90, volume=300, r=0.01, t_end=30)\n",
+    "error_func(0.01, coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "moderate-monkey",
+   "metadata": {},
+   "source": [
+    "The result is an error of 2.3 °C, because the final temperature with\n",
+    "this value of `r` is too high."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "fossil-israel",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error_func(0.02, coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "south-fabric",
+   "metadata": {},
+   "source": [
+    "With `r=0.02`, the error is  about -11°C, which means that the final temperature is too low. So we know that the correct value must be in between.\n",
+    "\n",
+    "So we can call `root_scalar` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "backed-while",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = root_scalar(error_func, coffee, bracket=[0.01, 0.02])\n",
+    "res.flag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exotic-development",
+   "metadata": {},
+   "source": [
+    "The first argument is the error function.\n",
+    "The second argument is the `System` object, which `root_scalar` passes as an argument to `error_func`.\n",
+    "The third argument is an interval that brackets the root.\n",
+    "\n",
+    "Here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "early-twelve",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r_coffee = res.root\n",
+    "r_coffee"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "greenhouse-designer",
+   "metadata": {},
+   "source": [
+    "In this example, `r_coffee` turns out to be about `0.0115`, in units of min$^{-1}$ (inverse minutes).\n",
+    "\n",
+    "We can confirm that this value is correct by setting `r` to the root we found and running the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "foster-lodging",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee.r = res.root\n",
+    "run_simulation(coffee, change_func)\n",
+    "coffee.T_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sweet-horizontal",
+   "metadata": {},
+   "source": [
+    "The final temperature is very close to 70 °C."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "imposed-complement",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "**Exercise:**  Simulate the temperature of 50 mL of milk with a starting temperature of 5 °C, in a vessel with `r=0.1`, for 15 minutes, and plot the results.\n",
+    "\n",
+    "By trial and error, find a value for `r` that makes the final temperature close to 20 °C."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "radical-accident",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "extraordinary-andrew",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "initial-parallel",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write an error function that simulates the temperature of the milk and returns the difference between the final temperature and 20 °C.  Use it to estimate the value of `r` for the milk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "yellow-single",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "cognitive-italian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "placed-concentrate",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "announced-continent",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap16.ipynb b/chapters/chap16.ipynb
new file mode 100644
index 00000000..1105131b
--- /dev/null
+++ b/chapters/chap16.ipynb
@@ -0,0 +1,642 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "involved-hampshire",
+   "metadata": {},
+   "source": [
+    "# Chapter 16"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "senior-paintball",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "straight-milan",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import code from previous notebooks\n",
+    "\n",
+    "from chap15 import change_func\n",
+    "from chap15 import run_simulation\n",
+    "from chap15 import make_system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "global-shooting",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we wrote a simulation of a cooling cup of\n",
+    "coffee. Given the initial temperature of the coffee, the temperature of the atmosphere, and the rate parameter, `r`, we can predict how the\n",
+    "temperature of the coffee will change over time.\n",
+    "\n",
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ceramic-calibration",
+   "metadata": {},
+   "source": [
+    "## Mixing liquids\n",
+    "\n",
+    "When we mix two liquids, the temperature of the mixture depends on the\n",
+    "temperatures of the ingredients, but it might not be obvious how to\n",
+    "compute it.\n",
+    "\n",
+    "Assuming there are no chemical reactions that either produce or consume heat, the total thermal energy of the system is the same before and after mixing; in other words, thermal energy is **conserved**.\n",
+    "\n",
+    "If the temperature of the first liquid is $T_1$, the temperature of the second liquid is $T_2$, and the final temperature of the mixture is $T$, the heat transfer into the first liquid is $C_1 (T - T_1)$ and the heat transfer into the second liquid is $C_2 (T - T_2)$, where $C_1$ and $C_2$ are the thermal masses of the liquids.\n",
+    "\n",
+    "In order to conserve energy, these heat transfers must add up to 0:\n",
+    "\n",
+    "$$C_1 (T - T_1) + C_2 (T - T_2) = 0$$ \n",
+    "\n",
+    "We can solve this equation for T:\n",
+    "\n",
+    "$$T = \\frac{C_1 T_1 + C_2 T_2}{C_1 + C_2}$$ \n",
+    "\n",
+    "For the coffee cooling problem, we have the volume of each liquid; if we also know the density, $\\rho$, and the specific heat capacity, $c_p$, we can compute thermal mass: \n",
+    "\n",
+    "$$C = \\rho V c_p$$ \n",
+    "\n",
+    "If the liquids have the same density and heat capacity, they drop out of the equation, and we can write:\n",
+    "\n",
+    "$$T = \\frac{V_1 T_1 + V_2 T_2}{V_1 + V_2}$$ \n",
+    "\n",
+    "where $V_1$ and $V_2$ are the volumes of the liquids.\n",
+    "\n",
+    "As an approximation, I'll assume that milk and coffee have the same\n",
+    "density and specific heat. As an exercise, you can look up these\n",
+    "quantities and see how good this assumption is.\n",
+    "\n",
+    "The following function takes two `System` objects that represent the\n",
+    "coffee and milk, and creates a new `System` to represent the mixture:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "orange-beaver",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mix(system1, system2):\n",
+    "    \n",
+    "    V1, V2 = system1.volume, system2.volume\n",
+    "    T1, T2 = system1.T_final, system2.T_final\n",
+    "    \n",
+    "    V_mix = V1 + V2\n",
+    "    T_mix = (V1 * T1 + V2 * T2) / V_mix\n",
+    "    \n",
+    "    return make_system(T_init=T_mix,\n",
+    "                       volume=V_mix,\n",
+    "                       r=system1.r,\n",
+    "                       t_end=30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "quiet-payroll",
+   "metadata": {},
+   "source": [
+    "The first two lines extract volume and temperature from the two `System` objects. Then the following two lines compute the volume and temperature of the mixture. Finally, `mix` makes a new `System` object and returns it.\n",
+    "\n",
+    "This function uses the value of `r` from `system1` as the value of `r`\n",
+    "for the mixture. If `system1` represents the coffee, and we are adding\n",
+    "the milk to the coffee, this is probably a reasonable choice. On the\n",
+    "other hand, when we increase the amount of liquid in the coffee cup,\n",
+    "that might change `r`. So this is an assumption we might want to\n",
+    "revisit.\n",
+    "\n",
+    "Now we have everything we need to solve the problem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "talented-appendix",
+   "metadata": {},
+   "source": [
+    "## Mix first or last?\n",
+    "\n",
+    "First I'll create objects to represent the coffee and milk.\n",
+    "For `r_coffee` and `r_milk`, I'll use the values we computed in the previous chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "breathing-pizza",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r_coffee = 0.0115\n",
+    "coffee = make_system(T_init=90, volume=300, r=r_coffee, t_end=30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "miniature-doctrine",
+   "metadata": {},
+   "source": [
+    "For `r_milk`, I'll use the value I estimated in the exercise from the previous chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "faced-start",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r_milk = 0.133\n",
+    "milk = make_system(T_init=5, volume=50, r=r_milk, t_end=15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "interior-alpha",
+   "metadata": {},
+   "source": [
+    "Now we can mix them and simulate 30 minutes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "stock-release",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mix_first = mix(coffee, milk)\n",
+    "run_simulation(mix_first, change_func)\n",
+    "\n",
+    "mix_first.T_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "committed-preference",
+   "metadata": {},
+   "source": [
+    "The final temperature is 61.5 °C which is still warm enough to be\n",
+    "enjoyable. Would we do any better if we added the milk last?\n",
+    "\n",
+    "I'll simulate the coffee and milk separately, and then mix them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "integral-character",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_simulation(coffee, change_func)\n",
+    "run_simulation(milk, change_func)\n",
+    "mix_last = mix(coffee, milk)\n",
+    "mix_last.T_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "trying-jacob",
+   "metadata": {},
+   "source": [
+    "After mixing, the temperature is 62.9 °C, so it looks like adding the\n",
+    "milk at the end is better. \n",
+    "But is that the best we can do?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "enclosed-shore",
+   "metadata": {},
+   "source": [
+    "## Optimization\n",
+    "\n",
+    "Adding the milk after 30 minutes is better than adding it immediately, but maybe there's something in between that's even better. To find out, I'll use the following function, which takes the time to add the milk, `t_add`, as a parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "accepting-consumer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_and_mix(t_add, t_total):\n",
+    "    coffee.t_end = t_add\n",
+    "    coffee_results = run_simulation(coffee, change_func)\n",
+    "    \n",
+    "    milk.t_end = t_add\n",
+    "    milk_results = run_simulation(milk, change_func)\n",
+    "    \n",
+    "    mixture = mix(coffee, milk)\n",
+    "    mixture.t_end = t_total - t_add\n",
+    "    results = run_simulation(mixture, change_func)\n",
+    "\n",
+    "    return mixture.T_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "critical-pound",
+   "metadata": {},
+   "source": [
+    "When `t_add` is`0`, we add the milk immediately; when `t_add` is `30`, we add it at the end. Now we can sweep the range of values in between:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "isolated-georgia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sweep = SweepSeries()\n",
+    "for t_add in linspace(0, 30, 11):\n",
+    "    sweep[t_add] = run_and_mix(t_add, 30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "latin-fireplace",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "possible-prevention",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "sweep.plot(label='mixture', color='C2')\n",
+    "\n",
+    "decorate(xlabel='Time until mixing (minutes)',\n",
+    "         ylabel='Final emperature (C)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "light-poland",
+   "metadata": {},
+   "source": [
+    "Note that this is a parameter sweep, not a time series.\n",
+    "\n",
+    "The final temperature is maximized when `t_add=30`, so adding the milk\n",
+    "at the end is optimal.\n",
+    "\n",
+    "As an exercise you will have a chance to explore a variation of the coffee cooling problem:\n",
+    "\n",
+    "> Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office. In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
+    "\n",
+    "But first, let's do some analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "industrial-circle",
+   "metadata": {},
+   "source": [
+    "## Analysis\n",
+    "\n",
+    "Simulating Newton's law of cooling is almost silly, because we can solve the differential equation analytically. If\n",
+    "\n",
+    "$$\\frac{dT}{dt} = -r (T - T_{env})$$ \n",
+    "\n",
+    "the general solution is\n",
+    "\n",
+    "$$T{\\left (t \\right )} = C \\exp(-r t) + T_{env}$$ \n",
+    "\n",
+    "and the particular solution where $T(0) = T_{init}$ is\n",
+    "\n",
+    "$$T_{env} + \\left(- T_{env} + T_{init}\\right) \\exp(-r t)$$ \n",
+    "\n",
+    "If you would like to see this solution done by hand, you can watch this video: <http://modsimpy.com/khan3>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "molecular-happiness",
+   "metadata": {},
+   "source": [
+    "Now we can use the observed data to estimate the parameter $r$. If we\n",
+    "observe the that temperature at $t_{end}$ is $T_{final}$, we can plug these values into the particular solution and solve for $r$. The result is:\n",
+    "\n",
+    "$$r = \\frac{1}{t_{end}} \\log{\\left (\\frac{T_{init} - T_{env}}{T_{final} - T_{env}} \\right )}$$\n",
+    "\n",
+    "The following function takes a `System` object and computes `r`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "dressed-supply",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import log\n",
+    "\n",
+    "def compute_r(system):\n",
+    "    t_end = system.t_end\n",
+    "    T_init = system.T_init\n",
+    "    T_final = system.T_final\n",
+    "    T_env = system.T_env\n",
+    "    \n",
+    "    r = log((T_init - T_env) / (T_final - T_env)) / t_end\n",
+    "    return r"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "young-shift",
+   "metadata": {},
+   "source": [
+    "We can use this function to compute `r` for the coffee, given the parameters of the problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "bacterial-tonight",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee2 = make_system(T_init=90, volume=300, r=0, t_end=30)\n",
+    "coffee2.T_final = 70\n",
+    "r_coffee2 = compute_r(coffee2)\n",
+    "r_coffee2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "removable-authority",
+   "metadata": {},
+   "source": [
+    "This value is close to the value of `r` we computed in the previous chapter, `0.115`, but not exactly the same.\n",
+    "That's because the simulations use discrete time steps, and the analysis uses continuous time.\n",
+    "\n",
+    "Nevertheless, the results of any analysis are consistent with the simulation.\n",
+    "To check, we'll use the following function, which takes a `System` object and uses the analytic result to compute a time series:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "civic-trail",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import exp\n",
+    "from pandas import Series\n",
+    "\n",
+    "def run_analysis(system):\n",
+    "    T_env, T_init, r = system.T_env, system.T_init, system.r\n",
+    "    \n",
+    "    t_array = linrange(system.t_0, system.t_end, system.dt)    \n",
+    "    T_array = T_env + (T_init - T_env) * exp(-r * t_array)\n",
+    "    \n",
+    "    system.T_final = T_array[-1]\n",
+    "    return make_series(t_array, T_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eleven-faculty",
+   "metadata": {},
+   "source": [
+    "The first line unpacks the system variables.\n",
+    "The next two lines compute `t_array`, which is a NumPy array of time stamps, and `T_array`, which is an array of the corresponding temperatures.\n",
+    "\n",
+    "The last two lines store the final temperature in the `System` object and return the results in a Pandas `Series`.\n",
+    "\n",
+    "We can run it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "terminal-carry",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee2.r = r_coffee2\n",
+    "results2 = run_analysis(coffee2)\n",
+    "coffee2.T_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "approved-subject",
+   "metadata": {},
+   "source": [
+    "The final temperature is 70 °C, as it should be. In fact, the results\n",
+    "are identical to what we got by simulation, with a small difference due to rounding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "grand-silence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee.r = 0.011543\n",
+    "results = run_simulation(coffee, change_func)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "monetary-sensitivity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import allclose\n",
+    "\n",
+    "allclose(results, results2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "polish-focus",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "respected-inclusion",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Use `compute_r` to compute `r_milk` according to the analytic solution.  Run the analysis with this value of `r_milk` and confirm that the results are consistent with the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "headed-circle",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "enormous-consolidation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "social-stamp",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "exclusive-puzzle",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "chinese-explanation",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
+    "\n",
+    "Hint: Think about the simplest way to represent the behavior of a refrigerator in this model.  The change you make to test this variation of the problem should be very small!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "terminal-billy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "nuclear-going",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap17.ipynb b/chapters/chap17.ipynb
new file mode 100644
index 00000000..b4521f95
--- /dev/null
+++ b/chapters/chap17.ipynb
@@ -0,0 +1,547 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "interracial-guitar",
+   "metadata": {},
+   "source": [
+    "# Chapter 17"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "restricted-thumb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "european-movement",
+   "metadata": {},
+   "source": [
+    "In this chapter we start a new example, a model of how glucose and insulin interact to control blood sugar.  We will implement a widely used model called the \"Minimal Model\" because it is intended to include only the elements essential to explain the observed behavior of the system.\n",
+    "\n",
+    "This chapter presents the model itself and some background information we need to understand it.\n",
+    "In the next chapter we'll implement the model and compare the results to real data.\n",
+    "\n",
+    "My presentation in this chapter follows Bergman (2005) \"Minimal Model\"\n",
+    "(abstract at <http://modsimpy.com/bergman>, PDF at\n",
+    "<http://modsimpy.com/minmod>)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tamil-helping",
+   "metadata": {},
+   "source": [
+    "## The Minimal Model\n",
+    "\n",
+    "**Pharmacokinetics** is the study of how drugs and other substances move around the body, react, and are eliminated. In this chapter, we\n",
+    "implement one of the most widely used pharmacokinetic models: the\n",
+    "so-called \"minimal model\" of glucose and insulin in the blood stream.\n",
+    "\n",
+    "**Glucose** is a form of sugar that circulates in the blood of animals; it is used as fuel for muscles, the brain, and other organs. The concentration of blood sugar is controlled by the hormone system, and especially by **insulin**, which is produced by the pancreas and has the effect of reducing blood sugar.\n",
+    "\n",
+    "In people with normal pancreatic function, the hormone system maintains **homeostasis**; that is, it keeps the concentration of blood sugar in a range that is neither too high or too low.\n",
+    "\n",
+    "But if the pancreas does not produce enough insulin, or if the cells\n",
+    "that should respond to insulin become insensitive, blood sugar can\n",
+    "become elevated, a condition called **hyperglycemia**. Long term, severe hyperglycemia is the defining symptom of **diabetes mellitus**, a serious disease that affects almost 10% of the population in the U.S. (see <http://modsimpy.com/cdc>)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sharp-yukon",
+   "metadata": {},
+   "source": [
+    "One of the most-used tests for hyperglycemia and diabetes is the\n",
+    "frequently sampled intravenous glucose tolerance test (FSIGT), in which glucose is injected into the blood stream of a fasting subject (someone who has not eaten recently); then blood samples are collected at intervals of 2--10 minutes for 3 hours. The samples are analyzed to\n",
+    "measure the concentrations of glucose and insulin.\n",
+    "\n",
+    "Using these measurements, we can estimate several parameters of\n",
+    "the subject's response; the most important is a parameter denoted $S_I$, which quantifies the effect of insulin on the rate of reduction in blood sugar."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "round-roommate",
+   "metadata": {},
+   "source": [
+    "## The glucose minimal model\n",
+    "\n",
+    "The \"minimal model\", as proposed by Bergman, Ider, Bowden, and\n",
+    "Cobelli, consists of two parts: the glucose model and the insulin model. I will present an implementation of the glucose model; as a case study, you will have the chance to implement the insulin model.\n",
+    "\n",
+    "The original model was developed in the 1970s; since then, many\n",
+    "variations and extensions have been proposed. Bergman's comments on the development of the model provide insight into their process:\n",
+    "\n",
+    "> We applied the principle of Occam's Razor, i.e. by asking what was the simplest model based upon known physiology that could account for the insulin-glucose relationship revealed in the data. Such a model must be simple enough to account totally for the measured glucose (given the insulin input), yet it must be possible, using mathematical techniques, to estimate all the characteristic parameters of the model from a single data set (thus avoiding unverifiable assumptions)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "accompanied-battery",
+   "metadata": {},
+   "source": [
+    "The most useful models are the ones that achieve this balance: including enough realism to capture the essential features of the system without so much complexity that they are impractical. In this example, the practical limit is the ability to estimate the parameters of the model using data, and to interpret the parameters meaningfully.\n",
+    "\n",
+    "Bergman discusses the features he and his colleagues thought were\n",
+    "essential:\n",
+    "\n",
+    "> (1) Glucose, once elevated by injection, returns to basal level due to two effects: the effect of glucose itself to normalize its own concentration \\[...\\] as well as the catalytic effect of insulin to allow glucose to self-normalize (2) Also, we discovered that the effect of insulin on net glucose disappearance must be sluggish --- that is, that insulin acts slowly because insulin must first move from plasma to a remote compartment \\[...\\] to exert its action on glucose disposal.\n",
+    "\n",
+    "To paraphrase the second point, the effect of insulin on glucose\n",
+    "disposal, as seen in the data, happens more slowly than we would expect if it depended primarily on the the concentration of insulin in the blood. Bergman's group hypothesized that insulin must move relatively slowly from the blood to a \"remote compartment\" where it has its effect."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bottom-extent",
+   "metadata": {},
+   "source": [
+    "At the time, the remote compartment was a modeling abstraction that\n",
+    "might, or might not, represent something physical. Later, according to\n",
+    "Bergman, it was \"shown to be interstitial fluid\", that is, the fluid\n",
+    "that surrounds tissue cells. In the history of mathematical modeling, it is common for hypothetical entities, added to models to achieve\n",
+    "particular effects, to be found later to correspond to physical\n",
+    "entities.  The most notable example, historically, might be [genes](https://en.wikipedia.org/wiki/Gene#Discovery_of_discrete_inherited_units).\n",
+    "\n",
+    "The glucose model consists of two differential equations:\n",
+    "\n",
+    "$$\\frac{dG}{dt} = -k_1 \\left[ G(t) - G_b \\right] - X(t) G(t)$$\n",
+    "\n",
+    "$$\\frac{dX}{dt} = k_3 \\left[I(t) - I_b \\right] - k_2 X(t)$$ \n",
+    "\n",
+    "where\n",
+    "\n",
+    "-   $G$ is the concentration of blood glucose as a function of time and $dG/dt$ is its rate of change.\n",
+    "\n",
+    "-   $X$ is the concentration of insulin in the tissue fluid as a\n",
+    "    function of time, and $dX/dt$ is its rate of change.\n",
+    "\n",
+    "-   $I$ is the concentration of insulin in the blood as a function of\n",
+    "    time, which is taken as an input into the model, based on\n",
+    "    measurements.\n",
+    "\n",
+    "-   $G_b$ is the basal concentration of blood glucose and $I_b$ is the\n",
+    "    basal concentration of blood insulin, that is, the concentrations at equilibrium. Both are constants estimated from measurements at the\n",
+    "    beginning or end of the test.\n",
+    "\n",
+    "-   $k_1$, $k_2$, and $k_3$ are positive-valued parameters that control the rates of appearance and disappearance for glucose and insulin."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dutch-retailer",
+   "metadata": {},
+   "source": [
+    "We can interpret the terms in the equations one by one:\n",
+    "\n",
+    "-   $-k_1 \\left[ G(t) - G_b \\right]$ is the rate of glucose\n",
+    "    disappearance due to the effect of glucose itself. When $G(t)$ is\n",
+    "    above basal level, $G_b$, this term is negative; when $G(t)$ is\n",
+    "    below basal level this term is positive. So in the absence of\n",
+    "    insulin, this term tends to restore blood glucose to basal level.\n",
+    "\n",
+    "-   $-X(t) G(t)$ models the interaction of glucose and insulin in tissue\n",
+    "    fluid, so the rate increases as either $X$ or $G$ increases. This\n",
+    "    term does not require a rate parameter because the units of $X$ are\n",
+    "    unspecified; we can consider $X$ to be in whatever units make the\n",
+    "    parameter of this term 1.\n",
+    "\n",
+    "-   $k_3 \\left[ I(t) - I_b \\right]$ is the rate at which insulin\n",
+    "    diffuses between blood and tissue fluid. When $I(t)$ is above basal\n",
+    "    level, insulin diffuses from the blood into the tissue fluid. When\n",
+    "    $I(t)$ is below basal level, insulin diffuses from tissue to the\n",
+    "    blood.\n",
+    "\n",
+    "-   $-k_2 X(t)$ is the rate of insulin disappearance in tissue fluid as\n",
+    "    it is consumed or broken down."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "regional-receptor",
+   "metadata": {},
+   "source": [
+    "The initial state of the model is $X(0) = I_b$ and $G(0) = G_0$, where\n",
+    "$G_0$ is a constant that represents the concentration of blood sugar\n",
+    "immediately after the injection. In theory we could estimate $G_0$ based on measurements, but in practice it takes time for the injected glucose to spread through the blood volume. Since $G_0$ is not measurable, it is treated as a **free parameter** of the model, which means that we are free to choose it to fit the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exact-heating",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "To develop and test the model, we'll use data from [Pacini and Bergman](https://www.researchgate.net/publication/13707725_Insulin_sensitivity_and_glucose_effectiveness_Minimal_model_analysis_of_regular_and_insulin-modified_FSIGT), \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 23: 113-122, (1986)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "separated-strip",
+   "metadata": {},
+   "source": [
+    "The following cell downloads the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "looking-needle",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'glucose_insulin.csv'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/glucose_insulin.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dressed-regard",
+   "metadata": {},
+   "source": [
+    "We can use Pandas to read the data file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "naval-geology",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_csv\n",
+    "\n",
+    "data = read_csv('glucose_insulin.csv', index_col='time')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "close-payday",
+   "metadata": {},
+   "source": [
+    "`data` has two columns: `glucose` is the concentration of blood glucose in mg/dL; `insulin` is concentration of insulin in the blood in μU/mL (a medical \"unit\", denoted U, is an amount defined by convention in context). The index is time in minutes.\n",
+    "\n",
+    "This dataset represents glucose and insulin concentrations over\n",
+    "182 min for a subject with normal insulin production and sensitivity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "burning-valuable",
+   "metadata": {},
+   "source": [
+    "## Interpolation\n",
+    "\n",
+    "Before we are ready to implement the model, there's one problem we have to solve. In the differential equations, $I$ is a function that can be evaluated at any time, $t$. But in the `DataFrame`, we only have measurements at discrete times. This is a job for interpolation!\n",
+    "\n",
+    "The ModSim library provides an `interpolate`, function that takes a `Series` as a parameter and returns a function. That's right, I said it returns a *function*.\n",
+    "\n",
+    "So we can call `interpolate` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "terminal-teaching",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "I = interpolate(data.insulin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "banner-shakespeare",
+   "metadata": {},
+   "source": [
+    "Then we can call the new function, `I`, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "intense-thursday",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "I(18)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "severe-desire",
+   "metadata": {},
+   "source": [
+    "The result is about 31.7, which is a linear interpolation between the actual measurements at `t=16` and `t=19`. \n",
+    "\n",
+    "We can also pass an array as an argument to `I`.  Here's an array of equally-spaced values from `t_0` to `t_end`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "least-pattern",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_0 = data.index[0]\n",
+    "t_end = data.index[-1]\n",
+    "t_array = linrange(t_0, t_end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "empirical-tackle",
+   "metadata": {},
+   "source": [
+    "And here are the corresponding values of `I`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "mounted-venice",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "I_array = I(t_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "searching-respect",
+   "metadata": {},
+   "source": [
+    "We can use `make_series` to put the results in a Pandas `Series`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "affecting-response",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "I_series = make_series(t_array, I_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "insured-textbook",
+   "metadata": {},
+   "source": [
+    "Here's what the interpolated values look like along with the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "engaging-watershed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.insulin.plot(style='o', color='C2', label='insulin data')\n",
+    "I_series.plot(color='C2', label='interpolated')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration ($\\mu$U/mL)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adopted-locking",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This chapter introduces a model of the interaction between glucose and insulin in the blood stream.\n",
+    "And it introduces a new tool, interpolation, which we'll need to implement the model\n",
+    "\n",
+    "In the next chapter, we will use measured concentrations of insulin to simulate the glucose-insulin system, and compare the results to measured concentrations of glucose.\n",
+    "\n",
+    "Then, you'll have a chance to implement the second part of the model, which uses measured concentrations of glucose to simulate the insulin response, and compare the results to the data.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "floppy-store",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hungarian-newman",
+   "metadata": {},
+   "source": [
+    "**Exercise:** `interpolate` is a wrapper for the SciPy function `interp1d`.\n",
+    "Read the documentation of `interp1d` at <http://modsimpy.com/interp>.\n",
+    "\n",
+    "In particular, notice the `kind` argument, which specifies a kind of interpolation.\n",
+    "The default is linear interpolation, which connects the data points with straight lines.\n",
+    "\n",
+    "Pass a keyword argument to `interpolate` to specify one of the other kinds of interpolation, and run the code again to see what it looks like. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "endless-network",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "entire-concern",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.insulin.plot(style='o', color='C2', label='insulin data')\n",
+    "I_series.plot(color='C2', label='interpolated')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration ($\\mu$U/mL)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "furnished-recognition",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Interpolate the glucose data and generate a plot, similar to the previous one, that shows the data points and the interpolated curve evaluated at the time values in `t_array`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "representative-acquisition",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "rocky-sydney",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "data.glucose.plot(style='o', alpha=0.5, label='glucose data')\n",
+    "G_series.plot(color='C0', label='interpolated')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration (mg/dL)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "essential-fishing",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap18.ipynb b/chapters/chap18.ipynb
new file mode 100644
index 00000000..0e0d245d
--- /dev/null
+++ b/chapters/chap18.ipynb
@@ -0,0 +1,783 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "electric-netherlands",
+   "metadata": {},
+   "source": [
+    "# Chapter 18"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tribal-blame",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "detected-welsh",
+   "metadata": {},
+   "source": [
+    "The previous chapter present the minimal model of the glucose-insulin system, and introduces a tool we will need to implement it: interpolation.\n",
+    "\n",
+    "In this chapter, we'll implement the model two ways:\n",
+    "\n",
+    "* We'll start by rewriting the differential equations as difference equations; then we'll solve the difference equations using a version of `run_simulation`, similar to what we have used in previous chapters.\n",
+    "\n",
+    "* Then we'll use a new SciPy function, called `solve_ivp`, to solve the differential equation.\n",
+    "\n",
+    "We'll see that `solve_ivp` is faster and more accurate than `run_simulation`.\n",
+    "As a result, we will us it for every model in the rest of the book."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "original-photographer",
+   "metadata": {},
+   "source": [
+    "The following cell downloads the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "fewer-weather",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'glucose_insulin.csv'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/glucose_insulin.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "postal-procedure",
+   "metadata": {},
+   "source": [
+    "We can use Pandas to read the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "computational-border",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_csv\n",
+    "\n",
+    "data = read_csv(filename, index_col='time');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "collective-orleans",
+   "metadata": {},
+   "source": [
+    "## Implementing the Model\n",
+    "\n",
+    "To get started, let's assume that the parameters of the model are known.\n",
+    "We'll implement the model and use it to generate time series for `G` and `X`. \n",
+    "Then we'll see how we can choose parameters that generate a series that fits the data.\n",
+    "\n",
+    "Here are the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "delayed-trance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G0 = 270\n",
+    "k1 = 0.02\n",
+    "k2 = 0.02\n",
+    "k3 = 1.5e-05"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sapphire-examination",
+   "metadata": {},
+   "source": [
+    "I'll put these values in a sequence which we'll pass to `make_system`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "following-alarm",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = G0, k1, k2, k3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "polished-burner",
+   "metadata": {},
+   "source": [
+    "Here's a version of `make_system` that takes `params` and `data` as parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "substantial-literacy",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(params, data):\n",
+    "    G0, k1, k2, k3 = params\n",
+    "    \n",
+    "    t_0 = data.index[0]\n",
+    "    t_end = data.index[-1]\n",
+    "    \n",
+    "    Gb = data.glucose[t_0]\n",
+    "    Ib = data.insulin[t_0]\n",
+    "    I = interpolate(data.insulin)\n",
+    "    \n",
+    "    init = State(G=G0, X=0)\n",
+    "    \n",
+    "    return System(init=init, params=params,\n",
+    "                  Gb=Gb, Ib=Ib, I=I,\n",
+    "                  t_0=t_0, t_end=t_end, dt=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afraid-friendly",
+   "metadata": {},
+   "source": [
+    "`make_system` gets `t_0` and `t_end` from the data. \n",
+    "It uses the measurements at `t=0` as the basal levels, `Gb` and `Ib`. \n",
+    "And it uses the parameter `G0` as the initial value for `G`. Then it \n",
+    "packs everything into a `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "legislative-richards",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stupid-retro",
+   "metadata": {},
+   "source": [
+    "## The Update Function\n",
+    "\n",
+    "The minimal model is expressed in terms of differential equations:\n",
+    "\n",
+    "$$\\frac{dG}{dt} = -k_1 \\left[ G(t) - G_b \\right] - X(t) G(t)$$\n",
+    "\n",
+    "$$\\frac{dX}{dt} = k_3 \\left[I(t) - I_b \\right] - k_2 X(t)$$ \n",
+    "\n",
+    "To simulate this system, we will rewriting them as difference equations. \n",
+    "If we multiply both sides by $dt$, we have:\n",
+    "\n",
+    "$$dG = \\left[ -k_1 \\left[ G(t) - G_b \\right] - X(t) G(t) \\right] dt$$\n",
+    "\n",
+    "$$dX = \\left[ k_3 \\left[I(t) - I_b \\right] - k_2 X(t) \\right] dt$$ \n",
+    "\n",
+    "If we think of $dt$ as a small step in time, these equations tell us how to compute the corresponding changes in $G$ and $X$.\n",
+    "\n",
+    "Here's an update function that computes these changes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "roman-archive",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_func(state, t, system):\n",
+    "    G, X = state\n",
+    "    G0, k1, k2, k3 = system.params \n",
+    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
+    "    dt = system.dt\n",
+    "        \n",
+    "    dGdt = -k1 * (G - Gb) - X*G\n",
+    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
+    "    \n",
+    "    G += dGdt * dt\n",
+    "    X += dXdt * dt\n",
+    "\n",
+    "    return State(G=G, X=X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "basic-subdivision",
+   "metadata": {},
+   "source": [
+    "As usual, the update function takes a `State` object, a time, and a\n",
+    "`System` object as parameters. The first line uses multiple assignment\n",
+    "to extract the current values of `G` and `X`.\n",
+    "\n",
+    "The following lines unpack the parameters we need from the `System`\n",
+    "object.\n",
+    "\n",
+    "Computing the derivatives `dGdt` and `dXdt` is straightforward; we just translate the equations from math notation to Python.\n",
+    "Then, to perform the update, we multiply each derivative by the discrete time step `dt`, which is 2 min in this example. \n",
+    "\n",
+    "The return value is a `State` object with the new values of `G` and `X`.\n",
+    "\n",
+    "Before running the simulation, it is a good idea to run the update\n",
+    "function with the initial conditions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "sapphire-shannon",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "update_func(system.init, system.t_0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "growing-hormone",
+   "metadata": {},
+   "source": [
+    "If it runs without errors and there is nothing obviously wrong with the results, we are ready to run the simulation. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "strange-citation",
+   "metadata": {},
+   "source": [
+    "## The Simulation\n",
+    "\n",
+    "We'll use the following version of `run_simulation`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "willing-masters",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):    \n",
+    "    t_array = linrange(system.t_0, system.t_end, system.dt)\n",
+    "    n = len(t_array)\n",
+    "    \n",
+    "    frame = TimeFrame(index=t_array, \n",
+    "                      columns=system.init.index)\n",
+    "    frame.iloc[0] = system.init\n",
+    "    \n",
+    "    for i in range(n-1):\n",
+    "        t = t_array[i]\n",
+    "        state = frame.iloc[i]\n",
+    "        frame.iloc[i+1] = update_func(state, t, system)\n",
+    "    \n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "musical-loading",
+   "metadata": {},
+   "source": [
+    "We can run it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "optional-burden",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, update_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ambient-video",
+   "metadata": {},
+   "source": [
+    "The result is a `TimeFrame` with a row for each time step and a column for each of the state variables, `G` and `X`.\n",
+    "Here are the first few time steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "international-germany",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "least-steal",
+   "metadata": {},
+   "source": [
+    "The following plot shows the simulated glucose levels from the model along with the measured data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "liked-asset",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.glucose.plot(style='o', alpha=0.5, label='glucose data')\n",
+    "results.G.plot(style='-', color='C0', label='simulation')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration (mg/dL)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stopped-excuse",
+   "metadata": {},
+   "source": [
+    "With the parameters I chose, the model fits the data well, except during the first few minutes after the injection.\n",
+    "But we don't expect the model to do well in this regime.\n",
+    "\n",
+    "The problem is that the model is **non-spatial**; that is, it does not\n",
+    "take into account different concentrations in different parts of the\n",
+    "body. Instead, it assumes that the concentrations of glucose and insulin in blood, and insulin in tissue fluid, are the same throughout the body. This way of representing the body is known among experts as the \"bag of blood\" model.\n",
+    "\n",
+    "Immediately after injection, it takes time for the injected glucose to\n",
+    "circulate. During that time, we don't expect a non-spatial model to be\n",
+    "accurate. For this reason, we should not take the estimated value of `G0` too seriously; it is useful for fitting the model, but not meant to correspond to a physical, measurable quantity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "internal-positive",
+   "metadata": {},
+   "source": [
+    "The following plot shows simulated insulin levels in the hypothetical \"remote compartment\", which is in unspecified units."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "damaged-protection",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.X.plot(color='C1', label='remote insulin')\n",
+    "\n",
+    "decorate(xlabel='Time (min)', \n",
+    "         ylabel='Concentration (arbitrary units)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "economic-spare",
+   "metadata": {},
+   "source": [
+    "Remember that `X` represents the concentration of insulin in the \"remote compartment\", which is believed to be tissue fluid, so we can't compare it to the measured concentration of insulin in the blood.\n",
+    "\n",
+    "`X` rises quickly after the initial injection and then declines as the concentration of glucose declines.  Qualitatively, this behavior is as expected, but because `X` is not an observable quantity, we can't validate this part of the model quantitatively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "spatial-scholar",
+   "metadata": {},
+   "source": [
+    "## Solving differential equations\n",
+    "\n",
+    "To implement the minimal model, we rewrote the differential equations as difference equations with a finite time step, `dt`.\n",
+    "\n",
+    "When $dt$ is very small, or more precisely **infinitesimal**, the difference equations are the same as the differential equations.\n",
+    "But in our simulations, $dt$ is 2 min, which is not very small, and definitely not infinitesimal. \n",
+    "\n",
+    "In effect, the simulations assume that the derivatives $dG/dt$ and $dX/dt$ are constant during each 2 min time step.\n",
+    "This method, evaluating derivatives at discrete time steps and assuming that they are constant in between, is called **Euler's method** (see <http://modsimpy.com/euler>).\n",
+    "\n",
+    "Euler's method is good enough for many simple problems, but sometimes it is not very accurate.\n",
+    "In that case, sometimes we can make it more accurate by decreasing the size of `dt`.\n",
+    "But then it is not very efficient.\n",
+    "\n",
+    "There are other methods that are more accurate and more efficient than Euler's method.\n",
+    "SciPy provides several of them as a function called `solve_ivp` \n",
+    "The \"ivp\" in `solve_ivp` stands for \"initial value problem\", which is the term for problems like the ones we've been solving, where we are given the initial conditions and try to predict what will happen.\n",
+    "\n",
+    "ModSim provides a function called `run_solve_ivp` that makes `solve_ivp` a little easier to use.\n",
+    "\n",
+    "To use it, we have to provide a \"slope function\", which is similar to an update function; in fact, it takes the same parameters: a time stamp, a `State` object, and a `System` object.\n",
+    "\n",
+    "Here's a slope function that evaluates the differential equations of the minimal model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "expected-collapse",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    G, X = state\n",
+    "    G0, k1, k2, k3 = system.params \n",
+    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
+    "        \n",
+    "    dGdt = -k1 * (G - Gb) - X*G\n",
+    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
+    "    \n",
+    "    return dGdt, dXdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "modified-surname",
+   "metadata": {},
+   "source": [
+    "`slope_func` is a little simpler than `update_func` because it only compute the derivatives, that is, the slopes. It doesn't do the updates; the solver does them for us.\n",
+    "\n",
+    "Now we can call `run_solve_ivp` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "intelligent-visitor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results2, details = run_solve_ivp(system, slope_func,\n",
+    "                                  t_eval=results.index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "promotional-result",
+   "metadata": {},
+   "source": [
+    "`run_solve_ivp` is similar to `run_simulation`: it takes a `System`\n",
+    "object and a slope function as parameters.\n",
+    "\n",
+    "The third argument, `t_eval`, is optional; it specifies where the solution should be evaluated.\n",
+    "\n",
+    "It returns two values: a `TimeFrame`, which we assign to `results2`, and an `OdeResult` object, which we assign to `details`.\n",
+    "\n",
+    "The `OdeResult` object contains information about how the solver ran, including a success code and a diagnostic message."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "industrial-focus",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "details.success"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "frequent-exhibit",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fatal-parliament",
+   "metadata": {},
+   "source": [
+    "It's important to check these messages after running the solver, in case anything went wrong.\n",
+    "\n",
+    "The `TimeFrame` has one row for each time step and one column for each state variable. In this example, the rows are time from 0 to 182 minutes; the columns are the state variables, `G` and `X`.\n",
+    "Here are the first few time steps:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "immediate-legislature",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results2.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "noticed-material",
+   "metadata": {},
+   "source": [
+    "Because we used `t_eval=results.index`, the time stamps in `results2` are the same as in `results`, which makes them easier to compare.\n",
+    "\n",
+    "Here are the results from `run_solve_ivp` along with the results from `run_simulation`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "peripheral-commission",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.G.plot(style='--', label='simulation')\n",
+    "results2.G.plot(style='-', label='solve ivp')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration (mg/dL)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "advanced-provider",
+   "metadata": {},
+   "source": [
+    "The differences are barely visible.\n",
+    "We can compute the relative differences like this:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "clear-neighbor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diff = results.G - results2.G\n",
+    "percent_diff = diff / results2.G * 100\n",
+    "percent_diff.abs().describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "temporal-threat",
+   "metadata": {},
+   "source": [
+    "The biggest differences are a little more than 1%.\n",
+    "So in this example, the results from Euler's method are probably good enough for practical purposes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "corrected-indonesia",
+   "metadata": {},
+   "source": [
+    "Here are the results for `X`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "happy-guess",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.X.plot(style='--', label='simulation')\n",
+    "results2.X.plot(style='-', label='solve ivp')\n",
+    "\n",
+    "decorate(xlabel='Time (min)', \n",
+    "         ylabel='Concentration (arbitrary units)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "electronic-navigation",
+   "metadata": {},
+   "source": [
+    "These differences are little bigger, especially at the beginning.\n",
+    "As an exercise, you can experiment with `dt` and see what effect it has on these results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prostate-psychology",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this chapter, we implemented the glucose minimal model two ways, using `run_simulation` and `run_solve_ivp`, and compared the results.\n",
+    "We found that in this example, `run_simulation`, which uses Euler's method, is probably good enough.\n",
+    "But soon we will see examples where it is not.\n",
+    "\n",
+    "So far, we have assumed that the parameters of the system are known, but in practice that's not true.\n",
+    "As one of the case studies in the next chapter, you'll have a chance to see where those parameters came from."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unusual-springer",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "substantial-grain",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Our solution to the differential equations is only approximate because we used a finite step size, `dt=2` minutes.\n",
+    "\n",
+    "If we make the step size smaller, we expect the solution to be more accurate.  Run the simulation with `dt=1` and compare the results.  What is the largest relative error between the two solutions?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "thermal-trance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "prompt-activity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "developed-collaboration",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "alpha-configuration",
+   "metadata": {},
+   "source": [
+    "## Under the hood\n",
+    "\n",
+    "The documentation for `solve_ivp` is at <https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html>.\n",
+    "\n",
+    "It uses a Runge-Kutta method...\n",
+    "[RK45](https://en.wikipedia.org/wiki/List_of_Runge–Kutta_methods)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "russian-qualification",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap19.ipynb b/chapters/chap19.ipynb
new file mode 100644
index 00000000..f2f3c588
--- /dev/null
+++ b/chapters/chap19.ipynb
@@ -0,0 +1,288 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "mighty-israeli",
+   "metadata": {},
+   "source": [
+    "# Chapter 19"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "passing-solid",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "structured-satellite",
+   "metadata": {},
+   "source": [
+    "## The glucose minimal model\n",
+    "\n",
+    "In the previous chapter we implemented the glucose minimal model using given parameters, but I didn't say where those parameters came from.\n",
+    "\n",
+    "In the repository for this book, you will find a notebook,\n",
+    "`glucose.ipynb`, that shows how we can find the parameters that best fit the data.\n",
+    "\n",
+    "It uses a SciPy function called `leastsq`, which stands for \"least squares\"; that is, it finds the parameters that minimize the sum of squared differences between the results of the model and the data.\n",
+    "\n",
+    "You can think of `leastsq` as an optional tool for this book.  We won't use it in the text itself, but it appears in a few of the case studies.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "laden-gathering",
+   "metadata": {},
+   "source": [
+    "## The insulin minimal model\n",
+    "\n",
+    "Along with the glucose minimal model, Berman et al. developed an insulin minimal model, in which the concentration of insulin, $I$, is governed by this differential equation:\n",
+    "\n",
+    "$$\\frac{dI}{dt} = -k I(t) + \\gamma \\left[ G(t) - G_T \\right] t$$ \n",
+    "\n",
+    "where\n",
+    "\n",
+    "-   $k$ is a parameter that controls the rate of insulin disappearance\n",
+    "    independent of blood glucose.\n",
+    "\n",
+    "-   $G(t)$ is the measured concentration of blood glucose at time $t$.\n",
+    "\n",
+    "-   $G_T$ is the glucose threshold; when blood glucose is above this\n",
+    "    level, it triggers an increase in blood insulin.\n",
+    "\n",
+    "-   $\\gamma$ is a parameter that controls the rate of increase (or\n",
+    "    decrease) in blood insulin when glucose is above (or below) $G_T$.\n",
+    "\n",
+    "The initial condition is $I(0) = I_0$. As in the glucose minimal model, we treat the initial condition as a parameter which we'll choose to fit the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "interstate-gibson",
+   "metadata": {},
+   "source": [
+    "The parameters of this model can be used to estimate $\\phi_1$ and\n",
+    "$\\phi_2$, which are quantities that \"describe the sensitivity to glucose of the first and second phase pancreatic responsivity\". These quantities are related to the parameters as follows:\n",
+    "\n",
+    "$$\\phi_1 = \\frac{I_{max} - I_b}{k (G_0 - G_b)}$$\n",
+    "\n",
+    "$$\\phi_2 = \\gamma \\times 10^4$$ \n",
+    "\n",
+    "where $I_{max}$ is the maximum measured insulin level, and $I_b$ and $G_b$ are the basal levels of insulin and glucose.\n",
+    "\n",
+    "In the repository for this book, you will find a notebook,\n",
+    "`insulin.ipynb`, that contains starter code for this case study. Use it to implement the insulin model, find the parameters that best fit the data, and estimate $\\phi_1$ and $\\phi_2$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "parallel-radical",
+   "metadata": {},
+   "source": [
+    "## Low-Pass Filter\n",
+    "\n",
+    "The following circuit diagram (from <http://modsimpy.com/divider>) shows a low-pass filter built with one resistor and one capacitor.\n",
+    "\n",
+    "![Circuit diagram of a low-pass filter](https://github.com/AllenDowney/ModSim/raw/main/figs/RC_Divider.svg)\n",
+    "\n",
+    "A \"filter\" is a circuit takes a signal, $V_{in}$, as input and produces a signal, $V_{out}$, as output. In this context, a \"signal\" is a voltage that changes over time.\n",
+    "\n",
+    "A filter is \"low-pass\" if it allows low-frequency signals to pass from\n",
+    "$V_{in}$ to $V_{out}$ unchanged, but it reduces the amplitude of\n",
+    "high-frequency signals.\n",
+    "\n",
+    "By applying the laws of circuit analysis, we can derive a differential\n",
+    "equation that describes the behavior of this system. By solving the\n",
+    "differential equation, we can predict the effect of this circuit on any input signal.\n",
+    "\n",
+    "Suppose we are given $V_{in}$ and $V_{out}$ at a particular instant in\n",
+    "time. By Ohm's law, which is a simple model of the behavior of\n",
+    "resistors, the instantaneous current through the resistor is:\n",
+    "\n",
+    "$$I_R = (V_{in} - V_{out}) / R$$ \n",
+    "\n",
+    "where $R$ is resistance in ohms (Ω).\n",
+    "\n",
+    "Assuming that no current flows through the output of the circuit,\n",
+    "Kirchhoff's current law implies that the current through the capacitor\n",
+    "is: \n",
+    "\n",
+    "$$I_C = I_R$$ \n",
+    "\n",
+    "According to a simple model of the behavior of\n",
+    "capacitors, current through the capacitor causes a change in the voltage across the capacitor: \n",
+    "\n",
+    "$$I_C = C \\frac{d V_{out}}{dt}$$ \n",
+    "\n",
+    "where $C$ is capacitance in farads (F). Combining these equations yields a differential equation for $V_{out}$:\n",
+    "\n",
+    "$$\\frac{d V_{out}}{dt} = \\frac{V_{in} - V_{out}}{R C}$$ \n",
+    "\n",
+    "In the repository for this book, you will find a notebook, `filter.ipynb`, which contains starter code for this case study. Follow the instructions to simulate the low-pass filter for input signals like this:\n",
+    "\n",
+    "$$V_{in}(t) = A \\cos (2 \\pi f t)$$ \n",
+    "\n",
+    "where $A$ is the amplitude of the input signal, say 5 V, and $f$ is the frequency of the signal in Hz.\n",
+    "\n",
+    "In the repository for this book, you will find a notebook,\n",
+    "`filter.ipynb`, which contains starter code for this case study. Read\n",
+    "the notebook, run the code, and work on the exercises."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "violent-directive",
+   "metadata": {},
+   "source": [
+    "## Thermal behavior of a wall\n",
+    "\n",
+    "This case study is based on a paper by Gori, et al[^2] that models the\n",
+    "thermal behavior of a brick wall, with the goal of understanding the\n",
+    "\"performance gap between the expected energy use of buildings and their measured energy use\".\n",
+    "\n",
+    "The following figure shows the scenario and their model of the wall:\n",
+    "\n",
+    "![Model of a wall as a series of thermal insulators](https://github.com/AllenDowney/ModSim/raw/main/figs/wall_model.png)\n",
+    "\n",
+    "On the interior and exterior surfaces of the wall, they measure\n",
+    "temperature and heat flux over a period of three days. They model the\n",
+    "wall using two thermal masses connected to the surfaces, and to each\n",
+    "other, by thermal resistors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "entire-stations",
+   "metadata": {},
+   "source": [
+    "The primary methodology of the paper is a Bayesian method for inferring the parameters of the system (two thermal masses and three thermal resistances).\n",
+    "\n",
+    "The primary result is a comparison of two models: the one shown here\n",
+    "with two thermal masses, and a simpler model with only one thermal mass. They find that the two-mass model is able to reproduce the measured fluxes substantially better.\n",
+    "\n",
+    "For this case study we will implement their model and run it with the\n",
+    "estimated parameters from the paper, and then use `leastsq` to see\n",
+    "if we can find parameters that yield lower errors.\n",
+    "\n",
+    "In the repository for this book, you will find a notebook, `wall.ipynb` with the code and results for this case study.\n",
+    "\n",
+    "The authors put their paper under a Creative Commons license, and\n",
+    "make their data available at <http://modsimpy.com/wall>. I thank them\n",
+    "for their commitment to open, reproducible science, which made this\n",
+    "case study possible.\n",
+    "\n",
+    "Gori, Marincioni, Biddulph, Elwell, \"Inferring the thermal resistance and effective thermal mass distribution of a wall from in situ measurements to characterise heat transfer at both the interior and exterior surfaces\", *Energy and Buildings*, Volume 135, pages 398-409, <http://modsimpy.com/wall2>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "narrow-voice",
+   "metadata": {},
+   "source": [
+    "## HIV\n",
+    "\n",
+    "During the initial phase of HIV infection, the concentration of the virus in the bloodstream typically increases quickly and then decreases.\n",
+    "The most obvious explanation for the decline is an immune response that destroys the virus or controls its replication.\n",
+    "However, at least in some patients, the decline occurs even without any detectable immune response.\n",
+    "\n",
+    "In 1996 Andrew Phillips proposed another explanation for the decline (\"Reduction of HIV Concentration During Acute Infection: Independence from a Specific Immune Response\", available from <https://people.math.gatech.edu/~weiss/uploads/5/8/6/1/58618765/phillips1996.pdf>).\n",
+    "\n",
+    "Phillips presents a system of differential equations that models the concentrations of the HIV virus and the CD4 cells it infects.\n",
+    "The model does not include an immune response; nevertheless, it demonstrates behavior that is qualitatively similar to what is seen in patients during the first few weeks after infection.\n",
+    "\n",
+    "His conclusion is that the observed decline in the concentration of HIV might not be caused by an immune response; it could be due to the dynamic interaction between HIV and the cells it infects.\n",
+    "\n",
+    "In the repository for this book, you will find a notebook, `hiv_model.ipynb`, which you can use to implement Phillips's model and consider whether it does the work it is meant to do."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "international-button",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap20.ipynb b/chapters/chap20.ipynb
new file mode 100644
index 00000000..fb46d858
--- /dev/null
+++ b/chapters/chap20.ipynb
@@ -0,0 +1,794 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "funded-utilization",
+   "metadata": {},
+   "source": [
+    "# Chapter 20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "furnished-portsmouth",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "embedded-gentleman",
+   "metadata": {},
+   "source": [
+    "So far the differential equations we've worked with have been **first\n",
+    "order**, which means they involve only first derivatives. In this\n",
+    "chapter, we turn our attention to second order ODEs, which can involve\n",
+    "both first and second derivatives.\n",
+    "\n",
+    "We'll revisit the falling penny example from\n",
+    "Chapter xxx, and use `run_solve_ivp` to find the position and velocity of the penny as it falls, with and without air resistance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "isolated-louis",
+   "metadata": {},
+   "source": [
+    "## Newton's second law of motion\n",
+    "\n",
+    "First order ODEs can be written \n",
+    "\n",
+    "$$\\frac{dy}{dx} = G(x, y)$$ \n",
+    "\n",
+    "where $G$ is some function of $x$ and $y$ (see <http://modsimpy.com/ode>). Second order ODEs can be written \n",
+    "\n",
+    "$$\\frac{d^2y}{dx^2} = H(x, y, \\frac{dy}{dt})$$\n",
+    "\n",
+    "where $H$ is a function of $x$, $y$, and $dy/dx$.\n",
+    "\n",
+    "In this chapter, we will work with one of the most famous and useful\n",
+    "second order ODEs, Newton's second law of motion: \n",
+    "\n",
+    "$$F = m a$$ \n",
+    "\n",
+    "where $F$ is a force or the total of a set of forces, $m$ is the mass of a moving object, and $a$ is its acceleration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "drawn-symphony",
+   "metadata": {},
+   "source": [
+    "Newton's law might not look like a differential equation, until we\n",
+    "realize that acceleration, $a$, is the second derivative of position,\n",
+    "$y$, with respect to time, $t$. With the substitution\n",
+    "\n",
+    "$$a = \\frac{d^2y}{dt^2}$$ \n",
+    "\n",
+    "Newton's law can be written\n",
+    "\n",
+    "$$\\frac{d^2y}{dt^2} = F / m$$ \n",
+    "\n",
+    "And that's definitely a second order ODE.\n",
+    "In general, $F$ can be a function of time, position, and velocity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "swiss-vietnam",
+   "metadata": {},
+   "source": [
+    "Of course, this \"law\" is really a model in the sense that it is a\n",
+    "simplification of the real world. Although it is often approximately\n",
+    "true:\n",
+    "\n",
+    "-   It only applies if $m$ is constant. If mass depends on time,\n",
+    "    position, or velocity, we have to use a more general form of\n",
+    "    Newton's law (see <http://modsimpy.com/varmass>).\n",
+    "\n",
+    "-   It is not a good model for very small things, which are better\n",
+    "    described by another model, quantum mechanics.\n",
+    "\n",
+    "-   And it is not a good model for things moving very fast, which are\n",
+    "    better described by yet another model, relativistic mechanics.\n",
+    "\n",
+    "However, for medium-sized things with constant mass, moving at\n",
+    "medium-sized speeds, Newton's model is extremely useful. If we can\n",
+    "quantify the forces that act on such an object, we can predict how it\n",
+    "will move."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "coordinate-three",
+   "metadata": {},
+   "source": [
+    "## Dropping pennies\n",
+    "\n",
+    "As a first example, let's get back to the penny falling from the Empire State Building, which we considered in\n",
+    "Chapter xxx. We will implement two models of this system: first without air resistance, then with.\n",
+    "\n",
+    "Given that the Empire State Building is 381 m high, and assuming that\n",
+    "the penny is dropped from a standstill, the initial conditions are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "compatible-increase",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "init = State(y=381, v=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "intellectual-radiation",
+   "metadata": {},
+   "source": [
+    "where `y` is height above the sidewalk and `v` is velocity. \n",
+    "\n",
+    "The units `m` and `s` are from the `units` object provided by Pint:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "wicked-thomas",
+   "metadata": {},
+   "source": [
+    "The only system parameter is the acceleration of gravity:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "reverse-authorization",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g = 9.8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "developing-newfoundland",
+   "metadata": {},
+   "source": [
+    "In addition, we'll specify the duration of the simulation and the step\n",
+    "size:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "square-toolbox",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_end = 10\n",
+    "dt = 0.1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "precise-tobago",
+   "metadata": {},
+   "source": [
+    "With these parameters, the number of time steps is 100, which is good\n",
+    "enough for many problems. Once we have a solution, we will increase the\n",
+    "number of steps and see what effect it has on the results.\n",
+    "\n",
+    "We need a `System` object to store the parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "built-piece",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "system = System(init=init, g=g, t_end=t_end, dt=dt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "heavy-boards",
+   "metadata": {},
+   "source": [
+    "Now we need a slope function, and here's where things get tricky. As we have seen, `run_solve_ivp` can solve systems of first order ODEs, but Newton's law is a second order ODE. However, if we recognize that\n",
+    "\n",
+    "1.  Velocity, $v$, is the derivative of position, $dy/dt$, and\n",
+    "\n",
+    "2.  Acceleration, $a$, is the derivative of velocity, $dv/dt$,\n",
+    "\n",
+    "we can rewrite Newton's law as a system of first order ODEs:\n",
+    "\n",
+    "$$\\frac{dy}{dt} = v$$ \n",
+    "\n",
+    "$$\\frac{dv}{dt} = a$$ \n",
+    "\n",
+    "And we can translate those\n",
+    "equations into a slope function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "occupied-mercury",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    y, v = state\n",
+    "\n",
+    "    dydt = v\n",
+    "    dvdt = -system.g\n",
+    "    \n",
+    "    return dydt, dvdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opening-adolescent",
+   "metadata": {},
+   "source": [
+    "The first parameter, `state`, contains the position and velocity of the\n",
+    "penny. The last parameter, `system`, contains the system parameter `g`,\n",
+    "which is the magnitude of acceleration due to gravity.\n",
+    "\n",
+    "The second parameter, `t`, is time. It is not used in this slope\n",
+    "function because none of the factors of the model are time dependent. I include it anyway because this function will be called by `run_solve_ivp`, which always provides the same arguments,\n",
+    "whether they are needed or not.\n",
+    "\n",
+    "The rest of the function is a straightforward translation of the\n",
+    "differential equations, with the substitution $a = -g$, which indicates that acceleration due to gravity is in the direction of decreasing $y$. `slope_func` returns a sequence containing the two derivatives.\n",
+    "\n",
+    "Before calling `run_solve_ivp`, it is a good idea to test the slope\n",
+    "function with the initial conditions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "positive-feeling",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dydt, dvdt = slope_func(0, system.init, system)\n",
+    "print(dydt)\n",
+    "print(dvdt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "false-charlotte",
+   "metadata": {},
+   "source": [
+    "The result is 0 m/s for velocity and 9.8 m/s$^2$ for acceleration. Now we call `run_solve_ivp` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "lovely-management",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "results, details = run_solve_ivp(system, slope_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "assisted-swimming",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "solved-chambers",
+   "metadata": {},
+   "source": [
+    "`results` is a `TimeFrame` with two columns: `y` contains the height of\n",
+    "the penny; `v` contains its velocity.\n",
+    "\n",
+    "We can plot the results like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "authorized-barrier",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "results.y.plot()\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Position (m)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "differential-airfare",
+   "metadata": {},
+   "source": [
+    "Since acceleration is constant, velocity increases linearly and position decreases quadratically; as a result, the height curve is a parabola.\n",
+    "\n",
+    "The last value of `results.y` is negative, which means we ran the simulation too long. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "protected-fiber",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_end = results.index[-1]\n",
+    "results.y[t_end]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "metallic-tamil",
+   "metadata": {},
+   "source": [
+    "One way to solve this problem is to use the results to\n",
+    "estimate the time when the penny hits the sidewalk.\n",
+    "\n",
+    "The ModSim library provides `crossings`, which takes a `TimeSeries` and a value, and returns a sequence of times when the series passes through the value. We can find the time when the height of the penny is `0` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "japanese-clear",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "t_crossings = crossings(results.y, 0)\n",
+    "t_crossings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "demonstrated-emission",
+   "metadata": {},
+   "source": [
+    "The result is an array with a single value, 8.818 s. Now, we could run\n",
+    "the simulation again with `t_end = 8.818`, but there's a better way."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "blind-dominant",
+   "metadata": {},
+   "source": [
+    "## Events\n",
+    "\n",
+    "As an option, `run_solve_ivp` can take an **event function**, which\n",
+    "detects an \"event\", like the penny hitting the sidewalk, and ends the\n",
+    "simulation.\n",
+    "\n",
+    "Event functions take the same parameters as slope functions, `state`,\n",
+    "`t`, and `system`. They should return a value that passes through `0`\n",
+    "when the event occurs. Here's an event function that detects the penny\n",
+    "hitting the sidewalk:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "comfortable-simple",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(t, state, system):\n",
+    "    y, v = state\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "closing-vehicle",
+   "metadata": {},
+   "source": [
+    "The return value is the height of the penny, `y`, which passes through\n",
+    "`0` when the penny hits the sidewalk.\n",
+    "\n",
+    "We pass the event function to `run_solve_ivp` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "exotic-shareware",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_solve_ivp(system, slope_func,\n",
+    "                                 events=event_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "recreational-blair",
+   "metadata": {},
+   "source": [
+    "Then we can get the flight time and final velocity like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "appropriate-roberts",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_end = results.index[-1]\n",
+    "t_end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "orange-retro",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y, v = results.iloc[-1]\n",
+    "print(y)\n",
+    "print(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cleared-jamaica",
+   "metadata": {},
+   "source": [
+    "If there were no air resistance, the penny would hit the sidewalk (or someone's head) at more than 300 km/h.\n",
+    "\n",
+    "So it's a good thing there is air resistance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "induced-albert",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "But air resistance...\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "straight-johns",
+   "metadata": {},
+   "source": [
+    "### Exercises\n",
+    "\n",
+    "**Exercise:** Here's a question from the web site [Ask an Astronomer](http://curious.astro.cornell.edu/about-us/39-our-solar-system/the-earth/other-catastrophes/57-how-long-would-it-take-the-earth-to-fall-into-the-sun-intermediate):\n",
+    "\n",
+    "\"If the Earth suddenly stopped orbiting the Sun, I know eventually it would be pulled in by the Sun's gravity and hit it. How long would it take the Earth to hit the Sun? I imagine it would go slowly at first and then pick up speed.\"\n",
+    "\n",
+    "Use `run_solve_ivp` to answer this question.\n",
+    "\n",
+    "Here are some suggestions about how to proceed:\n",
+    "\n",
+    "1.  Look up the Law of Universal Gravitation and any constants you need.  I suggest you work entirely in SI units: meters, kilograms, and Newtons.\n",
+    "\n",
+    "2.  When the distance between the Earth and the Sun gets small, this system behaves badly, so you should use an event function to stop when the surface of Earth reaches the surface of the Sun.\n",
+    "\n",
+    "3. Express your answer in days, and plot the results as millions of kilometers versus days.\n",
+    "\n",
+    "If you read the reply by Dave Rothstein, you will see other ways to solve the problem, and a good discussion of the modeling decisions behind them.\n",
+    "\n",
+    "You might also be interested to know that [it's actually not that easy to get to the Sun](https://www.theatlantic.com/science/archive/2018/08/parker-solar-probe-launch-nasa/567197/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "forbidden-distributor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "former-taxation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "biological-creek",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r_0 / r_final"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "oriental-riverside",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_end = 1e7  # seconds\n",
+    "\n",
+    "system = System(init=init,\n",
+    "                G=6.674e-11,   # N m^2 / kg^2\n",
+    "                m1=1.989e30,   # kg\n",
+    "                m2=5.972e24,   # kg\n",
+    "                r_final=radius_sun + radius_earth,\n",
+    "                t_end=t_end)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "radio-reproduction",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "heavy-cologne",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "little-electric",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "continental-details",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "suitable-traveler",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "upper-victory",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "transparent-treat",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "brutal-woman",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "gentle-burst",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "comfortable-galaxy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "satisfactory-latitude",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "significant-rebound",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "right-colleague",
+   "metadata": {},
+   "source": [
+    "## Under the hood\n",
+    "\n",
+    "`solve_ivp`\n",
+    "\n",
+    "Here is the source code for `crossings` so you can see what's happening under the hood:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "chief-error",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%psource crossings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "comparable-company",
+   "metadata": {},
+   "source": [
+    "The [documentation of InterpolatedUnivariateSpline is here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.InterpolatedUnivariateSpline.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "listed-shelter",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap21.ipynb b/chapters/chap21.ipynb
new file mode 100644
index 00000000..3cd917bd
--- /dev/null
+++ b/chapters/chap21.ipynb
@@ -0,0 +1,759 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "excited-advance",
+   "metadata": {},
+   "source": [
+    "# Chapter 21"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unauthorized-constitutional",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "superb-february",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we simulated a penny falling in a vacuum, that\n",
+    "is, without air resistance. But the computational framework we used is\n",
+    "very general; it is easy to add additional forces, including drag.\n",
+    "\n",
+    "In this chapter, I present a model of drag force and add it to the\n",
+    "simulation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "interesting-freeware",
+   "metadata": {},
+   "source": [
+    "## Drag force\n",
+    "\n",
+    "As an object moves through a fluid, like air, the object applies force\n",
+    "to the air and, in accordance with Newton's third law of motion, the air applies an equal and opposite force to the object (see\n",
+    "<http://modsimpy.com/newton>).\n",
+    "\n",
+    "The direction of this **drag force** is opposite the direction of\n",
+    "travel, and its magnitude is given by the drag equation (see\n",
+    "<http://modsimpy.com/drageq>): \n",
+    "\n",
+    "$$F_d = \\frac{1}{2}~\\rho~v^2~C_d~A$$\n",
+    "\n",
+    "where\n",
+    "\n",
+    "-   $F_d$ is force due to drag, in newtons (N).\n",
+    "\n",
+    "-   $\\rho$ is the density of the fluid in kg/m^3^.\n",
+    "\n",
+    "-   $v$ is the magnitude of velocity in m/s.\n",
+    "\n",
+    "-   $A$ is the **reference area** of the object, in m^2^. In this\n",
+    "    context, the reference area is the projected frontal area, that is, the visible area of the object as seen from a point on its line of\n",
+    "    travel (and far away).\n",
+    "\n",
+    "-   $C_d$ is the **drag coefficient**, a dimensionless quantity that\n",
+    "    depends on the shape of the object (including length but not frontal area), its surface properties, and how it interacts with the fluid."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unable-scheduling",
+   "metadata": {},
+   "source": [
+    "For objects moving at moderate speeds through air, typical drag\n",
+    "coefficients are between 0.1 and 1.0, with blunt objects at the high end of the range and streamlined objects at the low end (see\n",
+    "<http://modsimpy.com/dragco>).\n",
+    "\n",
+    "For simple geometric objects we can sometimes guess the drag coefficient with reasonable accuracy; for more complex objects we usually have to take measurements and estimate $C_d$ from data.\n",
+    "\n",
+    "Of course, the drag equation is itself a model, based on the assumption that $C_d$ does not depend on the other terms in the equation: density, velocity, and area. For objects moving in air at moderate speeds (below 45 mph or 20 m/s), this model might be good enough, but we should remember to revisit this assumption.\n",
+    "\n",
+    "For the falling penny, we can use measurements to estimate $C_d$. In\n",
+    "particular, we can measure **terminal velocity**, $v_{term}$, which is\n",
+    "the speed where drag force equals force due to gravity:\n",
+    "\n",
+    "$$\\frac{1}{2}~\\rho~v_{term}^2~C_d~A = m g$$ \n",
+    "\n",
+    "where $m$ is the mass of the object and $g$ is acceleration due to gravity. Solving this equation for\n",
+    "$C_d$ yields: \n",
+    "\n",
+    "$$C_d = \\frac{2~m g}{\\rho~v_{term}^2~A}$$ \n",
+    "\n",
+    "According to *Mythbusters*, the terminal velocity of a penny is between 35 and 65 mph (see <http://modsimpy.com/mythbust>). Using the low end of their range, 40 mph or about 18 m/s, the estimated value of $C_d$ is 0.44, which is close to the drag coefficient of a smooth sphere.\n",
+    "\n",
+    "Now we are ready to add air resistance to the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "human-cloud",
+   "metadata": {},
+   "source": [
+    "## The Params Object\n",
+    "\n",
+    "As the number of system parameters increases, and as we need to do more work to compute them, we will find it useful to define a `Params` object to contain the quantities we need to make a `System` object. `Params` objects are similar to `System` objects, and we initialize them the same way.\n",
+    "\n",
+    "Here's the `Params` object for the falling penny:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "established-knitting",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = Params(\n",
+    "    mass = 0.0025,      # kg\n",
+    "    diameter = 0.019,   # m\n",
+    "    rho = 1.2,     # kg/m**3\n",
+    "    g = 9.8,       # m/s**2\n",
+    "    v_init = 0,    # m / s\n",
+    "    v_term = 18,   # m / s\n",
+    "    height = 381,  # m\n",
+    "    t_end = 30,    # s\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "instrumental-gross",
+   "metadata": {},
+   "source": [
+    "The mass and diameter are from <http://modsimpy.com/penny>. The density\n",
+    "of air depends on temperature, barometric pressure (which depends on\n",
+    "altitude), humidity, and composition (<http://modsimpy.com/density>). I\n",
+    "chose a value that might be typical in Boston, Massachusetts at 20 °C.\n",
+    "\n",
+    "\n",
+    "Here's a version of `make_system` that takes the `Params` object and computes the inital state, `init`, the area, and the coefficient of drag.\n",
+    "Then it returns a `System` object with the quantities we'll need for the simulation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "published-jesus",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import pi\n",
+    "\n",
+    "def make_system(params):\n",
+    "    init = State(y=params.height, v=params.v_init)\n",
+    "\n",
+    "    area = pi * (params.diameter/2)**2\n",
+    "\n",
+    "    C_d = (2 * params.mass * params.g / \n",
+    "           (params.rho * area * params.v_term**2))\n",
+    "\n",
+    "    return System(init=init,\n",
+    "                  area=area,\n",
+    "                  C_d=C_d,\n",
+    "                  mass=params.mass,\n",
+    "                  rho=params.rho,\n",
+    "                  g=params.g,\n",
+    "                  t_end=params.t_end\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "personalized-kruger",
+   "metadata": {},
+   "source": [
+    "And here's how we call it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "executive-protection",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "portable-carbon",
+   "metadata": {},
+   "source": [
+    "Based on the mass and diameter of the penny, the density of air, and acceleration due to gravity, and the observed terminal velocity, we estimate that the coefficient of drag is about 0.44."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "quick-cedar",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.C_d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "inner-telescope",
+   "metadata": {},
+   "source": [
+    "It might not be obvious why it is useful to create a `Params` object just to create a `System` object.\n",
+    "In fact, if we only run one simulation, it might not be useful.  But it helps when we want to change or sweep the parameters.\n",
+    "\n",
+    "For example, suppose we learn that the terminal velocity of a penny is actually closer to 20 m/s.\n",
+    "We can make a `Params` object with the new value, and a corresponding `System` object, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "great-crime",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params2 = params.set(v_term=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "outstanding-truth",
+   "metadata": {},
+   "source": [
+    "The result from `set` is a new `Params` object that is identical to the original except for the given value of `v_term`. \n",
+    "\n",
+    "If we pass `params2` to `make_system`, we see that it computes a different value of `C_d`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "independent-trace",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system2 = make_system(params2)\n",
+    "system2.C_d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "charged-explorer",
+   "metadata": {},
+   "source": [
+    "If the terminal velocity of the penny is 20 m/s, rather than 18 m/s, that implies that the coefficient of drag is 0.36, rather than 0.44.\n",
+    "And that makes sense, since lower drag implies faster terminal velocity.\n",
+    "\n",
+    "Using `Params` objects to make `System` objects helps make sure that relationships like this are consistent.  And since we are always making new objects, rather than modifying existing objects, we are less likely to make a mistake."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "primary-advocate",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "Now let's get to the simulation.  Here's a version of the slope function that includes drag:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "noble-stick",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    y, v = state\n",
+    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
+    "    mass, g = system.mass, system.g\n",
+    "    \n",
+    "    f_drag = rho * v**2 * C_d * area / 2\n",
+    "    a_drag = f_drag / mass\n",
+    "    \n",
+    "    dydt = v\n",
+    "    dvdt = -g + a_drag\n",
+    "    \n",
+    "    return dydt, dvdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "baking-class",
+   "metadata": {},
+   "source": [
+    "`f_drag` is force due to drag, based on the drag equation. `a_drag` is\n",
+    "acceleration due to drag, based on Newton's second law.\n",
+    "\n",
+    "To compute total acceleration, we add accelerations due to gravity and\n",
+    "drag. `g` is negated because it is in the direction of decreasing `y`,\n",
+    "and `a_drag` is positive because it is in the direction of increasing\n",
+    "`y`. In the next chapter we will use `Vector` objects to keep track of\n",
+    "the direction of forces and add them up in a less error-prone way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "velvet-tunisia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "psychological-style",
+   "metadata": {},
+   "source": [
+    "To stop the simulation when the penny hits the sidewalk, we'll use the\n",
+    "event function from Section xxx:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "practical-nowhere",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(t, state, system):\n",
+    "    y, v = state\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "animated-clause",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "executive-there",
+   "metadata": {},
+   "source": [
+    "Now we can run the simulation like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "liberal-dictionary",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_solve_ivp(system, slope_func,\n",
+    "                                 events=event_func)\n",
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "incident-paradise",
+   "metadata": {},
+   "source": [
+    "Here are the last few time steps:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "coastal-anthropology",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "suburban-martial",
+   "metadata": {},
+   "source": [
+    "The final height is close to 0, as expected.\n",
+    "\n",
+    "Interestingly, the final velocity is not exactly terminal velocity, which suggests that there are some numerical errors.\n",
+    "\n",
+    "We can get the flight time from `results`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "interim-underground",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_sidewalk = results.index[-1]\n",
+    "t_sidewalk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "institutional-colors",
+   "metadata": {},
+   "source": [
+    "Here's the plot of position as a function of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "small-franchise",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_position(results):\n",
+    "    results.y.plot()\n",
+    "        \n",
+    "    decorate(xlabel='Time (s)',\n",
+    "         ylabel='Position (m)')\n",
+    "    \n",
+    "plot_position(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "plain-phone",
+   "metadata": {},
+   "source": [
+    "And velocity as a function of time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "analyzed-criticism",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_velocity(results):\n",
+    "\n",
+    "    results.v.plot(color='C1', label='v')\n",
+    "        \n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Velocity (m/s)')\n",
+    "    \n",
+    "plot_velocity(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "careful-causing",
+   "metadata": {},
+   "source": [
+    "From an initial velocity of 0, the penny accelerates downward until it reaches terminal velocity; after that, velocity is constant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "inside-confidence",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "planned-endorsement",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "respective-address",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Run the simulation with a downward initial velocity that exceeds the penny's terminal velocity.\n",
+    "\n",
+    "What do you expect to happen?  Plot velocity and position as a function of time, and see if they are consistent with your prediction.\n",
+    "\n",
+    "Hint: Use `params.set` to make a new `Params` object with a different initial velocity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "inclusive-twenty",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "vertical-judge",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "greenhouse-madagascar",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_sidewalk = results2.index[-1]\n",
+    "t_sidewalk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "sudden-details",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_position(results2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "opening-jurisdiction",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "smaller-millennium",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose we drop a quarter from the Empire State Building and find that its flight time is 19.1 seconds.  Use this measurement to estimate terminal velocity and coefficient of drag.\n",
+    "\n",
+    "You can get the relevant dimensions of a quarter from https://en.wikipedia.org/wiki/Quarter_(United_States_coin).\n",
+    "\n",
+    "1. Create a `Params` object with new values of `mass` and `diameter`. We don't know `v_term`, so we'll start with the initial guess 18 m/s.\n",
+    "\n",
+    "2. Use `make_system` to create a `System` object.  \n",
+    "\n",
+    "3. Call `run_solve_ivp` to simulate the system.  How does the flight time of the simulation compare to the measurement?\n",
+    "\n",
+    "4. Try a few different values of `v_term` and see if you can get the simulated flight time close to 19.1 seconds.\n",
+    "\n",
+    "5. Optionally, write an error function and use `root_scalar` to improve your estimate.\n",
+    "\n",
+    "6. Use your best estimate of `v_term` to compute `C_d`.\n",
+    "\n",
+    "Note: I fabricated the observed flight time, so don't take the results of this exercise too seriously."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "compact-bunny",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "shared-contrary",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "portable-account",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "arabic-shareware",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "valued-literature",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "sufficient-retail",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "comparable-lounge",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "ready-people",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "miniature-remark",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "frozen-termination",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "colored-oxygen",
+   "metadata": {},
+   "source": [
+    "## Under the Hood\n",
+    "\n",
+    "`Params` is a `SimpleNamespace`, like `System`.\n",
+    "\n",
+    "`Params` and `System` are actually the same; I have given them different names to document the different roles they play."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
new file mode 100644
index 00000000..5dae8f6f
--- /dev/null
+++ b/chapters/chap22.ipynb
@@ -0,0 +1,1248 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "collaborative-people",
+   "metadata": {},
+   "source": [
+    "# Chapter 22"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "connected-covering",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "advanced-guidance",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we modeled objects moving in one dimension, with and without drag. Now let's move on to two dimensions, and baseball!\n",
+    "\n",
+    "In this chapter we model the flight of a baseball including the effect\n",
+    "of air resistance. In the next chapter we use this model to solve an\n",
+    "optimization problem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "offensive-chinese",
+   "metadata": {},
+   "source": [
+    "## Baseball\n",
+    "\n",
+    "To model the flight of a baseball, we have to make some\n",
+    "decisions. To get started, we'll ignore any spin that might be on the ball, and the resulting Magnus force (see <http://modsimpy.com/magnus>). Under this assumption, the ball travels in a vertical plane, so we'll run simulations in two dimensions, rather than three.\n",
+    "\n",
+    "Air resistance has a substantial effect on most projectiles in air, so\n",
+    "we will include a drag force.\n",
+    "\n",
+    "To model air resistance, we'll need the mass, frontal area, and drag\n",
+    "coefficient of a baseball. Mass and diameter are easy to find (see\n",
+    "<http://modsimpy.com/baseball>). Drag coefficient is only a little\n",
+    "harder; according to *[The Physics of Baseball](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC)*, the drag coefficient of a baseball is approximately 0.33 (with no units).\n",
+    "\n",
+    "However, this value *does* depend on velocity. At low velocities it\n",
+    "might be as high as 0.5, and at high velocities as low as 0.28.\n",
+    "Furthermore, the transition between these regimes typically happens\n",
+    "exactly in the range of velocities we are interested in, between 20 m/s and 40 m/s.\n",
+    "\n",
+    "Nevertheless, we'll start with a simple model where the drag coefficient does not depend on velocity; as an exercise at the end of the chapter, you will have a chance to implement a more detailed model and see what effect is has on the results.\n",
+    "\n",
+    "But first we need a new computational tool, the `Vector` object."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "worthy-wheel",
+   "metadata": {},
+   "source": [
+    "## Vectors\n",
+    "\n",
+    "Now that we are working in two dimensions, it will be useful to\n",
+    "work with **vector quantities**, that is, quantities that represent both a magnitude and a direction. We will use vectors to represent positions, velocities, accelerations, and forces in two and three dimensions.\n",
+    "\n",
+    "ModSim provides a function called `Vector` the creates a Pandas `Series` that contains the **components** of the vector.\n",
+    "In a `Vector` that represents a position in space, the components are the $x$ and $y$ coordinates in 2-D, plus a $z$ coordinate if the `Vector` is in 3-D.\n",
+    "\n",
+    "You can create a `Vector` by specifying its components. The following\n",
+    "`Vector` represents a point 3 units to the right (or east) and 4 units up (or north) from an implicit origin:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "handy-terrain",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = Vector(3, 4)\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "settled-roommate",
+   "metadata": {},
+   "source": [
+    "You can access the components of a `Vector` by name using the dot\n",
+    "operator, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "controversial-shower",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A.x, A.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "earlier-contemporary",
+   "metadata": {},
+   "source": [
+    "You can also access them by index using brackets, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "automated-drove",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A[0], A[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "grave-burst",
+   "metadata": {},
+   "source": [
+    "`Vector` objects support most mathematical operations, including\n",
+    "addition and subtraction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "conditional-latitude",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "B = Vector(1, 2)\n",
+    "B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "crucial-opening",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A + B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "encouraging-cabinet",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A - B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "combined-command",
+   "metadata": {},
+   "source": [
+    "For the definition and graphical interpretation of these operations, see <http://modsimpy.com/vecops>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "later-adelaide",
+   "metadata": {},
+   "source": [
+    "We can specify a `Vector` with coordinates `x` and `y`, as in the previous examples.\n",
+    "Equivalently, we can specify a `Vector` with a magnitude and angle.\n",
+    "\n",
+    "**Magnitude** is the length of the vector: if the `Vector` represents a position, magnitude is the distance from the origin; if it represents a velocity, magnitude is speed, that is, how fast the object is moving, regardless of direction.\n",
+    "\n",
+    "The **angle** of a `Vector` is its direction, expressed as an angle in radians from the positive $x$ axis. In the Cartesian plane, the angle 0 rad is due east, and the angle $\\pi$ rad is due west.\n",
+    "\n",
+    "ModSim provides functions to compute the magnitude and angle of a `Vector`.  For example, here are the magnitude and angle of `A`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "peripheral-tattoo",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "mag = vector_mag(A)\n",
+    "theta = vector_angle(A)\n",
+    "mag, theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "great-advice",
+   "metadata": {},
+   "source": [
+    "The magnitude is 5 because the length of `A` is the hypotenuse of a 3-4-5 triangle.\n",
+    "\n",
+    "The result from `vector_angle` is in radians, and most Python functions, like `sin` and `cos`, work with radians. \n",
+    "But many people think more naturally in degrees. \n",
+    "Fortunately, NumPy provides a function to convert radians to degrees:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "strange-cleaning",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import rad2deg\n",
+    "\n",
+    "angle = rad2deg(theta)\n",
+    "angle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "assured-cutting",
+   "metadata": {},
+   "source": [
+    "And a function to convert degrees to radians:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cellular-community",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import deg2rad\n",
+    "\n",
+    "theta = deg2rad(angle)\n",
+    "theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "responsible-gentleman",
+   "metadata": {},
+   "source": [
+    "Following convention, I'll use `angle` for a value in degrees and `theta` for a value in radians.\n",
+    "\n",
+    "If you are given an angle and velocity, you can make a `Vector` using\n",
+    "`pol2cart`, which converts from polar to Cartesian coordinates. For example, here's a new `Vector` with the same angle and magnitude of `A`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "monetary-firmware",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = pol2cart(theta, mag)\n",
+    "Vector(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lucky-plastic",
+   "metadata": {},
+   "source": [
+    "Another way to represent the direction of `A` is a **unit vector**,\n",
+    "which is a vector with magnitude 1 that points in the same direction as\n",
+    "`A`. You can compute a unit vector by dividing a vector by its\n",
+    "magnitude:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "explicit-piano",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A / vector_mag(A)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "respected-oliver",
+   "metadata": {},
+   "source": [
+    "We can do the same thing using the `vector_hat` function, so named because unit vectors are conventionally decorated with a hat, like this: $\\hat{A}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "relative-republic",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vector_hat(A)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reduced-celebration",
+   "metadata": {},
+   "source": [
+    "Now let's get back to the game."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "similar-local",
+   "metadata": {},
+   "source": [
+    "## Simulating baseball flight\n",
+    "\n",
+    "Let's simulate the flight of a baseball that is batted from home plate\n",
+    "at an angle of 45° and initial speed 40 m/s. We'll use the center of home plate as the origin, a horizontal x-axis (parallel to the ground), and vertical y-axis (perpendicular to the ground). The initial height is about 1 m."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "narrative-latest",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "params = Params(\n",
+    "    x = 0,          # m\n",
+    "    y = 1,          # m\n",
+    "    angle = 45,     # degree\n",
+    "    velocity = 40,  # m / s\n",
+    "\n",
+    "    mass = 145e-3,    # kg \n",
+    "    diameter = 73e-3, # m \n",
+    "    C_d = 0.33,       # dimensionless\n",
+    "\n",
+    "    rho = 1.2,      # kg/m**3\n",
+    "    g = 9.8,        # m/s**2\n",
+    "    t_end = 10,     # s\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "metric-collins",
+   "metadata": {},
+   "source": [
+    "I got the mass and diameter of the baseball from [Wikipedia](https://en.wikipedia.org/wiki/Baseball_(ball)) and the coefficient of drag is from *[The Physics of Baseball](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC)*:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "increasing-combination",
+   "metadata": {},
+   "source": [
+    "The density of air, `rho`, is based on a temperature of 20 °C at sea level (see <http://modsimpy.com/tempress>). \n",
+    "And we'll need the acceleration of gravity, `g`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "detected-return",
+   "metadata": {},
+   "source": [
+    "The following function uses these quantities to make a `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "bored-billy",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from numpy import pi, deg2rad\n",
+    "\n",
+    "def make_system(params):\n",
+    "    \n",
+    "    # convert angle to degrees\n",
+    "    theta = deg2rad(params.angle)\n",
+    "    \n",
+    "    # compute x and y components of velocity\n",
+    "    vx, vy = pol2cart(theta, params.velocity)\n",
+    "    \n",
+    "    # make the initial state\n",
+    "    init = State(x=params.x, y=params.y, vx=vx, vy=vy)\n",
+    "    \n",
+    "    # compute the frontal area\n",
+    "    area = pi * (params.diameter/2)**2\n",
+    "\n",
+    "    return System(params,\n",
+    "                  init = init,\n",
+    "                  area = area,\n",
+    "                 )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "conscious-template",
+   "metadata": {},
+   "source": [
+    "`make_system` uses `deg2rad` to convert `angle` to radians and\n",
+    "`pol2cart` to compute the $x$ and $y$ components of the initial\n",
+    "velocity.\n",
+    "\n",
+    "`init` is a `State` object with four state variables:\n",
+    "\n",
+    "* `x` and `y` are the components of position.\n",
+    "\n",
+    "* `vx` and `vy` are the components of velocity.\n",
+    "\n",
+    "The `System` object also contains `t_end`, which is 10 seconds, long enough for the ball to land on the ground.\n",
+    "Here's the `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ethical-donna",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "convinced-fellow",
+   "metadata": {},
+   "source": [
+    "And here's the initial `State`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "legitimate-gossip",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "occasional-given",
+   "metadata": {},
+   "source": [
+    "Next we need a function to compute drag force:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "legal-terminal",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def drag_force(V, system):\n",
+    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
+    "    \n",
+    "    mag = rho * vector_mag(V)**2 * C_d * area / 2\n",
+    "    direction = -vector_hat(V)\n",
+    "    f_drag = mag * direction\n",
+    "    return f_drag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "false-confusion",
+   "metadata": {},
+   "source": [
+    "This function takes `V` as a `Vector` and returns `f_drag` as a\n",
+    "`Vector`. \n",
+    "\n",
+    "* It uses `vector_mag` to compute the magnitude of `V`, \n",
+    "and the drag equation to compute the magnitude of the drag force, `mag`.\n",
+    "\n",
+    "* Then it uses `vector_hat` to compute `direction`, which is a unit vector in the opposite direction of `V`.\n",
+    "\n",
+    "* Finally, it computes the drag force vector by multiplying `mag` and `direction`.\n",
+    "\n",
+    "We can test it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "frank-chick",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vx, vy = system.init.vx, system.init.vy\n",
+    "V_test = Vector(vx, vy)\n",
+    "drag_force(V_test, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "absent-vector",
+   "metadata": {},
+   "source": [
+    "The result is a `Vector` that represents the drag force on the baseball, in Newtons, under the initial conditions.\n",
+    "\n",
+    "Now we're ready for a slope function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "suitable-salem",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    x, y, vx, vy = state\n",
+    "    mass, g = system.mass, system.g\n",
+    "    \n",
+    "    V = Vector(vx, vy)\n",
+    "    a_drag = drag_force(V, system) / mass\n",
+    "    a_grav = g * Vector(0, -1)\n",
+    "    \n",
+    "    A = a_grav + a_drag\n",
+    "    \n",
+    "    return V.x, V.y, A.x, A.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "scheduled-courage",
+   "metadata": {},
+   "source": [
+    "As usual, the parameters of the slope function are a time, a `State` object, and a `System` object. \n",
+    "In this example, we don't use `t`, but we can't leave it out because when `run_solve_ivp` calls the slope function, it always provides the same arguments, whether they are needed or not.\n",
+    "\n",
+    "`slope_func` unpacks the `State` object into variables `x`, `y`, `vx`, and `vy`.\n",
+    "Then it packs `vx` and `vy` into a `Vector`, which it uses to compute drag force and acceleration due to drag, `a_drag`.\n",
+    "\n",
+    "To represent acceleration due to gravity, it makes a `Vector` with magnitude `g` in the negative $y$ direction.\n",
+    "\n",
+    "The total acceleration of the baseball, `A`, is the sum of accelerations due to gravity and drag."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "enclosed-favorite",
+   "metadata": {},
+   "source": [
+    "The return value is a sequence that contains:\n",
+    "\n",
+    "* The components of velocity, `V.x` and `V.y`.\n",
+    "\n",
+    "* The components of acceleration, `A.x` and `A.y`.\n",
+    "\n",
+    "Together, these components represent the slope of the state variables, because `V` is the derivative of position and `A` is the derivative of velocity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "crude-parcel",
+   "metadata": {},
+   "source": [
+    "As always, we can test the slope function by running it with the initial conditions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "closing-simon",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "regulated-railway",
+   "metadata": {},
+   "source": [
+    "Using vectors to represent forces and accelerations makes the code\n",
+    "concise, readable, and less error-prone. In particular, when we add\n",
+    "`a_grav` and `a_drag`, the directions are likely to be correct, because they are encoded in the `Vector` objects.\n",
+    "\n",
+    "We're almost ready to run the simulation.  The last thing we need is an event function that stops when the ball hits the ground."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "brief-level",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def event_func(t, state, system):\n",
+    "    x, y, vx, vy = state\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "novel-farmer",
+   "metadata": {},
+   "source": [
+    "The event function takes the same parameters as the slope function, and returns the $y$ coordinate of position. When the $y$ coordinate passes through 0, the simulation stops.\n",
+    "\n",
+    "As we did with `slope_func`, we can test `event_func` with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "threatened-alberta",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unlikely-dressing",
+   "metadata": {},
+   "source": [
+    "Now we're ready to run the simulation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "special-background",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "results, details = run_solve_ivp(system, slope_func,\n",
+    "                                 events=event_func)\n",
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "iraqi-appeal",
+   "metadata": {},
+   "source": [
+    "`details` contains information about the simulation, including a message that indicates that a \"termination event\" occurred; that is, the simulated ball reached the ground.\n",
+    "\n",
+    "`results` is a `TimeFrame` with one column for each of the state variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "prospective-external",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dominant-weekly",
+   "metadata": {},
+   "source": [
+    "We can get the flight time like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "medieval-calvin",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flight_time = results.index[-1]\n",
+    "flight_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "convenient-heading",
+   "metadata": {},
+   "source": [
+    "And the final state like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "gorgeous-survey",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_state = results.iloc[-1]\n",
+    "final_state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "needed-aruba",
+   "metadata": {},
+   "source": [
+    "The final value of `y` is close to 0, as it should be.  The final value of `x` tells us how far the ball flew, in meters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "failing-bangkok",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_dist = final_state.x\n",
+    "x_dist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "growing-england",
+   "metadata": {},
+   "source": [
+    "We can also get the final velocity, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "copyrighted-highway",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_V = Vector(final_state.vx, final_state.vy)\n",
+    "final_V"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "vietnamese-diagram",
+   "metadata": {},
+   "source": [
+    "The speed of the ball on impact is about 26 m/s, which is substantially slower than the initial velocity, 40 m/s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "structured-adams",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vector_mag(final_V)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "continuous-quick",
+   "metadata": {},
+   "source": [
+    "## Trajectories\n",
+    "\n",
+    "To visualize the results, we can plot the $x$ and $y$ components of position like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "spare-burst",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.x.plot(color='C4')\n",
+    "results.y.plot(color='C2', style='--')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Position (m)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "horizontal-bench",
+   "metadata": {},
+   "source": [
+    "As expected, the $x$ component increases as the ball moves away from home plate. The $y$ position climbs initially and then descends, falling to 0 m near 5.0 s.\n",
+    "\n",
+    "Another way to view the results is to plot the $x$ component on the\n",
+    "x-axis and the $y$ component on the y-axis, so the plotted line follows the trajectory of the ball through the plane:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "dated-browse",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_trajectory(results):\n",
+    "    x = results.x\n",
+    "    y = results.y\n",
+    "    make_series(x, y).plot(label='trajectory')\n",
+    "\n",
+    "    decorate(xlabel='x position (m)',\n",
+    "             ylabel='y position (m)')\n",
+    "\n",
+    "plot_trajectory(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "certified-synthetic",
+   "metadata": {},
+   "source": [
+    "This way of visualizing the results is called a **trajectory plot** (see <http://modsimpy.com/trajec>).\n",
+    "\n",
+    "A trajectory plot can be easier to interpret than a time series plot,\n",
+    "because it shows what the motion of the projectile would look like (at\n",
+    "least from one point of view). Both plots can be useful, but don't get\n",
+    "them mixed up! If you are looking at a time series plot and interpreting it as a trajectory, you will be very confused."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cosmetic-aircraft",
+   "metadata": {},
+   "source": [
+    "## Animation\n",
+    "\n",
+    "One of the best ways to visualize the results of a physical model is animation.  If there are problems with the model, animation can make them apparent.\n",
+    "\n",
+    "The ModSimPy library provides `animate`, which takes as parameters a `TimeSeries` and a draw function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "neither-craps",
+   "metadata": {},
+   "source": [
+    "The draw function should take as parameters a `State` object and the time.  It should draw a single frame of the animation.\n",
+    "\n",
+    "Inside the draw function, you almost always have to call `set_xlim` and `set_ylim`.  Otherwise `matplotlib` auto-scales the axes, which is usually not what you want."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "starting-fabric",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.pyplot import plot\n",
+    "\n",
+    "xlim = results.x.min(), results.x.max()\n",
+    "ylim = results.y.min(), results.y.max()\n",
+    "\n",
+    "def draw_func(t, state):\n",
+    "    plot(state.x, state.y, 'bo')\n",
+    "    decorate(xlabel='x position (m)',\n",
+    "             ylabel='y position (m)',\n",
+    "             xlim=xlim,\n",
+    "             ylim=ylim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "prescription-boutique",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# animate(results, draw_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lyric-harassment",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pressing-retrieval",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Run the simulation with and without air resistance.  How wrong would we be if we ignored drag?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "optical-weather",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hint\n",
+    "\n",
+    "system2 = make_system(params.set(C_d=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "acknowledged-belgium",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "spatial-ensemble",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "domestic-apparatus",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "correct-pittsburgh",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "generic-shelter",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / meter$^3$.  How much farther would a ball hit with the same velocity and launch angle travel?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "global-referral",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hint\n",
+    "\n",
+    "system3 = make_system(params.set(rho=1.0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "appointed-sugar",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "specialized-mediterranean",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "shaped-paragraph",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The model so far is based on the assumption that coefficient of drag does not depend on velocity, but in reality it does.  The following figure, from Adair, [*The Physics of Baseball*](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC), shows coefficient of drag as a function of velocity.\n",
+    "\n",
+    "<img src=\"https://github.com/AllenDowney/ModSimPy/raw/master/figs/baseball_drag.png\" width=\"400\">\n",
+    "\n",
+    "\n",
+    "I used [an online graph digitizer](https://automeris.io/WebPlotDigitizer/) to extract the data and save it in a CSV file.  Here's how we can read it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "therapeutic-onion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'baseball_drag.csv'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/baseball_drag.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "returning-fellowship",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_csv\n",
+    "\n",
+    "\n",
+    "baseball_drag = read_csv(filename)\n",
+    "mph = Quantity(baseball_drag['Velocity in mph'], units.mph)\n",
+    "mps = mph.to(units.meter / units.second)\n",
+    "baseball_drag.index = mps.magnitude\n",
+    "baseball_drag.index.name = 'Velocity in meters per second'\n",
+    "baseball_drag.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "heated-belfast",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drag_interp = interpolate(baseball_drag['Drag coefficient'])\n",
+    "vs = linspace(0, 60)\n",
+    "cds = drag_interp(vs)\n",
+    "make_series(vs, cds).plot()\n",
+    "decorate(xlabel='Velocity (m/s)', ylabel='C_d')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reverse-shock",
+   "metadata": {},
+   "source": [
+    "Modify the model to include the dependence of `C_d` on velocity, and see how much it affects the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "engaged-provision",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "directed-fiber",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "framed-dealer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "accomplished-elizabeth",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "going-techno",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "brief-saying",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "spare-pregnancy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "catholic-staff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "broad-sequence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "contained-diversity",
+   "metadata": {},
+   "source": [
+    "### Under the hood\n",
+    "\n",
+    "`Vector` "
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap23.ipynb b/chapters/chap23.ipynb
new file mode 100644
index 00000000..b5333cf2
--- /dev/null
+++ b/chapters/chap23.ipynb
@@ -0,0 +1,557 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "foreign-pepper",
+   "metadata": {},
+   "source": [
+    "# Chapter 23"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "competitive-backing",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "quantitative-montana",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from previous notebook\n",
+    "\n",
+    "from chap22 import params\n",
+    "from chap22 import make_system\n",
+    "from chap22 import slope_func\n",
+    "from chap22 import event_func"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "angry-pledge",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we developed a model of the flight of a\n",
+    "baseball, including gravity and a simple version of drag, but neglecting spin, Magnus force, and the dependence of the coefficient of drag on velocity.\n",
+    "\n",
+    "In this chapter we apply that model to an optimization problem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "decent-birth",
+   "metadata": {},
+   "source": [
+    "## The Manny Ramirez problem\n",
+    "\n",
+    "Manny Ramirez is a former member of the Boston Red Sox (an American\n",
+    "baseball team) who was notorious for his relaxed attitude and taste for practical jokes. Our objective in this chapter is to solve the following Manny-inspired problem:\n",
+    "\n",
+    "> What is the minimum effort required to hit a home run in Fenway Park?\n",
+    "\n",
+    "Fenway Park is a baseball stadium in Boston, Massachusetts. One of its\n",
+    "most famous features is the \"Green Monster\", which is a wall in left\n",
+    "field that is unusually close to home plate, only 310 feet away. To\n",
+    "compensate for the short distance, the wall is unusually high, at 37\n",
+    "feet (see <http://modsimpy.com/wally>)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exposed-diving",
+   "metadata": {},
+   "source": [
+    "We want to find the minimum velocity at which a ball can leave home\n",
+    "plate and still go over the Green Monster. We'll proceed in the\n",
+    "following steps:\n",
+    "\n",
+    "1.  For a given velocity, we'll find the optimal **launch angle**, that is, the angle the ball should leave home plate to maximize its height when it reaches the wall.\n",
+    "\n",
+    "2.  Then we'll find the minimal velocity that clears the wall, given\n",
+    "    that it has the optimal launch angle."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "received-diamond",
+   "metadata": {},
+   "source": [
+    "## Finding the range\n",
+    "\n",
+    "Suppose we want to find the launch angle that maximizes **range**, that is, the distance the ball travels in the air before landing. We'll use a function in the ModSim library, `maximize_scalar`, which takes a function and finds its maximum.\n",
+    "\n",
+    "The function we pass to `maximize_scalar` should take launch angle in degrees, simulate the flight of a ball launched at that angle, and return the distance the ball travels along the $x$ axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "forty-knitting",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def range_func(angle, params):\n",
+    "    params = params.set(angle=angle)\n",
+    "    system = make_system(params)\n",
+    "    results, details = run_solve_ivp(system, slope_func,\n",
+    "                                     events=event_func)\n",
+    "    x_dist = results.iloc[-1].x\n",
+    "    print(angle, x_dist)\n",
+    "    return x_dist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exact-cigarette",
+   "metadata": {},
+   "source": [
+    "`range_func` makes a new `System` object with the given value of\n",
+    "`angle`. Then it calls `run_solve_ivp` and\n",
+    "returns the final value of `x` from the results.\n",
+    "\n",
+    "We can call `range_func` directly like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "senior-counter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "range_func(45, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "paperback-passing",
+   "metadata": {},
+   "source": [
+    "And we can sweep a sequence of angles like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "biological-evans",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "angles = linspace(20, 80, 21)\n",
+    "sweep = SweepSeries()\n",
+    "\n",
+    "for angle in angles:\n",
+    "    x_dist = range_func(angle, params)\n",
+    "    sweep[angle] = x_dist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "premium-contribution",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "experienced-providence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sweep.plot()\n",
+    "\n",
+    "decorate(xlabel='Launch angle (degree)',\n",
+    "         ylabel='Range (meter)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "conscious-blade",
+   "metadata": {},
+   "source": [
+    "It looks like the optimal angle is near 40°.\n",
+    "\n",
+    "We can find the optimal angle more precisely and more efficiently using `maximize_scalar`, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "invisible-jaguar",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = maximize_scalar(range_func, params, bounds=[0, 90])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "czech-command",
+   "metadata": {},
+   "source": [
+    "The first parameter is the function we want to maximize. The second is\n",
+    "the range of values we want to search; in this case, it's the range of\n",
+    "angles from 0° to 90°. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "violent-explanation",
+   "metadata": {},
+   "source": [
+    "The return value from `maximize_scalar` is an object that contains the\n",
+    "results, including `x`, which is the angle that yielded the highest\n",
+    "range, and `fun`, which is the value of `range_func` when it's evaluated at `x`, that is, range when the baseball is launched at the optimal angle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "vocal-nerve",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "figured-uniform",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res.x, res.fun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "shaped-southeast",
+   "metadata": {},
+   "source": [
+    "For these parameters, the optimal angle is about 41°, which yields a\n",
+    "range of 100 m.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "temporal-extension",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "\n",
+    "\n",
+    "If you enjoy this exercise, you might be interested in this paper: \"How to hit home runs: Optimum baseball bat swing parameters for maximum range trajectories\", by Sawicki, Hubbard, and Stronge, at\n",
+    "<http://modsimpy.com/runs>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "processed-constitution",
+   "metadata": {},
+   "source": [
+    "## Exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "handmade-rhythm",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Let's finish off the Manny Ramirez problem:\n",
+    "\n",
+    "> What is the minimum effort required to hit a home run in Fenway Park?\n",
+    "\n",
+    "Although the problem asks for a minimum, it is not an optimization problem.  Rather, we want to solve for the initial velocity that just barely gets the ball to the top of the wall, given that it is launched at the optimal angle.\n",
+    "\n",
+    "And we have to be careful about what we mean by \"optimal\".  For this problem, we don't want the longest range, we want the maximum height at the point where it reaches the wall.\n",
+    "\n",
+    "If you are ready to solve the problem on your own, go ahead.  Otherwise I will walk you through the process with an outline and some starter code.\n",
+    "\n",
+    "As a first step, write an `event_func` that stops the simulation when the ball reaches the wall at 310 feet (94.5 m).\n",
+    "Test your function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "studied-association",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "developmental-alabama",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "novel-appointment",
+   "metadata": {},
+   "source": [
+    "Next, write a function called `height_func` that takes a launch angle, simulates the flight of a baseball, and returns the height of the baseball when it reaches the wall.\n",
+    "Test your function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ignored-decrease",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "western-communist",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "located-lawsuit",
+   "metadata": {},
+   "source": [
+    "Now use `maximize_scalar` to find the optimal angle.  Is it higher or lower than the angle that maximizes range?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "duplicate-madison",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "legislative-prospect",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "interpreted-telephone",
+   "metadata": {},
+   "source": [
+    "Even though we are finding the \"minimum\" velocity, we are not really solving a minimization problem.  Rather, we want to find the velocity that makes the height at the wall exactly 37 feet (11.3 m), given that it's launched at the optimal angle.  And that's a job for `root_scalar`.\n",
+    "\n",
+    "Write an error function that takes a velocity and a `System` object as parameters.  It should use `maximize_scalar` to find the highest possible height of the ball at the wall, for the given velocity.  Then it should return the difference between that optimal height and 11.3 meters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "egyptian-shadow",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "documentary-guidance",
+   "metadata": {},
+   "source": [
+    "Test your error function before you call `root_scalar`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "sustainable-supply",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reserved-shelter",
+   "metadata": {},
+   "source": [
+    "Then use `root_scalar` to find the answer to the problem, the minimum velocity that gets the ball out of the park."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "certified-webster",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "silver-bernard",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "absent-encoding",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "approved-fiction",
+   "metadata": {},
+   "source": [
+    "And just to check, run `error_func` with the value you found."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "thick-jungle",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "simple-steps",
+   "metadata": {},
+   "source": [
+    "## Under the Hood\n",
+    "\n",
+    "`maximize_scalar` uses a SciPy function called `minimize_scalar`, which provides several optimization methods.  By default, it uses `bounded`, a version of Brent's algorithm that is safe in the sense that it always uses values within the bounds you provide (including both ends).\n",
+    "You can read more about it at <http://modsimpy.com/minimize>)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dense-study",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap24.ipynb b/chapters/chap24.ipynb
new file mode 100644
index 00000000..b6fa9422
--- /dev/null
+++ b/chapters/chap24.ipynb
@@ -0,0 +1,901 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "august-parks",
+   "metadata": {},
+   "source": [
+    "# Chapter 24"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "suspended-puppy",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "confirmed-california",
+   "metadata": {},
+   "source": [
+    "In this chapter and the next we'll model systems that involve rotating objects.\n",
+    "\n",
+    "In general, rotation is complicated.\n",
+    "In three dimensions, objects can rotate around three axes and many objects are easier to spin around some axes than others.\n",
+    "\n",
+    "If the configuration of an object changes over time, it might become\n",
+    "easier or harder to spin, which explains the surprising dynamics of\n",
+    "gymnasts, divers, ice skaters, etc.\n",
+    "\n",
+    "And when you apply a twisting force to a rotating object, the effect is often contrary to intuition. \n",
+    "For an example, see this video on gyroscopic precession <http://modsimpy.com/precess>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "composed-carol",
+   "metadata": {},
+   "source": [
+    "In this book, we will not take on the physics of rotation in all its glory. \n",
+    "Rather, we will focus on simple scenarios where all rotation and all twisting forces are around a single axis. \n",
+    "In that case, we can treat some vector quantities as if they were scalars, in the same way that we sometimes treat velocity as a scalar with an implicit direction.\n",
+    "\n",
+    "The fundamental ideas in these examples are angular velocity, angular acceleration, torque, and moment of inertia.\n",
+    "If you are not already familiar with these concepts, I will define them as we go along, and I will point to additional reading."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "integral-welsh",
+   "metadata": {},
+   "source": [
+    "## The physics of toilet paper\n",
+    "\n",
+    "As an example of a system with rotation, we'll simulate the manufacture of a roll of toilet paper, as shown in this video <https://youtu.be/Z74OfpUbeac?t=231>. \n",
+    "Starting with a cardboard tube at the center, we will roll up 47 m of paper, a typical length for a roll of toilet paper in the U.S. (see <http://modsimpy.com/paper>).\n",
+    "\n",
+    "The following figure shows a diagram of the system: $r$ represents\n",
+    "the radius of the roll at a point in time. Initially, $r$ is the radius of the cardboard core, $R_{min}$. When the roll is complete, $r$ is $R_{max}$.\n",
+    "\n",
+    "![Diagram of a roll of toilet paper, showing change in paper length as a result of a small rotation, $d\\theta$.](https://github.com/AllenDowney/ModSim/raw/main/figs/paper_roll.png)\n",
+    "\n",
+    "I'll use $\\theta$ to represent the total rotation of the roll in\n",
+    "radians. In the diagram, $d\\theta$ represents a small increase in\n",
+    "$\\theta$, which corresponds to a distance along the circumference of $r~d\\theta$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "political-prerequisite",
+   "metadata": {},
+   "source": [
+    "I'll use $y$ to represent the total length of paper that's been rolled. \n",
+    "Initially, $\\theta=0$ and $y=0$. \n",
+    "For each small increase in $\\theta$, there is a corresponding increase in $y$: \n",
+    "\n",
+    "$$dy = r~d\\theta$$\n",
+    "\n",
+    "If we divide both sides by a small increase in time, $dt$, we get a\n",
+    "differential equation for $y$ as a function of time.\n",
+    "\n",
+    "$$\\frac{dy}{dt} = r \\frac{d\\theta}{dt}$$ \n",
+    "\n",
+    "As we roll up the paper, $r$ increases. Assuming it increases by a fixed amount per revolution, we can write \n",
+    "\n",
+    "$$dr = k~d\\theta$$ \n",
+    "\n",
+    "Where $k$ is an unknown constant we'll have to figure out. \n",
+    "Again, we can divide both sides by $dt$ to get a differential equation in time:\n",
+    "\n",
+    "$$\\frac{dr}{dt} = k \\frac{d\\theta}{dt}$$ \n",
+    "\n",
+    "Finally, let's assume that $\\theta$ increases at a constant rate of $\\omega = 300$ rad/s (about 2900 revolutions per minute): \n",
+    "\n",
+    "$$\\frac{d\\theta}{dt} = \\omega$$ \n",
+    "\n",
+    "This rate of change is called an **angular velocity**. Now we have a system of differential equations we can use to simulate the system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prostate-smell",
+   "metadata": {},
+   "source": [
+    "## Parameters\n",
+    "\n",
+    "Here are the parameters of the system:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "honey-translator",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Rmin = 0.02      # m\n",
+    "Rmax = 0.055     # m\n",
+    "L = 47           # m\n",
+    "omega = 300      # rad / s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "provincial-logistics",
+   "metadata": {},
+   "source": [
+    "`Rmin` and `Rmax` are the initial and final values for the radius, `r`.\n",
+    "`L` is the total length of the paper.\n",
+    "`omega` is the angular velocity in radians per second.\n",
+    "\n",
+    "Figuring out `k` is not easy, but we can estimate it by pretending that `r` is constant and equal to the average of `Rmin` and `Rmax`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "floppy-plane",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ravg = (Rmax + Rmin) / 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ordinary-leader",
+   "metadata": {},
+   "source": [
+    "In that case, the circumference of the roll is also be constant:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "funky-bandwidth",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Cavg = 2 * np.pi * Ravg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "square-radius",
+   "metadata": {},
+   "source": [
+    "And we can compute the number of revolutions to roll up length `L`, like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "front-conservation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "revs = L / Cavg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "headed-gibson",
+   "metadata": {},
+   "source": [
+    "Converting rotations to radians, we can estimate the final value of `theta`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "defined-plymouth",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "theta = 2 * np.pi * revs\n",
+    "theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "naughty-narrative",
+   "metadata": {},
+   "source": [
+    "Finally, `k` is the total change in `r` divided by the total change in `theta`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "subject-brief",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "k_est = (Rmax - Rmin) / theta\n",
+    "k_est"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "experienced-sharing",
+   "metadata": {},
+   "source": [
+    "At the end of the chapter, we'll derive `k` analytically, but this estimate is enough to get started."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "phantom-yacht",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "The state variables we'll use are, `theta`, `y`, and `r`.\n",
+    "Here are the initial conditions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ranging-graham",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init = State(theta=0, y=0, r=Rmin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "caring-unemployment",
+   "metadata": {},
+   "source": [
+    "And here's a `System` object with `init` and `t_end`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fresh-domestic",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = System(init=init, t_end=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beginning-artwork",
+   "metadata": {},
+   "source": [
+    "Now we can use the differential equations from the previous section to\n",
+    "write a slope function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "signed-eight",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    theta, y, r = state\n",
+    "    \n",
+    "    dydt = r * omega\n",
+    "    drdt = k_est * omega\n",
+    "    \n",
+    "    return omega, dydt, drdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "maritime-funds",
+   "metadata": {},
+   "source": [
+    "As usual, the slope function takes a time stamp, a `State` object, and a `System` object. \n",
+    "\n",
+    "The job of the slope function is to compute the time derivatives of the state variables.\n",
+    "\n",
+    "The derivative of `theta` is angular velocity, `omega`.\n",
+    "The derivatives of `y` and `r` are given by the differential equations we derived."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "trained-witness",
+   "metadata": {},
+   "source": [
+    "And as usual, we'll test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "exterior-water",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "russian-rochester",
+   "metadata": {},
+   "source": [
+    "We'd like to stop the simulation when the length of paper on the roll is `L`. We can do that with an event function that passes through 0 when `y` equals `L`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "strong-custody",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(t, state, system):\n",
+    "    theta, y, r = state\n",
+    "    return L - y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "catholic-workshop",
+   "metadata": {},
+   "source": [
+    "We can test it with the initial conditions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "moved-present",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "affecting-pathology",
+   "metadata": {},
+   "source": [
+    "Now let's run the simulation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "stable-lying",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_solve_ivp(system, slope_func,\n",
+    "                                  events=event_func)\n",
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "experienced-pathology",
+   "metadata": {},
+   "source": [
+    "Here are the last few time steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "careful-bahrain",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "underlying-variance",
+   "metadata": {},
+   "source": [
+    "The time it takes to complete one roll is about 4.2 seconds, which is consistent with what we see in the video."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "individual-patrick",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.index[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "informative-porter",
+   "metadata": {},
+   "source": [
+    "The final value of `y` is 47 meters, as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "indian-skirt",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_state = results.iloc[-1] \n",
+    "final_state.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "running-tutorial",
+   "metadata": {},
+   "source": [
+    "The final value of radius is 0.55 m, which is `Rmax`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "higher-conflict",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_state.r"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tamil-referral",
+   "metadata": {},
+   "source": [
+    "The total number of rotations is close to 200, which seems plausible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "employed-ordinary",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radians = final_state.theta\n",
+    "rotations = radians / 2 / np.pi\n",
+    "rotations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "otherwise-mississippi",
+   "metadata": {},
+   "source": [
+    "As an exercise, we'll see how fast the paper is moving.  But first, let's take a closer look at the results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lesser-consumer",
+   "metadata": {},
+   "source": [
+    "## Plotting\n",
+    "\n",
+    "Here's what `theta` looks like over time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "included-schema",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_theta(results):\n",
+    "    results.theta.plot(color='C0', label='theta')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Angle (rad)')\n",
+    "    \n",
+    "plot_theta(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "regulation-runner",
+   "metadata": {},
+   "source": [
+    "`theta` grows linearly, as we should expect with constant angular velocity.\n",
+    "\n",
+    "Here's what `r` looks like over time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "persistent-siemens",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_r(results):\n",
+    "    results.r.plot(color='C2', label='r')\n",
+    "\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Radius (m)')\n",
+    "    \n",
+    "plot_r(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dedicated-tuning",
+   "metadata": {},
+   "source": [
+    "`r` also increases linearly.\n",
+    "\n",
+    "But since the derivative of `y` depends on `r`, and `r` is increasing, `y` grows with increasing slope."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "contrary-typing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_y(results):\n",
+    "    results.y.plot(color='C1', label='y')\n",
+    "\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Length (m)')\n",
+    "    \n",
+    "plot_y(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "furnished-executive",
+   "metadata": {},
+   "source": [
+    "In the next section, we'll see that we could have solved the\n",
+    "differential equations analytically.\n",
+    "However, it is often useful to start with simulation as a way of exploring and checking assumptions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "better-asbestos",
+   "metadata": {},
+   "source": [
+    "## Analysis\n",
+    "\n",
+    "Since angular velocity is constant: \n",
+    "\n",
+    "$$\\frac{d\\theta}{dt} = \\omega \\quad\\quad (1)$$ \n",
+    "\n",
+    "We can find $\\theta$ as a function of time by integrating both sides:\n",
+    "\n",
+    "$$\\theta(t) = \\omega t$$ \n",
+    "\n",
+    "Similarly, we can solve this equation\n",
+    "\n",
+    "$$\\frac{dr}{dt} = k \\omega$$\n",
+    "\n",
+    "to find\n",
+    "\n",
+    "$$r(t) = k \\omega t + R_{min}$$ \n",
+    "\n",
+    "Then we can plug the solution for $r$ into the equation for $y$: \n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\frac{dy}{dt} & = r \\omega        \\quad\\quad (2)              \\\\\n",
+    "              & = \\left[ k \\omega t + R_{min} \\right] \\omega \\nonumber\\end{aligned}$$\n",
+    "              \n",
+    "Integrating both sides yields:\n",
+    "\n",
+    "$$y(t) = \\left[ k \\omega t^2 / 2 + R_{min} t \\right] \\omega$$ \n",
+    "\n",
+    "So $y$ is a parabola, as you might have guessed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "meaningful-kazakhstan",
+   "metadata": {},
+   "source": [
+    "We can also use these equations to find the relationship between $y$ and $r$, independent of time, which we can use to compute $k$.\n",
+    "Dividing Equations 1 and 2, yields\n",
+    "\n",
+    "$$\\frac{dr}{dy} = \\frac{k}{r}$$ \n",
+    "\n",
+    "Separating variables yields\n",
+    "\n",
+    "$$r~dr = k~dy$$ \n",
+    "\n",
+    "Integrating both sides yields \n",
+    "\n",
+    "$$r^2 / 2 = k y + C$$ \n",
+    "\n",
+    "Solving for $y$, we have \n",
+    "\n",
+    "$$y = \\frac{1}{2k} (r^2 - C)                 \\label{eqn3}$$\n",
+    "\n",
+    "When $y=0$, $r=R_{min}$, so \n",
+    "\n",
+    "$$R_{min}^2 / 2 = C$$ \n",
+    "\n",
+    "When $y=L$, $r=R_{max}$, so\n",
+    "\n",
+    "$$L = \\frac{1}{2k} (R_{max}^2 - R_{min}^2)$$ \n",
+    "\n",
+    "Solving for $k$ yields\n",
+    "\n",
+    "$$k =  \\frac{1}{2L} (R_{max}^2 - R_{min}^2)           \\label{eqn4}$$\n",
+    "\n",
+    "Plugging in the values of the parameters yields `2.8e-5` m/rad, the same as the \"estimate\" we computed in Section xxx. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "acknowledged-register",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "k = (Rmax**2 - Rmin**2) / (2 * L)\n",
+    "k"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "retired-skill",
+   "metadata": {},
+   "source": [
+    "In this case the estimate turns out to be exact."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "foreign-needle",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This chapter introduces rotation, starting with an example where angular velocity is constant.\n",
+    "\n",
+    "We simulated the manufacture of a roll of toilet paper, then we solved the same problem analytically.\n",
+    "\n",
+    "In the next chapter, we'll see a more interesting example where angular velocity is not constant.  And we'll introduce three new concepts: torque, angular acceleration, and moment of inertia.\n",
+    "\n",
+    "But first, you might want to work on the following exercises."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "thick-luther",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "**Exercise:** Since we keep `omega` constant, the linear velocity of the paper increases with radius.  We can use `gradient` to estimate the derivative of `results.y`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "cardiac-hospital",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dydt = gradient(results.y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "welsh-charleston",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dydt.plot(label='dydt')\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Linear velocity (m/s)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "becoming-birthday",
+   "metadata": {},
+   "source": [
+    "With constant angular velocity, linear velocity is increasing, reaching its maximum at the end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "informational-washer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_linear_velocity = dydt.iloc[-1]\n",
+    "max_linear_velocity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bizarre-variation",
+   "metadata": {},
+   "source": [
+    "Now suppose this peak velocity is the limiting factor; that is, we can't move the paper any faster than that.\n",
+    "\n",
+    "In that case, we might be able to speed up the process by keeping the linear velocity at the maximum all the time.\n",
+    "\n",
+    "Write a slope function that keeps the linear velocity, `dydt`, constant, and computes the angular velocity, `omega`, accordingly.\n",
+    "\n",
+    "Run the simulation and see how much faster we could finish rolling the paper."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "excellent-japanese",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "steady-member",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "applied-sacramento",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "adult-chuck",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "conditional-cliff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "arranged-queensland",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "similar-variance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "lovely-orange",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap25.ipynb b/chapters/chap25.ipynb
new file mode 100644
index 00000000..eaaa432f
--- /dev/null
+++ b/chapters/chap25.ipynb
@@ -0,0 +1,1076 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "unauthorized-winter",
+   "metadata": {},
+   "source": [
+    "# Chapter 25"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "complete-innocent",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "chief-delight",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we modeled a system with constant angular\n",
+    "velocity.\n",
+    "In this chapter we take the next step, modeling a system with constant angular acceleration and deceleration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "earned-motorcycle",
+   "metadata": {},
+   "source": [
+    "## Angular acceleration\n",
+    "\n",
+    "Just as linear acceleration is the derivative of velocity, **angular\n",
+    "acceleration** is the derivative of angular velocity. And just as linear acceleration is caused by force, angular acceleration is caused by the rotational version of force, **torque**. If you are not familiar with torque, you can read about it at <http://modsimpy.com/torque>.\n",
+    "\n",
+    "In general, torque is a vector quantity, defined as the **cross\n",
+    "product** of $\\vec{r}$ and $\\vec{F}$, where $\\vec{r}$ is the **lever\n",
+    "arm**, a vector from the center of rotation to the point where the force is applied, and $\\vec{F}$ is the vector that represents the magnitude and direction of the force."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "promotional-trigger",
+   "metadata": {},
+   "source": [
+    "However, for the problems in this chapter, we only need the *magnitude* of torque; we don't care about the direction. In that case, we can compute \n",
+    "\n",
+    "$$\\tau = r F \\sin \\theta$$ \n",
+    "\n",
+    "where $\\tau$ is torque, $r$ is the length of the lever arm, $F$ is the magnitude of force, and $\\theta$ is the angle between $\\vec{r}$ and $\\vec{F}$.\n",
+    "\n",
+    "Since torque is the product of a length and a force, it is expressed in newton meters (Nm)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "infrared-summit",
+   "metadata": {},
+   "source": [
+    "## Moment of inertia\n",
+    "\n",
+    "In the same way that linear acceleration is related to force by Newton's second law of motion, $F=ma$, angular acceleration is related to torque by another form of Newton's law: \n",
+    "\n",
+    "$$\\tau = I \\alpha$$ \n",
+    "\n",
+    "where $\\alpha$ is angular acceleration and $I$ is **moment of inertia**. Just as mass is what makes it hard to accelerate an object, moment of inertia is what makes it hard to spin an object.\n",
+    "\n",
+    "In the most general case, a 3-D object rotating around an arbitrary\n",
+    "axis, moment of inertia is a tensor, which is a function that takes a\n",
+    "vector as a parameter and returns a vector as a result.\n",
+    "\n",
+    "Fortunately, in a system where all rotation and torque happens around a single axis, we don't have to deal with the most general case. We can treat moment of inertia as a scalar quantity.\n",
+    "\n",
+    "For a small object with mass $m$, rotating around a point at distance\n",
+    "$r$, the moment of inertia is $I = m r^2$. For more complex objects, we can compute $I$ by dividing the object into small masses, computing\n",
+    "moments of inertia for each mass, and adding them up.\n",
+    "\n",
+    "However, for most simple shapes, people have already done the\n",
+    "calculations; you can just look up the answers. For example, see\n",
+    "<http://modsimpy.com/moment>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "julian-klein",
+   "metadata": {},
+   "source": [
+    "## Teapots and turntables\n",
+    "\n",
+    "Tables in Chinese restaurants often have a rotating tray or turntable\n",
+    "that makes it easy for customers to share dishes. These turntables are\n",
+    "supported by low-friction bearings that allow them to turn easily and\n",
+    "glide. However, they can be heavy, especially when they are loaded with food, so they have a high moment of inertia.\n",
+    "\n",
+    "Suppose I am sitting at a table with a pot of tea on the turntable\n",
+    "directly in front of me, and the person sitting directly opposite asks\n",
+    "me to pass the tea. I push on the edge of the turntable with 2 N of\n",
+    "force until it has turned 0.5 rad, then let go. The turntable glides\n",
+    "until it comes to a stop 1.5 rad from the starting position. How much\n",
+    "force should I apply for a second push so the teapot glides to a stop\n",
+    "directly opposite me?\n",
+    "\n",
+    "We'll answer this question in these steps:\n",
+    "\n",
+    "1.  I'll use the results from the first push to estimate the coefficient of friction for the turntable.\n",
+    "\n",
+    "2.  As an exercise, you'll use that coefficient of friction to estimate the force needed to rotate the turntable through the remaining angle.\n",
+    "\n",
+    "Our simulation will use the following parameters:\n",
+    "\n",
+    "1.  The radius of the turntable is 0.5 m, and its weight is 7 kg.\n",
+    "\n",
+    "2.  The teapot weights 0.3 kg, and it sits 0.4 m from the center of the turntable.\n",
+    "\n",
+    "The following figure shows the scenario, where $F$ is the force I apply to the turntable at the perimeter, perpendicular to the lever arm, $r$, and $\\tau$ is the resulting torque. The blue circle near the bottom is the teapot.\n",
+    "\n",
+    "![Diagram of a turntable with a\n",
+    "teapot.](https://github.com/AllenDowney/ModSim/raw/main/figs/teapot.png)\n",
+    "\n",
+    "Here are the parameters from the statement of the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "spiritual-disorder",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import pi\n",
+    "\n",
+    "radius_disk = 0.5     # m\n",
+    "mass_disk = 7         # kg\n",
+    "radius_pot = 0.4      # m\n",
+    "mass_pot = 0.3        # kg\n",
+    "force = 2             # N\n",
+    "\n",
+    "theta_push = 0.5        # radian\n",
+    "theta_test = 1.5        # radian\n",
+    "theta_target = pi       # radian"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bound-algorithm",
+   "metadata": {},
+   "source": [
+    "`theta_push` is the angle where I stop pushing on the turntable.\n",
+    "`theta_test` is how far the table turns during my test push.\n",
+    "`theta_target` is where we want the table to be after the second push.\n",
+    "\n",
+    "We can use these parameters to compute the moment of inertia of the turntable, using the formula for a horizontal disk revolving around a vertical axis through its center: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "recorded-administration",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "I_disk = mass_disk * radius_disk**2 / 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "economic-concord",
+   "metadata": {},
+   "source": [
+    "We can also compute the moment of inertia of the teapot, treating it as a point mass:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "present-termination",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "I_pot = mass_pot * radius_pot**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dominican-joseph",
+   "metadata": {},
+   "source": [
+    "The total moment of inertia is the sum of these parts:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "former-driver",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "I_total = I_disk + I_pot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "effective-danger",
+   "metadata": {},
+   "source": [
+    "Friction in the bearings probably depends on the weight of the turntable and its contents, but probably does not depend on angular velocity.\n",
+    "So we'll assume that it is a constant.\n",
+    "We don't know what it is, so I will start with a guess, and we will use `root_scalar` to improve it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "enclosed-happiness",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torque_friction = 0.3   # N*m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "balanced-method",
+   "metadata": {},
+   "source": [
+    "For this problem we'll treat friction as a torque.\n",
+    "\n",
+    "The state variables we'll use are `theta`, which is the angle of the table in rad, and `omega`, which is angular velocity in rad/s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "passing-sullivan",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init = State(theta=0, omega=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "second-leather",
+   "metadata": {},
+   "source": [
+    "Now we can make a `System` with the initial state, `init`, the maximum duration of the simulation, `t_end`, and the parameters we are going to vary, `force` and `torque_friction`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "acoustic-furniture",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = System(init=init, \n",
+    "                force=force,\n",
+    "                torque_friction=torque_friction,\n",
+    "                t_end=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "crucial-recognition",
+   "metadata": {},
+   "source": [
+    "Here's a slope function that takes the current state, which contains angle and angular velocity, and returns the derivatives, angular velocity and angular acceleration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ranking-local",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    theta, omega = state\n",
+    "    force = system.force\n",
+    "    torque_friction = system.torque_friction\n",
+    "    \n",
+    "    torque = radius_disk * force - torque_friction\n",
+    "    alpha = torque / I_total\n",
+    "    \n",
+    "    return omega, alpha "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exposed-court",
+   "metadata": {},
+   "source": [
+    "In this scenario, the force I apply to the turntable is always\n",
+    "perpendicular to the lever arm, so $\\sin \\theta = 1$ and the torque due\n",
+    "to force is $\\tau = r F$.\n",
+    "\n",
+    "`torque_friction` represents the torque due to friction. Because the\n",
+    "turntable is rotating in the direction of positive `theta`, friction\n",
+    "acts in the direction of negative `theta`.\n",
+    "\n",
+    "We can test the slope function with the initial conditions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "saved-purple",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "forbidden-legislature",
+   "metadata": {},
+   "source": [
+    "We are almost ready to run the simulation, but first there's a problem we have to address."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "decent-microwave",
+   "metadata": {},
+   "source": [
+    "## Two Phase Simulation\n",
+    "\n",
+    "When I stop pushing on the turntable, the angular acceleration changes\n",
+    "abruptly. We could implement the slope function with an `if` statement\n",
+    "that checks the value of `theta` and sets `force` accordingly. And for a coarse model like this one, that might be fine. But a more robust approach is to simulate the system in two phases:\n",
+    "\n",
+    "1.  During the first phase, force is constant, and we run until `theta` is 0.5 radians.\n",
+    "\n",
+    "2.  During the second phase, force is 0, and we run until `omega` is 0.\n",
+    "\n",
+    "Then we can combine the results of the two phases into a single\n",
+    "`TimeFrame`.\n",
+    "\n",
+    "Here's the event function I'll use for Phase 1; it stops the simulation when `theta` reaches `theta_push`, which is when I stop pushing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "black-wichita",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func1(t, state, system):\n",
+    "    theta, omega = state\n",
+    "    return theta - theta_push"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "separate-college",
+   "metadata": {},
+   "source": [
+    "We can test it with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ranking-google",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_func1(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reduced-sharp",
+   "metadata": {},
+   "source": [
+    "And run the first phase of the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "meaning-philosophy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results1, details1 = run_solve_ivp(system, slope_func,\n",
+    "                                   events=event_func1)\n",
+    "details1.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "spread-application",
+   "metadata": {},
+   "source": [
+    "Here are the last few time steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "focused-invention",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results1.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "willing-receipt",
+   "metadata": {},
+   "source": [
+    "It takes a little more than a second for me to rotate the table 0.5 rad.\n",
+    "When I release the table, the angular velocity is about 0.87 rad / s.\n",
+    "\n",
+    "Before we run the second phase, we have to extract the final time and\n",
+    "state of the first phase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "russian-experience",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_2 = results1.index[-1]\n",
+    "init2 = results1.iloc[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aware-generator",
+   "metadata": {},
+   "source": [
+    "Now we can make a `System` object for Phase 2 with the initial state\n",
+    "from Phase 1 and with `force=0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "chinese-cover",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system2 = system.set(t_0=t_2, init=init2, force=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "actual-monaco",
+   "metadata": {},
+   "source": [
+    "For the second phase, we need an event function that stops when the\n",
+    "turntable stops; that is, when angular velocity is 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "recovered-section",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func2(t, state, system):\n",
+    "    theta, omega = state\n",
+    "    return omega"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ordinary-miniature",
+   "metadata": {},
+   "source": [
+    "We'll test it with the initial conditions for Phase 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "corporate-taste",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_func2(system2.t_0, system2.init, system2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "administrative-major",
+   "metadata": {},
+   "source": [
+    "Now we can run the second phase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "destroyed-adrian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results2, details2 = run_solve_ivp(system2, slope_func,\n",
+    "                                   events=event_func2)\n",
+    "details2.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hindu-requirement",
+   "metadata": {},
+   "source": [
+    "`DataFrame` provides `append`, which appends `results2` to the end of\n",
+    "`results1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "frank-scene",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = results1.append(results2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "wooden-butterfly",
+   "metadata": {},
+   "source": [
+    "Here are the last few time steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "short-singer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "metropolitan-roommate",
+   "metadata": {},
+   "source": [
+    "At the end, angular velocity is close to 0, and the total rotation is about 1.7 rad, a little farther than we were aiming for.\n",
+    "\n",
+    "We can plot `theta` for both phases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "distributed-humanitarian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.theta.plot(label='theta')\n",
+    "decorate(xlabel='Time (s)',\n",
+    "             ylabel='Angle (rad)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "excess-confidence",
+   "metadata": {},
+   "source": [
+    "And `omega`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "proved-surfing",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "results.omega.plot(label='omega', color='C1')\n",
+    "decorate(xlabel='Time (s)',\n",
+    "             ylabel='Angular velocity (rad/s)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "handy-frontier",
+   "metadata": {},
+   "source": [
+    "Angular velocity, `omega`, increases linearly while I am pushing, and decreases linearly after I let go. The angle, `theta`, is the integral of angular velocity, so it forms a parabola during each phase.\n",
+    "\n",
+    "In the next section, we'll use this simulation to estimate the torque\n",
+    "due to friction."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "careful-minneapolis",
+   "metadata": {},
+   "source": [
+    "## Estimating Friction\n",
+    "\n",
+    "Let's take the code from the previous section and wrap it in a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "constant-wallace",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_two_phases(force, torque_friction, system):\n",
+    "    \n",
+    "    # put the specified parameters into the System object\n",
+    "    system1 = system.set(force=force, \n",
+    "                         torque_friction=torque_friction)\n",
+    "\n",
+    "    # run phase 1\n",
+    "    results1, details1 = run_solve_ivp(system1, slope_func, \n",
+    "                                       events=event_func1)\n",
+    "\n",
+    "    # get the final state from phase 1\n",
+    "    t_2 = results1.index[-1]\n",
+    "    init2 = results1.iloc[-1]\n",
+    "    \n",
+    "    # run phase 2\n",
+    "    system2 = system1.set(t_0=t_2, init=init2, force=0)\n",
+    "    results2, details2 = run_solve_ivp(system2, slope_func, \n",
+    "                                       events=event_func2)\n",
+    "    \n",
+    "    # combine and return the results\n",
+    "    results = results1.append(results2)\n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "positive-possible",
+   "metadata": {},
+   "source": [
+    "I'll test it with the same parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "played-ranking",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "force = 2\n",
+    "torque_friction = 0.3\n",
+    "results = run_two_phases(force, torque_friction, system)\n",
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "damaged-concert",
+   "metadata": {},
+   "source": [
+    "These results are the same as in the previous section.\n",
+    "\n",
+    "We can use `run_two_phases` to write an error function we can use, with `root_scalar`, to find the torque due to friction that yields the\n",
+    "observed results from the first push, a total rotation of 1.5 rad."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "skilled-diving",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def error_func1(torque_friction, system):\n",
+    "    force = system.force\n",
+    "    results = run_two_phases(force, torque_friction, system)\n",
+    "    theta_final = results.iloc[-1].theta\n",
+    "    print(torque_friction, theta_final)\n",
+    "    return theta_final - theta_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "generous-parcel",
+   "metadata": {},
+   "source": [
+    "With `torque_friction=0.3`, the table rotates a bit too far:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "united-ranch",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guess1 = 0.3\n",
+    "error_func1(guess1, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "heard-serial",
+   "metadata": {},
+   "source": [
+    "With `torque_friction=0.4`, it doesn't go far enough."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "rotary-brazilian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guess2 = 0.4\n",
+    "error_func1(guess2, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "comparable-physiology",
+   "metadata": {},
+   "source": [
+    "So we can use those two values as a bracket for `root_scalar`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "hungarian-cattle",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = root_scalar(error_func1, system, bracket=[guess1, guess2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abandoned-remedy",
+   "metadata": {},
+   "source": [
+    "The result is 0.333 N m, a little less than the initial guess."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "sorted-struggle",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actual_friction = res.root\n",
+    "actual_friction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "democratic-praise",
+   "metadata": {},
+   "source": [
+    "Now that we know the torque due to friction, we can compute the force\n",
+    "needed to rotate the turntable through the remaining angle, that is,\n",
+    "from 1.5 rad to 3.14 rad.\n",
+    "\n",
+    "But first, let's animate the results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "knowing-sleeve",
+   "metadata": {},
+   "source": [
+    "## Animation\n",
+    "\n",
+    "\n",
+    "Here's a function that takes the state of the system and draws it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "funky-affect",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.patches import Circle\n",
+    "from matplotlib.pyplot import gca, axis\n",
+    "\n",
+    "def draw_func(t, state):\n",
+    "    theta, omega = state\n",
+    "    \n",
+    "    # draw a circle for the table\n",
+    "    circle1 = Circle([0, 0], radius_disk)\n",
+    "    gca().add_patch(circle1)\n",
+    "    \n",
+    "    # draw a circle for the teapot\n",
+    "    center = pol2cart(theta, radius_pot)\n",
+    "    circle2 = Circle(center, 0.05, color='C1')\n",
+    "    gca().add_patch(circle2)\n",
+    "\n",
+    "    axis('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dangerous-envelope",
+   "metadata": {},
+   "source": [
+    "This function uses a few features we have not seen before, but you can read about them in the Matplotlib documentation.\n",
+    "\n",
+    "Here's what the initial condition looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "illegal-remainder",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state = results.iloc[0]\n",
+    "draw_func(0, state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efficient-summary",
+   "metadata": {},
+   "source": [
+    "And here's an animation of the first push."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "toxic-shark",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# animate(results, draw_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "horizontal-shade",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "The example in this chapter demonstrates the concepts of torque, angular acceleration, and moment of inertia.\n",
+    "\n",
+    "We used these concepts to simulate a turntable, using a hypothetical observation to estimating torque due to friction.\n",
+    "\n",
+    "As an exercise, you can finish off the example, estimating the force needed to rotate the table to a given target angle.\n",
+    "\n",
+    "The next chapter describes several case studies you can work on to practice the tools from the last few chapters, including projectiles, rotating objects, `root_scalar`, and `maximize_scalar`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pediatric-precipitation",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sixth-breach",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Continuing the example from this chapter, estimate the force that delivers the teapot to the desired position.\n",
+    "Use this `System` object, with the friction we computed in the previous section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "dental-density",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system3 = system.set(torque_friction=actual_friction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "practical-cabinet",
+   "metadata": {},
+   "source": [
+    "Write an error function that takes `force` and `system`, simulates the system, and returns the difference between `theta_final` and the remaining angle after the first push."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "grave-consent",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remaining_angle = theta_target - theta_test\n",
+    "remaining_angle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hundred-judgment",
+   "metadata": {},
+   "source": [
+    "Use your error function and `root_scalar` to find the force needed for the second push.\n",
+    "Run the simulation with the force you computed and confirm that the table stops at the target angle after both pushes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "apparent-lancaster",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "persistent-rings",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "previous-pittsburgh",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "governing-component",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "statistical-behalf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "unauthorized-equity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eight-prototype",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/chapters/chap26.ipynb b/chapters/chap26.ipynb
new file mode 100644
index 00000000..6abd4e82
--- /dev/null
+++ b/chapters/chap26.ipynb
@@ -0,0 +1,389 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "early-drove",
+   "metadata": {},
+   "source": [
+    "# Chapter 26"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "comic-convert",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "broken-procedure",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "massive-thong",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "experienced-junction",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acoustic-small",
+   "metadata": {},
+   "source": [
+    "You made it to the end of the book.  Congratulations!\n",
+    "\n",
+    "This last chapter is a collection of case studies you might want to read and work on.\n",
+    "They are based on the methods in the last few chapters, including Newtonian mechanics in 1-D and 2-D, and rotation around a single axis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "capital-vatican",
+   "metadata": {},
+   "source": [
+    "## Bungee jumping\n",
+    "\n",
+    "Suppose you want to set the world record for the highest \"bungee dunk\", which is a stunt in which a bungee jumper dunks a cookie in a cup of tea at the lowest point of a jump. An example is shown in this video: <http://modsimpy.com/dunk>.\n",
+    "\n",
+    "Since the record is 70 m, let's design a jump for 80 m. We'll start with the following modeling assumptions:\n",
+    "\n",
+    "-   Initially the bungee cord hangs from a crane with the attachment\n",
+    "    point 80 m above a cup of tea.\n",
+    "\n",
+    "-   Until the cord is fully extended, it applies no force to the jumper.  It turns out this might not be a good assumption; we'll revisit it in the next case study.\n",
+    "\n",
+    "-   After the cord is fully extended, it obeys Hooke's Law; that is, it applies a force to the jumper proportional to the extension of the cord beyond its resting length. See <http://modsimpy.com/hooke>.\n",
+    "\n",
+    "-   The mass of the jumper is 75 kg.\n",
+    "\n",
+    "-   The jumper is subject to drag force so that their terminal velocity is 60 m/s.\n",
+    "\n",
+    "Our objective is to choose the length of the cord, `L`, and its spring\n",
+    "constant, `k`, so that the jumper falls all the way to the tea cup, but no farther!\n",
+    "\n",
+    "In the repository for this book, you will find a notebook,\n",
+    "`bungee1.ipynb`, which contains starter code and exercises for this case study."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "recognized-bidding",
+   "metadata": {},
+   "source": [
+    "## Bungee dunk revisited\n",
+    "\n",
+    "In the previous case study, we assume that the cord applies no force to\n",
+    "the jumper until it is stretched. It is tempting to say that the cord\n",
+    "has no effect because it falls along with the jumper, but that intuition\n",
+    "is incorrect. As the cord falls, it transfers energy to the jumper.\n",
+    "\n",
+    "At <http://modsimpy.com/bungee> you'll find a paper (Heck, Uylings, and Kędzierska, \"Understanding the physics of bungee jumping\", Physics Education, Volume 45, Number 1, 2010.) that explains\n",
+    "this phenomenon and derives the acceleration of the jumper, $a$, as a\n",
+    "function of position, $y$, and velocity, $v$:\n",
+    "\n",
+    "$$a = g + \\frac{\\mu v^2/2}{\\mu(L+y) + 2L}$$ \n",
+    "\n",
+    "where $g$ is acceleration due to gravity, $L$ is the length of the cord, and $\\mu$ is the ratio of the mass of the cord, $m$, and the mass of the jumper, $M$.\n",
+    "\n",
+    "If you don't believe that their model is correct, this video might\n",
+    "convince you: <http://modsimpy.com/drop>.\n",
+    "\n",
+    "In the repository for this book, you will find a notebook,\n",
+    "`bungee2.ipynb`, which contains starter code and exercises for this case study. How does the behavior of the system change as we vary the mass of the cord? When the mass of the cord equals the mass of the jumper, what is the net effect on the lowest point in the jump?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "german-penetration",
+   "metadata": {},
+   "source": [
+    "## Orbiting the Sun\n",
+    "\n",
+    "In a previous example, we modeled the interaction between the Earth and the Sun, simulating what would happen if the Earth stopped in its orbit and fell straight into the Sun.\n",
+    "\n",
+    "Now let's extend the model to two dimensions and simulate one revolution of the Earth around the Sun, that is, one year.\n",
+    "\n",
+    "In the repository for this book, you will find a notebook,\n",
+    "`orbit.ipynb`, which contains starter code and exercises for this case study.\n",
+    "\n",
+    "Among other things, you will have a chance to experiment with different algorithms and see what effect they have on the accuracy of the results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "victorian-colonial",
+   "metadata": {},
+   "source": [
+    "## Spider-Man\n",
+    "\n",
+    "In this case study we'll develop a model of Spider-Man swinging from a\n",
+    "springy cable of webbing attached to the top of the Empire State\n",
+    "Building. Initially, Spider-Man is at the top of a nearby building, as\n",
+    "shown in this figure:\n",
+    "\n",
+    "![Diagram of the initial state for the Spider-Man case\n",
+    "study.](https://github.com/AllenDowney/ModSim/raw/main/figs/spiderman.png)\n",
+    "\n",
+    "The origin, `O`, is at the base of the Empire State Building. The vector `H` represents the position where the webbing is attached to the building, relative to `O`. The vector `P` is the position of Spider-Man relative to `O`. And `L` is the vector from the attachment point to Spider-Man.\n",
+    "\n",
+    "By following the arrows from `O`, along `H`, and along `L`, we can see\n",
+    "that\n",
+    "\n",
+    "```\n",
+    "H + L = P\n",
+    "```\n",
+    "\n",
+    "So we can compute `L` like this:\n",
+    "\n",
+    "```\n",
+    "L = P - H\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bizarre-strand",
+   "metadata": {},
+   "source": [
+    "The goals of this case study are:\n",
+    "\n",
+    "1.  Implement a model of this scenario to predict Spider-Man's\n",
+    "    trajectory.\n",
+    "\n",
+    "2.  Choose the right time for Spider-Man to let go of the webbing in\n",
+    "    order to maximize the distance he travels before landing.\n",
+    "\n",
+    "3.  Choose the best angle for Spider-Man to jump off the building, and\n",
+    "    let go of the webbing, to maximize range.\n",
+    "\n",
+    "We'll use the following parameters:\n",
+    "\n",
+    "1.  According to the Spider-Man Wiki (<http://modsimpy.com/spider>), Spider-Man weighs 76 kg.\n",
+    "\n",
+    "2.  Let's assume his terminal velocity is 60 m/s.\n",
+    "\n",
+    "3.  The length of the web is 100 m.\n",
+    "\n",
+    "4.  The initial angle of the web is 45° to the left of straight down.\n",
+    "\n",
+    "5.  The spring constant of the web is 40 N/m when the cord is stretched, and 0 when it's compressed.\n",
+    "\n",
+    "In the repository for this book, you will find a notebook,\n",
+    "`spiderman.ipynb`, which contains starter code. Read through the\n",
+    "notebook and run the code. It uses `minimize`, which is a SciPy function that can search for an optimal set of parameters (as contrasted with `minimize_scalar`, which can only search along a single axis)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "african-relative",
+   "metadata": {},
+   "source": [
+    "## Kittens\n",
+    "\n",
+    "If you have used the Internet, you have probably seen videos of kittens unrolling toilet paper.\n",
+    "And you might have wondered how long it would take a standard kitten to unroll 47 m of paper, the length of a standard roll.\n",
+    "\n",
+    "The interactions of the kitten and the paper rolls are complex.  To keep things simple, let's assume that the kitten pulls down on the free end of the roll with constant force.  And let's neglect the friction between the roll and the axle.\n",
+    "\n",
+    "This diagram shows the paper roll with the force applied by the kitten, $F$, the lever arm of the force around the axis of rotation, $r$, and the resulting torque, $\\tau$.\n",
+    "\n",
+    "![Diagram of a roll of toilet paper, showing a force, lever arm, and the resulting torque.](https://github.com/AllenDowney/ModSim/raw/main/figs/kitten.png)\n",
+    "\n",
+    "Assuming that the force applied by the kitten is 0.002 N, how long would it take to unroll a standard roll of toilet paper?\n",
+    "\n",
+    "In the repository for this book, you will find a notebook,\n",
+    "`kitten.ipynb`, which contains starter code for this case study. Use it to implement this model and check whether the results seem plausible."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "future-burlington",
+   "metadata": {},
+   "source": [
+    "## Simulating a yo-yo\n",
+    "\n",
+    "Suppose you are holding a yo-yo with a length of string wound around its axle, and you drop it while holding the end of the string stationary. As gravity accelerates the yo-yo downward, tension in the string exerts a force upward. Since this force acts on a point offset from the center of mass, it exerts a torque that causes the yo-yo to spin.\n",
+    "\n",
+    "The following diagram shows the forces on the yo-yo and the resulting torque. The outer shaded area shows the body of the yo-yo. The inner shaded area shows the rolled up string, the radius of which changes as the yo-yo unrolls.\n",
+    "\n",
+    "![Diagram of a yo-yo showing forces due to gravity and tension in the\n",
+    "string, the lever arm of tension, and the resulting\n",
+    "torque.](https://github.com/AllenDowney/ModSim/raw/main/figs/yoyo.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "turkish-result",
+   "metadata": {},
+   "source": [
+    "In this system, we can't figure out the linear and angular acceleration independently; we have to solve a system of equations: \n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\sum F &= m a \\\\\n",
+    "\\sum \\tau &= I \\alpha\\end{aligned}$$ \n",
+    "\n",
+    "where the summations indicate that we are adding up forces and torques.\n",
+    "\n",
+    "As in the previous examples, linear and angular velocity are related\n",
+    "because of the way the string unrolls:\n",
+    "\n",
+    "$$\\frac{dy}{dt} = -r \\frac{d \\theta}{dt}$$ \n",
+    "\n",
+    "In this example, the linear and angular accelerations have opposite sign. As the yo-yo rotates counter-clockwise, $\\theta$ increases and $y$, which is the length of the rolled part of the string, decreases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "surrounded-quilt",
+   "metadata": {},
+   "source": [
+    "Taking the derivative of both sides yields a similar relationship\n",
+    "between linear and angular acceleration:\n",
+    "\n",
+    "$$\\frac{d^2 y}{dt^2} = -r \\frac{d^2 \\theta}{dt^2}$$ \n",
+    "\n",
+    "Which we can write more concisely: $$a = -r \\alpha$$ This relationship is not a general law of nature; it is specific to scenarios like this where one object rolls along another without stretching or slipping.\n",
+    "\n",
+    "Because of the way we've set up the problem, $y$ actually has two\n",
+    "meanings: it represents the length of the rolled string and the height\n",
+    "of the yo-yo, which decreases as the yo-yo falls. Similarly, $a$\n",
+    "represents acceleration in the length of the rolled string and the\n",
+    "height of the yo-yo.\n",
+    "\n",
+    "We can compute the acceleration of the yo-yo by adding up the linear\n",
+    "forces: \n",
+    "\n",
+    "$$\\sum F = T - mg = ma$$ \n",
+    "\n",
+    "Where $T$ is positive because the tension force points up, and $mg$ is negative because gravity points down."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "finnish-disaster",
+   "metadata": {},
+   "source": [
+    "Because gravity acts on the center of mass, it creates no torque, so the only torque is due to tension: \n",
+    "\n",
+    "$$\\sum \\tau = T r = I \\alpha$$ \n",
+    "\n",
+    "Positive (upward) tension yields positive (counter-clockwise) angular\n",
+    "acceleration.\n",
+    "\n",
+    "Now we have three equations in three unknowns, $T$, $a$, and $\\alpha$,\n",
+    "with $I$, $m$, $g$, and $r$ as known parameters. We could solve these equations by hand, but we can also get SymPy to do it for us:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "crude-tribune",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sympy import symbols, Eq, solve\n",
+    "\n",
+    "T, a, alpha, I, m, g, r = symbols('T a alpha I m g r')\n",
+    "eq1 = Eq(a, -r * alpha)\n",
+    "eq2 = Eq(T - m*g, m * a)\n",
+    "eq3 = Eq(T * r, I * alpha)\n",
+    "soln = solve([eq1, eq2, eq3], [T, a, alpha])\n",
+    "soln"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "insured-indie",
+   "metadata": {},
+   "source": [
+    "The results are \n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "T      &= m g I / I^*   \\\\\n",
+    "a      &= -m g r^2 / I^* \\\\\n",
+    "\\alpha &= m g r / I^*    \\\\\\end{aligned}$$ \n",
+    "\n",
+    "where $I^*$ is the augmented moment of inertia, $I + m r^2$. \n",
+    "We can use these equations for $a$ and $\\alpha$ to write a slope function and simulate this system.\n",
+    "\n",
+    "In the repository for this book, you will find a notebook, `yoyo.ipynb`, which contains starter code for this case study. Use it to implement and test this model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "accompanied-southwest",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 9bd0df4f685719bb46c381c0546232a6e3d8519d Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sat, 13 Feb 2021 14:30:28 -0500
Subject: [PATCH 033/144] Updating chapters

---
 chapters/chap10.ipynb | 54 +++++++------------------------------------
 chapters/chap11.ipynb |  2 +-
 2 files changed, 9 insertions(+), 47 deletions(-)

diff --git a/chapters/chap10.ipynb b/chapters/chap10.ipynb
index 5ef4f184..3c12f900 100644
--- a/chapters/chap10.ipynb
+++ b/chapters/chap10.ipynb
@@ -280,7 +280,9 @@
     "This figure shows the three scenarios we'll consider:\n",
     "\n",
     "![One queue, one server (left), one queue, two servers (middle), two\n",
-    "queues, two servers (right).](figs/queue.pdf){width=\"4.5in\"}\n",
+    "queues, two servers (right).](https://github.com/AllenDowney/ModSim/raw/main/figs/queue.png)\n",
+    "*One queue, one server (left), one queue, two servers (middle), two\n",
+    "queues, two servers (right).*\n",
     "\n",
     "As we did in the bike share model, we'll assume that a customer is equally likely to arrive during any time step. I'll denote this probability using the Greek letter lambda, $\\lambda$, or the variable name `lam`. The value of $\\lambda$ probably varies from day to day, so we'll have to consider a range of possibilities.\n",
     "\n",
@@ -335,56 +337,16 @@
     "expected height of the tallest tree in a stand of Douglas firs as a\n",
     "function of age, for a stand where the trees are the same age.\n",
     "\n",
-    "Depending on the quality of the site, the trees might grow more quickly or slowing. So each curve is identified by a \"site index\\\" that indicates the quality of the site.\n",
-    "\n",
-    "The following figure shows site curves for three different site\n",
-    "indices. \n",
-    "\n",
-    "![Site index curves for tree\n",
-    "growth.](figs/trees-fig01.pdf){height=\"3in\"}\n",
+    "Depending on the quality of the site, the trees might grow more quickly or slowing. So each curve is identified by a \"site index\" that indicates the quality of the site.\n",
     "\n",
     "The goal of this case study is to explain the shape of these\n",
     "curves, that is, why trees grow the way they do.\n",
-    "\n",
-    "As a starting place, let's assume that the ability of the tree to gain\n",
-    "mass is limited by the area it exposes to sunlight, and that the growth rate (in mass) is proportional to that area. In that case we can write:\n",
-    "\n",
-    "$$m_{n+1} = m_n + \\alpha A$$\n",
-    "\n",
-    "where $m_n$ is the mass of the at time step $n$, $A$ is the area exposed to sunlight, and $\\alpha$ is an unknown growth parameter.\n",
-    "\n",
-    "To get from $m$ to $A$, I'll make the additional assumption that mass is proportional to height raised to an unknown power: $m = \\beta h^D$ where $h$ is height, $\\beta$ is an unknown constant of proportionality, and $D$ is the dimension that relates height and mass. I start by assuming $D=3$, but then revisit that assumption.\n",
-    "\n",
-    "Finally, we'll assume that area is proportional to height squared:\n",
-    "\n",
-    "$$A = \\gamma h^2$$\n",
-    "\n",
-    "I specify height in feet, and choose units for mass and area so that\n",
-    "$\\beta=1$ and $\\gamma=1$. Putting all that together, we can write a\n",
-    "difference equation for height:\n",
-    "\n",
-    "$$h_{n+1}^D = h_n^D + \\alpha h_n^2$$\n",
-    "\n",
-    "With $D=3$, the solution to this equation is close to a straight line,\n",
-    "which is not a bad model for the growth curves. But the model implies\n",
-    "that trees can grow forever, and we know that's not true. As trees get\n",
-    "taller, it gets harder for them to move water and nutrients against the force of gravity, and their growth slows.\n",
-    "\n",
-    "We can model this effect by adding a factor to the model similar to what we saw in the logistic model of population growth. Instead of assuming:\n",
-    "\n",
-    "$$m_{n+1} = m_n + \\alpha A$$\n",
-    "\n",
-    "Let's assume\n",
-    "\n",
-    "$$m_{n+1} = m_n + \\alpha A (1 - h / K)$$\n",
-    "\n",
-    "where $K$ is similar to the carrying capacity of the logistic model. As $h$ approaches $K$, the factor $(1 - h/K)$ goes to 0, causing growth to level off.\n",
+    "The answer I propose involved fractal dimensions, so you might find it interesting.\n",
     "\n",
     "In the repository for this book, you'll find a notebook called\n",
-    "`trees.ipynb` that implements both models and uses them to fit the data.\n",
+    "`trees.ipynb` that incrementally develops a model of tree growth and uses it to fit the data.\n",
     "\n",
-    "There are no exercises in this case study; it is mostly meant as an\n",
-    "example of what you can do with the tools we have so far, and a preview of what we will be able to do with the tools in the next few chapters."
+    "There are no exercises in this case study, but it is an example of what you can do with the tools we have so far and a preview of what you will be able to do with the tools in the next few chapters."
    ]
   },
   {
@@ -412,7 +374,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap11.ipynb b/chapters/chap11.ipynb
index 6cd6a696..32f797bc 100644
--- a/chapters/chap11.ipynb
+++ b/chapters/chap11.ipynb
@@ -210,7 +210,7 @@
     "model.\n",
     "\n",
     "![Stock and flow diagram for an SIR\n",
-    "model.](figs/stock_flow1.pdf){width=\"4in\"}\n",
+    "model.](https://github.com/AllenDowney/ModSim/raw/main/figs/stock_flow1.png)\n",
     "\n",
     "Stocks are represented by rectangles, flows by arrows. The widget in the middle of the arrows represents a valve that controls the rate of flow; the diagram shows the parameters that control the valves."
    ]

From a667229b146753fb9b1380804980f7570f30d662 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sat, 13 Feb 2021 19:36:29 -0500
Subject: [PATCH 034/144] Updating modsim.py

---
 modsim.py | 873 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 873 insertions(+)
 create mode 100644 modsim.py

diff --git a/modsim.py b/modsim.py
new file mode 100644
index 00000000..cd389049
--- /dev/null
+++ b/modsim.py
@@ -0,0 +1,873 @@
+"""
+Code from Modeling and Simulation in Python.
+
+Copyright 2020 Allen Downey
+
+MIT License: https://opensource.org/licenses/MIT
+"""
+
+import logging
+
+logger = logging.getLogger(name="modsim.py")
+
+# make sure we have Python 3.6 or better
+import sys
+
+if sys.version_info < (3, 6):
+    logger.warning("modsim.py depends on Python 3.6 features.")
+
+import inspect
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import scipy
+
+import scipy.optimize as spo
+
+from scipy.interpolate import interp1d
+from scipy.interpolate import InterpolatedUnivariateSpline
+
+from scipy.integrate import solve_ivp
+
+from types import SimpleNamespace
+from copy import copy
+
+import pint
+
+units = pint.UnitRegistry()
+#Quantity = units.Quantity
+
+
+def flip(p=0.5):
+    """Flips a coin with the given probability.
+
+    p: float 0-1
+
+    returns: boolean (True or False)
+    """
+    return np.random.random() < p
+
+
+def cart2pol(x, y, z=None):
+    """Convert Cartesian coordinates to polar.
+
+    x: number or sequence
+    y: number or sequence
+    z: number or sequence (optional)
+
+    returns: theta, rho OR theta, rho, z
+    """
+    x = np.asarray(x)
+    y = np.asarray(y)
+
+    rho = np.hypot(x, y)
+    theta = np.arctan2(y, x)
+
+    if z is None:
+        return theta, rho
+    else:
+        return theta, rho, z
+
+
+def pol2cart(theta, rho, z=None):
+    """Convert polar coordinates to Cartesian.
+
+    theta: number or sequence in radians
+    rho: number or sequence
+    z: number or sequence (optional)
+
+    returns: x, y OR x, y, z
+    """
+    x = rho * np.cos(theta)
+    y = rho * np.sin(theta)
+
+    if z is None:
+        return x, y
+    else:
+        return x, y, z
+
+from numpy import linspace
+
+def linrange(start, stop=None, step=1, **options):
+    """Make an array of equally spaced values.
+
+    start: first value
+    stop: last value (might be approximate)
+    step: difference between elements (should be consistent)
+
+    returns: NumPy array
+    """
+    if stop is None:
+        stop = start
+        start = 0
+    n = int(round((stop-start) / step))
+    return linspace(start, stop, n+1, **options)
+
+
+def root_scalar(func, *args, **kwargs):
+    """Finds the input value that minimizes `min_func`.
+
+    Wrapper for
+    https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root_scalar.html
+
+    func: computes the function to be minimized
+    bracket: sequence of two values, lower and upper bounds of the range to be searched
+    args: any additional positional arguments are passed to func
+    kwargs: any keyword arguments are passed to root_scalar
+
+    returns: RootResults object
+    """
+    bracket = kwargs.get('bracket', None)
+    if bracket is None or len(bracket) != 2:
+        msg = ("To run root_scalar, you have to provide a "
+               "`bracket` keyword argument with a sequence "
+               "of length 2.")
+        raise ValueError(msg)
+
+    try:
+        func(bracket[0], *args)
+    except Exception as e:
+        msg = ("Before running scipy.integrate.root_scalar "
+               "I tried running the function you provided "
+               "with `bracket[0]`, "
+               "and I got the following error:")
+        logger.error(msg)
+        raise (e)
+
+    underride(kwargs, rtol=1e-4)
+
+    res = spo.root_scalar(func, *args, **kwargs)
+
+    if not res.converged:
+        msg = ("scipy.optimize.root_scalar did not converge. "
+               "The message it returned is:\n" + res.flag)
+        raise ValueError(msg)
+
+    return res
+
+
+def minimize_scalar(func, *args, **kwargs):
+    """Finds the input value that minimizes `func`.
+
+    Wrapper for
+    https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
+
+    func: computes the function to be minimized
+    args: any additional positional arguments are passed to func
+    kwargs: any keyword arguments are passed to minimize_scalar
+
+    returns: OptimizeResult object
+    """
+    bounds = kwargs.get('bounds', None)
+
+    if bounds is None or len(bounds) != 2:
+        msg = ("To run maximize_scalar or minimize_scalar, "
+               "you have to provide a `bounds` "
+               "keyword argument with a sequence "
+               "of length 2.")
+        raise ValueError(msg)
+
+    try:
+        func(bounds[0], *args)
+    except Exception as e:
+        msg = ("Before running scipy.integrate.minimize_scalar, "
+               "I tried running the function you provided "
+               "with the lower bound, "
+               "and I got the following error:")
+        logger.error(msg)
+        raise (e)
+
+    underride(kwargs, method='bounded')
+
+    res = spo.minimize_scalar(func, args=args, **kwargs)
+
+    if not res.success:
+        msg = ("minimize_scalar did not succeed."
+               "The message it returned is: \n" +
+               res.message)
+        raise Exception(msg)
+
+    return res
+
+
+def maximize_scalar(max_func, *args, **kwargs):
+    """Finds the input value that maximizes `max_func`.
+
+    Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
+
+    min_func: computes the function to be maximized
+    args: any additional positional arguments are passed to max_func
+    options: any keyword arguments are passed as options to minimize_scalar
+
+    returns: ModSimSeries object
+    """
+    def min_func(*args):
+        return -max_func(*args)
+
+    res = minimize_scalar(min_func, *args, **kwargs)
+
+    # we have to negate the function value before returning res
+    res.fun = -res.fun
+    return res
+
+
+def run_solve_ivp(system, slope_func, **options):
+    """Computes a numerical solution to a differential equation.
+
+    `system` must contain `init` with initial conditions,
+    `t_end` with the end time.  Optionally, it can contain
+    `t_0` with the start time.
+
+    It should contain any other parameters required by the
+    slope function.
+
+    `options` can be any legal options of `scipy.integrate.solve_ivp`
+
+    system: System object
+    slope_func: function that computes slopes
+
+    returns: TimeFrame
+    """
+    system = remove_units(system)
+
+    # make sure `system` contains `init`
+    if not hasattr(system, "init"):
+        msg = """It looks like `system` does not contain `init`
+                 as a system variable.  `init` should be a State
+                 object that specifies the initial condition:"""
+        raise ValueError(msg)
+
+    # make sure `system` contains `t_end`
+    if not hasattr(system, "t_end"):
+        msg = """It looks like `system` does not contain `t_end`
+                 as a system variable.  `t_end` should be the
+                 final time:"""
+        raise ValueError(msg)
+
+    # the default value for t_0 is 0
+    t_0 = getattr(system, "t_0", 0)
+
+    # try running the slope function with the initial conditions
+    try:
+        slope_func(t_0, system.init, system)
+    except Exception as e:
+        msg = """Before running scipy.integrate.solve_ivp, I tried
+                 running the slope function you provided with the
+                 initial conditions in `system` and `t=t_0` and I got
+                 the following error:"""
+        logger.error(msg)
+        raise (e)
+
+    # get the list of event functions
+    events = options.get('events', [])
+
+    # if there's only one event function, put it in a list
+    try:
+        iter(events)
+    except TypeError:
+        events = [events]
+
+    for event_func in events:
+        # make events terminal unless otherwise specified
+        if not hasattr(event_func, 'terminal'):
+            event_func.terminal = True
+
+        # test the event function with the initial conditions
+        try:
+            event_func(t_0, system.init, system)
+        except Exception as e:
+            msg = """Before running scipy.integrate.solve_ivp, I tried
+                     running the event function you provided with the
+                     initial conditions in `system` and `t=t_0` and I got
+                     the following error:"""
+            logger.error(msg)
+            raise (e)
+
+    # get dense output unless otherwise specified
+    if not 't_eval' in options:
+        underride(options, dense_output=True)
+
+    # run the solver
+    bunch = solve_ivp(slope_func, [t_0, system.t_end], system.init,
+                      args=[system], **options)
+
+    # separate the results from the details
+    y = bunch.pop("y")
+    t = bunch.pop("t")
+
+    # get the column names from `init`, if possible
+    if hasattr(system.init, 'index'):
+        columns = system.init.index
+    else:
+        columns = range(len(system.init))
+
+    # evaluate the results at equally-spaced points
+    if options.get('dense_output', False):
+        try:
+            num = system.num
+        except AttributeError:
+            num = 101
+        t_final = t[-1]
+        t_array = linspace(t_0, t_final, num)
+        y_array = bunch.sol(t_array)
+
+        # pack the results into a TimeFrame
+        results = TimeFrame(y_array.T, index=t_array,
+                        columns=columns)
+    else:
+        results = TimeFrame(y.T, index=t,
+                        columns=columns)
+
+    return results, bunch
+
+
+def leastsq(error_func, x0, *args, **options):
+    """Find the parameters that yield the best fit for the data.
+
+    `x0` can be a sequence, array, Series, or Params
+
+    Positional arguments are passed along to `error_func`.
+
+    Keyword arguments are passed to `scipy.optimize.leastsq`
+
+    error_func: function that computes a sequence of errors
+    x0: initial guess for the best parameters
+    args: passed to error_func
+    options: passed to leastsq
+
+    :returns: Params object with best_params and ModSimSeries with details
+    """
+    # override `full_output` so we get a message if something goes wrong
+    options["full_output"] = True
+
+    # run leastsq
+    t = scipy.optimize.leastsq(error_func, x0=x0, args=args, **options)
+    best_params, cov_x, infodict, mesg, ier = t
+
+    # pack the results into a ModSimSeries object
+    details = SimpleNamespace(cov_x=cov_x,
+                              mesg=mesg,
+                              ier=ier,
+                              **infodict)
+    details.success = details.ier in [1,2,3,4]
+
+    # if we got a Params object, we should return a Params object
+    if isinstance(x0, Params):
+        best_params = Params(pd.Series(best_params, x0.index))
+
+    # return the best parameters and details
+    return best_params, details
+
+
+def crossings(series, value):
+    """Find the labels where the series passes through value.
+
+    The labels in series must be increasing numerical values.
+
+    series: Series
+    value: number
+
+    returns: sequence of labels
+    """
+    values = series.values - value
+    interp = InterpolatedUnivariateSpline(series.index, values)
+    return interp.roots()
+
+
+def has_nan(a):
+    """Checks whether the an array contains any NaNs.
+
+    :param a: NumPy array or Pandas Series
+    :return: boolean
+    """
+    return np.any(np.isnan(a))
+
+
+def is_strictly_increasing(a):
+    """Checks whether the elements of an array are strictly increasing.
+
+    :param a: NumPy array or Pandas Series
+    :return: boolean
+    """
+    return np.all(np.diff(a) > 0)
+
+
+def interpolate(series, **options):
+    """Creates an interpolation function.
+
+    series: Series object
+    options: any legal options to scipy.interpolate.interp1d
+
+    returns: function that maps from the index to the values
+    """
+    if has_nan(series.index):
+        msg = """The Series you passed to interpolate contains
+                 NaN values in the index, which would result in
+                 undefined behavior.  So I'm putting a stop to that."""
+        raise ValueError(msg)
+
+    if not is_strictly_increasing(series.index):
+        msg = """The Series you passed to interpolate has an index
+                 that is not strictly increasing, which would result in
+                 undefined behavior.  So I'm putting a stop to that."""
+        raise ValueError(msg)
+
+    # make the interpolate function extrapolate past the ends of
+    # the range, unless `options` already specifies a value for `fill_value`
+    underride(options, fill_value="extrapolate")
+
+    # call interp1d, which returns a new function object
+    x = series.index
+    y = series.values
+    interp_func = interp1d(x, y, **options)
+    return interp_func
+
+
+def interpolate_inverse(series, **options):
+    """Interpolate the inverse function of a Series.
+
+    series: Series object, represents a mapping from `a` to `b`
+    options: any legal options to scipy.interpolate.interp1d
+
+    returns: interpolation object, can be used as a function
+             from `b` to `a`
+    """
+    inverse = pd.Series(series.index, index=series.values)
+    interp_func = interpolate(inverse, **options)
+    return interp_func
+
+
+def gradient(series, **options):
+    """Computes the numerical derivative of a series.
+
+    If the elements of series have units, they are dropped.
+
+    series: Series object
+    options: any legal options to np.gradient
+
+    returns: Series, same subclass as series
+    """
+    x = series.index
+    y = series.values
+
+    a = np.gradient(y, x, **options)
+    return series.__class__(a, series.index)
+
+
+def source_code(obj):
+    """Prints the source code for a given object.
+
+    obj: function or method object
+    """
+    print(inspect.getsource(obj))
+
+
+def underride(d, **options):
+    """Add key-value pairs to d only if key is not in d.
+
+    If d is None, create a new dictionary.
+
+    d: dictionary
+    options: keyword args to add to d
+    """
+    if d is None:
+        d = {}
+
+    for key, val in options.items():
+        d.setdefault(key, val)
+
+    return d
+
+
+def contour(df, **options):
+    """Makes a contour plot from a DataFrame.
+
+    Wrapper for plt.contour
+    https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.contour.html
+
+    Note: columns and index must be numerical
+
+    df: DataFrame
+    options: passed to plt.contour
+    """
+    fontsize = options.pop("fontsize", 12)
+    underride(options, cmap="viridis")
+    x = df.columns
+    y = df.index
+    X, Y = np.meshgrid(x, y)
+    cs = plt.contour(X, Y, df, **options)
+    plt.clabel(cs, inline=1, fontsize=fontsize)
+
+
+def savefig(filename, **options):
+    """Save the current figure.
+
+    Keyword arguments are passed along to plt.savefig
+
+    https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html
+
+    filename: string
+    """
+    print("Saving figure to file", filename)
+    plt.savefig(filename, **options)
+
+
+def decorate(**options):
+    """Decorate the current axes.
+
+    Call decorate with keyword arguments like
+    decorate(title='Title',
+             xlabel='x',
+             ylabel='y')
+
+    The keyword arguments can be any of the axis properties
+    https://matplotlib.org/api/axes_api.html
+    """
+    ax = plt.gca()
+    ax.set(**options)
+
+    handles, labels = ax.get_legend_handles_labels()
+    if handles:
+        ax.legend(handles, labels)
+
+    plt.tight_layout()
+
+
+def remove_from_legend(bad_labels):
+    """Removes some labels from the legend.
+
+    bad_labels: sequence of strings
+    """
+    ax = plt.gca()
+    handles, labels = ax.get_legend_handles_labels()
+    handle_list, label_list = [], []
+    for handle, label in zip(handles, labels):
+        if label not in bad_labels:
+            handle_list.append(handle)
+            label_list.append(label)
+    ax.legend(handle_list, label_list)
+
+
+class SettableNamespace(SimpleNamespace):
+    """Contains a collection of parameters.
+
+    Used to make a System object.
+
+    Takes keyword arguments and stores them as attributes.
+    """
+    def __init__(self, namespace=None, **kwargs):
+        super().__init__()
+        if namespace:
+            self.__dict__.update(namespace.__dict__)
+        self.__dict__.update(kwargs)
+
+    def get(self, name, default=None):
+        """Look up a variable.
+
+        name: string varname
+        default: value returned if `name` is not present
+        """
+        try:
+            return self.__getattribute__(name, default)
+        except AttributeError:
+            return default
+
+    def set(self, **variables):
+        """Make a copy and update the given variables.
+
+        returns: Params
+        """
+        new = copy(self)
+        new.__dict__.update(variables)
+        return new
+
+
+def magnitude(x):
+    """Returns the magnitude of a Quantity or number.
+
+    x: Quantity or number
+
+    returns: number
+    """
+    return x.magnitude if hasattr(x, 'magnitude') else x
+
+
+def remove_units(namespace):
+    """Removes units from the values in a Namespace.
+
+    Only removes units from top-level values;
+    does not traverse nested values.
+
+    returns: new Namespace object
+    """
+    res = copy(namespace)
+    for label, value in res.__dict__.items():
+        if isinstance(value, pd.Series):
+            value = remove_units_series(value)
+        res.__dict__[label] = magnitude(value)
+    return res
+
+
+def remove_units_series(series):
+    """Removes units from the values in a Series.
+
+    Only removes units from top-level values;
+    does not traverse nested values.
+
+    returns: new Series object
+    """
+    res = copy(series)
+    for label, value in res.iteritems():
+        res[label] = magnitude(value)
+    return res
+
+
+class System(SettableNamespace):
+    """Contains system parameters and their values.
+
+    Takes keyword arguments and stores them as attributes.
+    """
+    pass
+
+
+class Params(SettableNamespace):
+    """Contains system parameters and their values.
+
+    Takes keyword arguments and stores them as attributes.
+    """
+    pass
+
+
+def State(**variables):
+    """Contains the values of state variables."""
+    return pd.Series(variables, name='state')
+
+
+def make_series(x, y, **options):
+    """Make a Pandas Series.
+
+    x: sequence used as the index
+    y: sequence used as the values
+
+    returns: Pandas Series
+    """
+    if isinstance(y, pd.Series):
+        y = y.values
+    return pd.Series(y, index=x, **options)
+
+
+def TimeSeries(*args, **kwargs):
+    """
+    """
+    if args or kwargs:
+        series = pd.Series(*args, **kwargs)
+    else:
+        series = pd.Series([], dtype=np.float64)
+
+    series.index.name = 'Time'
+    if 'name' not in kwargs:
+        series.name = 'Quantity'
+    return series
+
+
+def SweepSeries(*args, **kwargs):
+    """
+    """
+    if args or kwargs:
+        series = pd.Series(*args, **kwargs)
+    else:
+        series = pd.Series([], dtype=np.float64)
+
+    series.index.name = 'Parameter'
+    if 'name' not in kwargs:
+        series.name = 'Metric'
+    return series
+
+
+def show(obj):
+    """Display a Series or Namespace as a DataFrame."""
+    if isinstance(obj, pd.Series):
+        return pd.DataFrame(obj)
+    elif isinstance(obj, SimpleNamespace):
+        return pd.DataFrame(pd.Series(obj.__dict__),
+                            columns=['value'])
+    else:
+        return obj
+
+
+def TimeFrame(*args, **kwargs):
+    """DataFrame that maps from time to State.
+    """
+    underride(kwargs, dtype=float)
+    return pd.DataFrame(*args, **kwargs)
+
+
+def SweepFrame(*args, **kwargs):
+    """DataFrame that maps from parameter value to SweepSeries.
+    """
+    underride(kwargs, dtype=float)
+    return pd.DataFrame(*args, **kwargs)
+
+
+def Vector(x, y, z=None, **options):
+    """
+    """
+    if z is None:
+        return pd.Series(dict(x=x, y=y), **options)
+    else:
+        return pd.Series(dict(x=x, y=y, z=z), **options)
+
+
+## Vector functions (should work with any sequence)
+
+def vector_mag(v):
+    """Vector magnitude."""
+    return np.sqrt(np.dot(v, v))
+
+
+def vector_mag2(v):
+    """Vector magnitude squared."""
+    return np.dot(v, v)
+
+
+def vector_angle(v):
+    """Angle between v and the positive x axis.
+
+    Only works with 2-D vectors.
+
+    returns: angle in radians
+    """
+    assert len(v) == 2
+    x, y = v
+    return np.arctan2(y, x)
+
+
+def vector_polar(v):
+    """Vector magnitude and angle.
+
+    returns: (number, angle in radians)
+    """
+    return vector_mag(v), vector_angle(v)
+
+
+def vector_hat(v):
+    """Unit vector in the direction of v.
+
+    returns: Vector or array
+    """
+    # check if the magnitude of the Quantity is 0
+    mag = vector_mag(v)
+    if mag == 0:
+        return v
+    else:
+        return v / mag
+
+
+def vector_perp(v):
+    """Perpendicular Vector (rotated left).
+
+    Only works with 2-D Vectors.
+
+    returns: Vector
+    """
+    assert len(v) == 2
+    x, y = v
+    return Vector(-y, x)
+
+
+def vector_dot(v, w):
+    """Dot product of v and w.
+
+    returns: number or Quantity
+    """
+    return np.dot(v, w)
+
+
+def vector_cross(v, w):
+    """Cross product of v and w.
+
+    returns: number or Quantity for 2-D, Vector for 3-D
+    """
+    res = np.cross(v, w)
+
+    if len(v) == 3:
+        return Vector(*res)
+    else:
+        return res
+
+
+def vector_proj(v, w):
+    """Projection of v onto w.
+
+    returns: array or Vector with direction of w and units of v.
+    """
+    w_hat = vector_hat(w)
+    return vector_dot(v, w_hat) * w_hat
+
+
+def scalar_proj(v, w):
+    """Returns the scalar projection of v onto w.
+
+    Which is the magnitude of the projection of v onto w.
+
+    returns: scalar with units of v.
+    """
+    return vector_dot(v, vector_hat(w))
+
+
+def vector_dist(v, w):
+    """Euclidean distance from v to w, with units."""
+    if isinstance(v, list):
+        v = np.asarray(v)
+    return vector_mag(v - w)
+
+
+def vector_diff_angle(v, w):
+    """Angular difference between two vectors, in radians.
+    """
+    if len(v) == 2:
+        return vector_angle(v) - vector_angle(w)
+    else:
+        # TODO: see http://www.euclideanspace.com/maths/algebra/
+        # vectors/angleBetween/
+        raise NotImplementedError()
+
+
+def plot_segment(A, B, **options):
+    """Plots a line segment between two Vectors.
+
+    For 3-D vectors, the z axis is ignored.
+
+    Additional options are passed along to plot().
+
+    A: Vector
+    B: Vector
+    """
+    xs = A.x, B.x
+    ys = A.y, B.y
+    plt.plot(xs, ys, **options)
+
+
+from time import sleep
+from IPython.display import clear_output
+
+def animate(results, draw_func, *args, interval=None):
+    """Animate results from a simulation.
+
+    results: TimeFrame
+    draw_func: function that draws state
+    interval: time between frames in seconds
+    """
+    plt.figure()
+    try:
+        for t, state in results.iterrows():
+            draw_func(t, state, *args)
+            plt.show()
+            if interval:
+                sleep(interval)
+            clear_output(wait=True)
+        draw_func(t, state, *args)
+        plt.show()
+    except KeyboardInterrupt:
+        pass

From 6b397714aed91949d86753b7fd7b0e961262f2e5 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sat, 13 Feb 2021 19:37:26 -0500
Subject: [PATCH 035/144] Updating chapters

---
 chapters/chap01.ipynb | 80 ++++++++++++++-----------------------------
 chapters/chap02.ipynb | 31 +++++++++--------
 chapters/chap03.ipynb | 31 +++++++++--------
 chapters/chap04.ipynb | 31 +++++++++--------
 chapters/chap05.ipynb | 31 +++++++++--------
 chapters/chap06.ipynb | 31 +++++++++--------
 chapters/chap07.ipynb | 31 +++++++++--------
 chapters/chap08.ipynb | 31 +++++++++--------
 chapters/chap09.ipynb | 31 +++++++++--------
 chapters/chap10.ipynb | 33 ++++++++++--------
 chapters/chap11.ipynb | 31 +++++++++--------
 chapters/chap12.ipynb | 31 +++++++++--------
 chapters/chap13.ipynb | 31 +++++++++--------
 chapters/chap14.ipynb | 31 +++++++++--------
 chapters/chap15.ipynb | 31 +++++++++--------
 chapters/chap16.ipynb | 31 +++++++++--------
 chapters/chap17.ipynb | 31 +++++++++--------
 chapters/chap18.ipynb | 31 +++++++++--------
 chapters/chap19.ipynb | 37 +++++++++++---------
 chapters/chap20.ipynb | 31 +++++++++--------
 chapters/chap21.ipynb | 31 +++++++++--------
 chapters/chap22.ipynb | 31 +++++++++--------
 chapters/chap23.ipynb | 31 +++++++++--------
 chapters/chap24.ipynb | 31 +++++++++--------
 chapters/chap25.ipynb | 31 +++++++++--------
 chapters/chap26.ipynb | 37 +++++++++++---------
 26 files changed, 460 insertions(+), 409 deletions(-)

diff --git a/chapters/chap01.ipynb b/chapters/chap01.ipynb
index d2b12fcd..4d031bd7 100644
--- a/chapters/chap01.ipynb
+++ b/chapters/chap01.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "biblical-enhancement",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
@@ -77,7 +80,9 @@
   {
    "cell_type": "markdown",
    "id": "confirmed-budapest",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "## Jupyter\n",
     "\n",
@@ -89,51 +94,17 @@
   {
    "cell_type": "markdown",
    "id": "danish-scope",
-   "metadata": {},
-   "source": [
-    "To run a cell, hold down SHIFT and press ENTER.  \n",
-    "\n",
-    "* If you run a text cell, Jupyter formats the text and displays the result.\n",
-    "\n",
-    "* If you run a code cell, Jupyter runs the Python code in the cell and displays the result, if any.\n",
-    "\n",
-    "If you run the following cell, it checks whether the libraries you need are installed.  If so, the cell produces no output.  If not, you'll see updates from the installer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "asian-america",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
    "source": [
-    "# check if the libraries we need are installed\n",
+    "To run a cell, hold down SHIFT and press ENTER.  \n",
     "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    \n",
-    "# download modsim.py if necessary\n",
+    "* If you run a text cell, Jupyter formats the text and displays the result.\n",
     "\n",
-    "from os.path import exists\n",
+    "* If you run a code cell, Jupyter runs the Python code in the cell and displays the result, if any.\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "copyrighted-desperate",
-   "metadata": {},
-   "source": [
-    "If the previous cell runs without producing any error messages, you are all set.\n"
+    "The cells at the beginning of this notebook check whether the libraries we'll need are installed.  If so, the cells produce no output.  If not, you'll see updates from the installer."
    ]
   },
   {
@@ -141,7 +112,7 @@
    "id": "hourly-financing",
    "metadata": {},
    "source": [
-    "## Modeling\n",
+    "## Modeling and Simulation\n",
     "\n",
     "This book is about modeling and simulation of physical systems. The\n",
     "following diagram shows what I mean by \"modeling\":\n",
@@ -1231,6 +1202,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
diff --git a/chapters/chap02.ipynb b/chapters/chap02.ipynb
index f9bdf584..dc5e9f80 100644
--- a/chapters/chap02.ipynb
+++ b/chapters/chap02.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cathedral-glasgow",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap03.ipynb b/chapters/chap03.ipynb
index f905d62f..794b0d43 100644
--- a/chapters/chap03.ipynb
+++ b/chapters/chap03.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "welcome-gentleman",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap04.ipynb b/chapters/chap04.ipynb
index 03a177b0..da973561 100644
--- a/chapters/chap04.ipynb
+++ b/chapters/chap04.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hawaiian-detective",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap05.ipynb b/chapters/chap05.ipynb
index 99dd47af..b3266246 100644
--- a/chapters/chap05.ipynb
+++ b/chapters/chap05.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "inclusive-theater",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap06.ipynb b/chapters/chap06.ipynb
index d64d790c..0c18a0ac 100644
--- a/chapters/chap06.ipynb
+++ b/chapters/chap06.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "parental-symposium",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap07.ipynb b/chapters/chap07.ipynb
index 7bd8ec6e..40678cf7 100644
--- a/chapters/chap07.ipynb
+++ b/chapters/chap07.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "disciplinary-occasion",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap08.ipynb b/chapters/chap08.ipynb
index 7d794270..2aaa36bb 100644
--- a/chapters/chap08.ipynb
+++ b/chapters/chap08.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "compound-announcement",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap09.ipynb b/chapters/chap09.ipynb
index 9aa9f8e7..8e3c66ac 100644
--- a/chapters/chap09.ipynb
+++ b/chapters/chap09.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "gentle-purchase",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap10.ipynb b/chapters/chap10.ipynb
index 3c12f900..aa249823 100644
--- a/chapters/chap10.ipynb
+++ b/chapters/chap10.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "confused-bargain",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
-    "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "from os.path import basename, exists\n",
+    "\n",
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap11.ipynb b/chapters/chap11.ipynb
index 32f797bc..c1c16741 100644
--- a/chapters/chap11.ipynb
+++ b/chapters/chap11.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "detected-creation",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap12.ipynb b/chapters/chap12.ipynb
index a693d9d7..752291a4 100644
--- a/chapters/chap12.ipynb
+++ b/chapters/chap12.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "excellent-orchestra",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap13.ipynb b/chapters/chap13.ipynb
index f2ef27f3..07f38c02 100644
--- a/chapters/chap13.ipynb
+++ b/chapters/chap13.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ceramic-rendering",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap14.ipynb b/chapters/chap14.ipynb
index 5a8f9484..139bbcc6 100644
--- a/chapters/chap14.ipynb
+++ b/chapters/chap14.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "impressed-headline",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap15.ipynb b/chapters/chap15.ipynb
index 29ba9961..a13abf5a 100644
--- a/chapters/chap15.ipynb
+++ b/chapters/chap15.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "american-article",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap16.ipynb b/chapters/chap16.ipynb
index 1105131b..52b085f7 100644
--- a/chapters/chap16.ipynb
+++ b/chapters/chap16.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "senior-paintball",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap17.ipynb b/chapters/chap17.ipynb
index b4521f95..7db3f45e 100644
--- a/chapters/chap17.ipynb
+++ b/chapters/chap17.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "restricted-thumb",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap18.ipynb b/chapters/chap18.ipynb
index 0e0d245d..4fc84f93 100644
--- a/chapters/chap18.ipynb
+++ b/chapters/chap18.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "tribal-blame",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap19.ipynb b/chapters/chap19.ipynb
index f2f3c588..7b2c625e 100644
--- a/chapters/chap19.ipynb
+++ b/chapters/chap19.ipynb
@@ -10,8 +10,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "passing-solid",
-   "metadata": {},
+   "id": "imported-table",
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "*Modeling and Simulation in Python*\n",
     "\n",
@@ -22,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -39,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -48,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
-    "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "from os.path import basename, exists\n",
+    "\n",
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap20.ipynb b/chapters/chap20.ipynb
index fb46d858..e22a5571 100644
--- a/chapters/chap20.ipynb
+++ b/chapters/chap20.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "furnished-portsmouth",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap21.ipynb b/chapters/chap21.ipynb
index 3cd917bd..b827e286 100644
--- a/chapters/chap21.ipynb
+++ b/chapters/chap21.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "unauthorized-constitutional",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
index 5dae8f6f..031a2fdd 100644
--- a/chapters/chap22.ipynb
+++ b/chapters/chap22.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "connected-covering",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap23.ipynb b/chapters/chap23.ipynb
index b5333cf2..6763b952 100644
--- a/chapters/chap23.ipynb
+++ b/chapters/chap23.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "competitive-backing",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap24.ipynb b/chapters/chap24.ipynb
index b6fa9422..7334a31b 100644
--- a/chapters/chap24.ipynb
+++ b/chapters/chap24.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "suspended-puppy",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap25.ipynb b/chapters/chap25.ipynb
index eaaa432f..ed62cb68 100644
--- a/chapters/chap25.ipynb
+++ b/chapters/chap25.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "complete-innocent",
+   "id": "imported-table",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -50,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },
diff --git a/chapters/chap26.ipynb b/chapters/chap26.ipynb
index 6abd4e82..08252908 100644
--- a/chapters/chap26.ipynb
+++ b/chapters/chap26.ipynb
@@ -10,8 +10,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "comic-convert",
-   "metadata": {},
+   "id": "imported-table",
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "*Modeling and Simulation in Python*\n",
     "\n",
@@ -22,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "broken-procedure",
+   "execution_count": null,
+   "id": "electoral-turkey",
    "metadata": {
     "tags": []
    },
@@ -39,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "massive-thong",
+   "execution_count": 1,
+   "id": "meaning-public",
    "metadata": {
     "tags": []
    },
@@ -48,20 +50,23 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
-    "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "from os.path import basename, exists\n",
+    "\n",
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "experienced-junction",
+   "execution_count": null,
+   "id": "alike-thompson",
    "metadata": {
     "tags": []
    },

From 9e176ff314eda083c8be1d5634e4c01c239d5000 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sat, 13 Feb 2021 19:51:01 -0500
Subject: [PATCH 036/144] Updating chapters

---
 chapters/chap05.ipynb | 113 +++++++++++++++++-------------------------
 chapters/chap10.ipynb |  95 +++++++++++++++++++++--------------
 2 files changed, 104 insertions(+), 104 deletions(-)

diff --git a/chapters/chap05.ipynb b/chapters/chap05.ipynb
index b3266246..f1facb0b 100644
--- a/chapters/chap05.ipynb
+++ b/chapters/chap05.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "meaning-public",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "alike-thompson",
    "metadata": {
     "tags": []
@@ -77,25 +77,14 @@
     "from modsim import *"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "therapeutic-genealogy",
-   "metadata": {},
-   "source": [
-    "In this chapter..."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "informal-contractor",
    "metadata": {},
    "source": [
-    "# World population\n",
-    "\n",
     "In 1968 Paul Erlich published *The Population Bomb*, in which he\n",
     "predicted that world population would grow quickly during the 1970s,\n",
-    "that agricultural production could not keep up, and that mass starvation\n",
-    "in the next two decades was inevitable (see\n",
+    "that agricultural production could not keep up, and that mass starvation in the next two decades was inevitable (see\n",
     "<https://modsimpy.com/popbomb>). As someone who grew up during those\n",
     "decades, I am happy to report that those predictions were wrong.\n",
     "\n",
@@ -103,9 +92,7 @@
     "open question how many people the earth can sustain while maintaining\n",
     "and improving our quality of life.\n",
     "\n",
-    "In this chapter and the next, we use tools from the previous chapters to\n",
-    "explain world population growth since 1950 and generate predictions for\n",
-    "the next 50-100 years.\n",
+    "In this chapter and the next, we use tools from the previous chapters to model world population growth since 1950 and generate predictions for the next 50-100 years.\n",
     "\n",
     "For background on world population growth, watch this video from the\n",
     "American Museum of Natural History <https://modsimpy.com/human>."
@@ -116,7 +103,7 @@
    "id": "dying-browse",
    "metadata": {},
    "source": [
-    "## World Population Data\n",
+    "## World Population Growth\n",
     "\n",
     "The Wikipedia article on world population contains tables with estimates of world population from prehistory to the present, and projections for the future (<https://modsimpy.com/worldpop>)."
    ]
@@ -131,17 +118,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "convenient-stroke",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates.html'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/master/data/World_population_estimates.html')"
    ]
   },
   {
@@ -156,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "israeli-finding",
    "metadata": {},
    "outputs": [],
@@ -174,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "instant-beverage",
    "metadata": {},
    "outputs": [],
@@ -222,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "driving-wrapping",
    "metadata": {},
    "outputs": [],
@@ -243,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "running-alcohol",
    "metadata": {},
    "outputs": [],
@@ -274,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "engaging-regular",
    "metadata": {},
    "outputs": [],
@@ -297,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "amended-negative",
    "metadata": {},
    "outputs": [],
@@ -321,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "graduate-specialist",
    "metadata": {},
    "outputs": [],
@@ -344,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "linear-admission",
    "metadata": {},
    "outputs": [],
@@ -365,13 +348,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "id": "ordered-garage",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "def plot_estimates():\n",
     "    census.plot(style=':', label='US Census')\n",
     "    un.plot(style='--', label='UN DESA')\n",
@@ -392,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "id": "periodic-weekend",
    "metadata": {},
    "outputs": [],
@@ -425,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "id": "secondary-player",
    "metadata": {},
    "outputs": [],
@@ -435,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "id": "caring-garlic",
    "metadata": {},
    "outputs": [],
@@ -457,7 +438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "id": "corresponding-emerald",
    "metadata": {},
    "outputs": [],
@@ -478,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "id": "headed-witch",
    "metadata": {
     "scrolled": true
@@ -505,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "id": "advisory-complex",
    "metadata": {
     "scrolled": true
@@ -528,7 +509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "id": "frank-owner",
    "metadata": {},
    "outputs": [],
@@ -572,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "id": "undefined-sauce",
    "metadata": {},
    "outputs": [],
@@ -590,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "id": "postal-debate",
    "metadata": {},
    "outputs": [],
@@ -616,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "id": "functional-trick",
    "metadata": {},
    "outputs": [],
@@ -636,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "id": "acoustic-south",
    "metadata": {},
    "outputs": [],
@@ -657,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "id": "planned-remains",
    "metadata": {},
    "outputs": [],
@@ -676,7 +657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "id": "quarterly-aggregate",
    "metadata": {},
    "outputs": [],
@@ -695,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "id": "prescription-optics",
    "metadata": {},
    "outputs": [],
@@ -714,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "id": "convertible-patch",
    "metadata": {},
    "outputs": [],
@@ -746,14 +727,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 31,
    "id": "duplicate-leave",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
-    "results = TimeSeries(time='Year', quantity='Population')"
+    "results = TimeSeries()"
    ]
   },
   {
@@ -769,7 +748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 32,
    "id": "convenient-thong",
    "metadata": {},
    "outputs": [],
@@ -787,13 +766,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 33,
    "id": "israeli-surveillance",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "show(results)"
    ]
   },
@@ -807,7 +784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 34,
    "id": "attended-morris",
    "metadata": {},
    "outputs": [],
@@ -832,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 35,
    "id": "wrong-brooks",
    "metadata": {},
    "outputs": [],
@@ -892,7 +869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 36,
    "id": "terminal-reynolds",
    "metadata": {},
    "outputs": [],
@@ -916,7 +893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 37,
    "id": "organizational-memphis",
    "metadata": {},
    "outputs": [],
@@ -942,7 +919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 38,
    "id": "rising-anger",
    "metadata": {},
    "outputs": [],
@@ -952,7 +929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 41,
    "id": "false-handbook",
    "metadata": {},
    "outputs": [],
@@ -962,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 42,
    "id": "political-loading",
    "metadata": {},
    "outputs": [],
@@ -995,7 +972,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap10.ipynb b/chapters/chap10.ipynb
index aa249823..b642f49f 100644
--- a/chapters/chap10.ipynb
+++ b/chapters/chap10.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "id": "meaning-public",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "alike-thompson",
    "metadata": {
     "tags": []
@@ -82,9 +82,7 @@
    "id": "thirty-medication",
    "metadata": {},
    "source": [
-    "# Case studies\n",
-    "\n",
-    "This chapter presents case studies where you can practice the tools we have learned so far."
+    "This chapter presents case studies where you can apply the tools we have learned so far to problems involving the growth of human and salmon populations, queueing systems, and a model of tree growth. "
    ]
   },
   {
@@ -94,29 +92,29 @@
    "source": [
     "## Prehistoric World Population\n",
     "\n",
-    "On the Wikipedia page about world population estimates, the first table contains estimates for prehistoric populations.  The following cells process this table and plot some of the results."
+    "On the Wikipedia page about world population estimates, the second table contains estimates for prehistoric populations."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "id": "usual-penguin",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates.html'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/master/data/World_population_estimates.html')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "id": "thick-lincoln",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from pandas import read_html\n",
@@ -129,16 +127,20 @@
   {
    "cell_type": "markdown",
    "id": "disciplinary-chosen",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "Select `tables[1]`, which is the second table on the page."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "id": "conscious-orange",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "table1 = tables[1]\n",
@@ -148,7 +150,9 @@
   {
    "cell_type": "markdown",
    "id": "hungry-monster",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "Not all researchers provide estimates for the same dates.\n",
     "\n",
@@ -157,9 +161,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "id": "thick-blanket",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "table1.columns = ['PRB', 'UN', 'Maddison', 'HYDE', 'Tanton', \n",
@@ -169,16 +175,20 @@
   {
    "cell_type": "markdown",
    "id": "substantial-implement",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "Some of the estimates are in a form Pandas doesn't recognize as numbers, but we can coerce them to be numeric."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "id": "ranking-prescription",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "for col in table1.columns:\n",
@@ -188,17 +198,20 @@
   {
    "cell_type": "markdown",
    "id": "amazing-difference",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "Here are the results.  Notice that we are working in millions now, not billions."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "id": "metallic-offense",
    "metadata": {
-    "scrolled": false
+    "scrolled": false,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -212,14 +225,24 @@
   {
    "cell_type": "markdown",
    "id": "human-conservation",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "We can use `xlim` to zoom in on everything after Year 0."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "tired-tongue",
+   "metadata": {},
+   "source": [
+    "The following figure shows the estimates of several research groups from 1 CE to the near present."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "id": "solar-action",
    "metadata": {},
    "outputs": [],
@@ -235,14 +258,13 @@
    "id": "wanted-fantasy",
    "metadata": {},
    "source": [
-    "See if you can find a model that fits these data well from Year 0 to 1950.\n",
-    "\n",
-    "How well does your best model predict actual population growth from 1950 to the present?"
+    "See if you can find a model that fits these estimates.\n",
+    "How well does your best model predict actual population growth from 1940 to the present?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "id": "together-jackson",
    "metadata": {},
    "outputs": [],
@@ -252,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "id": "simple-verse",
    "metadata": {
     "scrolled": true
@@ -362,6 +384,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",

From 38089265c29c1c6a9429dda102e9ff6b78ed55f7 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sat, 13 Feb 2021 19:52:54 -0500
Subject: [PATCH 037/144] Updating chapters

---
 chapters/chap05.ipynb | 54 +++++++++++++++++++++----------------------
 1 file changed, 27 insertions(+), 27 deletions(-)

diff --git a/chapters/chap05.ipynb b/chapters/chap05.ipynb
index f1facb0b..b028809e 100644
--- a/chapters/chap05.ipynb
+++ b/chapters/chap05.ipynb
@@ -348,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "ordered-garage",
    "metadata": {},
    "outputs": [],
@@ -373,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "periodic-weekend",
    "metadata": {},
    "outputs": [],
@@ -406,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "secondary-player",
    "metadata": {},
    "outputs": [],
@@ -416,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "caring-garlic",
    "metadata": {},
    "outputs": [],
@@ -438,7 +438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "corresponding-emerald",
    "metadata": {},
    "outputs": [],
@@ -459,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "headed-witch",
    "metadata": {
     "scrolled": true
@@ -486,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "advisory-complex",
    "metadata": {
     "scrolled": true
@@ -509,7 +509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "frank-owner",
    "metadata": {},
    "outputs": [],
@@ -553,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "undefined-sauce",
    "metadata": {},
    "outputs": [],
@@ -571,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "postal-debate",
    "metadata": {},
    "outputs": [],
@@ -597,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "functional-trick",
    "metadata": {},
    "outputs": [],
@@ -617,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "acoustic-south",
    "metadata": {},
    "outputs": [],
@@ -638,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "planned-remains",
    "metadata": {},
    "outputs": [],
@@ -657,7 +657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "quarterly-aggregate",
    "metadata": {},
    "outputs": [],
@@ -676,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "prescription-optics",
    "metadata": {},
    "outputs": [],
@@ -695,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "id": "convertible-patch",
    "metadata": {},
    "outputs": [],
@@ -727,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 29,
    "id": "duplicate-leave",
    "metadata": {},
    "outputs": [],
@@ -748,7 +748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "id": "convenient-thong",
    "metadata": {},
    "outputs": [],
@@ -766,7 +766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "id": "israeli-surveillance",
    "metadata": {},
    "outputs": [],
@@ -784,7 +784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "id": "attended-morris",
    "metadata": {},
    "outputs": [],
@@ -809,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 33,
    "id": "wrong-brooks",
    "metadata": {},
    "outputs": [],
@@ -869,7 +869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "id": "terminal-reynolds",
    "metadata": {},
    "outputs": [],
@@ -884,7 +884,7 @@
     "total_growth = p_end - p_0\n",
     "annual_growth = total_growth / elapsed_time\n",
     "\n",
-    "results = TimeSeries(time='Year', quantity='Population')\n",
+    "results = TimeSeries()\n",
     "results[t_0] = p_0\n",
     "\n",
     "for t in range(t_0, t_end):\n",
@@ -893,7 +893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "id": "organizational-memphis",
    "metadata": {},
    "outputs": [],
@@ -919,7 +919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "id": "rising-anger",
    "metadata": {},
    "outputs": [],
@@ -929,7 +929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 38,
    "id": "false-handbook",
    "metadata": {},
    "outputs": [],
@@ -939,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 39,
    "id": "political-loading",
    "metadata": {},
    "outputs": [],

From 3d391db9371f09b65feb4dea355ffbb7871900cb Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sun, 14 Feb 2021 15:03:39 -0500
Subject: [PATCH 038/144] Updating chapters

---
 chapters/chap01.ipynb | 192 +++++++++++++++++---------------
 chapters/chap02.ipynb | 175 ++++++++++++++---------------
 chapters/chap03.ipynb | 128 +++++++++++-----------
 chapters/chap04.ipynb | 248 ++++++++++++++++++++++--------------------
 4 files changed, 381 insertions(+), 362 deletions(-)

diff --git a/chapters/chap01.ipynb b/chapters/chap01.ipynb
index 4d031bd7..426c9db5 100644
--- a/chapters/chap01.ipynb
+++ b/chapters/chap01.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "meaning-public",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "alike-thompson",
    "metadata": {
     "tags": []
@@ -287,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "eleven-marine",
    "metadata": {},
    "outputs": [],
@@ -307,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "following-launch",
    "metadata": {},
    "outputs": [],
@@ -325,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "mature-duration",
    "metadata": {},
    "outputs": [],
@@ -345,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "considered-inclusion",
    "metadata": {},
    "outputs": [],
@@ -363,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "valued-electricity",
    "metadata": {},
    "outputs": [],
@@ -382,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "closed-month",
    "metadata": {},
    "outputs": [],
@@ -401,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "nuclear-clone",
    "metadata": {},
    "outputs": [],
@@ -419,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "quarterly-nightmare",
    "metadata": {},
    "outputs": [],
@@ -438,7 +438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "quality-external",
    "metadata": {},
    "outputs": [],
@@ -472,7 +472,7 @@
    "source": [
     "## Computation with units\n",
     "\n",
-    "The computation in the previous section uses numbers without units.  \n",
+    "The computation in the previous section uses numbers without units.\n",
     "When we set `h=381`, we left out the meters, and when we set `a=9.8`, we left out the meters per second squared.\n",
     "And, when we got the result `v=86`, we added back the meters per second.\n",
     "\n",
@@ -481,29 +481,41 @@
     "\n",
     "To represent units, we'll use a Python library called [Pint](https://pint.readthedocs.io/en/latest/).\n",
     "The ModSim library initializes Pint and provides an object called `units` that contains variables representing pretty much every unit you've ever heard of.\n",
-    "\n",
-    "We can import it like this:"
+    "For example:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "based-belief",
+   "execution_count": 54,
+   "id": "promotional-lover",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "units.league"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "practical-throat",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "units.fortnight"
+   ]
   },
   {
    "cell_type": "markdown",
    "id": "unlike-opera",
    "metadata": {},
    "source": [
-    "Now we can create variables named `meter` and `second`."
+    "But leagues and fortnights are not SI units.\n",
+    "Instead, we will use `meter` and `second`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "russian-popularity",
    "metadata": {},
    "outputs": [],
@@ -514,7 +526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "endless-paint",
    "metadata": {},
    "outputs": [],
@@ -531,6 +543,14 @@
     "To find out what other units are defined, type `units.` (including the period) in the next cell and then press TAB.  You should see a pop-up menu with a list of units."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "developmental-majority",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "everyday-location",
@@ -541,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "earlier-bandwidth",
    "metadata": {},
    "outputs": [],
@@ -560,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "missing-privilege",
    "metadata": {},
    "outputs": [],
@@ -570,7 +590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "fourth-swedish",
    "metadata": {},
    "outputs": [],
@@ -583,12 +603,12 @@
    "id": "realistic-techno",
    "metadata": {},
    "source": [
-    "Now we can create a quantity that represents $3.4$ s."
+    "We can also create a quantity that represents $3.4$ s."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 56,
    "id": "apart-france",
    "metadata": {},
    "outputs": [],
@@ -607,7 +627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 57,
    "id": "alien-scout",
    "metadata": {},
    "outputs": [],
@@ -626,7 +646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 58,
    "id": "confidential-costs",
    "metadata": {},
    "outputs": [],
@@ -639,12 +659,12 @@
    "id": "hollow-programmer",
    "metadata": {},
    "source": [
-    "We can use it to compute the time to reach the sidewalk."
+    "We can use it to compute the time the penny would take to reach the sidewalk."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "studied-opera",
    "metadata": {},
    "outputs": [],
@@ -663,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "exterior-greek",
    "metadata": {},
    "outputs": [],
@@ -684,7 +704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "antique-landing",
    "metadata": {},
    "outputs": [],
@@ -695,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "included-failure",
    "metadata": {},
    "outputs": [],
@@ -721,16 +741,16 @@
     "\n",
     "This chapter introduces a modeling framework that consists of three steps:\n",
     "\n",
-    "* Abstraction is the process of defining a model, deciding which elements of the real world to include and which can be left out.\n",
+    "* Abstraction is the process of defining a model by deciding which elements of the real world to include and which can be left out.\n",
     "\n",
-    "* Analysis and simulation are ways to use a model to generate predictions, explain why things behave as they to, and design things that behave as we want.\n",
+    "* Analysis and simulation are ways to use a model to generate predictions, explain why things behave as they do, and design things that behave as we want.\n",
     "\n",
     "* Validation is how we test whether the model is right, often by comparing predictions with measurements from the real world.\n",
     "\n",
     "As a first example, we modeled a penny dropped from the Empire State building, including gravity but ignoring air resistance.\n",
     "In the exercises, you'll have a chance to try a better model, including air resistance.\n",
     "\n",
-    "This chapter also presents Pint, a library for doing computation with units, which is convenient for converting between different units and important for avoiding catastrophic errors."
+    "This chapter also presents Pint, a library for doing computation with units, which is convenient for converting between different units and helpful for avoiding catastrophic errors."
    ]
   },
   {
@@ -753,14 +773,13 @@
     "v = a t\n",
     "```\n",
     "\n",
-    "you'll get a **syntax error**, which means that something is wrong with the punctuation of the program.\n",
-    "\n",
+    "you'll get a **syntax error**, which means that something is wrong with the structure of the program.\n",
     "Try it out in the next cell so you see what the error message looks like."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 59,
    "id": "postal-marking",
    "metadata": {},
    "outputs": [],
@@ -783,7 +802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 60,
    "id": "legal-observer",
    "metadata": {},
    "outputs": [],
@@ -807,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 61,
    "id": "pressing-belgium",
    "metadata": {},
    "outputs": [],
@@ -829,7 +848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 62,
    "id": "inner-equivalent",
    "metadata": {},
    "outputs": [],
@@ -839,7 +858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 63,
    "id": "nuclear-thirty",
    "metadata": {},
    "outputs": [],
@@ -849,7 +868,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 64,
    "id": "available-steering",
    "metadata": {},
    "outputs": [],
@@ -867,7 +886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 65,
    "id": "documentary-doctrine",
    "metadata": {},
    "outputs": [],
@@ -889,13 +908,13 @@
    "id": "steady-chancellor",
    "metadata": {},
    "source": [
-    "In this example, you should get a `DimensionalityError`, which is defined by Pint to indicate that you have violated a rules of dimensional analysis: you cannot add quantities with different dimensions.\n",
+    "In this example, you should get a `DimensionalityError`, which indicates that you have violated a rule of dimensional analysis: you cannot add quantities with different dimensions.\n",
     "\n",
     "The error messages you get from Python are big and scary, but if you read them carefully, they contain a lot of useful information.\n",
     "\n",
-    "1.  Start from the bottom and read up.\n",
-    "2.  The last line usually tells you what type of error happened, and sometimes additional information.\n",
-    "3.  The previous lines are a \"traceback\" of what was happening when the error occurred.  The first section of the traceback shows the code you wrote.  The following sections are often from Python libraries.\n",
+    "The last line usually tells you what type of error happened, and sometimes additional information, so you might want to start from the bottom and read up.\n",
+    "\n",
+    "The previous lines are a \"traceback\" of what was happening when the error occurred.  The first section of the traceback shows the code you wrote.  The following sections are often from Python libraries.\n",
     "\n",
     "Before you go on, you might want to delete the erroneous code so the notebook can run without errors."
    ]
@@ -910,7 +929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 66,
    "id": "greenhouse-reason",
    "metadata": {},
    "outputs": [],
@@ -948,7 +967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 67,
    "id": "secure-crowd",
    "metadata": {},
    "outputs": [],
@@ -959,7 +978,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 68,
    "id": "thirty-minneapolis",
    "metadata": {},
    "outputs": [],
@@ -969,7 +988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 69,
    "id": "premier-seeking",
    "metadata": {},
    "outputs": [],
@@ -979,7 +998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 70,
    "id": "brave-laundry",
    "metadata": {},
    "outputs": [],
@@ -989,7 +1008,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 71,
    "id": "adjusted-consultation",
    "metadata": {},
    "outputs": [],
@@ -999,7 +1018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 72,
    "id": "understanding-consortium",
    "metadata": {},
    "outputs": [],
@@ -1012,8 +1031,8 @@
    "id": "composed-tunnel",
    "metadata": {},
    "source": [
-    "**Exercise:** When I was in high school, the pitcher on the baseball team claimed that, when he threw a fastball, he was throwing the ball down; that is, the ball was leaving his hand at a downward angle.\n",
-    "I was skeptical; watching from the side, I thought the ball was leaving his hand at an upward angle.\n",
+    "**Exercise:** When I was in high school, the pitcher on the baseball team claimed that when he threw a fastball he was throwing the ball down; that is, the ball left his hand at a downward angle.\n",
+    "I was skeptical; watching from the side, I thought the ball left his hand at an upward angle.\n",
     "\n",
     "Can you think of a simple model you could use to settle the argument?  What factors would you include and what could you ignore?  What quantities would you have to look up or estimate?\n",
     "\n",
@@ -1022,7 +1041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "id": "about-complex",
    "metadata": {},
    "outputs": [],
@@ -1032,7 +1051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "id": "unique-owner",
    "metadata": {},
    "outputs": [],
@@ -1042,7 +1061,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "id": "minus-batman",
    "metadata": {},
    "outputs": [],
@@ -1052,7 +1071,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "id": "exact-vegetable",
    "metadata": {},
    "outputs": [],
@@ -1062,7 +1081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "id": "neutral-lightning",
    "metadata": {},
    "outputs": [],
@@ -1075,12 +1094,12 @@
    "id": "jewish-secret",
    "metadata": {},
    "source": [
-    "**Exercise:** Suppose I run a 10K race in 44:52.  What is my average page in minutes per mile?"
+    "**Exercise:** Suppose I run a 10K race in 44:52 (44 minutes and 52 seconds).  What is my average page in minutes per mile?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 73,
    "id": "virgin-cambodia",
    "metadata": {},
    "outputs": [],
@@ -1092,7 +1111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 74,
    "id": "right-intention",
    "metadata": {},
    "outputs": [],
@@ -1102,7 +1121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 75,
    "id": "mineral-sally",
    "metadata": {},
    "outputs": [],
@@ -1112,8 +1131,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "id": "preceding-cricket",
+   "execution_count": 81,
+   "id": "front-logistics",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1122,18 +1141,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "id": "effective-rendering",
+   "execution_count": 82,
+   "id": "preceding-cricket",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "pace_per_mile = pace_per_km.to(minute / mile)\n",
+    "pace_per_mile"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "id": "printable-reply",
+   "execution_count": 83,
+   "id": "effective-rendering",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1141,18 +1161,22 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "environmental-wallet",
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "printable-reply",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## More Jupyter"
+    "# Solution goes here"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "quality-probe",
+   "id": "environmental-wallet",
    "metadata": {},
    "source": [
+    "## More Jupyter\n",
+    "\n",
     "You can add and remove cells from a notebook using the buttons in the toolbar and the items in the menu, both of which you should see at the top of this notebook.\n",
     "\n",
     "Try the following exercises:\n",
@@ -1181,15 +1205,9 @@
    "source": [
     "When you change the contents of a cell, you have to run it again for those changes to have an effect.  If you forget to do that, the results can be confusing, because the code you are looking at is not the code you ran.\n",
     "\n",
-    "If you ever lose track of which cells have run, and in what order, you should go to the Kernel menu and select \"Restart & Run All\".  Restarting the kernel means that all of your variables get deleted, and running all the cells means all of your code will run again, in the right order."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "suffering-intro",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Select \"Restart & Run All\" now and confirm that it does what you want."
+    "If you ever lose track of which cells have run, and in what order, you should go to the Kernel menu and select \"Restart & Run All\".  Restarting the kernel means that all of your variables get deleted, and running all the cells means all of your code will run again, in the right order.\n",
+    "\n",
+    "Select \"Restart & Run All\" now and confirm that it runs all of the cells."
    ]
   },
   {
diff --git a/chapters/chap02.ipynb b/chapters/chap02.ipynb
index dc5e9f80..d441523c 100644
--- a/chapters/chap02.ipynb
+++ b/chapters/chap02.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "meaning-public",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "alike-thompson",
    "metadata": {
     "tags": []
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "incorrect-comparison",
    "metadata": {},
    "outputs": [],
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "brief-diversity",
    "metadata": {},
    "outputs": [],
@@ -152,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "designed-brazilian",
    "metadata": {},
    "outputs": [],
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "impaired-potter",
    "metadata": {},
    "outputs": [],
@@ -190,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 8,
    "id": "basic-fabric",
    "metadata": {},
    "outputs": [],
@@ -203,7 +203,7 @@
    "id": "delayed-ocean",
    "metadata": {},
    "source": [
-    "You don't have to use `show`, but I think it looks better.\n",
+    "You don't have to use `show`, but I think the results look better.\n",
     "\n",
     "We can update the state by assigning new values to the variables. \n",
     "For example, if a student moves a bike from Olin to Wellesley, we can figure out the new values and assign them:"
@@ -211,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "floppy-trainer",
    "metadata": {},
    "outputs": [],
@@ -231,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "hungarian-bride",
    "metadata": {},
    "outputs": [],
@@ -255,14 +255,14 @@
    "source": [
     "## Defining functions\n",
     "\n",
-    "So far we have used functions defined in NumPy and ModSim. Now we're going to define our own function.\n",
+    "So far we have used functions defined in NumPy and the ModSim library. Now we're going to define our own functions.\n",
     "\n",
     "When you are developing code in Jupyter, it is often efficient to write a few lines of code, test them to confirm they do what you intend, and then use them to define a new function. For example, these lines move a bike from Olin to Wellesley:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "vertical-drawing",
    "metadata": {},
    "outputs": [],
@@ -282,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "significant-nutrition",
    "metadata": {},
    "outputs": [],
@@ -313,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "moving-jurisdiction",
    "metadata": {},
    "outputs": [],
@@ -333,12 +333,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "proper-symposium",
    "metadata": {},
    "outputs": [],
    "source": [
-    "bikeshare"
+    "show(bikeshare)"
    ]
   },
   {
@@ -352,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "identical-yacht",
    "metadata": {},
    "outputs": [],
@@ -365,10 +365,8 @@
    "id": "premier-youth",
    "metadata": {},
    "source": [
-    "This result indicates that `bike_to_wellesley` is a function. You don't\n",
-    "have to know what `__main__` means, but if you see something like this,\n",
-    "it probably means that you looked up a function but you didn't actually\n",
-    "call it. So don't forget the parentheses."
+    "This result indicates that `bike_to_wellesley` is a function. You don't have to know what `__main__` means, but if you see something like this, it probably means that you named a function but didn't actually call it.\n",
+    "So don't forget the parentheses."
    ]
   },
   {
@@ -388,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "heavy-patrol",
    "metadata": {},
    "outputs": [],
@@ -409,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "french-preference",
    "metadata": {},
    "outputs": [],
@@ -423,14 +421,14 @@
    "id": "original-hollywood",
    "metadata": {},
    "source": [
-    "When you call the `print` function, you can put a variable name in\n",
+    "When you call the `print` function, you can put a variable in\n",
     "parentheses, as in the previous example, or you can provide a sequence\n",
     "of variables separated by commas, like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "alternative-keyboard",
    "metadata": {},
    "outputs": [],
@@ -453,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "robust-holly",
    "metadata": {},
    "outputs": [],
@@ -480,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "fifteen-atmosphere",
    "metadata": {},
    "outputs": [],
@@ -501,7 +499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "matched-narrow",
    "metadata": {},
    "outputs": [],
@@ -533,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 22,
    "id": "illegal-metropolitan",
    "metadata": {},
    "outputs": [],
@@ -551,20 +549,18 @@
     "get `True` about 70 times and `False` about 30 times. But the results\n",
     "are random, so they might differ from these expectations.\n",
     "\n",
-    "`True` and `False` are special values defined by Python. Note that they\n",
-    "are not strings. There is a difference between `True`, which is a\n",
-    "special value, and `'True'`, which is a string.\n",
-    "\n",
-    "`True` and `False` are called **boolean** values because they are\n",
+    "`True` and `False` are special values defined by Python. \n",
+    "They are called **boolean** values because they are\n",
     "related to Boolean algebra (<https://modsimpy.com/boolean>).\n",
     "\n",
-    "We can use boolean values to control the behavior of the program, using\n",
-    "an **if statement**:"
+    "Note that they are not strings. There is a difference between `True`, which is a boolean value, and `'True'`, which is a string.\n",
+    "\n",
+    "We can use boolean values to control the behavior of the program, using an **if statement**:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 23,
    "id": "excessive-murder",
    "metadata": {},
    "outputs": [],
@@ -591,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 24,
    "id": "fundamental-nursing",
    "metadata": {},
    "outputs": [],
@@ -607,15 +603,17 @@
    "id": "recovered-chemical",
    "metadata": {},
    "source": [
+    "If you run the previous cell a few times, it should print `heads` about half the time, and `tails` about half the time.\n",
+    "\n",
     "Now we can use `flip` to simulate the arrival of students who want to\n",
     "borrow a bike. Suppose students arrive at the Olin station every 2\n",
-    "minutes, on average. In that case, the chance of an arrival during any\n",
-    "one-minute period is 50%, and we can simulate it like this:"
+    "minutes on average.\n",
+    "In that case, the chance of an arrival during any one-minute period is 50%, and we can simulate it like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 25,
    "id": "twenty-health",
    "metadata": {},
    "outputs": [],
@@ -636,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 26,
    "id": "played-character",
    "metadata": {},
    "outputs": [],
@@ -656,7 +654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 27,
    "id": "ecological-colon",
    "metadata": {},
    "outputs": [],
@@ -679,7 +677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 28,
    "id": "mediterranean-german",
    "metadata": {},
    "outputs": [],
@@ -692,7 +690,7 @@
    "id": "sought-mobile",
    "metadata": {},
    "source": [
-    "Even though there are no values in parentheses, we have to include them."
+    "Depending on the results from `flip`, this function might move a bike to Olin, or to Wellesley, or neither, or both."
    ]
   },
   {
@@ -703,16 +701,17 @@
     "## Parameters\n",
     "\n",
     "The previous version of `step` is fine if the arrival probabilities\n",
-    "never change, but in reality, these probabilities vary over time.\n",
+    "never change, but in reality they vary over time.\n",
     "\n",
-    "So instead of putting the constant values 0.5 and 0.33 in `step` we can replace them with **parameters**. Parameters are variables whose values are set when a function is called.\n",
+    "So instead of putting the constant values 0.5 and 0.33 in `step`, we can replace them with **parameters**.\n",
+    "Parameters are variables whose values are set when a function is called.\n",
     "\n",
     "Here's a version of `step` that takes two parameters, `p1` and `p2`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 29,
    "id": "hollywood-shopping",
    "metadata": {},
    "outputs": [],
@@ -736,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "id": "buried-alert",
    "metadata": {},
    "outputs": [],
@@ -757,7 +756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "id": "recognized-denmark",
    "metadata": {},
    "outputs": [],
@@ -796,7 +795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "id": "polish-river",
    "metadata": {},
    "outputs": [],
@@ -818,13 +817,13 @@
     "-   The words `for` and `in` are special words we have to use in a for\n",
     "    loop.\n",
     "\n",
-    "-   `range` is a Python function we're using here to control the number of times the loop runs.\n",
+    "-   `range` is a Python function we use to control the number of times the loop runs.\n",
     "\n",
     "-   `i` is a **loop variable** that gets created when the for loop runs.\n",
     "\n",
     "When this loop runs, it runs the statements inside the loop three times.  The first time, the value of `i` is `0`; the second time, it is `1`; the third time, it is `2`.\n",
     "\n",
-    "Each time through the loop, it prints the value of `i` and moves one bike Olin to Wellesley."
+    "Each time through the loop, it prints the value of `i` and moves one bike to Wellesley."
    ]
   },
   {
@@ -832,25 +831,19 @@
    "id": "breeding-groove",
    "metadata": {},
    "source": [
-    "## TimeSeries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "flexible-projection",
-   "metadata": {},
-   "source": [
+    "## TimeSeries\n",
+    "\n",
     "When we run a simulation, we often want to save the results for later analysis. The ModSim library provides a `TimeSeries` object for this purpose. A `TimeSeries` contains a sequence of time stamps and a\n",
     "corresponding sequence of quantities.\n",
     "\n",
-    "In this example, the time stamps are integers representing minutes, and the quantities are the number of bikes at one location.\n",
+    "In this example, the time stamps are integers representing minutes and the quantities are the number of bikes at one location.\n",
     "\n",
     "Since we have moved a number of bikes around, let's start again with a new `State` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 33,
    "id": "every-consultation",
    "metadata": {},
    "outputs": [],
@@ -868,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 34,
    "id": "changing-planet",
    "metadata": {},
    "outputs": [],
@@ -886,7 +879,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 35,
    "id": "aquatic-richardson",
    "metadata": {},
    "outputs": [],
@@ -906,7 +899,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 36,
    "id": "english-titanium",
    "metadata": {},
    "outputs": [],
@@ -937,12 +930,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 37,
    "id": "indonesian-singing",
    "metadata": {},
    "outputs": [],
    "source": [
-    "results"
+    "show(results)"
    ]
   },
   {
@@ -950,21 +943,7 @@
    "id": "small-encoding",
    "metadata": {},
    "source": [
-    "The left column is the time stamps; the right column is the quantities (which might be negative, depending on the state of the system).\n",
-    "\n",
-    "At the bottom, `dtype` is the type of the data in the `TimeSeries`; you can ignore this for now.\n",
-    "\n",
-    "The `show` function displays a `TimeSeries` as a table:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "verbal-bikini",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "show(results)"
+    "The left column is the time stamps; the right column is the quantities."
    ]
   },
   {
@@ -980,7 +959,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 38,
    "id": "saved-hands",
    "metadata": {},
    "outputs": [],
@@ -992,6 +971,15 @@
     "         ylabel='Number of bikes')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "egyptian-korea",
+   "metadata": {},
+   "source": [
+    "The result should be a plot with time on the $x$-axis and the number of bikes on the $y$-axis.\n",
+    "Since we only ran three time steps, it might not be very interesting."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "limited-interstate",
@@ -1003,7 +991,7 @@
     "\n",
     "We used a `State` object to represent the state of the system.\n",
     "Then we used the `flip` function and an `if` statement to simulate a single time step.\n",
-    "We used `for` loop to simulate a series of steps, and a `TimeSeries` to record the results.\n",
+    "We used a `for` loop to simulate a series of steps, and a `TimeSeries` to record the results.\n",
     "Finally, we used `plot` and `decorate` to plot the results.\n",
     "\n",
     "In the next chapter, we will extend this simulation to make it a little more realistic."
@@ -1029,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 39,
    "id": "helpful-zambia",
    "metadata": {},
    "outputs": [],
@@ -1049,7 +1037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 40,
    "id": "beneficial-mainland",
    "metadata": {},
    "outputs": [],
@@ -1080,12 +1068,12 @@
     "\n",
     "1. Create a `State` object with the initial state of the system.\n",
     "\n",
-    "2. Call `run_simulation` with appropriate parameters."
+    "2. Call `run_simulation` with parameters `p1=0.3`, `p2=0.2`, and `num_steps=60`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 41,
    "id": "former-frost",
    "metadata": {},
    "outputs": [],
@@ -1095,7 +1083,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 42,
    "id": "spare-honduras",
    "metadata": {},
    "outputs": [],
@@ -1112,7 +1100,8 @@
     "\n",
     "This section contains additional information about the functions we've used and pointers to their documentation.\n",
     "\n",
-    "You don't need to know anything in these sections, so if you are already feeling overwhelmed, you might want to skip them.  But if you are curious, read on."
+    "You don't need to know anything in this section, so if you are already feeling overwhelmed, you might want to skip it.\n",
+    "But if you are curious, read on."
    ]
   },
   {
@@ -1157,7 +1146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 43,
    "id": "agricultural-midwest",
    "metadata": {},
    "outputs": [],
diff --git a/chapters/chap03.ipynb b/chapters/chap03.ipynb
index 794b0d43..1132a70d 100644
--- a/chapters/chap03.ipynb
+++ b/chapters/chap03.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "meaning-public",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "alike-thompson",
    "metadata": {
     "tags": []
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 4,
    "id": "embedded-heavy",
    "metadata": {},
    "outputs": [],
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 5,
    "id": "unusual-advancement",
    "metadata": {},
    "outputs": [],
@@ -197,9 +197,8 @@
    "id": "serial-mortality",
    "metadata": {},
    "source": [
-    "The name of the parameter is `state` rather than `bikeshare` as a\n",
-    "reminder that the value of `state` could be any `State` object, not just\n",
-    "`bikeshare`.\n",
+    "The name of the parameter is `state`, rather than `bikeshare`, as a\n",
+    "reminder that the value of `state` could be any `State` object, not just the one we called `bikeshare`.\n",
     "\n",
     "This version of `bike_to_wellesley` requires a `State` object as a\n",
     "parameter, so we have to provide one when we call it:"
@@ -207,13 +206,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 6,
    "id": "packed-hungarian",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "bikeshare = State(olin=10, wellesley=2)\n",
     "bike_to_wellesley(bikeshare)"
    ]
@@ -237,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 7,
    "id": "right-assessment",
    "metadata": {},
    "outputs": [],
@@ -256,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 8,
    "id": "cleared-advocacy",
    "metadata": {},
    "outputs": [],
@@ -283,13 +280,14 @@
     "## Documentation\n",
     "\n",
     "Another problem with the code we have so far is that it contains no\n",
-    "**documentation**. Documentation is text we add to a program to help\n",
+    "**documentation**.\n",
+    "Documentation is text we add to a program to help\n",
     "other programmers read and understand it. It has no effect on the\n",
     "program when it runs.\n",
     "\n",
-    "There are two forms of documentation, **docstrings** and **comments**:\n",
+    "There are two kinds of documentation, **docstrings** and **comments**:\n",
     "\n",
-    "* A docstring is a string in triple-quotes that appears at the beginning of a function.\n",
+    "* A docstring is a string in triple quotes that appears at the beginning of a function.\n",
     "\n",
     "* A comment is a line of text that begins with a hash symbol, `#`.\n",
     "\n",
@@ -298,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 9,
    "id": "moral-parallel",
    "metadata": {},
    "outputs": [],
@@ -309,7 +307,7 @@
     "    state: bikeshare State object\n",
     "    \"\"\"\n",
     "    # We decrease one state variable and increase the\n",
-    "    # other, so the total number of bikes is unchanged.\n",
+    "    # other so the total number of bikes is unchanged.\n",
     "    state.wellesley -= 1\n",
     "    state.olin += 1"
    ]
@@ -339,16 +337,17 @@
     "## Negative bikes\n",
     "\n",
     "The changes we've made so far improve the quality of the code, but we\n",
-    "haven't done anything to improve the quality of the model yet. Let's do that now.\n",
+    "haven't done anything to improve the quality of the model. Let's do that now.\n",
     "\n",
     "Currently the simulation does not check whether a bike is available when a customer arrives, so the number of bikes at a location can be\n",
-    "negative. That's not very realistic. Here's a version of\n",
-    "`bike_to_olin` that fixes the problem:"
+    "negative. That's not very realistic.\n",
+    "\n",
+    "Here's a version of `bike_to_olin` that fixes the problem:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 10,
    "id": "divine-leisure",
    "metadata": {},
    "outputs": [],
@@ -372,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 11,
    "id": "choice-cooking",
    "metadata": {},
    "outputs": [],
@@ -391,12 +390,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 12,
    "id": "twelve-moderator",
    "metadata": {},
    "outputs": [],
    "source": [
-    "bikeshare"
+    "show(bikeshare)"
    ]
   },
   {
@@ -414,14 +413,14 @@
    "source": [
     "## Comparison operators\n",
     "\n",
-    "The updated version of `bike_to_olin` uses the equals operator, `==`, which compares two values and returns `True` if they are equal and `False` otherwise.\n",
+    "The updated version of `bike_to_olin` uses the equals operator, `==`, which compares two values and returns `True`, if they are equal, and `False` otherwise.\n",
     "\n",
-    "It is easy to confuse the equals operators with the assignment operator, `=`, which assigns a value to a variable. For example, the following statement creates a variable, `x`, if it doesn't already exist, and gives it the value `5`."
+    "It is easy to confuse the equals operator with the assignment operator, `=`, which assigns a value to a variable. For example, the following statement creates a variable, `x`, if it doesn't already exist, and gives it the value `5`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 13,
    "id": "level-burns",
    "metadata": {},
    "outputs": [],
@@ -440,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 14,
    "id": "civic-remains",
    "metadata": {},
    "outputs": [],
@@ -458,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 15,
    "id": "affecting-naples",
    "metadata": {},
    "outputs": [],
@@ -515,19 +514,16 @@
     "system will work, or to design a system that works better. First, we\n",
     "have to decide what we mean by \"how well\" and \"better\".\n",
     "\n",
-    "From the customer's point of view, we might like to know the probability of finding an available bike. From the system-owner's point of view, we\n",
-    "might want to minimize the number of customers who don't get a bike when\n",
-    "they want one, or maximize the number of bikes in use. Statistics like\n",
-    "these that quantify how well the system works are called **metrics**.\n",
+    "From the customer's point of view, we might like to know the probability of finding an available bike. From the system-owner's point of view, we might want to minimize the number of customers who don't get a bike when they want one, or maximize the number of bikes in use. Statistics like these that quantify how well the system works are called **metrics**.\n",
     "\n",
-    "As a simple example, let's measure the number of unhappy customers.\n",
+    "As an example, let's measure the number of unhappy customers.\n",
     "Here's a version of `bike_to_olin` that keeps track of the number of\n",
     "customers who arrive at a station with no bikes:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 16,
    "id": "arbitrary-ferry",
    "metadata": {},
    "outputs": [],
@@ -555,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 17,
    "id": "cardiovascular-montgomery",
    "metadata": {},
    "outputs": [],
@@ -574,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 18,
    "id": "cosmetic-above",
    "metadata": {
     "scrolled": true
@@ -589,17 +585,17 @@
    "id": "pleased-gasoline",
    "metadata": {},
    "source": [
-    "There should be 12 bikes at Olin, no bikes at Wellesley, and one unhappy customer."
+    "After this update, there should be 12 bikes at Olin, no bikes at Wellesley, and one unhappy customer."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 19,
    "id": "bulgarian-palestine",
    "metadata": {},
    "outputs": [],
    "source": [
-    "bikeshare"
+    "show(bikeshare)"
    ]
   },
   {
@@ -648,13 +644,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 20,
    "id": "wrong-internet",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "def run_simulation(state, p1, p2, num_steps):\n",
     "    \"\"\"Simulate the given number of time steps.\n",
     "    \n",
@@ -678,13 +672,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 21,
    "id": "instrumental-copyright",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "def step(state, p1, p2):\n",
     "    \"\"\"Simulate one time step.\n",
     "    \n",
@@ -701,9 +695,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 22,
    "id": "improved-renaissance",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "def bike_to_olin(state):\n",
@@ -720,7 +716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 23,
    "id": "unavailable-maker",
    "metadata": {},
    "outputs": [],
@@ -746,9 +742,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 24,
    "id": "phantom-carter",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Solution goes here"
@@ -756,7 +754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 25,
    "id": "adopted-contrary",
    "metadata": {},
    "outputs": [],
@@ -766,7 +764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 26,
    "id": "comparable-natural",
    "metadata": {},
    "outputs": [],
@@ -776,7 +774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 27,
    "id": "attractive-amendment",
    "metadata": {},
    "outputs": [],
@@ -789,31 +787,35 @@
    "id": "possible-initial",
    "metadata": {},
    "source": [
-    "**Exercise:** Now run the simulation a few times and confirm that the number of bikes is never negative.  What's the final state of the system?"
+    "**Exercise:** Now run the simulation with parameters `p1=0.3`, `p2=0.2`, and `num_steps=60`, and confirm that the number of bikes is never negative.\n",
+    "\n",
+    "Start with this initial state:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
-   "id": "immune-shock",
+   "execution_count": 28,
+   "id": "eleven-constraint",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "bikeshare = State(olin=10, wellesley=2,\n",
+    "                  olin_empty=0, wellesley_empty=0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
-   "id": "sporting-collectible",
+   "execution_count": 29,
+   "id": "immune-shock",
    "metadata": {},
    "outputs": [],
    "source": [
-    "bikeshare"
+    "# Solution goes here"
    ]
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
diff --git a/chapters/chap04.ipynb b/chapters/chap04.ipynb
index da973561..f32e2a55 100644
--- a/chapters/chap04.ipynb
+++ b/chapters/chap04.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "meaning-public",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "alike-thompson",
    "metadata": {
     "tags": []
@@ -80,70 +80,36 @@
   {
    "cell_type": "markdown",
    "id": "caring-gnome",
-   "metadata": {},
-   "source": [
-    "Here the code from previous chapters we'll reuse."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "stylish-raising",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "\n",
-    "\n",
-    "def step(state, p1, p2):\n",
-    "    \"\"\"Simulate one time step.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    p1: probability of an Olin->Wellesley ride\n",
-    "    p2: probability of a Wellesley->Olin ride\n",
-    "    \"\"\"\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley(state)\n",
-    "    \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin(state)"
+    "The following cells download the code from Chapter 3 and import the `step` function we defined."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "wound-bottle",
-   "metadata": {},
+   "execution_count": 4,
+   "id": "ranking-today",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.wellesley == 0:\n",
-    "        state.wellesley_empty += 1\n",
-    "        return\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1"
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/chap03.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "prescribed-affect",
-   "metadata": {},
+   "execution_count": 5,
+   "id": "stylish-raising",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.olin == 0:\n",
-    "        state.olin_empty += 1\n",
-    "        return\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1"
+    "from chap03 import step"
    ]
   },
   {
@@ -151,7 +117,8 @@
    "id": "atlantic-collectible",
    "metadata": {},
    "source": [
-    "In the previous chapter we defined metrics that quantify the performance of bike sharing this system. In this chapter we see how those metrics depend on the parameters of the system, like the Customer rate of customers at bike stations.\n",
+    "In the previous chapter we defined metrics that quantify the performance of a bike sharing system.\n",
+    "In this chapter we'll see how those metrics depend on the parameters of the system, like the arrival rate of customers at the stations.\n",
     "\n",
     "We also discuss a program development strategy, called incremental\n",
     "development, that might help you write programs faster and spend less\n",
@@ -165,12 +132,13 @@
    "source": [
     "## Functions that return values\n",
     "\n",
-    "We have seen several functions that return values; for example, when you run `sqrt`, it returns a number you can assign to a variable."
+    "We have used several functions that return values.\n",
+    "For example, when you run `sqrt`, it returns a number you can assign to a variable."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "imposed-pregnancy",
    "metadata": {},
    "outputs": [],
@@ -186,18 +154,16 @@
    "id": "unsigned-recipe",
    "metadata": {},
    "source": [
-    "When you run `State`, it returns a new `State` object:"
+    "And when you run `State`, it returns a new `State` object:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "accessible-wallace",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "bikeshare = State(olin=10, wellesley=2)\n",
     "bikeshare"
    ]
@@ -215,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "minimal-supervisor",
    "metadata": {},
    "outputs": [],
@@ -236,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "warming-program",
    "metadata": {},
    "outputs": [],
@@ -256,13 +222,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "sitting-cleveland",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "def run_simulation(p1, p2, num_steps):\n",
     "    state = State(olin=10, wellesley=2,\n",
     "                  olin_empty=0, wellesley_empty=0)\n",
@@ -283,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "difficult-shepherd",
    "metadata": {},
    "outputs": [],
@@ -301,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "tough-sweet",
    "metadata": {},
    "outputs": [],
@@ -319,7 +283,7 @@
     "These five values are **parameters of the model**, which are quantities that determine the behavior of the system.\n",
     "\n",
     "It is easy to get the parameters of a model confused with the parameters of a function. \n",
-    "In fact, it is common for the parameters of the model to appear as parameters in functions. \n",
+    "It is especially easy because the parameters of a model often appear as parameters of a function.\n",
     "\n",
     "For example, the previous version of `run_simulation` takes `p1`, `p2`, and `num_steps` as parameters.\n",
     "So we can call `run_simulation` with different parameters and see how\n",
@@ -341,7 +305,7 @@
     "        step(state, p1, p2)\n",
     "```\n",
     "\n",
-    "In this example, `range` creates a sequence of numbers from 0 to `num_steps` (including `0` but not `num_steps`). \n",
+    "In this example, `range` creates a sequence of numbers from `0` to `num_steps` (including `0` but not `num_steps`). \n",
     "Each time through the loop, the next number in the sequence gets assigned to the loop variable, `i`.\n",
     "\n",
     "But `range` only works with integers; to get a sequence of non-integer\n",
@@ -350,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "bound-juice",
    "metadata": {},
    "outputs": [],
@@ -378,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "commercial-methodology",
    "metadata": {},
    "outputs": [],
@@ -402,6 +366,8 @@
     "\n",
     "4.  Runs the body of the loop, which prints `p1`.\n",
     "\n",
+    "5. ...\n",
+    "\n",
     "And so on, until it gets to the end of the array. This will come in handy in the next section."
    ]
   },
@@ -428,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "working-chair",
    "metadata": {},
    "outputs": [],
@@ -460,13 +426,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "instrumental-session",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "sweep = SweepSeries()"
    ]
   },
@@ -480,11 +444,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "hollywood-technical",
    "metadata": {},
    "outputs": [],
    "source": [
+    "p1_array = linspace(0, 0.6, 31)\n",
+    "\n",
     "for p1 in p1_array:\n",
     "    final_state = run_simulation(p1, p2, num_steps)\n",
     "    sweep[p1] = final_state.olin_empty"
@@ -492,57 +458,68 @@
   },
   {
    "cell_type": "markdown",
-   "id": "instructional-showcase",
+   "id": "healthy-prime",
    "metadata": {},
    "source": [
     "The result is a `SweepSeries` that maps from each value of `p1` to the\n",
-    "resulting number of unhappy customers. Then we can plot the results:"
+    "resulting number of unhappy customers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "driven-theme",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "We can display the results like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "id": "hollywood-spirit",
-   "metadata": {},
+   "execution_count": 18,
+   "id": "recovered-buffalo",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
-    "sweep.plot(label='Olin')\n",
-    "\n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Customer rate at Olin (p1 in customers/min)', \n",
-    "         ylabel='Number of unhappy customers')"
+    "show(sweep)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "intense-start",
+   "id": "instructional-showcase",
    "metadata": {},
    "source": [
-    "NumPy provides functions that compute a variety of summary statistics, like `mean`, `median`, and `std` (which computes standard deviation).\n",
-    "\n",
-    "We can use `mean` to compute the average of the values in a series, like this:"
+    "We can plot the results like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "contemporary-enough",
+   "execution_count": 19,
+   "id": "hollywood-spirit",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from numpy import mean\n",
+    "sweep.plot(label='Olin', color='C1')\n",
     "\n",
-    "mean(sweep)"
+    "decorate(title='Olin-Wellesley Bikeshare',\n",
+    "         xlabel='Customer rate at Olin (p1 in customers/min)', \n",
+    "         ylabel='Number of unhappy customers at Olin')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "owned-senior",
+   "id": "educated-bloom",
    "metadata": {},
    "source": [
-    "In this example, computing the mean might not be useful, but in the exercises below, it will be."
+    "I use the keyword argument `color='C1'` to specify the color of the line.\n",
+    "The `TimeSeries` we have plotted so far use the default color, `C0`, which is blue.\n",
+    "I want a different color for `SweepSeries` to remind us that it is not a `TimeSeries`.\n",
+    "\n",
+    "When the arrival rate at Olin is low, there are plenty of bikes and no unhappy customers.\n",
+    "As the arrival rate increases, we are more likely to run out of bikes and the number of unhappy customers increases."
    ]
   },
   {
@@ -553,7 +530,7 @@
     "## Incremental development\n",
     "\n",
     "When you start writing programs that are more than a few lines, you\n",
-    "might find yourself spending more and more time debugging. The more code you write before you start debugging, the harder it is to find the problem.\n",
+    "might find yourself spending more time debugging. The more code you write before you start debugging, the harder it is to find the problem.\n",
     "\n",
     "**Incremental development** is a way of programming that tries to\n",
     "minimize the pain of debugging. The fundamental steps are:\n",
@@ -570,7 +547,7 @@
     "    check it by performing another computation.\n",
     "\n",
     "3.  Run the program and see if the change worked. If so, go back to\n",
-    "    Step 2. If not, you will have to do some debugging, but if the\n",
+    "    Step 2. If not, you have to do some debugging, but if the\n",
     "    change you made was small, it shouldn't take long to find the\n",
     "    problem.\n",
     "\n",
@@ -597,7 +574,17 @@
    "id": "nominated-assault",
    "metadata": {},
    "source": [
-    "## Summary"
+    "## Summary\n",
+    "\n",
+    "This chapter introduces functions that return values, which we use to write a version of `run_simulation` that returns a `State` object with the final state of the system.\n",
+    "\n",
+    "It also introduces `linspace`, which we use to create a NumPy array, and `SweepSeries`, which we use to store the results of a parameter sweep.\n",
+    "\n",
+    "We used a parameter sweep to explore the relationship between one of the parameters, `p1`, and the number of unhappy customers, which is a metric that quantifies how well (or badly) the system works.\n",
+    "\n",
+    "In the exercises, you'll have a chance to sweep other parameters and compute other metrics.\n",
+    "\n",
+    "In the next chapter, we'll move on to a new problem, modeling and predicting world population growth."
    ]
   },
   {
@@ -620,7 +607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "reflected-freedom",
    "metadata": {},
    "outputs": [],
@@ -630,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "north-formation",
    "metadata": {},
    "outputs": [],
@@ -643,13 +630,12 @@
    "id": "robust-blair",
    "metadata": {},
    "source": [
-    "**Exercise:** Read the documentation of `linspace` at <>.\n",
-    "Then use it to make an array of 101 equally spaced points between 0 and 1 (including both)."
+    "**Exercise:** Read the documentation of `linspace` at <https://numpy.org/doc/stable/reference/generated/numpy.linspace.html>. Then use it to make an array of 101 equally spaced points between 0 and 1 (including both)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "collected-butter",
    "metadata": {},
    "outputs": [],
@@ -669,7 +655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "authorized-sarah",
    "metadata": {},
    "outputs": [],
@@ -679,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "romance-wisdom",
    "metadata": {},
    "outputs": [],
@@ -697,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "norman-banana",
    "metadata": {},
    "outputs": [],
@@ -707,7 +693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "mexican-robert",
    "metadata": {},
    "outputs": [],
@@ -733,7 +719,6 @@
     "**Exercise:** Because our simulations are random, the results vary from one run to another, and the results of a parameter sweep tend to be noisy.  We can get a clearer picture of the relationship between a parameter and a metric by running multiple simulations with the same parameter and taking the average of the results.\n",
     "\n",
     "Write a function called `run_multiple_simulations` that takes as parameters `p1`, `p2`, `num_steps`, and `num_runs`.\n",
-    "\n",
     "`num_runs` specifies how many times it should call `run_simulation`.\n",
     "\n",
     "After each run, it should store the total number of unhappy customers (at Olin or Wellesley) in a `TimeSeries`.\n",
@@ -753,7 +738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 27,
    "id": "accredited-salmon",
    "metadata": {},
    "outputs": [],
@@ -763,7 +748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 28,
    "id": "visible-allowance",
    "metadata": {},
    "outputs": [],
@@ -773,7 +758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 29,
    "id": "spatial-fundamentals",
    "metadata": {},
    "outputs": [],
@@ -801,7 +786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 30,
    "id": "reverse-emphasis",
    "metadata": {
     "scrolled": true
@@ -813,7 +798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 32,
    "id": "broad-latitude",
    "metadata": {
     "scrolled": true
@@ -823,16 +808,41 @@
     "# Solution goes here"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "biblical-federal",
+   "metadata": {},
+   "source": [
+    "## Under the Hood\n",
+    "\n",
+    "The object you get when you call `SweepSeries` is actually a Pandas `Series`, the same as the object you get from `TimeSeries`.\n",
+    "I give them different names to help us remember that they play different roles.\n",
+    "\n",
+    "`Series` provides a number of functions, which you can read about at <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html>.\n",
+    "\n",
+    "They include `mean`, which computes the average of the values in the `Series`, so if you have a `Series` named `totals`, for example, you can compute the mean like this:\n",
+    "\n",
+    "```\n",
+    "    totals.mean()\n",
+    "```\n",
+    "\n",
+    "`Series` provides other statistical functions, like `std`, which computes the standard deviation of the values in the series.\n",
+    "\n",
+    "In this chapter I use the keyword argument `color` to specify the color of a line plot.\n",
+    "You can read about the other available colors at <https://matplotlib.org/3.3.2/tutorials/colors/colors.html>."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "powerful-planner",
+   "id": "federal-cemetery",
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -848,7 +858,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

From 3e2d30840ae5c6a2a5381fdc7da8f9122ed5de59 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 15 Feb 2021 13:29:54 -0500
Subject: [PATCH 039/144] Updating chapters

---
 chapters/chap05.ipynb |  10 --
 chapters/chap06.ipynb | 157 +++++++++++------------
 chapters/chap07.ipynb | 241 +++++++++++++++++++----------------
 chapters/chap08.ipynb | 288 +++++++++++++++++++++---------------------
 chapters/chap09.ipynb | 248 ++++++++++++++++++++++--------------
 chapters/chap10.ipynb |   8 +-
 6 files changed, 500 insertions(+), 452 deletions(-)

diff --git a/chapters/chap05.ipynb b/chapters/chap05.ipynb
index b028809e..b9ceafd6 100644
--- a/chapters/chap05.ipynb
+++ b/chapters/chap05.ipynb
@@ -824,8 +824,6 @@
    "id": "automated-albany",
    "metadata": {},
    "source": [
-    "The keyword argument `color='gray'` specifies the color of the line.  For details on color specification see <https://modsimpy.com/color>.\n",
-    "\n",
     "From 1950 to 1990, the model does not fit the data particularly well, but after that, it's pretty good."
    ]
   },
@@ -946,14 +944,6 @@
    "source": [
     "# Solution goes here"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "improved-sigma",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/chapters/chap06.ipynb b/chapters/chap06.ipynb
index 0c18a0ac..08e9df6a 100644
--- a/chapters/chap06.ipynb
+++ b/chapters/chap06.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "meaning-public",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "alike-thompson",
    "metadata": {
     "tags": []
@@ -79,49 +79,39 @@
   },
   {
    "cell_type": "markdown",
-   "id": "quality-spectrum",
-   "metadata": {},
-   "source": [
-    "In the previous chapter we simulated a model of world population with\n",
-    "constant growth. In this chapter we see if we can make a better model\n",
-    "with growth proportional to the population.\n",
-    "\n",
-    "But first, we'll improve the code from the previous chapter by\n",
-    "encapsulating it in a function and using `System` objects."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ranking-tamil",
-   "metadata": {},
+   "id": "amber-contrary",
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "Here's the code that reads the data."
+    "Here's the data from the previous chapter again."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "annoying-herald",
-   "metadata": {},
+   "execution_count": 7,
+   "id": "critical-addition",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates.html'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/master/data/World_population_estimates.html')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "shared-offense",
-   "metadata": {},
+   "execution_count": 8,
+   "id": "naughty-swing",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from pandas import read_html\n",
     "\n",
+    "filename = 'World_population_estimates.html'\n",
     "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
     "table2 = tables[2]\n",
     "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
@@ -129,6 +119,19 @@
     "                  'thomlinson', 'durand', 'clark']"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "quality-spectrum",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we simulated a model of world population with\n",
+    "constant growth. In this chapter we see if we can make a better model\n",
+    "with growth proportional to the population.\n",
+    "\n",
+    "But first, we'll improve the code from the previous chapter by\n",
+    "encapsulating it in a function and adding a new feature, a `System` object."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "therapeutic-merchant",
@@ -146,8 +149,7 @@
     "For example, in the bike share model, state variables include the number of bikes at each location, which get updated whenever a customer moves a bike. System parameters include the number of locations, total number of bikes, and arrival rates at each location.\n",
     "\n",
     "In the population model, the only state variable is the population.\n",
-    "System parameters include the annual growth rate, the initial time and\n",
-    "population, and the end time.\n",
+    "System parameters include the annual growth rate, the initial population, and the start and end times.\n",
     "\n",
     "Suppose we have the following variables, as computed in the previous\n",
     "chapter (assuming `table2` is the `DataFrame` we read from the file):"
@@ -155,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "id": "numerous-university",
    "metadata": {},
    "outputs": [],
@@ -180,18 +182,16 @@
    "metadata": {},
    "source": [
     "Some of these are parameters we need to simulate the system; others are temporary values we can discard. \n",
-    "To distinguish between them, we'll put the parameters we need into a `System` object like this:"
+    "To distinguish between them, we'll put the parameters we need in a `System` object like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "id": "colonial-domestic",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "system = System(t_0=t_0, \n",
     "                t_end=t_end,\n",
     "                p_0=p_0,\n",
@@ -211,12 +211,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "id": "floral-routine",
    "metadata": {},
    "outputs": [],
    "source": [
-    "system"
+    "show(system)"
    ]
   },
   {
@@ -229,13 +229,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "id": "pacific-challenge",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "def run_simulation1(system):\n",
     "    results = TimeSeries()\n",
     "    results[system.t_0] = system.p_0\n",
@@ -248,30 +246,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "technological-medicine",
+   "id": "rough-strain",
    "metadata": {},
    "source": [
     "`run_simulation1` takes a `System` object and uses the parameters in it to determine `t_0`, `t_end`, and `annual_growth`.\n",
     "\n",
-    "Inside the loop, it stores the results in a `TimeSeries` which it returns at the end.\n",
-    "\n",
-    "The following function plots the results along with the estimates\n",
-    "`census` and `un`:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "grateful-terror",
-   "metadata": {},
-   "source": [
-    "\n",
+    "It simulates population growth over time and returns the results in a `TimeSeries`.\n",
     "\n",
     "Here's how we call it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 17,
    "id": "electoral-breach",
    "metadata": {},
    "outputs": [],
@@ -289,13 +276,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 18,
    "id": "peripheral-cassette",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "def plot_estimates():\n",
     "    census.plot(style=':', label='US Census')\n",
     "    un.plot(style='--', label='UN DESA')\n",
@@ -313,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 19,
    "id": "capable-diana",
    "metadata": {},
    "outputs": [],
@@ -353,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 20,
    "id": "laughing-wesley",
    "metadata": {},
    "outputs": [],
@@ -375,15 +360,17 @@
    "id": "educated-portugal",
    "metadata": {},
    "source": [
-    "Now we can choose the values of `birth_rate` and `death_rate` that best fit the data. \n",
+    "Each time through the loop, we use the parameter `birth_rate` to compute the number of births, and `death_rate` to compute the number of deaths.\n",
+    "The rest of the function is the same as `run_simulation1`.\n",
     "\n",
+    "Now we can choose the values of `birth_rate` and `death_rate` that best fit the data. \n",
     "For the death rate, I'll use 7.7 deaths per 1000 people, which was roughly the global death rate in 2020 (see <https://www.indexmundi.com/world/death_rate.html>).\n",
     "I chose the birth rate by hand to fit the data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 21,
    "id": "wired-brief",
    "metadata": {},
    "outputs": [],
@@ -402,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 22,
    "id": "looking-trace",
    "metadata": {},
    "outputs": [],
@@ -439,17 +426,17 @@
     "`run_simulation1` and `run_simulation2` are nearly identical except for the body of the `for` loop, where we compute the population for the next year.\n",
     "\n",
     "Rather than repeat identical code, we can separate the things that\n",
-    "change from the things that don't. First, I'll pull out the update code from `run_simulation2` and make it a function:"
+    "change from the things that don't. First, I'll pull out the births and deaths from `run_simulation2` and make a function:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 23,
    "id": "handmade-permit",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def growth_func1(pop, t, system):\n",
+    "def growth_func1(t, pop, system):\n",
     "    births = system.birth_rate * pop\n",
     "    deaths = system.death_rate * pop\n",
     "    return births - deaths"
@@ -460,19 +447,20 @@
    "id": "fabulous-bankruptcy",
    "metadata": {},
    "source": [
-    "This function takes as arguments the current population, current year,\n",
-    "and a `System` object; it returns the net population growth during the current year.\n",
+    "`growth_func1` takes as arguments the current year, current population, and a `System` object; it returns the net population growth during the current year.\n",
     "\n",
-    "This update function does not use `t`, so we could leave it out. But we will see other functions that need it, and it is convenient if they all take the same parameters, used or not.\n",
+    "This function does not use `t`, so we could leave it out. But we will see other growth functions that need it, and it is convenient if they all take the same parameters, used or not.\n",
     "\n",
     "Now we can write a function that runs any model:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 24,
    "id": "civilian-accused",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "def run_simulation(system, growth_func):\n",
@@ -480,7 +468,7 @@
     "    results[system.t_0] = system.p_0\n",
     "    \n",
     "    for t in range(system.t_0, system.t_end):\n",
-    "        growth = growth_func(results[t], t, system)\n",
+    "        growth = growth_func(t, results[t], system)\n",
     "        results[t+1] = results[t] + growth\n",
     "        \n",
     "    return results"
@@ -501,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 25,
    "id": "wicked-seeking",
    "metadata": {},
    "outputs": [],
@@ -517,7 +505,7 @@
     "Passing a function as an argument is the same as passing any other\n",
     "value. The argument, which is `growth_func1` in this example, gets\n",
     "assigned to the parameter, which is called `growth_func`. Inside\n",
-    "`run_simulation`, we can run `growth_func` just like any other function.\n",
+    "`run_simulation`, we can call `growth_func` just like any other function.\n",
     "\n",
     "Each time through the loop, `run_simulation` calls `growth_func1` to compute net growth, and uses it to compute the population during the next year."
    ]
@@ -535,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 26,
    "id": "impressive-model",
    "metadata": {},
    "outputs": [],
@@ -556,12 +544,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 27,
    "id": "familiar-helena",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def growth_func2(pop, t, system):\n",
+    "def growth_func2(t, pop, system):\n",
     "    return system.alpha * pop"
    ]
   },
@@ -575,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 28,
    "id": "independent-effectiveness",
    "metadata": {},
    "outputs": [],
@@ -621,14 +609,14 @@
    "source": [
     "**Exercise:** Maybe the reason the proportional model doesn't work very well is that the growth rate, `alpha`, is changing over time.  So let's try a model with different growth rates before and after 1980 (as an arbitrary choice).\n",
     "\n",
-    "Write an update function that takes `pop`, `t`, and `system` as parameters.  The system object, `system`, should contain two parameters: the growth rate before 1980, `alpha1`, and the growth rate after 1980, `alpha2`.  It should use `t` to determine which growth rate to use.\n",
+    "Write an update function that takes `t`, `pop`, and `system` as parameters.  The system object, `system`, should contain two parameters: the growth rate before 1980, `alpha1`, and the growth rate after 1980, `alpha2`.  It should use `t` to determine which growth rate to use.\n",
     "\n",
     "Test your function by calling it directly, then pass it to `run_simulation`.  Plot the results.  Adjust the parameters `alpha1` and `alpha2` to fit the data as well as you can."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 29,
    "id": "minus-recommendation",
    "metadata": {
     "scrolled": false
@@ -640,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 30,
    "id": "headed-amsterdam",
    "metadata": {},
    "outputs": [],
@@ -650,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 31,
    "id": "preliminary-carnival",
    "metadata": {},
    "outputs": [],
@@ -678,6 +666,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
diff --git a/chapters/chap07.ipynb b/chapters/chap07.ipynb
index 40678cf7..f5859ad3 100644
--- a/chapters/chap07.ipynb
+++ b/chapters/chap07.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "meaning-public",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "alike-thompson",
    "metadata": {
     "tags": []
@@ -79,54 +79,39 @@
   },
   {
    "cell_type": "markdown",
-   "id": "indie-scoop",
-   "metadata": {},
-   "source": [
-    "In the previous chapter we developed a population model where net growth during each time step is proportional to the current population. This model seems more realistic than the constant growth model, but it does not fit the data as well.\n",
-    "\n",
-    "There are a few things we could try to improve the model:\n",
-    "\n",
-    "-   Maybe net growth depends on the current population, but the\n",
-    "    relationship is quadratic, not linear.\n",
-    "\n",
-    "-   Maybe the net growth rate varies over time.\n",
-    "\n",
-    "In this chapter, we'll explore the first option.\n",
-    "In the exercises, you will have a chance to try the second. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "qualified-houston",
-   "metadata": {},
+   "id": "straight-algeria",
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "Here's the code that reads the data."
+    "Here's the data from the previous chapter again."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "major-arkansas",
-   "metadata": {},
+   "execution_count": 4,
+   "id": "square-trademark",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates.html'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/master/data/World_population_estimates.html')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "intense-columbus",
-   "metadata": {},
+   "execution_count": 5,
+   "id": "arctic-toilet",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from pandas import read_html\n",
     "\n",
+    "filename = 'World_population_estimates.html'\n",
     "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
     "table2 = tables[2]\n",
     "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
@@ -136,9 +121,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "wound-necklace",
-   "metadata": {},
+   "execution_count": 6,
+   "id": "protective-pipeline",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "un = table2.un / 1e9\n",
@@ -147,53 +134,48 @@
   },
   {
    "cell_type": "markdown",
-   "id": "intended-matrix",
-   "metadata": {},
+   "id": "informational-hungary",
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "And here are the functions from the previous chapter."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "former-accuracy",
-   "metadata": {},
+   "execution_count": 7,
+   "id": "associate-novel",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
+    "from chap06 import run_simulation\n",
     "\n",
-    "\n",
-    "def run_simulation(system, growth_func):\n",
-    "    \"\"\"Simulate the system using any update function.\n",
-    "    \n",
-    "    system: System object\n",
-    "    growth_func: function that computes the population next year\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in range(system.t_0, system.t_end):\n",
-    "        growth = growth_func(results[t], t, system)\n",
-    "        results[t+1] = results[t] + growth\n",
-    "        \n",
-    "    return results"
+    "def plot_estimates():\n",
+    "    census.plot(style=':', label='US Census')\n",
+    "    un.plot(style='--', label='UN DESA')\n",
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)') "
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "duplicate-insert",
+   "cell_type": "markdown",
+   "id": "indie-scoop",
    "metadata": {},
-   "outputs": [],
    "source": [
+    "In the previous chapter we developed a population model where net growth during each time step is proportional to the current population. This model seems more realistic than the constant growth model, but it does not fit the data as well.\n",
     "\n",
+    "There are a few things we could try to improve the model:\n",
     "\n",
-    "def plot_estimates():\n",
-    "    census.plot(style=':', label='US Census')\n",
-    "    un.plot(style='--', label='UN DESA')\n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)')"
+    "-   Maybe net growth depends on the current population, but the\n",
+    "    relationship is quadratic, not linear.\n",
+    "\n",
+    "-   Maybe the net growth rate varies over time.\n",
+    "\n",
+    "In this chapter, we'll explore the first option.\n",
+    "In the exercises, you will have a chance to try the second. "
    ]
   },
   {
@@ -220,12 +202,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "sized-hawaiian",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def growth_func_quad(pop, t, system):\n",
+    "def growth_func_quad(t, pop, system):\n",
     "    return system.alpha * pop + system.beta * pop**2"
    ]
   },
@@ -239,13 +221,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "interstate-canal",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "t_0 = census.index[0]\n",
     "p_0 = census[t_0]\n",
     "t_end = census.index[-1]\n",
@@ -266,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "banned-dallas",
    "metadata": {},
    "outputs": [],
@@ -285,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "positive-scheme",
    "metadata": {},
    "outputs": [],
@@ -303,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "flush-fellowship",
    "metadata": {},
    "outputs": [],
@@ -348,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "excited-catch",
    "metadata": {},
    "outputs": [],
@@ -368,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "classical-badge",
    "metadata": {},
    "outputs": [],
@@ -382,17 +362,17 @@
    "id": "quantitative-differential",
    "metadata": {},
    "source": [
-    "To plot the growth rate versus population, we can import the `plot` function from Matplotlib:"
+    "In order to plot growth rate versus population, we'll use `make_series` to make a Pandas `Series`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "interim-consent",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from matplotlib.pyplot import plot"
+    "growth_series = make_series(pop_array, growth_array)"
    ]
   },
   {
@@ -400,17 +380,18 @@
    "id": "published-average",
    "metadata": {},
    "source": [
-    "And use it like this."
+    "Which we can plot like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "received-crossing",
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot(pop_array, growth_array, label='growth')\n",
+    "growth_series.plot(label='growth')\n",
+    "\n",
     "\n",
     "decorate(xlabel='Population (billions)',\n",
     "         ylabel='Net growth (billions)',\n",
@@ -466,7 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "lightweight-entrepreneur",
    "metadata": {},
    "outputs": [],
@@ -489,9 +470,9 @@
     "This is the same model; it's just a different way to **parameterize** it. Given $\\alpha$ and $\\beta$, we can compute $r=\\alpha$ and $K=-\\alpha/\\beta$.\n",
     "\n",
     "In this version, it is easier to interpret the parameters: $r$ is the\n",
-    "maximum growth rate, observed when $p$ is small, and $K$ is the\n",
-    "equilibrium point. $K$ is also called the **carrying capacity**, since\n",
-    "it indicates the maximum population the environment can sustain."
+    "unconstrained growth rate, observed when $p$ is small, and $K$ is the\n",
+    "equilibrium point. \n",
+    "$K$ is also called the **carrying capacity**, since it indicates the maximum population the environment can sustain."
    ]
   },
   {
@@ -529,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "lucky-insurance",
    "metadata": {},
    "outputs": [],
@@ -556,14 +537,12 @@
    "id": "transparent-cotton",
    "metadata": {},
    "source": [
-    "Dysfunction \\#1: Not using parameters. In the following version, the\n",
-    "function doesn't take any parameters; when `sys1` appears inside the\n",
-    "function, it refers to the object we create outside the function."
+    "**Dysfunction #1:** Not using parameters. In the following version, the function doesn't take any parameters; when `sys1` appears inside the function, it refers to the object we create outside the function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "aggregate-baker",
    "metadata": {},
    "outputs": [],
@@ -582,7 +561,7 @@
    "id": "otherwise-belarus",
    "metadata": {},
    "source": [
-    "This version actually works, but it is not as versatile as it could be.\n",
+    "This version works, but it is not as versatile as it could be.\n",
     "If there are several `System` objects, this function can only work with one of them, and only if it is named `sys1`."
    ]
   },
@@ -591,13 +570,13 @@
    "id": "naughty-crazy",
    "metadata": {},
    "source": [
-    "Dysfunction \\#2: Clobbering the parameters. When people first learn\n",
+    "**Dysfunction #2:** Clobbering the parameters. When people first learn\n",
     "about parameters, they often write functions like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "outdoor-petroleum",
    "metadata": {},
    "outputs": [],
@@ -635,13 +614,12 @@
    "id": "expired-detail",
    "metadata": {},
    "source": [
-    "Dysfunction \\#3: No return value. Here's a version that computes the\n",
-    "value of `K` but doesn't return it."
+    "**Dysfunction #3:** No return value. Here's a version that computes the value of `K` but doesn't return it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "increasing-database",
    "metadata": {},
    "outputs": [],
@@ -668,8 +646,7 @@
    "id": "meaning-thriller",
    "metadata": {},
    "source": [
-    "Dysfunction \\#4: Ignoring the return value. Finally, here's a version\n",
-    "where the function is correct, but the way it's used is not.\n",
+    "**Dysfunction #4:** Ignoring the return value. Finally, here's a version where the function is correct, but the way it's used is not.\n",
     "\n",
     "```\n",
     "def carrying_capacity(system):\n",
@@ -714,12 +691,12 @@
     "\n",
     "where $r=\\alpha$ and $K=-\\alpha/\\beta$.  \n",
     "\n",
-    "Write a version of `growth_func` that implements this version of the model.  Test it by computing the values of `r` and `K` that correspond to `alpha=0.025, beta=-0.0018`, and confirm that you get the same results. "
+    "Write a version of `growth_func` that implements this version of the model.  Test it by computing the values of `r` and `K` that correspond to `alpha=0.025` and `beta=-0.0018`, and confirm that you get the same results. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "unauthorized-settle",
    "metadata": {},
    "outputs": [],
@@ -729,7 +706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "primary-roman",
    "metadata": {},
    "outputs": [],
@@ -739,7 +716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "ordinary-solid",
    "metadata": {},
    "outputs": [],
@@ -763,7 +740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "intermediate-kazakhstan",
    "metadata": {},
    "outputs": [],
@@ -778,19 +755,61 @@
    "source": [
     "## Under the hood\n",
     "\n",
-    "Explain the difference between the Pandas and Matplotlib `plot` functions."
+    "ModSim provides `make_series` to make it easier to create a Pandas Series.  In this chapter, we used it like this: "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "described-funds",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "growth_series = make_series(pop_array, growth_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "least-password",
+   "metadata": {},
+   "source": [
+    "If you import `Series` from Pandas, you can make a `Series` yourself, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "sitting-quarterly",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import Series\n",
+    "\n",
+    "growth_series = Series(growth_array, pop_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "german-kennedy",
+   "metadata": {},
+   "source": [
+    "The difference is that the arguments are in reverse order: the first argument is stored as the values in the `Series`; the second argument is stored as the `index`.\n",
+    "\n",
+    "I find that order counterintuitive, which is why I use `make_series`.\n",
+    "`make_series` takes the same optional keyword arguments as `Series`, which you can read about at <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "mental-october",
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -806,7 +825,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap08.ipynb b/chapters/chap08.ipynb
index 2aaa36bb..50891a0d 100644
--- a/chapters/chap08.ipynb
+++ b/chapters/chap08.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "meaning-public",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "alike-thompson",
    "metadata": {
     "tags": []
@@ -79,71 +79,98 @@
   },
   {
    "cell_type": "markdown",
-   "id": "chicken-emphasis",
-   "metadata": {},
+   "id": "global-international",
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "In the previous chapter we developed a quadratic model of world\n",
-    "population growth from 1950 to 2016. It is a simple model, but it fits\n",
-    "the data well and the mechanisms it's based on are plausible.\n",
-    "\n",
-    "In this chapter we'll use the quadratic model to generate projections of future growth, and compare our results to projections from actual\n",
-    "demographers."
+    "Here's the data from the previous chapters, one last time."
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "global-international",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "necessary-factor",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
    "source": [
-    "Here's the code that downloads the data."
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/master/data/World_population_estimates.html')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "necessary-factor",
-   "metadata": {},
+   "execution_count": 5,
+   "id": "changed-desktop",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "import os\n",
+    "from pandas import read_html\n",
     "\n",
     "filename = 'World_population_estimates.html'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
+    "table2 = tables[2]\n",
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "metallic-inventory",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "un = table2.un / 1e9\n",
+    "census = table2.census / 1e9"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "designed-entrance",
-   "metadata": {},
+   "id": "current-canberra",
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "And here's the code that reads `table2`, which contains world populations estimates from the U.S. Census and U.N. DESA, among other organizations."
+    "And here are the functions from the previous chapter."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "changed-desktop",
-   "metadata": {},
+   "execution_count": 7,
+   "id": "cutting-financing",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "from pandas import read_html\n",
+    "from chap06 import run_simulation\n",
     "\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')"
+    "def plot_estimates():\n",
+    "    census.plot(style=':', label='US Census')\n",
+    "    un.plot(style='--', label='UN DESA')\n",
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)') "
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "metallic-inventory",
+   "cell_type": "markdown",
+   "id": "chicken-emphasis",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "table2 = tables[2]\n",
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']"
+    "In the previous chapter we developed a quadratic model of world\n",
+    "population growth from 1950 to 2016. It is a simple model, but it fits\n",
+    "the data well and the mechanisms it's based on are plausible.\n",
+    "\n",
+    "In this chapter we'll use the quadratic model to generate projections of future growth, and compare our results to projections from actual\n",
+    "demographers."
    ]
   },
   {
@@ -159,108 +186,80 @@
    "id": "concrete-lightning",
    "metadata": {},
    "source": [
-    "Now let's run the quadratic model, extending the results until 2100, and see how our projections compare to the professionals'.\n",
+    "Let's run the quadratic model, extending the results until 2100, and see how our projections compare to the professionals'.\n",
     "\n",
-    "Here's the code we'll need from the previous chapter."
+    "Here's the quadratic growth function again."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "extraordinary-shade",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "def run_simulation(system, growth_func):\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in range(system.t_0, system.t_end):\n",
-    "        growth = growth_func(results[t], t, system)\n",
-    "        results[t+1] = results[t] + growth\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "indirect-russia",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def growth_func_quad(pop, t, system):\n",
+    "def growth_func_quad(t, pop, system):\n",
     "    return system.alpha * pop + system.beta * pop**2"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "collaborative-schedule",
+   "cell_type": "markdown",
+   "id": "little-struggle",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "census = table2.census / 1e9\n",
-    "un = table2.un / 1e9"
+    "And here are the system parameters."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "comfortable-compression",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "t_0 = census.index[0]\n",
-    "p_0 = census[t_0]"
+    "p_0 = census[t_0]\n",
+    "\n",
+    "system = System(t_0 = t_0,\n",
+    "                p_0 = p_0,\n",
+    "                alpha = 25 / 1000,\n",
+    "                beta = -1.8 / 1000,\n",
+    "                t_end = 2100)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "working-cleveland",
+   "cell_type": "markdown",
+   "id": "legitimate-guess",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "system = System(t_0=t_0,\n",
-    "                p_0=p_0,\n",
-    "                t_end=2100)"
+    "With `t_end=2100`, we can generate the projection by calling `run_simulation` the usual way."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "sophisticated-adult",
+   "id": "broken-windsor",
    "metadata": {},
    "outputs": [],
    "source": [
-    "system.alpha = 25 / 1000\n",
-    "system.beta = -1.8 / 1000"
+    "results = run_simulation(system, growth_func_quad)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "broken-windsor",
+   "cell_type": "markdown",
+   "id": "provincial-competition",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "results = run_simulation(system, growth_func_quad)"
+    "Here are the last few values in the results."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "latest-function",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "show(results.tail())"
    ]
   },
@@ -269,18 +268,16 @@
    "id": "outer-ensemble",
    "metadata": {},
    "source": [
-    "And here are the results."
+    "Here's what the results look like."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "portable-pottery",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "results.plot(color='gray', label='model')\n",
     "decorate(xlabel='Year', \n",
     "         ylabel='World population (billion)',\n",
@@ -292,14 +289,14 @@
    "id": "macro-carroll",
    "metadata": {},
    "source": [
-    "According to the model, population growth will slow gradually after 2020, approaching 12.5 billion by 2100.\n",
+    "According to the model, population growth will slow gradually after 2020, approaching 12.6 billion by 2100.\n",
     "\n",
     "I am using the word \"projection\" deliberately, rather than\n",
     "\"prediction\", with the following distinction: \"prediction\" implies\n",
     "something like \"this is what we should reasonably expect to happen, at\n",
     "least approximately\"; \"projection\" implies something like \"if this\n",
-    "model is actually a good description of what is happening in this\n",
-    "system, and if nothing in the future causes the parameters of the model to change, this is what would happen.\"\n",
+    "model is a good description of what is happening in this\n",
+    "system, and if nothing in the future causes the system parameters to change, this is what would happen.\"\n",
     "\n",
     "Using \"projection\" leaves open the possibility that there are important things in the real world that are not captured in the model. It also suggests that, even if the model is good, the parameters we estimate based on the past might be different in the future.\n",
     "\n",
@@ -309,7 +306,7 @@
     "rates fall and death rates rise because resources become scarce.\n",
     "\n",
     "If that assumption is valid, we might be able to use actual population\n",
-    "growth to estimate carrying capacity, especially if we observe the\n",
+    "growth to estimate carrying capacity, provided we observe the\n",
     "transition into the regime where the growth rate starts to fall.\n",
     "\n",
     "But in the case of world population growth, those conditions don't\n",
@@ -340,20 +337,14 @@
    "id": "small-seminar",
    "metadata": {},
    "source": [
-    "## Projections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "greater-illness",
-   "metadata": {},
-   "source": [
-    "From the same page where we got the past population estimates, we'll read `table3`, which contains predictions for population growth over the next 50-100 years, generated by the U.S. Census, U.N. DESA, and the Population Reference Bureau."
+    "## Projections\n",
+    "\n",
+    "From the same Wikipedia page where we got the past population estimates, we'll read `table3`, which contains predictions for population growth over the next 50-100 years, generated by the U.S. Census, U.N. DESA, and the Population Reference Bureau."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "precious-contribution",
    "metadata": {},
    "outputs": [],
@@ -374,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "headed-tuner",
    "metadata": {},
    "outputs": [],
@@ -392,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "paperback-delay",
    "metadata": {},
    "outputs": [],
@@ -417,12 +408,12 @@
    "id": "prostate-matrix",
    "metadata": {},
    "source": [
-    "Here are their projections compared to the results of the quadratic model."
+    "Here are the professional projections compared to the results of the quadratic model."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "billion-dynamics",
    "metadata": {},
    "outputs": [],
@@ -439,12 +430,7 @@
    "source": [
     "The U.N. DESA expects the world population to reach 11 billion around 2100, and then level off.\n",
     "Projections by U.S. Census are a little lower, and they only go until 2050.\n",
-    "\n",
-    "Real demographers expect world population to grow more slowly than our model projects, probably because their models are broken down by region and country, where conditions are different, and they take into account expected economic development.\n",
-    "\n",
-    "Nevertheless, their projections are qualitatively similar to ours, and\n",
-    "theirs differ from each other almost as much as they differ from ours.\n",
-    "So the results from this model, simple as it is, are not entirely unreasonable.\n"
+    "\n"
    ]
   },
   {
@@ -454,8 +440,15 @@
    "source": [
     "## Summary\n",
     "\n",
+    "In this chapter we use the quadratic growth model to project world population growth between now and 2100.\n",
+    "\n",
+    "Real demographers expect world population to grow more slowly than our model, probably because their models are broken down by region and country, where conditions are different, and they take into account expected economic development.\n",
+    "\n",
+    "Nevertheless, their projections are qualitatively similar to ours, and\n",
+    "theirs differ from each other almost as much as they differ from ours.\n",
+    "So the results from the model, simple as it is, are not entirely unreasonable.\n",
     "\n",
-    "You might be interested in this [video by Hans Rosling about the demographic changes we expect in this century](https://www.youtube.com/watch?v=ezVk1ahRF78)."
+    "If you are interested in some of the factors that go into the professional predictions, you might like this [video by Hans Rosling about the demographic changes we expect this century](https://www.youtube.com/watch?v=ezVk1ahRF78)."
    ]
   },
   {
@@ -472,13 +465,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "handmade-funeral",
    "metadata": {},
    "outputs": [],
    "source": [
     "diff = census.diff()\n",
-    "diff.head()"
+    "show(diff.head())"
    ]
   },
   {
@@ -486,20 +479,20 @@
    "id": "discrete-scanner",
    "metadata": {},
    "source": [
-    "The first element is `NaN` because we don't have the data for 1945, so we can't compute the first difference.\n",
+    "The first element is `NaN` because we don't have the data for 1949, so we can't compute the first difference.\n",
     "\n",
     "If we divide these differences by the populations, the result is an estimate of the growth rate during each year: "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "objective-accused",
    "metadata": {},
    "outputs": [],
    "source": [
     "alpha = census.diff() / census\n",
-    "alpha.head()"
+    "show(alpha.head())"
    ]
   },
   {
@@ -512,7 +505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "unique-matrix",
    "metadata": {},
    "outputs": [],
@@ -532,12 +525,13 @@
    "id": "flexible-amateur",
    "metadata": {},
    "source": [
+    "It uses `style='.'` to plot each data point with a small circle.\n",
     "And here's what it looks like."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "pressing-proceeding",
    "metadata": {},
    "outputs": [],
@@ -553,13 +547,12 @@
     "Other than a bump around 1990, net growth rate has been declining roughly linearly since 1970.\n",
     "\n",
     "We can model the decline by fitting a line to this data and extrapolating into the future.\n",
-    "\n",
     "Here's a function that takes a time stamp and computes a line that roughly fits the growth rates since 1970."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "mexican-denver",
    "metadata": {},
    "outputs": [],
@@ -575,12 +568,12 @@
    "id": "handy-dubai",
    "metadata": {},
    "source": [
-    "To see what it looks like, I'll create an array of time stamps from 1960 to 2020 and use `alpha_func` to compute the corresponding growth rates."
+    "To see what it looks like, I'll create an array of time stamps from 1960 to 2020, use `alpha_func` to compute the corresponding growth rates, and put the results into a `Series`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "addressed-worker",
    "metadata": {},
    "outputs": [],
@@ -588,7 +581,8 @@
     "from numpy import linspace\n",
     "\n",
     "t_array = linspace(1960, 2020, 5)\n",
-    "alpha_array = alpha_func(t_array)"
+    "alpha_array = alpha_func(t_array)\n",
+    "alpha_series = make_series(t_array, alpha_array)"
    ]
   },
   {
@@ -601,7 +595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "cathedral-shakespeare",
    "metadata": {},
    "outputs": [],
@@ -609,7 +603,10 @@
     "from matplotlib.pyplot import plot\n",
     "\n",
     "plot_alpha()\n",
-    "plot(t_array, alpha_array, color='gray')"
+    "alpha_series.plot(label='model', color='gray')\n",
+    "\n",
+    "decorate(ylabel='Net growth rate',\n",
+    "         title='Linear model of net growth rate')"
    ]
   },
   {
@@ -619,20 +616,20 @@
    "source": [
     "If you don't like the `slope` and `intercept` I chose, feel free to adjust them.\n",
     "\n",
-    "Now, as an exercise, you can use this function to make a projection of world population until 2100.\n",
+    "Now, as an exercise, you can use this function project world population until 2100.\n",
     "\n",
-    "1. Create a `System` object that includes `alpha_func` as a system variable.\n",
+    "1. Create a `System` object that includes `alpha_func` as a system parameter.\n",
     "\n",
     "2. Define a growth function that uses `alpha_func` to compute the net growth rate at the given time `t`.\n",
     "\n",
-    "3. Run a simulation from 1960 to 2100 with your update function, and plot the results.\n",
+    "3. Run a simulation from 1960 to 2100 with your growth function, and plot the results.\n",
     "\n",
     "4. Compare your projections with those from the US Census and UN."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "common-april",
    "metadata": {},
    "outputs": [],
@@ -642,7 +639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "ignored-chain",
    "metadata": {},
    "outputs": [],
@@ -652,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "color-accountability",
    "metadata": {},
    "outputs": [],
@@ -662,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "numeric-raise",
    "metadata": {},
    "outputs": [],
@@ -672,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "id": "included-vehicle",
    "metadata": {},
    "outputs": [],
@@ -682,7 +679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "id": "brown-rating",
    "metadata": {},
    "outputs": [],
@@ -692,7 +689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "id": "laughing-cylinder",
    "metadata": {},
    "outputs": [],
@@ -702,7 +699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "id": "personalized-parking",
    "metadata": {},
    "outputs": [],
@@ -720,6 +717,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -735,7 +733,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap09.ipynb b/chapters/chap09.ipynb
index 8e3c66ac..a8579efe 100644
--- a/chapters/chap09.ipynb
+++ b/chapters/chap09.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "meaning-public",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "alike-thompson",
    "metadata": {
     "tags": []
@@ -84,8 +84,7 @@
    "source": [
     "In this chapter we express the models from previous chapters as\n",
     "difference equations and differential equations, solve the equations,\n",
-    "and derive the functional forms of the solutions. We also discuss the\n",
-    "complementary roles of mathematical analysis and simulation."
+    "and derive the functional forms of the solutions. And I present some thoughts about the complementary roles of mathematical analysis and simulation."
    ]
   },
   {
@@ -108,8 +107,9 @@
     "\n",
     "$$x_{n+1} = x_n + c$$ \n",
     "\n",
-    "where $x_n$ is the population during year $n$,\n",
-    "$x_0$ is a given initial population, and $c$ is constant annual growth.\n",
+    "where $x_n$ is the population during year $n$, \n",
+    "$x_{n+1}$ is the population during year $n+1$,\n",
+    "and $c$ is constant annual growth.\n",
     "This way of representing the model is a **recurrence relation**; see\n",
     "<http://modsimpy.com/recur>.\n",
     "\n",
@@ -126,7 +126,7 @@
    "source": [
     "In the case of constant growth we can see that $x_1 = x_0 + c$, and\n",
     "$x_2 = x_1 + c$. Combining these, we get $x_2 = x_0 + 2c$, then\n",
-    "$x_3 = x_0 + 3c$, and it is not hard to conclude that in general\n",
+    "$x_3 = x_0 + 3c$, and we can see that in general\n",
     "\n",
     "$$x_n = x_0 + nc$$ \n",
     "\n",
@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "motivated-consumer",
+   "id": "sufficient-tampa",
    "metadata": {},
    "source": [
     "We can also write the proportional model as a recurrence relation:\n",
@@ -148,29 +148,30 @@
     "\n",
     "$$x_{n+1} = x_n (1 + \\alpha)$$ \n",
     "\n",
-    "Now we can see that\n",
-    "\n",
-    "$x_1 = x_0 (1 + \\alpha)$, and $x_2 = x_0 (1 + \\alpha)^2$, and in general\n",
+    "Now we can see that $x_1 = x_0 (1 + \\alpha)$, and $x_2 = x_0 (1 + \\alpha)^2$, and in general\n",
     "\n",
     "$$x_n = x_0 (1 + \\alpha)^n$$ \n",
     "\n",
     "This result is a **geometric progression**;\n",
     "see <http://modsimpy.com/geom>. When $\\alpha$ is positive, the factor\n",
     "$1+\\alpha$ is greater than 1, so the elements of the sequence grow\n",
-    "without bound.\n",
-    "\n",
+    "without bound."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "motivated-consumer",
+   "metadata": {},
+   "source": [
     "Finally, we can write the quadratic model like this:\n",
     "\n",
     "$$x_{n+1} = x_n + \\alpha x_n + \\beta x_n^2$$ \n",
     "\n",
-    "or with the more\n",
-    "conventional parameterization like this:\n",
+    "or with the more conventional parameterization like this:\n",
     "\n",
     "$$x_{n+1} = x_n + r x_n (1 - x_n / K)$$ \n",
     "\n",
-    "There is no analytic solution to\n",
-    "this equation, but we can approximate it with a differential equation\n",
-    "and solve that, which is what we'll do in the next section."
+    "There is no analytic solution to this equation, but we can approximate it with a differential equation and solve that, which is what we'll do in the next section."
    ]
   },
   {
@@ -202,7 +203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "covered-defense",
+   "id": "latest-impression",
    "metadata": {},
    "source": [
     "We can make this model continuous by writing the rate of change in the\n",
@@ -219,8 +220,14 @@
     "\n",
     "And then integrate both sides: \n",
     "\n",
-    "$$x(t) = c t + x_0$$\n",
-    "\n",
+    "$$x(t) = c t + x_0$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "covered-defense",
+   "metadata": {},
+   "source": [
     "Similarly, we can write the proportional growth model like this:\n",
     "\n",
     "$$\\frac{\\Delta x}{\\Delta t} = \\alpha x$$ \n",
@@ -263,14 +270,13 @@
     "\n",
     "$$x = C \\exp(\\alpha t)$$ \n",
     "\n",
-    "where $C = \\exp(K)$. There are many solutions\n",
-    "to this differential equation, with different values of $C$. The\n",
-    "particular solution we want is the one that has the value $x_0$ when\n",
-    "$t=0$.\n",
+    "where $C = \\exp(K)$. There are many solutions to this differential equation, with different values of $C$. The particular solution we want is the one that has the value $x_0$ when $t=0$.\n",
     "\n",
     "When $t=0$, $x(t) = C$, so $C = x_0$ and the solution we want is\n",
-    "$$x(t) = x_0 \\exp(\\alpha t)$$ If you would like to see this derivation\n",
-    "done more carefully, you might like this video:\n",
+    "\n",
+    "$$x(t) = x_0 \\exp(\\alpha t)$$ \n",
+    "\n",
+    "If you would like to see this derivation done more carefully, you might like this video:\n",
     "<http://modsimpy.com/khan1>."
    ]
   },
@@ -297,10 +303,10 @@
    "source": [
     "-   With analysis we can sometimes compute, exactly and efficiently, a\n",
     "    value that we could only approximate, less efficiently, with\n",
-    "    simulation. For example, in the quadratic model we plotted growth rate versus population and saw net crossed through zero when the population is\n",
+    "    simulation. For example, in the quadratic model we plotted net growth versus population and saw it crosses through zero when the population is\n",
     "    near 14 billion.  We could estimate the crossing point using a\n",
     "    numerical search algorithm (more about that later). But with the\n",
-    "    analysis in Section xxx, we get the general result that\n",
+    "    analysis in Section xxx, we derived the general result that\n",
     "    $K=-\\alpha/\\beta$.\n",
     "\n",
     "-   Analysis often provides \"computational shortcuts\", that is, the\n",
@@ -325,7 +331,7 @@
    "metadata": {},
    "source": [
     "When people see what analysis can do, they sometimes get drunk with\n",
-    "power, and imagine that it gives them a special ability to see past the veil of the material world and discern the laws of mathematics that govern the universe. When they analyze a model of a physical system, they talk about \"the math behind it\" as if our world is the mere shadow of a world of ideal mathematical entities (I am not making this up; see <http://modsimpy.com/plato>.).\n",
+    "power and imagine that it gives them a special ability to see past the veil of the material world and discern the laws of mathematics that govern the universe. When they analyze a model of a physical system, they talk about \"the math behind it\" as if our world is the mere shadow of a world of ideal mathematical entities (I am not making this up; see <http://modsimpy.com/plato>.).\n",
     "\n",
     "This is, of course, nonsense. Mathematical notation is a language\n",
     "designed by humans for a purpose, specifically to facilitate symbolic\n",
@@ -356,12 +362,11 @@
     "\n",
     "WolframAlpha infers that `f(t)` is a function of `t` and `alpha` is a\n",
     "parameter; it classifies the query as a \"first-order linear ordinary\n",
-    "differential equation\\\", and reports the general solution:\n",
+    "differential equation\", and reports the general solution:\n",
     "\n",
     "$$f(t) = c_1 \\exp(\\alpha t)$$ \n",
     "\n",
-    "If you add a second equation to specify\n",
-    "the initial condition:\n",
+    "If you add a second equation to specify the initial condition:\n",
     "\n",
     "```\n",
     "df(t) / dt = alpha f(t),  f(0) = p_0\n",
@@ -386,16 +391,7 @@
     "tools similar to Mathematica. They are not as easy to use as\n",
     "WolframAlpha, but they have some other advantages.\n",
     "\n",
-    "Before we can use SymPy, we have to import it:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "civic-payday",
-   "metadata": {},
-   "source": [
-    "SymPy defines a `Symbol` object that represents symbolic variable names,\n",
-    "functions, and other mathematical entities.\n",
+    "SymPy defines a `Symbol` object that represents symbolic variable names, functions, and other mathematical entities.\n",
     "\n",
     "The `symbols` function takes a string and returns `Symbol` objects. So\n",
     "if we run this assignment:"
@@ -403,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "flush-balance",
    "metadata": {},
    "outputs": [],
@@ -424,7 +420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "ancient-anderson",
    "metadata": {},
    "outputs": [],
@@ -446,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "familiar-oakland",
    "metadata": {},
    "outputs": [],
@@ -464,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "incomplete-sleep",
    "metadata": {},
    "outputs": [],
@@ -485,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "inside-sally",
    "metadata": {},
    "outputs": [],
@@ -514,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "requested-program",
    "metadata": {},
    "outputs": [],
@@ -539,7 +535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "incomplete-parker",
    "metadata": {},
    "outputs": [],
@@ -562,7 +558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "described-overhead",
    "metadata": {},
    "outputs": [],
@@ -584,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "material-voice",
    "metadata": {},
    "outputs": [],
@@ -602,7 +598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "id": "supposed-domestic",
    "metadata": {},
    "outputs": [],
@@ -633,12 +629,12 @@
    "id": "arabic-victorian",
    "metadata": {},
    "source": [
-    "We'll use the (r, K) parameterization, so we'll need two more symbols:"
+    "To solve the quadratic growth curve, we'll use the `r, K` parameterization, so we'll need two more symbols:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "id": "political-chinese",
    "metadata": {},
    "outputs": [],
@@ -656,7 +652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "id": "lined-queen",
    "metadata": {},
    "outputs": [],
@@ -675,7 +671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "id": "printable-typing",
    "metadata": {},
    "outputs": [],
@@ -694,7 +690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "id": "final-treatment",
    "metadata": {},
    "outputs": [],
@@ -713,7 +709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "id": "freelance-admission",
    "metadata": {},
    "outputs": [],
@@ -734,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "id": "orange-glasgow",
    "metadata": {},
    "outputs": [],
@@ -746,15 +742,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "periodic-bikini",
+   "id": "aggressive-annual",
    "metadata": {},
    "source": [
-    "The result from `solve` is a list of solutions.  In this case, [we have reason to expect only one solution](https://en.wikipedia.org/wiki/Picard%E2%80%93Lindel%C3%B6f_theorem), but we still get a list, so we have to use the bracket operator, `[0]`, to select the first one."
+    "The result from `solve` is a list of solutions.  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "id": "expensive-palace",
    "metadata": {},
    "outputs": [],
@@ -762,9 +758,17 @@
     "type(solutions), len(solutions)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "periodic-bikini",
+   "metadata": {},
+   "source": [
+    "In this case, [we have reason to expect only one solution](https://en.wikipedia.org/wiki/Picard%E2%80%93Lindel%C3%B6f_theorem), but we still get a list, so we have to use the bracket operator, `[0]`, to select the first one."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "id": "worthy-sleeve",
    "metadata": {},
    "outputs": [],
@@ -783,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "id": "aggressive-queue",
    "metadata": {},
    "outputs": [],
@@ -802,12 +806,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 36,
    "id": "funded-tolerance",
    "metadata": {},
    "outputs": [],
    "source": [
-    "particular.simplify()"
+    "simpler = particular.simplify()\n",
+    "simpler"
    ]
   },
   {
@@ -827,16 +832,20 @@
   {
    "cell_type": "markdown",
    "id": "burning-sherman",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "We can use SymPy to confirm that these two forms are equivalent.  First we represent the alternative version of the logistic function:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "id": "institutional-finish",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "A = (K - p_0) / p_0\n",
@@ -845,9 +854,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "id": "noble-auditor",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from sympy import exp\n",
@@ -859,17 +870,20 @@
   {
    "cell_type": "markdown",
    "id": "brave-conditions",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "To see whether two expressions are equivalent, we can check whether their difference simplifies to 0."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "id": "industrial-administrator",
    "metadata": {
-    "scrolled": true
+    "scrolled": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -879,13 +893,39 @@
   {
    "cell_type": "markdown",
    "id": "therapeutic-topic",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "This test only works one way: if SymPy says the difference reduces to 0, the expressions are definitely equivalent (and not just numerically close).\n",
     "\n",
     "But if SymPy can't find a way to simplify the result to 0, that doesn't necessarily mean there isn't one.  Testing whether two expressions are equivalent is a surprisingly hard problem; in fact, there is no algorithm that can solve it in general."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "flying-bermuda",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "If you use SymPy to compute and expression, and then want to evaluate that expression in Python, SumPy provides a function called `pycode` that generates Python code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "bibliographic-sarah",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sympy.printing.pycode import pycode\n",
+    "\n",
+    "pycode(simpler)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "cutting-access",
@@ -896,7 +936,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "outstanding-launch",
+   "id": "selective-calvin",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -905,27 +945,30 @@
     "\n",
     "What I called the constant growth model is more commonly called **linear growth** because the solution is a line.  If we model time as continuous, the solution is\n",
     "\n",
-    "$$f(t) = p_0 + \\alpha t$$\n",
+    "$$f(t) = p_0 + c t$$\n",
     "\n",
-    "where $\\alpha$ is the growth rate.\n",
+    "where $c$ is net annual growth.\n",
     "\n",
     "Similarly, the proportional growth model is usually called **exponential growth** because the solution is exponential:\n",
     "\n",
-    "$$f(t) = p_0 \\exp{\\alpha t}$$\n",
-    "\n",
-    "Finally, the quadratic growth model is called **logistic growth** because the solution is the logistic function:\n",
-    "\n",
-    "$$f(t) = \\frac{K}{1 + A \\exp(-rt)}$$ \n",
-    "\n",
-    "where $A = (K - p_0) / p_0$."
+    "$$f(t) = p_0 \\exp{\\alpha t}$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "adjacent-tracy",
+   "id": "outstanding-launch",
    "metadata": {},
    "source": [
-    "I avoided these terms until now because they are based on results we had not derived yet."
+    "Finally, the quadratic growth model is called **logistic growth** because the solution is the logistic function:\n",
+    "\n",
+    "$$f(t) = \\frac{K}{1 + A \\exp(-rt)}$$ \n",
+    "\n",
+    "where $A = (K - p_0) / p_0$.\n",
+    "\n",
+    "I avoided these terms until now because they are based on results we had not derived yet.\n",
+    "\n",
+    "With that, we are done with modeling world population growth.\n",
+    "The next chapter presents case studies where you can apply the tools we have learned so far."
    ]
   },
   {
@@ -941,14 +984,14 @@
    "id": "ideal-ecology",
    "metadata": {},
    "source": [
-    "**Exercise:** Solve the quadratic growth equation using the alternative parameterization\n",
+    "**Exercise:** Use SymPy to solve the quadratic growth equation using the alternative parameterization\n",
     "\n",
     "$\\frac{df(t)}{dt} = \\alpha f(t) + \\beta f^2(t) $"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "id": "internal-knock",
    "metadata": {},
    "outputs": [],
@@ -958,7 +1001,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
    "id": "smooth-sunglasses",
    "metadata": {},
    "outputs": [],
@@ -968,7 +1011,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "id": "fundamental-arlington",
    "metadata": {},
    "outputs": [],
@@ -978,7 +1021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "id": "desirable-alpha",
    "metadata": {},
    "outputs": [],
@@ -988,7 +1031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 31,
    "id": "coupled-grounds",
    "metadata": {},
    "outputs": [],
@@ -998,7 +1041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 32,
    "id": "looking-ground",
    "metadata": {},
    "outputs": [],
@@ -1008,7 +1051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 33,
    "id": "anticipated-paragraph",
    "metadata": {},
    "outputs": [],
@@ -1031,9 +1074,18 @@
     "\n",
     "Find the general solution and also the particular solution where `f(0) = p_0`."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "purple-traffic",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -1049,7 +1101,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap10.ipynb b/chapters/chap10.ipynb
index b642f49f..b8ac84f3 100644
--- a/chapters/chap10.ipynb
+++ b/chapters/chap10.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 18,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 19,
    "id": "meaning-public",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 20,
    "id": "alike-thompson",
    "metadata": {
     "tags": []
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "tired-tongue",
+   "id": "conventional-nudist",
    "metadata": {},
    "source": [
     "The following figure shows the estimates of several research groups from 1 CE to the near present."

From 38d81481e89be807d32769d9c96ace00c4c685d0 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 15 Feb 2021 15:34:11 -0500
Subject: [PATCH 040/144] Updating chapters

---
 chapters/chap01.ipynb | 26 +++++++++++++++++---------
 chapters/chap02.ipynb | 18 +++++++++++++-----
 chapters/chap03.ipynb | 18 +++++++++++++-----
 chapters/chap04.ipynb | 20 ++++++++++++++------
 chapters/chap05.ipynb | 18 +++++++++++++-----
 chapters/chap06.ipynb | 18 +++++++++++++-----
 chapters/chap07.ipynb | 18 +++++++++++++-----
 chapters/chap08.ipynb | 18 +++++++++++++-----
 chapters/chap09.ipynb | 18 +++++++++++++-----
 chapters/chap10.ipynb | 20 ++++++++++++++------
 chapters/chap11.ipynb | 16 ++++++++++++----
 chapters/chap12.ipynb | 14 +++++++++++---
 chapters/chap13.ipynb | 14 +++++++++++---
 chapters/chap14.ipynb | 14 +++++++++++---
 chapters/chap15.ipynb | 14 +++++++++++---
 chapters/chap16.ipynb | 14 +++++++++++---
 chapters/chap17.ipynb | 14 +++++++++++---
 chapters/chap18.ipynb | 14 +++++++++++---
 chapters/chap19.ipynb | 18 +++++++++++++-----
 chapters/chap20.ipynb | 14 +++++++++++---
 chapters/chap21.ipynb | 14 +++++++++++---
 chapters/chap22.ipynb | 14 +++++++++++---
 chapters/chap23.ipynb | 14 +++++++++++---
 chapters/chap24.ipynb | 16 ++++++++++++----
 chapters/chap25.ipynb | 16 ++++++++++++----
 chapters/chap26.ipynb | 20 ++++++++++++++------
 26 files changed, 320 insertions(+), 112 deletions(-)

diff --git a/chapters/chap01.ipynb b/chapters/chap01.ipynb
index 426c9db5..93b0181a 100644
--- a/chapters/chap01.ipynb
+++ b/chapters/chap01.ipynb
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "meaning-public",
+   "execution_count": 1,
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "alike-thompson",
+   "execution_count": null,
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap01.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "confirmed-budapest",
@@ -487,7 +495,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "promotional-lover",
+   "id": "cardiac-class",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -497,7 +505,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "practical-throat",
+   "id": "phantom-copper",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -546,7 +554,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "developmental-majority",
+   "id": "saving-suggestion",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1132,7 +1140,7 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "id": "front-logistics",
+   "id": "coral-camel",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/chapters/chap02.ipynb b/chapters/chap02.ipynb
index d441523c..451e3519 100644
--- a/chapters/chap02.ipynb
+++ b/chapters/chap02.ipynb
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "meaning-public",
+   "execution_count": 1,
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "alike-thompson",
+   "execution_count": null,
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap02.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "electronic-radius",
diff --git a/chapters/chap03.ipynb b/chapters/chap03.ipynb
index 1132a70d..82185a21 100644
--- a/chapters/chap03.ipynb
+++ b/chapters/chap03.ipynb
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "meaning-public",
+   "execution_count": 1,
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "alike-thompson",
+   "execution_count": null,
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap03.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "coral-steering",
diff --git a/chapters/chap04.ipynb b/chapters/chap04.ipynb
index f32e2a55..2d0bdf40 100644
--- a/chapters/chap04.ipynb
+++ b/chapters/chap04.ipynb
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "meaning-public",
+   "execution_count": 1,
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "alike-thompson",
+   "execution_count": null,
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap04.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "caring-gnome",
@@ -97,7 +105,7 @@
    "outputs": [],
    "source": [
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/chap03.py')"
+    "         'ModSimPy/master/chap03.py')"
    ]
   },
   {
diff --git a/chapters/chap05.ipynb b/chapters/chap05.ipynb
index b9ceafd6..770d78ba 100644
--- a/chapters/chap05.ipynb
+++ b/chapters/chap05.ipynb
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "meaning-public",
+   "execution_count": 1,
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "alike-thompson",
+   "execution_count": null,
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap05.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "informal-contractor",
diff --git a/chapters/chap06.ipynb b/chapters/chap06.ipynb
index 08e9df6a..9d4935e4 100644
--- a/chapters/chap06.ipynb
+++ b/chapters/chap06.ipynb
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "meaning-public",
+   "execution_count": 1,
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "alike-thompson",
+   "execution_count": null,
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap06.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "amber-contrary",
diff --git a/chapters/chap07.ipynb b/chapters/chap07.ipynb
index f5859ad3..0c965728 100644
--- a/chapters/chap07.ipynb
+++ b/chapters/chap07.ipynb
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "meaning-public",
+   "execution_count": 1,
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "alike-thompson",
+   "execution_count": null,
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap07.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "straight-algeria",
diff --git a/chapters/chap08.ipynb b/chapters/chap08.ipynb
index 50891a0d..b8c4c5a7 100644
--- a/chapters/chap08.ipynb
+++ b/chapters/chap08.ipynb
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "meaning-public",
+   "execution_count": 1,
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "alike-thompson",
+   "execution_count": null,
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap08.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "global-international",
diff --git a/chapters/chap09.ipynb b/chapters/chap09.ipynb
index a8579efe..02d66d3c 100644
--- a/chapters/chap09.ipynb
+++ b/chapters/chap09.ipynb
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "meaning-public",
+   "execution_count": 1,
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "alike-thompson",
+   "execution_count": null,
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap09.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "biblical-murder",
diff --git a/chapters/chap10.ipynb b/chapters/chap10.ipynb
index b8ac84f3..5cccdbf6 100644
--- a/chapters/chap10.ipynb
+++ b/chapters/chap10.ipynb
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "meaning-public",
+   "execution_count": 1,
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "alike-thompson",
+   "execution_count": null,
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap10.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "thirty-medication",
@@ -305,7 +313,7 @@
     "This figure shows the three scenarios we'll consider:\n",
     "\n",
     "![One queue, one server (left), one queue, two servers (middle), two\n",
-    "queues, two servers (right).](https://github.com/AllenDowney/ModSim/raw/main/figs/queue.png)\n",
+    "queues, two servers (right).](https://github.com/AllenDowney/ModSim/raw/master/figs/queue.png)\n",
     "*One queue, one server (left), one queue, two servers (middle), two\n",
     "queues, two servers (right).*\n",
     "\n",
diff --git a/chapters/chap11.ipynb b/chapters/chap11.ipynb
index c1c16741..001c40d5 100644
--- a/chapters/chap11.ipynb
+++ b/chapters/chap11.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap11.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "persistent-carbon",
@@ -213,7 +221,7 @@
     "model.\n",
     "\n",
     "![Stock and flow diagram for an SIR\n",
-    "model.](https://github.com/AllenDowney/ModSim/raw/main/figs/stock_flow1.png)\n",
+    "model.](https://github.com/AllenDowney/ModSim/raw/master/figs/stock_flow1.png)\n",
     "\n",
     "Stocks are represented by rectangles, flows by arrows. The widget in the middle of the arrows represents a valve that controls the rate of flow; the diagram shows the parameters that control the valves."
    ]
diff --git a/chapters/chap12.ipynb b/chapters/chap12.ipynb
index 752291a4..a71639ac 100644
--- a/chapters/chap12.ipynb
+++ b/chapters/chap12.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap12.ipynb)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
diff --git a/chapters/chap13.ipynb b/chapters/chap13.ipynb
index 07f38c02..ab1d4886 100644
--- a/chapters/chap13.ipynb
+++ b/chapters/chap13.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap13.ipynb)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
diff --git a/chapters/chap14.ipynb b/chapters/chap14.ipynb
index 139bbcc6..09fad940 100644
--- a/chapters/chap14.ipynb
+++ b/chapters/chap14.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap14.ipynb)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
diff --git a/chapters/chap15.ipynb b/chapters/chap15.ipynb
index a13abf5a..e859829b 100644
--- a/chapters/chap15.ipynb
+++ b/chapters/chap15.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap15.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "italic-murder",
diff --git a/chapters/chap16.ipynb b/chapters/chap16.ipynb
index 52b085f7..0e8361da 100644
--- a/chapters/chap16.ipynb
+++ b/chapters/chap16.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap16.ipynb)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
diff --git a/chapters/chap17.ipynb b/chapters/chap17.ipynb
index 7db3f45e..7db8d0a4 100644
--- a/chapters/chap17.ipynb
+++ b/chapters/chap17.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap17.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "european-movement",
diff --git a/chapters/chap18.ipynb b/chapters/chap18.ipynb
index 4fc84f93..6a450990 100644
--- a/chapters/chap18.ipynb
+++ b/chapters/chap18.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap18.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "detected-welsh",
diff --git a/chapters/chap19.ipynb b/chapters/chap19.ipynb
index 7b2c625e..734b0ee9 100644
--- a/chapters/chap19.ipynb
+++ b/chapters/chap19.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap19.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "structured-satellite",
@@ -148,7 +156,7 @@
     "\n",
     "The following circuit diagram (from <http://modsimpy.com/divider>) shows a low-pass filter built with one resistor and one capacitor.\n",
     "\n",
-    "![Circuit diagram of a low-pass filter](https://github.com/AllenDowney/ModSim/raw/main/figs/RC_Divider.svg)\n",
+    "![Circuit diagram of a low-pass filter](https://github.com/AllenDowney/ModSim/raw/master/figs/RC_Divider.svg)\n",
     "\n",
     "A \"filter\" is a circuit takes a signal, $V_{in}$, as input and produces a signal, $V_{out}$, as output. In this context, a \"signal\" is a voltage that changes over time.\n",
     "\n",
@@ -207,7 +215,7 @@
     "\n",
     "The following figure shows the scenario and their model of the wall:\n",
     "\n",
-    "![Model of a wall as a series of thermal insulators](https://github.com/AllenDowney/ModSim/raw/main/figs/wall_model.png)\n",
+    "![Model of a wall as a series of thermal insulators](https://github.com/AllenDowney/ModSim/raw/master/figs/wall_model.png)\n",
     "\n",
     "On the interior and exterior surfaces of the wall, they measure\n",
     "temperature and heat flux over a period of three days. They model the\n",
diff --git a/chapters/chap20.ipynb b/chapters/chap20.ipynb
index e22a5571..aa7fee08 100644
--- a/chapters/chap20.ipynb
+++ b/chapters/chap20.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap20.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "embedded-gentleman",
diff --git a/chapters/chap21.ipynb b/chapters/chap21.ipynb
index b827e286..803a25b4 100644
--- a/chapters/chap21.ipynb
+++ b/chapters/chap21.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap21.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "superb-february",
diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
index 031a2fdd..96420aed 100644
--- a/chapters/chap22.ipynb
+++ b/chapters/chap22.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap22.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "advanced-guidance",
diff --git a/chapters/chap23.ipynb b/chapters/chap23.ipynb
index 6763b952..b8d7de76 100644
--- a/chapters/chap23.ipynb
+++ b/chapters/chap23.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap23.ipynb)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
diff --git a/chapters/chap24.ipynb b/chapters/chap24.ipynb
index 7334a31b..28cc37c4 100644
--- a/chapters/chap24.ipynb
+++ b/chapters/chap24.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap24.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "confirmed-california",
@@ -121,7 +129,7 @@
     "The following figure shows a diagram of the system: $r$ represents\n",
     "the radius of the roll at a point in time. Initially, $r$ is the radius of the cardboard core, $R_{min}$. When the roll is complete, $r$ is $R_{max}$.\n",
     "\n",
-    "![Diagram of a roll of toilet paper, showing change in paper length as a result of a small rotation, $d\\theta$.](https://github.com/AllenDowney/ModSim/raw/main/figs/paper_roll.png)\n",
+    "![Diagram of a roll of toilet paper, showing change in paper length as a result of a small rotation, $d\\theta$.](https://github.com/AllenDowney/ModSim/raw/master/figs/paper_roll.png)\n",
     "\n",
     "I'll use $\\theta$ to represent the total rotation of the roll in\n",
     "radians. In the diagram, $d\\theta$ represents a small increase in\n",
diff --git a/chapters/chap25.ipynb b/chapters/chap25.ipynb
index ed62cb68..68d2d92e 100644
--- a/chapters/chap25.ipynb
+++ b/chapters/chap25.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap25.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "chief-delight",
@@ -179,7 +187,7 @@
     "The following figure shows the scenario, where $F$ is the force I apply to the turntable at the perimeter, perpendicular to the lever arm, $r$, and $\\tau$ is the resulting torque. The blue circle near the bottom is the teapot.\n",
     "\n",
     "![Diagram of a turntable with a\n",
-    "teapot.](https://github.com/AllenDowney/ModSim/raw/main/figs/teapot.png)\n",
+    "teapot.](https://github.com/AllenDowney/ModSim/raw/master/figs/teapot.png)\n",
     "\n",
     "Here are the parameters from the statement of the problem:"
    ]
diff --git a/chapters/chap26.ipynb b/chapters/chap26.ipynb
index 08252908..bf0d7cee 100644
--- a/chapters/chap26.ipynb
+++ b/chapters/chap26.ipynb
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "meaning-public",
+   "id": "formal-context",
    "metadata": {
     "tags": []
    },
@@ -60,13 +60,13 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alike-thompson",
+   "id": "progressive-typing",
    "metadata": {
     "tags": []
    },
@@ -77,6 +77,14 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap26.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "acoustic-small",
@@ -174,7 +182,7 @@
     "shown in this figure:\n",
     "\n",
     "![Diagram of the initial state for the Spider-Man case\n",
-    "study.](https://github.com/AllenDowney/ModSim/raw/main/figs/spiderman.png)\n",
+    "study.](https://github.com/AllenDowney/ModSim/raw/master/figs/spiderman.png)\n",
     "\n",
     "The origin, `O`, is at the base of the Empire State Building. The vector `H` represents the position where the webbing is attached to the building, relative to `O`. The vector `P` is the position of Spider-Man relative to `O`. And `L` is the vector from the attachment point to Spider-Man.\n",
     "\n",
@@ -239,7 +247,7 @@
     "\n",
     "This diagram shows the paper roll with the force applied by the kitten, $F$, the lever arm of the force around the axis of rotation, $r$, and the resulting torque, $\\tau$.\n",
     "\n",
-    "![Diagram of a roll of toilet paper, showing a force, lever arm, and the resulting torque.](https://github.com/AllenDowney/ModSim/raw/main/figs/kitten.png)\n",
+    "![Diagram of a roll of toilet paper, showing a force, lever arm, and the resulting torque.](https://github.com/AllenDowney/ModSim/raw/master/figs/kitten.png)\n",
     "\n",
     "Assuming that the force applied by the kitten is 0.002 N, how long would it take to unroll a standard roll of toilet paper?\n",
     "\n",
@@ -260,7 +268,7 @@
     "\n",
     "![Diagram of a yo-yo showing forces due to gravity and tension in the\n",
     "string, the lever arm of tension, and the resulting\n",
-    "torque.](https://github.com/AllenDowney/ModSim/raw/main/figs/yoyo.png)"
+    "torque.](https://github.com/AllenDowney/ModSim/raw/master/figs/yoyo.png)"
    ]
   },
   {

From fa7d2dc4b4d5cec9fe5d871df8dfbacb27e80c2f Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 15 Feb 2021 15:42:11 -0500
Subject: [PATCH 041/144] Adding build files

---
 jb/_config.yml       |  18 ++++
 jb/_toc.yml          |  29 ++++++
 jb/build.sh          |  16 ++++
 jb/index.md          |  65 ++++++++++++++
 jb/preface.md        | 206 +++++++++++++++++++++++++++++++++++++++++++
 jb/prep_notebooks.py |  26 ++++++
 6 files changed, 360 insertions(+)
 create mode 100644 jb/_config.yml
 create mode 100644 jb/_toc.yml
 create mode 100644 jb/build.sh
 create mode 100644 jb/index.md
 create mode 100644 jb/preface.md
 create mode 100644 jb/prep_notebooks.py

diff --git a/jb/_config.yml b/jb/_config.yml
new file mode 100644
index 00000000..15b91be1
--- /dev/null
+++ b/jb/_config.yml
@@ -0,0 +1,18 @@
+# Book settings
+title: Modeling and Simulation in Python
+author: Allen B. Downey
+
+latex:
+  latex_documents:
+    targetname: book.tex
+
+execute:
+  execute_notebooks: 'off'
+
+repository:
+  url: https://github.com/AllenDowney/ModSimPy
+html:
+  use_repository_button: true
+
+parse:
+  myst_extended_syntax: true
diff --git a/jb/_toc.yml b/jb/_toc.yml
new file mode 100644
index 00000000..50015db5
--- /dev/null
+++ b/jb/_toc.yml
@@ -0,0 +1,29 @@
+file: index
+sections:
+- file: preface
+- file: chap01
+- file: chap02
+- file: chap03
+- file: chap04
+- file: chap05
+- file: chap06
+- file: chap07
+- file: chap08
+- file: chap09
+- file: chap10
+- file: chap11
+- file: chap12
+- file: chap13
+- file: chap14
+- file: chap15
+- file: chap16
+- file: chap17
+- file: chap18
+- file: chap19
+- file: chap20
+- file: chap21
+- file: chap22
+- file: chap23
+- file: chap24
+- file: chap25
+- file: chap26
diff --git a/jb/build.sh b/jb/build.sh
new file mode 100644
index 00000000..131c2418
--- /dev/null
+++ b/jb/build.sh
@@ -0,0 +1,16 @@
+# pip install jupyter-book
+# pip install ghp-import
+
+# Build the Jupyter book version
+
+# copy the chapter notebooks
+cp ../ModSimPySolutions/chapters/chap*.ipynb .
+
+# add tags to hide the solutions
+python prep_notebooks.py
+
+# build the HTML version
+jb build .
+
+# push it to GitHub
+ghp-import -n -p -f _build/html
diff --git a/jb/index.md b/jb/index.md
new file mode 100644
index 00000000..4804509e
--- /dev/null
+++ b/jb/index.md
@@ -0,0 +1,65 @@
+# Modeling and Simulation in Python
+
+by Allen B. Downey
+
+*Modeling and Simulation in Python* is a Free Book. It is available under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/), which means that you are free to copy and modify it, as long as you attribute the work and don’t use it for commercial purposes.
+
+Other Free Books by Allen Downey are available from [Green Tea Press](https://greenteapress.com/wp/).
+
+**Run the notebooks**
+
+[Download the notebooks as a Zip file](https://github.com/AllenDowney/ModSimPy/raw/master/ModSimPyNotebooks.zip)
+
+Or use these links to run the notebooks on Colab:
+
+* [Chapter 1](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap01.ipynb)
+
+* [Chapter 2](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap02.ipynb)
+
+* [Chapter 3](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap03.ipynb)
+
+* [Chapter 4](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap04.ipynb)
+
+* [Chapter 5](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap05.ipynb)
+
+* [Chapter 6](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap06.ipynb)
+
+* [Chapter 7](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap07.ipynb)
+
+* [Chapter 8](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap08.ipynb)
+
+* [Chapter 9](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap09.ipynb)
+
+* [Chapter 10](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap10.ipynb)
+
+* [Chapter 11](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap11.ipynb)
+
+* [Chapter 12](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap12.ipynb)
+
+* [Chapter 13](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap13.ipynb)
+
+* [Chapter 14](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap14.ipynb)
+
+* [Chapter 15](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap15.ipynb)
+
+* [Chapter 16](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap16.ipynb)
+
+* [Chapter 17](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap17.ipynb)
+
+* [Chapter 18](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap18.ipynb)
+
+* [Chapter 19](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap19.ipynb)
+
+* [Chapter 20](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap20.ipynb)
+
+* [Chapter 21](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap21.ipynb)
+
+* [Chapter 22](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap22.ipynb)
+
+* [Chapter 23](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap23.ipynb)
+
+* [Chapter 24](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap24.ipynb)
+
+* [Chapter 25](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap25.ipynb)
+
+* [Chapter 26](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap26.ipynb)
diff --git a/jb/preface.md b/jb/preface.md
new file mode 100644
index 00000000..cca775c2
--- /dev/null
+++ b/jb/preface.md
@@ -0,0 +1,206 @@
+# Preface
+
+The essential skills of modeling — abstraction, analysis, simulation,
+and validation — are central in engineering, natural sciences, social
+sciences, medicine, and many other fields. Some students learn these
+skills implicitly, but in most schools they are not taught explicitly,
+and students get little practice. That's the problem this book is meant to address.
+
+At Olin College, we teach these skills in a class called "Modeling and
+Simulation", which all students take in their first semester. My
+colleagues, John Geddes and Mark Somerville, and I developed this class and taught it for the first time in 2009.
+
+It is based on our belief that modeling should be taught explicitly,
+early, and throughout the curriculum. It is also based on our conviction that computation is an essential part of this process.
+
+If students are limited to the mathematical analysis they can do by
+hand, they are restricted to a small number of simple physical systems, like a projectile moving in a vacuum or a block on a frictionless plane.
+
+And they only see bad models; that is, models that are too
+simple for their intended purpose. In nearly every mechanical system,
+air resistance and friction are essential features; if we ignore them,
+our predictions will be wrong and our designs won’t work.
+
+In most introductory physics classes, students don't make modeling
+decisions; sometimes they are not even aware of the decisions that have been made for them. Our goal is to teach the entire modeling process and give students a chance to practice it.
+
+**How much programming do I need?**
+
+If you have never programmed before, you should be able to read this
+book, understand it, and do the exercises. I will do my best to explain everything you need to know; in particular, I have chosen carefully the vocabulary I introduce, and I try to define each term the first time it is used. If you find that I have used a term without defining it, let me know.
+
+If you have programmed before, you will have an easier time getting
+started, but you might be uncomfortable in some places. I take an
+approach to programming you have probably not seen before.
+
+Most programming classes have two big problems:
+
+1.  They go "bottom up", starting with basic language features and
+    gradually adding more powerful tools. As a result, it takes a long
+    time before students can do anything more interesting than convert
+    Fahrenheit to Celsius.
+
+2.  They have no context. Students learn to program with no particular
+    goal in mind, so the exercises span an incoherent collection of
+    topics, and the exercises tend to be unmotivated.
+
+In this book, you learn to program with an immediate goal in mind:
+writing simulations of physical systems. And we proceed "top down", by
+which I mean we use professional-strength data structures and language
+features right away. In particular, we use the following Python libraries:
+
+-   NumPy for basic numerical computation (see
+    <https://www.numpy.org/>).
+
+-   SciPy for scientific computation (see <https://www.scipy.org/>).
+
+-   Matplotlib for visualization (see <https://matplotlib.org/>).
+
+-   Pandas for working with data (see <https://pandas.pydata.org/>).
+
+-   SymPy for symbolic computation, (see <https://www.sympy.org>).
+
+-   Pint for units like kilograms and meters (see
+    <https://pint.readthedocs.io>).
+
+-   Jupyter for reading, running, and developing code (see
+    <https://jupyter.org>).
+
+These tools let you work on more interesting programs sooner, but there are some drawbacks: they can be hard to use, and it can be challenging to keep track of which library does what and how they interact.
+
+I have tried to mitigate these problems by providing a library that makes it easier to get started with these tools, and provides some
+additional capabilities.
+
+Some features in the ModSim library are like training wheels; at some
+point you will probably stop using them and start working with the
+underlying libraries directly. Other features you might find useful the whole time you are working through the book, and later.
+
+I encourage you to read the the ModSim library code. Most of it is not
+complicated, and I tried to make it readable. Particularly if you have
+some programming experience, you might learn something by reverse
+engineering my design decisions.
+
+**How much math and science do I need?**
+
+I assume that you know what derivatives and integrals are, but that's
+about all. In particular, you don’t need to know (or remember) much
+about finding derivatives or integrals of functions analytically. If you know the derivative of $x^2$ and you can integrate $2x~dx$, that will do it. More importantly, you should understand what those concepts *mean*; but if you don’t, this book might help you figure it out.
+
+You don't have to know anything about differential equations.
+
+As for science, we will cover topics from a variety of fields, including demography, epidemiology, medicine, thermodynamics, and mechanics. For the most part, I don’t assume you know anything about these topics. In fact, one of the skills you need to do modeling is the ability to learn enough about new fields to develop models and simulations.
+
+When we get to mechanics, I assume you understand the relationship
+between position, velocity, and acceleration, and that you are familiar with Newton’s laws of motion, especially the second law, which is often expressed as $F = ma$ (force equals mass times acceleration).
+
+I think that's everything you need, but if you find that I left
+something out, please let me know.
+
+**Getting started**
+
+To run the examples and work on the exercises in this book, you will need an environment where you can run Jupyter notebooks.
+
+Jupyter is a software development environment where you can run Python code, including the examples in this book, and write your own code.
+
+A Jupyter notebook is a document that contains text, code, and results from running the code.
+Each chapter of this book is a Jupyter notebook where you can run the examples and work on exercises.
+
+To run the notebooks, you have two options:
+
+1. You can install Python and Jupyter on your computer and download my notebooks.
+
+2. You can run the notebooks on Colab.
+
+To run the notebooks on Colab, go to [the landing page for this book](https://allendowney.github.io/ModSimPy/index.html) and follow the links to the chapters.
+
+To run the notebooks on your computer, there are three steps:
+
+1.  Download the notebooks and copy them to your computer.
+
+2.  Install Python, Jupyter, and some additional libraries.
+
+3.  Run Jupyter and open the notebooks.
+
+To get the notebooks, download [this Zip archive](http://modsimpy.com/zip). You will need a program like
+WinZip or gzip to unpack the Zip file. Make a note of the location of
+the files you download.
+
+The next two sections provide details for the other steps.
+Installing and running software can be challenging, especially if you are not familiar with the command line.
+If you run into problems, you might want to work on Colab, at least to get started.
+
+
+**Installing Python**
+
+You might already have Python installed on your computer, but you might not have the latest version. To use the code in this book, you need Python 3.6 or later. Even if you have the latest version, you probably don't have all of the libraries we need.
+
+You could update Python and install these libraries, but I strongly
+recommend that you don’t go down that road. I think you will find it
+easier to use **Anaconda**, which is a free Python distribution that includes all the libraries you need for this book (and more).
+
+Anaconda is available for Linux, macOS, and Windows. By default, it puts all files in your home directory, so you don’t need administrator (root) permission to install it, and if you have a version of Python already, Anaconda will not remove or modify it.
+
+Start at <https://www.anaconda.com/download>. Download the installer for your system and run it. I recommend you run the installer as a normal user, not as administrator or root.
+
+I suggest you accept the recommended options. On Windows you have the
+option to install Visual Studio Code, which is an interactive
+environment for writing programs. You won't need it for this book, but
+you might want it for other projects.
+
+By default, Anaconda installs most of the packages you need, but there
+are a few more you have to add. Once the installation is complete, open a command window. On macOS or Linux, you can use Terminal. On Windows, open the Anaconda Prompt that should be in your Start menu.
+
+Run the following command (copy and paste it if you can, to avoid
+typos):
+
+```
+conda install jupyter pandas sympy
+conda install beautifulsoup4 lxml html5lib
+conda install -c unidata pint
+```
+
+That should be everything you need.
+
+
+**Running Jupyter**
+
+ If you have not used Jupyter before, you can read about it at <https://jupyter.org>.
+
+To start Jupyter on macOS or Linux, open a Terminal; on Windows, open
+Git Bash. Use `cd` to “change directory" into the directory that contains the notebooks.
+
+```
+cd ModSimPy
+```
+
+Then launch the Jupyter notebook server:
+
+```
+jupyter notebook
+```
+
+Jupyter should open a window in a browser, and you should see the list
+of notebooks in my repository. Click on the first notebook, and follow
+the instructions to run the first few "cells". The first time you run a notebook, it might take several seconds to start, while some Python
+files get initialized. After that, it should run faster.
+
+You can also launch Jupyter from the Start menu on Windows, the Dock on macOS, or the Anaconda Navigator on any system. If you do that, Jupyter might start in your home directory or somewhere else in your file system, so you might have to navigate to find the directory with the notebooks.
+
+
+**Contributor List**
+
+If you have a suggestion or correction, send it to
+<downey@allendowney.com>. Or if you are a Git user, open an issue, or send me a pull request on [this repository](https://github.com/AllenDowney/ModSimPy).
+
+If I make a change based on your feedback, I will add you to the
+contributor list, unless you ask to be omitted.
+
+If you include at least part of the sentence the error appears in, that makes it easy for me to search. Page and section numbers are fine, too, but not as easy to work with. Thanks!
+
+-   My early work on this book benefited from conversations with my
+    colleagues at Olin College, including John Geddes, Mark Somerville, Alison Wood, Chris Lee, and Jason Woodard.
+
+-   I am grateful to Lisa Downey and Jason Woodard for their thoughtful and careful copy editing.
+
+-   Thanks to Alessandra Ferzoco, Erhardt Graeff, Emily Tow, Kelsey
+    Houston-Edwards, Linda Vanasupa, Matt Neal, Joanne Pratt, and Steve Matsumoto for their helpful suggestions.
diff --git a/jb/prep_notebooks.py b/jb/prep_notebooks.py
new file mode 100644
index 00000000..95067d96
--- /dev/null
+++ b/jb/prep_notebooks.py
@@ -0,0 +1,26 @@
+import nbformat as nbf
+from glob import glob
+
+# Collect a list of all notebooks in the content folder
+notebooks = glob("*.ipynb")
+
+# Text to look for in adding tags
+text_search_dict = {
+    "# Solution": "hide-cell",  # Hide the cell with a button to show
+}
+
+# Search through each notebook and look for the text, add a tag if necessary
+for ipath in notebooks:
+    ntbk = nbf.read(ipath, nbf.NO_CONVERT)
+
+    for cell in ntbk.cells:
+        cell_tags = cell.get('metadata', {}).get('tags', [])
+        cell_tags = []
+        for key, val in text_search_dict.items():
+            if key in cell['source']:
+                if val not in cell_tags:
+                    cell_tags.append(val)
+        if len(cell_tags) > 0:
+            cell['metadata']['tags'] = cell_tags
+
+    nbf.write(ntbk, ipath)

From 71248abc526a4b9ebe62de54de812708f7210c52 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 15 Feb 2021 15:46:08 -0500
Subject: [PATCH 042/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 0 -> 165534 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 ModSimPyNotebooks.zip

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HcmV?d00001


From 4a533cab3ce873f8d42ced92897342dc97f2e27d Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 16 Feb 2021 11:22:59 -0500
Subject: [PATCH 043/144] Updating modsim.py

---
 chap03.py | 43 ++++++++++++++++++++++++++++++++++
 chap06.py | 12 ++++++++++
 chap11.py | 43 ++++++++++++++++++++++++++++++++++
 chap12.py |  7 ++++++
 chap13.py | 27 +++++++++++++++++++++
 chap15.py | 36 ++++++++++++++++++++++++++++
 chap18.py | 30 ++++++++++++++++++++++++
 chap22.py | 70 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 8 files changed, 268 insertions(+)
 create mode 100644 chap03.py
 create mode 100644 chap06.py
 create mode 100644 chap11.py
 create mode 100644 chap12.py
 create mode 100644 chap13.py
 create mode 100644 chap15.py
 create mode 100644 chap18.py
 create mode 100644 chap22.py

diff --git a/chap03.py b/chap03.py
new file mode 100644
index 00000000..5b6f1626
--- /dev/null
+++ b/chap03.py
@@ -0,0 +1,43 @@
+from modsim import *
+
+def step(state, p1, p2):
+    """Simulate one time step.
+    
+    state: bikeshare State object
+    p1: probability of an Olin->Wellesley ride
+    p2: probability of a Wellesley->Olin ride
+    """
+    if flip(p1):
+        bike_to_wellesley(state)
+    
+    if flip(p2):
+        bike_to_olin(state)
+
+from modsim import *
+
+def bike_to_olin(state):
+    """Move one bike from Wellesley to Olin.
+    
+    state: bikeshare State object
+    """
+    if state.wellesley == 0:
+        state.wellesley_empty += 1
+        return
+    state.wellesley -= 1
+    state.olin += 1
+
+from modsim import *
+
+# Solution
+
+def bike_to_wellesley(state):
+    """Move one bike from Olin to Wellesley.
+    
+    state: bikeshare State object
+    """
+    if state.olin == 0:
+        state.olin_empty += 1
+        return
+    state.olin -= 1
+    state.wellesley += 1
+
diff --git a/chap06.py b/chap06.py
new file mode 100644
index 00000000..f18bcbe4
--- /dev/null
+++ b/chap06.py
@@ -0,0 +1,12 @@
+from modsim import *
+
+def run_simulation(system, growth_func):
+    results = TimeSeries()
+    results[system.t_0] = system.p_0
+    
+    for t in range(system.t_0, system.t_end):
+        growth = growth_func(t, results[t], system)
+        results[t+1] = results[t] + growth
+        
+    return results
+
diff --git a/chap11.py b/chap11.py
new file mode 100644
index 00000000..23d40dbb
--- /dev/null
+++ b/chap11.py
@@ -0,0 +1,43 @@
+from modsim import *
+
+def make_system(beta, gamma):
+    init = State(S=89, I=1, R=0)
+    init /= init.sum()
+
+    return System(init=init, t_end=7*14,
+                  beta=beta, gamma=gamma)
+
+from modsim import *
+
+def update_func(t, state, system):
+    s, i, r = state
+
+    infected = system.beta * i * s    
+    recovered = system.gamma * i
+    
+    s -= infected
+    i += infected - recovered
+    r += recovered
+    
+    return State(S=s, I=i, R=r)
+
+from modsim import *
+
+def plot_results(S, I, R):
+    S.plot(style='--', label='Susceptible')
+    I.plot(style='-', label='Infected')
+    R.plot(style=':', label='Resistant')
+    decorate(xlabel='Time (days)',
+             ylabel='Fraction of population')
+
+from modsim import *
+
+def run_simulation(system, update_func):
+    frame = TimeFrame(columns=system.init.index)
+    frame.loc[0] = system.init
+    
+    for t in range(0, system.t_end):
+        frame.loc[t+1] = update_func(t, frame.loc[t], system)
+    
+    return frame
+
diff --git a/chap12.py b/chap12.py
new file mode 100644
index 00000000..c389b767
--- /dev/null
+++ b/chap12.py
@@ -0,0 +1,7 @@
+from modsim import *
+
+def calc_total_infected(results, system):
+    s_0 = results.S[system.t0]
+    s_end = results.S[system.t_end]
+    return s_0 - s_end
+
diff --git a/chap13.py b/chap13.py
new file mode 100644
index 00000000..ca40c10b
--- /dev/null
+++ b/chap13.py
@@ -0,0 +1,27 @@
+from modsim import *
+
+# import code from previous notebooks
+
+from chap11 import make_system
+from chap11 import update_func
+from chap11 import run_simulation
+from chap12 import calc_total_infected
+
+from modsim import *
+
+def sweep_beta(beta_array, gamma):
+    sweep = SweepSeries()
+    for beta in beta_array:
+        system = make_system(beta, gamma)
+        results = run_simulation(system, update_func)
+        sweep[beta] = calc_total_infected(results, system)
+    return sweep
+
+from modsim import *
+
+def sweep_parameters(beta_array, gamma_array):
+    frame = SweepFrame(columns=gamma_array)
+    for gamma in gamma_array:
+        frame[gamma] = sweep_beta(beta_array, gamma)
+    return frame
+
diff --git a/chap15.py b/chap15.py
new file mode 100644
index 00000000..f9c28e9b
--- /dev/null
+++ b/chap15.py
@@ -0,0 +1,36 @@
+from modsim import *
+
+def make_system(T_init, volume, r, t_end):
+    return System(T_init=T_init,
+                  T_final=T_init,
+                  volume=volume,
+                  r=r,
+                  t_end=t_end,
+                  T_env=22,
+                  t_0=0,
+                  dt=1)
+
+from modsim import *
+
+def change_func(T, t, system):
+    r, T_env, dt = system.r, system.T_env, system.dt    
+    return -r * (T - T_env) * dt
+
+from modsim import *
+
+def run_simulation(system, change_func):
+    t_array = linrange(system.t_0, system.t_end, system.dt)
+    n = len(t_array)
+    
+    series = TimeSeries(index=t_array)
+    series.iloc[0] = system.T_init
+    
+    for i in range(n-1):
+        t = t_array[i]
+        T = series.iloc[i]
+        series.iloc[i+1] = T + change_func(T, t, system)
+    
+    system.t_end = t_array[-1]
+    system.T_final = series.iloc[-1]
+    return series
+
diff --git a/chap18.py b/chap18.py
new file mode 100644
index 00000000..7e3db625
--- /dev/null
+++ b/chap18.py
@@ -0,0 +1,30 @@
+from modsim import *
+
+def make_system(params, data):
+    G0, k1, k2, k3 = params
+    
+    t_0 = data.index[0]
+    t_end = data.index[-1]
+    
+    Gb = data.glucose[t_0]
+    Ib = data.insulin[t_0]
+    I = interpolate(data.insulin)
+    
+    init = State(G=G0, X=0)
+    
+    return System(init=init, params=params,
+                  Gb=Gb, Ib=Ib, I=I,
+                  t_0=t_0, t_end=t_end, dt=2)
+
+from modsim import *
+
+def slope_func(t, state, system):
+    G, X = state
+    G0, k1, k2, k3 = system.params 
+    I, Ib, Gb = system.I, system.Ib, system.Gb
+        
+    dGdt = -k1 * (G - Gb) - X*G
+    dXdt = k3 * (I(t) - Ib) - k2 * X
+    
+    return dGdt, dXdt
+
diff --git a/chap22.py b/chap22.py
new file mode 100644
index 00000000..040c12e3
--- /dev/null
+++ b/chap22.py
@@ -0,0 +1,70 @@
+from modsim import *
+
+params = Params(
+    x = 0,          # m
+    y = 1,          # m
+    angle = 45,     # degree
+    velocity = 40,  # m / s
+
+    mass = 145e-3,    # kg 
+    diameter = 73e-3, # m 
+    C_d = 0.33,       # dimensionless
+
+    rho = 1.2,      # kg/m**3
+    g = 9.8,        # m/s**2
+    t_end = 10,     # s
+)
+
+from modsim import *
+
+from numpy import pi, deg2rad
+
+def make_system(params):
+    
+    # convert angle to degrees
+    theta = deg2rad(params.angle)
+    
+    # compute x and y components of velocity
+    vx, vy = pol2cart(theta, params.velocity)
+    
+    # make the initial state
+    init = State(x=params.x, y=params.y, vx=vx, vy=vy)
+    
+    # compute the frontal area
+    area = pi * (params.diameter/2)**2
+
+    return System(params,
+                  init = init,
+                  area = area,
+                 )
+
+from modsim import *
+
+def drag_force(V, system):
+    rho, C_d, area = system.rho, system.C_d, system.area
+    
+    mag = rho * vector_mag(V)**2 * C_d * area / 2
+    direction = -vector_hat(V)
+    f_drag = mag * direction
+    return f_drag
+
+from modsim import *
+
+def slope_func(t, state, system):
+    x, y, vx, vy = state
+    mass, g = system.mass, system.g
+    
+    V = Vector(vx, vy)
+    a_drag = drag_force(V, system) / mass
+    a_grav = g * Vector(0, -1)
+    
+    A = a_grav + a_drag
+    
+    return V.x, V.y, A.x, A.y
+
+from modsim import *
+
+def event_func(t, state, system):
+    x, y, vx, vy = state
+    return y
+

From 27447686264ca061049def4872caaa3a5641c228 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 16 Feb 2021 12:14:31 -0500
Subject: [PATCH 044/144] Updating modsim.py

---
 chap12.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/chap12.py b/chap12.py
index c389b767..f72e7997 100644
--- a/chap12.py
+++ b/chap12.py
@@ -1,7 +1,7 @@
 from modsim import *
 
 def calc_total_infected(results, system):
-    s_0 = results.S[system.t0]
+    s_0 = results.S[0]
     s_end = results.S[system.t_end]
     return s_0 - s_end
 

From c1c7858d85be6b769a1ff172b7f101ea7d063178 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 16 Feb 2021 12:14:40 -0500
Subject: [PATCH 045/144] Updating chapters

---
 chapters/chap10.ipynb |  79 ++++--
 chapters/chap11.ipynb | 212 +++++++---------
 chapters/chap12.ipynb | 569 +++++++++---------------------------------
 chapters/chap13.ipynb | 160 +++++++-----
 4 files changed, 352 insertions(+), 668 deletions(-)

diff --git a/chapters/chap10.ipynb b/chapters/chap10.ipynb
index 5cccdbf6..b6a1d3c6 100644
--- a/chapters/chap10.ipynb
+++ b/chapters/chap10.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -100,12 +100,12 @@
    "source": [
     "## Prehistoric World Population\n",
     "\n",
-    "On the Wikipedia page about world population estimates, the second table contains estimates for prehistoric populations."
+    "On the Wikipedia page about world population growth, the second table contains estimates for prehistoric populations."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "usual-penguin",
    "metadata": {
     "tags": []
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "thick-lincoln",
    "metadata": {
     "tags": []
@@ -144,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "conscious-orange",
    "metadata": {
     "tags": []
@@ -162,14 +162,14 @@
     "tags": []
    },
    "source": [
-    "Not all researchers provide estimates for the same dates.\n",
+    "Some of the values are null because not all researchers provide estimates for the same dates.\n",
     "\n",
     "Again, we'll replace the long column names with more convenient abbreviations."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "thick-blanket",
    "metadata": {
     "tags": []
@@ -192,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "id": "ranking-prescription",
    "metadata": {
     "tags": []
@@ -215,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "id": "metallic-offense",
    "metadata": {
     "scrolled": false,
@@ -250,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "id": "solar-action",
    "metadata": {},
    "outputs": [],
@@ -272,7 +272,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
+   "id": "rural-express",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
+    "table2 = tables[2]\n",
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "preceding-sheep",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "un = table2.un / 1e9\n",
+    "census = table2.census / 1e9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
    "id": "together-jackson",
    "metadata": {},
    "outputs": [],
@@ -282,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "simple-verse",
    "metadata": {
     "scrolled": true
@@ -313,18 +342,20 @@
     "This figure shows the three scenarios we'll consider:\n",
     "\n",
     "![One queue, one server (left), one queue, two servers (middle), two\n",
-    "queues, two servers (right).](https://github.com/AllenDowney/ModSim/raw/master/figs/queue.png)\n",
+    "queues, two servers (right).](https://github.com/AllenDowney/ModSim/raw/main/figs/queue.png)\n",
     "*One queue, one server (left), one queue, two servers (middle), two\n",
     "queues, two servers (right).*\n",
     "\n",
-    "As we did in the bike share model, we'll assume that a customer is equally likely to arrive during any time step. I'll denote this probability using the Greek letter lambda, $\\lambda$, or the variable name `lam`. The value of $\\lambda$ probably varies from day to day, so we'll have to consider a range of possibilities.\n",
+    "As we did in the bike share model, we'll divide time into discrete time steps of one minute.\n",
+    "And we'll assume that a customer is equally likely to arrive during any time step. \n",
+    "I'll denote this probability using the Greek letter lambda, $\\lambda$, or the variable name `lam`. The value of $\\lambda$ probably varies from day to day, so we'll have to consider a range of possibilities.\n",
     "\n",
     "Based on data from other stores, you know that it takes 5 minutes for a customer to check out, on average. But checkout times are variable: most customers take less than 5 minutes, but some take substantially more. A simple way to model this variability is to assume that when a customer is checking out, they always have the same probability of finishing during the next time step, regardless of how long they have been checking out. I'll denote this probability using the Greek letter mu, $\\mu$, or the variable name `mu`.\n",
     "\n",
     "If we choose $\\mu=1/5$ per minute, the average time for each checkout\n",
     "will be 5 minutes, which is consistent with the data. Most people takes less than 5 minutes, but a few take substantially longer, which is probably not a bad model of the distribution in real stores.\n",
     "\n",
-    "Now we're ready to get started. In the repository for this book, you'll find a notebook called `queue.ipynb` that contains some code to get you started and instructions.\n",
+    "Now we're ready to implement the model. In the repository for this book, you'll find a notebook called `queue.ipynb` that contains some code to get you started and instructions.\n",
     "\n",
     "As always, you should practice incremental development: write no more\n",
     "than one or two lines of code a time, and test as you go!"
@@ -343,13 +374,11 @@
     "The goal of this case study is to model year-to-year changes in\n",
     "population, evaluate how predictable these changes are, and estimate the probability that a particular population will increase or decrease in the next 10 years.\n",
     "\n",
-    "As an example, I use data from page 18 of the 2017 report, which\n",
-    "provides population estimates for the Narraguagus and Sheepscot Rivers\n",
+    "As an example, I use data from the 2017 report, which provides population estimates for the Narraguagus and Sheepscot Rivers\n",
     "in Maine.\n",
     "\n",
     "In the repository for this book, you'll find a notebook called\n",
-    "`salmon.ipynb` that contains some code to get you started and\n",
-    "instructions.\n",
+    "`salmon.ipynb` that contains this data and some code to get you started.\n",
     "\n",
     "You should take my instructions as suggestions; if you want to try\n",
     "something different, please do!"
@@ -364,17 +393,17 @@
     "\n",
     "This case study is based on \"Height-Age Curves for Planted Stands of\n",
     "Douglas Fir, with Adjustments for Density\", a working paper by\n",
-    "Flewelling, Collier, Gonyea, Marshall, and Turnblom.\n",
+    "Flewelling et al.\n",
     "\n",
     "It provides \"site index curves\", which are curves that show the\n",
-    "expected height of the tallest tree in a stand of Douglas firs as a\n",
+    "expected height of the tallest tree in a stand of Douglas fir as a\n",
     "function of age, for a stand where the trees are the same age.\n",
     "\n",
-    "Depending on the quality of the site, the trees might grow more quickly or slowing. So each curve is identified by a \"site index\" that indicates the quality of the site.\n",
+    "Depending on the quality of the site, the trees might grow more quickly or slowly. So each curve is identified by a \"site index\" that indicates the quality of the site.\n",
     "\n",
     "The goal of this case study is to explain the shape of these\n",
     "curves, that is, why trees grow the way they do.\n",
-    "The answer I propose involved fractal dimensions, so you might find it interesting.\n",
+    "The answer I propose involves fractal dimensions, so you might find it interesting.\n",
     "\n",
     "In the repository for this book, you'll find a notebook called\n",
     "`trees.ipynb` that incrementally develops a model of tree growth and uses it to fit the data.\n",
diff --git a/chapters/chap11.ipynb b/chapters/chap11.ipynb
index 001c40d5..4865ec5d 100644
--- a/chapters/chap11.ipynb
+++ b/chapters/chap11.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -94,7 +94,7 @@
     "susceptible population, and use it to evaluate the effectiveness of\n",
     "possible interventions.\n",
     "\n",
-    "My presentation of the model in the next few chapters is based on an excellent article by David Smith and Lang Moore, [^1]: Smith and Moore, \"The SIR Model for Spread of Disease,\" Journal of Online Mathematics and its Applications, December 2001, available at <http://modsimpy.com/sir>."
+    "My presentation of the model in the next few chapters is based on an excellent article by David Smith and Lang Moore, \"The SIR Model for Spread of Disease,\" *Journal of Online Mathematics and its Applications*, December 2001, available at <http://modsimpy.com/sir>."
    ]
   },
   {
@@ -127,22 +127,21 @@
    "source": [
     "## The SIR model\n",
     "\n",
-    "The Kermack-McKendrick model is a simple version of an **SIR model**,\n",
-    "so-named because it considers three categories of people:\n",
+    "The Kermack-McKendrick model is an example of an **SIR model**,\n",
+    "so-named because it represents three categories of people:\n",
     "\n",
-    "-   **S**: People who are \"susceptible\\\", that is, capable of\n",
+    "-   **S**: People who are \"susceptible\", that is, capable of\n",
     "    contracting the disease if they come into contact with someone who\n",
     "    is infected.\n",
     "\n",
-    "-   **I**: People who are \"infectious\\\", that is, capable of passing\n",
+    "-   **I**: People who are \"infectious\", that is, capable of passing\n",
     "    along the disease if they come into contact with someone\n",
     "    susceptible.\n",
     "\n",
-    "-   **R**: People who are \"recovered\\\". In the basic version of the\n",
+    "-   **R**: People who are \"recovered\". In the basic version of the\n",
     "    model, people who have recovered are considered to be immune to\n",
     "    reinfection. That is a reasonable model for some diseases, but not\n",
-    "    for others, so it should be on the list of assumptions to reconsider\n",
-    "    later."
+    "    for others, so it should be on the list of assumptions to reconsider later."
    ]
   },
   {
@@ -192,7 +191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fourth-celtic",
+   "id": "accomplished-franklin",
    "metadata": {},
    "source": [
     "## The SIR equations\n",
@@ -215,13 +214,19 @@
     "\n",
     "In this example, there are three stocks---susceptible, infectious, and\n",
     "recovered---and two flows---new infections and recoveries. Compartment\n",
-    "models are often represented visually using stock and flow diagrams (see <http://modsimpy.com/stock>).\n",
-    "\n",
+    "models are often represented visually using stock and flow diagrams (see <http://modsimpy.com/stock>)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fourth-celtic",
+   "metadata": {},
+   "source": [
     "The following figure shows the stock and flow diagram for an SIR\n",
     "model.\n",
     "\n",
     "![Stock and flow diagram for an SIR\n",
-    "model.](https://github.com/AllenDowney/ModSim/raw/master/figs/stock_flow1.png)\n",
+    "model.](https://github.com/AllenDowney/ModSim/raw/main/figs/stock_flow1.png)\n",
     "\n",
     "Stocks are represented by rectangles, flows by arrows. The widget in the middle of the arrows represents a valve that controls the rate of flow; the diagram shows the parameters that control the valves."
    ]
@@ -249,13 +254,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 4,
    "id": "criminal-change",
    "metadata": {},
    "outputs": [],
    "source": [
     "init = State(S=89, I=1, R=0)\n",
-    "init"
+    "show(init)"
    ]
   },
   {
@@ -268,15 +273,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 5,
    "id": "pediatric-ratio",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from numpy import sum\n",
-    "\n",
-    "init /= sum(init)\n",
-    "init"
+    "init /= init.sum()\n",
+    "show(init)"
    ]
   },
   {
@@ -290,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 6,
    "id": "little-stylus",
    "metadata": {},
    "outputs": [],
@@ -309,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 7,
    "id": "veterinary-aerospace",
    "metadata": {},
    "outputs": [],
@@ -323,7 +326,7 @@
    "id": "lightweight-delta",
    "metadata": {},
    "source": [
-    "Now we need a `System` object to store the parameters and initial\n",
+    "I'll use a `System` object to store the parameters and initial\n",
     "conditions. The following function takes the system parameters and returns a new `System` object:"
    ]
   },
@@ -338,12 +341,9 @@
    "source": [
     "def make_system(beta, gamma):\n",
     "    init = State(S=89, I=1, R=0)\n",
-    "    init /= sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
+    "    init /= init.sum()\n",
     "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
+    "    return System(init=init, t_end=7*14,\n",
     "                  beta=beta, gamma=gamma)"
    ]
   },
@@ -368,7 +368,7 @@
    "outputs": [],
    "source": [
     "system = make_system(beta, gamma)\n",
-    "system"
+    "show(system)"
    ]
   },
   {
@@ -379,21 +379,20 @@
     "## The update function\n",
     "\n",
     "At any point in time, the state of the system is represented by a\n",
-    "`State` object with three variables, `S`, `I` and `R`. So I'll define an\n",
-    "update function that takes as parameters a `State` object, the current\n",
-    "time, and a `System` object:"
+    "`State` object with three variables, `S`, `I` and `R`. So I'll define an update function that takes as parameters the current\n",
+    "time, a `State` object, and a `System` object:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 10,
    "id": "wound-wayne",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "def update_func(state, t, system):\n",
+    "def update_func(t, state, system):\n",
     "    s, i, r = state\n",
     "\n",
     "    infected = system.beta * i * s    \n",
@@ -422,7 +421,7 @@
     "\n",
     "The update function computes `infected` and `recovered` as a fraction of the population, then updates `s`, `i` and `r`. The return value is a `State` that contains the updated values.\n",
     "\n",
-    "When we call `update_func` like this:"
+    "We can call `update_func` like this:"
    ]
   },
   {
@@ -432,8 +431,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "state = update_func(init, 0, system)\n",
-    "state"
+    "state = update_func(0, init, system)\n",
+    "show(state)"
    ]
   },
   {
@@ -441,6 +440,8 @@
    "id": "standard-search",
    "metadata": {},
    "source": [
+    "The result is the new `State` object.\n",
+    "\n",
     "You might notice that this version of `update_func` does not use one of its parameters, `t`. I include it anyway because update functions\n",
     "sometimes depend on time, and it is convenient if they all take the same parameters, whether they need them or not."
    ]
@@ -457,20 +458,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 12,
    "id": "occasional-pottery",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from numpy import arange\n",
-    "\n",
-    "def run_simulation(system, update_func):\n",
+    "def run_simulation1(system, update_func):\n",
     "    state = system.init\n",
     "\n",
-    "    for t in arange(system.t0, system.t_end):\n",
-    "        state = update_func(state, t, system)\n",
+    "    for t in range(0, system.t_end):\n",
+    "        state = update_func(t, state, system)\n",
     "\n",
     "    return state"
    ]
@@ -482,38 +479,20 @@
    "source": [
     "The parameters of `run_simulation` are the `System` object and the\n",
     "update function. The `System` object contains the parameters, initial\n",
-    "conditions, and values of `t0` and `t_end`.\n",
+    "conditions, and values of `0` and `t_end`.\n",
     "\n",
     "We can call `run_simulation` like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 13,
    "id": "differential-difference",
    "metadata": {},
    "outputs": [],
    "source": [
-    "system = make_system(beta, gamma)\n",
-    "final_state = run_simulation(system, update_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "incredible-marsh",
-   "metadata": {},
-   "source": [
-    "The result is the final state of the system:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "weekly-acrobat",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "final_state"
+    "final_state = run_simulation1(system, update_func)\n",
+    "show(final_state)"
    ]
   },
   {
@@ -521,9 +500,9 @@
    "id": "behind-removal",
    "metadata": {},
    "source": [
-    "This result indicates that after 14 weeks (98 days), about 52% of the\n",
+    "The result indicates that after 14 weeks (98 days), about 52% of the\n",
     "population is still susceptible, which means they were never infected,\n",
-    "less than 1% are actively infected, and 48% have recovered, which means they were infected at some point."
+    "almost 48% have recovered, which means they were infected at some point, and less than 1% are actively infected."
    ]
   },
   {
@@ -541,24 +520,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 14,
    "id": "advanced-recommendation",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
-    "def run_simulation(system, update_func):\n",
+    "def run_simulation2(system, update_func):\n",
     "    S = TimeSeries()\n",
     "    I = TimeSeries()\n",
     "    R = TimeSeries()\n",
     "\n",
     "    state = system.init\n",
-    "    t0 = system.t0\n",
-    "    S[t0], I[t0], R[t0] = state\n",
+    "    S[0], I[0], R[0] = state\n",
     "    \n",
-    "    for t in arange(system.t0, system.t_end):\n",
-    "        state = update_func(state, t, system)\n",
+    "    for t in range(0, system.t_end):\n",
+    "        state = update_func(t, state, system)\n",
     "        S[t+1], I[t+1], R[t+1] = state\n",
     "    \n",
     "    return S, I, R"
@@ -569,32 +545,25 @@
    "id": "behind-breathing",
    "metadata": {},
    "source": [
-    "First, we create `TimeSeries` objects to store the results. Notice that\n",
-    "the variables `S`, `I`, and `R` are `TimeSeries` objects now.\n",
-    "\n",
-    "Next we initialize `state`, `t0`, and the first elements of `S`, `I` and\n",
+    "First, we create `TimeSeries` objects to store the results.\n",
+    "Next we initialize `state` and the first elements of `S`, `I` and\n",
     "`R`.\n",
     "\n",
-    "Inside the loop, we use `update_func` to compute the state of the system\n",
-    "at the next time step, then use multiple assignment to unpack the\n",
-    "elements of `state`, assigning each to the corresponding `TimeSeries`.\n",
+    "Inside the loop, we use `update_func` to compute the state of the system at the next time step, then use multiple assignment to unpack the elements of `state`, assigning each to the corresponding `TimeSeries`.\n",
     "\n",
-    "At the end of the function, we return the values `S`, `I`, and `R`. This\n",
-    "is the first example we have seen where a function returns more than one\n",
-    "value.\n",
+    "At the end of the function, we return the values `S`, `I`, and `R`. This is the first example we have seen where a function returns more than one value.\n",
     "\n",
-    "Now we can run the function like this:"
+    "We can run the function like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 15,
    "id": "subtle-zambia",
    "metadata": {},
    "outputs": [],
    "source": [
-    "system = make_system(beta, gamma)\n",
-    "S, I, R = run_simulation(system, update_func)"
+    "S, I, R = run_simulation2(system, update_func)"
    ]
   },
   {
@@ -607,7 +576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 16,
    "id": "welcome-tractor",
    "metadata": {
     "tags": []
@@ -632,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 17,
    "id": "agricultural-joining",
    "metadata": {},
    "outputs": [],
@@ -645,7 +614,7 @@
    "id": "civic-pharmacy",
    "metadata": {},
    "source": [
-    "Notice that it takes about three weeks (21 days) for the outbreak to get going, and about six weeks (42 days) before it peaks. The fraction of the population that's infected is never very high, but it adds up. In total, almost half the population gets sick."
+    "It takes about three weeks (21 days) for the outbreak to get going, and about five weeks (35 days) to peak. The fraction of the population that's infected is never very high, but it adds up. In total, almost half the population gets sick."
    ]
   },
   {
@@ -658,15 +627,14 @@
     "If the number of state variables is small, storing them as separate\n",
     "`TimeSeries` objects might not be so bad. But a better alternative is to use a `TimeFrame`, which is another object defined in the ModSim\n",
     "library.\n",
-    "\n",
-    "A `TimeFrame` is a kind of a `DataFrame`, which we used in.\n",
+    "A `TimeFrame` is a kind of a `DataFrame`, which we used earlier to store world population estimates.\n",
     "\n",
     "Here's a more concise version of `run_simulation` using a `TimeFrame`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 18,
    "id": "native-central",
    "metadata": {
     "tags": []
@@ -675,10 +643,10 @@
    "source": [
     "def run_simulation(system, update_func):\n",
     "    frame = TimeFrame(columns=system.init.index)\n",
-    "    frame.loc[system.t0] = system.init\n",
+    "    frame.loc[0] = system.init\n",
     "    \n",
-    "    for t in arange(system.t0, system.t_end):\n",
-    "        frame.loc[t+1] = update_func(frame.loc[t], t, system)\n",
+    "    for t in range(0, system.t_end):\n",
+    "        frame.loc[t+1] = update_func(t, frame.loc[t], system)\n",
     "    \n",
     "    return frame"
    ]
@@ -690,19 +658,18 @@
    "source": [
     "The first line creates an empty `TimeFrame` with one column for each\n",
     "state variable. Then, before the loop starts, we store the initial\n",
-    "conditions in the `TimeFrame` at `t0`. Based on the way we've been using\n",
+    "conditions in the `TimeFrame` at `0`. Based on the way we've been using\n",
     "`TimeSeries` objects, it is tempting to write:\n",
     "\n",
     "```\n",
-    "frame[system.t0] = system.init\n",
+    "frame[0] = system.init\n",
     "```\n",
     "\n",
     "But when you use the bracket operator with a `TimeFrame` or `DataFrame`, it selects a column, not a row. \n",
-    "\n",
     "To select a row, we have to use `loc`, like this:\n",
     "\n",
     "```\n",
-    "frame.loc[system.t0] = system.init\n",
+    "frame.loc[0] = system.init\n",
     "```\n",
     "\n",
     "Since the value on the right side is a `State`, the assignment matches\n",
@@ -718,7 +685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 19,
    "id": "quick-fifty",
    "metadata": {},
    "outputs": [],
@@ -736,7 +703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 20,
    "id": "listed-enough",
    "metadata": {},
    "outputs": [],
@@ -759,7 +726,12 @@
    "metadata": {},
    "source": [
     "## Summary\n",
-    "\n"
+    "\n",
+    "This chapter presents an SIR model of infectious disease and two ways to collect the results, using several `TimeSeries` objects or a single `TimeFrame`.\n",
+    "\n",
+    "In the next chapter we'll use the model to explore the effect of immunization and other interventions.\n",
+    "\n",
+    "But first you might want to work on these exercises."
    ]
   },
   {
@@ -782,8 +754,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "id": "recovered-freeze",
+   "execution_count": 21,
+   "id": "parental-thunder",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -792,19 +764,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "interpreted-colony",
+   "execution_count": 22,
+   "id": "recovered-freeze",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# Solution goes here"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "given-canberra",
+   "execution_count": 23,
+   "id": "suited-spanking",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# Solution goes here"
+   ]
   }
  ],
  "metadata": {
diff --git a/chapters/chap12.ipynb b/chapters/chap12.ipynb
index a71639ac..fc23c876 100644
--- a/chapters/chap12.ipynb
+++ b/chapters/chap12.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -85,11 +85,26 @@
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap12.ipynb)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "breathing-hamilton",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'chap11.py')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
    "id": "growing-sperm",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# import code from previous notebooks\n",
@@ -104,7 +119,9 @@
    "id": "identical-steam",
    "metadata": {},
    "source": [
-    "In the previous chapter I presented the SIR model of infectious disease and used it to model the Freshman Plague at Olin. In this chapter we'll consider metrics intended to quantify the effects of the disease and interventions intended to reduce those effects."
+    "In the previous chapter I presented the SIR model of infectious disease and used it to model the Freshman Plague at Olin. In this chapter we'll consider metrics intended to quantify the effects of the disease and interventions intended to reduce those effects.\n",
+    "\n",
+    "We'll use some of the functions from the previous chapter: `make_system`, `update_func`, and the last version of `run_simulation`, which returns the results in a `DataFrame` object."
    ]
   },
   {
@@ -126,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "id": "recent-cooper",
    "metadata": {},
    "outputs": [],
@@ -142,12 +159,14 @@
    "metadata": {},
    "source": [
     "`add_immunization` moves the given fraction of the population from `S`\n",
-    "to `R`. "
+    "to `R`.\n",
+    "\n",
+    "As a basis for comparison, I'll run the model with the same parameters as in the previous chapter, with no immunization."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "id": "found-learning",
    "metadata": {},
    "outputs": [],
@@ -167,20 +186,36 @@
    "id": "unsigned-joseph",
    "metadata": {},
    "source": [
-    "If we assume that 10% of students are vaccinated at the\n",
-    "beginning of the semester, and the vaccine is 100% effective, we can\n",
-    "simulate the effect like this:"
+    "Now let's see what happens if 10% of students are immune.\n",
+    "I'll make another `System` object with the same parameters, then run `add_immunization` to modify the initial conditions."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "funny-copper",
+   "execution_count": 13,
+   "id": "enormous-abortion",
    "metadata": {},
    "outputs": [],
    "source": [
     "system2 = make_system(beta, gamma)\n",
-    "add_immunization(system2, 0.1)\n",
+    "add_immunization(system2, 0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "subject-ideal",
+   "metadata": {},
+   "source": [
+    "Now we can run the simulation like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "funny-copper",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "results2 = run_simulation(system2, update_func)"
    ]
   },
@@ -195,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 15,
    "id": "divided-biotechnology",
    "metadata": {},
    "outputs": [],
@@ -207,6 +242,15 @@
     "         ylabel='Fraction of population')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "passive-dance",
+   "metadata": {},
+   "source": [
+    "With immunization, there is a smaller change in `S`; that is, fewer people are infected.\n",
+    "In the next section we'll compute this change and use it to quantify the effect of immunization."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "postal-cemetery",
@@ -217,7 +261,7 @@
     "When we plot a time series, we get a view of everything that happened\n",
     "when the model ran, but often we want to boil it down to a few numbers\n",
     "that summarize the outcome. These summary statistics are called\n",
-    "**metrics**, as we saw in Section xxx.\n",
+    "**metrics**.\n",
     "\n",
     "In the SIR model, we might want to know the time until the peak of the\n",
     "outbreak, the number of people who are sick at the peak, the number of\n",
@@ -225,13 +269,12 @@
     "\n",
     "As an example, I will focus on the last one --- the total number of sick students --- and we will consider interventions intended to minimize it.\n",
     "\n",
-    "When a person gets infected, they move from `S` to `I`, so we can get\n",
-    "the total number of infections by computing the difference in `S` at the beginning and the end:"
+    "We can get the total number of infections by computing the difference in `S` at the beginning and the end of the simulation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 18,
    "id": "synthetic-element",
    "metadata": {
     "tags": []
@@ -239,14 +282,14 @@
    "outputs": [],
    "source": [
     "def calc_total_infected(results, system):\n",
-    "    s_0 = results.S[system.t0]\n",
+    "    s_0 = results.S[0]\n",
     "    s_end = results.S[system.t_end]\n",
     "    return s_0 - s_end"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 19,
    "id": "recovered-picnic",
    "metadata": {},
    "outputs": [],
@@ -256,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 20,
    "id": "american-transfer",
    "metadata": {},
    "outputs": [],
@@ -285,55 +328,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 27,
    "id": "progressive-architect",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def sweep_immunity(immunize_array):\n",
+    "def sweep_immunity(fraction_array):\n",
     "    sweep = SweepSeries()\n",
     "\n",
-    "    for fraction in immunize_array:\n",
-    "        sir = make_system(beta, gamma)\n",
-    "        add_immunization(sir, fraction)\n",
-    "        results = run_simulation(sir, update_func)\n",
-    "        sweep[fraction] = calc_total_infected(results, sir)\n",
+    "    for fraction in fraction_array:\n",
+    "        system = make_system(beta, gamma)\n",
+    "        add_immunization(system, fraction)\n",
+    "        results = run_simulation(system, update_func)\n",
+    "        sweep[fraction] = calc_total_infected(results, system)\n",
     "\n",
     "    return sweep"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "indie-seeker",
+   "id": "timely-industry",
    "metadata": {},
    "source": [
-    "The parameter of `sweep_immunity` is an array of immunization rates. The\n",
-    "result is a `SweepSeries` object that maps from each immunization rate\n",
-    "to the resulting fraction of students ever infected.\n",
+    "The parameter of `sweep_immunity` is an array of immunization rates. The result is a `SweepSeries` object that maps from each immunization rate to the resulting fraction of students ever infected.\n",
     "\n",
-    "The following figure shows a plot of the `SweepSeries`. Notice that\n",
-    "the x-axis is the immunization rate, not time."
+    "We can call it like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 28,
    "id": "measured-pavilion",
    "metadata": {},
    "outputs": [],
    "source": [
-    "immunize_array = linspace(0, 1, 21)\n",
-    "infected_sweep = sweep_immunity(immunize_array)"
+    "fraction_array = linspace(0, 1, 21)\n",
+    "infected_sweep = sweep_immunity(fraction_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "indie-seeker",
+   "metadata": {},
+   "source": [
+    "The following figure plots the `SweepSeries`. Notice that the $x$-axis is the immunization rate, not time."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 29,
    "id": "interior-humanitarian",
    "metadata": {},
    "outputs": [],
    "source": [
-    "infected_sweep.plot()\n",
+    "infected_sweep.plot(color='C2')\n",
     "\n",
     "decorate(xlabel='Fraction immunized',\n",
     "         ylabel='Total fraction infected',\n",
@@ -368,474 +416,83 @@
     "\n",
     "But it's a curse because a small decrease in immunization can cause a\n",
     "big increase in infections. In this example, if we drop from 80%\n",
-    "immunization to 60%, that might not be too bad. But if we drop from 40% to 20%, that would trigger a major outbreak, affecting more than 15% of the population. For a serious disease like measles, just to name one, that would be a public health catastrophe.\n",
-    "\n",
-    "One use of models like this is to demonstrate phenomena like herd\n",
-    "immunity and to predict the effect of interventions like vaccination.\n",
-    "Another use is to evaluate alternatives and guide decision making. We'll see an example in the next section."
+    "immunization to 60%, that might not be too bad. But if we drop from 40% to 20%, that would trigger a major outbreak, affecting more than 15% of the population. For a serious disease like measles, just to name one, that would be a public health catastrophe."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "changed-capability",
-   "metadata": {},
-   "source": [
-    "## Hand washing\n",
-    "\n",
-    "Suppose you are the Dean of Student Life, and you have a budget of just \\$1200 to combat the Freshman Plague. You have two options for spending this money:\n",
-    "\n",
-    "1.  You can pay for vaccinations, at a rate of \\$100 per dose.\n",
-    "\n",
-    "2.  You can spend money on a campaign to remind students to wash hands\n",
-    "    frequently.\n",
-    "\n",
-    "We have already seen how we can model the effect of vaccination. Now\n",
-    "let's think about the hand-washing campaign. We'll have to answer two\n",
-    "questions:\n",
-    "\n",
-    "1.  How should we incorporate the effect of hand washing in the model?\n",
-    "\n",
-    "2.  How should we quantify the effect of the money we spend on a\n",
-    "    hand-washing campaign?\n",
-    "\n",
-    "For the sake of simplicity, let's assume that we have data from a\n",
-    "similar campaign at another school showing that a well-funded campaign\n",
-    "can change student behavior enough to reduce the infection rate by 20%.\n",
-    "\n",
-    "In terms of the model, hand washing has the effect of reducing `beta`.\n",
-    "That's not the only way we could incorporate the effect, but it seems\n",
-    "reasonable and it's easy to implement."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "alpine-guest",
-   "metadata": {},
-   "source": [
-    "Now we have to model the relationship between the money we spend and the\n",
-    "effectiveness of the campaign. Again, let's suppose we have data from\n",
-    "another school that suggests:\n",
-    "\n",
-    "-   If we spend \\$500 on posters, materials, and staff time, we can\n",
-    "    change student behavior in a way that decreases the effective value of `beta` by 10%.\n",
-    "\n",
-    "-   If we spend \\$1000, the total decrease in `beta` is almost 20%.\n",
-    "\n",
-    "-   Above \\$1000, additional spending has little additional benefit."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "agricultural-trinidad",
-   "metadata": {},
-   "source": [
-    "## Logistic function"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cheap-structure",
-   "metadata": {},
-   "source": [
-    "To model the effect of a hand-washing campaign, I'll use a [generalized logistic function](https://en.wikipedia.org/wiki/Generalised_logistic_function) (GLF), which is a convenient function for modeling curves that have a generally sigmoid shape.  The parameters of the GLF correspond to various features of the curve in a way that makes it easy to find a function that has the shape you want, based on data or background information about the scenario."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "blond-armstrong",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from numpy import exp\n",
-    "\n",
-    "def logistic(x, A=0, B=1, C=1, M=0, K=1, Q=1, nu=1):\n",
-    "    \"\"\"Computes the generalize logistic function.\n",
-    "    \n",
-    "    A: controls the lower bound\n",
-    "    B: controls the steepness of the transition \n",
-    "    C: not all that useful, AFAIK\n",
-    "    M: controls the location of the transition\n",
-    "    K: controls the upper bound\n",
-    "    Q: shift the transition left or right\n",
-    "    nu: affects the symmetry of the transition\n",
-    "    \n",
-    "    returns: float or array\n",
-    "    \"\"\"\n",
-    "    exponent = -B * (x - M)\n",
-    "    denom = C + Q * exp(exponent)\n",
-    "    return A + (K-A) / denom ** (1/nu)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "seven-budget",
-   "metadata": {},
-   "source": [
-    "The following array represents the range of possible spending."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "disturbed-amount",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spending = linspace(0, 1200, 21)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "supreme-nutrition",
+   "id": "amino-excess",
    "metadata": {},
    "source": [
-    "`compute_factor` computes the reduction in `beta` for a given level of campaign spending.\n",
+    "## Summary\n",
     "\n",
-    "`M` is chosen so the transition happens around \\$500.\n",
+    "In general, models are used to predict, explain, and design.\n",
+    "In this chapter, we use an SIR predict the effect of immunization and to explain the phenomenon of herd immunity.\n",
     "\n",
-    "`K` is the maximum reduction in `beta`, 20%.\n",
+    "In the repository for this book, you will find a file called `plague.ipynb` that uses this model for design, that is, for making public health decisions intended to achieve a goal.\n",
     "\n",
-    "`B` is chosen by trial and error to yield a curve that seems feasible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "otherwise-answer",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_factor(spending):\n",
-    "    \"\"\"Reduction factor as a function of spending.\n",
-    "    \n",
-    "    spending: dollars from 0 to 1200\n",
-    "    \n",
-    "    returns: fractional reduction in beta\n",
-    "    \"\"\"\n",
-    "    return logistic(spending, M=500, K=0.2, B=0.01)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "expected-venture",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "seventh-strike",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "percent_reduction = compute_factor(spending) * 100\n",
-    "\n",
-    "make_series(spending, percent_reduction).plot()\n",
+    "In the next chapter, we'll explore the SIR model further by sweeping the parameters.\n",
     "\n",
-    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
-    "         ylabel='Percent reduction in infection rate',\n",
-    "         title='Effect of hand washing on infection rate')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "legal-michigan",
-   "metadata": {},
-   "source": [
-    "The result is the following function, which\n",
-    "takes spending as a parameter and returns `factor`, which is the factor\n",
-    "by which `beta` is reduced:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "obvious-congress",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_factor(spending):\n",
-    "    return logistic(spending, M=500, K=0.2, B=0.01)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sunset-investing",
-   "metadata": {},
-   "source": [
-    "I use `compute_factor` to write `add_hand_washing`, which takes a\n",
-    "`System` object and a budget, and modifies `system.beta` to model the\n",
-    "effect of hand washing:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "polish-multiple",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def add_hand_washing(system, spending):\n",
-    "    factor = compute_factor(spending)\n",
-    "    system.beta *= (1 - factor)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "worthy-fellowship",
-   "metadata": {},
-   "source": [
-    "Now we can sweep a range of values for `spending` and use the simulation\n",
-    "to compute the effect:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "statistical-garden",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_hand_washing(spending_array):\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for spending in spending_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        add_hand_washing(system, spending)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[spending] = calc_total_infected(results, system)\n",
-    "        \n",
-    "    return sweep"
+    "But first you might want to work on this exercise."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "clinical-surge",
-   "metadata": {},
-   "source": [
-    "Here's how we run it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "joined-graduation",
+   "id": "drawn-hindu",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from numpy import linspace\n",
+    "**Exercise:** Suppose we have the option to quarantine infected students.  For example, a student who feels ill might be moved to an infirmary or a private dorm room until they are no longer infectious.\n",
     "\n",
-    "spending_array = linspace(0, 1200, 20)\n",
-    "infected_sweep2 = sweep_hand_washing(spending_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "designing-smile",
-   "metadata": {},
-   "source": [
-    "The following figure shows the result. "
+    "How might you incorporate the effect of quarantine in the SIR model?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "cheap-decision",
+   "execution_count": 30,
+   "id": "impressive-librarian",
    "metadata": {},
    "outputs": [],
    "source": [
-    "infected_sweep2.plot()\n",
-    "\n",
-    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
-    "         ylabel='Total fraction infected',\n",
-    "         title='Effect of hand washing on total infections')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "selective-right",
-   "metadata": {},
-   "source": [
-    "Below \\$200, the campaign has little effect. \n",
-    "\n",
-    "At \\$800 it has a substantial effect, reducing total infections from more than 45% to about 20%. \n",
-    "\n",
-    "Above \\$800, the additional benefit is small."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "lucky-boulder",
-   "metadata": {},
-   "source": [
-    "## Optimization\n",
-    "\n",
-    "Let's put it all together. With a fixed budget of \\$1200, we have to\n",
-    "decide how many doses of vaccine to buy and how much to spend on the\n",
-    "hand-washing campaign.\n",
-    "\n",
-    "Here are the parameters:"
+    "# Solution goes here"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "surrounded-copying",
+   "id": "assumed-license",
    "metadata": {},
    "outputs": [],
    "source": [
-    "num_students = 90\n",
-    "budget = 1200\n",
-    "price_per_dose = 100\n",
-    "max_doses = int(budget / price_per_dose)\n",
-    "max_doses"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "expired-conditioning",
-   "metadata": {},
-   "source": [
-    "The fraction `budget/price_per_dose` might not be an integer. `int` is a\n",
-    "built-in function that converts numbers to integers, rounding down.\n",
-    "\n",
-    "We'll sweep the range of possible doses:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "shaped-utility",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dose_array = linrange(max_doses)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "occupational-reply",
-   "metadata": {},
-   "source": [
-    "In this example we call `linrange` with only one argument; it returns a NumPy array with the integers from 0 to `max_doses`, including both.\n",
-    "\n",
-    "Then we run the simulation for each element of `dose_array`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "recognized-release",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_doses(dose_array):\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for doses in dose_array:\n",
-    "        fraction = doses / num_students\n",
-    "        spending = budget - doses * price_per_dose\n",
-    "        \n",
-    "        system = make_system(beta, gamma)\n",
-    "        add_immunization(system, fraction)\n",
-    "        add_hand_washing(system, spending)\n",
-    "        \n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[doses] = calc_total_infected(results, system)\n",
-    "\n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cardiac-mitchell",
-   "metadata": {},
-   "source": [
-    "For each number of doses, we compute the fraction of students we can\n",
-    "immunize, `fraction` and the remaining budget we can spend on the\n",
-    "campaign, `spending`. Then we run the simulation with those quantities\n",
-    "and store the number of infections.\n",
-    "\n",
-    "The following figure shows the result."
+    "# Solution goes here"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "worth-mounting",
+   "execution_count": 32,
+   "id": "intended-premium",
    "metadata": {},
    "outputs": [],
    "source": [
-    "infected_sweep3 = sweep_doses(dose_array)"
+    "# Solution goes here"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "adjusted-highlight",
+   "execution_count": 34,
+   "id": "limiting-interest",
    "metadata": {},
    "outputs": [],
    "source": [
-    "infected_sweep3.plot()\n",
-    "\n",
-    "decorate(xlabel='Doses of vaccine',\n",
-    "         ylabel='Total fraction infected',\n",
-    "         title='Total infections vs. doses')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dynamic-easter",
-   "metadata": {},
-   "source": [
-    "If we buy no doses of vaccine and spend the entire budget on the campaign, the fraction infected is around 19%. At 4 doses, we have \\$800 left for the campaign, and this is the optimal point that minimizes the number of students who get sick.\n",
-    "\n",
-    "As we increase the number of doses, we have to cut campaign spending,\n",
-    "which turns out to make things worse. But interestingly, when we get\n",
-    "above 10 doses, the effect of herd immunity starts to kick in, and the\n",
-    "number of sick students goes down again."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "amino-excess",
-   "metadata": {},
-   "source": [
-    "## Summary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "timely-bottom",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Suppose the price of the vaccine drops to $50 per dose.  How does that affect the optimal allocation of the spending?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "drawn-hindu",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose we have the option to quarantine infected students.  For example, a student who feels ill might be moved to an infirmary, or a private dorm room, until they are no longer infectious.\n",
-    "\n",
-    "How might you incorporate the effect of quarantine in the SIR model?"
+    "# Solution goes here"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "id": "impressive-librarian",
+   "execution_count": null,
+   "id": "undefined-treasury",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
+   "source": []
   }
  ],
  "metadata": {
diff --git a/chapters/chap13.ipynb b/chapters/chap13.ipynb
index ab1d4886..46d86119 100644
--- a/chapters/chap13.ipynb
+++ b/chapters/chap13.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -88,6 +88,32 @@
   {
    "cell_type": "code",
    "execution_count": 4,
+   "id": "fantastic-recovery",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'chap11.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "excellent-harris",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'chap12.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "id": "apparent-horizon",
    "metadata": {
     "tags": []
@@ -107,11 +133,13 @@
    "id": "electoral-montgomery",
    "metadata": {},
    "source": [
-    "In the previous chapters I presented an SIR model of infectious disease, specifically the Kermack-McKendrick model. We extended the model to include vaccination and the effect of a hand-washing campaign, and used the extended model to allocate a limited budget optimally, that is, to minimize the number of infections.\n",
+    "In the previous chapter we extended the SIR model to include immunization and used it to demonstrate herd immunity.\n",
     "\n",
     "But we assumed that the parameters of the model, contact rate and\n",
     "recovery rate, were known. In this chapter, we explore the behavior of\n",
-    "the model as we vary these parameters, use analysis to understand these relationships better, and propose a method for using data to estimate parameters."
+    "the model as we vary these parameters.\n",
+    "\n",
+    "In the next chapter, we use analysis to understand these relationships better, and propose a method for using data to estimate parameters."
    ]
   },
   {
@@ -136,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "amazing-operations",
    "metadata": {},
    "outputs": [],
@@ -150,34 +178,16 @@
    "id": "adopted-selling",
    "metadata": {},
    "source": [
-    "Then run the simulation for each value and print the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "brief-minutes",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for beta in beta_array:\n",
-    "    system = make_system(beta, gamma)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    print(beta, calc_total_infected(results, system))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "understanding-teach",
-   "metadata": {},
-   "source": [
-    "We can wrap that code in a function and store the results in a\n",
-    "`SweepSeries` object:"
+    "The following function takes this array as a parameter and a single value for `gamma`.\n",
+    "\n",
+    "It runs the simulation for each value of `beta` and computes the same metric we used in the previous chapter, the fraction of the population that gets infected.\n",
+    "\n",
+    "The result is a `SweepSeries` that contains the values of `beta` and the corresponding metrics."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "single-burke",
    "metadata": {
     "tags": []
@@ -198,12 +208,12 @@
    "id": "wired-wrist",
    "metadata": {},
    "source": [
-    "Now we can run `sweep_beta` like this:"
+    "We can run `sweep_beta` like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "collected-practice",
    "metadata": {},
    "outputs": [],
@@ -216,12 +226,12 @@
    "id": "exempt-binary",
    "metadata": {},
    "source": [
-    "And plot the results:"
+    "Before we plot the results, I will use a formatted string literal, also called an **f-string** to assemble a label that includes the value of `gamma`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "competitive-amount",
    "metadata": {},
    "outputs": [],
@@ -235,28 +245,25 @@
    "id": "imported-huntington",
    "metadata": {},
    "source": [
-    "The first line uses a formatted string literal, also called an **f-string** to assemble a label for the plotted line:\n",
-    "\n",
-    "-   An f-string starts with the letter \"f\" followed by a string in single or double quote. \n",
-    "\n",
-    "-   The string can contain any number of format specifiers in squiggly brackets, `{}`.\n",
+    "An f-string starts with the letter \"f\" followed by a string in single or double quotes. \n",
+    "The string can contain any number of format specifiers in squiggly brackets, `{}`.\n",
+    "When a variable name appears in a format specifier, it is replaced with the value of the variable.\n",
     "\n",
-    "-   When a variable name appears in a format specifier, it is replaced with the value of the variable.\n",
-    "\n",
-    "In this example, if the value of `gamma` is `0.25`, the value of `label` is `'gamma = 0.25'`.\n",
+    "In this example, the value of `gamma` is `0.25`, so the value of `label` is `'gamma = 0.25'`.\n",
     "\n",
     "You can read more about f-strings at <https://docs.python.org/3/tutorial/inputoutput.html#tut-f-strings>.\n",
-    "\n"
+    "\n",
+    "Now let's see the results."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "certain-second",
    "metadata": {},
    "outputs": [],
    "source": [
-    "infected_sweep.plot(label=label)\n",
+    "infected_sweep.plot(label=label, color='C1')\n",
     "\n",
     "decorate(xlabel='Contact rate (beta)',\n",
     "         ylabel='Fraction infected')"
@@ -286,17 +293,18 @@
     "## Sweeping gamma\n",
     "\n",
     "Let's see what that looks like for a few different values of `gamma`.\n",
-    "Again, we'll use `linspace` to make an array of values:"
+    "We'll use `linspace` to make an array of values:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "advised-venice",
    "metadata": {},
    "outputs": [],
    "source": [
-    "gamma_array = linspace(0.1, 0.7, 4)"
+    "gamma_array = linspace(0.1, 0.7, 4)\n",
+    "gamma_array"
    ]
   },
   {
@@ -309,17 +317,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "determined-consent",
    "metadata": {},
    "outputs": [],
    "source": [
     "for gamma in gamma_array:\n",
     "    infected_sweep = sweep_beta(beta_array, gamma)\n",
-    "    label = 'gamma = ' + str(gamma)\n",
+    "    label = f'gamma = {gamma}'\n",
     "    infected_sweep.plot(label=label)\n",
     "    \n",
-    "decorate()"
+    "decorate(xlabel='Contact rate (beta)',\n",
+    "         ylabel='Fraction infected')"
    ]
   },
   {
@@ -354,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "aquatic-federal",
    "metadata": {
     "tags": []
@@ -392,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "julian-liberia",
    "metadata": {},
    "outputs": [],
@@ -410,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 16,
    "id": "superior-redhead",
    "metadata": {},
    "outputs": [],
@@ -433,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 17,
    "id": "regular-drama",
    "metadata": {},
    "outputs": [],
@@ -455,16 +464,14 @@
     "\n",
     "Another way to visualize the results of a two-dimensional sweep is a\n",
     "**contour plot**, which shows the parameters on the axes and contour\n",
-    "lines, that is, lines of constant value. In this example, the value is\n",
-    "the fraction of students infected.\n",
+    "lines where the value of the metric is constant.\n",
     "\n",
-    "The ModSim library provides `contour`, which takes a `SweepFrame` as a\n",
-    "parameter:"
+    "The ModSim library provides `contour`, which takes a `SweepFrame` and uses Matplotlib to generate a contour plot."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 18,
    "id": "logical-method",
    "metadata": {},
    "outputs": [],
@@ -481,11 +488,10 @@
    "id": "corresponding-headline",
    "metadata": {},
    "source": [
-    "Infection rates are lowest in the lower right, where the contact rate is and the recovery rate is high. They increase as we move to the upper left, where the contact rate is high and the recovery rate is low.\n",
+    "The values of `gamma` are on the $x$-axis, corresponding to the columns of the `SweepFrame`.\n",
+    "The values of `beta` are on the $y$-axis, corresponding to the rows of the `SweepFrame`.\n",
     "\n",
-    "This figure suggests that there might be a relationship between `beta`\n",
-    "and `gamma` that determines the outcome of the model. In fact, there is.\n",
-    "In the next chapter we'll explore it by running simulations, then derive it by analysis.\n",
+    "Infection rates are lowest in the lower right, where the contact rate is low and the recovery rate is high. They increase as we move to the upper left, where the contact rate is high and the recovery rate is low.\n",
     "\n"
    ]
   },
@@ -494,7 +500,21 @@
    "id": "located-double",
    "metadata": {},
    "source": [
-    "## Summary"
+    "## Summary\n",
+    "\n",
+    "This chapter demonstrates a two-dimensional parameter sweep using a `SweepFrame` to store the results.\n",
+    "\n",
+    "We plotted the results three ways: \n",
+    "\n",
+    "* First we plotted total infections versus `beta`, with one line for each value of `gamma`.\n",
+    "\n",
+    "* Then we plotted total infections versus `gamma`, with one line for each value of `beta`.\n",
+    "\n",
+    "* Finally, we made a contour plot with `beta` on the $y$-axis, `gamma` on the $x$-axis and contour lines where the metric is constant.\n",
+    "\n",
+    "These visualizations suggest that there is a relationship between `beta` and `gamma` that determines the outcome of the model. \n",
+    "In fact, there is.\n",
+    "In the next chapter we'll explore it by running simulations, then derive it by analysis."
    ]
   },
   {
@@ -510,12 +530,14 @@
    "id": "stopped-breathing",
    "metadata": {},
    "source": [
-    "**Exercise:**  Suppose the infectious period for the Freshman Plague is known to be 2 days on average, and suppose during one particularly bad year, 40% of the class is infected at some point.  Estimate the time between contacts."
+    "**Exercise:**  If we know `beta` and `gamma`, we can compute the fraction of the population that gets infected.  In general, we don't know these parameters, but sometimes we can estimate them based on the behavior of an outbreak.\n",
+    "\n",
+    "Suppose the infectious period for the Freshman Plague is known to be 2 days on average, and suppose during one particularly bad year, 40% of the class is infected at some point.  Estimate the time between contacts, `1/beta`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 19,
    "id": "talented-crack",
    "metadata": {},
    "outputs": [],
@@ -525,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 20,
    "id": "pressed-isaac",
    "metadata": {},
    "outputs": [],
@@ -535,7 +557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 21,
    "id": "equal-crazy",
    "metadata": {},
    "outputs": [],

From 041ab45c30482439ebfcb6c6ad501592277c0484 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 16 Feb 2021 12:14:40 -0500
Subject: [PATCH 046/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 165534 -> 163285 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index e0d387e9ff6fb88c38e596b9638bd4c83dbf0369..54410d0d620febdb0b81138697ce9f68f7edf706 100644
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B4>SM(


From a90bad047cf0bccb9b74c26273fbf4267eaf03d1 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 16 Feb 2021 19:53:43 -0500
Subject: [PATCH 047/144] Updating chapters

---
 chapters/chap07.ipynb | 187 +++++++-----------
 chapters/chap08.ipynb |  13 +-
 chapters/chap13.ipynb | 100 +++++-----
 chapters/chap14.ipynb | 427 ++++++++++++++++++++++++++++++------------
 chapters/chap16.ipynb | 147 ++++++++++-----
 5 files changed, 525 insertions(+), 349 deletions(-)

diff --git a/chapters/chap07.ipynb b/chapters/chap07.ipynb
index 0c965728..e3f69fc6 100644
--- a/chapters/chap07.ipynb
+++ b/chapters/chap07.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "regular-director",
+   "id": "progressive-travel",
    "metadata": {},
    "source": [
     "# Chapter 7"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "imported-table",
+   "id": "black-toolbox",
    "metadata": {
     "tags": []
    },
@@ -25,7 +25,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "electoral-turkey",
+   "id": "incorrect-probability",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "formal-context",
+   "execution_count": 2,
+   "id": "earlier-pride",
    "metadata": {
     "tags": []
    },
@@ -65,8 +65,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "progressive-typing",
+   "execution_count": 3,
+   "id": "bound-nature",
    "metadata": {
     "tags": []
    },
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "plastic-trigger",
+   "id": "found-pledge",
    "metadata": {},
    "source": [
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap07.ipynb)"
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "straight-algeria",
+   "id": "general-noise",
    "metadata": {
     "tags": []
    },
@@ -98,7 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "square-trademark",
+   "id": "affiliated-eleven",
    "metadata": {
     "tags": []
    },
@@ -111,7 +111,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "arctic-toilet",
+   "id": "recent-trouble",
    "metadata": {
     "tags": []
    },
@@ -130,7 +130,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "protective-pipeline",
+   "id": "western-blowing",
    "metadata": {
     "tags": []
    },
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "informational-hungary",
+   "id": "occasional-kitchen",
    "metadata": {
     "tags": []
    },
@@ -153,7 +153,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "associate-novel",
+   "id": "monetary-profile",
    "metadata": {
     "tags": []
    },
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "indie-scoop",
+   "id": "damaged-reservation",
    "metadata": {},
    "source": [
     "In the previous chapter we developed a population model where net growth during each time step is proportional to the current population. This model seems more realistic than the constant growth model, but it does not fit the data as well.\n",
@@ -188,7 +188,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "champion-retreat",
+   "id": "assigned-slovakia",
    "metadata": {},
    "source": [
     "## Quadratic growth\n",
@@ -211,7 +211,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "sized-hawaiian",
+   "id": "beginning-belly",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -221,7 +221,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "amateur-strand",
+   "id": "initial-factory",
    "metadata": {},
    "source": [
     "Here's the `System` object we'll use, initialized with `t_0`, `p_0`, and `t_end`."
@@ -230,7 +230,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "interstate-canal",
+   "id": "listed-florence",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "acting-transportation",
+   "id": "amber-context",
    "metadata": {},
    "source": [
     "Now we have to add the parameters `alpha` and `beta` .\n",
@@ -255,7 +255,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "banned-dallas",
+   "id": "signed-impossible",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,7 +265,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "supreme-wellington",
+   "id": "confidential-retreat",
    "metadata": {},
    "source": [
     "And here's how we run it:"
@@ -274,7 +274,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "positive-scheme",
+   "id": "italian-converter",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -283,7 +283,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "satisfied-density",
+   "id": "forbidden-brisbane",
    "metadata": {},
    "source": [
     "And here are the results."
@@ -292,7 +292,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "flush-fellowship",
+   "id": "simplified-sight",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -303,7 +303,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "organized-combining",
+   "id": "primary-ending",
    "metadata": {},
    "source": [
     "The model fits the data well over the whole range, with just a bit of space between them in the 1960s.\n",
@@ -324,7 +324,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "powerful-century",
+   "id": "sunset-underground",
    "metadata": {},
    "source": [
     "## Net growth\n",
@@ -337,7 +337,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "excited-catch",
+   "id": "neural-guinea",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -348,7 +348,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "tracked-guide",
+   "id": "heated-selling",
    "metadata": {},
    "source": [
     "Now I'll use the quadratic model to compute net growth for each population."
@@ -357,7 +357,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "classical-badge",
+   "id": "animal-spoke",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -367,48 +367,48 @@
   },
   {
    "cell_type": "markdown",
-   "id": "quantitative-differential",
+   "id": "engaging-parade",
    "metadata": {},
    "source": [
-    "In order to plot growth rate versus population, we'll use `make_series` to make a Pandas `Series`."
+    "To plot growth rate versus population, we'll use the `plot` function from Matplotlib.\n",
+    "First we have to import it:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "interim-consent",
+   "id": "informed-three",
    "metadata": {},
    "outputs": [],
    "source": [
-    "growth_series = make_series(pop_array, growth_array)"
+    "from matplotlib.pyplot import plot"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "published-average",
+   "id": "retained-deployment",
    "metadata": {},
    "source": [
-    "Which we can plot like this:"
+    "Now we can use it like this:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "received-crossing",
+   "id": "unexpected-nigeria",
    "metadata": {},
    "outputs": [],
    "source": [
-    "growth_series.plot(label='growth')\n",
-    "\n",
+    "plot(pop_array, growth_array, label='net growth')\n",
     "\n",
     "decorate(xlabel='Population (billions)',\n",
     "         ylabel='Net growth (billions)',\n",
-    "         title='Growth vs. Population')"
+    "         title='Net Growth vs. Population')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "plastic-wisdom",
+   "id": "precise-finish",
    "metadata": {},
    "source": [
     "Note that the x-axis is not time, as in the previous figures, but population. We can divide this curve into four regimes of behavior:\n",
@@ -433,7 +433,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "relevant-detection",
+   "id": "angry-voice",
    "metadata": {},
    "source": [
     "## Equilibrium\n",
@@ -456,7 +456,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "lightweight-entrepreneur",
+   "id": "ordinary-honolulu",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -465,7 +465,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "stuck-flood",
+   "id": "adaptive-pharmacy",
    "metadata": {},
    "source": [
     "With these parameters, net growth is 0 when the population is about 13.9 billion.\n",
@@ -485,7 +485,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "derived-oliver",
+   "id": "continental-image",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -501,7 +501,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bibliographic-handy",
+   "id": "eligible-pride",
    "metadata": {},
    "source": [
     "## Dysfunctions\n",
@@ -519,7 +519,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "lucky-insurance",
+   "id": "realistic-opinion",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -534,7 +534,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "accredited-fitting",
+   "id": "olive-information",
    "metadata": {},
    "source": [
     "Now let's see all the ways that can go wrong."
@@ -542,7 +542,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "transparent-cotton",
+   "id": "prostate-motorcycle",
    "metadata": {},
    "source": [
     "**Dysfunction #1:** Not using parameters. In the following version, the function doesn't take any parameters; when `sys1` appears inside the function, it refers to the object we create outside the function."
@@ -551,7 +551,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "aggregate-baker",
+   "id": "marine-entry",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -566,7 +566,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "otherwise-belarus",
+   "id": "dated-invalid",
    "metadata": {},
    "source": [
     "This version works, but it is not as versatile as it could be.\n",
@@ -575,7 +575,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "naughty-crazy",
+   "id": "meaningful-louisiana",
    "metadata": {},
    "source": [
     "**Dysfunction #2:** Clobbering the parameters. When people first learn\n",
@@ -585,7 +585,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "outdoor-petroleum",
+   "id": "moving-brazil",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -602,7 +602,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "afraid-street",
+   "id": "dietary-spectacular",
    "metadata": {},
    "source": [
     "In this example, we have a `System` object named `sys1` that gets passed\n",
@@ -619,7 +619,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "expired-detail",
+   "id": "present-estonia",
    "metadata": {},
    "source": [
     "**Dysfunction #3:** No return value. Here's a version that computes the value of `K` but doesn't return it."
@@ -628,7 +628,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "increasing-database",
+   "id": "sacred-physiology",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -643,7 +643,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "brutal-chile",
+   "id": "technological-incentive",
    "metadata": {},
    "source": [
     "A function that doesn't have a return statement actually returns a special value called `None`, so in this example the value of `pop` is `None`. If you are debugging a program and find that the value of a variable is `None` when it shouldn't be, a function without a return statement is a likely cause."
@@ -651,7 +651,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "meaning-thriller",
+   "id": "received-firewall",
    "metadata": {},
    "source": [
     "**Dysfunction #4:** Ignoring the return value. Finally, here's a version where the function is correct, but the way it's used is not.\n",
@@ -669,7 +669,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "opposed-vessel",
+   "id": "approximate-straight",
    "metadata": {},
    "source": [
     "In this example, `carrying_capacity` runs and returns `K`, but the\n",
@@ -682,7 +682,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "quarterly-ethiopia",
+   "id": "liable-mixture",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -690,7 +690,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "painted-samuel",
+   "id": "worst-builder",
    "metadata": {},
    "source": [
     "**Exercise:** In a previous section, we saw a different way to parameterize the quadratic model:\n",
@@ -705,7 +705,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "unauthorized-settle",
+   "id": "stretch-check",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -715,7 +715,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "primary-roman",
+   "id": "tender-treat",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -725,7 +725,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "ordinary-solid",
+   "id": "passive-certificate",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -734,7 +734,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ignored-architect",
+   "id": "understood-cancer",
    "metadata": {},
    "source": [
     "**Exercise:**  What happens if we start with an initial population above the carrying capacity, like 20 billion?  Run the model with initial populations between 1 and 20 billion, and plot the results on the same axes.\n",
@@ -749,68 +749,17 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "intermediate-kazakhstan",
+   "id": "agricultural-burke",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Solution goes here"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "positive-enforcement",
-   "metadata": {},
-   "source": [
-    "## Under the hood\n",
-    "\n",
-    "ModSim provides `make_series` to make it easier to create a Pandas Series.  In this chapter, we used it like this: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "described-funds",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "growth_series = make_series(pop_array, growth_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "least-password",
-   "metadata": {},
-   "source": [
-    "If you import `Series` from Pandas, you can make a `Series` yourself, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "sitting-quarterly",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas import Series\n",
-    "\n",
-    "growth_series = Series(growth_array, pop_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "german-kennedy",
-   "metadata": {},
-   "source": [
-    "The difference is that the arguments are in reverse order: the first argument is stored as the values in the `Series`; the second argument is stored as the `index`.\n",
-    "\n",
-    "I find that order counterintuitive, which is why I use `make_series`.\n",
-    "`make_series` takes the same optional keyword arguments as `Series`, which you can read about at <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html>."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "mental-october",
+   "id": "colored-globe",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -833,7 +782,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap08.ipynb b/chapters/chap08.ipynb
index b8c4c5a7..228360d3 100644
--- a/chapters/chap08.ipynb
+++ b/chapters/chap08.ipynb
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -576,7 +576,7 @@
    "id": "handy-dubai",
    "metadata": {},
    "source": [
-    "To see what it looks like, I'll create an array of time stamps from 1960 to 2020, use `alpha_func` to compute the corresponding growth rates, and put the results into a `Series`."
+    "To see what it looks like, I'll create an array of time stamps from 1960 to 2020 and use `alpha_func` to compute the corresponding growth rates."
    ]
   },
   {
@@ -586,11 +586,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from numpy import linspace\n",
-    "\n",
     "t_array = linspace(1960, 2020, 5)\n",
-    "alpha_array = alpha_func(t_array)\n",
-    "alpha_series = make_series(t_array, alpha_array)"
+    "alpha_array = alpha_func(t_array)"
    ]
   },
   {
@@ -611,7 +608,7 @@
     "from matplotlib.pyplot import plot\n",
     "\n",
     "plot_alpha()\n",
-    "alpha_series.plot(label='model', color='gray')\n",
+    "plot(t_array, alpha_array, label='model', color='gray')\n",
     "\n",
     "decorate(ylabel='Net growth rate',\n",
     "         title='Linear model of net growth rate')"
diff --git a/chapters/chap13.ipynb b/chapters/chap13.ipynb
index 46d86119..ed73cd5f 100644
--- a/chapters/chap13.ipynb
+++ b/chapters/chap13.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "artistic-wells",
+   "id": "colonial-jacob",
    "metadata": {},
    "source": [
     "# Chapter 13"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "imported-table",
+   "id": "modified-discretion",
    "metadata": {
     "tags": []
    },
@@ -25,7 +25,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "electoral-turkey",
+   "id": "complicated-retreat",
    "metadata": {
     "tags": []
    },
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "formal-context",
+   "id": "selected-there",
    "metadata": {
     "tags": []
    },
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "progressive-typing",
+   "id": "prepared-apparatus",
    "metadata": {
     "tags": []
    },
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "plastic-trigger",
+   "id": "consolidated-cherry",
    "metadata": {},
    "source": [
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap13.ipynb)"
@@ -88,7 +88,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "fantastic-recovery",
+   "id": "awful-martial",
    "metadata": {
     "tags": []
    },
@@ -101,7 +101,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "excellent-harris",
+   "id": "changing-burner",
    "metadata": {
     "tags": []
    },
@@ -114,7 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "apparent-horizon",
+   "id": "defensive-automation",
    "metadata": {
     "tags": []
    },
@@ -125,12 +125,14 @@
     "from chap11 import make_system\n",
     "from chap11 import update_func\n",
     "from chap11 import run_simulation\n",
+    "from chap11 import plot_results\n",
+    "\n",
     "from chap12 import calc_total_infected"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "electoral-montgomery",
+   "id": "structural-application",
    "metadata": {},
    "source": [
     "In the previous chapter we extended the SIR model to include immunization and used it to demonstrate herd immunity.\n",
@@ -144,7 +146,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "nervous-spread",
+   "id": "tracked-outreach",
    "metadata": {},
    "source": [
     "## Sweeping beta\n",
@@ -165,7 +167,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "amazing-operations",
+   "id": "forward-responsibility",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -175,7 +177,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adopted-selling",
+   "id": "eligible-shareware",
    "metadata": {},
    "source": [
     "The following function takes this array as a parameter and a single value for `gamma`.\n",
@@ -188,7 +190,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "single-burke",
+   "id": "dutch-chemistry",
    "metadata": {
     "tags": []
    },
@@ -205,7 +207,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "wired-wrist",
+   "id": "mathematical-process",
    "metadata": {},
    "source": [
     "We can run `sweep_beta` like this:"
@@ -214,7 +216,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "collected-practice",
+   "id": "christian-singer",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,7 +225,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "exempt-binary",
+   "id": "collectible-positive",
    "metadata": {},
    "source": [
     "Before we plot the results, I will use a formatted string literal, also called an **f-string** to assemble a label that includes the value of `gamma`:"
@@ -232,7 +234,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "competitive-amount",
+   "id": "proprietary-credit",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -242,7 +244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "imported-huntington",
+   "id": "invisible-bible",
    "metadata": {},
    "source": [
     "An f-string starts with the letter \"f\" followed by a string in single or double quotes. \n",
@@ -259,7 +261,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "certain-second",
+   "id": "developmental-video",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,7 +273,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "affiliated-honduras",
+   "id": "durable-isaac",
    "metadata": {},
    "source": [
     "Remember that this figure\n",
@@ -287,7 +289,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "worst-alexandria",
+   "id": "described-jacksonville",
    "metadata": {},
    "source": [
     "## Sweeping gamma\n",
@@ -299,7 +301,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "advised-venice",
+   "id": "chief-greece",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -309,7 +311,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sensitive-folder",
+   "id": "medium-greek",
    "metadata": {},
    "source": [
     "And run `sweep_beta` for each value of `gamma`:"
@@ -318,7 +320,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "determined-consent",
+   "id": "thermal-pulse",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -333,7 +335,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "tutorial-venture",
+   "id": "artistic-charge",
    "metadata": {},
    "source": [
     "When `gamma` is low, the recovery rate is low, which means people are infectious longer.\n",
@@ -344,7 +346,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "rising-flour",
+   "id": "metropolitan-collect",
    "metadata": {},
    "source": [
     "## SweepFrame\n",
@@ -364,7 +366,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "aquatic-federal",
+   "id": "scientific-dealer",
    "metadata": {
     "tags": []
    },
@@ -379,7 +381,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "level-distinction",
+   "id": "educated-congress",
    "metadata": {},
    "source": [
     "`sweep_parameters` takes as parameters an array of values for `beta` and\n",
@@ -402,7 +404,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "julian-liberia",
+   "id": "engaging-philosophy",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -411,7 +413,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sharing-contemporary",
+   "id": "realistic-hughes",
    "metadata": {},
    "source": [
     "With the results in a `SweepFrame`, we can plot each column like this:"
@@ -420,7 +422,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "superior-redhead",
+   "id": "false-forestry",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -429,12 +431,13 @@
     "    frame[gamma].plot(label=label)\n",
     "\n",
     "decorate(xlabel='Contact rate (beta)',\n",
-    "         ylabel='Fraction infected')"
+    "         ylabel='Fraction infected',\n",
+    "         title='Sweep beta, multiple values of gamma')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "english-photographer",
+   "id": "armed-spending",
    "metadata": {},
    "source": [
     "Alternatively, we can plot each row like this:"
@@ -443,7 +446,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "regular-drama",
+   "id": "silent-advantage",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -452,12 +455,13 @@
     "    frame.loc[beta].plot(label=label)\n",
     "    \n",
     "decorate(xlabel='Recovery rate (gamma)',\n",
-    "         ylabel='Fraction infected')"
+    "         ylabel='Fraction infected',\n",
+    "         title='Sweep gamma, multiple values of beta')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "selective-evaluation",
+   "id": "diverse-works",
    "metadata": {},
    "source": [
     "This example demonstrates one use of a `SweepFrame`: we can run the analysis once, save the results, and then generate different visualizations.\n",
@@ -472,7 +476,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "logical-method",
+   "id": "veterinary-cheese",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -480,12 +484,12 @@
     "\n",
     "decorate(xlabel='Recovery rate (gamma)',\n",
     "         ylabel='Contact rate (beta)',\n",
-    "         title='Fraction infected, contour plot')"
+    "         title='Contour plot, fraction infected')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "corresponding-headline",
+   "id": "operational-daughter",
    "metadata": {},
    "source": [
     "The values of `gamma` are on the $x$-axis, corresponding to the columns of the `SweepFrame`.\n",
@@ -497,7 +501,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "located-double",
+   "id": "personal-formula",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -519,7 +523,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "flying-tracker",
+   "id": "variable-capture",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -527,7 +531,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "stopped-breathing",
+   "id": "conscious-blues",
    "metadata": {},
    "source": [
     "**Exercise:**  If we know `beta` and `gamma`, we can compute the fraction of the population that gets infected.  In general, we don't know these parameters, but sometimes we can estimate them based on the behavior of an outbreak.\n",
@@ -538,7 +542,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "talented-crack",
+   "id": "democratic-driving",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -548,7 +552,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "pressed-isaac",
+   "id": "stretch-israel",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -558,7 +562,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "equal-crazy",
+   "id": "controlling-strand",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -568,7 +572,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "desirable-crystal",
+   "id": "instructional-hayes",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -591,7 +595,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap14.ipynb b/chapters/chap14.ipynb
index 09fad940..f91da633 100644
--- a/chapters/chap14.ipynb
+++ b/chapters/chap14.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "happy-minutes",
+   "id": "narrow-announcement",
    "metadata": {},
    "source": [
     "# Chapter 14"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "imported-table",
+   "id": "still-fellow",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "electoral-turkey",
+   "execution_count": 1,
+   "id": "complicated-nickname",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "formal-context",
+   "execution_count": 2,
+   "id": "pretty-ethics",
    "metadata": {
     "tags": []
    },
@@ -65,8 +65,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "progressive-typing",
+   "execution_count": 3,
+   "id": "approved-terminal",
    "metadata": {
     "tags": []
    },
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "plastic-trigger",
+   "id": "legal-attack",
    "metadata": {},
    "source": [
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap14.ipynb)"
@@ -88,8 +88,49 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "level-struggle",
-   "metadata": {},
+   "id": "lucky-enforcement",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'chap11.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "hairy-fifth",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'chap12.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "laden-thousand",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'chap13.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "future-baseball",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# import code from previous notebooks\n",
@@ -106,36 +147,33 @@
   },
   {
    "cell_type": "markdown",
-   "id": "disturbed-joshua",
+   "id": "apparent-country",
    "metadata": {},
    "source": [
-    "In the previous chapters we used simulation to predict the effect of an infectious disease in a susceptible population and to design\n",
-    "interventions that would minimize the effect.\n",
+    "In the previous chapter we swept the parameters of the SIR model: the contact rate, `beta`, and the recovery rate, `gamma`.\n",
+    "For each pair of parameters, we ran a simulation and computed the total fraction of the population infected.\n",
     "\n",
-    "In this chapter we use analysis to investigate the relationship between the parameters, `beta` and `gamma`, and the outcome of the simulation."
+    "In this chapter we investigate the relationship between the parameters and this metric, using both simulation and analysis."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "respiratory-twelve",
+   "id": "sharp-ladder",
    "metadata": {},
    "source": [
     "## Nondimensionalization\n",
     "\n",
-    "The figures in\n",
-    "Section [\\[sweepframe\\]](#sweepframe){reference-type=\"ref\"\n",
-    "reference=\"sweepframe\"} suggest that there is a relationship between the parameters of the SIR model, `beta` and `gamma`, that determines the outcome of the simulation, the fraction of students infected. Let's think what that relationship might be.\n",
+    "The figures in the previous chapter suggest that there is a relationship between the parameters of the SIR model, `beta` and `gamma`, and the fraction of the population that is infected. Let's think what that relationship might be.\n",
     "\n",
-    "-   When `beta` exceeds `gamma`, that means there are more contacts\n",
-    "    (that is, potential infections) than recoveries during each day (or other unit of time). The difference between `beta` and `gamma` might be called the \"excess contact rate\\\", in units of contacts per day.\n",
+    "-   When `beta` exceeds `gamma`, there are more contacts\n",
+    "    than recoveries during each day. The difference between `beta` and `gamma` might be called the \"excess contact rate\", in units of contacts per day.\n",
     "\n",
     "-   As an alternative, we might consider the ratio `beta/gamma`, which\n",
     "    is the number of contacts per recovery. Because the numerator and\n",
     "    denominator are in the same units, this ratio is **dimensionless**, which means it has no units.\n",
     "\n",
     "Describing physical systems using dimensionless parameters is often a\n",
-    "useful move in the modeling and simulation game. It is so useful, in\n",
-    "fact, that it has a name: **nondimensionalization** (see\n",
+    "useful move in the modeling and simulation game. In fact, it is so useful that it has a name: **nondimensionalization** (see\n",
     "<http://modsimpy.com/nondim>).\n",
     "\n",
     "So we'll try the second option first."
@@ -143,70 +181,95 @@
   },
   {
    "cell_type": "markdown",
-   "id": "caroline-compiler",
+   "id": "reasonable-letter",
    "metadata": {},
    "source": [
     "## Exploring the results\n",
     "\n",
-    "Suppose we have a `SweepFrame` with one row for each value of `beta` and one column for each value of `gamma`. Each element in the `SweepFrame` is the fraction of students infected in a simulation with a given pair of parameters.\n",
+    "In the previous chapter, we wrote a function, `sweep_parameters`,\n",
+    "that takes an array of values for `beta` and an array of values for `gamma`.\n",
+    "It runs a simulation for each pair of parameters and returns a `SweepFrame` with the results.\n",
     "\n",
-    "We can print the values in the `SweepFrame` like this:"
+    "I'll run it again with the following arrays of parameters."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "flying-chosen",
+   "execution_count": 8,
+   "id": "compliant-butler",
    "metadata": {},
    "outputs": [],
    "source": [
-    "beta_array = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 , 1.1]\n",
+    "beta_array = [0.1, 0.2, 0.3, 0.4, 0.5, \n",
+    "              0.6, 0.7, 0.8, 0.9, 1.0 , 1.1]\n",
     "gamma_array = [0.2, 0.4, 0.6, 0.8]\n",
-    "frame = sweep_parameters(beta_array, gamma_array)\n",
+    "frame = sweep_parameters(beta_array, gamma_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "temporal-danish",
+   "metadata": {},
+   "source": [
+    "Here's what the first few rows look like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "diagnostic-creation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "frame.head()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "senior-hazard",
+   "metadata": {},
+   "source": [
+    "The `SweepFrame` has one row for each value of `beta` and one column for each value of `gamma`. \n",
+    "\n",
+    "We can print the values in the `SweepFrame` like this:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "satisfied-japan",
+   "execution_count": 10,
+   "id": "tested-nation",
    "metadata": {},
    "outputs": [],
    "source": [
     "for gamma in frame.columns:\n",
     "    column = frame[gamma]\n",
     "    for beta in column.index:\n",
-    "        frac_infected = column[beta]\n",
-    "        print(beta, gamma, frac_infected)"
+    "        metric = column[beta]\n",
+    "        print(beta, gamma, metric)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "automotive-solution",
+   "id": "cloudy-volunteer",
    "metadata": {},
    "source": [
     "This is the first example we've seen with one `for` loop inside another:\n",
     "\n",
     "-   Each time the outer loop runs, it selects a value of `gamma` from\n",
-    "    the columns of the `DataFrame` and extracts the corresponding\n",
+    "    the columns of the `SweepFrame` and extracts the corresponding\n",
     "    column.\n",
     "\n",
-    "-   Each time the inner loop runs, it selects a value of `beta` from the\n",
-    "    column and selects the corresponding element, which is the fraction\n",
-    "    of students infected.\n",
+    "-   Each time the inner loop runs, it selects a value of `beta` from the index of the column and selects the corresponding element, which is the fraction of the population that got infected.\n",
     "\n",
-    "In the example from the previous chapter, `frame` has 4 columns, one for\n",
-    "each value of `gamma`, and 11 rows, one for each value of `beta`. So\n",
-    "these loops print 44 lines, one for each pair of parameters.\n",
+    "Since there are 11 rows and 4 columns, the total number of lines in the output is 44.\n",
     "\n",
-    "The following function encapulates the previous loop and plots the\n",
-    "fraction infected as a function of the ratio `beta/gamma`:"
+    "The following function uses the same loop to enumerate the elements of the `SweepFrame`, but instead of printing a line for each element, it plots a point."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "baking-tactics",
+   "execution_count": 11,
+   "id": "elder-retirement",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,17 +277,27 @@
     "\n",
     "def plot_sweep_frame(frame):\n",
     "    for gamma in frame.columns:\n",
-    "        series = frame[gamma]\n",
-    "        for beta in series.index:\n",
-    "            frac_infected = series[beta]\n",
-    "            plot(beta/gamma, frac_infected, 'o', \n",
-    "                 color='C1', alpha=0.4)"
+    "        column = frame[gamma]\n",
+    "        for beta in column.index:\n",
+    "            metric = column[beta]\n",
+    "            plot(beta/gamma, metric, '.', color='C1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "marine-stadium",
+   "metadata": {},
+   "source": [
+    "On the $x$-axis, it plots the ratio `beta/gamma`.\n",
+    "On the $y$-axis, it plots the fraction of the population that's infected.\n",
+    "\n",
+    "Here's what it looks like:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "dental-outreach",
+   "execution_count": 12,
+   "id": "legal-onion",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -236,15 +309,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "systematic-gossip",
+   "id": "purple-jersey",
    "metadata": {},
    "source": [
-    "The results fall on a single curve, at least approximately. That means that we can predict the fraction of students who will be infected based on a single parameter, the ratio `beta/gamma`. We don't need to know the values of `beta` and `gamma` separately."
+    "The results fall on a single curve, at least approximately. That means that we can predict the fraction of the population that will be infected based on a single parameter, the ratio `beta/gamma`. We don't need to know the values of `beta` and `gamma` separately."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "theoretical-arrow",
+   "id": "sublime-cargo",
    "metadata": {},
    "source": [
     "## Contact number\n",
@@ -256,12 +329,12 @@
     "(as a fraction of the population).\n",
     "\n",
     "When a new disease is introduced to a susceptible population, $s$ is\n",
-    "approximately 1, so the number of people infected by each sick person is $\\beta / \\gamma$. This ratio is called the \"contact number\\\" or \"basic reproduction number\\\" (see <http://modsimpy.com/contact>). By convention it is usually denoted $R_0$, but in the context of an SIR model, this notation is confusing, so we'll use $c$ instead."
+    "approximately 1, so the number of people infected by each sick person is $\\beta / \\gamma$. This ratio is called the \"contact number\\\" or \"basic reproduction number\\\" (see <http://modsimpy.com/contact>). By convention it is usually denoted $R_0$, but in the context of an SIR model, that notation is confusing, so we'll use $c$ instead."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "intimate-romance",
+   "id": "potential-curve",
    "metadata": {},
    "source": [
     "The results in the previous section suggest that there is a relationship between $c$ and the total number of infections. We can derive this relationship by analyzing the differential equations from\n",
@@ -276,14 +349,28 @@
     "contact rate by the infection rate to get the dimensionless quantity\n",
     "$c$, now we'll divide $di/dt$ by $ds/dt$ to get a ratio of rates:\n",
     "\n",
-    "$$\\frac{di}{ds} = -1 + \\frac{1}{cs}$$ \n",
+    "$$\\frac{di}{ds} = \\frac{\\beta s i - \\gamma i}{-\\beta s i}$$ \n",
+    "\n",
+    "Which we can simplify as\n",
     "\n",
+    "$$\\frac{di}{ds} = -1 + \\frac{\\gamma}{\\beta s}$$ \n",
+    "\n",
+    "Replacing $\\beta/\\gamma$ with $c$, we can write\n",
+    "\n",
+    "$$\\frac{di}{ds} = -1 + \\frac{1}{c s}$$ "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reverse-prize",
+   "metadata": {},
+   "source": [
     "Dividing one differential equation by another is not an obvious move, but in this case it is useful because it gives us a relationship between $i$, $s$ and $c$ that does not depend on time. From that relationship, we can derive an equation that relates $c$ to the final value of $s$. In theory, this equation makes it possible to infer $c$ by observing the course of an epidemic."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "revolutionary-motorcycle",
+   "id": "lightweight-upgrade",
    "metadata": {},
    "source": [
     "Here's how the derivation goes. We multiply both sides of the previous\n",
@@ -308,7 +395,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "spread-convert",
+   "id": "blessed-charger",
    "metadata": {},
    "source": [
     "Now, at the end of the epidemic, let's assume that $i(\\infty) = 0$, and $s(\\infty)$ is an unknown quantity, $s_{\\infty}$. Now we have:\n",
@@ -320,29 +407,27 @@
   },
   {
    "cell_type": "markdown",
-   "id": "intended-worker",
+   "id": "killing-flavor",
    "metadata": {},
    "source": [
     "## Analysis and simulation\n",
     "\n",
-    "Let's compare this analytic result to the results from simulation. I'll create an array of values for $s_{\\infty}$"
+    "Let's compare this analytic result to the results from simulation. I'll create an array of values for $s_{\\infty}$."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "previous-schedule",
+   "execution_count": 13,
+   "id": "temporal-murray",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from numpy import linspace\n",
-    "\n",
-    "s_inf_array = linspace(0.0001, 0.999, 31)"
+    "s_inf_array = linspace(0.003, 0.99, 50)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "positive-float",
+   "id": "engaged-activity",
    "metadata": {},
    "source": [
     "And compute the corresponding values of $c$:"
@@ -350,8 +435,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "valuable-landscape",
+   "execution_count": 14,
+   "id": "coordinated-sheffield",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -362,7 +447,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "premium-carry",
+   "id": "cleared-gregory",
    "metadata": {},
    "source": [
     "To get the total infected, we compute the difference between $s(0)$ and\n",
@@ -371,8 +456,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "composite-cabinet",
+   "execution_count": 15,
+   "id": "level-citizenship",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -381,17 +466,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "satisfactory-blink",
+   "id": "divine-polls",
    "metadata": {},
    "source": [
-    "We can use `make_series` to put `c_array`\n",
-    "and `frac_infected` in a Pandas `Series`."
+    "The ModSim library provides a function called `make_series` we can use to put `c_array` and `frac_infected` in a Pandas `Series`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "appropriate-geology",
+   "execution_count": 16,
+   "id": "mounted-patient",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -400,7 +484,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bottom-print",
+   "id": "emerging-insert",
    "metadata": {},
    "source": [
     "Now we can plot the results:"
@@ -408,8 +492,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "integral-reading",
+   "execution_count": 17,
+   "id": "adjusted-spread",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -422,51 +506,57 @@
   },
   {
    "cell_type": "markdown",
-   "id": "peaceful-interest",
+   "id": "adjustable-calculator",
    "metadata": {},
    "source": [
     "When the contact number exceeds 1, analysis and simulation agree. When\n",
     "the contact number is less than 1, they do not: analysis indicates there should be no infections; in the simulations there are a small number of infections.\n",
     "\n",
     "The reason for the discrepancy is that the simulation divides time into a discrete series of days, whereas the analysis treats time as a\n",
-    "continuous quantity. In other words, the two methods are actually based on different models. So which model is better?\n",
-    "\n",
-    "Probably neither. When the contact number is small, the early progress\n",
-    "of the epidemic depends on details of the scenario. If we are lucky, the original infected person, \"patient zero\", infects no one and there is no epidemic. If we are unlucky, patient zero might have a large number of close friends, or might work in the dining hall (and fail to observe safe food handling procedures).\n",
-    "\n",
-    "For contact numbers near or less than 1, we might need a more detailed\n",
-    "model. But for higher contact numbers the SIR model might be good\n",
-    "enough."
+    "continuous quantity.\n",
+    "When the contact number is large, these two models agree; when it is small, they diverge."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "beginning-capital",
+   "id": "southeast-comparative",
    "metadata": {},
    "source": [
     "## Estimating contact number\n",
     "\n",
-    "Figure xxx shows that if we know the contact number, we can compute the fraction infected. But we can also read the figure the other way; that is, at the end of an epidemic, if we can estimate the fraction of the population that was ever infected, we can use it to estimate the contact number.\n",
+    "The previous figure shows that if we know the contact number, we can estimate the fraction of the population that will be infected with just a few arithmetic operations.\n",
+    "We don't have to run a simulation.\n",
+    "\n",
+    "Also, we can read the figure the other way; if we know what fraction of the population was affected by a past outbreak, we can estimate the contact number.\n",
+    "Then, if we know one of the parameters, like `gamma`, we can use the contact number to estimate the other parameter, like `beta`.\n",
+    "\n",
+    "At least in theory, we can.\n",
+    "In practice, it might not work very well, because of the shape of the curve. \n",
     "\n",
-    "Well, in theory we can. In practice, it might not work very well,\n",
-    "because of the shape of the curve. When the contact number is near 2,\n",
-    "the curve is quite steep, which means that small changes in $c$ yield\n",
-    "big changes in the number of infections. If we observe that the total\n",
-    "fraction infected is anywhere from 20% to 80%, we would conclude that\n",
-    "$c$ is near 2.\n",
+    "* When the contact number is low, the curve is quite steep, which means that small changes in $c$ yield big changes in the number of infections. If we observe that the total fraction infected is anywhere from 20% to 80%, we would conclude that $c$ is near 2.\n",
+    "\n",
+    "* And when the contact number is high, the curve is nearly flat, which means that it's hard to see the difference between values of $c$ between 3 and 6.\n",
+    "\n",
+    "So it might not be practical to use this curve to estimate $c$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "greenhouse-harmony",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
     "\n",
-    "On the other hand, for larger contact numbers, nearly the entire\n",
-    "population is infected, so the curve is nearly flat. In that case we\n",
-    "would not be able to estimate $c$ precisely, because any value greater\n",
-    "than 3 would yield effectively the same results. Fortunately, this is\n",
-    "unlikely to happen in the real world; very few epidemics affect anything close to 90% of the population.\n",
+    "In this chapter we used simulations to explore the relationship between `beta`, `gamma`, and the fraction infected.\n",
+    "Then we used analysis to explain that relationship.\n",
     "\n",
-    "So the SIR model has limitations; nevertheless, it provides insight into the behavior of infectious disease, especially the phenomenon of herd immunity. As we saw in Chapter xxx, if we know the parameters of the model, we can use it to evaluate possible interventions. And as we saw in this chapter, we might be able to use data from earlier outbreaks to estimate the parameters.\n"
+    "With that, we are done with the SIR model.\n",
+    "In the next chapter we move on to thermal systems and the notorious coffee cooling problem."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "olive-niger",
+   "id": "buried-tracy",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -474,10 +564,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "upper-interim",
+   "id": "mineral-walnut",
    "metadata": {},
    "source": [
-    "**Exercise:**  If we didn't know about contact numbers, we might have explored other possibilities, like the difference between `beta` and `gamma`, rather than their ratio.\n",
+    "**Exercise:** At the beginning of this chapter, I suggested two ways to relate `beta` and `gamma`: we could compute their difference or their ratio.\n",
+    "\n",
+    "Because the ratio is dimensionless, I suggested we explore it first, and that led us to discover the contact number, which is `beta/gamma`.\n",
+    "When we plotted the fraction infected as a function of the contact number, we found that this metric falls on a single curve, at least approximately.\n",
+    "That indicates that the ratio is enough to predict the results; we don't have to know `beta` and `gamma` individually. \n",
+    "\n",
+    "But that leaves a question open: what happens if we do the same thing using the difference instead of the ratio?\n",
     "\n",
     "Write a version of `plot_sweep_frame`, called `plot_sweep_frame_difference`, that plots the fraction infected versus the difference `beta-gamma`.\n",
     "\n",
@@ -486,8 +582,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "talented-discovery",
+   "execution_count": 18,
+   "id": "right-official",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -496,8 +592,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "bright-label",
+   "execution_count": 19,
+   "id": "weekly-doctrine",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -506,8 +602,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "suspended-louisiana",
+   "execution_count": 20,
+   "id": "failing-treaty",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -516,20 +612,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "understanding-fault",
+   "id": "statistical-senate",
    "metadata": {},
    "source": [
     "**Exercise:** Suppose you run a survey at the end of the semester and find that 26% of students had the Freshman Plague at some point.\n",
     "\n",
     "What is your best estimate of `c`?\n",
     "\n",
-    "Hint: if you print `frac_infected_series`, you can read off the answer. "
+    "Hint: if you display `frac_infected_series`, you can read off the answer. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "another-timing",
+   "execution_count": 21,
+   "id": "manufactured-cache",
    "metadata": {
     "scrolled": true
    },
@@ -540,8 +636,94 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "gross-terry",
+   "execution_count": 22,
+   "id": "academic-chancellor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "functioning-resort",
+   "metadata": {},
+   "source": [
+    "**Exercise**: So far the only metric we have considered is the total fraction of the population that gets infected over the course of an epidemic.  That is an important metric, but it is not the only one we care about.\n",
+    "\n",
+    "For example, if we have limited resources to deal with infected people, we might also be concerned about the number of people who are sick at the peak of the epidemic, which is the maximum of `I`.\n",
+    "\n",
+    "Write a version of `sweep_beta` that computes this metric, and use it to compute a `SweepFrame` for a range of values of `beta` and `gamma`.\n",
+    "Make a contour plot that shows the value of this metric as a function of `beta` and `gamma`.\n",
+    "\n",
+    "Then use `plot_sweep_frame` to plot the maximum of `I` as a function of the contact number, `beta/gamma`.\n",
+    "Do the results fall on a single curve?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "decent-understanding",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "laughing-imperial",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "close-cache",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "annoying-alexander",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "charming-checkout",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "passive-slave",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "promising-spyware",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -551,13 +733,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "found-surgery",
+   "id": "passing-quarterly",
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -573,7 +756,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap16.ipynb b/chapters/chap16.ipynb
index 0e8361da..dfca2ad7 100644
--- a/chapters/chap16.ipynb
+++ b/chapters/chap16.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "involved-hampshire",
+   "id": "stopped-plymouth",
    "metadata": {},
    "source": [
     "# Chapter 16"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "imported-table",
+   "id": "endangered-sequence",
    "metadata": {
     "tags": []
    },
@@ -25,7 +25,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "electoral-turkey",
+   "id": "owned-renewal",
    "metadata": {
     "tags": []
    },
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "formal-context",
+   "id": "appreciated-prefix",
    "metadata": {
     "tags": []
    },
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "progressive-typing",
+   "id": "grave-feature",
    "metadata": {
     "tags": []
    },
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "plastic-trigger",
+   "id": "senior-malawi",
    "metadata": {},
    "source": [
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap16.ipynb)"
@@ -88,7 +88,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "straight-milan",
+   "id": "electrical-temple",
    "metadata": {
     "tags": []
    },
@@ -103,7 +103,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "global-shooting",
+   "id": "vocal-parking",
    "metadata": {},
    "source": [
     "In the previous chapter we wrote a simulation of a cooling cup of\n",
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ceramic-calibration",
+   "id": "extensive-peace",
    "metadata": {},
    "source": [
     "## Mixing liquids\n",
@@ -157,7 +157,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "orange-beaver",
+   "id": "front-major",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "quiet-payroll",
+   "id": "empirical-potter",
    "metadata": {},
    "source": [
     "The first two lines extract volume and temperature from the two `System` objects. Then the following two lines compute the volume and temperature of the mixture. Finally, `mix` makes a new `System` object and returns it.\n",
@@ -194,7 +194,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "talented-appendix",
+   "id": "pacific-tuesday",
    "metadata": {},
    "source": [
     "## Mix first or last?\n",
@@ -206,7 +206,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "breathing-pizza",
+   "id": "psychological-opera",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "miniature-doctrine",
+   "id": "interested-moses",
    "metadata": {},
    "source": [
     "For `r_milk`, I'll use the value I estimated in the exercise from the previous chapter."
@@ -225,7 +225,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "faced-start",
+   "id": "outstanding-attendance",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,7 +235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "interior-alpha",
+   "id": "american-channels",
    "metadata": {},
    "source": [
     "Now we can mix them and simulate 30 minutes:"
@@ -244,7 +244,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "stock-release",
+   "id": "certified-testament",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "committed-preference",
+   "id": "opposed-function",
    "metadata": {},
    "source": [
     "The final temperature is 61.5 °C which is still warm enough to be\n",
@@ -268,7 +268,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "integral-character",
+   "id": "military-receiver",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -280,7 +280,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "trying-jacob",
+   "id": "hundred-garbage",
    "metadata": {},
    "source": [
     "After mixing, the temperature is 62.9 °C, so it looks like adding the\n",
@@ -290,7 +290,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "enclosed-shore",
+   "id": "traditional-episode",
    "metadata": {},
    "source": [
     "## Optimization\n",
@@ -301,7 +301,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "accepting-consumer",
+   "id": "partial-strap",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -321,7 +321,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "critical-pound",
+   "id": "synthetic-bridal",
    "metadata": {},
    "source": [
     "When `t_add` is`0`, we add the milk immediately; when `t_add` is `30`, we add it at the end. Now we can sweep the range of values in between:"
@@ -330,7 +330,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "isolated-georgia",
+   "id": "naval-future",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -341,7 +341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "latin-fireplace",
+   "id": "welcome-piece",
    "metadata": {},
    "source": [
     "Here's what the results look like:"
@@ -350,7 +350,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "possible-prevention",
+   "id": "polished-module",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -364,7 +364,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "light-poland",
+   "id": "unauthorized-plate",
    "metadata": {},
    "source": [
     "Note that this is a parameter sweep, not a time series.\n",
@@ -381,7 +381,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "industrial-circle",
+   "id": "confirmed-donor",
    "metadata": {},
    "source": [
     "## Analysis\n",
@@ -403,7 +403,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "molecular-happiness",
+   "id": "unnecessary-variance",
    "metadata": {},
    "source": [
     "Now we can use the observed data to estimate the parameter $r$. If we\n",
@@ -417,7 +417,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "dressed-supply",
+   "id": "bridal-freight",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -435,7 +435,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "young-shift",
+   "id": "attractive-pennsylvania",
    "metadata": {},
    "source": [
     "We can use this function to compute `r` for the coffee, given the parameters of the problem."
@@ -444,7 +444,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "bacterial-tonight",
+   "id": "civil-raise",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -456,7 +456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "removable-authority",
+   "id": "cardiovascular-nickname",
    "metadata": {},
    "source": [
     "This value is close to the value of `r` we computed in the previous chapter, `0.115`, but not exactly the same.\n",
@@ -469,7 +469,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "civic-trail",
+   "id": "improved-optimization",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -488,13 +488,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eleven-faculty",
+   "id": "continuing-example",
    "metadata": {},
    "source": [
     "The first line unpacks the system variables.\n",
     "The next two lines compute `t_array`, which is a NumPy array of time stamps, and `T_array`, which is an array of the corresponding temperatures.\n",
     "\n",
-    "The last two lines store the final temperature in the `System` object and return the results in a Pandas `Series`.\n",
+    "The last two lines store the final temperature in the `System` object and use `make_series` to return the results in a Pandas `Series`.\n",
     "\n",
     "We can run it like this:"
    ]
@@ -502,7 +502,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "terminal-carry",
+   "id": "numeric-technique",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -513,7 +513,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "approved-subject",
+   "id": "located-consumption",
    "metadata": {},
    "source": [
     "The final temperature is 70 °C, as it should be. In fact, the results\n",
@@ -523,7 +523,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "grand-silence",
+   "id": "legendary-steal",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -534,7 +534,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "monetary-sensitivity",
+   "id": "conventional-islam",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -545,7 +545,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "polish-focus",
+   "id": "stopped-referral",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -553,7 +553,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "respected-inclusion",
+   "id": "exceptional-paper",
    "metadata": {},
    "source": [
     "**Exercise:** Use `compute_r` to compute `r_milk` according to the analytic solution.  Run the analysis with this value of `r_milk` and confirm that the results are consistent with the simulation."
@@ -562,7 +562,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "headed-circle",
+   "id": "municipal-sustainability",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -572,7 +572,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "enormous-consolidation",
+   "id": "radical-problem",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -582,7 +582,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "social-stamp",
+   "id": "cooked-revision",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -592,7 +592,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "exclusive-puzzle",
+   "id": "interstate-correspondence",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -601,7 +601,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "chinese-explanation",
+   "id": "medium-curtis",
    "metadata": {},
    "source": [
     "**Exercise:**  Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
@@ -612,20 +612,63 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "terminal-billy",
+   "id": "wicked-reasoning",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Solution goes here"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "corrected-dealing",
+   "metadata": {},
+   "source": [
+    "## Under the hood\n",
+    "\n",
+    "ModSim provides `make_series` to make it easier to create a Pandas Series.  In this chapter, we used it like this: "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "nuclear-going",
+   "execution_count": 26,
+   "id": "consistent-eleven",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "make_series(t_array, T_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exceptional-assembly",
+   "metadata": {},
+   "source": [
+    "If you import `Series` from Pandas, you can make a `Series` yourself, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "interpreted-intro",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from pandas import Series\n",
+    "\n",
+    "Series(T_array, t_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "right-louis",
+   "metadata": {},
+   "source": [
+    "The difference is that the arguments are in reverse order: the first argument is stored as the values in the `Series`; the second argument is stored as the `index`.\n",
+    "\n",
+    "I find that order counterintuitive, which is why I use `make_series`.\n",
+    "`make_series` takes the same optional keyword arguments as `Series`, which you can read about at <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html>."
+   ]
   }
  ],
  "metadata": {
@@ -645,7 +688,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

From 2b9e2687981701a73ad27fdeba562b1479353fb2 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 16 Feb 2021 19:53:43 -0500
Subject: [PATCH 048/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 163285 -> 163819 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index 54410d0d620febdb0b81138697ce9f68f7edf706..467f6348f19ab0528619212804aed19ab05bc17a 100644
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From d899084f013b2c32973f6c8c7b1e5ecb3994813a Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 16 Feb 2021 19:54:16 -0500
Subject: [PATCH 049/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 163819 -> 163819 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index 467f6348f19ab0528619212804aed19ab05bc17a..c272a5cec28a90b6d1a15d854db8e911f666e21b 100644
GIT binary patch
delta 928
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From 7a041674e5e3d7d9d63be79c1dda5b06b485075c Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Wed, 17 Feb 2021 13:57:57 -0500
Subject: [PATCH 050/144] Updating modsim.py

---
 chap13.py | 2 ++
 chap15.py | 5 ++---
 modsim.py | 5 ++++-
 3 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/chap13.py b/chap13.py
index ca40c10b..fd5aa080 100644
--- a/chap13.py
+++ b/chap13.py
@@ -5,6 +5,8 @@
 from chap11 import make_system
 from chap11 import update_func
 from chap11 import run_simulation
+from chap11 import plot_results
+
 from chap12 import calc_total_infected
 
 from modsim import *
diff --git a/chap15.py b/chap15.py
index f9c28e9b..f439ced2 100644
--- a/chap15.py
+++ b/chap15.py
@@ -12,7 +12,7 @@ def make_system(T_init, volume, r, t_end):
 
 from modsim import *
 
-def change_func(T, t, system):
+def change_func(t, T, system):
     r, T_env, dt = system.r, system.T_env, system.dt    
     return -r * (T - T_env) * dt
 
@@ -28,9 +28,8 @@ def run_simulation(system, change_func):
     for i in range(n-1):
         t = t_array[i]
         T = series.iloc[i]
-        series.iloc[i+1] = T + change_func(T, t, system)
+        series.iloc[i+1] = T + change_func(t, T, system)
     
-    system.t_end = t_array[-1]
     system.T_final = series.iloc[-1]
     return series
 
diff --git a/modsim.py b/modsim.py
index cd389049..9ed50260 100644
--- a/modsim.py
+++ b/modsim.py
@@ -652,9 +652,12 @@ def make_series(x, y, **options):
 
     returns: Pandas Series
     """
+    underride(options, name='values')
     if isinstance(y, pd.Series):
         y = y.values
-    return pd.Series(y, index=x, **options)
+    series = pd.Series(y, index=x, **options)
+    series.index.name = 'index'
+    return series
 
 
 def TimeSeries(*args, **kwargs):

From 340da69c55cc06c65acecd35069158cba6e544a2 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Wed, 17 Feb 2021 14:23:13 -0500
Subject: [PATCH 051/144] Updating chapters

---
 chapters/chap14.ipynb | 167 ++++++++++++++++----------
 chapters/chap15.ipynb | 264 ++++++++++++++++++++++++------------------
 chapters/chap16.ipynb | 260 +++++++++++++++++++----------------------
 chapters/chap17.ipynb |   6 +-
 4 files changed, 384 insertions(+), 313 deletions(-)

diff --git a/chapters/chap14.ipynb b/chapters/chap14.ipynb
index f91da633..b712c1b3 100644
--- a/chapters/chap14.ipynb
+++ b/chapters/chap14.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "narrow-announcement",
+   "id": "infrared-shower",
    "metadata": {},
    "source": [
     "# Chapter 14"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "still-fellow",
+   "id": "documentary-steal",
    "metadata": {
     "tags": []
    },
@@ -25,7 +25,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "complicated-nickname",
+   "id": "wanted-precipitation",
    "metadata": {
     "tags": []
    },
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "pretty-ethics",
+   "id": "earned-kidney",
    "metadata": {
     "tags": []
    },
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "approved-terminal",
+   "id": "forty-hammer",
    "metadata": {
     "tags": []
    },
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "legal-attack",
+   "id": "attractive-corner",
    "metadata": {},
    "source": [
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap14.ipynb)"
@@ -88,7 +88,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "lucky-enforcement",
+   "id": "middle-surge",
    "metadata": {
     "tags": []
    },
@@ -101,7 +101,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "hairy-fifth",
+   "id": "hybrid-making",
    "metadata": {
     "tags": []
    },
@@ -114,7 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "laden-thousand",
+   "id": "inclusive-characteristic",
    "metadata": {
     "tags": []
    },
@@ -127,7 +127,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "future-baseball",
+   "id": "grave-occasions",
    "metadata": {
     "tags": []
    },
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "apparent-country",
+   "id": "brief-trademark",
    "metadata": {},
    "source": [
     "In the previous chapter we swept the parameters of the SIR model: the contact rate, `beta`, and the recovery rate, `gamma`.\n",
@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sharp-ladder",
+   "id": "weekly-award",
    "metadata": {},
    "source": [
     "## Nondimensionalization\n",
@@ -181,7 +181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "reasonable-letter",
+   "id": "legendary-terrorism",
    "metadata": {},
    "source": [
     "## Exploring the results\n",
@@ -196,7 +196,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "compliant-butler",
+   "id": "genetic-morris",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -208,7 +208,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "temporal-danish",
+   "id": "hearing-cycle",
    "metadata": {},
    "source": [
     "Here's what the first few rows look like:"
@@ -217,7 +217,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "diagnostic-creation",
+   "id": "appointed-terrorist",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "senior-hazard",
+   "id": "catholic-alfred",
    "metadata": {},
    "source": [
     "The `SweepFrame` has one row for each value of `beta` and one column for each value of `gamma`. \n",
@@ -237,7 +237,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "tested-nation",
+   "id": "collected-waterproof",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -250,7 +250,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cloudy-volunteer",
+   "id": "unexpected-surgeon",
    "metadata": {},
    "source": [
     "This is the first example we've seen with one `for` loop inside another:\n",
@@ -269,7 +269,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "elder-retirement",
+   "id": "polar-flash",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -285,7 +285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "marine-stadium",
+   "id": "shared-boxing",
    "metadata": {},
    "source": [
     "On the $x$-axis, it plots the ratio `beta/gamma`.\n",
@@ -297,7 +297,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "legal-onion",
+   "id": "incorporate-launch",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -309,7 +309,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "purple-jersey",
+   "id": "tropical-colony",
    "metadata": {},
    "source": [
     "The results fall on a single curve, at least approximately. That means that we can predict the fraction of the population that will be infected based on a single parameter, the ratio `beta/gamma`. We don't need to know the values of `beta` and `gamma` separately."
@@ -317,7 +317,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sublime-cargo",
+   "id": "lightweight-surfing",
    "metadata": {},
    "source": [
     "## Contact number\n",
@@ -334,7 +334,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "potential-curve",
+   "id": "banner-egyptian",
    "metadata": {},
    "source": [
     "The results in the previous section suggest that there is a relationship between $c$ and the total number of infections. We can derive this relationship by analyzing the differential equations from\n",
@@ -362,7 +362,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "reverse-prize",
+   "id": "noticed-mouse",
    "metadata": {},
    "source": [
     "Dividing one differential equation by another is not an obvious move, but in this case it is useful because it gives us a relationship between $i$, $s$ and $c$ that does not depend on time. From that relationship, we can derive an equation that relates $c$ to the final value of $s$. In theory, this equation makes it possible to infer $c$ by observing the course of an epidemic."
@@ -370,7 +370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "lightweight-upgrade",
+   "id": "accessible-bernard",
    "metadata": {},
    "source": [
     "Here's how the derivation goes. We multiply both sides of the previous\n",
@@ -395,7 +395,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "blessed-charger",
+   "id": "instrumental-placement",
    "metadata": {},
    "source": [
     "Now, at the end of the epidemic, let's assume that $i(\\infty) = 0$, and $s(\\infty)$ is an unknown quantity, $s_{\\infty}$. Now we have:\n",
@@ -407,7 +407,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "killing-flavor",
+   "id": "under-dating",
    "metadata": {},
    "source": [
     "## Analysis and simulation\n",
@@ -418,7 +418,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "temporal-murray",
+   "id": "architectural-growth",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -427,7 +427,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "engaged-activity",
+   "id": "oriented-turtle",
    "metadata": {},
    "source": [
     "And compute the corresponding values of $c$:"
@@ -436,7 +436,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "coordinated-sheffield",
+   "id": "narrative-embassy",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -447,7 +447,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cleared-gregory",
+   "id": "assisted-public",
    "metadata": {},
    "source": [
     "To get the total infected, we compute the difference between $s(0)$ and\n",
@@ -457,7 +457,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "level-citizenship",
+   "id": "canadian-assumption",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -466,7 +466,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "divine-polls",
+   "id": "published-generation",
    "metadata": {},
    "source": [
     "The ModSim library provides a function called `make_series` we can use to put `c_array` and `frac_infected` in a Pandas `Series`."
@@ -475,7 +475,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "mounted-patient",
+   "id": "antique-watch",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -484,7 +484,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "emerging-insert",
+   "id": "conscious-bathroom",
    "metadata": {},
    "source": [
     "Now we can plot the results:"
@@ -493,7 +493,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "adjusted-spread",
+   "id": "fallen-router",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -506,7 +506,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adjustable-calculator",
+   "id": "billion-worthy",
    "metadata": {},
    "source": [
     "When the contact number exceeds 1, analysis and simulation agree. When\n",
@@ -519,7 +519,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "southeast-comparative",
+   "id": "alternate-surprise",
    "metadata": {},
    "source": [
     "## Estimating contact number\n",
@@ -542,7 +542,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "greenhouse-harmony",
+   "id": "related-council",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -556,7 +556,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "buried-tracy",
+   "id": "removed-conviction",
    "metadata": {},
    "source": [
     "## Exercises"
@@ -564,7 +564,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "mineral-walnut",
+   "id": "textile-program",
    "metadata": {},
    "source": [
     "**Exercise:** At the beginning of this chapter, I suggested two ways to relate `beta` and `gamma`: we could compute their difference or their ratio.\n",
@@ -583,7 +583,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "right-official",
+   "id": "current-tiger",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -593,7 +593,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "weekly-doctrine",
+   "id": "specialized-regression",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -603,7 +603,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "failing-treaty",
+   "id": "canadian-authentication",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -612,7 +612,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "statistical-senate",
+   "id": "angry-perfume",
    "metadata": {},
    "source": [
     "**Exercise:** Suppose you run a survey at the end of the semester and find that 26% of students had the Freshman Plague at some point.\n",
@@ -625,7 +625,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "manufactured-cache",
+   "id": "surgical-mouth",
    "metadata": {
     "scrolled": true
    },
@@ -637,7 +637,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "academic-chancellor",
+   "id": "focused-magnet",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -646,7 +646,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "functioning-resort",
+   "id": "charged-sample",
    "metadata": {},
    "source": [
     "**Exercise**: So far the only metric we have considered is the total fraction of the population that gets infected over the course of an epidemic.  That is an important metric, but it is not the only one we care about.\n",
@@ -663,7 +663,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "decent-understanding",
+   "id": "greatest-palestine",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -673,7 +673,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "laughing-imperial",
+   "id": "average-practitioner",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -683,7 +683,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "close-cache",
+   "id": "elegant-gamma",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -693,7 +693,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "annoying-alexander",
+   "id": "plain-dayton",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -703,7 +703,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "charming-checkout",
+   "id": "sexual-services",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -713,7 +713,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "passive-slave",
+   "id": "central-fever",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -723,17 +723,68 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "promising-spyware",
+   "id": "tropical-advancement",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Solution goes here"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "editorial-coating",
+   "metadata": {},
+   "source": [
+    "## Under the hood\n",
+    "\n",
+    "ModSim provides `make_series` to make it easier to create a Pandas Series.  In this chapter, we used it like this: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "corresponding-india",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "make_series(t_array, T_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "comparable-strap",
+   "metadata": {},
+   "source": [
+    "If you import `Series` from Pandas, you can make a `Series` yourself, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "constant-christianity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import Series\n",
+    "\n",
+    "Series(T_array, t_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "wireless-president",
+   "metadata": {},
+   "source": [
+    "The difference is that the arguments are in reverse order: the first argument is stored as the values in the `Series`; the second argument is stored as the `index`.\n",
+    "\n",
+    "I find that order counterintuitive, which is why I use `make_series`.\n",
+    "`make_series` takes the same optional keyword arguments as `Series`, which you can read about at <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html>."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "passing-quarterly",
+   "id": "hidden-recipient",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/chapters/chap15.ipynb b/chapters/chap15.ipynb
index e859829b..b8d79df4 100644
--- a/chapters/chap15.ipynb
+++ b/chapters/chap15.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "located-subscription",
+   "id": "romantic-speech",
    "metadata": {},
    "source": [
     "# Chapter 15"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "imported-table",
+   "id": "higher-button",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "electoral-turkey",
+   "execution_count": 1,
+   "id": "narrow-indication",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "formal-context",
+   "execution_count": 2,
+   "id": "dependent-monitoring",
    "metadata": {
     "tags": []
    },
@@ -65,8 +65,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "progressive-typing",
+   "execution_count": 3,
+   "id": "continental-decrease",
    "metadata": {
     "tags": []
    },
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "plastic-trigger",
+   "id": "derived-exhibit",
    "metadata": {},
    "source": [
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap15.ipynb)"
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "italic-murder",
+   "id": "capital-needle",
    "metadata": {},
    "source": [
     "So far the systems we have studied have been physical in the sense that they exist in the world, but they have not been physics, in the sense of what physics classes are usually about. In the next few chapters, we'll do some physics, starting with **thermal systems**, that is, systems where the temperature of objects changes as heat transfers from one to another."
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adolescent-phone",
+   "id": "working-senator",
    "metadata": {},
    "source": [
     "## The coffee cooling problem\n",
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "appreciated-ukraine",
+   "id": "backed-steel",
    "metadata": {},
    "source": [
     "To help answer this question, I made a trial run with the milk and\n",
@@ -135,14 +135,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "arranged-ridge",
+   "id": "retired-noise",
    "metadata": {},
    "source": [
     "## Temperature and heat\n",
     "\n",
     "To understand how coffee cools (and milk warms), we need a model of\n",
     "temperature and heat. **Temperature** is a property of an object or a\n",
-    "system; in SI units it is measured in degrees Celsius (°C). Temperature quantifies how hot or cold the object is, which is related to the average velocity of the particles that make up the object.\n",
+    "system; in SI units it is measured in degrees Celsius (°C). Temperature quantifies how hot or cold the object is, which is related to the average velocity of the particles that make it up.\n",
     "\n",
     "When particles in a hot object contact particles in a cold object, the\n",
     "hot object gets cooler and the cold object gets warmer as energy is\n",
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "purple-edward",
+   "id": "upper-johnson",
    "metadata": {},
    "source": [
     "For objects made primarily from one material, thermal mass can be\n",
@@ -188,7 +188,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "other-finnish",
+   "id": "closed-vegetable",
    "metadata": {},
    "source": [
     "## Heat transfer\n",
@@ -204,7 +204,7 @@
     "    place, they carry heat energy with them. Fluid flows can be caused\n",
     "    by external action, like stirring, or by internal differences in\n",
     "    temperature. For example, you might have heard that hot air rises,\n",
-    "    which is a form of \"natural convection\\\".\n",
+    "    which is a form of \"natural convection\".\n",
     "\n",
     "-   Radiation: As the particles in an object move due to thermal energy,\n",
     "    they emit electromagnetic radiation. The energy carried by this\n",
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "friendly-nurse",
+   "id": "valuable-shark",
    "metadata": {},
    "source": [
     "For objects like coffee in a car, the effect of radiation is much\n",
@@ -225,22 +225,21 @@
   },
   {
    "cell_type": "markdown",
-   "id": "heavy-forward",
+   "id": "freelance-target",
    "metadata": {},
    "source": [
     "## Newton's law of cooling\n",
     "\n",
-    "Newton's law of cooling asserts that the temperature rate of change for an object is proportional to the difference in temperature between the\n",
-    "object and the surrounding environment:\n",
+    "Newton's law of cooling asserts that the temperature rate of change for an object is proportional to the difference in temperature between the object and the surrounding environment:\n",
     "\n",
     "$$\\frac{dT}{dt} = -r (T - T_{env})$$ \n",
     "\n",
-    "where $T$, the temperature of the object, is a function of time, $t$; $T_{env}$ is the temperature of the environment, and $r$ is a constant that characterizes how quickly heat is transferred between the system and the environment."
+    "where $T$, the temperature of the object, is a function of time, $t$, $T_{env}$ is the temperature of the environment, and $r$ is a constant that characterizes how quickly heat is transferred between the system and the environment."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "educated-accommodation",
+   "id": "corrected-attachment",
    "metadata": {},
    "source": [
     "Newton's so-called \"law \" is really a model: it is a good approximation in some conditions and less good in others.\n",
@@ -262,7 +261,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "determined-seventh",
+   "id": "selective-video",
    "metadata": {},
    "source": [
     "## Implementation\n",
@@ -275,7 +274,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "geological-graph",
+   "id": "incredible-greeting",
    "metadata": {
     "tags": []
    },
@@ -294,16 +293,18 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cardiac-lesson",
+   "id": "gross-appeal",
    "metadata": {},
    "source": [
-    "The values of `T_init`, `volume`, and `t_end` come from the statement of the problem:"
+    "In addition to the parameters, `make_system` sets the temperature of the environment, `T_env`, the initial time stamp, `t_0`, and the time step, `dt`, which we will use use to simulate the cooling process.\n",
+    "\n",
+    "Here's a `System` object that represents the coffee."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "eastern-alignment",
+   "id": "fuzzy-support",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,12 +313,11 @@
   },
   {
    "cell_type": "markdown",
-   "id": "horizontal-background",
+   "id": "rental-pearl",
    "metadata": {},
    "source": [
-    "I chose the value of `r` arbitrarily for now; we will figure out how to estimate it soon.\n",
-    "\n",
-    "In addition, `make_system` sets the temperature of the environment, `T_env`, and the time step, `dt`, which we will use use to simulate the cooling process.\n",
+    "The values of `T_init`, `volume`, and `t_end` come from the statement of the problem.\n",
+    "I chose the value of `r` arbitrarily for now; we will see how to estimate it soon.\n",
     "\n",
     "Strictly speaking, Newton's law is a differential equation, but over a short period of time we can approximate it with a difference equation:\n",
     "\n",
@@ -329,26 +329,26 @@
     "environment, $T - T_{env}$. To minimize confusion, I avoid this second\n",
     "use.\n",
     "\n",
-    "The following function takes the current temperature, `T`, the current time `t`, and a `System` object, and computes the change in temperature during a time step:"
+    "The following function takes the current time `t`, the current temperature, `T`, and a `System` object, and computes the change in temperature during a time step:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "vulnerable-sarah",
+   "id": "tight-baptist",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "def change_func(T, t, system):\n",
+    "def change_func(t, T, system):\n",
     "    r, T_env, dt = system.r, system.T_env, system.dt    \n",
     "    return -r * (T - T_env) * dt"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "unknown-programming",
+   "id": "flexible-crest",
    "metadata": {},
    "source": [
     "We can test it with the initial temperature of the coffee, like this:"
@@ -357,19 +357,19 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "killing-tragedy",
+   "id": "fiscal-artwork",
    "metadata": {},
    "outputs": [],
    "source": [
-    "change_func(coffee.T_init, 0, coffee)"
+    "change_func(0, coffee.T_init, coffee)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "geographic-female",
+   "id": "sporting-morocco",
    "metadata": {},
    "source": [
-    "With `dt=1` minute, the temperature drops by about 0.7 °C/min, at least for this value of `r`.\n",
+    "With `dt=1` minute, the temperature drops by about 0.7 °C, at least for this value of `r`.\n",
     "\n",
     "Now here's a version of `run_simulation` that simulates a series of time steps from `t_0` to `t_end`:"
    ]
@@ -377,7 +377,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "wireless-handy",
+   "id": "cardiac-independence",
    "metadata": {
     "tags": []
    },
@@ -393,30 +393,48 @@
     "    for i in range(n-1):\n",
     "        t = t_array[i]\n",
     "        T = series.iloc[i]\n",
-    "        series.iloc[i+1] = T + change_func(T, t, system)\n",
+    "        series.iloc[i+1] = T + change_func(t, T, system)\n",
     "    \n",
-    "    system.t_end = t_array[-1]\n",
     "    system.T_final = series.iloc[-1]\n",
     "    return series"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "voluntary-velvet",
+   "id": "saved-dublin",
+   "metadata": {},
+   "source": [
+    "There are a two things here that are different from previous versions of `run_simulation`.\n",
+    "\n",
+    "First, we use  `linrange` to make an array of values from `t_0` to `t_end` with time step `dt`.  \n",
+    "`linrange` is similar to `linspace`; they both take a start value and an end value and return an array of equally spaced values.\n",
+    "The difference is the third argument: `linspace` takes an integer that indicates the number of points in the range; `linrange` takes a step size that indicates the interval between values.\n",
+    "When we make the `TimeSeries`, we use the keyword argument `index` to indicate that the index of the `TimeSeries` is the array of time stamps, `t_array`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fantastic-object",
    "metadata": {},
    "source": [
-    "This function is similar to previous versions of `run_simulation`.\n",
+    "Second, this version of `run_simulation` uses `iloc` rather than `loc` to specify the rows in the `TimeSeries`.\n",
+    "Here's the difference: \n",
+    "\n",
+    "* With `loc`, the label in brackets can be any kind of value, with any start, end, and time step.  For example, in the world population model, the labels are years starting in 1960 and ending in 2016.\n",
     "\n",
-    "One difference is that it uses `linrange` to make an array of values\n",
-    "from `t_0` to `t_end` with time step `dt`.\n",
+    "* With `iloc`, the label in brackets is always an integer starting at 0.  So we can always get the first element with `iloc[0]` and the last element with `iloc[-1]`, regardless of what the labels are.\n",
     "\n",
-    "We can run it like this:"
+    "In this version of `run_simulation`, the loop variable is an integer, `i`, that goes from `0` to `n-1`, including `0` but not including `n-1`.\n",
+    "So the first time through the loop, `i` is `0` and the value we add to the `TimeSeries` has index 1.\n",
+    "The last time through the loop, `i` is `n-2` and the value we add has index `n-1`.\n",
+    "\n",
+    "We can run the simulation like this:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "after-championship",
+   "id": "numerous-metabolism",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -425,39 +443,64 @@
   },
   {
    "cell_type": "markdown",
-   "id": "consecutive-party",
+   "id": "earned-primary",
    "metadata": {},
    "source": [
-    "The result is a `TimeSeries` with one row per time step. "
+    "The result is a `TimeSeries` with one row per time step. \n",
+    "Here are the first few rows:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "proof-guard",
+   "id": "infectious-carolina",
    "metadata": {},
    "outputs": [],
    "source": [
-    "results.head()"
+    "show(results.head())"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "limiting-legislation",
+   "id": "color-olive",
    "metadata": {},
    "source": [
-    "Here's what the results look like."
+    "And the last few rows:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "expected-company",
+   "id": "accompanied-melbourne",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "show(results.tail())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "formal-headset",
+   "metadata": {},
+   "source": [
+    "With `t_0=0`, `t_end=30`, and `dt=1`, the time stamps go from `0.0` to `30.0`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "documentary-standing",
+   "metadata": {},
+   "source": [
+    "Here's what the `TimeSeries` looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "important-constitution",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "results.plot(label='coffee')\n",
     "\n",
     "decorate(xlabel='Time (minute)',\n",
@@ -467,7 +510,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "incomplete-demonstration",
+   "id": "absent-arkansas",
    "metadata": {},
    "source": [
     "The temperature after 30 minutes is 72.3 °C, which is a little higher than what's stated in the problem, 70 °C. "
@@ -475,8 +518,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "protecting-picture",
+   "execution_count": 13,
+   "id": "absolute-desire",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -485,7 +528,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "tight-operation",
+   "id": "light-carpet",
    "metadata": {},
    "source": [
     "By trial and error, we could find the value of `r` where the final temperature is precisely 70 °C.\n",
@@ -494,16 +537,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "equipped-blackberry",
+   "id": "funny-weekly",
    "metadata": {},
    "source": [
     "## Finding roots\n",
     "\n",
-    "ModSim provides a function called `root_scalar` that finds the roots of non-linear equations. As a simple example, suppose you want to find the roots of the polynomial \n",
+    "The ModSim library provides a function called `root_scalar` that finds the roots of non-linear equations. As an example, suppose you want to find the roots of the polynomial \n",
     "\n",
     "$$f(x) = (x - 1)(x - 2)(x - 3)$$ \n",
     "\n",
-    "A **root** is a value of $x$ that makes $f(x)=0$. Because of the way I wrote the polynomial, we can see that if $x=1$, the first factor is 0; if $x=2$, the second factor is 0; and if $x=3$, the third factor is 0, so those are the roots.\n",
+    "A **root** is a value of $x$ that makes $f(x)=0$. Because of the way I wrote this polynomial, we can see that if $x=1$, the first factor is 0; if $x=2$, the second factor is 0; and if $x=3$, the third factor is 0, so those are the roots.\n",
     "\n",
     "I'll use this example to demonstrate `root_scalar`. First, we have to\n",
     "write a function that evaluates $f$:"
@@ -511,8 +554,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "round-budapest",
+   "execution_count": 14,
+   "id": "small-shark",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -522,7 +565,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "injured-limitation",
+   "id": "cleared-cylinder",
    "metadata": {},
    "source": [
     "Now we call `root_scalar` like this:"
@@ -530,8 +573,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "regulated-cloud",
+   "execution_count": 15,
+   "id": "defensive-content",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -541,17 +584,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "regulated-weekend",
+   "id": "adult-management",
    "metadata": {},
    "source": [
     "The first argument is the function whose roots we want. The second\n",
-    "argument is an interval that contains or \"brackets\" a root. The result is an object that contains several variables, including `root`, which stores the root that was found."
+    "argument is an interval that contains or \"brackets\" a root. The result is an object that contains several variables, including `root`, which is the root that was found."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "graphic-preliminary",
+   "execution_count": 16,
+   "id": "behind-perth",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -560,7 +603,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "changing-winter",
+   "id": "disturbed-shade",
    "metadata": {},
    "source": [
     "If we provide a different interval, we find a different root."
@@ -568,8 +611,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "authentic-hunter",
+   "execution_count": 17,
+   "id": "following-sentence",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -579,7 +622,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bound-freight",
+   "id": "eligible-updating",
    "metadata": {},
    "source": [
     "If the interval doesn't contain a root, you'll get a `ValueError` and a message like \"f(a) and f(b) must have different signs\".\n",
@@ -591,7 +634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "impossible-spanish",
+   "id": "ahead-lawyer",
    "metadata": {},
    "source": [
     "Now we can use `root_scalar` to estimate `r`."
@@ -599,7 +642,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "imperial-engineering",
+   "id": "commercial-correlation",
    "metadata": {},
    "source": [
     "## Estimating `r`\n",
@@ -611,7 +654,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "quality-colors",
+   "id": "genetic-compound",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -623,7 +666,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "random-loading",
+   "id": "happy-bridal",
    "metadata": {},
    "source": [
     "This is called an \"error function\" because it returns the\n",
@@ -636,7 +679,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "unknown-pharmacy",
+   "id": "french-financing",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -646,17 +689,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "moderate-monkey",
+   "id": "statutory-activation",
    "metadata": {},
    "source": [
-    "The result is an error of 2.3 °C, because the final temperature with\n",
-    "this value of `r` is too high."
+    "The result is an error of 2.3 °C, which means the final temperature with `r=0.01` is too high."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "fossil-israel",
+   "id": "excellent-fellow",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -665,18 +707,18 @@
   },
   {
    "cell_type": "markdown",
-   "id": "south-fabric",
+   "id": "unlimited-living",
    "metadata": {},
    "source": [
     "With `r=0.02`, the error is  about -11°C, which means that the final temperature is too low. So we know that the correct value must be in between.\n",
     "\n",
-    "So we can call `root_scalar` like this:"
+    "Now we can call `root_scalar` like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "backed-while",
+   "execution_count": 21,
+   "id": "french-decline",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -686,7 +728,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "exotic-development",
+   "id": "tamil-absorption",
    "metadata": {},
    "source": [
     "The first argument is the error function.\n",
@@ -698,8 +740,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "early-twelve",
+   "execution_count": 22,
+   "id": "afraid-ordering",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -709,7 +751,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "greenhouse-designer",
+   "id": "partial-definition",
    "metadata": {},
    "source": [
     "In this example, `r_coffee` turns out to be about `0.0115`, in units of min$^{-1}$ (inverse minutes).\n",
@@ -719,8 +761,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "foster-lodging",
+   "execution_count": 23,
+   "id": "derived-annual",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -731,7 +773,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sweet-horizontal",
+   "id": "prerequisite-conditioning",
    "metadata": {},
    "source": [
     "The final temperature is very close to 70 °C."
@@ -739,7 +781,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "imposed-complement",
+   "id": "scientific-attachment",
    "metadata": {},
    "source": [
     "## Exercises\n",
@@ -751,8 +793,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "radical-accident",
+   "execution_count": 24,
+   "id": "committed-angel",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -761,8 +803,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "extraordinary-andrew",
+   "execution_count": 25,
+   "id": "military-military",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -771,7 +813,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "initial-parallel",
+   "id": "logical-acoustic",
    "metadata": {},
    "source": [
     "**Exercise:** Write an error function that simulates the temperature of the milk and returns the difference between the final temperature and 20 °C.  Use it to estimate the value of `r` for the milk."
@@ -779,8 +821,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "id": "yellow-single",
+   "execution_count": 26,
+   "id": "agreed-excuse",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -789,8 +831,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "cognitive-italian",
+   "execution_count": 27,
+   "id": "defined-nudist",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -799,8 +841,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "placed-concentrate",
+   "execution_count": 28,
+   "id": "loose-rehabilitation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -810,7 +852,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "announced-continent",
+   "id": "alert-emerald",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/chapters/chap16.ipynb b/chapters/chap16.ipynb
index dfca2ad7..285ffa16 100644
--- a/chapters/chap16.ipynb
+++ b/chapters/chap16.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "stopped-plymouth",
+   "id": "medieval-johnston",
    "metadata": {},
    "source": [
     "# Chapter 16"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "endangered-sequence",
+   "id": "fifty-chicken",
    "metadata": {
     "tags": []
    },
@@ -24,8 +24,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "owned-renewal",
+   "execution_count": 2,
+   "id": "blank-belly",
    "metadata": {
     "tags": []
    },
@@ -41,8 +41,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "appreciated-prefix",
+   "execution_count": 3,
+   "id": "british-place",
    "metadata": {
     "tags": []
    },
@@ -65,8 +65,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "grave-feature",
+   "execution_count": 4,
+   "id": "determined-volunteer",
    "metadata": {
     "tags": []
    },
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "senior-malawi",
+   "id": "valuable-shannon",
    "metadata": {},
    "source": [
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap16.ipynb)"
@@ -87,8 +87,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "electrical-temple",
+   "execution_count": 5,
+   "id": "fossil-moisture",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'chap15.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cutting-scale",
    "metadata": {
     "tags": []
    },
@@ -103,26 +116,30 @@
   },
   {
    "cell_type": "markdown",
-   "id": "vocal-parking",
+   "id": "enhanced-copper",
    "metadata": {},
    "source": [
     "In the previous chapter we wrote a simulation of a cooling cup of\n",
     "coffee. Given the initial temperature of the coffee, the temperature of the atmosphere, and the rate parameter, `r`, we can predict how the\n",
     "temperature of the coffee will change over time.\n",
+    "Then we used a root finding algorithm to estimate `r` based on data I invented for the example.\n",
+    "\n",
+    "If you did the exercises, you simulated the temperature of the milk as it warmed, and estimated its rate parameter as well.\n",
     "\n",
-    "..."
+    "Now let's put it together.\n",
+    "In this chapter we'll write a function that simulates mixing the two liquids and use it to answer the question we started with: is it better to mix the coffee and milk at the beginning, the end, or somewhere in the middle?"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "extensive-peace",
+   "id": "hydraulic-belfast",
    "metadata": {},
    "source": [
     "## Mixing liquids\n",
     "\n",
     "When we mix two liquids, the temperature of the mixture depends on the\n",
-    "temperatures of the ingredients, but it might not be obvious how to\n",
-    "compute it.\n",
+    "temperatures of the ingredients as well as their volumes, densities, and specific heat capacities (as defined in the previous chapter).\n",
+    "In this section I'll explain how.\n",
     "\n",
     "Assuming there are no chemical reactions that either produce or consume heat, the total thermal energy of the system is the same before and after mixing; in other words, thermal energy is **conserved**.\n",
     "\n",
@@ -150,14 +167,14 @@
     "density and specific heat. As an exercise, you can look up these\n",
     "quantities and see how good this assumption is.\n",
     "\n",
-    "The following function takes two `System` objects that represent the\n",
+    "The following function takes two `System` objects, representing the\n",
     "coffee and milk, and creates a new `System` to represent the mixture:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "front-major",
+   "execution_count": 11,
+   "id": "extensive-happening",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,10 +194,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "empirical-potter",
+   "id": "overhead-architect",
    "metadata": {},
    "source": [
-    "The first two lines extract volume and temperature from the two `System` objects. Then the following two lines compute the volume and temperature of the mixture. Finally, `mix` makes a new `System` object and returns it.\n",
+    "The first two lines extract volume and temperature from the `System` objects. The next two lines compute the volume and temperature of the mixture. Finally, `mix` makes a new `System` object and returns it.\n",
     "\n",
     "This function uses the value of `r` from `system1` as the value of `r`\n",
     "for the mixture. If `system1` represents the coffee, and we are adding\n",
@@ -194,19 +211,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "pacific-tuesday",
+   "id": "foreign-campbell",
    "metadata": {},
    "source": [
     "## Mix first or last?\n",
     "\n",
     "First I'll create objects to represent the coffee and milk.\n",
-    "For `r_coffee` and `r_milk`, I'll use the values we computed in the previous chapter."
+    "For `r_coffee`, I'll use the value we computed in the previous chapter."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "psychological-opera",
+   "execution_count": 12,
+   "id": "polyphonic-specialist",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -216,7 +233,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "interested-moses",
+   "id": "authorized-system",
    "metadata": {},
    "source": [
     "For `r_milk`, I'll use the value I estimated in the exercise from the previous chapter."
@@ -224,8 +241,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "outstanding-attendance",
+   "execution_count": 13,
+   "id": "subtle-donna",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "american-channels",
+   "id": "satisfactory-rwanda",
    "metadata": {},
    "source": [
     "Now we can mix them and simulate 30 minutes:"
@@ -243,8 +260,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "certified-testament",
+   "execution_count": 14,
+   "id": "domestic-tours",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,7 +273,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "opposed-function",
+   "id": "accredited-diagnosis",
    "metadata": {},
    "source": [
     "The final temperature is 61.5 °C which is still warm enough to be\n",
@@ -267,8 +284,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "military-receiver",
+   "execution_count": 15,
+   "id": "subject-richardson",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -280,7 +297,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hundred-garbage",
+   "id": "divine-trance",
    "metadata": {},
    "source": [
     "After mixing, the temperature is 62.9 °C, so it looks like adding the\n",
@@ -290,7 +307,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "traditional-episode",
+   "id": "linear-republican",
    "metadata": {},
    "source": [
     "## Optimization\n",
@@ -300,8 +317,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "partial-strap",
+   "execution_count": 16,
+   "id": "mental-corporation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -321,16 +338,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "synthetic-bridal",
+   "id": "better-cemetery",
    "metadata": {},
    "source": [
+    "`run_and_mix` simulates both systems for the given time, `t_add`.\n",
+    "Then it mixes them and simulates the mixture fir the remaining time, `t_total - t_add`.\n",
+    "\n",
     "When `t_add` is`0`, we add the milk immediately; when `t_add` is `30`, we add it at the end. Now we can sweep the range of values in between:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "naval-future",
+   "execution_count": 17,
+   "id": "adverse-hanging",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -341,7 +361,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "welcome-piece",
+   "id": "practical-state",
    "metadata": {},
    "source": [
     "Here's what the results look like:"
@@ -349,13 +369,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "polished-module",
+   "execution_count": 18,
+   "id": "bright-proposal",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "sweep.plot(label='mixture', color='C2')\n",
     "\n",
     "decorate(xlabel='Time until mixing (minutes)',\n",
@@ -364,29 +382,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "unauthorized-plate",
+   "id": "experienced-tribute",
    "metadata": {},
    "source": [
     "Note that this is a parameter sweep, not a time series.\n",
     "\n",
     "The final temperature is maximized when `t_add=30`, so adding the milk\n",
-    "at the end is optimal.\n",
-    "\n",
-    "As an exercise you will have a chance to explore a variation of the coffee cooling problem:\n",
-    "\n",
-    "> Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office. In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
-    "\n",
-    "But first, let's do some analysis."
+    "at the end is optimal."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "confirmed-donor",
+   "id": "rough-investor",
    "metadata": {},
    "source": [
     "## Analysis\n",
     "\n",
-    "Simulating Newton's law of cooling is almost silly, because we can solve the differential equation analytically. If\n",
+    "Simulating Newton's law of cooling isn't really necessary because we can solve the differential equation analytically. If\n",
     "\n",
     "$$\\frac{dT}{dt} = -r (T - T_{env})$$ \n",
     "\n",
@@ -403,7 +415,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "unnecessary-variance",
+   "id": "chief-abortion",
    "metadata": {},
    "source": [
     "Now we can use the observed data to estimate the parameter $r$. If we\n",
@@ -416,8 +428,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "bridal-freight",
+   "execution_count": 30,
+   "id": "secondary-swift",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -435,7 +447,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "attractive-pennsylvania",
+   "id": "correct-spare",
    "metadata": {},
    "source": [
     "We can use this function to compute `r` for the coffee, given the parameters of the problem."
@@ -443,8 +455,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "civil-raise",
+   "execution_count": 31,
+   "id": "south-machinery",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -456,25 +468,24 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cardiovascular-nickname",
+   "id": "worthy-steal",
    "metadata": {},
    "source": [
     "This value is close to the value of `r` we computed in the previous chapter, `0.115`, but not exactly the same.\n",
     "That's because the simulations use discrete time steps, and the analysis uses continuous time.\n",
     "\n",
-    "Nevertheless, the results of any analysis are consistent with the simulation.\n",
+    "Nevertheless, the results of the analysis are consistent with the simulation.\n",
     "To check, we'll use the following function, which takes a `System` object and uses the analytic result to compute a time series:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "improved-optimization",
+   "execution_count": 32,
+   "id": "opening-transsexual",
    "metadata": {},
    "outputs": [],
    "source": [
     "from numpy import exp\n",
-    "from pandas import Series\n",
     "\n",
     "def run_analysis(system):\n",
     "    T_env, T_init, r = system.T_env, system.T_init, system.r\n",
@@ -488,7 +499,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "continuing-example",
+   "id": "fallen-spiritual",
    "metadata": {},
    "source": [
     "The first line unpacks the system variables.\n",
@@ -501,8 +512,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "numeric-technique",
+   "execution_count": 33,
+   "id": "agreed-bouquet",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -513,7 +524,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "located-consumption",
+   "id": "exact-juice",
    "metadata": {},
    "source": [
     "The final temperature is 70 °C, as it should be. In fact, the results\n",
@@ -522,9 +533,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "legendary-steal",
-   "metadata": {},
+   "execution_count": 34,
+   "id": "portuguese-sympathy",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "coffee.r = 0.011543\n",
@@ -533,9 +546,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "conventional-islam",
-   "metadata": {},
+   "execution_count": 35,
+   "id": "distinguished-regard",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from numpy import allclose\n",
@@ -545,34 +560,38 @@
   },
   {
    "cell_type": "markdown",
-   "id": "stopped-referral",
+   "id": "rapid-payroll",
    "metadata": {},
    "source": [
-    "## Exercises"
+    "## Summary\n",
+    "\n",
+    "In this chapter we finished the coffee cooling problem from the previous chapter, and found that it is better to add the milk at the end, at least for the version of the problem I posed.\n",
+    "\n",
+    "As an exercise you will have a chance to explore a variation of the problem where the answer might be different.\n",
+    "\n",
+    "In the next chapter we'll move on to a new example, a model of how glucose and insulin interact to control blood sugar."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "exceptional-paper",
+   "id": "gothic-clearance",
    "metadata": {},
    "source": [
-    "**Exercise:** Use `compute_r` to compute `r_milk` according to the analytic solution.  Run the analysis with this value of `r_milk` and confirm that the results are consistent with the simulation."
+    "## Exercises"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "municipal-sustainability",
+   "cell_type": "markdown",
+   "id": "foreign-permission",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Solution goes here"
+    "**Exercise:** Use `compute_r` to compute `r_milk` according to the analytic solution.  Run the analysis with this value of `r_milk` and confirm that the results are consistent with the simulation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "radical-problem",
+   "execution_count": 25,
+   "id": "acting-howard",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -581,8 +600,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "cooked-revision",
+   "execution_count": 26,
+   "id": "ahead-compensation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -591,28 +610,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "interstate-correspondence",
+   "execution_count": 27,
+   "id": "assumed-shock",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Solution goes here"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "medium-curtis",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
-    "\n",
-    "Hint: Think about the simplest way to represent the behavior of a refrigerator in this model.  The change you make to test this variation of the problem should be very small!"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "wicked-reasoning",
+   "execution_count": 28,
+   "id": "elementary-moral",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -621,53 +630,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "corrected-dealing",
+   "id": "western-mixture",
    "metadata": {},
    "source": [
-    "## Under the hood\n",
+    "**Exercise:**  Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
     "\n",
-    "ModSim provides `make_series` to make it easier to create a Pandas Series.  In this chapter, we used it like this: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "consistent-eleven",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "make_series(t_array, T_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "exceptional-assembly",
-   "metadata": {},
-   "source": [
-    "If you import `Series` from Pandas, you can make a `Series` yourself, like this:"
+    "Hint: Think about the simplest way to represent the behavior of a refrigerator in this model.  The change you make to test this variation of the problem should be very small!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "interpreted-intro",
+   "execution_count": 29,
+   "id": "super-citizenship",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pandas import Series\n",
-    "\n",
-    "Series(T_array, t_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "right-louis",
-   "metadata": {},
-   "source": [
-    "The difference is that the arguments are in reverse order: the first argument is stored as the values in the `Series`; the second argument is stored as the `index`.\n",
-    "\n",
-    "I find that order counterintuitive, which is why I use `make_series`.\n",
-    "`make_series` takes the same optional keyword arguments as `Series`, which you can read about at <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html>."
+    "# Solution goes here"
    ]
   }
  ],
diff --git a/chapters/chap17.ipynb b/chapters/chap17.ipynb
index 7db8d0a4..24a3bda2 100644
--- a/chapters/chap17.ipynb
+++ b/chapters/chap17.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []

From 2d217808a67d1275744363252970fb1f0894759f Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Wed, 17 Feb 2021 14:23:13 -0500
Subject: [PATCH 052/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 163819 -> 164848 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
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OAxUA%T_Jy6gZ~3drgX{x


From 03eae98b2c3c22e93b25589f722b27ca6681b29d Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 18 Feb 2021 09:44:44 -0500
Subject: [PATCH 053/144] Updating examples

---
 examples/bungee1.ipynb               |  714 +++++++++++++++++
 examples/bungee2.ipynb               |  754 ++++++++++++++++++
 examples/duck.ipynb                  |  209 +++++
 examples/filter.ipynb                |  929 +++++++++++++++++++++
 examples/glucose.ipynb               |  623 +++++++++++++++
 examples/header.ipynb                |   93 +++
 examples/hiv_model_soln.ipynb        |  311 ++++++++
 examples/insulin.ipynb               |  425 ++++++++++
 examples/kitten.ipynb                |  519 ++++++++++++
 examples/oem.ipynb                   | 1106 ++++++++++++++++++++++++++
 examples/orbit.ipynb                 |  377 +++++++++
 examples/plague.ipynb                |  623 +++++++++++++++
 examples/queue.ipynb                 |  592 ++++++++++++++
 examples/salmon.ipynb                |  695 ++++++++++++++++
 examples/spiderman.ipynb             |  848 ++++++++++++++++++++
 examples/throwingaxe.ipynb           |  482 +++++++++++
 examples/trees.ipynb                 | 1014 +++++++++++++++++++++++
 examples/wall.ipynb                  |  629 +++++++++++++++
 examples/wall_soln_with_fsolve.ipynb | 1021 ++++++++++++++++++++++++
 examples/yoyo.ipynb                  |  607 ++++++++++++++
 20 files changed, 12571 insertions(+)
 create mode 100644 examples/bungee1.ipynb
 create mode 100644 examples/bungee2.ipynb
 create mode 100644 examples/duck.ipynb
 create mode 100644 examples/filter.ipynb
 create mode 100644 examples/glucose.ipynb
 create mode 100644 examples/header.ipynb
 create mode 100644 examples/hiv_model_soln.ipynb
 create mode 100644 examples/insulin.ipynb
 create mode 100644 examples/kitten.ipynb
 create mode 100644 examples/oem.ipynb
 create mode 100644 examples/orbit.ipynb
 create mode 100644 examples/plague.ipynb
 create mode 100644 examples/queue.ipynb
 create mode 100644 examples/salmon.ipynb
 create mode 100644 examples/spiderman.ipynb
 create mode 100644 examples/throwingaxe.ipynb
 create mode 100644 examples/trees.ipynb
 create mode 100644 examples/wall.ipynb
 create mode 100644 examples/wall_soln_with_fsolve.ipynb
 create mode 100644 examples/yoyo.ipynb

diff --git a/examples/bungee1.ipynb b/examples/bungee1.ipynb
new file mode 100644
index 00000000..0a146f3d
--- /dev/null
+++ b/examples/bungee1.ipynb
@@ -0,0 +1,714 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bungee Dunk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Suppose you want to set the world record for the highest \"bungee dunk\", [as shown in this video](https://www.youtube.com/watch?v=UBf7WC19lpw).  Since the record is 70 m, let's design a jump for 80 m.\n",
+    "\n",
+    "We'll make the following modeling assumptions:\n",
+    "\n",
+    "1. Initially the bungee cord hangs from a crane with the attachment point 80 m above a cup of tea.\n",
+    "\n",
+    "2. Until the cord is fully extended, it applies no force to the jumper.  It turns out this might not be a good assumption; we will revisit it.\n",
+    "\n",
+    "3. After the cord is fully extended, it obeys [Hooke's Law](https://en.wikipedia.org/wiki/Hooke%27s_law); that is, it applies a force to the jumper proportional to the extension of the cord beyond its resting length.\n",
+    "\n",
+    "4. The jumper is subject to drag force proportional to the square of their velocity, in the opposite of their direction of motion.\n",
+    "\n",
+    "Our objective is to choose the length of the cord, `L`, and its spring constant, `k`, so that the jumper falls all the way to the tea cup, but no farther! \n",
+    "\n",
+    "First I'll create a `Param` object to contain the quantities we'll need:\n",
+    "\n",
+    "1. Let's assume that the jumper's mass is 75 kg.\n",
+    "\n",
+    "2. With a terminal velocity of 60 m/s.\n",
+    "\n",
+    "3. The length of the bungee cord is `L = 40 m`.\n",
+    "\n",
+    "4. The spring constant of the cord is `k = 20 N / m` when the cord is stretched, and 0 when it's compressed.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = Params(y_attach = 80,   # m,\n",
+    "                 v_init = 0,     # m / s,\n",
+    "                 g = 9.8,        # m/s**2,\n",
+    "                 mass = 75,      # kg,\n",
+    "                 area = 1,       # m**2,\n",
+    "                 rho = 1.2,      # kg/m**3,\n",
+    "                 v_term = 60,    # m / s,\n",
+    "                 L = 25,         # m,\n",
+    "                 k = 40,         # N / m\n",
+    "               )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now here's a version of `make_system` that takes a `Params` object as a parameter.\n",
+    "\n",
+    "`make_system` uses the given value of `v_term` to compute the drag coefficient `C_d`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(params):\n",
+    "    \"\"\"Makes a System object for the given params.\n",
+    "    \n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    area, mass = params.area, params.mass\n",
+    "    g, rho = params.g, params.rho\n",
+    "    v_init, v_term = params.v_init, params.v_term\n",
+    "    y_attach = params.y_attach\n",
+    "    \n",
+    "    C_d = 2 * mass * g / (rho * area * v_term**2)\n",
+    "    init = State(y=y_attach, v=v_init)\n",
+    "    t_end = 20\n",
+    "\n",
+    "    return System(params, C_d=C_d, \n",
+    "                  init=init, t_end=t_end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's make a `System`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`spring_force` computes the force of the cord on the jumper.\n",
+    "\n",
+    "If the spring is not extended, it returns `zero_force`, which is either 0 Newtons or 0, depending on whether the `System` object has units.  I did that so the slope function works correctly with and without units."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def spring_force(y, system):\n",
+    "    \"\"\"Computes the force of the bungee cord on the jumper:\n",
+    "    \n",
+    "    y: height of the jumper\n",
+    "    \n",
+    "    Uses these variables from system|\n",
+    "    y_attach: height of the attachment point\n",
+    "    L: resting length of the cord\n",
+    "    k: spring constant of the cord\n",
+    "    \n",
+    "    returns: force in N\n",
+    "    \"\"\"\n",
+    "    y_attach, L, k = system.y_attach, system.L, system.k\n",
+    "    \n",
+    "    distance_fallen = y_attach - y\n",
+    "    if distance_fallen <= L:\n",
+    "        return 0\n",
+    "    \n",
+    "    extension = distance_fallen - L\n",
+    "    f_spring = k * extension\n",
+    "    return f_spring"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The spring force is 0 until the cord is fully extended.  When it is extended 1 m, the spring force is 40 N. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spring_force(55, system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spring_force(54, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`drag_force` computes drag as a function of velocity:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drag_force(v, system):\n",
+    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
+    "    \n",
+    "    v: velocity\n",
+    "    system: System object\n",
+    "\n",
+    "    returns: drag force\n",
+    "    \"\"\"\n",
+    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
+    "    \n",
+    "    f_drag = -np.sign(v) * rho * v**2 * C_d * area / 2\n",
+    "    return f_drag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the drag force at 60 meters per second."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = -60\n",
+    "f_drag = drag_force(v, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Acceleration due to drag at 60 m/s is approximately g, which confirms that 60 m/s is terminal velocity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a_drag = f_drag / system.mass\n",
+    "a_drag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now here's the slope function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    \"\"\"Compute derivatives of the state.\n",
+    "    \n",
+    "    state: position, velocity\n",
+    "    t: time\n",
+    "    system: System object containing g, rho,\n",
+    "            C_d, area, and mass\n",
+    "    \n",
+    "    returns: derivatives of y and v\n",
+    "    \"\"\"\n",
+    "    y, v = state\n",
+    "    mass, g = system.mass, system.g\n",
+    "    \n",
+    "    a_drag = drag_force(v, system) / mass\n",
+    "    a_spring = spring_force(y, system) / mass\n",
+    "    \n",
+    "    dvdt = -g + a_drag + a_spring\n",
+    "    \n",
+    "    return v, dvdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, let's test the slope function with the initial params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_solve_ivp(system, slope_func)\n",
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the plot of position as a function of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_position(results):\n",
+    "    results.y.plot()\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Position (m)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_position(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After reaching the lowest point, the jumper springs back almost to almost 70 m and oscillates several times.  That looks like more oscillation that we expect from an actual jump, which suggests that there is some dissipation of energy in the real world that is not captured in our model.  To improve the model, that might be a good thing to investigate.\n",
+    "\n",
+    "But since we are primarily interested in the initial descent, the model might be good enough for now.\n",
+    "\n",
+    "We can use `min` to find the lowest point:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "min(results.y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At the lowest point, the jumper is still too high, so we'll need to increase `L` or decrease `k`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's velocity as a function of time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_velocity(results):\n",
+    "    results.v.plot(color='C1', label='v')\n",
+    "        \n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Velocity (m/s)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "plot_velocity(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Although we compute acceleration inside the slope function, we don't get acceleration as a result from `run_solve_ivp`.\n",
+    "\n",
+    "We can approximate it by computing the numerical derivative of `ys`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = gradient(results.v)\n",
+    "a.plot(color='C2')\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Acceleration (m/$s^2$)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can compute the maximum acceleration the jumper experiences:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_acceleration = max(a)\n",
+    "max_acceleration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Relative to the acceleration of gravity, the jumper \"pulls\" about \"1.7 g's\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_acceleration / system.g"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solving for length\n",
+    "\n",
+    "Assuming that `k` is fixed, let's find the length `L` that makes the minimum altitude of the jumper exactly 0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The metric we are interested in is the lowest point of the first oscillation.  For both efficiency and accuracy, it is better to stop the simulation when we reach this point, rather than run past it and then compute the minimum.\n",
+    "\n",
+    "Here's an event function that stops the simulation when velocity is 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(t, state, system):\n",
+    "    \"\"\"Return velocity.\n",
+    "    \"\"\"\n",
+    "    y, v = state\n",
+    "    return v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As usual, we should test it with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we call `run_solve_ivp` with this event function, we'll see that the simulation stops immediately because the initial velocity is 0.\n",
+    "\n",
+    "We could work around that by starting with a very small, non-zero initial velocity.\n",
+    "But we can also avoid it by setting the `direction` attribute of the `event_func`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_func.direction = 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The value 1 (or any positive value) indicates that the event should only occur if the result from `event_func` is increasing.\n",
+    "A negative value would indicate that the results should be decreasing.\n",
+    "\n",
+    "Now we can test it and confirm that it stops at the bottom of the jump."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_solve_ivp(system, slope_func, \n",
+    "                                 events=event_func)\n",
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_position(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the height of the jumper at the lowest point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "min(results.y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write an error function that takes `L` and `params` as arguments, simulates a bungee jump, and returns the lowest point.\n",
+    "\n",
+    "Test the error function with a guess of 25 m and confirm that the return value is about 5 meters.\n",
+    "\n",
+    "Use `root_scalar` with your error function to find the value of `L` that yields a perfect bungee dunk.\n",
+    "\n",
+    "Run a simulation with the result from `root_scalar` and confirm that it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/bungee2.ipynb b/examples/bungee2.ipynb
new file mode 100644
index 00000000..10c1131a
--- /dev/null
+++ b/examples/bungee2.ipynb
@@ -0,0 +1,754 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bungee Dunk Revisited"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the previous case study, we simulated a bungee jump with a model that took into account gravity, air resistance, and the spring force of the bungee cord, but we ignored the weight of the cord.\n",
+    "\n",
+    "It is tempting to say that the weight of the cord doesn't matter because it falls along with the jumper.  But that intuition is incorrect, as explained by [Heck, Uylings, and Kędzierska](http://iopscience.iop.org/article/10.1088/0031-9120/45/1/007).  As the cord falls, it transfers energy to the jumper.   They derive a differential equation that relates the acceleration of the jumper to position and velocity:\n",
+    "\n",
+    "$a = g + \\frac{\\mu v^2/2}{\\mu(L+y) + 2L}$ \n",
+    "\n",
+    "where $a$ is the net acceleration of the jumper, $g$ is acceleration due to gravity, $v$ is the velocity of the jumper, $y$ is the position of the jumper relative to the starting point (usually negative), $L$ is the length of the cord, and $\\mu$ is the mass ratio of the cord and jumper.\n",
+    "\n",
+    "If you don't believe this model is correct, [this video might convince you](https://www.youtube.com/watch?v=X-QFAB0gEtE).\n",
+    "\n",
+    "Following the previous case study, we'll model the jump with the following assumptions:\n",
+    "\n",
+    "1. Initially the bungee cord hangs from a crane with the attachment point 80 m above a cup of tea.\n",
+    "\n",
+    "2. Until the cord is fully extended, it applies a force to the jumper as explained above.\n",
+    "\n",
+    "3. After the cord is fully extended, it obeys [Hooke's Law](https://en.wikipedia.org/wiki/Hooke%27s_law); that is, it applies a force to the jumper proportional to the extension of the cord beyond its resting length.\n",
+    "\n",
+    "4. The jumper is subject to drag force proportional to the square of their velocity, in the opposite of their direction of motion.\n",
+    "\n",
+    "First I'll create a `Param` object to contain the quantities we'll need:\n",
+    "\n",
+    "1. Let's assume that the jumper's mass is 75 kg and the cord's mass is also 75 kg, so `mu=1`.\n",
+    "\n",
+    "2. The jumpers's frontal area is 1 square meter, and terminal velocity is 60 m/s.  I'll use these values to back out the coefficient of drag.\n",
+    "\n",
+    "3. The length of the bungee cord is `L = 25 m`.\n",
+    "\n",
+    "4. The spring constant of the cord is `k = 40 N / m` when the cord is stretched, and 0 when it's compressed.\n",
+    "\n",
+    "I adopt the coordinate system and most of the variable names from [Heck, Uylings, and Kędzierska](http://iopscience.iop.org/article/10.1088/0031-9120/45/1/007).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = Params(y_attach = 80,   # m,\n",
+    "                 v_init = 0,     # m / s,\n",
+    "                 g = 9.8,        # m/s**2,\n",
+    "                 M = 75,         # kg,\n",
+    "                 m_cord = 75,    # kg\n",
+    "                 area = 1,       # m**2,\n",
+    "                 rho = 1.2,      # kg/m**3,\n",
+    "                 v_term = 60,    # m / s,\n",
+    "                 L = 25,         # m,\n",
+    "                 k = 40,         # N / m\n",
+    "               )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now here's a version of `make_system` that takes a `Params` object as a parameter.\n",
+    "\n",
+    "`make_system` uses the given value of `v_term` to compute the drag coefficient `C_d`.\n",
+    "\n",
+    "It also computes `mu` and the initial `State` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(params):\n",
+    "    \"\"\"Makes a System object for the given params.\n",
+    "    \n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    M, m_cord = params.M, params.m_cord\n",
+    "    g, rho, area =  params.g, params.rho, params.area\n",
+    "    v_init, v_term = params.v_init, params.v_term\n",
+    "    \n",
+    "    # back out the coefficient of drag\n",
+    "    C_d = 2 * M * g / (rho * area * v_term**2)\n",
+    "    \n",
+    "    mu = m_cord / M\n",
+    "    init = State(y=params.y_attach, v=v_init)\n",
+    "    t_end = 8\n",
+    "\n",
+    "    return System(params, C_d=C_d, mu=mu,\n",
+    "                  init=init, t_end=t_end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's make a `System`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system1 = make_system(params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`drag_force` computes drag as a function of velocity:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drag_force(v, system):\n",
+    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
+    "    \n",
+    "    v: velocity\n",
+    "    \n",
+    "    returns: drag force in N\n",
+    "    \"\"\"\n",
+    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
+    "\n",
+    "    f_drag = -np.sign(v) * rho * v**2 * C_d * area / 2\n",
+    "    return f_drag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's drag force at 20 m/s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drag_force(20, system1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function computes the acceleration of the jumper due to tension in the cord.\n",
+    "\n",
+    "$a_{cord} = \\frac{\\mu v^2/2}{\\mu(L+y) + 2L}$ "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cord_acc(y, v, system):\n",
+    "    \"\"\"Computes the force of the bungee cord on the jumper:\n",
+    "    \n",
+    "    y: height of the jumper\n",
+    "    v: velocity of the jumpter\n",
+    "    \n",
+    "    returns: acceleration in m/s\n",
+    "    \"\"\"\n",
+    "    L, mu = system.L, system.mu\n",
+    "    \n",
+    "    a_cord = -v**2 / 2 / (2*L/mu + (L+y))\n",
+    "    return a_cord"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's acceleration due to tension in the cord if we're going 20 m/s after falling 20 m."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = -20\n",
+    "v = -20\n",
+    "cord_acc(y, v, system1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now here's the slope function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func1(t, state, system):\n",
+    "    \"\"\"Compute derivatives of the state.\n",
+    "    \n",
+    "    state: position, velocity\n",
+    "    t: time\n",
+    "    system: System object containing g, rho,\n",
+    "            C_d, area, and mass\n",
+    "    \n",
+    "    returns: derivatives of y and v\n",
+    "    \"\"\"\n",
+    "    y, v = state\n",
+    "    M, g = system.M, system.g\n",
+    "    \n",
+    "    a_drag = drag_force(v, system) / M\n",
+    "    a_cord = cord_acc(y, v, system)\n",
+    "    dvdt = -g + a_cord + a_drag\n",
+    "    \n",
+    "    return v, dvdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, let's test the slope function with the initial params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func1(0, system1.init, system1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll need an event function to stop the simulation when we get to the end of the cord."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func1(t, state, system):\n",
+    "    \"\"\"Run until y=-L.\n",
+    "    \n",
+    "    state: position, velocity\n",
+    "    t: time\n",
+    "    system: System object containing g, rho,\n",
+    "            C_d, area, and mass\n",
+    "    \n",
+    "    returns: difference between y and y_attach-L\n",
+    "    \"\"\"\n",
+    "    y, v = state   \n",
+    "    return y - (system.y_attach - system.L)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can test it with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_func1(0, system1.init, system1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results1, details1 = run_solve_ivp(system1, slope_func1, \n",
+    "                                  events=event_func1)\n",
+    "details1.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the plot of position as a function of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_position(results, **options):\n",
+    "    results.y.plot(**options)\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Position (m)')\n",
+    "    \n",
+    "plot_position(results1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `min` to find the lowest point:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "min(results1.y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As expected, Phase 1 ends when the jumper reaches an altitude of 55 m."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Phase 2\n",
+    "\n",
+    "Once the jumper has falled more than the length of the cord, acceleration due to energy transfer from the cord stops abruptly.  As the cord stretches, it starts to exert a spring force.  So let's simulate this second phase."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`spring_force` computes the force of the cord on the jumper:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def spring_force(y, system):\n",
+    "    \"\"\"Computes the force of the bungee cord on the jumper:\n",
+    "    \n",
+    "    y: height of the jumper\n",
+    "    \n",
+    "    Uses these variables from system:\n",
+    "    y_attach: height of the attachment point\n",
+    "    L: resting length of the cord\n",
+    "    k: spring constant of the cord\n",
+    "    \n",
+    "    returns: force in N\n",
+    "    \"\"\"\n",
+    "    L, k = system.L, system.k\n",
+    "    \n",
+    "    distance_fallen = system.y_attach - y\n",
+    "    extension = distance_fallen - L\n",
+    "    f_spring = k * extension\n",
+    "    return f_spring"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The spring force is 0 until the cord is fully extended.  When it is extended 1 m, the spring force is 40 N. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spring_force(55, system1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spring_force(56, system1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The slope function for Phase 2 includes the spring force, and drops the acceleration due to the cord."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func2(t, state, system):\n",
+    "    \"\"\"Compute derivatives of the state.\n",
+    "    \n",
+    "    state: position, velocity\n",
+    "    t: time\n",
+    "    system: System object containing g, rho,\n",
+    "            C_d, area, and mass\n",
+    "    \n",
+    "    returns: derivatives of y and v\n",
+    "    \"\"\"\n",
+    "    y, v = state\n",
+    "    M, g = system.M, system.g\n",
+    "    \n",
+    "    a_drag = drag_force(v, system) / M\n",
+    "    a_spring = spring_force(y, system) / M\n",
+    "    dvdt = -g + a_drag + a_spring\n",
+    "    \n",
+    "    return v, dvdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The initial state for Phase 2 is the final state from Phase 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_final = results1.index[-1]\n",
+    "t_final"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_final = results1.iloc[-1]\n",
+    "state_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And that gives me the starting conditions for Phase 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system2 = System(system1, t_0=t_final, init=state_final)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run Phase 2, setting the direction of the event function so it doesn't stop the simulation immediately. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results2, details2 = run_solve_ivp(system2, slope_func2)\n",
+    "details2.message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_final = results2.index[-1]\n",
+    "t_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the results on the same axes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_position(results1, label='Phase 1')\n",
+    "plot_position(results2, label='Phase 2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And get the lowest position from Phase 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "min(results2.y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To see how big the effect of the cord is, I'll collect the previous code in a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_two_phases(params):\n",
+    "    system1 = make_system(params)\n",
+    "    results1, details1 = run_solve_ivp(system1, slope_func1, \n",
+    "                                       events=event_func1)\n",
+    "    t_final = results1.index[-1]\n",
+    "    state_final = results1.iloc[-1]\n",
+    "    \n",
+    "    system2 = system1.set(t_0=t_final, init=state_final)\n",
+    "    results2, details2 = run_solve_ivp(system2, slope_func2)\n",
+    "    return results1.append(results2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run both phases and get the results in a single `TimeFrame`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_two_phases(params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_position(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params_no_cord = params.set(m_cord=1)\n",
+    "results_no_cord = run_two_phases(params_no_cord);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "plot_position(results, label='m_cord = 75 kg')\n",
+    "plot_position(results_no_cord, label='m_cord = 1 kg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "min(results_no_cord.y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diff = min(results.y) - min(results_no_cord.y)\n",
+    "diff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The difference is about a meter, which could certainly be the difference between a successful bungee dunk and a bad day."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/duck.ipynb b/examples/duck.ipynb
new file mode 100644
index 00000000..d141be53
--- /dev/null
+++ b/examples/duck.ipynb
@@ -0,0 +1,209 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Title"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Copyright 2018 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The duck problem\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g = UNITS.g\n",
+    "cm = UNITS.cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = System (\n",
+    "    density_duck = 0.3 * g/cm**3,\n",
+    "    density_water = 1  * g/cm**3,\n",
+    "    r = 5 * cm,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def error_func(d, system):\n",
+    "    # in order to work with root_scale, we have\n",
+    "    # to accept input that does not have units\n",
+    "    d = d * cm\n",
+    "    r = system.r\n",
+    "    \n",
+    "    volume_duck = 4 / 3 * pi * r**3\n",
+    "    mass_duck = volume_duck * system.density_duck\n",
+    "\n",
+    "    volume_water = pi / 3 * (3 * r * d**2 - d**3)\n",
+    "    mass_water = volume_water * system.density_water\n",
+    "\n",
+    "    # and return an error that does not have units\n",
+    "    error = mass_duck - mass_water\n",
+    "    return magnitude(error)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error_func(3, system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error_func(4, system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = root_scalar(error_func, [3., 4.], system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res.root * cm"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/filter.ipynb b/examples/filter.ipynb
new file mode 100644
index 00000000..b625bac2
--- /dev/null
+++ b/examples/filter.ipynb
@@ -0,0 +1,929 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Low-Pass Filter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following circuit diagram (from [Wikipedia](https://en.wikipedia.org/wiki/File:RC_Divider.svg)) shows a low-pass filter built with one resistor and one capacitor.  \n",
+    "\n",
+    "![Circuit diagram of a low-pass filter](https://github.com/AllenDowney/ModSim/raw/main/figs/RC_Divider.svg)\n",
+    "\n",
+    "A \"filter\" is a circuit takes a signal, $V_{in}$, as input and produces a signal, $V_{out}$, as output.  In this context, a \"signal\" is a voltage that changes over time.\n",
+    "\n",
+    "A filter is \"low-pass\" if it allows low-frequency signals to pass from $V_{in}$ to $V_{out}$ unchanged, but it reduces the amplitude of high-frequency signals.\n",
+    "\n",
+    "By applying the laws of circuit analysis, we can derive a differential equation that describes the behavior of this system.  By solving the differential equation, we can predict the effect of this circuit on any input signal.\n",
+    "\n",
+    "Suppose we are given $V_{in}$ and $V_{out}$ at a particular instant in time.  By Ohm's law, which is a simple model of the behavior of resistors, the instantaneous current through the resistor is:\n",
+    "\n",
+    "$ I_R = (V_{in} - V_{out}) / R $\n",
+    "\n",
+    "where $R$ is resistance in ohms.\n",
+    "\n",
+    "Assuming that no current flows through the output of the circuit, Kirchhoff's current law implies that the current through the capacitor is:\n",
+    "\n",
+    "$ I_C = I_R $\n",
+    "\n",
+    "According to a simple model of the behavior of capacitors, current through the capacitor causes a change in the voltage across the capacitor:\n",
+    "\n",
+    "$ I_C = C \\frac{d V_{out}}{dt} $\n",
+    "\n",
+    "where $C$ is capacitance in farads (F).\n",
+    "\n",
+    "Combining these equations yields a differential equation for $V_{out}$:\n",
+    "\n",
+    "$ \\frac{d }{dt} V_{out} = \\frac{V_{in} - V_{out}}{R C} $\n",
+    "\n",
+    "Follow the instructions below to simulate the low-pass filter for input signals like this:\n",
+    "\n",
+    "$ V_{in}(t) = A \\cos (2 \\pi f t) $\n",
+    "\n",
+    "where $A$ is the amplitude of the input signal, say 5 V, and $f$ is the frequency of the signal in Hz."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Params and System objects\n",
+    "\n",
+    "Here's a `Params` object to contain the quantities we need.  I've chosen values for `R1` and `C1` that might be typical for a circuit that works with audio signal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = Params(\n",
+    "    R1 = 1e6,   # * ohm\n",
+    "    C1 = 1e-9,  # * farad\n",
+    "    A = 5,      # * volt\n",
+    "    f = 1000,   # * Hz\n",
+    ")\n",
+    "params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can pass the `Params` object `make_system` which computes some additional parameters and defines `init`.\n",
+    "\n",
+    "* `omega` is the frequency of the input signal in radians/second.\n",
+    "\n",
+    "* `tau` is the time constant for this circuit, which is the time it takes to get from an initial startup phase to \n",
+    "\n",
+    "* `cutoff` is the cutoff frequency for this circuit (in Hz), which marks the transition from low frequency signals, which pass through the filter unchanged, to high frequency signals, which are attenuated.\n",
+    "\n",
+    "* `t_end` is chosen so we run the simulation for 4 cycles of the input signal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import pi\n",
+    "\n",
+    "def make_system(params):\n",
+    "    \"\"\"Makes a System object for the given conditions.\n",
+    "    \n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    f, R1, C1 = params.f, params.R1, params.C1\n",
+    "    \n",
+    "    init = State(V_out = 0)\n",
+    "    omega = 2 * pi * f\n",
+    "    tau = R1 * C1\n",
+    "    cutoff = 1 / R1 / C1 / 2 / pi\n",
+    "    t_end = 4 / f\n",
+    "    \n",
+    "    return System(params, \n",
+    "                  init=init, \n",
+    "                  t_end=t_end, num=401,\n",
+    "                  omega=omega, tau=tau, \n",
+    "                  cutoff=cutoff)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's make a `System`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params)\n",
+    "system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The system variable `num` controls how many time steps we get from `run_solve_ivp`.  The default is 101; in this case we increase it to 401 because the methods we'll use to analyze the results require high resolution in time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a slope function that takes as an input a `State` object that contains `V_out`, and returns the derivative of `V_out`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then run the simulation.  I suggest using `t_eval=ts` to make sure we have enough data points to plot and analyze the results. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_solve_ivp(system, slope_func)\n",
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a function you can use to plot `V_out` as a function of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(results):\n",
+    "    V_out = results.V_out.copy()\n",
+    "    t_end = results.index[-1]\n",
+    "    \n",
+    "    if t_end < 0.1:\n",
+    "        V_out.index *= 1000\n",
+    "        xlabel = 'Time (ms)'\n",
+    "    else:\n",
+    "        V_out = results.V_out\n",
+    "        xlabel = 'Time (s)'\n",
+    "        \n",
+    "    V_out.plot(label='_nolegend')\n",
+    "    decorate(xlabel=xlabel,\n",
+    "             ylabel='$V_{out}$ (volt)')\n",
+    "    \n",
+    "plot_results(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If things have gone according to plan, the amplitude of the output signal should be about 0.8 V.\n",
+    "\n",
+    "Also, you might notice that it takes a few cycles for the signal to get to the full amplitude. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sweeping frequency\n",
+    "\n",
+    "Here's what `V_out` looks like for a range of frequencies:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.pyplot import subplot\n",
+    "\n",
+    "fs = [1, 10, 100, 1000, 10000, 100000]\n",
+    "\n",
+    "for i, f in enumerate(fs):\n",
+    "    system = make_system(params.set(f=f))\n",
+    "    results, details = run_solve_ivp(system, slope_func)\n",
+    "    subplot(3, 2, i+1)\n",
+    "    plot_results(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At low frequencies, notice that there is an initial \"transient\" before the output gets to a steady-state sinusoidal output.  The duration of this transient is a small multiple of the time constant, `tau`, which is 1 ms."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Estimating the output ratio\n",
+    "\n",
+    "Let's compare the amplitudes of the input and output signals.  Below the cutoff frequency, we expect them to be about the same.  Above the cutoff, we expect the amplitude of the output signal to be smaller.\n",
+    "\n",
+    "We'll start with a signal at the cutoff frequency, `f=1000` Hz."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params.set(f=1000))\n",
+    "results, details = run_solve_ivp(system, slope_func)\n",
+    "V_out = results.V_out\n",
+    "plot_results(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function computes `V_in` as a `TimeSeries`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_vin(results, system):\n",
+    "    \"\"\"Computes V_in as a TimeSeries.\n",
+    "    \n",
+    "    results: TimeFrame with simulation results\n",
+    "    system: System object with A and omega\n",
+    "    \n",
+    "    returns: TimeSeries\n",
+    "    \"\"\"\n",
+    "    A, omega = system.A, system.omega\n",
+    "    \n",
+    "    ts = results.index\n",
+    "    V_in = A * np.cos(omega * ts)\n",
+    "    return TimeSeries(V_in, results.index, name='V_in')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the input and output look like.  Notice that the output is not just smaller; it is also \"out of phase\"; that is, the peaks of the output are shifted to the right, relative to the peaks of the input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "V_in = compute_vin(results, system)\n",
+    "\n",
+    "V_out.plot()\n",
+    "V_in.plot()\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='V (volt)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function estimates the amplitude of a signal by computing half the distance between the min and max."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def estimate_A(series):\n",
+    "    \"\"\"Estimate amplitude.\n",
+    "    \n",
+    "    series: TimeSeries\n",
+    "    \n",
+    "    returns: amplitude in volts\n",
+    "    \"\"\"\n",
+    "    return (series.max() - series.min()) / 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The amplitude of `V_in` should be near 5 (but not exact because we evaluated it at a finite number of points)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A_in = estimate_A(V_in)\n",
+    "A_in"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The amplitude of `V_out` should be lower."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A_out = estimate_A(V_out)\n",
+    "A_out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the ratio between them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ratio = A_out / A_in\n",
+    "ratio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Encapsulate the code we have so far in a function that takes two `TimeSeries` objects and returns the ratio between their amplitudes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And test your function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "estimate_ratio(V_out, V_in)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Estimating phase offset\n",
+    "\n",
+    "The delay between the peak of the input and the peak of the output is call a \"phase shift\" or \"phase offset\", usually measured in fractions of a cycle, degrees, or radians.\n",
+    "\n",
+    "To estimate the phase offset between two signals, we can use cross-correlation.  Here's what the cross-correlation looks like between `V_out` and `V_in`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr = np.correlate(V_out, V_in, mode='same')\n",
+    "corr_series = make_series(V_in.index, corr)\n",
+    "corr_series.plot(color='C4')\n",
+    "decorate(xlabel='Lag (s)',\n",
+    "         ylabel='Correlation')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The location of the peak in the cross correlation is the estimated shift between the two signals, in seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peak_time = corr_series.idxmax()\n",
+    "peak_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can express the phase offset as a multiple of the period of the input signal:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "period = 1 / system.f\n",
+    "period"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peak_time / period"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We don't care about whole period offsets, only the fractional part, which we can get using `modf`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frac, whole = np.modf(peak_time / period)\n",
+    "frac"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can convert from a fraction of a cycle to degrees:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frac * 360"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Encapsulate this code in a function that takes two `TimeSeries` objects and a `System` object, and returns the phase offset in degrees.\n",
+    "\n",
+    "Note: by convention, if the output is shifted to the right, the phase offset is negative."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "estimate_offset(V_out, V_in, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sweeping frequency again\n",
+    "\n",
+    "**Exercise:** Write a function that takes as parameters an array of input frequencies and a `Params` object.\n",
+    "\n",
+    "For each input frequency it should run a simulation and use the results to estimate the output ratio (dimensionless) and phase offset (in degrees).\n",
+    "\n",
+    "It should return two `SweepSeries` objects, one for the ratios and one for the offsets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run your function with these frequencies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fs = 10 ** linspace(0, 4, 9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ratios, offsets = sweep_frequency(fs, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot output ratios like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ratios.plot(color='C2', label='output ratio')\n",
+    "decorate(xlabel='Frequency (Hz)',\n",
+    "         ylabel='$V_{out} / V_{in}$')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But it is useful and conventional to plot ratios on a log-log scale.  The vertical gray line shows the cutoff frequency."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_ratios(ratios, system):\n",
+    "    \"\"\"Plot output ratios.\n",
+    "    \"\"\"\n",
+    "    # axvline can't handle a Quantity with units\n",
+    "    cutoff = magnitude(system.cutoff)\n",
+    "    plt.axvline(cutoff, color='gray', alpha=0.4)\n",
+    "    \n",
+    "    ratios.plot(color='C2', label='output ratio')\n",
+    "    decorate(xlabel='Frequency (Hz)',\n",
+    "             ylabel='$V_{out} / V_{in}$',\n",
+    "             xscale='log', yscale='log')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_ratios(ratios, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This plot shows the cutoff behavior more clearly.  Below the cutoff, the output ratio is close to 1.  Above the cutoff, it drops off linearly, on a log scale, which indicates that output ratios for high frequencies are practically 0.\n",
+    "\n",
+    "Here's the plot for phase offset, on a log-x scale:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_offsets(offsets, system):\n",
+    "    \"\"\"Plot phase offsets.\n",
+    "    \"\"\"\n",
+    "    # axvline can't handle a Quantity with units\n",
+    "    cutoff = magnitude(system.cutoff)\n",
+    "    plt.axvline(cutoff, color='gray', alpha=0.4)\n",
+    "    \n",
+    "    offsets.plot(color='C9', label='phase offset')\n",
+    "    decorate(xlabel='Frequency (Hz)',\n",
+    "             ylabel='Phase offset (degree)',\n",
+    "             xscale='log')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_offsets(offsets, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For low frequencies, the phase offset is near 0.  For high frequencies, it approaches 90 degrees."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis\n",
+    "\n",
+    "By analysis we can show that the output ratio for this signal is\n",
+    "\n",
+    "$A = \\frac{1}{\\sqrt{1 + (R C \\omega)^2}}$ \n",
+    "\n",
+    "where $\\omega = 2 \\pi f$, and the phase offset is\n",
+    "\n",
+    "$ \\phi = \\arctan (- R C \\omega)$ \n",
+    "\n",
+    "**Exercise:** Write functions that take an array of input frequencies and returns $A(f)$ and $\\phi(f)$ as `SweepSeries` objects.  Plot these objects and compare them with the results from the previous section.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = output_ratios(fs, system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi = phase_offsets(fs, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the theoretical results along with the simulation results and see if they agree."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A.plot(style=':', color='gray', label='analysis')\n",
+    "plot_ratios(ratios, system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi.plot(style=':', color='gray', label='analysis')\n",
+    "plot_offsets(offsets, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the phase offsets, there are differences between the theoretical results and our estimates, but that is probably because it is not easy to estimate phase offsets precisely from numerical results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Consider modifying this notebook to model a [first order high-pass filter](https://en.wikipedia.org/wiki/High-pass_filter#First-order_continuous-time_implementation), a [two-stage second-order low-pass filter](https://www.electronics-tutorials.ws/filter/filter_2.html), or a [passive band-pass filter](https://www.electronics-tutorials.ws/filter/filter_4.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/glucose.ipynb b/examples/glucose.ipynb
new file mode 100644
index 00000000..bde7bb82
--- /dev/null
+++ b/examples/glucose.ipynb
@@ -0,0 +1,623 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Glucose Minimal Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import basename, exists\n",
+    "\n",
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSim/main/modsim.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the previous chapter we implemented the glucose minimal model using given parameters, but I didn't say where those parameters came from.\n",
+    "This notebook solves the mystery.\n",
+    "\n",
+    "We'll use a SciPy function called `leastsq`, which stands for \"least squares\"; that is, it finds the parameters that minimize the sum of squared differences between the results of the model and the data.\n",
+    "\n",
+    "You can think of `leastsq` as optional material.  We won't use it in the book itself, but it appears in a few of the case studies.\n",
+    "\n",
+    "You can read more about `leastsq` in [the SciPy documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.leastsq.html).  It uses the Levenberg-Marquart algorithm, which you can read about [on Wikipedia](https://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cells download and read the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSim/main/data/glucose_insulin.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('glucose_insulin.csv', index_col='time');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll use `make_system` and `slope_func` as defined in Chapter 18."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from chap18 import make_system\n",
+    "from chap18 import slope_func"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Computing errors\n",
+    "\n",
+    "In this context, the \"errors\" are the differences between the results from the model and the data.\n",
+    "\n",
+    "To compute the errors, I'll start again with the parameters we used in Chapter 18."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G0 = 270\n",
+    "k1 = 0.02\n",
+    "k2 = 0.02\n",
+    "k3 = 1.5e-05"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = G0, k1, k2, k3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`make_system` takes the parameters and actual data and returns a `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run the ODE solver."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_solve_ivp(system, slope_func, \n",
+    "                                 t_eval=data.index)\n",
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because we specify `t_eval=data.index`, the results are evaluated at the some time stamps as the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the results like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.glucose.plot(style='o', alpha=0.5, label='data')\n",
+    "results.G.plot(style='-', color='C0', label='model')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration (mg/dL)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "During the first three time steps, the model does not fit the data. That's because it takes some time for the injected glucose to disperse.\n",
+    "\n",
+    "We can compute the errors like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "errors = results.G - data.glucose\n",
+    "errors.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first few errors are substantially larger than the rest.\n",
+    "\n",
+    "In the next section, we'll use `leastsq` to search for the parameters that minimize the sum of the squared errors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optimization\n",
+    "\n",
+    "To use `leastsq`, we need an \"error function\" that takes a sequence of parameters and returns an array of errors.\n",
+    "\n",
+    "Here's a function that does what we need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def error_func(params, data):\n",
+    "    \"\"\"Computes an array of errors to be minimized.\n",
+    "    \n",
+    "    params: sequence of parameters\n",
+    "    data: DataFrame of values to be matched\n",
+    "    \n",
+    "    returns: array of errors\n",
+    "    \"\"\"\n",
+    "    print(params)\n",
+    "    \n",
+    "    # make a System with the given parameters\n",
+    "    system = make_system(params, data)\n",
+    "    \n",
+    "    # solve the ODE\n",
+    "    results, details = run_solve_ivp(system, slope_func, \n",
+    "                                     t_eval=data.index)\n",
+    "    \n",
+    "    # compute the difference between the model\n",
+    "    # results and actual data\n",
+    "    errors = results.G - data.glucose\n",
+    "    return errors.iloc[3:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`error_func` uses the given parameters to make a `System` object, runs the simulation, and returns the errors.\n",
+    "\n",
+    "But notice that it does not return all of the errors; rather, it uses `iloc` to select only the elements with index 3 or more.\n",
+    "In other words, it omits the elements with index 0, 1, and 2.\n",
+    "Note: You can read more about this use of `iloc` [in the Pandas documentation](https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-integer).\n",
+    "\n",
+    "Since we don't expect the model to fit the data in this regime, we'll leave it out.\n",
+    "\n",
+    "We can call `error_func` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error_func(params, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we're ready to call `leastsq`.  As arguments, we pass `error_func`, the parameters where we want to start the search, and the data, which will be passed as an argument to `error_func`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_params, fit_details = leastsq(error_func, params, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each time `error_func` is called, it prints the parameters, so we can get a sense of how `leastsq` works.\n",
+    "\n",
+    "`leastsq` has two return values.\n",
+    "The is an array with the best parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second is an object with information about the results, including a success flag and a message."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fit_details.success"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fit_details.mesg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have `best_params`, we can use it to make a `System` object and run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system2 = make_system(best_params, data)\n",
+    "results2, details = run_solve_ivp(system2, slope_func, \n",
+    "                                  t_eval=data.index)\n",
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the results, along with the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.glucose.plot(style='o', alpha=0.5, label='data')\n",
+    "results.G.plot(style='-', color='C0', label='model')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration (mg/dL)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can compute the errors like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "errors2 = results2.G - data.glucose\n",
+    "errors2.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the sum of the squared errors like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import sum\n",
+    "\n",
+    "sum(errors2.iloc[3:]**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If things have gone according to plan, the sum of squared errors should be smaller now, compared to the parameters we started with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sum(errors.iloc[3:]**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpreting parameters\n",
+    "\n",
+    "So we found the parameters that best match the data.  You might wonder why this is useful.\n",
+    "\n",
+    "The parameters themselves don't mean very much, but we can use them to compute two quantities we care about:\n",
+    "\n",
+    "-   \"Glucose effectiveness\", $E$, which is the tendency of elevated\n",
+    "    glucose to cause depletion of glucose.\n",
+    "\n",
+    "-   \"Insulin sensitivity\", $S$, which is the ability of elevated blood\n",
+    "    insulin to enhance glucose effectiveness.\n",
+    "\n",
+    "Glucose effectiveness is defined as the change in $dG/dt$ as we vary\n",
+    "$G$: \n",
+    "\n",
+    "$$E \\equiv - \\frac{\\delta \\dot{G}}{\\delta G}$$ \n",
+    "\n",
+    "where $\\dot{G}$ is shorthand for $dG/dt$. Taking the derivative of $dG/dt$ with respect to $G$, we get \n",
+    "\n",
+    "$$E = k_1 + X$$ \n",
+    "\n",
+    "The **glucose effectiveness index**, $S_G$, is the value of $E$ when blood insulin is near its basal level, $I_b$.\n",
+    "In that case, $X$ approaches 0 and $E$ approaches $k_1$. So we can use\n",
+    "the best-fit value of $k_1$ as an estimate of $S_G$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insulin sensitivity is defined as the change in $E$ as we vary $I$:\n",
+    "\n",
+    "$$S \\equiv - \\frac{\\delta E}{\\delta I}$$ \n",
+    "\n",
+    "The **insulin sensitivity index**, $S_I$, is the value of $S$ when $E$ and $I$ are at steady state: \n",
+    "\n",
+    "$$S_I \\equiv \\frac{\\delta E_{SS}}{\\delta I_{SS}}$$ \n",
+    "\n",
+    "$E$ and $I$ are at steady state when $dG/dt$ and $dX/dt$ are 0, but we don't actually have to solve those equations to find $S_I$. \n",
+    "\n",
+    "If we set $dX/dt = 0$ and solve for $X$, we find the relation:\n",
+    "\n",
+    "$$X_{SS} = \\frac{k_3}{k_2} I_{SS}$$ \n",
+    "\n",
+    "And since $E = k_1 + X$, we have:\n",
+    "\n",
+    "$$S_I = \\frac{\\delta E_{SS}}{\\delta I_{SS}} = \\frac{\\delta X_{SS}}{\\delta I_{SS}}$$\n",
+    "\n",
+    "Taking the derivative of $X_{SS}$ with respect to $I_{SS}$, we have:\n",
+    "\n",
+    "$$S_I = k_3 / k_2$$ \n",
+    "\n",
+    "So if we find parameters that make the model fit the data, we can use $k_3 / k_2$ as an estimate of $S_I$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the parameters we found, here are the estimated values of $S_G$ and $S_I$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G0, k1, k2, k3 = best_params\n",
+    "indices = SimpleNamespace(S_G=k1, S_I=k3/k2)\n",
+    "show(indices)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "According to [Boston et al](https://www.researchgate.net/publication/8931437_MINMOD_Millennium_A_Computer_Program_to_Calculate_Glucose_Effectiveness_and_Insulin_Sensitivity_From_the_Frequently_Sampled_Intravenous_Glucose_Tolerance_Test), normal ranges for these values are..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "S_G_interval = 1.2e-3, 4.5e-2\n",
+    "S_G_interval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "S_I_interval = 5.0e-5, 2.2e-3\n",
+    "S_I_interval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The estimated quantities are within the normal intervals."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "**Exercise:** How sensitive are the results to the starting guess for the parameters?  If you try different values for the starting guess, do we get the same values for the best parameters?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/header.ipynb b/examples/header.ipynb
new file mode 100644
index 00000000..6b6acc86
--- /dev/null
+++ b/examples/header.ipynb
@@ -0,0 +1,93 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Title"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/hiv_model_soln.ipynb b/examples/hiv_model_soln.ipynb
new file mode 100644
index 00000000..d4dd128e
--- /dev/null
+++ b/examples/hiv_model_soln.ipynb
@@ -0,0 +1,311 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling HIV infection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "During the initial phase of HIV infection, the concentration of the virus in the bloodstream typically increases quickly and then decreases.\n",
+    "The most obvious explanation for the decline is an immune response that destroys the virus or controls its replication.\n",
+    "However, at least in some patients, the decline occurs even without any detectable immune response.\n",
+    "\n",
+    "In 1996 Andrew Phillips proposed another explanation for the decline (\"Reduction of HIV Concentration During Acute Infection: Independence from a Specific Immune Response\", available from <https://people.math.gatech.edu/~weiss/uploads/5/8/6/1/58618765/phillips1996.pdf>).\n",
+    "\n",
+    "Phillips presents a system of differential equations that models the concentrations of the HIV virus and the CD4 cells it infects.\n",
+    "The model does not include an immune response; nevertheless, it demonstrates behavior that is qualitatively similar to what is seen in patients during the first few weeks after infection.\n",
+    "\n",
+    "His conclusion is that the observed decline in the concentration of HIV might not be caused by an immune response; it could be due to the dynamic interaction between HIV and the cells it infects.\n",
+    "\n",
+    "In this notebook, we'll implement Phillips's model and consider whether it does the work it is meant to do."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Model\n",
+    "\n",
+    "The model has four state variables, `R`, `L`, `E`, and `V`.  Read the paper to understand what they represent.\n",
+    "\n",
+    "Here are the initial conditional we can glean from the paper. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init = State(R=200, L=0, E=0, V=4e-7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The behavior of the system is controlled by 9 parameters.\n",
+    "That might seem like a lot, but they are not entirely free parameters; their values are constrained by measurements and background knowledge (although some are more constrained than others).\n",
+    "Here are the values from Table 1.\n",
+    "\n",
+    "Note: the parameter $\\rho$ (the Greek letter \"rho\") in the table appears as $p$ in the equations.  Since it represents a proportion, we'll use $p$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamma = 1.36\n",
+    "mu = 1.36e-3\n",
+    "tau = 0.2\n",
+    "beta = 0.00027\n",
+    "p = 0.1\n",
+    "alpha = 3.6e-2\n",
+    "sigma = 2\n",
+    "delta = 0.33\n",
+    "pi = 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a `System` object with the initial conditions and the duration of the simulation (120 days).\n",
+    "Normally we would store the parameters in the `System` object, but the code will be less cluttered if we leave them as global variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = System(init=init,\n",
+    "                t_end=120,\n",
+    "                num=481)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Use the equations in the paper to write a slope function that takes a `State` object with the current values of `R`, `L`, `E`, and `V`, and returns their derivatives in the corresponding order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your slope function with the initial conditions.\n",
+    "The results should be approximately\n",
+    "\n",
+    "```\n",
+    "-2.16e-08, 2.16e-09, 1.944e-08, -8e-07\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Now use `run_solve_ivp` to simulate the system of equations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next few cells plot the results on the same scale as the figures in the paper.\n",
+    "\n",
+    "**Exericise:** Compare your results to the results in the paper.\n",
+    "Are they consistent?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.V.plot(label='V')\n",
+    "decorate(xlabel='Time (days)',\n",
+    "         ylabel='Free virions V',\n",
+    "         yscale='log',\n",
+    "         ylim=[0.1, 1e4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.R.plot(label='R', color='C1')\n",
+    "decorate(xlabel='Time (days)',\n",
+    "         ylabel='Number of cells',\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.L.plot(color='C2', label='L')\n",
+    "results.E.plot(color='C4', label='E')\n",
+    "decorate(xlabel='Time (days)',\n",
+    "         ylabel='Number of cells',\n",
+    "         yscale='log',\n",
+    "         ylim=[0.1, 100])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** What kind of work is this model doing?  Do you find the results convincing?  What are the strengths of the model?  What are the weaknesses?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Read [this response to Phillips's article](https://science.sciencemag.org/content/sci/272/5270/1960.full.pdf) and Phillips's response to the response.  What do you think of the arguments on both sides.  Do you think the model Phillips proposed is sufficient to make his argument, or do you think it leaves out essential features of the real world?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/insulin.ipynb b/examples/insulin.ipynb
new file mode 100644
index 00000000..374b7684
--- /dev/null
+++ b/examples/insulin.ipynb
@@ -0,0 +1,425 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Insulin Minimal Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import basename, exists\n",
+    "\n",
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSim/main/modsim.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cells download and read the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSim/main/data/glucose_insulin.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('glucose_insulin.csv', index_col='time');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In Chapter 17 I present the glucose minimal model; in Chapter 18 we implemented it using `run_simulation` and `run_solve_ivp`.\n",
+    "In this case study, we'll implement the other half of the Minimal Model, which describes the concentration of insulin.\n",
+    "\n",
+    "In the insulin minimal model, the concentration of insulin, $I$, is governed by this differential equation:\n",
+    "\n",
+    "$ \\frac{dI}{dt} = -k I(t) + \\gamma (G(t) - G_T) t $\n",
+    "\n",
+    "where $G(t)$ is the concentration of glucose at time $t$, and\n",
+    "$k$, $\\gamma$, and $G_T$ are positive-valued parameters:\n",
+    "\n",
+    "* $k$ controls the rate of insulin disappearance.\n",
+    "\n",
+    "* $G_T$ determines the glucose threshold: when $G(t)$ exceeds this threshold, it causes insulin to appear; when $G(t)$ is below this threshold, it causes insulin to disappear.\n",
+    "\n",
+    "* $\\gamma$ controls how quickly insulin appears or disappears when the concentration of glucose is elevated or depressed.\n",
+    " \n",
+    "Notice that this equation depends on time, $t$, since the initial injection.  It has the effect of increasing glucose sensitivity over time.  If you are familiar with control systems, the effect of this term is similar to the integral term in a [PID controller](https://en.wikipedia.org/wiki/PID_controller).\n",
+    "\n",
+    "In addition to the three parameters in the equation, we will also consider the initial concentration of insulin, $I_0$, to be a free parameter; that is, we will choose the value of $I_0$, and the other parameters, that best fit the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Write a version of `make_system` that takes the parameters of the model (`I0`, `k`, `gamma`, and `G_T`) as parameters, along with a `DataFrame` containing the measurements, and returns a `System` object suitable for use with `run_solve_ivp`.\n",
+    "\n",
+    "Use it to make a `System` object with the following parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "I0 = 360   \n",
+    "k = 0.25\n",
+    "gamma = 0.004\n",
+    "G_T = 80\n",
+    "\n",
+    "params = I0, k, gamma, G_T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a slope function that takes a time stamp, a `State` object, and a `System` object, and returns the derivative of `I` with respect to time.  Test your function with the initial conditions from `system`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Run `run_solve_ivp` with your `System` object and slope function, and plot the results, along with the measured insulin levels.  Use the keyword argument `t_eval=data.index` so the results are evaluated as the same time stamps as the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Write an error function that takes a sequence of parameters as an argument, along with the `DataFrame` containing the measurements.  It should make a `System` object with the given parameters, call `run_solve_ivp`, and compute the difference between the results of the simulation and the measured values.  Test your error function by calling it with the parameters from the previous exercise.\n",
+    "\n",
+    "Hint: As we did with the glucose model, you might want to drop the first 2-3 elements from the sequence of errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Use `leastsq` to find the parameters that best fit the data.  Make a `System` object with those parameters, run it, and plot the results along with the measurements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Using the best parameters, estimate the sensitivity to glucose of the first and second phase pancreatic responsivity:\n",
+    "\n",
+    "$ \\phi_1 = \\frac{I_{max} - I_b}{k (G_0 - G_b)} $\n",
+    "\n",
+    "$ \\phi_2 = \\gamma \\times 10^4 $\n",
+    "\n",
+    "For $G_0$, use the best estimate from the glucose model, 272 mg/dL.  For $G_b$ and $I_b$, use the initial measurements from the data.\n",
+    "For $I_{max}$ is the maximum measurement of insulin concentration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "According to [Pacini and Bergman](https://www.researchgate.net/publication/13707725_Insulin_sensitivity_and_glucose_effectiveness_Minimal_model_analysis_of_regular_and_insulin-modified_FSIGT), here are the normal ranges for these quantities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi_1_interval = 2, 4\n",
+    "phi_1_interval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi_2_interval = 20, 35\n",
+    "phi_2_interval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do your estimates fall in these ranges?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/kitten.ipynb b/examples/kitten.ipynb
new file mode 100644
index 00000000..e0c11988
--- /dev/null
+++ b/examples/kitten.ipynb
@@ -0,0 +1,519 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Kittens"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you have used the Internet, you have probably seen videos of kittens unrolling toilet paper.\n",
+    "And you might have wondered how long it would take a standard kitten to unroll 47 m of paper, the length of a standard roll.\n",
+    "\n",
+    "The interactions of the kitten and the paper rolls are complex.  To keep things simple, let's assume that the kitten pulls down on the free end of the roll with constant force.  And let's neglect the friction between the roll and the axle.\n",
+    "\n",
+    "This diagram shows the paper roll with the force applied by the kitten, $F$, the lever arm of the force around the axis of rotation, $r$, and the resulting torque, $\\tau$.\n",
+    "\n",
+    "![Diagram of a roll of toilet paper, showing a force, lever arm, and the resulting torque.](https://github.com/AllenDowney/ModSim/raw/main/figs/kitten.png)\n",
+    "\n",
+    "Assuming that the force applied by the kitten is 0.002 N, how long would it take to unroll a standard roll of toilet paper?\n",
+    "\n",
+    "We'll use the same parameters as in Chapter 24:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Rmin = 0.02      # m\n",
+    "Rmax = 0.055     # m\n",
+    "Mcore = 15e-3    # kg\n",
+    "Mroll = 215e-3   # kg\n",
+    "L = 47           # m\n",
+    "tension = 0.002  # N"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Rmin` and `Rmax` are the minimum and maximum radius of the roll, respectively.\n",
+    "`Mcore` is the weight of the core (the cardboard tube at the center) and `Mroll` is the total weight of the paper.\n",
+    "`L` is the unrolled length of the paper.\n",
+    "`tension` is the force the kitten applies by pulling on the loose end of the roll (I chose this value because it yields reasonable results)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In Chapter 24 we defined $k$ to be the constant that relates a change in the radius of the roll to a change in the rotation of the roll:\n",
+    "\n",
+    "$$dr = k~d\\theta$$ \n",
+    "\n",
+    "And we derived the equation for $k$ in terms of $R_{min}$, $R_{max}$, and $L$. \n",
+    "\n",
+    "$$k =  \\frac{1}{2L} (R_{max}^2 - R_{min}^2)$$\n",
+    "\n",
+    "So we can compute `k` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "k = (Rmax**2 - Rmin**2) / 2 / L    \n",
+    "k    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Moment of Inertia\n",
+    "\n",
+    "To compute angular acceleration, we'll need the moment of inertia for the roll.\n",
+    "\n",
+    "At <http://modsimpy.com/moment> you can find moments of inertia for\n",
+    "simple geometric shapes. I'll model the core as a \"thin cylindrical shell\", and the paper roll as a \"thick-walled cylindrical tube with open ends\".\n",
+    "\n",
+    "The moment of inertia for a thin shell is just $m r^2$, where $m$ is the mass and $r$ is the radius of the shell.\n",
+    "\n",
+    "For a thick-walled tube the moment of inertia is\n",
+    "\n",
+    "$$I = \\frac{\\pi \\rho h}{2} (r_2^4 - r_1^4)$$ \n",
+    "\n",
+    "where $\\rho$ is the density of the material, $h$ is the height of the tube (if we think of the roll oriented vertically), $r_2$ is the outer diameter, and $r_1$ is the inner diameter.\n",
+    "\n",
+    "Since the outer diameter changes as the kitten unrolls the paper, we\n",
+    "have to compute the moment of inertia, at each point in time, as a\n",
+    "function of the current radius, `r`, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def moment_of_inertia(r, system):\n",
+    "    \"\"\"Moment of inertia for a roll of toilet paper.\n",
+    "    \n",
+    "    r: current radius of roll in meters\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: moment of inertia in kg m**2\n",
+    "    \"\"\"    \n",
+    "    Icore = Mcore * Rmin**2   \n",
+    "    Iroll = np.pi * rho_h / 2 * (r**4 - Rmin**4)\n",
+    "    return Icore + Iroll"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Icore` is the moment of inertia of the core; `Iroll` is the moment of inertia of the paper.\n",
+    "\n",
+    "`rho_h` is the density of the paper in terms of mass per unit of area. \n",
+    "To compute `rho_h`, we compute the area of the complete roll like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "area = np.pi * (Rmax**2 - Rmin**2)\n",
+    "area"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And divide the mass of the roll by that area."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rho_h = Mroll / area\n",
+    "rho_h"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an example, here's the moment of inertia for the complete roll."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moment_of_inertia(Rmax, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As `r` decreases, so does `I`.  Here's the moment of inertia when the roll is empty."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moment_of_inertia(Rmin, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The way $I$ changes over time might be more of a problem than I have made it seem.  In the same way that $F = m a$ only applies when $m$ is constant, $\\tau = I \\alpha$ only applies when $I$ is constant.  When $I$ varies, we usually have to use a more general version of Newton's law.  However, I believe that in this example, mass and moment of inertia vary together in a way that makes the simple approach work out.\n",
+    "\n",
+    "A friend of mine who is a physicist is not convinced; nevertheless, let's proceed on the assumption that I am right."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "The state variables we'll use are\n",
+    "\n",
+    "* `theta`, the total rotation of the roll in radians, \n",
+    "\n",
+    "* `omega`, angular velocity in rad / s,\n",
+    "\n",
+    "* `r`, the radius of the roll, and\n",
+    "\n",
+    "* `y`, the length of the unrolled paper.\n",
+    "\n",
+    "Here's a `State` object with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init = State(theta=0, omega=0, y=0, r=Rmax)\n",
+    "init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's a `System` object with the starting conditions and `t_end`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = System(init=init, t_end=120)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can take it from here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**\n",
+    "\n",
+    "Write a slope function we can use to simulate this system.  Test it with the initial conditions.  The results should be approximately\n",
+    "\n",
+    "```\n",
+    "0.0, 0.294, 0.0, 0.0\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write an event function that stops the simulation when `y` equals `L`, that is, when the entire roll is unrolled.  Test your function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And check the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final value of `theta` should be about 200 rotations, the same as in Chapter 24.\n",
+    "\n",
+    "The final value of `omega` should be about 63 rad/s, which is about 10 revolutions per second.  That's pretty fast, but it might be plausible.\n",
+    "\n",
+    "The final value of `y` should be `L`, which is 47 m.\n",
+    "\n",
+    "The final value of `r` should be `Rmin`, which is 0.02 m.\n",
+    "\n",
+    "And the total unrolling time should be about 76 seconds, which seems plausible."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cells plot the results.\n",
+    "\n",
+    "`theta` increases slowly at first, then accelerates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.theta.plot(color='C0', label='theta')\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Angle (rad)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Angular velocity, `omega`, increases almost linearly at first, as constant force yields almost constant torque.  Then, as the radius decreases, the lever arm decreases, yielding lower torque, but moment of inertia decreases even more, yielding higher angular acceleration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.omega.plot(color='C2', label='omega')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Angular velocity (rad/s)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`y` increases slowly and then accelerates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.y.plot(color='C1', label='y')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Length (m)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`r` decreases slowly, then accelerates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.r.plot(color='C4', label='r')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Radius (m)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/oem.ipynb b/examples/oem.ipynb
new file mode 100644
index 00000000..d9edd314
--- /dev/null
+++ b/examples/oem.ipynb
@@ -0,0 +1,1106 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Title"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Case study.\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure Jupyter so figures appear in the notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Configure Jupyter to display the assigned value after an assignment\n",
+    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
+    "\n",
+    "# import functions from the modsim.py module\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Electric car"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Olin Electric Motorsports](https://www.olinelectricmotorsports.com/) is a club at Olin College that designs and builds electric cars, and participates in the [Formula SAE Electric](https://www.sae.org/attend/student-events/formula-sae-electric) competition.\n",
+    "\n",
+    "The goal of this case study is to use simulation to guide the design of a car intended to accelerate from standing to 100 kph as quickly as possible.  The [world record for this event](https://www.youtube.com/watch?annotation_id=annotation_2297602723&feature=iv&src_vid=I-NCH8ct24U&v=n2XiCYA3C9s), using a car that meets the competition requirements, is 1.513 seconds.\n",
+    "\n",
+    "We'll start with a simple model that takes into account the characteristics of the motor and vehicle:\n",
+    "\n",
+    "* The motor is an [Emrax 228 high voltage axial flux synchronous permanent magnet motor](http://emrax.com/products/emrax-228/); according to the [data sheet](http://emrax.com/wp-content/uploads/2017/01/emrax_228_technical_data_4.5.pdf), its maximum torque is 240 Nm, at 0 rpm.  But maximum torque decreases with motor speed; at 5000 rpm, maximum torque is 216 Nm.\n",
+    "\n",
+    "* The motor is connected to the drive axle with a chain drive with speed ratio 13:60 or 1:4.6; that is, the axle rotates once for each 4.6 rotations of the motor.\n",
+    "\n",
+    "* The radius of the tires is 0.26 meters.\n",
+    "\n",
+    "* The weight of the vehicle, including driver, is 300 kg.\n",
+    "\n",
+    "To start, we will assume no slipping between the tires and the road surface, no air resistance, and no rolling resistance.  Then we will relax these assumptions one at a time.\n",
+    "\n",
+    "* First we'll add drag, assuming that the frontal area of the vehicle is 0.6 square meters, with coefficient of drag 0.6.\n",
+    "\n",
+    "* Next we'll add rolling resistance, assuming a coefficient of 0.2.\n",
+    "\n",
+    "* Finally we'll compute the peak acceleration to see if the \"no slip\" assumption is credible.\n",
+    "\n",
+    "We'll use this model to estimate the potential benefit of possible design improvements, including decreasing drag and rolling resistance, or increasing the speed ratio.\n",
+    "\n",
+    "I'll start by loading the units we need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radian = UNITS.radian\n",
+    "m = UNITS.meter\n",
+    "s = UNITS.second\n",
+    "minute = UNITS.minute\n",
+    "hour = UNITS.hour\n",
+    "km = UNITS.kilometer\n",
+    "kg = UNITS.kilogram\n",
+    "N = UNITS.newton\n",
+    "rpm = UNITS.rpm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And store the parameters in a `Params` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = Params(r_wheel=0.26 * m,\n",
+    "                speed_ratio=13/60,\n",
+    "                C_rr=0.2,\n",
+    "                C_d=0.5,\n",
+    "                area=0.6*m**2,\n",
+    "                rho=1.2*kg/m**3,\n",
+    "                mass=300*kg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`make_system` creates the initial state, `init`, and constructs an `interp1d` object that represents torque as a function of motor speed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(params):\n",
+    "    \"\"\"Make a system object.\n",
+    "    \n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(x=0*m, v=0*m/s)\n",
+    "    \n",
+    "    rpms = [0, 2000, 5000]\n",
+    "    torques = [240, 240, 216]\n",
+    "    interpolate_torque = interpolate(Series(torques, rpms))\n",
+    "    \n",
+    "    return System(params, init=init,\n",
+    "                  interpolate_torque=interpolate_torque,\n",
+    "                  t_end=3*s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Testing `make_system`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Torque and speed\n",
+    "\n",
+    "The relationship between torque and motor speed is taken from the [Emrax 228 data sheet](http://emrax.com/wp-content/uploads/2017/01/emrax_228_technical_data_4.5.pdf).  The following functions reproduce the red dotted line that represents peak torque, which can only be sustained for a few seconds before the motor overheats."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_torque(omega, system):\n",
+    "    \"\"\"Maximum peak torque as a function of motor speed.\n",
+    "    \n",
+    "    omega: motor speed in radian/s\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: torque in Nm\n",
+    "    \"\"\"\n",
+    "    factor = (1 * radian / s).to(rpm)\n",
+    "    x = magnitude(omega * factor)\n",
+    "    return system.interpolate_torque(x) * N * m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "compute_torque(0*radian/s, system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "omega = (5000 * rpm).to(radian/s)\n",
+    "compute_torque(omega, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the whole curve."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xs = linspace(0, 525, 21) * radian / s\n",
+    "taus = [compute_torque(x, system) for x in xs]\n",
+    "plot(xs, taus)\n",
+    "decorate(xlabel='Motor speed (rpm)',\n",
+    "         ylabel='Available torque (N m)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simulation\n",
+    "\n",
+    "Here's the slope function that computes the maximum possible acceleration of the car as a function of it current speed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Computes the derivatives of the state variables.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time\n",
+    "    system: System object \n",
+    "    \n",
+    "    returns: sequence of derivatives\n",
+    "    \"\"\"\n",
+    "    x, v = state\n",
+    "    r_wheel, speed_ratio = system.r_wheel, system.speed_ratio\n",
+    "    mass = system.mass\n",
+    "    \n",
+    "    # use velocity, v, to compute angular velocity of the wheel\n",
+    "    omega2 = v / r_wheel\n",
+    "    \n",
+    "    # use the speed ratio to compute motor speed\n",
+    "    omega1 = omega2 / speed_ratio\n",
+    "    \n",
+    "    # look up motor speed to get maximum torque at the motor\n",
+    "    tau1 = compute_torque(omega1, system)\n",
+    "    \n",
+    "    # compute the corresponding torque at the axle\n",
+    "    tau2 = tau1 / speed_ratio\n",
+    "    \n",
+    "    # compute the force of the wheel on the ground\n",
+    "    F = tau2 / r_wheel\n",
+    "    \n",
+    "    # compute acceleration\n",
+    "    a = F/mass\n",
+    "\n",
+    "    return v, a      "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Testing `slope_func` at linear velocity 10 m/s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_state = State(x=0*m, v=10*m/s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(test_state, 0*s, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_ode_solver(system, slope_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And look at the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After 3 seconds, the vehicle could be at 40 meters per second, in theory, which is 144 kph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_final = get_last_value(results.v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_final.to(km/hour)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting `x`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_position(results):\n",
+    "    plot(results.x, label='x')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Position (m)')\n",
+    "    \n",
+    "plot_position(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting `v`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_velocity(results):\n",
+    "    plot(results.v, label='v')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Velocity (m/s)')\n",
+    "    \n",
+    "plot_velocity(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Stopping at 100 kph\n",
+    "\n",
+    "We'll use an event function to stop the simulation when we reach 100 kph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(state, t, system):\n",
+    "    \"\"\"Stops when we get to 100 km/hour.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time\n",
+    "    system: System object \n",
+    "    \n",
+    "    returns: difference from 100 km/hour\n",
+    "    \"\"\"\n",
+    "    x, v = state\n",
+    "    \n",
+    "    # convert to km/hour\n",
+    "    factor = (1 * m/s).to(km/hour)\n",
+    "    v = magnitude(v * factor)\n",
+    "    \n",
+    "    return v - 100     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subplot(2, 1, 1)\n",
+    "plot_position(results)\n",
+    "\n",
+    "subplot(2, 1, 2)\n",
+    "plot_velocity(results)\n",
+    "\n",
+    "savefig('figs/chap11-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "According to this model, we should be able to make this run in just over 2 seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_final = get_last_label(results) * s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At the end of the run, the car has gone about 28 meters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state = results.last_row()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we send the final state back to the slope function, we can see that the final acceleration is about 13 $m/s^2$, which is about 1.3 times the acceleration of gravity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v, a = slope_func(state, 0, system)\n",
+    "v.to(km/hour)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g = 9.8 * m/s**2\n",
+    "(a / g).to(UNITS.dimensionless)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's not easy for a vehicle to accelerate faster than `g`, because that implies a coefficient of friction between the wheels and the road surface that's greater than 1.  But racing tires on dry asphalt can do that; the OEM team at Olin has tested their tires and found a peak coefficient near 1.5.\n",
+    "\n",
+    "So it's possible that our no slip assumption is valid, but only under ideal conditions, where weight is distributed equally on four tires, and all tires are driving."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** How much time do we lose because maximum torque decreases as motor speed increases?  Run the model again with no drop off in torque and see how much time it saves."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Drag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section we'll see how much effect drag has on the results.\n",
+    "\n",
+    "Here's a function to compute drag force, as we saw in Chapter 21."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drag_force(v, system):\n",
+    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
+    "    \n",
+    "    v: velocity\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: drag force\n",
+    "    \"\"\"\n",
+    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
+    "    \n",
+    "    f_drag = -np.sign(v) * rho * v**2 * C_d * area / 2\n",
+    "    return f_drag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can test it with a velocity of 20 m/s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drag_force(20 * m/s, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the resulting acceleration of the vehicle due to drag.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drag_force(20 * m/s, system) / system.mass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the effect of drag is not huge, compared to the acceleration we computed in the previous section, but it is not negligible.\n",
+    "\n",
+    "Here's a modified slope function that takes drag into account."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func2(state, t, system):\n",
+    "    \"\"\"Computes the derivatives of the state variables.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time\n",
+    "    system: System object \n",
+    "    \n",
+    "    returns: sequence of derivatives\n",
+    "    \"\"\"\n",
+    "    x, v = state\n",
+    "    r_wheel, speed_ratio = system.r_wheel, system.speed_ratio\n",
+    "    mass = system.mass\n",
+    "    \n",
+    "    omega2 = v / r_wheel * radian\n",
+    "    omega1 = omega2 / speed_ratio\n",
+    "    tau1 = compute_torque(omega1, system)\n",
+    "    tau2 = tau1 / speed_ratio\n",
+    "    F = tau2 / r_wheel\n",
+    "    a_motor = F / mass\n",
+    "    a_drag = drag_force(v, system) / mass\n",
+    "    \n",
+    "    a = a_motor + a_drag\n",
+    "    return v, a "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the next run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results2, details = run_ode_solver(system, slope_func2, events=event_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The time to reach 100 kph is a bit higher."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_final2 = get_last_label(results2) * s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But the total effect of drag is only about 2/100 seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_final2 - t_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's not huge, which suggests we might not be able to save much time by decreasing the frontal area, or coefficient of drag, of the car."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rolling resistance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we'll consider [rolling resistance](https://en.wikipedia.org/wiki/Rolling_resistance), which the force that resists the motion of the car as it rolls on tires.  The cofficient of rolling resistance, `C_rr`, is the ratio of rolling resistance to the normal force between the car and the ground (in that way it is similar to a coefficient of friction).\n",
+    "\n",
+    "The following function computes rolling resistance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.set(unit_rr = 1 * N / kg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rolling_resistance(system):\n",
+    "    \"\"\"Computes force due to rolling resistance.\n",
+    "    \n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: force\n",
+    "    \"\"\"\n",
+    "    return -system.C_rr * system.mass * system.unit_rr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The acceleration due to rolling resistance is 0.2 (it is not a coincidence that it equals `C_rr`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rolling_resistance(system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rolling_resistance(system) / system.mass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a modified slope function that includes drag and rolling resistance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func3(state, t, system):\n",
+    "    \"\"\"Computes the derivatives of the state variables.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time\n",
+    "    system: System object \n",
+    "    \n",
+    "    returns: sequence of derivatives\n",
+    "    \"\"\"\n",
+    "    x, v = state\n",
+    "    r_wheel, speed_ratio = system.r_wheel, system.speed_ratio\n",
+    "    mass = system.mass\n",
+    "    \n",
+    "    omega2 = v / r_wheel * radian\n",
+    "    omega1 = omega2 / speed_ratio\n",
+    "    tau1 = compute_torque(omega1, system)\n",
+    "    tau2 = tau1 / speed_ratio\n",
+    "    F = tau2 / r_wheel\n",
+    "    a_motor = F / mass\n",
+    "    a_drag = drag_force(v, system) / mass\n",
+    "    a_roll = rolling_resistance(system) / mass\n",
+    "    \n",
+    "    a = a_motor + a_drag + a_roll\n",
+    "    return v, a "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results3, details = run_ode_solver(system, slope_func3, events=event_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final time is a little higher, but the total cost of rolling resistance is only 3/100 seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_final3 = get_last_label(results3) * s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_final3 - t_final2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So, again, there is probably not much to be gained by decreasing rolling resistance.\n",
+    "\n",
+    "In fact, it is hard to decrease rolling resistance without also decreasing traction, so that might not help at all."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optimal gear ratio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The gear ratio 13:60 is intended to maximize the acceleration of the car without causing the tires to slip.  In this section, we'll consider other gear ratios and estimate their effects on acceleration and time to reach 100 kph.\n",
+    "\n",
+    "Here's a function that takes a speed ratio as a parameter and returns time to reach 100 kph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def time_to_speed(speed_ratio, params):\n",
+    "    \"\"\"Computes times to reach 100 kph.\n",
+    "    \n",
+    "    speed_ratio: ratio of wheel speed to motor speed\n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: time to reach 100 kph, in seconds\n",
+    "    \"\"\"\n",
+    "    params = Params(params, speed_ratio=speed_ratio)\n",
+    "    system = make_system(params)\n",
+    "    system.set(unit_rr = 1 * N / kg)\n",
+    "    \n",
+    "    results, details = run_ode_solver(system, slope_func3, events=event_func)\n",
+    "    t_final = get_last_label(results)\n",
+    "    a_initial = slope_func(system.init, 0, system)\n",
+    "    return t_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can test it with the default ratio:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_to_speed(13/60, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can try it with different numbers of teeth on the motor gear (assuming that the axle gear has 60 teeth):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for teeth in linrange(8, 18):\n",
+    "    print(teeth, time_to_speed(teeth/60, params))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wow!  The speed ratio has a big effect on the results.  At first glance, it looks like we could break the world record (1.513 seconds) just by decreasing the number of teeth.\n",
+    "\n",
+    "But before we try it, let's see what effect that has on peak acceleration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def initial_acceleration(speed_ratio, params):\n",
+    "    \"\"\"Maximum acceleration as a function of speed ratio.\n",
+    "    \n",
+    "    speed_ratio: ratio of wheel speed to motor speed\n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: peak acceleration, in m/s^2\n",
+    "    \"\"\"\n",
+    "    params = Params(params, speed_ratio=speed_ratio)\n",
+    "    system = make_system(params)\n",
+    "    a_initial = slope_func(system.init, 0, system)[1] * m/s**2\n",
+    "    return a_initial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for teeth in linrange(8, 18):\n",
+    "    print(teeth, initial_acceleration(teeth/60, params))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we decrease the speed ratio, the peak acceleration increases.  With 8 teeth on the motor gear, we could break the world record, but only if we can accelerate at 2.3 times the acceleration of gravity, which is impossible without very sticky ties and a vehicle that generates a lot of downforce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "23.07 / 9.8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results suggest that the most promising way to improve the performance of the car (for this event) would be to improve traction."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/orbit.ipynb b/examples/orbit.ipynb
new file mode 100644
index 00000000..d2eae086
--- /dev/null
+++ b/examples/orbit.ipynb
@@ -0,0 +1,377 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Orbiting the Sun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In a previous example, we modeled the interaction between the Earth and the Sun, simulating what would happen if the Earth stopped in its orbit and fell straight into the Sun.\n",
+    "\n",
+    "Now let's extend the model to two dimensions and simulate one revolution of the Earth around the Sun, that is, one year.\n",
+    "\n",
+    "At perihelion, the distance from the Earth to the Sun is 147.09 million km and its velocity is 30,290 m/s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r_0 = 147.09e9     # initial distance m\n",
+    "v_0 = 30.29e3      # initial velocity m/s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the other constants we'll need, all with about 4 significant digits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = 6.6743e-11     # gravitational constant N / kg**2 * m**2\n",
+    "m1 = 1.989e30      # mass of the Sun kg\n",
+    "m2 = 5.972e24      # mass of the Earth kg\n",
+    "t_end = 3.154e7    # one year in seconds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Put the initial conditions in a `State` object with variables `x`, `y`, `vx`, and `vy`.\n",
+    "Create a `System` object with variables `init` and `t_end`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a function called `universal_gravitation` that takes a `State` and a `System` and returns the gravitational force of the Sun on the Earth as a `Vector`.\n",
+    "\n",
+    "Test your function with the initial conditions; the result should be a Vector with approximate components:\n",
+    "\n",
+    "```\n",
+    "x   -3.66e+22\n",
+    "y   0\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a slope function that takes a timestamp, a `State`, and a `System` and computes the derivatives of the state variables.\n",
+    "\n",
+    "Test your function with the initial conditions.  The result should be a sequence of four values, approximately\n",
+    "\n",
+    "```\n",
+    "0.0, -30290.0, -0.006, 0.0\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Use `run_solve_ivp` to run the simulation.\n",
+    "Save the return values in variables called `results` and `details`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use the following function to plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.pyplot import plot\n",
+    "\n",
+    "def plot_trajectory(results):\n",
+    "    x = results.x / 1e9\n",
+    "    y = results.y / 1e9\n",
+    "\n",
+    "    make_series(x, y).plot(label='orbit')\n",
+    "    plot(0, 0, 'yo')\n",
+    "\n",
+    "    decorate(xlabel='x distance (million km)',\n",
+    "             ylabel='y distance (million km)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_trajectory(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You will probably see that the earth does not end up back where it started, as we expect it to after one year.\n",
+    "The following cells compute the error, which is the distance between the initial and final positions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error = results.iloc[-1] - system.init\n",
+    "error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "offset = Vector(error.x, error.y)\n",
+    "vector_mag(offset) / 1e9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The problem is that the algorithm used by `run_solve_ivp` does not work very well with systems like this.\n",
+    "There are two ways we can improve it.\n",
+    "\n",
+    "`run_solve_ivp` takes a keyword argument, `rtol`, that specifies the \"relative tolerance\", which determines the size of the time steps in the simulation.  Lower values of `rtol` require smaller steps, which yield more accurate results.\n",
+    "The default value of `rtol` is `1e-3`.  \n",
+    "\n",
+    "**Exercise:** Try running the simulation again with smaller values, like `1e-4` or `1e-5`, and see what effect it has on the magnitude of `offset`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The other way to improve the results is to use a different algorithm.  `run_solve_ivp` takes a keyword argument, `method`, that specifies which algorithm it should use.  The default is `RK45`, which is a good, general-purpose algorithm, but not particularly good for this system.  One of the other options is `RK23`, which is usually less accurate than `RK45` (with the same step size), but for this system it turns out to be unreasonably good, [for reasons I don't entirely understand](https://mathoverflow.net/questions/314940/celestial-mechanics-and-runge-kutta-methods).\n",
+    "Yet another option is 'DOP853', which is particularly good when `rtol` is small.\n",
+    "\n",
+    "**Exercise:** Run the simulation with one of these methods and see what effect it has on the results.  To get a sense of how efficient the methods are, display `details.nfev`, which is the number of times `run_solve_ivp` called the slope function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "details.nfev"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Animation\n",
+    "\n",
+    "You can use the following draw function to animate the results, if you want to see what the orbit looks like (not in real time)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xlim = results.x.min(), results.x.max()\n",
+    "ylim = results.y.min(), results.y.max()\n",
+    "\n",
+    "def draw_func(t, state):\n",
+    "    x, y, vx, vy = state\n",
+    "    plot(x, y, 'b.')\n",
+    "    plot(0, 0, 'yo')\n",
+    "    decorate(xlabel='x distance (million km)',\n",
+    "             ylabel='y distance (million km)',\n",
+    "             xlim=xlim,\n",
+    "             ylim=ylim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# animate(results, draw_func)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/plague.ipynb b/examples/plague.ipynb
new file mode 100644
index 00000000..a2b391c5
--- /dev/null
+++ b/examples/plague.ipynb
@@ -0,0 +1,623 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "combined-semiconductor",
+   "metadata": {},
+   "source": [
+    "# The Freshman Plague"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "imported-table",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "electoral-turkey",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "formal-context",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import basename, exists\n",
+    "\n",
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/master/modsim.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "progressive-typing",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "undefined-miller",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'chap11.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "growing-sperm",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import code from previous notebooks\n",
+    "\n",
+    "from chap11 import make_system\n",
+    "from chap11 import update_func\n",
+    "from chap11 import run_simulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "[Click here to run this case study on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/plague.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "complex-renewal",
+   "metadata": {},
+   "source": [
+    "This case study picks up where Chapter 12 leaves off."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "recent-cooper",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_immunization(system, fraction):\n",
+    "    system.init.S -= fraction\n",
+    "    system.init.R += fraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "found-learning",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc = 3             # time between contacts in days \n",
+    "tr = 4             # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "system = make_system(beta, gamma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "synthetic-element",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_total_infected(results, system):\n",
+    "    s_0 = results.S[0]\n",
+    "    s_end = results.S[system.t_end]\n",
+    "    return s_0 - s_end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "changed-capability",
+   "metadata": {},
+   "source": [
+    "## Hand washing\n",
+    "\n",
+    "Suppose you are the Dean of Student Life, and you have a budget of just \\$1200 to combat the Freshman Plague. You have two options for spending this money:\n",
+    "\n",
+    "1.  You can pay for vaccinations, at a rate of \\$100 per dose.\n",
+    "\n",
+    "2.  You can spend money on a campaign to remind students to wash hands\n",
+    "    frequently.\n",
+    "\n",
+    "We have already seen how we can model the effect of vaccination. Now\n",
+    "let's think about the hand-washing campaign. We'll have to answer two\n",
+    "questions:\n",
+    "\n",
+    "1.  How should we incorporate the effect of hand washing in the model?\n",
+    "\n",
+    "2.  How should we quantify the effect of the money we spend on a\n",
+    "    hand-washing campaign?\n",
+    "\n",
+    "For the sake of simplicity, let's assume that we have data from a\n",
+    "similar campaign at another school showing that a well-funded campaign\n",
+    "can change student behavior enough to reduce the infection rate by 20%.\n",
+    "\n",
+    "In terms of the model, hand washing has the effect of reducing `beta`.\n",
+    "That's not the only way we could incorporate the effect, but it seems\n",
+    "reasonable and it's easy to implement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "alpine-guest",
+   "metadata": {},
+   "source": [
+    "Now we have to model the relationship between the money we spend and the\n",
+    "effectiveness of the campaign. Again, let's suppose we have data from\n",
+    "another school that suggests:\n",
+    "\n",
+    "-   If we spend \\$500 on posters, materials, and staff time, we can\n",
+    "    change student behavior in a way that decreases the effective value of `beta` by 10%.\n",
+    "\n",
+    "-   If we spend \\$1000, the total decrease in `beta` is almost 20%.\n",
+    "\n",
+    "-   Above \\$1000, additional spending has little additional benefit."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "agricultural-trinidad",
+   "metadata": {},
+   "source": [
+    "## Logistic function\n",
+    "\n",
+    "To model the effect of a hand-washing campaign, I'll use a [generalized logistic function](https://en.wikipedia.org/wiki/Generalised_logistic_function) (GLF), which is a convenient function for modeling curves that have a generally sigmoid shape.  The parameters of the GLF correspond to various features of the curve in a way that makes it easy to find a function that has the shape you want, based on data or background information about the scenario."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "blond-armstrong",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import exp\n",
+    "\n",
+    "def logistic(x, A=0, B=1, C=1, M=0, K=1, Q=1, nu=1):\n",
+    "    \"\"\"Computes the generalize logistic function.\n",
+    "    \n",
+    "    A: controls the lower bound\n",
+    "    B: controls the steepness of the transition \n",
+    "    C: not all that useful, AFAIK\n",
+    "    M: controls the location of the transition\n",
+    "    K: controls the upper bound\n",
+    "    Q: shift the transition left or right\n",
+    "    nu: affects the symmetry of the transition\n",
+    "    \n",
+    "    returns: float or array\n",
+    "    \"\"\"\n",
+    "    exponent = -B * (x - M)\n",
+    "    denom = C + Q * exp(exponent)\n",
+    "    return A + (K-A) / denom ** (1/nu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "seven-budget",
+   "metadata": {},
+   "source": [
+    "The following array represents the range of possible spending."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "disturbed-amount",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spending = linspace(0, 1200, 21)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "supreme-nutrition",
+   "metadata": {},
+   "source": [
+    "`compute_factor` computes the reduction in `beta` for a given level of campaign spending.\n",
+    "\n",
+    "`M` is chosen so the transition happens around \\$500.\n",
+    "\n",
+    "`K` is the maximum reduction in `beta`, 20%.\n",
+    "\n",
+    "`B` is chosen by trial and error to yield a curve that seems feasible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "otherwise-answer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_factor(spending):\n",
+    "    \"\"\"Reduction factor as a function of spending.\n",
+    "    \n",
+    "    spending: dollars from 0 to 1200\n",
+    "    \n",
+    "    returns: fractional reduction in beta\n",
+    "    \"\"\"\n",
+    "    return logistic(spending, M=500, K=0.2, B=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "expected-venture",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "seventh-strike",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "percent_reduction = compute_factor(spending) * 100\n",
+    "\n",
+    "make_series(spending, percent_reduction).plot()\n",
+    "\n",
+    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
+    "         ylabel='Percent reduction in infection rate',\n",
+    "         title='Effect of hand washing on infection rate')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "legal-michigan",
+   "metadata": {},
+   "source": [
+    "The result is the following function, which\n",
+    "takes spending as a parameter and returns `factor`, which is the factor\n",
+    "by which `beta` is reduced:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "obvious-congress",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_factor(spending):\n",
+    "    return logistic(spending, M=500, K=0.2, B=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sunset-investing",
+   "metadata": {},
+   "source": [
+    "I use `compute_factor` to write `add_hand_washing`, which takes a\n",
+    "`System` object and a budget, and modifies `system.beta` to model the\n",
+    "effect of hand washing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "polish-multiple",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_hand_washing(system, spending):\n",
+    "    factor = compute_factor(spending)\n",
+    "    system.beta *= (1 - factor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "worthy-fellowship",
+   "metadata": {},
+   "source": [
+    "Now we can sweep a range of values for `spending` and use the simulation\n",
+    "to compute the effect:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "statistical-garden",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_hand_washing(spending_array):\n",
+    "    sweep = SweepSeries()\n",
+    "    \n",
+    "    for spending in spending_array:\n",
+    "        system = make_system(beta, gamma)\n",
+    "        add_hand_washing(system, spending)\n",
+    "        results = run_simulation(system, update_func)\n",
+    "        sweep[spending] = calc_total_infected(results, system)\n",
+    "        \n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "clinical-surge",
+   "metadata": {},
+   "source": [
+    "Here's how we run it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "joined-graduation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import linspace\n",
+    "\n",
+    "spending_array = linspace(0, 1200, 20)\n",
+    "infected_sweep2 = sweep_hand_washing(spending_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "designing-smile",
+   "metadata": {},
+   "source": [
+    "The following figure shows the result. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "cheap-decision",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infected_sweep2.plot()\n",
+    "\n",
+    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
+    "         ylabel='Total fraction infected',\n",
+    "         title='Effect of hand washing on total infections')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "selective-right",
+   "metadata": {},
+   "source": [
+    "Below \\$200, the campaign has little effect. \n",
+    "\n",
+    "At \\$800 it has a substantial effect, reducing total infections from more than 45% to about 20%. \n",
+    "\n",
+    "Above \\$800, the additional benefit is small."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lucky-boulder",
+   "metadata": {},
+   "source": [
+    "## Optimization\n",
+    "\n",
+    "Let's put it all together. With a fixed budget of \\$1200, we have to\n",
+    "decide how many doses of vaccine to buy and how much to spend on the\n",
+    "hand-washing campaign.\n",
+    "\n",
+    "Here are the parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "surrounded-copying",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_students = 90\n",
+    "budget = 1200\n",
+    "price_per_dose = 100\n",
+    "max_doses = int(budget / price_per_dose)\n",
+    "max_doses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "expired-conditioning",
+   "metadata": {},
+   "source": [
+    "The fraction `budget/price_per_dose` might not be an integer. `int` is a\n",
+    "built-in function that converts numbers to integers, rounding down.\n",
+    "\n",
+    "We'll sweep the range of possible doses:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "shaped-utility",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dose_array = linrange(max_doses)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "occupational-reply",
+   "metadata": {},
+   "source": [
+    "In this example we call `linrange` with only one argument; it returns a NumPy array with the integers from 0 to `max_doses`, including both.\n",
+    "\n",
+    "Then we run the simulation for each element of `dose_array`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "recognized-release",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_doses(dose_array):\n",
+    "    sweep = SweepSeries()\n",
+    "    \n",
+    "    for doses in dose_array:\n",
+    "        fraction = doses / num_students\n",
+    "        spending = budget - doses * price_per_dose\n",
+    "        \n",
+    "        system = make_system(beta, gamma)\n",
+    "        add_immunization(system, fraction)\n",
+    "        add_hand_washing(system, spending)\n",
+    "        \n",
+    "        results = run_simulation(system, update_func)\n",
+    "        sweep[doses] = calc_total_infected(results, system)\n",
+    "\n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cardiac-mitchell",
+   "metadata": {},
+   "source": [
+    "For each number of doses, we compute the fraction of students we can\n",
+    "immunize, `fraction` and the remaining budget we can spend on the\n",
+    "campaign, `spending`. Then we run the simulation with those quantities\n",
+    "and store the number of infections.\n",
+    "\n",
+    "The following figure shows the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "worth-mounting",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infected_sweep3 = sweep_doses(dose_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "adjusted-highlight",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infected_sweep3.plot()\n",
+    "\n",
+    "decorate(xlabel='Doses of vaccine',\n",
+    "         ylabel='Total fraction infected',\n",
+    "         title='Total infections vs. doses')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dynamic-easter",
+   "metadata": {},
+   "source": [
+    "If we buy no doses of vaccine and spend the entire budget on the campaign, the fraction infected is around 19%. At 4 doses, we have \\$800 left for the campaign, and this is the optimal point that minimizes the number of students who get sick.\n",
+    "\n",
+    "As we increase the number of doses, we have to cut campaign spending,\n",
+    "which turns out to make things worse. But interestingly, when we get\n",
+    "above 10 doses, the effect of herd immunity starts to kick in, and the\n",
+    "number of sick students goes down again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "timely-bottom",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose the price of the vaccine drops to $50 per dose.  How does that affect the optimal allocation of the spending?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "swiss-preview",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/queue.ipynb b/examples/queue.ipynb
new file mode 100644
index 00000000..eed8b76c
--- /dev/null
+++ b/examples/queue.ipynb
@@ -0,0 +1,592 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# One Queue or Two"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook presents a case study from *Modeling and Simulation in Python*.  It explores a question related to queueing theory, which is the study of systems that involve waiting in lines, also known as \"queues\".\n",
+    "\n",
+    "Suppose you are designing the checkout area for a new store.  There is room for two checkout counters and a waiting area for customers.  You can make two lines, one for each counter, or one line that serves both counters.\n",
+    "\n",
+    "In theory, you might expect a single line to be better, but it has some practical drawbacks: in order to maintain a single line, you would have to install rope barriers, and customers might be put off by what seems to be a longer line, even if it moves faster.\n",
+    "\n",
+    "So you'd like to check whether the single line is really better and by how much.  Simulation can help answer this question."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we did in the bikeshare model, we'll assume that a customer is equally likely to arrive during any timestep.  I'll denote this probability using the Greek letter lambda, $\\lambda$, or the variable name `lam`.  The value of $\\lambda$ probably varies from day to day, so we'll have to consider a range of possibilities.\n",
+    "\n",
+    "Based on data from other stores, you know that it takes 5 minutes for a customer to check out, on average.  But checkout times are highly variable: most customers take less than 5 minutes, but some take substantially more.  A simple way to model this variability is to assume that when a customer is checking out, they have the same probability of finishing up during each time step.  I'll denote this probability using the Greek letter mu, $\\mu$, or the variable name `mu`.\n",
+    "\n",
+    "If we choose $\\mu=1/5$, the average number of time steps for each checkout will be 5 minutes, which is consistent with the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## One server, one queue\n",
+    "\n",
+    "Write a function called `make_system` that takes `lam` and `mu` as parameters and returns a `System` object with variables `lam`, `mu`, and `duration`.  Set `duration`, which is the number of time steps to simulate, to 10 hours, expressed in minutes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test this function by creating a `System` object with `lam=1/8` and `mu=1/5`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write an update function that takes as parameters `x`, which is the total number of customer in the store, including the one checking out; `t`, which is the number of minutes that have elapsed in the simulation, and `system`, which is a `System` object.\n",
+    "\n",
+    "If there's a customer checking out, it should use `flip` to decide whether they are done.  And it should use `flip` to decide if a new customer has arrived.\n",
+    "\n",
+    "It should return the total number of customers at the end of the time step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your function by calling it with `x=1`, `t=0`, and the `System` object you created.  If you run it a few times, you should see different results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run the simulation.  Here's a version of `run_simulation` that creates a `TimeSeries` with the total number of customers in the store, including the one checking out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Simulate a queueing system.\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function object\n",
+    "    \"\"\"\n",
+    "    x = 0\n",
+    "    results = TimeSeries()\n",
+    "    results[0] = x\n",
+    "    \n",
+    "    for t in linrange(0, system.duration):\n",
+    "        x = update_func(x, t, system)\n",
+    "        results[t+1] = x\n",
+    "\n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Call `run_simulation` with your update function and plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the simulation, we can compute `L`, which is the average number of customers in the system, and `W`, which is the average time customers spend in the store.  `L` and `W` are related by Little's Law:\n",
+    "\n",
+    "$L = \\lambda W$\n",
+    "\n",
+    "Where $\\lambda$ is the arrival rate.  Here's a function that computes them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_metrics(results, system):\n",
+    "    \"\"\"Compute average number of customers and wait time.\n",
+    "    \n",
+    "    results: TimeSeries of queue lengths\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: L, W\n",
+    "    \"\"\"\n",
+    "    L = results.mean()\n",
+    "    W = L / system.lam\n",
+    "    return L, W"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Call `compute_metrics` with the results from your simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Parameter sweep\n",
+    "\n",
+    "Since we don't know the actual value of $\\lambda$, we can sweep through a range of possibilities, from 10% to 80% of the completion rate, $\\mu$.  (If customers arrive faster than the completion rate, the queue grows without bound.  In that case the metrics `L` and `W` just depend on how long the store is open.)\n",
+    "\n",
+    "Create an array of values for `lam`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a function that takes an array of values for `lam`, a single value for `mu`, and an update function.\n",
+    "\n",
+    "For each value of `lam`, it should run a simulation, compute `L` and `W`, and store the value of `W` in a `SweepSeries`.\n",
+    "\n",
+    "It should return the `SweepSeries`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Call your function to generate a `SweepSeries`, and plot it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we imagine that this range of values represents arrival rates on different days, we can use the average value of `W`, for a range of values of `lam`, to compare different queueing strategies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis\n",
+    "\n",
+    "The model I chose for this system is a common model in queueing theory, in part because many of its properties can be derived analytically.\n",
+    "\n",
+    "In particular, we can derive the average time in the store as a function of $\\mu$ and $\\lambda$:\n",
+    "\n",
+    "$W = 1 / (\\mu - \\lambda)$\n",
+    "\n",
+    "The following function plots the theoretical value of $W$ as a function of $\\lambda$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_W(lam_array, mu):\n",
+    "    \"\"\"Plot the theoretical mean wait time.\n",
+    "    \n",
+    "    lam_array: array of values for `lam`\n",
+    "    mu: probability of finishing a checkout\n",
+    "    \"\"\"\n",
+    "    W_array = 1 / (mu - lam_array)\n",
+    "    W_series = make_series(lam_array, W_array)\n",
+    "    W_series.plot(style='-', label='analysis')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use this function to plot the theoretical results, then plot your simulation results again on the same graph.  How do they compare?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiple servers\n",
+    "\n",
+    "Now let's try the other two queueing strategies:\n",
+    "\n",
+    "1.  One queue with two checkout counters.\n",
+    "2.  Two queues, one for each counter.\n",
+    "\n",
+    "The following figure shows the three scenarios:\n",
+    "\n",
+    "![](https://github.com/AllenDowney/ModSim/raw/main/figs/queue.png)\n",
+    "\n",
+    "Write an update function for one queue with two servers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use this update function to simulate the system, plot the results, and print the metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we have two checkout counters now, we can consider values for $\\lambda$ that exceed $\\mu$.\n",
+    "\n",
+    "Create a new array of values for `lam` from 10% to 160% of `mu`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use your sweep function to simulate the two server, one queue scenario with a range of values for `lam`.\n",
+    "\n",
+    "Plot the results and print the average value of `W` across all values of `lam`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiple queues\n",
+    "\n",
+    "To simulate the scenario with two separate queues, we need two state variables to keep track of customers in each queue.\n",
+    "\n",
+    "Write an update function that takes `x1`, `x2`, `t`, and `system` as parameters and returns `x1` and `x2` as return values.  f you are not sure how to return more than one return value, see `compute_metrics`.\n",
+    "\n",
+    "When a customer arrives, which queue do they join?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a version of `run_simulation` that works with this update function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your functions by running a simulation with a single value of `lam`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sweep a range of values for `lam`, plot the results, and print the average wait time across all values of `lam`.\n",
+    "\n",
+    "How do the results compare to the scenario with two servers and one queue."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/salmon.ipynb b/examples/salmon.ipynb
new file mode 100644
index 00000000..4d59bbc1
--- /dev/null
+++ b/examples/salmon.ipynb
@@ -0,0 +1,695 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Salmon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Can we predict salmon populations?\n",
+    "\n",
+    "Each year the [U.S. Atlantic Salmon Assessment Committee](https://www.nefsc.noaa.gov/USASAC/Reports/USASAC2018-Report-30-2017-Activities.pdf) reports estimates of salmon populations in oceans and rivers in the northeastern United States.  The reports are useful for monitoring changes in these populations, but they generally do not include predictions.\n",
+    "\n",
+    "The goal of this case study is to model year-to-year changes in population, evaluate how predictable these changes are, and estimate the probability that a particular population will increase or decrease in the next 10 years.\n",
+    "\n",
+    "As an example, I'll use data from page 18 of the 2017 report, which provides population estimates for the Narraguagus and Sheepscot Rivers in Maine.\n",
+    "\n",
+    "![USASAC_Report_2017_Page18](https://github.com/AllenDowney/ModSim/raw/main/data/USASAC_Report_2017_Page18.png)\n",
+    "\n",
+    "There are tools for extracting data from a PDF document automatically, but for this example I will keep it simple and type it in.\n",
+    "\n",
+    "Here are the population estimates for the Narraguagus River:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pops = [2749, 2845, 4247, 1843, 2562, 1774, 1201, 1284, 1287, \n",
+    "        2339, 1177, 962, 1176, 2149, 1404, 969, 1237, 1615, 1201]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get this data into a Pandas Series, I'll also make a range of years to use as an index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "years = linrange(1997, 2015)\n",
+    "years"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pop_series = TimeSeries(pops, index=years)\n",
+    "pop_series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_population(series):\n",
+    "    series.plot(label='Estimated population')\n",
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='Population estimate', \n",
+    "             title='Narraguacus River',\n",
+    "             ylim=[0, 5000])\n",
+    "    \n",
+    "plot_population(pop_series)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modeling changes\n",
+    "\n",
+    "To see how the population changes from year-to-year, I'll use `diff` to compute the absolute difference between each year and the next.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "abs_diffs = pop_series.diff()\n",
+    "abs_diffs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can compute relative differences by dividing by the original series elementwise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rel_diffs = abs_diffs / pop_series\n",
+    "rel_diffs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These relative differences are observed annual net growth rates.  So let's drop the `0` and save them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rates = rel_diffs.dropna()\n",
+    "rates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A simple way to model this system is to draw a random value from this series of observed rates each year.  We can use the NumPy function `choice` to make a random choice from a series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.choice(rates)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "Now we can simulate the system by drawing random growth rates from the series of observed rates.\n",
+    "\n",
+    "I'll start the simulation in 2015."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_0 = 2015\n",
+    "p_0 = pop_series[t_0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I'll create a `System` object with variables `t_0`, `p_0`, `rates`, and `duration=10` years. \n",
+    "\n",
+    "The series of observed rates is one big parameter of the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = System(t_0=t_0,\n",
+    "                p_0=p_0,\n",
+    "                duration=10,\n",
+    "                rates=rates)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write an update functon that takes as parameters `pop`, `t`, and `system`.\n",
+    "It should choose a random growth rate, compute the change in population, and return the new population."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your update function and run it a few times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "update_func1(p_0, t_0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a version of `run_simulation` that stores the results in a `TimeSeries` and returns it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Simulate a queueing system.\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function object\n",
+    "    \"\"\"\n",
+    "    t_0 = system.t_0\n",
+    "    t_end = t_0 + system.duration\n",
+    "    \n",
+    "    results = TimeSeries()\n",
+    "    results[t_0] = system.p_0\n",
+    "    \n",
+    "    for t in linrange(t_0, t_end):\n",
+    "        results[t+1] = update_func(results[t], t, system)\n",
+    "\n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use `run_simulation` to run generate a prediction for the next 10 years.\n",
+    "\n",
+    "The plot your prediction along with the original data.  Your prediction should pick up where the data leave off."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get a sense of how much the results vary, we can run the model several times and plot all of the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_many_simulations(system, update_func, iters):\n",
+    "    \"\"\"Runs simulations and plots the results.\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function object\n",
+    "    iters: number of simulations to run\n",
+    "    \"\"\"\n",
+    "    for i in range(iters):\n",
+    "        results = run_simulation(system, update_func)\n",
+    "        results.plot(color='gray', label='_nolegend', \n",
+    "                     linewidth=1, alpha=0.3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plot option `alpha=0.1` makes the lines semi-transparent, so they are darker where they overlap.\n",
+    "\n",
+    "Run `plot_many_simulations` with your update function and `iters=30`.  Also plot the original data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are highly variable: according to this model, the population might continue to decline over the next 10 years, or it might recover and grow rapidly!\n",
+    "\n",
+    "It's hard to say how seriously we should take this model.  There are many factors that influence salmon populations that are not included in the model.  For example, if the population starts to grow quickly, it might be limited by resource limits, predators, or fishing.  If the population starts to fall, humans might restrict fishing and stock the river with farmed fish.\n",
+    "\n",
+    "So these results should probably not be considered useful predictions.  However, there might be something useful we can do, which is to estimate the probability that the population will increase or decrease in the next 10 years.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distribution of net changes\n",
+    "\n",
+    "To describe the distribution of net changes, write a function called `run_many_simulations` that runs many simulations, saves the final populations in a `ModSimSeries`, and returns the `ModSimSeries`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_many_simulations(system, update_func, iters):\n",
+    "    \"\"\"Runs simulations and report final populations.\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function object\n",
+    "    iters: number of simulations to run\n",
+    "    \n",
+    "    returns: series of final populations\n",
+    "    \"\"\"\n",
+    "    # FILL THIS IN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your function by running it with `iters=5`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_many_simulations(system, update_func1, 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run 1000 simulations and describe the distribution of the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "last_pops = run_many_simulations(system, update_func1, 1000)\n",
+    "last_pops.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we substract off the initial population, we get the distribution of changes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net_changes = last_pops - p_0\n",
+    "net_changes.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The median is negative, which indicates that the population decreases more often than it increases.\n",
+    "\n",
+    "We can be more specific by counting the number of runs where `net_changes` is positive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.sum(net_changes > 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or we can use `mean` to compute the fraction of runs where `net_changes` is positive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.mean(net_changes > 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the fraction where it's negative."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.mean(net_changes < 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So, based on observed past changes, this model predicts that the population is more likely to decrease than increase over the next 10 years, by about 2:1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A refined model\n",
+    "\n",
+    "There are a few ways we could improve the model.\n",
+    "\n",
+    "1.  It looks like there might be cyclic behavior in the past data, with a period of 4-5 years.  We could extend the model to include this effect.\n",
+    "\n",
+    "2.  Older data might not be as relevant for prediction as newer data, so we could give more weight to newer data.\n",
+    "\n",
+    "The second option is easier to implement, so let's try it.\n",
+    "\n",
+    "I'll use `linspace` to create an array of \"weights\" for the observed rates.  The probability that I choose each rate will be proportional to these weights.\n",
+    "\n",
+    "The weights have to add up to 1, so I divide through by the total."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weights = linspace(0, 1, len(rates))\n",
+    "weights /= sum(weights)\n",
+    "weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I'll add the weights to the `System` object, since they are parameters of the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.weights = weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can pass these weights as a parameter to `np.random.choice` (see the [documentation](https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.choice.html))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.choice(system.rates, p=system.weights)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write an update function that takes the weights into account."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use `plot_many_simulations` to plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use `run_many_simulations` to collect the results and `describe` to summarize the distribution of net changes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Does the refined model have much effect on the probability of population decline?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/spiderman.ipynb b/examples/spiderman.ipynb
new file mode 100644
index 00000000..39ecaa1e
--- /dev/null
+++ b/examples/spiderman.ipynb
@@ -0,0 +1,848 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Spider-Man"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case study we'll develop a model of Spider-Man swinging from a springy cable of webbing attached to the top of the Empire State Building.  Initially, Spider-Man is at the top of a nearby building, as shown in this diagram.\n",
+    "\n",
+    "![Diagram of the initial state for the Spider-Man case\n",
+    "study.](https://github.com/AllenDowney/ModSim/raw/main/figs/spiderman.png)\n",
+    "\n",
+    "The origin, `O⃗`, is at the base of the Empire State Building.  The vector `H⃗` represents the position where the webbing is attached to the building, relative to `O⃗`.  The vector `P⃗` is the position of Spider-Man relative to `O⃗`.  And `L⃗` is the vector from the attachment point to Spider-Man.\n",
+    "\n",
+    "By following the arrows from `O⃗`, along `H⃗`, and along `L⃗`, we can see that \n",
+    "\n",
+    "`H⃗ + L⃗ = P⃗`\n",
+    "\n",
+    "So we can compute `L⃗` like this:\n",
+    "\n",
+    "`L⃗ = P⃗ - H⃗`\n",
+    "\n",
+    "The goals of this case study are:\n",
+    "\n",
+    "1. Implement a model of this scenario to predict Spider-Man's trajectory.\n",
+    "\n",
+    "2. Choose the right time for Spider-Man to let go of the webbing in order to maximize the distance he travels before landing.\n",
+    "\n",
+    "3. Choose the best angle for Spider-Man to jump off the building, and let go of the webbing, to maximize range."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I'll create a `Params` object to contain the quantities we'll need:\n",
+    "\n",
+    "1. According to [the Spider-Man Wiki](http://spiderman.wikia.com/wiki/Peter_Parker_%28Earth-616%29), Spider-Man weighs 76 kg.\n",
+    "\n",
+    "2. Let's assume his terminal velocity is 60 m/s.\n",
+    "\n",
+    "3. The length of the web is 100 m.\n",
+    "\n",
+    "4. The initial angle of the web is 45 degrees to the left of straight down.\n",
+    "\n",
+    "5. The spring constant of the web is 40 N / m when the cord is stretched, and 0 when it's compressed.\n",
+    "\n",
+    "Here's a `Params` object with the parameters of the system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = Params(height = 381,    # m,\n",
+    "                g = 9.8,         # m/s**2,\n",
+    "                mass = 75,       # kg,\n",
+    "                area = 1,        # m**2,\n",
+    "                rho = 1.2,       # kg/m**3,\n",
+    "                v_term = 60,     # m / s,\n",
+    "                length = 100,          # m,\n",
+    "                angle = (270 - 45),    # degree,\n",
+    "                k = 40,                # N / m,\n",
+    "                t_0 = 0,               # s,\n",
+    "                t_end = 30,            # s\n",
+    "               )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute the initial position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def initial_condition(params):\n",
+    "    \"\"\"Compute the initial position and velocity.\n",
+    "    \n",
+    "    params: Params object\n",
+    "    \"\"\"\n",
+    "    H⃗ = Vector(0, params.height)\n",
+    "    theta = np.deg2rad(params.angle)\n",
+    "    x, y = pol2cart(theta, params.length)\n",
+    "    L⃗ = Vector(x, y)\n",
+    "    P⃗ = H⃗ + L⃗\n",
+    "    \n",
+    "    return State(x=P⃗.x, y=P⃗.y, vx=0, vy=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "initial_condition(params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now here's a version of `make_system` that takes a `Params` object as a parameter.\n",
+    "\n",
+    "`make_system` uses the given value of `v_term` to compute the drag coefficient `C_d`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(params):\n",
+    "    \"\"\"Makes a System object for the given conditions.\n",
+    "    \n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = initial_condition(params)\n",
+    "    \n",
+    "    mass, g = params.mass, params.g\n",
+    "    rho, area, v_term = params.rho, params.area, params.v_term\n",
+    "    C_d = 2 * mass * g / (rho * area * v_term**2)\n",
+    "    \n",
+    "    return System(params, init=init, C_d=C_d)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's make a `System`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Drag and spring forces\n",
+    "\n",
+    "Here's drag force, as we saw in Chapter 22."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drag_force(V⃗, system):\n",
+    "    \"\"\"Compute drag force.\n",
+    "    \n",
+    "    V⃗: velocity Vector\n",
+    "    system: `System` object\n",
+    "    \n",
+    "    returns: force Vector\n",
+    "    \"\"\"\n",
+    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
+    "    \n",
+    "    mag = rho * vector_mag(V⃗)**2 * C_d * area / 2\n",
+    "    direction = -vector_hat(V⃗)\n",
+    "    f_drag = direction * mag\n",
+    "    return f_drag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "V⃗_test = Vector(10, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drag_force(V⃗_test, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the 2-D version of spring force.  We saw the 1-D version in Chapter 21."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def spring_force(L⃗, system):\n",
+    "    \"\"\"Compute drag force.\n",
+    "    \n",
+    "    L⃗: Vector representing the webbing\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: force Vector\n",
+    "    \"\"\"\n",
+    "    extension = vector_mag(L⃗) - system.length\n",
+    "    if extension < 0:\n",
+    "        mag = 0\n",
+    "    else:\n",
+    "        mag = system.k * extension\n",
+    "        \n",
+    "    direction = -vector_hat(L⃗)\n",
+    "    f_spring = direction * mag\n",
+    "    return f_spring"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "L⃗_test = Vector(0, -system.length-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_spring = spring_force(L⃗_test, system)\n",
+    "f_spring"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the slope function, including acceleration due to gravity, drag, and the spring force of the webbing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    \"\"\"Computes derivatives of the state variables.\n",
+    "    \n",
+    "    state: State (x, y, x velocity, y velocity)\n",
+    "    t: time\n",
+    "    system: System object with g, rho, C_d, area, mass\n",
+    "    \n",
+    "    returns: sequence (vx, vy, ax, ay)\n",
+    "    \"\"\"\n",
+    "    x, y, vx, vy = state\n",
+    "    P⃗ = Vector(x, y)\n",
+    "    V⃗ = Vector(vx, vy)\n",
+    "    g, mass = system.g, system.mass\n",
+    "    \n",
+    "    H⃗ = Vector(0, system.height)\n",
+    "    L⃗ = P⃗ - H⃗\n",
+    "    \n",
+    "    a_grav = Vector(0, -g)\n",
+    "    a_spring = spring_force(L⃗, system) / mass\n",
+    "    a_drag = drag_force(V⃗, system) / mass\n",
+    "    \n",
+    "    A⃗ = a_grav + a_drag + a_spring\n",
+    "    \n",
+    "    return V⃗.x, V⃗.y, A⃗.x, A⃗.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, let's test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_solve_ivp(system, slope_func)\n",
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualizing the results\n",
+    "\n",
+    "We can extract the x and y components as `Series` objects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The simplest way to visualize the results is to plot x and y as functions of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_position(results):\n",
+    "    results.x.plot(label='x')\n",
+    "    results.y.plot(label='y')\n",
+    "\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Position (m)')\n",
+    "    \n",
+    "plot_position(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the velocities the same way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_velocity(results):\n",
+    "    results.vx.plot(label='vx')\n",
+    "    results.vy.plot(label='vy')\n",
+    "\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Velocity (m/s)')\n",
+    "    \n",
+    "plot_velocity(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another way to visualize the results is to plot y versus x.  The result is the trajectory through the plane of motion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_trajectory(results, label):\n",
+    "    x = results.x\n",
+    "    y = results.y\n",
+    "    make_series(x, y).plot(label=label)\n",
+    "\n",
+    "    decorate(xlabel='x position (m)',\n",
+    "             ylabel='y position (m)')\n",
+    "    \n",
+    "plot_trajectory(results, label='trajectory')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Letting go\n",
+    "\n",
+    "Now let's find the optimal time for Spider-Man to let go.  We have to run the simulation in two phases because the spring force changes abruptly when Spider-Man lets go, so we can't integrate through it.\n",
+    "\n",
+    "Here are the parameters for Phase 1, running for 9 seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params1 = params.set(t_end=9)\n",
+    "system1 = make_system(params1)\n",
+    "results1, details1 = run_solve_ivp(system1, slope_func)\n",
+    "plot_trajectory(results1, label='phase 1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final conditions from Phase 1 are the initial conditions for Phase 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_0 = results1.index[-1]\n",
+    "t_0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init = results1.iloc[-1]\n",
+    "init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_end = t_0 + 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is the `System` for Phase 2.  We can turn off the spring force by setting `k=0`, so we don't have to write a new slope function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system2 = system1.set(init=init, t_0=t_0, t_end=t_end, k=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's an event function that stops the simulation when Spider-Man reaches the ground."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def event_func(t, state, system):\n",
+    "    \"\"\"Stops when y=0.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: height\n",
+    "    \"\"\"\n",
+    "    x, y, vx, vy = state\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run Phase 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results2, details2 = run_solve_ivp(system2, slope_func, \n",
+    "                                   events=event_func)\n",
+    "details2.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_trajectory(results1, label='phase 1')\n",
+    "plot_trajectory(results2, label='phase 2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can gather all that into a function that takes `t_release` and `V_0`, runs both phases, and returns the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_two_phase(t_release, params):\n",
+    "    \"\"\"Run both phases.\n",
+    "    \n",
+    "    t_release: time when Spider-Man lets go of the webbing\n",
+    "    \"\"\"\n",
+    "    params1 = params.set(t_end=t_release)\n",
+    "    system1 = make_system(params1)\n",
+    "    results1, details1 = run_solve_ivp(system1, slope_func)\n",
+    "\n",
+    "    t_0 = results1.index[-1]\n",
+    "    t_end = t_0 + 10\n",
+    "    init = results1.iloc[-1]\n",
+    "\n",
+    "    system2 = system1.set(init=init, t_0=t_0, t_end=t_end, k=0)\n",
+    "    results2, details2 = run_solve_ivp(system2, slope_func, \n",
+    "                                       events=event_func)\n",
+    "\n",
+    "    return results1.append(results2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's a test run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_release = 9 \n",
+    "results = run_two_phase(t_release, params)\n",
+    "plot_trajectory(results, 'trajectory')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_final = results.iloc[-1].x\n",
+    "x_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Animation\n",
+    "\n",
+    "Here's a draw function we can use to animate the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.pyplot import plot\n",
+    "\n",
+    "xlim = results.x.min(), results.x.max()\n",
+    "ylim = results.y.min(), results.y.max()\n",
+    "\n",
+    "def draw_func(t, state):\n",
+    "    plot(state.x, state.y, 'bo')\n",
+    "    decorate(xlabel='x position (m)',\n",
+    "             ylabel='y position (m)',\n",
+    "             xlim=xlim,\n",
+    "             ylim=ylim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# animate(results, draw_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Maximizing range\n",
+    "\n",
+    "To find the best value of `t_release`, we need a function that takes possible values, runs the simulation, and returns the range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def range_func(t_release, params):\n",
+    "    \"\"\"Compute the final value of x.\n",
+    "    \n",
+    "    t_release: time to release web\n",
+    "    params: Params object\n",
+    "    \"\"\"\n",
+    "    results = run_two_phase(t_release, params)\n",
+    "    x_final = results.iloc[-1].x\n",
+    "    print(t_release, x_final)\n",
+    "    return x_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can test it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "range_func(9, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And run it for a few values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for t_release in linrange(3, 15, 3):\n",
+    "    range_func(t_release, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use `maximize_scalar` to find the optimum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bounds = [6, 12]\n",
+    "res = maximize_scalar(range_func, params, bounds=bounds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can run the simulation with the optimal value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_time = res.x\n",
+    "results = run_two_phase(best_time, params)\n",
+    "plot_trajectory(results, label='trajectory')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_final = results.iloc[-1].x\n",
+    "x_final"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/throwingaxe.ipynb b/examples/throwingaxe.ipynb
new file mode 100644
index 00000000..89f5bc3c
--- /dev/null
+++ b/examples/throwingaxe.ipynb
@@ -0,0 +1,482 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Throwing Axes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "My favorite event at Lumberjack Competitions is axe throwing.  The axes used for this event typically weigh 1.5 to 2 kg, with handles roughly 0.7 m long.  They are thrown overhead at a target typically 6 m away and 1.5 m off the ground.  Normally, the axe makes one full rotation in the air to hit the target blade first, with the handle close to vertical.\n",
+    "\n",
+    "![Diagram of throwing axe](https://github.com/AllenDowney/ModSim/raw/main/figs/throwingaxe1.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a version of `make_system` that sets the initial conditions.\n",
+    "\n",
+    "The state variables are x, y, theta, vx, vy, omega, where theta is the orientation (angle) of the axe in radians and omega is the angular velocity in radians per second.\n",
+    "\n",
+    "I chose initial conditions based on videos of axe throwing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g = 9.8       # m/s**2,\n",
+    "mass = 1.5    # kg,\n",
+    "length = 0.7  # m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = 0      # m\n",
+    "y = 2      # m\n",
+    "vx = 8     # m / s\n",
+    "vy = 4     # m / s\n",
+    "theta = 2            # rad\n",
+    "omega = -7           # rad / s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init = State(x=x, y=y, \n",
+    "             vx=vx, vy=vy, \n",
+    "             theta=theta, omega=omega)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = System(init=init, t_end=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a simple starting place, I ignore drag, so `vx` and `omega` are constant, and `ay` is just `-g`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    x, y, vx, vy, theta, omega = state\n",
+    "\n",
+    "    ax = 0\n",
+    "    ay = -g\n",
+    "    alpha = 0\n",
+    "\n",
+    "    return vx, vy, ax, ay, omega, alpha"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, let's test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope_func(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_solve_ivp(system, slope_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualizing the results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The simplest way to visualize the results is to plot the state variables as a function of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_position(results):\n",
+    "    results.x.plot(label='x')\n",
+    "    results.y.plot(label='y')\n",
+    "\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Position (m)')\n",
+    "    \n",
+    "plot_position(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the velocities the same way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_velocity(results):\n",
+    "    results.vx.plot(label='vx')\n",
+    "    results.vy.plot(label='vy')\n",
+    "\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Velocity (m/s)')\n",
+    "    \n",
+    "plot_velocity(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.theta.plot(label='theta', color='C2')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Angle (radian)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.omega.plot(label='omega', color='C2')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Angular velocity (rad/s)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another way to visualize the results is to plot y versus x.  The result is the trajectory through the plane of motion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_trajectory(results):\n",
+    "    x = results.x\n",
+    "    y = results.y\n",
+    "    make_series(x, y).plot(label='trajectory')\n",
+    "    \n",
+    "    decorate(xlabel='x position (m)',\n",
+    "             ylabel='y position (m)')\n",
+    "    \n",
+    "plot_trajectory(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Animation\n",
+    "\n",
+    "Animating this system is a little more complicated, if we want to show the shape and orientation of the axe.\n",
+    "\n",
+    "It is useful to construct a frame with $\\hat{r}$ along the handle of the axe and $\\hat{\\theta}$ perpendicular."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_frame(theta):\n",
+    "    x, y = pol2cart(theta, 1)\n",
+    "    rhat = Vector(x, y)\n",
+    "    that = vector_perp(rhat)\n",
+    "    return rhat, that"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "theta = 1\n",
+    "rhat, that = make_frame(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rhat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "that"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.dot(rhat, that)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "O = Vector(0, 0)\n",
+    "plot_segment(O, rhat)\n",
+    "plot_segment(O, that)\n",
+    "plt.axis('equal');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we're ready to animate the results.  The following figure shows the frame and the labeled points A, B, C, and D.\n",
+    "\n",
+    "![Diagram of the axe with reference frame](https://github.com/AllenDowney/ModSim/raw/main/figs//throwingaxe2.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "l1 = 0.6\n",
+    "l2 = 0.1\n",
+    "\n",
+    "def draw_func(t, state):   \n",
+    "    x, y, vx, vy, theta, omega = state\n",
+    "    P = Vector(x, y)\n",
+    "    rhat, that = make_frame(theta)\n",
+    "    \n",
+    "    # plot the handle\n",
+    "    A = P - l1 * rhat\n",
+    "    B = P + l2 * rhat\n",
+    "    plot_segment(A, B, color='red')\n",
+    "\n",
+    "    # plot the axe head\n",
+    "    C = B + l2 * that\n",
+    "    D = B - l2 * that\n",
+    "    plot_segment(C, D, color='black', linewidth=10)\n",
+    "\n",
+    "    # plot the COG\n",
+    "    plt.plot(x, y, 'bo')\n",
+    "\n",
+    "    decorate(xlabel='x position (m)',\n",
+    "             ylabel='y position (m)',\n",
+    "             xlim=[-0.3, 8.2],\n",
+    "             ylim=[0, 5]\n",
+    "            )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "During the animation, the parts of the axe seem to slide around relative to each other.  I think that's because the lines and circles get rounded off to the nearest pixel.\n",
+    "\n",
+    "Here's the final state of the axe at the point of impact (assuming the target is 8 m away)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "draw_func(0, system.init)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "animate(results, draw_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "**Exercise:**  Find the starting conditions that make the final height of the COG as close as possible to 1.5 m.  Ideally, the final angle should be a little past vertical."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/trees.ipynb b/examples/trees.ipynb
new file mode 100644
index 00000000..c8c4cbe6
--- /dev/null
+++ b/examples/trees.ipynb
@@ -0,0 +1,1014 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Trees"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modeling tree growth\n",
+    "\n",
+    "This case study is based on \"[Height-Age Curves for Planted Stands of Douglas Fir, with Adjustments for Density](http://www.cfr.washington.edu/research.smc/working_papers/smc_working_paper_1.pdf)\", a working paper by Flewelling, Collier, Gonyea, Marshall, and Turnblom.\n",
+    "\n",
+    "It provides \"site index curves\", which are curves that show the expected height of the tallest tree in a stand of Douglas firs as a function of age, for a stand where the trees are the same age.\n",
+    "\n",
+    "Depending on the quality of the site, the trees might grow more quickly or slowly.  So each curve is identified by a \"site index\" that indicates the quality of the site.\n",
+    "\n",
+    "I'll start with some of the data from their Table 1.  Here's the sequence of ages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "years = [2, 3, 4, 5, 6, 8, 10, 15, 20, 25, 30,\n",
+    "         35, 40, 45, 50, 55, 60, 65, 70]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the series of heights for a site with index 45, indicating that height at 30 years is 45 feet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "site45 = TimeSeries([1.4, 1.49, 1.75, 2.18, 2.78, 4.45, 6.74,\n",
+    "                    14.86, 25.39, 35.60, 45.00, 53.65, 61.60,\n",
+    "                    68.92, 75.66, 81.85, 87.56, 92.8, 97.63],\n",
+    "                    index=years)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the series for site index 65."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "site65 = TimeSeries([1.4, 1.56, 2.01, 2.76, 3.79, 6.64, 10.44, \n",
+    "                    23.26, 37.65, 51.66, 65.00, 77.50, 89.07, \n",
+    "                    99.66, 109.28, 117.96, 125.74, 132.68, 138.84],\n",
+    "                    index=years)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And for site index 85."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "site85 = TimeSeries([1.4, 1.8, 2.71, 4.09, 5.92, 10.73, 16.81, \n",
+    "                 34.03, 51.26, 68.54, 85, 100.34, 114.33,\n",
+    "                 126.91, 138.06, 147.86, 156.39, 163.76, 170.10],\n",
+    "               index=years)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the curves look like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "site85.plot(label='SI 85')\n",
+    "site65.plot(label='SI 65')\n",
+    "site45.plot(label='SI 45')\n",
+    "decorate(xlabel='Time (years)',\n",
+    "         ylabel='Height (feet)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For my examples I'll work with the SI 65 data; as an exercise, you can run the notebook again with either of the other curves."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = site65"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model 1\n",
+    "\n",
+    "As a starting place, let's assume that the ability of the tree to gain mass is limited by the area it exposes to sunlight, and that the growth rate (in mass) is proportional to that area.  In that case we can write:\n",
+    "\n",
+    "$$ m_{n+1} = m_n + \\alpha A$$\n",
+    "\n",
+    "where $m_n$ is the mass of the at time step $n$, $A$ is the area exposed to sunlight, and $\\alpha$ is an unknown growth parameter.\n",
+    "\n",
+    "To get from $m$ to $A$, I'll make the additional assumption that mass is proportional to height raised to an unknown power:\n",
+    "\n",
+    "$$ m = \\beta h^D $$\n",
+    "\n",
+    "where $h$ is height, $\\beta$ is an unknown constant of proportionality, and $D$ is the dimension that relates height and mass. We'll start by assuming $D=3$, but we'll revisit that assumption.\n",
+    "\n",
+    "Finally, we'll assume that area is proportional to height squared:\n",
+    "\n",
+    "$$ A = \\gamma h^2$$\n",
+    "\n",
+    "I'll specify height in feet, and choose units for mass and area so that $\\beta=1$ and $\\gamma=1$.\n",
+    "\n",
+    "Putting all that together, we can write a difference equation for height:\n",
+    "\n",
+    "$$ h_{n+1}^D = h_n^D + \\alpha h_n^2 $$\n",
+    "\n",
+    "Now let's simulate this system.  Here's a system object with the parameters and initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha = 7\n",
+    "dim = 3\n",
+    "\n",
+    "t_0 = data.index[0]\n",
+    "h_0 = data[t_0]\n",
+    "t_end = data.index[-1]\n",
+    "\n",
+    "system = System(alpha=alpha, \n",
+    "                dim=dim, \n",
+    "                h_0=h_0, \n",
+    "                t_0=t_0, \n",
+    "                t_end=t_end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's an update function that takes the current height as a parameter and returns the height during the next time step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update(height, t, system):\n",
+    "    \"\"\"Update height based on geometric model.\n",
+    "    \n",
+    "    height: current height in feet\n",
+    "    t: what year it is\n",
+    "    system: system object with model parameters\n",
+    "    \"\"\"\n",
+    "    area = height**2\n",
+    "    mass = height**system.dim\n",
+    "    mass += system.alpha * area\n",
+    "    return mass**(1/system.dim)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I'll test the update function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "update(h_0, t_0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's our usual version of `run_simulation`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Simulate the system using any update function.\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that computes the population next year\n",
+    "    \n",
+    "    returns: TimeSeries\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_0] = system.h_0\n",
+    "    \n",
+    "    for t in linrange(system.t_0, system.t_end-1):\n",
+    "        results[t+1] = update_func(results[t], t, system)\n",
+    "        \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = run_simulation(system, update)\n",
+    "results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(results, data):\n",
+    "    results.plot(style=':', label='model', color='gray')\n",
+    "    data.plot(label='data')\n",
+    "    decorate(xlabel='Time (years)',\n",
+    "             ylabel='Height (feet)')\n",
+    "    \n",
+    "plot_results(results, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model converges to a straight line.\n",
+    "\n",
+    "I chose the value of `alpha` to fit the data as well as I could, but it is clear that the data have curvature that's not captured by the model.\n",
+    "\n",
+    "Here are the errors, that is, the differences between the model and the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "errors = results - data\n",
+    "errors.dropna()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the mean absolute error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mean_abs_error(results, data):\n",
+    "    return (results-data).abs().mean()\n",
+    "\n",
+    "mean_abs_error(results, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This model might explain why the height of a tree grows roughly linearly:\n",
+    "\n",
+    "1. If area is proportional to $h^2$ and mass is proportional to $h^3$, and\n",
+    "\n",
+    "2. Change in mass is proportional to area, and\n",
+    "\n",
+    "3. Height grows linearly, then\n",
+    "\n",
+    "4. Area grows in proportion to $h^2$, and\n",
+    "\n",
+    "5. Mass grows in proportion to $h^3$.\n",
+    "\n",
+    "If the goal is to explain (approximate) linear growth, we might stop there.  But this model does not fit the data particularly well, and it implies that trees could keep growing forever.\n",
+    "\n",
+    "So we might want to do better."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a second attempt, let's suppose that we don't know $D$.  In fact, we don't, because trees are not like simple solids; they are more like fractals, which have [fractal dimension](https://en.wikipedia.org/wiki/Fractal_dimension).\n",
+    "\n",
+    "I would expect the fractal dimension of a tree to be between 2 and 3, so I'll guess 2.5."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha = 7\n",
+    "dim = 2.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I'll wrap the code from the previous section is a function that takes the parameters as inputs and makes a `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(params, data):\n",
+    "    \"\"\"Makes a System object.\n",
+    "    \n",
+    "    params: sequence of alpha, dim\n",
+    "    data: Series\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    alpha, dim = params\n",
+    "    \n",
+    "    t_0 = data.index[0]\n",
+    "    t_end = data.index[-1]\n",
+    "    h_0 = data[t_0]\n",
+    "\n",
+    "    return System(alpha=alpha, dim=dim, \n",
+    "                  h_0=h_0, t_0=t_0, t_end=t_end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we use it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = alpha, dim\n",
+    "system = make_system(params, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With different values for the parameters, we get curves with different behavior.  Here are a few that I chose by hand."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_and_plot(alpha, dim, data):\n",
+    "    params = alpha, dim\n",
+    "    system = make_system(params, data)\n",
+    "    results = run_simulation(system, update)\n",
+    "    results.plot(style=':', color='gray', label='_nolegend')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_and_plot(0.145, 2, data)\n",
+    "run_and_plot(0.58, 2.4, data)\n",
+    "run_and_plot(2.8, 2.8, data)\n",
+    "run_and_plot(6.6, 3, data)\n",
+    "run_and_plot(15.5, 3.2, data)\n",
+    "run_and_plot(38, 3.4, data)\n",
+    "\n",
+    "data.plot(label='data')\n",
+    "decorate(xlabel='Time (years)',\n",
+    "             ylabel='Height (feet)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To find the parameters that best fit the data, I'll use `leastsq`.\n",
+    "\n",
+    "We need an error function that takes parameters and returns errors:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def error_func(params, data, update_func):\n",
+    "    \"\"\"Runs the model and returns errors.\n",
+    "    \n",
+    "    params: sequence of alpha, dim\n",
+    "    data: Series\n",
+    "    update_func: function object\n",
+    "    \n",
+    "    returns: Series of errors\n",
+    "    \"\"\"\n",
+    "    print(params)\n",
+    "    system = make_system(params, data)\n",
+    "    results = run_simulation(system, update_func)\n",
+    "    return (results - data).dropna()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we use it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "errors = error_func(params, data, update)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can pass `error_func` to `leastsq`, which finds the parameters that minimize the squares of the errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_params, details = leastsq(error_func, params, data, update)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(details.success)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the best parameters we found, we can run the model and plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(best_params, data)\n",
+    "results = run_simulation(system, update)\n",
+    "plot_results(results, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The mean absolute error is better than for Model 1, but that doesn't mean much.  The model still doesn't fit the data well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_abs_error(results, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the estimated fractal dimension is 3.11, which doesn't seem likely.\n",
+    "\n",
+    "Let's try one more thing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Models 1 and 2 imply that trees can grow forever, but we know that's not true.  As trees get taller, it gets harder for them to move water and nutrients against the force of gravity, and their growth slows.\n",
+    "\n",
+    "We can model this effect by adding a term to the model similar to what we saw in the logistic model of population growth.  Instead of assuming:\n",
+    "\n",
+    "$ m_{n+1} = m_n + \\alpha A $ \n",
+    "\n",
+    "Let's assume\n",
+    "\n",
+    "$ m_{n+1} = m_n + \\alpha A (1 - h / K) $\n",
+    "\n",
+    "where $K$ is similar to the carrying capacity of the logistic model.  As $h$ approaches $K$, the factor $(1 - h/K)$ goes to 0, causing growth to level off.\n",
+    "\n",
+    "Here's what the implementation of this model looks like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha = 2.0\n",
+    "dim = 2.5\n",
+    "K = 150\n",
+    "\n",
+    "params = [alpha, dim, K]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's an updated version of `make_system`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(params, data):\n",
+    "    \"\"\"Makes a System object.\n",
+    "    \n",
+    "    params: sequence of alpha, dim, K\n",
+    "    data: Series\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    alpha, dim, K = params\n",
+    "    \n",
+    "    t_0 = data.index[0]\n",
+    "    t_end = data.index[-1]\n",
+    "    h_0 = data[t_0]\n",
+    "\n",
+    "    return System(alpha=alpha, dim=dim, K=K, \n",
+    "                  h_0=h_0, t_0=t_0, t_end=t_end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the new `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the new update function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update3(height, t, system):\n",
+    "    \"\"\"Update height based on geometric model with growth limiting term.\n",
+    "    \n",
+    "    height: current height in feet\n",
+    "    t: what year it is\n",
+    "    system: system object with model parameters\n",
+    "    \"\"\"\n",
+    "    area = height**2\n",
+    "    mass = height**system.dim\n",
+    "    mass += system.alpha * area * (1 - height/system.K)\n",
+    "    return mass**(1/system.dim)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, we'll test the update function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "update3(h_0, t_0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we'll test the error function with the new update function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error_func(params, data, update3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's search for the best parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_params, details = leastsq(error_func, params, data, update3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "details.success"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With these parameters, we can fit the data much better."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(best_params, data)\n",
+    "results = run_simulation(system, update3)\n",
+    "plot_results(results, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the mean absolute error is substantially smaller."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_abs_error(results, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The estimated fractal dimension is about 2.6, which is plausible; it suggests that if you double the height of the tree, the mass grows by a factor of $2^{2.6}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "2**2.6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In other words, the mass of the tree scales faster than area, but not as fast as it would for a solid 3-D object.\n",
+    "\n",
+    "What is this model good for?\n",
+    "\n",
+    "1) It offers a possible explanation for the shape of tree growth curves.\n",
+    "\n",
+    "2) It provides a way to estimate the fractal dimension of a tree based on a growth curve (probably with different values for different species).\n",
+    "\n",
+    "3) It might provide a way to predict future growth of a tree, based on measurements of past growth.  As with the logistic population model, this would probably only work if we have observed the part of the curve where the growth rate starts to decline."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysis\n",
+    "\n",
+    "With some help from my colleague, John Geddes, we can do some analysis.\n",
+    "\n",
+    "Starting with the difference equation in terms of mass:\n",
+    " \n",
+    "$m_{n+1} = m_n + \\alpha A (1 - h / K) $\n",
+    "\n",
+    "We can write the corresponding differential equation:\n",
+    "\n",
+    "(1) $ \\frac{dm}{dt} = \\alpha A (1 - h / K) $\n",
+    "\n",
+    "With\n",
+    "\n",
+    "(2) $A = h^2$\n",
+    "\n",
+    "and\n",
+    "\n",
+    "(3) $m = h^D$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Taking the derivative of the last equation yields\n",
+    "\n",
+    "(4) $\\frac{dm}{dt} = D h^{D-1} \\frac{dh}{dt}$\n",
+    "\n",
+    "Combining (1), (2), and (4), we can write a differential equation for $h$:\n",
+    "\n",
+    "(5) $\\frac{dh}{dt} = \\frac{\\alpha}{D} h^{3-D} (1 - h/K)$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's consider two cases:\n",
+    "\n",
+    "* With infinite $K$, the factor $(1 - h/K)$ approaches 1, so we have Model 2.\n",
+    "\n",
+    "* With finite $K$, we have Model 3."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model 2\n",
+    "\n",
+    "Within Model 2, we'll consider two special cases, with $D=2$ and $D=3$.\n",
+    "\n",
+    "With $D=2$, we have\n",
+    "\n",
+    "$\\frac{dh}{dt} = \\frac{\\alpha}{2} h$\n",
+    "\n",
+    "which yields exponential growth with parameter $\\alpha/2$.\n",
+    "\n",
+    "With $D=3$, we have Model 1, with this equation:\n",
+    "\n",
+    "$\\frac{dh}{dt} = \\frac{\\alpha}{3}$\n",
+    "\n",
+    "which yields linear growth with parameter $\\alpha/3$. This result explains why Model 1 is linear."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model 3\n",
+    "\n",
+    "Within Model 3, we'll consider two special cases, with $D=2$ and $D=3$.\n",
+    "\n",
+    "With $D=2$, we have\n",
+    "\n",
+    "$\\frac{dh}{dt} = \\frac{\\alpha}{2} h (1 - h/K)$\n",
+    "\n",
+    "which yields logisitic growth with parameters $r = \\alpha/2$ and $K$.\n",
+    "\n",
+    "With $D=3$, we have\n",
+    "\n",
+    "$\\frac{dh}{dt} = \\frac{\\alpha}{3} (1 - h/K)$\n",
+    "\n",
+    "which yields a first order step response; that is, it converges to $K$ like a negative exponential:\n",
+    "\n",
+    "$ h(t) = c \\exp(-\\frac{\\alpha}{3K} t) + K $\n",
+    "\n",
+    "where $c$ is a constant that depends on the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Open Exercise** Find an analytic solution when $D$ is between 2 and 3, and compare it to the data.  Note: The parameters we estimated for the difference equation might not be right for the differential equation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Additional resources:\n",
+    "\n",
+    "Garcia, [A stochastic differential equation model for the\n",
+    "height growth of forest stands](http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=664FED1E46ABCBF6E16741C294B79976?doi=10.1.1.608.81&rep=rep1&type=pdf)\n",
+    "\n",
+    "[EasySDE software and data](http://forestgrowth.unbc.ca/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/wall.ipynb b/examples/wall.ipynb
new file mode 100644
index 00000000..2fd4d5ba
--- /dev/null
+++ b/examples/wall.ipynb
@@ -0,0 +1,629 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Thermal behavior of a wall"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import basename, exists\n",
+    "\n",
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/main/modsim.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This case study is based on Gori, Marincioni, Biddulph, Elwell, \"Inferring the thermal resistance and effective thermal mass distribution of a wall from in situ measurements to characterise heat transfer at both the interior and exterior surfaces\", *Energy and Buildings*, Volume 135, 15 January 2017, Pages 398-409, [which I downloaded here](https://www.sciencedirect.com/science/article/pii/S0378778816313056).\n",
+    "    \n",
+    "The authors put their paper under a Creative Commons license, and [make their data available here](http://discovery.ucl.ac.uk/1526521).  I thank them for their commitment to open, reproducible science, which made this case study possible.\n",
+    "\n",
+    "The goal of their paper is to model the thermal behavior of a wall as a step toward understanding the \"performance gap between the expected energy use of buildings and their measured energy use\".  The wall they study is identified as the exterior wall of an office building in central London, [not unlike this one](https://www.google.com/maps/@51.5269375,-0.1303666,3a,75y,90h,88.17t/data=!3m6!1e1!3m4!1sAoAXzN0mbGF9acaVEgUdDA!2e0!7i13312!8i6656)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following figure shows the scenario and their model:\n",
+    "\n",
+    "![Figure 2](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr2.jpg)\n",
+    "\n",
+    "On the interior and exterior surfaces of the wall, they measure temperature and heat flux over a period of three days.  They model the wall using two thermal masses connected to the surfaces, and to each other, by thermal resistors.\n",
+    "\n",
+    "The primary methodology of the paper is a Bayesian method for inferring the parameters of the system (two thermal masses and three thermal resistances).\n",
+    "\n",
+    "The primary result is a comparison of two models: the one shown here with two thermal masses, and a simpler model with only one thermal mass.  They find that the two-mass model is able to reproduce the measured fluxes substantially better."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tempting as it is, I will not replicate their method for estimating the parameters.  Rather, I will implement their model and run it with their estimated parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cells download and read the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSim/main/data/DataOWall.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('DataOWall.csv', \n",
+    "                   parse_dates=[0], index_col=0, \n",
+    "                   header=0, skiprows=[1,2])\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The index contains Pandas `Timestamp` objects, which is good for dealing with real-world dates and times, but not as good for running the simulations, so I'm going to convert to seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timestamp_0 = data.index[0]\n",
+    "timestamp_0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Subtracting the first `Timestamp` yields `Timedelta` objects:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_deltas = data.index - timestamp_0\n",
+    "time_deltas.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we can convert to seconds and replace the index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.index = time_deltas.days * 86400 + time_deltas.seconds\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The timesteps are all 5 minutes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.all(np.diff(data.index) == 300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the measured fluxes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.Q_in.plot(color='C2')\n",
+    "data.Q_out.plot(color='C0')\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Heat flux (W/$m^2$)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the measured temperatures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.T_int.plot(color='C2')\n",
+    "data.T_ext.plot(color='C0')\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Temperature (degC)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Making the System object\n",
+    "\n",
+    "`params` is a sequence with the [estimated parameters from the paper](https://www.sciencedirect.com/science/article/pii/S0378778816313056#tbl0005)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "R1 = 0.076   # m**2 * K / W,\n",
+    "R2 = 0.272   # m**2 * K / W,\n",
+    "R3 = 0.078   # m**2 * K / W,\n",
+    "C1 = 212900  # J / m**2 / K,\n",
+    "C2 = 113100  # J / m**2 / K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = R1, R2, R3, C1, C2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll pass `params` to `make_system`, which computes `init`, packs the parameters into `Series` objects, and computes the interpolation functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(params, data):\n",
+    "    \"\"\"Makes a System object for the given conditions.\n",
+    "    \n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    R1, R2, R3, C1, C2 = params\n",
+    "    \n",
+    "    init = State(T_C1 = 16.11, T_C2 = 15.27)\n",
+    "    \n",
+    "    t_end = data.index[-1]\n",
+    "    \n",
+    "    return System(init=init,\n",
+    "                  R=(R1, R2, R3),\n",
+    "                  C=(C1, C2),\n",
+    "                  T_int_func=interpolate(data.T_int),\n",
+    "                  T_ext_func=interpolate(data.T_ext),\n",
+    "                  t_end=t_end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a `System` object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test the interpolation function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.T_ext_func(0), system.T_ext_func(150), system.T_ext_func(300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implementing the model\n",
+    "\n",
+    "Next we need a slope function that takes instantaneous values of the two internal temperatures and computes their time rates of change.\n",
+    "\n",
+    "The slope function gets called two ways.\n",
+    "\n",
+    "* When we call it directly, `state` is a `State` object and the values it contains have units.\n",
+    "\n",
+    "* When `run_solve_ivp` calls it, `state` is an array and the values it contains don't have units.\n",
+    "\n",
+    "In the second case, we have to apply the units before attempting the computation.  `require_units` applies units if necessary:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function computes the fluxes between the four zones."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_flux(t, state, system):\n",
+    "    \"\"\"Compute the fluxes between the walls surfaces and the internal masses.\n",
+    "    \n",
+    "    state: State with T_C1 and T_C2\n",
+    "    t: time in seconds\n",
+    "    system: System with interpolated measurements and the R Series\n",
+    "    \n",
+    "    returns: Series of fluxes\n",
+    "    \"\"\"    \n",
+    "    # unpack the temperatures\n",
+    "    T_C1, T_C2 = state\n",
+    "        \n",
+    "    # compute a series of temperatures from inside out\n",
+    "    T_int = system.T_int_func(t)\n",
+    "    T_ext = system.T_ext_func(t)\n",
+    "    \n",
+    "    T = [T_int, T_C1, T_C2, T_ext]\n",
+    "    \n",
+    "    # compute differences of adjacent temperatures\n",
+    "    T_diff = np.diff(T)\n",
+    "\n",
+    "    # compute fluxes between adjacent compartments\n",
+    "    Q = T_diff / system.R\n",
+    "    return Q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can test it like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "compute_flux(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a slope function that computes derivatives of `T_C1` and `T_C2`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    \"\"\"Compute derivatives of the state.\n",
+    "    \n",
+    "    state: position, velocity\n",
+    "    t: time\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: derivatives of y and v\n",
+    "    \"\"\"\n",
+    "    Q = compute_flux(t, state, system)\n",
+    "\n",
+    "    # compute the net flux in each node\n",
+    "    Q_diff = np.diff(Q)\n",
+    "        \n",
+    "    # compute the rate of change of temperature\n",
+    "    dQdt = Q_diff / system.C\n",
+    "    return dQdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slopes = slope_func(0, system.init, system)\n",
+    "slopes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's run the simulation, generating estimates for the time steps in the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_solve_ivp(system, slope_func,\n",
+    "                                 t_eval=data.index)\n",
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(results, data):\n",
+    "    data.T_int.plot(color='C2')\n",
+    "    results.T_C1.plot(color='C3')\n",
+    "    results.T_C2.plot(color='C1')\n",
+    "    data.T_ext.plot(color='C0')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Temperature (degC)')\n",
+    "    \n",
+    "plot_results(results, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results are similar to what's in the paper:\n",
+    "\n",
+    "![Figure 5](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr5.jpg).  \n",
+    "\n",
+    "To get the estimated fluxes, we have to go through the results and basically do the flux calculation again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def recompute_fluxes(results, system):\n",
+    "    \"\"\"Compute fluxes between wall surfaces and internal masses.\n",
+    "    \n",
+    "    results: Timeframe with T_C1 and T_C2\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: Timeframe with Q_in and Q_out\n",
+    "    \"\"\"\n",
+    "    Q_frame = TimeFrame(index=results.index, \n",
+    "                        columns=['Q_in', 'Q_out'])\n",
+    "    \n",
+    "    for t, row in results.iterrows():\n",
+    "        Q = compute_flux(t, row, system)\n",
+    "        Q_frame.loc[t] = (-Q[0], -Q[2])\n",
+    "        \n",
+    "    return Q_frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q_frame = recompute_fluxes(results, system)\n",
+    "Q_frame.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see how the estimates compare to the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_Q_in(frame, data):\n",
+    "    frame.Q_in.plot(color='gray')\n",
+    "    data.Q_in.plot(color='C2')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Heat flux (W/$m^2$)')\n",
+    "    \n",
+    "plot_Q_in(Q_frame, data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_Q_out(frame, data):\n",
+    "    frame.Q_out.plot(color='gray')\n",
+    "    data.Q_out.plot(color='C0')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Heat flux (W/$m^2$)')\n",
+    "    \n",
+    "plot_Q_out(Q_frame, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results are also similar to what's in the paper (the bottom row):\n",
+    "\n",
+    "![Figure 3](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr3.jpg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/wall_soln_with_fsolve.ipynb b/examples/wall_soln_with_fsolve.ipynb
new file mode 100644
index 00000000..e5d972e6
--- /dev/null
+++ b/examples/wall_soln_with_fsolve.ipynb
@@ -0,0 +1,1021 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Thermal behavior of a wall"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import basename, exists\n",
+    "\n",
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSim/main/modsim.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This case study is based on Gori, Marincioni, Biddulph, Elwell, \"Inferring the thermal resistance and effective thermal mass distribution of a wall from in situ measurements to characterise heat transfer at both the interior and exterior surfaces\", *Energy and Buildings*, Volume 135, 15 January 2017, Pages 398-409, [which I downloaded here](https://www.sciencedirect.com/science/article/pii/S0378778816313056).\n",
+    "    \n",
+    "The authors put their paper under a Creative Commons license, and [make their data available here](http://discovery.ucl.ac.uk/1526521).  I thank them for their commitment to open, reproducible science, which made this case study possible.\n",
+    "\n",
+    "The goal of their paper is to model the thermal behavior of a wall as a step toward understanding the \"performance gap between the expected energy use of buildings and their measured energy use\".  The wall they study is identified as the exterior wall of an office building in central London, [not unlike this one](https://www.google.com/maps/@51.5269375,-0.1303666,3a,75y,90h,88.17t/data=!3m6!1e1!3m4!1sAoAXzN0mbGF9acaVEgUdDA!2e0!7i13312!8i6656)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following figure shows the scenario and their model:\n",
+    "\n",
+    "![Figure 2](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr2.jpg)\n",
+    "\n",
+    "On the interior and exterior surfaces of the wall, they measure temperature and heat flux over a period of three days.  They model the wall using two thermal masses connected to the surfaces, and to each other, by thermal resistors.\n",
+    "\n",
+    "The primary methodology of the paper is a Bayesian method for inferring the parameters of the system (two thermal masses and three thermal resistances).\n",
+    "\n",
+    "The primary result is a comparison of two models: the one shown here with two thermal masses, and a simpler model with only one thermal mass.  They find that the two-mass model is able to reproduce the measured fluxes substantially better."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tempting as it is, I will not replicate their method for estimating the parameters.  Rather, I will\n",
+    "\n",
+    "1. Implement their model and run it with their estimated parameters, to replicate the results, and \n",
+    "\n",
+    "2. Use SciPy's `leastsq` to see if I can find parameters that a yield lower sum of squared errors.\n",
+    "\n",
+    "`leastsq` is a wrapper for some venerable FORTRAN code that runs [\"a modification of the Levenberg-Marquardt algorithm\"](https://www.math.utah.edu/software/minpack/minpack/lmdif.html), which is one of my favorites ([really](http://allendowney.com/research/model)).\n",
+    "\n",
+    "Implementing their model in the ModSimPy framework turns out to be straightforward.  The simulations run fast enough even when we carry units through the computation.  And the results are visually similar to the ones in the original paper.\n",
+    "\n",
+    "I find that `leastsq` is not able to find parameters that yield substantially better results, which suggest that the estimates in the paper are at least locally optimal."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the data\n",
+    "\n",
+    "First I'll load the units we need from Pint."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = units.meter\n",
+    "K = units.kelvin\n",
+    "W = units.watt\n",
+    "J = units.joule\n",
+    "degC = units.celsius\n",
+    "s = units.second"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSim/main/data/DataOWall.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('DataOWall.csv', \n",
+    "                   parse_dates=[0], index_col=0, \n",
+    "                   header=0, skiprows=[1,2])\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The index contains Pandas `Timestamp` objects, which is good for dealing with real-world dates and times, but not as good for running the simulations, so I'm going to convert to seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timestamp_0 = data.index[0]\n",
+    "timestamp_0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Subtracting the first `Timestamp` yields `Timedelta` objects:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_deltas = data.index - timestamp_0\n",
+    "time_deltas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we can convert to seconds and replace the index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.index = time_deltas.days * 86400 + time_deltas.seconds\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The timesteps are all 5 minutes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.all(np.diff(data.index) == 300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mark the columns of the `Dataframe` with units."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.Q_in.units = W / m**2\n",
+    "data.Q_out.units = W / m**2\n",
+    "data.T_int.units = degC\n",
+    "data.T_ext.units = degC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the measured fluxes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.Q_in.plot(color='C2')\n",
+    "data.Q_out.plot(color='C0')\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Heat flux (W/$m^2$)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the measured temperatures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.T_int.plot(color='C2')\n",
+    "data.T_ext.plot(color='C0')\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Temperature (degC)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Params and System objects\n",
+    "\n",
+    "Here's a `Params` object with the [estimated parameters from the paper](https://www.sciencedirect.com/science/article/pii/S0378778816313056#tbl0005)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "R1 = 0.076   # m**2 * K / W,\n",
+    "R2 = 0.272   # m**2 * K / W,\n",
+    "R3 = 0.078   # m**2 * K / W,\n",
+    "C1 = 212900  # J / m**2 / K,\n",
+    "C2 = 113100  # J / m**2 / K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = R1, R2, R3, C1, C2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = 0.07, R2, R3, C1, C2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I'll pass the `Params` object `make_system`, which computes `init`, packs the parameters into `Series` objects, and computes the interpolation functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_system(params, data):\n",
+    "    \"\"\"Makes a System object for the given conditions.\n",
+    "    \n",
+    "    params: Params object\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    R1, R2, R3, C1, C2 = params\n",
+    "    \n",
+    "    init = State(T_C1 = 16.11, T_C2 = 15.27)\n",
+    "    \n",
+    "    ts = data.index\n",
+    "    t_end = ts[-1] * s\n",
+    "    \n",
+    "    return System(init=init,\n",
+    "                  R= (R1, R2, R3),\n",
+    "                  C= (C1, C2),\n",
+    "                  T_int_func=interpolate(data.T_int),\n",
+    "                  T_ext_func=interpolate(data.T_ext),\n",
+    "                  unit_temp=degC,\n",
+    "                  t_end=t_end, ts=ts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a `System` object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(params, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test the interpolation function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system.T_ext_func(0), system.T_ext_func(150), system.T_ext_func(300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implementing the model\n",
+    "\n",
+    "Next we need a slope function that takes instantaneous values of the two internal temperatures and computes their time rates of change.\n",
+    "\n",
+    "The slope function gets called two ways.\n",
+    "\n",
+    "* When we call it directly, `state` is a `State` object and the values it contains have units.\n",
+    "\n",
+    "* When `run_solve_ivp` calls it, `state` is an array and the values it contains don't have units.\n",
+    "\n",
+    "In the second case, we have to apply the units before attempting the computation.  `require_units` applies units if necessary:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function computes the fluxes between the four zones."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_flux(t, state, system):\n",
+    "    \"\"\"Compute the fluxes between the walls surfaces and the internal masses.\n",
+    "    \n",
+    "    state: State with T_C1 and T_C2\n",
+    "    t: time in seconds\n",
+    "    system: System with interpolated measurements and the R Series\n",
+    "    \n",
+    "    returns: Series of fluxes\n",
+    "    \"\"\"    \n",
+    "    # unpack the temperatures\n",
+    "    T_C1, T_C2 = state\n",
+    "        \n",
+    "    # compute a series of temperatures from inside out\n",
+    "    T_int = system.T_int_func(t)\n",
+    "    T_ext = system.T_ext_func(t)\n",
+    "    \n",
+    "    T = [T_int, T_C1, T_C2, T_ext]\n",
+    "    \n",
+    "    # compute differences of adjacent temperatures\n",
+    "    T_diff = np.diff(T)\n",
+    "\n",
+    "    # compute fluxes between adjacent compartments\n",
+    "    Q = T_diff / system.R\n",
+    "    return Q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can test it like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "compute_flux(0, system.init, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a slope function that computes derivatives of `T_C1` and `T_C2`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    \"\"\"Compute derivatives of the state.\n",
+    "    \n",
+    "    state: position, velocity\n",
+    "    t: time\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: derivatives of y and v\n",
+    "    \"\"\"\n",
+    "    Q = compute_flux(t, state, system)\n",
+    "\n",
+    "    # compute the net flux in each node\n",
+    "    Q_diff = np.diff(Q)\n",
+    "        \n",
+    "    # compute the rate of change of temperature\n",
+    "    dQdt = Q_diff / system.C\n",
+    "    return dQdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slopes = slope_func(system.ts[0], system.init, system)\n",
+    "slopes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's run the simulation, generating estimates for the time steps in the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_solve_ivp(system, slope_func,\n",
+    "                                 t_eval=data.index)\n",
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(results, data):\n",
+    "    data.T_int.plot(color='C2')\n",
+    "    results.T_C1.plot(color='C3')\n",
+    "    results.T_C2.plot(color='C1')\n",
+    "    data.T_ext.plot(color='C0')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Temperature (degC)')\n",
+    "    \n",
+    "plot_results(results, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results are similar to what's in the paper:\n",
+    "\n",
+    "![Figure 5](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr5.jpg).  \n",
+    "\n",
+    "To get the estimated fluxes, we have to go through the results and basically do the flux calculation again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def recompute_fluxes(results, system):\n",
+    "    \"\"\"Compute fluxes between wall surfaces and internal masses.\n",
+    "    \n",
+    "    results: Timeframe with T_C1 and T_C2\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: Timeframe with Q_in and Q_out\n",
+    "    \"\"\"\n",
+    "    Q_frame = TimeFrame(index=results.index, \n",
+    "                        columns=['Q_in', 'Q_out'])\n",
+    "    \n",
+    "    for t, row in results.iterrows():\n",
+    "        Q = compute_flux(t, row, system)\n",
+    "        Q_frame.loc[t] = (-Q[0], -Q[2])\n",
+    "        \n",
+    "    return Q_frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q_frame = recompute_fluxes(results, system)\n",
+    "Q_frame.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see how the estimates compare to the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_Q_in(frame, data):\n",
+    "    frame.Q_in.plot(color='gray')\n",
+    "    data.Q_in.plot(color='C2')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Heat flux (W/$m^2$)')\n",
+    "    \n",
+    "plot_Q_in(Q_frame, data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_Q_out(frame, data):\n",
+    "    frame.Q_out.plot(color='gray')\n",
+    "    data.Q_out.plot(color='C0')\n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Heat flux (W/$m^2$)')\n",
+    "    \n",
+    "plot_Q_out(Q_frame, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results are also similar to what's in the paper (the bottom row):\n",
+    "\n",
+    "![Figure 3](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr3.jpg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error_Q_in = Q_frame.Q_in - data.Q_in\n",
+    "error_Q_out = Q_frame.Q_out - data.Q_out\n",
+    "\n",
+    "errors = np.hstack([error_Q_in, error_Q_out])\n",
+    "errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the sum of the squared errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.sum(errors**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Estimating parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's see if we can do any better than the parameters in the paper."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = R1, R2, R3, C1, C2\n",
+    "params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = R1, 0.27, R3, C1, C2\n",
+    "params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's an error function that takes a hypothetical set of parameters, runs the simulation, and returns an array of errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def error_func(params, data):\n",
+    "    \"\"\"Run a simulation and return an array of errors.\n",
+    "    \n",
+    "    params: Params object or array\n",
+    "    data: DataFrame\n",
+    "    \n",
+    "    returns: array of float\n",
+    "    \"\"\"\n",
+    "    print(params)\n",
+    "    system = make_system(params, data)    \n",
+    "    results, details = run_solve_ivp(system, slope_func, \n",
+    "                                     t_eval=data.index)\n",
+    "    Q_frame = recompute_fluxes(results, system)\n",
+    "    error_Q_in = Q_frame.Q_in - data.Q_in\n",
+    "    error_Q_out = Q_frame.Q_out - data.Q_out\n",
+    "\n",
+    "    errors = np.hstack([error_Q_in, error_Q_out])\n",
+    "    print(np.sum(errors**2))\n",
+    "    return errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Testing `error_func`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "errors = error_func(params, data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.sum(errors**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pass `error_func` to `leastsq` to see if it can do any better."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_params, details = leastsq(error_func, params, data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "details.ier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(details.mesg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The best params are only slightly different from the starting place. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_params - params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like with the `best_params`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(best_params, data)\n",
+    "system = remove_units(system)\n",
+    "\n",
+    "results, details = run_solve_ivp(system, slope_func, t_eval=system.ts)\n",
+    "Q_frame = recompute_fluxes(results, system)\n",
+    "errors = compute_error(Q_frame, data)\n",
+    "print(np.sum(errors**2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The sum of squared errors is only slightly smaller.\n",
+    "\n",
+    "And the results are visually similar."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_Q_in(Q_frame, data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_Q_out(Q_frame, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Try starting the model with a different set of parameters and see if it moves toward the parameters in the paper.\n",
+    "\n",
+    "I found that no matter where I start, `leastsq` doesn't move far, which suggests that it is not able to optimize the parameters effectively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Notes\n",
+    "\n",
+    "Notes on working with degC.\n",
+    "\n",
+    "Usually I construct a `Quantity` object by multiplying a number and a unit.  With degC, that doesn't work; you get\n",
+    "\n",
+    "```\n",
+    "OffsetUnitCalculusError: Ambiguous operation with offset unit (degC).\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#16.11 * C "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The problem is that it doesn't know whether you want a temperature measurement or a temperature difference.\n",
+    "\n",
+    "You can create a temperature measurement like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "T = Quantity(16.11, degC)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you convert to Kelvin, it does the right thing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "T.to(K)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you subtract temperatures, the results is a temperature difference, indicated by the units."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diff = T - 273.15 * K"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you convert a temperature difference to Kelvin, it does the right thing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diff.to(K)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/yoyo.ipynb b/examples/yoyo.ipynb
new file mode 100644
index 00000000..8ecf3111
--- /dev/null
+++ b/examples/yoyo.ipynb
@@ -0,0 +1,607 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simulating a Yo-Yo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# install Pint if necessary\n",
+    "\n",
+    "try:\n",
+    "    import pint\n",
+    "except ImportError:\n",
+    "    !pip install pint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import exists\n",
+    "\n",
+    "filename = 'modsim.py'\n",
+    "if not exists(filename):\n",
+    "    from urllib.request import urlretrieve\n",
+    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
+    "    local, _ = urlretrieve(url+filename, filename)\n",
+    "    print('Downloaded ' + local)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Yo-yo\n",
+    "\n",
+    "Suppose you are holding a yo-yo with a length of string wound around its axle, and you drop it while holding the end of the string stationary. As gravity accelerates the yo-yo downward, tension in the string exerts a force upward. Since this force acts on a point offset from the center of mass, it exerts a torque that causes the yo-yo to spin.\n",
+    "\n",
+    "The following diagram shows the forces on the yo-yo and the resulting torque. The outer shaded area shows the body of the yo-yo. The inner shaded area shows the rolled up string, the radius of which changes as the yo-yo unrolls.\n",
+    "\n",
+    "![Diagram of a yo-yo showing forces due to gravity and tension in the\n",
+    "string, the lever arm of tension, and the resulting\n",
+    "torque.](https://github.com/AllenDowney/ModSim/raw/main/figs/yoyo.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this system, we can't figure out the linear and angular acceleration independently; we have to solve a system of equations:\n",
+    "\n",
+    "$\\sum F = m a $\n",
+    "\n",
+    "$\\sum \\tau = I \\alpha$\n",
+    "\n",
+    "where the summations indicate that we are adding up forces and torques.\n",
+    "\n",
+    "As in the previous examples, linear and angular velocity are related because of the way the string unrolls:\n",
+    "\n",
+    "$\\frac{dy}{dt} = -r \\frac{d \\theta}{dt} $\n",
+    "\n",
+    "In this example, the linear and angular accelerations have opposite sign.  As the yo-yo rotates counter-clockwise, $\\theta$ increases and $y$, which is the length of the rolled part of the string, decreases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Taking the derivative of both sides yields a similar relationship between linear and angular acceleration:\n",
+    "\n",
+    "$\\frac{d^2 y}{dt^2} = -r \\frac{d^2 \\theta}{dt^2} $\n",
+    "\n",
+    "Which we can write more concisely:\n",
+    "\n",
+    "$ a = -r \\alpha $\n",
+    "\n",
+    "This relationship is not a general law of nature; it is specific to scenarios like this where there is rolling without stretching or slipping.\n",
+    "\n",
+    "Because of the way we've set up the problem, $y$ actually has two meanings: it represents the length of the rolled string and the height of the yo-yo, which decreases as the yo-yo falls.  Similarly, $a$ represents acceleration in the length of the rolled string and the height of the yo-yo.\n",
+    "\n",
+    "We can compute the acceleration of the yo-yo by adding up the linear forces:\n",
+    "\n",
+    "$\\sum F = T - mg = ma $\n",
+    "\n",
+    "Where $T$ is positive because the tension force points up, and $mg$ is negative because gravity points down."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because gravity acts on the center of mass, it creates no torque, so the only torque is due to tension:\n",
+    "\n",
+    "$\\sum \\tau = T r = I \\alpha $\n",
+    "\n",
+    "Positive (upward) tension yields positive (counter-clockwise) angular acceleration.\n",
+    "\n",
+    "Now we have three equations in three unknowns, $T$, $a$, and $\\alpha$, with $I$, $m$, $g$, and $r$ as known parameters.  We could solve these equations by hand, but we can also get SymPy to do it for us."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sympy import symbols, Eq, solve\n",
+    "\n",
+    "T, a, alpha, I, m, g, r = symbols('T a alpha I m g r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eq1 = Eq(a, -r * alpha)\n",
+    "eq1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eq2 = Eq(T - m * g, m * a)\n",
+    "eq2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eq3 = Eq(T * r, I * alpha)\n",
+    "eq3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "soln = solve([eq1, eq2, eq3], [T, a, alpha])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "soln[T]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "soln[a]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "soln[alpha]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are\n",
+    "\n",
+    "$T      = m g I / I^*   $\n",
+    "\n",
+    "$a      = -m g r^2 / I^* $\n",
+    "\n",
+    "$\\alpha = m g r / I^*    $\n",
+    "\n",
+    "where $I^*$ is the augmented moment of inertia, $I + m r^2$.\n",
+    "\n",
+    "You can also see [the derivation of these equations in this video](https://www.youtube.com/watch?v=chC7xVDKl4Q)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use these equations for $a$ and $\\alpha$ to write a slope function and simulate this system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Simulate the descent of a yo-yo.  How long does it take to reach the end of the string?\n",
+    "\n",
+    "Here are the system parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Rmin = 8e-3     # m\n",
+    "Rmax = 16e-3    # m\n",
+    "Rout = 35e-3    # m\n",
+    "mass = 50e-3    # kg\n",
+    "L = 1           # m\n",
+    "g = 9.8         # m / s**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* `Rmin` is the radius of the axle.  `Rmax` is the radius of the axle plus rolled string.\n",
+    "\n",
+    "* `Rout` is the radius of the yo-yo body.  `mass` is the total mass of the yo-yo, ignoring the string.  \n",
+    "\n",
+    "* `L` is the length of the string.\n",
+    "\n",
+    "* `g` is the acceleration of gravity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "1 / (Rmax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Based on these parameters, we can compute the moment of inertia for the yo-yo, modeling it as a solid cylinder with uniform density ([see here](https://en.wikipedia.org/wiki/List_of_moments_of_inertia)).\n",
+    "\n",
+    "In reality, the distribution of weight in a yo-yo is often designed to achieve desired effects.  But we'll keep it simple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "I = mass * Rout**2 / 2\n",
+    "I"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can compute `k`, which is the constant that determines how the radius of the spooled string decreases as it unwinds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "k = (Rmax**2 - Rmin**2) / 2 / L   \n",
+    "k"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The state variables we'll use are angle, `theta`, angular velocity, `omega`, the length of the spooled string, `y`, and the linear velocity of the yo-yo, `v`.\n",
+    "\n",
+    "Here is a `State` object with the the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init = State(theta=0, omega=0, y=L, v=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's a `System` object with `init` and `t_end` (chosen to be longer than I expect for the yo-yo to drop 1 m)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = System(init=init, t_end=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a slope function for this system, using these results from the book:\n",
+    "\n",
+    "$ r = \\sqrt{2 k y + R_{min}^2} $ \n",
+    "\n",
+    "$ T      = m g I / I^*  $\n",
+    "\n",
+    "$ a      = -m g r^2 / I^* $\n",
+    "\n",
+    "$ \\alpha  = m g r / I^*  $\n",
+    "\n",
+    "where $I^*$ is the augmented moment of inertia, $I + m r^2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your slope function with the initial conditions.\n",
+    "The results should be approximately\n",
+    "\n",
+    "```\n",
+    "0, 180.5, 0, -2.9\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the initial acceleration is substantially smaller than `g` because the yo-yo has to start spinning before it can fall.\n",
+    "\n",
+    "Write an event function that will stop the simulation when `y` is 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your event function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check the final state.  If things have gone according to plan, the final value of `y` should be close to 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How long does it take for the yo-yo to fall 1 m?  Does the answer seem reasonable?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cells plot the results.\n",
+    "\n",
+    "`theta` should increase and accelerate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.theta.plot(color='C0', label='theta')\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Angle (rad)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`y` should decrease and accelerate down."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.y.plot(color='C1', label='y')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Length (m)')\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot velocity as a function of time; is the acceleration constant?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.v.plot(label='velocity', color='C3')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Velocity (m/s)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `gradient` to estimate the derivative of `v`.  How does the acceleration of the yo-yo compare to `g`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = gradient(results.v)\n",
+    "a.plot(label='acceleration', color='C4')\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Acceleration (m/$s^2$)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can use the formula for `r` to plot the radius of the spooled thread over time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = np.sqrt(2*k*results.y + Rmin**2)\n",
+    "r.plot(label='radius')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Radius of spooled thread (m)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 95a8bc6987ad467cfc6bed3745265c19cb0a8d0a Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 18 Feb 2021 09:47:43 -0500
Subject: [PATCH 054/144] Updating examples

---
 .../{hiv_model_soln.ipynb => hiv_model.ipynb} |    0
 examples/wall_soln_with_fsolve.ipynb          | 1021 -----------------
 2 files changed, 1021 deletions(-)
 rename examples/{hiv_model_soln.ipynb => hiv_model.ipynb} (100%)
 delete mode 100644 examples/wall_soln_with_fsolve.ipynb

diff --git a/examples/hiv_model_soln.ipynb b/examples/hiv_model.ipynb
similarity index 100%
rename from examples/hiv_model_soln.ipynb
rename to examples/hiv_model.ipynb
diff --git a/examples/wall_soln_with_fsolve.ipynb b/examples/wall_soln_with_fsolve.ipynb
deleted file mode 100644
index e5d972e6..00000000
--- a/examples/wall_soln_with_fsolve.ipynb
+++ /dev/null
@@ -1,1021 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Thermal behavior of a wall"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# download modsim.py if necessary\n",
-    "\n",
-    "from os.path import basename, exists\n",
-    "\n",
-    "def download(url):\n",
-    "    filename = basename(url)\n",
-    "    if not exists(filename):\n",
-    "        from urllib.request import urlretrieve\n",
-    "        local, _ = urlretrieve(url, filename)\n",
-    "        print('Downloaded ' + local)\n",
-    "    \n",
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSim/main/modsim.py')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# import functions from modsim\n",
-    "\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This case study is based on Gori, Marincioni, Biddulph, Elwell, \"Inferring the thermal resistance and effective thermal mass distribution of a wall from in situ measurements to characterise heat transfer at both the interior and exterior surfaces\", *Energy and Buildings*, Volume 135, 15 January 2017, Pages 398-409, [which I downloaded here](https://www.sciencedirect.com/science/article/pii/S0378778816313056).\n",
-    "    \n",
-    "The authors put their paper under a Creative Commons license, and [make their data available here](http://discovery.ucl.ac.uk/1526521).  I thank them for their commitment to open, reproducible science, which made this case study possible.\n",
-    "\n",
-    "The goal of their paper is to model the thermal behavior of a wall as a step toward understanding the \"performance gap between the expected energy use of buildings and their measured energy use\".  The wall they study is identified as the exterior wall of an office building in central London, [not unlike this one](https://www.google.com/maps/@51.5269375,-0.1303666,3a,75y,90h,88.17t/data=!3m6!1e1!3m4!1sAoAXzN0mbGF9acaVEgUdDA!2e0!7i13312!8i6656)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following figure shows the scenario and their model:\n",
-    "\n",
-    "![Figure 2](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr2.jpg)\n",
-    "\n",
-    "On the interior and exterior surfaces of the wall, they measure temperature and heat flux over a period of three days.  They model the wall using two thermal masses connected to the surfaces, and to each other, by thermal resistors.\n",
-    "\n",
-    "The primary methodology of the paper is a Bayesian method for inferring the parameters of the system (two thermal masses and three thermal resistances).\n",
-    "\n",
-    "The primary result is a comparison of two models: the one shown here with two thermal masses, and a simpler model with only one thermal mass.  They find that the two-mass model is able to reproduce the measured fluxes substantially better."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Tempting as it is, I will not replicate their method for estimating the parameters.  Rather, I will\n",
-    "\n",
-    "1. Implement their model and run it with their estimated parameters, to replicate the results, and \n",
-    "\n",
-    "2. Use SciPy's `leastsq` to see if I can find parameters that a yield lower sum of squared errors.\n",
-    "\n",
-    "`leastsq` is a wrapper for some venerable FORTRAN code that runs [\"a modification of the Levenberg-Marquardt algorithm\"](https://www.math.utah.edu/software/minpack/minpack/lmdif.html), which is one of my favorites ([really](http://allendowney.com/research/model)).\n",
-    "\n",
-    "Implementing their model in the ModSimPy framework turns out to be straightforward.  The simulations run fast enough even when we carry units through the computation.  And the results are visually similar to the ones in the original paper.\n",
-    "\n",
-    "I find that `leastsq` is not able to find parameters that yield substantially better results, which suggest that the estimates in the paper are at least locally optimal."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading the data\n",
-    "\n",
-    "First I'll load the units we need from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = units.meter\n",
-    "K = units.kelvin\n",
-    "W = units.watt\n",
-    "J = units.joule\n",
-    "degC = units.celsius\n",
-    "s = units.second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Read the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSim/main/data/DataOWall.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv('DataOWall.csv', \n",
-    "                   parse_dates=[0], index_col=0, \n",
-    "                   header=0, skiprows=[1,2])\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The index contains Pandas `Timestamp` objects, which is good for dealing with real-world dates and times, but not as good for running the simulations, so I'm going to convert to seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "timestamp_0 = data.index[0]\n",
-    "timestamp_0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Subtracting the first `Timestamp` yields `Timedelta` objects:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "time_deltas = data.index - timestamp_0\n",
-    "time_deltas"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we can convert to seconds and replace the index."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data.index = time_deltas.days * 86400 + time_deltas.seconds\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The timesteps are all 5 minutes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.all(np.diff(data.index) == 300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Mark the columns of the `Dataframe` with units."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data.Q_in.units = W / m**2\n",
-    "data.Q_out.units = W / m**2\n",
-    "data.T_int.units = degC\n",
-    "data.T_ext.units = degC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the measured fluxes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data.Q_in.plot(color='C2')\n",
-    "data.Q_out.plot(color='C0')\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Heat flux (W/$m^2$)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the measured temperatures."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data.T_int.plot(color='C2')\n",
-    "data.T_ext.plot(color='C0')\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Temperature (degC)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Params and System objects\n",
-    "\n",
-    "Here's a `Params` object with the [estimated parameters from the paper](https://www.sciencedirect.com/science/article/pii/S0378778816313056#tbl0005)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "R1 = 0.076   # m**2 * K / W,\n",
-    "R2 = 0.272   # m**2 * K / W,\n",
-    "R3 = 0.078   # m**2 * K / W,\n",
-    "C1 = 212900  # J / m**2 / K,\n",
-    "C2 = 113100  # J / m**2 / K"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = R1, R2, R3, C1, C2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = 0.07, R2, R3, C1, C2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll pass the `Params` object `make_system`, which computes `init`, packs the parameters into `Series` objects, and computes the interpolation functions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params, data):\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    R1, R2, R3, C1, C2 = params\n",
-    "    \n",
-    "    init = State(T_C1 = 16.11, T_C2 = 15.27)\n",
-    "    \n",
-    "    ts = data.index\n",
-    "    t_end = ts[-1] * s\n",
-    "    \n",
-    "    return System(init=init,\n",
-    "                  R= (R1, R2, R3),\n",
-    "                  C= (C1, C2),\n",
-    "                  T_int_func=interpolate(data.T_int),\n",
-    "                  T_ext_func=interpolate(data.T_ext),\n",
-    "                  unit_temp=degC,\n",
-    "                  t_end=t_end, ts=ts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Make a `System` object"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test the interpolation function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.T_ext_func(0), system.T_ext_func(150), system.T_ext_func(300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Implementing the model\n",
-    "\n",
-    "Next we need a slope function that takes instantaneous values of the two internal temperatures and computes their time rates of change.\n",
-    "\n",
-    "The slope function gets called two ways.\n",
-    "\n",
-    "* When we call it directly, `state` is a `State` object and the values it contains have units.\n",
-    "\n",
-    "* When `run_solve_ivp` calls it, `state` is an array and the values it contains don't have units.\n",
-    "\n",
-    "In the second case, we have to apply the units before attempting the computation.  `require_units` applies units if necessary:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function computes the fluxes between the four zones."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_flux(t, state, system):\n",
-    "    \"\"\"Compute the fluxes between the walls surfaces and the internal masses.\n",
-    "    \n",
-    "    state: State with T_C1 and T_C2\n",
-    "    t: time in seconds\n",
-    "    system: System with interpolated measurements and the R Series\n",
-    "    \n",
-    "    returns: Series of fluxes\n",
-    "    \"\"\"    \n",
-    "    # unpack the temperatures\n",
-    "    T_C1, T_C2 = state\n",
-    "        \n",
-    "    # compute a series of temperatures from inside out\n",
-    "    T_int = system.T_int_func(t)\n",
-    "    T_ext = system.T_ext_func(t)\n",
-    "    \n",
-    "    T = [T_int, T_C1, T_C2, T_ext]\n",
-    "    \n",
-    "    # compute differences of adjacent temperatures\n",
-    "    T_diff = np.diff(T)\n",
-    "\n",
-    "    # compute fluxes between adjacent compartments\n",
-    "    Q = T_diff / system.R\n",
-    "    return Q"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compute_flux(0, system.init, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a slope function that computes derivatives of `T_C1` and `T_C2`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(t, state, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    Q = compute_flux(t, state, system)\n",
-    "\n",
-    "    # compute the net flux in each node\n",
-    "    Q_diff = np.diff(Q)\n",
-    "        \n",
-    "    # compute the rate of change of temperature\n",
-    "    dQdt = Q_diff / system.C\n",
-    "    return dQdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slopes = slope_func(system.ts[0], system.init, system)\n",
-    "slopes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's run the simulation, generating estimates for the time steps in the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_solve_ivp(system, slope_func,\n",
-    "                                 t_eval=data.index)\n",
-    "details.message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(results, data):\n",
-    "    data.T_int.plot(color='C2')\n",
-    "    results.T_C1.plot(color='C3')\n",
-    "    results.T_C2.plot(color='C1')\n",
-    "    data.T_ext.plot(color='C0')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Temperature (degC)')\n",
-    "    \n",
-    "plot_results(results, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These results are similar to what's in the paper:\n",
-    "\n",
-    "![Figure 5](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr5.jpg).  \n",
-    "\n",
-    "To get the estimated fluxes, we have to go through the results and basically do the flux calculation again."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def recompute_fluxes(results, system):\n",
-    "    \"\"\"Compute fluxes between wall surfaces and internal masses.\n",
-    "    \n",
-    "    results: Timeframe with T_C1 and T_C2\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: Timeframe with Q_in and Q_out\n",
-    "    \"\"\"\n",
-    "    Q_frame = TimeFrame(index=results.index, \n",
-    "                        columns=['Q_in', 'Q_out'])\n",
-    "    \n",
-    "    for t, row in results.iterrows():\n",
-    "        Q = compute_flux(t, row, system)\n",
-    "        Q_frame.loc[t] = (-Q[0], -Q[2])\n",
-    "        \n",
-    "    return Q_frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Q_frame = recompute_fluxes(results, system)\n",
-    "Q_frame.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see how the estimates compare to the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_Q_in(frame, data):\n",
-    "    frame.Q_in.plot(color='gray')\n",
-    "    data.Q_in.plot(color='C2')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Heat flux (W/$m^2$)')\n",
-    "    \n",
-    "plot_Q_in(Q_frame, data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_Q_out(frame, data):\n",
-    "    frame.Q_out.plot(color='gray')\n",
-    "    data.Q_out.plot(color='C0')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Heat flux (W/$m^2$)')\n",
-    "    \n",
-    "plot_Q_out(Q_frame, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These results are also similar to what's in the paper (the bottom row):\n",
-    "\n",
-    "![Figure 3](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr3.jpg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "error_Q_in = Q_frame.Q_in - data.Q_in\n",
-    "error_Q_out = Q_frame.Q_out - data.Q_out\n",
-    "\n",
-    "errors = np.hstack([error_Q_in, error_Q_out])\n",
-    "errors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the sum of the squared errors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.sum(errors**2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Estimating parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's see if we can do any better than the parameters in the paper."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = R1, R2, R3, C1, C2\n",
-    "params"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = R1, 0.27, R3, C1, C2\n",
-    "params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an error function that takes a hypothetical set of parameters, runs the simulation, and returns an array of errors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func(params, data):\n",
-    "    \"\"\"Run a simulation and return an array of errors.\n",
-    "    \n",
-    "    params: Params object or array\n",
-    "    data: DataFrame\n",
-    "    \n",
-    "    returns: array of float\n",
-    "    \"\"\"\n",
-    "    print(params)\n",
-    "    system = make_system(params, data)    \n",
-    "    results, details = run_solve_ivp(system, slope_func, \n",
-    "                                     t_eval=data.index)\n",
-    "    Q_frame = recompute_fluxes(results, system)\n",
-    "    error_Q_in = Q_frame.Q_in - data.Q_in\n",
-    "    error_Q_out = Q_frame.Q_out - data.Q_out\n",
-    "\n",
-    "    errors = np.hstack([error_Q_in, error_Q_out])\n",
-    "    print(np.sum(errors**2))\n",
-    "    return errors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `error_func`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "errors = error_func(params, data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.sum(errors**2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pass `error_func` to `leastsq` to see if it can do any better."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_params, details = leastsq(error_func, params, data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "details.ier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(details.mesg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The best params are only slightly different from the starting place. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_params"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_params - params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like with the `best_params`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(best_params, data)\n",
-    "system = remove_units(system)\n",
-    "\n",
-    "results, details = run_solve_ivp(system, slope_func, t_eval=system.ts)\n",
-    "Q_frame = recompute_fluxes(results, system)\n",
-    "errors = compute_error(Q_frame, data)\n",
-    "print(np.sum(errors**2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The sum of squared errors is only slightly smaller.\n",
-    "\n",
-    "And the results are visually similar."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_Q_in(Q_frame, data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_Q_out(Q_frame, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Try starting the model with a different set of parameters and see if it moves toward the parameters in the paper.\n",
-    "\n",
-    "I found that no matter where I start, `leastsq` doesn't move far, which suggests that it is not able to optimize the parameters effectively."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Notes\n",
-    "\n",
-    "Notes on working with degC.\n",
-    "\n",
-    "Usually I construct a `Quantity` object by multiplying a number and a unit.  With degC, that doesn't work; you get\n",
-    "\n",
-    "```\n",
-    "OffsetUnitCalculusError: Ambiguous operation with offset unit (degC).\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#16.11 * C "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The problem is that it doesn't know whether you want a temperature measurement or a temperature difference.\n",
-    "\n",
-    "You can create a temperature measurement like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "T = Quantity(16.11, degC)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you convert to Kelvin, it does the right thing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "T.to(K)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When you subtract temperatures, the results is a temperature difference, indicated by the units."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diff = T - 273.15 * K"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you convert a temperature difference to Kelvin, it does the right thing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diff.to(K)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From ff530af634b269fdba8e8ff6c5778fecbe3dd5f4 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 18 Feb 2021 13:43:29 -0500
Subject: [PATCH 055/144] Updating modsim.py

---
 chap22.py |  7 +++----
 modsim.py | 11 ++++-------
 2 files changed, 7 insertions(+), 11 deletions(-)

diff --git a/chap22.py b/chap22.py
index 040c12e3..00fe5be2 100644
--- a/chap22.py
+++ b/chap22.py
@@ -4,7 +4,7 @@
     x = 0,          # m
     y = 1,          # m
     angle = 45,     # degree
-    velocity = 40,  # m / s
+    speed = 40,  # m / s
 
     mass = 145e-3,    # kg 
     diameter = 73e-3, # m 
@@ -25,7 +25,7 @@ def make_system(params):
     theta = deg2rad(params.angle)
     
     # compute x and y components of velocity
-    vx, vy = pol2cart(theta, params.velocity)
+    vx, vy = pol2cart(theta, params.speed)
     
     # make the initial state
     init = State(x=params.x, y=params.y, vx=vx, vy=vy)
@@ -35,8 +35,7 @@ def make_system(params):
 
     return System(params,
                   init = init,
-                  area = area,
-                 )
+                  area = area)
 
 from modsim import *
 
diff --git a/modsim.py b/modsim.py
index 9ed50260..4613793e 100644
--- a/modsim.py
+++ b/modsim.py
@@ -33,11 +33,6 @@
 from types import SimpleNamespace
 from copy import copy
 
-import pint
-
-units = pint.UnitRegistry()
-#Quantity = units.Quantity
-
 
 def flip(p=0.5):
     """Flips a coin with the given probability.
@@ -691,8 +686,9 @@ def SweepSeries(*args, **kwargs):
 def show(obj):
     """Display a Series or Namespace as a DataFrame."""
     if isinstance(obj, pd.Series):
-        return pd.DataFrame(obj)
-    elif isinstance(obj, SimpleNamespace):
+        df = pd.DataFrame(obj)
+        return df
+    elif hasattr(obj, __dict__):
         return pd.DataFrame(pd.Series(obj.__dict__),
                             columns=['value'])
     else:
@@ -716,6 +712,7 @@ def SweepFrame(*args, **kwargs):
 def Vector(x, y, z=None, **options):
     """
     """
+    underride(options, name='component')
     if z is None:
         return pd.Series(dict(x=x, y=y), **options)
     else:

From 24c68860b5a14aa7f7d9f41aff3efcd40c7b48d1 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 18 Feb 2021 14:17:21 -0500
Subject: [PATCH 056/144] Updating modsim.py

---
 modsim.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modsim.py b/modsim.py
index 4613793e..02bdb103 100644
--- a/modsim.py
+++ b/modsim.py
@@ -688,7 +688,7 @@ def show(obj):
     if isinstance(obj, pd.Series):
         df = pd.DataFrame(obj)
         return df
-    elif hasattr(obj, __dict__):
+    elif hasattr(obj, '__dict__'):
         return pd.DataFrame(pd.Series(obj.__dict__),
                             columns=['value'])
     else:

From 85e2cf0a35f11aeb2e23502e1936bc17f0e6baac Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 18 Feb 2021 14:17:26 -0500
Subject: [PATCH 057/144] Updating chapters

---
 chapters/chap10.ipynb |  35 +++-
 chapters/chap14.ipynb |   2 +-
 chapters/chap15.ipynb |   6 +-
 chapters/chap16.ipynb |   6 +-
 chapters/chap17.ipynb | 129 ++++++------
 chapters/chap18.ipynb | 150 ++++++++------
 chapters/chap19.ipynb | 108 +++++++---
 chapters/chap20.ipynb | 224 ++++++---------------
 chapters/chap21.ipynb | 126 ++++++------
 chapters/chap22.ipynb | 448 +++++++++++++++++++++++++++---------------
 chapters/chap23.ipynb | 135 ++++++++-----
 11 files changed, 774 insertions(+), 595 deletions(-)

diff --git a/chapters/chap10.ipynb b/chapters/chap10.ipynb
index b6a1d3c6..727b3d4a 100644
--- a/chapters/chap10.ipynb
+++ b/chapters/chap10.ipynb
@@ -272,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "rural-express",
    "metadata": {
     "tags": []
@@ -288,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "id": "preceding-sheep",
    "metadata": {
     "tags": []
@@ -301,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "id": "together-jackson",
    "metadata": {},
    "outputs": [],
@@ -311,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "id": "simple-verse",
    "metadata": {
     "scrolled": true
@@ -361,6 +361,15 @@
     "than one or two lines of code a time, and test as you go!"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "norwegian-watch",
+   "metadata": {},
+   "source": [
+    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/queue.ipynb) or\n",
+    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/queue.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "forward-point",
@@ -384,6 +393,15 @@
     "something different, please do!"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "copyrighted-smooth",
+   "metadata": {},
+   "source": [
+    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/salmon.ipynb) or\n",
+    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/salmon.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "limiting-moore",
@@ -411,6 +429,15 @@
     "There are no exercises in this case study, but it is an example of what you can do with the tools we have so far and a preview of what you will be able to do with the tools in the next few chapters."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "solid-summit",
+   "metadata": {},
+   "source": [
+    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/trees.ipynb) or\n",
+    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/trees.ipynb)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/chapters/chap14.ipynb b/chapters/chap14.ipynb
index b712c1b3..6b82fe40 100644
--- a/chapters/chap14.ipynb
+++ b/chapters/chap14.ipynb
@@ -807,7 +807,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap15.ipynb b/chapters/chap15.ipynb
index b8d79df4..1018b6c1 100644
--- a/chapters/chap15.ipynb
+++ b/chapters/chap15.ipynb
@@ -59,8 +59,8 @@
     "        local, _ = urlretrieve(url, filename)\n",
     "        print('Downloaded ' + local)\n",
     "    \n",
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/master/modsim.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'modsim.py')"
    ]
   },
   {
@@ -401,7 +401,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "saved-dublin",
+   "id": "together-adapter",
    "metadata": {},
    "source": [
     "There are a two things here that are different from previous versions of `run_simulation`.\n",
diff --git a/chapters/chap16.ipynb b/chapters/chap16.ipynb
index 285ffa16..81e97912 100644
--- a/chapters/chap16.ipynb
+++ b/chapters/chap16.ipynb
@@ -59,8 +59,8 @@
     "        local, _ = urlretrieve(url, filename)\n",
     "        print('Downloaded ' + local)\n",
     "    \n",
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/master/modsim.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'modsim.py')"
    ]
   },
   {
@@ -666,7 +666,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap17.ipynb b/chapters/chap17.ipynb
index 24a3bda2..fe6a8163 100644
--- a/chapters/chap17.ipynb
+++ b/chapters/chap17.ipynb
@@ -59,8 +59,8 @@
     "        local, _ = urlretrieve(url, filename)\n",
     "        print('Downloaded ' + local)\n",
     "    \n",
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/master/modsim.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'modsim.py')"
    ]
   },
   {
@@ -90,9 +90,10 @@
    "id": "european-movement",
    "metadata": {},
    "source": [
-    "In this chapter we start a new example, a model of how glucose and insulin interact to control blood sugar.  We will implement a widely used model called the \"Minimal Model\" because it is intended to include only the elements essential to explain the observed behavior of the system.\n",
+    "In this chapter we start a new example, a model of how glucose and insulin interact to control blood sugar.  \n",
+    "We will implement a widely used model called the \"Minimal Model\" because it is intended to include only the elements essential to explain the observed behavior of the system.\n",
     "\n",
-    "This chapter presents the model itself and some background information we need to understand it.\n",
+    "This chapter presents the model and some background information we need to understand it.\n",
     "In the next chapter we'll implement the model and compare the results to real data.\n",
     "\n",
     "My presentation in this chapter follows Bergman (2005) \"Minimal Model\"\n",
@@ -107,9 +108,7 @@
    "source": [
     "## The Minimal Model\n",
     "\n",
-    "**Pharmacokinetics** is the study of how drugs and other substances move around the body, react, and are eliminated. In this chapter, we\n",
-    "implement one of the most widely used pharmacokinetic models: the\n",
-    "so-called \"minimal model\" of glucose and insulin in the blood stream.\n",
+    "**Pharmacokinetics** is the study of how drugs and other substances move around the body, react, and are eliminated. In this chapter, I present a widely used pharmacokinetic models: the so-called \"minimal model\" of glucose and insulin in the blood stream.\n",
     "\n",
     "**Glucose** is a form of sugar that circulates in the blood of animals; it is used as fuel for muscles, the brain, and other organs. The concentration of blood sugar is controlled by the hormone system, and especially by **insulin**, which is produced by the pancreas and has the effect of reducing blood sugar.\n",
     "\n",
@@ -125,7 +124,7 @@
    "id": "sharp-yukon",
    "metadata": {},
    "source": [
-    "One of the most-used tests for hyperglycemia and diabetes is the\n",
+    "A widely used test for hyperglycemia and diabetes is the\n",
     "frequently sampled intravenous glucose tolerance test (FSIGT), in which glucose is injected into the blood stream of a fasting subject (someone who has not eaten recently); then blood samples are collected at intervals of 2--10 minutes for 3 hours. The samples are analyzed to\n",
     "measure the concentrations of glucose and insulin.\n",
     "\n",
@@ -140,7 +139,7 @@
    "source": [
     "## The glucose minimal model\n",
     "\n",
-    "The \"minimal model\", as proposed by Bergman, Ider, Bowden, and\n",
+    "The minimal model, as proposed by Bergman, Ider, Bowden, and\n",
     "Cobelli, consists of two parts: the glucose model and the insulin model. I will present an implementation of the glucose model; as a case study, you will have the chance to implement the insulin model.\n",
     "\n",
     "The original model was developed in the 1970s; since then, many\n",
@@ -162,21 +161,27 @@
     "> (1) Glucose, once elevated by injection, returns to basal level due to two effects: the effect of glucose itself to normalize its own concentration \\[...\\] as well as the catalytic effect of insulin to allow glucose to self-normalize (2) Also, we discovered that the effect of insulin on net glucose disappearance must be sluggish --- that is, that insulin acts slowly because insulin must first move from plasma to a remote compartment \\[...\\] to exert its action on glucose disposal.\n",
     "\n",
     "To paraphrase the second point, the effect of insulin on glucose\n",
-    "disposal, as seen in the data, happens more slowly than we would expect if it depended primarily on the the concentration of insulin in the blood. Bergman's group hypothesized that insulin must move relatively slowly from the blood to a \"remote compartment\" where it has its effect."
+    "disposal, as seen in the data, happens more slowly than we would expect if it depended primarily on the the concentration of insulin in the blood. Bergman's group hypothesized that insulin must move relatively slowly from the blood to a remote compartment where it has its effect."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bottom-extent",
+   "id": "growing-logistics",
    "metadata": {},
    "source": [
-    "At the time, the remote compartment was a modeling abstraction that\n",
+    "At the time, the \"remote compartment\" was a modeling abstraction that\n",
     "might, or might not, represent something physical. Later, according to\n",
     "Bergman, it was \"shown to be interstitial fluid\", that is, the fluid\n",
-    "that surrounds tissue cells. In the history of mathematical modeling, it is common for hypothetical entities, added to models to achieve\n",
-    "particular effects, to be found later to correspond to physical\n",
-    "entities.  The most notable example, historically, might be [genes](https://en.wikipedia.org/wiki/Gene#Discovery_of_discrete_inherited_units).\n",
+    "that surrounds tissue cells. \n",
     "\n",
+    "In the history of mathematical modeling, it is common for hypothetical entities, added to models to achieve particular effects, to be found later to correspond to physical entities.  One notable example is [the gene] https://en.wikipedia.org/wiki/Gene#Discovery_of_discrete_inherited_units)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bottom-extent",
+   "metadata": {},
+   "source": [
     "The glucose model consists of two differential equations:\n",
     "\n",
     "$$\\frac{dG}{dt} = -k_1 \\left[ G(t) - G_b \\right] - X(t) G(t)$$\n",
@@ -220,14 +225,10 @@
     "    unspecified; we can consider $X$ to be in whatever units make the\n",
     "    parameter of this term 1.\n",
     "\n",
-    "-   $k_3 \\left[ I(t) - I_b \\right]$ is the rate at which insulin\n",
-    "    diffuses between blood and tissue fluid. When $I(t)$ is above basal\n",
-    "    level, insulin diffuses from the blood into the tissue fluid. When\n",
-    "    $I(t)$ is below basal level, insulin diffuses from tissue to the\n",
+    "-   $k_3 \\left[ I(t) - I_b \\right]$ is the rate at which insulin diffuses between blood and tissue fluid. When $I(t)$ is above basal level, insulin diffuses from the blood into the tissue fluid. When $I(t)$ is below basal level, insulin diffuses from tissue to the\n",
     "    blood.\n",
     "\n",
-    "-   $-k_2 X(t)$ is the rate of insulin disappearance in tissue fluid as\n",
-    "    it is consumed or broken down."
+    "-   $-k_2 X(t)$ is the rate of insulin disappearance in tissue fluid as it is consumed or broken down."
    ]
   },
   {
@@ -242,37 +243,36 @@
   },
   {
    "cell_type": "markdown",
-   "id": "exact-heating",
+   "id": "apart-giant",
    "metadata": {},
    "source": [
     "## Data\n",
     "\n",
-    "To develop and test the model, we'll use data from [Pacini and Bergman](https://www.researchgate.net/publication/13707725_Insulin_sensitivity_and_glucose_effectiveness_Minimal_model_analysis_of_regular_and_insulin-modified_FSIGT), \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 23: 113-122, (1986)."
+    "To develop and test the model, we'll use data from [Pacini and Bergman](https://www.researchgate.net/publication/13707725_Insulin_sensitivity_and_glucose_effectiveness_Minimal_model_analysis_of_regular_and_insulin-modified_FSIGT), \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 1986."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "separated-strip",
-   "metadata": {},
+   "id": "exact-heating",
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "The following cell downloads the data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 4,
    "id": "looking-needle",
    "metadata": {
-    "scrolled": true
+    "scrolled": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
-    "import os\n",
-    "\n",
-    "filename = 'glucose_insulin.csv'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/glucose_insulin.csv"
+    "download('https://github.com/AllenDowney/ModSim/raw/master/' +\n",
+    "         'glucose_insulin.csv')"
    ]
   },
   {
@@ -285,14 +285,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "id": "naval-geology",
+   "execution_count": 5,
+   "id": "cosmetic-matrix",
    "metadata": {},
    "outputs": [],
    "source": [
     "from pandas import read_csv\n",
     "\n",
-    "data = read_csv('glucose_insulin.csv', index_col='time')\n",
+    "data = read_csv('glucose_insulin.csv', index_col='time')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "written-beast",
+   "metadata": {},
+   "source": [
+    "Here are the first few rows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "naval-geology",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "data.head()"
    ]
   },
@@ -301,7 +318,7 @@
    "id": "close-payday",
    "metadata": {},
    "source": [
-    "`data` has two columns: `glucose` is the concentration of blood glucose in mg/dL; `insulin` is concentration of insulin in the blood in μU/mL (a medical \"unit\", denoted U, is an amount defined by convention in context). The index is time in minutes.\n",
+    "`data` has two columns: `glucose` is the concentration of blood glucose in mg/dL; `insulin` is the concentration of insulin in the blood in μU/mL (a medical \"unit\", denoted U, is an amount defined by convention in context). The index is time in minutes.\n",
     "\n",
     "This dataset represents glucose and insulin concentrations over\n",
     "182 min for a subject with normal insulin production and sensitivity."
@@ -316,14 +333,14 @@
     "\n",
     "Before we are ready to implement the model, there's one problem we have to solve. In the differential equations, $I$ is a function that can be evaluated at any time, $t$. But in the `DataFrame`, we only have measurements at discrete times. This is a job for interpolation!\n",
     "\n",
-    "The ModSim library provides an `interpolate`, function that takes a `Series` as a parameter and returns a function. That's right, I said it returns a *function*.\n",
+    "The ModSim library provides an `interpolate` function that takes a `Series` as a parameter and returns a function. That's right, I said it returns a *function*.\n",
     "\n",
-    "So we can call `interpolate` like this:"
+    "We can call `interpolate` like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 7,
    "id": "terminal-teaching",
    "metadata": {},
    "outputs": [],
@@ -336,12 +353,12 @@
    "id": "banner-shakespeare",
    "metadata": {},
    "source": [
-    "Then we can call the new function, `I`, like this:"
+    "The result is a function we can call like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 8,
    "id": "intense-thursday",
    "metadata": {},
    "outputs": [],
@@ -354,14 +371,14 @@
    "id": "severe-desire",
    "metadata": {},
    "source": [
-    "The result is about 31.7, which is a linear interpolation between the actual measurements at `t=16` and `t=19`. \n",
+    "In this example the interpolated value is about 31.7, which is a linear interpolation between the actual measurements at `t=16` and `t=19`. \n",
     "\n",
     "We can also pass an array as an argument to `I`.  Here's an array of equally-spaced values from `t_0` to `t_end`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 9,
    "id": "least-pattern",
    "metadata": {},
    "outputs": [],
@@ -381,7 +398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 10,
    "id": "mounted-venice",
    "metadata": {},
    "outputs": [],
@@ -399,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 11,
    "id": "affecting-response",
    "metadata": {},
    "outputs": [],
@@ -417,13 +434,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 12,
    "id": "engaging-watershed",
    "metadata": {},
    "outputs": [],
    "source": [
     "data.insulin.plot(style='o', color='C2', label='insulin data')\n",
-    "I_series.plot(color='C2', label='interpolated')\n",
+    "I_series.plot(color='C2', label='interpolation')\n",
     "\n",
     "decorate(xlabel='Time (min)',\n",
     "         ylabel='Concentration ($\\mu$U/mL)')"
@@ -437,12 +454,11 @@
     "## Summary\n",
     "\n",
     "This chapter introduces a model of the interaction between glucose and insulin in the blood stream.\n",
-    "And it introduces a new tool, interpolation, which we'll need to implement the model\n",
+    "And it introduces a new tool, interpolation, which we'll need to implement the model.\n",
     "\n",
     "In the next chapter, we will use measured concentrations of insulin to simulate the glucose-insulin system, and compare the results to measured concentrations of glucose.\n",
     "\n",
-    "Then, you'll have a chance to implement the second part of the model, which uses measured concentrations of glucose to simulate the insulin response, and compare the results to the data.\n",
-    "\n"
+    "Then you'll have a chance to implement the second part of the model, which uses measured concentrations of glucose to simulate the insulin response, and compare the results to the data."
    ]
   },
   {
@@ -469,7 +485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 13,
    "id": "endless-network",
    "metadata": {},
    "outputs": [],
@@ -479,13 +495,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 14,
    "id": "entire-concern",
    "metadata": {},
    "outputs": [],
    "source": [
     "data.insulin.plot(style='o', color='C2', label='insulin data')\n",
-    "I_series.plot(color='C2', label='interpolated')\n",
+    "I_series.plot(color='C2', label='interpolation')\n",
     "\n",
     "decorate(xlabel='Time (min)',\n",
     "         ylabel='Concentration ($\\mu$U/mL)')"
@@ -501,7 +517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 15,
    "id": "representative-acquisition",
    "metadata": {},
    "outputs": [],
@@ -511,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 16,
    "id": "rocky-sydney",
    "metadata": {
     "scrolled": true
@@ -535,6 +551,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
diff --git a/chapters/chap18.ipynb b/chapters/chap18.ipynb
index 6a450990..a69372fc 100644
--- a/chapters/chap18.ipynb
+++ b/chapters/chap18.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -59,13 +59,13 @@
     "        local, _ = urlretrieve(url, filename)\n",
     "        print('Downloaded ' + local)\n",
     "    \n",
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/master/modsim.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -90,22 +90,24 @@
    "id": "detected-welsh",
    "metadata": {},
    "source": [
-    "The previous chapter present the minimal model of the glucose-insulin system, and introduces a tool we will need to implement it: interpolation.\n",
+    "The previous chapter presents the minimal model of the glucose-insulin system and introduces a tool we will need to implement it, interpolation.\n",
     "\n",
     "In this chapter, we'll implement the model two ways:\n",
     "\n",
-    "* We'll start by rewriting the differential equations as difference equations; then we'll solve the difference equations using a version of `run_simulation`, similar to what we have used in previous chapters.\n",
+    "* We'll start by rewriting the differential equations as difference equations; then we'll solve the difference equations using a version of `run_simulation` similar to what we have used in previous chapters.\n",
     "\n",
-    "* Then we'll use a new SciPy function, called `solve_ivp`, to solve the differential equation.\n",
+    "* Then we'll use a new SciPy function, called `solve_ivp`, to solve the differential equation using a better algorithm.\n",
     "\n",
     "We'll see that `solve_ivp` is faster and more accurate than `run_simulation`.\n",
-    "As a result, we will us it for every model in the rest of the book."
+    "As a result, we will use it for the models in the rest of the book."
    ]
   },
   {
    "cell_type": "markdown",
    "id": "original-photographer",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "The following cell downloads the data."
    ]
@@ -114,21 +116,21 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "fewer-weather",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "import os\n",
-    "\n",
-    "filename = 'glucose_insulin.csv'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/glucose_insulin.csv"
+    "download('https://github.com/AllenDowney/ModSim/raw/master/' +\n",
+    "         'glucose_insulin.csv')"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "postal-procedure",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "We can use Pandas to read the data."
    ]
@@ -137,12 +139,14 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "computational-border",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from pandas import read_csv\n",
     "\n",
-    "data = read_csv(filename, index_col='time');"
+    "data = read_csv('glucose_insulin.csv', index_col='time');"
    ]
   },
   {
@@ -154,7 +158,7 @@
     "\n",
     "To get started, let's assume that the parameters of the model are known.\n",
     "We'll implement the model and use it to generate time series for `G` and `X`. \n",
-    "Then we'll see how we can choose parameters that generate a series that fits the data.\n",
+    "Then we'll see how we can choose parameters that make the simulation fit the data.\n",
     "\n",
     "Here are the parameters."
    ]
@@ -258,7 +262,7 @@
     "\n",
     "$$\\frac{dX}{dt} = k_3 \\left[I(t) - I_b \\right] - k_2 X(t)$$ \n",
     "\n",
-    "To simulate this system, we will rewriting them as difference equations. \n",
+    "To simulate this system, we will rewrite them as difference equations. \n",
     "If we multiply both sides by $dt$, we have:\n",
     "\n",
     "$$dG = \\left[ -k_1 \\left[ G(t) - G_b \\right] - X(t) G(t) \\right] dt$$\n",
@@ -277,7 +281,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def update_func(state, t, system):\n",
+    "def update_func(t, state, system):\n",
     "    G, X = state\n",
     "    G0, k1, k2, k3 = system.params \n",
     "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
@@ -297,15 +301,13 @@
    "id": "basic-subdivision",
    "metadata": {},
    "source": [
-    "As usual, the update function takes a `State` object, a time, and a\n",
-    "`System` object as parameters. The first line uses multiple assignment\n",
-    "to extract the current values of `G` and `X`.\n",
+    "As usual, the update function takes a time stamp, a `State` object, and a `System` object as parameters. The first line uses multiple assignment to extract the current values of `G` and `X`.\n",
     "\n",
     "The following lines unpack the parameters we need from the `System`\n",
     "object.\n",
     "\n",
-    "Computing the derivatives `dGdt` and `dXdt` is straightforward; we just translate the equations from math notation to Python.\n",
-    "Then, to perform the update, we multiply each derivative by the discrete time step `dt`, which is 2 min in this example. \n",
+    "To compute the derivatives `dGdt` and `dXdt` we translate the equations from math notation to Python.\n",
+    "Then, to perform the update, we multiply each derivative by the time step `dt`, which is 2 min in this example. \n",
     "\n",
     "The return value is a `State` object with the new values of `G` and `X`.\n",
     "\n",
@@ -320,7 +322,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "update_func(system.init, system.t_0, system)"
+    "update_func(system.t_0, system.init, system)"
    ]
   },
   {
@@ -359,7 +361,7 @@
     "    for i in range(n-1):\n",
     "        t = t_array[i]\n",
     "        state = frame.iloc[i]\n",
-    "        frame.iloc[i+1] = update_func(state, t, system)\n",
+    "        frame.iloc[i+1] = update_func(t, state, system)\n",
     "    \n",
     "    return frame"
    ]
@@ -369,6 +371,11 @@
    "id": "musical-loading",
    "metadata": {},
    "source": [
+    "This version is similar to the one we used for the coffee cooling problem.\n",
+    "The biggest difference is that it makes and returns a `TimeFrame` rather than a `TimeSeries`.\n",
+    "\n",
+    "When we make the `TimeSeries`, we use `index` to indicate that the index is the array of time stamps, `t_array`, and `columns` to indicate that the column names are the state variables we get from `init`.\n",
+    "\n",
     "We can run it like this:"
    ]
   },
@@ -428,7 +435,7 @@
    "id": "stopped-excuse",
    "metadata": {},
    "source": [
-    "With the parameters I chose, the model fits the data well, except during the first few minutes after the injection.\n",
+    "With the parameters I chose, the model fits the data well except during the first few minutes after the injection.\n",
     "But we don't expect the model to do well in this regime.\n",
     "\n",
     "The problem is that the model is **non-spatial**; that is, it does not\n",
@@ -486,15 +493,15 @@
     "In effect, the simulations assume that the derivatives $dG/dt$ and $dX/dt$ are constant during each 2 min time step.\n",
     "This method, evaluating derivatives at discrete time steps and assuming that they are constant in between, is called **Euler's method** (see <http://modsimpy.com/euler>).\n",
     "\n",
-    "Euler's method is good enough for many simple problems, but sometimes it is not very accurate.\n",
-    "In that case, sometimes we can make it more accurate by decreasing the size of `dt`.\n",
+    "Euler's method is good enough for many problems, but sometimes it is not very accurate.\n",
+    "In that case, we can usually make it more accurate by decreasing the size of `dt`.\n",
     "But then it is not very efficient.\n",
     "\n",
     "There are other methods that are more accurate and more efficient than Euler's method.\n",
-    "SciPy provides several of them as a function called `solve_ivp` \n",
+    "SciPy provides several of them wrapped in a function called `solve_ivp`.\n",
     "The \"ivp\" in `solve_ivp` stands for \"initial value problem\", which is the term for problems like the ones we've been solving, where we are given the initial conditions and try to predict what will happen.\n",
     "\n",
-    "ModSim provides a function called `run_solve_ivp` that makes `solve_ivp` a little easier to use.\n",
+    "The ModSim library provides a function called `run_solve_ivp` that makes `solve_ivp` a little easier to use.\n",
     "\n",
     "To use it, we have to provide a \"slope function\", which is similar to an update function; in fact, it takes the same parameters: a time stamp, a `State` object, and a `System` object.\n",
     "\n",
@@ -533,7 +540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 18,
    "id": "intelligent-visitor",
    "metadata": {},
    "outputs": [],
@@ -559,7 +566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 19,
    "id": "industrial-focus",
    "metadata": {},
    "outputs": [],
@@ -569,7 +576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 20,
    "id": "frequent-exhibit",
    "metadata": {},
    "outputs": [],
@@ -590,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 21,
    "id": "immediate-legislature",
    "metadata": {},
    "outputs": [],
@@ -605,12 +612,12 @@
    "source": [
     "Because we used `t_eval=results.index`, the time stamps in `results2` are the same as in `results`, which makes them easier to compare.\n",
     "\n",
-    "Here are the results from `run_solve_ivp` along with the results from `run_simulation`:"
+    "The following figure shows the results from `run_solve_ivp` along with the results from `run_simulation`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 22,
    "id": "peripheral-commission",
    "metadata": {},
    "outputs": [],
@@ -634,13 +641,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "clear-neighbor",
+   "execution_count": 23,
+   "id": "diagnostic-lawsuit",
    "metadata": {},
    "outputs": [],
    "source": [
     "diff = results.G - results2.G\n",
-    "percent_diff = diff / results2.G * 100\n",
+    "percent_diff = diff / results2.G * 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "comparative-boulder",
+   "metadata": {},
+   "source": [
+    "And we can use `describe` to compute summary statistics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "clear-neighbor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "percent_diff.abs().describe()"
    ]
   },
@@ -649,7 +673,8 @@
    "id": "temporal-threat",
    "metadata": {},
    "source": [
-    "The biggest differences are a little more than 1%.\n",
+    "The mean relative difference is about 0.65%;\n",
+    "the maximum is a little more than 1%.\n",
     "So in this example, the results from Euler's method are probably good enough for practical purposes."
    ]
   },
@@ -663,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 25,
    "id": "happy-guess",
    "metadata": {},
    "outputs": [],
@@ -684,6 +709,20 @@
     "As an exercise, you can experiment with `dt` and see what effect it has on these results."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "julian-dublin",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "diff = results.G - results2.X\n",
+    "percent_diff = diff / results2.X * 100\n",
+    "percent_diff.abs().describe()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "prostate-psychology",
@@ -719,7 +758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 27,
    "id": "thermal-trance",
    "metadata": {},
    "outputs": [],
@@ -729,7 +768,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 28,
    "id": "prompt-activity",
    "metadata": {},
    "outputs": [],
@@ -739,7 +778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 29,
    "id": "developed-collaboration",
    "metadata": {},
    "outputs": [],
@@ -747,19 +786,6 @@
     "# Solution goes here"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "alpha-configuration",
-   "metadata": {},
-   "source": [
-    "## Under the hood\n",
-    "\n",
-    "The documentation for `solve_ivp` is at <https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html>.\n",
-    "\n",
-    "It uses a Runge-Kutta method...\n",
-    "[RK45](https://en.wikipedia.org/wiki/List_of_Runge–Kutta_methods)."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/chapters/chap19.ipynb b/chapters/chap19.ipynb
index 734b0ee9..26182b16 100644
--- a/chapters/chap19.ipynb
+++ b/chapters/chap19.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -85,6 +85,14 @@
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap19.ipynb)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "reflected-sitting",
+   "metadata": {},
+   "source": [
+    "This chapter presents case studies where you can apply the tools we have learned so far to the glucose-insulin minimal model, an electronic circuit, a thermal model of a wall, and the interaction of HIV and T cells."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "structured-satellite",
@@ -97,11 +105,20 @@
     "In the repository for this book, you will find a notebook,\n",
     "`glucose.ipynb`, that shows how we can find the parameters that best fit the data.\n",
     "\n",
-    "It uses a SciPy function called `leastsq`, which stands for \"least squares\"; that is, it finds the parameters that minimize the sum of squared differences between the results of the model and the data.\n",
+    "It uses a SciPy function called `leastsq`, which stands for \"least squares\"; so-named because it finds the parameters that minimize the sum of squared differences between the results of the model and the data.\n",
     "\n",
     "You can think of `leastsq` as an optional tool for this book.  We won't use it in the text itself, but it appears in a few of the case studies.\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "everyday-stopping",
+   "metadata": {},
+   "source": [
+    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/glucose.ipynb) or\n",
+    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/glucose.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "laden-gathering",
@@ -126,7 +143,7 @@
     "-   $\\gamma$ is a parameter that controls the rate of increase (or\n",
     "    decrease) in blood insulin when glucose is above (or below) $G_T$.\n",
     "\n",
-    "The initial condition is $I(0) = I_0$. As in the glucose minimal model, we treat the initial condition as a parameter which we'll choose to fit the data."
+    "The initial condition is $I(0) = I_0$. As in the glucose minimal model, we treat this initial concentration as a free parameter; that is, we'll choose it to fit the data."
    ]
   },
   {
@@ -149,21 +166,36 @@
   },
   {
    "cell_type": "markdown",
-   "id": "parallel-radical",
+   "id": "sustainable-breath",
+   "metadata": {},
+   "source": [
+    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/insulin.ipynb) or\n",
+    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/insulin.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hybrid-vienna",
    "metadata": {},
    "source": [
     "## Low-Pass Filter\n",
     "\n",
     "The following circuit diagram (from <http://modsimpy.com/divider>) shows a low-pass filter built with one resistor and one capacitor.\n",
     "\n",
-    "![Circuit diagram of a low-pass filter](https://github.com/AllenDowney/ModSim/raw/master/figs/RC_Divider.svg)\n",
+    "![Circuit diagram of a low-pass filter](https://github.com/AllenDowney/ModSim/raw/main/figs/RC_Divider.svg)\n",
     "\n",
     "A \"filter\" is a circuit takes a signal, $V_{in}$, as input and produces a signal, $V_{out}$, as output. In this context, a \"signal\" is a voltage that changes over time.\n",
     "\n",
     "A filter is \"low-pass\" if it allows low-frequency signals to pass from\n",
     "$V_{in}$ to $V_{out}$ unchanged, but it reduces the amplitude of\n",
-    "high-frequency signals.\n",
-    "\n",
+    "high-frequency signals."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "visible-estonia",
+   "metadata": {},
+   "source": [
     "By applying the laws of circuit analysis, we can derive a differential\n",
     "equation that describes the behavior of this system. By solving the\n",
     "differential equation, we can predict the effect of this circuit on any input signal.\n",
@@ -180,8 +212,14 @@
     "Kirchhoff's current law implies that the current through the capacitor\n",
     "is: \n",
     "\n",
-    "$$I_C = I_R$$ \n",
-    "\n",
+    "$$I_C = I_R$$ "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "parallel-radical",
+   "metadata": {},
+   "source": [
     "According to a simple model of the behavior of\n",
     "capacitors, current through the capacitor causes a change in the voltage across the capacitor: \n",
     "\n",
@@ -202,6 +240,15 @@
     "the notebook, run the code, and work on the exercises."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "twenty-sight",
+   "metadata": {},
+   "source": [
+    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/filter.ipynb) or\n",
+    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/filter.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "violent-directive",
@@ -209,18 +256,14 @@
    "source": [
     "## Thermal behavior of a wall\n",
     "\n",
-    "This case study is based on a paper by Gori, et al[^2] that models the\n",
-    "thermal behavior of a brick wall, with the goal of understanding the\n",
-    "\"performance gap between the expected energy use of buildings and their measured energy use\".\n",
+    "This case study is based on a paper that models the thermal behavior of a brick wall with the goal of understanding the \"performance gap between the expected energy use of buildings and their measured energy use\".\n",
     "\n",
     "The following figure shows the scenario and their model of the wall:\n",
     "\n",
-    "![Model of a wall as a series of thermal insulators](https://github.com/AllenDowney/ModSim/raw/master/figs/wall_model.png)\n",
+    "![Model of a wall as a series of thermal insulators](https://github.com/AllenDowney/ModSim/raw/main/figs/wall_model.png)\n",
     "\n",
     "On the interior and exterior surfaces of the wall, they measure\n",
-    "temperature and heat flux over a period of three days. They model the\n",
-    "wall using two thermal masses connected to the surfaces, and to each\n",
-    "other, by thermal resistors."
+    "temperature and heat flux (rate of heat flow) over a period of three days. They model the wall using two thermal masses connected to the surfaces, and to each other, by thermal resistors."
    ]
   },
   {
@@ -239,17 +282,27 @@
     "\n",
     "In the repository for this book, you will find a notebook, `wall.ipynb` with the code and results for this case study.\n",
     "\n",
+    "The paper this case study is based on is\n",
+    "Gori, Marincioni, Biddulph, Elwell, \"Inferring the thermal resistance and effective thermal mass distribution of a wall from in situ measurements to characterise heat transfer at both the interior and exterior surfaces\", *Energy and Buildings*, 2017, available from <http://modsimpy.com/wall2>.\n",
+    "\n",
     "The authors put their paper under a Creative Commons license, and\n",
     "make their data available at <http://modsimpy.com/wall>. I thank them\n",
     "for their commitment to open, reproducible science, which made this\n",
-    "case study possible.\n",
-    "\n",
-    "Gori, Marincioni, Biddulph, Elwell, \"Inferring the thermal resistance and effective thermal mass distribution of a wall from in situ measurements to characterise heat transfer at both the interior and exterior surfaces\", *Energy and Buildings*, Volume 135, pages 398-409, <http://modsimpy.com/wall2>."
+    "case study possible."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "narrow-voice",
+   "id": "wireless-advocacy",
+   "metadata": {},
+   "source": [
+    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/owall.ipynb) or\n",
+    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/owall.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "duplicate-joshua",
    "metadata": {},
    "source": [
     "## HIV\n",
@@ -258,7 +311,7 @@
     "The most obvious explanation for the decline is an immune response that destroys the virus or controls its replication.\n",
     "However, at least in some patients, the decline occurs even without any detectable immune response.\n",
     "\n",
-    "In 1996 Andrew Phillips proposed another explanation for the decline (\"Reduction of HIV Concentration During Acute Infection: Independence from a Specific Immune Response\", available from <https://people.math.gatech.edu/~weiss/uploads/5/8/6/1/58618765/phillips1996.pdf>).\n",
+    "In 1996 Andrew Phillips proposed another explanation for the decline in this paper: \"Reduction of HIV Concentration During Acute Infection: Independence from a Specific Immune Response\", available from <https://people.math.gatech.edu/~weiss/uploads/5/8/6/1/58618765/phillips1996.pdf>).\n",
     "\n",
     "Phillips presents a system of differential equations that models the concentrations of the HIV virus and the CD4 cells it infects.\n",
     "The model does not include an immune response; nevertheless, it demonstrates behavior that is qualitatively similar to what is seen in patients during the first few weeks after infection.\n",
@@ -268,6 +321,15 @@
     "In the repository for this book, you will find a notebook, `hiv_model.ipynb`, which you can use to implement Phillips's model and consider whether it does the work it is meant to do."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "narrow-voice",
+   "metadata": {},
+   "source": [
+    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/hiv_model.ipynb) or\n",
+    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/hiv_model.ipynb)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/chapters/chap20.ipynb b/chapters/chap20.ipynb
index aa7fee08..0ac9851f 100644
--- a/chapters/chap20.ipynb
+++ b/chapters/chap20.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -92,11 +92,9 @@
    "source": [
     "So far the differential equations we've worked with have been **first\n",
     "order**, which means they involve only first derivatives. In this\n",
-    "chapter, we turn our attention to second order ODEs, which can involve\n",
-    "both first and second derivatives.\n",
+    "chapter, we turn our attention to second order differential equations, which can involve both first and second derivatives.\n",
     "\n",
-    "We'll revisit the falling penny example from\n",
-    "Chapter xxx, and use `run_solve_ivp` to find the position and velocity of the penny as it falls, with and without air resistance."
+    "We'll revisit the falling penny example from Chapter 1, and use `run_solve_ivp` to find the position and velocity of the penny as it falls, with and without air resistance."
    ]
   },
   {
@@ -106,18 +104,18 @@
    "source": [
     "## Newton's second law of motion\n",
     "\n",
-    "First order ODEs can be written \n",
+    "First order differential equations (DEs) can be written \n",
     "\n",
     "$$\\frac{dy}{dx} = G(x, y)$$ \n",
     "\n",
-    "where $G$ is some function of $x$ and $y$ (see <http://modsimpy.com/ode>). Second order ODEs can be written \n",
+    "where $G$ is some function of $x$ and $y$ (see <http://modsimpy.com/ode>). Second order DEs can be written \n",
     "\n",
     "$$\\frac{d^2y}{dx^2} = H(x, y, \\frac{dy}{dt})$$\n",
     "\n",
     "where $H$ is a function of $x$, $y$, and $dy/dx$.\n",
     "\n",
     "In this chapter, we will work with one of the most famous and useful\n",
-    "second order ODEs, Newton's second law of motion: \n",
+    "second order DEs, Newton's second law of motion: \n",
     "\n",
     "$$F = m a$$ \n",
     "\n",
@@ -139,7 +137,7 @@
     "\n",
     "$$\\frac{d^2y}{dt^2} = F / m$$ \n",
     "\n",
-    "And that's definitely a second order ODE.\n",
+    "And that's definitely a second order DE.\n",
     "In general, $F$ can be a function of time, position, and velocity."
    ]
   },
@@ -175,8 +173,7 @@
    "source": [
     "## Dropping pennies\n",
     "\n",
-    "As a first example, let's get back to the penny falling from the Empire State Building, which we considered in\n",
-    "Chapter xxx. We will implement two models of this system: first without air resistance, then with.\n",
+    "As a first example, let's get back to the penny falling from the Empire State Building, which we considered in Chapter 1. We will implement two models of this system: first without air resistance, then with.\n",
     "\n",
     "Given that the Empire State Building is 381 m high, and assuming that\n",
     "the penny is dropped from a standstill, the initial conditions are:"
@@ -184,13 +181,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "id": "compatible-increase",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "init = State(y=381, v=0)"
    ]
   },
@@ -201,69 +196,19 @@
    "source": [
     "where `y` is height above the sidewalk and `v` is velocity. \n",
     "\n",
-    "The units `m` and `s` are from the `units` object provided by Pint:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "wicked-thomas",
-   "metadata": {},
-   "source": [
-    "The only system parameter is the acceleration of gravity:"
+    "I'll put the initial conditions in a `System` object, along with the magnitude of acceleration due to gravity, `g`, and the duration of the simulations, `t_end`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 19,
    "id": "reverse-authorization",
    "metadata": {},
    "outputs": [],
    "source": [
-    "g = 9.8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "developing-newfoundland",
-   "metadata": {},
-   "source": [
-    "In addition, we'll specify the duration of the simulation and the step\n",
-    "size:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "square-toolbox",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_end = 10\n",
-    "dt = 0.1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "precise-tobago",
-   "metadata": {},
-   "source": [
-    "With these parameters, the number of time steps is 100, which is good\n",
-    "enough for many problems. Once we have a solution, we will increase the\n",
-    "number of steps and see what effect it has on the results.\n",
-    "\n",
-    "We need a `System` object to store the parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "built-piece",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "system = System(init=init, g=g, t_end=t_end, dt=dt)"
+    "system = System(init=init, \n",
+    "                g=9.8, \n",
+    "                t_end=10)"
    ]
   },
   {
@@ -271,7 +216,7 @@
    "id": "heavy-boards",
    "metadata": {},
    "source": [
-    "Now we need a slope function, and here's where things get tricky. As we have seen, `run_solve_ivp` can solve systems of first order ODEs, but Newton's law is a second order ODE. However, if we recognize that\n",
+    "Now we need a slope function, and here's where things get tricky. As we have seen, `run_solve_ivp` can solve systems of first order DEs, but Newton's law is a second order DE. However, if we recognize that\n",
     "\n",
     "1.  Velocity, $v$, is the derivative of position, $dy/dt$, and\n",
     "\n",
@@ -289,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 20,
    "id": "occupied-mercury",
    "metadata": {},
    "outputs": [],
@@ -308,16 +253,15 @@
    "id": "opening-adolescent",
    "metadata": {},
    "source": [
-    "The first parameter, `state`, contains the position and velocity of the\n",
-    "penny. The last parameter, `system`, contains the system parameter `g`,\n",
-    "which is the magnitude of acceleration due to gravity.\n",
+    "As usual, the parameters are a time stamp, a `State` object, and a `System` object.\n",
+    "\n",
+    "The first line unpacks the state variables, `y` and `v`.\n",
     "\n",
-    "The second parameter, `t`, is time. It is not used in this slope\n",
-    "function because none of the factors of the model are time dependent. I include it anyway because this function will be called by `run_solve_ivp`, which always provides the same arguments,\n",
-    "whether they are needed or not.\n",
+    "The next two lines compute the derivatives of the state variables, `dydt` and `dvdt`.\n",
+    "The derivative of position is velocity, and the derivative of velocity is acceleration.\n",
+    "In this case, $a = -g$, which indicates that acceleration due to gravity is in the direction of decreasing $y$. \n",
     "\n",
-    "The rest of the function is a straightforward translation of the\n",
-    "differential equations, with the substitution $a = -g$, which indicates that acceleration due to gravity is in the direction of decreasing $y$. `slope_func` returns a sequence containing the two derivatives.\n",
+    "`slope_func` returns a sequence containing the two derivatives.\n",
     "\n",
     "Before calling `run_solve_ivp`, it is a good idea to test the slope\n",
     "function with the initial conditions:"
@@ -325,14 +269,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 21,
    "id": "positive-feeling",
    "metadata": {},
    "outputs": [],
    "source": [
     "dydt, dvdt = slope_func(0, system.init, system)\n",
-    "print(dydt)\n",
-    "print(dvdt)"
+    "dydt, dvdt"
    ]
   },
   {
@@ -340,25 +283,34 @@
    "id": "false-charlotte",
    "metadata": {},
    "source": [
-    "The result is 0 m/s for velocity and 9.8 m/s$^2$ for acceleration. Now we call `run_solve_ivp` like this:"
+    "The result is 0 m/s for velocity and -9.8 m/s$^2$ for acceleration.\n",
+    "\n",
+    "Now we call `run_solve_ivp` like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 22,
    "id": "lovely-management",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "results, details = run_solve_ivp(system, slope_func)\n",
-    "details"
+    "details.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ranging-lingerie",
+   "metadata": {},
+   "source": [
+    "`results` is a `TimeFrame` with two columns: `y` contains the height of the penny; `v` contains its velocity.\n",
+    "Here are the first few rows."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 23,
    "id": "assisted-swimming",
    "metadata": {},
    "outputs": [],
@@ -371,21 +323,16 @@
    "id": "solved-chambers",
    "metadata": {},
    "source": [
-    "`results` is a `TimeFrame` with two columns: `y` contains the height of\n",
-    "the penny; `v` contains its velocity.\n",
-    "\n",
     "We can plot the results like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 24,
    "id": "authorized-barrier",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "results.y.plot()\n",
     "\n",
     "decorate(xlabel='Time (s)',\n",
@@ -404,13 +351,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 25,
    "id": "protected-fiber",
    "metadata": {},
    "outputs": [],
    "source": [
-    "t_end = results.index[-1]\n",
-    "results.y[t_end]"
+    "results.iloc[-1].y"
    ]
   },
   {
@@ -426,13 +372,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 26,
    "id": "japanese-clear",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "t_crossings = crossings(results.y, 0)\n",
     "t_crossings"
    ]
@@ -457,15 +401,12 @@
     "detects an \"event\", like the penny hitting the sidewalk, and ends the\n",
     "simulation.\n",
     "\n",
-    "Event functions take the same parameters as slope functions, `state`,\n",
-    "`t`, and `system`. They should return a value that passes through `0`\n",
-    "when the event occurs. Here's an event function that detects the penny\n",
-    "hitting the sidewalk:"
+    "Event functions take the same parameters as slope functions, `t`, `state`, and `system`. They should return a value that passes through `0` when the event occurs. Here's an event function that detects the penny hitting the sidewalk:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 27,
    "id": "comfortable-simple",
    "metadata": {},
    "outputs": [],
@@ -488,14 +429,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 28,
    "id": "exotic-shareware",
    "metadata": {},
    "outputs": [],
    "source": [
     "results, details = run_solve_ivp(system, slope_func,\n",
     "                                 events=event_func)\n",
-    "details"
+    "details.message"
    ]
   },
   {
@@ -519,14 +460,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 29,
    "id": "orange-retro",
    "metadata": {},
    "outputs": [],
    "source": [
     "y, v = results.iloc[-1]\n",
-    "print(y)\n",
-    "print(v)"
+    "y, v"
    ]
   },
   {
@@ -534,9 +474,7 @@
    "id": "cleared-jamaica",
    "metadata": {},
    "source": [
-    "If there were no air resistance, the penny would hit the sidewalk (or someone's head) at more than 300 km/h.\n",
-    "\n",
-    "So it's a good thing there is air resistance."
+    "If there were no air resistance, the penny would hit the sidewalk (or someone's head) at about 86 m/s. So it's a good thing there is air resistance."
    ]
   },
   {
@@ -546,7 +484,12 @@
    "source": [
     "## Summary\n",
     "\n",
-    "But air resistance...\n"
+    "In this chapter, we wrote Newton's second law, which is a second order DE, as a system of first order DEs.\n",
+    "Then we used `run_solve_ivp` to simulate a penny dropping from the Empire State Building in the absence of air resistance.\n",
+    "And we used an event function to stop the simulation when the penny reaches the sidewalk.\n",
+    "\n",
+    "In the next chapter we'll add air resistance to the model.\n",
+    "But first you might want to work on this exercise."
    ]
   },
   {
@@ -595,16 +538,6 @@
     "# Solution goes here"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "biological-creek",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r_0 / r_final"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 38,
@@ -612,14 +545,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "t_end = 1e7  # seconds\n",
-    "\n",
-    "system = System(init=init,\n",
-    "                G=6.674e-11,   # N m^2 / kg^2\n",
-    "                m1=1.989e30,   # kg\n",
-    "                m2=5.972e24,   # kg\n",
-    "                r_final=radius_sun + radius_earth,\n",
-    "                t_end=t_end)"
+    "# Solution goes here"
    ]
   },
   {
@@ -742,36 +668,6 @@
     "# Solution goes here"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "right-colleague",
-   "metadata": {},
-   "source": [
-    "## Under the hood\n",
-    "\n",
-    "`solve_ivp`\n",
-    "\n",
-    "Here is the source code for `crossings` so you can see what's happening under the hood:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "chief-error",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%psource crossings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "comparable-company",
-   "metadata": {},
-   "source": [
-    "The [documentation of InterpolatedUnivariateSpline is here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.InterpolatedUnivariateSpline.html)."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/chapters/chap21.ipynb b/chapters/chap21.ipynb
index 803a25b4..a74f2afe 100644
--- a/chapters/chap21.ipynb
+++ b/chapters/chap21.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -119,11 +119,11 @@
     "\n",
     "-   $F_d$ is force due to drag, in newtons (N).\n",
     "\n",
-    "-   $\\rho$ is the density of the fluid in kg/m^3^.\n",
+    "-   $\\rho$ is the density of the fluid in kg/m$^3$.\n",
     "\n",
     "-   $v$ is the magnitude of velocity in m/s.\n",
     "\n",
-    "-   $A$ is the **reference area** of the object, in m^2^. In this\n",
+    "-   $A$ is the **reference area** of the object, in m$^2$. In this\n",
     "    context, the reference area is the projected frontal area, that is, the visible area of the object as seen from a point on its line of\n",
     "    travel (and far away).\n",
     "\n",
@@ -182,12 +182,12 @@
     "params = Params(\n",
     "    mass = 0.0025,      # kg\n",
     "    diameter = 0.019,   # m\n",
-    "    rho = 1.2,     # kg/m**3\n",
-    "    g = 9.8,       # m/s**2\n",
-    "    v_init = 0,    # m / s\n",
-    "    v_term = 18,   # m / s\n",
-    "    height = 381,  # m\n",
-    "    t_end = 30,    # s\n",
+    "    rho = 1.2,          # kg/m**3\n",
+    "    g = 9.8,            # m/s**2\n",
+    "    v_init = 0,         # m / s\n",
+    "    v_term = 18,        # m / s\n",
+    "    height = 381,       # m\n",
+    "    t_end = 30,         # s\n",
     ")"
    ]
   },
@@ -229,8 +229,7 @@
     "                  mass=params.mass,\n",
     "                  rho=params.rho,\n",
     "                  g=params.g,\n",
-    "                  t_end=params.t_end\n",
-    "    )"
+    "                  t_end=params.t_end)"
    ]
   },
   {
@@ -359,14 +358,20 @@
    "id": "baking-class",
    "metadata": {},
    "source": [
+    "As usual, the parameters of the slope function are a time stamp, a `State` object, and a `System` object. \n",
+    "We don't use `t` in this example, but we can't leave it out because when `run_solve_ivp` calls the slope function, it always provides the same arguments, whether they are needed or not.\n",
+    "\n",
     "`f_drag` is force due to drag, based on the drag equation. `a_drag` is\n",
     "acceleration due to drag, based on Newton's second law.\n",
     "\n",
     "To compute total acceleration, we add accelerations due to gravity and\n",
-    "drag. `g` is negated because it is in the direction of decreasing `y`,\n",
-    "and `a_drag` is positive because it is in the direction of increasing\n",
-    "`y`. In the next chapter we will use `Vector` objects to keep track of\n",
-    "the direction of forces and add them up in a less error-prone way."
+    "drag. \n",
+    "`g` is negated because it is in the direction of decreasing `y`; `a_drag` is positive because it is in the direction of increasing\n",
+    "`y`. \n",
+    "In the next chapter we will use `Vector` objects to keep track of\n",
+    "the direction of forces and add them up in a less error-prone way.\n",
+    "\n",
+    "As usual, let's test the slope function with the initial conditions."
    ]
   },
   {
@@ -384,8 +389,10 @@
    "id": "psychological-style",
    "metadata": {},
    "source": [
+    "Because the initial velocity is 0, so is the drag force, so the initial acceleration is still `g`. \n",
+    "\n",
     "To stop the simulation when the penny hits the sidewalk, we'll use the\n",
-    "event function from Section xxx:"
+    "event function from the previous chapter."
    ]
   },
   {
@@ -400,16 +407,6 @@
     "    return y"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "animated-clause",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "event_func(0, system.init, system)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "executive-there",
@@ -420,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "liberal-dictionary",
    "metadata": {},
    "outputs": [],
@@ -440,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "coastal-anthropology",
    "metadata": {},
    "outputs": [],
@@ -455,14 +452,14 @@
    "source": [
     "The final height is close to 0, as expected.\n",
     "\n",
-    "Interestingly, the final velocity is not exactly terminal velocity, which suggests that there are some numerical errors.\n",
+    "Interestingly, the final velocity is not exactly terminal velocity, which is a reminder that the simulation results are only approximate.\n",
     "\n",
     "We can get the flight time from `results`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "interim-underground",
    "metadata": {},
    "outputs": [],
@@ -476,12 +473,14 @@
    "id": "institutional-colors",
    "metadata": {},
    "source": [
-    "Here's the plot of position as a function of time."
+    "With air resistance, it takes about 22 seconds for the penny to reach the sidewalk.\n",
+    "\n",
+    "Here's a plot of position as a function of time."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "small-franchise",
    "metadata": {},
    "outputs": [],
@@ -505,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "analyzed-criticism",
    "metadata": {},
    "outputs": [],
@@ -533,7 +532,13 @@
    "id": "inside-confidence",
    "metadata": {},
    "source": [
-    "## Summary"
+    "## Summary\n",
+    "\n",
+    "This chapter presents a model of drag force, which we use to estimate the coefficient of drag for a penny, and then simulate, one more time, dropping a penny from the Empire State building.\n",
+    "\n",
+    "In the next chapter we'll move from one dimension to two, simulating the flight of a baseball.\n",
+    "\n",
+    "But first you might want to work on these exercises."
    ]
   },
   {
@@ -558,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "inclusive-twenty",
    "metadata": {},
    "outputs": [],
@@ -568,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "vertical-judge",
    "metadata": {},
    "outputs": [],
@@ -578,28 +583,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "greenhouse-madagascar",
    "metadata": {},
    "outputs": [],
    "source": [
-    "t_sidewalk = results2.index[-1]\n",
-    "t_sidewalk"
+    "# Solution goes here"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "sudden-details",
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_position(results2)"
+    "# Solution goes here"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "opening-jurisdiction",
    "metadata": {
     "scrolled": false
@@ -630,12 +634,12 @@
     "\n",
     "6. Use your best estimate of `v_term` to compute `C_d`.\n",
     "\n",
-    "Note: I fabricated the observed flight time, so don't take the results of this exercise too seriously."
+    "Note: I fabricated the \"observed\" flight time, so don't take the results of this exercise too seriously."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "compact-bunny",
    "metadata": {},
    "outputs": [],
@@ -645,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "shared-contrary",
    "metadata": {},
    "outputs": [],
@@ -655,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "portable-account",
    "metadata": {},
    "outputs": [],
@@ -665,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "arabic-shareware",
    "metadata": {},
    "outputs": [],
@@ -675,7 +679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "valued-literature",
    "metadata": {},
    "outputs": [],
@@ -685,7 +689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "id": "sufficient-retail",
    "metadata": {},
    "outputs": [],
@@ -695,7 +699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "id": "comparable-lounge",
    "metadata": {},
    "outputs": [],
@@ -705,7 +709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "id": "ready-people",
    "metadata": {},
    "outputs": [],
@@ -715,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "id": "miniature-remark",
    "metadata": {},
    "outputs": [],
@@ -725,25 +729,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "id": "frozen-termination",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Solution goes here"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "colored-oxygen",
-   "metadata": {},
-   "source": [
-    "## Under the Hood\n",
-    "\n",
-    "`Params` is a `SimpleNamespace`, like `System`.\n",
-    "\n",
-    "`Params` and `System` are actually the same; I have given them different names to document the different roles they play."
-   ]
   }
  ],
  "metadata": {
diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
index 96420aed..366e22f7 100644
--- a/chapters/chap22.ipynb
+++ b/chapters/chap22.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -36,12 +36,16 @@
     "try:\n",
     "    import pint\n",
     "except ImportError:\n",
-    "    !pip install pint"
+    "    !pip install pint\n",
+    "    \n",
+    "# import units\n",
+    "from pint import UnitRegistry\n",
+    "units = UnitRegistry()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -90,7 +94,7 @@
    "id": "advanced-guidance",
    "metadata": {},
    "source": [
-    "In the previous chapter we modeled objects moving in one dimension, with and without drag. Now let's move on to two dimensions, and baseball!\n",
+    "In the previous chapter we modeled an object moving in one dimension, with and without drag. Now let's move on to two dimensions, and baseball!\n",
     "\n",
     "In this chapter we model the flight of a baseball including the effect\n",
     "of air resistance. In the next chapter we use this model to solve an\n",
@@ -107,9 +111,6 @@
     "To model the flight of a baseball, we have to make some\n",
     "decisions. To get started, we'll ignore any spin that might be on the ball, and the resulting Magnus force (see <http://modsimpy.com/magnus>). Under this assumption, the ball travels in a vertical plane, so we'll run simulations in two dimensions, rather than three.\n",
     "\n",
-    "Air resistance has a substantial effect on most projectiles in air, so\n",
-    "we will include a drag force.\n",
-    "\n",
     "To model air resistance, we'll need the mass, frontal area, and drag\n",
     "coefficient of a baseball. Mass and diameter are easy to find (see\n",
     "<http://modsimpy.com/baseball>). Drag coefficient is only a little\n",
@@ -120,7 +121,7 @@
     "Furthermore, the transition between these regimes typically happens\n",
     "exactly in the range of velocities we are interested in, between 20 m/s and 40 m/s.\n",
     "\n",
-    "Nevertheless, we'll start with a simple model where the drag coefficient does not depend on velocity; as an exercise at the end of the chapter, you will have a chance to implement a more detailed model and see what effect is has on the results.\n",
+    "Nevertheless, we'll start with a simple model where the drag coefficient does not depend on velocity; as an exercise at the end of the chapter, you can implement a more detailed model and see what effect it has on the results.\n",
     "\n",
     "But first we need a new computational tool, the `Vector` object."
    ]
@@ -135,7 +136,7 @@
     "Now that we are working in two dimensions, it will be useful to\n",
     "work with **vector quantities**, that is, quantities that represent both a magnitude and a direction. We will use vectors to represent positions, velocities, accelerations, and forces in two and three dimensions.\n",
     "\n",
-    "ModSim provides a function called `Vector` the creates a Pandas `Series` that contains the **components** of the vector.\n",
+    "ModSim provides a function called `Vector` that creates a Pandas `Series` that contains the **components** of the vector.\n",
     "In a `Vector` that represents a position in space, the components are the $x$ and $y$ coordinates in 2-D, plus a $z$ coordinate if the `Vector` is in 3-D.\n",
     "\n",
     "You can create a `Vector` by specifying its components. The following\n",
@@ -150,7 +151,7 @@
    "outputs": [],
    "source": [
     "A = Vector(3, 4)\n",
-    "A"
+    "show(A)"
    ]
   },
   {
@@ -207,27 +208,17 @@
    "outputs": [],
    "source": [
     "B = Vector(1, 2)\n",
-    "B"
+    "show(A + B)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "crucial-opening",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A + B"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
    "id": "encouraging-cabinet",
    "metadata": {},
    "outputs": [],
    "source": [
-    "A - B"
+    "show(A - B)"
    ]
   },
   {
@@ -235,18 +226,12 @@
    "id": "combined-command",
    "metadata": {},
    "source": [
-    "For the definition and graphical interpretation of these operations, see <http://modsimpy.com/vecops>."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "later-adelaide",
-   "metadata": {},
-   "source": [
+    "For the definition and graphical interpretation of these operations, see <http://modsimpy.com/vecops>.\n",
+    "\n",
     "We can specify a `Vector` with coordinates `x` and `y`, as in the previous examples.\n",
     "Equivalently, we can specify a `Vector` with a magnitude and angle.\n",
     "\n",
-    "**Magnitude** is the length of the vector: if the `Vector` represents a position, magnitude is the distance from the origin; if it represents a velocity, magnitude is speed, that is, how fast the object is moving, regardless of direction.\n",
+    "**Magnitude** is the length of the vector: if the `Vector` represents a position, magnitude is its distance from the origin; if it represents a velocity, magnitude is its speed.\n",
     "\n",
     "The **angle** of a `Vector` is its direction, expressed as an angle in radians from the positive $x$ axis. In the Cartesian plane, the angle 0 rad is due east, and the angle $\\pi$ rad is due west.\n",
     "\n",
@@ -255,13 +240,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 61,
    "id": "peripheral-tattoo",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "mag = vector_mag(A)\n",
     "theta = vector_angle(A)\n",
     "mag, theta"
@@ -274,14 +257,15 @@
    "source": [
     "The magnitude is 5 because the length of `A` is the hypotenuse of a 3-4-5 triangle.\n",
     "\n",
-    "The result from `vector_angle` is in radians, and most Python functions, like `sin` and `cos`, work with radians. \n",
-    "But many people think more naturally in degrees. \n",
+    "The result from `vector_angle` is in radians.\n",
+    "Most Python functions, like `sin` and `cos`, work with radians, \n",
+    "but many people find it more natural to work with degrees. \n",
     "Fortunately, NumPy provides a function to convert radians to degrees:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "strange-cleaning",
    "metadata": {},
    "outputs": [],
@@ -302,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "cellular-community",
    "metadata": {},
    "outputs": [],
@@ -318,21 +302,22 @@
    "id": "responsible-gentleman",
    "metadata": {},
    "source": [
-    "Following convention, I'll use `angle` for a value in degrees and `theta` for a value in radians.\n",
+    "To avoid confusion, I'll use the variable name `angle` for a value in degrees and `theta` for a value in radians.\n",
     "\n",
-    "If you are given an angle and velocity, you can make a `Vector` using\n",
+    "If you are given an angle and magnitude, you can make a `Vector` using\n",
     "`pol2cart`, which converts from polar to Cartesian coordinates. For example, here's a new `Vector` with the same angle and magnitude of `A`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "monetary-firmware",
    "metadata": {},
    "outputs": [],
    "source": [
     "x, y = pol2cart(theta, mag)\n",
-    "Vector(x, y)"
+    "C = Vector(x, y)\n",
+    "show(C)"
    ]
   },
   {
@@ -348,12 +333,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "explicit-piano",
    "metadata": {},
    "outputs": [],
    "source": [
-    "A / vector_mag(A)"
+    "show(A / vector_mag(A))"
    ]
   },
   {
@@ -361,17 +346,18 @@
    "id": "respected-oliver",
    "metadata": {},
    "source": [
-    "We can do the same thing using the `vector_hat` function, so named because unit vectors are conventionally decorated with a hat, like this: $\\hat{A}$."
+    "ModSim provides a function that does the same thing, called `vector_hat` because unit vectors are conventionally decorated with a hat, like this: $\\hat{A}$."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "relative-republic",
    "metadata": {},
    "outputs": [],
    "source": [
-    "vector_hat(A)"
+    "A_hat = vector_hat(A)\n",
+    "show(A_hat)"
    ]
   },
   {
@@ -390,12 +376,14 @@
     "## Simulating baseball flight\n",
     "\n",
     "Let's simulate the flight of a baseball that is batted from home plate\n",
-    "at an angle of 45° and initial speed 40 m/s. We'll use the center of home plate as the origin, a horizontal x-axis (parallel to the ground), and vertical y-axis (perpendicular to the ground). The initial height is about 1 m."
+    "at an angle of 45° and initial speed 40 m/s. We'll use the center of home plate as the origin, a horizontal x-axis (parallel to the ground), and a vertical y-axis (perpendicular to the ground). The initial height is 1 m.\n",
+    "\n",
+    "Here's a `Params` object with the parameters we'll need."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "narrative-latest",
    "metadata": {
     "tags": []
@@ -406,7 +394,7 @@
     "    x = 0,          # m\n",
     "    y = 1,          # m\n",
     "    angle = 45,     # degree\n",
-    "    velocity = 40,  # m / s\n",
+    "    speed = 40,  # m / s\n",
     "\n",
     "    mass = 145e-3,    # kg \n",
     "    diameter = 73e-3, # m \n",
@@ -423,29 +411,19 @@
    "id": "metric-collins",
    "metadata": {},
    "source": [
-    "I got the mass and diameter of the baseball from [Wikipedia](https://en.wikipedia.org/wiki/Baseball_(ball)) and the coefficient of drag is from *[The Physics of Baseball](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC)*:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "increasing-combination",
-   "metadata": {},
-   "source": [
+    "I got the mass and diameter of the baseball from [Wikipedia](https://en.wikipedia.org/wiki/Baseball_(ball)) and the coefficient of drag from *[The Physics of Baseball](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC)*:\n",
+    "\n",
     "The density of air, `rho`, is based on a temperature of 20 °C at sea level (see <http://modsimpy.com/tempress>). \n",
-    "And we'll need the acceleration of gravity, `g`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "detected-return",
-   "metadata": {},
-   "source": [
+    "As usual, `g` is acceleration due to gravity.\n",
+    "\n",
+    "`t_end` is 10 seconds, which is long enough for the ball to land on the ground.\n",
+    "\n",
     "The following function uses these quantities to make a `System` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "bored-billy",
    "metadata": {
     "tags": []
@@ -460,7 +438,7 @@
     "    theta = deg2rad(params.angle)\n",
     "    \n",
     "    # compute x and y components of velocity\n",
-    "    vx, vy = pol2cart(theta, params.velocity)\n",
+    "    vx, vy = pol2cart(theta, params.speed)\n",
     "    \n",
     "    # make the initial state\n",
     "    init = State(x=params.x, y=params.y, vx=vx, vy=vy)\n",
@@ -470,8 +448,7 @@
     "\n",
     "    return System(params,\n",
     "                  init = init,\n",
-    "                  area = area,\n",
-    "                 )"
+    "                  area = area)"
    ]
   },
   {
@@ -489,13 +466,14 @@
     "\n",
     "* `vx` and `vy` are the components of velocity.\n",
     "\n",
-    "The `System` object also contains `t_end`, which is 10 seconds, long enough for the ball to land on the ground.\n",
-    "Here's the `System` object."
+    "When we call `System`, we pass `Params` as the first argument, which means that the variables in `Params` are copied to the new `System` object.\n",
+    "\n",
+    "Here's how we make the `System` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "ethical-donna",
    "metadata": {},
    "outputs": [],
@@ -513,12 +491,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "legitimate-gossip",
    "metadata": {},
    "outputs": [],
    "source": [
-    "system.init"
+    "show(system.init)"
    ]
   },
   {
@@ -526,12 +504,14 @@
    "id": "occasional-given",
    "metadata": {},
    "source": [
+    "## Drag Force\n",
+    "\n",
     "Next we need a function to compute drag force:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "legal-terminal",
    "metadata": {
     "tags": []
@@ -567,14 +547,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "frank-chick",
    "metadata": {},
    "outputs": [],
    "source": [
     "vx, vy = system.init.vx, system.init.vy\n",
     "V_test = Vector(vx, vy)\n",
-    "drag_force(V_test, system)"
+    "f_drag = drag_force(V_test, system)\n",
+    "show(f_drag)"
    ]
   },
   {
@@ -589,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "suitable-salem",
    "metadata": {
     "tags": []
@@ -614,11 +595,11 @@
    "id": "scheduled-courage",
    "metadata": {},
    "source": [
-    "As usual, the parameters of the slope function are a time, a `State` object, and a `System` object. \n",
-    "In this example, we don't use `t`, but we can't leave it out because when `run_solve_ivp` calls the slope function, it always provides the same arguments, whether they are needed or not.\n",
+    "As usual, the parameters of the slope function are a time stamp, a `State` object, and a `System` object. \n",
+    "We don't use `t` in this example, but we can't leave it out because when `run_solve_ivp` calls the slope function, it always provides the same arguments, whether they are needed or not.\n",
     "\n",
     "`slope_func` unpacks the `State` object into variables `x`, `y`, `vx`, and `vy`.\n",
-    "Then it packs `vx` and `vy` into a `Vector`, which it uses to compute drag force and acceleration due to drag, `a_drag`.\n",
+    "Then it packs `vx` and `vy` into a `Vector`, which it uses to compute drag force, `f_drag`,  and acceleration due to drag, `a_drag`.\n",
     "\n",
     "To represent acceleration due to gravity, it makes a `Vector` with magnitude `g` in the negative $y$ direction.\n",
     "\n",
@@ -636,7 +617,7 @@
     "\n",
     "* The components of acceleration, `A.x` and `A.y`.\n",
     "\n",
-    "Together, these components represent the slope of the state variables, because `V` is the derivative of position and `A` is the derivative of velocity."
+    "These components represent the slope of the state variables, because `V` is the derivative of position and `A` is the derivative of velocity."
    ]
   },
   {
@@ -649,7 +630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "closing-simon",
    "metadata": {},
    "outputs": [],
@@ -659,19 +640,27 @@
   },
   {
    "cell_type": "markdown",
-   "id": "regulated-railway",
+   "id": "selective-letter",
    "metadata": {},
    "source": [
     "Using vectors to represent forces and accelerations makes the code\n",
     "concise, readable, and less error-prone. In particular, when we add\n",
-    "`a_grav` and `a_drag`, the directions are likely to be correct, because they are encoded in the `Vector` objects.\n",
+    "`a_grav` and `a_drag`, the directions are likely to be correct, because they are encoded in the `Vector` objects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "regulated-railway",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
     "\n",
     "We're almost ready to run the simulation.  The last thing we need is an event function that stops when the ball hits the ground."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "brief-level",
    "metadata": {
     "tags": []
@@ -695,7 +684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "threatened-alberta",
    "metadata": {},
    "outputs": [],
@@ -713,13 +702,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "special-background",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "results, details = run_solve_ivp(system, slope_func,\n",
     "                                 events=event_func)\n",
     "details.message"
@@ -730,14 +717,15 @@
    "id": "iraqi-appeal",
    "metadata": {},
    "source": [
-    "`details` contains information about the simulation, including a message that indicates that a \"termination event\" occurred; that is, the simulated ball reached the ground.\n",
+    "The message in `details` indicates that a \"termination event\" occurred; that is, the simulated ball reached the ground.\n",
     "\n",
-    "`results` is a `TimeFrame` with one column for each of the state variables:"
+    "`results` is a `TimeFrame` with one row for each time step and one column for each of the state variables.\n",
+    "Here are the last few rows."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "prospective-external",
    "metadata": {},
    "outputs": [],
@@ -755,7 +743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "medieval-calvin",
    "metadata": {},
    "outputs": [],
@@ -774,13 +762,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "id": "gorgeous-survey",
    "metadata": {},
    "outputs": [],
    "source": [
     "final_state = results.iloc[-1]\n",
-    "final_state"
+    "show(final_state)"
    ]
   },
   {
@@ -793,7 +781,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "id": "failing-bangkok",
    "metadata": {},
    "outputs": [],
@@ -812,26 +800,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "id": "copyrighted-highway",
    "metadata": {},
    "outputs": [],
    "source": [
     "final_V = Vector(final_state.vx, final_state.vy)\n",
-    "final_V"
+    "show(final_V)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "vietnamese-diagram",
+   "id": "official-handle",
    "metadata": {},
    "source": [
-    "The speed of the ball on impact is about 26 m/s, which is substantially slower than the initial velocity, 40 m/s."
+    "The magnitude of final velocity is the speed of the ball when it lands."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "id": "structured-adams",
    "metadata": {},
    "outputs": [],
@@ -839,6 +827,14 @@
     "vector_mag(final_V)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "vietnamese-diagram",
+   "metadata": {},
+   "source": [
+    "The final speed is about 26 m/s, which is substantially slower than the initial speed, 40 m/s."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "continuous-quick",
@@ -851,7 +847,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "id": "spare-burst",
    "metadata": {},
    "outputs": [],
@@ -871,12 +867,12 @@
     "As expected, the $x$ component increases as the ball moves away from home plate. The $y$ position climbs initially and then descends, falling to 0 m near 5.0 s.\n",
     "\n",
     "Another way to view the results is to plot the $x$ component on the\n",
-    "x-axis and the $y$ component on the y-axis, so the plotted line follows the trajectory of the ball through the plane:"
+    "$x$-axis and the $y$ component on the $y$-axis, so the plotted line follows the trajectory of the ball through the plane:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "id": "dated-browse",
    "metadata": {},
    "outputs": [],
@@ -902,34 +898,39 @@
     "A trajectory plot can be easier to interpret than a time series plot,\n",
     "because it shows what the motion of the projectile would look like (at\n",
     "least from one point of view). Both plots can be useful, but don't get\n",
-    "them mixed up! If you are looking at a time series plot and interpreting it as a trajectory, you will be very confused."
+    "them mixed up! If you are looking at a time series plot and interpreting it as a trajectory, you will be very confused.\n",
+    "\n",
+    "Notice that the trajectory is not symmetric.\n",
+    "With a launch angle of 45°, the landing angle is closer to vertical, about 57° degrees."
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "cosmetic-aircraft",
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "atmospheric-logan",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Animation\n",
-    "\n",
-    "One of the best ways to visualize the results of a physical model is animation.  If there are problems with the model, animation can make them apparent.\n",
-    "\n",
-    "The ModSimPy library provides `animate`, which takes as parameters a `TimeSeries` and a draw function."
+    "rad2deg(vector_angle(final_V))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "neither-craps",
+   "id": "cosmetic-aircraft",
    "metadata": {},
    "source": [
-    "The draw function should take as parameters a `State` object and the time.  It should draw a single frame of the animation.\n",
+    "## Animation\n",
+    "\n",
+    "One of the best ways to visualize the results of a physical model is animation.  If there are problems with the model, animation can make them apparent.\n",
+    "\n",
+    "The ModSimPy library provides `animate`, which takes as parameters a `TimeSeries` and a draw function.\n",
     "\n",
-    "Inside the draw function, you almost always have to call `set_xlim` and `set_ylim`.  Otherwise `matplotlib` auto-scales the axes, which is usually not what you want."
+    "The draw function should take as parameters a time stamps and a `State`.  It should draw a single frame of the animation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 35,
    "id": "starting-fabric",
    "metadata": {},
    "outputs": [],
@@ -947,9 +948,20 @@
     "             ylim=ylim)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "oriental-inclusion",
+   "metadata": {},
+   "source": [
+    "Inside the draw function, should use `decorate` to set the limits of the $x$ and $y$ axes.\n",
+    "Otherwise `matplotlib` auto-scales the axes, which is usually not what you want.\n",
+    "\n",
+    "To run the animation, uncomment the following line of code and run the cell."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 36,
    "id": "prescription-boutique",
    "metadata": {},
    "outputs": [],
@@ -1030,7 +1042,7 @@
    "id": "generic-shelter",
    "metadata": {},
    "source": [
-    "**Exercise:** The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / meter$^3$.  How much farther would a ball hit with the same velocity and launch angle travel?"
+    "**Exercise:** The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / m$^3$.  How much farther would a ball hit with the same initial speed and launch angle travel?"
    ]
   },
   {
@@ -1074,55 +1086,169 @@
     "\n",
     "<img src=\"https://github.com/AllenDowney/ModSimPy/raw/master/figs/baseball_drag.png\" width=\"400\">\n",
     "\n",
-    "\n",
-    "I used [an online graph digitizer](https://automeris.io/WebPlotDigitizer/) to extract the data and save it in a CSV file.  Here's how we can read it:"
+    "I used [an online graph digitizer](https://automeris.io/WebPlotDigitizer/) to extract the data and save it in a CSV file.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "governmental-error",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "The following cell downloads the data file."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "therapeutic-onion",
-   "metadata": {},
+   "id": "understanding-mason",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "import os\n",
-    "\n",
-    "filename = 'baseball_drag.csv'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/baseball_drag.csv"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'baseball_drag.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "familiar-bargain",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "We can use Pandas to read it."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "returning-fellowship",
-   "metadata": {},
+   "id": "spatial-finish",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from pandas import read_csv\n",
     "\n",
-    "\n",
-    "baseball_drag = read_csv(filename)\n",
-    "mph = Quantity(baseball_drag['Velocity in mph'], units.mph)\n",
-    "mps = mph.to(units.meter / units.second)\n",
-    "baseball_drag.index = mps.magnitude\n",
-    "baseball_drag.index.name = 'Velocity in meters per second'\n",
+    "baseball_drag = read_csv('baseball_drag.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "weekly-flavor",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Here are the first few rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "returning-fellowship",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
     "baseball_drag.head()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "spare-thompson",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "I'll use Pint to convert miles per hour to meters per second."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
+   "id": "liable-concord",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "mph_to_mps = (1 * units.mph).to(units.mps).magnitude\n",
+    "speed = baseball_drag['Velocity in mph'] * mph_to_mps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "forward-acrobat",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "I'll put the results in a `Series`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "designed-inclusion",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "C_d_series = make_series(speed, baseball_drag['Drag coefficient'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "consolidated-offset",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "We can plot the `Series` like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "sunset-kennedy",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "C_d_series.plot(label='C_d')\n",
+    "decorate(xlabel='Speed (m/s)', \n",
+    "         ylabel='C_d',\n",
+    "         title='Coefficient of drag versus speed')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "forbidden-inside",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "And, for use in the slope function, we can make a function that interpolates the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
    "id": "heated-belfast",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "drag_interp = interpolate(baseball_drag['Drag coefficient'])\n",
-    "vs = linspace(0, 60)\n",
-    "cds = drag_interp(vs)\n",
-    "make_series(vs, cds).plot()\n",
-    "decorate(xlabel='Velocity (m/s)', ylabel='C_d')"
+    "drag_interp = interpolate(C_d_series)\n",
+    "drag_interp(30)"
    ]
   },
   {
@@ -1135,7 +1261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "id": "engaged-provision",
    "metadata": {},
    "outputs": [],
@@ -1145,7 +1271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "id": "directed-fiber",
    "metadata": {},
    "outputs": [],
@@ -1155,7 +1281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "id": "framed-dealer",
    "metadata": {},
    "outputs": [],
@@ -1165,7 +1291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "id": "accomplished-elizabeth",
    "metadata": {},
    "outputs": [],
@@ -1175,7 +1301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "id": "going-techno",
    "metadata": {},
    "outputs": [],
@@ -1185,7 +1311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "id": "brief-saying",
    "metadata": {},
    "outputs": [],
@@ -1195,7 +1321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "id": "spare-pregnancy",
    "metadata": {},
    "outputs": [],
@@ -1205,7 +1331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "id": "catholic-staff",
    "metadata": {},
    "outputs": [],
@@ -1215,7 +1341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "id": "broad-sequence",
    "metadata": {},
    "outputs": [],
@@ -1224,14 +1350,12 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "contained-diversity",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "strategic-background",
    "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "`Vector` "
-   ]
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/chapters/chap23.ipynb b/chapters/chap23.ipynb
index b8d7de76..46777e94 100644
--- a/chapters/chap23.ipynb
+++ b/chapters/chap23.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -36,12 +36,16 @@
     "try:\n",
     "    import pint\n",
     "except ImportError:\n",
-    "    !pip install pint"
+    "    !pip install pint\n",
+    "    \n",
+    "# import units\n",
+    "from pint import UnitRegistry\n",
+    "units = UnitRegistry()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -129,7 +133,26 @@
     "most famous features is the \"Green Monster\", which is a wall in left\n",
     "field that is unusually close to home plate, only 310 feet away. To\n",
     "compensate for the short distance, the wall is unusually high, at 37\n",
-    "feet (see <http://modsimpy.com/wally>)."
+    "feet (see <http://modsimpy.com/wally>).\n",
+    "\n",
+    "Starting with `params` from the previous chapter, I'll make a new `Params` object with two additional parameters, `wall_distance` and `wall_height`, in meters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cardiac-attraction",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feet_to_meter = (1 * units.feet).to(units.meter).magnitude\n",
+    "\n",
+    "params = params.set(\n",
+    "    wall_distance = 310 * feet_to_meter,\n",
+    "    wall_height = 37 * feet_to_meter,\n",
+    ")\n",
+    "\n",
+    "show(params)"
    ]
   },
   {
@@ -137,13 +160,12 @@
    "id": "exposed-diving",
    "metadata": {},
    "source": [
-    "We want to find the minimum velocity at which a ball can leave home\n",
-    "plate and still go over the Green Monster. We'll proceed in the\n",
+    "The answer we want is the minimum speed at which a ball can leave home plate and still go over the Green Monster. We'll proceed in the\n",
     "following steps:\n",
     "\n",
-    "1.  For a given velocity, we'll find the optimal **launch angle**, that is, the angle the ball should leave home plate to maximize its height when it reaches the wall.\n",
+    "1.  For a given speed, we'll find the optimal **launch angle**, that is, the angle the ball should leave home plate to maximize its height when it reaches the wall.\n",
     "\n",
-    "2.  Then we'll find the minimal velocity that clears the wall, given\n",
+    "2.  Then we'll find the minimum speed that clears the wall, given\n",
     "    that it has the optimal launch angle."
    ]
   },
@@ -161,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "forty-knitting",
    "metadata": {},
    "outputs": [],
@@ -190,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "senior-counter",
    "metadata": {},
    "outputs": [],
@@ -203,14 +225,18 @@
    "id": "paperback-passing",
    "metadata": {},
    "source": [
-    "And we can sweep a sequence of angles like this:"
+    "With launch angle 45°, the ball lands about 99 meters from home plate.\n",
+    "\n",
+    "Now we can sweep a sequence of angles like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "biological-evans",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "angles = linspace(20, 80, 21)\n",
@@ -231,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "experienced-providence",
    "metadata": {},
    "outputs": [],
@@ -247,14 +273,13 @@
    "id": "conscious-blade",
    "metadata": {},
    "source": [
-    "It looks like the optimal angle is near 40°.\n",
-    "\n",
+    "It looks like the range is maximized when the initial angle is near 40°.\n",
     "We can find the optimal angle more precisely and more efficiently using `maximize_scalar`, like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "invisible-jaguar",
    "metadata": {},
    "outputs": [],
@@ -269,22 +294,16 @@
    "source": [
     "The first parameter is the function we want to maximize. The second is\n",
     "the range of values we want to search; in this case, it's the range of\n",
-    "angles from 0° to 90°. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "violent-explanation",
-   "metadata": {},
-   "source": [
+    "angles from 0° to 90°. \n",
+    "\n",
     "The return value from `maximize_scalar` is an object that contains the\n",
-    "results, including `x`, which is the angle that yielded the highest\n",
-    "range, and `fun`, which is the value of `range_func` when it's evaluated at `x`, that is, range when the baseball is launched at the optimal angle."
+    "results, including `x`, which is the angle that yielded the maximum\n",
+    "range, and `fun`, which is the range when the ball is launched at the optimal angle."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "vocal-nerve",
    "metadata": {},
    "outputs": [],
@@ -294,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "figured-uniform",
    "metadata": {},
    "outputs": [],
@@ -319,10 +338,13 @@
    "source": [
     "## Summary\n",
     "\n",
-    "\n",
+    "This chapter introduces the Manny Ramirez problem and starts to solve it.\n",
+    "As an exercise, you can finish it off.\n",
     "\n",
     "If you enjoy this exercise, you might be interested in this paper: \"How to hit home runs: Optimum baseball bat swing parameters for maximum range trajectories\", by Sawicki, Hubbard, and Stronge, at\n",
-    "<http://modsimpy.com/runs>."
+    "<http://modsimpy.com/runs>.\n",
+    "\n",
+    "In the next chapter, we start a new topic: rotation!"
    ]
   },
   {
@@ -342,19 +364,19 @@
     "\n",
     "> What is the minimum effort required to hit a home run in Fenway Park?\n",
     "\n",
-    "Although the problem asks for a minimum, it is not an optimization problem.  Rather, we want to solve for the initial velocity that just barely gets the ball to the top of the wall, given that it is launched at the optimal angle.\n",
+    "Although the problem asks for a minimum, it is not an optimization problem.  Rather, we want to solve for the initial speed that just barely gets the ball to the top of the wall, given that it is launched at the optimal angle.\n",
     "\n",
-    "And we have to be careful about what we mean by \"optimal\".  For this problem, we don't want the longest range, we want the maximum height at the point where it reaches the wall.\n",
+    "And we have to be careful about what we mean by \"optimal\".  For this problem, we don't want the longest range; we want the maximum height at the point where it reaches the wall.\n",
     "\n",
     "If you are ready to solve the problem on your own, go ahead.  Otherwise I will walk you through the process with an outline and some starter code.\n",
     "\n",
-    "As a first step, write an `event_func` that stops the simulation when the ball reaches the wall at 310 feet (94.5 m).\n",
+    "As a first step, write an `event_func` that stops the simulation when the ball reaches the wall at `wall_distance`, which is a parameter in `params`.\n",
     "Test your function with the initial conditions."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "studied-association",
    "metadata": {},
    "outputs": [],
@@ -364,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "developmental-alabama",
    "metadata": {},
    "outputs": [],
@@ -383,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "ignored-decrease",
    "metadata": {},
    "outputs": [],
@@ -393,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "western-communist",
    "metadata": {},
    "outputs": [],
@@ -411,7 +433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "duplicate-madison",
    "metadata": {},
    "outputs": [],
@@ -421,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "legislative-prospect",
    "metadata": {},
    "outputs": [],
@@ -434,14 +456,17 @@
    "id": "interpreted-telephone",
    "metadata": {},
    "source": [
-    "Even though we are finding the \"minimum\" velocity, we are not really solving a minimization problem.  Rather, we want to find the velocity that makes the height at the wall exactly 37 feet (11.3 m), given that it's launched at the optimal angle.  And that's a job for `root_scalar`.\n",
+    "The angle that maximizes the height at the wall is a little higher than the angle that maximizes range.\n",
+    "\n",
+    "Now, let's find the initial speed that makes the height at the wall exactly 37 feet, given that it's launched at the optimal angle. \n",
+    "This is a root-finding problem, so we'll use `root_scalar`.\n",
     "\n",
-    "Write an error function that takes a velocity and a `System` object as parameters.  It should use `maximize_scalar` to find the highest possible height of the ball at the wall, for the given velocity.  Then it should return the difference between that optimal height and 11.3 meters."
+    "Write an error function that takes a speed and a `System` object as parameters.  It should use `maximize_scalar` to find the highest possible height of the ball at the wall, for the given speed.  Then it should return the difference between that optimal height and `wall_height`, which is a parameter in `params`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "egyptian-shadow",
    "metadata": {},
    "outputs": [],
@@ -459,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "sustainable-supply",
    "metadata": {},
    "outputs": [],
@@ -467,17 +492,27 @@
     "# Solution goes here"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "curious-liberia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "reserved-shelter",
    "metadata": {},
    "source": [
-    "Then use `root_scalar` to find the answer to the problem, the minimum velocity that gets the ball out of the park."
+    "Then use `root_scalar` to find the answer to the problem, the minimum speed that gets the ball out of the park."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "id": "certified-webster",
    "metadata": {},
    "outputs": [],
@@ -487,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "id": "silver-bernard",
    "metadata": {},
    "outputs": [],
@@ -497,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "id": "absent-encoding",
    "metadata": {},
    "outputs": [],
@@ -515,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "id": "thick-jungle",
    "metadata": {},
    "outputs": [],

From 76481d757eaceee656d125c588024c77a2a51dd8 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 18 Feb 2021 14:17:26 -0500
Subject: [PATCH 058/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 164848 -> 238714 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
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LtkE0d?bH7OE5wtl


From bf0b15c43801e1012c592c32b5e51a2a203a1c79 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 18 Feb 2021 15:50:56 -0500
Subject: [PATCH 059/144] Updating chapters

---
 chapters/chap01.ipynb | 126 +++++++++++++++++++++++++++---------------
 chapters/chap10.ipynb |  25 ++-------
 chapters/chap14.ipynb |  68 +++++++++--------------
 chapters/chap21.ipynb |  17 ------
 chapters/chap22.ipynb |  38 ++++++-------
 chapters/chap23.ipynb |   6 +-
 chapters/chap24.ipynb |  29 ++--------
 chapters/chap25.ipynb | 107 +++++++++++++++--------------------
 chapters/chap26.ipynb | 123 +++++++++++++++++++----------------------
 9 files changed, 238 insertions(+), 301 deletions(-)

diff --git a/chapters/chap01.ipynb b/chapters/chap01.ipynb
index 93b0181a..f3bea000 100644
--- a/chapters/chap01.ipynb
+++ b/chapters/chap01.ipynb
@@ -34,14 +34,14 @@
     "# install Pint if necessary\n",
     "\n",
     "try:\n",
-    "    import pint\n",
+    "    from pint import UnitRegistry\n",
     "except ImportError:\n",
     "    !pip install pint"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -475,7 +475,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "consecutive-sleeve",
+   "id": "judicial-capability",
    "metadata": {},
    "source": [
     "## Computation with units\n",
@@ -488,13 +488,34 @@
     "When possible, it is better to include units in the computation.\n",
     "\n",
     "To represent units, we'll use a Python library called [Pint](https://pint.readthedocs.io/en/latest/).\n",
-    "The ModSim library initializes Pint and provides an object called `units` that contains variables representing pretty much every unit you've ever heard of.\n",
+    "\n",
+    "To use it, we have to import a function called `UnitRegistry` and call it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "threatened-mediterranean",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pint import UnitRegistry\n",
+    "\n",
+    "units = UnitRegistry()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "consecutive-sleeve",
+   "metadata": {},
+   "source": [
+    "The result is an object that contains variables representing pretty much every unit you've heard of.\n",
     "For example:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 14,
    "id": "cardiac-class",
    "metadata": {},
    "outputs": [],
@@ -504,7 +525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 15,
    "id": "phantom-copper",
    "metadata": {},
    "outputs": [],
@@ -523,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "id": "russian-popularity",
    "metadata": {},
    "outputs": [],
@@ -534,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "id": "endless-paint",
    "metadata": {},
    "outputs": [],
@@ -548,7 +569,10 @@
    "id": "spiritual-scenario",
    "metadata": {},
    "source": [
-    "To find out what other units are defined, type `units.` (including the period) in the next cell and then press TAB.  You should see a pop-up menu with a list of units."
+    "To find out what other units are defined, type `units.` (including the period) in the next cell.\n",
+    "\n",
+    "If you are on Colab, a pop-up menu should appear with a list of units.\n",
+    "In other Jupyter environments, you might have to press `TAB` to get the menu."
    ]
   },
   {
@@ -569,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "id": "earlier-bandwidth",
    "metadata": {},
    "outputs": [],
@@ -588,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "id": "missing-privilege",
    "metadata": {},
    "outputs": [],
@@ -598,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "id": "fourth-swedish",
    "metadata": {},
    "outputs": [],
@@ -616,7 +640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 21,
    "id": "apart-france",
    "metadata": {},
    "outputs": [],
@@ -635,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 22,
    "id": "alien-scout",
    "metadata": {},
    "outputs": [],
@@ -654,7 +678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 23,
    "id": "confidential-costs",
    "metadata": {},
    "outputs": [],
@@ -672,7 +696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "id": "studied-opera",
    "metadata": {},
    "outputs": [],
@@ -691,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "id": "exterior-greek",
    "metadata": {},
    "outputs": [],
@@ -700,6 +724,16 @@
     "v"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "beautiful-digit",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert abs(v.magnitude - 86.41527642726142) < 1e7"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "superior-biography",
@@ -712,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "id": "antique-landing",
    "metadata": {},
    "outputs": [],
@@ -723,7 +757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "id": "included-failure",
    "metadata": {},
    "outputs": [],
@@ -787,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 28,
    "id": "postal-marking",
    "metadata": {},
    "outputs": [],
@@ -810,7 +844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 29,
    "id": "legal-observer",
    "metadata": {},
    "outputs": [],
@@ -834,7 +868,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 30,
    "id": "pressing-belgium",
    "metadata": {},
    "outputs": [],
@@ -856,7 +890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 31,
    "id": "inner-equivalent",
    "metadata": {},
    "outputs": [],
@@ -866,7 +900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 32,
    "id": "nuclear-thirty",
    "metadata": {},
    "outputs": [],
@@ -876,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 33,
    "id": "available-steering",
    "metadata": {},
    "outputs": [],
@@ -894,7 +928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 34,
    "id": "documentary-doctrine",
    "metadata": {},
    "outputs": [],
@@ -937,7 +971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 35,
    "id": "greenhouse-reason",
    "metadata": {},
    "outputs": [],
@@ -975,7 +1009,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 36,
    "id": "secure-crowd",
    "metadata": {},
    "outputs": [],
@@ -986,7 +1020,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 37,
    "id": "thirty-minneapolis",
    "metadata": {},
    "outputs": [],
@@ -996,7 +1030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 38,
    "id": "premier-seeking",
    "metadata": {},
    "outputs": [],
@@ -1006,7 +1040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 39,
    "id": "brave-laundry",
    "metadata": {},
    "outputs": [],
@@ -1016,7 +1050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 40,
    "id": "adjusted-consultation",
    "metadata": {},
    "outputs": [],
@@ -1026,7 +1060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 41,
    "id": "understanding-consortium",
    "metadata": {},
    "outputs": [],
@@ -1049,7 +1083,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 42,
    "id": "about-complex",
    "metadata": {},
    "outputs": [],
@@ -1059,7 +1093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 43,
    "id": "unique-owner",
    "metadata": {},
    "outputs": [],
@@ -1069,7 +1103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 44,
    "id": "minus-batman",
    "metadata": {},
    "outputs": [],
@@ -1079,7 +1113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 45,
    "id": "exact-vegetable",
    "metadata": {},
    "outputs": [],
@@ -1089,7 +1123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 46,
    "id": "neutral-lightning",
    "metadata": {},
    "outputs": [],
@@ -1107,7 +1141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 47,
    "id": "virgin-cambodia",
    "metadata": {},
    "outputs": [],
@@ -1119,7 +1153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 48,
    "id": "right-intention",
    "metadata": {},
    "outputs": [],
@@ -1129,7 +1163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 49,
    "id": "mineral-sally",
    "metadata": {},
    "outputs": [],
@@ -1139,7 +1173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 50,
    "id": "coral-camel",
    "metadata": {},
    "outputs": [],
@@ -1149,7 +1183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 51,
    "id": "preceding-cricket",
    "metadata": {},
    "outputs": [],
@@ -1160,7 +1194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 52,
    "id": "effective-rendering",
    "metadata": {},
    "outputs": [],
@@ -1170,7 +1204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 53,
    "id": "printable-reply",
    "metadata": {},
    "outputs": [],
diff --git a/chapters/chap10.ipynb b/chapters/chap10.ipynb
index 727b3d4a..6118041e 100644
--- a/chapters/chap10.ipynb
+++ b/chapters/chap10.ipynb
@@ -22,23 +22,6 @@
     "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "electoral-turkey",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -363,11 +346,11 @@
   },
   {
    "cell_type": "markdown",
-   "id": "norwegian-watch",
+   "id": "wicked-dispute",
    "metadata": {},
    "source": [
     "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/queue.ipynb) or\n",
-    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/queue.ipynb)"
+    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/queue.ipynb)."
    ]
   },
   {
@@ -395,7 +378,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "copyrighted-smooth",
+   "id": "guided-radio",
    "metadata": {},
    "source": [
     "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/salmon.ipynb) or\n",
@@ -431,7 +414,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "solid-summit",
+   "id": "acknowledged-vault",
    "metadata": {},
    "source": [
     "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/trees.ipynb) or\n",
diff --git a/chapters/chap14.ipynb b/chapters/chap14.ipynb
index 6b82fe40..4d7fb778 100644
--- a/chapters/chap14.ipynb
+++ b/chapters/chap14.ipynb
@@ -25,23 +25,6 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "wanted-precipitation",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
    "id": "earned-kidney",
    "metadata": {
     "tags": []
@@ -65,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "forty-hammer",
    "metadata": {
     "tags": []
@@ -87,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "middle-surge",
    "metadata": {
     "tags": []
@@ -100,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "hybrid-making",
    "metadata": {
     "tags": []
@@ -113,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "inclusive-characteristic",
    "metadata": {
     "tags": []
@@ -126,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 6,
    "id": "grave-occasions",
    "metadata": {
     "tags": []
@@ -138,6 +121,7 @@
     "from chap11 import make_system\n",
     "from chap11 import update_func\n",
     "from chap11 import run_simulation\n",
+    "from chap11 import plot_results\n",
     "\n",
     "from chap12 import calc_total_infected\n",
     "\n",
@@ -195,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "genetic-morris",
    "metadata": {},
    "outputs": [],
@@ -216,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "appointed-terrorist",
    "metadata": {},
    "outputs": [],
@@ -236,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "collected-waterproof",
    "metadata": {},
    "outputs": [],
@@ -268,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "polar-flash",
    "metadata": {},
    "outputs": [],
@@ -296,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "incorporate-launch",
    "metadata": {},
    "outputs": [],
@@ -417,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "architectural-growth",
    "metadata": {},
    "outputs": [],
@@ -435,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "narrative-embassy",
    "metadata": {},
    "outputs": [],
@@ -456,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "canadian-assumption",
    "metadata": {},
    "outputs": [],
@@ -474,7 +458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "antique-watch",
    "metadata": {},
    "outputs": [],
@@ -492,7 +476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "fallen-router",
    "metadata": {},
    "outputs": [],
@@ -582,7 +566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "current-tiger",
    "metadata": {},
    "outputs": [],
@@ -592,7 +576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "specialized-regression",
    "metadata": {},
    "outputs": [],
@@ -602,7 +586,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "canadian-authentication",
    "metadata": {},
    "outputs": [],
@@ -624,7 +608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "surgical-mouth",
    "metadata": {
     "scrolled": true
@@ -636,7 +620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "focused-magnet",
    "metadata": {},
    "outputs": [],
@@ -702,7 +686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "sexual-services",
    "metadata": {},
    "outputs": [],
@@ -712,7 +696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 27,
    "id": "central-fever",
    "metadata": {},
    "outputs": [],
@@ -722,7 +706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 28,
    "id": "tropical-advancement",
    "metadata": {},
    "outputs": [],
@@ -742,7 +726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "id": "corresponding-india",
    "metadata": {},
    "outputs": [],
@@ -760,7 +744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "constant-christianity",
    "metadata": {},
    "outputs": [],
diff --git a/chapters/chap21.ipynb b/chapters/chap21.ipynb
index a74f2afe..bf0da871 100644
--- a/chapters/chap21.ipynb
+++ b/chapters/chap21.ipynb
@@ -22,23 +22,6 @@
     "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "electoral-turkey",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
index 366e22f7..a556d27f 100644
--- a/chapters/chap22.ipynb
+++ b/chapters/chap22.ipynb
@@ -34,7 +34,7 @@
     "# install Pint if necessary\n",
     "\n",
     "try:\n",
-    "    import pint\n",
+    "    from pint import UnitRegistry\n",
     "except ImportError:\n",
     "    !pip install pint\n",
     "    \n",
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 9,
    "id": "peripheral-tattoo",
    "metadata": {},
    "outputs": [],
@@ -640,7 +640,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "selective-letter",
+   "id": "fluid-equilibrium",
    "metadata": {},
    "source": [
     "Using vectors to represent forces and accelerations makes the code\n",
@@ -811,7 +811,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "official-handle",
+   "id": "expressed-monday",
    "metadata": {},
    "source": [
     "The magnitude of final velocity is the speed of the ball when it lands."
@@ -907,7 +907,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "atmospheric-logan",
+   "id": "moving-handling",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -950,7 +950,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "oriental-inclusion",
+   "id": "current-costume",
    "metadata": {},
    "source": [
     "Inside the draw function, should use `decorate` to set the limits of the $x$ and $y$ axes.\n",
@@ -1091,7 +1091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "governmental-error",
+   "id": "exotic-adjustment",
    "metadata": {
     "tags": []
    },
@@ -1102,7 +1102,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "understanding-mason",
+   "id": "hungarian-vulnerability",
    "metadata": {
     "tags": []
    },
@@ -1114,7 +1114,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "familiar-bargain",
+   "id": "complete-momentum",
    "metadata": {
     "tags": []
    },
@@ -1125,7 +1125,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "spatial-finish",
+   "id": "horizontal-steam",
    "metadata": {
     "tags": []
    },
@@ -1138,7 +1138,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "weekly-flavor",
+   "id": "narrative-telling",
    "metadata": {
     "tags": []
    },
@@ -1160,7 +1160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "spare-thompson",
+   "id": "vulnerable-shore",
    "metadata": {
     "tags": []
    },
@@ -1171,7 +1171,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "liable-concord",
+   "id": "continental-defendant",
    "metadata": {
     "tags": []
    },
@@ -1183,7 +1183,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "forward-acrobat",
+   "id": "alpha-warrior",
    "metadata": {
     "tags": []
    },
@@ -1194,7 +1194,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "designed-inclusion",
+   "id": "actual-sherman",
    "metadata": {
     "tags": []
    },
@@ -1205,7 +1205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "consolidated-offset",
+   "id": "random-alignment",
    "metadata": {
     "tags": []
    },
@@ -1216,7 +1216,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "sunset-kennedy",
+   "id": "chinese-optimization",
    "metadata": {
     "tags": []
    },
@@ -1230,7 +1230,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "forbidden-inside",
+   "id": "involved-behalf",
    "metadata": {
     "tags": []
    },
@@ -1352,7 +1352,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "strategic-background",
+   "id": "existing-lighter",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/chapters/chap23.ipynb b/chapters/chap23.ipynb
index 46777e94..493c75a1 100644
--- a/chapters/chap23.ipynb
+++ b/chapters/chap23.ipynb
@@ -34,7 +34,7 @@
     "# install Pint if necessary\n",
     "\n",
     "try:\n",
-    "    import pint\n",
+    "    from pint import UnitRegistry\n",
     "except ImportError:\n",
     "    !pip install pint\n",
     "    \n",
@@ -141,7 +141,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "cardiac-attraction",
+   "id": "published-requirement",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -495,7 +495,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "curious-liberia",
+   "id": "corrected-parker",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/chapters/chap24.ipynb b/chapters/chap24.ipynb
index 28cc37c4..b1d88682 100644
--- a/chapters/chap24.ipynb
+++ b/chapters/chap24.ipynb
@@ -24,24 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "electoral-turkey",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -91,7 +74,6 @@
    "metadata": {},
    "source": [
     "In this chapter and the next we'll model systems that involve rotating objects.\n",
-    "\n",
     "In general, rotation is complicated.\n",
     "In three dimensions, objects can rotate around three axes and many objects are easier to spin around some axes than others.\n",
     "\n",
@@ -108,8 +90,7 @@
    "id": "composed-carol",
    "metadata": {},
    "source": [
-    "In this book, we will not take on the physics of rotation in all its glory. \n",
-    "Rather, we will focus on simple scenarios where all rotation and all twisting forces are around a single axis. \n",
+    "We will not take on the physics of rotation in all its glory; rather, we will focus on simple scenarios where all rotation and all twisting forces are around a single axis. \n",
     "In that case, we can treat some vector quantities as if they were scalars, in the same way that we sometimes treat velocity as a scalar with an implicit direction.\n",
     "\n",
     "The fundamental ideas in these examples are angular velocity, angular acceleration, torque, and moment of inertia.\n",
@@ -129,7 +110,7 @@
     "The following figure shows a diagram of the system: $r$ represents\n",
     "the radius of the roll at a point in time. Initially, $r$ is the radius of the cardboard core, $R_{min}$. When the roll is complete, $r$ is $R_{max}$.\n",
     "\n",
-    "![Diagram of a roll of toilet paper, showing change in paper length as a result of a small rotation, $d\\theta$.](https://github.com/AllenDowney/ModSim/raw/master/figs/paper_roll.png)\n",
+    "![Diagram of a roll of toilet paper, showing change in paper length as a result of a small rotation, $d\\theta$.](https://github.com/AllenDowney/ModSim/raw/main/figs/paper_roll.png)\n",
     "\n",
     "I'll use $\\theta$ to represent the total rotation of the roll in\n",
     "radians. In the diagram, $d\\theta$ represents a small increase in\n",
@@ -741,7 +722,7 @@
     "\n",
     "In the next chapter, we'll see a more interesting example where angular velocity is not constant.  And we'll introduce three new concepts: torque, angular acceleration, and moment of inertia.\n",
     "\n",
-    "But first, you might want to work on the following exercises."
+    "But first, you might want to work on the following exercise."
    ]
   },
   {
diff --git a/chapters/chap25.ipynb b/chapters/chap25.ipynb
index 68d2d92e..8e307091 100644
--- a/chapters/chap25.ipynb
+++ b/chapters/chap25.ipynb
@@ -22,23 +22,6 @@
     "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "electoral-turkey",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -65,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -92,7 +75,7 @@
    "source": [
     "In the previous chapter we modeled a system with constant angular\n",
     "velocity.\n",
-    "In this chapter we take the next step, modeling a system with constant angular acceleration and deceleration."
+    "In this chapter we take the next step, modeling a system with angular acceleration and deceleration."
    ]
   },
   {
@@ -187,14 +170,14 @@
     "The following figure shows the scenario, where $F$ is the force I apply to the turntable at the perimeter, perpendicular to the lever arm, $r$, and $\\tau$ is the resulting torque. The blue circle near the bottom is the teapot.\n",
     "\n",
     "![Diagram of a turntable with a\n",
-    "teapot.](https://github.com/AllenDowney/ModSim/raw/master/figs/teapot.png)\n",
+    "teapot.](https://github.com/AllenDowney/ModSim/raw/main/figs/teapot.png)\n",
     "\n",
     "Here are the parameters from the statement of the problem:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "spiritual-disorder",
    "metadata": {},
    "outputs": [],
@@ -226,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "recorded-administration",
    "metadata": {},
    "outputs": [],
@@ -244,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "present-termination",
    "metadata": {},
    "outputs": [],
@@ -262,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "former-driver",
    "metadata": {},
    "outputs": [],
@@ -282,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "enclosed-happiness",
    "metadata": {},
    "outputs": [],
@@ -302,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "passing-sullivan",
    "metadata": {},
    "outputs": [],
@@ -320,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "acoustic-furniture",
    "metadata": {},
    "outputs": [],
@@ -341,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "ranking-local",
    "metadata": {},
    "outputs": [],
@@ -375,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "saved-purple",
    "metadata": {},
    "outputs": [],
@@ -414,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "black-wichita",
    "metadata": {},
    "outputs": [],
@@ -434,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "ranking-google",
    "metadata": {},
    "outputs": [],
@@ -452,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "meaning-philosophy",
    "metadata": {},
    "outputs": [],
@@ -472,7 +455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "focused-invention",
    "metadata": {},
    "outputs": [],
@@ -494,7 +477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "russian-experience",
    "metadata": {},
    "outputs": [],
@@ -514,7 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "chinese-cover",
    "metadata": {},
    "outputs": [],
@@ -533,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "recovered-section",
    "metadata": {},
    "outputs": [],
@@ -553,7 +536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "corporate-taste",
    "metadata": {},
    "outputs": [],
@@ -571,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "destroyed-adrian",
    "metadata": {},
    "outputs": [],
@@ -592,7 +575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "frank-scene",
    "metadata": {},
    "outputs": [],
@@ -610,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "short-singer",
    "metadata": {},
    "outputs": [],
@@ -630,7 +613,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "distributed-humanitarian",
    "metadata": {},
    "outputs": [],
@@ -650,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "proved-surfing",
    "metadata": {
     "scrolled": true
@@ -685,7 +668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "constant-wallace",
    "metadata": {},
    "outputs": [],
@@ -724,7 +707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "played-ranking",
    "metadata": {},
    "outputs": [],
@@ -748,7 +731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "skilled-diving",
    "metadata": {},
    "outputs": [],
@@ -771,7 +754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "id": "united-ranch",
    "metadata": {},
    "outputs": [],
@@ -790,7 +773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "id": "rotary-brazilian",
    "metadata": {},
    "outputs": [],
@@ -809,7 +792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "id": "hungarian-cattle",
    "metadata": {},
    "outputs": [],
@@ -827,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "id": "sorted-struggle",
    "metadata": {},
    "outputs": [],
@@ -861,7 +844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "id": "funky-affect",
    "metadata": {},
    "outputs": [],
@@ -896,7 +879,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "id": "illegal-remainder",
    "metadata": {},
    "outputs": [],
@@ -915,7 +898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "id": "toxic-shark",
    "metadata": {},
    "outputs": [],
@@ -931,12 +914,10 @@
     "## Summary\n",
     "\n",
     "The example in this chapter demonstrates the concepts of torque, angular acceleration, and moment of inertia.\n",
-    "\n",
     "We used these concepts to simulate a turntable, using a hypothetical observation to estimating torque due to friction.\n",
-    "\n",
     "As an exercise, you can finish off the example, estimating the force needed to rotate the table to a given target angle.\n",
     "\n",
-    "The next chapter describes several case studies you can work on to practice the tools from the last few chapters, including projectiles, rotating objects, `root_scalar`, and `maximize_scalar`.\n"
+    "The next chapter describes several case studies you can work on to practice the tools from the last few chapters, including projectiles, rotating objects, `root_scalar`, and `maximize_scalar`."
    ]
   },
   {
@@ -958,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "id": "dental-density",
    "metadata": {},
    "outputs": [],
@@ -976,7 +957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "id": "grave-consent",
    "metadata": {},
    "outputs": [],
@@ -996,7 +977,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "id": "apparent-lancaster",
    "metadata": {},
    "outputs": [],
@@ -1006,7 +987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "id": "persistent-rings",
    "metadata": {},
    "outputs": [],
@@ -1016,7 +997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "id": "previous-pittsburgh",
    "metadata": {},
    "outputs": [],
@@ -1026,7 +1007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "id": "governing-component",
    "metadata": {},
    "outputs": [],
@@ -1036,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "id": "statistical-behalf",
    "metadata": {},
    "outputs": [],
@@ -1046,7 +1027,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "id": "unauthorized-equity",
    "metadata": {},
    "outputs": [],
diff --git a/chapters/chap26.ipynb b/chapters/chap26.ipynb
index bf0d7cee..091fb691 100644
--- a/chapters/chap26.ipynb
+++ b/chapters/chap26.ipynb
@@ -22,69 +22,6 @@
     "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "electoral-turkey",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "formal-context",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# download modsim.py if necessary\n",
-    "\n",
-    "from os.path import basename, exists\n",
-    "\n",
-    "def download(url):\n",
-    "    filename = basename(url)\n",
-    "    if not exists(filename):\n",
-    "        from urllib.request import urlretrieve\n",
-    "        local, _ = urlretrieve(url, filename)\n",
-    "        print('Downloaded ' + local)\n",
-    "    \n",
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/master/modsim.py')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "progressive-typing",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# import functions from modsim\n",
-    "\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "plastic-trigger",
-   "metadata": {},
-   "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap26.ipynb)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "acoustic-small",
@@ -125,6 +62,15 @@
     "`bungee1.ipynb`, which contains starter code and exercises for this case study."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "given-album",
+   "metadata": {},
+   "source": [
+    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/bungee1.ipynb) or\n",
+    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/bungee1.ipynb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "recognized-bidding",
@@ -152,6 +98,15 @@
     "`bungee2.ipynb`, which contains starter code and exercises for this case study. How does the behavior of the system change as we vary the mass of the cord? When the mass of the cord equals the mass of the jumper, what is the net effect on the lowest point in the jump?"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "sporting-armenia",
+   "metadata": {},
+   "source": [
+    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/bungee2.ipynb) or\n",
+    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/bungee2.ipynb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "german-penetration",
@@ -169,6 +124,15 @@
     "Among other things, you will have a chance to experiment with different algorithms and see what effect they have on the accuracy of the results."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "parliamentary-louis",
+   "metadata": {},
+   "source": [
+    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/orbit.ipynb) or\n",
+    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/orbit.ipynb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "victorian-colonial",
@@ -233,6 +197,15 @@
     "notebook and run the code. It uses `minimize`, which is a SciPy function that can search for an optimal set of parameters (as contrasted with `minimize_scalar`, which can only search along a single axis)."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ambient-recall",
+   "metadata": {},
+   "source": [
+    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/spiderman.ipynb) or\n",
+    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/spiderman.ipynb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "african-relative",
@@ -247,7 +220,7 @@
     "\n",
     "This diagram shows the paper roll with the force applied by the kitten, $F$, the lever arm of the force around the axis of rotation, $r$, and the resulting torque, $\\tau$.\n",
     "\n",
-    "![Diagram of a roll of toilet paper, showing a force, lever arm, and the resulting torque.](https://github.com/AllenDowney/ModSim/raw/master/figs/kitten.png)\n",
+    "![Diagram of a roll of toilet paper, showing a force, lever arm, and the resulting torque.](https://github.com/AllenDowney/ModSim/raw/main/figs/kitten.png)\n",
     "\n",
     "Assuming that the force applied by the kitten is 0.002 N, how long would it take to unroll a standard roll of toilet paper?\n",
     "\n",
@@ -255,6 +228,15 @@
     "`kitten.ipynb`, which contains starter code for this case study. Use it to implement this model and check whether the results seem plausible."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "flying-forge",
+   "metadata": {},
+   "source": [
+    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/kitten.ipynb) or\n",
+    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/kitten.ipynb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "future-burlington",
@@ -268,7 +250,7 @@
     "\n",
     "![Diagram of a yo-yo showing forces due to gravity and tension in the\n",
     "string, the lever arm of tension, and the resulting\n",
-    "torque.](https://github.com/AllenDowney/ModSim/raw/master/figs/yoyo.png)"
+    "torque.](https://github.com/AllenDowney/ModSim/raw/main/figs/yoyo.png)"
    ]
   },
   {
@@ -336,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "crude-tribune",
    "metadata": {},
    "outputs": [],
@@ -369,6 +351,15 @@
     "In the repository for this book, you will find a notebook, `yoyo.ipynb`, which contains starter code for this case study. Use it to implement and test this model."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "excessive-equity",
+   "metadata": {},
+   "source": [
+    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/yoyo.ipynb) or\n",
+    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/yoyo.ipynb)."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 1a7995195e0c1be14625042099a3d28dddd97eb4 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 18 Feb 2021 15:50:57 -0500
Subject: [PATCH 060/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 238714 -> 238412 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index 02c0c6b587a5e182db303b8bbb1ebc43855afca9..e3375844a56545654ed3d9d545eb4954717de90b 100644
GIT binary patch
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P6ab}Qz9ax&?veiwP&pt+


From 7a12a7a7861b3e8605e547844766bd19cdd3f2a5 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 18 Feb 2021 16:10:14 -0500
Subject: [PATCH 061/144] Updating chapters

---
 chapters/chap01.ipynb |  9 +++--
 chapters/chap07.ipynb | 76 ++++++++++++++++++++-----------------------
 chapters/chap08.ipynb | 69 +++++++++++++++++++++++----------------
 chapters/chap12.ipynb | 55 +++++++++++--------------------
 chapters/chap14.ipynb |  8 ++---
 chapters/chap17.ipynb |  2 +-
 chapters/chap18.ipynb |  2 +-
 chapters/chap20.ipynb |  8 +++--
 chapters/chap22.ipynb | 44 ++++++++++++-------------
 chapters/chap23.ipynb |  8 ++---
 10 files changed, 138 insertions(+), 143 deletions(-)

diff --git a/chapters/chap01.ipynb b/chapters/chap01.ipynb
index f3bea000..af6790bf 100644
--- a/chapters/chap01.ipynb
+++ b/chapters/chap01.ipynb
@@ -475,7 +475,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "judicial-capability",
+   "id": "clinical-blackjack",
    "metadata": {},
    "source": [
     "## Computation with units\n",
@@ -495,7 +495,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "threatened-mediterranean",
+   "id": "lovely-declaration",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -727,7 +727,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "beautiful-digit",
+   "id": "impaired-puzzle",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1188,8 +1188,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pace_per_mile = pace_per_km.to(minute / mile)\n",
-    "pace_per_mile"
+    "# Solution goes here"
    ]
   },
   {
diff --git a/chapters/chap07.ipynb b/chapters/chap07.ipynb
index e3f69fc6..2290abf5 100644
--- a/chapters/chap07.ipynb
+++ b/chapters/chap07.ipynb
@@ -22,23 +22,6 @@
     "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "incorrect-probability",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -59,8 +42,8 @@
     "        local, _ = urlretrieve(url, filename)\n",
     "        print('Downloaded ' + local)\n",
     "    \n",
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/master/modsim.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'modsim.py')"
    ]
   },
   {
@@ -104,8 +87,8 @@
    },
    "outputs": [],
    "source": [
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/master/data/World_population_estimates.html')"
+    "download('https://github.com/AllenDowney/ModSim/raw/main/' +\n",
+    "         'World_population_estimates.html')"
    ]
   },
   {
@@ -153,6 +136,19 @@
   {
    "cell_type": "code",
    "execution_count": 7,
+   "id": "simple-coupon",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'chap06.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "id": "monetary-profile",
    "metadata": {
     "tags": []
@@ -210,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "beginning-belly",
    "metadata": {},
    "outputs": [],
@@ -229,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "listed-florence",
    "metadata": {},
    "outputs": [],
@@ -254,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "signed-impossible",
    "metadata": {},
    "outputs": [],
@@ -273,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "italian-converter",
    "metadata": {},
    "outputs": [],
@@ -291,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "simplified-sight",
    "metadata": {},
    "outputs": [],
@@ -336,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "neural-guinea",
    "metadata": {},
    "outputs": [],
@@ -356,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "animal-spoke",
    "metadata": {},
    "outputs": [],
@@ -376,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "informed-three",
    "metadata": {},
    "outputs": [],
@@ -394,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "unexpected-nigeria",
    "metadata": {},
    "outputs": [],
@@ -455,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "ordinary-honolulu",
    "metadata": {},
    "outputs": [],
@@ -518,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "realistic-opinion",
    "metadata": {},
    "outputs": [],
@@ -550,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "marine-entry",
    "metadata": {},
    "outputs": [],
@@ -584,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "moving-brazil",
    "metadata": {},
    "outputs": [],
@@ -627,7 +623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "sacred-physiology",
    "metadata": {},
    "outputs": [],
@@ -704,7 +700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "stretch-check",
    "metadata": {},
    "outputs": [],
@@ -714,7 +710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "tender-treat",
    "metadata": {},
    "outputs": [],
@@ -724,7 +720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "passive-certificate",
    "metadata": {},
    "outputs": [],
@@ -748,7 +744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "agricultural-burke",
    "metadata": {},
    "outputs": [],
@@ -782,7 +778,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap08.ipynb b/chapters/chap08.ipynb
index 228360d3..c96210af 100644
--- a/chapters/chap08.ipynb
+++ b/chapters/chap08.ipynb
@@ -59,8 +59,8 @@
     "        local, _ = urlretrieve(url, filename)\n",
     "        print('Downloaded ' + local)\n",
     "    \n",
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/master/modsim.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'modsim.py')"
    ]
   },
   {
@@ -104,8 +104,8 @@
    },
    "outputs": [],
    "source": [
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/master/data/World_population_estimates.html')"
+    "download('https://github.com/AllenDowney/ModSim/raw/main/' +\n",
+    "         'World_population_estimates.html')"
    ]
   },
   {
@@ -153,6 +153,19 @@
   {
    "cell_type": "code",
    "execution_count": 7,
+   "id": "measured-arthur",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'chap06.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "id": "cutting-financing",
    "metadata": {
     "tags": []
@@ -201,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "indirect-russia",
    "metadata": {},
    "outputs": [],
@@ -220,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "comfortable-compression",
    "metadata": {},
    "outputs": [],
@@ -245,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "broken-windsor",
    "metadata": {},
    "outputs": [],
@@ -263,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "latest-function",
    "metadata": {},
    "outputs": [],
@@ -281,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "portable-pottery",
    "metadata": {},
    "outputs": [],
@@ -352,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "precious-contribution",
    "metadata": {},
    "outputs": [],
@@ -373,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "headed-tuner",
    "metadata": {},
    "outputs": [],
@@ -391,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "paperback-delay",
    "metadata": {},
    "outputs": [],
@@ -421,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "billion-dynamics",
    "metadata": {},
    "outputs": [],
@@ -473,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "handmade-funeral",
    "metadata": {},
    "outputs": [],
@@ -494,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "objective-accused",
    "metadata": {},
    "outputs": [],
@@ -513,7 +526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "unique-matrix",
    "metadata": {},
    "outputs": [],
@@ -539,7 +552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "pressing-proceeding",
    "metadata": {},
    "outputs": [],
@@ -560,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "mexican-denver",
    "metadata": {},
    "outputs": [],
@@ -581,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "addressed-worker",
    "metadata": {},
    "outputs": [],
@@ -600,7 +613,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "cathedral-shakespeare",
    "metadata": {},
    "outputs": [],
@@ -634,7 +647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "common-april",
    "metadata": {},
    "outputs": [],
@@ -644,7 +657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "ignored-chain",
    "metadata": {},
    "outputs": [],
@@ -654,7 +667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "color-accountability",
    "metadata": {},
    "outputs": [],
@@ -664,7 +677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "id": "numeric-raise",
    "metadata": {},
    "outputs": [],
@@ -674,7 +687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "id": "included-vehicle",
    "metadata": {},
    "outputs": [],
@@ -684,7 +697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "id": "brown-rating",
    "metadata": {},
    "outputs": [],
@@ -694,7 +707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "id": "laughing-cylinder",
    "metadata": {},
    "outputs": [],
@@ -704,7 +717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "id": "personalized-parking",
    "metadata": {},
    "outputs": [],
diff --git a/chapters/chap12.ipynb b/chapters/chap12.ipynb
index fc23c876..06c7386b 100644
--- a/chapters/chap12.ipynb
+++ b/chapters/chap12.ipynb
@@ -25,23 +25,6 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "electoral-turkey",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -59,13 +42,13 @@
     "        local, _ = urlretrieve(url, filename)\n",
     "        print('Downloaded ' + local)\n",
     "    \n",
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/master/modsim.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -87,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "breathing-hamilton",
    "metadata": {
     "tags": []
@@ -143,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "id": "recent-cooper",
    "metadata": {},
    "outputs": [],
@@ -166,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "id": "found-learning",
    "metadata": {},
    "outputs": [],
@@ -192,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "id": "enormous-abortion",
    "metadata": {},
    "outputs": [],
@@ -211,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "id": "funny-copper",
    "metadata": {},
    "outputs": [],
@@ -230,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "id": "divided-biotechnology",
    "metadata": {},
    "outputs": [],
@@ -274,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 10,
    "id": "synthetic-element",
    "metadata": {
     "tags": []
@@ -289,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 11,
    "id": "recovered-picnic",
    "metadata": {},
    "outputs": [],
@@ -299,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "id": "american-transfer",
    "metadata": {},
    "outputs": [],
@@ -328,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 13,
    "id": "progressive-architect",
    "metadata": {},
    "outputs": [],
@@ -357,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 14,
    "id": "measured-pavilion",
    "metadata": {},
    "outputs": [],
@@ -376,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 15,
    "id": "interior-humanitarian",
    "metadata": {},
    "outputs": [],
@@ -448,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 16,
    "id": "impressive-librarian",
    "metadata": {},
    "outputs": [],
@@ -458,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 17,
    "id": "assumed-license",
    "metadata": {},
    "outputs": [],
@@ -468,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 18,
    "id": "intended-premium",
    "metadata": {},
    "outputs": [],
@@ -478,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 19,
    "id": "limiting-interest",
    "metadata": {},
    "outputs": [],
diff --git a/chapters/chap14.ipynb b/chapters/chap14.ipynb
index 4d7fb778..39e7d841 100644
--- a/chapters/chap14.ipynb
+++ b/chapters/chap14.ipynb
@@ -726,12 +726,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "id": "corresponding-india",
    "metadata": {},
    "outputs": [],
    "source": [
-    "make_series(t_array, T_array)"
+    "frac_infected_series = make_series(c_array, frac_infected)"
    ]
   },
   {
@@ -744,14 +744,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "id": "constant-christianity",
    "metadata": {},
    "outputs": [],
    "source": [
     "from pandas import Series\n",
     "\n",
-    "Series(T_array, t_array)"
+    "frac_infected_series = Series(frac_infected, c_array)"
    ]
   },
   {
diff --git a/chapters/chap17.ipynb b/chapters/chap17.ipynb
index fe6a8163..f1dbacb1 100644
--- a/chapters/chap17.ipynb
+++ b/chapters/chap17.ipynb
@@ -271,7 +271,7 @@
    },
    "outputs": [],
    "source": [
-    "download('https://github.com/AllenDowney/ModSim/raw/master/' +\n",
+    "download('https://github.com/AllenDowney/ModSim/raw/main/' +\n",
     "         'glucose_insulin.csv')"
    ]
   },
diff --git a/chapters/chap18.ipynb b/chapters/chap18.ipynb
index a69372fc..0f4de3ee 100644
--- a/chapters/chap18.ipynb
+++ b/chapters/chap18.ipynb
@@ -121,7 +121,7 @@
    },
    "outputs": [],
    "source": [
-    "download('https://github.com/AllenDowney/ModSim/raw/master/' +\n",
+    "download('https://github.com/AllenDowney/ModSim/raw/main/' +\n",
     "         'glucose_insulin.csv')"
    ]
   },
diff --git a/chapters/chap20.ipynb b/chapters/chap20.ipynb
index 0ac9851f..172ad017 100644
--- a/chapters/chap20.ipynb
+++ b/chapters/chap20.ipynb
@@ -34,9 +34,13 @@
     "# install Pint if necessary\n",
     "\n",
     "try:\n",
-    "    import pint\n",
+    "    from pint import UnitRegistry\n",
     "except ImportError:\n",
-    "    !pip install pint"
+    "    !pip install pint\n",
+    "    \n",
+    "# import units\n",
+    "from pint import UnitRegistry\n",
+    "units = UnitRegistry()"
    ]
   },
   {
diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
index a556d27f..b9f926ca 100644
--- a/chapters/chap22.ipynb
+++ b/chapters/chap22.ipynb
@@ -63,8 +63,8 @@
     "        local, _ = urlretrieve(url, filename)\n",
     "        print('Downloaded ' + local)\n",
     "    \n",
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/master/modsim.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'modsim.py')"
    ]
   },
   {
@@ -640,7 +640,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fluid-equilibrium",
+   "id": "received-winner",
    "metadata": {},
    "source": [
     "Using vectors to represent forces and accelerations makes the code\n",
@@ -811,7 +811,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "expressed-monday",
+   "id": "higher-spirituality",
    "metadata": {},
    "source": [
     "The magnitude of final velocity is the speed of the ball when it lands."
@@ -907,7 +907,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "moving-handling",
+   "id": "abandoned-census",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -950,7 +950,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "current-costume",
+   "id": "alpine-compromise",
    "metadata": {},
    "source": [
     "Inside the draw function, should use `decorate` to set the limits of the $x$ and $y$ axes.\n",
@@ -1091,7 +1091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "exotic-adjustment",
+   "id": "primary-choice",
    "metadata": {
     "tags": []
    },
@@ -1102,19 +1102,19 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "hungarian-vulnerability",
+   "id": "educational-isolation",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "download('https://github.com/AllenDowney/ModSim/raw/main/' +\n",
     "         'baseball_drag.csv')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "complete-momentum",
+   "id": "virtual-albuquerque",
    "metadata": {
     "tags": []
    },
@@ -1125,7 +1125,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "horizontal-steam",
+   "id": "personal-database",
    "metadata": {
     "tags": []
    },
@@ -1138,7 +1138,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "narrative-telling",
+   "id": "assumed-harrison",
    "metadata": {
     "tags": []
    },
@@ -1160,7 +1160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "vulnerable-shore",
+   "id": "backed-access",
    "metadata": {
     "tags": []
    },
@@ -1170,20 +1170,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "id": "continental-defendant",
+   "execution_count": 61,
+   "id": "spectacular-trash",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "mph_to_mps = (1 * units.mph).to(units.mps).magnitude\n",
+    "mph_to_mps = (1 * units.mph).to(units.m/units.s).magnitude\n",
     "speed = baseball_drag['Velocity in mph'] * mph_to_mps"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "alpha-warrior",
+   "id": "finnish-switch",
    "metadata": {
     "tags": []
    },
@@ -1194,7 +1194,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "actual-sherman",
+   "id": "right-burton",
    "metadata": {
     "tags": []
    },
@@ -1205,7 +1205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "random-alignment",
+   "id": "mobile-flashing",
    "metadata": {
     "tags": []
    },
@@ -1216,7 +1216,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "chinese-optimization",
+   "id": "labeled-closing",
    "metadata": {
     "tags": []
    },
@@ -1230,7 +1230,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "involved-behalf",
+   "id": "indoor-zealand",
    "metadata": {
     "tags": []
    },
@@ -1352,7 +1352,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "existing-lighter",
+   "id": "ignored-conditions",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/chapters/chap23.ipynb b/chapters/chap23.ipynb
index 493c75a1..20612664 100644
--- a/chapters/chap23.ipynb
+++ b/chapters/chap23.ipynb
@@ -63,8 +63,8 @@
     "        local, _ = urlretrieve(url, filename)\n",
     "        print('Downloaded ' + local)\n",
     "    \n",
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/master/modsim.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'modsim.py')"
    ]
   },
   {
@@ -141,7 +141,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "published-requirement",
+   "id": "finite-warrant",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -495,7 +495,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "corrected-parker",
+   "id": "american-biodiversity",
    "metadata": {},
    "outputs": [],
    "source": [

From 006631b3b9d4af203d7f22da4cc1fedffc1820f0 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 18 Feb 2021 16:10:14 -0500
Subject: [PATCH 062/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 238412 -> 238281 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index e3375844a56545654ed3d9d545eb4954717de90b..b15f4f82e33e290aba04d11f6d356c47b9d6549d 100644
GIT binary patch
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From da4cd89806b0b267a97256e2454a951d96b464a9 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 18 Feb 2021 16:17:16 -0500
Subject: [PATCH 063/144] Updating chapters

---
 chapters/chap17.ipynb | 14 --------------
 chapters/chap18.ipynb |  2 +-
 chapters/chap22.ipynb | 36 ++++++++++++++++++------------------
 chapters/chap23.ipynb | 13 +++++++++++++
 4 files changed, 32 insertions(+), 33 deletions(-)

diff --git a/chapters/chap17.ipynb b/chapters/chap17.ipynb
index f1dbacb1..b12365d3 100644
--- a/chapters/chap17.ipynb
+++ b/chapters/chap17.ipynb
@@ -261,20 +261,6 @@
     "The following cell downloads the data."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "looking-needle",
-   "metadata": {
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "download('https://github.com/AllenDowney/ModSim/raw/main/' +\n",
-    "         'glucose_insulin.csv')"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "dressed-regard",
diff --git a/chapters/chap18.ipynb b/chapters/chap18.ipynb
index 0f4de3ee..862e549f 100644
--- a/chapters/chap18.ipynb
+++ b/chapters/chap18.ipynb
@@ -121,7 +121,7 @@
    },
    "outputs": [],
    "source": [
-    "download('https://github.com/AllenDowney/ModSim/raw/main/' +\n",
+    "download('https://github.com/AllenDowney/ModSim/raw/main/data/' +\n",
     "         'glucose_insulin.csv')"
    ]
   },
diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
index b9f926ca..e8ec0010 100644
--- a/chapters/chap22.ipynb
+++ b/chapters/chap22.ipynb
@@ -640,7 +640,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "received-winner",
+   "id": "adolescent-westminster",
    "metadata": {},
    "source": [
     "Using vectors to represent forces and accelerations makes the code\n",
@@ -811,7 +811,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "higher-spirituality",
+   "id": "statewide-middle",
    "metadata": {},
    "source": [
     "The magnitude of final velocity is the speed of the ball when it lands."
@@ -907,7 +907,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "abandoned-census",
+   "id": "resistant-vegetation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -950,7 +950,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "alpine-compromise",
+   "id": "incomplete-beijing",
    "metadata": {},
    "source": [
     "Inside the draw function, should use `decorate` to set the limits of the $x$ and $y$ axes.\n",
@@ -1091,7 +1091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "primary-choice",
+   "id": "german-niagara",
    "metadata": {
     "tags": []
    },
@@ -1102,19 +1102,19 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "educational-isolation",
+   "id": "directed-moisture",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "download('https://github.com/AllenDowney/ModSim/raw/main/' +\n",
+    "download('https://github.com/AllenDowney/ModSim/raw/main/data/' +\n",
     "         'baseball_drag.csv')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "virtual-albuquerque",
+   "id": "imported-chosen",
    "metadata": {
     "tags": []
    },
@@ -1125,7 +1125,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "personal-database",
+   "id": "fuzzy-register",
    "metadata": {
     "tags": []
    },
@@ -1138,7 +1138,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "assumed-harrison",
+   "id": "progressive-buyer",
    "metadata": {
     "tags": []
    },
@@ -1160,7 +1160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "backed-access",
+   "id": "bronze-acoustic",
    "metadata": {
     "tags": []
    },
@@ -1171,7 +1171,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "spectacular-trash",
+   "id": "reasonable-swaziland",
    "metadata": {
     "tags": []
    },
@@ -1183,7 +1183,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "finnish-switch",
+   "id": "industrial-architecture",
    "metadata": {
     "tags": []
    },
@@ -1194,7 +1194,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "right-burton",
+   "id": "attached-shower",
    "metadata": {
     "tags": []
    },
@@ -1205,7 +1205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "mobile-flashing",
+   "id": "amazing-horse",
    "metadata": {
     "tags": []
    },
@@ -1216,7 +1216,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "labeled-closing",
+   "id": "christian-camcorder",
    "metadata": {
     "tags": []
    },
@@ -1230,7 +1230,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "indoor-zealand",
+   "id": "computational-alloy",
    "metadata": {
     "tags": []
    },
@@ -1352,7 +1352,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ignored-conditions",
+   "id": "strong-design",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/chapters/chap23.ipynb b/chapters/chap23.ipynb
index 20612664..6d38cabc 100644
--- a/chapters/chap23.ipynb
+++ b/chapters/chap23.ipynb
@@ -89,6 +89,19 @@
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap23.ipynb)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bearing-magnet",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'chap22.py')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,

From 6d95a869747e5a486430d801b927d28d1c139d77 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 18 Feb 2021 16:17:16 -0500
Subject: [PATCH 064/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 238281 -> 238277 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index b15f4f82e33e290aba04d11f6d356c47b9d6549d..05b0bfcf1c9d7f42bc0f1768f8dd501939f017b5 100644
GIT binary patch
delta 22457
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From a5ca0b593a4d0d0d1fdb852c0901fdfc8ade106a Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 18 Feb 2021 16:32:40 -0500
Subject: [PATCH 065/144] Updating chapters

---
 chapters/chap17.ipynb | 49 +++++++++++++++++++++++++------------------
 chapters/chap23.ipynb |  2 +-
 2 files changed, 30 insertions(+), 21 deletions(-)

diff --git a/chapters/chap17.ipynb b/chapters/chap17.ipynb
index b12365d3..4b275880 100644
--- a/chapters/chap17.ipynb
+++ b/chapters/chap17.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -261,6 +261,19 @@
     "The following cell downloads the data."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "mental-wrong",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download('https://github.com/AllenDowney/ModSim/raw/main/data/' +\n",
+    "         'glucose_insulin.csv')\n",
+    "\n",
+    "'https://github.com/AllenDowney/ModSim/raw/main/data/glucose_insulin.csv'"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "dressed-regard",
@@ -271,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "cosmetic-matrix",
    "metadata": {},
    "outputs": [],
@@ -291,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "naval-geology",
    "metadata": {},
    "outputs": [],
@@ -326,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "terminal-teaching",
    "metadata": {},
    "outputs": [],
@@ -344,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "intense-thursday",
    "metadata": {},
    "outputs": [],
@@ -364,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "least-pattern",
    "metadata": {},
    "outputs": [],
@@ -384,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "mounted-venice",
    "metadata": {},
    "outputs": [],
@@ -402,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "affecting-response",
    "metadata": {},
    "outputs": [],
@@ -420,7 +433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "engaging-watershed",
    "metadata": {},
    "outputs": [],
@@ -471,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "endless-network",
    "metadata": {},
    "outputs": [],
@@ -481,7 +494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "entire-concern",
    "metadata": {},
    "outputs": [],
@@ -503,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "representative-acquisition",
    "metadata": {},
    "outputs": [],
@@ -513,18 +526,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "rocky-sydney",
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "data.glucose.plot(style='o', alpha=0.5, label='glucose data')\n",
-    "G_series.plot(color='C0', label='interpolated')\n",
-    "\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration (mg/dL)')"
+    "# Solution goes here"
    ]
   },
   {
diff --git a/chapters/chap23.ipynb b/chapters/chap23.ipynb
index 6d38cabc..309a3936 100644
--- a/chapters/chap23.ipynb
+++ b/chapters/chap23.ipynb
@@ -92,7 +92,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bearing-magnet",
+   "id": "usual-institution",
    "metadata": {
     "tags": []
    },

From 544a221b9d1bd38592e2d98a4bec2f98bcfcc71a Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 18 Feb 2021 16:32:40 -0500
Subject: [PATCH 066/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 238277 -> 238229 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index 05b0bfcf1c9d7f42bc0f1768f8dd501939f017b5..4d55b87d6f4885ceb470823f3d73d0150739dd92 100644
GIT binary patch
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Of;r*vW1p500sjwbq%pJr


From c7289100a23990c87a68caa341f721e4439fd0fb Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 18 Feb 2021 16:37:44 -0500
Subject: [PATCH 067/144] Updating examples

---
 examples/kitten.ipynb | 82 ++++++++++++++++++-------------------------
 1 file changed, 34 insertions(+), 48 deletions(-)

diff --git a/examples/kitten.ipynb b/examples/kitten.ipynb
index e0c11988..5b87b1a5 100644
--- a/examples/kitten.ipynb
+++ b/examples/kitten.ipynb
@@ -20,23 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 1,
    "metadata": {
     "tags": []
    },
@@ -44,19 +28,22 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 2,
    "metadata": {
     "tags": []
    },
@@ -87,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,15 +147,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def moment_of_inertia(r, system):\n",
+    "def moment_of_inertia(r):\n",
     "    \"\"\"Moment of inertia for a roll of toilet paper.\n",
     "    \n",
     "    r: current radius of roll in meters\n",
-    "    system: System object\n",
     "    \n",
     "    returns: moment of inertia in kg m**2\n",
     "    \"\"\"    \n",
@@ -189,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -206,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,11 +209,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "moment_of_inertia(Rmax, system)"
+    "moment_of_inertia(Rmax)"
    ]
   },
   {
@@ -239,11 +225,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "moment_of_inertia(Rmin, system)"
+    "moment_of_inertia(Rmin)"
    ]
   },
   {
@@ -276,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -293,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -322,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -331,7 +317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -347,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -356,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -372,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -388,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -421,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -439,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -458,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -477,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [

From 75cad57ce526a3a41b7f888a00408a787e8e69d7 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 19 Feb 2021 14:40:59 -0500
Subject: [PATCH 068/144] Updating modsim.py

---
 examples/duck.ipynb |  209 --------
 examples/oem.ipynb  | 1106 -------------------------------------------
 2 files changed, 1315 deletions(-)
 delete mode 100644 examples/duck.ipynb
 delete mode 100644 examples/oem.ipynb

diff --git a/examples/duck.ipynb b/examples/duck.ipynb
deleted file mode 100644
index d141be53..00000000
--- a/examples/duck.ipynb
+++ /dev/null
@@ -1,209 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Title"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# download modsim.py if necessary\n",
-    "\n",
-    "from os.path import exists\n",
-    "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# import functions from modsim\n",
-    "\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Copyright 2018 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The duck problem\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g = UNITS.g\n",
-    "cm = UNITS.cm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = System (\n",
-    "    density_duck = 0.3 * g/cm**3,\n",
-    "    density_water = 1  * g/cm**3,\n",
-    "    r = 5 * cm,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func(d, system):\n",
-    "    # in order to work with root_scale, we have\n",
-    "    # to accept input that does not have units\n",
-    "    d = d * cm\n",
-    "    r = system.r\n",
-    "    \n",
-    "    volume_duck = 4 / 3 * pi * r**3\n",
-    "    mass_duck = volume_duck * system.density_duck\n",
-    "\n",
-    "    volume_water = pi / 3 * (3 * r * d**2 - d**3)\n",
-    "    mass_water = volume_water * system.density_water\n",
-    "\n",
-    "    # and return an error that does not have units\n",
-    "    error = mass_duck - mass_water\n",
-    "    return magnitude(error)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "error_func(3, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "error_func(4, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = root_scalar(error_func, [3., 4.], system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res.root * cm"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/oem.ipynb b/examples/oem.ipynb
deleted file mode 100644
index d9edd314..00000000
--- a/examples/oem.ipynb
+++ /dev/null
@@ -1,1106 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Title"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# download modsim.py if necessary\n",
-    "\n",
-    "from os.path import exists\n",
-    "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# import functions from modsim\n",
-    "\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case study.\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Electric car"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Olin Electric Motorsports](https://www.olinelectricmotorsports.com/) is a club at Olin College that designs and builds electric cars, and participates in the [Formula SAE Electric](https://www.sae.org/attend/student-events/formula-sae-electric) competition.\n",
-    "\n",
-    "The goal of this case study is to use simulation to guide the design of a car intended to accelerate from standing to 100 kph as quickly as possible.  The [world record for this event](https://www.youtube.com/watch?annotation_id=annotation_2297602723&feature=iv&src_vid=I-NCH8ct24U&v=n2XiCYA3C9s), using a car that meets the competition requirements, is 1.513 seconds.\n",
-    "\n",
-    "We'll start with a simple model that takes into account the characteristics of the motor and vehicle:\n",
-    "\n",
-    "* The motor is an [Emrax 228 high voltage axial flux synchronous permanent magnet motor](http://emrax.com/products/emrax-228/); according to the [data sheet](http://emrax.com/wp-content/uploads/2017/01/emrax_228_technical_data_4.5.pdf), its maximum torque is 240 Nm, at 0 rpm.  But maximum torque decreases with motor speed; at 5000 rpm, maximum torque is 216 Nm.\n",
-    "\n",
-    "* The motor is connected to the drive axle with a chain drive with speed ratio 13:60 or 1:4.6; that is, the axle rotates once for each 4.6 rotations of the motor.\n",
-    "\n",
-    "* The radius of the tires is 0.26 meters.\n",
-    "\n",
-    "* The weight of the vehicle, including driver, is 300 kg.\n",
-    "\n",
-    "To start, we will assume no slipping between the tires and the road surface, no air resistance, and no rolling resistance.  Then we will relax these assumptions one at a time.\n",
-    "\n",
-    "* First we'll add drag, assuming that the frontal area of the vehicle is 0.6 square meters, with coefficient of drag 0.6.\n",
-    "\n",
-    "* Next we'll add rolling resistance, assuming a coefficient of 0.2.\n",
-    "\n",
-    "* Finally we'll compute the peak acceleration to see if the \"no slip\" assumption is credible.\n",
-    "\n",
-    "We'll use this model to estimate the potential benefit of possible design improvements, including decreasing drag and rolling resistance, or increasing the speed ratio.\n",
-    "\n",
-    "I'll start by loading the units we need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radian = UNITS.radian\n",
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "minute = UNITS.minute\n",
-    "hour = UNITS.hour\n",
-    "km = UNITS.kilometer\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton\n",
-    "rpm = UNITS.rpm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And store the parameters in a `Params` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(r_wheel=0.26 * m,\n",
-    "                speed_ratio=13/60,\n",
-    "                C_rr=0.2,\n",
-    "                C_d=0.5,\n",
-    "                area=0.6*m**2,\n",
-    "                rho=1.2*kg/m**3,\n",
-    "                mass=300*kg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system` creates the initial state, `init`, and constructs an `interp1d` object that represents torque as a function of motor speed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(x=0*m, v=0*m/s)\n",
-    "    \n",
-    "    rpms = [0, 2000, 5000]\n",
-    "    torques = [240, 240, 216]\n",
-    "    interpolate_torque = interpolate(Series(torques, rpms))\n",
-    "    \n",
-    "    return System(params, init=init,\n",
-    "                  interpolate_torque=interpolate_torque,\n",
-    "                  t_end=3*s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `make_system`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Torque and speed\n",
-    "\n",
-    "The relationship between torque and motor speed is taken from the [Emrax 228 data sheet](http://emrax.com/wp-content/uploads/2017/01/emrax_228_technical_data_4.5.pdf).  The following functions reproduce the red dotted line that represents peak torque, which can only be sustained for a few seconds before the motor overheats."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_torque(omega, system):\n",
-    "    \"\"\"Maximum peak torque as a function of motor speed.\n",
-    "    \n",
-    "    omega: motor speed in radian/s\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: torque in Nm\n",
-    "    \"\"\"\n",
-    "    factor = (1 * radian / s).to(rpm)\n",
-    "    x = magnitude(omega * factor)\n",
-    "    return system.interpolate_torque(x) * N * m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compute_torque(0*radian/s, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "omega = (5000 * rpm).to(radian/s)\n",
-    "compute_torque(omega, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the whole curve."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xs = linspace(0, 525, 21) * radian / s\n",
-    "taus = [compute_torque(x, system) for x in xs]\n",
-    "plot(xs, taus)\n",
-    "decorate(xlabel='Motor speed (rpm)',\n",
-    "         ylabel='Available torque (N m)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Simulation\n",
-    "\n",
-    "Here's the slope function that computes the maximum possible acceleration of the car as a function of it current speed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    x, v = state\n",
-    "    r_wheel, speed_ratio = system.r_wheel, system.speed_ratio\n",
-    "    mass = system.mass\n",
-    "    \n",
-    "    # use velocity, v, to compute angular velocity of the wheel\n",
-    "    omega2 = v / r_wheel\n",
-    "    \n",
-    "    # use the speed ratio to compute motor speed\n",
-    "    omega1 = omega2 / speed_ratio\n",
-    "    \n",
-    "    # look up motor speed to get maximum torque at the motor\n",
-    "    tau1 = compute_torque(omega1, system)\n",
-    "    \n",
-    "    # compute the corresponding torque at the axle\n",
-    "    tau2 = tau1 / speed_ratio\n",
-    "    \n",
-    "    # compute the force of the wheel on the ground\n",
-    "    F = tau2 / r_wheel\n",
-    "    \n",
-    "    # compute acceleration\n",
-    "    a = F/mass\n",
-    "\n",
-    "    return v, a      "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `slope_func` at linear velocity 10 m/s."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_state = State(x=0*m, v=10*m/s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(test_state, 0*s, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And look at the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After 3 seconds, the vehicle could be at 40 meters per second, in theory, which is 144 kph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_final = get_last_value(results.v)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_final.to(km/hour)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `x`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_position(results):\n",
-    "    plot(results.x, label='x')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "    \n",
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `v`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_velocity(results):\n",
-    "    plot(results.v, label='v')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')\n",
-    "    \n",
-    "plot_velocity(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Stopping at 100 kph\n",
-    "\n",
-    "We'll use an event function to stop the simulation when we reach 100 kph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Stops when we get to 100 km/hour.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: difference from 100 km/hour\n",
-    "    \"\"\"\n",
-    "    x, v = state\n",
-    "    \n",
-    "    # convert to km/hour\n",
-    "    factor = (1 * m/s).to(km/hour)\n",
-    "    v = magnitude(v * factor)\n",
-    "    \n",
-    "    return v - 100     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subplot(2, 1, 1)\n",
-    "plot_position(results)\n",
-    "\n",
-    "subplot(2, 1, 2)\n",
-    "plot_velocity(results)\n",
-    "\n",
-    "savefig('figs/chap11-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "According to this model, we should be able to make this run in just over 2 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_final = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At the end of the run, the car has gone about 28 meters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = results.last_row()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we send the final state back to the slope function, we can see that the final acceleration is about 13 $m/s^2$, which is about 1.3 times the acceleration of gravity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v, a = slope_func(state, 0, system)\n",
-    "v.to(km/hour)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g = 9.8 * m/s**2\n",
-    "(a / g).to(UNITS.dimensionless)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's not easy for a vehicle to accelerate faster than `g`, because that implies a coefficient of friction between the wheels and the road surface that's greater than 1.  But racing tires on dry asphalt can do that; the OEM team at Olin has tested their tires and found a peak coefficient near 1.5.\n",
-    "\n",
-    "So it's possible that our no slip assumption is valid, but only under ideal conditions, where weight is distributed equally on four tires, and all tires are driving."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** How much time do we lose because maximum torque decreases as motor speed increases?  Run the model again with no drop off in torque and see how much time it saves."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section we'll see how much effect drag has on the results.\n",
-    "\n",
-    "Here's a function to compute drag force, as we saw in Chapter 21."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(v, system):\n",
-    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
-    "    \n",
-    "    v: velocity\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: drag force\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    f_drag = -np.sign(v) * rho * v**2 * C_d * area / 2\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it with a velocity of 20 m/s."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drag_force(20 * m/s, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the resulting acceleration of the vehicle due to drag.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drag_force(20 * m/s, system) / system.mass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the effect of drag is not huge, compared to the acceleration we computed in the previous section, but it is not negligible.\n",
-    "\n",
-    "Here's a modified slope function that takes drag into account."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func2(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    x, v = state\n",
-    "    r_wheel, speed_ratio = system.r_wheel, system.speed_ratio\n",
-    "    mass = system.mass\n",
-    "    \n",
-    "    omega2 = v / r_wheel * radian\n",
-    "    omega1 = omega2 / speed_ratio\n",
-    "    tau1 = compute_torque(omega1, system)\n",
-    "    tau2 = tau1 / speed_ratio\n",
-    "    F = tau2 / r_wheel\n",
-    "    a_motor = F / mass\n",
-    "    a_drag = drag_force(v, system) / mass\n",
-    "    \n",
-    "    a = a_motor + a_drag\n",
-    "    return v, a "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the next run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2, details = run_ode_solver(system, slope_func2, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The time to reach 100 kph is a bit higher."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_final2 = get_last_label(results2) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But the total effect of drag is only about 2/100 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_final2 - t_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That's not huge, which suggests we might not be able to save much time by decreasing the frontal area, or coefficient of drag, of the car."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Rolling resistance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we'll consider [rolling resistance](https://en.wikipedia.org/wiki/Rolling_resistance), which the force that resists the motion of the car as it rolls on tires.  The cofficient of rolling resistance, `C_rr`, is the ratio of rolling resistance to the normal force between the car and the ground (in that way it is similar to a coefficient of friction).\n",
-    "\n",
-    "The following function computes rolling resistance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.set(unit_rr = 1 * N / kg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def rolling_resistance(system):\n",
-    "    \"\"\"Computes force due to rolling resistance.\n",
-    "    \n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: force\n",
-    "    \"\"\"\n",
-    "    return -system.C_rr * system.mass * system.unit_rr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The acceleration due to rolling resistance is 0.2 (it is not a coincidence that it equals `C_rr`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rolling_resistance(system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rolling_resistance(system) / system.mass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a modified slope function that includes drag and rolling resistance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func3(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    x, v = state\n",
-    "    r_wheel, speed_ratio = system.r_wheel, system.speed_ratio\n",
-    "    mass = system.mass\n",
-    "    \n",
-    "    omega2 = v / r_wheel * radian\n",
-    "    omega1 = omega2 / speed_ratio\n",
-    "    tau1 = compute_torque(omega1, system)\n",
-    "    tau2 = tau1 / speed_ratio\n",
-    "    F = tau2 / r_wheel\n",
-    "    a_motor = F / mass\n",
-    "    a_drag = drag_force(v, system) / mass\n",
-    "    a_roll = rolling_resistance(system) / mass\n",
-    "    \n",
-    "    a = a_motor + a_drag + a_roll\n",
-    "    return v, a "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results3, details = run_ode_solver(system, slope_func3, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final time is a little higher, but the total cost of rolling resistance is only 3/100 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_final3 = get_last_label(results3) * s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_final3 - t_final2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So, again, there is probably not much to be gained by decreasing rolling resistance.\n",
-    "\n",
-    "In fact, it is hard to decrease rolling resistance without also decreasing traction, so that might not help at all."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optimal gear ratio"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The gear ratio 13:60 is intended to maximize the acceleration of the car without causing the tires to slip.  In this section, we'll consider other gear ratios and estimate their effects on acceleration and time to reach 100 kph.\n",
-    "\n",
-    "Here's a function that takes a speed ratio as a parameter and returns time to reach 100 kph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def time_to_speed(speed_ratio, params):\n",
-    "    \"\"\"Computes times to reach 100 kph.\n",
-    "    \n",
-    "    speed_ratio: ratio of wheel speed to motor speed\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: time to reach 100 kph, in seconds\n",
-    "    \"\"\"\n",
-    "    params = Params(params, speed_ratio=speed_ratio)\n",
-    "    system = make_system(params)\n",
-    "    system.set(unit_rr = 1 * N / kg)\n",
-    "    \n",
-    "    results, details = run_ode_solver(system, slope_func3, events=event_func)\n",
-    "    t_final = get_last_label(results)\n",
-    "    a_initial = slope_func(system.init, 0, system)\n",
-    "    return t_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it with the default ratio:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "time_to_speed(13/60, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can try it with different numbers of teeth on the motor gear (assuming that the axle gear has 60 teeth):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for teeth in linrange(8, 18):\n",
-    "    print(teeth, time_to_speed(teeth/60, params))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Wow!  The speed ratio has a big effect on the results.  At first glance, it looks like we could break the world record (1.513 seconds) just by decreasing the number of teeth.\n",
-    "\n",
-    "But before we try it, let's see what effect that has on peak acceleration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def initial_acceleration(speed_ratio, params):\n",
-    "    \"\"\"Maximum acceleration as a function of speed ratio.\n",
-    "    \n",
-    "    speed_ratio: ratio of wheel speed to motor speed\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: peak acceleration, in m/s^2\n",
-    "    \"\"\"\n",
-    "    params = Params(params, speed_ratio=speed_ratio)\n",
-    "    system = make_system(params)\n",
-    "    a_initial = slope_func(system.init, 0, system)[1] * m/s**2\n",
-    "    return a_initial"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for teeth in linrange(8, 18):\n",
-    "    print(teeth, initial_acceleration(teeth/60, params))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we decrease the speed ratio, the peak acceleration increases.  With 8 teeth on the motor gear, we could break the world record, but only if we can accelerate at 2.3 times the acceleration of gravity, which is impossible without very sticky ties and a vehicle that generates a lot of downforce."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "23.07 / 9.8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These results suggest that the most promising way to improve the performance of the car (for this event) would be to improve traction."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 53ddbaed3657dd01e32b477ae1df3f319c332223 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 19 Feb 2021 14:44:37 -0500
Subject: [PATCH 069/144] Updating chapters

---
 chapters/chap01.ipynb |  8 ++++----
 chapters/chap05.ipynb | 14 +++++++++-----
 chapters/chap09.ipynb |  4 ++--
 chapters/chap10.ipynb |  2 +-
 chapters/chap11.ipynb |  2 +-
 chapters/chap17.ipynb | 21 ++-------------------
 chapters/chap19.ipynb |  8 ++++----
 chapters/chap22.ipynb |  4 ++--
 8 files changed, 25 insertions(+), 38 deletions(-)

diff --git a/chapters/chap01.ipynb b/chapters/chap01.ipynb
index af6790bf..eaa6b3c9 100644
--- a/chapters/chap01.ipynb
+++ b/chapters/chap01.ipynb
@@ -59,8 +59,8 @@
     "        local, _ = urlretrieve(url, filename)\n",
     "        print('Downloaded ' + local)\n",
     "    \n",
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/master/modsim.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'modsim.py')"
    ]
   },
   {
@@ -125,7 +125,7 @@
     "This book is about modeling and simulation of physical systems. The\n",
     "following diagram shows what I mean by \"modeling\":\n",
     "\n",
-    "<img src =\"https://github.com/AllenDowney/ModSimPy/raw/master/book/figs/modeling_framework.png\" width=\"400\">"
+    "![Diagram of the modeling framework](https://github.com/AllenDowney/ModSim/raw/main/figs/modeling_framework.png)"
    ]
   },
   {
@@ -1277,7 +1277,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap05.ipynb b/chapters/chap05.ipynb
index 770d78ba..d6b9856f 100644
--- a/chapters/chap05.ipynb
+++ b/chapters/chap05.ipynb
@@ -119,7 +119,9 @@
   {
    "cell_type": "markdown",
    "id": "continuing-cancellation",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "The following cell downloads a copy of https://en.wikipedia.org/wiki/World_population_estimates"
    ]
@@ -128,7 +130,9 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "convenient-stroke",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
@@ -141,8 +145,7 @@
    "metadata": {},
    "source": [
     "To read this data, we will use Pandas, which provides functions for\n",
-    "working with data. The function we'll use is `read_html`, which can read a web page and extract data from any tables it contains. Before we can use it, we have to import it. You have already seen this import\n",
-    "statement:"
+    "working with data. The function we'll use is `read_html`, which can read a web page and extract data from any tables it contains. Before we can use it, we have to import it."
    ]
   },
   {
@@ -955,6 +958,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -970,7 +974,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap09.ipynb b/chapters/chap09.ipynb
index 02d66d3c..d552d700 100644
--- a/chapters/chap09.ipynb
+++ b/chapters/chap09.ipynb
@@ -994,7 +994,7 @@
    "source": [
     "**Exercise:** Use SymPy to solve the quadratic growth equation using the alternative parameterization\n",
     "\n",
-    "$\\frac{df(t)}{dt} = \\alpha f(t) + \\beta f^2(t) $"
+    "$$ \\frac{df(t)}{dt} = \\alpha f(t) + \\beta f^2(t) $$\n"
    ]
   },
   {
@@ -1109,7 +1109,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap10.ipynb b/chapters/chap10.ipynb
index 6118041e..4476b1b7 100644
--- a/chapters/chap10.ipynb
+++ b/chapters/chap10.ipynb
@@ -447,7 +447,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap11.ipynb b/chapters/chap11.ipynb
index 4865ec5d..c5732fbb 100644
--- a/chapters/chap11.ipynb
+++ b/chapters/chap11.ipynb
@@ -800,7 +800,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap17.ipynb b/chapters/chap17.ipynb
index 4b275880..9f04bf2b 100644
--- a/chapters/chap17.ipynb
+++ b/chapters/chap17.ipynb
@@ -22,23 +22,6 @@
     "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "electoral-turkey",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -317,7 +300,7 @@
    "id": "close-payday",
    "metadata": {},
    "source": [
-    "`data` has two columns: `glucose` is the concentration of blood glucose in mg/dL; `insulin` is the concentration of insulin in the blood in μU/mL (a medical \"unit\", denoted U, is an amount defined by convention in context). The index is time in minutes.\n",
+    "`data` has two columns: `glucose` is the concentration of blood glucose in mg/dL; `insulin` is the concentration of insulin in the blood in $\\mu$U/mL (a medical \"unit\", denoted U, is an amount defined by convention in context). The index is time in minutes.\n",
     "\n",
     "This dataset represents glucose and insulin concentrations over\n",
     "182 min for a subject with normal insulin production and sensitivity."
@@ -562,7 +545,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap19.ipynb b/chapters/chap19.ipynb
index 26182b16..fc246406 100644
--- a/chapters/chap19.ipynb
+++ b/chapters/chap19.ipynb
@@ -180,9 +180,9 @@
    "source": [
     "## Low-Pass Filter\n",
     "\n",
-    "The following circuit diagram (from <http://modsimpy.com/divider>) shows a low-pass filter built with one resistor and one capacitor.\n",
+    "The following circuit diagram (from [Wikipedia](https://commons.wikimedia.org/wiki/File:1st_Order_Lowpass_Filter_RC.svg)) shows a low-pass filter built with one resistor and one capacitor.\n",
     "\n",
-    "![Circuit diagram of a low-pass filter](https://github.com/AllenDowney/ModSim/raw/main/figs/RC_Divider.svg)\n",
+    "![Circuit diagram of a low-pass filter](https://github.com/AllenDowney/ModSim/raw/main/figs/Lowpass_Filter_RC.png)\n",
     "\n",
     "A \"filter\" is a circuit takes a signal, $V_{in}$, as input and produces a signal, $V_{out}$, as output. In this context, a \"signal\" is a voltage that changes over time.\n",
     "\n",
@@ -206,7 +206,7 @@
     "\n",
     "$$I_R = (V_{in} - V_{out}) / R$$ \n",
     "\n",
-    "where $R$ is resistance in ohms (Ω).\n",
+    "where $R$ is resistance in ohms ($\\Omega$).\n",
     "\n",
     "Assuming that no current flows through the output of the circuit,\n",
     "Kirchhoff's current law implies that the current through the capacitor\n",
@@ -355,7 +355,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
index e8ec0010..e2c8ce41 100644
--- a/chapters/chap22.ipynb
+++ b/chapters/chap22.ipynb
@@ -1084,7 +1084,7 @@
    "source": [
     "**Exercise:** The model so far is based on the assumption that coefficient of drag does not depend on velocity, but in reality it does.  The following figure, from Adair, [*The Physics of Baseball*](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC), shows coefficient of drag as a function of velocity.\n",
     "\n",
-    "<img src=\"https://github.com/AllenDowney/ModSimPy/raw/master/figs/baseball_drag.png\" width=\"400\">\n",
+    "![Graph of drag coefficient versus velocity](https://github.com/AllenDowney/ModSimPy/raw/master/figs/baseball_drag.png)\n",
     "\n",
     "I used [an online graph digitizer](https://automeris.io/WebPlotDigitizer/) to extract the data and save it in a CSV file.  "
    ]
@@ -1375,7 +1375,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

From fe80a994689dafd282c3d479b19c90c34c590eb5 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 19 Feb 2021 14:44:37 -0500
Subject: [PATCH 070/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 238229 -> 230688 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index 4d55b87d6f4885ceb470823f3d73d0150739dd92..ad00ad4036ac1e3df29a0ef2779637cb5ea39721 100644
GIT binary patch
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SjuHBSK%drIZ=oK!fd2tKs_$X|


From bc60f27de72c7a02560fc4e458defa524e4674fe Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 17 May 2021 11:16:43 -0400
Subject: [PATCH 071/144] Updating modsim.py

---
 chap11.py | 6 +++---
 chap12.py | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/chap11.py b/chap11.py
index 23d40dbb..d8569569 100644
--- a/chap11.py
+++ b/chap11.py
@@ -1,7 +1,7 @@
 from modsim import *
 
 def make_system(beta, gamma):
-    init = State(S=89, I=1, R=0)
+    init = State(s=89, i=1, r=0)
     init /= init.sum()
 
     return System(init=init, t_end=7*14,
@@ -10,7 +10,7 @@ def make_system(beta, gamma):
 from modsim import *
 
 def update_func(t, state, system):
-    s, i, r = state
+    s, i, r = state.s, state.i, state.r
 
     infected = system.beta * i * s    
     recovered = system.gamma * i
@@ -19,7 +19,7 @@ def update_func(t, state, system):
     i += infected - recovered
     r += recovered
     
-    return State(S=s, I=i, R=r)
+    return State(s=s, i=i, r=r)
 
 from modsim import *
 
diff --git a/chap12.py b/chap12.py
index f72e7997..7cfc3909 100644
--- a/chap12.py
+++ b/chap12.py
@@ -1,7 +1,7 @@
 from modsim import *
 
 def calc_total_infected(results, system):
-    s_0 = results.S[0]
-    s_end = results.S[system.t_end]
+    s_0 = results.s[0]
+    s_end = results.s[system.t_end]
     return s_0 - s_end
 

From e14809bc6bb0e67e1b204239cae7b63033e8844b Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 17 May 2021 14:37:52 -0400
Subject: [PATCH 072/144] Updating chapters

---
 chapters/chap01.ipynb | 235 +++++++++++++++++++++++++-----------------
 chapters/chap02.ipynb | 179 +++++++++++++++++---------------
 chapters/chap03.ipynb |  75 ++++++++------
 chapters/chap04.ipynb |  74 +++++++------
 chapters/chap05.ipynb |  75 ++++++++------
 chapters/chap06.ipynb |  31 +++---
 chapters/chap07.ipynb | 102 +++++++++---------
 chapters/chap08.ipynb |  33 +++---
 chapters/chap09.ipynb | 115 +++++++++------------
 chapters/chap10.ipynb |  90 ++++++----------
 chapters/chap11.ipynb | 162 +++++++++++++++++------------
 chapters/chap12.ipynb |  68 +++++++-----
 chapters/chap13.ipynb |  50 +++++----
 chapters/chap14.ipynb |  84 ++++++++-------
 chapters/chap15.ipynb | 128 ++++++++++-------------
 chapters/chap16.ipynb |  37 +++----
 chapters/chap17.ipynb |  35 ++++---
 chapters/chap18.ipynb |  45 ++------
 chapters/chap19.ipynb |  50 ++-------
 chapters/chap20.ipynb |  10 +-
 chapters/chap21.ipynb |  18 ++--
 chapters/chap22.ipynb |  26 +++--
 chapters/chap23.ipynb |  12 ++-
 chapters/chap24.ipynb |   8 +-
 chapters/chap25.ipynb |  16 +--
 chapters/chap26.ipynb |   8 +-
 26 files changed, 902 insertions(+), 864 deletions(-)

diff --git a/chapters/chap01.ipynb b/chapters/chap01.ipynb
index eaa6b3c9..ac87c061 100644
--- a/chapters/chap01.ipynb
+++ b/chapters/chap01.ipynb
@@ -2,15 +2,15 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "uniform-persian",
+   "id": "affecting-malta",
    "metadata": {},
    "source": [
-    "# Chapter 1"
+    "# Modeling"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "imported-table",
+   "id": "pressed-palestinian",
    "metadata": {
     "tags": []
    },
@@ -22,10 +22,40 @@
     "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "confirmed-budapest",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Jupyter\n",
+    "\n",
+    "Welcome to *Modeling and Simulation in Python*, and welcome to Jupyter.\n",
+    "\n",
+    "This is a Jupyter notebook, which is a development environment where you can write and run Python code.  Each notebook is divided into cells.  Each cell contains either text (like this cell) or Python code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "danish-scope",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "To run a cell, hold down SHIFT and press ENTER.  \n",
+    "\n",
+    "* If you run a text cell, Jupyter formats the text and displays the result.\n",
+    "\n",
+    "* If you run a code cell, Jupyter runs the Python code in the cell and displays the result, if any.\n",
+    "\n",
+    "The following cells check whether the libraries we need are installed.  If so, the cells produce no output.  If not, you'll see updates from the installer."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "electoral-turkey",
+   "id": "passive-dayton",
    "metadata": {
     "tags": []
    },
@@ -42,7 +72,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "formal-context",
+   "id": "prompt-committee",
    "metadata": {
     "tags": []
    },
@@ -66,7 +96,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "progressive-typing",
+   "id": "fifteen-train",
    "metadata": {
     "tags": []
    },
@@ -79,40 +109,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "plastic-trigger",
+   "id": "chronic-moore",
    "metadata": {},
    "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap01.ipynb)"
+    "This chapter introduces the modeling framework we will use throughout the book, and works through our first example, using a simple model of physics to evaluate the claim that a penny falling from the height of the Empire State Building could kill someone if it hit them on the head. Also, you'll see how to do computation in Python with units like meters and seconds."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "confirmed-budapest",
-   "metadata": {
-    "tags": []
-   },
+   "id": "extensive-proposition",
+   "metadata": {},
    "source": [
-    "## Jupyter\n",
-    "\n",
-    "Welcome to *Modeling and Simulation in Python*, and welcome to Jupyter.\n",
-    "\n",
-    "This is a Jupyter notebook, which is a development environment where you can write and run Python code.  Each notebook is divided into cells.  Each cell contains either text (like this cell) or Python code."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "danish-scope",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "To run a cell, hold down SHIFT and press ENTER.  \n",
-    "\n",
-    "* If you run a text cell, Jupyter formats the text and displays the result.\n",
-    "\n",
-    "* If you run a code cell, Jupyter runs the Python code in the cell and displays the result, if any.\n",
-    "\n",
-    "The cells at the beginning of this notebook check whether the libraries we'll need are installed.  If so, the cells produce no output.  If not, you'll see updates from the installer."
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -120,12 +129,12 @@
    "id": "hourly-financing",
    "metadata": {},
    "source": [
-    "## Modeling and Simulation\n",
+    "## The Modeling Framework\n",
     "\n",
-    "This book is about modeling and simulation of physical systems. The\n",
-    "following diagram shows what I mean by \"modeling\":\n",
+    "This book is about modeling and simulating physical systems. The\n",
+    "following diagram shows what I mean by *modeling*:\n",
     "\n",
-    "![Diagram of the modeling framework](https://github.com/AllenDowney/ModSim/raw/main/figs/modeling_framework.png)"
+    "![Diagram of the modeling framework.](https://github.com/AllenDowney/ModSim/raw/main/figs/modeling_framework.png)"
    ]
   },
   {
@@ -133,22 +142,22 @@
    "id": "structured-receiver",
    "metadata": {},
    "source": [
-    "Starting in the lower left, the **system** is something in the real\n",
+    "Starting in the lower left, the *system* is something in the real\n",
     "world we are interested in. \n",
     "To model the system, we have to decide which elements of the real world to include and which we can leave out.\n",
-    "This process is called **abstraction**.\n",
+    "This process is called *abstraction*.\n",
     "\n",
-    "The result of abstraction is a **model**, which is a description of the system that includes only the features we think are essential. A model\n",
+    "The result of abstraction is a *model*, which is a description of the system that includes only the features we think are essential. A model\n",
     "can be represented in the form of diagrams and equations, which can be\n",
-    "used for mathematical **analysis**. It can also be implemented in the\n",
-    "form of a computer program, which can run **simulations**.\n",
+    "used for mathematical *analysis*. It can also be implemented in the\n",
+    "form of a computer program, which can run *simulations*.\n",
     "\n",
-    "The result of analysis and simulation might be a **prediction** about\n",
-    "what the system will do, an **explanation** of why it behaves the way it\n",
-    "does, or a **design** intended to achieve a purpose.\n",
+    "The result of analysis and simulation might be a *prediction* about\n",
+    "what the system will do, an *explanation* of why it behaves the way it\n",
+    "does, or a *design* intended to achieve a purpose.\n",
     "\n",
-    "We can **validate** predictions and test designs by taking\n",
-    "**measurements** from the real world and comparing the **data** we get\n",
+    "We can *validate* predictions and test designs by taking\n",
+    "*measurements* from the real world and comparing the *data* we get\n",
     "with the results from analysis and simulation."
    ]
   },
@@ -165,7 +174,6 @@
     "Sometimes the best model is the most detailed. If we include more\n",
     "features, the model is more realistic, and we expect its predictions to\n",
     "be more accurate.\n",
-    "\n",
     "But often a simpler model is better. If we include only the essential\n",
     "features and leave out the rest, we get models that are easier to work\n",
     "with, and the explanations they provide can be clearer and more\n",
@@ -183,12 +191,10 @@
     "Newton's law of universal gravitation, and compute the resulting orbit\n",
     "using Newton's laws of motion, you can show that the result is an\n",
     "ellipse.\n",
-    "\n",
     "Of course, the actual orbit of Earth is not a perfect ellipse, because\n",
     "of the gravitational forces of the Moon, Jupiter, and other objects in\n",
     "the solar system, and because Newton's laws of motion are only\n",
     "approximately true (they don't take into account relativistic effects).\n",
-    "\n",
     "But adding these features to the model would not improve the\n",
     "explanation; more detail would only be a distraction from the\n",
     "fundamental cause. However, if the goal is to predict the position of\n",
@@ -205,15 +211,17 @@
     "a good idea to start with a simple model, even if it is likely to be too\n",
     "simple, and test whether it is good enough for its purpose. Then you can\n",
     "add features gradually, starting with the ones you expect to be most\n",
-    "essential. This process is called **iterative modeling**.\n",
+    "essential. This process is called *iterative modeling*.\n",
     "\n",
-    "Comparing results of successive models provides a form of **internal\n",
-    "validation**, so you can catch conceptual, mathematical, and software\n",
+    "Comparing results of successive models provides a form of *internal\n",
+    "validation*, so you can catch conceptual, mathematical, and software\n",
     "errors. And by adding and removing features, you can tell which ones\n",
     "have the biggest effect on the results, and which can be ignored.\n",
     "\n",
-    "Comparing results to data from the real world provides **external\n",
-    "validation**, which is generally the strongest test."
+    "Comparing results to data from the real world provides *external\n",
+    "validation*, which is generally the strongest test.\n",
+    "\n",
+    "The modeling framework is pretty abstract; the following example might make it clearer."
    ]
   },
   {
@@ -221,9 +229,9 @@
    "id": "criminal-lunch",
    "metadata": {},
    "source": [
-    "## The falling penny myth\n",
+    "## The Falling Penny Myth\n",
     "\n",
-    "Let's see an example of how models are used. You might have heard that a\n",
+    "You might have heard that a\n",
     "penny dropped from the top of the Empire State Building would be going\n",
     "so fast when it hit the pavement that it would be embedded in the\n",
     "concrete; or if it hit a person, it would break their skull.\n",
@@ -233,7 +241,7 @@
     "If air resistance is negligible, the primary force acting on the penny\n",
     "is gravity, which causes the penny to accelerate downward.\n",
     "\n",
-    "If the initial velocity is 0, the velocity after $t$ seconds is \n",
+    "If the initial velocity is 0 and the acceleration is $a$, the velocity after $t$ seconds is \n",
     "\n",
     "$$v = a t$$\n",
     "\n",
@@ -262,7 +270,7 @@
     "On the other hand, ignoring air resistance is not a good choice. Once\n",
     "the penny gets to about 29 m/s, the upward force of air resistance\n",
     "equals the downward force of gravity, so the penny stops accelerating.\n",
-    "This is the **terminal velocity** of the penny in air.\n",
+    "This is the *terminal velocity* of the penny in air.\n",
     "\n",
     "Once the penny reaches terminal velocity, it doesn't matter how much farther it falls; it hits the sidewalk at about 29 m/s.\n",
     "That's much less than 86 m/s, as the simple model predicts."
@@ -278,7 +286,7 @@
     "\n",
     "The television show *Mythbusters* has tested the myth of the falling\n",
     "penny more carefully; you can view the results at\n",
-    "<http://modsimpy.com/myth>. Their work is based on a mathematical model of motion, measurements to determine the force of air resistance on a penny, and a physical model of a human head."
+    "<https://modsimpy.com/myth/>. Their work is based on a mathematical model of motion, measurements to determine the force of air resistance on a penny, and a physical model of a human head."
    ]
   },
   {
@@ -286,11 +294,11 @@
    "id": "brief-zoning",
    "metadata": {},
    "source": [
-    "## Computation\n",
+    "## Computation In Python\n",
     "\n",
     "Let me show you how I computed the results from the\n",
     "previous section using Python.\n",
-    "First we'll create a variable to represent acceleration due to gravity in meters per second squared (m/s^2)."
+    "First we'll create a variable to represent acceleration due to gravity in meters per second squared (m/s$^2$)."
    ]
   },
   {
@@ -308,7 +316,7 @@
    "id": "upset-myanmar",
    "metadata": {},
    "source": [
-    "A **variable** is a name that corresponds to a value. In this example, the name is `a` and the value is the number `9.8`.\n",
+    "A *variable* is a name that corresponds to a value. In this example, the name is `a` and the value is the number `9.8`.\n",
     "\n",
     "Suppose we let the penny drop for $3.4$ seconds (s).  I'll create a variable to represent this time:"
    ]
@@ -403,7 +411,7 @@
    "id": "fuzzy-lease",
    "metadata": {},
    "source": [
-    "For this computation, we need the square root function, which is provided by a library called NumPy.\n",
+    "For this computation, we need the square root function, `sqrt`, which is provided by a library called NumPy.\n",
     "Before we can use it, we have to import it like this:"
    ]
   },
@@ -455,17 +463,25 @@
     "v"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "active-lobby",
+   "metadata": {},
+   "source": [
+    "And its velocity on impact would be about $86$ m/s."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "human-phase",
    "metadata": {},
    "source": [
-    "And its velocity on impact would be about $86$ m/s.\n",
+    "### False Precision\n",
     "\n",
     "Python displays results with about 16 digits, which gives the impression that they are very precise, but don't be fooled.\n",
     "The numbers we get out are only as good as the numbers we put in.\n",
     "\n",
-    "For example, the \"roof height\" of the Empire State Building is $380$ m [according to Wikipedia](https://en.wikipedia.org/wiki/Empire_State_Building).\n",
+    "For example, the \"roof height\" of the Empire State Building is $380$ m (according to Wikipedia: <https://en.wikipedia.org/wiki/Empire_State_Building>).\n",
     "I chose $h=381$ m for this example on the assumption that the observation deck is on the roof and you drop the penny from a 1 meter railing.\n",
     "But that's probably not right, so we should treat this value as an approximation where only the first two digits are likely to be right.\n",
     "\n",
@@ -478,17 +494,16 @@
    "id": "clinical-blackjack",
    "metadata": {},
    "source": [
-    "## Computation with units\n",
+    "### Computation With Units\n",
     "\n",
-    "The computation in the previous section uses numbers without units.\n",
+    "The computations we just did use numbers without units.\n",
     "When we set `h=381`, we left out the meters, and when we set `a=9.8`, we left out the meters per second squared.\n",
     "And, when we got the result `v=86`, we added back the meters per second.\n",
     "\n",
-    "Leaving units of out computation is common practice, but it tends to cause errors, including the very expensive failure of the [Mars Climate Orbiter](https://en.wikipedia.org/wiki/Mars_Climate_Orbiter) in 1999.\n",
+    "Leaving units of out computation is common practice, but it tends to cause errors, including the very expensive failure of the Mars Climate Orbiter in 1999 (see <https://en.wikipedia.org/wiki/Mars_Climate_Orbiter>).\n",
     "When possible, it is better to include units in the computation.\n",
     "\n",
-    "To represent units, we'll use a Python library called [Pint](https://pint.readthedocs.io/en/latest/).\n",
-    "\n",
+    "To represent units, we'll use a library called Pint (<https://pint.readthedocs.io/>).\n",
     "To use it, we have to import a function called `UnitRegistry` and call it like this:"
    ]
   },
@@ -567,7 +582,9 @@
   {
    "cell_type": "markdown",
    "id": "spiritual-scenario",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "To find out what other units are defined, type `units.` (including the period) in the next cell.\n",
     "\n",
@@ -579,7 +596,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "saving-suggestion",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": []
   },
@@ -607,7 +626,7 @@
    "id": "generic-bowling",
    "metadata": {},
    "source": [
-    "The result is a **quantity** with two parts, called `magnitude` and `units`, which we can access like this:"
+    "The result is a *quantity* with two parts, called `magnitude` and `units`, which we can access like this:"
    ]
   },
   {
@@ -728,7 +747,9 @@
    "cell_type": "code",
    "execution_count": 57,
    "id": "impaired-puzzle",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "assert abs(v.magnitude - 86.41527642726142) < 1e7"
@@ -800,7 +821,9 @@
    "id": "thousand-equation",
    "metadata": {},
    "source": [
-    "## Exercises"
+    "## Exercises\n",
+    "\n",
+    "Before you go on, you might want to work on the following exercises."
    ]
   },
   {
@@ -808,14 +831,16 @@
    "id": "handmade-zoning",
    "metadata": {},
    "source": [
-    "**Exercise** In mathematical notation, we can write an equation like $v = a t$ and it's understood that we are multiplying $a$ and $t$.\n",
+    "### Exercise 1\n",
+    "\n",
+    "In mathematical notation, we can write an equation like $v = a t$ and it's understood that we are multiplying $a$ and $t$.\n",
     "But that doesn't work in Python.  If you put two variables side-by-side, like this:\n",
     "\n",
     "```\n",
     "v = a t\n",
     "```\n",
     "\n",
-    "you'll get a **syntax error**, which means that something is wrong with the structure of the program.\n",
+    "you'll get a *syntax error*, which means that something is wrong with the structure of the program.\n",
     "Try it out in the next cell so you see what the error message looks like."
    ]
   },
@@ -838,7 +863,9 @@
    "id": "pressing-sugar",
    "metadata": {},
    "source": [
-    "**Exercise:** In this chapter we used the `sqrt` function from the NumPy library.  NumPy also provides a variable named `pi` that contains an approximation of the mathematical constant $\\pi$.\n",
+    "### Exercise 2\n",
+    "\n",
+    "In this chapter we used the `sqrt` function from the NumPy library.  NumPy also provides a variable named `pi` that contains an approximation of the mathematical constant $\\pi$.\n",
     "We can import it like this:"
    ]
   },
@@ -861,7 +888,7 @@
     "NumPy provides other functions we'll use, including `log`, `exp`, `sin`, and `cos`.\n",
     "Import `sin` and `cos` from NumPy and compute\n",
     "\n",
-    "$$sin^2 \\pi/4 + cos^2 \\pi/4$$\n",
+    "$$sin^2 (\\pi/4) + cos^2 (\\pi/4)$$\n",
     "\n",
     "Note: A mathematical identity tells us that the answer should be $1$."
    ]
@@ -881,7 +908,9 @@
    "id": "unlimited-delivery",
    "metadata": {},
    "source": [
-    "**Exercise:** Suppose you bring a 10 foot pole to the top of the Empire State Building and use it to drop the penny from `h` plus 10 feet.\n",
+    "### Exercise 3\n",
+    "\n",
+    "Suppose you bring a 10 foot pole to the top of the Empire State Building and use it to drop the penny from `h` plus 10 feet.\n",
     "\n",
     "Define a variable named `foot` that contains the unit `foot` provided by `UNITS`.  Define a variable named `pole_height` and give it the value 10 feet.\n",
     "\n",
@@ -923,7 +952,9 @@
    "id": "aggressive-climate",
    "metadata": {},
    "source": [
-    "**Exercise**: Why would it be nonsensical to add `a` and `t`? What happens if you try?"
+    "### Exercise 4\n",
+    "\n",
+    "Why would it be nonsensical to add `a` and `t`? What happens if you try?"
    ]
   },
   {
@@ -947,7 +978,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "steady-chancellor",
+   "id": "legal-audio",
    "metadata": {},
    "source": [
     "In this example, you should get a `DimensionalityError`, which indicates that you have violated a rule of dimensional analysis: you cannot add quantities with different dimensions.\n",
@@ -956,8 +987,16 @@
     "\n",
     "The last line usually tells you what type of error happened, and sometimes additional information, so you might want to start from the bottom and read up.\n",
     "\n",
-    "The previous lines are a \"traceback\" of what was happening when the error occurred.  The first section of the traceback shows the code you wrote.  The following sections are often from Python libraries.\n",
-    "\n",
+    "The previous lines are a *traceback* of what was happening when the error occurred.  The first section of the traceback shows the code you wrote.  The following sections are often from Python libraries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "steady-chancellor",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
     "Before you go on, you might want to delete the erroneous code so the notebook can run without errors."
    ]
   },
@@ -966,7 +1005,9 @@
    "id": "valid-empire",
    "metadata": {},
    "source": [
-    "**Exercise:** Suppose instead of dropping the penny, you throw it downward at its terminal velocity, $29$ m/s.  How long would it take to fall $381$ m?"
+    "### Exercise 5\n",
+    "\n",
+    "Suppose instead of dropping the penny, you throw it downward at its terminal velocity, $29$ m/s.  How long would it take to fall $381$ m?"
    ]
   },
   {
@@ -984,7 +1025,9 @@
    "id": "asian-murray",
    "metadata": {},
    "source": [
-    "**Exercise:** So far we have considered two models of a falling penny:\n",
+    "### Exercise 6:\n",
+    "\n",
+    "So far we have considered two models of a falling penny:\n",
     "\n",
     "* If we ignore air resistance, the penny falls with constant acceleration, and we can compute the time to reach the sidewalk and the velocity of the penny when it gets there.\n",
     "\n",
@@ -1073,12 +1116,14 @@
    "id": "composed-tunnel",
    "metadata": {},
    "source": [
-    "**Exercise:** When I was in high school, the pitcher on the baseball team claimed that when he threw a fastball he was throwing the ball down; that is, the ball left his hand at a downward angle.\n",
+    "### Exercise 7\n",
+    "\n",
+    "When I was in high school, the pitcher on the baseball team claimed that when he threw a fastball he was throwing the ball down; that is, the ball left his hand at a downward angle.\n",
     "I was skeptical; watching from the side, I thought the ball left his hand at an upward angle.\n",
     "\n",
     "Can you think of a simple model you could use to settle the argument?  What factors would you include and what could you ignore?  What quantities would you have to look up or estimate?\n",
     "\n",
-    "I suggest you convert all quantities to [SI units](https://en.wikipedia.org/wiki/International_System_of_Units): meters and seconds."
+    "I suggest you convert all quantities to SI units like meters and seconds (see <https://en.wikipedia.org/wiki/International_System_of_Units>)."
    ]
   },
   {
@@ -1136,7 +1181,9 @@
    "id": "jewish-secret",
    "metadata": {},
    "source": [
-    "**Exercise:** Suppose I run a 10K race in 44:52 (44 minutes and 52 seconds).  What is my average page in minutes per mile?"
+    "### Exercise 8\n",
+    "\n",
+    "Suppose I run a 10K race in 44:52 (44 minutes and 52 seconds).  What is my average page in minutes per mile?"
    ]
   },
   {
@@ -1214,7 +1261,9 @@
   {
    "cell_type": "markdown",
    "id": "environmental-wallet",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "## More Jupyter\n",
     "\n",
@@ -1242,7 +1291,9 @@
   {
    "cell_type": "markdown",
    "id": "successful-kentucky",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "When you change the contents of a cell, you have to run it again for those changes to have an effect.  If you forget to do that, the results can be confusing, because the code you are looking at is not the code you ran.\n",
     "\n",
diff --git a/chapters/chap02.ipynb b/chapters/chap02.ipynb
index 451e3519..0b49a1fe 100644
--- a/chapters/chap02.ipynb
+++ b/chapters/chap02.ipynb
@@ -5,7 +5,7 @@
    "id": "victorian-latitude",
    "metadata": {},
    "source": [
-    "# Chapter 2"
+    "# Bike Share System"
    ]
   },
   {
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -79,10 +79,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "plastic-trigger",
+   "id": "unlimited-antenna",
    "metadata": {},
    "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/chapters/chap02.ipynb)"
+    "This chapter presents a simple model of a bike share system and\n",
+    "demonstrates the features of Python we'll use to develop simulations of real-world systems.\n",
+    "\n",
+    "Along the way, we'll make decisions about how to model the system. In\n",
+    "the next chapter we'll review these decisions and gradually improve the model."
    ]
   },
   {
@@ -90,11 +94,8 @@
    "id": "electronic-radius",
    "metadata": {},
    "source": [
-    "This chapter presents a simple model of a bike share system and\n",
-    "demonstrates the features of Python we'll use to develop simulations of real-world systems.\n",
-    "\n",
-    "Along the way, we'll make decisions about how to model the system. In\n",
-    "the next chapter we'll review these decisions and gradually improve the model."
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -104,7 +105,7 @@
    "source": [
     "## Modeling a Bike Share System\n",
     "\n",
-    "Imagine a bike share system for students traveling between Olin College and Wellesley College, which are about 3 miles apart in eastern Massachusetts.\n",
+    "Imagine a bike share system for students traveling between Olin College and Wellesley College, which are about three miles apart in eastern Massachusetts.\n",
     "\n",
     "Suppose the system contains 12 bikes and two bike racks, one at Olin and one at Wellesley, each with the capacity to hold 12 bikes.\n",
     "\n",
@@ -126,18 +127,16 @@
    "id": "living-wayne",
    "metadata": {},
    "source": [
-    "The expressions in parentheses are **keyword arguments**.\n",
+    "The expressions in parentheses are *keyword arguments*.\n",
     "They create two variables, `olin` and `wellesley`, and give them values.\n",
-    "\n",
     "Then we call the `State` function.\n",
-    "The result is a `State` object, which is a collection of **state variables**.\n",
+    "The result is a `State` object, which is a collection of *state variables*.\n",
     "\n",
     "In this example, the state variables represent the number of\n",
-    "bikes at each location. The initial values are 10 and 2, indicating that there are 10 bikes at Olin and 2 at Wellesley. \n",
+    "bikes at each location. The initial values are `10` and `2`, indicating that there are 10 bikes at Olin and 2 at Wellesley. \n",
     "\n",
     "The `State` object is assigned to a new variable named `bikeshare`.\n",
-    "We can read the variables inside a `State` object using the **dot\n",
-    "operator**, like this:"
+    "We can read the variables inside a `State` object using the *dot operator*, like this:"
    ]
   },
   {
@@ -173,7 +172,7 @@
    "id": "phantom-oklahoma",
    "metadata": {},
    "source": [
-    "Or, to display the state variables and their values, you can just type the name of the object:"
+    "Or, to display the state variables and their values, you can just enter the name of the object:"
    ]
   },
   {
@@ -188,11 +187,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fleet-beijing",
+   "id": "vital-journal",
    "metadata": {},
    "source": [
-    "These values make up the **state** of the system.\n",
-    "\n",
+    "These values make up the *state* of the system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fleet-beijing",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
     "The ModSim library provides a function called `show` that displays a `State` object as a table."
    ]
   },
@@ -200,19 +207,29 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "basic-fabric",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "show(bikeshare)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "specified-definition",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "You don't have to use `show`, but I think the results look better."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "delayed-ocean",
    "metadata": {},
    "source": [
-    "You don't have to use `show`, but I think the results look better.\n",
-    "\n",
     "We can update the state by assigning new values to the variables. \n",
     "For example, if a student moves a bike from Olin to Wellesley, we can figure out the new values and assign them:"
    ]
@@ -233,7 +250,7 @@
    "id": "natural-gossip",
    "metadata": {},
    "source": [
-    "Or we can use **update operators**, `-=` and `+=`, to subtract 1 from\n",
+    "Or we can use *update operators*, `-=` and `+=`, to subtract 1 from\n",
     "`olin` and add 1 to `wellesley`:"
    ]
   },
@@ -261,7 +278,7 @@
    "id": "controversial-opportunity",
    "metadata": {},
    "source": [
-    "## Defining functions\n",
+    "## Defining Functions\n",
     "\n",
     "So far we have used functions defined in NumPy and the ModSim library. Now we're going to define our own functions.\n",
     "\n",
@@ -309,13 +326,13 @@
     "function. The name of the function is `bike_to_wellesley`. The empty\n",
     "parentheses indicate that this function requires no additional\n",
     "information when it runs. The colon indicates the beginning of an\n",
-    "indented **code block**.\n",
+    "indented *code block*.\n",
     "\n",
-    "The next two lines are the **body** of the function. They have to be\n",
-    "indented; by convention, the indentation is 4 spaces.\n",
+    "The next two lines are the *body* of the function. They have to be\n",
+    "indented; by convention, the indentation is four spaces.\n",
     "\n",
     "When you define a function, it has no immediate effect. The body of the\n",
-    "function doesn't run until you **call** the function. Here's how to call\n",
+    "function doesn't run until you *call* the function. Here's how to call\n",
     "this function:"
    ]
   },
@@ -382,11 +399,10 @@
    "id": "brazilian-medicare",
    "metadata": {},
    "source": [
-    "## Print statements\n",
+    "## Print Statements\n",
     "\n",
     "As you write more complicated programs, it is easy to lose track of what\n",
-    "is going on. One of the most useful tools for debugging is the **print\n",
-    "statement**, which displays text in the Jupyter notebook.\n",
+    "is going on. One of the most useful tools for debugging is the *print statement*, which displays text in the Jupyter notebook.\n",
     "\n",
     "Normally when Jupyter runs the code in a cell, it displays the value of\n",
     "the last line of code. For example, if you run:"
@@ -477,7 +493,7 @@
    "source": [
     "Each time we call this version of the function, it displays a message,\n",
     "which can help us keep track of what the program is doing.\n",
-    "The message in this example is a **string**, which is a sequence of\n",
+    "The message in this example is a *string*, which is a sequence of\n",
     "letters and other symbols in quotes.\n",
     "\n",
     "Just like `bike_to_wellesley`, we can define a function that moves a\n",
@@ -531,7 +547,9 @@
    "id": "enhanced-maintenance",
    "metadata": {},
    "source": [
-    "## If statements\n",
+    "## If Statements\n",
+    "\n",
+    "At this point we have functions that simulate moving bikes; now let's think about simulating customers. As a simple model of customer behavior, I will use a random number generator to determine when customers arrive at each station.\n",
     "\n",
     "The ModSim library provides a function called `flip` that generates random \"coin tosses\".\n",
     "When you call it, you provide a probability between 0 and 1, like this:"
@@ -558,12 +576,12 @@
     "are random, so they might differ from these expectations.\n",
     "\n",
     "`True` and `False` are special values defined by Python. \n",
-    "They are called **boolean** values because they are\n",
+    "They are called *boolean* values because they are\n",
     "related to Boolean algebra (<https://modsimpy.com/boolean>).\n",
     "\n",
     "Note that they are not strings. There is a difference between `True`, which is a boolean value, and `'True'`, which is a string.\n",
     "\n",
-    "We can use boolean values to control the behavior of the program, using an **if statement**:"
+    "We can use boolean values to control the behavior of the program, using an *if statement*:"
    ]
   },
   {
@@ -589,7 +607,7 @@
     "function definitions: the first line has to end with a colon, and the\n",
     "lines inside the `if` statement have to be indented.\n",
     "\n",
-    "Optionally, you can add an **else clause** to indicate what should\n",
+    "Optionally, you can add an *else clause* to indicate what should\n",
     "happen if the result is `False`:"
    ]
   },
@@ -613,8 +631,8 @@
    "source": [
     "If you run the previous cell a few times, it should print `heads` about half the time, and `tails` about half the time.\n",
     "\n",
-    "Now we can use `flip` to simulate the arrival of students who want to\n",
-    "borrow a bike. Suppose students arrive at the Olin station every 2\n",
+    "Now we can use `flip` to simulate the arrival of customers who want to\n",
+    "borrow a bike. Suppose students arrive at the Olin station every two\n",
     "minutes on average.\n",
     "In that case, the chance of an arrival during any one-minute period is 50%, and we can simulate it like this:"
    ]
@@ -635,7 +653,7 @@
    "id": "difficult-construction",
    "metadata": {},
    "source": [
-    "If students arrive at the Wellesley station every 3 minutes, on average,\n",
+    "If students arrive at the Wellesley station every three minutes, on average,\n",
     "the chance of an arrival during any one-minute period is 33%, and we can\n",
     "simulate it like this:"
    ]
@@ -656,8 +674,7 @@
    "id": "standard-party",
    "metadata": {},
    "source": [
-    "We can combine these snippets into a function that simulates a **time\n",
-    "step**, which is an interval of time, in this case one minute:"
+    "We can combine these snippets into a function that simulates a *time step*, which is an interval of time, in this case one minute:"
    ]
   },
   {
@@ -711,7 +728,7 @@
     "The previous version of `step` is fine if the arrival probabilities\n",
     "never change, but in reality they vary over time.\n",
     "\n",
-    "So instead of putting the constant values 0.5 and 0.33 in `step`, we can replace them with **parameters**.\n",
+    "So instead of putting the constant values 0.5 and 0.33 in `step`, we can replace them with *parameters*.\n",
     "Parameters are variables whose values are set when a function is called.\n",
     "\n",
     "Here's a version of `step` that takes two parameters, `p1` and `p2`:"
@@ -757,7 +774,7 @@
    "metadata": {},
    "source": [
     "The values you provide when you call the function are called\n",
-    "**arguments**. The arguments, `0.5` and `0.33` in this example, get\n",
+    "*arguments*. The arguments, `0.5` and `0.33` in this example, get\n",
     "assigned to the parameters, `p1` and `p2`, in order. So running this\n",
     "function has the same effect as:"
    ]
@@ -786,7 +803,7 @@
    "source": [
     "The advantage of using parameters is that you can call the same function many times, providing different arguments each time.\n",
     "\n",
-    "Adding parameters to a function is called **generalization**, because it makes the function more general, that is, less specialized."
+    "Adding parameters to a function is called *generalization*, because it makes the function more general; without parameters, the function always does the same thing; with parameters, it can do a range of things."
    ]
   },
   {
@@ -794,16 +811,15 @@
    "id": "scenic-african",
    "metadata": {},
    "source": [
-    "## For loops\n",
+    "## For Loops\n",
     "\n",
     "At some point you will get sick of running cells over and over.\n",
-    "Fortunately, there is an easy way to repeat a chunk of code, the **for\n",
-    "loop**. Here's an example:"
+    "Fortunately, there is an easy way to repeat a chunk of code, the *for loop*. Here's an example:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 35,
    "id": "polish-river",
    "metadata": {},
    "outputs": [],
@@ -827,7 +843,7 @@
     "\n",
     "-   `range` is a Python function we use to control the number of times the loop runs.\n",
     "\n",
-    "-   `i` is a **loop variable** that gets created when the for loop runs.\n",
+    "-   `i` is a *loop variable* that gets created when the for loop runs.\n",
     "\n",
     "When this loop runs, it runs the statements inside the loop three times.  The first time, the value of `i` is `0`; the second time, it is `1`; the third time, it is `2`.\n",
     "\n",
@@ -839,7 +855,7 @@
    "id": "breeding-groove",
    "metadata": {},
    "source": [
-    "## TimeSeries\n",
+    "## Timeseries\n",
     "\n",
     "When we run a simulation, we often want to save the results for later analysis. The ModSim library provides a `TimeSeries` object for this purpose. A `TimeSeries` contains a sequence of time stamps and a\n",
     "corresponding sequence of quantities.\n",
@@ -851,7 +867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "id": "every-consultation",
    "metadata": {},
    "outputs": [],
@@ -869,7 +885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "id": "changing-planet",
    "metadata": {},
    "outputs": [],
@@ -887,7 +903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "id": "aquatic-richardson",
    "metadata": {},
    "outputs": [],
@@ -900,14 +916,14 @@
    "id": "searching-funeral",
    "metadata": {},
    "source": [
-    "The number in brackets is the time stamp, also called a **label**.\n",
+    "The number in brackets is the time stamp, also called a *label*.\n",
     "\n",
     "We can use a `TimeSeries` inside a for loop to store the results of the simulation:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "id": "english-titanium",
    "metadata": {},
    "outputs": [],
@@ -938,7 +954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "indonesian-singing",
    "metadata": {},
    "outputs": [],
@@ -967,7 +983,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "id": "saved-hands",
    "metadata": {},
    "outputs": [],
@@ -1010,7 +1026,9 @@
    "id": "fallen-surprise",
    "metadata": {},
    "source": [
-    "## Exercises"
+    "## Exercises\n",
+    "\n",
+    "Before you go on, you might want to work on the following exercises."
    ]
   },
   {
@@ -1018,14 +1036,16 @@
    "id": "capital-internship",
    "metadata": {},
    "source": [
-    "**Exercise:** What happens if you spell the name of a state variable wrong?  Edit the following cell, change the spelling of `wellesley`, and run it.\n",
+    "### Exercise 1\n",
     "\n",
-    "The error message uses the word \"attribute\", which is another name for what we are calling a state variable. "
+    "What happens if you spell the name of a state variable wrong?  Edit the following cell, change the spelling of `wellesley`, and run it.\n",
+    "\n",
+    "The error message uses the word *attribute*, which is another name for what we are calling a state variable. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "id": "helpful-zambia",
    "metadata": {},
    "outputs": [],
@@ -1040,12 +1060,14 @@
    "id": "dirty-multiple",
    "metadata": {},
    "source": [
-    "**Exercise:** Make a `State` object with a third state variable, called `babson`, with initial value 0, and display the state of the system."
+    "### Exercise 2\n",
+    "\n",
+    "Make a `State` object with a third state variable, called `downtown`, with initial value 0, and display the state of the system."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 37,
    "id": "beneficial-mainland",
    "metadata": {},
    "outputs": [],
@@ -1058,7 +1080,9 @@
    "id": "christian-madrid",
    "metadata": {},
    "source": [
-    "**Exercise:** Wrap the code in the chapter in a function named `run_simulation` that takes three parameters, named `p1`, `p2`, and `num_steps`.\n",
+    "### Exercise 3\n",
+    "\n",
+    "Wrap the code in the chapter in a function named `run_simulation` that takes three parameters, named `p1`, `p2`, and `num_steps`.\n",
     "\n",
     "It should:\n",
     "\n",
@@ -1081,7 +1105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "id": "former-frost",
    "metadata": {},
    "outputs": [],
@@ -1091,7 +1115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "id": "spare-honduras",
    "metadata": {},
    "outputs": [],
@@ -1117,13 +1141,10 @@
    "id": "quick-citizen",
    "metadata": {},
    "source": [
-    "`State` and `TimeSeries` objects are based on the `Series` object defined by a the Pandas library.\n",
+    "`State` and `TimeSeries` objects are based on the `Series` object defined by the Pandas library.\n",
     "The documentation is at <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html>.\n",
     "\n",
-    "`show` works by creating another Pandas object, called a `DataFrame`, which can be displayed as a table.\n",
-    "We'll use `DataFrame` objects in future chapters.\n",
-    "\n",
-    "`Series` objects provide their own `plot` function which is why we call it like this:\n",
+    "`Series` objects provide their own `plot` function, which is why we call it like this:\n",
     "\n",
     "```\n",
     "results.plot()\n",
@@ -1154,7 +1175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "id": "agricultural-midwest",
    "metadata": {},
    "outputs": [],
@@ -1162,14 +1183,6 @@
     "source_code(flip)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "running-membership",
-   "metadata": {},
-   "source": [
-    "You might not understand everything in this function yet, but you will."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1196,7 +1209,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap03.ipynb b/chapters/chap03.ipynb
index 82185a21..13638e4f 100644
--- a/chapters/chap03.ipynb
+++ b/chapters/chap03.ipynb
@@ -5,7 +5,7 @@
    "id": "signal-format",
    "metadata": {},
    "source": [
-    "# Chapter 3"
+    "# Iteration"
    ]
   },
   {
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -77,21 +77,13 @@
     "from modsim import *"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "plastic-trigger",
-   "metadata": {},
-   "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap03.ipynb)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "coral-steering",
    "metadata": {},
    "source": [
     "To paraphrase two Georges, \"All models are wrong, but some models are\n",
-    "more wrong than others.\" In this chapter, I demonstrate the process we\n",
+    "more wrong than others.\" This chapter demonstrates the process we\n",
     "use to make models less wrong.\n",
     "\n",
     "As an example, we'll review the bikeshare model from the previous\n",
@@ -100,12 +92,21 @@
     "the system and evaluate designs intended to make it work better."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "twelve-length",
    "metadata": {},
    "source": [
-    "## Iterative modeling\n",
+    "## Iterative Modeling\n",
     "\n",
     "The model we have so far is simple, but it is based on unrealistic\n",
     "assumptions. Before you go on, take a minute to review the model from\n",
@@ -139,7 +140,7 @@
     "\n",
     "This process, starting with a simple model, identifying the most\n",
     "important problems, and making gradual improvements, is called\n",
-    "**iterative modeling**.\n",
+    "*iterative modeling*.\n",
     "\n",
     "For any physical system, there are many possible models, based on\n",
     "different assumptions and simplifications. It often takes several\n",
@@ -152,7 +153,7 @@
    "id": "female-salem",
    "metadata": {},
    "source": [
-    "## More than one State object\n",
+    "## More Than One State Object\n",
     "\n",
     "Before we go on, I want to make a few changes to the code from the\n",
     "previous chapter. First I'll generalize the functions we wrote so they\n",
@@ -288,12 +289,12 @@
     "## Documentation\n",
     "\n",
     "Another problem with the code we have so far is that it contains no\n",
-    "**documentation**.\n",
+    "*documentation*.\n",
     "Documentation is text we add to a program to help\n",
     "other programmers read and understand it. It has no effect on the\n",
     "program when it runs.\n",
     "\n",
-    "There are two kinds of documentation, **docstrings** and **comments**:\n",
+    "There are two kinds of documentation, *docstrings* and *comments*:\n",
     "\n",
     "* A docstring is a string in triple quotes that appears at the beginning of a function.\n",
     "\n",
@@ -342,7 +343,7 @@
    "id": "american-clear",
    "metadata": {},
    "source": [
-    "## Negative bikes\n",
+    "## Negative Bikes\n",
     "\n",
     "The changes we've made so far improve the quality of the code, but we\n",
     "haven't done anything to improve the quality of the model. Let's do that now.\n",
@@ -372,7 +373,7 @@
    "id": "decimal-denver",
    "metadata": {},
    "source": [
-    "The first line checks whether the number of bikes at Wellesley is zero. If so, it uses a **return statement**, which causes the function to end immediately, without running the rest of the statements. So if there are no bikes at Wellesley, we \"return\" from `bike_to_olin` without changing the state.\n",
+    "The first line checks whether the number of bikes at Wellesley is zero. If so, it uses a *return statement*, which causes the function to end immediately, without running the rest of the statements. So if there are no bikes at Wellesley, we return from `bike_to_olin` without changing the state.\n",
     "\n",
     "We can test it by initializing the state with no bikes at Wellesley And calling `bike_to_olin`."
    ]
@@ -419,7 +420,7 @@
    "id": "gorgeous-found",
    "metadata": {},
    "source": [
-    "## Comparison operators\n",
+    "## Comparison Operators\n",
     "\n",
     "The updated version of `bike_to_olin` uses the equals operator, `==`, which compares two values and returns `True`, if they are equal, and `False` otherwise.\n",
     "\n",
@@ -486,7 +487,7 @@
     "    print('yes, x is 5')\n",
     "```\n",
     "\n",
-    "That's a **syntax error**, which means that the structure of the program is invalid. Python will print an error message and the program won't run."
+    "That's a *syntax error*, which means that the structure of the program is invalid. Python will print an error message and the program won't run."
    ]
   },
   {
@@ -494,7 +495,7 @@
    "id": "twelve-defensive",
    "metadata": {},
    "source": [
-    "The equals operator is one of the **comparison operators**. The others\n",
+    "The equals operator is one of Python's *comparison operators*. The others\n",
     "are:\n",
     "\n",
     "| Operation             \t| Symbol \t|\n",
@@ -515,14 +516,14 @@
     "## Metrics\n",
     "\n",
     "Getting back to the bike share system, at this point we have the ability to simulate the behavior of the system. Since the arrival of customers is random, the state of the system is different each time we run a\n",
-    "simulation. Models like this are called random or **stochastic**; models\n",
-    "that do the same thing every time they run are **deterministic**.\n",
+    "simulation. Models like this are called random or *stochastic*; models\n",
+    "that do the same thing every time they run are *deterministic*.\n",
     "\n",
     "Suppose we want to use our model to predict how well the bike share\n",
     "system will work, or to design a system that works better. First, we\n",
     "have to decide what we mean by \"how well\" and \"better\".\n",
     "\n",
-    "From the customer's point of view, we might like to know the probability of finding an available bike. From the system-owner's point of view, we might want to minimize the number of customers who don't get a bike when they want one, or maximize the number of bikes in use. Statistics like these that quantify how well the system works are called **metrics**.\n",
+    "From the customer's point of view, we might like to know the probability of finding an available bike. From the system-owner's point of view, we might want to minimize the number of customers who don't get a bike when they want one, or maximize the number of bikes in use. Statistics like these that quantify how well the system works are called *metrics*.\n",
     "\n",
     "As an example, let's measure the number of unhappy customers.\n",
     "Here's a version of `bike_to_olin` that keeps track of the number of\n",
@@ -550,7 +551,7 @@
    "metadata": {},
    "source": [
     "If a customer arrives at the Wellesley station and finds no bike\n",
-    "available, `bike_to_olin` updates `wellesley_empty` which counts the\n",
+    "available, `bike_to_olin` updates `wellesley_empty`, which counts the\n",
     "number of unhappy customers.\n",
     "\n",
     "This function only works if we initialize `wellesley_empty` when we\n",
@@ -645,7 +646,9 @@
   {
    "cell_type": "markdown",
    "id": "careful-hacker",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "Here's the code we have so far, with docstrings, all in one place."
    ]
@@ -654,7 +657,9 @@
    "cell_type": "code",
    "execution_count": 20,
    "id": "wrong-internet",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "def run_simulation(state, p1, p2, num_steps):\n",
@@ -726,7 +731,9 @@
    "cell_type": "code",
    "execution_count": 23,
    "id": "unavailable-maker",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "def bike_to_wellesley(state):\n",
@@ -743,7 +750,9 @@
    "id": "bigger-rapid",
    "metadata": {},
    "source": [
-    "**Exercise:** Modify `bike_to_wellesley` so it checks whether a bike is available at Olin.  If not, it should add one to `olin_empty`.\n",
+    "### Exercise 1\n",
+    "\n",
+    " Modify `bike_to_wellesley` so it checks whether a bike is available at Olin.  If not, it should add one to `olin_empty`.\n",
     "\n",
     "To test it, create a `State` that initializes `olin` and `olin_empty` to `0`, run `bike_to_wellesley`, and check the result."
    ]
@@ -795,7 +804,9 @@
    "id": "possible-initial",
    "metadata": {},
    "source": [
-    "**Exercise:** Now run the simulation with parameters `p1=0.3`, `p2=0.2`, and `num_steps=60`, and confirm that the number of bikes is never negative.\n",
+    "### Exercise 2\n",
+    "\n",
+    " Now run the simulation with parameters `p1=0.3`, `p2=0.2`, and `num_steps=60`, and confirm that the number of bikes is never negative.\n",
     "\n",
     "Start with this initial state:"
    ]
@@ -839,7 +850,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap04.ipynb b/chapters/chap04.ipynb
index 2d0bdf40..c0634758 100644
--- a/chapters/chap04.ipynb
+++ b/chapters/chap04.ipynb
@@ -5,7 +5,7 @@
    "id": "existing-guidance",
    "metadata": {},
    "source": [
-    "# Chapter 4"
+    "# Sweeping Parameters"
    ]
   },
   {
@@ -77,14 +77,6 @@
     "from modsim import *"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "plastic-trigger",
-   "metadata": {},
-   "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap04.ipynb)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "caring-gnome",
@@ -128,17 +120,26 @@
     "In the previous chapter we defined metrics that quantify the performance of a bike sharing system.\n",
     "In this chapter we'll see how those metrics depend on the parameters of the system, like the arrival rate of customers at the stations.\n",
     "\n",
-    "We also discuss a program development strategy, called incremental\n",
+    "And I will present a program development strategy, called incremental\n",
     "development, that might help you write programs faster and spend less\n",
     "time debugging."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "strategic-newspaper",
    "metadata": {},
    "source": [
-    "## Functions that return values\n",
+    "## Functions That Return Values\n",
     "\n",
     "We have used several functions that return values.\n",
     "For example, when you run `sqrt`, it returns a number you can assign to a variable."
@@ -288,7 +289,7 @@
    "metadata": {},
    "source": [
     "The simulation we just ran starts with `olin=10` and `wellesley=2`, and uses the values `p1=0.3`, `p2=0.2`, and `num_steps=60`. \n",
-    "These five values are **parameters of the model**, which are quantities that determine the behavior of the system.\n",
+    "These five values are *parameters of the model*, which are quantities that determine the behavior of the system.\n",
     "\n",
     "It is easy to get the parameters of a model confused with the parameters of a function. \n",
     "It is especially easy because the parameters of a model often appear as parameters of a function.\n",
@@ -304,7 +305,7 @@
    "id": "valuable-aircraft",
    "metadata": {},
    "source": [
-    "## Loops and arrays\n",
+    "## Loops and Arrays\n",
     "\n",
     "In `run_simulation`, we use this `for` loop:\n",
     "\n",
@@ -342,7 +343,7 @@
     "many elements it should contain. In this example, the sequence contains\n",
     "`5` equally-spaced numbers, starting at `0` and ending at `1`.\n",
     "\n",
-    "The result is a NumPy **array**, which is a new kind of object we have\n",
+    "The result is a NumPy *array*, which is a new kind of object we have\n",
     "not seen before. An array is a container for a sequence of numbers.\n",
     "\n",
     "We can use an array in a `for` loop like this:"
@@ -384,7 +385,7 @@
    "id": "crazy-belize",
    "metadata": {},
    "source": [
-    "## Sweeping parameters\n",
+    "## Sweeping Parameters\n",
     "\n",
     "If we know the actual values of parameters like `p1` and `p2`, we can\n",
     "use them to make specific predictions, like how many bikes will be at\n",
@@ -395,7 +396,7 @@
     "designs. For example, if we observe the system and notice that we often run out of bikes at a particular time, we could use the model to figure out why that happens. And if we are considering adding more bikes, or another station, we could evaluate the effect of various \"what if\" scenarios.\n",
     "\n",
     "As an example, suppose we have enough data to estimate that `p2` is\n",
-    "about `0.2`, but we don't have any information about `p1`. We could run simulations with a range of values for `p1` and see how the results vary. This process is called **sweeping** a parameter, in the sense that the value of the parameter \"sweeps\" through a range of possible values.\n",
+    "about `0.2`, but we don't have any information about `p1`. We could run simulations with a range of values for `p1` and see how the results vary. This process is called *sweeping* a parameter, in the sense that the value of the parameter \"sweeps\" through a range of possible values.\n",
     "\n",
     "Now that we know about loops and arrays, we can use them like this:"
    ]
@@ -522,12 +523,12 @@
    "id": "educated-bloom",
    "metadata": {},
    "source": [
-    "I use the keyword argument `color='C1'` to specify the color of the line.\n",
+    "The keyword argument `color='C1'` specifies the color of the line.\n",
     "The `TimeSeries` we have plotted so far use the default color, `C0`, which is blue.\n",
-    "I want a different color for `SweepSeries` to remind us that it is not a `TimeSeries`.\n",
+    "I use a different color for `SweepSeries` to remind us that it is not a `TimeSeries`.\n",
     "\n",
     "When the arrival rate at Olin is low, there are plenty of bikes and no unhappy customers.\n",
-    "As the arrival rate increases, we are more likely to run out of bikes and the number of unhappy customers increases."
+    "As the arrival rate increases, we are more likely to run out of bikes and the number of unhappy customers increases. The line is jagged because the simulation is based on random numbers. Sometime we get lucky and there are relatively few unhappy customers; other times are are unlucky and there are more. "
    ]
   },
   {
@@ -535,12 +536,12 @@
    "id": "korean-christianity",
    "metadata": {},
    "source": [
-    "## Incremental development\n",
+    "## Incremental Development\n",
     "\n",
     "When you start writing programs that are more than a few lines, you\n",
     "might find yourself spending more time debugging. The more code you write before you start debugging, the harder it is to find the problem.\n",
     "\n",
-    "**Incremental development** is a way of programming that tries to\n",
+    "*Incremental development* is a way of programming that tries to\n",
     "minimize the pain of debugging. The fundamental steps are:\n",
     "\n",
     "1.  Always start with a working program. If you have an example from a\n",
@@ -562,7 +563,7 @@
     "When this process works, your changes usually work the first time, or if they don't, the problem is obvious. In practice, there are two problems with incremental development:\n",
     "\n",
     "-   Sometimes you have to write extra code to generate visible output\n",
-    "    that you can check. This extra code is called **scaffolding**\n",
+    "    that you can check. This extra code is called *scaffolding*\n",
     "    because you use it to build the program and then remove it when you\n",
     "    are done. That might seem like a waste, but time you spend on\n",
     "    scaffolding is almost always time you save on debugging.\n",
@@ -608,7 +609,9 @@
    "id": "primary-quest",
    "metadata": {},
    "source": [
-    "**Exercise:** Write a function called `make_state` that creates a `State` object with the state variables `olin=10` and `wellesley=2`, and then returns the new `State` object.\n",
+    "### Exercise 1\n",
+    "\n",
+    " Write a function called `make_state` that creates a `State` object with the state variables `olin=10` and `wellesley=2`, and then returns the new `State` object.\n",
     "\n",
     "Write a line of code that calls `make_state` and assigns the result to a variable named `init`."
    ]
@@ -638,7 +641,9 @@
    "id": "robust-blair",
    "metadata": {},
    "source": [
-    "**Exercise:** Read the documentation of `linspace` at <https://numpy.org/doc/stable/reference/generated/numpy.linspace.html>. Then use it to make an array of 101 equally spaced points between 0 and 1 (including both)."
+    "### Exercise 2\n",
+    "\n",
+    "Read the documentation of `linspace` at <https://numpy.org/doc/stable/reference/generated/numpy.linspace.html>. Then use it to make an array of 101 equally spaced points between 0 and 1 (including both)."
    ]
   },
   {
@@ -656,7 +661,10 @@
    "id": "fleet-debut",
    "metadata": {},
    "source": [
-    "**Exercise:** Wrap the code from this chapter in a function named `sweep_p1` that takes an array called `p1_array` as a parameter.  It should create a new `SweepSeries`, run a simulation for each value of `p1` in `p1_array`, store the results in the `SweepSeries`, and return the `SweepSeries`.\n",
+    "### Exercise 3\n",
+    "\n",
+    " Wrap the code from this chapter in a function named `sweep_p1` that takes an array called `p1_array` as a parameter.  It should create a new `SweepSeries` and run a simulation for each value of `p1` in `p1_array`, with `p2=0.2` and `num_steps=60`.\n",
+    "It should store the results in the `SweepSeries` and return it.  \n",
     "\n",
     "Use your function to plot the number of unhappy customers at Olin as a function of `p1`.  Label the axes."
    ]
@@ -686,7 +694,9 @@
    "id": "developmental-broad",
    "metadata": {},
    "source": [
-    "**Exercise:** Write a function called `sweep_p2` that runs simulations with `p1=0.5` and a range of values for `p2`.  It should store the results in a `SweepSeries` and return the `SweepSeries`.\n"
+    "### Exercise 4\n",
+    "\n",
+    " Write a function called `sweep_p2` that runs simulations with `p1=0.5` and a range of values for `p2`.  It should store the results in a `SweepSeries` and return the `SweepSeries`.\n"
    ]
   },
   {
@@ -714,7 +724,7 @@
    "id": "separate-mention",
    "metadata": {},
    "source": [
-    "## Optional Exercises\n",
+    "## Challenge Exercises\n",
     "\n",
     "The following two exercises are a little more challenging.  If you are comfortable with what you have learned so far, you should give them a try.  If you feel like you have your hands full, you might want to skip them for now."
    ]
@@ -724,7 +734,9 @@
    "id": "bearing-orbit",
    "metadata": {},
    "source": [
-    "**Exercise:** Because our simulations are random, the results vary from one run to another, and the results of a parameter sweep tend to be noisy.  We can get a clearer picture of the relationship between a parameter and a metric by running multiple simulations with the same parameter and taking the average of the results.\n",
+    "### Exercise 5\n",
+    "\n",
+    " Because our simulations are random, the results vary from one run to another, and the results of a parameter sweep tend to be noisy.  We can get a clearer picture of the relationship between a parameter and a metric by running multiple simulations with the same parameter and taking the average of the results.\n",
     "\n",
     "Write a function called `run_multiple_simulations` that takes as parameters `p1`, `p2`, `num_steps`, and `num_runs`.\n",
     "`num_runs` specifies how many times it should call `run_simulation`.\n",
@@ -779,7 +791,9 @@
    "id": "structural-expense",
    "metadata": {},
    "source": [
-    "**Exercise:**  Continuting the previous exercise, use `run_multiple_simulations` to run simulations with a range of values for `p1` and\n",
+    "### Exercise 6\n",
+    "\n",
+    "Continuing the previous exercise, use `run_multiple_simulations` to run simulations with a range of values for `p1` and\n",
     "\n",
     "```\n",
     "p2 = 0.3\n",
@@ -866,7 +880,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap05.ipynb b/chapters/chap05.ipynb
index d6b9856f..a82967d0 100644
--- a/chapters/chap05.ipynb
+++ b/chapters/chap05.ipynb
@@ -5,7 +5,7 @@
    "id": "wicked-mozambique",
    "metadata": {},
    "source": [
-    "# Chapter 5"
+    "# World Population"
    ]
   },
   {
@@ -77,14 +77,6 @@
     "from modsim import *"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "plastic-trigger",
-   "metadata": {},
-   "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap05.ipynb)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "informal-contractor",
@@ -101,11 +93,19 @@
     "and improving our quality of life.\n",
     "\n",
     "In this chapter and the next, we use tools from the previous chapters to model world population growth since 1950 and generate predictions for the next 50-100 years.\n",
-    "\n",
     "For background on world population growth, watch this video from the\n",
     "American Museum of Natural History <https://modsimpy.com/human>."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "plastic-trigger",
+   "metadata": {},
+   "source": [
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "dying-browse",
@@ -144,7 +144,7 @@
    "id": "acknowledged-bracelet",
    "metadata": {},
    "source": [
-    "To read this data, we will use Pandas, which provides functions for\n",
+    "To read this data, we will use the Pandas library, which provides functions for\n",
     "working with data. The function we'll use is `read_html`, which can read a web page and extract data from any tables it contains. Before we can use it, we have to import it."
    ]
   },
@@ -191,11 +191,11 @@
     "    as a string. This argument can also be a URL starting with `http`.\n",
     "\n",
     "-   `header`: Indicates which row of each table should be considered the\n",
-    "    header, that is, the set of labels that identify the columns. In\n",
+    "    *header*, that is, the set of labels that identify the columns. In\n",
     "    this case it is the first row (numbered 0).\n",
     "\n",
     "-   `index_col`: Indicates which column of each table should be\n",
-    "    considered the **index**, that is, the set of labels that identify\n",
+    "    considered the *index*, that is, the set of labels that identify\n",
     "    the rows. In this case it is the first column, which contains the\n",
     "    years.\n",
     "\n",
@@ -250,7 +250,7 @@
    "id": "valid-editing",
    "metadata": {},
    "source": [
-    "The first column, which is labeled `Year`, is special.  It is the **index** for this `DataFrame`, which means it contains the labels for the rows.\n",
+    "The first column, which is labeled `Year`, is special.  It is the *index* for this `DataFrame`, which means it contains the labels for the rows.\n",
     "\n",
     "Some of the values use scientific notation; for example, `2.544000e+09` is shorthand for $2.544 \\cdot 10^9$ or 2.544 billion.\n",
     "\n",
@@ -328,8 +328,8 @@
    "id": "brilliant-brook",
    "metadata": {},
    "source": [
-    "The left column is the **index** of the `Series`; in this example it contains the dates.\n",
-    "The right column contains the **values**, which are population estimates.\n",
+    "The left column is the *index* of the `Series`; in this example it contains the dates.\n",
+    "The right column contains the *values*, which are population estimates.\n",
     "In 2016 the world population was about 7.3 billion.\n",
     "\n",
     "Here are the estimates from the United Nations\n",
@@ -407,12 +407,12 @@
    "id": "tender-summer",
    "metadata": {},
    "source": [
-    "## Absolute and relative errors\n",
+    "## Absolute and Relative Errors\n",
     "\n",
     "Estimates of world population from the U.S. Census and the U.N. DESA differ slightly.\n",
-    "One way to characterize this difference is **absolute error**, which is the absolute value of the difference between the two.\n",
+    "One way to characterize this difference is *absolute error*, which is the absolute value of the difference between the estimates.\n",
     "\n",
-    "To compute absolute value, we can import `abs` from NumPy:"
+    "To compute absolute errors, we can import `abs` from NumPy:"
    ]
   },
   {
@@ -425,6 +425,14 @@
     "from numpy import abs"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "frozen-scotland",
+   "metadata": {},
+   "source": [
+    "And use it like this:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -442,9 +450,9 @@
    "metadata": {},
    "source": [
     "When you subtract two `Series` objects, the result is a new `Series`.\n",
-    "Because one of the estimates from 2016 is `NaN`, the result is `NaN`.\n",
+    "Because one of the estimates for 2016 is `NaN`, the result for 2016 is `NaN`.\n",
     "\n",
-    "To summarize the results, we can compute the **mean absolute error**."
+    "To summarize the results, we can compute the *mean absolute error*."
    ]
   },
   {
@@ -492,7 +500,7 @@
     "Now 0.1 billion is a lot of people, so that might sound like a serious discrepancy.\n",
     "But counting everyone is the world is hard, and we should not expect the estimates to be exact.\n",
     "\n",
-    "Another way to quantify the magnitude of the difference is **relative error**, which is the size of the error divided by the estimates themselves."
+    "Another way to quantify the magnitude of the difference is *relative error*, which is the size of the error divided by the estimates themselves."
    ]
   },
   {
@@ -595,12 +603,9 @@
    "id": "dutch-sample",
    "metadata": {},
    "source": [
-    "The numbers in brackets are called **labels**, because they label the\n",
-    "rows of the `Series`.\n",
-    "\n",
     "In this example, the labels 2016 and 1950 are part of the data, so it\n",
     "would be better not to make them part of the program. \n",
-    "Putting values like these in the program is called **hard coding**; it is considered bad practice because if the data change in the future, we have to change the program (see <https://modsimpy.com/hardcode>).\n",
+    "Putting values like these in the program is called *hard coding*; it is considered bad practice because if the data change in the future, we have to change the program (see <https://modsimpy.com/hardcode>).\n",
     "\n",
     "It would be better to get the labels from the `Series`.\n",
     "We can do that by selecting the index from `census` and then selecting the first element."
@@ -729,7 +734,7 @@
    "id": "public-verification",
    "metadata": {},
    "source": [
-    "## Simulation\n",
+    "## Simulating Population Growth\n",
     "\n",
     "Our simulation will start with the observed population in 1950, `p_0`,\n",
     "and add `annual_growth` each year. To store the results, we'll use a\n",
@@ -871,7 +876,9 @@
   {
    "cell_type": "markdown",
    "id": "hearing-today",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "Here's the code from this chapter all in one place."
    ]
@@ -880,7 +887,9 @@
    "cell_type": "code",
    "execution_count": 35,
    "id": "terminal-reynolds",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "t_0 = census.index[0]\n",
@@ -904,7 +913,9 @@
    "cell_type": "code",
    "execution_count": 36,
    "id": "organizational-memphis",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "results.plot(color='gray', label='model')\n",
@@ -917,7 +928,9 @@
    "id": "ecological-welsh",
    "metadata": {},
    "source": [
-    "**Exercise:**  Try fitting the model using data from 1970 to the present, and see if that does a better job.\n",
+    "### Exercise 1\n",
+    "\n",
+    "  Try fitting the model using data from 1970 to the present, and see if that does a better job.\n",
     "\n",
     "Suggestions: \n",
     "\n",
diff --git a/chapters/chap06.ipynb b/chapters/chap06.ipynb
index 9d4935e4..efaffaeb 100644
--- a/chapters/chap06.ipynb
+++ b/chapters/chap06.ipynb
@@ -5,7 +5,7 @@
    "id": "creative-motel",
    "metadata": {},
    "source": [
-    "# Chapter 6"
+    "# Proportional Growth"
    ]
   },
   {
@@ -82,7 +82,8 @@
    "id": "plastic-trigger",
    "metadata": {},
    "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap06.ipynb)"
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -133,7 +134,7 @@
    "metadata": {},
    "source": [
     "In the previous chapter we simulated a model of world population with\n",
-    "constant growth. In this chapter we see if we can make a better model\n",
+    "constant growth. In this chapter we'll see if we can make a better model\n",
     "with growth proportional to the population.\n",
     "\n",
     "But first, we'll improve the code from the previous chapter by\n",
@@ -145,14 +146,14 @@
    "id": "therapeutic-merchant",
    "metadata": {},
    "source": [
-    "## System objects\n",
+    "## System Objects\n",
     "\n",
     "Like a `State` object, a `System` object contains variables and their\n",
     "values. The difference is:\n",
     "\n",
     "-   `State` objects contain state variables that get updated in the course of a simulation.\n",
     "\n",
-    "-   `System` objects contain **system parameters**, which usually don't get updated over the course of a simulation.\n",
+    "-   `System` objects contain *system parameters*, which usually don't get updated over the course of a simulation.\n",
     "\n",
     "For example, in the bike share model, state variables include the number of bikes at each location, which get updated whenever a customer moves a bike. System parameters include the number of locations, total number of bikes, and arrival rates at each location.\n",
     "\n",
@@ -260,7 +261,6 @@
     "`run_simulation1` takes a `System` object and uses the parameters in it to determine `t_0`, `t_end`, and `annual_growth`.\n",
     "\n",
     "It simulates population growth over time and returns the results in a `TimeSeries`.\n",
-    "\n",
     "Here's how we call it."
    ]
   },
@@ -323,7 +323,7 @@
    "source": [
     "It might not be obvious that using functions and `System` objects is a\n",
     "big improvement, and for a simple model that we run only once, maybe\n",
-    "it's not. But as we work with more complex models, and when we run many simulations with different parameters, we'll see that the organization of the code makes a big difference.\n",
+    "it's not. But as we work with more complex models, and when we run many simulations with different parameters, we'll see that this way of organizing the code makes a big difference.\n",
     "\n",
     "Now let's see if we can improve the model."
    ]
@@ -333,7 +333,7 @@
    "id": "geographic-hormone",
    "metadata": {},
    "source": [
-    "## Proportional growth model\n",
+    "## Proportional Growth Model\n",
     "\n",
     "The biggest problem with the constant growth model is that it doesn't\n",
     "make any sense. It is hard to imagine how people all over the world\n",
@@ -373,7 +373,7 @@
     "\n",
     "Now we can choose the values of `birth_rate` and `death_rate` that best fit the data. \n",
     "For the death rate, I'll use 7.7 deaths per 1000 people, which was roughly the global death rate in 2020 (see <https://www.indexmundi.com/world/death_rate.html>).\n",
-    "I chose the birth rate by hand to fit the data."
+    "I chose the birth rate by hand to fit the population data."
    ]
   },
   {
@@ -415,7 +415,7 @@
    "source": [
     "The proportional model fits\n",
     "the data well from 1950 to 1965, but not so well after that. Overall,\n",
-    "the **quality of fit** is not as good as the constant growth model,\n",
+    "the *quality of fit* is not as good as the constant growth model,\n",
     "which is surprising, because it seems like the proportional model is\n",
     "more realistic.\n",
     "\n",
@@ -429,7 +429,7 @@
    "id": "appropriate-checkout",
    "metadata": {},
    "source": [
-    "## Factoring out the update function\n",
+    "## Factoring Out the Update Function\n",
     "\n",
     "`run_simulation1` and `run_simulation2` are nearly identical except for the body of the `for` loop, where we compute the population for the next year.\n",
     "\n",
@@ -458,7 +458,6 @@
     "`growth_func1` takes as arguments the current year, current population, and a `System` object; it returns the net population growth during the current year.\n",
     "\n",
     "This function does not use `t`, so we could leave it out. But we will see other growth functions that need it, and it is convenient if they all take the same parameters, used or not.\n",
-    "\n",
     "Now we can write a function that runs any model:"
    ]
   },
@@ -523,7 +522,7 @@
    "id": "spectacular-paradise",
    "metadata": {},
    "source": [
-    "## Combining birth and death\n",
+    "## Combining Birth and Death\n",
     "\n",
     "We can simplify the code slightly by combining births and deaths to compute the net growth rate. \n",
     "Instead of two parameters, `birth_rate` and `death_rate`, we can write the update function in terms of a single parameter that represents the difference:"
@@ -615,7 +614,9 @@
    "id": "compliant-preserve",
    "metadata": {},
    "source": [
-    "**Exercise:** Maybe the reason the proportional model doesn't work very well is that the growth rate, `alpha`, is changing over time.  So let's try a model with different growth rates before and after 1980 (as an arbitrary choice).\n",
+    "### Exercise 1\n",
+    "\n",
+    " Maybe the reason the proportional model doesn't work very well is that the growth rate, `alpha`, is changing over time.  So let's try a model with different growth rates before and after 1980 (as an arbitrary choice).\n",
     "\n",
     "Write an update function that takes `t`, `pop`, and `system` as parameters.  The system object, `system`, should contain two parameters: the growth rate before 1980, `alpha1`, and the growth rate after 1980, `alpha2`.  It should use `t` to determine which growth rate to use.\n",
     "\n",
@@ -690,7 +691,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap07.ipynb b/chapters/chap07.ipynb
index 2290abf5..efb6bb0e 100644
--- a/chapters/chap07.ipynb
+++ b/chapters/chap07.ipynb
@@ -5,7 +5,7 @@
    "id": "progressive-travel",
    "metadata": {},
    "source": [
-    "# Chapter 7"
+    "# Limits to Growth"
    ]
   },
   {
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "earlier-pride",
    "metadata": {
     "tags": []
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "bound-nature",
    "metadata": {
     "tags": []
@@ -65,7 +65,8 @@
    "id": "found-pledge",
    "metadata": {},
    "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap07.ipynb)"
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -80,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "affiliated-eleven",
    "metadata": {
     "tags": []
@@ -93,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "recent-trouble",
    "metadata": {
     "tags": []
@@ -112,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "western-blowing",
    "metadata": {
     "tags": []
@@ -135,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "simple-coupon",
    "metadata": {
     "tags": []
@@ -148,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "monetary-profile",
    "metadata": {
     "tags": []
@@ -187,7 +188,7 @@
    "id": "assigned-slovakia",
    "metadata": {},
    "source": [
-    "## Quadratic growth\n",
+    "## Quadratic Growth\n",
     "\n",
     "It makes sense that net growth should depend on the current population, but maybe it's not a linear relationship, like this:\n",
     "\n",
@@ -206,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "beginning-belly",
    "metadata": {},
    "outputs": [],
@@ -225,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "listed-florence",
    "metadata": {},
    "outputs": [],
@@ -250,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "signed-impossible",
    "metadata": {},
    "outputs": [],
@@ -269,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "italian-converter",
    "metadata": {},
    "outputs": [],
@@ -282,12 +283,12 @@
    "id": "forbidden-brisbane",
    "metadata": {},
    "source": [
-    "And here are the results."
+    "Here are the results."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "simplified-sight",
    "metadata": {},
    "outputs": [],
@@ -304,9 +305,9 @@
    "source": [
     "The model fits the data well over the whole range, with just a bit of space between them in the 1960s.\n",
     "\n",
-    "Of course, we should expect the quadratic model to fit better than the\n",
-    "constant and proportional models because it has two parameters we can\n",
-    "choose, where the other models have only one. In general, the more\n",
+    "It is not entirely surprising that the quadratic model fits better than the\n",
+    "constant and proportional models, because it has two parameters we can\n",
+    "choose where the other models have only one. In general, the more\n",
     "parameters you have to play with, the better you should expect the model\n",
     "to fit.\n",
     "\n",
@@ -323,16 +324,15 @@
    "id": "sunset-underground",
    "metadata": {},
    "source": [
-    "## Net growth\n",
+    "## Net Growth\n",
     "\n",
     "Let's plot the relationship between growth and population in the quadratic model.\n",
-    "\n",
     "I'll use `linspace` to make an array of 101 populations from 0 to 15 billion."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "neural-guinea",
    "metadata": {},
    "outputs": [],
@@ -352,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "animal-spoke",
    "metadata": {},
    "outputs": [],
@@ -372,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "informed-three",
    "metadata": {},
    "outputs": [],
@@ -390,12 +390,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "unexpected-nigeria",
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot(pop_array, growth_array, label='net growth')\n",
+    "plot(pop_array, growth_array, label='net growth', color='C2')\n",
     "\n",
     "decorate(xlabel='Population (billions)',\n",
     "         ylabel='Net growth (billions)',\n",
@@ -407,13 +407,13 @@
    "id": "precise-finish",
    "metadata": {},
    "source": [
-    "Note that the x-axis is not time, as in the previous figures, but population. We can divide this curve into four regimes of behavior:\n",
+    "Note that the x-axis is not time, as in the previous figures, but population. We can divide this curve into four kinds of behavior:\n",
     "\n",
-    "-   When the population is less than 3-4 billion, net growth is\n",
+    "-   When the population is less than 3 billion, net growth is\n",
     "    proportional to population, as in the proportional model. In this\n",
-    "    regime, the population grows slowly because the population is small.\n",
+    "    range, the population grows slowly because the population is small.\n",
     "\n",
-    "-   Between 4 billion and 10 billion, the population grows quickly\n",
+    "-   Between 3 billion and 10 billion, the population grows quickly\n",
     "    because there are a lot of people.\n",
     "\n",
     "-   Above 10 billion, population grows more slowly; this behavior models\n",
@@ -424,7 +424,7 @@
     "    exceeds the birth rate and net growth becomes negative.\n",
     "\n",
     "Just below 14 billion, there is a point where net growth is 0, which\n",
-    "means that the population does not change. At this point, the birth and death rates are equal, so the population is in **equilibrium**."
+    "means that the population does not change. At this point, the birth and death rates are equal, so the population is in *equilibrium*."
    ]
   },
   {
@@ -432,21 +432,20 @@
    "id": "angry-voice",
    "metadata": {},
    "source": [
-    "## Equilibrium\n",
+    "## Finding Equilibrium\n",
     "\n",
-    "To find the equilibrium point, we can find the roots, or zeros, of this equation: \n",
+    "The equilibrium point is the population, $p$, where net population growth, $\\Delta p$, is 0.\n",
+    "We can compute it by finding the roots, or zeros, of this equation: \n",
     "\n",
     "$$\\Delta p = \\alpha p + \\beta p^2$$ \n",
     "\n",
-    "where $\\Delta p$ is net\n",
-    "population growth, $p$ is current population, and $\\alpha$ and $\\beta$\n",
-    "are the parameters of the model. We can rewrite the right hand side like\n",
-    "this: \n",
+    "where $\\alpha$ and $\\beta$ are the parameters of the model. \n",
+    "If rewrite the right hand side like this: \n",
     "\n",
     "$$\\Delta p = p (\\alpha + \\beta p)$$ \n",
     "\n",
-    "which is $0$ when $p=0$ or $p=-\\alpha/\\beta$.\n",
-    "We can use the parameters of the system to compute the second equilibrium point:"
+    "we can see that net growth is $0$ when $p=0$ or $p=-\\alpha/\\beta$.\n",
+    "So we can compute the (non-zero) equilibrium point like this:"
    ]
   },
   {
@@ -471,12 +470,12 @@
     "\n",
     "$$\\Delta p = r p (1 - p / K)$$ \n",
     "\n",
-    "This is the same model; it's just a different way to **parameterize** it. Given $\\alpha$ and $\\beta$, we can compute $r=\\alpha$ and $K=-\\alpha/\\beta$.\n",
+    "This is the same model; it's just a different way to *parameterize* it. Given $\\alpha$ and $\\beta$, we can compute $r=\\alpha$ and $K=-\\alpha/\\beta$.\n",
     "\n",
     "In this version, it is easier to interpret the parameters: $r$ is the\n",
     "unconstrained growth rate, observed when $p$ is small, and $K$ is the\n",
     "equilibrium point. \n",
-    "$K$ is also called the **carrying capacity**, since it indicates the maximum population the environment can sustain."
+    "$K$ is also called the *carrying capacity*, since it indicates the maximum population the environment can sustain."
    ]
   },
   {
@@ -487,7 +486,6 @@
     "## Summary\n",
     "\n",
     "In this chapter we implemented a quadratic growth model where net growth depends on the current population and the population squared.\n",
-    "\n",
     "This model fits the data well, and we saw one reason why: it is based on the assumption that there is a limit to the number of people the Earth can support.\n",
     "\n",
     "In the next chapter we'll use the models we have developed to generate\n",
@@ -503,7 +501,7 @@
     "## Dysfunctions\n",
     "\n",
     "When people learn about functions, there are a few things they often\n",
-    "find confusing. In this section I present and explain some common\n",
+    "find confusing. In this section I'll present and explain some common\n",
     "problems.\n",
     "\n",
     "As an example, suppose you want a function that takes a\n",
@@ -541,7 +539,7 @@
    "id": "prostate-motorcycle",
    "metadata": {},
    "source": [
-    "**Dysfunction #1:** Not using parameters. In the following version, the function doesn't take any parameters; when `sys1` appears inside the function, it refers to the object we create outside the function."
+    "*Dysfunction #1:* Not using parameters. In the following version, the function doesn't take any parameters; when `sys1` appears inside the function, it refers to the object we create outside the function."
    ]
   },
   {
@@ -574,7 +572,7 @@
    "id": "meaningful-louisiana",
    "metadata": {},
    "source": [
-    "**Dysfunction #2:** Clobbering the parameters. When people first learn\n",
+    "*Dysfunction #2:* Clobbering the parameters. When people first learn\n",
     "about parameters, they often write functions like this:"
    ]
   },
@@ -618,7 +616,7 @@
    "id": "present-estonia",
    "metadata": {},
    "source": [
-    "**Dysfunction #3:** No return value. Here's a version that computes the value of `K` but doesn't return it."
+    "*Dysfunction #3:* No return value. Here's a version that computes the value of `K` but doesn't return it."
    ]
   },
   {
@@ -650,7 +648,7 @@
    "id": "received-firewall",
    "metadata": {},
    "source": [
-    "**Dysfunction #4:** Ignoring the return value. Finally, here's a version where the function is correct, but the way it's used is not.\n",
+    "*Dysfunction #4:* Ignoring the return value. Finally, here's a version where the function is correct, but the way it's used is not.\n",
     "\n",
     "```\n",
     "def carrying_capacity(system):\n",
@@ -689,7 +687,9 @@
    "id": "worst-builder",
    "metadata": {},
    "source": [
-    "**Exercise:** In a previous section, we saw a different way to parameterize the quadratic model:\n",
+    "### Exercise 1\n",
+    "\n",
+    " In a previous section, we saw a different way to parameterize the quadratic model:\n",
     "\n",
     "$$ \\Delta p = r p (1 - p / K) $$\n",
     "\n",
@@ -733,7 +733,9 @@
    "id": "understood-cancer",
    "metadata": {},
    "source": [
-    "**Exercise:**  What happens if we start with an initial population above the carrying capacity, like 20 billion?  Run the model with initial populations between 1 and 20 billion, and plot the results on the same axes.\n",
+    "### Exercise 2\n",
+    "\n",
+    "  What happens if we start with an initial population above the carrying capacity, like 20 billion?  Run the model with initial populations between 1 and 20 billion, and plot the results on the same axes.\n",
     "\n",
     "Hint: If there are too many labels in the legend, you can plot results like this:\n",
     "\n",
@@ -778,7 +780,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap08.ipynb b/chapters/chap08.ipynb
index c96210af..87ae7238 100644
--- a/chapters/chap08.ipynb
+++ b/chapters/chap08.ipynb
@@ -5,7 +5,7 @@
    "id": "external-reward",
    "metadata": {},
    "source": [
-    "# Chapter 8"
+    "# Projecting Population Growth"
    ]
   },
   {
@@ -77,14 +77,6 @@
     "from modsim import *"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "plastic-trigger",
-   "metadata": {},
-   "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap08.ipynb)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "global-international",
@@ -328,7 +320,7 @@
     "\n",
     "If that assumption is valid, we might be able to use actual population\n",
     "growth to estimate carrying capacity, provided we observe the\n",
-    "transition into the regime where the growth rate starts to fall.\n",
+    "transition into the population range where the growth rate starts to fall.\n",
     "\n",
     "But in the case of world population growth, those conditions don't\n",
     "apply. Over the last 50 years, the net growth rate has leveled off, but not yet started to fall, so we don't have enough data to make a credible estimate of carrying capacity. And resource limitations are probably *not* the primary reason growth has slowed. As evidence, consider:\n",
@@ -358,7 +350,7 @@
    "id": "small-seminar",
    "metadata": {},
    "source": [
-    "## Projections\n",
+    "## Comparing Projections\n",
     "\n",
     "From the same Wikipedia page where we got the past population estimates, we'll read `table3`, which contains predictions for population growth over the next 50-100 years, generated by the U.S. Census, U.N. DESA, and the Population Reference Bureau."
    ]
@@ -469,17 +461,28 @@
     "theirs differ from each other almost as much as they differ from ours.\n",
     "So the results from the model, simple as it is, are not entirely unreasonable.\n",
     "\n",
-    "If you are interested in some of the factors that go into the professional predictions, you might like this [video by Hans Rosling about the demographic changes we expect this century](https://www.youtube.com/watch?v=ezVk1ahRF78)."
+    "If you are interested in some of the factors that go into the professional predictions, you might like this video by Hans Rosling about the demographic changes we expect this century: <https://www.youtube.com/watch?v=ezVk1ahRF78>."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "authorized-suggestion",
+   "id": "automated-simpson",
    "metadata": {},
    "source": [
     "## Exercises\n",
     "\n",
-    "**Exercise:** The net growth rate of world population has been declining for several decades.  That observation suggests one more way to generate more realistic projections, by extrapolating observed changes in growth rate.\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "authorized-suggestion",
+   "metadata": {},
+   "source": [
+    "### Exercise 1\n",
+    "\n",
+    " The net growth rate of world population has been declining for several decades.  That observation suggests one more way to generate more realistic projections, by extrapolating observed changes in growth rate.\n",
     "\n",
     "To compute past growth rates, we'll use a function called `diff`, which computes the difference between successive elements in a `Series`.  For example, here are the changes from one year to the next in `census`:"
    ]
@@ -751,7 +754,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap09.ipynb b/chapters/chap09.ipynb
index d552d700..6a4b6a95 100644
--- a/chapters/chap09.ipynb
+++ b/chapters/chap09.ipynb
@@ -5,7 +5,7 @@
    "id": "asian-customer",
    "metadata": {},
    "source": [
-    "# Chapter 9"
+    "# Analysis of Population Growth"
    ]
   },
   {
@@ -77,22 +77,14 @@
     "from modsim import *"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "plastic-trigger",
-   "metadata": {},
-   "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap09.ipynb)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "biblical-murder",
    "metadata": {},
    "source": [
-    "In this chapter we express the models from previous chapters as\n",
+    "In this chapter we'll express the models from previous chapters as\n",
     "difference equations and differential equations, solve the equations,\n",
-    "and derive the functional forms of the solutions. And I present some thoughts about the complementary roles of mathematical analysis and simulation."
+    "and derive the functional forms of the solutions. And I'll present some thoughts about the complementary roles of mathematical analysis and simulation."
    ]
   },
   {
@@ -100,7 +92,7 @@
    "id": "hybrid-retrieval",
    "metadata": {},
    "source": [
-    "## Recurrence relations\n",
+    "## Recurrence Relations\n",
     "\n",
     "The population models in the previous chapter and this one are simple\n",
     "enough that we didn't really need to run simulations. We could have\n",
@@ -118,7 +110,7 @@
     "where $x_n$ is the population during year $n$, \n",
     "$x_{n+1}$ is the population during year $n+1$,\n",
     "and $c$ is constant annual growth.\n",
-    "This way of representing the model is a **recurrence relation**; see\n",
+    "This way of representing the model, where future population is a function of current population, is a *recurrence relation*; see\n",
     "<http://modsimpy.com/recur>.\n",
     "\n",
     "Sometimes it is possible to solve a recurrence relation by writing an\n",
@@ -158,9 +150,9 @@
     "\n",
     "Now we can see that $x_1 = x_0 (1 + \\alpha)$, and $x_2 = x_0 (1 + \\alpha)^2$, and in general\n",
     "\n",
-    "$$x_n = x_0 (1 + \\alpha)^n$$ \n",
+    "$$x_n = x_0 (1 + \\alpha)^n$$\n",
     "\n",
-    "This result is a **geometric progression**;\n",
+    "A sequence with this functional form is called a *geometric progression*;\n",
     "see <http://modsimpy.com/geom>. When $\\alpha$ is positive, the factor\n",
     "$1+\\alpha$ is greater than 1, so the elements of the sequence grow\n",
     "without bound."
@@ -187,7 +179,7 @@
    "id": "decreased-location",
    "metadata": {},
    "source": [
-    "## Differential equations\n",
+    "## Differential Equations\n",
     "\n",
     "Starting again with the constant growth model \n",
     "\n",
@@ -203,10 +195,11 @@
     "\n",
     "$$\\frac{\\Delta x}{\\Delta t} = c$$ \n",
     "\n",
-    "This model is **discrete**, which\n",
-    "means it is only defined at integer values of $n$ and not in between.\n",
+    "This is a *discrete* model, which\n",
+    "means time is only defined at integer values of $n$ and not in between.\n",
     "But in reality, people are born and die all the time, not once a year,\n",
-    "so a **continuous** model might be more realistic."
+    "so it might be more realistic use a *continuous* model, which means\n",
+    "time is defined at all values of $t$, not just integers. "
    ]
   },
   {
@@ -214,15 +207,13 @@
    "id": "latest-impression",
    "metadata": {},
    "source": [
-    "We can make this model continuous by writing the rate of change in the\n",
+    "In a continuous model, we write the rate of change in the\n",
     "form of a derivative: \n",
     "\n",
     "$$\\frac{dx}{dt} = c$$ \n",
     "\n",
-    "This way of representing the model is a **differential equation**; see <http://modsimpy.com/diffeq>.\n",
-    "\n",
-    "We can solve this differential equation if we multiply both sides by\n",
-    "$dt$: \n",
+    "This way of representing the model is a *differential equation*, which is an equation that involves at least one derivative (see <http://modsimpy.com/diffeq>).\n",
+    "To solve this equation, we multiply both sides by $dt$: \n",
     "\n",
     "$$dx = c dt$$ \n",
     "\n",
@@ -268,7 +259,6 @@
     "$$\\exp(\\ln(x)) = \\exp(\\alpha t + K)$$ \n",
     "\n",
     "The exponential function can be written $\\exp(x)$ or $e^x$. In this book I use the first form because it resembles the Python code.\n",
-    "\n",
     "We can rewrite the previous equation as\n",
     "\n",
     "$$x = \\exp(\\alpha t) \\exp(K)$$ \n",
@@ -293,13 +283,12 @@
    "id": "czech-scratch",
    "metadata": {},
    "source": [
-    "## Analysis and simulation\n",
+    "## Analysis and Simulation\n",
     "\n",
-    "Once you have designed a model, there are generally two ways to proceed: simulation and analysis. Simulation often comes in the form of a computer program that models changes in a system over time, like births and deaths, or bikes moving from place to place. Analysis often comes in the form of algebra; that is, symbolic manipulation using mathematical notation.\n",
+    "Once you have designed a model, there are generally two ways to proceed: simulation and analysis. Simulation often comes in the form of a computer program that models changes in a system over time, like births and deaths, or bikes moving from place to place. Analysis often comes in the form of algebra and calculus; that is, symbolic manipulation using mathematical notation.\n",
     "\n",
     "Analysis and simulation have different capabilities and limitations.\n",
-    "Simulation is generally more versatile; it is easy to add and remove\n",
-    "parts of a program and test many versions of a model, as we have done in the previous examples.\n",
+    "Simulation is generally more versatile; it is easy to add and remove parts of a program and test many versions of a model, as we have done in the previous examples.\n",
     "\n",
     "But there are several things we can do with analysis that are harder or impossible with simulations:"
    ]
@@ -313,11 +302,9 @@
     "    value that we could only approximate, less efficiently, with\n",
     "    simulation. For example, in the quadratic model we plotted net growth versus population and saw it crosses through zero when the population is\n",
     "    near 14 billion.  We could estimate the crossing point using a\n",
-    "    numerical search algorithm (more about that later). But with the\n",
-    "    analysis in Section xxx, we derived the general result that\n",
-    "    $K=-\\alpha/\\beta$.\n",
+    "    numerical search algorithm (more about that later). But with a bit of algebra, we derived the general result that $K=-\\alpha/\\beta$.\n",
     "\n",
-    "-   Analysis often provides \"computational shortcuts\", that is, the\n",
+    "-   Analysis sometimes provides \"computational shortcuts\", that is, the\n",
     "    ability to jump forward in time to compute the state of a system\n",
     "    many time steps in the future without computing the intervening\n",
     "    states.\n",
@@ -362,7 +349,7 @@
     "alternative is symbolic computation. If you have used services like\n",
     "WolframAlpha, you have used symbolic computation.\n",
     "\n",
-    "For example, if you go to <https://www.wolframalpha.com/> and type\n",
+    "For example, if you go to <https://www.wolframalpha.com/> and enter\n",
     "\n",
     "```\n",
     "df(t) / dt = alpha f(t)\n",
@@ -399,15 +386,14 @@
     "tools similar to Mathematica. They are not as easy to use as\n",
     "WolframAlpha, but they have some other advantages.\n",
     "\n",
-    "SymPy defines a `Symbol` object that represents symbolic variable names, functions, and other mathematical entities.\n",
-    "\n",
-    "The `symbols` function takes a string and returns `Symbol` objects. So\n",
-    "if we run this assignment:"
+    "To use it, we'll define `Symbol` objects that represent variable names and functions.\n",
+    "The `symbols` function takes a string and returns `Symbol` objects.\n",
+    "So if we run this assignment:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "flush-balance",
    "metadata": {},
    "outputs": [],
@@ -422,13 +408,12 @@
    "id": "faced-praise",
    "metadata": {},
    "source": [
-    "Python understands that `t` is a symbol, not a numerical value. If we\n",
-    "now run"
+    "Now when we use `t`, Python understands that it is a symbol, not a numerical value. For example, if we use `t` as part of an expression, like this,"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "ancient-anderson",
    "metadata": {},
    "outputs": [],
@@ -511,7 +496,7 @@
    "id": "indian-trout",
    "metadata": {},
    "source": [
-    "## Differential equations in SymPy\n",
+    "## Differential Equations In SymPy\n",
     "\n",
     "SymPy provides a function, `diff`, that can differentiate a function. We can apply it to `f(t)` like this:"
    ]
@@ -582,8 +567,8 @@
    "id": "inside-medicine",
    "metadata": {},
    "source": [
-    "The result is the **general\n",
-    "solution**, which still contains an unspecified constant, $C_1$. To get the **particular solution** where $f(0) = p_0$, we substitute `p_0` for `C1`. First, we have to create two more symbols:"
+    "The result is the *general\n",
+    "solution*, which still contains an unspecified constant, $C_1$. To get the *particular solution* where $f(0) = p_0$, we substitute `p_0` for `C1`. First, we have to create two more symbols:"
    ]
   },
   {
@@ -620,8 +605,7 @@
    "id": "modern-raleigh",
    "metadata": {},
    "source": [
-    "The result is called the **exponential growth curve**; see\n",
-    "<http://modsimpy.com/expo>."
+    "When $t=0$, $e^{\\alpha t}$ is $1$, so $f(t)$ is $p_0$, which is what we wanted."
    ]
   },
   {
@@ -629,14 +613,8 @@
    "id": "negative-chance",
    "metadata": {},
    "source": [
-    "## Solving the quadratic growth model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "arabic-victorian",
-   "metadata": {},
-   "source": [
+    "## Solving the Quadratic Growth Model\n",
+    "\n",
     "To solve the quadratic growth curve, we'll use the `r, K` parameterization, so we'll need two more symbols:"
    ]
   },
@@ -771,7 +749,7 @@
    "id": "periodic-bikini",
    "metadata": {},
    "source": [
-    "In this case, [we have reason to expect only one solution](https://en.wikipedia.org/wiki/Picard%E2%80%93Lindel%C3%B6f_theorem), but we still get a list, so we have to use the bracket operator, `[0]`, to select the first one."
+    "In this case, there is only one solution, but we still get a list, so we have to use the bracket operator, `[0]`, to select the first one."
    ]
   },
   {
@@ -828,7 +806,7 @@
    "id": "scenic-preparation",
    "metadata": {},
    "source": [
-    "This function is called the **logistic growth curve**; see\n",
+    "This function is called the *logistic growth curve*; see\n",
     "<http://modsimpy.com/logistic>. In the context of growth models, the\n",
     "logistic function is often written like this:\n",
     "\n",
@@ -949,15 +927,15 @@
    "source": [
     "## Summary\n",
     "\n",
-    "In this chapter we wrote the growth models from the previous chapters in terms of difference and differential equations.\n",
+    "In this chapter we wrote the growth models from the previous chapters in terms of difference and differential equations. We solved some of these equations by hand; for others, we used WolframAlpha and SymPy.\n",
     "\n",
-    "What I called the constant growth model is more commonly called **linear growth** because the solution is a line.  If we model time as continuous, the solution is\n",
+    "What I called the \"constant growth\" model is more commonly called *linear growth* because the solution is a line.  If we model time as continuous, the solution is\n",
     "\n",
     "$$f(t) = p_0 + c t$$\n",
     "\n",
     "where $c$ is net annual growth.\n",
     "\n",
-    "Similarly, the proportional growth model is usually called **exponential growth** because the solution is exponential:\n",
+    "Similarly, the proportional growth model is usually called *exponential growth* because the solution is an exponential function:\n",
     "\n",
     "$$f(t) = p_0 \\exp{\\alpha t}$$"
    ]
@@ -967,7 +945,7 @@
    "id": "outstanding-launch",
    "metadata": {},
    "source": [
-    "Finally, the quadratic growth model is called **logistic growth** because the solution is the logistic function:\n",
+    "Finally, the quadratic growth model is called *logistic growth* because the solution is a logistic function:\n",
     "\n",
     "$$f(t) = \\frac{K}{1 + A \\exp(-rt)}$$ \n",
     "\n",
@@ -975,7 +953,7 @@
     "\n",
     "I avoided these terms until now because they are based on results we had not derived yet.\n",
     "\n",
-    "With that, we are done with modeling world population growth.\n",
+    "With that, we are done modeling world population growth.\n",
     "The next chapter presents case studies where you can apply the tools we have learned so far."
    ]
   },
@@ -984,7 +962,10 @@
    "id": "offshore-passage",
    "metadata": {},
    "source": [
-    "## Exercises"
+    "## Exercises\n",
+    "\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -992,7 +973,9 @@
    "id": "ideal-ecology",
    "metadata": {},
    "source": [
-    "**Exercise:** Use SymPy to solve the quadratic growth equation using the alternative parameterization\n",
+    "### Exercise 1\n",
+    "\n",
+    " Use SymPy to solve the quadratic growth equation using the alternative parameterization\n",
     "\n",
     "$$ \\frac{df(t)}{dt} = \\alpha f(t) + \\beta f^2(t) $$\n"
    ]
@@ -1072,7 +1055,9 @@
    "id": "residential-spanking",
    "metadata": {},
    "source": [
-    "**Exercise:**  Use [WolframAlpha](https://www.wolframalpha.com/) to solve the quadratic growth model, using either or both forms of parameterization:\n",
+    "### Exercise 2\n",
+    "\n",
+    "  Use [WolframAlpha](https://www.wolframalpha.com/) to solve the quadratic growth model, using either or both forms of parameterization:\n",
     "\n",
     "    df(t) / dt = alpha f(t) + beta f(t)^2\n",
     "\n",
diff --git a/chapters/chap10.ipynb b/chapters/chap10.ipynb
index 4476b1b7..ae02aeb4 100644
--- a/chapters/chap10.ipynb
+++ b/chapters/chap10.ipynb
@@ -5,7 +5,7 @@
    "id": "dense-storm",
    "metadata": {},
    "source": [
-    "# Chapter 10"
+    "# Case Studies Part 1"
    ]
   },
   {
@@ -62,28 +62,33 @@
   },
   {
    "cell_type": "markdown",
-   "id": "plastic-trigger",
+   "id": "thirty-medication",
    "metadata": {},
    "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap10.ipynb)"
+    "This chapter presents case studies where you can apply the tools we have learned so far to problems involving population growth, queueing systems, and tree growth. \n",
+    "\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "thirty-medication",
+   "id": "industrial-mercy",
    "metadata": {},
    "source": [
-    "This chapter presents case studies where you can apply the tools we have learned so far to problems involving the growth of human and salmon populations, queueing systems, and a model of tree growth. "
+    "## Historical World Population\n",
+    "\n",
+    "The Wikipedia page about world population growth includes estimates for world population from 12,000 years ago to the present (see <https://en.wikipedia.org/wiki/World_population_estimates.html>)."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "industrial-mercy",
-   "metadata": {},
+   "id": "weekly-class",
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "## Prehistoric World Population\n",
-    "\n",
-    "On the Wikipedia page about world population growth, the second table contains estimates for prehistoric populations."
+    "The following cells download an archived version of this page and read the data into a Pandas `DataFrame`."
    ]
   },
   {
@@ -115,16 +120,6 @@
     "len(tables)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "disciplinary-chosen",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "Select `tables[1]`, which is the second table on the page."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -309,9 +304,9 @@
    "id": "ruled-spain",
    "metadata": {},
    "source": [
-    "## One queue or two?\n",
+    "## One Queue Or Two?\n",
     "\n",
-    "This case study is related to **queueing theory**, which is the study of systems that involve waiting in lines, also known as \"queues\".\n",
+    "This case study is related to *queueing theory*, which is the study of systems that involve waiting in lines, also known as \"queues\".\n",
     "\n",
     "Suppose you are designing the checkout area for a new store. There is\n",
     "enough room in the store for two checkout counters and a waiting area\n",
@@ -336,29 +331,21 @@
     "Based on data from other stores, you know that it takes 5 minutes for a customer to check out, on average. But checkout times are variable: most customers take less than 5 minutes, but some take substantially more. A simple way to model this variability is to assume that when a customer is checking out, they always have the same probability of finishing during the next time step, regardless of how long they have been checking out. I'll denote this probability using the Greek letter mu, $\\mu$, or the variable name `mu`.\n",
     "\n",
     "If we choose $\\mu=1/5$ per minute, the average time for each checkout\n",
-    "will be 5 minutes, which is consistent with the data. Most people takes less than 5 minutes, but a few take substantially longer, which is probably not a bad model of the distribution in real stores.\n",
+    "will be 5 minutes, which is consistent with the data. Most people take less than 5 minutes, but a few take substantially longer, which is probably not a bad model of the distribution in real stores.\n",
     "\n",
-    "Now we're ready to implement the model. In the repository for this book, you'll find a notebook called `queue.ipynb` that contains some code to get you started and instructions.\n",
+    "Now we're ready to implement the model. In the repository for this book, you'll find a notebook called *queue.ipynb* that contains some code to get you started and instructions.\n",
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/queue.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/queue.ipynb>.\n",
     "\n",
     "As always, you should practice incremental development: write no more\n",
     "than one or two lines of code a time, and test as you go!"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "wicked-dispute",
-   "metadata": {},
-   "source": [
-    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/queue.ipynb) or\n",
-    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/queue.ipynb)."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "forward-point",
    "metadata": {},
    "source": [
-    "## Predicting salmon populations\n",
+    "## Predicting Salmon Populations\n",
     "\n",
     "Each year the U.S. Atlantic Salmon Assessment Committee reports\n",
     "estimates of salmon populations in oceans and rivers in the northeastern United States. The reports are useful for monitoring changes in these populations, but they generally do not include predictions.\n",
@@ -370,19 +357,12 @@
     "in Maine.\n",
     "\n",
     "In the repository for this book, you'll find a notebook called\n",
-    "`salmon.ipynb` that contains this data and some code to get you started.\n",
+    "*salmon.ipynb* that contains this data and some code to get you started.\n",
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/salmon.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/salmon.ipynb>.\n",
     "\n",
     "You should take my instructions as suggestions; if you want to try\n",
-    "something different, please do!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "guided-radio",
-   "metadata": {},
-   "source": [
-    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/salmon.ipynb) or\n",
-    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/salmon.ipynb)"
+    "something different, please do!\n",
+    "\n"
    ]
   },
   {
@@ -390,37 +370,27 @@
    "id": "limiting-moore",
    "metadata": {},
    "source": [
-    "## Tree growth\n",
+    "## Tree Growth\n",
     "\n",
     "This case study is based on \"Height-Age Curves for Planted Stands of\n",
     "Douglas Fir, with Adjustments for Density\", a working paper by\n",
     "Flewelling et al.\n",
-    "\n",
-    "It provides \"site index curves\", which are curves that show the\n",
+    "It provides *site index curves*, which are curves that show the\n",
     "expected height of the tallest tree in a stand of Douglas fir as a\n",
     "function of age, for a stand where the trees are the same age.\n",
-    "\n",
-    "Depending on the quality of the site, the trees might grow more quickly or slowly. So each curve is identified by a \"site index\" that indicates the quality of the site.\n",
+    "Depending on the quality of the site, the trees might grow more quickly or slowly. So each curve is identified by a *site index* that indicates the quality of the site.\n",
     "\n",
     "The goal of this case study is to explain the shape of these\n",
     "curves, that is, why trees grow the way they do.\n",
     "The answer I propose involves fractal dimensions, so you might find it interesting.\n",
     "\n",
     "In the repository for this book, you'll find a notebook called\n",
-    "`trees.ipynb` that incrementally develops a model of tree growth and uses it to fit the data.\n",
+    "*trees.ipynb* that incrementally develops a model of tree growth and uses it to fit the data.\n",
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/trees.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/trees.ipynb>.\n",
     "\n",
     "There are no exercises in this case study, but it is an example of what you can do with the tools we have so far and a preview of what you will be able to do with the tools in the next few chapters."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "acknowledged-vault",
-   "metadata": {},
-   "source": [
-    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/trees.ipynb) or\n",
-    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/trees.ipynb)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/chapters/chap11.ipynb b/chapters/chap11.ipynb
index c5732fbb..5c6e0ef9 100644
--- a/chapters/chap11.ipynb
+++ b/chapters/chap11.ipynb
@@ -5,7 +5,7 @@
    "id": "pressing-munich",
    "metadata": {},
    "source": [
-    "# Chapter 11"
+    "# Epidemiology"
    ]
   },
   {
@@ -77,20 +77,12 @@
     "from modsim import *"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "plastic-trigger",
-   "metadata": {},
-   "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap11.ipynb)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "persistent-carbon",
    "metadata": {},
    "source": [
-    "In this chapter, we develop a model of an epidemic as it spreads in a\n",
+    "In this chapter, we'll develop a model of an epidemic as it spreads in a\n",
     "susceptible population, and use it to evaluate the effectiveness of\n",
     "possible interventions.\n",
     "\n",
@@ -105,7 +97,7 @@
     "## The Freshman Plague\n",
     "\n",
     "Every year at Olin College, about 90 new students come to campus from\n",
-    "around the country and the world. Most of them arrive healthy and happy, but usually at least one brings with them some kind of infectious disease. A few weeks later, predictably, some fraction of the incoming class comes down with what we call \"The Freshman Plague\".\n",
+    "around the country and the world. Most of them arrive healthy and happy, but usually at least one brings with them some kind of infectious disease. A few weeks later, predictably, some fraction of the incoming class comes down with what we call the \"Freshman Plague\".\n",
     "\n",
     "In this chapter we introduce a well-known model of infectious disease,\n",
     "the Kermack-McKendrick model, and use it to explain the progression of\n",
@@ -125,20 +117,20 @@
    "id": "distant-expense",
    "metadata": {},
    "source": [
-    "## The SIR model\n",
+    "## The Kermack-McKendrick Model\n",
     "\n",
-    "The Kermack-McKendrick model is an example of an **SIR model**,\n",
+    "The Kermack-McKendrick (KM) model is an example of an *SIR model*,\n",
     "so-named because it represents three categories of people:\n",
     "\n",
-    "-   **S**: People who are \"susceptible\", that is, capable of\n",
+    "-   *S*: People who are \"susceptible\", that is, capable of\n",
     "    contracting the disease if they come into contact with someone who\n",
     "    is infected.\n",
     "\n",
-    "-   **I**: People who are \"infectious\", that is, capable of passing\n",
+    "-   *I*: People who are \"infectious\", that is, capable of passing\n",
     "    along the disease if they come into contact with someone\n",
     "    susceptible.\n",
     "\n",
-    "-   **R**: People who are \"recovered\". In the basic version of the\n",
+    "-   *R*: People who are \"recovered\". In the basic version of the\n",
     "    model, people who have recovered are considered to be immune to\n",
     "    reinfection. That is a reasonable model for some diseases, but not\n",
     "    for others, so it should be on the list of assumptions to reconsider later."
@@ -163,7 +155,7 @@
    "id": "important-yugoslavia",
    "metadata": {},
    "source": [
-    "Now let's think about the number of new infections. Suppose we know that each susceptible person comes into contact with 1 person every 3 days, on average, in a way that would cause them to become infected if the other person is infected. We'll denote this contact rate with the Greek letter beta, $\\beta$.\n",
+    "Now let's think about the number of new infections. Suppose we know that each susceptible person comes into contact with 1 person every 3 days, on average, in a way that would cause them to become infected if the other person is infected. We'll denote this contact rate with the Greek letter beta, $\\beta$, or the variables name `beta`.\n",
     "\n",
     "It's probably not reasonable to assume that we know $\\beta$ ahead of\n",
     "time, but later we'll see how to estimate it based on data from previous outbreaks.\n",
@@ -185,7 +177,7 @@
     "    dividing by $N$ yields the fraction of the population that gets\n",
     "    infected in a day, which is $\\beta s i$.\n",
     "\n",
-    "This model assumes that the population is closed; that is, no one\n",
+    "The KM model assumes that the population is closed; that is, no one\n",
     "arrives or departs, so the size of the population, $N$, is constant."
    ]
   },
@@ -194,7 +186,7 @@
    "id": "accomplished-franklin",
    "metadata": {},
    "source": [
-    "## The SIR equations\n",
+    "## The KM Equations\n",
     "\n",
     "If we treat time as a continuous quantity, we can write differential\n",
     "equations that describe the rates of change for $s$, $i$, and $r$ (where $r$ is the fraction of the population that has recovered):\n",
@@ -206,11 +198,11 @@
     "\n",
     "To avoid cluttering the equations, I leave it implied that $s$ is a function of time, $s(t)$, and likewise for $i$ and $r$.\n",
     "\n",
-    "SIR models are examples of **compartment models**, so-called because\n",
+    "SIR models are examples of *compartment models*, so-called because\n",
     "they divide the world into discrete categories, or compartments, and\n",
     "describe transitions from one compartment to another. Compartments are\n",
-    "also called **stocks** and transitions between them are called\n",
-    "**flows**.\n",
+    "also called *stocks* and transitions between them are called\n",
+    "*flows*.\n",
     "\n",
     "In this example, there are three stocks---susceptible, infectious, and\n",
     "recovered---and two flows---new infections and recoveries. Compartment\n",
@@ -222,8 +214,7 @@
    "id": "fourth-celtic",
    "metadata": {},
    "source": [
-    "The following figure shows the stock and flow diagram for an SIR\n",
-    "model.\n",
+    "The following figure shows the stock and flow diagram for the KM model.\n",
     "\n",
     "![Stock and flow diagram for an SIR\n",
     "model.](https://github.com/AllenDowney/ModSim/raw/main/figs/stock_flow1.png)\n",
@@ -236,20 +227,20 @@
    "id": "martial-details",
    "metadata": {},
    "source": [
-    "## Implementation\n",
+    "## Implementing the KM model\n",
     "\n",
     "For a given physical system, there are many possible models, and for a\n",
     "given model, there are many ways to represent it. For example, we can\n",
     "represent an SIR model as a stock-and-flow diagram, as a set of\n",
     "differential equations, or as a Python program. The process of\n",
-    "representing a model in these forms is called **implementation**. In\n",
-    "this section, we implement the SIR model in Python.\n",
+    "representing a model in these forms is called *implementation*. In\n",
+    "this section, we implement the KM model in Python.\n",
     "\n",
     "I'll represent the initial state of the system using a `State` object\n",
-    "with state variables `S`, `I`, and `R`; they represent the fraction of\n",
+    "with state variables `s`, `i`, and `r`; they represent the fraction of\n",
     "the population in each compartment.\n",
     "\n",
-    "We can initialize the `State` object with the *number* of people in each compartment, assuming there is one infected student in a class of 90:"
+    "We can initialize the `State` object with the *number* of people in each compartment; for example, here is the initial state with one infected student in a class of 90:"
    ]
   },
   {
@@ -259,7 +250,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "init = State(S=89, I=1, R=0)\n",
+    "init = State(s=89, i=1, r=0)\n",
     "show(init)"
    ]
   },
@@ -268,7 +259,7 @@
    "id": "chronic-jonathan",
    "metadata": {},
    "source": [
-    "And then convert the numbers to fractions by dividing by the total:"
+    "We can convert the numbers to fractions by dividing by the total:"
    ]
   },
   {
@@ -340,7 +331,7 @@
    "outputs": [],
    "source": [
     "def make_system(beta, gamma):\n",
-    "    init = State(S=89, I=1, R=0)\n",
+    "    init = State(s=89, i=1, r=0)\n",
     "    init /= init.sum()\n",
     "\n",
     "    return System(init=init, t_end=7*14,\n",
@@ -371,16 +362,23 @@
     "show(system)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "requested-turning",
+   "metadata": {},
+   "source": [
+    "Now that we have object to represent the system and it's state, we are ready for the update function."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "alone-desire",
    "metadata": {},
    "source": [
-    "## The update function\n",
+    "## The Update Function\n",
     "\n",
-    "At any point in time, the state of the system is represented by a\n",
-    "`State` object with three variables, `S`, `I` and `R`. So I'll define an update function that takes as parameters the current\n",
-    "time, a `State` object, and a `System` object:"
+    "The purpose of an update function is to take the current state of a system and compute the state during the next time step.\n",
+    "Here's the update function we'll use for the KM model:"
    ]
   },
   {
@@ -393,7 +391,7 @@
    "outputs": [],
    "source": [
     "def update_func(t, state, system):\n",
-    "    s, i, r = state\n",
+    "    s, i, r = state.s, state.i, state.r\n",
     "\n",
     "    infected = system.beta * i * s    \n",
     "    recovered = system.gamma * i\n",
@@ -402,7 +400,7 @@
     "    i += infected - recovered\n",
     "    r += recovered\n",
     "    \n",
-    "    return State(S=s, I=i, R=r)"
+    "    return State(s=s, i=i, r=r)"
    ]
   },
   {
@@ -410,14 +408,13 @@
    "id": "aboriginal-malpractice",
    "metadata": {},
    "source": [
-    "The first line uses a feature we have not seen before, **multiple\n",
-    "assignment**. The value on the right side is a `State` object that\n",
-    "contains three values. The left side is a sequence of three variable\n",
-    "names. The assignment does just what we want: it assigns the three\n",
-    "values from the `State` object to the three variables, in order.\n",
+    "`update_func` takes as parameters the current time, a `State` object, and a `System` object.\n",
     "\n",
-    "The variables `s`, `i` and `r`, are lowercase to distinguish them\n",
-    "from the state variables, `S`, `I` and `R`.\n",
+    "The first line unpacks the `State` object, assigning the values of the state variables to new variables with the same names.\n",
+    "This is an example of *multiple assignment*.\n",
+    "The left side is a sequence of variables; the right side is a sequence of expressions.\n",
+    "The values on the right side are assigned to the variables on the left side, in order.\n",
+    "By creating these variables, we avoid repeating `state` several times, which makes the code easier to read.\n",
     "\n",
     "The update function computes `infected` and `recovered` as a fraction of the population, then updates `s`, `i` and `r`. The return value is a `State` that contains the updated values.\n",
     "\n",
@@ -451,7 +448,7 @@
    "id": "informational-cisco",
    "metadata": {},
    "source": [
-    "## Running the simulation\n",
+    "## Running the Simulation\n",
     "\n",
     "Now we can simulate the model over a sequence of time steps:"
    ]
@@ -510,7 +507,7 @@
    "id": "serial-genius",
    "metadata": {},
    "source": [
-    "## Collecting the results\n",
+    "## Collecting the Results\n",
     "\n",
     "The previous version of `run_simulation` only returns the final state,\n",
     "but we might want to see how the state changes over time. We'll consider two ways to do that: first, using three `TimeSeries` objects, then using a new object called a `TimeFrame`.\n",
@@ -535,7 +532,7 @@
     "    \n",
     "    for t in range(0, system.t_end):\n",
     "        state = update_func(t, state, system)\n",
-    "        S[t+1], I[t+1], R[t+1] = state\n",
+    "        S[t+1], I[t+1], R[t+1] = state.s, state.i, state.r\n",
     "    \n",
     "    return S, I, R"
    ]
@@ -622,7 +619,7 @@
    "id": "unexpected-empire",
    "metadata": {},
    "source": [
-    "## Now with a TimeFrame\n",
+    "## Now With a TimeFrame\n",
     "\n",
     "If the number of state variables is small, storing them as separate\n",
     "`TimeSeries` objects might not be so bad. But a better alternative is to use a `TimeFrame`, which is another object defined in the ModSim\n",
@@ -674,13 +671,13 @@
     "\n",
     "Since the value on the right side is a `State`, the assignment matches\n",
     "up the index of the `State` with the columns of the `TimeFrame`; that\n",
-    "is, it assigns the `S` value from `system.init` to the `S` column of\n",
-    "`frame`, and likewise with `I` and `R`.\n",
+    "is, it assigns the `s` value from `system.init` to the `s` column of\n",
+    "`frame`, and likewise with `i` and `r`.\n",
     "\n",
-    "We use the same feature to write the loop more concisely, assigning the `State` we get from `update_func` directly to the next row of\n",
-    "`frame`.\n",
+    "Each time through the loop, we assign the `State` we get from `update_func` to the next row of `frame`.\n",
+    "At the end, we return `frame`. \n",
     "\n",
-    "Finally, we return `frame`. We can call this version of `run_simulation` like this:"
+    "We can call this version of `run_simulation` like this:"
    ]
   },
   {
@@ -695,29 +692,48 @@
   },
   {
    "cell_type": "markdown",
-   "id": "global-complement",
+   "id": "small-liability",
    "metadata": {},
    "source": [
-    "And plot the results like this:"
+    "Here are the first few rows of the results."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "listed-enough",
+   "id": "appropriate-keyboard",
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_results(results.S, results.I, results.R)"
+    "results.head()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "welsh-distinction",
+   "id": "global-complement",
    "metadata": {},
    "source": [
+    "The columns in the `TimeFrame` correspond to the state variables, `s`, `i`, and `r`.\n",
     "As with a `DataFrame`, we can use the dot operator to select columns\n",
-    "from a `TimeFrame`.\n"
+    "from a `TimeFrame`, so we can plot the results like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "listed-enough",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_results(results.s, results.i, results.r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "statutory-begin",
+   "metadata": {},
+   "source": [
+    "The results are the same as before, now in a more convenient form."
    ]
   },
   {
@@ -728,8 +744,7 @@
     "## Summary\n",
     "\n",
     "This chapter presents an SIR model of infectious disease and two ways to collect the results, using several `TimeSeries` objects or a single `TimeFrame`.\n",
-    "\n",
-    "In the next chapter we'll use the model to explore the effect of immunization and other interventions.\n",
+    "In the next chapter we'll use the model to explore the effect of immunization.\n",
     "\n",
     "But first you might want to work on these exercises."
    ]
@@ -739,7 +754,10 @@
    "id": "induced-tunnel",
    "metadata": {},
    "source": [
-    "## Exercises"
+    "## Exercises\n",
+    "\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -747,14 +765,16 @@
    "id": "floral-remove",
    "metadata": {},
    "source": [
-    "**Exercise**  Suppose the time between contacts is 4 days and the recovery time is 5 days.  After 14 weeks, how many students, total, have been infected?\n",
+    "### Exercise 1  \n",
+    "\n",
+    "Suppose the time between contacts is 4 days and the recovery time is 5 days.  After 14 weeks, how many students, total, have been infected?\n",
     "\n",
     "Hint: what is the change in `S` between the beginning and the end of the simulation?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "id": "parental-thunder",
    "metadata": {},
    "outputs": [],
@@ -764,7 +784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "id": "recovered-freeze",
    "metadata": {},
    "outputs": [],
@@ -774,13 +794,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 27,
    "id": "suited-spanking",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Solution goes here"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "derived-mauritius",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/chapters/chap12.ipynb b/chapters/chap12.ipynb
index 06c7386b..913a1c29 100644
--- a/chapters/chap12.ipynb
+++ b/chapters/chap12.ipynb
@@ -5,7 +5,7 @@
    "id": "combined-semiconductor",
    "metadata": {},
    "source": [
-    "# Chapter 12"
+    "# Modeling Vaccination"
    ]
   },
   {
@@ -60,14 +60,6 @@
     "from modsim import *"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "plastic-trigger",
-   "metadata": {},
-   "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap12.ipynb)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -77,8 +69,7 @@
    },
    "outputs": [],
    "source": [
-    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
-    "         'chap11.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/chap11.py')"
    ]
   },
   {
@@ -102,7 +93,7 @@
    "id": "identical-steam",
    "metadata": {},
    "source": [
-    "In the previous chapter I presented the SIR model of infectious disease and used it to model the Freshman Plague at Olin. In this chapter we'll consider metrics intended to quantify the effects of the disease and interventions intended to reduce those effects.\n",
+    "In the previous chapter I presented the Kermack-McKendrick (KM) model of infectious disease and used it to model the Freshman Plague at Olin. In this chapter we'll consider metrics intended to quantify the effects of the disease and interventions intended to reduce those effects.\n",
     "\n",
     "We'll use some of the functions from the previous chapter: `make_system`, `update_func`, and the last version of `run_simulation`, which returns the results in a `DataFrame` object."
    ]
@@ -132,8 +123,8 @@
    "outputs": [],
    "source": [
     "def add_immunization(system, fraction):\n",
-    "    system.init.S -= fraction\n",
-    "    system.init.R += fraction"
+    "    system.init.s -= fraction\n",
+    "    system.init.r += fraction"
    ]
   },
   {
@@ -207,7 +198,7 @@
    "id": "bronze-techno",
    "metadata": {},
    "source": [
-    "The following figure shows `S` as a function of time, with and\n",
+    "The following figure shows `s` as a function of time, with and\n",
     "without immunization."
    ]
   },
@@ -218,8 +209,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "results.S.plot(label='No immunization')\n",
-    "results2.S.plot(label='10% immunization')\n",
+    "results.s.plot(style='--', label='No immunization')\n",
+    "results2.s.plot(label='10% immunization')\n",
     "\n",
     "decorate(xlabel='Time (days)',\n",
     "         ylabel='Fraction of population')"
@@ -230,7 +221,7 @@
    "id": "passive-dance",
    "metadata": {},
    "source": [
-    "With immunization, there is a smaller change in `S`; that is, fewer people are infected.\n",
+    "With immunization, there is a smaller change in `s`; that is, fewer people are infected.\n",
     "In the next section we'll compute this change and use it to quantify the effect of immunization."
    ]
   },
@@ -244,15 +235,15 @@
     "When we plot a time series, we get a view of everything that happened\n",
     "when the model ran, but often we want to boil it down to a few numbers\n",
     "that summarize the outcome. These summary statistics are called\n",
-    "**metrics**.\n",
+    "*metrics*.\n",
     "\n",
-    "In the SIR model, we might want to know the time until the peak of the\n",
+    "In the KM model, we might want to know the time until the peak of the\n",
     "outbreak, the number of people who are sick at the peak, the number of\n",
     "students who will still be sick at the end of the semester, or the total number of students who get sick at any point.\n",
     "\n",
     "As an example, I will focus on the last one --- the total number of sick students --- and we will consider interventions intended to minimize it.\n",
     "\n",
-    "We can get the total number of infections by computing the difference in `S` at the beginning and the end of the simulation."
+    "We can get the total number of infections by computing the difference in `s` at the beginning and the end of the simulation."
    ]
   },
   {
@@ -265,11 +256,19 @@
    "outputs": [],
    "source": [
     "def calc_total_infected(results, system):\n",
-    "    s_0 = results.S[0]\n",
-    "    s_end = results.S[system.t_end]\n",
+    "    s_0 = results.s[0]\n",
+    "    s_end = results.s[system.t_end]\n",
     "    return s_0 - s_end"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "surrounded-niagara",
+   "metadata": {},
+   "source": [
+    "And here are the results from the two simulations."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 11,
@@ -384,7 +383,7 @@
     "Reducing the number of \"susceptibles\" and increasing the number of\n",
     "\"resistants\" makes it harder for the disease to spread, because some\n",
     "fraction of contacts are wasted on people who cannot be infected. This\n",
-    "phenomenon is called **herd immunity**, and it is an important element\n",
+    "phenomenon is called *herd immunity*, and it is an important element\n",
     "of public health (see <http://modsimpy.com/herd>)."
    ]
   },
@@ -410,21 +409,34 @@
     "## Summary\n",
     "\n",
     "In general, models are used to predict, explain, and design.\n",
-    "In this chapter, we use an SIR predict the effect of immunization and to explain the phenomenon of herd immunity.\n",
+    "In this chapter, we use an SIR model to predict the effect of immunization and to explain the phenomenon of herd immunity.\n",
     "\n",
-    "In the repository for this book, you will find a file called `plague.ipynb` that uses this model for design, that is, for making public health decisions intended to achieve a goal.\n",
+    "In the repository for this book, you will find a file called *plague.ipynb* that uses this model for design, that is, for making public health decisions intended to achieve a goal.\n",
     "\n",
     "In the next chapter, we'll explore the SIR model further by sweeping the parameters.\n",
     "\n",
     "But first you might want to work on this exercise."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "institutional-memory",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "drawn-hindu",
    "metadata": {},
    "source": [
-    "**Exercise:** Suppose we have the option to quarantine infected students.  For example, a student who feels ill might be moved to an infirmary or a private dorm room until they are no longer infectious.\n",
+    "### Exercise 1\n",
+    "\n",
+    " Suppose we have the option to quarantine infected students.  For example, a student who feels ill might be moved to an infirmary or a private dorm room until they are no longer infectious.\n",
     "\n",
     "How might you incorporate the effect of quarantine in the SIR model?"
    ]
@@ -495,7 +507,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap13.ipynb b/chapters/chap13.ipynb
index ed73cd5f..a741bf4c 100644
--- a/chapters/chap13.ipynb
+++ b/chapters/chap13.ipynb
@@ -5,7 +5,7 @@
    "id": "colonial-jacob",
    "metadata": {},
    "source": [
-    "# Chapter 13"
+    "# Sweeping Parameters"
    ]
   },
   {
@@ -77,14 +77,6 @@
     "from modsim import *"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "consolidated-cherry",
-   "metadata": {},
-   "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap13.ipynb)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -94,8 +86,7 @@
    },
    "outputs": [],
    "source": [
-    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
-    "         'chap11.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/chap11.py')"
    ]
   },
   {
@@ -107,8 +98,7 @@
    },
    "outputs": [],
    "source": [
-    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
-    "         'chap12.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/chap12.py')"
    ]
   },
   {
@@ -135,13 +125,13 @@
    "id": "structural-application",
    "metadata": {},
    "source": [
-    "In the previous chapter we extended the SIR model to include immunization and used it to demonstrate herd immunity.\n",
+    "In the previous chapter we extended the Kermack-McKendrick (KM) model to include immunization and used it to demonstrate herd immunity.\n",
     "\n",
     "But we assumed that the parameters of the model, contact rate and\n",
-    "recovery rate, were known. In this chapter, we explore the behavior of\n",
+    "recovery rate, were known. In this chapter, we'll explore the behavior of\n",
     "the model as we vary these parameters.\n",
     "\n",
-    "In the next chapter, we use analysis to understand these relationships better, and propose a method for using data to estimate parameters."
+    "In the next chapter, we'll use analysis to understand these relationships better, and propose a method for using data to estimate parameters."
    ]
   },
   {
@@ -149,7 +139,7 @@
    "id": "tracked-outreach",
    "metadata": {},
    "source": [
-    "## Sweeping beta\n",
+    "## Sweeping Beta\n",
     "\n",
     "Recall that $\\beta$ is the contact rate, which captures both the\n",
     "frequency of interaction between people and the fraction of those\n",
@@ -180,8 +170,9 @@
    "id": "eligible-shareware",
    "metadata": {},
    "source": [
-    "The following function takes this array as a parameter and a single value for `gamma`.\n",
+    "We'll start with a single value for `gamma`, which is the recovery rate, that is, the fraction of infected people who recover per day.\n",
     "\n",
+    "The following function takes `beta_array` and `gamma` as parameters.\n",
     "It runs the simulation for each value of `beta` and computes the same metric we used in the previous chapter, the fraction of the population that gets infected.\n",
     "\n",
     "The result is a `SweepSeries` that contains the values of `beta` and the corresponding metrics."
@@ -228,7 +219,7 @@
    "id": "collectible-positive",
    "metadata": {},
    "source": [
-    "Before we plot the results, I will use a formatted string literal, also called an **f-string** to assemble a label that includes the value of `gamma`:"
+    "Before we plot the results, I will use a formatted string literal, also called an *f-string* to assemble a label that includes the value of `gamma`:"
    ]
   },
   {
@@ -247,7 +238,7 @@
    "id": "invisible-bible",
    "metadata": {},
    "source": [
-    "An f-string starts with the letter \"f\" followed by a string in single or double quotes. \n",
+    "An f-string starts with the letter `f` followed by a string in single or double quotes. \n",
     "The string can contain any number of format specifiers in squiggly brackets, `{}`.\n",
     "When a variable name appears in a format specifier, it is replaced with the value of the variable.\n",
     "\n",
@@ -292,7 +283,7 @@
    "id": "described-jacksonville",
    "metadata": {},
    "source": [
-    "## Sweeping gamma\n",
+    "## Sweeping Gamma\n",
     "\n",
     "Let's see what that looks like for a few different values of `gamma`.\n",
     "We'll use `linspace` to make an array of values:"
@@ -349,11 +340,11 @@
    "id": "metropolitan-collect",
    "metadata": {},
    "source": [
-    "## SweepFrame\n",
+    "## Using a SweepFrame\n",
     "\n",
     "In the previous section, we swept a range of values for `gamma`, and for\n",
-    "each value, we swept a range of values for `beta`. This process is a\n",
-    "**two-dimensional sweep**.\n",
+    "each value of `gamma`, we swept a range of values for `beta`. This process is a\n",
+    "*two-dimensional sweep*.\n",
     "\n",
     "If we want to store the results, rather than plot them, we can use a\n",
     "`SweepFrame`, which is a kind of `DataFrame` where the rows sweep one\n",
@@ -467,7 +458,7 @@
     "This example demonstrates one use of a `SweepFrame`: we can run the analysis once, save the results, and then generate different visualizations.\n",
     "\n",
     "Another way to visualize the results of a two-dimensional sweep is a\n",
-    "**contour plot**, which shows the parameters on the axes and contour\n",
+    "*contour plot*, which shows the parameters on the axes and contour\n",
     "lines where the value of the metric is constant.\n",
     "\n",
     "The ModSim library provides `contour`, which takes a `SweepFrame` and uses Matplotlib to generate a contour plot."
@@ -526,7 +517,10 @@
    "id": "variable-capture",
    "metadata": {},
    "source": [
-    "## Exercises"
+    "## Exercises\n",
+    "\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -534,7 +528,9 @@
    "id": "conscious-blues",
    "metadata": {},
    "source": [
-    "**Exercise:**  If we know `beta` and `gamma`, we can compute the fraction of the population that gets infected.  In general, we don't know these parameters, but sometimes we can estimate them based on the behavior of an outbreak.\n",
+    "### Exercise 1\n",
+    "\n",
+    "  If we know `beta` and `gamma`, we can compute the fraction of the population that gets infected.  In general, we don't know these parameters, but sometimes we can estimate them based on the behavior of an outbreak.\n",
     "\n",
     "Suppose the infectious period for the Freshman Plague is known to be 2 days on average, and suppose during one particularly bad year, 40% of the class is infected at some point.  Estimate the time between contacts, `1/beta`."
    ]
diff --git a/chapters/chap14.ipynb b/chapters/chap14.ipynb
index 39e7d841..13c116d5 100644
--- a/chapters/chap14.ipynb
+++ b/chapters/chap14.ipynb
@@ -5,7 +5,7 @@
    "id": "infrared-shower",
    "metadata": {},
    "source": [
-    "# Chapter 14"
+    "# Nondimensionalization"
    ]
   },
   {
@@ -60,14 +60,6 @@
     "from modsim import *"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "attractive-corner",
-   "metadata": {},
-   "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap14.ipynb)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -134,10 +126,10 @@
    "id": "brief-trademark",
    "metadata": {},
    "source": [
-    "In the previous chapter we swept the parameters of the SIR model: the contact rate, `beta`, and the recovery rate, `gamma`.\n",
+    "In the previous chapter we swept the parameters of the Kermack-McKendrick (KM) model: the contact rate, `beta`, and the recovery rate, `gamma`.\n",
     "For each pair of parameters, we ran a simulation and computed the total fraction of the population infected.\n",
     "\n",
-    "In this chapter we investigate the relationship between the parameters and this metric, using both simulation and analysis."
+    "In this chapter we'll investigate the relationship between the parameters and this metric, using both simulation and analysis."
    ]
   },
   {
@@ -145,19 +137,17 @@
    "id": "weekly-award",
    "metadata": {},
    "source": [
-    "## Nondimensionalization\n",
-    "\n",
-    "The figures in the previous chapter suggest that there is a relationship between the parameters of the SIR model, `beta` and `gamma`, and the fraction of the population that is infected. Let's think what that relationship might be.\n",
+    "The figures in the previous chapter suggest that there is a relationship between the parameters of the KM model, `beta` and `gamma`, and the fraction of the population that is infected. Let's think what that relationship might be.\n",
     "\n",
     "-   When `beta` exceeds `gamma`, there are more contacts\n",
-    "    than recoveries during each day. The difference between `beta` and `gamma` might be called the \"excess contact rate\", in units of contacts per day.\n",
+    "    than recoveries during each day. The difference between `beta` and `gamma` might be called the *excess contact rate*, in units of contacts per day.\n",
     "\n",
     "-   As an alternative, we might consider the ratio `beta/gamma`, which\n",
     "    is the number of contacts per recovery. Because the numerator and\n",
-    "    denominator are in the same units, this ratio is **dimensionless**, which means it has no units.\n",
+    "    denominator are in the same units, this ratio is *dimensionless*, which means it has no units.\n",
     "\n",
     "Describing physical systems using dimensionless parameters is often a\n",
-    "useful move in the modeling and simulation game. In fact, it is so useful that it has a name: **nondimensionalization** (see\n",
+    "useful move in the modeling and simulation game. In fact, it is so useful that it has a name: *nondimensionalization* (see\n",
     "<http://modsimpy.com/nondim>).\n",
     "\n",
     "So we'll try the second option first."
@@ -168,7 +158,7 @@
    "id": "legendary-terrorism",
    "metadata": {},
    "source": [
-    "## Exploring the results\n",
+    "## Exploring the Results\n",
     "\n",
     "In the previous chapter, we wrote a function, `sweep_parameters`,\n",
     "that takes an array of values for `beta` and an array of values for `gamma`.\n",
@@ -214,7 +204,6 @@
    "metadata": {},
    "source": [
     "The `SweepFrame` has one row for each value of `beta` and one column for each value of `gamma`. \n",
-    "\n",
     "We can print the values in the `SweepFrame` like this:"
    ]
   },
@@ -304,7 +293,7 @@
    "id": "lightweight-surfing",
    "metadata": {},
    "source": [
-    "## Contact number\n",
+    "## Contact Number\n",
     "\n",
     "From Section xxx, recall that the number of new infections in a\n",
     "given day is $\\beta s i N$, and the number of recoveries is\n",
@@ -313,7 +302,7 @@
     "(as a fraction of the population).\n",
     "\n",
     "When a new disease is introduced to a susceptible population, $s$ is\n",
-    "approximately 1, so the number of people infected by each sick person is $\\beta / \\gamma$. This ratio is called the \"contact number\\\" or \"basic reproduction number\\\" (see <http://modsimpy.com/contact>). By convention it is usually denoted $R_0$, but in the context of an SIR model, that notation is confusing, so we'll use $c$ instead."
+    "approximately 1, so the number of people infected by each sick person is $\\beta / \\gamma$. This ratio is called the *contact number* or *basic reproduction number* (see <http://modsimpy.com/contact>). By convention it is usually denoted $R_0$, but in the context of an SIR model, that notation is confusing, so we'll use $c$ instead."
    ]
   },
   {
@@ -394,7 +383,7 @@
    "id": "under-dating",
    "metadata": {},
    "source": [
-    "## Analysis and simulation\n",
+    "## Analysis and Simulation\n",
     "\n",
     "Let's compare this analytic result to the results from simulation. I'll create an array of values for $s_{\\infty}$."
    ]
@@ -506,12 +495,12 @@
    "id": "alternate-surprise",
    "metadata": {},
    "source": [
-    "## Estimating contact number\n",
+    "## Estimating Contact Number\n",
     "\n",
     "The previous figure shows that if we know the contact number, we can estimate the fraction of the population that will be infected with just a few arithmetic operations.\n",
     "We don't have to run a simulation.\n",
     "\n",
-    "Also, we can read the figure the other way; if we know what fraction of the population was affected by a past outbreak, we can estimate the contact number.\n",
+    "We can also read the figure the other way; if we know what fraction of the population was affected by a past outbreak, we can estimate the contact number.\n",
     "Then, if we know one of the parameters, like `gamma`, we can use the contact number to estimate the other parameter, like `beta`.\n",
     "\n",
     "At least in theory, we can.\n",
@@ -521,7 +510,8 @@
     "\n",
     "* And when the contact number is high, the curve is nearly flat, which means that it's hard to see the difference between values of $c$ between 3 and 6.\n",
     "\n",
-    "So it might not be practical to use this curve to estimate $c$."
+    "With the uncertainty of real data, we might not be able to estimate $c$ with much precision.\n",
+    "But as one of the exercises below, you'll have a chance to try."
    ]
   },
   {
@@ -534,8 +524,8 @@
     "In this chapter we used simulations to explore the relationship between `beta`, `gamma`, and the fraction infected.\n",
     "Then we used analysis to explain that relationship.\n",
     "\n",
-    "With that, we are done with the SIR model.\n",
-    "In the next chapter we move on to thermal systems and the notorious coffee cooling problem."
+    "With that, we are done with the Kermack-McKendrick model.\n",
+    "In the next chapter we'll move on to thermal systems and the notorious coffee cooling problem."
    ]
   },
   {
@@ -543,7 +533,10 @@
    "id": "removed-conviction",
    "metadata": {},
    "source": [
-    "## Exercises"
+    "## Exercises\n",
+    "\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -551,7 +544,9 @@
    "id": "textile-program",
    "metadata": {},
    "source": [
-    "**Exercise:** At the beginning of this chapter, I suggested two ways to relate `beta` and `gamma`: we could compute their difference or their ratio.\n",
+    "### Exercise 1\n",
+    "\n",
+    " At the beginning of this chapter, I suggested two ways to relate `beta` and `gamma`: we could compute their difference or their ratio.\n",
     "\n",
     "Because the ratio is dimensionless, I suggested we explore it first, and that led us to discover the contact number, which is `beta/gamma`.\n",
     "When we plotted the fraction infected as a function of the contact number, we found that this metric falls on a single curve, at least approximately.\n",
@@ -599,8 +594,9 @@
    "id": "angry-perfume",
    "metadata": {},
    "source": [
-    "**Exercise:** Suppose you run a survey at the end of the semester and find that 26% of students had the Freshman Plague at some point.\n",
+    "### Exercise 2\n",
     "\n",
+    " Suppose you run a survey at the end of the semester and find that 26% of students had the Freshman Plague at some point.\n",
     "What is your best estimate of `c`?\n",
     "\n",
     "Hint: if you display `frac_infected_series`, you can read off the answer. "
@@ -633,7 +629,9 @@
    "id": "charged-sample",
    "metadata": {},
    "source": [
-    "**Exercise**: So far the only metric we have considered is the total fraction of the population that gets infected over the course of an epidemic.  That is an important metric, but it is not the only one we care about.\n",
+    "### Exercise 3\n",
+    "\n",
+    "So far the only metric we have considered is the total fraction of the population that gets infected over the course of an epidemic.  That is an important metric, but it is not the only one we care about.\n",
     "\n",
     "For example, if we have limited resources to deal with infected people, we might also be concerned about the number of people who are sick at the peak of the epidemic, which is the maximum of `I`.\n",
     "\n",
@@ -646,7 +644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "id": "greatest-palestine",
    "metadata": {},
    "outputs": [],
@@ -656,7 +654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "id": "average-practitioner",
    "metadata": {},
    "outputs": [],
@@ -666,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "id": "elegant-gamma",
    "metadata": {},
    "outputs": [],
@@ -676,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "id": "plain-dayton",
    "metadata": {},
    "outputs": [],
@@ -686,7 +684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 30,
    "id": "sexual-services",
    "metadata": {},
    "outputs": [],
@@ -696,7 +694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 31,
    "id": "central-fever",
    "metadata": {},
    "outputs": [],
@@ -706,7 +704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 32,
    "id": "tropical-advancement",
    "metadata": {},
    "outputs": [],
@@ -719,14 +717,14 @@
    "id": "editorial-coating",
    "metadata": {},
    "source": [
-    "## Under the hood\n",
+    "## Under the Hood\n",
     "\n",
     "ModSim provides `make_series` to make it easier to create a Pandas Series.  In this chapter, we used it like this: "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 33,
    "id": "corresponding-india",
    "metadata": {},
    "outputs": [],
@@ -744,7 +742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 34,
    "id": "constant-christianity",
    "metadata": {},
    "outputs": [],
@@ -759,7 +757,7 @@
    "id": "wireless-president",
    "metadata": {},
    "source": [
-    "The difference is that the arguments are in reverse order: the first argument is stored as the values in the `Series`; the second argument is stored as the `index`.\n",
+    "The difference is that the arguments are in reverse order: the first argument is stored as the values in the `Series`; the second argument is stored as the index.\n",
     "\n",
     "I find that order counterintuitive, which is why I use `make_series`.\n",
     "`make_series` takes the same optional keyword arguments as `Series`, which you can read about at <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html>."
@@ -791,7 +789,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap15.ipynb b/chapters/chap15.ipynb
index 1018b6c1..7c861373 100644
--- a/chapters/chap15.ipynb
+++ b/chapters/chap15.ipynb
@@ -25,23 +25,6 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "narrow-indication",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
    "id": "dependent-monitoring",
    "metadata": {
     "tags": []
@@ -65,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "continental-decrease",
    "metadata": {
     "tags": []
@@ -82,7 +65,8 @@
    "id": "derived-exhibit",
    "metadata": {},
    "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap15.ipynb)"
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -90,7 +74,7 @@
    "id": "capital-needle",
    "metadata": {},
    "source": [
-    "So far the systems we have studied have been physical in the sense that they exist in the world, but they have not been physics, in the sense of what physics classes are usually about. In the next few chapters, we'll do some physics, starting with **thermal systems**, that is, systems where the temperature of objects changes as heat transfers from one to another."
+    "So far the systems we have studied have been physical in the sense that they exist in the world, but they have not been physics in the sense of what physics classes are usually about. In the next few chapters, we'll do some physics, starting with *thermal systems*, that is, systems where the temperature of objects changes as heat transfers from one to another."
    ]
   },
   {
@@ -98,7 +82,7 @@
    "id": "working-senator",
    "metadata": {},
    "source": [
-    "## The coffee cooling problem\n",
+    "## The Coffee Cooling Problem\n",
     "\n",
     "The coffee cooling problem was discussed by Jearl Walker in \n",
     "\"The Amateur Scientist\", *Scientific American*, Volume 237, Issue 5, November 1977. Since then it has become a standard example of modeling and simulation.\n",
@@ -138,23 +122,23 @@
    "id": "retired-noise",
    "metadata": {},
    "source": [
-    "## Temperature and heat\n",
+    "## Temperature and Heat\n",
     "\n",
     "To understand how coffee cools (and milk warms), we need a model of\n",
-    "temperature and heat. **Temperature** is a property of an object or a\n",
+    "temperature and heat. *Temperature* is a property of an object or a\n",
     "system; in SI units it is measured in degrees Celsius (°C). Temperature quantifies how hot or cold the object is, which is related to the average velocity of the particles that make it up.\n",
     "\n",
     "When particles in a hot object contact particles in a cold object, the\n",
     "hot object gets cooler and the cold object gets warmer as energy is\n",
     "transferred from one to the other. The transferred energy is called\n",
-    "**heat**; in SI units it is measured in joules (J).\n",
+    "*heat*; in SI units it is measured in joules (J).\n",
     "\n",
     "Heat is related to temperature by the following equation (see\n",
     "<http://modsimpy.com/thermass>): \n",
     "\n",
     "$$Q = C~\\Delta T$$ \n",
     "\n",
-    "where $Q$ is the amount of heat transferred to an object, $\\Delta T$ is resulting change in temperature, and $C$ is the **thermal mass** of the object, which quantifies how much energy it takes to heat or cool it. In SI units, thermal mass is measured in joules per degree Celsius (J/°C)."
+    "where $Q$ is the amount of heat transferred to an object, $\\Delta T$ is resulting change in temperature, and $C$ is the *thermal mass* of the object, which quantifies how much energy it takes to heat or cool it. In SI units, thermal mass is measured in joules per degree Celsius (J/°C)."
    ]
   },
   {
@@ -167,7 +151,7 @@
     "\n",
     "$$C = m c_p$$ \n",
     "\n",
-    "where $m$ is the mass of the object and $c_p$ is the **specific heat capacity** of the material (see <http://modsimpy.com/specheat>).\n",
+    "where $m$ is the mass of the object and $c_p$ is the *specific heat capacity* of the material (see <http://modsimpy.com/specheat>).\n",
     "\n",
     "We can use these equations to estimate the thermal mass of a cup of\n",
     "coffee. The specific heat capacity of coffee is probably close to that\n",
@@ -191,7 +175,7 @@
    "id": "closed-vegetable",
    "metadata": {},
    "source": [
-    "## Heat transfer\n",
+    "## Heat Transfer\n",
     "\n",
     "In a situation like the coffee cooling problem, there are three ways\n",
     "heat transfers from one object to another (see <http://modsimpy.com/transfer>):\n",
@@ -228,7 +212,7 @@
    "id": "freelance-target",
    "metadata": {},
    "source": [
-    "## Newton's law of cooling\n",
+    "## Newton's Law of Cooling\n",
     "\n",
     "Newton's law of cooling asserts that the temperature rate of change for an object is proportional to the difference in temperature between the object and the surrounding environment:\n",
     "\n",
@@ -266,14 +250,13 @@
    "source": [
     "## Implementation\n",
     "\n",
-    "To get started, let's forget about the milk temporarily and focus on the coffee. \n",
-    "\n",
+    "To get started, let's forget about the milk temporarily and focus on the coffee.\n",
     "Here's a function that takes the parameters of the system and makes a `System` object:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "incredible-greeting",
    "metadata": {
     "tags": []
@@ -297,13 +280,12 @@
    "metadata": {},
    "source": [
     "In addition to the parameters, `make_system` sets the temperature of the environment, `T_env`, the initial time stamp, `t_0`, and the time step, `dt`, which we will use use to simulate the cooling process.\n",
-    "\n",
     "Here's a `System` object that represents the coffee."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "fuzzy-support",
    "metadata": {},
    "outputs": [],
@@ -334,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "tight-baptist",
    "metadata": {
     "tags": []
@@ -356,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "fiscal-artwork",
    "metadata": {},
    "outputs": [],
@@ -376,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "cardiac-independence",
    "metadata": {
     "tags": []
@@ -433,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "numerous-metabolism",
    "metadata": {},
    "outputs": [],
@@ -452,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "infectious-carolina",
    "metadata": {},
    "outputs": [],
@@ -470,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "accompanied-melbourne",
    "metadata": {},
    "outputs": [],
@@ -483,20 +465,14 @@
    "id": "formal-headset",
    "metadata": {},
    "source": [
-    "With `t_0=0`, `t_end=30`, and `dt=1`, the time stamps go from `0.0` to `30.0`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "documentary-standing",
-   "metadata": {},
-   "source": [
+    "With `t_0=0`, `t_end=30`, and `dt=1`, the time stamps go from `0.0` to `30.0`.\n",
+    "\n",
     "Here's what the `TimeSeries` looks like."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "important-constitution",
    "metadata": {},
    "outputs": [],
@@ -518,7 +494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "absolute-desire",
    "metadata": {},
    "outputs": [],
@@ -540,13 +516,13 @@
    "id": "funny-weekly",
    "metadata": {},
    "source": [
-    "## Finding roots\n",
+    "## Finding Roots\n",
     "\n",
     "The ModSim library provides a function called `root_scalar` that finds the roots of non-linear equations. As an example, suppose you want to find the roots of the polynomial \n",
     "\n",
     "$$f(x) = (x - 1)(x - 2)(x - 3)$$ \n",
     "\n",
-    "A **root** is a value of $x$ that makes $f(x)=0$. Because of the way I wrote this polynomial, we can see that if $x=1$, the first factor is 0; if $x=2$, the second factor is 0; and if $x=3$, the third factor is 0, so those are the roots.\n",
+    "A *root* is a value of $x$ that makes $f(x)=0$. Because of the way I wrote this polynomial, we can see that if $x=1$, the first factor is 0; if $x=2$, the second factor is 0; and if $x=3$, the third factor is 0, so those are the roots.\n",
     "\n",
     "I'll use this example to demonstrate `root_scalar`. First, we have to\n",
     "write a function that evaluates $f$:"
@@ -554,7 +530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "small-shark",
    "metadata": {},
    "outputs": [],
@@ -573,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "defensive-content",
    "metadata": {},
    "outputs": [],
@@ -593,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "behind-perth",
    "metadata": {},
    "outputs": [],
@@ -611,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "following-sentence",
    "metadata": {},
    "outputs": [],
@@ -653,7 +629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "genetic-compound",
    "metadata": {},
    "outputs": [],
@@ -678,7 +654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "french-financing",
    "metadata": {},
    "outputs": [],
@@ -697,7 +673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "excellent-fellow",
    "metadata": {},
    "outputs": [],
@@ -717,7 +693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "french-decline",
    "metadata": {},
    "outputs": [],
@@ -740,7 +716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "afraid-ordering",
    "metadata": {},
    "outputs": [],
@@ -755,13 +731,12 @@
    "metadata": {},
    "source": [
     "In this example, `r_coffee` turns out to be about `0.0115`, in units of min$^{-1}$ (inverse minutes).\n",
-    "\n",
     "We can confirm that this value is correct by setting `r` to the root we found and running the simulation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "derived-annual",
    "metadata": {},
    "outputs": [],
@@ -779,21 +754,28 @@
     "The final temperature is very close to 70 °C."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "smart-yeast",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "scientific-attachment",
    "metadata": {},
    "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:**  Simulate the temperature of 50 mL of milk with a starting temperature of 5 °C, in a vessel with `r=0.1`, for 15 minutes, and plot the results.\n",
+    "### Exercise 1\n",
     "\n",
+    "Simulate the temperature of 50 mL of milk with a starting temperature of 5 °C, in a vessel with `r=0.1`, for 15 minutes, and plot the results.\n",
     "By trial and error, find a value for `r` that makes the final temperature close to 20 °C."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "committed-angel",
    "metadata": {},
    "outputs": [],
@@ -803,7 +785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "military-military",
    "metadata": {},
    "outputs": [],
@@ -816,12 +798,14 @@
    "id": "logical-acoustic",
    "metadata": {},
    "source": [
-    "**Exercise:** Write an error function that simulates the temperature of the milk and returns the difference between the final temperature and 20 °C.  Use it to estimate the value of `r` for the milk."
+    "### Exercise 2\n",
+    "\n",
+    "Write an error function that simulates the temperature of the milk and returns the difference between the final temperature and 20 °C.  Use it to estimate the value of `r` for the milk."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "agreed-excuse",
    "metadata": {},
    "outputs": [],
@@ -831,7 +815,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "defined-nudist",
    "metadata": {},
    "outputs": [],
@@ -841,7 +825,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "loose-rehabilitation",
    "metadata": {},
    "outputs": [],
@@ -875,7 +859,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap16.ipynb b/chapters/chap16.ipynb
index 81e97912..da35c2a5 100644
--- a/chapters/chap16.ipynb
+++ b/chapters/chap16.ipynb
@@ -22,23 +22,6 @@
     "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "blank-belly",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -82,7 +65,8 @@
    "id": "valuable-shannon",
    "metadata": {},
    "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap16.ipynb)"
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -135,13 +119,13 @@
    "id": "hydraulic-belfast",
    "metadata": {},
    "source": [
-    "## Mixing liquids\n",
+    "## Mixing Liquids\n",
     "\n",
     "When we mix two liquids, the temperature of the mixture depends on the\n",
     "temperatures of the ingredients as well as their volumes, densities, and specific heat capacities (as defined in the previous chapter).\n",
     "In this section I'll explain how.\n",
     "\n",
-    "Assuming there are no chemical reactions that either produce or consume heat, the total thermal energy of the system is the same before and after mixing; in other words, thermal energy is **conserved**.\n",
+    "Assuming there are no chemical reactions that either produce or consume heat, the total thermal energy of the system is the same before and after mixing; in other words, thermal energy is *conserved*.\n",
     "\n",
     "If the temperature of the first liquid is $T_1$, the temperature of the second liquid is $T_2$, and the final temperature of the mixture is $T$, the heat transfer into the first liquid is $C_1 (T - T_1)$ and the heat transfer into the second liquid is $C_2 (T - T_2)$, where $C_1$ and $C_2$ are the thermal masses of the liquids.\n",
     "\n",
@@ -214,7 +198,7 @@
    "id": "foreign-campbell",
    "metadata": {},
    "source": [
-    "## Mix first or last?\n",
+    "## Mix First or Last?\n",
     "\n",
     "First I'll create objects to represent the coffee and milk.\n",
     "For `r_coffee`, I'll use the value we computed in the previous chapter."
@@ -504,7 +488,6 @@
    "source": [
     "The first line unpacks the system variables.\n",
     "The next two lines compute `t_array`, which is a NumPy array of time stamps, and `T_array`, which is an array of the corresponding temperatures.\n",
-    "\n",
     "The last two lines store the final temperature in the `System` object and use `make_series` to return the results in a Pandas `Series`.\n",
     "\n",
     "We can run it like this:"
@@ -585,7 +568,9 @@
    "id": "foreign-permission",
    "metadata": {},
    "source": [
-    "**Exercise:** Use `compute_r` to compute `r_milk` according to the analytic solution.  Run the analysis with this value of `r_milk` and confirm that the results are consistent with the simulation."
+    "### Exercise 1\n",
+    "\n",
+    "Use `compute_r` to compute `r_milk` according to the analytic solution.  Run the analysis with this value of `r_milk` and confirm that the results are consistent with the simulation."
    ]
   },
   {
@@ -633,7 +618,9 @@
    "id": "western-mixture",
    "metadata": {},
    "source": [
-    "**Exercise:**  Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
+    "### Exercise 2\n",
+    "\n",
+    "Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
     "\n",
     "Hint: Think about the simplest way to represent the behavior of a refrigerator in this model.  The change you make to test this variation of the problem should be very small!"
    ]
@@ -666,7 +653,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap17.ipynb b/chapters/chap17.ipynb
index 9f04bf2b..2df9f137 100644
--- a/chapters/chap17.ipynb
+++ b/chapters/chap17.ipynb
@@ -65,7 +65,8 @@
    "id": "plastic-trigger",
    "metadata": {},
    "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap17.ipynb)"
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -91,15 +92,15 @@
    "source": [
     "## The Minimal Model\n",
     "\n",
-    "**Pharmacokinetics** is the study of how drugs and other substances move around the body, react, and are eliminated. In this chapter, I present a widely used pharmacokinetic models: the so-called \"minimal model\" of glucose and insulin in the blood stream.\n",
+    "*Pharmacokinetics* is the study of how drugs and other substances move around the body, react, and are eliminated. In this chapter, I present a widely used pharmacokinetic models: the so-called \"minimal model\" of glucose and insulin in the blood stream.\n",
     "\n",
-    "**Glucose** is a form of sugar that circulates in the blood of animals; it is used as fuel for muscles, the brain, and other organs. The concentration of blood sugar is controlled by the hormone system, and especially by **insulin**, which is produced by the pancreas and has the effect of reducing blood sugar.\n",
+    "*Glucose* is a form of sugar that circulates in the blood of animals; it is used as fuel for muscles, the brain, and other organs. The concentration of blood sugar is controlled by the hormone system, and especially by *insulin*, which is produced by the pancreas and has the effect of reducing blood sugar.\n",
     "\n",
-    "In people with normal pancreatic function, the hormone system maintains **homeostasis**; that is, it keeps the concentration of blood sugar in a range that is neither too high or too low.\n",
+    "In people with normal pancreatic function, the hormone system maintains *homeostasis*; that is, it keeps the concentration of blood sugar in a range that is neither too high or too low.\n",
     "\n",
     "But if the pancreas does not produce enough insulin, or if the cells\n",
     "that should respond to insulin become insensitive, blood sugar can\n",
-    "become elevated, a condition called **hyperglycemia**. Long term, severe hyperglycemia is the defining symptom of **diabetes mellitus**, a serious disease that affects almost 10% of the population in the U.S. (see <http://modsimpy.com/cdc>)."
+    "become elevated, a condition called *hyperglycemia*. Long term, severe hyperglycemia is the defining symptom of *diabetes mellitus*, a serious disease that affects almost 10% of the population in the U.S. (see <http://modsimpy.com/cdc>)."
    ]
   },
   {
@@ -120,7 +121,7 @@
    "id": "round-roommate",
    "metadata": {},
    "source": [
-    "## The glucose minimal model\n",
+    "## The Glucose Minimal Model\n",
     "\n",
     "The minimal model, as proposed by Bergman, Ider, Bowden, and\n",
     "Cobelli, consists of two parts: the glucose model and the insulin model. I will present an implementation of the glucose model; as a case study, you will have the chance to implement the insulin model.\n",
@@ -157,7 +158,7 @@
     "Bergman, it was \"shown to be interstitial fluid\", that is, the fluid\n",
     "that surrounds tissue cells. \n",
     "\n",
-    "In the history of mathematical modeling, it is common for hypothetical entities, added to models to achieve particular effects, to be found later to correspond to physical entities.  One notable example is [the gene] https://en.wikipedia.org/wiki/Gene#Discovery_of_discrete_inherited_units)."
+    "In the history of mathematical modeling, it is common for hypothetical entities, added to models to achieve particular effects, to be found later to correspond to physical entities.  One notable example is the gene (see <https://en.wikipedia.org/wiki/Gene#Discovery_of_discrete_inherited_units>)."
    ]
   },
   {
@@ -221,7 +222,7 @@
    "source": [
     "The initial state of the model is $X(0) = I_b$ and $G(0) = G_0$, where\n",
     "$G_0$ is a constant that represents the concentration of blood sugar\n",
-    "immediately after the injection. In theory we could estimate $G_0$ based on measurements, but in practice it takes time for the injected glucose to spread through the blood volume. Since $G_0$ is not measurable, it is treated as a **free parameter** of the model, which means that we are free to choose it to fit the data."
+    "immediately after the injection. In theory we could estimate $G_0$ based on measurements, but in practice it takes time for the injected glucose to spread through the blood volume. Since $G_0$ is not measurable, it is treated as a *free parameter* of the model, which means that we are free to choose it to fit the data."
    ]
   },
   {
@@ -231,7 +232,7 @@
    "source": [
     "## Data\n",
     "\n",
-    "To develop and test the model, we'll use data from [Pacini and Bergman](https://www.researchgate.net/publication/13707725_Insulin_sensitivity_and_glucose_effectiveness_Minimal_model_analysis_of_regular_and_insulin-modified_FSIGT), \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 1986."
+    "To develop and test the model, we'll use data from Pacini and Bergman, \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 1986 (see <https://www.researchgate.net/publication/13707725_Insulin_sensitivity_and_glucose_effectiveness_Minimal_model_analysis_of_regular_and_insulin-modified_FSIGT>)."
    ]
   },
   {
@@ -248,7 +249,9 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "mental-wrong",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "download('https://github.com/AllenDowney/ModSim/raw/main/data/' +\n",
@@ -316,7 +319,6 @@
     "Before we are ready to implement the model, there's one problem we have to solve. In the differential equations, $I$ is a function that can be evaluated at any time, $t$. But in the `DataFrame`, we only have measurements at discrete times. This is a job for interpolation!\n",
     "\n",
     "The ModSim library provides an `interpolate` function that takes a `Series` as a parameter and returns a function. That's right, I said it returns a *function*.\n",
-    "\n",
     "We can call `interpolate` like this:"
    ]
   },
@@ -353,8 +355,7 @@
    "id": "severe-desire",
    "metadata": {},
    "source": [
-    "In this example the interpolated value is about 31.7, which is a linear interpolation between the actual measurements at `t=16` and `t=19`. \n",
-    "\n",
+    "In this example the interpolated value is about 31.7, which is a linear interpolation between the actual measurements at `t=16` and `t=19`.\n",
     "We can also pass an array as an argument to `I`.  Here's an array of equally-spaced values from `t_0` to `t_end`."
    ]
   },
@@ -456,7 +457,9 @@
    "id": "hungarian-newman",
    "metadata": {},
    "source": [
-    "**Exercise:** `interpolate` is a wrapper for the SciPy function `interp1d`.\n",
+    "### Exercise 1\n",
+    "\n",
+    "`interpolate` is a wrapper for the SciPy function `interp1d`.\n",
     "Read the documentation of `interp1d` at <http://modsimpy.com/interp>.\n",
     "\n",
     "In particular, notice the `kind` argument, which specifies a kind of interpolation.\n",
@@ -494,7 +497,9 @@
    "id": "furnished-recognition",
    "metadata": {},
    "source": [
-    "**Exercise:** Interpolate the glucose data and generate a plot, similar to the previous one, that shows the data points and the interpolated curve evaluated at the time values in `t_array`."
+    "### Exercise 2\n",
+    "\n",
+    " Interpolate the glucose data and generate a plot, similar to the previous one, that shows the data points and the interpolated curve evaluated at the time values in `t_array`."
    ]
   },
   {
diff --git a/chapters/chap18.ipynb b/chapters/chap18.ipynb
index 862e549f..a40e9224 100644
--- a/chapters/chap18.ipynb
+++ b/chapters/chap18.ipynb
@@ -22,23 +22,6 @@
     "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "electoral-turkey",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -82,7 +65,8 @@
    "id": "plastic-trigger",
    "metadata": {},
    "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap18.ipynb)"
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -270,7 +254,6 @@
     "$$dX = \\left[ k_3 \\left[I(t) - I_b \\right] - k_2 X(t) \\right] dt$$ \n",
     "\n",
     "If we think of $dt$ as a small step in time, these equations tell us how to compute the corresponding changes in $G$ and $X$.\n",
-    "\n",
     "Here's an update function that computes these changes:"
    ]
   },
@@ -438,7 +421,7 @@
     "With the parameters I chose, the model fits the data well except during the first few minutes after the injection.\n",
     "But we don't expect the model to do well in this regime.\n",
     "\n",
-    "The problem is that the model is **non-spatial**; that is, it does not\n",
+    "The problem is that the model is *non-spatial*; that is, it does not\n",
     "take into account different concentrations in different parts of the\n",
     "body. Instead, it assumes that the concentrations of glucose and insulin in blood, and insulin in tissue fluid, are the same throughout the body. This way of representing the body is known among experts as the \"bag of blood\" model.\n",
     "\n",
@@ -483,15 +466,14 @@
    "id": "spatial-scholar",
    "metadata": {},
    "source": [
-    "## Solving differential equations\n",
+    "## Solving Differential Equations\n",
     "\n",
     "To implement the minimal model, we rewrote the differential equations as difference equations with a finite time step, `dt`.\n",
-    "\n",
-    "When $dt$ is very small, or more precisely **infinitesimal**, the difference equations are the same as the differential equations.\n",
+    "When $dt$ is very small, or more precisely *infinitesimal*, the difference equations are the same as the differential equations.\n",
     "But in our simulations, $dt$ is 2 min, which is not very small, and definitely not infinitesimal. \n",
     "\n",
     "In effect, the simulations assume that the derivatives $dG/dt$ and $dX/dt$ are constant during each 2 min time step.\n",
-    "This method, evaluating derivatives at discrete time steps and assuming that they are constant in between, is called **Euler's method** (see <http://modsimpy.com/euler>).\n",
+    "This method, evaluating derivatives at discrete time steps and assuming that they are constant in between, is called *Euler's method* (see <http://modsimpy.com/euler>).\n",
     "\n",
     "Euler's method is good enough for many problems, but sometimes it is not very accurate.\n",
     "In that case, we can usually make it more accurate by decreasing the size of `dt`.\n",
@@ -675,14 +657,8 @@
    "source": [
     "The mean relative difference is about 0.65%;\n",
     "the maximum is a little more than 1%.\n",
-    "So in this example, the results from Euler's method are probably good enough for practical purposes."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "corrected-indonesia",
-   "metadata": {},
-   "source": [
+    "So in this example, the results from Euler's method are probably good enough for practical purposes.\n",
+    "\n",
     "Here are the results for `X`."
    ]
   },
@@ -751,8 +727,9 @@
    "id": "substantial-grain",
    "metadata": {},
    "source": [
-    "**Exercise:**  Our solution to the differential equations is only approximate because we used a finite step size, `dt=2` minutes.\n",
+    "### Exercise 1\n",
     "\n",
+    "Our solution to the differential equations is only approximate because we used a finite step size, `dt=2` minutes.\n",
     "If we make the step size smaller, we expect the solution to be more accurate.  Run the simulation with `dt=1` and compare the results.  What is the largest relative error between the two solutions?"
    ]
   },
@@ -812,7 +789,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap19.ipynb b/chapters/chap19.ipynb
index fc246406..c7bbc9bb 100644
--- a/chapters/chap19.ipynb
+++ b/chapters/chap19.ipynb
@@ -22,23 +22,6 @@
     "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "electoral-turkey",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -77,14 +60,6 @@
     "from modsim import *"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "plastic-trigger",
-   "metadata": {},
-   "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap19.ipynb)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "reflected-sitting",
@@ -98,7 +73,7 @@
    "id": "structured-satellite",
    "metadata": {},
    "source": [
-    "## The glucose minimal model\n",
+    "## The Glucose Minimal Model\n",
     "\n",
     "In the previous chapter we implemented the glucose minimal model using given parameters, but I didn't say where those parameters came from.\n",
     "\n",
@@ -115,8 +90,7 @@
    "id": "everyday-stopping",
    "metadata": {},
    "source": [
-    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/glucose.ipynb) or\n",
-    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/glucose.ipynb)"
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/glucose.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/glucose.ipynb>."
    ]
   },
   {
@@ -124,7 +98,7 @@
    "id": "laden-gathering",
    "metadata": {},
    "source": [
-    "## The insulin minimal model\n",
+    "## The Insulin Minimal Model\n",
     "\n",
     "Along with the glucose minimal model, Berman et al. developed an insulin minimal model, in which the concentration of insulin, $I$, is governed by this differential equation:\n",
     "\n",
@@ -169,8 +143,7 @@
    "id": "sustainable-breath",
    "metadata": {},
    "source": [
-    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/insulin.ipynb) or\n",
-    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/insulin.ipynb)"
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/insulin.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/insulin.ipynb>."
    ]
   },
   {
@@ -178,9 +151,9 @@
    "id": "hybrid-vienna",
    "metadata": {},
    "source": [
-    "## Low-Pass Filter\n",
+    "## Low-pass Filter\n",
     "\n",
-    "The following circuit diagram (from [Wikipedia](https://commons.wikimedia.org/wiki/File:1st_Order_Lowpass_Filter_RC.svg)) shows a low-pass filter built with one resistor and one capacitor.\n",
+    "The following circuit diagram (from <https://commons.wikimedia.org/wiki/File:1st_Order_Lowpass_Filter_RC.svg>) shows a low-pass filter built with one resistor and one capacitor.\n",
     "\n",
     "![Circuit diagram of a low-pass filter](https://github.com/AllenDowney/ModSim/raw/main/figs/Lowpass_Filter_RC.png)\n",
     "\n",
@@ -245,8 +218,7 @@
    "id": "twenty-sight",
    "metadata": {},
    "source": [
-    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/filter.ipynb) or\n",
-    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/filter.ipynb)"
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/filter.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/filter.ipynb>."
    ]
   },
   {
@@ -254,7 +226,7 @@
    "id": "violent-directive",
    "metadata": {},
    "source": [
-    "## Thermal behavior of a wall\n",
+    "## Thermal Behavior of a Wall\n",
     "\n",
     "This case study is based on a paper that models the thermal behavior of a brick wall with the goal of understanding the \"performance gap between the expected energy use of buildings and their measured energy use\".\n",
     "\n",
@@ -296,8 +268,7 @@
    "id": "wireless-advocacy",
    "metadata": {},
    "source": [
-    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/owall.ipynb) or\n",
-    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/owall.ipynb)"
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/owall.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/owall.ipynb>."
    ]
   },
   {
@@ -326,8 +297,7 @@
    "id": "narrow-voice",
    "metadata": {},
    "source": [
-    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/hiv_model.ipynb) or\n",
-    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/hiv_model.ipynb)"
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/hiv_model.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/hiv_model.ipynb>."
    ]
   },
   {
diff --git a/chapters/chap20.ipynb b/chapters/chap20.ipynb
index 172ad017..7d994520 100644
--- a/chapters/chap20.ipynb
+++ b/chapters/chap20.ipynb
@@ -106,7 +106,7 @@
    "id": "isolated-louis",
    "metadata": {},
    "source": [
-    "## Newton's second law of motion\n",
+    "## Newton's Second Law of Motion\n",
     "\n",
     "First order differential equations (DEs) can be written \n",
     "\n",
@@ -175,7 +175,7 @@
    "id": "coordinate-three",
    "metadata": {},
    "source": [
-    "## Dropping pennies\n",
+    "## Dropping Pennies\n",
     "\n",
     "As a first example, let's get back to the penny falling from the Empire State Building, which we considered in Chapter 1. We will implement two models of this system: first without air resistance, then with.\n",
     "\n",
@@ -401,7 +401,7 @@
    "source": [
     "## Events\n",
     "\n",
-    "As an option, `run_solve_ivp` can take an **event function**, which\n",
+    "As an option, `run_solve_ivp` can take an *event function*, which\n",
     "detects an \"event\", like the penny hitting the sidewalk, and ends the\n",
     "simulation.\n",
     "\n",
@@ -503,7 +503,9 @@
    "source": [
     "### Exercises\n",
     "\n",
-    "**Exercise:** Here's a question from the web site [Ask an Astronomer](http://curious.astro.cornell.edu/about-us/39-our-solar-system/the-earth/other-catastrophes/57-how-long-would-it-take-the-earth-to-fall-into-the-sun-intermediate):\n",
+    "### Exercise\n",
+    "\n",
+    " Here's a question from the web site [Ask an Astronomer](http://curious.astro.cornell.edu/about-us/39-our-solar-system/the-earth/other-catastrophes/57-how-long-would-it-take-the-earth-to-fall-into-the-sun-intermediate):\n",
     "\n",
     "\"If the Earth suddenly stopped orbiting the Sun, I know eventually it would be pulled in by the Sun's gravity and hit it. How long would it take the Earth to hit the Sun? I imagine it would go slowly at first and then pick up speed.\"\n",
     "\n",
diff --git a/chapters/chap21.ipynb b/chapters/chap21.ipynb
index bf0da871..6998bb46 100644
--- a/chapters/chap21.ipynb
+++ b/chapters/chap21.ipynb
@@ -86,13 +86,13 @@
    "id": "interesting-freeware",
    "metadata": {},
    "source": [
-    "## Drag force\n",
+    "## Drag Force\n",
     "\n",
     "As an object moves through a fluid, like air, the object applies force\n",
     "to the air and, in accordance with Newton's third law of motion, the air applies an equal and opposite force to the object (see\n",
     "<http://modsimpy.com/newton>).\n",
     "\n",
-    "The direction of this **drag force** is opposite the direction of\n",
+    "The direction of this *drag force* is opposite the direction of\n",
     "travel, and its magnitude is given by the drag equation (see\n",
     "<http://modsimpy.com/drageq>): \n",
     "\n",
@@ -106,11 +106,11 @@
     "\n",
     "-   $v$ is the magnitude of velocity in m/s.\n",
     "\n",
-    "-   $A$ is the **reference area** of the object, in m$^2$. In this\n",
+    "-   $A$ is the *reference area* of the object, in m$^2$. In this\n",
     "    context, the reference area is the projected frontal area, that is, the visible area of the object as seen from a point on its line of\n",
     "    travel (and far away).\n",
     "\n",
-    "-   $C_d$ is the **drag coefficient**, a dimensionless quantity that\n",
+    "-   $C_d$ is the *drag coefficient*, a dimensionless quantity that\n",
     "    depends on the shape of the object (including length but not frontal area), its surface properties, and how it interacts with the fluid."
    ]
   },
@@ -128,7 +128,7 @@
     "Of course, the drag equation is itself a model, based on the assumption that $C_d$ does not depend on the other terms in the equation: density, velocity, and area. For objects moving in air at moderate speeds (below 45 mph or 20 m/s), this model might be good enough, but we should remember to revisit this assumption.\n",
     "\n",
     "For the falling penny, we can use measurements to estimate $C_d$. In\n",
-    "particular, we can measure **terminal velocity**, $v_{term}$, which is\n",
+    "particular, we can measure *terminal velocity*, $v_{term}$, which is\n",
     "the speed where drag force equals force due to gravity:\n",
     "\n",
     "$$\\frac{1}{2}~\\rho~v_{term}^2~C_d~A = m g$$ \n",
@@ -537,7 +537,9 @@
    "id": "respective-address",
    "metadata": {},
    "source": [
-    "**Exercise:** Run the simulation with a downward initial velocity that exceeds the penny's terminal velocity.\n",
+    "### Exercise\n",
+    "\n",
+    " Run the simulation with a downward initial velocity that exceeds the penny's terminal velocity.\n",
     "\n",
     "What do you expect to happen?  Plot velocity and position as a function of time, and see if they are consistent with your prediction.\n",
     "\n",
@@ -601,7 +603,9 @@
    "id": "smaller-millennium",
    "metadata": {},
    "source": [
-    "**Exercise:** Suppose we drop a quarter from the Empire State Building and find that its flight time is 19.1 seconds.  Use this measurement to estimate terminal velocity and coefficient of drag.\n",
+    "### Exercise\n",
+    "\n",
+    " Suppose we drop a quarter from the Empire State Building and find that its flight time is 19.1 seconds.  Use this measurement to estimate terminal velocity and coefficient of drag.\n",
     "\n",
     "You can get the relevant dimensions of a quarter from https://en.wikipedia.org/wiki/Quarter_(United_States_coin).\n",
     "\n",
diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
index e2c8ce41..619c0c5d 100644
--- a/chapters/chap22.ipynb
+++ b/chapters/chap22.ipynb
@@ -134,9 +134,9 @@
     "## Vectors\n",
     "\n",
     "Now that we are working in two dimensions, it will be useful to\n",
-    "work with **vector quantities**, that is, quantities that represent both a magnitude and a direction. We will use vectors to represent positions, velocities, accelerations, and forces in two and three dimensions.\n",
+    "work with *vector quantities*, that is, quantities that represent both a magnitude and a direction. We will use vectors to represent positions, velocities, accelerations, and forces in two and three dimensions.\n",
     "\n",
-    "ModSim provides a function called `Vector` that creates a Pandas `Series` that contains the **components** of the vector.\n",
+    "ModSim provides a function called `Vector` that creates a Pandas `Series` that contains the *components* of the vector.\n",
     "In a `Vector` that represents a position in space, the components are the $x$ and $y$ coordinates in 2-D, plus a $z$ coordinate if the `Vector` is in 3-D.\n",
     "\n",
     "You can create a `Vector` by specifying its components. The following\n",
@@ -231,9 +231,9 @@
     "We can specify a `Vector` with coordinates `x` and `y`, as in the previous examples.\n",
     "Equivalently, we can specify a `Vector` with a magnitude and angle.\n",
     "\n",
-    "**Magnitude** is the length of the vector: if the `Vector` represents a position, magnitude is its distance from the origin; if it represents a velocity, magnitude is its speed.\n",
+    "*Magnitude* is the length of the vector: if the `Vector` represents a position, magnitude is its distance from the origin; if it represents a velocity, magnitude is its speed.\n",
     "\n",
-    "The **angle** of a `Vector` is its direction, expressed as an angle in radians from the positive $x$ axis. In the Cartesian plane, the angle 0 rad is due east, and the angle $\\pi$ rad is due west.\n",
+    "The *angle* of a `Vector` is its direction, expressed as an angle in radians from the positive $x$ axis. In the Cartesian plane, the angle 0 rad is due east, and the angle $\\pi$ rad is due west.\n",
     "\n",
     "ModSim provides functions to compute the magnitude and angle of a `Vector`.  For example, here are the magnitude and angle of `A`:"
    ]
@@ -325,7 +325,7 @@
    "id": "lucky-plastic",
    "metadata": {},
    "source": [
-    "Another way to represent the direction of `A` is a **unit vector**,\n",
+    "Another way to represent the direction of `A` is a *unit vector*,\n",
     "which is a vector with magnitude 1 that points in the same direction as\n",
     "`A`. You can compute a unit vector by dividing a vector by its\n",
     "magnitude:"
@@ -373,7 +373,7 @@
    "id": "similar-local",
    "metadata": {},
    "source": [
-    "## Simulating baseball flight\n",
+    "## Simulating Baseball Flight\n",
     "\n",
     "Let's simulate the flight of a baseball that is batted from home plate\n",
     "at an angle of 45° and initial speed 40 m/s. We'll use the center of home plate as the origin, a horizontal x-axis (parallel to the ground), and a vertical y-axis (perpendicular to the ground). The initial height is 1 m.\n",
@@ -893,7 +893,7 @@
    "id": "certified-synthetic",
    "metadata": {},
    "source": [
-    "This way of visualizing the results is called a **trajectory plot** (see <http://modsimpy.com/trajec>).\n",
+    "This way of visualizing the results is called a *trajectory plot* (see <http://modsimpy.com/trajec>).\n",
     "\n",
     "A trajectory plot can be easier to interpret than a time series plot,\n",
     "because it shows what the motion of the projectile would look like (at\n",
@@ -982,7 +982,9 @@
    "id": "pressing-retrieval",
    "metadata": {},
    "source": [
-    "**Exercise:** Run the simulation with and without air resistance.  How wrong would we be if we ignored drag?"
+    "### Exercise\n",
+    "\n",
+    " Run the simulation with and without air resistance.  How wrong would we be if we ignored drag?"
    ]
   },
   {
@@ -1042,7 +1044,9 @@
    "id": "generic-shelter",
    "metadata": {},
    "source": [
-    "**Exercise:** The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / m$^3$.  How much farther would a ball hit with the same initial speed and launch angle travel?"
+    "### Exercise\n",
+    "\n",
+    " The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / m$^3$.  How much farther would a ball hit with the same initial speed and launch angle travel?"
    ]
   },
   {
@@ -1082,7 +1086,9 @@
    "id": "shaped-paragraph",
    "metadata": {},
    "source": [
-    "**Exercise:** The model so far is based on the assumption that coefficient of drag does not depend on velocity, but in reality it does.  The following figure, from Adair, [*The Physics of Baseball*](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC), shows coefficient of drag as a function of velocity.\n",
+    "### Exercise\n",
+    "\n",
+    " The model so far is based on the assumption that coefficient of drag does not depend on velocity, but in reality it does.  The following figure, from Adair, [*The Physics of Baseball*](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC), shows coefficient of drag as a function of velocity.\n",
     "\n",
     "![Graph of drag coefficient versus velocity](https://github.com/AllenDowney/ModSimPy/raw/master/figs/baseball_drag.png)\n",
     "\n",
diff --git a/chapters/chap23.ipynb b/chapters/chap23.ipynb
index 309a3936..3dbcf551 100644
--- a/chapters/chap23.ipynb
+++ b/chapters/chap23.ipynb
@@ -135,7 +135,7 @@
    "id": "decent-birth",
    "metadata": {},
    "source": [
-    "## The Manny Ramirez problem\n",
+    "## The Manny Ramirez Problem\n",
     "\n",
     "Manny Ramirez is a former member of the Boston Red Sox (an American\n",
     "baseball team) who was notorious for his relaxed attitude and taste for practical jokes. Our objective in this chapter is to solve the following Manny-inspired problem:\n",
@@ -176,7 +176,7 @@
     "The answer we want is the minimum speed at which a ball can leave home plate and still go over the Green Monster. We'll proceed in the\n",
     "following steps:\n",
     "\n",
-    "1.  For a given speed, we'll find the optimal **launch angle**, that is, the angle the ball should leave home plate to maximize its height when it reaches the wall.\n",
+    "1.  For a given speed, we'll find the optimal *launch angle*, that is, the angle the ball should leave home plate to maximize its height when it reaches the wall.\n",
     "\n",
     "2.  Then we'll find the minimum speed that clears the wall, given\n",
     "    that it has the optimal launch angle."
@@ -187,9 +187,9 @@
    "id": "received-diamond",
    "metadata": {},
    "source": [
-    "## Finding the range\n",
+    "## Finding the Range\n",
     "\n",
-    "Suppose we want to find the launch angle that maximizes **range**, that is, the distance the ball travels in the air before landing. We'll use a function in the ModSim library, `maximize_scalar`, which takes a function and finds its maximum.\n",
+    "Suppose we want to find the launch angle that maximizes *range*, that is, the distance the ball travels in the air before landing. We'll use a function in the ModSim library, `maximize_scalar`, which takes a function and finds its maximum.\n",
     "\n",
     "The function we pass to `maximize_scalar` should take launch angle in degrees, simulate the flight of a ball launched at that angle, and return the distance the ball travels along the $x$ axis."
    ]
@@ -373,7 +373,9 @@
    "id": "handmade-rhythm",
    "metadata": {},
    "source": [
-    "**Exercise:** Let's finish off the Manny Ramirez problem:\n",
+    "### Exercise\n",
+    "\n",
+    " Let's finish off the Manny Ramirez problem:\n",
     "\n",
     "> What is the minimum effort required to hit a home run in Fenway Park?\n",
     "\n",
diff --git a/chapters/chap24.ipynb b/chapters/chap24.ipynb
index b1d88682..916ebe70 100644
--- a/chapters/chap24.ipynb
+++ b/chapters/chap24.ipynb
@@ -102,7 +102,7 @@
    "id": "integral-welsh",
    "metadata": {},
    "source": [
-    "## The physics of toilet paper\n",
+    "## The Physics of Toilet Paper\n",
     "\n",
     "As an example of a system with rotation, we'll simulate the manufacture of a roll of toilet paper, as shown in this video <https://youtu.be/Z74OfpUbeac?t=231>. \n",
     "Starting with a cardboard tube at the center, we will roll up 47 m of paper, a typical length for a roll of toilet paper in the U.S. (see <http://modsimpy.com/paper>).\n",
@@ -146,7 +146,7 @@
     "\n",
     "$$\\frac{d\\theta}{dt} = \\omega$$ \n",
     "\n",
-    "This rate of change is called an **angular velocity**. Now we have a system of differential equations we can use to simulate the system."
+    "This rate of change is called an *angular velocity*. Now we have a system of differential equations we can use to simulate the system."
    ]
   },
   {
@@ -732,7 +732,9 @@
    "source": [
     "## Exercises\n",
     "\n",
-    "**Exercise:** Since we keep `omega` constant, the linear velocity of the paper increases with radius.  We can use `gradient` to estimate the derivative of `results.y`."
+    "### Exercise\n",
+    "\n",
+    " Since we keep `omega` constant, the linear velocity of the paper increases with radius.  We can use `gradient` to estimate the derivative of `results.y`."
    ]
   },
   {
diff --git a/chapters/chap25.ipynb b/chapters/chap25.ipynb
index 8e307091..b34fb451 100644
--- a/chapters/chap25.ipynb
+++ b/chapters/chap25.ipynb
@@ -83,13 +83,13 @@
    "id": "earned-motorcycle",
    "metadata": {},
    "source": [
-    "## Angular acceleration\n",
+    "## Angular Acceleration\n",
     "\n",
     "Just as linear acceleration is the derivative of velocity, **angular\n",
-    "acceleration** is the derivative of angular velocity. And just as linear acceleration is caused by force, angular acceleration is caused by the rotational version of force, **torque**. If you are not familiar with torque, you can read about it at <http://modsimpy.com/torque>.\n",
+    "acceleration* is the derivative of angular velocity. And just as linear acceleration is caused by force, angular acceleration is caused by the rotational version of force, *torque**. If you are not familiar with torque, you can read about it at <http://modsimpy.com/torque>.\n",
     "\n",
     "In general, torque is a vector quantity, defined as the **cross\n",
-    "product** of $\\vec{r}$ and $\\vec{F}$, where $\\vec{r}$ is the **lever\n",
+    "product* of $\\vec{r}$ and $\\vec{F}$, where $\\vec{r}$ is the *lever\n",
     "arm**, a vector from the center of rotation to the point where the force is applied, and $\\vec{F}$ is the vector that represents the magnitude and direction of the force."
    ]
   },
@@ -112,13 +112,13 @@
    "id": "infrared-summit",
    "metadata": {},
    "source": [
-    "## Moment of inertia\n",
+    "## Moment of Inertia\n",
     "\n",
     "In the same way that linear acceleration is related to force by Newton's second law of motion, $F=ma$, angular acceleration is related to torque by another form of Newton's law: \n",
     "\n",
     "$$\\tau = I \\alpha$$ \n",
     "\n",
-    "where $\\alpha$ is angular acceleration and $I$ is **moment of inertia**. Just as mass is what makes it hard to accelerate an object, moment of inertia is what makes it hard to spin an object.\n",
+    "where $\\alpha$ is angular acceleration and $I$ is *moment of inertia*. Just as mass is what makes it hard to accelerate an object, moment of inertia is what makes it hard to spin an object.\n",
     "\n",
     "In the most general case, a 3-D object rotating around an arbitrary\n",
     "axis, moment of inertia is a tensor, which is a function that takes a\n",
@@ -140,7 +140,7 @@
    "id": "julian-klein",
    "metadata": {},
    "source": [
-    "## Teapots and turntables\n",
+    "## Teapots and Turntables\n",
     "\n",
     "Tables in Chinese restaurants often have a rotating tray or turntable\n",
     "that makes it easy for customers to share dishes. These turntables are\n",
@@ -933,7 +933,9 @@
    "id": "sixth-breach",
    "metadata": {},
    "source": [
-    "**Exercise:** Continuing the example from this chapter, estimate the force that delivers the teapot to the desired position.\n",
+    "### Exercise\n",
+    "\n",
+    " Continuing the example from this chapter, estimate the force that delivers the teapot to the desired position.\n",
     "Use this `System` object, with the friction we computed in the previous section."
    ]
   },
diff --git a/chapters/chap26.ipynb b/chapters/chap26.ipynb
index 091fb691..1ab7ef95 100644
--- a/chapters/chap26.ipynb
+++ b/chapters/chap26.ipynb
@@ -38,7 +38,7 @@
    "id": "capital-vatican",
    "metadata": {},
    "source": [
-    "## Bungee jumping\n",
+    "## Bungee Jumping\n",
     "\n",
     "Suppose you want to set the world record for the highest \"bungee dunk\", which is a stunt in which a bungee jumper dunks a cookie in a cup of tea at the lowest point of a jump. An example is shown in this video: <http://modsimpy.com/dunk>.\n",
     "\n",
@@ -76,7 +76,7 @@
    "id": "recognized-bidding",
    "metadata": {},
    "source": [
-    "## Bungee dunk revisited\n",
+    "## Bungee Dunk Revisited\n",
     "\n",
     "In the previous case study, we assume that the cord applies no force to\n",
     "the jumper until it is stretched. It is tempting to say that the cord\n",
@@ -138,7 +138,7 @@
    "id": "victorian-colonial",
    "metadata": {},
    "source": [
-    "## Spider-Man\n",
+    "## Spider-man\n",
     "\n",
     "In this case study we'll develop a model of Spider-Man swinging from a\n",
     "springy cable of webbing attached to the top of the Empire State\n",
@@ -242,7 +242,7 @@
    "id": "future-burlington",
    "metadata": {},
    "source": [
-    "## Simulating a yo-yo\n",
+    "## Simulating a Yo-yo\n",
     "\n",
     "Suppose you are holding a yo-yo with a length of string wound around its axle, and you drop it while holding the end of the string stationary. As gravity accelerates the yo-yo downward, tension in the string exerts a force upward. Since this force acts on a point offset from the center of mass, it exerts a torque that causes the yo-yo to spin.\n",
     "\n",

From bd7af7c86ccc993c287c469584ace6a2df792ae7 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 17 May 2021 14:37:52 -0400
Subject: [PATCH 073/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 230688 -> 231584 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
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From cc14dbb22c8c11ab3f25477d04575709a955812a Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 17 May 2021 14:57:16 -0400
Subject: [PATCH 074/144] Updating chapters

---
 chapters/chap03.py | 43 +++++++++++++++++++++++++++++
 chapters/chap06.py | 12 ++++++++
 chapters/chap11.py | 43 +++++++++++++++++++++++++++++
 chapters/chap12.py |  7 +++++
 chapters/chap13.py | 29 +++++++++++++++++++
 chapters/chap15.py | 35 +++++++++++++++++++++++
 chapters/chap18.py | 30 ++++++++++++++++++++
 chapters/chap22.py | 69 ++++++++++++++++++++++++++++++++++++++++++++++
 8 files changed, 268 insertions(+)
 create mode 100644 chapters/chap03.py
 create mode 100644 chapters/chap06.py
 create mode 100644 chapters/chap11.py
 create mode 100644 chapters/chap12.py
 create mode 100644 chapters/chap13.py
 create mode 100644 chapters/chap15.py
 create mode 100644 chapters/chap18.py
 create mode 100644 chapters/chap22.py

diff --git a/chapters/chap03.py b/chapters/chap03.py
new file mode 100644
index 00000000..5b6f1626
--- /dev/null
+++ b/chapters/chap03.py
@@ -0,0 +1,43 @@
+from modsim import *
+
+def step(state, p1, p2):
+    """Simulate one time step.
+    
+    state: bikeshare State object
+    p1: probability of an Olin->Wellesley ride
+    p2: probability of a Wellesley->Olin ride
+    """
+    if flip(p1):
+        bike_to_wellesley(state)
+    
+    if flip(p2):
+        bike_to_olin(state)
+
+from modsim import *
+
+def bike_to_olin(state):
+    """Move one bike from Wellesley to Olin.
+    
+    state: bikeshare State object
+    """
+    if state.wellesley == 0:
+        state.wellesley_empty += 1
+        return
+    state.wellesley -= 1
+    state.olin += 1
+
+from modsim import *
+
+# Solution
+
+def bike_to_wellesley(state):
+    """Move one bike from Olin to Wellesley.
+    
+    state: bikeshare State object
+    """
+    if state.olin == 0:
+        state.olin_empty += 1
+        return
+    state.olin -= 1
+    state.wellesley += 1
+
diff --git a/chapters/chap06.py b/chapters/chap06.py
new file mode 100644
index 00000000..f18bcbe4
--- /dev/null
+++ b/chapters/chap06.py
@@ -0,0 +1,12 @@
+from modsim import *
+
+def run_simulation(system, growth_func):
+    results = TimeSeries()
+    results[system.t_0] = system.p_0
+    
+    for t in range(system.t_0, system.t_end):
+        growth = growth_func(t, results[t], system)
+        results[t+1] = results[t] + growth
+        
+    return results
+
diff --git a/chapters/chap11.py b/chapters/chap11.py
new file mode 100644
index 00000000..d8569569
--- /dev/null
+++ b/chapters/chap11.py
@@ -0,0 +1,43 @@
+from modsim import *
+
+def make_system(beta, gamma):
+    init = State(s=89, i=1, r=0)
+    init /= init.sum()
+
+    return System(init=init, t_end=7*14,
+                  beta=beta, gamma=gamma)
+
+from modsim import *
+
+def update_func(t, state, system):
+    s, i, r = state.s, state.i, state.r
+
+    infected = system.beta * i * s    
+    recovered = system.gamma * i
+    
+    s -= infected
+    i += infected - recovered
+    r += recovered
+    
+    return State(s=s, i=i, r=r)
+
+from modsim import *
+
+def plot_results(S, I, R):
+    S.plot(style='--', label='Susceptible')
+    I.plot(style='-', label='Infected')
+    R.plot(style=':', label='Resistant')
+    decorate(xlabel='Time (days)',
+             ylabel='Fraction of population')
+
+from modsim import *
+
+def run_simulation(system, update_func):
+    frame = TimeFrame(columns=system.init.index)
+    frame.loc[0] = system.init
+    
+    for t in range(0, system.t_end):
+        frame.loc[t+1] = update_func(t, frame.loc[t], system)
+    
+    return frame
+
diff --git a/chapters/chap12.py b/chapters/chap12.py
new file mode 100644
index 00000000..7cfc3909
--- /dev/null
+++ b/chapters/chap12.py
@@ -0,0 +1,7 @@
+from modsim import *
+
+def calc_total_infected(results, system):
+    s_0 = results.s[0]
+    s_end = results.s[system.t_end]
+    return s_0 - s_end
+
diff --git a/chapters/chap13.py b/chapters/chap13.py
new file mode 100644
index 00000000..fd5aa080
--- /dev/null
+++ b/chapters/chap13.py
@@ -0,0 +1,29 @@
+from modsim import *
+
+# import code from previous notebooks
+
+from chap11 import make_system
+from chap11 import update_func
+from chap11 import run_simulation
+from chap11 import plot_results
+
+from chap12 import calc_total_infected
+
+from modsim import *
+
+def sweep_beta(beta_array, gamma):
+    sweep = SweepSeries()
+    for beta in beta_array:
+        system = make_system(beta, gamma)
+        results = run_simulation(system, update_func)
+        sweep[beta] = calc_total_infected(results, system)
+    return sweep
+
+from modsim import *
+
+def sweep_parameters(beta_array, gamma_array):
+    frame = SweepFrame(columns=gamma_array)
+    for gamma in gamma_array:
+        frame[gamma] = sweep_beta(beta_array, gamma)
+    return frame
+
diff --git a/chapters/chap15.py b/chapters/chap15.py
new file mode 100644
index 00000000..f439ced2
--- /dev/null
+++ b/chapters/chap15.py
@@ -0,0 +1,35 @@
+from modsim import *
+
+def make_system(T_init, volume, r, t_end):
+    return System(T_init=T_init,
+                  T_final=T_init,
+                  volume=volume,
+                  r=r,
+                  t_end=t_end,
+                  T_env=22,
+                  t_0=0,
+                  dt=1)
+
+from modsim import *
+
+def change_func(t, T, system):
+    r, T_env, dt = system.r, system.T_env, system.dt    
+    return -r * (T - T_env) * dt
+
+from modsim import *
+
+def run_simulation(system, change_func):
+    t_array = linrange(system.t_0, system.t_end, system.dt)
+    n = len(t_array)
+    
+    series = TimeSeries(index=t_array)
+    series.iloc[0] = system.T_init
+    
+    for i in range(n-1):
+        t = t_array[i]
+        T = series.iloc[i]
+        series.iloc[i+1] = T + change_func(t, T, system)
+    
+    system.T_final = series.iloc[-1]
+    return series
+
diff --git a/chapters/chap18.py b/chapters/chap18.py
new file mode 100644
index 00000000..7e3db625
--- /dev/null
+++ b/chapters/chap18.py
@@ -0,0 +1,30 @@
+from modsim import *
+
+def make_system(params, data):
+    G0, k1, k2, k3 = params
+    
+    t_0 = data.index[0]
+    t_end = data.index[-1]
+    
+    Gb = data.glucose[t_0]
+    Ib = data.insulin[t_0]
+    I = interpolate(data.insulin)
+    
+    init = State(G=G0, X=0)
+    
+    return System(init=init, params=params,
+                  Gb=Gb, Ib=Ib, I=I,
+                  t_0=t_0, t_end=t_end, dt=2)
+
+from modsim import *
+
+def slope_func(t, state, system):
+    G, X = state
+    G0, k1, k2, k3 = system.params 
+    I, Ib, Gb = system.I, system.Ib, system.Gb
+        
+    dGdt = -k1 * (G - Gb) - X*G
+    dXdt = k3 * (I(t) - Ib) - k2 * X
+    
+    return dGdt, dXdt
+
diff --git a/chapters/chap22.py b/chapters/chap22.py
new file mode 100644
index 00000000..00fe5be2
--- /dev/null
+++ b/chapters/chap22.py
@@ -0,0 +1,69 @@
+from modsim import *
+
+params = Params(
+    x = 0,          # m
+    y = 1,          # m
+    angle = 45,     # degree
+    speed = 40,  # m / s
+
+    mass = 145e-3,    # kg 
+    diameter = 73e-3, # m 
+    C_d = 0.33,       # dimensionless
+
+    rho = 1.2,      # kg/m**3
+    g = 9.8,        # m/s**2
+    t_end = 10,     # s
+)
+
+from modsim import *
+
+from numpy import pi, deg2rad
+
+def make_system(params):
+    
+    # convert angle to degrees
+    theta = deg2rad(params.angle)
+    
+    # compute x and y components of velocity
+    vx, vy = pol2cart(theta, params.speed)
+    
+    # make the initial state
+    init = State(x=params.x, y=params.y, vx=vx, vy=vy)
+    
+    # compute the frontal area
+    area = pi * (params.diameter/2)**2
+
+    return System(params,
+                  init = init,
+                  area = area)
+
+from modsim import *
+
+def drag_force(V, system):
+    rho, C_d, area = system.rho, system.C_d, system.area
+    
+    mag = rho * vector_mag(V)**2 * C_d * area / 2
+    direction = -vector_hat(V)
+    f_drag = mag * direction
+    return f_drag
+
+from modsim import *
+
+def slope_func(t, state, system):
+    x, y, vx, vy = state
+    mass, g = system.mass, system.g
+    
+    V = Vector(vx, vy)
+    a_drag = drag_force(V, system) / mass
+    a_grav = g * Vector(0, -1)
+    
+    A = a_grav + a_drag
+    
+    return V.x, V.y, A.x, A.y
+
+from modsim import *
+
+def event_func(t, state, system):
+    x, y, vx, vy = state
+    return y
+

From bd358b0ec30f08c8be1b034382aac4f1640be4e4 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Wed, 23 Jun 2021 14:51:49 -0400
Subject: [PATCH 075/144] Adding secant figure

---
 figs/secant.fig |  19 +++++++++++++++++++
 figs/secant.png | Bin 0 -> 289 bytes
 2 files changed, 19 insertions(+)
 create mode 100644 figs/secant.fig
 create mode 100644 figs/secant.png

diff --git a/figs/secant.fig b/figs/secant.fig
new file mode 100644
index 00000000..2c9f8000
--- /dev/null
+++ b/figs/secant.fig
@@ -0,0 +1,19 @@
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+4 0 0 50 0 16 14 0.0000 4 165 450 4425 5400 f(x2)\001
+4 0 0 50 0 16 14 0.0000 4 165 450 1875 3225 f(x1)\001
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From a1128e83039a8210f9078e512d28c6bf944c776a Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Wed, 23 Jun 2021 14:53:04 -0400
Subject: [PATCH 076/144] Adding figures

---
 figs/golden1.fig |  23 +++++++++++++++++++++++
 figs/golden1.png | Bin 0 -> 296 bytes
 figs/golden2.fig |  45 +++++++++++++++++++++++++++++++++++++++++++++
 figs/golden2.png | Bin 0 -> 657 bytes
 4 files changed, 68 insertions(+)
 create mode 100644 figs/golden1.fig
 create mode 100644 figs/golden1.png
 create mode 100644 figs/golden2.fig
 create mode 100644 figs/golden2.png

diff --git a/figs/golden1.fig b/figs/golden1.fig
new file mode 100644
index 00000000..ef10ce9f
--- /dev/null
+++ b/figs/golden1.fig
@@ -0,0 +1,23 @@
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HcmV?d00001


From dc7ebd6772c4564106001c5cee4cba418aca3c86 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Wed, 14 Jul 2021 09:43:18 -0400
Subject: [PATCH 077/144] Updating chapters

---
 chapters/chap01.ipynb |   2 +-
 chapters/chap09.ipynb |  18 +-
 chapters/chap12.ipynb |   2 +-
 chapters/chap14.ipynb |   6 +-
 chapters/chap15.ipynb | 138 ++++++------
 chapters/chap16.ipynb |  39 +++-
 chapters/chap17.ipynb |  81 ++++---
 chapters/chap18.ipynb |  37 +--
 chapters/chap19.ipynb |  87 +++----
 chapters/chap20.ipynb | 109 +++++----
 chapters/chap21.ipynb |  97 ++++----
 chapters/chap22.ipynb | 219 +++++++++++-------
 chapters/chap23.ipynb |  30 +--
 chapters/chap24.ipynb | 104 +++++----
 chapters/chap25.ipynb |  12 +-
 chapters/chap26.ipynb |  92 +++-----
 chapters/chap27.ipynb | 512 ++++++++++++++++++++++++++++++++++++++++++
 17 files changed, 1083 insertions(+), 502 deletions(-)
 create mode 100644 chapters/chap27.ipynb

diff --git a/chapters/chap01.ipynb b/chapters/chap01.ipynb
index ac87c061..2f358775 100644
--- a/chapters/chap01.ipynb
+++ b/chapters/chap01.ipynb
@@ -1183,7 +1183,7 @@
    "source": [
     "### Exercise 8\n",
     "\n",
-    "Suppose I run a 10K race in 44:52 (44 minutes and 52 seconds).  What is my average page in minutes per mile?"
+    "Suppose I run a 10K race in 44:52 (44 minutes and 52 seconds).  What is my average pace in minutes per mile?"
    ]
   },
   {
diff --git a/chapters/chap09.ipynb b/chapters/chap09.ipynb
index 6a4b6a95..3c5a7249 100644
--- a/chapters/chap09.ipynb
+++ b/chapters/chap09.ipynb
@@ -92,7 +92,7 @@
    "id": "hybrid-retrieval",
    "metadata": {},
    "source": [
-    "## Recurrence Relations\n",
+    "## Difference Equations\n",
     "\n",
     "The population models in the previous chapter and this one are simple\n",
     "enough that we didn't really need to run simulations. We could have\n",
@@ -110,13 +110,11 @@
     "where $x_n$ is the population during year $n$, \n",
     "$x_{n+1}$ is the population during year $n+1$,\n",
     "and $c$ is constant annual growth.\n",
-    "This way of representing the model, where future population is a function of current population, is a *recurrence relation*; see\n",
-    "<http://modsimpy.com/recur>.\n",
+    "This way of representing the model, where future population is a function of current population, is a *difference equation*; see\n",
+    "<https://en.wikipedia.org/wiki/Linear_difference_equation>.\n",
     "\n",
-    "Sometimes it is possible to solve a recurrence relation by writing an\n",
-    "equation that computes $x_n$, for a given value of $n$, directly; that\n",
-    "is, without computing the intervening values from $x_1$ through\n",
-    "$x_{n-1}$."
+    "Sometimes it is possible to compute $x_n$, for a given value of $n$, directly; that\n",
+    "is, without computing the intervening values from $x_1$ through $x_{n-1}$."
    ]
   },
   {
@@ -130,9 +128,7 @@
     "\n",
     "$$x_n = x_0 + nc$$ \n",
     "\n",
-    "So if we want to know $x_{100}$ and we don't care\n",
-    "about the other values, we can compute it with one multiplication and\n",
-    "one addition."
+    "So if we want to know $x_{100}$ and we don't care about the other values, we can compute it with one multiplication and one addition."
    ]
   },
   {
@@ -140,7 +136,7 @@
    "id": "sufficient-tampa",
    "metadata": {},
    "source": [
-    "We can also write the proportional model as a recurrence relation:\n",
+    "We can also write the proportional model as a difference equation:\n",
     "\n",
     "$$x_{n+1} = x_n + \\alpha x_n$$ \n",
     "\n",
diff --git a/chapters/chap12.ipynb b/chapters/chap12.ipynb
index 913a1c29..61be3d58 100644
--- a/chapters/chap12.ipynb
+++ b/chapters/chap12.ipynb
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "surrounded-niagara",
+   "id": "parallel-pipeline",
    "metadata": {},
    "source": [
     "And here are the results from the two simulations."
diff --git a/chapters/chap14.ipynb b/chapters/chap14.ipynb
index 13c116d5..f9e4255b 100644
--- a/chapters/chap14.ipynb
+++ b/chapters/chap14.ipynb
@@ -375,7 +375,11 @@
     "\n",
     "$$q = 1 = 0 + s_{\\infty}- \\frac{1}{c} \\log s_{\\infty}$$ \n",
     "\n",
-    "Solving for $c$, we get $$c = \\frac{\\log s_{\\infty}}{s_{\\infty}- 1}$$ By relating $c$ and $s_{\\infty}$, this equation makes it possible to estimate $c$ based on data, and possibly predict the behavior of future epidemics."
+    "Solving for $c$, we get \n",
+    "\n",
+    "$$c = \\frac{\\log s_{\\infty}}{s_{\\infty}- 1}$$ \n",
+    "\n",
+    "By relating $c$ and $s_{\\infty}$, this equation makes it possible to estimate $c$ based on data, and possibly predict the behavior of future epidemics."
    ]
   },
   {
diff --git a/chapters/chap15.ipynb b/chapters/chap15.ipynb
index 7c861373..5cf0fb8b 100644
--- a/chapters/chap15.ipynb
+++ b/chapters/chap15.ipynb
@@ -5,7 +5,7 @@
    "id": "romantic-speech",
    "metadata": {},
    "source": [
-    "# Chapter 15"
+    "# Cooling Coffee"
    ]
   },
   {
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "dependent-monitoring",
    "metadata": {
     "tags": []
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "continental-decrease",
    "metadata": {
     "tags": []
@@ -62,8 +62,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "derived-exhibit",
-   "metadata": {},
+   "id": "moving-trademark",
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
     "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
@@ -89,7 +91,7 @@
     "\n",
     "Here is my version of the problem:\n",
     "\n",
-    "> Suppose I stop on the way to work to pick up a cup of coffee, which I take with milk. Assuming that I want the coffee to be as hot as possible when I arrive at work, should I add the milk at the coffee shop, wait until I get to work, or add the milk at some point in between?"
+    "> Suppose I stop on the way to work to pick up a cup of coffee and a small container of milk. Assuming that I want the coffee to be as hot as possible when I arrive at work, should I add the milk at the coffee shop, wait until I get to work, or add the milk at some point in between?"
    ]
   },
   {
@@ -138,7 +140,7 @@
     "\n",
     "$$Q = C~\\Delta T$$ \n",
     "\n",
-    "where $Q$ is the amount of heat transferred to an object, $\\Delta T$ is resulting change in temperature, and $C$ is the *thermal mass* of the object, which quantifies how much energy it takes to heat or cool it. In SI units, thermal mass is measured in joules per degree Celsius (J/°C)."
+    "where $Q$ is the amount of heat transferred to an object, $\\Delta T$ is resulting change in temperature, and $C$ is the object's *thermal mass*, which is a property of the object that determines how much energy it takes to heat or cool it. In SI units, thermal mass is measured in joules per degree Celsius (J/°C)."
    ]
   },
   {
@@ -151,7 +153,7 @@
     "\n",
     "$$C = m c_p$$ \n",
     "\n",
-    "where $m$ is the mass of the object and $c_p$ is the *specific heat capacity* of the material (see <http://modsimpy.com/specheat>).\n",
+    "where $m$ is the mass of the object and $c_p$ is the *specific heat capacity* of the material, which is the amount of thermal mass per gram (see <http://modsimpy.com/specheat>).\n",
     "\n",
     "We can use these equations to estimate the thermal mass of a cup of\n",
     "coffee. The specific heat capacity of coffee is probably close to that\n",
@@ -160,14 +162,12 @@
     "\n",
     "So when a cup of coffee cools from 90 °C to 70 °C, the change in\n",
     "temperature, $\\Delta T$ is 20 °C, which means that 25 200 J of heat\n",
-    "energy was transferred from the coffee to the surrounding environment\n",
+    "energy was transferred from the cup and the coffee to the surrounding environment\n",
     "(the cup holder and air in my car).\n",
     "\n",
     "To give you a sense of how much energy that is, if you were able to\n",
     "harness all of that heat to do work (which you cannot), you could\n",
-    "use it to lift a cup of coffee from sea level to 8571 m, just shy of the height of Mount Everest, 8848 m.\n",
-    "\n",
-    "Assuming that the cup has less mass than the coffee, and is made from a material with lower specific heat, we can ignore the thermal mass of the cup. For a cup with substantial thermal mass, like a ceramic mug, we might consider a model that computes the temperature of coffee and cup separately."
+    "use it to lift a cup of coffee from sea level to 8571 m, just shy of the height of Mount Everest, 8848 m."
    ]
   },
   {
@@ -214,11 +214,11 @@
    "source": [
     "## Newton's Law of Cooling\n",
     "\n",
-    "Newton's law of cooling asserts that the temperature rate of change for an object is proportional to the difference in temperature between the object and the surrounding environment:\n",
+    "*Newton's law of cooling* asserts that the temperature rate of change for an object is proportional to the difference in temperature between the object and the surrounding environment:\n",
     "\n",
     "$$\\frac{dT}{dt} = -r (T - T_{env})$$ \n",
     "\n",
-    "where $T$, the temperature of the object, is a function of time, $t$, $T_{env}$ is the temperature of the environment, and $r$ is a constant that characterizes how quickly heat is transferred between the system and the environment."
+    "where $t$ is time, $T$ is the temperature of the object, $T_{env}$ is the temperature of the environment, and $r$ is a constant that characterizes how quickly heat is transferred between the object and the environment."
    ]
   },
   {
@@ -239,8 +239,9 @@
     "law is not a good model at all. This is the case for objects in space or in a vacuum, and for objects at high temperatures (more than a few\n",
     "hundred degrees Celsius, say).\n",
     "\n",
-    "However, for a situation like the coffee cooling problem, we expect\n",
-    "Newton's model to be quite good."
+    "However, for a situation like the coffee cooling problem, we expect Newton's model to be quite good.\n",
+    "\n",
+    "With that, we have just one more modeling decision to make: whether to treat the coffee and the cup as separate objects or a single object. If the cup is made of paper, it has less mass than the coffee, and the specific heat capacity of paper is lower, too. In that case, it would be reasonable to treat the cup and coffee as a single object. For a cup with substantial thermal mass, like a ceramic mug, we might consider a model that computes the temperature of coffee and cup separately."
    ]
   },
   {
@@ -248,15 +249,16 @@
    "id": "selective-video",
    "metadata": {},
    "source": [
-    "## Implementation\n",
+    "## Implementing Newtonian Cooling\n",
+    "\n",
+    "To get started, we'll focus on the coffee. Then, as an exercise, you can simulate the milk. In the next chapter, we'll put them together, literally.\n",
     "\n",
-    "To get started, let's forget about the milk temporarily and focus on the coffee.\n",
     "Here's a function that takes the parameters of the system and makes a `System` object:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "incredible-greeting",
    "metadata": {
     "tags": []
@@ -285,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "fuzzy-support",
    "metadata": {},
    "outputs": [],
@@ -316,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "tight-baptist",
    "metadata": {
     "tags": []
@@ -338,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "fiscal-artwork",
    "metadata": {},
    "outputs": [],
@@ -358,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "cardiac-independence",
    "metadata": {
     "tags": []
@@ -388,7 +390,7 @@
    "source": [
     "There are a two things here that are different from previous versions of `run_simulation`.\n",
     "\n",
-    "First, we use  `linrange` to make an array of values from `t_0` to `t_end` with time step `dt`.  \n",
+    "First, we use  `linrange` to make an array of values from `t_0` to `t_end` with time step `dt`.\n",
     "`linrange` is similar to `linspace`; they both take a start value and an end value and return an array of equally spaced values.\n",
     "The difference is the third argument: `linspace` takes an integer that indicates the number of points in the range; `linrange` takes a step size that indicates the interval between values.\n",
     "When we make the `TimeSeries`, we use the keyword argument `index` to indicate that the index of the `TimeSeries` is the array of time stamps, `t_array`."
@@ -415,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "numerous-metabolism",
    "metadata": {},
    "outputs": [],
@@ -434,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "infectious-carolina",
    "metadata": {},
    "outputs": [],
@@ -452,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "accompanied-melbourne",
    "metadata": {},
    "outputs": [],
@@ -472,7 +474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "important-constitution",
    "metadata": {},
    "outputs": [],
@@ -489,12 +491,12 @@
    "id": "absent-arkansas",
    "metadata": {},
    "source": [
-    "The temperature after 30 minutes is 72.3 °C, which is a little higher than what's stated in the problem, 70 °C. "
+    "The temperature after 30 minutes is 72.3 °C, which is a little higher than the measurement we're trying to match, which is 70 °C. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "absolute-desire",
    "metadata": {},
    "outputs": [],
@@ -507,8 +509,7 @@
    "id": "light-carpet",
    "metadata": {},
    "source": [
-    "By trial and error, we could find the value of `r` where the final temperature is precisely 70 °C.\n",
-    "But it is more efficient to use a root-finding algorithm."
+    "To find the value of `r` where the final temperature is precisely 70 °C, we could proceed by by trial and error, but it is more efficient to use a root-finding algorithm."
    ]
   },
   {
@@ -530,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "small-shark",
    "metadata": {},
    "outputs": [],
@@ -549,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "defensive-content",
    "metadata": {},
    "outputs": [],
@@ -564,12 +565,12 @@
    "metadata": {},
    "source": [
     "The first argument is the function whose roots we want. The second\n",
-    "argument is an interval that contains or \"brackets\" a root. The result is an object that contains several variables, including `root`, which is the root that was found."
+    "argument is an interval that contains or *brackets* a root. The result is an object that contains several variables, including the Boolean value `converged`, which is `True` if the  search converged successfully on a root, and `root`, which is the root that was found."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "behind-perth",
    "metadata": {},
    "outputs": [],
@@ -587,7 +588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "following-sentence",
    "metadata": {},
    "outputs": [],
@@ -601,18 +602,8 @@
    "id": "eligible-updating",
    "metadata": {},
    "source": [
-    "If the interval doesn't contain a root, you'll get a `ValueError` and a message like \"f(a) and f(b) must have different signs\".\n",
+    "If the interval doesn't contain a root, you'll get a `ValueError` and a message like `f(a) and f(b) must have different signs`.\n",
     "\n",
-    "```\n",
-    "res = root_scalar(func, bracket=[4, 5])\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ahead-lawyer",
-   "metadata": {},
-   "source": [
     "Now we can use `root_scalar` to estimate `r`."
    ]
   },
@@ -621,7 +612,7 @@
    "id": "commercial-correlation",
    "metadata": {},
    "source": [
-    "## Estimating `r`\n",
+    "## Estimating r\n",
     "\n",
     "What we want is the value of `r` that yields a final temperature of\n",
     "70 °C. To use `root_scalar`, we need a function that takes `r` as a parameter and returns the difference between the final temperature and the goal:"
@@ -629,7 +620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "genetic-compound",
    "metadata": {},
    "outputs": [],
@@ -645,7 +636,7 @@
    "id": "happy-bridal",
    "metadata": {},
    "source": [
-    "This is called an \"error function\" because it returns the\n",
+    "This is called an *error function* because it returns the\n",
     "difference between what we got and what we wanted, that is, the error.\n",
     "With the right value of `r`, the error is 0.\n",
     "\n",
@@ -654,7 +645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "french-financing",
    "metadata": {},
    "outputs": [],
@@ -673,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "excellent-fellow",
    "metadata": {},
    "outputs": [],
@@ -693,13 +684,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "french-decline",
    "metadata": {},
    "outputs": [],
    "source": [
     "res = root_scalar(error_func, coffee, bracket=[0.01, 0.02])\n",
-    "res.flag"
+    "res"
    ]
   },
   {
@@ -711,12 +702,12 @@
     "The second argument is the `System` object, which `root_scalar` passes as an argument to `error_func`.\n",
     "The third argument is an interval that brackets the root.\n",
     "\n",
-    "Here are the results."
+    "Here's the root we found."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "afraid-ordering",
    "metadata": {},
    "outputs": [],
@@ -736,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "derived-annual",
    "metadata": {},
    "outputs": [],
@@ -754,12 +745,29 @@
     "The final temperature is very close to 70 °C."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "outer-powder",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This chapter presents the basics of heat, temperature, and Newton's law of cooling, which is a model that is accurate when most heat transfer is by conduction and convection, not radiation.\n",
+    "\n",
+    "To simulate a hot cup of coffee, we wrote Newton's law as a difference equation, then wrote a version of `run_simulation` that implements it. Then we used `root_scalar` to find the value of `r` that matches the measurement from my hypothetical experiment.\n",
+    "\n",
+    "All that is the first step toward solving the coffee cooling problem I posed at the beginning of the chapter. As an exercise, you'll do the next step, which is simulating the milk. In the next chapter, we'll model the mixing process and finish off the problem."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "smart-yeast",
    "metadata": {},
    "source": [
-    "## Exercises"
+    "## Exercises\n",
+    "\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -769,13 +777,13 @@
    "source": [
     "### Exercise 1\n",
     "\n",
-    "Simulate the temperature of 50 mL of milk with a starting temperature of 5 °C, in a vessel with `r=0.1`, for 15 minutes, and plot the results.\n",
+    "Simulate the temperature of 50 mL of milk with a starting temperature of 5 °C, in a vessel with `r=0.1`, for 15 minutes, and plot the results. Use `make_system` to make a `System` object that represents the milk, and use `run_simulation` to simulate it.\n",
     "By trial and error, find a value for `r` that makes the final temperature close to 20 °C."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "committed-angel",
    "metadata": {},
    "outputs": [],
@@ -785,7 +793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "military-military",
    "metadata": {},
    "outputs": [],
@@ -805,7 +813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "agreed-excuse",
    "metadata": {},
    "outputs": [],
@@ -815,7 +823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "defined-nudist",
    "metadata": {},
    "outputs": [],
@@ -825,7 +833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "id": "loose-rehabilitation",
    "metadata": {},
    "outputs": [],
diff --git a/chapters/chap16.ipynb b/chapters/chap16.ipynb
index da35c2a5..ca0b41cc 100644
--- a/chapters/chap16.ipynb
+++ b/chapters/chap16.ipynb
@@ -5,7 +5,7 @@
    "id": "medieval-johnston",
    "metadata": {},
    "source": [
-    "# Chapter 16"
+    "# Adding Milk"
    ]
   },
   {
@@ -63,7 +63,9 @@
   {
    "cell_type": "markdown",
    "id": "valuable-shannon",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
     "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
@@ -104,14 +106,14 @@
    "metadata": {},
    "source": [
     "In the previous chapter we wrote a simulation of a cooling cup of\n",
-    "coffee. Given the initial temperature of the coffee, the temperature of the atmosphere, and the rate parameter, `r`, we can predict how the\n",
-    "temperature of the coffee will change over time.\n",
-    "Then we used a root finding algorithm to estimate `r` based on data I invented for the example.\n",
+    "coffee. \n",
+    "Given the initial temperature of the coffee, the temperature of the atmosphere, and the rate parameter, `r`, we predicted the temperature of the coffee over time.\n",
+    "Then we used a root finding algorithm to estimate `r` based on data.\n",
     "\n",
     "If you did the exercises, you simulated the temperature of the milk as it warmed, and estimated its rate parameter as well.\n",
     "\n",
     "Now let's put it together.\n",
-    "In this chapter we'll write a function that simulates mixing the two liquids and use it to answer the question we started with: is it better to mix the coffee and milk at the beginning, the end, or somewhere in the middle?"
+    "In this chapter we'll write a function that simulates mixing the two liquids, and use it to answer the question we started with: is it better to mix the coffee and milk at the beginning, the end, or somewhere in the middle?"
    ]
   },
   {
@@ -148,9 +150,10 @@
     "where $V_1$ and $V_2$ are the volumes of the liquids.\n",
     "\n",
     "As an approximation, I'll assume that milk and coffee have the same\n",
-    "density and specific heat. As an exercise, you can look up these\n",
+    "density and specific heat. If you are interested, you can look up these\n",
     "quantities and see how good this assumption is.\n",
     "\n",
+    "Now let's simulate the mixing process.\n",
     "The following function takes two `System` objects, representing the\n",
     "coffee and milk, and creates a new `System` to represent the mixture:"
    ]
@@ -294,7 +297,7 @@
    "id": "linear-republican",
    "metadata": {},
    "source": [
-    "## Optimization\n",
+    "## Optimal Timing\n",
     "\n",
     "Adding the milk after 30 minutes is better than adding it immediately, but maybe there's something in between that's even better. To find out, I'll use the following function, which takes the time to add the milk, `t_add`, as a parameter:"
    ]
@@ -326,7 +329,7 @@
    "metadata": {},
    "source": [
     "`run_and_mix` simulates both systems for the given time, `t_add`.\n",
-    "Then it mixes them and simulates the mixture fir the remaining time, `t_total - t_add`.\n",
+    "Then it mixes them and simulates the mixture for the remaining time, `t_total - t_add`.\n",
     "\n",
     "When `t_add` is`0`, we add the milk immediately; when `t_add` is `30`, we add it at the end. Now we can sweep the range of values in between:"
    ]
@@ -380,7 +383,7 @@
    "id": "rough-investor",
    "metadata": {},
    "source": [
-    "## Analysis\n",
+    "## Analytic Solution\n",
     "\n",
     "Simulating Newton's law of cooling isn't really necessary because we can solve the differential equation analytically. If\n",
     "\n",
@@ -541,6 +544,17 @@
     "allclose(results, results2)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "floral-homework",
+   "metadata": {},
+   "source": [
+    "Since we can solve this problem analytically, you might wonder why we bothered writing a simulation. \n",
+    "One reason is validation: since we solved the same problem two ways, we can be more confident that the answer is correct. \n",
+    "The other reason is flexibility: now that we have a working simulation, it would be easy to add more features. For example, the temperature of the environment might change over time, or we could simulate the coffee and container as two objects. \n",
+    "If the coffee and milk are next to each other, we could include the heat flow between them. A model with these features would be difficult or impossible to solve analytically. "
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "rapid-payroll",
@@ -560,7 +574,10 @@
    "id": "gothic-clearance",
    "metadata": {},
    "source": [
-    "## Exercises"
+    "## Exercises\n",
+    "\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
diff --git a/chapters/chap17.ipynb b/chapters/chap17.ipynb
index 2df9f137..7228d016 100644
--- a/chapters/chap17.ipynb
+++ b/chapters/chap17.ipynb
@@ -5,7 +5,7 @@
    "id": "interracial-guitar",
    "metadata": {},
    "source": [
-    "# Chapter 17"
+    "# Pharmacokinetics"
    ]
   },
   {
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -63,7 +63,9 @@
   {
    "cell_type": "markdown",
    "id": "plastic-trigger",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
     "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
@@ -74,8 +76,8 @@
    "id": "european-movement",
    "metadata": {},
    "source": [
-    "In this chapter we start a new example, a model of how glucose and insulin interact to control blood sugar.  \n",
-    "We will implement a widely used model called the \"Minimal Model\" because it is intended to include only the elements essential to explain the observed behavior of the system.\n",
+    "In this chapter we'll start a new example, a model of how glucose and insulin interact to control blood sugar.\n",
+    "We will implement a widely used model called the \"minimal model\" because it is intended to include only the elements essential to explain the observed behavior of the system.\n",
     "\n",
     "This chapter presents the model and some background information we need to understand it.\n",
     "In the next chapter we'll implement the model and compare the results to real data.\n",
@@ -92,7 +94,7 @@
    "source": [
     "## The Minimal Model\n",
     "\n",
-    "*Pharmacokinetics* is the study of how drugs and other substances move around the body, react, and are eliminated. In this chapter, I present a widely used pharmacokinetic models: the so-called \"minimal model\" of glucose and insulin in the blood stream.\n",
+    "*Pharmacokinetics* is the study of how drugs and other substances move around the body, react, and are eliminated. In this chapter, I present a widely used pharmacokinetic model of glucose and insulin in the blood stream.\n",
     "\n",
     "*Glucose* is a form of sugar that circulates in the blood of animals; it is used as fuel for muscles, the brain, and other organs. The concentration of blood sugar is controlled by the hormone system, and especially by *insulin*, which is produced by the pancreas and has the effect of reducing blood sugar.\n",
     "\n",
@@ -109,8 +111,7 @@
    "metadata": {},
    "source": [
     "A widely used test for hyperglycemia and diabetes is the\n",
-    "frequently sampled intravenous glucose tolerance test (FSIGT), in which glucose is injected into the blood stream of a fasting subject (someone who has not eaten recently); then blood samples are collected at intervals of 2--10 minutes for 3 hours. The samples are analyzed to\n",
-    "measure the concentrations of glucose and insulin.\n",
+    "frequently sampled intravenous glucose tolerance test (FSIGT), in which glucose is injected into the blood stream of a fasting subject (someone who has not eaten recently); then blood samples are collected at intervals of 2--10 minutes for 3 hours. The samples are analyzed to measure the concentrations of glucose and insulin.\n",
     "\n",
     "Using these measurements, we can estimate several parameters of\n",
     "the subject's response; the most important is a parameter denoted $S_I$, which quantifies the effect of insulin on the rate of reduction in blood sugar."
@@ -142,7 +143,8 @@
     "Bergman discusses the features he and his colleagues thought were\n",
     "essential:\n",
     "\n",
-    "> (1) Glucose, once elevated by injection, returns to basal level due to two effects: the effect of glucose itself to normalize its own concentration \\[...\\] as well as the catalytic effect of insulin to allow glucose to self-normalize (2) Also, we discovered that the effect of insulin on net glucose disappearance must be sluggish --- that is, that insulin acts slowly because insulin must first move from plasma to a remote compartment \\[...\\] to exert its action on glucose disposal.\n",
+    "> 1. Glucose, once elevated by injection, returns to basal level due to two effects: the effect of glucose itself to normalize its own concentration \\[...\\] as well as the catalytic effect of insulin to allow glucose to self-normalize.\n",
+    "> 2. Also, we discovered that the effect of insulin on net glucose disappearance must be sluggish --- that is, that insulin acts slowly because insulin must first move from plasma to a remote compartment \\[...\\] to exert its action on glucose disposal.\n",
     "\n",
     "To paraphrase the second point, the effect of insulin on glucose\n",
     "disposal, as seen in the data, happens more slowly than we would expect if it depended primarily on the the concentration of insulin in the blood. Bergman's group hypothesized that insulin must move relatively slowly from the blood to a remote compartment where it has its effect."
@@ -230,7 +232,7 @@
    "id": "apart-giant",
    "metadata": {},
    "source": [
-    "## Data\n",
+    "## Getting the Data\n",
     "\n",
     "To develop and test the model, we'll use data from Pacini and Bergman, \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 1986 (see <https://www.researchgate.net/publication/13707725_Insulin_sensitivity_and_glucose_effectiveness_Minimal_model_analysis_of_regular_and_insulin-modified_FSIGT>)."
    ]
@@ -247,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "mental-wrong",
    "metadata": {
     "tags": []
@@ -270,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "cosmetic-matrix",
    "metadata": {},
    "outputs": [],
@@ -290,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "naval-geology",
    "metadata": {},
    "outputs": [],
@@ -316,7 +318,7 @@
    "source": [
     "## Interpolation\n",
     "\n",
-    "Before we are ready to implement the model, there's one problem we have to solve. In the differential equations, $I$ is a function that can be evaluated at any time, $t$. But in the `DataFrame`, we only have measurements at discrete times. This is a job for interpolation!\n",
+    "Before we are ready to implement the model, there's one problem we have to solve. In the differential equations, $I$ is a function that can be evaluated at any time, $t$. But in the `DataFrame`, we only have measurements at discrete times. The solution is interpolation, which estimates the value of $I$ for values of $t$ between the measurements.\n",
     "\n",
     "The ModSim library provides an `interpolate` function that takes a `Series` as a parameter and returns a function. That's right, I said it returns a *function*.\n",
     "We can call `interpolate` like this:"
@@ -324,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "terminal-teaching",
    "metadata": {},
    "outputs": [],
@@ -342,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "intense-thursday",
    "metadata": {},
    "outputs": [],
@@ -361,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "least-pattern",
    "metadata": {},
    "outputs": [],
@@ -381,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "mounted-venice",
    "metadata": {},
    "outputs": [],
@@ -399,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "affecting-response",
    "metadata": {},
    "outputs": [],
@@ -417,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "engaging-watershed",
    "metadata": {},
    "outputs": [],
@@ -429,6 +431,14 @@
     "         ylabel='Concentration ($\\mu$U/mL)')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "blond-prince",
+   "metadata": {},
+   "source": [
+    "Linear interpolation connects the dots with straight lines, and for this dataset that's probably good enough. As an exercise, below, you can try out other kinds of interpolation."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "adopted-locking",
@@ -449,7 +459,10 @@
    "id": "floppy-store",
    "metadata": {},
    "source": [
-    "## Exercises"
+    "## Exercises\n",
+    "\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -470,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "endless-network",
    "metadata": {},
    "outputs": [],
@@ -480,11 +493,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "entire-concern",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
+    "# Here's the plotting code again.\n",
+    "\n",
     "data.insulin.plot(style='o', color='C2', label='insulin data')\n",
     "I_series.plot(color='C2', label='interpolation')\n",
     "\n",
@@ -492,6 +509,16 @@
     "         ylabel='Concentration ($\\mu$U/mL)')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "invisible-activation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "furnished-recognition",
@@ -504,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "representative-acquisition",
    "metadata": {},
    "outputs": [],
@@ -514,7 +541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "rocky-sydney",
    "metadata": {
     "scrolled": true
diff --git a/chapters/chap18.ipynb b/chapters/chap18.ipynb
index a40e9224..6af7974b 100644
--- a/chapters/chap18.ipynb
+++ b/chapters/chap18.ipynb
@@ -5,7 +5,7 @@
    "id": "electric-netherlands",
    "metadata": {},
    "source": [
-    "# Chapter 18"
+    "# Glucose and Insulin"
    ]
   },
   {
@@ -62,8 +62,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "plastic-trigger",
-   "metadata": {},
+   "id": "preliminary-mexico",
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
     "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
@@ -321,7 +323,7 @@
    "id": "strange-citation",
    "metadata": {},
    "source": [
-    "## The Simulation\n",
+    "## Running the Simulation\n",
     "\n",
     "We'll use the following version of `run_simulation`:"
    ]
@@ -355,9 +357,9 @@
    "metadata": {},
    "source": [
     "This version is similar to the one we used for the coffee cooling problem.\n",
-    "The biggest difference is that it makes and returns a `TimeFrame` rather than a `TimeSeries`.\n",
+    "The biggest difference is that it makes and returns a `TimeFrame`, which contains one column for each state variable, rather than a `TimeSeries`, which can only store one state variable.\n",
     "\n",
-    "When we make the `TimeSeries`, we use `index` to indicate that the index is the array of time stamps, `t_array`, and `columns` to indicate that the column names are the state variables we get from `init`.\n",
+    "When we make the `TimeFrame`, we use `index` to indicate that the index is the array of time stamps, `t_array`, and `columns` to indicate that the column names are the state variables we get from `init`.\n",
     "\n",
     "We can run it like this:"
    ]
@@ -419,7 +421,7 @@
    "metadata": {},
    "source": [
     "With the parameters I chose, the model fits the data well except during the first few minutes after the injection.\n",
-    "But we don't expect the model to do well in this regime.\n",
+    "But we don't expect the model to do well in this part of the time series.\n",
     "\n",
     "The problem is that the model is *non-spatial*; that is, it does not\n",
     "take into account different concentrations in different parts of the\n",
@@ -481,11 +483,11 @@
     "\n",
     "There are other methods that are more accurate and more efficient than Euler's method.\n",
     "SciPy provides several of them wrapped in a function called `solve_ivp`.\n",
-    "The \"ivp\" in `solve_ivp` stands for \"initial value problem\", which is the term for problems like the ones we've been solving, where we are given the initial conditions and try to predict what will happen.\n",
+    "The `ivp` stands for *initial value problem*, which is the term for problems like the ones we've been solving, where we are given the initial conditions and try to predict what will happen.\n",
     "\n",
     "The ModSim library provides a function called `run_solve_ivp` that makes `solve_ivp` a little easier to use.\n",
     "\n",
-    "To use it, we have to provide a \"slope function\", which is similar to an update function; in fact, it takes the same parameters: a time stamp, a `State` object, and a `System` object.\n",
+    "To use it, we have to provide a *slope function*, which is similar to an update function; in fact, it takes the same parameters: a time stamp, a `State` object, and a `System` object.\n",
     "\n",
     "Here's a slope function that evaluates the differential equations of the minimal model."
    ]
@@ -655,10 +657,7 @@
    "id": "temporal-threat",
    "metadata": {},
    "source": [
-    "The mean relative difference is about 0.65%;\n",
-    "the maximum is a little more than 1%.\n",
-    "So in this example, the results from Euler's method are probably good enough for practical purposes.\n",
-    "\n",
+    "The mean relative difference is about 0.65% and the maximum is a little more than 1%.\n",
     "Here are the results for `X`."
    ]
   },
@@ -682,7 +681,12 @@
    "metadata": {},
    "source": [
     "These differences are little bigger, especially at the beginning.\n",
-    "As an exercise, you can experiment with `dt` and see what effect it has on these results."
+    "\n",
+    "If we use `run_simulation` with smaller time steps, the results are more accurate, but they take longer to compute.\n",
+    "For some problems, we can find a value of `dt` that produces accurate results in a reasonable time. However, if `dt` is *too* small, the results can be inaccurate again. So it can be tricky to get it right.\n",
+    "\n",
+    "The advantage of `run_solve_ivp` is that it chooses the step size automatically in order to balance accuracy and efficiency.\n",
+    "You can use keyword arguments to adjust this balance, but most of the time the results are accurate enough, and the computation is fast enough, without any intervention."
    ]
   },
   {
@@ -719,7 +723,10 @@
    "id": "unusual-springer",
    "metadata": {},
    "source": [
-    "## Exercises"
+    "## Exercises\n",
+    "\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
diff --git a/chapters/chap19.ipynb b/chapters/chap19.ipynb
index c7bbc9bb..d5c7dd21 100644
--- a/chapters/chap19.ipynb
+++ b/chapters/chap19.ipynb
@@ -5,7 +5,7 @@
    "id": "mighty-israeli",
    "metadata": {},
    "source": [
-    "# Chapter 19"
+    "# Case Studies Part 2"
    ]
   },
   {
@@ -60,6 +60,17 @@
     "from modsim import *"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "dense-seattle",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "reflected-sitting",
@@ -78,19 +89,12 @@
     "In the previous chapter we implemented the glucose minimal model using given parameters, but I didn't say where those parameters came from.\n",
     "\n",
     "In the repository for this book, you will find a notebook,\n",
-    "`glucose.ipynb`, that shows how we can find the parameters that best fit the data.\n",
+    "*glucose.ipynb*, that shows how we can find the parameters that best fit the data.\n",
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/glucose.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/glucose.ipynb>.\n",
     "\n",
     "It uses a SciPy function called `leastsq`, which stands for \"least squares\"; so-named because it finds the parameters that minimize the sum of squared differences between the results of the model and the data.\n",
     "\n",
-    "You can think of `leastsq` as an optional tool for this book.  We won't use it in the text itself, but it appears in a few of the case studies.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "everyday-stopping",
-   "metadata": {},
-   "source": [
-    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/glucose.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/glucose.ipynb>."
+    "You can think of `leastsq` as an optional tool for this book.  We won't use it in the text itself, but it appears in a few of the case studies."
    ]
   },
   {
@@ -135,14 +139,7 @@
     "where $I_{max}$ is the maximum measured insulin level, and $I_b$ and $G_b$ are the basal levels of insulin and glucose.\n",
     "\n",
     "In the repository for this book, you will find a notebook,\n",
-    "`insulin.ipynb`, that contains starter code for this case study. Use it to implement the insulin model, find the parameters that best fit the data, and estimate $\\phi_1$ and $\\phi_2$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sustainable-breath",
-   "metadata": {},
-   "source": [
+    "*insulin.ipynb*, that contains starter code for this case study. Use it to implement the insulin model, find the parameters that best fit the data, and estimate $\\phi_1$ and $\\phi_2$.\n",
     "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/insulin.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/insulin.ipynb>."
    ]
   },
@@ -157,9 +154,9 @@
     "\n",
     "![Circuit diagram of a low-pass filter](https://github.com/AllenDowney/ModSim/raw/main/figs/Lowpass_Filter_RC.png)\n",
     "\n",
-    "A \"filter\" is a circuit takes a signal, $V_{in}$, as input and produces a signal, $V_{out}$, as output. In this context, a \"signal\" is a voltage that changes over time.\n",
+    "A *filter* is a circuit takes a signal, $V_{in}$, as input and produces a signal, $V_{out}$, as output. In this context, a *signal* is a voltage that changes over time.\n",
     "\n",
-    "A filter is \"low-pass\" if it allows low-frequency signals to pass from\n",
+    "A filter is *low-pass* if it allows low-frequency signals to pass from\n",
     "$V_{in}$ to $V_{out}$ unchanged, but it reduces the amplitude of\n",
     "high-frequency signals."
    ]
@@ -202,23 +199,12 @@
     "\n",
     "$$\\frac{d V_{out}}{dt} = \\frac{V_{in} - V_{out}}{R C}$$ \n",
     "\n",
-    "In the repository for this book, you will find a notebook, `filter.ipynb`, which contains starter code for this case study. Follow the instructions to simulate the low-pass filter for input signals like this:\n",
+    "In the repository for this book, you will find a notebook, *filter.ipynb*, which contains starter code for this case study. You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/filter.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/filter.ipynb>.\n",
+    "Follow the instructions to simulate the low-pass filter for input signals like this:\n",
     "\n",
     "$$V_{in}(t) = A \\cos (2 \\pi f t)$$ \n",
     "\n",
-    "where $A$ is the amplitude of the input signal, say 5 V, and $f$ is the frequency of the signal in Hz.\n",
-    "\n",
-    "In the repository for this book, you will find a notebook,\n",
-    "`filter.ipynb`, which contains starter code for this case study. Read\n",
-    "the notebook, run the code, and work on the exercises."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "twenty-sight",
-   "metadata": {},
-   "source": [
-    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/filter.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/filter.ipynb>."
+    "where $A$ is the amplitude of the input signal, say 5 V, and $f$ is the frequency of the signal in Hz."
    ]
   },
   {
@@ -243,32 +229,21 @@
    "id": "entire-stations",
    "metadata": {},
    "source": [
-    "The primary methodology of the paper is a Bayesian method for inferring the parameters of the system (two thermal masses and three thermal resistances).\n",
+    "The primary methodology of the paper is a statistical method for inferring the parameters of the system (two thermal masses and three thermal resistances).\n",
     "\n",
-    "The primary result is a comparison of two models: the one shown here\n",
-    "with two thermal masses, and a simpler model with only one thermal mass. They find that the two-mass model is able to reproduce the measured fluxes substantially better.\n",
+    "The primary result is a comparison of two models: the one shown here with two thermal masses, and a simpler model with only one thermal mass. They find that the two-mass model is able to reproduce the measured fluxes substantially better.\n",
     "\n",
     "For this case study we will implement their model and run it with the\n",
     "estimated parameters from the paper, and then use `leastsq` to see\n",
     "if we can find parameters that yield lower errors.\n",
     "\n",
-    "In the repository for this book, you will find a notebook, `wall.ipynb` with the code and results for this case study.\n",
+    "In the repository for this book, you will find a notebook, *wall.ipynb* with the code and results for this case study.\n",
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/wall.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/wall.ipynb>.\n",
     "\n",
     "The paper this case study is based on is\n",
     "Gori, Marincioni, Biddulph, Elwell, \"Inferring the thermal resistance and effective thermal mass distribution of a wall from in situ measurements to characterise heat transfer at both the interior and exterior surfaces\", *Energy and Buildings*, 2017, available from <http://modsimpy.com/wall2>.\n",
     "\n",
-    "The authors put their paper under a Creative Commons license, and\n",
-    "make their data available at <http://modsimpy.com/wall>. I thank them\n",
-    "for their commitment to open, reproducible science, which made this\n",
-    "case study possible."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "wireless-advocacy",
-   "metadata": {},
-   "source": [
-    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/owall.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/owall.ipynb>."
+    "The authors put their paper under a Creative Commons license and made their data available at <http://modsimpy.com/wall>. I thank them for their commitment to open, reproducible science, which made this case study possible."
    ]
   },
   {
@@ -289,14 +264,7 @@
     "\n",
     "His conclusion is that the observed decline in the concentration of HIV might not be caused by an immune response; it could be due to the dynamic interaction between HIV and the cells it infects.\n",
     "\n",
-    "In the repository for this book, you will find a notebook, `hiv_model.ipynb`, which you can use to implement Phillips's model and consider whether it does the work it is meant to do."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "narrow-voice",
-   "metadata": {},
-   "source": [
+    "In the repository for this book, you will find a notebook, *hiv_model.ipynb*, which you can use to implement Phillips's model and consider whether it does the work it is meant to do.\n",
     "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/hiv_model.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/hiv_model.ipynb>."
    ]
   },
@@ -310,6 +278,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
diff --git a/chapters/chap20.ipynb b/chapters/chap20.ipynb
index 7d994520..ed187056 100644
--- a/chapters/chap20.ipynb
+++ b/chapters/chap20.ipynb
@@ -5,7 +5,7 @@
    "id": "funded-utilization",
    "metadata": {},
    "source": [
-    "# Chapter 20"
+    "# Pennies"
    ]
   },
   {
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 18,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 19,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 20,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -83,8 +83,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "plastic-trigger",
-   "metadata": {},
+   "id": "removable-zoning",
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap20.ipynb)"
    ]
@@ -94,9 +96,8 @@
    "id": "embedded-gentleman",
    "metadata": {},
    "source": [
-    "So far the differential equations we've worked with have been **first\n",
-    "order**, which means they involve only first derivatives. In this\n",
-    "chapter, we turn our attention to second order differential equations, which can involve both first and second derivatives.\n",
+    "So far the differential equations we've worked with have been *first order*, which means they involve only first derivatives. In this\n",
+    "chapter, we turn our attention to *second order* differential equations, which can involve both first and second derivatives.\n",
     "\n",
     "We'll revisit the falling penny example from Chapter 1, and use `run_solve_ivp` to find the position and velocity of the penny as it falls, with and without air resistance."
    ]
@@ -185,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 21,
    "id": "compatible-increase",
    "metadata": {},
    "outputs": [],
@@ -205,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "id": "reverse-authorization",
    "metadata": {},
    "outputs": [],
@@ -238,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "id": "occupied-mercury",
    "metadata": {},
    "outputs": [],
@@ -273,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "id": "positive-feeling",
    "metadata": {},
    "outputs": [],
@@ -294,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "id": "lovely-management",
    "metadata": {},
    "outputs": [],
@@ -314,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "id": "assisted-swimming",
    "metadata": {},
    "outputs": [],
@@ -332,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "id": "authorized-barrier",
    "metadata": {},
    "outputs": [],
@@ -355,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "id": "protected-fiber",
    "metadata": {},
    "outputs": [],
@@ -376,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "id": "japanese-clear",
    "metadata": {},
    "outputs": [],
@@ -410,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "id": "comfortable-simple",
    "metadata": {},
    "outputs": [],
@@ -433,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
    "id": "exotic-shareware",
    "metadata": {},
    "outputs": [],
@@ -448,12 +449,12 @@
    "id": "recreational-blair",
    "metadata": {},
    "source": [
-    "Then we can get the flight time and final velocity like this:"
+    "Then we can get the flight time like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 32,
    "id": "appropriate-roberts",
    "metadata": {},
    "outputs": [],
@@ -462,9 +463,17 @@
     "t_end"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "pediatric-portal",
+   "metadata": {},
+   "source": [
+    "And the final velocity like this:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 33,
    "id": "orange-retro",
    "metadata": {},
    "outputs": [],
@@ -498,16 +507,25 @@
   },
   {
    "cell_type": "markdown",
-   "id": "straight-johns",
+   "id": "operational-bhutan",
    "metadata": {},
    "source": [
-    "### Exercises\n",
+    "## Exercises\n",
     "\n",
-    "### Exercise\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "straight-johns",
+   "metadata": {},
+   "source": [
+    "### Exercise 1\n",
     "\n",
-    " Here's a question from the web site [Ask an Astronomer](http://curious.astro.cornell.edu/about-us/39-our-solar-system/the-earth/other-catastrophes/57-how-long-would-it-take-the-earth-to-fall-into-the-sun-intermediate):\n",
+    "Here's a question from the web site *Ask an Astronomer* (see http://curious.astro.cornell.edu/about-us/39-our-solar-system/the-earth/other-catastrophes/57-how-long-would-it-take-the-earth-to-fall-into-the-sun-intermediate):\n",
     "\n",
-    "\"If the Earth suddenly stopped orbiting the Sun, I know eventually it would be pulled in by the Sun's gravity and hit it. How long would it take the Earth to hit the Sun? I imagine it would go slowly at first and then pick up speed.\"\n",
+    "> \"If the Earth suddenly stopped orbiting the Sun, I know eventually it would be pulled in by the Sun's gravity and hit it. How long would it take the Earth to hit the Sun? I imagine it would go slowly at first and then pick up speed.\"\n",
     "\n",
     "Use `run_solve_ivp` to answer this question.\n",
     "\n",
@@ -521,12 +539,12 @@
     "\n",
     "If you read the reply by Dave Rothstein, you will see other ways to solve the problem, and a good discussion of the modeling decisions behind them.\n",
     "\n",
-    "You might also be interested to know that [it's actually not that easy to get to the Sun](https://www.theatlantic.com/science/archive/2018/08/parker-solar-probe-launch-nasa/567197/)."
+    "You might also be interested to know that it's not that easy to get to the Sun; see https://www.theatlantic.com/science/archive/2018/08/parker-solar-probe-launch-nasa/567197/."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 34,
    "id": "forbidden-distributor",
    "metadata": {},
    "outputs": [],
@@ -536,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 35,
    "id": "former-taxation",
    "metadata": {},
    "outputs": [],
@@ -546,7 +564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 36,
    "id": "oriental-riverside",
    "metadata": {},
    "outputs": [],
@@ -556,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 37,
    "id": "radio-reproduction",
    "metadata": {},
    "outputs": [],
@@ -566,7 +584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 38,
    "id": "heavy-cologne",
    "metadata": {},
    "outputs": [],
@@ -576,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 39,
    "id": "little-electric",
    "metadata": {},
    "outputs": [],
@@ -586,7 +604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 40,
    "id": "continental-details",
    "metadata": {},
    "outputs": [],
@@ -596,7 +614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 41,
    "id": "suitable-traveler",
    "metadata": {},
    "outputs": [],
@@ -606,7 +624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 42,
    "id": "upper-victory",
    "metadata": {},
    "outputs": [],
@@ -616,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 43,
    "id": "transparent-treat",
    "metadata": {},
    "outputs": [],
@@ -626,7 +644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 44,
    "id": "brutal-woman",
    "metadata": {},
    "outputs": [],
@@ -636,7 +654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 45,
    "id": "gentle-burst",
    "metadata": {},
    "outputs": [],
@@ -646,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 46,
    "id": "comfortable-galaxy",
    "metadata": {},
    "outputs": [],
@@ -656,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 47,
    "id": "satisfactory-latitude",
    "metadata": {},
    "outputs": [],
@@ -666,7 +684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 48,
    "id": "significant-rebound",
    "metadata": {},
    "outputs": [],
@@ -684,6 +702,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -699,7 +718,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap21.ipynb b/chapters/chap21.ipynb
index 6998bb46..e272f5da 100644
--- a/chapters/chap21.ipynb
+++ b/chapters/chap21.ipynb
@@ -5,7 +5,7 @@
    "id": "excited-advance",
    "metadata": {},
    "source": [
-    "# Chapter 21"
+    "# Drag"
    ]
   },
   {
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 32,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 33,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -63,7 +63,9 @@
   {
    "cell_type": "markdown",
    "id": "plastic-trigger",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap21.ipynb)"
    ]
@@ -125,7 +127,7 @@
     "\n",
     "For simple geometric objects we can sometimes guess the drag coefficient with reasonable accuracy; for more complex objects we usually have to take measurements and estimate $C_d$ from data.\n",
     "\n",
-    "Of course, the drag equation is itself a model, based on the assumption that $C_d$ does not depend on the other terms in the equation: density, velocity, and area. For objects moving in air at moderate speeds (below 45 mph or 20 m/s), this model might be good enough, but we should remember to revisit this assumption.\n",
+    "Of course, the drag equation is itself a model, based on the assumption that $C_d$ does not depend on the other terms in the equation: density, velocity, and area. For objects moving in air at moderate speeds (below 45 mph or 20 m/s), this model might be good enough, but we will revisit this assumption in the next chapter.\n",
     "\n",
     "For the falling penny, we can use measurements to estimate $C_d$. In\n",
     "particular, we can measure *terminal velocity*, $v_{term}$, which is\n",
@@ -140,7 +142,7 @@
     "\n",
     "According to *Mythbusters*, the terminal velocity of a penny is between 35 and 65 mph (see <http://modsimpy.com/mythbust>). Using the low end of their range, 40 mph or about 18 m/s, the estimated value of $C_d$ is 0.44, which is close to the drag coefficient of a smooth sphere.\n",
     "\n",
-    "Now we are ready to add air resistance to the model."
+    "Now we are ready to add air resistance to the model. But first I want to introduce one more computational tool, the `Params` object."
    ]
   },
   {
@@ -157,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 34,
    "id": "established-knitting",
    "metadata": {},
    "outputs": [],
@@ -181,9 +183,8 @@
    "source": [
     "The mass and diameter are from <http://modsimpy.com/penny>. The density\n",
     "of air depends on temperature, barometric pressure (which depends on\n",
-    "altitude), humidity, and composition (<http://modsimpy.com/density>). I\n",
-    "chose a value that might be typical in Boston, Massachusetts at 20 °C.\n",
-    "\n",
+    "altitude), humidity, and composition (see <http://modsimpy.com/density>). \n",
+    "I chose a value that might be typical in New York City at 20 °C.\n",
     "\n",
     "Here's a version of `make_system` that takes the `Params` object and computes the inital state, `init`, the area, and the coefficient of drag.\n",
     "Then it returns a `System` object with the quantities we'll need for the simulation. "
@@ -191,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 35,
    "id": "published-jesus",
    "metadata": {},
    "outputs": [],
@@ -225,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 36,
    "id": "executive-protection",
    "metadata": {},
    "outputs": [],
@@ -243,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 37,
    "id": "quick-cedar",
    "metadata": {},
    "outputs": [],
@@ -265,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 38,
    "id": "great-crime",
    "metadata": {},
    "outputs": [],
@@ -279,13 +280,12 @@
    "metadata": {},
    "source": [
     "The result from `set` is a new `Params` object that is identical to the original except for the given value of `v_term`. \n",
-    "\n",
     "If we pass `params2` to `make_system`, we see that it computes a different value of `C_d`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 39,
    "id": "independent-trace",
    "metadata": {},
    "outputs": [],
@@ -310,14 +310,14 @@
    "id": "primary-advocate",
    "metadata": {},
    "source": [
-    "## Simulation\n",
+    "## Simulating the Penny Drop\n",
     "\n",
     "Now let's get to the simulation.  Here's a version of the slope function that includes drag:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 40,
    "id": "noble-stick",
    "metadata": {},
    "outputs": [],
@@ -359,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 41,
    "id": "velvet-tunisia",
    "metadata": {},
    "outputs": [],
@@ -380,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 42,
    "id": "practical-nowhere",
    "metadata": {},
    "outputs": [],
@@ -400,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 43,
    "id": "liberal-dictionary",
    "metadata": {},
    "outputs": [],
@@ -420,7 +420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 44,
    "id": "coastal-anthropology",
    "metadata": {},
    "outputs": [],
@@ -442,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 45,
    "id": "interim-underground",
    "metadata": {},
    "outputs": [],
@@ -463,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 46,
    "id": "small-franchise",
    "metadata": {},
    "outputs": [],
@@ -487,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 47,
    "id": "analyzed-criticism",
    "metadata": {},
    "outputs": [],
@@ -517,10 +517,9 @@
    "source": [
     "## Summary\n",
     "\n",
-    "This chapter presents a model of drag force, which we use to estimate the coefficient of drag for a penny, and then simulate, one more time, dropping a penny from the Empire State building.\n",
+    "This chapter presents a model of drag force, which we use to estimate the coefficient of drag for a penny, and then simulate, one more time, dropping a penny from the Empire State Building.\n",
     "\n",
     "In the next chapter we'll move from one dimension to two, simulating the flight of a baseball.\n",
-    "\n",
     "But first you might want to work on these exercises."
    ]
   },
@@ -529,7 +528,10 @@
    "id": "planned-endorsement",
    "metadata": {},
    "source": [
-    "## Exercises"
+    "## Exercises\n",
+    "\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -537,7 +539,7 @@
    "id": "respective-address",
    "metadata": {},
    "source": [
-    "### Exercise\n",
+    "### Exercise 1\n",
     "\n",
     " Run the simulation with a downward initial velocity that exceeds the penny's terminal velocity.\n",
     "\n",
@@ -548,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 48,
    "id": "inclusive-twenty",
    "metadata": {},
    "outputs": [],
@@ -558,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 49,
    "id": "vertical-judge",
    "metadata": {},
    "outputs": [],
@@ -568,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 50,
    "id": "greenhouse-madagascar",
    "metadata": {},
    "outputs": [],
@@ -578,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 51,
    "id": "sudden-details",
    "metadata": {},
    "outputs": [],
@@ -588,7 +590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 52,
    "id": "opening-jurisdiction",
    "metadata": {
     "scrolled": false
@@ -603,11 +605,11 @@
    "id": "smaller-millennium",
    "metadata": {},
    "source": [
-    "### Exercise\n",
+    "### Exercise 2\n",
     "\n",
     " Suppose we drop a quarter from the Empire State Building and find that its flight time is 19.1 seconds.  Use this measurement to estimate terminal velocity and coefficient of drag.\n",
     "\n",
-    "You can get the relevant dimensions of a quarter from https://en.wikipedia.org/wiki/Quarter_(United_States_coin).\n",
+    "You can get the relevant dimensions of a quarter from <https://en.wikipedia.org/wiki/Quarter_(United_States_coin)>.\n",
     "\n",
     "1. Create a `Params` object with new values of `mass` and `diameter`. We don't know `v_term`, so we'll start with the initial guess 18 m/s.\n",
     "\n",
@@ -626,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 53,
    "id": "compact-bunny",
    "metadata": {},
    "outputs": [],
@@ -636,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 54,
    "id": "shared-contrary",
    "metadata": {},
    "outputs": [],
@@ -646,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 55,
    "id": "portable-account",
    "metadata": {},
    "outputs": [],
@@ -656,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 56,
    "id": "arabic-shareware",
    "metadata": {},
    "outputs": [],
@@ -666,7 +668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 57,
    "id": "valued-literature",
    "metadata": {},
    "outputs": [],
@@ -676,7 +678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 58,
    "id": "sufficient-retail",
    "metadata": {},
    "outputs": [],
@@ -686,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 59,
    "id": "comparable-lounge",
    "metadata": {},
    "outputs": [],
@@ -696,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 60,
    "id": "ready-people",
    "metadata": {},
    "outputs": [],
@@ -706,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 61,
    "id": "miniature-remark",
    "metadata": {},
    "outputs": [],
@@ -716,7 +718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 62,
    "id": "frozen-termination",
    "metadata": {},
    "outputs": [],
@@ -726,6 +728,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -741,7 +744,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
index 619c0c5d..ba9b76ed 100644
--- a/chapters/chap22.ipynb
+++ b/chapters/chap22.ipynb
@@ -5,7 +5,7 @@
    "id": "collaborative-people",
    "metadata": {},
    "source": [
-    "# Chapter 22"
+    "# Baseball"
    ]
   },
   {
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 64,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 65,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 66,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -84,7 +84,9 @@
   {
    "cell_type": "markdown",
    "id": "plastic-trigger",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap22.ipynb)"
    ]
@@ -108,13 +110,12 @@
    "source": [
     "## Baseball\n",
     "\n",
-    "To model the flight of a baseball, we have to make some\n",
-    "decisions. To get started, we'll ignore any spin that might be on the ball, and the resulting Magnus force (see <http://modsimpy.com/magnus>). Under this assumption, the ball travels in a vertical plane, so we'll run simulations in two dimensions, rather than three.\n",
+    "To model the flight of a baseball, we have to make some decisions. To get started, we'll ignore any spin that might be on the ball, and the resulting Magnus force (see <http://modsimpy.com/magnus>). Under this assumption, the ball travels in a vertical plane, so we'll run simulations in two dimensions, rather than three.\n",
     "\n",
     "To model air resistance, we'll need the mass, frontal area, and drag\n",
     "coefficient of a baseball. Mass and diameter are easy to find (see\n",
     "<http://modsimpy.com/baseball>). Drag coefficient is only a little\n",
-    "harder; according to *[The Physics of Baseball](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC)*, the drag coefficient of a baseball is approximately 0.33 (with no units).\n",
+    "harder; according to *The Physics of Baseball* (see https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC), the drag coefficient of a baseball is approximately 0.33 (with no units).\n",
     "\n",
     "However, this value *does* depend on velocity. At low velocities it\n",
     "might be as high as 0.5, and at high velocities as low as 0.28.\n",
@@ -145,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 67,
    "id": "handy-terrain",
    "metadata": {},
    "outputs": [],
@@ -165,12 +166,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 68,
+   "id": "vocal-latino",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A.x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
    "id": "controversial-shower",
    "metadata": {},
    "outputs": [],
    "source": [
-    "A.x, A.y"
+    "A.y"
    ]
   },
   {
@@ -183,12 +194,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 70,
+   "id": "digital-channels",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
    "id": "automated-drove",
    "metadata": {},
    "outputs": [],
    "source": [
-    "A[0], A[1]"
+    "A[1]"
    ]
   },
   {
@@ -197,12 +218,12 @@
    "metadata": {},
    "source": [
     "`Vector` objects support most mathematical operations, including\n",
-    "addition and subtraction:"
+    "addition:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 72,
    "id": "conditional-latitude",
    "metadata": {},
    "outputs": [],
@@ -211,9 +232,17 @@
     "show(A + B)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "charming-reviewer",
+   "metadata": {},
+   "source": [
+    "And subtraction:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 73,
    "id": "encouraging-cabinet",
    "metadata": {},
    "outputs": [],
@@ -240,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 74,
    "id": "peripheral-tattoo",
    "metadata": {},
    "outputs": [],
@@ -265,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 75,
    "id": "strange-cleaning",
    "metadata": {},
    "outputs": [],
@@ -286,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 76,
    "id": "cellular-community",
    "metadata": {},
    "outputs": [],
@@ -310,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 77,
    "id": "monetary-firmware",
    "metadata": {},
    "outputs": [],
@@ -333,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 78,
    "id": "explicit-piano",
    "metadata": {},
    "outputs": [],
@@ -351,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 79,
    "id": "relative-republic",
    "metadata": {},
    "outputs": [],
@@ -383,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 80,
    "id": "narrative-latest",
    "metadata": {
     "tags": []
@@ -411,11 +440,9 @@
    "id": "metric-collins",
    "metadata": {},
    "source": [
-    "I got the mass and diameter of the baseball from [Wikipedia](https://en.wikipedia.org/wiki/Baseball_(ball)) and the coefficient of drag from *[The Physics of Baseball](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC)*:\n",
-    "\n",
+    "I got the mass and diameter of the baseball from Wikipedia (see <https://en.wikipedia.org/wiki/Baseball_(ball)>) and the coefficient of drag from *The Physics of Baseball* (see <https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC>):\n",
     "The density of air, `rho`, is based on a temperature of 20 °C at sea level (see <http://modsimpy.com/tempress>). \n",
     "As usual, `g` is acceleration due to gravity.\n",
-    "\n",
     "`t_end` is 10 seconds, which is long enough for the ball to land on the ground.\n",
     "\n",
     "The following function uses these quantities to make a `System` object."
@@ -423,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 81,
    "id": "bored-billy",
    "metadata": {
     "tags": []
@@ -473,7 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 82,
    "id": "ethical-donna",
    "metadata": {},
    "outputs": [],
@@ -491,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 83,
    "id": "legitimate-gossip",
    "metadata": {},
    "outputs": [],
@@ -511,7 +538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 84,
    "id": "legal-terminal",
    "metadata": {
     "tags": []
@@ -547,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 85,
    "id": "frank-chick",
    "metadata": {},
    "outputs": [],
@@ -560,17 +587,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "absent-vector",
+   "id": "ultimate-upgrade",
    "metadata": {},
    "source": [
     "The result is a `Vector` that represents the drag force on the baseball, in Newtons, under the initial conditions.\n",
     "\n",
-    "Now we're ready for a slope function:"
+    "Now we can add drag to the slope function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 86,
    "id": "suitable-salem",
    "metadata": {
     "tags": []
@@ -630,7 +657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 87,
    "id": "closing-simon",
    "metadata": {},
    "outputs": [],
@@ -653,14 +680,14 @@
    "id": "regulated-railway",
    "metadata": {},
    "source": [
-    "## Simulation\n",
+    "## Adding an Event Function\n",
     "\n",
     "We're almost ready to run the simulation.  The last thing we need is an event function that stops when the ball hits the ground."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 88,
    "id": "brief-level",
    "metadata": {
     "tags": []
@@ -684,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 89,
    "id": "threatened-alberta",
    "metadata": {},
    "outputs": [],
@@ -697,12 +724,12 @@
    "id": "unlikely-dressing",
    "metadata": {},
    "source": [
-    "Now we're ready to run the simulation:"
+    "Here's how we run the simulation with this event function:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 90,
    "id": "special-background",
    "metadata": {},
    "outputs": [],
@@ -717,7 +744,7 @@
    "id": "iraqi-appeal",
    "metadata": {},
    "source": [
-    "The message in `details` indicates that a \"termination event\" occurred; that is, the simulated ball reached the ground.\n",
+    "The message in indicates that a \"termination event\" occurred; that is, the simulated ball reached the ground.\n",
     "\n",
     "`results` is a `TimeFrame` with one row for each time step and one column for each of the state variables.\n",
     "Here are the last few rows."
@@ -725,7 +752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 91,
    "id": "prospective-external",
    "metadata": {},
    "outputs": [],
@@ -743,7 +770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 92,
    "id": "medieval-calvin",
    "metadata": {},
    "outputs": [],
@@ -762,7 +789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 93,
    "id": "gorgeous-survey",
    "metadata": {},
    "outputs": [],
@@ -781,7 +808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 94,
    "id": "failing-bangkok",
    "metadata": {},
    "outputs": [],
@@ -800,7 +827,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 95,
    "id": "copyrighted-highway",
    "metadata": {},
    "outputs": [],
@@ -819,7 +846,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 96,
    "id": "structured-adams",
    "metadata": {},
    "outputs": [],
@@ -840,14 +867,14 @@
    "id": "continuous-quick",
    "metadata": {},
    "source": [
-    "## Trajectories\n",
+    "## Visualizing Trajectories\n",
     "\n",
     "To visualize the results, we can plot the $x$ and $y$ components of position like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 97,
    "id": "spare-burst",
    "metadata": {},
    "outputs": [],
@@ -872,7 +899,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 98,
    "id": "dated-browse",
    "metadata": {},
    "outputs": [],
@@ -894,7 +921,6 @@
    "metadata": {},
    "source": [
     "This way of visualizing the results is called a *trajectory plot* (see <http://modsimpy.com/trajec>).\n",
-    "\n",
     "A trajectory plot can be easier to interpret than a time series plot,\n",
     "because it shows what the motion of the projectile would look like (at\n",
     "least from one point of view). Both plots can be useful, but don't get\n",
@@ -906,7 +932,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 99,
    "id": "resistant-vegetation",
    "metadata": {},
    "outputs": [],
@@ -919,7 +945,7 @@
    "id": "cosmetic-aircraft",
    "metadata": {},
    "source": [
-    "## Animation\n",
+    "## Animating the Baseball\n",
     "\n",
     "One of the best ways to visualize the results of a physical model is animation.  If there are problems with the model, animation can make them apparent.\n",
     "\n",
@@ -930,7 +956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 100,
    "id": "starting-fabric",
    "metadata": {},
    "outputs": [],
@@ -950,20 +976,36 @@
   },
   {
    "cell_type": "markdown",
-   "id": "incomplete-beijing",
+   "id": "sustained-slide",
    "metadata": {},
    "source": [
     "Inside the draw function, should use `decorate` to set the limits of the $x$ and $y$ axes.\n",
     "Otherwise `matplotlib` auto-scales the axes, which is usually not what you want.\n",
     "\n",
+    "Now we can run the animation like this:\n",
+    "\n",
+    "```\n",
+    "animate(results, draw_func)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "incomplete-beijing",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
     "To run the animation, uncomment the following line of code and run the cell."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 101,
    "id": "prescription-boutique",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# animate(results, draw_func)"
@@ -974,7 +1016,10 @@
    "id": "lyric-harassment",
    "metadata": {},
    "source": [
-    "## Exercises"
+    "## Exercises\n",
+    "\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -982,14 +1027,14 @@
    "id": "pressing-retrieval",
    "metadata": {},
    "source": [
-    "### Exercise\n",
+    "### Exercise 1\n",
     "\n",
     " Run the simulation with and without air resistance.  How wrong would we be if we ignored drag?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 102,
    "id": "optical-weather",
    "metadata": {},
    "outputs": [],
@@ -1001,7 +1046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 103,
    "id": "acknowledged-belgium",
    "metadata": {},
    "outputs": [],
@@ -1011,7 +1056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 104,
    "id": "spatial-ensemble",
    "metadata": {},
    "outputs": [],
@@ -1021,7 +1066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 105,
    "id": "domestic-apparatus",
    "metadata": {},
    "outputs": [],
@@ -1031,7 +1076,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 106,
    "id": "correct-pittsburgh",
    "metadata": {},
    "outputs": [],
@@ -1044,14 +1089,14 @@
    "id": "generic-shelter",
    "metadata": {},
    "source": [
-    "### Exercise\n",
+    "### Exercise 2\n",
     "\n",
     " The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / m$^3$.  How much farther would a ball hit with the same initial speed and launch angle travel?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 107,
    "id": "global-referral",
    "metadata": {},
    "outputs": [],
@@ -1063,7 +1108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 108,
    "id": "appointed-sugar",
    "metadata": {},
    "outputs": [],
@@ -1073,7 +1118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 109,
    "id": "specialized-mediterranean",
    "metadata": {},
    "outputs": [],
@@ -1086,13 +1131,13 @@
    "id": "shaped-paragraph",
    "metadata": {},
    "source": [
-    "### Exercise\n",
+    "### Exercise 3\n",
     "\n",
-    " The model so far is based on the assumption that coefficient of drag does not depend on velocity, but in reality it does.  The following figure, from Adair, [*The Physics of Baseball*](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC), shows coefficient of drag as a function of velocity.\n",
+    " The model so far is based on the assumption that coefficient of drag does not depend on velocity, but in reality it does.  The following figure, from Adair, *The Physics of Baseball*, shows coefficient of drag as a function of velocity (see <https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC>).\n",
     "\n",
     "![Graph of drag coefficient versus velocity](https://github.com/AllenDowney/ModSimPy/raw/master/figs/baseball_drag.png)\n",
     "\n",
-    "I used [an online graph digitizer](https://automeris.io/WebPlotDigitizer/) to extract the data and save it in a CSV file.  "
+    "I used an online graph digitizer (<https://automeris.io/WebPlotDigitizer>) to extract the data and save it in a CSV file.  "
    ]
   },
   {
@@ -1107,7 +1152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 110,
    "id": "directed-moisture",
    "metadata": {
     "tags": []
@@ -1130,7 +1175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 111,
    "id": "fuzzy-register",
    "metadata": {
     "tags": []
@@ -1154,7 +1199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 112,
    "id": "returning-fellowship",
    "metadata": {
     "tags": []
@@ -1176,7 +1221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 113,
    "id": "reasonable-swaziland",
    "metadata": {
     "tags": []
@@ -1199,7 +1244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 114,
    "id": "attached-shower",
    "metadata": {
     "tags": []
@@ -1221,7 +1266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 115,
    "id": "christian-camcorder",
    "metadata": {
     "tags": []
@@ -1246,7 +1291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 116,
    "id": "heated-belfast",
    "metadata": {
     "tags": []
@@ -1267,7 +1312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 117,
    "id": "engaged-provision",
    "metadata": {},
    "outputs": [],
@@ -1277,7 +1322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 118,
    "id": "directed-fiber",
    "metadata": {},
    "outputs": [],
@@ -1287,7 +1332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 119,
    "id": "framed-dealer",
    "metadata": {},
    "outputs": [],
@@ -1297,7 +1342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 120,
    "id": "accomplished-elizabeth",
    "metadata": {},
    "outputs": [],
@@ -1307,7 +1352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 121,
    "id": "going-techno",
    "metadata": {},
    "outputs": [],
@@ -1317,7 +1362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 122,
    "id": "brief-saying",
    "metadata": {},
    "outputs": [],
@@ -1327,7 +1372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 123,
    "id": "spare-pregnancy",
    "metadata": {},
    "outputs": [],
@@ -1337,7 +1382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 124,
    "id": "catholic-staff",
    "metadata": {},
    "outputs": [],
@@ -1347,7 +1392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 125,
    "id": "broad-sequence",
    "metadata": {},
    "outputs": [],
diff --git a/chapters/chap23.ipynb b/chapters/chap23.ipynb
index 3dbcf551..7fd7a805 100644
--- a/chapters/chap23.ipynb
+++ b/chapters/chap23.ipynb
@@ -5,7 +5,7 @@
    "id": "foreign-pepper",
    "metadata": {},
    "source": [
-    "# Chapter 23"
+    "# Optimal Baseball"
    ]
   },
   {
@@ -84,7 +84,9 @@
   {
    "cell_type": "markdown",
    "id": "plastic-trigger",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap23.ipynb)"
    ]
@@ -127,7 +129,7 @@
     "In the previous chapter we developed a model of the flight of a\n",
     "baseball, including gravity and a simple version of drag, but neglecting spin, Magnus force, and the dependence of the coefficient of drag on velocity.\n",
     "\n",
-    "In this chapter we apply that model to an optimization problem."
+    "In this chapter we apply that model to an optimization problem. In general, *optimization* is a process for improving a design by searching for the parameters that maximize a benefit or minimize a cost. For example, in this chapter we'll find the angle you should hit a baseball to maximize the distance it travels. And we'll use a new function, called `maximize_scalar` that searches for this angle efficiently."
    ]
   },
   {
@@ -339,9 +341,8 @@
    "id": "shaped-southeast",
    "metadata": {},
    "source": [
-    "For these parameters, the optimal angle is about 41°, which yields a\n",
-    "range of 100 m.\n",
-    "\n"
+    "For these parameters, the optimal angle is about 41°, which yields a range of 100 m.\n",
+    "Now we have what we need to finish the problem; the last step is to find the minimum velocity needed to get the ball over the wall. In the exercises at the end of the chapter, I provide some suggestions. Then it's up to you!"
    ]
   },
   {
@@ -351,11 +352,9 @@
    "source": [
     "## Summary\n",
     "\n",
-    "This chapter introduces the Manny Ramirez problem and starts to solve it.\n",
-    "As an exercise, you can finish it off.\n",
+    "This chapter introduces an new tool, `maximize_scalar`, that provides an efficient way to search for the maximum of a function. We used it to find the launch angle that maximizes the distance a baseball flies through the air, given its initial velocity.\n",
     "\n",
-    "If you enjoy this exercise, you might be interested in this paper: \"How to hit home runs: Optimum baseball bat swing parameters for maximum range trajectories\", by Sawicki, Hubbard, and Stronge, at\n",
-    "<http://modsimpy.com/runs>.\n",
+    "If you enjoy this example, you might be interested in this paper: \"How to hit home runs: Optimum baseball bat swing parameters for maximum range trajectories\", by Sawicki, Hubbard, and Stronge, at <http://modsimpy.com/runs>.\n",
     "\n",
     "In the next chapter, we start a new topic: rotation!"
    ]
@@ -365,7 +364,10 @@
    "id": "processed-constitution",
    "metadata": {},
    "source": [
-    "## Exercise"
+    "## Exercises\n",
+    "\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -373,9 +375,9 @@
    "id": "handmade-rhythm",
    "metadata": {},
    "source": [
-    "### Exercise\n",
+    "### Exercise 1\n",
     "\n",
-    " Let's finish off the Manny Ramirez problem:\n",
+    "Let's finish off the Manny Ramirez problem:\n",
     "\n",
     "> What is the minimum effort required to hit a home run in Fenway Park?\n",
     "\n",
@@ -610,7 +612,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap24.ipynb b/chapters/chap24.ipynb
index 916ebe70..8d50d082 100644
--- a/chapters/chap24.ipynb
+++ b/chapters/chap24.ipynb
@@ -5,7 +5,7 @@
    "id": "august-parks",
    "metadata": {},
    "source": [
-    "# Chapter 24"
+    "# Rotation"
    ]
   },
   {
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -63,7 +63,9 @@
   {
    "cell_type": "markdown",
    "id": "plastic-trigger",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap24.ipynb)"
    ]
@@ -82,7 +84,7 @@
     "gymnasts, divers, ice skaters, etc.\n",
     "\n",
     "And when you apply a twisting force to a rotating object, the effect is often contrary to intuition. \n",
-    "For an example, see this video on gyroscopic precession <http://modsimpy.com/precess>."
+    "For an example, see this video on gyroscopic precession: <http://modsimpy.com/precess>."
    ]
   },
   {
@@ -104,7 +106,7 @@
    "source": [
     "## The Physics of Toilet Paper\n",
     "\n",
-    "As an example of a system with rotation, we'll simulate the manufacture of a roll of toilet paper, as shown in this video <https://youtu.be/Z74OfpUbeac?t=231>. \n",
+    "As an example of a system with rotation, we'll simulate the manufacture of a roll of toilet paper, as shown in this video: <https://youtu.be/Z74OfpUbeac?t=231>. \n",
     "Starting with a cardboard tube at the center, we will roll up 47 m of paper, a typical length for a roll of toilet paper in the U.S. (see <http://modsimpy.com/paper>).\n",
     "\n",
     "The following figure shows a diagram of the system: $r$ represents\n",
@@ -112,9 +114,7 @@
     "\n",
     "![Diagram of a roll of toilet paper, showing change in paper length as a result of a small rotation, $d\\theta$.](https://github.com/AllenDowney/ModSim/raw/main/figs/paper_roll.png)\n",
     "\n",
-    "I'll use $\\theta$ to represent the total rotation of the roll in\n",
-    "radians. In the diagram, $d\\theta$ represents a small increase in\n",
-    "$\\theta$, which corresponds to a distance along the circumference of $r~d\\theta$."
+    "I'll use $\\theta$ to represent the total rotation of the roll in radians. In the diagram, $d\\theta$ represents a small increase in $\\theta$, which corresponds to a distance along the circumference of $r~d\\theta$."
    ]
   },
   {
@@ -154,14 +154,14 @@
    "id": "prostate-smell",
    "metadata": {},
    "source": [
-    "## Parameters\n",
+    "## Setting Parameters\n",
     "\n",
     "Here are the parameters of the system:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "honey-translator",
    "metadata": {},
    "outputs": [],
@@ -186,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "floppy-plane",
    "metadata": {},
    "outputs": [],
@@ -204,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "funky-bandwidth",
    "metadata": {},
    "outputs": [],
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "front-conservation",
    "metadata": {},
    "outputs": [],
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "defined-plymouth",
    "metadata": {},
    "outputs": [],
@@ -259,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "subject-brief",
    "metadata": {},
    "outputs": [],
@@ -281,7 +281,7 @@
    "id": "phantom-yacht",
    "metadata": {},
    "source": [
-    "## Simulation\n",
+    "## Simulating the System\n",
     "\n",
     "The state variables we'll use are, `theta`, `y`, and `r`.\n",
     "Here are the initial conditions:"
@@ -289,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "ranging-graham",
    "metadata": {},
    "outputs": [],
@@ -307,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "fresh-domestic",
    "metadata": {},
    "outputs": [],
@@ -326,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "signed-eight",
    "metadata": {},
    "outputs": [],
@@ -363,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "exterior-water",
    "metadata": {},
    "outputs": [],
@@ -381,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "strong-custody",
    "metadata": {},
    "outputs": [],
@@ -401,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "moved-present",
    "metadata": {},
    "outputs": [],
@@ -419,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "stable-lying",
    "metadata": {},
    "outputs": [],
@@ -439,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "careful-bahrain",
    "metadata": {},
    "outputs": [],
@@ -457,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "individual-patrick",
    "metadata": {},
    "outputs": [],
@@ -475,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "indian-skirt",
    "metadata": {},
    "outputs": [],
@@ -494,7 +494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "higher-conflict",
    "metadata": {},
    "outputs": [],
@@ -512,7 +512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "employed-ordinary",
    "metadata": {},
    "outputs": [],
@@ -535,14 +535,14 @@
    "id": "lesser-consumer",
    "metadata": {},
    "source": [
-    "## Plotting\n",
+    "## Plotting the Results\n",
     "\n",
     "Here's what `theta` looks like over time."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "included-schema",
    "metadata": {},
    "outputs": [],
@@ -567,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "persistent-siemens",
    "metadata": {},
    "outputs": [],
@@ -593,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "contrary-typing",
    "metadata": {},
    "outputs": [],
@@ -692,7 +692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "acknowledged-register",
    "metadata": {},
    "outputs": [],
@@ -727,19 +727,28 @@
   },
   {
    "cell_type": "markdown",
-   "id": "thick-luther",
+   "id": "variable-lambda",
    "metadata": {},
    "source": [
     "## Exercises\n",
     "\n",
-    "### Exercise\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "thick-luther",
+   "metadata": {},
+   "source": [
+    "### Exercise 1\n",
     "\n",
     " Since we keep `omega` constant, the linear velocity of the paper increases with radius.  We can use `gradient` to estimate the derivative of `results.y`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "cardiac-hospital",
    "metadata": {},
    "outputs": [],
@@ -749,7 +758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "welsh-charleston",
    "metadata": {},
    "outputs": [],
@@ -769,7 +778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "informational-washer",
    "metadata": {},
    "outputs": [],
@@ -794,7 +803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "id": "excellent-japanese",
    "metadata": {},
    "outputs": [],
@@ -804,7 +813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "id": "steady-member",
    "metadata": {},
    "outputs": [],
@@ -814,7 +823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "id": "applied-sacramento",
    "metadata": {},
    "outputs": [],
@@ -824,7 +833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "id": "adult-chuck",
    "metadata": {},
    "outputs": [],
@@ -834,7 +843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "id": "conditional-cliff",
    "metadata": {},
    "outputs": [],
@@ -844,7 +853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "id": "arranged-queensland",
    "metadata": {},
    "outputs": [],
@@ -854,7 +863,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "id": "similar-variance",
    "metadata": {},
    "outputs": [],
@@ -872,6 +881,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -887,7 +897,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap25.ipynb b/chapters/chap25.ipynb
index b34fb451..dfedacc0 100644
--- a/chapters/chap25.ipynb
+++ b/chapters/chap25.ipynb
@@ -5,7 +5,7 @@
    "id": "unauthorized-winter",
    "metadata": {},
    "source": [
-    "# Chapter 25"
+    "# Torque"
    ]
   },
   {
@@ -925,7 +925,10 @@
    "id": "pediatric-precipitation",
    "metadata": {},
    "source": [
-    "## Exercises"
+    "## Exercises\n",
+    "\n",
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "You can access the notebooks at <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -933,7 +936,7 @@
    "id": "sixth-breach",
    "metadata": {},
    "source": [
-    "### Exercise\n",
+    "### Exercise 1\n",
     "\n",
     " Continuing the example from this chapter, estimate the force that delivers the teapot to the desired position.\n",
     "Use this `System` object, with the friction we computed in the previous section."
@@ -1047,6 +1050,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -1062,7 +1066,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap26.ipynb b/chapters/chap26.ipynb
index 1ab7ef95..94196b18 100644
--- a/chapters/chap26.ipynb
+++ b/chapters/chap26.ipynb
@@ -5,7 +5,7 @@
    "id": "early-drove",
    "metadata": {},
    "source": [
-    "# Chapter 26"
+    "# Case Studies Part 3"
    ]
   },
   {
@@ -27,9 +27,7 @@
    "id": "acoustic-small",
    "metadata": {},
    "source": [
-    "You made it to the end of the book.  Congratulations!\n",
-    "\n",
-    "This last chapter is a collection of case studies you might want to read and work on.\n",
+    "This chapter is a collection of case studies you might want to read and work on.\n",
     "They are based on the methods in the last few chapters, including Newtonian mechanics in 1-D and 2-D, and rotation around a single axis."
    ]
   },
@@ -59,16 +57,8 @@
     "constant, `k`, so that the jumper falls all the way to the tea cup, but no farther!\n",
     "\n",
     "In the repository for this book, you will find a notebook,\n",
-    "`bungee1.ipynb`, which contains starter code and exercises for this case study."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "given-album",
-   "metadata": {},
-   "source": [
-    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/bungee1.ipynb) or\n",
-    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/bungee1.ipynb)."
+    "*bungee1.ipynb*, which contains starter code and exercises for this case study.\n",
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/bungee1.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/bungee1.ipynb>."
    ]
   },
   {
@@ -95,16 +85,10 @@
     "convince you: <http://modsimpy.com/drop>.\n",
     "\n",
     "In the repository for this book, you will find a notebook,\n",
-    "`bungee2.ipynb`, which contains starter code and exercises for this case study. How does the behavior of the system change as we vary the mass of the cord? When the mass of the cord equals the mass of the jumper, what is the net effect on the lowest point in the jump?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sporting-armenia",
-   "metadata": {},
-   "source": [
-    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/bungee2.ipynb) or\n",
-    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/bungee2.ipynb)."
+    "*bungee2.ipynb*, which contains starter code and exercises for this case study. \n",
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/bungee2.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/bungee2.ipynb>.\n",
+    "\n",
+    "How does the behavior of the system change as we vary the mass of the cord? When the mass of the cord equals the mass of the jumper, what is the net effect on the lowest point in the jump?"
    ]
   },
   {
@@ -119,20 +103,12 @@
     "Now let's extend the model to two dimensions and simulate one revolution of the Earth around the Sun, that is, one year.\n",
     "\n",
     "In the repository for this book, you will find a notebook,\n",
-    "`orbit.ipynb`, which contains starter code and exercises for this case study.\n",
+    "*orbit.ipynb*, which contains starter code and exercises for this case study.\n",
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/orbit.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/orbit.ipynb>.\n",
     "\n",
     "Among other things, you will have a chance to experiment with different algorithms and see what effect they have on the accuracy of the results."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "parliamentary-louis",
-   "metadata": {},
-   "source": [
-    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/orbit.ipynb) or\n",
-    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/orbit.ipynb)."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "victorian-colonial",
@@ -146,7 +122,7 @@
     "shown in this figure:\n",
     "\n",
     "![Diagram of the initial state for the Spider-Man case\n",
-    "study.](https://github.com/AllenDowney/ModSim/raw/master/figs/spiderman.png)\n",
+    "study.](https://github.com/AllenDowney/ModSim/raw/main/figs/spiderman.png)\n",
     "\n",
     "The origin, `O`, is at the base of the Empire State Building. The vector `H` represents the position where the webbing is attached to the building, relative to `O`. The vector `P` is the position of Spider-Man relative to `O`. And `L` is the vector from the attachment point to Spider-Man.\n",
     "\n",
@@ -193,19 +169,13 @@
     "5.  The spring constant of the web is 40 N/m when the cord is stretched, and 0 when it's compressed.\n",
     "\n",
     "In the repository for this book, you will find a notebook,\n",
-    "`spiderman.ipynb`, which contains starter code. Read through the\n",
+    "*spiderman.ipynb*, which contains starter code. \n",
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/spiderman.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/spiderman.ipynb>.\n",
+    "\n",
+    "Read through the\n",
     "notebook and run the code. It uses `minimize`, which is a SciPy function that can search for an optimal set of parameters (as contrasted with `minimize_scalar`, which can only search along a single axis)."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "ambient-recall",
-   "metadata": {},
-   "source": [
-    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/spiderman.ipynb) or\n",
-    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/spiderman.ipynb)."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "african-relative",
@@ -225,16 +195,8 @@
     "Assuming that the force applied by the kitten is 0.002 N, how long would it take to unroll a standard roll of toilet paper?\n",
     "\n",
     "In the repository for this book, you will find a notebook,\n",
-    "`kitten.ipynb`, which contains starter code for this case study. Use it to implement this model and check whether the results seem plausible."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "flying-forge",
-   "metadata": {},
-   "source": [
-    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/kitten.ipynb) or\n",
-    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/kitten.ipynb)."
+    "*kitten.ipynb*, which contains starter code for this case study. Use it to implement this model and check whether the results seem plausible.\n",
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/kitten.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/kitten.ipynb>."
    ]
   },
   {
@@ -284,7 +246,11 @@
     "\n",
     "$$\\frac{d^2 y}{dt^2} = -r \\frac{d^2 \\theta}{dt^2}$$ \n",
     "\n",
-    "Which we can write more concisely: $$a = -r \\alpha$$ This relationship is not a general law of nature; it is specific to scenarios like this where one object rolls along another without stretching or slipping.\n",
+    "Which we can write more concisely: \n",
+    "\n",
+    "$$a = -r \\alpha$$ \n",
+    "\n",
+    "This relationship is not a general law of nature; it is specific to scenarios like this where one object rolls along another without stretching or slipping.\n",
     "\n",
     "Because of the way we've set up the problem, $y$ actually has two\n",
     "meanings: it represents the length of the rolled string and the height\n",
@@ -348,16 +314,8 @@
     "where $I^*$ is the augmented moment of inertia, $I + m r^2$. \n",
     "We can use these equations for $a$ and $\\alpha$ to write a slope function and simulate this system.\n",
     "\n",
-    "In the repository for this book, you will find a notebook, `yoyo.ipynb`, which contains starter code for this case study. Use it to implement and test this model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "excessive-equity",
-   "metadata": {},
-   "source": [
-    "[Click here to download it](https://github.com/AllenDowney/ModSimPy/raw/master/examples/yoyo.ipynb) or\n",
-    "[click here to run it on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/yoyo.ipynb)."
+    "In the repository for this book, you will find a notebook, *yoyo.ipynb*, which contains starter code you can use to implement and test this model.\n",
+    "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/yoyo.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/yoyo.ipynb>."
    ]
   },
   {
@@ -385,7 +343,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap27.ipynb b/chapters/chap27.ipynb
new file mode 100644
index 00000000..543d856e
--- /dev/null
+++ b/chapters/chap27.ipynb
@@ -0,0 +1,512 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "early-drove",
+   "metadata": {},
+   "source": [
+    "# Under the Hood"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "imported-table",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "approximate-working",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import basename, exists\n",
+    "\n",
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/master/modsim.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "suspended-occasion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import functions from modsim\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "younger-bullet",
+   "metadata": {},
+   "source": [
+    "In this chapter we \"open the hood,\" looking more closely at how some of\n",
+    "the tools we have used---`run_solve_ivp`, `root_scalar`, and `maximize_scalar`---work.\n",
+    "\n",
+    "Most of the time you don't need to know, which is why I left this chapter for last.\n",
+    "But you might be curious. And if nothing else, I have found that I can remember how to use these tools more easily because I know something about how they work."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "african-collector",
+   "metadata": {},
+   "source": [
+    "## How run_solve_ivp Works\n",
+    "\n",
+    "`run_solve_ivp` is a function in the ModSimPy library that checks for common errors in the parameters and then calls `solve_ip`, which is the function in the SciPy library that does the actual work.\n",
+    "\n",
+    "By default, `solve_ivp` uses the Dormand-Prince method, which is a kind of Runge-Kutta method. You can read about it at\n",
+    "<https://en.wikipedia.org/wiki/Dormand-Prince_method>, but I'll give you a sense of\n",
+    "it here.\n",
+    "\n",
+    "The key idea behind all Runge-Kutta methods is to evaluate the slope function several times at each time step and use a weighted average of the computed slopes to estimate the value at the next time step.\n",
+    "Different methods evaluate the slope function in different places and compute the average with different weights.\n",
+    "\n",
+    "So let's see if we can figure out how `solve_ivp` works.\n",
+    "As an example, we'll solve the following differential equation:\n",
+    "\n",
+    "$$\\frac{dy}{dt}(t) = y \\sin t$$\n",
+    "\n",
+    "Here's the slope function we'll use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "coastal-cameroon",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def slope_func(t, state, system):\n",
+    "    y, = state\n",
+    "    dydt = y * np.sin(t)\n",
+    "    return dydt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "egyptian-inventory",
+   "metadata": {},
+   "source": [
+    "I'll create a `State` object with the initial state and a `System` object with the end time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "recovered-makeup",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init = State(y=1)\n",
+    "system = System(init=init, t_end=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "resident-document",
+   "metadata": {},
+   "source": [
+    "Now we can call `run_solve_ivp`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "younger-affect",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results, details = run_solve_ivp(system, slope_func)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sophisticated-person",
+   "metadata": {},
+   "source": [
+    "One of the variables in `details` is `nfev`, which stands for \"number of function evaluations\", that is, the number of times `solve_ivp` called the slope function.\n",
+    "This example took 50 evaluations.\n",
+    "Keep that in mind.\n",
+    "\n",
+    "Here are the first few time steps in `results`: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "suspended-tumor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "removable-queens",
+   "metadata": {},
+   "source": [
+    "And here is the number of time steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "noticed-portable",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unable-visibility",
+   "metadata": {},
+   "source": [
+    "`results` contains 101 points that are equally spaced in time.\n",
+    "Now you might wonder, if `solve_ivp` ran the slope function 50 times, how did we get 101 time steps?\n",
+    "\n",
+    "To answer that question, we need to know more how the solver works.\n",
+    "There are actually three steps:\n",
+    "\n",
+    "1. For each time step, `solve_ivp` evaluates the slope function seven times, with different values of `t` and `y`.\n",
+    "\n",
+    "2. Using the results, it computes the best estimate for the value `y` at the next time step.\n",
+    "\n",
+    "3. After computing all of the time steps, it uses interpolation to compute equally spaced points that connect the estimates from the previous step.\n",
+    "\n",
+    "So we can see what's happening, I will run `run_solve_ivp` with the keyword argument `dense_output=False`, which skips the interpolation step and returns time steps that are not equally spaced (that is, not \"dense\").\n",
+    "\n",
+    "While we're at it, I'll modify the slope function so that every time it runs, it adds the values of `t`, `y`, and `dydt` to a list called `evals`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "efficient-aluminum",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(t, state, system):\n",
+    "    y, = state\n",
+    "    dydt = y * np.sin(t)\n",
+    "    evals.append((t, y, dydt))\n",
+    "    return dydt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "compliant-sampling",
+   "metadata": {},
+   "source": [
+    "Now, before we call `run_solve_ivp` again, I'll initialize `evals` with an empty list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "handed-gothic",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evals = []\n",
+    "results2, details = run_solve_ivp(system, slope_func, dense_output=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mathematical-terrace",
+   "metadata": {},
+   "source": [
+    "Here are the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "accessory-wrapping",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "rapid-parks",
+   "metadata": {},
+   "source": [
+    "It turns out there are only eight time steps, and the first five of them only cover 0.11 seconds.\n",
+    "\n",
+    "The time steps are not equal because the Dormand-Prince method is *adaptive*.\n",
+    "At each time step, it actually computes two estimates of the next\n",
+    "value. By comparing them, it can estimate the magnitude of the error,\n",
+    "which it uses to adjust the time step. If the error is too big, it uses\n",
+    "a smaller time step; if the error is small enough, it uses a bigger time\n",
+    "step. By adjusting the time step in this way, it minimizes the number\n",
+    "of times it calls the slope function to achieve a given level of\n",
+    "accuracy.\n",
+    "\n",
+    "Because we saved the values of `y` and `t`, we can plot the locations where the slope function was evaluated.\n",
+    "I'll need to use a couple of features we have not seen before, if you don't mind.\n",
+    "\n",
+    "First we'll unpack the values from `evals` using `np.transpose`.\n",
+    "Then we can use trigonometry to convert the slope, `dydt`, to components called `u` and `v`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "indie-antigua",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t, y, slope = np.transpose(evals)\n",
+    "theta = np.arctan(slope)\n",
+    "u = np.cos(theta)\n",
+    "v = np.sin(theta)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sublime-significance",
+   "metadata": {},
+   "source": [
+    "Using these values, we can generate a *quiver plot* that shows an arrow for each time the slope function ran.\n",
+    "The location of the each arrow represents the values of `t` and `y`; the orientation of the arrow shows the slope that was computed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "fossil-librarian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.quiver(t, y, u, v, pivot='middle', \n",
+    "           color='C1', alpha=0.4, label='evaluation points')\n",
+    "results2['y'].plot(style='o', color='C0', label='solution points')\n",
+    "results['y'].plot(lw=1, label='interpolation')\n",
+    "\n",
+    "decorate()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "uniform-cable",
+   "metadata": {},
+   "source": [
+    "In this figure, the arrows show where the slope function was executed;\n",
+    "the dots show the best estimate of `y` for each time step; and the line shows the interpolation that connects the estimates.\n",
+    "\n",
+    "Notice that many of the arrows do not fall on the line; `solve_ivp` evaluated the slope function at these locations in order to compute the solution, but as it turned out, they are not part of the solution.\n",
+    "\n",
+    "This is good to know when you are writing a slope function; you should not assume that the time and state you get as input variables are correct."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "split-arabic",
+   "metadata": {},
+   "source": [
+    "## How root_scalar Works\n",
+    "\n",
+    "`root_scalar` in the ModSim library is a wrapper for a function in the SciPy library with the same name.\n",
+    "Like `run_solve_ivp`, it checks for common errors and changes some of the parameters in a way that makes the SciPy function easier to use (I hope).\n",
+    "\n",
+    "According to the documentation, `root_scalar` uses \"a combination of bisection, secant, and inverse quadratic interpolation methods.\" (See\n",
+    "<https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root_scalar.html>)\n",
+    "\n",
+    "To understand what that means, suppose we're trying to find a root of a\n",
+    "function of one variable, $f(x)$, and assume we have evaluated the\n",
+    "function at two places, $x_1$ and $x_2$, and found that the results have\n",
+    "opposite signs. Specifically, assume $f(x_1) > 0$ and $f(x_2) < 0$, as\n",
+    "shown in the following diagram:\n",
+    "\n",
+    "![Initial state of a root-finding search](https://github.com/AllenDowney/ModSim/raw/main/figs/secant.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "divided-sailing",
+   "metadata": {},
+   "source": [
+    "If $f$ is a continuous function, there must be at least one root in this\n",
+    "interval. In this case we would say that $x_1$ and $x_2$ *bracket* a\n",
+    "root.\n",
+    "\n",
+    "If this were all you knew about $f$, where would you go looking for a\n",
+    "root? If you said \"halfway between $x_1$ and $x_2$,\" congratulations!\n",
+    "`You just invented a numerical method called *bisection*!\n",
+    "\n",
+    "If you said, \"I would connect the dots with a straight line and compute\n",
+    "the zero of the line,\" congratulations! You just invented the *secant\n",
+    "method*!\n",
+    "\n",
+    "And if you said, \"I would evaluate $f$ at a third point, find the\n",
+    "parabola that passes through all three points, and compute the zeros of\n",
+    "the parabola,\" congratulations, you just invented *inverse quadratic\n",
+    "interpolation*!\n",
+    "\n",
+    "That's most of how `root_scalar` works. The details of how these methods are\n",
+    "combined are interesting, but beyond the scope of this book. You can\n",
+    "read more at <https://en.wikipedia.org/wiki/Brents_method>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dental-archives",
+   "metadata": {},
+   "source": [
+    "## How maximize_scalar Works \n",
+    "\n",
+    "`maximize_scalar` in the ModSim library is a wrapper for a function in the SciPy library called `minimize_scalar`.\n",
+    "You can read about it at <https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html>.\n",
+    "\n",
+    "By default, it uses Brent's method, which is related to the method I described in the previous section for root-finding.\n",
+    "Brent's method for finding a maximum or minimum is based on a simpler algorithm:\n",
+    "the *golden-section search*, which I will explain.\n",
+    "\n",
+    "Suppose we're trying to find the minimum of a function of a single variable, $f(x)$.\n",
+    "As a starting place, assume that we have evaluated the function at three\n",
+    "places, $x_1$, $x_2$, and $x_3$, and found that $x_2$ yields the lowest\n",
+    "value. The following diagram shows this initial state.\n",
+    "\n",
+    "![Initial state of a golden-section\n",
+    "search](https://github.com/AllenDowney/ModSim/raw/main/figs/golden1.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "alpine-metro",
+   "metadata": {},
+   "source": [
+    "We will assume that $f(x)$ is continuous and *unimodal* in this range,\n",
+    "which means that there is exactly one minimum between $x_1$ and $x_3$.\n",
+    "\n",
+    "The next step is to choose a fourth point, $x_4$, and evaluate $f(x_4)$.\n",
+    "There are two possible outcomes, depending on whether $f(x_4)$ is\n",
+    "greater than $f(x_2)$ or not.\n",
+    "The following figure shows the two possible states.\n",
+    "\n",
+    "![Possible states of a golden-section search after evaluating\n",
+    "$f(x_4)$](https://github.com/AllenDowney/ModSim/raw/main/figs/golden2.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "african-check",
+   "metadata": {},
+   "source": [
+    "If $f(x_4)$ is less than $f(x_2)$ (shown on the left), the minimum must\n",
+    "be between $x_2$ and $x_3$, so we would discard $x_1$ and proceed with\n",
+    "the new bracket $(x_2, x_4, x_3)$.\n",
+    "\n",
+    "If $f(x_4)$ is greater than $f(x_2)$ (shown on the right), the local\n",
+    "minimum must be between $x_1$ and $x_4$, so we would discard $x_3$ and\n",
+    "proceed with the new bracket $(x_1, x_2, x_4)$.\n",
+    "\n",
+    "Either way, the range gets smaller and our estimate of the optimal value\n",
+    "of $x$ gets better.\n",
+    "\n",
+    "This method works for almost any value of $x_4$, but some choices are\n",
+    "better than others. You might be tempted to bisect the interval between\n",
+    "$x_2$ and $x_3$, but that turns out not to be optimal. You can\n",
+    "read about a better option at <https://greenteapress.com/matlab/golden>."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "broken-preparation",
+   "metadata": {},
+   "source": [
+    "## Chapter Review\n",
+    "\n",
+    "The information in this chapter is not strictly necessary; you can use\n",
+    "these methods without knowing much about how they work. But there are\n",
+    "two reasons you might want to know.\n",
+    "\n",
+    "One reason is pure curiosity. If you use these methods, and especially\n",
+    "if you come to rely on them, you might find it unsatisfying to treat\n",
+    "them as \"black boxes.\" At the risk of mixing metaphors, I hope you\n",
+    "enjoyed opening the hood.\n",
+    "\n",
+    "The other reason is that these methods are not infallible; sometimes\n",
+    "things go wrong. If you know how they work, at least in a general sense,\n",
+    "you might find it easier to debug them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "noted-interview",
+   "metadata": {},
+   "source": [
+    "With that, you have reached the end of the book, so congratulations! I\n",
+    "hope you enjoyed it and learned a lot. I think the tools in this book\n",
+    "are useful, and the ways of thinking are important, not just in\n",
+    "engineering and science, but in practically every field of inquiry.\n",
+    "\n",
+    "Models are the tools we use to understand the world: if you build good\n",
+    "models, you are more likely to get things right. Good luck!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "suspended-apparel",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From e3b3a278bafeee7bbb1bf17cd2cc92de4d7f5e2a Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Wed, 14 Jul 2021 09:43:18 -0400
Subject: [PATCH 078/144] Updating the notebook zip file

---
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 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
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From 98c50c6ca248eb64586887547342a99ce0a46f83 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 15 Jul 2021 15:22:22 -0400
Subject: [PATCH 079/144] Updating chapters

---
 chapters/chap24.ipynb | 35 ++++++++++++++++--------------
 chapters/chap25.ipynb | 50 ++++++++++++++++++++++++++++++-------------
 chapters/chap26.ipynb |  2 +-
 chapters/chap27.ipynb | 36 ++++++++++++++++++-------------
 4 files changed, 76 insertions(+), 47 deletions(-)

diff --git a/chapters/chap24.ipynb b/chapters/chap24.ipynb
index 8d50d082..91a8c07f 100644
--- a/chapters/chap24.ipynb
+++ b/chapters/chap24.ipynb
@@ -78,11 +78,9 @@
     "In this chapter and the next we'll model systems that involve rotating objects.\n",
     "In general, rotation is complicated.\n",
     "In three dimensions, objects can rotate around three axes and many objects are easier to spin around some axes than others.\n",
-    "\n",
     "If the configuration of an object changes over time, it might become\n",
     "easier or harder to spin, which explains the surprising dynamics of\n",
     "gymnasts, divers, ice skaters, etc.\n",
-    "\n",
     "And when you apply a twisting force to a rotating object, the effect is often contrary to intuition. \n",
     "For an example, see this video on gyroscopic precession: <http://modsimpy.com/precess>."
    ]
@@ -93,10 +91,10 @@
    "metadata": {},
    "source": [
     "We will not take on the physics of rotation in all its glory; rather, we will focus on simple scenarios where all rotation and all twisting forces are around a single axis. \n",
-    "In that case, we can treat some vector quantities as if they were scalars, in the same way that we sometimes treat velocity as a scalar with an implicit direction.\n",
+    "In that case, we can treat some vector quantities as if they were scalars; that is, simple numbers.\n",
     "\n",
     "The fundamental ideas in these examples are angular velocity, angular acceleration, torque, and moment of inertia.\n",
-    "If you are not already familiar with these concepts, I will define them as we go along, and I will point to additional reading."
+    "If you are not already familiar with these concepts, don't worry; I will define them as we go along, and I will point to additional reading."
    ]
   },
   {
@@ -199,7 +197,7 @@
    "id": "ordinary-leader",
    "metadata": {},
    "source": [
-    "In that case, the circumference of the roll is also be constant:"
+    "In that case, the circumference of the roll is also constant:"
    ]
   },
   {
@@ -348,7 +346,6 @@
     "As usual, the slope function takes a time stamp, a `State` object, and a `System` object. \n",
     "\n",
     "The job of the slope function is to compute the time derivatives of the state variables.\n",
-    "\n",
     "The derivative of `theta` is angular velocity, `omega`.\n",
     "The derivatives of `y` and `r` are given by the differential equations we derived."
    ]
@@ -583,12 +580,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dedicated-tuning",
+   "id": "smoking-spine",
    "metadata": {},
    "source": [
     "`r` also increases linearly.\n",
     "\n",
-    "But since the derivative of `y` depends on `r`, and `r` is increasing, `y` grows with increasing slope."
+    "Here's what `y` looks like over time."
    ]
   },
   {
@@ -609,11 +606,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "furnished-executive",
+   "id": "dedicated-tuning",
    "metadata": {},
    "source": [
-    "In the next section, we'll see that we could have solved the\n",
-    "differential equations analytically.\n",
+    "Since the derivative of `y` depends on `r`, and `r` is increasing, `y` grows with increasing slope.\n",
+    "\n",
+    "In the next section, we'll see that we could have solved these differential equations analytically.\n",
     "However, it is often useful to start with simulation as a way of exploring and checking assumptions."
    ]
   },
@@ -622,7 +620,7 @@
    "id": "better-asbestos",
    "metadata": {},
    "source": [
-    "## Analysis\n",
+    "## Analytic Solution\n",
     "\n",
     "Since angular velocity is constant: \n",
     "\n",
@@ -717,7 +715,6 @@
     "## Summary\n",
     "\n",
     "This chapter introduces rotation, starting with an example where angular velocity is constant.\n",
-    "\n",
     "We simulated the manufacture of a roll of toilet paper, then we solved the same problem analytically.\n",
     "\n",
     "In the next chapter, we'll see a more interesting example where angular velocity is not constant.  And we'll introduce three new concepts: torque, angular acceleration, and moment of inertia.\n",
@@ -756,6 +753,14 @@
     "dydt = gradient(results.y)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "atomic-montana",
+   "metadata": {},
+   "source": [
+    "Here's what the result looks like."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 26,
@@ -793,12 +798,10 @@
    "metadata": {},
    "source": [
     "Now suppose this peak velocity is the limiting factor; that is, we can't move the paper any faster than that.\n",
-    "\n",
     "In that case, we might be able to speed up the process by keeping the linear velocity at the maximum all the time.\n",
     "\n",
     "Write a slope function that keeps the linear velocity, `dydt`, constant, and computes the angular velocity, `omega`, accordingly.\n",
-    "\n",
-    "Run the simulation and see how much faster we could finish rolling the paper."
+    "Then, run the simulation and see how much faster we could finish rolling the paper."
    ]
   },
   {
diff --git a/chapters/chap25.ipynb b/chapters/chap25.ipynb
index dfedacc0..cc17ba05 100644
--- a/chapters/chap25.ipynb
+++ b/chapters/chap25.ipynb
@@ -63,7 +63,9 @@
   {
    "cell_type": "markdown",
    "id": "plastic-trigger",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap25.ipynb)"
    ]
@@ -85,12 +87,12 @@
    "source": [
     "## Angular Acceleration\n",
     "\n",
-    "Just as linear acceleration is the derivative of velocity, **angular\n",
-    "acceleration* is the derivative of angular velocity. And just as linear acceleration is caused by force, angular acceleration is caused by the rotational version of force, *torque**. If you are not familiar with torque, you can read about it at <http://modsimpy.com/torque>.\n",
+    "Just as linear acceleration is the derivative of velocity, *angular\n",
+    "acceleration* is the derivative of angular velocity. And just as linear acceleration is caused by force, angular acceleration is caused by the rotational version of force, *torque*. If you are not familiar with torque, you can read about it at <http://modsimpy.com/torque>.\n",
     "\n",
-    "In general, torque is a vector quantity, defined as the **cross\n",
+    "In general, torque is a vector quantity, defined as the *cross\n",
     "product* of $\\vec{r}$ and $\\vec{F}$, where $\\vec{r}$ is the *lever\n",
-    "arm**, a vector from the center of rotation to the point where the force is applied, and $\\vec{F}$ is the vector that represents the magnitude and direction of the force."
+    "arm*, a vector from the center of rotation to the point where the force is applied, and $\\vec{F}$ is the vector that represents the magnitude and direction of the force."
    ]
   },
   {
@@ -98,7 +100,7 @@
    "id": "promotional-trigger",
    "metadata": {},
    "source": [
-    "However, for the problems in this chapter, we only need the *magnitude* of torque; we don't care about the direction. In that case, we can compute \n",
+    "However, for the problems in this chapter, we only need the *magnitude* of torque; we don't care about the direction. In that case, we can compute this product of scalar quantities:\n",
     "\n",
     "$$\\tau = r F \\sin \\theta$$ \n",
     "\n",
@@ -121,7 +123,7 @@
     "where $\\alpha$ is angular acceleration and $I$ is *moment of inertia*. Just as mass is what makes it hard to accelerate an object, moment of inertia is what makes it hard to spin an object.\n",
     "\n",
     "In the most general case, a 3-D object rotating around an arbitrary\n",
-    "axis, moment of inertia is a tensor, which is a function that takes a\n",
+    "axis, moment of inertia is a *tensor*, which is a function that takes a\n",
     "vector as a parameter and returns a vector as a result.\n",
     "\n",
     "Fortunately, in a system where all rotation and torque happens around a single axis, we don't have to deal with the most general case. We can treat moment of inertia as a scalar quantity.\n",
@@ -167,7 +169,7 @@
     "\n",
     "2.  The teapot weights 0.3 kg, and it sits 0.4 m from the center of the turntable.\n",
     "\n",
-    "The following figure shows the scenario, where $F$ is the force I apply to the turntable at the perimeter, perpendicular to the lever arm, $r$, and $\\tau$ is the resulting torque. The blue circle near the bottom is the teapot.\n",
+    "The following figure shows the scenario, where $F$ is the force I apply to the turntable at the perimeter, perpendicular to the lever arm, $r$, and $\\tau$ is the resulting torque. The  circle near the bottom is the teapot.\n",
     "\n",
     "![Diagram of a turntable with a\n",
     "teapot.](https://github.com/AllenDowney/ModSim/raw/main/figs/teapot.png)\n",
@@ -376,7 +378,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "decent-microwave",
+   "id": "tribal-disclosure",
    "metadata": {},
    "source": [
     "## Two Phase Simulation\n",
@@ -390,7 +392,15 @@
     "2.  During the second phase, force is 0, and we run until `omega` is 0.\n",
     "\n",
     "Then we can combine the results of the two phases into a single\n",
-    "`TimeFrame`.\n",
+    "`TimeFrame`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "decent-microwave",
+   "metadata": {},
+   "source": [
+    "### Phase 1\n",
     "\n",
     "Here's the event function I'll use for Phase 1; it stops the simulation when `theta` reaches `theta_push`, which is when I stop pushing:"
    ]
@@ -491,6 +501,8 @@
    "id": "aware-generator",
    "metadata": {},
    "source": [
+    "### Phase 2\n",
+    "\n",
     "Now we can make a `System` object for Phase 2 with the initial state\n",
     "from Phase 1 and with `force=0`."
    ]
@@ -549,6 +561,8 @@
    "id": "administrative-major",
    "metadata": {},
    "source": [
+    "The result is the angular velocity at the beginning of Phase 2, in rad/s.\n",
+    "\n",
     "Now we can run the second phase."
    ]
   },
@@ -569,6 +583,8 @@
    "id": "hindu-requirement",
    "metadata": {},
    "source": [
+    "### Combining the Results\n",
+    "\n",
     "`DataFrame` provides `append`, which appends `results2` to the end of\n",
     "`results1`."
    ]
@@ -749,6 +765,12 @@
    "id": "generous-parcel",
    "metadata": {},
    "source": [
+    "This error function takes torque due to friction as an input.\n",
+    "It extracts `force` from the `System` object and runs the simulation.\n",
+    "From the results, it extracts the last value of `theta` and returns the difference between the result of the simulation and the result of the experiment.\n",
+    "When this difference is 0, the value of `torque_friction` is an estimate for the friction in the experiment.\n",
+    "\n",
+    "To bracket the root, we need one value that's too low and one that's too high.\n",
     "With `torque_friction=0.3`, the table rotates a bit too far:"
    ]
   },
@@ -827,8 +849,7 @@
     "Now that we know the torque due to friction, we can compute the force\n",
     "needed to rotate the turntable through the remaining angle, that is,\n",
     "from 1.5 rad to 3.14 rad.\n",
-    "\n",
-    "But first, let's animate the results."
+    "You'll have a chance to do that as an exercise, but first, let's animate the results."
    ]
   },
   {
@@ -836,8 +857,7 @@
    "id": "knowing-sleeve",
    "metadata": {},
    "source": [
-    "## Animation\n",
-    "\n",
+    "## Animating the Turntable\n",
     "\n",
     "Here's a function that takes the state of the system and draws it."
    ]
@@ -893,7 +913,7 @@
    "id": "efficient-summary",
    "metadata": {},
    "source": [
-    "And here's an animation of the first push."
+    "And here's how we call it."
    ]
   },
   {
diff --git a/chapters/chap26.ipynb b/chapters/chap26.ipynb
index 94196b18..758cd3c6 100644
--- a/chapters/chap26.ipynb
+++ b/chapters/chap26.ipynb
@@ -98,7 +98,7 @@
    "source": [
     "## Orbiting the Sun\n",
     "\n",
-    "In a previous example, we modeled the interaction between the Earth and the Sun, simulating what would happen if the Earth stopped in its orbit and fell straight into the Sun.\n",
+    "In the exercise at the end of Chapter 20, we modeled the interaction between the Earth and the Sun, simulating what would happen if the Earth stopped in its orbit and fell straight into the Sun.\n",
     "\n",
     "Now let's extend the model to two dimensions and simulate one revolution of the Earth around the Sun, that is, one year.\n",
     "\n",
diff --git a/chapters/chap27.ipynb b/chapters/chap27.ipynb
index 543d856e..c5f52c3d 100644
--- a/chapters/chap27.ipynb
+++ b/chapters/chap27.ipynb
@@ -24,9 +24,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 2,
    "id": "approximate-working",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# download modsim.py if necessary\n",
@@ -46,9 +48,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 3,
    "id": "suspended-occasion",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# import functions from modsim\n",
@@ -94,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 4,
    "id": "coastal-cameroon",
    "metadata": {},
    "outputs": [],
@@ -117,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "recovered-makeup",
    "metadata": {},
    "outputs": [],
@@ -136,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 6,
    "id": "younger-affect",
    "metadata": {},
    "outputs": [],
@@ -159,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 7,
    "id": "suspended-tumor",
    "metadata": {},
    "outputs": [],
@@ -177,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 8,
    "id": "noticed-portable",
    "metadata": {},
    "outputs": [],
@@ -209,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 9,
    "id": "efficient-aluminum",
    "metadata": {},
    "outputs": [],
@@ -231,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 10,
    "id": "handed-gothic",
    "metadata": {},
    "outputs": [],
@@ -250,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 11,
    "id": "accessory-wrapping",
    "metadata": {},
    "outputs": [],
@@ -283,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 12,
    "id": "indie-antigua",
    "metadata": {},
    "outputs": [],
@@ -305,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 13,
    "id": "fossil-librarian",
    "metadata": {},
    "outputs": [],
@@ -317,7 +321,8 @@
     "results2['y'].plot(style='o', color='C0', label='solution points')\n",
     "results['y'].plot(lw=1, label='interpolation')\n",
     "\n",
-    "decorate()"
+    "decorate(xlabel='Time (t)',\n",
+    "         ylabel='Quantity (y)')"
    ]
   },
   {
@@ -489,6 +494,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",

From 9c164caf9a444f3715b8801c514f26be327c4e8d Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 15 Jul 2021 15:22:22 -0400
Subject: [PATCH 080/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 239267 -> 239582 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index dbaa662e32e2f5d1441ed8744e0210e97c0e05e6..7dd8b9a7d34198fc4b21df597d6d55ff2984761d 100644
GIT binary patch
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C=Q{QP


From 32672cd91a2cfc9cdd82455db9beb78770006484 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 10 Feb 2022 14:36:40 -0500
Subject: [PATCH 081/144] Update chap01.ipynb

---
 chapters/chap01.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/chapters/chap01.ipynb b/chapters/chap01.ipynb
index 2f358775..844e7204 100644
--- a/chapters/chap01.ipynb
+++ b/chapters/chap01.ipynb
@@ -752,7 +752,7 @@
    },
    "outputs": [],
    "source": [
-    "assert abs(v.magnitude - 86.41527642726142) < 1e7"
+    "assert abs(v.magnitude - 86.41527642726142) < 1e-7"
    ]
   },
   {

From 1fe1cee5ebbca47fde1c7114ba343ced1bbc5806 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sun, 20 Mar 2022 19:07:24 -0400
Subject: [PATCH 082/144] Removing old versions

---
 colab/chap01.ipynb    |  532 -----------------
 colab/chap02.ipynb    |  893 -----------------------------
 colab/chap03.ipynb    |  606 --------------------
 colab/chap04.ipynb    |  681 ----------------------
 colab/chap05.ipynb    |  617 --------------------
 colab/chap06.ipynb    |  560 ------------------
 colab/chap07.ipynb    |  547 ------------------
 colab/chap08.ipynb    |  609 --------------------
 colab/chap09.ipynb    |  657 ---------------------
 colab/chap10.ipynb    |  474 ----------------
 colab/chap11.ipynb    |  476 ----------------
 colab/chap12.ipynb    |  887 -----------------------------
 colab/chap13.ipynb    |  526 -----------------
 colab/chap13ode.ipynb |  194 -------
 colab/chap14.ipynb    |  487 ----------------
 colab/chap15.ipynb    |  332 -----------
 colab/chap16.ipynb    |  854 ----------------------------
 colab/chap17.ipynb    |  299 ----------
 colab/chap18.ipynb    |  559 ------------------
 colab/chap20.ipynb    |  656 ---------------------
 colab/chap21.ipynb    |  527 -----------------
 colab/chap22.ipynb    | 1264 -----------------------------------------
 colab/chap23.ipynb    |  584 -------------------
 colab/chap24.ipynb    |  653 ---------------------
 colab/chap25.ipynb    |  957 -------------------------------
 25 files changed, 15431 deletions(-)
 delete mode 100644 colab/chap01.ipynb
 delete mode 100644 colab/chap02.ipynb
 delete mode 100644 colab/chap03.ipynb
 delete mode 100644 colab/chap04.ipynb
 delete mode 100644 colab/chap05.ipynb
 delete mode 100644 colab/chap06.ipynb
 delete mode 100644 colab/chap07.ipynb
 delete mode 100644 colab/chap08.ipynb
 delete mode 100644 colab/chap09.ipynb
 delete mode 100644 colab/chap10.ipynb
 delete mode 100644 colab/chap11.ipynb
 delete mode 100644 colab/chap12.ipynb
 delete mode 100644 colab/chap13.ipynb
 delete mode 100644 colab/chap13ode.ipynb
 delete mode 100644 colab/chap14.ipynb
 delete mode 100644 colab/chap15.ipynb
 delete mode 100644 colab/chap16.ipynb
 delete mode 100644 colab/chap17.ipynb
 delete mode 100644 colab/chap18.ipynb
 delete mode 100644 colab/chap20.ipynb
 delete mode 100644 colab/chap21.ipynb
 delete mode 100644 colab/chap22.ipynb
 delete mode 100644 colab/chap23.ipynb
 delete mode 100644 colab/chap24.ipynb
 delete mode 100644 colab/chap25.ipynb

diff --git a/colab/chap01.ipynb b/colab/chap01.ipynb
deleted file mode 100644
index 7a39ec16..00000000
--- a/colab/chap01.ipynb
+++ /dev/null
@@ -1,532 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Jupyter\n",
-    "\n",
-    "Welcome to *Modeling and Simulation in Python*, and welcome to Jupyter.\n",
-    "\n",
-    "This is a Jupyter notebook, which is a development environment where you can write and run Python code.  Each notebook is divided into cells.  Each cell contains either text (like this cell) or Python code."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Selecting and running cells\n",
-    "\n",
-    "To select a cell, click in the left margin next to the cell.  You should see a blue frame surrounding the selected cell.\n",
-    "\n",
-    "To edit a code cell, click inside the cell.  You should see a green frame around the selected cell, and you should see a cursor inside the cell.\n",
-    "\n",
-    "To edit a text cell, double-click inside the cell.  Again, you should see a green frame around the selected cell, and you should see a cursor inside the cell.\n",
-    "\n",
-    "To run a cell, hold down SHIFT and press ENTER.  \n",
-    "\n",
-    "* If you run a text cell, Jupyter formats the text and displays the result.\n",
-    "\n",
-    "* If you run a code cell, Jupyter runs the Python code in the cell and displays the result, if any.\n",
-    "\n",
-    "To try it out, edit this cell, change some of the text, and then press SHIFT-ENTER to format it."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Adding and removing cells\n",
-    "\n",
-    "You can add and remove cells from a notebook using the buttons in the toolbar and the items in the menu, both of which you should see at the top of this notebook.\n",
-    "\n",
-    "Try the following exercises:\n",
-    "\n",
-    "1.  From the Insert menu select \"Insert cell below\" to add a cell below this one.  By default, you get a code cell, as you can see in the pulldown menu that says \"Code\".\n",
-    "\n",
-    "2.  In the new cell, add a print statement like `print('Hello')`, and run it.\n",
-    "\n",
-    "3.  Add another cell, select the new cell, and then click on the pulldown menu that says \"Code\" and select \"Markdown\".  This makes the new cell a text cell.\n",
-    "\n",
-    "4.  In the new cell, type some text, and then run it.\n",
-    "\n",
-    "5.  Use the arrow buttons in the toolbar to move cells up and down.\n",
-    "\n",
-    "6.  Use the cut, copy, and paste buttons to delete, add, and move cells.\n",
-    "\n",
-    "7.  As you make changes, Jupyter saves your notebook automatically, but if you want to make sure, you can press the save button, which looks like a floppy disk from the 1990s.\n",
-    "\n",
-    "8.  Finally, when you are done with a notebook, select \"Close and Halt\" from the File menu."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installing modules\n",
-    "\n",
-    "These notebooks use standard Python modules like NumPy and SciPy.  I assume you already have them installed in your environment.\n",
-    "\n",
-    "They also use two less common modules: Pint, which provides units, and `modsimpy`, which contains code I wrote specifically for this book.\n",
-    "\n",
-    "The following cells check whether you have these modules already and tries to install them if you don't."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first time you run this on a new installation of Python, it might produce a warning message in pink.  That's probably ok, but if you get a message that says `modsim.py depends on Python 3.7 features`, that means you have an older version of Python, and some features in `modsim.py` won't work correctly.\n",
-    "\n",
-    "If you need a newer version of Python, I recommend installing Anaconda.  You'll find more information in the preface of the book.\n",
-    "\n",
-    "You can find out what version of Python and Jupyter you have by running the following cells."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python --version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!jupyter-notebook --version"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The penny myth\n",
-    "\n",
-    "The following cells contain code from the beginning of Chapter 1.\n",
-    "\n",
-    "`modsim` defines `UNITS`, which contains variables representing pretty much every unit you've ever heard of.  It uses [Pint](https://pint.readthedocs.io/en/latest/), which is a Python library that provides tools for computing with units.\n",
-    "\n",
-    "The following lines create new variables named `meter` and `second`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "meter = UNITS.meter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "second = UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To find out what other units are defined, type `UNITS.` (including the period) in the next cell and then press TAB.  You should see a pop-up menu with a list of units."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a variable named `a` and give it the value of acceleration due to gravity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = 9.8 * meter / second**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create `t` and give it the value 4 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = 4 * second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute the distance a penny would fall after `t` seconds with constant acceleration `a`.  Notice that the units of the result are correct."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a * t**2 / 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise**: Compute the velocity of the penny after `t` seconds.  Check that the units of the result are correct."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise**: Why would it be nonsensical to add `a` and `t`? What happens if you try?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The error messages you get from Python are big and scary, but if you read them carefully, they contain a lot of useful information.\n",
-    "\n",
-    "1.  Start from the bottom and read up.\n",
-    "2.  The last line usually tells you what type of error happened, and sometimes additional information.\n",
-    "3.  The previous lines are a \"traceback\" of what was happening when the error occurred.  The first section of the traceback shows the code you wrote.  The following sections are often from Python libraries.\n",
-    "\n",
-    "In this example, you should get a `DimensionalityError`, which is defined by Pint to indicate that you have violated a rules of dimensional analysis: you cannot add quantities with different dimensions.\n",
-    "\n",
-    "Before you go on, you might want to delete the erroneous code so the notebook can run without errors."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Falling pennies\n",
-    "\n",
-    "Now let's solve the falling penny problem.\n",
-    "\n",
-    "Set `h` to the height of the Empire State Building:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h = 381 * meter"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute the time it would take a penny to fall, assuming constant acceleration.\n",
-    "\n",
-    "$ a t^2 / 2 = h $\n",
-    "\n",
-    "$ t = \\sqrt{2 h / a}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = sqrt(2 * h / a)\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Given `t`, we can compute the velocity of the penny when it lands.\n",
-    "\n",
-    "$v = a t$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v = a * t\n",
-    "v"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can convert from one set of units to another like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mile = UNITS.mile\n",
-    "hour = UNITS.hour"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v.to(mile/hour)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose you bring a 10 foot pole to the top of the Empire State Building and use it to drop the penny from `h` plus 10 feet.\n",
-    "\n",
-    "Define a variable named `foot` that contains the unit `foot` provided by `UNITS`.  Define a variable named `pole_height` and give it the value 10 feet.\n",
-    "\n",
-    "What happens if you add `h`, which is in units of meters, to `pole_height`, which is in units of feet?  What happens if you write the addition the other way around?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** In reality, air resistance limits the velocity of the penny.  At about 18 m/s, the force of air resistance equals the force of gravity and the penny stops accelerating.\n",
-    "\n",
-    "As a simplification, let's assume that the acceleration of the penny is `a` until the penny reaches 18 m/s, and then 0 afterwards.  What is the total time for the penny to fall 381 m?\n",
-    "\n",
-    "You can break this question into three parts:\n",
-    "\n",
-    "1. How long until the penny reaches 18 m/s with constant acceleration `a`.\n",
-    "2. How far would the penny fall during that time?\n",
-    "3. How long to fall the remaining distance with constant velocity 18 m/s?\n",
-    "\n",
-    "Suggestion: Assign each intermediate result to a variable with a meaningful name.  And assign units to all quantities!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Restart and run all\n",
-    "\n",
-    "When you change the contents of a cell, you have to run it again for those changes to have an effect.  If you forget to do that, the results can be confusing, because the code you are looking at is not the code you ran.\n",
-    "\n",
-    "If you ever lose track of which cells have run, and in what order, you should go to the Kernel menu and select \"Restart & Run All\".  Restarting the kernel means that all of your variables get deleted, and running all the cells means all of your code will run again, in the right order.\n",
-    "\n",
-    "**Exercise:**  Select \"Restart & Run All\" now and confirm that it does what you want."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap02.ipynb b/colab/chap02.ipynb
deleted file mode 100644
index 52250e76..00000000
--- a/colab/chap02.ipynb
+++ /dev/null
@@ -1,893 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Modeling a bikeshare system"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll start with a `State` object that represents the number of bikes at each station.\n",
-    "\n",
-    "When you display a `State` object, it lists the state variables and their values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can access the state variables using dot notation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare.olin"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "bikeshare.wellesley"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** What happens if you spell the name of a state variable wrong?  Edit the previous cell, change the spelling of `wellesley`, and run the cell again.\n",
-    "\n",
-    "The error message uses the word \"attribute\", which is another name for what we are calling a state variable. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Add a third attribute called `babson` with initial value 0, and display the state of `bikeshare` again."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Updating\n",
-    "\n",
-    "We can use the update operators `+=` and `-=` to change state variables."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare.olin -= 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we display `bikeshare`, we should see the change."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Of course, if we subtract a bike from `olin`, we should add it to `wellesley`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare.wellesley += 1\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Functions\n",
-    "\n",
-    "We can take the code we've written so far and encapsulate it in a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_wellesley():\n",
-    "    bikeshare.olin -= 1\n",
-    "    bikeshare.wellesley += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When you define a function, it doesn't run the statements inside the function, yet.  When you call the function, it runs the statements inside."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bike_to_wellesley()\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "One common error is to omit the parentheses, which has the effect of looking up the function, but not calling it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bike_to_wellesley"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The output indicates that `bike_to_wellesley` is a function defined in a \"namespace\" called `__main__`, but you don't have to understand what that means."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Define a function called `bike_to_olin` that moves a bike from Wellesley to Olin.  Call the new function and display `bikeshare`  to confirm that it works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Conditionals"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`modsim.py` provides `flip`, which takes a probability and returns either `True` or `False`, which are special values defined by Python.\n",
-    "\n",
-    "The Python function `help` looks up a function and displays its documentation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(flip)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following example, the probability is 0.7 or 70%.  If you run this cell several times, you should get `True` about 70% of the time and `False` about 30%."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "flip(0.7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following example, we use `flip` as part of an if statement.  If the result from `flip` is `True`, we print `heads`; otherwise we do nothing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if flip(0.7):\n",
-    "    print('heads')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With an else clause, we can print heads or tails depending on whether `flip` returns `True` or `False`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if flip(0.7):\n",
-    "    print('heads')\n",
-    "else:\n",
-    "    print('tails')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step\n",
-    "\n",
-    "Now let's get back to the bikeshare state.  Again let's start with a new `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Suppose that in any given minute, there is a 50% chance that a student picks up a bike at Olin and rides to Wellesley.  We can simulate that like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if flip(0.5):\n",
-    "    bike_to_wellesley()\n",
-    "    print('Moving a bike to Wellesley')\n",
-    "\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And maybe at the same time, there is also a 40% chance that a student at Wellesley rides to Olin."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if flip(0.4):\n",
-    "    bike_to_olin()\n",
-    "    print('Moving a bike to Olin')\n",
-    "\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can wrap that code in a function called `step` that simulates one time step.  In any given minute, a student might ride from Olin to Wellesley, from Wellesley to Olin, or both, or neither, depending on the results of `flip`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step():\n",
-    "    if flip(0.5):\n",
-    "        bike_to_wellesley()\n",
-    "        print('Moving a bike to Wellesley')\n",
-    "        \n",
-    "    if flip(0.4):\n",
-    "        bike_to_olin()\n",
-    "        print('Moving a bike to Olin')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since this function takes no parameters, we call it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "step()\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Parameters\n",
-    "\n",
-    "As defined in the previous section, `step` is not as useful as it could be, because the probabilities `0.5` and `0.4` are \"hard coded\".\n",
-    "\n",
-    "It would be better to generalize this function so it takes the probabilities `p1` and `p2` as parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step(p1, p2):\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley()\n",
-    "        print('Moving a bike to Wellesley')\n",
-    "        \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin()\n",
-    "        print('Moving a bike to Olin')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can call it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "step(0.5, 0.4)\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** At the beginning of `step`, add a print statement that displays the values of `p1` and `p2`.  Call it again with values `0.3`, and `0.2`, and confirm that the values of the parameters are what you expect. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## For loop"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before we go on, I'll redefine `step` without the print statements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step(p1, p2):\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley()\n",
-    "    \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And let's start again with a new `State` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use a `for` loop to move 4 bikes from Olin to Wellesley."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(4):\n",
-    "    bike_to_wellesley()\n",
-    "    \n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or we can simulate 4 random time steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(4):\n",
-    "    step(0.3, 0.2)\n",
-    "    \n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If each step corresponds to a minute, we can simulate an entire hour like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(60):\n",
-    "    step(0.3, 0.2)\n",
-    "\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After 60 minutes, you might see that the number of bike at Olin is negative.  We'll fix that problem in the next notebook.\n",
-    "\n",
-    "But first, we want to plot the results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## TimeSeries\n",
-    "\n",
-    "`modsim.py` provides an object called a `TimeSeries` that can contain a sequence of values changing over time.\n",
-    "\n",
-    "We can create a new, empty `TimeSeries` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = TimeSeries()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can add a value to the `TimeSeries` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results[0] = bikeshare.olin\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `0` in brackets is an `index` that indicates that this value is associated with time step 0.\n",
-    "\n",
-    "Now we'll use a for loop to save the results of the simulation.  I'll start one more time with a new `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a for loop that runs 10 steps and stores the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(10):\n",
-    "    step(0.3, 0.2)\n",
-    "    results[i] = bikeshare.olin"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can display the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `TimeSeries` is a specialized version of a Pandas `Series`, so we can use any of the functions provided by `Series`, including several that compute summary statistics:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can read the documentation of `Series` [here](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting\n",
-    "\n",
-    "We can also plot the results like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results, label='Olin')\n",
-    "\n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Time step (min)', \n",
-    "         ylabel='Number of bikes')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`decorate`, which is defined in the `modsim` library, adds a title and labels the axes."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Wrap the code from this section in a function named `run_simulation` that takes three parameters, named `p1`, `p2`, and `num_steps`.\n",
-    "\n",
-    "It should:\n",
-    "\n",
-    "1. Create a `TimeSeries` object to hold the results.\n",
-    "2. Use a for loop to run `step` the number of times specified by `num_steps`, passing along the specified values of `p1` and `p2`.\n",
-    "3. After each step, it should save the number of bikes at Olin in the `TimeSeries`.\n",
-    "4. After the for loop, it should plot the results and\n",
-    "5. Decorate the axes.\n",
-    "\n",
-    "To test your function:\n",
-    "\n",
-    "1. Create a `State` object with the initial state of the system.\n",
-    "2. Call `run_simulation` with appropriate parameters.\n",
-    "3. Save the resulting figure.\n",
-    "\n",
-    "Optional:\n",
-    "\n",
-    "1. Extend your solution so it creates two `TimeSeries` objects, keeps track of the number of bikes at Olin *and* at Wellesley, and plots both series at the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Opening the hood\n",
-    "\n",
-    "The functions in `modsim.py` are built on top of several widely-used Python libraries, especially NumPy, SciPy, and Pandas.  These libraries are powerful but can be hard to use.  The intent of `modsim.py` is to give you the power of these libraries while making it easy to get started.\n",
-    "\n",
-    "In the future, you might want to use these libraries directly, rather than using `modsim.py`.  So we will pause occasionally to open the hood and let you see how `modsim.py` works.\n",
-    "\n",
-    "You don't need to know anything in these sections, so if you are already feeling overwhelmed, you might want to skip them.  But if you are curious, read on."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pandas\n",
-    "\n",
-    "This chapter introduces two objects, `State` and `TimeSeries`.  Both are based on the `Series` object defined by Pandas, which is a library primarily used for data science.\n",
-    "\n",
-    "You can read the documentation of the `Series` object [here](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html)\n",
-    "\n",
-    "The primary differences between `TimeSeries` and `Series` are:\n",
-    "\n",
-    "1. I made it easier to create a new, empty `Series` while avoiding a [confusing inconsistency](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html).\n",
-    "\n",
-    "2. I provide a function so the `Series` looks good when displayed in Jupyter.\n",
-    "\n",
-    "3. I provide a function called `set` that we'll use later.\n",
-    "\n",
-    "`State` has all of those capabilities; in addition, it provides an easier way to initialize state variables, and it provides functions called `T` and `dt`, which will help us avoid a confusing error later."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pyplot\n",
-    "\n",
-    "The `plot` function in `modsim.py` is based on the `plot` function in Pyplot, which is part of Matplotlib.  You can read the documentation of `plot` [here](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html).\n",
-    "\n",
-    "`decorate` provides a convenient way to call the `pyplot` functions `title`, `xlabel`, and `ylabel`, and `legend`.  It also avoids an annoying warning message if you try to make a legend when you don't have any labelled lines."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(decorate)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### NumPy\n",
-    "\n",
-    "The `flip` function in `modsim.py` uses NumPy's `random` function to generate a random number between 0 and 1.\n",
-    "\n",
-    "You can get the source code for `flip` by running the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_code(flip)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "celltoolbar": "Tags",
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap03.ipynb b/colab/chap03.ipynb
deleted file mode 100644
index 9aae1691..00000000
--- a/colab/chap03.ipynb
+++ /dev/null
@@ -1,606 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## More than one State object\n",
-    "\n",
-    "Here's the code from the previous chapter, with two changes:\n",
-    "\n",
-    "1. I've added DocStrings that explain what each function does, and what parameters it takes.\n",
-    "\n",
-    "2. I've added a parameter named `state` to the functions so they work with whatever `State` object we give them, instead of always using `bikeshare`.  That makes it possible to work with more than one `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step(state, p1, p2):\n",
-    "    \"\"\"Simulate one minute of time.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    p1: probability of an Olin->Wellesley customer arrival\n",
-    "    p2: probability of a Wellesley->Olin customer arrival\n",
-    "    \"\"\"\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley(state)\n",
-    "    \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin(state)\n",
-    "        \n",
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1\n",
-    "    \n",
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1\n",
-    "    \n",
-    "def decorate_bikeshare():\n",
-    "    \"\"\"Add a title and label the axes.\"\"\"\n",
-    "    decorate(title='Olin-Wellesley Bikeshare',\n",
-    "             xlabel='Time step (min)', \n",
-    "             ylabel='Number of bikes')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's `run_simulation`, which is a solution to the exercise at the end of the previous notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(state, p1, p2, num_steps):\n",
-    "    \"\"\"Simulate the given number of time steps.\n",
-    "    \n",
-    "    state: State object\n",
-    "    p1: probability of an Olin->Wellesley customer arrival\n",
-    "    p2: probability of a Wellesley->Olin customer arrival\n",
-    "    num_steps: number of time steps\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()    \n",
-    "    for i in range(num_steps):\n",
-    "        step(state, p1, p2)\n",
-    "        results[i] = state.olin\n",
-    "        \n",
-    "    plot(results, label='Olin')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can create more than one `State` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare1 = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare2 = State(olin=2, wellesley=10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Whenever we call a function, we indicate which `State` object to work with:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bike_to_olin(bikeshare1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bike_to_wellesley(bikeshare2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And you can confirm that the different objects are getting updated independently:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Negative bikes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the code we have so far, the number of bikes at one of the locations can go negative, and the number of bikes at the other location can exceed the actual number of bikes in the system.\n",
-    "\n",
-    "If you run this simulation a few times, it happens often."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)\n",
-    "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
-    "decorate_bikeshare()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can fix this problem using the `return` statement to exit the function early if an update would cause negative bikes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.olin == 0:\n",
-    "        return\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1\n",
-    "    \n",
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.wellesley == 0:\n",
-    "        return\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now if you run the simulation again, it should behave."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)\n",
-    "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
-    "decorate_bikeshare()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Comparison operators"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `if` statements in the previous section used the comparison operator `==`.  The other comparison operators are listed in the book.\n",
-    "\n",
-    "It is easy to confuse the comparison operator `==` with the assignment operator `=`.\n",
-    "\n",
-    "Remember that `=` creates a variable or gives an existing variable a new value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Whereas `==` compares two values and returns `True` if they are equal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x == 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can use `==` in an `if` statement."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if x == 5:\n",
-    "    print('yes, x is 5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But if you use `=` in an `if` statement, you get an error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# If you remove the # from the if statement and run it, you'll get\n",
-    "# SyntaxError: invalid syntax\n",
-    "\n",
-    "#if x = 5:\n",
-    "#    print('yes, x is 5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Add an `else` clause to the `if` statement above, and print an appropriate message.\n",
-    "\n",
-    "Replace the `==` operator with one or two of the other comparison operators, and confirm they do what you expect."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have a working simulation, we'll use it to evaluate alternative designs and see how good or bad they are.  The metric we'll use is the number of customers who arrive and find no bikes available, which might indicate a design problem."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we'll make a new `State` object that creates and initializes additional state variables to keep track of the metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2, \n",
-    "                  olin_empty=0, wellesley_empty=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we need versions of `bike_to_wellesley` and `bike_to_olin` that update the metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.olin == 0:\n",
-    "        state.olin_empty += 1\n",
-    "        return\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1\n",
-    "    \n",
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.wellesley == 0:\n",
-    "        state.wellesley_empty += 1\n",
-    "        return\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now when we run a simulation, it keeps track of unhappy customers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
-    "decorate_bikeshare()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After the simulation, we can print the number of unhappy customers at each location."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare.wellesley_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:** As another metric, we might be interested in the time until the first customer arrives and doesn't find a bike.  To make that work, we have to add a \"clock\" to keep track of how many time steps have elapsed:\n",
-    "\n",
-    "1. Create a new `State` object with an additional state variable, `clock`, initialized to 0. \n",
-    "\n",
-    "2. Write a modified version of `step` that adds one to the clock each time it is invoked.\n",
-    "\n",
-    "Test your code by running the simulation and check the value of `clock` at the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2, \n",
-    "                  olin_empty=0, wellesley_empty=0,\n",
-    "                  clock=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Continuing the previous exercise, let's record the time when the first customer arrives and doesn't find a bike.\n",
-    "\n",
-    "1. Create a new `State` object with an additional state variable, `t_first_empty`, initialized to -1 as a special value to indicate that it has not been set. \n",
-    "\n",
-    "2. Write a modified version of `step` that checks whether`olin_empty` and `wellesley_empty` are 0.  If not, it should set `t_first_empty` to `clock` (but only if `t_first_empty` has not already been set).\n",
-    "\n",
-    "Test your code by running the simulation and printing the values of `olin_empty`, `wellesley_empty`, and `t_first_empty` at the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap04.ipynb b/colab/chap04.ipynb
deleted file mode 100644
index 5a790b56..00000000
--- a/colab/chap04.ipynb
+++ /dev/null
@@ -1,681 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Returning values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a simple function that returns a value:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def add_five(x):\n",
-    "    return x + 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we call it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y = add_five(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you run a function on the last line of a cell, Jupyter displays the result:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "add_five(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But that can be a bad habit, because usually if you call a function and don't assign the result in a variable, the result gets discarded.\n",
-    "\n",
-    "In the following example, Jupyter shows the second result, but the first result just disappears."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "add_five(3)\n",
-    "add_five(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When you call a function that returns a variable, it is generally a good idea to assign the result to a variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y1 = add_five(3)\n",
-    "y2 = add_five(5)\n",
-    "\n",
-    "print(y1, y2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write a function called `make_state` that creates a `State` object with the state variables `olin=10` and `wellesley=2`, and then returns the new `State` object.\n",
-    "\n",
-    "Write a line of code that calls `make_state` and assigns the result to a variable named `init`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Running simulations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the code from the previous notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step(state, p1, p2):\n",
-    "    \"\"\"Simulate one minute of time.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    p1: probability of an Olin->Wellesley customer arrival\n",
-    "    p2: probability of a Wellesley->Olin customer arrival\n",
-    "    \"\"\"\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley(state)\n",
-    "    \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin(state)\n",
-    "        \n",
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.olin == 0:\n",
-    "        state.olin_empty += 1\n",
-    "        return\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1\n",
-    "    \n",
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.wellesley == 0:\n",
-    "        state.wellesley_empty += 1\n",
-    "        return\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1\n",
-    "    \n",
-    "def decorate_bikeshare():\n",
-    "    \"\"\"Add a title and label the axes.\"\"\"\n",
-    "    decorate(title='Olin-Wellesley Bikeshare',\n",
-    "             xlabel='Time step (min)', \n",
-    "             ylabel='Number of bikes')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a modified version of `run_simulation` that creates a `State` object, runs the simulation, and returns the `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(p1, p2, num_steps):\n",
-    "    \"\"\"Simulate the given number of time steps.\n",
-    "    \n",
-    "    p1: probability of an Olin->Wellesley customer arrival\n",
-    "    p2: probability of a Wellesley->Olin customer arrival\n",
-    "    num_steps: number of time steps\n",
-    "    \"\"\"\n",
-    "    state = State(olin=10, wellesley=2, \n",
-    "                  olin_empty=0, wellesley_empty=0)\n",
-    "                    \n",
-    "    for i in range(num_steps):\n",
-    "        step(state, p1, p2)\n",
-    "        \n",
-    "    return state"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now `run_simulation` doesn't plot anything:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = run_simulation(0.4, 0.2, 60)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But after the simulation, we can read the metrics from the `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run simulations with different values for the parameters.  When `p1` is small, we probably don't run out of bikes at Olin."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = run_simulation(0.2, 0.2, 60)\n",
-    "state.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When `p1` is large, we probably do."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = run_simulation(0.6, 0.2, 60)\n",
-    "state.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## More for loops"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`linspace` creates a NumPy array of equally spaced numbers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p1_array = linspace(0, 1, 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use an array in a `for` loop, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for p1 in p1_array:\n",
-    "    print(p1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This will come in handy in the next section.\n",
-    "\n",
-    "`linspace` is defined in `modsim.py`.  You can get the documentation using `help`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(linspace)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`linspace` is based on a NumPy function with the same name.  [Click here](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html) to read more about how to use it."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** \n",
-    "Use `linspace` to make an array of 10 equally spaced numbers from 1 to 10 (including both)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** The `modsim` library provides a related function called `linrange`.  You can view the documentation by running the following cell:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(linrange)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use `linrange` to make an array of numbers from 1 to 11 with a step size of 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sweeping parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`p1_array` contains a range of values for `p1`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p2 = 0.2\n",
-    "num_steps = 60\n",
-    "p1_array = linspace(0, 1, 11)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following loop runs a simulation for each value of `p1` in `p1_array`; after each simulation, it prints the number of unhappy customers at the Olin station:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for p1 in p1_array:\n",
-    "    state = run_simulation(p1, p2, num_steps)\n",
-    "    print(p1, state.olin_empty)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can do the same thing, but storing the results in a `SweepSeries` instead of printing them.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sweep = SweepSeries()\n",
-    "\n",
-    "for p1 in p1_array:\n",
-    "    state = run_simulation(p1, p2, num_steps)\n",
-    "    sweep[p1] = state.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then we can plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(sweep, label='Olin')\n",
-    "\n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Arrival rate at Olin (p1 in customers/min)', \n",
-    "         ylabel='Number of unhappy customers')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:** Wrap this code in a function named `sweep_p1` that takes an array called `p1_array` as a parameter.  It should create a new `SweepSeries`, run a simulation for each value of `p1` in `p1_array`, store the results in the `SweepSeries`, and return the `SweepSeries`.\n",
-    "\n",
-    "Use your function to plot the number of unhappy customers at Olin as a function of `p1`.  Label the axes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write a function called `sweep_p2` that runs simulations with `p1=0.5` and a range of values for `p2`.  It should store the results in a `SweepSeries` and return the `SweepSeries`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Optional Exercises\n",
-    "\n",
-    "The following two exercises are a little more challenging.  If you are comfortable with what you have learned so far, you should give them a try.  If you feel like you have your hands full, you might want to skip them for now.\n",
-    "\n",
-    "**Exercise:** Because our simulations are random, the results vary from one run to another, and the results of a parameter sweep tend to be noisy.  We can get a clearer picture of the relationship between a parameter and a metric by running multiple simulations with the same parameter and taking the average of the results.\n",
-    "\n",
-    "Write a function called `run_multiple_simulations` that takes as parameters `p1`, `p2`, `num_steps`, and `num_runs`.\n",
-    "\n",
-    "`num_runs` specifies how many times it should call `run_simulation`.\n",
-    "\n",
-    "After each run, it should store the total number of unhappy customers (at Olin or Wellesley) in a `TimeSeries`.  At the end, it should return the `TimeSeries`.\n",
-    "\n",
-    "Test your function with parameters\n",
-    "\n",
-    "```\n",
-    "p1 = 0.3\n",
-    "p2 = 0.3\n",
-    "num_steps = 60\n",
-    "num_runs = 10\n",
-    "```\n",
-    "\n",
-    "Display the resulting `TimeSeries` and use the `mean` function provided by the `TimeSeries` object to compute the average number of unhappy customers (see Section 2.7)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Continuting the previous exercise, use `run_multiple_simulations` to run simulations with a range of values for `p1` and\n",
-    "\n",
-    "```\n",
-    "p2 = 0.3\n",
-    "num_steps = 60\n",
-    "num_runs = 20\n",
-    "```\n",
-    "\n",
-    "Store the results in a `SweepSeries`, then plot the average number of unhappy customers as a function of `p1`.  Label the axes.\n",
-    "\n",
-    "What value of `p1` minimizes the average number of unhappy customers?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap05.ipynb b/colab/chap05.ipynb
deleted file mode 100644
index 2dca482e..00000000
--- a/colab/chap05.ipynb
+++ /dev/null
@@ -1,617 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reading data\n",
-    "\n",
-    "Pandas is a library that provides tools for reading and processing data.  `read_html` reads a web page from a file or the Internet and creates one `DataFrame` for each table on the page."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas import read_html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following cell downloads a copy of https://en.wikipedia.org/wiki/World_population_estimates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates.html'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "len(tables)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The arguments of `read_html` specify the file to read and how to interpret the tables in the file.  The result, `tables`, is a sequence of `DataFrame` objects; `len(tables)` reports the length of the sequence.\n",
-    "\n",
-    "We can select the `DataFrame` we want using the bracket operator.  The tables are numbered from 0, so `tables[2]` is actually the third table on the page.\n",
-    "\n",
-    "`head` selects the header and the first five rows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "table2 = tables[2]\n",
-    "table2.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`tail` selects the last five rows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table2.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Long column names are awkard to work with, but we can replace them with abbreviated names."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the DataFrame looks like now.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table2.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first column, which is labeled `Year`, is special.  It is the **index** for this `DataFrame`, which means it contains the labels for the rows.\n",
-    "\n",
-    "Some of the values use scientific notation; for example, `2.544000e+09` is shorthand for $2.544 \\cdot 10^9$ or 2.544 billion.\n",
-    "\n",
-    "`NaN` is a special value that indicates missing data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Series\n",
-    "\n",
-    "We can use dot notation to select a column from a `DataFrame`.  The result is a `Series`, which is like a `DataFrame` with a single column."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census = table2.census\n",
-    "census.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Like a `DataFrame`, a `Series` contains an index, which labels the rows.\n",
-    "\n",
-    "`1e9` is scientific notation for $1 \\cdot 10^9$ or 1 billion."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From here on, we will work in units of billions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "un.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census = table2.census / 1e9\n",
-    "census.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what these estimates look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "plot(census, ':', label='US Census')\n",
-    "plot(un, '--', label='UN DESA')\n",
-    "    \n",
-    "decorate(xlabel='Year',\n",
-    "         ylabel='World population (billion)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following expression computes the elementwise differences between the two series, then divides through by the UN value to produce [relative errors](https://en.wikipedia.org/wiki/Approximation_error), then finds the largest element.\n",
-    "\n",
-    "So the largest relative error between the estimates is about 1.3%."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max(abs(census - un) / un) * 100"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Break down that expression into smaller steps and display the intermediate results, to make sure you understand how it works.\n",
-    "\n",
-    "1.  Compute the elementwise differences, `census - un`\n",
-    "2.  Compute the absolute differences, `abs(census - un)`\n",
-    "3.  Compute the relative differences, `abs(census - un) / un`\n",
-    "4.  Compute the percent differences, `abs(census - un) / un * 100`\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`max` and `abs` are built-in functions provided by Python, but NumPy also provides version that are a little more general.  When you import `modsim`, you get the NumPy versions of these functions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Constant growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can select a value from a `Series` using bracket notation.  Here's the first element:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census[1950]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the last value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census[2016]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But rather than \"hard code\" those dates, we can get the first and last labels from the `Series`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = get_first_label(census)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_end = get_last_label(census)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "elapsed_time = t_end - t_0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can get the first and last values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_0 = get_first_value(census)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_end = get_last_value(census)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we can compute the average annual growth in billions of people per year."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "total_growth = p_end - p_0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "annual_growth = total_growth / elapsed_time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### TimeSeries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's create a `TimeSeries` to contain values generated by a linear growth model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = TimeSeries()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initially the `TimeSeries` is empty, but we can initialize it so the starting value, in 1950, is the 1950 population estimated by the US Census."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results[t_0] = census[t_0]\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After that, the population in the model grows by a constant amount each year."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for t in linrange(t_0, t_end):\n",
-    "    results[t+1] = results[t] + annual_growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results looks like, compared to the actual data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(census, ':', label='US Census')\n",
-    "plot(un, '--', label='UN DESA')\n",
-    "plot(results, color='gray', label='model')\n",
-    "\n",
-    "decorate(xlabel='Year', \n",
-    "         ylabel='World population (billion)',\n",
-    "         title='Constant growth')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model fits the data pretty well after 1990, but not so well before."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Optional Exercise:**  Try fitting the model using data from 1970 to the present, and see if that does a better job.\n",
-    "\n",
-    "Hint: \n",
-    "\n",
-    "1. Copy the code from above and make a few changes.  Test your code after each small change.\n",
-    "\n",
-    "2. Make sure your `TimeSeries` starts in 1950, even though the estimated annual growth is based on later data.\n",
-    "\n",
-    "3. You might want to add a constant to the starting value to match the data better."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census.loc[1960:1970]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap06.ipynb b/colab/chap06.ipynb
deleted file mode 100644
index 6d32eff3..00000000
--- a/colab/chap06.ipynb
+++ /dev/null
@@ -1,560 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 6"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates.html'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas import read_html\n",
-    "\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "table2 = tables[2]\n",
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "un.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census = table2.census / 1e9\n",
-    "census.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = get_first_label(census)\n",
-    "t_end = get_last_label(census)\n",
-    "elapsed_time = t_end - t_0\n",
-    "\n",
-    "p_0 = get_first_value(census)\n",
-    "p_end = get_last_value(census)\n",
-    "total_growth = p_end - p_0\n",
-    "\n",
-    "annual_growth = total_growth / elapsed_time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### System objects"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can rewrite the code from the previous chapter using system objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                annual_growth=annual_growth)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can encapsulate the code that runs the model in a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation1(system):\n",
-    "    \"\"\"Runs the constant growth model.\n",
-    "    \n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        results[t+1] = results[t] + system.annual_growth\n",
-    "    \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also encapsulate the code that plots the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(census, un, timeseries, title):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    census: TimeSeries of population estimates\n",
-    "    un: TimeSeries of population estimates\n",
-    "    timeseries: TimeSeries of simulation results\n",
-    "    title: string\n",
-    "    \"\"\"\n",
-    "    plot(census, ':', label='US Census')\n",
-    "    plot(un, '--', label='UN DESA')\n",
-    "    plot(timeseries, color='gray', label='model')\n",
-    "    \n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation1(system)\n",
-    "plot_results(census, un, results, 'Constant growth model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Proportional growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a more realistic model where the number of births and deaths is proportional to the current population."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation2(system):\n",
-    "    \"\"\"Run a model with proportional birth and death.\n",
-    "    \n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        births = system.birth_rate * results[t]\n",
-    "        deaths = system.death_rate * results[t]\n",
-    "        results[t+1] = results[t] + births - deaths\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I picked a death rate that seemed reasonable and then adjusted the birth rate to fit the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.death_rate = 0.01\n",
-    "system.birth_rate = 0.027"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation2(system)\n",
-    "plot_results(census, un, results, 'Proportional model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model fits the data pretty well for the first 20 years, but not so well after that."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Factoring out the update function"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`run_simulation1` and `run_simulation2` are nearly identical except the body of the loop.  So we can factor that part out into a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func1(pop, t, system):\n",
-    "    \"\"\"Compute the population next year.\n",
-    "    \n",
-    "    pop: current population\n",
-    "    t: current year\n",
-    "    system: system object containing parameters of the model\n",
-    "    \n",
-    "    returns: population next year\n",
-    "    \"\"\"\n",
-    "    births = system.birth_rate * pop\n",
-    "    deaths = system.death_rate * pop\n",
-    "    return pop + births - deaths"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The name `update_func` refers to a function object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "update_func1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Which we can confirm by checking its type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(update_func1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`run_simulation` takes the update function as a parameter and calls it just like any other function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate the system using any update function.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that computes the population next year\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        results[t+1] = update_func(results[t], t, system)\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we use it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = get_first_label(census)\n",
-    "t_end = get_last_label(census)\n",
-    "p_0 = census[t_0]\n",
-    "\n",
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                birth_rate=0.027,\n",
-    "                death_rate=0.01)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func1)\n",
-    "plot_results(census, un, results, 'Proportional model, factored')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Remember not to put parentheses after `update_func1`.  What happens if you try?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** When you run `run_simulation`, it runs `update_func1` once for each year between `t_0` and `t_end`.  To see that for yourself, add a print statement at the beginning of `update_func1` that prints the values of `t` and `pop`, then run `run_simulation` again."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Combining birth and death"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since births and deaths get added up, we don't have to compute them separately.  We can combine the birth and death rates into a single net growth rate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func2(pop, t, system):\n",
-    "    \"\"\"Compute the population next year.\n",
-    "    \n",
-    "    pop: current population\n",
-    "    t: current year\n",
-    "    system: system object containing parameters of the model\n",
-    "    \n",
-    "    returns: population next year\n",
-    "    \"\"\"\n",
-    "    net_growth = system.alpha  * pop\n",
-    "    return pop + net_growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how it works:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.alpha = system.birth_rate - system.death_rate\n",
-    "\n",
-    "results = run_simulation(system, update_func2)\n",
-    "plot_results(census, un, results, 'Proportional model, combined birth and death')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Maybe the reason the proportional model doesn't work very well is that the growth rate, `alpha`, is changing over time.  So let's try a model with different growth rates before and after 1980 (as an arbitrary choice).\n",
-    "\n",
-    "Write an update function that takes `pop`, `t`, and `system` as parameters.  The system object, `system`, should contain two parameters: the growth rate before 1980, `alpha1`, and the growth rate after 1980, `alpha2`.  It should use `t` to determine which growth rate to use.  Note: Don't forget the `return` statement.\n",
-    "\n",
-    "Test your function by calling it directly, then pass it to `run_simulation`.  Plot the results.  Adjust the parameters `alpha1` and `alpha2` to fit the data as well as you can.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap07.ipynb b/colab/chap07.ipynb
deleted file mode 100644
index e8ed937c..00000000
--- a/colab/chap07.ipynb
+++ /dev/null
@@ -1,547 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 7"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates.html'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas import read_html\n",
-    "\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "table2 = tables[2]\n",
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "un.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census = table2.census / 1e9\n",
-    "census.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(census, un, timeseries, title):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    census: TimeSeries of population estimates\n",
-    "    un: TimeSeries of population estimates\n",
-    "    timeseries: TimeSeries of simulation results\n",
-    "    title: string\n",
-    "    \"\"\"\n",
-    "    plot(census, ':', label='US Census')\n",
-    "    plot(un, '--', label='UN DESA')\n",
-    "    plot(timeseries, color='gray', label='model')\n",
-    "    \n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate the system using any update function.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that computes the population next year\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        results[t+1] = update_func(results[t], t, system)\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Quadratic growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the implementation of the quadratic growth model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func_quad(pop, t, system):\n",
-    "    \"\"\"Compute the population next year with a quadratic model.\n",
-    "    \n",
-    "    pop: current population\n",
-    "    t: current year\n",
-    "    system: system object containing parameters of the model\n",
-    "    \n",
-    "    returns: population next year\n",
-    "    \"\"\"\n",
-    "    net_growth = system.alpha * pop + system.beta * pop**2\n",
-    "    return pop + net_growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a `System` object with the parameters `alpha` and `beta`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = get_first_label(census)\n",
-    "t_end = get_last_label(census)\n",
-    "p_0 = census[t_0]\n",
-    "\n",
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                alpha=0.025,\n",
-    "                beta=-0.0018)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here are the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func_quad)\n",
-    "plot_results(census, un, results, 'Quadratic model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Can you find values for the parameters that make the model fit better?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Equilibrium\n",
-    "\n",
-    "To understand the quadratic model better, let's plot net growth as a function of population."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pop_array = linspace(0, 15, 100)\n",
-    "net_growth_array = system.alpha * pop_array + system.beta * pop_array**2\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sns.set_style('whitegrid')\n",
-    "\n",
-    "plot(pop_array, net_growth_array)\n",
-    "decorate(xlabel='Population (billions)',\n",
-    "         ylabel='Net growth (billions)')\n",
-    "\n",
-    "sns.set_style('white')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like.  Remember that the x axis is population now, not time."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It looks like the growth rate passes through 0 when the population is a little less than 14 billion.\n",
-    "\n",
-    "In the book we found that the net growth is 0 when the population is $-\\alpha/\\beta$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "-system.alpha / system.beta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is the equilibrium the population tends toward."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`sns` is a library called Seaborn which provides functions that control the appearance of plots.  In this case I want a grid to make it easier to estimate the population where the growth rate crosses through 0."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dysfunctions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When people first learn about functions, there are a few things they often find confusing.  In this section I present and explain some common problems with functions.\n",
-    "\n",
-    "As an example, suppose you want a function that takes a `System` object, with variables `alpha` and `beta`, as a parameter and computes the carrying capacity, `-alpha/beta`.  Here's a good solution:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def carrying_capacity(system):\n",
-    "    K = -system.alpha / system.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "pop = carrying_capacity(sys1)\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's see all the ways that can go wrong.\n",
-    "\n",
-    "**Dysfunction #1:** Not using parameters.  In the following version, the function doesn't take any parameters; when `sys1` appears inside the function, it refers to the object we created outside the function.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def carrying_capacity():\n",
-    "    K = -sys1.alpha / sys1.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "pop = carrying_capacity()\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This version actually works, but it is not as versatile as it could be.  If there are several `System` objects, this function can only work with one of them, and only if it is named `system`.\n",
-    "\n",
-    "**Dysfunction #2:** Clobbering the parameters.  When people first learn about parameters, they often write functions like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def carrying_capacity(system):\n",
-    "    system = System(alpha=0.025, beta=-0.0018)\n",
-    "    K = -system.alpha / system.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "pop = carrying_capacity(sys1)\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this example, we have a `System` object named `sys1` that gets passed as an argument to `carrying_capacity`.  But when the function runs, it ignores the argument and immediately replaces it with a new `System` object.  As a result, this function always returns the same value, no matter what argument is passed.\n",
-    "\n",
-    "When you write a function, you generally don't know what the values of the parameters will be.  Your job is to write a function that works for any valid values.  If you assign your own values to the parameters, you defeat the whole purpose of functions.\n",
-    "\n",
-    "\n",
-    "**Dysfunction #3:** No return value.  Here's a version that computes the value of `K` but doesn't return it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def carrying_capacity(system):\n",
-    "    K = -system.alpha / system.beta\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "pop = carrying_capacity(sys1)\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A function that doesn't have a return statement always returns a special value called `None`, so in this example the value of `pop` is `None`.  If you are debugging a program and find that the value of a variable is `None` when it shouldn't be, a function without a return statement is a likely cause.\n",
-    "\n",
-    "**Dysfunction #4:** Ignoring the return value.  Finally, here's a version where the function is correct, but the way it's used is not."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def carrying_capacity(system):\n",
-    "    K = -system.alpha / system.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys2 = System(alpha=0.025, beta=-0.0018)\n",
-    "carrying_capacity(sys2)\n",
-    "\n",
-    "# print(K)     This line won't work because K only exists inside the function."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this example, `carrying_capacity` runs and returns `K`, but the return value is dropped.\n",
-    "\n",
-    "When you call a function that returns a value, you should do something with the result.  Often you assign it to a variable, as in the previous examples, but you can also use it as part of an expression.\n",
-    "\n",
-    "For example, you could eliminate the temporary variable `pop` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(carrying_capacity(sys1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or if you had more than one system, you could compute the total carrying capacity like this:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "total = carrying_capacity(sys1) + carrying_capacity(sys2)\n",
-    "total"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:** In the book, I present a different way to parameterize the quadratic model:\n",
-    "\n",
-    "$ \\Delta p = r p (1 - p / K) $\n",
-    "\n",
-    "where $r=\\alpha$ and $K=-\\alpha/\\beta$.  Write a version of `update_func` that implements this version of the model.  Test it by computing the values of `r` and `K` that correspond to `alpha=0.025, beta=-0.0018`, and confirm that you get the same results. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap08.ipynb b/colab/chap08.ipynb
deleted file mode 100644
index a77508ed..00000000
--- a/colab/chap08.ipynb
+++ /dev/null
@@ -1,609 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Functions from the previous chapter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(census, un, timeseries, title):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    census: TimeSeries of population estimates\n",
-    "    un: TimeSeries of population estimates\n",
-    "    timeseries: TimeSeries of simulation results\n",
-    "    title: string\n",
-    "    \"\"\"\n",
-    "    plot(census, ':', label='US Census')\n",
-    "    plot(un, '--', label='UN DESA')\n",
-    "    plot(timeseries, color='gray', label='model')\n",
-    "    \n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate the system using any update function.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that computes the population next year\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        results[t+1] = update_func(results[t], t, system)\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Reading the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the data file\n",
-    "\n",
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates2.csv'\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates2.csv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table2 = pd.read_csv('World_population_estimates2.csv')\n",
-    "table2.index = table2.Year\n",
-    "table2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "census = table2.census / 1e9\n",
-    "plot(census, ':', label='US Census')\n",
-    "plot(un, '--', label='UN DESA')\n",
-    "    \n",
-    "decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)',\n",
-    "             title='Estimated world population')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Running the quadratic model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the update function for the quadratic growth model with parameters `alpha` and `beta`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func_quad(pop, t, system):\n",
-    "    \"\"\"Update population based on a quadratic model.\n",
-    "    \n",
-    "    pop: current population in billions\n",
-    "    t: what year it is\n",
-    "    system: system object with model parameters\n",
-    "    \"\"\"\n",
-    "    net_growth = system.alpha * pop + system.beta * pop**2\n",
-    "    return pop + net_growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Extract the starting time and population."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = get_first_label(census)\n",
-    "t_end = get_last_label(census)\n",
-    "p_0 = get_first_value(census)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initialize the system object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                alpha=0.025,\n",
-    "                beta=-0.0018)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the model and plot results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func_quad)\n",
-    "plot_results(census, un, results, 'Quadratic model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Generating projections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To generate projections, all we have to do is change `t_end`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "system.t_end = 2250\n",
-    "results = run_simulation(system, update_func_quad)\n",
-    "plot_results(census, un, results, 'World population projection')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The population in the model converges on the equilibrium population, `-alpha/beta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results[system.t_end]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "-system.alpha / system.beta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  What happens if we start with an initial population above the carrying capacity, like 20 billion?  Run the model with initial populations between 1 and 20 billion, and plot the results on the same axes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Comparing projections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can compare the projection from our model with projections produced by people who know what they are doing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the data file\n",
-    "\n",
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates3.csv'\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates3.csv\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def read_table3(filename = 'data/World_population_estimates.html'):\n",
-    "    tables = pd.read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "    table3 = tables[3]\n",
-    "    table3.columns = ['census', 'prb', 'un']\n",
-    "    return table3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#table3 = read_table3()\n",
-    "#table3.to_csv('data/World_population_estimates3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table3 = pd.read_csv('World_population_estimates3.csv')\n",
-    "table3.index = table3.Year\n",
-    "table3.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`NaN` is a special value that represents missing data, in this case because some agencies did not publish projections for some years."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This function plots projections from the UN DESA and U.S. Census.  It uses `dropna` to remove the `NaN` values from each series before plotting it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_projections(table):\n",
-    "    \"\"\"Plot world population projections.\n",
-    "    \n",
-    "    table: DataFrame with columns 'un' and 'census'\n",
-    "    \"\"\"\n",
-    "    census_proj = table.census / 1e9\n",
-    "    un_proj = table.un / 1e9\n",
-    "    \n",
-    "    plot(census_proj.dropna(), ':', color='C0', label='US Census')\n",
-    "    plot(un_proj.dropna(), '--', color='C1', label='UN DESA')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the model until 2100, which is as far as the other projections go."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = System(t_0=t_0, \n",
-    "                t_end=2100,\n",
-    "                p_0=p_0,\n",
-    "                alpha=0.025,\n",
-    "                beta=-0.0018)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func_quad)\n",
-    "\n",
-    "plt.axvspan(1950, 2016, color='C0', alpha=0.05)\n",
-    "plot_results(census, un, results, 'World population projections')\n",
-    "plot_projections(table3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "People who know what they are doing expect the growth rate to decline more sharply than our model projects."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:** The net growth rate of world population has been declining for several decades.  That observation suggests one more way to generate projections, by extrapolating observed changes in growth rate.\n",
-    "\n",
-    "The `modsim` library provides a function, `compute_rel_diff`, that computes relative differences of the elements in a sequence.\n",
-    "\n",
-    "Here's how we can use it to compute the relative differences in the `census` and `un` estimates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "alpha_census = compute_rel_diff(census)\n",
-    "plot(alpha_census, label='US Census')\n",
-    "\n",
-    "alpha_un = compute_rel_diff(un)\n",
-    "plot(alpha_un, label='UN DESA')\n",
-    "\n",
-    "decorate(xlabel='Year', label='Net growth rate')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Other than a bump around 1990, net growth rate has been declining roughly linearly since 1965.  As an exercise, you can use this data to make a projection of world population until 2100.\n",
-    "\n",
-    "1. Define a function, `alpha_func`, that takes `t` as a parameter and returns an estimate of the net growth rate at time `t`, based on a linear function `alpha = intercept + slope * t`.  Choose values of `slope` and `intercept` to fit the observed net growth rates since 1965.\n",
-    "\n",
-    "2. Call your function with a range of `ts` from 1960 to 2020 and plot the results.\n",
-    "\n",
-    "3. Create a `System` object that includes `alpha_func` as a system variable.\n",
-    "\n",
-    "4. Define an update function that uses `alpha_func` to compute the net growth rate at the given time `t`.\n",
-    "\n",
-    "5. Test your update function with `t_0 = 1960` and `p_0 = census[t_0]`.\n",
-    "\n",
-    "6. Run a simulation from 1960 to 2100 with your update function, and plot the results.\n",
-    "\n",
-    "7. Compare your projections with those from the US Census and UN."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Related viewing:** You might be interested in this [video by Hans Rosling about the demographic changes we expect in this century](https://www.youtube.com/watch?v=ezVk1ahRF78)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap09.ipynb b/colab/chap09.ipynb
deleted file mode 100644
index 9e3b0cd5..00000000
--- a/colab/chap09.ipynb
+++ /dev/null
@@ -1,657 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 9"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following displays SymPy expressions and provides the option of showing results in LaTeX format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sympy.printing import latex\n",
-    "\n",
-    "def show(expr, show_latex=False):\n",
-    "    \"\"\"Display a SymPy expression.\n",
-    "    \n",
-    "    expr: SymPy expression\n",
-    "    show_latex: boolean\n",
-    "    \"\"\"\n",
-    "    if show_latex:\n",
-    "        print(latex(expr))\n",
-    "    return expr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis with SymPy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a symbol for time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sympy as sp\n",
-    "\n",
-    "t = sp.symbols('t')\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you combine symbols and numbers, you get symbolic expressions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "expr = t + 1\n",
-    "expr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is an `Add` object, which just represents the sum without trying to compute it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(expr)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`subs` can be used to replace a symbol with a number, which allows the addition to proceed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "expr.subs(t, 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`f` is a special class of symbol that represents a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f = sp.Function('f')\n",
-    "f"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The type of `f` is `UndefinedFunction`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(f)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "SymPy understands that `f(t)` means `f` evaluated at `t`, but it doesn't try to evaluate it yet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f(t)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`diff` returns a `Derivative` object that represents the time derivative of `f`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dfdt = sp.diff(f(t), t)\n",
-    "dfdt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(dfdt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We need a symbol for `alpha`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "alpha = sp.symbols('alpha')\n",
-    "alpha"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can write the differential equation for proportional growth."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eq1 = sp.Eq(dfdt, alpha*f(t))\n",
-    "eq1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And use `dsolve` to solve it.  The result is the general solution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "solution_eq = sp.dsolve(eq1)\n",
-    "solution_eq"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can tell it's a general solution because it contains an unspecified constant, `C1`.\n",
-    "\n",
-    "In this example, finding the particular solution is easy: we just replace `C1` with `p_0`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "C1, p_0 = sp.symbols('C1 p_0')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "particular = solution_eq.subs(C1, p_0)\n",
-    "particular"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the next example, we have to work a little harder to find the particular solution."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Solving the quadratic growth equation \n",
-    "\n",
-    "We'll use the (r, K) parameterization, so we'll need two more symbols:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r, K = sp.symbols('r K')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can write the differential equation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eq2 = sp.Eq(sp.diff(f(t), t), r * f(t) * (1 - f(t)/K))\n",
-    "eq2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And solve it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "solution_eq = sp.dsolve(eq2)\n",
-    "solution_eq"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result, `solution_eq`, contains `rhs`, which is the right-hand side of the solution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "general = solution_eq.rhs\n",
-    "general"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can evaluate the right-hand side at $t=0$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "at_0 = general.subs(t, 0)\n",
-    "at_0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we want to find the value of `C1` that makes `f(0) = p_0`.\n",
-    "\n",
-    "So we'll create the equation `at_0 = p_0` and solve for `C1`.  Because this is just an algebraic identity, not a differential equation, we use `solve`, not `dsolve`.\n",
-    "\n",
-    "The result from `solve` is a list of solutions.  In this case, [we have reason to expect only one solution](https://en.wikipedia.org/wiki/Picard%E2%80%93Lindel%C3%B6f_theorem), but we still get a list, so we have to use the bracket operator, `[0]`, to select the first one."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "solutions = sp.solve(sp.Eq(at_0, p_0), C1)\n",
-    "type(solutions), len(solutions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "value_of_C1 = solutions[0]\n",
-    "value_of_C1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now in the general solution, we want to replace `C1` with the value of `C1` we just figured out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "particular = general.subs(C1, value_of_C1)\n",
-    "particular"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is complicated, but SymPy provides a method that tries to simplify it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "particular = sp.simplify(particular)\n",
-    "particular"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Often simplicity is in the eye of the beholder, but that's about as simple as this expression gets.\n",
-    "\n",
-    "Just to double-check, we can evaluate it at `t=0` and confirm that we get `p_0`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "particular.subs(t, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This solution is called the [logistic function](https://en.wikipedia.org/wiki/Population_growth#Logistic_equation).\n",
-    "\n",
-    "In some places you'll see it written in a different form:\n",
-    "\n",
-    "$f(t) = \\frac{K}{1 + A e^{-rt}}$\n",
-    "\n",
-    "where $A = (K - p_0) / p_0$.\n",
-    "\n",
-    "We can use SymPy to confirm that these two forms are equivalent.  First we represent the alternative version of the logistic function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = (K - p_0) / p_0\n",
-    "A"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "logistic = K / (1 + A * sp.exp(-r*t))\n",
-    "logistic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To see whether two expressions are equivalent, we can check whether their difference simplifies to 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "sp.simplify(particular - logistic)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This test only works one way: if SymPy says the difference reduces to 0, the expressions are definitely equivalent (and not just numerically close).\n",
-    "\n",
-    "But if SymPy can't find a way to simplify the result to 0, that doesn't necessarily mean there isn't one.  Testing whether two expressions are equivalent is a surprisingly hard problem; in fact, there is no algorithm that can solve it in general."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Solve the quadratic growth equation using the alternative parameterization\n",
-    "\n",
-    "$\\frac{df(t)}{dt} = \\alpha f(t) + \\beta f^2(t) $"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Use [WolframAlpha](https://www.wolframalpha.com/) to solve the quadratic growth model, using either or both forms of parameterization:\n",
-    "\n",
-    "    df(t) / dt = alpha f(t) + beta f(t)^2\n",
-    "\n",
-    "or\n",
-    "\n",
-    "    df(t) / dt = r f(t) (1 - f(t)/K)\n",
-    "\n",
-    "Find the general solution and also the particular solution where `f(0) = p_0`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap10.ipynb b/colab/chap10.ipynb
deleted file mode 100644
index f2e903ab..00000000
--- a/colab/chap10.ipynb
+++ /dev/null
@@ -1,474 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 10"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "To get a `DataFrame` and a `Series`, I'll read the world population data and select a column.\n",
-    "\n",
-    "`DataFrame` and `Series` contain a variable called `shape` that indicates the number of rows and columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates.html'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/World_population_estimates.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas import read_html\n",
-    "\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "table2 = tables[2]\n",
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']\n",
-    "table2.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census = table2.census / 1e9\n",
-    "census.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "un.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `DataFrame` contains `index`, which labels the rows.  It is an `Int64Index`, which is similar to a NumPy array."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "table2.index"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And `columns`, which labels the columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "table2.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And `values`, which is an array of values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "table2.values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `Series` does not have `columns`, but it does have `name`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "census.name"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It contains `values`, which is an array."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census.values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And it contains `index`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census.index"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you ever wonder what kind of object a variable refers to, you can use the `type` function.  The result indicates what type the object is, and the module where that type is defined.\n",
-    "\n",
-    "`DataFrame`, `Int64Index`, `Index`, and `Series` are defined by Pandas.\n",
-    "\n",
-    "`ndarray` is defined by NumPy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(table2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(table2.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(table2.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(table2.values)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(census)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(census.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "type(census.values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Optional exercise\n",
-    "\n",
-    "The following exercise provides a chance to practice what you have learned so far, and maybe develop a different growth model.  If you feel comfortable with what we have done so far, you might want to give it a try.\n",
-    "\n",
-    "**Optional Exercise:** On the Wikipedia page about world population estimates, the first table contains estimates for prehistoric populations.  The following cells process this table and plot some of the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = 'World_population_estimates.html'\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "len(tables)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Select `tables[1]`, which is the second table on the page."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table1 = tables[1]\n",
-    "table1.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Not all agencies and researchers provided estimates for the same dates.  Again `NaN` is the special value that indicates missing data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table1.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Again, we'll replace the long column names with more convenient abbreviations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table1.columns = ['PRB', 'UN', 'Maddison', 'HYDE', 'Tanton', \n",
-    "                  'Biraben', 'McEvedy & Jones', 'Thomlinson', 'Durand', 'Clark']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Some of the estimates are in a form Pandas doesn't recognize as numbers, but we can coerce them to be numeric."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in table1.columns:\n",
-    "    table1[col] = pd.to_numeric(table1[col], errors='coerce')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results.  Notice that we are working in millions now, not billions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "table1.plot()\n",
-    "decorate(xlim=[-10000, 2000], xlabel='Year', \n",
-    "         ylabel='World population (millions)',\n",
-    "         title='Prehistoric population estimates')\n",
-    "plt.legend(fontsize='small');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use `xlim` to zoom in on everything after Year 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table1.plot()\n",
-    "decorate(xlim=[0, 2000], xlabel='Year', \n",
-    "         ylabel='World population (millions)',\n",
-    "         title='CE population estimates')\n",
-    "plt.legend(fontsize='small');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "See if you can find a model that fits these data well from Year 0 to 1950.\n",
-    "\n",
-    "How well does your best model predict actual population growth from 1950 to the present?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap11.ipynb b/colab/chap11.ipynb
deleted file mode 100644
index 887d7df7..00000000
--- a/colab/chap11.ipynb
+++ /dev/null
@@ -1,476 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 11"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### SIR implementation\n",
-    "\n",
-    "We'll use a `State` object to represent the number (or fraction) of people in each compartment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "init = State(S=89, I=1, R=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To convert from number of people to fractions, we divide through by the total."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "init /= sum(init)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system` creates a `System` object with the given parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an example with hypothetical values for `beta` and `gamma`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tc = 3      # time between contacts in days \n",
-    "tr = 4      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The update function takes the state during the current time step and returns the state during the next time step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State with variables S, I, R\n",
-    "    t: time step\n",
-    "    system: System with beta and gamma\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = system.beta * i * s    \n",
-    "    recovered = system.gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To run a single time step, we call it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = update_func(init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run a simulation by calling the update function for each time step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: State object for final state\n",
-    "    \"\"\"\n",
-    "    state = system.init\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        state = update_func(state, t, system)\n",
-    "        \n",
-    "    return state"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is the state of the system at `t_end`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "run_simulation(system, update_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise**  Suppose the time between contacts is 4 days and the recovery time is 5 days.  After 14 weeks, how many students, total, have been infected?\n",
-    "\n",
-    "Hint: what is the change in `S` between the beginning and the end of the simulation?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using TimeSeries objects"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we want to store the state of the system at each time step, we can use one `TimeSeries` object for each state variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "    \n",
-    "    Add three Series objects to the System: S, I, R\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \"\"\"\n",
-    "    S = TimeSeries()\n",
-    "    I = TimeSeries()\n",
-    "    R = TimeSeries()\n",
-    "\n",
-    "    state = system.init\n",
-    "    t0 = system.t0\n",
-    "    S[t0], I[t0], R[t0] = state\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        state = update_func(state, t, system)\n",
-    "        S[t+1], I[t+1], R[t+1] = state\n",
-    "    \n",
-    "    return S, I, R"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we call it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tc = 3      # time between contacts in days \n",
-    "tr = 4      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)\n",
-    "S, I, R = run_simulation(system, update_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then we can plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(S, I, R):\n",
-    "    \"\"\"Plot the results of a SIR model.\n",
-    "    \n",
-    "    S: TimeSeries\n",
-    "    I: TimeSeries\n",
-    "    R: TimeSeries\n",
-    "    \"\"\"\n",
-    "    plot(S, '--', label='Susceptible')\n",
-    "    plot(I, '-', label='Infected')\n",
-    "    plot(R, ':', label='Recovered')\n",
-    "    decorate(xlabel='Time (days)',\n",
-    "             ylabel='Fraction of population')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what they look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_results(S, I, R)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using a DataFrame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Instead of making three `TimeSeries` objects, we can use one `DataFrame`.\n",
-    "\n",
-    "We have to use `row` to selects rows, rather than columns.  But then Pandas does the right thing, matching up the state variables with the columns of the `DataFrame`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    frame = TimeFrame(columns=system.init.index)\n",
-    "    frame.row[system.t0] = system.init\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it, and what the result looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tc = 3      # time between contacts in days \n",
-    "tr = 4      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)\n",
-    "results = run_simulation(system, update_func)\n",
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can extract the results and plot them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_results(results.S, results.I, results.R)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise**  Suppose the time between contacts is 4 days and the recovery time is 5 days.  Simulate this scenario for 14 weeks and plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap12.ipynb b/colab/chap12.ipynb
deleted file mode 100644
index ef6f4478..00000000
--- a/colab/chap12.ipynb
+++ /dev/null
@@ -1,887 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 12"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code\n",
-    "\n",
-    "Here's the code from the previous notebook that we'll need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State with variables S, I, R\n",
-    "    t: time step\n",
-    "    system: System with beta and gamma\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = system.beta * i * s    \n",
-    "    recovered = system.gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    frame = TimeFrame(columns=system.init.index)\n",
-    "    frame.row[system.t0] = system.init\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Given the results, we can compute metrics that quantify whatever we are interested in, like the total number of sick students, for example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_total_infected(results):\n",
-    "    \"\"\"Fraction of population infected during the simulation.\n",
-    "    \n",
-    "    results: DataFrame with columns S, I, R\n",
-    "    \n",
-    "    returns: fraction of population\n",
-    "    \"\"\"\n",
-    "    return get_first_value(results.S) - get_last_value(results.S)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an example.|"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "beta = 0.333\n",
-    "gamma = 0.25\n",
-    "system = make_system(beta, gamma)\n",
-    "\n",
-    "results = run_simulation(system, update_func)\n",
-    "print(beta, gamma, calc_total_infected(results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write functions that take a `TimeFrame` object as a parameter and compute the other metrics mentioned in the book:\n",
-    "\n",
-    "1.  The fraction of students who are sick at the peak of the outbreak.\n",
-    "\n",
-    "2.  The day the outbreak peaks.\n",
-    "\n",
-    "3.  The fraction of students who are sick at the end of the semester.\n",
-    "\n",
-    "Note: Not all of these functions require the `System` object, but when you write a set of related functons, it is often convenient if they all take the same parameters.\n",
-    "\n",
-    "Hint:  If you have a `TimeSeries` called `I`, you can compute the largest value of the series like this:\n",
-    "\n",
-    "    I.max()\n",
-    "\n",
-    "And the index of the largest value like this:\n",
-    "\n",
-    "    I.idxmax()\n",
-    "\n",
-    "You can read about these functions in the `Series` [documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### What if?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use this model to evaluate \"what if\" scenarios.  For example, this function models the effect of immunization by moving some fraction of the population from S to R before the simulation starts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def add_immunization(system, fraction):\n",
-    "    \"\"\"Immunize a fraction of the population.\n",
-    "    \n",
-    "    Moves the given fraction from S to R.\n",
-    "    \n",
-    "    system: System object\n",
-    "    fraction: number from 0 to 1\n",
-    "    \"\"\"\n",
-    "    system.init.S -= fraction\n",
-    "    system.init.R += fraction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's start again with the system we used in the previous sections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tc = 3      # time between contacts in days \n",
-    "tr = 4      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And run the model without immunization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func)\n",
-    "calc_total_infected(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now with 10% immunization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system2 = make_system(beta, gamma)\n",
-    "add_immunization(system2, 0.1)\n",
-    "results2 = run_simulation(system2, update_func)\n",
-    "calc_total_infected(results2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "10% immunization leads to a drop in infections of 16 percentage points.\n",
-    "\n",
-    "Here's what the time series looks like for S, with and without immunization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results.S, '-', label='No immunization')\n",
-    "plot(results2.S, '--', label='10% immunization')\n",
-    "\n",
-    "decorate(xlabel='Time (days)',\n",
-    "         ylabel='Fraction susceptible')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can sweep through a range of values for the fraction of the population who are immunized."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "immunize_array = linspace(0, 1, 11)\n",
-    "for fraction in immunize_array:\n",
-    "    system = make_system(beta, gamma)\n",
-    "    add_immunization(system, fraction)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    print(fraction, calc_total_infected(results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This function does the same thing and stores the results in a `Sweep` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_immunity(immunize_array):\n",
-    "    \"\"\"Sweeps a range of values for immunity.\n",
-    "    \n",
-    "    immunize_array: array of fraction immunized\n",
-    "    \n",
-    "    returns: Sweep object\n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for fraction in immunize_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        add_immunization(system, fraction)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[fraction] = calc_total_infected(results)\n",
-    "        \n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "immunize_array = linspace(0, 1, 21)\n",
-    "infected_sweep = sweep_immunity(immunize_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(infected_sweep)\n",
-    "\n",
-    "decorate(xlabel='Fraction immunized',\n",
-    "         ylabel='Total fraction infected',\n",
-    "         title='Fraction infected vs. immunization rate',\n",
-    "         legend=False)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If 40% of the population is immunized, less than 4% of the population gets sick."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Logistic function"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To model the effect of a hand-washing campaign, I'll use a [generalized logistic function](https://en.wikipedia.org/wiki/Generalised_logistic_function) (GLF), which is a convenient function for modeling curves that have a generally sigmoid shape.  The parameters of the GLF correspond to various features of the curve in a way that makes it easy to find a function that has the shape you want, based on data or background information about the scenario."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def logistic(x, A=0, B=1, C=1, M=0, K=1, Q=1, nu=1):\n",
-    "    \"\"\"Computes the generalize logistic function.\n",
-    "    \n",
-    "    A: controls the lower bound\n",
-    "    B: controls the steepness of the transition \n",
-    "    C: not all that useful, AFAIK\n",
-    "    M: controls the location of the transition\n",
-    "    K: controls the upper bound\n",
-    "    Q: shift the transition left or right\n",
-    "    nu: affects the symmetry of the transition\n",
-    "    \n",
-    "    returns: float or array\n",
-    "    \"\"\"\n",
-    "    exponent = -B * (x - M)\n",
-    "    denom = C + Q * exp(exponent)\n",
-    "    return A + (K-A) / denom ** (1/nu)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following array represents the range of possible spending."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spending = linspace(0, 1200, 21)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`compute_factor` computes the reduction in `beta` for a given level of campaign spending.\n",
-    "\n",
-    "`M` is chosen so the transition happens around \\$500.\n",
-    "\n",
-    "`K` is the maximum reduction in `beta`, 20%.\n",
-    "\n",
-    "`B` is chosen by trial and error to yield a curve that seems feasible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_factor(spending):\n",
-    "    \"\"\"Reduction factor as a function of spending.\n",
-    "    \n",
-    "    spending: dollars from 0 to 1200\n",
-    "    \n",
-    "    returns: fractional reduction in beta\n",
-    "    \"\"\"\n",
-    "    return logistic(spending, M=500, K=0.2, B=0.01)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "percent_reduction = compute_factor(spending) * 100\n",
-    "\n",
-    "plot(spending, percent_reduction)\n",
-    "\n",
-    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
-    "         ylabel='Percent reduction in infection rate',\n",
-    "         title='Effect of hand washing on infection rate',\n",
-    "         legend=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Modify the parameters `M`, `K`, and `B`, and see what effect they have on the shape of the curve.  Read about the [generalized logistic function on Wikipedia](https://en.wikipedia.org/wiki/Generalised_logistic_function).  Modify the other parameters and see what effect they have."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Hand washing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can model the effect of a hand-washing campaign by modifying `beta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def add_hand_washing(system, spending):\n",
-    "    \"\"\"Modifies system to model the effect of hand washing.\n",
-    "    \n",
-    "    system: System object\n",
-    "    spending: campaign spending in USD\n",
-    "    \"\"\"\n",
-    "    factor = compute_factor(spending)\n",
-    "    system.beta *= (1 - factor)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's start with the same values of `beta` and `gamma` we've been using."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tc = 3      # time between contacts in days \n",
-    "tr = 4      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "beta, gamma"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can sweep different levels of campaign spending."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spending_array = linspace(0, 1200, 13)\n",
-    "\n",
-    "for spending in spending_array:\n",
-    "    system = make_system(beta, gamma)\n",
-    "    add_hand_washing(system, spending)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    print(spending, system.beta, calc_total_infected(results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a function that sweeps a range of spending and stores the results in a `SweepSeries`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_hand_washing(spending_array):\n",
-    "    \"\"\"Run simulations with a range of spending.\n",
-    "    \n",
-    "    spending_array: array of dollars from 0 to 1200\n",
-    "    \n",
-    "    returns: Sweep object\n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for spending in spending_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        add_hand_washing(system, spending)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[spending] = calc_total_infected(results)\n",
-    "        \n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spending_array = linspace(0, 1200, 20)\n",
-    "infected_sweep = sweep_hand_washing(spending_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(infected_sweep)\n",
-    "\n",
-    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
-    "         ylabel='Total fraction infected',\n",
-    "         title='Effect of hand washing on total infections',\n",
-    "         legend=False)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's put it all together to make some public health spending decisions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optimization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Suppose we have \\$1200 to spend on any combination of vaccines and a hand-washing campaign."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_students = 90\n",
-    "budget = 1200\n",
-    "price_per_dose = 100\n",
-    "max_doses = int(budget / price_per_dose)\n",
-    "dose_array = linrange(max_doses, endpoint=True)\n",
-    "max_doses"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can sweep through a range of doses from, 0 to `max_doses`, model the effects of immunization and the hand-washing campaign, and run simulations.\n",
-    "\n",
-    "For each scenario, we compute the fraction of students who get sick."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for doses in dose_array:\n",
-    "    fraction = doses / num_students\n",
-    "    spending = budget - doses * price_per_dose\n",
-    "    \n",
-    "    system = make_system(beta, gamma)\n",
-    "    add_immunization(system, fraction)\n",
-    "    add_hand_washing(system, spending)\n",
-    "    \n",
-    "    results = run_simulation(system, update_func)\n",
-    "    print(doses, system.init.S, system.beta, calc_total_infected(results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function wraps that loop and stores the results in a `Sweep` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_doses(dose_array):\n",
-    "    \"\"\"Runs simulations with different doses and campaign spending.\n",
-    "    \n",
-    "    dose_array: range of values for number of vaccinations\n",
-    "    \n",
-    "    return: Sweep object with total number of infections \n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for doses in dose_array:\n",
-    "        fraction = doses / num_students\n",
-    "        spending = budget - doses * price_per_dose\n",
-    "        \n",
-    "        system = make_system(beta, gamma)\n",
-    "        add_immunization(system, fraction)\n",
-    "        add_hand_washing(system, spending)\n",
-    "        \n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[doses] = calc_total_infected(results)\n",
-    "\n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can compute the number of infected students for each possible allocation of the budget."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "infected_sweep = sweep_doses(dose_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(infected_sweep)\n",
-    "\n",
-    "decorate(xlabel='Doses of vaccine',\n",
-    "         ylabel='Total fraction infected',\n",
-    "         title='Total infections vs. doses',\n",
-    "         legend=False)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Suppose the price of the vaccine drops to $50 per dose.  How does that affect the optimal allocation of the spending?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose we have the option to quarantine infected students.  For example, a student who feels ill might be moved to an infirmary, or a private dorm room, until they are no longer infectious.\n",
-    "\n",
-    "How might you incorporate the effect of quarantine in the SIR model?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap13.ipynb b/colab/chap13.ipynb
deleted file mode 100644
index bff8f833..00000000
--- a/colab/chap13.ipynb
+++ /dev/null
@@ -1,526 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 13"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from previous chapters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system`, `plot_results`, and `calc_total_infected` are unchanged."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= np.sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(S, I, R):\n",
-    "    \"\"\"Plot the results of a SIR model.\n",
-    "    \n",
-    "    S: TimeSeries\n",
-    "    I: TimeSeries\n",
-    "    R: TimeSeries\n",
-    "    \"\"\"\n",
-    "    plot(S, '--', label='Susceptible')\n",
-    "    plot(I, '-', label='Infected')\n",
-    "    plot(R, ':', label='Recovered')\n",
-    "    decorate(xlabel='Time (days)',\n",
-    "             ylabel='Fraction of population')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_total_infected(results):\n",
-    "    \"\"\"Fraction of population infected during the simulation.\n",
-    "    \n",
-    "    results: DataFrame with columns S, I, R\n",
-    "    \n",
-    "    returns: fraction of population\n",
-    "    \"\"\"\n",
-    "    return get_first_value(results.S) - get_last_value(results.S)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    init, t0, t_end = system.init, system.t0, system.t_end\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t0] = init\n",
-    "    \n",
-    "    for t in linrange(t0, t_end):\n",
-    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State (s, i, r)\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State (sir)\n",
-    "    \"\"\"\n",
-    "    beta, gamma = system.beta, system.gamma\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = beta * i * s    \n",
-    "    recovered = gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sweeping beta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Make a range of values for `beta`, with constant `gamma`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "beta_array = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 , 1.1]\n",
-    "gamma = 0.2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the simulation once for each value of `beta` and print total infections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for beta in beta_array:\n",
-    "    system = make_system(beta, gamma)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    print(system.beta, calc_total_infected(results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Wrap that loop in a function and return a `SweepSeries` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_beta(beta_array, gamma):\n",
-    "    \"\"\"Sweep a range of values for beta.\n",
-    "    \n",
-    "    beta_array: array of beta values\n",
-    "    gamma: recovery rate\n",
-    "    \n",
-    "    returns: SweepSeries that maps from beta to total infected\n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    for beta in beta_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[system.beta] = calc_total_infected(results)\n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sweep `beta` and plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "infected_sweep = sweep_beta(beta_array, gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "label = 'gamma = ' + str(gamma)\n",
-    "plot(infected_sweep, label=label)\n",
-    "\n",
-    "decorate(xlabel='Contact rate (beta)',\n",
-    "         ylabel='Fraction infected')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sweeping gamma"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using the same array of values for `beta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "beta_array"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And now an array of values for `gamma`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gamma_array = [0.2, 0.4, 0.6, 0.8]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For each value of `gamma`, sweep `beta` and plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(7, 4))\n",
-    "\n",
-    "for gamma in gamma_array:\n",
-    "    infected_sweep = sweep_beta(beta_array, gamma)\n",
-    "    label = 'gamma = ' + str(gamma)\n",
-    "    plot(infected_sweep, label=label)\n",
-    "    \n",
-    "decorate(xlabel='Contact rate (beta)',\n",
-    "         ylabel='Fraction infected',\n",
-    "         loc='upper left')\n",
-    "\n",
-    "plt.legend(bbox_to_anchor=(1.02, 1.02))\n",
-    "plt.tight_layout()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Suppose the infectious period for the Freshman Plague is known to be 2 days on average, and suppose during one particularly bad year, 40% of the class is infected at some point.  Estimate the time between contacts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SweepFrame\n",
-    "\n",
-    "The following sweeps two parameters and stores the results in a `SweepFrame`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_parameters(beta_array, gamma_array):\n",
-    "    \"\"\"Sweep a range of values for beta and gamma.\n",
-    "    \n",
-    "    beta_array: array of infection rates\n",
-    "    gamma_array: array of recovery rates\n",
-    "    \n",
-    "    returns: SweepFrame with one row for each beta\n",
-    "             and one column for each gamma\n",
-    "    \"\"\"\n",
-    "    frame = SweepFrame(columns=gamma_array)\n",
-    "    for gamma in gamma_array:\n",
-    "        frame[gamma] = sweep_beta(beta_array, gamma)\n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the `SweepFrame` look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frame = sweep_parameters(beta_array, gamma_array)\n",
-    "frame.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we can plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for gamma in gamma_array:\n",
-    "    label = 'gamma = ' + str(gamma)\n",
-    "    plot(frame[gamma], label=label)\n",
-    "    \n",
-    "decorate(xlabel='Contact rate (beta)',\n",
-    "         ylabel='Fraction infected',\n",
-    "         title='',\n",
-    "         loc='upper left')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also plot one line for each value of `beta`, although there are a lot of them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(7, 4))\n",
-    "\n",
-    "\n",
-    "for beta in [1.1, 0.9, 0.7, 0.5, 0.3]:\n",
-    "    label = 'beta = ' + str(beta)\n",
-    "    plot(frame.row[beta], label=label)\n",
-    "    \n",
-    "decorate(xlabel='Recovery rate (gamma)',\n",
-    "         ylabel='Fraction infected')\n",
-    "\n",
-    "plt.legend(bbox_to_anchor=(1.02, 1.02))\n",
-    "plt.tight_layout()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's often useful to separate the code that generates results from the code that plots the results, so we can run the simulations once, save the results, and then use them for different analysis, visualization, etc.\n",
-    "\n",
-    "After running `sweep_parameters`, we have a `SweepFrame` with one row for each value of `beta` and one column for each value of `gamma`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "contour(frame)\n",
-    "\n",
-    "decorate(xlabel='Recovery rate (gamma)',\n",
-    "         ylabel='Contact rate (beta)',\n",
-    "         title='Fraction infected, contour plot')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap13ode.ipynb b/colab/chap13ode.ipynb
deleted file mode 100644
index c376d88b..00000000
--- a/colab/chap13ode.ipynb
+++ /dev/null
@@ -1,194 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 13"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from previous chapters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system`, `plot_results`, and `calc_total_infected` are unchanged."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= np.sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(S, I, R):\n",
-    "    \"\"\"Plot the results of a SIR model.\n",
-    "    \n",
-    "    S: TimeSeries\n",
-    "    I: TimeSeries\n",
-    "    R: TimeSeries\n",
-    "    \"\"\"\n",
-    "    plot(S, '--', label='Susceptible')\n",
-    "    plot(I, '-', label='Infected')\n",
-    "    plot(R, ':', label='Recovered')\n",
-    "    decorate(xlabel='Time (days)',\n",
-    "             ylabel='Fraction of population')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_total_infected(results):\n",
-    "    \"\"\"Fraction of population infected during the simulation.\n",
-    "    \n",
-    "    results: DataFrame with columns S, I, R\n",
-    "    \n",
-    "    returns: fraction of population\n",
-    "    \"\"\"\n",
-    "    return get_first_value(results.S) - get_last_value(results.S)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Write a slope function for the SIR model and test it with `run_ode_solver`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(0.333, 0.25)\n",
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, max_step=3)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_results(results.S, results.I, results.R)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap14.ipynb b/colab/chap14.ipynb
deleted file mode 100644
index c8522091..00000000
--- a/colab/chap14.ipynb
+++ /dev/null
@@ -1,487 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 14"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from previous chapters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= np.sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State (s, i, r)\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State (sir)\n",
-    "    \"\"\"\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = system.beta * i * s    \n",
-    "    recovered = system.gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    init, t0, t_end = system.init, system.t0, system.t_end\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t0] = init\n",
-    "    \n",
-    "    for t in linrange(t0, t_end):\n",
-    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_total_infected(results):\n",
-    "    \"\"\"Fraction of population infected during the simulation.\n",
-    "    \n",
-    "    results: DataFrame with columns S, I, R\n",
-    "    \n",
-    "    returns: fraction of population\n",
-    "    \"\"\"\n",
-    "    return get_first_value(results.S) - get_last_value(results.S)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_beta(beta_array, gamma):\n",
-    "    \"\"\"Sweep a range of values for beta.\n",
-    "    \n",
-    "    beta_array: array of beta values\n",
-    "    gamma: recovery rate\n",
-    "    \n",
-    "    returns: SweepSeries that maps from beta to total infected\n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    for beta in beta_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[system.beta] = calc_total_infected(results)\n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_parameters(beta_array, gamma_array):\n",
-    "    \"\"\"Sweep a range of values for beta and gamma.\n",
-    "    \n",
-    "    beta_array: array of infection rates\n",
-    "    gamma_array: array of recovery rates\n",
-    "    \n",
-    "    returns: SweepFrame with one row for each beta\n",
-    "             and one column for each gamma\n",
-    "    \"\"\"\n",
-    "    frame = SweepFrame(columns=gamma_array)\n",
-    "    for gamma in gamma_array:\n",
-    "        frame[gamma] = sweep_beta(beta_array, gamma)\n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Contact number"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the `SweepFrame` from the previous chapter, with one row for each value of `beta` and one column for each value of `gamma`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "beta_array = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 , 1.1]\n",
-    "gamma_array = [0.2, 0.4, 0.6, 0.8]\n",
-    "frame = sweep_parameters(beta_array, gamma_array)\n",
-    "frame.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frame.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following loop shows how we can loop through the columns and rows of the `SweepFrame`.  With 11 rows and 4 columns, there are 44 elements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for gamma in frame.columns:\n",
-    "    column = frame[gamma]\n",
-    "    for beta in column.index:\n",
-    "        frac_infected = column[beta]\n",
-    "        print(beta, gamma, frac_infected)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can wrap that loop in a function and plot the results.  For each element of the `SweepFrame`, we have `beta`, `gamma`, and `frac_infected`, and we plot `beta/gamma` on the x-axis and `frac_infected` on the y-axis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_sweep_frame(frame):\n",
-    "    \"\"\"Plot the values from a SweepFrame.\n",
-    "    \n",
-    "    For each (beta, gamma), compute the contact number,\n",
-    "    beta/gamma\n",
-    "    \n",
-    "    frame: SweepFrame with one row per beta, one column per gamma\n",
-    "    \"\"\"\n",
-    "    for gamma in frame.columns:\n",
-    "        column = frame[gamma]\n",
-    "        for beta in column.index:\n",
-    "            frac_infected = column[beta]\n",
-    "            plot(beta/gamma, frac_infected, 'ro')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_sweep_frame(frame)\n",
-    "\n",
-    "decorate(xlabel='Contact number (beta/gamma)',\n",
-    "         ylabel='Fraction infected')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It turns out that the ratio `beta/gamma`, called the \"contact number\" is sufficient to predict the total number of infections; we don't have to know `beta` and `gamma` separately.\n",
-    "\n",
-    "We can see that in the previous plot: when we plot the fraction infected versus the contact number, the results fall close to a curve."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the book we figured out the relationship between $c$ and $s_{\\infty}$ analytically.  Now we can compute it for a range of values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s_inf_array = linspace(0.0001, 0.9999, 101);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c_array = log(s_inf_array) / (s_inf_array - 1);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`total_infected` is the change in $s$ from the beginning to the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frac_infected = 1 - s_inf_array\n",
-    "frac_infected_series = Series(frac_infected, index=c_array);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can plot the analytic results and compare them to the simulations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_sweep_frame(frame)\n",
-    "plot(frac_infected_series, label='Analysis')\n",
-    "\n",
-    "decorate(xlabel='Contact number (c)',\n",
-    "         ylabel='Fraction infected')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The agreement is generally good, except for values of `c` less than 1."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  If we didn't know about contact numbers, we might have explored other possibilities, like the difference between `beta` and `gamma`, rather than their ratio.\n",
-    "\n",
-    "Write a version of `plot_sweep_frame`, called `plot_sweep_frame_difference`, that plots the fraction infected versus the difference `beta-gamma`.\n",
-    "\n",
-    "What do the results look like, and what does that imply? "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose you run a survey at the end of the semester and find that 26% of students had the Freshman Plague at some point.\n",
-    "\n",
-    "What is your best estimate of `c`?\n",
-    "\n",
-    "Hint: if you print `frac_infected_series`, you can read off the answer. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap15.ipynb b/colab/chap15.ipynb
deleted file mode 100644
index 66c8b36a..00000000
--- a/colab/chap15.ipynb
+++ /dev/null
@@ -1,332 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 15"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The coffee cooling problem\n",
-    "\n",
-    "I'll use a `State` object to store the initial temperature.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "init = State(T=90)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And a `System` object to contain the system parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee = System(init=init,\n",
-    "                volume=300,\n",
-    "                r=0.01,\n",
-    "                T_env=22,\n",
-    "                t_0=0,\n",
-    "                t_end=30,\n",
-    "                dt=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The update function implements Newton's law of cooling."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the thermal transfer model.\n",
-    "    \n",
-    "    state: State (temp)\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State (temp)\n",
-    "    \"\"\"\n",
-    "    r, T_env, dt = system.r, system.T_env, system.dt\n",
-    "    \n",
-    "    T = state.T\n",
-    "    T += -r * (T - T_env) * dt\n",
-    "    \n",
-    "    return State(T=T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how it works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "update_func(init, 0, coffee)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a version of `run_simulation` that uses `linrange` to make an array of time steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "    \n",
-    "    Add a TimeFrame to the System: results\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \"\"\"\n",
-    "    init = system.init\n",
-    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t_0] = init\n",
-    "    ts = linrange(t_0, t_end, dt)\n",
-    "    \n",
-    "    for t in ts:\n",
-    "        frame.row[t+dt] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how it works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(coffee, update_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results.T, label='coffee')\n",
-    "decorate(xlabel='Time (minutes)',\n",
-    "         ylabel='Temperature (C)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the final temperature:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee.T_final = get_last_value(results.T)\n",
-    "T_final = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Encapsulation\n",
-    "\n",
-    "Before we go on, let's define a function to initialize `System` objects with relevant parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(T_init, r, volume, t_end):\n",
-    "    \"\"\"Makes a System object with the given parameters.\n",
-    "\n",
-    "    T_init: initial temperature in degC\n",
-    "    r: heat transfer rate, in 1/min\n",
-    "    volume: volume of liquid in mL\n",
-    "    t_end: end time of simulation\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(T=T_init)\n",
-    "                   \n",
-    "    return System(init=init,\n",
-    "                  r=r, \n",
-    "                  volume=volume,\n",
-    "                  temp=T_init,\n",
-    "                  t_0=0, \n",
-    "                  t_end=t_end, \n",
-    "                  dt=1,\n",
-    "                  T_env=22)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we use it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee = make_system(T_init=90, r=0.01, volume=300, t_end=30)\n",
-    "results = run_simulation(coffee, update_func)\n",
-    "T_final = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:**  Simulate the temperature of 50 mL of milk with a starting temperature of 5 degC, in a vessel with the same insulation, for 15 minutes, and plot the results.\n",
-    "\n",
-    "By trial and error, find a value for `r` that makes the final temperature close to 20 C."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results.T, label='milk')\n",
-    "decorate(xlabel='Time (minutes)',\n",
-    "         ylabel='Temperature (C)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap16.ipynb b/colab/chap16.ipynb
deleted file mode 100644
index 163d9611..00000000
--- a/colab/chap16.ipynb
+++ /dev/null
@@ -1,854 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 16"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Code from previous notebooks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the thermal transfer model.\n",
-    "    \n",
-    "    state: State (temp)\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State (temp)\n",
-    "    \"\"\"\n",
-    "    r, T_env, dt = system.r, system.T_env, system.dt\n",
-    "    \n",
-    "    T = state.T\n",
-    "    T += -r * (T - T_env) * dt\n",
-    "    \n",
-    "    return State(T=T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "    \n",
-    "    Add a TimeFrame to the System: results\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \"\"\"\n",
-    "    init = system.init\n",
-    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t_0] = init\n",
-    "    ts = linrange(t_0, t_end, dt)\n",
-    "    \n",
-    "    for t in ts:\n",
-    "        frame.row[t+dt] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(T_init, r, volume, t_end):\n",
-    "    \"\"\"Makes a System object with the given parameters.\n",
-    "\n",
-    "    T_init: initial temperature in degC\n",
-    "    r: heat transfer rate, in 1/min\n",
-    "    volume: volume of liquid in mL\n",
-    "    t_end: end time of simulation\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(T=T_init)\n",
-    "                   \n",
-    "    return System(init=init,\n",
-    "                  r=r, \n",
-    "                  volume=volume,\n",
-    "                  temp=T_init,\n",
-    "                  t_0=0, \n",
-    "                  t_end=t_end, \n",
-    "                  dt=1,\n",
-    "                  T_env=22)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using `root_bisect`\n",
-    "\n",
-    "As a simple example, let's find the roots of this function; that is, the values of `x` that make the result 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def func(x):\n",
-    "    return (x-1) * (x-2) * (x-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`modsim.py` provides `root_bisect`, which searches for a root by bisection.  The first argument is the function whose roots we want.  The second argument is an interval that contains a root."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = root_bisect(func, [0.5, 1.5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is an object that contains the root that was found and other information."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we provide a different interval, we find a different root."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = root_bisect(func, [1.5, 2.5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If the interval doesn't contain a root, the results explain the error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = root_bisect(func, [4, 5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We want to find the value of `r` that makes the final temperature 70, so we define an \"error function\" that takes `r` as a parameter and returns the difference between the final temperature and the goal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func1(r):\n",
-    "    \"\"\"Runs a simulation and returns the `error`.\n",
-    "    \n",
-    "    r: heat transfer rate, in 1/min\n",
-    "    \n",
-    "    returns: difference between final temp and 70 C\n",
-    "    \"\"\"\n",
-    "    system = make_system(T_init=90, r=r, volume=300, t_end=30)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    T_final = get_last_value(results.T)\n",
-    "    return T_final - 70"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With `r=0.01`, we end up a little too warm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "error_func1(r=0.01)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With `r=0.02`, we end up too cold."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "error_func1(r=0.02)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The return value from `root_bisect` is an array with a single element, the estimated value of `r`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = root_bisect(error_func1, [0.01, 0.02])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r_coffee = res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we run the simulation with the estimated value of `r`, the final temperature is 70 C, as expected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=30)\n",
-    "results = run_simulation(coffee, update_func)\n",
-    "T_final = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  When you call `root_bisect`, it calls `error_func1` several times.  To see how this works, add a print statement to `error_func1` and run `root_bisect` again."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Repeat this process to estimate `r_milk`, given that it starts at 5 C and reaches 20 C after 15 minutes.  \n",
-    "\n",
-    "Before you use `root_bisect`, you might want to try a few values for `r_milk` and see how close you can get by trial and error.  Here's an initial guess to get you started:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r_milk = 0.1\n",
-    "milk = make_system(T_init=5, r=r_milk, volume=50, t_end=15)\n",
-    "results = run_simulation(milk, update_func)\n",
-    "T_final = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Mixing liquids"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function takes `System` objects that represent two liquids, computes the temperature of the mixture, and returns a new `System` object that represents the mixture."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mix(system1, system2):\n",
-    "    \"\"\"Simulates the mixture of two liquids.\n",
-    "    \n",
-    "    system1: System representing coffee\n",
-    "    system2: System representing milk\n",
-    "    \n",
-    "    returns: System representing the mixture\n",
-    "    \"\"\"\n",
-    "    assert system1.t_end == system2.t_end\n",
-    "    \n",
-    "    V1, V2 = system1.volume, system2.volume\n",
-    "    T1, T2 = system1.temp, system2.temp\n",
-    "    \n",
-    "    V_mix = V1 + V2\n",
-    "    T_mix = (V1 * T1 + V2 * T2) / V_mix\n",
-    "    \n",
-    "    return make_system(T_init=T_mix,\n",
-    "                       r=system1.r,\n",
-    "                       volume=V_mix,\n",
-    "                       t_end=30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`mix` requires the `System` objects to have `temp` as a system variable.  `make_system` initializes this variable;\n",
-    "the following function makes sure it gets updated when we run a simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_and_set(system):\n",
-    "    \"\"\"Run a simulation and set the final temperature.\n",
-    "    \n",
-    "    system: System\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    system.temp = get_last_value(results.T)\n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mixing immediately\n",
-    "\n",
-    "Next here's what we get if we add the milk immediately."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=30)\n",
-    "coffee.temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "milk = make_system(T_init=5, r=r_milk, volume=50, t_end=30)\n",
-    "milk.temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mix_first = mix(coffee, milk)\n",
-    "mix_first.temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mix_results = run_and_set(mix_first)\n",
-    "mix_first.temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mixing at the end\n",
-    "\n",
-    "First we'll see what happens if we add the milk at the end.  We'll simulate the coffee and the milk separately."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee_results = run_and_set(coffee)\n",
-    "coffee.temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "milk_results = run_and_set(milk)\n",
-    "milk.temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(coffee_results.T, label='coffee')\n",
-    "plot(milk_results.T, '--', label='milk')\n",
-    "\n",
-    "decorate(xlabel='Time (minutes)',\n",
-    "         ylabel='Temperature (C)',\n",
-    "         loc='center left')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what happens when we mix them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mix_last = mix(coffee, milk)\n",
-    "mix_last.temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mix_last.temp - mix_first.temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function takes `t_add`, which is the time when the milk is added, and returns the final temperature."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_and_mix(t_add, t_total):\n",
-    "    \"\"\"Simulates two liquids and them mixes them at t_add.\n",
-    "    \n",
-    "    t_add: time in minutes\n",
-    "    t_total: total time to simulate, min\n",
-    "    \n",
-    "    returns: final temperature\n",
-    "    \"\"\"\n",
-    "    coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=t_add)\n",
-    "    coffee_results = run_and_set(coffee)\n",
-    "    \n",
-    "    milk = make_system(T_init=5, r=r_milk, volume=50, t_end=t_add)\n",
-    "    milk_results = run_and_set(milk)\n",
-    "    \n",
-    "    mixture = mix(coffee, milk)\n",
-    "    mixture.t_end = t_total - t_add\n",
-    "    results = run_and_set(mixture)\n",
-    "\n",
-    "    return mixture.temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can try it out with a few values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_and_mix(t_add=0, t_total=30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_and_mix(t_add=15, t_total=30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_and_mix(t_add=30, t_total=30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then sweep a range of values for `t_add`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sweep = SweepSeries()\n",
-    "for t_add in linspace(0, 30, 11):\n",
-    "    sweep[t_add] = run_and_mix(t_add, 30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the result looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(sweep, label='final temp', color='C2')\n",
-    "decorate(xlabel='Time added (min)',\n",
-    "         ylabel='Final temperature (C)')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can use the analytic result to compute temperature as a function of time.  The following function is similar to `run_simulation`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_analysis(system):\n",
-    "    \"\"\"Computes temperature using the analytic solution.\n",
-    "        \n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    T_env, r = system.T_env, system.r\n",
-    "    \n",
-    "    T_init = system.init.T    \n",
-    "    ts = linspace(0, system.t_end)\n",
-    "    \n",
-    "    T_array = T_env + (T_init - T_env) * exp(-r * ts)\n",
-    "    \n",
-    "    # to be consistent with run_simulation,\n",
-    "    # we put the array into a TimeFrame\n",
-    "    return TimeFrame(T_array, index=ts, columns=['T'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it.  From the analysis (see `chap16sympy.ipynb`), we have the computed value of `r_coffee2`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r_coffee2 = 0.011610223142273859\n",
-    "coffee2 = make_system(T_init=90, r=r_coffee2, volume=300, t_end=30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_analysis(coffee2)\n",
-    "T_final_analysis = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can compare to the results from simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=30)\n",
-    "results = run_simulation(coffee, update_func)\n",
-    "T_final_simulation = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "They are identical except for a small roundoff error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "T_final_analysis - T_final_simulation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:**  Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
-    "\n",
-    "Hint: Think about the simplest way to represent the behavior of a refrigerator in this model.  The change you make to test this variation of the problem should be very small!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap17.ipynb b/colab/chap17.ipynb
deleted file mode 100644
index 4e65e064..00000000
--- a/colab/chap17.ipynb
+++ /dev/null
@@ -1,299 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 17"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data\n",
-    "\n",
-    "We have data from Pacini and Bergman (1986), \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 23: 113-122.."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "filename = 'glucose_insulin.csv'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/glucose_insulin.csv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv(filename, index_col='time')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the glucose time series looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(data.glucose, 'bo', label='glucose')\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration (mg/dL)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the insulin time series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(data.insulin, 'go', label='insulin')\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the book, I put them in a single figure, using `subplot`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subplot(2, 1, 1)\n",
-    "plot(data.glucose, 'bo', label='glucose')\n",
-    "decorate(ylabel='Concentration (mg/dL)')\n",
-    "\n",
-    "subplot(2, 1, 2)\n",
-    "plot(data.insulin, 'go', label='insulin')\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Interpolation\n",
-    "\n",
-    "We have measurements of insulin concentration at discrete points in time, but we need to estimate it at intervening points.  We'll use `interpolate`, which takes a `Series` and returns a function:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The return value from `interpolate` is a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "I = interpolate(data.insulin)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use the result, `I`, to estimate the insulin level at any point in time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "I(7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`I` can also take an array of time and return an array of estimates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = get_first_label(data)\n",
-    "t_end = get_last_label(data)\n",
-    "ts = linrange(t_0, t_end, endpoint=True)\n",
-    "I(ts)\n",
-    "type(ts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the interpolated values look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(data.insulin, 'go', label='insulin data')\n",
-    "plot(ts, I(ts), color='green', label='interpolated')\n",
-    "\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** [Read the documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html) of `scipy.interpolate.interp1d`.  Pass a keyword argument to `interpolate` to specify one of the other kinds of interpolation, and run the code again to see what it looks like. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Interpolate the glucose data and generate a plot, similar to the previous one, that shows the data points and the interpolated curve evaluated at the time values in `ts`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_code(interpolate)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap18.ipynb b/colab/chap18.ipynb
deleted file mode 100644
index 089fb4b3..00000000
--- a/colab/chap18.ipynb
+++ /dev/null
@@ -1,559 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 18"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter\n",
-    "\n",
-    "Read the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "filename = 'glucose_insulin.csv'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/glucose_insulin.csv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv(filename, index_col='time');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Interpolate the insulin data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "I = interpolate(data.insulin)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The glucose minimal model\n",
-    "\n",
-    "I'll cheat by starting with parameters that fit the data roughly; then we'll see how to improve them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(G0 = 290,\n",
-    "                k1 = 0.03,\n",
-    "                k2 = 0.02,\n",
-    "                k3 = 1e-05)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a version of `make_system` that takes the parameters and data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params, data):\n",
-    "    \"\"\"Makes a System object with the given parameters.\n",
-    "    \n",
-    "    params: sequence of G0, k1, k2, k3\n",
-    "    data: DataFrame with `glucose` and `insulin`\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    G0, k1, k2, k3 = params\n",
-    "    \n",
-    "    Gb = data.glucose[0]\n",
-    "    Ib = data.insulin[0]\n",
-    "    I = interpolate(data.insulin)\n",
-    "    \n",
-    "    t_0 = get_first_label(data)\n",
-    "    t_end = get_last_label(data)\n",
-    "\n",
-    "    init = State(G=G0, X=0)\n",
-    "    \n",
-    "    return System(params,\n",
-    "                  init=init, Gb=Gb, Ib=Ib, I=I,\n",
-    "                  t_0=t_0, t_end=t_end, dt=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the update function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Updates the glucose minimal model.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time in min\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    G, X = state\n",
-    "    k1, k2, k3 = system.k1, system.k2, system.k3 \n",
-    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
-    "    dt = system.dt\n",
-    "        \n",
-    "    dGdt = -k1 * (G - Gb) - X*G\n",
-    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
-    "    \n",
-    "    G += dGdt * dt\n",
-    "    X += dXdt * dt\n",
-    "\n",
-    "    return State(G=G, X=X)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before running the simulation, it is always a good idea to test the update function using the initial conditions.  In this case we can veryify that the results are at least qualitatively correct."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "update_func(system.init, system.t_0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now `run_simulation` is pretty much the same as it always is."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    init = system.init\n",
-    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t_0] = init\n",
-    "    ts = linrange(t_0, t_end, dt)\n",
-    "    \n",
-    "    for t in ts:\n",
-    "        frame.row[t+dt] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The results are in a `TimeFrame` object with one column per state variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following plot shows the results of the simulation along with the actual glucose data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subplot(2, 1, 1)\n",
-    "\n",
-    "plot(results.G, 'b-', label='simulation')\n",
-    "plot(data.glucose, 'bo', label='glucose data')\n",
-    "decorate(ylabel='Concentration (mg/dL)')\n",
-    "\n",
-    "subplot(2, 1, 2)\n",
-    "\n",
-    "plot(results.X, 'C1', label='remote insulin')\n",
-    "\n",
-    "decorate(xlabel='Time (min)', \n",
-    "         ylabel='Concentration (arbitrary units)')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Numerical solution\n",
-    "\n",
-    "Now let's solve the differential equation numerically using `run_ode_solver`, which is an implementation of Ralston's method.\n",
-    "\n",
-    "Instead of an update function, we provide a slope function that evaluates the right-hand side of the differential equations.\n",
-    "\n",
-    "We don't have to do the update part; the solver does it for us."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the glucose minimal model.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time in min\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: derivatives of G and X\n",
-    "    \"\"\"\n",
-    "    G, X = state\n",
-    "    k1, k2, k3 = system.k1, system.k2, system.k3 \n",
-    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
-    "    \n",
-    "    dGdt = -k1 * (G - Gb) - X*G\n",
-    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
-    "    \n",
-    "    return dGdt, dXdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run the ODE solver."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2, details = run_ode_solver(system, slope_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`details` is a `ModSimSeries` object with information about how the solver worked."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`results` is a `TimeFrame` with one row for each time step and one column for each state variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting the results from `run_simulation` and `run_ode_solver`, we can see that they are not very different."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results.G, 'C0', label='run_simulation')\n",
-    "plot(results2.G, 'C2--', label='run_ode_solver')\n",
-    "\n",
-    "decorate(xlabel='Time (min)', ylabel='Concentration (mg/dL)')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The differences in `G` are less than 2%."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diff = results.G - results2.G\n",
-    "percent_diff = diff / results2.G * 100\n",
-    "percent_diff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max(abs(percent_diff))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:**  Our solution to the differential equations is only approximate because we used a finite step size, `dt=2` minutes.\n",
-    "\n",
-    "If we make the step size smaller, we expect the solution to be more accurate.  Run the simulation with `dt=1` and compare the results.  What is the largest relative error between the two solutions?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "Here's the source code for `run_ode_solver` if you'd like to know how it works.\n",
-    "\n",
-    "Notice that `run_ode_solver` is another name for `run_ralston`, which implements [Ralston's method](https://en.wikipedia.org/wiki/List_of_Runge–Kutta_methods)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_code(run_ode_solver)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Related reading:** You might be interested in this article about [people making a DIY artificial pancreas](https://www.bloomberg.com/news/features/2018-08-08/the-250-biohack-that-s-revolutionizing-life-with-diabetes)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap20.ipynb b/colab/chap20.ipynb
deleted file mode 100644
index f1968b01..00000000
--- a/colab/chap20.ipynb
+++ /dev/null
@@ -1,656 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 20"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dropping pennies\n",
-    "\n",
-    "I'll start by getting the units we need from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And defining the initial state."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "init = State(y=381 * m, \n",
-    "             v=0 * m/s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Acceleration due to gravity is about 9.8 m / s$^2$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g = 9.8 * m/s**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll start with a duration of 10 seconds and step size 0.1 second."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_end = 10 * s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dt = 0.1 * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we make a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = System(init=init, g=g, t_end=t_end, dt=dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And define the slope function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object containing `g`\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    g = system.g    \n",
-    "\n",
-    "    dydt = v\n",
-    "    dvdt = -g\n",
-    "    \n",
-    "    return dydt, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's always a good idea to test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dydt, dvdt = slope_func(system.init, 0, system)\n",
-    "print(dydt)\n",
-    "print(dvdt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we're ready to call `run_ode_solver`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's position as a function of time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_position(results):\n",
-    "    plot(results.y, label='y')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "\n",
-    "plot_position(results)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Onto the sidewalk\n",
-    "\n",
-    "To figure out when the penny hit the sidewalk, we can use `crossings`, which finds the times where a `Series` passes through a given value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_crossings = crossings(results.y, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For this example there should be just one crossing, the time when the penny hits the sidewalk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_sidewalk = t_crossings[0] * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can compare that to the exact result.  Without air resistance, we have\n",
-    "\n",
-    "$v = -g t$\n",
-    "\n",
-    "and\n",
-    "\n",
-    "$y = 381 - g t^2 / 2$\n",
-    "\n",
-    "Setting $y=0$ and solving for $t$ yields\n",
-    "\n",
-    "$t = \\sqrt{\\frac{2 y_{init}}{g}}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sqrt(2 * init.y / g)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The estimate is accurate to about 9 decimal places."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Events\n",
-    "\n",
-    "Instead of running the simulation until the penny goes through the sidewalk, it would be better to detect the point where the penny hits the sidewalk and stop.  `run_ode_solver` provides exactly the tool we need, **event functions**.\n",
-    "\n",
-    "Here's an event function that returns the height of the penny above the sidewalk:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Return the height of the penny above the sidewalk.\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we pass it to `run_ode_solver`.  The solver should run until the event function returns 0, and then terminate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The message from the solver indicates the solver stopped because the event we wanted to detect happened.\n",
-    "\n",
-    "Here are the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the `events` option, the solver returns the actual time steps it computed, which are not necessarily equally spaced. \n",
-    "\n",
-    "The last time step is when the event occurred:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_sidewalk = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is accurate to about 4 decimal places.\n",
-    "\n",
-    "We can also check the velocity of the penny when it hits the sidewalk:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_sidewalk = get_last_value(results.v)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And convert to kilometers per hour."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "km = UNITS.kilometer\n",
-    "h = UNITS.hour\n",
-    "v_sidewalk.to(km / h)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If there were no air resistance, the penny would hit the sidewalk (or someone's head) at more than 300 km/h.\n",
-    "\n",
-    "So it's a good thing there is air resistance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Under the hood\n",
-    "\n",
-    "Here is the source code for `crossings` so you can see what's happening under the hood:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_code(crossings)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The [documentation of InterpolatedUnivariateSpline is here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.InterpolatedUnivariateSpline.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Here's a question from the web site [Ask an Astronomer](http://curious.astro.cornell.edu/about-us/39-our-solar-system/the-earth/other-catastrophes/57-how-long-would-it-take-the-earth-to-fall-into-the-sun-intermediate):\n",
-    "\n",
-    "\"If the Earth suddenly stopped orbiting the Sun, I know eventually it would be pulled in by the Sun's gravity and hit it. How long would it take the Earth to hit the Sun? I imagine it would go slowly at first and then pick up speed.\"\n",
-    "\n",
-    "Use `run_ode_solver` to answer this question.\n",
-    "\n",
-    "Here are some suggestions about how to proceed:\n",
-    "\n",
-    "1.  Look up the Law of Universal Gravitation and any constants you need.  I suggest you work entirely in SI units: meters, kilograms, and Newtons.\n",
-    "\n",
-    "2.  When the distance between the Earth and the Sun gets small, this system behaves badly, so you should use an event function to stop when the surface of Earth reaches the surface of the Sun.\n",
-    "\n",
-    "3. Express your answer in days, and plot the results as millions of kilometers versus days.\n",
-    "\n",
-    "If you read the reply by Dave Rothstein, you will see other ways to solve the problem, and a good discussion of the modeling decisions behind them.\n",
-    "\n",
-    "You might also be interested to know that [it's actually not that easy to get to the Sun](https://www.theatlantic.com/science/archive/2018/08/parker-solar-probe-launch-nasa/567197/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap21.ipynb b/colab/chap21.ipynb
deleted file mode 100644
index ae52a9b9..00000000
--- a/colab/chap21.ipynb
+++ /dev/null
@@ -1,527 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 21"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### With air resistance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we'll add air resistance using the [drag equation](https://en.wikipedia.org/wiki/Drag_equation)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll start by getting the units we'll need from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now I'll create a `Params` object to contain the quantities we need.  Using a Params object is convenient for grouping the system parameters in a way that's easy to read (and double-check)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(height = 381 * m,\n",
-    "                v_init = 0 * m / s,\n",
-    "                g = 9.8 * m/s**2,\n",
-    "                mass = 2.5e-3 * kg,\n",
-    "                diameter = 19e-3 * m,\n",
-    "                rho = 1.2 * kg/m**3,\n",
-    "                v_term = 18 * m / s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can pass the `Params` object `make_system` which computes some additional parameters and defines `init`.\n",
-    "\n",
-    "`make_system` uses the given radius to compute `area` and the given `v_term` to compute the drag coefficient `C_d`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    diameter, mass = params.diameter, params.mass\n",
-    "    g, rho = params.g, params.rho, \n",
-    "    v_init, v_term = params.v_init, params.v_term\n",
-    "    height = params.height\n",
-    "    \n",
-    "    area = np.pi * (diameter/2)**2\n",
-    "    C_d = 2 * mass * g / (rho * area * v_term**2)\n",
-    "    init = State(y=height, v=v_init)\n",
-    "    t_end = 30 * s\n",
-    "    dt = t_end / 100\n",
-    "    \n",
-    "    return System(params, area=area, C_d=C_d, \n",
-    "                  init=init, t_end=t_end, dt=dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the slope function, including acceleration due to gravity and drag."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    rho, C_d, g = system.rho, system.C_d, system.g\n",
-    "    area, mass = system.area, system.mass\n",
-    "    \n",
-    "    f_drag = rho * v**2 * C_d * area / 2\n",
-    "    a_drag = f_drag / mass\n",
-    "    \n",
-    "    dydt = v\n",
-    "    dvdt = -g + a_drag\n",
-    "    \n",
-    "    return dydt, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, let's test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use the same event function as in the previous chapter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Return the height of the penny above the sidewalk.\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final height is close to 0, as expected.\n",
-    "\n",
-    "Interestingly, the final velocity is not exactly terminal velocity, which suggests that there are some numerical errors.\n",
-    "\n",
-    "We can get the flight time from `results`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_sidewalk = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the plot of position as a function of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_position(results):\n",
-    "    plot(results.y)\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "    \n",
-    "plot_position(results)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And velocity as a function of time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_velocity(results):\n",
-    "    plot(results.v, color='C1', label='v')\n",
-    "        \n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')\n",
-    "    \n",
-    "plot_velocity(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From an initial velocity of 0, the penny accelerates downward until it reaches terminal velocity; after that, velocity is constant."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Run the simulation with an initial velocity, downward, that exceeds the penny's terminal velocity.  Hint: You can create a new `Params` object based on an existing one, like this:\n",
-    "\n",
-    "`params2 = Params(params, v_init=-30 * m/s)`\n",
-    "\n",
-    "What do you expect to happen?  Plot velocity and position as a function of time, and see if they are consistent with your prediction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose we drop a quarter from the Empire State Building and find that its flight time is 19.1 seconds.  Use this measurement to estimate the terminal velocity.\n",
-    "\n",
-    "1. You can get the relevant dimensions of a quarter from https://en.wikipedia.org/wiki/Quarter_(United_States_coin).\n",
-    "\n",
-    "2. Create a `Params` object with the system parameters.  We don't know `v_term`, so we'll start with the inital guess `v_term = 18 * m / s`.\n",
-    "\n",
-    "3. Use `make_system` to create a `System` object.\n",
-    "\n",
-    "4. Call `run_ode_solver` to simulate the system.  How does the flight time of the simulation compare to the measurement?\n",
-    "\n",
-    "5. Try a few different values of `t_term` and see if you can get the simulated flight time close to 19.1 seconds.\n",
-    "\n",
-    "6. Optionally, write an error function and use `root_scalar` to improve your estimate.\n",
-    "\n",
-    "7. Use your best estimate of `v_term` to compute `C_d`.\n",
-    "\n",
-    "Note: I fabricated the observed flight time, so don't take the results of this exercise too seriously."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap22.ipynb b/colab/chap22.ipynb
deleted file mode 100644
index 10bc4bcf..00000000
--- a/colab/chap22.ipynb
+++ /dev/null
@@ -1,1264 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 22"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Vectors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `Vector` object represents a vector quantity.  In the context of mechanics, vector quantities include position, velocity, acceleration, and force, all of which might be in 2D or 3D.\n",
-    "\n",
-    "You can define a `Vector` object without units, but if it represents a physical quantity, you will often want to attach units to it.\n",
-    "\n",
-    "I'll start by grabbing the units we'll need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a two dimensional `Vector` in meters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = Vector(3, 4) * m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can access the elements by name."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A.x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A.y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The magnitude is the length of the vector."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A.mag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The angle is the number of radians between the vector and the positive x axis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A.angle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we make another `Vector` with the same units,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "B = Vector(1, 2) * m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can add `Vector` objects like this"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A + B"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And subtract like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A - B"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can compute the Euclidean distance between two Vectors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A.dist(B)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the difference in angle"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A.diff_angle(B)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we are given the magnitude and angle of a vector, what we have is the representation of the vector in polar coordinates."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mag = A.mag\n",
-    "angle = A.angle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use `pol2cart` to convert from polar to Cartesian coordinates, and then use the Cartesian coordinates to make a `Vector` object.\n",
-    "\n",
-    "In this example, the `Vector` we get should have the same components as `A`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x, y = pol2cart(angle, mag)\n",
-    "Vector(x, y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another way to represent the direction of `A` is a unit vector, which is a vector with magnitude 1 that points in the same direction as `A`.  You can compute a unit vector by dividing a vector by its magnitude:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A / A.mag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or by using the `hat` function, so named because unit vectors are conventionally decorated with a hat, like this: $\\hat{A}$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A.hat()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Create a `Vector` named `a_grav` that represents acceleration due to gravity, with x component 0 and y component $-9.8$ meters / second$^2$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Degrees and radians\n",
-    "\n",
-    "Pint provides units to represent degree and radians."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "degree = UNITS.degree\n",
-    "radian = UNITS.radian"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you have an angle in degrees,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "angle = 45 * degree\n",
-    "angle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can convert to radians."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "angle_rad = angle.to(radian)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If it's already in radians, `to` does the right thing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "angle_rad.to(radian)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also convert from radians to degrees."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "angle_rad.to(degree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As an alterative, you can use `np.deg2rad`, which works with Pint quantities, but it also works with simple numbers and NumPy arrays:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.deg2rad(angle)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Create a `Vector` named `a_force` that represents acceleration due to a force of 0.5 Newton applied to an object with mass 0.3 kilograms, in a direction 45 degrees up from the positive x-axis.\n",
-    "\n",
-    "Add `a_force` to `a_grav` from the previous exercise.  If that addition succeeds, that means that the units are compatible.  Confirm that the total acceleration seems to make sense."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Baseball"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a `Params` object that contains parameters for the flight of a baseball."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_end = 10 * s\n",
-    "dt = t_end / 100\n",
-    "\n",
-    "params = Params(x = 0 * m, \n",
-    "                y = 1 * m,\n",
-    "                g = 9.8 * m/s**2,\n",
-    "                mass = 145e-3 * kg,\n",
-    "                diameter = 73e-3 * m,\n",
-    "                rho = 1.2 * kg/m**3,\n",
-    "                C_d = 0.33,\n",
-    "                angle = 45 * degree,\n",
-    "                velocity = 40 * m / s,\n",
-    "                t_end=t_end, dt=dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the function that uses the `Params` object to make a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params object with angle, velocity, x, y,\n",
-    "               diameter, duration, g, mass, rho, and C_d\n",
-    "               \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    angle, velocity = params.angle, params.velocity\n",
-    "    \n",
-    "    # convert angle to degrees\n",
-    "    theta = np.deg2rad(angle)\n",
-    "    \n",
-    "    # compute x and y components of velocity\n",
-    "    vx, vy = pol2cart(theta, velocity)\n",
-    "    \n",
-    "    # make the initial state\n",
-    "    R = Vector(params.x, params.y)\n",
-    "    V = Vector(vx, vy)\n",
-    "    init = State(R=R, V=V)\n",
-    "    \n",
-    "    # compute area from diameter\n",
-    "    diameter = params.diameter\n",
-    "    area = np.pi * (diameter/2)**2\n",
-    "    \n",
-    "    return System(params, init=init, area=area)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we use it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a function that computes drag force using vectors:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(V, system):\n",
-    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
-    "    \n",
-    "    V: velocity Vector\n",
-    "    system: System object with rho, C_d, area\n",
-    "    \n",
-    "    returns: Vector drag force\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    mag = rho * V.mag**2 * C_d * area / 2\n",
-    "    direction = -V.hat()\n",
-    "    f_drag = direction * mag\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "V_test = Vector(10, 10) * m/s\n",
-    "drag_force(V_test, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the slope function that computes acceleration due to gravity and drag."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the state variables.\n",
-    "    \n",
-    "    state: State (x, y, x velocity, y velocity)\n",
-    "    t: time\n",
-    "    system: System object with g, rho, C_d, area, mass\n",
-    "    \n",
-    "    returns: sequence (vx, vy, ax, ay)\n",
-    "    \"\"\"\n",
-    "    R, V = state\n",
-    "    mass, g = system.mass, system.g\n",
-    "    \n",
-    "    a_drag = drag_force(V, system) / mass\n",
-    "    a_grav = Vector(0, -g)\n",
-    "    \n",
-    "    A = a_grav + a_drag\n",
-    "    \n",
-    "    return V, A"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Always test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use an event function to stop the simulation when the ball hits the ground:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Stop when the y coordinate is 0.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: y coordinate\n",
-    "    \"\"\"\n",
-    "    R, V = state\n",
-    "    return R.y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "event_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can call `run_ode_solver`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final label tells us the flight time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "flight_time = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final value of `x` tells us the how far the ball landed from home plate:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "R_final = get_last_value(results.R)\n",
-    "x_dist = R_final.x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing the results\n",
-    "\n",
-    "The simplest way to visualize the results is to plot x and y as functions of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xs = results.R.extract('x')\n",
-    "ys = results.R.extract('y')\n",
-    "\n",
-    "xs.plot()\n",
-    "ys.plot()\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Position (m)')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot the velocities the same way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vx = results.V.extract('x')\n",
-    "vy = results.V.extract('y')\n",
-    "\n",
-    "vx.plot(label='vx')\n",
-    "vy.plot(label='vy')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The x velocity slows down due to drag.\n",
-    "\n",
-    "The y velocity drops quickly while drag and gravity are in the same direction, then more slowly after the ball starts to fall.\n",
-    "\n",
-    "Another way to visualize the results is to plot y versus x.  The result is the trajectory of the ball through its plane of motion."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_trajectory(results):\n",
-    "    xs = results.R.extract('x')\n",
-    "    ys = results.R.extract('y')\n",
-    "    plot(xs, ys, color='C2', label='trajectory')\n",
-    "\n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)')\n",
-    "\n",
-    "plot_trajectory(results)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Animation\n",
-    "\n",
-    "One of the best ways to visualize the results of a physical model is animation.  If there are problems with the model, animation can make them apparent.\n",
-    "\n",
-    "The ModSimPy library provides `animate`, which takes as parameters a `TimeSeries` and a draw function.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The draw function should take as parameters a `State` object and the time.  It should draw a single frame of the animation.\n",
-    "\n",
-    "Inside the draw function, you almost always have to call `set_xlim` and `set_ylim`.  Otherwise `matplotlib` auto-scales the axes, which is usually not what you want."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xs = results.R.extract('x')\n",
-    "ys = results.R.extract('y')\n",
-    "\n",
-    "def draw_func(state, t):\n",
-    "    set_xlim(xs)\n",
-    "    set_ylim(ys)\n",
-    "    x, y = state.R\n",
-    "    plot(x, y, 'bo')\n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "animate(results, draw_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Delete the lines that set the x and y axes (or [comment them out](https://en.wiktionary.org/wiki/comment_out)) and see what the animation does."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "`Vector` is a function that returns a `ModSimVector` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "V = Vector(3, 4)\n",
-    "type(V)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `ModSimVector` is a specialized kind of Pint `Quantity`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "isinstance(V, Quantity)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There's one gotcha you might run into with Vectors and Quantities.  If you multiply a `ModSimVector` and a `Quantity`, you get a `ModSimVector`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "V1 = V * m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(V1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But if you multiply a `Quantity` and a `Vector`, you get a `Quantity`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "V2 = m * V"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(V2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With a `ModSimVector` you can get the coordinates using dot notation, as well as `mag`, `mag2`, and `angle`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "V1.x, V1.y, V1.mag, V1.angle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With a `Quantity`, you can't.  But you can use indexing to get the coordinates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "V2[0], V2[1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And you can use vector functions to get the magnitude and angle."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vector_mag(V2), vector_angle(V2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And often you can avoid the whole issue by doing the multiplication with the `ModSimVector` on the left."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Run the simulation with and without air resistance.  How wrong would we be if we ignored drag?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Hint\n",
-    "\n",
-    "system_no_drag = System(system, C_d=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / meter$^3$.  How much farther would a ball hit with the same velocity and launch angle travel?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Hint\n",
-    "\n",
-    "system2 = System(system, rho=1.0*kg/m**3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** The model so far is based on the assumption that coefficient of drag does not depend on velocity, but in reality it does.  The following figure, from Adair, [*The Physics of Baseball*](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC), shows coefficient of drag as a function of velocity.\n",
-    "\n",
-    "<img src=\"data/baseball_drag.png\" width=\"400\">\n",
-    "\n",
-    "\n",
-    "I used [an online graph digitizer](https://automeris.io/WebPlotDigitizer/) to extract the data and save it in a CSV file.  Here's how we can read it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "filename = 'baseball_drag.csv'\n",
-    "\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/data/baseball_drag.csv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "baseball_drag = pd.read_csv(filename)\n",
-    "mph = Quantity(baseball_drag['Velocity in mph'], UNITS.mph)\n",
-    "mps = mph.to(m/s)\n",
-    "baseball_drag.index = magnitude(mps)\n",
-    "baseball_drag.index.name = 'Velocity in meters per second'\n",
-    "baseball_drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Modify the model to include the dependence of `C_d` on velocity, and see how much it affects the results.  Hint: use `interpolate`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "C_d = drag_interp(43 * m / s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap23.ipynb b/colab/chap23.ipynb
deleted file mode 100644
index 713d90ae..00000000
--- a/colab/chap23.ipynb
+++ /dev/null
@@ -1,584 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 23"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "degree = UNITS.degree"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_end = 20 * s\n",
-    "dt = t_end / 100\n",
-    "\n",
-    "params = Params(x = 0 * m, \n",
-    "                y = 1 * m,\n",
-    "                g = 9.8 * m/s**2,\n",
-    "                mass = 145e-3 * kg,\n",
-    "                diameter = 73e-3 * m,\n",
-    "                rho = 1.2 * kg/m**3,\n",
-    "                C_d = 0.3,\n",
-    "                angle = 45 * degree,\n",
-    "                velocity = 40 * m / s,\n",
-    "                t_end=t_end,\n",
-    "                dt=dt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params object with angle, velocity, x, y,\n",
-    "               diameter, duration, g, mass, rho, and C_d\n",
-    "               \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    angle, velocity = params.angle, params.velocity\n",
-    "    \n",
-    "    # convert angle to degrees\n",
-    "    theta = np.deg2rad(angle)\n",
-    "    \n",
-    "    # compute x and y components of velocity\n",
-    "    vx, vy = pol2cart(theta, velocity)\n",
-    "    \n",
-    "    # make the initial state\n",
-    "    R = Vector(params.x, params.y)\n",
-    "    V = Vector(vx, vy)\n",
-    "    init = State(R=R, V=V)\n",
-    "    \n",
-    "    # compute area from diameter\n",
-    "    diameter = params.diameter\n",
-    "    area = np.pi * (diameter/2)**2\n",
-    "    \n",
-    "    return System(params, init=init, area=area)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(V, system):\n",
-    "    \"\"\"Computes drag force in the opposite direction of `V`.\n",
-    "    \n",
-    "    V: velocity Vector\n",
-    "    system: System object with rho, C_d, area\n",
-    "    \n",
-    "    returns: Vector drag force\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    mag = rho * V.mag**2 * C_d * area / 2\n",
-    "    direction = -V.hat()\n",
-    "    f_drag = direction * mag\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the state variables.\n",
-    "    \n",
-    "    state: State (x, y, x velocity, y velocity)\n",
-    "    t: time\n",
-    "    system: System object with g, rho, C_d, area, mass\n",
-    "    \n",
-    "    returns: sequence (vx, vy, ax, ay)\n",
-    "    \"\"\"\n",
-    "    R, V = state\n",
-    "    mass, g = system.mass, system.g\n",
-    "    \n",
-    "    a_drag = drag_force(V, system) / mass\n",
-    "    a_grav = Vector(0, -g)\n",
-    "    \n",
-    "    A = a_grav + a_drag\n",
-    "    \n",
-    "    return V, A"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Stop when the y coordinate is 0.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: y coordinate\n",
-    "    \"\"\"\n",
-    "    R, V = state\n",
-    "    return R.y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optimal launch angle\n",
-    "\n",
-    "To find the launch angle that maximizes distance from home plate, we need a function that takes launch angle and returns range."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def range_func(angle, params):  \n",
-    "    \"\"\"Computes range for a given launch angle.\n",
-    "    \n",
-    "    angle: launch angle in degrees\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: distance in meters\n",
-    "    \"\"\"\n",
-    "    params = Params(params, angle=angle)\n",
-    "    system = make_system(params)\n",
-    "    results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "    x_dist = get_last_value(results.R).x\n",
-    "    print(angle, x_dist)\n",
-    "    return x_dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's test `range_func`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "range_func(45, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And sweep through a range of angles."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "angles = linspace(20, 80, 21)\n",
-    "sweep = SweepSeries()\n",
-    "\n",
-    "for angle in angles:\n",
-    "    x_dist = range_func(angle, params)\n",
-    "    sweep[angle] = x_dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting the `Sweep` object, it looks like the peak is between 40 and 45 degrees."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(sweep, color='C2')\n",
-    "decorate(xlabel='Launch angle (degree)',\n",
-    "         ylabel='Range (m)',\n",
-    "         title='Range as a function of launch angle',\n",
-    "         legend=False)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use `maximize` to search for the peak efficiently."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bounds = [0, 90] * degree\n",
-    "res = maximize(range_func, bounds, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`res` is an `ModSimSeries` object with detailed results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`x` is the optimal angle and `fun` the optional range."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimal_angle = res.x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_x_dist = res.fun"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "Read the source code for `maximize` and `minimize_scalar`, below.\n",
-    "\n",
-    "Add a print statement to `range_func` that prints `angle`.  Then run `maximize` again so you can see how many times it calls `range_func` and what the arguments are."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_code(maximize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_code(minimize_scalar)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The Manny Ramirez problem\n",
-    "\n",
-    "Finally, let's solve the Manny Ramirez problem:\n",
-    "\n",
-    "*What is the minimum effort required to hit a home run in Fenway Park?*\n",
-    "\n",
-    "Fenway Park is a baseball stadium in Boston, Massachusetts.  One of its most famous features is the \"Green Monster\", which is a wall in left field that is unusually close to home plate, only 310 feet along the left field line.  To compensate for the short distance, the wall is unusually high, at 37 feet.\n",
-    "\n",
-    "Although the problem asks for a minimum, it is not an optimization problem.  Rather, we want to solve for the initial velocity that just barely gets the ball to the top of the wall, given that it is launched at the optimal angle.\n",
-    "\n",
-    "And we have to be careful about what we mean by \"optimal\".  For this problem, we don't want the longest range, we want the maximum height at the point where it reaches the wall.\n",
-    "\n",
-    "If you are ready to solve the problem on your own, go ahead.  Otherwise I will walk you through the process with an outline and some starter code.\n",
-    "\n",
-    "As a first step, write a function called `height_func` that takes a launch angle and a params as parameters, simulates the flights of a baseball, and returns the height of the baseball when it reaches a point 94.5 meters (310 feet) from home plate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Always test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your function with a launch angle of 45 degrees:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now use `maximize` to find the optimal angle.  Is it higher or lower than the angle that maximizes range?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With initial velocity 40 m/s and an optimal launch angle, the ball clears the Green Monster with a little room to spare.\n",
-    "\n",
-    "Which means we can get over the wall with a lower initial velocity."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Finding the minimum velocity\n",
-    "\n",
-    "Even though we are finding the \"minimum\" velocity, we are not really solving a minimization problem.  Rather, we want to find the velocity that makes the height at the wall exactly 11 m, given given that it's launched at the optimal angle.  And that's a job for `root_bisect`.\n",
-    "\n",
-    "Write an error function that takes a velocity and a `Params` object as parameters.  It should use `maximize` to find the highest possible height of the ball at the wall, for the given velocity.  Then it should return the difference between that optimal height and 11 meters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your error function before you call `root_bisect`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then use `root_bisect` to find the answer to the problem, the minimum velocity that gets the ball out of the park."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And just to check, run `error_func` with the value you found."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap24.ipynb b/colab/chap24.ipynb
deleted file mode 100644
index a2273574..00000000
--- a/colab/chap24.ipynb
+++ /dev/null
@@ -1,653 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 24"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Rolling paper\n",
-    "\n",
-    "We'll start by loading the units we need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radian = UNITS.radian\n",
-    "m = UNITS.meter\n",
-    "s = UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And creating a `Params` object with the system parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(Rmin = 0.02 * m,\n",
-    "                Rmax = 0.055 * m,\n",
-    "                L = 47 * m,\n",
-    "                omega = 10 * radian / s,\n",
-    "                t_end = 130 * s,\n",
-    "                dt = 1*s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function estimates the parameter `k`, which is the increase in the radius of the roll for each radian of rotation. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def estimate_k(params):\n",
-    "    \"\"\"Estimates the parameter `k`.\n",
-    "    \n",
-    "    params: Params with Rmin, Rmax, and L\n",
-    "    \n",
-    "    returns: k in meters per radian\n",
-    "    \"\"\"\n",
-    "    Rmin, Rmax, L = params.Rmin, params.Rmax, params.L\n",
-    "    \n",
-    "    Ravg = (Rmax + Rmin) / 2\n",
-    "    Cavg = 2 * pi * Ravg\n",
-    "    revs = L / Cavg\n",
-    "    rads = 2 * pi * revs\n",
-    "    k = (Rmax - Rmin) / rads\n",
-    "    return k"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As usual, `make_system` takes a `Params` object and returns a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params with Rmin, Rmax, and L\n",
-    "    \n",
-    "    returns: System with init, k, and ts\n",
-    "    \"\"\"\n",
-    "    init = State(theta = 0 * radian,\n",
-    "                 y = 0 * m,\n",
-    "                 r = params.Rmin)\n",
-    "    \n",
-    "    k = estimate_k(params)\n",
-    "\n",
-    "    return System(params, init=init, k=k)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `make_system`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can write a slope function based on the differential equations\n",
-    "\n",
-    "$\\omega = \\frac{d\\theta}{dt} = 10$\n",
-    "\n",
-    "$\\frac{dy}{dt} = r \\frac{d\\theta}{dt}$\n",
-    "\n",
-    "$\\frac{dr}{dt} = k \\frac{d\\theta}{dt}$\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object with theta, y, r\n",
-    "    t: time\n",
-    "    system: System object with r, k\n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    theta, y, r = state\n",
-    "    k, omega = system.k, system.omega\n",
-    "    \n",
-    "    dydt = r * omega\n",
-    "    drdt = k * omega\n",
-    "    \n",
-    "    return omega, dydt, drdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `slope_func`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll use an event function to stop when `y=L`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Detects when we've rolled length `L`.\n",
-    "    \n",
-    "    state: State object with theta, y, r\n",
-    "    t: time\n",
-    "    system: System object with L\n",
-    "    \n",
-    "    returns: difference between `y` and `L`\n",
-    "    \"\"\"\n",
-    "    theta, y, r = state\n",
-    "    \n",
-    "    return y - system.L"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "event_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And look at the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final value of `y` is 47 meters, as expected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unrolled = get_last_value(results.y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final value of radius is `R_max`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radius = get_last_value(results.r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The total number of rotations is close to 200, which seems plausible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radians = get_last_value(results.theta) \n",
-    "rotations = magnitude(radians) / 2 / np.pi"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The elapsed time is about 2 minutes, which is also plausible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_final = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `theta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_theta(results):\n",
-    "    plot(results.theta, color='C0', label='theta')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angle (rad)')\n",
-    "    \n",
-    "plot_theta(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `y`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_y(results):\n",
-    "    plot(results.y, color='C1', label='y')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Length (m)')\n",
-    "    \n",
-    "plot_y(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `r`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_r(results):\n",
-    "    plot(results.r, color='C2', label='r')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Radius (m)')\n",
-    "    \n",
-    "plot_r(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also see the relationship between `y` and `r`, which I derive analytically in the book."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results.r, results.y, color='C3')\n",
-    "\n",
-    "decorate(xlabel='Radius (m)',\n",
-    "         ylabel='Length (m)',\n",
-    "         legend=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the figure from the book."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_three(results):\n",
-    "    subplot(3, 1, 1)\n",
-    "    plot_theta(results)\n",
-    "\n",
-    "    subplot(3, 1, 2)\n",
-    "    plot_y(results)\n",
-    "\n",
-    "    subplot(3, 1, 3)\n",
-    "    plot_r(results)\n",
-    "\n",
-    "plot_three(results)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Animation\n",
-    "\n",
-    "Here's a draw function that animates the results using `matplotlib` patches."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib.patches import Circle\n",
-    "from matplotlib.patches import Arrow\n",
-    "\n",
-    "def draw_func(state, t):\n",
-    "    # get radius in mm\n",
-    "    theta, y, r = state\n",
-    "    radius = r.magnitude * 1000\n",
-    "    \n",
-    "    # draw a circle with\n",
-    "    circle = Circle([0, 0], radius, fill=True)\n",
-    "    plt.gca().add_patch(circle)\n",
-    "    \n",
-    "    # draw an arrow to show rotation\n",
-    "    dx, dy = pol2cart(theta, radius)\n",
-    "    arrow = Arrow(0, 0, dx, dy)\n",
-    "    plt.gca().add_patch(arrow)\n",
-    "\n",
-    "    # make the aspect ratio 1\n",
-    "    plt.axis('equal')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "animate(results, draw_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Run the simulation again with a smaller step size to smooth out the animation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Since we keep `omega` constant, the linear velocity of the paper increases with radius.  Use `gradient` to estimate the derivative of `results.y`.  What is the peak linear velocity?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(dydt, label='dydt')\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Linear velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now suppose the peak velocity is the limit; that is, we can't move the paper any faster than that.\n",
-    "\n",
-    "Nevertheless, we might be able to speed up the process by keeping the linear velocity at the maximum all the time.\n",
-    "\n",
-    "Write a slope function that keeps the linear velocity, `dydt`, constant, and computes the angular velocity, `omega`, accordingly.\n",
-    "\n",
-    "Run the simulation and see how much faster we could finish rolling the paper."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/colab/chap25.ipynb b/colab/chap25.ipynb
deleted file mode 100644
index 1516eaff..00000000
--- a/colab/chap25.ipynb
+++ /dev/null
@@ -1,957 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 25"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "*Modeling and Simulation in Python*\n",
-    "\n",
-    "Copyright 2021 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# check if the libraries we need are installed\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint\n",
-    "    \n",
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Teapots and Turntables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Tables in Chinese restaurants often have a rotating tray or turntable that makes it easy for customers to share dishes.  These turntables are supported by low-friction bearings that allow them to turn easily and glide.  However, they can be heavy, especially when they are loaded with food, so they have a high moment of inertia.\n",
-    "\n",
-    "Suppose I am sitting at a table with a pot of tea on the turntable directly in front of me, and the person sitting directly opposite asks me to pass the tea.  I push on the edge of the turntable with 1 Newton of force until it has turned 0.5 radians, then let go.  The turntable glides until it comes to a stop 1.5 radians from the starting position.  How much force should I apply for a second push so the teapot glides to a stop directly opposite me?\n",
-    "\n",
-    "The following figure shows the scenario, where `F` is the force I apply to the turntable at the perimeter, perpendicular to the moment arm, `r`, and `tau` is the resulting torque.  The blue circle near the bottom is the teapot.\n",
-    "\n",
-    "![](diagrams/teapot.png)\n",
-    "\n",
-    "We'll answer this question in these steps:\n",
-    "\n",
-    "1.  We'll use the results from the first push to estimate the coefficient of friction for the turntable.\n",
-    "\n",
-    "2.  We'll use that coefficient of friction to estimate the force needed to rotate the turntable through the remaining angle.\n",
-    "\n",
-    "Our simulation will use the following parameters:\n",
-    "\n",
-    "1.  The radius of the turntable is 0.5 meters, and its weight is 7 kg.\n",
-    "\n",
-    "2.  The teapot weights 0.3 kg, and it sits 0.4 meters from the center of the turntable.\n",
-    "\n",
-    "As usual, I'll get units from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radian = UNITS.radian\n",
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And store the parameters in a `Params` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(radius_disk=0.5*m,\n",
-    "                mass_disk=7*kg,\n",
-    "                radius_pot=0.4*m,\n",
-    "                mass_pot=0.3*kg,\n",
-    "                force=1*N,\n",
-    "                torque_friction=0.2*N*m,\n",
-    "                theta_end=0.5*radian,\n",
-    "                t_end=20*s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system` creates the initial state, `init`, and computes the total moment of inertia for the turntable and the teapot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    mass_disk, mass_pot = params.mass_disk, params.mass_pot\n",
-    "    radius_disk, radius_pot = params.radius_disk, params.radius_pot\n",
-    "    \n",
-    "    init = State(theta=0*radian, omega=0*radian/s)\n",
-    "    \n",
-    "    I_disk = mass_disk * radius_disk**2 / 2\n",
-    "    I_pot = mass_pot * radius_pot**2\n",
-    "    \n",
-    "    return System(params, init=init, I=I_disk+I_pot)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the `System` object we'll use for the first phase of the simulation, while I am pushing the turntable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system1 = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Simulation\n",
-    "\n",
-    "When I stop pushing on the turntable, the angular acceleration changes abruptly.  We could implement the slope function with an `if` statement that checks the value of `theta` and sets `force` accordingly.  And for a coarse model like this one, that might be fine.  But we will get more accurate results if we simulate the system in two phases:\n",
-    "\n",
-    "1.  During the first phase, force is constant, and we run until `theta` is 0.5 radians.\n",
-    "\n",
-    "2.  During the second phase, force is 0, and we run until `omega` is 0.\n",
-    "\n",
-    "Then we can combine the results of the two phases into a single `TimeFrame`.\n",
-    "\n",
-    "Here's the slope function we'll use:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    theta, omega = state\n",
-    "    radius_disk, force = system.radius_disk, system.force\n",
-    "    torque_friction, I = system.torque_friction, system.I\n",
-    "    \n",
-    "    torque = radius_disk * force - torque_friction\n",
-    "    alpha = torque / I\n",
-    "    \n",
-    "    return omega, alpha "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, we'll test the slope function before running the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system1.init, 0, system1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an event function that stops the simulation when `theta` reaches `theta_end`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func1(state, t, system):\n",
-    "    \"\"\"Stops when theta reaches theta_end.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: difference from target\n",
-    "    \"\"\"\n",
-    "    theta, omega = state\n",
-    "    return theta - system.theta_end "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "event_func1(system1.init, 0, system1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the first phase."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results1, details1 = run_ode_solver(system1, slope_func, events=event_func1)\n",
-    "details1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And look at the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results1.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Phase 2\n",
-    "\n",
-    "Before we run the second phase, we have to extract the final time and state of the first phase."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = results1.last_label() * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And make an initial `State` object for Phase 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "init2 = results1.last_row()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And a new `System` object with zero force."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system2 = System(system1, t_0=t_0, init=init2, force=0*N)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an event function that stops when angular velocity is 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func2(state, t, system):\n",
-    "    \"\"\"Stops when omega is 0.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: omega\n",
-    "    \"\"\"\n",
-    "    theta, omega = state\n",
-    "    return omega"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "event_func2(system2.init, 0, system2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the second phase."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system2.init, system2.t_0, system2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2, details2 = run_ode_solver(system2, slope_func, events=event_func2)\n",
-    "details2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And check the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pandas provides `combine_first`, which combines `results1` and `results2`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = results1.combine_first(results2)\n",
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can plot `theta` for both phases."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_theta(results):\n",
-    "    plot(results.theta, label='theta')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angle (rad)')\n",
-    "    \n",
-    "plot_theta(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And `omega`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_omega(results):\n",
-    "    plot(results.omega, label='omega', color='C1')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angular velocity (rad/s)')\n",
-    "    \n",
-    "plot_omega(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subplot(2, 1, 1)\n",
-    "plot_theta(results)\n",
-    "subplot(2, 1, 2)\n",
-    "plot_omega(results)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Estimating friction\n",
-    "\n",
-    "Let's take the code from the previous section and wrap it in a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_two_phases(force, torque_friction, params):\n",
-    "    \"\"\"Run both phases.\n",
-    "    \n",
-    "    force: force applied to the turntable\n",
-    "    torque_friction: friction due to torque\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: TimeFrame of simulation results\n",
-    "    \"\"\"\n",
-    "    # put the specified parameters into the Params object\n",
-    "    params = Params(params, force=force, torque_friction=torque_friction)\n",
-    "\n",
-    "    # run phase 1\n",
-    "    system1 = make_system(params)\n",
-    "    results1, details1 = run_ode_solver(system1, slope_func, \n",
-    "                                          events=event_func1)\n",
-    "\n",
-    "    # get the final state from phase 1\n",
-    "    t_0 = results1.last_label() * s\n",
-    "    init2 = results1.last_row()\n",
-    "    \n",
-    "    # run phase 2\n",
-    "    system2 = System(system1, t_0=t_0, init=init2, force=0*N)\n",
-    "    results2, details2 = run_ode_solver(system2, slope_func, \n",
-    "                                        events=event_func2)\n",
-    "    \n",
-    "    # combine and return the results\n",
-    "    results = results1.combine_first(results2)\n",
-    "    return TimeFrame(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's test it with the same parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "force = 1*N\n",
-    "torque_friction = 0.2*N*m\n",
-    "results = run_two_phases(force, torque_friction, params)\n",
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And check the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "theta_final = results.last_row().theta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the error function we'll use with `root_bisect`.\n",
-    "\n",
-    "It takes a hypothetical value for `torque_friction` and returns the difference between `theta_final` and the observed duration of the first push, 1.5 radian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func1(torque_friction, params):\n",
-    "    \"\"\"Error function for root_scalar.\n",
-    "    \n",
-    "    torque_friction: hypothetical value\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: offset from target value\n",
-    "    \"\"\"\n",
-    "    force = 1 * N\n",
-    "    results = run_two_phases(force, torque_friction, params)\n",
-    "    theta_final = results.last_row().theta\n",
-    "    print(torque_friction, theta_final)\n",
-    "    return theta_final - 1.5 * radian"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing the error function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "guess1 = 0.1*N*m\n",
-    "error_func1(guess1, params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "guess2 = 0.3*N*m\n",
-    "error_func1(guess2, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And running `root_scalar`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = root_bisect(error_func1, [guess1, guess2], params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is the coefficient of friction that yields a total rotation of 1.5 radian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torque_friction = res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a test run with the estimated value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "force = 1 * N\n",
-    "results = run_two_phases(force, torque_friction, params)\n",
-    "theta_final = get_last_value(results.theta)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looks good."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Animation\n",
-    "\n",
-    "\n",
-    "Here's a draw function we can use to animate the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib.patches import Circle\n",
-    "from matplotlib.patches import Arrow\n",
-    "\n",
-    "def draw_func(state, t):\n",
-    "    theta, omega = state\n",
-    "    \n",
-    "    # draw a circle for the table\n",
-    "    radius_disk = magnitude(params.radius_disk)\n",
-    "    circle1 = Circle([0, 0], radius_disk)\n",
-    "    plt.gca().add_patch(circle1)\n",
-    "    \n",
-    "    # draw a circle for the teapot\n",
-    "    radius_pot = magnitude(params.radius_pot)\n",
-    "    center = pol2cart(theta, radius_pot)\n",
-    "    circle2 = Circle(center, 0.05, color='C1')\n",
-    "    plt.gca().add_patch(circle2)\n",
-    "\n",
-    "    # make the aspect ratio 1\n",
-    "    plt.axis('equal')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = results.first_row()\n",
-    "draw_func(state, 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "animate(results, draw_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "### Exercises\n",
-    "\n",
-    "Now finish off the example by estimating the force that delivers the teapot to the desired position.\n",
-    "\n",
-    "Write an error function that takes `force` and `params` and returns the offset from the desired angle."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test the error function with `force=1`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And run `root_bisect` to find the desired force."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "force = res.root\n",
-    "results = run_two_phases(force, torque_friction, params)\n",
-    "theta_final = get_last_value(results.theta)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "remaining_angle = np.pi - 1.5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Now suppose my friend pours 0.1 kg of tea and puts the teapot back on the turntable at distance 0.3 meters from the center.  If I ask for the tea back, how much force should they apply, over an arc of 0.5 radians, to make the teapot glide to a stop back in front of me?  You can assume that torque due to friction is proportional to the total mass of the teapot and the turntable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 294666b7292117cbd632fa09143c49bbbe325e7c Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sun, 20 Mar 2022 19:42:37 -0400
Subject: [PATCH 083/144] Update README.md

---
 README.md | 233 ++----------------------------------------------------
 1 file changed, 5 insertions(+), 228 deletions(-)

diff --git a/README.md b/README.md
index 5d66e33b..518d976f 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,9 @@
 # Modeling and Simulation in Python
 
+[Read the book and run the code](https://allendowney.github.io/ModSimPy/).
+
+[Pre-order the books from No Starch Press](https://nostarch.com/modeling-and-simulation-python).
+
 *Modeling and Simulation in Python* is an introduction to physical modeling using a computational approach.  It is organized in three parts:
 
 * The first part presents discrete models, including a bikeshare system and world population growth.
@@ -14,232 +18,5 @@ Python is an ideal programming language for this material.  It is a good first l
 
 *Modeling and Simulation in Python* is a Free Book. It is available under the [Creative Commons Attribution-NonCommercial 4.0 Unported License](https://creativecommons.org/licenses/by-nc/4.0/), which means that you are free to copy, distribute, and modify it, as long as you attribute the work and don’t use it for commercial purposes.
 
-[This](http://greenteapress.com/wp/modsimpy/) and other Free Books by Allen Downey are available from [Green Tea Press](http://greenteapress.com/wp).
-
-
-## Getting started
-
-There are three ways to work with the code:
-
-* You can run the notebooks on Colab.
-
-* You can run them on Binder.
-
-* You can download them and run them in your own Jupyter environment.
-
-### Colab
-
-Use these links to run the notebooks on Colab:
-
-* [chap01.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap01.ipynb)
-
-* [chap02.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap02.ipynb)
-
-* [chap03.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap03.ipynb)
-
-* [chap04.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap04.ipynb)
-
-* [chap05.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap05.ipynb)
-
-* [chap06.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap06.ipynb)
-
-* [chap07.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap07.ipynb)
-
-* [chap08.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap08.ipynb)
-
-* [chap09.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap09.ipynb)
-
-* [chap10.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap10.ipynb)
-
-* [chap11.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap11.ipynb)
-
-* [chap12.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap12.ipynb)
-
-* [chap13.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap13.ipynb)
-
-* [chap13ode.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap13ode.ipynb)
-
-* [chap14.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap14.ipynb)
-
-* [chap15.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap15.ipynb)
-
-* [chap16.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap16.ipynb)
-
-* [chap17.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap17.ipynb)
-
-* [chap18.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap18.ipynb)
-
-* [chap20.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap20.ipynb)
-
-* [chap21.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap21.ipynb)
-
-* [chap22.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap22.ipynb)
-
-* [chap23.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap23.ipynb)
-
-* [chap24.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap24.ipynb)
-
-* [chap25.ipynb](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/colab/chap25.ipynb)
-
-### Binder
-
-You can run the code from the repository in a browser by pressing the Binder button below.
-
-[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/AllenDowney/ModSimPy/master?filepath=notebooks)
-
-
-### Install Jupyter
-
-To run the examples and work on the exercises in this book, you have to:
-
-1.  Install Python on your computer, along with the libraries we will
-    use.
-
-2.  Copy my files onto your computer.
-
-3.  Run Jupyter, which is a tool for running and writing programs, and
-    load a **notebook**, which is a file that contains code and
-    text.
-
-The next three sections provide details for these steps. I wish there
-were an easier way to get started; it’s regrettable that you have to do so much work before you write your first program. Be persistent!
-
-#### Installing Python
-
-You might already have Python installed on your computer, but you might
-not have the latest version. To use the code in this book, you need
-Python 3.6 or later. Even if you have the latest version, you probably
-don’t have all of the libraries we need.
-
-You could update Python and install these libraries, but I strongly
-recommend that you don’t go down that road. I think you will find it
-easier to use **Anaconda**, which is a free Python distribution that
-includes all the libraries you need for this book (and more).
-
-Anaconda is available for Linux, macOS, and Windows. By default, it puts
-all files in your home directory, so you don’t need administrator (root)
-permission to install it, and if you have a version of Python already,
-Anaconda will not remove or modify it.
-
-Start at [the Anaconda download page](https://www.anaconda.com/distribution/#download-section).
-Download the installer for
-your system and run it. You don’t need administrative privileges to
-install Anaconda, so I recommend you run the installer as a normal user,
-not as administrator or root.
-
-I suggest you accept the recommended options. On Windows you have the
-option to install Visual Studio Code, which is an interactive
-environment for writing programs. You won’t need it for this book, but
-you might want it for other projects.
-
-By default, Anaconda installs most of the packages you need, but there
-are a few more you have to add. Once the installation is complete, open
-a command window. On macOS or Linux, you can use Terminal. On Windows,
-open the Anaconda Prompt that should be in your Start menu.
-
-Run the following command (copy and paste it if you can, to avoid
-typos):
-```
-conda install jupyterlab pandas seaborn sympy beautifulsoup4 lxml html5lib pytables
-```
-To install Pint, run this command:
-```
-conda install -c conda-forge pint
-```
-And to install the ModSim library, run this command:
-```
-pip install modsimpy
-```
-That should be everything you need.
-
-#### Copying my files
-
-The simplest way to get the files for this book is to download a [Zip
-archive from GitHub](https://github.com/AllenDowney/ModSimPy/archive/master.zip).
-You will need a program like
-WinZip or gzip to unpack the Zip file. Make a note of the location of
-the files you download.
-
-If you download the Zip file, you can skip the rest of this section,
-which explains how to use Git.
-
-The code for this book is available from
-<https://github.com/AllenDowney/ModSimPy>, which is a **Git
-repository**. Git is a software tool that helps you keep track of the
-programs and other files that make up a project. A collection of files
-under Git’s control is called a repository (the cool kids call it a
-“repo"). GitHub is a hosting service that provides storage for Git
-repositories and a convenient web interface.
-
-Before you download these files, I suggest you copy my repository on
-GitHub, which is called **forking**. If you don’t already have a
-GitHub account, you’ll need to create one.
-
-Use a browser to view the homepage of my repository at
-<https://github.com/AllenDowney/ModSimPy>. You should see a gray button
-in the upper right that says [Fork]{}. If you press it, GitHub will
-create a copy of my repository that belongs to you.
-
-Now, the best way to download the files is to use a **Git client**,
-which is a program that manages git repositories. You can get
-installation instructions for Windows, macOS, and Linux at
-<http://modsimpy.com/getgit>.
-
-In Windows, I suggest you accept the options recommended by the
-installer, with two exceptions:
-
--   As the default editor, choose instead of .
-
--   For “Configuring line ending conversions", select “Check out as is,
-    commit as is".
-
-For macOS and Linux, I suggest you accept the recommended options.
-
-Once the installation is complete, open a command window. On Windows,
-open Git Bash, which should be in your Start menu. On macOS or Linux,
-you can use Terminal.
-
-To find out what directory you are in, type , which stands for “print
-working directory". On Windows, most likely you are in . On MacOS or
-Linux, you are probably in your home directory, .
-
-The next step is to copy files from your repository on GitHub to your
-computer; in Git vocabulary, this process is called **cloning**. Run
-this command:
-
-```
-git clone https://github.com/YourGitHubUserName/ModSimPy
-```
-
-Of course, you should replace with your GitHub user name. After cloning,
-you should have a new directory called .
-
-#### Running Jupyter
-
-The code for each chapter, and starter code for the exercises, is in
-Jupyter notebooks. If you have not used Jupyter before, you can read
-about it at <https://jupyter.org>.
-
-To start Jupyter on macOS or Linux, open a Terminal; on Windows, open
-Git Bash. Use to “change directory" into the code directory in the
-repository:
-```
-cd ModSimPy/notebooks
-```
-Then launch the Jupyter notebook server:
-```
-jupyter notebook
-```
-Jupyter should open a window in a browser, and you should see the list
-of notebooks in my repository. Click on the first notebook, and follow
-the instructions to run the first few “cells". The first time you run a
-notebook, it might take several seconds to start, while some Python
-files get initialized. After that, it should run faster.
-
-Feel free to read through the notebook, but it might not make sense
-until you read Chapter 1.
+This and other Free Books by Allen Downey are available from [Green Tea Press](http://greenteapress.com/wp).
 
-You can also launch Jupyter from the Start menu on Windows, the Dock on
-macOS, or the Anaconda Navigator on any system. If you do that, Jupyter
-might start in your home directory or somewhere else in your file
-system, so you might have to navigate to find the directory.

From 85cd9a43a52ebdbd395e63b0ab5af6132dd02692 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sun, 20 Mar 2022 19:42:48 -0400
Subject: [PATCH 084/144] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 518d976f..5339bbf3 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 
 [Read the book and run the code](https://allendowney.github.io/ModSimPy/).
 
-[Pre-order the books from No Starch Press](https://nostarch.com/modeling-and-simulation-python).
+[Pre-order the book from No Starch Press](https://nostarch.com/modeling-and-simulation-python).
 
 *Modeling and Simulation in Python* is an introduction to physical modeling using a computational approach.  It is organized in three parts:
 

From 7db4ddaf11b6fd91ff00d2fd5a38486b22576b43 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 10 Jun 2022 15:39:16 -0400
Subject: [PATCH 085/144] Updating chapters

---
 chapters/chap01.ipynb | 117 ++++++++++++++++++++++--------------------
 chapters/chap02.ipynb | 108 +++++++++++++++++++++-----------------
 chapters/chap03.ipynb |  14 +++--
 chapters/chap04.ipynb |  22 ++++----
 chapters/chap05.ipynb |  22 ++++----
 chapters/chap06.ipynb |  13 +++--
 chapters/chap07.ipynb |  17 +++---
 chapters/chap08.ipynb | 117 ++++++++++++++++++++++++------------------
 chapters/chap09.ipynb |  94 ++++++++++++++++++---------------
 chapters/chap10.ipynb |   4 +-
 chapters/chap11.ipynb |  18 +++----
 chapters/chap12.ipynb |   4 +-
 chapters/chap13.ipynb |   5 +-
 chapters/chap14.ipynb |  16 +++---
 chapters/chap15.ipynb |   4 +-
 chapters/chap16.ipynb |   6 +--
 chapters/chap17.ipynb |  12 ++---
 chapters/chap18.ipynb |  10 ++--
 chapters/chap19.ipynb |   4 +-
 chapters/chap20.ipynb |   9 ++--
 chapters/chap21.ipynb |  11 ++--
 chapters/chap22.ipynb |  15 +++---
 chapters/chap23.ipynb |   9 ++--
 chapters/chap24.ipynb |  13 ++---
 chapters/chap25.ipynb |  14 ++---
 chapters/chap26.ipynb |  22 ++++++--
 chapters/chap27.ipynb |  89 ++++++++++++++------------------
 27 files changed, 423 insertions(+), 366 deletions(-)

diff --git a/chapters/chap01.ipynb b/chapters/chap01.ipynb
index 844e7204..3f4722ec 100644
--- a/chapters/chap01.ipynb
+++ b/chapters/chap01.ipynb
@@ -236,12 +236,10 @@
     "so fast when it hit the pavement that it would be embedded in the\n",
     "concrete; or if it hit a person, it would break their skull.\n",
     "\n",
-    "We can test this myth by making and analyzing a model. To get started,\n",
-    "we'll assume that the effect of air resistance is small. This will turn out to be a bad assumption, but bear with me.\n",
-    "If air resistance is negligible, the primary force acting on the penny\n",
-    "is gravity, which causes the penny to accelerate downward.\n",
+    "We can test this myth by making and analyzing two models. For the first model,\n",
+    "we'll assume that the effect of air resistance is small. In that case, the primary force acting on the penny is gravity, which causes the penny to accelerate downward.\n",
     "\n",
-    "If the initial velocity is 0 and the acceleration is $a$, the velocity after $t$ seconds is \n",
+    "If the initial velocity is 0 and the acceleration, $a$, is constant, the velocity after $t$ seconds is \n",
     "\n",
     "$$v = a t$$\n",
     "\n",
@@ -263,17 +261,18 @@
    "id": "documentary-diagnosis",
    "metadata": {},
    "source": [
-    "Of course, these results are not exact because the model is based on\n",
-    "simplifications. For example, we assume that gravity is constant. In\n",
-    "fact, the force of gravity is different on different parts of the globe, and it gets weaker as you move away from the surface. But these differences are small, so ignoring them is probably a good choice for this scenario.\n",
+    "Of course, these results are not exact because the model is based on simplifications. For example, we assume that gravity is constant. \n",
+    "In fact, the force of gravity is different on different parts of the globe, and it gets weaker as you move away from the surface. \n",
+    "But these differences are small, so ignoring them is probably a good choice for this problem.\n",
     "\n",
-    "On the other hand, ignoring air resistance is not a good choice. Once\n",
-    "the penny gets to about 29 m/s, the upward force of air resistance\n",
-    "equals the downward force of gravity, so the penny stops accelerating.\n",
+    "On the other hand, ignoring air resistance is not a good choice, because in this scenario its effect is substantial.\n",
+    "Once the penny gets to about 29 m/s, the upward force of air resistance equals the downward force of gravity, so the penny stops accelerating.\n",
     "This is the *terminal velocity* of the penny in air.\n",
     "\n",
-    "Once the penny reaches terminal velocity, it doesn't matter how much farther it falls; it hits the sidewalk at about 29 m/s.\n",
-    "That's much less than 86 m/s, as the simple model predicts."
+    "And that suggests a second model, where the penny accelerates until it reaches terminal velocity; after that, acceleration is 0 and velocity is constant.\n",
+    "In this model, the penny hits the sidewalk at about 29 m/s.\n",
+    "That's much less than 86 m/s, which is what the first model predicts.\n",
+    "Getting hit with a penny at that speed might hurt, but it would be unlikely to cause real harm. And it would not damage concrete."
    ]
   },
   {
@@ -349,14 +348,20 @@
     "v = a * t"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "serial-pilot",
+   "metadata": {},
+   "source": [
+    "Python uses the symbol `*` for multiplication.  The other arithmetic operators are `+` for addition, `-` for subtraction, `/` for division, and `**` for exponentiation."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "qualified-diabetes",
    "metadata": {},
    "source": [
-    "Python uses the symbol `*` for multiplication.  The other arithmetic operators are `+` for addition, `-` for subtraction, `/` for division, and `**` for exponentiation.\n",
-    "\n",
-    "When you assign a value to a variable, Jupyter does not show the result automatically, but you can display the value of a variable like this:"
+    "After you assign a value to a variable, you can display the value like this:"
    ]
   },
   {
@@ -500,7 +505,7 @@
     "When we set `h=381`, we left out the meters, and when we set `a=9.8`, we left out the meters per second squared.\n",
     "And, when we got the result `v=86`, we added back the meters per second.\n",
     "\n",
-    "Leaving units of out computation is common practice, but it tends to cause errors, including the very expensive failure of the Mars Climate Orbiter in 1999 (see <https://en.wikipedia.org/wiki/Mars_Climate_Orbiter>).\n",
+    "Leaving units out of computation is a common practice, but it tends to cause errors, including the very expensive failure of the Mars Climate Orbiter in 1999 (see <https://en.wikipedia.org/wiki/Mars_Climate_Orbiter>).\n",
     "When possible, it is better to include units in the computation.\n",
     "\n",
     "To represent units, we'll use a library called Pint (<https://pint.readthedocs.io/>).\n",
@@ -553,7 +558,8 @@
    "id": "unlike-opera",
    "metadata": {},
    "source": [
-    "But leagues and fortnights are not SI units.\n",
+    "But leagues and fortnights are not part of the International System of Units\n",
+    "(see <https://en.wikipedia.org/wiki/International_System_of_Units>); in the jargon, they are not \"SI units\".\n",
     "Instead, we will use `meter` and `second`."
    ]
   },
@@ -745,7 +751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 26,
    "id": "impaired-puzzle",
    "metadata": {
     "tags": []
@@ -767,7 +773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "antique-landing",
    "metadata": {},
    "outputs": [],
@@ -778,7 +784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "id": "included-failure",
    "metadata": {},
    "outputs": [],
@@ -828,7 +834,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "handmade-zoning",
+   "id": "periodic-objective",
    "metadata": {},
    "source": [
     "### Exercise 1\n",
@@ -840,15 +846,16 @@
     "v = a t\n",
     "```\n",
     "\n",
-    "you'll get a *syntax error*, which means that something is wrong with the structure of the program.\n",
-    "Try it out in the next cell so you see what the error message looks like."
+    "you'll get a *syntax error*, which means that something is wrong with the structure of the program. Try it out so you see what the error message looks like."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "postal-marking",
-   "metadata": {},
+   "execution_count": 29,
+   "id": "exempt-delight",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "a = 9.8 * meter / second**2\n",
@@ -871,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "id": "legal-observer",
    "metadata": {},
    "outputs": [],
@@ -895,7 +902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "id": "pressing-belgium",
    "metadata": {},
    "outputs": [],
@@ -919,7 +926,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "id": "inner-equivalent",
    "metadata": {},
    "outputs": [],
@@ -929,7 +936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "id": "nuclear-thirty",
    "metadata": {},
    "outputs": [],
@@ -939,7 +946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "id": "available-steering",
    "metadata": {},
    "outputs": [],
@@ -959,7 +966,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "id": "documentary-doctrine",
    "metadata": {},
    "outputs": [],
@@ -1012,7 +1019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "id": "greenhouse-reason",
    "metadata": {},
    "outputs": [],
@@ -1033,7 +1040,7 @@
     "\n",
     "* If we take air resistance into account, and drop the penny at its terminal velocity, it falls with constant velocity. \n",
     "\n",
-    "Now let's consider a third model that includes elements of the first two: let's assume that the acceleration of the penny is `a` until the penny reaches $29$ m/s, and then $0$ m/s afterwards.  What is the total time for the penny to fall $381$ m?"
+    "Now let's consider a third model that includes elements of the first two: let's assume that the acceleration of the penny is `a` until the penny reaches $29$ m/s, and then $0$ m/s$^2$ afterwards.  What is the total time for the penny to fall $381$ m?"
    ]
   },
   {
@@ -1052,7 +1059,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "id": "secure-crowd",
    "metadata": {},
    "outputs": [],
@@ -1063,7 +1070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "id": "thirty-minneapolis",
    "metadata": {},
    "outputs": [],
@@ -1073,7 +1080,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "id": "premier-seeking",
    "metadata": {},
    "outputs": [],
@@ -1083,7 +1090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "id": "brave-laundry",
    "metadata": {},
    "outputs": [],
@@ -1093,7 +1100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "id": "adjusted-consultation",
    "metadata": {},
    "outputs": [],
@@ -1103,7 +1110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "id": "understanding-consortium",
    "metadata": {},
    "outputs": [],
@@ -1128,7 +1135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "id": "about-complex",
    "metadata": {},
    "outputs": [],
@@ -1138,7 +1145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "id": "unique-owner",
    "metadata": {},
    "outputs": [],
@@ -1148,7 +1155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "id": "minus-batman",
    "metadata": {},
    "outputs": [],
@@ -1158,7 +1165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "id": "exact-vegetable",
    "metadata": {},
    "outputs": [],
@@ -1168,7 +1175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "id": "neutral-lightning",
    "metadata": {},
    "outputs": [],
@@ -1188,7 +1195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "id": "virgin-cambodia",
    "metadata": {},
    "outputs": [],
@@ -1200,7 +1207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "id": "right-intention",
    "metadata": {},
    "outputs": [],
@@ -1210,7 +1217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "id": "mineral-sally",
    "metadata": {},
    "outputs": [],
@@ -1220,7 +1227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "id": "coral-camel",
    "metadata": {},
    "outputs": [],
@@ -1230,7 +1237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "id": "preceding-cricket",
    "metadata": {},
    "outputs": [],
@@ -1240,7 +1247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "id": "effective-rendering",
    "metadata": {},
    "outputs": [],
@@ -1250,7 +1257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "id": "printable-reply",
    "metadata": {},
    "outputs": [],
@@ -1314,7 +1321,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1328,7 +1335,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap02.ipynb b/chapters/chap02.ipynb
index 0b49a1fe..d6c54436 100644
--- a/chapters/chap02.ipynb
+++ b/chapters/chap02.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -114,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "incorrect-comparison",
    "metadata": {},
    "outputs": [],
@@ -136,12 +136,12 @@
     "bikes at each location. The initial values are `10` and `2`, indicating that there are 10 bikes at Olin and 2 at Wellesley. \n",
     "\n",
     "The `State` object is assigned to a new variable named `bikeshare`.\n",
-    "We can read the variables inside a `State` object using the *dot operator*, like this:"
+    "We can get the value of a variable in a `State` object using the *dot operator*, like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "brief-diversity",
    "metadata": {},
    "outputs": [],
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "designed-brazilian",
    "metadata": {},
    "outputs": [],
@@ -172,12 +172,12 @@
    "id": "phantom-oklahoma",
    "metadata": {},
    "source": [
-    "Or, to display the state variables and their values, you can just enter the name of the object:"
+    "Or, to display all of the state variables and their values, you can enter just the name of the object:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "impaired-potter",
    "metadata": {},
    "outputs": [],
@@ -205,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "basic-fabric",
    "metadata": {
     "tags": []
@@ -236,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "floppy-trainer",
    "metadata": {},
    "outputs": [],
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "hungarian-bride",
    "metadata": {},
    "outputs": [],
@@ -287,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "vertical-drawing",
    "metadata": {},
    "outputs": [],
@@ -307,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "significant-nutrition",
    "metadata": {},
    "outputs": [],
@@ -338,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "moving-jurisdiction",
    "metadata": {},
    "outputs": [],
@@ -358,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "proper-symposium",
    "metadata": {},
    "outputs": [],
@@ -377,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "identical-yacht",
    "metadata": {},
    "outputs": [],
@@ -410,7 +410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "heavy-patrol",
    "metadata": {},
    "outputs": [],
@@ -431,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "french-preference",
    "metadata": {},
    "outputs": [],
@@ -452,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "alternative-keyboard",
    "metadata": {},
    "outputs": [],
@@ -475,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "robust-holly",
    "metadata": {},
    "outputs": [],
@@ -502,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "fifteen-atmosphere",
    "metadata": {},
    "outputs": [],
@@ -523,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "matched-narrow",
    "metadata": {},
    "outputs": [],
@@ -557,7 +557,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 57,
+   "id": "29c1f41a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# this line sets the random number generator so the results in\n",
+    "# the book are the same every time we run it\n",
+    "np.random.seed(17)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
    "id": "illegal-metropolitan",
    "metadata": {},
    "outputs": [],
@@ -586,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 59,
    "id": "excessive-murder",
    "metadata": {},
    "outputs": [],
@@ -613,7 +627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 60,
    "id": "fundamental-nursing",
    "metadata": {},
    "outputs": [],
@@ -639,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 61,
    "id": "twenty-health",
    "metadata": {},
    "outputs": [],
@@ -660,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 62,
    "id": "played-character",
    "metadata": {},
    "outputs": [],
@@ -679,7 +693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 63,
    "id": "ecological-colon",
    "metadata": {},
    "outputs": [],
@@ -702,7 +716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 64,
    "id": "mediterranean-german",
    "metadata": {},
    "outputs": [],
@@ -736,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 65,
    "id": "hollywood-shopping",
    "metadata": {},
    "outputs": [],
@@ -760,7 +774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 66,
    "id": "buried-alert",
    "metadata": {},
    "outputs": [],
@@ -781,7 +795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 67,
    "id": "recognized-denmark",
    "metadata": {},
    "outputs": [],
@@ -819,7 +833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 68,
    "id": "polish-river",
    "metadata": {},
    "outputs": [],
@@ -867,7 +881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "id": "every-consultation",
    "metadata": {},
    "outputs": [],
@@ -885,7 +899,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 70,
    "id": "changing-planet",
    "metadata": {},
    "outputs": [],
@@ -903,7 +917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "id": "aquatic-richardson",
    "metadata": {},
    "outputs": [],
@@ -923,7 +937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "id": "english-titanium",
    "metadata": {},
    "outputs": [],
@@ -954,7 +968,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "id": "indonesian-singing",
    "metadata": {},
    "outputs": [],
@@ -983,7 +997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "id": "saved-hands",
    "metadata": {},
    "outputs": [],
@@ -1045,7 +1059,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "id": "helpful-zambia",
    "metadata": {},
    "outputs": [],
@@ -1067,7 +1081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 41,
    "id": "beneficial-mainland",
    "metadata": {},
    "outputs": [],
@@ -1105,7 +1119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "id": "former-frost",
    "metadata": {},
    "outputs": [],
@@ -1115,7 +1129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "id": "spare-honduras",
    "metadata": {},
    "outputs": [],
@@ -1175,7 +1189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "id": "agricultural-midwest",
    "metadata": {},
    "outputs": [],
@@ -1195,7 +1209,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1209,7 +1223,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap03.ipynb b/chapters/chap03.ipynb
index 13638e4f..0afd2830 100644
--- a/chapters/chap03.ipynb
+++ b/chapters/chap03.ipynb
@@ -134,9 +134,8 @@
     "the first assumption might be reasonable if we simulate the system for a short period of time, like one hour.\n",
     "\n",
     "The second assumption is not very realistic, but it might not affect the results very much, depending on what we use the model for.\n",
-    "\n",
-    "On the other hand, the third assumption seems problematic, and it is\n",
-    "relatively easy to fix. In this chapter, we will.\n",
+    "On the other hand, the third assumption seems more problematic.\n",
+    "It is relatively easy to fix, though; in this chapter, we'll fix it.\n",
     "\n",
     "This process, starting with a simple model, identifying the most\n",
     "important problems, and making gradual improvements, is called\n",
@@ -495,8 +494,7 @@
    "id": "twelve-defensive",
    "metadata": {},
    "source": [
-    "The equals operator is one of Python's *comparison operators*. The others\n",
-    "are:\n",
+    "The equals operator is one of Python's *comparison operators*; the complete list is in the following table.\n",
     "\n",
     "| Operation             \t| Symbol \t|\n",
     "|-----------------------\t|--------\t|\n",
@@ -752,7 +750,7 @@
    "source": [
     "### Exercise 1\n",
     "\n",
-    " Modify `bike_to_wellesley` so it checks whether a bike is available at Olin.  If not, it should add one to `olin_empty`.\n",
+    " Modify `bike_to_wellesley` so it checks whether a bike is available at Olin.  If not, it should add `1` to `olin_empty`.\n",
     "\n",
     "To test it, create a `State` that initializes `olin` and `olin_empty` to `0`, run `bike_to_wellesley`, and check the result."
    ]
@@ -836,7 +834,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -850,7 +848,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap04.ipynb b/chapters/chap04.ipynb
index c0634758..44186d1e 100644
--- a/chapters/chap04.ipynb
+++ b/chapters/chap04.ipynb
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -318,7 +318,7 @@
     "Each time through the loop, the next number in the sequence gets assigned to the loop variable, `i`.\n",
     "\n",
     "But `range` only works with integers; to get a sequence of non-integer\n",
-    "values, we can use `linspace`, which is defined NumPy:"
+    "values, we can use `linspace`, which is provided by NumPy:"
    ]
   },
   {
@@ -501,7 +501,7 @@
    "id": "instructional-showcase",
    "metadata": {},
    "source": [
-    "We can plot the results like this:"
+    "We can plot the elements of the `SweepSeries` like this:"
    ]
   },
   {
@@ -524,11 +524,11 @@
    "metadata": {},
    "source": [
     "The keyword argument `color='C1'` specifies the color of the line.\n",
-    "The `TimeSeries` we have plotted so far use the default color, `C0`, which is blue.\n",
+    "The `TimeSeries` we have plotted so far use the default color, `C0`, which is blue (see <https://matplotlib.org/stable/tutorials/colors/colors.html> for the other colors defined by Matplotlib).\n",
     "I use a different color for `SweepSeries` to remind us that it is not a `TimeSeries`.\n",
     "\n",
     "When the arrival rate at Olin is low, there are plenty of bikes and no unhappy customers.\n",
-    "As the arrival rate increases, we are more likely to run out of bikes and the number of unhappy customers increases. The line is jagged because the simulation is based on random numbers. Sometime we get lucky and there are relatively few unhappy customers; other times are are unlucky and there are more. "
+    "As the arrival rate increases, we are more likely to run out of bikes and the number of unhappy customers increases. The line is jagged because the simulation is based on random numbers. Sometime we get lucky and there are relatively few unhappy customers; other times we are unlucky and there are more. "
    ]
   },
   {
@@ -560,7 +560,7 @@
     "    change you made was small, it shouldn't take long to find the\n",
     "    problem.\n",
     "\n",
-    "When this process works, your changes usually work the first time, or if they don't, the problem is obvious. In practice, there are two problems with incremental development:\n",
+    "When this process works, your changes usually work the first time or, if they don't, the problem is obvious. In practice, there are two problems with incremental development:\n",
     "\n",
     "-   Sometimes you have to write extra code to generate visible output\n",
     "    that you can check. This extra code is called *scaffolding*\n",
@@ -666,7 +666,7 @@
     " Wrap the code from this chapter in a function named `sweep_p1` that takes an array called `p1_array` as a parameter.  It should create a new `SweepSeries` and run a simulation for each value of `p1` in `p1_array`, with `p2=0.2` and `num_steps=60`.\n",
     "It should store the results in the `SweepSeries` and return it.  \n",
     "\n",
-    "Use your function to plot the number of unhappy customers at Olin as a function of `p1`.  Label the axes."
+    "Use your function to generate a `SweepSeries` and then plot the number of unhappy customers at Olin as a function of `p1`.  Label the axes."
    ]
   },
   {
@@ -820,7 +820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "id": "broad-latitude",
    "metadata": {
     "scrolled": true
@@ -866,7 +866,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -880,7 +880,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap05.ipynb b/chapters/chap05.ipynb
index a82967d0..0e1bd458 100644
--- a/chapters/chap05.ipynb
+++ b/chapters/chap05.ipynb
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -252,7 +252,7 @@
    "source": [
     "The first column, which is labeled `Year`, is special.  It is the *index* for this `DataFrame`, which means it contains the labels for the rows.\n",
     "\n",
-    "Some of the values use scientific notation; for example, `2.544000e+09` is shorthand for $2.544 \\cdot 10^9$ or 2.544 billion.\n",
+    "Some of the values use scientific notation; for example, `2.516000e+09` is shorthand for $2.516 \\cdot 10^9$ or 2.544 billion.\n",
     "\n",
     "`NaN` is a special value that indicates missing data."
    ]
@@ -512,7 +512,7 @@
    },
    "outputs": [],
    "source": [
-    "rel_error = abs_error / census * 100\n",
+    "rel_error = 100 * abs_error / census\n",
     "rel_error.tail()"
    ]
   },
@@ -885,7 +885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "id": "terminal-reynolds",
    "metadata": {
     "tags": []
@@ -911,7 +911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "id": "organizational-memphis",
    "metadata": {
     "tags": []
@@ -941,7 +941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "id": "rising-anger",
    "metadata": {},
    "outputs": [],
@@ -951,7 +951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "id": "false-handbook",
    "metadata": {},
    "outputs": [],
@@ -961,7 +961,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "id": "political-loading",
    "metadata": {},
    "outputs": [],
@@ -973,7 +973,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -987,7 +987,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap06.ipynb b/chapters/chap06.ipynb
index efaffaeb..5dc999b5 100644
--- a/chapters/chap06.ipynb
+++ b/chapters/chap06.ipynb
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "plastic-trigger",
+   "id": "375794f9",
    "metadata": {},
    "source": [
     "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
@@ -215,7 +215,10 @@
     "`t0` and `t_end` are the first and last years; `p_0` is the initial\n",
     "population, and `annual_growth` is the estimated annual growth.\n",
     "\n",
-    "Here's what `system` looks like."
+    "The assignment `t_0=t_0` reads the value of the existing variable named `t_0`, which we created previously, and stores it in a new system variable, also named `t_0`.\n",
+    "The variables inside the `System` object are distinct from other variables, so you can change one without affecting the other, even if they have the same name.\n",
+    "\n",
+    "So this `System` object contains four new variables; here's what they look like."
    ]
   },
   {
@@ -258,7 +261,7 @@
    "id": "rough-strain",
    "metadata": {},
    "source": [
-    "`run_simulation1` takes a `System` object and uses the parameters in it to determine `t_0`, `t_end`, and `annual_growth`.\n",
+    "`run_simulation1` takes a `System` object and reads from it the values of `t_0`, `t_end`, and `annual_growth`.\n",
     "\n",
     "It simulates population growth over time and returns the results in a `TimeSeries`.\n",
     "Here's how we call it."
@@ -677,7 +680,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -691,7 +694,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap07.ipynb b/chapters/chap07.ipynb
index efb6bb0e..8a89f9a3 100644
--- a/chapters/chap07.ipynb
+++ b/chapters/chap07.ipynb
@@ -81,15 +81,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "affiliated-eleven",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "download('https://github.com/AllenDowney/ModSim/raw/main/' +\n",
-    "         'World_population_estimates.html')"
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/master/data/World_population_estimates.html')"
    ]
   },
   {
@@ -440,7 +440,7 @@
     "$$\\Delta p = \\alpha p + \\beta p^2$$ \n",
     "\n",
     "where $\\alpha$ and $\\beta$ are the parameters of the model. \n",
-    "If rewrite the right hand side like this: \n",
+    "If we rewrite the right hand side like this: \n",
     "\n",
     "$$\\Delta p = p (\\alpha + \\beta p)$$ \n",
     "\n",
@@ -463,7 +463,8 @@
    "id": "adaptive-pharmacy",
    "metadata": {},
    "source": [
-    "With these parameters, net growth is 0 when the population is about 13.9 billion.\n",
+    "With these parameters, net growth is 0 when the population is about 13.9 billion\n",
+    "(the result is positive because `beta` is negative).\n",
     "\n",
     "In the context of population modeling, the quadratic model is more\n",
     "conventionally written like this: \n",
@@ -564,7 +565,7 @@
    "metadata": {},
    "source": [
     "This version works, but it is not as versatile as it could be.\n",
-    "If there are several `System` objects, this function can only work with one of them, and only if it is named `sys1`."
+    "If there are several `System` objects, this function can work with only one of them, and only if it is named `sys1`."
    ]
   },
   {
@@ -766,7 +767,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -780,7 +781,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap08.ipynb b/chapters/chap08.ipynb
index 87ae7238..dafd3e12 100644
--- a/chapters/chap08.ipynb
+++ b/chapters/chap08.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -89,20 +89,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "necessary-factor",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "download('https://github.com/AllenDowney/ModSim/raw/main/' +\n",
-    "         'World_population_estimates.html')"
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/master/data/World_population_estimates.html')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "changed-desktop",
    "metadata": {
     "tags": []
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "metallic-inventory",
    "metadata": {
     "tags": []
@@ -144,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "measured-arthur",
    "metadata": {
     "tags": []
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "cutting-financing",
    "metadata": {
     "tags": []
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "indirect-russia",
    "metadata": {},
    "outputs": [],
@@ -225,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "comfortable-compression",
    "metadata": {},
    "outputs": [],
@@ -250,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "broken-windsor",
    "metadata": {},
    "outputs": [],
@@ -268,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "latest-function",
    "metadata": {},
    "outputs": [],
@@ -286,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "portable-pottery",
    "metadata": {},
    "outputs": [],
@@ -304,12 +304,11 @@
    "source": [
     "According to the model, population growth will slow gradually after 2020, approaching 12.6 billion by 2100.\n",
     "\n",
-    "I am using the word \"projection\" deliberately, rather than\n",
-    "\"prediction\", with the following distinction: \"prediction\" implies\n",
-    "something like \"this is what we should reasonably expect to happen, at\n",
+    "I am using the word *projection* deliberately, rather than\n",
+    "*prediction*, with the following distinction: \"prediction\" implies\n",
+    "something like \"this is what we expect to happen, at\n",
     "least approximately\"; \"projection\" implies something like \"if this\n",
-    "model is a good description of what is happening in this\n",
-    "system, and if nothing in the future causes the system parameters to change, this is what would happen.\"\n",
+    "model is a good description of the system, and if nothing in the future causes the system parameters to change, this is what would happen.\"\n",
     "\n",
     "Using \"projection\" leaves open the possibility that there are important things in the real world that are not captured in the model. It also suggests that, even if the model is good, the parameters we estimate based on the past might be different in the future.\n",
     "\n",
@@ -352,18 +351,17 @@
    "source": [
     "## Comparing Projections\n",
     "\n",
-    "From the same Wikipedia page where we got the past population estimates, we'll read `table3`, which contains predictions for population growth over the next 50-100 years, generated by the U.S. Census, U.N. DESA, and the Population Reference Bureau."
+    "From the same Wikipedia page where we got the past population estimates, we'll read `table3`, which contains projections for population growth over the next 50-100 years, generated by the U.S. Census, U.N. DESA, and the Population Reference Bureau."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "precious-contribution",
+   "execution_count": 15,
+   "id": "20e6f166",
    "metadata": {},
    "outputs": [],
    "source": [
-    "table3 = tables[3]\n",
-    "table3.head()"
+    "table3 = tables[3]"
    ]
   },
   {
@@ -371,14 +369,12 @@
    "id": "coated-smoke",
    "metadata": {},
    "source": [
-    "Some values are `NaN`, which indicates missing data, because some organizations did not publish projections for some years.\n",
-    "\n",
     "The column names are long strings; for convenience, I'll replace them with abbreviations."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "headed-tuner",
    "metadata": {},
    "outputs": [],
@@ -388,15 +384,34 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ceramic-scroll",
+   "id": "c2a91ffb",
+   "metadata": {},
+   "source": [
+    "Here are the first few rows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "precious-contribution",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table3.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f62861c1",
    "metadata": {},
    "source": [
+    "Some values are `NaN`, which indicates missing data, because some organizations did not publish projections for some years.\n",
     "The following function plots projections from the U.N. DESA and U.S. Census.  It uses `dropna` to remove the `NaN` values from each series before plotting it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "id": "paperback-delay",
    "metadata": {},
    "outputs": [],
@@ -426,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "id": "billion-dynamics",
    "metadata": {},
    "outputs": [],
@@ -461,7 +476,7 @@
     "theirs differ from each other almost as much as they differ from ours.\n",
     "So the results from the model, simple as it is, are not entirely unreasonable.\n",
     "\n",
-    "If you are interested in some of the factors that go into the professional predictions, you might like this video by Hans Rosling about the demographic changes we expect this century: <https://www.youtube.com/watch?v=ezVk1ahRF78>."
+    "If you are interested in some of the factors that go into the professional projections, you might like this video by Hans Rosling about the demographic changes we expect this century: <https://www.youtube.com/watch?v=ezVk1ahRF78>."
    ]
   },
   {
@@ -489,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "id": "handmade-funeral",
    "metadata": {},
    "outputs": [],
@@ -510,7 +525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "id": "objective-accused",
    "metadata": {},
    "outputs": [],
@@ -529,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "id": "unique-matrix",
    "metadata": {},
    "outputs": [],
@@ -555,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "id": "pressing-proceeding",
    "metadata": {},
    "outputs": [],
@@ -568,7 +583,7 @@
    "id": "cross-reducing",
    "metadata": {},
    "source": [
-    "Other than a bump around 1990, net growth rate has been declining roughly linearly since 1970.\n",
+    "Other than a bump around 1990, the net growth rate has been declining roughly linearly since 1970.\n",
     "\n",
     "We can model the decline by fitting a line to this data and extrapolating into the future.\n",
     "Here's a function that takes a time stamp and computes a line that roughly fits the growth rates since 1970."
@@ -576,7 +591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "id": "mexican-denver",
    "metadata": {},
    "outputs": [],
@@ -597,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "id": "addressed-worker",
    "metadata": {},
    "outputs": [],
@@ -616,7 +631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "id": "cathedral-shakespeare",
    "metadata": {},
    "outputs": [],
@@ -637,7 +652,7 @@
    "source": [
     "If you don't like the `slope` and `intercept` I chose, feel free to adjust them.\n",
     "\n",
-    "Now, as an exercise, you can use this function project world population until 2100.\n",
+    "Now, as an exercise, you can use this function to project world population until 2100.\n",
     "\n",
     "1. Create a `System` object that includes `alpha_func` as a system parameter.\n",
     "\n",
@@ -650,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "id": "common-april",
    "metadata": {},
    "outputs": [],
@@ -660,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
    "id": "ignored-chain",
    "metadata": {},
    "outputs": [],
@@ -670,7 +685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "id": "color-accountability",
    "metadata": {},
    "outputs": [],
@@ -680,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "id": "numeric-raise",
    "metadata": {},
    "outputs": [],
@@ -690,7 +705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 31,
    "id": "included-vehicle",
    "metadata": {},
    "outputs": [],
@@ -700,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 32,
    "id": "brown-rating",
    "metadata": {},
    "outputs": [],
@@ -710,7 +725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 33,
    "id": "laughing-cylinder",
    "metadata": {},
    "outputs": [],
@@ -720,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 34,
    "id": "personalized-parking",
    "metadata": {},
    "outputs": [],
@@ -740,7 +755,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -754,7 +769,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap09.ipynb b/chapters/chap09.ipynb
index 3c5a7249..d04ff587 100644
--- a/chapters/chap09.ipynb
+++ b/chapters/chap09.ipynb
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -113,7 +113,7 @@
     "This way of representing the model, where future population is a function of current population, is a *difference equation*; see\n",
     "<https://en.wikipedia.org/wiki/Linear_difference_equation>.\n",
     "\n",
-    "Sometimes it is possible to compute $x_n$, for a given value of $n$, directly; that\n",
+    "For a given value of $n$, sometimes it is possible to compute $x_n$ directly; that\n",
     "is, without computing the intervening values from $x_1$ through $x_{n-1}$."
    ]
   },
@@ -194,7 +194,7 @@
     "This is a *discrete* model, which\n",
     "means time is only defined at integer values of $n$ and not in between.\n",
     "But in reality, people are born and die all the time, not once a year,\n",
-    "so it might be more realistic use a *continuous* model, which means\n",
+    "so it might be more realistic to use a *continuous* model, which means\n",
     "time is defined at all values of $t$, not just integers. "
    ]
   },
@@ -311,8 +311,8 @@
     "    find counterexamples, but it is hard to write proofs.\n",
     "\n",
     "-   Analysis can provide insight into models and the systems they\n",
-    "    describe; for example, sometimes we can identify regimes of\n",
-    "    qualitatively different behavior and key parameters that control\n",
+    "    describe; for example, sometimes we can identify\n",
+    "    qualitatively different ways the system can behave and key parameters that control\n",
     "    those behaviors."
    ]
   },
@@ -382,14 +382,14 @@
     "tools similar to Mathematica. They are not as easy to use as\n",
     "WolframAlpha, but they have some other advantages.\n",
     "\n",
-    "To use it, we'll define `Symbol` objects that represent variable names and functions.\n",
+    "To use it, we'll define `Symbol` objects that represent names of variables and functions.\n",
     "The `symbols` function takes a string and returns `Symbol` objects.\n",
     "So if we run this assignment:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "flush-balance",
    "metadata": {},
    "outputs": [],
@@ -404,12 +404,13 @@
    "id": "faced-praise",
    "metadata": {},
    "source": [
-    "Now when we use `t`, Python understands that it is a symbol, not a numerical value. For example, if we use `t` as part of an expression, like this,"
+    "Now when we use `t`, Python treats it like a variable name rather than a specific number. \n",
+    "For example, if we use `t` as part of an expression, like this,"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "ancient-anderson",
    "metadata": {},
    "outputs": [],
@@ -563,8 +564,9 @@
    "id": "inside-medicine",
    "metadata": {},
    "source": [
-    "The result is the *general\n",
-    "solution*, which still contains an unspecified constant, $C_1$. To get the *particular solution* where $f(0) = p_0$, we substitute `p_0` for `C1`. First, we have to create two more symbols:"
+    "The result is the *general solution*, which still contains an unspecified constant, $C_1$.\n",
+    "To get the *particular solution* where $f(0) = p_0$, we substitute $p_0$ for `C1`. \n",
+    "First, we have to tell Python that `C1` is a symbol."
    ]
   },
   {
@@ -574,7 +576,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "C1, p_0 = symbols('C1 p_0')"
+    "C1 = symbols('C1')"
    ]
   },
   {
@@ -582,7 +584,8 @@
    "id": "serious-license",
    "metadata": {},
    "source": [
-    "Now we can perform the substitution:"
+    "Now we can substitute the value of $p_0$ for `C1`.\n",
+    "For example, if $p_0$ is 1000:"
    ]
   },
   {
@@ -592,7 +595,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "particular = solution_eq.subs(C1, p_0)\n",
+    "particular = solution_eq.subs(C1, 1000)\n",
     "particular"
    ]
   },
@@ -601,7 +604,17 @@
    "id": "modern-raleigh",
    "metadata": {},
    "source": [
-    "When $t=0$, $e^{\\alpha t}$ is $1$, so $f(t)$ is $p_0$, which is what we wanted."
+    "When $t=0$, the value of $f(0)$ is $p_0$, which confirms that this is the solution we want."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "394e9410",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "particular.subs(t, 0)"
    ]
   },
   {
@@ -616,7 +629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "political-chinese",
    "metadata": {},
    "outputs": [],
@@ -634,7 +647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "lined-queen",
    "metadata": {},
    "outputs": [],
@@ -653,7 +666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "printable-typing",
    "metadata": {},
    "outputs": [],
@@ -672,7 +685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "final-treatment",
    "metadata": {},
    "outputs": [],
@@ -691,7 +704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "freelance-admission",
    "metadata": {},
    "outputs": [],
@@ -707,18 +720,19 @@
    "source": [
     "Now we want to find the value of `C1` that makes `f(0) = p_0`.\n",
     "\n",
-    "So we'll create the equation `at_0 = p_0` and solve for `C1`.  Because this is just an algebraic identity, not a differential equation, we use `solve`, not `dsolve`."
+    "So we'll create the equation `at_0 = p_0` and solve for `C1`.  Because this is just an algebraic equation, not a differential equation, we use `solve`, not `dsolve`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "orange-glasgow",
    "metadata": {},
    "outputs": [],
    "source": [
     "from sympy import solve\n",
     "\n",
+    "p_0 = symbols('p_0')\n",
     "solutions = solve(Eq(at_0, p_0), C1)"
    ]
   },
@@ -732,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "expensive-palace",
    "metadata": {},
    "outputs": [],
@@ -750,7 +764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "worthy-sleeve",
    "metadata": {},
    "outputs": [],
@@ -769,7 +783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "aggressive-queue",
    "metadata": {},
    "outputs": [],
@@ -788,7 +802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 24,
    "id": "funded-tolerance",
    "metadata": {},
    "outputs": [],
@@ -823,7 +837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "institutional-finish",
    "metadata": {
     "tags": []
@@ -836,7 +850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "noble-auditor",
    "metadata": {
     "tags": []
@@ -861,7 +875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "industrial-administrator",
    "metadata": {
     "scrolled": true,
@@ -896,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 28,
    "id": "bibliographic-sarah",
    "metadata": {
     "tags": []
@@ -978,7 +992,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "id": "internal-knock",
    "metadata": {},
    "outputs": [],
@@ -988,7 +1002,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "id": "smooth-sunglasses",
    "metadata": {},
    "outputs": [],
@@ -998,7 +1012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 31,
    "id": "fundamental-arlington",
    "metadata": {},
    "outputs": [],
@@ -1008,7 +1022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 32,
    "id": "desirable-alpha",
    "metadata": {},
    "outputs": [],
@@ -1018,7 +1032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 33,
    "id": "coupled-grounds",
    "metadata": {},
    "outputs": [],
@@ -1028,7 +1042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 34,
    "id": "looking-ground",
    "metadata": {},
    "outputs": [],
@@ -1038,7 +1052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 35,
    "id": "anticipated-paragraph",
    "metadata": {},
    "outputs": [],
@@ -1076,7 +1090,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1090,7 +1104,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap10.ipynb b/chapters/chap10.ipynb
index ae02aeb4..76c63317 100644
--- a/chapters/chap10.ipynb
+++ b/chapters/chap10.ipynb
@@ -403,7 +403,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -417,7 +417,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap11.ipynb b/chapters/chap11.ipynb
index 5c6e0ef9..2c9e09bc 100644
--- a/chapters/chap11.ipynb
+++ b/chapters/chap11.ipynb
@@ -131,9 +131,8 @@
     "    susceptible.\n",
     "\n",
     "-   *R*: People who are \"recovered\". In the basic version of the\n",
-    "    model, people who have recovered are considered to be immune to\n",
-    "    reinfection. That is a reasonable model for some diseases, but not\n",
-    "    for others, so it should be on the list of assumptions to reconsider later."
+    "    model, people who have recovered are considered to be no longer\n",
+    "    infectious and immune to reinfection. That is a reasonable model for some diseases, but not for others, so it should be on the list of assumptions to reconsider later."
    ]
   },
   {
@@ -141,9 +140,8 @@
    "id": "reasonable-kitchen",
    "metadata": {},
    "source": [
-    "Let's think about how the number of people in each category changes over time. Suppose we know that people with the disease are infectious for a period of 4 days, on average. If 100 people are infectious at a\n",
-    "particular point in time, and we ignore the particular time each one\n",
-    "became infected, we expect about 1 out of 4 to recover on any particular day.\n",
+    "Let's think about how the number of people in each category changes over time. Suppose we know that people with the disease are infectious for a period of 4 days, on average.\n",
+    "If 100 people are infectious at a particular point in time, and we ignore the particular time each one became infected, we expect about 1 out of 4 to recover on any particular day.\n",
     "\n",
     "Putting that a different way, if the time between recoveries is 4 days, the recovery rate is about 0.25 recoveries per day, which we'll denote with the Greek letter gamma, $\\gamma$, or the variable name `gamma`.\n",
     "\n",
@@ -367,7 +365,7 @@
    "id": "requested-turning",
    "metadata": {},
    "source": [
-    "Now that we have object to represent the system and it's state, we are ready for the update function."
+    "Now that we have object to represent the system and its state, we are ready for the update function."
    ]
   },
   {
@@ -509,7 +507,7 @@
    "source": [
     "## Collecting the Results\n",
     "\n",
-    "The previous version of `run_simulation` only returns the final state,\n",
+    "The previous version of `run_simulation` returns only the final state,\n",
     "but we might want to see how the state changes over time. We'll consider two ways to do that: first, using three `TimeSeries` objects, then using a new object called a `TimeFrame`.\n",
     "\n",
     "Here's the first version:"
@@ -814,7 +812,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -828,7 +826,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap12.ipynb b/chapters/chap12.ipynb
index 61be3d58..2f576a70 100644
--- a/chapters/chap12.ipynb
+++ b/chapters/chap12.ipynb
@@ -493,7 +493,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -507,7 +507,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap13.ipynb b/chapters/chap13.ipynb
index a741bf4c..0f04b715 100644
--- a/chapters/chap13.ipynb
+++ b/chapters/chap13.ipynb
@@ -485,6 +485,7 @@
    "source": [
     "The values of `gamma` are on the $x$-axis, corresponding to the columns of the `SweepFrame`.\n",
     "The values of `beta` are on the $y$-axis, corresponding to the rows of the `SweepFrame`.\n",
+    "Each line follows a contour where the infection rate is constant.\n",
     "\n",
     "Infection rates are lowest in the lower right, where the contact rate is low and the recovery rate is high. They increase as we move to the upper left, where the contact rate is high and the recovery rate is low.\n",
     "\n"
@@ -577,7 +578,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -591,7 +592,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap14.ipynb b/chapters/chap14.ipynb
index f9e4255b..59533809 100644
--- a/chapters/chap14.ipynb
+++ b/chapters/chap14.ipynb
@@ -149,8 +149,7 @@
     "Describing physical systems using dimensionless parameters is often a\n",
     "useful move in the modeling and simulation game. In fact, it is so useful that it has a name: *nondimensionalization* (see\n",
     "<http://modsimpy.com/nondim>).\n",
-    "\n",
-    "So we'll try the second option first."
+    "So that's what we'll try first."
    ]
   },
   {
@@ -236,7 +235,7 @@
     "\n",
     "Since there are 11 rows and 4 columns, the total number of lines in the output is 44.\n",
     "\n",
-    "The following function uses the same loop to enumerate the elements of the `SweepFrame`, but instead of printing a line for each element, it plots a point."
+    "The following function uses the same loops to enumerate the elements of the `SweepFrame`, but instead of printing a line for each element, it plots a point."
    ]
   },
   {
@@ -261,8 +260,7 @@
    "id": "shared-boxing",
    "metadata": {},
    "source": [
-    "On the $x$-axis, it plots the ratio `beta/gamma`.\n",
-    "On the $y$-axis, it plots the fraction of the population that's infected.\n",
+    "For each element of the `SweepFrame` is plots a point with the ratio `beta/gamma` as the $x$ coordinate and `metric` -- which is the fraction of the population that's infected -- as the $y$ coordinate.\n",
     "\n",
     "Here's what it looks like:"
    ]
@@ -295,7 +293,7 @@
    "source": [
     "## Contact Number\n",
     "\n",
-    "From Section xxx, recall that the number of new infections in a\n",
+    "From Chapter 11, recall that the number of new infections in a\n",
     "given day is $\\beta s i N$, and the number of recoveries is\n",
     "$\\gamma i N$. If we divide these quantities, the result is\n",
     "$\\beta s / \\gamma$, which is the number of new infections per recovery\n",
@@ -311,7 +309,7 @@
    "metadata": {},
    "source": [
     "The results in the previous section suggest that there is a relationship between $c$ and the total number of infections. We can derive this relationship by analyzing the differential equations from\n",
-    "Section xxx:\n",
+    "Chapter 11:\n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{ds}{dt} &= -\\beta s i \\\\\n",
@@ -779,7 +777,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -793,7 +791,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap15.ipynb b/chapters/chap15.ipynb
index 5cf0fb8b..6df8c173 100644
--- a/chapters/chap15.ipynb
+++ b/chapters/chap15.ipynb
@@ -853,7 +853,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -867,7 +867,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap16.ipynb b/chapters/chap16.ipynb
index ca0b41cc..d4d8ce57 100644
--- a/chapters/chap16.ipynb
+++ b/chapters/chap16.ipynb
@@ -331,7 +331,7 @@
     "`run_and_mix` simulates both systems for the given time, `t_add`.\n",
     "Then it mixes them and simulates the mixture for the remaining time, `t_total - t_add`.\n",
     "\n",
-    "When `t_add` is`0`, we add the milk immediately; when `t_add` is `30`, we add it at the end. Now we can sweep the range of values in between:"
+    "When `t_add` is `0`, we add the milk immediately; when `t_add` is `30`, we add it at the end. Now we can sweep the range of values in between:"
    ]
   },
   {
@@ -656,7 +656,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -670,7 +670,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap17.ipynb b/chapters/chap17.ipynb
index 7228d016..cd0d3bc0 100644
--- a/chapters/chap17.ipynb
+++ b/chapters/chap17.ipynb
@@ -160,7 +160,7 @@
     "Bergman, it was \"shown to be interstitial fluid\", that is, the fluid\n",
     "that surrounds tissue cells. \n",
     "\n",
-    "In the history of mathematical modeling, it is common for hypothetical entities, added to models to achieve particular effects, to be found later to correspond to physical entities.  One notable example is the gene (see <https://en.wikipedia.org/wiki/Gene#Discovery_of_discrete_inherited_units>)."
+    "In the history of mathematical modeling, it is common for hypothetical entities, added to models to achieve particular effects, to be found later to correspond to physical entities.  One notable example is the gene, which was defined as an inheritable unit several decades before we learned that genes are encoded in DNA  (see <https://en.wikipedia.org/wiki/Gene#Discovery_of_discrete_inherited_units>)."
    ]
   },
   {
@@ -318,9 +318,9 @@
    "source": [
     "## Interpolation\n",
     "\n",
-    "Before we are ready to implement the model, there's one problem we have to solve. In the differential equations, $I$ is a function that can be evaluated at any time, $t$. But in the `DataFrame`, we only have measurements at discrete times. The solution is interpolation, which estimates the value of $I$ for values of $t$ between the measurements.\n",
+    "Before we are ready to implement the model, there's one problem we have to solve. In the differential equations, $I$ is a function that can be evaluated at any time $t$. But in the `DataFrame`, we have measurements only at discrete times. The solution is interpolation, which estimates the value of $I$ for values of $t$ between the measurements.\n",
     "\n",
-    "The ModSim library provides an `interpolate` function that takes a `Series` as a parameter and returns a function. That's right, I said it returns a *function*.\n",
+    "To interpolate the values in `I`, we can use `interpolate`, which takes a `Series` as a parameter and returns a function. That's right, I said it returns a *function*.\n",
     "We can call `interpolate` like this:"
    ]
   },
@@ -478,7 +478,7 @@
     "In particular, notice the `kind` argument, which specifies a kind of interpolation.\n",
     "The default is linear interpolation, which connects the data points with straight lines.\n",
     "\n",
-    "Pass a keyword argument to `interpolate` to specify one of the other kinds of interpolation, and run the code again to see what it looks like. "
+    "Pass a keyword argument to `interpolate` to specify one of the other kinds of interpolation and plot the results."
    ]
   },
   {
@@ -563,7 +563,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -577,7 +577,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap18.ipynb b/chapters/chap18.ipynb
index 6af7974b..0afbc8bf 100644
--- a/chapters/chap18.ipynb
+++ b/chapters/chap18.ipynb
@@ -76,7 +76,7 @@
    "id": "detected-welsh",
    "metadata": {},
    "source": [
-    "The previous chapter presents the minimal model of the glucose-insulin system and introduces a tool we will need to implement it, interpolation.\n",
+    "The previous chapter presents the minimal model of the glucose-insulin system and introduces a tool we need to implement it: interpolation.\n",
     "\n",
     "In this chapter, we'll implement the model two ways:\n",
     "\n",
@@ -517,7 +517,7 @@
    "id": "modified-surname",
    "metadata": {},
    "source": [
-    "`slope_func` is a little simpler than `update_func` because it only compute the derivatives, that is, the slopes. It doesn't do the updates; the solver does them for us.\n",
+    "`slope_func` is a little simpler than `update_func` because it computes only the derivatives, that is, the slopes. It doesn't do the updates; the solver does them for us.\n",
     "\n",
     "Now we can call `run_solve_ivp` like this:"
    ]
@@ -680,7 +680,7 @@
    "id": "electronic-navigation",
    "metadata": {},
    "source": [
-    "These differences are little bigger, especially at the beginning.\n",
+    "These differences are a little bigger, especially at the beginning.\n",
     "\n",
     "If we use `run_simulation` with smaller time steps, the results are more accurate, but they take longer to compute.\n",
     "For some problems, we can find a value of `dt` that produces accurate results in a reasonable time. However, if `dt` is *too* small, the results can be inaccurate again. So it can be tricky to get it right.\n",
@@ -782,7 +782,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -796,7 +796,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap19.ipynb b/chapters/chap19.ipynb
index d5c7dd21..5f255895 100644
--- a/chapters/chap19.ipynb
+++ b/chapters/chap19.ipynb
@@ -280,7 +280,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -294,7 +294,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap20.ipynb b/chapters/chap20.ipynb
index ed187056..0ce7dfe4 100644
--- a/chapters/chap20.ipynb
+++ b/chapters/chap20.ipynb
@@ -5,7 +5,7 @@
    "id": "funded-utilization",
    "metadata": {},
    "source": [
-    "# Pennies"
+    "# The Empire State Building Strikes Back"
    ]
   },
   {
@@ -88,7 +88,8 @@
     "tags": []
    },
    "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap20.ipynb)"
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -704,7 +705,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -718,7 +719,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap21.ipynb b/chapters/chap21.ipynb
index e272f5da..240aa9a2 100644
--- a/chapters/chap21.ipynb
+++ b/chapters/chap21.ipynb
@@ -67,7 +67,8 @@
     "tags": []
    },
    "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap21.ipynb)"
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -102,7 +103,7 @@
     "\n",
     "where\n",
     "\n",
-    "-   $F_d$ is force due to drag, in newtons (N).\n",
+    "-   $F_d$ is force due to drag, in newtons (N), which are the SI units of force. A newton is 1 kg m/s$^2$.\n",
     "\n",
     "-   $\\rho$ is the density of the fluid in kg/m$^3$.\n",
     "\n",
@@ -258,7 +259,7 @@
    "metadata": {},
    "source": [
     "It might not be obvious why it is useful to create a `Params` object just to create a `System` object.\n",
-    "In fact, if we only run one simulation, it might not be useful.  But it helps when we want to change or sweep the parameters.\n",
+    "In fact, if we run only one simulation, it might not be useful.  But it helps when we want to change or sweep the parameters.\n",
     "\n",
     "For example, suppose we learn that the terminal velocity of a penny is actually closer to 20 m/s.\n",
     "We can make a `Params` object with the new value, and a corresponding `System` object, like this:"
@@ -730,7 +731,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -744,7 +745,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
index ba9b76ed..c15b45a3 100644
--- a/chapters/chap22.ipynb
+++ b/chapters/chap22.ipynb
@@ -88,7 +88,8 @@
     "tags": []
    },
    "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap22.ipynb)"
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -119,7 +120,7 @@
     "\n",
     "However, this value *does* depend on velocity. At low velocities it\n",
     "might be as high as 0.5, and at high velocities as low as 0.28.\n",
-    "Furthermore, the transition between these regimes typically happens\n",
+    "Furthermore, the transition between these values typically happens\n",
     "exactly in the range of velocities we are interested in, between 20 m/s and 40 m/s.\n",
     "\n",
     "Nevertheless, we'll start with a simple model where the drag coefficient does not depend on velocity; as an exercise at the end of the chapter, you can implement a more detailed model and see what effect it has on the results.\n",
@@ -744,7 +745,7 @@
    "id": "iraqi-appeal",
    "metadata": {},
    "source": [
-    "The message in indicates that a \"termination event\" occurred; that is, the simulated ball reached the ground.\n",
+    "The message indicates that a \"termination event\" occurred; that is, the simulated ball reached the ground.\n",
     "\n",
     "`results` is a `TimeFrame` with one row for each time step and one column for each of the state variables.\n",
     "Here are the last few rows."
@@ -951,7 +952,7 @@
     "\n",
     "The ModSimPy library provides `animate`, which takes as parameters a `TimeSeries` and a draw function.\n",
     "\n",
-    "The draw function should take as parameters a time stamps and a `State`.  It should draw a single frame of the animation."
+    "The draw function should take as parameters a time stamp and a `State`.  It should draw a single frame of the animation."
    ]
   },
   {
@@ -1091,7 +1092,7 @@
    "source": [
     "### Exercise 2\n",
     "\n",
-    " The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / m$^3$.  How much farther would a ball hit with the same initial speed and launch angle travel?"
+    "The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / m$^3$.  Compared with the example near sea level, how much farther would a ball travel if hit with the same initial speed and launch angle?"
    ]
   },
   {
@@ -1412,7 +1413,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1426,7 +1427,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap23.ipynb b/chapters/chap23.ipynb
index 7fd7a805..3b7e9663 100644
--- a/chapters/chap23.ipynb
+++ b/chapters/chap23.ipynb
@@ -88,7 +88,8 @@
     "tags": []
    },
    "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap23.ipynb)"
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -352,7 +353,7 @@
    "source": [
     "## Summary\n",
     "\n",
-    "This chapter introduces an new tool, `maximize_scalar`, that provides an efficient way to search for the maximum of a function. We used it to find the launch angle that maximizes the distance a baseball flies through the air, given its initial velocity.\n",
+    "This chapter introduces a new tool, `maximize_scalar`, that provides an efficient way to search for the maximum of a function. We used it to find the launch angle that maximizes the distance a baseball flies through the air, given its initial velocity.\n",
     "\n",
     "If you enjoy this example, you might be interested in this paper: \"How to hit home runs: Optimum baseball bat swing parameters for maximum range trajectories\", by Sawicki, Hubbard, and Stronge, at <http://modsimpy.com/runs>.\n",
     "\n",
@@ -598,7 +599,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -612,7 +613,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap24.ipynb b/chapters/chap24.ipynb
index 91a8c07f..68cfc0aa 100644
--- a/chapters/chap24.ipynb
+++ b/chapters/chap24.ipynb
@@ -67,7 +67,8 @@
     "tags": []
    },
    "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap24.ipynb)"
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -135,7 +136,7 @@
     "\n",
     "$$dr = k~d\\theta$$ \n",
     "\n",
-    "Where $k$ is an unknown constant we'll have to figure out. \n",
+    "where $k$ is an unknown constant we'll have to figure out. \n",
     "Again, we can divide both sides by $dt$ to get a differential equation in time:\n",
     "\n",
     "$$\\frac{dr}{dt} = k \\frac{d\\theta}{dt}$$ \n",
@@ -281,7 +282,7 @@
    "source": [
     "## Simulating the System\n",
     "\n",
-    "The state variables we'll use are, `theta`, `y`, and `r`.\n",
+    "The state variables we'll use are `theta`, `y`, and `r`.\n",
     "Here are the initial conditions:"
    ]
   },
@@ -657,7 +658,7 @@
    "metadata": {},
    "source": [
     "We can also use these equations to find the relationship between $y$ and $r$, independent of time, which we can use to compute $k$.\n",
-    "Dividing Equations 1 and 2, yields\n",
+    "Dividing Equations 1 and 2 yields\n",
     "\n",
     "$$\\frac{dr}{dy} = \\frac{k}{r}$$ \n",
     "\n",
@@ -886,7 +887,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -900,7 +901,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap25.ipynb b/chapters/chap25.ipynb
index cc17ba05..8053abc9 100644
--- a/chapters/chap25.ipynb
+++ b/chapters/chap25.ipynb
@@ -67,7 +67,8 @@
     "tags": []
    },
    "source": [
-    "[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master//chapters/chap25.ipynb)"
+    "This chapter is available as a Jupyter notebook where you can read the text, run the code, and work on the exercises. \n",
+    "Click here to access the notebooks: <https://allendowney.github.io/ModSimPy/>."
    ]
   },
   {
@@ -100,7 +101,7 @@
    "id": "promotional-trigger",
    "metadata": {},
    "source": [
-    "However, for the problems in this chapter, we only need the *magnitude* of torque; we don't care about the direction. In that case, we can compute this product of scalar quantities:\n",
+    "For the problems in this chapter, however, we only need the *magnitude* of torque; we don't care about the direction. In that case, we can compute this product of scalar quantities:\n",
     "\n",
     "$$\\tau = r F \\sin \\theta$$ \n",
     "\n",
@@ -131,8 +132,7 @@
     "For a small object with mass $m$, rotating around a point at distance\n",
     "$r$, the moment of inertia is $I = m r^2$. For more complex objects, we can compute $I$ by dividing the object into small masses, computing\n",
     "moments of inertia for each mass, and adding them up.\n",
-    "\n",
-    "However, for most simple shapes, people have already done the\n",
+    "For most simple shapes, people have already done the\n",
     "calculations; you can just look up the answers. For example, see\n",
     "<http://modsimpy.com/moment>."
    ]
@@ -934,7 +934,7 @@
     "## Summary\n",
     "\n",
     "The example in this chapter demonstrates the concepts of torque, angular acceleration, and moment of inertia.\n",
-    "We used these concepts to simulate a turntable, using a hypothetical observation to estimating torque due to friction.\n",
+    "We used these concepts to simulate a turntable, using a hypothetical observation to estimate torque due to friction.\n",
     "As an exercise, you can finish off the example, estimating the force needed to rotate the table to a given target angle.\n",
     "\n",
     "The next chapter describes several case studies you can work on to practice the tools from the last few chapters, including projectiles, rotating objects, `root_scalar`, and `maximize_scalar`."
@@ -1072,7 +1072,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1086,7 +1086,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap26.ipynb b/chapters/chap26.ipynb
index 758cd3c6..a5fcd00b 100644
--- a/chapters/chap26.ipynb
+++ b/chapters/chap26.ipynb
@@ -318,10 +318,26 @@
     "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/yoyo.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/yoyo.ipynb>."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "professional-meeting",
+   "metadata": {},
+   "source": [
+    "## Congratulations\n",
+    "\n",
+    "With that, you have reached the end of the book, so congratulations! I\n",
+    "hope you enjoyed it and learned a lot. I think the tools in this book\n",
+    "are useful, and the ways of thinking are important, not just in\n",
+    "engineering and science, but in practically every field of inquiry.\n",
+    "\n",
+    "Models are the tools we use to understand the world: if you build good\n",
+    "models, you are more likely to get things right. Good luck!"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "accompanied-southwest",
+   "id": "leading-paint",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -329,7 +345,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -343,7 +359,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap27.ipynb b/chapters/chap27.ipynb
index c5f52c3d..3ccf1585 100644
--- a/chapters/chap27.ipynb
+++ b/chapters/chap27.ipynb
@@ -65,11 +65,20 @@
    "id": "younger-bullet",
    "metadata": {},
    "source": [
-    "In this chapter we \"open the hood,\" looking more closely at how some of\n",
-    "the tools we have used---`run_solve_ivp`, `root_scalar`, and `maximize_scalar`---work.\n",
+    "In this appendix we \"open the hood,\" looking more closely at how some of\n",
+    "the tools we have used work: specifically, `run_solve_ivp`, `root_scalar`, and `maximize_scalar`.\n",
     "\n",
-    "Most of the time you don't need to know, which is why I left this chapter for last.\n",
-    "But you might be curious. And if nothing else, I have found that I can remember how to use these tools more easily because I know something about how they work."
+    "Most of the time you don't need to know, you can use these methods without knowing much about how they work.\n",
+    "But there are a few reasons you might *want* to know.\n",
+    "\n",
+    "One reason is pure curiosity. \n",
+    "If you use these methods, and especially if you come to rely on them, you might find it unsatisfying to treat them as black boxes. \n",
+    "In that case, you might enjoy opening the hood.\n",
+    "\n",
+    "Another is that these methods are not infallible; sometimes things go wrong. \n",
+    "If you know how they work, at least in a general sense, you might find it easier to debug them.\n",
+    "\n",
+    "And if nothing else, I have found that I can remember how to use these tools more easily because I know something about how they work."
    ]
   },
   {
@@ -81,7 +90,7 @@
     "\n",
     "`run_solve_ivp` is a function in the ModSimPy library that checks for common errors in the parameters and then calls `solve_ip`, which is the function in the SciPy library that does the actual work.\n",
     "\n",
-    "By default, `solve_ivp` uses the Dormand-Prince method, which is a kind of Runge-Kutta method. You can read about it at\n",
+    "By default, `solve_ivp` uses the *Dormand-Prince method*, which is a kind of *Runge-Kutta method*. You can read about it at\n",
     "<https://en.wikipedia.org/wiki/Dormand-Prince_method>, but I'll give you a sense of\n",
     "it here.\n",
     "\n",
@@ -198,7 +207,7 @@
     "Now you might wonder, if `solve_ivp` ran the slope function 50 times, how did we get 101 time steps?\n",
     "\n",
     "To answer that question, we need to know more how the solver works.\n",
-    "There are actually three steps:\n",
+    "There are actually three stages:\n",
     "\n",
     "1. For each time step, `solve_ivp` evaluates the slope function seven times, with different values of `t` and `y`.\n",
     "\n",
@@ -206,14 +215,12 @@
     "\n",
     "3. After computing all of the time steps, it uses interpolation to compute equally spaced points that connect the estimates from the previous step.\n",
     "\n",
-    "So we can see what's happening, I will run `run_solve_ivp` with the keyword argument `dense_output=False`, which skips the interpolation step and returns time steps that are not equally spaced (that is, not \"dense\").\n",
-    "\n",
-    "While we're at it, I'll modify the slope function so that every time it runs, it adds the values of `t`, `y`, and `dydt` to a list called `evals`."
+    "To show the first two steps, I'll modify the slope function so that every time it runs, it adds the values of `t`, `y`, and `dydt` to a list called `evals`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "id": "efficient-aluminum",
    "metadata": {},
    "outputs": [],
@@ -230,7 +237,8 @@
    "id": "compliant-sampling",
    "metadata": {},
    "source": [
-    "Now, before we call `run_solve_ivp` again, I'll initialize `evals` with an empty list."
+    "Before we call `run_solve_ivp`, I'll initialize `evals` with an empty list.\n",
+    "And I'll use the keyword argument `dense_output=False`, which skips the interpolation step and returns time steps that are not equally spaced (that is, not \"dense\")."
    ]
   },
   {
@@ -264,20 +272,29 @@
   },
   {
    "cell_type": "markdown",
-   "id": "rapid-parks",
+   "id": "worth-baseline",
    "metadata": {},
    "source": [
-    "It turns out there are only eight time steps, and the first five of them only cover 0.11 seconds.\n",
+    "Because we skipped the interpolation step, we can see that `solve_ivp` computed only seven time steps, not including the initial condition.\n",
+    "Also, we see that the time steps are different sizes. \n",
+    "The first is only 100 microseconds, the second is about 10 times bigger, and the third is 10 times bigger than that.\n",
     "\n",
     "The time steps are not equal because the Dormand-Prince method is *adaptive*.\n",
-    "At each time step, it actually computes two estimates of the next\n",
+    "At each time step, it computes two estimates of the next\n",
     "value. By comparing them, it can estimate the magnitude of the error,\n",
     "which it uses to adjust the time step. If the error is too big, it uses\n",
     "a smaller time step; if the error is small enough, it uses a bigger time\n",
     "step. By adjusting the time step in this way, it minimizes the number\n",
     "of times it calls the slope function to achieve a given level of\n",
     "accuracy.\n",
-    "\n",
+    "In this example, it takes five steps to simulate the first second, but then only two more steps to compute the remaining two seconds."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "rapid-parks",
+   "metadata": {},
+   "source": [
     "Because we saved the values of `y` and `t`, we can plot the locations where the slope function was evaluated.\n",
     "I'll need to use a couple of features we have not seen before, if you don't mind.\n",
     "\n",
@@ -330,8 +347,8 @@
    "id": "uniform-cable",
    "metadata": {},
    "source": [
-    "In this figure, the arrows show where the slope function was executed;\n",
-    "the dots show the best estimate of `y` for each time step; and the line shows the interpolation that connects the estimates.\n",
+    "In this figure, there are 50 arrows, one for each time the slope function was evaluated, and 8 dots, one for each time step (although several of them overlap).\n",
+    "The line shows the 101 points in the interpolation that connects the estimates.\n",
     "\n",
     "Notice that many of the arrows do not fall on the line; `solve_ivp` evaluated the slope function at these locations in order to compute the solution, but as it turned out, they are not part of the solution.\n",
     "\n",
@@ -423,8 +440,7 @@
     "greater than $f(x_2)$ or not.\n",
     "The following figure shows the two possible states.\n",
     "\n",
-    "![Possible states of a golden-section search after evaluating\n",
-    "$f(x_4)$](https://github.com/AllenDowney/ModSim/raw/main/figs/golden2.png)"
+    "![](https://github.com/AllenDowney/ModSim/raw/main/figs/golden2.png)"
    ]
   },
   {
@@ -453,36 +469,7 @@
    "cell_type": "markdown",
    "id": "broken-preparation",
    "metadata": {},
-   "source": [
-    "## Chapter Review\n",
-    "\n",
-    "The information in this chapter is not strictly necessary; you can use\n",
-    "these methods without knowing much about how they work. But there are\n",
-    "two reasons you might want to know.\n",
-    "\n",
-    "One reason is pure curiosity. If you use these methods, and especially\n",
-    "if you come to rely on them, you might find it unsatisfying to treat\n",
-    "them as \"black boxes.\" At the risk of mixing metaphors, I hope you\n",
-    "enjoyed opening the hood.\n",
-    "\n",
-    "The other reason is that these methods are not infallible; sometimes\n",
-    "things go wrong. If you know how they work, at least in a general sense,\n",
-    "you might find it easier to debug them."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "noted-interview",
-   "metadata": {},
-   "source": [
-    "With that, you have reached the end of the book, so congratulations! I\n",
-    "hope you enjoyed it and learned a lot. I think the tools in this book\n",
-    "are useful, and the ways of thinking are important, not just in\n",
-    "engineering and science, but in practically every field of inquiry.\n",
-    "\n",
-    "Models are the tools we use to understand the world: if you build good\n",
-    "models, you are more likely to get things right. Good luck!"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -496,7 +483,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -510,7 +497,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,

From b713ed30cedcf7d1dde431186da90a2830ea7857 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 10 Jun 2022 15:39:17 -0400
Subject: [PATCH 086/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 239582 -> 240226 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index 7dd8b9a7d34198fc4b21df597d6d55ff2984761d..2a72aca2aee678f8a67cc4630ab668dc721930a5 100644
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From 4cc77ece4bf07208415f3f2158bb99d90410e22e Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 5 Sep 2022 14:09:06 -0400
Subject: [PATCH 087/144] Adding Makefile and dev requirements

---
 Makefile             | 108 +++++++++++++++++++++++++++++++++++++++++++
 requirements-dev.txt |   4 ++
 requirements.txt     |   1 -
 3 files changed, 112 insertions(+), 1 deletion(-)
 create mode 100644 Makefile
 create mode 100644 requirements-dev.txt

diff --git a/Makefile b/Makefile
new file mode 100644
index 00000000..358c4272
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,108 @@
+.PHONY: clean data lint format requirements create_environment
+
+#################################################################################
+# GLOBALS                                                                       #
+#################################################################################
+
+PROJECT_NAME = ModSimPy
+PYTHON_VERSION = 3.10
+PYTHON_INTERPRETER = python
+
+
+#################################################################################
+# COMMANDS                                                                      #
+#################################################################################
+
+
+## Set up python interpreter environment
+create_environment:
+	conda create -y --name $(PROJECT_NAME) python=$(PYTHON_VERSION)
+	@echo ">>> conda env created. Activate with:\nconda activate $(PROJECT_NAME)"
+
+
+## Install Python Dependencies
+requirements:
+	$(PYTHON_INTERPRETER) -m pip install -U pip setuptools wheel
+	$(PYTHON_INTERPRETER) -m pip install -r requirements-dev.txt
+
+
+## Lint using flake8 and black (use `make format` to do formatting)
+lint:
+	flake8 pacs
+	black --check --config pyproject.toml pacs
+
+
+## Format source code with black
+format:
+	black --config pyproject.toml pacs
+
+
+## Delete all compiled Python files
+clean:
+	find . -type f -name "*.py[co]" -delete
+	find . -type d -name "__pycache__" -delete
+
+
+
+#################################################################################
+# Self Documenting Commands                                                     #
+#################################################################################
+
+.DEFAULT_GOAL := help
+
+# Inspired by <http://marmelab.com/blog/2016/02/29/auto-documented-makefile.html>
+# sed script explained:
+# /^##/:
+# 	* save line in hold space
+# 	* purge line
+# 	* Loop:
+# 		* append newline + line to hold space
+# 		* go to next line
+# 		* if line starts with doc comment, strip comment character off and loop
+# 	* remove target prerequisites
+# 	* append hold space (+ newline) to line
+# 	* replace newline plus comments by `---`
+# 	* print line
+# Separate expressions are necessary because labels cannot be delimited by
+# semicolon; see <http://stackoverflow.com/a/11799865/1968>
+.PHONY: help
+help:
+	@echo "$$(tput bold)Available rules:$$(tput sgr0)"
+	@echo
+	@sed -n -e "/^## / { \
+		h; \
+		s/.*//; \
+		:doc" \
+		-e "H; \
+		n; \
+		s/^## //; \
+		t doc" \
+		-e "s/:.*//; \
+		G; \
+		s/\\n## /---/; \
+		s/\\n/ /g; \
+		p; \
+	}" ${MAKEFILE_LIST} \
+	| LC_ALL='C' sort --ignore-case \
+	| awk -F '---' \
+		-v ncol=$$(tput cols) \
+		-v indent=19 \
+		-v col_on="$$(tput setaf 6)" \
+		-v col_off="$$(tput sgr0)" \
+	'{ \
+		printf "%s%*s%s ", col_on, -indent, $$1, col_off; \
+		n = split($$2, words, " "); \
+		line_length = ncol - indent; \
+		for (i = 1; i <= n; i++) { \
+			line_length -= length(words[i]) + 1; \
+			if (line_length <= 0) { \
+				line_length = ncol - indent - length(words[i]) - 1; \
+				printf "\n%*s ", -indent, " "; \
+			} \
+			printf "%s ", words[i]; \
+		} \
+		printf "\n"; \
+	}' \
+	| more $(shell test $(shell uname) = Darwin && echo '--no-init --raw-control-chars')
+
+
diff --git a/requirements-dev.txt b/requirements-dev.txt
new file mode 100644
index 00000000..5b829f89
--- /dev/null
+++ b/requirements-dev.txt
@@ -0,0 +1,4 @@
+pandoc
+jupyter-book
+ghp-import
+nbformat
diff --git a/requirements.txt b/requirements.txt
index cd11ff06..40465b84 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -11,5 +11,4 @@ html5lib
 beautifulsoup4
 tables
 yapf
-pip
 modsimpy

From 19d11341d00206695480e2de4d4095c9e01967a2 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 5 Sep 2022 14:09:32 -0400
Subject: [PATCH 088/144] Adding Makefile and dev requirements

---
 requirements-dev.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/requirements-dev.txt b/requirements-dev.txt
index 5b829f89..fd2b2219 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,4 +1,3 @@
 pandoc
 jupyter-book
 ghp-import
-nbformat

From d23d565300e4395fcd0e5e535d446b229215aa93 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 5 Sep 2022 14:33:31 -0400
Subject: [PATCH 089/144] Updating chapters

---
 chapters/chap02.ipynb |  9 ++++-----
 chapters/chap05.ipynb |  3 ---
 chapters/chap07.ipynb |  2 +-
 chapters/chap22.ipynb | 23 ++++++++++++++++++++---
 4 files changed, 25 insertions(+), 12 deletions(-)

diff --git a/chapters/chap02.ipynb b/chapters/chap02.ipynb
index d6c54436..a142d5b8 100644
--- a/chapters/chap02.ipynb
+++ b/chapters/chap02.ipynb
@@ -127,13 +127,12 @@
    "id": "living-wayne",
    "metadata": {},
    "source": [
-    "The expressions in parentheses are *keyword arguments*.\n",
-    "They create two variables, `olin` and `wellesley`, and give them values.\n",
-    "Then we call the `State` function.\n",
-    "The result is a `State` object, which is a collection of *state variables*.\n",
+    "The equations in parentheses create two variables, `olin` and `wellesley`, and give them the values `10` and `2`.\n",
+    "The `State` function stores these variables and their values in a `State` object, which gets assigned to a new variable named `bikeshare`.\n",
     "\n",
+    "Variables stored inside a `State` object are called *state variables*.\n",
     "In this example, the state variables represent the number of\n",
-    "bikes at each location. The initial values are `10` and `2`, indicating that there are 10 bikes at Olin and 2 at Wellesley. \n",
+    "bikes at each location. Their values indicate that there are 10 bikes at Olin and 2 at Wellesley. \n",
     "\n",
     "The `State` object is assigned to a new variable named `bikeshare`.\n",
     "We can get the value of a variable in a `State` object using the *dot operator*, like this:"
diff --git a/chapters/chap05.ipynb b/chapters/chap05.ipynb
index 0e1bd458..f0c280f8 100644
--- a/chapters/chap05.ipynb
+++ b/chapters/chap05.ipynb
@@ -756,9 +756,6 @@
    "id": "expired-salmon",
    "metadata": {},
    "source": [
-    "In this example, I use the keyword arguments `time` and `quantity` to give names to the index and the values.\n",
-    "These names don't affect the computation, but they appear when we display or plot the `TimeSeries`.\n",
-    "\n",
     "We can set the first value in the new `TimeSeries` like this."
    ]
   },
diff --git a/chapters/chap07.ipynb b/chapters/chap07.ipynb
index 8a89f9a3..2c6b2efd 100644
--- a/chapters/chap07.ipynb
+++ b/chapters/chap07.ipynb
@@ -657,7 +657,7 @@
     "    return K\n",
     "    \n",
     "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "carrying_capacity(sys1)\n",
+    "carrying_capacity(sys1)   # WRONG\n",
     "print(K)\n",
     "```"
    ]
diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
index c15b45a3..09f59fc2 100644
--- a/chapters/chap22.ipynb
+++ b/chapters/chap22.ipynb
@@ -951,7 +951,6 @@
     "One of the best ways to visualize the results of a physical model is animation.  If there are problems with the model, animation can make them apparent.\n",
     "\n",
     "The ModSimPy library provides `animate`, which takes as parameters a `TimeSeries` and a draw function.\n",
-    "\n",
     "The draw function should take as parameters a time stamp and a `State`.  It should draw a single frame of the animation."
    ]
   },
@@ -987,7 +986,9 @@
     "\n",
     "```\n",
     "animate(results, draw_func)\n",
-    "```"
+    "```\n",
+    "\n",
+    "You can see the results when you run the code from this chapter."
    ]
   },
   {
@@ -1012,6 +1013,22 @@
     "# animate(results, draw_func)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3bede5d5",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This chapter introduces `Vector` objects, which we use to represent position, velocity, and acceleration in two dimensions.\n",
+    "We also represent forces using vectors, which make it easier to add up forces acting in different directions.\n",
+    "\n",
+    "Our ODE solver doesn't work with `Vector` objects, so it takes some work to pack and unpack their components.\n",
+    "Nevertheless, we were able to run simulations with vectors and display the results.\n",
+    "\n",
+    "In the next chapter we'll use these simulations to solve an optimization problem."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "lyric-harassment",
@@ -1427,7 +1444,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

From 8d7274f010d865a70e5f81fb514fb6bca69be052 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 5 Sep 2022 14:33:31 -0400
Subject: [PATCH 090/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 240226 -> 240360 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index 2a72aca2aee678f8a67cc4630ab668dc721930a5..ba4a3e5bab94478eb6d4f6a405567f31a64fa098 100644
GIT binary patch
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From 4ad186d73a7219cd1eb336bfdaf82906fa2b9f9e Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 5 Sep 2022 14:57:04 -0400
Subject: [PATCH 091/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 240360 -> 240360 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index ba4a3e5bab94478eb6d4f6a405567f31a64fa098..4897783c8a067e127b7c1ffee7a2544f42878c73 100644
GIT binary patch
delta 1258
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From f8e73b9040b11504fe17d429e6c38e732ea3bc23 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 30 Sep 2022 20:01:25 -0400
Subject: [PATCH 092/144] Updates

---
 environment.yml | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/environment.yml b/environment.yml
index d3445ea7..56d42fc4 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,13 +1,11 @@
 name: ModSimPy
 channels:
   - conda-forge
-  - defaults
 dependencies:
-  - python=3.7
+  - python
   - jupyter
   - numpy
   - matplotlib
-  - seaborn
   - pandas
   - scipy
   - pint
@@ -16,7 +14,8 @@ dependencies:
   - html5lib
   - beautifulsoup4
   - pytables
-  - yapf
   - pip
-  - pip:
-      - modsimpy
+
+# pip install pytest nbmake
+# pip install jupyter-book ghp-import
+# pip install pypandoc fastdoc

From 8c0a1f1e0c7acfa2a145ed5fd4fa60e92499ba92 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 30 Sep 2022 20:06:34 -0400
Subject: [PATCH 093/144] Setting rcParams and dtypes

---
 modsim.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/modsim.py b/modsim.py
index 02bdb103..0af5c309 100644
--- a/modsim.py
+++ b/modsim.py
@@ -19,6 +19,11 @@
 import inspect
 
 import matplotlib.pyplot as plt
+
+plt.rcParams['figure.dpi'] = 75
+plt.rcParams['savefig.dpi'] = 300
+plt.rcParams['figure.figsize'] = 6, 4
+
 import numpy as np
 import pandas as pd
 import scipy
@@ -656,9 +661,10 @@ def make_series(x, y, **options):
 
 
 def TimeSeries(*args, **kwargs):
-    """
+    """Make a pd.Series object to represent a time series.
     """
     if args or kwargs:
+        underride(kwargs, dtype=float)
         series = pd.Series(*args, **kwargs)
     else:
         series = pd.Series([], dtype=np.float64)
@@ -670,9 +676,10 @@ def TimeSeries(*args, **kwargs):
 
 
 def SweepSeries(*args, **kwargs):
-    """
+    """Make a pd.Series object to store results from a parameter sweep.
     """
     if args or kwargs:
+        underride(kwargs, dtype=float)
         series = pd.Series(*args, **kwargs)
     else:
         series = pd.Series([], dtype=np.float64)

From b358869e8ac6f6f90ada9dbc7015a207bc19b10f Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 30 Sep 2022 20:09:44 -0400
Subject: [PATCH 094/144] Updating chapters

---
 chapters/chap01.ipynb |   4 +-
 chapters/chap02.ipynb | 104 +++++++++++++++++++++---------------------
 chapters/chap03.ipynb |   8 ++--
 chapters/chap04.ipynb |   6 +--
 chapters/chap05.ipynb |   8 ++--
 chapters/chap06.ipynb |  52 ++++++++++-----------
 chapters/chap07.ipynb |  32 ++++++-------
 chapters/chap08.ipynb |  82 ++++++++++++++++-----------------
 chapters/chap09.ipynb |   5 +-
 chapters/chap10.ipynb |  30 ++++++------
 chapters/chap14.ipynb |   6 +--
 chapters/chap15.ipynb |  66 +++++++++++++--------------
 chapters/chap16.ipynb |  54 +++++++++++-----------
 chapters/chap17.ipynb |   8 ++--
 chapters/chap18.ipynb |   4 +-
 chapters/chap19.ipynb |   4 +-
 chapters/chap23.ipynb |  52 ++++++++++-----------
 chapters/chap25.ipynb |  10 ++--
 chapters/chap27.ipynb |  38 ++++++---------
 19 files changed, 282 insertions(+), 291 deletions(-)

diff --git a/chapters/chap01.ipynb b/chapters/chap01.ipynb
index 3f4722ec..9d0da03b 100644
--- a/chapters/chap01.ipynb
+++ b/chapters/chap01.ipynb
@@ -919,7 +919,7 @@
     "\n",
     "Suppose you bring a 10 foot pole to the top of the Empire State Building and use it to drop the penny from `h` plus 10 feet.\n",
     "\n",
-    "Define a variable named `foot` that contains the unit `foot` provided by `UNITS`.  Define a variable named `pole_height` and give it the value 10 feet.\n",
+    "Define a variable named `foot` that contains the unit `foot` provided by the `UnitRegistry` we called `units`.  Define a variable named `pole_height` and give it the value 10 feet.\n",
     "\n",
     "What happens if you add `h`, which is in units of meters, to `pole_height`, which is in units of feet?  What happens if you write the addition the other way around?"
    ]
@@ -1335,7 +1335,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap02.ipynb b/chapters/chap02.ipynb
index a142d5b8..f1fc36d1 100644
--- a/chapters/chap02.ipynb
+++ b/chapters/chap02.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -114,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "incorrect-comparison",
    "metadata": {},
    "outputs": [],
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "brief-diversity",
    "metadata": {},
    "outputs": [],
@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "designed-brazilian",
    "metadata": {},
    "outputs": [],
@@ -176,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "impaired-potter",
    "metadata": {},
    "outputs": [],
@@ -204,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "basic-fabric",
    "metadata": {
     "tags": []
@@ -235,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "floppy-trainer",
    "metadata": {},
    "outputs": [],
@@ -255,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "hungarian-bride",
    "metadata": {},
    "outputs": [],
@@ -286,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "vertical-drawing",
    "metadata": {},
    "outputs": [],
@@ -306,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "significant-nutrition",
    "metadata": {},
    "outputs": [],
@@ -337,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "moving-jurisdiction",
    "metadata": {},
    "outputs": [],
@@ -357,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "proper-symposium",
    "metadata": {},
    "outputs": [],
@@ -376,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "identical-yacht",
    "metadata": {},
    "outputs": [],
@@ -409,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "heavy-patrol",
    "metadata": {},
    "outputs": [],
@@ -430,7 +430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "french-preference",
    "metadata": {},
    "outputs": [],
@@ -451,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "alternative-keyboard",
    "metadata": {},
    "outputs": [],
@@ -469,12 +469,12 @@
     "them.\n",
     "\n",
     "Print statements are useful for debugging functions. For example, we can\n",
-    "add a print statement to `move_bike`, like this:"
+    "add a print statement to `bike_to_wellesley`, like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "robust-holly",
    "metadata": {},
    "outputs": [],
@@ -501,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "fifteen-atmosphere",
    "metadata": {},
    "outputs": [],
@@ -522,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "matched-narrow",
    "metadata": {},
    "outputs": [],
@@ -556,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 22,
    "id": "29c1f41a",
    "metadata": {
     "tags": []
@@ -570,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 23,
    "id": "illegal-metropolitan",
    "metadata": {},
    "outputs": [],
@@ -599,7 +599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 24,
    "id": "excessive-murder",
    "metadata": {},
    "outputs": [],
@@ -626,7 +626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 25,
    "id": "fundamental-nursing",
    "metadata": {},
    "outputs": [],
@@ -652,7 +652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 26,
    "id": "twenty-health",
    "metadata": {},
    "outputs": [],
@@ -673,7 +673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 27,
    "id": "played-character",
    "metadata": {},
    "outputs": [],
@@ -692,7 +692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 28,
    "id": "ecological-colon",
    "metadata": {},
    "outputs": [],
@@ -715,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 29,
    "id": "mediterranean-german",
    "metadata": {},
    "outputs": [],
@@ -749,7 +749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 30,
    "id": "hollywood-shopping",
    "metadata": {},
    "outputs": [],
@@ -773,7 +773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 31,
    "id": "buried-alert",
    "metadata": {},
    "outputs": [],
@@ -794,7 +794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 32,
    "id": "recognized-denmark",
    "metadata": {},
    "outputs": [],
@@ -832,7 +832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 33,
    "id": "polish-river",
    "metadata": {},
    "outputs": [],
@@ -868,19 +868,19 @@
    "id": "breeding-groove",
    "metadata": {},
    "source": [
-    "## Timeseries\n",
+    "## TimeSeries\n",
     "\n",
-    "When we run a simulation, we often want to save the results for later analysis. The ModSim library provides a `TimeSeries` object for this purpose. A `TimeSeries` contains a sequence of time stamps and a\n",
+    "When we run a simulation, we often want to save the results for later analysis. The ModSim library provides a `TimeSeries` object for this purpose. A `TimeSeries` contains a sequence of timestamps and a\n",
     "corresponding sequence of quantities.\n",
     "\n",
-    "In this example, the time stamps are integers representing minutes and the quantities are the number of bikes at one location.\n",
+    "In this example, the timestamps are integers representing minutes and the quantities are the number of bikes at one location.\n",
     "\n",
     "Since we have moved a number of bikes around, let's start again with a new `State` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 34,
    "id": "every-consultation",
    "metadata": {},
    "outputs": [],
@@ -898,7 +898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 35,
    "id": "changing-planet",
    "metadata": {},
    "outputs": [],
@@ -916,7 +916,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 36,
    "id": "aquatic-richardson",
    "metadata": {},
    "outputs": [],
@@ -929,14 +929,14 @@
    "id": "searching-funeral",
    "metadata": {},
    "source": [
-    "The number in brackets is the time stamp, also called a *label*.\n",
+    "The number in brackets is the timestamp, also called a *label*.\n",
     "\n",
     "We can use a `TimeSeries` inside a for loop to store the results of the simulation:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 37,
    "id": "english-titanium",
    "metadata": {},
    "outputs": [],
@@ -954,12 +954,12 @@
    "source": [
     "Each time through the loop, we print the value of `i` and call `step`, which updates `bikeshare`.\n",
     "Then we store the number of bikes at Olin in `results`. \n",
-    "We use the loop variable, `i`, to compute the time stamp, `i+1`.\n",
+    "We use the loop variable, `i`, to compute the timestamp, `i+1`.\n",
     "\n",
-    "The first time through the loop, the value of `i` is `0`, so the time stamp is `1`.\n",
-    "The last time, the value of `i` is `2`, so the time stamp is `3`.\n",
+    "The first time through the loop, the value of `i` is `0`, so the timestamp is `1`.\n",
+    "The last time, the value of `i` is `2`, so the timestamp is `3`.\n",
     "\n",
-    "When the loop exits, `results` contains 4 time stamps, from 0 through\n",
+    "When the loop exits, `results` contains 4 timestamps, from 0 through\n",
     "3, and the number of bikes at Olin at the end of each time step.\n",
     "\n",
     "We can display the `TimeSeries` like this:"
@@ -967,7 +967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 38,
    "id": "indonesian-singing",
    "metadata": {},
    "outputs": [],
@@ -980,7 +980,7 @@
    "id": "small-encoding",
    "metadata": {},
    "source": [
-    "The left column is the time stamps; the right column is the quantities."
+    "The left column is the timestamps; the right column is the quantities."
    ]
   },
   {
@@ -996,14 +996,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 39,
    "id": "saved-hands",
    "metadata": {},
    "outputs": [],
    "source": [
     "results.plot()\n",
     "\n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
+    "decorate(title='Olin-Wellesley bikeshare',\n",
     "         xlabel='Time step (min)', \n",
     "         ylabel='Number of bikes')"
    ]
@@ -1177,7 +1177,7 @@
    "id": "digital-stretch",
    "metadata": {},
    "source": [
-    "`decorate` is based on Matplotlib, which is a widely-used plotting library for Python.  Matplotlib provides separate functions for `title`, `xlabel`, and `ylabel`.\n",
+    "`decorate` is based on Matplotlib, which is a widely used plotting library for Python.  Matplotlib provides separate functions for `title`, `xlabel`, and `ylabel`.\n",
     "`decorate` makes them a little easier to use.\n",
     "For the list of keyword arguments you can pass to `decorate`, see <https://matplotlib.org/3.2.2/api/axes_api.html?highlight=axes#module-matplotlib.axes>.\n",
     "\n",
@@ -1222,7 +1222,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap03.ipynb b/chapters/chap03.ipynb
index 0afd2830..dbb7f229 100644
--- a/chapters/chap03.ipynb
+++ b/chapters/chap03.ipynb
@@ -86,7 +86,7 @@
     "more wrong than others.\" This chapter demonstrates the process we\n",
     "use to make models less wrong.\n",
     "\n",
-    "As an example, we'll review the bikeshare model from the previous\n",
+    "As an example, we'll review the bike share model from the previous\n",
     "chapter, consider its strengths and weaknesses, and gradually improve\n",
     "it. We'll also see ways to use the model to understand the behavior of\n",
     "the system and evaluate designs intended to make it work better."
@@ -374,7 +374,7 @@
    "source": [
     "The first line checks whether the number of bikes at Wellesley is zero. If so, it uses a *return statement*, which causes the function to end immediately, without running the rest of the statements. So if there are no bikes at Wellesley, we return from `bike_to_olin` without changing the state.\n",
     "\n",
-    "We can test it by initializing the state with no bikes at Wellesley And calling `bike_to_olin`."
+    "We can test it by initializing the state with no bikes at Wellesley and calling `bike_to_olin`."
    ]
   },
   {
@@ -421,7 +421,7 @@
    "source": [
     "## Comparison Operators\n",
     "\n",
-    "The updated version of `bike_to_olin` uses the equals operator, `==`, which compares two values and returns `True`, if they are equal, and `False` otherwise.\n",
+    "The updated version of `bike_to_olin` uses the equals operator, `==`, which compares two values and returns `True` if they are equal, and `False` otherwise.\n",
     "\n",
     "It is easy to confuse the equals operator with the assignment operator, `=`, which assigns a value to a variable. For example, the following statement creates a variable, `x`, if it doesn't already exist, and gives it the value `5`."
    ]
@@ -848,7 +848,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap04.ipynb b/chapters/chap04.ipynb
index 44186d1e..20361f9d 100644
--- a/chapters/chap04.ipynb
+++ b/chapters/chap04.ipynb
@@ -513,7 +513,7 @@
    "source": [
     "sweep.plot(label='Olin', color='C1')\n",
     "\n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
+    "decorate(title='Olin-Wellesley bikeshare',\n",
     "         xlabel='Customer rate at Olin (p1 in customers/min)', \n",
     "         ylabel='Number of unhappy customers at Olin')"
    ]
@@ -528,7 +528,7 @@
     "I use a different color for `SweepSeries` to remind us that it is not a `TimeSeries`.\n",
     "\n",
     "When the arrival rate at Olin is low, there are plenty of bikes and no unhappy customers.\n",
-    "As the arrival rate increases, we are more likely to run out of bikes and the number of unhappy customers increases. The line is jagged because the simulation is based on random numbers. Sometime we get lucky and there are relatively few unhappy customers; other times we are unlucky and there are more. "
+    "As the arrival rate increases, we are more likely to run out of bikes and the number of unhappy customers increases. The line is jagged because the simulation is based on random numbers. Sometimes we get lucky and there are relatively few unhappy customers; other times we are unlucky and there are more. "
    ]
   },
   {
@@ -880,7 +880,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap05.ipynb b/chapters/chap05.ipynb
index f0c280f8..3f630bb1 100644
--- a/chapters/chap05.ipynb
+++ b/chapters/chap05.ipynb
@@ -89,7 +89,7 @@
     "decades, I am happy to report that those predictions were wrong.\n",
     "\n",
     "But world population growth is still a topic of concern, and it is an\n",
-    "open question how many people the earth can sustain while maintaining\n",
+    "open question how many people Earth can sustain while maintaining\n",
     "and improving our quality of life.\n",
     "\n",
     "In this chapter and the next, we use tools from the previous chapters to model world population growth since 1950 and generate predictions for the next 50-100 years.\n",
@@ -368,7 +368,7 @@
     "    census.plot(style=':', label='US Census')\n",
     "    un.plot(style='--', label='UN DESA')\n",
     "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)') "
+    "             ylabel='World population (billions)') "
    ]
   },
   {
@@ -811,7 +811,7 @@
    "id": "signed-colleague",
    "metadata": {},
    "source": [
-    "The values of `t` go from from `t_0` to `t_end`, including the first but not the last.\n",
+    "The values of `t` go from `t_0` to `t_end`, including the first but not the last.\n",
     "\n",
     "Inside the loop, we compute the population for the next year by adding the population for the current year and `annual_growth`. \n",
     "\n",
@@ -984,7 +984,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap06.ipynb b/chapters/chap06.ipynb
index 5dc999b5..058a6412 100644
--- a/chapters/chap06.ipynb
+++ b/chapters/chap06.ipynb
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "critical-addition",
    "metadata": {
     "tags": []
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "naughty-swing",
    "metadata": {
     "tags": []
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "id": "numerous-university",
    "metadata": {},
    "outputs": [],
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "id": "colonial-domestic",
    "metadata": {},
    "outputs": [],
@@ -223,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "id": "floral-routine",
    "metadata": {},
    "outputs": [],
@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "id": "pacific-challenge",
    "metadata": {},
    "outputs": [],
@@ -269,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 10,
    "id": "electoral-breach",
    "metadata": {},
    "outputs": [],
@@ -287,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "id": "peripheral-cassette",
    "metadata": {},
    "outputs": [],
@@ -309,14 +309,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 12,
    "id": "capable-diana",
    "metadata": {},
    "outputs": [],
    "source": [
     "results1.plot(label='model', color='gray')\n",
     "plot_estimates()\n",
-    "decorate(title='Constant Growth Model')"
+    "decorate(title='Constant growth model')"
    ]
   },
   {
@@ -349,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 13,
    "id": "laughing-wesley",
    "metadata": {},
    "outputs": [],
@@ -381,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 14,
    "id": "wired-brief",
    "metadata": {},
    "outputs": [],
@@ -400,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 15,
    "id": "looking-trace",
    "metadata": {},
    "outputs": [],
@@ -408,7 +408,7 @@
     "results2 = run_simulation2(system)\n",
     "results2.plot(label='model', color='gray')\n",
     "plot_estimates()\n",
-    "decorate(title='Proportional Growth Model')"
+    "decorate(title='Proportional growth model')"
    ]
   },
   {
@@ -442,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 16,
    "id": "handmade-permit",
    "metadata": {},
    "outputs": [],
@@ -466,7 +466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 17,
    "id": "civilian-accused",
    "metadata": {
     "tags": []
@@ -499,7 +499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
    "id": "wicked-seeking",
    "metadata": {},
    "outputs": [],
@@ -533,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 19,
    "id": "impressive-model",
    "metadata": {},
    "outputs": [],
@@ -554,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 20,
    "id": "familiar-helena",
    "metadata": {},
    "outputs": [],
@@ -573,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 21,
    "id": "independent-effectiveness",
    "metadata": {},
    "outputs": [],
@@ -628,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 22,
    "id": "minus-recommendation",
    "metadata": {
     "scrolled": false
@@ -640,7 +640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 23,
    "id": "headed-amsterdam",
    "metadata": {},
    "outputs": [],
@@ -650,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 24,
    "id": "preliminary-carnival",
    "metadata": {},
    "outputs": [],
@@ -694,7 +694,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap07.ipynb b/chapters/chap07.ipynb
index 2c6b2efd..e463dcaf 100644
--- a/chapters/chap07.ipynb
+++ b/chapters/chap07.ipynb
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "affiliated-eleven",
    "metadata": {
     "tags": []
@@ -162,7 +162,7 @@
     "    census.plot(style=':', label='US Census')\n",
     "    un.plot(style='--', label='UN DESA')\n",
     "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)') "
+    "             ylabel='World population (billions)') "
    ]
   },
   {
@@ -295,7 +295,7 @@
    "source": [
     "results.plot(color='gray', label='model')\n",
     "plot_estimates()\n",
-    "decorate(title='Quadratic Growth Model')"
+    "decorate(title='Quadratic growth model')"
    ]
   },
   {
@@ -307,7 +307,7 @@
     "\n",
     "It is not entirely surprising that the quadratic model fits better than the\n",
     "constant and proportional models, because it has two parameters we can\n",
-    "choose where the other models have only one. In general, the more\n",
+    "choose, where the other models have only one. In general, the more\n",
     "parameters you have to play with, the better you should expect the model\n",
     "to fit.\n",
     "\n",
@@ -399,7 +399,7 @@
     "\n",
     "decorate(xlabel='Population (billions)',\n",
     "         ylabel='Net growth (billions)',\n",
-    "         title='Net Growth vs. Population')"
+    "         title='Net growth vs. population')"
    ]
   },
   {
@@ -440,7 +440,7 @@
     "$$\\Delta p = \\alpha p + \\beta p^2$$ \n",
     "\n",
     "where $\\alpha$ and $\\beta$ are the parameters of the model. \n",
-    "If we rewrite the right hand side like this: \n",
+    "If we rewrite the right-hand side like this: \n",
     "\n",
     "$$\\Delta p = p (\\alpha + \\beta p)$$ \n",
     "\n",
@@ -450,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "ordinary-honolulu",
    "metadata": {},
    "outputs": [],
@@ -513,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "realistic-opinion",
    "metadata": {},
    "outputs": [],
@@ -545,7 +545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "marine-entry",
    "metadata": {},
    "outputs": [],
@@ -579,7 +579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "moving-brazil",
    "metadata": {},
    "outputs": [],
@@ -622,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "sacred-physiology",
    "metadata": {},
    "outputs": [],
@@ -701,7 +701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "stretch-check",
    "metadata": {},
    "outputs": [],
@@ -711,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "tender-treat",
    "metadata": {},
    "outputs": [],
@@ -721,7 +721,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "passive-certificate",
    "metadata": {},
    "outputs": [],
@@ -747,7 +747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "agricultural-burke",
    "metadata": {},
    "outputs": [],
@@ -781,7 +781,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap08.ipynb b/chapters/chap08.ipynb
index dafd3e12..d93ef6a4 100644
--- a/chapters/chap08.ipynb
+++ b/chapters/chap08.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "necessary-factor",
    "metadata": {
     "tags": []
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "changed-desktop",
    "metadata": {
     "tags": []
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "metallic-inventory",
    "metadata": {
     "tags": []
@@ -144,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "measured-arthur",
    "metadata": {
     "tags": []
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "cutting-financing",
    "metadata": {
     "tags": []
@@ -170,7 +170,7 @@
     "    census.plot(style=':', label='US Census')\n",
     "    un.plot(style='--', label='UN DESA')\n",
     "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)') "
+    "             ylabel='World population (billions)') "
    ]
   },
   {
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "indirect-russia",
    "metadata": {},
    "outputs": [],
@@ -225,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "comfortable-compression",
    "metadata": {},
    "outputs": [],
@@ -250,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "broken-windsor",
    "metadata": {},
    "outputs": [],
@@ -268,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "latest-function",
    "metadata": {},
    "outputs": [],
@@ -286,15 +286,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "portable-pottery",
    "metadata": {},
    "outputs": [],
    "source": [
     "results.plot(color='gray', label='model')\n",
     "decorate(xlabel='Year', \n",
-    "         ylabel='World population (billion)',\n",
-    "         title='Quadratic Model Projection')"
+    "         ylabel='World population (billions)',\n",
+    "         title='Quadratic model projection')"
    ]
   },
   {
@@ -356,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "20e6f166",
    "metadata": {},
    "outputs": [],
@@ -374,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "headed-tuner",
    "metadata": {},
    "outputs": [],
@@ -392,7 +392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "precious-contribution",
    "metadata": {},
    "outputs": [],
@@ -411,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "paperback-delay",
    "metadata": {},
    "outputs": [],
@@ -428,7 +428,7 @@
     "    un_proj.plot(style='--', label='UN DESA')\n",
     "    \n",
     "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)')"
+    "             ylabel='World population (billions)')"
    ]
   },
   {
@@ -441,14 +441,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "billion-dynamics",
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_projections(table3)\n",
     "results.plot(color='gray', label='model')\n",
-    "decorate(title='Quadratic Model Projection')"
+    "plot_projections(table3)\n",
+    "decorate(title='Quadratic model projection, with UN and Census')"
    ]
   },
   {
@@ -504,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "handmade-funeral",
    "metadata": {},
    "outputs": [],
@@ -525,7 +525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "objective-accused",
    "metadata": {},
    "outputs": [],
@@ -544,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "unique-matrix",
    "metadata": {},
    "outputs": [],
@@ -556,7 +556,7 @@
     "    alpha_un = un.diff() / un\n",
     "    alpha_un.plot(style='.', label='UN DESA')\n",
     "\n",
-    "    decorate(xlabel='Year', label='Net growth rate')"
+    "    decorate(xlabel='Year', ylabel='Net growth rate')"
    ]
   },
   {
@@ -570,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "pressing-proceeding",
    "metadata": {},
    "outputs": [],
@@ -591,7 +591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "mexican-denver",
    "metadata": {},
    "outputs": [],
@@ -612,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "addressed-worker",
    "metadata": {},
    "outputs": [],
@@ -631,7 +631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "cathedral-shakespeare",
    "metadata": {},
    "outputs": [],
@@ -665,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "common-april",
    "metadata": {},
    "outputs": [],
@@ -675,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "ignored-chain",
    "metadata": {},
    "outputs": [],
@@ -685,7 +685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "id": "color-accountability",
    "metadata": {},
    "outputs": [],
@@ -695,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "id": "numeric-raise",
    "metadata": {},
    "outputs": [],
@@ -705,7 +705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "id": "included-vehicle",
    "metadata": {},
    "outputs": [],
@@ -715,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "id": "brown-rating",
    "metadata": {},
    "outputs": [],
@@ -725,7 +725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "id": "laughing-cylinder",
    "metadata": {},
    "outputs": [],
@@ -735,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "id": "personalized-parking",
    "metadata": {},
    "outputs": [],
@@ -769,7 +769,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap09.ipynb b/chapters/chap09.ipynb
index d04ff587..26e145d2 100644
--- a/chapters/chap09.ipynb
+++ b/chapters/chap09.ipynb
@@ -383,8 +383,7 @@
     "WolframAlpha, but they have some other advantages.\n",
     "\n",
     "To use it, we'll define `Symbol` objects that represent names of variables and functions.\n",
-    "The `symbols` function takes a string and returns `Symbol` objects.\n",
-    "So if we run this assignment:"
+    "The `symbols` function takes a string and returns `Symbol` objects."
    ]
   },
   {
@@ -1104,7 +1103,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap10.ipynb b/chapters/chap10.ipynb
index 76c63317..f35a6820 100644
--- a/chapters/chap10.ipynb
+++ b/chapters/chap10.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "usual-penguin",
    "metadata": {
     "tags": []
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "thick-lincoln",
    "metadata": {
     "tags": []
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "conscious-orange",
    "metadata": {
     "tags": []
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "thick-blanket",
    "metadata": {
     "tags": []
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "ranking-prescription",
    "metadata": {
     "tags": []
@@ -193,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "metallic-offense",
    "metadata": {
     "scrolled": false,
@@ -228,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "solar-action",
    "metadata": {},
    "outputs": [],
@@ -250,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "rural-express",
    "metadata": {
     "tags": []
@@ -266,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "preceding-sheep",
    "metadata": {
     "tags": []
@@ -279,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "together-jackson",
    "metadata": {},
    "outputs": [],
@@ -289,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "simple-verse",
    "metadata": {
     "scrolled": true
@@ -337,7 +337,7 @@
     "You can download it from <https://github.com/AllenDowney/ModSimPy/raw/master/examples/queue.ipynb> or run it on Colab at <https://colab.research.google.com/github/AllenDowney/ModSimPy/blob/master/examples/queue.ipynb>.\n",
     "\n",
     "As always, you should practice incremental development: write no more\n",
-    "than one or two lines of code a time, and test as you go!"
+    "than one or two lines of code at a time, and test as you go!"
    ]
   },
   {
@@ -417,7 +417,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap14.ipynb b/chapters/chap14.ipynb
index 59533809..f8283be9 100644
--- a/chapters/chap14.ipynb
+++ b/chapters/chap14.ipynb
@@ -260,7 +260,7 @@
    "id": "shared-boxing",
    "metadata": {},
    "source": [
-    "For each element of the `SweepFrame` is plots a point with the ratio `beta/gamma` as the $x$ coordinate and `metric` -- which is the fraction of the population that's infected -- as the $y$ coordinate.\n",
+    "For each element of the `SweepFrame` it plots a point with the ratio `beta/gamma` as the $x$ coordinate and `metric` -- which is the fraction of the population that's infected -- as the $y$ coordinate.\n",
     "\n",
     "Here's what it looks like:"
    ]
@@ -336,7 +336,7 @@
    "id": "noticed-mouse",
    "metadata": {},
    "source": [
-    "Dividing one differential equation by another is not an obvious move, but in this case it is useful because it gives us a relationship between $i$, $s$ and $c$ that does not depend on time. From that relationship, we can derive an equation that relates $c$ to the final value of $s$. In theory, this equation makes it possible to infer $c$ by observing the course of an epidemic."
+    "Dividing one differential equation by another is not an obvious move, but in this case it is useful because it gives us a relationship between $i$, $s$, and $c$ that does not depend on time. From that relationship, we can derive an equation that relates $c$ to the final value of $s$. In theory, this equation makes it possible to infer $c$ by observing the course of an epidemic."
    ]
   },
   {
@@ -791,7 +791,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap15.ipynb b/chapters/chap15.ipynb
index 6df8c173..92d125d9 100644
--- a/chapters/chap15.ipynb
+++ b/chapters/chap15.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "dependent-monitoring",
    "metadata": {
     "tags": []
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "continental-decrease",
    "metadata": {
     "tags": []
@@ -140,7 +140,7 @@
     "\n",
     "$$Q = C~\\Delta T$$ \n",
     "\n",
-    "where $Q$ is the amount of heat transferred to an object, $\\Delta T$ is resulting change in temperature, and $C$ is the object's *thermal mass*, which is a property of the object that determines how much energy it takes to heat or cool it. In SI units, thermal mass is measured in joules per degree Celsius (J/°C)."
+    "where $Q$ is the amount of heat transferred to an object, $\\Delta T$ is the resulting change in temperature, and $C$ is the object's *thermal mass*, which is a property of the object that determines how much energy it takes to heat or cool it. In SI units, thermal mass is measured in joules per degree Celsius (J/°C)."
    ]
   },
   {
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "incredible-greeting",
    "metadata": {
     "tags": []
@@ -281,13 +281,13 @@
    "id": "gross-appeal",
    "metadata": {},
    "source": [
-    "In addition to the parameters, `make_system` sets the temperature of the environment, `T_env`, the initial time stamp, `t_0`, and the time step, `dt`, which we will use use to simulate the cooling process.\n",
+    "In addition to the parameters, `make_system` sets the temperature of the environment, `T_env`, the initial time stamp, `t_0`, and the time step, `dt`, which we will use to simulate the cooling process.\n",
     "Here's a `System` object that represents the coffee."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "fuzzy-support",
    "metadata": {},
    "outputs": [],
@@ -318,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "tight-baptist",
    "metadata": {
     "tags": []
@@ -340,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "fiscal-artwork",
    "metadata": {},
    "outputs": [],
@@ -360,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "cardiac-independence",
    "metadata": {
     "tags": []
@@ -388,7 +388,7 @@
    "id": "together-adapter",
    "metadata": {},
    "source": [
-    "There are a two things here that are different from previous versions of `run_simulation`.\n",
+    "There are two things here that are different from previous versions of `run_simulation`.\n",
     "\n",
     "First, we use  `linrange` to make an array of values from `t_0` to `t_end` with time step `dt`.\n",
     "`linrange` is similar to `linspace`; they both take a start value and an end value and return an array of equally spaced values.\n",
@@ -417,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "numerous-metabolism",
    "metadata": {},
    "outputs": [],
@@ -436,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "infectious-carolina",
    "metadata": {},
    "outputs": [],
@@ -454,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "accompanied-melbourne",
    "metadata": {},
    "outputs": [],
@@ -474,14 +474,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "important-constitution",
    "metadata": {},
    "outputs": [],
    "source": [
     "results.plot(label='coffee')\n",
     "\n",
-    "decorate(xlabel='Time (minute)',\n",
+    "decorate(xlabel='Time (min)',\n",
     "         ylabel='Temperature (C)',\n",
     "         title='Coffee Cooling')"
    ]
@@ -496,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "absolute-desire",
    "metadata": {},
    "outputs": [],
@@ -509,7 +509,7 @@
    "id": "light-carpet",
    "metadata": {},
    "source": [
-    "To find the value of `r` where the final temperature is precisely 70 °C, we could proceed by by trial and error, but it is more efficient to use a root-finding algorithm."
+    "To find the value of `r` where the final temperature is precisely 70 °C, we could proceed by trial and error, but it is more efficient to use a root-finding algorithm."
    ]
   },
   {
@@ -531,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "small-shark",
    "metadata": {},
    "outputs": [],
@@ -550,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "defensive-content",
    "metadata": {},
    "outputs": [],
@@ -570,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "behind-perth",
    "metadata": {},
    "outputs": [],
@@ -588,7 +588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "following-sentence",
    "metadata": {},
    "outputs": [],
@@ -620,7 +620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "genetic-compound",
    "metadata": {},
    "outputs": [],
@@ -645,7 +645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "french-financing",
    "metadata": {},
    "outputs": [],
@@ -664,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "excellent-fellow",
    "metadata": {},
    "outputs": [],
@@ -684,7 +684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "french-decline",
    "metadata": {},
    "outputs": [],
@@ -707,7 +707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "afraid-ordering",
    "metadata": {},
    "outputs": [],
@@ -727,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "derived-annual",
    "metadata": {},
    "outputs": [],
@@ -783,7 +783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "committed-angel",
    "metadata": {},
    "outputs": [],
@@ -793,7 +793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "military-military",
    "metadata": {},
    "outputs": [],
@@ -813,7 +813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "agreed-excuse",
    "metadata": {},
    "outputs": [],
@@ -823,7 +823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "defined-nudist",
    "metadata": {},
    "outputs": [],
@@ -833,7 +833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "loose-rehabilitation",
    "metadata": {},
    "outputs": [],
@@ -867,7 +867,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap16.ipynb b/chapters/chap16.ipynb
index d4d8ce57..562342c3 100644
--- a/chapters/chap16.ipynb
+++ b/chapters/chap16.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "british-place",
    "metadata": {
     "tags": []
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "determined-volunteer",
    "metadata": {
     "tags": []
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "fossil-moisture",
    "metadata": {
     "tags": []
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "cutting-scale",
    "metadata": {
     "tags": []
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "id": "extensive-happening",
    "metadata": {},
    "outputs": [],
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "id": "polyphonic-specialist",
    "metadata": {},
    "outputs": [],
@@ -228,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "id": "subtle-donna",
    "metadata": {},
    "outputs": [],
@@ -247,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "id": "domestic-tours",
    "metadata": {},
    "outputs": [],
@@ -271,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "id": "subject-richardson",
    "metadata": {},
    "outputs": [],
@@ -304,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "id": "mental-corporation",
    "metadata": {},
    "outputs": [],
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "id": "adverse-hanging",
    "metadata": {},
    "outputs": [],
@@ -356,15 +356,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "id": "bright-proposal",
    "metadata": {},
    "outputs": [],
    "source": [
     "sweep.plot(label='mixture', color='C2')\n",
     "\n",
-    "decorate(xlabel='Time until mixing (minutes)',\n",
-    "         ylabel='Final emperature (C)')"
+    "decorate(xlabel='Time until mixing (min)',\n",
+    "         ylabel='Final temperature (C)')"
    ]
   },
   {
@@ -406,7 +406,7 @@
    "metadata": {},
    "source": [
     "Now we can use the observed data to estimate the parameter $r$. If we\n",
-    "observe the that temperature at $t_{end}$ is $T_{final}$, we can plug these values into the particular solution and solve for $r$. The result is:\n",
+    "observe the that the temperature at $t_{end}$ is $T_{final}$, we can plug these values into the particular solution and solve for $r$. The result is:\n",
     "\n",
     "$$r = \\frac{1}{t_{end}} \\log{\\left (\\frac{T_{init} - T_{env}}{T_{final} - T_{env}} \\right )}$$\n",
     "\n",
@@ -415,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 13,
    "id": "secondary-swift",
    "metadata": {},
    "outputs": [],
@@ -442,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 14,
    "id": "south-machinery",
    "metadata": {},
    "outputs": [],
@@ -467,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 15,
    "id": "opening-transsexual",
    "metadata": {},
    "outputs": [],
@@ -498,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 16,
    "id": "agreed-bouquet",
    "metadata": {},
    "outputs": [],
@@ -519,7 +519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 17,
    "id": "portuguese-sympathy",
    "metadata": {
     "tags": []
@@ -532,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 18,
    "id": "distinguished-regard",
    "metadata": {
     "tags": []
@@ -592,7 +592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 19,
    "id": "acting-howard",
    "metadata": {},
    "outputs": [],
@@ -602,7 +602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 20,
    "id": "ahead-compensation",
    "metadata": {},
    "outputs": [],
@@ -612,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 21,
    "id": "assumed-shock",
    "metadata": {},
    "outputs": [],
@@ -622,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 22,
    "id": "elementary-moral",
    "metadata": {},
    "outputs": [],
@@ -644,7 +644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 23,
    "id": "super-citizenship",
    "metadata": {},
    "outputs": [],
@@ -670,7 +670,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap17.ipynb b/chapters/chap17.ipynb
index cd0d3bc0..46c74d6b 100644
--- a/chapters/chap17.ipynb
+++ b/chapters/chap17.ipynb
@@ -147,7 +147,7 @@
     "> 2. Also, we discovered that the effect of insulin on net glucose disappearance must be sluggish --- that is, that insulin acts slowly because insulin must first move from plasma to a remote compartment \\[...\\] to exert its action on glucose disposal.\n",
     "\n",
     "To paraphrase the second point, the effect of insulin on glucose\n",
-    "disposal, as seen in the data, happens more slowly than we would expect if it depended primarily on the the concentration of insulin in the blood. Bergman's group hypothesized that insulin must move relatively slowly from the blood to a remote compartment where it has its effect."
+    "disposal, as seen in the data, happens more slowly than we would expect if it depended primarily on the concentration of insulin in the blood. Bergman's group hypothesized that insulin must move relatively slowly from the blood to a remote compartment where it has its effect."
    ]
   },
   {
@@ -428,7 +428,7 @@
     "I_series.plot(color='C2', label='interpolation')\n",
     "\n",
     "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')"
+    "         ylabel='Concentration (μU/mL)')"
    ]
   },
   {
@@ -506,7 +506,7 @@
     "I_series.plot(color='C2', label='interpolation')\n",
     "\n",
     "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')"
+    "         ylabel='Concentration (μU/mL)')"
    ]
   },
   {
@@ -577,7 +577,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap18.ipynb b/chapters/chap18.ipynb
index 0afbc8bf..80bb5739 100644
--- a/chapters/chap18.ipynb
+++ b/chapters/chap18.ipynb
@@ -286,7 +286,7 @@
    "id": "basic-subdivision",
    "metadata": {},
    "source": [
-    "As usual, the update function takes a time stamp, a `State` object, and a `System` object as parameters. The first line uses multiple assignment to extract the current values of `G` and `X`.\n",
+    "As usual, the update function takes a timestamp, a `State` object, and a `System` object as parameters. The first line uses multiple assignment to extract the current values of `G` and `X`.\n",
     "\n",
     "The following lines unpack the parameters we need from the `System`\n",
     "object.\n",
@@ -796,7 +796,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap19.ipynb b/chapters/chap19.ipynb
index 5f255895..70050688 100644
--- a/chapters/chap19.ipynb
+++ b/chapters/chap19.ipynb
@@ -154,7 +154,7 @@
     "\n",
     "![Circuit diagram of a low-pass filter](https://github.com/AllenDowney/ModSim/raw/main/figs/Lowpass_Filter_RC.png)\n",
     "\n",
-    "A *filter* is a circuit takes a signal, $V_{in}$, as input and produces a signal, $V_{out}$, as output. In this context, a *signal* is a voltage that changes over time.\n",
+    "A *filter* is a circuit that takes a signal, $V_{in}$, as input and produces a signal, $V_{out}$, as output. In this context, a *signal* is a voltage that changes over time.\n",
     "\n",
     "A filter is *low-pass* if it allows low-frequency signals to pass from\n",
     "$V_{in}$ to $V_{out}$ unchanged, but it reduces the amplitude of\n",
@@ -294,7 +294,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap23.ipynb b/chapters/chap23.ipynb
index 3b7e9663..6c1d23f8 100644
--- a/chapters/chap23.ipynb
+++ b/chapters/chap23.ipynb
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "usual-institution",
    "metadata": {
     "tags": []
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "quantitative-montana",
    "metadata": {
     "tags": []
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "finite-warrant",
    "metadata": {},
    "outputs": [],
@@ -199,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "forty-knitting",
    "metadata": {},
    "outputs": [],
@@ -228,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "senior-counter",
    "metadata": {},
    "outputs": [],
@@ -248,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "biological-evans",
    "metadata": {
     "tags": []
@@ -273,15 +273,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "experienced-providence",
    "metadata": {},
    "outputs": [],
    "source": [
     "sweep.plot()\n",
     "\n",
-    "decorate(xlabel='Launch angle (degree)',\n",
-    "         ylabel='Range (meter)')"
+    "decorate(xlabel='Launch angle (degrees)',\n",
+    "         ylabel='Range (m)')"
    ]
   },
   {
@@ -295,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "invisible-jaguar",
    "metadata": {},
    "outputs": [],
@@ -319,7 +319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "vocal-nerve",
    "metadata": {},
    "outputs": [],
@@ -329,7 +329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "figured-uniform",
    "metadata": {},
    "outputs": [],
@@ -394,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "studied-association",
    "metadata": {},
    "outputs": [],
@@ -404,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "developmental-alabama",
    "metadata": {},
    "outputs": [],
@@ -423,7 +423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "ignored-decrease",
    "metadata": {},
    "outputs": [],
@@ -433,7 +433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "western-communist",
    "metadata": {},
    "outputs": [],
@@ -451,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "duplicate-madison",
    "metadata": {},
    "outputs": [],
@@ -461,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "legislative-prospect",
    "metadata": {},
    "outputs": [],
@@ -484,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "egyptian-shadow",
    "metadata": {},
    "outputs": [],
@@ -502,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "sustainable-supply",
    "metadata": {},
    "outputs": [],
@@ -512,7 +512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "american-biodiversity",
    "metadata": {},
    "outputs": [],
@@ -530,7 +530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "certified-webster",
    "metadata": {},
    "outputs": [],
@@ -540,7 +540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "silver-bernard",
    "metadata": {},
    "outputs": [],
@@ -550,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "absent-encoding",
    "metadata": {},
    "outputs": [],
@@ -568,7 +568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "thick-jungle",
    "metadata": {},
    "outputs": [],
@@ -613,7 +613,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap25.ipynb b/chapters/chap25.ipynb
index 8053abc9..4d0155ee 100644
--- a/chapters/chap25.ipynb
+++ b/chapters/chap25.ipynb
@@ -585,8 +585,8 @@
    "source": [
     "### Combining the Results\n",
     "\n",
-    "`DataFrame` provides `append`, which appends `results2` to the end of\n",
-    "`results1`."
+    "Pandas provides a function called `concat`, which makes a \n",
+    "`DataFrame` with the rows from `results1` followed by the rows from `results2`."
    ]
   },
   {
@@ -596,7 +596,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = results1.append(results2)"
+    "results = pd.concat([results1, results2])"
    ]
   },
   {
@@ -709,7 +709,7 @@
     "                                       events=event_func2)\n",
     "    \n",
     "    # combine and return the results\n",
-    "    results = results1.append(results2)\n",
+    "    results = pd.concat([results1, results2])\n",
     "    return results"
    ]
   },
@@ -1086,7 +1086,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap27.ipynb b/chapters/chap27.ipynb
index 3ccf1585..5046d8ff 100644
--- a/chapters/chap27.ipynb
+++ b/chapters/chap27.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "approximate-working",
    "metadata": {
     "tags": []
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "suspended-occasion",
    "metadata": {
     "tags": []
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "coastal-cameroon",
    "metadata": {},
    "outputs": [],
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "recovered-makeup",
    "metadata": {},
    "outputs": [],
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "younger-affect",
    "metadata": {},
    "outputs": [],
@@ -172,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "suspended-tumor",
    "metadata": {},
    "outputs": [],
@@ -190,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "noticed-portable",
    "metadata": {},
    "outputs": [],
@@ -206,7 +206,7 @@
     "`results` contains 101 points that are equally spaced in time.\n",
     "Now you might wonder, if `solve_ivp` ran the slope function 50 times, how did we get 101 time steps?\n",
     "\n",
-    "To answer that question, we need to know more how the solver works.\n",
+    "To answer that question, we need to know more about how the solver works.\n",
     "There are actually three stages:\n",
     "\n",
     "1. For each time step, `solve_ivp` evaluates the slope function seven times, with different values of `t` and `y`.\n",
@@ -220,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "efficient-aluminum",
    "metadata": {},
    "outputs": [],
@@ -243,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "handed-gothic",
    "metadata": {},
    "outputs": [],
@@ -262,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "accessory-wrapping",
    "metadata": {},
    "outputs": [],
@@ -304,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "indie-antigua",
    "metadata": {},
    "outputs": [],
@@ -321,12 +321,12 @@
    "metadata": {},
    "source": [
     "Using these values, we can generate a *quiver plot* that shows an arrow for each time the slope function ran.\n",
-    "The location of the each arrow represents the values of `t` and `y`; the orientation of the arrow shows the slope that was computed."
+    "The location of each arrow represents the values of `t` and `y`; the orientation of the arrow shows the slope that was computed."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "fossil-librarian",
    "metadata": {},
    "outputs": [],
@@ -470,14 +470,6 @@
    "id": "broken-preparation",
    "metadata": {},
    "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "suspended-apparel",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -497,7 +489,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

From 8ca65359e5ac51004c81e0b973dfb37d7bd73d48 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 30 Sep 2022 20:09:44 -0400
Subject: [PATCH 095/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 240360 -> 240367 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index 4897783c8a067e127b7c1ffee7a2544f42878c73..12baf6af19630b6099d634238170e16a0961b3a2 100644
GIT binary patch
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mP%mcUK0v@au-$?8=VF7v&*B8Lpu=Yd0!Bqhh=)#lf&L5ce$e~?


From ab06023298d96292c107e96fa5f3fbc04b8a7c6e Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 27 Apr 2023 15:29:44 -0400
Subject: [PATCH 096/144] Updates

---
 Makefile             | 82 ++------------------------------------------
 requirements-dev.txt |  6 ++--
 requirements.txt     | 13 +++----
 3 files changed, 12 insertions(+), 89 deletions(-)

diff --git a/Makefile b/Makefile
index 358c4272..4d980441 100644
--- a/Makefile
+++ b/Makefile
@@ -1,38 +1,23 @@
-.PHONY: clean data lint format requirements create_environment
-
-#################################################################################
-# GLOBALS                                                                       #
-#################################################################################
-
 PROJECT_NAME = ModSimPy
-PYTHON_VERSION = 3.10
+PYTHON_VERSION = 3.8
 PYTHON_INTERPRETER = python
 
 
-#################################################################################
-# COMMANDS                                                                      #
-#################################################################################
-
-
-## Set up python interpreter environment
 create_environment:
 	conda create -y --name $(PROJECT_NAME) python=$(PYTHON_VERSION)
 	@echo ">>> conda env created. Activate with:\nconda activate $(PROJECT_NAME)"
 
 
-## Install Python Dependencies
 requirements:
 	$(PYTHON_INTERPRETER) -m pip install -U pip setuptools wheel
 	$(PYTHON_INTERPRETER) -m pip install -r requirements-dev.txt
 
 
-## Lint using flake8 and black (use `make format` to do formatting)
 lint:
 	flake8 pacs
 	black --check --config pyproject.toml pacs
 
 
-## Format source code with black
 format:
 	black --config pyproject.toml pacs
 
@@ -43,66 +28,5 @@ clean:
 	find . -type d -name "__pycache__" -delete
 
 
-
-#################################################################################
-# Self Documenting Commands                                                     #
-#################################################################################
-
-.DEFAULT_GOAL := help
-
-# Inspired by <http://marmelab.com/blog/2016/02/29/auto-documented-makefile.html>
-# sed script explained:
-# /^##/:
-# 	* save line in hold space
-# 	* purge line
-# 	* Loop:
-# 		* append newline + line to hold space
-# 		* go to next line
-# 		* if line starts with doc comment, strip comment character off and loop
-# 	* remove target prerequisites
-# 	* append hold space (+ newline) to line
-# 	* replace newline plus comments by `---`
-# 	* print line
-# Separate expressions are necessary because labels cannot be delimited by
-# semicolon; see <http://stackoverflow.com/a/11799865/1968>
-.PHONY: help
-help:
-	@echo "$$(tput bold)Available rules:$$(tput sgr0)"
-	@echo
-	@sed -n -e "/^## / { \
-		h; \
-		s/.*//; \
-		:doc" \
-		-e "H; \
-		n; \
-		s/^## //; \
-		t doc" \
-		-e "s/:.*//; \
-		G; \
-		s/\\n## /---/; \
-		s/\\n/ /g; \
-		p; \
-	}" ${MAKEFILE_LIST} \
-	| LC_ALL='C' sort --ignore-case \
-	| awk -F '---' \
-		-v ncol=$$(tput cols) \
-		-v indent=19 \
-		-v col_on="$$(tput setaf 6)" \
-		-v col_off="$$(tput sgr0)" \
-	'{ \
-		printf "%s%*s%s ", col_on, -indent, $$1, col_off; \
-		n = split($$2, words, " "); \
-		line_length = ncol - indent; \
-		for (i = 1; i <= n; i++) { \
-			line_length -= length(words[i]) + 1; \
-			if (line_length <= 0) { \
-				line_length = ncol - indent - length(words[i]) - 1; \
-				printf "\n%*s ", -indent, " "; \
-			} \
-			printf "%s ", words[i]; \
-		} \
-		printf "\n"; \
-	}' \
-	| more $(shell test $(shell uname) = Darwin && echo '--no-init --raw-control-chars')
-
-
+tests:
+	cd chapters; pytest --nbmake chap01.ipynb
diff --git a/requirements-dev.txt b/requirements-dev.txt
index fd2b2219..38916bad 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,3 +1,5 @@
+-r requirements.txt
 pandoc
-jupyter-book
-ghp-import
+pypandoc
+pytest
+nbmake
diff --git a/requirements.txt b/requirements.txt
index 40465b84..002783d6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,14 +1,11 @@
+beautifulsoup4
+html5lib
 jupyter
-numpy
+lxml
 matplotlib
-seaborn
+numpy
 pandas
-scipy
 pint
+scipy
 sympy
-lxml
-html5lib
-beautifulsoup4
 tables
-yapf
-modsimpy

From 8b12e5c7eac71003f3382c5dd0536ddcf381eb92 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 27 Apr 2023 15:38:53 -0400
Subject: [PATCH 097/144] Removing iteritems

---
 modsim.py | 881 +-----------------------------------------------------
 1 file changed, 1 insertion(+), 880 deletions(-)
 mode change 100644 => 120000 modsim.py

diff --git a/modsim.py b/modsim.py
deleted file mode 100644
index 0af5c309..00000000
--- a/modsim.py
+++ /dev/null
@@ -1,880 +0,0 @@
-"""
-Code from Modeling and Simulation in Python.
-
-Copyright 2020 Allen Downey
-
-MIT License: https://opensource.org/licenses/MIT
-"""
-
-import logging
-
-logger = logging.getLogger(name="modsim.py")
-
-# make sure we have Python 3.6 or better
-import sys
-
-if sys.version_info < (3, 6):
-    logger.warning("modsim.py depends on Python 3.6 features.")
-
-import inspect
-
-import matplotlib.pyplot as plt
-
-plt.rcParams['figure.dpi'] = 75
-plt.rcParams['savefig.dpi'] = 300
-plt.rcParams['figure.figsize'] = 6, 4
-
-import numpy as np
-import pandas as pd
-import scipy
-
-import scipy.optimize as spo
-
-from scipy.interpolate import interp1d
-from scipy.interpolate import InterpolatedUnivariateSpline
-
-from scipy.integrate import solve_ivp
-
-from types import SimpleNamespace
-from copy import copy
-
-
-def flip(p=0.5):
-    """Flips a coin with the given probability.
-
-    p: float 0-1
-
-    returns: boolean (True or False)
-    """
-    return np.random.random() < p
-
-
-def cart2pol(x, y, z=None):
-    """Convert Cartesian coordinates to polar.
-
-    x: number or sequence
-    y: number or sequence
-    z: number or sequence (optional)
-
-    returns: theta, rho OR theta, rho, z
-    """
-    x = np.asarray(x)
-    y = np.asarray(y)
-
-    rho = np.hypot(x, y)
-    theta = np.arctan2(y, x)
-
-    if z is None:
-        return theta, rho
-    else:
-        return theta, rho, z
-
-
-def pol2cart(theta, rho, z=None):
-    """Convert polar coordinates to Cartesian.
-
-    theta: number or sequence in radians
-    rho: number or sequence
-    z: number or sequence (optional)
-
-    returns: x, y OR x, y, z
-    """
-    x = rho * np.cos(theta)
-    y = rho * np.sin(theta)
-
-    if z is None:
-        return x, y
-    else:
-        return x, y, z
-
-from numpy import linspace
-
-def linrange(start, stop=None, step=1, **options):
-    """Make an array of equally spaced values.
-
-    start: first value
-    stop: last value (might be approximate)
-    step: difference between elements (should be consistent)
-
-    returns: NumPy array
-    """
-    if stop is None:
-        stop = start
-        start = 0
-    n = int(round((stop-start) / step))
-    return linspace(start, stop, n+1, **options)
-
-
-def root_scalar(func, *args, **kwargs):
-    """Finds the input value that minimizes `min_func`.
-
-    Wrapper for
-    https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root_scalar.html
-
-    func: computes the function to be minimized
-    bracket: sequence of two values, lower and upper bounds of the range to be searched
-    args: any additional positional arguments are passed to func
-    kwargs: any keyword arguments are passed to root_scalar
-
-    returns: RootResults object
-    """
-    bracket = kwargs.get('bracket', None)
-    if bracket is None or len(bracket) != 2:
-        msg = ("To run root_scalar, you have to provide a "
-               "`bracket` keyword argument with a sequence "
-               "of length 2.")
-        raise ValueError(msg)
-
-    try:
-        func(bracket[0], *args)
-    except Exception as e:
-        msg = ("Before running scipy.integrate.root_scalar "
-               "I tried running the function you provided "
-               "with `bracket[0]`, "
-               "and I got the following error:")
-        logger.error(msg)
-        raise (e)
-
-    underride(kwargs, rtol=1e-4)
-
-    res = spo.root_scalar(func, *args, **kwargs)
-
-    if not res.converged:
-        msg = ("scipy.optimize.root_scalar did not converge. "
-               "The message it returned is:\n" + res.flag)
-        raise ValueError(msg)
-
-    return res
-
-
-def minimize_scalar(func, *args, **kwargs):
-    """Finds the input value that minimizes `func`.
-
-    Wrapper for
-    https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
-
-    func: computes the function to be minimized
-    args: any additional positional arguments are passed to func
-    kwargs: any keyword arguments are passed to minimize_scalar
-
-    returns: OptimizeResult object
-    """
-    bounds = kwargs.get('bounds', None)
-
-    if bounds is None or len(bounds) != 2:
-        msg = ("To run maximize_scalar or minimize_scalar, "
-               "you have to provide a `bounds` "
-               "keyword argument with a sequence "
-               "of length 2.")
-        raise ValueError(msg)
-
-    try:
-        func(bounds[0], *args)
-    except Exception as e:
-        msg = ("Before running scipy.integrate.minimize_scalar, "
-               "I tried running the function you provided "
-               "with the lower bound, "
-               "and I got the following error:")
-        logger.error(msg)
-        raise (e)
-
-    underride(kwargs, method='bounded')
-
-    res = spo.minimize_scalar(func, args=args, **kwargs)
-
-    if not res.success:
-        msg = ("minimize_scalar did not succeed."
-               "The message it returned is: \n" +
-               res.message)
-        raise Exception(msg)
-
-    return res
-
-
-def maximize_scalar(max_func, *args, **kwargs):
-    """Finds the input value that maximizes `max_func`.
-
-    Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
-
-    min_func: computes the function to be maximized
-    args: any additional positional arguments are passed to max_func
-    options: any keyword arguments are passed as options to minimize_scalar
-
-    returns: ModSimSeries object
-    """
-    def min_func(*args):
-        return -max_func(*args)
-
-    res = minimize_scalar(min_func, *args, **kwargs)
-
-    # we have to negate the function value before returning res
-    res.fun = -res.fun
-    return res
-
-
-def run_solve_ivp(system, slope_func, **options):
-    """Computes a numerical solution to a differential equation.
-
-    `system` must contain `init` with initial conditions,
-    `t_end` with the end time.  Optionally, it can contain
-    `t_0` with the start time.
-
-    It should contain any other parameters required by the
-    slope function.
-
-    `options` can be any legal options of `scipy.integrate.solve_ivp`
-
-    system: System object
-    slope_func: function that computes slopes
-
-    returns: TimeFrame
-    """
-    system = remove_units(system)
-
-    # make sure `system` contains `init`
-    if not hasattr(system, "init"):
-        msg = """It looks like `system` does not contain `init`
-                 as a system variable.  `init` should be a State
-                 object that specifies the initial condition:"""
-        raise ValueError(msg)
-
-    # make sure `system` contains `t_end`
-    if not hasattr(system, "t_end"):
-        msg = """It looks like `system` does not contain `t_end`
-                 as a system variable.  `t_end` should be the
-                 final time:"""
-        raise ValueError(msg)
-
-    # the default value for t_0 is 0
-    t_0 = getattr(system, "t_0", 0)
-
-    # try running the slope function with the initial conditions
-    try:
-        slope_func(t_0, system.init, system)
-    except Exception as e:
-        msg = """Before running scipy.integrate.solve_ivp, I tried
-                 running the slope function you provided with the
-                 initial conditions in `system` and `t=t_0` and I got
-                 the following error:"""
-        logger.error(msg)
-        raise (e)
-
-    # get the list of event functions
-    events = options.get('events', [])
-
-    # if there's only one event function, put it in a list
-    try:
-        iter(events)
-    except TypeError:
-        events = [events]
-
-    for event_func in events:
-        # make events terminal unless otherwise specified
-        if not hasattr(event_func, 'terminal'):
-            event_func.terminal = True
-
-        # test the event function with the initial conditions
-        try:
-            event_func(t_0, system.init, system)
-        except Exception as e:
-            msg = """Before running scipy.integrate.solve_ivp, I tried
-                     running the event function you provided with the
-                     initial conditions in `system` and `t=t_0` and I got
-                     the following error:"""
-            logger.error(msg)
-            raise (e)
-
-    # get dense output unless otherwise specified
-    if not 't_eval' in options:
-        underride(options, dense_output=True)
-
-    # run the solver
-    bunch = solve_ivp(slope_func, [t_0, system.t_end], system.init,
-                      args=[system], **options)
-
-    # separate the results from the details
-    y = bunch.pop("y")
-    t = bunch.pop("t")
-
-    # get the column names from `init`, if possible
-    if hasattr(system.init, 'index'):
-        columns = system.init.index
-    else:
-        columns = range(len(system.init))
-
-    # evaluate the results at equally-spaced points
-    if options.get('dense_output', False):
-        try:
-            num = system.num
-        except AttributeError:
-            num = 101
-        t_final = t[-1]
-        t_array = linspace(t_0, t_final, num)
-        y_array = bunch.sol(t_array)
-
-        # pack the results into a TimeFrame
-        results = TimeFrame(y_array.T, index=t_array,
-                        columns=columns)
-    else:
-        results = TimeFrame(y.T, index=t,
-                        columns=columns)
-
-    return results, bunch
-
-
-def leastsq(error_func, x0, *args, **options):
-    """Find the parameters that yield the best fit for the data.
-
-    `x0` can be a sequence, array, Series, or Params
-
-    Positional arguments are passed along to `error_func`.
-
-    Keyword arguments are passed to `scipy.optimize.leastsq`
-
-    error_func: function that computes a sequence of errors
-    x0: initial guess for the best parameters
-    args: passed to error_func
-    options: passed to leastsq
-
-    :returns: Params object with best_params and ModSimSeries with details
-    """
-    # override `full_output` so we get a message if something goes wrong
-    options["full_output"] = True
-
-    # run leastsq
-    t = scipy.optimize.leastsq(error_func, x0=x0, args=args, **options)
-    best_params, cov_x, infodict, mesg, ier = t
-
-    # pack the results into a ModSimSeries object
-    details = SimpleNamespace(cov_x=cov_x,
-                              mesg=mesg,
-                              ier=ier,
-                              **infodict)
-    details.success = details.ier in [1,2,3,4]
-
-    # if we got a Params object, we should return a Params object
-    if isinstance(x0, Params):
-        best_params = Params(pd.Series(best_params, x0.index))
-
-    # return the best parameters and details
-    return best_params, details
-
-
-def crossings(series, value):
-    """Find the labels where the series passes through value.
-
-    The labels in series must be increasing numerical values.
-
-    series: Series
-    value: number
-
-    returns: sequence of labels
-    """
-    values = series.values - value
-    interp = InterpolatedUnivariateSpline(series.index, values)
-    return interp.roots()
-
-
-def has_nan(a):
-    """Checks whether the an array contains any NaNs.
-
-    :param a: NumPy array or Pandas Series
-    :return: boolean
-    """
-    return np.any(np.isnan(a))
-
-
-def is_strictly_increasing(a):
-    """Checks whether the elements of an array are strictly increasing.
-
-    :param a: NumPy array or Pandas Series
-    :return: boolean
-    """
-    return np.all(np.diff(a) > 0)
-
-
-def interpolate(series, **options):
-    """Creates an interpolation function.
-
-    series: Series object
-    options: any legal options to scipy.interpolate.interp1d
-
-    returns: function that maps from the index to the values
-    """
-    if has_nan(series.index):
-        msg = """The Series you passed to interpolate contains
-                 NaN values in the index, which would result in
-                 undefined behavior.  So I'm putting a stop to that."""
-        raise ValueError(msg)
-
-    if not is_strictly_increasing(series.index):
-        msg = """The Series you passed to interpolate has an index
-                 that is not strictly increasing, which would result in
-                 undefined behavior.  So I'm putting a stop to that."""
-        raise ValueError(msg)
-
-    # make the interpolate function extrapolate past the ends of
-    # the range, unless `options` already specifies a value for `fill_value`
-    underride(options, fill_value="extrapolate")
-
-    # call interp1d, which returns a new function object
-    x = series.index
-    y = series.values
-    interp_func = interp1d(x, y, **options)
-    return interp_func
-
-
-def interpolate_inverse(series, **options):
-    """Interpolate the inverse function of a Series.
-
-    series: Series object, represents a mapping from `a` to `b`
-    options: any legal options to scipy.interpolate.interp1d
-
-    returns: interpolation object, can be used as a function
-             from `b` to `a`
-    """
-    inverse = pd.Series(series.index, index=series.values)
-    interp_func = interpolate(inverse, **options)
-    return interp_func
-
-
-def gradient(series, **options):
-    """Computes the numerical derivative of a series.
-
-    If the elements of series have units, they are dropped.
-
-    series: Series object
-    options: any legal options to np.gradient
-
-    returns: Series, same subclass as series
-    """
-    x = series.index
-    y = series.values
-
-    a = np.gradient(y, x, **options)
-    return series.__class__(a, series.index)
-
-
-def source_code(obj):
-    """Prints the source code for a given object.
-
-    obj: function or method object
-    """
-    print(inspect.getsource(obj))
-
-
-def underride(d, **options):
-    """Add key-value pairs to d only if key is not in d.
-
-    If d is None, create a new dictionary.
-
-    d: dictionary
-    options: keyword args to add to d
-    """
-    if d is None:
-        d = {}
-
-    for key, val in options.items():
-        d.setdefault(key, val)
-
-    return d
-
-
-def contour(df, **options):
-    """Makes a contour plot from a DataFrame.
-
-    Wrapper for plt.contour
-    https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.contour.html
-
-    Note: columns and index must be numerical
-
-    df: DataFrame
-    options: passed to plt.contour
-    """
-    fontsize = options.pop("fontsize", 12)
-    underride(options, cmap="viridis")
-    x = df.columns
-    y = df.index
-    X, Y = np.meshgrid(x, y)
-    cs = plt.contour(X, Y, df, **options)
-    plt.clabel(cs, inline=1, fontsize=fontsize)
-
-
-def savefig(filename, **options):
-    """Save the current figure.
-
-    Keyword arguments are passed along to plt.savefig
-
-    https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html
-
-    filename: string
-    """
-    print("Saving figure to file", filename)
-    plt.savefig(filename, **options)
-
-
-def decorate(**options):
-    """Decorate the current axes.
-
-    Call decorate with keyword arguments like
-    decorate(title='Title',
-             xlabel='x',
-             ylabel='y')
-
-    The keyword arguments can be any of the axis properties
-    https://matplotlib.org/api/axes_api.html
-    """
-    ax = plt.gca()
-    ax.set(**options)
-
-    handles, labels = ax.get_legend_handles_labels()
-    if handles:
-        ax.legend(handles, labels)
-
-    plt.tight_layout()
-
-
-def remove_from_legend(bad_labels):
-    """Removes some labels from the legend.
-
-    bad_labels: sequence of strings
-    """
-    ax = plt.gca()
-    handles, labels = ax.get_legend_handles_labels()
-    handle_list, label_list = [], []
-    for handle, label in zip(handles, labels):
-        if label not in bad_labels:
-            handle_list.append(handle)
-            label_list.append(label)
-    ax.legend(handle_list, label_list)
-
-
-class SettableNamespace(SimpleNamespace):
-    """Contains a collection of parameters.
-
-    Used to make a System object.
-
-    Takes keyword arguments and stores them as attributes.
-    """
-    def __init__(self, namespace=None, **kwargs):
-        super().__init__()
-        if namespace:
-            self.__dict__.update(namespace.__dict__)
-        self.__dict__.update(kwargs)
-
-    def get(self, name, default=None):
-        """Look up a variable.
-
-        name: string varname
-        default: value returned if `name` is not present
-        """
-        try:
-            return self.__getattribute__(name, default)
-        except AttributeError:
-            return default
-
-    def set(self, **variables):
-        """Make a copy and update the given variables.
-
-        returns: Params
-        """
-        new = copy(self)
-        new.__dict__.update(variables)
-        return new
-
-
-def magnitude(x):
-    """Returns the magnitude of a Quantity or number.
-
-    x: Quantity or number
-
-    returns: number
-    """
-    return x.magnitude if hasattr(x, 'magnitude') else x
-
-
-def remove_units(namespace):
-    """Removes units from the values in a Namespace.
-
-    Only removes units from top-level values;
-    does not traverse nested values.
-
-    returns: new Namespace object
-    """
-    res = copy(namespace)
-    for label, value in res.__dict__.items():
-        if isinstance(value, pd.Series):
-            value = remove_units_series(value)
-        res.__dict__[label] = magnitude(value)
-    return res
-
-
-def remove_units_series(series):
-    """Removes units from the values in a Series.
-
-    Only removes units from top-level values;
-    does not traverse nested values.
-
-    returns: new Series object
-    """
-    res = copy(series)
-    for label, value in res.iteritems():
-        res[label] = magnitude(value)
-    return res
-
-
-class System(SettableNamespace):
-    """Contains system parameters and their values.
-
-    Takes keyword arguments and stores them as attributes.
-    """
-    pass
-
-
-class Params(SettableNamespace):
-    """Contains system parameters and their values.
-
-    Takes keyword arguments and stores them as attributes.
-    """
-    pass
-
-
-def State(**variables):
-    """Contains the values of state variables."""
-    return pd.Series(variables, name='state')
-
-
-def make_series(x, y, **options):
-    """Make a Pandas Series.
-
-    x: sequence used as the index
-    y: sequence used as the values
-
-    returns: Pandas Series
-    """
-    underride(options, name='values')
-    if isinstance(y, pd.Series):
-        y = y.values
-    series = pd.Series(y, index=x, **options)
-    series.index.name = 'index'
-    return series
-
-
-def TimeSeries(*args, **kwargs):
-    """Make a pd.Series object to represent a time series.
-    """
-    if args or kwargs:
-        underride(kwargs, dtype=float)
-        series = pd.Series(*args, **kwargs)
-    else:
-        series = pd.Series([], dtype=np.float64)
-
-    series.index.name = 'Time'
-    if 'name' not in kwargs:
-        series.name = 'Quantity'
-    return series
-
-
-def SweepSeries(*args, **kwargs):
-    """Make a pd.Series object to store results from a parameter sweep.
-    """
-    if args or kwargs:
-        underride(kwargs, dtype=float)
-        series = pd.Series(*args, **kwargs)
-    else:
-        series = pd.Series([], dtype=np.float64)
-
-    series.index.name = 'Parameter'
-    if 'name' not in kwargs:
-        series.name = 'Metric'
-    return series
-
-
-def show(obj):
-    """Display a Series or Namespace as a DataFrame."""
-    if isinstance(obj, pd.Series):
-        df = pd.DataFrame(obj)
-        return df
-    elif hasattr(obj, '__dict__'):
-        return pd.DataFrame(pd.Series(obj.__dict__),
-                            columns=['value'])
-    else:
-        return obj
-
-
-def TimeFrame(*args, **kwargs):
-    """DataFrame that maps from time to State.
-    """
-    underride(kwargs, dtype=float)
-    return pd.DataFrame(*args, **kwargs)
-
-
-def SweepFrame(*args, **kwargs):
-    """DataFrame that maps from parameter value to SweepSeries.
-    """
-    underride(kwargs, dtype=float)
-    return pd.DataFrame(*args, **kwargs)
-
-
-def Vector(x, y, z=None, **options):
-    """
-    """
-    underride(options, name='component')
-    if z is None:
-        return pd.Series(dict(x=x, y=y), **options)
-    else:
-        return pd.Series(dict(x=x, y=y, z=z), **options)
-
-
-## Vector functions (should work with any sequence)
-
-def vector_mag(v):
-    """Vector magnitude."""
-    return np.sqrt(np.dot(v, v))
-
-
-def vector_mag2(v):
-    """Vector magnitude squared."""
-    return np.dot(v, v)
-
-
-def vector_angle(v):
-    """Angle between v and the positive x axis.
-
-    Only works with 2-D vectors.
-
-    returns: angle in radians
-    """
-    assert len(v) == 2
-    x, y = v
-    return np.arctan2(y, x)
-
-
-def vector_polar(v):
-    """Vector magnitude and angle.
-
-    returns: (number, angle in radians)
-    """
-    return vector_mag(v), vector_angle(v)
-
-
-def vector_hat(v):
-    """Unit vector in the direction of v.
-
-    returns: Vector or array
-    """
-    # check if the magnitude of the Quantity is 0
-    mag = vector_mag(v)
-    if mag == 0:
-        return v
-    else:
-        return v / mag
-
-
-def vector_perp(v):
-    """Perpendicular Vector (rotated left).
-
-    Only works with 2-D Vectors.
-
-    returns: Vector
-    """
-    assert len(v) == 2
-    x, y = v
-    return Vector(-y, x)
-
-
-def vector_dot(v, w):
-    """Dot product of v and w.
-
-    returns: number or Quantity
-    """
-    return np.dot(v, w)
-
-
-def vector_cross(v, w):
-    """Cross product of v and w.
-
-    returns: number or Quantity for 2-D, Vector for 3-D
-    """
-    res = np.cross(v, w)
-
-    if len(v) == 3:
-        return Vector(*res)
-    else:
-        return res
-
-
-def vector_proj(v, w):
-    """Projection of v onto w.
-
-    returns: array or Vector with direction of w and units of v.
-    """
-    w_hat = vector_hat(w)
-    return vector_dot(v, w_hat) * w_hat
-
-
-def scalar_proj(v, w):
-    """Returns the scalar projection of v onto w.
-
-    Which is the magnitude of the projection of v onto w.
-
-    returns: scalar with units of v.
-    """
-    return vector_dot(v, vector_hat(w))
-
-
-def vector_dist(v, w):
-    """Euclidean distance from v to w, with units."""
-    if isinstance(v, list):
-        v = np.asarray(v)
-    return vector_mag(v - w)
-
-
-def vector_diff_angle(v, w):
-    """Angular difference between two vectors, in radians.
-    """
-    if len(v) == 2:
-        return vector_angle(v) - vector_angle(w)
-    else:
-        # TODO: see http://www.euclideanspace.com/maths/algebra/
-        # vectors/angleBetween/
-        raise NotImplementedError()
-
-
-def plot_segment(A, B, **options):
-    """Plots a line segment between two Vectors.
-
-    For 3-D vectors, the z axis is ignored.
-
-    Additional options are passed along to plot().
-
-    A: Vector
-    B: Vector
-    """
-    xs = A.x, B.x
-    ys = A.y, B.y
-    plt.plot(xs, ys, **options)
-
-
-from time import sleep
-from IPython.display import clear_output
-
-def animate(results, draw_func, *args, interval=None):
-    """Animate results from a simulation.
-
-    results: TimeFrame
-    draw_func: function that draws state
-    interval: time between frames in seconds
-    """
-    plt.figure()
-    try:
-        for t, state in results.iterrows():
-            draw_func(t, state, *args)
-            plt.show()
-            if interval:
-                sleep(interval)
-            clear_output(wait=True)
-        draw_func(t, state, *args)
-        plt.show()
-    except KeyboardInterrupt:
-        pass
diff --git a/modsim.py b/modsim.py
new file mode 120000
index 00000000..8049cfc8
--- /dev/null
+++ b/modsim.py
@@ -0,0 +1 @@
+modsim/modsim.py
\ No newline at end of file

From ca36cb58b7ab069b8a0906961062367f8545dd12 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 27 Apr 2023 15:42:19 -0400
Subject: [PATCH 098/144] No link

---
 modsim.py | 881 +++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 880 insertions(+), 1 deletion(-)
 mode change 120000 => 100644 modsim.py

diff --git a/modsim.py b/modsim.py
deleted file mode 120000
index 8049cfc8..00000000
--- a/modsim.py
+++ /dev/null
@@ -1 +0,0 @@
-modsim/modsim.py
\ No newline at end of file
diff --git a/modsim.py b/modsim.py
new file mode 100644
index 00000000..b65c951a
--- /dev/null
+++ b/modsim.py
@@ -0,0 +1,880 @@
+"""
+Code from Modeling and Simulation in Python.
+
+Copyright 2020 Allen Downey
+
+MIT License: https://opensource.org/licenses/MIT
+"""
+
+import logging
+
+logger = logging.getLogger(name="modsim.py")
+
+# make sure we have Python 3.6 or better
+import sys
+
+if sys.version_info < (3, 6):
+    logger.warning("modsim.py depends on Python 3.6 features.")
+
+import inspect
+
+import matplotlib.pyplot as plt
+
+plt.rcParams['figure.dpi'] = 75
+plt.rcParams['savefig.dpi'] = 300
+plt.rcParams['figure.figsize'] = 6, 4
+
+import numpy as np
+import pandas as pd
+import scipy
+
+import scipy.optimize as spo
+
+from scipy.interpolate import interp1d
+from scipy.interpolate import InterpolatedUnivariateSpline
+
+from scipy.integrate import solve_ivp
+
+from types import SimpleNamespace
+from copy import copy
+
+
+def flip(p=0.5):
+    """Flips a coin with the given probability.
+
+    p: float 0-1
+
+    returns: boolean (True or False)
+    """
+    return np.random.random() < p
+
+
+def cart2pol(x, y, z=None):
+    """Convert Cartesian coordinates to polar.
+
+    x: number or sequence
+    y: number or sequence
+    z: number or sequence (optional)
+
+    returns: theta, rho OR theta, rho, z
+    """
+    x = np.asarray(x)
+    y = np.asarray(y)
+
+    rho = np.hypot(x, y)
+    theta = np.arctan2(y, x)
+
+    if z is None:
+        return theta, rho
+    else:
+        return theta, rho, z
+
+
+def pol2cart(theta, rho, z=None):
+    """Convert polar coordinates to Cartesian.
+
+    theta: number or sequence in radians
+    rho: number or sequence
+    z: number or sequence (optional)
+
+    returns: x, y OR x, y, z
+    """
+    x = rho * np.cos(theta)
+    y = rho * np.sin(theta)
+
+    if z is None:
+        return x, y
+    else:
+        return x, y, z
+
+from numpy import linspace
+
+def linrange(start, stop=None, step=1):
+    """Make an array of equally spaced values.
+
+    start: first value
+    stop: last value (might be approximate)
+    step: difference between elements (should be consistent)
+
+    returns: NumPy array
+    """
+    if stop is None:
+        stop = start
+        start = 0
+    n = int(round((stop-start) / step))
+    return linspace(start, stop, n+1)
+
+
+def root_scalar(func, *args, **kwargs):
+    """Finds the input value that minimizes `min_func`.
+
+    Wrapper for
+    https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root_scalar.html
+
+    func: computes the function to be minimized
+    bracket: sequence of two values, lower and upper bounds of the range to be searched
+    args: any additional positional arguments are passed to func
+    kwargs: any keyword arguments are passed to root_scalar
+
+    returns: RootResults object
+    """
+    bracket = kwargs.get('bracket', None)
+    if bracket is None or len(bracket) != 2:
+        msg = ("To run root_scalar, you have to provide a "
+               "`bracket` keyword argument with a sequence "
+               "of length 2.")
+        raise ValueError(msg)
+
+    try:
+        func(bracket[0], *args)
+    except Exception as e:
+        msg = ("Before running scipy.integrate.root_scalar "
+               "I tried running the function you provided "
+               "with `bracket[0]`, "
+               "and I got the following error:")
+        logger.error(msg)
+        raise (e)
+
+    underride(kwargs, rtol=1e-4)
+
+    res = spo.root_scalar(func, *args, **kwargs)
+
+    if not res.converged:
+        msg = ("scipy.optimize.root_scalar did not converge. "
+               "The message it returned is:\n" + res.flag)
+        raise ValueError(msg)
+
+    return res
+
+
+def minimize_scalar(func, *args, **kwargs):
+    """Finds the input value that minimizes `func`.
+
+    Wrapper for
+    https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
+
+    func: computes the function to be minimized
+    args: any additional positional arguments are passed to func
+    kwargs: any keyword arguments are passed to minimize_scalar
+
+    returns: OptimizeResult object
+    """
+    bounds = kwargs.get('bounds', None)
+
+    if bounds is None or len(bounds) != 2:
+        msg = ("To run maximize_scalar or minimize_scalar, "
+               "you have to provide a `bounds` "
+               "keyword argument with a sequence "
+               "of length 2.")
+        raise ValueError(msg)
+
+    try:
+        func(bounds[0], *args)
+    except Exception as e:
+        msg = ("Before running scipy.integrate.minimize_scalar, "
+               "I tried running the function you provided "
+               "with the lower bound, "
+               "and I got the following error:")
+        logger.error(msg)
+        raise (e)
+
+    underride(kwargs, method='bounded')
+
+    res = spo.minimize_scalar(func, args=args, **kwargs)
+
+    if not res.success:
+        msg = ("minimize_scalar did not succeed."
+               "The message it returned is: \n" +
+               res.message)
+        raise Exception(msg)
+
+    return res
+
+
+def maximize_scalar(max_func, *args, **kwargs):
+    """Finds the input value that maximizes `max_func`.
+
+    Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
+
+    min_func: computes the function to be maximized
+    args: any additional positional arguments are passed to max_func
+    options: any keyword arguments are passed as options to minimize_scalar
+
+    returns: ModSimSeries object
+    """
+    def min_func(*args):
+        return -max_func(*args)
+
+    res = minimize_scalar(min_func, *args, **kwargs)
+
+    # we have to negate the function value before returning res
+    res.fun = -res.fun
+    return res
+
+
+def run_solve_ivp(system, slope_func, **options):
+    """Computes a numerical solution to a differential equation.
+
+    `system` must contain `init` with initial conditions,
+    `t_end` with the end time.  Optionally, it can contain
+    `t_0` with the start time.
+
+    It should contain any other parameters required by the
+    slope function.
+
+    `options` can be any legal options of `scipy.integrate.solve_ivp`
+
+    system: System object
+    slope_func: function that computes slopes
+
+    returns: TimeFrame
+    """
+    system = remove_units(system)
+
+    # make sure `system` contains `init`
+    if not hasattr(system, "init"):
+        msg = """It looks like `system` does not contain `init`
+                 as a system variable.  `init` should be a State
+                 object that specifies the initial condition:"""
+        raise ValueError(msg)
+
+    # make sure `system` contains `t_end`
+    if not hasattr(system, "t_end"):
+        msg = """It looks like `system` does not contain `t_end`
+                 as a system variable.  `t_end` should be the
+                 final time:"""
+        raise ValueError(msg)
+
+    # the default value for t_0 is 0
+    t_0 = getattr(system, "t_0", 0)
+
+    # try running the slope function with the initial conditions
+    try:
+        slope_func(t_0, system.init, system)
+    except Exception as e:
+        msg = """Before running scipy.integrate.solve_ivp, I tried
+                 running the slope function you provided with the
+                 initial conditions in `system` and `t=t_0` and I got
+                 the following error:"""
+        logger.error(msg)
+        raise (e)
+
+    # get the list of event functions
+    events = options.get('events', [])
+
+    # if there's only one event function, put it in a list
+    try:
+        iter(events)
+    except TypeError:
+        events = [events]
+
+    for event_func in events:
+        # make events terminal unless otherwise specified
+        if not hasattr(event_func, 'terminal'):
+            event_func.terminal = True
+
+        # test the event function with the initial conditions
+        try:
+            event_func(t_0, system.init, system)
+        except Exception as e:
+            msg = """Before running scipy.integrate.solve_ivp, I tried
+                     running the event function you provided with the
+                     initial conditions in `system` and `t=t_0` and I got
+                     the following error:"""
+            logger.error(msg)
+            raise (e)
+
+    # get dense output unless otherwise specified
+    if not 't_eval' in options:
+        underride(options, dense_output=True)
+
+    # run the solver
+    bunch = solve_ivp(slope_func, [t_0, system.t_end], system.init,
+                      args=[system], **options)
+
+    # separate the results from the details
+    y = bunch.pop("y")
+    t = bunch.pop("t")
+
+    # get the column names from `init`, if possible
+    if hasattr(system.init, 'index'):
+        columns = system.init.index
+    else:
+        columns = range(len(system.init))
+
+    # evaluate the results at equally-spaced points
+    if options.get('dense_output', False):
+        try:
+            num = system.num
+        except AttributeError:
+            num = 101
+        t_final = t[-1]
+        t_array = linspace(t_0, t_final, num)
+        y_array = bunch.sol(t_array)
+
+        # pack the results into a TimeFrame
+        results = TimeFrame(y_array.T, index=t_array,
+                        columns=columns)
+    else:
+        results = TimeFrame(y.T, index=t,
+                        columns=columns)
+
+    return results, bunch
+
+
+def leastsq(error_func, x0, *args, **options):
+    """Find the parameters that yield the best fit for the data.
+
+    `x0` can be a sequence, array, Series, or Params
+
+    Positional arguments are passed along to `error_func`.
+
+    Keyword arguments are passed to `scipy.optimize.leastsq`
+
+    error_func: function that computes a sequence of errors
+    x0: initial guess for the best parameters
+    args: passed to error_func
+    options: passed to leastsq
+
+    :returns: Params object with best_params and ModSimSeries with details
+    """
+    # override `full_output` so we get a message if something goes wrong
+    options["full_output"] = True
+
+    # run leastsq
+    t = scipy.optimize.leastsq(error_func, x0=x0, args=args, **options)
+    best_params, cov_x, infodict, mesg, ier = t
+
+    # pack the results into a ModSimSeries object
+    details = SimpleNamespace(cov_x=cov_x,
+                              mesg=mesg,
+                              ier=ier,
+                              **infodict)
+    details.success = details.ier in [1,2,3,4]
+
+    # if we got a Params object, we should return a Params object
+    if isinstance(x0, Params):
+        best_params = Params(pd.Series(best_params, x0.index))
+
+    # return the best parameters and details
+    return best_params, details
+
+
+def crossings(series, value):
+    """Find the labels where the series passes through value.
+
+    The labels in series must be increasing numerical values.
+
+    series: Series
+    value: number
+
+    returns: sequence of labels
+    """
+    values = series.values - value
+    interp = InterpolatedUnivariateSpline(series.index, values)
+    return interp.roots()
+
+
+def has_nan(a):
+    """Checks whether the an array contains any NaNs.
+
+    :param a: NumPy array or Pandas Series
+    :return: boolean
+    """
+    return np.any(np.isnan(a))
+
+
+def is_strictly_increasing(a):
+    """Checks whether the elements of an array are strictly increasing.
+
+    :param a: NumPy array or Pandas Series
+    :return: boolean
+    """
+    return np.all(np.diff(a) > 0)
+
+
+def interpolate(series, **options):
+    """Creates an interpolation function.
+
+    series: Series object
+    options: any legal options to scipy.interpolate.interp1d
+
+    returns: function that maps from the index to the values
+    """
+    if has_nan(series.index):
+        msg = """The Series you passed to interpolate contains
+                 NaN values in the index, which would result in
+                 undefined behavior.  So I'm putting a stop to that."""
+        raise ValueError(msg)
+
+    if not is_strictly_increasing(series.index):
+        msg = """The Series you passed to interpolate has an index
+                 that is not strictly increasing, which would result in
+                 undefined behavior.  So I'm putting a stop to that."""
+        raise ValueError(msg)
+
+    # make the interpolate function extrapolate past the ends of
+    # the range, unless `options` already specifies a value for `fill_value`
+    underride(options, fill_value="extrapolate")
+
+    # call interp1d, which returns a new function object
+    x = series.index
+    y = series.values
+    interp_func = interp1d(x, y, **options)
+    return interp_func
+
+
+def interpolate_inverse(series, **options):
+    """Interpolate the inverse function of a Series.
+
+    series: Series object, represents a mapping from `a` to `b`
+    options: any legal options to scipy.interpolate.interp1d
+
+    returns: interpolation object, can be used as a function
+             from `b` to `a`
+    """
+    inverse = pd.Series(series.index, index=series.values)
+    interp_func = interpolate(inverse, **options)
+    return interp_func
+
+
+def gradient(series, **options):
+    """Computes the numerical derivative of a series.
+
+    If the elements of series have units, they are dropped.
+
+    series: Series object
+    options: any legal options to np.gradient
+
+    returns: Series, same subclass as series
+    """
+    x = series.index
+    y = series.values
+
+    a = np.gradient(y, x, **options)
+    return series.__class__(a, series.index)
+
+
+def source_code(obj):
+    """Prints the source code for a given object.
+
+    obj: function or method object
+    """
+    print(inspect.getsource(obj))
+
+
+def underride(d, **options):
+    """Add key-value pairs to d only if key is not in d.
+
+    If d is None, create a new dictionary.
+
+    d: dictionary
+    options: keyword args to add to d
+    """
+    if d is None:
+        d = {}
+
+    for key, val in options.items():
+        d.setdefault(key, val)
+
+    return d
+
+
+def contour(df, **options):
+    """Makes a contour plot from a DataFrame.
+
+    Wrapper for plt.contour
+    https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.contour.html
+
+    Note: columns and index must be numerical
+
+    df: DataFrame
+    options: passed to plt.contour
+    """
+    fontsize = options.pop("fontsize", 12)
+    underride(options, cmap="viridis")
+    x = df.columns
+    y = df.index
+    X, Y = np.meshgrid(x, y)
+    cs = plt.contour(X, Y, df, **options)
+    plt.clabel(cs, inline=1, fontsize=fontsize)
+
+
+def savefig(filename, **options):
+    """Save the current figure.
+
+    Keyword arguments are passed along to plt.savefig
+
+    https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html
+
+    filename: string
+    """
+    print("Saving figure to file", filename)
+    plt.savefig(filename, **options)
+
+
+def decorate(**options):
+    """Decorate the current axes.
+
+    Call decorate with keyword arguments like
+    decorate(title='Title',
+             xlabel='x',
+             ylabel='y')
+
+    The keyword arguments can be any of the axis properties
+    https://matplotlib.org/api/axes_api.html
+    """
+    ax = plt.gca()
+    ax.set(**options)
+
+    handles, labels = ax.get_legend_handles_labels()
+    if handles:
+        ax.legend(handles, labels)
+
+    plt.tight_layout()
+
+
+def remove_from_legend(bad_labels):
+    """Removes some labels from the legend.
+
+    bad_labels: sequence of strings
+    """
+    ax = plt.gca()
+    handles, labels = ax.get_legend_handles_labels()
+    handle_list, label_list = [], []
+    for handle, label in zip(handles, labels):
+        if label not in bad_labels:
+            handle_list.append(handle)
+            label_list.append(label)
+    ax.legend(handle_list, label_list)
+
+
+class SettableNamespace(SimpleNamespace):
+    """Contains a collection of parameters.
+
+    Used to make a System object.
+
+    Takes keyword arguments and stores them as attributes.
+    """
+    def __init__(self, namespace=None, **kwargs):
+        super().__init__()
+        if namespace:
+            self.__dict__.update(namespace.__dict__)
+        self.__dict__.update(kwargs)
+
+    def get(self, name, default=None):
+        """Look up a variable.
+
+        name: string varname
+        default: value returned if `name` is not present
+        """
+        try:
+            return self.__getattribute__(name, default)
+        except AttributeError:
+            return default
+
+    def set(self, **variables):
+        """Make a copy and update the given variables.
+
+        returns: Params
+        """
+        new = copy(self)
+        new.__dict__.update(variables)
+        return new
+
+
+def magnitude(x):
+    """Returns the magnitude of a Quantity or number.
+
+    x: Quantity or number
+
+    returns: number
+    """
+    return x.magnitude if hasattr(x, 'magnitude') else x
+
+
+def remove_units(namespace):
+    """Removes units from the values in a Namespace.
+
+    Only removes units from top-level values;
+    does not traverse nested values.
+
+    returns: new Namespace object
+    """
+    res = copy(namespace)
+    for label, value in res.__dict__.items():
+        if isinstance(value, pd.Series):
+            value = remove_units_series(value)
+        res.__dict__[label] = magnitude(value)
+    return res
+
+
+def remove_units_series(series):
+    """Removes units from the values in a Series.
+
+    Only removes units from top-level values;
+    does not traverse nested values.
+
+    returns: new Series object
+    """
+    res = copy(series)
+    for label, value in res.iteritems():
+        res[label] = magnitude(value)
+    return res
+
+
+class System(SettableNamespace):
+    """Contains system parameters and their values.
+
+    Takes keyword arguments and stores them as attributes.
+    """
+    pass
+
+
+class Params(SettableNamespace):
+    """Contains system parameters and their values.
+
+    Takes keyword arguments and stores them as attributes.
+    """
+    pass
+
+
+def State(**variables):
+    """Contains the values of state variables."""
+    return pd.Series(variables, name='state')
+
+
+def make_series(x, y, **options):
+    """Make a Pandas Series.
+
+    x: sequence used as the index
+    y: sequence used as the values
+
+    returns: Pandas Series
+    """
+    underride(options, name='values')
+    if isinstance(y, pd.Series):
+        y = y.values
+    series = pd.Series(y, index=x, **options)
+    series.index.name = 'index'
+    return series
+
+
+def TimeSeries(*args, **kwargs):
+    """Make a pd.Series object to represent a time series.
+    """
+    if args or kwargs:
+        underride(kwargs, dtype=float)
+        series = pd.Series(*args, **kwargs)
+    else:
+        series = pd.Series([], dtype=float)
+
+    series.index.name = 'Time'
+    if 'name' not in kwargs:
+        series.name = 'Quantity'
+    return series
+
+
+def SweepSeries(*args, **kwargs):
+    """Make a pd.Series object to store results from a parameter sweep.
+    """
+    if args or kwargs:
+        underride(kwargs, dtype=float)
+        series = pd.Series(*args, **kwargs)
+    else:
+        series = pd.Series([], dtype=np.float64)
+
+    series.index.name = 'Parameter'
+    if 'name' not in kwargs:
+        series.name = 'Metric'
+    return series
+
+
+def show(obj):
+    """Display a Series or Namespace as a DataFrame."""
+    if isinstance(obj, pd.Series):
+        df = pd.DataFrame(obj)
+        return df
+    elif hasattr(obj, '__dict__'):
+        return pd.DataFrame(pd.Series(obj.__dict__),
+                            columns=['value'])
+    else:
+        return obj
+
+
+def TimeFrame(*args, **kwargs):
+    """DataFrame that maps from time to State.
+    """
+    underride(kwargs, dtype=float)
+    return pd.DataFrame(*args, **kwargs)
+
+
+def SweepFrame(*args, **kwargs):
+    """DataFrame that maps from parameter value to SweepSeries.
+    """
+    underride(kwargs, dtype=float)
+    return pd.DataFrame(*args, **kwargs)
+
+
+def Vector(x, y, z=None, **options):
+    """
+    """
+    underride(options, name='component')
+    if z is None:
+        return pd.Series(dict(x=x, y=y), **options)
+    else:
+        return pd.Series(dict(x=x, y=y, z=z), **options)
+
+
+## Vector functions (should work with any sequence)
+
+def vector_mag(v):
+    """Vector magnitude."""
+    return np.sqrt(np.dot(v, v))
+
+
+def vector_mag2(v):
+    """Vector magnitude squared."""
+    return np.dot(v, v)
+
+
+def vector_angle(v):
+    """Angle between v and the positive x axis.
+
+    Only works with 2-D vectors.
+
+    returns: angle in radians
+    """
+    assert len(v) == 2
+    x, y = v
+    return np.arctan2(y, x)
+
+
+def vector_polar(v):
+    """Vector magnitude and angle.
+
+    returns: (number, angle in radians)
+    """
+    return vector_mag(v), vector_angle(v)
+
+
+def vector_hat(v):
+    """Unit vector in the direction of v.
+
+    returns: Vector or array
+    """
+    # check if the magnitude of the Quantity is 0
+    mag = vector_mag(v)
+    if mag == 0:
+        return v
+    else:
+        return v / mag
+
+
+def vector_perp(v):
+    """Perpendicular Vector (rotated left).
+
+    Only works with 2-D Vectors.
+
+    returns: Vector
+    """
+    assert len(v) == 2
+    x, y = v
+    return Vector(-y, x)
+
+
+def vector_dot(v, w):
+    """Dot product of v and w.
+
+    returns: number or Quantity
+    """
+    return np.dot(v, w)
+
+
+def vector_cross(v, w):
+    """Cross product of v and w.
+
+    returns: number or Quantity for 2-D, Vector for 3-D
+    """
+    res = np.cross(v, w)
+
+    if len(v) == 3:
+        return Vector(*res)
+    else:
+        return res
+
+
+def vector_proj(v, w):
+    """Projection of v onto w.
+
+    returns: array or Vector with direction of w and units of v.
+    """
+    w_hat = vector_hat(w)
+    return vector_dot(v, w_hat) * w_hat
+
+
+def scalar_proj(v, w):
+    """Returns the scalar projection of v onto w.
+
+    Which is the magnitude of the projection of v onto w.
+
+    returns: scalar with units of v.
+    """
+    return vector_dot(v, vector_hat(w))
+
+
+def vector_dist(v, w):
+    """Euclidean distance from v to w, with units."""
+    if isinstance(v, list):
+        v = np.asarray(v)
+    return vector_mag(v - w)
+
+
+def vector_diff_angle(v, w):
+    """Angular difference between two vectors, in radians.
+    """
+    if len(v) == 2:
+        return vector_angle(v) - vector_angle(w)
+    else:
+        # TODO: see http://www.euclideanspace.com/maths/algebra/
+        # vectors/angleBetween/
+        raise NotImplementedError()
+
+
+def plot_segment(A, B, **options):
+    """Plots a line segment between two Vectors.
+
+    For 3-D vectors, the z axis is ignored.
+
+    Additional options are passed along to plot().
+
+    A: Vector
+    B: Vector
+    """
+    xs = A.x, B.x
+    ys = A.y, B.y
+    plt.plot(xs, ys, **options)
+
+
+from time import sleep
+from IPython.display import clear_output
+
+def animate(results, draw_func, *args, interval=None):
+    """Animate results from a simulation.
+
+    results: TimeFrame
+    draw_func: function that draws state
+    interval: time between frames in seconds
+    """
+    plt.figure()
+    try:
+        for t, state in results.iterrows():
+            draw_func(t, state, *args)
+            plt.show()
+            if interval:
+                sleep(interval)
+            clear_output(wait=True)
+        draw_func(t, state, *args)
+        plt.show()
+    except KeyboardInterrupt:
+        pass

From ca68034482e5bd5e0f953ff3758fc29049577a24 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 27 Apr 2023 15:47:34 -0400
Subject: [PATCH 099/144] No iteritems

---
 modsim/modsim.py | 1705 +++++++++-------------------------------------
 1 file changed, 319 insertions(+), 1386 deletions(-)

diff --git a/modsim/modsim.py b/modsim/modsim.py
index 1d412a9b..ed012ec6 100644
--- a/modsim/modsim.py
+++ b/modsim/modsim.py
@@ -1,17 +1,15 @@
 """
 Code from Modeling and Simulation in Python.
 
-Copyright 2017 Allen Downey
+Copyright 2020 Allen Downey
 
-License: https://creativecommons.org/licenses/by/4.0)
+MIT License: https://opensource.org/licenses/MIT
 """
 
 import logging
 
 logger = logging.getLogger(name="modsim.py")
 
-# TODO: Make this Python 3.7 when conda is ready
-
 # make sure we have Python 3.6 or better
 import sys
 
@@ -19,41 +17,26 @@
     logger.warning("modsim.py depends on Python 3.6 features.")
 
 import inspect
+
 import matplotlib.pyplot as plt
+
+plt.rcParams['figure.dpi'] = 75
+plt.rcParams['savefig.dpi'] = 300
+plt.rcParams['figure.figsize'] = 6, 4
+
 import numpy as np
 import pandas as pd
 import scipy
-import sympy
-
-import seaborn as sns
-
-sns.set(style="white", font_scale=1.2)
-
-import pint
-
-UNITS = pint.UnitRegistry()
-Quantity = UNITS.Quantity
-
-# TODO: Consider making this optional
-from pint.errors import UnitStrippedWarning
-import warnings
-
-warnings.simplefilter("error", UnitStrippedWarning)
 
-# expose some names so we can use them without dot notation
-from copy import copy
-from numpy import sqrt, log, exp, pi
-from pandas import DataFrame, Series
-from time import sleep
+import scipy.optimize as spo
 
 from scipy.interpolate import interp1d
 from scipy.interpolate import InterpolatedUnivariateSpline
 
-from scipy.integrate import odeint
 from scipy.integrate import solve_ivp
 
-# from scipy.optimize import leastsq
-# from scipy.optimize import minimize_scalar
+from types import SimpleNamespace
+from copy import copy
 
 
 def flip(p=0.5):
@@ -66,17 +49,6 @@ def flip(p=0.5):
     return np.random.random() < p
 
 
-# For all the built-in Python functions that do math,
-# let's use the NumPy version instead.
-
-abs = np.abs
-min = np.min
-max = np.max
-pow = np.power
-sum = np.sum
-round = np.round
-
-
 def cart2pol(x, y, z=None):
     """Convert Cartesian coordinates to polar.
 
@@ -115,447 +87,140 @@ def pol2cart(theta, rho, z=None):
     else:
         return x, y, z
 
+from numpy import linspace
 
-def linspace(start, stop, num=50, **options):
-    """Returns an array of evenly-spaced values in the interval [start, stop].
+def linrange(start, stop=None, step=1):
+    """Make an array of equally spaced values.
 
     start: first value
-    stop: last value
-    num: number of values
-
-    Also accepts the same keyword arguments as np.linspace.  See
-    https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html
-
-    returns: array or Quantity
-    """
-    # drop the units
-    start = magnitude(start)
-    stop = magnitude(stop)
-
-    underride(options, dtype=np.float64)
-
-    array = np.linspace(start, stop, num, **options)
-    return array
-
-
-def linrange(start=0, stop=None, step=1, endpoint=False, **options):
-    """Returns an array of evenly-spaced values in an interval.
-
-    By default, the last value in the array is `stop-step`
-    (at least approximately).
-    If you provide the keyword argument `endpoint=True`,
-    the last value in the array is `stop`.
-
-    This function works best if the space between start and stop
-    is divisible by step; otherwise the results might be surprising.
-
-    start: first value
-    stop: last value
-    step: space between values
+    stop: last value (might be approximate)
+    step: difference between elements (should be consistent)
 
     returns: NumPy array
     """
     if stop is None:
         stop = start
         start = 0
+    n = int(round((stop-start) / step))
+    return linspace(start, stop, n+1)
 
-    # drop the units
-    start = magnitude(start)
-    stop = magnitude(stop)
-    step = magnitude(step)
-
-    n = np.round((stop - start) / step)
-    if endpoint:
-        n += 1
-
-    array = np.full(int(n), step, **options)
-    if n:
-        array[0] = start
-
-    # TODO: restore units?
-    return np.cumsum(array)
-
-
-def magnitude(x):
-    """Returns the magnitude of a Quantity or number.
-
-    x: Quantity or number
-
-    returns: number
-    """
-    return x.magnitude if isinstance(x, Quantity) else x
-
-
-def magnitudes(x):
-    """Returns the magnitude of a Quantity or number, or sequence.
-
-    x: Quantity, number, or sequence
-
-    returns: number or list or same type as x
-    """
-    if isinstance(x, Quantity):
-        return x.magnitude
-    try:
-        t = [magnitude(elt) for elt in x]
-
-        # if x is an array, return an array
-        if isinstance(x, np.ndarray):
-            return np.array(t)
-
-        # if x is a Series, return a Series of the same subtype
-        if isinstance(x, pd.Series):
-            return x.__class__(t, x.index)
-
-        return t
-    except TypeError:  # not iterable
-        return x
 
+def root_scalar(func, *args, **kwargs):
+    """Finds the input value that minimizes `min_func`.
 
-def get_unit(x):
-    """Returns the units of a Quantity or number.
+    Wrapper for
+    https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root_scalar.html
 
-    x: Quantity or number
+    func: computes the function to be minimized
+    bracket: sequence of two values, lower and upper bounds of the range to be searched
+    args: any additional positional arguments are passed to func
+    kwargs: any keyword arguments are passed to root_scalar
 
-    returns: Unit object or 1
+    returns: RootResults object
     """
-    return x.units if isinstance(x, Quantity) else 1
-
-
-def get_units(x):
-    """Returns the units of a Quantity, number, or sequence.
-
-    x: Quantity, number, or sequence
+    bracket = kwargs.get('bracket', None)
+    if bracket is None or len(bracket) != 2:
+        msg = ("To run root_scalar, you have to provide a "
+               "`bracket` keyword argument with a sequence "
+               "of length 2.")
+        raise ValueError(msg)
 
-    returns: Unit object or list or same type as x
-    """
-    if isinstance(x, Quantity):
-        return x.units
     try:
-        t = [get_unit(elt) for elt in x]
-
-        # if x is an array, return an array
-        if isinstance(x, np.ndarray):
-            return np.array(t)
-
-        # if x is a Series, return a Series of the same subtype
-        if isinstance(x, pd.Series):
-            return x.__class__(t, x.index)
-
-        return t
-    except TypeError:  # not iterable
-        return 1
-
-
-def get_first_unit(x):
-    """Returns the units of a Quantity, number, or sequence.
-
-    If x is a sequence, returns the units of the first element.
-
-    :param x: Quantity, number, or sequence
-
-    :return: Unit object or 1
-    """
-    units = get_units(x)
-    if hasattr(units, "__getitem__"):
-        units = units[0]
-    return units
+        func(bracket[0], *args)
+    except Exception as e:
+        msg = ("Before running scipy.integrate.root_scalar "
+               "I tried running the function you provided "
+               "with `bracket[0]`, "
+               "and I got the following error:")
+        logger.error(msg)
+        raise (e)
 
+    underride(kwargs, rtol=1e-4)
 
-def remove_units(series):
-    """Removes units from the values in a Series.
+    res = spo.root_scalar(func, *args, **kwargs)
 
-    Only removes units from top-level values;
-    does not traverse nested values.
+    if not res.converged:
+        msg = ("scipy.optimize.root_scalar did not converge. "
+               "The message it returned is:\n" + res.flag)
+        raise ValueError(msg)
 
-    returns: new Series object
-    """
-    res = copy(series)
-    for label, value in res.iteritems():
-        res[label] = magnitude(value)
     return res
 
 
-def require_units(x, units):
-    """Apply units to `x`, if necessary.
-
-    x: Quantity or number
-    units: Pint Units object
-
-    returns: Quantity
-    """
-    if isinstance(x, Quantity):
-        return x.to(units)
-    else:
-        return Quantity(x, units)
-
-
-def leastsq(error_func, x0, *args, **options):
-    """Find the parameters that yield the best fit for the data.
-
-    `x0` can be a sequence, array, Series, or Params
-
-    Positional arguments are passed along to `error_func`.
-
-    Keyword arguments are passed to `scipy.optimize.leastsq`
-
-    error_func: function that computes a sequence of errors
-    x0: initial guess for the best parameters
-    args: passed to error_func
-    options: passed to leastsq
-
-    :returns: Params object with best_params and ModSimSeries with details
-    """
-    # override `full_output` so we get a message if something goes wrong
-    options["full_output"] = True
-
-    # run leastsq
-    t = scipy.optimize.leastsq(error_func, x0=x0, args=args, **options)
-    best_params, cov_x, infodict, mesg, ier = t
-
-    # pack the results into a ModSimSeries object
-    details = ModSimSeries(infodict)
-    details.set(cov_x=cov_x, mesg=mesg, ier=ier)
-
-    # if we got a Params object, we should return a Params object
-    if isinstance(x0, Params):
-        best_params = Params(Series(best_params, x0.index))
-
-    # return the best parameters and details
-    return best_params, details
-
-
-def minimize_scalar(min_func, bounds, *args, **options):
-    """Finds the input value that minimizes `min_func`.
+def minimize_scalar(func, *args, **kwargs):
+    """Finds the input value that minimizes `func`.
 
     Wrapper for
     https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
 
-    min_func: computes the function to be minimized
-    bounds: sequence of two values, lower and upper bounds of the range to be searched
-    args: any additional positional arguments are passed to min_func
-    options: any keyword arguments are passed as options to minimize_scalar
+    func: computes the function to be minimized
+    args: any additional positional arguments are passed to func
+    kwargs: any keyword arguments are passed to minimize_scalar
 
-    returns: ModSimSeries object
+    returns: OptimizeResult object
     """
+    bounds = kwargs.get('bounds', None)
+
+    if bounds is None or len(bounds) != 2:
+        msg = ("To run maximize_scalar or minimize_scalar, "
+               "you have to provide a `bounds` "
+               "keyword argument with a sequence "
+               "of length 2.")
+        raise ValueError(msg)
+
     try:
-        min_func(bounds[0], *args)
+        func(bounds[0], *args)
     except Exception as e:
-        msg = """Before running scipy.integrate.minimize_scalar, I tried
-                 running the slope function you provided with the
-                 initial conditions in system and t=0, and I got
-                 the following error:"""
+        msg = ("Before running scipy.integrate.minimize_scalar, "
+               "I tried running the function you provided "
+               "with the lower bound, "
+               "and I got the following error:")
         logger.error(msg)
         raise (e)
 
-    underride(options, xatol=1e-3)
+    underride(kwargs, method='bounded')
 
-    res = scipy.optimize.minimize_scalar(
-        min_func,
-        bracket=bounds,
-        bounds=bounds,
-        args=args,
-        method="bounded",
-        options=options,
-    )
+    res = spo.minimize_scalar(func, args=args, **kwargs)
 
     if not res.success:
-        msg = (
-            """scipy.optimize.minimize_scalar did not succeed.
-                 The message it returned is %s"""
-            % res.message
-        )
+        msg = ("minimize_scalar did not succeed."
+               "The message it returned is: \n" +
+               res.message)
         raise Exception(msg)
 
-    return ModSimSeries(res)
+    return res
 
 
-def maximize_scalar(max_func, bounds, *args, **options):
+def maximize_scalar(max_func, *args, **kwargs):
     """Finds the input value that maximizes `max_func`.
 
     Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
 
     min_func: computes the function to be maximized
-    bounds: sequence of two values, lower and upper bounds of the
-            range to be searched
     args: any additional positional arguments are passed to max_func
     options: any keyword arguments are passed as options to minimize_scalar
 
     returns: ModSimSeries object
     """
-
     def min_func(*args):
         return -max_func(*args)
 
-    res = minimize_scalar(min_func, bounds, *args, **options)
+    res = minimize_scalar(min_func, *args, **kwargs)
 
     # we have to negate the function value before returning res
     res.fun = -res.fun
     return res
 
 
-def minimize_golden(min_func, bracket, *args, **options):
-    """Find the minimum of a function by golden section search.
-
-    Based on
-    https://en.wikipedia.org/wiki/Golden-section_search#Iterative_algorithm
-
-    :param min_func: function to be minimized
-    :param bracket: interval containing a minimum
-    :param args: arguments passes to min_func
-    :param options: rtol and maxiter
-
-    :return: ModSimSeries
-    """
-    maxiter = options.get("maxiter", 100)
-    rtol = options.get("rtol", 1e-3)
-
-    def success(**kwargs):
-        return ModSimSeries(dict(success=True, **kwargs))
-
-    def failure(**kwargs):
-        return ModSimSeries(dict(success=False, **kwargs))
-
-    a, b = bracket
-    ya = min_func(a, *args)
-    yb = min_func(b, *args)
-
-    phi = 2 / (np.sqrt(5) - 1)
-    h = b - a
-    c = b - h / phi
-    yc = min_func(c, *args)
-
-    d = a + h / phi
-    yd = min_func(d, *args)
-
-    if yc > ya or yc > yb:
-        return failure(message="The bracket is not well-formed.")
-
-    for i in range(maxiter):
-
-        # check for convergence
-        if abs(h / c) < rtol:
-            return success(x=c, fun=yc)
-
-        if yc < yd:
-            b, yb = d, yd
-            d, yd = c, yc
-            h = b - a
-            c = b - h / phi
-            yc = min_func(c, *args)
-        else:
-            a, ya = c, yc
-            c, yc = d, yd
-            h = b - a
-            d = a + h / phi
-            yd = min_func(d, *args)
-
-    # if we exited the loop, too many iterations
-    return failure(root=c, message="maximum iterations = %d exceeded" % maxiter)
-
-
-def maximize_golden(max_func, bracket, *args, **options):
-    """Find the maximum of a function by golden section search.
-
-    :param min_func: function to be maximized
-    :param bracket: interval containing a maximum
-    :param args: arguments passes to min_func
-    :param options: rtol and maxiter
-
-    :return: ModSimSeries
-    """
-
-    def min_func(*args):
-        return -max_func(*args)
-
-    res = minimize_golden(min_func, bracket, *args, **options)
-
-    # we have to negate the function value before returning res
-    res.fun = -res.fun
-    return res
-
-
-def minimize_powell(min_func, x0, *args, **options):
-    """Finds the input value that minimizes `min_func`.
-    Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
-    min_func: computes the function to be minimized
-    x0: initial guess
-    args: any additional positional arguments are passed to min_func
-    options: any keyword arguments are passed as options to minimize_scalar
-    returns: ModSimSeries object
-    """
-    underride(options, tol=1e-3)
-
-    res = scipy.optimize.minimize(min_func, x0, *args, **options)
-
-    return ModSimSeries(res)
-
-
-# make aliases for minimize and maximize
-minimize = minimize_golden
-maximize = maximize_golden
-
-
-def run_odeint(system, slope_func, **options):
-    """Integrates an ordinary differential equation.
-
-    `system` should contain system parameters and `ts`, which
-    is an array or Series that specifies the time when the
-    solution will be computed.
-
-    system: System object
-    slope_func: function that computes slopes
-
-    returns: TimeFrame
-    """
-    # make sure `system` contains `ts`
-    if not hasattr(system, "ts"):
-        msg = """It looks like `system` does not contain `ts`
-                 as a system variable.  `ts` should be an array
-                 or Series that specifies the times when the
-                 solution will be computed:"""
-        raise ValueError(msg)
-
-    # make sure `system` contains `init`
-    if not hasattr(system, "init"):
-        msg = """It looks like `system` does not contain `init`
-                 as a system variable.  `init` should be a State
-                 object that specifies the initial condition:"""
-        raise ValueError(msg)
-
-    # try running the slope function with the initial conditions
-    try:
-        slope_func(system.init, system.ts[0], system)
-    except Exception as e:
-        msg = """Before running scipy.integrate.odeint, I tried
-                 running the slope function you provided with the
-                 initial conditions in system and t=0, and I got
-                 the following error:"""
-        logger.error(msg)
-        raise (e)
-
-    # when odeint calls slope_func, it should pass `system` as
-    # the third argument.  To make that work, we have to make a
-    # tuple with a single element and pass the tuple to odeint as `args`
-    args = (system,)
-
-    # now we're ready to run `odeint` with `init` and `ts` from `system`
-    array = odeint(slope_func, list(system.init), system.ts, args, **options)
-
-    # the return value from odeint is an array, so let's pack it into
-    # a TimeFrame with appropriate columns and index
-    frame = TimeFrame(
-        array, columns=system.init.index, index=system.ts, dtype=np.float64
-    )
-    return frame
-
-
 def run_solve_ivp(system, slope_func, **options):
     """Computes a numerical solution to a differential equation.
 
     `system` must contain `init` with initial conditions,
-    `t_0` with the start time, and `t_end` with the end time.
+    `t_end` with the end time.  Optionally, it can contain
+    `t_0` with the start time.
 
-    It can contain any other parameters required by the slope function.
+    It should contain any other parameters required by the
+    slope function.
 
     `options` can be any legal options of `scipy.integrate.solve_ivp`
 
@@ -564,85 +229,8 @@ def run_solve_ivp(system, slope_func, **options):
 
     returns: TimeFrame
     """
-    # make sure `system` contains `init`
-    if not hasattr(system, "init"):
-        msg = """It looks like `system` does not contain `init`
-                 as a system variable.  `init` should be a State
-                 object that specifies the initial condition:"""
-        raise ValueError(msg)
-
-    # make sure `system` contains `t_end`
-    if not hasattr(system, "t_end"):
-        msg = """It looks like `system` does not contain `t_end`
-                 as a system variable.  `t_end` should be the
-                 final time:"""
-        raise ValueError(msg)
-
-    # remove units from the system object
     system = remove_units(system)
-    system.init = remove_units(system.init)
-
-    # the default value for t_0 is 0
-    t_0 = getattr(system, "t_0", 0)
-
-    # remove units from max_step
-    # if not specified, require 50 steps
-    max_step = options.pop("max_step", None)
-    if max_step is None:
-        max_step = system.t_end - system.t_0 / 50
-    options["max_step"] = magnitude(max_step)
-
-    # try running the slope function with the initial conditions
-    try:
-        slope_func(system.init, t_0, system)
-    except Exception as e:
-        msg = """Before running scipy.integrate.solve_ivp, I tried
-                 running the slope function you provided with the
-                 initial conditions in `system` and `t=t_0` and I got
-                 the following error:"""
-        logger.error(msg)
-        raise (e)
-
-    # wrap the slope function to reverse the arguments and add `system`
-    f = lambda t, y: slope_func(y, t, system)
-
-    def wrap_event(event):
-        """Wrap the event functions.
-
-        Make events terminal by default.
-        """
-        wrapped = lambda t, y: event(y, t, system)
-        wrapped.terminal = getattr(event, "terminal", True)
-        wrapped.direction = getattr(event, "direction", 0)
-        return wrapped
-
-    # wrap the event functions so they take the right arguments
-    events = options.pop("events", [])
-    try:
-        events = [wrap_event(event) for event in events]
-    except TypeError:
-        events = wrap_event(events)
-
-    # run the solver
-    bunch = solve_ivp(f, [t_0, system.t_end], system.init, events=events, **options)
-
-    # separate the results from the details
-    y = bunch.pop("y")
-    t = bunch.pop("t")
-    details = ModSimSeries(bunch)
-
-    # pack the results into a TimeFrame
-    results = TimeFrame(np.transpose(y), index=t, columns=system.init.index)
-    return results, details
 
-
-def check_system(system, slope_func):
-    """Make sure the system object has the fields we need for run_ode_solver.
-
-    :param system:
-    :param slope_func:
-    :return:
-    """
     # make sure `system` contains `init`
     if not hasattr(system, "init"):
         msg = """It looks like `system` does not contain `init`
@@ -655,276 +243,121 @@ def check_system(system, slope_func):
         msg = """It looks like `system` does not contain `t_end`
                  as a system variable.  `t_end` should be the
                  final time:"""
-        raise ValueError(msg)
-
-    # the default value for t_0 is 0
-    t_0 = getattr(system, "t_0", 0)
-
-    # get the initial conditions
-    init = system.init
-
-    # get t_end
-    t_end = system.t_end
-
-    # if dt is not specified, take 100 steps
-    try:
-        dt = system.dt
-    except KeyError:
-        dt = t_end / 100
-
-    return init, t_0, t_end, dt
-
-
-def run_euler(system, slope_func, **options):
-    """Computes a numerical solution to a differential equation.
-
-    `system` must contain `init` with initial conditions,
-    `t_end` with the end time, and `dt` with the time step.
-
-    `system` may contain `t_0` to override the default, 0
-
-    It can contain any other parameters required by the slope function.
-
-    `options` can be ...
-
-    system: System object
-    slope_func: function that computes slopes
-
-    returns: TimeFrame
-    """
-    # the default message if nothing changes
-    msg = "The solver successfully reached the end of the integration interval."
-
-    # get parameters from system
-    init, t_0, t_end, dt = check_system(system, slope_func)
-
-    # make the TimeFrame
-    frame = TimeFrame(columns=init.index)
-    frame.row[t_0] = init
-    ts = linrange(t_0, t_end, dt) * get_units(t_end)
-
-    # run the solver
-    for t1 in ts:
-        y1 = frame.row[t1]
-        slopes = slope_func(y1, t1, system)
-        y2 = [y + slope * dt for y, slope in zip(y1, slopes)]
-        t2 = t1 + dt
-        frame.row[t2] = y2
-
-    details = ModSimSeries(dict(message="Success"))
-    return frame, details
-
-
-def run_ralston(system, slope_func, **options):
-    """Computes a numerical solution to a differential equation.
-
-    `system` must contain `init` with initial conditions,
-     and `t_end` with the end time.
-
-     `system` may contain `t_0` to override the default, 0
-
-    It can contain any other parameters required by the slope function.
-
-    `options` can be ...
-
-    system: System object
-    slope_func: function that computes slopes
-
-    returns: TimeFrame
-    """
-    # the default message if nothing changes
-    msg = "The solver successfully reached the end of the integration interval."
-
-    # get parameters from system
-    init, t_0, t_end, dt = check_system(system, slope_func)
-
-    # make the TimeFrame
-    frame = TimeFrame(columns=init.index)
-    frame.row[t_0] = init
-    ts = linrange(t_0, t_end, dt) * get_units(t_end)
-
-    event_func = options.get("events", None)
-    z1 = np.nan
-
-    def project(y1, t1, slopes, dt):
-        t2 = t1 + dt
-        y2 = [y + slope * dt for y, slope in zip(y1, slopes)]
-        return y2, t2
-
-    # run the solver
-    for t1 in ts:
-        y1 = frame.row[t1]
-
-        # evaluate the slopes at the start of the time step
-        slopes1 = slope_func(y1, t1, system)
-
-        # evaluate the slopes at the two-thirds point
-        y_mid, t_mid = project(y1, t1, slopes1, 2 * dt / 3)
-        slopes2 = slope_func(y_mid, t_mid, system)
-
-        # compute the weighted sum of the slopes
-        slopes = [(k1 + 3 * k2) / 4 for k1, k2 in zip(slopes1, slopes2)]
-
-        # compute the next time stamp
-        y2, t2 = project(y1, t1, slopes, dt)
-
-        # check for a terminating event
-        if event_func:
-            z2 = event_func(y2, t2, system)
-            if z1 * z2 < 0:
-                scale = magnitude(z1 / (z1 - z2))
-                y2, t2 = project(y1, t1, slopes, scale * dt)
-                frame.row[t2] = y2
-                msg = "A termination event occurred."
-                break
-            else:
-                z1 = z2
-
-        # store the results
-        frame.row[t2] = y2
-
-    details = ModSimSeries(dict(success=True, message=msg))
-    return frame, details
-
-
-run_ode_solver = run_ralston
-
-# TODO: Implement leapfrog
-
-
-def fsolve(func, x0, *args, **options):
-    """Return the roots of the (non-linear) equations
-    defined by func(x) = 0 given a starting estimate.
-
-    Uses scipy.optimize.fsolve, with extra error-checking.
-
-    func: function to find the roots of
-    x0: scalar or array, initial guess
-    args: additional positional arguments are passed along to fsolve,
-          which passes them along to func
-
-    returns: solution as an array
-    """
-    # make sure we can run the given function with x0
-    try:
-        func(x0, *args)
-    except Exception as e:
-        msg = """Before running scipy.optimize.fsolve, I tried
-                 running the error function you provided with the x0
-                 you provided, and I got the following error:"""
-        logger.error(msg)
-        raise (e)
-
-    # make the tolerance more forgiving than the default
-    underride(options, xtol=1e-6)
-
-    # run fsolve
-    result = scipy.optimize.fsolve(func, x0, args=args, **options)
-
-    return result
-
-
-def root_scalar(func, bracket, *args, **options):
-    """Return the roots of the (non-linear) equations
-    defined by func(x) = 0 given a starting estimate.
-
-    Uses scipy.optimize.root_scalar, with extra error-checking.
-
-    func: function to find the roots of
-    bracket:
-    args: additional positional arguments are passed along to root_scalar,
-          which passes them along to func
+        raise ValueError(msg)
 
-    returns: solution as an array
-    """
-    x0 = bracket[0]
+    # the default value for t_0 is 0
+    t_0 = getattr(system, "t_0", 0)
 
-    # make sure we can run the given function with x0
+    # try running the slope function with the initial conditions
     try:
-        error = func(x0, *args)
+        slope_func(t_0, system.init, system)
     except Exception as e:
-        msg = """Before running scipy.optimize.root_scalar, I tried
-                 running the error function you provided with the x0
-                 you provided, and I got the following error:"""
+        msg = """Before running scipy.integrate.solve_ivp, I tried
+                 running the slope function you provided with the
+                 initial conditions in `system` and `t=t_0` and I got
+                 the following error:"""
         logger.error(msg)
         raise (e)
 
-    if isinstance(error, Quantity):
-        msg = """It looks like your error function returns a Quantity
-                 with units.  In order to work with root_scalar, it
-                 has to return a number.  You can use magnitude()
-                 to get the unitless part of a Quantity."""
-        raise ValueError(msg)
-
-    # add the bracket to the options
-    underride(options, bracket=bracket)
-
-    # run root_scalar
-    res = scipy.optimize.root_scalar(func, args=args, **options)
+    # get the list of event functions
+    events = options.get('events', [])
 
-    return res
-
-
-def root_bisect(error_func, bracket, *args, **options):
-    """Return the roots of the (non-linear) equations
-    defined by error_func(x) = 0 given a starting bracket.
+    # if there's only one event function, put it in a list
+    try:
+        iter(events)
+    except TypeError:
+        events = [events]
+
+    for event_func in events:
+        # make events terminal unless otherwise specified
+        if not hasattr(event_func, 'terminal'):
+            event_func.terminal = True
+
+        # test the event function with the initial conditions
+        try:
+            event_func(t_0, system.init, system)
+        except Exception as e:
+            msg = """Before running scipy.integrate.solve_ivp, I tried
+                     running the event function you provided with the
+                     initial conditions in `system` and `t=t_0` and I got
+                     the following error:"""
+            logger.error(msg)
+            raise (e)
+
+    # get dense output unless otherwise specified
+    if not 't_eval' in options:
+        underride(options, dense_output=True)
 
-    error_func: function to find the roots of
-    bracket: interval that brackets at least one root
-    args: additional positional arguments are passed along to error_func
+    # run the solver
+    bunch = solve_ivp(slope_func, [t_0, system.t_end], system.init,
+                      args=[system], **options)
 
-    returns: ModSimSeries with results
-    """
-    maxiter = options.get("maxiter", 100)
-    rtol = options.get("rtol", 1e-7)
+    # separate the results from the details
+    y = bunch.pop("y")
+    t = bunch.pop("t")
 
-    def success(**kwargs):
-        return ModSimSeries(dict(converged=True, **kwargs))
+    # get the column names from `init`, if possible
+    if hasattr(system.init, 'index'):
+        columns = system.init.index
+    else:
+        columns = range(len(system.init))
+
+    # evaluate the results at equally-spaced points
+    if options.get('dense_output', False):
+        try:
+            num = system.num
+        except AttributeError:
+            num = 101
+        t_final = t[-1]
+        t_array = linspace(t_0, t_final, num)
+        y_array = bunch.sol(t_array)
+
+        # pack the results into a TimeFrame
+        results = TimeFrame(y_array.T, index=t_array,
+                        columns=columns)
+    else:
+        results = TimeFrame(y.T, index=t,
+                        columns=columns)
 
-    def failure(**kwargs):
-        return ModSimSeries(dict(converged=False, **kwargs))
+    return results, bunch
 
-    x0, x1 = bracket
 
-    y0 = error_func(x0, *args)
-    if y0 == 0:
-        return success(root=x0)
+def leastsq(error_func, x0, *args, **options):
+    """Find the parameters that yield the best fit for the data.
 
-    y1 = error_func(x1, *args)
-    if y1 == 0:
-        return success(root=x1)
+    `x0` can be a sequence, array, Series, or Params
 
-    for i in range(maxiter):
+    Positional arguments are passed along to `error_func`.
 
-        # check the bracket
-        if np.sign(y0 * y1) > 0:
-            return failure(flag="%f and %f do not bracket a root" % (x0, x1))
+    Keyword arguments are passed to `scipy.optimize.leastsq`
 
-        # bisection
-        x2 = (x0 + x1) / 2
+    error_func: function that computes a sequence of errors
+    x0: initial guess for the best parameters
+    args: passed to error_func
+    options: passed to leastsq
 
-        # secant
-        # x2 = x1 - y1 * (x1 - x0) / (y1 - y0)
+    :returns: Params object with best_params and ModSimSeries with details
+    """
+    # override `full_output` so we get a message if something goes wrong
+    options["full_output"] = True
 
-        # check for convergence
-        if abs(x1 - x0) / x2 < rtol:
-            return success(root=x2)
+    # run leastsq
+    t = scipy.optimize.leastsq(error_func, x0=x0, args=args, **options)
+    best_params, cov_x, infodict, mesg, ier = t
 
-        # evaluate the error function
-        y2 = error_func(x2, *args)
-        if y2 == 0:
-            return success(root=x2)
+    # pack the results into a ModSimSeries object
+    details = SimpleNamespace(cov_x=cov_x,
+                              mesg=mesg,
+                              ier=ier,
+                              **infodict)
+    details.success = details.ier in [1,2,3,4]
 
-        # make the new bracket
-        if np.sign(y0 * y2) > 0:
-            x0 = x2
-            y0 = y2
-        else:
-            x1 = x2
-            y1 = y2
+    # if we got a Params object, we should return a Params object
+    if isinstance(x0, Params):
+        best_params = Params(pd.Series(best_params, x0.index))
 
-    # if we exited the loop, too many iterations
-    return failure(root=x2, flag="maximum iterations = %d exceeded" % maxiter)
+    # return the best parameters and details
+    return best_params, details
 
 
 def crossings(series, value):
@@ -937,7 +370,7 @@ def crossings(series, value):
 
     returns: sequence of labels
     """
-    values = magnitudes(series - value)
+    values = series.values - value
     interp = InterpolatedUnivariateSpline(series.index, values)
     return interp.roots()
 
@@ -966,7 +399,7 @@ def interpolate(series, **options):
     series: Series object
     options: any legal options to scipy.interpolate.interp1d
 
-    returns: function that maps from the index of the series to values
+    returns: function that maps from the index to the values
     """
     if has_nan(series.index):
         msg = """The Series you passed to interpolate contains
@@ -985,15 +418,10 @@ def interpolate(series, **options):
     underride(options, fill_value="extrapolate")
 
     # call interp1d, which returns a new function object
-    x = magnitudes(series.index)
-    y = magnitudes(series.values)
+    x = series.index
+    y = series.values
     interp_func = interp1d(x, y, **options)
-    units = get_units(series.values[0])
-
-    def wrapper(x):
-        return interp_func(magnitudes(x)) * units
-
-    return wrapper
+    return interp_func
 
 
 def interpolate_inverse(series, **options):
@@ -1005,7 +433,7 @@ def interpolate_inverse(series, **options):
     returns: interpolation object, can be used as a function
              from `b` to `a`
     """
-    inverse = Series(series.index, index=series.values)
+    inverse = pd.Series(series.index, index=series.values)
     interp_func = interpolate(inverse, **options)
     return interp_func
 
@@ -1020,46 +448,13 @@ def gradient(series, **options):
 
     returns: Series, same subclass as series
     """
-    x = magnitudes(series.index)
-    y = magnitudes(series.values)
-    # units = get_units(series)
+    x = series.index
+    y = series.values
 
     a = np.gradient(y, x, **options)
     return series.__class__(a, series.index)
 
 
-def correlate(s1, s2, **options):
-    """Computes the numerical derivative of a series.
-
-    If the elements of series have units, they are dropped.
-
-    s1: sequence or Series
-    options: any legal options to np.correlate
-
-    returns: NumPy array
-    """
-    # TODO: Check that they have the same units.
-    # TODO: Check that they have the same index.
-    x = magnitudes(s1)
-    y = magnitudes(s2)
-
-    corr = np.correlate(x, y, **options)
-    return corr
-
-
-def unpack(series):
-    """Make the names in `series` available as globals.
-
-    series: Series with variables names in the index
-    """
-    # TODO: Make this a context manager, so the syntax is
-    # with series:
-    # and maybe even add an __exit__ that copies changes back
-    frame = inspect.currentframe()
-    caller = frame.f_back
-    caller.f_globals.update(series)
-
-
 def source_code(obj):
     """Prints the source code for a given object.
 
@@ -1085,63 +480,6 @@ def underride(d, **options):
     return d
 
 
-def plot(*args, **options):
-    """Makes line plots.
-
-    args can be:
-      plot(y)
-      plot(y, style_string)
-      plot(x, y)
-      plot(x, y, style_string)
-
-    options are the same as for pyplot.plot
-    """
-    x, y, style = parse_plot_args(*args, **options)
-
-    if isinstance(x, pd.DataFrame) or isinstance(y, pd.DataFrame):
-        raise ValueError("modsimpy.plot can't handle DataFrames.")
-
-    if x is None:
-        if isinstance(y, Quantity):
-            y = y.magnitude
-
-        if isinstance(y, (list, np.ndarray)):
-            x = np.arange(len(y))
-
-        if isinstance(y, pd.Series):
-            x = y.index
-            y = y.values
-
-    x = magnitudes(x)
-    y = magnitudes(y)
-    underride(options, linewidth=2)
-
-    if style is not None:
-        lines = plt.plot(x, y, style, **options)
-    else:
-        lines = plt.plot(x, y, **options)
-    return lines
-
-
-def parse_plot_args(*args, **options):
-    """Parse the args the same way plt.plot does."""
-    x = None
-    y = None
-    style = None
-
-    if len(args) == 1:
-        y = args[0]
-    elif len(args) == 2:
-        if isinstance(args[1], str):
-            y, style = args
-        else:
-            x, y = args
-    elif len(args) == 3:
-        x, y, style = args
-
-    return x, y, style
-
-
 def contour(df, **options):
     """Makes a contour plot from a DataFrame.
 
@@ -1179,41 +517,21 @@ def decorate(**options):
     """Decorate the current axes.
 
     Call decorate with keyword arguments like
-
     decorate(title='Title',
              xlabel='x',
              ylabel='y')
 
     The keyword arguments can be any of the axis properties
-
     https://matplotlib.org/api/axes_api.html
-
-    In addition, you can use `legend=False` to suppress the legend.
-
-    And you can use `loc` to indicate the location of the legend
-    (the default value is 'best')
-    """
-    loc = options.pop("loc", "best")
-    if options.pop("legend", True):
-        legend(loc=loc)
-
-    plt.gca().set(**options)
-    plt.tight_layout()
-
-
-def legend(**options):
-    """Draws a legend only if there is at least one labeled item.
-
-    options are passed to plt.legend()
-    https://matplotlib.org/api/_as_gen/matplotlib.pyplot.legend.html
-
     """
-    underride(options, loc="best", frameon=False)
-
     ax = plt.gca()
+    ax.set(**options)
+
     handles, labels = ax.get_legend_handles_labels()
     if handles:
-        ax.legend(handles, labels, **options)
+        ax.legend(handles, labels)
+
+    plt.tight_layout()
 
 
 def remove_from_legend(bad_labels):
@@ -1231,500 +549,193 @@ def remove_from_legend(bad_labels):
     ax.legend(handle_list, label_list)
 
 
-def subplot(nrows, ncols, plot_number, **options):
-    figsize = {(2, 1): (8, 8), (3, 1): (8, 10)}
-    key = nrows, ncols
-    default = (8, 5.5)
-    width, height = figsize.get(key, default)
-
-    plt.subplot(nrows, ncols, plot_number, **options)
-    fig = plt.gcf()
-    fig.set_figwidth(width)
-    fig.set_figheight(height)
-
-
-class ModSimSeries(pd.Series):
-    """Modified version of a Pandas Series,
-    with a few changes to make it more suited to our purpose.
+class SettableNamespace(SimpleNamespace):
+    """Contains a collection of parameters.
 
-    In particular:
+    Used to make a System object.
 
-    1. I provide a more consistent __init__ method.
-
-    2. Series provides two special variables called
-       `dt` and `T` that cause problems if we try to use those names
-        as variables.  I override them so they can be used variable names.
-
-    3. Series doesn't provide a good _repr_html, so it doesn't look
-       good in Jupyter notebooks.
-
-    4. ModSimSeries provides a set() method that takes keyword arguments.
+    Takes keyword arguments and stores them as attributes.
     """
+    def __init__(self, namespace=None, **kwargs):
+        super().__init__()
+        if namespace:
+            self.__dict__.update(namespace.__dict__)
+        self.__dict__.update(kwargs)
 
-    def __init__(self, *args, **kwargs):
-        """Initialize a Series.
-
-        Note: this cleans up a weird Series behavior, which is
-        that Series() and Series([]) yield different results.
-        See: https://github.com/pandas-dev/pandas/issues/16737
-        """
-        if args or kwargs:
-            underride(kwargs, copy=True)
-            super().__init__(*args, **kwargs)
-        else:
-            super().__init__([], dtype=np.float64)
-
-    def _repr_html_(self):
-        """Returns an HTML representation of the series.
-
-        Mostly used for Jupyter notebooks.
-        """
-        df = pd.DataFrame(self.values, index=self.index, columns=["values"])
-        return df._repr_html_()
-
-    def __copy__(self, deep=True):
-        series = super().copy(deep=deep)
-        return self.__class__(series)
-
-    copy = __copy__
-
-    def __getitem__(self, key):
-        """
-
-        If the key is a Quantity, its units are stripped.
-
-        :param key:
-        :return:
-        """
-        key = magnitude(key)
-        return super().__getitem__(key)
-
-    def __setitem__(self, key, value):
-        """
-
-        If the key is a Quantity, its units are stripped.
-
-        :param key:
-        :param value:
-        """
-        key = magnitude(key)
-        super().__setitem__(key, value)
-
-    def first_label(self):
-        """Returns the first element of the index.
-        """
-        return self.index[0]
-
-    def last_label(self):
-        """Returns the last element of the index.
-        """
-        return self.index[-1]
-
-    def first_value(self):
-        """Returns the first element of the index.
-        """
-        return self[self.index[0]]
-
-    def last_value(self):
-        """Returns the last element of the index.
-        """
-        return self[self.index[-1]]
-
-    def set(self, **kwargs):
-        """Uses keyword arguments to update the Series in place.
-
-        Example: series.set(a=1, b=2)
-        """
-        for name, value in kwargs.items():
-            self[name] = value
-
-    def extract(self, var):
-        """Extract a variable from each element of a Series.
+    def get(self, name, default=None):
+        """Look up a variable.
 
-        Example: to extract the x-coordinate from a Series of Vectors
-
-        x_series = series.extract('x')
-
-        :param var: string variable name
-        :return: ModSimSeries, same subtype as `self`
-        """
-        t = [getattr(V, var) for V in self]
-        return self.__class__(t, self.index, name=var)
-
-    def plot(self, *args, **kwargs):
-        """Plot a Series.
-
-        :param args: arguments passed to plt.plot
-        :param kwargs: keyword argumentspassed to plt.plot
-        :return:
-        """
-        x = magnitudes(self.index)
-        y = magnitudes(self.values)
-
-        underride(kwargs, linewidth=2)
-        if self.name:
-            underride(kwargs, label=self.name)
-        plt.plot(x, y, *args, **kwargs)
-
-    @property
-    def dt(self):
-        """Intercept the Series accessor object so we can use `dt`
-        as a row label and access it using dot notation.
-
-        https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.dt.html
+        name: string varname
+        default: value returned if `name` is not present
         """
-        return self.loc["dt"]
+        try:
+            return self.__getattribute__(name, default)
+        except AttributeError:
+            return default
 
-    @property
-    def T(self):
-        """Intercept the Series accessor object so we can use `T`
-        as a row label and access it using dot notation.
+    def set(self, **variables):
+        """Make a copy and update the given variables.
 
-        https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.T.html
+        returns: Params
         """
-        return self.loc["T"]
-
-
-def get_first_label(x):
-    """Returns the label of the first element.
-
-    :param x: Series or DataFrame
-    """
-    return x.index[0]
-
-
-def get_last_label(x):
-    """Returns the label of the last element.
-
-    :param x: Series or DataFrame
-    """
-    return x.index[-1]
+        new = copy(self)
+        new.__dict__.update(variables)
+        return new
 
 
-def get_first_value(x):
-    """Returns the value of the first element.
+def magnitude(x):
+    """Returns the magnitude of a Quantity or number.
 
-    Does not work with DataFrames; use first_row().
+    x: Quantity or number
 
-    :param x: Series
+    returns: number
     """
-    return x[x.index[0]]
+    return x.magnitude if hasattr(x, 'magnitude') else x
 
 
-def get_last_value(x):
-    """Returns the value of the last element.
+def remove_units(namespace):
+    """Removes units from the values in a Namespace.
 
-    Does not work with DataFrames; use last_row()
+    Only removes units from top-level values;
+    does not traverse nested values.
 
-    :param x: Series
+    returns: new Namespace object
     """
-    return x[x.index[-1]]
-
-
-class TimeSeries(ModSimSeries):
-    """Represents a mapping from times to values."""
-
-    pass
-
-
-class SweepSeries(ModSimSeries):
-    """Represents a mapping from parameter values to metrics."""
+    res = copy(namespace)
+    for label, value in res.__dict__.items():
+        if isinstance(value, pd.Series):
+            value = remove_units_series(value)
+        res.__dict__[label] = magnitude(value)
+    return res
 
-    pass
 
+def remove_units_series(series):
+    """Removes units from the values in a Series.
 
-class System(ModSimSeries):
-    """Contains system variables and their values.
+    Only removes units from top-level values;
+    does not traverse nested values.
 
-    Takes keyword arguments and stores them as rows.
+    returns: new Series object
     """
-
-    def __init__(self, *args, **kwargs):
-        """Initialize the series.
-
-        If there are no positional arguments, use kwargs.
-
-        If there is one positional argument, copy it and add
-        in the kwargs.
-
-        More than one positional argument is an error.
-        """
-        if len(args) == 0:
-            super().__init__(list(kwargs.values()), index=kwargs)
-        elif len(args) == 1:
-            super().__init__(*args, copy=True)
-            self.set(**kwargs)
-        else:
-            msg = "__init__() takes at most one positional argument"
-            raise TypeError(msg)
+    res = copy(series)
+    for label, value in res.items():
+        res[label] = magnitude(value)
+    return res
 
 
-class State(System):
-    """Contains state variables and their values.
+class System(SettableNamespace):
+    """Contains system parameters and their values.
 
-    Takes keyword arguments and stores them as rows.
+    Takes keyword arguments and stores them as attributes.
     """
-
     pass
 
 
-class Condition(System):
-    """Represents the condition of a system.
+class Params(SettableNamespace):
+    """Contains system parameters and their values.
 
-    Condition objects are often used to construct a System object.
+    Takes keyword arguments and stores them as attributes.
     """
-
     pass
 
 
-class Params(System):
-    """Represents a set of parameters.
-    """
+def State(**variables):
+    """Contains the values of state variables."""
+    return pd.Series(variables, name='state')
 
-    pass
 
+def make_series(x, y, **options):
+    """Make a Pandas Series.
 
-def compute_abs_diff(seq):
-    """Compute absolute differences between successive elements.
+    x: sequence used as the index
+    y: sequence used as the values
 
-    :param seq:
-    :return: Series is seq is a Series, otherwise NumPy array
+    returns: Pandas Series
     """
-    xs = np.asarray(seq)
-
-    # The right thing to put at the end is np.nan, but at
-    # the moment edfiff1d is broken
-    # https://github.com/numpy/numpy/issues/13103
-    # So I'm working around by appending 0 instead.
-    # to_end = np.array([np.nan], dtype=np.float64)
-    to_end = np.array([0], dtype=np.float64)
-    diff = np.ediff1d(xs, to_end)
-
-    if isinstance(seq, Series):
-        return Series(diff, seq.index)
-    else:
-        return diff
+    underride(options, name='values')
+    if isinstance(y, pd.Series):
+        y = y.values
+    series = pd.Series(y, index=x, **options)
+    series.index.name = 'index'
+    return series
 
 
-def compute_rel_diff(seq):
-    """Compute absolute differences between successive elements.
-
-    :param seq: any sequence
-    :return: Series is seq is a Series, otherwise NumPy array
+def TimeSeries(*args, **kwargs):
+    """Make a pd.Series object to represent a time series.
     """
-    diff = compute_abs_diff(seq)
-    return diff / seq
-
-
-class ModSimDataFrame(pd.DataFrame):
-    """ModSimDataFrame is a modified version of a Pandas DataFrame,
-    with a few changes to make it more suited to our purpose.
+    if args or kwargs:
+        underride(kwargs, dtype=float)
+        series = pd.Series(*args, **kwargs)
+    else:
+        series = pd.Series([], dtype=float)
 
-    In particular:
+    series.index.name = 'Time'
+    if 'name' not in kwargs:
+        series.name = 'Quantity'
+    return series
 
-    1. DataFrame provides two special variables called
-       `dt` and `T` that cause problems if we try to use those names
-        as variables.    I override them so they can be used as row labels.
 
-    2.  When you select a row or column from a ModSimDataFrame, you get
-        back an appropriate subclass of Series: TimeSeries, SweepSeries,
-        or ModSimSeries.
+def SweepSeries(*args, **kwargs):
+    """Make a pd.Series object to store results from a parameter sweep.
     """
-
-    column_constructor = ModSimSeries
-    row_constructor = ModSimSeries
-
-    def __init__(self, *args, **options):
-        # TODO: currently ModSimDataFrame underrides to float64 and
-        # ModSimSeries does not.  Does this inconsistency make sense?
-        # underride(options, dtype=np.float64)
-        super().__init__(*args, **options)
-
-    def __getitem__(self, key):
-        """Intercept the column getter to return the right subclass of Series.
-        """
-        obj = super().__getitem__(key)
-        if isinstance(obj, Series):
-            obj = self.column_constructor(obj)
+    if args or kwargs:
+        underride(kwargs, dtype=float)
+        series = pd.Series(*args, **kwargs)
+    else:
+        series = pd.Series([], dtype=np.float64)
+
+    series.index.name = 'Parameter'
+    if 'name' not in kwargs:
+        series.name = 'Metric'
+    return series
+
+
+def show(obj):
+    """Display a Series or Namespace as a DataFrame."""
+    if isinstance(obj, pd.Series):
+        df = pd.DataFrame(obj)
+        return df
+    elif hasattr(obj, '__dict__'):
+        return pd.DataFrame(pd.Series(obj.__dict__),
+                            columns=['value'])
+    else:
         return obj
 
-    def plot(self, *args, **kwargs):
-        """Plot the columns of a DataFrame.
-
-        :param args: arguments passed to plt.plot
-        :param kwargs: keyword argumentspassed to plt.plot
-        :return:
-        """
-        for col in self.columns:
-            self[col].plot(*args, **kwargs)
-
-    @property
-    def dt(self):
-        """Intercept the Series accessor object so we can use `dt`
-        as a column label and access it using dot notation.
-
-        https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.dt.html
-        """
-        return self["dt"]
-
-    @property
-    def T(self):
-        """Intercept the Series accessor object so we can use `T`
-        as a column label and access it using dot notation.
 
-        https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.T.html
-        """
-        return self["T"]
-
-    @property
-    def row(self):
-        """Gets or sets a row.
-
-        Returns a wrapper for the Pandas LocIndexer, so when we look up a row
-        we get the right kind of ModSimSeries.
-
-        returns ModSimLocIndexer
-        """
-        li = self.loc
-        return ModSimLocIndexer(li, self.row_constructor)
-
-    def first_row(self):
-        """Returns the first row
-
-        :return: some kind of ModSimSeries
-        """
-        return self.row[self.index[0]]
-
-    def last_row(self):
-        """Returns the first row
-
-        :return: some kind of ModSimSeries
-        """
-        return self.row[self.index[-1]]
-
-    def first_label(self):
-        """Returns the first element of the index.
-        """
-        return self.index[0]
-
-    def last_label(self):
-        """Returns the last element of the index.
-        """
-        return self.index[-1]
-
-
-class ModSimLocIndexer:
-    """Wraps a Pandas LocIndexer."""
-
-    def __init__(self, li, constructor):
-        """Save the LocIndexer and constructor.
-        """
-        self.li = li
-        self.constructor = constructor
-
-    def __getitem__(self, key):
-        """Get a row and return the appropriate type of Series.
-
-        If key is a Quantity, its units are stripped
-
-        :key: scalar or Quantity
-
-        :return: Series
-        """
-        key = magnitude(key)
-        result = self.li[key]
-        if isinstance(result, Series):
-            result = self.constructor(result)
-        return result
-
-    def __setitem__(self, key, value):
-        """Setting a row in a DataFrame.
-
-        If key is a Quantity, its units are stripped
-
-        :key: scalar or Quantity
-        :value: sequence or Series
-        """
-        key = magnitude(key)
-        self.li[key] = value
-
-
-class TimeFrame(ModSimDataFrame):
-    """A DataFrame that maps from time to State.
+def TimeFrame(*args, **kwargs):
+    """DataFrame that maps from time to State.
     """
+    underride(kwargs, dtype=float)
+    return pd.DataFrame(*args, **kwargs)
 
-    column_constructor = TimeSeries
-    row_constructor = State
 
-
-class SweepFrame(ModSimDataFrame):
-    """A DataFrame that maps from a parameter value to a SweepSeries.
+def SweepFrame(*args, **kwargs):
+    """DataFrame that maps from parameter value to SweepSeries.
     """
-
-    column_constructor = SweepSeries
-    row_constructor = SweepSeries
+    underride(kwargs, dtype=float)
+    return pd.DataFrame(*args, **kwargs)
 
 
-def Vector(*args, units=None):
-    """Make a ModSimVector.
-
-    args: can be a single argument or sequence
-    units: Pint Unit object or Quantity
-
-    If there's only one argument, it should be a sequence.
-
-    Otherwise, the arguments are treated as coordinates.
-
-    returns: ModSimVector
+def Vector(x, y, z=None, **options):
     """
-    if len(args) == 1:
-        args = args[0]
-
-        # if it's a series, pull out the values
-        if isinstance(args, Series):
-            args = args.values
-
-    # see if any of the arguments have units; if so, save the first one
-    for elt in args:
-        found_units = getattr(elt, "units", None)
-        if found_units:
-            break
-
-    if found_units:
-        # if there are units, remove them
-        args = [float(magnitude(elt)) for elt in args]
+    """
+    underride(options, name='component')
+    if z is None:
+        return pd.Series(dict(x=x, y=y), **options)
     else:
-        # otherwise, just ensure that all elements are floats (to avoid overflow issues in numpy)
-        args = [float(elt) for elt in args]
-
-    # if the units keyword is provided, it overrides the units in args
-    if units is not None:
-        found_units = units
-
-    return ModSimVector(args, found_units)
+        return pd.Series(dict(x=x, y=y, z=z), **options)
 
 
 ## Vector functions (should work with any sequence)
 
-
 def vector_mag(v):
-    """Vector magnitude with units.
-
-    returns: number or Quantity
-    """
-    a = magnitude(v)
-    units = get_first_unit(v)
-    return np.sqrt(np.dot(a, a)) * units
+    """Vector magnitude."""
+    return np.sqrt(np.dot(v, v))
 
 
 def vector_mag2(v):
-    """Vector magnitude squared with units.
-
-    returns: number of Quantity
-    """
-    a = magnitude(v)
-    units = get_first_unit(v)
-    return np.dot(a, a) * units * units
+    """Vector magnitude squared."""
+    return np.dot(v, v)
 
 
 def vector_angle(v):
@@ -1732,7 +743,7 @@ def vector_angle(v):
 
     Only works with 2-D vectors.
 
-    returns: number in radians
+    returns: angle in radians
     """
     assert len(v) == 2
     x, y = v
@@ -1742,7 +753,7 @@ def vector_angle(v):
 def vector_polar(v):
     """Vector magnitude and angle.
 
-    returns: (number or quantity, number in radians)
+    returns: (number, angle in radians)
     """
     return vector_mag(v), vector_angle(v)
 
@@ -1750,19 +761,12 @@ def vector_polar(v):
 def vector_hat(v):
     """Unit vector in the direction of v.
 
-    The result should have no units.
-
     returns: Vector or array
     """
-    # get the size of the vector
-    mag = vector_mag(v)
-
     # check if the magnitude of the Quantity is 0
-    if magnitude(mag) == 0:
-        if isinstance(v, ModSimVector):
-            return Vector(magnitude(v))
-        else:
-            return magnitude(np.asarray(v))
+    mag = vector_mag(v)
+    if mag == 0:
+        return v
     else:
         return v / mag
 
@@ -1784,9 +788,7 @@ def vector_dot(v, w):
 
     returns: number or Quantity
     """
-    a1 = magnitude(v)
-    a2 = magnitude(w)
-    return np.dot(a1, a2) * get_first_unit(v) * get_first_unit(w)
+    return np.dot(v, w)
 
 
 def vector_cross(v, w):
@@ -1794,22 +796,17 @@ def vector_cross(v, w):
 
     returns: number or Quantity for 2-D, Vector for 3-D
     """
-    a1 = magnitude(v)
-    a2 = magnitude(w)
-    res = np.cross(a1, a2)
+    res = np.cross(v, w)
 
-    if len(v) == 3 and (isinstance(v, ModSimVector) or isinstance(w, ModSimVector)):
-        return ModSimVector(res, get_first_unit(v) * get_first_unit(w))
+    if len(v) == 3:
+        return Vector(*res)
     else:
-        return res * get_first_unit(v) * get_first_unit(w)
+        return res
 
 
 def vector_proj(v, w):
     """Projection of v onto w.
 
-    Results has the units of v, but that might not make sense unless
-    v and w have the same units.
-
     returns: array or Vector with direction of w and units of v.
     """
     w_hat = vector_hat(w)
@@ -1821,9 +818,6 @@ def scalar_proj(v, w):
 
     Which is the magnitude of the projection of v onto w.
 
-    Results has the units of v, but that might not make sense unless
-    v and w have the same units.
-
     returns: scalar with units of v.
     """
     return vector_dot(v, vector_hat(w))
@@ -1847,54 +841,6 @@ def vector_diff_angle(v, w):
         raise NotImplementedError()
 
 
-class ModSimVector(Quantity):
-    """Represented as a Pint Quantity with a NumPy array
-
-    x, y, z, mag, mag2, and angle are accessible as attributes.
-    """
-
-    @property
-    def x(self):
-        """Returns the x component with units."""
-        return self[0]
-
-    @property
-    def y(self):
-        """Returns the y component with units."""
-        return self[1]
-
-    @property
-    def z(self):
-        """Returns the z component with units."""
-        return self[2]
-
-    @property
-    def mag(self):
-        """Returns the magnitude with units."""
-        return vector_mag(self)
-
-    @property
-    def mag2(self):
-        """Returns the magnitude squared with units."""
-        return vector_mag2(self)
-
-    @property
-    def angle(self):
-        """Returns the angle between self and the positive x axis."""
-        return vector_angle(self)
-
-    # make the vector functions available as methods
-    polar = vector_polar
-    hat = vector_hat
-    perp = vector_perp
-    dot = vector_dot
-    cross = vector_cross
-    proj = vector_proj
-    comp = scalar_proj
-    dist = vector_dist
-    diff_angle = vector_diff_angle
-
-
 def plot_segment(A, B, **options):
     """Plots a line segment between two Vectors.
 
@@ -1907,12 +853,13 @@ def plot_segment(A, B, **options):
     """
     xs = A.x, B.x
     ys = A.y, B.y
-    plot(xs, ys, **options)
+    plt.plot(xs, ys, **options)
+
 
 from time import sleep
 from IPython.display import clear_output
 
-def animate(results, draw_func, interval=None):
+def animate(results, draw_func, *args, interval=None):
     """Animate results from a simulation.
 
     results: TimeFrame
@@ -1922,26 +869,12 @@ def animate(results, draw_func, interval=None):
     plt.figure()
     try:
         for t, state in results.iterrows():
-            draw_func(state, t)
+            draw_func(t, state, *args)
             plt.show()
             if interval:
                 sleep(interval)
             clear_output(wait=True)
-        draw_func(state, t)
+        draw_func(t, state, *args)
         plt.show()
     except KeyboardInterrupt:
         pass
-
-def set_xlim(seq):
-    """Set the limits of the x-axis.
-
-    seq: sequence of numbers or Quantities
-    """
-    plt.xlim(magnitude(min(seq)), magnitude(max(seq)))
-
-def set_ylim(seq):
-    """Set the limits of the y-axis.
-
-    seq: sequence of numbers or Quantities
-    """
-    plt.ylim(magnitude(min(seq)), magnitude(max(seq)))

From d05a61f2962bb810ea139fe692107209713508ef Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 27 Apr 2023 15:47:57 -0400
Subject: [PATCH 100/144] No iteritems

---
 modsim.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modsim.py b/modsim.py
index b65c951a..ed012ec6 100644
--- a/modsim.py
+++ b/modsim.py
@@ -618,7 +618,7 @@ def remove_units_series(series):
     returns: new Series object
     """
     res = copy(series)
-    for label, value in res.iteritems():
+    for label, value in res.items():
         res[label] = magnitude(value)
     return res
 

From 1e2f797373139af33fcb7260406d7dca0037bca1 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 27 Apr 2023 16:41:02 -0400
Subject: [PATCH 101/144] Updating examples

---
 examples/bungee1.ipynb     |  20 ++---
 examples/bungee2.ipynb     |  36 +++++----
 examples/filter.ipynb      |  26 +++---
 examples/glucose.ipynb     |  43 +++++-----
 examples/header.ipynb      |  20 ++---
 examples/hiv_model.ipynb   |  20 ++---
 examples/insulin.ipynb     |   7 +-
 examples/kitten.ipynb      |   7 +-
 examples/orbit.ipynb       |  18 +++--
 examples/queue.ipynb       | 159 +++++++++++++++++--------------------
 examples/salmon.ipynb      |  18 +++--
 examples/spiderman.ipynb   |  24 +++---
 examples/throwingaxe.ipynb |  20 ++---
 examples/trees.ipynb       |  22 ++---
 examples/wall.ipynb        |   6 +-
 examples/yoyo.ipynb        |  70 ++++++++--------
 16 files changed, 256 insertions(+), 260 deletions(-)

diff --git a/examples/bungee1.ipynb b/examples/bungee1.ipynb
index 0a146f3d..5c67e507 100644
--- a/examples/bungee1.ipynb
+++ b/examples/bungee1.ipynb
@@ -44,14 +44,16 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
@@ -692,7 +694,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -706,7 +708,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/examples/bungee2.ipynb b/examples/bungee2.ipynb
index 10c1131a..ff6dc9ad 100644
--- a/examples/bungee2.ipynb
+++ b/examples/bungee2.ipynb
@@ -44,14 +44,16 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
@@ -633,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -646,7 +648,7 @@
     "    \n",
     "    system2 = system1.set(t_0=t_final, init=state_final)\n",
     "    results2, details2 = run_solve_ivp(system2, slope_func2)\n",
-    "    return results1.append(results2)"
+    "    return pd.concat([results1, results2])"
    ]
   },
   {
@@ -658,7 +660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -667,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -676,7 +678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -686,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 37,
    "metadata": {
     "scrolled": true
    },
@@ -698,7 +700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -707,7 +709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -732,7 +734,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -746,7 +748,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/examples/filter.ipynb b/examples/filter.ipynb
index b625bac2..a7aab5db 100644
--- a/examples/filter.ipynb
+++ b/examples/filter.ipynb
@@ -44,14 +44,16 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
-    "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "from os.path import basename, exists\n",
+    "\n",
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
@@ -71,9 +73,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The following circuit diagram (from [Wikipedia](https://en.wikipedia.org/wiki/File:RC_Divider.svg)) shows a low-pass filter built with one resistor and one capacitor.  \n",
+    "The following circuit diagram (from [Wikipedia](https://commons.wikimedia.org/wiki/File:1st_Order_Lowpass_Filter_RC.svg)) shows a low-pass filter built with one resistor and one capacitor.  \n",
     "\n",
-    "![Circuit diagram of a low-pass filter](https://github.com/AllenDowney/ModSim/raw/main/figs/RC_Divider.svg)\n",
+    "![Circuit diagram of a low-pass filter](https://github.com/AllenDowney/ModSim/raw/main/figs/Lowpass_Filter_RC.png)\n",
     "\n",
     "A \"filter\" is a circuit takes a signal, $V_{in}$, as input and produces a signal, $V_{out}$, as output.  In this context, a \"signal\" is a voltage that changes over time.\n",
     "\n",
@@ -907,7 +909,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -921,7 +923,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/examples/glucose.ipynb b/examples/glucose.ipynb
index bde7bb82..e6fefbc2 100644
--- a/examples/glucose.ipynb
+++ b/examples/glucose.ipynb
@@ -25,22 +25,6 @@
     "tags": []
    },
    "outputs": [],
-   "source": [
-    "# install Pint if necessary\n",
-    "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
@@ -53,13 +37,12 @@
     "        local, _ = urlretrieve(url, filename)\n",
     "        print('Downloaded ' + local)\n",
     "    \n",
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSim/main/modsim.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "tags": []
    },
@@ -93,17 +76,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSim/main/data/glucose_insulin.csv')"
+    "download('https://github.com/AllenDowney/ModSim/raw/main/data/' +\n",
+    "         'glucose_insulin.csv')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,6 +100,16 @@
     "We'll use `make_system` and `slope_func` as defined in Chapter 18."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
+    "         'chap18.py')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -601,7 +594,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -615,7 +608,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/examples/header.ipynb b/examples/header.ipynb
index 6b6acc86..be0a4822 100644
--- a/examples/header.ipynb
+++ b/examples/header.ipynb
@@ -44,14 +44,16 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
@@ -71,7 +73,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -85,7 +87,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/examples/hiv_model.ipynb b/examples/hiv_model.ipynb
index d4dd128e..f1abf75c 100644
--- a/examples/hiv_model.ipynb
+++ b/examples/hiv_model.ipynb
@@ -44,14 +44,16 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
@@ -289,7 +291,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -303,7 +305,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/examples/insulin.ipynb b/examples/insulin.ipynb
index 374b7684..3366b38f 100644
--- a/examples/insulin.ipynb
+++ b/examples/insulin.ipynb
@@ -53,8 +53,7 @@
     "        local, _ = urlretrieve(url, filename)\n",
     "        print('Downloaded ' + local)\n",
     "    \n",
-    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSim/main/modsim.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
@@ -403,7 +402,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -417,7 +416,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/examples/kitten.ipynb b/examples/kitten.ipynb
index 5b87b1a5..f9a5804c 100644
--- a/examples/kitten.ipynb
+++ b/examples/kitten.ipynb
@@ -37,8 +37,7 @@
     "        local, _ = urlretrieve(url, filename)\n",
     "        print('Downloaded ' + local)\n",
     "    \n",
-    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
-    "         'modsim.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
@@ -483,7 +482,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -497,7 +496,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/examples/orbit.ipynb b/examples/orbit.ipynb
index d2eae086..32d03430 100644
--- a/examples/orbit.ipynb
+++ b/examples/orbit.ipynb
@@ -44,14 +44,16 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
@@ -355,7 +357,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/examples/queue.ipynb b/examples/queue.ipynb
index eed8b76c..743a2097 100644
--- a/examples/queue.ipynb
+++ b/examples/queue.ipynb
@@ -11,98 +11,69 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "*Modeling and Simulation in Python*\n",
+    "This notebook presents a case study from *Modeling and Simulation in Python*.  It explores a question related to queueing theory, which is the study of systems that involve waiting in lines, also known as \"queues\".\n",
     "\n",
-    "Copyright 2021 Allen Downey\n",
+    "Suppose you are designing the checkout area for a new store.  There is room for two checkout counters and a waiting area for customers.  You can make two lines, one for each counter, or one line that serves both counters.\n",
     "\n",
-    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+    "In theory, you might expect a single line to be better, but it has some practical drawbacks: in order to maintain a single line, you would have to install rope barriers, and customers might be put off by what seems to be a longer line, even if it moves faster.\n",
+    "\n",
+    "So you'd like to check whether the single line is really better and by how much.  Simulation can help answer this question."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "# install Pint if necessary\n",
+    "As we did in the bikeshare model, we'll assume that a customer is equally likely to arrive during any timestep.  I'll denote this probability using the Greek letter lambda, $\\lambda$, or the variable name `lam`.  The value of $\\lambda$ probably varies from day to day, so we'll have to consider a range of possibilities.\n",
+    "\n",
+    "Based on data from other stores, you know that it takes 5 minutes for a customer to check out, on average.  But checkout times are highly variable: most customers take less than 5 minutes, but some take substantially more.  A simple way to model this variability is to assume that when a customer is checking out, they have the same probability of finishing up during each time step.  I'll denote this probability using the Greek letter mu, $\\mu$, or the variable name `mu`.\n",
     "\n",
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint"
+    "If we choose $\\mu=1/5$, the average number of time steps for each checkout will be 5 minutes, which is consistent with the data."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "# download modsim.py if necessary\n",
-    "\n",
-    "from os.path import exists\n",
+    "## One server, one queue\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "Write a function called `make_system` that takes `lam` and `mu` as parameters and returns a `System` object with variables `lam`, `mu`, and `duration`.  Set `duration`, which is the number of time steps to simulate, to 10 hours, expressed in minutes. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# import functions from modsim\n",
-    "\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook presents a case study from *Modeling and Simulation in Python*.  It explores a question related to queueing theory, which is the study of systems that involve waiting in lines, also known as \"queues\".\n",
-    "\n",
-    "Suppose you are designing the checkout area for a new store.  There is room for two checkout counters and a waiting area for customers.  You can make two lines, one for each counter, or one line that serves both counters.\n",
-    "\n",
-    "In theory, you might expect a single line to be better, but it has some practical drawbacks: in order to maintain a single line, you would have to install rope barriers, and customers might be put off by what seems to be a longer line, even if it moves faster.\n",
-    "\n",
-    "So you'd like to check whether the single line is really better and by how much.  Simulation can help answer this question."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we did in the bikeshare model, we'll assume that a customer is equally likely to arrive during any timestep.  I'll denote this probability using the Greek letter lambda, $\\lambda$, or the variable name `lam`.  The value of $\\lambda$ probably varies from day to day, so we'll have to consider a range of possibilities.\n",
+    "# download modsim.py if necessary\n",
     "\n",
-    "Based on data from other stores, you know that it takes 5 minutes for a customer to check out, on average.  But checkout times are highly variable: most customers take less than 5 minutes, but some take substantially more.  A simple way to model this variability is to assume that when a customer is checking out, they have the same probability of finishing up during each time step.  I'll denote this probability using the Greek letter mu, $\\mu$, or the variable name `mu`.\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "If we choose $\\mu=1/5$, the average number of time steps for each checkout will be 5 minutes, which is consistent with the data."
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## One server, one queue\n",
-    "\n",
-    "Write a function called `make_system` that takes `lam` and `mu` as parameters and returns a `System` object with variables `lam`, `mu`, and `duration`.  Set `duration`, which is the number of time steps to simulate, to 10 hours, expressed in minutes. "
+    "from modsim import *"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -181,7 +152,7 @@
     "    update_func: function object\n",
     "    \"\"\"\n",
     "    x = 0\n",
-    "    results = TimeSeries()\n",
+    "    results = TimeSeries(name='Queue length')\n",
     "    results[0] = x\n",
     "    \n",
     "    for t in linrange(0, system.duration):\n",
@@ -200,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -220,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -266,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -286,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -311,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -327,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -351,7 +322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -375,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -402,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -418,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -436,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -454,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -463,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -485,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -501,7 +472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -517,7 +488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -535,7 +506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -544,7 +515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -553,13 +524,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Solution goes here"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -569,8 +551,9 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -584,7 +567,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/examples/salmon.ipynb b/examples/salmon.ipynb
index 4d59bbc1..b63b753c 100644
--- a/examples/salmon.ipynb
+++ b/examples/salmon.ipynb
@@ -44,14 +44,16 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
@@ -673,7 +675,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/examples/spiderman.ipynb b/examples/spiderman.ipynb
index 39ecaa1e..35154f0b 100644
--- a/examples/spiderman.ipynb
+++ b/examples/spiderman.ipynb
@@ -44,14 +44,16 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
-    "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "from os.path import basename, exists\n",
+    "\n",
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
@@ -643,7 +645,7 @@
     "    results2, details2 = run_solve_ivp(system2, slope_func, \n",
     "                                       events=event_func)\n",
     "\n",
-    "    return results1.append(results2)"
+    "    return pd.concat([results1, results2])"
    ]
   },
   {
@@ -826,7 +828,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -840,7 +842,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/examples/throwingaxe.ipynb b/examples/throwingaxe.ipynb
index 89f5bc3c..6ca67e19 100644
--- a/examples/throwingaxe.ipynb
+++ b/examples/throwingaxe.ipynb
@@ -44,14 +44,16 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
@@ -460,7 +462,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -474,7 +476,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/examples/trees.ipynb b/examples/trees.ipynb
index c8c4cbe6..5dad9e80 100644
--- a/examples/trees.ipynb
+++ b/examples/trees.ipynb
@@ -44,14 +44,16 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
-    "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "from os.path import basename, exists\n",
+    "\n",
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
@@ -992,7 +994,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1006,7 +1008,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/examples/wall.ipynb b/examples/wall.ipynb
index 2fd4d5ba..4814fe1d 100644
--- a/examples/wall.ipynb
+++ b/examples/wall.ipynb
@@ -54,7 +54,7 @@
     "        print('Downloaded ' + local)\n",
     "    \n",
     "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
-    "         'ModSimPy/main/modsim.py')"
+    "         'ModSimPy/master/modsim.py')"
    ]
   },
   {
@@ -607,7 +607,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -621,7 +621,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/examples/yoyo.ipynb b/examples/yoyo.ipynb
index 8ecf3111..316e3e0e 100644
--- a/examples/yoyo.ipynb
+++ b/examples/yoyo.ipynb
@@ -44,14 +44,16 @@
    "source": [
     "# download modsim.py if necessary\n",
     "\n",
-    "from os.path import exists\n",
+    "from os.path import basename, exists\n",
     "\n",
-    "filename = 'modsim.py'\n",
-    "if not exists(filename):\n",
-    "    from urllib.request import urlretrieve\n",
-    "    url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'\n",
-    "    local, _ = urlretrieve(url+filename, filename)\n",
-    "    print('Downloaded ' + local)"
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
@@ -139,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -180,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -189,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -198,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -207,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -249,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -276,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -294,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -311,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -330,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -346,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -372,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -393,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -411,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -427,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 21,
    "metadata": {
     "scrolled": false
    },
@@ -445,7 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 22,
    "metadata": {
     "scrolled": false
    },
@@ -463,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -488,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -506,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -526,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -545,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -564,7 +566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -585,7 +587,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -599,7 +601,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,

From 67bbab01e13a50f722fa2badb05cc369e46aa5ad Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 27 Apr 2023 16:46:59 -0400
Subject: [PATCH 102/144] Updating chapters

---
 chapters/chap01.ipynb |   2 +-
 chapters/chap04.ipynb |   2 +-
 chapters/chap05.ipynb |   6 +-
 chapters/chap11.ipynb |  10 +--
 chapters/chap18.ipynb |   7 +-
 chapters/chap22.ipynb | 147 ++++++++++++++++++++----------------------
 chapters/chap24.ipynb |   4 +-
 chapters/chap27.ipynb |   2 +-
 8 files changed, 87 insertions(+), 93 deletions(-)

diff --git a/chapters/chap01.ipynb b/chapters/chap01.ipynb
index 9d0da03b..7bb182f1 100644
--- a/chapters/chap01.ipynb
+++ b/chapters/chap01.ipynb
@@ -1335,7 +1335,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap04.ipynb b/chapters/chap04.ipynb
index 20361f9d..f233a1f8 100644
--- a/chapters/chap04.ipynb
+++ b/chapters/chap04.ipynb
@@ -793,7 +793,7 @@
    "source": [
     "### Exercise 6\n",
     "\n",
-    "Continuing the previous exercise, use `run_multiple_simulations` to run simulations with a range of values for `p1` and\n",
+    "Continuing the previous exercise, use `run_multiple_simulations` to run simulations with a range of values for `p1` and `p2`.\n",
     "\n",
     "```\n",
     "p2 = 0.3\n",
diff --git a/chapters/chap05.ipynb b/chapters/chap05.ipynb
index 3f630bb1..04ddeaab 100644
--- a/chapters/chap05.ipynb
+++ b/chapters/chap05.ipynb
@@ -390,7 +390,7 @@
    "outputs": [],
    "source": [
     "plot_estimates()\n",
-    "decorate(title='World Population Estimates')"
+    "decorate(title='World population estimates')"
    ]
   },
   {
@@ -829,7 +829,7 @@
    "source": [
     "results.plot(color='gray', label='model')\n",
     "plot_estimates()\n",
-    "decorate(title='Constant Growth Model')"
+    "decorate(title='Constant growth model')"
    ]
   },
   {
@@ -917,7 +917,7 @@
    "source": [
     "results.plot(color='gray', label='model')\n",
     "plot_estimates()\n",
-    "decorate(title='Constant Growth Model')"
+    "decorate(title='Constant growth model')"
    ]
   },
   {
diff --git a/chapters/chap11.ipynb b/chapters/chap11.ipynb
index 2c9e09bc..cddab379 100644
--- a/chapters/chap11.ipynb
+++ b/chapters/chap11.ipynb
@@ -581,7 +581,7 @@
     "def plot_results(S, I, R):\n",
     "    S.plot(style='--', label='Susceptible')\n",
     "    I.plot(style='-', label='Infected')\n",
-    "    R.plot(style=':', label='Resistant')\n",
+    "    R.plot(style=':', label='Recovered')\n",
     "    decorate(xlabel='Time (days)',\n",
     "             ylabel='Fraction of population')"
    ]
@@ -772,7 +772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "parental-thunder",
    "metadata": {},
    "outputs": [],
@@ -782,7 +782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "id": "recovered-freeze",
    "metadata": {},
    "outputs": [],
@@ -792,7 +792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 24,
    "id": "suited-spanking",
    "metadata": {},
    "outputs": [],
@@ -826,7 +826,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap18.ipynb b/chapters/chap18.ipynb
index 80bb5739..b82e3a16 100644
--- a/chapters/chap18.ipynb
+++ b/chapters/chap18.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -42,8 +42,7 @@
     "        local, _ = urlretrieve(url, filename)\n",
     "        print('Downloaded ' + local)\n",
     "    \n",
-    "download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
-    "         'modsim.py')"
+    "download('https://github.com/AllenDowney/ModSimPy/raw/master/modsim.py')"
    ]
   },
   {
@@ -796,7 +795,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
index 09f59fc2..83941ba4 100644
--- a/chapters/chap22.ipynb
+++ b/chapters/chap22.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 4,
    "id": "handy-terrain",
    "metadata": {},
    "outputs": [],
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 5,
    "id": "vocal-latino",
    "metadata": {},
    "outputs": [],
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 6,
    "id": "controversial-shower",
    "metadata": {},
    "outputs": [],
@@ -195,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 7,
    "id": "digital-channels",
    "metadata": {},
    "outputs": [],
@@ -205,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 8,
    "id": "automated-drove",
    "metadata": {},
    "outputs": [],
@@ -224,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 9,
    "id": "conditional-latitude",
    "metadata": {},
    "outputs": [],
@@ -243,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 10,
    "id": "encouraging-cabinet",
    "metadata": {},
    "outputs": [],
@@ -270,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 11,
    "id": "peripheral-tattoo",
    "metadata": {},
    "outputs": [],
@@ -295,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 12,
    "id": "strange-cleaning",
    "metadata": {},
    "outputs": [],
@@ -316,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 13,
    "id": "cellular-community",
    "metadata": {},
    "outputs": [],
@@ -340,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 14,
    "id": "monetary-firmware",
    "metadata": {},
    "outputs": [],
@@ -363,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 15,
    "id": "explicit-piano",
    "metadata": {},
    "outputs": [],
@@ -381,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 16,
    "id": "relative-republic",
    "metadata": {},
    "outputs": [],
@@ -413,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 17,
    "id": "narrative-latest",
    "metadata": {
     "tags": []
@@ -451,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 18,
    "id": "bored-billy",
    "metadata": {
     "tags": []
@@ -462,7 +462,7 @@
     "\n",
     "def make_system(params):\n",
     "    \n",
-    "    # convert angle to degrees\n",
+    "    # convert angle to radians\n",
     "    theta = deg2rad(params.angle)\n",
     "    \n",
     "    # compute x and y components of velocity\n",
@@ -494,14 +494,14 @@
     "\n",
     "* `vx` and `vy` are the components of velocity.\n",
     "\n",
-    "When we call `System`, we pass `Params` as the first argument, which means that the variables in `Params` are copied to the new `System` object.\n",
+    "When we call `System`, we pass `params` as the first argument, which means that the variables in `params` are copied to the new `System` object.\n",
     "\n",
     "Here's how we make the `System` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 19,
    "id": "ethical-donna",
    "metadata": {},
    "outputs": [],
@@ -519,7 +519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 20,
    "id": "legitimate-gossip",
    "metadata": {},
    "outputs": [],
@@ -539,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 21,
    "id": "legal-terminal",
    "metadata": {
     "tags": []
@@ -575,7 +575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 22,
    "id": "frank-chick",
    "metadata": {},
    "outputs": [],
@@ -598,7 +598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 23,
    "id": "suitable-salem",
    "metadata": {
     "tags": []
@@ -627,7 +627,7 @@
     "We don't use `t` in this example, but we can't leave it out because when `run_solve_ivp` calls the slope function, it always provides the same arguments, whether they are needed or not.\n",
     "\n",
     "`slope_func` unpacks the `State` object into variables `x`, `y`, `vx`, and `vy`.\n",
-    "Then it packs `vx` and `vy` into a `Vector`, which it uses to compute drag force, `f_drag`,  and acceleration due to drag, `a_drag`.\n",
+    "Then it packs `vx` and `vy` into a `Vector`, which it uses to compute acceleration due to drag, `a_drag`.\n",
     "\n",
     "To represent acceleration due to gravity, it makes a `Vector` with magnitude `g` in the negative $y$ direction.\n",
     "\n",
@@ -658,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 24,
    "id": "closing-simon",
    "metadata": {},
    "outputs": [],
@@ -688,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 25,
    "id": "brief-level",
    "metadata": {
     "tags": []
@@ -712,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 26,
    "id": "threatened-alberta",
    "metadata": {},
    "outputs": [],
@@ -730,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 27,
    "id": "special-background",
    "metadata": {},
    "outputs": [],
@@ -753,7 +753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 28,
    "id": "prospective-external",
    "metadata": {},
    "outputs": [],
@@ -771,7 +771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 29,
    "id": "medieval-calvin",
    "metadata": {},
    "outputs": [],
@@ -790,7 +790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 30,
    "id": "gorgeous-survey",
    "metadata": {},
    "outputs": [],
@@ -809,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 31,
    "id": "failing-bangkok",
    "metadata": {},
    "outputs": [],
@@ -828,7 +828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 32,
    "id": "copyrighted-highway",
    "metadata": {},
    "outputs": [],
@@ -847,7 +847,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 33,
    "id": "structured-adams",
    "metadata": {},
    "outputs": [],
@@ -875,7 +875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 34,
    "id": "spare-burst",
    "metadata": {},
    "outputs": [],
@@ -900,7 +900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 35,
    "id": "dated-browse",
    "metadata": {},
    "outputs": [],
@@ -933,7 +933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 36,
    "id": "resistant-vegetation",
    "metadata": {},
    "outputs": [],
@@ -956,7 +956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 37,
    "id": "starting-fabric",
    "metadata": {},
    "outputs": [],
@@ -1003,7 +1003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 38,
    "id": "prescription-boutique",
    "metadata": {
     "tags": []
@@ -1052,7 +1052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 39,
    "id": "optical-weather",
    "metadata": {},
    "outputs": [],
@@ -1064,7 +1064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 40,
    "id": "acknowledged-belgium",
    "metadata": {},
    "outputs": [],
@@ -1074,7 +1074,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 41,
    "id": "spatial-ensemble",
    "metadata": {},
    "outputs": [],
@@ -1084,7 +1084,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 42,
    "id": "domestic-apparatus",
    "metadata": {},
    "outputs": [],
@@ -1094,7 +1094,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 43,
    "id": "correct-pittsburgh",
    "metadata": {},
    "outputs": [],
@@ -1114,7 +1114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 44,
    "id": "global-referral",
    "metadata": {},
    "outputs": [],
@@ -1126,7 +1126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 45,
    "id": "appointed-sugar",
    "metadata": {},
    "outputs": [],
@@ -1136,7 +1136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 46,
    "id": "specialized-mediterranean",
    "metadata": {},
    "outputs": [],
@@ -1170,7 +1170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 47,
    "id": "directed-moisture",
    "metadata": {
     "tags": []
@@ -1193,7 +1193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 48,
    "id": "fuzzy-register",
    "metadata": {
     "tags": []
@@ -1217,7 +1217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 49,
    "id": "returning-fellowship",
    "metadata": {
     "tags": []
@@ -1239,7 +1239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 50,
    "id": "reasonable-swaziland",
    "metadata": {
     "tags": []
@@ -1262,7 +1262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 51,
    "id": "attached-shower",
    "metadata": {
     "tags": []
@@ -1275,26 +1275,21 @@
   {
    "cell_type": "markdown",
    "id": "amazing-horse",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "source": [
-    "We can plot the `Series` like this."
+    "Here's what it looks like."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 64,
    "id": "christian-camcorder",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "C_d_series.plot(label='C_d')\n",
+    "C_d_series.plot(label='$C_d$')\n",
     "decorate(xlabel='Speed (m/s)', \n",
-    "         ylabel='C_d',\n",
-    "         title='Coefficient of drag versus speed')"
+    "         ylabel='Coefficient of drag')"
    ]
   },
   {
@@ -1309,7 +1304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 53,
    "id": "heated-belfast",
    "metadata": {
     "tags": []
@@ -1330,7 +1325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 54,
    "id": "engaged-provision",
    "metadata": {},
    "outputs": [],
@@ -1340,7 +1335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 55,
    "id": "directed-fiber",
    "metadata": {},
    "outputs": [],
@@ -1350,7 +1345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 56,
    "id": "framed-dealer",
    "metadata": {},
    "outputs": [],
@@ -1360,7 +1355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 57,
    "id": "accomplished-elizabeth",
    "metadata": {},
    "outputs": [],
@@ -1370,7 +1365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 58,
    "id": "going-techno",
    "metadata": {},
    "outputs": [],
@@ -1380,7 +1375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 59,
    "id": "brief-saying",
    "metadata": {},
    "outputs": [],
@@ -1390,7 +1385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 60,
    "id": "spare-pregnancy",
    "metadata": {},
    "outputs": [],
@@ -1400,7 +1395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 61,
    "id": "catholic-staff",
    "metadata": {},
    "outputs": [],
@@ -1410,7 +1405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 62,
    "id": "broad-sequence",
    "metadata": {},
    "outputs": [],
@@ -1444,7 +1439,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap24.ipynb b/chapters/chap24.ipynb
index 68cfc0aa..fa25bd5a 100644
--- a/chapters/chap24.ipynb
+++ b/chapters/chap24.ipynb
@@ -487,7 +487,7 @@
    "id": "running-tutorial",
    "metadata": {},
    "source": [
-    "The final value of radius is 0.55 m, which is `Rmax`."
+    "The final value of `r` is 0.55 m, which is `Rmax`."
    ]
   },
   {
@@ -901,7 +901,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap27.ipynb b/chapters/chap27.ipynb
index 5046d8ff..6f3a40f3 100644
--- a/chapters/chap27.ipynb
+++ b/chapters/chap27.ipynb
@@ -88,7 +88,7 @@
    "source": [
     "## How run_solve_ivp Works\n",
     "\n",
-    "`run_solve_ivp` is a function in the ModSimPy library that checks for common errors in the parameters and then calls `solve_ip`, which is the function in the SciPy library that does the actual work.\n",
+    "`run_solve_ivp` is a function in the ModSimPy library that checks for common errors in the parameters and then calls `solve_ivp`, which is the function in the SciPy library that does the actual work.\n",
     "\n",
     "By default, `solve_ivp` uses the *Dormand-Prince method*, which is a kind of *Runge-Kutta method*. You can read about it at\n",
     "<https://en.wikipedia.org/wiki/Dormand-Prince_method>, but I'll give you a sense of\n",

From 32aaa7ba695795b0ae443edfe76f6d9b9e6d185e Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 27 Apr 2023 16:46:59 -0400
Subject: [PATCH 103/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 240367 -> 240629 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index 12baf6af19630b6099d634238170e16a0961b3a2..9949a6f52dc14595d072564f21e27856a9192209 100644
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From 2ef797ae0912232bfa9129c66c8c5c80a21a056a Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 27 Apr 2023 17:03:55 -0400
Subject: [PATCH 104/144] Removing obsolete directory

---
 soln/chap01soln.ipynb                     |  849 -----
 soln/chap02soln.ipynb                     | 2078 ------------
 soln/chap03soln.ipynb                     | 1274 --------
 soln/chap04soln.ipynb                     | 1186 -------
 soln/chap05soln.ipynb                     | 1732 ----------
 soln/chap06soln.ipynb                     |  808 -----
 soln/chap07soln.ipynb                     |  779 -----
 soln/chap08soln.ipynb                     | 1067 ------
 soln/chap09soln.ipynb                     | 1147 -------
 soln/chap10soln.ipynb                     | 1208 -------
 soln/chap11soln.ipynb                     |  900 -----
 soln/chap12soln.ipynb                     | 1634 ----------
 soln/chap13ode.ipynb                      |  302 --
 soln/chap13soln.ipynb                     |  963 ------
 soln/chap14soln.ipynb                     |  763 -----
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diff --git a/soln/chap01soln.ipynb b/soln/chap01soln.ipynb
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+++ /dev/null
@@ -1,849 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 1\n",
-    "\n",
-    "Copyright 2020 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Jupyter\n",
-    "\n",
-    "Welcome to *Modeling and Simulation*, welcome to Python, and welcome to Jupyter.\n",
-    "\n",
-    "This is a Jupyter notebook, which is a development environment where you can write and run Python code.  Each notebook is divided into cells.  Each cell contains either text (like this cell) or Python code.\n",
-    "\n",
-    "### Selecting and running cells\n",
-    "\n",
-    "To select a cell, click in the left margin next to the cell.  You should see a blue frame surrounding the selected cell.\n",
-    "\n",
-    "To edit a code cell, click inside the cell.  You should see a green frame around the selected cell, and you should see a cursor inside the cell.\n",
-    "\n",
-    "To edit a text cell, double-click inside the cell.  Again, you should see a green frame around the selected cell, and you should see a cursor inside the cell.\n",
-    "\n",
-    "To run a cell, hold down SHIFT and press ENTER.  \n",
-    "\n",
-    "* If you run a text cell, Jupyter formats the text and displays the result.\n",
-    "\n",
-    "* If you run a code cell, Jupyter runs the Python code in the cell and displays the result, if any.\n",
-    "\n",
-    "To try it out, edit this cell, change some of the text, and then press SHIFT-ENTER to format it."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Adding and removing cells\n",
-    "\n",
-    "You can add and remove cells from a notebook using the buttons in the toolbar and the items in the menu, both of which you should see at the top of this notebook.\n",
-    "\n",
-    "Try the following exercises:\n",
-    "\n",
-    "1.  From the Insert menu select \"Insert cell below\" to add a cell below this one.  By default, you get a code cell, as you can see in the pulldown menu that says \"Code\".\n",
-    "\n",
-    "2.  In the new cell, add a print statement like `print('Hello')`, and run it.\n",
-    "\n",
-    "3.  Add another cell, select the new cell, and then click on the pulldown menu that says \"Code\" and select \"Markdown\".  This makes the new cell a text cell.\n",
-    "\n",
-    "4.  In the new cell, type some text, and then run it.\n",
-    "\n",
-    "5.  Use the arrow buttons in the toolbar to move cells up and down.\n",
-    "\n",
-    "6.  Use the cut, copy, and paste buttons to delete, add, and move cells.\n",
-    "\n",
-    "7.  As you make changes, Jupyter saves your notebook automatically, but if you want to make sure, you can press the save button, which looks like a floppy disk from the 1990s.\n",
-    "\n",
-    "8.  Finally, when you are done with a notebook, select \"Close and Halt\" from the File menu."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using the notebooks\n",
-    "\n",
-    "The notebooks for each chapter contain the code from the chapter along with additional examples, explanatory text, and exercises.  I recommend you \n",
-    "\n",
-    "1. Read the chapter first to understand the concepts and vocabulary, \n",
-    "2. Run the notebook to review what you learned and see it in action, and then\n",
-    "3. Attempt the exercises.\n",
-    "\n",
-    "If you try to work through the notebooks without reading the book, you're gonna have a bad time.  The notebooks contain some explanatory text, but it is probably not enough to make sense if you have not read the book.  If you are working through a notebook and you get stuck, you might want to re-read (or read!) the corresponding section of the book."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installing modules\n",
-    "\n",
-    "These notebooks use standard Python modules like NumPy and SciPy.  I assume you already have them installed in your environment.\n",
-    "\n",
-    "They also use two less common modules: Pint, which provides units, and modsim, which contains code I wrote specifically for this book.\n",
-    "\n",
-    "The following cells check whether you have these modules already and tries to install them if you don't."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first time you run this on a new installation of Python, it might produce a warning message in pink.  That's probably ok, but if you get a message that says `modsim.py depends on Python 3.7 features`, that means you have an older version of Python, and some features in `modsim.py` won't work correctly.\n",
-    "\n",
-    "If you need a newer version of Python, I recommend installing Anaconda.  You'll find more information in the preface of the book.\n",
-    "\n",
-    "You can find out what version of Python and Jupyter you have by running the following cells."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Python 3.7.5\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!python --version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6.0.2\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!jupyter-notebook --version"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Configuring Jupyter\n",
-    "\n",
-    "The following cell:\n",
-    "\n",
-    "1.  Uses a Jupyter \"magic command\" to specify whether figures should appear in the notebook, or pop up in a new window.\n",
-    "\n",
-    "2.  Configures Jupyter to display some values that would otherwise be invisible.  \n",
-    "\n",
-    "Select the following cell and press SHIFT-ENTER to run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The penny myth\n",
-    "\n",
-    "The following cells contain code from the beginning of Chapter 1.\n",
-    "\n",
-    "`modsim` defines `UNITS`, which contains variables representing pretty much every unit you've ever heard of.  It uses [Pint](https://pint.readthedocs.io/en/latest/), which is a Python library that provides tools for computing with units.\n",
-    "\n",
-    "The following lines create new variables named `meter` and `second`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "meter"
-      ],
-      "text/latex": [
-       "$\\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "<Unit('meter')>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "meter = UNITS.meter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "second"
-      ],
-      "text/latex": [
-       "$\\mathrm{second}$"
-      ],
-      "text/plain": [
-       "<Unit('second')>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "second = UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To find out what other units are defined, type `UNITS.` (including the period) in the next cell and then press TAB.  You should see a pop-up menu with a list of units."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a variable named `a` and give it the value of acceleration due to gravity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "9.8 meter/second<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$9.8\\ \\frac{\\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "9.8 <Unit('meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = 9.8 * meter / second**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create `t` and give it the value 4 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "4 second"
-      ],
-      "text/latex": [
-       "$4\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "4 <Unit('second')>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t = 4 * second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute the distance a penny would fall after `t` seconds with constant acceleration `a`.  Notice that the units of the result are correct."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "78.4 meter"
-      ],
-      "text/latex": [
-       "$78.4\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "78.4 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a * t**2 / 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise**: Compute the velocity of the penny after `t` seconds.  Check that the units of the result are correct."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "39.2 meter/second"
-      ],
-      "text/latex": [
-       "$39.2\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "39.2 <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "a * t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise**: Why would it be nonsensical to add `a` and `t`? What happens if you try?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# a + t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The error messages you get from Python are big and scary, but if you read them carefully, they contain a lot of useful information.\n",
-    "\n",
-    "1.  Start from the bottom and read up.\n",
-    "2.  The last line usually tells you what type of error happened, and sometimes additional information.\n",
-    "3.  The previous lines are a \"traceback\" of what was happening when the error occurred.  The first section of the traceback shows the code you wrote.  The following sections are often from Python libraries.\n",
-    "\n",
-    "In this example, you should get a `DimensionalityError`, which is defined by Pint to indicate that you have violated a rules of dimensional analysis: you cannot add quantities with different dimensions.\n",
-    "\n",
-    "Before you go on, you might want to delete the erroneous code so the notebook can run without errors."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Falling pennies\n",
-    "\n",
-    "Now let's solve the falling penny problem.\n",
-    "\n",
-    "Set `h` to the height of the Empire State Building:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "381 meter"
-      ],
-      "text/latex": [
-       "$381\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "381 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "h = 381 * meter"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute the time it would take a penny to fall, assuming constant acceleration.\n",
-    "\n",
-    "$ a t^2 / 2 = h $\n",
-    "\n",
-    "$ t = \\sqrt{2 h / a}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "8.817885349720552 second"
-      ],
-      "text/latex": [
-       "$8.817885349720552\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "8.817885349720552 <Unit('second')>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t = sqrt(2 * h / a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Given `t`, we can compute the velocity of the penny when it lands.\n",
-    "\n",
-    "$v = a t$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "86.41527642726142 meter/second"
-      ],
-      "text/latex": [
-       "$86.41527642726142\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "86.41527642726142 <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v = a * t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can convert from one set of units to another like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "hour"
-      ],
-      "text/latex": [
-       "$\\mathrm{hour}$"
-      ],
-      "text/plain": [
-       "<Unit('hour')>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mile = UNITS.mile\n",
-    "hour = UNITS.hour"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "193.30546802805438 mile/hour"
-      ],
-      "text/latex": [
-       "$193.30546802805438\\ \\frac{\\mathrm{mile}}{\\mathrm{hour}}$"
-      ],
-      "text/plain": [
-       "193.30546802805438 <Unit('mile / hour')>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v.to(mile/hour)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose you bring a 10 foot pole to the top of the Empire State Building and use it to drop the penny from `h` plus 10 feet.\n",
-    "\n",
-    "Define a variable named `foot` that contains the unit `foot` provided by `UNITS`.  Define a variable named `pole_height` and give it the value 10 feet.\n",
-    "\n",
-    "What happens if you add `h`, which is in units of meters, to `pole_height`, which is in units of feet?  What happens if you write the addition the other way around?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "384.048 meter"
-      ],
-      "text/latex": [
-       "$384.048\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "384.048 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "foot = UNITS.foot\n",
-    "pole_height = 10 * foot\n",
-    "\n",
-    "h + pole_height"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "1260.0 foot"
-      ],
-      "text/latex": [
-       "$1260.0\\ \\mathrm{foot}$"
-      ],
-      "text/plain": [
-       "1260.0 <Unit('foot')>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "pole_height + h"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** In reality, air resistance limits the velocity of the penny.  At about 18 m/s, the force of air resistance equals the force of gravity and the penny stops accelerating.\n",
-    "\n",
-    "As a simplification, let's assume that the acceleration of the penny is `a` until the penny reaches 18 m/s, and then 0 afterwards.  What is the total time for the penny to fall 381 m?\n",
-    "\n",
-    "You can break this question into three parts:\n",
-    "\n",
-    "1. How long until the penny reaches 18 m/s with constant acceleration `a`.\n",
-    "2. How far would the penny fall during that time?\n",
-    "3. How long to fall the remaining distance with constant velocity 18 m/s?\n",
-    "\n",
-    "Suggestion: Assign each intermediate result to a variable with a meaningful name.  And assign units to all quantities!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "18.0 meter/second"
-      ],
-      "text/latex": [
-       "$18.0\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "18.0 <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "v_terminal = 18 * meter / second "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time to reach terminal velocity 1.8367346938775508 second\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "t1 = v_terminal / a\n",
-    "print('Time to reach terminal velocity', t1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Height fallen in t1 16.530612244897956 meter\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "h1 = a * t1**2 / 2\n",
-    "print('Height fallen in t1', h1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time to fall remaining distance 20.24829931972789 second\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "t2 = (h - h1) / v_terminal\n",
-    "print('Time to fall remaining distance', t2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total falling time 22.085034013605444 second\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "t_total = t1 + t2\n",
-    "print('Total falling time', t_total)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Restart and run all\n",
-    "\n",
-    "When you change the contents of a cell, you have to run it again for those changes to have an effect.  If you forget to do that, the results can be confusing, because the code you are looking at is not the code you ran.\n",
-    "\n",
-    "If you ever lose track of which cells have run, and in what order, you should go to the Kernel menu and select \"Restart & Run All\".  Restarting the kernel means that all of your variables get deleted, and running all the cells means all of your code will run again, in the right order.\n",
-    "\n",
-    "**Exercise:**  Select \"Restart & Run All\" now and confirm that it does what you want."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap02soln.ipynb b/soln/chap02soln.ipynb
deleted file mode 100644
index fc263c0c..00000000
--- a/soln/chap02soln.ipynb
+++ /dev/null
@@ -1,2078 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 2\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim library\n",
-    "from modsim import *\n",
-    "\n",
-    "# set the random number generator\n",
-    "np.random.seed(7)\n",
-    "\n",
-    "# If this cell runs successfully, it produces no output."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Modeling a bikeshare system"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll start with a `State` object that represents the number of bikes at each station.\n",
-    "\n",
-    "When you display a `State` object, it lists the state variables and their values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         10\n",
-       "wellesley     2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can access the state variables using dot notation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare.olin"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare.wellesley"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** What happens if you spell the name of a state variable wrong?  Edit the previous cell, change the spelling of `wellesley`, and run the cell again.\n",
-    "\n",
-    "The error message uses the word \"attribute\", which is another name for what we are calling a state variable. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Add a third attribute called `babson` with initial value 0, and display the state of `bikeshare` again."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Updating\n",
-    "\n",
-    "We can use the update operators `+=` and `-=` to change state variables."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare.olin -= 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we display `bikeshare`, we should see the change."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         9\n",
-       "wellesley    2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Of course, if we subtract a bike from `olin`, we should add it to `wellesley`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         9\n",
-       "wellesley    3\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare.wellesley += 1\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Functions\n",
-    "\n",
-    "We can take the code we've written so far and encapsulate it in a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_wellesley():\n",
-    "    bikeshare.olin -= 1\n",
-    "    bikeshare.wellesley += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When you define a function, it doesn't run the statements inside the function, yet.  When you call the function, it runs the statements inside."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         8\n",
-       "wellesley    4\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bike_to_wellesley()\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "One common error is to omit the parentheses, which has the effect of looking up the function, but not calling it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.bike_to_wellesley()>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bike_to_wellesley"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The output indicates that `bike_to_wellesley` is a function defined in a \"namespace\" called `__main__`, but you don't have to understand what that means."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Define a function called `bike_to_olin` that moves a bike from Wellesley to Olin.  Call the new function and display `bikeshare`  to confirm that it works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def bike_to_olin():\n",
-    "    bikeshare.wellesley -= 1\n",
-    "    bikeshare.olin += 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         9\n",
-       "wellesley    3\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "bike_to_olin()\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Conditionals"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`modsim.py` provides `flip`, which takes a probability and returns either `True` or `False`, which are special values defined by Python.\n",
-    "\n",
-    "The Python function `help` looks up a function and displays its documentation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on function flip in module modsim.modsim:\n",
-      "\n",
-      "flip(p=0.5)\n",
-      "    Flips a coin with the given probability.\n",
-      "    \n",
-      "    p: float 0-1\n",
-      "    \n",
-      "    returns: boolean (True or False)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "help(flip)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following example, the probability is 0.7 or 70%.  If you run this cell several times, you should get `True` about 70% of the time and `False` about 30%."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flip(0.7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following example, we use `flip` as part of an if statement.  If the result from `flip` is `True`, we print `heads`; otherwise we do nothing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if flip(0.7):\n",
-    "    print('heads')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With an else clause, we can print heads or tails depending on whether `flip` returns `True` or `False`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "heads\n"
-     ]
-    }
-   ],
-   "source": [
-    "if flip(0.7):\n",
-    "    print('heads')\n",
-    "else:\n",
-    "    print('tails')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step\n",
-    "\n",
-    "Now let's get back to the bikeshare state.  Again let's start with a new `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         10\n",
-       "wellesley     2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Suppose that in any given minute, there is a 50% chance that a student picks up a bike at Olin and rides to Wellesley.  We can simulate that like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         10\n",
-       "wellesley     2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "if flip(0.5):\n",
-    "    bike_to_wellesley()\n",
-    "    print('Moving a bike to Wellesley')\n",
-    "\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And maybe at the same time, there is also a 40% chance that a student at Wellesley rides to Olin."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         10\n",
-       "wellesley     2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "if flip(0.4):\n",
-    "    bike_to_olin()\n",
-    "    print('Moving a bike to Olin')\n",
-    "\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can wrap that code in a function called `step` that simulates one time step.  In any given minute, a student might ride from Olin to Wellesley, from Wellesley to Olin, or both, or neither, depending on the results of `flip`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step():\n",
-    "    if flip(0.5):\n",
-    "        bike_to_wellesley()\n",
-    "        print('Moving a bike to Wellesley')\n",
-    "        \n",
-    "    if flip(0.4):\n",
-    "        bike_to_olin()\n",
-    "        print('Moving a bike to Olin')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since this function takes no parameters, we call it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         10\n",
-       "wellesley     2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "step()\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Parameters\n",
-    "\n",
-    "As defined in the previous section, `step` is not as useful as it could be, because the probabilities `0.5` and `0.4` are \"hard coded\".\n",
-    "\n",
-    "It would be better to generalize this function so it takes the probabilities `p1` and `p2` as parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step(p1, p2):\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley()\n",
-    "        print('Moving a bike to Wellesley')\n",
-    "        \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin()\n",
-    "        print('Moving a bike to Olin')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can call it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Moving a bike to Wellesley\n",
-      "Moving a bike to Olin\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         10\n",
-       "wellesley     2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "step(0.5, 0.4)\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** At the beginning of `step`, add a print statement that displays the values of `p1` and `p2`.  Call it again with values `0.3`, and `0.2`, and confirm that the values of the parameters are what you expect. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.3 0.2\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def step(p1, p2):\n",
-    "    print(p1, p2)\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley()\n",
-    "        print('Moving a bike to Wellesley')\n",
-    "        \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin()\n",
-    "        print('Moving a bike to Olin')\n",
-    "        \n",
-    "step(0.3, 0.2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## For loop"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before we go on, I'll redefine `step` without the print statements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step(p1, p2):\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley()\n",
-    "    \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And let's start again with a new `State` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         10\n",
-       "wellesley     2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use a `for` loop to move 4 bikes from Olin to Wellesley."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         6\n",
-       "wellesley    6\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(4):\n",
-    "    bike_to_wellesley()\n",
-    "    \n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or we can simulate 4 random time steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         5\n",
-       "wellesley    7\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(4):\n",
-    "    step(0.3, 0.2)\n",
-    "    \n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If each step corresponds to a minute, we can simulate an entire hour like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         4\n",
-       "wellesley    8\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(60):\n",
-    "    step(0.3, 0.2)\n",
-    "\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After 60 minutes, you might see that the number of bike at Olin is negative.  We'll fix that problem in the next notebook.\n",
-    "\n",
-    "But first, we want to plot the results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## TimeSeries\n",
-    "\n",
-    "`modsim.py` provides an object called a `TimeSeries` that can contain a sequence of values changing over time.\n",
-    "\n",
-    "We can create a new, empty `TimeSeries` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Series([], dtype: float64)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = TimeSeries()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can add a value to the `TimeSeries` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "0    4\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results[0] = bikeshare.olin\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `0` in brackets is an `index` that indicates that this value is associated with time step 0.\n",
-    "\n",
-    "Now we'll use a for loop to save the results of the simulation.  I'll start one more time with a new `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         10\n",
-       "wellesley     2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a for loop that runs 10 steps and stores the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(10):\n",
-    "    step(0.3, 0.2)\n",
-    "    results[i] = bikeshare.olin"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can display the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "0    10\n",
-       "1    10\n",
-       "2     9\n",
-       "3     9\n",
-       "4    10\n",
-       "5    10\n",
-       "6    10\n",
-       "7    11\n",
-       "8    11\n",
-       "9    11\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `TimeSeries` is a specialized version of a Pandas `Series`, so we can use any of the functions provided by `Series`, including several that compute summary statistics:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10.1"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    10.000000\n",
-       "mean     10.100000\n",
-       "std       0.737865\n",
-       "min       9.000000\n",
-       "25%      10.000000\n",
-       "50%      10.000000\n",
-       "75%      10.750000\n",
-       "max      11.000000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can read the documentation of `Series` [here](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting\n",
-    "\n",
-    "We can also plot the results like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap02-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(results, label='Olin')\n",
-    "\n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Time step (min)', \n",
-    "         ylabel='Number of bikes')\n",
-    "\n",
-    "savefig('figs/chap02-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`decorate`, which is defined in the `modsim` library, adds a title and labels the axes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on function decorate in module modsim.modsim:\n",
-      "\n",
-      "decorate(**options)\n",
-      "    Decorate the current axes.\n",
-      "    \n",
-      "    Call decorate with keyword arguments like\n",
-      "    \n",
-      "    decorate(title='Title',\n",
-      "             xlabel='x',\n",
-      "             ylabel='y')\n",
-      "    \n",
-      "    The keyword arguments can be any of the axis properties\n",
-      "    \n",
-      "    https://matplotlib.org/api/axes_api.html\n",
-      "    \n",
-      "    In addition, you can use `legend=False` to suppress the legend.\n",
-      "    \n",
-      "    And you can use `loc` to indicate the location of the legend\n",
-      "    (the default value is 'best')\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "help(decorate)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`savefig()` saves a figure in a file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on function savefig in module modsim.modsim:\n",
-      "\n",
-      "savefig(filename, **options)\n",
-      "    Save the current figure.\n",
-      "    \n",
-      "    Keyword arguments are passed along to plt.savefig\n",
-      "    \n",
-      "    https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n",
-      "    \n",
-      "    filename: string\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "help(savefig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The suffix of the filename indicates the format you want.  This example saves the current figure in a PDF file named `chap01-fig01.pdf`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Wrap the code from this section in a function named `run_simulation` that takes three parameters, named `p1`, `p2`, and `num_steps`.\n",
-    "\n",
-    "It should:\n",
-    "\n",
-    "1. Create a `TimeSeries` object to hold the results.\n",
-    "2. Use a for loop to run `step` the number of times specified by `num_steps`, passing along the specified values of `p1` and `p2`.\n",
-    "3. After each step, it should save the number of bikes at Olin in the `TimeSeries`.\n",
-    "4. After the for loop, it should plot the results and\n",
-    "5. Decorate the axes.\n",
-    "\n",
-    "To test your function:\n",
-    "\n",
-    "1. Create a `State` object with the initial state of the system.\n",
-    "2. Call `run_simulation` with appropriate parameters.\n",
-    "3. Save the resulting figure.\n",
-    "\n",
-    "Optional:\n",
-    "\n",
-    "1. Extend your solution so it creates two `TimeSeries` objects, keeps track of the number of bikes at Olin *and* at Wellesley, and plots both series at the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def run_simulation(p1, p2, num_steps):\n",
-    "    olin = TimeSeries()\n",
-    "    wellesley = TimeSeries()\n",
-    "    \n",
-    "    for i in range(num_steps):\n",
-    "        step(p1, p2)\n",
-    "        olin[i] = bikeshare.olin\n",
-    "        wellesley[i] = bikeshare.wellesley\n",
-    "        \n",
-    "    plot(olin, label='Olin')\n",
-    "    plot(wellesley, label='Wellesley')\n",
-    "    decorate(title='Olin-Wellesley Bikeshare',\n",
-    "             xlabel='Time step (min)', \n",
-    "             ylabel='Number of bikes')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "bikeshare = State(olin=10, wellesley=2)\n",
-    "run_simulation(0.3, 0.2, 60)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Opening the hood\n",
-    "\n",
-    "The functions in `modsim.py` are built on top of several widely-used Python libraries, especially NumPy, SciPy, and Pandas.  These libraries are powerful but can be hard to use.  The intent of `modsim.py` is to give you the power of these libraries while making it easy to get started.\n",
-    "\n",
-    "In the future, you might want to use these libraries directly, rather than using `modsim.py`.  So we will pause occasionally to open the hood and let you see how `modsim.py` works.\n",
-    "\n",
-    "You don't need to know anything in these sections, so if you are already feeling overwhelmed, you might want to skip them.  But if you are curious, read on."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pandas\n",
-    "\n",
-    "This chapter introduces two objects, `State` and `TimeSeries`.  Both are based on the `Series` object defined by Pandas, which is a library primarily used for data science.\n",
-    "\n",
-    "You can read the documentation of the `Series` object [here](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html)\n",
-    "\n",
-    "The primary differences between `TimeSeries` and `Series` are:\n",
-    "\n",
-    "1. I made it easier to create a new, empty `Series` while avoiding a [confusing inconsistency](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html).\n",
-    "\n",
-    "2. I provide a function so the `Series` looks good when displayed in Jupyter.\n",
-    "\n",
-    "3. I provide a function called `set` that we'll use later.\n",
-    "\n",
-    "`State` has all of those capabilities; in addition, it provides an easier way to initialize state variables, and it provides functions called `T` and `dt`, which will help us avoid a confusing error later."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pyplot\n",
-    "\n",
-    "The `plot` function in `modsim.py` is based on the `plot` function in Pyplot, which is part of Matplotlib.  You can read the documentation of `plot` [here](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html).\n",
-    "\n",
-    "`decorate` provides a convenient way to call the `pyplot` functions `title`, `xlabel`, and `ylabel`, and `legend`.  It also avoids an annoying warning message if you try to make a legend when you don't have any labelled lines."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on function decorate in module modsim.modsim:\n",
-      "\n",
-      "decorate(**options)\n",
-      "    Decorate the current axes.\n",
-      "    \n",
-      "    Call decorate with keyword arguments like\n",
-      "    \n",
-      "    decorate(title='Title',\n",
-      "             xlabel='x',\n",
-      "             ylabel='y')\n",
-      "    \n",
-      "    The keyword arguments can be any of the axis properties\n",
-      "    \n",
-      "    https://matplotlib.org/api/axes_api.html\n",
-      "    \n",
-      "    In addition, you can use `legend=False` to suppress the legend.\n",
-      "    \n",
-      "    And you can use `loc` to indicate the location of the legend\n",
-      "    (the default value is 'best')\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "help(decorate)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### NumPy\n",
-    "\n",
-    "The `flip` function in `modsim.py` uses NumPy's `random` function to generate a random number between 0 and 1.\n",
-    "\n",
-    "You can get the source code for `flip` by running the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "def flip(p=0.5):\n",
-      "    \"\"\"Flips a coin with the given probability.\n",
-      "\n",
-      "    p: float 0-1\n",
-      "\n",
-      "    returns: boolean (True or False)\n",
-      "    \"\"\"\n",
-      "    return np.random.random() < p\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "source_code(flip)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap03soln.ipynb b/soln/chap03soln.ipynb
deleted file mode 100644
index d0c36ba3..00000000
--- a/soln/chap03soln.ipynb
+++ /dev/null
@@ -1,1274 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 3\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim library\n",
-    "from modsim import *\n",
-    "\n",
-    "# set the random number generator\n",
-    "np.random.seed(7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## More than one State object\n",
-    "\n",
-    "Here's the code from the previous chapter, with two changes:\n",
-    "\n",
-    "1. I've added DocStrings that explain what each function does, and what parameters it takes.\n",
-    "\n",
-    "2. I've added a parameter named `state` to the functions so they work with whatever `State` object we give them, instead of always using `bikeshare`.  That makes it possible to work with more than one `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step(state, p1, p2):\n",
-    "    \"\"\"Simulate one minute of time.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    p1: probability of an Olin->Wellesley customer arrival\n",
-    "    p2: probability of a Wellesley->Olin customer arrival\n",
-    "    \"\"\"\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley(state)\n",
-    "    \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin(state)\n",
-    "        \n",
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1\n",
-    "    \n",
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1\n",
-    "    \n",
-    "def decorate_bikeshare():\n",
-    "    \"\"\"Add a title and label the axes.\"\"\"\n",
-    "    decorate(title='Olin-Wellesley Bikeshare',\n",
-    "             xlabel='Time step (min)', \n",
-    "             ylabel='Number of bikes')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's `run_simulation`, which is a solution to the exercise at the end of the previous notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(state, p1, p2, num_steps):\n",
-    "    \"\"\"Simulate the given number of time steps.\n",
-    "    \n",
-    "    state: State object\n",
-    "    p1: probability of an Olin->Wellesley customer arrival\n",
-    "    p2: probability of a Wellesley->Olin customer arrival\n",
-    "    num_steps: number of time steps\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()    \n",
-    "    for i in range(num_steps):\n",
-    "        step(state, p1, p2)\n",
-    "        results[i] = state.olin\n",
-    "        \n",
-    "    plot(results, label='Olin')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can create more than one `State` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         10\n",
-       "wellesley     2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare1 = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin          2\n",
-       "wellesley    10\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare2 = State(olin=2, wellesley=10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Whenever we call a function, we indicate which `State` object to work with:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bike_to_olin(bikeshare1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bike_to_wellesley(bikeshare2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And you can confirm that the different objects are getting updated independently:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         11\n",
-       "wellesley     1\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin          1\n",
-       "wellesley    11\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Negative bikes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the code we have so far, the number of bikes at one of the locations can go negative, and the number of bikes at the other location can exceed the actual number of bikes in the system.\n",
-    "\n",
-    "If you run this simulation a few times, it happens often."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)\n",
-    "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
-    "decorate_bikeshare()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can fix this problem using the `return` statement to exit the function early if an update would cause negative bikes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.olin == 0:\n",
-    "        return\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1\n",
-    "    \n",
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.wellesley == 0:\n",
-    "        return\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now if you run the simulation again, it should behave."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)\n",
-    "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
-    "decorate_bikeshare()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Comparison operators"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `if` statements in the previous section used the comparison operator `==`.  The other comparison operators are listed in the book.\n",
-    "\n",
-    "It is easy to confuse the comparison operator `==` with the assignment operator `=`.\n",
-    "\n",
-    "Remember that `=` creates a variable or gives an existing variable a new value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Whereas `==` compares two values and returns `True` if they are equal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x == 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can use `==` in an `if` statement."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "yes, x is 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "if x == 5:\n",
-    "    print('yes, x is 5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But if you use `=` in an `if` statement, you get an error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# If you remove the # from the if statement and run it, you'll get\n",
-    "# SyntaxError: invalid syntax\n",
-    "\n",
-    "#if x = 5:\n",
-    "#    print('yes, x is 5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Add an `else` clause to the `if` statement above, and print an appropriate message.\n",
-    "\n",
-    "Replace the `==` operator with one or two of the other comparison operators, and confirm they do what you expect."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have a working simulation, we'll use it to evaluate alternative designs and see how good or bad they are.  The metric we'll use is the number of customers who arrive and find no bikes available, which might indicate a design problem."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we'll make a new `State` object that creates and initializes additional state variables to keep track of the metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>olin_empty</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley_empty</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin               10\n",
-       "wellesley           2\n",
-       "olin_empty          0\n",
-       "wellesley_empty     0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2, \n",
-    "                  olin_empty=0, wellesley_empty=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we need versions of `bike_to_wellesley` and `bike_to_olin` that update the metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.olin == 0:\n",
-    "        state.olin_empty += 1\n",
-    "        return\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1\n",
-    "    \n",
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.wellesley == 0:\n",
-    "        state.wellesley_empty += 1\n",
-    "        return\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now when we run a simulation, it keeps track of unhappy customers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
-    "decorate_bikeshare()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After the simulation, we can print the number of unhappy customers at each location."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare.wellesley_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:** As another metric, we might be interested in the time until the first customer arrives and doesn't find a bike.  To make that work, we have to add a \"clock\" to keep track of how many time steps have elapsed:\n",
-    "\n",
-    "1. Create a new `State` object with an additional state variable, `clock`, initialized to 0. \n",
-    "\n",
-    "2. Write a modified version of `step` that adds one to the clock each time it is invoked.\n",
-    "\n",
-    "Test your code by running the simulation and check the value of `clock` at the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>olin_empty</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley_empty</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>clock</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin               10\n",
-       "wellesley           2\n",
-       "olin_empty          0\n",
-       "wellesley_empty     0\n",
-       "clock               0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2, \n",
-    "                  olin_empty=0, wellesley_empty=0,\n",
-    "                  clock=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def step(state, p1, p2):\n",
-    "    \"\"\"Simulate one minute of time.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    p1: probability of an Olin->Wellesley customer arrival\n",
-    "    p2: probability of a Wellesley->Olin customer arrival\n",
-    "    \"\"\"\n",
-    "    state.clock += 1\n",
-    "    \n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley(state)\n",
-    "    \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin(state)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
-    "decorate_bikeshare()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>olin_empty</th>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley_empty</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>clock</th>\n",
-       "      <td>60</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin                1\n",
-       "wellesley          11\n",
-       "olin_empty          5\n",
-       "wellesley_empty     0\n",
-       "clock              60\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Continuing the previous exercise, let's record the time when the first customer arrives and doesn't find a bike.\n",
-    "\n",
-    "1. Create a new `State` object with an additional state variable, `t_first_empty`, initialized to -1 as a special value to indicate that it has not been set. \n",
-    "\n",
-    "2. Write a modified version of `step` that checks whether`olin_empty` and `wellesley_empty` are 0.  If not, it should set `t_first_empty` to `clock` (but only if `t_first_empty` has not already been set).\n",
-    "\n",
-    "Test your code by running the simulation and printing the values of `olin_empty`, `wellesley_empty`, and `t_first_empty` at the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>olin_empty</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley_empty</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>clock</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_first_empty</th>\n",
-       "      <td>-1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin               10\n",
-       "wellesley           2\n",
-       "olin_empty          0\n",
-       "wellesley_empty     0\n",
-       "clock               0\n",
-       "t_first_empty      -1\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "bikeshare = State(olin=10, wellesley=2, \n",
-    "                  olin_empty=0, wellesley_empty=0,\n",
-    "                  clock=0, t_first_empty=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def step(state, p1, p2):\n",
-    "    \"\"\"Simulate one minute of time.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    p1: probability of an Olin->Wellesley customer arrival\n",
-    "    p2: probability of a Wellesley->Olin customer arrival\n",
-    "    \"\"\"\n",
-    "    state.clock += 1\n",
-    "    \n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley(state)\n",
-    "    \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin(state)\n",
-    "        \n",
-    "    if state.t_first_empty != -1:\n",
-    "        return\n",
-    "    \n",
-    "    if state.olin_empty + state.wellesley_empty > 0:\n",
-    "        state.t_first_empty = state.clock"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
-    "decorate_bikeshare()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>olin_empty</th>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley_empty</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>clock</th>\n",
-       "      <td>60</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_first_empty</th>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin                0\n",
-       "wellesley          12\n",
-       "olin_empty          8\n",
-       "wellesley_empty     0\n",
-       "clock              60\n",
-       "t_first_empty      30\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "bikeshare"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap04soln.ipynb b/soln/chap04soln.ipynb
deleted file mode 100644
index cd178939..00000000
--- a/soln/chap04soln.ipynb
+++ /dev/null
@@ -1,1186 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 4\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim library\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Returning values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a simple function that returns a value:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def add_five(x):\n",
-    "    return x + 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we call it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y = add_five(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you run a function on the last line of a cell, Jupyter displays the result:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "add_five(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But that can be a bad habit, because usually if you call a function and don't assign the result in a variable, the result gets discarded.\n",
-    "\n",
-    "In the following example, Jupyter shows the second result, but the first result just disappears."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "add_five(3)\n",
-    "add_five(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When you call a function that returns a variable, it is generally a good idea to assign the result to a variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8 10\n"
-     ]
-    }
-   ],
-   "source": [
-    "y1 = add_five(3)\n",
-    "y2 = add_five(5)\n",
-    "\n",
-    "print(y1, y2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write a function called `make_state` that creates a `State` object with the state variables `olin=10` and `wellesley=2`, and then returns the new `State` object.\n",
-    "\n",
-    "Write a line of code that calls `make_state` and assigns the result to a variable named `init`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def make_state():\n",
-    "    state = State(olin=10, wellesley=2)\n",
-    "    return state"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin         10\n",
-       "wellesley     2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "init = make_state()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Running simulations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the code from the previous notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step(state, p1, p2):\n",
-    "    \"\"\"Simulate one minute of time.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    p1: probability of an Olin->Wellesley customer arrival\n",
-    "    p2: probability of a Wellesley->Olin customer arrival\n",
-    "    \"\"\"\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley(state)\n",
-    "    \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin(state)\n",
-    "        \n",
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.olin == 0:\n",
-    "        state.olin_empty += 1\n",
-    "        return\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1\n",
-    "    \n",
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.wellesley == 0:\n",
-    "        state.wellesley_empty += 1\n",
-    "        return\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1\n",
-    "    \n",
-    "def decorate_bikeshare():\n",
-    "    \"\"\"Add a title and label the axes.\"\"\"\n",
-    "    decorate(title='Olin-Wellesley Bikeshare',\n",
-    "             xlabel='Time step (min)', \n",
-    "             ylabel='Number of bikes')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a modified version of `run_simulation` that creates a `State` object, runs the simulation, and returns the `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(p1, p2, num_steps):\n",
-    "    \"\"\"Simulate the given number of time steps.\n",
-    "    \n",
-    "    p1: probability of an Olin->Wellesley customer arrival\n",
-    "    p2: probability of a Wellesley->Olin customer arrival\n",
-    "    num_steps: number of time steps\n",
-    "    \"\"\"\n",
-    "    state = State(olin=10, wellesley=2, \n",
-    "                  olin_empty=0, wellesley_empty=0)\n",
-    "                    \n",
-    "    for i in range(num_steps):\n",
-    "        step(state, p1, p2)\n",
-    "        \n",
-    "    return state"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now `run_simulation` doesn't plot anything:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>olin</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley</th>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>olin_empty</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wellesley_empty</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "olin                1\n",
-       "wellesley          11\n",
-       "olin_empty          2\n",
-       "wellesley_empty     2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "state = run_simulation(0.4, 0.2, 60)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But after the simulation, we can read the metrics from the `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "state.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run simulations with different values for the parameters.  When `p1` is small, we probably don't run out of bikes at Olin."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "state = run_simulation(0.2, 0.2, 60)\n",
-    "state.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When `p1` is large, we probably do."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "state = run_simulation(0.6, 0.2, 60)\n",
-    "state.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## More for loops"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`linspace` creates a NumPy array of equally spaced numbers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.  , 0.25, 0.5 , 0.75, 1.  ])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p1_array = linspace(0, 1, 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use an array in a `for` loop, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0\n",
-      "0.25\n",
-      "0.5\n",
-      "0.75\n",
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "for p1 in p1_array:\n",
-    "    print(p1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This will come in handy in the next section.\n",
-    "\n",
-    "`linspace` is defined in `modsim.py`.  You can get the documentation using `help`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on function linspace in module modsim.modsim:\n",
-      "\n",
-      "linspace(start, stop, num=50, **options)\n",
-      "    Returns an array of evenly-spaced values in the interval [start, stop].\n",
-      "    \n",
-      "    start: first value\n",
-      "    stop: last value\n",
-      "    num: number of values\n",
-      "    \n",
-      "    Also accepts the same keyword arguments as np.linspace.  See\n",
-      "    https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html\n",
-      "    \n",
-      "    returns: array or Quantity\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "help(linspace)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`linspace` is based on a NumPy function with the same name.  [Click here](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html) to read more about how to use it."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** \n",
-    "Use `linspace` to make an array of 10 equally spaced numbers from 1 to 10 (including both)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "linspace(1, 10, 10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** The `modsim` library provides a related function called `linrange`.  You can view the documentation by running the following cell:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on function linrange in module modsim.modsim:\n",
-      "\n",
-      "linrange(start=0, stop=None, step=1, endpoint=False, **options)\n",
-      "    Returns an array of evenly-spaced values in an interval.\n",
-      "    \n",
-      "    By default, the last value in the array is `stop-step`\n",
-      "    (at least approximately).\n",
-      "    If you provide the keyword argument `endpoint=True`,\n",
-      "    the last value in the array is `stop`.\n",
-      "    \n",
-      "    This function works best if the space between start and stop\n",
-      "    is divisible by step; otherwise the results might be surprising.\n",
-      "    \n",
-      "    start: first value\n",
-      "    stop: last value\n",
-      "    step: space between values\n",
-      "    \n",
-      "    returns: NumPy array\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "help(linrange)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use `linrange` to make an array of numbers from 1 to 11 with a step size of 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1,  3,  5,  7,  9, 11])"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "linrange(1, 11, 2, endpoint=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sweeping parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`p1_array` contains a range of values for `p1`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p2 = 0.2\n",
-    "num_steps = 60\n",
-    "p1_array = linspace(0, 1, 11)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following loop runs a simulation for each value of `p1` in `p1_array`; after each simulation, it prints the number of unhappy customers at the Olin station:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0 0\n",
-      "0.1 0\n",
-      "0.2 0\n",
-      "0.30000000000000004 4\n",
-      "0.4 0\n",
-      "0.5 14\n",
-      "0.6000000000000001 5\n",
-      "0.7000000000000001 23\n",
-      "0.8 31\n",
-      "0.9 32\n",
-      "1.0 39\n"
-     ]
-    }
-   ],
-   "source": [
-    "for p1 in p1_array:\n",
-    "    state = run_simulation(p1, p2, num_steps)\n",
-    "    print(p1, state.olin_empty)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can do the same thing, but storing the results in a `SweepSeries` instead of printing them.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sweep = SweepSeries()\n",
-    "\n",
-    "for p1 in p1_array:\n",
-    "    state = run_simulation(p1, p2, num_steps)\n",
-    "    sweep[p1] = state.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then we can plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(sweep, label='Olin')\n",
-    "\n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Arrival rate at Olin (p1 in customers/min)', \n",
-    "         ylabel='Number of unhappy customers')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:** Wrap this code in a function named `sweep_p1` that takes an array called `p1_array` as a parameter.  It should create a new `SweepSeries`, run a simulation for each value of `p1` in `p1_array`, store the results in the `SweepSeries`, and return the `SweepSeries`.\n",
-    "\n",
-    "Use your function to plot the number of unhappy customers at Olin as a function of `p1`.  Label the axes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def sweep_p1(p1_array):\n",
-    "    p2 = 0.2\n",
-    "    num_steps = 60\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for p1 in p1_array:\n",
-    "        state = run_simulation(p1, p2, num_steps)\n",
-    "        sweep[p1] = state.olin_empty\n",
-    "        \n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "p1_array = linspace(0, 1, 101)\n",
-    "sweep = sweep_p1(p1_array)\n",
-    "plot(sweep, 'bo', label='Olin')\n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Arrival rate at Olin (p1 in customers/min)', \n",
-    "         ylabel='Number of unhappy customers')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write a function called `sweep_p2` that runs simulations with `p1=0.5` and a range of values for `p2`.  It should store the results in a `SweepSeries` and return the `SweepSeries`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def sweep_p2(p2_array):\n",
-    "    p1 = 0.5\n",
-    "    num_steps = 60\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for p2 in p2_array:\n",
-    "        state = run_simulation(p1, p2, num_steps)\n",
-    "        sweep[p2] = state.olin_empty\n",
-    "        \n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "p2_array = linspace(0, 1, 101)\n",
-    "sweep = sweep_p2(p2_array)\n",
-    "plot(sweep, 'bo', label='Olin')\n",
-    "\n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Arrival rate at Wellesley (p2 in customers/min)', \n",
-    "         ylabel='Number of unhappy customers')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Optional Exercises\n",
-    "\n",
-    "The following two exercises are a little more challenging.  If you are comfortable with what you have learned so far, you should give them a try.  If you feel like you have your hands full, you might want to skip them for now.\n",
-    "\n",
-    "**Exercise:** Because our simulations are random, the results vary from one run to another, and the results of a parameter sweep tend to be noisy.  We can get a clearer picture of the relationship between a parameter and a metric by running multiple simulations with the same parameter and taking the average of the results.\n",
-    "\n",
-    "Write a function called `run_multiple_simulations` that takes as parameters `p1`, `p2`, `num_steps`, and `num_runs`.\n",
-    "\n",
-    "`num_runs` specifies how many times it should call `run_simulation`.\n",
-    "\n",
-    "After each run, it should store the total number of unhappy customers (at Olin or Wellesley) in a `TimeSeries`.  At the end, it should return the `TimeSeries`.\n",
-    "\n",
-    "Test your function with parameters\n",
-    "\n",
-    "```\n",
-    "p1 = 0.3\n",
-    "p2 = 0.3\n",
-    "num_steps = 60\n",
-    "num_runs = 10\n",
-    "```\n",
-    "\n",
-    "Display the resulting `TimeSeries` and use the `mean` function provided by the `TimeSeries` object to compute the average number of unhappy customers (see Section 2.7)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def run_multiple_simulations(p1, p2, num_steps, num_runs):\n",
-    "    results = TimeSeries()\n",
-    "    \n",
-    "    for i in range(num_runs):\n",
-    "        state = run_simulation(p1, p2, num_steps)\n",
-    "        results[i] = state.olin_empty + state.wellesley_empty\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "0    0\n",
-       "1    0\n",
-       "2    0\n",
-       "3    0\n",
-       "4    4\n",
-       "5    2\n",
-       "6    0\n",
-       "7    4\n",
-       "8    4\n",
-       "9    0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "p1 = 0.3\n",
-    "p2 = 0.3\n",
-    "num_steps = 60\n",
-    "num_runs = 10\n",
-    "run_multiple_simulations(p1, p2, num_steps, num_runs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Continuting the previous exercise, use `run_multiple_simulations` to run simulations with a range of values for `p1` and\n",
-    "\n",
-    "```\n",
-    "p2 = 0.3\n",
-    "num_steps = 60\n",
-    "num_runs = 20\n",
-    "```\n",
-    "\n",
-    "Store the results in a `SweepSeries`, then plot the average number of unhappy customers as a function of `p1`.  Label the axes.\n",
-    "\n",
-    "What value of `p1` minimizes the average number of unhappy customers?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "p1_array = linspace(0, 1, 20)\n",
-    "p2 = 0.3\n",
-    "num_steps = 60\n",
-    "num_runs = 20\n",
-    "\n",
-    "sweep = SweepSeries()\n",
-    "for p1 in p1_array:\n",
-    "    results = run_multiple_simulations(p1, p2, num_steps, num_runs)\n",
-    "    sweep[p1] = results.mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot(sweep, label='total', color='green')\n",
-    "    \n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Arrival rate at Olin (p1 in customers/min)', \n",
-    "         ylabel='Average total unhappy customers')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap05soln.ipynb b/soln/chap05soln.ipynb
deleted file mode 100644
index 2de345b2..00000000
--- a/soln/chap05soln.ipynb
+++ /dev/null
@@ -1,1732 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 5\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reading data\n",
-    "\n",
-    "Pandas is a library that provides tools for reading and processing data.  `read_html` reads a web page from a file or the Internet and creates one `DataFrame` for each table on the page."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas import read_html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The data directory contains a downloaded copy of https://en.wikipedia.org/wiki/World_population_estimates\n",
-    "\n",
-    "The arguments of `read_html` specify the file to read and how to interpret the tables in the file.  The result, `tables`, is a sequence of `DataFrame` objects; `len(tables)` reports the length of the sequence."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filename = 'data/World_population_estimates.html'\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "len(tables)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can select the `DataFrame` we want using the bracket operator.  The tables are numbered from 0, so `tables[2]` is actually the third table on the page.\n",
-    "\n",
-    "`head` selects the header and the first five rows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>United States Census Bureau (2017)[28]</th>\n",
-       "      <th>Population Reference Bureau (1973–2016)[15]</th>\n",
-       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
-       "      <th>Maddison (2008)[17]</th>\n",
-       "      <th>HYDE (2007)[24]</th>\n",
-       "      <th>Tanton (1994)[18]</th>\n",
-       "      <th>Biraben (1980)[19]</th>\n",
-       "      <th>McEvedy &amp; Jones (1978)[20]</th>\n",
-       "      <th>Thomlinson (1975)[21]</th>\n",
-       "      <th>Durand (1974)[22]</th>\n",
-       "      <th>Clark (1967)[23]</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1950</th>\n",
-       "      <td>2557628654</td>\n",
-       "      <td>2.516000e+09</td>\n",
-       "      <td>2.525149e+09</td>\n",
-       "      <td>2.544000e+09</td>\n",
-       "      <td>2.527960e+09</td>\n",
-       "      <td>2.400000e+09</td>\n",
-       "      <td>2.527000e+09</td>\n",
-       "      <td>2.500000e+09</td>\n",
-       "      <td>2.400000e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.486000e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1951</th>\n",
-       "      <td>2594939877</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.572851e+09</td>\n",
-       "      <td>2.571663e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1952</th>\n",
-       "      <td>2636772306</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.619292e+09</td>\n",
-       "      <td>2.617949e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1953</th>\n",
-       "      <td>2682053389</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.665865e+09</td>\n",
-       "      <td>2.665959e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1954</th>\n",
-       "      <td>2730228104</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.713172e+09</td>\n",
-       "      <td>2.716927e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      United States Census Bureau (2017)[28]  \\\n",
-       "Year                                           \n",
-       "1950                              2557628654   \n",
-       "1951                              2594939877   \n",
-       "1952                              2636772306   \n",
-       "1953                              2682053389   \n",
-       "1954                              2730228104   \n",
-       "\n",
-       "      Population Reference Bureau (1973–2016)[15]  \\\n",
-       "Year                                                \n",
-       "1950                                 2.516000e+09   \n",
-       "1951                                          NaN   \n",
-       "1952                                          NaN   \n",
-       "1953                                          NaN   \n",
-       "1954                                          NaN   \n",
-       "\n",
-       "      United Nations Department of Economic and Social Affairs (2015)[16]  \\\n",
-       "Year                                                                        \n",
-       "1950                                       2.525149e+09                     \n",
-       "1951                                       2.572851e+09                     \n",
-       "1952                                       2.619292e+09                     \n",
-       "1953                                       2.665865e+09                     \n",
-       "1954                                       2.713172e+09                     \n",
-       "\n",
-       "      Maddison (2008)[17]  HYDE (2007)[24]  Tanton (1994)[18]  \\\n",
-       "Year                                                            \n",
-       "1950         2.544000e+09     2.527960e+09       2.400000e+09   \n",
-       "1951         2.571663e+09              NaN                NaN   \n",
-       "1952         2.617949e+09              NaN                NaN   \n",
-       "1953         2.665959e+09              NaN                NaN   \n",
-       "1954         2.716927e+09              NaN                NaN   \n",
-       "\n",
-       "      Biraben (1980)[19]  McEvedy & Jones (1978)[20]  Thomlinson (1975)[21]  \\\n",
-       "Year                                                                          \n",
-       "1950        2.527000e+09                2.500000e+09           2.400000e+09   \n",
-       "1951                 NaN                         NaN                    NaN   \n",
-       "1952                 NaN                         NaN                    NaN   \n",
-       "1953                 NaN                         NaN                    NaN   \n",
-       "1954                 NaN                         NaN                    NaN   \n",
-       "\n",
-       "     Durand (1974)[22]  Clark (1967)[23]  \n",
-       "Year                                      \n",
-       "1950               NaN      2.486000e+09  \n",
-       "1951               NaN               NaN  \n",
-       "1952               NaN               NaN  \n",
-       "1953               NaN               NaN  \n",
-       "1954               NaN               NaN  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table2 = tables[2]\n",
-    "table2.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`tail` selects the last five rows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>United States Census Bureau (2017)[28]</th>\n",
-       "      <th>Population Reference Bureau (1973–2016)[15]</th>\n",
-       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
-       "      <th>Maddison (2008)[17]</th>\n",
-       "      <th>HYDE (2007)[24]</th>\n",
-       "      <th>Tanton (1994)[18]</th>\n",
-       "      <th>Biraben (1980)[19]</th>\n",
-       "      <th>McEvedy &amp; Jones (1978)[20]</th>\n",
-       "      <th>Thomlinson (1975)[21]</th>\n",
-       "      <th>Durand (1974)[22]</th>\n",
-       "      <th>Clark (1967)[23]</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2012</th>\n",
-       "      <td>7013871313</td>\n",
-       "      <td>7.057075e+09</td>\n",
-       "      <td>7.080072e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2013</th>\n",
-       "      <td>7092128094</td>\n",
-       "      <td>7.136796e+09</td>\n",
-       "      <td>7.162119e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014</th>\n",
-       "      <td>7169968185</td>\n",
-       "      <td>7.238184e+09</td>\n",
-       "      <td>7.243784e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015</th>\n",
-       "      <td>7247892788</td>\n",
-       "      <td>7.336435e+09</td>\n",
-       "      <td>7.349472e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016</th>\n",
-       "      <td>7325996709</td>\n",
-       "      <td>7.418152e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      United States Census Bureau (2017)[28]  \\\n",
-       "Year                                           \n",
-       "2012                              7013871313   \n",
-       "2013                              7092128094   \n",
-       "2014                              7169968185   \n",
-       "2015                              7247892788   \n",
-       "2016                              7325996709   \n",
-       "\n",
-       "      Population Reference Bureau (1973–2016)[15]  \\\n",
-       "Year                                                \n",
-       "2012                                 7.057075e+09   \n",
-       "2013                                 7.136796e+09   \n",
-       "2014                                 7.238184e+09   \n",
-       "2015                                 7.336435e+09   \n",
-       "2016                                 7.418152e+09   \n",
-       "\n",
-       "      United Nations Department of Economic and Social Affairs (2015)[16]  \\\n",
-       "Year                                                                        \n",
-       "2012                                       7.080072e+09                     \n",
-       "2013                                       7.162119e+09                     \n",
-       "2014                                       7.243784e+09                     \n",
-       "2015                                       7.349472e+09                     \n",
-       "2016                                                NaN                     \n",
-       "\n",
-       "      Maddison (2008)[17]  HYDE (2007)[24]  Tanton (1994)[18]  \\\n",
-       "Year                                                            \n",
-       "2012                  NaN              NaN                NaN   \n",
-       "2013                  NaN              NaN                NaN   \n",
-       "2014                  NaN              NaN                NaN   \n",
-       "2015                  NaN              NaN                NaN   \n",
-       "2016                  NaN              NaN                NaN   \n",
-       "\n",
-       "      Biraben (1980)[19]  McEvedy & Jones (1978)[20]  Thomlinson (1975)[21]  \\\n",
-       "Year                                                                          \n",
-       "2012                 NaN                         NaN                    NaN   \n",
-       "2013                 NaN                         NaN                    NaN   \n",
-       "2014                 NaN                         NaN                    NaN   \n",
-       "2015                 NaN                         NaN                    NaN   \n",
-       "2016                 NaN                         NaN                    NaN   \n",
-       "\n",
-       "     Durand (1974)[22]  Clark (1967)[23]  \n",
-       "Year                                      \n",
-       "2012               NaN               NaN  \n",
-       "2013               NaN               NaN  \n",
-       "2014               NaN               NaN  \n",
-       "2015               NaN               NaN  \n",
-       "2016               NaN               NaN  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table2.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Long column names are awkard to work with, but we can replace them with abbreviated names."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the DataFrame looks like now.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>census</th>\n",
-       "      <th>prb</th>\n",
-       "      <th>un</th>\n",
-       "      <th>maddison</th>\n",
-       "      <th>hyde</th>\n",
-       "      <th>tanton</th>\n",
-       "      <th>biraben</th>\n",
-       "      <th>mj</th>\n",
-       "      <th>thomlinson</th>\n",
-       "      <th>durand</th>\n",
-       "      <th>clark</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1950</th>\n",
-       "      <td>2557628654</td>\n",
-       "      <td>2.516000e+09</td>\n",
-       "      <td>2.525149e+09</td>\n",
-       "      <td>2.544000e+09</td>\n",
-       "      <td>2.527960e+09</td>\n",
-       "      <td>2.400000e+09</td>\n",
-       "      <td>2.527000e+09</td>\n",
-       "      <td>2.500000e+09</td>\n",
-       "      <td>2.400000e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.486000e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1951</th>\n",
-       "      <td>2594939877</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.572851e+09</td>\n",
-       "      <td>2.571663e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1952</th>\n",
-       "      <td>2636772306</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.619292e+09</td>\n",
-       "      <td>2.617949e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1953</th>\n",
-       "      <td>2682053389</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.665865e+09</td>\n",
-       "      <td>2.665959e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1954</th>\n",
-       "      <td>2730228104</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.713172e+09</td>\n",
-       "      <td>2.716927e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          census           prb            un      maddison          hyde  \\\n",
-       "Year                                                                       \n",
-       "1950  2557628654  2.516000e+09  2.525149e+09  2.544000e+09  2.527960e+09   \n",
-       "1951  2594939877           NaN  2.572851e+09  2.571663e+09           NaN   \n",
-       "1952  2636772306           NaN  2.619292e+09  2.617949e+09           NaN   \n",
-       "1953  2682053389           NaN  2.665865e+09  2.665959e+09           NaN   \n",
-       "1954  2730228104           NaN  2.713172e+09  2.716927e+09           NaN   \n",
-       "\n",
-       "            tanton       biraben            mj    thomlinson durand  \\\n",
-       "Year                                                                  \n",
-       "1950  2.400000e+09  2.527000e+09  2.500000e+09  2.400000e+09    NaN   \n",
-       "1951           NaN           NaN           NaN           NaN    NaN   \n",
-       "1952           NaN           NaN           NaN           NaN    NaN   \n",
-       "1953           NaN           NaN           NaN           NaN    NaN   \n",
-       "1954           NaN           NaN           NaN           NaN    NaN   \n",
-       "\n",
-       "             clark  \n",
-       "Year                \n",
-       "1950  2.486000e+09  \n",
-       "1951           NaN  \n",
-       "1952           NaN  \n",
-       "1953           NaN  \n",
-       "1954           NaN  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table2.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first column, which is labeled `Year`, is special.  It is the **index** for this `DataFrame`, which means it contains the labels for the rows.\n",
-    "\n",
-    "Some of the values use scientific notation; for example, `2.544000e+09` is shorthand for $2.544 \\cdot 10^9$ or 2.544 billion.\n",
-    "\n",
-    "`NaN` is a special value that indicates missing data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Series\n",
-    "\n",
-    "We can use dot notation to select a column from a `DataFrame`.  The result is a `Series`, which is like a `DataFrame` with a single column."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "1950    2557628654\n",
-       "1951    2594939877\n",
-       "1952    2636772306\n",
-       "1953    2682053389\n",
-       "1954    2730228104\n",
-       "Name: census, dtype: int64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "census = table2.census\n",
-    "census.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "2012    7013871313\n",
-       "2013    7092128094\n",
-       "2014    7169968185\n",
-       "2015    7247892788\n",
-       "2016    7325996709\n",
-       "Name: census, dtype: int64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "census.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Like a `DataFrame`, a `Series` contains an index, which labels the rows.\n",
-    "\n",
-    "`1e9` is scientific notation for $1 \\cdot 10^9$ or 1 billion."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From here on, we will work in units of billions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "1950    2.525149\n",
-       "1951    2.572851\n",
-       "1952    2.619292\n",
-       "1953    2.665865\n",
-       "1954    2.713172\n",
-       "Name: un, dtype: float64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "un.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "1950    2.557629\n",
-       "1951    2.594940\n",
-       "1952    2.636772\n",
-       "1953    2.682053\n",
-       "1954    2.730228\n",
-       "Name: census, dtype: float64"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "census = table2.census / 1e9\n",
-    "census.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what these estimates look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap05-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(census, ':', label='US Census')\n",
-    "plot(un, '--', label='UN DESA')\n",
-    "    \n",
-    "decorate(xlabel='Year',\n",
-    "         ylabel='World population (billion)')\n",
-    "\n",
-    "savefig('figs/chap05-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following expression computes the elementwise differences between the two series, then divides through by the UN value to produce [relative errors](https://en.wikipedia.org/wiki/Approximation_error), then finds the largest element.\n",
-    "\n",
-    "So the largest relative error between the estimates is about 1.3%."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.3821293828998855"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max(abs(census - un) / un) * 100"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Break down that expression into smaller steps and display the intermediate results, to make sure you understand how it works.\n",
-    "\n",
-    "1.  Compute the elementwise differences, `census - un`\n",
-    "2.  Compute the absolute differences, `abs(census - un)`\n",
-    "3.  Compute the relative differences, `abs(census - un) / un`\n",
-    "4.  Compute the percent differences, `abs(census - un) / un * 100`\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "1950    0.032480\n",
-       "1951    0.022089\n",
-       "1952    0.017480\n",
-       "1953    0.016188\n",
-       "1954    0.017056\n",
-       "1955    0.020448\n",
-       "1956    0.023728\n",
-       "1957    0.028307\n",
-       "1958    0.032107\n",
-       "1959    0.030321\n",
-       "1960    0.016999\n",
-       "1961    0.001137\n",
-       "1962   -0.000978\n",
-       "1963    0.008650\n",
-       "1964    0.017462\n",
-       "1965    0.021303\n",
-       "1966    0.023203\n",
-       "1967    0.021812\n",
-       "1968    0.020639\n",
-       "1969    0.021050\n",
-       "1970    0.021525\n",
-       "1971    0.023573\n",
-       "1972    0.023695\n",
-       "1973    0.022914\n",
-       "1974    0.021304\n",
-       "1975    0.018063\n",
-       "1976    0.014049\n",
-       "1977    0.011268\n",
-       "1978    0.008441\n",
-       "1979    0.007486\n",
-       "          ...   \n",
-       "1987   -0.018115\n",
-       "1988   -0.023658\n",
-       "1989   -0.028560\n",
-       "1990   -0.031861\n",
-       "1991   -0.037323\n",
-       "1992   -0.038763\n",
-       "1993   -0.040597\n",
-       "1994   -0.042404\n",
-       "1995   -0.042619\n",
-       "1996   -0.041576\n",
-       "1997   -0.040716\n",
-       "1998   -0.040090\n",
-       "1999   -0.039403\n",
-       "2000   -0.039129\n",
-       "2001   -0.038928\n",
-       "2002   -0.038837\n",
-       "2003   -0.039401\n",
-       "2004   -0.040006\n",
-       "2005   -0.041050\n",
-       "2006   -0.041964\n",
-       "2007   -0.043192\n",
-       "2008   -0.044599\n",
-       "2009   -0.046508\n",
-       "2010   -0.057599\n",
-       "2011   -0.061999\n",
-       "2012   -0.066201\n",
-       "2013   -0.069991\n",
-       "2014   -0.073816\n",
-       "2015   -0.101579\n",
-       "2016         NaN\n",
-       "Length: 67, dtype: float64"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "census - un"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "1950    0.032480\n",
-       "1951    0.022089\n",
-       "1952    0.017480\n",
-       "1953    0.016188\n",
-       "1954    0.017056\n",
-       "1955    0.020448\n",
-       "1956    0.023728\n",
-       "1957    0.028307\n",
-       "1958    0.032107\n",
-       "1959    0.030321\n",
-       "1960    0.016999\n",
-       "1961    0.001137\n",
-       "1962    0.000978\n",
-       "1963    0.008650\n",
-       "1964    0.017462\n",
-       "1965    0.021303\n",
-       "1966    0.023203\n",
-       "1967    0.021812\n",
-       "1968    0.020639\n",
-       "1969    0.021050\n",
-       "1970    0.021525\n",
-       "1971    0.023573\n",
-       "1972    0.023695\n",
-       "1973    0.022914\n",
-       "1974    0.021304\n",
-       "1975    0.018063\n",
-       "1976    0.014049\n",
-       "1977    0.011268\n",
-       "1978    0.008441\n",
-       "1979    0.007486\n",
-       "          ...   \n",
-       "1987    0.018115\n",
-       "1988    0.023658\n",
-       "1989    0.028560\n",
-       "1990    0.031861\n",
-       "1991    0.037323\n",
-       "1992    0.038763\n",
-       "1993    0.040597\n",
-       "1994    0.042404\n",
-       "1995    0.042619\n",
-       "1996    0.041576\n",
-       "1997    0.040716\n",
-       "1998    0.040090\n",
-       "1999    0.039403\n",
-       "2000    0.039129\n",
-       "2001    0.038928\n",
-       "2002    0.038837\n",
-       "2003    0.039401\n",
-       "2004    0.040006\n",
-       "2005    0.041050\n",
-       "2006    0.041964\n",
-       "2007    0.043192\n",
-       "2008    0.044599\n",
-       "2009    0.046508\n",
-       "2010    0.057599\n",
-       "2011    0.061999\n",
-       "2012    0.066201\n",
-       "2013    0.069991\n",
-       "2014    0.073816\n",
-       "2015    0.101579\n",
-       "2016         NaN\n",
-       "Length: 67, dtype: float64"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "abs(census - un)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "1950    0.012862\n",
-       "1951    0.008585\n",
-       "1952    0.006674\n",
-       "1953    0.006072\n",
-       "1954    0.006286\n",
-       "1955    0.007404\n",
-       "1956    0.008439\n",
-       "1957    0.009887\n",
-       "1958    0.011011\n",
-       "1959    0.010208\n",
-       "1960    0.005617\n",
-       "1961    0.000369\n",
-       "1962    0.000311\n",
-       "1963    0.002702\n",
-       "1964    0.005350\n",
-       "1965    0.006399\n",
-       "1966    0.006829\n",
-       "1967    0.006289\n",
-       "1968    0.005827\n",
-       "1969    0.005821\n",
-       "1970    0.005832\n",
-       "1971    0.006258\n",
-       "1972    0.006166\n",
-       "1973    0.005847\n",
-       "1974    0.005332\n",
-       "1975    0.004437\n",
-       "1976    0.003388\n",
-       "1977    0.002670\n",
-       "1978    0.001965\n",
-       "1979    0.001712\n",
-       "          ...   \n",
-       "1987    0.003591\n",
-       "1988    0.004604\n",
-       "1989    0.005461\n",
-       "1990    0.005988\n",
-       "1991    0.006900\n",
-       "1992    0.007054\n",
-       "1993    0.007277\n",
-       "1994    0.007490\n",
-       "1995    0.007423\n",
-       "1996    0.007142\n",
-       "1997    0.006903\n",
-       "1998    0.006709\n",
-       "1999    0.006511\n",
-       "2000    0.006386\n",
-       "2001    0.006274\n",
-       "2002    0.006183\n",
-       "2003    0.006197\n",
-       "2004    0.006216\n",
-       "2005    0.006302\n",
-       "2006    0.006365\n",
-       "2007    0.006473\n",
-       "2008    0.006604\n",
-       "2009    0.006805\n",
-       "2010    0.008328\n",
-       "2011    0.008860\n",
-       "2012    0.009350\n",
-       "2013    0.009772\n",
-       "2014    0.010190\n",
-       "2015    0.013821\n",
-       "2016         NaN\n",
-       "Length: 67, dtype: float64"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "abs(census - un) / un"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.4014999251669376"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "max(abs(census - un) / census) * 100"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`max` and `abs` are built-in functions provided by Python, but NumPy also provides version that are a little more general.  When you import `modsim`, you get the NumPy versions of these functions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Constant growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can select a value from a `Series` using bracket notation.  Here's the first element:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.557628654"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "census[1950]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the last value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7.325996709"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "census[2016]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But rather than \"hard code\" those dates, we can get the first and last labels from the `Series`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1950"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_0 = get_first_label(census)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2016"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_end = get_last_label(census)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "66"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "elapsed_time = t_end - t_0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can get the first and last values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.557628654"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p_0 = get_first_value(census)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7.325996709"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p_end = get_last_value(census)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we can compute the average annual growth in billions of people per year."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.768368055"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "total_growth = p_end - p_0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.07224800083333333"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "annual_growth = total_growth / elapsed_time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### TimeSeries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's create a `TimeSeries` to contain values generated by a linear growth model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Series([], dtype: float64)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = TimeSeries()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initially the `TimeSeries` is empty, but we can initialize it so the starting value, in 1950, is the 1950 population estimated by the US Census."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1950</th>\n",
-       "      <td>2.557629</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "1950    2.557629\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results[t_0] = census[t_0]\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After that, the population in the model grows by a constant amount each year."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for t in linrange(t_0, t_end):\n",
-    "    results[t+1] = results[t] + annual_growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results looks like, compared to the actual data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap05-fig02.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(census, ':', label='US Census')\n",
-    "plot(un, '--', label='UN DESA')\n",
-    "plot(results, color='gray', label='model')\n",
-    "\n",
-    "decorate(xlabel='Year', \n",
-    "         ylabel='World population (billion)',\n",
-    "         title='Constant growth')\n",
-    "\n",
-    "savefig('figs/chap05-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model fits the data pretty well after 1990, but not so well before."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Optional Exercise:**  Try fitting the model using data from 1970 to the present, and see if that does a better job.\n",
-    "\n",
-    "Hint: \n",
-    "\n",
-    "1. Copy the code from above and make a few changes.  Test your code after each small change.\n",
-    "\n",
-    "2. Make sure your `TimeSeries` starts in 1950, even though the estimated annual growth is based on later data.\n",
-    "\n",
-    "3. You might want to add a constant to the starting value to match the data better."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def compute_annual_growth(t_0, t_end):\n",
-    "    \"\"\"Average annual growth over given period.\n",
-    "    \n",
-    "    t_0: start date\n",
-    "    t_end: end_date\n",
-    "    \n",
-    "    returns: average annual growth\n",
-    "    \"\"\"\n",
-    "    elapsed_time = t_end - t_0\n",
-    "    p_0 = census[t_0]\n",
-    "    p_end = census[t_end]\n",
-    "    total_growth = p_end - p_0\n",
-    "    annual_growth = total_growth / elapsed_time\n",
-    "    return annual_growth\n",
-    "\n",
-    "# compute annual growth using data from 1970 to the end\n",
-    "t_0 = 1970\n",
-    "t_end = get_last_label(census)\n",
-    "annual_growth = compute_annual_growth(t_0, t_end)\n",
-    "\n",
-    "# Run the simulation over the whole time range.\n",
-    "# I subtract 0.45 from the initial value to shift\n",
-    "# the fitted curve down so it fits the data better.\n",
-    "t_0 = get_first_label(census)\n",
-    "t_end = get_last_label(census)\n",
-    "p_0 = get_first_value(census) - 0.45\n",
-    "\n",
-    "# initialize the result\n",
-    "results = TimeSeries()\n",
-    "results[t_0] = p_0\n",
-    "\n",
-    "# run the simulation\n",
-    "for t in linrange(t_0, t_end):\n",
-    "    results[t+1] = results[t] + annual_growth\n",
-    "    \n",
-    "# plot the results\n",
-    "plot(census, ':', label='US Census')\n",
-    "plot(un, '--', label='UN DESA')\n",
-    "plot(results, '--', color='gray', label='model')\n",
-    "\n",
-    "decorate(xlabel='Year', \n",
-    "         ylabel='World population (billion)',\n",
-    "         title='Constant growth')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "1960    3.043002\n",
-       "1961    3.083967\n",
-       "1962    3.140093\n",
-       "1963    3.209828\n",
-       "1964    3.281201\n",
-       "1965    3.350426\n",
-       "1966    3.420678\n",
-       "1967    3.490334\n",
-       "1968    3.562314\n",
-       "1969    3.637159\n",
-       "1970    3.712698\n",
-       "Name: census, dtype: float64"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "census.loc[1960:1970]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap06soln.ipynb b/soln/chap06soln.ipynb
deleted file mode 100644
index f4f35bfe..00000000
--- a/soln/chap06soln.ipynb
+++ /dev/null
@@ -1,808 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 6\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *\n",
-    "\n",
-    "from pandas import read_html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = 'data/World_population_estimates.html'\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "table2 = tables[2]\n",
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "1950    2.525149\n",
-       "1951    2.572851\n",
-       "1952    2.619292\n",
-       "1953    2.665865\n",
-       "1954    2.713172\n",
-       "Name: un, dtype: float64"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "un.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "1950    2.557629\n",
-       "1951    2.594940\n",
-       "1952    2.636772\n",
-       "1953    2.682053\n",
-       "1954    2.730228\n",
-       "Name: census, dtype: float64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "census = table2.census / 1e9\n",
-    "census.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.07224800083333333"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_0 = get_first_label(census)\n",
-    "t_end = get_last_label(census)\n",
-    "elapsed_time = t_end - t_0\n",
-    "\n",
-    "p_0 = get_first_value(census)\n",
-    "p_end = get_last_value(census)\n",
-    "total_growth = p_end - p_0\n",
-    "\n",
-    "annual_growth = total_growth / elapsed_time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### System objects"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can rewrite the code from the previous chapter using system objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>1950.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>2016.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p_0</th>\n",
-       "      <td>2.557629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>annual_growth</th>\n",
-       "      <td>0.072248</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "t_0              1950.000000\n",
-       "t_end            2016.000000\n",
-       "p_0                 2.557629\n",
-       "annual_growth       0.072248\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                annual_growth=annual_growth)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can encapsulate the code that runs the model in a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation1(system):\n",
-    "    \"\"\"Runs the constant growth model.\n",
-    "    \n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        results[t+1] = results[t] + system.annual_growth\n",
-    "    \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also encapsulate the code that plots the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(census, un, timeseries, title):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    census: TimeSeries of population estimates\n",
-    "    un: TimeSeries of population estimates\n",
-    "    timeseries: TimeSeries of simulation results\n",
-    "    title: string\n",
-    "    \"\"\"\n",
-    "    plot(census, ':', label='US Census')\n",
-    "    plot(un, '--', label='UN DESA')\n",
-    "    plot(timeseries, color='gray', label='model')\n",
-    "    \n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results = run_simulation1(system)\n",
-    "plot_results(census, un, results, 'Constant growth model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Proportional growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a more realistic model where the number of births and deaths is proportional to the current population."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation2(system):\n",
-    "    \"\"\"Run a model with proportional birth and death.\n",
-    "    \n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        births = system.birth_rate * results[t]\n",
-    "        deaths = system.death_rate * results[t]\n",
-    "        results[t+1] = results[t] + births - deaths\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I picked a death rate that seemed reasonable and then adjusted the birth rate to fit the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.death_rate = 0.01\n",
-    "system.birth_rate = 0.027"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap06-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results = run_simulation2(system)\n",
-    "plot_results(census, un, results, 'Proportional model')\n",
-    "savefig('figs/chap06-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model fits the data pretty well for the first 20 years, but not so well after that."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Factoring out the update function"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`run_simulation1` and `run_simulation2` are nearly identical except the body of the loop.  So we can factor that part out into a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func1(pop, t, system):\n",
-    "    \"\"\"Compute the population next year.\n",
-    "    \n",
-    "    pop: current population\n",
-    "    t: current year\n",
-    "    system: system object containing parameters of the model\n",
-    "    \n",
-    "    returns: population next year\n",
-    "    \"\"\"\n",
-    "    births = system.birth_rate * pop\n",
-    "    deaths = system.death_rate * pop\n",
-    "    return pop + births - deaths"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The name `update_func` refers to a function object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.update_func1(pop, t, system)>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "update_func1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Which we can confirm by checking its type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "function"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(update_func1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`run_simulation` takes the update function as a parameter and calls it just like any other function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate the system using any update function.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that computes the population next year\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        results[t+1] = update_func(results[t], t, system)\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we use it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>1950.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>2016.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p_0</th>\n",
-       "      <td>2.557629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>birth_rate</th>\n",
-       "      <td>0.027000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>death_rate</th>\n",
-       "      <td>0.010000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "t_0           1950.000000\n",
-       "t_end         2016.000000\n",
-       "p_0              2.557629\n",
-       "birth_rate       0.027000\n",
-       "death_rate       0.010000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_0 = get_first_label(census)\n",
-    "t_end = get_last_label(census)\n",
-    "p_0 = census[t_0]\n",
-    "\n",
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                birth_rate=0.027,\n",
-    "                death_rate=0.01)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results = run_simulation(system, update_func1)\n",
-    "plot_results(census, un, results, 'Proportional model, factored')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Remember not to put parentheses after `update_func1`.  What happens if you try?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** When you run `run_simulation`, it runs `update_func1` once for each year between `t_0` and `t_end`.  To see that for yourself, add a print statement at the beginning of `update_func1` that prints the values of `t` and `pop`, then run `run_simulation` again."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Combining birth and death"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since births and deaths get added up, we don't have to compute them separately.  We can combine the birth and death rates into a single net growth rate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func2(pop, t, system):\n",
-    "    \"\"\"Compute the population next year.\n",
-    "    \n",
-    "    pop: current population\n",
-    "    t: current year\n",
-    "    system: system object containing parameters of the model\n",
-    "    \n",
-    "    returns: population next year\n",
-    "    \"\"\"\n",
-    "    net_growth = system.alpha  * pop\n",
-    "    return pop + net_growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how it works:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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8OYEkqFIpOjqa119/nUmTJrFjxw42btxIq1ateOyxx4iLi8u1zLfffsuLL77IyJEj2bhxI5s2bWLgwIE89dRTbN++vZjfgRDiXhEfH88nn3zC2bNnqVChAr/97W+pXr16sRxbElQptGfPHurWrUvr1q2x2+14e3vz9NNP079//1wTVHJyMv/617/429/+Ro8ePfD09MTT05Nhw4YxduxYTpw4AZj1xzNmzKB79+60bduWZ599Nmt/W7dupX///kydOpW2bdvSqVMnpk2blnWM+fPn06NHD8LDw3nggQfYsGFDVrmwsLCb4unWrRurV6/Ot5wQovS7ePEiM2fOJDY2luDgYMaMGUNgYGCxHf+eTVADnv+BAc/fPEjFX2duYcDzPxB94ELWsuU/n2TA8z/w7rzdWctiryUz4PkfeGzy8pvK//4/6xjw/A8cPRNfpLF36dKFw4cPM2rUKD7//HMOHDiAw+Fg8uTJNGzY8Jbtd+3aRVpaGp07d75l3dNPP82wYcMA+Pzzz1m4cCGffvop69evJyAggOeeey5r26NHj+Lh4cGmTZv4xz/+wXvvvcexY8eIi4vj1Vdf5f3332fbtm0MHz6c119/nYImu7zTckKIknfy5Ek+/fRTrl+/Tp06dXj88cfx8/Mr1hju2QR1N2vUqBELFiygSZMmzJkzh6FDh9KhQwfef//9XL/c4+LiqFChAh4eHvnud+7cuUyYMIHatWvj7e3NCy+8wLZt2zh58iQANpuNcePG4eHhQWRkJEFBQZw6dQpPT0/c3d359ttv2bdvH0OHDmXFihXYbLZ8j3en5YQQJevAgQN8+eWXpKam0rRpU0aOHIm3t3exx3HP9uJb9NagW5ZNGtPulmV9IurSJ6LuTcsCK/jkWv7t57r86rg8PT3JyMi4ZbnD4bjpeYI6derw5z//GTAT0MqVK5kyZQqVKlVi+PDhN5UNCgoiPj6e9PT0W5JUYmIiXl5eeHp6cu7cOV5++WUmTZqUtd7d3Z2zZ8/i7u5O+fLlb4rBw8MDp9NJ+fLlmTVrFh988AGjRo3C29ubxx57jLFjx+b7XvMrJ0lKiNJp69atLF9u1h6Fh4fTp08f7PaSuZe5ZxNUaVW1alXOnTtHixYtspZlZGRw7ty5rNmCH3nkEXr16sXo0aMBCAgIYPjw4ezbtw+t9S37DA0Nxdvbm/Xr19OjR4+b1r3xxhucOnWKWbNmERwczKRJk+jYsWPW+sOHD1O3bl127dqVZ8zXrl3Lar9KT0/np59+4plnniEsLAw3NzfS0/83Y4phGFy7dq3AcjnbrYQQJcswDFatWsXPP/8MQPfu3enQoUOJXkxKFV8x69+/P++++y7Hjh0DzB4yU6dOJSgoiJCQEAD69u3LjBkzWL16NWlpaaSlpbF161bWr19P9+7db9mnp6cnEydOZNKkSaxZs4aMjAySkpL49NNPWbBgAePHjwdg8ODBvPfee5w/fx6Hw8GHH37IiBEjSElJyTfmuLg4xowZQ3R0NB4eHlSpUgWbzUaFChWoXbs2aWlpLF26FIfDwaxZs7hx40aB5YQQpUdGRgbz58/n559/xm63M3jwYCIjI0u8pkPuoIrZ+PHjcXNzY+zYscTGxuLl5UW7du347LPPsqrnHn30Uby9vZk+fTovvvgihmHQoEEDJk2aRGRkZK77feihh/Dz8+ODDz7gpZdewjAMmjZtysyZMwkPDwfgqaeeIiMjgxEjRhAfH0/jxo2ZOXMm/v7++cZcr149Jk+ezKuvvsqlS5eoVKkSr7zyCo0aNQLglVde4Y033uAvf/kLgwYNolWrVpbKCSFKXnJyMnPmzMlqb/7Nb35DgwYNSjosAGx3Q48qpVRd4MSaNWuoWbNmSYcjhBD3hGvXrvHVV19x+fJl/Pz8eOSRR7KaGopLTExMZs1QPa31yezr5A5KCCHKoPPnz/PNN9+QmJhIUFAQI0aMKHXV75KghBCijDly5Ajz5s0jPT2dOnXq8NBDD+Hj41PSYd1CEpQQQpQh27dvZ+nSpRiGQUhICAMGDMDdPe9U4ExPxe5x65Qa5y5fJ6iSLx7uRdfXTnrxCSFEGZDZjXzJkiUYhkGnTp0YPHhwnsnJcGQQ++MXnP34eZypSTetm//jEcZN/ZHlP58s0pjlDkoIIe5x6enpLFiwgF9++QW73U5UVBShoaF5bp9x7TIXv/8PqWc12Owkn9xPOdUma32N4PIYhkHsteQijdtSglJKuQGtgTAgGHAAF4BtWuvd+ZUVQghRcq5fv87s2bM5e/YsXl5e/OY3v8lzqgzDMLhxcDNXln2IM+U6bn4BVB70e366UJ6MzSfo174eAG2bVeX9P3WneuXyRRp7vglKKVUJ+B3wNBAIHAdiATegMlBHKXUemAG8p7Uu2lFUhRBCWHbp0iW++eYb4uPjqVChAo888gjBwcG5bpt68SSxK2eScvoXAHwbtiZowAQOXUhn2txN+Hi50yGkOhXKe2Gz2Yo8OUE+CUopNQr4K7AGGAOs1lqn5tjGH+gIjAD2K6Ve1lrPKsJ4hRBCWHDs2DHmzZtHamoqNWrUYPjw4ZQvn3dScdyIJ+X0L9h9/fFu+yBVIvphs9loVt8cs7RZ/UD8fD2L8R3kfwcVDoRrrS/ntYHWOgFYAixRSlUDXgYkQQkhRAnasWNHVmeIpk2bMnjw4FsGkjYy0kk+uQ/fhubIL7717yeg7//x2X4fNi6MZ8Z9KQRVMruej3+wZbG/B8inF5/W+pn8klMu25/XWk8onLDuXVamfJ82bRohISEcPXr0pm1ymxww03fffcd9991HaGgooaGhtGzZkr59+zJz5sybpujo1q0bISEhWdtl/vTu3Ttrm1OnTjF+/HjCw8MJDQ0lKiqKL7/8Mtfj9uvXj06dOt00YKwQomQ4nU5WrFjB4sWLMQyDyMhIHnzwwZuSk2EY3NDRnPnw91yY8w9Szx/PWlexVU/SbN4YhoE+nfvs3cXJci8+pVQwEAJ4ADeNIKi1XlrIcZV5qampTJw4kblz5+Lpae22unHjxvzwgzlJo2EY7Nq1i4kTJ5KQkHDTxIT//ve/bxn1PJPT6eTJJ58kKiqKN998E29vb3bv3s348eOzxunKlDkIbFBQECtXrqR///6/4h0LIX6N1NRU5s+fz5EjR/LsqZd26TSxqz4h+eQ+ANwDa/Bj9HFaRARRM9icjPDxqGaMjmpKcCXfYn8POVl6DkopNQY4A6zErNJbnO1nUZFFV4ZFRESQlJTE22+/fUflbTYbrVq14vXXX2fmzJkkJCRYKnf16lVOnTpFVFQUPj4+2Gw2QkNDefHFF2/Zds6cOfTs2ZMHH3yQL7744o7iFEL8evHx8XzyySccOXIEHx8fHn300ZuSkyMpkSvLPyLm4+dJPrkPu3d5AnuNYXW1MUzfcIPPl/6vVieokk+pSE5g/Q7qBeAj4CWtdeKdHEgpNQL4IMdiH2CN1rrXnewzP8f//kCe6yr3HYt/K/OQCTtXcmVZzrD+p/7L87N+j5n5AmkXjue5vjD5+voydepURo4cSefOnWnbtu0d7SciIgK73c6uXbtynRI+p8DAQNq0acPjjz/OwIEDs6r5Bg8efNN2cXFxrFmzhmXLluHn58fUqVPZt2/fTfNcCSGK3pkzZ5g9ezZJSUlUrlyZhx9+mICAgJu2iVv/NYk7V4LNju/9vQnq+jBuvn4MupbModMJt0zaWlpYTVC1gP/eaXIC0Fp/BXyV+VopFYp5R/bCne7zXnf//fczduxY/vSnP2VV3d2uzPmXrl+/nrVs4sSJtzw9Pnr0aCZMMJsQZ86cydy5c1m1ahWff/45GRkZdO7cmUmTJmWNdPz9998TERFBtWrVABg4cCBffvklU6ZMuaM4hRC3b8+ePSxatAiHw0H9+vUZNmxY1tTsjpQbuHmXA6BS5DCS4q4wP74FV89U5q8+Zm++wAo+TH2mY577L2lWE9RKoDtwpDAOqpTywExWr2mt9xTGPnOyemfj36pX1t1UQWqOeePXhARYn/I907hx49i0aROTJ0++qf3HKofDQUJCQlYiAXjzzTfzbIPKjHHkyJGMHDmStLQ0duzYwdtvv82zzz7LnDlzMAyDuXPncunSJTp06ACY9d+pqam8+OKLBAYG3nacQgjrnE4na9asYfPmzQCEhYXRt29f7HY7abFniVs9i/Rrl6n5xJvY7G64+wVQafALRP9jFQZXuXw1meCA0lGNlx+rCWoP8G+l1EDgMJCWfaXW+tYGivyNB5KB6bdZ7q5nZcr37Nzc3HjjjTcYNGgQvr63/4GKjo7GMAwaN25safs5c+Ywe/Zsvv/+e8BMVhEREXh4ePDEE08AsGXLFuLj41m+fDl2+/+aMceOHcvs2bOzZvAVQhS+7J0hbDYbffv2JTw8HEfKDWI3zuXa9mXgdGDz9GHVis307GPOjOtfzpOXRrehbjV/KpS/9WK4NLI6WGxnYCtmm1FLXM9IuX5y7/ecB6WUJ2a13mta69I/W2IhszLle061a9fmz3/+M3PnzrV8HKfTSXR0NK+++ipjx47N9wG97Dp37szp06eZMmUKsbGxGIbBmTNn+PTTT7Omm58zZw59+vShSpUqBAUFZf0MGTKE2bNnS5dzIYpIXFwcM2fOvKkzRFjrViTsXMmZ9ydwLXoxOJ2Ub9md95wPMW11HJv3ns8q37JR0F2TnMDiHZTWumshHrMP4MTsDVjmWJnyPTfDhg1j/fr1bNmyJc9tDh8+nNVzx93dnRo1avDEE08wfPjwm7b7wx/+gJub2y3lly9fTtWqVfn666+ZNm0aUVFRJCcnU6lSJfr06cPvfvc7YmNjWb16NZ999tkt5aOiopgyZQorVqwgKirK4hkRQlhx4sQJ5s2bR3JyMkFBQQwfPpyAgADOfz2Z5BN7AfCqdR+Ve/4Wr2r16R5wAs/d56gaWPqr8vJiecp3pVQVYALQDPPO6yDwkdb6eL4Fb93PTCBea/38bZSpi0z5LoQogwzDIDo6mhUrVmRV1w8dOjSrzTph50oubfiW5enhqI496dmuLgBOp4HNZnaUKs3ym/Ld6nNQbTDbnoYAV4DLQBSwVyl1W1V8QDvgp9ssI4QQZU5GRgYLFy5k+fLlGIZB+7Zt6BGURvLOZVnb+N3fnbMd/sSKC8Gs2Ho6a+QYu91W6pNTQax2kngL+AZ4Onu7kVLqXeAN4HaqAOsC525jeyGEKHMSExOZM2cOZ8+exd3dnV4h9Qg6OJfEpAQMdy8OeDSnQ1hDbHY3OrauR4rDTudWNe/6pJSd1QQVBjyRS6eGacCO2zmg1rrc7WwvhBBlTUxMDHPmzOH69ev4l/Ohq+8l/PfuxAkYQQ1562hj4n84TGjzOvh6e2C32+jtqtq7l1hNUOcx73x0juX1gTt+eFcIIcTNdu7cydKlS3E4HFTztdEhZTs+qQ7cKwQR0O1RfJtEEDRzK+1qVCjpUIuc1QT1BfChUur3QGY3sgjgP651QgghfgWHw8GyZcvYscOslAoLCyPkyhaSz7qxIvV+HnpkAuUD/AF47Yl291RVXl6sJqi/A9WBuZgdK2xAOmYV38tFE5oQQpQNiYmJzJ07l5iYGNzc3Ojfvz+hoaGkx7Xi7W9/YdORGzQ7lUAnV4IqC8kJrD8HlQY8qZSaCCjMUSCOaq2TizI4IYS4150+fZq5s7/mRnIqvqTRpqKd6nUUAB4B1Xj0AX8edjizpsMoS/Kb8r0fsEprne76PadaSpknUeaDEkKI22MYBlvXrmTVxi04gWCu08Yngfkxzdm29CATR7YGoGpg2e1Xlt8d1GKgKnDJ9XteDODWYQmEEELkKiU+lgVffoyOTQHgPo94unftSnrDLsTNjKZX0yolHGHpkGeC0lrbc/tdlE7Tpk3j4MGDTJ+e//i7mU9tb9u2DX9//2KKTgiR6erVq8z5ZjYXY1Nww0ldHw8emDAZN1/z/3H6i92x28tGG1NBLE/5LoQQ4s4YhpOkw9s5Z6/Ed999R0pKCv6+3hyPD2L79cr0SHajqmvIPElO/5NfG9RlzOq7AmmtgwstontcTEwMgwcP5tlnn2XGjBmkp6fzzDPPYLfbmTFjBmlpaTz99KYAD/UAACAASURBVNOMHj2azZs389Zbb3HixAlq1KjB+PHj6dOnT9Z+Xn75Zfbu3Uv9+vWpX7/+TceZO3cuM2fOJC4ujpCQEF577TVq1apVEm9ZiDIt+eQ+rqyaxY4LN9jjNKfUady4MUOGDGGHjqNudf8y3c6Un/zuoF7AYoIqbb7++muOHCmUuRUL1KhRIx555JHbKpOYmMj+/ftZu3Ytq1evZuLEiQwaNIg1a9awceNGnnnmGVq0aMHYsWOZMmUKvXr1Ytu2bYwbN46goCBat27Ns88+S9OmTfnoo4/QWjNmzBjCwsxhEVeuXMk777zDBx98QKNGjfj000958sknWbw4v6ZEIURhSrsSQ9yaz7l6ZDebnLU4Z1TFMKBx8zCGP9APm81Gh5bVSzrMUi2/NqjPijGOMmfcuHFZkwE6HA5GjRqFp6cnXbt2xeFw8Mknn9C2bVv69TM7UEZERDBgwAC+//57goOD2b9/Px9//DGenp60aNGCQYMGcfbsWcC8exo1ahTNmjUD4KmnnuKLL75g69at1KlTp8TesxBlQcb1q1zdMJfE3au54vRig7MxNwwP3Nw9Oe9syIDmYWXmOaZfK78qPsuz42mtb38u8iJ0u3c0JaFixYoAWfMy+fmZzzhkzlCbkpJCjRo1bipTs2ZNtm7dyuXLl/Hy8qJSpUo3rctMUOfOnWP69Ol8+OGHWevT09M5d+6cJCghitj1A5tI2LkSbQSyw1kDJ1CjRg0GD3mAChX88XCXTs9W5VfFd6PYoiiDCrqCCgsLY/v27TctO3PmDJUrV6ZKlSqkpqYSGxtLYGAgABcvXszaLjg4mFGjRt00UeGxY8eoXr06sbGxhfguhBCG00H6lRg8g82LP58W3Vi7YQ9nkxwAhIeH06tXL9zdpU/a7cqviu/x4gxE3CwyMpIZM2awdOlSevfuTXR0NIsWLeLdd9+lRo0atGnThilTpjB58mROnTrFd999R6tWrQAYPHgw77//PuHh4dSvX5/Fixfz0ksvsXjxYvknEaKQGIZB0tEdxP34BRmJV3EM+Qf+fp7MmzePK0kObHY3uvXsQ2S7250yT2TKr4pvKjBZa33D9XteDK31Hws/tLLN3d2d6dOn89Zbb/Hyyy9TpUoVJk+eTGRkJAD/+c9/ePnll2nfvj3Vq1enR48exMXFAWaCSkhIYNy4cVy6dInatWvz3nvvUbduXWJiYkrybQlxT0g5e4S4Hz8n5fQvAFw1yjP/i4WU87hKRkYGQUFBDBs2jKCgoBKO9O6W55TvSqm1wBCtdbzr97wYWutuRRLd/2Kpi0z5LoQoYelx54lb9zU3Dm4GwO5TnvLthvL2uvN4pJvV7C1btqRfv354enqWZKh3jfymfM+viq9rbr8LIURZdWnRu6TGHCIDd/zC+2Fv2pV5i5bgkX4Jd3d3+vXrx/333y+99AqJ5QYJpVQ5YDjQDEgDDgBzXCOdCyHEPceZloyRnoZbOXNywIAuD7Nh3ly+vNiEiIuViN/+Jenp6QQGBvLggw9StWrVEo743mIpQSmlWgDrMBPTXsw5oR4H/qqU6qu1PlRkEQohRDEzHBkk7l7D1Y1zsVVrgn+/31HRzwufOs2p80AVQpcu5fLxaABatGhB//798fLyKuGo7z1W76A+AhYA/6e1TgdQSvkAHwMfAJ2LJjwhhCg+hmFw49DPXF33Nelx5wE4degoB9nHuIfCuHTpEmuWziPxyhXc3d3p27cvoaGhUqVXRKwmqJbAqMzkBKC1TlZK/Q3YVSSRCSFEMUo+uY+4H78k9fxRADwCqmOEDuG/8xPpkAbbt29nxYoVZGRkULlyZYYNG0ZwsAxDWpSsJqidQEfgcI7lYcAvhRqREEIUs4yEK5z/+q9gOEl1L0+NniPwa9kNm5s779a9ys8bV7NkiflVd//999O3b1/ppVcM8nsOaly2l9HAu0qpMGAL4ABCgKeBKUUaoRBCFIGMhCu4+QVis9lw96+M5/19WfDzedYmKd6s2hZ/N3diYmKYP38+8fHxeHp60r9/f0JCQko69DKjoNHMs7sA9HH9ZLoC/BZ4vZDjEkKIIuG4cY2rm74lYedK7N3GUbet2YRes99vqVnuKM/4eVO7ih+bNm1i7dq1OJ1OqlWrxoMPPkhAQEAJR1+25PccVL3iDEQIIYqSMzWZ+K0LubZ1IUZaCgawZNE6OgY2JaShOeLD4M4NSUhI4KuvvuTEiROAOZNA9+7dswZ2FsUnvyq+x7TWs6zuSCllAx7XWn9SKJEJIUQhMDLSSdi1kqubvsWZlACAb8PWRPt0YO3PiTSKTSKkobntoUOHWLhwIcnJyfj6+jJ48GAaNWpUgtGXbflV8YUqpZ4HZgDfa63P57aRUioYeAR4Clid38GUUtWA94GuQArwodb61TsJXAghrEjYuYLYVZ8CkB5Qn9r9H8endlN6pWbQsUsGlfy9SU9PZ+XKlVkzCDRo0IDBgwdTvnz5kgy9zMuviu/3rk4Rk4C3lVK/YI4ecQWwAUGY3c8bA8uA0Vrr6AKO9wOwA6gCVAPWK6UOaq2//tXvRAghMJ9lciTG4u5fGQC/+3twettGvj5Ti2vu9/FuzfsA8PZyx9vLnYsXLzJ//nwuX76M3W6nR48etGvXTp5tKgXy7Wautd4ODFRK1QP6YnYrbwg4MTtNvAMs1VqfLuhASqm2QH2gg+t5qhNKqS5A8q96B0II4ZISo4n78QvSYs/i8dBUalQPwu7pTZOx/yR43m7GRNbHbjcTj2EYbNmyhTVr1uBwOAgMDOSBBx6gWrVqJfwuRCZLz0FprU8A03/lsVoD+4DXlFKjMav4pmut3/qV+xVClHFpl88Qt+4rkg5vAyAZL2Z/soxX/zgCTw83PNztPPdwq6ztExMTWbBgAcePHwegdevW9OrVS55tKmWKc/a6AMyHfddj3kk1AZYrpc5LFZ8Q4k5kJMRydcNsEveuA8OJzcMLv/Ao3tseQEK6GxfjkqhVxe+mMgcPHmTRokVZHSEGDBhAkyZNSuYNiHwVZ4JKBRK01q+5Xu9RSn0MDAUkQQkhbtvF+W+Qeu4ITmyUa9mDoM7DcferxAshNwis4IOHuz1r29TUVJYvX87u3bsBsyPEoEGD8PPzy2v3ooQVZ4I6BPgqpTyzTdEh848LISxzZqSZ01/4mL3rKkUO46fv5/HN5Wb09AjnYb9KAFQNLHdTudOnT/P9998THx+Pm5sbPXr0oG3bttIRopQrzgSxCrgMvOXqvq6AMZjDJQkhRJ4Mp4Pr+zcSt/4bPGo1x7/3/1HexwPfRq2pMawuYTtj6BNR95ZyDoeDdevW8dNPP2EYBlWrVmXo0KEyFftd4nYmLAzF7OjggdnNPIvWusAOFFrrFKVUZ2AacB6zk8RUrfX824pYCFFmGIZB8vHdZs+8S6cAOLNvH4eM/TwxJBSApvUCaVov8Jayly5d4vvvv+fChQsAREZG0qVLFxkR4i5idcLCl4G/AXFAYo7VBhZ7+GmtjwP9bydAIUTZlHrhBHE/fk7yib0AuPlXxhEyiDcWpRJyMRmn08jqMp6d0+lky5Yt/PjjjzgcDipWrMiQIUOoXbt2cb8F8StZvYN6AnhVa/33ogxGCCHAHGn87CcvguEkw82H4M4P4h/eD7u7J281iKdBjQq5th9dvXqVH374gVOnzLut0NBQevfuLbPd3qWsJqhAYG5RBiKEKNuc6anYPcxE4u5fGc/7OrFy5yVWpbTgn3W7UdHdfEapYc2Kt5Q1DINdu3axYsUK0tLSKFeuHAMHDqRx48bF+h5E4bKaoL4FRgCvFV0oQoiyyHA6SNy7lqvrvqFS/wn4NzLblmoMnoDd4xf6udkJruSTZ/nExEQWLVrEkSNHAGjatCn9+/fH19e3WOIXRcdqgkoGXlJKDQOOAGnZV2qtf1PYgQkh7n3JJ/cRu3oWaRfNqS1WfjOXFo/VQdUJwGazMTqqWZ5lDcNg3759LFu2jJSUFLy9venXrx/NmzeX7uP3CKsJyhd5mFYIUUjS484Ru+bzrKGJ3Pwrc6BSV2bt8iZqZwyqTv4TA16/fp3FixejtQagUaNGDBgwQB66vcdYHYvv8aIORAhRNtw4vI2L898EZwaGuxcBHYZSoe0Aghx2KoRcpn2LvAdrNQyDAwcOsHTpUpKTk/Hy8qJ3797cf//9ctd0D7qd56BaAC8CzQA75sgQ72itNxdRbEKIe5B37aY4PXzZlhDMXq9OvN6+H3a7DV8P6BBSPc9y169fZ8mSJRw6dAiA+vXrM3DgQCpUqFBcoYtiZi94E1BK9QV2Yvbm+xaYB/hhzufUq+jCE0Lc7ZJP/8KFef/CmZ4KgJt3OaqP/S+rvXpQq25N0tMd+ZY3DIO9e/cyffp0Dh06hKenJ1FRUYwcOVKS0z3O6h3U34HXtdaTsy9USr2C+QDvysIOTAhxd8tIiCV2zSxu/PITAN9O/5ChE8bj7mbH18+f917oirdn/l9BiYmJLFmyJKutqUGDBgwYMEASUxlhNUHdB+TWU2828OfCC0cIcbczMtKJ37qI+J++xUhPxebuwcaMliw4XZ2AHWfo0aYOQL7JyTAMdu/ezcqVK0lJSZG2pjLKaoI6DYQCR3Msbw1cKtSIhBB3raQTe7iy7EMyrprj35Vr0o6AHo/R9JxBYFwSXVvXKnAf8fHxLFq0KGsywUaNGhEVFYW/v3+Rxi5KH6sJ6j1ghlKqJrDFtSwCeBmYWhSBCSHuPs6UG2RcvcBFZ0USmj1A76H9AAizUCNnGAbR0dGsWbOG9PR0fHx86NOnDy1atJC7pjLKajfzd5RSfsBLQGXX4nPAX7TW7xZVcEKI0s2ZkUbKmYP41msJQLkmESS1/S3/WpZG5I1gelvcz+XLl1m0aBFnzpwBoFmzZvTt25dy5coVUFLcyyx3M3cNFPt3pVQwkKy1zjmquRCiDEk6upMrK2eSce0yFyP/SETH1thsNpr36M+b6iqNalUqcB8Oh4NNmzaxceNGHA4H5cuXp3///jIFuwDySVBKqXHAJ655nMblsj7rdyvzQQkh7g0ZCVe4svITkvRWAC44KjJ76V7qN7uPKgHm+HdWklNMTAwLFy7k8uXLgDnyeM+ePfHxyXvcPVG25HcH9QIwB3NiwRfy2c7yfFBCiLuX4XRwbdsSrq6fg5Gegs3Tm0odH+K7I1Wo5+6Bl4e1iQBTU1NZu3YtW7eaCS4gIICoqCjq1atXlOGLu1CeCUprXS+333NSSknrpRBlQOzqz0jYthQAe90wag54Cnf/QJ5rY+CWy8SBudFas3TpUhISErDZbLRv357OnTvj4eFRlKGLu5TVGXWPA2Fa67gcy6sDu4HgIohNCFGKVGgTxdnd0XwTF0KQrR3P+5vTrFtJTomJiSxbtoyDBw8CUK1aNQYMGEC1anmPuydEfm1QA4FI18u6wF+VUkk5NmtYRHEJIUrYjUNbSfxlEz69xlOhvDceFatQc+x/aLT+OA/1sDYRoGEYbN++nTVr1pCamoqHhwfdunWjTZs22O2WRloTZVh+d1B7gN8DmZdHodw8D5QBXAceK5rQhBAlISMxjisrPs7qBLEkJoDfPjMam81GYAVfxgxsbmk/Fy5cYPHixZw9exaAxo0b069fPxmmSFiWXxvUKaAbgFLqU+BZrXVCcQUmhChehuEkcddq4n78AmdqEnh4s+hGKHsTqjEkMZUAf29L+0lLS8vqBGEYBn5+fvTp04f77rtPHrgVt8XyfFBKKXelVA0gs6uODfACWmutvymqAIUQRS8t9hxXlr5PyulfAPBt2JrKfZ+iz1U7Y6v64+1l7ZHJQ4cOsWzZsqxOEG3atKFbt254eXkVZfjiHmW1k0QU8CmQ2zSXVwFJUELcxZL0FlJO/0Ki05u0Vg9Rr98AbDYbyuLwd/Hx8SxbtozDhw8DZieIqKgoqlfPe34nIQpidSSJf2JOqfFvYA0wEKgK/Ad4vmhCE0IUJWdqMnYv86HYCm0HcuTEJT7QVRhds7XlqriMjAx+/vlnNmzYQEZGBp6ennTr1o3w8HDpBCF+NasJqhHwoNZaK6V2AuW01nOVUunAK5jTbggh7gLO9FTiN80jbsdqYru8RNswhc3NndYPj+XtlHTK+3pa2s/x48dZunQpsbGxADRv3pxevXrh5+dXlOGLMsRqgkoGnK7fDwMtgWXADsBaf1MhRIlLOXOQy4unkx53DgzYsGQlzZrVp7yPB3a7zVJySkhIYNWqVezfvx+AwMBA+vXrR/369Ys6fFHGWE1QG4BXlVK/A7YDTyql3gK6AJZ79imlfgt8AKRmWzxeaz3L6j6EELfPmZZM3NqvSNi+HDDwqFyThY7OqBYt8fG0NkSRw+Fg69atrF+/nrS0NNzd3enUqRMRERG4u1sed1oIy6x+qp4HFgG/BWZgPh+VAHhizgllVSvgLa31n24nSCHEnUs+/Qtn57+NPSkW7G5UjBhCpcgHGefmbrmt6cSJEyxbtixrYNcmTZrQu3dvKlasWJShizLOajfzo8B9SikfrXWyUqoN0BW4orXraT5rWgP/vYM4hRB3ymbHlhTLmYwAYps/zNAu3SwXvXbtGqtWreLAgQOAObBr3759adhQBpERRS+/oY58C1i+NvO11jrnEEi5lXMDQoBHlVL/BpKAj4EpWmvjdgMXQuTOMAySYw5jD26At5c7PrWa4NZnIueuVOCBbqrgHWD2ztu8eTObNm0iPT0dd3d3OnbsSPv27aU6TxSb/D5p1zGHM7LCSiV2EGb71SxgKHAf8ANmVaFM1yFEIchIiOX0D9Ph9G6iazzC8NEPAFC3dQR1LZQ3DIPDhw+zYsUKrl69CkDTpk3p2bOnVOeJYpdfgupamAfSWl8AOmdbtFspNQ14AElQQvwqhtNBwo4VxK37GtKSSTY8OBtzkaSUdHy9rU1lceXKFVasWMHRo0cBCAoKok+fPtI7T5SY/MbiW1+YB1JKNQN+o7X+S7bFnpgTIgoh7lDy+ROcWfAu7nEnAfBtHE58wyGMb9rA0hBFKSkprF+/nujoaJxOJ15eXnTp0oXw8HDc3Kz18BOiKFgd6mgb+VT3aa3bWNhNPPC8UioGmIk5OvrvgAlWYhBC3Crx0FYuzH8Td5wYPhWp2u8pyjVpS1ULZZ1OJ7t372bNmjUkJZnNyKGhoXTr1o3y5csXbeBCWGC1tXNxLuXqA/2B16zsQGt91jXH1FTMIZKuAH/TWn9rMQYhRA6+dZuT4VGenem1adJnDOWa1LZU7tSpUyxfvpwLFy4AULt2bfr06SMTCIpSxWo388m5LXc9eDsQeNvifn4EwixHJ4S4yY3LF9jz/Sz8Oj1KiybVcfMuR91x06jv5YOXR8HVcVevXmXVqlVZM9v6+/vTs2dPmjVrJlNhiFLn1/YX/RGYVhiBCCHyZjgyuBa9mMvr5lDVmcaWBQbNXvwjdrsNHwvVcampqWzcuJEtW7bgcDjw8PCgffv2dOjQAQ8Pa50ohChuVtugcnsmqgLmQLHnCzUiIcRNrh7ZzfW1n5F++QxuwGlvRcveD2K3F3zH43Q62bVrF2vXruXGjRsAhISE0L17d/z9Lc6lIUQJsXoHldczUSnA44UXjhAi09WLF9jz1bvUTjar49wrVqFy7yeo37CVpfJHjx5l5cqVWcMT1apVi969e1OjRo0ii1mIwmQ1QeV8JsoA0oADWuvEwg1JCAHgFnuC2skHSTfcsLeMom7f4djdCx5t/NKlS6xcuZJjx44BULFiRXr06EHTpk2lnUncVax2klgPoJQqDyjAYS7WyUUYmxBlSnqGk/XrttGpcxieHm743deOSycGExDSmaBaBffOS0xMZN26dezatQvDMPDy8qJTp060adNGhicSdyWrbVBemKM9jMB8uBYgWSn1EfC81tpRRPEJUSakx51jyydvUyflOMuTnmXgwI7YbDYa9n+0wLJpaWls3ryZzZs3k56ejs1mIzw8nC5duuDrm+uQmkLcFaxeVr2DOffTQ0A0YAfaAm9izu30x6IIToh7nSPlBvE/zeda9BJqODNItXlQt9wNS2UzO0CsW7eO69evA+Y0GN27d6dy5cpFGbYQxcJqgvoNMEBrvSnbsu+UUnHAXCRBCXFb4q4lsWHeHJrErsMz4zpgw69lN2p2fgRPv0r5ljUMA601a9as4cqVKwDUqFGDXr16Ubu2tQd1hbgb3M6U7+m5LL9WiLEIUWbErvmSkIsrAPCo3pjg3mPwql7wHEunT59m9erVnDlzBoBKlSrRvXt36QAh7klWE9RLwMdKqbHAFq21UynVHLNd6u/Zn5OyMjeUEGWNYRgcP3OVBrUDAKjbbRBHTu6kXLsHqRnRvcDkcvnyZdasWYPWGgBfX186depEWFiYDOgq7llWE9TbQHlgI+BQSjkBD8AGtAH+nW1b+W8RIpuUxGusmPEO5ZPO4vX0VGoG++NRsQr3PTcdm82eb9lr166xbt069uzZg2EYeHh4EBERQfv27fHy8iqmdyBEybCaoAYXaRRC3IOMjHSubV9K/KZvaZGWhNMN4o4coGZwBEC+ySkpKYlNmzYRHR2Nw+HAZrPRunVrOnfujJ+fX3G9BSFK1O0+B+UDNMLsxXdMHtIV4lbpGQ42LVhAnbMr4brZicGjdgv8u4ykQq3825nS0tLYsmULmzdvJjU1FYBmzZrRtWtXAgMDizx2IUoTq89BuQH/AJ7lf1V7aUqpz4AJWuuMIotQiLvM/hmvUOfaYQA8gmoT2H0Uvg1C8y2TkZHB9u3b2bhxY9bcTA0aNKB79+4yBYYos6xW8f0dGAmMAjZhJqgOmM9BTXL9CFEmOZwG8YkpBFbwAaBaSDiXN8XA/UNo3mcQNnvezbKZkwauX7+ehIQEAGrWrEm3bt2oV69escQvRGllNUGNAp7QWi/NtmyuUioR+BBJUKKMOnnsJLvnfESiRxAjJz6LzWajSvsBBLfri93TJ89yhmGwf/9+1q1bR1xcHADBwcF069aNxo0bS5dxIbCeoMoDR3NZfhyQR9ZFmeNITiR+8/c4ty8lxEjnRtpJ4uKvE1jJD5u7BzZyn2PJMAwOHTrEunXruHTpEgABAQF06dKF5s2bS2ISIhurCWobMB6zDSq7CcCOQo1IiFLswOFznFg9n2ZJ0Riprkf+6obRsNcofCrl3bvOMAyOHj3K2rVrOX/enELN39+fzp0707JlS3mWSYhcWE1QfwTWKaW6AFtcy9oBdYE+hR+WEKVPauwFHPP+SFOSMACfei0J6PJIviNAGIbB8ePHWbduHTExMQCUL1+ejh070qpVKxllXIh8WO1mvl0p1Qp4CmiKOfTRIuA9rbXMqCvuWUdPX6VWNX+8PNzwDKiCZ4XKXE93UKvfaCqpvCcONAyDEydOsG7duqxhiXx9fYmMjCQsLEymWRfCAsuXb1rrw8BEpVQg4NBaxxddWEKULMMwWPz1fPyPruBouyfp07sNNpuNRo+/ht3XP8+2IsMwOHnyJOvXr+fUqVMA+Pj40L59e9q0aYOnZ8ETDgohTJYTlFLqVeBpoIrr9Rngba3120UUmxDFzjAMUk7tJ27d1zQ7exjc4dKZDZgjeoFbuQp5lsuZmLy9vbMSkwxLJMTts/qg7t8xq/de5+b5oF5RSnlord8ouhCFKHoxlxJZvGANbVI2UzHRnCrd7lsB7/BBtG3XL89ymVV569ev5/Tp04CZmNq1a0fbtm3x9vYulviFuBdZvYN6Angsx3NQPymljgLvAZKgxF3txvbF9Lr8LQA2L18qRgymQni/PJ9lMgyDY8eOsWHDhqw2Jm9vbyIiImjTpo0kJiEKgdUE5Q6cyWX5UUBGrhR3nVPnEzh8Kpae7czRGuq16cipvYvwbdmLql0ewM0n94+1YRgcOXKEDRs2cPbsWcBsY8q8Y5KqPCEKj9UE9QYwXSn1iNb6DIBSKgD4F+ZwR0LcNS6fO8e6Ge9Sze0alxq/QXCAL56Va9LguZnYvfK+Yzp06BAbNmzgwoULgNkrLyIigvDwcElMQhQBqwnqIeA+4Lirc0QGUBvwBNoppX6XuaHWOrjQoxTiV4q9lkxFLyfXfl7A9W1L6OiVigHY489AgALINTk5nU7279/Ppk2buHz5MmA+x9S+fXtat24tvfKEKEK3M2FhoVBKVQT2ApO01p8V1n6FyI1hGLz3zTacv6wiqsIhSDNHf/Bt3IaALg/jGVQ713IZGRns2bOHn376iatXrwLmyA8dOnQgNDRUnmMSohhYfVB3ViEecwZQoxD3J0TeDCedzs6kvM8VSAPvOs0J6DoC7xqNc908LS2NHTt28PPPP5OYaE53FhAQQGRkJCEhITIkkRDFqFjHWVFKPQb4A/uK87ii7Ii9lsyclZpurWvQpH4QNrsbQa26kHp0O8E9R+FTLyTXh2yTk5OJjo5m69atJCcnA+bo4pGRkTRr1gy7Pf+p2YUQha/YEpRSqh7wF6A9sLy4jivKls0rVtPs4EJ2xLSkyXMTAKja5UHo9lCuU6wnJCSwZcsWduzYQVpaGmDOxxQZGSnTXghRwoolQblm5P0SmKi1vqCUKo7DijIgKSWduIQUKmdc5OraL2l2ch+4Qy2f4xiGgc1mw+Z2a3vRlStX+Omnn9i7dy9OpxMwZ7CNjIykTp06kpiEKAWK6w7qVUBrrb8rpuOJMuDomXje+Xg5/b130chpTldm9/KlYvsh+If3zzXJxMTE8NNPP3Ho0CH+v707j4+rug44/psZWbIly7bkRbaxjSVjHyFhWYsleQECdlhDQpKyhC0JCWmS0uRTUpI2oQlNS0ICDS1Jm4ZQaBYClKVQ1gQ+sQ1e5VXyJh9bNsI2kjfJtrzI2ub1j/skDwIPsmxpRtL5fj764Hlv3ptzeDNz5r737r0AgUCAnJwc5s6dy/jx43s1fmNMdKcshWVpHAAAFulJREFUUCLyeFd3oqpf+oinfA4YLyKf9R+n4vpVlajqX3X1dYyJlBGu5ZuDniMY9iA0iOHFVzNizmc+0MnW8zy2bt3KsmXLOoYjCoVC5OfnM2fOHNLT02MRvjHmI0RrQUV+yhOBTwJbcJMXNgOFwAzgyY96EVXNjnwsIuW4gWZ/c5rxmgHK8zxWbNzDn8uq+fvbS0kIBRk6cRp1oyeRPOE80i+6kYRhI9+3TWtrK+vXr2f58uUcOHAAgKSkJIqLiyktLWXo0KGxSMUY00WnLFCqen37v0Xk17iC8q3I54jIfUB2522NOdvampvY/PIfuLqtgsVLU7j04jwCwRDn3vHTD1xjamxsZPXq1axcuZKjR48Crg/TrFmzKCwstFEfjOkjunoN6iZci6mz3wLlp/uiqpp/utuYgae6toGMtCRatizh4Nv/w6VeHQTh3NB2IA/gfcWpvr6eFStWUF5eTktLCwAZGRnMmTOH3Nxc68NkTB/T1QK1B5gHbOu0/Brg3bMakTHA/7yxhfUL3+Tm0ZsZ0rgPgMQxk0mfdytDsk7+vvE8j927d7N8+XIqKys7lk+ZMoXZs2eTlZVld+QZ00d1tUD9E/CYiMwD1gIB3HxQVwPXR9vQmO7IO7SA4qGLoBESRowh/WM3k5I7t6MvU1tbG5WVlaxYsaJjVPFgMEheXh6zZs0iIyMjhtEbY86Grg519HsR2Q18FbjNX7weuFhVy3oqODMwNLe08erSd/BaW/jsx88HIOviK6l5bzXpF17HsMLLOk7lnThxgjVr1rBy5UoaGhoAN91FUVERJSUlpKba7C/G9Bdd7gelqguBhT0YixmgqnUrbQseZWiohUOl/8KI1CSSxmYx+ZuPdBSmAwcOUFZWRkVFRcf1pZEjRzJr1ixmzJhhg7ca0w9F6wf1QFd3oqrfOTvhmIHA8zzeqWlgUmoLB99+hoT1CylMCuMFE0huOQiMdU8MJlBVVUVZWRlVVVUd22dmZjJ79mzOO+88u75kTD8WrQVV3MV9eGcjEDMwtLaF+eEv3iCzbimXpGyDthYIBEnN/zhpF11PwrBRNDc3U1FRwcqVKzv6LyUkJJCXl0dpaSljxtiUY8YMBNH6QV3a/m8RuQ14XVUP9EpUpt8KeW3cdOJJBg8+Bm2QkjOXtIs/R+LI8dTX17Pyj3+kvLycpqYmAFJTUykuLqaoqIjk5OQYR2+M6U1dvQb1c2AWYAXKnJbq2gaeem09N16eQ9bEdAIJgxiRPw+vfhej5t1CYkYm27dvZ+WfnmTbtpO9GCZOnEhpaSnZ2dnWf8mYAaqrBaoM+Azwkx6MxfQzXlsLla8/xxU1Cyh76RKy7rwDgHGX30ZTcwvrystZ9eyr1NfXA258vOnTp1NSUsK4ceNiGboxJg50tUCFgR+LyD8A7wCNkStVteRsB2b6ppr9RznR1MzounIOLn4WObwPgnBh+n4A9u7dy6pVq1i/fn3H3XjDhg1j5syZFBYWkpKSEsvwjTFx5HRaUNbfyUS1clMNr//hGT6ZugHChwAYNGoCqRfewM62VN58/HF27drV8fzMzExKSkqYNm2azVhrjPmArnbU/WH7v0VkGBBU1UM9FpXpM5pb2kgc5K4RTWlWbktZDGEIjcggVPQpthwJse61ZRw/fhxwo4nn5eVRXFzM6NGjYxm6MSbOdbmjroh8HfgeMN5/vA94WFXtutQA1HCsmUeer+BIbTX3fvs6QsEA6XkXcWz9QurG5LK5roWqP51sdGdkZFBcXMz06dNJTEyMXeDGmD6jSwVKRO7GzYr7I2AJbiy+ucDfi0ijqj7ccyGaeON5YahexUU7f8NwGtixvZSxY9NYu3Yta+vTaKh2s9WGQiFycnIoLi5mwoQJ1qnWGHNautqCuhP4mqo+FbFsqYi8C9wHWIHq56p2HeK1Jdv5fM5RjpS9QMv+XYwC9gwez84lb1C1qwbPc32209PTKSoqIj8/3/ouGWO6rasFajRuJt3O1gATzl44Jh6Fw2EWPPkEpU2rqd9xhONeAjsSJ1PFKI4ca4Zj7xEMBsnOzmbmzJlkZmZaa8kYc8a6WqA24qbVuL/T8htx08CbfmbTjjrOHZvK0OREgsEgFw6vYf9+WBgQ3mtNwmsEaCYtLY3CwkLy8/NtCnVjzFnV1QL1A+BVEZkNLPeXzQauBD7bE4GZ2Pn9C2UcXfM6E4ou4sJLClm3bh3rjozgaDgJwm7epWwRioqKbEJAY0yP6ept5m+IyHzgG7j5oBqBSqBYVSt6MD7TCzzPo7UtDA17ObTiZUorF7IrKYUtWxbz8/VLOp43cuRICgoKyM/Ptw61xpgeF226jSuBxap6DEBV3wbe7q3ATO+o2nmQl557nTkJG0lo2ElVOI13vKk0kwDNbhTxnJwcCgsLmTRpkrWWjDG9JloL6lWgVURWAwv8v2Wq2tQrkZle0bLhdeTYIlaG0znI1I7l48aNo6CggOnTpzN48OAYRmiMGaiiFahRuL5Os4ELgbuAoIis4GTBKlPVth6P0pwVnuexZt1Waqp3cv70yZSXl7Nly3bC4XMAGDw4iby8GRQUFDB27NgYR2uMGeiizQd1EHjF/0NEQkAhrmDNBr4EjBKRxar6iV6I1XST53k07d7C1kUvseOdXezw0li3YRkAgUCAKVOmUFBQgIiQkNDlwUWMMaZHdfnbSFXbRGQ3UAvsA3YCY4CsHorNnKHGY8fY8ObLHKouZ+thjzqScV3aYMSIERQWFjJjxgyGDRsW20CNMeZDRC1QIjIUuAT4OHAZkA3sAhYCjwE3q2pND8doTlNbWxubVy6m7M1XqPVSCDMEgMRQkJzzsyksmWVDDxlj4l60u/gWAyXAQWARbjijBapa1TuhmdNRs6ee1UuWEBqSwKZNG2lsbARSAY+xI4Yx55JLyc7JZdCgQbEO1RhjuiRaC2ousBt4HHdDxHJVbTmTFxORa4AfA5m404QPqOojZ7LPgaqtLUwoFGTfjkrWLvoTm3bt5ygnRwkfM2YMudnTKJhZQmpqagwjNcaY7olWoLJwp/bmA18HUkRkCfBnXMFaq6peV19IRMYBzwGfUdXXRaQQN+DsKlVd2+0MBhjP83jw8bdo3rmSkUOOsa+pfaK/RJJoY2LmNOZf/nEyMjLsFJ4xpk+LdhdfNfBf/h8iMgOYhytY3wdaRGQR8GdV/eVHvZCq1orIaFU9IiJBYCTQChw50yT6s9oDx1i7ZS+Xl05EVVlfvo4Tu7fjBQPsawqSQJjM9GQKSucgM+fazLTGmH7jdO7iqwAqRORhoAi4HTfs0aeBjyxQ/j6OiEgycNh/7Z+q6rbTjnqAaGxq4R//9VnGhWqoeBtaWtwZ1gAwPqmV6ednkz/vGganDo9toMYY0wM+skCJyASgFJjl/7cQ1/JZhpsLatFpvuYJIAXIA14TkW2q+thp7qPfCYc91lftZ8XGWq4qGk7F8sVUbn+H8YnuLGpLC5xzzjlMnz6d86dNYVjaqBhHbIwxPSvaXXzP4wrSONxpuKXAy8DdwJrujiChqmGgGVgtIr8GrsXdsj5geZ5HTW0NTz35AkOp4zcbTq4bxgmmpidROP+TjM8pil2QxhjTy6K1oJKAf8O1kNb6haXbRORjwEOqGvktmwQcOpP99kWe57FO97OwrJL8CW1UVm6mrq6O5ACEgWSayRrczAUXXEDmnCtITLNhh4wxA0+0mySuOcuvVQ6cIyLfwvWpKgW+DHzmLL9OXKurq2Pjhg289fYyPK+FJdVueUpKClnDQmQltzF1zrUkZ+YRCNgND8aYgavXBl5T1cMicjXwc+Be3IgUd6jqW70VQ6wsWKEsXbGG9NABDtQf7FieSCtZ54yjeN6VTJ482e7AM8aYCL06Mqjf3+nC3nzNWKmvr2fTpk1s3ryZPXv2AHAAGEQbEwOHyUoNMq2glBH58xg0YkxsgzXGmDhkQ1efRXV1dbzw+lJ27lBC3vGO5QkBjwkcIjOpCZlewPC8W0g6Z5p1pDXGmCisQJ0Bz/PY/d4etlcplZs3sW//AQBCQDAYIjc3h9zcXMYFjxHy2kiekk8gZGPhGWNMV1iBOk2e51FbW0tlZSVlq9bR0nSsY1376buJwSPkXnYDo0uvjGGkxhjTt1mB6oJwOEz1uztZsmItdXvepaGhoWNdEq1MDDQwKdjA5MnnMjznE6RkzyKUbHMsGWPMmbACdQqtra3s2LGDLVu2oKocP37ymlJqairZ2dmk1ymjm/YxKv9KUrJnkTA0LYYRG2NM/2IFKsKJEyfYtm0bS8vK2VvzLngnB8sYShOTAg2kZ89h3vU3EQgE8Lwrra+SMcb0kAFfoA4fPkzZmvXsfa+a6upqwuGTA2ak0cik4GEmBhrImJBJau7VpJw/u+PuOytOxhjTcwZcgfI8j71796KqqCq1tbUd6wKBAOeeey5pdVuZ3PgOIyZOJe2Ca0mWUjt9Z4wxvWzAFKg9e/fxxHNvcLT+PQLhEx3LQ4QZHzjC6NyLmX/V5SQnJ9O8byehlOGEUmwaC2OMiZUBU6BefeUljh14jwAwhBYmBBqYEGhgbEITqVPySJ+TR1JyMgCJYybFNlhjjDEDp0DNu/RSKp56mPFePSNDLaScV8DQ868neWoRwaTkWIdnjDGmkwFToDKzppB+xVUEB6eQMnWmFSVjjIlzA6ZAAQyfeVWsQzDGGNNFdp+0McaYuGQFyhhjTFyyAmWMMSYuWYEyxhgTl6xAGWOMiUtWoIwxxsSlvnKbeQhgz549sY7DGGPMWRTxvR7qvK6vFKhxALfcckus4zDGGNMzxgHbIxf0lQK1CrgIqAXaPuK5xhhj+o4Qrjit6rwi4Hle74djjDHGfAS7ScIYY0xcsgJljDEmLlmBMsYYE5esQBljjIlLVqCMMcbEJStQxhhj4pIVKGOMMXHJCpQxxpi41FdGkjglESkBXlHVMf7j0cDDwBVAE/A4cK+qtvnrfwfcALRG7CZPVXeIyCTgMWAWsA/4hqq+1mvJ0K185vjrzwdqgO+p6nP+upjmczq5iMivgFs77SIFuEdVfxzrXKBbx+Z24B+AUcAW4G9VdYm/ri/m8w3gLmAksAy4U1V3xDIfEbkM+Akw1X/dB1X1ERFJBP4duA43+sxDqnp/xHY3AD/GjWDwFvBFVd0Xy1zOJJ+I7e8CPqaqn45YFvP3Wnf12RaUiARE5A7gDSAxYtVvgTG4L+wLgBLgnyLWFwKfVtWhEX87/HVPA+txH8CvAE+LSFYPpwJ0Lx8RGQe8invjpgJ3Ak/4b0iIUT7dyUVVvxZ5TIBvA5v93GKWS3fzEZE84CHgWmAE8ATwooi0f+b6Wj43AD8Cbvdjfgl4U0QG+9v2ej4iMhF4HrgP9//4JuB+EbkC+CEgwBSgGPiCiHze3y4H94X9RT/ebX787WL1uelWPv62Q0XkQeBnH7LrmL3XzlSfLVC4A/Z13MEEQESSgSuBu1R1n6rWA98HvuJ/KIcA2UB5552JyDRgJvADVW1W1QW4D+GXez4VoBv5AJ8H3lbV36qqp6pv4r5UDsY4n+7kQsRzpwAPADerakMfPTZTOfn5CuB+9Tb62/bFfP4CeFRV31LVVlX9T6AZmB/DfCYDT6rqC6oaVtVVwCJgLvAF4EeqelBVq4F/Ab7qb3cr8LKqLlHVE8B3gbkiMjXGx6a7+YD7oZoJPBK5wzh4r52RvlygfqWqRcDqiGXt+RyLWNYGjMb9IsnHndp7VET2i8haEbnGf14OsFNVI7fdAkzvkeg/qDv5FAHVIvK0iBwQkXXAWFU9Qmzz6U4ukX6G+zKs8B/3xWPzJ2AjsAH3Rf5T4EZVDdM38wl2Wte+fhoxykdVF6vq19ofi0g6blDpdbhTd5tPEU9O5DpVPQ7s8tfH7NicQT4AN6nqdcDeTruN9XvtjPTZAqWqNR+y7CjutMUDIpIuIiOBH/irh+BOgy3G/YIcjztl8YyIzACGAsc77fI4kNwzGXwg9u7kk45rsj+BewPfD7zgt0Bilk83cwFARC4ALsN9obfri8dmMKC48/4pwN/hTvGNpW/m8zzwlyIyU0QGichXcGcjhhDjfABEZDiuZVAGrImI4cPiiRZvzHOB087nQ4+pLy7y6a4+W6CiuA33i7USd/HzRX/5IVV9Q1UvU9XVqtqiqs8DC4BP4X4dDum0r2TgaC/FfSqnzAd3IfuPqvqKn88zwFrgKuIzn2i5tPsS8KKqRv4SjMdcIHo+/wjsUdUyVW1S1V8C1cD19MF8VPVp3A+gp3GtjQuAN4GDxDgf/zTWClzr4TrgiL8qMqbIeKLFG/Nj0418ool5PmeiPxaoccBXVTVDVS8A3gMqVfW4iHxSRL7Q6fmJwAlc83mSf52qXTbvb1bHwinzwTXV0zo9v/3OzHjMJ1ou7a7l/ResIT5zgej5TASSOj2/FWihD+bj35Dzsqqep6pjgW/hitQaYpiPiFyMa2W8CFynqidU9SCwB3dTwYfFszlynX/9bZK/PKbHppv5RBOv77Uu6fO3mX+Ih4CNInI37k33U07eCRYCHhaRStwH60ZgDnCHqu4UkQrgRyLyXX/5tcDs3k6gk2j5/A74axG5FXgS9+s8D7hBVXfFYT7Rcmm/zTkLWBq5kapqHOYC0fN5BXe67BncRGy3ALnAq3F6bCB6PvOBe0XkIqABd/qvBlilql4s8vFPZb+C64rwi06rf+/Hux53mutu3C304D4rS0TkEmA5rmW4TlW3+vuNybE5g3xOKY4/O13SHwvUV4BHgXrcqZZf+KdXUNUXReQe4ClgLK4Fco2q7vS3/Qvg17i+AgeAL6vqxl6Ov7No+VSIyNW4L5JfAjuBz6rqLn/beMvnlLn4JgNN/h1kncVbLhD92DzqX0d4EtcPajNwdRwfG4h+fP6Au7BegTvr8GfgU6raPuNpLPK5E3dd+X4RiewT9B+4AvozYBPuTNGvgV8BqOoGEfmS//gcXIvl+ojtY3VsupVPF8Tje61LbEZdY4wxcak/XoMyxhjTD1iBMsYYE5esQBljjIlLVqCMMcbEJStQxhhj4pIVKGOMMXHJCpQxPcQfxHe/P+hn53X3iMhREZkcg9CM6ROsQBnTc/4G1xn+fRPLiUgmcA9uxIDqGMRlTJ9gBcqYHqKqe3BzDd0hIqURq36Bm5Os83A2xpgINpKEMT3In+xvKW54oGLcyPnPAAWqutl/zu3A93DD7lTiWlZ/9NclAP8M3IybIqYON1TX3araJiJPAGHcLLhTcENdLeq1BI3pQdaCMqYH+WPV/SVuEN/bcTOh3hdRnD6BG6T1Hv85j+Hm9Crxd/Fd3NTft+Fm6f0u8E3gmoiXuRU3cOh83DQNxvQL/XGwWGPiiqpuFJGHcNNxVwI/iVj9PeAn/lxeAFUiMhM3ncXncDPyflFV3/bX/7eIfAc3U+r/+cs2q+oTPZ2HMb3NCpQxveOHuFl1/1lVWyKW5wBFIvL9iGWD8Ofr8Ufgny8iD+KmV8/Djfoeinj+9p4M3JhYsVN8xvQCVW30/9nYaVUC8G0gP+IvFzdnDyJyH/As7rP6LHA50HmqhM77NKZfsBaUMbG1BThXVavaF/itqRPAg8BfAX+jqr/z1w3BTSYYiEGsxvQqK1DGxNYDwO9ERIEFwGXAvbjrT+AmD7xGRJYBw3GnCofxwenkjel37BSfMTGkqs8CdwHfwV13ugv4qqo+5z/l87i79zYA/wtUAb8Bino9WGN6mfWDMsYYE5esBWWMMSYuWYEyxhgTl6xAGWOMiUtWoIwxxsQlK1DGGGPikhUoY4wxcckKlDHGmLhkBcoYY0xc+n+IrPDfuheGWQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "system.alpha = system.birth_rate - system.death_rate\n",
-    "\n",
-    "results = run_simulation(system, update_func2)\n",
-    "plot_results(census, un, results, 'Proportional model, combined birth and death')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Maybe the reason the proportional model doesn't work very well is that the growth rate, `alpha`, is changing over time.  So let's try a model with different growth rates before and after 1980 (as an arbitrary choice).\n",
-    "\n",
-    "Write an update function that takes `pop`, `t`, and `system` as parameters.  The system object, `system`, should contain two parameters: the growth rate before 1980, `alpha1`, and the growth rate after 1980, `alpha2`.  It should use `t` to determine which growth rate to use.  Note: Don't forget the `return` statement.\n",
-    "\n",
-    "Test your function by calling it directly, then pass it to `run_simulation`.  Plot the results.  Adjust the parameters `alpha1` and `alpha2` to fit the data as well as you can.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def update_func3(pop, t, system):\n",
-    "    \"\"\"Compute the population next year.\n",
-    "    \n",
-    "    pop: current population\n",
-    "    t: current year\n",
-    "    system: system object containing parameters of the model\n",
-    "    \n",
-    "    returns: population next year\n",
-    "    \"\"\"\n",
-    "    if t < 1980:\n",
-    "        net_growth = system.alpha1 * pop\n",
-    "    else:\n",
-    "        net_growth = system.alpha2 * pop\n",
-    "    return pop + net_growth"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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IKz4hhBDlkiQoIYQQ5ZIkKCGEEOWSJCghhBDlkiQoIYQQ5ZIkKCGEEFelrN9EJAlKCCFEiTgcDlasWMG//vUvDhxwt/uwkqsoz0EJIYQoB9LS0pg/fz6xsbFYLBZ8fK62g+orkwQlhBDCLcePH2fevHmkp6cTEBDAHXfcQYMGDcpsfZKghBBCFMvpdLJhwwZWrVqF0+mkYcOG/OEPfyAgIKBM1yt1UNeZUor9+/f/bviIESP47rvv8v9euHAhI0aMoG3btkRFRfHYY4+xb9++YpcdExPDQw89RKdOnejYsSP3338/W7ZsKfVtEELcODIyMvjmm29YuXIlTqeTrl27cv/995d5cgJJUOXSli1beO2113jppZfYvn07MTExtGvXjgcffJCkpKRC55k3bx4TJkxg9OjRxMTEsH79eoYPH87jjz/Otm3brvMWCCEqg7i4OGbMmMHBgwfx8/PjnnvuoU+fPlit1yd1SIIqh3bt2kXDhg1p3749VqsVPz8/nnjiCYYMGVJogsrIyODf//43r776Kn379sXHxwcfHx/uvPNOxowZw7FjxwCw2+1Mnz6dPn360KlTJ55++un85W3evJkhQ4YwefJkOnXqRPfu3fnggw/y1zF//nz69u1LVFQUf/jDH1i3bl3+fB06dLgsnt69e7NixYpi5xNClF9Op5NNmzYxa9YsUlJSqFOnDmPGjKFp06bXNY5Km6CGPfMDw5754bJhr8zcxLBnfmDLvjP5w5b+fJxhz/zAlG935g9LTMlg2DM/8OCkpZfN/5d31zDsmR84fDK5TGPv2bMnBw8e5IEHHuDzzz9n37592O12Jk2aRJMmTX43/S+//EJ2djY9evT43bgnnniCO++8E4DPP/+cH3/8kVmzZrF27VpCQ0P561//mj/t4cOHsdlsrF+/nn/+8598+OGHHDlyhKSkJF588UWmTZvG1q1bGTVqFK+99toVn4G42vmEEJ6TmZnJ3Llz+emnn3A4HHRs14ZBgQn429Mvm85ud2B3yHNQN5zIyEi+//57br75ZubMmcPtt99Oly5dmDZtWqEn96SkJIKDg7HZbMUud+7cuTz55JPUr18fPz8/nn32WbZu3crx48cBsFgsjB07FpvNRteuXalRowaxsbH4+Pjg7e3NvHnz2LNnD7fffjs//fQTFoul2PVd7XxCCM84deoUM2bM4MCBA/j6+nL74H40P7qIrMPbSfzpk8um/XqZZsWW2DKNp9K24lv49ojfDXvpkc6/GzYwuiEDoxteNiwsuEqh87/3157XHJePjw+5ubm/G2632/H19c3/u0GDBjz//POAkYCWLVvGG2+8QUhICKNGjbps3ho1apCcnExOTs7vklRaWhq+vr74+PgQHx/PxIkTeemll/LHe3t7c+rUKby9vQkICLgsBpvNhsPhICAggNmzZzNjxgweeOAB/Pz8ePDBBxkzZkyx21rcfJKkhCg/nE4nmzdvZvny5TgcDiIiIrjjjjsIqRbM6QNLsYWEU2PYn3E6nfnH7uAujXjnq+3079SgzI7nSpugyqtatWoRHx9Py5Yt84fl5uYSHx+f31vwvffeS//+/XnooYcACA0NZdSoUezZswet9e+W2bZtW/z8/Fi7di19+/a9bNybb75JbGwss2fPpmbNmrz00kt069Ytf/zBgwdp2LAhv/zyS5Exp6Sk5Ndf5eTksGHDBp566ik6dOiAl5cXOTk5+dM6nU5SUlKuOF/BeishhGdkZGTwww8/5J9botq3p0+PLvgGhgAQfscEspzefLXmKLGnD/LCHztisVgIDfLj1TG3lunFphTxXWdDhgxhypQpHDlyBIDk5GQmT55MjRo1aNWqFQCDBg1i+vTprFixguzsbLKzs9m8eTNr166lT58+v1umj48P48eP56WXXmLlypXk5uZy6dIlZs2axffff8+4ceMAGDlyJB9++CGnT5/Gbrfz0Ucfcd9995GZmVlszElJSTzyyCNs2bIFm81GeHg4FouF4OBg6tevT3Z2NosXL8ZutzN79mzS09OvOJ8QwvNOnjzJjBkz0Frj6+vLiG7tueXEEpIWfoDTYXRw61UlgByHhcUbj7Pl1zMcOZWSP39Zl4TIHdR1Nm7cOLy8vBgzZgyJiYn4+vrSuXNnPvvss/ziufvvvx8/Pz+mTp3KhAkTcDqd3HTTTbz00kt07dq10OXefffdBAYGMmPGDJ577jmcTifNmzdn5syZREVFAfD444+Tm5vLfffdR3JyMk2bNmXmzJkEBQUVG3OjRo2YNGkSL774IgkJCYSEhPDCCy8QGRkJwAsvvMCbb77JP/7xD0aMGEG7du3cmk8I4RkFH7yNCK9Jz8BkvH+eRS5g8bZx9PBJGkcaxXfBAb48dWcbqlfzo0ndatctTktFaFGllGoIHFu5ciV169b1dDhCCFFhXbx4ke+//z6/FKdNvTBaJGzAas/G4uNHSLe7mXuqHgs3nmDC/R3o1qZOmcYTFxeXVzLUSGt93HWc3EEJIcQN4ujRo3z33Xekp6fj7+9PF78Eap3eDUDV5l0I6/Mg3kFh1P/5OD7ecSSnZXk0XklQQghRydntdtasWcP69esBaNiwIbfddhv2nUu5+GsWcY1H4AhvRnhQGAD9OjWg/c3h1Aip4smwJUEJIURlduHCBebPn8+pU6ewANHNG9HnD6OxWq04b72NQyG38s/PthMSuJt2N9ekiq83XlaLx5MTXMcEpZS6D5hRYHAVYKXWuv/1ikMIIW4Ue/bsYdGiRWRnZ1PVaqeL5RgRccfJzboDnyr+WLxtdGhRh863nKJTiwh8bV6eDvky1y1Baa2/BL7M+1sp1RZYBjx7vWIQQogbQVZWFkuWLGHXrl0A1Lck09lyiqqhNdlXcwCvvr2e//ytJwH+PlitFiY+3MnDERfOrQSllPIC2gMdgJqAHTgDbNVa7yxu3iKWZ8NIVi9rrXeVdH4hhBCFi4+PZ/78+SQlJeGFgw7WeJraLhLS9S6COw1n2sdbSLiQyMY9p+nfqew6GywNxSYopVQI8GfgCSAMOAokAl5AdaCBUuo0MB34UGvt7ltUxwEZwNSrjFsIIYQLh8PBxo0bWb16NQ6HgxDvXLo6jxBcvxnV+jxESJ16APzptlakpGfRqkkNzwbshiITlFLqAeAVYCXwCLBCa51VYJogoBtwH7BXKTVRaz27uBUqpXwwivX+pLUu/w9hCSFEOZeamsp3874l9mQcAB07dqTrzXXZsSuWl9fbGeCfxLg7jATVIKL4B/PLk+LuoKKAKK31uaIm0FqnAv8D/qeUigAmAsUmKGAg4DDnE0IIcQ1+3bePH39YQFaOnSreFm676578t7U09m+IbdNaqvh6X/ai14qiyHfxaa2fKi45FTL9aa31k25MOgKYq7V2uLvsysSdLt8/+OADWrVqxeHDhy+bprDOAfN89913NGvWjLZt29K2bVtat27NoEGDmDlz5mVddPTu3ZtWrVrlT5f3b8CAAfnTxMbGMm7cOKKiomjbti1Dhw7lv//9b6HrHTx4MN27d7/shbFCiLKXnZ3Ngq+/4Nt588jKsVPbkkq/sBx+PvjbuzXrhQcy68X+/HFYiwqXnKAErfiUUjWBVoANuGxLtdaLS7DOzsCLJZj+hpSVlcX48eOZO3cuPj4+bs3TtGlTfvjB6KTR6XTyyy+/MH78eFJTUy/rmPCdd9753VvP8zgcDh577DGGDh3KW2+9hZ+fHzt37mTcuHH4+Phw11135U+b9xLYGjVqsGzZMoYMGXINWyyEcNeJwweYP+9bUrMcWHEQ5Z9G694jGDsnhdxDR7m1VT2a1DPemRdU1b3zR3nkbiu+RzAaNBTWI54To9GEuxoC8SWY/oYUHR1NfHw87733HhMmTCjx/BaLhXbt2vHaa6/x+OOP88gjj1zxpbBgPNQXGxvL0KFDqVLFeFCvbdu2TJgwgezs7MumnTNnDv369SMsLIwvvvhCEpQQZczhcLB21QpiNmzEiYUQSyaD2kVyU/97sfr4cW/yQfx9vWlUu+LUMxXH3TuoZ4GPgee01mnXskKtddVrmd9dR1//Q5Hjqg8aQ1A749ng1B3LOL+k4PPDv2k8cX7+57iZz5J95miR40uTv78/kydPZvTo0fTo0YNOna7uOYXo6GisViu//PJLoV3CFxQWFkbHjh15+OGHGT58eH4x38iRIy+bLikpiZUrV7JkyRICAwOZPHkye/bsuayfKyFE6blw4QLfffcdcXFxgIWb/HKISW5Oz5sHYvXxA+DOPk09G2Qpc7c/qHrAf641OYmSadOmDWPGjOHvf/87qampV7WMvP6XLl68mD9s/Pjx+Z0G5v2bMmVK/viZM2fy2GOPsXv3bp566ik6d+7M2LFjOXPmTP40CxYsIDo6moiICAICAhg+fHiR9VRCiKuXezGZNbPeZtrUD4mLiyMwMJDR996Dre1o4rOD2HM40dMhlhl376CWAX2AQ2UYS6ly984mqF3//LupK6n7yJvXEhLgfpfvecaOHcv69euZNGnSZfU/7rLb7aSmphIREZE/7K233iqyDiovxtGjRzN69Giys7PZvn077733Hk8//TRz5szB6XQyd+5cEhIS6NKlC2DUmWVlZTFhwgTCwsJKHKcQ4nJOh53TGxexZM0G4uxGwVNE3cbcf+8dVKlShYh6ObRrFk5kvRAPR1p23E1Qu4B3lFLDgYPAZZURWuuSV5LcoNzp8t2Vl5cXb775JiNGjMDf37/E69uyZQtOp5OmTd279Z8zZw7ffPMNCxYsAIxkFR0djc1m49FHHwVg06ZNJCcns3TpUqzW327Cx4wZwzfffJPfg68Q4upknjzA9gWziLngQxZVsVmcxGeEc/Z8bR7xNYrz/P1slTo5gftFfD2AzRgvd22N+YyU+a/wds+iUO50+V5Q/fr1ef7555k7d67b63E4HGzZsoUXX3yRMWPGEBAQ4NZ8PXr04MSJE7zxxhskJibidDo5efIks2bNyu9ufs6cOQwcOJDw8HBq1KiR/++2227jm2++kSbnQlwl+6VU4ha8z/xZ01hxwZ8svKlfM5THx/2ZGje15eFhLbBWvNbiV82tOyitda+yDuRG4U6X74W58847Wbt2LZs2bSpymoMHD9K2bVsAvL29qVOnDo8++iijRo26bLq//e1veHn9vuHl0qVLqVWrFl999RUffPABQ4cOJSMjg5CQEAYOHMif//xnEhMTWbFiBZ999tnv5h86dChvvPEGP/30E0OHDnVzjwgh8hw/eYoFexJId4ZiAXr16kXXbt2wWCz849HOng7vunO7y3elVDjwJNAC485rP/Cx1vposTOWAunyXQhRWWXE7sNasyFr1q3PvwB1WKrwa1p9xj/Yg063RFxhCRXbNXf5rpTqCCwHTgIbMR7UHQo8pZTqqbXeVqoRCyFEJZebco7EFbM5sX8n623NSc3MwWKx0K1bN2o1vAWr1QvVINTTYXqUu40k3ga+Bp5wfcGrUmoK8CYgRYBCCOEGR04WKZt+IHHDAnZnh7DX2QSnPQebXyAPjr6bOnXqeDrEcsPdBNUBeLSQt49/AGwv3ZCEEKLycTqdXDq4hcTln3EuOZUN9gZcwHhbS0JOTTp16CrJqQB3E9RpjFcU6QLDGwPy8K4QQlxBZuxeTn87mX3OmuxyROLEQrVq1Rg5ciRBIeGEBPl5OsRyx90E9QXwkVLqL0BeM7Jo4F1znBBCiAKc9lwsXsZpNs0/nJ+8W3M+yyiIataiNSOHD3b7ZdA3IncT1OtAbWAuRgs+C5CDUcQ3sWxCE0KIisnpsJO2axUX1s0l7K6J7DwSx5o1a7DbnVhtVWjYLJrbhnXB5u3uo6g3Jnefg8oGHlNKjQcURnfth7XWGWUZnBBCVDQZJ34lcdmnZJ89RorTl68+mp3/6p22bdvSv39//PykOM8dxXX5PhhYrrXOMT8XVE8pBZS4PyghhKh0clISSFr5Ben7N+JwwgGfhuzICMaJEy9bFUbddTtNmjTxdJgVSnF3UIuAWkCC+bkoJe0PSgghKpWLv27g3MIpOHOzuWAJYGuV5pxNywSc1G6gGH33iPz+1YT7ikxQWmtrYZ9F+fTBBx+wf9iaHkwAACAASURBVP9+pk6dWux0eU9tb9261a0ODIUQV+YbcRO5dgcrshUJVj/IySQoKIhhw4bJXdM1cLvLdyGEEIbMUwdJ272a6gMfx2KxcD7TyfLgbpw7nwg4adO2LQMHDCi0Cx3hvuLqoM5hFN9dkda6ZqlFVMnFxcUxcuRInn76aaZPn05OTg5PPfUUVquV6dOnk52dzRNPPMFDDz3Exo0befvttzl27Bh16tRh3LhxDBw4MH85EydOZPfu3TRu3JjGjRtftp65c+cyc+ZMkpKSaNWqFS+//DL16tXzxCYLUWnkpiaStPq/XNy7DoDlsb7UjKzHzxs34nA4CA6uxogRw2nUqJGHI60ciruDehY3E1R589VXX3Ho0PXpWzEyMpJ77723RPOkpaWxd+9eVq9ezYoVKxg/fjwjRoxg5cqVxMTE8NRTT9GyZUvGjBnDG2+8Qf/+/dm6dStjx46lRo0atG/fnqeffprmzZvz8ccfo7XmkUceoUMHo+eTZcuW8f777zNjxgwiIyOZNWsWjz32GIsWFVeVKIQoiiM7k+RNP5Dy8/c4c7PBy8aK7OYciz+N39lYADp27EifPn3kuaZSVFwd1GfXMY4bztixY/M7A7Tb7TzwwAP4+PjQq1cv7HY7n376KZ06dWLwYKMBZXR0NMOGDWPBggXUrFmTvXv38sknn+Dj40PLli0ZMWIEp06dAoy7pwceeIAWLVoA8Pjjj/PFF1+wefNmGjRo4LFtFqIiurh/I4nLZ2FPSwLAJ7ITu3xu4vSuvfh5ZRMWFsbw4cOpX7++hyOtfIor4nO7dzytdcn7Ii9DJb2j8YRq1aoB5PfLFBgYCJDfQ21mZubv3stVt25dNm/ezLlz5/D19SUkJOSycXkJKj4+nqlTp/LRRx/lj8/JySE+Pl4SlBAllJucgD0tiVOOMBJu6sfJ+FOkpOzFarXSpUsXunfvjre3VOeXheL2avp1i+IGZLEU3y1mhw4d2Lbt8l5MTp48SfXq1QkPDycrK4vExETCwsIAOHv2bP50NWvW5IEHHriso8IjR45Qu3ZtEhMTS3ErhKh8cpITyDkfh3+TdgAERw3hwDkH326OI+zQrwBEREQwfPhwatWq5clQK73iivgevp6BiMt17dqV6dOns3jxYgYMGMCWLVtYuHAhU6ZMoU6dOnTs2JE33niDSZMmERsby3fffUe7dsYBNXLkSKZNm0ZUVBSNGzdm0aJFPPfccyxatEiu9IQogiPrEskbvyNl8yKcXjZO93yRWztEsufX/cToI4T5XMLb25uePXsSHR2dX9ohyk5xRXyTgUla63Tzc1GcWuv/K/3Qbmze3t5MnTqVt99+m4kTJxIeHs6kSZPo2rUrAO+++y4TJ07k1ltvpXbt2vTt25ekJKOMfOTIkaSmpjJ27FgSEhKoX78+H374IQ0bNiQuLs6TmyVEueN02EnbuZIL677Bnp4CwLZLdVm+YBuH9m0iNvYYAI0aNWLo0KGEht7YnQheT0V2+a6UWg3cprVONj8Xxam17l0m0f0WS0Oky3chRCm7dHQXSSs/IzvhBAC+dRUhvR/krfm/QNphnA47fn5+9O/fnzZt2lyxaF6U3FV1+a617lXYZyGEqAycTgdJq/9LdsIJkgkkov+DOGo35YtFi3CmnAGgRYsWDBw4kICAAA9He2Nyu0JCKVUVGAW0ALKBfcAc803nQghR7tnTU3A6HHgHhmCxWKne/48s/X4FXxyvRdftp0lbEoPT6SQ4OJghQ4YQGRnp6ZBvaG4lKKVUS2ANRmLajdEn1MPAK0qpQVrrA2UWoRBCXCNnbg4p2xaTvH4elnqtqNJ/HDVD/PGr14xa0dl0uLSc1LPpWCwWoqOj6dmzpzxwWw64ewf1MfA98CetdQ6AUqoK8AkwA+hRNuEJIcTVczqdpB/4maRVX5CbnADA/v0n2XNpF0/c3pylS5dy4IBxfR0REcGwYcOIiIjwZMjChbsJqjXwQF5yAtBaZyilXgV+cXdlSqkIYBrQC8gEPtJav1iCeIUQwi2Zpw6SuGI2WXFGArLVqIet8z189nUC7dLjmDp1DdnZ2flvcOnYsaM0HS9n3E1QO4BuwMECwzsAv5ZgfT8A24FwIAJYq5Tar7X+qgTLEEKIYuWmJhL/+QvgsJPlVZU6/UcT2KYP8afP0Kv+L5xLMB5sb9asGQMHDpSuZ8qp4p6DGuvy5xZgilKqA7AJsAOtgCeAN9xZkVKqE9AY6GLeiR1TSvXE6D5eCCGuiSM7E4vNF4vFgndQGP5t+rN48ykWJzVnvK0ZiUuW5r+dJTg4mMGDB9O0aVMPRy2Kc6W3mbs6Aww0/+U5D/wReM2NdbUH9gAvK6Uewijim6q1ftvtaIUQogDXB20D+j5OaItOWCwWag16lPCgoww8eZiYpXO4dCkdq9VK586d6dGjhzSCqACKew6qtDs0CcUoJlyLcSd1M7BUKXVaiviEEFfj0tGdJK6YTc4540Hb1fMXEOGoT5dWtTl37hznD8dw6vhxAOrXr8+QIUOoWVO6r6soiivie1BrPdvdBSmlLMDDWutPi5gkC0jVWr9s/r1LKfUJcDsgCUoI4bbs83EkrphNxpEdAHgH1yS27gBmx0Dffae4dHYfmzZtwuFw4O/vT79+/WjdurW8CaKCKa6Ir61S6hlgOrBAa326sImUUjWBe4HHgRXFLO8A4K+U8nF5uFfeXCqEKJH0Q9s4++0b4HTg9PYjrPudBEUNpjZe/ClgCwf3rGfjwVQA2rVrR58+ffD39/dw1OJqFFfE9xezUcRLwHtKqV8x3h5xHrAANTCanzcFlgAPaa23FLOu5cA54G0z8SngEYyGFkII4ZYqDVrg9K/GxqTqbMztxLvthpKcmsqSJUs4fPgwYDzTNGTIkN/1qSYqlmLvYLTW24DhSqlGwCCMZuVNAAdGo4n3gcVa6xNXWpHWOlMp1QP4ADiN0UhistZ6/rVtghCiMrt0eAfJm36g1p1/x+pbBatPFRr86X2mfbSVVrUDWLNmNdu2bsZuN17s2rt3b9q3by/PNFUCbhWxaa2PAVOvdWVa66PAkGtdjhCi8stOPEXi8llkHDHeBTBnynSGj32SqlVsePn68VDvMJYvX8bmVKM4r3Xr1vTr14+qVat6MmxRiqQOSAhRrjiyLnFh/bekbPkfOOxYfP3ZaGnPt/H1sW44Sp82YSxZsoSjR48CUKtWLQYPHky9evU8HLkobZKghBDlRrrezPklH2FPTwYsBLbpS2jPe7FfcOKnT1M15zjTps3F4XDg5+dHnz59aNeunRTnVVKSoIQQ5YbF6o09PZmTzprENRrOfUMG4XQ6uXhoF8d+WUF6ejogrfNuFJKghBAeY7+USsaxXQS06AaAf2R7rAPG886cBJomVqXbyTiWL/uJuLg4AOrVq8egQYPkjeM3iJJ0WNgW43VFNoxm5vm01tfcgEIIceNwOuyk7ljOhbVf48hKZ9c5G116dgagYYdoXgmI47jezqxPZwIQEBBA3759adWqlTxsewNxt8PCicCrQBKQVmC0k1Jo4SeEuDFkxmnOL/2Y7LPHADjiqMPXiw9Ro7HipjpBbN68mbVr15KdnY3VaqVTp0706NEDX19fD0curjd376AeBV7UWr9elsEIISov+6VUklZ9QdquVQB4B9cgrO/DrDtUlfCTyZw/c4JlP8aQmJgIQNOmTenfvz9hYWGeDFt4kLsJKgyYW5aBCCEqt7zk5MCKo8VAGg4ZjdXmS//gs3in7GT5kiMAVK9enQEDBtCkSRMPRyw8zd0ENQ+4D3i57EIRQlQ2TnsuFi/jNBPSfRSxsaeZdrwpIbENmZSdy9rlK9m2bRtOpxNfX1969OhBx44d8fLy8mzgolxwN0FlAM8ppe4EDgHZriO11neVdmBCiIrLkZVB0tqvyYrThNzzCv5VfPEOCuOWx16mxbydtKiZzpQpU8jMzMRisdChQwd69eolzcbFZdxNUP5IlxhCiCtwOp2k600kLvsUe1oSTiz85/1veXb8vXhZLZw4fhSfpE1sOmTUMzVu3JgBAwZIH02iUO6+i+/hsg5ECFGx5SQncH7px/l9NNkimjD9dBsOJAWwbfchDu7ZzLFjRsu90NBQ+vfvT9OmTaXZuChSSZ6DaglMAFoAVoz+nd7XWm8so9iEEBVE6valJK78HGdOFhZff8J63Udg237coeM5uHcrP/34DQB+fn706NGDqKgoqWcSV+Tuc1CDgB8x+nSah/Gg7q3AWqXUEK31srILUQhR3jmdTpw5WezIaoCt/WgGtbyFdTHr2bBhAzk5OVitVqKioujRowdVqlTxdLiignD3Dup14DWt9STXgUqpFzAe4JUEJcQNxJGdSfbZY/jVawZAULv+xGYE8uXCJAYlJjBlyhTS0oxn+pVS9O3bl+rVq3syZFEBuZugmgGFtdT7Bni+9MIRQpR3Gcd2c27xNHLTUznT/Tlu7XwLFqsXAXVq07fBHk4fTgCMXm379+9Pw4YNPRuwqLDcTVAngLbA4QLD2wMJpRqREKJcsmemk7RiNmm7VgJwyh7CVz/uJDgkgJ3bNuR3tx4UFESfPn1o2bKlNIAQ18TdBPUhMF0pVRfYZA6LBiYCk8siMCFE+ZGuN3N+6cfYL14AL29Cut3FPF2deilHmPfNbAB8fX3p0qULnTt3xmazeThiURm428z8faVUIPAckFeQHA/8Q2s9payCE0J4XtKar0neMA8Aa3gkoYMeZ+uBY+ScXk9ubi5Wq5UOHTrQvXt36W5dlCq3m5mbL4p9XSlVE8jQWhd8q7kQohKq2iyahJ8X8UPaLeRcaozPl/PIyMgAoHnz5vTp04fQ0FAPRykqoyITlFJqLPCp1jrT/FxwfP5n6Q9KiMrDfimVi/vWE9RhEBaLBZ+aDUjq/hRZa9bhvHCADKBBgwb07duXunXrejpcUYkVdwf1LDAHyDQ/F0X6gxKikkg/sJlzS2bguJTC91sSiO53K6tWreLs2bMA1KxZkz59+hAZGSkNIESZKzJBaa0bFfa5IKWU/EqFqODsGWkkLvuUi3vXAbArty4Hzp7l+NdfA0bLvJ49e9K6dWusVqsnQxU3EHffJHEU6KC1TiowvDawE5A3PQpRQV06vJ2ERdNwpF8gxRrAvpB2HDmbjJ9XJn5+fnTr1o2oqChpmSeuu+LqoIYDXc0/GwKvKKUuFZhMehQTogK7uDeGhB/e46LTxi/ezYjN9sF5NhmbzUanTp3o0qULfn5+ng5T3KCKu4PaBfwF4717YDyo69oPlBO4CDxYNqEJIcqao05zfrY24XCWP9jBarXQvn17unfvTmBgoKfDEze44uqgYoHeAEqpWcDTWuvU6xWYEKL0OXKySN64gIuNe3D40H42bdpETo4/WOCWW1rSq1dPaTIuyg23+4NSSnkrpeoAee/ItwC+QHut9ddlFaAQonRknjpI3Pfvsy8pl91rDuAwC0eUUvTq1Yvw8HAPRyjE5dxtJDEUmAUUdml1AXArQSml/gjMALJcBo/TWs92Z34hRMk57TmcWzuHrRvWs9dRnUyMxg41a9Vl6OD+1KtXz8MRClE4d98k8S+MLjXeAVYCw4FawLvAMyVYXzvgba3130sSpBDi6lyKP8K6b2awJ83GJSIAqFkznK7de9GyhbrC3EJ4lrsJKhK4Q2utlVI7gKpa67lKqRzgBYxuN9zRHvjPVcQphCgBh8PBjo3rWLNyOekY78erGRpMnwGD5SFbUWG4m6AyAIf5+SDQGlgCbAeaurMApZQX0Aq4Xyn1DnAJ+AR4Q2vtLEnQQojCORwO9u3bx5o1a0hKSgJ8sDmdBNRvx5iHhspDtqJCcTdBrQNeVEr9GdgGPKaUehvoCbjbsq+GOe9s4HaMThB/MOeXVyUJcQ2cTif79u5h1dLFXLhkVPGGhobSvVs3ajeIpEaIvGVcVDzuJqhngIXAH4HpGM9HpQI+GH1CXZHW+gzQw2XQTqXUB8AfkAQlxFVxOp3s37+fNStXcC7pAgB+5HJL534M6tdV7phEheZuM/PDQDOlVBWtdYZSqiPQCzivtd7szjKUUi2Au7TW/3AZ7IPxMlohRAk4nU4OHDjA2jVrOJtgdGpdlWxutqVy3K8Drdq0leQkKrziXnXkf4Xhq/P+1loXfAVSYZKBZ5RSccBMjDdT/Bl4skQRC3EDy7tjWrduXf4bxv3JpqU1gTatWxPS++/Y/AOwWqURhKj4iruDuojxOiN3eF1pAq31KfP9fpMxmqefB17VWs9zcx1C3LDyEtPatWtJMO+YqlhyaWk5S7gli6Z3P01AZDsPRylE6SouQfUq7ZVprVcBHUp7uUJUVg6Hg19//ZWYmJj8xBQUFETXrl1p6Exie8wmbIP/SECkPGwrKp/i3sW39noGIoT4jcPhYO/evcTExHD+/HkA/LwsBFh9eWTsOPx8fQAY2HGAJ8MUoky5+6qjrRRT3Ke17lhqEQlxA7Pb7ezevZv169ebzzFBUNUq3OKMp2FWHNl2b3b9Gkento09HKkQZc/dZuaLCpmvMTAEeLk0AxLiRpSbm8vOnTtZv349KSkpAAQFBtI2MIO6ZzdjtYA9tAE57UdLchI3DHebmU8qbLj58tfhwHulGZQQN4rs7Gx27NjBxo0bSUtLA6B6WBjhNgs3J6zDPyMXi68foT3vJaj9QCzWK7ZHEqLScPcOqiirgA9KIxAhbiSZmZls3bqVTZs2cemS8ZRGeHg43bp14+abFUc+fh5vay5nA28m6qG/4R0U5uGIhbj+3K2DKuyZqGCMF8WeLtWIhKjE0tPT2bRpE1u3biUry3glUZ06dYhs2gKLNYAWLVoA0PAPT3Hm+DGio7p7MlwhPMrdO6iinonKBB4uvXCEqJxSUlL4+eef2b59O7m5uQA0bNiQrl274nPmEEmrphDnqM6ZZv+kVlhVfGrUo34NaToubmzuJqiCz0Q5gWxgn9Y6rXRDEqLyOH/+PBs2bGD37t04HEaHAE2bNqVr166E+zo5/9PHZJ74lUAr1Kpqxd8r18MRC1F+uNtIYi2AUioAUIDdGKwzyjA2ISqsU6dOsWHDBvbv3w+AxWLhlltuoWvXrqRezGXHnFm0zN4FTgdW/yBCez9Ao1Y9pZ8mIVy4Wwfli/HG8fswXvAKkKGU+hh4RmttL6P4hKhQ0tLSWLJkSX5i8vLyok2bNtx6662EhobiyM3m3KwxtLSn4sRCcPuBhPS4B68qAR6OXIjyx90ivvcx+n66G9gCWIFOwFtAFvB/ZRGcEBWF0+nkl19+YdmyZWRlZWGz2YiKiqJz5874Vw3gUmYOAFZvH0Lb9ODsgV00vn0swfUjPRy5EOWXuwnqLmCY1nq9y7DvlFJJwFwkQYkbWFJSEgsXLuT48eMAREZGMmTIEIKDgzl1Ip41U9/kfJUGPPTUH7FYLET0u4+IAQ9KcZ4QV1CSLt9zChmeUoqxCFGh2O12Nm3axJo1a8jNzcXf359BgwYZTcUddlK2LCJ77Rxa5F7iQuoJEpPvpnpIABYvm6dDF6JCcDdBPQd8opQaA2zSWjuUUrdg1Eu97vqclJt9QwlRoZ06dYqFCxfm98nUqlUrBgwYgK9fFbavXEX44R/JSYwDwBFxC5EDHiYoROqZhCgJdxPUe0AAEAPYlVIOwAZYgI7AOy7TyrtYRKWVlZXFqlWr2LJlCwDVqlVjyJAhNGnSBHt6Cuvf+gf1co6SA3iH1CKs38P4N2kvxXlCXAV3E9TIMo1CiApAa83ixYtJTU3FYrEQHR1Nz5498fY2DiOrnz/h3qlkZdvIaTGYlsPuweItxXlCXK2SPgdVBYjEaMV3RB7SFTeClJQUli5dyoEDBwCoXbs2w4YNI9vpy7xpn1C9TXf6dW+BxctG/bvHYw0IxS9Y3p0nxLVy9zkoL+CfwNP8VrSXrZT6DHhSay2Pv4tKx+FwsHnzZlavXk1OTg4+Pj707t2bDh06kHlkB3GLP6VTegI71p/B2e1lLBYL/nWk2bgQpcXdIr7XgdHAA8B6jATVBeM5qJfMf0JUGnFxcSxatCi/EUTz5s3p0asPF06e4OzXr5AZuxcbkOkXRvc+/aWOSYgy4G6CegB4VGu92GXYXKVUGvARkqBEJZGRkcGKFSvYsWMHYDSCGDx4MNX8/YmZNplWlkNkWsDqF0BItzsJaj9Amo0LUUbcTVABwOFChh8FqpdeOEJ4htPpZOfOnaxYsYJLly5htVqJjo6mR48e2Gw2Mk7up431EHaseLXsR91+9+BVJdDTYQtRqbmboLYC4zDqoFw9CWwv1YiEuM7OnDnD4sWLOXnyJGB0g1G/aQf2/ryN9h3thNhsVKnXjKo97iekWUd8wmp7OGIhbgzuJqj/A9YopXoCm8xhnYGGwMDSD0uIspeZmcnq1avZunUrTqeTgIAA+vXrR2OfSxz+/l2G2C+wZklNbrt7EADhXeVpCyGuJ3ebmW9TSrUDHgeaY7z6aCHwodZaetQVFUrB4jyLxcLNLdrQObIutp1zSTi5nyAgyy+MPu3lbkkIT3H3Dgqt9UFgvFIqDLBrrZPLLiwhykZ8fDxLliwhLs54DVH9+vWpXr0B2dsWYdex2AGrfxAhXe8kqF1/LF5uHyJCiFLm9tGnlHoReAIIN/8+CbyntX6vjGITotSkp6ezatWq/NZ5ecV5LVu25OTCj8j1icVu8SY0ehgh0bdh9avq4YiFEO4+qPs6RvHea1zeH9QLSimb1vrNsgtRiKvncDjYunUra9asITMzE6vVSkBYI7y9q9GqVSsA6vS9hwSLnRo97sI7SBqlClFeuHsH9SjwYIHnoDYopQ4DHwKSoES5c/ToUZYuXcq5c+cAaNy4MR3rhZIWMxe708KJ+C7Urx2Cl38QEcPGejhaIURB7iYob+BkIcMPAyV6GEQpVQ3YDbyktf6sJPMK4Y4LFy6wfPny/G7X/fwDGRTditCDK8jecIhQKzir1SHCP9vDkQohiuNugnoTmKqUuldrfRJAKRUK/BvjdUclMR2oU8J5hLiirKwsYmJi2LRpE3a7HZvNRo61Ojdd3E/A2g1kA15VqxHS8x4CW/XCYpWeYYQoz9xNUHcDzYCjZuOIXKA+4AN0Vkr9OW9CrXXNohailHoQCAL2XHXEQhTgcDjYuXMnq1atIj09HYDWrVvTu1dPTn3yLL72JJxeNkKiR1ItegRWnyoejlgI4Y6SdFh4TZRSjYB/ALcCS691eUIAHDt2jGXLlnHmzBljgC2Y9lG3MrRfRwCcfe8jM3YPYT3vxTtIusAQoiJx90Hd2deyErO7jv8C47XWZ5RS17I4IUhMTGT58uVorQEICgqifmgodY6vJnZ7Os6+UVgsFoJb9yS4dU/PBiuEuCrX6ynEFwGttf7uOq1PVFKXLl1i7dq1bNu2DYfDgbe3jVvbNOem89vJjVsP3hARkghOB1ikjkmIiux6JahRQG2l1O3m34EYjS46aq2lfa+4otzcXLZs2UJMTAyZmZkAZDir0dZ+mgY7vyAXsFYJJKT73QS17ScNIISoBK5LgtJa3+z6t1JqJ8ZbKD67HusXFZfT6WTv3r2sWrWK5GTj7VqNGjWiV+f2XPr2FWzkgtWb4I6DqdblDrzkDRBCVBryojFRbsXGxrJs2TLi4+MBsNgCGDl8MC1b3IzFYuFkgxb4VPEntPdobCG1PBytEKK0FZmglFKfursQrfUfS7JSrXWbkkwvbiwJCQmsXLmSgwcPAsZ783BUpd2l7ehfW9PqlmYA1B31HBZv6c1WiMqquDso1zdE+ADDgAMYnRdmA+2A1sBXZRaduKGkpKSwZs0adu3ahdPpxGbzoXPbW4hM3k3O0Y3gDZGWXUBfAElOQlRyRSYorfWdeZ+VUh9h1Bn9zXUapdRrwM0F5xWiJDIyMli/fj1btmwhNzcXi8VKhiWMTgEpNPzlc3KcDiw+flS79XaCOw71dLhCiOvE3TqoezDumAqaDewsvXDEjSQ7O5vNmzezYcMGsrKyAGjRogWqRjDWmJlUScsBi4XAtv0I6T4K74BqHo5YCHE9uZugzgC9gUMFhg8FYks1IlHp2e12duzYwbp167h48SIAwWER3HnbEOrUqUNuaiKxP3+KX/02VO/7ID4163s4YiGEJ7iboF4BZiqlegM7AAtGf1CDgTuLm1GIPA6Hg71797JmzRouXLgAQK1aEZw4ZadWwiHSsn0B8A4Ko/6Yd6VlnhA3OHdfdfSFUioOGAPcbw7eDXTXWm8uq+BE5eB0Ojlw4ACrV6/O75spLCyM7lFtqXFiPRnntwBgi98GjQYanyU5CXHDc/s5KK31amB1GcYiKhmn08nRo0dZtWpV/rNMVfwDSM6tTu/qKfit+g8ZDjsWmy/BnUdQLaqHhyMWQpQnxT0HNdndhWitJ5ROOKKyOH78OGvWrCE21qiirFq1Kt26dSPt0AEiji3B70gOWKwEtulLSPe78Q4M9XDEQojyprg7qCg3l+EsjUBE5XDy5ElWr17NsWPHAPD19aNZy/b/3969x0dVngkc/80kkzvhjgRQSLg8EJAA4RaDF7SotXitl613W23tut1Pbe3uqrVuW2/Vra1127W1tlu1rcXa6lZ70Wq5iUASkoAIj0BJAwgCIULIhdxm/3hPwhhhDIHkzCTP9/PJh+ScOWeehzMzz7znvOd9+eSCM0hKSqKKKvb/o4nknOkMPed66wBhjDmqaPdBzW/7XUSuA/6kqnt7JCoTd3bs2MHixYvZvHkzAMnJyUzInUbRqq1sL99I8PyzARg081zShp9C6ugpfoZrjIkDnb0G9QNgLmAFynzIjh07WLJkCZs2uTsQkpKSmDNnDvk5WdS+uYhpaWtpIkTtvivpP3QYgYREK07GmE7pbIFaBVwKPNSNsZg48t5777F48eL2whQKhRgyUthZFWBS1Dq6TAAAE+VJREFUdSlVv/whAIHkNE4q/DT9BtpNtsaYY9PZAtUKPCAiXwe2AvWRK1V19okOzMSmHTt2sHTp0vaBXEOhELNnz2b2zJn89YnvM7/5beo3hgkkhMic9UkGnHYZCan9PmavxhjzUcfSgrL7nfqwbdu2sWTJErZs2QK4wjRizCQuOG8+wwYPIBwOc+qgeoJ7IOPU+Qw68yoS+w/1OWpjTDzr7I2632z7XUQygaCqftBtUZmYUVFRwdKlS9t75SUlJTFr1ize3Z3KB+sXszx1EJddeiaBQIDRl7jJkZOGjfYzZGNML9HpG3VF5IvAXcAI7+/dwGOqateleplwOMyWLVtYtmwZlZWVgOuVN3PWbApm5dP87gqyy39JIO0AtXsaAXeDrRUmY8yJ1KkCJSJ3APcA9wPLcWPxFQL/ISL1qvpY94VoekrbkETLli1j586dAKSkpDBx8nRWbw1yqGIre9c/R0tNFQEgNHwsY+df6m/Qxpheq7MtqNuAW1X11xHL3hSRfwD3AVag4ljbIK7Lly9vHysvPT2dgoICZs6cyc715YwqfZyBB+poAUJDT2HQGVeRJnMIBAL+Bm+M6bU6W6CG4mbS7agEGHXiwjE9qampibKyMlasWMEHH7hLipmZmcjkGRwKDaewUAAYmTOGloQGEgaNYvCZV5E+cS6BQNDP0I0xfUBnC9TbuGk1Huyw/CrcNPAmjjQ0NFBcXMzKlSupra0F3OjihYWFjD55DM889gQTEnZQMfE+xmQNIDRgGKNufJCkrBwrTMaYHtPZAvUN4BURKQDe8pYVAOcDl3VHYObEq6mpYeXKlZSUlLTPYJuVlcW0GbOZPimHg6Wvsv/px7gwpQaAtKp3Icvd4pY8YpxvcRtj+qbOdjN/VUTOAb6Emw+qHtgAzFLV8m6Mz5wAe/fuZcWKFaxdu5aWlhYAxowZw7x583irdBdvv/QCg1/fQqDZFa3kkRMYWHg5qeNm+Bm2MaaPizbdxvnAMlWtBVDVpcDSngrMHL/KykreeustNm48fBZ20qRJFBYWMnLkSMLhVhp/dz/JKfuhGVKzpzLgtMtIGT3FOj8YY3wXrQX1CtAsIsXAG97PClU91CORmS5pbW1FVVmxYgXbt28HICEhgby8PKbNmMXaN4t4v+oQI0dCIBBk8NwLqd2+iRFnXU5yVo7P0RtjzGHRCtQQ3L1OBcA84HYgKCIrOVywVqlqS7dHaT5WY2Mj5eXlrFy5kn379gHuHqZZs2Yxc8Y0qFjD9kXfYlLd+7y5tYDpp36VQCDAsNPtPiZjTGyKNh9UNfCy94OIJAAzcAWrAPgsMERElqnqp3ogVnMENTU1rF69mpKSEurr3Ri+AwYMoKCggNFZWRwo/Sv7fv4MrXUHSAHqgxnMmpZjp/CMMTGv00MdqWqLiGwHdgK7gUpgGGDnhXywa9cuVq5cybp162htbQVg5MiRFBQUMGnSJCpeW8SBVx8mIRCmFUgankP/2QvJyD2NQELI3+CNMaYTohYoEckAzgI+ASwAJgLbgL8BTwFXq+p73Ryj8bS2tvLuu++yatUqKioqAAgEAuTm5jJeptDSksjkyeMBGJo9jveLoTJlAlMWXsXgCXnWajLGxJVovfiWAbOBamAxbjijN1R1c1efTEQWAg8A2bhW2MOq+uOu7q+vOHToEKWlpaxevZrq6mrAjSo+ffp08nPHcWj9cqpeuo/K1iymnvowocQgGeNnELz1h4wbfJLP0RtjTNdEa0EVAtuBn+E6RLylqk1dfSIRyQJ+C1yqqn8SkRm48fyKVHVNV/fbm1VVVbF69WrKyspobGwEIDOzP80poxgSbGFaTTk1zz4F4Vb6BWFUSgM1tfUM6p9OIBAk3YqTMSaORStQObhTe+cAXwTSRWQ58DquYK1R1XBnn0hVd4rIUFWtEZEgMBhoBmq6HH0v1DbVxapVq9i8+XBjdfTo0cydO5fBHGLLou8xLKGGur1AMJH03NPIzD+f7FET7TSeMabXiNaLrwL4qfeDiOQBZ+MK1j1Ak4gsBl5X1R915sm84pQG7Pee+zuquul4EugtGhoaKC8vp6ioiKqqKsDdv7S3cQCp/YZz442XA9D0wW6GJhyE9IEMyD+XzOkLSMwY6GfoxhjTLY6lF185UC4ijwH5wE24YY8uATpVoDwNQDowFfijiGxS1aeOYfteZffu3RQVFVFeXk5TkzuDmpmZyczpeWQn1vCPv71MSnMZhxovITkpkdCAYYy84X6SR4wjEEzwOXpjjOk+H1ugRGQUMAeY6/07A3dqbgVuLqjFx/KEqtoKNALFIvIT4GJcj8A+o6WlBVWlqKiovTcewMGWDFJDaXxmbAv1q39KY9MhsoIQTMkkWFcFSe6aUsoo8SlyY4zpOdF68b2AK0hZuOtEbwJ/AO4ASo51BAkRORN4VFXzIxYnAx8ca9Dx6sCBA6xZs4aSkhIOHjwIQCgUIi8vj7zx2Wx//nsMYx9169zjU0ZPIXPGuaRPmE0g0e5dMsb0LdFaUMnA93EtpDVey+d4lAEjReQruC7rc4DPAb16rJ1wOMzWrVspLi5m48aNhMOuX0ljOIXG1n58/cvX079/BuGWJprTWoBM+k09i37TFpA0eIS/wRtjjI+idZJYeCKfSFX3i8gFwA+Ae3E3/N6sqktO5PPEitraWsrKylizZk372HjBYBAZl0NuRhNN65aSQS37919G//4ZBBJCjLjmXkKDR9hID8YYwzF0kjgRvPud5vXkc/akcDhMZWUlxcXFbNiwoX3upRaSSGwOcEV2HUlbXwRcKyqh32CGJdQAwwFIGjbap8iNMSb29GiB6q3q6uraW0ttXcQBxo8fz/SpUwi/9BApiYdgJwQSQqRNnEO/qWeTOmaK9cQzxpijsALVReFwmIqKCkpKSti4cWN7aylMIknNrVx83Y1MnnAyAJXF2QRbm8nMm0967jwSUjP8DN0YY+KCFahjVFNTQ1lZGaWlpe3j4gFkD+3PuPBuhlWvJZgI9dXvA65Ajbr6HoJJKT5FbIwx8ckKVCe0tLSwadMmSktL2bRpU3tPvCBBTgofZG5oGxnVbqLhQFIK6RPnMGrs8PbtrTgZY8yxswIVxZ49eygtLWXt2rXU1tYCrifepEmTmJY3leYX7iO5tY5wIEja2HwyppxB2oRZBEPJPkdujDHxzwpUBw0NDaxfv57S0lJ27NjRvjwlECaHvQyefytnz5sCQPX8KwgmhsjILSQhLdOvkI0xpleyAoWbCHDr1q2Ul5ezYcMGmpubAQgFA2QnHCCndRdDqCcQgFDS3vbtBs69yK+QjTGm1+vTBaqqqoqSkjWUlpXTUF/bvnxIoB4J7OHkwH5C4TChwVmk536KjMmnkzRklI8RG2NM39HnClRdXR3r16+nvLz8Q6fwMvplMjN/Bnl5eez9zQME6oP0P/Ui+k2eR9JJ2TbPkjHG9LA+U6C2bd/BM795hcaDuwh4IzkkBsKMppqxwWoGnnk7E/OnA9Dv2rsJpmVaUTLGGB/1mQL12l/+TNPBnQQIMyJwkJxANScH9pOUlkH6pEIGZB+eHj0hvb+PkRpjjIE+VKDOPfcTrH3mYU5p3Ut6UiLpMpuMyaeTmj2VQEKf+W8wxpi40Wc+mUedPJrM8y4kmNqPtHH5dvOsMcbEuD5ToAAy88/3OwRjjDGdFPQ7AGOMMeZIrEAZY4yJSVagjDHGxCQrUMYYY2KSFShjjDExyQqUMcaYmBQv3cwTAHbt2uV3HMYYY06giM/1hI7r4qVAZQFcc801fsdhjDGme2QBWyIXxEuBKgJOB3YCLT7HYowx5sRJwBWnoo4rAuFwuOfDMcYYYz6GdZIwxhgTk6xAGWOMiUlWoIwxxsQkK1DGGGNikhUoY4wxMckKlDHGmJhkBcoYY0xMsgJljDEmJsXLSBJHJSKzgZdVdZj391DgMeA84BDwM+BeVW3x1j8NXAk0R+xmqqr+XUROAZ4C5gK7gS+p6h97LBm6lM9p3vpJwHvAXar6W2+dr/kcSy4i8gRwbYddpAN3q+oDfucCXTo2NwFfB4YAG4Gvqupyb1085vMl4HZgMLACuE1V/+5nPiKyAHgIGO897yOq+mMRSQL+G7gcN/rMo6r6YMR2VwIP4EYwWALcqKq7/czlePKJ2P524ExVvSRime+vta6K2xaUiARE5GbgVSApYtUvgGG4D+wpwGzgWxHrZwCXqGpGxM/fvXXPAWtxb8BbgOdEJKebUwG6lo+IZAGv4F64/YDbgGe9FyT4lE9XclHVWyOPCfA14B0vN99y6Wo+IjIVeBS4GBgAPAu8KCJt77l4y+dK4H7gJi/m/wNeE5EUb9sez0dETgZeAO7D/R9/BnhQRM4DvgkIMBaYBdwgItd72+XiPrBv9OLd5MXfxq/3TZfy8bbNEJFHgO8eYde+vdaOV9wWKNwB+yLuYAIgImnA+cDtqrpbVfcB9wC3eG/KVGAiUNZxZyIyAZgJfENVG1X1Ddyb8HPdnwrQhXyA64GlqvoLVQ2r6mu4D5Vqn/PpSi5EPHYs8DBwtaoeiNNjM57D768A7ltvvbdtPObzaeBJVV2iqs2q+j9AI3COj/mMAX6lqr9X1VZVLQIWA4XADcD9qlqtqhXAfwFf8La7FviDqi5X1QbgTqBQRMb7fGy6mg+4L6rZwI8jdxgDr7XjEs8F6glVzQeKI5a15VMbsawFGIr7RjINd2rvSRHZIyJrRGSh97hcoFJVI7fdCJzaLdF/VFfyyQcqROQ5EdkrIqXAcFWtwd98upJLpO/iPgzLvb/j8dj8BXgbWIf7IP8OcJWqthKf+QQ7rGtbPwGf8lHVZap6a9vfIjIIN6h0Ke7U3TtHiSc3cp2q1gHbvPW+HZvjyAfgM6p6OfB+h936/Vo7LnFboFT1vSMsO4g7bfGwiAwSkcHAN7zVqbjTYMtw3yBH4E5ZLBKRPCADqOuwyzogrXsy+EjsXclnEK7J/izuBfwg8HuvBeJbPl3MBQARmQIswH2gt4nHY5MCKO68fzrw77hTfMOJz3xeAD4vIjNFJCQit+DORqTicz4AItIf1zJYBZRExHCkeKLF63sucMz5HPGYemIin66K2wIVxXW4b6wbcBc/X/SWf6Cqr6rqAlUtVtUmVX0BeAO4CPftMLXDvtKAgz0U99EcNR/chew/q+rLXj6LgDXAJ4nNfKLl0uazwIuqGvlNMBZzgej5/CewS1VXqeohVf0RUAFcQRzmo6rP4b4APYdrbUwBXgOq8Tkf7zTWSlzr4XKgxlsVGVNkPNHi9f3YdCGfaHzP53j0xgKVBXxBVU9S1SnADmCDqtaJyIUickOHxycBDbjm8ynedao2E/lws9oPR80H11Qf2OHxbT0zYzGfaLm0uZgPX7CG2MwFoudzMpDc4fHNQBNxmI/XIecPqjpOVYcDX8EVqRJ8zEdEzsC1Ml4ELlfVBlWtBnbhOhUcKZ53Itd5199O8Zb7emy6mE80sfpa65S472Z+BI8Cb4vIHbgX3Xc43BMsAXhMRDbg3lhXAacBN6tqpYiUA/eLyJ3e8ouBgp5OoINo+TwN/IuIXAv8CvftfCpwpapui8F8ouXS1s05B3gzciNV1RjMBaLn8zLudNki3ERs1wCTgVdi9NhA9HzOAe4VkdOBA7jTf+8BRaoa9iMf71T2y7hbER7vsPoZL961uNNcd+C60IN7rywXkbOAt3Atw1JVfdfbry/H5jjyOaoYfu90Sm8sULcATwL7cKdaHvdOr6CqL4rI3cCvgeG4FshCVa30tv008BPcvQJ7gc+p6ts9HH9H0fIpF5ELcB8kPwIqgctUdZu3bazlc9RcPGOAQ14Pso5iLReIfmye9K4j/Ap3H9Q7wAUxfGwg+vH5Je7CejnurMPrwEWq2jbjqR/53Ia7rvygiETeE/RDXAH9LrAed6boJ8ATAKq6TkQ+6/09EtdiuSJie7+OTZfy6YRYfK11is2oa4wxJib1xmtQxhhjegErUMYYY2KSFShjjDExyQqUMcaYmGQFyhhjTEyyAmWMMSYmWYEyppt4g/ju8Qb97LjubhE5KCJjfAjNmLhgBcqY7vNl3M3wH5pYTkSygbtxIwZU+BCXMXHBCpQx3URVd+HmGrpZROZErHocNydZx+FsjDERbCQJY7qRN9nfm7jhgWbhRs5fBExX1Xe8x9wE3IUbdmcDrmX1Z29dIvBt4GrcFDFVuKG67lDVFhF5FmjFzYI7FjfU1eIeS9CYbmQtKGO6kTdW3edxg/jehJsJ9b6I4vQp3CCtd3uPeQo3p9dsbxd34qb+vg43S++dwL8CCyOe5lrcwKHn4KZpMKZX6I2DxRoTU1T1bRF5FDcd9wbgoYjVdwEPeXN5AWwWkZm46Sz+CTcj742qutRb/3MR+TfcTKkvecveUdVnuzsPY3qaFShjesY3cbPqfltVmyKW5wL5InJPxLIQ3nw93gj854jII7jp1afiRn1PiHj8lu4M3Bi/2Ck+Y3qAqtZ7v9Z3WJUIfA2YFvEzGTdnDyJyH/A87r36PHAu0HGqhI77NKZXsBaUMf7aCIxW1c1tC7zWVAPwCPDPwJdV9WlvXSpuMsGAD7Ea06OsQBnjr4eBp0VEgTeABcC9uOtP4CYPXCgiK4D+uFOFmXx0Onljeh07xWeMj1T1eeB24N9w151uB76gqr/1HnI9rvfeOuB3wGbgf4H8Hg/WmB5m90EZY4yJSdaCMsYYE5OsQBljjIlJVqCMMcbEJCtQxhhjYpIVKGOMMTHJCpQxxpiYZAXKGGNMTLICZYwxJib9P1DdBIyhWFgqAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system.alpha1 = 0.0195\n",
-    "system.alpha2 = 0.014\n",
-    "\n",
-    "results = run_simulation(system, update_func3)\n",
-    "plot_results(census, un, results, 'Proportional model, parameter changes over time')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap07soln.ipynb b/soln/chap07soln.ipynb
deleted file mode 100644
index ab58bf90..00000000
--- a/soln/chap07soln.ipynb
+++ /dev/null
@@ -1,779 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 7\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *\n",
-    "\n",
-    "from pandas import read_html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = 'data/World_population_estimates.html'\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "table2 = tables[2]\n",
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "1950    2.525149\n",
-       "1951    2.572851\n",
-       "1952    2.619292\n",
-       "1953    2.665865\n",
-       "1954    2.713172\n",
-       "Name: un, dtype: float64"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "un.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Year\n",
-       "1950    2.557629\n",
-       "1951    2.594940\n",
-       "1952    2.636772\n",
-       "1953    2.682053\n",
-       "1954    2.730228\n",
-       "Name: census, dtype: float64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "census = table2.census / 1e9\n",
-    "census.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(census, un, timeseries, title):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    census: TimeSeries of population estimates\n",
-    "    un: TimeSeries of population estimates\n",
-    "    timeseries: TimeSeries of simulation results\n",
-    "    title: string\n",
-    "    \"\"\"\n",
-    "    plot(census, ':', label='US Census')\n",
-    "    plot(un, '--', label='UN DESA')\n",
-    "    plot(timeseries, color='gray', label='model')\n",
-    "    \n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate the system using any update function.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that computes the population next year\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        results[t+1] = update_func(results[t], t, system)\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Quadratic growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the implementation of the quadratic growth model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func_quad(pop, t, system):\n",
-    "    \"\"\"Compute the population next year with a quadratic model.\n",
-    "    \n",
-    "    pop: current population\n",
-    "    t: current year\n",
-    "    system: system object containing parameters of the model\n",
-    "    \n",
-    "    returns: population next year\n",
-    "    \"\"\"\n",
-    "    net_growth = system.alpha * pop + system.beta * pop**2\n",
-    "    return pop + net_growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a `System` object with the parameters `alpha` and `beta`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>1950.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>2016.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p_0</th>\n",
-       "      <td>2.557629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>alpha</th>\n",
-       "      <td>0.025000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>beta</th>\n",
-       "      <td>-0.001800</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "t_0      1950.000000\n",
-       "t_end    2016.000000\n",
-       "p_0         2.557629\n",
-       "alpha       0.025000\n",
-       "beta       -0.001800\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_0 = get_first_label(census)\n",
-    "t_end = get_last_label(census)\n",
-    "p_0 = census[t_0]\n",
-    "\n",
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                alpha=0.025,\n",
-    "                beta=-0.0018)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here are the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap07-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results = run_simulation(system, update_func_quad)\n",
-    "plot_results(census, un, results, 'Quadratic model')\n",
-    "savefig('figs/chap07-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Can you find values for the parameters that make the model fit better?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Equilibrium\n",
-    "\n",
-    "To understand the quadratic model better, let's plot net growth as a function of population."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pop_array = linspace(0, 15, 100)\n",
-    "net_growth_array = system.alpha * pop_array + system.beta * pop_array**2\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap07-fig02.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.set_style('whitegrid')\n",
-    "\n",
-    "plot(pop_array, net_growth_array)\n",
-    "decorate(xlabel='Population (billions)',\n",
-    "         ylabel='Net growth (billions)')\n",
-    "\n",
-    "sns.set_style('white')\n",
-    "\n",
-    "savefig('figs/chap07-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like.  Remember that the x axis is population now, not time."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It looks like the growth rate passes through 0 when the population is a little less than 14 billion.\n",
-    "\n",
-    "In the book we found that the net growth is 0 when the population is $-\\alpha/\\beta$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13.88888888888889"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "-system.alpha / system.beta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is the equilibrium the population tends toward."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`sns` is a library called Seaborn which provides functions that control the appearance of plots.  In this case I want a grid to make it easier to estimate the population where the growth rate crosses through 0."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dysfunctions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When people first learn about functions, there are a few things they often find confusing.  In this section I present and explain some common problems with functions.\n",
-    "\n",
-    "As an example, suppose you want a function that takes a `System` object, with variables `alpha` and `beta`, as a parameter and computes the carrying capacity, `-alpha/beta`.  Here's a good solution:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "13.88888888888889\n"
-     ]
-    }
-   ],
-   "source": [
-    "def carrying_capacity(system):\n",
-    "    K = -system.alpha / system.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "pop = carrying_capacity(sys1)\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's see all the ways that can go wrong.\n",
-    "\n",
-    "**Dysfunction #1:** Not using parameters.  In the following version, the function doesn't take any parameters; when `sys1` appears inside the function, it refers to the object we created outside the function.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "13.88888888888889\n"
-     ]
-    }
-   ],
-   "source": [
-    "def carrying_capacity():\n",
-    "    K = -sys1.alpha / sys1.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "pop = carrying_capacity()\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This version actually works, but it is not as versatile as it could be.  If there are several `System` objects, this function can only work with one of them, and only if it is named `system`.\n",
-    "\n",
-    "**Dysfunction #2:** Clobbering the parameters.  When people first learn about parameters, they often write functions like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "13.88888888888889\n"
-     ]
-    }
-   ],
-   "source": [
-    "def carrying_capacity(system):\n",
-    "    system = System(alpha=0.025, beta=-0.0018)\n",
-    "    K = -system.alpha / system.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "pop = carrying_capacity(sys1)\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this example, we have a `System` object named `sys1` that gets passed as an argument to `carrying_capacity`.  But when the function runs, it ignores the argument and immediately replaces it with a new `System` object.  As a result, this function always returns the same value, no matter what argument is passed.\n",
-    "\n",
-    "When you write a function, you generally don't know what the values of the parameters will be.  Your job is to write a function that works for any valid values.  If you assign your own values to the parameters, you defeat the whole purpose of functions.\n",
-    "\n",
-    "\n",
-    "**Dysfunction #3:** No return value.  Here's a version that computes the value of `K` but doesn't return it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "def carrying_capacity(system):\n",
-    "    K = -system.alpha / system.beta\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "pop = carrying_capacity(sys1)\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A function that doesn't have a return statement always returns a special value called `None`, so in this example the value of `pop` is `None`.  If you are debugging a program and find that the value of a variable is `None` when it shouldn't be, a function without a return statement is a likely cause.\n",
-    "\n",
-    "**Dysfunction #4:** Ignoring the return value.  Finally, here's a version where the function is correct, but the way it's used is not."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13.88888888888889"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def carrying_capacity(system):\n",
-    "    K = -system.alpha / system.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys2 = System(alpha=0.025, beta=-0.0018)\n",
-    "carrying_capacity(sys2)\n",
-    "\n",
-    "# print(K)     This line won't work because K only exists inside the function."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this example, `carrying_capacity` runs and returns `K`, but the return value is dropped.\n",
-    "\n",
-    "When you call a function that returns a value, you should do something with the result.  Often you assign it to a variable, as in the previous examples, but you can also use it as part of an expression.\n",
-    "\n",
-    "For example, you could eliminate the temporary variable `pop` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "13.88888888888889\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(carrying_capacity(sys1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or if you had more than one system, you could compute the total carrying capacity like this:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "27.77777777777778"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "total = carrying_capacity(sys1) + carrying_capacity(sys2)\n",
-    "total"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:** In the book, I present a different way to parameterize the quadratic model:\n",
-    "\n",
-    "$ \\Delta p = r p (1 - p / K) $\n",
-    "\n",
-    "where $r=\\alpha$ and $K=-\\alpha/\\beta$.  Write a version of `update_func` that implements this version of the model.  Test it by computing the values of `r` and `K` that correspond to `alpha=0.025, beta=-0.0018`, and confirm that you get the same results. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.025, 13.88888888888889)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                alpha=0.025,\n",
-    "                beta=-0.0018)\n",
-    "\n",
-    "system.r = system.alpha\n",
-    "system.K = -system.alpha/system.beta\n",
-    "\n",
-    "system.r, system.K"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def update_func_quad2(pop, t, system):\n",
-    "    \"\"\"Compute the population next year.\n",
-    "    \n",
-    "    pop: current population\n",
-    "    t: current year\n",
-    "    system: system object containing parameters of the model\n",
-    "    \n",
-    "    returns: population next year\n",
-    "    \"\"\"\n",
-    "    net_growth = system.r * pop * (1 - pop / system.K)\n",
-    "    return pop + net_growth"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results = run_simulation(system, update_func_quad2)\n",
-    "plot_results(census, un, results, 'Quadratic model')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap08soln.ipynb b/soln/chap08soln.ipynb
deleted file mode 100644
index 4ce822af..00000000
--- a/soln/chap08soln.ipynb
+++ /dev/null
@@ -1,1067 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 8\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# If we're running on Colab, install modsimpy\n",
-    "# https://pypi.org/project/modsimpy/\n",
-    "\n",
-    "import sys\n",
-    "IN_COLAB = 'google.colab' in sys.modules\n",
-    "\n",
-    "if IN_COLAB:\n",
-    "    !pip install pint\n",
-    "    !pip install modsimpy\n",
-    "    !mkdir figs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Functions from the previous chapter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(census, un, timeseries, title):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    census: TimeSeries of population estimates\n",
-    "    un: TimeSeries of population estimates\n",
-    "    timeseries: TimeSeries of simulation results\n",
-    "    title: string\n",
-    "    \"\"\"\n",
-    "    plot(census, ':', label='US Census')\n",
-    "    plot(un, '--', label='UN DESA')\n",
-    "    plot(timeseries, color='gray', label='model')\n",
-    "    \n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate the system using any update function.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that computes the population next year\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        results[t+1] = update_func(results[t], t, system)\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Reading the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the data file\n",
-    "\n",
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates2.csv'\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/notebooks/data/World_population_estimates2.csv\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def read_table2(filename):\n",
-    "    tables = pd.read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "    table2 = tables[2]\n",
-    "    table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']\n",
-    "    return table2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#table2 = read_table2()\n",
-    "#table2.to_csv('data/World_population_estimates2.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Year</th>\n",
-       "      <th>census</th>\n",
-       "      <th>prb</th>\n",
-       "      <th>un</th>\n",
-       "      <th>maddison</th>\n",
-       "      <th>hyde</th>\n",
-       "      <th>tanton</th>\n",
-       "      <th>biraben</th>\n",
-       "      <th>mj</th>\n",
-       "      <th>thomlinson</th>\n",
-       "      <th>durand</th>\n",
-       "      <th>clark</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1950</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2557628654</td>\n",
-       "      <td>2.516000e+09</td>\n",
-       "      <td>2.525149e+09</td>\n",
-       "      <td>2.544000e+09</td>\n",
-       "      <td>2.527960e+09</td>\n",
-       "      <td>2.400000e+09</td>\n",
-       "      <td>2.527000e+09</td>\n",
-       "      <td>2.500000e+09</td>\n",
-       "      <td>2.400000e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.486000e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1951</th>\n",
-       "      <td>1951</td>\n",
-       "      <td>2594939877</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.572851e+09</td>\n",
-       "      <td>2.571663e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1952</th>\n",
-       "      <td>1952</td>\n",
-       "      <td>2636772306</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.619292e+09</td>\n",
-       "      <td>2.617949e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1953</th>\n",
-       "      <td>1953</td>\n",
-       "      <td>2682053389</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.665865e+09</td>\n",
-       "      <td>2.665959e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1954</th>\n",
-       "      <td>1954</td>\n",
-       "      <td>2730228104</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.713172e+09</td>\n",
-       "      <td>2.716927e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Year      census           prb            un      maddison  \\\n",
-       "Year                                                               \n",
-       "1950  1950  2557628654  2.516000e+09  2.525149e+09  2.544000e+09   \n",
-       "1951  1951  2594939877           NaN  2.572851e+09  2.571663e+09   \n",
-       "1952  1952  2636772306           NaN  2.619292e+09  2.617949e+09   \n",
-       "1953  1953  2682053389           NaN  2.665865e+09  2.665959e+09   \n",
-       "1954  1954  2730228104           NaN  2.713172e+09  2.716927e+09   \n",
-       "\n",
-       "              hyde        tanton       biraben            mj    thomlinson  \\\n",
-       "Year                                                                         \n",
-       "1950  2.527960e+09  2.400000e+09  2.527000e+09  2.500000e+09  2.400000e+09   \n",
-       "1951           NaN           NaN           NaN           NaN           NaN   \n",
-       "1952           NaN           NaN           NaN           NaN           NaN   \n",
-       "1953           NaN           NaN           NaN           NaN           NaN   \n",
-       "1954           NaN           NaN           NaN           NaN           NaN   \n",
-       "\n",
-       "     durand         clark  \n",
-       "Year                       \n",
-       "1950    NaN  2.486000e+09  \n",
-       "1951    NaN           NaN  \n",
-       "1952    NaN           NaN  \n",
-       "1953    NaN           NaN  \n",
-       "1954    NaN           NaN  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table2 = pd.read_csv('World_population_estimates2.csv')\n",
-    "table2.index = table2.Year\n",
-    "table2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "census = table2.census / 1e9\n",
-    "plot(census, ':', label='US Census')\n",
-    "plot(un, '--', label='UN DESA')\n",
-    "    \n",
-    "decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)',\n",
-    "             title='Estimated world population')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Running the quadratic model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the update function for the quadratic growth model with parameters `alpha` and `beta`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func_quad(pop, t, system):\n",
-    "    \"\"\"Update population based on a quadratic model.\n",
-    "    \n",
-    "    pop: current population in billions\n",
-    "    t: what year it is\n",
-    "    system: system object with model parameters\n",
-    "    \"\"\"\n",
-    "    net_growth = system.alpha * pop + system.beta * pop**2\n",
-    "    return pop + net_growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Extract the starting time and population."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = get_first_label(census)\n",
-    "t_end = get_last_label(census)\n",
-    "p_0 = get_first_value(census)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initialize the system object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                alpha=0.025,\n",
-    "                beta=-0.0018)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the model and plot results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results = run_simulation(system, update_func_quad)\n",
-    "plot_results(census, un, results, 'Quadratic model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Generating projections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To generate projections, all we have to do is change `t_end`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap08-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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h4MSJExw9epSjR49mJKWzZ8/muE50dDQVK1akQoUKGS/351KlSunfRDFVLBNUMGvYsCGLFi1i1qxZzJ07lxdffJEKFSowfPhwHn744Yv+I8fHx1OuXLlL3taIi4tj9OjR1K5dG4C///3vtGnTht9//x2AkJAQRo4cSUREBNdeey0xMTHs37+fqlWrEh4ezieffEKfPn3o378/AwYMuOQFJTIy8rLWU4ElNTWVY8eOcfToUY4cOcLRo0c5fvx4tg0RwsLCqFy5MpUrVyYmJoaYmBgqVapExYoV9babylaxTFCeJSe38fd3uGhaz4516dmxbqZplcqVzHb9Nx7vdtG0vJaeIiMjs30mwuFwEBUVlfG5Tp06PPXUU4CVgJYtW8bEiROpUKECgwcPzrRuTEwMp0+fJi0t7aKLwLlz5zIqfw8fPszYsWMZP358xvzw8HD++OMPwsPDiY6OzhRDREQETqeT6OhoPvzwQ9555x2GDRtGiRIlGD58OCNGjMh1X3NbT5NUYHK5XCQkJHDw4EEOHTrEoUOHOHbsGC6X66JlK1asSLVq1ahWrRpVqlShcuXKVKhQQRsZqDwplgkqUFWrVo3Dhw/TrFmzjGnp6ekcPnw4Y7Tgu+66ix49enDPPfcA1oVg8ODBbN26FWPMRdts2bIlJUqUYPXq1ZnqoABeeeUV9u/fz4cffkiVKlUYP348nTt3zpi/c+dO6taty5YtW3KM+cyZMzgcDqZMmUJaWhrfffcdo0ePpk2bNoSFhWVqXeVyuThz5swl19NGG4HB6XRy5MgR9u3bl5GUkpKSMi0TEhJC1apVM5JRbGws1apVy/RjRqnLpQkqgNx888289dZbNGjQgCuuuILTp08zefJkYmJiaN68OQC9evVi0qRJ1KxZM6NBwZYtW1i9ejUvvfTSRduMjIxkzJgxjB8/npCQELp27Upqaipz585l0aJFTJtmdX/Yr18/Jk2aRIMGDahSpQrTp09n6tSprFixIteY4+Pjuf/++5k6dSrt2rWjatWqhISEUK5cOcqVK0dqaipLly7lpptuYsaMGZw/f/6S6yn/cLlcHDt2jH379vH777+zf//+i1rQlS5dmpo1a1KzZk1q1apF9erV9fac8hlNUAFk1KhRhIWFMWLECE6dOkVUVBQdOnTggw8+yLgI3H333ZQoUYLJkyfzxBNP4HK5uOKKKxg/fjzXXnttttu94447KFOmDO+88w5PPvkkLpeLJk2aMH36dNq2bQvAQw89RHp6OkOGDOH06dM0atSI6dOnU7Zs2VxjrlevHhMmTGDcuHEcP36cChUq8PTTT9OwYUMAnn76aV555RWeeeYZ+vbtS6tWrbxaTxWOpKQkdu3axa5du9i7dy/JycmZ5lesWJG6detSp04datWqRfny5fUWrCo0IdndPw4mIlIX2LdixQpq1qzp73CUCmjuUtLOnTvZtWsXhw4dyjS/bNmy1KtXj3r16lG3bl0t0SqfO3ToEDfccANAPWPM757ztASlVBHndDo5cOAAv/32Gzt27ODcuXMZ88LCwqhbty4NGzakQYMGVKxYUUtIKmBoglKqCHI6nezfvz8jKSUmJmbMK1OmDA0aNKBRo0bUr1+fyMhIP0aqVM40QSlVhBw7doyffvqJbdu2ZUpKFSpUoEmTJjRp0oTY2FgtJamgoAlKqSCXmJjI1q1b+fnnnzl27FjG9IoVK2YkpWrVqmlSUkFHE5RSQcjlcrFnzx42b96MMSbjYdkSJUpw1VVX0aJFC2rUqKFJSQU1rxKUiIQBrYE2WKPfOoCjwCZjzE++C08p5SkpKYktW7awefNmEhISAGt000aNGtGiRQsaNmxIeLj+7lRFQ65/ySJSAfgr8AhQCdgLnALCgMpAHRE5AkwBJhljTvs2XKWKpyNHjrB+/Xp+/fXXjH7uypUrR+vWrWnZsqVPh91Wyl9yTFAiMgx4DlgB3A98bYxJybJMWaAzMATYJiJjjTEf+jBepYoN9228devWsW/fvozpDRs2pE2bNjRo0ED7tlNFWm4lqLZAW2PMiZwWMMacBT4HPheRWGAsoAlKqXxwOBxs27aNdevWcfz4ccDqnLdVq1a0b9+eChUq+DlCpQpHjgnKGDM6LxsyxhwB/pLviIoxb4Z8f/PNN5k6dSoLFiygQYMGGctkNzig24IFCxg7diwlSpQArGdkqlevzoABA7jvvvsyKtKvv/56Tp48SVhYWKb1q1SpkjG+0/79+3n55ZfZuHEj6enp1KhRg8GDBzN06NCLvrd3794kJiayYsUK7a/NCw6Hg59++ok1a9ZkdKobHR1N+/btad26NSVLlvRzhEoVLq9rU0WkCtAciAAyNQ0yxiwt4LhULlJSUhgzZgxxcXFeP2TZqFEjFi+2Bmp0uVxs2bKFMWPGcPbsWR5//PGM5V5//fWLej13czqdPPjgg/Tp04dXX32VEiVK8NNPPzFq1CgiIyMZNGhQxrIbN24kIiKCmJgYli1bxs0335yPPS7asktMMTExdOrUiauuukobPahiy9tWfPcDk7GSU1YurEYTqpB07NiRw4cP88Ybb/DEE0/kef2QkBBatWrFCy+8wEMPPcT9999/yU5hARISEti/fz99+vTJ+DXfsmVLnnjiCVJTUzMtO3fuXG688UYqVarEjBkzNEFlw+l0ZiSm06et9kUxMTF07dqVJk2aaBNxVex5+9Ps78BU4EljzLlLLRzo9r54e47zKvcaQdlWPQA4++MyTn7xTo7L1h87P+P9oel/J/Xo3lyXKSilSpXi5ZdfZujQoXTt2pX27dtf1nY6duxIaGgoW7ZsyXZI+KwqVapEu3btuPfee7n11ltp27YtLVu2pF+/fpmWi4+PZ8WKFXzxxReUKVOGl19+ma1bt2Ya56o4c7lc7Nq1i+XLl3Py5EkAKleunJGYtOGDUhZvE1Qt4L9FITkVFVdffTUjRozgn//8Z8atu7xyj7/k2SXOmDFjLrqldM899/CXv1jVi9OnTycuLo7ly5fz0UcfkZ6eTteuXRk/fnzGoIoLFy6kY8eOxMbGAnDrrbcyc+ZMJk6ceFlxFiWHDx9m+fLl/P7774DVBdF1111H06ZNNTEplYW3CWoZcAOwy4exFBpvSzVlW/XIKE1dSs37X8lPSID3Q767jRw5krVr1zJhwoRM9T/ecjgcnD17NiORALz66qs51kG5Yxw6dChDhw4lNTWVzZs388Ybb/Doo48yd+5cXC4XcXFxHD9+nGuuuQaw6sxSUlJ44oknqFSpUp7jLArOnDnDypUr+eWXXwAoWbIkXbp0oW3bthc1SlFKWbxNUD8Dr4vIrcBOIFOFgzEmTxUhItIOWGKMqWJ/rgL8FysJhgBfAI8aYxLyst1g582Q757CwsJ45ZVX6Nu3L6VKlcrz923cuBGXy0WjRo28Wn7u3LnMmTOHhQsXAlay6tixIxERETzwwAMArF+/ntOnT/Pll19mKhGMGDGCOXPmMGrUqDzHGcwcDgfr1q3j22+/JT09nbCwMNq1a0fnzp21VZ5Sl+DtPYWuwAagJNAC+xkp+9XG2y8TkRAReQCrRObZ/GwakA7UAxoCFYBJ3m63qHAP+b5nzx4ATp8+zcsvv5xpyPesateuzVNPPUVcXJzX3+N0Otm4cSPjxo1jxIgRXvdC0LVrVw4cOMDEiRM5deoULpeLgwcP8v7777sHHGPu3Ln07NmTqlWrEhMTk/G67bbbmDNnDmlpaV7HGez27dvHlClTWLlyJenp6TRt2pRRo0bRo0cPTU5KecGrEpQx5roC+r4JwM3AC8DTACISCjiBCcaY8/a0qcBbBfSdQcObId+zM3DgQFavXs369etzXGbnzp20bNkSgPDwcGrUqMEDDzzA4MGDMy33t7/9LdtbTl9++SXVqlVj9uzZvPnmm/Tp04fk5GQqVKhAz549+etf/8qpU6f4+uuv+eCDDy5av0+fPkycOJGvvvqKPn36eHlEgtO5c+dYvnw5W7duBazGJb1796Z+/fp+jkyp4OL1kO8iUhXrQdymWCWv7cBUY8zFTddy3kZ1Y8xhEekGLDLGlM9hufeASsaYvl5ssy465LsKAC6Xi82bN/P111+TkpJCeHg4nTt3plOnTvosk1I5yPeQ73ad0XLgILAOq56oDzBaRLoZYy7uviAbxpjDXnzXGKAf0MmbbSoVCE6fPs2nn36a0Wdeo0aN6Nmzp3ZLpFQ+ePuz7jXgY+ARY0xGkUtE3gJeAfJ9C1BEIoA3gVuA640xO/K7TaV8zV1qWr58OampqZQqVYrevXvTtGlTf4emVNDzNkG1AR7wTE62N4HN+Q1CRMoAnwFlgHbGmD/yu02lfC1rqalx48bcfPPNlC5d2s+RKVU0eJugjgB1AZNlen2gIB7enYNVr9XZGJNUANtTyqd++eUXPv/8cy01KeVD3iaoGcC7IvIY4G4q1hH4jz3vsolIc6A3kAIcFxH3rNPGGG31oAJKSkoKS5cuzXjgVktNSvmOtwnqRaA6EIdV0gkB0rBu8Y3N65caY1YB5e33v5Cld3SlAtHhw4eZP38+8fHxhIeH06tXL1q2bKmduirlI94+B5UKPGi3sBMgGdhtjEn2ZXBKBQKXy8X333/PihUrcDqdVK1aldtvv52YmBh/h6ZUkZbbkO+9geXGmDT7fVa13LfjdDwoVVQlJyezcOFCdu2yuqFs164dN954oz7XpFQhyO1/2RKgGnDcfp8THQ9KFUnHjh1j7ty5JCQkULJkSfr27YtHHalSysdyG/I9NLv3Kvi8+eabbN++ncmTJ+e6nPuJ7k2bNnk1gGFRtnXrVj799FPS09OJjY1l0KBBlC+fbccnSikf0fsUSnlwOBwsX76cDRs2ANa4W7179861L0SllG/kVgd1Auv23SW5h81Q+XPo0CH69evHo48+ypQpU0hLS2P06NGEhoYyZcoUUlNTeeSRR7jnnntYt24dr732Gvv27aNGjRqMGjWKnj17Zmxn7Nix/PLLL9SvX/+iTkrj4uKYPn068fHxNG/enGeffZZatWr5Y5cDyvnz55k3bx779+8nNDSUnj170qZNG22lp5Sf5FaC+jteJqhgMnv27IwK78LQsGFD7rrrLq+XP3fuHNu2beObb77h66+/ZsyYMfTt25cVK1awZs0aRo8eTbNmzRgxYgQTJ06kR48ebNq0iZEjRxITE0Pr1q159NFHadKkCVOnTsUYw/3330+bNtaoKMuWLeN///sf77zzDg0bNuT999/nwQcfZMmS3KoZi74TJ04we/ZsTp8+TXR0NIMGDdKkrZSf5VYH9UEhxqE8jBw5MmMwQIfDwbBhw4iMjOS6667D4XDw3nvv0b59e3r3thpXduzYkVtuuYWFCxdSpUoVtm3bxrRp04iMjKRZs2b07duXP/6weo+Ki4tj2LBhGb0ePPTQQ8yYMYMNGzZQp04dv+2zP+3du5e4uDhSUlKoXr06gwcPpkyZMv4OS6liL7dbfF6PgGeMyft4436Sl9KMv7gr493jMrkvlu4Rai9cuECNGjUyrVOzZk02bNjAiRMniIqKytSLds2aNTMS1OHDh5k8eTLvvvtuxvy0tDQOHz5cLBPU5s2b+fzzz3G5XDRu3JjbbrtN65uUChC53eI7X2hRqEwuVefRpk0bfvgh8wgnBw8epHLlylStWpWUlBROnTpFpUqVAKu5tFuVKlUYNmxYpoEK9+zZQ/Xq1Tl16lQB7kVgc7lcLF++nO+//x6Aa665hhtuuEHrm5QKILnd4ru3MANR3rv22muZMmUKS5cu5aabbmLjxo189tlnvPXWW9SoUYN27doxceJEJkyYwP79+1mwYAGtWrUCoF+/frz99tu0bduW+vXrs2TJEp588kmWLFlSbB4+TU9PZ8GCBWzfvp3Q0FBuvvnmjOOjlAocud3iexl7GHb7fU5cxph/FHxoKifh4eFMnjyZ1157jbFjx1K1alUmTJjAtddeC8B//vMfxo4dS6dOnahevTrdu3cnPj4esBLU2bNnGTlyJMePH6d27dpMmjSJunXrcujQIX/uVqG4cOECc+bMYf/+/URFRXHHHXdQr149f4ellMpGjkO+i8g3wG3GmNP2+5y4jHTGDpUAACAASURBVDHX+yQ6L+iQ78pbiYmJzJo1i6NHjxIdHc3QoUOpWrWqv8NSqli7rCHfjTHXZfdeqWCUkJDAjBkzSEhIoGLFitx9993aM4RSAc7rSgcRKQ0MBpoCqcCvwFy7p3OlAtaxY8eYOXMmiYmJxMbGMmTIEB2/Sakg4FUfeyLSDDgAvICVoFoDrwJGRK70XXhK5c/Bgwd5//33SUxMpG7dugwfPlyTk1JBwtsS1FRgEfCwMSYNQERKAtOAd4CuvglPqcu3f/9+Zs2aRVpaGo0bN6Z///7FpqWiUkWBt72UtwAmupMTgD1Y4fNAO18EplR+7N27l5kzZ5KWlkbz5s0ZMGCAJielgoy3CepHoHM209sAvxVcOErl3+7du/n4449JT0/n6quvpm/fvhm9cCilgkduz0GN9Pi4EXhLRNoA6wEH0Bx4BJjo0wiVyoOdO3cSFxeHw+GgVatW9OnTR3uHUCpIXao3c09HgZ72y+0kcB9W4wml/GrHjh3MmzcPp9NJ27Zt6dWrlyYnpYJYbs9B6eP1Kmjs3LkzIzl16NCBHj16aHJSKsjleGNeRIbnZUMiEiIi9+U/JKXyZs+ePcTFxeF0OunYsaMmJ6WKiNxu8bUUkf8DpgALjTFHsltIRKoAdwEPAV9786Ui0g5Y4h6JV0QigbeAAVj1W68bY/7t9V6oYmv//v3MmTMHh8NB27ZtufHGGzU5KVVE5HaL7zG7UcR44A0R+Q2r94iTQAgQg9X8vBHwBXCPMWZjbl8mIiHA/VgP+XqaAAhwBVAO+FJE/jDGfHRZe6WKhUOHDjF79uyM1npa56RU0ZLrgyHGmB+AW0WkHtALq1l5A8CJ1Wjif8BSY8wBL79vAnAzVqOKpz2mD8dKcAlAgoi8CowANEGpbB05coRZs2aRmppKs2bNuOWWWzQ5KVXEePXkojFmHzC5AL5vijFmvIh0c08QkfJALJmfp9oBNCuA71NF0PHjx5kxYwYXLlygcePG9OvXT59zUqoIKtT/1caYw9lMjrb/TfKYlgSU8n1EKticPn2amTNnkpycTMOGDbn99ts1OSlVRAXC/2z30PIlPaaVAhL9EIsKYOfPn2fmzJmcO3eOOnXqMHDgQMLCwvwdllLKR/yeoOx6p6NYjSTcrkS7UFIeUlJSmD17NqdOnaJq1aoMHjyYiIgIf4ellPKhQOk9cwbwjIj8gnXLbwzwX/+GpAKFw+EgLi6Ow4cPU758eYYMGUKJEiX8HZZSysfyMmBhS6xxoCKwmplnMMbktwHFeOA1rGbsocC7WM9fqWLO5XKxcOFC9u7dS+nSpbn77rspU6aMv8NSShUCrxKUiIzFGlojHjiXZbaLPLbwM8asAsp7fL4AjLJfSgFWcvriiy/49ddfiYyMZMiQIVSsWNHfYSmlCom3JagHgHHGmBd9GYxSntatW8emTZsICwtj8ODBxMbG+jskpVQh8raRRCUgzpeBKOXp119/5euvrZ6zbrvtNurV076LlSpuvE1QnwBDfBmIUm4HDx5k4cKFAHTv3p2mTZv6OSKllD94e4svGXhSRAYCu4BUz5nGmEEFHZgqnuLj4zM6f23dujWdOnXyd0hKKT/xNkGVAmb7MhClkpKSmDVrFklJSTRo0IDevXtr/3pKFWPe9sV3r68DUcVbeno6c+fOJT4+nmrVqjFgwADtwkipYi4vz0E1A54AmmLVXe0A/meMWeej2FQx4XK5WLx4MQcOHKBMmTLceeedREVF+TsspZSfefUTVUR6AT9iteb7BJgHlAFWi0gP34WnioOVK1eybdu2jGedypYt6++QlFIBwNsS1IvAC8aYCZ4TReRprAd4lxV0YKp4+OWXX1i7di0hISEMHDiQqlWr+jskpVSA8PYmf2NgVjbT56DjNqnLdOjQIT799FMAevXqRYMGDfwckVIqkHiboA4ALbOZ3ho4XnDhqOLi7NmzzJ07F4fDQZs2bWjbtq2/Q1JKBRhvb/FNAqaISE1gvT2tIzAWeNkXgamiKy0tjTlz5pCYmEjdunXp2bOnv0NSSgUgb5uZ/09EygBPApXtyYeBZ4wxb/kqOFX0uFvsHTlyhAoVKuigg0qpHHndzNzuKPZFEakCJBtjsvZqrtQlffvttxm9k995552UKlXK3yEppQJUjglKREYC7xljLtjvs87PeF8A40GpYuC3335j1apVAAwYMICYmBj/BqSUCmi5laD+DswFLtjvc5Ln8aBU8XP06FEWLVoEwI033kjDhg39HJFSKtDlmKCMMfWye5+ViGhnaSpXSUlJzJkzh7S0NFq0aEHHjh39HZJSKgh425PEXhG5aChTEakOHCvwqFSR4XQ6+eSTTzhz5gzVq1enT58+2gGsUsorudVB3Qpca3+sCzwnIklZFtMnK1WuVqxYwb59+yhdujSDBg0iPNzrdjlKqWIut6vFz8BjgPvnbksyjwPlAhKB4b4JTQW7X3/9lXXr1hEaGsrAgQMpV66cv0NSSgWR3Oqg9gPXA4jI+8CjxpizhRWYCm7Hjh1j8eLFAPTo0YM6der4OSKlVLDxejwoEQkXkRqA+6nKECAKaG2M+dhXAargk5yczNy5c0lLS6N58+a0a9fO3yEppYKQVwlKRPoA7wMXNZQAEgBNUAqwGkUsWLCAhIQEqlWrpo0ilFKXzdvOYv+NNaRGO+AccB1wJ3AU+ItvQlPBaNWqVezevZuSJUtyxx13EBER4e+QlFJBytsE1RB4zhizGWvgwtLGmDis5JTbQ7xeE5EOIrJRRM6IyG4ReaAgtqsKz/bt21mzZg0hISEMGDCA8uXL+zskpVQQ8zZBJQNO+/1OoIX9fjPQKL9BiEgosBhrCPlyWKWzt0SkRe5rqkBx4sSJjJ4iunfvTv369f0ckVIq2HmboL4FxolIeeAH4DYRiQC6AQXRsq8CUAUIsXumcAHpZG7WrgJUamoqcXFxpKam0rRpU+0pQilVILxNUP8HtAXuA2YDpbES0/vAm/kNwhhzCngL+BBIAzYBTxljtud328q3XC4Xn332GSdPnqRy5crceuut2ihCKVUgvEpQxpjdxpjGwNvGmCSsxhIDgE7GmJfyG4R9i+8CcBdQEqtk9oyI9MjvtpVvbdq0iW3bthEREcGgQYOIjIz0d0hKqSIit66Osh2ox2P6N+7PdtLKj/7ANcYYd4OL1SIyHRiB1XpQBaBDhw7x1VdfAXDrrbfq8BlKqQKV23NQiVh1Qd7I75CotbAe+vWUjnW7TwWgpKQk5s2bh9PppF27dlx11VX+DkkpVcTklqCuK7QorFLSv0XkIWAq0Ap4ENCm5gHI/TDu2bNnqVmzJj166J1YpVTBy60vvtWFFYQx5lcR6Q88D7yC9QDwP40xiwsrBuW9b7/9lj179lCqVCkGDBhAWFh+C9BKKXUxb7s62kQut/uMMfnubM0YsxRYmt/tKN/avXs3q1dbv1369++vPZQrpXzG28F5lmSzXn3gZuDZggxIBa4zZ86wYMECALp168YVV1zh54iUUkWZt72ZT8huuojcB9wKvFGQQanA43A4mDdvHsnJyTRo0IAuXbr4OySlVBHn7YO6OVkJ3FgQgajA9tVXX/HHH39Qrlw5brvtNn0YVynlc97WQWX3TFQ54GngSIFGpALO1q1b2bRpU8bIuKVKZfuInFJKFShv66ByeibqAnBvwYWjAs2JEyf4bLHVCWyPG7pRo0YNP0eklCouvE1QWZ+JcmF15PqrMeZcwYakAkVqaipzP55NmsNJ3ZAErqpWxt8hKaWKEW/74lttPxe1Gas0dRb4WZNT0eXuBPZUwmnKcYHI9Ai+O1HB32EppYoRrxKUiETZfeOdxOpp/EfgpIi8ISL6lGYR5O4ENjIigtYxJVlf6jraN63m77CUUsWIt7f4/ofVw/gdwEasxNYeeBVIAf7hi+CUf2TqBLZvX5o2bUqHdAcR4fpbRClVeLxNUIOAW4wxaz2mLRCReCAOTVBFRlJSEnEfz8LpdNLi6tY0bdoUQJOTUqrQ5WXI9+x6Fj9TgLEoP3M6nXwyZzbnki4Qw3nMLwdJTNYO5ZVS/uFtCepJYJqIjADWG2OcInIVMBl40fM5qQIYG0r5yeqVX7Pv4B9EkU6tqDBONe1MdMkIf4ellCqmvE1QbwDRwBrAISJOIAIIwRpd93WPZfVeUBDatWsn3373PeDiuooptLjvGSJL6gO5Sin/8TZB9fNpFMqvzpw5w/y4uQBcHXWaVnc/RUTp0n6OSilV3HnbWexqABEpCTTEqrvao89BBT+Hw8Gcj94jJd1JLOeIkl5ElK/i77CUUsrrvvjCgH8Bj/Lnrb1UEfkA+IsxJt1nESqfWrZsGUfjz1Iq1Emyqzqtunb2d0hKKQV4f4vvRWAoMAxYi5WgrsF6Dmq8/VJBZtu2bWzcuJHQ0FDuHH4PVWJrEhmhVYhKqcDgbYIaBjxgj3rrFici54B30QQVdI4dPsTihdbggzfddBM1a9fxc0RKKZWZtwkqGtidzfS9QOWCC0cVhpQLyXz8wXTSnRCNi7oNrvJ3SEopdRFvH9TdBIzKZvpfsDqQVUHC5XIx/71JnEmDsqQQVb0tlcuX9HdYSil1EW9LUP8AVolIN2C9Pa0DUBfoWfBhKV9Zu3AGu06cJxwHt93Sm9hmHQkN1dFxlVKBx9vhNn4AWgHLgVpYt/U+A640xnzvu/BUQdq1fiWrtu4BoEfrJtRtdS1R2ihCKRWgvC1BYYzZCYwRkUqAwxhz2ndhqYJ2+uBOFn21EicRVC8VSds+g/0dklJK5crbOihEZJyIHAaOA6dE5HcRecx3oamC4nK5WPLN9yQRQSnSadNnuL9DUkqpS/L2Qd0XgYeAF8g8HtTTIhJhjHklv4GISCzwNtbw8heAd40x4/K7XQXffvste/b9TsmSJbnrrqHUqFnd3yEppdQleXuL7wFgeJbnoL4Tkd3AJCDfCQpYjNUisCoQC6wWke3GmNkFsO1iyeV08N3C2azatheA/v37a3JSSgUNbxNUOHAwm+m7gTL5DUJE2gP1gWuMMWnAPrvFYHJ+t11cuVwudsyfzNrfjgPhXN26Aw0aNPB3WEop5TVv66BeASaLSC33BBGpCLyE1d1RfrUGtgLPisgfIrIHuM0Yc6QAtl0snfpuISt+O0QK4YSHl6Znj+v9HZJSSuWJtyWoO4DGwF4ROQikA7WBSKCDiPzVvaAx5nK6wq4IdAZWY5WkrgS+FJEjeosv7xJ3fM9XK1dxikqULVWCYQ88SFSkDjyolAoueRmw0JdSgLPGmGftzz+LyDSgP6AJKg8uHN7N2k8+ZLcrlrDQEAYPHUalCuX8HZZSSuWZt+NBfejjOHYApUQk0hiTak/z+hktZUk/e4ofP3qFTY5qANxya19iY2P9HJVSSl0er5+D8rHlwAngNRGJFJFmwP3Ax/4NK7icT01nbVosTkKpXkdo0aKFv0NSSqnLFhAJyhhzAeiKVf90BPgSeNkYM9+vgQURh8PBwiVfkOwMITY2lvvuHujvkJRSKl8C5jaaMWYvcLO/4whG8eZHvvntEPv37yc6Opo777yTsDDtY08pFdwCJkGpy5O452d+jJvENmdtQkJCGThwIGXK5PvRNKWU8rscE5SIvOftRowx9xVMOCov0s+cYOeCt1jvrAlAp87XUbt2bT9HpZRSBSO3OqgyHq9KwD1YY0CFAQ6gBdZQ8FoK8wOXI439817jm6QYHITSUJpwQ7dr/B2WUkoVmByTizEmo5ZdRN4F3jDG/M1zGRF5AeuhWlXIDn3xAV8fTieRMlSrWoWBt/cjJEQHHlRKFR3eln7uxBqwMKsPgZ8KLhzljfht37Pux1846qpMqRJRDL7zLiIitKcIpVTR4m0z86NAdp259QH2F1w4yhsbvl3JDldlAG4bMJhy5bSnCKVU0eNtCeo5YLqIXA/8CIRgjQfVG9AHbgrRgQMH+OGUE4DO191Igyvq+jcgpZTyEa9KUMaYGcBNgAu4GxiKNRRGF2PMp74LT3lKSDhNXFwcTqeT9u3bc32XTv4OSSmlfMbrFnjGmG+Ab3wYi8rFMbOVmXPjOO8Kp379+vTo0cPfISmllE/l9hzUy95uxBjzRMGEo7LjuJDEkk9mkugqRWQI9O9/O6GhAdFLlVJK+UxuJai2Xm7DVRCBqJx99dFbHEovRThO+t0xnNKlS/k7JKWU8rncnoO6zv1eRO4GvjDGnCyUqFSGTZ/HselIMuCi38030Vjq+zskpZQqFN7eJ/ofVm8SqhBtWfcdX/7wKwBdr6pH0zbaKEIpVXx4m6A2ALf5MhCV2cljR/hy+Vc4CaVGFHS57W5/h6SUUoXK21Z8TuBfIvI0sA+riXkGY0y7gg6sOEtOTmbOvPmkEkZM6AUGPfxPbRShlCp2vE1QG+yX8jGHw0FcXBynTp2iSpUq3Dv8bkqUivZ3WEopVei8SlDGmAnu9yJSFgg1xpz2WVTFlMvl4r13p3L4+DFKly7NXXfdpclJKVVseX3fSEQeEZGDQAJwSkSOiMg/fRda8bN08SIOHz9GGE6uaX+N9rGnlCrWvEpQIjIGeAl4E+gMdAH+AzwhIo/6Lrzi4+fNG/nh518AF1eVdtHh2vb+DkkppfzK2zqoUcDDxpiPPaZ9JyL7gReA/xZ4ZMXInl07+XTJUiCEduVSuWnkeEJCtFGEUqp48/YqGANsymb6ZqBmwYVT/Bw4eIiPP56NkxCaRJ2j+71/IzSyhL/DUkopv/M2QW0j+2E17gB2FFw4xUtCQgIzP3gPhyuEWiFnuGX4SCLKVfZ3WEopFRC8vcU3HvhcRDoC39vTOgI9gf6+CKyoO3/+PDNnziTN6aJqSCLX3tiXErHajZFSSrl5Ox7UMuAGIAVrPKgBwFmgrTFmSUEGJCLlReSAiNxTkNsNJKmpqcyePZv4+HiqVavG0Icfp1HHrv4OSymlAkpuw230BNYYY84DGGO+Bb4thJimADUK4Xv8wuFw8PakKZw+m0D58uUZMmQI0dH6rJNSSmWV2y2+z4F0EfkBWGm/1hljUnwVjIgMB8oCW331Hf7kdDqZ8eGHnD6bQCTpdOvYSZOTUkrlILcEVRm4Bquu6VrgcSBURNbzZ8LaYIxxFEQgIlIPeAboBHxZENsMJC6Xi8UL57P/4EEicNCxvJPmbVr5OyyllApYuY0HlQAssV+ISBjQCithdQTuAyqLyBpjzM35CcLe9kxgjDHmqIjkZ3MBx+Vy8eXSz/ll22+E4eSmyim0fPBZQkLD/B2aUkoFLK+fBrVLSoeAI8Bx4IC9fkE0PRtnfYVZUADbCjhLli5j4w+bCcXJDWUSaDHsH4RGlvR3WEopFdBybWYuItFAN6A7cCNwJXAQ+AaYDtxljDlcAHEMBqqLiLvJehlgsoi0M8aMLIDt+813333Hjz+sJwQXncKO0Hro04SXqejvsJRSKuDl1opvDdAOq3PYVVjdGa00xuwu6CCMMVdm+e6fgDeMMR8U9HcVpo0bN/L1118D0D7sMC0HPExkldp+jkoppYJDbiWoa7Bu6b2H1SDie2NMWqFEVQRs3LiRL774AoDevXvTqkkjwkpr7+RKKeWt3BJUfaxbezcAjwClRWQtsAIrYf1ojHH5IihjzNW+2G5h8UxOvXr1om3btn6OSCmlgk9urfh+B6bZL0SkBXA9VsIaB6SJyCpghTFmss8jDRKeyallyFGqlC7t54iUUio45aUV38/GmP8At2IlqblAD6wxohSwYcOGjOTULvQP6sZUpM6VRavJvFJKFZZLdhYrIjWB9kAH+99WQDqwDmssqFU+jC9obNiwgS+/tJ4vbhf6B81rVCB26NOEhHnbH69SSilPubXim4+VkGKBc8B3wGfAGGBzQfUgURSsWbOGlStXAlZyuqpyFNXueEqfdVJKqXzI7ed9FPAGVgnpR2OMs1AiCiIul4sVK1bw3XffAS7ah/5B7SgHsXe9qC32lFIqn3JrJNGnMAMJNk6nk6VLl7J582ZCQ0O5oeWVVPjtAPXufZ7wcjH+Dk8ppYKeVpBcBofDwaJFi9i2bRvh4eEMHDiQRo0a4bzpVkIjovwdnlJKFQmaoPIoLS2NefPmsWvXLsJwckUdoVGjRgCanJRSqgBpgsqDpKQk5syZw8GDB4nCwQ1he7lwMJ6U1H5EReqhVEqpgqRXVS/Fx8cza9Ys4uPjKRWSxg2heylZqSYNB/1Tk5NSSvmAXlm98McffzBj5ixSLiRTgWSuD91Hlas6EtNnFCHhEf4OTymliiRNUJewY8cO5s+fT3p6OtVDztE5dD9VuwykfOeBhISE+Ds8pZQqsjRB5cKzX70m9WrS/NDXVLvlr5Rr1tnPkSmlVNGnCSobDoeDz5d+wZYfNwPQrVs3unTpgjN5EGGlyvg5OqWUKh40QWVx/vx54uLmcuDAQUJxUqv6FXTt2hVAk5NSShUiTVAejh49ysezZ3H2XCIlSaNL2H6qXtHS32EppVSxpAnK9u26H1j99VKcLheVOc8NFc5Tq99TlK7d2N+hKaVUsaQJClixfAVr160F4IqQeK5vXp9qPR8gtIQONqiUUv6iCQo4ceIIIbi4OuwE7XsNpGrrbv4OSSmlij1NUMCAQYOJ37OVirG1CC9b2d/hKKWUQhMUAOHh4VQRbQyhlFKBJNTfASillFLZ0QSllFIqIGmCUkopFZACpg5KRG4EXgIaAseBV4wx7/g3KqWUUv4SECUoEakFzAdeAMoDdwL/FpGb/BqYUkopvwmUElRdYLYxZqH9eZOIrAKuAb7yV1BKKaX8JyASlDFmDbDG/VlEKgKdgRlerB4GVj96SimlgovHtTss67yASFCeRKQc8CmwAVjsxSqxAEOGDPFlWEoppXwrFtjjOSGgEpSINMJKSr8BQ4wxTi9W24RV2joCOHwYnlJKqYIXhpWcNmWdEeJyuQo/nGyISBes5DQFeMoYExiBKaWU8ouASFAicgWwBRhrjHnT3/EopZTyv0BJUK8DjwPns8yaZIz5hx9CUkop5WcBkaCUUkqprALiQV2llFIqK01QSimlApImKKWUUgFJE5RSSqmApAlKKaVUQAqoniQKmoi0A5YYY6rYn2OA/wI3ASnAe8AzxhiHPf8jYBCQ7rGZ5saYvSJSG5gOdMAaDmS0MWZpoe2Mh5yGJhGRSOAtYABWrxqvG2P+7bHeIOBfWE9trwbuMcYct+cFxP7lY9+C+tx5zC8BrAJeMsYs8pge8OfOjuVy9y+oz5+IVMG6ttwAhABfAI8aYxLs9QL+/OVj33x27opkCUpEQkTkAWAZEOkx60OgCtAYuApoBzznMb8V0M8YE+3x2mvPmwP8AlQCHgTmiEh9H+/KRS4xNMkEQIArgLbAcBEZZq/XBOsP5R57H3Zh7ZOb3/fvcvfNFuznDhFpjnXxbp9lvYA/d3D5+2cL9vM3DesCXQ/rAl8BmGSvF/Dn73L3zeazc1dUS1ATgJuxDvbTACJSCugJtPD45TIO+ExEngZKAFcCP2XdmN1HYBvgRmNMKrBSRD4F7gfG+n53MqlLzkOTDMf6ZZYAJIjIq8AI4CNgKPCZMWYtgIg8aS/TEOtXUSDsX10uY99EpCRBfu5EZB+wgj9/ZXsKhnMHl7l/ReH8AU5ggjHmvB33VKwSPwTH+avLZeybr89dUU1QU4wx40Wkm8c0d2nRs7cKBxCD9YvhSqxfCVNFpANwEBhvjFkCNAEOuE+QbQdWCaxQXWJoklisjnbddgDN7PdNgB88tpMkIgft+U4CYP/ysW9XE/zn7jBQ3xhzTkQezbJqwJ87O67L3b+gP3/GmPFZFu+H1X0bBMH5y8e++fTcFckEZYw5nM20RBFZBrwsIg9h/XJxH/iSQBmsEzQB+Bm4FYgTkY5ANJCUZZNJQCnf7IF3sgxNstme7BmnZ4yX2oeA2r887luwn7vFl+i5P6jOHeR5/4L+/GWZNwbrIt7JnhRU5y+P++bTc1ckE1Qu7saq7NsOnABeB24BThtjlmHVWbnNF5F7sQ74Nqwk5qkUkOjziHMgWYYm4c/4POP0jPE8Oe9DSC7zCl1e9y3Yz50Xw8oEzbmDvO9fUTl/IhIBvIl1TbneGLPDXiVozl9e983X565INpLIRSwwwhhT1RhzFfAHsN0uct8iIsOzLB8JXMA6WbXt+61uV5L5llOhEWtokg3AImCAMeaCXTdzFKshgZtnjL95zrPr5Grb0wNm/y5n34L93HmxWlCcOzu2PO9fUTh/IlIGWI7VgKedMcazTiYozt/l7Juvz11xK0G9Dmyzi6m1gYn8WZEZBvxXRLZj3VK6A6sY+4Ax5oCI/Ay8aFdwdgL6Ah0LewfEGppkCdkPTTIDeEZEfsEqXo/BKjECzAbW2vVy3wP/BrYYY3ba2/X7/uVj34rCuctNwJ87O47L3b+icP7mYP3g72yMyXpbK+DPXz72zafnrrglqAeBqUA8cBp40xgzGcAYs0hExgIfA9WwKvP6GGMO2OveDryL1Zb/JHC/MWZbIccPMArrvu+/ReTfHtMnYdWpvQb8ivUH9S7WAJAYY7aKyH325xpYv5QGeqwfCPt3ufsW9OfO5DKsTJCcO7j8/Qv287cc6I31bOVxkYzC0mljTM0gOX+Xu28+PXc63IZSSqmAVNzqoJRSSgUJTVBKKaUCkiYopZRSAUkTlFJKqYCkCUoppVRA0gSllFIqIGmCUqoQiMgcETlhd8KZdd5YEUkUkbp+CE2pgKUJSqnC8RjWg/GeD0EiIvWwhh4Ya4z53Q9xKRWwNEEpVQiMMUeBJ4EHRMRzwL43scbSyUvXQEoVC9qThFKFRERCgO+wOtNsiz00AdDSGOPu+PZe4CmsLnG2Y5WsvrTnhQPPA3cB1YFTWF3MjDHGOERkJtb4Qo2xRh7ub4xZVWg7qFQB0xKUUoXEGOMCHgKaFybMlgAAAblJREFUA/cCrwIveCSnm7E6NB5rLzMdWCgi7gHensQaivturKG3nwT+CvTx+JqhWJ3o3gD8f3t3rNpUFMdx/Cu0FJf6CHXyD3UR6huUOvkATq4Fp0ZoBEFKaYfQgLuTEpxM6SN0cCjdI9I/xOeI0MXh3MAl+7050u8HLiTnhHDucPnzPze5v9uOT0nq1EN7WKy0Vpn5KyI+A18oHdKoNf0RGGXmj+b9PCJeAu+BN8CMEnv/s5n/GhFDSnLpMljud2Z+7/o8pD5YoKT+nQIfgLPMvG+N7wJ7EfGpNbZJk5/TPDl6PyLGwDNKl/WUEnmw9KfLhUt9cotP6llmLpqXi5WpDeAYeNE6nlMydIiIc2BKuW6nwCtKamnb6ndK/y07KKked8BOZs6XA0039RcYA++Ao8ycNHOPKcGbj9awVqlzFiipHhfAJCISuAYOgBPK/ScoQZuvI+IGeELZKtwGttawVqlzbvFJlcjMKTAAhpT7TgPgMDMvm4+8pfx6bwZcAXPgG7DX+2KlHvg/KElSleygJElVskBJkqpkgZIkVckCJUmqkgVKklQlC5QkqUoWKElSlSxQkqQq/QOwN3B4aRbPtgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "system.t_end = 2250\n",
-    "results = run_simulation(system, update_func_quad)\n",
-    "plot_results(census, un, results, 'World population projection')\n",
-    "savefig('figs/chap08-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The population in the model converges on the equilibrium population, `-alpha/beta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13.856665141368708"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results[system.t_end]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13.88888888888889"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "-system.alpha / system.beta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  What happens if we start with an initial population above the carrying capacity, like 20 billion?  Run the model with initial populations between 1 and 20 billion, and plot the results on the same axes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "p0_array = linspace(1, 25, 11)\n",
-    "\n",
-    "for system.p_0 in p0_array:\n",
-    "    results = run_simulation(system, update_func_quad)\n",
-    "    plot(results)\n",
-    "\n",
-    "decorate(xlabel='Year',\n",
-    "         ylabel='Population (billions)',\n",
-    "         title='Projections with hypothetical starting populations')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Comparing projections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can compare the projection from our model with projections produced by people who know what they are doing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the data file\n",
-    "\n",
-    "import os\n",
-    "\n",
-    "filename = 'World_population_estimates3.csv'\n",
-    "if not os.path.exists(filename):\n",
-    "    !wget https://raw.githubusercontent.com/AllenDowney/ModSimPy/master/notebooks/data/World_population_estimates3.csv\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def read_table3(filename = 'data/World_population_estimates.html'):\n",
-    "    tables = pd.read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "    table3 = tables[3]\n",
-    "    table3.columns = ['census', 'prb', 'un']\n",
-    "    return table3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#table3 = read_table3()\n",
-    "#table3.to_csv('data/World_population_estimates3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Year</th>\n",
-       "      <th>census</th>\n",
-       "      <th>prb</th>\n",
-       "      <th>un</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2016</th>\n",
-       "      <td>2016</td>\n",
-       "      <td>7.334772e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7.432663e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017</th>\n",
-       "      <td>2017</td>\n",
-       "      <td>7.412779e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018</th>\n",
-       "      <td>2018</td>\n",
-       "      <td>7.490428e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019</th>\n",
-       "      <td>2019</td>\n",
-       "      <td>7.567403e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020</th>\n",
-       "      <td>2020</td>\n",
-       "      <td>7.643402e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7.758157e+09</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Year        census  prb            un\n",
-       "Year                                       \n",
-       "2016  2016  7.334772e+09  NaN  7.432663e+09\n",
-       "2017  2017  7.412779e+09  NaN           NaN\n",
-       "2018  2018  7.490428e+09  NaN           NaN\n",
-       "2019  2019  7.567403e+09  NaN           NaN\n",
-       "2020  2020  7.643402e+09  NaN  7.758157e+09"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table3 = pd.read_csv('World_population_estimates3.csv')\n",
-    "table3.index = table3.Year\n",
-    "table3.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`NaN` is a special value that represents missing data, in this case because some agencies did not publish projections for some years."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This function plots projections from the UN DESA and U.S. Census.  It uses `dropna` to remove the `NaN` values from each series before plotting it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_projections(table):\n",
-    "    \"\"\"Plot world population projections.\n",
-    "    \n",
-    "    table: DataFrame with columns 'un' and 'census'\n",
-    "    \"\"\"\n",
-    "    census_proj = table.census / 1e9\n",
-    "    un_proj = table.un / 1e9\n",
-    "    \n",
-    "    plot(census_proj.dropna(), ':', color='C0', label='US Census')\n",
-    "    plot(un_proj.dropna(), '--', color='C1', label='UN DESA')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the model until 2100, which is as far as the other projections go."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = System(t_0=t_0, \n",
-    "                t_end=2100,\n",
-    "                p_0=p_0,\n",
-    "                alpha=0.025,\n",
-    "                beta=-0.0018)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap08-fig02.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results = run_simulation(system, update_func_quad)\n",
-    "\n",
-    "plt.axvspan(1950, 2016, color='C0', alpha=0.05)\n",
-    "plot_results(census, un, results, 'World population projections')\n",
-    "plot_projections(table3)\n",
-    "savefig('figs/chap08-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "People who know what they are doing expect the growth rate to decline more sharply than our model projects."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:** The net growth rate of world population has been declining for several decades.  That observation suggests one more way to generate projections, by extrapolating observed changes in growth rate.\n",
-    "\n",
-    "The `modsim` library provides a function, `compute_rel_diff`, that computes relative differences of the elements in a sequence.\n",
-    "\n",
-    "Here's how we can use it to compute the relative differences in the `census` and `un` estimates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "alpha_census = compute_rel_diff(census)\n",
-    "plot(alpha_census, label='US Census')\n",
-    "\n",
-    "alpha_un = compute_rel_diff(un)\n",
-    "plot(alpha_un, label='UN DESA')\n",
-    "\n",
-    "decorate(xlabel='Year', label='Net growth rate')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Other than a bump around 1990, net growth rate has been declining roughly linearly since 1965.  As an exercise, you can use this data to make a projection of world population until 2100.\n",
-    "\n",
-    "1. Define a function, `alpha_func`, that takes `t` as a parameter and returns an estimate of the net growth rate at time `t`, based on a linear function `alpha = intercept + slope * t`.  Choose values of `slope` and `intercept` to fit the observed net growth rates since 1965.\n",
-    "\n",
-    "2. Call your function with a range of `ts` from 1960 to 2020 and plot the results.\n",
-    "\n",
-    "3. Create a `System` object that includes `alpha_func` as a system variable.\n",
-    "\n",
-    "4. Define an update function that uses `alpha_func` to compute the net growth rate at the given time `t`.\n",
-    "\n",
-    "5. Test your update function with `t_0 = 1960` and `p_0 = census[t_0]`.\n",
-    "\n",
-    "6. Run a simulation from 1960 to 2100 with your update function, and plot the results.\n",
-    "\n",
-    "7. Compare your projections with those from the US Census and UN."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def alpha_func(t):\n",
-    "    intercept = 0.02\n",
-    "    slope = -0.00021\n",
-    "    return intercept + slope * (t - 1970)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "ts = linrange(1960, 2020)\n",
-    "alpha_model = TimeSeries(alpha_func(ts), ts)\n",
-    "plot(alpha_model, color='gray', label='model')\n",
-    "plot(alpha_census)\n",
-    "plot(alpha_un)\n",
-    "\n",
-    "decorate(xlabel='Year', ylabel='Net growth rate')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "t_0 = 1960\n",
-    "t_end = 2100\n",
-    "p_0 = census[t_0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                alpha_func=alpha_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def update_func_alpha(pop, t, system):\n",
-    "    \"\"\"Update population based on a quadratic model.\n",
-    "    \n",
-    "    pop: current population in billions\n",
-    "    t: what year it is\n",
-    "    system: system object with model parameters\n",
-    "    \"\"\"\n",
-    "    net_growth = system.alpha_func(t) * pop\n",
-    "    return pop + net_growth"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.1102518413268"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "update_func_alpha(p_0, t_0, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results = run_simulation(system, update_func_alpha);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_results(census, un, results, 'World population projections')\n",
-    "plot_projections(table3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Related viewing:** You might be interested in this [video by Hans Rosling about the demographic changes we expect in this century](https://www.youtube.com/watch?v=ezVk1ahRF78)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap09soln.ipynb b/soln/chap09soln.ipynb
deleted file mode 100644
index aa6929e0..00000000
--- a/soln/chap09soln.ipynb
+++ /dev/null
@@ -1,1147 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 9\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import everything from SymPy.\n",
-    "from sympy import *\n",
-    "\n",
-    "# Set up Jupyter notebook to display math.\n",
-    "init_printing() "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following displays SymPy expressions and provides the option of showing results in LaTeX format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sympy.printing import latex\n",
-    "\n",
-    "def show(expr, show_latex=False):\n",
-    "    \"\"\"Display a SymPy expression.\n",
-    "    \n",
-    "    expr: SymPy expression\n",
-    "    show_latex: boolean\n",
-    "    \"\"\"\n",
-    "    if show_latex:\n",
-    "        print(latex(expr))\n",
-    "    return expr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis with SymPy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a symbol for time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAcAAAANCAYAAABlyXS1AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnElEQVQYGV2QCxHCMBAFGxSgIRqQQB3UAzIaG2ABCcUBQyWgAQdhNyRAmpnX++xd7tKQcx7aSSnt8R9oxH/uGqj2iI0C4y0cya0CzxbaeSuET5jn2cQJOa/BF/59cCFF0YRwP7H2/9puHp0dbFeaL6d01vdFMkvN/yDewYii76Y2tJndPMBEbWzQZ5S/YocAuwZXxnHeGV2R8UX7Bq+TPY8f0jS0AAAAAElFTkSuQmCC\n",
-      "text/latex": [
-       "$\\displaystyle t$"
-      ],
-      "text/plain": [
-       "t"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t = symbols('t')\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you combine symbols and numbers, you get symbolic expressions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle t + 1$"
-      ],
-      "text/plain": [
-       "t + 1"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expr = t + 1\n",
-    "expr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is an `Add` object, which just represents the sum without trying to compute it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "sympy.core.add.Add"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(expr)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`subs` can be used to replace a symbol with a number, which allows the addition to proceed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOCAYAAAAWo42rAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVQoFXWR0RGCQAxEwbEALUFKsAZKkBagBH/5cygBW5ASKAHpAFvQDvBtuDDHjGYmbLLZy11COs9zIqvrOgMqS5bPGWjhO6WphEF0BVch8YX6A6+I7zupMZFlKBpBbJ1IriJcOBJ/gotPEB4sWPhkH8gePIaCQx6CVmhv9Ipj6PYk74jtausYCXxyvVm33Lz2s6OKdGqAEi+I+7/CIH4LsZNNzYlMvnCb70Cm6XNfz0QyIfaVbNRKfBjtcEAojM1vGV1oK4gVHNLkEurXvtZhSLTgIhJL1MBrTckXilpU0lDNGcIAAAAASUVORK5CYII=\n",
-      "text/latex": [
-       "$\\displaystyle 3$"
-      ],
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expr.subs(t, 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`f` is a special class of symbol that represents a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "f"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f = Function('f')\n",
-    "f"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The type of `f` is `UndefinedFunction`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "sympy.core.function.UndefinedFunction"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(f)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "SymPy understands that `f(t)` means `f` evaluated at `t`, but it doesn't try to evaluate it yet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle f{\\left(t \\right)}$"
-      ],
-      "text/plain": [
-       "f(t)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f(t)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`diff` returns a `Derivative` object that represents the time derivative of `f`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)}$"
-      ],
-      "text/plain": [
-       "d       \n",
-       "──(f(t))\n",
-       "dt      "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dfdt = diff(f(t), t)\n",
-    "dfdt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "sympy.core.function.Derivative"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(dfdt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We need a symbol for `alpha`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAA0AAAAJCAYAAADpeqZqAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVQYGXWQ2w2CQBREhViAiR1gB2gLdmAsQUvwd/+MdqAtSAfSgY8OwApI7GA9s+4lG6KTDHOfc4HMez9K4Zw7kHexNkMv1Op0JrMlGgWNK9zaEDohf8E5cRtnypyC4UFQ2YKKxG/kDnfKwQrWYYnmiUSuezhES2ERi1MZ2aU1xTo6D5d0raCnbw2mOYkuiE/4D+rfzFRLchKar/x+MldZZxyDM7qE0h4Mbkh0JYC8JGjTX653FvqLDAUTVD9Kb9QRHz9smkzvIogUTAAAAABJRU5ErkJggg==\n",
-      "text/latex": [
-       "$\\displaystyle \\alpha$"
-      ],
-      "text/plain": [
-       "α"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alpha = symbols('alpha')\n",
-    "alpha"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can write the differential equation for proportional growth."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)} = \\alpha f{\\left(t \\right)}$"
-      ],
-      "text/plain": [
-       "d                \n",
-       "──(f(t)) = α⋅f(t)\n",
-       "dt               "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eq1 = Eq(dfdt, alpha*f(t))\n",
-    "eq1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And use `dsolve` to solve it.  The result is the general solution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHIAAAAYCAYAAAAmsqlBAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAFVklEQVRoBe2ab1IUOxDAF8oD+OcE4g2eegO8AeoJwBtAvU/wjYIboCdQvAF6AoEbqCd4ljfg/X4hmZrJJjOzC7K7JV3Vm6TT6XS6O+nMzK5dXV1N7mE1LXBwcHCB5tuUl+uruYR7rXHeQ6zwD/hDa9w7UiusJmyi9g8c+lv11+6P1uXyIo45QqP/wCfgQ9rv1JDS+m9wg+YeqCN14jl4MtGRN8H9/f0NcGeMDPnkH8P7N/Jgm+/gZlo79VNw1zblUaLH9gW0rUR7gDeLYATQYXR8B59ZhxbOY+oBYnScUL5KtL4SvvfgKbgHdmT1jbvrPnRz7Tug6wpHV9ThjD7X4K54R+nOuBVA1gmCPCq/tASeUXeeS0rrbTA/Sg9QdSS9H0CNLepQBeXGP4X2GpwCJtcY3qpeUW+P24b2FXwOLh2g6y5K/QsabJ0Apb0FavA39oO3CcrUNm3QbgbNa+YNR6yd1MOxStnYtXjZgUEnbIE6zwjRGZ8pG6BtxJ63hTWd1xUn28j7aRvhXyg12NIA+piDXK8Ge0r9fa4ctGQD7dPeOTnrPG1lNjssCvhFKT0PGndjmD/qvVHbkTphAlNSNpWSEyi8E7GpI5b25YollkMqP8HjRFiC0lNCAz1i3QZbDTyF3sDT7IYa44x0beXuC3KRrwPfKkN9bFvaBkx130KN0wP6XnFHwvASrCrKQCecUFZ56DYYSgHgOBUyH4SAob5QQA+PS53oEZaMVdPJXfKp1nkDuinK+XdBT7sdSjeLOdnU1raVwfRSXsqPYPfxIwpwJzlIhY2SX9Cb85n2JAp4VqA7Tl6jybqO1DDf4O3sPtoqZ5R1ZEO7U2B+g9ILnYFlpPcCPK7tMWVfEE/JiOPMvT5aCMUL5HXX7L8P2kOYzLxgBPje7pCyY/wWr842KjoAv44z/5lfNyn7jl6NN9qJyMqjsjN3pWEOH5oj9bsrBwF5BqbYADR38wfK4gUOukGt/ObiB80Acu48/0GaHTqOdHicwGrxWLQDUAl3bA368mMa43ijexSg160suDCZQSf0rfeaI/tFJx2oMx6DxbVEexr0vhNt5z/XMyp44BuEKUcyQuUmTOqxWgMV70RlxmgEphte1tU0XZQBsWhwLcKooxK7NJeOaCOf8wyGYLcgqfvjSSJ4HKeburbz8abPhmHQ2J+SI3svOkOCUc7I1EFe5VcBwskwxqjw6DAd3hfk+Zod85mxU48zOeNN2iVHGllDiobFVyZ+IR3Fm6OKehPFrTHuhFG7IMozsjXKLHDJ3MUXFi0h6rkD39Qzb4snVc1xKacm2phy9DrHCCvxlBypIw5LzC2aiqUjqUUO1U5+ZOEavxTF7trRRwtyzCl/Ik8aIF73ey8ezC/fPPNX14hMHzVqF0qmGw/rbVaEeiyKQzvS/uINLY4PERjlGekleY5vdi31hQC6qau7Nj2/dfSgP72W8xZfdUpnULfhpvDO0AByPKG86AzdI5oxQ5V8R4ZjkUHnAwPNf7Ubl5Hri3SjfEJZizjn2pZn0YCO5rBH6OGHAdeWHGYK8SX/PMdpWBZjj8EJqL185Aqyac8tMwjOf9JnkPhp5IhPI2dtWq0On59c5vok5TjH12SvGp21bC16PetEhkdHerj3CEj13Od5e96coRyjsbaj83lWpV27M9yJ/mtGEjOlPHGBUwdfUyXN4A1fCyhDTkz0vhJec/BXylqO7Ru+VH2swQubFyA3gHVz3tTrSGh/HMyR7iyNa9n3So3uKUg7a5Zx7vihR4KpiZaREAP4dnPdnAu98X92YlRuUg4+8MLjBch3saN38Jzr+uuG/Q++SmmKe4mwdAAAAABJRU5ErkJggg==\n",
-      "text/latex": [
-       "$\\displaystyle f{\\left(t \\right)} = C_{1} e^{\\alpha t}$"
-      ],
-      "text/plain": [
-       "           α⋅t\n",
-       "f(t) = C₁⋅ℯ   "
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "solution_eq = dsolve(eq1)\n",
-    "solution_eq"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can tell it's a general solution because it contains an unspecified constant, `C1`.\n",
-    "\n",
-    "In this example, finding the particular solution is easy: we just replace `C1` with `p_0`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "C1, p_0 = symbols('C1 p_0')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle f{\\left(t \\right)} = p_{0} e^{\\alpha t}$"
-      ],
-      "text/plain": [
-       "           α⋅t\n",
-       "f(t) = p₀⋅ℯ   "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "particular = solution_eq.subs(C1, p_0)\n",
-    "particular"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the next example, we have to work a little harder to find the particular solution."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Solving the quadratic growth equation \n",
-    "\n",
-    "We'll use the (r, K) parameterization, so we'll need two more symbols:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r, K = symbols('r K')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can write the differential equation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPMAAAAyCAYAAACJQu0uAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAM/klEQVR4Ae2d/5XdtBLHb/ZsAQEqeI8OQqgA6CAhFSR0AIf/8l8OdACpICEdABWElw7gVUDYDsL3o9Xc4ytLtmT7ytZdzzle2fJImhnPL8nW3nsfPnw47NCmBJ4/f/6DKP9V5W9tcrAe1ZLZfY3+UuXj9ahYduSrZbvbe6slAW/I9++CIYvH/+p4liNb8MAfwxXOjXB+Uvm/MdxW7t8ricxi/IEYIxo81PFa19+0wugl0Sm58wxQ8KajiugnOsLLnzo+5Vx1f6k8gq4xTIzuq2PlyIlwfxHKd2FfsWbCwUl8o/Kz2P2W6ooisxh+pwOh8hB+bYnRS6FV8n8kXlDApxfA00vxQITEgOGJYBEChhkNGpIFmcmfOsJIjGxoNwpq+7OQ3qvEqTQNRcYMp2L6S8/xPk+r/Ogle5woBvBY5xhBs+B5wTERFNClr1T3psuQrjHwP1SeROsODrpIhnJyX9fI5jeV33Zwh07JcEjPoadZuJ5AOZH5Ly+wCc33JjMkQLRhenMJjtQFhQ4vMZ6+E7/oWwq49y5x84Xq/6/jx8T9YzW6rINojqM8cShHpAZOiiOzeOIhxATfALvtkihlI2ogexT8EuBzMXESUbtMiV+XOqtM4gg/qYtq59J3lc5pdPuOnQsPI2463b6OMWZ1YhCBojwI5m8dGDHzGrzeDnUlwJzuZ6+kdUdecDTRT+pMRMXIMB6yDcpwXozz6gUN4dEO3Ps60M8Hvo+3KsMoTHtS6F4/qosBuv6L+mHBbciJxNquXpc0ZjGD0BD0Z8aYSuY3QK5wbrH3v7MkILljAChu84s04oUFJ5wSHzi8UBkaoKodYPC9RSzho3vMh12mohK8FLBKHjqJFO5Bfb3RgRFj1Nntkh1WvnEVG08M4fUQZLi8D6PvdJ9IvUM9CaBcKHBz0SImIvGBYwKGggI47x1W/A9GnJovWwvao8slgMN85m2gpN3quFFjFlUwhBBeBxQSrYceQIC+X86VgJSKaQ2K3XxU7sjCvYISb0PG+LHwh4JGji7i/MxxdIYfPDWdJxtqClLG/LW4IBIchek9FYKxVLspRhsmlnSP1dZLcqKDi19jz+qcuuh1HifzZIyOrd3vGbMXFFE59Jp4woPuX5JSbe15xOhxjjV2o+E6InOoXyE771WBHsbgIZVdXdR5DJfoPmVq8krtWFgrjeqQtRr0jLlDydvOOadECPcAxOSj1hgNeGniUjJG6VHSS8uGMMZQv8JnghFijDE4mS+jj0KKGR51N7EORursXbMLYCO4m7ndM2YJBuYx2qNwVGerqeblPlednW+GmQskxJTpYrIh6Q3OiWMsMnM/9b007Z3++f74CizWH+2LZed1GzsYWinX7W1Bz5g9eV+oxGC/1cHCyx86HGPU6bxKpNBYi++WEe0tATJnvnxJjtOlyOILnRoCdMycWYjnFmglF4LM1ypTr7cYi5R5CkAfmVE1mKvvRbumluBKBONVeRjJnTKMA2MqzrZbhjG2DpLBP6KRb5ObihBDchUvPHvmo6M8CQcd4ZvtYmemNugPe73ZjVUMagedBLN7xY07DdS+mr6nInOHnMVPX6pHUhgeEJ415f14zx19cY+AdMzaLaO+Nw1eCVCEYkWGMbVnXeMf38+qvHpaeJ4A0dbOXcXAHwyKd+xTAN35aUpD3wZHchDtqewgt+tq+l7VmL1isVhBCsVcprdTBgkJDyOvsVuG4bYKRBbAKdXt6fBf5KvDfY4oTF6t4Ay2ABgltEEPJV+BjYLHY6plshhtA4LwGedLlan0O6cfmwakgs1oH56Oavp+PUrRsgjOy4lJW5SwMhwFbzyUhnEvtuBBPy90ZO2WAXnDYAqcHZkl1xvxw7fIB52ztoEibQGcMYsQyqHnGqPVImxJOyK/k0Osw8w6k/ukNN2PUVXfrzIZWwpt9GMBKaFTYpUmzNjYCCnqCNQOhWaLphNkrHEjdfZaZkgOTbCiZ8G32D/q4D96FPHj8WlHtjYKHq94nLBj9YMeAfYcbq/K/lbV9+sy2qZhewHjWTGwoZ0yDEA06Rmq+qAtXpoUCoNfcreMulsWPL3wDK1PdTzUYdHlb90fSwHhEzClur26g38lKxxASWq+pJTsOWT3KXpxPNX1vZYx5+6UQWAIobdAIgFh4IvvllG/pH44ihJgPo9jGQLWA9iowvrA7zrYIcQ1/0AOAx8z5k+Es8O6EsCRFkdmPeNV9L2KMfM8xKBLn3Xai7rc7wB47zvX4SnGnpovGy7tszyq6Jq6Wmpj9Ur1yaLJW38Dfthp9sZfM94Qfx7tSH8OrrXZy+UlkKVH4bBr6Pu1Bl3sH2err6F3cij4QThjhognxCOmgChqhpHCIS0z55HCOWc9c3YMGEWAjmMUV92YMzO6XEQQ/pAsDHd26Wklg8hVXuh6qnaDz1P3F9Ov2Uz6DkTTkJ52h8GRuufQrcw8r67vGHMuY5k8JNFGFwOSLf0N0WrGQeq6WRCdZoAufdd1rgGvxpOnOfX55GS61G8t/ZpM45kaVtf3amm2BIanGvTiXqh4w1R0YBHp0DUOnfPe0ozHd+G8adaqqdoyZy59hUPUfWyDDZQ5U4JUc+Rw0Dgx/lJt9vrlJeCew4Ruq+t7TWPGEF9kCAUjTKU2J8YhRccIwQ+dBKltaOCq6oP6YA67+LzZj8T2xaxV2D5lR/qRRRYvkT72qnkSQPZZQSEyTHV9v4oQsXgV0UWdcoRGFxsLnFS6Rx9OuL7PRXfLxIiZWif6cCjQO3VKwD9QBOhjh/UkUByZ19L3WpEZLwXYJ3K3V/G/KH/qm1pSYjZfuA8IVKZe7zDe03j31WoxZhbCps6XbzylqSwlyojGQ3a0cfN1lb+rDgfIpoOpWYKa30k4Bo9C7lfR98FdU3r4D8QEBgRxk39bSv3QR9ZOGeEdhM/3yKvslmH8LYBk8Eh08L4dOUx1CFtgpYgG8cpnqCwewT9ApoYzYuXcHNxB58imizO6si78bFD/GDK71vhyrWgaJvxV9H0wMosoBIky8XrhJF1UHczyAQT3e/MK1SHoJypZKCJKpKKtbvUAYSDA4yudHka6gjYlY6V7WveOyRSHepeM2WVb0ht0joXG1JTrle6jg7M/3VQfMSCzAmy6c3uV+LsFfR+dM4tIS9dChaKeOaspXcgmBlm8U4ZO1Cfp4Fq7ZSBhdZAMcKTAnA/9b3to7K94T+mc40T3bZoVDSQLsWvGnNLvcJjV9f06pChyzQpy7LelTlaWI+0cc6qnBLcULMKWtCX1ynllVErLWvgo0sO1Bl9xXHvmsWzwe9FV4xcnSPUBc6q3V+m/q+t7jjHjJcOoDEvUJ7/E8tEVvEmg9jgQt1smpy/h4K3PlXJN4mGBRsjdRaEF+mqpC3TroGd61DudM91g2lY0f53BtIvMGi8rMgtv1uIi4+iYpe8nC2DqDAYQFgsNzBUQJvNifkKUn+5AyERM5ipm5ODGfudH1TvMkYDkjSEz//9I58j5ToB4PZkveznwM0lT1lAmyUxjNfcvm66NUxGPcZKmJn9bSjgYd+7OJet6L6dLwF7l8WySWdD07rfX0ushhLmorGvSV1a4syIkDeeCxiSoEbBO0vy5/Z67/RUDiHgIx5DZotcVGuesKIZRYWy+rGY7zJWA5M58Ddk/mdtXQ+1tvsye7x90kCnagqi9ijo3OzZOUw7UGbMkY5P314GULJUOqo8pdli/Xy8vAZ6JKdfyvW+vR3QOuPGGzDn6CbD4VQNwKMxhu4GtxrizxjBj5hvirN+WEoNEcdKQplKQWVJatzEZ00FyNyVfl5rzj+7eq4vf44KSzjEq0m4+POL+2UD9o9/IurlvFa488TAQLsE75dF9N3fpSM+9KunW+z46KPvpUhLwckaZL+mVW1Q84tUcVixQ1IrO9vbg6EyixG6w0iIzpL0N6GPl0Bm4hNz9bamT+TL3hEek3uF8EkCRn0nWON1LBpsvhwHkIN6pw6mhi+eUA6k8b25uWhM0kRmiMdqjQaoO78S1zRm6vy2FIF29FypfaoVRXSg7LCUByZcowXOy6LRU11vrx60NDOiTpb5nmTtrXMZHv2u9y15U/u49s5iAgZc6iM78I7lXOkx5qGNF23lLlRg5QrW5XHPpiGhvDryiIfOLeufc0T30yubDrCKffLsgPF5PkS1a0EEfF/1ISGPwTUXOP2sU2vbg5KOR7ZG3U9SVQOvK1uVla+eSLVGZgPYfnRPImoOr5ii+2wSzCMbc2SLY3ZbGQtxLnpaZ8qVjk4aMKHZjXkghanQjRbNVbTfFqTHmHRkDebJfv7fw1hL/uzG39LREqxSO+STfBOwGvcCzkxzdqyiV1b77XoDsaBe7MUfFsu1KUzyVLArtMFECkh/vtVlEs1diE3vaRrPdmLfxHIqpkAIyf/7UK2Rx+7veQHJjnkw0/uJSZPEvMCgnET+4Ce4AAAAASUVORK5CYII=\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)} = r \\left(1 - \\frac{f{\\left(t \\right)}}{K}\\right) f{\\left(t \\right)}$"
-      ],
-      "text/plain": [
-       "d            ⎛    f(t)⎞     \n",
-       "──(f(t)) = r⋅⎜1 - ────⎟⋅f(t)\n",
-       "dt           ⎝     K  ⎠     "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eq2 = Eq(diff(f(t), t), r * f(t) * (1 - f(t)/K))\n",
-    "eq2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And solve it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle f{\\left(t \\right)} = \\frac{K e^{C_{1} K + r t}}{e^{C_{1} K + r t} - 1}$"
-      ],
-      "text/plain": [
-       "           C₁⋅K + r⋅t \n",
-       "        K⋅ℯ           \n",
-       "f(t) = ───────────────\n",
-       "        C₁⋅K + r⋅t    \n",
-       "       ℯ           - 1"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "solution_eq = dsolve(eq2)\n",
-    "solution_eq"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result, `solution_eq`, contains `rhs`, which is the right-hand side of the solution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{K e^{C_{1} K + r t}}{e^{C_{1} K + r t} - 1}$"
-      ],
-      "text/plain": [
-       "    C₁⋅K + r⋅t \n",
-       " K⋅ℯ           \n",
-       "───────────────\n",
-       " C₁⋅K + r⋅t    \n",
-       "ℯ           - 1"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "general = solution_eq.rhs\n",
-    "general"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can evaluate the right-hand side at $t=0$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{K e^{C_{1} K}}{e^{C_{1} K} - 1}$"
-      ],
-      "text/plain": [
-       "    C₁⋅K \n",
-       " K⋅ℯ     \n",
-       "─────────\n",
-       " C₁⋅K    \n",
-       "ℯ     - 1"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "at_0 = general.subs(t, 0)\n",
-    "at_0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we want to find the value of `C1` that makes `f(0) = p_0`.\n",
-    "\n",
-    "So we'll create the equation `at_0 = p_0` and solve for `C1`.  Because this is just an algebraic identity, not a differential equation, we use `solve`, not `dsolve`.\n",
-    "\n",
-    "The result from `solve` is a list of solutions.  In this case, [we have reason to expect only one solution](https://en.wikipedia.org/wiki/Picard%E2%80%93Lindel%C3%B6f_theorem), but we still get a list, so we have to use the bracket operator, `[0]`, to select the first one."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(list, 1)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "solutions = solve(Eq(at_0, p_0), C1)\n",
-    "type(solutions), len(solutions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{\\log{\\left(- \\frac{p_{0}}{K - p_{0}} \\right)}}{K}$"
-      ],
-      "text/plain": [
-       "   ⎛ -p₀  ⎞\n",
-       "log⎜──────⎟\n",
-       "   ⎝K - p₀⎠\n",
-       "───────────\n",
-       "     K     "
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "value_of_C1 = solutions[0]\n",
-    "value_of_C1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now in the general solution, we want to replace `C1` with the value of `C1` we just figured out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle - \\frac{K p_{0} e^{r t}}{\\left(K - p_{0}\\right) \\left(- \\frac{p_{0} e^{r t}}{K - p_{0}} - 1\\right)}$"
-      ],
-      "text/plain": [
-       "             r⋅t        \n",
-       "      -K⋅p₀⋅ℯ           \n",
-       "────────────────────────\n",
-       "         ⎛      r⋅t    ⎞\n",
-       "         ⎜  p₀⋅ℯ       ⎟\n",
-       "(K - p₀)⋅⎜- ─────── - 1⎟\n",
-       "         ⎝   K - p₀    ⎠"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "particular = general.subs(C1, value_of_C1)\n",
-    "particular"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is complicated, but SymPy provides a method that tries to simplify it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{K p_{0} e^{r t}}{K + p_{0} e^{r t} - p_{0}}$"
-      ],
-      "text/plain": [
-       "         r⋅t    \n",
-       "   K⋅p₀⋅ℯ       \n",
-       "────────────────\n",
-       "        r⋅t     \n",
-       "K + p₀⋅ℯ    - p₀"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "particular = simplify(particular)\n",
-    "particular"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Often simplicity is in the eye of the beholder, but that's about as simple as this expression gets.\n",
-    "\n",
-    "Just to double-check, we can evaluate it at `t=0` and confirm that we get `p_0`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle p_{0}$"
-      ],
-      "text/plain": [
-       "p₀"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "particular.subs(t, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This solution is called the [logistic function](https://en.wikipedia.org/wiki/Population_growth#Logistic_equation).\n",
-    "\n",
-    "In some places you'll see it written in a different form:\n",
-    "\n",
-    "$f(t) = \\frac{K}{1 + A e^{-rt}}$\n",
-    "\n",
-    "where $A = (K - p_0) / p_0$.\n",
-    "\n",
-    "We can use SymPy to confirm that these two forms are equivalent.  First we represent the alternative version of the logistic function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{K - p_{0}}{p_{0}}$"
-      ],
-      "text/plain": [
-       "K - p₀\n",
-       "──────\n",
-       "  p₀  "
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A = (K - p_0) / p_0\n",
-    "A"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIQAAAA5CAYAAAD3EIMMAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAHQ0lEQVR4Ae2c7XHcNhCGKU8KcNRB5A7kuILIHdhRBXI6sCe/pH+euAM5FSROB7IrkOMO4g7sUQfK+0BYDO4I6Hh3xB0pYGcgfBJc7L5YAMSeDm5vb7va6OLi4rXG/EzhhR/7F8VfFc5Ud+PLOqU/KB23oZ62D5YOagSEaVPKZTZ8UfzUyuJY5YDhN4LSAObB0w8PfoSZAUrBJ77qY6qJ6l+p/Lvi56n6h1pWLSCkUFP0VaxcAeCx8r8rXCpdhVWIx18zIJyFkNKDhVD6WMI5VfwmFlJN6ZoBgfLDBlEgYIl4qpg9Q7VUJSCkdGcdpHVnHZT/Q2lOHtUtEcvIf7RcUEne9g/fAIMCS8R7hSOl7ZhZiSgWh1krIMxC3HgwIBWsBMSGslqqFRDsHz4KDFgFR0qzXLCEHCtNfZVUHSCkbLMOC8dNr/3qrUR1gJDibf8QjptmCgQWyrAUL5Tme0R1VCMg3KZRCg9HziWtX/p8lXuJKu4y/Gz/U4o+UrD9wT9KX6vunQdApzRHT75D0A7CYlRzj8GAqwAEA200TAI1LhnDJFNpqwaIShWfG3YDRE4ylZY3QFSq+NywGyBykqm0vAGiUsXnht0AkZNMpeUH5+fn9bldV6rsIcNuH6aGSKmiNm3JqEjZQ4Y6O0DovuFoyMCm2Ea82z3KFNlzPM3Kp1ICNXe3IFCV4d+ACxwxl1G4z3Nx5Ujpf5VwDjGKXyoffpl112Knf3HRO1ToXb3vkgu9n0mFLF8uv3c2FkLM87M6u5oO4/DCxdkFd7jnCjEYGDj+DU983T7B0HneAOW+rRyT51qhR7OwEBIgM/yr4pxXNL/T/ByPTm3xe2BG9mZB3G4PaawZV/FF+NJ43c8J1D+WMTj5qNxd8yvGKwzZ4EL4SiG4Eaqsm4uFwFnlLxjOEIgPLnEaJH4NWIzg65B5bufF4gkrBVBLWYnv6v8/BX5jwvixmGEiqcz9CEkxfh4LYFC7bhYWQnzi+Jr0cFI5s4DAj3aJWVo+KD3qOq3+4pmnV7h3Ypneqi7JG40yBLgBcVCI5x3gf7NnVGazmnGZv6dVx/GV2rqlklgBGbylgdKAIQYEfd1Ql6LJA0IDWjWTEKwRAj1UwBwHYVvlCDGCxhKxV8HkspRR9kShUx5hwwMzlDLAkhI+CjpVcDz65z4p/wvtfZ5lxZHvY51flJ3omdyShLzc8qo2s1wyAERA+J2IFv4ySyHMMOaQgEBQ1iikvuDhbwX6ZAaa9UHZMWDdzFQ9imaGkk8RzwEeI5TPGFEQluhXxTmF2jPJWM/Rr/GXaoM146TDstprN3kL4UeEAHME4t9rgDbbmLkIl5maFKrq2VQNmXHumOr76xQDvmsFIwAS8wYQXZ5Y4WeFx1ZmDylmnT+M8oyBd/UUFLUZlPTvSo6bDlSPbLL1PUDoAYQFYn/ynSu5V0J4oD5HKMWtl1EDrAT7CKxGz7qojPXWrbnRM0OSKC5+F2Yfy9HxLkUxOCiGd8qZlTEBhpgvxkfbQOovBaRQXyrhAMHL9QJAAFMwe58CVL2a1Kcd+9zGaPUT2RYILjbLoaHeARigBYGrHLOOcgDGEEtAH/eS+oMH5EK/nfK8m5D87zO0uYfoKwYEliEAR33zHvSRncmqK0KP6FUMYN4wWQgvNolFXrpOp/BGe8UIKZDy7LptjX6jPAJ1pDRghFiTL++SW/9F+QCPH/GYuz5HO+MPBS/wqPyhQqx4ZR0BoiufJkLx/F+K175v9hFnVOyaekvGrhkY+D6UirkOZl6CY/YTeqS6TZeEXl9RAb/4Yn9yn8Wj3pl6YrX/rNgBJuqHJHuLYLl8m51bgyWeXNZZiFTFlMokMJQQBLgn3gDkKuuJUrkjYIazqe3xrDqs11hWS12NS3OxEIyaS6veuXlccaR703tRIEsSZh0rkJr1nS/vgcB6VT1Wg28Y2TbWdl/xbAAhIbJRZP1OnhxKCtArcAwlYjmSy1xJ/tfpu+cxpcGzYWLD9qPSyZkQv0BtaIs5XSZmQ25TxUXVyjVTbZp737JUC+Ul6wO63tpCqKMk4lW+9bHTmCwkg9ZtQgKz2FQm+G5FhSTQAFFIsHPttgFirporxHfYQ2i95mjFJtA2iJ9Uxlc27tpLXCUXGlLrdhsJxIAY41i1DS+zeVYThOMjn59xU4M4UT1T2MRZhucnQwEQBTjiwgaL81CJexTuI7Z1lpmUfIoBQrOIbxgrv2NMShoDmNG4+GLJlTdWYpWzjPlTYEEAkP0HPCWnSW1TuaZeBAg+qgF0loj4buNY+XgCnPh2nY+ds8yar9t58waIzUXO5jv2cDpVHssBALAiMTgoxteE8klTsSVj0qPekjmvcJYBp3TlsQ6ETZxltuRm3MebhdhMnih/LGeZzTgo9FSzEJsJdkxnmc04KPRUA8RmgmX/kLzUi7ozZxm+VfAbjVl85+ldf0cDasmEBLRf4IuuO3IqPlN+efOYeGo+Rf8Dk2x+5YE1OQQAAAAASUVORK5CYII=\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{K}{1 + \\frac{\\left(K - p_{0}\\right) e^{- r t}}{p_{0}}}$"
-      ],
-      "text/plain": [
-       "        K         \n",
-       "──────────────────\n",
-       "              -r⋅t\n",
-       "    (K - p₀)⋅ℯ    \n",
-       "1 + ──────────────\n",
-       "          p₀      "
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "logistic = K / (1 + A * exp(-r*t))\n",
-    "logistic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To see whether two expressions are equivalent, we can check whether their difference simplifies to 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOCAYAAAAWo42rAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVQoFW2S3Q2CQBCEkVgA0Q7ODvypQEpQO9AWjG+8GVuwA4MdCBWY0IF2ILED/AZvyYWwyTKze7M75GDUNE2kyLIsAS7kl3yRM/JGvwKjWA8fJfjg4Ehe4Wcyhzudt0KKA9yBdzUVcG1Wnau2jVt4oUYvntRzhhITpj2BldqqSGOp/zyqPQ6B08aJP7HpUGjDU7PWoW0OhR2X0Ka6ZkDM7aN3NEtrBrrO5W3WupohaxsuTKhLXYarPF+AlVxbIUSfrAY3JobLYUfu1Rvr4UPTJwTO1ytwTd3+FD9d+zu+IBCKGAAAAABJRU5ErkJggg==\n",
-      "text/latex": [
-       "$\\displaystyle 0$"
-      ],
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "simplify(particular - logistic)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This test only works one way: if SymPy says the difference reduces to 0, the expressions are definitely equivalent (and not just numerically close).\n",
-    "\n",
-    "But if SymPy can't find a way to simplify the result to 0, that doesn't necessarily mean there isn't one.  Testing whether two expressions are equivalent is a surprisingly hard problem; in fact, there is no algorithm that can solve it in general."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Solve the quadratic growth equation using the alternative parameterization\n",
-    "\n",
-    "$\\frac{df(t)}{dt} = \\alpha f(t) + \\beta f^2(t) $"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "alpha, beta = symbols('alpha beta')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{d}{d t} f{\\left(t \\right)} = \\alpha f{\\left(t \\right)} + \\beta f^{2}{\\left(t \\right)}$"
-      ],
-      "text/plain": [
-       "d                      2   \n",
-       "──(f(t)) = α⋅f(t) + β⋅f (t)\n",
-       "dt                         "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "eq3 = Eq(diff(f(t), t), alpha*f(t) + beta*f(t)**2)\n",
-    "eq3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAANIAAAA5CAYAAABDGnUDAAAACXBIWXMAAA7EAAAOxAGVKw4bAAANa0lEQVR4Ae2d65UctRKAhz0EYOwIrsnA+EaAycDgCNZkAMe/7H97IAMgggUyACKwLxkAEbBnM/D9Pm2rLWla/Zid567qHI1epVd1lapK6plZvX//ftVCo0HKA69fv36c5o8xzRyfHNO8Pl41aBRIKPDmzZvvCN8mRX2S8pdkviRcE666+ILY8l+p/5t4X/CY8R4Sft/XgGPjfKRUN2gUkAIw5S9E3xJnAkH+MeXWXZL+njgA6QckLH9K+pOb0t180v//6Pmc+M84AukfSCv42Xxj/T7js30O1sY6XgrAjE+Y3d8lU5JXiP4iKGC9ELkS8momBemd+U2APmw/CuAosGF+BaKa87ui7CDZJkgHIftRDvqKWV0OzOw3yr6HmWsmlEIkzqagkEzBMxAUcgW3hy6viaewHxSaj3RQ8u9+cJjMHftfwiPCA/JfOyqx6ZQxn5DvzaYO5xtimVQ/qAaaVT/WKm9TznwcW62jIF2T15T7oZinwm/9TuZAv7OgCdIsMp0mEgynSfY1cdAmxL8QviFooqmBwqEC+dqOrtD9Tn0qcBR9gKE6yjTX9Geq7T70UE/RXiF1/vpHF8S/DmCL84LQBGmAOK3olhSA6dy9NYdSk0wTTMZU86TmmII05LBbnrYnWwf6VTN8QTB+SLiVINE+gv5Rpi1jBbFjPEjyB0k2jXQQsu9l0K8Y5bwYSWFROL6E6YOJl9QPMb34Q+WhGX3Y1zPioA2IFTo12POAUHxQrqayTQr6OGqcFPrTQeoUSs26IUG3zRVBoT0oNEE6KPl3Ori7dLmLy3SWB5MuGT2WJ0UhqWCoDWqgdiv7quGuwPUOKgPKfiOoxWrg+EErgufcvTtKhUohSvO1fnZa3gRpp+Sd3znMEQ8FbPQpQX+malZ1TKWf40GCYJv0TkUhcvcPTNbh60usSLvDp4cN4pSaQjwF5S/CS0Lvg5CWoR1b83HX4LredoO8YuxScPs17noiY/03QRqjzh7qYAwZIfou6c77D3WfEfRzxPFULTjbxJo7MvEX1hOviMXRXIuM5u7v3Y87+jVBwTFvrNDKnLE/BWvV1YnbA2WfEhRQzTI1l/UKr85/hkvZLsBxFWhPED2hK+EzCsQ5KDRBOij5w+D6Bz/CKL32kUEJ3s8oFAqHPkf0QxQYGcdTsShED8iL22uIrq70g1aUr5XRTrCtArp2MkabKJzi7RUYW7r0tBkY3LcqamsaQN9NUROk3dB1Vq8wgMyrEFwMNFBInnblj8CNu7/aRNBXcJcWrFPbRJxQuOSDtl66qhnXBGluP7RX+ymMCrvz0f/ZuL+pcenbDabfPKbwd1nfBGmX1J3u25O12j2NQuGJloKTCprM4wuivc9CflvgZWfmDy3pmLb6ZYbsVaKJPjbSJoz1gH41bTdqPzGnxdVni1u0BluhQMcIMoOMVwPr34KrUKUQTLq0YBtpxlF7XBGrUfYCjLXpWnzj/GAmZ0mcJkglRfaUT4TDtw+q0DF3Wl8KVV8HbjT1+rKlCccjbMrcS4fbGJ85aopWabFxxxs2bKbdhoTbUjPNM+9QMjMNBnG3VRsFIK/vEV/a1Mx7cVNz80m9uJqA0X9Kq1t6DxS49feReIiaAf3t9ticOwbRJzj6HW9sHdusgxaR+XvNRFk8odORdtf9l7Le7yCt5vF+xTZhV45tyDc4AAWqgsSDibucD6u87AtTBUch0kEdu5nOlgWuR7ee6DRhyijTMqdMgTHT7icWJrMb3DU9Gi2ZX6FYe+2DshWCoiB6R9JfGloO+P7XHwQv0ho0CtwJCgweNnRC4DGrwuNlmMKQ3QeQ145/R1wKF8UBwn1CWU9eU0Tz7taOcTdOixoFDk6BmkZSCFYwe7xRjnE6YY8ex0w662pHuzrM/xB6u590g0aBk6XAoEZiNf8l1DSNAqZvZFzFoVphHBJA26mVPIUKAku6QaPASVMg00gwtuaamkQG92JOH8i4vD3W7FsTkk4wxNU/Uth80dI+vFQstY/t9a/W+qGsQaPASVGgFCSPXX2B0t/o8u3ekvnj4hQ2BSQD8BWK+MUuj8THTD9PA0sBzfpLM/TlgcdSDaYPN3uMdLyWbhRYQoFMkGwI4wWzjeSYphDnSvwKjPlHsYnt1VyzgHkdzesgsybckO4VBc4GVustugJVOyiw+iFBP6cGao4xQbSd/lUUWvMNGgVOlgJrGomVjB40TK0UAYz+kUfnJwHMuf3c7Ek8qeOd5JAgqZHGtJGrGTPLnooAc/YaSeEilBpMrTZ26mc3PdBeH8lDjiXwJ+0GL4zTTsD5KM23dKPAUgoMCZKCcDHRkQKgIAxB5h/BpDK/+KVwataVwjXUXyijH32k5idVKdQqDkmBzEeCWTXLDCXTl3O0vvaKj+2Dpun688tpQ/3Zvtda5QB3Jc/avQLQZ7y3wPqP3hf2OdUeEHWTb+GUGimYZXT4rtZpV67/U/uKryZY+KaluEyidoTuWOfi3FXoHo4/HDJ2DXBXlx/Wxdpd/6AlQbn3lpreWiZXXaw1ZPm+vxflhu/X94c2d10Ef9Wp6iZkb3+DqBC4g04+eHC8BypfSKVoGmjrDuX3+X2r/M4C6/Ol3c+JZZR7B6zbu8a1N/0p9/lbd0m632hJa81YfnR/E8PclA1/miz77hhlAc6oeE6Il6uaIDHdoVQjOx7caaotPlR4SVrTaB+wjiAFbdypXnZhtokCvuuTUe6rEGkq+RpYdqBEXhq6CStgvRCRX5GXVvLflEUk+iDQxyT/gqPAhvkVncjP8vUa0MY652zbNTijxIaeqolgPChxZcsOTyabzVz2Ab7jPCPOiFj2fwx55qhtrIaWJj8TNFmrtnScMzjSRPNkFi1juzsWv2I9lwNr0i041b+JcXMcFDR9pCBIXTxp0oGXQtQsS9q5Y1RtzbTzQ6YRBgXB153CQQmxvzUnrQxT65Uuvmp1TVwF6j3R/Inwnyncaid7ruho4A9EPiK48brWFbHpdL1uQNkhE3k3JjeZC9tUQA22kw2I8R1bzaLl5fNUMNwc03ledvVDc/CrRIOvtn1MJ0MNwJ8G2qq6/RVMTZ/JfsSjV/EzdT890n4xmJ8EXxGnBLZIbRrqzIyA6zwfqqdP+1B4rggPCeZPAph7YCLi4JAT64Dfm7+JYa3BVCVe4/fy1G7xA7VzGk0KkR2DOwtv8SS230C/ccj0fMFQpXBlo9NO00/hGDr9kQbX1AWNTNodWq109MBc3b1lpHRdmmlujNLEdAQ3m6HN0vK0fcQfjOlXzaH2N3bTkXbbAJ9R7Tk6hs+vBs7f55fx8q0FqTbaXSvvHqoPtHZ/Fpfssb5mw7Yeeuz30PFXTKDUsgqLwnGf/iZGrSwtMmiClJEjaE2FRd9IBtGelnBqInepOb6Mgja0G1N8vMB6XZ8HBPo/QvmDN9aXu/gVZZZLpxRieVpm2t1cbVADtVvZVw13Be6ar03Zrv8mRhroD2Y+4Vl1lve3IjrJMsM1Qafa41hNC4VrCsSx7ckATOHm4Z2Xjrcnapq1HqqEgwRiQQbq1y8jkXeDWZFWA5uPEDVVzIcYHPsL1wlphW0Jjqf5uGtwg3jbDfKKcctNzzWWZemc4rPtaWFl00gpiW7SCs4KAitE/e5IXs3kIcGUaafAjT0Iqo8HWJcM4UnqOekwb2KFwrWnjO3u7z2KGkXayPzhXoVYIZA5PdVakVewjMURtwfyp/43MXE9PucemiD1pOgT0bTpC7qEO7IMMwUyYdy1pnAX1cuYNPiDYDwHfOgKiHOvQVzTQ/A8/BBsp5AYByCtkKUaKpavld20CEKopgvC1ZXFNv0GlZbvI806NC/HDjzm/k1M9gyaICVPDyL74GtEdufuGStpVibFyXarEmHTfMfYUxpxafeeGvpeW3YKtbSTEp/+7vrfxGS80AQp5wDfHaztlh7D1oQs7+X0crsyRfW5Xm4qpLRTkxqGriJqVK5pyBp+KGcsNYzPf6q9eEJGs7ObsvbZUUCNtAYQV21kXU3I0jYSOBI7LT/WdLazppNk3dHUS4tnp2mvWeevUEm/vQBjZQy+YFAv0ec832htZOZ700g5pX3o2Q5KXuc6vNY08yHpdHuMfCpwwURfpJNlnW4E+k7Rf0qrF6Xpa81HWtTBnpCZ51ytF0x88LMNKPsaxZ7mfJTDQBg1jruMF6oekcb3yUjO/+Phrh9v+T8piW1HAuWehrmzOaZMq/niTuodyFZ9FfqcBMZU87hmTyYDgxxiHpMTPQKE7tl5hK+p30PTSD0pgn2sapepNwYI7GWuzKhADvpU1E/Z4RuPv0lD5jN3N96k+7vWxud6WS6q+UglRbaT/5lush1rO922Xg5JATYcrQdN/TWroQkSVOkIVLs/2uTZabrpvDa4WxTwHTuvCoL5my6tCdINNapmWEqsuWkIrXkYf7p5brOGd/wU0PT3cGYNmo8ESWD8QV9mjVrLCnxT2rcQTuLUatnS7h82PKKFoTYa9KHbqd0OeQKiP6f7OZd8O5xF6/q2FOA5euTt5XLV722m3W2pPNIewquNPNL2mLvB6VJAk27tKxvpcppGSqnR0o0CG1Lg/xiUgrleV3h5AAAAAElFTkSuQmCC\n",
-      "text/latex": [
-       "$\\displaystyle f{\\left(t \\right)} = \\frac{\\alpha e^{\\alpha \\left(C_{1} + t\\right)}}{\\beta \\left(1 - e^{\\alpha \\left(C_{1} + t\\right)}\\right)}$"
-      ],
-      "text/plain": [
-       "             α⋅(C₁ + t)   \n",
-       "          α⋅ℯ             \n",
-       "f(t) = ───────────────────\n",
-       "         ⎛     α⋅(C₁ + t)⎞\n",
-       "       β⋅⎝1 - ℯ          ⎠"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "solution_eq = dsolve(eq3)\n",
-    "solution_eq"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{\\alpha e^{\\alpha \\left(C_{1} + t\\right)}}{\\beta \\left(1 - e^{\\alpha \\left(C_{1} + t\\right)}\\right)}$"
-      ],
-      "text/plain": [
-       "      α⋅(C₁ + t)   \n",
-       "   α⋅ℯ             \n",
-       "───────────────────\n",
-       "  ⎛     α⋅(C₁ + t)⎞\n",
-       "β⋅⎝1 - ℯ          ⎠"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "general = solution_eq.rhs\n",
-    "general"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "at_0 = general.subs(t, 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAG8AAAA8CAYAAABo3+Q5AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAJEElEQVR4Ae2c6ZHUOhCAZ7cIYOuRAWTAEQFLBhwRABksxS/4R0EGQAQcGUAGHBnAi4AjA973ad1+sj32eMbjtWdqukqrwy2p1ZdalmeP/v79u5g7PHv27KE0kr+eO62b0sfaTuj7hvxu3zGO+yJOhcdizpj70T4LTt6yvj9kr8i/Wu8DsxYeCzllEU9It/osZtdxWO8n1vCW/H2ftRzN1W2yAN3Iv6RblL/1Wcy+4LBerU8r7NwmLs14wW+g7dNYgmNc3bFwlfR1FaMS5sX9cd/7Dk3vSLrTpTBLtwnB16D2DunxUqoHNjL+C4Z4Tf6S9IjyC3ItfRYALT8g5ANJBW6FWQoPaiX6Q7GIVuI3ecCYRq66pFaN3mTcEfo8Z8w70NmqVLMTHsRegWgtbxSrY9yrzKFmJ6CshT8nn5Uwocd93pSOSYnY2p857nkK7UfO4BrNG1cZU8X4TG4Uq+t8S7pN/TZ5CdRl2HVShO1q/02SQr7I4OkVc0rnS1ID5ii8e1CpyxgDtOhvCEDLUzgLypdJuif3mBwM1z+SFK6Bk31tM8BZUFegHmO+k2wbw3o9Opww1ynJcgWOabxGek8y4lLKkwHzaxkypUHoloi6yRylyyzG/EleWl5BwzvaFJb7btCiW5W+AAWpwAznVbZeZ7Po3CcvaHXekr68n8LTDTwgSVhOXI53UWXd2aKgaYw5FVQdtBoZlECGkazrJj+ft6a/CrPEo6w1pHqR3yA/yfC3VVR5El/qAx7bUEz+pf5wgrqurG4ZWyGjg7G6afe+OsiwsDqf3SdpkfJLJc8FafMv0hjKrwKpOA1Iwmu0Ttcgw0YRHuPeIKX9KpaHEAwIPO9VgpBCOFpRWJbMM40VATN0KyR+FDRVkC5VatNX1Nxc27dJkcz3MB5vVi5T/0i9Hqg4p7gK1ECGLAn9OuUQpq5V4ebwD5UxFC/GlKYop3lXCq8g0qgq9otYdIXJ4Gk1vq1wAhdmmB0LbITjPGsDo7dRABqlbWnYXZvQAMEIswvX50aCf8zB/2K5Ns42qiEwlaMCnW4TYpS2QvCNhK+STLqOx+ShwQvKCs6w+oHPSQpRPBcmA3zWCeCFoMdgQOfcSx66njxYWYKy8P2jluyZUOV2zVsHxg9+BH/KOVZZnuHvstdUCsajRbw4ThaXTeQEuh3x0jtEG1ZAQ7NW4Pd+DF0qYWVfa+sMrvug7vt+sb5gXqVLsdZRBFaZ6P+KHq8CrcIrFuwiGhrIMw+6DnSfJFN+kYYyPzTLsVYC86/9CUBB88qxCwRfm8We17fPRnjQddSjY/CnRG0VHhgKTliqeeeP0sZu0cP9Q4jwwK9gneiU5PlxXeilBD0XvO7cO4XfJbzYKBsSz1YYODbpInU1Ck0TX/cStUtJGG56YG2xz3vkuJA7wMIQXHyDP63Co5MWZAcjr0o4TbsCEtwThbSn0F6JQM8f9f4b7rJLWXoPtm1E1qZ3Kd9fUv9N6rws3RIN4YkaUXg92qwz7hYE3INIhZODCzHyDGEpZCMvXxnpOk3hdvN+rWXwHUNobMznzdv/y5yhfJ2DgzflHWDIJJS7pPUShCkYQ91kTcWCDPc9iGp913lmPZirUKyH4BaWSTRXjwS02Uft7BuVib+W0J10AARjVg3hHWD5PQnli7wDDMsL/pe0Krxv1DyzLAWeu691Mh8cz3G6lOReyWWKk6oY6a0GbV0HXtAS+H51beExtp7AlwharQfnRK90kBqLBqc30F96Wu8AeT723V/wQ95UoHXPq2B1VCBeLVyQl/siZRlm0noVosrRR3gqkszoDYzvXvCIPHkCcq+3zkjOp0cxkBoCKqAeSCXWCy0o1+8Adb9j3f2lWw/mbCjhscQMBJl2hcFd5DJQcB58+4BnSq3lpA8yeI6rgpQunLpMjKh35ZudHvO03gEyr1bxjuTax7r70zjy9VE9h21Ynq/A1Mgn5B7a83egCsH9UYvqA0Gk1zTlHtPRUbz6WVILkal3mbd095S1jnBBFBOodL7+y+EtbbmXiPXkOGEN6agE/k0eqngBCjO3lFNwUt2clO7+oi061XOeyz9pdltowGDhOWJBxFD3lMZhLAXo8aSP8FxcXTF+0WZ7hR7GbezrtHmr4FxLgWeOswxUGiPxgFMKz6NCrhJrkQvGkPl/LGcgjbbXac9QUtFxhVDq81rx97hSm0dFCwmiV1Hk4mVCgoLZMk6mqeFtzD/vsPrvDVC0shIYU1dd3gFSd37nSQKirtWZKspDfRNQsWK/bfTfiuU1Rh3WoMZ6i+F7xTIIahlSa9Ith5tyv7RurqvRla0ao2Xo1Oy4KVoukIxm63eA4qhE8R5UYW/r7k8Lb1cCf6swt/T06dMz0tex6WKOj11zSEfXc5+B84r0ogvPeUgnBf6J9S78Ak8e/O7Cm6PbXGA1BgxdESyPZwO6+DxYWUaYHkILXufub+UxZ45uMxavu3hDSmeraNxyXkaj9XFhdLjD+qOyDo77n3ueRxPfMtUDk4RbtLfOVQ5YFMA/o+gRqDNom+1PvFwHxBvG10N3H+0tsGaVwXW7b6ajSNtiZ+k2M2INx317ohXsPbBOo1ZfLPg5SafgZMashccCdEOGy77yUiP3HTwmuTf2ipBnLTwlxULUQAWoVu4tsE7Xp+A697mcAbPe83JCD+UmB2ZveU2SDy3BgSMOgmt/hRWdD/m0HDi4zWn5P2j2g9scxL5pOx+ENy3/B81+EN4g9k3b+SC8afk/aPaD8Aaxb9rOB+FNy/9Bsx+EN4h903Y+CG9a/g+a/SC8QeybtvOcb9I35gxv5uPzd8fwgyCvlJZ+PrfxJDPouFevxxDQFXjqZWb++btXLf7T1XQzXeD4K6Zed2b0my3sm9v084H8s/MFQvJC9wspPqFr/Xx8tlJqIWxvhIeQ/BhIK8u/XI5le6HrB7SCPxJRoDsPeyM8JOEHqm1fcCksPyV0L1wmXJp3D/ZCeAhFizN1ffvvc39npyD3AvZFeCGQxu+2cykhuJ0PUvL17IXwigX54U7jFz8IzK+UtboE1I00y3q072K+b0eF+B1baYEIKn2NRW5Ao4X+pJz//m4X5ZZo/g+Y8ZVSGQ+ydQAAAABJRU5ErkJggg==\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{\\log{\\left(\\frac{\\beta p_{0}}{\\alpha + \\beta p_{0}} \\right)}}{\\alpha}$"
-      ],
-      "text/plain": [
-       "   ⎛  β⋅p₀  ⎞\n",
-       "log⎜────────⎟\n",
-       "   ⎝α + β⋅p₀⎠\n",
-       "─────────────\n",
-       "      α      "
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "solutions = solve(Eq(at_0, p_0), C1)\n",
-    "value_of_C1 = solutions[0]\n",
-    "value_of_C1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{\\alpha p_{0} e^{\\alpha t}}{\\alpha - \\beta p_{0} e^{\\alpha t} + \\beta p_{0}}$"
-      ],
-      "text/plain": [
-       "           α⋅t      \n",
-       "     α⋅p₀⋅ℯ         \n",
-       "────────────────────\n",
-       "          α⋅t       \n",
-       "α - β⋅p₀⋅ℯ    + β⋅p₀"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "particular = general.subs(C1, value_of_C1)\n",
-    "particular.simplify()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Use [WolframAlpha](https://www.wolframalpha.com/) to solve the quadratic growth model, using either or both forms of parameterization:\n",
-    "\n",
-    "    df(t) / dt = alpha f(t) + beta f(t)^2\n",
-    "\n",
-    "or\n",
-    "\n",
-    "    df(t) / dt = r f(t) (1 - f(t)/K)\n",
-    "\n",
-    "Find the general solution and also the particular solution where `f(0) = p_0`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap10soln.ipynb b/soln/chap10soln.ipynb
deleted file mode 100644
index 912b67ac..00000000
--- a/soln/chap10soln.ipynb
+++ /dev/null
@@ -1,1208 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 10\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *\n",
-    "\n",
-    "from pandas import read_html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "To get a `DataFrame` and a `Series`, I'll read the world population data and select a column.\n",
-    "\n",
-    "`DataFrame` and `Series` contain a variable called `shape` that indicates the number of rows and columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(67, 11)"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filename = 'data/World_population_estimates.html'\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "table2 = tables[2]\n",
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']\n",
-    "table2.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(67,)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "census = table2.census / 1e9\n",
-    "census.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(67,)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "un.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `DataFrame` contains `index`, which labels the rows.  It is an `Int64Index`, which is similar to a NumPy array."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Int64Index([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960,\n",
-       "            1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971,\n",
-       "            1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982,\n",
-       "            1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993,\n",
-       "            1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,\n",
-       "            2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,\n",
-       "            2016],\n",
-       "           dtype='int64', name='Year')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table2.index"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And `columns`, which labels the columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['census', 'prb', 'un', 'maddison', 'hyde', 'tanton', 'biraben', 'mj',\n",
-       "       'thomlinson', 'durand', 'clark'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table2.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And `values`, which is an array of values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
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-       "        nan, nan, nan],\n",
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-       "        nan, nan, nan, nan, nan],\n",
-       "       [6395699509, 6396000000.0, 6435705595.0, 6374056000.0, nan, nan,\n",
-       "        nan, nan, nan, nan, nan],\n",
-       "       [6473044732, 6477000000.0, 6514094605.0, 6462987000.0, nan, nan,\n",
-       "        nan, nan, nan, nan, nan],\n",
-       "       [6551263534, 6555000000.0, 6593227977.0, 6540214000.0, nan, nan,\n",
-       "        nan, nan, nan, nan, nan],\n",
-       "       [6629913759, 6625000000.0, 6673105937.0, 6616689000.0, nan, nan,\n",
-       "        nan, nan, nan, nan, nan],\n",
-       "       [6709049780, 6705000000.0, 6753649228.0, 6694832000.0, nan, nan,\n",
-       "        nan, nan, nan, nan, nan],\n",
-       "       [6788214394, 6809972000.0, 6834721933.0, 6764086000.0, nan, nan,\n",
-       "        nan, nan, nan, nan, nan],\n",
-       "       [6858584755, 6892319000.0, 6916183482.0, nan, nan, nan, nan, nan,\n",
-       "        nan, nan, nan],\n",
-       "       [6935999491, 6986951000.0, 6997998760.0, nan, nan, nan, nan, nan,\n",
-       "        nan, nan, nan],\n",
-       "       [7013871313, 7057075000.0, 7080072417.0, nan, nan, nan, nan, nan,\n",
-       "        nan, nan, nan],\n",
-       "       [7092128094, 7136796000.0, 7162119434.0, nan, nan, nan, nan, nan,\n",
-       "        nan, nan, nan],\n",
-       "       [7169968185, 7238184000.0, 7243784000.0, nan, nan, nan, nan, nan,\n",
-       "        nan, nan, nan],\n",
-       "       [7247892788, 7336435000.0, 7349472000.0, nan, nan, nan, nan, nan,\n",
-       "        nan, nan, nan],\n",
-       "       [7325996709, 7418151841.0, nan, nan, nan, nan, nan, nan, nan, nan,\n",
-       "        nan]], dtype=object)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table2.values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `Series` does not have `columns`, but it does have `name`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'census'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "census.name"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It contains `values`, which is an array."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2.55762865, 2.59493988, 2.63677231, 2.68205339, 2.7302281 ,\n",
-       "       2.78209894, 2.83529967, 2.89134972, 2.94813725, 3.00071659,\n",
-       "       3.04300151, 3.08396693, 3.14009322, 3.20982788, 3.28120131,\n",
-       "       3.35042579, 3.42067792, 3.49033371, 3.56231382, 3.63715905,\n",
-       "       3.71269774, 3.79032695, 3.86656865, 3.94209644, 4.01660881,\n",
-       "       4.08908323, 4.16018501, 4.23208458, 4.30410575, 4.37901394,\n",
-       "       4.45136274, 4.53441012, 4.61456656, 4.69573674, 4.77456939,\n",
-       "       4.8564627 , 4.94057123, 5.02720049, 5.11455717, 5.20144011,\n",
-       "       5.28895593, 5.37158592, 5.45613628, 5.53826832, 5.61868213,\n",
-       "       5.69920299, 5.77944059, 5.85797254, 5.93521325, 6.01207492,\n",
-       "       6.08857138, 6.16521925, 6.24201635, 6.31859096, 6.39569951,\n",
-       "       6.47304473, 6.55126353, 6.62991376, 6.70904978, 6.78821439,\n",
-       "       6.85858475, 6.93599949, 7.01387131, 7.09212809, 7.16996819,\n",
-       "       7.24789279, 7.32599671])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "census.values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And it contains `index`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Int64Index([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960,\n",
-       "            1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971,\n",
-       "            1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982,\n",
-       "            1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993,\n",
-       "            1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,\n",
-       "            2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,\n",
-       "            2016],\n",
-       "           dtype='int64', name='Year')"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "census.index"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you ever wonder what kind of object a variable refers to, you can use the `type` function.  The result indicates what type the object is, and the module where that type is defined.\n",
-    "\n",
-    "`DataFrame`, `Int64Index`, `Index`, and `Series` are defined by Pandas.\n",
-    "\n",
-    "`ndarray` is defined by NumPy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.frame.DataFrame"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(table2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.indexes.numeric.Int64Index"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(table2.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.indexes.base.Index"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(table2.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(table2.values)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.series.Series"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(census)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.indexes.numeric.Int64Index"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(census.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(census.values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Optional exercise\n",
-    "\n",
-    "The following exercise provides a chance to practice what you have learned so far, and maybe develop a different growth model.  If you feel comfortable with what we have done so far, you might want to give it a try.\n",
-    "\n",
-    "**Optional Exercise:** On the Wikipedia page about world population estimates, the first table contains estimates for prehistoric populations.  The following cells process this table and plot some of the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filename = 'data/World_population_estimates.html'\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "len(tables)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Select `tables[1]`, which is the second table on the page."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Population Reference Bureau (1973–2016)[15]</th>\n",
-       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
-       "      <th>Maddison (2008)[17]</th>\n",
-       "      <th>HYDE (2010)[citation needed]</th>\n",
-       "      <th>Tanton (1994)[18]</th>\n",
-       "      <th>Biraben (1980)[19]</th>\n",
-       "      <th>McEvedy &amp; Jones (1978)[20]</th>\n",
-       "      <th>Thomlinson (1975)[21]</th>\n",
-       "      <th>Durand (1974)[22]</th>\n",
-       "      <th>Clark (1967)[23]</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>-10000</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2M[24]</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1–10M</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-9000</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-8000</th>\n",
-       "      <td>5.</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5–10M</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-7000</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8.</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-6000</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>11.</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Population Reference Bureau (1973–2016)[15]  \\\n",
-       "Year                                                 \n",
-       "-10000                                         NaN   \n",
-       "-9000                                          NaN   \n",
-       "-8000                                           5.   \n",
-       "-7000                                          NaN   \n",
-       "-6000                                          NaN   \n",
-       "\n",
-       "        United Nations Department of Economic and Social Affairs (2015)[16]  \\\n",
-       "Year                                                                          \n",
-       "-10000                                                NaN                     \n",
-       "-9000                                                 NaN                     \n",
-       "-8000                                                 NaN                     \n",
-       "-7000                                                 NaN                     \n",
-       "-6000                                                 NaN                     \n",
-       "\n",
-       "       Maddison (2008)[17] HYDE (2010)[citation needed]  Tanton (1994)[18]  \\\n",
-       "Year                                                                         \n",
-       "-10000                 NaN                       2M[24]                NaN   \n",
-       "-9000                  NaN                           4.                NaN   \n",
-       "-8000                  NaN                           5.                NaN   \n",
-       "-7000                  NaN                           8.                NaN   \n",
-       "-6000                  NaN                          11.                NaN   \n",
-       "\n",
-       "        Biraben (1980)[19]  McEvedy & Jones (1978)[20] Thomlinson (1975)[21]  \\\n",
-       "Year                                                                           \n",
-       "-10000                 NaN                         4.0                 1–10M   \n",
-       "-9000                  NaN                         NaN                   NaN   \n",
-       "-8000                  NaN                         NaN                   NaN   \n",
-       "-7000                  NaN                         NaN                   NaN   \n",
-       "-6000                  NaN                         NaN                   NaN   \n",
-       "\n",
-       "       Durand (1974)[22] Clark (1967)[23]  \n",
-       "Year                                       \n",
-       "-10000               NaN              NaN  \n",
-       "-9000                NaN              NaN  \n",
-       "-8000              5–10M              NaN  \n",
-       "-7000                NaN              NaN  \n",
-       "-6000                NaN              NaN  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table1 = tables[1]\n",
-    "table1.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Not all agencies and researchers provided estimates for the same dates.  Again `NaN` is the special value that indicates missing data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Population Reference Bureau (1973–2016)[15]</th>\n",
-       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
-       "      <th>Maddison (2008)[17]</th>\n",
-       "      <th>HYDE (2010)[citation needed]</th>\n",
-       "      <th>Tanton (1994)[18]</th>\n",
-       "      <th>Biraben (1980)[19]</th>\n",
-       "      <th>McEvedy &amp; Jones (1978)[20]</th>\n",
-       "      <th>Thomlinson (1975)[21]</th>\n",
-       "      <th>Durand (1974)[22]</th>\n",
-       "      <th>Clark (1967)[23]</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1913</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1793.</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1920</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1860.0</td>\n",
-       "      <td>1863.</td>\n",
-       "      <td>1912.</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1968.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1925</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1930</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2070.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2092.</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2145.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1940</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2300.0</td>\n",
-       "      <td>2299.</td>\n",
-       "      <td>2307.</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2340.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Population Reference Bureau (1973–2016)[15]  \\\n",
-       "Year                                               \n",
-       "1913                                         NaN   \n",
-       "1920                                         NaN   \n",
-       "1925                                         NaN   \n",
-       "1930                                         NaN   \n",
-       "1940                                         NaN   \n",
-       "\n",
-       "      United Nations Department of Economic and Social Affairs (2015)[16]  \\\n",
-       "Year                                                                        \n",
-       "1913                                                NaN                     \n",
-       "1920                                             1860.0                     \n",
-       "1925                                                NaN                     \n",
-       "1930                                             2070.0                     \n",
-       "1940                                             2300.0                     \n",
-       "\n",
-       "     Maddison (2008)[17] HYDE (2010)[citation needed]  Tanton (1994)[18]  \\\n",
-       "Year                                                                       \n",
-       "1913               1793.                          NaN                NaN   \n",
-       "1920               1863.                        1912.                NaN   \n",
-       "1925                 NaN                          NaN                NaN   \n",
-       "1930                 NaN                        2092.                NaN   \n",
-       "1940               2299.                        2307.                NaN   \n",
-       "\n",
-       "      Biraben (1980)[19]  McEvedy & Jones (1978)[20] Thomlinson (1975)[21]  \\\n",
-       "Year                                                                         \n",
-       "1913                 NaN                         NaN                   NaN   \n",
-       "1920                 NaN                         NaN                   NaN   \n",
-       "1925                 NaN                      2000.0                   NaN   \n",
-       "1930                 NaN                         NaN                   NaN   \n",
-       "1940                 NaN                         NaN                   NaN   \n",
-       "\n",
-       "     Durand (1974)[22] Clark (1967)[23]  \n",
-       "Year                                     \n",
-       "1913               NaN              NaN  \n",
-       "1920               NaN            1968.  \n",
-       "1925               NaN              NaN  \n",
-       "1930               NaN            2145.  \n",
-       "1940               NaN            2340.  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table1.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Again, we'll replace the long column names with more convenient abbreviations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table1.columns = ['PRB', 'UN', 'Maddison', 'HYDE', 'Tanton', \n",
-    "                  'Biraben', 'McEvedy & Jones', 'Thomlinson', 'Durand', 'Clark']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Some of the estimates are in a form Pandas doesn't recognize as numbers, but we can coerce them to be numeric."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in table1.columns:\n",
-    "    table1[col] = pd.to_numeric(table1[col], errors='coerce')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results.  Notice that we are working in millions now, not billions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "table1.plot()\n",
-    "decorate(xlim=[-10000, 2000], xlabel='Year', \n",
-    "         ylabel='World population (millions)',\n",
-    "         title='Prehistoric population estimates')\n",
-    "plt.legend(fontsize='small');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use `xlim` to zoom in on everything after Year 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "table1.plot()\n",
-    "decorate(xlim=[0, 2000], xlabel='Year', \n",
-    "         ylabel='World population (millions)',\n",
-    "         title='CE population estimates')\n",
-    "plt.legend(fontsize='small');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "See if you can find a model that fits these data well from Year 0 to 1950.\n",
-    "\n",
-    "How well does your best model predict actual population growth from 1950 to the present?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# The function I found that best matches the data has the form\n",
-    "# a + b / (c - x)\n",
-    "\n",
-    "# This function is hard to explain physically; that is, it doesn't\n",
-    "# correspond to a growth model that makes sense in terms of human behavior.\n",
-    "\n",
-    "# And it implies that the population goes to infinity in 2040.\n",
-    "\n",
-    "xs = linspace(100, 1950)\n",
-    "ys = 110 + 200000 / (2040 - xs)\n",
-    "table1.plot()\n",
-    "plot(xs, ys, color='gray', label='model')\n",
-    "\n",
-    "decorate(xlim=[0, 2000], xlabel='Year', \n",
-    "         ylabel='World population (millions)',\n",
-    "         title='CE population estimates')\n",
-    "plt.legend(fontsize='small');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# And it doesn't do a particularly good job of predicting\n",
-    "# actual growth from 1940 to the present.\n",
-    "\n",
-    "plot(census, ':', label='US Census')\n",
-    "plot(un, '--', label='UN DESA')\n",
-    "\n",
-    "xs = linspace(1940, 2020)\n",
-    "ys = 110 + 200000 / (2040 - xs)\n",
-    "plot(xs, ys/1000, color='gray', label='model')\n",
-    "\n",
-    "decorate(xlim=[1950, 2016], xlabel='Year', \n",
-    "         ylabel='World population (billions)',\n",
-    "         title='Prehistoric population estimates')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap11soln.ipynb b/soln/chap11soln.ipynb
deleted file mode 100644
index 11bb3039..00000000
--- a/soln/chap11soln.ipynb
+++ /dev/null
@@ -1,900 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 11\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### SIR implementation\n",
-    "\n",
-    "We'll use a `State` object to represent the number (or fraction) of people in each compartment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>S</th>\n",
-       "      <td>89</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>I</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>R</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "S    89\n",
-       "I     1\n",
-       "R     0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init = State(S=89, I=1, R=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To convert from number of people to fractions, we divide through by the total."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>S</th>\n",
-       "      <td>0.988889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>I</th>\n",
-       "      <td>0.011111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>R</th>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "S    0.988889\n",
-       "I    0.011111\n",
-       "R    0.000000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init /= sum(init)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system` creates a `System` object with the given parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an example with hypothetical values for `beta` and `gamma`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>S    0.988889\n",
-       "I    0.011111\n",
-       "R    0.000000\n",
-       "dtyp...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t0</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>98</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>beta</th>\n",
-       "      <td>0.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gamma</th>\n",
-       "      <td>0.25</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "init     S    0.988889\n",
-       "I    0.011111\n",
-       "R    0.000000\n",
-       "dtyp...\n",
-       "t0                                                       0\n",
-       "t_end                                                   98\n",
-       "beta                                              0.333333\n",
-       "gamma                                                 0.25\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tc = 3      # time between contacts in days \n",
-    "tr = 4      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The update function takes the state during the current time step and returns the state during the next time step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State with variables S, I, R\n",
-    "    t: time step\n",
-    "    system: System with beta and gamma\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = system.beta * i * s    \n",
-    "    recovered = system.gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To run a single time step, we call it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>S</th>\n",
-       "      <td>0.985226</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>I</th>\n",
-       "      <td>0.011996</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>R</th>\n",
-       "      <td>0.002778</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "S    0.985226\n",
-       "I    0.011996\n",
-       "R    0.002778\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "state = update_func(init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run a simulation by calling the update function for each time step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: State object for final state\n",
-    "    \"\"\"\n",
-    "    state = system.init\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        state = update_func(state, t, system)\n",
-    "        \n",
-    "    return state"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is the state of the system at `t_end`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>S</th>\n",
-       "      <td>0.520568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>I</th>\n",
-       "      <td>0.000666</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>R</th>\n",
-       "      <td>0.478766</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "S    0.520568\n",
-       "I    0.000666\n",
-       "R    0.478766\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run_simulation(system, update_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise**  Suppose the time between contacts is 4 days and the recovery time is 5 days.  After 14 weeks, how many students, total, have been infected?\n",
-    "\n",
-    "Hint: what is the change in `S` between the beginning and the end of the simulation?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.3787177442414792"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "tc = 4      # time between contacts in days \n",
-    "tr = 5      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)\n",
-    "s0 = system.init.S\n",
-    "\n",
-    "final = run_simulation(system, update_func)\n",
-    "s_end = final.S\n",
-    "s0 - s_end"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using TimeSeries objects"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we want to store the state of the system at each time step, we can use one `TimeSeries` object for each state variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "    \n",
-    "    Add three Series objects to the System: S, I, R\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \"\"\"\n",
-    "    S = TimeSeries()\n",
-    "    I = TimeSeries()\n",
-    "    R = TimeSeries()\n",
-    "\n",
-    "    state = system.init\n",
-    "    t0 = system.t0\n",
-    "    S[t0], I[t0], R[t0] = state\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        state = update_func(state, t, system)\n",
-    "        S[t+1], I[t+1], R[t+1] = state\n",
-    "    \n",
-    "    return S, I, R"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we call it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tc = 3      # time between contacts in days \n",
-    "tr = 4      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)\n",
-    "S, I, R = run_simulation(system, update_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then we can plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(S, I, R):\n",
-    "    \"\"\"Plot the results of a SIR model.\n",
-    "    \n",
-    "    S: TimeSeries\n",
-    "    I: TimeSeries\n",
-    "    R: TimeSeries\n",
-    "    \"\"\"\n",
-    "    plot(S, '--', label='Susceptible')\n",
-    "    plot(I, '-', label='Infected')\n",
-    "    plot(R, ':', label='Recovered')\n",
-    "    decorate(xlabel='Time (days)',\n",
-    "             ylabel='Fraction of population')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what they look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap11-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_results(S, I, R)\n",
-    "savefig('figs/chap11-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using a DataFrame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Instead of making three `TimeSeries` objects, we can use one `DataFrame`.\n",
-    "\n",
-    "We have to use `row` to selects rows, rather than columns.  But then Pandas does the right thing, matching up the state variables with the columns of the `DataFrame`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    frame = TimeFrame(columns=system.init.index)\n",
-    "    frame.row[system.t0] = system.init\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it, and what the result looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>S</th>\n",
-       "      <th>I</th>\n",
-       "      <th>R</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.988889</td>\n",
-       "      <td>0.011111</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.985226</td>\n",
-       "      <td>0.011996</td>\n",
-       "      <td>0.002778</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.981287</td>\n",
-       "      <td>0.012936</td>\n",
-       "      <td>0.005777</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.977055</td>\n",
-       "      <td>0.013934</td>\n",
-       "      <td>0.009011</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.972517</td>\n",
-       "      <td>0.014988</td>\n",
-       "      <td>0.012494</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          S         I         R\n",
-       "0  0.988889  0.011111  0.000000\n",
-       "1  0.985226  0.011996  0.002778\n",
-       "2  0.981287  0.012936  0.005777\n",
-       "3  0.977055  0.013934  0.009011\n",
-       "4  0.972517  0.014988  0.012494"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tc = 3      # time between contacts in days \n",
-    "tr = 4      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)\n",
-    "results = run_simulation(system, update_func)\n",
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can extract the results and plot them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_results(results.S, results.I, results.R)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise**  Suppose the time between contacts is 4 days and the recovery time is 5 days.  Simulate this scenario for 14 weeks and plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "tc = 4      # time between contacts in days \n",
-    "tr = 5      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)\n",
-    "results = run_simulation(system, update_func)\n",
-    "\n",
-    "plot_results(results.S, results.I, results.R)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap12soln.ipynb b/soln/chap12soln.ipynb
deleted file mode 100644
index 24817afc..00000000
--- a/soln/chap12soln.ipynb
+++ /dev/null
@@ -1,1634 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 12\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code\n",
-    "\n",
-    "Here's the code from the previous notebook that we'll need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State with variables S, I, R\n",
-    "    t: time step\n",
-    "    system: System with beta and gamma\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = system.beta * i * s    \n",
-    "    recovered = system.gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    frame = TimeFrame(columns=system.init.index)\n",
-    "    frame.row[system.t0] = system.init\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Given the results, we can compute metrics that quantify whatever we are interested in, like the total number of sick students, for example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_total_infected(results):\n",
-    "    \"\"\"Fraction of population infected during the simulation.\n",
-    "    \n",
-    "    results: DataFrame with columns S, I, R\n",
-    "    \n",
-    "    returns: fraction of population\n",
-    "    \"\"\"\n",
-    "    return get_first_value(results.S) - get_last_value(results.S)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an example.|"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.333 0.25 0.46716293183605073\n"
-     ]
-    }
-   ],
-   "source": [
-    "beta = 0.333\n",
-    "gamma = 0.25\n",
-    "system = make_system(beta, gamma)\n",
-    "\n",
-    "results = run_simulation(system, update_func)\n",
-    "print(beta, gamma, calc_total_infected(results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write functions that take a `TimeFrame` object as a parameter and compute the other metrics mentioned in the book:\n",
-    "\n",
-    "1.  The fraction of students who are sick at the peak of the outbreak.\n",
-    "\n",
-    "2.  The day the outbreak peaks.\n",
-    "\n",
-    "3.  The fraction of students who are sick at the end of the semester.\n",
-    "\n",
-    "Note: Not all of these functions require the `System` object, but when you write a set of related functons, it is often convenient if they all take the same parameters.\n",
-    "\n",
-    "Hint:  If you have a `TimeSeries` called `I`, you can compute the largest value of the series like this:\n",
-    "\n",
-    "    I.max()\n",
-    "\n",
-    "And the index of the largest value like this:\n",
-    "\n",
-    "    I.idxmax()\n",
-    "\n",
-    "You can read about these functions in the `Series` [documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.043536202687592354"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def fraction_sick_at_peak(results):\n",
-    "    return results.I.max()\n",
-    "\n",
-    "fraction_sick_at_peak(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "30"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def time_of_peak(results):\n",
-    "    return results.I.idxmax()\n",
-    "\n",
-    "time_of_peak(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.0006741943156034474"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def sick_at_end(results):\n",
-    "    return get_last_value(results.I)\n",
-    "\n",
-    "sick_at_end(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### What if?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use this model to evaluate \"what if\" scenarios.  For example, this function models the effect of immunization by moving some fraction of the population from S to R before the simulation starts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def add_immunization(system, fraction):\n",
-    "    \"\"\"Immunize a fraction of the population.\n",
-    "    \n",
-    "    Moves the given fraction from S to R.\n",
-    "    \n",
-    "    system: System object\n",
-    "    fraction: number from 0 to 1\n",
-    "    \"\"\"\n",
-    "    system.init.S -= fraction\n",
-    "    system.init.R += fraction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's start again with the system we used in the previous sections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>S    0.988889\n",
-       "I    0.011111\n",
-       "R    0.000000\n",
-       "dtyp...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t0</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>98</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>beta</th>\n",
-       "      <td>0.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gamma</th>\n",
-       "      <td>0.25</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "init     S    0.988889\n",
-       "I    0.011111\n",
-       "R    0.000000\n",
-       "dtyp...\n",
-       "t0                                                       0\n",
-       "t_end                                                   98\n",
-       "beta                                              0.333333\n",
-       "gamma                                                 0.25\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tc = 3      # time between contacts in days \n",
-    "tr = 4      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And run the model without immunization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.468320811028781"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = run_simulation(system, update_func)\n",
-    "calc_total_infected(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now with 10% immunization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.30650802853979753"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system2 = make_system(beta, gamma)\n",
-    "add_immunization(system2, 0.1)\n",
-    "results2 = run_simulation(system2, update_func)\n",
-    "calc_total_infected(results2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "10% immunization leads to a drop in infections of 16 percentage points.\n",
-    "\n",
-    "Here's what the time series looks like for S, with and without immunization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap12-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(results.S, '-', label='No immunization')\n",
-    "plot(results2.S, '--', label='10% immunization')\n",
-    "\n",
-    "decorate(xlabel='Time (days)',\n",
-    "         ylabel='Fraction susceptible')\n",
-    "\n",
-    "savefig('figs/chap12-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can sweep through a range of values for the fraction of the population who are immunized."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0 0.468320811028781\n",
-      "0.1 0.30650802853979753\n",
-      "0.2 0.16136545700638427\n",
-      "0.30000000000000004 0.0728155898425179\n",
-      "0.4 0.03552021675299155\n",
-      "0.5 0.019688715782459176\n",
-      "0.6000000000000001 0.011622057998337987\n",
-      "0.7000000000000001 0.006838737800619332\n",
-      "0.8 0.003696496253713877\n",
-      "0.9 0.0014815326722661948\n",
-      "1.0 -0.00016121210941239666\n"
-     ]
-    }
-   ],
-   "source": [
-    "immunize_array = linspace(0, 1, 11)\n",
-    "for fraction in immunize_array:\n",
-    "    system = make_system(beta, gamma)\n",
-    "    add_immunization(system, fraction)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    print(fraction, calc_total_infected(results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This function does the same thing and stores the results in a `Sweep` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_immunity(immunize_array):\n",
-    "    \"\"\"Sweeps a range of values for immunity.\n",
-    "    \n",
-    "    immunize_array: array of fraction immunized\n",
-    "    \n",
-    "    returns: Sweep object\n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for fraction in immunize_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        add_immunization(system, fraction)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[fraction] = calc_total_infected(results)\n",
-    "        \n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.00</th>\n",
-       "      <td>0.468321</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.05</th>\n",
-       "      <td>0.387288</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.10</th>\n",
-       "      <td>0.306508</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.15</th>\n",
-       "      <td>0.229234</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.20</th>\n",
-       "      <td>0.161365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.25</th>\n",
-       "      <td>0.108791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.30</th>\n",
-       "      <td>0.072816</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.35</th>\n",
-       "      <td>0.049938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.40</th>\n",
-       "      <td>0.035520</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.45</th>\n",
-       "      <td>0.026121</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.50</th>\n",
-       "      <td>0.019689</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.55</th>\n",
-       "      <td>0.015072</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.60</th>\n",
-       "      <td>0.011622</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.65</th>\n",
-       "      <td>0.008956</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.70</th>\n",
-       "      <td>0.006839</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.75</th>\n",
-       "      <td>0.005119</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.80</th>\n",
-       "      <td>0.003696</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.85</th>\n",
-       "      <td>0.002500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.90</th>\n",
-       "      <td>0.001482</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.95</th>\n",
-       "      <td>0.000603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.00</th>\n",
-       "      <td>-0.000161</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "0.00    0.468321\n",
-       "0.05    0.387288\n",
-       "0.10    0.306508\n",
-       "0.15    0.229234\n",
-       "0.20    0.161365\n",
-       "0.25    0.108791\n",
-       "0.30    0.072816\n",
-       "0.35    0.049938\n",
-       "0.40    0.035520\n",
-       "0.45    0.026121\n",
-       "0.50    0.019689\n",
-       "0.55    0.015072\n",
-       "0.60    0.011622\n",
-       "0.65    0.008956\n",
-       "0.70    0.006839\n",
-       "0.75    0.005119\n",
-       "0.80    0.003696\n",
-       "0.85    0.002500\n",
-       "0.90    0.001482\n",
-       "0.95    0.000603\n",
-       "1.00   -0.000161\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "immunize_array = linspace(0, 1, 21)\n",
-    "infected_sweep = sweep_immunity(immunize_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap12-fig02.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(infected_sweep)\n",
-    "\n",
-    "decorate(xlabel='Fraction immunized',\n",
-    "         ylabel='Total fraction infected',\n",
-    "         title='Fraction infected vs. immunization rate',\n",
-    "         legend=False)\n",
-    "\n",
-    "savefig('figs/chap12-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If 40% of the population is immunized, less than 4% of the population gets sick."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Logistic function"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To model the effect of a hand-washing campaign, I'll use a [generalized logistic function](https://en.wikipedia.org/wiki/Generalised_logistic_function) (GLF), which is a convenient function for modeling curves that have a generally sigmoid shape.  The parameters of the GLF correspond to various features of the curve in a way that makes it easy to find a function that has the shape you want, based on data or background information about the scenario."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def logistic(x, A=0, B=1, C=1, M=0, K=1, Q=1, nu=1):\n",
-    "    \"\"\"Computes the generalize logistic function.\n",
-    "    \n",
-    "    A: controls the lower bound\n",
-    "    B: controls the steepness of the transition \n",
-    "    C: not all that useful, AFAIK\n",
-    "    M: controls the location of the transition\n",
-    "    K: controls the upper bound\n",
-    "    Q: shift the transition left or right\n",
-    "    nu: affects the symmetry of the transition\n",
-    "    \n",
-    "    returns: float or array\n",
-    "    \"\"\"\n",
-    "    exponent = -B * (x - M)\n",
-    "    denom = C + Q * exp(exponent)\n",
-    "    return A + (K-A) / denom ** (1/nu)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following array represents the range of possible spending."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([   0.,   60.,  120.,  180.,  240.,  300.,  360.,  420.,  480.,\n",
-       "        540.,  600.,  660.,  720.,  780.,  840.,  900.,  960., 1020.,\n",
-       "       1080., 1140., 1200.])"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spending = linspace(0, 1200, 21)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`compute_factor` computes the reduction in `beta` for a given level of campaign spending.\n",
-    "\n",
-    "`M` is chosen so the transition happens around \\$500.\n",
-    "\n",
-    "`K` is the maximum reduction in `beta`, 20%.\n",
-    "\n",
-    "`B` is chosen by trial and error to yield a curve that seems feasible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_factor(spending):\n",
-    "    \"\"\"Reduction factor as a function of spending.\n",
-    "    \n",
-    "    spending: dollars from 0 to 1200\n",
-    "    \n",
-    "    returns: fractional reduction in beta\n",
-    "    \"\"\"\n",
-    "    return logistic(spending, M=500, K=0.2, B=0.01)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "percent_reduction = compute_factor(spending) * 100\n",
-    "\n",
-    "plot(spending, percent_reduction)\n",
-    "\n",
-    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
-    "         ylabel='Percent reduction in infection rate',\n",
-    "         title='Effect of hand washing on infection rate',\n",
-    "         legend=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Modify the parameters `M`, `K`, and `B`, and see what effect they have on the shape of the curve.  Read about the [generalized logistic function on Wikipedia](https://en.wikipedia.org/wiki/Generalised_logistic_function).  Modify the other parameters and see what effect they have."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Hand washing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can model the effect of a hand-washing campaign by modifying `beta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def add_hand_washing(system, spending):\n",
-    "    \"\"\"Modifies system to model the effect of hand washing.\n",
-    "    \n",
-    "    system: System object\n",
-    "    spending: campaign spending in USD\n",
-    "    \"\"\"\n",
-    "    factor = compute_factor(spending)\n",
-    "    system.beta *= (1 - factor)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's start with the same values of `beta` and `gamma` we've been using."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.3333333333333333, 0.25)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tc = 3      # time between contacts in days \n",
-    "tr = 4      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "beta, gamma"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can sweep different levels of campaign spending."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0 0.3328871432717143 0.4667702312363652\n",
-      "100.0 0.3321342526691939 0.46414165040064037\n",
-      "200.0 0.33017160845482885 0.4572170063132055\n",
-      "300.0 0.32538647186519215 0.4398872029120663\n",
-      "400.0 0.3154039052420003 0.40163064627138245\n",
-      "500.0 0.3 0.3370342594898199\n",
-      "600.0 0.28459609475799963 0.26731703056804546\n",
-      "700.0 0.2746135281348078 0.22184699045990752\n",
-      "800.0 0.26982839154517113 0.20079159841614402\n",
-      "900.0 0.2678657473308061 0.1923921833925878\n",
-      "1000.0 0.26711285672828566 0.18921320781833872\n",
-      "1100.0 0.26683150821044227 0.18803175228016467\n",
-      "1200.0 0.26672740341296003 0.1875955039953746\n"
-     ]
-    }
-   ],
-   "source": [
-    "spending_array = linspace(0, 1200, 13)\n",
-    "\n",
-    "for spending in spending_array:\n",
-    "    system = make_system(beta, gamma)\n",
-    "    add_hand_washing(system, spending)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    print(spending, system.beta, calc_total_infected(results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a function that sweeps a range of spending and stores the results in a `SweepSeries`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_hand_washing(spending_array):\n",
-    "    \"\"\"Run simulations with a range of spending.\n",
-    "    \n",
-    "    spending_array: array of dollars from 0 to 1200\n",
-    "    \n",
-    "    returns: Sweep object\n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for spending in spending_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        add_hand_washing(system, spending)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[spending] = calc_total_infected(results)\n",
-    "        \n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.000000</th>\n",
-       "      <td>0.466770</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63.157895</th>\n",
-       "      <td>0.465418</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>126.315789</th>\n",
-       "      <td>0.462905</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>189.473684</th>\n",
-       "      <td>0.458291</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>252.631579</th>\n",
-       "      <td>0.449980</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>315.789474</th>\n",
-       "      <td>0.435540</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>378.947368</th>\n",
-       "      <td>0.411960</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>442.105263</th>\n",
-       "      <td>0.377183</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>505.263158</th>\n",
-       "      <td>0.333171</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>568.421053</th>\n",
-       "      <td>0.287633</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>631.578947</th>\n",
-       "      <td>0.249745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>694.736842</th>\n",
-       "      <td>0.223529</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>757.894737</th>\n",
-       "      <td>0.207480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>821.052632</th>\n",
-       "      <td>0.198306</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>884.210526</th>\n",
-       "      <td>0.193244</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>947.368421</th>\n",
-       "      <td>0.190500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1010.526316</th>\n",
-       "      <td>0.189027</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1073.684211</th>\n",
-       "      <td>0.188239</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1136.842105</th>\n",
-       "      <td>0.187819</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1200.000000</th>\n",
-       "      <td>0.187596</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "0.000000       0.466770\n",
-       "63.157895      0.465418\n",
-       "126.315789     0.462905\n",
-       "189.473684     0.458291\n",
-       "252.631579     0.449980\n",
-       "315.789474     0.435540\n",
-       "378.947368     0.411960\n",
-       "442.105263     0.377183\n",
-       "505.263158     0.333171\n",
-       "568.421053     0.287633\n",
-       "631.578947     0.249745\n",
-       "694.736842     0.223529\n",
-       "757.894737     0.207480\n",
-       "821.052632     0.198306\n",
-       "884.210526     0.193244\n",
-       "947.368421     0.190500\n",
-       "1010.526316    0.189027\n",
-       "1073.684211    0.188239\n",
-       "1136.842105    0.187819\n",
-       "1200.000000    0.187596\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spending_array = linspace(0, 1200, 20)\n",
-    "infected_sweep = sweep_hand_washing(spending_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap12-fig03.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(infected_sweep)\n",
-    "\n",
-    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
-    "         ylabel='Total fraction infected',\n",
-    "         title='Effect of hand washing on total infections',\n",
-    "         legend=False)\n",
-    "\n",
-    "savefig('figs/chap12-fig03.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's put it all together to make some public health spending decisions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optimization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Suppose we have \\$1200 to spend on any combination of vaccines and a hand-washing campaign."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "num_students = 90\n",
-    "budget = 1200\n",
-    "price_per_dose = 100\n",
-    "max_doses = int(budget / price_per_dose)\n",
-    "dose_array = linrange(max_doses, endpoint=True)\n",
-    "max_doses"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can sweep through a range of doses from, 0 to `max_doses`, model the effects of immunization and the hand-washing campaign, and run simulations.\n",
-    "\n",
-    "For each scenario, we compute the fraction of students who get sick."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 0.9888888888888889 0.26672740341296003 0.1875955039953746\n",
-      "1 0.9777777777777779 0.26683150821044227 0.17458071882622528\n",
-      "2 0.9666666666666667 0.26711285672828566 0.16290983834857686\n",
-      "3 0.9555555555555556 0.2678657473308061 0.15350834947768177\n",
-      "4 0.9444444444444445 0.26982839154517113 0.1485650923152827\n",
-      "5 0.9333333333333333 0.2746135281348078 0.15294595061102179\n",
-      "6 0.9222222222222223 0.28459609475799963 0.1749644150235239\n",
-      "7 0.9111111111111112 0.3 0.21734316168444845\n",
-      "8 0.9 0.3154039052420003 0.2590710444883414\n",
-      "9 0.888888888888889 0.32538647186519215 0.27840288410342784\n",
-      "10 0.8777777777777778 0.33017160845482885 0.2779145346228302\n",
-      "11 0.8666666666666667 0.3321342526691939 0.2673574966927026\n",
-      "12 0.8555555555555556 0.3328871432717143 0.25279694563572175\n"
-     ]
-    }
-   ],
-   "source": [
-    "for doses in dose_array:\n",
-    "    fraction = doses / num_students\n",
-    "    spending = budget - doses * price_per_dose\n",
-    "    \n",
-    "    system = make_system(beta, gamma)\n",
-    "    add_immunization(system, fraction)\n",
-    "    add_hand_washing(system, spending)\n",
-    "    \n",
-    "    results = run_simulation(system, update_func)\n",
-    "    print(doses, system.init.S, system.beta, calc_total_infected(results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function wraps that loop and stores the results in a `Sweep` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_doses(dose_array):\n",
-    "    \"\"\"Runs simulations with different doses and campaign spending.\n",
-    "    \n",
-    "    dose_array: range of values for number of vaccinations\n",
-    "    \n",
-    "    return: Sweep object with total number of infections \n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for doses in dose_array:\n",
-    "        fraction = doses / num_students\n",
-    "        spending = budget - doses * price_per_dose\n",
-    "        \n",
-    "        system = make_system(beta, gamma)\n",
-    "        add_immunization(system, fraction)\n",
-    "        add_hand_washing(system, spending)\n",
-    "        \n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[doses] = calc_total_infected(results)\n",
-    "\n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can compute the number of infected students for each possible allocation of the budget."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.187596</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.174581</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.162910</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.153508</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.148565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.152946</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0.174964</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0.217343</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0.259071</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0.278403</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0.277915</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>0.267357</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>0.252797</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "0     0.187596\n",
-       "1     0.174581\n",
-       "2     0.162910\n",
-       "3     0.153508\n",
-       "4     0.148565\n",
-       "5     0.152946\n",
-       "6     0.174964\n",
-       "7     0.217343\n",
-       "8     0.259071\n",
-       "9     0.278403\n",
-       "10    0.277915\n",
-       "11    0.267357\n",
-       "12    0.252797\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "infected_sweep = sweep_doses(dose_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap12-fig04.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(infected_sweep)\n",
-    "\n",
-    "decorate(xlabel='Doses of vaccine',\n",
-    "         ylabel='Total fraction infected',\n",
-    "         title='Total infections vs. doses',\n",
-    "         legend=False)\n",
-    "\n",
-    "savefig('figs/chap12-fig04.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Suppose the price of the vaccine drops to $50 per dose.  How does that affect the optimal allocation of the spending?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose we have the option to quarantine infected students.  For example, a student who feels ill might be moved to an infirmary, or a private dorm room, until they are no longer infectious.\n",
-    "\n",
-    "How might you incorporate the effect of quarantine in the SIR model?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "\"\"\"There is no unique best answer to this question,\n",
-    "but one simple option is to model quarantine as an\n",
-    "effective reduction in gamma, on the assumption that\n",
-    "quarantine reduces the number of infectious contacts\n",
-    "per infected student.\n",
-    "\n",
-    "Another option would be to add a fourth compartment\n",
-    "to the model to track the fraction of the population\n",
-    "in quarantine at each point in time.  This approach\n",
-    "would be more complex, and it is not obvious that it\n",
-    "is substantially better.\n",
-    "\n",
-    "The following function could be used, like \n",
-    "add_immunization and add_hand_washing, to adjust the\n",
-    "parameters in order to model various interventions.\n",
-    "\n",
-    "In this example, `high` is the highest duration of\n",
-    "the infection period, with no quarantine.  `low` is\n",
-    "the lowest duration, on the assumption that it takes\n",
-    "some time to identify infectious students.\n",
-    "\n",
-    "`fraction` is the fraction of infected students who \n",
-    "are quarantined as soon as they are identified.\n",
-    "\"\"\"\n",
-    "\n",
-    "def add_quarantine(system, fraction):\n",
-    "    \"\"\"Model the effect of quarantine by adjusting gamma.\n",
-    "    \n",
-    "    system: System object\n",
-    "    fraction: fraction of students quarantined\n",
-    "    \"\"\"\n",
-    "    # `low` represents the number of days a student \n",
-    "    # is infectious if quarantined.\n",
-    "    # `high` is the number of days they are infectious\n",
-    "    # if not quarantined\n",
-    "    low = 1\n",
-    "    high = 4\n",
-    "    tr = high - fraction * (high-low)\n",
-    "    system.gamma = 1 / tr"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap13ode.ipynb b/soln/chap13ode.ipynb
deleted file mode 100644
index e8bc0731..00000000
--- a/soln/chap13ode.ipynb
+++ /dev/null
@@ -1,302 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 13\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from previous chapters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system`, `plot_results`, and `calc_total_infected` are unchanged."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= np.sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(S, I, R):\n",
-    "    \"\"\"Plot the results of a SIR model.\n",
-    "    \n",
-    "    S: TimeSeries\n",
-    "    I: TimeSeries\n",
-    "    R: TimeSeries\n",
-    "    \"\"\"\n",
-    "    plot(S, '--', label='Susceptible')\n",
-    "    plot(I, '-', label='Infected')\n",
-    "    plot(R, ':', label='Recovered')\n",
-    "    decorate(xlabel='Time (days)',\n",
-    "             ylabel='Fraction of population')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_total_infected(results):\n",
-    "    \"\"\"Fraction of population infected during the simulation.\n",
-    "    \n",
-    "    results: DataFrame with columns S, I, R\n",
-    "    \n",
-    "    returns: fraction of population\n",
-    "    \"\"\"\n",
-    "    return get_first_value(results.S) - get_last_value(results.S)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Write a slope function for the SIR model and test it with `run_ode_solver`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State (s, i, r)\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State (sir)\n",
-    "    \"\"\"\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = system.beta * i * s    \n",
-    "    recovered = system.gamma * i\n",
-    "    \n",
-    "    dSdt = -infected\n",
-    "    dIdt = infected - recovered\n",
-    "    dRdt = recovered\n",
-    "    \n",
-    "    return dSdt, dIdt, dRdt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-0.003658888888888889, 0.0008811111111111112, 0.002777777777777778)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(0.333, 0.25)\n",
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>sol</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_events</th>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nfev</th>\n",
-       "      <td>212</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>njev</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nlu</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>status</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "sol                                                      None\n",
-       "t_events                                                   []\n",
-       "nfev                                                      212\n",
-       "njev                                                        0\n",
-       "nlu                                                         0\n",
-       "status                                                      0\n",
-       "message     The solver successfully reached the end of the...\n",
-       "success                                                  True\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, max_step=3)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_results(results.S, results.I, results.R)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap13soln.ipynb b/soln/chap13soln.ipynb
deleted file mode 100644
index 0e09644f..00000000
--- a/soln/chap13soln.ipynb
+++ /dev/null
@@ -1,963 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 13\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from previous chapters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system`, `plot_results`, and `calc_total_infected` are unchanged."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= np.sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(S, I, R):\n",
-    "    \"\"\"Plot the results of a SIR model.\n",
-    "    \n",
-    "    S: TimeSeries\n",
-    "    I: TimeSeries\n",
-    "    R: TimeSeries\n",
-    "    \"\"\"\n",
-    "    plot(S, '--', label='Susceptible')\n",
-    "    plot(I, '-', label='Infected')\n",
-    "    plot(R, ':', label='Recovered')\n",
-    "    decorate(xlabel='Time (days)',\n",
-    "             ylabel='Fraction of population')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_total_infected(results):\n",
-    "    \"\"\"Fraction of population infected during the simulation.\n",
-    "    \n",
-    "    results: DataFrame with columns S, I, R\n",
-    "    \n",
-    "    returns: fraction of population\n",
-    "    \"\"\"\n",
-    "    return get_first_value(results.S) - get_last_value(results.S)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    init, t0, t_end = system.init, system.t0, system.t_end\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t0] = init\n",
-    "    \n",
-    "    for t in linrange(t0, t_end):\n",
-    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State (s, i, r)\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State (sir)\n",
-    "    \"\"\"\n",
-    "    beta, gamma = system.beta, system.gamma\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = beta * i * s    \n",
-    "    recovered = gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sweeping beta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Make a range of values for `beta`, with constant `gamma`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.2"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "beta_array = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 , 1.1]\n",
-    "gamma = 0.2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the simulation once for each value of `beta` and print total infections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.1 0.010756340768063644\n",
-      "0.2 0.11898421353185373\n",
-      "0.3 0.5890954199973404\n",
-      "0.4 0.8013385277185551\n",
-      "0.5 0.8965769637207062\n",
-      "0.6 0.942929291399791\n",
-      "0.7 0.966299311298026\n",
-      "0.8 0.9781518959989762\n",
-      "0.9 0.9840568957948106\n",
-      "1.0 0.9868823507202488\n",
-      "1.1 0.988148177093735\n"
-     ]
-    }
-   ],
-   "source": [
-    "for beta in beta_array:\n",
-    "    system = make_system(beta, gamma)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    print(system.beta, calc_total_infected(results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Wrap that loop in a function and return a `SweepSeries` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_beta(beta_array, gamma):\n",
-    "    \"\"\"Sweep a range of values for beta.\n",
-    "    \n",
-    "    beta_array: array of beta values\n",
-    "    gamma: recovery rate\n",
-    "    \n",
-    "    returns: SweepSeries that maps from beta to total infected\n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    for beta in beta_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[system.beta] = calc_total_infected(results)\n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sweep `beta` and plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.1</th>\n",
-       "      <td>0.010756</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.2</th>\n",
-       "      <td>0.118984</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.3</th>\n",
-       "      <td>0.589095</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.4</th>\n",
-       "      <td>0.801339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.5</th>\n",
-       "      <td>0.896577</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.6</th>\n",
-       "      <td>0.942929</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.7</th>\n",
-       "      <td>0.966299</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.8</th>\n",
-       "      <td>0.978152</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.9</th>\n",
-       "      <td>0.984057</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.0</th>\n",
-       "      <td>0.986882</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.1</th>\n",
-       "      <td>0.988148</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "0.1    0.010756\n",
-       "0.2    0.118984\n",
-       "0.3    0.589095\n",
-       "0.4    0.801339\n",
-       "0.5    0.896577\n",
-       "0.6    0.942929\n",
-       "0.7    0.966299\n",
-       "0.8    0.978152\n",
-       "0.9    0.984057\n",
-       "1.0    0.986882\n",
-       "1.1    0.988148\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "infected_sweep = sweep_beta(beta_array, gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap13-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "label = 'gamma = ' + str(gamma)\n",
-    "plot(infected_sweep, label=label)\n",
-    "\n",
-    "decorate(xlabel='Contact rate (beta)',\n",
-    "         ylabel='Fraction infected')\n",
-    "\n",
-    "savefig('figs/chap13-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sweeping gamma"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using the same array of values for `beta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "beta_array"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And now an array of values for `gamma`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0.2, 0.4, 0.6, 0.8]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gamma_array = [0.2, 0.4, 0.6, 0.8]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For each value of `gamma`, sweep `beta` and plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap13-fig02.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(7, 4))\n",
-    "\n",
-    "for gamma in gamma_array:\n",
-    "    infected_sweep = sweep_beta(beta_array, gamma)\n",
-    "    label = 'gamma = ' + str(gamma)\n",
-    "    plot(infected_sweep, label=label)\n",
-    "    \n",
-    "decorate(xlabel='Contact rate (beta)',\n",
-    "         ylabel='Fraction infected',\n",
-    "         loc='upper left')\n",
-    "\n",
-    "plt.legend(bbox_to_anchor=(1.02, 1.02))\n",
-    "plt.tight_layout()\n",
-    "savefig('figs/chap13-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Suppose the infectious period for the Freshman Plague is known to be 2 days on average, and suppose during one particularly bad year, 40% of the class is infected at some point.  Estimate the time between contacts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.1</th>\n",
-       "      <td>0.002736</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.2</th>\n",
-       "      <td>0.007235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.3</th>\n",
-       "      <td>0.015929</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.4</th>\n",
-       "      <td>0.038603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.5</th>\n",
-       "      <td>0.132438</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.6</th>\n",
-       "      <td>0.346765</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.7</th>\n",
-       "      <td>0.530585</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.8</th>\n",
-       "      <td>0.661553</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.9</th>\n",
-       "      <td>0.754595</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.0</th>\n",
-       "      <td>0.821534</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.1</th>\n",
-       "      <td>0.870219</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "0.1    0.002736\n",
-       "0.2    0.007235\n",
-       "0.3    0.015929\n",
-       "0.4    0.038603\n",
-       "0.5    0.132438\n",
-       "0.6    0.346765\n",
-       "0.7    0.530585\n",
-       "0.8    0.661553\n",
-       "0.9    0.754595\n",
-       "1.0    0.821534\n",
-       "1.1    0.870219\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Sweep beta with fixed gamma\n",
-    "gamma = 1/2\n",
-    "infected_sweep = sweep_beta(beta_array, gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.62548698])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Interpolating by eye, we can see that the infection rate passes through 0.4\n",
-    "# when beta is between 0.6 and 0.7\n",
-    "# We can use the `crossings` function to interpolate more precisely\n",
-    "# (although we don't know about it yet :)\n",
-    "beta_estimate = crossings(infected_sweep, 0.4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1.59875429])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Time between contacts is 1/beta\n",
-    "time_between_contacts = 1/beta_estimate"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SweepFrame\n",
-    "\n",
-    "The following sweeps two parameters and stores the results in a `SweepFrame`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_parameters(beta_array, gamma_array):\n",
-    "    \"\"\"Sweep a range of values for beta and gamma.\n",
-    "    \n",
-    "    beta_array: array of infection rates\n",
-    "    gamma_array: array of recovery rates\n",
-    "    \n",
-    "    returns: SweepFrame with one row for each beta\n",
-    "             and one column for each gamma\n",
-    "    \"\"\"\n",
-    "    frame = SweepFrame(columns=gamma_array)\n",
-    "    for gamma in gamma_array:\n",
-    "        frame[gamma] = sweep_beta(beta_array, gamma)\n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the `SweepFrame` look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0.2</th>\n",
-       "      <th>0.4</th>\n",
-       "      <th>0.6</th>\n",
-       "      <th>0.8</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.1</th>\n",
-       "      <td>0.010756</td>\n",
-       "      <td>0.003642</td>\n",
-       "      <td>0.002191</td>\n",
-       "      <td>0.001567</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.2</th>\n",
-       "      <td>0.118984</td>\n",
-       "      <td>0.010763</td>\n",
-       "      <td>0.005447</td>\n",
-       "      <td>0.003644</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.3</th>\n",
-       "      <td>0.589095</td>\n",
-       "      <td>0.030185</td>\n",
-       "      <td>0.010771</td>\n",
-       "      <td>0.006526</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.4</th>\n",
-       "      <td>0.801339</td>\n",
-       "      <td>0.131563</td>\n",
-       "      <td>0.020917</td>\n",
-       "      <td>0.010780</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.5</th>\n",
-       "      <td>0.896577</td>\n",
-       "      <td>0.396409</td>\n",
-       "      <td>0.046140</td>\n",
-       "      <td>0.017640</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          0.2       0.4       0.6       0.8\n",
-       "0.1  0.010756  0.003642  0.002191  0.001567\n",
-       "0.2  0.118984  0.010763  0.005447  0.003644\n",
-       "0.3  0.589095  0.030185  0.010771  0.006526\n",
-       "0.4  0.801339  0.131563  0.020917  0.010780\n",
-       "0.5  0.896577  0.396409  0.046140  0.017640"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "frame = sweep_parameters(beta_array, gamma_array)\n",
-    "frame.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we can plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for gamma in gamma_array:\n",
-    "    label = 'gamma = ' + str(gamma)\n",
-    "    plot(frame[gamma], label=label)\n",
-    "    \n",
-    "decorate(xlabel='Contact rate (beta)',\n",
-    "         ylabel='Fraction infected',\n",
-    "         title='',\n",
-    "         loc='upper left')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also plot one line for each value of `beta`, although there are a lot of them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap13-fig03.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(7, 4))\n",
-    "\n",
-    "\n",
-    "for beta in [1.1, 0.9, 0.7, 0.5, 0.3]:\n",
-    "    label = 'beta = ' + str(beta)\n",
-    "    plot(frame.row[beta], label=label)\n",
-    "    \n",
-    "decorate(xlabel='Recovery rate (gamma)',\n",
-    "         ylabel='Fraction infected')\n",
-    "\n",
-    "plt.legend(bbox_to_anchor=(1.02, 1.02))\n",
-    "plt.tight_layout()\n",
-    "savefig('figs/chap13-fig03.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's often useful to separate the code that generates results from the code that plots the results, so we can run the simulations once, save the results, and then use them for different analysis, visualization, etc.\n",
-    "\n",
-    "After running `sweep_parameters`, we have a `SweepFrame` with one row for each value of `beta` and one column for each value of `gamma`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap13-fig04.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "contour(frame)\n",
-    "\n",
-    "decorate(xlabel='Recovery rate (gamma)',\n",
-    "         ylabel='Contact rate (beta)',\n",
-    "         title='Fraction infected, contour plot')\n",
-    "\n",
-    "savefig('figs/chap13-fig04.pdf')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap14soln.ipynb b/soln/chap14soln.ipynb
deleted file mode 100644
index c284087c..00000000
--- a/soln/chap14soln.ipynb
+++ /dev/null
@@ -1,763 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 14\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from previous chapters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= np.sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State (s, i, r)\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State (sir)\n",
-    "    \"\"\"\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = system.beta * i * s    \n",
-    "    recovered = system.gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    init, t0, t_end = system.init, system.t0, system.t_end\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t0] = init\n",
-    "    \n",
-    "    for t in linrange(t0, t_end):\n",
-    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_total_infected(results):\n",
-    "    \"\"\"Fraction of population infected during the simulation.\n",
-    "    \n",
-    "    results: DataFrame with columns S, I, R\n",
-    "    \n",
-    "    returns: fraction of population\n",
-    "    \"\"\"\n",
-    "    return get_first_value(results.S) - get_last_value(results.S)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_beta(beta_array, gamma):\n",
-    "    \"\"\"Sweep a range of values for beta.\n",
-    "    \n",
-    "    beta_array: array of beta values\n",
-    "    gamma: recovery rate\n",
-    "    \n",
-    "    returns: SweepSeries that maps from beta to total infected\n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    for beta in beta_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[system.beta] = calc_total_infected(results)\n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_parameters(beta_array, gamma_array):\n",
-    "    \"\"\"Sweep a range of values for beta and gamma.\n",
-    "    \n",
-    "    beta_array: array of infection rates\n",
-    "    gamma_array: array of recovery rates\n",
-    "    \n",
-    "    returns: SweepFrame with one row for each beta\n",
-    "             and one column for each gamma\n",
-    "    \"\"\"\n",
-    "    frame = SweepFrame(columns=gamma_array)\n",
-    "    for gamma in gamma_array:\n",
-    "        frame[gamma] = sweep_beta(beta_array, gamma)\n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Contact number"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the `SweepFrame` from the previous chapter, with one row for each value of `beta` and one column for each value of `gamma`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0.2</th>\n",
-       "      <th>0.4</th>\n",
-       "      <th>0.6</th>\n",
-       "      <th>0.8</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.1</th>\n",
-       "      <td>0.010756</td>\n",
-       "      <td>0.003642</td>\n",
-       "      <td>0.002191</td>\n",
-       "      <td>0.001567</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.2</th>\n",
-       "      <td>0.118984</td>\n",
-       "      <td>0.010763</td>\n",
-       "      <td>0.005447</td>\n",
-       "      <td>0.003644</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.3</th>\n",
-       "      <td>0.589095</td>\n",
-       "      <td>0.030185</td>\n",
-       "      <td>0.010771</td>\n",
-       "      <td>0.006526</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.4</th>\n",
-       "      <td>0.801339</td>\n",
-       "      <td>0.131563</td>\n",
-       "      <td>0.020917</td>\n",
-       "      <td>0.010780</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.5</th>\n",
-       "      <td>0.896577</td>\n",
-       "      <td>0.396409</td>\n",
-       "      <td>0.046140</td>\n",
-       "      <td>0.017640</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          0.2       0.4       0.6       0.8\n",
-       "0.1  0.010756  0.003642  0.002191  0.001567\n",
-       "0.2  0.118984  0.010763  0.005447  0.003644\n",
-       "0.3  0.589095  0.030185  0.010771  0.006526\n",
-       "0.4  0.801339  0.131563  0.020917  0.010780\n",
-       "0.5  0.896577  0.396409  0.046140  0.017640"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "beta_array = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 , 1.1]\n",
-    "gamma_array = [0.2, 0.4, 0.6, 0.8]\n",
-    "frame = sweep_parameters(beta_array, gamma_array)\n",
-    "frame.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(11, 4)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "frame.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following loop shows how we can loop through the columns and rows of the `SweepFrame`.  With 11 rows and 4 columns, there are 44 elements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.1 0.2 0.010756340768063644\n",
-      "0.2 0.2 0.11898421353185373\n",
-      "0.3 0.2 0.5890954199973404\n",
-      "0.4 0.2 0.8013385277185551\n",
-      "0.5 0.2 0.8965769637207062\n",
-      "0.6 0.2 0.942929291399791\n",
-      "0.7 0.2 0.966299311298026\n",
-      "0.8 0.2 0.9781518959989762\n",
-      "0.9 0.2 0.9840568957948106\n",
-      "1.0 0.2 0.9868823507202488\n",
-      "1.1 0.2 0.988148177093735\n",
-      "0.1 0.4 0.0036416926514175607\n",
-      "0.2 0.4 0.010763463373360094\n",
-      "0.3 0.4 0.030184952469116566\n",
-      "0.4 0.4 0.131562924303259\n",
-      "0.5 0.4 0.3964094037932606\n",
-      "0.6 0.4 0.5979016626615987\n",
-      "0.7 0.4 0.7284704154876106\n",
-      "0.8 0.4 0.8144604459153759\n",
-      "0.9 0.4 0.8722697237137128\n",
-      "1.0 0.4 0.9116692168795855\n",
-      "1.1 0.4 0.9386802509510287\n",
-      "0.1 0.6 0.002190722188881611\n",
-      "0.2 0.6 0.005446688837466351\n",
-      "0.3 0.6 0.010771139974975585\n",
-      "0.4 0.6 0.020916599304195316\n",
-      "0.5 0.6 0.04614035896610047\n",
-      "0.6 0.6 0.13288938996079536\n",
-      "0.7 0.6 0.3118432512847451\n",
-      "0.8 0.6 0.47832565854255393\n",
-      "0.9 0.6 0.605687582114665\n",
-      "1.0 0.6 0.7014254793376209\n",
-      "1.1 0.6 0.7738176405451065\n",
-      "0.1 0.8 0.0015665254038139675\n",
-      "0.2 0.8 0.003643953969662994\n",
-      "0.3 0.8 0.006526163529085194\n",
-      "0.4 0.8 0.010779807499500693\n",
-      "0.5 0.8 0.017639902596349066\n",
-      "0.6 0.8 0.030291868201986594\n",
-      "0.7 0.8 0.05882382948158804\n",
-      "0.8 0.8 0.13358889291095588\n",
-      "0.9 0.8 0.2668895539427739\n",
-      "1.0 0.8 0.40375121210421994\n",
-      "1.1 0.8 0.519583469821867\n"
-     ]
-    }
-   ],
-   "source": [
-    "for gamma in frame.columns:\n",
-    "    column = frame[gamma]\n",
-    "    for beta in column.index:\n",
-    "        frac_infected = column[beta]\n",
-    "        print(beta, gamma, frac_infected)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can wrap that loop in a function and plot the results.  For each element of the `SweepFrame`, we have `beta`, `gamma`, and `frac_infected`, and we plot `beta/gamma` on the x-axis and `frac_infected` on the y-axis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_sweep_frame(frame):\n",
-    "    \"\"\"Plot the values from a SweepFrame.\n",
-    "    \n",
-    "    For each (beta, gamma), compute the contact number,\n",
-    "    beta/gamma\n",
-    "    \n",
-    "    frame: SweepFrame with one row per beta, one column per gamma\n",
-    "    \"\"\"\n",
-    "    for gamma in frame.columns:\n",
-    "        column = frame[gamma]\n",
-    "        for beta in column.index:\n",
-    "            frac_infected = column[beta]\n",
-    "            plot(beta/gamma, frac_infected, 'ro')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap14-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_sweep_frame(frame)\n",
-    "\n",
-    "decorate(xlabel='Contact number (beta/gamma)',\n",
-    "         ylabel='Fraction infected')\n",
-    "\n",
-    "savefig('figs/chap14-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It turns out that the ratio `beta/gamma`, called the \"contact number\" is sufficient to predict the total number of infections; we don't have to know `beta` and `gamma` separately.\n",
-    "\n",
-    "We can see that in the previous plot: when we plot the fraction infected versus the contact number, the results fall close to a curve."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the book we figured out the relationship between $c$ and $s_{\\infty}$ analytically.  Now we can compute it for a range of values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s_inf_array = linspace(0.0001, 0.9999, 101);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c_array = log(s_inf_array) / (s_inf_array - 1);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`total_infected` is the change in $s$ from the beginning to the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frac_infected = 1 - s_inf_array\n",
-    "frac_infected_series = Series(frac_infected, index=c_array);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can plot the analytic results and compare them to the simulations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap14-fig02.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_sweep_frame(frame)\n",
-    "plot(frac_infected_series, label='Analysis')\n",
-    "\n",
-    "decorate(xlabel='Contact number (c)',\n",
-    "         ylabel='Fraction infected')\n",
-    "\n",
-    "savefig('figs/chap14-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The agreement is generally good, except for values of `c` less than 1."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  If we didn't know about contact numbers, we might have explored other possibilities, like the difference between `beta` and `gamma`, rather than their ratio.\n",
-    "\n",
-    "Write a version of `plot_sweep_frame`, called `plot_sweep_frame_difference`, that plots the fraction infected versus the difference `beta-gamma`.\n",
-    "\n",
-    "What do the results look like, and what does that imply? "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def plot_sweep_frame_difference(frame):\n",
-    "    for gamma in frame.columns:\n",
-    "        column = frame[gamma]\n",
-    "        for beta in column.index:\n",
-    "            frac_infected = column[beta]\n",
-    "            plot(beta - gamma, frac_infected, 'ro')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_sweep_frame_difference(frame)\n",
-    "\n",
-    "decorate(xlabel='Excess infection rate (infections-recoveries per day)',\n",
-    "         ylabel='Fraction infected',\n",
-    "         legend=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# The results don't fall on a line, which means that if we know the difference between\n",
-    "# `beta` and `gamma`, but not their ratio, that's not enough to predict the fraction infected."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose you run a survey at the end of the semester and find that 26% of students had the Freshman Plague at some point.\n",
-    "\n",
-    "What is your best estimate of `c`?\n",
-    "\n",
-    "Hint: if you print `frac_infected_series`, you can read off the answer. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9.211261    0.999900\n",
-       "4.642296    0.989902\n",
-       "3.987365    0.979904\n",
-       "3.612133    0.969906\n",
-       "3.350924    0.959908\n",
-       "3.151808    0.949910\n",
-       "2.991711    0.939912\n",
-       "2.858363    0.929914\n",
-       "2.744467    0.919916\n",
-       "2.645332    0.909918\n",
-       "2.557767    0.899920\n",
-       "2.479505    0.889922\n",
-       "2.408879    0.879924\n",
-       "2.344627    0.869926\n",
-       "2.285771    0.859928\n",
-       "2.231541    0.849930\n",
-       "2.181315    0.839932\n",
-       "2.134590    0.829934\n",
-       "2.090947    0.819936\n",
-       "2.050040    0.809938\n",
-       "2.011573    0.799940\n",
-       "1.975299    0.789942\n",
-       "1.941002    0.779944\n",
-       "1.908499    0.769946\n",
-       "1.877628    0.759948\n",
-       "1.848249    0.749950\n",
-       "1.820238    0.739952\n",
-       "1.793487    0.729954\n",
-       "1.767898    0.719956\n",
-       "1.743384    0.709958\n",
-       "              ...   \n",
-       "1.181034    0.290042\n",
-       "1.173263    0.280044\n",
-       "1.165630    0.270046\n",
-       "1.158132    0.260048\n",
-       "1.150765    0.250050\n",
-       "1.143524    0.240052\n",
-       "1.136407    0.230054\n",
-       "1.129409    0.220056\n",
-       "1.122527    0.210058\n",
-       "1.115758    0.200060\n",
-       "1.109099    0.190062\n",
-       "1.102547    0.180064\n",
-       "1.096099    0.170066\n",
-       "1.089751    0.160068\n",
-       "1.083503    0.150070\n",
-       "1.077350    0.140072\n",
-       "1.071291    0.130074\n",
-       "1.065323    0.120076\n",
-       "1.059444    0.110078\n",
-       "1.053651    0.100080\n",
-       "1.047943    0.090082\n",
-       "1.042317    0.080084\n",
-       "1.036772    0.070086\n",
-       "1.031305    0.060088\n",
-       "1.025914    0.050090\n",
-       "1.020598    0.040092\n",
-       "1.015356    0.030094\n",
-       "1.010185    0.020096\n",
-       "1.005083    0.010098\n",
-       "1.000050    0.000100\n",
-       "Length: 101, dtype: float64"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "frac_infected_series"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# It looks like the fraction infected is 0.26 when the contact number is about 1.16"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap15soln.ipynb b/soln/chap15soln.ipynb
deleted file mode 100644
index f3a0031b..00000000
--- a/soln/chap15soln.ipynb
+++ /dev/null
@@ -1,735 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 15\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The coffee cooling problem\n",
-    "\n",
-    "I'll use a `State` object to store the initial temperature.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>T</th>\n",
-       "      <td>90</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "T    90\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init = State(T=90)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And a `System` object to contain the system parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>T    90\n",
-       "dtype: int64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>volume</th>\n",
-       "      <td>300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>r</th>\n",
-       "      <td>0.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>T_env</th>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "init      T    90\n",
-       "dtype: int64\n",
-       "volume                     300\n",
-       "r                         0.01\n",
-       "T_env                       22\n",
-       "t_0                          0\n",
-       "t_end                       30\n",
-       "dt                           1\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee = System(init=init,\n",
-    "                volume=300,\n",
-    "                r=0.01,\n",
-    "                T_env=22,\n",
-    "                t_0=0,\n",
-    "                t_end=30,\n",
-    "                dt=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The update function implements Newton's law of cooling."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the thermal transfer model.\n",
-    "    \n",
-    "    state: State (temp)\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State (temp)\n",
-    "    \"\"\"\n",
-    "    r, T_env, dt = system.r, system.T_env, system.dt\n",
-    "    \n",
-    "    T = state.T\n",
-    "    T += -r * (T - T_env) * dt\n",
-    "    \n",
-    "    return State(T=T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how it works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>T</th>\n",
-       "      <td>89.32</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "T    89.32\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "update_func(init, 0, coffee)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a version of `run_simulation` that uses `linrange` to make an array of time steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "    \n",
-    "    Add a TimeFrame to the System: results\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \"\"\"\n",
-    "    init = system.init\n",
-    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t_0] = init\n",
-    "    ts = linrange(t_0, t_end, dt)\n",
-    "    \n",
-    "    for t in ts:\n",
-    "        frame.row[t+dt] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how it works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>T</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>89.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>88.6468</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>87.9803</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>87.3205</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>86.6673</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>86.0207</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>85.3804</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>84.7466</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>84.1192</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>83.498</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>82.883</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>82.2742</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>81.6714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>81.0747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>80.484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>79.8991</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>79.3201</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>78.7469</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>78.1795</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>77.6177</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>77.0615</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>76.5109</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>75.9658</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>75.4261</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>74.8919</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>74.3629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>73.8393</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>73.3209</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>72.8077</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>72.2996</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          T\n",
-       "0        90\n",
-       "1     89.32\n",
-       "2   88.6468\n",
-       "3   87.9803\n",
-       "4   87.3205\n",
-       "5   86.6673\n",
-       "6   86.0207\n",
-       "7   85.3804\n",
-       "8   84.7466\n",
-       "9   84.1192\n",
-       "10   83.498\n",
-       "11   82.883\n",
-       "12  82.2742\n",
-       "13  81.6714\n",
-       "14  81.0747\n",
-       "15   80.484\n",
-       "16  79.8991\n",
-       "17  79.3201\n",
-       "18  78.7469\n",
-       "19  78.1795\n",
-       "20  77.6177\n",
-       "21  77.0615\n",
-       "22  76.5109\n",
-       "23  75.9658\n",
-       "24  75.4261\n",
-       "25  74.8919\n",
-       "26  74.3629\n",
-       "27  73.8393\n",
-       "28  73.3209\n",
-       "29  72.8077\n",
-       "30  72.2996"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = run_simulation(coffee, update_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(results.T, label='coffee')\n",
-    "decorate(xlabel='Time (minutes)',\n",
-    "         ylabel='Temperature (C)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the final temperature:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "72.2996253904031"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee.T_final = get_last_value(results.T)\n",
-    "T_final = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Encapsulation\n",
-    "\n",
-    "Before we go on, let's define a function to initialize `System` objects with relevant parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(T_init, r, volume, t_end):\n",
-    "    \"\"\"Makes a System object with the given parameters.\n",
-    "\n",
-    "    T_init: initial temperature in degC\n",
-    "    r: heat transfer rate, in 1/min\n",
-    "    volume: volume of liquid in mL\n",
-    "    t_end: end time of simulation\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(T=T_init)\n",
-    "                   \n",
-    "    return System(init=init,\n",
-    "                  r=r, \n",
-    "                  volume=volume,\n",
-    "                  temp=T_init,\n",
-    "                  t_0=0, \n",
-    "                  t_end=t_end, \n",
-    "                  dt=1,\n",
-    "                  T_env=22)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we use it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "72.2996253904031"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee = make_system(T_init=90, r=0.01, volume=300, t_end=30)\n",
-    "results = run_simulation(coffee, update_func)\n",
-    "T_final = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:**  Simulate the temperature of 50 mL of milk with a starting temperature of 5 degC, in a vessel with the same insulation, for 15 minutes, and plot the results.\n",
-    "\n",
-    "By trial and error, find a value for `r` that makes the final temperature close to 20 C."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20.00135627897414"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "milk = make_system(T_init=5, t_end=15, r=0.133, volume=50)\n",
-    "results = run_simulation(milk, update_func)\n",
-    "T_final = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(results.T, label='milk')\n",
-    "decorate(xlabel='Time (minutes)',\n",
-    "         ylabel='Temperature (C)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap16soln.ipynb b/soln/chap16soln.ipynb
deleted file mode 100644
index b49cc932..00000000
--- a/soln/chap16soln.ipynb
+++ /dev/null
@@ -1,1506 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 16\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Code from previous notebooks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the thermal transfer model.\n",
-    "    \n",
-    "    state: State (temp)\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State (temp)\n",
-    "    \"\"\"\n",
-    "    r, T_env, dt = system.r, system.T_env, system.dt\n",
-    "    \n",
-    "    T = state.T\n",
-    "    T += -r * (T - T_env) * dt\n",
-    "    \n",
-    "    return State(T=T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "    \n",
-    "    Add a TimeFrame to the System: results\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \"\"\"\n",
-    "    init = system.init\n",
-    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t_0] = init\n",
-    "    ts = linrange(t_0, t_end, dt)\n",
-    "    \n",
-    "    for t in ts:\n",
-    "        frame.row[t+dt] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(T_init, r, volume, t_end):\n",
-    "    \"\"\"Makes a System object with the given parameters.\n",
-    "\n",
-    "    T_init: initial temperature in degC\n",
-    "    r: heat transfer rate, in 1/min\n",
-    "    volume: volume of liquid in mL\n",
-    "    t_end: end time of simulation\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(T=T_init)\n",
-    "                   \n",
-    "    return System(init=init,\n",
-    "                  r=r, \n",
-    "                  volume=volume,\n",
-    "                  temp=T_init,\n",
-    "                  t_0=0, \n",
-    "                  t_end=t_end, \n",
-    "                  dt=1,\n",
-    "                  T_env=22)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using `root_bisect`\n",
-    "\n",
-    "As a simple example, let's find the roots of this function; that is, the values of `x` that make the result 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def func(x):\n",
-    "    return (x-1) * (x-2) * (x-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`modsim.py` provides `root_bisect`, which searches for a root by bisection.  The first argument is the function whose roots we want.  The second argument is an interval that contains a root."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>converged</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>root</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "converged    True\n",
-       "root            1\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res = root_bisect(func, [0.5, 1.5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is an object that contains the root that was found and other information."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.0"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we provide a different interval, we find a different root."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>converged</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>root</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "converged    True\n",
-       "root            2\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res = root_bisect(func, [1.5, 2.5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If the interval doesn't contain a root, the results explain the error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>converged</th>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>flag</th>\n",
-       "      <td>4.000000 and 5.000000 do not bracket a root</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "converged                                          False\n",
-       "flag         4.000000 and 5.000000 do not bracket a root\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res = root_bisect(func, [4, 5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We want to find the value of `r` that makes the final temperature 70, so we define an \"error function\" that takes `r` as a parameter and returns the difference between the final temperature and the goal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func1(r):\n",
-    "    \"\"\"Runs a simulation and returns the `error`.\n",
-    "    \n",
-    "    r: heat transfer rate, in 1/min\n",
-    "    \n",
-    "    returns: difference between final temp and 70 C\n",
-    "    \"\"\"\n",
-    "    system = make_system(T_init=90, r=r, volume=300, t_end=30)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    T_final = get_last_value(results.T)\n",
-    "    return T_final - 70"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With `r=0.01`, we end up a little too warm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.2996253904030937"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "error_func1(r=0.01)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With `r=0.02`, we end up too cold."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-10.907066281994297"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "error_func1(r=0.02)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The return value from `root_bisect` is an array with a single element, the estimated value of `r`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>converged</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>root</th>\n",
-       "      <td>0.0115431</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "converged         True\n",
-       "root         0.0115431\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res = root_bisect(error_func1, [0.01, 0.02])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.011543084681034089"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r_coffee = res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we run the simulation with the estimated value of `r`, the final temperature is 70 C, as expected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "69.99999985860761"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=30)\n",
-    "results = run_simulation(coffee, update_func)\n",
-    "T_final = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  When you call `root_bisect`, it calls `error_func1` several times.  To see how this works, add a print statement to `error_func1` and run `root_bisect` again."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Repeat this process to estimate `r_milk`, given that it starts at 5 C and reaches 20 C after 15 minutes.  \n",
-    "\n",
-    "Before you use `root_bisect`, you might want to try a few values for `r_milk` and see how close you can get by trial and error.  Here's an initial guess to get you started:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18.499850754390966"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r_milk = 0.1\n",
-    "milk = make_system(T_init=5, r=r_milk, volume=50, t_end=15)\n",
-    "results = run_simulation(milk, update_func)\n",
-    "T_final = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def error_func2(r):\n",
-    "    \"\"\"Runs a simulation and returns the `error`.\n",
-    "    \n",
-    "    r: heat transfer rate, in 1/min\n",
-    "    \n",
-    "    returns: difference between final temp and 20C\n",
-    "    \"\"\"\n",
-    "    system = make_system(T_init=5, r=r, volume=50, t_end=15)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    T_final = get_last_value(results.T)\n",
-    "    return T_final - 20"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-1.500149245609034"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "error_func2(r=0.1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.4018656744898585"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "error_func2(r=0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>converged</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>root</th>\n",
-       "      <td>0.132961</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "converged        True\n",
-       "root         0.132961\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "res = root_bisect(error_func2, [0.1, 0.2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.13296079039573666"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "r_milk = res.root"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20.00000003602163"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "milk = make_system(T_init=5, r=r_milk, volume=50, t_end=15)\n",
-    "results = run_simulation(milk, update_func)\n",
-    "T_final = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Mixing liquids"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function takes `System` objects that represent two liquids, computes the temperature of the mixture, and returns a new `System` object that represents the mixture."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mix(system1, system2):\n",
-    "    \"\"\"Simulates the mixture of two liquids.\n",
-    "    \n",
-    "    system1: System representing coffee\n",
-    "    system2: System representing milk\n",
-    "    \n",
-    "    returns: System representing the mixture\n",
-    "    \"\"\"\n",
-    "    assert system1.t_end == system2.t_end\n",
-    "    \n",
-    "    V1, V2 = system1.volume, system2.volume\n",
-    "    T1, T2 = system1.temp, system2.temp\n",
-    "    \n",
-    "    V_mix = V1 + V2\n",
-    "    T_mix = (V1 * T1 + V2 * T2) / V_mix\n",
-    "    \n",
-    "    return make_system(T_init=T_mix,\n",
-    "                       r=system1.r,\n",
-    "                       volume=V_mix,\n",
-    "                       t_end=30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`mix` requires the `System` objects to have `temp` as a system variable.  `make_system` initializes this variable;\n",
-    "the following function makes sure it gets updated when we run a simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_and_set(system):\n",
-    "    \"\"\"Run a simulation and set the final temperature.\n",
-    "    \n",
-    "    system: System\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    system.temp = get_last_value(results.T)\n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mixing immediately\n",
-    "\n",
-    "Next here's what we get if we add the milk immediately."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "90"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=30)\n",
-    "coffee.temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "milk = make_system(T_init=5, r=r_milk, volume=50, t_end=30)\n",
-    "milk.temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "77.85714285714286"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mix_first = mix(coffee, milk)\n",
-    "mix_first.temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "61.4285713124277"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mix_results = run_and_set(mix_first)\n",
-    "mix_first.temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mixing at the end\n",
-    "\n",
-    "First we'll see what happens if we add the milk at the end.  We'll simulate the coffee and the milk separately."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "69.99999985860761"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee_results = run_and_set(coffee)\n",
-    "coffee.temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "21.76470589082862"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "milk_results = run_and_set(milk)\n",
-    "milk.temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap16-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(coffee_results.T, label='coffee')\n",
-    "plot(milk_results.T, '--', label='milk')\n",
-    "\n",
-    "decorate(xlabel='Time (minutes)',\n",
-    "         ylabel='Temperature (C)',\n",
-    "         loc='center left')\n",
-    "\n",
-    "savefig('figs/chap16-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what happens when we mix them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "63.10924357749633"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mix_last = mix(coffee, milk)\n",
-    "mix_last.temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.6806722650686297"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mix_last.temp - mix_first.temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function takes `t_add`, which is the time when the milk is added, and returns the final temperature."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_and_mix(t_add, t_total):\n",
-    "    \"\"\"Simulates two liquids and them mixes them at t_add.\n",
-    "    \n",
-    "    t_add: time in minutes\n",
-    "    t_total: total time to simulate, min\n",
-    "    \n",
-    "    returns: final temperature\n",
-    "    \"\"\"\n",
-    "    coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=t_add)\n",
-    "    coffee_results = run_and_set(coffee)\n",
-    "    \n",
-    "    milk = make_system(T_init=5, r=r_milk, volume=50, t_end=t_add)\n",
-    "    milk_results = run_and_set(milk)\n",
-    "    \n",
-    "    mixture = mix(coffee, milk)\n",
-    "    mixture.t_end = t_total - t_add\n",
-    "    results = run_and_set(mixture)\n",
-    "\n",
-    "    return mixture.temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can try it out with a few values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "61.4285713124277"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run_and_mix(t_add=0, t_total=30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "62.9028090119359"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run_and_mix(t_add=15, t_total=30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "63.10924357749633"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run_and_mix(t_add=30, t_total=30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then sweep a range of values for `t_add`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sweep = SweepSeries()\n",
-    "for t_add in linspace(0, 30, 11):\n",
-    "    sweep[t_add] = run_and_mix(t_add, 30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the result looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap16-fig02.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(sweep, label='final temp', color='C2')\n",
-    "decorate(xlabel='Time added (min)',\n",
-    "         ylabel='Final temperature (C)')\n",
-    "\n",
-    "savefig('figs/chap16-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can use the analytic result to compute temperature as a function of time.  The following function is similar to `run_simulation`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_analysis(system):\n",
-    "    \"\"\"Computes temperature using the analytic solution.\n",
-    "        \n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    T_env, r = system.T_env, system.r\n",
-    "    \n",
-    "    T_init = system.init.T    \n",
-    "    ts = linspace(0, system.t_end)\n",
-    "    \n",
-    "    T_array = T_env + (T_init - T_env) * exp(-r * ts)\n",
-    "    \n",
-    "    # to be consistent with run_simulation,\n",
-    "    # we put the array into a TimeFrame\n",
-    "    return TimeFrame(T_array, index=ts, columns=['T'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it.  From the analysis (see `chap16sympy.ipynb`), we have the computed value of `r_coffee2`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>T    90\n",
-       "dtype: int64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>r</th>\n",
-       "      <td>0.0116102</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>volume</th>\n",
-       "      <td>300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>temp</th>\n",
-       "      <td>90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>T_env</th>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "init      T    90\n",
-       "dtype: int64\n",
-       "r                    0.0116102\n",
-       "volume                     300\n",
-       "temp                        90\n",
-       "t_0                          0\n",
-       "t_end                       30\n",
-       "dt                           1\n",
-       "T_env                       22\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r_coffee2 = 0.011610223142273859\n",
-    "coffee2 = make_system(T_init=90, r=r_coffee2, volume=300, t_end=30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "70.0"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = run_analysis(coffee2)\n",
-    "T_final_analysis = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can compare to the results from simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "69.99999985860761"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=30)\n",
-    "results = run_simulation(coffee, update_func)\n",
-    "T_final_simulation = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "They are identical except for a small roundoff error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.4139239112864743e-07"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "T_final_analysis - T_final_simulation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:**  Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
-    "\n",
-    "Hint: Think about the simplest way to represent the behavior of a refrigerator in this model.  The change you make to test this variation of the problem should be very small!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "## A refrigerator keeps the milk at a constant temperature,\n",
-    "## so it is like a container with r = 0.\n",
-    "\n",
-    "## With r_milk = 0, it is best to add the milk at the beginning."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap17soln.ipynb b/soln/chap17soln.ipynb
deleted file mode 100644
index ee8707e9..00000000
--- a/soln/chap17soln.ipynb
+++ /dev/null
@@ -1,642 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 17\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data\n",
-    "\n",
-    "We have data from Pacini and Bergman (1986), \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 23: 113-122.."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>glucose</th>\n",
-       "      <th>insulin</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>time</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>92</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>350</td>\n",
-       "      <td>26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>287</td>\n",
-       "      <td>130</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>251</td>\n",
-       "      <td>85</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>240</td>\n",
-       "      <td>51</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>216</td>\n",
-       "      <td>49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>211</td>\n",
-       "      <td>45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>205</td>\n",
-       "      <td>41</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>196</td>\n",
-       "      <td>35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>192</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>172</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>163</td>\n",
-       "      <td>27</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>142</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>124</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>105</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62</th>\n",
-       "      <td>92</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>84</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>77</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>82</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>102</th>\n",
-       "      <td>81</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>122</th>\n",
-       "      <td>82</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142</th>\n",
-       "      <td>82</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>162</th>\n",
-       "      <td>85</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>182</th>\n",
-       "      <td>90</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      glucose  insulin\n",
-       "time                  \n",
-       "0          92       11\n",
-       "2         350       26\n",
-       "4         287      130\n",
-       "6         251       85\n",
-       "8         240       51\n",
-       "10        216       49\n",
-       "12        211       45\n",
-       "14        205       41\n",
-       "16        196       35\n",
-       "19        192       30\n",
-       "22        172       30\n",
-       "27        163       27\n",
-       "32        142       30\n",
-       "42        124       22\n",
-       "52        105       15\n",
-       "62         92       15\n",
-       "72         84       11\n",
-       "82         77       10\n",
-       "92         82        8\n",
-       "102        81       11\n",
-       "122        82        7\n",
-       "142        82        8\n",
-       "162        85        8\n",
-       "182        90        7"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = pd.read_csv('data/glucose_insulin.csv', index_col='time')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the glucose time series looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(data.glucose, 'bo', label='glucose')\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration (mg/dL)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the insulin time series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(data.insulin, 'go', label='insulin')\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the book, I put them in a single figure, using `subplot`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap17-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "subplot(2, 1, 1)\n",
-    "plot(data.glucose, 'bo', label='glucose')\n",
-    "decorate(ylabel='Concentration (mg/dL)')\n",
-    "\n",
-    "subplot(2, 1, 2)\n",
-    "plot(data.insulin, 'go', label='insulin')\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')\n",
-    "\n",
-    "savefig('figs/chap17-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Interpolation\n",
-    "\n",
-    "We have measurements of insulin concentration at discrete points in time, but we need to estimate it at intervening points.  We'll use `interpolate`, which takes a `Series` and returns a function:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The return value from `interpolate` is a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function modsim.modsim.interpolate.<locals>.wrapper(x)>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "I = interpolate(data.insulin)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use the result, `I`, to estimate the insulin level at any point in time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "68.0"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "I(7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`I` can also take an array of time and return an array of estimates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_0 = get_first_label(data)\n",
-    "t_end = get_last_label(data)\n",
-    "ts = linrange(t_0, t_end, endpoint=True)\n",
-    "I(ts)\n",
-    "type(ts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the interpolated values look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap17-fig02.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(data.insulin, 'go', label='insulin data')\n",
-    "plot(ts, I(ts), color='green', label='interpolated')\n",
-    "\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')\n",
-    "\n",
-    "savefig('figs/chap17-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** [Read the documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html) of `scipy.interpolate.interp1d`.  Pass a keyword argument to `interpolate` to specify one of the other kinds of interpolation, and run the code again to see what it looks like. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "I = interpolate(data.insulin, kind='cubic')\n",
-    "plot(data.insulin, 'go', label='insulin data')\n",
-    "plot(ts, I(ts), color='green', label='interpolated')\n",
-    "\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Interpolate the glucose data and generate a plot, similar to the previous one, that shows the data points and the interpolated curve evaluated at the time values in `ts`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "G = interpolate(data.glucose)\n",
-    "plot(data.glucose, 'bo', label='gluciose data')\n",
-    "plot(ts, G(ts), color='blue', label='interpolated')\n",
-    "\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration (mg/dL)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "def interpolate(series, **options):\n",
-      "    \"\"\"Creates an interpolation function.\n",
-      "\n",
-      "    series: Series object\n",
-      "    options: any legal options to scipy.interpolate.interp1d\n",
-      "\n",
-      "    returns: function that maps from the index of the series to values\n",
-      "    \"\"\"\n",
-      "    if has_nan(series.index):\n",
-      "        msg = \"\"\"The Series you passed to interpolate contains\n",
-      "                 NaN values in the index, which would result in\n",
-      "                 undefined behavior.  So I'm putting a stop to that.\"\"\"\n",
-      "        raise ValueError(msg)\n",
-      "\n",
-      "    if not is_strictly_increasing(series.index):\n",
-      "        msg = \"\"\"The Series you passed to interpolate has an index\n",
-      "                 that is not strictly increasing, which would result in\n",
-      "                 undefined behavior.  So I'm putting a stop to that.\"\"\"\n",
-      "        raise ValueError(msg)\n",
-      "\n",
-      "    # make the interpolate function extrapolate past the ends of\n",
-      "    # the range, unless `options` already specifies a value for `fill_value`\n",
-      "    underride(options, fill_value='extrapolate')\n",
-      "\n",
-      "    # call interp1d, which returns a new function object\n",
-      "    x = magnitudes(series.index)\n",
-      "    y = magnitudes(series.values)\n",
-      "    interp_func = interp1d(x, y, **options)\n",
-      "    units = get_units(series.values[0])\n",
-      "\n",
-      "    def wrapper(x):\n",
-      "        return interp_func(magnitudes(x)) * units\n",
-      "    return wrapper\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "source_code(interpolate)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap18soln.ipynb b/soln/chap18soln.ipynb
deleted file mode 100644
index 85d815f1..00000000
--- a/soln/chap18soln.ipynb
+++ /dev/null
@@ -1,1981 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 18\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter\n",
-    "\n",
-    "Read the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv('data/glucose_insulin.csv', index_col='time');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Interpolate the insulin data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function modsim.modsim.interpolate.<locals>.wrapper(x)>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "I = interpolate(data.insulin)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The glucose minimal model\n",
-    "\n",
-    "I'll cheat by starting with parameters that fit the data roughly; then we'll see how to improve them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>G0</th>\n",
-       "      <td>290.00000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k1</th>\n",
-       "      <td>0.03000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k2</th>\n",
-       "      <td>0.02000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k3</th>\n",
-       "      <td>0.00001</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "G0    290.00000\n",
-       "k1      0.03000\n",
-       "k2      0.02000\n",
-       "k3      0.00001\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = Params(G0 = 290,\n",
-    "                k1 = 0.03,\n",
-    "                k2 = 0.02,\n",
-    "                k3 = 1e-05)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a version of `make_system` that takes the parameters and data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params, data):\n",
-    "    \"\"\"Makes a System object with the given parameters.\n",
-    "    \n",
-    "    params: sequence of G0, k1, k2, k3\n",
-    "    data: DataFrame with `glucose` and `insulin`\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    G0, k1, k2, k3 = params\n",
-    "    \n",
-    "    Gb = data.glucose[0]\n",
-    "    Ib = data.insulin[0]\n",
-    "    I = interpolate(data.insulin)\n",
-    "    \n",
-    "    t_0 = get_first_label(data)\n",
-    "    t_end = get_last_label(data)\n",
-    "\n",
-    "    init = State(G=G0, X=0)\n",
-    "    \n",
-    "    return System(params,\n",
-    "                  init=init, Gb=Gb, Ib=Ib, I=I,\n",
-    "                  t_0=t_0, t_end=t_end, dt=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>G0</th>\n",
-       "      <td>290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k1</th>\n",
-       "      <td>0.03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k2</th>\n",
-       "      <td>0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k3</th>\n",
-       "      <td>1e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>G    290.0\n",
-       "X      0.0\n",
-       "dtype: float64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gb</th>\n",
-       "      <td>92</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Ib</th>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>I</th>\n",
-       "      <td>&lt;function interpolate.&lt;locals&gt;.wrapper at 0x7f...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>182</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "G0                                                     290\n",
-       "k1                                                    0.03\n",
-       "k2                                                    0.02\n",
-       "k3                                                   1e-05\n",
-       "init                  G    290.0\n",
-       "X      0.0\n",
-       "dtype: float64\n",
-       "Gb                                                      92\n",
-       "Ib                                                      11\n",
-       "I        <function interpolate.<locals>.wrapper at 0x7f...\n",
-       "t_0                                                      0\n",
-       "t_end                                                  182\n",
-       "dt                                                       2\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the update function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Updates the glucose minimal model.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time in min\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    G, X = state\n",
-    "    k1, k2, k3 = system.k1, system.k2, system.k3 \n",
-    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
-    "    dt = system.dt\n",
-    "        \n",
-    "    dGdt = -k1 * (G - Gb) - X*G\n",
-    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
-    "    \n",
-    "    G += dGdt * dt\n",
-    "    X += dXdt * dt\n",
-    "\n",
-    "    return State(G=G, X=X)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before running the simulation, it is always a good idea to test the update function using the initial conditions.  In this case we can veryify that the results are at least qualitatively correct."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>G</th>\n",
-       "      <td>278.12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>X</th>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "G    278.12\n",
-       "X      0.00\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "update_func(system.init, system.t_0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now `run_simulation` is pretty much the same as it always is."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    init = system.init\n",
-    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t_0] = init\n",
-    "    ts = linrange(t_0, t_end, dt)\n",
-    "    \n",
-    "    for t in ts:\n",
-    "        frame.row[t+dt] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The results are in a `TimeFrame` object with one column per state variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>G</th>\n",
-       "      <th>X</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>290.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>278.120000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>266.952800</td>\n",
-       "      <td>0.000300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>256.295460</td>\n",
-       "      <td>0.002668</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>245.070140</td>\n",
-       "      <td>0.004041</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>233.905138</td>\n",
-       "      <td>0.004680</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>223.201651</td>\n",
-       "      <td>0.005252</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>212.984844</td>\n",
-       "      <td>0.005722</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>203.288207</td>\n",
-       "      <td>0.006093</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>194.133461</td>\n",
-       "      <td>0.006330</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>185.547835</td>\n",
-       "      <td>0.006490</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>177.526606</td>\n",
-       "      <td>0.006610</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>170.048014</td>\n",
-       "      <td>0.006726</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>163.077697</td>\n",
-       "      <td>0.006813</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>156.590997</td>\n",
-       "      <td>0.006872</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>150.563255</td>\n",
-       "      <td>0.006929</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>144.962829</td>\n",
-       "      <td>0.007008</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>139.753191</td>\n",
-       "      <td>0.007108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>134.901294</td>\n",
-       "      <td>0.007172</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>130.392302</td>\n",
-       "      <td>0.007201</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>126.210925</td>\n",
-       "      <td>0.007197</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>122.341665</td>\n",
-       "      <td>0.007161</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>118.769029</td>\n",
-       "      <td>0.007094</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>115.477698</td>\n",
-       "      <td>0.007003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>112.451743</td>\n",
-       "      <td>0.006887</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>109.675836</td>\n",
-       "      <td>0.006747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>107.135308</td>\n",
-       "      <td>0.006585</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>104.816180</td>\n",
-       "      <td>0.006402</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>102.705192</td>\n",
-       "      <td>0.006226</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58</th>\n",
-       "      <td>100.784058</td>\n",
-       "      <td>0.006057</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>124</th>\n",
-       "      <td>86.390694</td>\n",
-       "      <td>0.001095</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>126</th>\n",
-       "      <td>86.538102</td>\n",
-       "      <td>0.000973</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>128</th>\n",
-       "      <td>86.697421</td>\n",
-       "      <td>0.000858</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>130</th>\n",
-       "      <td>86.866797</td>\n",
-       "      <td>0.000750</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>132</th>\n",
-       "      <td>87.044539</td>\n",
-       "      <td>0.000648</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>134</th>\n",
-       "      <td>87.229105</td>\n",
-       "      <td>0.000552</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>136</th>\n",
-       "      <td>87.419090</td>\n",
-       "      <td>0.000462</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>87.613215</td>\n",
-       "      <td>0.000377</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>140</th>\n",
-       "      <td>87.810314</td>\n",
-       "      <td>0.000298</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142</th>\n",
-       "      <td>88.009328</td>\n",
-       "      <td>0.000224</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>144</th>\n",
-       "      <td>88.209296</td>\n",
-       "      <td>0.000155</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>146</th>\n",
-       "      <td>88.409343</td>\n",
-       "      <td>0.000089</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>148</th>\n",
-       "      <td>88.609033</td>\n",
-       "      <td>0.000026</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>150</th>\n",
-       "      <td>88.807970</td>\n",
-       "      <td>-0.000036</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>152</th>\n",
-       "      <td>89.005799</td>\n",
-       "      <td>-0.000094</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>154</th>\n",
-       "      <td>89.202200</td>\n",
-       "      <td>-0.000150</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>156</th>\n",
-       "      <td>89.396887</td>\n",
-       "      <td>-0.000204</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>158</th>\n",
-       "      <td>89.589604</td>\n",
-       "      <td>-0.000256</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>160</th>\n",
-       "      <td>89.780123</td>\n",
-       "      <td>-0.000306</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>162</th>\n",
-       "      <td>89.968242</td>\n",
-       "      <td>-0.000354</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>164</th>\n",
-       "      <td>90.153784</td>\n",
-       "      <td>-0.000400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>166</th>\n",
-       "      <td>90.336592</td>\n",
-       "      <td>-0.000446</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>168</th>\n",
-       "      <td>90.516892</td>\n",
-       "      <td>-0.000492</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>170</th>\n",
-       "      <td>90.694895</td>\n",
-       "      <td>-0.000538</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>172</th>\n",
-       "      <td>90.870797</td>\n",
-       "      <td>-0.000585</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>174</th>\n",
-       "      <td>91.044781</td>\n",
-       "      <td>-0.000631</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>91.217018</td>\n",
-       "      <td>-0.000678</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>178</th>\n",
-       "      <td>91.387668</td>\n",
-       "      <td>-0.000725</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>180</th>\n",
-       "      <td>91.556880</td>\n",
-       "      <td>-0.000772</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>182</th>\n",
-       "      <td>91.724792</td>\n",
-       "      <td>-0.000819</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>92 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              G         X\n",
-       "0    290.000000  0.000000\n",
-       "2    278.120000  0.000000\n",
-       "4    266.952800  0.000300\n",
-       "6    256.295460  0.002668\n",
-       "8    245.070140  0.004041\n",
-       "10   233.905138  0.004680\n",
-       "12   223.201651  0.005252\n",
-       "14   212.984844  0.005722\n",
-       "16   203.288207  0.006093\n",
-       "18   194.133461  0.006330\n",
-       "20   185.547835  0.006490\n",
-       "22   177.526606  0.006610\n",
-       "24   170.048014  0.006726\n",
-       "26   163.077697  0.006813\n",
-       "28   156.590997  0.006872\n",
-       "30   150.563255  0.006929\n",
-       "32   144.962829  0.007008\n",
-       "34   139.753191  0.007108\n",
-       "36   134.901294  0.007172\n",
-       "38   130.392302  0.007201\n",
-       "40   126.210925  0.007197\n",
-       "42   122.341665  0.007161\n",
-       "44   118.769029  0.007094\n",
-       "46   115.477698  0.007003\n",
-       "48   112.451743  0.006887\n",
-       "50   109.675836  0.006747\n",
-       "52   107.135308  0.006585\n",
-       "54   104.816180  0.006402\n",
-       "56   102.705192  0.006226\n",
-       "58   100.784058  0.006057\n",
-       "..          ...       ...\n",
-       "124   86.390694  0.001095\n",
-       "126   86.538102  0.000973\n",
-       "128   86.697421  0.000858\n",
-       "130   86.866797  0.000750\n",
-       "132   87.044539  0.000648\n",
-       "134   87.229105  0.000552\n",
-       "136   87.419090  0.000462\n",
-       "138   87.613215  0.000377\n",
-       "140   87.810314  0.000298\n",
-       "142   88.009328  0.000224\n",
-       "144   88.209296  0.000155\n",
-       "146   88.409343  0.000089\n",
-       "148   88.609033  0.000026\n",
-       "150   88.807970 -0.000036\n",
-       "152   89.005799 -0.000094\n",
-       "154   89.202200 -0.000150\n",
-       "156   89.396887 -0.000204\n",
-       "158   89.589604 -0.000256\n",
-       "160   89.780123 -0.000306\n",
-       "162   89.968242 -0.000354\n",
-       "164   90.153784 -0.000400\n",
-       "166   90.336592 -0.000446\n",
-       "168   90.516892 -0.000492\n",
-       "170   90.694895 -0.000538\n",
-       "172   90.870797 -0.000585\n",
-       "174   91.044781 -0.000631\n",
-       "176   91.217018 -0.000678\n",
-       "178   91.387668 -0.000725\n",
-       "180   91.556880 -0.000772\n",
-       "182   91.724792 -0.000819\n",
-       "\n",
-       "[92 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following plot shows the results of the simulation along with the actual glucose data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap18-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "subplot(2, 1, 1)\n",
-    "\n",
-    "plot(results.G, 'b-', label='simulation')\n",
-    "plot(data.glucose, 'bo', label='glucose data')\n",
-    "decorate(ylabel='Concentration (mg/dL)')\n",
-    "\n",
-    "subplot(2, 1, 2)\n",
-    "\n",
-    "plot(results.X, 'C1', label='remote insulin')\n",
-    "\n",
-    "decorate(xlabel='Time (min)', \n",
-    "         ylabel='Concentration (arbitrary units)')\n",
-    "\n",
-    "savefig('figs/chap18-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Numerical solution\n",
-    "\n",
-    "Now let's solve the differential equation numerically using `run_ode_solver`, which is an implementation of Ralston's method.\n",
-    "\n",
-    "Instead of an update function, we provide a slope function that evaluates the right-hand side of the differential equations.\n",
-    "\n",
-    "We don't have to do the update part; the solver does it for us."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the glucose minimal model.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time in min\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: derivatives of G and X\n",
-    "    \"\"\"\n",
-    "    G, X = state\n",
-    "    k1, k2, k3 = system.k1, system.k2, system.k3 \n",
-    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
-    "    \n",
-    "    dGdt = -k1 * (G - Gb) - X*G\n",
-    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
-    "    \n",
-    "    return dGdt, dXdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-5.9399999999999995, 0.0)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run the ODE solver."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2, details = run_ode_solver(system, slope_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`details` is a `ModSimSeries` object with information about how the solver worked."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                                                 True\n",
-       "message    The solver successfully reached the end of the...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`results` is a `TimeFrame` with one row for each time step and one column for each state variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>G</th>\n",
-       "      <th>X</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>290.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>278.476400</td>\n",
-       "      <td>0.000150</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>267.464465</td>\n",
-       "      <td>0.001478</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>255.898956</td>\n",
-       "      <td>0.003303</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>244.423982</td>\n",
-       "      <td>0.004284</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>233.420932</td>\n",
-       "      <td>0.004880</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>222.905305</td>\n",
-       "      <td>0.005393</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>212.909446</td>\n",
-       "      <td>0.005808</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>203.454111</td>\n",
-       "      <td>0.006108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>194.566003</td>\n",
-       "      <td>0.006306</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>186.240256</td>\n",
-       "      <td>0.006439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>178.458757</td>\n",
-       "      <td>0.006559</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>171.185070</td>\n",
-       "      <td>0.006662</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>164.395933</td>\n",
-       "      <td>0.006738</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>158.067772</td>\n",
-       "      <td>0.006793</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>152.171731</td>\n",
-       "      <td>0.006864</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>146.670366</td>\n",
-       "      <td>0.006956</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>141.531725</td>\n",
-       "      <td>0.007040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>136.741833</td>\n",
-       "      <td>0.007089</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>132.285774</td>\n",
-       "      <td>0.007105</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>128.148400</td>\n",
-       "      <td>0.007088</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>124.314528</td>\n",
-       "      <td>0.007042</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>120.769107</td>\n",
-       "      <td>0.006967</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>117.496427</td>\n",
-       "      <td>0.006868</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>114.481355</td>\n",
-       "      <td>0.006746</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>111.709352</td>\n",
-       "      <td>0.006601</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>109.166524</td>\n",
-       "      <td>0.006434</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>106.839640</td>\n",
-       "      <td>0.006260</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>104.710408</td>\n",
-       "      <td>0.006093</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58</th>\n",
-       "      <td>102.762318</td>\n",
-       "      <td>0.005932</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>124</th>\n",
-       "      <td>86.966171</td>\n",
-       "      <td>0.001074</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>126</th>\n",
-       "      <td>87.088335</td>\n",
-       "      <td>0.000957</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>128</th>\n",
-       "      <td>87.222428</td>\n",
-       "      <td>0.000846</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>130</th>\n",
-       "      <td>87.366704</td>\n",
-       "      <td>0.000741</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>132</th>\n",
-       "      <td>87.519569</td>\n",
-       "      <td>0.000642</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>134</th>\n",
-       "      <td>87.679564</td>\n",
-       "      <td>0.000549</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>136</th>\n",
-       "      <td>87.845355</td>\n",
-       "      <td>0.000462</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>88.015722</td>\n",
-       "      <td>0.000381</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>140</th>\n",
-       "      <td>88.189550</td>\n",
-       "      <td>0.000304</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142</th>\n",
-       "      <td>88.365824</td>\n",
-       "      <td>0.000232</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>144</th>\n",
-       "      <td>88.543616</td>\n",
-       "      <td>0.000164</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>146</th>\n",
-       "      <td>88.722428</td>\n",
-       "      <td>0.000099</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>148</th>\n",
-       "      <td>88.901796</td>\n",
-       "      <td>0.000036</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>150</th>\n",
-       "      <td>89.081302</td>\n",
-       "      <td>-0.000024</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>152</th>\n",
-       "      <td>89.260563</td>\n",
-       "      <td>-0.000082</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>154</th>\n",
-       "      <td>89.439233</td>\n",
-       "      <td>-0.000137</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>156</th>\n",
-       "      <td>89.617001</td>\n",
-       "      <td>-0.000191</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>158</th>\n",
-       "      <td>89.793584</td>\n",
-       "      <td>-0.000242</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>160</th>\n",
-       "      <td>89.968729</td>\n",
-       "      <td>-0.000291</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>162</th>\n",
-       "      <td>90.142209</td>\n",
-       "      <td>-0.000339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>164</th>\n",
-       "      <td>90.313821</td>\n",
-       "      <td>-0.000385</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>166</th>\n",
-       "      <td>90.483737</td>\n",
-       "      <td>-0.000432</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>168</th>\n",
-       "      <td>90.652108</td>\n",
-       "      <td>-0.000479</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>170</th>\n",
-       "      <td>90.819076</td>\n",
-       "      <td>-0.000526</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>172</th>\n",
-       "      <td>90.984773</td>\n",
-       "      <td>-0.000573</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>174</th>\n",
-       "      <td>91.149324</td>\n",
-       "      <td>-0.000620</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>91.312844</td>\n",
-       "      <td>-0.000667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>178</th>\n",
-       "      <td>91.475442</td>\n",
-       "      <td>-0.000714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>180</th>\n",
-       "      <td>91.637217</td>\n",
-       "      <td>-0.000762</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>182</th>\n",
-       "      <td>91.798265</td>\n",
-       "      <td>-0.000809</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>92 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              G         X\n",
-       "0    290.000000  0.000000\n",
-       "2    278.476400  0.000150\n",
-       "4    267.464465  0.001478\n",
-       "6    255.898956  0.003303\n",
-       "8    244.423982  0.004284\n",
-       "10   233.420932  0.004880\n",
-       "12   222.905305  0.005393\n",
-       "14   212.909446  0.005808\n",
-       "16   203.454111  0.006108\n",
-       "18   194.566003  0.006306\n",
-       "20   186.240256  0.006439\n",
-       "22   178.458757  0.006559\n",
-       "24   171.185070  0.006662\n",
-       "26   164.395933  0.006738\n",
-       "28   158.067772  0.006793\n",
-       "30   152.171731  0.006864\n",
-       "32   146.670366  0.006956\n",
-       "34   141.531725  0.007040\n",
-       "36   136.741833  0.007089\n",
-       "38   132.285774  0.007105\n",
-       "40   128.148400  0.007088\n",
-       "42   124.314528  0.007042\n",
-       "44   120.769107  0.006967\n",
-       "46   117.496427  0.006868\n",
-       "48   114.481355  0.006746\n",
-       "50   111.709352  0.006601\n",
-       "52   109.166524  0.006434\n",
-       "54   106.839640  0.006260\n",
-       "56   104.710408  0.006093\n",
-       "58   102.762318  0.005932\n",
-       "..          ...       ...\n",
-       "124   86.966171  0.001074\n",
-       "126   87.088335  0.000957\n",
-       "128   87.222428  0.000846\n",
-       "130   87.366704  0.000741\n",
-       "132   87.519569  0.000642\n",
-       "134   87.679564  0.000549\n",
-       "136   87.845355  0.000462\n",
-       "138   88.015722  0.000381\n",
-       "140   88.189550  0.000304\n",
-       "142   88.365824  0.000232\n",
-       "144   88.543616  0.000164\n",
-       "146   88.722428  0.000099\n",
-       "148   88.901796  0.000036\n",
-       "150   89.081302 -0.000024\n",
-       "152   89.260563 -0.000082\n",
-       "154   89.439233 -0.000137\n",
-       "156   89.617001 -0.000191\n",
-       "158   89.793584 -0.000242\n",
-       "160   89.968729 -0.000291\n",
-       "162   90.142209 -0.000339\n",
-       "164   90.313821 -0.000385\n",
-       "166   90.483737 -0.000432\n",
-       "168   90.652108 -0.000479\n",
-       "170   90.819076 -0.000526\n",
-       "172   90.984773 -0.000573\n",
-       "174   91.149324 -0.000620\n",
-       "176   91.312844 -0.000667\n",
-       "178   91.475442 -0.000714\n",
-       "180   91.637217 -0.000762\n",
-       "182   91.798265 -0.000809\n",
-       "\n",
-       "[92 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting the results from `run_simulation` and `run_ode_solver`, we can see that they are not very different."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap18-fig02.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(results.G, 'C0', label='run_simulation')\n",
-    "plot(results2.G, 'C2--', label='run_ode_solver')\n",
-    "\n",
-    "decorate(xlabel='Time (min)', ylabel='Concentration (mg/dL)')\n",
-    "\n",
-    "savefig('figs/chap18-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The differences in `G` are less than 2%."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      0.000000\n",
-       "2     -0.127982\n",
-       "4     -0.191302\n",
-       "6      0.154946\n",
-       "8      0.264360\n",
-       "10     0.207439\n",
-       "12     0.132947\n",
-       "14     0.035413\n",
-       "16    -0.081544\n",
-       "18    -0.222312\n",
-       "20    -0.371789\n",
-       "22    -0.522334\n",
-       "24    -0.664226\n",
-       "26    -0.801867\n",
-       "28    -0.934267\n",
-       "30    -1.057014\n",
-       "32    -1.164200\n",
-       "34    -1.256633\n",
-       "36    -1.345996\n",
-       "38    -1.431350\n",
-       "40    -1.511900\n",
-       "42    -1.586993\n",
-       "44    -1.656117\n",
-       "46    -1.718120\n",
-       "48    -1.772875\n",
-       "50    -1.820363\n",
-       "52    -1.860658\n",
-       "54    -1.893922\n",
-       "56    -1.915011\n",
-       "58    -1.925084\n",
-       "         ...   \n",
-       "124   -0.661724\n",
-       "126   -0.631811\n",
-       "128   -0.601917\n",
-       "130   -0.572193\n",
-       "132   -0.542770\n",
-       "134   -0.513756\n",
-       "136   -0.485244\n",
-       "138   -0.457312\n",
-       "140   -0.430024\n",
-       "142   -0.403431\n",
-       "144   -0.377577\n",
-       "146   -0.352881\n",
-       "148   -0.329311\n",
-       "150   -0.306834\n",
-       "152   -0.285416\n",
-       "154   -0.265021\n",
-       "156   -0.245616\n",
-       "158   -0.227165\n",
-       "160   -0.209635\n",
-       "162   -0.192991\n",
-       "164   -0.177201\n",
-       "166   -0.162620\n",
-       "168   -0.149158\n",
-       "170   -0.136734\n",
-       "172   -0.125270\n",
-       "174   -0.114695\n",
-       "176   -0.104943\n",
-       "178   -0.095953\n",
-       "180   -0.087669\n",
-       "182   -0.080037\n",
-       "Name: G, Length: 92, dtype: float64"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diff = results.G - results2.G\n",
-    "percent_diff = diff / results2.G * 100\n",
-    "percent_diff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.9252541088148694"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max(abs(percent_diff))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:**  Our solution to the differential equations is only approximate because we used a finite step size, `dt=2` minutes.\n",
-    "\n",
-    "If we make the step size smaller, we expect the solution to be more accurate.  Run the simulation with `dt=1` and compare the results.  What is the largest relative error between the two solutions?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                                                 True\n",
-       "message    The solver successfully reached the end of the...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system3 = System(system, dt=1)\n",
-    "results3, details = run_ode_solver(system3, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot(results2.G, 'C2--', label='run_ode_solver (dt=2)')\n",
-    "plot(results3.G, 'C3:', label='run_ode_solver (dt=1)')\n",
-    "\n",
-    "decorate(xlabel='Time (m)', ylabel='mg/dL')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      0.000000\n",
-       "2      0.009372\n",
-       "4      0.061820\n",
-       "6      0.031912\n",
-       "8      0.012039\n",
-       "10     0.011405\n",
-       "12     0.010124\n",
-       "14     0.009322\n",
-       "16     0.007946\n",
-       "18     0.008430\n",
-       "20     0.009144\n",
-       "22     0.011609\n",
-       "24     0.013346\n",
-       "26     0.015050\n",
-       "28     0.016702\n",
-       "30     0.019412\n",
-       "32     0.021838\n",
-       "34     0.022601\n",
-       "36     0.023359\n",
-       "38     0.024093\n",
-       "40     0.024789\n",
-       "42     0.025433\n",
-       "44     0.026113\n",
-       "46     0.026703\n",
-       "48     0.027200\n",
-       "50     0.027601\n",
-       "52     0.027907\n",
-       "54     0.028811\n",
-       "56     0.029488\n",
-       "58     0.029959\n",
-       "         ...   \n",
-       "124    0.010964\n",
-       "126    0.010626\n",
-       "128    0.010277\n",
-       "130    0.009921\n",
-       "132    0.009561\n",
-       "134    0.009198\n",
-       "136    0.008834\n",
-       "138    0.008472\n",
-       "140    0.008113\n",
-       "142    0.007758\n",
-       "144    0.007359\n",
-       "146    0.006976\n",
-       "148    0.006608\n",
-       "150    0.006255\n",
-       "152    0.005917\n",
-       "154    0.005594\n",
-       "156    0.005284\n",
-       "158    0.004988\n",
-       "160    0.004705\n",
-       "162    0.004434\n",
-       "164    0.004127\n",
-       "166    0.003841\n",
-       "168    0.003574\n",
-       "170    0.003326\n",
-       "172    0.003096\n",
-       "174    0.002881\n",
-       "176    0.002681\n",
-       "178    0.002495\n",
-       "180    0.002322\n",
-       "182    0.002161\n",
-       "Name: G, Length: 92, dtype: float64"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "diff = (results2.G - results3.G).dropna()\n",
-    "percent_diff = diff / results2.G * 100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.061819954534205544"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "max(abs(percent_diff))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "Here's the source code for `run_ode_solver` if you'd like to know how it works.\n",
-    "\n",
-    "Notice that `run_ode_solver` is another name for `run_ralston`, which implements [Ralston's method](https://en.wikipedia.org/wiki/List_of_Runge–Kutta_methods)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "def run_ralston(system, slope_func, **options):\n",
-      "    \"\"\"Computes a numerical solution to a differential equation.\n",
-      "\n",
-      "    `system` must contain `init` with initial conditions,\n",
-      "     and `t_end` with the end time.\n",
-      "\n",
-      "     `system` may contain `t_0` to override the default, 0\n",
-      "\n",
-      "    It can contain any other parameters required by the slope function.\n",
-      "\n",
-      "    `options` can be ...\n",
-      "\n",
-      "    system: System object\n",
-      "    slope_func: function that computes slopes\n",
-      "\n",
-      "    returns: TimeFrame\n",
-      "    \"\"\"\n",
-      "    # the default message if nothing changes\n",
-      "    msg = \"The solver successfully reached the end of the integration interval.\"\n",
-      "\n",
-      "    # get parameters from system\n",
-      "    init, t_0, t_end, dt = check_system(system, slope_func)\n",
-      "\n",
-      "    # make the TimeFrame\n",
-      "    frame = TimeFrame(columns=init.index)\n",
-      "    frame.row[t_0] = init\n",
-      "    ts = linrange(t_0, t_end, dt) * get_units(t_end)\n",
-      "\n",
-      "    event_func = options.get('events', None)\n",
-      "    z1 = np.nan\n",
-      "\n",
-      "    def project(y1, t1, slopes, dt):\n",
-      "        t2 = t1 + dt\n",
-      "        y2 = [y + slope * dt for y, slope in zip(y1, slopes)]\n",
-      "        return y2, t2\n",
-      "\n",
-      "    # run the solver\n",
-      "    for t1 in ts:\n",
-      "        y1 = frame.row[t1]\n",
-      "\n",
-      "        # evaluate the slopes at the start of the time step\n",
-      "        slopes1 = slope_func(y1, t1, system)\n",
-      "\n",
-      "        # evaluate the slopes at the two-thirds point\n",
-      "        y_mid, t_mid = project(y1, t1, slopes1, 2 * dt / 3)\n",
-      "        slopes2 = slope_func(y_mid, t_mid, system)\n",
-      "\n",
-      "        # compute the weighted sum of the slopes\n",
-      "        slopes = [(k1 + 3 * k2) / 4 for k1, k2 in zip(slopes1, slopes2)]\n",
-      "\n",
-      "        # compute the next time stamp\n",
-      "        y2, t2 = project(y1, t1, slopes, dt)\n",
-      "\n",
-      "        # check for a terminating event\n",
-      "        if event_func:\n",
-      "            z2 = event_func(y2, t2, system)\n",
-      "            if z1 * z2 < 0:\n",
-      "                scale = magnitude(z1 / (z1 - z2))\n",
-      "                y2, t2 = project(y1, t1, slopes, scale * dt)\n",
-      "                frame.row[t2] = y2\n",
-      "                msg = \"A termination event occurred.\"\n",
-      "                break\n",
-      "            else:\n",
-      "                z1 = z2\n",
-      "\n",
-      "        # store the results\n",
-      "        frame.row[t2] = y2\n",
-      "\n",
-      "    details = ModSimSeries(dict(success=True, message=msg))\n",
-      "    return frame, details\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "source_code(run_ode_solver)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Related reading:** You might be interested in this article about [people making a DIY artificial pancreas](https://www.bloomberg.com/news/features/2018-08-08/the-250-biohack-that-s-revolutionizing-life-with-diabetes)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap20soln.ipynb b/soln/chap20soln.ipynb
deleted file mode 100644
index 1c6cb450..00000000
--- a/soln/chap20soln.ipynb
+++ /dev/null
@@ -1,1624 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 20\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dropping pennies\n",
-    "\n",
-    "I'll start by getting the units we need from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "second"
-      ],
-      "text/latex": [
-       "$\\mathrm{second}$"
-      ],
-      "text/plain": [
-       "<Unit('second')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And defining the initial state."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>381 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "y             381 meter\n",
-       "v    0.0 meter / second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init = State(y=381 * m, \n",
-    "             v=0 * m/s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Acceleration due to gravity is about 9.8 m / s$^2$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "9.8 meter/second<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$9.8\\ \\frac{\\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "9.8 <Unit('meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "g = 9.8 * m/s**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll start with a duration of 10 seconds and step size 0.1 second."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "10 second"
-      ],
-      "text/latex": [
-       "$10\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "10 <Unit('second')>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_end = 10 * s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.1 second"
-      ],
-      "text/latex": [
-       "$0.1\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "0.1 <Unit('second')>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dt = 0.1 * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we make a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>y             381 meter\n",
-       "v    0.0 meter / secon...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>10 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>0.1 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "init     y             381 meter\n",
-       "v    0.0 meter / secon...\n",
-       "g                                  9.8 meter / second ** 2\n",
-       "t_end                                            10 second\n",
-       "dt                                              0.1 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = System(init=init, g=g, t_end=t_end, dt=dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And define the slope function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object containing `g`\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    g = system.g    \n",
-    "\n",
-    "    dydt = v\n",
-    "    dvdt = -g\n",
-    "    \n",
-    "    return dydt, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's always a good idea to test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0 meter / second\n",
-      "-9.8 meter / second ** 2\n"
-     ]
-    }
-   ],
-   "source": [
-    "dydt, dvdt = slope_func(system.init, 0, system)\n",
-    "print(dydt)\n",
-    "print(dvdt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we're ready to call `run_ode_solver`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                                                 True\n",
-       "message    The solver successfully reached the end of the...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>y</th>\n",
-       "      <th>v</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.0</th>\n",
-       "      <td>381 meter</td>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1</th>\n",
-       "      <td>380.951 meter</td>\n",
-       "      <td>-0.9800000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.2</th>\n",
-       "      <td>380.80400000000003 meter</td>\n",
-       "      <td>-1.9600000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.3</th>\n",
-       "      <td>380.559 meter</td>\n",
-       "      <td>-2.9400000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.4</th>\n",
-       "      <td>380.216 meter</td>\n",
-       "      <td>-3.9200000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                            y                                   v\n",
-       "0.0                 381 meter                  0.0 meter / second\n",
-       "0.1             380.951 meter  -0.9800000000000001 meter / second\n",
-       "0.2  380.80400000000003 meter  -1.9600000000000002 meter / second\n",
-       "0.3             380.559 meter  -2.9400000000000004 meter / second\n",
-       "0.4             380.216 meter  -3.9200000000000004 meter / second"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>y</th>\n",
-       "      <th>v</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>9.6</th>\n",
-       "      <td>-70.58399999999979 meter</td>\n",
-       "      <td>-94.08000000000003 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.7</th>\n",
-       "      <td>-80.0409999999998 meter</td>\n",
-       "      <td>-95.06000000000003 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.8</th>\n",
-       "      <td>-89.5959999999998 meter</td>\n",
-       "      <td>-96.04000000000003 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.9</th>\n",
-       "      <td>-99.24899999999981 meter</td>\n",
-       "      <td>-97.02000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10.0</th>\n",
-       "      <td>-108.99999999999982 meter</td>\n",
-       "      <td>-98.00000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                              y                                  v\n",
-       "9.6    -70.58399999999979 meter  -94.08000000000003 meter / second\n",
-       "9.7     -80.0409999999998 meter  -95.06000000000003 meter / second\n",
-       "9.8     -89.5959999999998 meter  -96.04000000000003 meter / second\n",
-       "9.9    -99.24899999999981 meter  -97.02000000000004 meter / second\n",
-       "10.0  -108.99999999999982 meter  -98.00000000000004 meter / second"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's position as a function of time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap20-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_position(results):\n",
-    "    plot(results.y, label='y')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "\n",
-    "plot_position(results)\n",
-    "savefig('figs/chap20-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Onto the sidewalk\n",
-    "\n",
-    "To figure out when the penny hit the sidewalk, we can use `crossings`, which finds the times where a `Series` passes through a given value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([8.81788535])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_crossings = crossings(results.y, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For this example there should be just one crossing, the time when the penny hits the sidewalk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "8.81788534972054 second"
-      ],
-      "text/latex": [
-       "$8.81788534972054\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "8.81788534972054 <Unit('second')>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_sidewalk = t_crossings[0] * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can compare that to the exact result.  Without air resistance, we have\n",
-    "\n",
-    "$v = -g t$\n",
-    "\n",
-    "and\n",
-    "\n",
-    "$y = 381 - g t^2 / 2$\n",
-    "\n",
-    "Setting $y=0$ and solving for $t$ yields\n",
-    "\n",
-    "$t = \\sqrt{\\frac{2 y_{init}}{g}}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "8.817885349720552 second"
-      ],
-      "text/latex": [
-       "$8.817885349720552\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "8.817885349720552 <Unit('second')>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sqrt(2 * init.y / g)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The estimate is accurate to about 9 decimal places."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Events\n",
-    "\n",
-    "Instead of running the simulation until the penny goes through the sidewalk, it would be better to detect the point where the penny hits the sidewalk and stop.  `run_ode_solver` provides exactly the tool we need, **event functions**.\n",
-    "\n",
-    "Here's an event function that returns the height of the penny above the sidewalk:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Return the height of the penny above the sidewalk.\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we pass it to `run_ode_solver`.  The solver should run until the event function returns 0, and then terminate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The message from the solver indicates the solver stopped because the event we wanted to detect happened.\n",
-    "\n",
-    "Here are the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>y</th>\n",
-       "      <th>v</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>8.500000</th>\n",
-       "      <td>26.97500000000022 meter</td>\n",
-       "      <td>-83.29999999999998 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.600000</th>\n",
-       "      <td>18.596000000000224 meter</td>\n",
-       "      <td>-84.27999999999999 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.700000</th>\n",
-       "      <td>10.119000000000225 meter</td>\n",
-       "      <td>-85.25999999999999 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.800000</th>\n",
-       "      <td>1.5440000000002243 meter</td>\n",
-       "      <td>-86.24 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.817802</th>\n",
-       "      <td>-4.440892098500626e-16 meter</td>\n",
-       "      <td>-86.41446327683617 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                     y                                  v\n",
-       "8.500000       26.97500000000022 meter  -83.29999999999998 meter / second\n",
-       "8.600000      18.596000000000224 meter  -84.27999999999999 meter / second\n",
-       "8.700000      10.119000000000225 meter  -85.25999999999999 meter / second\n",
-       "8.800000      1.5440000000002243 meter              -86.24 meter / second\n",
-       "8.817802  -4.440892098500626e-16 meter  -86.41446327683617 meter / second"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the `events` option, the solver returns the actual time steps it computed, which are not necessarily equally spaced. \n",
-    "\n",
-    "The last time step is when the event occurred:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "8.81780237518735 second"
-      ],
-      "text/latex": [
-       "$8.81780237518735\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "8.81780237518735 <Unit('second')>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_sidewalk = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is accurate to about 4 decimal places.\n",
-    "\n",
-    "We can also check the velocity of the penny when it hits the sidewalk:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-86.41446327683617 meter/second"
-      ],
-      "text/latex": [
-       "$-86.41446327683617\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "-86.41446327683617 <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v_sidewalk = get_last_value(results.v)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And convert to kilometers per hour."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-311.09206779661025 kilometer/hour"
-      ],
-      "text/latex": [
-       "$-311.09206779661025\\ \\frac{\\mathrm{kilometer}}{\\mathrm{hour}}$"
-      ],
-      "text/plain": [
-       "-311.09206779661025 <Unit('kilometer / hour')>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "km = UNITS.kilometer\n",
-    "h = UNITS.hour\n",
-    "v_sidewalk.to(km / h)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If there were no air resistance, the penny would hit the sidewalk (or someone's head) at more than 300 km/h.\n",
-    "\n",
-    "So it's a good thing there is air resistance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Under the hood\n",
-    "\n",
-    "Here is the source code for `crossings` so you can see what's happening under the hood:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "def crossings(series, value):\n",
-      "    \"\"\"Find the labels where the series passes through value.\n",
-      "\n",
-      "    The labels in series must be increasing numerical values.\n",
-      "\n",
-      "    series: Series\n",
-      "    value: number\n",
-      "\n",
-      "    returns: sequence of labels\n",
-      "    \"\"\"\n",
-      "    values = magnitudes(series - value)\n",
-      "    interp = InterpolatedUnivariateSpline(series.index, values)\n",
-      "    return interp.roots()\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "source_code(crossings)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The [documentation of InterpolatedUnivariateSpline is here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.InterpolatedUnivariateSpline.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Here's a question from the web site [Ask an Astronomer](http://curious.astro.cornell.edu/about-us/39-our-solar-system/the-earth/other-catastrophes/57-how-long-would-it-take-the-earth-to-fall-into-the-sun-intermediate):\n",
-    "\n",
-    "\"If the Earth suddenly stopped orbiting the Sun, I know eventually it would be pulled in by the Sun's gravity and hit it. How long would it take the Earth to hit the Sun? I imagine it would go slowly at first and then pick up speed.\"\n",
-    "\n",
-    "Use `run_ode_solver` to answer this question.\n",
-    "\n",
-    "Here are some suggestions about how to proceed:\n",
-    "\n",
-    "1.  Look up the Law of Universal Gravitation and any constants you need.  I suggest you work entirely in SI units: meters, kilograms, and Newtons.\n",
-    "\n",
-    "2.  When the distance between the Earth and the Sun gets small, this system behaves badly, so you should use an event function to stop when the surface of Earth reaches the surface of the Sun.\n",
-    "\n",
-    "3. Express your answer in days, and plot the results as millions of kilometers versus days.\n",
-    "\n",
-    "If you read the reply by Dave Rothstein, you will see other ways to solve the problem, and a good discussion of the modeling decisions behind them.\n",
-    "\n",
-    "You might also be interested to know that [it's actually not that easy to get to the Sun](https://www.theatlantic.com/science/archive/2018/08/parker-solar-probe-launch-nasa/567197/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "astronomical_unit"
-      ],
-      "text/latex": [
-       "$\\mathrm{astronomical_unit}$"
-      ],
-      "text/plain": [
-       "<Unit('astronomical_unit')>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "N = UNITS.newton\n",
-    "kg = UNITS.kilogram\n",
-    "m = UNITS.meter\n",
-    "AU = UNITS.astronomical_unit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>r</th>\n",
-       "      <td>149597870691.0 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "r    149597870691.0 meter\n",
-       "v      0.0 meter / second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "r_0 = (1 * AU).to_base_units()\n",
-    "v_0 = 0 * m / s\n",
-    "init = State(r=r_0,\n",
-    "             v=v_0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>r    149597870691.0 meter\n",
-       "v      0.0 meter / s...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>G</th>\n",
-       "      <td>6.674e-11 meter ** 2 * newton / kilogram ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>m1</th>\n",
-       "      <td>1.989e+30 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>r_final</th>\n",
-       "      <td>701879000.0 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>m2</th>\n",
-       "      <td>5.972e+24 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>10000000.0 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>100000.0 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "init       r    149597870691.0 meter\n",
-       "v      0.0 meter / s...\n",
-       "G              6.674e-11 meter ** 2 * newton / kilogram ** 2\n",
-       "m1                                        1.989e+30 kilogram\n",
-       "r_final                                    701879000.0 meter\n",
-       "m2                                        5.972e+24 kilogram\n",
-       "t_end                                      10000000.0 second\n",
-       "dt                                           100000.0 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "radius_earth = 6.371e6 * m\n",
-    "radius_sun = 695.508e6 * m\n",
-    "\n",
-    "t_end = 1e7 * s\n",
-    "dt = t_end / 100\n",
-    "\n",
-    "system = System(init=init,\n",
-    "                G=6.674e-11 * N / kg**2 * m**2,\n",
-    "                m1=1.989e30 * kg,\n",
-    "                r_final=radius_sun + radius_earth,\n",
-    "                m2=5.972e24 * kg,\n",
-    "                t_end=t_end,\n",
-    "                dt=dt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def universal_gravitation(state, system):\n",
-    "    \"\"\"Computes gravitational force.\n",
-    "    \n",
-    "    state: State object with distance r\n",
-    "    system: System object with m1, m2, and G\n",
-    "    \"\"\"\n",
-    "    r, v = state\n",
-    "    G, m1, m2 = system.G, system.m1, system.m2\n",
-    "    \n",
-    "    force = G * m1 * m2 / r**2\n",
-    "    return force"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "3.5423376937972604×10<sup>22</sup> newton"
-      ],
-      "text/latex": [
-       "$3.5423376937972604\\times 10^{22}\\ \\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "3.5423376937972604e+22 <Unit('newton')>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "universal_gravitation(init, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object containing `m2`\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    m2 = system.m2    \n",
-    "\n",
-    "    force = universal_gravitation(state, system)\n",
-    "    dydt = v\n",
-    "    dvdt = -force / m2\n",
-    "    \n",
-    "    return dydt, dvdt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0 <Unit('meter / second')>,\n",
-       " -0.005931576848287442 <Unit('newton / kilogram')>)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def event_func(state, t, system):\n",
-    "    r, v = state\n",
-    "    return r - system.r_final"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "148895991691.0 meter"
-      ],
-      "text/latex": [
-       "$148895991691.0\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "148895991691.0 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "event_func(init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5600110.3392641265"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "t_event = get_last_label(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "64.81609188963108 day"
-      ],
-      "text/latex": [
-       "$64.81609188963108\\ \\mathrm{day}$"
-      ],
-      "text/plain": [
-       "64.81609188963108 <Unit('day')>"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "seconds = t_event * s\n",
-    "days = seconds.to(UNITS.day)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results.index /= 60 * 60 * 24"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results.r /= 1e9"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot(results.r, label='r')\n",
-    "\n",
-    "decorate(xlabel='Time (day)',\n",
-    "         ylabel='Distance from sun (million km)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap21soln.ipynb b/soln/chap21soln.ipynb
deleted file mode 100644
index e1678e07..00000000
--- a/soln/chap21soln.ipynb
+++ /dev/null
@@ -1,1526 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 21\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### With air resistance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we'll add air resistance using the [drag equation](https://en.wikipedia.org/wiki/Drag_equation)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll start by getting the units we'll need from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "kilogram"
-      ],
-      "text/latex": [
-       "$\\mathrm{kilogram}$"
-      ],
-      "text/plain": [
-       "<Unit('kilogram')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now I'll create a `Params` object to contain the quantities we need.  Using a Params object is convenient for grouping the system parameters in a way that's easy to read (and double-check)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>height</th>\n",
-       "      <td>381 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_init</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>0.0025 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>diameter</th>\n",
-       "      <td>0.019 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>18.0 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "height                      381 meter\n",
-       "v_init             0.0 meter / second\n",
-       "g             9.8 meter / second ** 2\n",
-       "mass                  0.0025 kilogram\n",
-       "diameter                  0.019 meter\n",
-       "rho         1.2 kilogram / meter ** 3\n",
-       "v_term            18.0 meter / second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = Params(height = 381 * m,\n",
-    "                v_init = 0 * m / s,\n",
-    "                g = 9.8 * m/s**2,\n",
-    "                mass = 2.5e-3 * kg,\n",
-    "                diameter = 19e-3 * m,\n",
-    "                rho = 1.2 * kg/m**3,\n",
-    "                v_term = 18 * m / s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can pass the `Params` object `make_system` which computes some additional parameters and defines `init`.\n",
-    "\n",
-    "`make_system` uses the given radius to compute `area` and the given `v_term` to compute the drag coefficient `C_d`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    diameter, mass = params.diameter, params.mass\n",
-    "    g, rho = params.g, params.rho, \n",
-    "    v_init, v_term = params.v_init, params.v_term\n",
-    "    height = params.height\n",
-    "    \n",
-    "    area = np.pi * (diameter/2)**2\n",
-    "    C_d = 2 * mass * g / (rho * area * v_term**2)\n",
-    "    init = State(y=height, v=v_init)\n",
-    "    t_end = 30 * s\n",
-    "    dt = t_end / 100\n",
-    "    \n",
-    "    return System(params, area=area, C_d=C_d, \n",
-    "                  init=init, t_end=t_end, dt=dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>height</th>\n",
-       "      <td>381 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_init</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>0.0025 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>diameter</th>\n",
-       "      <td>0.019 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>18.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>0.0002835287369864788 meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.4445009981135434 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>y             381 meter\n",
-       "v    0.0 meter / secon...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>30 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>0.3 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "height                                              381 meter\n",
-       "v_init                                     0.0 meter / second\n",
-       "g                                     9.8 meter / second ** 2\n",
-       "mass                                          0.0025 kilogram\n",
-       "diameter                                          0.019 meter\n",
-       "rho                                 1.2 kilogram / meter ** 3\n",
-       "v_term                                    18.0 meter / second\n",
-       "area                         0.0002835287369864788 meter ** 2\n",
-       "C_d                          0.4445009981135434 dimensionless\n",
-       "init        y             381 meter\n",
-       "v    0.0 meter / secon...\n",
-       "t_end                                               30 second\n",
-       "dt                                                 0.3 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the slope function, including acceleration due to gravity and drag."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    rho, C_d, g = system.rho, system.C_d, system.g\n",
-    "    area, mass = system.area, system.mass\n",
-    "    \n",
-    "    f_drag = rho * v**2 * C_d * area / 2\n",
-    "    a_drag = f_drag / mass\n",
-    "    \n",
-    "    dydt = v\n",
-    "    dvdt = -g + a_drag\n",
-    "    \n",
-    "    return dydt, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, let's test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0 <Unit('meter / second')>, -9.8 <Unit('meter / second ** 2')>)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use the same event function as in the previous chapter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Return the height of the penny above the sidewalk.\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>y</th>\n",
-       "      <th>v</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.0</th>\n",
-       "      <td>381 meter</td>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.3</th>\n",
-       "      <td>380.559 meter</td>\n",
-       "      <td>-2.913855777777778 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.6</th>\n",
-       "      <td>379.2553998560887 meter</td>\n",
-       "      <td>-5.676321799192868 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.9</th>\n",
-       "      <td>377.15535917269835 meter</td>\n",
-       "      <td>-8.166374221525693 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.2</th>\n",
-       "      <td>374.35521895425103 meter</td>\n",
-       "      <td>-10.311720070623485 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                            y                                   v\n",
-       "0.0                 381 meter                  0.0 meter / second\n",
-       "0.3             380.559 meter   -2.913855777777778 meter / second\n",
-       "0.6   379.2553998560887 meter   -5.676321799192868 meter / second\n",
-       "0.9  377.15535917269835 meter   -8.166374221525693 meter / second\n",
-       "1.2  374.35521895425103 meter  -10.311720070623485 meter / second"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>y</th>\n",
-       "      <th>v</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>21.300000</th>\n",
-       "      <td>20.486949941310563 meter</td>\n",
-       "      <td>-17.99999999508715 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21.600000</th>\n",
-       "      <td>15.086949942543686 meter</td>\n",
-       "      <td>-17.99999999642989 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21.900000</th>\n",
-       "      <td>9.686949943439785 meter</td>\n",
-       "      <td>-17.99999999740564 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22.200000</th>\n",
-       "      <td>4.286949944090969 meter</td>\n",
-       "      <td>-17.999999998114706 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22.438164</th>\n",
-       "      <td>0.0 meter</td>\n",
-       "      <td>-17.99999999852377 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  y                                   v\n",
-       "21.300000  20.486949941310563 meter   -17.99999999508715 meter / second\n",
-       "21.600000  15.086949942543686 meter   -17.99999999642989 meter / second\n",
-       "21.900000   9.686949943439785 meter   -17.99999999740564 meter / second\n",
-       "22.200000   4.286949944090969 meter  -17.999999998114706 meter / second\n",
-       "22.438164                 0.0 meter   -17.99999999852377 meter / second"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final height is close to 0, as expected.\n",
-    "\n",
-    "Interestingly, the final velocity is not exactly terminal velocity, which suggests that there are some numerical errors.\n",
-    "\n",
-    "We can get the flight time from `results`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "22.438163885803732 second"
-      ],
-      "text/latex": [
-       "$22.438163885803732\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "22.438163885803732 <Unit('second')>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_sidewalk = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the plot of position as a function of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap21-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_position(results):\n",
-    "    plot(results.y)\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "    \n",
-    "plot_position(results)\n",
-    "savefig('figs/chap21-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And velocity as a function of time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_velocity(results):\n",
-    "    plot(results.v, color='C1', label='v')\n",
-    "        \n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')\n",
-    "    \n",
-    "plot_velocity(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From an initial velocity of 0, the penny accelerates downward until it reaches terminal velocity; after that, velocity is constant."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Run the simulation with an initial velocity, downward, that exceeds the penny's terminal velocity.  Hint: You can create a new `Params` object based on an existing one, like this:\n",
-    "\n",
-    "`params2 = Params(params, v_init=-30 * m/s)`\n",
-    "\n",
-    "What do you expect to happen?  Plot velocity and position as a function of time, and see if they are consistent with your prediction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>height</th>\n",
-       "      <td>381 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_init</th>\n",
-       "      <td>-30.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>0.0025 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>diameter</th>\n",
-       "      <td>0.019 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>18.0 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "height                      381 meter\n",
-       "v_init           -30.0 meter / second\n",
-       "g             9.8 meter / second ** 2\n",
-       "mass                  0.0025 kilogram\n",
-       "diameter                  0.019 meter\n",
-       "rho         1.2 kilogram / meter ** 3\n",
-       "v_term            18.0 meter / second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "v_init = -30 * m / s\n",
-    "params2 = Params(params, v_init=v_init)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A termination event occurred.'"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system2 = make_system(params2)\n",
-    "results, details = run_ode_solver(system2, slope_func, events=event_func, max_step=0.5)\n",
-    "details.message"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_velocity(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose we drop a quarter from the Empire State Building and find that its flight time is 19.1 seconds.  Use this measurement to estimate the terminal velocity.\n",
-    "\n",
-    "1. You can get the relevant dimensions of a quarter from https://en.wikipedia.org/wiki/Quarter_(United_States_coin).\n",
-    "\n",
-    "2. Create a `Params` object with the system parameters.  We don't know `v_term`, so we'll start with the inital guess `v_term = 18 * m / s`.\n",
-    "\n",
-    "3. Use `make_system` to create a `System` object.\n",
-    "\n",
-    "4. Call `run_ode_solver` to simulate the system.  How does the flight time of the simulation compare to the measurement?\n",
-    "\n",
-    "5. Try a few different values of `t_term` and see if you can get the simulated flight time close to 19.1 seconds.\n",
-    "\n",
-    "6. Optionally, write an error function and use `root_scalar` to improve your estimate.\n",
-    "\n",
-    "7. Use your best estimate of `v_term` to compute `C_d`.\n",
-    "\n",
-    "Note: I fabricated the observed flight time, so don't take the results of this exercise too seriously."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>height</th>\n",
-       "      <td>381 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_init</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>0.00567 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>diameter</th>\n",
-       "      <td>0.02426 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>18.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>flight_time</th>\n",
-       "      <td>19.1 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "height                         381 meter\n",
-       "v_init                0.0 meter / second\n",
-       "g                9.8 meter / second ** 2\n",
-       "mass                    0.00567 kilogram\n",
-       "diameter                   0.02426 meter\n",
-       "rho            1.2 kilogram / meter ** 3\n",
-       "v_term               18.0 meter / second\n",
-       "flight_time                  19.1 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Here's a `Params` object with the dimensions of a quarter,\n",
-    "# the observed flight time and our initial guess for `v_term`\n",
-    "\n",
-    "params3 = Params(params,\n",
-    "                 mass = 5.67e-3 * kg,\n",
-    "                 diameter = 24.26e-3 * m,\n",
-    "                 v_term = 18 * m / s,\n",
-    "                 flight_time = 19.1 * s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>height</th>\n",
-       "      <td>381 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_init</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>0.00567 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>diameter</th>\n",
-       "      <td>0.02426 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>18.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>flight_time</th>\n",
-       "      <td>19.1 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>0.000462244204111976 meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.6183600157463346 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>y             381 meter\n",
-       "v    0.0 meter / secon...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>30 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>0.3 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "height                                                 381 meter\n",
-       "v_init                                        0.0 meter / second\n",
-       "g                                        9.8 meter / second ** 2\n",
-       "mass                                            0.00567 kilogram\n",
-       "diameter                                           0.02426 meter\n",
-       "rho                                    1.2 kilogram / meter ** 3\n",
-       "v_term                                       18.0 meter / second\n",
-       "flight_time                                          19.1 second\n",
-       "area                             0.000462244204111976 meter ** 2\n",
-       "C_d                             0.6183600157463346 dimensionless\n",
-       "init           y             381 meter\n",
-       "v    0.0 meter / secon...\n",
-       "t_end                                                  30 second\n",
-       "dt                                                    0.3 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Now we can make a `System` object\n",
-    "\n",
-    "system3 = make_system(params3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Run the simulation\n",
-    "\n",
-    "results, details = run_ode_solver(system3, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "22.438163885803736 second"
-      ],
-      "text/latex": [
-       "$22.438163885803736\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "22.438163885803736 <Unit('second')>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# And get the flight time\n",
-    "\n",
-    "flight_time = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# The flight time is a little long, so we could increase `v_term` and try again.\n",
-    "\n",
-    "# Or we could write an error function\n",
-    "\n",
-    "def error_func(guess, params):\n",
-    "    \"\"\"Final height as a function of C_d.\n",
-    "    \n",
-    "    guess: guess at v_term\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: height in m\n",
-    "    \"\"\"\n",
-    "    print(guess)\n",
-    "    params = Params(params, v_term=guess)\n",
-    "    system = make_system(params)\n",
-    "    results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "    flight_time = get_last_label(results) * s\n",
-    "    error = flight_time - params.flight_time\n",
-    "    return magnitude(error)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "18.0 meter / second\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "3.338163885803734"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# We can test the error function like this\n",
-    "v_guess1 = 18 * m / s\n",
-    "error_func(v_guess1, params3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "22.0 meter / second\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "-0.22714064382192944"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "v_guess2 = 22 * m / s\n",
-    "error_func(v_guess2, params3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "18.0 meter / second\n",
-      "22.0 meter / second\n",
-      "20.0 meter / second\n",
-      "21.0 meter / second\n",
-      "21.5 meter / second\n",
-      "21.75 meter / second\n",
-      "21.625 meter / second\n",
-      "21.6875 meter / second\n",
-      "21.71875 meter / second\n",
-      "21.703125 meter / second\n",
-      "21.6953125 meter / second\n",
-      "21.69140625 meter / second\n",
-      "21.689453125 meter / second\n",
-      "21.6884765625 meter / second\n",
-      "21.68798828125 meter / second\n",
-      "21.687744140625 meter / second\n",
-      "21.6878662109375 meter / second\n",
-      "21.68792724609375 meter / second\n",
-      "21.687896728515625 meter / second\n",
-      "21.687881469726562 meter / second\n",
-      "21.68787384033203 meter / second\n",
-      "21.687877655029297 meter / second\n",
-      "21.68787956237793 meter / second\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>converged</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>root</th>\n",
-       "      <td>21.687878608703613 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "converged                                 True\n",
-       "root         21.687878608703613 meter / second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Now we can use `root_scalar` to find the value of `v_term` that yields the measured flight time.\n",
-    "\n",
-    "res = root_bisect(error_func, [v_guess1, v_guess2], params3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "21.687878608703613 meter/second"
-      ],
-      "text/latex": [
-       "$21.687878608703613\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "21.687878608703613 <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "v_term_solution = res.root"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.4259437619496639 dimensionless"
-      ],
-      "text/latex": [
-       "$0.4259437619496639\\ dimensionless$"
-      ],
-      "text/plain": [
-       "0.4259437619496639 <Unit('dimensionless')>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Plugging in the estimated value, we can use `make_system` to compute `C_d`\n",
-    "\n",
-    "params_solution = Params(params3, v_term=v_term_solution)\n",
-    "system = make_system(params_solution)\n",
-    "system.C_d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap22soln.ipynb b/soln/chap22soln.ipynb
deleted file mode 100644
index 889338bf..00000000
--- a/soln/chap22soln.ipynb
+++ /dev/null
@@ -1,2957 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 22\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Vectors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `Vector` object represents a vector quantity.  In the context of mechanics, vector quantities include position, velocity, acceleration, and force, all of which might be in 2D or 3D.\n",
-    "\n",
-    "You can define a `Vector` object without units, but if it represents a physical quantity, you will often want to attach units to it.\n",
-    "\n",
-    "I'll start by grabbing the units we'll need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "kilogram"
-      ],
-      "text/latex": [
-       "$\\mathrm{kilogram}$"
-      ],
-      "text/plain": [
-       "<Unit('kilogram')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a two dimensional `Vector` in meters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}3.0 & 4.0\\end{pmatrix} meter\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}3.0 & 4.0\\end{pmatrix}\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "array([3., 4.]) <Unit('meter')>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A = Vector(3, 4) * m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can access the elements by name."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "3.0 meter"
-      ],
-      "text/latex": [
-       "$3.0\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "3.0 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A.x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "4.0 meter"
-      ],
-      "text/latex": [
-       "$4.0\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "4.0 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A.y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The magnitude is the length of the vector."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "5.0 meter"
-      ],
-      "text/latex": [
-       "$5.0\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "5.0 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A.mag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The angle is the number of radians between the vector and the positive x axis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.9272952180016122 radian"
-      ],
-      "text/latex": [
-       "$0.9272952180016122\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "0.9272952180016122 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A.angle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we make another `Vector` with the same units,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}1.0 & 2.0\\end{pmatrix} meter\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}1.0 & 2.0\\end{pmatrix}\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "array([1., 2.]) <Unit('meter')>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "B = Vector(1, 2) * m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can add `Vector` objects like this"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}4.0 & 6.0\\end{pmatrix} meter\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}4.0 & 6.0\\end{pmatrix}\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "array([4., 6.]) <Unit('meter')>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A + B"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And subtract like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}2.0 & 2.0\\end{pmatrix} meter\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}2.0 & 2.0\\end{pmatrix}\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "array([2., 2.]) <Unit('meter')>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A - B"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can compute the Euclidean distance between two Vectors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "2.8284271247461903 meter"
-      ],
-      "text/latex": [
-       "$2.8284271247461903\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "2.8284271247461903 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A.dist(B)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the difference in angle"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-0.17985349979247822 radian"
-      ],
-      "text/latex": [
-       "$-0.17985349979247822\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "-0.17985349979247822 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A.diff_angle(B)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we are given the magnitude and angle of a vector, what we have is the representation of the vector in polar coordinates."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.9272952180016122 radian"
-      ],
-      "text/latex": [
-       "$0.9272952180016122\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "0.9272952180016122 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mag = A.mag\n",
-    "angle = A.angle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use `pol2cart` to convert from polar to Cartesian coordinates, and then use the Cartesian coordinates to make a `Vector` object.\n",
-    "\n",
-    "In this example, the `Vector` we get should have the same components as `A`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}3.0000000000000004 & 3.9999999999999996\\end{pmatrix} meter\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}3.0000000000000004 & 3.9999999999999996\\end{pmatrix}\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "array([3., 4.]) <Unit('meter')>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x, y = pol2cart(angle, mag)\n",
-    "Vector(x, y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another way to represent the direction of `A` is a unit vector, which is a vector with magnitude 1 that points in the same direction as `A`.  You can compute a unit vector by dividing a vector by its magnitude:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}0.6 & 0.8\\end{pmatrix} dimensionless\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}0.6 & 0.8\\end{pmatrix}\\ dimensionless$"
-      ],
-      "text/plain": [
-       "array([0.6, 0.8]) <Unit('dimensionless')>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A / A.mag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or by using the `hat` function, so named because unit vectors are conventionally decorated with a hat, like this: $\\hat{A}$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}0.6 & 0.8\\end{pmatrix} dimensionless\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}0.6 & 0.8\\end{pmatrix}\\ dimensionless$"
-      ],
-      "text/plain": [
-       "array([0.6, 0.8]) <Unit('dimensionless')>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A.hat()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Create a `Vector` named `a_grav` that represents acceleration due to gravity, with x component 0 and y component $-9.8$ meters / second$^2$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}0.0 & -9.8\\end{pmatrix} meter/second<sup>2</sup>\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}0.0 & -9.8\\end{pmatrix}\\ \\frac{\\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "array([ 0. , -9.8]) <Unit('meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "a_grav = Vector(0, -9.8) * m / s**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Degrees and radians\n",
-    "\n",
-    "Pint provides units to represent degree and radians."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "radian"
-      ],
-      "text/latex": [
-       "$\\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "<Unit('radian')>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "degree = UNITS.degree\n",
-    "radian = UNITS.radian"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you have an angle in degrees,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "45 degree"
-      ],
-      "text/latex": [
-       "$45\\ \\mathrm{degree}$"
-      ],
-      "text/plain": [
-       "45 <Unit('degree')>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "angle = 45 * degree\n",
-    "angle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can convert to radians."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.7853981633974483 radian"
-      ],
-      "text/latex": [
-       "$0.7853981633974483\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "0.7853981633974483 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "angle_rad = angle.to(radian)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If it's already in radians, `to` does the right thing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.7853981633974483 radian"
-      ],
-      "text/latex": [
-       "$0.7853981633974483\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "0.7853981633974483 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "angle_rad.to(radian)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also convert from radians to degrees."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "45.0 degree"
-      ],
-      "text/latex": [
-       "$45.0\\ \\mathrm{degree}$"
-      ],
-      "text/plain": [
-       "45.0 <Unit('degree')>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "angle_rad.to(degree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As an alterative, you can use `np.deg2rad`, which works with Pint quantities, but it also works with simple numbers and NumPy arrays:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.7853981633974483 radian"
-      ],
-      "text/latex": [
-       "$0.7853981633974483\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "0.7853981633974483 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.deg2rad(angle)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Create a `Vector` named `a_force` that represents acceleration due to a force of 0.5 Newton applied to an object with mass 0.3 kilograms, in a direction 45 degrees up from the positive x-axis.\n",
-    "\n",
-    "Add `a_force` to `a_grav` from the previous exercise.  If that addition succeeds, that means that the units are compatible.  Confirm that the total acceleration seems to make sense."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}1.1785113019775793 & 1.1785113019775793\\end{pmatrix} newton/kilogram\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}1.1785113019775793 & 1.1785113019775793\\end{pmatrix}\\ \\frac{\\mathrm{newton}}{\\mathrm{kilogram}}$"
-      ],
-      "text/plain": [
-       "array([1.1785113, 1.1785113]) <Unit('newton / kilogram')>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "N = UNITS.newton\n",
-    "mag = 0.5 * N\n",
-    "angle = 45 * degree\n",
-    "theta = angle.to(radian)\n",
-    "x, y = pol2cart(theta, mag)\n",
-    "force = Vector(x, y)\n",
-    "\n",
-    "mass = 0.3 * kg\n",
-    "a_force = force / mass\n",
-    "a_force"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}1.1785113019775793 & -8.621488698022421\\end{pmatrix} newton/kilogram\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}1.1785113019775793 & -8.621488698022421\\end{pmatrix}\\ \\frac{\\mathrm{newton}}{\\mathrm{kilogram}}$"
-      ],
-      "text/plain": [
-       "array([ 1.1785113, -8.6214887]) <Unit('newton / kilogram')>"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "a_force + a_grav"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Baseball"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a `Params` object that contains parameters for the flight of a baseball."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>x</th>\n",
-       "      <td>0 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>1 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>0.145 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>diameter</th>\n",
-       "      <td>0.073 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.33</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>angle</th>\n",
-       "      <td>45 degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>velocity</th>\n",
-       "      <td>40.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>10 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>0.1 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "x                             0 meter\n",
-       "y                             1 meter\n",
-       "g             9.8 meter / second ** 2\n",
-       "mass                   0.145 kilogram\n",
-       "diameter                  0.073 meter\n",
-       "rho         1.2 kilogram / meter ** 3\n",
-       "C_d                              0.33\n",
-       "angle                       45 degree\n",
-       "velocity          40.0 meter / second\n",
-       "t_end                       10 second\n",
-       "dt                         0.1 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_end = 10 * s\n",
-    "dt = t_end / 100\n",
-    "\n",
-    "params = Params(x = 0 * m, \n",
-    "                y = 1 * m,\n",
-    "                g = 9.8 * m/s**2,\n",
-    "                mass = 145e-3 * kg,\n",
-    "                diameter = 73e-3 * m,\n",
-    "                rho = 1.2 * kg/m**3,\n",
-    "                C_d = 0.33,\n",
-    "                angle = 45 * degree,\n",
-    "                velocity = 40 * m / s,\n",
-    "                t_end=t_end, dt=dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the function that uses the `Params` object to make a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params object with angle, velocity, x, y,\n",
-    "               diameter, duration, g, mass, rho, and C_d\n",
-    "               \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    angle, velocity = params.angle, params.velocity\n",
-    "    \n",
-    "    # convert angle to degrees\n",
-    "    theta = np.deg2rad(angle)\n",
-    "    \n",
-    "    # compute x and y components of velocity\n",
-    "    vx, vy = pol2cart(theta, velocity)\n",
-    "    \n",
-    "    # make the initial state\n",
-    "    R = Vector(params.x, params.y)\n",
-    "    V = Vector(vx, vy)\n",
-    "    init = State(R=R, V=V)\n",
-    "    \n",
-    "    # compute area from diameter\n",
-    "    diameter = params.diameter\n",
-    "    area = np.pi * (diameter/2)**2\n",
-    "    \n",
-    "    return System(params, init=init, area=area)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we use it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>x</th>\n",
-       "      <td>0 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>1 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>0.145 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>diameter</th>\n",
-       "      <td>0.073 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.33</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>angle</th>\n",
-       "      <td>45 degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>velocity</th>\n",
-       "      <td>40.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>10 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>0.1 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>R                                   [0 meter, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>0.004185386812745002 meter ** 2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "x                                                     0 meter\n",
-       "y                                                     1 meter\n",
-       "g                                     9.8 meter / second ** 2\n",
-       "mass                                           0.145 kilogram\n",
-       "diameter                                          0.073 meter\n",
-       "rho                                 1.2 kilogram / meter ** 3\n",
-       "C_d                                                      0.33\n",
-       "angle                                               45 degree\n",
-       "velocity                                  40.0 meter / second\n",
-       "t_end                                               10 second\n",
-       "dt                                                 0.1 second\n",
-       "init        R                                   [0 meter, ...\n",
-       "area                          0.004185386812745002 meter ** 2\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a function that computes drag force using vectors:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(V, system):\n",
-    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
-    "    \n",
-    "    V: velocity Vector\n",
-    "    system: System object with rho, C_d, area\n",
-    "    \n",
-    "    returns: Vector drag force\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    mag = rho * V.mag**2 * C_d * area / 2\n",
-    "    direction = -V.hat()\n",
-    "    f_drag = direction * mag\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}-0.11719680972835739 & -0.11719680972835739\\end{pmatrix} kilogram meter/second<sup>2</sup>\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}-0.11719680972835739 & -0.11719680972835739\\end{pmatrix}\\ \\frac{\\mathrm{kilogram} \\cdot \\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "array([-0.11719681, -0.11719681]) <Unit('kilogram * meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "V_test = Vector(10, 10) * m/s\n",
-    "drag_force(V_test, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the slope function that computes acceleration due to gravity and drag."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the state variables.\n",
-    "    \n",
-    "    state: State (x, y, x velocity, y velocity)\n",
-    "    t: time\n",
-    "    system: System object with g, rho, C_d, area, mass\n",
-    "    \n",
-    "    returns: sequence (vx, vy, ax, ay)\n",
-    "    \"\"\"\n",
-    "    R, V = state\n",
-    "    mass, g = system.mass, system.g\n",
-    "    \n",
-    "    a_drag = drag_force(V, system) / mass\n",
-    "    a_grav = Vector(0, -g)\n",
-    "    \n",
-    "    A = a_grav + a_drag\n",
-    "    \n",
-    "    return V, A"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Always test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([28.28427125, 28.28427125]) <Unit('meter / second')>,\n",
-       " array([ -6.46603088, -16.26603088]) <Unit('meter / second ** 2')>)"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use an event function to stop the simulation when the ball hits the ground:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Stop when the y coordinate is 0.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: y coordinate\n",
-    "    \"\"\"\n",
-    "    R, V = state\n",
-    "    return R.y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "1 meter"
-      ],
-      "text/latex": [
-       "$1\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "1 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "event_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can call `run_ode_solver`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final label tells us the flight time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "5.004505488017051 second"
-      ],
-      "text/latex": [
-       "$5.004505488017051\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "5.004505488017051 <Unit('second')>"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flight_time = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final value of `x` tells us the how far the ball landed from home plate:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "99.30497406350605 meter"
-      ],
-      "text/latex": [
-       "$99.30497406350605\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "99.30497406350605 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "R_final = get_last_value(results.R)\n",
-    "x_dist = R_final.x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing the results\n",
-    "\n",
-    "The simplest way to visualize the results is to plot x and y as functions of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap22-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xs = results.R.extract('x')\n",
-    "ys = results.R.extract('y')\n",
-    "\n",
-    "xs.plot()\n",
-    "ys.plot()\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Position (m)')\n",
-    "\n",
-    "savefig('figs/chap22-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot the velocities the same way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "vx = results.V.extract('x')\n",
-    "vy = results.V.extract('y')\n",
-    "\n",
-    "vx.plot(label='vx')\n",
-    "vy.plot(label='vy')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The x velocity slows down due to drag.\n",
-    "\n",
-    "The y velocity drops quickly while drag and gravity are in the same direction, then more slowly after the ball starts to fall.\n",
-    "\n",
-    "Another way to visualize the results is to plot y versus x.  The result is the trajectory of the ball through its plane of motion."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap22-fig02.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_trajectory(results):\n",
-    "    xs = results.R.extract('x')\n",
-    "    ys = results.R.extract('y')\n",
-    "    plot(xs, ys, color='C2', label='trajectory')\n",
-    "\n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)')\n",
-    "\n",
-    "plot_trajectory(results)\n",
-    "savefig('figs/chap22-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Animation\n",
-    "\n",
-    "One of the best ways to visualize the results of a physical model is animation.  If there are problems with the model, animation can make them apparent.\n",
-    "\n",
-    "The ModSimPy library provides `animate`, which takes as parameters a `TimeSeries` and a draw function.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The draw function should take as parameters a `State` object and the time.  It should draw a single frame of the animation.\n",
-    "\n",
-    "Inside the draw function, you almost always have to call `set_xlim` and `set_ylim`.  Otherwise `matplotlib` auto-scales the axes, which is usually not what you want."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xs = results.R.extract('x')\n",
-    "ys = results.R.extract('y')\n",
-    "\n",
-    "def draw_func(state, t):\n",
-    "    set_xlim(xs)\n",
-    "    set_ylim(ys)\n",
-    "    x, y = state.R\n",
-    "    plot(x, y, 'bo')\n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "animate(results, draw_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Delete the lines that set the x and y axes (or [comment them out](https://en.wiktionary.org/wiki/comment_out)) and see what the animation does."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "`Vector` is a function that returns a `ModSimVector` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "modsim.modsim.ModSimVector"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "V = Vector(3, 4)\n",
-    "type(V)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `ModSimVector` is a specialized kind of Pint `Quantity`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(V, Quantity)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There's one gotcha you might run into with Vectors and Quantities.  If you multiply a `ModSimVector` and a `Quantity`, you get a `ModSimVector`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}3.0 & 4.0\\end{pmatrix} meter\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}3.0 & 4.0\\end{pmatrix}\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "array([3., 4.]) <Unit('meter')>"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "V1 = V * m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "modsim.modsim.ModSimVector"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(V1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But if you multiply a `Quantity` and a `Vector`, you get a `Quantity`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}3.0 & 4.0\\end{pmatrix} meter\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}3.0 & 4.0\\end{pmatrix}\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "array([3., 4.]) <Unit('meter')>"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "V2 = m * V"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pint.quantity.build_quantity_class.<locals>.Quantity"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(V2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With a `ModSimVector` you can get the coordinates using dot notation, as well as `mag`, `mag2`, and `angle`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3.0 <Unit('meter')>,\n",
-       " 4.0 <Unit('meter')>,\n",
-       " 5.0 <Unit('meter')>,\n",
-       " 0.9272952180016122 <Unit('radian')>)"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "V1.x, V1.y, V1.mag, V1.angle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With a `Quantity`, you can't.  But you can use indexing to get the coordinates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3.0 <Unit('meter')>, 4.0 <Unit('meter')>)"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "V2[0], V2[1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And you can use vector functions to get the magnitude and angle."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5.0 <Unit('meter')>, 0.9272952180016122 <Unit('radian')>)"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vector_mag(V2), vector_angle(V2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And often you can avoid the whole issue by doing the multiplication with the `ModSimVector` on the left."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Run the simulation with and without air resistance.  How wrong would we be if we ignored drag?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>x</th>\n",
-       "      <td>0 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>1 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>0.145 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>diameter</th>\n",
-       "      <td>0.073 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>angle</th>\n",
-       "      <td>45 degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>velocity</th>\n",
-       "      <td>40.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>10 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>0.1 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>R                                   [0 meter, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>0.004185386812745002 meter ** 2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "x                                                     0 meter\n",
-       "y                                                     1 meter\n",
-       "g                                     9.8 meter / second ** 2\n",
-       "mass                                           0.145 kilogram\n",
-       "diameter                                          0.073 meter\n",
-       "rho                                 1.2 kilogram / meter ** 3\n",
-       "C_d                                                         0\n",
-       "angle                                               45 degree\n",
-       "velocity                                  40.0 meter / second\n",
-       "t_end                                               10 second\n",
-       "dt                                                 0.1 second\n",
-       "init        R                                   [0 meter, ...\n",
-       "area                          0.004185386812745002 meter ** 2\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Hint\n",
-    "\n",
-    "system_no_drag = System(system, C_d=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results_no_drag, details = run_ode_solver(system_no_drag, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_trajectory(results)\n",
-    "plot_trajectory(results_no_drag)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "99.30497406350605 meter"
-      ],
-      "text/latex": [
-       "$99.30497406350605\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "99.30497406350605 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "x_dist = get_last_value(results.R).x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "164.25596844639244 meter"
-      ],
-      "text/latex": [
-       "$164.25596844639244\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "164.25596844639244 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "xdist_no_drag = get_last_value(results_no_drag.R).x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "64.95099438288639 meter"
-      ],
-      "text/latex": [
-       "$64.95099438288639\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "64.95099438288639 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "xdist_no_drag - x_dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / meter$^3$.  How much farther would a ball hit with the same velocity and launch angle travel?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>x</th>\n",
-       "      <td>0 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>1 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>0.145 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>diameter</th>\n",
-       "      <td>0.073 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.0 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.33</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>angle</th>\n",
-       "      <td>45 degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>velocity</th>\n",
-       "      <td>40.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>10 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>0.1 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>R                                   [0 meter, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>0.004185386812745002 meter ** 2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "x                                                     0 meter\n",
-       "y                                                     1 meter\n",
-       "g                                     9.8 meter / second ** 2\n",
-       "mass                                           0.145 kilogram\n",
-       "diameter                                          0.073 meter\n",
-       "rho                                 1.0 kilogram / meter ** 3\n",
-       "C_d                                                      0.33\n",
-       "angle                                               45 degree\n",
-       "velocity                                  40.0 meter / second\n",
-       "t_end                                               10 second\n",
-       "dt                                                 0.1 second\n",
-       "init        R                                   [0 meter, ...\n",
-       "area                          0.004185386812745002 meter ** 2\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Hint\n",
-    "\n",
-    "system2 = System(system, rho=1.0*kg/m**3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "105.77787365390012 meter"
-      ],
-      "text/latex": [
-       "$105.77787365390012\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "105.77787365390012 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results2, details2 = run_ode_solver(system2, slope_func, events=event_func)\n",
-    "x = results2.R.extract('x')\n",
-    "x_dist2 = get_last_value(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "6.472899590394064 meter"
-      ],
-      "text/latex": [
-       "$6.472899590394064\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "6.472899590394064 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "x_dist2 - x_dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** The model so far is based on the assumption that coefficient of drag does not depend on velocity, but in reality it does.  The following figure, from Adair, [*The Physics of Baseball*](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC), shows coefficient of drag as a function of velocity.\n",
-    "\n",
-    "<img src=\"data/baseball_drag.png\" width=\"400\">\n",
-    "\n",
-    "\n",
-    "I used [an online graph digitizer](https://automeris.io/WebPlotDigitizer/) to extract the data and save it in a CSV file.  Here's how we can read it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Velocity in mph</th>\n",
-       "      <th>Drag coefficient</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Velocity in meters per second</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.026146</th>\n",
-       "      <td>0.058486</td>\n",
-       "      <td>0.49965</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.871509</th>\n",
-       "      <td>19.845000</td>\n",
-       "      <td>0.49878</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17.647351</th>\n",
-       "      <td>39.476000</td>\n",
-       "      <td>0.49704</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22.432914</th>\n",
-       "      <td>50.181000</td>\n",
-       "      <td>0.48225</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26.882303</th>\n",
-       "      <td>60.134000</td>\n",
-       "      <td>0.45004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30.636992</th>\n",
-       "      <td>68.533000</td>\n",
-       "      <td>0.40914</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32.977694</th>\n",
-       "      <td>73.769000</td>\n",
-       "      <td>0.38042</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34.604472</th>\n",
-       "      <td>77.408000</td>\n",
-       "      <td>0.36562</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37.497268</th>\n",
-       "      <td>83.879000</td>\n",
-       "      <td>0.34822</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40.460249</th>\n",
-       "      <td>90.507000</td>\n",
-       "      <td>0.33081</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43.492522</th>\n",
-       "      <td>97.290000</td>\n",
-       "      <td>0.31427</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46.733562</th>\n",
-       "      <td>104.540000</td>\n",
-       "      <td>0.30035</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50.886563</th>\n",
-       "      <td>113.830000</td>\n",
-       "      <td>0.28816</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54.047136</th>\n",
-       "      <td>120.900000</td>\n",
-       "      <td>0.28381</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56.926074</th>\n",
-       "      <td>127.340000</td>\n",
-       "      <td>0.28033</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60.086646</th>\n",
-       "      <td>134.410000</td>\n",
-       "      <td>0.28207</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               Velocity in mph  Drag coefficient\n",
-       "Velocity in meters per second                                   \n",
-       "0.026146                              0.058486           0.49965\n",
-       "8.871509                             19.845000           0.49878\n",
-       "17.647351                            39.476000           0.49704\n",
-       "22.432914                            50.181000           0.48225\n",
-       "26.882303                            60.134000           0.45004\n",
-       "30.636992                            68.533000           0.40914\n",
-       "32.977694                            73.769000           0.38042\n",
-       "34.604472                            77.408000           0.36562\n",
-       "37.497268                            83.879000           0.34822\n",
-       "40.460249                            90.507000           0.33081\n",
-       "43.492522                            97.290000           0.31427\n",
-       "46.733562                           104.540000           0.30035\n",
-       "50.886563                           113.830000           0.28816\n",
-       "54.047136                           120.900000           0.28381\n",
-       "56.926074                           127.340000           0.28033\n",
-       "60.086646                           134.410000           0.28207"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "baseball_drag = pd.read_csv('data/baseball_drag.csv')\n",
-    "mph = Quantity(baseball_drag['Velocity in mph'], UNITS.mph)\n",
-    "mps = mph.to(m/s)\n",
-    "baseball_drag.index = magnitude(mps)\n",
-    "baseball_drag.index.name = 'Velocity in meters per second'\n",
-    "baseball_drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Modify the model to include the dependence of `C_d` on velocity, and see how much it affects the results.  Hint: use `interpolate`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "drag_interp = interpolate(baseball_drag['Drag coefficient'])\n",
-    "vs = linspace(0, 60)\n",
-    "cds = drag_interp(vs)\n",
-    "plot(vs, cds)\n",
-    "decorate(xlabel='Velocity (m/s)', ylabel='C_d')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def drag_force(V, system):\n",
-    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
-    "    \n",
-    "    v: velocity\n",
-    "    system: System object with rho, C_d, area\n",
-    "    \n",
-    "    returns: Vector drag force\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    C_d = drag_interp(V.mag)\n",
-    "    mag = -rho * V.mag**2 * C_d * area / 2\n",
-    "    direction = V.hat()\n",
-    "    f_drag = direction * mag\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.31695653551010883"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "C_d = drag_interp(43 * m / s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}-1.023081126128183 & -1.023081126128183\\end{pmatrix} kilogram meter/second<sup>2</sup>\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}-1.023081126128183 & -1.023081126128183\\end{pmatrix}\\ \\frac{\\mathrm{kilogram} \\cdot \\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "array([-1.02308113, -1.02308113]) <Unit('kilogram * meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system = System(system, drag_interp=drag_interp)\n",
-    "V = Vector(30, 30) * m/s\n",
-    "f_drag = drag_force(V, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([28.28427125, 28.28427125]) <Unit('meter / second')>,\n",
-       " array([ -6.53489118, -16.33489118]) <Unit('meter / second ** 2')>)"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>R</th>\n",
-       "      <th>V</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4.600000</th>\n",
-       "      <td>[86.37265447523836 meter, 6.352840864406531 me...</td>\n",
-       "      <td>[12.388380175637913 meter / second, -18.940140...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.700000</th>\n",
-       "      <td>[87.59981910596807 meter, 4.4276738010043495 m...</td>\n",
-       "      <td>[12.155784727055282 meter / second, -19.555228...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.800000</th>\n",
-       "      <td>[88.8038132288331 meter, 2.4417868717825417 me...</td>\n",
-       "      <td>[11.925028455712733 meter / second, -20.154598...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.900000</th>\n",
-       "      <td>[89.98482652030708 meter, 0.396745649345958 me...</td>\n",
-       "      <td>[11.696226535097686 meter / second, -20.738387...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.918869</th>\n",
-       "      <td>[90.20337227180956 meter, 0.0 meter]</td>\n",
-       "      <td>[11.653444424369289 meter / second, -20.845633...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                          R  \\\n",
-       "4.600000  [86.37265447523836 meter, 6.352840864406531 me...   \n",
-       "4.700000  [87.59981910596807 meter, 4.4276738010043495 m...   \n",
-       "4.800000  [88.8038132288331 meter, 2.4417868717825417 me...   \n",
-       "4.900000  [89.98482652030708 meter, 0.396745649345958 me...   \n",
-       "4.918869               [90.20337227180956 meter, 0.0 meter]   \n",
-       "\n",
-       "                                                          V  \n",
-       "4.600000  [12.388380175637913 meter / second, -18.940140...  \n",
-       "4.700000  [12.155784727055282 meter / second, -19.555228...  \n",
-       "4.800000  [11.925028455712733 meter / second, -20.154598...  \n",
-       "4.900000  [11.696226535097686 meter / second, -20.738387...  \n",
-       "4.918869  [11.653444424369289 meter / second, -20.845633...  "
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "90.20337227180956 meter"
-      ],
-      "text/latex": [
-       "$90.20337227180956\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "90.20337227180956 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "x = results.R.extract('x')\n",
-    "x_dist3 = get_last_value(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "9.101601791696496 meter"
-      ],
-      "text/latex": [
-       "$9.101601791696496\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "9.101601791696496 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "x_dist - x_dist3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(17.461046752327743 <Unit('meter / second')>, 40.0 <Unit('meter / second')>)"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Here are the highest and lowest speeds\n",
-    "\n",
-    "vs = results.V.extract('mag')\n",
-    "interval = min(vs), max(vs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.49707694, 0.33351435])"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# And here are the drag coefficients at the highest and lowest speed.\n",
-    "# They are substantially different.\n",
-    "\n",
-    "drag_interp(interval)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap23soln.ipynb b/soln/chap23soln.ipynb
deleted file mode 100644
index 793812fe..00000000
--- a/soln/chap23soln.ipynb
+++ /dev/null
@@ -1,1311 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 23\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "degree"
-      ],
-      "text/latex": [
-       "$\\mathrm{degree}$"
-      ],
-      "text/plain": [
-       "<Unit('degree')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "degree = UNITS.degree"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>x</th>\n",
-       "      <td>0 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>1 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>0.145 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>diameter</th>\n",
-       "      <td>0.073 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>angle</th>\n",
-       "      <td>45 degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>velocity</th>\n",
-       "      <td>40.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>20 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>0.2 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "x                             0 meter\n",
-       "y                             1 meter\n",
-       "g             9.8 meter / second ** 2\n",
-       "mass                   0.145 kilogram\n",
-       "diameter                  0.073 meter\n",
-       "rho         1.2 kilogram / meter ** 3\n",
-       "C_d                               0.3\n",
-       "angle                       45 degree\n",
-       "velocity          40.0 meter / second\n",
-       "t_end                       20 second\n",
-       "dt                         0.2 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_end = 20 * s\n",
-    "dt = t_end / 100\n",
-    "\n",
-    "params = Params(x = 0 * m, \n",
-    "                y = 1 * m,\n",
-    "                g = 9.8 * m/s**2,\n",
-    "                mass = 145e-3 * kg,\n",
-    "                diameter = 73e-3 * m,\n",
-    "                rho = 1.2 * kg/m**3,\n",
-    "                C_d = 0.3,\n",
-    "                angle = 45 * degree,\n",
-    "                velocity = 40 * m / s,\n",
-    "                t_end=t_end,\n",
-    "                dt=dt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params object with angle, velocity, x, y,\n",
-    "               diameter, duration, g, mass, rho, and C_d\n",
-    "               \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    angle, velocity = params.angle, params.velocity\n",
-    "    \n",
-    "    # convert angle to degrees\n",
-    "    theta = np.deg2rad(angle)\n",
-    "    \n",
-    "    # compute x and y components of velocity\n",
-    "    vx, vy = pol2cart(theta, velocity)\n",
-    "    \n",
-    "    # make the initial state\n",
-    "    R = Vector(params.x, params.y)\n",
-    "    V = Vector(vx, vy)\n",
-    "    init = State(R=R, V=V)\n",
-    "    \n",
-    "    # compute area from diameter\n",
-    "    diameter = params.diameter\n",
-    "    area = np.pi * (diameter/2)**2\n",
-    "    \n",
-    "    return System(params, init=init, area=area)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(V, system):\n",
-    "    \"\"\"Computes drag force in the opposite direction of `V`.\n",
-    "    \n",
-    "    V: velocity Vector\n",
-    "    system: System object with rho, C_d, area\n",
-    "    \n",
-    "    returns: Vector drag force\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    mag = rho * V.mag**2 * C_d * area / 2\n",
-    "    direction = -V.hat()\n",
-    "    f_drag = direction * mag\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the state variables.\n",
-    "    \n",
-    "    state: State (x, y, x velocity, y velocity)\n",
-    "    t: time\n",
-    "    system: System object with g, rho, C_d, area, mass\n",
-    "    \n",
-    "    returns: sequence (vx, vy, ax, ay)\n",
-    "    \"\"\"\n",
-    "    R, V = state\n",
-    "    mass, g = system.mass, system.g\n",
-    "    \n",
-    "    a_drag = drag_force(V, system) / mass\n",
-    "    a_grav = Vector(0, -g)\n",
-    "    \n",
-    "    A = a_grav + a_drag\n",
-    "    \n",
-    "    return V, A"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Stop when the y coordinate is 0.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: y coordinate\n",
-    "    \"\"\"\n",
-    "    R, V = state\n",
-    "    return R.y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optimal launch angle\n",
-    "\n",
-    "To find the launch angle that maximizes distance from home plate, we need a function that takes launch angle and returns range."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def range_func(angle, params):  \n",
-    "    \"\"\"Computes range for a given launch angle.\n",
-    "    \n",
-    "    angle: launch angle in degrees\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: distance in meters\n",
-    "    \"\"\"\n",
-    "    params = Params(params, angle=angle)\n",
-    "    system = make_system(params)\n",
-    "    results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "    x_dist = get_last_value(results.R).x\n",
-    "    print(angle, x_dist)\n",
-    "    return x_dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's test `range_func`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "45 102.67621633303261 meter\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "102.67621633303261 meter"
-      ],
-      "text/latex": [
-       "$102.67621633303261\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "102.67621633303261 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "range_func(45, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And sweep through a range of angles."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "20.0 79.83369234082717 meter\n",
-      "23.0 86.20478395495141 meter\n",
-      "26.0 91.4709452133534 meter\n",
-      "29.0 95.83255039259141 meter\n",
-      "32.0 99.10671362732461 meter\n",
-      "35.0 101.51073514079798 meter\n",
-      "38.0 102.9023803583573 meter\n",
-      "41.0 103.40314791198459 meter\n",
-      "44.0 103.02100653301224 meter\n",
-      "47.0 101.73693469942923 meter\n",
-      "50.0 99.56858789440345 meter\n",
-      "53.0 96.55360348120037 meter\n",
-      "56.0 92.7050051463521 meter\n",
-      "59.0 88.04022482508677 meter\n",
-      "62.0 82.58080180144869 meter\n",
-      "65.0 76.32548350395584 meter\n",
-      "68.0 69.32998156572299 meter\n",
-      "71.0 61.63270335890529 meter\n",
-      "74.0 53.24654950607608 meter\n",
-      "77.0 44.252471292784826 meter\n",
-      "80.0 34.68860100116735 meter\n"
-     ]
-    }
-   ],
-   "source": [
-    "angles = linspace(20, 80, 21)\n",
-    "sweep = SweepSeries()\n",
-    "\n",
-    "for angle in angles:\n",
-    "    x_dist = range_func(angle, params)\n",
-    "    sweep[angle] = x_dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting the `Sweep` object, it looks like the peak is between 40 and 45 degrees."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap23-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(sweep, color='C2')\n",
-    "decorate(xlabel='Launch angle (degree)',\n",
-    "         ylabel='Range (m)',\n",
-    "         title='Range as a function of launch angle',\n",
-    "         legend=False)\n",
-    "\n",
-    "savefig('figs/chap23-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use `maximize` to search for the peak efficiently."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 degree 17.15514421874411 meter\n",
-      "90 degree 1.2569667261582053e-14 meter\n",
-      "34.376941012509455 degree 101.06319487191678 meter\n",
-      "55.623058987490545 degree 93.24073676688748 meter\n",
-      "21.246117974981075 degree 82.63917923456437 meter\n",
-      "42.49223594996215 degree 103.32194321543766 meter\n",
-      "47.507764050037856 degree 101.41737997289113 meter\n",
-      "39.39246911258517 degree 103.27504916143039 meter\n",
-      "44.40799721266088 degree 102.90451237123467 meter\n",
-      "41.308230375283905 degree 103.41115569950708 meter\n",
-      "40.576474687263406 degree 103.38512107429113 meter\n",
-      "41.760480261941645 degree 103.4149581469886 meter\n",
-      "42.03998606330441 degree 103.38949811498512 meter\n",
-      "41.58773617664667 degree 103.41463212933446 meter\n",
-      "41.867241978009446 degree 103.41261934653042 meter\n",
-      "41.69449789271447 degree 103.41499899416574 meter\n",
-      "41.65371854587384 degree 103.41492192196334 meter\n",
-      "41.71970091510101 degree 103.41500757170726 meter\n",
-      "41.73527723955511 degree 103.41499793513337 meter\n",
-      "41.710074217168575 degree 103.41500782026304 meter\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>x</th>\n",
-       "      <td>41.710074217168575 degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fun</th>\n",
-       "      <td>103.41500782026304 meter</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                         True\n",
-       "x          41.710074217168575 degree\n",
-       "fun         103.41500782026304 meter\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bounds = [0, 90] * degree\n",
-    "res = maximize(range_func, bounds, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`res` is an `ModSimSeries` object with detailed results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>x</th>\n",
-       "      <td>41.710074217168575 degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fun</th>\n",
-       "      <td>103.41500782026304 meter</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                         True\n",
-       "x          41.710074217168575 degree\n",
-       "fun         103.41500782026304 meter\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`x` is the optimal angle and `fun` the optional range."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "41.710074217168575 degree"
-      ],
-      "text/latex": [
-       "$41.710074217168575\\ \\mathrm{degree}$"
-      ],
-      "text/plain": [
-       "41.710074217168575 <Unit('degree')>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "optimal_angle = res.x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "103.41500782026304 meter"
-      ],
-      "text/latex": [
-       "$103.41500782026304\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "103.41500782026304 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max_x_dist = res.fun"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "Read the source code for `maximize` and `minimize_scalar`, below.\n",
-    "\n",
-    "Add a print statement to `range_func` that prints `angle`.  Then run `maximize` again so you can see how many times it calls `range_func` and what the arguments are."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "def maximize_golden(max_func, bracket, *args, **options):\n",
-      "    \"\"\"Find the maximum of a function by golden section search.\n",
-      "\n",
-      "    :param min_func: function to be maximized\n",
-      "    :param bracket: interval containing a maximum\n",
-      "    :param args: arguments passes to min_func\n",
-      "    :param options: rtol and maxiter\n",
-      "\n",
-      "    :return: ModSimSeries\n",
-      "    \"\"\"\n",
-      "    def min_func(*args):\n",
-      "        return -max_func(*args)\n",
-      "\n",
-      "    res = minimize_golden(min_func, bracket, *args, **options)\n",
-      "\n",
-      "    # we have to negate the function value before returning res\n",
-      "    res.fun = -res.fun\n",
-      "    return res\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "source_code(maximize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "def minimize_scalar(min_func, bounds, *args, **options):\n",
-      "    \"\"\"Finds the input value that minimizes `min_func`.\n",
-      "\n",
-      "    Wrapper for\n",
-      "    https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html\n",
-      "\n",
-      "    min_func: computes the function to be minimized\n",
-      "    bounds: sequence of two values, lower and upper bounds of the range to be searched\n",
-      "    args: any additional positional arguments are passed to min_func\n",
-      "    options: any keyword arguments are passed as options to minimize_scalar\n",
-      "\n",
-      "    returns: ModSimSeries object\n",
-      "    \"\"\"\n",
-      "    try:\n",
-      "        min_func(bounds[0], *args)\n",
-      "    except Exception as e:\n",
-      "        msg = \"\"\"Before running scipy.integrate.minimize_scalar, I tried\n",
-      "                 running the slope function you provided with the\n",
-      "                 initial conditions in system and t=0, and I got\n",
-      "                 the following error:\"\"\"\n",
-      "        logger.error(msg)\n",
-      "        raise(e)\n",
-      "\n",
-      "    underride(options, xatol=1e-3)\n",
-      "\n",
-      "    res = scipy.optimize.minimize_scalar(min_func,\n",
-      "                                         bracket=bounds,\n",
-      "                                         bounds=bounds,\n",
-      "                                         args=args,\n",
-      "                                         method='bounded',\n",
-      "                                         options=options)\n",
-      "\n",
-      "    if not res.success:\n",
-      "        msg = \"\"\"scipy.optimize.minimize_scalar did not succeed.\n",
-      "                 The message it returned is %s\"\"\" % res.message\n",
-      "        raise Exception(msg)\n",
-      "\n",
-      "    return ModSimSeries(res)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "source_code(minimize_scalar)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The Manny Ramirez problem\n",
-    "\n",
-    "Finally, let's solve the Manny Ramirez problem:\n",
-    "\n",
-    "*What is the minimum effort required to hit a home run in Fenway Park?*\n",
-    "\n",
-    "Fenway Park is a baseball stadium in Boston, Massachusetts.  One of its most famous features is the \"Green Monster\", which is a wall in left field that is unusually close to home plate, only 310 feet along the left field line.  To compensate for the short distance, the wall is unusually high, at 37 feet.\n",
-    "\n",
-    "Although the problem asks for a minimum, it is not an optimization problem.  Rather, we want to solve for the initial velocity that just barely gets the ball to the top of the wall, given that it is launched at the optimal angle.\n",
-    "\n",
-    "And we have to be careful about what we mean by \"optimal\".  For this problem, we don't want the longest range, we want the maximum height at the point where it reaches the wall.\n",
-    "\n",
-    "If you are ready to solve the problem on your own, go ahead.  Otherwise I will walk you through the process with an outline and some starter code.\n",
-    "\n",
-    "As a first step, write a function called `height_func` that takes a launch angle and a params as parameters, simulates the flights of a baseball, and returns the height of the baseball when it reaches a point 94.5 meters (310 feet) from home plate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Stop when the y coordinate is 0.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: y coordinate\n",
-    "    \"\"\"\n",
-    "    R, V = state\n",
-    "    return R.x - system.x_wall"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Always test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-94.5 meter"
-      ],
-      "text/latex": [
-       "$-94.5\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "-94.5 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system = make_system(params)\n",
-    "system.set(x_wall = 94.5 * m)\n",
-    "event_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def height_func(angle, params):\n",
-    "    \"\"\"Computes the height of the ball at the wall.\n",
-    "    \n",
-    "    angle: launch angle in degrees\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: height in meters\n",
-    "    \"\"\"\n",
-    "    params = Params(params, angle=angle)\n",
-    "    system = make_system(params)\n",
-    "    system.set(x_wall = 94.5 * m)\n",
-    "\n",
-    "    results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "    height = get_last_value(results.R).y\n",
-    "    \n",
-    "    return height"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your function with a launch angle of 45 degrees:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "10.953200948514532 meter"
-      ],
-      "text/latex": [
-       "$10.953200948514532\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "10.953200948514532 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "height_func(45 * degree, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now use `maximize` to find the optimal angle.  Is it higher or lower than the angle that maximizes range?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>x</th>\n",
-       "      <td>44.25082945182002 degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fun</th>\n",
-       "      <td>10.967930469934105 meter</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                        True\n",
-       "x          44.25082945182002 degree\n",
-       "fun        10.967930469934105 meter\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "bounds = [0, 90] * degree\n",
-    "res = maximize(height_func, bounds, params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "44.25082945182002 degree"
-      ],
-      "text/latex": [
-       "$44.25082945182002\\ \\mathrm{degree}$"
-      ],
-      "text/plain": [
-       "44.25082945182002 <Unit('degree')>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "optimal_angle = res.x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "10.967930469934105 meter"
-      ],
-      "text/latex": [
-       "$10.967930469934105\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "10.967930469934105 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "optimal_height = res.fun"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With initial velocity 40 m/s and an optimal launch angle, the ball clears the Green Monster with a little room to spare.\n",
-    "\n",
-    "Which means we can get over the wall with a lower initial velocity."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Finding the minimum velocity\n",
-    "\n",
-    "Even though we are finding the \"minimum\" velocity, we are not really solving a minimization problem.  Rather, we want to find the velocity that makes the height at the wall exactly 11 m, given given that it's launched at the optimal angle.  And that's a job for `root_bisect`.\n",
-    "\n",
-    "Write an error function that takes a velocity and a `Params` object as parameters.  It should use `maximize` to find the highest possible height of the ball at the wall, for the given velocity.  Then it should return the difference between that optimal height and 11 meters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def error_func(velocity, params):\n",
-    "    \"\"\"Returns the optimal height at the wall minus the target height.\n",
-    "    \n",
-    "    velocity: initial velocity in m/s\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: height difference in meters\n",
-    "    \"\"\"\n",
-    "    print(velocity)\n",
-    "    params = Params(params, velocity=velocity)\n",
-    "    bounds = [0, 90] * degree\n",
-    "    res = maximize(height_func, bounds, params)\n",
-    "    return res.fun - 11*m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your error function before you call `root_bisect`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "40.0 meter / second\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "-0.03206953006589508 meter"
-      ],
-      "text/latex": [
-       "$-0.03206953006589508\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "-0.03206953006589508 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "error_func(40*m/s, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then use `root_bisect` to find the answer to the problem, the minimum velocity that gets the ball out of the park."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "30.0 meter / second\n",
-      "50.0 meter / second\n",
-      "40.0 meter / second\n",
-      "45.0 meter / second\n",
-      "42.5 meter / second\n",
-      "41.25 meter / second\n",
-      "40.625 meter / second\n",
-      "40.3125 meter / second\n",
-      "40.15625 meter / second\n",
-      "40.078125 meter / second\n",
-      "40.0390625 meter / second\n",
-      "40.01953125 meter / second\n",
-      "40.009765625 meter / second\n",
-      "40.0048828125 meter / second\n",
-      "40.00732421875 meter / second\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>converged</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>root</th>\n",
-       "      <td>40.008544921875 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "converged                              True\n",
-       "root         40.008544921875 meter / second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "bounds = [30, 50] * m/s\n",
-    "res = root_bisect(error_func, bounds, params, rtol=1e-4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "40.008544921875 meter/second"
-      ],
-      "text/latex": [
-       "$40.008544921875\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "40.008544921875 <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "min_velocity = res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And just to check, run `error_func` with the value you found."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "40.008544921875 meter / second\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "0.002204447936565046 meter"
-      ],
-      "text/latex": [
-       "$0.002204447936565046\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "0.002204447936565046 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "error_func(min_velocity, params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap24soln.ipynb b/soln/chap24soln.ipynb
deleted file mode 100644
index 77094fc0..00000000
--- a/soln/chap24soln.ipynb
+++ /dev/null
@@ -1,1319 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 24\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Rolling paper\n",
-    "\n",
-    "We'll start by loading the units we need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "second"
-      ],
-      "text/latex": [
-       "$\\mathrm{second}$"
-      ],
-      "text/plain": [
-       "<Unit('second')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "radian = UNITS.radian\n",
-    "m = UNITS.meter\n",
-    "s = UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And creating a `Params` object with the system parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Rmin</th>\n",
-       "      <td>0.02 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rmax</th>\n",
-       "      <td>0.055 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>L</th>\n",
-       "      <td>47 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>omega</th>\n",
-       "      <td>10.0 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>130 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>1 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Rmin               0.02 meter\n",
-       "Rmax              0.055 meter\n",
-       "L                    47 meter\n",
-       "omega    10.0 radian / second\n",
-       "t_end              130 second\n",
-       "dt                   1 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = Params(Rmin = 0.02 * m,\n",
-    "                Rmax = 0.055 * m,\n",
-    "                L = 47 * m,\n",
-    "                omega = 10 * radian / s,\n",
-    "                t_end = 130 * s,\n",
-    "                dt = 1*s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function estimates the parameter `k`, which is the increase in the radius of the roll for each radian of rotation. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def estimate_k(params):\n",
-    "    \"\"\"Estimates the parameter `k`.\n",
-    "    \n",
-    "    params: Params with Rmin, Rmax, and L\n",
-    "    \n",
-    "    returns: k in meters per radian\n",
-    "    \"\"\"\n",
-    "    Rmin, Rmax, L = params.Rmin, params.Rmax, params.L\n",
-    "    \n",
-    "    Ravg = (Rmax + Rmin) / 2\n",
-    "    Cavg = 2 * pi * Ravg\n",
-    "    revs = L / Cavg\n",
-    "    rads = 2 * pi * revs\n",
-    "    k = (Rmax - Rmin) / rads\n",
-    "    return k"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As usual, `make_system` takes a `Params` object and returns a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params with Rmin, Rmax, and L\n",
-    "    \n",
-    "    returns: System with init, k, and ts\n",
-    "    \"\"\"\n",
-    "    init = State(theta = 0 * radian,\n",
-    "                 y = 0 * m,\n",
-    "                 r = params.Rmin)\n",
-    "    \n",
-    "    k = estimate_k(params)\n",
-    "\n",
-    "    return System(params, init=init, k=k)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `make_system`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Rmin</th>\n",
-       "      <td>0.02 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rmax</th>\n",
-       "      <td>0.055 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>L</th>\n",
-       "      <td>47 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>omega</th>\n",
-       "      <td>10.0 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>130 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>1 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>theta      0 radian\n",
-       "y           0 meter\n",
-       "r     ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k</th>\n",
-       "      <td>2.7925531914893616e-05 meter</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Rmin                                            0.02 meter\n",
-       "Rmax                                           0.055 meter\n",
-       "L                                                 47 meter\n",
-       "omega                                 10.0 radian / second\n",
-       "t_end                                           130 second\n",
-       "dt                                                1 second\n",
-       "init     theta      0 radian\n",
-       "y           0 meter\n",
-       "r     ...\n",
-       "k                             2.7925531914893616e-05 meter\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>theta</th>\n",
-       "      <td>0 radian</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>0 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>r</th>\n",
-       "      <td>0.02 meter</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "theta      0 radian\n",
-       "y           0 meter\n",
-       "r        0.02 meter\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can write a slope function based on the differential equations\n",
-    "\n",
-    "$\\omega = \\frac{d\\theta}{dt} = 10$\n",
-    "\n",
-    "$\\frac{dy}{dt} = r \\frac{d\\theta}{dt}$\n",
-    "\n",
-    "$\\frac{dr}{dt} = k \\frac{d\\theta}{dt}$\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object with theta, y, r\n",
-    "    t: time\n",
-    "    system: System object with r, k\n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    theta, y, r = state\n",
-    "    k, omega = system.k, system.omega\n",
-    "    \n",
-    "    dydt = r * omega\n",
-    "    drdt = k * omega\n",
-    "    \n",
-    "    return omega, dydt, drdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `slope_func`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10.0 <Unit('radian / second')>,\n",
-       " 0.2 <Unit('meter * radian / second')>,\n",
-       " 0.0002792553191489362 <Unit('meter * radian / second')>)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll use an event function to stop when `y=L`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Detects when we've rolled length `L`.\n",
-    "    \n",
-    "    state: State object with theta, y, r\n",
-    "    t: time\n",
-    "    system: System object with L\n",
-    "    \n",
-    "    returns: difference between `y` and `L`\n",
-    "    \"\"\"\n",
-    "    theta, y, r = state\n",
-    "    \n",
-    "    return y - system.L"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-47 meter"
-      ],
-      "text/latex": [
-       "$-47\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "-47 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "event_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And look at the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>theta</th>\n",
-       "      <th>y</th>\n",
-       "      <th>r</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>122.00000</th>\n",
-       "      <td>1220.0 radian</td>\n",
-       "      <td>45.18218085106379 meter</td>\n",
-       "      <td>0.05406914893617014 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>123.00000</th>\n",
-       "      <td>1230.0 radian</td>\n",
-       "      <td>45.72426861702124 meter</td>\n",
-       "      <td>0.054348404255319074 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>124.00000</th>\n",
-       "      <td>1240.0 radian</td>\n",
-       "      <td>46.269148936170176 meter</td>\n",
-       "      <td>0.05462765957446801 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>125.00000</th>\n",
-       "      <td>1250.0 radian</td>\n",
-       "      <td>46.8168218085106 meter</td>\n",
-       "      <td>0.054906914893616945 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>125.33277</th>\n",
-       "      <td>1253.3276965817133 radian</td>\n",
-       "      <td>47.0 meter</td>\n",
-       "      <td>0.054999842590712666 meter</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               theta                         y  \\\n",
-       "122.00000              1220.0 radian   45.18218085106379 meter   \n",
-       "123.00000              1230.0 radian   45.72426861702124 meter   \n",
-       "124.00000              1240.0 radian  46.269148936170176 meter   \n",
-       "125.00000              1250.0 radian    46.8168218085106 meter   \n",
-       "125.33277  1253.3276965817133 radian                47.0 meter   \n",
-       "\n",
-       "                                    r  \n",
-       "122.00000   0.05406914893617014 meter  \n",
-       "123.00000  0.054348404255319074 meter  \n",
-       "124.00000   0.05462765957446801 meter  \n",
-       "125.00000  0.054906914893616945 meter  \n",
-       "125.33277  0.054999842590712666 meter  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final value of `y` is 47 meters, as expected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "47.0 meter"
-      ],
-      "text/latex": [
-       "$47.0\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "47.0 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unrolled = get_last_value(results.y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final value of radius is `R_max`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.054999842590712666 meter"
-      ],
-      "text/latex": [
-       "$0.054999842590712666\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "0.054999842590712666 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "radius = get_last_value(results.r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The total number of rotations is close to 200, which seems plausible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "199.47329822495885"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "radians = get_last_value(results.theta) \n",
-    "rotations = magnitude(radians) / 2 / np.pi"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The elapsed time is about 2 minutes, which is also plausible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "125.33276965817134 second"
-      ],
-      "text/latex": [
-       "$125.33276965817134\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "125.33276965817134 <Unit('second')>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_final = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `theta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_theta(results):\n",
-    "    plot(results.theta, color='C0', label='theta')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angle (rad)')\n",
-    "    \n",
-    "plot_theta(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `y`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_y(results):\n",
-    "    plot(results.y, color='C1', label='y')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Length (m)')\n",
-    "    \n",
-    "plot_y(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `r`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_r(results):\n",
-    "    plot(results.r, color='C2', label='r')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Radius (m)')\n",
-    "    \n",
-    "plot_r(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also see the relationship between `y` and `r`, which I derive analytically in the book."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(results.r, results.y, color='C3')\n",
-    "\n",
-    "decorate(xlabel='Radius (m)',\n",
-    "         ylabel='Length (m)',\n",
-    "         legend=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the figure from the book."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap24-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x720 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_three(results):\n",
-    "    subplot(3, 1, 1)\n",
-    "    plot_theta(results)\n",
-    "\n",
-    "    subplot(3, 1, 2)\n",
-    "    plot_y(results)\n",
-    "\n",
-    "    subplot(3, 1, 3)\n",
-    "    plot_r(results)\n",
-    "\n",
-    "plot_three(results)\n",
-    "savefig('figs/chap24-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Animation\n",
-    "\n",
-    "Here's a draw function that animates the results using `matplotlib` patches."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib.patches import Circle\n",
-    "from matplotlib.patches import Arrow\n",
-    "\n",
-    "def draw_func(state, t):\n",
-    "    # get radius in mm\n",
-    "    theta, y, r = state\n",
-    "    radius = r.magnitude * 1000\n",
-    "    \n",
-    "    # draw a circle with\n",
-    "    circle = Circle([0, 0], radius, fill=True)\n",
-    "    plt.gca().add_patch(circle)\n",
-    "    \n",
-    "    # draw an arrow to show rotation\n",
-    "    dx, dy = pol2cart(theta, radius)\n",
-    "    arrow = Arrow(0, 0, dx, dy)\n",
-    "    plt.gca().add_patch(arrow)\n",
-    "\n",
-    "    # make the aspect ratio 1\n",
-    "    plt.axis('equal')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "animate(results, draw_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Run the simulation again with a smaller step size to smooth out the animation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Since we keep `omega` constant, the linear velocity of the paper increases with radius.  Use `gradient` to estimate the derivative of `results.y`.  What is the peak linear velocity?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "dydt = gradient(results.y);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(dydt, label='dydt')\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Linear velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.5504654255319086 meter/second"
-      ],
-      "text/latex": [
-       "$0.5504654255319086\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "0.5504654255319086 <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "linear_velocity = get_last_value(dydt) * m/s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now suppose the peak velocity is the limit; that is, we can't move the paper any faster than that.\n",
-    "\n",
-    "Nevertheless, we might be able to speed up the process by keeping the linear velocity at the maximum all the time.\n",
-    "\n",
-    "Write a slope function that keeps the linear velocity, `dydt`, constant, and computes the angular velocity, `omega`, accordingly.\n",
-    "\n",
-    "Run the simulation and see how much faster we could finish rolling the paper."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object with theta, y, r\n",
-    "    t: time\n",
-    "    system: System object with r, k\n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    theta, y, r = state\n",
-    "    k, omega = system.k, system.omega\n",
-    "    \n",
-    "    dydt = linear_velocity\n",
-    "    omega = dydt / r\n",
-    "    drdt = k * omega\n",
-    "    \n",
-    "    return omega, dydt, drdt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(27.523271276595427 <Unit('1 / second')>,\n",
-       " 0.5504654255319086 <Unit('meter / second')>,\n",
-       " 0.0007686019904368403 <Unit('meter / second')>)"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "85.38229254741002 second"
-      ],
-      "text/latex": [
-       "$85.38229254741002\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "85.38229254741002 <Unit('second')>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "t_final = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x720 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_three(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/chap25soln.ipynb b/soln/chap25soln.ipynb
deleted file mode 100644
index 01b67093..00000000
--- a/soln/chap25soln.ipynb
+++ /dev/null
@@ -1,2577 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 25\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Teapots and Turntables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Tables in Chinese restaurants often have a rotating tray or turntable that makes it easy for customers to share dishes.  These turntables are supported by low-friction bearings that allow them to turn easily and glide.  However, they can be heavy, especially when they are loaded with food, so they have a high moment of inertia.\n",
-    "\n",
-    "Suppose I am sitting at a table with a pot of tea on the turntable directly in front of me, and the person sitting directly opposite asks me to pass the tea.  I push on the edge of the turntable with 1 Newton of force until it has turned 0.5 radians, then let go.  The turntable glides until it comes to a stop 1.5 radians from the starting position.  How much force should I apply for a second push so the teapot glides to a stop directly opposite me?\n",
-    "\n",
-    "The following figure shows the scenario, where `F` is the force I apply to the turntable at the perimeter, perpendicular to the moment arm, `r`, and `tau` is the resulting torque.  The blue circle near the bottom is the teapot.\n",
-    "\n",
-    "![](diagrams/teapot.png)\n",
-    "\n",
-    "We'll answer this question in these steps:\n",
-    "\n",
-    "1.  We'll use the results from the first push to estimate the coefficient of friction for the turntable.\n",
-    "\n",
-    "2.  We'll use that coefficient of friction to estimate the force needed to rotate the turntable through the remaining angle.\n",
-    "\n",
-    "Our simulation will use the following parameters:\n",
-    "\n",
-    "1.  The radius of the turntable is 0.5 meters, and its weight is 7 kg.\n",
-    "\n",
-    "2.  The teapot weights 0.3 kg, and it sits 0.4 meters from the center of the turntable.\n",
-    "\n",
-    "As usual, I'll get units from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "newton"
-      ],
-      "text/latex": [
-       "$\\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "<Unit('newton')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "radian = UNITS.radian\n",
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And store the parameters in a `Params` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>radius_disk</th>\n",
-       "      <td>0.5 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass_disk</th>\n",
-       "      <td>7 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>radius_pot</th>\n",
-       "      <td>0.4 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass_pot</th>\n",
-       "      <td>0.3 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>force</th>\n",
-       "      <td>1 newton</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>torque_friction</th>\n",
-       "      <td>0.2 meter * newton</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>theta_end</th>\n",
-       "      <td>0.5 radian</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>20 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "radius_disk                 0.5 meter\n",
-       "mass_disk                  7 kilogram\n",
-       "radius_pot                  0.4 meter\n",
-       "mass_pot                 0.3 kilogram\n",
-       "force                        1 newton\n",
-       "torque_friction    0.2 meter * newton\n",
-       "theta_end                  0.5 radian\n",
-       "t_end                       20 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = Params(radius_disk=0.5*m,\n",
-    "                mass_disk=7*kg,\n",
-    "                radius_pot=0.4*m,\n",
-    "                mass_pot=0.3*kg,\n",
-    "                force=1*N,\n",
-    "                torque_friction=0.2*N*m,\n",
-    "                theta_end=0.5*radian,\n",
-    "                t_end=20*s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system` creates the initial state, `init`, and computes the total moment of inertia for the turntable and the teapot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    mass_disk, mass_pot = params.mass_disk, params.mass_pot\n",
-    "    radius_disk, radius_pot = params.radius_disk, params.radius_pot\n",
-    "    \n",
-    "    init = State(theta=0*radian, omega=0*radian/s)\n",
-    "    \n",
-    "    I_disk = mass_disk * radius_disk**2 / 2\n",
-    "    I_pot = mass_pot * radius_pot**2\n",
-    "    \n",
-    "    return System(params, init=init, I=I_disk+I_pot)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the `System` object we'll use for the first phase of the simulation, while I am pushing the turntable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>radius_disk</th>\n",
-       "      <td>0.5 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass_disk</th>\n",
-       "      <td>7 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>radius_pot</th>\n",
-       "      <td>0.4 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass_pot</th>\n",
-       "      <td>0.3 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>force</th>\n",
-       "      <td>1 newton</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>torque_friction</th>\n",
-       "      <td>0.2 meter * newton</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>theta_end</th>\n",
-       "      <td>0.5 radian</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>20 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>theta               0 radian\n",
-       "omega    0.0 radi...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>I</th>\n",
-       "      <td>0.923 kilogram * meter ** 2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "radius_disk                                                0.5 meter\n",
-       "mass_disk                                                 7 kilogram\n",
-       "radius_pot                                                 0.4 meter\n",
-       "mass_pot                                                0.3 kilogram\n",
-       "force                                                       1 newton\n",
-       "torque_friction                                   0.2 meter * newton\n",
-       "theta_end                                                 0.5 radian\n",
-       "t_end                                                      20 second\n",
-       "init               theta               0 radian\n",
-       "omega    0.0 radi...\n",
-       "I                                        0.923 kilogram * meter ** 2\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system1 = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Simulation\n",
-    "\n",
-    "When I stop pushing on the turntable, the angular acceleration changes abruptly.  We could implement the slope function with an `if` statement that checks the value of `theta` and sets `force` accordingly.  And for a coarse model like this one, that might be fine.  But we will get more accurate results if we simulate the system in two phases:\n",
-    "\n",
-    "1.  During the first phase, force is constant, and we run until `theta` is 0.5 radians.\n",
-    "\n",
-    "2.  During the second phase, force is 0, and we run until `omega` is 0.\n",
-    "\n",
-    "Then we can combine the results of the two phases into a single `TimeFrame`.\n",
-    "\n",
-    "Here's the slope function we'll use:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    theta, omega = state\n",
-    "    radius_disk, force = system.radius_disk, system.force\n",
-    "    torque_friction, I = system.torque_friction, system.I\n",
-    "    \n",
-    "    torque = radius_disk * force - torque_friction\n",
-    "    alpha = torque / I\n",
-    "    \n",
-    "    return omega, alpha "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, we'll test the slope function before running the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0 <Unit('radian / second')>,\n",
-       " 0.32502708559046584 <Unit('newton / kilogram / meter')>)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(system1.init, 0, system1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an event function that stops the simulation when `theta` reaches `theta_end`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func1(state, t, system):\n",
-    "    \"\"\"Stops when theta reaches theta_end.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: difference from target\n",
-    "    \"\"\"\n",
-    "    theta, omega = state\n",
-    "    return theta - system.theta_end "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-0.5 radian"
-      ],
-      "text/latex": [
-       "$-0.5\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "-0.5 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "event_func1(system1.init, 0, system1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the first phase."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results1, details1 = run_ode_solver(system1, slope_func, events=event_func1)\n",
-    "details1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And look at the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>theta</th>\n",
-       "      <th>omega</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1.000000</th>\n",
-       "      <td>0.16251354279523292 radian</td>\n",
-       "      <td>0.32502708559046584 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.200000</th>\n",
-       "      <td>0.2340195016251354 radian</td>\n",
-       "      <td>0.390032502708559 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.400000</th>\n",
-       "      <td>0.31852654387865653 radian</td>\n",
-       "      <td>0.45503791982665215 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.600000</th>\n",
-       "      <td>0.41603466955579627 radian</td>\n",
-       "      <td>0.5200433369447454 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.751961</th>\n",
-       "      <td>0.5 radian</td>\n",
-       "      <td>0.569434707794277 radian / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               theta                                omega\n",
-       "1.000000  0.16251354279523292 radian  0.32502708559046584 radian / second\n",
-       "1.200000   0.2340195016251354 radian    0.390032502708559 radian / second\n",
-       "1.400000  0.31852654387865653 radian  0.45503791982665215 radian / second\n",
-       "1.600000  0.41603466955579627 radian   0.5200433369447454 radian / second\n",
-       "1.751961                  0.5 radian    0.569434707794277 radian / second"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results1.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Phase 2\n",
-    "\n",
-    "Before we run the second phase, we have to extract the final time and state of the first phase."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "1.7519607843137255 second"
-      ],
-      "text/latex": [
-       "$1.7519607843137255\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "1.7519607843137255 <Unit('second')>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_0 = results1.last_label() * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And make an initial `State` object for Phase 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>theta</th>\n",
-       "      <td>0.5 radian</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>omega</th>\n",
-       "      <td>0.569434707794277 radian / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "theta                           0.5 radian\n",
-       "omega    0.569434707794277 radian / second\n",
-       "Name: 1.7519607843137255, dtype: object"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init2 = results1.last_row()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And a new `System` object with zero force."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>radius_disk</th>\n",
-       "      <td>0.5 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass_disk</th>\n",
-       "      <td>7 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>radius_pot</th>\n",
-       "      <td>0.4 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass_pot</th>\n",
-       "      <td>0.3 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>force</th>\n",
-       "      <td>0 newton</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>torque_friction</th>\n",
-       "      <td>0.2 meter * newton</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>theta_end</th>\n",
-       "      <td>0.5 radian</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>20 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>theta                           0.5 radian\n",
-       "ome...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>I</th>\n",
-       "      <td>0.923 kilogram * meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>1.7519607843137255 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "radius_disk                                                0.5 meter\n",
-       "mass_disk                                                 7 kilogram\n",
-       "radius_pot                                                 0.4 meter\n",
-       "mass_pot                                                0.3 kilogram\n",
-       "force                                                       0 newton\n",
-       "torque_friction                                   0.2 meter * newton\n",
-       "theta_end                                                 0.5 radian\n",
-       "t_end                                                      20 second\n",
-       "init               theta                           0.5 radian\n",
-       "ome...\n",
-       "I                                        0.923 kilogram * meter ** 2\n",
-       "t_0                                        1.7519607843137255 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system2 = System(system1, t_0=t_0, init=init2, force=0*N)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an event function that stops when angular velocity is 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func2(state, t, system):\n",
-    "    \"\"\"Stops when omega is 0.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: omega\n",
-    "    \"\"\"\n",
-    "    theta, omega = state\n",
-    "    return omega"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.569434707794277 radian/second"
-      ],
-      "text/latex": [
-       "$0.569434707794277\\ \\frac{\\mathrm{radian}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "0.569434707794277 <Unit('radian / second')>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "event_func2(system2.init, 0, system2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the second phase."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.569434707794277 <Unit('radian / second')>,\n",
-       " -0.21668472372697725 <Unit('newton / kilogram / meter')>)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(system2.init, system2.t_0, system2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results2, details2 = run_ode_solver(system2, slope_func, events=event_func2)\n",
-    "details2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And check the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>theta</th>\n",
-       "      <th>omega</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3.751961</th>\n",
-       "      <td>1.2054999681345993 radian</td>\n",
-       "      <td>0.13606526034032235 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.951961</th>\n",
-       "      <td>1.2283793257281241 radian</td>\n",
-       "      <td>0.09272831559492689 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.151961</th>\n",
-       "      <td>1.2425912943725699 radian</td>\n",
-       "      <td>0.049391370849531435 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.351961</th>\n",
-       "      <td>1.2481358740679367 radian</td>\n",
-       "      <td>0.006054426104135979 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.379902</th>\n",
-       "      <td>1.247699599245727 radian</td>\n",
-       "      <td>0.0 radian / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                              theta                                 omega\n",
-       "3.751961  1.2054999681345993 radian   0.13606526034032235 radian / second\n",
-       "3.951961  1.2283793257281241 radian   0.09272831559492689 radian / second\n",
-       "4.151961  1.2425912943725699 radian  0.049391370849531435 radian / second\n",
-       "4.351961  1.2481358740679367 radian  0.006054426104135979 radian / second\n",
-       "4.379902   1.247699599245727 radian                   0.0 radian / second"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results2.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pandas provides `combine_first`, which combines `results1` and `results2`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>theta</th>\n",
-       "      <th>omega</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3.751961</th>\n",
-       "      <td>1.2054999681345993 radian</td>\n",
-       "      <td>0.13606526034032235 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.951961</th>\n",
-       "      <td>1.2283793257281241 radian</td>\n",
-       "      <td>0.09272831559492689 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.151961</th>\n",
-       "      <td>1.2425912943725699 radian</td>\n",
-       "      <td>0.049391370849531435 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.351961</th>\n",
-       "      <td>1.2481358740679367 radian</td>\n",
-       "      <td>0.006054426104135979 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.379902</th>\n",
-       "      <td>1.247699599245727 radian</td>\n",
-       "      <td>0.0 radian / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                              theta                                 omega\n",
-       "3.751961  1.2054999681345993 radian   0.13606526034032235 radian / second\n",
-       "3.951961  1.2283793257281241 radian   0.09272831559492689 radian / second\n",
-       "4.151961  1.2425912943725699 radian  0.049391370849531435 radian / second\n",
-       "4.351961  1.2481358740679367 radian  0.006054426104135979 radian / second\n",
-       "4.379902   1.247699599245727 radian                   0.0 radian / second"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = results1.combine_first(results2)\n",
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can plot `theta` for both phases."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_theta(results):\n",
-    "    plot(results.theta, label='theta')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angle (rad)')\n",
-    "    \n",
-    "plot_theta(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And `omega`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_omega(results):\n",
-    "    plot(results.omega, label='omega', color='C1')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angular velocity (rad/s)')\n",
-    "    \n",
-    "plot_omega(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap25-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "subplot(2, 1, 1)\n",
-    "plot_theta(results)\n",
-    "subplot(2, 1, 2)\n",
-    "plot_omega(results)\n",
-    "savefig('figs/chap25-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Estimating friction\n",
-    "\n",
-    "Let's take the code from the previous section and wrap it in a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_two_phases(force, torque_friction, params):\n",
-    "    \"\"\"Run both phases.\n",
-    "    \n",
-    "    force: force applied to the turntable\n",
-    "    torque_friction: friction due to torque\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: TimeFrame of simulation results\n",
-    "    \"\"\"\n",
-    "    # put the specified parameters into the Params object\n",
-    "    params = Params(params, force=force, torque_friction=torque_friction)\n",
-    "\n",
-    "    # run phase 1\n",
-    "    system1 = make_system(params)\n",
-    "    results1, details1 = run_ode_solver(system1, slope_func, \n",
-    "                                          events=event_func1)\n",
-    "\n",
-    "    # get the final state from phase 1\n",
-    "    t_0 = results1.last_label() * s\n",
-    "    init2 = results1.last_row()\n",
-    "    \n",
-    "    # run phase 2\n",
-    "    system2 = System(system1, t_0=t_0, init=init2, force=0*N)\n",
-    "    results2, details2 = run_ode_solver(system2, slope_func, \n",
-    "                                        events=event_func2)\n",
-    "    \n",
-    "    # combine and return the results\n",
-    "    results = results1.combine_first(results2)\n",
-    "    return TimeFrame(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's test it with the same parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>theta</th>\n",
-       "      <th>omega</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3.751961</th>\n",
-       "      <td>1.2054999681345993 radian</td>\n",
-       "      <td>0.13606526034032235 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.951961</th>\n",
-       "      <td>1.2283793257281241 radian</td>\n",
-       "      <td>0.09272831559492689 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.151961</th>\n",
-       "      <td>1.2425912943725699 radian</td>\n",
-       "      <td>0.049391370849531435 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.351961</th>\n",
-       "      <td>1.2481358740679367 radian</td>\n",
-       "      <td>0.006054426104135979 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.379902</th>\n",
-       "      <td>1.247699599245727 radian</td>\n",
-       "      <td>0.0 radian / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                              theta                                 omega\n",
-       "3.751961  1.2054999681345993 radian   0.13606526034032235 radian / second\n",
-       "3.951961  1.2283793257281241 radian   0.09272831559492689 radian / second\n",
-       "4.151961  1.2425912943725699 radian  0.049391370849531435 radian / second\n",
-       "4.351961  1.2481358740679367 radian  0.006054426104135979 radian / second\n",
-       "4.379902   1.247699599245727 radian                   0.0 radian / second"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "force = 1*N\n",
-    "torque_friction = 0.2*N*m\n",
-    "results = run_two_phases(force, torque_friction, params)\n",
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And check the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "1.247699599245727 radian"
-      ],
-      "text/latex": [
-       "$1.247699599245727\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "1.247699599245727 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "theta_final = results.last_row().theta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the error function we'll use with `root_bisect`.\n",
-    "\n",
-    "It takes a hypothetical value for `torque_friction` and returns the difference between `theta_final` and the observed duration of the first push, 1.5 radian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func1(torque_friction, params):\n",
-    "    \"\"\"Error function for root_scalar.\n",
-    "    \n",
-    "    torque_friction: hypothetical value\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: offset from target value\n",
-    "    \"\"\"\n",
-    "    force = 1 * N\n",
-    "    results = run_two_phases(force, torque_friction, params)\n",
-    "    theta_final = results.last_row().theta\n",
-    "    print(torque_friction, theta_final)\n",
-    "    return theta_final - 1.5 * radian"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing the error function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.1 meter * newton 2.491081016010591 radian\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "0.991081016010591 radian"
-      ],
-      "text/latex": [
-       "$0.991081016010591\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "0.991081016010591 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "guess1 = 0.1*N*m\n",
-    "error_func1(guess1, params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.3 meter * newton 0.8319164068661051 radian\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "-0.6680835931338949 radian"
-      ],
-      "text/latex": [
-       "$-0.6680835931338949\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "-0.6680835931338949 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "guess2 = 0.3*N*m\n",
-    "error_func1(guess2, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And running `root_scalar`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.1 meter * newton 2.491081016010591 radian\n",
-      "0.3 meter * newton 0.8319164068661051 radian\n",
-      "0.2 meter * newton 1.247699599245727 radian\n",
-      "0.15000000000000002 meter * newton 1.664679140408952 radian\n",
-      "0.17500000000000002 meter * newton 1.4245119372074873 radian\n",
-      "0.16250000000000003 meter * newton 1.5351133644066652 radian\n",
-      "0.16875 meter * newton 1.4775125382367449 radian\n",
-      "0.16562500000000002 meter * newton 1.5057134910749645 radian\n",
-      "0.16718750000000002 meter * newton 1.4913395920086545 radian\n",
-      "0.16640625000000003 meter * newton 1.4984572231242101 radian\n",
-      "0.16601562500000003 meter * newton 1.502067905168568 radian\n",
-      "0.16621093750000004 meter * newton 1.5002582165443015 radian\n",
-      "0.16630859375000004 meter * newton 1.4993566348484146 radian\n",
-      "0.16625976562500006 meter * newton 1.4998071542109142 radian\n",
-      "0.16623535156250005 meter * newton 1.500032617476343 radian\n",
-      "0.16624755859375007 meter * newton 1.499919868872052 radian\n",
-      "0.16624145507812504 meter * newton 1.4999762389308366 radian\n",
-      "0.16623840332031253 meter * newton 1.5000044271426904 radian\n",
-      "0.1662399291992188 meter * newton 1.4999903327715458 radian\n",
-      "0.16623916625976565 meter * newton 1.499997379890813 radian\n",
-      "0.1662387847900391 meter * newton 1.5000009035001758 radian\n",
-      "0.16623897552490235 meter * newton 1.4999991416913503 radian\n",
-      "0.16623888015747074 meter * newton 1.5000000225947272 radian\n",
-      "0.16623892784118655 meter * newton 1.4999995821427796 radian\n",
-      "0.16623890399932864 meter * newton 1.4999998023686882 radian\n",
-      "0.1662388920783997 meter * newton 1.4999999124816914 radian\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>converged</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>root</th>\n",
-       "      <td>0.16623888611793522 meter * newton</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "converged                                  True\n",
-       "root         0.16623888611793522 meter * newton\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res = root_bisect(error_func1, [guess1, guess2], params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is the coefficient of friction that yields a total rotation of 1.5 radian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.16623888611793522 meter newton"
-      ],
-      "text/latex": [
-       "$0.16623888611793522\\ \\mathrm{meter} \\cdot \\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "0.16623888611793522 <Unit('meter * newton')>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "torque_friction = res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a test run with the estimated value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "1.4999999675382056 radian"
-      ],
-      "text/latex": [
-       "$1.4999999675382056\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "1.4999999675382056 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "force = 1 * N\n",
-    "results = run_two_phases(force, torque_friction, params)\n",
-    "theta_final = get_last_value(results.theta)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looks good."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Animation\n",
-    "\n",
-    "\n",
-    "Here's a draw function we can use to animate the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib.patches import Circle\n",
-    "from matplotlib.patches import Arrow\n",
-    "\n",
-    "def draw_func(state, t):\n",
-    "    theta, omega = state\n",
-    "    \n",
-    "    # draw a circle for the table\n",
-    "    radius_disk = magnitude(params.radius_disk)\n",
-    "    circle1 = Circle([0, 0], radius_disk)\n",
-    "    plt.gca().add_patch(circle1)\n",
-    "    \n",
-    "    # draw a circle for the teapot\n",
-    "    radius_pot = magnitude(params.radius_pot)\n",
-    "    center = pol2cart(theta, radius_pot)\n",
-    "    circle2 = Circle(center, 0.05, color='C1')\n",
-    "    plt.gca().add_patch(circle2)\n",
-    "\n",
-    "    # make the aspect ratio 1\n",
-    "    plt.axis('equal')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "state = results.first_row()\n",
-    "draw_func(state, 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "animate(results, draw_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "### Exercises\n",
-    "\n",
-    "Now finish off the example by estimating the force that delivers the teapot to the desired position.\n",
-    "\n",
-    "Write an error function that takes `force` and `params` and returns the offset from the desired angle."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def error_func2(force, params):\n",
-    "    \"\"\"Error function for root_scalar.\n",
-    "    \n",
-    "    force: hypothetical value\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: offset from target value\n",
-    "    \"\"\"\n",
-    "    # notice that this function uses the global value of torque_friction\n",
-    "    results = run_two_phases(force, torque_friction, params)\n",
-    "    theta_final = get_last_value(results.theta)\n",
-    "    print(force, theta_final)\n",
-    "    remaining_angle = np.pi - 1.5\n",
-    "    return theta_final - remaining_angle * radian"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test the error function with `force=1`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.5 newton 0.7508796257362771 radian\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "-0.890713027853516 radian"
-      ],
-      "text/latex": [
-       "$-0.890713027853516\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "-0.890713027853516 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "guess1 = 0.5 * N\n",
-    "error_func2(guess1, params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2 newton 2.990287714114223 radian\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "1.3486950605244297 radian"
-      ],
-      "text/latex": [
-       "$1.3486950605244297\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "1.3486950605244297 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "guess2 = 2 * N\n",
-    "error_func2(guess2, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And run `root_bisect` to find the desired force."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.5 newton 0.7508796257362771 radian\n",
-      "2 newton 2.990287714114223 radian\n",
-      "1.25 newton 1.8767666580336735 radian\n",
-      "0.875 newton 1.314142751543798 radian\n",
-      "1.0625 newton 1.5961003377501604 radian\n",
-      "1.15625 newton 1.7324316654000937 radian\n",
-      "1.109375 newton 1.6640846390801893 radian\n",
-      "1.0859375 newton 1.6295935099707282 radian\n",
-      "1.09765625 newton 1.6467143299143472 radian\n",
-      "1.091796875 newton 1.6381227337897593 radian\n",
-      "1.0947265625 newton 1.6424107353138588 radian\n",
-      "1.09326171875 newton 1.6402647854172603 radian\n",
-      "1.093994140625 newton 1.6413372730819231 radian\n",
-      "1.0943603515625 newton 1.6418738823769812 radian\n",
-      "1.09417724609375 newton 1.641605547274225 radian\n",
-      "1.094085693359375 newton 1.6414714025642667 radian\n",
-      "1.0941314697265625 newton 1.641538473015794 radian\n",
-      "1.0941543579101562 newton 1.6415720096691466 radian\n",
-      "1.0941658020019531 newton 1.6415887783527199 radian\n",
-      "1.0941715240478516 newton 1.6415971627837305 radian\n",
-      "1.0941686630249023 newton 1.6415929705607897 radian\n",
-      "1.0941672325134277 newton 1.641590874454896 radian\n",
-      "1.094167947769165 newton 1.6415919225073785 radian\n",
-      "1.0941683053970337 newton 1.6415924465339684 radian\n",
-      "1.094168484210968 newton 1.6415927085473503 radian\n",
-      "1.0941683948040009 newton 1.641592577540652 radian\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>converged</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>root</th>\n",
-       "      <td>1.0941684395074844 newton</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
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-       "converged                         True\n",
-       "root         1.0941684395074844 newton\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "res = root_bisect(error_func2, [guess1, guess2], params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "1.6415926430439989 radian"
-      ],
-      "text/latex": [
-       "$1.6415926430439989\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "1.6415926430439989 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "force = res.root\n",
-    "results = run_two_phases(force, torque_friction, params)\n",
-    "theta_final = get_last_value(results.theta)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.6415926535897931"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "remaining_angle = np.pi - 1.5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Now suppose my friend pours 0.1 kg of tea and puts the teapot back on the turntable at distance 0.3 meters from the center.  If I ask for the tea back, how much force should they apply, over an arc of 0.5 radians, to make the teapot glide to a stop back in front of me?  You can assume that torque due to friction is proportional to the total mass of the teapot and the turntable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "7.3 kilogram"
-      ],
-      "text/latex": [
-       "$7.3\\ \\mathrm{kilogram}$"
-      ],
-      "text/plain": [
-       "7.3 <Unit('kilogram')>"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "mass_before = params.mass_pot + params.mass_disk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>radius_disk</th>\n",
-       "      <td>0.5 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass_disk</th>\n",
-       "      <td>7 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>radius_pot</th>\n",
-       "      <td>0.3 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass_pot</th>\n",
-       "      <td>0.2 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>force</th>\n",
-       "      <td>1 newton</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>torque_friction</th>\n",
-       "      <td>0.2 meter * newton</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>theta_end</th>\n",
-       "      <td>0.5 radian</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>20 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "radius_disk                 0.5 meter\n",
-       "mass_disk                  7 kilogram\n",
-       "radius_pot                  0.3 meter\n",
-       "mass_pot                 0.2 kilogram\n",
-       "force                        1 newton\n",
-       "torque_friction    0.2 meter * newton\n",
-       "theta_end                  0.5 radian\n",
-       "t_end                       20 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "params2 = Params(params, mass_pot=0.2*kg, radius_pot=0.3*m)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "7.2 kilogram"
-      ],
-      "text/latex": [
-       "$7.2\\ \\mathrm{kilogram}$"
-      ],
-      "text/plain": [
-       "7.2 <Unit('kilogram')>"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "mass_after = params2.mass_pot + params2.mass_disk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.16623888611793522 meter newton"
-      ],
-      "text/latex": [
-       "$0.16623888611793522\\ \\mathrm{meter} \\cdot \\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "0.16623888611793522 <Unit('meter * newton')>"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "torque_friction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.16396164110262107 meter newton"
-      ],
-      "text/latex": [
-       "$0.16396164110262107\\ \\mathrm{meter} \\cdot \\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "0.16396164110262107 <Unit('meter * newton')>"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "torque_friction2 = torque_friction * mass_after / mass_before"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>theta</th>\n",
-       "      <th>omega</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5.630969</th>\n",
-       "      <td>2.9973839879919186 radian</td>\n",
-       "      <td>0.12061194274576453 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5.830969</th>\n",
-       "      <td>3.0178342233248876 radian</td>\n",
-       "      <td>0.0838904105839233 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.030969</th>\n",
-       "      <td>3.030940152225488 radian</td>\n",
-       "      <td>0.04716887842208207 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.230969</th>\n",
-       "      <td>3.03670177469372 radian</td>\n",
-       "      <td>0.010447346260240839 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.287870</th>\n",
-       "      <td>3.036251498073426 radian</td>\n",
-       "      <td>0.0 radian / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                              theta                                 omega\n",
-       "5.630969  2.9973839879919186 radian   0.12061194274576453 radian / second\n",
-       "5.830969  3.0178342233248876 radian    0.0838904105839233 radian / second\n",
-       "6.030969   3.030940152225488 radian   0.04716887842208207 radian / second\n",
-       "6.230969    3.03670177469372 radian  0.010447346260240839 radian / second\n",
-       "6.287870   3.036251498073426 radian                   0.0 radian / second"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "guess = 2 * N\n",
-    "results = run_two_phases(guess, torque_friction2, params2)\n",
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "subplot(2, 1, 1)\n",
-    "plot_theta(results)\n",
-    "subplot(2, 1, 2)\n",
-    "plot_omega(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# Note: this is so similar to error_func2, it would be better\n",
-    "# to generalize it, but for expediency, I will make a modified\n",
-    "# verison.\n",
-    "\n",
-    "def error_func3(force, params):\n",
-    "    \"\"\"Error function for root_scalar.\n",
-    "    \n",
-    "    force: hypothetical value\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: offset from target value\n",
-    "    \"\"\"\n",
-    "    # notice that this function uses the global value of torque_friction2\n",
-    "    results = run_two_phases(force, torque_friction2, params)\n",
-    "    theta_final = get_last_value(results.theta)\n",
-    "    print(force, theta_final)\n",
-    "    remaining_angle = np.pi * radian\n",
-    "    return theta_final - remaining_angle"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 newton 1.521633703251964 radian\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "-1.619958950337829 radian"
-      ],
-      "text/latex": [
-       "$-1.619958950337829\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "-1.619958950337829 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "guess1 = 1 * N\n",
-    "error_func3(guess1, params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3 newton 4.544952149083414 radian\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "1.4033594954936213 radian"
-      ],
-      "text/latex": [
-       "$1.4033594954936213\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "1.4033594954936213 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "guess2 = 3 * N\n",
-    "error_func3(guess2, params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 newton 1.5220674118848017 radian\n",
-      "3 newton 4.555863285616827 radian\n",
-      "2.0 newton 3.036251498073426 radian\n",
-      "2.5 newton 3.7711230348952873 radian\n",
-      "2.25 newton 3.410270734411078 radian\n",
-      "2.125 newton 3.237449910464716 radian\n",
-      "2.0625 newton 3.137756144960873 radian\n",
-      "2.09375 newton 3.189525229335694 radian\n",
-      "2.078125 newton 3.1637524234581544 radian\n",
-      "2.0703125 newton 3.1507232783707426 radian\n",
-      "2.06640625 newton 3.144231960206115 radian\n",
-      "2.064453125 newton 3.1409921147185713 radian\n",
-      "2.0654296875 newton 3.142611552996112 radian\n",
-      "2.06494140625 newton 3.141801712740783 radian\n",
-      "2.064697265625 newton 3.1413968834505375 radian\n",
-      "2.0648193359375 newton 3.1415992905258765 radian\n",
-      "2.06475830078125 newton 3.141498085095761 radian\n",
-      "2.064788818359375 newton 3.1415486873377074 radian\n",
-      "2.0648040771484375 newton 3.1415739888135135 radian\n",
-      "2.0648117065429688 newton 3.141586639640125 radian\n",
-      "2.0648155212402344 newton 3.1415929650756085 radian\n",
-      "2.0648136138916016 newton 3.1415898023560187 radian\n",
-      "2.064814567565918 newton 3.1415913837153515 radian\n",
-      "2.064815044403076 newton 3.141592174395364 radian\n",
-      "2.0648152828216553 newton 3.1415925697354568 radian\n",
-      "2.064815402030945 newton 3.141592767405525 radian\n"
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-    {
-     "data": {
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-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>converged</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>root</th>\n",
-       "      <td>2.0648153424263 newton</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "converged                      True\n",
-       "root         2.0648153424263 newton\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "res = root_bisect(error_func3, [guess1, guess2], params2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.0648153424263 newton 3.135475479073576 radian\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "-0.006117174516217183 radian"
-      ],
-      "text/latex": [
-       "$-0.006117174516217183\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "-0.006117174516217183 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "error_func3(res.root, params)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
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- },
- "nbformat": 4,
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diff --git a/soln/data/World_population_estimates2.csv b/soln/data/World_population_estimates2.csv
deleted file mode 100644
index ed7de057..00000000
--- a/soln/data/World_population_estimates2.csv
+++ /dev/null
@@ -1,68 +0,0 @@
-Year,census,prb,un,maddison,hyde,tanton,biraben,mj,thomlinson,durand,clark
-1950,2557628654,2516000000.0,2525149000.0,2544000000.0,2527960000.0,2400000000.0,2527000000.0,2500000000.0,2400000000.0,,2486000000.0
-1951,2594939877,,2572850917.0,2571663000.0,,,,,,,
-1952,2636772306,,2619292068.0,2617949000.0,,,,,,,
-1953,2682053389,,2665865392.0,2665959000.0,,,,,,,
-1954,2730228104,,2713172027.0,2716927000.0,,,,,,,
-1955,2782098943,,2761650981.0,2769074000.0,,,,,,,
-1956,2835299673,,2811572031.0,2822502000.0,,,,,,,
-1957,2891349717,,2863042795.0,2879934000.0,,,,,,,
-1958,2948137248,,2916030167.0,2939254000.0,,,,,,,
-1959,3000716593,,2970395814.0,2995909000.0,,,,,,,
-1960,3043001508,,3026002942.0,3041507000.0,3042000000.0,,,,,,
-1961,3083966929,,3082830266.0,3082161000.0,,,,,,,
-1962,3140093217,,3141071531.0,3135787000.0,,,,,,,3036000000.0
-1963,3209827882,,3201178277.0,3201354000.0,,,,,,,
-1964,3281201306,,3263738832.0,3266477000.0,,,,,,,
-1965,3350425793,,3329122479.0,3333138000.0,,,,,,,
-1966,3420677923,,3397475247.0,3402224000.0,,,,,,,3288000000.0
-1967,3490333715,,3468521724.0,3471464000.0,,,,,,,
-1968,3562313822,,3541674891.0,3543086000.0,,,,,,,
-1969,3637159050,,3616108749.0,3615743000.0,,,,,,,
-1970,3712697742,,3691172616.0,3691157000.0,3710000000.0,,3637000000.0,,3600000000.0,"3,600,000,000– 3,700,000,000",3632000000.0
-1971,3790326948,,3766754345.0,3769818000.0,,,,,,,
-1972,3866568653,,3842873611.0,3846499000.0,,,,,,,
-1973,3942096442,,3919182332.0,3922793000.0,3923000000.0,,,,,,3860000000.0
-1974,4016608813,,3995304922.0,3997677000.0,,,,,,,
-1975,4089083233,,4071020434.0,4070671000.0,,,,3900000000.0,4000000000.0,,
-1976,4160185010,,4146135850.0,4141445000.0,,,,,,,
-1977,4232084578,,4220816737.0,4213539000.0,,,,,,,
-1978,4304105753,,4295664825.0,4286317000.0,,,,,,,
-1979,4379013942,,4371527871.0,4363144000.0,,,,,,,
-1980,4451362735,,4449048798.0,4439529000.0,4461000000.0,,,,,,
-1981,4534410125,,4528234634.0,4514838000.0,,,,,,,
-1982,4614566561,,4608962418.0,4587307000.0,,,,,,,
-1983,4695736743,,4691559840.0,4676388000.0,,,,,,,
-1984,4774569391,,4776392828.0,4756521000.0,,,,,,,
-1985,4856462699,,4863601517.0,4837719000.0,,5000000000.0,,,,,
-1986,4940571232,,4953376710.0,4920968000.0,,,,,,,
-1987,5027200492,,5045315871.0,5006672000.0,,,,,,,
-1988,5114557167,,5138214688.0,5093306000.0,,,,,,,
-1989,5201440110,,5230000000.0,5180540000.0,,,,,,,
-1990,5288955934,,5320816667.0,5269029000.0,5308000000.0,,,,,,
-1991,5371585922,,5408908724.0,5351922000.0,,,,,,,
-1992,5456136278,,5494899570.0,5435722000.0,,,,,,,
-1993,5538268316,,5578865109.0,5518127000.0,,,,,,,
-1994,5618682132,,5661086346.0,5599396000.0,,,,,,,
-1995,5699202985,5760000000.0,5741822412.0,5681575000.0,,,,,,,
-1996,5779440593,,5821016750.0,5762212000.0,,,,,,,
-1997,5857972543,5840000000.0,5898688337.0,5842122000.0,,,,,,,
-1998,5935213248,,5975303657.0,5921366000.0,,,,,,,
-1999,6012074922,,6051478010.0,5999622000.0,,,,,,,
-2000,6088571383,6067000000.0,6127700428.0,6076558000.0,6145000000.0,,,5750000000.0,,,
-2001,6165219247,6137000000.0,6204147026.0,6154791000.0,,,,,,,
-2002,6242016348,6215000000.0,6280853817.0,6231704000.0,,,,,,,
-2003,6318590956,6314000000.0,6357991749.0,6308364000.0,,,,,,,
-2004,6395699509,6396000000.0,6435705595.0,6374056000.0,,,,,,,
-2005,6473044732,6477000000.0,6514094605.0,6462987000.0,,,,,,,
-2006,6551263534,6555000000.0,6593227977.0,6540214000.0,,,,,,,
-2007,6629913759,6625000000.0,6673105937.0,6616689000.0,,,,,,,
-2008,6709049780,6705000000.0,6753649228.0,6694832000.0,,,,,,,
-2009,6788214394,6809972000.0,6834721933.0,6764086000.0,,,,,,,
-2010,6858584755,6892319000.0,6916183482.0,,,,,,,,
-2011,6935999491,6986951000.0,6997998760.0,,,,,,,,
-2012,7013871313,7057075000.0,7080072417.0,,,,,,,,
-2013,7092128094,7136796000.0,7162119434.0,,,,,,,,
-2014,7169968185,7238184000.0,7243784000.0,,,,,,,,
-2015,7247892788,7336435000.0,7349472000.0,,,,,,,,
-2016,7325996709,7418151841.0,,,,,,,,,
diff --git a/soln/data/World_population_estimates3.csv b/soln/data/World_population_estimates3.csv
deleted file mode 100644
index 2ab604fe..00000000
--- a/soln/data/World_population_estimates3.csv
+++ /dev/null
@@ -1,46 +0,0 @@
-Year,census,prb,un
-2016,7334771614.0,,7432663280.0
-2017,7412778971.0,,
-2018,7490427640.0,,
-2019,7567402977.0,,
-2020,7643402123.0,,7758157000.0
-2021,7718256830.0,,
-2022,7792021317.0,,
-2023,7864725370.0,,
-2024,7936271554.0,,
-2025,8006580553.0,8000000000.0,8141661000.0
-2026,8075716000.0,,
-2027,8143729466.0,,
-2028,8210559895.0,,
-2029,8276190519.0,,
-2030,8340606590.0,8505000000.0,8500766000.0
-2031,8403880343.0,,
-2032,8466094022.0,,
-2033,8527246205.0,,
-2034,8587325154.0,,
-2035,8646304704.0,,8838908000.0
-2036,8704239274.0,,
-2037,8761189197.0,,
-2038,8817138785.0,,
-2039,8872066537.0,,
-2040,8925949679.0,,9157234000.0
-2041,8978822945.0,,
-2042,9030723366.0,,
-2043,9081617002.0,,
-2044,9131462326.0,,
-2045,9180225214.0,,9453892000.0
-2046,9227935007.0,,
-2047,9274616811.0,,
-2048,9320232984.0,,
-2049,9364750182.0,,
-2050,9408141302.0,9804000000.0,9725148000.0
-2055,,,9968809000.0
-2060,,,10184290000.0
-2065,,,10375719000.0
-2070,,,10547989000.0
-2075,,,10701653000.0
-2080,,,10836635000.0
-2085,,,10953525000.0
-2090,,,11055270000.0
-2095,,,11142461000.0
-2100,,,11213317000.0
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diff --git a/soln/duck_soln.ipynb b/soln/duck_soln.ipynb
deleted file mode 100644
index 9197ff79..00000000
--- a/soln/duck_soln.ipynb
+++ /dev/null
@@ -1,266 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Copyright 2018 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The duck problem\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "centimeter"
-      ],
-      "text/latex": [
-       "$\\mathrm{centimeter}$"
-      ],
-      "text/plain": [
-       "<Unit('centimeter')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "g = UNITS.g\n",
-    "cm = UNITS.cm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>density_duck</th>\n",
-       "      <td>0.3 gram / centimeter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>density_water</th>\n",
-       "      <td>1.0 gram / centimeter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>r</th>\n",
-       "      <td>5 centimeter</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "density_duck     0.3 gram / centimeter ** 3\n",
-       "density_water    1.0 gram / centimeter ** 3\n",
-       "r                              5 centimeter\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = System (\n",
-    "    density_duck = 0.3 * g/cm**3,\n",
-    "    density_water = 1  * g/cm**3,\n",
-    "    r = 5 * cm,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func(d, system):\n",
-    "    # in order to work with root_scale, we have\n",
-    "    # to accept input that does not have units\n",
-    "    d = d * cm\n",
-    "    r = system.r\n",
-    "    \n",
-    "    volume_duck = 4 / 3 * pi * r**3\n",
-    "    mass_duck = volume_duck * system.density_duck\n",
-    "\n",
-    "    volume_water = pi / 3 * (3 * r * d**2 - d**3)\n",
-    "    mass_water = volume_water * system.density_water\n",
-    "\n",
-    "    # and return an error that does not have units\n",
-    "    error = mass_duck - mass_water\n",
-    "    return magnitude(error)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "43.982297150257125"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "error_func(3, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-27.227136331111524"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "error_func(4, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "      converged: True\n",
-       "           flag: 'converged'\n",
-       " function_calls: 7\n",
-       "     iterations: 6\n",
-       "           root: 3.6325749109061265"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res = root_scalar(error_func, [3., 4.], system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "3.6325749109061265 centimeter"
-      ],
-      "text/latex": [
-       "$3.6325749109061265\\ \\mathrm{centimeter}$"
-      ],
-      "text/plain": [
-       "3.6325749109061265 <Unit('centimeter')>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res.root * cm"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/filter_soln.ipynb b/soln/filter_soln.ipynb
deleted file mode 100644
index d7fb0f44..00000000
--- a/soln/filter_soln.ipynb
+++ /dev/null
@@ -1,1774 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Low pass filter\n",
-    "\n",
-    "The following circuit diagram (from [Wikipedia](https://en.wikipedia.org/wiki/File:RC_Divider.svg)) shows a low-pass filter built with one resistor and one capacitor.  \n",
-    "\n",
-    "![Low-pass filter circuit diagram](diagrams/RC_Divider.png)\n",
-    "\n",
-    "A \"filter\" is a circuit takes a signal, $V_{in}$, as input and produces a signal, $V_{out}$, as output.  In this context, a \"signal\" is a voltage that changes over time.\n",
-    "\n",
-    "A filter is \"low-pass\" if it allows low-frequency signals to pass from $V_{in}$ to $V_{out}$ unchanged, but it reduces the amplitude of high-frequency signals.\n",
-    "\n",
-    "By applying the laws of circuit analysis, we can derive a differential equation that describes the behavior of this system.  By solving the differential equation, we can predict the effect of this circuit on any input signal.\n",
-    "\n",
-    "Suppose we are given $V_{in}$ and $V_{out}$ at a particular instant in time.  By Ohm's law, which is a simple model of the behavior of resistors, the instantaneous current through the resistor is:\n",
-    "\n",
-    "$ I_R = (V_{in} - V_{out}) / R $\n",
-    "\n",
-    "where $R$ is resistance in ohms.\n",
-    "\n",
-    "Assuming that no current flows through the output of the circuit, Kirchhoff's current law implies that the current through the capacitor is:\n",
-    "\n",
-    "$ I_C = I_R $\n",
-    "\n",
-    "According to a simple model of the behavior of capacitors, current through the capacitor causes a change in the voltage across the capacitor:\n",
-    "\n",
-    "$ I_C = C \\frac{d V_{out}}{dt} $\n",
-    "\n",
-    "where $C$ is capacitance in farads (F).\n",
-    "\n",
-    "Combining these equations yields a differential equation for $V_{out}$:\n",
-    "\n",
-    "$ \\frac{d }{dt} V_{out} = \\frac{V_{in} - V_{out}}{R C} $\n",
-    "\n",
-    "Follow the instructions blow to simulate the low-pass filter for input signals like this:\n",
-    "\n",
-    "$ V_{in}(t) = A \\cos (2 \\pi f t) $\n",
-    "\n",
-    "where $A$ is the amplitude of the input signal, say 5 V, and $f$ is the frequency of the signal in Hz."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Params and System objects\n",
-    "\n",
-    "Here's a `Params` object to contain the quantities we need.  I've chosen values for `R1` and `C1` that might be typical for a circuit that works with audio signal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "second"
-      ],
-      "text/latex": [
-       "$\\mathrm{second}$"
-      ],
-      "text/plain": [
-       "<Unit('second')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ohm = UNITS.ohm\n",
-    "farad = UNITS.farad\n",
-    "volt = UNITS.volt\n",
-    "Hz = UNITS.Hz\n",
-    "second = UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>R1</th>\n",
-       "      <td>1000000.0 ohm</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C1</th>\n",
-       "      <td>1e-09 farad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>A</th>\n",
-       "      <td>5 volt</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>f</th>\n",
-       "      <td>1000 hertz</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "R1    1000000.0 ohm\n",
-       "C1      1e-09 farad\n",
-       "A            5 volt\n",
-       "f        1000 hertz\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = Params(\n",
-    "    R1 = 1e6 * ohm,\n",
-    "    C1 = 1e-9 * farad,\n",
-    "    A = 5 * volt,\n",
-    "    f = 1000 * Hz)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can pass the `Params` object `make_system` which computes some additional parameters and defines `init`.\n",
-    "\n",
-    "* `omega` is the frequency of the input signal in radians/second.\n",
-    "\n",
-    "* `tau` is the time constant for this circuit, which is the time it takes to get from an initial startup phase to \n",
-    "\n",
-    "* `cutoff` is the cutoff frequency for this circuit (in Hz), which marks the transition from low frequency signals, which pass through the filter unchanged, to high frequency signals, which are attenuated.\n",
-    "\n",
-    "* `t_end` is chosen so we run the simulation for 4 cycles of the input signal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    f, R1, C1 = params.f, params.R1, params.C1\n",
-    "    \n",
-    "    init = State(V_out = 0)\n",
-    "    omega = 2 * np.pi * f\n",
-    "    tau = R1 * C1\n",
-    "    cutoff = 1 / R1 / C1 / 2 / np.pi\n",
-    "    t_end = 4 / f\n",
-    "    dt = t_end / 4000\n",
-    "    \n",
-    "    return System(params, init=init, t_end=t_end, dt=dt,\n",
-    "                  omega=omega, tau=tau, cutoff=cutoff.to(Hz))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>R1</th>\n",
-       "      <td>1000000.0 ohm</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C1</th>\n",
-       "      <td>1e-09 farad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>A</th>\n",
-       "      <td>5 volt</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>f</th>\n",
-       "      <td>1000 hertz</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>V_out    0\n",
-       "dtype: int64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>0.004 / hertz</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>1e-06 / hertz</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>omega</th>\n",
-       "      <td>6283.185307179586 hertz</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>tau</th>\n",
-       "      <td>0.001 farad * ohm</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cutoff</th>\n",
-       "      <td>159.15494309189532 hertz</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "R1                   1000000.0 ohm\n",
-       "C1                     1e-09 farad\n",
-       "A                           5 volt\n",
-       "f                       1000 hertz\n",
-       "init       V_out    0\n",
-       "dtype: int64\n",
-       "t_end                0.004 / hertz\n",
-       "dt                   1e-06 / hertz\n",
-       "omega      6283.185307179586 hertz\n",
-       "tau              0.001 farad * ohm\n",
-       "cutoff    159.15494309189532 hertz\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write a slope function that takes as an input a `State` object that contains `V_out`, and returns the derivative of `V_out`.\n",
-    "\n",
-    "Note: The ODE solver requires the return value from slope_func to be a sequence, even if there is only one element.  The simplest way to do that is to return a list with a single element:\n",
-    "\n",
-    "```\n",
-    "    return [dV_out_dt]\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: V_out\n",
-    "    t: time\n",
-    "    system: System object with A, omega, R1 and C1\n",
-    "    \n",
-    "    returns: dV_out/dt\n",
-    "    \"\"\"\n",
-    "    [V_out] = state\n",
-    "    \n",
-    "    R1, C1 = system.R1, system.C1\n",
-    "    A, omega = system.A, system.omega\n",
-    "    \n",
-    "    V_in = A * np.cos(omega * t)\n",
-    "    \n",
-    "    V_R1 = V_in - V_out\n",
-    "\n",
-    "    I_R1 = V_R1 / R1\n",
-    "    I_C1 = I_R1\n",
-    "\n",
-    "    dV_out_dt = I_C1 / C1\n",
-    "\n",
-    "    return [dV_out_dt]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[5000.0 <Unit('volt / farad / ohm')>]"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(system.init, 0*UNITS.s, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then run the simulation.  I suggest using `t_eval=ts` to make sure we have enough data points to plot and analyze the results. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                                                 True\n",
-       "message    The solver successfully reached the end of the...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>V_out</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.000000</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.000001</th>\n",
-       "      <td>0.004997467101366766 volt / farad / hertz / ohm</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.000002</th>\n",
-       "      <td>0.00998974189353765 volt / farad / hertz / ohm</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.000003</th>\n",
-       "      <td>0.014976632282572247 volt / farad / hertz / ohm</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.000004</th>\n",
-       "      <td>0.01995794638210714 volt / farad / hertz / ohm</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                    V_out\n",
-       "0.000000                                                0\n",
-       "0.000001  0.004997467101366766 volt / farad / hertz / ohm\n",
-       "0.000002   0.00998974189353765 volt / farad / hertz / ohm\n",
-       "0.000003  0.014976632282572247 volt / farad / hertz / ohm\n",
-       "0.000004   0.01995794638210714 volt / farad / hertz / ohm"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a function you can use to plot `V_out` as a function of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_results(results):\n",
-    "    xs = results.V_out.index\n",
-    "    ys = results.V_out.values\n",
-    "\n",
-    "    t_end = get_last_label(results)\n",
-    "    if t_end < 10:\n",
-    "        xs *= 1000\n",
-    "        xlabel = 'Time (ms)'\n",
-    "    else:\n",
-    "        xlabel = 'Time (s)'\n",
-    "        \n",
-    "    plot(xs, ys)\n",
-    "    decorate(xlabel=xlabel,\n",
-    "             ylabel='$V_{out}$ (volt)',\n",
-    "             legend=False)\n",
-    "    \n",
-    "plot_results(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If things have gone according to plan, the amplitude of the output signal should be about 0.8 V.\n",
-    "\n",
-    "Also, you might notice that it takes a few cycles for the signal to get to the full amplitude. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sweeping frequency\n",
-    "\n",
-    "Plot `V_out` looks like for a range of frequencies:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x396 with 6 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fs = [1, 10, 100, 1000, 10000, 100000] * Hz\n",
-    "\n",
-    "for i, f in enumerate(fs):\n",
-    "    system = make_system(Params(params, f=f))\n",
-    "    results, details = run_ode_solver(system, slope_func)\n",
-    "    subplot(3, 2, i+1)\n",
-    "    plot_results(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At low frequencies, notice that there is an initial \"transient\" before the output gets to a steady-state sinusoidal output.  The duration of this transient is a small multiple of the time constant, `tau`, which is 1 ms."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Estimating the output ratio\n",
-    "\n",
-    "Let's compare the amplitudes of the input and output signals.  Below the cutoff frequency, we expect them to be about the same.  Above the cutoff, we expect the amplitude of the output signal to be smaller.\n",
-    "\n",
-    "We'll start with a signal at the cutoff frequency, `f=1000` Hz."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "system = make_system(Params(params, f=1000*Hz))\n",
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "V_out = results.V_out\n",
-    "plot_results(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function computes `V_in` as a `TimeSeries`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_vin(results, system):\n",
-    "    \"\"\"Computes V_in as a TimeSeries.\n",
-    "    \n",
-    "    results: TimeFrame with simulation results\n",
-    "    system: System object with A and omega\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    A, omega = system.A, system.omega\n",
-    "    \n",
-    "    ts = results.index.values * UNITS.second\n",
-    "    V_in = A * np.cos(omega * ts)\n",
-    "    return TimeSeries(V_in, results.index, name='V_in')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the input and output look like.  Notice that the output is not just smaller; it is also \"out of phase\"; that is, the peaks of the output are shifted to the right, relative to the peaks of the input."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "V_in = compute_vin(results, system)\n",
-    "\n",
-    "plot(V_out)\n",
-    "plot(V_in)\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='V (volt)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function estimates the amplitude of a signal by computing half the distance between the min and max."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def estimate_A(series):\n",
-    "    \"\"\"Estimate amplitude.\n",
-    "    \n",
-    "    series: TimeSeries\n",
-    "    \n",
-    "    returns: amplitude in volts\n",
-    "    \"\"\"\n",
-    "    return (series.max() - series.min()) / 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The amplitude of `V_in` should be near 5 (but not exact because we evaluated it at a finite number of points)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "5.0 volt"
-      ],
-      "text/latex": [
-       "$5.0\\ \\mathrm{volt}$"
-      ],
-      "text/plain": [
-       "5.0 <Unit('volt')>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A_in = estimate_A(V_in)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The amplitude of `V_out` should be lower."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.8133731785983502 volt/(farad hertz ohm)"
-      ],
-      "text/latex": [
-       "$0.8133731785983502\\ \\frac{\\mathrm{volt}}{\\left(\\mathrm{farad} \\cdot \\mathrm{hertz} \\cdot \\mathrm{ohm}\\right)}$"
-      ],
-      "text/plain": [
-       "0.8133731785983502 <Unit('volt / farad / hertz / ohm')>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A_out = estimate_A(V_out)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the ratio between them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.16267463571967006 1/(farad hertz ohm)"
-      ],
-      "text/latex": [
-       "$0.16267463571967006\\ \\frac{1}{\\left(\\mathrm{farad} \\cdot \\mathrm{hertz} \\cdot \\mathrm{ohm}\\right)}$"
-      ],
-      "text/plain": [
-       "0.16267463571967006 <Unit('1 / farad / hertz / ohm')>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ratio = A_out / A_in"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.16267463571967006 dimensionless"
-      ],
-      "text/latex": [
-       "$0.16267463571967006\\ dimensionless$"
-      ],
-      "text/plain": [
-       "0.16267463571967006 <Unit('dimensionless')>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ratio.to_base_units()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Encapsulate the code we have so far in a function that takes two TimeSeries objects and returns the ratio between their amplitudes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def estimate_ratio(V1, V2):\n",
-    "    \"\"\"Estimate the ratio of amplitudes.\n",
-    "    \n",
-    "    V1: TimeSeries\n",
-    "    V2: TimeSeries\n",
-    "    \n",
-    "    returns: amplitude ratio\n",
-    "    \"\"\"\n",
-    "    a1 = estimate_A(V1)\n",
-    "    a2 = estimate_A(V2)\n",
-    "    return a1 / a2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And test your function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.16267463571967006 1/(farad hertz ohm)"
-      ],
-      "text/latex": [
-       "$0.16267463571967006\\ \\frac{1}{\\left(\\mathrm{farad} \\cdot \\mathrm{hertz} \\cdot \\mathrm{ohm}\\right)}$"
-      ],
-      "text/plain": [
-       "0.16267463571967006 <Unit('1 / farad / hertz / ohm')>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "estimate_ratio(V_out, V_in)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Estimating phase offset\n",
-    "\n",
-    "The delay between the peak of the input and the peak of the output is call a \"phase shift\" or \"phase offset\", usually measured in fractions of a cycle, degrees, or radians.\n",
-    "\n",
-    "To estimate the phase offset between two signals, we can use cross-correlation.  Here's what the cross-correlation looks like between `V_out` and `V_in`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "corr = correlate(V_out, V_in, mode='same')\n",
-    "corr = TimeSeries(corr, V_in.index)\n",
-    "plot(corr, color='C4')\n",
-    "decorate(xlabel='Lag (s)',\n",
-    "         ylabel='Correlation')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The location of the peak in the cross correlation is the estimated shift between the two signals, in seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.00221799999999997 second"
-      ],
-      "text/latex": [
-       "$0.00221799999999997\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "0.00221799999999997 <Unit('second')>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "peak_time = corr.idxmax() * UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can express the phase offset as a multiple of the period of the input signal:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.001 1/hertz"
-      ],
-      "text/latex": [
-       "$0.001\\ \\frac{1}{\\mathrm{hertz}}$"
-      ],
-      "text/plain": [
-       "0.001 <Unit('1 / hertz')>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "period = 1 / system.f"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "2.2179999999999698 dimensionless"
-      ],
-      "text/latex": [
-       "$2.2179999999999698\\ dimensionless$"
-      ],
-      "text/plain": [
-       "2.2179999999999698 <Unit('dimensionless')>"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(peak_time / period).to_reduced_units()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We don't care about whole period offsets, only the fractional part, which we can get using `modf`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.21799999999996977 dimensionless"
-      ],
-      "text/latex": [
-       "$0.21799999999996977\\ dimensionless$"
-      ],
-      "text/plain": [
-       "0.21799999999996977 <Unit('dimensionless')>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "frac, whole = np.modf(peak_time / period)\n",
-    "frac = frac.to_reduced_units()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we can convert from a fraction of a cycle to degrees:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "78.47999999998912 degree"
-      ],
-      "text/latex": [
-       "$78.47999999998912\\ \\mathrm{degree}$"
-      ],
-      "text/plain": [
-       "78.47999999998912 <Unit('degree')>"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "frac * 360 * UNITS.degree"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Encapsulate this code in a function that takes two `TimeSeries` objects and a `System` object, and returns the phase offset in degrees.\n",
-    "\n",
-    "Note: by convention, if the output is shifted to the right, the phase offset is negative."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def estimate_offset(V1, V2, system):\n",
-    "    \"\"\"Estimate phase offset.\n",
-    "    \n",
-    "    V1: TimeSeries\n",
-    "    V2: TimeSeries\n",
-    "    system: System object with f\n",
-    "    \n",
-    "    returns: amplitude ratio\n",
-    "    \"\"\"\n",
-    "    corr = correlate(V1, V2, mode='same')\n",
-    "    corr = TimeSeries(corr, V1.index)\n",
-    "    peak_time = corr.idxmax() * UNITS.second\n",
-    "    period = 1 / system.f\n",
-    "    frac, whole = np.modf(peak_time / period)\n",
-    "    frac = frac.to_reduced_units()\n",
-    "    return -frac * 360 * UNITS.degree"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-78.47999999998912 degree"
-      ],
-      "text/latex": [
-       "$-78.47999999998912\\ \\mathrm{degree}$"
-      ],
-      "text/plain": [
-       "-78.47999999998912 <Unit('degree')>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "estimate_offset(V_out, V_in, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sweeping frequency again\n",
-    "\n",
-    "**Exercise:** Write a function that takes as parameters an array of input frequencies and a `Params` object.\n",
-    "\n",
-    "For each input frequency it should run a simulation and use the results to estimate the output ratio (dimensionless) and phase offset (in degrees).\n",
-    "\n",
-    "It should return two `SweepSeries` objects, one for the ratios and one for the offsets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def sweep_frequency(fs, params):\n",
-    "    ratios = SweepSeries()\n",
-    "    offsets = SweepSeries()\n",
-    "\n",
-    "    for i, f in enumerate(fs):\n",
-    "        system = make_system(Params(params, f=f))\n",
-    "        results, details = run_ode_solver(system, slope_func)\n",
-    "        V_out = results.V_out\n",
-    "        V_in = compute_vin(results, system)\n",
-    "        \n",
-    "        f = magnitude(f)\n",
-    "        ratios[f] = estimate_ratio(V_out, V_in)\n",
-    "        offsets[f] = estimate_offset(V_out, V_in, system)\n",
-    "    return ratios, offsets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run your function with these frequencies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}1.0 & 3.1622776601683795 & 10.0 & 31.622776601683793 & 100.0 & 316.22776601683796 & 1000.0 & 3162.2776601683795 & 10000.0\\end{pmatrix} hertz\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}1.0 & 3.1622776601683795 & 10.0 & 31.622776601683793 & 100.0 & 316.22776601683796 & 1000.0 & 3162.2776601683795 & 10000.0\\end{pmatrix}\\ \\mathrm{hertz}$"
-      ],
-      "text/plain": [
-       "array([1.00000000e+00, 3.16227766e+00, 1.00000000e+01, 3.16227766e+01,\n",
-       "       1.00000000e+02, 3.16227766e+02, 1.00000000e+03, 3.16227766e+03,\n",
-       "       1.00000000e+04]) <Unit('hertz')>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fs = 10 ** linspace(0, 4, 9) * Hz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ratios, offsets = sweep_frequency(fs, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot output ratios like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(ratios, color='C2', label='output ratio')\n",
-    "decorate(xlabel='Frequency (Hz)',\n",
-    "         ylabel='$V_{out} / V_{in}$')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But it is useful and conventional to plot ratios on a log-log scale.  The vertical gray line shows the cutoff frequency."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_ratios(ratios, system):\n",
-    "    \"\"\"Plot output ratios.\n",
-    "    \"\"\"\n",
-    "    # axvline can't handle a Quantity with units\n",
-    "    cutoff = magnitude(system.cutoff)\n",
-    "    plt.axvline(cutoff, color='gray', alpha=0.4)\n",
-    "    \n",
-    "    plot(ratios, color='C2', label='output ratio')\n",
-    "    decorate(xlabel='Frequency (Hz)',\n",
-    "             ylabel='$V_{out} / V_{in}$',\n",
-    "             xscale='log', yscale='log')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_ratios(ratios, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This plot shows the cutoff behavior more clearly.  Below the cutoff, the output ratio is close to 1.  Above the cutoff, it drops off linearly, on a log scale, which indicates that output ratios for high frequencies are practically 0.\n",
-    "\n",
-    "Here's the plot for phase offset, on a log-x scale:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_offsets(offsets, system):\n",
-    "    \"\"\"Plot phase offsets.\n",
-    "    \"\"\"\n",
-    "    # axvline can't handle a Quantity with units\n",
-    "    cutoff = magnitude(system.cutoff)\n",
-    "    plt.axvline(cutoff, color='gray', alpha=0.4)\n",
-    "    \n",
-    "    plot(offsets, color='C9')\n",
-    "    decorate(xlabel='Frequency (Hz)',\n",
-    "             ylabel='Phase offset (degree)',\n",
-    "             xscale='log')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_offsets(offsets, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For low frequencies, the phase offset is near 0.  For high frequencies, it approaches 90 degrees."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis\n",
-    "\n",
-    "By analysis we can show that the output ratio for this signal is\n",
-    "\n",
-    "$A = \\frac{1}{\\sqrt{1 + (R C \\omega)^2}}$ \n",
-    "\n",
-    "where $\\omega = 2 \\pi f$, and the phase offset is\n",
-    "\n",
-    "$ \\phi = \\arctan (- R C \\omega)$ \n",
-    "\n",
-    "**Exercise:** Write functions that take an array of input frequencies and returns $A(f)$ and $\\phi(f)$ as `SweepSeries` objects.  Plot these objects and compare them with the results from the previous section.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def output_ratios(fs, system):\n",
-    "    R1, C1, omega = system.R1, system.C1, system.omega\n",
-    "    \n",
-    "    omegas = 2 * np.pi * fs\n",
-    "    rco = R1 * C1 * omegas\n",
-    "    A = 1 / np.sqrt(1 + rco**2)\n",
-    "    return SweepSeries(A, magnitude(fs))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1.000000</th>\n",
-       "      <td>0.9999802613756332 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.162278</th>\n",
-       "      <td>0.9998026663382117 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10.000000</th>\n",
-       "      <td>0.9980319045036448 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31.622777</th>\n",
-       "      <td>0.9808266593818514 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100.000000</th>\n",
-       "      <td>0.8467330159648304 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>316.227766</th>\n",
-       "      <td>0.4495645925385784 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000.000000</th>\n",
-       "      <td>0.15717672547758985 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3162.277660</th>\n",
-       "      <td>0.050265590254250286 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10000.000000</th>\n",
-       "      <td>0.015913478971147695 dimensionless</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "1.000000          0.9999802613756332 dimensionless\n",
-       "3.162278          0.9998026663382117 dimensionless\n",
-       "10.000000         0.9980319045036448 dimensionless\n",
-       "31.622777         0.9808266593818514 dimensionless\n",
-       "100.000000        0.8467330159648304 dimensionless\n",
-       "316.227766        0.4495645925385784 dimensionless\n",
-       "1000.000000      0.15717672547758985 dimensionless\n",
-       "3162.277660     0.050265590254250286 dimensionless\n",
-       "10000.000000    0.015913478971147695 dimensionless\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A = output_ratios(fs, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def phase_offsets(fs, system):\n",
-    "    R1, C1, omega = system.R1, system.C1, system.omega\n",
-    "\n",
-    "    omegas = 2 * np.pi * fs\n",
-    "    rco = R1 * C1 * omegas\n",
-    "    phi = np.arctan(-rco).to(UNITS.degree)\n",
-    "    return SweepSeries(phi, magnitude(fs))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1.000000</th>\n",
-       "      <td>-0.3599952627020996 degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.162278</th>\n",
-       "      <td>-1.1382701830745394 degree</td>\n",
-       "    </tr>\n",
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-       "      <th>10.000000</th>\n",
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-       "    <tr>\n",
-       "      <th>31.622777</th>\n",
-       "      <td>-11.237840984624153 degree</td>\n",
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-       "    <tr>\n",
-       "      <th>100.000000</th>\n",
-       "      <td>-32.14190763534206 degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>316.227766</th>\n",
-       "      <td>-63.284247907949336 degree</td>\n",
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-       "      <th>3162.277660</th>\n",
-       "      <td>-87.1187796579127 degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10000.000000</th>\n",
-       "      <td>-89.08818633038616 degree</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "1.000000        -0.3599952627020996 degree\n",
-       "3.162278        -1.1382701830745394 degree\n",
-       "10.000000       -3.5952737798681755 degree\n",
-       "31.622777       -11.237840984624153 degree\n",
-       "100.000000       -32.14190763534206 degree\n",
-       "316.227766      -63.284247907949336 degree\n",
-       "1000.000000      -80.95693892096232 degree\n",
-       "3162.277660       -87.1187796579127 degree\n",
-       "10000.000000     -89.08818633038616 degree\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "phi = phase_offsets(fs, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the theoretical results along with the simulation results and see if they agree."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(A, ':', color='gray')\n",
-    "plot_ratios(ratios, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(phi, ':', color='gray')\n",
-    "plot_offsets(offsets, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the phase offsets, there are small differences between the theoretical results and our estimates, but that is probably because it is not easy to estimate phase offsets precisely from numerical results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Consider modifying this notebook to model a [first order high-pass filter](https://en.wikipedia.org/wiki/High-pass_filter#First-order_continuous-time_implementation), a [two-stage second-order low-pass filter](https://www.electronics-tutorials.ws/filter/filter_2.html), or a [passive band-pass filter](https://www.electronics-tutorials.ws/filter/filter_4.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/glucose_soln.ipynb b/soln/glucose_soln.ipynb
deleted file mode 100644
index 51d1f3ca..00000000
--- a/soln/glucose_soln.ipynb
+++ /dev/null
@@ -1,2104 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 18\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter\n",
-    "\n",
-    "Read the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv('data/glucose_insulin.csv', index_col='time');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Interpolate the insulin data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function modsim.modsim.interpolate.<locals>.wrapper(x)>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "I = interpolate(data.insulin)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initialize the parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1e-05"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "G0 = 290\n",
-    "k1 = 0.03\n",
-    "k2 = 0.02\n",
-    "k3 = 1e-05"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To estimate basal levels, we'll use the concentrations at `t=0`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Gb = data.glucose[0]\n",
-    "Ib = data.insulin[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create the initial condtions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>G</th>\n",
-       "      <td>290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>X</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "G    290\n",
-       "X      0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init = State(G=G0, X=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Make the `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "182"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_0 = get_first_label(data)\n",
-    "t_end = get_last_label(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>G0</th>\n",
-       "      <td>290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k1</th>\n",
-       "      <td>0.03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k2</th>\n",
-       "      <td>0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k3</th>\n",
-       "      <td>1e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>G    290\n",
-       "X      0\n",
-       "dtype: int64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gb</th>\n",
-       "      <td>92</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Ib</th>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>I</th>\n",
-       "      <td>&lt;function interpolate.&lt;locals&gt;.wrapper at 0x7f...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>182</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "G0                                                     290\n",
-       "k1                                                    0.03\n",
-       "k2                                                    0.02\n",
-       "k3                                                   1e-05\n",
-       "init                        G    290\n",
-       "X      0\n",
-       "dtype: int64\n",
-       "Gb                                                      92\n",
-       "Ib                                                      11\n",
-       "I        <function interpolate.<locals>.wrapper at 0x7f...\n",
-       "t_0                                                      0\n",
-       "t_end                                                  182\n",
-       "dt                                                       2\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = System(G0=G0, k1=k1, k2=k2, k3=k3,\n",
-    "                init=init, Gb=Gb, Ib=Ib, I=I,\n",
-    "                t_0=t_0, t_end=t_end, dt=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Updates the glucose minimal model.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time in min\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    G, X = state\n",
-    "    k1, k2, k3 = system.k1, system.k2, system.k3 \n",
-    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
-    "    dt = system.dt\n",
-    "    \n",
-    "    dGdt = -k1 * (G - Gb) - X*G\n",
-    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
-    "    \n",
-    "    G += dGdt * dt\n",
-    "    X += dXdt * dt\n",
-    "\n",
-    "    return State(G=G, X=X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t_0] = init\n",
-    "    ts = linrange(t_0, t_end, dt)\n",
-    "    \n",
-    "    for t in ts:\n",
-    "        frame.row[t+dt] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Numerical solution\n",
-    "\n",
-    "In the previous chapter, we approximated the differential equations with difference equations, and solved them using `run_simulation`.\n",
-    "\n",
-    "In this chapter, we solve the differential equation numerically using `run_ode_solver`, which is a wrapper for the SciPy ODE solver.\n",
-    "\n",
-    "Instead of an update function, we provide a slope function that evaluates the right-hand side of the differential equations.  We don't have to do the update part; the solver does it for us."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the glucose minimal model.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time in min\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: derivatives of G and X\n",
-    "    \"\"\"\n",
-    "    G, X = state\n",
-    "    k1, k2, k3 = system.k1, system.k2, system.k3 \n",
-    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
-    "    \n",
-    "    dGdt = -k1 * (G - Gb) - X*G\n",
-    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
-    "    \n",
-    "    return dGdt, dXdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-5.9399999999999995, 0.0)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run the ODE solver."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2, details = run_ode_solver(system, slope_func, t_eval=data.index);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`details` is a `ModSimSeries` object with information about how the solver worked."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
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-      "text/plain": [
-       "success                                                 True\n",
-       "message    The solver successfully reached the end of the...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`results` is a `TimeFrame` with one row for each time step and one column for each state variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>G</th>\n",
-       "      <th>X</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>290</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>278.476</td>\n",
-       "      <td>0.00015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>267.464</td>\n",
-       "      <td>0.00147812</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>255.899</td>\n",
-       "      <td>0.00330258</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>244.424</td>\n",
-       "      <td>0.00428352</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>233.421</td>\n",
-       "      <td>0.0048796</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>222.905</td>\n",
-       "      <td>0.00539312</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>212.909</td>\n",
-       "      <td>0.00580811</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>203.454</td>\n",
-       "      <td>0.00610843</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>194.566</td>\n",
-       "      <td>0.00630605</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>186.24</td>\n",
-       "      <td>0.00643892</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>178.459</td>\n",
-       "      <td>0.00655891</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>171.185</td>\n",
-       "      <td>0.0066622</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>164.396</td>\n",
-       "      <td>0.00673793</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>158.068</td>\n",
-       "      <td>0.00679316</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>152.172</td>\n",
-       "      <td>0.00686423</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>146.67</td>\n",
-       "      <td>0.00695603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>141.532</td>\n",
-       "      <td>0.00703975</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>136.742</td>\n",
-       "      <td>0.00708884</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>132.286</td>\n",
-       "      <td>0.00710463</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>128.148</td>\n",
-       "      <td>0.00708845</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>124.315</td>\n",
-       "      <td>0.00704154</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>120.769</td>\n",
-       "      <td>0.00696712</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>117.496</td>\n",
-       "      <td>0.00686816</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>114.481</td>\n",
-       "      <td>0.00674565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>111.709</td>\n",
-       "      <td>0.0066005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>109.167</td>\n",
-       "      <td>0.0064336</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>106.84</td>\n",
-       "      <td>0.00625981</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>104.71</td>\n",
-       "      <td>0.00609282</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58</th>\n",
-       "      <td>102.762</td>\n",
-       "      <td>0.00593238</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>124</th>\n",
-       "      <td>86.9662</td>\n",
-       "      <td>0.00107406</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>126</th>\n",
-       "      <td>87.0883</td>\n",
-       "      <td>0.000956516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>128</th>\n",
-       "      <td>87.2224</td>\n",
-       "      <td>0.000845541</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>130</th>\n",
-       "      <td>87.3667</td>\n",
-       "      <td>0.000740875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>132</th>\n",
-       "      <td>87.5196</td>\n",
-       "      <td>0.000642273</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>134</th>\n",
-       "      <td>87.6796</td>\n",
-       "      <td>0.000549496</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>136</th>\n",
-       "      <td>87.8454</td>\n",
-       "      <td>0.000462316</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>88.0157</td>\n",
-       "      <td>0.000380513</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>140</th>\n",
-       "      <td>88.1896</td>\n",
-       "      <td>0.000303877</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142</th>\n",
-       "      <td>88.3658</td>\n",
-       "      <td>0.000232205</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>144</th>\n",
-       "      <td>88.5436</td>\n",
-       "      <td>0.000164302</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>146</th>\n",
-       "      <td>88.7224</td>\n",
-       "      <td>9.90618e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>148</th>\n",
-       "      <td>88.9018</td>\n",
-       "      <td>3.63786e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>150</th>\n",
-       "      <td>89.0813</td>\n",
-       "      <td>-2.38474e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>152</th>\n",
-       "      <td>89.2606</td>\n",
-       "      <td>-8.17126e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>154</th>\n",
-       "      <td>89.4392</td>\n",
-       "      <td>-0.000137309</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>156</th>\n",
-       "      <td>89.617</td>\n",
-       "      <td>-0.000190727</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>158</th>\n",
-       "      <td>89.7936</td>\n",
-       "      <td>-0.00024205</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>160</th>\n",
-       "      <td>89.9687</td>\n",
-       "      <td>-0.000291362</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>162</th>\n",
-       "      <td>90.1422</td>\n",
-       "      <td>-0.000338741</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>164</th>\n",
-       "      <td>90.3138</td>\n",
-       "      <td>-0.000385262</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>166</th>\n",
-       "      <td>90.4837</td>\n",
-       "      <td>-0.00043192</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>168</th>\n",
-       "      <td>90.6521</td>\n",
-       "      <td>-0.000478709</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>170</th>\n",
-       "      <td>90.8191</td>\n",
-       "      <td>-0.000525623</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>172</th>\n",
-       "      <td>90.9848</td>\n",
-       "      <td>-0.000572659</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>174</th>\n",
-       "      <td>91.1493</td>\n",
-       "      <td>-0.00061981</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>91.3128</td>\n",
-       "      <td>-0.000667074</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>178</th>\n",
-       "      <td>91.4754</td>\n",
-       "      <td>-0.000714445</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>180</th>\n",
-       "      <td>91.6372</td>\n",
-       "      <td>-0.000761918</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>182</th>\n",
-       "      <td>91.7983</td>\n",
-       "      <td>-0.000809491</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>92 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           G            X\n",
-       "0        290            0\n",
-       "2    278.476      0.00015\n",
-       "4    267.464   0.00147812\n",
-       "6    255.899   0.00330258\n",
-       "8    244.424   0.00428352\n",
-       "10   233.421    0.0048796\n",
-       "12   222.905   0.00539312\n",
-       "14   212.909   0.00580811\n",
-       "16   203.454   0.00610843\n",
-       "18   194.566   0.00630605\n",
-       "20    186.24   0.00643892\n",
-       "22   178.459   0.00655891\n",
-       "24   171.185    0.0066622\n",
-       "26   164.396   0.00673793\n",
-       "28   158.068   0.00679316\n",
-       "30   152.172   0.00686423\n",
-       "32    146.67   0.00695603\n",
-       "34   141.532   0.00703975\n",
-       "36   136.742   0.00708884\n",
-       "38   132.286   0.00710463\n",
-       "40   128.148   0.00708845\n",
-       "42   124.315   0.00704154\n",
-       "44   120.769   0.00696712\n",
-       "46   117.496   0.00686816\n",
-       "48   114.481   0.00674565\n",
-       "50   111.709    0.0066005\n",
-       "52   109.167    0.0064336\n",
-       "54    106.84   0.00625981\n",
-       "56    104.71   0.00609282\n",
-       "58   102.762   0.00593238\n",
-       "..       ...          ...\n",
-       "124  86.9662   0.00107406\n",
-       "126  87.0883  0.000956516\n",
-       "128  87.2224  0.000845541\n",
-       "130  87.3667  0.000740875\n",
-       "132  87.5196  0.000642273\n",
-       "134  87.6796  0.000549496\n",
-       "136  87.8454  0.000462316\n",
-       "138  88.0157  0.000380513\n",
-       "140  88.1896  0.000303877\n",
-       "142  88.3658  0.000232205\n",
-       "144  88.5436  0.000164302\n",
-       "146  88.7224  9.90618e-05\n",
-       "148  88.9018  3.63786e-05\n",
-       "150  89.0813 -2.38474e-05\n",
-       "152  89.2606 -8.17126e-05\n",
-       "154  89.4392 -0.000137309\n",
-       "156   89.617 -0.000190727\n",
-       "158  89.7936  -0.00024205\n",
-       "160  89.9687 -0.000291362\n",
-       "162  90.1422 -0.000338741\n",
-       "164  90.3138 -0.000385262\n",
-       "166  90.4837  -0.00043192\n",
-       "168  90.6521 -0.000478709\n",
-       "170  90.8191 -0.000525623\n",
-       "172  90.9848 -0.000572659\n",
-       "174  91.1493  -0.00061981\n",
-       "176  91.3128 -0.000667074\n",
-       "178  91.4754 -0.000714445\n",
-       "180  91.6372 -0.000761918\n",
-       "182  91.7983 -0.000809491\n",
-       "\n",
-       "[92 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting the results from `run_simulation` and `run_ode_solver`, we can see that they are not very different."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f0d543fc240>]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(results.G, '-')\n",
-    "plot(results2.G, '-')\n",
-    "plot(data.glucose, 'bo')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The differences in `G` are less than 1%."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0              0\n",
-       "2      -0.127982\n",
-       "4      -0.191302\n",
-       "6       0.154946\n",
-       "8        0.26436\n",
-       "10      0.207439\n",
-       "12      0.132947\n",
-       "14      0.035413\n",
-       "16    -0.0815439\n",
-       "18     -0.222312\n",
-       "20     -0.371789\n",
-       "22     -0.522334\n",
-       "24     -0.664226\n",
-       "26     -0.801867\n",
-       "28     -0.934267\n",
-       "30      -1.05701\n",
-       "32       -1.1642\n",
-       "34      -1.25663\n",
-       "36        -1.346\n",
-       "38      -1.43135\n",
-       "40       -1.5119\n",
-       "42      -1.58699\n",
-       "44      -1.65612\n",
-       "46      -1.71812\n",
-       "48      -1.77288\n",
-       "50      -1.82036\n",
-       "52      -1.86066\n",
-       "54      -1.89392\n",
-       "56      -1.91501\n",
-       "58      -1.92508\n",
-       "         ...    \n",
-       "124    -0.661724\n",
-       "126    -0.631811\n",
-       "128    -0.601917\n",
-       "130    -0.572193\n",
-       "132     -0.54277\n",
-       "134    -0.513756\n",
-       "136    -0.485244\n",
-       "138    -0.457312\n",
-       "140    -0.430024\n",
-       "142    -0.403431\n",
-       "144    -0.377577\n",
-       "146    -0.352881\n",
-       "148    -0.329311\n",
-       "150    -0.306834\n",
-       "152    -0.285416\n",
-       "154    -0.265021\n",
-       "156    -0.245616\n",
-       "158    -0.227165\n",
-       "160    -0.209635\n",
-       "162    -0.192991\n",
-       "164    -0.177201\n",
-       "166     -0.16262\n",
-       "168    -0.149158\n",
-       "170    -0.136734\n",
-       "172     -0.12527\n",
-       "174    -0.114695\n",
-       "176    -0.104943\n",
-       "178   -0.0959529\n",
-       "180   -0.0876686\n",
-       "182   -0.0800372\n",
-       "Name: G, Length: 92, dtype: object"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diff = results.G - results2.G\n",
-    "percent_diff = diff / results2.G * 100\n",
-    "percent_diff.dropna()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optimization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's find the parameters that yield the best fit for the data.  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll use these values as an initial estimate and iteratively improve them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>G0</th>\n",
-       "      <td>290.00000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k1</th>\n",
-       "      <td>0.03000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k2</th>\n",
-       "      <td>0.02000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k3</th>\n",
-       "      <td>0.00001</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "G0    290.00000\n",
-       "k1      0.03000\n",
-       "k2      0.02000\n",
-       "k3      0.00001\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = Params(G0 = 290,\n",
-    "                k1 = 0.03,\n",
-    "                k2 = 0.02,\n",
-    "                k3 = 1e-05)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system` takes the parameters and actual data and returns a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params, data):\n",
-    "    \"\"\"Makes a System object with the given parameters.\n",
-    "    \n",
-    "    params: sequence of G0, k1, k2, k3\n",
-    "    data: DataFrame with `glucose` and `insulin`\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    # params might be a Params object or an array,\n",
-    "    # so we have to unpack it like this\n",
-    "    G0, k1, k2, k3 = params\n",
-    "    \n",
-    "    Gb = data.glucose[0]\n",
-    "    Ib = data.insulin[0]\n",
-    "    I = interpolate(data.insulin)\n",
-    "    \n",
-    "    t_0 = get_first_label(data)\n",
-    "    t_end = get_last_label(data)\n",
-    "\n",
-    "    init = State(G=G0, X=0)\n",
-    "    \n",
-    "    return System(G0=G0, k1=k1, k2=k2, k3=k3,\n",
-    "                  init=init, Gb=Gb, Ib=Ib, I=I,\n",
-    "                  t_0=t_0, t_end=t_end, dt=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>G0</th>\n",
-       "      <td>290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k1</th>\n",
-       "      <td>0.03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k2</th>\n",
-       "      <td>0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k3</th>\n",
-       "      <td>1e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>G    290.0\n",
-       "X      0.0\n",
-       "dtype: float64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gb</th>\n",
-       "      <td>92</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Ib</th>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>I</th>\n",
-       "      <td>&lt;function interpolate.&lt;locals&gt;.wrapper at 0x7f...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>182</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "G0                                                     290\n",
-       "k1                                                    0.03\n",
-       "k2                                                    0.02\n",
-       "k3                                                   1e-05\n",
-       "init                  G    290.0\n",
-       "X      0.0\n",
-       "dtype: float64\n",
-       "Gb                                                      92\n",
-       "Ib                                                      11\n",
-       "I        <function interpolate.<locals>.wrapper at 0x7f...\n",
-       "t_0                                                      0\n",
-       "t_end                                                  182\n",
-       "dt                                                       2\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`error_func` takes the parameters and actual data, makes a `System` object, and runs `odeint`, then compares the results to the data.  It returns an array of errors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                                                 True\n",
-       "message    The solver successfully reached the end of the...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params, data)\n",
-    "results, details = run_ode_solver(system, slope_func, t_eval=data.index)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func(params, data):\n",
-    "    \"\"\"Computes an array of errors to be minimized.\n",
-    "    \n",
-    "    params: sequence of parameters\n",
-    "    data: DataFrame of values to be matched\n",
-    "    \n",
-    "    returns: array of errors\n",
-    "    \"\"\"\n",
-    "    print(params)\n",
-    "    \n",
-    "    # make a System with the given parameters\n",
-    "    system = make_system(params, data)\n",
-    "    \n",
-    "    # solve the ODE\n",
-    "    results, details = run_ode_solver(system, slope_func, t_eval=data.index)\n",
-    "    \n",
-    "    # compute the difference between the model\n",
-    "    # results and actual data\n",
-    "    errors = results.G - data.glucose\n",
-    "    return errors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When we call `error_func`, we provide a sequence of parameters as a single object."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how that works:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G0    290.00000\n",
-      "k1      0.03000\n",
-      "k2      0.02000\n",
-      "k3      0.00001\n",
-      "dtype: float64\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0      198.000000\n",
-       "2      -71.523600\n",
-       "4      -19.535535\n",
-       "6        4.898956\n",
-       "8        4.423982\n",
-       "10      17.420932\n",
-       "12      11.905305\n",
-       "14       7.909446\n",
-       "16       7.454111\n",
-       "18            NaN\n",
-       "19            NaN\n",
-       "20            NaN\n",
-       "22       6.458757\n",
-       "24            NaN\n",
-       "26            NaN\n",
-       "27            NaN\n",
-       "28            NaN\n",
-       "30            NaN\n",
-       "32       4.670366\n",
-       "34            NaN\n",
-       "36            NaN\n",
-       "38            NaN\n",
-       "40            NaN\n",
-       "42       0.314528\n",
-       "44            NaN\n",
-       "46            NaN\n",
-       "48            NaN\n",
-       "50            NaN\n",
-       "52       4.166524\n",
-       "54            NaN\n",
-       "          ...    \n",
-       "124           NaN\n",
-       "126           NaN\n",
-       "128           NaN\n",
-       "130           NaN\n",
-       "132           NaN\n",
-       "134           NaN\n",
-       "136           NaN\n",
-       "138           NaN\n",
-       "140           NaN\n",
-       "142      6.365824\n",
-       "144           NaN\n",
-       "146           NaN\n",
-       "148           NaN\n",
-       "150           NaN\n",
-       "152           NaN\n",
-       "154           NaN\n",
-       "156           NaN\n",
-       "158           NaN\n",
-       "160           NaN\n",
-       "162      5.142209\n",
-       "164           NaN\n",
-       "166           NaN\n",
-       "168           NaN\n",
-       "170           NaN\n",
-       "172           NaN\n",
-       "174           NaN\n",
-       "176           NaN\n",
-       "178           NaN\n",
-       "180           NaN\n",
-       "182      1.798265\n",
-       "Length: 94, dtype: float64"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "error_func(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`leastsq` is a wrapper for `scipy.optimize.leastsq`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we call it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2.9e+02 3.0e-02 2.0e-02 1.0e-05]\n",
-      "[2.9e+02 3.0e-02 2.0e-02 1.0e-05]\n",
-      "[2.9e+02 3.0e-02 2.0e-02 1.0e-05]\n",
-      "[2.90000004e+02 3.00000000e-02 2.00000000e-02 1.00000000e-05]\n",
-      "[2.90000000e+02 3.00000004e-02 2.00000000e-02 1.00000000e-05]\n",
-      "[2.90000000e+02 3.00000000e-02 2.00000003e-02 1.00000000e-05]\n",
-      "[2.90000000e+02 3.00000000e-02 2.00000000e-02 1.00000001e-05]\n"
-     ]
-    }
-   ],
-   "source": [
-    "best_params, fit_details = leastsq(error_func, params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first return value is a `Params` object with the best parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>G0</th>\n",
-       "      <td>290.00000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k1</th>\n",
-       "      <td>0.03000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k2</th>\n",
-       "      <td>0.02000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k3</th>\n",
-       "      <td>0.00001</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "G0    290.00000\n",
-       "k1      0.03000\n",
-       "k2      0.02000\n",
-       "k3      0.00001\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "best_params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The second return value is a `ModSimSeries` object with information about the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>fvec</th>\n",
-       "      <td>[198.0, -71.52359999999999, -19.53553501808568...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nfev</th>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fjac</th>\n",
-       "      <td>[[nan, nan, nan, nan, nan, nan, nan, nan, nan,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ipvt</th>\n",
-       "      <td>[1, 2, 3, 4]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>qtf</th>\n",
-       "      <td>[nan, nan, nan, nan]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cov_x</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mesg</th>\n",
-       "      <td>The cosine of the angle between func(x) and an...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ier</th>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "fvec     [198.0, -71.52359999999999, -19.53553501808568...\n",
-       "nfev                                                     5\n",
-       "fjac     [[nan, nan, nan, nan, nan, nan, nan, nan, nan,...\n",
-       "ipvt                                          [1, 2, 3, 4]\n",
-       "qtf                                   [nan, nan, nan, nan]\n",
-       "cov_x                                                 None\n",
-       "mesg     The cosine of the angle between func(x) and an...\n",
-       "ier                                                      4\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fit_details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have `best_params`, we can use it to make a `System` object and run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The solver successfully reached the end of the integration interval.'"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(best_params, data)\n",
-    "results, details = run_ode_solver(system, slope_func, t_eval=data.index)\n",
-    "details.message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results, along with the data.  The first few points of the model don't fit the data, but we don't expect them to."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap08-fig04.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(results.G, label='simulation')\n",
-    "plot(data.glucose, 'bo', label='glucose data')\n",
-    "\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration (mg/dL)')\n",
-    "\n",
-    "savefig('figs/chap08-fig04.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Interpreting parameters\n",
-    "\n",
-    "Based on the parameters of the model, we can estimate glucose effectiveness and insulin sensitivity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def indices(params):\n",
-    "    \"\"\"Compute glucose effectiveness and insulin sensitivity.\n",
-    "    \n",
-    "    params: sequence of G0, k1, k2, k3\n",
-    "    data: DataFrame with `glucose` and `insulin`\n",
-    "    \n",
-    "    returns: State object containing S_G and S_I\n",
-    "    \"\"\"\n",
-    "    G0, k1, k2, k3 = params\n",
-    "    return State(S_G=k1, S_I=k3/k2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>S_G</th>\n",
-       "      <td>0.0300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S_I</th>\n",
-       "      <td>0.0005</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "S_G    0.0300\n",
-       "S_I    0.0005\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "indices(best_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "Here's the source code for `run_ode_solver` and `leastsq`, if you'd like to know how they work."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "def run_ralston(system, slope_func, **options):\n",
-      "    \"\"\"Computes a numerical solution to a differential equation.\n",
-      "\n",
-      "    `system` must contain `init` with initial conditions,\n",
-      "     and `t_end` with the end time.\n",
-      "\n",
-      "     `system` may contain `t_0` to override the default, 0\n",
-      "\n",
-      "    It can contain any other parameters required by the slope function.\n",
-      "\n",
-      "    `options` can be ...\n",
-      "\n",
-      "    system: System object\n",
-      "    slope_func: function that computes slopes\n",
-      "\n",
-      "    returns: TimeFrame\n",
-      "    \"\"\"\n",
-      "    # the default message if nothing changes\n",
-      "    msg = \"The solver successfully reached the end of the integration interval.\"\n",
-      "\n",
-      "    # get parameters from system\n",
-      "    init, t_0, t_end, dt = check_system(system, slope_func)\n",
-      "\n",
-      "    # make the TimeFrame\n",
-      "    frame = TimeFrame(columns=init.index)\n",
-      "    frame.row[t_0] = init\n",
-      "    ts = linrange(t_0, t_end, dt) * get_units(t_end)\n",
-      "\n",
-      "    event_func = options.get('events', None)\n",
-      "    z1 = np.nan\n",
-      "\n",
-      "    def project(y1, t1, slopes, dt):\n",
-      "        t2 = t1 + dt\n",
-      "        y2 = [y + slope * dt for y, slope in zip(y1, slopes)]\n",
-      "        return y2, t2\n",
-      "\n",
-      "    # run the solver\n",
-      "    for t1 in ts:\n",
-      "        y1 = frame.row[t1]\n",
-      "\n",
-      "        # evaluate the slopes at the start of the time step\n",
-      "        slopes1 = slope_func(y1, t1, system)\n",
-      "\n",
-      "        # evaluate the slopes at the two-thirds point\n",
-      "        y_mid, t_mid = project(y1, t1, slopes1, 2 * dt / 3)\n",
-      "        slopes2 = slope_func(y_mid, t_mid, system)\n",
-      "\n",
-      "        # compute the weighted sum of the slopes\n",
-      "        slopes = [(k1 + 3 * k2) / 4 for k1, k2 in zip(slopes1, slopes2)]\n",
-      "\n",
-      "        # compute the next time stamp\n",
-      "        y2, t2 = project(y1, t1, slopes, dt)\n",
-      "\n",
-      "        # check for a terminating event\n",
-      "        if event_func:\n",
-      "            z2 = event_func(y2, t2, system)\n",
-      "            if z1 * z2 < 0:\n",
-      "                scale = magnitude(z1 / (z1 - z2))\n",
-      "                y2, t2 = project(y1, t1, slopes, scale * dt)\n",
-      "                frame.row[t2] = y2\n",
-      "                msg = \"A termination event occurred.\"\n",
-      "                break\n",
-      "            else:\n",
-      "                z1 = z2\n",
-      "\n",
-      "        # store the results\n",
-      "        frame.row[t2] = y2\n",
-      "\n",
-      "    details = ModSimSeries(dict(success=True, message=msg))\n",
-      "    return frame, details\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "source_code(run_ode_solver)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "def leastsq(error_func, x0, *args, **options):\n",
-      "    \"\"\"Find the parameters that yield the best fit for the data.\n",
-      "\n",
-      "    `x0` can be a sequence, array, Series, or Params\n",
-      "\n",
-      "    Positional arguments are passed along to `error_func`.\n",
-      "\n",
-      "    Keyword arguments are passed to `scipy.optimize.leastsq`\n",
-      "\n",
-      "    error_func: function that computes a sequence of errors\n",
-      "    x0: initial guess for the best parameters\n",
-      "    args: passed to error_func\n",
-      "    options: passed to leastsq\n",
-      "\n",
-      "    :returns: Params object with best_params and ModSimSeries with details\n",
-      "    \"\"\"\n",
-      "    # override `full_output` so we get a message if something goes wrong\n",
-      "    options['full_output'] = True\n",
-      "\n",
-      "    # run leastsq\n",
-      "    t = scipy.optimize.leastsq(error_func, x0=x0, args=args, **options)\n",
-      "    best_params, cov_x, infodict, mesg, ier = t\n",
-      "\n",
-      "    # pack the results into a ModSimSeries object\n",
-      "    details = ModSimSeries(infodict)\n",
-      "    details.set(cov_x=cov_x, mesg=mesg, ier=ier)\n",
-      "\n",
-      "    # if we got a Params object, we should return a Params object\n",
-      "    if isinstance(x0, Params):\n",
-      "        best_params = Params(Series(best_params, x0.index))\n",
-      "\n",
-      "    # return the best parameters and details\n",
-      "    return best_params, details\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "source_code(leastsq)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:** Since we don't expect the first few points to agree, it's probably better not to make them part of the optimization process.  We can ignore them by leaving them out of the `Series` returned by `error_func`.  Modify the last line of `error_func` to return `errors.loc[8:]`, which includes only the elements of the `Series` from `t=8` and up.\n",
-    "\n",
-    "Does that improve the quality of the fit?  Does it change the best parameters by much?\n",
-    "\n",
-    "Note: You can read more about this use of `loc` [in the Pandas documentation](https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-integer)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** How sensitive are the results to the starting guess for the parameters?  If you try different values for the starting guess, do we get the same values for the best parameters?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Related reading:** You might be interested in this article about [people making a DIY artificial pancreas](https://www.bloomberg.com/news/features/2018-08-08/the-250-biohack-that-s-revolutionizing-life-with-diabetes)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/insulin_soln.ipynb b/soln/insulin_soln.ipynb
deleted file mode 100644
index f3dbc3ae..00000000
--- a/soln/insulin_soln.ipynb
+++ /dev/null
@@ -1,1125 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Insulin minimal model\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data\n",
-    "\n",
-    "We have data from Pacini and Bergman (1986), \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 23: 113-122.."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv('data/glucose_insulin.csv', index_col='time');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The insulin minimal model\n",
-    "\n",
-    "In addition to the glucose minimal mode, Pacini and Bergman present an insulin minimal model, in which the concentration of insulin, $I$, is governed by this differential equation:\n",
-    "\n",
-    "$ \\frac{dI}{dt} = -k I(t) + \\gamma (G(t) - G_T) t $"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Write a version of `make_system` that takes the parameters of this model, `I0`, `k`, `gamma`, and `G_T` as parameters, along with a `DataFrame` containing the measurements, and returns a `System` object suitable for use with `run_simulation` or `run_odeint`.\n",
-    "\n",
-    "Use it to make a `System` object with the following parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>I0</th>\n",
-       "      <td>360.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k</th>\n",
-       "      <td>0.250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gamma</th>\n",
-       "      <td>0.004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>G_T</th>\n",
-       "      <td>80.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "I0       360.000\n",
-       "k          0.250\n",
-       "gamma      0.004\n",
-       "G_T       80.000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = Params(I0 = 360,\n",
-    "                k = 0.25,\n",
-    "                gamma = 0.004,\n",
-    "                G_T = 80)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def make_system(params, data):\n",
-    "    # params might be a Params object or an array,\n",
-    "    # so we have to unpack it like this\n",
-    "    I0, k, gamma, G_T = params\n",
-    "    \n",
-    "    init = State(I=I0)\n",
-    "    \n",
-    "    t_0 = get_first_label(data)\n",
-    "    t_end = get_last_label(data)\n",
-    "    G=interpolate(data.glucose)\n",
-    "    \n",
-    "    system = System(I0=I0, k=k, gamma=gamma, G_T=G_T, G=G,\n",
-    "                    init=init, t_0=t_0, t_end=t_end, dt=1)\n",
-    "\n",
-    "    return system"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>I0</th>\n",
-       "      <td>360</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k</th>\n",
-       "      <td>0.25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gamma</th>\n",
-       "      <td>0.004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>G_T</th>\n",
-       "      <td>80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>G</th>\n",
-       "      <td>&lt;function interpolate.&lt;locals&gt;.wrapper at 0x7f...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>I    360.0\n",
-       "dtype: float64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>182</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "I0                                                     360\n",
-       "k                                                     0.25\n",
-       "gamma                                                0.004\n",
-       "G_T                                                     80\n",
-       "G        <function interpolate.<locals>.wrapper at 0x7f...\n",
-       "init                             I    360.0\n",
-       "dtype: float64\n",
-       "t_0                                                      0\n",
-       "t_end                                                  182\n",
-       "dt                                                       1\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system = make_system(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write a slope function that takes state, t, system as parameters and returns the derivative of `I` with respect to time.  Test your function with the initial condition $I(0)=360$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def slope_func(state, t, system):\n",
-    "    [I] = state\n",
-    "    k, gamma = system.k, system.gamma\n",
-    "    G, G_T = system.G, system.G_T\n",
-    "        \n",
-    "    dIdt = -k * I + gamma * (G(t) - G_T) * t\n",
-    "    \n",
-    "    return [dIdt]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-90.0]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "slope_func(system.init, system.t_0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Run `run_ode_solver` with your `System` object and slope function, and plot the results, along with the measured insulin levels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                                                 True\n",
-       "message    The solver successfully reached the end of the...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>I</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>178</th>\n",
-       "      <td>22.356671</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>179</th>\n",
-       "      <td>23.180483</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>180</th>\n",
-       "      <td>24.013211</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>181</th>\n",
-       "      <td>24.854654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>182</th>\n",
-       "      <td>25.704657</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             I\n",
-       "178  22.356671\n",
-       "179  23.180483\n",
-       "180  24.013211\n",
-       "181  24.854654\n",
-       "182  25.704657"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot(results.I, 'g-', label='simulation')\n",
-    "plot(data.insulin, 'go', label='insulin data')\n",
-    "\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Write an error function that takes a sequence of parameters as an argument, along with the `DataFrame` containing the measurements.  It should make a `System` object with the given parameters, run it, and compute the difference between the results of the simulation and the measured values.  Test your error function by calling it with the parameters from the previous exercise.\n",
-    "\n",
-    "Hint: As we did in a previous exercise, you might want to drop the errors for times prior to `t=8`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def error_func(params, data):\n",
-    "    \"\"\"Computes an array of errors to be minimized.\n",
-    "    \n",
-    "    params: sequence of parameters\n",
-    "    actual: array of values to be matched\n",
-    "    \n",
-    "    returns: array of errors\n",
-    "    \"\"\"\n",
-    "    print(params)\n",
-    "    \n",
-    "    # make a System with the given parameters\n",
-    "    system = make_system(params, data)\n",
-    "\n",
-    "    # solve the ODE\n",
-    "    results, details = run_ode_solver(system, slope_func)\n",
-    "\n",
-    "    # compute the difference between the model\n",
-    "    # results and actual data\n",
-    "    errors = (results.I - data.insulin).dropna()\n",
-    "    return TimeSeries(errors.loc[8:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I0       360.000\n",
-      "k          0.250\n",
-      "gamma      0.004\n",
-      "G_T       80.000\n",
-      "dtype: float64\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>11.892725</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>-2.299827</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>-7.258477</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>-7.522652</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>-3.263455</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>2.001941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>2.652451</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>7.047398</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>3.742283</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>8.929934</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>9.543487</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62</th>\n",
-       "      <td>0.764077</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>-3.217837</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>-10.385020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>-7.384116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>102</th>\n",
-       "      <td>-9.062852</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>122</th>\n",
-       "      <td>-3.583691</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142</th>\n",
-       "      <td>-3.586581</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>162</th>\n",
-       "      <td>3.171719</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>182</th>\n",
-       "      <td>18.704657</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "8      11.892725\n",
-       "10     -2.299827\n",
-       "12     -7.258477\n",
-       "14     -7.522652\n",
-       "16     -3.263455\n",
-       "19      2.001941\n",
-       "22      2.652451\n",
-       "27      7.047398\n",
-       "32      3.742283\n",
-       "42      8.929934\n",
-       "52      9.543487\n",
-       "62      0.764077\n",
-       "72     -3.217837\n",
-       "82    -10.385020\n",
-       "92     -7.384116\n",
-       "102    -9.062852\n",
-       "122    -3.583691\n",
-       "142    -3.586581\n",
-       "162     3.171719\n",
-       "182    18.704657\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "error_func(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Use `leastsq` to find the parameters that best fit the data.  Make a `System` object with those parameters, run it, and plot the results along with the measurements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[3.6e+02 2.5e-01 4.0e-03 8.0e+01]\n",
-      "[3.6e+02 2.5e-01 4.0e-03 8.0e+01]\n",
-      "[3.6e+02 2.5e-01 4.0e-03 8.0e+01]\n",
-      "[3.60000005e+02 2.50000000e-01 4.00000000e-03 8.00000000e+01]\n",
-      "[3.60000000e+02 2.50000004e-01 4.00000000e-03 8.00000000e+01]\n",
-      "[3.60000000e+02 2.50000000e-01 4.00000006e-03 8.00000000e+01]\n",
-      "[3.60000000e+02 2.50000000e-01 4.00000000e-03 8.00000012e+01]\n",
-      "[-5.30683629e+02 -6.79500345e-02 -2.29098225e-03  7.86040674e+01]\n",
-      "[2.86020929e+02 2.22835658e-01 2.97772381e-03 7.96421423e+01]\n",
-      "[2.86020933e+02 2.22835658e-01 2.97772381e-03 7.96421423e+01]\n",
-      "[2.86020929e+02 2.22835661e-01 2.97772381e-03 7.96421423e+01]\n",
-      "[2.86020929e+02 2.22835658e-01 2.97772385e-03 7.96421423e+01]\n",
-      "[2.86020929e+02 2.22835658e-01 2.97772381e-03 7.96421435e+01]\n",
-      "[1.40631803e+02 1.53621683e-01 1.89393929e-03 7.90793326e+01]\n",
-      "[1.40631805e+02 1.53621683e-01 1.89393929e-03 7.90793326e+01]\n",
-      "[1.40631803e+02 1.53621685e-01 1.89393929e-03 7.90793326e+01]\n",
-      "[1.40631803e+02 1.53621683e-01 1.89393932e-03 7.90793326e+01]\n",
-      "[1.40631803e+02 1.53621683e-01 1.89393929e-03 7.90793338e+01]\n",
-      "[1.01576311e+02 8.86883215e-02 9.10961079e-04 7.82514786e+01]\n",
-      "[1.01576313e+02 8.86883215e-02 9.10961079e-04 7.82514786e+01]\n",
-      "[1.01576311e+02 8.86883229e-02 9.10961079e-04 7.82514786e+01]\n",
-      "[1.01576311e+02 8.86883215e-02 9.10961092e-04 7.82514786e+01]\n",
-      "[1.01576311e+02 8.86883215e-02 9.10961079e-04 7.82514798e+01]\n",
-      "[5.41795211e+01 3.19558519e-02 1.64722970e-06 7.52821022e+01]\n",
-      "[9.41838679e+01 7.81822807e-02 7.18446369e-04 7.71896713e+01]\n",
-      "[9.41838693e+01 7.81822807e-02 7.18446369e-04 7.71896713e+01]\n",
-      "[9.41838679e+01 7.81822818e-02 7.18446369e-04 7.71896713e+01]\n",
-      "[9.41838679e+01 7.81822807e-02 7.18446380e-04 7.71896713e+01]\n",
-      "[9.41838679e+01 7.81822807e-02 7.18446369e-04 7.71896724e+01]\n",
-      "[7.78825680e+01 5.77937352e-02 4.15373341e-04 7.56865719e+01]\n",
-      "[7.78825692e+01 5.77937352e-02 4.15373341e-04 7.56865719e+01]\n",
-      "[7.78825680e+01 5.77937361e-02 4.15373341e-04 7.56865719e+01]\n",
-      "[7.78825680e+01 5.77937352e-02 4.15373347e-04 7.56865719e+01]\n",
-      "[7.78825680e+01 5.77937352e-02 4.15373341e-04 7.56865730e+01]\n",
-      "[6.35497242e+01 3.52871788e-02 1.00914602e-04 7.17166904e+01]\n",
-      "[7.00853351e+01 4.50180592e-02 2.47464050e-04 7.34214050e+01]\n",
-      "[7.00853362e+01 4.50180592e-02 2.47464050e-04 7.34214050e+01]\n",
-      "[7.00853351e+01 4.50180598e-02 2.47464050e-04 7.34214050e+01]\n",
-      "[7.00853351e+01 4.50180592e-02 2.47464054e-04 7.34214050e+01]\n",
-      "[7.00853351e+01 4.50180592e-02 2.47464050e-04 7.34214061e+01]\n",
-      "[6.37912891e+01 3.44851857e-02 9.62260771e-05 6.83462066e+01]\n",
-      "[6.67348714e+01 3.95606416e-02 1.77689353e-04 7.08245914e+01]\n",
-      "[6.67348724e+01 3.95606416e-02 1.77689353e-04 7.08245914e+01]\n",
-      "[6.67348714e+01 3.95606422e-02 1.77689353e-04 7.08245914e+01]\n",
-      "[6.67348714e+01 3.95606416e-02 1.77689356e-04 7.08245914e+01]\n",
-      "[6.67348714e+01 3.95606416e-02 1.77689353e-04 7.08245925e+01]\n",
-      "[6.34063138e+01 3.36912594e-02 8.92271739e-05 6.53604365e+01]\n",
-      "[6.54581258e+01 3.75386018e-02 1.53145128e-04 6.89998360e+01]\n",
-      "[6.54581268e+01 3.75386018e-02 1.53145128e-04 6.89998360e+01]\n",
-      "[6.54581258e+01 3.75386023e-02 1.53145128e-04 6.89998360e+01]\n",
-      "[6.54581258e+01 3.75386018e-02 1.53145131e-04 6.89998360e+01]\n",
-      "[6.54581258e+01 3.75386018e-02 1.53145128e-04 6.89998370e+01]\n",
-      "[6.37967075e+01 3.44948737e-02 1.05359671e-04 6.52279435e+01]\n",
-      "[6.37967084e+01 3.44948737e-02 1.05359671e-04 6.52279435e+01]\n",
-      "[6.37967075e+01 3.44948743e-02 1.05359671e-04 6.52279435e+01]\n",
-      "[6.37967075e+01 3.44948737e-02 1.05359673e-04 6.52279435e+01]\n",
-      "[6.37967075e+01 3.44948737e-02 1.05359671e-04 6.52279444e+01]\n",
-      "[6.34409307e+01 3.39762874e-02 1.00577649e-04 6.31260404e+01]\n",
-      "[6.34409316e+01 3.39762874e-02 1.00577649e-04 6.31260404e+01]\n",
-      "[6.34409307e+01 3.39762879e-02 1.00577649e-04 6.31260404e+01]\n",
-      "[6.34409307e+01 3.39762874e-02 1.00577650e-04 6.31260404e+01]\n",
-      "[6.34409307e+01 3.39762874e-02 1.00577649e-04 6.31260413e+01]\n",
-      "[6.28791031e+01 3.28824014e-02 8.29220012e-05 6.00474015e+01]\n",
-      "[6.30778597e+01 3.32816079e-02 8.95761488e-05 6.11905313e+01]\n",
-      "[6.30778606e+01 3.32816079e-02 8.95761488e-05 6.11905313e+01]\n",
-      "[6.30778597e+01 3.32816084e-02 8.95761488e-05 6.11905313e+01]\n",
-      "[6.30778597e+01 3.32816079e-02 8.95761502e-05 6.11905313e+01]\n",
-      "[6.30778597e+01 3.32816079e-02 8.95761488e-05 6.11905322e+01]\n",
-      "[6.28214056e+01 3.28104757e-02 8.25234881e-05 5.95114505e+01]\n",
-      "[6.28214065e+01 3.28104757e-02 8.25234881e-05 5.95114505e+01]\n",
-      "[6.28214056e+01 3.28104762e-02 8.25234881e-05 5.95114505e+01]\n",
-      "[6.28214056e+01 3.28104757e-02 8.25234894e-05 5.95114505e+01]\n",
-      "[6.28214056e+01 3.28104757e-02 8.25234881e-05 5.95114514e+01]\n",
-      "[6.27778545e+01 3.27546603e-02 8.21350019e-05 5.93234986e+01]\n",
-      "[6.27778555e+01 3.27546603e-02 8.21350019e-05 5.93234986e+01]\n",
-      "[6.27778545e+01 3.27546608e-02 8.21350019e-05 5.93234986e+01]\n",
-      "[6.27778545e+01 3.27546603e-02 8.21350032e-05 5.93234986e+01]\n",
-      "[6.27778545e+01 3.27546603e-02 8.21350019e-05 5.93234995e+01]\n",
-      "[6.27660608e+01 3.27361764e-02 8.19166237e-05 5.92757881e+01]\n",
-      "[6.27660617e+01 3.27361764e-02 8.19166237e-05 5.92757881e+01]\n",
-      "[6.27660608e+01 3.27361769e-02 8.19166237e-05 5.92757881e+01]\n",
-      "[6.27660608e+01 3.27361764e-02 8.19166250e-05 5.92757881e+01]\n",
-      "[6.27660608e+01 3.27361764e-02 8.19166237e-05 5.92757890e+01]\n",
-      "[6.27629251e+01 3.27314482e-02 8.18644601e-05 5.92648489e+01]\n",
-      "[6.27629260e+01 3.27314482e-02 8.18644601e-05 5.92648489e+01]\n",
-      "[6.27629251e+01 3.27314487e-02 8.18644601e-05 5.92648489e+01]\n",
-      "[6.27629251e+01 3.27314482e-02 8.18644614e-05 5.92648489e+01]\n",
-      "[6.27629251e+01 3.27314482e-02 8.18644601e-05 5.92648498e+01]\n",
-      "[6.27620378e+01 3.27300853e-02 8.18488907e-05 5.92615370e+01]\n",
-      "Both actual and predicted relative reductions in the sum of squares\n",
-      "  are at most 0.000000\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "best_params, details = leastsq(error_func, params, data)\n",
-    "print(details.mesg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "    <tr>\n",
-       "      <th>I0</th>\n",
-       "      <td>62.762</td>\n",
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-       "      <th>k</th>\n",
-       "      <td>0.0327301</td>\n",
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-       "    <tr>\n",
-       "      <th>gamma</th>\n",
-       "      <td>8.18489e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>G_T</th>\n",
-       "      <td>59.2615</td>\n",
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-       "    <tr>\n",
-       "      <th>G</th>\n",
-       "      <td>&lt;function interpolate.&lt;locals&gt;.wrapper at 0x7f...</td>\n",
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-       "      <th>t_0</th>\n",
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-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>182</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "I0                                                  62.762\n",
-       "k                                                0.0327301\n",
-       "gamma                                          8.18489e-05\n",
-       "G_T                                                59.2615\n",
-       "G        <function interpolate.<locals>.wrapper at 0x7f...\n",
-       "init                         I    62.762038\n",
-       "dtype: float64\n",
-       "t_0                                                      0\n",
-       "t_end                                                  182\n",
-       "dt                                                       1\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system = make_system(best_params, data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                                                 True\n",
-       "message    The solver successfully reached the end of the...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results, details = run_ode_solver(system, slope_func, t_eval=data.index)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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u/qD9fw7cB3QHPgaucM5l1ufYIiISnUIdvrsfeBo4HC85BH/1CPXgZuYzs6uB94CEoE13Awb0B0YAl5fPVGFmgwMxXAGkAt8BL4d6bBERiW6hDvG1Ax53zm0M0/HvBs4G/grcHtR+OV6vKBtvKY8pwCS8mSp+CbzpnPsUwMxuDewzwDkX3vWGRUQkYkLtQb2A13MJlyedc8OBr8obzCwFr0e2Kmi/NXjTKQEMDt7mnMvDm019CCIi0myE2oN6CFhqZhOBDUBZ8Ebn3CmhvJlzLqOa5uTAY15QWx6QFLQ9j8qCt4uISDMQaoJ6AdgHvMWBSSJc9gceE4PakgLHLd+eSGXB20VEpBkINUGNAH7qnFveEMEAOOeyzWw7XpHE1kDzQH4c1lsV2AZUlL0fRuUhQRERaeJCTVAOSGmIQKp4AbjTzJbjDendDDwa2PYS8KmZnQR8jldZuExLzIuINC+hJqj7gefM7HHge6DSAi3OuX+HKa47gIeBb/EKOZ7Cu/8K59wKM7sq8Lwn8CXwszAdV0REokSoCWp24HFKNdvqfaOuc+4jgnpmzrkC4LrAV3X7zwHm1OdYIiLSNISUoDQvn4iINJa6LLfxHd5MD+8D851zuQ0elYiItHh16UGdDZwGXAk8Y2ar+TFhfeGcK6vtxSIiIvVRl+U21gJrgelmFgsch5ewHgYGmNlCAgnLObeuIYMVEZGWI9RrUKXAp4GvO82sPXAqXsK6CRgQ9ghFRKRFqss1qME1bCoGcpxzrwKvhjUqERFp8erSg1qJV0Jedf0nAL+ZbQMecM49HtbIRESkRatLgupXQ3sM0AE4AbjbzPY5554LV2AiItKy1aVIora1n37Am908H7gReC5McYmISAsXrhtvP0EFEiIiEkbhSlAxQGGY3ktERCRsCeoKYEmY3ktERKROZebX1rApBmgPHI93L9SpYYxLRERauLpU8f2hhvZiIBtYBoxwzq0IW1QiItLi1aWKr6YycxERkQYT0lRHZvZrYCmwwjmXH9SeCBDcJiIicihCXbDwVqA3UGpmDm94bylQAtyASs0rLNy4iNnL57I7L4vUpI5MSBvPqD7pkQ5LRKTJCHWy2D5m1hEYDgwDRgB3AcnAhnAH11Qt3LiIGYtnUVRaBMCuvCxmLJ4FoCQlIlJHofagcM5l4a0F9T6AmaXgTRb79/CG1nTNXj63IjmVKyotYvbyuUpQIiJ1dMj3QTnncoC/APcdejjNw+68rJDaRUTkQCElKDPrVcOmnUBN21qc1KSOIbWLiMiBQh3i22Rmu/BmjVga+NoKXAe8EubYmqwJaeMrXYMCSIhNYELa+AhGJSLStISaoNKAoYGv44Fr8WaTAPiPmT0GfA187ZxbGrYom5jy60yq4hMRqb9Qq/hW4i1g+EJ5m5n148ekNRS4EOgGxIYvzKZnVJ90JSQRkUNQl7n4UgKFENVyzv2Aty7Uq0Gv6XKogZnZSOAxwPCucT3gnJtpZgnA48BFQCnwiHPu/kM9noiIRJe6FEl8bGa3mFn7g+1oZp3M7DZg/qEEZWYxwFzgMedce2AC8LiZHQ3cjZe0+uPdh3W5mV12KMcTEZHoU5chvhOAvwJbzOw/wDvAt8AuwAd0Bo4GxgCjgP8LvOZQdAC6AD4z8wF+vNkqioDLgSucc9lAtplNASYBzx/iMUVEJIrUZbLYfcBvzewBvERwCd61pvJrTMV4Ux69BfyPcy7jUINyzu02s8fxkt2zgWPdCGwDugOrgnZfAww51GOKiEh0qXORhHNuO97w2t2BIbhUoMw5tzvcQQXevwAvGc7Bqxh8FSi/FpYXtHsekBTuGEREJLJCnuoIwDlXhle40FAuAE5wzpWvRfWxmT2NN7wHkBi0bxKwrwFjERGRCAjXku/h1htoVaWtBC8pbscrkig3kMpDfiIi0gzUqwfVCN4D7jezXwH/wJs5/X+Aq4FNwJ1mthxvFvWbgUcjFaiIiDSMqOxBOee+xRvmm4R33ekl4E/OubnAHXg3C38LLMa7RvVkhEIVEZEGUq8elJnFB17rC253zuVV/4rQOef+Dfy7mvYCvLn/rgvXsUREJPqEuuT7SGAGcFQNu7To6Y1ERCR8Qu1BTQX2AOcBe8MfjoiIiCfUBDUEGOmcW9EQwYiIiJQLtUhiNdCjIQIREREJFmoPahrwDzObBnyHNzdehUBhg4iIyCELNUE9G3h8sJptflQkISIiYRLqgoVRed+UiIg0P/W9D+pU4Ei8a1irgfnOuZJwBiYiIi1bqPdBdQNex5t6aAPejbp9gDVmNtY5lxn2CEVEpEUKdcjuUbxJW/s5545wzg0A+gJZwCNhjk1ERFqwUBPUGcANzrmt5Q2BBQp/D5wVzsBERKRlCzVBFeBV61WlCj4REQmrUBPUe8AjZta1vCHw/cPAu+EMTEREWrZQq/j+AHwIbDSzjYG2PsByvOXZRUREwiLU+6C2m1kacDpemXk+sNo590FDBCciIi3XQROUmZ0FvO+cKw58D941p5WB7xPK2zXVkYiIhEtdelDzgG5AZuD7mqhQQkREwuagCSp4eiNNdSQiIo0lpIRjZh+aWUo17Z3NbEn4whIRkZauLtegTgIGB56OASaZWW6V3QYB/cMbmoiItGR1uQa1G7gZb949H3AdUBq03Q/sw5tNQkREJCzqcg1qBXA4gJktAC5wzmU3dGDR6tVVb7O3cB+XH3MRPp8v0uGIiDRbod4HdXJ17WaWAAx3zn0elqii2MKNi9i6dzvW6XCO6z080uGIiDRboS63MRKYgXdNqmqBhT/U9zvIsboDfwdOxpsD8Cnn3F8CyfBx4CK8ocZHnHP3h+u4B3P2Eafw1FcvMXv5XEb0PIa4GFXWi4g0hPost7ET+AXeLBK/BG7FuwY1IbyhMRfYBnQFRgKXm9klwN2A4RVljAi0XxbmY9fo5H7H071tF7bv28n87z9trMOKiLQ4oSaoNOAm59wcYBmwwzn3v8CNwG/DFZSZ/RTvutcNzrkC59wPwEnAAuByYLJzLts5twGYAkwK17EPJjYmlkvSzgPglW/foqC4oLEOLSLSooSaoEqAvYHv1wLHBL5fgDc3X7gMB1YAd5nZVjP7Hjgfr9fWHVgVtO8aYEgYj31Q6T2PYUBqP/YU5vKmpiEUEWkQoSaoL4FrzSwG+AY4M9B+FFAUxrg6AqOAYrye1AV4pe7nBrbnBe2bBySF8dgH5fP5mJh2PgBvug/IKdh7kFeIiEioQk1QtwJXAjcBLwCDzWw98C/gpTDGVQjsdc7d5ZwrdM59A8zEG94DSAzaNwnvGlijGtxlAMN6DKGgpJD/t7K2KQpFRKQ+QkpQzrnFQF/g/wL3Qg3HuwZ0FfC7MMa1BkgKVOyViwOyge14RRLlBlJ5yK/RTEw7jxhfDB+s/5QN2ZsjEYKISLMV6lx884DDnHM7wVsfyjn3hHPun8656paCr6/38aoFHzazBDMbAvw3MBuv53anmXUys754Q38vhPHYdda7fQ/OGHASfr+fp5f+E78/nKdARKRlC3WIbyTedaEG5ZwrwJv373C8UvN3gP8NVA/egbcW1bfAYmAO8GRDx1STnx95Du1btcXt+p6FGxdFKgwRkWYn1Btr/wY8b2Z/A9bjVdVVcM6FbajNObceOLua9gK8+QCvC9exDkVSQiITjz6fJxY9z6xvXmNEz6NJjG8d6bBERJq8UHtQ9+L1ov6J13tZiVcOXv7YIo3u+1MGpPYju2APc1ZpUWERkXAINUH1q+br8KDHFinGF8NVwy7Gh4+33Hw278mIdEgiIk1eqAnqWWCPc25j8BfevUivhj+8pqN/xz6c2v9ESv1lzFg8izJ/WaRDEhFp0uqzYOGvzKzqfUdasBD4Zdr5LNm6nLW71/Peuk84Y8BJkQ5JRKTJqs+Chb9BCxZWKykhkf8e/gum/GcGLy1/nWN7ptEpqWOkwxIRaZK0YGGYpfc6hvSex7Bo69fMXPIyt5x4jRY2FBGph3otWGhm8YHX+qpsz6vudS3NVcMvZkXmGpZmrODzzUso9Zcxe/lcdudlkZrUkQlp4xnVJz3SYYqIRLVQFyz8KfAU3uSwwXx4Q31avQ/omJjCpUdfwFNfvcTfF72InzKKSr37m3flZTFj8SwAJSkRkVqEeqPuo8Ae4Dx+XHZDqnHK4Sfw+ealrNix5oBtRaVFzF4+VwlKRKQWoSaoIcDIwHUpqUWML4Zr0y/jmjf/XO32XXlZXPzPazTkJyJSg1Dvg1oN9GiIQJqj1KQOtE1oU+N2Pz8O+WkePxGRykLtQU0D/mFm04DvqLJIoXNO8/xUcfnQn/HEov+jrJaZzjXkJyJyoFAT1LOBxwer2aYiiWqM7vtTCksKeXrJy5RRc5LanZfViFGJiES/UMvMQx0SFOC0n4ymW9su/PWjx/DXkKRSdUOviEglofagADCzwXir2r4HdAE2hHnBwmZnSNeBXDD4zGpnO0+ITWBC2vgIRCUiEr1CXVG3nZn9G295jVeArnhrRH1jZr0aIL5m5WdHns3gzgMAiI+Jwwd0SurIpBETdf1JRKSKUIfsHgZaAb3wZjAHuAHIAaaGMa5mKSYmhhuOu4p2rZIpLivhZ0eNY0LaeGYvn8vF/7yGa9+8TdV8IiIBoSaos4E/OOcqFjxyzm0CrgdOCWdgzVXHxBSuH3klPnz8a+Wb/H3RC+zKy1LJuYhIFaEmqGSqLPMeEFuP92qxju42mJ8ddQ4AJWUllbaVl5yLiLR0oSaVd4C7ApPFAvjNrDMwBXg/rJE1cxcMPqPGbSo5FxEJPUFdD/TGWyMqCfgA2Ay0B24Mb2jNW4wvhtTEDtVuU8m5iEiICco5t8M5dzxwLl5xxGPAOODY4OtSUjeXHH0e8THxldpUci4i4gl1uY044A5gq3NueqDtS2CkmU12zpU1QIzNVnlp+fPLXmFPYS4AJ/QerpJzERFCv1F3CjAe+FVQ23TgTqA1cFuY4moxRvVJZ1SfdD5c/x+eXPwiCzZ8zpJtK8gt3KeZzkWkRQv1GtTFwCXOuYqCCOfc88DlwBVhjKuCmaWY2SYzuyLwPMHMnjKzLDPbaWa3NsRxG9sph5/AsT3SANhbuE9l5yLS4oWaoJKAfdW0Z+MVSjSEJ4GeQc/vxptmqT8wArjczC5roGM3qg05Ww5oU9m5iLRUoSaoBcD/mllFmZmZpQCTgY/CGFf5e18OtAOCF0i8HJjsnMt2zm3AG3acFO5jR0JN5eUqOxeRlijUa1A34JWWbzWzjXhLbPQB1uFdmwobM+uHd23reLz7r8qTYXdgVdCua/BW+m3yUpM6squaZNQhMSUC0YiIRFaoZeabgKOAi4CZwN+BC4BjnHM/hCsoM4sFXgRuds5tD9qUHHjMC2rLwxt6bPImpI0nITah2m37Cvc3cjQiIpEV8vREzrkivGU2puMlqU+A1mYWziTxF+9Q7tUq7eW/pROD2mq6LtbkjOqTzqQRE+mU1BEf3rx9Ka3akZWfw70fPUpuYbP4Z4qI1Emo90GNBGbg9aKC+Qjvirq/AHqY2QWB522BJ4B0YDtekcTWwLaBVB7ya9LKy87LZeXlcNeCR/ghZzN3L5jKX066gfat20UwQhGRxhHqNaipwB7gPGBv+MPxOOcGBj83s6+Bqc6558xsH3CnmS3HG/K7GXi0oWKJtI5JKdx1yk3cs2Aqm/Zs5e4FU7njpBtJSWyookkRkegQaoIaAox0zq046J4N5w68dam+xRuifAqvFL3Z6pjoJal7F0xl895t3LngEW4fcwOd26RGOjQRkQYTaoJaDfSgctl3g3POHRP0fQFwXeCr2Vu4cRGzl89ld14WHRJTSE3swLbcTG6f/xC3jb6ew1J6HvxNRESaoFAT1DTgH2Y2DfgOKAre6Jz7d7gCEy85zVg8i6JS7zRn5eeQEBtPj7ZdycjdwR0fPswfT7yGwV0GRDhSEZHwCzVBPRt4fLCabeEskhBg9vK5FcmpXFFpMYWlRaT3OoZFW75m8seP8ZuRV3Bc7+ERilJEpGGElKCcc1o1txHVNINEVl4208/+K88s+yfvrfuEv302k61HbefCwWfh8/kaOUoRkYYRag8KADM7FTgSr0hhNfAho94AABlASURBVDDfOVdS+6skVDXNLJGa1JGYmBj+e9gv6NqmMy9+8yr/WjmPzXu2cW36ZbSKq/5mXxGRpiSkHpGZdTOzL4C3gd/gFSq8CSw1sy4NEF+LVt3MEsELGvp8PsYNHMsto64lMa41n29ewp0fPkxWXk4kwhURCatQh+weBUqAfs65I5xzA4C+QBbwSJhja/GqzizRKakjk0ZMPGB9qGE9jmLy2D/StU0n1mdv4tb3H2Dd7g0RiVlEJFxCHeI7AzjZOVc+iwPOuQwz+z3wfs0vk/qqOrNETXq1787k027hkf88xaqd33HHhw9zxdCfcVr/UbouJSJNUqgJqgCvWq8qVfA1ouB7o8pX3QWv6m9XXhat41pRUFLIzCWzWbXzOyYdO5HE+Nb1fm+t6CsikRBqgnoPeMTMfuGc2wFgZl3xZnZ4N9zByYGq3hu1Ky+LJ758Hp8PSspKASgoKSQuJhb88Nmmr/ghexO/O+5q+nboHfJ7z1g8C0BJSkQaXajXoP4AdAU2mpkzMwdsBNoAN4Y7ODlQdfdGlfpLK5JTuZKyUtq2Suaw9j3ZlpvJrR88yNzV71FWVhbSe2tFXxGJlFDXg9oOpOGtB/U08DhwjnMu3Tm3rQHikypCWV03p2APk8f+kf/6yWhKy0qZtfw17v5oKjv37w7pvbWir4hEQp0SlJm1MrNfm1kH51yJc26ec+5/gVbeZtONN40kNaljSPu2ikvg6uETuHX0daS0bsfqnd9x8zt/5b11H1PmLztg/0M9pohIuBw0QZlZe7xFCf+Gtw5TsO540x59aGZtwx+eVFXdvVGxvljvmlOQ4PulAIZ2P4qLjjybhNh48ksKmLnkZW56+24y9v64YPHB7rsSEWlMdelB/QVv1doBzrkvgjc4536Pt3hhZ+DP4Q9Pqqru3qhrf3oZ16RfVuv9Ugs3LuL5r+dQVFpc0ZaRm8nv3r6HV1e9TUlpSZ3vuxIRaQx1qeK7AJjknNtS3Ubn3AYzuwV4CLg1nMFFu0iVZNd0b1Rtx66uAALAj5+XV7zBZ5uW8D/HTqjzfVciIg2tLj2obsDag+zzNd46US1GeUn2rrws/PxYkr1w46JIh1at2goduiZ3ZtOerfxl/hSmffEs2fl7GjEyEZHq1SVBbQaOOMg+A4DtB9mnWWlqJdk1FTp0SurIlNNv58LBZxEfE8fCjYu48d938saa9ygp1fy/IhI5dUlQ/wLuqqlSL9B+FzAvjHFFvaZWkl1bAUSruAQuHjKOR868g2N7Hk1BSSEvfvMav3/3XpZkrMDvr27yEBGRhlWXa1D3A+cBS8zsMeArYA/QARgBXI83zdG9DRVkNKptKYxoVH5dqbZrZl2TO/PHE3/N19tW8dyyf5GRu4MHFz7BoM4/4ZK087BO/SMVvoi0QL66/HUcKCF/ELgEKC8n9wG7gReAe51z2Q0VZDiZWV/gh/nz59OrV696v0/VaYHA65E0l6q3ktIS3ln3Ma+tepvcov0AHNsjjV8MOZfDUnpGODoRaS62bNnCqaeeCt4qGRuCt9VpLj7nXC5wrZn9Fjgcr/e0C1jnnGuR4z916ZE0ZXGxcZxjp3JKv+N5033AvLXz+SpjOUsyVjCqTzoXDD6DHu261fh6TTorIoeqTj2o5iRcPaiWJqdgL6+uepv3v19IaVkpPnz8tPdQLhh0xgGT0Db33qWIhE9tPahQJ4uVFiqldTuuGnYxj555F2MPP5HYmFi+2LyUP753Hw98Mp21u9ZX7NvUKhxFJDqFutxGozGz04AH8ErYM4GHnHMzAlWDj+NNWFsKPOKcuz9ykbYsXZI78asRE7noyLN5w73PB98vZOm2lSzdthLr1J+zjji52uIRiN4KRxGJTlGZoMysNzAHuByYCwwH3jWzDcBJeHMC9gfaA++Y2Vbn3PMRCbaF6piUwhVDf8YFg87grbUf8u66j3G7vsft+p4YX8wBE9FC9FY4ikh0isoEBfQFXnLOvRZ4vtjMPgJOwEtaVwSqBrPNbAowCVCCioB2rdsyIW085w86nY82fMHb3y1gW27mAftp0lkRCVVUJijn3EJgYflzM+sIjMIrae8OrArafQ0wpFEDlAO0jm/NGQNO4r9+Mpqvt61i1jevsXlvRsX2dq2SKSguJK84n6T4xAhGKiJNRVQmqGCB5T7eAL4ElgSa84J2ycObbV2iQIwvhmE9jmJYj6PYsncb87//Dx9v+IJdeVn8Y8lLPP/NHI7vPZwxfX/KwM4/IcanOh0RqV5UJygzOwLvGtQqYCJQ/qd38J/gScC+Rg5N6qBXu+5cPvQiJqSNZ9GWr5m//lO+zVzLgh8+Y8EPn9E5qSMn9klnVN90erXrHulwRSTKRG2CMrPReMnpSeDPgRuCC8xsO16RxNbArgOpPOQnUSYhNp4T+4zgxD4jyMjdwcc/fMHCjYvYmZfFa6vf4bXV73B4h8MY1SedE/qMIKV1u0iHLCJRICoTlJn1x5t89jbn3LQqm18A7jSz5UAycDPwaCOHKPXUo21XJqSN5+Ih41iz83s+2fglX2xeyvrsTazP3sTz38zhyM5HMLL3UNJ7HkNKYvtIhywiERKVCQq4Dm/Ov/vNLPgep+nAHcDDwLd4Nxo/hdfLkiYkxhfD4C4DGNxlAFcNu5ilGSv4ZMOXLNv+LSszHSszHU8v+ScDO/+Ekb2G8tPeQ+mYmBLpsEWkEWmqI4kq+4r2s2TrCr7YspRvtq+mpOzHNaniYuJI73kMFx11Fj3bdsPn89X5fTU3oEh0OuTJYkUaS3JCG8b0G8mYfiP54PtPeWbpy5SUlQJQUlbCZ5u/4rPNX9G1TSeG9RjC8B5DGNx5AHGxNX+Uq84NWL76MaAkJRLFlKAkar266u2K5BTMh48d+3fx9ncLePu7BSTGtSat2yCO6TaYtG6D6NwmtdL+tc0NqAQlEr2UoCRq1Tx3n597T72ZJRkrWJKxgs17MvhyyzK+3LIMgO7JXRjSbSBpXQdxZJcjmtzqxyLiUYKSqFXbqsXWqT/WqT+XpJ1H5v7dLMtYyfIdq1mZ6di2L5Nt6zJ5b90n+Hw+YmNiq+2JaW5AkeimBCVRa0La+GrXlao6p1+XNqmcPmAMpw8YQ2lZKd9nbWT5jtUs376a73b/UG1yivXFcMJhx5JfXEBifOsG/7eISOiUoCRq1WfV4tiYWI7odDhHdDqci448m/ziAlbt/I631y7g20xHaWCW9VJ/GXPXvMeb7gP6pvRiUOcBDOr8EwZ26k+71m0b5d8nIrVTggqBSpUb36g+6Yd0jhPjWzM8UO0HkFu4jzW7vmf1znWs2bmu4gbh9dmbeGvtfAC6JnfmJx378JOOfRmQ2o++Kb1IiEtotj//5vrvkqZPCaqOVKrcPLRtlcyInkczoufRABSUFPLd7h9YvXMdq3d+x3e7f2DHvp3s2LeT/2z6CvCGAzskppCVn1OxztWh/PyjKSHocy3RTAmqjlSq3Dy1jmvFkK4DGdJ1IAClZaVs3rONdVkbWLf7B9ZlbWTz3oxqizWKSov4x1cvkV+cT9+U3hyW0pPWca1qPV60JQR9riWaKUHVkUqVW4bYmFj6duhF3w69GNv/RAAKigu47NXfVbt/QUkhM5e8DHj3Z3Vv24W+Kb3o26E3fVN60yelJymt21XMehFtCUGfazmYMn8Zewv3sTsvm9152WTl51R8vzs/B/Bz/cgr6dQAVbFKUHVUW8mzNG+t41vTqYaff5v4JIb3HMLG7C1s2buNjNwdZOTu4LPNS37cJyGJ3u2606td92rfAyKXEPS5brn8fj+5RfvJyd9DTsFecgr2kh34PrtgD9n5OewKJKTSaiphy/l8PnbnZStBRVJdS56learp53/V8Isrej7FpcVs3rONDTlb2JCzmQ3Zm9m0J4P9RXms2fU9a3Z9X+P7J8UnsWzbSrq06UTnNqkkxMYfNKZwXMvS57p20XS9sC7xFJUWs69wP3sL95FbtI/cwn3sLdzH3sJccvK9xJNTsJec/L3kFO6tNfEEa5vQho5JHUhN6kBqYgqpSR3oGHjs0bYrqUkdGuTfq8liQxBtH1ZpXPX5+fv9frIL9rBlzzY278lg8dblrN75HX5q/3/XoXV7urRJpXNyJ7q06UhqYkdSEtuR0tr7+jbTMXPJPw9ILJNGTGzSRRvRpOr1Qqj/OQ6F3++noKSQ/cV55BXle4/FBSzNWMGH6z+j1P9jUvHho3NSR/w+r0K1oKQwpGO1iU8kJbE9Ka3b0aG191j+vGNiCp0CiSghLiHc/8wKtU0WqwQl0sjKE8KuvCzat2pLWrdBtIpNYMf+nWTu282uvKyK+7VClRjXmnMHnkZyQhvaJCSRnNCG5ISkwPdJJMYnEhcTG+Z/UfhEU7K89s3bqh3+7JTUkSfGTa7UVlpWSmFJEQUlhRSUFAQeD/JVHEhCxfkViWh/cT55xfnU9/dybEws7RKSadsqmbat2tC2VXLF8w6J7UipkoTq0lOvSbh+VprNXCSKHOzertKyUrLyc8jcv5vMfbvI3L+b7PyciusEOQV7ycrPqfa1+SUF/HPlm7UePy4mjsS4VrSOa0Xr+Na0jmtFYlzrwPNWlZ63imtFfEwc8bHxgcc44mLiSIiNJy7wPD4mjrjYOOJ8sfh8PmJ8McT4Yip9H0P59z58QY/4/ZThB7+fTzct5pml/6SotBgor3B8kfziAkb2GlqxXxl+ysrKKPGXUloW+PKXUVpWSklZKWV+77HUX0ppWVngsXxbmbctsL24tITismLvsbSY4rIfH2u6XrgrL4vr5t3u7VdaTFHgNeHUOq4VSfGJtIlPJCkhiTbxiSzdtrLG/aedfQ9tWyWTGNc6pGVo6quxqlGVoESiTGxMLJ3bpNK5TSpHdjmi2n2uffPP7MrLPqC9TXwip/1kNPuK8thflMe+ov3sD/o+v6SQkrIScotKyC3a39D/lENWVFrMzCWzmblkdqRDqWTn/t2VnvvweQk++CuQ7FtVba/4ak2bhMQDElFSfCKx1fRya+vRdU3u3GD/1uo0VjWqEpRIEzQh7bwaijZ+UesvCL/fT3FZCQXF3jBUfmA4Kr/4wKGp/OICCksKvV5FWQklpSUUlRVTUlpSqadR3l5WVub1bvxllPnL8Pv9lR7Lyns/Qdt9Ph8+fPh8vlp7IW1bJeMDfL4YfECsL5bYmBhiY2KJ88USE3iMjQm0V3wfS6wveL+YH/fzxXo9wIreYeXH77M28sH6TystmhkfE8fPjxrHyN5DiY+NJyEm3nuMjW/wnks0FbQ01u0JSlAiTVB95ikEryQ4IfALtR3RNedgKNd8GsOYfiMZ0Klf1FwTq+/PvCE01u0JSlAiTdShzlMYbaKph1Au2s5xtMTTWD8rJSgRiQrR1EOQ2jXWz0oJSkSiRrT0EOTgGuNnFdOg7y4iIlJPSlAiIhKVlKBERCQqKUGJiEhUaolFErEA27dvj3QcIiItXtDv4gOmz2iJCao7wMSJEyMdh4iI/Kg7UGlNmpaYoBYDo4BtQN0WQxERkYYSi5ecFlfd0OKW2xARkaZBRRIiIhKVlKBERCQqKUGJiEhUUoISEZGopAQlIiJRSQlKRESikhKUiIhEJSUoERGJSi1xJol6M7OjgSeBNGA9cJVz7oC7nyPJzE4DHgAGAJnAQ865GWbWCsgFioJ2/8w5918RCLOCmV0FzAAKg5qvA2YDjwMX4c348Yhz7v7Gj7AyM5uIF2+wRGA+MI4oO8dmlg7Mc851CTxPoJbzamY/B+7Du7P/Y+AK51xmBOPtAjwKnAr4gLeBG51z2YHtzwM/B0qC3ibNObc+gjHX+n8tCs/xviq7xAGtgJ7OuYxInmMlqDoK/MeeC0wFRgMXAu+ZWR/n3N6IBhdgZr2BOcDleLEOB941sw3AbiDLOdctYgFWbxjwsHPuT8GNZnY/YEB/oD3wjpltdc49H4EYKzjnZgGzyp+b2VDgPeAPwBCi5BybmQ/4b2BKlU13U8N5NbPBwNPAmcBXwIPAy8ApEYx3JrAH6AfEAy8A04FLAtuHAec5595p6BirqiXmGj8H0XiOnXPJQfvEAQuAj5xzGYHmiJ1jDfHV3UlAvHNuqnOu2Dn3MvAtcHFkw6qkL/CSc+4151xZoHf3EXACXrL6OoKx1aSmuC4HJjvnsp1zG/D+U01qzMAOxszi8ZLVXc65b4iuc3w3cA3w1yrttZ3XXwJvOuc+dc4VALcCJ5jZgEjEa2YxQBlwt3Nuv3MuB/gHcGJgeyIwkMid85rOcW2fg6g6x9W4Be8PgTsh8udYCaruBgOrq7StwftrKSo45xY6535d/tzMOuJNjLsM76+gLma23Mx2mNn/M7OekYo1EF8s3nDppWaWYWbrzOxPZtYBb/hjVdDuUXWuA64D8oEnAs+j6Rw/6ZwbjvdXOgBmlkLt53Vw8DbnXB6wmcY57wfEG/gj6zzn3Lqg/c7D+zwDHIM37PQPM9tpZkvN7JxGiLXcATEH1PY5iKpzHMzMegB/Bn7tnCsLNEf0HCtB1V0ykFelLQ9IikAsB2Vm7YE3gC/xhvv2A//BG8s3vF+sr0UsQE9nvP8s/4c3hHMR3l941we2B5/vqDrXgSHfP+D1nspnXI6acxw0PBOsfCinpvMasc94DfFWYmY34yWoWwJNbYGFeD2DHsBk4F+Ba8UNrpaYa/scRPM5/h3wjnMuuLcU0XOsa1B1tx/vYniwJKDqBcaIM7Mj8JLSKmBi4K+hm6rscxOw08x6O+c2RyBMnHPbgTFBTV+b2TS88XmofL6j7VyfgTf89FZ5g3Mu6s5xFfsDjzWd16j8jAeGUqfhFaGc4pxbA+Ccew/v+l+5OWZ2JXAu8E2jBxpQ2+eA6D3HsXjDv5UWyov0OVYPqu5W4f01FGwglYdLIs7MRuP1ml4HLgqMc2Nm95jZoKBdEwKPBY0cYgUzO9LM7q7SnIAX03Yqn+9oO9fjgX8FDYVE5TkOFqh8q+28VvqMm1kScBgRPO9m1hZ4HxgBpAf/dW9m48zs8iovKf/8RMxBPgdRd44Djg88zg9ujPQ5Vg+q7hYAPjP7HV6Z7oV4108iPUxWwcz6A/OA25xz06psTgOONbPy6qdHgbecczsbM8YqcoDfm9kWvMqmocANwG/wClDuNLPleMMiN+PFHC1GAn+p0haN57iqF6j5vL4EfGpmJwGfA/cDy5xzayMRaMDLeH9IjwpcrwkWCzxqZquBJXgFS8cDVzduiAeo8XNgZtF4jsH7PH8R/AdXQETPsXpQdeScK8IberoQyAJuwyu9jKZfPtfhjRnfb2b7gr4exCsvzQbWARvw7tG4NGKRAs65rXhDBZOAvXgl8vc6514B7gBW4iWqxYFtT0Yo1Or0BaqO6UfdOa5GjefVObcCuCrwfBdwJPCzyIQJZpYGnAWkA5lBn+ctgXhfx/t/OBvv8/N74Bzn3KZIxRxQ4+cg2s5xkL4c+HmO+DnWiroiIhKV1IMSEZGopAQlIiJRSQlKRESikhKUiIhEJSUoERGJSkpQIiISlXSjrkiIzOw5vGlhanI33izyC4C2zrlGmcYmMF3Nf4DL6nPjp5n5gXHOuXl12PdxYLFz7v9Cj1SkbtSDEgndjXizgnfHW4YFvJtJy9umAJ8Fvt9fzesbyg3AN4cwK0F3vGmF6uIe4B4zS63nsUQOSjfqihwCMzsKWAH0C6yvFKk4WgOb8CZTXdlIx3wG2OScu6sxjictj4b4RBpAYK61iiG+wPDZBLwF6gxvmZFf4i3ZcSneNDK3OudeCLy+LfAw3hIkfuBDvKXOa1oy4RdATnlyMrO+wA94U0k9AvQCPsBbzmQK3szg24BrAzNWVxriM7OP8JYjPxr4L7w1ix5yzs0MOuarwNNmNtk5V1zvkyVSAw3xiTSeB4Df4k3MeRiwFC8xjcD7ZT/DzMrXbHoKL5GdjrckiR94N7Akd3XOBqpbkvtevOXRT8MbjlyON/w4HG/hv5nVvKbcLXhDfkPxktvfzSx4GfMPgNTAe4mEnRKUSOOZ7pxbEFgyYh7eGkB/ds45vF5OItDPzA7H6xFd4pxbHOgVXYo3oecZNbz3sXgTwFY1OfAen+ItPLfKOfdYYE2l6UDvQG+tOh8556YH4rsVb8QlrXxjYCmX9YFji4SdEpRI4wleujwP2BC0Gm/5+jqt8JYFB3DlM3gDu4E2HLgmWbmueLNjH+yY64OeBx+zOhXFFs65vYFv46vssxvoUsPrRQ6JrkGJNJ6q12mqrr1TLi6w71C8ob1gWTW8pgzwHcIxq1NUTVvVY8QCpSG8p0idqQclEn1W4/VU2jjn1jnn1uEVNDwEHFHDa7YDnRspvmCdAscWCTslKJEoE7jm8wbwvJmNMrOBwPN4xRVranjZEryKu0ZjZu2BPngLH4qEnRKUSHS6HK8U/XW8BNAeOM05l1PD/m/hVfs1phPxek/LGvm40kLoRl2RZsDMkvCWFz/DObe0kY45G68q8N7GOJ60POpBiTQDzrk8vGtU1zXG8cysO16PbXpjHE9aJiUokebjb0CamdVUih5OtwO3O+dqqioUOWQa4hMRkaikHpSIiEQlJSgREYlKSlAiIhKVlKBERCQqKUGJiEhU+v/RtT2C8cH46AAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot(results.I, 'g-', label='simulation')\n",
-    "plot(data.insulin, 'go', label='insulin data')\n",
-    "\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Using the best parameters, estimate the sensitivity to glucose of the first and second phase pancreatic responsivity:\n",
-    "\n",
-    "$ \\phi_1 = \\frac{I_{max} - I_b}{k (G_0 - G_b)} $\n",
-    "\n",
-    "$ \\phi_2 = \\gamma \\times 10^4 $\n",
-    "\n",
-    "For $G_0$, use the best estimate from the glucose model, 290.  For $G_b$ and $I_b$, use the inital measurements from the data.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "I0, k, gamma, G_T = best_params"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(130, 11)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "I_max = data.insulin.max()\n",
-    "Ib = data.insulin[0]\n",
-    "I_max, Ib"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(289, 92)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "# The value of G0 is the best estimate from the glucose model\n",
-    "G0 = 289\n",
-    "Gb = data.glucose[0]\n",
-    "G0, Gb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18.455830724497748"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "phi_1 = (I_max - Ib) / k / (G0 - Gb)\n",
-    "phi_1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8184889071339871"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "phi_2 = gamma * 1e4\n",
-    "phi_2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/interest.ipynb b/soln/interest.ipynb
deleted file mode 100644
index d8fe740d..00000000
--- a/soln/interest.ipynb
+++ /dev/null
@@ -1,373 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case study\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *\n",
-    "\n",
-    "from pandas import read_html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exponential, geometric, and polynomial growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Suppose there are two banks across the street from each other, The First Geometric Bank (FGB) and Exponential Savings and Loan (ESL).  They offer the same interest rate on checking accounts, 3%, but at FGB, they compute and pay interest at the end of each year, and at ESL they compound interest continuously.\n",
-    "\n",
-    "If you deposit $p_0$ dollars at FGB at the beginning of Year 0, the balanace of your account at the end of Year $n$ is\n",
-    "\n",
-    "$ x_n = p_0 (1 + \\alpha)^n $\n",
-    "\n",
-    "where $\\alpha = 0.03$.  At ESL, your balance at any time $t$ would be\n",
-    "\n",
-    "$ x(t) = p_0 \\exp(\\alpha t) $\n",
-    "\n",
-    "If you deposit \\$1000 at each back at the beginning of Year 0, how much would you have in each account after 10 years?\n",
-    "\n",
-    "Is there an interest rate FGB could pay so that your balance at the end of each year would be the same at both banks?  What is it?\n",
-    "\n",
-    "Hint: `modsim` provides a function called `exp`, which is a wrapper for the NumPy function `exp`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.03"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "p_0 = 1000\n",
-    "alpha = 0.03"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "ts = linrange(11)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1000.        , 1030.        , 1060.9       , 1092.727     ,\n",
-       "       1125.50881   , 1159.2740743 , 1194.05229653, 1229.87386542,\n",
-       "       1266.77008139, 1304.77318383, 1343.91637934])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "geometric = p_0 * (1 + alpha) ** ts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1000.        , 1030.45453395, 1061.83654655, 1094.17428371,\n",
-       "       1127.49685158, 1161.83424273, 1197.21736312, 1233.67805996,\n",
-       "       1271.24915032, 1309.96445073, 1349.85880758])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "exponential = p_0 * exp(alpha * ts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.030454533953516938"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "alpha2 = exp(alpha) - 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1000.        , 1030.45453395, 1061.83654655, 1094.17428371,\n",
-       "       1127.49685158, 1161.83424273, 1197.21736312, 1233.67805996,\n",
-       "       1271.24915032, 1309.96445073, 1349.85880758])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "geometric = p_0 * (1 + alpha2) ** ts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot(ts, exponential, '-', label='Exponential')\n",
-    "plot(ts, geometric, 's', label='Geometric')\n",
-    "\n",
-    "decorate(xlabel='Time (years)',\n",
-    "         ylabel='Value (dollars)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose a new bank opens called the Polynomial Credit Union (PCU).  In order to compete with First Geometric Bank and Exponential Savings and Loan, PCU offers a parabolic savings account where the balance is a polynomial function of time:\n",
-    "\n",
-    "$ x(t) = p_0 + \\beta_1 t + \\beta_2 t^2 $\n",
-    "\n",
-    "As a special deal, they offer an account with $\\beta_1 = 30$ and $\\beta_2 = 0.5$, with those parameters guaranteed for life.\n",
-    "\n",
-    "Suppose you deposit \\$1000 at all three banks at the beginning of Year 0.  How much would you have in each account at the end of Year 10?  How about Year 20?  And Year 100?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "number_of_years = 100\n",
-    "ts = linrange(number_of_years+1)\n",
-    "geometric = p_0 * (1 + alpha2) ** ts\n",
-    "exponential = p_0 * exp(alpha * ts)\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "beta1 = 30\n",
-    "beta2 = 0.5\n",
-    "parabolic = p_0 + beta1 * ts + beta2 * ts**2\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def plot_results():\n",
-    "    plot(ts, exponential, '-', label='Exponential')\n",
-    "    plot(ts, geometric, 's', label='Geometric')\n",
-    "    plot(ts, parabolic, 'o', label='Parabolic')\n",
-    "    \n",
-    "    decorate(xlabel='Time (years)',\n",
-    "             ylabel='Value (dollars)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_results()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_results()\n",
-    "plt.yscale('log')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/jump2_soln.ipynb b/soln/jump2_soln.ipynb
deleted file mode 100644
index fadfae6d..00000000
--- a/soln/jump2_soln.ipynb
+++ /dev/null
@@ -1,1685 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Bungee dunk example, taking into account the mass of the bungee cord\n",
-    "\n",
-    "Copyright 2019 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# If we're running on Colab, install modsimpy\n",
-    "# https://pypi.org/project/modsimpy/\n",
-    "\n",
-    "import sys\n",
-    "IN_COLAB = 'google.colab' in sys.modules\n",
-    "\n",
-    "if IN_COLAB:\n",
-    "    !pip install pint==0.9\n",
-    "    !pip install modsimpy\n",
-    "    !mkdir figs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Bungee jumping"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the previous case study, we simulated a bungee jump with a model that took into account gravity, air resistance, and the spring force of the bungee cord, but we ignored the weight of the cord.\n",
-    "\n",
-    "It is tempting to say that the weight of the cord doesn't matter, because it falls along with the jumper.  But that intuition is incorrect, as explained by [Heck, Uylings, and Kędzierska](http://iopscience.iop.org/article/10.1088/0031-9120/45/1/007).  As the cord falls, it transfers energy to the jumper.   They derive a differential equation that relates the acceleration of the jumper to position and velocity:\n",
-    "\n",
-    "$a = g + \\frac{\\mu v^2/2}{\\mu(L+y) + 2L}$ \n",
-    "\n",
-    "where $a$ is the net acceleration of the number, $g$ is acceleration due to gravity, $v$ is the velocity of the jumper, $y$ is the position of the jumper relative to the starting point (usually negative), $L$ is the length of the cord, and $\\mu$ is the mass ratio of the cord and jumper.\n",
-    "\n",
-    "If you don't believe this model is correct, [this video might convince you](https://www.youtube.com/watch?v=X-QFAB0gEtE).\n",
-    "\n",
-    "Following the example in Chapter 21, we'll model the jump with the following modeling assumptions:\n",
-    "\n",
-    "1. Initially the bungee cord hangs from a crane with the attachment point 80 m above a cup of tea.\n",
-    "\n",
-    "2. Until the cord is fully extended, it applies a force to the jumper as explained above.\n",
-    "\n",
-    "3. After the cord is fully extended, it obeys [Hooke's Law](https://en.wikipedia.org/wiki/Hooke%27s_law); that is, it applies a force to the jumper proportional to the extension of the cord beyond its resting length.\n",
-    "\n",
-    "4. The jumper is subject to drag force proportional to the square of their velocity, in the opposite of their direction of motion.\n",
-    "\n",
-    "First I'll create a `Param` object to contain the quantities we'll need:\n",
-    "\n",
-    "1. Let's assume that the jumper's mass is 75 kg and the cord's mass is also 75 kg, so `mu=1`.\n",
-    "\n",
-    "2. The jumpers's frontal area is 1 square meter, and terminal velocity is 60 m/s.  I'll use these values to back out the coefficient of drag.\n",
-    "\n",
-    "3. The length of the bungee cord is `L = 25 m`.\n",
-    "\n",
-    "4. The spring constant of the cord is `k = 40 N / m` when the cord is stretched, and 0 when it's compressed.\n",
-    "\n",
-    "I adopt the coordinate system and most of the variable names from [Heck, Uylings, and Kędzierska](http://iopscience.iop.org/article/10.1088/0031-9120/45/1/007).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(v_init = 0 * m / s,\n",
-    "                g = 9.8 * m/s**2,\n",
-    "                M = 75 * kg,                # mass of jumper\n",
-    "                m_cord = 75 * kg,           # mass of cord\n",
-    "                area = 1 * m**2,            # frontal area of jumper\n",
-    "                rho = 1.2 * kg/m**3,    # density of air\n",
-    "                v_term = 60 * m / s,        # terminal velocity of jumper\n",
-    "                L = 25 * m,                 # length of cord\n",
-    "                k = 40 * N / m)             # spring constant of cord"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now here's a version of `make_system` that takes a `Params` object as a parameter.\n",
-    "\n",
-    "`make_system` uses the given value of `v_term` to compute the drag coefficient `C_d`.\n",
-    "\n",
-    "It also computes `mu` and the initial `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Makes a System object for the given params.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    M, m_cord = params.M, params.m_cord\n",
-    "    g, rho, area =  params.g, params.rho, params.area\n",
-    "    v_init, v_term = params.v_init, params.v_term\n",
-    "    \n",
-    "    # back out the coefficient of drag\n",
-    "    C_d = 2 * M * g / (rho * area * v_term**2)\n",
-    "    \n",
-    "    mu = m_cord / M\n",
-    "    init = State(y=0*m, v=v_init)\n",
-    "    t_end = 10 * s\n",
-    "\n",
-    "    return System(params, C_d=C_d, mu=mu,\n",
-    "                  init=init, t_end=t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>v_init</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>M</th>\n",
-       "      <td>75 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>m_cord</th>\n",
-       "      <td>75 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>1 meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>60.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>L</th>\n",
-       "      <td>25 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k</th>\n",
-       "      <td>40.0 newton / meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.3402777777777778 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mu</th>\n",
-       "      <td>1.0 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>y               0 meter\n",
-       "v    0.0 meter / secon...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>10 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "v_init                                   0.0 meter / second\n",
-       "g                                   9.8 meter / second ** 2\n",
-       "M                                               75 kilogram\n",
-       "m_cord                                          75 kilogram\n",
-       "area                                           1 meter ** 2\n",
-       "rho                               1.2 kilogram / meter ** 3\n",
-       "v_term                                  60.0 meter / second\n",
-       "L                                                  25 meter\n",
-       "k                                       40.0 newton / meter\n",
-       "C_d                        0.3402777777777778 dimensionless\n",
-       "mu                                        1.0 dimensionless\n",
-       "init      y               0 meter\n",
-       "v    0.0 meter / secon...\n",
-       "t_end                                             10 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params)\n",
-    "system"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`drag_force` computes drag as a function of velocity:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(v, system):\n",
-    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
-    "    \n",
-    "    v: velocity\n",
-    "    \n",
-    "    returns: drag force in N\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "\n",
-    "    f_drag = -np.sign(v) * rho * v**2 * C_d * area / 2\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's drag force at 20 m/s."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-81.66666666666667 kilogram meter/second<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$-81.66666666666667\\ \\frac{\\mathrm{kilogram} \\cdot \\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "-81.66666666666667 <Unit('kilogram * meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drag_force(20 * m/s, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function computes the acceleration of the jumper due to tension in the cord.\n",
-    "\n",
-    "$a_{cord} = \\frac{\\mu v^2/2}{\\mu(L+y) + 2L}$ "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cord_acc(y, v, system):\n",
-    "    \"\"\"Computes the force of the bungee cord on the jumper:\n",
-    "    \n",
-    "    y: height of the jumper\n",
-    "    v: velocity of the jumpter\n",
-    "    \n",
-    "    returns: acceleration in m/s\n",
-    "    \"\"\"\n",
-    "    L, mu = system.L, system.mu\n",
-    "    \n",
-    "    a_cord = -v**2 / 2 / (2*L/mu + (L+y))\n",
-    "    return a_cord"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's acceleration due to tension in the cord if we're going 20 m/s after falling 20 m."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-3.6363636363636362 meter/second<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$-3.6363636363636362\\ \\frac{\\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "-3.6363636363636362 <Unit('meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y = -20 * m\n",
-    "v = -20 * m/s\n",
-    "cord_acc(y, v, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now here's the slope function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func1(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object containing g, rho,\n",
-    "            C_d, area, and mass\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    M, g = system.M, system.g\n",
-    "    \n",
-    "    a_drag = drag_force(v, system) / M\n",
-    "    a_cord = cord_acc(y, v, system)\n",
-    "    dvdt = -g + a_cord + a_drag\n",
-    "    \n",
-    "    return v, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, let's test the slope function with the initial params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0 <Unit('meter / second')>, -9.8 <Unit('meter / second ** 2')>)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func1(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll need an event function to stop the simulation when we get to the end of the cord."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Run until y=-L.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object containing g, rho,\n",
-    "            C_d, area, and mass\n",
-    "    \n",
-    "    returns: difference between y and -L\n",
-    "    \"\"\"\n",
-    "    y, v = state   \n",
-    "    return y + system.L"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "25 meter"
-      ],
-      "text/latex": [
-       "$25\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "25 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "event_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A termination event occurred.'"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func1, events=event_func)\n",
-    "details.message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how long it takes to drop 25 meters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.2118255911654763"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_final = get_last_label(results)\n",
-    "t_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the plot of position as a function of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_position(results, **options):\n",
-    "    plot(results.y, **options)\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "    \n",
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use `min` to find the lowest point:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-25.0 meter"
-      ],
-      "text/latex": [
-       "$-25.0\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "-25.0 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "min(results.y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's velocity as a function of time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_velocity(results):\n",
-    "    plot(results.v, color='C1', label='v')\n",
-    "        \n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')\n",
-    "    \n",
-    "plot_velocity(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Velocity when we reach the end of the cord."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-23.876881009672466 meter/second"
-      ],
-      "text/latex": [
-       "$-23.876881009672466\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "-23.876881009672466 <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "min(results.v)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Although we compute acceleration inside the slope function, we don't get acceleration as a result from `run_ode_solver`.\n",
-    "\n",
-    "We can approximate it by computing the numerical derivative of `v`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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I43d8g+H909l/sJQH/7mEu5+dT8Ge4nCHJxGkIS2oHwOjnXMXAVOAX5nZ7YFtqhkVkaB0S0/ivhsm8LPLR5IU72OJ28FNU2fy1icbKC+vCHd4EgEakqCinXMHAZxzm4BTgbPN7CGUoESkCTweD2eM7cWTd57G5JFZlJSW8/y0Vfz3Y3PI3rYv3OFJmDUkQeWb2ciqF865A8D5QDowrLkCq4+Z3WZmb9Wzrb+Z7TOzpJaOS0SCl9ohjjuuPonfXDeO9JR4Nmzdx22PfMLfpqskvT1rSIL6Hv5RIqo55444574HTG6WqOpgZklmNhV4sJ7tlwJzgOSWiklEQmvM4Ez+csc3+OakvlRWVvLGrA3c8sBslq3bGe7QJAyOm6Ccc1udc/n1bPs89CHV612gD/B07Q1mdj3wB1S0IdLqJcT5uP7iYUy9eRK9uyazffdBfvX0XJ59ewVlR3Rtqj0Jaiw+M0sH9jjnQna2mFkM0KmOTZXOuQLgCudcnpndDXSttc804Dmgb6jiEZHwsl6dePi2Kbwxaz2vfOCYNiebNZv28P+uPonMtMRwhyctINjRzP8F3Fb1wswuMLP7zGx0E2KZAGyvY9kG4JzLq+9A51x+KJOliESGaG8Ul59h/Omnp9A5NZ71W/Zy60Oz+Xx5vb8OpA0JdjTzYfgHjMXMegBvAA641cwucM7NbuwbBo5RVaCIfI316sSjt5/Ko68tZf7KfP7490WcP7EP135zCDE+b7jDk2YSbAsqDtgTeH4ZsMA5Nxz4FfCbUAQmIlJTUkIM/3PNWK6/eBjR3ije/XwTdzz+KXm7isIdmjSTYBNUNjA48PwS4NXA82nAiKYGJSJSF4/Hwzcn9WXqzZPITEsge9s+bn3oE+Ys3Rru0KQZBJugngH+ambP4R8Q9p3A+nggJhSBiYjUp3+PFB657VQmjujGoZIjTP3fxTzx+peUlJWHOzQJoaCuQTnnHjezSuAc4KfOuS2BTeOAnFAFV89n332MbRvQdSyRdiEx3sedV5/E+/038+zbK/lgfg4up5A7v3cS3Tt3OP4bSMQLesp359wTwBO1VqfxVXefiEiz8ng8nDuhD9arE396aRGbt+/ntoc/4cbLRvCN0T3CHZ40UYMTlJmtBz4EZgAznXP7a+/jnJsawthERBqkb1ZHHr5tCk/+azmfLN3KQ/9cwvL1u/jxt4YRFxv03+ESZo35lzsfOBP4AfCCma3hq4Q1X/chiUg4JcT5+PlVJzJ8QDpP/3s5Hy3KxeX6u/x6ZWoEtNaowUUSzrl1zrm/BKbdyADuDBz/ILDDzN40s5+YWf9milVE5Jg8Hg9njevFQ7dOoUeXJLYUHOCOxz5lqdsR7tAkCEFV8Tnnyp1znznnfuucGw/0A14GhgP/CWWAIiKN1atrMg/9bAqnBKr87nluPh8tbNb6LWkGQXfOmlkC/sFbYwOrNgPPBhYRkbCKi43mju+eRJdOq3lj1gYefe1LdhQe4oqzDI9Hxb6tQbCDxX4XeAr/fU9V/9KVgeeVgMYeEZGwi4rycM0FQ+jcKYGn/72cVz50FOwp5qffHokvOtjbQKWlBPsv9Afgefyjh3cNLN1qPIqIRIzzJvThl9eOIzbGy8wvtnDvc/M5eKgs3GHJcQTbxZcMPOGcU6euiLQKYwdn8ocbJ3Lv8wv4cv1OfvGXz/jtD08mPSU+3KFJPYJtQb0MXBPCOEREmt2AHqk8cMtkundOYvP2/fz80TlsytsX7rCkHsG2oKYCS8zsKvzFEUfdA+WcO62JcYmINIsunRKYevMk7ntxIauyd3PnE5/xi++P4UTrHO7QpJamtKCK8E/DvghYXGsREYlYSQkx/O7H45k8Kqu6DH3GAl2xiDTBtqDGAOOcc8tDGYyISEvxRXv5+ZWj6dIpgdc/Xs9j//clBYXFXHX2IJWhR4hgW1AOSAllICIiLS0qysP3zhvMjZeNIMoDr81Yx8OvLKHsiEZuiwTBtqD+APzNzJ4ANgJH1Ws6595ramAiIi3l3PG9yUiJ508vLWLW4q3s3neYu64ZS1K8L9yhtWvBtqBeAXoDDwBvAtNrLO/Uf5iISGQ66YQu/OGmU0jtEMvyDbu484lP2b3vULjDatcaM91GinNuL4BzrkGJzcxSnXOFwQYnItKS+ndP4YFbJnP3c/PIzT/Ar56ay+9vnEhqh7hwh9YuNaYF9YmZ3WlmHY+3o5mlm9mvgDnBhyYi0vI6d0rgjzdNonfXZLbuKOLXT81lX1FJuMNqlxpzDWoicB+w1cw+B94HVgG78I/BlwGMAKYAk4C/B44REWlVkhNj+N2PJ/A/f/2MnPwD/OaZedx/wwSSEmLCHVq70pj5oIqcc7cCA4B5wJXAe/jve/oCmAZcAswHBjnnbq5r1l0RkdYgpUMs990wka7piWRv28dvn51H8WGN39eSGl3F55zLB+4B7jGzKCANqHDO7Q51cCIi4dQpOY77b5jIL578jHW5e7n72fncc/144jWNfIto0njzzrkK59xOJScRaasyUuO5/4YJpKfEs2bzHn73/AIOlx4Jd1jtgiZEERE5jsy0RO6/YQKdkmNZsXEXv39xIaVl5eEOq81TghIRaYBuGUncd8NEUpJiWbpuJ398aZFGnGhmSlAiIg3Uo0sHfnfDBDok+Fi0uoCp//sFR8qVpJpLkxKUmfnMLN7MEmouoQpORCTS9O6azL0/nkBiXDTzVmzn4X8uobyiMtxhtUlBJSgzO9nMlgGH8U+7caDWIiLSZvXvnlJdzTfny2089tpSKpSkQi7YWslHgH3AxYDudRKRdsd6deK3PzyZ3z47j5lfbMEXHcVNl43QVB0hFGyCGgac7JxbEcpgRERakyF90/j1teO497n5fDA/B190FNdfPExJKkSCvQa1BugWykBERFqjEQMy+OUPxhHtjWL6Z5t4cfpqKivV3RcKwbagHgeeNbPHgfVAac2Nmg9KRNqTEwd15q7vj+H3f1vIm7M3EBfj5cqzB4U7rFYv2AT1YuDxT3VsqwS8Qb6viEirNHZIJndcfRJ/fvkLXvnQkZWRxJQTu4c7rFYtqATV0PmgRETak4nDu/HDC4fyzFsreOy1pWRlJNG/R0q4w2q1mjTioZmdDgzBfy1rDfCxc06DVIlIu3XBKX3YlLePGQtzue/FBTx86xRSkzXhYTCCvQ8q08zmA/8BfgrchH+q9yVm1jmE8YmItCoej4efXDqcE3p3Yve+w/z+bwspO6Jx+4IRbFfdo8ARoI9zbqBzbgDQG9gDPBSi2EREWiVftJe7rhlDeko8a3MKefJfy1XZF4RgE9Q5wC3OuW1VK5xzecDPgfNCEVh9zOw2M3ur1rrRZjbHzPaaWa6Z/cbMdCOCiIRNaoc4fvmDscT4vHy0KJd3Ps0Od0itTrAJ6jD+ar3amq2Cz8ySzGwq8GCt9QnAu8D/4Z888XTgGuBHzRGHiEhD9e+ewq2XjwLg+WkrWep2hDmi1iXYBPUh8JCZdalaEXj+IPBBKAKrw7tAH+DpWut7APOcc08458qdc+uBt4BTmikOEZEGmzQqi/86YyAVlfDnl78gb1dRuENqNYKt4rsDmAnkmFlOYF0vYDlwZTBvaGYxQKc6NlU65wqAK5xzeWZ2N9C1aqNzzgHfqvU+5wLPBBOHiEioXXX2IHK272fBqnzue2EBD9wymYQ4X7jDinhBtaCcc/nAcOBS4HngCeAC59xY59z2IGOZAGyvY9kW+My8472BmcUCrwDFwFNBxiEiElJRUR5uv/JEemZ2YEtBEQ/8Y7Gm6GiABregzOw8YIZzrizwHPzXnFYGnsdUrQ9mqCPn3Gwg6MIGM8sE3gAqgDOcc4eCfS8RkVBLiPPxqx+M4+ePfsKi1QX84/01fO+8weEOK6I1potvOpAJ7Ag8r0+LD3VkZoPxX/uaCVzvnCtpyc8XEWmIrumJ3Hn1GH7z7Dxe/3g9vTKTNRzSMTQ4QdUc3iiShjoys1T8RRuvOuf+O9zxiIgcy4iBGRoOqYGCHUlippl97Rs1swwzW9z0sBrlaiAL+ImZFdVYXmnhOEREGuSCU/pw5tielB6p4P4XF1C4/3C4Q4pIjbkGdSpQ1WE6BfixmdWe3v0EoF9oQqubc+7uWq8fAx5rzs8UEQmlquGQtu4oYs3mPfz+bwv5/Y0T8UVrIoiaGnMNajfw3/gLGTz4x9+rOcBUJVCEfzQJERE5hqrhkG5/ZE71cEi3XD5Ss/HW0JhrUCuAvgBmNgu4xDlX2FyBiYi0dVXDId35xGd8tCiXPt2SuXBys3ZCtSrB3gf1jbqSk5nFmNn4poclItI+1B4OaeXGXWGOKHIENZKEmZ2Mf8ihwXw9yVUG+74iIu3RpFFZZOft418z1/Pwq0t5/OenaqQJmjbdxk7gO8Ah4LvAXfivQV0RmtBERNqPK88eRN+sjuzYU8wL76wKdzgRIdgENRy43Tn3BrAUKHDO/Rn4GXBrqIITEWkvfNFR3H7lifiio/hgfg5frCkId0hhF2yCOgLsDzxfB4wMPJ+Ffwp4ERFppF6ZyXz3nBMAeOy1pew/WBrmiMIr2AS1ALjRzKKAZfhHDwcYCrTvb1REpAkumtKPIX3TKDxQwlP/Xh7ucMIq2AR1F/AD4HbgZWCwmWXjnzTwnyGKTUSk3fFGebj1O6OIi/Hy6Zfb+HTptuMf1EYFW2a+COgN/D1Qbj4aeAC4FrgtZNGJiLRDmWmJXHfhUAD++u9l7N7XPidnCHYsvulAT+fcTvDPD+Wce9I595pzTpOciIg00dkn92L0oM4cKC7j8f/7ksrK9verNdguvpOBslAGIiIiX/F4PNz8XyNJivexeO0OPlyQc/yD2phgb6h9GHjJzB4GsvHfC1XNObe6qYGJiLR3aR3jueGS4Tzwj8U8P20lIwZkkJmWGO6wWkywLajf4W9FvQYswj+rbtWyIjShiYjI5FFZnDKiG4dKynnk1aVUtKOp4oNNUH2OsfQNTWgiIuKfmmMEqR1iWZW9m2mfbgx3SC0m2Cq+HOdcDpAInAjsCrxXbmC9iIiESHJiDDf/l388hJfeW0NO/v7jHNE2BFvFl2xm7+Hv0nsd6IL/utQyM+sewvhERAQYMziTM8f2pOxIBQ+/soQj5RXhDqnZBdvF9yAQC3TnqwKJW4C9wCMhiEtERGr54UVD6dwpgY1b9/F/H60LdzjNLtgEdT5wh3Mur2qFcy4XuBk4LRSBiYjI0RLifNz6nVF4PPDaR+tYv6VtzxkbbIJKolZpeYC3Ce8pIiLHMaxfOhdO6kdFRSUPv7KEkrLycIfUbIJNJu8Dd5tZ1YxalWaWgX+4oxkhiUxEROp09Xkn0KNLElsKinj5vTXhDqfZBJugbgZ6ALuBBOAjIBfoiH9OKBERaSaxPi+3XXEiUVEepn26kRVtdJr4YMvMC5xzE4CL8BdHPBZ4flLN61IiItI8BvRI5fIzBlJZCY+8upTiw21v9LkGD3VkZoPrWF0QWKqcYGYa6khEpAX81xkDWbQ6nw1b9/HCO6v46bdHHv+gVqQxLaiqYYxW1rGsqLFNQx2JiLSAaG8Ut11xItHeKD5ckMOGLXvDHVJINSZBVQ1jVN/wRn3RUEciIi2qZ2Yy35zUl8pKeG7ayjY1LUeDu/jqGsIo0O1nwIdAZ2Cz5oMSEWlZl58xkJlf5LIqezdzV2xn4vBu4Q4pJDTUkYhIK5cY7+OqswcB8OI7qyhtI/dGaagjEZE24KxxveiZ2YGCPcW882l2uMMJCQ11JCLSBni9UfzwwqGAfxikwgOHwxxR02moIxGRNmKUdeakE7pwqOQI/3h/bbjDaTINdSQi0oZc+80heKM8zFiQw+btrXveqFAPdZSMhjoSEQmbHl06cN7EPlRUwnNvr2jVZeehHupojIY6EhEJryvOMpLifSxbv4tFqwuOf0CEavB9UHVxzs0CZoUoFhERCYEOCTFccZbx7NsreeGdlYyyzviiW195QLD3QUWb2b1m9uMa6xaY2a/NrPV9CyIibcx5E/uQlZHEtp0H+c/cTeEOJyjBJpMHgKuBmsX2fwGuAX7XxJiOycxuM7O3aq0bb2bzzWy/mW0NJEpPc8YhIhLJor1RXHvhEJOb8OoAAA6mSURBVAD++aFj/8HSMEfUeMEmqMuBK51z1RV7zrmXgO/jT1IhZ2ZJZjYV/03CNdfHAu8AL+Cfj2oycCPwreaIQ0SktRhzQhdGDsjg4KEyXvmw9ZWdB5ugEoCiOtYX4k8SzeFd/IPRPl1zpXOuBOjjnHsmsCoN//1Ye5opDhGRVsHj8XDdRUOJ8sB7czezpeBAuENqlGAT1Czgz2bWqWqFmaUA9wOzg3lDM4sxs8w6li6BXa5wzl3G0fNPAeCcq/rWtwEL8Ze9zwkmDhGRtqR312TOOrk3FRWVvPDOqnCH0yjBJqhbgH7ANjNba2ZrgO2BdTcH+Z4TAu9Re9kG0MDy9b7AQGA0cE+QcYiItClXnT2IhLhovlhTwJK1O8IdToMFVWbunMs1s6HAGcBgoBRwwAznXEWQ7zkbaFJhg3PuMLA+cK3qFuDXTXk/EZG2IKVDLJefMZAXp6/muWkreXzAqXi9kV9wHXSZOfAroIdz7gHn3GPAvcAvW7rM3MwGmNkGM0ussToW/8jqIiICfHNSXzLTEthScIAPFnxter+I1OrKzOuwESgB7jMzX2ASxf8Gnm3hOEREIpYv2ss1F/jLzv/x/lqKDpWFOaLjazVl5vUJdCleCAwCdgJvA392zr3cknGIiES6CcO6MqRvGvsPlvLaDBfucI4r2KGOwlFmDoBz7u461m0Ezm3OzxURae08Hg8/vGgotz/yCdM/y+bcCb3plp4U7rDqFeoy898Dn4QiMBERCb3+3VM47aQeHCmv5G/TV4c7nGMKZZl5Hv4y75+GKjgREQm9q889gbgYL/NWbGf5hp3hDqdewU63kQsMBS4DngP+ClwCTME/HbyIiESotI7xXHbaAACef3sV5RWROWdU0CXhzrlS59y7zrkHgLX4iyO2AY+EKDYREWkmF5/an/SUeLLz9jF/5fZwh1OnoBOUmfU2s3vMLAf4D/4ihWfx37grIiIRLNbn5aLJfQH47MttYY6mbo2q4guMHH4ZcC3+7rwK/GPvZQGTnXPLQh2giIg0j/HDuvH8tFV8saaAkrJyYn3ecId0lAa3oMzsSSAfeAY4AFwHdHHOnQVUApF/15eIiFTr0imB/t07cri0nKUu8sboa0wX3w34RxK/AbjOOfd351xh84QlIiItYcLwbgDMXd6Q8bhbVmMS1DfwT2HxKJBvZrPN7GYz6948oYmISHOrSlALV+VTdiSosb6bTYMTlHPuE+fc9UAm8B38EwJOBXIC73OJmSU3S5QiItIssjKS6JXZgYOHj0TcPVGNruILlJe/4Zy7BH+yuhH4HP/8S3lm9lyIYxQRkWY0MdCK+nxZZHXzNWlqDOfcXufc0865yfinY/8DMD4kkYmISIuo6uabvzKf8vLI6eYLdrDYrwmMLnF/YBERkVaiZ2YHsjIS2bbzICuzdzNiQEa4QwKa2IISEZHWz+PxRGQ1nxKUiIgwYVhVN992KiJkbD4lKBERoV/3jnTulMCe/SWszdkT7nAAJSgRESHQzTesKwBzl0fG4LFKUCIiAnzVzTd3RR6VleHv5lOCEhERAKxXKp2S49hZeIj1W/aGOxwlKBER8YuKqtnNF/5qPiUoERGpVl1uvmJ72Lv5lKBERKTa4L5pdEyKYfuug2zevj+ssShBiYhINW+Uh5OHRkY1nxKUiIgcpWY1XzgpQYmIyFGG9U8nMd5Hbv4Btu44ELY4lKBEROQovugoxg3JBMLbzacEJSIiX1Ndbh7Gbj4lKBER+ZpR1pn4WC8bt+4jf/fBsMSgBCUiIl8T4/My5oTwdvMpQYmISJ2+umk3PN18SlAiIlKn0YM6E+Pz4nIK2bX3UIt/vhKUiIjUKS42mtGDOgMwb0XLd/MpQYmISL3CWc2nBCUiIvUaMziTaK+H1dm72XugpEU/WwlKRETqlRjvY+TAzlRUwvyVLdvNpwQlIiLHNHG4v5vv8xaeI0oJSkREjmnskK5ERXlYsWEXB4pLW+xzW12CMrPbzOyterb5zGyxmd3dwmGJiLRZyYkxDO+XTnlFJQtW5rfY57aaBGVmSWY2FXjwGLvdB4xsoZBERNqNCcNbvpqv1SQo4F2gD/B0XRvN7FTgTOCDFoxJRKRdOHloVzweWOp2Uny4rEU+M7pFPqUBzCwG6FTHpkrnXAFwhXMuL9B917XWsanAs8C38LeiREQkhFKT4xjcJ41V2btZtLqAKSd2b/bPjKQW1ARgex3LNgDn3LHalU8BTzrnVjZ3kCIi7VVL37QbMS0o59xswNPY48zsGiAdeCTEIYmISA3jh3Xj2bdXsnjtDg6XHCEutnlTSCS1oIJ1BTAWKDSzvcD5wC/MbHp4wxIRaVsyUuOxnqmUlJaz2O1o9s+LmBZUsJxzZ9d8HShB/9I5d3d4IhIRabsmDO+Kyy1k7vI8Jgam42gubaEFJSIiLaRqjqhFqwsoO1LerJ/V6lpQx2sZOecubqFQRETancy0RPp260h23j6WrtvJ2MGZzfZZakGJiEijVN+028xj8ylBiYhIo1R1832+LK9Zx+ZTghIRkUbp0aUDowZmcLi0nOmfZjfb5yhBiYhIo3379IEAvPNZNodKjjTLZyhBiYhIow3tl4b1SuVAcRmzl2xtls9QghIRkUbzeDz84IIhdEqOIy05rlk+o9WVmYuISGQY0jeNv//27OPvGCS1oEREJCIpQYmISERSghIRkYikBCUiIhFJCUpERCKSEpSIiEQkJSgREYlIug8KvAD5+fnhjkNEpN2p8bvXW3ubEhR0BbjqqqvCHYeISHvWFdhYc4USFCwCJgHbgeadHlJERGrz4k9Oi2pv8FRWVrZ8OCIiIsehIgkREYlISlAiIhKRlKBERCQiKUGJiEhEUoISEZGIpAQlIiIRSQlKREQikhKUiIhEJI0k0QBmNgJ4ChgOZAPXOue+dtezmfUEngdOBnYANzvn3mvJWMOlEd/RacAM4FCN1X9yzv2uRQINMzMbC0x3znWuZ3u7PYeqNOA7apfnkJmdCfwRGID/3JjqnHu6jv3azDmkBHUcZhYDvA08AkwGLgU+NLNezrn9tXZ/FZgHnA+cArxlZiOdc9ktGXNLa+R3dCLwunPuOy0cZliZmQe4DnjgOLu2y3MIGvUdtbtzyMx6AG8A38f/f2008IGZbXbOfVBr9zZzDqmL7/hOBXzOuUecc2XOuVeBVcDlNXcys4HAScBvnHOlzrmZwDT8/+HaulNpwHcUMBr4siWDixD3AD8B7qtvh3Z+DkEDvqOA9ngO9Qb+6Zx70zlXEeidmA1MrLlTWzuHlKCObzCwpta6tcCwOvbLdc4dPM5+bVFDvyPw//V7hpnlmFmumU01s9hmjzD8nnLOjQa+OMY+7fkcgoZ9R9AOzyHn3KfOuRuqXptZJ/yDXC+ttWubOoeUoI4vCSiuta4YSAhyv7aoQT+7mUUDW4E3gROA04AzgDZ97QDAOZfXgN3a8znUoO+oPZ9DVcysI/5W0QL83X01talzSNegju8gEF9rXQJQFOR+bVGDfnbn3BHg9BqrNpjZ/cCfgP/XrBG2Du35HGqQ9n4OBbrw3gZWA1c55ypq7dKmziG1oI5vNWC11g0KrK+9X08ziz/Ofm1Rg74jM8syswcCRRVVYoDDzRxfa9Gez6EGac/nkJlNxt9qegu4zDlX18/cps4htaCObxbgMbPbgCfwV6gNx9/FUM0558xsGXC/md0FTAAuAsa3cLzh0KDvCNgNXAUUm9m9QB/gV8ALLRhrxGrn51BDtctzyMz6AdOBXzrnHq9vv7Z2DqkFdRzOuVLgXPy/dPcAvwQuds7tNLOrzKxm0/lS/P3iO4DngOuccytbOuaW1tDvKPAX37n4S9F3A3OA14GHwhJ4BNA5dHw6hwC4CegA/MHMimosf2rL55Bm1BURkYikFpSIiEQkJSgREYlISlAiIhKRlKBERCQiKUGJiEhEUoISEZGIpBt1RZqJmf0N//QI9bkH/4jUs4AOzrkWGY7GzLzA58D3nHPrjrFfFDAfuNo551oiNpGa1IISaT4/A7oGllMD68bWWPcAMDfw/GAdxzeXW4Blx0pOAIFx3u7FPxGlSIvTjboiLcDMhgIrgD7Ouc1hjCMOyAVOa+joAma2Ef9oBLObMzaR2tTFJxJGZnYqNbr4zKwSuAK4C/8AvF8A3wXuAK4G9gN3OedeDhzfAXgQuAyoBGYCPzvG1BXfAfbWTE5m9mvgeiAD/7xe/+Oc+0+NY97E3xqcHYIfWaTB1MUnEnn+CNwKnAz0BJbgT0xjgH8DT5tZUmDfZ/AnsrOBKfiT1AeBeZPqcj7wftULM/tW4LO+i3/U63eB180sucYx7+OfIFB/0EqLUoISiTx/cc7Ncs59iX8E6yL8rRqHf1DUeKCPmfXF3yK60jm3KNAquhr/9ODn1PPeJwGrarzuDZQAOYGux3uBS4CyGvusxj8R3qCQ/HQiDaS/iEQiz4Yaz4uBzc65qovFVXMAxQK9As+d2VHTcSXgb1VNr+O9uwC7arz+X/yVhtlmthj/TK0vOucO1dhnd+CxcyN/DpEmUQtKJPKU1Xpde9bUKtGBfUcBI2ssA4EX6zmmAvBUvXDO7QRG429xzQWuAZYHijqqVP2eKG/wTyASAkpQIq3XGsAHJDrnNjjnNgDbgan4k1Rd8vEXQwBgZpcAP3bOfeic+xn+ltcB4Lwax2TUOFakxaiLT6SVCsyeOg14ycxuAnYC9+Mvrlhbz2GLgRE1XnuBqWZWgL9i8GQgM/C8ygigkKO7HkWanVpQIq3b9/Enk7eARUBH4Ezn3N569n8Xf7UfAM6514Hf4m91rQPuA37qnJtZ45jJwPvOOXXxSYvSjboi7YiZJQCbgXOcc0sasH8UkIO/UvDTZg5P5ChqQYm0I865YvytpZsaeMhFQLaSk4SDEpRI+/MwMNxq1abXFmg9/RK4oUWiEqlFXXwiIhKR1IISEZGIpAQlIiIRSQlKREQikhKUiIhEJCUoERGJSP8f0LW+O37ZvbcAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "a = gradient(results.v)\n",
-    "plot(a)\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Acceleration (m/$s^2$)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The maximum downward acceleration, as a factor of `g`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_acceleration = max(abs(a)) * m/s**2 / params.g"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using Equation (1) from [Heck, Uylings, and Kędzierska](http://iopscience.iop.org/article/10.1088/0031-9120/45/1/007), we can compute the peak acceleration due to interaction with the cord, neglecting drag."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "1.625 dimensionless"
-      ],
-      "text/latex": [
-       "$1.625\\ dimensionless$"
-      ],
-      "text/plain": [
-       "1.625 <Unit('dimensionless')>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def max_acceleration(system):\n",
-    "    mu = system.mu\n",
-    "    return 1 + mu * (4+mu) / 8\n",
-    "\n",
-    "max_acceleration(system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you set `C_d=0`, the simulated acceleration approaches the theoretical result, although you might have to reduce `max_step` to get a good numerical estimate."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sweeping cord weight\n",
-    "\n",
-    "Now let's see how velocity at the crossover point depends on the weight of the cord."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_m_cord(m_cord_array, params):\n",
-    "    sweep = SweepSeries()\n",
-    "\n",
-    "    for m_cord in m_cord_array:\n",
-    "        system = make_system(Params(params, m_cord=m_cord))\n",
-    "        results, details = run_ode_solver(system, slope_func1, events=event_func)\n",
-    "        min_velocity = min(results.v) * m/s\n",
-    "        sweep[m_cord.magnitude] = min_velocity\n",
-    "        \n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m_cord_array = linspace(1, 201, 21) * kg\n",
-    "sweep = sweep_m_cord(m_cord_array, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like.  As expected, a heavier cord gets the jumper going faster.\n",
-    "\n",
-    "There's a hitch near 25 kg that seems to be due to numerical error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(sweep)\n",
-    "\n",
-    "decorate(xlabel='Mass of cord (kg)',\n",
-    "         ylabel='Fastest downward velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Phase 2\n",
-    "\n",
-    "Once the jumper falls past the length of the cord, acceleration due to energy transfer from the cord stops abruptly.  As the cord stretches, it starts to exert a spring force.  So let's simulate this second phase."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`spring_force` computes the force of the cord on the jumper:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def spring_force(y, system):\n",
-    "    \"\"\"Computes the force of the bungee cord on the jumper:\n",
-    "    \n",
-    "    y: height of the jumper\n",
-    "    \n",
-    "    Uses these variables from system:\n",
-    "    y_attach: height of the attachment point\n",
-    "    L: resting length of the cord\n",
-    "    k: spring constant of the cord\n",
-    "    \n",
-    "    returns: force in N\n",
-    "    \"\"\"\n",
-    "    L, k = system.L, system.k\n",
-    "    \n",
-    "    distance_fallen = -y\n",
-    "    extension = distance_fallen - L\n",
-    "    f_spring = k * extension\n",
-    "    return f_spring"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The spring force is 0 until the cord is fully extended.  When it is extended 1 m, the spring force is 40 N. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.0 newton"
-      ],
-      "text/latex": [
-       "$0.0\\ \\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "0.0 <Unit('newton')>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spring_force(-25*m, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "40.0 newton"
-      ],
-      "text/latex": [
-       "$40.0\\ \\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "40.0 <Unit('newton')>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spring_force(-26*m, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The slope function for Phase 2 includes the spring force, and drops the acceleration due to the cord."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func2(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object containing g, rho,\n",
-    "            C_d, area, and mass\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    M, g = system.M, system.g\n",
-    "    \n",
-    "    a_drag = drag_force(v, system) / M\n",
-    "    a_spring = spring_force(y, system) / M\n",
-    "    dvdt = -g + a_drag + a_spring\n",
-    "    \n",
-    "    return v, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll run Phase 1 again so we can get the final state."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>v_init</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>M</th>\n",
-       "      <td>75 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>m_cord</th>\n",
-       "      <td>75 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>1 meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>60.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>L</th>\n",
-       "      <td>25 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k</th>\n",
-       "      <td>40.0 newton / meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.3402777777777778 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mu</th>\n",
-       "      <td>1.0 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>y               0 meter\n",
-       "v    0.0 meter / secon...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>10 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "v_init                                   0.0 meter / second\n",
-       "g                                   9.8 meter / second ** 2\n",
-       "M                                               75 kilogram\n",
-       "m_cord                                          75 kilogram\n",
-       "area                                           1 meter ** 2\n",
-       "rho                               1.2 kilogram / meter ** 3\n",
-       "v_term                                  60.0 meter / second\n",
-       "L                                                  25 meter\n",
-       "k                                       40.0 newton / meter\n",
-       "C_d                        0.3402777777777778 dimensionless\n",
-       "mu                                        1.0 dimensionless\n",
-       "init      y               0 meter\n",
-       "v    0.0 meter / secon...\n",
-       "t_end                                             10 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system1 = make_system(params)\n",
-    "system1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A termination event occurred.'"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "event_func.direction=-1\n",
-    "results1, details1 = run_ode_solver(system1, slope_func1, events=event_func)\n",
-    "details1.message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now I need the final time, position, and velocity from Phase 1."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.2118255911654763"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_final = get_last_label(results1)\n",
-    "t_final"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>-25.0 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v</th>\n",
-       "      <td>-23.876881009672466 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "y                           -25.0 meter\n",
-       "v    -23.876881009672466 meter / second\n",
-       "Name: 2.2118255911654763, dtype: object"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init2 = results1.row[t_final]\n",
-    "init2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And that gives me the starting conditions for Phase 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>v_init</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>M</th>\n",
-       "      <td>75 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>m_cord</th>\n",
-       "      <td>75 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>1 meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>60.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>L</th>\n",
-       "      <td>25 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k</th>\n",
-       "      <td>40.0 newton / meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.3402777777777778 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mu</th>\n",
-       "      <td>1.0 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>y                           -25.0 meter\n",
-       "v    -...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>10 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>2.21183</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "v_init                                   0.0 meter / second\n",
-       "g                                   9.8 meter / second ** 2\n",
-       "M                                               75 kilogram\n",
-       "m_cord                                          75 kilogram\n",
-       "area                                           1 meter ** 2\n",
-       "rho                               1.2 kilogram / meter ** 3\n",
-       "v_term                                  60.0 meter / second\n",
-       "L                                                  25 meter\n",
-       "k                                       40.0 newton / meter\n",
-       "C_d                        0.3402777777777778 dimensionless\n",
-       "mu                                        1.0 dimensionless\n",
-       "init      y                           -25.0 meter\n",
-       "v    -...\n",
-       "t_end                                             10 second\n",
-       "t_0                                                 2.21183\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system2 = System(system1, t_0=t_final, init=init2)\n",
-    "system2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run Phase 2, setting the direction of the event function so it doesn't stop the simulation immediately. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A termination event occurred.'"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "event_func.direction=+1\n",
-    "results2, details2 = run_ode_solver(system2, slope_func2, events=event_func)\n",
-    "details2.message"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8.09952482949752"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_final = get_last_label(results2)\n",
-    "t_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot the results on the same axes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_position(results1, label='Phase 1')\n",
-    "plot_position(results2, label='Phase 2')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And get the lowest position from Phase 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-77.04609533183535 meter"
-      ],
-      "text/latex": [
-       "$-77.04609533183535\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "-77.04609533183535 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "min(results2.y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To see how big the effect of the cord is, I'll collect the previous code in a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def simulate_system2(params):\n",
-    "    \n",
-    "    system1 = make_system(params)\n",
-    "    event_func.direction=-1\n",
-    "    results1, details1 = run_ode_solver(system1, slope_func1, events=event_func)\n",
-    "\n",
-    "    t_final = get_last_label(results1)\n",
-    "    init2 = results1.row[t_final]\n",
-    "    \n",
-    "    system2 = System(system1, t_0=t_final, init=init2)\n",
-    "    results2, details2 = run_ode_solver(system2, slope_func2, events=event_func)\n",
-    "    t_final = get_last_label(results2)\n",
-    "    return TimeFrame(pd.concat([results1, results2]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run both phases and get the results in a single `TimeFrame`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = simulate_system2(params);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params_no_cord = Params(params, m_cord=1*kg)\n",
-    "results_no_cord = simulate_system2(params_no_cord);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/jump.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_position(results, label='m_cord = 75 kg')\n",
-    "plot_position(results_no_cord, label='m_cord = 1 kg')\n",
-    "\n",
-    "savefig('figs/jump.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-74.62920556283255 meter"
-      ],
-      "text/latex": [
-       "$-74.62920556283255\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "-74.62920556283255 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "min(results_no_cord.y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-2.4168897690027933 meter"
-      ],
-      "text/latex": [
-       "$-2.4168897690027933\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "-2.4168897690027933 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diff = min(results.y) - min(results_no_cord.y)\n",
-    "diff"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The difference is more than 2 meters, which could certainly be the difference between a successful bungee dunk and a bad day."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/jump_soln.ipynb b/soln/jump_soln.ipynb
deleted file mode 100644
index f8dd95de..00000000
--- a/soln/jump_soln.ipynb
+++ /dev/null
@@ -1,1368 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Bungee jump case study\n",
-    "\n",
-    "Copyright 2018 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Bungee jumping"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Suppose you want to set the world record for the highest \"bungee dunk\", [as shown in this video](https://www.youtube.com/watch?v=UBf7WC19lpw).  Since the record is 70 m, let's design a jump for 80 m.\n",
-    "\n",
-    "We'll make the following modeling assumptions:\n",
-    "\n",
-    "1. Initially the bungee cord hangs from a crane with the attachment point 80 m above a cup of tea.\n",
-    "\n",
-    "2. Until the cord is fully extended, it applies no force to the jumper.  It turns out this might not be a good assumption; we will revisit it.\n",
-    "\n",
-    "3. After the cord is fully extended, it obeys [Hooke's Law](https://en.wikipedia.org/wiki/Hooke%27s_law); that is, it applies a force to the jumper proportional to the extension of the cord beyond its resting length.\n",
-    "\n",
-    "4. The jumper is subject to drag force proportional to the square of their velocity, in the opposite of their direction of motion.\n",
-    "\n",
-    "Our objective is to choose the length of the cord, `L`, and its spring constant, `k`, so that the jumper falls all the way to the tea cup, but no farther! \n",
-    "\n",
-    "First I'll create a `Param` object to contain the quantities we'll need:\n",
-    "\n",
-    "1. Let's assume that the jumper's mass is 75 kg.\n",
-    "\n",
-    "2. With a terminal velocity of 60 m/s.\n",
-    "\n",
-    "3. The length of the bungee cord is `L = 40 m`.\n",
-    "\n",
-    "4. The spring constant of the cord is `k = 20 N / m` when the cord is stretched, and 0 when it's compressed.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "newton"
-      ],
-      "text/latex": [
-       "$\\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "<Unit('newton')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>y_attach</th>\n",
-       "      <td>80 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_init</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>75 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>1 meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>60.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>L</th>\n",
-       "      <td>25 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k</th>\n",
-       "      <td>40.0 newton / meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>zero_force</th>\n",
-       "      <td>0 newton</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "y_attach                       80 meter\n",
-       "v_init               0.0 meter / second\n",
-       "g               9.8 meter / second ** 2\n",
-       "mass                        75 kilogram\n",
-       "area                       1 meter ** 2\n",
-       "rho           1.2 kilogram / meter ** 3\n",
-       "v_term              60.0 meter / second\n",
-       "L                              25 meter\n",
-       "k                   40.0 newton / meter\n",
-       "zero_force                     0 newton\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = Params(y_attach = 80 * m,\n",
-    "                 v_init = 0 * m / s,\n",
-    "                 g = 9.8 * m/s**2,\n",
-    "                 mass = 75 * kg,\n",
-    "                 area = 1 * m**2,\n",
-    "                 rho = 1.2 * kg/m**3,\n",
-    "                 v_term = 60 * m / s,\n",
-    "                 L = 25 * m,\n",
-    "                 k = 40 * N / m,\n",
-    "                 zero_force = 0 * N)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now here's a version of `make_system` that takes a `Params` object as a parameter.\n",
-    "\n",
-    "`make_system` uses the given value of `v_term` to compute the drag coefficient `C_d`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Makes a System object for the given params.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    area, mass = params.area, params.mass\n",
-    "    g, rho = params.g, params.rho\n",
-    "    v_init, v_term = params.v_init, params.v_term\n",
-    "    y_attach = params.y_attach\n",
-    "    \n",
-    "    C_d = 2 * mass * g / (rho * area * v_term**2)\n",
-    "    init = State(y=y_attach, v=v_init)\n",
-    "    t_end = 20 * s\n",
-    "\n",
-    "    return System(params, C_d=C_d, \n",
-    "                  init=init, t_end=t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>y_attach</th>\n",
-       "      <td>80 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_init</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>75 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>1 meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>60.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>L</th>\n",
-       "      <td>25 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k</th>\n",
-       "      <td>40.0 newton / meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>zero_force</th>\n",
-       "      <td>0 newton</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.3402777777777778 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>y              80 meter\n",
-       "v    0.0 meter / secon...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>20 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "y_attach                                               80 meter\n",
-       "v_init                                       0.0 meter / second\n",
-       "g                                       9.8 meter / second ** 2\n",
-       "mass                                                75 kilogram\n",
-       "area                                               1 meter ** 2\n",
-       "rho                                   1.2 kilogram / meter ** 3\n",
-       "v_term                                      60.0 meter / second\n",
-       "L                                                      25 meter\n",
-       "k                                           40.0 newton / meter\n",
-       "zero_force                                             0 newton\n",
-       "C_d                            0.3402777777777778 dimensionless\n",
-       "init          y              80 meter\n",
-       "v    0.0 meter / secon...\n",
-       "t_end                                                 20 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`spring_force` computes the force of the cord on the jumper.\n",
-    "\n",
-    "If the spring is not extended, it returns `zero_force`, which is either 0 Newtons or 0, depending on whether the `System` object has units.  I did that so the slope function works correctly with and without units."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def spring_force(y, system):\n",
-    "    \"\"\"Computes the force of the bungee cord on the jumper:\n",
-    "    \n",
-    "    y: height of the jumper\n",
-    "    \n",
-    "    Uses these variables from system|\n",
-    "    y_attach: height of the attachment point\n",
-    "    L: resting length of the cord\n",
-    "    k: spring constant of the cord\n",
-    "    \n",
-    "    returns: force in N\n",
-    "    \"\"\"\n",
-    "    y_attach, L, k = system.y_attach, system.L, system.k\n",
-    "    \n",
-    "    distance_fallen = y_attach - y\n",
-    "    if distance_fallen <= L:\n",
-    "        return system.zero_force\n",
-    "    \n",
-    "    extension = distance_fallen - L\n",
-    "    f_spring = k * extension\n",
-    "    return f_spring"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The spring force is 0 until the cord is fully extended.  When it is extended 1 m, the spring force is 40 N. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0 newton"
-      ],
-      "text/latex": [
-       "$0\\ \\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "0 <Unit('newton')>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spring_force(80*m, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0 newton"
-      ],
-      "text/latex": [
-       "$0\\ \\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "0 <Unit('newton')>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spring_force(55*m, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "40.0 newton"
-      ],
-      "text/latex": [
-       "$40.0\\ \\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "40.0 <Unit('newton')>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spring_force(54*m, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`drag_force` computes drag as a function of velocity:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(v, system):\n",
-    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
-    "    \n",
-    "    v: velocity\n",
-    "    system: System object\n",
-    "\n",
-    "    returns: drag force\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    f_drag = -np.sign(v) * rho * v**2 * C_d * area / 2\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the drag force at 60 meters per second."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "735.0 kilogram meter/second<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$735.0\\ \\frac{\\mathrm{kilogram} \\cdot \\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "735.0 <Unit('kilogram * meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v = -60 * m/s\n",
-    "f_drag = drag_force(v, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Acceleration due to drag at 60 m/s is approximately g, which confirms that 60 m/s is terminal velocity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "9.8 meter/second<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$9.8\\ \\frac{\\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "9.8 <Unit('meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a_drag = f_drag / system.mass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now here's the slope function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object containing g, rho,\n",
-    "            C_d, area, and mass\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    mass, g = system.mass, system.g\n",
-    "    \n",
-    "    a_drag = drag_force(v, system) / mass\n",
-    "    a_spring = spring_force(y, system) / mass\n",
-    "    \n",
-    "    dvdt = -g + a_drag + a_spring\n",
-    "    \n",
-    "    return v, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, let's test the slope function with the initial params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0 <Unit('meter / second')>, -9.8 <Unit('meter / second ** 2')>)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                                                 True\n",
-       "message    The solver successfully reached the end of the...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the plot of position as a function of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_position(results):\n",
-    "    plot(results.y)\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After reaching the lowest point, the jumper springs back almost to almost 70 m, and oscillates several times.  That looks like more osciallation that we expect from an actual jump, which suggests that there some dissipation of energy in the real world that is not captured in our model.  To improve the model, that might be a good thing to investigate.\n",
-    "\n",
-    "But since we are primarily interested in the initial descent, the model might be good enough for now.\n",
-    "\n",
-    "We can use `min` to find the lowest point:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "5.413731910623594 meter"
-      ],
-      "text/latex": [
-       "$5.413731910623594\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "5.413731910623594 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "min(results.y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At the lowest point, the jumper is still too high, so we'll need to increase `L` or decrease `k`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's velocity as a function of time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_velocity(results):\n",
-    "    plot(results.v, color='C1', label='v')\n",
-    "        \n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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IH9tjdzglJwlKOFqi+yTJsydw+YP4J8y0O5yLkp0sEd4m3XyVJNl71po16vZQv2CN3eGcx3C5c0mzt229zdGUniQo4Wixg5nW0/RFjixvNJz6BVeCy03fga0kw112hyMKFN75HJhpgrNXOHLNXbabL7LzedLJfnuDKTHHJSil1Gql1Mm8n31Kqe8opc4opU4ppT5tZ3yivCpl/dNg3MEGq3abmSayUwrIVopc916rs7r3snwt0/GNn0k6FslN5KhWjklQSilDKfXHwOOAL+9XXwQUMBu4DHi/UuoPbQhRlJlpmhWdoODcGIbM5qsM/Z3H6D+xH8MfdGzNRzjXisouJK5WjklQWInoI8BXBrz+fuBurfVZrfVB4B7gw2WOTdgg0XmUVPgs7vpmvGOn2h3OiATnrsLwB4kf31eT2yVUmqjeCFiVG1we3wWOtk9o0dVW9/H+10iGz9odTsk4KUF9S2u9EtiUfUEp1QxMBHbmHbcbqMzHaXFR+vLHnxywUHIkXF4/ofnZCufSinK6iH4ZgHr1JpsjGZ67vong7BVgpgnvqN7K+Y5JUFrrY4O8nB2hzK8xHwWCpY9I2C12xHouCUxbZHMklya/m8800zZHI4aS7OkkfmwPhsfn6Ir5WaElmcXgVTxL1DEJagiRzD8Dea8FgbANsYgyMk2T2OFdANRNW2BzNJembvoi3I1jSXafInZkt93hiCFE9EsABGYvx+X12xzNhdXPWYWrrp7+jgP0nzxsdzgl4egEpbU+C5zAmiSRNZ/zu/xEFUp2dZAKn8EVCOEdO8XucC6JYbhoyG4HXyMlaipRNkE5vXsvy/B4c+u0snUDq42jE1TGj4HPK6XGKqVmAJ/IvCaqWOxIpvU0dQGGUQkf0+Fla/NFdr1Q9WtXKlEq2kPs8E5wuR09e2+ghtZzNR+rsXJ+Jfyf/zlgO7ADeAV4APiWrRGJkst1701daHMkxeEbN9VauxKP5rauF84R3bMJzDSBGYsduTh3KP4p8/E0t5DqPUPs0A67wyk6j90BDKS13gA05/0cAz6a+SNqRH4LqlqEWq/hTMcBwtt+R2j+FXaHI/LkuvfmVUb3XpZVOX8tXc/9nN7tvyMws3LqVRaiElpQosYkw10kzhzD8Porrv7ecEKLrgLDRXTvFlLRXrvDERnpeB99+7cCBsF5q+0O56Jlu/kiuzeSTsRtjqa4JEEJx4m1Z1pPk+dhuB3XyB8xT2gUgZlLIZ0kvPN5u8MRGdH9WzBTCfxT5uFpGGV3OBfNO3oS/klzMftjRDPruKqFJCjhONU2/pSvIbd2ZYO9gYic6B5rTLC+AltPWaFMK6raNjIs6PFUKdUK3AysAlqAFNb071eAh7TWe0sWoag5ufGnCl//NJjgvNUYvgDxY3vo7zyKb8xku0OqaaaZJrrvVYCKmr03UGjhVXQ+8YNM5fyzeEKV1xIczLAtKKXUWqXUeqzyQ7cAZ4EXMj/HgPcBO5VSTyilnFn6V1SUdDxKf8dBcLnxT55ndzhF5/L6qc9MkKjmCgCVIn5sL+loD56mlopeb+cONhCcU32lj4ZsQSml/i+wCPh34Dat9aAb2iilGoE7gHuVUm1a6w+UIlBRG2LtGsw0/olzK2I1/0g0tK4l3PY04e2/Y9Q176mKdV6VKjvlPzhnRcXWe8xqaF1H9PWXCbf9juY3vd3ucIpiuC6+h7XWH7zQCbTWPcC3gW8rpW4vWmSiJlVz917WeaWPDu8iML2yaw1Wsujeyu/eywrOWYGrLkT/yYPEOw7iHz/D7pAu2ZCPblrrBy72ZFrrn19aOKLWxdqtWnXVOEEiyyp9lN0OvroGtStJsvestfeTx0ddFTwkGB4voYVXAhCuktJHBfUtKKWCSqkvKqXmZn7+tlIqrJR6Wik1sbQhilphppLEj1nzbeqmqAscXdmys67Cu1+surUrlSK6z+reC8xorZru5FBmI8PwtuoofVRo5/e/YE2I8CmlbsXaRPCvgD7gX0sUm6gx/ScPYSbieEdPxB1stDuckvKNnYJ/4hzMeLTqt+12qmrq3svyT5qLd/REUpEu+g602R3OJSs0Qd0K3KG13gH8PvCE1vq/gE8CbylVcKK2xNo1AP4qbz1lnVu7ssHeQGqQmUzQd2ArYI3dVAur9NG5ArKVrtAEFQA6lFIu4Ebg0czrJtaaKCEuWW78aXKNJKhFV2W27d5a1dt2O1HfkZ2Y/TF8LdPwNI2zO5yiCrVa45uR3RtJx/tsjubSFJqgXgH+Fvg8MAr4pVJqEvBlYGOJYhM1Jn70dQDqpsy3OZLycAcbq3LtSiWoxu69LG/zeOqmLcRM9hPZ/aLd4VySQhPU/wbWAB8D/iyzPfunsTYS/IsSxSZqSLKnk2T3KQx/EO+4yl0webEaWq8FINxWHbOuKkVfpnpEYHb1dO/lq5bu4+EW6q4BNmqt01rrncCyAYd8RmstJZlFUcSyrafJc2tq4Wpwzgpcgepau+J0ie6TJDqPYfiD1FVhtRKA0II1dD72PWKHdpDoOom3ucXukEZkuG+Ce4BTSqlfKaX+TCk1J/+XkpxEMcVz40+10b2XZa1duQqQArLl0rfvNcCaXl5N1fLzufxBgsoqflvJkyWGW6i7BpgJ/BBYDDyqlDqglPqOUup2pVR1VCMUjlBrM/jyhVrXARDe/mxVrF1xuuh+K0EFZw3sFKouDdnP1bYNmKZpbzAjNOzjQ6aM0S8zf1BKzcSaVv5e4FtKqX1YU84/W+pARfVKJ/uJnzgAGNRNnmt3OGXnnzQH75hJJDqP0bd/a1VNe3YaM50idnAbAIEqT1CBmUtwh0aROHOc+NHXK3Lx+0V19mutD2itv621vh1r242/wKpqLsSI9R/fD+kkvpapuPxBu8MpO8Mwcq2oSh/Udrr40T2k41G8oydV7LhMoQyXm9Diq4HK/VwV3AGrlFqHVd18YE2QaDEDErUnu/7JX2PjT/kaFq/l7Ib/IapfJhWL4K6rtzukqhTdvwWo/tZTVkPrOro3/obIzucZc8Mf4fL47A7pohS6YeG9wJ8Dh3lji8kEvlHkuEQNyY4/VWIXRLF4msZRN2MxsYPbiOx6gcblN9gdUlXq25+pHlEjCcrXMh3f+Jn0dxwgumcToQVr7A7pohTagvpD4INa6x+WMhhRe0zTJH40m6Cqc8pvoRpa1xE7uI3etg2SoEogFe21ihG7PVVRvbxQDUvW0fnEAcJtGyouQRU6BhUFXi5lIKI2JbtPkop04wo24hlV24Xx6+e/CcNbR7x9N4kzx+0Op+pYtfdM6qYuwOWrszucsgktuhoMF9F9W0iGB9131rEKTVBfAe7JzOITomhy5Y0mza34HU0vlcsXoH7+5UDlDmo7Wa1MLx/IXd9EcPbyiiypVWiC2gVcAexVSqUG/ilhfKLKZStI+Kt0Rf/Fasjfz8dM2xtMFTFNMzf+VCsTJPKFlmRKalXYBpmFjkF9B6so7PeRWXuiiOJH9wBUbcmZi1U3fRGexrEku0/KdvBFlDh9hFT4DO76Znwt0+0Op+zq566ytoPvOFBRJbUKTVBTgZu11vtLGYyoLfkLdP2T5lzw+FpgGC5CrdfQ9fwD9LZtkARVJNnN+wIzl9RkV3J2O/ieVx8jvG0D/vEfsDukghTaxfcEsLaUgYja03/CWqDrHVebC3SHkl20G9n9Aul+WQdfDNFs997MpTZHYp/cdvAVVFKr0BbUS8C/K6XeBewFEvm/1Fr/TbEDE9XvXAVz6d7L5xszCf/kecSPvk5Ev0RDZusEMTJmKkHs8E7AakHVKv+kuXkltV6riL2wCm1B3YC1aWEIa9uNy/L+rCpNaKLaxXMTJGqv/t6F5Bf6FJcm1v46ZiKGd9xUPA2j7Q7HNueV1GrbYGsshSqoBaW1vrbUgYjaE5MJEkOqX3glnU98n74D20j2nMbTONbukCqWtf6ptrv3shpar7FKar3+Cqm+MO5AyO6QhjVkC0op9XmlVKDQEymlGpRSXypOWKLaJXs6SfWctnbQHVs7O+gWyh0IEZy3CjDp3Va5+/k4QXaCRLCGu/eyPI1jCcxYjJlKENn1gt3hXNBwXXzdwA6l1NeVUpcPdoBSylBKXaaU+hdgJ1BZy5SFbeLHMq2nSXNqagfdi5HbDn7b+ordz8duqb4w8eP7wOWhbprMiIRzkyUqoZtvyC4+rfW9SqmfA38DPK6USmIt2D0NGMA4rOrmBvAD4Eqt9eGSRyyqQm6B7iTp3htKYPYy3PXNJDqPET+2R7pCR6Dv0DYw0zVX3mg49epyTvv+i/hRTX/nMXxjJtkd0pCGfXTVWrdrrf8CmAjcBTwGtAOHgIeAdwNjtdZ/LslJXIy4zOC7oPz9fMIV8LTrRH37z61/EhaXr476+VcAzp+EU+gkiQjwcOaPLZRSS4FvAUuA/VjV1V+xKx4xcmYqaXW7ICWOLiTUuo7ulx4kvPN5Rt/wgYrbz8duMkFicA1L1hFuW0/vtt8x6pr3Orab3ZlRDaCU8gG/Bn4KNAN3Y3U7NtoamBiR/pOHMJP9eEZNwB1ssDscR/OPn4Fv/EzSsTDRPZvtDqeiJM6eINnVgasuhH/iLLvDcZS6aQvxNLWQ6jlN7OB2u8MZUkUkKGAd4NVa36u1Tmit7wN2AO+xNywxErnp5TW8QeHFyBWQbVtvbyAVJjt7r276IgyX2+ZonCVbUgucXTm/UhLUQqwJGvl2A62lvnD8+H4O3ftBerfLVN9iiR/LTpCQBbqFCC26GlzuitzPx07Z7r2gdO8NKvvgE9m9kXS8z95ghlBQglJKzShxHBcS4o1V1KNAyQu4paLdpCLddD3/gEz1LRKZIHFxKnk/H7uY6RR9ma6rwCxJUIPxjppA3dQFmIk4kd0v2h3OoAptQe1VSj2nlPpTpdSYkkY0uAgwcNFwEAiX+sKBGa24go0kTrfnBvbFyKWivSTOHMfw+Gpy24ORyhX6lNl8BYmfOEA6FsbT3IJ31AS7w3GsXOkjh3bzFZqgZmFNK/8IcEwp9aBS6r0XU2niEu0EBg5YzM+8XlKG20PDYquQu4wBXLpc997E2RjuQmsVi/o5mf18Th4k3nHQ7nAcT2bvFSa04AoMj4/YoR0kuk7aHc4bFJSgtNaHtdb/oLVeCqwEtgKfBjqUUj9USl1fyiCB9YChlPorpZRXKfVerOnmvyzxdYG83Sh3PIeZTFzgaDGcmBSIHRHD4yW06CpAHpQKkb//kxiaq66eoFoNOHO33ZFMkmgH9mGtRfJgVTf/kVJKK6WuKGZwWVrrfuBm4F3AGeDvgHdorU+V4noD+cfPwNcyg3QsTGTvpnJcsmplSxzJ+qeLl+2OCe94FjOVtDcYB0sn4sTadwMGgekln0dV8RryuvmcNs5eUB+LUqoeuBV4L/AWoAP4CfBZrfUOpZQL+E+sdUrTShGo1no7cFUpzl2I0JJ1nHnyB4TbNhCaX5I8XPVMM31ui3cpcXTR/JPm4B0zmUTnUaL7X6N+rux0M5jY4Z2QSuKfOFvW2RUgMHMJ7tBokmdPEG/X1E2db3dIOYW2oE4C/wGcAm7SWk/XWn9aa70DQGudBh4HeksTpv1Ci64Gw0V03xZSkW67w6lIic5jpONR3A1j8DTaMdemshmGkbcmaoOtsTjZue49GX8qhOFyE2q1xtl7HdZ9XGiC+gAwQWv9Ia31hvxfKKVaALTWv9BaV225YE+o2Zrqm07JVN8ROje9XMafRiq0+BowXET2vEKqr2qfBy/JuQkSMv5UqNwGmbteIJ2I2xtMnkIT1H3AG8oKKaWmYY1F1YTsZInerc56yqgU5yZISPfeSHkaxxCY2QqpJOEdz9sdjuMkw130nzyE4fFRN8U5XVVO5xs3Ff/EOZjxKNHXX7Y7nJwhx6CUUncAt2V+NIDvKqUGptbpWJMWakL93POn+vrHz7A7pIoiC3SLo6H1Wvr2byW8bQNNq26yOxxHiR3cBli15gyP1+ZoKktoyTrix/fS27bBGtJwgOFaUE9gLYSNZH7uy/w9+ycMvAS8o5QBOolM9R25dH8f/aeOgMuNb4IU7rwUQbUawxcgfmwP/afb7Q7HUaLSvTdioYVXgctD34E2kr3OaHcMtwiMsO0AACAASURBVGHhaeCDAEqpg8A/aq0HlhuqOaHWdfRsfpTwjmcZfd1dsti0QPHj+8BM4xs/G5fXb3c4Fc3l9RNasIberU/R27aeMdfdZXdIjmCaJn37M/X3Zi2zOZrK4w42UD9vFZHdGwlvf4bmK+xvewzXxfdW4AmtdQJ4BVin1ODVp7XWvy1NeM7jnzQH79gpJE63y1TfiyATJIqrYem19G59ivC2Zxi97g+kWjeQOH2EVPgM7vpmvONKstql6oVa1xHZvZHetvU0XX4rhmHYGs9wj/8PAROwppg/NMxxJlAz/3cYhkFD6zrOrP9/hNvWS4IqUKxdJkgUk3/KfDyjJpA8e4K+g9ukxQBEM62nwKyltn+xVqrg7OXn1R6tmzTH1niG6+JzDfZ3AaHFazmz4SdE9mwi1deLOyCLAYdjmiaxoxqQPaCKxXpQuoazz/yU3rb1kqAg170n659GLlt7tPvlhwi3rbc9QRWceJRSH1RK3Z7388+UUjXZ+W1N9V0iU30LlOzqIB3twRVsxNM83u5wqka29FFUv0w6Fhn+4CqXTvYTO7wDkAkSlypXe3Sn/bVHC90P6u+Aezi/K28bcK9S6i9LEZjT5Vb0O7RMvZPE8qaXS9dL8XibW6ibvggz2U941wt2h2Or+JHdmMl+fC0z8IRG2R1ORcvVHu2zv/ZooS2oDwPv1Vr/NPuC1vrLwPuAj5UiMKcLzluN4Q/KVN8CxNut7j3/ZOneK7aG7OLxGi99lJteLpsTFkXD0kwryubPVaEJahRwaJDX9wE12WeTneoLzqtf5TQxmcFXMvXzL8fw1hFv303izDG7w7GNjD8VV2jR1eByE937Kslwl21xFJqgNgKfUkrlJlUopdzAx7GmoNek7NNreNszmOmUzdE4U7o/Rn/HQTBc+G0ecK1GLl+A+gWXA9Db5rz9fMohGe6iv+OAVd7IQZW4K5m7vong7BVgpm2tPVpogvoEcAtwWCn1qFLqEawW1a1ATY5BAfinKDyjJpAKn8lVUBbni5/ILNBtmY7LV64NmGtLrptv2wZMM21zNOV3rrzRAlkEXkS5B/C2p23bJ6rQHXW3Ym25fjewF9gFfAWYq7XeUrrwnM3a/iA7BiDdfIOJt0v9vVKrm7YQT9M4Uj2niR3cbnc4ZRc98BoAgZky1b6YgnNX4Ao00H/yMP0dB2yJoeBp5lrrTuAxrH2fngE2aK1rvt5/Q+s1gEFUv0yqxqf6Dia7/kkW6JaOYbhyU857a2xWqWma9O3LJiiZXl5MhtubKxpr1wN4odPMQ0qpnwIa+BnWzrk7Mt199aUM0Ok8TeMIzFiMmUoQ2SlrovKZpnmuxNEUSVCllF32ENn1Iul47ZTM7O84SCrShTs0Gl/LdLvDqTq5br4dz2Gmyr8mqtAW1DeAVuAKIADUZf4+CfhaaUKrHKHMl4N0850v2X2SVKTbWqA7aqLd4VQ176gJ1E1dkFkT9aLd4ZRN335rhCE4e5mssSsB34SZeMdNIx3tIbr31bJfv9AE9U7gw1rrl7XWZubPy8BHgd8vXXiVoV5djuGrI370dfo7j9odjmPkxp8mzZUvjzLIVQCooQelaLZ7b/YKmyOpTueNs9uwUWuhCcoFnB7k9TNAqHjhVCaXry63JqqWvhwuRMafyiu0YA2G10/syC4SZ47bHU7JpeNRYu27wXDJ+FMJhRZfDYaL6L5XSUW6y3rtQhPUM8AXlFK+7AtKKT/wecC+SfIOktsOvu13siYqI1vBXArElofLH6B+/hVAbUyW6DuwDdIp/JPn4a6r6aHwkvKERhGcvRzSqbKvibqYdVDXAEeUUo8ppR7DWge1mhpeB5WvbuoCWROVJ93fZ01NlQW6ZZWrEdlW/Wuiornxp+U2R1L9GpZeB0Dv1vKuiSp0HdReYAHwZazyRtuBzwILtNa7Sxde5ZA1UeeLH9ub2UF3hizQLaO66YvwNI0j2XOa2KEddodTMtb0cklQ5RKcuzKzJupQWddEFbxfudb6LPDvJYyl4jW0XsPZ391nrYnqC+MO1O7wXKw9u/+TlJ4pp+yaqK7n7qe3bT2BGa12h1QSic6jJHtO465vwjdhpt3hVL3smqieTb+lt209/gmzynLd4bZ8fwVrt9wL0lqvLlpEFSy7Jqrv4DYiO5+nceWNdodkm9gRq2Et40/l17DESlCRXS+SvvGPcfmDdodUdNF91pTnwKxlGIbsp1oODUuupWfTbwlvf5Yxb/5DDLe35Ne80Jbv4iKFll5H38Ft9Latr9kEZZpp4tkddKV4Z9l5R02gbtpCYod3Et71Ao3Lrrc7pKLLVo8IzpLuvXLxTZiJr2U6/ScPEd2zmfr5l5f8msNt+f7Fkl+9CtWrN3HaF8jtE+UbO8XukMoucaqddDyKu3EsnsaxdodTkxqWXEvs8E56t66vugSV7o8RO7wTMGT/pzIyDIPQkms58+QP6N36dFkS1MVs+f5updQrSqkupdQspdQ9SqlPlDK4SuTy+gktvBKwZrzUoli7dO/ZrX7BFbl9ovo7q2ufqL4DbZipBP5Jc3AHG+0Op6Y0LF5r7RO1bwvJ8NmSX6/QWnwfAP4T+AWQXQu1G/icUupTpQmtcuV2o9xWm2uiZIKE/ax9oqw1UdW2eDy6dzMAwbmrbI6k9rjrmwjOyewTtf2Zkl+v0BbUx4GPaK2/CqQAtNbfBf4Iazt4kcc/WeEdM4lUpCvXV15LzrWgJEHZKfug1LttQ9U8KJlmmuieTYAkKLs0LCnfmqhCE9RsYNMgr78GTCheONXh/DVRtdXNlwx3kTx7AsNbh2+8VJe2U93Uhdbi8d7qWTzef3y/Vb28caxUL7dJcM4K3PVNJE63W+sdS6jQBKWBwUZa343V1ScGCLWuA8NF5PVNpKI9dodTNvFs62nyXAyX2+Zoatv5hT6r40Epkmk91c9ZKQWIbWK4PYQWrwVK/7kqNEF9BrhXKfVtrJl/f6qU+gXwRax6fGIAT8NoArOWQTpZlr5ap8iOP/llgoQjWKWPDCKvv0yqr/L3F5XxJ2fIdvNFdj5HOhEv2XUKLXX0CFbdPT9WmaMbgBhwudb6NyWLrsKdq19VXYPUw5HxJ2fxNI61Kn2nkoR3PGd3OJck2dNJ/4n9GF4/dTMW2x1OTfO1TMM/cTbpeJSofrlk1xmuksRbgUe11mkArfUO4AMli+Tcdf8KuEZr/Y6816YB3wMuB04Cf661/m2pY7lU9XNX4QqE6D95kPiJ/WUrD2KXdCJO/Ph+wKBOtthwjIal19J3YCu9W9fTtOpmu8MZsWzrKTBzCS6P7wJHi1ILLbmO+PF99LY9bW3JUQLDtaB+DRxTSv2zUmpZSa6eJ7Ot/D8C/zTIr+8D2oAxwJ8A9ymlHP9tb3i8hBZl+2qrvxUVP7YX0kl8LdNxyfYHjhGctxpXXT39J/YR7zhodzgjlpu9N0e695wgtOgqDLeXvgPbSIa7SnKN4RLUZODvgTcBryqltimlPqGUmlSSSOBhYCbw7fwXlVLzgFXA57TW/Vrrp4HfAB8qURxFle3mC+94BjOZsDma0rJW90PdtIU2RyLyubx+QousJ9xKrbSfTsTpO7gNgOCclTZHIwDcgRBNl9+Kr2Uqhqc0dfmGK3V0EvhX4F+VUjOAO4C7gK8qpdYDPwIe0Fr3FXKhzGaHowf5lam17gDu0FofU0p9AZiY9/uFwGGtdSTvtd1YY2KO558wE9/4mfR3HCDy+su5KhPVKHZEEpRTNSy9jp7NjxLe9jvGXPe+shT6LKa+A22YyX78E2fjaRhldzgiY/S6Oxi97o6Snb/QSRIHtdZf1VovBZYCL2PN7OtQSn2/wGutAY4P8udo5hpD1WMJAdEBr0WBiinRnL/ZV7UyU8lzFSSmLrA5GjGQb8IsfC3TSPf15qZqV5KIfgmwuitF7bjoOvVa653APcBXgT1YrapC3rdBa20M8udCe1JFgIE73gWB8MXGbpfQ4qvB7aFv/1aSPaftDqck4if2YybieMdMwhNqtjscMYBhGDQsfTMAva9V1oOSmUoSff0VgLIUKBXOcTHFYpuUUu9XSj0MnAA+jVWbb3apgsvYCUxTSuUnqfmZ1yuCO9BAvXoTYNLbtsHucEoiN/40Vbr3nCq06Gpweejb/xrJnk67wylY36EdpGNhvGOn1OTuALVs2ASVl5QeAjqAf8Ta8v1qrfVCrfXdWutDpQxQa62BrcDdSim/Uupa4FbgJ6W8brHld/OZZtrmaIpPJkg4n7u+ifp5qzKFPn9ndzgFi+zeCEC9ktZTrRkyQWVaSh3AN7G62W4HJmmt/0Jr/UqZ4st6F7AAaw3Ud4EPaa23lzmGSxKY0Yq7cSzJro7cl3m1MNMpYkd2ARCQBOVo2QelnteeKnmhz2Iw0ymir1vjT9K9V3uGG/8JAR8F7tdal62YnNb6C4O8dgSo3BWGgOFy07DkWrqeu5/erU8TmF49K+H7Tx0hHY/iaRqHp2mc3eGIYQRmLcMdGk3y7AliR3YSmLbI7pCGFWvXpCLdeJpb8I2fYXc4osyGbEFpra/RWn+vnMmp2mW3P4jsepF0LHKBoyuHdO9VDutBaR1QGZMlct178y+X4rA16KJn8YmR8zaPp25GK2ayv+LrouWTCRKVpWGZNZsvsusFRz8omaaZm15eP/8Km6MRdpAEVWaN2am+W5+yOZLiME1TFuhWGO+oCdRNX2Q9KO183u5whhQ/tpdUz2ncDaPxT5pjdzjCBpKgyiyorLpo8eOVXRctK3HmGKlIN+76ZryjJ174DcIRzq2Jcu6DUkSfm71nGPJVVYvkv3qZubz+c5t9OfjLoVCxQzsAq3qEjBFUjvr5l+PyB4kf30v/yZKuFBkR00wT3v4sAPULpHuvVkmCskH26TW8/RnSyX6bo7k0fQetrcQDsj9PRckvINvjwAel2KEdpHo78TS1UDdV9harVZKgbOCfMBPfhNmkY2GimUHgSmSaafoOWsvR6mYssTkacbGykyXC251XaT+7C3Vo8Vrp3qth8l/eJo3Lzi2YrFT9HYdI9/Xibhwr408VyCogO8MqIPt66XZFvVjpRJxwZnp5qHWtzdEIO0mCskn9oqsxPD5iB7eROHvC7nBGJLs/T2BGq4w/VSDDMGhYfj0Ava89aXM050T3bMKMR/FPnINvzGS7wxE2kgRlE3ddfW7wt1InS5wbf2q1ORIxUqHMg1LfgTbHPCiFt1l1AqX1JCRB2Sg7BtDbtgEznbI5motjphLEDmfq70mCqljuQChX484J+5WlIt1E978GhovQwqvsDkfYTBKUjeqmLsQ7ehKp8Bmie1+1O5yLEj+2FzMRwzt2Cp6GwTZKFpUi1823db3tD0rhnc9DOmXVDKxvsjUWYT9JUDYyDONcK2rLEzZHc3H6DpwbfxKVzUkPStnZew2t19gah3AGSVA2a1hyLbg8RPdtqaxN5GT8qWqc96Bk42SJ+PH9xI/tweUPEpx3mW1xCOeQBGUzaxO5y8BMO2IMoBDp/hixo3vAcFFXRduG1LJQ6zpwuYnufdW2B6WezY9YsSy9DpfXb0sMwlkkQTnAuTGApypit93YkV2QTuKfMAt3Xb3d4Ygi8ISaCc5dlXlQKv+s0lRfb67Cf9PKG8t+feFMkqAcIDBzCZ6mFpLdp+g70GZ3OBeU696bKd171aRplbUnaM+rj2OmkmW9du/WpzGT/QRmLcM7elJZry2cSxKUAxiGq6ImS0T3bQEgMHOpzZGIYqqbvhjv2Cmkwmdz+zCVg2mm6dn8GACNK28q23WF80mCcoiGJdeC4SLy+iskw112hzOkRPdJEqeOYPgCUsSzyhiGQePKTCtq0yNlu27fvi0kuzrwNI0jOGdF2a4rnE8SlEN4GscQnLsS0inCbc6dLNGXmYYcnLUUw+21ORpRbA2t12D4AsSO7CrbfmXdmWTYuOJGDJe7LNcUlUESlIM0Lr8BgJ4tTzp2skR2nUxgtjzpViOXP2C15ilPK6q/8yh9+17DcHtz3dxCZEmCcpDArGV4GseS7OrILYR1knQinisQG5QEVbUaM7PowtufIdUXLum1zj77M8Ak1HoN7mBjSa8lKo8kKAcxXG4almWmnG953OZo3ih2eCdmsh/fhFl4GkbZHY4oEd/YKQRmLsVM9pd0ynn/ycNEdjwPbg+jrrq9ZNcRlUsSlMM0LL0ub7LEWbvDOU9072YAGciuAU2rfw+Arhd/RTreV5JrnH32p4BJ47Lr8TSNK8k1RGWTBOUw+ZMlereutzucHNM0c+NPwTkrbY5GlFpg9gr8UxTpaA/dLz9Y9PPHTxwgsnsjhttL85p3Fv38ojpIgnKgxuVvAaD3tSccM1ki0XmUZFcHrmAj/omz7Q5HlJhhGIy+9n0AdG38NalId1HPf/aZ+wBoWHkjnsYxRT23qB6SoBwoMGspnqZxJLtO0rd/q93hABDdl51evkymAteIwLSFBGavwOyPcfaFXxTtvLGjrxPdswnD66f5ituKdl5RfSRBOZDhctOQnXL+6mM2R2OR7r3aNPraOwGDns2Pkug+ecnnM5MJTj38TQAaV92MJ9R8yecU1UsSlEM1LH2zVV16z2bbt+FIRXuJHdoBLjeBWVLeqJb4x88gtPhqSCU5u+F/Lvl8Z5/9GYlTh/GMmsCoq36/CBGKaiYJyqE8oWbq1ZvATNNjc32+iH4JzDSBGUtwBxpsjUWU36i17wG3h/D2Z3IVx0cidvR1ul78FWDQcsuf4/LVFS9IUZUkQTlYdsFk72tPlr26dL7IrhcACC1cY1sMwj7eURMYe8MfAXDqt9+kv/PoRZ8jnYhz6jf/BmaapsvfLnUcRUEkQTlY3bRFeMdMJhU+S3TPJltiSEW6reoRLg/BeattiUHYr2HFjdQvvBKzP0bHA/eQTsQLfq9ppul87LskzhzDO3YKo655bwkjFdVEEpSDGYZB4wprynnPq/ZUlsh1781sxR0I2RKDsJ9hGIx760fwjp5E4tRhTj/6HUzTvOD7zHSKUw/9B71bn8Zwe62uPY+vDBGLaiAJyuFCreswPD76DmwlceZY2a+f695bIN17tc7lDzD+XZ/A8PgIt22g42dfJRXtHfJ4M5Xg5K/+mXDbBgyvnwnv+Qz+SXPKGLGodJKgHM4dCFG/8Cqg/K2oVKSbvkM7pHtP5PhapjP+XZ/EVVdPdO9m2r/7cWJHdp93jGmmie7bwvGffJnIrhcx/EEm3vE5AjOX2BS1qFQeuwPIUkp9DPgYMAbQwMe11s9mfjcN+B5wOXAS+HOt9W/tirXcmlbdRLjtaXq3Ps2oa+7A5fWX5bqR3RvBTBOcvVy690ROcM4KJv/xPZz85T8TP/o6x370WbyjJ+AdOwVP4ziiezeR7LLWTLkCISbe8TmpPiJGxBEtKKXUO4G/Ad4GjAK+CTyklMpWkLwPaMNKXn8C3KeUmmVHrHbwT5yNf9Jc0rHIJU3zvVjhTPdevXTviQG8TS1MuuvLNF3xDnC5SZw5TvT1V+jZ9FuSXSfxNI1j1Lo/YOqH/1WSkxgxp7SgJgJ/r7Xemfn5+0qpfwJalVLtwCrgBq11P/C0Uuo3wIeAv7Mn3PJrXHkTp47toWfzozQsvQ7DMEp6vWTvWWKHd4LbQ3DeZSW9lqhMhtvDmOvuYvTa95I4c4z+0+0kzhzHP34mgdlSEktcurIlKKWUDxg9yK9MrfV/DDh2LRACdgBXAIe11pG8Q3YDNTUoUr9wDZ1P/oD+E/uJH9tD3eR5Jb1e79anrO69OZfhrqsv6bVEZTM8Xnwt0/G1TLc7FFFlytnFtwY4Psif81b9KaUWAz8FPqu17sBKVNEB54oCwVIH7CQujy+3JXapt+I20yl6M9UrstPchRCi3MrWgtJabwCG7ZdSSr0N+DHwD1rrr2dejgCBAYcGgdLuRe1AjSveQveLvya86wXGXP8B3PVNJblOdN8Wkj2n8TSPl5lXQgjbOGKSBORm8f0P8Mda66/l/WonME0plZ+k5mderyne5vHWbrapJD2vlW4r7t7MdPbGFW/BMBzzERFC1BhHfPsopd4N/D1wvdb6gfzfaa01sBW4WynlV0pdC9wK/KT8kdqvcdXNAPRsfgQzlSj6+RPdJ62tNdweGpZcW/TzCyFEoZwyi+9TgB94SimV//p7tdYPAe8CvoO1Buo08CGt9fayR+kAgVnL8I6dQuJ0O+FdL9KweG1Rz9+75UnAJDT/ipJ1IQohRCEckaC01isu8PsjwM1lCsfRDMOg6U23cPrhb9K98TeEFl1dtCnnZipBb6brsEEmRwghbOaILj5xcUKL1+Kub6K/4wCxwzuKdt7I7pdIRbrwjp1C3dQFRTuvEEKMhCSoCuTy+GhceRMA3Rt/U5RzmukUZ5/9KQBNq95a8oXAQghxIZKgKlTjihsx3F6iezePaAO5gXq3Pk2i8xieURNy662EEMJOkqAqlLu+iVDrNQB0v/TQJZ0rnYhz9pmfATD6mjsw3I4YmhRC1DhJUBWs6U23AAa9W5+m/9ThEZ+nZ9MjpMJn8I2fSb1s6y6EcAhJUBXMN3YKDStugHSSU7/9NqaZvuhzpPrCdL3wCwBGX/c+WZgrhHAM+TaqcKOvfR/u+mbi7btzU8Qvxtnn7icdi1A3o5XAzKUliFAIIUZGElSFc9fVM+YtHwTgzNM/Jhk+W/B7w7tepOflh8BwMeba98nMPSGEo0iCqgL1C9YQmL2cdCxC5xPfL+g98Y6DnHrw3wAY/ea78E+aU8oQhRDiokmCqgKGYTD2pj/B8PiI7Hyezqd+OOx4VCraS8fPv4aZiBNqvYam1beUMVohhCiMJKgq4W0ez7i3/Rm43HRv/A0nf/kN0sn+NxzXf+oIJ3729yS7TuKfOJuxN39YuvaEEI4kC16qSGjR1biCjXQ8cA+RXS+S7D1D44q34B09CXewka4Xf03va0+CmcYdGsX42/8Wl9dvd9hCCDEoSVBVJjhzKZP/8Cscv+8rxNs1p9r1+QcYLhpX3Miote+RauVCCEeTBFWFfC3TmfxHX6dny+MkTreT6DxGsvskddMWMvq6u/CNnWJ3iEIIcUGSoKqUp2EUo9e+x+4whBBixGSShBBCCEeSBCWEEMKRJEEJIYRwJElQQgghHEkSlBBCCEeSBCWEEMKRJEEJIYRwpFpYB+UGOHHihN1xCCGEGCDvu9k98He1kKAmAtx55512xyGEEGJoE4F9+S/UQoJ6BbgaOA6kbI5FCCHE+dxYyemVgb8wTNMsfzhCCCHEBcgkCSGEEI4kCUoIIYQjSYISQgjhSJKghBBCOJIkKCGEEI4kCUoIIYQjSYISQgjhSJKghBBCOFItVJIoiFJqKfAtYAmwH/ig1voNK5uVUtOA7wGXAyeBP9da/7ZMMd4A/AMwN3Ptf9Raf3uQ464DngD68l7+mtb6y2WI8YPAt4F43ssf1Vr/cMBxttxHpdSdmfjyBYCntNZvGXBs2e+jUmo18JDWuiXzsw/4d+B2rEoo39Baf3WI9xrAl4H/BfiA7wOf1FonSxxjC/AvwJsBA3gE+JjW+uwQ7z8MjAGyVQKOaq1ViWP0A71Af95hLwz8b5451q77GB5wiAfwA5O11scGeX/J7uNQ3zXl/jxKgiL3JfBr4F5gLfAu4HGl1HStdc+Aw+8DXgR+D7gK+JVSapnWen+JY5wKPAC8PxPrSuAxpdRBrfVjAw5fAdyvtX5vKWMawgrgn7TWn7rAcbbcR631fwP/nf1ZKbUceBz45CCHl+0+Zv5n/hBwz4BffRFQwGygCXhUKXVUa/2jQU7zv4B3YsUdB34JfAb4Uolj/C7QDcwEvMCPgf8A/mCQc4wFJgONWutIMeIqMMZW4IzWekIBp7HlPmqtQ3nHeID1wIYhklPJ7uNw3zXAOsr4eZQuPss6wKu1vldrndBa3wfsAN6Tf5BSah6wCvic1rpfa/008BusD1upzQB+orX+pdY6nWndbQCuHOTYlcBrZYhpMBe8ts33MT8OL1ay+oLWeusgh5TzPn4R+AjwlQGvvx+4W2t9Vmt9EOtL7cNDnOP9wL1a63at9SngC8McW5QYlVIuIA18UWsd0Vp3Af+F9dAxmJXAnlIkp6FizLtuof8ty34fB/G3WMn+80P8vpT3cQZDf9eU9fMoLSjLQmDXgNd2Yz11DTzu8IAPxW5gdQljA0Br/SzwbPZnpdRorCK4Px7k8BXAOKXUR7C6XH4KfFZrHR/k2KJRSrmxukjvUkp9A4hiPV1/TWudX/TRtvs4wEexuu/+c4jfl/M+fktr/Tml1LrsC0qpZqwimjvzjhvsc5m1cJBjJymlRmutz5QiRq11GnjHgOPeAWwZ4hwrAJdS6mWsFterwF9qrQf+/1e0GPOu26KUagPGA89krnt0kHOU/T7mU0pNwmppXJm5v4Mp2X28wHdNWT+P0oKyhLC+TPNFgeAIjysppVQTVovjJawmeP7vPEA7VnN6AXAdcD1WX3CpjQM2AT/E+p/mdqwnxY8MOM72+5jp1v0kVuvpDRWTy30fB+vGwbpPcP69Gu4+Dbyv2b8X5b4OEeN5lFKfwEpQfzvEISngZayun+lYiewRpVSpY4wAz2ONkymsB5NfDnGs3ffxr4BHtdbDtfhKeh+zBnzXbM68XLbPo7SgLBGsgfJ8QWDgoGWhx5VMpnvs11hPJncOfMLKDEC+Oe+lvUqpu4GvAX9Tyti01ieAa/Jeek0p9W9YY3r5rRTb7yNwE1bX1MOD/dLO+5gn28LMv1fD3aeB9zX7RVDy+5rpLv034BbgOq317sGO01p/fcD7Pg38GVaX1bODvacYtNZ/PeC6fw2cUkpN1VofGXC4nffRjdU1NuwGduW4jwO/azh3T8r2eZQWlGUn1lNVvvmc3zzNHjdNKRW4wHEloZRai/Uk8yvgdq11bJBjJiulWc9tyQAABWBJREFU7sm0ELJ8wBuOLUF8i5RSXxzw8mDXtvU+ZtwK/GyoLhQ772NWZhbcCc7/bA53nwZ+jucDxzPjQiWjlGrAmu14GbB6uCd/pdRfKqXyx6fcWA/KJb2vSqkvKaUW5L2U/e862HVtuY8ZazL/fGq4g0p9Hwf7rrHj8ygtKMt6wFBK/RXWFMp3YY2lnNcFoLXWSqmtwN2ZJ5Y1WF90V5Q6QKXUbOAh4O+01v82zKGdWE87UaXUl7C62j4L/N9Sxwh0AR9XSrVjTSFfDvwF8L/zD7LzPua5HPg/w/zezvuY78fA5zNjJyHgE1hTuoc69hNKqaewnl6/wOBjlMV2H9bD7tVa64FdtwPNwBqjfBvW5+VrwB6sMZRSWgKsUkplZxb+C/BwZvB+ILvuI1ify43DjD1lzaBE9/EC3zVl/TxKCwrQWvcDN2MlpjPA3wHv0FqfUkrdOWB9wruwxiROYk0A+JDWensZwvwo0AB8VSkVzvvztfwYM62qm7Gmy3diDQbfD3yj1AFmBpzfjjVTpwdrquqXtdY/d9B9zJoBnDcW4JT7OMDngO1Ys0pfwbqn38qLOZxZ20Xm9fuBF7C+rHZm3l8ySqklwFuxJriczPtctg8R46eAjVhjJieBWcAtWutS73b9IeAssBc4iLUe6q4hYiz7fcwzgwGfy6wy3schv2so8+dRdtQVQgjhSNKCEkII4UiSoIQQQjiSJCghhBCOJAlKCCGEI0mCEkII4UiSoIQQQjiSLNQVokiUUj/AKlMzlC9iVYVeDzRorctS2ilTPud54A+11q8Pc5wLa23NXVprXY7YhBiOtKCEKJ6PYVV7noi1hQtYC1izr92DtWhxIufq7JXDXwBbh0tOkKtM/iXyFl4KYSdZqCtECSilFgPbgJmZfXPsiqMOOIxVwLWgSh1KqX1YlT02lDI2IS5EuviEKKPMHkC5Lj6llAncAXwaq7DmJuB9WFuB3IVVMurTWusfZ97fAPwT1lYmJvA01vbqQ23h8F6gKz85KaX+D9Zup+Ow9kH7jNb6kbz3/BKrNbihCP/KQoyYdPEJYb9/AP4Sq1DoNKyCnz1Y1cF/AXxbKZXdG+o7WInsRqytTUys7biHetj8PeDR7A9Kqdsy13ofVnXph4H7lVKNee95FLh+mHMKURaSoISw339orddntql4CGu/nM9kJip8A2tPnZlKqVlYLaI/0Fq/kmkV3YVVYPSmIc69CquwZ9YMIA4cynQ9fglr07tE3jE7sSpVzy/Kv50QIyRPSELYb2/e36PAwbxdfrP7+/ixdk4F0Eqdt31ZEKtV9dAg5x4PnM77+f9hzTTcr5TajLVb6ve11n15x3Rm/tlykf8eQhSVtKCEsF9iwM9D7QXkyRy7HFiW92ce8P0h3pMGjOwPmf2PVmK1uF4APgC0ZSZ1ZGW/F0q9BYYQw5IEJUTl2AV4gXqt9V6t9V7gOPCPWElqMCewJkMAoJR6J/BhrfXjWuuPYbW8erH2dMoal/deIWwjXXxCVIjMTsS/AX6klPoocAq4G2tyxe4h3rYZWJr3sxv4R6VUB9aMwcuBCZm/Zy3l3OZ+QthGWlBCVJb3YyWTX2HtaNoE3KC17hri+IexZvsBoLW+H/g8VqvrdeArwP/WWj+d9561wKNl2OVWiGHJQl0hqphSKoi1xflNWutXCzjeBRzCmin4bInDE2JY0oISoopprf9/u3ZswyAMAFH0tqFjFdZAyggMkQ0zBjUFqdxAhU7ovd6Su6+zvOdcS5+bR5YkP3GigUDB+32TzNPwN330X09bkvWRW8EFT3wAVLKgAKgkUABUEigAKgkUAJUECoBKBwCM+k7ps1wSAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_velocity(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Although we compute acceleration inside the slope function, we don't get acceleration as a result from `run_ode_solver`.\n",
-    "\n",
-    "We can approximate it by computing the numerical derivative of `ys`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "a = gradient(results.v)\n",
-    "plot(a)\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Acceleration (m/$s^2$)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can compute the maximum acceleration the jumper experiences:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "16.64087716292061 meter/second<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$16.64087716292061\\ \\frac{\\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "16.64087716292061 <Unit('meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max_acceleration = max(a) * m/s**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Relative to the acceleration of gravity, the jumper \"pulls\" about \"1.7 g's\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "1.6980486900939398 dimensionless"
-      ],
-      "text/latex": [
-       "$1.6980486900939398\\ dimensionless$"
-      ],
-      "text/plain": [
-       "1.6980486900939398 <Unit('dimensionless')>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max_acceleration / system.g"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "The gradient function in `modsim.py` adapts the NumPy function of the same name so it works with `Series` objects.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "def gradient(series, **options):\n",
-      "    \"\"\"Computes the numerical derivative of a series.\n",
-      "\n",
-      "    If the elements of series have units, they are dropped.\n",
-      "\n",
-      "    series: Series object\n",
-      "    options: any legal options to np.gradient\n",
-      "\n",
-      "    returns: Series, same subclass as series\n",
-      "    \"\"\"\n",
-      "    x = magnitudes(series.index)\n",
-      "    y = magnitudes(series.values)\n",
-      "    # units = get_units(series.values[0])\n",
-      "\n",
-      "    a = np.gradient(y, x, **options)\n",
-      "    return series.__class__(a, series.index)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "source_code(gradient)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Solving for length\n",
-    "\n",
-    "Assuming that `k` is fixed, let's find the length `L` that makes the minimum altitude of the jumper exactly 0."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The metric we are interested in is the lowest point of the first oscillation.  For both efficiency and accuracy, it is better to stop the simulation when we reach this point, rather than run past it and the compute the minimum.\n",
-    "\n",
-    "Here's an event function that stops the simulation when velocity is 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Return velocity.\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    return v"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As usual, we should test it with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.0 meter/second"
-      ],
-      "text/latex": [
-       "$0.0\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "0.0 <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "event_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can test it and confirm that it stops at the bottom of the jump."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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8REqZCkrkFP2lUz0eu74jYSFefpi1jmc/nENObr7bsUQqjGLfB2WMScBZTy8eZ0uMTGCetVZ7Z0ul1aF5bZ7qn8aQd2YyY1EGT749k0ev70hURKjb0UTKvRMWlDEmBLgauBtoA+TgrPzgw1lbD2PMLOA14F/W2oIyTSsShFo0iGPYbWcw+M0ZLFy5nUdfn8YTN3ehWnS429FEyrXjnuIzxpwJLAR6A+8ATYAoa22StTYBCAPaAp8AtwPLjDE9yjyxSBBKTarGs7d3o3ZcFCs37uGhUVO0GrrIn3SiEdR9wBXW2kXH+qS1thBn9YffgNeMMW2Bp4BJpR1SpDxIrFmFZ2/vxuA3Z7A2Yy8PvjKFp/p1oW7tqm5HEymXjjuCstb2Ol45Hef1v1pr/146sUTKpxpVIxh22xk0T63Bjj3ZPPzqVOy6nW7HEimXSjJJIgpIBf5wYt1aO680Q4mUZ9GRoTzZrwvPfphO+tItPDZ6OgP7dqStiXc7mki5Uqxp5saYa4GtONek0o96zCmzdCLlVERYCI9e35EepyeTnZPPU+/MZOqCTW7HEilXinsf1DCciRINcLZnL/pIKptoIuVbiM/LPVe2o1f3BuTlF/LcmHTGT1/jdiyRcqO4p/iqAqOstevKMoxIReP1eripV0uqVQlnzPilvDZuIXuzcri8ZxM8Ho/b8USCWnFHUGOAvmWYQ6TC8ng8XH5OE269tA0eD3w0fhnvfa31+0ROprgjqOeBecaYa4C1wO9uyLXWnl3KuUQqnPO71CcmKpQRH83li0kryS8o4KZeLTWSEjmO4hbUGGA/8A2guw9FTtEZbeoQ4vPy7Idz+O8vqynIL6Tfxa1UUiLHUNyC6gB0stYuLMswIpVB55aJPNK3I8Pen8PX09aQX1jIgItb4/WqpESKKu41KAvElmUQkcqkY/PaPHZDR0JDvIyfvpZXP1tAgbbrEPmd4o6ghgHvG2NGAauA3KKftNZ+W9rBRCq605smMOiGTgx9dxY/zFpHfkEBd1zeFp9GUiJA8QvqU/+fI47xuUKc1c1FpITamngev6kzQ96dxYQ5GygoKOSuK9uppEQoZkFZa7WxoUgZadO4FoNv6sxTb89k4tyNFBTAPVe1xefTf3ZSuZ1su40SMcZournIKWjVsCZP3NyFyHAfk3/dyIiP55KXr+3VpHI70T/R7jHGjDfGnG+MOe72oMaYEGPMRcaYn3A2NhSRU9CiQRxP9UsjKiKEqQs28/xH6SopqdSOe4rPWnuRMeZiYDhQzxgzCVgMbAc8QC2cXXa7AOuBIdbaz8o8sUgF1rR+DYb0T+PxN6YzfWEGz344hwev60BoiE73SeVzwr/11tovrLVtgIuA5ThldBNwPdAeZ3XzXtbaNionkdLRpG51hg7oSnRkKDN/y2T4B3PIzct3O5ZIwBV3ksQktFOuSMA0Soll6IA0Br0xg9lLMnn6vdkM7NuRsFBNmJXKQ+cNRIJUw+RYnr4ljapVwpi7bCvPvD9bIympVFRQIkEsNakaz9zS9UhJDf8gndw8TZyQykEFJRLk6iVWZeiANGKiQpm9JFOz+6TSUEGJlAOpSdV4qn8aVSJDmbEogxc+mUe+SkoquOIudYQxJh5oDYTiTDM/QmvxiZS9RsmxPNWvC4PemM6U+ZvweT3cfZWWRZKKq1gFZYy5EXgNp5yOprX4RAKkSd3qPHFTFwa/NZ1J8zbi83m48/K22qpDKqTijqAeAN4CHrHW7ivDPCJyEs1Sa/D4jZ154u2ZTJizgRCfl1v/r41KSiqc4l6DSgFeVjmJBIeWDWsy6IZOhIV4+X7mOt74YiGFhdpPSiqW4hbUD0DPsgwiIiXTpnEtHr2hE6EhXr6dvpa3v/pNJSUVSnFP8S0AXjDG9MJZ8iin6CettQ+WdjARObl2Jp6BfTvy9Huz+O+U1fh8Xq7/W3M8Hp3uk/KvuCOoM4FZQCTOArEdijzal000ESmO9s0SeLh3B3xeD19MWsmY8Us1kpIKobhr8Z1V1kFE5NR1apnIA9e157kx6YydsIJQn5erzmvqdiyRP6Uk90ElALcDLXBGXkuBt6y1q8som4iUQNfWSdx3dTtGfjyXT36w+HxeLj+niduxRE5ZsU7xGWM64lx7uhhnP6htwN+AhcYYneITCRLd2yZz91Xt8HhgzPilfDFppduRRE5ZcUdQI4FPgVustUdObhtjRgHPAzoFKBIkzjo9hfz8Al7+93ze/d9iwkJ9XNg11e1YIiVW3EkS7YEXi5aT3ys4EyVEJIic07EeAy5pDcDozxfy46x1LicSKbniFlQGUP8YxxsAunlXJAhd2DWVG3u1AOCVsfOZPG+jy4lESqa4p/jGAG8aY+4GZvqPdQFe9H9ORILQRWc24lBuPh+NX8YLn84jNMRLWuskt2OJFEtxR1BP46wm8R9gI7AJ55rUWODRsokmIqXhinMMl/VsTEFBIc9/lE760i1uRxIpluLeB5UD3GyMuR8wwEFgpbX2YEm/oTEmFlgIPG6tfd8YEwaMAi4F8oEXrLXDSvp1ReT4rju/GTm5BXz1yyqeeX82g2/sTJsmtdyOJXJCxx1BGWMuMMaEFvn4AqArUBNn8dizihwvidFAnSLPn8QpvYY4Ey76GGN6l/BrisgJeDwebuzVgvO71Cc3r4Ah781i8eodbscSOaETjaC+BmoDW/0fH0+x94MyxvQBqgKLihzuA/S11u4CdhljRgD9gQ+L8zVFpHg8Hg8DLmnNodx8fk7fwJNvz2TogDSa1K3udjSRYzpuQVlrvcf6+FQZY1KBwUAa8J3/WCyQCCwp8tJlQKs/+/1E5I+8Xg93XtGW3LwCpszfxONvzuCZW7rSoE41t6OJ/EFxV5L42V8mRx+vZYyZW4z3+4CPgPuttZlFPhXt/zOryLEsIKo4uUSk5HxeD/de3Y5OLWpz4GAug96YzvrMvW7HEvmD446gjDE9gOb+p2cC/Y0xR9/z1Azn2tHJDAKstfbzo44f8P8ZWeRYFLC/GF9TRE5RiM/LQ73bM/S92cxbtpVBb0xn2G1nkFQz+uRvFgmQE12D2gHcD3j8j9twZtkdVohTJPcV4/tcCSQZYy7xP48BXgM6Apk4kyQ2+T/XlN+f8hORMhAa4mNg34489fZMFq7czqOvT+fZ284gvoZOYEhwONE1qEU4K0VgjJkIXOKfyFBi1trfrftvjJkPvOSfZr4fGGyMWYhzyu9+4OVT+T4iUjLhoT4eu6ETg9+cwdK1O3l09DSG33YGcdUiT/5mkTJ2omnmRf8ZdSFwyBgTdazHn8zwOPAbsBiYA4zDmYouIgEQGR7C4Js60ygllswdWTz6+jR27c12O5bICU/x7TPGJFprt+KcyjvWFp0eSjDN/DBr7WlFPs7GOX14W0m+hoiUniqRoTzVrwuPvj6NNZv38tgb03nmlq5Uiw53O5pUYieaxXc2sNP/8Vn+50c/Dh8XkXIuJiqMIf3TSEmIYX3mPh5/Ywb7s3LcjiWV2ImuQU0+1scA/uWJWgPLrbWanypSQVSLDmfogDQeeXUqqzfv4fE3ZzB0QBpREaFuR5NKqLj3QTUyxkw2xnT2X3Oa7X+sM8Z0LtOEIhJQNapG8PQtXUmoEcWKDbt54q2ZHDyU53YsqYSKu0LEKzj7Pq0FrgOScaaGvw68UCbJRMQ1NWMjefqWrtSMjWTp2p0MeWcW2TkqKQms4hZUN+Ae/yoQFwHfWGtXAG8Bp53wnSJSLiXUiOLpAWnUqBrOolXbeea92eTk5p/8jSKlpLgFlQ2EGmOq4KwqMd5/vDawpyyCiYj7kmpFM3RAV2Kjw/l1+TaGfziH3LwCt2NJJVHcgvoeZ7Q0DmetvP8ZY3r6j/23jLKJSBBISYhhyIA0YqJCmbNkC89/lE5+vkpKyl5xC6o/kI4zkrrQWnsAZ++mScDdZRNNRIJF/cSqPNU/jSoRIcxYlMELn84jv+BYt0aKlJ7i7qi7H7gLwBhT1RgTa60dXqbJRCSoNEqO5cl+XRj0xnR++XUToSFe7ry8LV6vx+1oUkEVe58nY8wtxpgNwC5ghzEmwxjzcNlFE5FgY+rVYPBNXQgP8zFhzgZe/3whhYUaSUnZKO59UPcDw3Gmm3cDugMvAg8aY+4qu3giEmxaNIhj0A2dCAvx8t2Mtbz11W8qKSkTxTrFh7NO3gBr7adFjk0zxqwDhqLVx0UqlTaNazHw+o4MfXc2/5uyGp/Xww1/b4HHo9N9UnqKe4qvFs5K40ebi3PTrohUMqc3TeCRPh0I8Xn4cvIq3vt6iUZSUqqKW1C/AZcd4/gVwLLSiyMi5UnHFrV5qLdTUl9MWsn7KikpRcU9xfc48I0xpgsww3+sC/BX4JLjvktEKrzOLRN5qHcHhn8wh88nrcTjgT4XNtfpPvnTijWCstb+APQEDuGsxXcpsBfoYK39uuziiUh5cLikfF4P4yau5INvNJKSP6+4Iyistb8Av5RhFhEpx7q0SuSh3u159sN0xk1cicfjofcFzTSSklN23ILyb6vxEs5o6RDwBfCw9n8SkePp0iqJB69rz3Nj0vns5xV4PHDd+SopOTUnOsX3JPB34DmcLTUuxFl7T0TkuNJaJ/HAde3xej2MnbCCMeOX6nSfnJITFdSlwNXW2uHW2udxZvH9wxijrTVF5IS6tnZGUodL6qPvlqmkpMROVFDJ/H4K+Rz/6xPKNJGIVAhdWyfx4LVOSf3np+V8rJKSEjpRQfmAI7uTWWsLca5FhZV1KBGpGLq2SeKBa0/H6/Xw75+W8/H3KikpvmIvFisicirOaFOH+6/xl9SPy/nke+t2JCknTjbNvK8xZv9Rr7/WGLO96Iusta+VejIRqTC6nVYHCmHEJ3P5148WjweuPq+p27EkyJ2ooNYDtxx1LBO4/qhjhYAKSkROqFvbOgCM+DidT3+w5OUXaAq6nNBxC8paWz+AOUSkEjhSUp/MZeyEFWTn5HNTr5ba9FCOSdegRCSgurWt418F3cv/pqxm1Nj52j5ejkkFJSIB17llIoNu7ERYqI8fZ6/nhU/mkpdf4HYsCTIqKBFxRTsTz1P9uhAZHsIvv25i+AdzyM3LP/kbpdJQQYmIa1o0iGPogDSiI0OZtTiTIe/MIjsnz+1YEiRUUCLiqiZ1q/PMrV2JjQ7n1+XbeOKtmWRl57odS4KACkpEXJeaVI1nbu1KXLUIFq/ewWOjp7MvK8ftWOIyFZSIBIWUhBiG33YGCTWiWLFhNwNfm8aufdluxxIXqaBEJGjUjqvC8NvOoE6taNZm7OWRV6exffdBt2OJS1RQIhJUasZGMuy2rtRPrMqmbft56NWpZO444HYscYEKSkSCTvWYCJ65tSuNU2LZujOLh1+dyoYt+9yOJQGmghKRoBQTFcbQAWm0aBDHjj3ZPPLaVFZs2OV2LAkgFZSIBK2oiFCeuLkzbZvUYs/+HAa+No30pVvcjiUBooISkaAWERbCoBs70+P0ZLJz8hny7ix+mr3O7VgSACooEQl6oSFe7r2qHZee3ZiCgkJe/vd8Pv3BanfeCk4FJSLlgsfjoc+FzRlwSWs8Hvjk+2W8+tkC8rXIbIWlghKRcuXCrqk80qcjYSFevp+5jqHvzSb7kNbvq4hUUCJS7nRplcjQAV2JiQolfekWBr4+jd37DrkdS0qZCkpEyqVmqTV47o5uxPuXRnrwlSls3r7f7VhSilRQIlJuJcfHMOKObjSoU42MHQd48JUpLF+ve6UqioAWlDHmXGPMXGPMXmPMSmNMf//xMGPMm8aYncaYbcaYRwKZS0TKr+pVIxh2a1famXjnXqnXpzF7SabbsaQUBKygjDEpwDhgKBALXAUMM8acBzwJGKAh0AHoY4zpHahsIlK+RUWEMujGTpzdPoVDOfk8/e4svp+51u1Y8icFcgRVH/jEWvuFtbbAWjsHmAR0BfoAT1trd1lr1wIjgP4BzCYi5VyIz8vdV7blinOaUFAIo8Yu4KPvllJQoHulyquQQH0ja+0UYMrh58aYGkA3YAyQCCwp8vJlQKtAZRORisHj8XDt+c2Ii41k9LgF/PvH5azP3MfdV7YlKiLU7XhSQq5MkjDGVAP+C8wC5voPZxV5SdUDqa8AAA4rSURBVBYQFehcIlIxnN+lPo/d0ImoiBBmLMrgAc3wK5cCXlDGmCbATGALcClweA39yCIviwL0t0lETlmH5rUZeVd3kuOjWZ+5j3tf+oW5y7TQbHkS6Fl83XFGTV8Cl1prs621u4BMnEkShzXl96f8RERKLDk+hpF3dadTi9ocOJjLk2/PZOyE5VrDr5wI5Cy+hsDXwOPW2kestUX/howBBhtjahpj6gP3+4+JiPwpURGhDOzbkav/YigshA+/XcpzY9K1PFI5ELBJEsBtQAzO1PJhRY6/CjwOjAQW45Tmm8DoAGYTkQrM6/Vw1XlNaVCnGiM/mcfUBZvZuHU/j17fkdpxVdyOJ8fhKe9DXf+Ia82ECRNITk52O46IBLkNW/bx9Huz2LTtANGRoTx4XXvamni3Y1VaGzdupGfPngCp/tuMjtBSRyJSqaQkxDDyrjPp0DyB/QdzeeKtGXw+caWuSwUhFZSIVDpVIkN57PpOXHGuc1Pve18vZsTHc8nO0XWpYKKCEpFKyev1cO1fmzGwbwciw3388usmHnplKlt2Zp38zRIQKigRqdS6tEpixJ3dSapZhdWb93DPi5OYtnCz27EEFZSICHVrV2Xk3c51qX1ZuQz/YA4v/WseWdm5bker1FRQIiJAdGQog27oxIBLWhMW4mXCnA3cOXISS9bscDtapaWCEhHx83g8XNg1lZfu7UHD5Gps2ZnFI69OZcz4peTlF7gdr9JRQYmIHCUlIYbn7+jOZT0bUwj856flPPDKFDZu3XfS90rpUUGJiBxDaIiX3hc0Z9itZxBfPZKVG3Zz1wuT+Xb6Gt0zFSAqKBGRE2jRII5/3ncWZ7dPISc3n9fHLeSpd2axa1+229EqPBWUiMhJVIkM5Z6r2vFQ7/ZER4aSvnQLd4yYyKzfMtyOVqGpoEREiumMNnUY9cBZnNa4Fnv25zD0vdmMGjufg1oZvUyooERESiCuWiRP9uvCzf9oSWiIl+9nruPOkROZvThT16ZKmQpKRKSEvF4Pvbo35MW7zyQ1qSqZO7IY8u4snnh7Jpu2aTPw0qKCEhE5RfUSq/Li3Wdy80UtqRIRwrxlW7n9+Z95/+vFWoWiFKigRET+BJ/PS69uDRn98Dn8pVM98gsKGTdxJbc8O4GJczfotN+foIISESkFsTHh3HH5aYy4szumbnV27j3EC5/M46FRU1m5cbfb8colFZSISClqUrc6z93RjbuvbEtsTDhL1+7k3pcm8+pnC9iz/5Db8coVFZSISCnzej307FCX0Q/15KIzG+L1ePhuxloGDJ/AN1NXk691/YpFBSUiUkaqRIZyY6+WvHL/WZzWpBb7D+Yy+otF3P3iZBas2KbrUyehghIRKWMpCTE81a8LA/t2JL5GFGsz9vLY6Ok88M8pzFi0mYICFdWxhLgdQESkMvB4PHRplUi7pvF8OXklX01ejV2/i2fen0OdWlW4uEdjzm6fTGiIz+2oQUMjKBGRAAoP9XHFOYZ3HzuXfhe1Ir56JJu2HWDU2Pnc9PSPjPt5BQcO6h4q0AhKRMQVEeEh/L1bA85Pq8/U+ZsYN3ElazP28v43S/jPhOWc36U+vbo3pEbVCLejukYFJSLiohCflx6np3Bmu2Tm2a2M+3kli1ZtZ9zElXz1y2rObp/CJWc1ok6taLejBpwKSkQkCHg8Hk5vmsDpTROw63YybuJKZv6WwQ+z1vHj7HV0bplIr24NaJ4ah9frcTtuQKigRESCjKlXg4F9O7Jx6z6+mLSKn9M3MGNRBjMWZVCjajhprZLo2iaJZqlx+CpwWamgRESCVHJ8DHdcfhpXn2f4ZtoaJs/byNZdB/l62hq+nraG6jHhpLV2yqp5BSwrFZSISJCLqxZJ7wuac935zVixYTfTFmxm6sLNbN2ZxTfT1vDNtDXExoST1iqRM9rUoXmDilFWKigRkXLC4/HQpG51mtStTt+/NWflRn9ZLdjMlp1ZfDt9Ld9OX0tsdDhdWiXStU0SLRvE4fOVzzuKVFAiIuWQx+OhcUp1GqdUp8+FzVm1aQ/TFmxm2oLNZOw4wPgZaxk/Yy1hoT5SE6vSoE41GiZXo0GdatSrXZWw0OC/IVgFJSJSznk8Hholx9IoOZbeFzRj9aY9TFu4mekLM9i0bT92/S7s+l1HXu/zekhJiDlSWg3rxJKaVJWoiFAXf4o/UkGJiFQgHo+HhsmxNEyOpfcFzdmflcPqzXtYtXEPqzftYdWm3Wzaup+1GXtZm7GXn9M3HHlvUs0qNKhTjZqxkUSFhxAZEUJk+B8fURGhzscRIYSX4UhMBSUiUoFFR4XRulEtWjeqdeRY9qE81mbsZdWmPazauJvVm/ewLmMfm7cfYPP2AyX6+ud3qc+tl7Yp7diACkpEpNKJCA+haf0aNK1f48ix3LwCNmzZx+pNe9h74BBZh/I4mJ3HwUN5zsdFnh9+ZOfkExJSdhMwVFAiIkJoiJcGdZxJFMGifM49FBGRCk8FJSIiQUkFJSIiQUkFJSIiQUkFJSIiQUkFJSIiQUkFJSIiQaki3AflA8jMzHQ7h4iIlFCR391/WDOpIhRUIsA111zjdg4RETl1icCqogcqQkHNAboBGUC+y1lERKRkfDjlNOfoT3gKCwsDH0dEROQkNElCRESCkgpKRESCkgpKRESCkgpKRESCkgpKRESCkgpKRESCkgpKRESCkgpKRESCUkVYSeJPMca0AUYDrYHVwA3W2j/c0VwZGGM6Al9ba+PdzhJIxphzgeFAY2Ar8Ly19g13UwWWMeZvwDNAKs7/Bs9Vtv8NDjPGxAILgcette+7HCegjDE3AG8Ah4ocvs1a+4EbeSr1CMoYEwZ8BfwbiAWeBn4wxlR1NViAGWM8xpibgB+AMLfzBJIxJgUYBwzF+TtwFTDMGHOeq8ECyBiTCHwGPGStjQEuA14yxrRzN5lrRgN13A7hknbASGttdJGHK+UElbyggB5AqLX2JWttrrX2X8Bi4Ap3YwXck8AtOL+kK5v6wCfW2i+stQX+0fMkoKurqQLIWpsB1LLWjjfGeIE4IA/Y526ywDPG9AGqAovczuKS04H5boc4rLIXVHNg6VHHlgGtXMjiptHW2tOBdLeDBJq1doq1dsDh58aYGjiLD//qXqrAs9buM8ZE4Zza+QF41Vq7wuVYAWWMSQUGAze4ncUNxhgfzqWO64wxm40xK40xDxtjPG5lquwFFQ1kHXUsC4hyIYtrrLWb3c4QDIwx1YD/ArNwTv1WNtlAFaADcIMx5kaX8wSM/5fzR8D91trKurlcLZx/pH6Acy3yUpwzK7e4FaiyT5I4AEQedSwK2O9CFnGRMaYJTiktAa6x1ha4HCng/D9zDpBujHkT+AfwjrupAmYQYK21n7sdxC3+Yj6zyKH5xphXgP8DXnMjU2UfQS0BzFHHmvqPSyVhjOmOM2r6ErjUWpvtcqSAMsacaYyZe9ThcGC3G3lcciVwqTFmtzFmN85p/teMMa78YnaDMaaFMebJow6H4YysXVHZR1ATAY8x5h5gFM6/FFoDX7iaSgLGGNMQ+Bp41Fr7itt5XDIfqGOMuRd4GegE3Ahc7GqqALLWNi363BgzH3ipkk0z3w3cZ4zZiDNybgvcCdzuVqBKPYKy1uYA5+MU007gUeAia+02V4NJIN0GxOBMLd9f5PGs28ECxVq7B7gAuATnv4M3gZustZNdDSYBZa3dBPQC+gN7cW6/GGKt/cytTNpRV0REglKlHkGJiEjwUkGJiEhQUkGJiEhQUkGJiEhQUkGJiEhQUkGJiEhQquw36oqcMmPM+0CfE7zkSZyV0ScCMdbagCyh5V9XbhrQ21q7/ASv8wIzgeustTYQ2URKQiMokVN3F5Dof/TwH+tY5NgIYLr/4wMBzHUnsOBE5QRH1t57Cmf/I5Ggoxt1RUqBMaYlzh5CqdbatS7miADWA2dba38r5ntWATdaayeVZTaRktIpPpEyZIzpQZFTfMaYQpxdex/BWag4HbgWeAC4DmeJmUestWP8748BRuJsfVAI/AzcdYItUq4EdhctJ2PMIKAfznYKS4GB1trxRd7zBc5ocFIp/MgipUan+EQCbzhwN9AZqAvMwymmDsDnwBvGmGj/a9/EKbLzcLZCKAS+N8Yc7x+XFwLfHX5ijLnY/72uxVmp/xtgrDGmapH3fAecc4KvKeIKFZRI4L1qrZ1orZ2Ps5L6fpxRjQVewNmjLNUY0wBnRHS1tXaOf1R0Hc429X89ztduDywu8rw+zi656/ynHp/CWRQ2t8hrluBs3vm7Fb1F3KZ/MYkE3soiH2cBa621hy8GH957Jxyo5//YGvO7bcuicEZVXx/jaycA24s8/whnpuFq/55P/wXes9YeLPKaHf4/40v4c4iUKY2gRAIv96jnx9u9N8T/2rbAaUUeTYD3jvOeAsBz+Il/65jTcUZc04G+wEL/pI7DDv8eyC/2TyASACookeC1FAgFqlhrV1prVwIZwPM4JXUsmTiTIQAwxlwC9LfW/mCtvQtn5LUPZ/+nw2oVea9I0NApPpEgZa21xpj/Ah8aY24DtgFP40yuWHact80F2hR57gOeN8ZswZkx2Bmo7f/4sDbALn5/6lHEdRpBiQS3Pjhl8iUwB6gGnGut3X2c13+DM9sPAGvtWGAwzqhrOTAUuN1a+3OR93QHvrPW6hSfBBXdqCtSgRhjooC1wF+ttfOK8XovsA5npuCUMo4nUiIaQYlUINbaLJzR0m3FfMs/gNUqJwlGKiiRiudFoLU5am760fyjp0eBAQFJJVJCOsUnIiJBSSMoEREJSiooEREJSiooEREJSiooEREJSiooEREJSv8Pk0BOE6/GAn8AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "5.448756012469637 meter"
-      ],
-      "text/latex": [
-       "$5.448756012469637\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "5.448756012469637 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "min(results.y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write an error function that takes `L` and `params` as arguments, simulates a bungee jump, and returns the lowest point.\n",
-    "\n",
-    "Test the error function with a guess of 25 m and confirm that the return value is about 5 meters.\n",
-    "\n",
-    "Use `root_scalar` with your error function to find the value of `L` that yields a perfect bungee dunk.  Hint: before calling `root_scalar`, make a version of `params` with no dimensions.\n",
-    "\n",
-    "Run a simulation with the result from `root_scalar` and confirm that it works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def error_func(L, params):\n",
-    "    \"\"\"Minimum height as a function of length.\n",
-    "    \n",
-    "    L: length in m\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: height in m\n",
-    "    \"\"\"\n",
-    "    params = Params(params, L=L)\n",
-    "    system = make_system(params)\n",
-    "\n",
-    "    results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "    min_height = get_last_value(results.y)\n",
-    "    return min_height"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "5.448756012469637 meter"
-      ],
-      "text/latex": [
-       "$5.448756012469637\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "5.448756012469637 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "guess1 = 25 * m\n",
-    "error_func(guess1, params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-1.398018644734631 meter"
-      ],
-      "text/latex": [
-       "$-1.398018644734631\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "-1.398018644734631 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "guess2 = 30 * m\n",
-    "error_func(guess2, params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>converged</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>root</th>\n",
-       "      <td>28.996669054031372 meter</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "converged                        True\n",
-       "root         28.996669054031372 meter\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "res = root_bisect(error_func, [guess1, guess2], params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "L = res.root\n",
-    "params_solution = Params(params, L=L)\n",
-    "system_solution = make_system(params_solution)\n",
-    "\n",
-    "results, details = run_ode_solver(system_solution, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "4.1424875280200724×10<sup>-7</sup> meter<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$4.1424875280200724\\times 10^{-7}\\ \\mathrm{meter}^{2}$"
-      ],
-      "text/plain": [
-       "4.1424875280200724e-07 <Unit('meter ** 2')>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "min_height = get_last_value(results.y) * m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/kitten_soln.ipynb b/soln/kitten_soln.ipynb
deleted file mode 100644
index af88784b..00000000
--- a/soln/kitten_soln.ipynb
+++ /dev/null
@@ -1,878 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case study.\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Unrolling\n",
-    "\n",
-    "Let's simulate a kitten unrolling toilet paper.  As reference material, see [this video](http://modsimpy.com/kitten).\n",
-    "\n",
-    "The interactions of the kitten and the paper roll are complex.  To keep things simple, let's assume that the kitten pulls down on the free end of the roll with constant force.  Also, we will neglect the friction between the roll and the axle.  \n",
-    "\n",
-    "![](diagrams/kitten.png)\n",
-    "\n",
-    "This figure shows the paper roll with $r$, $F$, and $\\tau$.  As a vector quantity, the direction of $\\tau$ is into the page, but we only care about its magnitude for now."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll start by loading the units we need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "newton"
-      ],
-      "text/latex": [
-       "$\\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "<Unit('newton')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "radian = UNITS.radian\n",
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And a few more parameters in the `Params` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Rmin</th>\n",
-       "      <td>0.02 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rmax</th>\n",
-       "      <td>0.055 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Mcore</th>\n",
-       "      <td>0.015 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Mroll</th>\n",
-       "      <td>0.215 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>L</th>\n",
-       "      <td>47 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>tension</th>\n",
-       "      <td>0.0002 newton</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>120 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Rmin           0.02 meter\n",
-       "Rmax          0.055 meter\n",
-       "Mcore      0.015 kilogram\n",
-       "Mroll      0.215 kilogram\n",
-       "L                47 meter\n",
-       "tension     0.0002 newton\n",
-       "t_end          120 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = Params(Rmin = 0.02 * m,\n",
-    "                Rmax = 0.055 * m,\n",
-    "                Mcore = 15e-3 * kg,\n",
-    "                Mroll = 215e-3 * kg,\n",
-    "                L = 47 * m,\n",
-    "                tension = 2e-4 * N,\n",
-    "                t_end = 120 * s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system` computes `rho_h`, which we'll need to compute moment of inertia, and `k`, which we'll use to compute `r`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params with Rmin, Rmax, Mcore, Mroll,\n",
-    "                              L, tension, and t_end\n",
-    "    \n",
-    "    returns: System with init, k, rho_h, Rmin, Rmax,\n",
-    "                         Mcore, Mroll, ts\n",
-    "    \"\"\"\n",
-    "    L, Rmax, Rmin = params.L, params.Rmax, params.Rmin\n",
-    "    Mroll = params.Mroll\n",
-    "    \n",
-    "    init = State(theta = 0 * radian,\n",
-    "                 omega = 0 * radian/s,\n",
-    "                 y = L)\n",
-    "    \n",
-    "    area = pi * (Rmax**2 - Rmin**2)\n",
-    "    rho_h = Mroll / area\n",
-    "    k = (Rmax**2 - Rmin**2) / 2 / L / radian    \n",
-    "    \n",
-    "    return System(params, init=init, area=area, rho_h=rho_h, k=k)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `make_system`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Rmin</th>\n",
-       "      <td>0.02 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rmax</th>\n",
-       "      <td>0.055 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Mcore</th>\n",
-       "      <td>0.015 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Mroll</th>\n",
-       "      <td>0.215 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>L</th>\n",
-       "      <td>47 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>tension</th>\n",
-       "      <td>0.0002 newton</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>120 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>theta               0 radian\n",
-       "omega    0.0 radi...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>0.008246680715673206 meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho_h</th>\n",
-       "      <td>26.07109543981524 kilogram / meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k</th>\n",
-       "      <td>2.7925531914893616e-05 meter / radian</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Rmin                                              0.02 meter\n",
-       "Rmax                                             0.055 meter\n",
-       "Mcore                                         0.015 kilogram\n",
-       "Mroll                                         0.215 kilogram\n",
-       "L                                                   47 meter\n",
-       "tension                                        0.0002 newton\n",
-       "t_end                                             120 second\n",
-       "init       theta               0 radian\n",
-       "omega    0.0 radi...\n",
-       "area                         0.008246680715673206 meter ** 2\n",
-       "rho_h                26.07109543981524 kilogram / meter ** 2\n",
-       "k                      2.7925531914893616e-05 meter / radian\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>theta</th>\n",
-       "      <td>0 radian</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>omega</th>\n",
-       "      <td>0.0 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>47 meter</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "theta               0 radian\n",
-       "omega    0.0 radian / second\n",
-       "y                   47 meter\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we compute `I` as a function of `r`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def moment_of_inertia(r, system):\n",
-    "    \"\"\"Moment of inertia for a roll of toilet paper.\n",
-    "    \n",
-    "    r: current radius of roll in meters\n",
-    "    system: System object with Mcore, rho, Rmin, Rmax\n",
-    "    \n",
-    "    returns: moment of inertia in kg m**2\n",
-    "    \"\"\"\n",
-    "    Mcore, Rmin, rho_h = system.Mcore, system.Rmin, system.rho_h\n",
-    "    \n",
-    "    Icore = Mcore * Rmin**2   \n",
-    "    Iroll = pi * rho_h / 2 * (r**4 - Rmin**4)\n",
-    "    return Icore + Iroll"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When `r` is `Rmin`, `I` is small."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "6×10<sup>-6</sup> kilogram meter<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$6\\times 10^{-6}\\ \\mathrm{kilogram} \\cdot \\mathrm{meter}^{2}$"
-      ],
-      "text/plain": [
-       "6e-06 <Unit('kilogram * meter ** 2')>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "moment_of_inertia(system.Rmin, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As `r` increases, so does `I`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.00037418750000000006 kilogram meter<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$0.00037418750000000006\\ \\mathrm{kilogram} \\cdot \\mathrm{meter}^{2}$"
-      ],
-      "text/plain": [
-       "0.00037418750000000006 <Unit('kilogram * meter ** 2')>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "moment_of_inertia(system.Rmax, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "Write a slope function we can use to simulate this system.  Here are some suggestions and hints:\n",
-    "\n",
-    "* `r` is no longer part of the `State` object.  Instead, we compute `r` at each time step, based on the current value of `y`, using\n",
-    "\n",
-    "$y = \\frac{1}{2k} (r^2 - R_{min}^2)$\n",
-    "\n",
-    "* Angular velocity, `omega`, is no longer constant.  Instead, we compute torque, `tau`, and angular acceleration, `alpha`, at each time step.\n",
-    "\n",
-    "* I changed the definition of `theta` so positive values correspond to clockwise rotation, so `dydt = -r * omega`; that is, positive values of `omega` yield decreasing values of `y`, the amount of paper still on the roll.\n",
-    "\n",
-    "* Your slope function should return `omega`, `alpha`, and `dydt`, which are the derivatives of `theta`, `omega`, and `y`, respectively.\n",
-    "\n",
-    "* Because `r` changes over time, we have to compute moment of inertia, `I`, at each time step.\n",
-    "\n",
-    "That last point might be more of a problem than I have made it seem.  In the same way that $F = m a$ only applies when $m$ is constant, $\\tau = I \\alpha$ only applies when $I$ is constant.  When $I$ varies, we usually have to use a more general version of Newton's law.  However, I believe that in this example, mass and moment of inertia vary together in a way that makes the simple approach work out.  Not all of my collegues are convinced."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object with theta, omega, y\n",
-    "    t: time\n",
-    "    system: System object with Rmin, k, Mcore, rho_h, tension\n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    theta, omega, y = state\n",
-    "    k, Rmin, tension = system.k, system.Rmin, system.tension\n",
-    "    \n",
-    "    r = sqrt(2*k*y + Rmin**2)\n",
-    "    I = moment_of_inertia(r, system)\n",
-    "    tau = r * tension\n",
-    "    alpha = tau / I\n",
-    "    dydt = -r * omega\n",
-    "    \n",
-    "    return omega, alpha, dydt      "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test `slope_func` with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0 <Unit('radian / second')>,\n",
-       " 0.02939702689159846 <Unit('newton / kilogram / meter / radian ** 0.5')>,\n",
-       " -0.0 <Unit('meter * radian ** 0.5 / second')>)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "slope_func(system.init, 0*s, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                                                 True\n",
-       "message    The solver successfully reached the end of the...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And look at the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>theta</th>\n",
-       "      <th>omega</th>\n",
-       "      <th>y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>115.2</th>\n",
-       "      <td>205.83885219911747 radian</td>\n",
-       "      <td>3.7827035471318 radian / second</td>\n",
-       "      <td>36.27041961153422 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>116.4</th>\n",
-       "      <td>210.40759585863975 radian</td>\n",
-       "      <td>3.8320604323461227 radian / second</td>\n",
-       "      <td>36.04569112433749 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>117.6</th>\n",
-       "      <td>215.0357989373311 radian</td>\n",
-       "      <td>3.88180711854165 radian / second</td>\n",
-       "      <td>35.818632262441746 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>118.8</th>\n",
-       "      <td>219.72393467957122 radian</td>\n",
-       "      <td>3.9319528720968493 radian / second</td>\n",
-       "      <td>35.589242955595836 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>120.0</th>\n",
-       "      <td>224.47248760394635 radian</td>\n",
-       "      <td>3.982507222010697 radian / second</td>\n",
-       "      <td>35.357523131304944 meter</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           theta                               omega  \\\n",
-       "115.2  205.83885219911747 radian     3.7827035471318 radian / second   \n",
-       "116.4  210.40759585863975 radian  3.8320604323461227 radian / second   \n",
-       "117.6   215.0357989373311 radian    3.88180711854165 radian / second   \n",
-       "118.8  219.72393467957122 radian  3.9319528720968493 radian / second   \n",
-       "120.0  224.47248760394635 radian   3.982507222010697 radian / second   \n",
-       "\n",
-       "                              y  \n",
-       "115.2   36.27041961153422 meter  \n",
-       "116.4   36.04569112433749 meter  \n",
-       "117.6  35.818632262441746 meter  \n",
-       "118.8  35.589242955595836 meter  \n",
-       "120.0  35.357523131304944 meter  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Check the results to see if they seem plausible:\n",
-    "\n",
-    "* The final value of `theta` should be about 220 radians.\n",
-    "\n",
-    "* The final value of `omega` should be near 4 radians/second, which is less one revolution per second, so that seems plausible.\n",
-    "\n",
-    "* The final value of `y` should be about 35 meters of paper left on the roll, which means the kitten pulls off 12 meters in two minutes.  That doesn't seem impossible, although it is based on a level of consistency and focus that is unlikely in a kitten.\n",
-    "\n",
-    "* Angular velocity, `omega`, should increase almost linearly at first, as constant force yields almost constant torque.  Then, as the radius decreases, the lever arm decreases, yielding lower torque, but moment of inertia decreases even more, yielding higher angular acceleration."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot `theta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_theta(results):\n",
-    "    plot(results.theta, color='C0', label='theta')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angle (rad)')\n",
-    "    \n",
-    "plot_theta(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot `omega`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_omega(results):\n",
-    "    plot(results.omega, color='C2', label='omega')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angular velocity (rad/s)')\n",
-    "    \n",
-    "plot_omega(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot `y`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_y(results):\n",
-    "    plot(results.y, color='C1', label='y')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Length (m)')\n",
-    "    \n",
-    "plot_y(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/oem_soln.ipynb b/soln/oem_soln.ipynb
deleted file mode 100644
index d018877c..00000000
--- a/soln/oem_soln.ipynb
+++ /dev/null
@@ -1,2027 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case study.\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Electric car"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Olin Electric Motorsports](https://www.olinelectricmotorsports.com/) is a club at Olin College that designs and builds electric cars, and participates in the [Formula SAE Electric](https://www.sae.org/attend/student-events/formula-sae-electric) competition.\n",
-    "\n",
-    "The goal of this case study is to use simulation to guide the design of a car intended to accelerate from standing to 100 kph as quickly as possible.  The [world record for this event](https://www.youtube.com/watch?annotation_id=annotation_2297602723&feature=iv&src_vid=I-NCH8ct24U&v=n2XiCYA3C9s), using a car that meets the competition requirements, is 1.513 seconds.\n",
-    "\n",
-    "We'll start with a simple model that takes into account the characteristics of the motor and vehicle:\n",
-    "\n",
-    "* The motor is an [Emrax 228 high voltage axial flux synchronous permanent magnet motor](http://emrax.com/products/emrax-228/); according to the [data sheet](http://emrax.com/wp-content/uploads/2017/01/emrax_228_technical_data_4.5.pdf), its maximum torque is 240 Nm, at 0 rpm.  But maximum torque decreases with motor speed; at 5000 rpm, maximum torque is 216 Nm.\n",
-    "\n",
-    "* The motor is connected to the drive axle with a chain drive with speed ratio 13:60 or 1:4.6; that is, the axle rotates once for each 4.6 rotations of the motor.\n",
-    "\n",
-    "* The radius of the tires is 0.26 meters.\n",
-    "\n",
-    "* The weight of the vehicle, including driver, is 300 kg.\n",
-    "\n",
-    "To start, we will assume no slipping between the tires and the road surface, no air resistance, and no rolling resistance.  Then we will relax these assumptions one at a time.\n",
-    "\n",
-    "* First we'll add drag, assuming that the frontal area of the vehicle is 0.6 square meters, with coefficient of drag 0.6.\n",
-    "\n",
-    "* Next we'll add rolling resistance, assuming a coefficient of 0.2.\n",
-    "\n",
-    "* Finally we'll compute the peak acceleration to see if the \"no slip\" assumption is credible.\n",
-    "\n",
-    "We'll use this model to estimate the potential benefit of possible design improvements, including decreasing drag and rolling resistance, or increasing the speed ratio.\n",
-    "\n",
-    "I'll start by loading the units we need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "revolutions_per_minute"
-      ],
-      "text/latex": [
-       "$\\mathrm{revolutions_per_minute}$"
-      ],
-      "text/plain": [
-       "<Unit('revolutions_per_minute')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "radian = UNITS.radian\n",
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "minute = UNITS.minute\n",
-    "hour = UNITS.hour\n",
-    "km = UNITS.kilometer\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton\n",
-    "rpm = UNITS.rpm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And store the parameters in a `Params` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>r_wheel</th>\n",
-       "      <td>0.26 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>speed_ratio</th>\n",
-       "      <td>0.216667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_rr</th>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>0.6 meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>300 kilogram</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "r_wheel                       0.26 meter\n",
-       "speed_ratio                     0.216667\n",
-       "C_rr                                 0.2\n",
-       "C_d                                  0.5\n",
-       "area                      0.6 meter ** 2\n",
-       "rho            1.2 kilogram / meter ** 3\n",
-       "mass                        300 kilogram\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = Params(r_wheel=0.26 * m,\n",
-    "                speed_ratio=13/60,\n",
-    "                C_rr=0.2,\n",
-    "                C_d=0.5,\n",
-    "                area=0.6*m**2,\n",
-    "                rho=1.2*kg/m**3,\n",
-    "                mass=300*kg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system` creates the initial state, `init`, and constructs an `interp1d` object that represents torque as a function of motor speed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(x=0*m, v=0*m/s)\n",
-    "    \n",
-    "    rpms = [0, 2000, 5000]\n",
-    "    torques = [240, 240, 216]\n",
-    "    interpolate_torque = interpolate(Series(torques, rpms))\n",
-    "    \n",
-    "    return System(params, init=init,\n",
-    "                  interpolate_torque=interpolate_torque,\n",
-    "                  t_end=3*s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `make_system`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>r_wheel</th>\n",
-       "      <td>0.26 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>speed_ratio</th>\n",
-       "      <td>0.216667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_rr</th>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>0.6 meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>300 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>x               0 meter\n",
-       "v    0.0 meter / secon...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>interpolate_torque</th>\n",
-       "      <td>&lt;function interpolate.&lt;locals&gt;.wrapper at 0x7f...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>unit_torque</th>\n",
-       "      <td>1.0 meter * newton</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>3 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "r_wheel                                                      0.26 meter\n",
-       "speed_ratio                                                    0.216667\n",
-       "C_rr                                                                0.2\n",
-       "C_d                                                                 0.5\n",
-       "area                                                     0.6 meter ** 2\n",
-       "rho                                           1.2 kilogram / meter ** 3\n",
-       "mass                                                       300 kilogram\n",
-       "init                  x               0 meter\n",
-       "v    0.0 meter / secon...\n",
-       "interpolate_torque    <function interpolate.<locals>.wrapper at 0x7f...\n",
-       "unit_torque                                          1.0 meter * newton\n",
-       "t_end                                                          3 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>x</th>\n",
-       "      <td>0 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "x               0 meter\n",
-       "v    0.0 meter / second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Torque and speed\n",
-    "\n",
-    "The relationship between torque and motor speed is taken from the [Emrax 228 data sheet](http://emrax.com/wp-content/uploads/2017/01/emrax_228_technical_data_4.5.pdf).  The following functions reproduce the red dotted line that represents peak torque, which can only be sustained for a few seconds before the motor overheats."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_torque(omega, system):\n",
-    "    \"\"\"Maximum peak torque as a function of motor speed.\n",
-    "    \n",
-    "    omega: motor speed in radian/s\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: torque in Nm\n",
-    "    \"\"\"\n",
-    "    factor = (1 * radian / s).to(rpm)\n",
-    "    x = magnitude(omega * factor)\n",
-    "    return system.interpolate_torque(x) * N * m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "240.0 meter newton"
-      ],
-      "text/latex": [
-       "$240.0\\ \\mathrm{meter} \\cdot \\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "240.0 <Unit('meter * newton')>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_torque(0*radian/s, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "216.0 meter newton"
-      ],
-      "text/latex": [
-       "$216.0\\ \\mathrm{meter} \\cdot \\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "216.0 <Unit('meter * newton')>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "omega = (5000 * rpm).to(radian/s)\n",
-    "compute_torque(omega, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the whole curve."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xs = linspace(0, 525, 21) * radian / s\n",
-    "taus = [compute_torque(x, system) for x in xs]\n",
-    "plot(xs, taus)\n",
-    "decorate(xlabel='Motor speed (rpm)',\n",
-    "         ylabel='Available torque (N m)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Simulation\n",
-    "\n",
-    "Here's the slope function that computes the maximum possible acceleration of the car as a function of it current speed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    x, v = state\n",
-    "    r_wheel, speed_ratio = system.r_wheel, system.speed_ratio\n",
-    "    mass = system.mass\n",
-    "    \n",
-    "    # use velocity, v, to compute angular velocity of the wheel\n",
-    "    omega2 = v / r_wheel\n",
-    "    \n",
-    "    # use the speed ratio to compute motor speed\n",
-    "    omega1 = omega2 / speed_ratio\n",
-    "    \n",
-    "    # look up motor speed to get maximum torque at the motor\n",
-    "    tau1 = compute_torque(omega1, system)\n",
-    "    \n",
-    "    # compute the corresponding torque at the axle\n",
-    "    tau2 = tau1 / speed_ratio\n",
-    "    \n",
-    "    # compute the force of the wheel on the ground\n",
-    "    F = tau2 / r_wheel\n",
-    "    \n",
-    "    # compute acceleration\n",
-    "    a = F/mass\n",
-    "\n",
-    "    return v, a      "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `slope_func` at linear velocity 10 m/s."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>x</th>\n",
-       "      <td>0 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v</th>\n",
-       "      <td>10.0 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "x                0 meter\n",
-       "v    10.0 meter / second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_state = State(x=0*m, v=10*m/s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10.0 <Unit('meter / second')>, 14.201183431952662 <Unit('newton / kilogram')>)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(test_state, 0*s, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                                                 True\n",
-       "message    The solver successfully reached the end of the...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And look at the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>v</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2.88</th>\n",
-       "      <td>57.32626824256294 meter</td>\n",
-       "      <td>38.632749274430566 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.91</th>\n",
-       "      <td>58.49067227876934 meter</td>\n",
-       "      <td>38.99375142852514 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.94</th>\n",
-       "      <td>59.66589334398368 meter</td>\n",
-       "      <td>39.353885587648904 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.97</th>\n",
-       "      <td>60.851905429699734 meter</td>\n",
-       "      <td>39.713153838812325 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.00</th>\n",
-       "      <td>62.0486825899462 meter</td>\n",
-       "      <td>40.071558264007834 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                             x                                  v\n",
-       "2.88   57.32626824256294 meter  38.632749274430566 meter / second\n",
-       "2.91   58.49067227876934 meter   38.99375142852514 meter / second\n",
-       "2.94   59.66589334398368 meter  39.353885587648904 meter / second\n",
-       "2.97  60.851905429699734 meter  39.713153838812325 meter / second\n",
-       "3.00    62.0486825899462 meter  40.071558264007834 meter / second"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After 3 seconds, the vehicle could be at 40 meters per second, in theory, which is 144 kph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "40.071558264007834 meter/second"
-      ],
-      "text/latex": [
-       "$40.071558264007834\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "40.071558264007834 <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v_final = get_last_value(results.v)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "144.2576097504282 kilometer/hour"
-      ],
-      "text/latex": [
-       "$144.2576097504282\\ \\frac{\\mathrm{kilometer}}{\\mathrm{hour}}$"
-      ],
-      "text/plain": [
-       "144.2576097504282 <Unit('kilometer / hour')>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v_final.to(km/hour)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `x`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_position(results):\n",
-    "    plot(results.x, label='x')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "    \n",
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `v`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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IpAE1EhhmZkudc78B3jazJ5xznwCfRa88EUlEP7qc1yiTG0dl069HC58rk0QSaUBlAFtCrdovAP43tD2Id0+UiMhhL+eNPrsLY87tQq0aaX6XJwkm0oCah/ce1DbgOGCqc64V8Afg8yjVJiIJJHeNdzlvzWZdzpOqEWlA3Qq8CLQHbjazzc65B/GaDOZEqTYRSQC79x3kmVlLeXfe97PzbghdztPsPDkWFd2o2x/43MwCZpYL9Cm3y2/NbF9UqxORuFUWCDL7s7VMfHNZ6GbbVEafo9l5UnUq+im6F3DOubnAHGBO+Jp7CieR6svW7eTfUxaxauP3N9vemJNNK91sK1Wooht1+4eWMToXGAT8wjmXBryNF1jvmtmu2JQpIvFg74FinnsjlzlfrCMYhCYNM7h+ZE9Oy26py3lS5Soch4eWMZoa+sA51wE4H7gUeNQ5twpvyvnvol2oiPgnEAjy9pfreXZW7netMEYN7MSlg5zWzpOoqdRPlpmt4ft191KBk/FGVyKSpFZu3M2jry3C1nsXTHp1bsLPL+pF2+b1fK5Mkl3EAeWcG4i3unmtcl8qqMqCRCQ+7C8s4YU3l/HGp2sIBKFR/VpcO6InZ/Zprct5EhORrsV3P97SRuuBonJfDgL3VXFdIuKTYDDIh19v5MkZS9m97yCpqSmMPLMjl1+gzrYSW5GOoH4GXGNmz0azGBHx1/r8vTw6ZTGLV20HoFv7Rtw0uhcdWjXwuTKpjiINqAK8lctFJAkVHSzl5beNaR+uoiwQpH6dmlw9rAfnnNSW1FRdzhN/RBpQfwTudc7dGpooISJJIBgM8vmSfJ6YvphtuwpJSYEhp7XnyqHdqJdZ0+/ypJqLNKCWAX8GVjrnfvRFM9MqkCIJJn/HAR6bupj5y7YA0KlNA24e3Zuuxx/nc2UinkgD6nG8RWGfRrP2RBJaSWkZUz5YyatvL6e4NEBm7XSuHNKNIf07kKbLeRJHIg2otsAQM1sdzWJEJLoWrtjGv19bxKZt+wE4q28brh3Rg+Pq1/a5MpEfizSg3gYGAAookQS0a28RT76+lA+/2QhA66Z1uWl0L3p3aepzZSJHFmlAfQE85JwbDawESsK/aGb/XdWFicixKwsEmf3pGm/F8aJSaqanMnaQI2dgJ2qk661jiW+RBtQgvKaFdflx241glVYkIlVixYZdPPLaIlZu2A14DQRvzMmmRWM1EJTEEFFAmdnZ0S5ERKrGgcISJoaWKAoGoUmD2lw/KlsrjkvCqahh4V3A38ysMJIncs7VA/7LzH5fVcWJSOSCwSBzF2xiwvQl7Dq0RNGAjlx+QRYZWnFcElBFP7V7gKXOucnAFDP7vPwOzrkU4CTgCuAi4J9RqVJEKrR5237+PWURC5ZvAyCr3XHcPKa3liiShFZRw8L7Q+H038Ac51wp3g2724EUoCne6uYpwDPA6Wa2PuoVi8h3SkrLmPzuCia9t4KS0gB1M2owflh3BvVrpyWKJOH9VMPCjcDtzrnfAAOBE4HmQABvZt/dwPtmdjDKdYpIOQuXb+PfUxayadsBAM45qS3XDO9Bg7rlO+KIJKZIJ0kcAGaFPkTER7v2FfHk9O/vaWrTrC43j+5NducmPlcmUrX0zqlIgggEgrz1+VqenZX73T1NlwzqykUDu1AjPdXv8kSqnAJKJAGs2byHhycvxNZ5bddPyGrGTRf10j1NktQUUCJxrOhgKS/OMaZ/tIpAIEij+rW4bmQ2Z/RupXuaJOlF2vK9vZmtjXItIhLmy6X5PDp10Xd9moad3oErhnSjTobarkv1EOkIaqVz7nPgeWCSme2IYk0i1dr23YU8Pm0xny3+FoCOrRtwyxj1aZLqJ9KA6ghcDtwEPOCcmwO8AEyPdKUJAOfcIOAvQBdgK/B3M3vMOVcTeAgYA5QB95nZPZEfhkjiKwsEmfXxap6fvYzCg2Vk1Epj3OBuDDu9A2lpmgQh1U+k08zX4wXLX5xzPYFLgd8AjzvnpgITzeydip7DOdcWeA24CpiOd0/VW865tXj3WDmgE9AAmO2c22Rmzx3FMYkknJUbdvPw5AWs3LgHgFN7tuCGUb1oelyGz5WJ+OdoJklsBFbh9Ybqgre6+SDn3D5gvJl9doTvaw+8aGZTQ4/nOec+AE7HC63xZrYL2OWcuxe4EVBASVIrKCrhhdl5zPx4NYEgNGmYwc9zsjmlZ0u/SxPxXaSTJOoAI/FGTucDW4AXgd+Z2VLnXCrwCPAKcPzhnsPM5gJzw56zEXAmMBFoCeSG7Z4HZFf2YEQSyWeLv+WxqYvYsaeI1NQURmlhV5EfiPT/hK1AMTAFGGxmH4R/0cwCofelzozkyZxzDYDX8ZZL+iq0uSBslwIgM8LaRBLKtl2FPDZ1EV8szQegS9uG3DKmN53aNPS5MpH4EmlAjQdeP9yae865Zma21cym4AVYhZxzXfHeg8oFxgGHLrKHX2zPBPZHWJtIQvjxJIh0rhzSjaGndyBNC7uK/EikAfUy0ALYFr7ROXc8XtDUjeRJnHMD8MLpUeC3ZhYEipxz+XiTJDaFds3ih5f8RBLayo27eXjS95MgTstuyQ2jsmnSUJMgRI6kooaFlwE5oYcpwATnXPkRVDtgZyQv5JzrBMwE7jSzB8t9eSJwl3NuEV7Y/Qp4IJLnFYlnhQdLeWF2HjPmrtIkCJFKqmgE9TYwCC+cAApDH4cE8d5DeibC17oFqAfc45wLv8fpYeD3wD+ApUAq8DjeKEskYX2Zm8+jU7yVIFJTYMSAjlwxuJsmQYhEqKKGhduBawBC9yr93cwKjrT/TzGzXwC/qGCXW0IfIglt594iHp+2mE8Wbga8lSBuu7gPndtqEoRIZVR0iW8o8LaZlQDzgIHOucPua2ZvRKc8kcRRvh1G7ZreShDDz9BKECJHo6JrDTPxJkZsDX1+JEEgrSqLEkk06/L38vCkhSxb670le3L35vw8pxfNGuluCZGjVdElvtTDfS4i3ysuKePVd5bz2vsrKC0Lcly9WtyQk83pvdQOQ+RYRfxurXPuGmCvmU0OPX4VmGFmE6NVnEg8W7RyGw9PWsjm7QcAGHJae352YXfqqh2GSJWIdKmjO4Ff4q1mfshi4H7nXGMzuz8axYnEo70Hinl6xlLembcegLbN63LLmD706NjY58pEkkukI6gbgUvNbM6hDWb2B+fcfLw1+BRQkvSCwSAffrOJCdMXs2d/MelpqYwd1JXRZ3emRrrehhWpapEG1HHAusNsXwU0r7pyROLTlp0FPPLaQr7O2wpAj46NufXi3rRpVs/nykSSV6QB9Tnwa+fc9WZWCuCcS8O77DcvWsWJ+K2sLMDrc1fzwlt5HCwuo05GDa4Z3oPzTj6eVK2fJxJVkQbUr4B3gfWh5YiCeO0w0oEhUapNxFerNu7mobD1887o3YobRmVzXP3aPlcmUj1E2lF3ofPu0r0U6IbXemM68IKZ7YtifSIxV1RcyktvGdM+WkUgEKRJwwxuHt2Lk7u38Ls0kWol4mnmZrbDOfcWsAFvvbw8hZMkmwXLt/Lw5IXk7yggJQVGnNmRK4Zo/TwRP0Q6zbwu8CQwBijBW0A23Tn3NjDazA5Er0SR6Nt7oJgnX1/Ce/M3ANC+ZX1uu6QPXY8/zufKRKqvSP8svA/vPafT+H5SxMnABOCvwK1VX5pI9JWfOl4jPZXLznfkDOxMutbPE/FVpAF1EZBjZl+GbfvSOXcLMBkFlCSgraGp41+Fpo5nd2rCrRf3plXTiPpvikiURRpQqcD2w2zfSYTddEXixaHW6xPfXEZR2NTxQf2O1/p5InEk0oD6CPhf59yVZlYM4JyrBdwFzI1WcSJVbd23e3nw1QXY+l0AnN67FTdq6rhIXKrMfVAfAxuccwtC23oDRcDgaBQmUpVKSst45Z3lTH53BWWBII0b1ObnF/XiVLVeF4lbkd4HtdI51w0YB3THa/0+Ce8+qMIKv1nEZ7lrdvDgqwvYuHU/AEP6t+eqod2po1XHReJaZe6D2gU8FMVaRKpUQVEJz87K5Y1P1wLQumldbrtEq46LJIqKWr7Pw1vS6CeZWb8qq0ikCszLzeeRyQvZvqeItNQURp/ThbHndaVmDa06LpIofqrlu0hC2b3vIE9MW8xHCzYB0KVtQ267pA8dWjXwuTIRqayKWr7fHctCRI5FMBjkg6838sS0JewrKKZmjTSuHJLF8DM7kaZVx0USUmVavl8C/BfQBTgBuBnIN7N7o1SbSETK33Dbp0tTbrm4Ny0a1/G5MhE5FpGuxTceuBf4B/A/oc15wH3OuXQz+0t0yhM5skAgyBufruHZWbnf3XB73YgenHuybrgVSQaRjqB+CdxkZpOcc78FMLMJzrldeMGlgJKY2rBlHw++uoBla3cCcHqvVtyYoxtuRZJJpAHVCZh/mO0LADXJkZgpLQsw5f2VvDTHKC0LcFy9Wtw0uhenZbfyuzQRqWKRBpQB5wFPlNt+Cd6lPpGoW7lxN/965RvWbN4LwKB+x3PN8B7Uzazpc2UiEg2RBtRvgcnOuZNC3/Nz51xnYBhejyiRqDlYUsZLb+Ux9UOvw23zRpncdnEfendt6ndpIhJFkS519KZzrh/eLL4lwCBgGXCqmX0dxfqkmlu6egcPvvoNm7YdICUFRg7oxBWDs6itDrciSa+ilSSGArPNLABgZkuB8TGqS6q58ssUtW1ej9vH9iGrXSN/CxORmKnoz9DpwA7n3EvAs2a2oIJ9RarM13lbeWjyArbtKiQtNYWLz+3KJed1oUa6likSqU4qCqjWwKWhjzucc0uBZ4EXzWxzLIqT6mVfQTETpi/hvfkbAOjcpgG3j+2rZYpEqqmKljraCvwL+Jdzrj1wGXAlcI9z7n3gOeA1tduQqvDpos38e8oidu87SM30VC6/IItRZ3UiLS3V79JExCeRTpJYC9yDF07dgcvxZvY94px7zcyujl6Jksx27SvisSmL+WSRNyjv3qERt4/tS+umdX2uTET8VumpUGaW65y7F+/eqP/AG1UpoKRSvl/cdTH7CkqoXTON8Rd2Z0j/DqRqcVcRoXKLxTYARuHdnHsusBp4AbgoOqVJstq+u5CHJy9k/rItAPTt2pRbL+5Ds0aZPlcmIvGkwoAKC6WL8VaS2Au8DPyvmc2LfnmSTILBIHO+WMdTM5ZSUFSqxV1FpEIV3Qc1C2+kFABm4K0YMdvMSmNUmySR/B0HeGjSAhau2A7AKT1acNPoXjRukOFzZSISryoaQdUFbgEmmdneGNUjSSYQCDLrkzU8+0YuB4vLqF+nJjeMymZA39YaNYlIhSqaZn5WLAuR5LNp234eePmb71piDOjTmhtysmlQt5bPlYlIItCCZlLlysoCTPtwFS++lUdx6aGWGL05Lbul36WJSAJRQEmVWvvtXh545RtWbtgNwLknt+W6ET3VEkNEKs2XgAqtjD7TzJqFHtcEHsKbiFEG3Gdm9/hRmxydktIAk99dzqvvLqe0LEiThhncdnEfTshq5ndpIpKgYhpQzrkU4Fq8NvHh7gYcXufeBsBs59wmM3sulvXJ0Vm5YTcPvPINa7/15tIM6d+e8Rd2J7N2DZ8rE5FEFusR1N3AhcAfgd+Fbb8KGG9mu4BdoZUqbsRb70/iVHFJGS++lcfUD1YSCELLxnW4bWwfsjs18bs0EUkCsQ6oR83s9865gYc2OOcaAi2B3LD98oDsGNcmlbBszU4eeOUbNm3bT0oKjDqrE+MGZ1G7pt7WFJGqEdPfJkdo03FoVdCCsG0FgNa9iUNFB0t57s1lzPx4NcEgtG1el9vH9lUjQRGpcvHw5+6B0L/hSwpkAvt9qEUqsHDFNh58dQFbdhaQmprCmHM6c+kgR80aaiQoIlXP94Ays13OuXy8SRKbQpuz+OElP/HRgcISnp65lLc+XwdAh1b1uWNsXzq1aehzZSKSzHwPqJCJwF3OuUV4l/x+BTzgb0kCMC83n4cnL2THniLS01K5dFBXRp/ThXQ1EhSRKIuXgPo98A9gKZAKPA486mtF1dzeA8VMmL6Y97/aCEDX4xty+9i+tGtR3+fKRKS68CWgzOwDoGHY4yK8hWlv8aMe+aFPFm3m0dcWsXu/13593NNIstYAAAx+SURBVOBujDyrE2lqJCgiMRQvIyiJA+Xbr/fo2JjbL+lDK7VfFxEfKKDk8O3Xh/VgyGnt1X5dRHyjgKrmduzx2q/Py1X7dRGJLwqoasprv76ep2Ys8dqv107nupE91X5dROKGAqoa2rKzgAdf/ea79uv9urfg5jFqvy4i8UUBVY0car/+3Bu5FBWXUS+zJjfmqP26iMQnBVQ1sWnbfv71yjfkrvHar5/RuxU35vSiYT21XxeR+KSASnKH2q+/8FYeJWq/LiIJRAGVxMq3Xz/npLZcP1Lt10UkMSigklBJaYDJ763g1Xfsu/brt17cmxOzmvtdmohIxBRQSaZ8+/XBp7Xn6mFqvy4iiUcBlSSKS8p4aY4x5YOVBAJBWjTO5PZL+pLdWe3XRSQxKaCSQPn26yMHdOKKwVnUrqXTKyKJS7/BEljRwVImvrmMGeHt1y/pS1Z7tV8XkcSngEpQar8uIslOAZVgCopKeHpmLrM/Wwt47ddvH9uXzmq/LiJJRgGVQOYv28LDkxawfU8R6WkpXDrIqf26iCQtBVQC2FdQzBPTvm+/3qVtQ+4Y25d2LdV+XUSSlwIqzn26aDP/nrKI3fvC2q8P6EiaRk0ikuQUUHFq174iHpu6mE8Weu3Xu3doxO1j+9Ja7ddFpJpQQMWZYDDIh99s4vGpi9lXUOy1X7+wO0P6d1D7dRGpVhRQcaR8+/U+ofbrzdV+XUSqIQVUHAgGg7z95Xqeen0JB0Lt168d0ZPz+qn9uohUXwoon23ZWcBDry5gwYptgNqvi4gcooDySSAQ5I1P1/DsLLVfFxE5HAWUD9R+XUTkpymgYqisLMD0j1bxwuw8iksDNKxXi5su6kX/Xq38Lk1EJO4ooGLkcO3XrxvZk3pqvy4iclgKqCg7XPv1W8b05qRuar8uIlIRBVQUlW+/PuS09oxX+3URkYgooKKguKSMl982Xnv/+/brt13Sh16dm/pdmohIwlBAVbFla3byr1e/YeNWtV8XETkW+q1ZRYoOljJx9jJmzPXar7dpVpc7xqr9uojI0VJAVYFFK7326/k71H5dRKSqKKCOQfn26+1b1ueOsX3p3Fbt10VEjpUC6ijNX7aFhycvZPvuQtLTUhg7yDH67C7USFcjQRGRqqCAqqR9BcVMmL6E9+ZvANR+XUQkWhRQlfDZ4s088lp4+/UsRg7opPbrIiJRoICKwO59B3l06qLv2q/36NiY2y7po/brIiJRpICqwOHar191YXeGqv26iEjUKaCOYMeeQh6ZvIgvc/MB6NOlKbdeovbrIiKxEjcB5ZzrDTwK9AJWA9eY2bxY1xEMBnnny/U8GWq/nlk7nWuG9+T8U9R+XUQkluIioJxzNYHpwP3AAGA0MMc5187M9saqji07C3ho0gIWLFf7dRERv8VFQAEDgRpmdn/o8cvOuVuBscAT0X7xQCDIm5+u4Zmw9us35GRzltqvi4j4Jl4CqjuwrNy2PCA7Fi/+xLTFzPxkDaD26yIi8SJeAqouUFBuWwEQkxkJGbXTadWkDldd2F3t10VE4kS8BNQBoPwbPZnA/li8+M+GdudnQ7vH4qVERCRC8bIEQi7gym3LCm0XEZFqKF5GUO8DKc65/wQewpvF1wuY6mtVIiLim7gYQZlZMTAEL5h2AncCo8xsm6+FiYiIb+JlBIWZLQHO8LsOERGJD3ExghIRESlPASUiInFJASUiInEpbt6DOgZpAPn5+X7XISIilRT2uzut/NeSIaBaAowbN87vOkRE5Oi1BFaFb0iGgJoHnAl8C5T5XIuIiFROGl44/ai9UkowGIx9OSIiIj9BkyRERCQuKaBERCQuKaBERCQuKaBERCQuKaBERCQuKaBERCQuKaBERCQuKaBERCQuJcNKEhFxzvUGHsXr1LsauMbMfnTnsnPueOBJ4FRgK3Cbmb0Ry1qPRiWO7xzgbaAwbPNfzewPMSn0GDnn+gEzzazZEb6ekOfvkAiOL+HOn3NuEPAXoAveOfm7mT12mP0S8txV4vgS7twBOOeGAX8GOuAd399idf6qxQjKOVcTmA68AjQE/gTMcc7VP8zuLwOLgMbA9cDLzrmOsar1aFTy+E4AJplZ3bCPuP4fBMA5l+Kcuw6YA9SsYNeEO39QqeNLqPPnnGsLvAb8Ee9n8zLgHufcBYfZPeHOXSWPL6HOHYBzriUwGfh/ZlYPuBi43zl3wmF2r/LzVy0CChgI1DCz+82sxMxeBpYCY8N3cs51BU4Cfm9mxWb2HvA6cG2sC66kgURwfCEnAgtiWVwVuRu4Ce8XwWEl8PmDCI4vJNHOX3vgRTObamaB0Kj+A+D08J0S+Ny1J4LjC0m0c4eZfQs0NbM3nXOpeOFTCuwL3y9a56+6XOLrDiwrty0PyD7MfuvN7EC5/fpFsbaqEOnxgfdXXFPn3E1ACt6o63dmdjC6JR6zR83s9865gRXsk6jnDyI7Pkiw82dmc4G5hx475xrhLe48sdyuCXnuKnF8kGDn7hAz2+ecywT24GXGX81sRbndonL+qssIqi5QUG5bAZB5lPvFm4jqds6lAxuBqUA34BzgPCCuLzMAmNnmCHZL1PMX0fEl8vkDcM41wPur+gu8S9LhEvbcHVLR8SX6uQOKgDrAycA1zrnyI6OonL/qMoI6AGSU25YJ7D/K/eJNRHWbWSlwbtimlc65PwF/Bf47qhXGRqKev4gk8vkLXQKaDuQC48wsUG6XhD53P3V8iXzuAELHUwzMd849DozEmxBxSFTOX3UZQeUCrty2rND28vsd75zL+In94k1Ex+eca+2cuzc0qeKQmnh/HSWDRD1/EUnU8+ecG4A3qpgGjDGzw9WbsOcukuNL4HN3lnPuq3KbawG7y22LyvmrLiOo94EU59x/Ag8Bo/GmY08N38nMzDm3EPiTc+43QH+8vxROi3G9lRXR8QE7gHFAgXPu//Cmjf4OeCqGtUZNAp+/SCXc+XPOdQJmAnea2YNH2i9Rz12kx0cCnruQBUBr59wvgAeAU/AmPuSE7xSt81ctRlBmVgwMwfvFvRO4ExhlZtucc+Occ+HD0NF414i3AhOAa81sSaxrroxIjy/0l90QYADe/zAfAZOA+3wpvAokw/mrSBKcv1uAenhTr/eHffw1Sc5dRMeXoOcOM9sDDAUuwvvd8jhwnZl9GIvzp466IiISl6rFCEpERBKPAkpEROKSAkpEROKSAkpEROKSAkpEROKSAkpEROJSdblRV6TKOeeeAa6qYJe78Va2fh+oZ2YxWbbHOZcGfAL8zMyWV7BfKvA5cKWZWSxqE6kMjaBEjt4dQMvQx8DQtn5h2+4FPg19fuAw3x8ttwMLKwon+G59tf/Da3QpEnd0o65IFXDO9QQWAx3MbK2PddQG1gPnRHoXv3NuFd5d/x9EszaRytIlPpEoCvV3+u4Sn3MuiNd19Td4C/zOB64A/gu4EtgL/MbMJoa+vx7wD2AMEATeA+6ooD3HpcDu8HByzv0PcAPQFK9v2G/N7M2w75mKNxr8oAoOWaTK6BKfSOz9BfgP4FTgeOBrvGA6GZgCPOacqxva93G8ILsAOAsvpN4K9Rc6nAuB2YceOOdyQq91Bd7q0rOASc65+mHfMxs4r4LnFPGFAkok9h42s/fNbAHeStj78UY1hrd4aAbQwTnXEW9EdLmZzQuNiq7EazM++AjPfRKwNOxxe+AgsC506fH/8Bb+LAnbJxev4VxWlRydSBXRX0wisbcy7PMCYK2ZHXoz+FB/oFpAu9Dn5twP2n1l4o2qZh7muZsD28MeP48303B1qK/P68DTZlYYts+O0L/NKnkcIlGlEZRI7JWUe1y+u+wh6aF9+wJ9wj66Ak8f4XsCQMqhB2a2DTgRb8T1KTAeWBSa1HHIod8DZREfgUgMKKBE4tcyoAZQx8xWmtlK4Fvg73ghdTj5eJMhAHDOXQTcaGZzzOwOvJHXPrweP4c0DftekbihS3wicSrUpfR14Dnn3C3ANuBPeJMr8o7wbV8BvcMepwF/d85twZsxeCrQIvT5Ib2BXfzw0qOI7zSCEolvV+GFyTRgHtAAGGRmu4+w/yy82X4AmNkk4C68Uddy4I/ArWb2Xtj3DABmm5ku8Ulc0Y26IknEOZcJrAUGm9nXEeyfCqzDmyk4N8rliVSKRlAiScTMCvBGS7dE+C0jgdUKJ4lHCiiR5PNPoJcrNze9vNDo6U7g5zGpSqSSdIlPRETikkZQIiISlxRQIiISlxRQIiISlxRQIiISlxRQIiISl/4/QnrtbXEnEGQAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_velocity(results):\n",
-    "    plot(results.v, label='v')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')\n",
-    "    \n",
-    "plot_velocity(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Stopping at 100 kph\n",
-    "\n",
-    "We'll use an event function to stop the simulation when we reach 100 kph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Stops when we get to 100 km/hour.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: difference from 100 km/hour\n",
-    "    \"\"\"\n",
-    "    x, v = state\n",
-    "    \n",
-    "    # convert to km/hour\n",
-    "    factor = (1 * m/s).to(km/hour)\n",
-    "    v = magnitude(v * factor)\n",
-    "    \n",
-    "    return v - 100     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap11-fig02.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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ZR1F+Fg/fOUPhJoacqQXnm8BnrbUbTvVkcIr3xuCf3xpjRgP3AQvbu0gREZH2cqSmkYefXcPqreUAzJ5WxE1XnEtiQrzLlUl7Om3AsdbObssLWWvfB2addUUiIiIdZM3Wcn797BqO1DSSmZbI1z87monDerldlnSAtgwyTgP6A8mtn7PWrmnPokRERNqTt9nH0y9v4YV3dgIwfEAP7rxuDD26pn7ET0q0CingGGOux1mXJhVovRhAAGfQsYiISMTZV17Dg39ZTfH+o3g8cVx/yWCuvuAc4j1a2yaWhdqC8wDOQONfAQ0dV46IiEj7CAQCvL7CWdumsclHXvc07pozFtMv2+3SJAxCDThdgN9Ya0s6shgREZH2UFPXxGPPr2PpugMAzBhbwO1Xj9B2C51IqAHnGeAm4J6OK0VEROTsrd9xiF/9bQ2VRxtITY7n9mu0tk1nFGrAeRBYY4yZA+wGTlrQz1p7YTvXJSIi0ibeZh9/+c9W5r6zg0AABvfrxp3XjaVXj3S3SxMXtKUF5xiwAKjruHJERETabu/BGn75l9UUH3AGEv/XzEFc+8lBxMefdj1biXGhBpzxwERr7fqOLEZERKQtAoEALy/bzZ/mbaSp2U9e9zS+ed1YBhdqIHFnF2rAsUDXjixERESkLaqqG/i/59ayastBAD4xvg9funK4BhIL0LZp4k8aY34D7AS8LZ+01r7c3oWJiIiczrsbSvnNP9dSXdtEemoiX/nMSKaOzHe7LIkgoQacvwe//vIUz2mhPxERCYu6Bi9/eGEjb6zcA8Coc3ry9c+N1orE8iEhBRxrrUZpiYiIqzYVV/Krv6+h/HAdSQkebrpiKJdP6Y9HKxLLKZw2uBhjzm/rixljNF1cRETalbfZx5MvbeI7v11C+eE6BhRk8fCdM5g1rUjhRk7rTC04/2OMuRv4P+ANa633VCcZYxKAK4Cv4Ewhf6vdqxQRkU5p14Gj/Prva9h1oBpPHFz7yUF8dqYhMUEdC3Jmpw041torjTFXAT8D+hljFgKbgAqcDTd7AiOBycAe4H5r7fMdXrGIiMQ8n8/Pvxfu4G+vbqXZF6BX93TuvG6Mpn9LyM44BsdaOxeYa4yZAVyGE2ZycVYyLgNWAw9Yaxd3cJ0iItJJ7D90jF//fQ22pAqAy84r5KYrhpKaHOq8GJHQBxkvBBZ2aCUiItKp+f0BFizdxZMLNtPk9dE9K4WvfXY0Y0yO26VJFFIcFhER15VX1fHIs++zfkcFABeMLeBLVw4nIy3J5cokWingiIiIawKBAK8t38Mf522kvrGZrIwk/vuakZw3orfbpUmUU8ARERFXVByp59Hn1rLGlgMwaVged3x6FF0zk12uTGKBAo6IiIRVIBDgjRV7eGLeRuoamslMS+RLV43g/NH5xMVpXRtpHyEHHGNMDjACSMSZJn6C9qISEZFQVB51Wm1Wb3VabSYOzeOOT4+kW5cUlyuTWBNSwDHG3Ar8FifctNbmvaiMMROAl6y1OcHHyUAN0NTitGXW2ova8roiIhKZAoEAb67cyxMvbqC2oZmM1ES+dNVwZowpUKuNdIhQW3C+BfwB+I61tubjvpkxJg64lQ9v2jkcOGytzfu4ry0iIpGpvKqOx/657sRYmwnn5nHHZ0aSrVYb6UChBpw+wCNnE26C7gUuB34MfK/F8bHA2rN8bRERiSB+f4BXl5fw5/mbqG90Wm2+eOVwLhirVhvpeKEGnNeATwDbz/L9HrfW/iC4MnJLY4AcY8x6nJWSFwHfsNbuP8v3ExERF5RV1vLoc2tPrGszeXgvbr96hMbaSNiEGnDWAb8yxswGtnHyWBmstd8O5UWstQdO81QtsBS4D/DibPA5F5gQYn0iIhIBfP4AC5YU8/R/ttDY5CMrI4nbrh7BlBG91WojYRVqwDkfWA6k4myw2VLgbIuw1t7Z8rEx5k7gkDGmj7V279m+voiIdLw9ZdU8+txatgb3kDp/dAFfvHIYWRla10bCL9S9qC7oyCKMMfcBf7fWbgkeOr42d0NHvq+IiJw9b7Of59/aznNvbKPZ5ye7SzL/fc1IJg7r5XZp0om1ZR2cXOArwFDAA2wB/mCtLW6HOkYA44wx1wUfPwIssNYeaofXFhGRDrK15DCPPreWPWXOHJSLJ/XjpiuGkpF6qlVFRMIn1HVwJgCvA3uBZTgL/V0BfNUYM8Nau+os67gVZ9zNjmBNC4AvneVriohIB6lvbOYv/9nC/CXFBALQq0c6X/3MKIYP7OF2aSJA6C04DwF/B2631p4Yc2OM+Q3wINCmLixr7UKga4vHlcCctryGiIi4Y/XWg/z2+XWUV9Xj8cRx9YwB/NfFg0lObNOaryIdKtSAMw74QstwE/QosLp9SxIRkUhUVdPAEy9sZNFaZwWPAQVZfPUzoxhQ0PUjflIk/EINOKVAIWBbHS/C2WJBRERilN8f4PUVe/jzS5uorfeSlBjPnIsNn5o+gPh4j9vliZxSqAHnGeD3xphvAO8Fj00Gfh18TkREYtDegzU89vw6NhVXAjBmcA63Xz2CvO7pLlcmcmahBpyfAL2B53BmUMXhLMj3KHBPx5QmIiJu8Tb7eP7N7Tz35naafX66ZiTzhU8NY/rofC3YJ1Eh1HVwmoAvGmPuAgxQD+yw1tZ3ZHEiIhJ+67Yf4v/9az37Dx0DYOaEvtw8ayiZaUkf8ZMikeO0AccYcxnwurXWG/y+tT7GGACstS93UH0iIhImR2oa+eP8jSxcvQ+A/J4Z3PGZkQwfoKnfEn3O1ILzEpAHlAe/P50AoLmBIiJRyu8P8NryEp5csNkZRJzg4dqZg7h6xkASE/TrXaLTaQOOtdZzqu9FRCR27DpwlMeeX4cN7h81xuRw29Uj6NVDg4gluoW6kvFbwNXW2iOtjvcEXrHWju2I4kREpGPUNXj526uW+UuK8fsDZHdJ5otXDteu3xIzzjQGZwZwbvDh+cCXjTGt17wZAgzomNJERKS9BQIBFq/dzx/nbeRwdSOeOLhian9uuHQIaSnaP0pix5lacCqBu3CmhMcBdwC+Fs8HgGPANzusOhERaTd7D9bw+L/Xs35HBQCmbzduu2YEA7USscSgM43B2YCzUjHGmLdxuqiqwlWYiIi0j4bGZp593fLiop00+wJkpiVx4+XnMnNCXzwedUdJbDpTF1WatbYu+PDy48dOdW6L80REJEIEAgGWrS/liXkbqThST1wcXDypH5+/7Fy6pGtNG4ltZ+qiqjHG9LLWluN0RbXeaBOcritNExcRiTB7yqr5/QsbWLfd6Y4aWJDF7deMZFDfbi5XJhIeZwo4FwKHg99fEIZaRETkLNXWe/n7a5aXlhTj8wfITEvkhkuHcNGkQuLVHSWdyJnG4Lxzqu8BjDFJwAhgm7W2uuPKExGRUPj9Ad5evZcnF2zmSI0zO+rS8wq5/pIh6o6STinUdXAGAn8E/hdYDyzDCThHjTGXWmvfO9PPi4hIx9m+t4rfzd1wYrG+IYXZfPmq4QzQ7CjpxELdTfxRoAbYDdwAFOBsunkz8CvgvI4oTkRETq+quoGnX97CGyv3ANAtM5mbZw1lxpgCLdYnnV6oAWcaMNpaW2aMuRJYYK3dboz5A/CNjitPRERa8zb7mLeomH+8Yalv9JEQH8enpg/g2k8O0mJ9IkGhBpwGINEYk46zqvEtweN5wNGOKExERE4WCARYvqmMP83bRGllLQATh+Zxy6yh9O6Z4XJ1IpEl1IDzKvAHnG6qOmC+MeYTwCPAvA6qTUREgkpKq3li3kbWbjsEQJ/cDL7wqeGMMTkuVyYSmUINOF8GfgL0Ay631tYaY8YDC4FvdVBtIiKdXlVNA3971fLae7vxByA9NZE5Fw/m0vMKSYj3uF2eSMQKKeBYa48BXwcwxnQxxnS11v6sQysTEenEmrw+Xly0k3++uZ36xmY8njiuOK+Qz11kyMpIdrs8kYgXagsOxpjbge8CvYOPy4FHFHRERNpPIBBgyboDPLlgM+WHnV1wxg3J5ZZZQ+mTm+lydSLRI9R1cO4Cvo/TTbUEZ4uGKcDdxph6a+0jHVeiiEjnsLXkMH+at4ktu51F5PvlZXLL7GEaZyPyMYTagnMHcJu19u8tji01xpQAP8YZbCwiIh9DaUUtT728maXrDgCQlZHEnEuGcNGEvsRrnI3IxxJqwOkJrDzF8dU4i/6JiEgbVdc28Y/XLS8v20WzL0BSgodPnT+Aay44h/RUrWcjcjZCDTgbgc8AD7Q6/llga1vf1BgzAXjJWpsTfJwE/Ab4NOADfmWtbf1eIiIxocnr46UlxTz3xjZqG5qJi4MLx/Xh+kuG0LNbqtvlicSEUAPOD4AFxpjJwLvBY5OBS4CrQ30zY0wccCvwy1ZP3Yuz9cMAIAt4xRiz31r7dKivLSIS6Xz+AO+s2cdfX9lCeVU9AKPO6cnNs4ZSlJ/lcnUisSXUaeKvBRf2+yrOXlT1wBZgvLV2XRve717gcpxxO99rcfxG4CZrbRVQZYz5Jc7aOwo4IhL1AoEAq7eW89SCzewurQacAcQ3zxrKGJOjfaNEOkDI08SttYuARWf5fo9ba39gjJlx/IAxpivQC9jc4rytwPCzfC8REddt21PFky9tZsPOCgB6dE1lzsWDuWBcH+I9CjYiHeW0AccYkwY8jDMuphGYC9xtra3+uG9mrT1wisPHN1Cpa3GsDkj7uO8jIuK2/YeO8czLW1i63vm1l5GayLWfHMTlU/qTlBjvcnUise9MLTj3ArOAX+AM/P0K0B1nYHF7qg1+bTmyLg041s7vIyLS4Q5V1fOPNyyvr9iD3+/MjJo9fQDXXHgOGZoZJRI2Zwo4nwaus9a+DWCMeQdYZIxJtNZ626sAa22VMaYMZ5Dx/uDhwZzcZSUiEtGOHmvkn29u5+Vlu/A2+/F44rhoYj+uu9jQPUszo0TC7UwBp4CTp4CvBDxALrCvnet4BvihMWY9TpfVXWjxQBGJArX1Xl54ZycvLtpBfaMPgGmj8rnuYkNBjrZWEHHLmQJOPE7XFADW2oAxphFI6oA6fgA8BGzCCVG/Bx7vgPcREWkXDU3NvLx0N8+/tY2aOqdRe9yQXG64dIimfItEgJBnUbUna+1CoGuLxw0420Hc4UY9IiKhavL6eOW93Tz/5naqahoBGFrUnRsuHcLQou4uVycix31UwLnJGNNysG8CcL0xpqLlSdba37Z7ZSIiEcTb7OeNFSX8441tVB5tAGBgn65cf8lgrWUjEoHOFHD2ALe3OlYG3NzqWABQwBGRmNTs8/PWqr3843V7YvXhwl5dmHPJYCYOzVOwEYlQpw041trCMNYhIhJRfD4/C9fs4x9vbKO0wlnNok9uBtddPJjzhvfGo0X6RCKaK2NwREQilc/n5+3V+3jujW2UVjrBpnePdP7rIsO00QVafVgkSijgiIjgdEUtXL2Xf7yxjbJKZ2H13j3S+exMw/mj84mP97hcoYi0hQKOiHRqzT4/b6/ay3NvfhBs8ns6wWb6KAUbkWilgCMinVKT18frK/bwr7e3cyg4eDi/Zzqfm6muKJFYoIAjIp1KQ2Mzr7y3m7kLd3C42lnHpk9uBtd+YpCCjUgMUcARkU6hrsHLgqW7eOGdnVTXNgFQ1DuLa2cOYvKwXpoVJRJjFHBEJKYdqWlk3uKdvLx0F7UNzQCYvt24duYgxg/J1To2IjFKAUdEYtLBw3XMXbiD15eX0NTsB5wtFT43cxAjz+mpYCMS4xRwRCSmlJRV8/xb21n0/n78/gAAE87N49MXnsOQ/tkuVyci4aKAIyJRLxAIsHnXYeYu3MHyTWUAeDxxzBhbwDUXnENhry4uVygi4aaAIyJRy+cPsGJTKf96ewe2pAqAxAQPMyf05aoZA8nrnu5yhSLiFgUcEYk6jV4fb63aywsLd3AguE9URmoil0/pz+VT+9MtM8XlCkXEbQo4IhI1jh5r5JV3d/PSkl0cOeasYZOTncaV0wcwc0JfUpL1K01EHPptICIRb+/BGl5ctJO3V+09MSNqQEEWV88YyJQRvbWdgoh8iAKOiESkQCDAuu2HeHFRMau2HDxxfNyQXK48fwAjBvbQVG8ROS0FHBGJKE1eH4ve38eLi6OFXxIAACAASURBVIrZXVoNQFJiPJ8Y14dZ04rok5vpcoUiEg0UcEQkIlQereflZbt55d3dJ7ZS6JaZzOVT+3PJpEKyMpLdLVBEoooCjoi4JhAIsHV3FfOXFLNs/QF8wYX5ivKzmD2tiOmj80lMiHe5ShGJRgo4IhJ2TV4fS9btZ/7iYnbsOwo4C/NNHdmbWdOKGFKYrfE1InJWFHBEJGzKKmt55d3dvLZ8DzV1TjdUZloSl0zux6WT+9OzW6q7BYpIzFDAEZEO5fcHeH9bOQuW7mLVloMEnF4oivKzuHxKf84fU0ByorqhRKR9KeCISIc4eqyRN1fu5ZV3d1Na6aw2nBDvYeqo3lx+Xn9Mv27qhhKRDqOAIyLtJhAIsKm4klfeLWHp+gM0+5xF+Xp2S+XSyYXMnNCPrpmaDSUiHU8BR0TO2rG6Jt5atZdX3tvN3oPHAIiLg7GDc7hkciHjz80j3qPWGhEJn4gJOMaYW4DfAY0tDt9hrX3KpZJE5AwCgQCbdx3mteUlLFm7/8QWCt0yk5k5sR8XTexHbnaay1WKSGcVMQEHGAM8ZK292+1CROT0qmoaeGvlXl5fUcL+Q7Unjo8a1JNLJhcycWgeCdobSkRcFkkBZyzwiNtFiMiH+fwB3rflvLa8hBWbyk4syJfdJZlPjO/LzAn96NUj3eUqRUQ+EBEBxxgTD4wAbjDG/AqoA54Afm6tDbhanEgntvdgDW+t2stbq/ZyuLoBcBbkmzg0j4sm9WOsydFO3iISkSIi4AA9gVXAU8DVwBDgRaAa+K2LdYl0OsfqvSxZu583Vu7BllSdON6rRzozJ/TlE+P7kt0lxcUKRUQ+WkQEHGttGXB+i0NrjTGPAteggCPS4Xw+P+u2V/Dmqj28t6H0xIDh1OQEpo7szScn9NX2CSISVSIi4BhjhgLXWmt/2OJwEtDgUkkiMS8QCFC8/yhvr97Hovf3UVXzwQTGEQN78MkJfZk8rBcpyRHxa0JEpE0i5TfXEeCbxph9wB+B0cDXgK+4WpVIDDpUVc877+/j7dV72VNWc+J47x7pXDCuDxeO7UOOpneLSJSLiIBjrd1vjJkN/AL4NVAB3G+tfd7dykRiQ3VtE8vWH+Cd9/exqbjyxH5QmWlJTB+dzwVjCxjUV1sniEjsiIiAA2CtfQsY53YdIrGivrGZ5ZvKeGfNPt635SemdicmeJhwbh4XjC1gzOBcEhM0C0pEYk/EBBwROXtNXh+rt5azZO1+lm8uo7HJB4AnDkYP6sn00QVMHt6L9NRElysVEelYCjgiUa7J62ONLWfJ2gOs2FxGfWPzieeGFGYzfXQ+U0b2plumpnaLSOehgCMShZq8Pt635SxZf4DlG08ONQMLspgyMp/po/I1WFhEOi0FHJEoUd/YzKotB3l3QymrtpRR3+g78VxRfhZTR/Zm6sh8bZkgIoICjkhEq6lrYsWmMt7dUMoaW443uAAfQFHvLKaO6s2Ukb3p3SPDxSpFRCKPAo5IhCk/XMd7m0pZvrGMTcWVJ2Y/gTOmZvLwXkwe3ou87mqpERE5HQUcEZcFAgF27jvK8k1lLN9Uyq4D1See83jiGHlODyYP782kYXl0z0p1sVIRkeihgCPigoamZtbvqGDV5oOs3FxGxdEPdiVJTY5njMll4rA8xg3JJTMtycVKRUSikwKOSJiUH65j5ZaDrNpykPXbD53Y0BIgu0sKE4fmMXFYHiMG9iAxId7FSkVEop8CjkgH8Tb72Fx8mNW2nDVbD1LSYt8ngHP6dGX8kFzGnZvLgPyueDzaJkFEpL0o4Ii0owMVx3h/azmrbTnrd1ScWEkYIDU5gdGmJ+OH5DJ2cC7dumjhPRGRjqKAI3IWauqaWL+jgnXbDrF22yFKK2tPer6wVxfGDs5htMnh3P7dte+TiEiYKOCItEGT18eW3YdZt90JNDv2HTmxMzdARmoio00OY0xPRpsczXoSEXGJAo7IGXib/WzfW8WGHRWs31HB1t2HTxocnBAfx5DC7owc1INR5/RkYJ9uxGssjYiI6xRwRFrwNvvZue8IG3Y6gWbL7sMnjaMBp9tp1KCejB6Uw7n9s0lJ1m0kIhJp9JtZOrWGxma2lhxmU/FhNu+qZGtJFU3ekwNNn9xMRgzswfCBPRhW1J2sjGSXqhURkVAp4Eincri6ga27D7NltxNodu47etJWCAB9cjMYWtSDEQN6MGxgd7plaraTiEi0UcCRmOXz+dldWh0MNFVsKTlM+eG6k87xxMHAgiyGFvVgaFE25/ZXC42ISCxQwJGYEAgEOFRVz7a9VWzbc4Rte6rYse/Ih8bPpCbHY/pmM7gwmyGF2Qwu7EZaSqJLVYuISEdRwJGoVFXTwM59R9mx7wjb9xxh294qjtQ0fui8vO5pDDkRZrLpm9dFs5xERDoBBRyJaIFAgMPVDezcf5Sde4+wIxhqDlc3fOjczLREBvXtduLPOX26qrtJRKSTUsCRiOFt9rOvvIZdB46y60D1ia/VtU0fOjc1OYEBBVkMyO/KwD5dMX27kdc9jbg4tc6IiIgCjrjA7w9QXlXHnrIaSsqqKSl1vu4rr6HZF/jQ+empiRT1zmJAQRYDC5xA06t7ujanFBGR01LAkQ7T7PNTVlnLvvJjwT817CmrYe/BGhpaDf49rlePdPr37kL/3lkU9c6isHcXenZNVcuMiIi0iQKOnJXjY2RKK2o5UFHLgUPHOFBRy77yGkorak/ZIgOQ3SWZvnld6JuXSb+8LvTLy6RPbqZmNImISLtQwJGP5G32c6iqjrLDdRw8XMfBylrKKus4UHGM0ora07bGAOR0S6UgJ5OC3AwKcjLpk5NB37wudElPCuMViIhIZ6OAI9Q3NnOoqo5DR+o5VFVPxZF6Dh2pd8LM4Toqj9aftGN2a5lpifTukUGvHun07pFOr54Z9MnJIL9nhvZpEhERV0TMvz7GmJHA48AIoBi4xVq70t2qoluT18eRY40cqWmk8mgDh6uDf442UHm0nsPVDVQebeBYvfeMr+OJgx7dUsnNTiMvO53c7mnkZqfRu0c6vXtmkJmm1hgREYksERFwjDFJwIvAw8B04BrgNWNMP2tttavFRQi/P0B9YzM1dU1U1zZRU9dETa3zfXXw2NFgmDlS08iRY43UNTSH9NqJCR56dk2lZ7dUenZNC351Hudmp9OjayqJCZ4OvkIREZH2ExEBB5gBJFprHw4+ftYY8xXgs8AfXKvqY/D5A/h8fpp9fpq8fpqafTR5fR/6vqGpmfqGZuqbmmlo9J14XNfYTF2Dl9r6ZmrrvdQ2eKmt91LX4MV/hm6iU4n3xJGVkUzXzGS6Z6WQ3SWF7l1SyM5KoXtWKtldnGNZGUmapSQiIjElUgLOucCWVse2AsNdqOWEugYv9/9pOYeq6gkEAgSAgD+AP+DMHgoEwOd3wkyzzwk2bQ0hbZGSFE+X9CQy05PITEuiS1rSSY+7ZibTNRhoumYmk5GaqOAiIiKdUqQEnAygrtWxOiDNhVpOqKnzYkuq8Db72/RzCfEeEuLjSEqMJynB43xNjCc5MZ7EROdxalICKcnxpCYnkJqcQMrxx0kJpKcmfvAn5fjXBOLj1U0kIiISikgJOLVAaqtjacAxF2o5ITc7jad/dAnH6pytAjxxccTFxeEJ5oy4uDjiPXEkJniIj/eQ4InD44lTq4mIiIjLIiXgbAb+p9WxwcDTLtRykozURDJStficiIhINImUgPM2EGeM+R/gNzizqEYAc12tSkRERKJSRAzqsNY2AZfiBJvDwD3AldbaQ64WJiIiIlEpUlpwsNZuBKa6XYeIiIhEv4howRERERFpTwo4IiIiEnMUcERERCTmKOCIiIhIzImYQcZnIR6grKzM7TpEREQkjFr82x/f+rlYCDi9AObMmeN2HSIiIuKOXsDOlgdiIeCsBKYBpYDP5VpEREQkfOJxws3K1k/EBQIduP21iIiIiAs0yFhERERijgKOiIiIxBwFHBEREYk5CjgiIiIScxRwREREJOYo4IiIiEjMUcARERGRmBMLC/21mTFmJPA4MAIoBm6x1n5okSBjTF/gj8AkoBz4qrX25eBzccD9wJeAJODPwLestc1huYgQtOE6xwK/Dp5XDTwB3G+tDQSf3wN0B44vmrTfWms6/gpC04brvBB4Hahvcfjn1tr7Y+XzNMZMA/7T6keTgV3W2kHBcyL68zzOGDMBeMlam3Oa56P6/jwuhOuM6vvzuBCuM6rvz+POdJ3Rfn8aY2YCPwPOwbnnHrTW/u4U50XEvdnpAo4xJgl4EXgYmA5cA7xmjOlnra1udfqzwLvA5cBU4AVjzChrbTHOh3M1MAZoBOYC3wXuC8uFfIRQr9MYkwYsAH4MXAAUAa8CZcDvjTE9gHygi7W2NrxX8dHa+HmOAf5prf3cKV4qJj5Pa+1iIKPFz/XBWeHzK8HHEf15wolfgLcCv/yIU6P2/oTQrjPa709o0+cZtfcnhHad0Xx/Bmv9F3Ajzu+iscCrxpjd1tpXW50eEfdmZ+yimgEkWmsfttZ6rbXPApuAz7Y8yRgzCBgH/MBa22StfQuYh/MXGJwP+WFr7T5r7SHgR8CXw3QNoZhBCNcJ9AHetdb+xlrrs9ZuB17A+UsJzl/i7ZF2s7Uwg9CuE5xrWXua14mVz7O1PwHPWGtfCz6O9M8T4F7gdpx/1E8pBu5PCOE6if77E0K7Toju+xNCv86Woun+LAT+Zq2da631B1uPFwJTWp4USfdmZww45wJbWh3bCgw/xXl7Wv1Fa3neucDmVs/1NsZkt2OtZyOk67SOq44/DrYUXAq8Hzw0BvAYY1YYYw4ZY141xgzpwLrbKtTPE5xr+aQxpsQYs8cY86AxJrnF60T959mSMebK4M/9oMXhSP88AR631o4FVp3hnGi/PyGE64yB+xNC+zwhuu9PCP06gei7P621i621tx1/HPxvP40P/i4eFzH3ZmcMOBlAXatjdUBaG89r/fzx71u/jltCvc4Tgr9M/h487/HgYR+wAqdJsR/OX+b/BJvOI0FI12mMSQD24TSHDgEuBD6J0xd8qteJ+s8TuAf4mbW25ZiGSP88sdYeCOG0aL8/Q73OE6L0/gzpOmPg/mzz50mU3p8AxpgsnFaZ5TjdVS1FzL3Z6cbgALVAaqtjacCxNp7X+vnjH07r13FLqNcJgDEmD6d/1Q988vhNZ639RavzvgP8N05T6uJ2rvnjCOk6gwPYPtHi0A5jzE+AnwPfPsXrRPvnOQIYCjzV8ngUfJ6hivb7s02i+P4MSQzcn20SzfdnsAvqRZxWmDnWWn+rUyLm3uyMLTibgdYj0gdzcpPZ8fP6GmNST3Ne69cZDJRaa4+0Y61nI9TrxBhzLs5Atx04vzyrWjz3DWPM1Banx+ME44Z2r/jjCek6jTH5xphfBpv4j0vig+uImc8z6FPAf1oPtI6CzzNU0X5/hizK78+QxMD92VZReX8aY6bjtNq8AHzaWnuquiLm3uyMLThvA3HGmP8BfoMzG2UETtPoCdZaa4xZB/wkmKLPw/lLOTl4yjPAXcaYN3ES6Y+CxyJFSNdpjOkGvAY8a6296xSvUwjcYIy5AjiC839U24E1HVd6m4R0nUAlMAeoM8bcB/QHvoczyA9i5PNsYRLw5imOFxLZn2dIYuD+DEkM3J+hivb7s62i7v40xgwAXgLusdY+errzIune7HQtONbaJpxBetcAh3H6Qa+01h4yxswxxrRsJrsGpz+4HGftiVuttRuDzz0O/BNYhvMXcDMnDxZzVRuu8wacaYm3G2OOtfjz9+DzdwPv4fQFl+NMU51lrfWF83pOJ9TrDP6fxqU4U6wrgUU4n9+vgi8VK5/ncYXAqcYERPTneSaxdH+eSSzdn2cSS/fnmcTQ/XkHkAk80Orv4s8j9d6MCwQCH32WiIiISBTpdC04IiIiEvsUcERERCTmKOCIiIhIzFHAERERkZijgCMiIiIxRwFHREREYk5nXOhPRMLAGPMkzs7Bp3Mvzm7EbwOZ1tqwLLtvjIkHlgKft9ZuO8N5Hpw1SW6w1tpw1CYi7UctOCLSUb4O9Ar+mRE8NqHFsV/iLPbVC2dF03D5GrDuTOEGILjHzn18sLGliEQRLfQnIh3OGDMM2AD0t9budrGOFGAPcGGLlVU/6md24qzEurAjaxOR9qUuKhFxjTFmBi26qIwxAeC/gO/gbMi3Crge+BbOtgXVwHestc8Efz4TeAj4NBAA3gK+bq091TL4AJ8DjrQMN8aY7wNfAnoCW4DvWmv/0+Jn5uK0Ri1sh0sWkTBRF5WIRJqfAd/A2ZCwL85Gg9XAeODfwO+MMRnBc3+PE4QuBs7HCTmvGmNO9z9vlwOvHH9gjLkq+F7X4+xqvAD4pzGmS4ufeQX45BleU0QikAKOiESax6y1b1tr1+LsXnwMp1XF4my+mAr0N8YU4bTIXGetXRlslbkBZyPDS07z2uOATS0eFwKNQEmw6+w+4GrA2+KczUAGTgASkSih/yMRkUizo8X3dcBua+3xwYINwa/JQL/g99YY0/Ln03BadV46xWvnAhUtHv8FZ6ZXsTFmNTAP+LO1tr7FOZXBrzltvA4RcZFacEQk0nhbPfaf5ryE4LmjgVEt/gwC/nyan/EDcccfWGsPAWNxWnyWATcB64ODoo87/nvSF/IViIjrFHBEJFptARKBdGvtDmvtDqAUeBAn5JxKGc5gYgCMMVcDX7bWvmat/TpOy08NcFmLn+nZ4mdFJEqoi0pEopK11hpj5gFPG2PuAA4BP8EZnLz1ND+2GhjZ4nE88KAx5iDOjK1JQF7w++NGAlWc3HUmIhFOLTgiEs1uxAkjLwArgSxgprX2yGnOX4Az2woAa+0/gR/itPpsA34MfMVa+1aLn5kOvGKtVReVSBTRQn8i0mkYY9KA3cAl1to1IZzvAUpwZmot7uDyRKQdqQVHRDoNa20dTmvNHSH+yKeAYoUbkeijgCMinc2vgRGm1dzy1oKtN/cAt4WlKhFpV+qiEhERkZijFhwRERGJOQo4IiIiEnMUcERERCTmKOCIiIhIzFHAERERkZijgCMiIiIxRwFHREREYo4CjoiIiMScqN9N3BiTDIwHSgFthiciItJ5xAO9gJXW2saWT0R9wMEJN9onRkREpPOaBixpeSAWAk4pwF//+lfy8vLcrkVERETCpKysjDlz5kAwC7QUCwHHB5CXl0dBQYHbtYiIiEj4fWiIigYZi4iISMxRwBEREZGYo4AjIiIiMUcBR0RERGJOLAwyPiO/309FRQVHjhzB54uNZXJSUlIoKCggMTHR7VJEREQiUswHnH379hEXF0dhYSGJiYnExcW5XdJZCQQCVFZWsm/fPvr37+92OSIiIhEp5ruoamtryc/PJykpKerDDUBcXBzdu3enoaHB7VJERETOaNeBoyxdf4BAIBD29475FhwAjye2clwsBDUREYlNzT4/724oZcHSXWwqrgTg51+Zyrn9u4e1jk4RcERERKRjVdU08Np7Jfzn3d1UHnV6GVKTE7hkciGD+nYLez0KOCIiIvKxbdtTxfwlxSxZe4Bmnx+APrkZXD6liAvGFpCW4s6EGAUcERERaRNvs4/Faw/w0pJitu89AkBcHEwcmscVU/sz8pyerg+nUMARERGRkFQerec/y3bz6nslHDnWCEBGaiIXTezHZVP6k5ud5nKFH1DAiRDf/OY3yc7O5p577gHA5/Mxbdo0fv3rXzNx4kSXqxMRkc4qEAiweddhXlpSzLsbSvH5nRlRhb26cMXUIs4fk09KUuTFicirqIPd+8R7rNpyMGzvN25ILj/8wqSPPO+qq67i7rvv5u677yY+Pp6lS5eSkpLChAkTwlCliIjIyRq9Pha/v4/5i3dRfOAoAB5PHFNG9OaKqf0ZWtTd9W6oM+l0ASdSnXfeeXg8HpYvX855553H/PnzmTVrVkT/5RERkdhTXlV3ohuqpq4JgC7pSVw8qR+XTu5Pz26pLlcYmk4XcEJpTXGDx+Nh9uzZzJ8/n9GjR/PGG2/w73//2+2yRESkEwgEAmwsruSlJcW8t6GUYC8UAwuyuGJqEdNG5ZOUGO9ukW3U6QJOJLvqqqv43Oc+x6RJkxg0aJC2YhARkQ7V0NTMO2v289KSYnaXVgMQ74lj+sjezJpWhOnXLWp7EhRwIsiAAQPo168fDz/8MF/84hfdLkdERGJU+eE6Xl62i9eWl1BT5wWga0YyF0/ux6WTC+meFR3dUGeigBNhrrrqKh544AEuu+wyt0sREZEYEggE2LizkvlLilm+8YNuqHP6dGXWtCKmjuxNYkJ0dUOdiQJOhJkzZw5z5sxxuwwREYkRp+uGOn9UPrOm9cf0y3a5wo6hgCMiIhKDTtkNlZnMJZMKufS8QrK7pLhcYcdSwBEREYkRx2dDzV/84W6o2dOKmBJj3VBnooAjIiIS5Rq9Pt5Zs4/5iz/ohkqIj2PaiNjuhjoTBRwREZEo9cGifLs7ZTfUmXSKgOP3+/F4PG6X0W4CgYDbJYiIiEuO7w01f3Ex724sxR/sh+qM3VBnEtaAY4yZCfwMOAcoBx601v7OGJMM1ABNLU5fZq296GzfMz09nf3795Obm0tiYmLULlh0XCAQoLKykpSUzpvKRUQ6oyavj0Wt9oaK98Rx/uiCTtsNdSZhCzjGmD7Av4AbgReBscCrxpjdQCVw2Fqb197vW1BQQEVFBSUlJTQ3N7f3y7siJSWFgoICt8sQEZEwqDxaz4Klu3j1vRKqa512gKyMJC6ZXBgzi/J1hHC24BQCf7PWzg0+XmmMWQhMAfYDazviTT0eDzk5OeTk5HTEy4uIiLS7QCDA1t1VzF9SzLL1B/AFu6EGFGQxe1oRU0dG395Q4Ra2gGOtXQwsPv7YGJMNTAOeAS4Bcowx64FcYBHwDWvt/nDVJyIi4jZvs4/Faw8wf0kxO/YeAcDjiWPKyN7MnlbEkMLsqB9qES6uDDI2xmQB84DlON1V04ClwH2AF/g/YC4wwY36REREwqmqpoFXlu3m5Xd3c6SmEYDMtEQunlTIZef1p2c3dUO1VdgDjjFmEE6o2QzMsdb6gTtbnXMncMgY08dauzfcNYqIiITDjr1HmLd4J4vXHqDZ5wegsFcXZk0r4vwxBSSrG+pjC/csquk44eZx4LvW2kDw+H3A3621W4KnJgW/NoSzPhERkY7m8/l5d2Mp8xYVs2X3YQA8cTBpWB6zpw1g2IDu6oZqB+GcRTUAeAm4x1r7aKunRwDjjDHXBR8/Aiyw1h4KV30iIiIdqbq2iVff283LS3dRcdT5//f0lARmTuzH5VP6k9c93eUKY0s4W3DuADKBB4wxD7Q4/hhwK864mx3BmhYAXwpjbSIiIh2ipLSa+UuKeXv1Ppq8PgAKcjKYNa2IC8b2ITW5U6y5G3bhnEV1J63G2rQyJ1y1iIiIdCSfP8CqzWXMW1zM+h0VJ46PHZzD7GkDGDWoJx6PuqE6kmKjiIhIO6lr8PL6ij28tKSYsso6AFKS4vnE+L5cMbU/BTmZLlfYeSjgiIiInKUDh44xf0kxb67cQ32j0w2Vk53GrKn9+eSEfmSkJrpcYeejgCMiIvIxBAIB1m0/xLzFxazacpDj+yAPH9CDWdOKmDA0j3h1Q7lGAUdERKQNGpqaeWfNPuYtLmZPWQ0AiQkeZowpYNa0Ivr3znK5QgEFHBERkZBUHKnn5WW7eOXd3dTUeQHI7pLMZef155LJhWRlJLtboJxEAUdEROQMbMlh5i0qZmmLTS/P6dOV2dOKmDIyn8QEj8sVyqko4IiIiLTS7POzbP0B5i0qxu6pApxNL6eO7M3saQMYXNhNqw1HOAUcERGRoOOrDS9YuovK4GrDGamJXDypH5dPKdKml1FEAUdERDq9PWXVzFt88mrDfXIz/n97dx5e1XXee/wrCSQk5nkGjV4Y29hmMmYQkjzFI4NomsRJ45s8t2nqtEnTpLcZbhLH6c3kJM7QPE6aNk3dJmmNGAx4wDYSAszkCdtgXutIQmIehJg0oeHcP/aROVaEfAQ6OoN+n+fRY/Y++2y92/ss6dXa612L+xdlkT9zEgM023DM0R0TEZE+qa3Nz2t2gqdLy3n93UtLH7bPNnyzG63HUDFMCY6IiPQpjU0tvPTKQdZtKefwyToAUpKTKJg9mfsXZjJ5rGYbjgdKcEREpE84UVvPM9sqeW5HFXUNXpn3qGGp3LcggzvnTWVwWnKEI5SepARHRETilt/vx6pqWVtazstvHaUtUOZ9bfoI7l+UyfwbxpOUpDLveKQER0RE4k5Laxvb9hzh6S3lvFt9BoCkxAQW3zyJB3IzuWbK8AhHKOGmBEdEROLG+fqLPLf9/WXeg9P686Fb07lnfgajhqnMu69QgiMiIjHv4PHzrNtSwUuvHPzTMu9ZkxiQrF93fY3uuIiIxCS/388b755kbWk5r+4/8d7+mdPGsERl3n2eEhwREYkpTc2tlLx6iKe3lL+3mndy//Yy7wymjBsS4QglGijBERGRmHD6XCPPbKvk2e0HOFd3EYARQwZw7wJvNe8hA1XmLZcowRERkahWfugMT2+poPT1Q7S0emXe2ZOHsSQ3iwUzJmg1b+mUEhwREYk6rW1+du87xtrSct4urwEgMQFuvWE8S3KzmJ4xQuNrpEtKcEREJGo0NLXw4q5q1m2p4GiNt4xCako/7rxlKvctzGDcyIERjlBihRIcERGJuBO19azfWsnGHQeoa2wBYOyINO5flMkdc6eQNqB/hCOUWKMER0REImZ/1WnWbn7/MgrTM0awJDeLW64fT1KiHkPJlVGCIyIivaq1tY3tbx9l7eZy9lfVApeWUViyOJOcyVpGQa6eEhwREekVdQ3NvLCrinVbKjhR2wDAoNT+3DVvKvctzNQyCtKjlOCIiEhYHaupY92WCl7YVUVDDtoDTgAAIABJREFUk7eMwoRRA3kgN4vbZk9mQIp+FUnP06dKRER6nN/vZ1/ladaWlrPz7aMEhtcwI3sUSxZnMXvaWBI1vkbCqFcTHOfcHcD3gBzgBPBDM/uVcy4Z+AWwAmgFfmxm3+3N2ERE5Oq1tLaxbc8R1paWU3bwDAD9khLIu3kSS3KzyJw4NMIRSl/RawmOc24yUAR8ElgLzAKed84dAPIAB2QBQ4HnnHOHzew/eis+ERG5chcamtm44wDrtlRw6mwjAIPTkrlnfjr3LMhgxJABEY5Q+pre7MFJB35vZqsD27udcyXAAryk5yEzqwVqnXOPAZ8BlOCIiESxo6fqWLe1ghd2VtF40RtfM2nMIJbkZpE3axIDkjUSQiKj1z55ZrYF2NK+7ZwbASwCngTGA/uCDt8P3NBbsYmISOiCx9fsePso/sD4mptyRrNkcRYz3RiNr5GIi0hq7ZwbCjwN7AReDeyuDzqkHkjr7bhEROTyWlrbePnNI6zZ/P7xNYtneuNrMiZofI1Ej15PcJxz1+CNwdkHPAi0T3wQPAFCGnChl0MTEZFOeONrqli3tYJTZ7z5azS+RqJdb1dR5eIlN08AXzUzP9DonDuGN8j4cODQabz/kZWIiPSyYzV1PL2lgheD5q+ZOHoQSxdrfI1Ev96sosoC1gNfM7Ofd3j5SeCbzrk3gUHAl4Cf9lZsIiLi8fv97D9Qy+rNvvfNX3NjziiWLs7W+BqJGb2Zfj8MDAa+65wLnuPmn4FvAD8C9gKJwK/xenlERKQXtLa28fJb3vpQVu2tD9U+f83SxRpfI7EnpATHOXcDcDcwGxiDNxnfMWA3sN7MfB90DjP7IvDFLg55OPAlIiK9pL6xmY07q1m3pfx960PdPT+d+xZmanyNxKwuE5zAmJlHgPnALrxxMT4gCRgFfBz4gXNuM/ComZWGN1wREekJJ2rrWbelgo07q6hvbAG89aGWLM6iYJbWh5LYd9lPsHPu34Dr8JZQWGZmZy5z3BDgo8Djzrk3zeyhcAQqIiJXr+xgLWtKytn65hHaAgNsrs8aydLcLOZMH6fxNRI3ukrRN5jZpz7oBGZ2DvgV8Cvn3Ioei0xERHpEa5uf3fuOsWZzOXsragBITExg8c2TWLI4k5zJwyMcoUjPu2yCY2ZF3T2Zma28unBERKSnNF5s4aXdB3m6tJwjp+oASBvQj7vmpXP/wkxGD0/9gDOIxK5QBxmnAf8H+E8zK3PO/Qpvkr5dwINmdjSMMYqISDfUnmtk/bZKnn25kvP1zQCMGZ7KA7lZ3DF3CmkD+kc4QpHwC3UU2U+BAuB/nHNL8BbH/BtgKfAz4M/CE56IiISq6ug51mwup+S1Q7S0tgFwzZRhLF2czfwbxpOUlBjhCEV6T6gJzhLgPjPb65z7CvCCmf2Lc24bsD184YmISFf8fj9vvHuSNZvLec1OAJCQAPOuH8fSxdlMzxhBQoIGDkvfE2qCkwocd84lAncB3wrs9+PNiSMiIr2ouaWN0tcPsWZzOQeOngMgJTmJ2+dM4YFFmUwYPSjCEYpEVqgJzm68MTgngeHAaufcBOBRYEeYYhMRkQ4u1F/k2e0HWL+1gtPnmgAYPjiF+xZmcvf8dAanJUc2QJEoEWqC8zng90A68NdmdsQ593O8BTKXhSk2EREJOFZTx9rScl7cVU3jRa/jfOq4wSxdnM3imRPp3y8pwhGKRJeuJvqbD+wwszYz2wfc1OGQr5rZ+bBGJyLSx+2vOs3qEh873rq08OVN14xm2eJsbnajNb5G5DK66sF5DHDOuS3ARmBj8JpTSm5ERMKjtc3Prr1HWV1SzjsHTgNa+FKku7qa6G9+YBmG24A7gC8655KAF/ASnpfMrLZ3whQRiX/tE/Ot3VzO0RpvYr6Bqf25+9Z07luYwcihmphPJFRdjsEJLMOwOvCFcy4DuBP4CPCEc64cr2T86+EOVEQkXtWeb2TD1kqeCZqYb+yINB7IzeSOuVNJ1cKXIt3WrVZjZpVcWncqEZiD17sjIiLdVH3s0sR8zS2XJuZblpfNrddrYj6RqxFyguOcy8NbXTylw0v1PRmQiEg88/v9vF1ew6oSH6+8cxzwJua75bpxLMvTxHwiPSXUtagex1uaoRpo7PCyH/hxD8clIhJXWlvb2PbmEVaX+PAdOgtAcr9EbpszhSWLs5ioiflEelSoPTh/AXzKzH4XzmBEROJNfWMzL+yqZm1pOSdrGwAYOiiZe+dncM+CDIYO6tgpLiI9IdQEpx5v5XAREQlBzdkG1m2p4LntB6hrbAFg4uiBLF2cTf7syaT018R8IuEUaoLzHeAx59znAgONRUSkEweOnmN1iY/S1w/R0urNzHdd5kiWLc5izvRxJCZqfI1Ibwg1wXkH+H+Azzn3Jy+amf4UEZE+y+/3s6fsJKtLLq3onZgAC26cwPK8bK6ZMjzCEYr0PaEmOL/GW1Tzt6hqSkQEgJbWNra+cZjVJeVUHPEGDqckJ3HH3Cksyc1i3MiBEY5QpO8KNcGZDNxtZhXhDEZEJBbUNzazcWcVa0srOHXGGzg8bHAK92tFb5GoEWqC8wKQCyjBEZE+q33g8LPbD1AfGDg8acwgluVlkzdzEskaOCwSNUJNcHYCv3DOFQI+oDn4RTP7h54OTEQkWnQ2cPj6rJEsy8tm9rSxGjgsEoVCTXDuAHYDg4CbOrzm79GIRESigN/v503fKVaV+HhtvwYOi8SakBIcM8sPdyAiItGgtbWNrXuOsHqzj/JDQQOHAzMOa+CwSGy4bILjnPsm8AMzawjlRM65wcCXzewbPRWciEhvaWhq4YWdVawtLedE0IzD9y3M5J75GQwZqIHDIrGkqx6cs8Be59xKYJWZ7eh4gHMuAZgNfBxYDvwklG/qnJsLrDezMYHtFOA8cDHosJfN7M6QrkJE5ArVnmtk3dYKnnn5AHUN3vBCzTgsEvsum+CY2eOB5OYfgI3OuRa8Cf9OAQnAaLzVxROAfwcWmFl1V98skBB9Gnisw0s3AKfNbNwVXoeISLccPH6eNZvL2fTKQVpa2wC4Nn0Ey/KyueU6zTgsEuu6HINjZoeAv3XOfQXIA2YBY4E2vMqqR4BiM2sK8fs9AtyLt/TD14P2zwLe6FbkIiLd5Pf72Vd5mtUlPnbuPQZAQgLMu34cy/NyuDZjRIQjFJGeEuog4zpgQ+DrajxhZt9wzuV12D8TGOOcexMvgSoFvmBmh6/y+4mI0NrmZ+fbR1lV4sOqagHo3y+R2+ZMYeniLCaOHhThCEWkp4VaJt4jzOzIZV6qA7YB38abY+dnwGpgbi+FJiJxqKm5lU2vHGRNiY8jp+oAGJzWn3sWZHDfgkyGDU6JcIQiEi69muBcjpl9MXjbOfdF4KRzbrKZHYxQWCISo87XX+SZbZWs21rB2Qte7cKYEWkszc3ijrlTGJASFT/6RCSMoqKVO+e+DfzBzN4J7Gqvx2yMUEgiEoOOn65nbWk5G3dW0XSxFYDsSUNZnpfD/BnjSUpKjHCEItJbQkpwnHPpZnYgjHHMAGY75z4W2P4psMHMTobxe4pInCg/dIZVJT627jlCW5s3ufpMN4bl+dnMyB5FQoIqokT6mlB7cHzOuR3AfwJPmVlND8fxabxxN75ATBuAv+zh7yEiccTv9/P6uydZXezjjTLvb6GkxATyZk1ieV42GROGRjhCEYmkUBOcTOBjwGeBnzrnNgL/BawNdabjYGZWAgwL2q4BHuzueUSk72kJLKWwqriMyiPnAEhNSeLOW9J5IDeTMcPTIhyhiESDUMvEq4HvAd9zzl0PfAT4CvBr59xq4EkzezF8YYpIX9fZUgrDBqfwwKJM7r41nUFpWkpBRC65kkHGh4ByoALIwVtd/A7n3HngITPb3oPxiUgfd+Z8E+u3VrBhWyUXgpZSWJaXQ/6sSSRrKQUR6USog4wHAkvwem7uBI4Dvwe+bmZ7nXOJwC+B/wamhClWEelDjpy6wJrN5by0q5qLLd5SCtOmDqewIIe507WUgoh0LdQenBN4C2GuAj4UGEPzHjNrC4zLWdSz4YlIX/NudS2rSnxsf/MIgYIobrluHMvzs5meMTKywYlIzAg1wXkIeLqzNaecc2PM7ISZrcJLgEREusXv9/OanWBVsY83facA6JeUwG2zJrMsL5vJYwdHOEIRiTWhJjh/BMYB75uXxjk3BdgHaCEXEem2ltY2tr5xmKJiHweOehVRaQP6cfet6dy/KJORQ1MjHKGIxKrLJjjOuY8CywKbCcBvnHMde3CmAqfDFJuIxKnGphY27qpizeZyTgYqokYMSeGBRVl86NZ0Bqb2j3CEIhLruurBeQG4Ay+5AWgIfLXzAzuBfw9LZCISd85eaGLd1gqe2VbJ+XqvImrSmEEsz8smb9Yk+vdTRZSI9IzLJjhmdgr4FIBz7gDwQzOr752wRCSeHKupY3WJjxdVESUivaSrR1T3AC+YWTOwG8hzznV6rJk9E57wRCSW+Q6dYVWxj217Dr9XETVn+lgK83O4LlMVUSISPl09olqPN7D4RODfl+MH1K8sIoBXEbWn7CRFm96/RlTBrEksz89m6rghEY5QRPqCrh5RJXb2bxGRzrS2+Xn5TW+NKN+hs8ClNaKW5GYxergqokSk94S8VINz7lPAOTNbGdj+H2CdmT0ZruBEJPo1NbeyaXc1q0vKOVpTB8CwQSncvyiTe+ZrjSgRiYxQl2r4GvD3eKuJt3sLeNw5N9LMHg9HcCISvS7UX+SZlw+wbksFZy54M0iMHzmQZXlZFMyZQorWiBKRCAq1B+czwEfMbGP7DjN71Dn3Ct4aVEpwRPqImrMNrNlczvM7DtDQ1ApA1qShFObnMH/GBJJUESUiUSDUBGc4UNXJ/nJgbM+FIyLR6uDx86wq9lHy2kFaWr2SqJtyRrOiIIcZOaNISFBiIyLRI9QEZwfwj865/21mLQDOuSS8x1a7wxWciETe/qrTFG0qY+feY/j9kJgAC2+cQGF+DtmTh0U6PBGRToWa4HwJeAmods69iVcafkPg/XeHKTYRiRC/38+r+0+wclMZeytqAOjfL5Hb5kxhWV4WE0Zp+TkRiW4hJThmtsd5s/x9BLgWuAisBf7LzM6HMT4R6UWtrW1s6bD45cAB/bhnQQb3L8xk+JABEY5QRCQ0IZeJm1mNc+554CCQCOxXciMSHxovtvDirmpWby7nxGlvRZYRQ1JYkustfpk2QItfikhsCbVMfBDwr8AKoBlvAc5+zrkXgEIzqwtfiCISLufqLrJhWyXrt1Zwru4iABNHD2R5fg75WvxSRGJYqD04P8Ybc3MrlwYVzwF+A3wf+FzPhyYi4XKytoE1pT427qii8aJX6n3NlGEU5udwy/XjVeotIjEv1ARnObDMzHYF7dvlnHsYWIkSHJGYUH3sHEXFPja/dojWwOqXM90YVhTkcH3WSJV6i0jcCDXBSQROdbL/NKByCpEot6+yhqJNPnbtOwZ4pd65N0+kMD+HzIlDIxydiEjPCzXBKQW+5Zz7hJldBHDOpQDfBLaEKzgRuXJtbX5e2X+cok1l7Ks8DUByv0RunzuFZXnZjBs5MMIRioiET3fmwdkKHHTOvRHYdyPQCHwoHIGJyJVpaW2j9PXDFBWXUX3MK3QcmNqf+xZkcN/CTIYNTolwhCIi4RfqPDg+59y1wIPAdKABeApvHpyGMMYnIiFqbGph464q1mwu52St1yxHDh3A0sVZ3HnLVJV6i0if0p15cGqBX4QxFhG5AmcvNAVKvSs5X++Vek8aM4jC/GwWz5xM/36JEY5QRKT3XTbBcc7txluS4QOZ2dzufFPn3FxgvZmNCWwn4yVPK4BW4Mdm9t3unFOkrzlxup41peVs3FlFU6DU200dzoqCHOZOH0eiSr1FpA/rqgdnfU9/M+dcAvBp4LEOLz0COCALGAo855w7bGb/0dMxiMS6qqPnKCouo/T1w++Ves+a5pV6X5epUm8REegiwTGzR8Lw/R4B7gW+A3w9aP8ngYcCj8FqnXOPAZ8BlOCIBOytqKGouIzd+44DkJiYwOKbJ1FYkE3GBJV6i4gEC3kMjnPuw8CXgRxgJvDXwDEz69gb05UnzOwbzrm8oPMOA8YD+4KO2483c7JIn9bW5mf3vmMUFft450Cg1Lt/EnfOncLSvGzGjkiLcIQiItEp1LWoHsJ7rPQj4P8Gdu8Hfuyc62dm3wvlPGZ2pJPd7RMF1gftqwf0k1v6rOaWNkpfP0RRsY+Dx71S70Gp/bl3obeq99BBKvUWEelKqD04fw981syecs59FcDMfuOcq8VLfEJKcC6jfaHO1KB9acCFqzinSExqaGrh+R1VrN3s49TZRgBGDR3AksXZ3DVvKqkpIXe6ioj0aaH+tMwCXulk/xvAuKsJwMxqnXPH8AYZHw7snsb7H1mJxLWzF5pYt7WCDVsrudDQDMDksYMpzM8m9+ZJKvUWEemmUBMcA24H/qXD/g/jPaq6Wk8C33TOvYn3yOpLwE974LwiUe346XrWlPjYuKuai81eqfe16SMozM9mjkq9RUSuWKgJzleBlc652YH3/JVzLhu4D2/umqv1DbzxPXvxFvb8NfBED5xXJCpVHjlL0SYfW/Ycpi1Q6j1n+lgK871SbxERuTqhLtXwbGByvi8DbwN3AO8A88zste5+UzMrAYYFbTcCDwe+ROKS3+/n7YoaijaV8er+E4BX6p0/axLL83NIHz8kwhGKiMSPrmYyvgd4zszaAMxsL/BQL8UlEjfa2vzs3HuMouIyrKoWgJTkJO68ZSpLc7MYo1JvEZEe11UPzlqgxjn3B+B3ZvZGF8eKSAfNLW2UvHqQVSU+Dp3wigIHp/XnvoWZ3LsgQ6XeIiJh1FWCMxH4SODr8865vcDvgN9fZj4bEQHqG5u9Uu/ScmraS72HpbIssKr3AJV6i4iEXVdLNZwAfgb8zDmXDnwU+ATwXedcMd4yCkVm1tAbgYpEuzPnA6Xe2yqpC5R6Tx03mOX5OeTePJF+SSr1FhHpLaEOMj4AfBcvuZkOfAyvsuqXzrkiM/tf4QtRJLodq6ljVYmPl3ZVc7GlDYDpGSNYUZDD7GvHavFLEZEI6HZfuZntCyyGacAX8Hp1lOBIn1Nx+CxFm8rYuucwgUpv5k4fx4qCHK7NGBHZ4ERE+rjuLLY5FFiKN7nfbUAF8F/A8vCEJhJ9/H4/b5WfYuVLZbz+7kkAkhITKJg1ieX52Uwdp1JvEZFo0GWCE5TU/BneTMbngD8C3zKz3eEPTyQ6tLX52fH2UYqKy3i3+gwAA5KTuHPeVJbmZjN6eOoHnEFERHpTV/PgbMDrqWkD1uHNWPycmbX0UmwiEdfc0krxq4dYVVzG4ZPeurBDBiZz/yKv1HtwWnKEIxQRkc501YMzCG9m4afM7FwvxSMSFeobm3luu1fqffqcV+o9Zngqy/KyuX3uFAYkq9RbRCSadVUmvrg3AxGJBrXnG1m3pYJntlVS1+h1VqaPH0JhfjYLb1Kpt4hIrNCfoSLA0VN1rC7x8eLuapoDpd7XZY5kRUEOs6aNUam3iEiMUYIjfVr5oTMUFfvYFlTqfct1Xqn3tHSVeouIxColONLn+P1+3vSdYuWmMt4IlHr3S0qgYOZkludnM3ns4AhHKCIiV0sJjvQZrW1+drx1lJXFZfgOeqXeqSlJ3DUvnSW5WYwaplJvEZF4oQRH4t7F5laKXz3IqmIfR055pd5DByVz/8JM7lGpt4hIXFKCI3GrrqGZZ7cf4OnScmrPNwEwZkQaywOl3in9kyIboIiIhI0SHIk7p8818nRpOc9uP0B9oNQ7Y8IQCvNzWHjjBJJU6i0iEveU4EjcOHLygreq9+6DtLR6pd43ZI1iRUEON7vRKvUWEelDlOBIzCs7WEvRJh8vv3UEvx8SEuDWG8azoiCHa6YMj3R4IiISAUpwJCb5/X7eePckRcVl7Ck7BXil3vmzvFLvSWNU6i0i0pcpwZGY0trm5+U9R1hZXEbF4bMApKb04+5b03kgN5ORQ1XqLSIiSnAkRlxsbuWl3dWsLinnaI1X6j1scAoPLMrk7vkZDErtH+EIRUQkmijBkah2oaGZZ1+u5OktFZwJlHqPHzmQZfnZ3DZ7Mskq9RYRkU4owZGoVHO2gbWlFTy3/QANTV6pd9akoRTm5zB/xgSSElURJSIil6cER6LKoRPnWVXso/jVQ++Vet+YM4rC/Bxuukal3iIiEholOBIV3q2uZeWmMna8ffS9Uu8FN06gMD+bnMkq9RYRke5RgiMR4/f7ed1OsnJTGW+Vt5d6J3LbnMksz8tmwuhBEY5QRERiVdQkOM65TwG/ApqCdj9sZr+LUEgSJq2tbWx78whFm3xUHPFKvdMGtJd6ZzFiyIAIRygiIrEuahIcYCbwIzP7x0gHIuHRFCj1XlXs4/jpegCGD07hgdws7r41nYEq9RYRkR4STQnOLOCnkQ5Cet6F+otseLmSdVsqOHvhIgDjRw1keV42BSr1FhGRMIiKBMc5lwTMAD7hnPsxUA/8Bvi+mfkjGpxcsZqzDazZXM7zOw7Q0NQKeKXeKwpyuPUGlXqLiEj4REWCA4wGXgF+BywHrgXWAueAX0YwLrkCB497pd4lrx2kpdXLT2/KGc2Kghxm5IxSqbeIiIRdVCQ4ZnYMWBy06w3n3M+BQpTgxIz9Vacp2lTGzr3H8PshMVDqvSI/h+zJwyIdnoiI9CFRkeA4564DPmxm3wzanQw0RigkCZHf7+fV/ScoKi7j7fIaAPr3S+S2OVNYlpfFhFEq9RYRkd4XFQkOcAb4e+fcIeBfgZuBvwU+F9Go5LJaW9vYsucIRZvKOHD0HOCVet8zP4MHFmUyXKXeIiISQVGR4JjZYefcA8APgJ8Ap4BHzWxlZCOTjhovtvDirmpWby7nRKDUe8SQFJbkZvGhW9NJG6BSbxERibyoSHAAzGwTMDvScUjnztdfZMM2r9T7XJ1X6j1x9ECW5eVQMHsS/fup1FtERKJH1CQ4Ep1O1jawptTHxh1VNF70Sr1zJg+jsCCHedePV6m3iIhEJSU40qnqY+coKvax+bVDtLZ5pd4z3RgKC7K5IUul3iIiEt2U4Mj7vFN5mqJir9QbvFLv3Jsmsjw/m6xJKvUWEZHYoARH8Pv9vPLOcVZuKmNf5WkAkvslctvcKSzPy2bcyIERjlBERKR7lOD0YS2tbZS+fphVxWVUHTsPwMDU/ty7IIP7F2YybHBKhCMUERG5Mkpw+qDGphY27qpizeZyTtY2ADBy6ACW5GZx17ypKvUWEZGYpwSnDzl7oYkN2ypZv7WS8/Xtpd6DWFGQzeKZk+nfLzHCEYqIiPQMJTh9wInT9awpLWfjziqaAqXebupwCvNzuOW6cSSq1FtEROKMEpw4VnX0HEXFZWx+/TBtgVLvWdPGsKIgh+syR6rUW0RE4pYSnDi0t6KGouIydu87DkBiYgJ5MyexPD+bjAlDIxydiIhI+CnBiRNtbZdKvd85ECj17p/EnXOnsDQvm7Ej0iIcoYiISO9RghPjvFLvQxQV+6gOlHoPSu3PvQu9Uu+hg1TqLSIifY8SnBjV0NTCxp1eqfepM16p96ihA1iyOJu75k0lNUW3VkRE+i79FowxZy80sX5rJRu2VXC+vhmAyWMHsTwvh8UzJ6nUW0REBCU4MeP46XrWbPaxcWc1F5u9Uu9pU4ezoiCHOdNV6i0iIhJMCU6UqzxyllXFPkrfuFTqPfvasawoyGF6xgiVeouIiHRCCU4U8vv97K2oYeWmMl7dfwIIlHrPmkRhfg7p44dEOEIREZHopgQnirS1+dm17xgrN5VhVbWAV+p917ypLM3NYoxKvUVEREKiBCcKNLe0sfm1gxQV+zh04gIAg9P6c9/CTO5dkKFSbxERkW5SghNB9Y3N75V615xtBGDUsFSW5WVx59ypDFCpt4iIyBXRb9AIOHO+iXVbK9iwrZK6Bq/Ue8q4wRTm55B780T6JanUW0RE5GoowelFx2rqWF3i48Vd1VxsaQNgesYICgtymD1trEq9RUREeogSnF5QcfgsRZvK2LrnMIFKb+ZOH0dhQTbTM0ZGNjgREZE4pAQnTPx+P2+Vn6Jok4/XzCv1TkpMoGCWt6r31HEq9RYREQkXJTg9rLXNz863j1JUXMa71WcAGJCcxJ3zprIkN4sxw1XqLSIiEm5KcHpIc0srm145xOqSMg6frANgcFoy9y/ySr2HDEyOcIQiIiJ9hxKcq1Tf2Mxz2w+wtrSc0+eaABgzPJWli7O5Y+4UlXqLiIhEgH77XqHa842s21LBM9sqqWtsASB9/BAK87NZeJNKvUVERCIpahIc59yNwBPADKAC+JSZ7Y5sVH/q6KlAqffuapoDpd7XZY5kRUEOs6aN0eKXIiIiUSAqEhznXDKwFngcyAUKgY3Oualmdi6iwQWUHzpDUbGPbUGl3rdcN47C/ByuzRgR2eBERETkfaIiwQHygP5m9nhg+4/Ouc8Bfw78S8SiAvZW1PDfLxivv3sS8Eq9b5s9ieV52UxRqbeIiEhUipYEZzrwTod9+4EbIhDLe46cvMBXf7mVNr9X6n3XvHSW5GYxenhqJMMSERGRDxAtCc4goL7DvnogopPGDB8ygNvnTmXMiFTumZ/B4DSVeouIiMSCaElw6oCO3SJpwIUIxPKe1JR+/M2Hb4pkCCIiInIFoqWWeR/gOuybFtgvIiIi0i3R0oNTDCQ45/4O+AVeFdUMYHVEoxIREZGYFBU9OGZ2EbgbL7E5DXwNWGpmJyMamIiIiMSkaOnBwczeBhZGOg4RERGJfVHRgyMiIiLSk5TgiIiISNxRgiMiIiJxJ2rG4FyFJIBjx45FOg4RERHpRUG/+5M6vhYPCc54gAcffDDScYiIiEhkjAfKg3fEQ4KzG1gEHAVaIxyLiIiI9J6oLxbZAAAJDUlEQVQkvORmd8cXEvx+f++HIyIiIhJGGmQsIiIicUcJjoiIiMQdJTgiIiISd5TgiIiISNxRgiMiIiJxRwmOiIiIxB0lOCIiIhJ3lOCIiIhI3ImHmYy7zTl3I/AEMAOoAD5lZn8yC6Jzbgrwr8A84ATwN2b2TOC1BOBR4C+BZOC3wJfNrKVXLiIE3bjOWcBPAsedA34DPGpm/sDr1cBIoH1WyMNm5sJ/BaHpxnUWAC8ADUG7v29mj8bL/XTOLQKe7fDWFKDSzK4JHBPV97Odc24usN7Mxlzm9Zhun+1CuM6Ybp/tQrjOmG6f7bq6zlhvn865O4DvATl4be6HZvarTo6LirbZ5xIc51wysBZ4HMgFCoGNzrmpZnauw+F/BLYD9wILgTXOuZvMrALv5iwHZgJNwGrgq8C3e+VCPkCo1+mcSwM2AN8B8oFM4HngGPBr59woYCIwxMzqevcqPlg37+dM4Ckz+0gnp4qL+2lmW4BBQe+bjDeF+ecC21F9P+G9H4CfBh77gENjtn1CaNcZ6+0TunU/Y7Z9QmjXGcvtMxBrEfBJvJ9Fs4DnnXMHzOz5DodHRdvsi4+o8oD+Zva4mTWb2R+BvcCfBx/knLsGmA18w8wumtkm4Gm8DzB4N/lxMztkZieBbwGf6aVrCEUeIVwnMBnYbma/MLNWMysD1uB9KMH7EJdFW2MLkkdo1wnetbxxmfPEy/3s6N+AJ81sY2A72u8nwCPAZ/F+qXcqDtonhHCdxH77hNCuE2K7fULo1xksltpnOvB7M1ttZm2B3uMSYEHwQdHUNvtigjMdeKfDvv3ADZ0cV93hgxZ83HRgX4fXJjjnRvRgrFcjpOs0z7L27UBPwd3A64FdM4FE59wu59xJ59zzzrlrwxh3d4V6P8G7ltudc1XOuWrn3A+dcylB54n5+xnMObc08L5vBO2O9vsJ8ISZzQJe6eKYWG+fEMJ1xkH7hNDuJ8R2+4TQrxOIvfZpZlvM7K/atwP/7xdx6bPYLmraZl9McAYB9R321QNp3Tyu4+vt/+54nkgJ9TrfE/hh8ofAcU8EdrcCu/C6FKfifZifDXSdR4OQrtM51w84hNcdei1QANyO9yy4s/PE/P0EvgZ8z8yCxzRE+/3EzI6EcFist89Qr/M9Mdo+Q7rOOGif3b6fxGj7BHDODcXrldmJ97gqWNS0zT43BgeoA1I77EsDLnTzuI6vt9+cjueJlFCvEwDn3Di856ttwO3tjc7MftDhuK8Af43Xlbqlh2O+EiFdZ2AA221Bu3zOuX8Cvg/8QyfnifX7OQO4Dvhd8P4YuJ+hivX22S0x3D5DEgfts1tiuX0GHkGtxeuFedDM2jocEjVtsy/24OwDOo5In8b7u8zaj5vinEu9zHEdzzMNOGpmZ3ow1qsR6nXinJuON9DNh/fDszbotS845xYGHZ6Elxg39njEVyak63TOTXTOPRbo4m+XzKXriJv7GbAEeLbjQOsYuJ+hivX2GbIYb58hiYP22V0x2T6dc7l4vTZrgBVm1llcUdM2+2IPTjGQ4Jz7O+AXeNUoM/C6Rt9jZuac2wP8UyCLno/3obw1cMiTwJeccy/hZaTfCuyLFiFdp3NuOLAR+KOZfamT86QDn3DO3QecwfuLqgx4LXyhd0tI1wnUAA8C9c65bwMZwNfxBvlBnNzPIPOAlzrZn05038+QxEH7DEkctM9QxXr77K6Ya5/OuSxgPfA1M/v55Y6LprbZ53pwzOwi3iC9QuA03nPQpWZ20jn3oHMuuJusEO958Am8uSc+bWZvB157AngKeBnvA7iP9w8Wi6huXOcn8MoSP+ucuxD09YfA6/8I7MB7FnwCr0z1fjNr7c3ruZxQrzPwl8bdeCXWNUAp3v37ceBU8XI/26UDnY0JiOr72ZV4ap9diaf22ZV4ap9diaP2+TAwGPhuh8/i96O1bSb4/f4PPkpEREQkhvS5HhwRERGJf0pwREREJO4owREREZG4owRHRERE4o4SHBEREYk7SnBEREQk7vTFif5EpBc45/4db+Xgy3kEbzXiYmCwmfXKtPvOuSRgG/AXZvZuF8cl4s1J8gkzs96ITUR6jnpwRCRcPg+MD3zlBfbNDdr3GN5kX+PxZjTtLX8L7OkquQEIrLHzbS4tbCkiMUQT/YlI2DnnrgfeAjLM7EAE4xgAVAMFQTOrftB7yvFmYi0JZ2wi0rP0iEpEIsY5l0fQIyrnnB/4KPAVvAX5XgE+DnwZb9mCc8BXzOzJwPsHAz8CVgB+YBPweTPrbBp8gI8AZ4KTG+fc/wX+EhgNvAN81cyeDXrParzeqJIeuGQR6SV6RCUi0eZ7wBfwFiScgrfQ4DlgDrAK+JVzblDg2F/jJUJ3AYvxkpznnXOX++PtXuC59g3n3LLA9/o43qrGG4CnnHNDgt7zHHB7F+cUkSikBEdEos0/m1mxmb2Bt3rxBbxeFcNbfDEVyHDOZeL1yHzMzHYHemU+gbeQ4Ycuc+7ZwN6g7XSgCagKPDr7NrAcaA46Zh8wCC8BEpEYob9IRCTa+IL+XQ8cMLP2wYKNgf+mAFMD/zbnXPD70/B6ddZ3cu6xwKmg7f/Eq/SqcM69CjwN/NbMGoKOqQn8d0w3r0NEIkg9OCISbZo7bLdd5rh+gWNvBm4K+roG+O1l3tMGJLRvmNlJYBZej8/LwEPAm4FB0e3af062hnwFIhJxSnBEJFa9A/QHBpqZz8x8wFHgh3hJTmeO4Q0mBsA5txz4jJltNLPP4/X8nAfuCXrP6KD3ikiM0CMqEYlJZmbOuaeB/3DOPQycBP4Jb3Dy/su87VXgxqDtJOCHzrnjeBVb84BxgX+3uxGo5f2PzkQkyqkHR0Ri2SfxkpE1wG5gKHCHmZ25zPEb8KqtADCzp4Bv4vX6vAt8B/icmW0Kek8u8JyZ6RGVSAzRRH8i0mc459KAA8CHzOy1EI5PBKrwKrW2hDk8EelB6sERkT7DzOrxemseDvEtS4AKJTcisUcJjoj0NT8BZrgOteUdBXpvvgb8Va9EJSI9So+oREREJO6oB0dERETijhIcERERiTtKcERERCTuKMERERGRuKMER0REROLO/wdkT47KCCgaNwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 576x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "subplot(2, 1, 1)\n",
-    "plot_position(results)\n",
-    "\n",
-    "subplot(2, 1, 2)\n",
-    "plot_velocity(results)\n",
-    "\n",
-    "savefig('figs/chap11-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "According to this model, we should be able to make this run in just over 2 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "2.010095574667565 second"
-      ],
-      "text/latex": [
-       "$2.010095574667565\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "2.010095574667565 <Unit('second')>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_final = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At the end of the run, the car has gone about 28 meters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>x</th>\n",
-       "      <td>28.385825236293645 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v</th>\n",
-       "      <td>27.777777777777775 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "x             28.385825236293645 meter\n",
-       "v    27.777777777777775 meter / second\n",
-       "Name: 2.010095574667565, dtype: object"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "state = results.last_row()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we send the final state back to the slope function, we can see that the final acceleration is about 13 $m/s^2$, which is about 1.3 times the acceleration of gravity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "99.99999999999999 kilometer/hour"
-      ],
-      "text/latex": [
-       "$99.99999999999999\\ \\frac{\\mathrm{kilometer}}{\\mathrm{hour}}$"
-      ],
-      "text/plain": [
-       "99.99999999999999 <Unit('kilometer / hour')>"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v, a = slope_func(state, 0, system)\n",
-    "v.to(km/hour)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "12.918946212080874 newton/kilogram"
-      ],
-      "text/latex": [
-       "$12.918946212080874\\ \\frac{\\mathrm{newton}}{\\mathrm{kilogram}}$"
-      ],
-      "text/plain": [
-       "12.918946212080874 <Unit('newton / kilogram')>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "1.3182598175592728 dimensionless"
-      ],
-      "text/latex": [
-       "$1.3182598175592728\\ dimensionless$"
-      ],
-      "text/plain": [
-       "1.3182598175592728 <Unit('dimensionless')>"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "g = 9.8 * m/s**2\n",
-    "(a / g).to(UNITS.dimensionless)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's not easy for a vehicle to accelerate faster than `g`, because that implies a coefficient of friction between the wheels and the road surface that's greater than 1.  But racing tires on dry asphalt can do that; the OEM team at Olin has tested their tires and found a peak coefficient near 1.5.\n",
-    "\n",
-    "So it's possible that our no slip assumption is valid, but only under ideal conditions, where weight is distributed equally on four tires, and all tires are driving."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** How much time do we lose because maximum torque decreases as motor speed increases?  Run the model again with no drop off in torque and see how much time it saves."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section we'll see how much effect drag has on the results.\n",
-    "\n",
-    "Here's a function to compute drag force, as we saw in Chapter 21."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(v, system):\n",
-    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
-    "    \n",
-    "    v: velocity\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: drag force\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    f_drag = -np.sign(v) * rho * v**2 * C_d * area / 2\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it with a velocity of 20 m/s."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-72.0 kilogram meter/second<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$-72.0\\ \\frac{\\mathrm{kilogram} \\cdot \\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "-72.0 <Unit('kilogram * meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drag_force(20 * m/s, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the resulting acceleration of the vehicle due to drag.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-0.24 meter/second<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$-0.24\\ \\frac{\\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "-0.24 <Unit('meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drag_force(20 * m/s, system) / system.mass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the effect of drag is not huge, compared to the acceleration we computed in the previous section, but it is not negligible.\n",
-    "\n",
-    "Here's a modified slope function that takes drag into account."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func2(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    x, v = state\n",
-    "    r_wheel, speed_ratio = system.r_wheel, system.speed_ratio\n",
-    "    mass = system.mass\n",
-    "    \n",
-    "    omega2 = v / r_wheel * radian\n",
-    "    omega1 = omega2 / speed_ratio\n",
-    "    tau1 = compute_torque(omega1, system)\n",
-    "    tau2 = tau1 / speed_ratio\n",
-    "    F = tau2 / r_wheel\n",
-    "    a_motor = F / mass\n",
-    "    a_drag = drag_force(v, system) / mass\n",
-    "    \n",
-    "    a = a_motor + a_drag\n",
-    "    return v, a "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the next run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results2, details = run_ode_solver(system, slope_func2, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The time to reach 100 kph is a bit higher."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "2.034354320417196 second"
-      ],
-      "text/latex": [
-       "$2.034354320417196\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "2.034354320417196 <Unit('second')>"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_final2 = get_last_label(results2) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But the total effect of drag is only about 2/100 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.02425874574963105 second"
-      ],
-      "text/latex": [
-       "$0.02425874574963105\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "0.02425874574963105 <Unit('second')>"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_final2 - t_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That's not huge, which suggests we might not be able to save much time by decreasing the frontal area, or coefficient of drag, of the car."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Rolling resistance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we'll consider [rolling resistance](https://en.wikipedia.org/wiki/Rolling_resistance), which the force that resists the motion of the car as it rolls on tires.  The cofficient of rolling resistance, `C_rr`, is the ratio of rolling resistance to the normal force between the car and the ground (in that way it is similar to a coefficient of friction).\n",
-    "\n",
-    "The following function computes rolling resistance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.set(unit_rr = 1 * N / kg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def rolling_resistance(system):\n",
-    "    \"\"\"Computes force due to rolling resistance.\n",
-    "    \n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: force\n",
-    "    \"\"\"\n",
-    "    return -system.C_rr * system.mass * system.unit_rr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The acceleration due to rolling resistance is 0.2 (it is not a coincidence that it equals `C_rr`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-60.0 newton"
-      ],
-      "text/latex": [
-       "$-60.0\\ \\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "-60.0 <Unit('newton')>"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rolling_resistance(system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-0.2 newton/kilogram"
-      ],
-      "text/latex": [
-       "$-0.2\\ \\frac{\\mathrm{newton}}{\\mathrm{kilogram}}$"
-      ],
-      "text/plain": [
-       "-0.2 <Unit('newton / kilogram')>"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rolling_resistance(system) / system.mass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a modified slope function that includes drag and rolling resistance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func3(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    x, v = state\n",
-    "    r_wheel, speed_ratio = system.r_wheel, system.speed_ratio\n",
-    "    mass = system.mass\n",
-    "    \n",
-    "    omega2 = v / r_wheel * radian\n",
-    "    omega1 = omega2 / speed_ratio\n",
-    "    tau1 = compute_torque(omega1, system)\n",
-    "    tau2 = tau1 / speed_ratio\n",
-    "    F = tau2 / r_wheel\n",
-    "    a_motor = F / mass\n",
-    "    a_drag = drag_force(v, system) / mass\n",
-    "    a_roll = rolling_resistance(system) / mass\n",
-    "    \n",
-    "    a = a_motor + a_drag + a_roll\n",
-    "    return v, a "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results3, details = run_ode_solver(system, slope_func3, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final time is a little higher, but the total cost of rolling resistance is only 3/100 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "2.0646544476416953 second"
-      ],
-      "text/latex": [
-       "$2.0646544476416953\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "2.0646544476416953 <Unit('second')>"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_final3 = get_last_label(results3) * s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.030300127224499374 second"
-      ],
-      "text/latex": [
-       "$0.030300127224499374\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "0.030300127224499374 <Unit('second')>"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_final3 - t_final2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So, again, there is probably not much to be gained by decreasing rolling resistance.\n",
-    "\n",
-    "In fact, it is hard to decrease rolling resistance without also decreasing traction, so that might not help at all."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optimal gear ratio"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The gear ratio 13:60 is intended to maximize the acceleration of the car without causing the tires to slip.  In this section, we'll consider other gear ratios and estimate their effects on acceleration and time to reach 100 kph.\n",
-    "\n",
-    "Here's a function that takes a speed ratio as a parameter and returns time to reach 100 kph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def time_to_speed(speed_ratio, params):\n",
-    "    \"\"\"Computes times to reach 100 kph.\n",
-    "    \n",
-    "    speed_ratio: ratio of wheel speed to motor speed\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: time to reach 100 kph, in seconds\n",
-    "    \"\"\"\n",
-    "    params = Params(params, speed_ratio=speed_ratio)\n",
-    "    system = make_system(params)\n",
-    "    system.set(unit_rr = 1 * N / kg)\n",
-    "    \n",
-    "    results, details = run_ode_solver(system, slope_func3, events=event_func)\n",
-    "    t_final = get_last_label(results)\n",
-    "    a_initial = slope_func(system.init, 0, system)\n",
-    "    return t_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it with the default ratio:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0646544476416953"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "time_to_speed(13/60, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can try it with different numbers of teeth on the motor gear (assuming that the axle gear has 60 teeth):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8 1.3230554808694261\n",
-      "9 1.4683740716590767\n",
-      "10 1.6154033363003908\n",
-      "11 1.763893473709603\n",
-      "12 1.913673186217739\n",
-      "13 2.0646544476416953\n",
-      "14 2.216761311453768\n",
-      "15 2.369962929121199\n",
-      "16 2.5242340753735495\n",
-      "17 2.6795453467447845\n"
-     ]
-    }
-   ],
-   "source": [
-    "for teeth in linrange(8, 18):\n",
-    "    print(teeth, time_to_speed(teeth/60, params))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Wow!  The speed ratio has a big effect on the results.  At first glance, it looks like we could break the world record (1.513 seconds) just by decreasing the number of teeth.\n",
-    "\n",
-    "But before we try it, let's see what effect that has on peak acceleration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def initial_acceleration(speed_ratio, params):\n",
-    "    \"\"\"Maximum acceleration as a function of speed ratio.\n",
-    "    \n",
-    "    speed_ratio: ratio of wheel speed to motor speed\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: peak acceleration, in m/s^2\n",
-    "    \"\"\"\n",
-    "    params = Params(params, speed_ratio=speed_ratio)\n",
-    "    system = make_system(params)\n",
-    "    a_initial = slope_func(system.init, 0, system)[1] * m/s**2\n",
-    "    return a_initial"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8 23.076923076923077 meter * newton / kilogram / second ** 2\n",
-      "9 20.51282051282051 meter * newton / kilogram / second ** 2\n",
-      "10 18.46153846153846 meter * newton / kilogram / second ** 2\n",
-      "11 16.783216783216787 meter * newton / kilogram / second ** 2\n",
-      "12 15.384615384615385 meter * newton / kilogram / second ** 2\n",
-      "13 14.201183431952662 meter * newton / kilogram / second ** 2\n",
-      "14 13.186813186813184 meter * newton / kilogram / second ** 2\n",
-      "15 12.307692307692308 meter * newton / kilogram / second ** 2\n",
-      "16 11.538461538461538 meter * newton / kilogram / second ** 2\n",
-      "17 10.85972850678733 meter * newton / kilogram / second ** 2\n"
-     ]
-    }
-   ],
-   "source": [
-    "for teeth in linrange(8, 18):\n",
-    "    print(teeth, initial_acceleration(teeth/60, params))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we decrease the speed ratio, the peak acceleration increases.  With 8 teeth on the motor gear, we could break the world record, but only if we can accelerate at 2.3 times the acceleration of gravity, which is impossible without very sticky ties and a vehicle that generates a lot of downforce."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.354081632653061"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "23.07 / 9.8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These results suggest that the most promising way to improve the performance of the car (for this event) would be to improve traction."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/orbit_soln.ipynb b/soln/orbit_soln.ipynb
deleted file mode 100644
index b55e14f8..00000000
--- a/soln/orbit_soln.ipynb
+++ /dev/null
@@ -1,767 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Earth orbit\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "year"
-      ],
-      "text/latex": [
-       "$\\mathrm{year}$"
-      ],
-      "text/plain": [
-       "<Unit('year')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Here are the units we'll need\n",
-    "\n",
-    "s = UNITS.second\n",
-    "N = UNITS.newton\n",
-    "kg = UNITS.kilogram\n",
-    "m = UNITS.meter\n",
-    "year = UNITS.year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>R</th>\n",
-       "      <td>[147000000000.0 meter, 0.0 meter]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>V</th>\n",
-       "      <td>[0.0 meter / second, -30300.0 meter / second]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "R                [147000000000.0 meter, 0.0 meter]\n",
-       "V    [0.0 meter / second, -30300.0 meter / second]\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# And an inition condition (with everything in SI units)\n",
-    "\n",
-    "# distance from the sun to the earth at perihelion\n",
-    "R = Vector(147e9, 0) * m\n",
-    "\n",
-    "# initial velocity\n",
-    "V = Vector(0, -30300) * m/s\n",
-    "\n",
-    "init = State(R=R, V=V)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>R                [147000000000.0 meter, 0.0 me...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>G</th>\n",
-       "      <td>6.674e-11 meter ** 2 * newton / kilogram ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>m1</th>\n",
-       "      <td>1.989e+30 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>r_final</th>\n",
-       "      <td>701879000.0 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>m2</th>\n",
-       "      <td>5.972e+24 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>31556925.9747 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "init       R                [147000000000.0 meter, 0.0 me...\n",
-       "G              6.674e-11 meter ** 2 * newton / kilogram ** 2\n",
-       "m1                                        1.989e+30 kilogram\n",
-       "r_final                                    701879000.0 meter\n",
-       "m2                                        5.972e+24 kilogram\n",
-       "t_end                                   31556925.9747 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Making a system object\n",
-    "\n",
-    "r_earth = 6.371e6 * m\n",
-    "r_sun = 695.508e6 * m\n",
-    "\n",
-    "t_end = (1 * year).to_base_units()\n",
-    "\n",
-    "system = System(init=init,\n",
-    "                G=6.674e-11 * N / kg**2 * m**2,\n",
-    "                m1=1.989e30 * kg,\n",
-    "                r_final=r_sun + r_earth,\n",
-    "                m2=5.972e24 * kg,\n",
-    "                t_end=t_end)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Here's a function that computes the force of gravity\n",
-    "\n",
-    "def universal_gravitation(state, system):\n",
-    "    \"\"\"Computes gravitational force.\n",
-    "    \n",
-    "    state: State object with distance r\n",
-    "    system: System object with m1, m2, and G\n",
-    "    \n",
-    "    returns: Vector\n",
-    "    \"\"\"\n",
-    "    R, V = state\n",
-    "    G, m1, m2 = system.G, system.m1, system.m2\n",
-    "        \n",
-    "    # make sure the result is a vector, either\n",
-    "    # by forcing it, or by putting v.hat() on\n",
-    "    # the left\n",
-    "    \n",
-    "    force = -G * m1 * m2 / R.mag2 * R.hat()\n",
-    "    return Vector(force)\n",
-    "\n",
-    "    force = -R.hat() * G * m1 * m2 / R.mag2\n",
-    "    return force"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}-3.6686485997501037×10<sup>22</sup> & -0.0\\end{pmatrix} newton\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}-3.6686485997501037\\times 10^{22} & -0.0\\end{pmatrix}\\ \\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "array([-3.6686486e+22, -0.0000000e+00]) <Unit('newton')>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "universal_gravitation(init, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The slope function\n",
-    "\n",
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object containing `g`\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    R, V = state\n",
-    "\n",
-    "    F = universal_gravitation(state, system)\n",
-    "    A = F / system.m2\n",
-    "    \n",
-    "    return V, A"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([     0., -30300.]) <Unit('meter / second')>,\n",
-       " array([-0.00614308, -0.        ]) <Unit('newton / kilogram')>)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Always test the slope function!\n",
-    "\n",
-    "slope_func(init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Here's an event function that stops the simulation\n",
-    "# before the collision\n",
-    "\n",
-    "def event_func(state, t, system):\n",
-    "    R, V = state\n",
-    "    return R.mag - system.r_final"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "146298121000.0 meter"
-      ],
-      "text/latex": [
-       "$146298121000.0\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "146298121000.0 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Always test the event function!\n",
-    "\n",
-    "event_func(init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>Success</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "message    Success\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Finally we can run the simulation\n",
-    "\n",
-    "system.set(dt=system.t_end/1000)\n",
-    "results, details = run_euler(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_trajectory(R):\n",
-    "    x = R.extract('x') / 1e9\n",
-    "    y = R.extract('y') / 1e9\n",
-    "\n",
-    "    plot(x, y)\n",
-    "    plot(0, 0, 'yo')\n",
-    "\n",
-    "    decorate(xlabel='x distance (million km)',\n",
-    "             ylabel='y distance (million km)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_trajectory(results.R)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "R    [1993548154.0907593 meter, 53558254525.37807 m...\n",
-       "V    [9671.881139414403 meter / second, 2805.422229...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "error = results.last_row() - system.init"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "53595343660.133934 meter"
-      ],
-      "text/latex": [
-       "$53595343660.133934\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "53595343660.133934 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "error.R.mag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "10070.535172486145 meter/second"
-      ],
-      "text/latex": [
-       "$10070.535172486145\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "10070.535172486145 <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "error.V.mag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                                                 True\n",
-       "message    The solver successfully reached the end of the...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Ralston\n",
-    "\n",
-    "system.set(dt=system.t_end/1000)\n",
-    "results, details = run_ralston(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_trajectory(results.R)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "R    [-3723637.7203674316 meter, -1060434466.552573...\n",
-       "V    [-214.86614651603597 meter / second, 0.7785432...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "error = results.last_row() - system.init"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "1060441004.1725632 meter"
-      ],
-      "text/latex": [
-       "$1060441004.1725632\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "1060441004.1725632 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "error.R.mag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "214.8675569931363 meter/second"
-      ],
-      "text/latex": [
-       "$214.8675569931363\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "214.8675569931363 <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "error.V.mag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xs = results.R.extract('x') * 1.1\n",
-    "ys = results.R.extract('y') * 1.1\n",
-    "\n",
-    "def draw_func(state, t):\n",
-    "    set_xlim(xs)\n",
-    "    set_ylim(ys)\n",
-    "    x, y = state.R\n",
-    "    plot(x, y, 'b.')\n",
-    "    plot(0, 0, 'yo')\n",
-    "    decorate(xlabel='x distance (million km)',\n",
-    "             ylabel='y distance (million km)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "system.set(dt=system.t_end/100)\n",
-    "results, details = run_ralston(system, slope_func)\n",
-    "animate(results, draw_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/penny_model_comparison.ipynb b/soln/penny_model_comparison.ipynb
deleted file mode 100644
index d85e73a7..00000000
--- a/soln/penny_model_comparison.ipynb
+++ /dev/null
@@ -1,1177 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Comparison of the penny models from Chapters 1, 20, and 21\n",
-    "\n",
-    "Copyright 2018 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### With air resistance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we'll add air resistance using the [drag equation](https://en.wikipedia.org/wiki/Drag_equation)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll start by getting the units we'll need from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "kilogram"
-      ],
-      "text/latex": [
-       "$\\mathrm{kilogram}$"
-      ],
-      "text/plain": [
-       "<Unit('kilogram')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now I'll create a `Params` object to contain the quantities we need.  Using a Params object is convenient for grouping the system parameters in a way that's easy to read (and double-check)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>height</th>\n",
-       "      <td>381 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_init</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>0.0025 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>diameter</th>\n",
-       "      <td>0.019 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>18.0 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "height                      381 meter\n",
-       "v_init             0.0 meter / second\n",
-       "g             9.8 meter / second ** 2\n",
-       "mass                  0.0025 kilogram\n",
-       "diameter                  0.019 meter\n",
-       "rho         1.2 kilogram / meter ** 3\n",
-       "v_term            18.0 meter / second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = Params(height = 381 * m,\n",
-    "                v_init = 0 * m / s,\n",
-    "                g = 9.8 * m/s**2,\n",
-    "                mass = 2.5e-3 * kg,\n",
-    "                diameter = 19e-3 * m,\n",
-    "                rho = 1.2 * kg/m**3,\n",
-    "                v_term = 18 * m / s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can pass the `Params` object `make_system` which computes some additional parameters and defines `init`.\n",
-    "\n",
-    "`make_system` uses the given radius to compute `area` and the given `v_term` to compute the drag coefficient `C_d`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    unpack(params)\n",
-    "    \n",
-    "    area = np.pi * (diameter/2)**2\n",
-    "    C_d = 2 * mass * g / (rho * area * v_term**2)\n",
-    "    init = State(y=height, v=v_init)\n",
-    "    t_end = 30 * s\n",
-    "    \n",
-    "    return System(params, area=area, C_d=C_d, \n",
-    "                  init=init, t_end=t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>height</th>\n",
-       "      <td>381 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_init</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>0.0025 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>diameter</th>\n",
-       "      <td>0.019 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>18.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>0.0002835287369864788 meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.4445009981135434 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>y             381 meter\n",
-       "v    0.0 meter / secon...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>30 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "height                                              381 meter\n",
-       "v_init                                     0.0 meter / second\n",
-       "g                                     9.8 meter / second ** 2\n",
-       "mass                                          0.0025 kilogram\n",
-       "diameter                                          0.019 meter\n",
-       "rho                                 1.2 kilogram / meter ** 3\n",
-       "v_term                                    18.0 meter / second\n",
-       "area                         0.0002835287369864788 meter ** 2\n",
-       "C_d                          0.4445009981135434 dimensionless\n",
-       "init        y             381 meter\n",
-       "v    0.0 meter / secon...\n",
-       "t_end                                               30 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the slope function, including acceleration due to gravity and drag."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    mass = system.mass\n",
-    "    g = system.g\n",
-    "    \n",
-    "    f_drag = rho * v**2 * C_d * area / 2\n",
-    "    a_drag = f_drag / mass\n",
-    "    \n",
-    "    dydt = v\n",
-    "    dvdt = -g + a_drag\n",
-    "    \n",
-    "    return dydt, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, let's test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0 <Unit('meter / second')>, -9.8 <Unit('meter / second ** 2')>)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use the same event function as in the previous chapter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Return the height of the penny above the sidewalk.\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                             True\n",
-       "message    A termination event occurred.\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>y</th>\n",
-       "      <th>v</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.000000</th>\n",
-       "      <td>381 meter</td>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.300000</th>\n",
-       "      <td>380.559 meter</td>\n",
-       "      <td>-2.913855777777778 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.600000</th>\n",
-       "      <td>379.2553998560887 meter</td>\n",
-       "      <td>-5.676321799192868 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.900000</th>\n",
-       "      <td>377.15535917269835 meter</td>\n",
-       "      <td>-8.166374221525693 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.200000</th>\n",
-       "      <td>374.35521895425103 meter</td>\n",
-       "      <td>-10.311720070623485 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.500000</th>\n",
-       "      <td>370.96543201556204 meter</td>\n",
-       "      <td>-12.090247228814045 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.800000</th>\n",
-       "      <td>367.09631700871336 meter</td>\n",
-       "      <td>-13.518952241005275 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.100000</th>\n",
-       "      <td>362.8483908201627 meter</td>\n",
-       "      <td>-14.638370780659383 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.400000</th>\n",
-       "      <td>358.3075410597563 meter</td>\n",
-       "      <td>-15.498718976734299 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.700000</th>\n",
-       "      <td>353.54387826134905 meter</td>\n",
-       "      <td>-16.150356583635052 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.000000</th>\n",
-       "      <td>348.6127953660127 meter</td>\n",
-       "      <td>-16.638537486499228 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.300000</th>\n",
-       "      <td>343.5570453854738 meter</td>\n",
-       "      <td>-17.001300692432412 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.600000</th>\n",
-       "      <td>338.4090764843133 meter</td>\n",
-       "      <td>-17.269254229780273 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.900000</th>\n",
-       "      <td>333.1932204915177 meter</td>\n",
-       "      <td>-17.466306425809933 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.200000</th>\n",
-       "      <td>327.92756526232625 meter</td>\n",
-       "      <td>-17.610750940510915 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.500000</th>\n",
-       "      <td>322.62547300477706 meter</td>\n",
-       "      <td>-17.716383148064374 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.800000</th>\n",
-       "      <td>317.2967703203745 meter</td>\n",
-       "      <td>-17.793499099432996 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5.100000</th>\n",
-       "      <td>311.94866008776336 meter</td>\n",
-       "      <td>-17.84972641814655 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5.400000</th>\n",
-       "      <td>306.586409493623 meter</td>\n",
-       "      <td>-17.890685845112163 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5.700000</th>\n",
-       "      <td>301.21386361121205 meter</td>\n",
-       "      <td>-17.920503377109643 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.000000</th>\n",
-       "      <td>295.8338258653892 meter</td>\n",
-       "      <td>-17.94219936904363 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.300000</th>\n",
-       "      <td>290.448338371113 meter</td>\n",
-       "      <td>-17.957980371929946 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.600000</th>\n",
-       "      <td>285.05888770100324 meter</td>\n",
-       "      <td>-17.96945605745975 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.900000</th>\n",
-       "      <td>279.66655550040554 meter</td>\n",
-       "      <td>-17.977799435972766 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.200000</th>\n",
-       "      <td>274.2721285128205 meter</td>\n",
-       "      <td>-17.9838646561385 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.500000</th>\n",
-       "      <td>268.87617883849407 meter</td>\n",
-       "      <td>-17.988273335755586 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.800000</th>\n",
-       "      <td>263.47912241839214 meter</td>\n",
-       "      <td>-17.99147768251337 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.100000</th>\n",
-       "      <td>258.08126161893864 meter</td>\n",
-       "      <td>-17.99380656697738 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.400000</th>\n",
-       "      <td>252.68281622283766 meter</td>\n",
-       "      <td>-17.99549911112701 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.700000</th>\n",
-       "      <td>247.28394597351817 meter</td>\n",
-       "      <td>-17.996729153771838 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13.800000</th>\n",
-       "      <td>155.48693674391328 meter</td>\n",
-       "      <td>-17.99998562459072 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14.100000</th>\n",
-       "      <td>150.08694035214128 meter</td>\n",
-       "      <td>-17.99998955354878 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14.400000</th>\n",
-       "      <td>144.6869429742007 meter</td>\n",
-       "      <td>-17.99999240867943 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14.700000</th>\n",
-       "      <td>139.28694487962224 meter</td>\n",
-       "      <td>-17.999994483471404 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15.000000</th>\n",
-       "      <td>133.88694626427096 meter</td>\n",
-       "      <td>-17.999995991199814 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15.300000</th>\n",
-       "      <td>128.48694727047982 meter</td>\n",
-       "      <td>-17.99999708684937 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15.600000</th>\n",
-       "      <td>123.08694800168064 meter</td>\n",
-       "      <td>-17.99999788304576 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15.900000</th>\n",
-       "      <td>117.68694853303616 meter</td>\n",
-       "      <td>-17.999998461632856 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16.200000</th>\n",
-       "      <td>112.28694891916632 meter</td>\n",
-       "      <td>-17.999998882085677 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16.500000</th>\n",
-       "      <td>106.88694919976282 meter</td>\n",
-       "      <td>-17.999999187624077 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16.800000</th>\n",
-       "      <td>101.48694940366919 meter</td>\n",
-       "      <td>-17.99999940965544 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17.100000</th>\n",
-       "      <td>96.08694955184568 meter</td>\n",
-       "      <td>-17.999999571003165 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17.400000</th>\n",
-       "      <td>90.68694965952389 meter</td>\n",
-       "      <td>-17.999999688252768 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17.700000</th>\n",
-       "      <td>85.28694973777245 meter</td>\n",
-       "      <td>-17.99999977345675 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18.000000</th>\n",
-       "      <td>79.8869497946348 meter</td>\n",
-       "      <td>-17.999999835373536 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18.300000</th>\n",
-       "      <td>74.48694983595604 meter</td>\n",
-       "      <td>-17.999999880367778 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18.600000</th>\n",
-       "      <td>69.08694986598373 meter</td>\n",
-       "      <td>-17.999999913064592 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18.900000</th>\n",
-       "      <td>63.686949887804516 meter</td>\n",
-       "      <td>-17.999999936825006 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19.200000</th>\n",
-       "      <td>58.28694990366144 meter</td>\n",
-       "      <td>-17.999999954091432 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19.500000</th>\n",
-       "      <td>52.88694991518449 meter</td>\n",
-       "      <td>-17.999999966638754 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19.800000</th>\n",
-       "      <td>47.48694992355816 meter</td>\n",
-       "      <td>-17.999999975756754 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20.100000</th>\n",
-       "      <td>42.08694992964321 meter</td>\n",
-       "      <td>-17.999999982382704 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20.400000</th>\n",
-       "      <td>36.686949934065154 meter</td>\n",
-       "      <td>-17.999999987197707 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20.700000</th>\n",
-       "      <td>31.28694993727853 meter</td>\n",
-       "      <td>-17.999999990696715 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21.000000</th>\n",
-       "      <td>25.886949939613654 meter</td>\n",
-       "      <td>-17.999999993239406 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21.300000</th>\n",
-       "      <td>20.486949941310563 meter</td>\n",
-       "      <td>-17.99999999508715 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21.600000</th>\n",
-       "      <td>15.086949942543686 meter</td>\n",
-       "      <td>-17.99999999642989 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21.900000</th>\n",
-       "      <td>9.686949943439785 meter</td>\n",
-       "      <td>-17.99999999740564 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22.200000</th>\n",
-       "      <td>4.286949944090969 meter</td>\n",
-       "      <td>-17.999999998114706 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22.438164</th>\n",
-       "      <td>0.0 meter</td>\n",
-       "      <td>-17.99999999852377 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>76 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  y                                   v\n",
-       "0.000000                  381 meter                  0.0 meter / second\n",
-       "0.300000              380.559 meter   -2.913855777777778 meter / second\n",
-       "0.600000    379.2553998560887 meter   -5.676321799192868 meter / second\n",
-       "0.900000   377.15535917269835 meter   -8.166374221525693 meter / second\n",
-       "1.200000   374.35521895425103 meter  -10.311720070623485 meter / second\n",
-       "1.500000   370.96543201556204 meter  -12.090247228814045 meter / second\n",
-       "1.800000   367.09631700871336 meter  -13.518952241005275 meter / second\n",
-       "2.100000    362.8483908201627 meter  -14.638370780659383 meter / second\n",
-       "2.400000    358.3075410597563 meter  -15.498718976734299 meter / second\n",
-       "2.700000   353.54387826134905 meter  -16.150356583635052 meter / second\n",
-       "3.000000    348.6127953660127 meter  -16.638537486499228 meter / second\n",
-       "3.300000    343.5570453854738 meter  -17.001300692432412 meter / second\n",
-       "3.600000    338.4090764843133 meter  -17.269254229780273 meter / second\n",
-       "3.900000    333.1932204915177 meter  -17.466306425809933 meter / second\n",
-       "4.200000   327.92756526232625 meter  -17.610750940510915 meter / second\n",
-       "4.500000   322.62547300477706 meter  -17.716383148064374 meter / second\n",
-       "4.800000    317.2967703203745 meter  -17.793499099432996 meter / second\n",
-       "5.100000   311.94866008776336 meter   -17.84972641814655 meter / second\n",
-       "5.400000     306.586409493623 meter  -17.890685845112163 meter / second\n",
-       "5.700000   301.21386361121205 meter  -17.920503377109643 meter / second\n",
-       "6.000000    295.8338258653892 meter   -17.94219936904363 meter / second\n",
-       "6.300000     290.448338371113 meter  -17.957980371929946 meter / second\n",
-       "6.600000   285.05888770100324 meter   -17.96945605745975 meter / second\n",
-       "6.900000   279.66655550040554 meter  -17.977799435972766 meter / second\n",
-       "7.200000    274.2721285128205 meter    -17.9838646561385 meter / second\n",
-       "7.500000   268.87617883849407 meter  -17.988273335755586 meter / second\n",
-       "7.800000   263.47912241839214 meter   -17.99147768251337 meter / second\n",
-       "8.100000   258.08126161893864 meter   -17.99380656697738 meter / second\n",
-       "8.400000   252.68281622283766 meter   -17.99549911112701 meter / second\n",
-       "8.700000   247.28394597351817 meter  -17.996729153771838 meter / second\n",
-       "...                             ...                                 ...\n",
-       "13.800000  155.48693674391328 meter   -17.99998562459072 meter / second\n",
-       "14.100000  150.08694035214128 meter   -17.99998955354878 meter / second\n",
-       "14.400000   144.6869429742007 meter   -17.99999240867943 meter / second\n",
-       "14.700000  139.28694487962224 meter  -17.999994483471404 meter / second\n",
-       "15.000000  133.88694626427096 meter  -17.999995991199814 meter / second\n",
-       "15.300000  128.48694727047982 meter   -17.99999708684937 meter / second\n",
-       "15.600000  123.08694800168064 meter   -17.99999788304576 meter / second\n",
-       "15.900000  117.68694853303616 meter  -17.999998461632856 meter / second\n",
-       "16.200000  112.28694891916632 meter  -17.999998882085677 meter / second\n",
-       "16.500000  106.88694919976282 meter  -17.999999187624077 meter / second\n",
-       "16.800000  101.48694940366919 meter   -17.99999940965544 meter / second\n",
-       "17.100000   96.08694955184568 meter  -17.999999571003165 meter / second\n",
-       "17.400000   90.68694965952389 meter  -17.999999688252768 meter / second\n",
-       "17.700000   85.28694973777245 meter   -17.99999977345675 meter / second\n",
-       "18.000000    79.8869497946348 meter  -17.999999835373536 meter / second\n",
-       "18.300000   74.48694983595604 meter  -17.999999880367778 meter / second\n",
-       "18.600000   69.08694986598373 meter  -17.999999913064592 meter / second\n",
-       "18.900000  63.686949887804516 meter  -17.999999936825006 meter / second\n",
-       "19.200000   58.28694990366144 meter  -17.999999954091432 meter / second\n",
-       "19.500000   52.88694991518449 meter  -17.999999966638754 meter / second\n",
-       "19.800000   47.48694992355816 meter  -17.999999975756754 meter / second\n",
-       "20.100000   42.08694992964321 meter  -17.999999982382704 meter / second\n",
-       "20.400000  36.686949934065154 meter  -17.999999987197707 meter / second\n",
-       "20.700000   31.28694993727853 meter  -17.999999990696715 meter / second\n",
-       "21.000000  25.886949939613654 meter  -17.999999993239406 meter / second\n",
-       "21.300000  20.486949941310563 meter   -17.99999999508715 meter / second\n",
-       "21.600000  15.086949942543686 meter   -17.99999999642989 meter / second\n",
-       "21.900000   9.686949943439785 meter   -17.99999999740564 meter / second\n",
-       "22.200000   4.286949944090969 meter  -17.999999998114706 meter / second\n",
-       "22.438164                 0.0 meter   -17.99999999852377 meter / second\n",
-       "\n",
-       "[76 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final height is close to 0, as expected.\n",
-    "\n",
-    "Interestingly, the final velocity is not exactly terminal velocity, which suggests that there are some numerical errors.\n",
-    "\n",
-    "We can get the flight time from `results`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22.438163885803732"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_sidewalk = get_last_label(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the plot of position as a function of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_position(results):\n",
-    "    plot(results.y)\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "    \n",
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And velocity as a function of time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_velocity(results):\n",
-    "    plot(results.v, color='C1', label='v')\n",
-    "        \n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')\n",
-    "    \n",
-    "plot_velocity(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From an initial velocity of 0, the penny accelerates downward until it reaches terminal velocity; after that, velocity is constant."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Back to Chapter 1\n",
-    "\n",
-    "We have now considered three models of a falling penny:\n",
-    "\n",
-    "1.  In Chapter 1, we started with the simplest model, which includes gravity and ignores drag.\n",
-    "\n",
-    "2.  As an exercise in Chapter 1, we did a \"back of the envelope\" calculation assuming constant acceleration until the penny reaches terminal velocity, and then constant velocity until it reaches the sidewalk.\n",
-    "\n",
-    "3.  In this chapter, we model the interaction of gravity and drag during the acceleration phase.\n",
-    "\n",
-    "We can compare the models by plotting velocity as a function of time.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22.4"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "g = 9.8\n",
-    "v_term = 18\n",
-    "t_end = 22.4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ts = linspace(0, t_end, 201)\n",
-    "model1 = -g * ts;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model2 = TimeSeries()\n",
-    "for t in ts:\n",
-    "    v = -g * t\n",
-    "    if v < -v_term:\n",
-    "        model2[t] = -v_term\n",
-    "    else:\n",
-    "        model2[t] = v"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func, max_step=0.5)\n",
-    "model3 = results.v;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(ts, model1, label='model1', color='gray')\n",
-    "plot(model2, label='model2', color='C0')\n",
-    "plot(model3, label='model3', color='C1')\n",
-    "        \n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(model2, label='model2', color='C0')\n",
-    "plot(results.v, label='model3', color='C1')\n",
-    "        \n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Clearly Model 1 is very different from the other two, which are almost identical except during the transition from constant acceleration to constant velocity.\n",
-    "\n",
-    "We can also compare the predictions:\n",
-    "\n",
-    "* According to Model 1, the penny takes 8.8 seconds to reach the sidewalk, and lands at 86 meters per second.\n",
-    "\n",
-    "* According to Model 2, the penny takes 22.1 seconds and lands at terminal velocity, 18 m/s.\n",
-    "\n",
-    "* According to Model 3, the penny takes 22.4 seconds and lands at terminal velocity.\n",
-    "\n",
-    "So what can we conclude?  The results from Model 1 are clearly unrealistic; it is probably not a useful model of this system.  The results from Model 2 are off by about 1%, which is probably good enough for most purposes.\n",
-    "\n",
-    "In fact, our estimate of the terminal velocity could by off by 10% or more, and the figure we are using for the height of the Empire State Building is not precise either.\n",
-    "\n",
-    "So the difference between Models 2 and 3 is swamped by other uncertainties."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/queue_soln.ipynb b/soln/queue_soln.ipynb
deleted file mode 100644
index 02122d5d..00000000
--- a/soln/queue_soln.ipynb
+++ /dev/null
@@ -1,1331 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case Study: Queueing theory\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *\n",
-    "\n",
-    "# set the random number generator\n",
-    "np.random.seed(7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## One queue or two?\n",
-    "\n",
-    "This notebook presents a solution to an exercise from *Modeling and Simulation in Python*.  It uses features from the first four chapters to answer a question related to queueing theory, which is the study of systems that involve waiting in lines, also known as \"queues\".\n",
-    "\n",
-    "Suppose you are designing the checkout area for a new store.  There is room for two checkout counters and a waiting area for customers.  You can make two lines, one for each counter, or one line that serves both counters.\n",
-    "\n",
-    "In theory, you might expect a single line to be better, but it has some practical drawbacks: in order to maintain a single line, you would have to install rope barriers, and customers might be put off by what seems to be a longer line, even if it moves faster.\n",
-    "\n",
-    "So you'd like to check whether the single line is really better and by how much.  Simulation can help answer this question.\n",
-    "\n",
-    "As we did in the bikeshare model, we'll assume that a customer is equally likely to arrive during any timestep.  I'll denote this probability using the Greek letter lambda, $\\lambda$, or the variable name `lam`.  The value of $\\lambda$ probably varies from day to day, so we'll have to consider a range of possibilities.\n",
-    "\n",
-    "Based on data from other stores, you know that it takes 5 minutes for a customer to check out, on average.  But checkout times are highly variable: most customers take less than 5 minutes, but some take substantially more.  A simple way to model this variability is to assume that when a customer is checking out, they have the same probability of finishing up during each time step.  I'll denote this probability using the Greek letter mu, $\\mu$, or the variable name `mu`.\n",
-    "\n",
-    "If we choose $\\mu=1/5$, the average number of time steps for each checkout will be 5 minutes, which is consistent with the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### One server, one queue\n",
-    "\n",
-    "Write a function called `make_system` that takes `lam` and `mu` as parameters and returns a `System` object with variables `lam`, `mu`, and `duration`.  Set `duration`, which is the number of time steps to simulate, to 10 hours, expressed in minutes. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def make_system(lam, mu):\n",
-    "    \"\"\"Make a System object.\n",
-    "    \n",
-    "    lam: arrival rate, per minute\n",
-    "    mu: service completion rate, per minute\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    # duration is 10 hours, expressed in minutes\n",
-    "    return System(lam=lam, mu=mu, duration=10*60)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test this function by creating a `System` object with `lam=1/8` and `mu=1/5`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>lam</th>\n",
-       "      <td>0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mu</th>\n",
-       "      <td>0.200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>duration</th>\n",
-       "      <td>600.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "lam           0.125\n",
-       "mu            0.200\n",
-       "duration    600.000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "interarrival_time = 8\n",
-    "service_time = 5\n",
-    "\n",
-    "lam = 1 / interarrival_time\n",
-    "mu = 1 / service_time\n",
-    "\n",
-    "system = make_system(lam, mu)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write an update function that takes as parameters `x`, which is the total number of customer in the store, including the one checking out; `t`, which is the number of minutes that have elapsed in the simulation, and `system`, which is a `System` object.\n",
-    "\n",
-    "If there's a customer checking out, it should use `flip` to decide whether they are done.  And it should use `flip` to decide if a new customer has arrived.\n",
-    "\n",
-    "It should return the total number of customers at the end of the time step.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def update_func1(x, t, system):\n",
-    "    \"\"\"Simulate one time step.\n",
-    "    \n",
-    "    x: number of people in the shop\n",
-    "    t: time step\n",
-    "    system: System object\n",
-    "    \"\"\"\n",
-    "    # if there's a customer in service, check if they're done\n",
-    "    if x > 0:\n",
-    "        if flip(system.mu):\n",
-    "            x -= 1\n",
-    "            \n",
-    "    # check for an arrival\n",
-    "    if flip(system.lam):\n",
-    "        x += 1\n",
-    "        \n",
-    "    return x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your function by calling it with `x=1`, `t=0`, and the `System` object you created.  If you run it a few times, you should see different results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "update_func1(1, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the simulation.  Here's a version of `run_simulation` that creates a `TimeSeries` with the total number of customers in the store, including the one checking out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate a queueing system.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function object\n",
-    "    \"\"\"\n",
-    "    x = 0\n",
-    "    results = TimeSeries()\n",
-    "    results[0] = x\n",
-    "    \n",
-    "    for t in linrange(0, system.duration):\n",
-    "        x = update_func(x, t, system)\n",
-    "        results[t+1] = x\n",
-    "\n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Call `run_simulation` with your update function and plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results = run_simulation(system, update_func1)\n",
-    "plot(results)\n",
-    "decorate(xlabel='Time (min)', ylabel='Customers')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After the simulation, we can compute `L`, which is the average number of customers in the system, and `W`, which is the average time customers spend in the store.  `L` and `W` are related by Little's Law:\n",
-    "\n",
-    "$L = \\lambda W$\n",
-    "\n",
-    "Where $\\lambda$ is the arrival rate.  Here's a function that computes them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_metrics(results, system):\n",
-    "    \"\"\"Compute average number of customers and wait time.\n",
-    "    \n",
-    "    results: TimeSeries of queue lengths\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: L, W\n",
-    "    \"\"\"\n",
-    "    L = results.mean()\n",
-    "    W = L / system.lam\n",
-    "    return L, W"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Call `compute_metrics` with the results from your simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.8552412645590682, 6.841930116472546)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "compute_metrics(results, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Parameter sweep\n",
-    "\n",
-    "Since we don't know the actual value of $\\lambda$, we can sweep through a range of possibilities, from 10% to 80% of the completion rate, $\\mu$.  (If customers arrive faster than the completion rate, the queue grows without bound.  In that case the metrics `L` and `W` just depend on how long the store is open.)\n",
-    "\n",
-    "Create an array of values for `lam`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.02  , 0.0214, 0.0228, 0.0242, 0.0256, 0.027 , 0.0284, 0.0298,\n",
-       "       0.0312, 0.0326, 0.034 , 0.0354, 0.0368, 0.0382, 0.0396, 0.041 ,\n",
-       "       0.0424, 0.0438, 0.0452, 0.0466, 0.048 , 0.0494, 0.0508, 0.0522,\n",
-       "       0.0536, 0.055 , 0.0564, 0.0578, 0.0592, 0.0606, 0.062 , 0.0634,\n",
-       "       0.0648, 0.0662, 0.0676, 0.069 , 0.0704, 0.0718, 0.0732, 0.0746,\n",
-       "       0.076 , 0.0774, 0.0788, 0.0802, 0.0816, 0.083 , 0.0844, 0.0858,\n",
-       "       0.0872, 0.0886, 0.09  , 0.0914, 0.0928, 0.0942, 0.0956, 0.097 ,\n",
-       "       0.0984, 0.0998, 0.1012, 0.1026, 0.104 , 0.1054, 0.1068, 0.1082,\n",
-       "       0.1096, 0.111 , 0.1124, 0.1138, 0.1152, 0.1166, 0.118 , 0.1194,\n",
-       "       0.1208, 0.1222, 0.1236, 0.125 , 0.1264, 0.1278, 0.1292, 0.1306,\n",
-       "       0.132 , 0.1334, 0.1348, 0.1362, 0.1376, 0.139 , 0.1404, 0.1418,\n",
-       "       0.1432, 0.1446, 0.146 , 0.1474, 0.1488, 0.1502, 0.1516, 0.153 ,\n",
-       "       0.1544, 0.1558, 0.1572, 0.1586, 0.16  ])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "num_vals = 101\n",
-    "lam_array = linspace(0.1*mu, 0.8*mu, num_vals)\n",
-    "lam_array"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write a function that takes an array of values for `lam`, a single value for `mu`, and an update function.\n",
-    "\n",
-    "For each value of `lam`, it should run a simulation, compute `L` and `W`, and store the value of `W` in a `SweepSeries`.\n",
-    "\n",
-    "It should return the `SweepSeries`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def sweep_lam(lam_array, mu, update_func):\n",
-    "    \"\"\"Run simulations with a range of values for `lam`\n",
-    "    \n",
-    "    lam_array: array of values for `lam`\n",
-    "    mu: probability of finishing a checkout\n",
-    "    update_func: passed along to run_simulation\n",
-    "    \n",
-    "    returns: SweepSeries of average wait time vs lam\n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for lam in lam_array:\n",
-    "        system = make_system(lam, mu)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        L, W = compute_metrics(results, system)\n",
-    "        sweep[lam] = W\n",
-    "        \n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Call your function to generate a `SweepSeries`, and plot it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.0200</th>\n",
-       "      <td>4.658902</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0214</th>\n",
-       "      <td>5.209386</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0228</th>\n",
-       "      <td>3.065067</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0242</th>\n",
-       "      <td>5.706742</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0256</th>\n",
-       "      <td>8.059484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0270</th>\n",
-       "      <td>9.613607</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0284</th>\n",
-       "      <td>3.280917</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0298</th>\n",
-       "      <td>4.075981</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0312</th>\n",
-       "      <td>6.932890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0326</th>\n",
-       "      <td>5.563325</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0340</th>\n",
-       "      <td>6.704512</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0354</th>\n",
-       "      <td>10.340581</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0368</th>\n",
-       "      <td>7.279534</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0382</th>\n",
-       "      <td>4.225070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0396</th>\n",
-       "      <td>4.453856</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0410</th>\n",
-       "      <td>3.449535</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0424</th>\n",
-       "      <td>5.376260</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0438</th>\n",
-       "      <td>7.483722</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0452</th>\n",
-       "      <td>4.491040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0466</th>\n",
-       "      <td>6.641292</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0480</th>\n",
-       "      <td>3.986412</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0494</th>\n",
-       "      <td>5.119672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0508</th>\n",
-       "      <td>6.386993</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0522</th>\n",
-       "      <td>8.861349</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0536</th>\n",
-       "      <td>6.394815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0550</th>\n",
-       "      <td>6.534564</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0564</th>\n",
-       "      <td>10.060065</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0578</th>\n",
-       "      <td>4.001405</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0592</th>\n",
-       "      <td>7.110896</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.0606</th>\n",
-       "      <td>3.459581</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1194</th>\n",
-       "      <td>14.687971</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1208</th>\n",
-       "      <td>6.942072</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1222</th>\n",
-       "      <td>12.472393</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1236</th>\n",
-       "      <td>9.827203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1250</th>\n",
-       "      <td>27.873544</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1264</th>\n",
-       "      <td>10.978538</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1278</th>\n",
-       "      <td>7.121673</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1292</th>\n",
-       "      <td>12.543593</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1306</th>\n",
-       "      <td>17.976675</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1320</th>\n",
-       "      <td>8.042152</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1334</th>\n",
-       "      <td>13.545640</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1348</th>\n",
-       "      <td>14.701018</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1362</th>\n",
-       "      <td>36.649637</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1376</th>\n",
-       "      <td>28.150756</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1390</th>\n",
-       "      <td>13.957553</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1404</th>\n",
-       "      <td>5.119672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1418</th>\n",
-       "      <td>17.166969</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1432</th>\n",
-       "      <td>9.527882</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1446</th>\n",
-       "      <td>13.750711</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1460</th>\n",
-       "      <td>7.783831</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1474</th>\n",
-       "      <td>10.249765</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1488</th>\n",
-       "      <td>6.988800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1502</th>\n",
-       "      <td>10.058690</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1516</th>\n",
-       "      <td>13.071881</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1530</th>\n",
-       "      <td>12.800017</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1544</th>\n",
-       "      <td>10.776512</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1558</th>\n",
-       "      <td>23.281694</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1572</th>\n",
-       "      <td>10.351704</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1586</th>\n",
-       "      <td>9.242687</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1600</th>\n",
-       "      <td>23.356905</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>101 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "0.0200     4.658902\n",
-       "0.0214     5.209386\n",
-       "0.0228     3.065067\n",
-       "0.0242     5.706742\n",
-       "0.0256     8.059484\n",
-       "0.0270     9.613607\n",
-       "0.0284     3.280917\n",
-       "0.0298     4.075981\n",
-       "0.0312     6.932890\n",
-       "0.0326     5.563325\n",
-       "0.0340     6.704512\n",
-       "0.0354    10.340581\n",
-       "0.0368     7.279534\n",
-       "0.0382     4.225070\n",
-       "0.0396     4.453856\n",
-       "0.0410     3.449535\n",
-       "0.0424     5.376260\n",
-       "0.0438     7.483722\n",
-       "0.0452     4.491040\n",
-       "0.0466     6.641292\n",
-       "0.0480     3.986412\n",
-       "0.0494     5.119672\n",
-       "0.0508     6.386993\n",
-       "0.0522     8.861349\n",
-       "0.0536     6.394815\n",
-       "0.0550     6.534564\n",
-       "0.0564    10.060065\n",
-       "0.0578     4.001405\n",
-       "0.0592     7.110896\n",
-       "0.0606     3.459581\n",
-       "            ...    \n",
-       "0.1194    14.687971\n",
-       "0.1208     6.942072\n",
-       "0.1222    12.472393\n",
-       "0.1236     9.827203\n",
-       "0.1250    27.873544\n",
-       "0.1264    10.978538\n",
-       "0.1278     7.121673\n",
-       "0.1292    12.543593\n",
-       "0.1306    17.976675\n",
-       "0.1320     8.042152\n",
-       "0.1334    13.545640\n",
-       "0.1348    14.701018\n",
-       "0.1362    36.649637\n",
-       "0.1376    28.150756\n",
-       "0.1390    13.957553\n",
-       "0.1404     5.119672\n",
-       "0.1418    17.166969\n",
-       "0.1432     9.527882\n",
-       "0.1446    13.750711\n",
-       "0.1460     7.783831\n",
-       "0.1474    10.249765\n",
-       "0.1488     6.988800\n",
-       "0.1502    10.058690\n",
-       "0.1516    13.071881\n",
-       "0.1530    12.800017\n",
-       "0.1544    10.776512\n",
-       "0.1558    23.281694\n",
-       "0.1572    10.351704\n",
-       "0.1586     9.242687\n",
-       "0.1600    23.356905\n",
-       "Length: 101, dtype: float64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "sweep = sweep_lam(lam_array, mu, update_func1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot(sweep, 'bo')\n",
-    "\n",
-    "decorate(xlabel='Arrival late, lambda (per min)',\n",
-    "         ylabel='Average time in system',\n",
-    "         title='Single server, single queue')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we imagine that this range of values represents arrival rates on different days, we can use the average value of `W`, for a range of values of `lam`, to compare different queueing strategies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# W_avg = sweep.mean()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis\n",
-    "\n",
-    "The model I chose for this system is a common model in queueing theory, in part because many of its properties can be derived analytically.\n",
-    "\n",
-    "In particular, we can derive the average time in the store as a function of $\\mu$ and $\\lambda$:\n",
-    "\n",
-    "$W = 1 / (\\mu - \\lambda)$\n",
-    "\n",
-    "The following function plots the theoretical value of $W$ as a function of $\\lambda$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_W(lam_array, mu):\n",
-    "    \"\"\"Plot the theoretical mean wait time.\n",
-    "    \n",
-    "    lam_array: array of values for `lam`\n",
-    "    mu: probability of finishing a checkout\n",
-    "    \"\"\"\n",
-    "    W = 1 / (mu - lam_array)\n",
-    "    plot(lam_array, W, 'g-')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use this function to plot the theoretical results, then plot your simulation results again on the same graph.  How do they compare?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_W(lam_array, mu)\n",
-    "plot(sweep, 'bo')\n",
-    "\n",
-    "decorate(xlabel='Arrival late, lambda (per min)',\n",
-    "         ylabel='Average time in system',\n",
-    "         title='Single server, single queue')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multiple servers\n",
-    "\n",
-    "Now let's try the other two queueing strategies:\n",
-    "\n",
-    "1.  One queue with two checkout counters.\n",
-    "2.  Two queues, one for each counter.\n",
-    "\n",
-    "The following figure shows the three scenarios:\n",
-    "\n",
-    "![](diagrams/queue.png)\n",
-    "\n",
-    "Write an update function for one queue with two servers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def update_func2(x, t, system):\n",
-    "    \"\"\"Simulate a single queue with two servers.\n",
-    "    \n",
-    "    system: System object\n",
-    "    \"\"\"\n",
-    "    # if both servers are busy, check whether the\n",
-    "    # second is complete\n",
-    "    if x > 1 and flip(system.mu):\n",
-    "        x -= 1\n",
-    "            \n",
-    "    # check whether the first is complete\n",
-    "    if x > 0 and flip(system.mu):\n",
-    "        x -= 1\n",
-    "    \n",
-    "    # check for an arrival\n",
-    "    if flip(system.lam):\n",
-    "        x += 1\n",
-    "        \n",
-    "    return x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use this update function to simulate the system, plot the results, and print the metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.8552412645590682, 6.841930116472546)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system = make_system(lam, mu)\n",
-    "run_simulation(system, update_func2)\n",
-    "plot(results)\n",
-    "decorate(xlabel='Time (min)', ylabel='Customers')\n",
-    "compute_metrics(results, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since we have two checkout counters now, we can consider values for $\\lambda$ that exceed $\\mu$.\n",
-    "\n",
-    "Create a new array of values for `lam` from 10% to 160% of `mu`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.02 , 0.023, 0.026, 0.029, 0.032, 0.035, 0.038, 0.041, 0.044,\n",
-       "       0.047, 0.05 , 0.053, 0.056, 0.059, 0.062, 0.065, 0.068, 0.071,\n",
-       "       0.074, 0.077, 0.08 , 0.083, 0.086, 0.089, 0.092, 0.095, 0.098,\n",
-       "       0.101, 0.104, 0.107, 0.11 , 0.113, 0.116, 0.119, 0.122, 0.125,\n",
-       "       0.128, 0.131, 0.134, 0.137, 0.14 , 0.143, 0.146, 0.149, 0.152,\n",
-       "       0.155, 0.158, 0.161, 0.164, 0.167, 0.17 , 0.173, 0.176, 0.179,\n",
-       "       0.182, 0.185, 0.188, 0.191, 0.194, 0.197, 0.2  , 0.203, 0.206,\n",
-       "       0.209, 0.212, 0.215, 0.218, 0.221, 0.224, 0.227, 0.23 , 0.233,\n",
-       "       0.236, 0.239, 0.242, 0.245, 0.248, 0.251, 0.254, 0.257, 0.26 ,\n",
-       "       0.263, 0.266, 0.269, 0.272, 0.275, 0.278, 0.281, 0.284, 0.287,\n",
-       "       0.29 , 0.293, 0.296, 0.299, 0.302, 0.305, 0.308, 0.311, 0.314,\n",
-       "       0.317, 0.32 ])"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "lam_array = linspace(0.1*mu, 1.6*mu, num_vals)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use your sweep function to simulate the two server, one queue scenario with a range of values for `lam`.\n",
-    "\n",
-    "Plot the results and print the average value of `W` across all values of `lam`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average of averages =  6.34676127667513 minutes\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "sweep = sweep_lam(lam_array, mu, update_func2)\n",
-    "W_avg = sweep.mean()\n",
-    "print('Average of averages = ', W_avg, 'minutes')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot(sweep, 'bo')\n",
-    "decorate(xlabel='Arrival late, lambda (per min)',\n",
-    "         ylabel='Average time in system',\n",
-    "         title='Multiple server, single queue')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multiple queues\n",
-    "\n",
-    "To simulate the scenario with two separate queues, we need two state variables to keep track of customers in each queue.\n",
-    "\n",
-    "Write an update function that takes `x1`, `x2`, `t`, and `system` as parameters and returns `x1` and `x2` as return values.  f you are not sure how to return more than one return value, see `compute_metrics`.\n",
-    "\n",
-    "When a customer arrives, which queue do they join?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def update_func3(x1, x2, t, system):\n",
-    "    \"\"\"Simulate two queues with one server each.\n",
-    "    \n",
-    "    x1: number of customers in queue 1\n",
-    "    x2: number of customers in queue 2\n",
-    "    t: time step\n",
-    "    system: System object\n",
-    "    \"\"\"\n",
-    "    # if the first servers is busy, check it it's done\n",
-    "    if x1 > 0 and flip(system.mu):\n",
-    "        x1 -= 1\n",
-    "            \n",
-    "    # if the second queue is busy, check if it's done\n",
-    "    if x2 > 0 and flip(system.mu):\n",
-    "        x2 -= 1\n",
-    "            \n",
-    "    # check for an arrival\n",
-    "    if flip(system.lam):\n",
-    "        # join whichever queue is shorter\n",
-    "        if x1 < x2:\n",
-    "            x1 += 1\n",
-    "        else:\n",
-    "            x2 += 1\n",
-    "            \n",
-    "    return x1, x2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write a version of `run_simulation` that works with this update function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate a queueing system.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function object\n",
-    "    \"\"\"\n",
-    "    x1, x2 = 0, 0\n",
-    "    results = TimeSeries()\n",
-    "    results[0] = x1 + x2\n",
-    "    \n",
-    "    for t in linrange(0, system.duration):\n",
-    "        x1, x2 = update_func(x1, x2, t, system)\n",
-    "        results[t+1] = x1 + x2\n",
-    "\n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your functions by running a simulation with a single value of `lam`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.8552412645590682, 6.841930116472546)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system = make_system(lam, mu)\n",
-    "run_simulation(system, update_func3)\n",
-    "plot(results)\n",
-    "decorate(xlabel='Time (min)', ylabel='Customers')\n",
-    "compute_metrics(results, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sweep a range of values for `lam`, plot the results, and print the average wait time across all values of `lam`.\n",
-    "\n",
-    "How do the results compare to the scenario with two servers and one queue."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average of averages =  6.753981831564981 minutes\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "sweep = sweep_lam(lam_array, mu, update_func3)\n",
-    "W_avg = sweep.mean()\n",
-    "print('Average of averages = ', W_avg, 'minutes')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot(sweep, 'bo')\n",
-    "decorate(xlabel='Arrival late, lambda (per min)',\n",
-    "         ylabel='Average time in system',\n",
-    "         title='Multiple server, multiple queue')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "\"\"\"\n",
-    "With two queues, the average of averages is slightly higher, most of the time.  But the difference is small.\n",
-    "\n",
-    "The two configurations are equally good as long as both servers are busy; the only time two lines is worse is if one queue is empty and the other contains more than one customer.  In real life, if we allow customers to change lanes, that disadvantage can be eliminated.\n",
-    "\n",
-    "From a theoretical point of view, one line is better.  From a practical point of view, the difference is small and can be mitigated.  So the best choice depends on practical considerations.\n",
-    "\n",
-    "On the other hand, you can do substantially better with an express line for customers with short service times.  But that's a topic for another notebook.\n",
-    "\"\"\";"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/rabbits2soln.ipynb b/soln/rabbits2soln.ipynb
deleted file mode 100644
index e4457bd2..00000000
--- a/soln/rabbits2soln.ipynb
+++ /dev/null
@@ -1,528 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Rabbit example\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Rabbit Redux\n",
-    "\n",
-    "This notebook starts with a version of the rabbit population growth model and walks through some steps for extending it.\n",
-    "\n",
-    "In the original model, we treat all rabbits as adults; that is, we assume that a rabbit is able to breed in the season after it is born.  In this notebook, we extend the model to include both juvenile and adult rabbits.\n",
-    "\n",
-    "As an example, let's assume that rabbits take 3 seasons to mature.  We could model that process explicitly by counting the number of rabbits that are 1, 2, or 3 seasons old.  As an alternative, we can model just two stages, juvenile and adult.  In the simpler model, the maturation rate is 1/3 of the juveniles per season.\n",
-    "\n",
-    "To implement this model, make these changes in the System object:\n",
-    "\n",
-    "1.  Replace `p0` with two initial populations: `juvenile_pop0` and `adult_pop0`, with values `0` and `10`.\n",
-    "\n",
-    "2.  Add an additional variable, `mature_rate`, with the value `0.33`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>t0</th>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>30.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>juvenile_pop0</th>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>adult_pop0</th>\n",
-       "      <td>10.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>birth_rate</th>\n",
-       "      <td>0.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mature_rate</th>\n",
-       "      <td>0.33</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>death_rate</th>\n",
-       "      <td>0.50</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "t0                0.00\n",
-       "t_end            30.00\n",
-       "juvenile_pop0     0.00\n",
-       "adult_pop0       10.00\n",
-       "birth_rate        0.90\n",
-       "mature_rate       0.33\n",
-       "death_rate        0.50\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = System(t0 = 0, \n",
-    "                t_end = 30,\n",
-    "                juvenile_pop0 = 0,\n",
-    "                adult_pop0 = 10,\n",
-    "                birth_rate = 0.9,\n",
-    "                mature_rate = 0.33,\n",
-    "                death_rate = 0.5)\n",
-    "\n",
-    "system"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now update `run_simulation` with the following changes:\n",
-    "\n",
-    "1.  Add a second TimeSeries, named `juveniles`, to keep track of the juvenile population, and initialize it with `juvenile_pop0`.\n",
-    "\n",
-    "2.  Inside the for loop, compute the number of juveniles that mature during each time step.\n",
-    "\n",
-    "3.  Also inside the for loop, add a line that stores the number of juveniles in the new `TimeSeries`.  For simplicity, let's assume that only adult rabbits die.\n",
-    "\n",
-    "4.  During each time step, subtract the number of maturations from the juvenile population and add it to the adult population.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def run_simulation(system):\n",
-    "    \"\"\"Runs a proportional growth model.\n",
-    "    \n",
-    "    Adds TimeSeries to `system` as `results`.\n",
-    "    \n",
-    "    system: System object with t0, t_end, p0,\n",
-    "            birth_rate and death_rate\n",
-    "    \"\"\"\n",
-    "    juveniles = TimeSeries()\n",
-    "    juveniles[system.t0] = system.juvenile_pop0\n",
-    "    \n",
-    "    adults = TimeSeries()\n",
-    "    adults[system.t0] = system.adult_pop0\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        maturations = system.mature_rate * juveniles[t]\n",
-    "        births = system.birth_rate * adults[t]\n",
-    "        deaths = system.death_rate * adults[t]\n",
-    "        \n",
-    "        if adults[t] > 30:\n",
-    "            market = adults[t] - 30\n",
-    "        else:\n",
-    "            market = 0\n",
-    "            \n",
-    "        juveniles[t+1] = juveniles[t] + births - maturations\n",
-    "        adults[t+1] = adults[t] + maturations - deaths - market\n",
-    "        \n",
-    "    system.adults = adults\n",
-    "    system.juveniles = juveniles"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your changes in `run_simulation`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>5.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5.470000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>6.209900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.057723</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>8.021560</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>9.117031</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>10.362107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>11.777219</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>13.385586</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>15.213601</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>17.291261</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>19.652658</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>22.336542</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>25.386953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>28.853947</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>32.794414</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>34.478600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>36.487431</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>37.893339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>39.401924</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>40.546917</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>41.694992</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>42.613800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>43.495581</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>44.226171</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>44.907656</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>45.485241</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>46.014130</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>46.469075</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>46.880673</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>47.238170</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "0     10.000000\n",
-       "1      5.000000\n",
-       "2      5.470000\n",
-       "3      6.209900\n",
-       "4      7.057723\n",
-       "5      8.021560\n",
-       "6      9.117031\n",
-       "7     10.362107\n",
-       "8     11.777219\n",
-       "9     13.385586\n",
-       "10    15.213601\n",
-       "11    17.291261\n",
-       "12    19.652658\n",
-       "13    22.336542\n",
-       "14    25.386953\n",
-       "15    28.853947\n",
-       "16    32.794414\n",
-       "17    34.478600\n",
-       "18    36.487431\n",
-       "19    37.893339\n",
-       "20    39.401924\n",
-       "21    40.546917\n",
-       "22    41.694992\n",
-       "23    42.613800\n",
-       "24    43.495581\n",
-       "25    44.226171\n",
-       "26    44.907656\n",
-       "27    45.485241\n",
-       "28    46.014130\n",
-       "29    46.469075\n",
-       "30    46.880673\n",
-       "31    47.238170\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run_simulation(system)\n",
-    "system.adults"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, update `plot_results` to plot both the adult and juvenile `TimeSeries`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def plot_results(system, title=None):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    system: System object with `results`\n",
-    "    \"\"\"\n",
-    "    newfig()\n",
-    "    plot(system.adults, 'bo-', label='adults')\n",
-    "    plot(system.juveniles, 'gs-', label='juveniles')\n",
-    "    decorate(xlabel='Season', \n",
-    "             ylabel='Rabbit population',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And test your updated version of `plot_results`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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nTsTV1RWAnj178tlnn6EoygNv1dm+fTvW1tZMmTIFADs7O2bNmkX79u0JDQ0l\nICAAjUaDra0tlpaWqNVqZs+eLbf/CFFOKCjcTr9NRHIEOboco3X1nOoxyG8QTd2byv/Zck7C/wH2\n3thbrOAvSZo8DXtv7C2V8O/SpQtqtZq9e/cyZMgQdu/ejaurK0FBQSQmJpKcnMwnn3zCnDlzDPso\nioK7u7vR63h5/T1Kt1q1auTm5qLVarGyKvqf1Y0bN4iPj6d58+ZG7RYWFkRFRdGxY0fefPNNPvjg\nA5YtW0bHjh3p1asXQUFBJXT0QohHlaJJ4Xri9QJP2bO3smdc63G09mgtoV9BSPg/QK8GvYp95l9S\nbK1s6dWg5IJfp/v7Opy1tTX9+vVj9+7dDBkyhF27djFgwAAsLCyws7MD4KuvvqJXrwe/v4VF8YeM\n2NnZ4ePjw7Zt24rcJjg4mOeee47Dhw9z6NAhJk6cSPfu3fn3v/9d7PcTQjy+5Oxkfrz0I2fjzhq1\n21ra4u3kTS11LdrUaWOm6sSjkPB/gF4NexX7zPtBXftldd3L1taW7Oxso7aIiAjs7e0NywMHDmT4\n8OFcu3aNkydP8vHHHwP6MQFubm5cunTJKPzj4uJwcXHBxubxbtF54okn2LRpE+np6ajVakDfqxAV\nFUW9evUA/biAGjVq8Mwzz/DMM88wcOBARo8ezezZs3F2lud2C1FW8nR57Luxj11/7jI6CbJUWeLl\n5IWngycWKhk3XhHJT60SatCgAX/++Sfh4eHk5uayefNmoqOjjbZp0aIFnp6efPLJJ/j7+9OwYUPD\nulGjRrFu3TqOHj2KVqslPDycl156iZUrVz52bf3796datWp88sknJCUlkZWVxYIFC3juuedIT0/n\n5MmT9OjRg5CQELRaLTk5OZw5cwY3NzecnJwe+/2FEKa5cOcC/zzwT/53+X9Gwe9u706bOm2o51hP\ngr8CkzP/ElYeRrU+99xzhIWF8dJLL2FjY8Pzzz/PoEGDuHDhgtF2/fv3Z9GiRXzwwQdG7a+88gpZ\nWVnMnDmThIQEatasyaBBgxg//vFvV1Sr1axYsYLPP/+cbt26YW1tjb+/P6tWrUKtVtO6dWtmzJjB\n3LlziYmJwc7OjqZNm/LNN9/ItUQhysDdjLtsvriZc3HnjNrrONShRa0WONnKh/DKQKXc/yzFSigq\nKooePXrw22+/UbduXXOXU2p8fX2ZM2cOQ4cONXcpQogKJkebw+4/d/Pr9V/J0+UZ2qtZV2OA7wCe\neuIpOdO3PLoBAAAgAElEQVSvYB6UfXLmX0ncuHEDQK6JCyEe6t6xSQoK8Znx3Ei6gUarMdyvr1Kp\nCKoXxCC/QTjYOpirVFFKJPwrgW3btjFr1izatWtHp06dzF2OEKKCyMzN5FrSNZKzk43an3B+gmHN\nh/GE8xPmKUyUOgn/SmDAgAEMGDDA3GUIISoIBYWo1CgikiPQ8fdtwDYWNoxqOYqOdTvKGJtKTsJf\nCCGqkLj0OM7ePktqTqqhTYWKOg518HbyJqieTKhVFUj4CyFEFaAoCr/d/I2fw382Cn4HGwd8XH2o\nbl3djNWJsibhL4QQldzdjLv89+x/+TPhT0ObBRZ4O3tT17EuKqSLv6qR8BdCiEpKURQORhxky+Ut\nRhP1qG3U+Lr6ytl+FSbhL4QQlVBiViKrz67m8t3LhjYLlQV9G/dlyTNLsLKQX/9Vmfz0hRCiElEU\nhSORR9h8cTPZeX8/48PDwYMxLcfg7extxupEeSHhL4QQFdi9E/ZotBr+TPiTxOxEALp6dUWlUtG7\nYW/6+/TH2tLaXGWKckbCXwghKjgFhbsZd7mWdM1oat6a1WsyJnAMDVwamLE6UR5J+AshRAWWp8vj\nz8Q/uZt516jd08GT2U/Oxsby8R7DLSqnMn9KQ2RkJCNHjsTX15eoqCijdTt27GDQoEEEBgbSu3dv\nvvrqK7RardG+EyZMICgoiI4dOzJhwgQiIyPL+hCEEKJc+CvlL07fPm0U/HZWdrSo1YKGLg0l+EWR\nyjT89+7dywsvvECdOnUKrAsNDWXGjBm8+uqrHD9+nEWLFrFt2za+/vprAHJzcxk3bhyOjo7s2LGD\nX375BRcXF4KDg8nNzS3LwxBCCLNSFIWDtw7yecjnZOVlGdo91B609mgtj90VD1Wm4Z+cnMy6desY\nOHBggXVr166la9eu9O3bFxsbG3x9fRk9ejRr1qxBp9MREhJCREQEM2fOpEaNGjg6OvLuu+8SGRnJ\nwYMHy/IwhBDCbLLzsllxagXrz683XN+3UlnRxK0JjWs0xlJlaeYKRUVQpuE/dOhQ6tevX+i6M2fO\nEBAQYNQWEBBAcnIyt27d4syZM3h5eeHi4mJY7+zsTL169Th79myp1i2EEOVBVGoUc/+Yy4mYE4Y2\ntbWaQI9A3O3dzViZqGjKzYC/xMREnJyMu6rygz4xMZGkpKQC6/O3SUhIKJMahRDCHBRFIeSvEDZe\n2Gg0mr+LdxcW91sst/CJYis34f845NGTQojKSpOnYe25tYRGhxrabK1sGREwgnae7cxYmajIyk34\nu7m5kZycbNSWlJQEgLu7O66urgXW52/j5uZWJjUKIURZikqNYvnJ5cSlxxnaPB09Gd96PLXUtcxY\nmajoyvxWv6IEBgYWuHZ/8uRJ3N3d8fLyIjAwkMjISKMu/vj4eP766y/atGlT1uUKIUSpye/mnxcy\nzyj4O3l1YkbnGRL84rGVmzP/UaNGMWLECHbt2kXPnj25cuUKq1atYuzYsahUKjp16kSjRo2YO3cu\ns2fPRlEU5syZg4+PD0FBQeYuXwghHkv+NL1aRcu1xGvEZfwd+j3r92R4wHA61O1grvJEJVOm4d+n\nTx9iYmJQFAWAp59+GpVKxcCBA5kzZw7z589n4cKFvPPOO7i5uTFy5EjGjh0LgKWlJcuXL+fjjz+m\ne/fuqFQqgoKCWL58OZaWcmuLEKLiy8zN5NLdS2TmZRraqltXZ1aXWXg4eJixMlHZlGn4//LLLw9c\n37t3b3r37l3keg8PD8OkP0IIUZkkZydzKf6S0Wj+2tVr07BGQwl+UeLKTbe/EEJUVceijnHhzgV0\n6ACwUFnQuEZjalWXa/uidEj4CyGEmSiKwo6rO9hxdYch+G0tbWnm3gy1jdrM1YnKTMJfCCHMIE+X\nx5qzazgWdczQVt26Ov41/bG1tDVjZaIqkPAXQogylpmbyTcnvuFK/BVDm4udC03dm8rc/KJMSPgL\nIUQZSshMYFHoImLTYg1tnbw6sfSZpVhaSPCLsiHhL4QQZSQiOYLFoYtJ1aQa2p71e5anGz0t05SL\nMiXhL4QQZeDs7bOsOLWCHG0OAFYWVoxuOZq2nm3NXJmoiiT8hRCilP1+83c2XdxkmODM3tqe19u+\nTmPXxmauTFRVEv5CCFFKdIqOHy/9yG83fjO0udm7MaX9FJmfX5iVhL8QQpSge+fovxJ/hfiseMO6\nUS1GMbHtRBxsHcxVnhCAhL8QQpS4XF0uF+5cIC0nzdDmZu/G9I7Tsba0NmNlQuhJ+AshRAnSaDWc\njztv9HCeuo51qe9cX4JflBsS/kIIUULiM+M5G3eW7LxsAFSoaFijIXXUdcxcmRDGJPyFEKIExKXH\n8dWxr4yC38/ND3d7dzNXJkRBEv5CCPGYYtJi+OroV4bJeyywoIl7E1yruZq5MiEKJ+EvhBCPISI5\nggXHF5CRkwGApcqSpu5NcbFzMXNlQhRNwl8IIR7R9cTrLDy+0NDVb2dlx88v/kyjGo3MXJkQD2ZS\n+GdmZrJ69WrOnDlDcnJyodts3LixRAsTQojyLDw+nCWhSwzT9dpb2zO1w1SecH7CvIUJYQKTwv+j\njz5i27ZtNGzYkBo1apR2TUIIUa6djzvPNye+IU+XB4CDrQNvdHiDuo51zVyZEKYxKfz/+OMP5s2b\nx7PPPlva9QghRLl2KvYUK06tQKvTAuBs58y0jtOora5t5sqEMJ1J4a/VamnTpk1p1yKEEOXa8ajj\nrDqzyvCAHld7V97s+CZu9m5mrkyI4rEwZaOuXbty/Pjx0q5FCCHKrUMRh4yCv5a6Fm8HvS3BLyok\nk878hw0bxqeffsqNGzdo0aIF9vb2Bbbp3LlziRcnhBDmkv+AHoDotGiuJ103LL/Y7EWmdZyGo62j\nOUoT4rGZFP4jRowA4NKlS0btKpUKRVFQqVRcvny55KsTQggz+yv1L24l3zIsO9g48FbQW1S3qW6+\nooR4TCaF/+rVq0u7DiGEKHciUyONgt/R1hF/d38JflHhmRT+7dq1K+06hBCiXInLiONm8k3DsrOd\nM83cm2GpsjRjVUKUDJNn+Dt9+jTr16/n8uXLZGRk4ODgQEBAAKNHj6ZRI5nNSghReVy8c5GrCVcN\ny062Tvi7+2OhMmmMtBDlnkn/kg8cOMDw4cMJDQ3F29ubtm3b4unpyYEDBxgyZAinT58u7TqFEKJM\nRCRHsOzkMhT0o/qrW1enmXszCX5RqZh05v/1118zaNAgPvnkEyws/v4PoNVqefvtt/nqq69kXIAQ\nosK7m3GXRaGL0ORpALC1tMW/pj9WFvIYFFG5mPQv+sqVK3z66adGwQ9gaWnJ+PHjefHFF0ulOCGE\nKCtpmjQWHF9AmiYNgKcbPs07nd7Bw8HDzJUJUfJM6sdSqVTk5eUV/gIW0hUmhKjYNHkaFoUu4m7G\nXQCsLa2Z1G6SBL+otExKbn9/f5YuXVrgA0Bubi5LlizB39+/VIoTQojSptVpWXZyGRHJEYD+ZGdc\nq3E0rNHQzJUJUXpM6vafOnUqY8aMoUuXLvj7+6NWq0lLS+PChQtkZ2fz3XfflXadQghR4hRFYfXZ\n1Vy8c9HQ9lLzl2hRu4UZqxKi9Jl05t+mTRt++uknevbsSUJCAhcvXiQxMZHevXvz008/0apVq9Ku\nUwghStzP4T9zLOqYYfkfPv+gq3dXM1YkRNkweQirj48Pn3zySWnWIoQQZeb3m7+z59oew3IX7y78\nw+cfZqxIiLJTZPiHhITQoUMHrKysCAkJeegLldSDfW7cuMGXX37JmTNnyM3NpUGDBrz22mt069YN\ngB07drBy5Upu3bqFu7s7ffv2ZcqUKVhayqxbQgjTnIw5yaaLmwzLAbUCeKn5S6hUKjNWJUTZKTL8\ng4ODOXz4MK6urgQHBxse4lOYknqwj06nIzg4mBYtWrB7927s7e1Zt24dkydPZtu2bcTHxzNjxgy+\n/PJLevTowc2bN5kwYQLW1tZMmjTpsd9fCFH5XU24ynenvzP8Pmvg0oBxrcfJJD6iSiky/FevXo2T\nk5Ph72UhMTGR6OhoPvzwQ5ydnQF46aWXmDdvHuHh4ezZs4euXbvSt29fAHx9fRk9ejRLly7l9ddf\nl9sOhRAPFJUaxZLQJeTp9Hcu1VbXZlK7SdhY2pi5MiHKVpHhf+/DfGJiYujXrx82NgX/g9y+fZs9\ne/aUyMN/3NzcaN26NT/++CPNmzfHwcGBDRs24OLiQvv27Zk3bx4vvfSS0T4BAQEkJydz69YtGjRo\n8Ng1CCEql/HbxwOg0Wo4c/sMGq1+9j4bSxv2v7xfntAnqiSTTpVnzpxJenp6oevu3r3LV199VWIF\nLVq0iOjoaDp27Ejz5s1ZtmwZCxYswNXVlcTERENvRD4XFxdA32sghBCFydPlcf7OeUPwW6msaF6z\nOa72rmauTAjzeOBo/5EjRxqu9U+cOBFra2uj9YqicOvWLRwdHUukmJycHIKDg2nQoAHLli2jWrVq\nbN26lQkTJvDDDz+UyHsIIaoWBYXL8ZfJzM0EwAILmro3pbq1nPGLquuBZ/6DBg3C29sb0D/EJy8v\nz+hLq9XSrFkzvvjiixIp5tixY1y6dIlZs2bh7u6OWq1m+PDh1K1bl59++gk3NzeSk5ON9klKSgLA\n3d29RGoQQlQuN5JukJSdZFj2dfPF2c7ZjBUJYX4PPPMfPHgwgwcP5tatWyxZsqTEzvCLotPpAP0H\njXtptVoURSEwMJCzZ88arTt58iTu7u54eXmVam1CiIrn8F+HiU6LNix7O3njbi8nCkKYdM1/zZo1\nRQZ/TEyMYfT942rVqhVubm7861//IikpCY1Gw+bNm7l58yZPP/00o0aNIiQkhF27dpGTk8P58+dZ\ntWoVY8aMkftzhRBGriVeY935dYZld3t3vJzkJEEIKMYMfwcOHODQoUNG3e6KonDt2jXu3r1bIsU4\nOjqycuVK5s+fzzPPPENaWhoNGjRg8eLFtGzZEoD58+ezcOFC3nnnHdzc3Bg5ciRjx44tkfcXQlQO\nCZkJfHPiG7Q6fS+i2lqNj6sPKuQkQQgwMfw3b97MBx98gJubG4mJibi7u5OSkkJ2djYtW7Ys0Wl/\n/fz8WL58eZHre/fuTe/evUvs/YQQlYsmT8PSsKWkadIAeKbxM8zsPFNG9gtxD5O6/VevXs3s2bMJ\nCQnB1taWtWvXcvr0af71r39hYWFBmzZtSrtOIYR4KEVR+P7M90SlRgFgaWHJhDYTJPiFuI9J4R8Z\nGWmYW1+lUqHValGpVPzjH/9gyJAhfPTRR6VZoxBCmGTH1R2cij1lWB7efDiNajQyY0VClE8mhb+V\nlRXZ2dkAODk5cfv2bcO6Dh06cPz48dKpTgghTHQq9hQ7ru4wLPdo0INOXp3MWJEQ5ZdJ4d+yZUvm\nz59PWloavr6+fPvtt4YPA/v27cPW1rZUixRCiAeJTIlk1elVhuUm7k14rulzZqxIiPLNpAF/kydP\nJjg4mMTEREaPHs0rr7xCu3btsLGxISMjg1GjRpV2nUIIUahUTSpLwpaQo80BoGb1mrza+lV5Sp8Q\nD2BS+Lds2ZIDBw5gZ2eHt7c3GzduZOfOneTl5dGyZUueeeaZ0q5TCCEKyNPl8c2Jb0jK0s/gZ2dl\nx8R2E7G3tjdzZUKUbybf569Wqw1/b968Oc2bNy+VgoQQwhSKorD+/HquJ14H9IORx7UeR211bTNX\nJkT5V2T4z58/3+QXUalUTJs2rUQKEkIIU+y/uZ/Dfx02LA9pMgT/mv5mrEiIiqPI8H/QRDv3k/AX\nQpS28dvHG/6elJ3EhTsXUFAAeCfoHXo26Gmu0oSocIoM//Dw8LKsQwghTJKVl8Xl+MuG4He0cWRE\nwAh5vocQxSDDYYUQFYZW0XLxzkXydHkA2Fra0rRmU6wtrc1cmRAVi0kD/l5++eWHbrN69erHLkYI\nIR7kRtINMvMyAbBQWdDUvSk2FjZmrkqIisek8M/NzS3QpZaRkcGtW7eoXbs2fn5+pVKcEELkS8hK\nIDY91rDcuEZjHGwczFiREBWXSeG/YcOGQtuTkpJ499136dOnT4kWJYQQ90rTpPFnwp+GZXd7d2pW\nr2nGioSo2B7rmr+LiwtvvPEGCxcuLKl6hBDCiKIorDm3hhydfgY/G0sbGtVohAoZ4CfEozJ5kp+i\nWFtbExsb+/ANhRDiERyJPMLZ22fp6tUVgCntp9CsZjMzVyVExWZS+IeEhBRoUxSFlJQU1q1bR506\ndUq8MCGEiM+MZ9PFTYblp554SoJfiBJgUvgHBwejUqlQFKXAOkdHR7744osSL0wIUbXpFB3fnf4O\nTZ4GgFrqWgxpOsTMVQlROZgU/oXdxqdSqXBwcMDb25tq1aqVeGFCiKrt1+u/Gubtt1BZ8ErgK9hY\nym19QpQEk8K/Xbt2pV2HEEIYRKZEsu3KNsPyP3z+gbeztxkrEqJyMXnA3969e9m+fTuRkZGkpKTg\n7OxMw4YNGTx4MB07dizNGoUQVUiuNpeVp1ei1WkBqO9Sn76N+5q5KiEqF5Nu9Vu5ciWTJ0/mwoUL\n1KlTh9atW1O7dm3CwsIYO3Ys//3vf0u7TiFEFfG/8P8Rm6a/g8jG0oaxgWOxUMlM5EKUJJOv+Y8b\nN47p06cXWPf555/z3XffMWrUqBIvTghRtYTHh/Pbjd8My0ObDZXJfIQoBSZ9nE5OTua5554rdN3z\nzz9PcnJyiRYlhKh6MnMz+f7M94Zl/5r+dPHqYr6ChKjETAp/X19fbt++Xei627dv06RJkxItSghR\n9Ww4v4GkrCQAqttUZ1TLUfKYXiFKiUnd/h9//DFz584lLS2Nli1b4uDgQGZmJidOnOD7779nxowZ\n5OTkGLa3sZHbcYQQpguLDiM0OtSwPDJgJI62jmasSIjKzaTwf+GFF9BoNJw4caLAOkVRGDZsmGFZ\npVJx6dKlkqtQCFGpJWcns/78esNyUL0gAj0CzViREJVfsWb4E0KIkqQoCt+f+Z7M3EwAXO1decH/\nBTNXJUTlZ1L4T548ubTrEEJUEeO3jzf8PTotmutJ+ln8VKjY+uJW7KzszFWaEFWGyZP8pKens3v3\nbi5fvkxGRgYODg4EBATQp08fbG1tS7NGIUQllJmbyc3km4bluo51aeza2IwVCVF1mBT+169fZ9So\nUcTHx+Pg4ED16tVJT09n7dq1LFmyhNWrV1OrVq3SrlUIUUnoFB3hCeHoFB0Aamu1TN8rRBky6Va/\nf//733h6erJ7927CwsI4cOAAJ06cYNu2bVSrVk2e6ieEMJmCwrXEa6TnpANggQW+br5YmPbrSAhR\nAkz633bixAnee+896tevb9Tu4+PD+++/T0hISKkUJ4SofG6n3+Z2xt/zhjSo0YDq1tXNWJEQVY9J\n4Z+VlYWjY+H33NasWZPMzMwSLUoIUTldT7xueEwvQK3qtfBQe5ixIiGqJpPC39vbm927dxe6bufO\nnXh7y7U6IcSDpWSnsOzkMnT8/3V+GzWNazRGhdxGLERZM2nA38svv8wHH3zA+fPnCQwMRK1Wk5aW\nxqlTpzh48CBz5swp0aK2bNnC8uXLiY6OpmbNmowcOZLRo0cDsGPHDlauXMmtW7dwd3enb9++TJky\nBUtLyxKtQQhRcvJ0eSw7uYyU7BS6enWluk113uvyHq72ruYuTYgqyaTwf/755wH9o333799vaH/i\niSeYO3cugwcPLrGCdu7cyeeff878+fNp27Ytp0+f5qOPPqJNmzZkZmYyY8YMvvzyS3r06MHNmzeZ\nMGEC1tbWTJo0qcRqEEKUrB8v/Wjo7lepVIxrNU6CXwgzMvk+/+eff57nn3+e9PR0MjIyqF69Omq1\nusQLWrJkCcHBwXTq1AmA9u3bGy45TJkyha5du9K3b19A/8Ch0aNHs3TpUl5//XUsLGS0sBDlzbGo\nY/x+83fD8iC/QTRxl4eBCWFOxUrLq1evcvz4cY4cOUJYWBiRkZElWsydO3e4fv069vb2DBs2jFat\nWtG/f3+2b98OwJkzZwgICDDaJyAggOTkZG7dulWitQghHt9fKX+x9txaw3Irj1b0btjbjBUJIcDE\nM//IyEgmT57MlStXUBTF0K5SqQgMDOTLL7/E09PzsYvJf2zwpk2b+PLLL6lXrx4//vgjb731Fh4e\nHiQmJuLk5GS0j4uLCwCJiYk0aNDgsWsQQpSMjJwMvjnxDbnaXAA8HDzkMb1ClBMmhf8HH3xAamoq\nc+bMoVmzZtjb25ORkcGFCxdYunQpH3zwAStXrnzsYvI/WIwcORJfX19AP9hw69atbNmy5bFfXwhR\nNnSKjm9PfUtCZgIAdlZ2vNbmNZm3X4hywqTwP3XqFCtWrKBt27ZG7U2aNKFevXpMmDChRIqpWbMm\n8PfZfD4vLy/i4uJwc3MjOTnZaF1SUhIA7u7uJVKDEOLxbQ3fyuW7lw3LYwPHUkstU4ALUV6YdM1f\nrVYXGa61atWievWSmZ2rZs2aODs7c/78eaP2iIgIPD09CQwM5OzZs0brTp48ibu7O15eXiVSgxDi\n8ZyKPcWea3sMy/0a96NF7RZmrEgIcT+Twn/w4MH89NNPha778ccfGTJkSIkUY2lpyZgxY1i7di1H\njhwhJyeHdevWcfnyZYYNG8aoUaMICQlh165d5OTkcP78eVatWsWYMWPkOqIQ5UBsWizfn/nesOxf\n05/+vv3NV5AQolAmdfs7ODiwceNGDh48SGBgIA4ODmRlZREWFkZKSgr9+/dn/vz5gH4Q4LRp0x65\noPHjx5OXl8fMmTNJSEigfv36fPvttzRpor81aP78+SxcuJB33nkHNzc3Ro4cydixYx/5/YQQJSMr\nN4uvT3yNJk8DgJu9G2MDx2KhkltwhShvVMq9w/eL4OfnZ/oLqlRcvnz54RuWoaioKHr06MFvv/1G\n3bp1zV2OEJWOoih8feJrzt7WX5aztrRmRucZ1HWU/29CmMuDss+kM//w8PBSKUwIUbGN3z4egL9S\n/+JW8i1Du5+bnwS/EOWY9McJIR5LYnYiEckRhmVPB09q2tc0Y0VCiIeR8BdCPLL0nHTC74ajoL96\n6GzrTAMXmWxLiPJOwl8I8UjiM+O5cOcCeUoeALaWtvi5+8kjeoWoACT8hRDFlqZJY8GxBeTocgCw\nsrDCv6Y/NhY2Zq5MCGGKxw5/jUZDXFxcSdQihKgANHkaFocu5k7GHQAssKCZezOqW5fMZF9CiNJn\nUvg3adKEhISEQtfdvHmTgQMHlmhRQojySavTsvzkcsPIfhUq/Nz8cLJ1evCOQohy5YG3+v3888+A\n/h7e3bt3o1arjdYrikJoaCgajab0KhRClAuKorDm3Bou3LlgaFv6zFKeeuIp8xUlhHgkDwz/n376\niQsXLqBSqZgzZ06R240cObLECxNClC9br2zlaORRw3K/xv0k+IWooB4Y/mvWrCEvLw9/f382bdpU\n4Gl7AI6Ojjg7O5dagUII8/v95u/s/nO3YbmTVycG+A4wY0VCiMfx0Bn+rKys+O2336hTp448PEeI\nKuhkzEk2XdxkWA6oFcCIgBHy+0CICqzI8J8/fz6vvfYa1apVY9OmTUVtBjz+w3yEEOXT1YSrfHf6\nO/IfAdLApQHjWo+Th/UIUcEVGf7Lly9n1KhRVKtWjeXLlz/wRST8hah8olKjWBK6hDydfhKfWupa\nTGw3ERtLuZdfiIquyPC/92E+8mAfIaqWhMwEFh5fSHZeNgBOdk5MbT8VtY36IXsKISoCk57qd6/4\n+HiysrKoXr06NWrUKI2ahBBmlJGTwYLjC0jJTgHAzsqOKe2n4GrvaubKhBAlxaTw12g0fPnll2zf\nvp3U1FRDu4uLC4MGDeKNN97A2tq61IoUQpSu/EfzahUt5+POk5qj/39ugQVbXtgij+cVopIxKfw/\n/PBDdu3axcCBA/H19aVatWpkZmZy8eJFVq9eTVpaGh9//HFp1yqEKEUKCuHx4YbgV6HC180XXzdf\nM1cmhChpJoX/vn37mDNnDgMGFLyvt23btsybN0/CX4gKTEHhasJVErL+nsa7oUtD3O3dzViVEKK0\nmBT+Op2Oli1bFrquXbt2aLXaEi1KCFF2dIqOK/FXuJN5x9BWz7EedRzqmLEqIURpMin8n3zySY4e\nPYqXl1eBdaGhoXTu3LnECxNClD6tTsuKUyuMgt9D7cETzk+YryghxAOFhcHu3RAbCx4e0LcvtG1b\nvNcoMvxDQkIMf+/ZsycLFy7k2rVrBAYGolarycrKIiwsjEOHDjFz5sxHPgghhHnk6fJYfnI5Z2+f\nNbR5OnjSwKUBKmT2PiFKW3FCXKeDrCw4cgS+/x60WrC1hehoWLFCv01xPgAUGf7BwcGoVCoURTH8\nuWbNGtasWVNg29dee43Lly+b/q5CCLPK1eay7OQyzsedN7TVdahLfZf6EvxCFJMpIa4o+vDOzISM\nDDh+HDZuhLw8/df163DgAHTsCLVr67fLyvr7K//huSdP6vfP17QpuLnBnj0lFP6rV68uxqELISqK\nXG0uS8OWcunuJUPb3O5zGeQ3SObrF1Xew4I8P8TT0/UhfOwYbNqkD/DcXLh6Ffbt0+/j6vp32Gdl\n6ffNd3+I5/v1V2jVquj6MjONlzMy9OEfE1O84ywy/Nu1a1e8VxJClHuaPA1Lw5YSHv/3rJ39Gvdj\ngO8ACX5RqZjapZ6Xpw/y1FR9l/rGjfoQz82F8HDYuxcCA6FGjb8DX6f7e/+iQvyPP4oX4vkKey0A\nlUrfze/qqv8gYWUF1atDnf8fl1unmONzTZ7hb+/evWzdupXr168bZvhr1KgRgwcP5sknnyzeuwoh\nypwmT8Oi0EX8mfCnoa2/b3/+4fMPM1YlRMk7ehS++QZycvRf0dH6YO/cGdzdIS1NH/ZpacYhXFSQ\nHz1adJAXJ8Tt7PSBbW8PXl76fa2s/v6ytNSH+MSJ+m2qVfv7y85O/wEgLOzva/z3evrph39f7mVS\n+Hj4nP8AACAASURBVH/77bf8+9//xtvb22iSnwsXLvDrr78ya9YsRo4cWbx3FkKUmey8bBYeX8j1\nxOuGtmf9nqVv475mrEqIh8s/g4+J0XdvBwVB/fqQkqIP8JQU46/UVAgJKTx8ExJK5mw8P8TVaoiM\n1K+3ttYHeP6fnp4wfbo+xPMD3+Keh2EWFeLBwRAQUHSN+b0Xe/bovyd16uiDv8RG+99r9erVvPrq\nq7z55psF1s2bN48VK1ZI+AtRTmXmZrLw+EJuJt00tA1pOoTeDXubsSpR1TyoG15R9F3qiYmQlKT/\nSkyEU6f018/zz+B1OvjpJ/Dzg5o1i36v4pyNq1Tg4ACOjuDtre9St7Y2DvO6deHdd/UhXr26vv3e\n4yosxEeN0n9IKcrjhHjbtsUP+/uZFP4pKSkMGTKk0HUvvvgiGzZseLwqhBClIv8hPRHJEYa255s9\nT48GPcxYlahqDh+Gr7/Wj1jXaODGDdi/H1q00AdvUpL+2vv9iuqGj4p6cPhXr66/Zm9jo/+yttb/\nWaeO/sw6P+wdHPTb5p+RFxXkI0YUfU3d3CH+qEwK/6ZNmxIREYG3t3eBdbGxsfj5+ZV4YUKIx5Oe\nk85/jv2HyJRIQ9uw5sN46omnzFeUqPDuP4N/+mlo1kzfpZ6QoD9jv/fPhAT94LfCQvzIkeJ1w1tZ\n/R3mbdqAk5Pxl6Oj/s9Ll2DlyoKvFxz84LB91CA3Z4g/qiLDPycnx/D3WbNm8emnn5KTk0PLli1x\ncHAgMzOTEydO8P333/PPf/6zTIoVQjxY/tP5cnW5nIs7R0au/jeuChXL/rGMLt5dzFmeqKB0On2Y\n79sH69b9fe95WBhs3gyNGz9+N3y1auDioh9Vn/+nTqe/hm9rqw99S0v9tnXrwrhxRb9fu3b67vyK\ndjZelooM/4CAAKNbfxRFYfLkyQW2UxSFoUOHcv78+QLrhBBlT6PVcP7OeTJz9b9xVajwcfWR4BdG\nCjuD9/GBO3cgLs74z7t39d3yj9oNr1br97e11Q+Ws7XVf3l5waxZ+rC3syu4n7v7o49sryoh/qiK\nDP+JEyfKfb9CVDAZuRlcuHMBjfb/2rvzuKjq/Y/jr2Fn2GGGRQQRFERxN80WSy27XNNumWlmad5M\n61r31s+9btm+aN77y9L0Zmbpr25lVpqaWveWdruKJGgugAuKCy7s67DM+f3xbQZGFpeQAebzfDzm\nAZxzZvg6jyPv+e5qOTAdOuKC4gj2auQvs3Ao5eXwzTdqiVjLKnK7dv22Gnx5ufoAERSkauxBQTWP\nwEC18E19zfAPPKCe15CmGtku6mow/Our5denvLyc1NTUi18ohLiq9p3dR2p2KlWaGjmlQ0cXQxfZ\nltcBXFiLv+02iI6G7GxVe6/9tbDwymrwvr4QEaFG3deee+7uDlFR8MwzDZdPmuFbnkte5Mei9lgA\ngKSkJB5//HF2797dZIUSQlyeH479wEd7P7IGv4vOhXhjPAEeAXYumbiaNE3V4pctU2FeWgo7d6rl\nZmNjGw7yhmrwJpMK8pAQ9QgOrvnq4dHwaPjES1guQkK8Zbmk8M/Pz+eZZ55h+/btlJWV1TkfExPT\n5AUTQlycpml8fuBzNh/ebD3m7uxOQnACXq5ediyZaGpVVWqluqws9Th+vGblusupxbu4qECvqlK1\n99oryUVFQWObtEozfNtxSeE/f/589u/fz3333ceKFSsYO3YsFRUVbNmyhVtvvZUnnnjiqhQuOTmZ\n8ePH8+ijj1q7IdavX8/y5cvJzMzEaDSSmJjI448/jrNlGKgQDqKyupL3dr/Hz6d/th7zcfOhm7Eb\nbs5udiyZuFKW5vsTJ1QYx8aqeehZWSpsa68pb9FQLb6yUj0/NFSFveVrUJBq9pcavGO7pPDfvn07\nb7zxBv369WPVqlVMmDCBiIgIZs6cyR//+EdSU1O5+eabm7Rg5eXlzJ07Fy+vmtrLzp07mT17NvPn\nz2fo0KEcPXqUqVOn4urqyrRp05r09wvRkhWaClmctNhm1b6eoT15M/FN3F3c7VgycbkKClS4f/ut\nWr2uuFgNwgO1w9vFVrML+LVnx9u7phav16vV6v7nf+p/jtTgxSWFf05ODhEREeoJLi6Yft1Y2Nvb\nm9mzZ/Pss882efgvXLiQjh07Elzrrl+1ahWDBg0i8dePp3FxcUycOJHFixfz6KOP4lR74WQh2qjT\nRadZtHMROaU51mNDo4dyd9e7cdLJ/4GWoL6lbPv1U1Pmjh+vabrPylID8ODSBuEZjWrQXWRkzde0\ntCubDic1eMd2SeEfEBDA0aNHCQkJwWAwsG/fPjp16mQ9d/z48SYt1K5du/jyyy/56quvmD59uvV4\nSkoK48aNs7m2R48e5Ofnk5mZSXR0dJOWQ4iWJu18Gu/seqdmDr9Ox5huYxjccbCdSyYskpLgH/9Q\nzfFFRZCRAV9/rfrT/f0bfl7t5nudrmYdeR8fmD5dLWzj6Vn3eVKLF1fiksLf0q//6aefcuONN/LK\nK69QWVmJv78/q1evJjw8vMkKVFZWxty5c5k1axYhISE253Jzc/Hz87M5FvBrm1dubq6Ev2jT/pP1\nHz5M/RCzpjp+3ZzdmNx3Mj1CGtkCTDQLTVM19PR0+Nvf4NixumvVHzlS/1K27u4q2HNz1TQ6S+hb\nGjLbt1fz7xsjtXhxuS4p/KdPn05ZWRkeHh5MmTKFHTt28PTTTwPg5+fHG2+80WQFWrhwIVFRUdx1\n111N9ppCtGaapvFV2ldsyNhgPebn4ce0/tOI9Iu0Y8kcl6apkfZpaeqRkVFTcz9yRJ2/UEmJqsVH\nRqpAtzTdBwc37T7tQlyKSwp/vV7PK6+8Yv35yy+/JD09ncrKSqKjo/Gsry3qClia+9etW1fveYPB\nQH5+vs2xvLw8AIxGWchEtB2WNfrNmEk/n87Z0rPWc/cm3Mtj/R8jwFPm8F9ttfeS9/ZWC+c4Oaka\nfn3986AG25WUqLXofX3V87y91cj7F19UQV8fab4XzemyF/mxiI2NtX5fUVGBm9tvn1q0Zs0aSktL\nGTlypPVYcXExe/bs4bvvvqN37951VhNMTk7GaDQSGSk1ING2VJgrOHDuAAWmAuuxQI9AZl4/Ew+X\nehZCF02muhrWr1d99wUF6lFZqc41NPre11cFfJ8+agtbDw/boP/DHxoOfgtpvhfNpdHwT0tLY/Xq\n1Zw+fZp27dpx77331tm+d9euXfz1r39l48aNv7kws2fP5s9//rPNsT//+c/06tWLhx56iJMnTzJ+\n/Hg2bNjALbfcQlpaGitWrGDSpEmyD4FoUwpMBRw4d4AKc82KmmHeYXQK7CTBfxWUl8Phw3DokPp6\n5Aj897+Nj7738YG4OPWIjVVz6C1/hvr0kRq8aNkaDP89e/Zw//334+rqSmRkJKmpqXz++ecsW7aM\ngQMHUlxczPz58/nkk0+sI/9/Kz8/vzoD+tzc3PD29sZoNGI0Glm4cCFvvvkmM2fOxGAwcP/99zNp\n0qQm+f1C2JumaWw6tIk9Z/agoTqOdejo6N+RcN9wdMiH3Ctx4dS7669X+74fOqQeJ07U7ae/cPEc\nNzf1nIAAmDdPLZrTWBO+hL1oyRoM/7fffpt+/fqxaNEi9Ho95eXlPPXUUyxcuJBHHnmEefPmUVRU\nxBNPPHFVw/fDDz+0+XnYsGEMGzbsqv0+IeylpKKE93a/xy9nf7EGv6uTK10MXWSN/t8gKQmWLFGj\n6fPzVY3+o48uvniOwaD693191UOvV2Hfvn3jO9EJ0Ro0GP67d+9myZIl6PV6ADw8PJg9ezY33ngj\nf/rTn7j55pt5+umnm3SanxCO6kjeEZYlLyOvLM96zM/djy6GLrg7y4p9V6K0FFJS1CC7zMy6Nfva\ni+dYQr1Tp5pHRoaMvhdtV4PhX1hYaF3Vz8JoNOLh4cFzzz3HHXfccdULJ0Rbp2ka3x79ljX711jn\n7wNE+EYQ5R8lzfyXyRL4yclw4IAauFdf8Ds5gasrDB+ugj46Wg3Qq01G34u2rNEBf/VtlqPT6ehT\n30oVQojLUlpZygepH7D7dM122HpXPQ/2flAW7rkMpaWQmgq7dtUEfm2WqXc+Pqop399fTb2LiIBa\nE4vqJX33oq264ql+Qogrdyz/GMuSl3G+9Lz1WJR/FA/3fZggfZAdS9ayWQbuZWWpn/39VbBfGPgW\nUVGQkKA+GFxYs5fme+HIGgx/nU4n0+eEaGKapvHDsR/4ZN8nVJlr1n8d0nEIo7qOwsVJPo/XR9NU\n8/vbb0Nenhq4Z9ne9sKBe1FR0LevegT9+jkqKUma74WorcG/NJqmMWLEiDofAMrLyxkzZozNDno6\nnY5t27ZdvVIK0UpZVuoDqNaqycjJsK7WNyhyEB4uHkzoNYE+YdKVdqGyMjh4EPbtU48tWxqed9+/\nvwr7Pn1U0/6FpPleCFsNhv+dd97ZnOUQok0rqSzhwLkDlFbVTB6P8Ivg4b4PE+zVyHyzNqqhLW+P\nH68J+yNHamr3UHfevY+P2uI2OBjmzGne8gvR2jUY/rXX8hdCXBkNjZOFJ8nMz8RMTZKFeYcx6/pZ\nuDq72rF09lF7A5vKSti9G777Tk218/Jq+Hm+vmqEfkAABAaq3fBAPU8IcXmkg1GIqyS7OJvU7FQK\nKwqtx5x1znQO7EywV7BDBj+oGn9FhVpG99y5muMlJXW3vO3QAbp1U4/z52HFirqvJwP3hLh8Ev5C\nNDGzZmbrka18efBLm+D3dvOmS1AX9K56O5bOvjRNzcPPyKi7371lOl7Xrirsu3ZVP1t06gTOzjJw\nT4imIOEvRBPKLs5mZcpKjuQdsR5zwolIv0gi/CIcetGevDxYtUr169cOfj8/1YwfHw/z5ze+850M\n3BOiaUj4C9EEatf2a0/h83bzJi4oDi/XRjqz2zhNg23bYM0atXteRIQaxe/hoXbD8/dX140de/Et\nb4UQTUPCX4jfqL7avrOTM8M7D2fx8MU4O9VdKdNRnD8PH3wAaWk1x0JCVO3dZIKzZ6X5Xgh7kPAX\n4go1VNuP8ItgYq+JtPd13GHoZjP861/wxRdqcJ9FSAhMmAAxMfYrmxBCwl+IS1J7sR6A0qpS0s+n\nU1hRyKDIQUBNbf93nX7n0LX97GxV2z98uOaYTgfDhsGIEWq6nhDCviT8hbgMDc3bl9q+qu1v2QJf\nfWU7oK9dO1Xbj4qyW9GEEBeQ8BfiEhVVFHEo9xBFFUXWY044MTJupEPX9pOS4J//VIP6qqvVgL7g\nYLVt7vDhqj/fRf7SCNGiyH9JIS6i0FRIek462SXZNsctI/mHxw63U8nsb/t2eOEFtcuepqljBw+q\nJXvnzpXV94RoqST8hWhAtbmaf2X+i3Vp62yCX+btq6D/739h1izIza057uSkVuULDZXgF6Ilk/AX\noh4Hzh3g418+JrvYtrYf5BlETEAMHi4eDTyz7TtyRDXzZ2aqhXssfH0hLg48PdWGPUKIlkvCX4ha\nzpee59N9n5KSnWJzXO+iJyYwhgCPADuVzP7y8mDtWtixo+aYXq8G90VFqX5+yyI97drZpYhCiEsk\n4S8EYKoysenQJjYf3mwzZ9/DxYPbY2/n7eFv4+LkmP9dKith82a1pn7tOfsuLmpVvoMH1Zr7tclm\nO0K0bI7510yIX2maRvLpZD7b/xl5ZXk2566LuI474+/E193XTqWzL02Dn39Wy/Lm5Nie69MHRo0C\ng0GN9pfNdoRoXST8hcOxLNhTUlnC4dzD5JvyrecGRQ4iyj+KsQlj6RjQ0V5FtLusLNWvn5Fhe7x9\nexgzRq3JbyGb7QjR+kj4C4djqjZxvOA42cXZaGjW425ObjzQ8wGui7gOnQPuMJOUpJbj/e9/obBQ\nBX1wsDrn7Q1/+ANcf70a0S+EaN0k/IXDKDQVsjFjI0knk2xW59OhI9wnnEi/SK6PvN6OJbSf7dvh\npZfgxIma1fkOHlRBP3asWqxHr7dvGYUQTUfCX7R5JRUlfHP4G747+h2V1ZU2wR/gEUBMQAx6V8dM\nNpNJbcDz/PO20/YAAgPVBjyjR9unbEKIq0fCX7RZZZVlbD2yla1HtlJeVW5zztfNlyj/KPw8/Bxy\noR6TCb7/Hr75BoqLIb9m2AN6PURHq/AvKmr4NYQQrZeEv2hzTFUm/pX5L7459A2llaU25yL8Ikgw\nJhDgGeCQoV9ZqUJ/0ybbYNfr1br8HTrIfH0hHIGEv2gzKqsr+eHYD2w8tJEik22VNcwnjJFxI+kd\n2pup66faqYT2U1mp+vU3boSCAttzQUEwdSrs3FkT+hYyX1+ItknCX7Ralil7ZsycKT7D8YLjmKpN\ngJqyB2D0MjIidgTXhF+Dk04NU186Yql9CmwHVVXw44+wYYNt0z5AQAD8/vdw3XVqwZ6ePWW+vhCO\nQsJftFpmzcyZkjNkFWbV6dMP8Azg9tjbGdh+oMNttZuUBF9/DSkpanEeg6Fmyh6Avz8kJsINN9hu\ntSvz9YVwHBL+otUpMhXx/bHv2XFyB5XmSptzbk5uRPpF8uKQFx1yOd4ff4RXX1VT9sp//Tx0/rz6\n2qmTqs0PGgSurvYroxDC/hzvr6Notc6WnGXL4S38dOInKqsrbYLf1cmVCN8IwnzCcNY5O1zw5+Wp\nKXvz59dt3ndzUzvuvfSS+l4IIRzrL6RolQ7nHmbLkS2kZKegaZrNOQ9nD8J9wwn1DsVZ51jN+wDH\nj8PWraqp32y2Hczn6qpW6WvXTn0vwS+EsGhx4Z+Tk8OCBQvYtm0bpaWldOrUiSeeeIKBAwcCsH79\nepYvX05mZiZGo5HExEQef/xxnC/cVky0ambNTGp2KpsPb+ZI3pE65yP9Iok3xGPQGxxuyp6mwd69\nsGULpKfbntPr1YeA8HAICanZbU+m7Akhamtx4f/oo4/i7e3N2rVr8fX15a233uLRRx9l06ZNHDt2\njNmzZzN//nyGDh3K0aNHmTp1Kq6urkybNs3eRRdXyDJqH6Baq+ZsyVlOFJ6grKrMOmrfIiE4gWEx\nw4gNinW4KXsVFWrd/a1b4cyZuuc7d1b9+d9/L1P2hBCNa1HhX1RURExMDH/84x8xGo0ATJ48mWXL\nlrFnzx7WrVvHoEGDSExMBCAuLo6JEyeyePFiHn30UZxkx5FWy1RtIrs4m1NFp+oM4nN2cmZA+ABu\njbmVdj41Vdi2PmUvKUnNyz92DMrK1DEfH9trnJygXz+45Ra1QA+oHfdkyp4QojEtKvx9fHx4+eWX\nbY5lZWUBEBoaSkpKCuPGjbM536NHD/Lz88nMzCQ6OrrZyip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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fcb06505128>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_results(system, title='Proportional growth model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook demonstrates the steps we recommend for starting your project:\n",
-    "\n",
-    "1.  Start with one of the examples from the book, either by copying a notebook or pasting code into a new notebook.  Get the code working before you make any changes.\n",
-    "\n",
-    "2.  Make one small change, and run the code again.\n",
-    "\n",
-    "3.  Repeat step 2 until you have a basic implementation of your model.\n",
-    "\n",
-    "If you start with working code that you understand and make small changes, you can avoid spending a lot of time debugging.\n",
-    "\n",
-    "One you have a basic model working, you can think about what metrics to measure, what parameters to sweep, and how to use the model to predict, explain, or design."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Bonus question\n",
-    "\n",
-    "Suppose you only have room for 30 adult rabbits.  Whenever the adult population exceeds 30, you take any excess rabbits to market (as pets for kind children, of course).  Modify `run_simulation` to model this strategy.  What effect does it have on the behavior of the system?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What happens if rabbits only take one season to mature?  Change `mature_rate` to 1.0 and run the simulation again."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/soln/rabbits3soln.ipynb b/soln/rabbits3soln.ipynb
deleted file mode 100644
index 431fd14f..00000000
--- a/soln/rabbits3soln.ipynb
+++ /dev/null
@@ -1,863 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Rabbit example\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Rabbit is Rich\n",
-    "\n",
-    "This notebook starts with a version of the rabbit population growth model.  You will modify it using some of the tools in Chapter 5.  Before you attempt this diagnostic, you should have a good understanding of State objects, as presented in Section 5.4.  And you should understand the version of `run_simulation` in Section 5.7.\n",
-    "\n",
-    "### Separating the `State` from the `System`\n",
-    "\n",
-    "Here's the `System` object from the previous diagnostic.  Notice that it includes system parameters, which don't change while the simulation is running, and population variables, which do.  We're going to improve that by pulling the population variables into a `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>t0</th>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>20.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>juvenile_pop0</th>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>adult_pop0</th>\n",
-       "      <td>10.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>birth_rate</th>\n",
-       "      <td>0.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mature_rate</th>\n",
-       "      <td>0.33</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>death_rate</th>\n",
-       "      <td>0.50</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "t0                0.00\n",
-       "t_end            20.00\n",
-       "juvenile_pop0     0.00\n",
-       "adult_pop0       10.00\n",
-       "birth_rate        0.90\n",
-       "mature_rate       0.33\n",
-       "death_rate        0.50\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = System(t0 = 0, \n",
-    "                t_end = 20,\n",
-    "                juvenile_pop0 = 0,\n",
-    "                adult_pop0 = 10,\n",
-    "                birth_rate = 0.9,\n",
-    "                mature_rate = 0.33,\n",
-    "                death_rate = 0.5)\n",
-    "\n",
-    "system"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following cells, define a `State` object named `init` that contains two state variables, `juveniles` and `adults`, with initial values `0` and `10`.  Make a version of the `System` object that does NOT contain `juvenile_pop0` and `adult_pop0`, but DOES contain `init`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>juveniles</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>adults</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "juveniles     0\n",
-       "adults       10\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "init = State(juveniles=0, adults=10)\n",
-    "init"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>t0</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>juveniles     0\n",
-       "adults       10\n",
-       "dtype: int64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>birth_rate</th>\n",
-       "      <td>0.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mature_rate</th>\n",
-       "      <td>0.33</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>death_rate</th>\n",
-       "      <td>0.5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "t0                                                        0\n",
-       "t_end                                                    20\n",
-       "init           juveniles     0\n",
-       "adults       10\n",
-       "dtype: int64\n",
-       "birth_rate                                              0.9\n",
-       "mature_rate                                            0.33\n",
-       "death_rate                                              0.5\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "system = System(t0 = 0, \n",
-    "                t_end = 20,\n",
-    "                init = init,\n",
-    "                birth_rate = 0.9,\n",
-    "                mature_rate = 0.33,\n",
-    "                death_rate = 0.5)\n",
-    "\n",
-    "system"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Updating `run_simulation`\n",
-    "\n",
-    "Here's the version of `run_simulation` from last time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def run_simulation(system):\n",
-    "    \"\"\"Runs a proportional growth model.\n",
-    "    \n",
-    "    Adds TimeSeries to `system` as `results`.\n",
-    "    \n",
-    "    system: System object\n",
-    "    \"\"\"\n",
-    "    juveniles = TimeSeries()\n",
-    "    juveniles[system.t0] = system.juvenile_pop0\n",
-    "    \n",
-    "    adults = TimeSeries()\n",
-    "    adults[system.t0] = system.adult_pop0\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        maturations = system.mature_rate * juveniles[t]\n",
-    "        births = system.birth_rate * adults[t]\n",
-    "        deaths = system.death_rate * adults[t]\n",
-    "        \n",
-    "        if adults[t] > 30:\n",
-    "            market = adults[t] - 30\n",
-    "        else:\n",
-    "            market = 0\n",
-    "            \n",
-    "        juveniles[t+1] = juveniles[t] + births - maturations\n",
-    "        adults[t+1] = adults[t] + maturations - deaths - market\n",
-    "        \n",
-    "    system.adults = adults\n",
-    "    system.juveniles = juveniles"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the cell below, write a version of `run_simulation` that works with the new `System` object (the one that contains a `State` object named `init`).\n",
-    "\n",
-    "Hint: you only have to change two lines."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def run_simulation(system):\n",
-    "    \"\"\"Runs a proportional growth model.\n",
-    "    \n",
-    "    Adds TimeSeries to `system` as `results`.\n",
-    "    \n",
-    "    system: System object \n",
-    "    \"\"\"\n",
-    "    juveniles = TimeSeries()\n",
-    "    juveniles[system.t0] = system.init.juveniles\n",
-    "    \n",
-    "    adults = TimeSeries()\n",
-    "    adults[system.t0] = system.init.adults\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        maturations = system.mature_rate * juveniles[t]\n",
-    "        births = system.birth_rate * adults[t]\n",
-    "        deaths = system.death_rate * adults[t]\n",
-    "        \n",
-    "        if adults[t] > 30:\n",
-    "            market = adults[t] - 30\n",
-    "        else:\n",
-    "            market = 0\n",
-    "            \n",
-    "        juveniles[t+1] = juveniles[t] + births - maturations\n",
-    "        adults[t+1] = adults[t] + maturations - deaths - market\n",
-    "        \n",
-    "    system.adults = adults\n",
-    "    system.juveniles = juveniles"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your changes in `run_simulation`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>5.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5.470000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>6.209900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.057723</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>8.021560</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>9.117031</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>10.362107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>11.777219</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>13.385586</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>15.213601</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>17.291261</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>19.652658</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>22.336542</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>25.386953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>28.853947</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>32.794414</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>34.478600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>36.487431</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>37.893339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>39.401924</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>40.546917</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "0     10.000000\n",
-       "1      5.000000\n",
-       "2      5.470000\n",
-       "3      6.209900\n",
-       "4      7.057723\n",
-       "5      8.021560\n",
-       "6      9.117031\n",
-       "7     10.362107\n",
-       "8     11.777219\n",
-       "9     13.385586\n",
-       "10    15.213601\n",
-       "11    17.291261\n",
-       "12    19.652658\n",
-       "13    22.336542\n",
-       "14    25.386953\n",
-       "15    28.853947\n",
-       "16    32.794414\n",
-       "17    34.478600\n",
-       "18    36.487431\n",
-       "19    37.893339\n",
-       "20    39.401924\n",
-       "21    40.546917\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run_simulation(system)\n",
-    "system.adults"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting the results\n",
-    "\n",
-    "Here's a version of `plot_results` that plots both the adult and juvenile `TimeSeries`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def plot_results(system, title=None):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    system: System object with `results`\n",
-    "    \"\"\"\n",
-    "    newfig()\n",
-    "    plot(system.adults, 'bo-', label='adults')\n",
-    "    plot(system.juveniles, 'gs-', label='juveniles')\n",
-    "    decorate(xlabel='Season', \n",
-    "             ylabel='Rabbit population',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If your changes in the previous section were successful, you should be able to run this new version of `plot_results`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Q/yF0btTZhFWZltxzrwQ2btxIv379aNu2LR07Vo1JDYQQojTczr7Nlwe+VINd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\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7feed5ab5908>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_results(system, title='Proportional growth model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That's the end of the diagnostic.  If you were able to get it done quickly, and you would like a challenge, here are two bonus questions:\n",
-    "\n",
-    "\n",
-    "### Bonus question #1\n",
-    "\n",
-    "Write a version of `run_simulation` that puts the results into a single `TimeFrame` named `results`, rather than two `TimeSeries` objects.\n",
-    "\n",
-    "Write a version of `plot_results` that can plot the results in this form.\n",
-    "\n",
-    "WARNING: This question is substantially harder, and requires you to have a good understanding of everything in Chapter 5.  We don't expect most people to be able to do this exercise at this point."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def run_simulation(system):\n",
-    "    \"\"\"Runs a proportional growth model.\n",
-    "    \n",
-    "    Adds TimeSeries to `system` as `results`.\n",
-    "    \n",
-    "    system: System object \n",
-    "    \"\"\"\n",
-    "    results = TimeFrame(columns = system.init.index)\n",
-    "    results.loc[system.t0] = system.init\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        juveniles, adults = results.loc[t]\n",
-    "\n",
-    "        maturations = system.mature_rate * juveniles\n",
-    "        births = system.birth_rate * adults\n",
-    "        deaths = system.death_rate * adults\n",
-    "        \n",
-    "        if adults > 30:\n",
-    "            market = adults - 30\n",
-    "        else:\n",
-    "            market = 0\n",
-    "            \n",
-    "        juveniles += births - maturations\n",
-    "        adults += maturations - deaths - market\n",
-    "        \n",
-    "        results.loc[t+1] = juveniles, adults\n",
-    "        \n",
-    "    system.results = results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_simulation(system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def plot_results(system, title=None):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    system: System object with `results`\n",
-    "    \"\"\"\n",
-    "    newfig()\n",
-    "    plot(system.results.adults, 'bo-', label='adults')\n",
-    "    plot(system.results.juveniles, 'gs-', label='juveniles')\n",
-    "    decorate(xlabel='Season', \n",
-    "             ylabel='Rabbit population',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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pohJYdOUeFRXF6tWr6dGjB+np6Rw5coSMjAx69erF6tWradeuXVXXKYQQopIV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\nSxe2bdvGb37zGz755BPCwq6+XXzmjpkAZJVkkZJXv9JLn4A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9noSEBEpLS1vgNyWEHdKE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\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7feed32bf940>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_results(system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Bonus question #2\n",
-    "\n",
-    "Factor out the update function.\n",
-    "\n",
-    "1.  Write a function called `update` that takes a `State` object and a `System` object and returns a new `State` object that represents the state of the system after one time step.\n",
-    "\n",
-    "2.  Write a version of `run_simulation` that takes an update function as a parameter and uses it to compute the update.\n",
-    "\n",
-    "3.  Run your new version of `run_simulation` and plot the results.\n",
-    "\n",
-    "WARNING: This question is substantially harder, and requires you to have a good understanding of everything in Chapter 5.  We don't expect most people to be able to do this exercise at this point."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def update(state, system):\n",
-    "    \"\"\"Compute the state of the system after one time step.\n",
-    "    \n",
-    "    state: State object with juveniles and adults\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    juveniles, adults = state\n",
-    "    \n",
-    "    maturations = system.mature_rate * juveniles\n",
-    "    births = system.birth_rate * adults\n",
-    "    deaths = system.death_rate * adults\n",
-    "        \n",
-    "    if adults > 30:\n",
-    "        market = adults - 30\n",
-    "    else:\n",
-    "        market = 0\n",
-    "            \n",
-    "    juveniles += births - maturations\n",
-    "    adults += maturations - deaths - market\n",
-    "    \n",
-    "    return State(juveniles=juveniles, adults=adults)\n",
-    "\n",
-    "\n",
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a proportional growth model.\n",
-    "    \n",
-    "    Adds TimeSeries to `system` as `results`.\n",
-    "    \n",
-    "    system: System object \n",
-    "    \"\"\"\n",
-    "    results = TimeFrame(columns = system.init.index)\n",
-    "    results.loc[system.t0] = system.init\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        results.loc[t+1] = update_func(results.loc[t], system)\n",
-    "        \n",
-    "    system.results = results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_simulation(system, update)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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pohJYdOUeFRXF6tWr6dGjB+np6Rw5coSMjAx69erF6tWradeuXVXXKYQQopIV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\nSxe2bdvGb37zGz755BPCwq6+XXzmjpkAZJVkkZJXv9JLn4A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9noSEBEpLS1vgNyWEHdKE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\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7feed32bf320>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_results(system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/soln/salmon_soln.ipynb b/soln/salmon_soln.ipynb
deleted file mode 100644
index d5d8a90e..00000000
--- a/soln/salmon_soln.ipynb
+++ /dev/null
@@ -1,1636 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case Study: Predicting salmon returns\n",
-    "\n",
-    "This case study is based on a ModSim student project by Josh Deng and Erika Lu.\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Can we predict salmon populations?\n",
-    "\n",
-    "Each year the [U.S. Atlantic Salmon Assessment Committee](https://www.nefsc.noaa.gov/USASAC/Reports/USASAC2018-Report-30-2017-Activities.pdf) reports estimates of salmon populations in oceans and rivers in the northeastern United States.  The reports are useful for monitoring changes in these populations, but they generally do not include predictions.\n",
-    "\n",
-    "The goal of this case study is to model year-to-year changes in population, evaluate how predictable these changes are, and estimate the probability that a particular population will increase or decrease in the next 10 years.\n",
-    "\n",
-    "As an example, I'll use data from page 18 of the 2017 report, which provides population estimates for the Narraguagus and Sheepscot Rivers in Maine.\n",
-    "\n",
-    "![USASAC_Report_2017_Page18](data/USASAC_Report_2017_Page18.png)\n",
-    "\n",
-    "At the end of this notebook, I make some suggestions for extracting data from a PDF document automatically, but for this example I will keep it simple and type it in.\n",
-    "\n",
-    "Here are the population estimates for the Narraguagus River:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pops = [2749, 2845, 4247, 1843, 2562, 1774, 1201, 1284, 1287, 2339, 1177, 962, 1176, 2149, 1404, 969, 1237, 1615, 1201];"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get this data into a Pandas Series, I'll also make a range of years to use as an index."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "range(1997, 2016)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "years = range(1997, 2016)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1997</th>\n",
-       "      <td>2749.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1998</th>\n",
-       "      <td>2845.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999</th>\n",
-       "      <td>4247.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000</th>\n",
-       "      <td>1843.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2001</th>\n",
-       "      <td>2562.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2002</th>\n",
-       "      <td>1774.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2003</th>\n",
-       "      <td>1201.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2004</th>\n",
-       "      <td>1284.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2005</th>\n",
-       "      <td>1287.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2006</th>\n",
-       "      <td>2339.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2007</th>\n",
-       "      <td>1177.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2008</th>\n",
-       "      <td>962.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2009</th>\n",
-       "      <td>1176.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2010</th>\n",
-       "      <td>2149.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2011</th>\n",
-       "      <td>1404.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2012</th>\n",
-       "      <td>969.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2013</th>\n",
-       "      <td>1237.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014</th>\n",
-       "      <td>1615.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015</th>\n",
-       "      <td>1201.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "1997    2749.0\n",
-       "1998    2845.0\n",
-       "1999    4247.0\n",
-       "2000    1843.0\n",
-       "2001    2562.0\n",
-       "2002    1774.0\n",
-       "2003    1201.0\n",
-       "2004    1284.0\n",
-       "2005    1287.0\n",
-       "2006    2339.0\n",
-       "2007    1177.0\n",
-       "2008     962.0\n",
-       "2009    1176.0\n",
-       "2010    2149.0\n",
-       "2011    1404.0\n",
-       "2012     969.0\n",
-       "2013    1237.0\n",
-       "2014    1615.0\n",
-       "2015    1201.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pop_series = TimeSeries(pops, index=years, dtype=np.float64)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_population(series):\n",
-    "    plot(series, label='Estimated population')\n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='Population estimate', \n",
-    "             title='Narraguacus River',\n",
-    "             ylim=[0, 5000])\n",
-    "    \n",
-    "plot_population(pop_series)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Modeling changes\n",
-    "\n",
-    "To see how the population changes from year-to-year, I'll use `ediff1d` to compute the absolute difference between each year and the next.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([   96.,  1402., -2404.,   719.,  -788.,  -573.,    83.,     3.,\n",
-       "        1052., -1162.,  -215.,   214.,   973.,  -745.,  -435.,   268.,\n",
-       "         378.,  -414.,     0.])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "abs_diffs = np.ediff1d(pop_series, to_end=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can compute relative differences by dividing by the original series elementwise."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1997    0.034922\n",
-       "1998    0.492794\n",
-       "1999   -0.566047\n",
-       "2000    0.390125\n",
-       "2001   -0.307572\n",
-       "2002   -0.322999\n",
-       "2003    0.069109\n",
-       "2004    0.002336\n",
-       "2005    0.817405\n",
-       "2006   -0.496794\n",
-       "2007   -0.182668\n",
-       "2008    0.222453\n",
-       "2009    0.827381\n",
-       "2010   -0.346673\n",
-       "2011   -0.309829\n",
-       "2012    0.276574\n",
-       "2013    0.305578\n",
-       "2014   -0.256347\n",
-       "2015    0.000000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rel_diffs = abs_diffs / pop_series"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or we can use the `modsim` function `compute_rel_diff`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1997    0.034922\n",
-       "1998    0.492794\n",
-       "1999   -0.566047\n",
-       "2000    0.390125\n",
-       "2001   -0.307572\n",
-       "2002   -0.322999\n",
-       "2003    0.069109\n",
-       "2004    0.002336\n",
-       "2005    0.817405\n",
-       "2006   -0.496794\n",
-       "2007   -0.182668\n",
-       "2008    0.222453\n",
-       "2009    0.827381\n",
-       "2010   -0.346673\n",
-       "2011   -0.309829\n",
-       "2012    0.276574\n",
-       "2013    0.305578\n",
-       "2014   -0.256347\n",
-       "2015    0.000000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rel_diffs = compute_rel_diff(pop_series)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These relative differences are observed annual net growth rates.  So let's drop the `0` and save them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1997    0.034922\n",
-       "1998    0.492794\n",
-       "1999   -0.566047\n",
-       "2000    0.390125\n",
-       "2001   -0.307572\n",
-       "2002   -0.322999\n",
-       "2003    0.069109\n",
-       "2004    0.002336\n",
-       "2005    0.817405\n",
-       "2006   -0.496794\n",
-       "2007   -0.182668\n",
-       "2008    0.222453\n",
-       "2009    0.827381\n",
-       "2010   -0.346673\n",
-       "2011   -0.309829\n",
-       "2012    0.276574\n",
-       "2013    0.305578\n",
-       "2014   -0.256347\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rates = rel_diffs.drop(2015)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A simple way to model this system is to draw a random value from this series of observed rates each year.  We can use the NumPy function `choice` to make a random choice from a series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.06910907577019151"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.random.choice(rates)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simulation\n",
-    "\n",
-    "Now we can simulate the system by drawing random growth rates from the series of observed rates.\n",
-    "\n",
-    "I'll start the simulation in 2015."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1201.0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_0 = 2015\n",
-    "p_0 = pop_series[t_0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a `System` object with variables `t_0`, `p_0`, `rates`, and `duration=10` years. \n",
-    "\n",
-    "The series of observed rates is one big parameter of the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p_0</th>\n",
-       "      <td>1201</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>duration</th>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rates</th>\n",
-       "      <td>1997    0.034922\n",
-       "1998    0.492794\n",
-       "1999   -0.56...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "t_0                                                      2015\n",
-       "p_0                                                      1201\n",
-       "duration                                                   10\n",
-       "rates       1997    0.034922\n",
-       "1998    0.492794\n",
-       "1999   -0.56...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = System(t_0=t_0,\n",
-    "                p_0=p_0,\n",
-    "                duration=10,\n",
-    "                rates=rates)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write an update functon that takes as parameters `pop`, `t`, and `system`.\n",
-    "It should choose a random growth rate, compute the change in population, and return the new population."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def update_func1(pop, t, system):\n",
-    "    \"\"\"Simulate one time step.\n",
-    "    \n",
-    "    pop: population\n",
-    "    t: time step\n",
-    "    system: System object\n",
-    "    \"\"\"\n",
-    "    rate = np.random.choice(system.rates)\n",
-    "    pop += rate * pop\n",
-    "    return pop"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your update function and run it a few times"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1203.806074766355"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "update_func1(p_0, t_0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a version of `run_simulation` that stores the results in a `TimeSeries` and returns it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate a queueing system.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function object\n",
-    "    \"\"\"\n",
-    "    t_0 = system.t_0\n",
-    "    t_end = t_0 + system.duration\n",
-    "    \n",
-    "    results = TimeSeries()\n",
-    "    results[t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(t_0, t_end):\n",
-    "        results[t+1] = update_func(results[t], t, system)\n",
-    "\n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use `run_simulation` to run generate a prediction for the next 10 years.\n",
-    "\n",
-    "The plot your prediction along with the original data.  Your prediction should pick up where the data leave off."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results = run_simulation(system, update_func1)\n",
-    "plot(results, label='Simulation')\n",
-    "plot_population(pop_series)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get a sense of how much the results vary, we can run the model several times and plot all of the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_many_simulations(system, update_func, iters):\n",
-    "    \"\"\"Runs simulations and plots the results.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function object\n",
-    "    iters: number of simulations to run\n",
-    "    \"\"\"\n",
-    "    for i in range(iters):\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        plot(results, color='gray', linewidth=5, alpha=0.1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The plot option `alpha=0.1` makes the lines semi-transparent, so they are darker where they overlap.\n",
-    "\n",
-    "Run `plot_many_simulations` with your update function and `iters=30`.  Also plot the original data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_many_simulations(system, update_func1, 30)\n",
-    "plot_population(pop_series)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The results are highly variable: according to this model, the population might continue to decline over the next 10 years, or it might recover and grow rapidly!\n",
-    "\n",
-    "It's hard to say how seriously we should take this model.  There are many factors that influence salmon populations that are not included in the model.  For example, if the population starts to grow quickly, it might be limited by resource limits, predators, or fishing.  If the population starts to fall, humans might restrict fishing and stock the river with farmed fish.\n",
-    "\n",
-    "So these results should probably not be considered useful predictions.  However, there might be something useful we can do, which is to estimate the probability that the population will increase or decrease in the next 10 years.  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Distribution of net changes\n",
-    "\n",
-    "To describe the distribution of net changes, write a function called `run_many_simulations` that runs many simulations, saves the final populations in a `ModSimSeries`, and returns the `ModSimSeries`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_many_simulations(system, update_func, iters):\n",
-    "    \"\"\"Runs simulations and report final populations.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function object\n",
-    "    iters: number of simulations to run\n",
-    "    \n",
-    "    returns: series of final populations\n",
-    "    \"\"\"\n",
-    "    # FILL THIS IN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def run_many_simulations(system, update_func, iters):\n",
-    "    \"\"\"Runs simulations and report final populations.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function object\n",
-    "    iters: number of simulations to run\n",
-    "    \n",
-    "    returns: series of final populations\n",
-    "    \"\"\"\n",
-    "    last_pops = ModSimSeries()\n",
-    "    \n",
-    "    for i in range(iters):\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        last_pops[i] = get_last_value(results)\n",
-    "        \n",
-    "    return last_pops"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your function by running it with `iters=5`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>892.723387</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>99.036567</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>943.549004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>290.634834</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1003.147033</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "0     892.723387\n",
-       "1      99.036567\n",
-       "2     943.549004\n",
-       "3     290.634834\n",
-       "4    1003.147033\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run_many_simulations(system, update_func1, 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run 1000 simulations and describe the distribution of the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count     1000.000000\n",
-       "mean      1605.829424\n",
-       "std       2799.595802\n",
-       "min         13.400432\n",
-       "25%        306.557442\n",
-       "50%        748.584171\n",
-       "75%       1686.740121\n",
-       "max      47316.744869\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "last_pops = run_many_simulations(system, update_func1, 1000)\n",
-    "last_pops.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we substract off the initial population, we get the distribution of changes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count     1000.000000\n",
-       "mean       404.829424\n",
-       "std       2799.595802\n",
-       "min      -1187.599568\n",
-       "25%       -894.442558\n",
-       "50%       -452.415829\n",
-       "75%        485.740121\n",
-       "max      46115.744869\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "net_changes = last_pops - p_0\n",
-    "net_changes.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The median is negative, which indicates that the population decreases more often than it increases.\n",
-    "\n",
-    "We can be more specific by counting the number of runs where `net_changes` is positive."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "348"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sum(net_changes > 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or we can use `mean` to compute the fraction of runs where `net_changes` is positive."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.348"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(net_changes > 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the fraction where it's negative."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.652"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(net_changes < 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So, based on observed past changes, this model predicts that the population is more likely to decrease than increase over the next 10 years, by about 2:1."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## A refined model\n",
-    "\n",
-    "There are a few ways we could improve the model.\n",
-    "\n",
-    "1.  It looks like there might be cyclic behavior in the past data, with a period of 4-5 years.  We could extend the model to include this effect.\n",
-    "\n",
-    "2.  Older data might not be as relevant for prediction as newer data, so we could give more weight to newer data.\n",
-    "\n",
-    "The second option is easier to implement, so let's try it.\n",
-    "\n",
-    "I'll use `linspace` to create an array of \"weights\" for the observed rates.  The probability that I choose each rate will be proportional to these weights.\n",
-    "\n",
-    "The weights have to add up to 1, so I divide through by the total."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "weights = linspace(0, 1, len(rates))\n",
-    "weights /= sum(weights)\n",
-    "plot(weights)\n",
-    "decorate(xlabel='Index into the rates array',\n",
-    "         ylabel='Weight')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll add the weights to the `System` object, since they are parameters of the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.weights = weights"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can pass these weights as a parameter to `np.random.choice` (see the [documentation](https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.choice.html))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.3466728711028385"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.random.choice(system.rates, p=system.weights)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write an update function that takes the weights into account."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def update_func2(pop, t, system):\n",
-    "    \"\"\"Simulate one time step.\n",
-    "    \n",
-    "    pop: population\n",
-    "    t: time step\n",
-    "    system: System object\n",
-    "    \"\"\"\n",
-    "    rate = np.random.choice(system.rates, p=system.weights)\n",
-    "    pop += rate * pop\n",
-    "    return pop"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use `plot_many_simulations` to plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "plot_many_simulations(system, update_func2, 30)\n",
-    "plot_population(pop_series)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use `run_many_simulations` to collect the results and `describe` to summarize the distribution of net changes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count     1000.000000\n",
-       "mean       742.390577\n",
-       "std       3254.531642\n",
-       "min      -1167.871448\n",
-       "25%       -828.100752\n",
-       "50%       -291.246535\n",
-       "75%        958.199207\n",
-       "max      35927.186006\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "last_pops = run_many_simulations(system, update_func2, 1000)\n",
-    "net_changes = last_pops - p_0\n",
-    "net_changes.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Does the refined model have much effect on the probability of population decline?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.582"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "np.mean(net_changes < 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extracting data from a PDF document\n",
-    "\n",
-    "The following section uses `tabula-py` to get data from a PDF document.\n",
-    "\n",
-    "If you don't already have it installed, and you are using Anaconda, you can install it by running the following command in a Terminal or Git Bash:\n",
-    "\n",
-    "```\n",
-    "conda install -c conda-forge tabula-py\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tabula import read_pdf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>1 SW</th>\n",
-       "      <th>2SW</th>\n",
-       "      <th>3SW</th>\n",
-       "      <th>Repeat</th>\n",
-       "      <th>Total</th>\n",
-       "      <th>Hatchery</th>\n",
-       "      <th>Natural</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1997</td>\n",
-       "      <td>278</td>\n",
-       "      <td>1,492</td>\n",
-       "      <td>8</td>\n",
-       "      <td>36</td>\n",
-       "      <td>1,814</td>\n",
-       "      <td>1,296</td>\n",
-       "      <td>518</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1998</td>\n",
-       "      <td>340</td>\n",
-       "      <td>1,477</td>\n",
-       "      <td>3</td>\n",
-       "      <td>42</td>\n",
-       "      <td>1,862</td>\n",
-       "      <td>1,146</td>\n",
-       "      <td>716</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>402</td>\n",
-       "      <td>1,136</td>\n",
-       "      <td>3</td>\n",
-       "      <td>26</td>\n",
-       "      <td>1,567</td>\n",
-       "      <td>959</td>\n",
-       "      <td>608</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2000</td>\n",
-       "      <td>292</td>\n",
-       "      <td>535</td>\n",
-       "      <td>0</td>\n",
-       "      <td>20</td>\n",
-       "      <td>847</td>\n",
-       "      <td>562</td>\n",
-       "      <td>285</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2001</td>\n",
-       "      <td>269</td>\n",
-       "      <td>804</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1,084</td>\n",
-       "      <td>833</td>\n",
-       "      <td>251</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>2002</td>\n",
-       "      <td>437</td>\n",
-       "      <td>505</td>\n",
-       "      <td>2</td>\n",
-       "      <td>23</td>\n",
-       "      <td>967</td>\n",
-       "      <td>832</td>\n",
-       "      <td>135</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>2003</td>\n",
-       "      <td>233</td>\n",
-       "      <td>1,185</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1,427</td>\n",
-       "      <td>1,238</td>\n",
-       "      <td>189</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>2004</td>\n",
-       "      <td>319</td>\n",
-       "      <td>1,266</td>\n",
-       "      <td>21</td>\n",
-       "      <td>24</td>\n",
-       "      <td>1,630</td>\n",
-       "      <td>1,395</td>\n",
-       "      <td>235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>2005</td>\n",
-       "      <td>317</td>\n",
-       "      <td>945</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1,272</td>\n",
-       "      <td>1,019</td>\n",
-       "      <td>253</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>2006</td>\n",
-       "      <td>442</td>\n",
-       "      <td>1,007</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1,456</td>\n",
-       "      <td>1,167</td>\n",
-       "      <td>289</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>2007</td>\n",
-       "      <td>299</td>\n",
-       "      <td>958</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1,261</td>\n",
-       "      <td>940</td>\n",
-       "      <td>321</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>2008</td>\n",
-       "      <td>812</td>\n",
-       "      <td>1,758</td>\n",
-       "      <td>12</td>\n",
-       "      <td>23</td>\n",
-       "      <td>2,605</td>\n",
-       "      <td>2,191</td>\n",
-       "      <td>414</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>2009</td>\n",
-       "      <td>243</td>\n",
-       "      <td>2,065</td>\n",
-       "      <td>16</td>\n",
-       "      <td>16</td>\n",
-       "      <td>2,340</td>\n",
-       "      <td>2,017</td>\n",
-       "      <td>323</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2010</td>\n",
-       "      <td>552</td>\n",
-       "      <td>1,081</td>\n",
-       "      <td>2</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1,651</td>\n",
-       "      <td>1,468</td>\n",
-       "      <td>183</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2011</td>\n",
-       "      <td>1,084</td>\n",
-       "      <td>3,053</td>\n",
-       "      <td>26</td>\n",
-       "      <td>15</td>\n",
-       "      <td>4,178</td>\n",
-       "      <td>3,560</td>\n",
-       "      <td>618</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>2012</td>\n",
-       "      <td>26</td>\n",
-       "      <td>879</td>\n",
-       "      <td>31</td>\n",
-       "      <td>5</td>\n",
-       "      <td>941</td>\n",
-       "      <td>731</td>\n",
-       "      <td>210</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2013</td>\n",
-       "      <td>78</td>\n",
-       "      <td>525</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>611</td>\n",
-       "      <td>413</td>\n",
-       "      <td>198</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>2014</td>\n",
-       "      <td>110</td>\n",
-       "      <td>334</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>450</td>\n",
-       "      <td>304</td>\n",
-       "      <td>146</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>2015</td>\n",
-       "      <td>150</td>\n",
-       "      <td>761</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1</td>\n",
-       "      <td>921</td>\n",
-       "      <td>739</td>\n",
-       "      <td>182</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>2016</td>\n",
-       "      <td>232</td>\n",
-       "      <td>389</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>626</td>\n",
-       "      <td>448</td>\n",
-       "      <td>178</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>2017</td>\n",
-       "      <td>363</td>\n",
-       "      <td>663</td>\n",
-       "      <td>13</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1041</td>\n",
-       "      <td>806</td>\n",
-       "      <td>235</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Unnamed: 0   1 SW    2SW  3SW  Repeat  Total Hatchery  Natural\n",
-       "0         1997    278  1,492    8      36  1,814    1,296      518\n",
-       "1         1998    340  1,477    3      42  1,862    1,146      716\n",
-       "2         1999    402  1,136    3      26  1,567      959      608\n",
-       "3         2000    292    535    0      20    847      562      285\n",
-       "4         2001    269    804    7       4  1,084      833      251\n",
-       "5         2002    437    505    2      23    967      832      135\n",
-       "6         2003    233  1,185    3       6  1,427    1,238      189\n",
-       "7         2004    319  1,266   21      24  1,630    1,395      235\n",
-       "8         2005    317    945    0      10  1,272    1,019      253\n",
-       "9         2006    442  1,007    2       5  1,456    1,167      289\n",
-       "10        2007    299    958    3       1  1,261      940      321\n",
-       "11        2008    812  1,758   12      23  2,605    2,191      414\n",
-       "12        2009    243  2,065   16      16  2,340    2,017      323\n",
-       "13        2010    552  1,081    2      16  1,651    1,468      183\n",
-       "14        2011  1,084  3,053   26      15  4,178    3,560      618\n",
-       "15        2012     26    879   31       5    941      731      210\n",
-       "16        2013     78    525    3       5    611      413      198\n",
-       "17        2014    110    334    3       3    450      304      146\n",
-       "18        2015    150    761    9       1    921      739      182\n",
-       "19        2016    232    389    2       3    626      448      178\n",
-       "20        2017    363    663   13       2   1041      806      235"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = read_pdf('data/USASAC2018-Report-30-2017-Activities-Page11.pdf')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/spiderman_soln.ipynb b/soln/spiderman_soln.ipynb
deleted file mode 100644
index 3c037852..00000000
--- a/soln/spiderman_soln.ipynb
+++ /dev/null
@@ -1,1536 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case study: Spider-Man\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "I'll start by getting the units we'll need from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "radian"
-      ],
-      "text/latex": [
-       "$\\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "<Unit('radian')>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton\n",
-    "degree = UNITS.degree\n",
-    "radian = UNITS.radian"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Spider-Man"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this case study we'll develop a model of Spider-Man swinging from a springy cable of webbing attached to the top of the Empire State Building.  Initially, Spider-Man is at the top of a nearby building, as shown in this diagram.\n",
-    "\n",
-    "![](diagrams/spiderman.png)\n",
-    "\n",
-    "The origin, `O⃗`, is at the base of the Empire State Building.  The vector `H⃗` represents the position where the webbing is attached to the building, relative to `O⃗`.  The vector `P⃗` is the position of Spider-Man relative to `O⃗`.  And `L⃗` is the vector from the attachment point to Spider-Man.\n",
-    "\n",
-    "By following the arrows from `O⃗`, along `H⃗`, and along `L⃗`, we can see that \n",
-    "\n",
-    "`H⃗ + L⃗ = P⃗`\n",
-    "\n",
-    "So we can compute `L⃗` like this:\n",
-    "\n",
-    "`L⃗ = P⃗ - H⃗`\n",
-    "\n",
-    "The goals of this case study are:\n",
-    "\n",
-    "1. Implement a model of this scenario to predict Spider-Man's trajectory.\n",
-    "\n",
-    "2. Choose the right time for Spider-Man to let go of the webbing in order to maximize the distance he travels before landing.\n",
-    "\n",
-    "3. Choose the best angle for Spider-Man to jump off the building, and let go of the webbing, to maximize range."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll create a `Params` object to contain the quantities we'll need:\n",
-    "\n",
-    "1. According to [the Spider-Man Wiki](http://spiderman.wikia.com/wiki/Peter_Parker_%28Earth-616%29), Spider-Man weighs 76 kg.\n",
-    "\n",
-    "2. Let's assume his terminal velocity is 60 m/s.\n",
-    "\n",
-    "3. The length of the web is 100 m.\n",
-    "\n",
-    "4. The initial angle of the web is 45 degrees to the left of straight down.\n",
-    "\n",
-    "5. The spring constant of the web is 40 N / m when the cord is stretched, and 0 when it's compressed.\n",
-    "\n",
-    "Here's a `Params` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>height</th>\n",
-       "      <td>381 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>75 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>1 meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>60.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>length</th>\n",
-       "      <td>100 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>angle</th>\n",
-       "      <td>225 degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k</th>\n",
-       "      <td>40.0 newton / meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>0 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>30 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "height                    381 meter\n",
-       "g           9.8 meter / second ** 2\n",
-       "mass                    75 kilogram\n",
-       "area                   1 meter ** 2\n",
-       "rho       1.2 kilogram / meter ** 3\n",
-       "v_term          60.0 meter / second\n",
-       "length                    100 meter\n",
-       "angle                    225 degree\n",
-       "k               40.0 newton / meter\n",
-       "t_0                        0 second\n",
-       "t_end                     30 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = Params(height = 381 * m,\n",
-    "                g = 9.8 * m/s**2,\n",
-    "                mass = 75 * kg,\n",
-    "                area = 1 * m**2,\n",
-    "                rho = 1.2 * kg/m**3,\n",
-    "                v_term = 60 * m / s,\n",
-    "                length = 100 * m,\n",
-    "                angle = (270 - 45) * degree,\n",
-    "                k = 40 * N / m,\n",
-    "                t_0 = 0 * s,\n",
-    "                t_end = 30 * s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute the initial position"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def initial_condition(params):\n",
-    "    \"\"\"Compute the initial position and velocity.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \"\"\"\n",
-    "    height, length, angle = params.height, params.length, params.angle\n",
-    "    \n",
-    "    H⃗ = Vector(0, height)\n",
-    "    theta = angle.to(radian)\n",
-    "    x, y = pol2cart(theta, length)\n",
-    "    L⃗ = Vector(x, y)\n",
-    "    P⃗ = H⃗ + L⃗\n",
-    "    V⃗ = Vector(0, 0) * m/s\n",
-    "    \n",
-    "    return State(P⃗=P⃗, V⃗=V⃗)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>P⃗</th>\n",
-       "      <td>[-70.71067811865477 meter, 310.28932188134524 ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>V⃗</th>\n",
-       "      <td>[0.0 meter / second, 0.0 meter / second]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "P⃗    [-70.71067811865477 meter, 310.28932188134524 ...\n",
-       "V⃗             [0.0 meter / second, 0.0 meter / second]\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "initial_condition(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now here's a version of `make_system` that takes a `Params` object as a parameter.\n",
-    "\n",
-    "`make_system` uses the given value of `v_term` to compute the drag coefficient `C_d`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = initial_condition(params)\n",
-    "    \n",
-    "    mass, g = params.mass, params.g\n",
-    "    rho, area, v_term = params.rho, params.area, params.v_term\n",
-    "    C_d = 2 * mass * g / (rho * area * v_term**2)\n",
-    "    \n",
-    "    return System(params, init=init, C_d=C_d)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>height</th>\n",
-       "      <td>381 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>75 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>1 meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>60.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>length</th>\n",
-       "      <td>100 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>angle</th>\n",
-       "      <td>225 degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k</th>\n",
-       "      <td>40.0 newton / meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>0 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>30 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>P⃗    [-70.71067811865477 meter, 310.289321881...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.3402777777777778 dimensionless</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "height                                            381 meter\n",
-       "g                                   9.8 meter / second ** 2\n",
-       "mass                                            75 kilogram\n",
-       "area                                           1 meter ** 2\n",
-       "rho                               1.2 kilogram / meter ** 3\n",
-       "v_term                                  60.0 meter / second\n",
-       "length                                            100 meter\n",
-       "angle                                            225 degree\n",
-       "k                                       40.0 newton / meter\n",
-       "t_0                                                0 second\n",
-       "t_end                                             30 second\n",
-       "init      P⃗    [-70.71067811865477 meter, 310.289321881...\n",
-       "C_d                        0.3402777777777778 dimensionless\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>P⃗</th>\n",
-       "      <td>[-70.71067811865477 meter, 310.28932188134524 ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>V⃗</th>\n",
-       "      <td>[0.0 meter / second, 0.0 meter / second]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "P⃗    [-70.71067811865477 meter, 310.28932188134524 ...\n",
-       "V⃗             [0.0 meter / second, 0.0 meter / second]\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Drag and spring forces\n",
-    "\n",
-    "Here's drag force, as we saw in Chapter 22."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(V⃗, system):\n",
-    "    \"\"\"Compute drag force.\n",
-    "    \n",
-    "    V⃗: velocity Vector\n",
-    "    system: `System` object\n",
-    "    \n",
-    "    returns: force Vector\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    mag = rho * V⃗.mag**2 * C_d * area / 2\n",
-    "    direction = -V⃗.hat()\n",
-    "    f_drag = direction * mag\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}10.0 & 10.0\\end{pmatrix} meter/second\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}10.0 & 10.0\\end{pmatrix}\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "array([10., 10.]) <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "V⃗_test = Vector(10, 10) * m/s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}-28.873526898450695 & -28.873526898450695\\end{pmatrix} kilogram meter/second<sup>2</sup>\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}-28.873526898450695 & -28.873526898450695\\end{pmatrix}\\ \\frac{\\mathrm{kilogram} \\cdot \\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "array([-28.8735269, -28.8735269]) <Unit('kilogram * meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drag_force(V⃗_test, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the 2-D version of spring force.  We saw the 1-D version in Chapter 21."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def spring_force(L⃗, system):\n",
-    "    \"\"\"Compute drag force.\n",
-    "    \n",
-    "    L⃗: Vector representing the webbing\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: force Vector\n",
-    "    \"\"\"\n",
-    "    extension = L⃗.mag - system.length\n",
-    "    if magnitude(extension) < 0:\n",
-    "        mag = 0\n",
-    "    else:\n",
-    "        mag = system.k * extension\n",
-    "        \n",
-    "    direction = -L⃗.hat()\n",
-    "    f_spring = direction * mag\n",
-    "    return f_spring"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}0 & -101\\end{pmatrix} meter\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}0 & -101\\end{pmatrix}\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "array([   0, -101]) <Unit('meter')>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "L⃗_test = Vector(0, -system.length-1*m)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}-0.0 & 40.0\\end{pmatrix} newton\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}-0.0 & 40.0\\end{pmatrix}\\ \\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "array([-0., 40.]) <Unit('newton')>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f_spring = spring_force(L⃗_test, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the slope function, including acceleration due to gravity, drag, and the spring force of the webbing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the state variables.\n",
-    "    \n",
-    "    state: State (x, y, x velocity, y velocity)\n",
-    "    t: time\n",
-    "    system: System object with g, rho, C_d, area, mass\n",
-    "    \n",
-    "    returns: sequence (vx, vy, ax, ay)\n",
-    "    \"\"\"\n",
-    "    P⃗, V⃗ = state\n",
-    "    g, mass = system.g, system.mass\n",
-    "    \n",
-    "    H⃗ = Vector(0, system.height)\n",
-    "    L⃗ = P⃗ - H⃗\n",
-    "    \n",
-    "    a_grav = Vector(0, -g)\n",
-    "    a_spring = spring_force(L⃗, system) / mass\n",
-    "    a_drag = drag_force(V⃗, system) / mass\n",
-    "    \n",
-    "    A⃗ = a_grav + a_drag + a_spring\n",
-    "    \n",
-    "    return V⃗, A⃗"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, let's test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([0., 0.]) <Unit('meter / second')>,\n",
-       " array([ 5.35924893e-15, -9.80000000e+00]) <Unit('meter / second ** 2')>)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                                                 True\n",
-       "message    The solver successfully reached the end of the...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing the results\n",
-    "\n",
-    "We can extract the x and y components as `Series` objects."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The simplest way to visualize the results is to plot x and y as functions of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_position(P⃗):\n",
-    "    x = P⃗.extract('x')\n",
-    "    y = P⃗.extract('y')\n",
-    "    plot(x, label='x')\n",
-    "    plot(y, label='y')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "    \n",
-    "plot_position(results.P⃗)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot the velocities the same way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_velocity(V⃗):\n",
-    "    vx = V⃗.extract('x')\n",
-    "    vy = V⃗.extract('y')\n",
-    "    plot(vx, label='vx')\n",
-    "    plot(vy, label='vy')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')\n",
-    "    \n",
-    "plot_velocity(results.V⃗)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another way to visualize the results is to plot y versus x.  The result is the trajectory through the plane of motion."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_trajectory(P⃗, **options):\n",
-    "    x = P⃗.extract('x')\n",
-    "    y = P⃗.extract('y')\n",
-    "    plot(x, y, **options)\n",
-    "    \n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)')\n",
-    "    \n",
-    "plot_trajectory(results.P⃗, label='trajectory')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Letting go\n",
-    "\n",
-    "Now let's find the optimal time for Spider-Man to let go.  We have to run the simulation in two phases because the spring force changes abruptly when Spider-Man lets go, so we can't integrate through it.\n",
-    "\n",
-    "Here are the parameters for Phase 1, running for 9 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "params1 = Params(params, t_end=9*s)\n",
-    "system1 = make_system(params1)\n",
-    "results1, details1 = run_ode_solver(system1, slope_func)\n",
-    "plot_trajectory(results1.P⃗, label='Phase 1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final conditions from Phase 1 are the initial conditions for Phase 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "8.999999999999991 second"
-      ],
-      "text/latex": [
-       "$8.999999999999991\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "8.999999999999991 <Unit('second')>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_final = get_last_label(results1) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the position Vector."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}42.09220704823149 & 273.0520143333356\\end{pmatrix} meter\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}42.09220704823149 & 273.0520143333356\\end{pmatrix}\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "array([ 42.09220705, 273.05201433]) <Unit('meter')>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init = results1.last_row()\n",
-    "init.P⃗"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the velocity Vector."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}14.625507683978038 & 7.937765075110924\\end{pmatrix} meter/second\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}14.625507683978038 & 7.937765075110924\\end{pmatrix}\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "array([14.62550768,  7.93776508]) <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init.V⃗"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here is the `System` for Phase 2.  We can turn off the spring force by setting `k=0`, so we don't have to write a new slope function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>height</th>\n",
-       "      <td>381 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>75 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>1 meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2 kilogram / meter ** 3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v_term</th>\n",
-       "      <td>60.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>length</th>\n",
-       "      <td>100 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>angle</th>\n",
-       "      <td>225 degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k</th>\n",
-       "      <td>0.0 newton / meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>8.999999999999991 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>18.999999999999993 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>P⃗    [42.09220704823149 meter, 273.0520143333...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.3402777777777778 dimensionless</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "height                                            381 meter\n",
-       "g                                   9.8 meter / second ** 2\n",
-       "mass                                            75 kilogram\n",
-       "area                                           1 meter ** 2\n",
-       "rho                               1.2 kilogram / meter ** 3\n",
-       "v_term                                  60.0 meter / second\n",
-       "length                                            100 meter\n",
-       "angle                                            225 degree\n",
-       "k                                        0.0 newton / meter\n",
-       "t_0                                8.999999999999991 second\n",
-       "t_end                             18.999999999999993 second\n",
-       "init      P⃗    [42.09220704823149 meter, 273.0520143333...\n",
-       "C_d                        0.3402777777777778 dimensionless\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system2 = System(system1, t_0=t_final, t_end=t_final+10*s, init=init, k=0*N/m)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an event function that stops the simulation when Spider-Man reaches the ground."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Stops when y=0.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: height\n",
-    "    \"\"\"\n",
-    "    P⃗, V⃗ = state\n",
-    "    return P⃗.y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run Phase 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2, details2 = run_ode_solver(system2, slope_func, events=event_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_trajectory(results1.P⃗, label='Phase 1')\n",
-    "plot_trajectory(results2.P⃗, label='Phase 2')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can gather all that into a function that takes `t_release` and `V_0`, runs both phases, and returns the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_two_phase(t_release, V⃗_0, params):\n",
-    "    \"\"\"Run both phases.\n",
-    "    \n",
-    "    t_release: time when Spider-Man lets go of the webbing\n",
-    "    V_0: initial velocity\n",
-    "    \"\"\"\n",
-    "    params1 = Params(params, t_end=t_release, V⃗_0=V⃗_0)\n",
-    "    system1 = make_system(params1)\n",
-    "    results1, details1 = run_ode_solver(system1, slope_func)\n",
-    "\n",
-    "    t_0 = get_last_label(results1) * s\n",
-    "    t_end = t_0 + 10 * s\n",
-    "    init = results1.last_row()\n",
-    "\n",
-    "    system2 = System(system1, t_0=t_0, t_end=t_end, init=init, k=0*N/m)\n",
-    "    results2, details2 = run_ode_solver(system2, slope_func, events=event_func)\n",
-    "\n",
-    "    results = results1.combine_first(results2)\n",
-    "    return TimeFrame(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's a test run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "143.42923899746137 meter"
-      ],
-      "text/latex": [
-       "$143.42923899746137\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "143.42923899746137 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "t_release = 9 * s\n",
-    "V⃗_0 = Vector(0, 0) * m/s\n",
-    "\n",
-    "results = run_two_phase(t_release, V⃗_0, params)\n",
-    "plot_trajectory(results.P⃗)\n",
-    "x_final = results.P⃗.last_value().x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Animation\n",
-    "\n",
-    "Here's a draw function we can use to animate the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xs = results.P⃗.extract('x')\n",
-    "ys = results.P⃗.extract('y')\n",
-    "\n",
-    "def draw_func(state, t):\n",
-    "    set_xlim(xs)\n",
-    "    set_ylim(ys)\n",
-    "    x, y = state.P⃗\n",
-    "    plot(x, y, 'bo')\n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "animate(results, draw_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Maximizing range\n",
-    "\n",
-    "To find the best value of `t_release`, we need a function that takes possible values, runs the simulation, and returns the range."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def range_func(t_release, params):\n",
-    "    \"\"\"Compute the final value of x.\n",
-    "    \n",
-    "    t_release: time to release web\n",
-    "    params: Params object\n",
-    "    \"\"\"\n",
-    "    V_0 = Vector(0, 0) * m/s\n",
-    "    results = run_two_phase(t_release, V_0, params)\n",
-    "    x_final = results.P⃗.last_value().x\n",
-    "    print(t_release, x_final)\n",
-    "    return x_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "range_func(9*s, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And run it for a few values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for t_release in linrange(3, 15, 3) * s:\n",
-    "    range_func(t_release, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can use `maximize_scalar` to find the optimum."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bounds = [6, 12] * s\n",
-    "res = maximize_golden(range_func, bounds, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we can run the simulation with the optimal value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_time = res.x\n",
-    "V⃗_0 = Vector(0, 0) * m/s\n",
-    "results = run_two_phase(best_time, V⃗_0, params)\n",
-    "plot_trajectory(results.P⃗)\n",
-    "x_final = results.P⃗.last_value().x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/test_deg2rad.ipynb b/soln/test_deg2rad.ipynb
deleted file mode 100644
index c5e6e148..00000000
--- a/soln/test_deg2rad.ipynb
+++ /dev/null
@@ -1,146 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from pint import UnitRegistry\n",
-    "ureg = UnitRegistry()\n",
-    "Q_ = ureg.Quantity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.7853981633974483"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "angle = 45\n",
-    "np.deg2rad(angle)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.7853981633974483 radian"
-      ],
-      "text/latex": [
-       "$0.7853981633974483\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "0.7853981633974483 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "angle = 45 * ureg.degree\n",
-    "np.deg2rad(angle)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.785 radian"
-      ],
-      "text/latex": [
-       "$0.785\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "0.785 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "angle = 0.785 * ureg.radian\n",
-    "np.deg2rad(angle)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "45.0 radian"
-      ],
-      "text/latex": [
-       "$45.0\\ \\mathrm{radian}$"
-      ],
-      "text/plain": [
-       "45.0 <Unit('radian')>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "angle = 45 * ureg.dimensionless\n",
-    "np.deg2rad(angle)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/test_ediff1d.ipynb b/soln/test_ediff1d.ipynb
deleted file mode 100644
index ce852ea6..00000000
--- a/soln/test_ediff1d.ipynb
+++ /dev/null
@@ -1,87 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "cannot convert float NaN to integer",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-13abd29cafa9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mediff1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/anaconda3/envs/ModSimPy/lib/python3.7/site-packages/numpy/lib/arraysetops.py\u001b[0m in \u001b[0;36mediff1d\u001b[0;34m(ary, to_end, to_begin)\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0ml_end\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0m_to_end\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_end\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_req\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m         \u001b[0;31m# check that casting has not overflowed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_to_end\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mto_end\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/envs/ModSimPy/lib/python3.7/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masanyarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    589\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m     \"\"\"\n\u001b[0;32m--> 591\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "a = np.array([1, 2, 3])\n",
-    "np.ediff1d(a, np.nan)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "cannot convert float NaN to integer",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-2-6f1cfd3684be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
-     ]
-    }
-   ],
-   "source": [
-    "a[2] = np.nan\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/test_euler.ipynb b/soln/test_euler.ipynb
deleted file mode 100644
index 1b8197c8..00000000
--- a/soln/test_euler.ipynb
+++ /dev/null
@@ -1,3610 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Experiments with different ODE solvers\n",
-    "\n",
-    "Copyright 2019 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>y    2\n",
-       "dtype: int64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "init     y    2\n",
-       "dtype: int64\n",
-       "t_0                        1\n",
-       "t_end                      3\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init = State(y = 2)\n",
-    "system = System(init=init, t_0=1, t_end=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    [y] = state\n",
-    "    dydt = y + t\n",
-    "    return [dydt]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_euler(system, slope_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "24.973714732487576"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "get_last_value(results.y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Glucose minimal model\n",
-    "\n",
-    "Read the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv('data/glucose_insulin.csv', index_col='time');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Interpolate the insulin data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<scipy.interpolate.interpolate.interp1d at 0x7f0b8f890908>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "I = interpolate(data.insulin)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initialize the parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1e-05"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "G0 = 290\n",
-    "k1 = 0.03\n",
-    "k2 = 0.02\n",
-    "k3 = 1e-05"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To estimate basal levels, we'll use the concentrations at `t=0`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Gb = data.glucose[0]\n",
-    "Ib = data.insulin[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create the initial condtions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>G</th>\n",
-       "      <td>290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>X</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "G    290\n",
-       "X      0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init = State(G=G0, X=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Make the `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "182"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_0 = get_first_label(data)\n",
-    "t_end = get_last_label(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>G0</th>\n",
-       "      <td>290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k1</th>\n",
-       "      <td>0.03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k2</th>\n",
-       "      <td>0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k3</th>\n",
-       "      <td>1e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>G    290\n",
-       "X      0\n",
-       "dtype: int64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gb</th>\n",
-       "      <td>92</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Ib</th>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>I</th>\n",
-       "      <td>&lt;scipy.interpolate.interpolate.interp1d object...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>182</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "G0                                                     290\n",
-       "k1                                                    0.03\n",
-       "k2                                                    0.02\n",
-       "k3                                                   1e-05\n",
-       "init                        G    290\n",
-       "X      0\n",
-       "dtype: int64\n",
-       "Gb                                                      92\n",
-       "Ib                                                      11\n",
-       "I        <scipy.interpolate.interpolate.interp1d object...\n",
-       "t_0                                                      0\n",
-       "t_end                                                  182\n",
-       "dt                                                       2\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = System(G0=G0, k1=k1, k2=k2, k3=k3,\n",
-    "                init=init, Gb=Gb, Ib=Ib, I=I,\n",
-    "                t_0=t_0, t_end=t_end, dt=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Updates the glucose minimal model.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time in min\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    G, X = state\n",
-    "    k1, k2, k3 = system.k1, system.k2, system.k3 \n",
-    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
-    "    dt = system.dt\n",
-    "    \n",
-    "    dGdt = -k1 * (G - Gb) - X*G\n",
-    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
-    "    \n",
-    "    G += dGdt * dt\n",
-    "    X += dXdt * dt\n",
-    "\n",
-    "    return State(G=G, X=X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t_0] = init\n",
-    "    ts = linrange(t_0, t_end, dt)\n",
-    "    \n",
-    "    for t in ts:\n",
-    "        frame.row[t+dt] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 166 ms, sys: 4.08 ms, total: 170 ms\n",
-      "Wall time: 167 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time results = run_simulation(system, update_func);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>G</th>\n",
-       "      <th>X</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>290</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>278.12</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>266.953</td>\n",
-       "      <td>0.0003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>256.295</td>\n",
-       "      <td>0.002668</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>245.07</td>\n",
-       "      <td>0.00404128</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>233.905</td>\n",
-       "      <td>0.00467963</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>223.202</td>\n",
-       "      <td>0.00525244</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>212.985</td>\n",
-       "      <td>0.00572235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>203.288</td>\n",
-       "      <td>0.00609345</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>194.133</td>\n",
-       "      <td>0.00632971</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>185.548</td>\n",
-       "      <td>0.00648986</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>177.527</td>\n",
-       "      <td>0.00661026</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>170.048</td>\n",
-       "      <td>0.00672585</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>163.078</td>\n",
-       "      <td>0.00681282</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>156.591</td>\n",
-       "      <td>0.00687231</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>150.563</td>\n",
-       "      <td>0.00692941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>144.963</td>\n",
-       "      <td>0.00700824</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>139.753</td>\n",
-       "      <td>0.00710791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>134.901</td>\n",
-       "      <td>0.00717159</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>130.392</td>\n",
-       "      <td>0.00720073</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>126.211</td>\n",
-       "      <td>0.0071967</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>122.342</td>\n",
-       "      <td>0.00716083</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>118.769</td>\n",
-       "      <td>0.0070944</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>115.478</td>\n",
-       "      <td>0.00700262</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>112.452</td>\n",
-       "      <td>0.00688652</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>109.676</td>\n",
-       "      <td>0.00674706</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>107.135</td>\n",
-       "      <td>0.00658517</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>104.816</td>\n",
-       "      <td>0.00640177</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>102.705</td>\n",
-       "      <td>0.0062257</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58</th>\n",
-       "      <td>100.784</td>\n",
-       "      <td>0.00605667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>124</th>\n",
-       "      <td>86.3907</td>\n",
-       "      <td>0.00109474</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>126</th>\n",
-       "      <td>86.5381</td>\n",
-       "      <td>0.000972951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>128</th>\n",
-       "      <td>86.6974</td>\n",
-       "      <td>0.000858033</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>130</th>\n",
-       "      <td>86.8668</td>\n",
-       "      <td>0.000749712</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>132</th>\n",
-       "      <td>87.0445</td>\n",
-       "      <td>0.000647723</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>134</th>\n",
-       "      <td>87.2291</td>\n",
-       "      <td>0.000551815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>136</th>\n",
-       "      <td>87.4191</td>\n",
-       "      <td>0.000461742</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>87.6132</td>\n",
-       "      <td>0.000377272</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>140</th>\n",
-       "      <td>87.8103</td>\n",
-       "      <td>0.000298181</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142</th>\n",
-       "      <td>88.0093</td>\n",
-       "      <td>0.000224254</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>144</th>\n",
-       "      <td>88.2093</td>\n",
-       "      <td>0.000155284</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>146</th>\n",
-       "      <td>88.4093</td>\n",
-       "      <td>8.90726e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>148</th>\n",
-       "      <td>88.609</td>\n",
-       "      <td>2.55097e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>150</th>\n",
-       "      <td>88.808</td>\n",
-       "      <td>-3.55107e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>152</th>\n",
-       "      <td>89.0058</td>\n",
-       "      <td>-9.40903e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>154</th>\n",
-       "      <td>89.2022</td>\n",
-       "      <td>-0.000150327</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>156</th>\n",
-       "      <td>89.3969</td>\n",
-       "      <td>-0.000204314</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>158</th>\n",
-       "      <td>89.5896</td>\n",
-       "      <td>-0.000256141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>160</th>\n",
-       "      <td>89.7801</td>\n",
-       "      <td>-0.000305895</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>162</th>\n",
-       "      <td>89.9682</td>\n",
-       "      <td>-0.00035366</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>164</th>\n",
-       "      <td>90.1538</td>\n",
-       "      <td>-0.000399513</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>166</th>\n",
-       "      <td>90.3366</td>\n",
-       "      <td>-0.000445533</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>168</th>\n",
-       "      <td>90.5169</td>\n",
-       "      <td>-0.000491711</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>170</th>\n",
-       "      <td>90.6949</td>\n",
-       "      <td>-0.000538043</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>172</th>\n",
-       "      <td>90.8708</td>\n",
-       "      <td>-0.000584521</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>174</th>\n",
-       "      <td>91.0448</td>\n",
-       "      <td>-0.00063114</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>91.217</td>\n",
-       "      <td>-0.000677895</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>178</th>\n",
-       "      <td>91.3877</td>\n",
-       "      <td>-0.000724779</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>180</th>\n",
-       "      <td>91.5569</td>\n",
-       "      <td>-0.000771788</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>182</th>\n",
-       "      <td>91.7248</td>\n",
-       "      <td>-0.000818916</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>92 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           G            X\n",
-       "0        290            0\n",
-       "2     278.12            0\n",
-       "4    266.953       0.0003\n",
-       "6    256.295     0.002668\n",
-       "8     245.07   0.00404128\n",
-       "10   233.905   0.00467963\n",
-       "12   223.202   0.00525244\n",
-       "14   212.985   0.00572235\n",
-       "16   203.288   0.00609345\n",
-       "18   194.133   0.00632971\n",
-       "20   185.548   0.00648986\n",
-       "22   177.527   0.00661026\n",
-       "24   170.048   0.00672585\n",
-       "26   163.078   0.00681282\n",
-       "28   156.591   0.00687231\n",
-       "30   150.563   0.00692941\n",
-       "32   144.963   0.00700824\n",
-       "34   139.753   0.00710791\n",
-       "36   134.901   0.00717159\n",
-       "38   130.392   0.00720073\n",
-       "40   126.211    0.0071967\n",
-       "42   122.342   0.00716083\n",
-       "44   118.769    0.0070944\n",
-       "46   115.478   0.00700262\n",
-       "48   112.452   0.00688652\n",
-       "50   109.676   0.00674706\n",
-       "52   107.135   0.00658517\n",
-       "54   104.816   0.00640177\n",
-       "56   102.705    0.0062257\n",
-       "58   100.784   0.00605667\n",
-       "..       ...          ...\n",
-       "124  86.3907   0.00109474\n",
-       "126  86.5381  0.000972951\n",
-       "128  86.6974  0.000858033\n",
-       "130  86.8668  0.000749712\n",
-       "132  87.0445  0.000647723\n",
-       "134  87.2291  0.000551815\n",
-       "136  87.4191  0.000461742\n",
-       "138  87.6132  0.000377272\n",
-       "140  87.8103  0.000298181\n",
-       "142  88.0093  0.000224254\n",
-       "144  88.2093  0.000155284\n",
-       "146  88.4093  8.90726e-05\n",
-       "148   88.609  2.55097e-05\n",
-       "150   88.808 -3.55107e-05\n",
-       "152  89.0058 -9.40903e-05\n",
-       "154  89.2022 -0.000150327\n",
-       "156  89.3969 -0.000204314\n",
-       "158  89.5896 -0.000256141\n",
-       "160  89.7801 -0.000305895\n",
-       "162  89.9682  -0.00035366\n",
-       "164  90.1538 -0.000399513\n",
-       "166  90.3366 -0.000445533\n",
-       "168  90.5169 -0.000491711\n",
-       "170  90.6949 -0.000538043\n",
-       "172  90.8708 -0.000584521\n",
-       "174  91.0448  -0.00063114\n",
-       "176   91.217 -0.000677895\n",
-       "178  91.3877 -0.000724779\n",
-       "180  91.5569 -0.000771788\n",
-       "182  91.7248 -0.000818916\n",
-       "\n",
-       "[92 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Numerical solution\n",
-    "\n",
-    "In the previous chapter, we approximated the differential equations with difference equations, and solved them using `run_simulation`.\n",
-    "\n",
-    "In this chapter, we solve the differential equation numerically using `run_euler`...\n",
-    "\n",
-    "Instead of an update function, we provide a slope function that evaluates the right-hand side of the differential equations.  We don't have to do the update part; the solver does it for us."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the glucose minimal model.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time in min\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: derivatives of G and X\n",
-    "    \"\"\"\n",
-    "    G, X = state\n",
-    "    k1, k2, k3 = system.k1, system.k2, system.k3 \n",
-    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
-    "    \n",
-    "    dGdt = -k1 * (G - Gb) - X*G\n",
-    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
-    "    \n",
-    "    return dGdt, dXdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-5.9399999999999995, 0.0)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run the ODE solver."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 276 ms, sys: 368 µs, total: 277 ms\n",
-      "Wall time: 274 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "system = System(G0=G0, k1=k1, k2=k2, k3=k3,\n",
-    "                init=init, Gb=Gb, Ib=Ib, I=I,\n",
-    "                t_0=t_0, t_end=t_end, dt=1)\n",
-    "\n",
-    "%time results2, details = run_euler(system, slope_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`results` is a `TimeFrame` with one row for each time step and one column for each state variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>G</th>\n",
-       "      <th>X</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>290</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>284.06</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>278.298</td>\n",
-       "      <td>7.5e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>272.688</td>\n",
-       "      <td>0.0002235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>267.207</td>\n",
-       "      <td>0.00088903</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>261.713</td>\n",
-       "      <td>0.00206125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>256.082</td>\n",
-       "      <td>0.00298502</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>250.395</td>\n",
-       "      <td>0.00366532</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>244.726</td>\n",
-       "      <td>0.00416202</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>239.125</td>\n",
-       "      <td>0.00447878</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>233.641</td>\n",
-       "      <td>0.0047792</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>228.275</td>\n",
-       "      <td>0.00506362</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>223.031</td>\n",
-       "      <td>0.00532235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>217.913</td>\n",
-       "      <td>0.0055559</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>212.925</td>\n",
-       "      <td>0.00576478</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>208.069</td>\n",
-       "      <td>0.00594948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>203.349</td>\n",
-       "      <td>0.0061005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>198.768</td>\n",
-       "      <td>0.00621849</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>194.329</td>\n",
-       "      <td>0.00631745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>190.032</td>\n",
-       "      <td>0.00639777</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>185.875</td>\n",
-       "      <td>0.00645981</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>181.858</td>\n",
-       "      <td>0.00652061</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>177.976</td>\n",
-       "      <td>0.0065802</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>174.226</td>\n",
-       "      <td>0.0066386</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>170.603</td>\n",
-       "      <td>0.00668983</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>167.103</td>\n",
-       "      <td>0.00673403</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>163.725</td>\n",
-       "      <td>0.00677135</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>160.465</td>\n",
-       "      <td>0.00680192</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>157.319</td>\n",
-       "      <td>0.00682588</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>154.286</td>\n",
-       "      <td>0.00685537</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>153</th>\n",
-       "      <td>89.2245</td>\n",
-       "      <td>-0.000116549</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>154</th>\n",
-       "      <td>89.3181</td>\n",
-       "      <td>-0.000144218</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>155</th>\n",
-       "      <td>89.4115</td>\n",
-       "      <td>-0.000171334</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>156</th>\n",
-       "      <td>89.5045</td>\n",
-       "      <td>-0.000197907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>157</th>\n",
-       "      <td>89.597</td>\n",
-       "      <td>-0.000223949</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>158</th>\n",
-       "      <td>89.6892</td>\n",
-       "      <td>-0.00024947</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>159</th>\n",
-       "      <td>89.7809</td>\n",
-       "      <td>-0.000274481</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>160</th>\n",
-       "      <td>89.8721</td>\n",
-       "      <td>-0.000298991</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>161</th>\n",
-       "      <td>89.9628</td>\n",
-       "      <td>-0.000323011</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>162</th>\n",
-       "      <td>90.053</td>\n",
-       "      <td>-0.000346551</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>163</th>\n",
-       "      <td>90.1426</td>\n",
-       "      <td>-0.00036962</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>164</th>\n",
-       "      <td>90.2316</td>\n",
-       "      <td>-0.000392728</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>165</th>\n",
-       "      <td>90.3201</td>\n",
-       "      <td>-0.000415873</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>166</th>\n",
-       "      <td>90.4081</td>\n",
-       "      <td>-0.000439056</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>167</th>\n",
-       "      <td>90.4955</td>\n",
-       "      <td>-0.000462274</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>168</th>\n",
-       "      <td>90.5825</td>\n",
-       "      <td>-0.000485529</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>169</th>\n",
-       "      <td>90.669</td>\n",
-       "      <td>-0.000508818</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>170</th>\n",
-       "      <td>90.7551</td>\n",
-       "      <td>-0.000532142</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>171</th>\n",
-       "      <td>90.8407</td>\n",
-       "      <td>-0.000555499</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>172</th>\n",
-       "      <td>90.926</td>\n",
-       "      <td>-0.000578889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>173</th>\n",
-       "      <td>91.0108</td>\n",
-       "      <td>-0.000602311</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>174</th>\n",
-       "      <td>91.0953</td>\n",
-       "      <td>-0.000625765</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>175</th>\n",
-       "      <td>91.1795</td>\n",
-       "      <td>-0.00064925</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>91.2633</td>\n",
-       "      <td>-0.000672765</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>177</th>\n",
-       "      <td>91.3468</td>\n",
-       "      <td>-0.00069631</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>178</th>\n",
-       "      <td>91.43</td>\n",
-       "      <td>-0.000719883</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>179</th>\n",
-       "      <td>91.5129</td>\n",
-       "      <td>-0.000743486</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>180</th>\n",
-       "      <td>91.5955</td>\n",
-       "      <td>-0.000767116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>181</th>\n",
-       "      <td>91.6779</td>\n",
-       "      <td>-0.000790774</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>182</th>\n",
-       "      <td>91.7601</td>\n",
-       "      <td>-0.000814458</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>183 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           G            X\n",
-       "0        290            0\n",
-       "1     284.06            0\n",
-       "2    278.298      7.5e-05\n",
-       "3    272.688    0.0002235\n",
-       "4    267.207   0.00088903\n",
-       "5    261.713   0.00206125\n",
-       "6    256.082   0.00298502\n",
-       "7    250.395   0.00366532\n",
-       "8    244.726   0.00416202\n",
-       "9    239.125   0.00447878\n",
-       "10   233.641    0.0047792\n",
-       "11   228.275   0.00506362\n",
-       "12   223.031   0.00532235\n",
-       "13   217.913    0.0055559\n",
-       "14   212.925   0.00576478\n",
-       "15   208.069   0.00594948\n",
-       "16   203.349    0.0061005\n",
-       "17   198.768   0.00621849\n",
-       "18   194.329   0.00631745\n",
-       "19   190.032   0.00639777\n",
-       "20   185.875   0.00645981\n",
-       "21   181.858   0.00652061\n",
-       "22   177.976    0.0065802\n",
-       "23   174.226    0.0066386\n",
-       "24   170.603   0.00668983\n",
-       "25   167.103   0.00673403\n",
-       "26   163.725   0.00677135\n",
-       "27   160.465   0.00680192\n",
-       "28   157.319   0.00682588\n",
-       "29   154.286   0.00685537\n",
-       "..       ...          ...\n",
-       "153  89.2245 -0.000116549\n",
-       "154  89.3181 -0.000144218\n",
-       "155  89.4115 -0.000171334\n",
-       "156  89.5045 -0.000197907\n",
-       "157   89.597 -0.000223949\n",
-       "158  89.6892  -0.00024947\n",
-       "159  89.7809 -0.000274481\n",
-       "160  89.8721 -0.000298991\n",
-       "161  89.9628 -0.000323011\n",
-       "162   90.053 -0.000346551\n",
-       "163  90.1426  -0.00036962\n",
-       "164  90.2316 -0.000392728\n",
-       "165  90.3201 -0.000415873\n",
-       "166  90.4081 -0.000439056\n",
-       "167  90.4955 -0.000462274\n",
-       "168  90.5825 -0.000485529\n",
-       "169   90.669 -0.000508818\n",
-       "170  90.7551 -0.000532142\n",
-       "171  90.8407 -0.000555499\n",
-       "172   90.926 -0.000578889\n",
-       "173  91.0108 -0.000602311\n",
-       "174  91.0953 -0.000625765\n",
-       "175  91.1795  -0.00064925\n",
-       "176  91.2633 -0.000672765\n",
-       "177  91.3468  -0.00069631\n",
-       "178    91.43 -0.000719883\n",
-       "179  91.5129 -0.000743486\n",
-       "180  91.5955 -0.000767116\n",
-       "181  91.6779 -0.000790774\n",
-       "182  91.7601 -0.000814458\n",
-       "\n",
-       "[183 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting the results from `run_simulation` and `run_euler`, we can see that they are not very different."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f0b8f7954e0>]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(results.G, '-')\n",
-    "plot(results2.G, '-')\n",
-    "plot(data.glucose, 'bo')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The differences in `G` are less than 1%."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.970403647921524"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diff = results.G - results2.G\n",
-    "percent_diff = diff / results2.G * 100\n",
-    "\n",
-    "max(abs(percent_diff.dropna()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dropping pennies\n",
-    "\n",
-    "I'll start by getting the units we need from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "second"
-      ],
-      "text/latex": [
-       "$\\mathrm{second}$"
-      ],
-      "text/plain": [
-       "<Unit('second')>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And defining the initial state."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>381 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "y             381 meter\n",
-       "v    0.0 meter / second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init = State(y=381 * m, \n",
-    "             v=0 * m/s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Acceleration due to gravity is about 9.8 m / s$^2$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "9.8 meter/second<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$9.8\\ \\frac{\\mathrm{meter}}{\\mathrm{second}^{2}}$"
-      ],
-      "text/plain": [
-       "9.8 <Unit('meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "g = 9.8 * m/s**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When we call `odeint`, we need an array of timestamps where we want to compute the solution.\n",
-    "\n",
-    "I'll start with a duration of 10 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "10 second"
-      ],
-      "text/latex": [
-       "$10\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "10 <Unit('second')>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_end = 10 * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we make a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>y             381 meter\n",
-       "v    0.0 meter / secon...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>10 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "init     y             381 meter\n",
-       "v    0.0 meter / secon...\n",
-       "g                                  9.8 meter / second ** 2\n",
-       "t_end                                            10 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = System(init=init, g=g, t_end=t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And define the slope function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object containing `g`\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    g = system.g    \n",
-    "\n",
-    "    dydt = v\n",
-    "    dvdt = -g\n",
-    "    \n",
-    "    return dydt, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's always a good idea to test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0 meter / second\n",
-      "-9.8 meter / second ** 2\n"
-     ]
-    }
-   ],
-   "source": [
-    "dydt, dvdt = slope_func(system.init, 0, system)\n",
-    "print(dydt)\n",
-    "print(dvdt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we're ready to call `run_euler`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Success'"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.set(dt=0.1*s)\n",
-    "results, details = run_euler(system, slope_func, max_step=0.5)\n",
-    "details.message"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>y</th>\n",
-       "      <th>v</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.0</th>\n",
-       "      <td>381 meter</td>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1</th>\n",
-       "      <td>381.0 meter</td>\n",
-       "      <td>-0.9800000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.2</th>\n",
-       "      <td>380.902 meter</td>\n",
-       "      <td>-1.9600000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.3</th>\n",
-       "      <td>380.70599999999996 meter</td>\n",
-       "      <td>-2.9400000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.4</th>\n",
-       "      <td>380.412 meter</td>\n",
-       "      <td>-3.9200000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.5</th>\n",
-       "      <td>380.02 meter</td>\n",
-       "      <td>-4.9 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.6</th>\n",
-       "      <td>379.53 meter</td>\n",
-       "      <td>-5.880000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.7</th>\n",
-       "      <td>378.94199999999995 meter</td>\n",
-       "      <td>-6.860000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.8</th>\n",
-       "      <td>378.256 meter</td>\n",
-       "      <td>-7.840000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.9</th>\n",
-       "      <td>377.472 meter</td>\n",
-       "      <td>-8.820000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.0</th>\n",
-       "      <td>376.59 meter</td>\n",
-       "      <td>-9.800000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.1</th>\n",
-       "      <td>375.60999999999996 meter</td>\n",
-       "      <td>-10.780000000000003 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.2</th>\n",
-       "      <td>374.532 meter</td>\n",
-       "      <td>-11.760000000000003 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.3</th>\n",
-       "      <td>373.356 meter</td>\n",
-       "      <td>-12.740000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.4</th>\n",
-       "      <td>372.082 meter</td>\n",
-       "      <td>-13.720000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.5</th>\n",
-       "      <td>370.71 meter</td>\n",
-       "      <td>-14.700000000000005 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.6</th>\n",
-       "      <td>369.23999999999995 meter</td>\n",
-       "      <td>-15.680000000000005 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.7</th>\n",
-       "      <td>367.67199999999997 meter</td>\n",
-       "      <td>-16.660000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.8</th>\n",
-       "      <td>366.006 meter</td>\n",
-       "      <td>-17.640000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.9</th>\n",
-       "      <td>364.24199999999996 meter</td>\n",
-       "      <td>-18.620000000000005 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.0</th>\n",
-       "      <td>362.37999999999994 meter</td>\n",
-       "      <td>-19.600000000000005 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.1</th>\n",
-       "      <td>360.41999999999996 meter</td>\n",
-       "      <td>-20.580000000000005 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.2</th>\n",
-       "      <td>358.36199999999997 meter</td>\n",
-       "      <td>-21.560000000000006 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.3</th>\n",
-       "      <td>356.20599999999996 meter</td>\n",
-       "      <td>-22.540000000000006 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.4</th>\n",
-       "      <td>353.95199999999994 meter</td>\n",
-       "      <td>-23.520000000000007 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.5</th>\n",
-       "      <td>351.59999999999997 meter</td>\n",
-       "      <td>-24.500000000000007 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.6</th>\n",
-       "      <td>349.15 meter</td>\n",
-       "      <td>-25.480000000000008 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.7</th>\n",
-       "      <td>346.602 meter</td>\n",
-       "      <td>-26.460000000000008 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.8</th>\n",
-       "      <td>343.95599999999996 meter</td>\n",
-       "      <td>-27.44000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.9</th>\n",
-       "      <td>341.21199999999993 meter</td>\n",
-       "      <td>-28.42000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.1</th>\n",
-       "      <td>137.4700000000001 meter</td>\n",
-       "      <td>-69.57999999999993 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.2</th>\n",
-       "      <td>130.5120000000001 meter</td>\n",
-       "      <td>-70.55999999999993 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.3</th>\n",
-       "      <td>123.45600000000012 meter</td>\n",
-       "      <td>-71.53999999999994 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.4</th>\n",
-       "      <td>116.30200000000012 meter</td>\n",
-       "      <td>-72.51999999999994 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.5</th>\n",
-       "      <td>109.05000000000013 meter</td>\n",
-       "      <td>-73.49999999999994 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.6</th>\n",
-       "      <td>101.70000000000013 meter</td>\n",
-       "      <td>-74.47999999999995 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.7</th>\n",
-       "      <td>94.25200000000014 meter</td>\n",
-       "      <td>-75.45999999999995 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.8</th>\n",
-       "      <td>86.70600000000015 meter</td>\n",
-       "      <td>-76.43999999999996 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.9</th>\n",
-       "      <td>79.06200000000015 meter</td>\n",
-       "      <td>-77.41999999999996 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.0</th>\n",
-       "      <td>71.32000000000016 meter</td>\n",
-       "      <td>-78.39999999999996 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.1</th>\n",
-       "      <td>63.48000000000017 meter</td>\n",
-       "      <td>-79.37999999999997 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.2</th>\n",
-       "      <td>55.54200000000017 meter</td>\n",
-       "      <td>-80.35999999999997 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.3</th>\n",
-       "      <td>47.50600000000017 meter</td>\n",
-       "      <td>-81.33999999999997 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.4</th>\n",
-       "      <td>39.37200000000017 meter</td>\n",
-       "      <td>-82.31999999999998 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.5</th>\n",
-       "      <td>31.14000000000017 meter</td>\n",
-       "      <td>-83.29999999999998 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.6</th>\n",
-       "      <td>22.810000000000173 meter</td>\n",
-       "      <td>-84.27999999999999 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.7</th>\n",
-       "      <td>14.382000000000174 meter</td>\n",
-       "      <td>-85.25999999999999 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.8</th>\n",
-       "      <td>5.856000000000174 meter</td>\n",
-       "      <td>-86.24 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.9</th>\n",
-       "      <td>-2.7679999999998266 meter</td>\n",
-       "      <td>-87.22 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.0</th>\n",
-       "      <td>-11.489999999999826 meter</td>\n",
-       "      <td>-88.2 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.1</th>\n",
-       "      <td>-20.309999999999825 meter</td>\n",
-       "      <td>-89.18 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.2</th>\n",
-       "      <td>-29.227999999999824 meter</td>\n",
-       "      <td>-90.16000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.3</th>\n",
-       "      <td>-38.24399999999983 meter</td>\n",
-       "      <td>-91.14000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.4</th>\n",
-       "      <td>-47.357999999999834 meter</td>\n",
-       "      <td>-92.12000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.5</th>\n",
-       "      <td>-56.56999999999984 meter</td>\n",
-       "      <td>-93.10000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.6</th>\n",
-       "      <td>-65.87999999999984 meter</td>\n",
-       "      <td>-94.08000000000003 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.7</th>\n",
-       "      <td>-75.28799999999984 meter</td>\n",
-       "      <td>-95.06000000000003 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.8</th>\n",
-       "      <td>-84.79399999999984 meter</td>\n",
-       "      <td>-96.04000000000003 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.9</th>\n",
-       "      <td>-94.39799999999984 meter</td>\n",
-       "      <td>-97.02000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10.0</th>\n",
-       "      <td>-104.09999999999985 meter</td>\n",
-       "      <td>-98.00000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                              y                                   v\n",
-       "0.0                   381 meter                  0.0 meter / second\n",
-       "0.1                 381.0 meter  -0.9800000000000001 meter / second\n",
-       "0.2               380.902 meter  -1.9600000000000002 meter / second\n",
-       "0.3    380.70599999999996 meter  -2.9400000000000004 meter / second\n",
-       "0.4               380.412 meter  -3.9200000000000004 meter / second\n",
-       "0.5                380.02 meter                 -4.9 meter / second\n",
-       "0.6                379.53 meter   -5.880000000000001 meter / second\n",
-       "0.7    378.94199999999995 meter   -6.860000000000001 meter / second\n",
-       "0.8               378.256 meter   -7.840000000000002 meter / second\n",
-       "0.9               377.472 meter   -8.820000000000002 meter / second\n",
-       "1.0                376.59 meter   -9.800000000000002 meter / second\n",
-       "1.1    375.60999999999996 meter  -10.780000000000003 meter / second\n",
-       "1.2               374.532 meter  -11.760000000000003 meter / second\n",
-       "1.3               373.356 meter  -12.740000000000004 meter / second\n",
-       "1.4               372.082 meter  -13.720000000000004 meter / second\n",
-       "1.5                370.71 meter  -14.700000000000005 meter / second\n",
-       "1.6    369.23999999999995 meter  -15.680000000000005 meter / second\n",
-       "1.7    367.67199999999997 meter  -16.660000000000004 meter / second\n",
-       "1.8               366.006 meter  -17.640000000000004 meter / second\n",
-       "1.9    364.24199999999996 meter  -18.620000000000005 meter / second\n",
-       "2.0    362.37999999999994 meter  -19.600000000000005 meter / second\n",
-       "2.1    360.41999999999996 meter  -20.580000000000005 meter / second\n",
-       "2.2    358.36199999999997 meter  -21.560000000000006 meter / second\n",
-       "2.3    356.20599999999996 meter  -22.540000000000006 meter / second\n",
-       "2.4    353.95199999999994 meter  -23.520000000000007 meter / second\n",
-       "2.5    351.59999999999997 meter  -24.500000000000007 meter / second\n",
-       "2.6                349.15 meter  -25.480000000000008 meter / second\n",
-       "2.7               346.602 meter  -26.460000000000008 meter / second\n",
-       "2.8    343.95599999999996 meter   -27.44000000000001 meter / second\n",
-       "2.9    341.21199999999993 meter   -28.42000000000001 meter / second\n",
-       "...                         ...                                 ...\n",
-       "7.1     137.4700000000001 meter   -69.57999999999993 meter / second\n",
-       "7.2     130.5120000000001 meter   -70.55999999999993 meter / second\n",
-       "7.3    123.45600000000012 meter   -71.53999999999994 meter / second\n",
-       "7.4    116.30200000000012 meter   -72.51999999999994 meter / second\n",
-       "7.5    109.05000000000013 meter   -73.49999999999994 meter / second\n",
-       "7.6    101.70000000000013 meter   -74.47999999999995 meter / second\n",
-       "7.7     94.25200000000014 meter   -75.45999999999995 meter / second\n",
-       "7.8     86.70600000000015 meter   -76.43999999999996 meter / second\n",
-       "7.9     79.06200000000015 meter   -77.41999999999996 meter / second\n",
-       "8.0     71.32000000000016 meter   -78.39999999999996 meter / second\n",
-       "8.1     63.48000000000017 meter   -79.37999999999997 meter / second\n",
-       "8.2     55.54200000000017 meter   -80.35999999999997 meter / second\n",
-       "8.3     47.50600000000017 meter   -81.33999999999997 meter / second\n",
-       "8.4     39.37200000000017 meter   -82.31999999999998 meter / second\n",
-       "8.5     31.14000000000017 meter   -83.29999999999998 meter / second\n",
-       "8.6    22.810000000000173 meter   -84.27999999999999 meter / second\n",
-       "8.7    14.382000000000174 meter   -85.25999999999999 meter / second\n",
-       "8.8     5.856000000000174 meter               -86.24 meter / second\n",
-       "8.9   -2.7679999999998266 meter               -87.22 meter / second\n",
-       "9.0   -11.489999999999826 meter                -88.2 meter / second\n",
-       "9.1   -20.309999999999825 meter               -89.18 meter / second\n",
-       "9.2   -29.227999999999824 meter   -90.16000000000001 meter / second\n",
-       "9.3    -38.24399999999983 meter   -91.14000000000001 meter / second\n",
-       "9.4   -47.357999999999834 meter   -92.12000000000002 meter / second\n",
-       "9.5    -56.56999999999984 meter   -93.10000000000002 meter / second\n",
-       "9.6    -65.87999999999984 meter   -94.08000000000003 meter / second\n",
-       "9.7    -75.28799999999984 meter   -95.06000000000003 meter / second\n",
-       "9.8    -84.79399999999984 meter   -96.04000000000003 meter / second\n",
-       "9.9    -94.39799999999984 meter   -97.02000000000004 meter / second\n",
-       "10.0  -104.09999999999985 meter   -98.00000000000004 meter / second\n",
-       "\n",
-       "[101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def crossings(series, value):\n",
-    "    \"\"\"Find the labels where the series passes through value.\n",
-    "\n",
-    "    The labels in series must be increasing numerical values.\n",
-    "\n",
-    "    series: Series\n",
-    "    value: number\n",
-    "\n",
-    "    returns: sequence of labels\n",
-    "    \"\"\"\n",
-    "    units = get_units(series.values[0])\n",
-    "    values = magnitudes(series - value)\n",
-    "    interp = InterpolatedUnivariateSpline(series.index, values)\n",
-    "    return interp.roots()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([8.86802711])"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_crossings = crossings(results.y, 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Success'"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.set(dt=0.1*s)\n",
-    "results, details = run_ralston(system, slope_func, max_step=0.5)\n",
-    "details.message"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([8.81788535])"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_crossings = crossings(results.y, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>y</th>\n",
-       "      <th>v</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.0</th>\n",
-       "      <td>381 meter</td>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1</th>\n",
-       "      <td>380.951 meter</td>\n",
-       "      <td>-0.9800000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.2</th>\n",
-       "      <td>380.80400000000003 meter</td>\n",
-       "      <td>-1.9600000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.3</th>\n",
-       "      <td>380.559 meter</td>\n",
-       "      <td>-2.9400000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.4</th>\n",
-       "      <td>380.216 meter</td>\n",
-       "      <td>-3.9200000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.5</th>\n",
-       "      <td>379.77500000000003 meter</td>\n",
-       "      <td>-4.9 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.6</th>\n",
-       "      <td>379.23600000000005 meter</td>\n",
-       "      <td>-5.880000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.7</th>\n",
-       "      <td>378.59900000000005 meter</td>\n",
-       "      <td>-6.860000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.8</th>\n",
-       "      <td>377.86400000000003 meter</td>\n",
-       "      <td>-7.840000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.9</th>\n",
-       "      <td>377.031 meter</td>\n",
-       "      <td>-8.820000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.0</th>\n",
-       "      <td>376.1 meter</td>\n",
-       "      <td>-9.800000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.1</th>\n",
-       "      <td>375.071 meter</td>\n",
-       "      <td>-10.780000000000003 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.2</th>\n",
-       "      <td>373.944 meter</td>\n",
-       "      <td>-11.760000000000003 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.3</th>\n",
-       "      <td>372.719 meter</td>\n",
-       "      <td>-12.740000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.4</th>\n",
-       "      <td>371.396 meter</td>\n",
-       "      <td>-13.720000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.5</th>\n",
-       "      <td>369.975 meter</td>\n",
-       "      <td>-14.700000000000005 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.6</th>\n",
-       "      <td>368.456 meter</td>\n",
-       "      <td>-15.680000000000005 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.7</th>\n",
-       "      <td>366.839 meter</td>\n",
-       "      <td>-16.660000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.8</th>\n",
-       "      <td>365.124 meter</td>\n",
-       "      <td>-17.640000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.9</th>\n",
-       "      <td>363.31100000000004 meter</td>\n",
-       "      <td>-18.620000000000005 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.0</th>\n",
-       "      <td>361.40000000000003 meter</td>\n",
-       "      <td>-19.600000000000005 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.1</th>\n",
-       "      <td>359.391 meter</td>\n",
-       "      <td>-20.580000000000005 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.2</th>\n",
-       "      <td>357.284 meter</td>\n",
-       "      <td>-21.560000000000006 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.3</th>\n",
-       "      <td>355.079 meter</td>\n",
-       "      <td>-22.540000000000006 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.4</th>\n",
-       "      <td>352.776 meter</td>\n",
-       "      <td>-23.520000000000007 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.5</th>\n",
-       "      <td>350.375 meter</td>\n",
-       "      <td>-24.500000000000007 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.6</th>\n",
-       "      <td>347.876 meter</td>\n",
-       "      <td>-25.480000000000008 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.7</th>\n",
-       "      <td>345.279 meter</td>\n",
-       "      <td>-26.460000000000008 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.8</th>\n",
-       "      <td>342.584 meter</td>\n",
-       "      <td>-27.44000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.9</th>\n",
-       "      <td>339.791 meter</td>\n",
-       "      <td>-28.42000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.1</th>\n",
-       "      <td>133.99100000000016 meter</td>\n",
-       "      <td>-69.57999999999993 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.2</th>\n",
-       "      <td>126.98400000000017 meter</td>\n",
-       "      <td>-70.55999999999993 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.3</th>\n",
-       "      <td>119.87900000000018 meter</td>\n",
-       "      <td>-71.53999999999994 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.4</th>\n",
-       "      <td>112.67600000000019 meter</td>\n",
-       "      <td>-72.51999999999994 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.5</th>\n",
-       "      <td>105.3750000000002 meter</td>\n",
-       "      <td>-73.49999999999994 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.6</th>\n",
-       "      <td>97.9760000000002 meter</td>\n",
-       "      <td>-74.47999999999995 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.7</th>\n",
-       "      <td>90.4790000000002 meter</td>\n",
-       "      <td>-75.45999999999995 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.8</th>\n",
-       "      <td>82.8840000000002 meter</td>\n",
-       "      <td>-76.43999999999996 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.9</th>\n",
-       "      <td>75.1910000000002 meter</td>\n",
-       "      <td>-77.41999999999996 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.0</th>\n",
-       "      <td>67.4000000000002 meter</td>\n",
-       "      <td>-78.39999999999996 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.1</th>\n",
-       "      <td>59.51100000000021 meter</td>\n",
-       "      <td>-79.37999999999997 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.2</th>\n",
-       "      <td>51.524000000000214 meter</td>\n",
-       "      <td>-80.35999999999997 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.3</th>\n",
-       "      <td>43.43900000000022 meter</td>\n",
-       "      <td>-81.33999999999997 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.4</th>\n",
-       "      <td>35.25600000000022 meter</td>\n",
-       "      <td>-82.31999999999998 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.5</th>\n",
-       "      <td>26.97500000000022 meter</td>\n",
-       "      <td>-83.29999999999998 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.6</th>\n",
-       "      <td>18.596000000000224 meter</td>\n",
-       "      <td>-84.27999999999999 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.7</th>\n",
-       "      <td>10.119000000000225 meter</td>\n",
-       "      <td>-85.25999999999999 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.8</th>\n",
-       "      <td>1.5440000000002243 meter</td>\n",
-       "      <td>-86.24 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.9</th>\n",
-       "      <td>-7.128999999999776 meter</td>\n",
-       "      <td>-87.22 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.0</th>\n",
-       "      <td>-15.899999999999777 meter</td>\n",
-       "      <td>-88.2 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.1</th>\n",
-       "      <td>-24.768999999999778 meter</td>\n",
-       "      <td>-89.18 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.2</th>\n",
-       "      <td>-33.73599999999978 meter</td>\n",
-       "      <td>-90.16000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.3</th>\n",
-       "      <td>-42.80099999999978 meter</td>\n",
-       "      <td>-91.14000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.4</th>\n",
-       "      <td>-51.963999999999785 meter</td>\n",
-       "      <td>-92.12000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.5</th>\n",
-       "      <td>-61.22499999999979 meter</td>\n",
-       "      <td>-93.10000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.6</th>\n",
-       "      <td>-70.58399999999979 meter</td>\n",
-       "      <td>-94.08000000000003 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.7</th>\n",
-       "      <td>-80.0409999999998 meter</td>\n",
-       "      <td>-95.06000000000003 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.8</th>\n",
-       "      <td>-89.5959999999998 meter</td>\n",
-       "      <td>-96.04000000000003 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9.9</th>\n",
-       "      <td>-99.24899999999981 meter</td>\n",
-       "      <td>-97.02000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10.0</th>\n",
-       "      <td>-108.99999999999982 meter</td>\n",
-       "      <td>-98.00000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>101 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                              y                                   v\n",
-       "0.0                   381 meter                  0.0 meter / second\n",
-       "0.1               380.951 meter  -0.9800000000000001 meter / second\n",
-       "0.2    380.80400000000003 meter  -1.9600000000000002 meter / second\n",
-       "0.3               380.559 meter  -2.9400000000000004 meter / second\n",
-       "0.4               380.216 meter  -3.9200000000000004 meter / second\n",
-       "0.5    379.77500000000003 meter                 -4.9 meter / second\n",
-       "0.6    379.23600000000005 meter   -5.880000000000001 meter / second\n",
-       "0.7    378.59900000000005 meter   -6.860000000000001 meter / second\n",
-       "0.8    377.86400000000003 meter   -7.840000000000002 meter / second\n",
-       "0.9               377.031 meter   -8.820000000000002 meter / second\n",
-       "1.0                 376.1 meter   -9.800000000000002 meter / second\n",
-       "1.1               375.071 meter  -10.780000000000003 meter / second\n",
-       "1.2               373.944 meter  -11.760000000000003 meter / second\n",
-       "1.3               372.719 meter  -12.740000000000004 meter / second\n",
-       "1.4               371.396 meter  -13.720000000000004 meter / second\n",
-       "1.5               369.975 meter  -14.700000000000005 meter / second\n",
-       "1.6               368.456 meter  -15.680000000000005 meter / second\n",
-       "1.7               366.839 meter  -16.660000000000004 meter / second\n",
-       "1.8               365.124 meter  -17.640000000000004 meter / second\n",
-       "1.9    363.31100000000004 meter  -18.620000000000005 meter / second\n",
-       "2.0    361.40000000000003 meter  -19.600000000000005 meter / second\n",
-       "2.1               359.391 meter  -20.580000000000005 meter / second\n",
-       "2.2               357.284 meter  -21.560000000000006 meter / second\n",
-       "2.3               355.079 meter  -22.540000000000006 meter / second\n",
-       "2.4               352.776 meter  -23.520000000000007 meter / second\n",
-       "2.5               350.375 meter  -24.500000000000007 meter / second\n",
-       "2.6               347.876 meter  -25.480000000000008 meter / second\n",
-       "2.7               345.279 meter  -26.460000000000008 meter / second\n",
-       "2.8               342.584 meter   -27.44000000000001 meter / second\n",
-       "2.9               339.791 meter   -28.42000000000001 meter / second\n",
-       "...                         ...                                 ...\n",
-       "7.1    133.99100000000016 meter   -69.57999999999993 meter / second\n",
-       "7.2    126.98400000000017 meter   -70.55999999999993 meter / second\n",
-       "7.3    119.87900000000018 meter   -71.53999999999994 meter / second\n",
-       "7.4    112.67600000000019 meter   -72.51999999999994 meter / second\n",
-       "7.5     105.3750000000002 meter   -73.49999999999994 meter / second\n",
-       "7.6      97.9760000000002 meter   -74.47999999999995 meter / second\n",
-       "7.7      90.4790000000002 meter   -75.45999999999995 meter / second\n",
-       "7.8      82.8840000000002 meter   -76.43999999999996 meter / second\n",
-       "7.9      75.1910000000002 meter   -77.41999999999996 meter / second\n",
-       "8.0      67.4000000000002 meter   -78.39999999999996 meter / second\n",
-       "8.1     59.51100000000021 meter   -79.37999999999997 meter / second\n",
-       "8.2    51.524000000000214 meter   -80.35999999999997 meter / second\n",
-       "8.3     43.43900000000022 meter   -81.33999999999997 meter / second\n",
-       "8.4     35.25600000000022 meter   -82.31999999999998 meter / second\n",
-       "8.5     26.97500000000022 meter   -83.29999999999998 meter / second\n",
-       "8.6    18.596000000000224 meter   -84.27999999999999 meter / second\n",
-       "8.7    10.119000000000225 meter   -85.25999999999999 meter / second\n",
-       "8.8    1.5440000000002243 meter               -86.24 meter / second\n",
-       "8.9    -7.128999999999776 meter               -87.22 meter / second\n",
-       "9.0   -15.899999999999777 meter                -88.2 meter / second\n",
-       "9.1   -24.768999999999778 meter               -89.18 meter / second\n",
-       "9.2    -33.73599999999978 meter   -90.16000000000001 meter / second\n",
-       "9.3    -42.80099999999978 meter   -91.14000000000001 meter / second\n",
-       "9.4   -51.963999999999785 meter   -92.12000000000002 meter / second\n",
-       "9.5    -61.22499999999979 meter   -93.10000000000002 meter / second\n",
-       "9.6    -70.58399999999979 meter   -94.08000000000003 meter / second\n",
-       "9.7     -80.0409999999998 meter   -95.06000000000003 meter / second\n",
-       "9.8     -89.5959999999998 meter   -96.04000000000003 meter / second\n",
-       "9.9    -99.24899999999981 meter   -97.02000000000004 meter / second\n",
-       "10.0  -108.99999999999982 meter   -98.00000000000004 meter / second\n",
-       "\n",
-       "[101 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's position as a function of time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/chap09-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_position(results):\n",
-    "    plot(results.y, label='y')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "\n",
-    "plot_position(results)\n",
-    "savefig('figs/chap09-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Onto the sidewalk\n",
-    "\n",
-    "To figure out when the penny hit the sidewalk, we can use `crossings`, which finds the times where a `Series` passes through a given value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def crossings(series, value):\n",
-    "    \"\"\"Find the labels where the series passes through value.\n",
-    "\n",
-    "    The labels in series must be increasing numerical values.\n",
-    "\n",
-    "    series: Series\n",
-    "    value: number\n",
-    "\n",
-    "    returns: sequence of labels\n",
-    "    \"\"\"\n",
-    "    units = get_units(series.values[0])\n",
-    "    values = magnitudes(series - value)\n",
-    "    interp = InterpolatedUnivariateSpline(series.index, values)\n",
-    "    return interp.roots()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([8.81788535])"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_crossings = crossings(results.y, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For this example there should be just one crossing, the time when the penny hits the sidewalk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "8.81788534972054 second"
-      ],
-      "text/latex": [
-       "$8.81788534972054\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "8.81788534972054 <Unit('second')>"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_sidewalk = t_crossings[0] * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can compare that to the exact result.  Without air resistance, we have\n",
-    "\n",
-    "$v = -g t$\n",
-    "\n",
-    "and\n",
-    "\n",
-    "$y = 381 - g t^2 / 2$\n",
-    "\n",
-    "Setting $y=0$ and solving for $t$ yields\n",
-    "\n",
-    "$t = \\sqrt{\\frac{2 y_{init}}{g}}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "8.817885349720552 second"
-      ],
-      "text/latex": [
-       "$8.817885349720552\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "8.817885349720552 <Unit('second')>"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sqrt(2 * init.y / g)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The estimate is accurate to about 10 decimal places."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Events\n",
-    "\n",
-    "Instead of running the simulation until the penny goes through the sidewalk, it would be better to detect the point where the penny hits the sidewalk and stop.  `run_ralston` provides exactly the tool we need, **event functions**.\n",
-    "\n",
-    "Here's an event function that returns the height of the penny above the sidewalk:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Return the height of the penny above the sidewalk.\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we pass it to `run_ralston`.  The solver should run until the event function returns 0, and then terminate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>Success</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "message    Success\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ralston(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The message from the solver indicates the solver stopped because the event we wanted to detect happened.\n",
-    "\n",
-    "Here are the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>y</th>\n",
-       "      <th>v</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.000000</th>\n",
-       "      <td>381 meter</td>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.100000</th>\n",
-       "      <td>380.951 meter</td>\n",
-       "      <td>-0.9800000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.200000</th>\n",
-       "      <td>380.80400000000003 meter</td>\n",
-       "      <td>-1.9600000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.300000</th>\n",
-       "      <td>380.559 meter</td>\n",
-       "      <td>-2.9400000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.400000</th>\n",
-       "      <td>380.216 meter</td>\n",
-       "      <td>-3.9200000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.500000</th>\n",
-       "      <td>379.77500000000003 meter</td>\n",
-       "      <td>-4.9 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.600000</th>\n",
-       "      <td>379.23600000000005 meter</td>\n",
-       "      <td>-5.880000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.700000</th>\n",
-       "      <td>378.59900000000005 meter</td>\n",
-       "      <td>-6.860000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.800000</th>\n",
-       "      <td>377.86400000000003 meter</td>\n",
-       "      <td>-7.840000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.900000</th>\n",
-       "      <td>377.031 meter</td>\n",
-       "      <td>-8.820000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.000000</th>\n",
-       "      <td>376.1 meter</td>\n",
-       "      <td>-9.800000000000002 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.100000</th>\n",
-       "      <td>375.071 meter</td>\n",
-       "      <td>-10.780000000000003 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.200000</th>\n",
-       "      <td>373.944 meter</td>\n",
-       "      <td>-11.760000000000003 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.300000</th>\n",
-       "      <td>372.719 meter</td>\n",
-       "      <td>-12.740000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.400000</th>\n",
-       "      <td>371.396 meter</td>\n",
-       "      <td>-13.720000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.500000</th>\n",
-       "      <td>369.975 meter</td>\n",
-       "      <td>-14.700000000000005 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.600000</th>\n",
-       "      <td>368.456 meter</td>\n",
-       "      <td>-15.680000000000005 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.700000</th>\n",
-       "      <td>366.839 meter</td>\n",
-       "      <td>-16.660000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.800000</th>\n",
-       "      <td>365.124 meter</td>\n",
-       "      <td>-17.640000000000004 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.900000</th>\n",
-       "      <td>363.31100000000004 meter</td>\n",
-       "      <td>-18.620000000000005 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.000000</th>\n",
-       "      <td>361.40000000000003 meter</td>\n",
-       "      <td>-19.600000000000005 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.100000</th>\n",
-       "      <td>359.391 meter</td>\n",
-       "      <td>-20.580000000000005 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.200000</th>\n",
-       "      <td>357.284 meter</td>\n",
-       "      <td>-21.560000000000006 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.300000</th>\n",
-       "      <td>355.079 meter</td>\n",
-       "      <td>-22.540000000000006 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.400000</th>\n",
-       "      <td>352.776 meter</td>\n",
-       "      <td>-23.520000000000007 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.500000</th>\n",
-       "      <td>350.375 meter</td>\n",
-       "      <td>-24.500000000000007 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.600000</th>\n",
-       "      <td>347.876 meter</td>\n",
-       "      <td>-25.480000000000008 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.700000</th>\n",
-       "      <td>345.279 meter</td>\n",
-       "      <td>-26.460000000000008 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.800000</th>\n",
-       "      <td>342.584 meter</td>\n",
-       "      <td>-27.44000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.900000</th>\n",
-       "      <td>339.791 meter</td>\n",
-       "      <td>-28.42000000000001 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.000000</th>\n",
-       "      <td>204.60000000000005 meter</td>\n",
-       "      <td>-58.799999999999926 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.100000</th>\n",
-       "      <td>198.67100000000005 meter</td>\n",
-       "      <td>-59.77999999999992 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.200000</th>\n",
-       "      <td>192.64400000000006 meter</td>\n",
-       "      <td>-60.75999999999992 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.300000</th>\n",
-       "      <td>186.51900000000006 meter</td>\n",
-       "      <td>-61.73999999999992 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.400000</th>\n",
-       "      <td>180.29600000000008 meter</td>\n",
-       "      <td>-62.719999999999914 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.500000</th>\n",
-       "      <td>173.97500000000008 meter</td>\n",
-       "      <td>-63.69999999999991 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.600000</th>\n",
-       "      <td>167.5560000000001 meter</td>\n",
-       "      <td>-64.67999999999991 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.700000</th>\n",
-       "      <td>161.0390000000001 meter</td>\n",
-       "      <td>-65.65999999999991 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.800000</th>\n",
-       "      <td>154.42400000000012 meter</td>\n",
-       "      <td>-66.63999999999992 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6.900000</th>\n",
-       "      <td>147.71100000000013 meter</td>\n",
-       "      <td>-67.61999999999992 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.000000</th>\n",
-       "      <td>140.90000000000015 meter</td>\n",
-       "      <td>-68.59999999999992 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.100000</th>\n",
-       "      <td>133.99100000000016 meter</td>\n",
-       "      <td>-69.57999999999993 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.200000</th>\n",
-       "      <td>126.98400000000017 meter</td>\n",
-       "      <td>-70.55999999999993 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.300000</th>\n",
-       "      <td>119.87900000000018 meter</td>\n",
-       "      <td>-71.53999999999994 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.400000</th>\n",
-       "      <td>112.67600000000019 meter</td>\n",
-       "      <td>-72.51999999999994 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.500000</th>\n",
-       "      <td>105.3750000000002 meter</td>\n",
-       "      <td>-73.49999999999994 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.600000</th>\n",
-       "      <td>97.9760000000002 meter</td>\n",
-       "      <td>-74.47999999999995 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.700000</th>\n",
-       "      <td>90.4790000000002 meter</td>\n",
-       "      <td>-75.45999999999995 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.800000</th>\n",
-       "      <td>82.8840000000002 meter</td>\n",
-       "      <td>-76.43999999999996 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.900000</th>\n",
-       "      <td>75.1910000000002 meter</td>\n",
-       "      <td>-77.41999999999996 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.000000</th>\n",
-       "      <td>67.4000000000002 meter</td>\n",
-       "      <td>-78.39999999999996 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.100000</th>\n",
-       "      <td>59.51100000000021 meter</td>\n",
-       "      <td>-79.37999999999997 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.200000</th>\n",
-       "      <td>51.524000000000214 meter</td>\n",
-       "      <td>-80.35999999999997 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.300000</th>\n",
-       "      <td>43.43900000000022 meter</td>\n",
-       "      <td>-81.33999999999997 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.400000</th>\n",
-       "      <td>35.25600000000022 meter</td>\n",
-       "      <td>-82.31999999999998 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.500000</th>\n",
-       "      <td>26.97500000000022 meter</td>\n",
-       "      <td>-83.29999999999998 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.600000</th>\n",
-       "      <td>18.596000000000224 meter</td>\n",
-       "      <td>-84.27999999999999 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.700000</th>\n",
-       "      <td>10.119000000000225 meter</td>\n",
-       "      <td>-85.25999999999999 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.800000</th>\n",
-       "      <td>1.5440000000002243 meter</td>\n",
-       "      <td>-86.24 meter / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8.817802</th>\n",
-       "      <td>-4.440892098500626e-16 meter</td>\n",
-       "      <td>-86.41446327683617 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>90 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                     y                                   v\n",
-       "0.000000                     381 meter                  0.0 meter / second\n",
-       "0.100000                 380.951 meter  -0.9800000000000001 meter / second\n",
-       "0.200000      380.80400000000003 meter  -1.9600000000000002 meter / second\n",
-       "0.300000                 380.559 meter  -2.9400000000000004 meter / second\n",
-       "0.400000                 380.216 meter  -3.9200000000000004 meter / second\n",
-       "0.500000      379.77500000000003 meter                 -4.9 meter / second\n",
-       "0.600000      379.23600000000005 meter   -5.880000000000001 meter / second\n",
-       "0.700000      378.59900000000005 meter   -6.860000000000001 meter / second\n",
-       "0.800000      377.86400000000003 meter   -7.840000000000002 meter / second\n",
-       "0.900000                 377.031 meter   -8.820000000000002 meter / second\n",
-       "1.000000                   376.1 meter   -9.800000000000002 meter / second\n",
-       "1.100000                 375.071 meter  -10.780000000000003 meter / second\n",
-       "1.200000                 373.944 meter  -11.760000000000003 meter / second\n",
-       "1.300000                 372.719 meter  -12.740000000000004 meter / second\n",
-       "1.400000                 371.396 meter  -13.720000000000004 meter / second\n",
-       "1.500000                 369.975 meter  -14.700000000000005 meter / second\n",
-       "1.600000                 368.456 meter  -15.680000000000005 meter / second\n",
-       "1.700000                 366.839 meter  -16.660000000000004 meter / second\n",
-       "1.800000                 365.124 meter  -17.640000000000004 meter / second\n",
-       "1.900000      363.31100000000004 meter  -18.620000000000005 meter / second\n",
-       "2.000000      361.40000000000003 meter  -19.600000000000005 meter / second\n",
-       "2.100000                 359.391 meter  -20.580000000000005 meter / second\n",
-       "2.200000                 357.284 meter  -21.560000000000006 meter / second\n",
-       "2.300000                 355.079 meter  -22.540000000000006 meter / second\n",
-       "2.400000                 352.776 meter  -23.520000000000007 meter / second\n",
-       "2.500000                 350.375 meter  -24.500000000000007 meter / second\n",
-       "2.600000                 347.876 meter  -25.480000000000008 meter / second\n",
-       "2.700000                 345.279 meter  -26.460000000000008 meter / second\n",
-       "2.800000                 342.584 meter   -27.44000000000001 meter / second\n",
-       "2.900000                 339.791 meter   -28.42000000000001 meter / second\n",
-       "...                                ...                                 ...\n",
-       "6.000000      204.60000000000005 meter  -58.799999999999926 meter / second\n",
-       "6.100000      198.67100000000005 meter   -59.77999999999992 meter / second\n",
-       "6.200000      192.64400000000006 meter   -60.75999999999992 meter / second\n",
-       "6.300000      186.51900000000006 meter   -61.73999999999992 meter / second\n",
-       "6.400000      180.29600000000008 meter  -62.719999999999914 meter / second\n",
-       "6.500000      173.97500000000008 meter   -63.69999999999991 meter / second\n",
-       "6.600000       167.5560000000001 meter   -64.67999999999991 meter / second\n",
-       "6.700000       161.0390000000001 meter   -65.65999999999991 meter / second\n",
-       "6.800000      154.42400000000012 meter   -66.63999999999992 meter / second\n",
-       "6.900000      147.71100000000013 meter   -67.61999999999992 meter / second\n",
-       "7.000000      140.90000000000015 meter   -68.59999999999992 meter / second\n",
-       "7.100000      133.99100000000016 meter   -69.57999999999993 meter / second\n",
-       "7.200000      126.98400000000017 meter   -70.55999999999993 meter / second\n",
-       "7.300000      119.87900000000018 meter   -71.53999999999994 meter / second\n",
-       "7.400000      112.67600000000019 meter   -72.51999999999994 meter / second\n",
-       "7.500000       105.3750000000002 meter   -73.49999999999994 meter / second\n",
-       "7.600000        97.9760000000002 meter   -74.47999999999995 meter / second\n",
-       "7.700000        90.4790000000002 meter   -75.45999999999995 meter / second\n",
-       "7.800000        82.8840000000002 meter   -76.43999999999996 meter / second\n",
-       "7.900000        75.1910000000002 meter   -77.41999999999996 meter / second\n",
-       "8.000000        67.4000000000002 meter   -78.39999999999996 meter / second\n",
-       "8.100000       59.51100000000021 meter   -79.37999999999997 meter / second\n",
-       "8.200000      51.524000000000214 meter   -80.35999999999997 meter / second\n",
-       "8.300000       43.43900000000022 meter   -81.33999999999997 meter / second\n",
-       "8.400000       35.25600000000022 meter   -82.31999999999998 meter / second\n",
-       "8.500000       26.97500000000022 meter   -83.29999999999998 meter / second\n",
-       "8.600000      18.596000000000224 meter   -84.27999999999999 meter / second\n",
-       "8.700000      10.119000000000225 meter   -85.25999999999999 meter / second\n",
-       "8.800000      1.5440000000002243 meter               -86.24 meter / second\n",
-       "8.817802  -4.440892098500626e-16 meter   -86.41446327683617 meter / second\n",
-       "\n",
-       "[90 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the `events` option, the solver returns the actual time steps it computed, which are not necessarily equally spaced. \n",
-    "\n",
-    "The last time step is when the event occurred:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "8.81780237518735 second"
-      ],
-      "text/latex": [
-       "$8.81780237518735\\ \\mathrm{second}$"
-      ],
-      "text/plain": [
-       "8.81780237518735 <Unit('second')>"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_sidewalk = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is accurate to about 15 decimal places.\n",
-    "\n",
-    "We can also check the velocity of the penny when it hits the sidewalk:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-86.41446327683617 meter/second"
-      ],
-      "text/latex": [
-       "$-86.41446327683617\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
-      ],
-      "text/plain": [
-       "-86.41446327683617 <Unit('meter / second')>"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v_sidewalk = get_last_value(results.v)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And convert to kilometers per hour."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "-311.09206779661025 kilometer/hour"
-      ],
-      "text/latex": [
-       "$-311.09206779661025\\ \\frac{\\mathrm{kilometer}}{\\mathrm{hour}}$"
-      ],
-      "text/plain": [
-       "-311.09206779661025 <Unit('kilometer / hour')>"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "km = UNITS.kilometer\n",
-    "h = UNITS.hour\n",
-    "v_sidewalk.to(km / h)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If there were no air resistance, the penny would hit the sidewalk (or someone's head) at more than 300 km/h.\n",
-    "\n",
-    "So it's a good thing there is air resistance."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/test_plot_quantity.ipynb b/soln/test_plot_quantity.ipynb
deleted file mode 100644
index 1efbad4e..00000000
--- a/soln/test_plot_quantity.ipynb
+++ /dev/null
@@ -1,387 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 25\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "import pint\n",
-    "UNITS = pint.UnitRegistry()\n",
-    "Quantity = UNITS.Quantity\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "x and y must not be None",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-3-e6f346678a7c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3356\u001b[0m                       mplDeprecation)\n\u001b[1;32m   3357\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3358\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3359\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3360\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1853\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1854\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1855\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1856\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1857\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1527\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_grab_next_args\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    404\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    364\u001b[0m         \u001b[0;31m# downstream.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 366\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must not be None\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    367\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    368\u001b[0m         \u001b[0mkw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: x and y must not be None"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADYBJREFUeJzt3HGI33d9x/Hny8ROprWO5QRJou1YuhrKoO7oOoRZ0Y20fyT/FEmguEppwK0OZhE6HCr1rylDELJptolT0Fr9Qw+J5A9X6RAjudJZmpTALTpzROhZu/5TtGZ774/fT++4XHLf3v3uLt77+YDA7/v7fX6/e+fD3TO/fH/3+6WqkCRtf6/a6gEkSZvD4EtSEwZfkpow+JLUhMGXpCYMviQ1sWrwk3wuyXNJnrnC7Uny6SRzSZ5O8rbJjylJWq8hz/A/Dxy4yu13AfvGf44C/7T+sSRJk7Zq8KvqCeBnV1lyCPhCjZwC3pDkTZMaUJI0GTsn8Bi7gQtLjufH1/1k+cIkRxn9L4DXvva1f3TLLbdM4MtLUh9PPvnkT6tqai33nUTws8J1K35eQ1UdB44DTE9P1+zs7AS+vCT1keS/13rfSfyWzjywd8nxHuDiBB5XkjRBkwj+DPDe8W/r3AG8WFWXnc6RJG2tVU/pJPkycCewK8k88FHg1QBV9RngBHA3MAe8BLxvo4aVJK3dqsGvqiOr3F7AX01sIknShvCdtpLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDUxKPhJDiQ5l2QuycMr3P7mJI8neSrJ00nunvyokqT1WDX4SXYAx4C7gP3AkST7ly37O+CxqroNOAz846QHlSStz5Bn+LcDc1V1vqpeBh4FDi1bU8Drx5dvAC5ObkRJ0iQMCf5u4MKS4/nxdUt9DLg3yTxwAvjASg+U5GiS2SSzCwsLaxhXkrRWQ4KfFa6rZcdHgM9X1R7gbuCLSS577Ko6XlXTVTU9NTX1yqeVJK3ZkODPA3uXHO/h8lM29wOPAVTV94DXALsmMaAkaTKGBP80sC/JTUmuY/Si7MyyNT8G3gWQ5K2Mgu85G0m6hqwa/Kq6BDwInASeZfTbOGeSPJLk4HjZQ8ADSX4AfBm4r6qWn/aRJG2hnUMWVdUJRi/GLr3uI0sunwXePtnRJEmT5DttJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNDAp+kgNJziWZS/LwFda8J8nZJGeSfGmyY0qS1mvnaguS7ACOAX8GzAOnk8xU1dkla/YBfwu8vapeSPLGjRpYkrQ2Q57h3w7MVdX5qnoZeBQ4tGzNA8CxqnoBoKqem+yYkqT1GhL83cCFJcfz4+uWuhm4Ocl3k5xKcmClB0pyNMlsktmFhYW1TSxJWpMhwc8K19Wy453APuBO4AjwL0necNmdqo5X1XRVTU9NTb3SWSVJ6zAk+PPA3iXHe4CLK6z5RlX9sqp+CJxj9A+AJOkaMST4p4F9SW5Kch1wGJhZtubrwDsBkuxidIrn/CQHlSStz6rBr6pLwIPASeBZ4LGqOpPkkSQHx8tOAs8nOQs8Dnyoqp7fqKElSa9cqpafjt8c09PTNTs7uyVfW5J+UyV5sqqm13Jf32krSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSE4OCn+RAknNJ5pI8fJV19ySpJNOTG1GSNAmrBj/JDuAYcBewHziSZP8K664H/hr4/qSHlCSt35Bn+LcDc1V1vqpeBh4FDq2w7uPAJ4CfT3A+SdKEDAn+buDCkuP58XW/luQ2YG9VffNqD5TkaJLZJLMLCwuveFhJ0toNCX5WuK5+fWPyKuBTwEOrPVBVHa+q6aqanpqaGj6lJGndhgR/Hti75HgPcHHJ8fXArcB3kvwIuAOY8YVbSbq2DAn+aWBfkpuSXAccBmZ+dWNVvVhVu6rqxqq6ETgFHKyq2Q2ZWJK0JqsGv6ouAQ8CJ4Fngceq6kySR5Ic3OgBJUmTsXPIoqo6AZxYdt1HrrD2zvWPJUmaNN9pK0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqYlDwkxxIci7JXJKHV7j9g0nOJnk6ybeTvGXyo0qS1mPV4CfZARwD7gL2A0eS7F+27Clguqr+EPga8IlJDypJWp8hz/BvB+aq6nxVvQw8ChxauqCqHq+ql8aHp4A9kx1TkrReQ4K/G7iw5Hh+fN2V3A98a6UbkhxNMptkdmFhYfiUkqR1GxL8rHBdrbgwuReYBj650u1VdbyqpqtqempqaviUkqR12zlgzTywd8nxHuDi8kVJ3g18GHhHVf1iMuNJkiZlyDP808C+JDcluQ44DMwsXZDkNuCzwMGqem7yY0qS1mvV4FfVJeBB4CTwLPBYVZ1J8kiSg+NlnwReB3w1yX8mmbnCw0mStsiQUzpU1QngxLLrPrLk8rsnPJckacJ8p60kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNDAp+kgNJziWZS/LwCrf/VpKvjG//fpIbJz2oJGl9Vg1+kh3AMeAuYD9wJMn+ZcvuB16oqt8HPgX8/aQHlSStz5Bn+LcDc1V1vqpeBh4FDi1bcwj4t/HlrwHvSpLJjSlJWq+dA9bsBi4sOZ4H/vhKa6rqUpIXgd8Ffrp0UZKjwNHx4S+SPLOWobehXSzbq8bci0XuxSL3YtEfrPWOQ4K/0jP1WsMaquo4cBwgyWxVTQ/4+tuee7HIvVjkXixyLxYlmV3rfYec0pkH9i453gNcvNKaJDuBG4CfrXUoSdLkDQn+aWBfkpuSXAccBmaWrZkB/mJ8+R7g36vqsmf4kqSts+opnfE5+QeBk8AO4HNVdSbJI8BsVc0A/wp8Mckco2f2hwd87ePrmHu7cS8WuReL3ItF7sWiNe9FfCIuST34TltJasLgS1ITGx58P5Zh0YC9+GCSs0meTvLtJG/Zijk3w2p7sWTdPUkqybb9lbwhe5HkPePvjTNJvrTZM26WAT8jb07yeJKnxj8nd2/FnBstyeeSPHel9ypl5NPjfXo6ydsGPXBVbdgfRi/y/hfwe8B1wA+A/cvW/CXwmfHlw8BXNnKmrfozcC/eCfz2+PL7O+/FeN31wBPAKWB6q+fewu+LfcBTwO+Mj9+41XNv4V4cB94/vrwf+NFWz71Be/GnwNuAZ65w+93Atxi9B+oO4PtDHnejn+H7sQyLVt2Lqnq8ql4aH55i9J6H7WjI9wXAx4FPAD/fzOE22ZC9eAA4VlUvAFTVc5s842YZshcFvH58+QYuf0/QtlBVT3D19zIdAr5QI6eANyR502qPu9HBX+ljGXZfaU1VXQJ+9bEM282QvVjqfkb/gm9Hq+5FktuAvVX1zc0cbAsM+b64Gbg5yXeTnEpyYNOm21xD9uJjwL1J5oETwAc2Z7RrzivtCTDsoxXWY2Ify7ANDP57JrkXmAbesaETbZ2r7kWSVzH61NX7NmugLTTk+2Ino9M6dzL6X99/JLm1qv5ng2fbbEP24gjw+ar6hyR/wuj9P7dW1f9t/HjXlDV1c6Of4fuxDIuG7AVJ3g18GDhYVb/YpNk222p7cT1wK/CdJD9idI5yZpu+cDv0Z+QbVfXLqvohcI7RPwDbzZC9uB94DKCqvge8htEHq3UzqCfLbXTw/ViGRavuxfg0xmcZxX67nqeFVfaiql6sql1VdWNV3cjo9YyDVbXmD426hg35Gfk6oxf0SbKL0Sme85s65eYYshc/Bt4FkOStjIK/sKlTXhtmgPeOf1vnDuDFqvrJanfa0FM6tXEfy/AbZ+BefBJ4HfDV8evWP66qg1s29AYZuBctDNyLk8CfJzkL/C/woap6fuum3hgD9+Ih4J+T/A2jUxj3bccniEm+zOgU3q7x6xUfBV4NUFWfYfT6xd3AHPAS8L5Bj7sN90qStALfaStJTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ18f+GmWq6NWLIwgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(1, 1, None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "newton"
-      ],
-      "text/latex": [
-       "$newton$"
-      ],
-      "text/plain": [
-       "<Unit('newton')>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "meter = UNITS.meter\n",
-    "minute = UNITS.minute\n",
-    "newton = UNITS.newton"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And a few more parameters in the `Params` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "240.0 meter newton"
-      ],
-      "text/latex": [
-       "$240.0 meter \\cdot newton$"
-      ],
-      "text/plain": [
-       "<Quantity(240.0, 'meter * newton')>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max_torque = 240 * newton * meter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "5500 1/minute"
-      ],
-      "text/latex": [
-       "$5500 \\frac{1}{minute}$"
-      ],
-      "text/plain": [
-       "<Quantity(5500, '1 / minute')>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max_rpm = 5500 / minute"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def available_torque(rpm):\n",
-    "    if rpm > max_rpm:\n",
-    "        return 0 * newton * meter\n",
-    "    return max_torque * (1 - rpm/max_rpm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "240.0 meter newton"
-      ],
-      "text/latex": [
-       "$240.0 meter \\cdot newton$"
-      ],
-      "text/plain": [
-       "<Quantity(240.0, 'meter * newton')>"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "available_torque(0 / minute)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "[   0.   302.5  605.   907.5 1210.  1512.5 1815.  2117.5 2420.  2722.5 3025.  3327.5 3630.  3932.5 4235.  4537.5 4840.  5142.5 5445.  5747.5 6050. ] 1/minute"
-      ],
-      "text/latex": [
-       "$[   0.   302.5  605.   907.5 1210.  1512.5 1815.  2117.5 2420.  2722.5 3025.  3327.5 3630.  3932.5 4235.  4537.5 4840.  5142.5 5445.  5747.5 6050. ] \\frac{1}{minute}$"
-      ],
-      "text/plain": [
-       "<Quantity([   0.   302.5  605.   907.5 1210.  1512.5 1815.  2117.5 2420.  2722.5\n",
-       " 3025.  3327.5 3630.  3932.5 4235.  4537.5 4840.  5142.5 5445.  5747.5\n",
-       " 6050. ], '1 / minute')>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rpms = np.linspace(0, max_rpm*1.1, 21) / minute"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<Quantity(240.0, 'meter * newton')>,\n",
-       " <Quantity(226.79999999999998, 'meter * newton')>,\n",
-       " <Quantity(213.6, 'meter * newton')>,\n",
-       " <Quantity(200.39999999999998, 'meter * newton')>,\n",
-       " <Quantity(187.20000000000002, 'meter * newton')>,\n",
-       " <Quantity(174.0, 'meter * newton')>,\n",
-       " <Quantity(160.79999999999998, 'meter * newton')>,\n",
-       " <Quantity(147.6, 'meter * newton')>,\n",
-       " <Quantity(134.39999999999998, 'meter * newton')>,\n",
-       " <Quantity(121.19999999999997, 'meter * newton')>,\n",
-       " <Quantity(107.99999999999999, 'meter * newton')>,\n",
-       " <Quantity(94.79999999999998, 'meter * newton')>,\n",
-       " <Quantity(81.59999999999997, 'meter * newton')>,\n",
-       " <Quantity(68.39999999999995, 'meter * newton')>,\n",
-       " <Quantity(55.19999999999997, 'meter * newton')>,\n",
-       " <Quantity(41.99999999999996, 'meter * newton')>,\n",
-       " <Quantity(28.799999999999972, 'meter * newton')>,\n",
-       " <Quantity(15.59999999999996, 'meter * newton')>,\n",
-       " <Quantity(2.399999999999949, 'meter * newton')>,\n",
-       " <Quantity(0.0, 'meter * newton')>,\n",
-       " <Quantity(0.0, 'meter * newton')>]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "taus = [available_torque(rpm) for rpm in rpms]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.0                    240.0 meter * newton\n",
-       "302.5     226.79999999999998 meter * newton\n",
-       "605.0                  213.6 meter * newton\n",
-       "907.5     200.39999999999998 meter * newton\n",
-       "1210.0    187.20000000000002 meter * newton\n",
-       "1512.5                 174.0 meter * newton\n",
-       "1815.0    160.79999999999998 meter * newton\n",
-       "2117.5                 147.6 meter * newton\n",
-       "2420.0    134.39999999999998 meter * newton\n",
-       "2722.5    121.19999999999997 meter * newton\n",
-       "3025.0    107.99999999999999 meter * newton\n",
-       "3327.5     94.79999999999998 meter * newton\n",
-       "3630.0     81.59999999999997 meter * newton\n",
-       "3932.5     68.39999999999995 meter * newton\n",
-       "4235.0     55.19999999999997 meter * newton\n",
-       "4537.5     41.99999999999996 meter * newton\n",
-       "4840.0    28.799999999999972 meter * newton\n",
-       "5142.5     15.59999999999996 meter * newton\n",
-       "5445.0     2.399999999999949 meter * newton\n",
-       "5747.5                   0.0 meter * newton\n",
-       "6050.0                   0.0 meter * newton\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "series = pd.Series(taus, index=rpms.magnitude)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "setting an array element with a sequence.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-34-be25389df638>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Motor speed (rad/s)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Available torque (N m)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3356\u001b[0m                       mplDeprecation)\n\u001b[1;32m   3357\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3358\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3359\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3360\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1853\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1854\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1855\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1856\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1857\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1527\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1528\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1529\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_line\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1930\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1931\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1932\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_line_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1933\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1934\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_line%d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_update_line_limits\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1952\u001b[0m         \u001b[0mFigures\u001b[0m \u001b[0mout\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdating\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataLim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1953\u001b[0m         \"\"\"\n\u001b[0;32m-> 1954\u001b[0;31m         \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1955\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1956\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mget_path\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    949\u001b[0m         \"\"\"\n\u001b[1;32m    950\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 951\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    952\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    953\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mrecache\u001b[0;34m(self, always)\u001b[0m\n\u001b[1;32m    655\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0malways\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    656\u001b[0m             \u001b[0myconv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_yorig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 657\u001b[0;31m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_unmasked_float_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myconv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    658\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    659\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_to_unmasked_float_array\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   2048\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2049\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2050\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2051\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2052\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m     \"\"\"\n\u001b[0;32m--> 492\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence."
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(series)\n",
-    "plt.xlabel('Motor speed (rad/s)')\n",
-    "plt.ylabel('Available torque (N m)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/test_timestamp.ipynb b/soln/test_timestamp.ipynb
deleted file mode 100644
index 6f125adb..00000000
--- a/soln/test_timestamp.ipynb
+++ /dev/null
@@ -1,173 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas._libs.tslibs.timestamps.Timestamp"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "timestamp = pd.Timestamp(1412526600000000000)\n",
-    "series = pd.Series([], dtype=object)\n",
-    "series['timestamp'] = timestamp\n",
-    "type(series.timestamp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "int"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "series = pd.Series([], dtype=object)\n",
-    "series['anything'] = 300.0\n",
-    "series['timestamp'] = timestamp\n",
-    "type(series.timestamp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dtype('O')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "series.dtype"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "INSTALLED VERSIONS\n",
-      "------------------\n",
-      "commit: None\n",
-      "python: 3.6.5.final.0\n",
-      "python-bits: 64\n",
-      "OS: Linux\n",
-      "OS-release: 4.4.0-128-generic\n",
-      "machine: x86_64\n",
-      "processor: x86_64\n",
-      "byteorder: little\n",
-      "LC_ALL: None\n",
-      "LANG: en_US.utf8\n",
-      "LOCALE: en_US.UTF-8\n",
-      "\n",
-      "pandas: 0.23.0\n",
-      "pytest: 3.5.1\n",
-      "pip: 10.0.1\n",
-      "setuptools: 39.1.0\n",
-      "Cython: 0.28.2\n",
-      "numpy: 1.14.3\n",
-      "scipy: 1.1.0\n",
-      "pyarrow: None\n",
-      "xarray: None\n",
-      "IPython: 6.4.0\n",
-      "sphinx: 1.7.4\n",
-      "patsy: 0.5.0\n",
-      "dateutil: 2.7.3\n",
-      "pytz: 2018.4\n",
-      "blosc: None\n",
-      "bottleneck: 1.2.1\n",
-      "tables: 3.4.3\n",
-      "numexpr: 2.6.5\n",
-      "feather: None\n",
-      "matplotlib: 2.2.2\n",
-      "openpyxl: 2.5.3\n",
-      "xlrd: 1.1.0\n",
-      "xlwt: 1.3.0\n",
-      "xlsxwriter: 1.0.4\n",
-      "lxml: 4.2.1\n",
-      "bs4: 4.6.0\n",
-      "html5lib: 1.0.1\n",
-      "sqlalchemy: 1.2.7\n",
-      "pymysql: None\n",
-      "psycopg2: None\n",
-      "jinja2: 2.10\n",
-      "s3fs: None\n",
-      "fastparquet: None\n",
-      "pandas_gbq: None\n",
-      "pandas_datareader: None\n"
-     ]
-    }
-   ],
-   "source": [
-    "pd.show_versions()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/test_units_off.ipynb b/soln/test_units_off.ipynb
deleted file mode 100644
index 705705a4..00000000
--- a/soln/test_units_off.ipynb
+++ /dev/null
@@ -1,731 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Low pass filter"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "A 0.0 dimensionless\n",
-      "Bd 0.0 dimensionless\n",
-      "Bq 0.0 dimensionless\n",
-      "C 0.0 dimensionless\n",
-      "Gy 0.0 dimensionless\n",
-      "Hz 0.0 dimensionless\n",
-      "K 0.0 dimensionless\n",
-      "Sv 0.0 dimensionless\n",
-      "amp 0.0 dimensionless\n",
-      "ampere 0.0 dimensionless\n",
-      "ampere_turn 0.0 dimensionless\n",
-      "baud 0.0 dimensionless\n",
-      "becquerel 0.0 dimensionless\n",
-      "bit 0.0 dimensionless\n",
-      "bps 0.0 dimensionless\n",
-      "candela 0.0 dimensionless\n",
-      "candle 0.0 dimensionless\n",
-      "cd 0.0 dimensionless\n",
-      "coulomb 0.0 dimensionless\n",
-      "count 0.0 dimensionless\n",
-      "counts_per_second 0.0 dimensionless\n",
-      "cps 0.0 dimensionless\n",
-      "degK 0.0 dimensionless\n",
-      "delta_degC 0.0 dimensionless\n",
-      "fine_structure_constant 0.0 dimensionless\n",
-      "g 0.0 dimensionless\n",
-      "gram 0.0 dimensionless\n",
-      "gray 0.0 dimensionless\n",
-      "hertz 0.0 dimensionless\n",
-      "kelvin 0.0 dimensionless\n",
-      "lm 0.0 dimensionless\n",
-      "lumen 0.0 dimensionless\n",
-      "lux 0.0 dimensionless\n",
-      "lx 0.0 dimensionless\n",
-      "m 0.0 dimensionless\n",
-      "meter 0.0 dimensionless\n",
-      "metre 0.0 dimensionless\n",
-      "mol 0.0 dimensionless\n",
-      "mole 0.0 dimensionless\n",
-      "rad 0.0 dimensionless\n",
-      "radian 0.0 dimensionless\n",
-      "rps 0.0 dimensionless\n",
-      "s 0.0 dimensionless\n",
-      "sec 0.0 dimensionless\n",
-      "second 0.0 dimensionless\n",
-      "sievert 0.0 dimensionless\n",
-      "sr 0.0 dimensionless\n",
-      "steradian 0.0 dimensionless\n",
-      "stere 0.0 dimensionless\n",
-      "Δcelsius 0.0 dimensionless\n"
-     ]
-    }
-   ],
-   "source": [
-    "with units_off():\n",
-    "    for i, name in enumerate(dir(UNITS)):\n",
-    "        unit = getattr(UNITS, name)\n",
-    "        try:\n",
-    "            res = 1*unit - 1\n",
-    "            if res == 0:\n",
-    "                print(name, 1*unit - 1)\n",
-    "        except TypeError:\n",
-    "            pass\n",
-    "        if i > 10000:\n",
-    "            break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-0.998 dimensionless\n"
-     ]
-    }
-   ],
-   "source": [
-    "with units_off():\n",
-    "    print(2 * UNITS.farad - 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1999.0 dimensionless\n"
-     ]
-    }
-   ],
-   "source": [
-    "with units_off():\n",
-    "    print(2 * UNITS.volt - 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1999.0 dimensionless\n"
-     ]
-    }
-   ],
-   "source": [
-    "with units_off():\n",
-    "    print(2 * UNITS.newton - 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "gram meter/second<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$\\frac{gram \\cdot meter}{second^{2}}$"
-      ],
-      "text/plain": [
-       "<Unit('gram * meter / second ** 2')>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mN = UNITS.gram * UNITS.meter / UNITS.second**2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0 dimensionless\n"
-     ]
-    }
-   ],
-   "source": [
-    "with units_off():\n",
-    "    print(2 * mN - 1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now I'll create a `Params` object to contain the quantities we need.  Using a Params object is convenient for grouping the system parameters in a way that's easy to read (and double-check)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>R1</th>\n",
-       "      <td>1.000000e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C1</th>\n",
-       "      <td>1.000000e-09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>A</th>\n",
-       "      <td>5.000000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>f</th>\n",
-       "      <td>1.000000e+03</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "R1    1.000000e+06\n",
-       "C1    1.000000e-09\n",
-       "A     5.000000e+00\n",
-       "f     1.000000e+03\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = Params(\n",
-    "    R1 = 1e6, # ohm\n",
-    "    C1 = 1e-9, # farad\n",
-    "    A = 5, # volt\n",
-    "    f = 1000, # Hz \n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can pass the `Params` object `make_system` which computes some additional parameters and defines `init`.\n",
-    "\n",
-    "`make_system` uses the given radius to compute `area` and the given `v_term` to compute the drag coefficient `C_d`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    unpack(params)\n",
-    "    \n",
-    "    init = State(V_out = 0)\n",
-    "    omega = 2 * np.pi * f\n",
-    "    tau = R1 * C1\n",
-    "    cutoff = 1 / R1 / C1\n",
-    "    t_end = 3 / f\n",
-    "    \n",
-    "    return System(params, init=init, t_end=t_end,\n",
-    "                  omega=omega, cutoff=cutoff)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>R1</th>\n",
-       "      <td>1e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C1</th>\n",
-       "      <td>1e-09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>A</th>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>f</th>\n",
-       "      <td>1000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>V_out    0\n",
-       "dtype: int64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>0.003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>omega</th>\n",
-       "      <td>6283.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cutoff</th>\n",
-       "      <td>1000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "R1                          1e+06\n",
-       "C1                          1e-09\n",
-       "A                               5\n",
-       "f                            1000\n",
-       "init      V_out    0\n",
-       "dtype: int64\n",
-       "t_end                       0.003\n",
-       "omega                     6283.19\n",
-       "cutoff                       1000\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the slope function,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    V_out, = state\n",
-    "    unpack(system)\n",
-    "    \n",
-    "    V_in = A * np.cos(omega * t)\n",
-    "    \n",
-    "    V_R1 = V_in - V_out\n",
-    "    \n",
-    "    I_R1 = V_R1 / R1    \n",
-    "    I_C1 = I_R1\n",
-    "\n",
-    "    dV_out = I_C1 / C1\n",
-    "    \n",
-    "    return dV_out"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, let's test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5000.0"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The solver successfully reached the end of the integration interval.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>sol</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_events</th>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nfev</th>\n",
-       "      <td>86</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>njev</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nlu</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>status</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "sol                                                      None\n",
-       "t_events                                                   []\n",
-       "nfev                                                       86\n",
-       "njev                                                        0\n",
-       "nlu                                                         0\n",
-       "status                                                      0\n",
-       "message     The solver successfully reached the end of the...\n",
-       "success                                                  True\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ts = linspace(0, system.t_end, 301)\n",
-    "results, details = run_ode_solver(system, slope_func, t_eval=ts)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the plot of position as a function of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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NixP6RyD3Qanqo8CjJS9PAnvPMQZVTQH3nOVzjwA/Vb2ZVhdP/GmDP4sfmIA6X7LZLGOTxSKumwb9VCK6mF1wylGdmlxko0/CsNFwCws/XOR5hvs7iUYiZLJZpuYSJFZSVSl3ZXgJnAXVrKQzWa/7aEP1g7t5SgWUZSGVh9t91N7WQl/3+bdAORcjg24N3ZJZyuXUhL9JRnlaW6IM9Rc36I6ZoucLJqACwtRsglQu1tDd0Xpe7d3LpberjY64o+2tJNNMWRZSWZxyuY82Dnb45j4CrwvJrNzySKbSjM8UMyz9csHmGRm0OJTfmIAKCO4fuN8PViQS8Wjop8yHXhbuezTio3sPYLi/w1P1I9+51zg7Y1NLBW/AYE+cNh8yLN1YHMp/TEAFhNNTxR/48ID/8YZNQ27tz1xI5VBLJSIWi3p+B6ahnxu3K3STj+69wneYq9x3TEAFhLEpd/C9BgLKEiXWRTqdYXy6eI9qkbTguUcWhzontUqQyNPfHS9s2E2spJiZtx5r1cYEVABIpzOMzxQXv+GB6hWIPRsbBzsLMZSJmQTJlO21WYvxmQTpjKMhu2N4fuIWUCdNiTgnbi9ELZS80jiXeSKqjwmoADAxkyCTW/z6uuM1SVeNt8YKTdwy2Synp20BXIsxj3vPf/cReLPQxsyFtCaLiWShC0BLLMpAT21aYFiihL+YgAoAYy7Nb2MNrKc87lYebveVcSZuAb5psDb3qKeztaCsLCfThX1RxpmcdrnIN/R3VLULwFq4vR32DFUfE1ABYKzGCRLF7yo+XO4H3DiT0gWwFkQiEe89sgXwrLj/barZQ+1cuJ/X09NLZuVWGRNQAaDWCRJ5NpgFVRbpdKbQ5RZqJ6DAu9iaEnF2vFmwtbs/Xe0thXhkMpVhet72FFYTE1B1JpnKMOnaXFhL7c+90E7MJqwo6VmYnF0uxAh7u9pqWtLGa0FZjONsuC2ojTX0Qpxh5ZoSUVVMQNWZiZklMtl8gkSb75sL3bS3tRSKkmYyWSZnTftbDbdgqKULFpyab4V5TJkLaTUSy6lCfC4WjTDQW5sEiTyee2SeiKpiAqrOuCtVu3/otcLjQjINfVXcWnEtLVzwKi1LyykWlqx/VyluobDBVYGjVpgF5R8moOqM9+GqrebnfKfFoc5FvQLwcKbb1zT0M/EkGdX4/pR+57glSlQVE1B1ZmK69tlhbjZYEH5NMpms5x7VMgC/2nfaPToTj4VbYxcsOHHJfGflxEqKuUWzcquFCag6ks1mPRUkNvTVY/ErPtDjM6b9lTIzv0wylzzS1d5KZ7t/VebPhjeTz9ywpYzX0cKF1RIl7B5VCxNQdWRmfqVQYqgj3kJne+0bnlma7NqUxjfqgVuJGDMLykMylWYmlyARjUQY7Ku9mxwsUcIvAtkCUkRuBj4GxIFngA+q6mw5Y0QkBtwH3IRzffeq6gMl790FfB+4UVW/5/f1nA2P9dTvb3+hs5GPcRw5NefMaXqpZmViwsDpOrv3wClK2hKLkkpnWEgkWUwk62LJBZGJmUTB6u/vcf6d6oE7fjxhAqpqBM6CEpFhYB9wq6oKcAj4+DrG3AXsAS4HrgHuEZFrXe9tBz4D+NcOtUzGA6CdAwy5vnvKUs09TNTZBQsQjUYYdKVOuzM/mx33v8VQne4PnLmn0KgOgRNQwI3AAVU9mDu+H7hdRCJljrkF2KeqKVWdAh4C7nC995PAp4FxH6+hLDwJEnVyTQAM9bkXP9P+3Ey6F8A6ZFkWvtt1jyZNQBVwK3lDdXyG+nvaC/X/ZhdWWE6m6zaXRiKIAmo7cNR1PAr0Aj1ljlnt3DYAEfkloFVVP1X9aa+fIMQ3ANPOz0JiOeWpkN3XFa/bXNzW28SsKRF5vPsI6/cMxaIRj2vclIjqEEQBFQVWSyVLlzmm9FwESIvIG4C7c//VHffiV/rjrjWDve2F+NfMwgopK3kEeF01A73xmlXIXo3BPlMiSslmsx6Lv54WFHi9IOPmiagKQRRQR4AtruOtwJSqLpQ5pvTcFhwr6n04Vta3ROSp3OufFZH3VP8Szo178Rvsa6/r4udYB05ILpvNMmk+dMDr7hzqrZ92Dme6+PK1AZuZucVkwZUWb4vR1VHfxBF3DMyUiOoQRAH1GHCdiOzOHd8N7F/HmP3AnSLSIiL9wG3AI6p6j6ruUdW9qroXOA7crqpf8PVqzkKQFj+wGMdquBeZelT5cNPp2oOVTGesNxRnJrDUIwvWjfcZMguqGgROQKnqGPAB4GEReQG4AviQiFyds3zOOib3EfcDrwBPAweAB1X18RpfxjlxC4F67d1w49H+zIICgpMhVpyDJbO48SgQgbs/Cdv0XgUCuQ9KVR8FHi15eRLYe44xqGoKuKeM79hZ2Swrw+1Gq7fvHEoTJWzxC1p8Iz+Ho7n9ahOzCS6q83zqjef+1NnCBejqaCXeFmN5Jc1yMs38UpKezrrvZgk1gbOgmoFsNuuxUoZq3B5gNQbNxedhdsFb5SNfbaOeuF3BthkUxqeDZeFGIhHPPbLiy5VjAqoOLCRSLK84wd221voHd8GpVpBvUzC/lCSxkqrzjOqL18Ktf3wDvFZCswfh0+liWa5IxLuRuZ5ssHtUVUxA1QF3ANWd4l1PoiWN3po9k88bfwrG4le6HSBv4TUj0/PLhRhPT2crrS3BWMosk6+6BOOuNhlBiz/lcbsam93NF5QKBW5aYlH6um07AMDUXLEkV5BqR3oy+Zr4/lQLE1B1wP3DDYprAmwzqJugZfDl8VSUaOJklmm3gOqtX4WPUvp7inOZnrP9apViAqoOuBe/IAkoc084pAIa34AzU5mbFbeSFyQLqr2the5cTDmdyTKzYMWXK8EEVI0pdc0ExX0EXmE5Odu8+zjc197X1RaY+AaYEpFnas4toIJjQQGeWK51B6iM4Dx5TcLcYrIQ3G5vC0b6ch53sDmxkmIx0ZyZfJMBTJDIU7pZtxmViGw2y/Ss28UXrHs02GNxqGphAqrGlFpPQcjgyxOJRDwaerM+XEGNPwH0uiy6peUUS8vNp0TMLyVJpoOp5IE3JjbVpM9QtTABVWMmAxp/ymPldLztLIJQhspNaUysGZUIb5JRsNx7cKar3Dh/TEDVGPeiH7TFD0rdE83pP3fHDYKoRAw2eYwjyO49KLk/c8uWyVcBJqBqzGTAShyV0t/k7olkKs3colMpPBqJ0NcdPA292TdUBzlBAqDdVRorlc4Ufk/G+jEBVUMymWxg90DlKdX+mi0I77ZI+nuK5Z+CRLO7kIK6SddNs9+jamECqobMLCyTzpn7Xe2ttAcsuAvQ3eHN5Gu2IPxkwLVz8M6rGRc/9zX3B/QeNbsbtlqYgKohQesBtRqRiLf9vFtbbQamPG3eg3mPervaaIk1ZyZfwnW9LbEovV3BbGdhFlR1MAFVQ9yLfRDde3ncGnqzxaEmA54gAXklojnvkfsZ6u+JB2qbhhtPqvlc89yfamMCqoZ4tPOAuiaguXfCTwW0hE4pzaqhexMkwnN/mi2WWy1MQNUQT3A3oNo5lFhQTaT9pdMZZhacjKtIJBKoIqSlNKsSMRXQIrGldMRbaG9zYszJVIaFpWSdZxROghelB0TkZuBjQBx4Bvigqs6WM0ZEYsB9wE0413evqj6Qe881wB8DXUAM+ANV/UwtrimbzQY+PTZPs2rnpT2G8nGeIOK5R02kRITFC+FsqI5zfNyJl03MJui29u/rJnBPoIgMA/uAW1VVgEPAx9cx5i5gD3A5cA1wj4hcKyIR4B+AD6vqXuBdwB+JyO4aXBYLS8GtwVdKb3ecaKTYXTeZStd5RrUh6FsA3DRrOZ0wpJjn8Vq5zXOPqkngBBRwI3BAVQ/mju8Hbs8JmHLG3ALsU9WUqk4BDwF34FhaH1HVLwOo6ihwGtjm+xVR2h4guMFdgFg04knfbRYX0lTAKxS46esq7tGaX0qynGx8JSKVzjDrcsEGNcU8j1VlqZx1qfE5a+OXgLfiLOxp4CjwJeCzqvpKFea0PfeZeUaBXqAHmC1jzGrnrlTVBPCg61p+OTf+O1WY8zkJi+88z0BPvCBUJ+cSbBzsrPOM/Mftgh0MuHYejUbo744zkbtHU7MJRoa66jwrf5kJkQsWvFtJzII6P8oSUCIyCPwJ8DbgC8BfACdw4jibgeuAr4vIl4APqepEBXOKAqulvKTLHFN6LlLyXkTkN4FfA25S1ZpURA1Ldliegd52ODYDNI8F5d6nFgYlYrCvvSCgJptAQIXJBQslJanmnEy+IHtOgki5FtR+4I+AX1TVzCrnHxSRFuDncQTYmyuY0xHgTa7jrcCUqi6UM0ZEjgBbXOe24FhRiEgc+DRwGXC9qr5awTzXRVgy+PI0WyZfJpMtdNGFkNyjJsvkC1P8CaCrvYW21hgryTTLK2mWllN0trfWe1qholwb+SdV9fNnEU4A5GI+fwP8eIVzegy4zpW8cDeOgCx3zH7gThFpEZF+4Dbgkdy5z+C4Am+opXCC0ocr+Nr5QJM1XXOXoeruaCXeGqvzjM5NszXG88YIg/8MnbGhusmqslSDsgSUqiYBROQ3VjsvIv+1dOz5oqpjwAeAh0XkBeAK4EMicrWIPLXWmNxH3A+8AjwNHAAeVNXHReR64OeAi4FvishTuf/eWcl8y8HpTuv8s8SiEXpCkG7qXgBm51dIp8+qmzQE0yGzcKH5qhVMh2STrptmr5tYKed08YnIRpwYE8BHRERx4jp5+nDiOf+5WpNS1UeBR0tengT2nmMMqpoC7lnl9W/jnXfNKM0OiwawQnYprS0xejrbmFtcIZPNMrOwEgq///ni7qIbBgsXoD+3HSCTzTK7sEIylaa1JfiW3/ng7CMMlxcCLNW8UsqJQc0CvwNsBNqBPy05vwz8fpXn1VCEZYNuKQO98UIvm8nZREMLKLd2HpbrjMWi9HXHC7+vqdnlhs22nFtMkspZ8R3xlkB2AliNZi68XA3OeZdz6dlvAhCR/62qP+v7rBqMsAV38wz0tHPk5BzgdYE1IpMh2gPlZrC3KKAaeTtA2LJg8zTrhupqUW6aeW/uz/e7/vZQWorIKOJt4RAeC6pZSh6FqQxVKf097UDjbwfw3J8QPUO9XXGi0QiZTJb5pSQryTRtIUjACQrl2snTnLm3yE0WZ0+UsQrhtaCaQ/ubLylDFaZUYPdiPd3AiRKeVjUheoZiuQ3VeQVveq5x3bB+UK6A2uXrLBqYsJVncTOwSvv3RtxoGLYNoG6apZyO2zrsD5EFBc1ZlaValCWgVPW1/N+5dO3345QUOgV8RlW/4svsGoDpuXCVZ3HTEXeK2i4tp0ilM8wtJgPbwbQSwuqCBe98Z+advVyxEGSJrpew9IFajWasylIt1rVaisitOJtkM8DjwArwiIj8gg9zawimQpgd5saThdSgbj5PF92QLX6tLTG6OxyXZCabZXa+8RZAd1v71liUns7wuGDB6ypvZDesH6w3V/N3gZ9W1a/mXxCRzwF/DvxtNSfWKIQ1/pRnoDfO8fF5wBG2F2xeNUcm1ITZggJHQ5/PNcSbnE2EKguxHNxKXpDbvJ+NgSZxw/rBev1NO4Gvlbz2NZxaeMYqhH3xa/QYRzab9TT8C6OVO9jge23C1AZlNVZzwxrlsV4BpTj9ltz8LPBSdabTeITdgupv8CyxpeUUyytOsfvWlihdHeFyH4H3HjWiGzasWwDyNIMb1i/W6+L7beAfReRO4DUci+qtwE9XeV4NQSaT9dZ4C+HD5d0L1XgPVmkGX9jcR+C9R2ZBBZPBBnfD+sW6LKhcN9qrge/n3vsE8HrL4luducWVUJZncdPd0UprLvMwsVIMVjcKnsUvhBYunNkaJZ812iiE3YKCM7dsGOWx3o66b1XVr+MkSxjnYDrk7j3I7d3qjXN6yunrODWboGO4u86zqh5h3gOVpyPeQrwtxvJKmmQqw8JSku4QVMwvh/z2Bsj9FrtDKqAsk++8WG8M6u9E5JCIfFRELvJlRg2Ed/EL54MFjR2E9zaSDOc9cvoONeY9cu8j7OtqIxaifYRuBhrcVe4X673bW4BfwYk9/VBEvikid+UaAxolhD1BIo/XPdFY2t9kSIuQluJWgBqpbqL0UVGzAAAgAElEQVT3/oRTgYDGd8P6xbpcfLmOuv8C/IuIdOBk8P0+8MdAR/WnF27cGVVhK8/ipt9Tk69xtL/SRpJhrpLR36Abqt1u8v6QumChsd2wfrLuqL2IRICfAP5PHAF1EPjDKs8r9JQ2WAtrfAPO1P4ahTA2kjwbjdpa3FOJJcQWbiQSYbCnnRMTC4Bzj0xAnZv1Jkl8Avh5IA18FrhBVV/0Y2JhZ2k5RWIlV56lJVrYBxFG3J1b5xadyt+tLeGMBbgJc323Uho11bwRYoR5BnrjBQE1OZtg+6aeOs8o+KzXghrGKRT7ZVX1zYkqIjcDHwPiwDPAB0v7TZ1tjIjEgPuAm3Cu715VfSD3nt3Ag8AGYB54n18C1uOaCGF5FjexWJTerjam552A9fTcMsMD4ffoToa8yoebns42YtEI6UyWxUSSxEqK9rbwbWtwk8lkvW7yEMegoMQN20BKhJ+UpQaLyJ+ISL+q3qGqXzqbcBKRIRH5ZCUTEpFhYB9wq6oKcAj4+DrG3AXsAS4HrgHuEZFrc+c+CzygqpcBHwYezrksq44ngy/k2jk0ZqKE28UXZhcsQDQa8d6jBogVzi2uFMoCdba3hl7geqzcBokTJlMZnnz+JM++PE7GhxJO5fppvgJ8R0Q+JSLvEJG+/AkRGRCRd4vIXwJPAv9a4ZxuBA6o6sHc8f3A7SWCZK0xtwD7VDWlqlPAQ8AdIrIVuCR3jKr+M9ANXFXhfFdlJtcDCsK7+92Ndx9H+Bc/aIwNoG4aLVY4FfIqLKU0YpzwqZfGePK5kzz+w1FO5tyX1aQsAaWqX8CpIPEi8KfApIgsiMgSMI7jansBuCI3thK2A0ddx6NAL9BT5pjVzm3LvX48l4lYeq7qDPc7LrBoJMKuLeGvAO6tyBz+xS+ZShcaSUZDvAHUTaPthZpqkBTzPHk3LFBww4ad4+NFoeRHlZmybWZVnceJ7dwnIptwFvwMcFRVT1dxTlG87eXzpMscU3oucpbX3eeqzp4dA/R0thFvi4XefQTeGE1DLH6ua+jtDu8GUDcDDVY01mPhNsAzlHfDjk87VVmm55YZGQq323LK50os5/Wvo6qncLrp+sER4E2u463AlKoulDNGRI7gbCjOswXHUjoCbBaRiCuGlj/nC5s3dPn10TXHvUBMzyXIZLKhTsv2+8GqB41nQTVOjDDPQE+8IKAmZxOMDIV3jVhJpgsFcKORCL0+eCGCqDY+BlyXy7gDuBvYv44x+4E7RaQlV+HiNuARVR0FXgbeCyAi78SxAJ/17UoaiHhrjK52J1U+nckyt7hyjncEm8kGKBJbijtbdHahWKg4jJTuI2wEFx80lhLhjkX3dccL7stqEjgBpapjwAdwMuxeAK4APiQiV4vIU2uNyX3E/cArwNPAAeBBVX08d+4XgLtF5Ec4FTD+fUlMyliDgQYqp+PZABryFPM8La526PntAGGldB9hGPt0rYb7GZq2Z+icBNIBqqqPAo+WvDwJ7D3HGFQ1Bdxzls89CPx41SbaZAz0tDM6lm//vsyuOs+nErx7oBrDggLnHuWTP6bmEmzoD+d+tdJOAGHeR+jGk2wUYgUCvBZgv09eiIosKBHZV3JstTsamIEG6a6bTmeYnXdtA2gQ9xE0TkWJRikSW0ojuWGnarDR/bwsKBG5AScxYXfJqX8BfrLSSRnBxJtqHt7Fb3p+mUyumnRvVxutLbE6z6h6NEomn7fEUeNYuHk37OzCCtlslpn5ZYb6wmnl1qJbw/m6+OaAy4BLReQHwCzwPE4JIaNBKa0mkc1mQ+l6aYQuumejUYLwjbaJ2s1gb9ENOzmbCKWAymSyTM/7n8RyXi4+VX1WVT8C/JyqvgF4D/C3wM3VnJwRLLraW2hrdayN5ZV0aNu/T841Xop5ntKKH36Un6kF0w1qQUFjKBEzC8XfVndHa2FdqDaVJkkMiciFqnoI+EY1JmQEF6dza5xTk4uA83B1tocvu6oWvvN60R5voSPewtJyKtcufYW+kFXJKK3y0RfiPl2r4XXDhlNATdcgQQIqTzO/FPiEiDwnIl8Rkf9ejUkZwcVTTyykMY5G6dN1NsKeKDE9V0xgaZQqH268FlRInyHPJmr/FKCKLChV/b383yKSb2JoNDBhbxnQaC0cVmOgJ86x07ntALMJdm4OVy3IRurTtRqlbtgwxnK9WZbBtaAKqOq/4V/5IyMghN2Cml1orBYOqxF2Db3RisSWknfDAqTSmYI7M0y4f1d+KnkVPZ0i8jxwAvg+8CrwDuA/Vz4tI6iE3X001cAJEnnCHuOYbHAXLDjXlbdyp+eWQxUnLK1S4mcSS0UWVK7x30dw6tm1AP+hGpMygktvrv07OA3lkilfisH7xsRM45U4KsW9qE/mtgOEiekGd8FCuHt3LSZSLCed576tNUZXu39eiEotqD/D6V6bBjqBz1djUkZwiUUj9HXHCw/V1NwyGwc66zyr8mnEKualdHW00toSJZnKFLYDhCXb8oz9NQ16j8K86b10j5qf8bNKY1BXqeo7VfXdOMVa76/CnIyAMxjiagWNvAcqj7MdIJyuWHeMsKu9lbhP+2vqTZgrfng3uvtr4VYqoGIi0gGgqt/H6WprNDhhzeTLZrPeh6tBBRSEN5ml0ZoUno2BEMdya3mPKnUe/gHwmIjcD7Szepdbo8EIa3ddd3HODlcmVSMS1gWwEXtArUa3yw2bWEmxmEiGxg3rqWLuc3LHOS0oEfndvJVUiqo+AtwJ7AR24PRbMhocj/soVNp542eH5QmtBdXAVT7clLphw9S7yxPH7au/BfV+4JdF5L8An3a1SwcKPZb+mw9zMwKKZ6Ph/HJo2r9PNkGCRB63BRWm5pK1qJAdFAZ64oxNFcuGbRnurvOMzs2yu817NEJfV/1jUALcB/wh8JSIvMPXGRmBp601Rneuw2kmk2VmIRzaXzNk8OXpc20HmF9KhmI7gNPmvTliUBBOJcJj4XbHfVdMz2lBqWoSp97eg8BvAY+IyDeA31DVH1V7QiJyM/AxIA48A3xQVWfLHSciMRyBehPO9d2rqg/k3nMN8MdAFxAD/kBVP1Pta2gGBnrbC5rU9NxyKLTdySZxH4GzHaC3u63gOpqaXWbjYLC3Aywtp1heqc3+miAQxr1QkzV078E6svhUdVZVfwtn39Nx4Psi8ikR2VytyYjIMLAPuFVVBTgEfHyd4+7KzfFy4BrgHhG5VkQiwD8AH1bVvcC7gD8SkdKmi0YZeGMcwbegstlsU7n4oLTqR/AXwNIEibDVp1svbgsqLDGoWmfBrjvNXFWPqeqdwFXACPCSiHy4SvO5ETiQi2uBs6/q9pxwKXfcLcA+VU2p6hTwEHAHjqX1EVX9cu46RoHTwLYqzb2pCFu9t4WlJMmUk8HX3tbYGXx5wrYZtFHbvJ+Nvq62ght2diEcVVk8Sl4NvCbrfkpzGX2X4LTaeAbHUvldnJJH5X7Gu4EvrHLqo8BR1/Eozt6qHpyuvXm2rzFutXNXqmoCeNA1h1/Ojf9OufM2irhdZGHwn094rKfG187Be4+mQ6BEeKvMN76FG4tFvW7YEFRl8cYI/VcizimgROROHGF0We7/O4AI8BrwHPC53P/LRlUfXe27ReS3WX0vValqEV1jXOm5SOn7ReQ3gV8DblLVpfJnbuQprVQQ9JYB3vTlxl/8IHydW91W3lAN4htBYKCnvSCgpgMuoEobSfq9BwrKs6D+X+BHOELo73P/f15VF3yYzxHgTa7jrcDUKt911nEicgTY4jq3BceKQkTiwKdxhO31qvpqVWffRHS2txBvi7G8kmYlmWZhKUl3Z3A7n9baNREESvsOpTNZYgHeDjBVox5DQWKwN87h487fQfdEuONPfd3xmjSSLCeLb4fvsyjyGHCfiOzOxZfuBvavc9x+4E4R+UegG7gtdx7gMzhFbW/wScA2DZFIhKHeDo6POy0DJmYTARdQrk26TaKd57cDzC8lyWSzzC4EN9sysZJiIeFkhcaiEXobrM372QhTxQ9vHcvaxAgD1UtZVceADwAPi8gLwBU4RWgRkatF5KlzjcNJmHgFeBo4ADyoqo+LyPXAzwEXA98Ukady/72zdlfYWLh/pO42FkHDqcHXfC4+KFkAA5woUZodFoaN39XAU00i8BZU7Z+hwKUy5eJTj67y+veAvWWMSwH3rPL6t3HiUUaVGOorVsAKcjmdBVf/mngT7K9xM9AT5+ipOcBxIV24ta/OM1qdWrUQDxqe7RoBr8ri8ULUSEAFyoIywoXbVRZkC6pU8wtyMke18e61CfA9cs2tWRIkIFxVWepRicUElHHelBYkDWrn1snZ2vvOg4K3WkFwF7/JmebaA+WmPwRFY1PpDDO5DL5IJFKzTscmoIzzprO9tbDhNZnOFFJQg0Yz1eArZbD3zO0AQaTZqny48TYADaaAmnb9dvq62mipQQYfmIAyKsTtjglqmuxkkzQpXI2OuLMdAChsBwgaKyUVsntrsL8mSHgrfgT1GapPkpEJKKMi3NpuEONQzViDz00Y2r9PllTIDvJeLT/oD0HR2Hq5yU1AGRUxGPCWAUvLKRIrKQBaW6KFgHQzEfSq2bUuQBo0wuCGrdc2DRNQRkW4U82DKKDcVt1gk2Xw5fH2HQqgBeXeANpEGXx5OttbiLe63LCJVJ1ndCaeFPMabgMwAWVUxECvN5MvkwmW9ufODmum9GU3His3gG5Y95yapQyVm9KsuKDtKUylM8zMOwIqEonUtJeaCSijItrbWgpus3QA93FMzBZrAQ/1dqwxsnHxxAlnlwLnQqp1hewgEmRX+dTsMpncb6a3q43WlljNvtsElFExQU6U8Lj4mtSC6ulspS3nQlpeCZYLqR4VsoPIBperfGImWA0W3Erehho/QyagjIoZDGiqeSaT9QioZnXxOYV93UpEcBbAelTIDiJBrsrifYZq64Vozl+DUVWCGuOYXVghlXa66Ha2t9LZ3nwZfHmGAroA1qNCdhDx7CecCVYs163Q1NoLYQLKqJig+s/dD1azWk95PNmWgbKgmrPKfCluBSpoVVkmpuvnhTABZVTMoKcg6TLpnNVSb8y9VySoFlTpNoBmxh3fGQ+IEpFYLvbpaolF6euqrZVrAsqomLbWWKHBXCabDUy1Ao8F1aQZfHlK44RBcSGNT5uVm2eo323lBkOJmCipwlLrViAmoIyq4HYhBUX781hQ/c29+JVuB5ier78SkVhJFWrwxaIRT1XvZmQogBZUvd3kJqCMqjDs0v7cWnG9SKbSnvYAze4+gtJMsfrfo8kS916z1eArxW3lB+EZgvq7yU1AGVXBo/1N1989MTnrag/QXbv2AEFmyLPXpv73qN6LX9AY7I0TzZXiml1YYSXXBbqeeF2wtXeTB673tYjcDHwMiAPPAB9U1dlyx4lIDLgPuAnn+u5V1QdK3rsL+D5wY66VvFEhG/q9Gw2z2Wxd6965LYQNdXiwgkjQEiXGZ+q7+AWNWCzKQE+8EPeZnE0wMtRVt/mUdgJoegtKRIaBfcCtqirAIeDj6xx3F7AHuBy4BrhHRK51vbcd+AzQ5uOlNB1OCRTn57S0nKp7tQLTzs/E7UIKgovP7tGZDAbIyp1dWCGZcjJyO+ItheaktSRQAgq4ETigqgdzx/cDt4tIqSq+1rhbgH2qmlLVKeAh4A7Xez8JfBoY9+kampJIJOIt11JnH7r74XZbd81MkFxI2WzWa+XaPQJgQ7/bVV7fZ8hrPXXUxSNSFwElIu8WkVTpf8BFwFHX0FGgF+gp+Yjta4xb7dy23Pf+EtCqqp+q6gUZgDdNtp5ZSKWLnyVIOMRiUU/V7Hpuqg6Cdh5ENgTIggqChVuXX4WqPrrad4vIbwOrbdAoVfWia4wrPRcB0iLyBuBu4K3nM2fj3GwISKLE0nKKpeVik8L8Hi3DWWjygmlipn4xjtL6bs3Yp2s1hkoyLesZyw3CHrWgufiOAFtcx1uBKVVdWMe40nNbcKyo9+FYWd8Skadyr39WRN5T3UtoXkoTJeqFLX5nZyggVbPHPe49s3DzdHW0Em/LVZ5Ppgv7xOpBPYvE5gmagHoMuE5EdueO7wb2r3PcfuBOEWkRkX7gNuARVb1HVfeo6l5V3QscB25X1S/4djVNxlBfsWPt9NxyoVBrrTkdAM0vqAQlk88do2z2Kh9unMrz9d8PVdqksF6FfAMloFR1DPgA8LCIvABcAXwIQESuzlk+a47DSZh4BXgaOAA8qKqP1/RCmpTWlhh9rpJH9VoAT08VH+phC757KK34Ua/mhW4lwhIkvLgtyno9QxMziUKTwr7u2jYpdBO4yGQuPvXoKq9/D9hbxrgUcE8Z37OzookaqzI80FEoo3N6apFNg501n8Pp6cXC37b4eenpdFxIyytpllecZoF9NW4SmFhJFZsURuunnQcV92/29NTiGiP9w/29w/21f4bzBMqCMsLP8EDxxzw2VXv3RDKVZma+2KHVBJSXSCTiWXDq4ULyBN9725u2SeHZcN+f03Vy8bm/d3igfs+Q/TKMqjJcZ+1vfDpRcFsN9MStxNEquBeceiyAHhdsHRe/oDLYV6xLOLuwQmK59pveg+Imt6fXqCruBWdiNlHz3lBu954tfqvjVSLqa0HV030UVGJRr+U/VmNFL53x7iM0AWU0DO1tLYWYRiZT+0QJ94Jr7r3V8QioelhQAXEfBZl63qOp2QTpXL+w3q422uu4idoElFF1Ng7UT/vzLn6mna9GX3e8UDdxMZFkoYZ7bZKpdKGhZSQSsW0AZ6GesdwgKXkmoIyqU68gbzqd8fQYqvfDFVSiUW/dxFomSkzMeGOE9UpfDjobXQKq1rFcrwvWBJTRYAzXyYJy793o7Woj3mqL39moV6JEUILvQWewN163RAlvHLe+XggTUEbV8SRKzNQuUcLce+XjsXJrqETYHrXyiMWink3VtVIistlsoDZRm4Ayqk57W0uhQGstEyXGPJsLbfFbiw11CsKPBSi+EXQ8Vm6N4lDTc8ueKvNd7fWt5WACyvAFdwWJU5O10dDd31OPChZhonSvzWLC/0SJZCpdUFYikYjdo3PgjkOdmiytl+0P7mdoZLCz7oWWTUAZvjAyWGzjcHLC/4fLFr/1EYtGPG7QWigRp6eWPAkSbRYjXJORoeL9OTmxWJO6iSfdSl4d283nMQFl+MIm18N1ogYCyha/9VNrK/ekWbjrYqCnvfA7Xkgka9J645TrWQ3CPTIBZfjCcH9HTV1Itvitn1oLKHPBro9oNOJx8/ntiUimMh4vxMYA3CMTUIYvxGLRmrqQxmzxWzelAspvF5JXO6+/+ygMlLr5/OT09GJhm8ZATzwQ2zRMQBm+4X24/NX+3Bl8tviVR29XGx25MjYryWKFBz+YXyq6qFpjUasgUSYjQ7WL5Z6aCJ6SZwLK8A3vw+Wf9reYSBb6C7XEogza4lcWkUiEEbcV5eM9cltPwwOdRKP1zQ4LC+77c3p6ydcu1UF0wZqAMnzD/XCNTS6SyfjjQnI/WO7Yl3Fu3JlafqYyexa/oWAsfmGgPd5Cf0+x+LKfZam8AioYXggTUIZvdHe20d3RCkAynfFtw67bOrPFb33UKlEiiNp5WHAren65+RYTSeYWi16IoLhgA9fyXURuBj4GxIFngA+q6my540QkBtwH3IRzffeq6gO59wwCfwZcBnQAv6+qf+3/VTUvI0NdvDw6DcDx8Xlf2iucGC8+tCMB0fzCQmlZqmQqXfUCrulM1pPEMmICal2MDHXx4mtTgPNb37un+t9x/HTxGdoYIBdsoCwoERkG9gG3qqoAh4CPr3PcXcAe4HLgGuAeEbk2d+7TwKiqXgW8HfhTEdnm3xUZW4e7C38fPz1f9c9PpTMe19SWYRNQ66G9raVQ8y2TzXqEfbU4PbVIMhc76e1qo7uzrerf0chs3lD8TR87veBLtuUx17MZpGcoUAIKuBE4oKoHc8f3A7eLSKk4X2vcLcA+VU2p6hTwEHBHznp6B/ARAFUdBd4ETPp6RU3O1o1FAeXHwzU2uVhortbfE6ezvbWqn98MbB32LoDVxrP4beheY6SxGoO97YVsy8RKyhdXuVt5dCuV9aYuLj4ReTfwhVVOfRQ46joeBXqBHsDt5tu+xrjVzl0JXAycAP6TiLwLxzV4r6q+VNHFGGsykBMai4kkiZUU49OJqrr5jrs0flv8zo8tw9088/I44I+V63YfBWnxCwuRSIStw90FV/mxsfmqFtpdWk4xMesIvWgk4tkeUm/qYkGp6qOq2lL6H5ACVlOx0yXH0TXGlZ6L5F5vBXYBs6r6ZuA24BMi8sYKL8dYA+fhcmvoc1X9/OMBdU2EiS0uF9KpqcVCNetqkMlkPaWu7B6dH25PxGiVlQj3M7RxsDNQTSSD5uI7AmxxHW8FplS11O+w1rjSc1twrKjjueN9AKr6MvAEcC2Gr7i15mq6kM5Y/MyCOi8621sZ7HWytjKZbFUzxcanl1hJOvpld0droQ2LsT62uWO54/NV3bLh9UIES4EImoB6DLhORHbnju8G9q9z3H7gThFpEZF+HEvpEVU9DPwA+EUAEdkE3AB8z5crMQq4tb/jp6v3cJ2eXipo+z2dbbb4VcAW1wJYzUSJYyWxjXq3bwgr7vjq8kqa8Znq7YcKavwJAiagVHUM+ADwsIi8AFwBfAhARK4WkafONQ4nYeIV4GngAPCgqj6eO3cL8E4ReQ74GvBRVT1Qi2trZvq743TlH65kumqbDT3uvYBpfmHD64atngvJ64IN1uIXJvJxqDzHxqpzjxIrKcZdBWI3B+w5Ctw+KFV9FHh0lde/B+wtY1wKuOcsn30E+KmqTdYoi0gkwtaN3bx0xNnLcXRsriqVkkfHbPGrFu7F7+TEAql0hpZYZfprJpPluMWfqsa2jd0cPOo8Q6Nj81wlGyv+zBPjxcza4f6OwLWpCZQFZTQuO0Z6Cn+/evyMfdfrJpXOeDT9bRtNQFVCZ3srAz1OHCqdyVbFijo5ucDySjH+1N8dr/gzm5ltni0b81VJZnn1RPFZDOIzZALKqAk7NvUU4g8nJxdZWk5V9HnHxuYLhTMHetrps8WvYi7YXFQiXjtRuRLh/owLNvda/KlC+rrjhWSWVDrD6FhlGbHZbNZzj3Zu7q3o8/zABJRREzrbWwslbrLZLK+drGwBdL/fvbAa588FI8UF6tUTsxVvqn71RHEBDeLiF0Z2bSn+Ox4+PlPRZ41PJwotUOJtMU/3gaBgAsqoGTtdD1elbr4jJ4uLn3thNc6fLRu6CjGI2YWVivpDzS2uMJHLNItFI4F0H4WRnZv7Cn+/emKuIiXCreTt2NQbmPp7bkxAGTXDrUUfOTVH+jx720zPLTM97yyerbGoZfBViVgsyvZNrlhhBW4+t+to63B3oDZ/hplNg52FskeLiWRFFehf9bj3gumFMAFl1IzB3vbCXqWVZNqzQXA9uDW/bRu7iVWYbWYU2emyRiuJQ5XGn4zqEI1GPIre+SoRS8upgnCLRCLsCKgXwp5so2ZEIt6H65Vj5+dDdz+UO2zxqyrueN6J8QUSK+tPZkmmMhx1bQEwF2x1cT9Dh47NnJeb77WTxRij2yoLGiagjJpy0bb+wt+vjE4XKpGXy2Ii6dmkaMH36tLZ3lpoKJjJZjl8bP0a+qsnZjwZlvmOsEZ12DHSU9ijNjmbOK/q5gePTBf+dideBA0TUEZN2bKhq9Bld2k5xeip9aXKHj4+Syan+Y0MddFjvYWqzsUuJeKl3MbQ9fDy0eLit3tH/xojjfOhtSXGri3FZAk9sr57lFhOcdT13Lnvd9AwAWXUlEgkwu4dA4XjF19bXzuul1wP4+4AP1hhZvf2/sKepdGxeRYTybLfm1hJeVywu7fbPfIDuaD4DB08MrWu+pYHj057lLwg7yE0AWXUnEtcD9crx2bKXgCn55YLFQ4ikQgX2eLnC92dbYXMyGw2y4uvlq+hv3RkquC2HR7oKFSnMKrL9k09heKx80vJspMlstkszx2eKBzvCbiFawLKqDlDfR2FTYGZTJYXXi3PinKP2znSU3AVGtXn0l2Dhb+fPzxRViA+m83y/OHiPbps55AvczOcvWWX7izeo2dfGS/rfacmFwvFmltiUfa4vBlBxASUURcuv7C4eD1zcPyce6JS6QzPuzS/S3fZ4ucnF2/rJ97m7F2anl/mSBmxwhMTC97F74JgL35h53UXDhVcsUdPzXF66txdAp4+eLrw9+7t/bS3BTN7L48JKKMu7N7eX2jBsZBI8uJra7uRXnh1slC/r6ezzfbW+ExLLMolFxQ19B/q2Dnf88MXi2P27OgnHrDK2I1Gb1cbF20tJkv8QE+tOX56bpmXR4tbO664eINvc6sWJqCMuhCLRXn9nuHC8YHnT561OnMqneEHrsVv7+5hYgEsy9JovH73MFFXssTx8bNXOD89tcThXBwkEolUpRWEcW7eeMmmwt8Hj06vWVniu8+dKLhqd4z0sHGg8pY3fmMCyqgbl1845An0fv/F1TXAp146zdziCgAd8RYuu3Bw1XFGdentavME0b/59PFVY1HZbJZvPnOscHzhll5LjqgRwwMdXOiyor7+w9FVM/qOnZ7noCv9/5pLR2oyv0oxAWXUjbbWGG96XfFB+cGLY5yc8JY/Oj21xIHnTxaO3/S6EavrVkOuuWykYK2emlxcNRj/4qtTheaR0UiE6y7fXNM5Njs3XLHFc4+edD0v4Ox7+vKTRwrHu7f3B65z7tkIXIRMRG4GPgbEgWeAD6rqGTmUZxsnIjHgPuAmnOu7V1UfyL3nMuB/At1AFvhNVf1X/6/KOBuX7hxEX5vk+PgCmWyWLz5xmPe85UI2DnYyNZvgn755qJi23N/BZZYcUVP6uuPs3bOxYN1+8+njbOjrKHQwPjW5yNefGi2Mv/yiIQZ6zQ8lPloAAAtcSURBVHqqJf09ca65bITv/OgEAN974RQtsShvkI0sJpL807cOFzwQ8bYYb75ySz2nuy4CZUGJyDCwD7hVVQU4BHx8nePuAvYAlwPXAPeIyLW5c/8D+EtV3QvcCfydiAROSDcT0WiEt197QSFjLLGS4u+/epCHvqT87WNa6FfT2hLlxusuCGRLgEbn2ss2MdzfATjddr/wjUN8+9kTPPncSfZ//ZVC7LC/J871V4Rn8Wsk3iAbPZXov/OjE3xq/7P81aMveLL73n7NDrpDVH0lUAIKuBE4oKoHc8f3A7eLSOmqtNa4W4B9qppS1SngIeCO3LgYkM997QHWX8TKqDq9XW285y0XFXoRZbNZxqeXCrvdW2NR3n3DLotr1IlYLMo7r9tZiBem0hm+/+Ipnnz+JCtJp6V7e1sL77p+J60tQVtSmoNoNMK7rt/Jlg3FvlvJVKYQM4xEIvzk1ds9JZLCQF2sBxF5N/CFVU59FDjqOh4FenGEidvNt32NcauduzL3968CXxWRXwc2ArepamW9x42qsGmwk59/2x6+9oNRTyvrkaEu3nrV1lBkHDUy/T1xbvnxi/jnb73K5GzijHM3XbeTob6OOs3OACem+9NvvZAfvnSap146XahEv2mwkzdfuaXglg0TdRFQqvroat8tIr+NExsqJV1yHF1jXOm5CJAWkXbgc8D7VfWLInId8I8ickBVj67yWUaN6e+J8zM/dhHziytMzy/T09lGb1dbYTOiUV8Getp57zuEg0emOD4+TzYLmzd0ITsGrCdXQIjFolx96SbeeMlG5haTtLVGA78Zdy2CNvMjwJtcx1uBKVUt7Wx31nEicgRwO8K34FhRlwOdqvpFAFX9jog8l/scE1ABoruzLVR+8mYiFo1wyc5BLtlpqf5BJhKJFJqDhpmgqT2PAdeJyO7c8d3A/nWO2w/cKSItItIP3AY8ArwM9InIDQAichFwGfBDX67EMAzDqIhACShVHQM+ADwsIi8AVwAfAhCRq0XkqXONw0mYeAV4GjgAPKiqj6vqNE4CxZ+IyLPAw8Avq+orNbtAwzAMo2wi59MuuFkQkZ3A4a985Sts27at3tMxDMNoOEZHR3nb294GsEtVX3WfC5QFZRiGYRh5TEAZhmEYgcQElGEYhhFITEAZhmEYgSRo+6CCRgzg5MmT5xpnGIZhnAeu9fWMNgUmoNZmM8Dtt99e73kYhmE0OptxtggVMAG1NgeAtwAnOLPckmEYhlE5MRzhdKD0hO2DMgzDMAKJJUkYhmEYgcQElGEYhhFITEAZhmEYgcQElGEYhhFITEAZhmEYgcTSzMtERG4GPgbEgWeAD6rqbDljRCQG3AfchPNvfq+qPpB7z27gQWADMA+8T1VfDPk1/RTwVziNJfO8RVXn8JFKrsd1fjvwHeD1qjqee60u98jH66nL/SlnvmuNEZEO4JPAtTidsr8L/KqqLonIMPD/ARcAGZxWOt8K8fVcAXwbp49dnveqqgb4evpwnpNLcIyfv1LVP8i957yeIbOgyiD3498H3KqqAhwCPr6OMXcBe3C6+l4D3CMi1+bOfRZ4QFUvAz6M0+PK9x7nPl/TDTgCa6/rP7+FU6XXg4i8D/g63o7MUId75PP11Pz+lDPfMsb8Do4ydGXuvw7gt3LnPgl8I3eP7gD+XkQ6Q3w9NwB/U3KP/BZOlV7P7wGjqppfE/6DiFyfO3dez5AJqPK4ETigqgdzx/cDt5f8A6815hZgn6qmVHUKeAi4Q0S24mgbDwGo6j8D3cBVvl+RT9eUG3cD8JMi8rSIfENE3ur71VR4PSKyBfgZ4J3uD63jPfLlenLU4/6sOd8yx3wd+K+qmlHVNE437AtEpAX4P4BPAajqU8BBHOs+dNeTG3cDcKmI/EBEnhSRn/X5Ws4113LG/BrwG7nXN+NYWDOVPEMmoMpjO3DUdTwK9AI9ZY5Z7dy23OvHVTWzyjm/8euaACaAB4C9OBrh50XE72uq6HpU9biq/qyqvrTK59bjHvl1PVCf+wOVX9Nj+esRkQuAe4C/x3EbRVX1dMn7gn6PznY9AAvA3+JYIr8IPCAiV/txEeXMtZwxqppV1ZSIfAb4EfA1QKngGbIYVHlEgdVKbqTLHFN6LnKW193n/Mava0JV3dreEyLyLeAdOK4Bv6j0etbzubW4R35dT73uD1TpmkTkjcDngT9X1S/mrMXQ3qPS6wFQ1V9xjX1BRD4H/BTwvUonvQZVuR5VvUNE7gb+Afhd4F9XeU9Z98csqPI4gtePvxWYUtWFMseUntuCo0EcATaXmND5c37jyzWJSL+I/HbJNUWAZFVnfyaVXs9an1uPe+TL9dTx/kAVrklEbgO+BPymqv633JgxICIig673heIerXY9IhITkd8REbflEvhnSETemVMWUNV5HAvwDVTwDJmAKo/HgOtymSgAdwP71zFmP3CniLSISD9wG/CIqo7iZOm8F0BE3omTgfSsb1dS3nzLGbPqNQFzwK8CPwsgIlfhZCn9i18XUsZc1zPGQx3vkS/XQ/3uD1R4Tbnswz8FblTVv8m/QVVTwD8Bv5wbdyVwGY6LyU/8up408B6K13MBcCuOReInlf7mfh74cC4GGs8df7WSZ8iKxZaJiLwbJ7WyDack/PuAC4H/pap7zzZGVSdzQdx7cdwobcBfqOq9uffsxgnubgASOOmxPwj5NV0N/BmO7zoF/Lqq/luQr6fkc7LAsHrTzGt+j3y8nrrcn0qvSUQUGASOuT7ym6r6qyKyCfhfwC4cd9KHVPWxEF/PxThxwk041b4/oqqfC/j19OfmfHnu4z4PfFhVM+f7DJmAMgzDMAKJufgMwzCMQGICyjAMwwgkJqAMwzCMQGICyjAMwwgkJqAMwzCMQGKVJAzDJ0Tkn4G35A7jOOnPK7njb+AU3H0e2KqqMz7P5T3AO1T1P1bp89qArwI/k09fN4xqY2nmhlEDROTTwLSq3lOH7+7BKZFzg6pOVPFzbwVuUdU7zjnYMM4Ds6AMo06IyE7gMDAA9ANPAf8Fpw1DG05bgnTuuB2n8vUncu+9DKcKwRuBU8BH3dUISrgLeCIvnETkazjldX4Op2XK14CPAH8OXAo8Afy8qs6J0/vnD3FK2hwF/lBV/zr3uY8Afy4i4ncrCKM5sRiUYQSHPuBqYAdOCZlP4FSzvhD4APDfRWRARLpxBMy/ABuB/wv4hIi8ZdVPhV8C/q7ktV/BKXe0HXh97vwv4LR7uBh4v4hEcfr4/Iaq9gO/DvxZ7vvzJXm+kPt8w6g6JqAMI1j8nqquAF/BKXHzJ6q6DHwxd7wduBmYVdV7VTWpqgeAv8QRah5EZAQQ4MmSU/tU9XCuLNIPcWpDvpI7/jawK9ceYQ74hZzw+xowmCsEmuc7QK36SRlNhgkowwgW+RhRvhXBNICrl06UnJUjItP5/4D/m9X762wHErmmkqt9T/67pl3HGYprwztwEjz2595zn4i0usaeOMv3GkbFWAzKMIJFOVlLx4HvqWq+nTZn6YkEXmGzru8Rp2X6VlV9b87d92bgYeD7wGdyw2LUpn+Z0YSYBWUY4eNR4CIReX+u3ckuHPfbXauMPQq0icjQeXxPC/CFXM+ibO6zsoC7WvpmvB1WDaNqmIAyjJCRixPdBLwfOA18C6e1we+tMnYMp/329aXnyvieWZw+RL8FzALfxOn6+qhr2HU4CRuGUXVsH5RhNDgi8v8Al6rqB6r8ua3Aa8CPqerBan62YYBZUIbRDHwS+HERGa7y5/574MsmnAy/MAFlGA2Oqi7g7GH6aLU+M1fq6D8CH6rWZxpGKebiMwzDMAKJWVCGYRhGIDEBZRiGYQQSE1CGYRhGIDEBZRiGYQQSE1CGYRhGIPn/AUoH0lFelEjqAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_results(results):\n",
-    "    xs = results.V_out.index\n",
-    "    ys = results.V_out.values\n",
-    "\n",
-    "    t_end = get_last_label(results)\n",
-    "    if t_end < 10:\n",
-    "        xs *= 1000\n",
-    "        xlabel = 'Time (ms)'\n",
-    "    else:\n",
-    "        xlabel = 'Time (s)'\n",
-    "        \n",
-    "    plot(xs, ys)\n",
-    "    decorate(xlabel=xlabel,\n",
-    "             ylabel='$V_{out}$ (volt)',\n",
-    "             legend=False)\n",
-    "    \n",
-    "plot_results(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And velocity as a function of time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The solver successfully reached the end of the integration interval.\n",
-      "The solver successfully reached the end of the integration interval.\n",
-      "The solver successfully reached the end of the integration interval.\n",
-      "The solver successfully reached the end of the integration interval.\n",
-      "The solver successfully reached the end of the integration interval.\n",
-      "The solver successfully reached the end of the integration interval.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x396 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fs = [1, 10, 100, 1000, 10000, 100000]\n",
-    "for i, f in enumerate(fs):\n",
-    "    system = make_system(Params(params, f=f))\n",
-    "    ts = linspace(0, system.t_end, 301)\n",
-    "    results, details = run_ode_solver(system, slope_func, t_eval=ts)\n",
-    "    subplot(3, 2, i+1)\n",
-    "    plot_results(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/test_vector.ipynb b/soln/test_vector.ipynb
deleted file mode 100644
index 611fef30..00000000
--- a/soln/test_vector.ipynb
+++ /dev/null
@@ -1,211 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "1 meter"
-      ],
-      "text/latex": [
-       "$1 meter$"
-      ],
-      "text/plain": [
-       "<Quantity(1, 'meter')>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = 1 * UNITS.meter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "[0.5547002  0.83205029] dimensionless"
-      ],
-      "text/latex": [
-       "$[0.5547002  0.83205029] dimensionless$"
-      ],
-      "text/plain": [
-       "<Quantity([0.5547002  0.83205029], 'dimensionless')>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v = Vector(2,3).hat()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "[0.5547002  0.83205029] meter"
-      ],
-      "text/latex": [
-       "$[0.5547002  0.83205029] meter$"
-      ],
-      "text/plain": [
-       "<Quantity([0.5547002  0.83205029], 'meter')>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vp = x * v"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "modsim._Vector"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(v)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "modsim._Vector"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(v * 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "modsim._Vector"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(v * x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pint.unit.build_quantity_class.<locals>.Quantity"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(x * v)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/throwingaxe_soln.ipynb b/soln/throwingaxe_soln.ipynb
deleted file mode 100644
index 0354bdbc..00000000
--- a/soln/throwingaxe_soln.ipynb
+++ /dev/null
@@ -1,991 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case study: Throwing Axe\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Throwing axe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our favorite event at Lumberjack Competitions is axe throwing.  The axes used for this event typically weigh 1.5 to 2 kg, with handles roughly 0.7 m long.  They are thrown overhead at a target typically 6 m away and 1.5 m off the ground.  Normally, the axe makes one full rotation in the air to hit the target blade first, with the handle close to vertical.\n",
-    "\n",
-    "![Diagram of throwing axe](diagrams/throwingaxe1.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a version of `make_system` that sets the initial conditions.\n",
-    "\n",
-    "The state variables are x, y, theta, vx, vy, omega, where theta is the orientation (angle) of the axe in radians and omega is the angular velocity in radians per second.\n",
-    "\n",
-    "I chose initial conditions based on videos of axe throwing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "radian = UNITS.radian\n",
-    "\n",
-    "def make_system():\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    returns: System with init, ...\n",
-    "    \"\"\"\n",
-    "    P = Vector(0, 2) * m\n",
-    "    V = Vector(8, 4) * m/s\n",
-    "    theta = 2 * radian\n",
-    "    omega = -7 * radian/s\n",
-    "    \n",
-    "    init = State(P=P, V=V, theta=theta, omega=omega)\n",
-    "\n",
-    "    t_end = 1.0 * s\n",
-    "    \n",
-    "    return System(init=init, t_end=t_end,\n",
-    "                  g = 9.8 * m/s**2,\n",
-    "                  mass = 1.5 * kg,\n",
-    "                  length = 0.7 * m)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>P                          [0.0 meter, 2.0 met...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>1.0 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>1.5 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>length</th>\n",
-       "      <td>0.7 meter</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "init      P                          [0.0 meter, 2.0 met...\n",
-       "t_end                                            1.0 second\n",
-       "g                                   9.8 meter / second ** 2\n",
-       "mass                                           1.5 kilogram\n",
-       "length                                            0.7 meter\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>P</th>\n",
-       "      <td>[0.0 meter, 2.0 meter]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>V</th>\n",
-       "      <td>[8.0 meter / second, 4.0 meter / second]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>theta</th>\n",
-       "      <td>2 radian</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>omega</th>\n",
-       "      <td>-7.0 radian / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "P                          [0.0 meter, 2.0 meter]\n",
-       "V        [8.0 meter / second, 4.0 meter / second]\n",
-       "theta                                    2 radian\n",
-       "omega                        -7.0 radian / second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As a simple starting place, I ignore drag, so `vx` and `omega` are constant, and `ay` is just `-g`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the state variables.\n",
-    "    \n",
-    "    state: State (x, y, x velocity, y velocity)\n",
-    "    t: time\n",
-    "    system: System object with length0, m, k\n",
-    "    \n",
-    "    returns: sequence (vx, vy, ax, ay)\n",
-    "    \"\"\"\n",
-    "    P, V, theta, omega = state\n",
-    "\n",
-    "    A = Vector(0, -system.g)\n",
-    "    alpha = 0 * radian / s**2\n",
-    "\n",
-    "    return V, A, omega, alpha"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, let's test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([8., 4.]) <Unit('meter / second')>,\n",
-       " array([ 0. , -9.8]) <Unit('meter / second ** 2')>,\n",
-       " -7.0 <Unit('radian / second')>,\n",
-       " 0.0 <Unit('radian / second ** 2')>)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                                                 True\n",
-       "message    The solver successfully reached the end of the...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>P</th>\n",
-       "      <th>V</th>\n",
-       "      <th>theta</th>\n",
-       "      <th>omega</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.96</th>\n",
-       "      <td>[7.680000000000005 meter, 1.3241600000000022 m...</td>\n",
-       "      <td>[8.0 meter / second, -5.407999999999994 meter ...</td>\n",
-       "      <td>-4.7200000000000015 radian</td>\n",
-       "      <td>-7.0 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.97</th>\n",
-       "      <td>[7.760000000000005 meter, 1.2695900000000022 m...</td>\n",
-       "      <td>[8.0 meter / second, -5.505999999999994 meter ...</td>\n",
-       "      <td>-4.790000000000002 radian</td>\n",
-       "      <td>-7.0 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.98</th>\n",
-       "      <td>[7.840000000000005 meter, 1.2140400000000022 m...</td>\n",
-       "      <td>[8.0 meter / second, -5.603999999999994 meter ...</td>\n",
-       "      <td>-4.860000000000002 radian</td>\n",
-       "      <td>-7.0 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.99</th>\n",
-       "      <td>[7.920000000000005 meter, 1.1575100000000023 m...</td>\n",
-       "      <td>[8.0 meter / second, -5.701999999999994 meter ...</td>\n",
-       "      <td>-4.930000000000002 radian</td>\n",
-       "      <td>-7.0 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.00</th>\n",
-       "      <td>[8.000000000000005 meter, 1.1000000000000023 m...</td>\n",
-       "      <td>[8.0 meter / second, -5.799999999999994 meter ...</td>\n",
-       "      <td>-5.000000000000003 radian</td>\n",
-       "      <td>-7.0 radian / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                      P  \\\n",
-       "0.96  [7.680000000000005 meter, 1.3241600000000022 m...   \n",
-       "0.97  [7.760000000000005 meter, 1.2695900000000022 m...   \n",
-       "0.98  [7.840000000000005 meter, 1.2140400000000022 m...   \n",
-       "0.99  [7.920000000000005 meter, 1.1575100000000023 m...   \n",
-       "1.00  [8.000000000000005 meter, 1.1000000000000023 m...   \n",
-       "\n",
-       "                                                      V  \\\n",
-       "0.96  [8.0 meter / second, -5.407999999999994 meter ...   \n",
-       "0.97  [8.0 meter / second, -5.505999999999994 meter ...   \n",
-       "0.98  [8.0 meter / second, -5.603999999999994 meter ...   \n",
-       "0.99  [8.0 meter / second, -5.701999999999994 meter ...   \n",
-       "1.00  [8.0 meter / second, -5.799999999999994 meter ...   \n",
-       "\n",
-       "                           theta                 omega  \n",
-       "0.96  -4.7200000000000015 radian  -7.0 radian / second  \n",
-       "0.97   -4.790000000000002 radian  -7.0 radian / second  \n",
-       "0.98   -4.860000000000002 radian  -7.0 radian / second  \n",
-       "0.99   -4.930000000000002 radian  -7.0 radian / second  \n",
-       "1.00   -5.000000000000003 radian  -7.0 radian / second  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing the results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The simplest way to visualize the results is to plot the state variables as a function of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_position(P):\n",
-    "    x = P.extract('x')\n",
-    "    y = P.extract('y')\n",
-    "    plot(x, label='x')\n",
-    "    plot(y, label='y')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "    \n",
-    "plot_position(results.P)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot the velocities the same way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_velocity(V):\n",
-    "    vx = V.extract('x')\n",
-    "    vy = V.extract('y')\n",
-    "    plot(vx, label='vx')\n",
-    "    plot(vy, label='vy')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')\n",
-    "    \n",
-    "plot_velocity(results.V)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(results.theta, label='theta', color='C2')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Angle (radian)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(results.omega, label='omega', color='C2')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Angular velocity (rad/s)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another way to visualize the results is to plot y versus x.  The result is the trajectory through the plane of motion."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_trajectory(P, **options):\n",
-    "    x = P.extract('x')\n",
-    "    y = P.extract('y')\n",
-    "    plot(x, y, **options)\n",
-    "    \n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)')\n",
-    "    \n",
-    "plot_trajectory(results.P, label='trajectory')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Animation\n",
-    "\n",
-    "Animating this system is a little more complicated, if we want to show the shape and orientation of the axe.\n",
-    "\n",
-    "It is useful to construct a frame with $\\hat{r}$ along the handle of the axe and $\\hat{\\theta}$ perpendicular."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we're ready to animate the results.  The following figure shows the frame and the labeled points A, B, C, and D.\n",
-    "\n",
-    "![Diagram of the axe with reference frame](diagrams/throwingaxe2.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_frame(theta):\n",
-    "    rhat = Vector(pol2cart(theta, 1))\n",
-    "    that = rhat.perp()\n",
-    "    return rhat, that"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P, V, theta, omega = results.first_row()\n",
-    "rhat, that = make_frame(theta)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}-0.4161468365471424 & 0.9092974268256817\\end{pmatrix} dimensionless\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}-0.4161468365471424 & 0.9092974268256817\\end{pmatrix}\\ dimensionless$"
-      ],
-      "text/plain": [
-       "array([-0.41614684,  0.90929743]) <Unit('dimensionless')>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rhat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\\[\\begin{pmatrix}-0.9092974268256817 & -0.4161468365471424\\end{pmatrix} dimensionless\\]"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix}-0.9092974268256817 & -0.4161468365471424\\end{pmatrix}\\ dimensionless$"
-      ],
-      "text/plain": [
-       "array([-0.90929743, -0.41614684]) <Unit('dimensionless')>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "that"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.0 dimensionless"
-      ],
-      "text/latex": [
-       "$0.0\\ dimensionless$"
-      ],
-      "text/plain": [
-       "0.0 <Unit('dimensionless')>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.dot(rhat, that)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-0.9547622981669658,\n",
-       " 0.04546487134128409,\n",
-       " -0.48241904971578364,\n",
-       " 0.9755696399943229)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "O = Vector(0, 0)\n",
-    "plot_segment(O, rhat)\n",
-    "plot_segment(O, that)\n",
-    "plt.axis('equal')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xs = results.P.extract('x')\n",
-    "ys = results.P.extract('y')\n",
-    "\n",
-    "l1 = 0.6 * m\n",
-    "l2 = 0.1 * m\n",
-    "\n",
-    "def draw_func(state, t):\n",
-    "    plt.axis('equal')\n",
-    "    set_xlim([0,8])\n",
-    "    set_ylim([0,6])\n",
-    "    \n",
-    "    P, V, theta, omega = state\n",
-    "    rhat, that = make_frame(theta)\n",
-    "    \n",
-    "    # plot the handle\n",
-    "    A = P - l1 * rhat\n",
-    "    B = P + l2 * rhat\n",
-    "    plot_segment(A, B, color='red')\n",
-    "\n",
-    "    # plot the axe head\n",
-    "    C = B + l2 * that\n",
-    "    D = B - l2 * that\n",
-    "    plot_segment(C, D, color='black', linewidth=10)\n",
-    "\n",
-    "    # plot the COG\n",
-    "    x, y = P\n",
-    "    plot(x, y, 'bo')\n",
-    "\n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "During the animation, the parts of the axe seem to slide around relative to each other.  I think that's because the lines and circles get rounded off to the nearest pixel.\n",
-    "\n",
-    "Here's the final state of the axe at the point of impact (assuming the target is 8 m away)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>P</th>\n",
-       "      <td>[0.0 meter, 2.0 meter]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>V</th>\n",
-       "      <td>[8.0 meter / second, 4.0 meter / second]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>theta</th>\n",
-       "      <td>2 radian</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>omega</th>\n",
-       "      <td>-7.0 radian / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "P                          [0.0 meter, 2.0 meter]\n",
-       "V        [8.0 meter / second, 4.0 meter / second]\n",
-       "theta                                    2 radian\n",
-       "omega                        -7.0 radian / second\n",
-       "Name: 0.0, dtype: object"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "state = results.first_row()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "draw_func(state, 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "animate(results, draw_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:**  Find the starting conditions that make the final height of the COG as close as possible to 1.5 m.  Ideally, the final angle should be a little past vertical."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/trees_soln.ipynb b/soln/trees_soln.ipynb
deleted file mode 100644
index 12100a69..00000000
--- a/soln/trees_soln.ipynb
+++ /dev/null
@@ -1,1981 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Copyright 2018 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Modeling tree growth\n",
-    "\n",
-    "This case study is based on \"[Height-Age Curves for Planted Stands of Douglas Fir, with Adjustments for Density](http://www.cfr.washington.edu/research.smc/working_papers/smc_working_paper_1.pdf)\", a working paper by Flewelling, Collier, Gonyea, Marshall, and Turnblom.\n",
-    "\n",
-    "It provides \"site index curves\", which are curves that show the expected height of the tallest tree in a stand of Douglas firs as a function of age, for a stand where the trees are the same age.\n",
-    "\n",
-    "Depending on the quality of the site, the trees might grow more quickly or slowing.  So each curve is identified by a \"site index\" that indicates the quality of the site.\n",
-    "\n",
-    "I'll start with some of the data from their Table 1.  Here's the sequence of ages."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[2, 3, 4, 5, 6, 8, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "years = [2, 3, 4, 5, 6, 8, 10, 15, 20, 25, 30,\n",
-    "         35, 40, 45, 50, 55, 60, 65, 70]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the series of heights for a site with index 45, indicating that height at 30 years is 45 feet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>2.18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>2.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>4.45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>6.74</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>14.86</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>25.39</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>35.60</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>45.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>53.65</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>61.60</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>68.92</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>75.66</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>81.85</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>87.56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>65</th>\n",
-       "      <td>92.80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>70</th>\n",
-       "      <td>97.63</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "2      1.40\n",
-       "3      1.49\n",
-       "4      1.75\n",
-       "5      2.18\n",
-       "6      2.78\n",
-       "8      4.45\n",
-       "10     6.74\n",
-       "15    14.86\n",
-       "20    25.39\n",
-       "25    35.60\n",
-       "30    45.00\n",
-       "35    53.65\n",
-       "40    61.60\n",
-       "45    68.92\n",
-       "50    75.66\n",
-       "55    81.85\n",
-       "60    87.56\n",
-       "65    92.80\n",
-       "70    97.63\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "site45 = TimeSeries([1.4, 1.49, 1.75, 2.18, 2.78, 4.45, 6.74,\n",
-    "                    14.86, 25.39, 35.60, 45.00, 53.65, 61.60,\n",
-    "                    68.92, 75.66, 81.85, 87.56, 92.8, 97.63],\n",
-    "                    index=years)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the series for site index 65."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>2.76</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>3.79</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>6.64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>10.44</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>23.26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>37.65</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>51.66</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>65.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>77.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>89.07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>99.66</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>109.28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>117.96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>125.74</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>65</th>\n",
-       "      <td>132.68</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>70</th>\n",
-       "      <td>138.84</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "2       1.40\n",
-       "3       1.56\n",
-       "4       2.01\n",
-       "5       2.76\n",
-       "6       3.79\n",
-       "8       6.64\n",
-       "10     10.44\n",
-       "15     23.26\n",
-       "20     37.65\n",
-       "25     51.66\n",
-       "30     65.00\n",
-       "35     77.50\n",
-       "40     89.07\n",
-       "45     99.66\n",
-       "50    109.28\n",
-       "55    117.96\n",
-       "60    125.74\n",
-       "65    132.68\n",
-       "70    138.84\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "site65 = TimeSeries([1.4, 1.56, 2.01, 2.76, 3.79, 6.64, 10.44, \n",
-    "                    23.26, 37.65, 51.66, 65.00, 77.50, 89.07, \n",
-    "                    99.66, 109.28, 117.96, 125.74, 132.68, 138.84],\n",
-    "                    index=years)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And for site index 85."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2       1.40\n",
-       "3       1.80\n",
-       "4       2.71\n",
-       "5       4.09\n",
-       "6       5.92\n",
-       "8      10.73\n",
-       "10     16.81\n",
-       "15     34.03\n",
-       "20     51.26\n",
-       "25     68.54\n",
-       "30     85.00\n",
-       "35    100.34\n",
-       "40    114.33\n",
-       "45    126.91\n",
-       "50    138.06\n",
-       "55    147.86\n",
-       "60    156.39\n",
-       "65    163.76\n",
-       "70    170.10\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "site85 = Series([1.4, 1.8, 2.71, 4.09, 5.92, 10.73, 16.81, \n",
-    "                 34.03, 51.26, 68.54, 85, 100.34, 114.33,\n",
-    "                 126.91, 138.06, 147.86, 156.39, 163.76, 170.10],\n",
-    "               index=years)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the curves look like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file figs/trees-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(site85, label='SI 85')\n",
-    "plot(site65, label='SI 65')\n",
-    "plot(site45, label='SI 45')\n",
-    "decorate(xlabel='Time (years)',\n",
-    "         ylabel='Height (feet)')\n",
-    "\n",
-    "savefig('figs/trees-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For my examples I'll work with the SI 65 data; as an exercise, you can run the notebook again with either of the other curves."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = site65;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Model 1\n",
-    "\n",
-    "As a starting place, let's assume that the ability of the tree to gain mass is limited by the area it exposes to sunlight, and that the growth rate (in mass) is proportional to that area.  In that case we can write:\n",
-    "\n",
-    "$ m_{n+1} = m_n + \\alpha A$\n",
-    "\n",
-    "where $m_n$ is the mass of the at time step $n$, $A$ is the area exposed to sunlight, and $\\alpha$ is an unknown growth parameter.\n",
-    "\n",
-    "To get from $m$ to $A$, I'll make the additional assumption that mass is proportional to height raised to an unknown power:\n",
-    "\n",
-    "$ m = \\beta h^D $\n",
-    "\n",
-    "where $h$ is height, $\\beta$ is an unknown constant of proportionality, and $D$ is the dimension that relates height and mass.  \n",
-    "\n",
-    "We'll start by assuming $D=3$, but we'll revisit that assumption.\n",
-    "\n",
-    "Finally, we'll assume that area is proportional to height squared:\n",
-    "\n",
-    "$ A = \\gamma h^2$\n",
-    "\n",
-    "I'll specify height in feet, and choose units for mass and area so that $\\beta=1$ and $\\gamma=1$.\n",
-    "\n",
-    "Putting all that together, we can write a difference equation for height:\n",
-    "\n",
-    "$ h_{n+1}^D = h_n^D + \\alpha h_n^2 $\n",
-    "\n",
-    "Now let's solve it.  Here's a system object with the parameters and initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>alpha</th>\n",
-       "      <td>7.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dim</th>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>h_0</th>\n",
-       "      <td>1.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>70.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "alpha     7.0\n",
-       "dim       3.0\n",
-       "h_0       1.4\n",
-       "t_0       2.0\n",
-       "t_end    70.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alpha = 7\n",
-    "dim = 3\n",
-    "\n",
-    "t_0 = get_first_label(data)\n",
-    "t_end = get_last_label(data)\n",
-    "\n",
-    "h_0 = get_first_value(data)\n",
-    "\n",
-    "system = System(alpha=alpha, dim=dim, \n",
-    "                h_0=h_0, t_0=t_0, t_end=t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's an update function that takes the current height as a parameter and returns the height during the next time step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update(height, t, system):\n",
-    "    \"\"\"Update height based on geometric model.\n",
-    "    \n",
-    "    height: current height in feet\n",
-    "    t: what year it is\n",
-    "    system: system object with model parameters\n",
-    "    \"\"\"\n",
-    "    area = height**2\n",
-    "    mass = height**system.dim\n",
-    "    mass += system.alpha * area\n",
-    "    return mass**(1/system.dim)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test the update function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.5439688299649954"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "update(h_0, t_0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's our usual version of `run_simulation`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate the system using any update function.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that computes the population next year\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.h_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        results[t+1] = update_func(results[t], t, system)\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "66    140.497321\n",
-       "67    142.792941\n",
-       "68    145.089152\n",
-       "69    147.385935\n",
-       "70    149.683273\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = run_simulation(system, update)\n",
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_results(results, data):\n",
-    "    plot(results, ':', label='model', color='gray')\n",
-    "    plot(data, label='data')\n",
-    "    decorate(xlabel='Time (years)',\n",
-    "             ylabel='Height (feet)')\n",
-    "    \n",
-    "plot_results(results, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result converges to a straight line.\n",
-    "\n",
-    "I chose the value of `alpha` to fit the data as well as I could, but it is clear that the data have curvature that's not captured by the model.\n",
-    "\n",
-    "Here are the errors:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2      0.000000\n",
-       "3      0.983969\n",
-       "4      1.942915\n",
-       "5      2.792070\n",
-       "6      3.496882\n",
-       "8      4.387116\n",
-       "10     4.557437\n",
-       "15     2.194275\n",
-       "20    -1.346851\n",
-       "25    -4.304519\n",
-       "30    -6.468590\n",
-       "35    -7.709926\n",
-       "40    -7.962119\n",
-       "45    -7.189989\n",
-       "50    -5.413433\n",
-       "55    -2.669382\n",
-       "60     0.997125\n",
-       "65     5.522310\n",
-       "70    10.843273\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "errors = results - data\n",
-    "errors.dropna()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the mean absolute error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.251693738559428"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def mean_abs_error(results, data):\n",
-    "    return np.mean(np.abs(results-data))\n",
-    "\n",
-    "mean_abs_error(results, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This model might explain why the height of a tree grows roughly linearly:\n",
-    "\n",
-    "1. If area is proportional to $h^2$ and mass is proportional to $h^3$, and\n",
-    "\n",
-    "2. Change in mass is proportional to area, and\n",
-    "\n",
-    "3. Height grows linearly, then\n",
-    "\n",
-    "4. Area grows in proportion to $h^2$, and\n",
-    "\n",
-    "5. Mass grows in proportion to $h^3$.\n",
-    "\n",
-    "If the goal is to explain (approximate) linear growth, we might stop there.  But this model does not fit the data particularly well, and it implies that trees could keep growing forever.\n",
-    "\n",
-    "So we might want to do better."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Model 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As a second attempt, let's suppose that we don't know $D$.  In fact, we don't, because trees are not like simple solids; they are more like fractals, which have [fractal dimension](https://en.wikipedia.org/wiki/Fractal_dimension).\n",
-    "\n",
-    "I would expect the fractal dimension of a tree to be between 2 and 3, so I'll guess 2.5."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(7, 2.8)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alpha = 7\n",
-    "dim = 2.8\n",
-    "\n",
-    "params = alpha, dim"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll wrap the code from the previous section is a function that takes the parameters as inputs and makes a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params, data):\n",
-    "    \"\"\"Makes a System object.\n",
-    "    \n",
-    "    params: sequence of alpha, dim\n",
-    "    data: Series\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    alpha, dim = params\n",
-    "    \n",
-    "    t_0 = get_first_label(data)\n",
-    "    t_end = get_last_label(data)\n",
-    "\n",
-    "    h_0 = get_first_value(data)\n",
-    "\n",
-    "    return System(alpha=alpha, dim=dim, \n",
-    "                  h_0=h_0, t_0=t_0, t_end=t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we use it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>alpha</th>\n",
-       "      <td>7.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dim</th>\n",
-       "      <td>2.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>h_0</th>\n",
-       "      <td>1.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>70.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "alpha     7.0\n",
-       "dim       2.8\n",
-       "h_0       1.4\n",
-       "t_0       2.0\n",
-       "t_end    70.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With different values for the parameters, we get curves with different behavior.  Here are a few that I chose by hand."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_and_plot(alpha, dim, data):\n",
-    "    params = alpha, dim\n",
-    "    system = make_system(params, data)\n",
-    "    results = run_simulation(system, update)\n",
-    "    plot(results, ':', color='gray')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "run_and_plot(0.145, 2, data)\n",
-    "run_and_plot(0.58, 2.4, data)\n",
-    "run_and_plot(2.8, 2.8, data)\n",
-    "run_and_plot(6.6, 3, data)\n",
-    "run_and_plot(15.5, 3.2, data)\n",
-    "run_and_plot(38, 3.4, data)\n",
-    "\n",
-    "plot(data, label='data')\n",
-    "decorate(xlabel='Time (years)',\n",
-    "             ylabel='Height (feet)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To find the parameters that best fit the data, I'll use `leastsq`.\n",
-    "\n",
-    "We need an error function that takes parameters and returns errors:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func(params, data, update_func):\n",
-    "    \"\"\"Runs the model and returns errors.\n",
-    "    \n",
-    "    params: sequence of alpha, dim\n",
-    "    data: Series\n",
-    "    update_func: function object\n",
-    "    \n",
-    "    returns: Series of errors\n",
-    "    \"\"\"\n",
-    "    print(params)\n",
-    "    system = make_system(params, data)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    return (results - data).dropna()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we use it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(7, 2.8)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "2       0.000000\n",
-       "3       1.148852\n",
-       "4       2.494726\n",
-       "5       3.987474\n",
-       "6       5.596116\n",
-       "8       9.023410\n",
-       "10     12.588184\n",
-       "15     21.692189\n",
-       "20     32.897280\n",
-       "25     47.207627\n",
-       "30     64.370278\n",
-       "35     84.203833\n",
-       "40    106.551950\n",
-       "45    131.281973\n",
-       "50    158.242758\n",
-       "55    187.291743\n",
-       "60    218.296906\n",
-       "65    251.121491\n",
-       "70    285.640417\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "errors = error_func(params, data, update)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can pass `error_func` to `leastsq`, which finds the parameters that minimize the squares of the errors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[7.  2.8]\n",
-      "[7.  2.8]\n",
-      "[7.  2.8]\n",
-      "[7.0000001 2.8      ]\n",
-      "[7.         2.80000004]\n",
-      "[6.92221276 2.90250933]\n",
-      "[6.92221286 2.90250933]\n",
-      "[6.92221276 2.90250937]\n",
-      "[9.09216731 3.04344997]\n",
-      "[9.09216745 3.04344997]\n",
-      "[9.09216731 3.04345002]\n",
-      "[11.32004198  3.11949616]\n",
-      "[11.32004214  3.11949616]\n",
-      "[11.32004198  3.11949621]\n",
-      "[11.45136365  3.11757872]\n",
-      "[11.45136382  3.11757872]\n",
-      "[11.45136365  3.11757877]\n",
-      "[11.46621698  3.11792121]\n",
-      "[11.46621715  3.11792121]\n",
-      "[11.46621698  3.11792126]\n",
-      "[11.46393831  3.11787315]\n",
-      "[11.46393848  3.11787315]\n",
-      "[11.46393831  3.1178732 ]\n",
-      "[11.46425733  3.11787985]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>fvec</th>\n",
-       "      <td>[0.0, 1.259430004271867, 2.5891995571210504, 3...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nfev</th>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fjac</th>\n",
-       "      <td>[[-1211.616390427339, -0.0007452893651874872, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ipvt</th>\n",
-       "      <td>[2, 1]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>qtf</th>\n",
-       "      <td>[4.18653877720665e-05, 0.00060508849225549]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cov_x</th>\n",
-       "      <td>[[0.2779654957988092, 0.005806517068509026], [...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mesg</th>\n",
-       "      <td>Both actual and predicted relative reductions ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ier</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "fvec     [0.0, 1.259430004271867, 2.5891995571210504, 3...\n",
-       "nfev                                                    22\n",
-       "fjac     [[-1211.616390427339, -0.0007452893651874872, ...\n",
-       "ipvt                                                [2, 1]\n",
-       "qtf            [4.18653877720665e-05, 0.00060508849225549]\n",
-       "cov_x    [[0.2779654957988092, 0.005806517068509026], [...\n",
-       "mesg     Both actual and predicted relative reductions ...\n",
-       "ier                                                      1\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "best_params, details = leastsq(error_func, params, data, update)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using the best parameters we found, we can run the model and plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "system = make_system(best_params, data)\n",
-    "results = run_simulation(system, update)\n",
-    "\n",
-    "plot_results(results, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The mean absolute error is better than for Model 1, but that doesn't mean much.  The model still doesn't fit the data well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.618490489889673"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mean_abs_error(results, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the estimated fractal dimension is 3.11, which doesn't seem likely.\n",
-    "\n",
-    "Let's try one more thing."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Model 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Models 1 and 2 imply that trees can grow forever, but we know that's not true.  As trees get taller, it gets harder for them to move water and nutrients against the force of gravity, and their growth slows.\n",
-    "\n",
-    "We can model this effect by adding a term to the model similar to what we saw in the logistic model of population growth.  Instead of assuming:\n",
-    "\n",
-    "$ m_{n+1} = m_n + \\alpha A $ \n",
-    "\n",
-    "Let's assume\n",
-    "\n",
-    "$ m_{n+1} = m_n + \\alpha A (1 - h / K) $\n",
-    "\n",
-    "where $K$ is similar to the carrying capacity of the logistic model.  As $h$ approaches $K$, the factor $(1 - h/K)$ goes to 0, causing growth to level off.\n",
-    "\n",
-    "Here's what the implementation of this model looks like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[2.0, 2.5, 150]"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alpha = 2.0\n",
-    "dim = 2.5\n",
-    "K = 150\n",
-    "\n",
-    "params = [alpha, dim, K]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an updated version of `make_system`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params, data):\n",
-    "    \"\"\"Makes a System object.\n",
-    "    \n",
-    "    params: sequence of alpha, dim, K\n",
-    "    data: Series\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    alpha, dim, K = params\n",
-    "    \n",
-    "    t_0 = get_first_label(data)\n",
-    "    t_end = get_last_label(data)\n",
-    "\n",
-    "    h_0 = get_first_value(data)\n",
-    "\n",
-    "    return System(alpha=alpha, dim=dim, K=K, \n",
-    "                  h_0=h_0, t_0=t_0, t_end=t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the new `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>alpha</th>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dim</th>\n",
-       "      <td>2.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>K</th>\n",
-       "      <td>150.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>h_0</th>\n",
-       "      <td>1.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_0</th>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>70.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "alpha      2.0\n",
-       "dim        2.5\n",
-       "K        150.0\n",
-       "h_0        1.4\n",
-       "t_0        2.0\n",
-       "t_end     70.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the new update function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update3(height, t, system):\n",
-    "    \"\"\"Update height based on geometric model with growth limiting term.\n",
-    "    \n",
-    "    height: current height in feet\n",
-    "    t: what year it is\n",
-    "    system: system object with model parameters\n",
-    "    \"\"\"\n",
-    "    area = height**2\n",
-    "    mass = height**system.dim\n",
-    "    mass += system.alpha * area * (1 - height/system.K)\n",
-    "    return mass**(1/system.dim)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, we'll test the update function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.075043351397076"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "update3(h_0, t_0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can test the error function with the new update function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2.0, 2.5, 150]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "2      0.000000\n",
-       "3      0.515043\n",
-       "4      0.920001\n",
-       "5      1.216367\n",
-       "6      1.431972\n",
-       "8      1.685640\n",
-       "10     1.801190\n",
-       "15     1.975039\n",
-       "20     3.929748\n",
-       "25     7.701256\n",
-       "30    11.775782\n",
-       "35    15.027748\n",
-       "40    16.865948\n",
-       "45    17.173291\n",
-       "50    16.106566\n",
-       "55    13.967791\n",
-       "60    11.095254\n",
-       "65     7.785693\n",
-       "70     4.284147\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "error_func(params, data, update3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And search for the best parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[  2.    2.5 150. ]\n",
-      "[  2.    2.5 150. ]\n",
-      "[  2.    2.5 150. ]\n",
-      "[  2.00000003   2.5        150.        ]\n",
-      "[  2.           2.50000004 150.        ]\n",
-      "[  2.           2.5        150.00000224]\n",
-      "[  2.57150998   2.62640233 152.85619548]\n",
-      "[  2.57151002   2.62640233 152.85619548]\n",
-      "[  2.57150998   2.62640237 152.85619548]\n",
-      "[  2.57150998   2.62640233 152.85619775]\n",
-      "[  2.69480841   2.64180464 164.38502718]\n",
-      "[  2.69480845   2.64180464 164.38502718]\n",
-      "[  2.69480841   2.64180467 164.38502718]\n",
-      "[  2.69480841   2.64180464 164.38502963]\n",
-      "[  2.69504971   2.64141865 165.06670862]\n",
-      "[  2.69504975   2.64141865 165.06670862]\n",
-      "[  2.69504971   2.64141869 165.06670862]\n",
-      "[  2.69504971   2.64141865 165.06671108]\n",
-      "[  2.69600956   2.64152837 165.08460657]\n",
-      "[  2.6960096    2.64152837 165.08460657]\n",
-      "[  2.69600956   2.64152841 165.08460657]\n",
-      "[  2.69600956   2.64152837 165.08460903]\n",
-      "[  2.69593577   2.64152036 165.08358693]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>fvec</th>\n",
-       "      <td>[0.0, 0.6026560697086136, 1.1051308784106184, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nfev</th>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fjac</th>\n",
-       "      <td>[[-935.0854735477023, -0.0005819515032659498, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ipvt</th>\n",
-       "      <td>[2, 1, 3]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>qtf</th>\n",
-       "      <td>[-1.8155922127449653e-05, -0.00029942939542415...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cov_x</th>\n",
-       "      <td>[[0.034808802477415246, 0.003865949037799679, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mesg</th>\n",
-       "      <td>Both actual and predicted relative reductions ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ier</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "fvec     [0.0, 0.6026560697086136, 1.1051308784106184, ...\n",
-       "nfev                                                    21\n",
-       "fjac     [[-935.0854735477023, -0.0005819515032659498, ...\n",
-       "ipvt                                             [2, 1, 3]\n",
-       "qtf      [-1.8155922127449653e-05, -0.00029942939542415...\n",
-       "cov_x    [[0.034808802477415246, 0.003865949037799679, ...\n",
-       "mesg     Both actual and predicted relative reductions ...\n",
-       "ier                                                      1\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "best_params, details = leastsq(error_func, params, data, update3)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With these parameters, we can fit the data much better."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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hJKcJFjWz+JRSdqXUOuAbwFqt9b7goV5g9NXFRKBnMuOLtLS0NB577DH+8Ic/sGLFCp599llWrVpFYmIi3/ve97jnnntYvnw5d9xxB9dffz319fX09vaSnZ3NmjVruOSSS3jrrbe47777+PWvf83SpUv56le/yhVXXIHX66WmpibSL1GIqHH48GFeefU1Xt+0j//+/WY2bKulp+/IF7mMjBTOP2M2N106nzMXFkjlhzCJinVQSqlk4DkgGVipta4LObwXUCHnJgIzgu3TypIlS445dHfttdeOuH/zzTcP3163bt2IY+edd96I+1/84hcnJkAhpoBAwOBw8wCb9g8y4AOrxU1hggurzUZCfBxL5+SwYFYmDrst0qFOeVGRoDBn5VmBc7TWo4fzfgu8G7xutQm4H9imtd4/uSEKIaaiQCCA1WrFMAyqGtxs2tVAu7sfw+rAbjdITU0lOcnJMpXDvLJM7HFRM/A05UU8QSmlFgGXAANAs1LDnaVOrXWR1nqXUupLwDqgEPgAuC4iwQohpgy/3091dTWVlZWUzlnEjko3DW29w8dzsnNITorntHl5zCvNwGaTxDTZIpKgtNZvAWnB2zsxF+eOdf4zwDPhj0wIMV3s3LmT/ZU1HGqF9w7sIisra/iYw25j2YJ8Fs/Owh4nQ3mREvEelBBCTLaePi913Yl8VGVgAHFxAxgBA1uclYWzslg+N0cmPkSBaZ+gDMOQSsITQBb7imjW2dlJWloa/YM+tulmdlS04vMHcCUnY7fbSXa5mFOSwenz82SBbRSZ1gnKbrfT19dHYuK0WlIVFn19fbKdh4g6XV1dlJeX09TUTFrBPCrq++kf9A0fz8jIoDg3mVULC6b97rXRaFonqJycHOrq6igsLCQhIUF6UifAMAz6+vqoq6v7WCkmISKt8uBBdh9oprrNwDh8gLy8vOFj2WkJrFpUQHFucgQjFGOZ1gkqJSUFMAusSjWFE2e328nNzR3+9xQi0gzD4HBjN3sa7FQ0WTCApCQ7hmGQ6ornjAX5zC5Oky+lUW5aJygwk5R8sAoR2wKBAPX19RQWFtLc0cemXfXUNpvFZjIyM3A4HKS4EjhtXi7zSzNlyniMmPYJSggR25qbm9mzZw8t7d34E1po6x3ZK0pLSWbJnGyWqhyp/hBjJEEJIWJaQ1Mr2yu7aegysFnrKSgowGK1YLVYmFeWycp5uTJlPEZJghJCxCSvz8823cLW/V6au21YLAGSU5KxWGBWURpnLMiTzQJjnCQoIURMCAQC1NTUUFBQQFVjD+/tqB+uMJ6VnYU9Lo6i3FRWLconLzMpwtGKiSAJSggR9dra2ti1axdNrW7cRj0Do3bgKchOZdWiAmbkJcvMvClEEpQQIup1dLrZcdBNXYeBhVbyC/KJi4sjIT6OVQsLUCXpWK2SmKYaSVBCiKhlGAYVNZ28t9dDS68Di8VLamoqcXFxLD4lm9Pm5+J0yMfYVCX/s0KIqNLc3Exqaiq9AwZvb6sdXs+UmZmJ1WqlODeFc5cWkZUmpYmmOklQQoio0NfXx549e6ipraeXTNw+F4GQIsSpyYmsWpSPmpEu15mmCUlQQoio4Ha72aHrOdRiMOBrJS83jnhnPFaLhYWnZLFyfh7xstB2WpEEJYSIuLauPjaWd3O4K4EBnweXy4XdHkdBlovVywrJTJXhvOlIEpQQYtJ5vV58Ph+2OAcf7m1kZ0UrAcMgIz2DlOQU0tNcnLUonzkynDetSYISQkwawzCor69n9+7duL1Ouo1MPP1H9mey2+NYPi+PlfPypG6ekAQlhJg8vb29vPv+VioaDbr6+snKjCPJZVZ9KMx2ce5SGc4TR0QkQSmlVgLPa61zgvcdwE+BawE/8LDW+v6Q8z8DfB/IBzYAN2mtmyc9cCHECRvw+tl2oIsD7cl09XURZ4vDarOS5LRz1uIC2Z9JfMykJiillAX4MvDgqEP3AAqYBaQC65VSdVrrp5RS84AngYuBLcADwO+AtZMWuBDihAQCASwWC/sPd/DezgY8/V5SUlMBC6lpKSxVuZx2aq4M54mjmuwe1D3ApcC9wP8Lab8Rs1fUAXQopR4EbgaeAj4PPKe1fhdAKXVH8JzZWuuKSY1eCDEufr+fiooKDlQ1MOAooqGtd/iYxWJh/pxCzl1aREaKVBsXxzbZ20qu01ovx+wJAaCUSsMcutsbct4+YGHw9rzQY1prD1ATclwIEUUMw+Cdd9/j5Y37eXuPm32V9cPHXAl2Pn1GCVeeO0uSkziuSe1Baa3rj9LsCv7tCWnzAIkhxz2MFHpcCBFFGtp62V1vp7bdrAIxMDiAxQJL5uTIcJ74RKJhFt9Q3z906k4i0BNyfPS0ntDjQogo4PX5eX9XIzsrWyEugYSEBBITE5lblifDeeKERDxBaa07lFKNmJMk6oLNczkyrLc3eAwApVQiMIORQ4JCiAjw+XwcOHAAZ0oub29vwN07OHysqCCPsxYXMq80Q2bniRMS8QQV9DTwHaXUTswhvduBHwWP/RZ4Vyl1HrAJuB/YprXeH4lAhRCm9vZ2Nm/Zyp7DHrq9dWRmZQ4fK8lLYc3yIlyJjghGKGLdZE+SOJa7gd3AHmAz8AywDkBrvQv4UvB+KzAfuC4yYQohhhyq6+Dd8l4aOg16ensYHBgg3mHjgpUzuOzsUklO4qRFpAeltX4LSAu53w/cGvxztPOfwUxaQogI6x/0sXFnPXsPdWNzJGH195GRkYEqzea8ZcUkJdgjHaKYIqJliE8IEcUCgQA+n4+61j42bK2lp88LQEZGBg67lbUrSqQShJhw40pQSqnLMSs5rAByMMsRNWIOxz2vtX4tbBEKISLK7Xbz4ZZtHGgK4ItLH3FszowMzl1aSKJTek1i4o2ZoJRSN2BeH3IBrwLPAm2ADcgCFgNPK6XcwH1a66fCG64QYjL19/fz7EvvsL/Bx6APMjPicCUnkxAfx+plRZxSlHb8BxHiBB0zQSmlXgPagS8C72qtjWOcZwEuBL6mlLpJay018oSYAjz9Xt7Z3sjhrkQGfe7h4Ts1I51zlhTijJcrBCK8xnqH/ZvW+qPjPUAwcb0MvKyUWjFhkQkhIsIwDA7UdvL2tjr6Bnykp6URCATIz8ngU2eUUVqQGukQxTRxzAQVmpyCQ33/p7UeCD1HKZUEfEVr/UjwZ7YghIhJXq+XPeX7qetO4FC9+8gBi4Wzl8/mrMUFOB3SaxKTZ6whPkfwuAX4BbBBKdUy6rTlmAtnHwlbhEKIsGttbeWlNz9ib20/Sa5U0tLMa0uuBDtrVhRTkpcS4QjFdDTW16EbgMeBoWtPB49x3gsTGpEQYlL1eAZ5/t2D7KnuB8xZey6XiyVzclm1qECKu4qIGWuI7wml1H7MahNvANdgTpoYYmAWbN0V1giFEGFhGAblVe28u6OeQa+DhIQEBgYGKCnK5fLViuLc5EiHKKa5MQeUtdZvAyilSoHDx5rJJ4SILb19g7yxpZbqxiPXmjIzM5lfms7q5SXY46TXJCJvXFc8tdbVSqnPKKW+BcwGlmGWJWrQWo/evl0IEaV8Ph+vv7uV7ZW9JKUcWXSb5opn7YpiCrJdY/y0EJNrXMVilVI3AY8CfwKGKkCWA3crpf49PKEJISZSX18/T/zfG7y2pYHWDjcej7kP6JI52Vz/KSXJSUSd8VYz/xfga1rr+zHLHKG1fgJzEe/NYYpNCDFB2t39/PmdKhrdIb/y/kGuPHcWZy8uxB4XLRsbCHHEeBc1zAKOtsZpO5A3ceEIISaSYRjsPdTOO9vr8PkDZGRk4PV6ObUsl6svWCg19ERUG2+C0sAFwM9HtX8G2DehEQkhJkRrexcflrdzsL5ruM0eZ+P6i1ew8JQsqTwuot54E9S3gT8GSxnFAf+glDoFuAy4NlzBCSE+OcMw2PTRXl58rxJXSgYul3ltKTPFyYVnlJCZmhDhCIUYn3ENPGutXwJWAvGYO99+CugHztBa/zV84QkhPgl/wOCvb+zgz28dYMBr0NHegXfQy8JZWVx3wRxJTiKmjLuwltZ6D3BT+EIRQpyMrp4BXvmgmsY2A7vDweDgIIkJDj59xgzmzcqNdHhCfGLjTlBKqc8AtwNzMNdB3QI0yjooISJPV7ezYVsdg14/FouF7KwsEu1ePnvJclKS4iMdnhAnZLw76t4EPAg8BNwVbN4HPKyUitNa/yA84QkhxtLr6eel9/bT2BkYbrNaLJy7vISlc7JlIoSIaePtQQ2tg/qDUurbMFyrrwMzcZ10glJKnQH8GFBAC/CD4HM4gJ9iTsbwAw8H12MJMa1VVDXyuxe30d3nJTc3l/j4eNJc8Vx4egk5GYmRDk+Ikzbe1XlhXQellLICfwF+rLVOBf4W+KlSajFwD2bSmgWcBtwY3J9KiGkpEDDYsreRp5/fgdsziGEYtLa0MmdGGtd/ao4kJzFljDdBDa2DGm2i1kGlAzmAJbiFvAH4gEHgRuA+rXWH1roKs8cm1SvEtNTjGeQvb1fy/p5GMjOzsNls2OOsXL56NheePlOKvIopJSrWQWmt25RSPwV+hbk5og24DWgA8oG9IafvAxae7HMKEWsqazt546MaBgb9ANjibMyfXcwFK2eQn5N+nJ8WIvaMt5r5S0qplcC3OLIOqhxzHdTWkw0iOMTXD/wd8AywCrMwbWfwFE/I6R5AxjDEtOH1BXjpnXL213bjdDoBsFgsnHZqLitOzcVqlYkQYmoaa8v3Q8BZWut6pdTdwINa65vCFMfVwef6VvD+BqXUk5jDewChqwsTMTdKFGLKa+nw8L8vbqW2sR2r1Up+Xj6pyU4uPL1Eqo+LKW+sa1A5wILg7e8ASWGMoxizSkUoH+ZsvkbMSRJD5jJyyE+IKccwDHYeaOH/XtXUN3diGAZ+vx+npYfPXihbY4jpYawhvj8A65VSQ7voNiqljnqi1vpkr8y+AtyvlPoqZkHaZcBXgL8HDgPfUUrtBFyYi4V/dJLPJ0TU8vr8vLGlhoqaTrBYyM7KpqWliWWzU7niU6fjdIx7fb0QMW2sd/oXgccxZ9g9B3yBI9eEJpTWeo9S6mrgP4AfYvaa/l1r/Rel1MuYC4T3YPb4HgfWhSMOISKtw93PS5uqaHf3D7cV5adx9eoSZhbnysJbMa0cM0FprQ1gI4BS6ovAH7XWA+EKRGv9IvDiUdr7MbeXvzVczy1ENDhQ08EfX92JMyGJuDjzV3NBWSbnLCnEZpMNBcX0M9YkideBO7TWH2qtf3W8B1JKnQX8h9Z67UQGKMRUFwgYvL31MK+/v4/+/n4cDg9FhfmsWTGDuSUZkQ5PiIgZa4jv34DHlFJ+zCnf64G9WutBAKVUPLAYWA18PvgzXwljrEJMOZ5+L+s3VVNV18bAgDlAYTMGOX12giQnMe2NNcS3RSl1GvA3wD9iXh+yKKV6AQvmrD4f8G7w2DPBYUEhxDjUt/awflM1nn4vjvh4MjIyYKCdy86Zw/x5cyIdnhARN+Z0IK11AHPh7DNKqWRgKZALBDAnMuzUWneHPUohphDDMNhZ0cp7O+sJGOZ3OovFwgVnzGZ2gZPU1NQIRyhEdPgkGxZ2A2+HMRYhpjyvz88r71fx0Z5q0tPSsVgtJMTHceHpJRTnJkc6PCGiiiyoEGKSdLj7+fNbmv0Ha/H5fPj9fubPKeHiVTNJTnREOjwhoo4kKCEmwYHaTl7ffJiOzm58Ph8AqY4+zlucIclJiGOQBCVEGPkDBu/vamDb/mYAXC4Xfp+XgmQPF61eRk52VoQjFCJ6jXfL96FisZ5R7SnAd7XW3wxHcELEsqEp5PWtR2obpyQ5+Mz5K0l0GLhcUk9PiLGMtVC3EBiaTvQd4A2lVPuo05YA/wBIghIiRH1rD8+/c4Dm1k7S0tIAKM1P4fyVM6SWnhDjNNZvymmYC3SH1jYdawbfkxMakRAxbGgK+eubD9Lc3ILf78dqsXDhWXNZPjdHaukJ8QmMtVD3z0qpmZgFWg8CKzG3vxhiAD1a69G9KiGmpdAq5L29Hvx+P3YbzEjpZv7MFElOQnxCx1uoezh4UypVCjGG0VXI09PTSbT7OSXbx9lnnkZCQsLYDyCE+JjxTpJIBv4ds/ZeAmapo2CbMn8AAB63SURBVGFSIFZMZ0NTyL2+wHDbgrJMTp83F8MIkJiYGMHohIhd471a+ySwFniWMO0JJUSs8QcMNu2qZ8veBvr7+3G5XMTZrJy3vEgKvQoxAcaboC4BLtZavxPOYISIFZ5+Ly+/X83BmjZaWs3JEClJDq5du5CsNBnOE2IijDdBdTFygoQQ01ZbVx8vvHcId+8gXe4u/H4/mS4LxUltpLnskQ5PiCljvJMfHgYeUEqlhzMYIaLdofou/vhGBe7eQQCys7KYW5TA0tJ4zjrz9OGdcIUQJ2+shbotHFkDBZAFtCilugB/6Lla65zwhCdEdDAMg237W9i0qwEjuEWGPc7KhaeXkp0yB6vVKpMhhJhgY33du33SohAiivn9Ad78qIY9la14fV6cTicpSQ4uWVUq15uECKOxFur+ajIDUUrlA/8NrAH6gce11ncppRzAT4FrMXtuD2ut75/M2MT05en38uLGKg43dNDS3ELACLD41FKuWjObRKdcbxIinMa7Dup/jnHIAAaBOswt38tPIpa/AB9h7tibD2xQSpUDCwEFzMKsDbheKVWntX7qJJ5LiONq7TQnQ3R7BmlrbcPn95GXaiHH3oTTMS/S4Qkx5Y13kkQ3cCNwKuY6qE5gNvBFzIRyBrBFKXXRiQShlDodKAP+SWvdr7U+BJwHvBl83vu01h1a6yrgQeDmE3keIcarsraTZ96ooNsTnAyRncXsvDjmFdlZsmQxVqsUVxEi3MY75agM+IHW+s7QRqXUXcAyrfVlSqmvAvcC608gjuXALuC7SqmbMIf4HsVcIJwP7A05dx9mr0qICWcYBh/ta+b93Q3DbQ67jcvPLiPJXorT6ZRtMoSYJONNUGs5+pYavwPuCN5eD/zXCcaRAZwDbMBMhnODjze09ip0HyoPINOlxITz+QO8saWGfVVtBAIB4uLiSElycNnZZWSkOCMdnhDTzngTVA3waaBiVPtFQGPwdgnQcYJxDABurfV3g/d3KKWewBzeA7P+35BEoAchJlBvn5cXNx6irtlNc3MzhmGwbMEsLj/nFJzxsrZJiEgY72/e3cCvlVLnAZsxr10tBy4HvqiUmgf8BvjfE4xjH5ColHJorQdDYuvATIAKcyIGmL2rvR9/CCFOTHOHhxffO0SPZ5CmpiZ8Ph/5qRby4luJd6hIhyfEtDWuBKW1/r1Sqha4Ffg84AV2A6u01h8ppVYC/4l53ehEvIo5nPeQUupfMBPSl4GvYe5F9R2l1E7Ahbk+60cn+DxCjHCgppPXNh/G5w+AxUJGRgYpllaKMqyccsos2cNJiAga99iF1nojsPEYxz4EPjzRILTW/Uqp1cBPgAbMSRL/qbV+Rin1AvAQsAez5/Y4sO5En0sIMCdDbC5v4sM9jcNt8XYbV5wzH2Ogg9TU1OGt2oUQkTFWqaPfA3+vtXYHbx+T1vozJxuI1vogcOlR2vsxe263nuxzCAHg9QV4Y8th9h/uwDAMrFYraa54Lj27lPRkJ5AS6RCFEIzdg+rlSC2+3kmIRYiw6+nz8uJ7h2hs66GluQUssGxBGRefWSqTIYSIMmOVOvri0W4LEaua2oOTIfoGaWxsxOv1UpBmoSSlh3iHLdLhCSFGGfdXRqVUCeYw2xzMyQufBvZprd8PU2xCTJj9hzt4Y0sNPn8Ai8VCsstFhqOTwnQraWmpkQ5PCHEU46rXEixFtAdYDFyMuS5pCfC2Uury8IUnxMkxDIP3dzfwygfV5kw9IN5h4/OXLWHVklJWrFhBWVmZzNYTIgqNt6DYD4F7tdafxiwOi9b6n4HvAf8RptiEOClen5/1m6rYUt403Jae7OS6tXMozk1h4cKF5OXlRS5AIcSYxpuglgJ/OEr7bzCH/ISIKj2eQf705gEqaztpa22jo72DGXnJXLP2FNKS4yMdnhBiHMaboNowq5ePdhrQdJR2ISKmtbOPP75RQXO7h6bmZnp6e0iO62Z+oQWnQ2bqCRErxvvb+lPgMaXUvwMWYIlS6jLMEkgPhis4IT6pw41u1r9fzaDXj8VqIc5mY3auhfw0C709PRiGIdebhIgR4y119KBSqhu4H7NY6x8xa+Tdi5QdElGi/FA7b35UQ8Awl+857DZuvHIFdYf2kJeXJ5MhhIgxn6TU0WOYvagkwKa1docvLCHGzzAMPtzTyOaQyRCuBDuXn1NGZmoCJfmrJDEJEYPGKnU05p7WSh2p8qy1luriIiL8/gBvflTDvuoOurq6sNlszCzK5rKzy3Al2AEkOQkRo8bqQe3GLHUU+ts9VPpodJsswxeTrn/Qx/pNVdQ2ddPW3k5PTw8ZLgvnLpg9nJyEELFrrARVOuq+BTNpXQJUhy0iIcbB3TvIC+8epM3dj2HA4OAgeakWTsmF+roaCvJzIx2iEOIkjVWL72NJSCllALVHOybEZGnu8PD8u4fw9HsBsFgtXLZ6Ad1N5eTk5LBo0aIIRyiEmAiyKETElOoGN+vfr8LrM8sWWa0Wzl9RjCrJoL8/m/j4eLnmJMQUIQlKxIzdla1s2FZHb28vNpuNlORELllVSmG2CwCn0xnhCIUQE0kSlIh6hmGwaVcDW3Uzbrebjo4Okpw2rr9g1XByEkJMPWNNM7/lGOd/XinVGtqotX50ogMTAsDnD/D65sNU1HQS8Ptxu90kOy3MLwxQV11BUd7pkQ5RCBEmY/WgvnWUtkZg9OaFBiAJSky4/gEfL248RH2ruaGz1WZj+YJSnANVZGaks3Tp0ghHKIQIp7Fm8Y2eZi7EpOnqGeC5dw/S2T0w3LZwVhbnLCmkq6uA1NRUrNbx1joWQsSiqLsGpZRKA3YCd2utf6mUcmAWq70W8AMPa63vj2SMIrya2j08/+5Buro9WK1W4uLiWLWogKVzsrFYLKSnp0c6RCHEJIjGr6DrgMKQ+/cACpiFub3HjUqpGyIRmAi/g3VdPPvWAdo63DQ1NtHW2sKnTitmmcqR6eNCTDNRlaCUUjcCKcCukOYbgfu01h1a6yrM7T1ujkB4Isx2VLTw0qYqBga9tLa2YrMGmJvrpbdd1oULMR1FzRCfUqoU+A6wClgfbEsD8oHQYrT7gIWTHqAIm0DA4L2d9eyoaAHAZrMxozCHbHszORnJIwoTCyGmj6hIUEopG/Br4HatdWPIB9LQIhdPyOkezD2pxBTg9QV47cNqKuu6htvyMpO4ZNV82loayc3NxeFwRDBCIUSkREWCAu4CtNb6T6Pae4N/J4S0JQI9kxKVCCtPv5cX3jtEXbMbm9WGxWphVmEqnzq9hDiblcTi4kiHKISIoGhJUJ8FCpRSVwfvJ2OurVqJufZKAXXBY3MZOeQnYlBntzmNvLnVTXNLM06nkwtWzeOsRQVYrTIZQggRJQlKaz039L5SajvwSHCaeQ/wHaXUTswhv9uRbeZj2tA08u6ePpqam8AwyHf1ke3sluQkhBgWFQnqOO4GHgL2YM46fBxzKrqIQdUNbtZvqsLrDxBnt5OWmkJ+opvCLCd5eXmRDk8IEUWiMkFprZeE3O4Hbg3+ETGs/FA7b35UQ8AwN2Z2OuK4+soVdLYcprS0lISEhOM8ghBiOonKBCWmFsMw+GhfMxt31GK12QBITnRwxTllpKc4KcieF+EIhRDRSBKUCKtAwOCd7XVs2VtHS0sLLpeLU2bmc9nZZbgS7JEOTwgRxSRBibDx+QO8+kE15YdaaGpqwjAMLF43K2dLchJCHJ8kKBEW/YM+XnyvivrWHhwOB06nE1dcPwtnOEhLkU0GhRDHJwlKTLgezyDPvXOQNnf/cNv5Z8wlMdDE/PnzSUyUQiBCiOOTBCUmVFtXH3/ecIC+Af9w21mLCliqcoAZkQtMCBFzJEGJCVPf0sMfX9tLfWMzGRkZuJKSOP+0YlRJRqRDE0LEIElQYkJU1nby5zfLaWpuwTAMOtvbuHRViSQnIcQJkwQlTtquA628vb0OuyOeuLg4LIaX5WUOCrOTIh2aECKGSYISJ8wwDN7f3chH+5oAsFqtnDKzkNJ0D2euXIrT6YxwhEKIWCYJSpwQf8Dg9Q+r2F9zZB+n3IxELj2rlESnrHESQpw8SVDiExv0+vjfF7ext/LIhoIleSlcdGYJ9jhbpMMTQkwR1kgHIGKLp9/Lk3/6kN0V9QQCAVpaWphdlMKlZ5VKchJCTCjpQYlx6+oZ4Ll3DjIQcGC1WgkEApySH885i/NkHychxISTBCXGpbndw3PvHqRvwEdcXBzZWVnMyrVwxQWnYbNJz0kIMfEkQYkxGYZBRXULb25rxOsLAGCzWvibtacyqygtwtEJIaYySVDimLxeL8+/9gGbdRd5eXlYbTbiHTYuPauUgiwp+CqECC+ZJCGOKhAI8Lvn3mXjnja8Ph8tra0kJdi5Zs1sSU5CiEkhCUp8jN8f4K2tdTT1JmCxmJMfstISuXbNKWSkyOJbIcTkkCE+MUJPn5eXNh6iqd2D0+kkLS2NwmwX11+0CKdD3i5CiMkTNZ84SqlPAT8AZgPNwA+11o8ppRzAT4FrAT/wsNb6/shFOjW53W5augbYsL0ZT793uP20BSWsWVFMnE0620KIyRUVCUopVQw8A9wI/AVYDryslKoCzgMUMAtIBdYrpeq01k9FJNgpxjAMqqurefW93RxujyM3NxcsFqwWC2ctKmDR7KzhYT4hhJhMUZGggJnAb7XWzwbvb1ZKvQWchZm0btJadwAdSqkHgZsBSVATwN3dw59e20VDZwDw09HZSUFeNhedOZPCbJkMIYSInKhIUFrrd4B3hu4rpTKAc4CngXxgb8jp+4CFkxrgFNXjGeTlDxsYsKQB7TjsDmYWZnHVmjkkJzoiHZ4QYpqLigQVSimVCvwV+AD4KNjsCTnFAyROdlxTTV1LD+s3VdE34CM5JRmLBZbNK2btaTPkepMQIipEVYJSSs3BvAa1F/gckBA8lBByWiLQM8mhTQkej4fy8nIsSYW8v7uJgGEAYLVYuOTcuSycJdebhBDRI2oSlFLqXMzktA74ttbaAPqVUo2YkyTqgqfOZeSQnxiHuro6tm3fwb46H72BTjIzMwFIiI/j4jNnUiDXm4QQUSYqEpRSahbwPHCn1vonow4/DXxHKbUTcAG3Az+a5BBjXm+fj48O+ejpN7BYeklNSaEwN5WLz5yJS643CSGiUFQkKOBWIBm4XykVusbpZ8DdwEPAHszKF49j9rLEONU2d/PO3m4MWyJ2+yBZWVksnJ3L6mVFcr1JCBG1oiJBaa2/CXxzjFNuDf4R4zA4OEggECA+Pp6dFa28t7OegGGQmZGJxQqrlxazYFamXG8SQkS1qEhQYuI0NTWxY8cOklzJ9NoK2H+4Y/hYUqKDi84skWKvQoiYIAlqCvF4PGzevJm+wQCbdD/xLj8ul5mMcjMSuXhVKa4Ee4SjFEKI8ZEENYUkJibiyihi4wfVBLCRGNzpdl5pJquXFmKT601CiBgiCSqGGYYxfB3JMAy2729hV61BQlIKqampxMXZWL20iPllmRGOVAghPjlJUDHI7/dz8OBBGhoaOPvss/EH4I0tNVTUdGCxWEhPTyfRaefiM2eSn5UU6XCFEOKESIKKMYZhsHHjRjo7OwHYvmsf+5tttHb2DZ+Tl5nERWfOlOtNQoiYJgkqxlgsFoqLi+ns7KSj12DrezVkZucOH19Qlsk5S+R6kxAi9smnWJQzgvXyQuUVFNHS76K+L2M4OdmsFtYsL+a85cWSnIQQU4L0oKJUIBDg8OHDVFZWctZZZ+F0OjEMA13dwcZdDXhIJziDHFeCnYvOnEleplxvEkJMHZKgotTWrVtpaGgAYP/+/eQVncLb2+tobOsdcV5RjosLTy8h0SnXm4QQU4skqChVUlJCQ0MDXp/B29vrYZ8fQkoTuRLsrFpUwOziNClZJISYkiRBRZhhGLjdblJTU0e0Z2ZmMWDL4FB7AGeCazgJWa0Wls7JZsWpudjjbJEIWQghJoUkqAhqbm6moqKC9vZ2zjnnHNLS0jAMg/rWXt7ZXkdrbxIJIXsHl+SlcM6SQtKS4yMXtBBCTBJJUBFUX19Pe3s7AOXl+8gsVOyoaKGp3TPivJQkB+csKWRmfooM5wkhpg1JUJMkEAhgtY6c/j1r1iwOVR2myW1hX2s/CbXVI47H2aysODWXJXOyZd8mIcS0IwkqjAzDoL29naqqKrq6ulizZs1wD6irZ4CdB9xUurOw2+NJSDhyPclmtaBK0jltXh7JstutEGKakgQVRoZhsGXLFgYHBwFzr6aAzcWOihYO1rsxDANnyEWmhPg4Fp6SxYKyTJk2LoSY9iRBTZChXWydTudwm9Vqpbi4mO27D9DaAwdercDuTP7Yz2amOFk8J5s5M9JlKE8IIYIkQZ2ktrY2tNa0t7czc+ZMFixYgKffS0NrL3UtPeyv9lLf4SLZlYzdMbJXNCMvmaVzcijKccnkByGEGEUS1Cfg9Xqx248kGcMw6O33UVnTSt8gVLYcZluNja6egRE/l5GRMXzbHmdlzox0Fs/OJiPFiRBCiKOLiQSllFoMrAMWAQeBL2mtN0/Gc9e3dPPWpl10dnXTP+hFzZ2H12fg6ffS1TOAP2BQW2/F7/cTH2+FTg+2UQtonY44SgtSKCtMpTg3WYbxhBBiHKI+QSmlHMBfgEeAc4FrgFeUUiVaa3c4n9vT7+XZtyqpre3A5/eZjVWtOBwjZ9ZlZ2Vjt8dhDW6xbrNayM1IJD8riaKcZAqzXVitMoQnhBCfRNQnKOA8wK61fiR4/3dKqX8Ergd+Hs4nNgyztFC8Mx5frw+rxYrP5xuRoBLi48jLTCLN5SAjJYH8rCRy0hNkywshhDhJsZCg5gHlo9r2AQvD/cRJCXauXTsHfSgFiwXSUpOJd8Rhj7OSEB9HqiueeLvUwxNCiHCIhQTlAjyj2jxA4lHOnXDZ6Qlkp5dOxlMJIYQIEQvjUL1Awqi2RKAnArEIIYSYJLGQoPYCalTb3GC7EEKIKSoWhvjeBCxKqW8AP8WcxbcIeDaiUQkhhAirqO9Baa0HgYsxE1M7cCdwlda6JaKBCSGECKtY6EGhtd4NnB3pOIQQQkyeqO9BCSGEmJ5iogd1kmwAjY2NkY5DCCHEKCGfzR9bVDodElQ+wOc+97lIxyGEEOLY8oHK0IbpkKA2A+cADYA/wrEIIYQYyYaZnD5WANxiGMbkhyOEEEIch0ySEEIIEZUkQQkhhIhKkqCEEEJEJUlQQgghopIkKCGEEFFJEpQQQoioJAlKCCFEVJIEJYQQIipNh0oSH6OUWgysw9xX6iDwJa31x1YxRyOl1Ergea11TvC+A3OfrGsxK2U8rLW+P4IhHpVS6lPAD4DZQDPwQ631YzEU/2XA94FSzPj/M5biH6KUSgN2AndrrX8ZS/Erpb4EPAYMhDTfCvwvMfAalFL5wH8Da4B+4HGt9V2x8H+glPoc5r99qATgdeAywhT/tEtQwTfDX4BHgHMx95l6RSlVorV2RzS4MSilLMCXgQdHHboHc8fhWUAqsF4pVae1fmqSQzwmpVQx8AxwI+a//XLgZaVUFXAe0R9/PvBH4G+01i8ppZYB7ymlNgPXEeXxj7IOKAy5H/XvnxDLgIe01v8e2qiUup/YeA1/AT4CcjFL+2xQSpUDC4ny+LXWvwF+M3RfKbUUeAX4FmF8D03HIb7zALvW+hGttVdr/TtgD3B9ZMM6rnuArwH3jmq/EbhPa92hta7CTGA3T3JsxzMT+K3W+lmtdSDYW30LOIsYiF9r3QBkB5OTFcgEfEA3MRD/EKXUjUAKsCukOWbix/xis/0o7VH/GpRSpwNlwD9prfu11ocwP4veJAbiD6WUsmMmq+9qrXcQxvinXQ8KmAeUj2rbh/ktJpqt01rfrZQ6b6ghOFyTD+wNOS/qXovW+h3gnaH7SqkMzAK+TxMD8QNorbuVUolAF+bvzQNACzESv1KqFPgOsApYH2yLifcPgFLKhjkk/wWl1MOAB3gCc9gpFl7DcswvBt9VSt2EOcT3KPAksRF/qFuBPuDRcL+HpmOCcmG+uUN5gMQIxDJuWuv6ozS7gn+Hvp6ofi1KqVTgr8AHmMMdEDvx9wNJmB+UL2L+kkKUxx/8cP81cLvWulEpNXQolt4/2cAW4FfA1cCpmENmjuDxaH8NQ1/KNmD2pOZiflFoCR6P9viB4Usk3wL+QWttKKXC+h6ajgmqF/PiXqhEoCcCsZys3uDfoa8nal+LUmoO5ofKXuBzHIk7JuLXWgeAQWCLUupxYEXwULTHfxegtdZ/GtUeM+8frXUjsDqkabtS6ifAxcH70f4aBgC31vq7wfs7lFJPYA6PQfTHP+QiIAC8ELwf1vfQdLwGtRfzgl6ouYzsosYErXUH0MjI1xOVr0UpdS5mr+nPwLXBcfiYiF8ptVop9dGo5nggJuIHPgtcq5TqVEp1Yg6/PArcR2zEj1JqvlLqnlHNDsxebSy8hn1AYrAHMiSO2HkPDbkS+H3wy1rYP4OmYw/qTcCilPoG5tTIazCHbJ6NaFQn7mngO0qpnZhDNrcDP4psSCMppWYBzwN3aq1/Mupw1MePeWG+UCn1TczYTsecUfk3mL+cUR2/1npu6H2l1HbgkeA08x6iPP6gTuBflFK1mNdtlgL/BPwj5iSnaH8Nr2IO5z2klPoXzA/0L2NOfDpI9Mc/5AzMHnmosP0OT7selNZ6EHNY4BqgHbgTuEpr3TLmD0avu4HdmL+kmzGnc6+LaEQfdyuQDNyvlOoJ+fMAMRC/1roLuATz2kc78Djw91rrDcRA/McRE/FrreuAKzBnh7kx4/wPrfUfiYHXoLXuxxyiLMPc3Xs95lq6Z4iB+EPMBEZfDw9b/LKjrhBCiKg07XpQQgghYoMkKCGEEFFJEpQQQoioJAlKCCFEVJIEJYQQIipJghJCCBGVpuNCXSHGpJT6JUdK0BzNPZjV2N8EkrXWk1KWJlhT7z3gBq31/sl4zvEIVnh/H/iC1lpHOh4xdUgPSoiPuw2zQnM+5pYIACtD2h4ENgZv9x7l58Pln4Ad0ZScYLhG4feI3sWlIkbJQl0hxqCUWoC5TUJpcK+bSMXhBA4Da7XWuyMVx1iUUpXAl7XWb0U6FjE1yBCfECcguC/X8BCfUsoA/ha4A7PO2hbg85hbE3wBszzPHVrrp4M/nww8hLlNtgG8Adx2jG1VwCz42jmUnJRSLwJtWusvhMR0N3C+1nr18R5fKTU7ePxczErUFcC3tdZ/DR43MDfH/CpmvcFlwLeD97Mx91T7ttb6pZAYn8Xsfb71Sf4thTgWGeITYuL8APhnzIKaM4CtmInpNOBPwGMh++c8jpnIPo1Zo80AXlZKHetL46UENxoM+jVwpVIqdJuDv+XIttzHfHyllAV4DnNH4DOAJZi9xF+Mqrb9OWAt5vW4K4Kv7fOY1apfAP6glEoJOX89cMEYr0GIT0QSlBAT52da6ze11tsxq7f3YPYyNPAwZk+lVClVhtkj+jut9eZgr+gLmIU4LzrGY6/ALMY55M+ABTNxoZRahlmI9I/jePwEzN1ov6613qe1Lse8rpYB5IY8x+Na6z3B1zMTc0+j6uBQ5/cwi+d6Q87fi1nNekT1dCFOlCQoISbOgZDbHqBKaz10kbc/+Hc8MC94Ww9VdgfaMHfrHb1X2ZBcoHXojtbag9kr+2yw6e+Al7TW7cd7/ODP/jdwjVLqMaXUm5hDgAC2kOesDLn9a6AZOKiU2gz8G7BXa90Xck5b8O+cY7wGIT4R6YoLMXG8o+4HjnFeXPDcpZhDb6Haj/EzAcweU6hfA38NXm+6HvjmeB5fKZWEuXlkH+Z1o79i9vbeGnXucPLRWrcopZYD52P22m4C/n97d+xSVRyGcfwLEYmBDi4uDg7yOOXQ0uQmCA1CoI6tgS4NTUFNQXGHmqQtiCihRQRBBCNoaam13lBJcLCp/oGk4f0duly9XAXxnCvPZ7rcc87vnHuH+9xzfu85731J021FG9Uf3r9dPoPZmTigzC7eN+AqcL1cPqOExhvgGXlPUadDsjih3TYZaA+AIXJe6TTjjwATwHDpU4SkhbJtZwhSlt8BRiNiBdgqTfd2yT5ZVUBVx3fY+ysw680BZXbBIiIkrQOvJS2RnVafkAUL37ts9gWY6hjnSNI7MqBWq7A5xfiTZLv0RUkfyQq9F2XYa132fwVoSfpFVijeAkbL68oU2cJ85/jmZmfnOSizetwlf9zXyC6kw8BMRPzpsv4GWY3X6S0wwP/qvZ7jR8RnspP0U7Kw4RHZpvs3cPOknUfEe+Ax0AJ+kCXoyxHxoW21aWAzInyJz86Fb9Q16wOSBoGfwGxEfG17/zZZUj5WnuhQi/K4o32ycvBTXcdhl4vPoMz6QKm8awFLAJLGJc2Tc0ov6wynYg7YczjZeXJAmfWP58ANSQLGgFfAAflEiNqUs6eHwL06j8MuH1/iMzOzRvIZlJmZNZIDyszMGskBZWZmjeSAMjOzRnJAmZlZI/0DrDbIPD17d1kAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "system = make_system(best_params, data)\n",
-    "results = run_simulation(system, update3)\n",
-    "\n",
-    "plot_results(results, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the mean absolute error is substantually smaller."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8895329463347671"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mean_abs_error(results, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The estimated fractal dimension is about 2.6, which is plausible for a tree.\n",
-    "\n",
-    "Basically, it suggests that if you double the height of the tree, the mass grows by a factor of $2^{2.6}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6.062866266041593"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2**2.6"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In other words, the mass of the tree scales faster than area, but not as fast as it would for a solid 3-D object.\n",
-    "\n",
-    "What is this model good for?\n",
-    "\n",
-    "1) It offers a possible explanation for the shape of tree growth curves.\n",
-    "\n",
-    "2) It provides a way to estimate the fractal dimension of a tree based on a growth curve (probably with different values for different species).\n",
-    "\n",
-    "3) It might provide a way to predict future growth of a tree, based on measurements of past growth.  As with the logistic population model, this would probably only work if we have observed the part of the curve where the growth rate starts to decline."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis\n",
-    "\n",
-    "With some help from my colleague, John Geddes, we can do some analysis.\n",
-    "\n",
-    "Starting with the difference equation in terms of mass:\n",
-    " \n",
-    "$m_{n+1} = m_n + \\alpha A (1 - h / K) $\n",
-    "\n",
-    "We can write the corresponding differential equation:\n",
-    "\n",
-    "(1) $ \\frac{dm}{dt} = \\alpha A (1 - h / K) $\n",
-    "\n",
-    "With\n",
-    "\n",
-    "(2) $A = h^2$\n",
-    "\n",
-    "and\n",
-    "\n",
-    "(3) $m = h^D$\n",
-    "\n",
-    "Taking the derivative of the last equation yields\n",
-    "\n",
-    "(4) $\\frac{dm}{dt} = D h^{D-1} \\frac{dh}{dt}$\n",
-    "\n",
-    "Combining (1), (2), and (4), we can write a differential equation for $h$:\n",
-    "\n",
-    "(5) $\\frac{dh}{dt} = \\frac{\\alpha}{D} h^{3-D} (1 - h/K)$\n",
-    "\n",
-    "Now let's consider two cases:\n",
-    "\n",
-    "* With infinite $K$, the factor $(1 - h/K)$ approaches 1, so we have Model 2.\n",
-    "* With finite $K$, we have Model 3.\n",
-    "\n",
-    "#### Model 2\n",
-    "\n",
-    "Within Model 2, we'll consider two special cases, with $D=2$ and $D=3$.\n",
-    "\n",
-    "With $D=2$, we have\n",
-    "\n",
-    "$\\frac{dh}{dt} = \\frac{\\alpha}{2} h$\n",
-    "\n",
-    "which yields exponential growth with parameter $\\alpha/2$.\n",
-    "\n",
-    "With $D=3$, we have Model 1, with this equation:\n",
-    "\n",
-    "$\\frac{dh}{dt} = \\frac{\\alpha}{3}$\n",
-    "\n",
-    "which yields linear growth with parameter $\\alpha/3$.\n",
-    "\n",
-    "This result explains why Model 1 is linear.\n",
-    "\n",
-    "\n",
-    "\n",
-    "#### Model 3\n",
-    "\n",
-    "Within Model 3, we'll consider two special cases, with $D=2$ and $D=3$.\n",
-    "\n",
-    "With $D=2$, we have\n",
-    "\n",
-    "$\\frac{dh}{dt} = \\frac{\\alpha}{2} h (1 - h/K)$\n",
-    "\n",
-    "which yields logisitic growth with parameters $r = \\alpha/2$ and $K$.\n",
-    "\n",
-    "With $D=3$, we have\n",
-    "\n",
-    "$\\frac{dh}{dt} = \\frac{\\alpha}{3} (1 - h/K)$\n",
-    "\n",
-    "which yields a first order step response; that is, it converges to $K$ like a negative exponential:\n",
-    "\n",
-    "$ h(t) = c \\exp(-\\frac{\\alpha}{3K} t) + K $\n",
-    "\n",
-    "where $c$ is a constant that depends on the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "alpha = 10\n",
-    "D = 3\n",
-    "K = 200\n",
-    "params = alpha, D, K\n",
-    "system = make_system(params, data)\n",
-    "results = run_simulation(system, update3);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "t = results.index\n",
-    "a = alpha/3\n",
-    "h = (-220) * exp(-a * t / K) + K\n",
-    "plot(t, h)\n",
-    "plot(results)\n",
-    "decorate(xlabel='Time (years)',\n",
-    "         ylabel='Height (feet)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Additional resources:\n",
-    "\n",
-    "Garcia, [A stochastic differential equation model for the\n",
-    "height growth of forest stands](http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=664FED1E46ABCBF6E16741C294B79976?doi=10.1.1.608.81&rep=rep1&type=pdf)\n",
-    "\n",
-    "[EasySDE software and data](http://forestgrowth.unbc.ca/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/wall_soln.ipynb b/soln/wall_soln.ipynb
deleted file mode 100644
index 4442ecd9..00000000
--- a/soln/wall_soln.ipynb
+++ /dev/null
@@ -1,1879 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "\n",
-    "Copyright 2018 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Inferring thermal resistance and thermal mass of a wall\n",
-    "\n",
-    "This case study is based on Gori, Marincioni, Biddulph, Elwell, \"Inferring the thermal resistance and effective thermal mass distribution of a wall from in situ measurements to characterise heat transfer at both the interior and exterior surfaces\", *Energy and Buildings*, Volume 135, 15 January 2017, Pages 398-409, [which I downloaded here](https://www.sciencedirect.com/science/article/pii/S0378778816313056).\n",
-    "    \n",
-    "The authors put their paper under a Creative Commons license, and [make their data available here](http://discovery.ucl.ac.uk/1526521).  I thank them for their commitment to open, reproducible science, which made this case study possible.\n",
-    "\n",
-    "The goal of their paper is to model the thermal behavior of a wall as a step toward understanding the \"performance gap between the expected energy use of buildings and their measured energy use\".  The wall they study is identified as the exterior wall of an office building in central London, [not unlike this one](https://www.google.com/maps/@51.5269375,-0.1303666,3a,75y,90h,88.17t/data=!3m6!1e1!3m4!1sAoAXzN0mbGF9acaVEgUdDA!2e0!7i13312!8i6656).\n",
-    "\n",
-    "The following figure shows the scenario and their model:\n",
-    "\n",
-    "![Figure 2](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr2.jpg)\n",
-    "\n",
-    "On the interior and exterior surfaces of the wall, they measure temperature and heat flux over a period of three days.  They model the wall using two thermal masses connected to the surfaces, and to each other, by thermal resistors.\n",
-    "\n",
-    "The primary methodology of the paper is a Bayesian method for inferring the parameters of the system (two thermal masses and three thermal resistances).\n",
-    "\n",
-    "The primary result is a comparison of two models: the one shown here with two thermal masses, and a simpler model with only one thermal mass.  They find that the two-mass model is able to reproduce the measured fluxes substantially better.\n",
-    "\n",
-    "Tempting as it is, I will not replicate their method for estimating the parameters.  Rather, I will\n",
-    "\n",
-    "1. Implement their model and run it with their estimated parameters, to replicate the results, and \n",
-    "\n",
-    "2. Use SciPy's `leastsq` to see if I can find parameters that yield lower errors (root mean square).\n",
-    "\n",
-    "`leastsq` is a wrapper for some venerable FORTRAN code that runs [\"a modification of the Levenberg-Marquardt algorithm\"](https://www.math.utah.edu/software/minpack/minpack/lmdif.html), which is one of my favorites ([really](http://allendowney.com/research/model)).\n",
-    "\n",
-    "Implementing their model in the ModSimPy framework turns out to be straightforward.  The simulations run fast enough even when we carry units through the computation.  And the results are visually similar to the ones in the original paper.\n",
-    "\n",
-    "I find that `leastsq` is not able to find parameters that yield substantially better results, which suggest that the estimates in the paper are at least locally optimal.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Loading the data\n",
-    "\n",
-    "First I'll load the units we need from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "second"
-      ],
-      "text/latex": [
-       "$\\mathrm{second}$"
-      ],
-      "text/plain": [
-       "<Unit('second')>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m = UNITS.meter\n",
-    "K = UNITS.kelvin\n",
-    "W = UNITS.watt\n",
-    "J = UNITS.joule\n",
-    "degC = UNITS.celsius\n",
-    "s = UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Read the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Q_in</th>\n",
-       "      <th>Q_out</th>\n",
-       "      <th>T_int</th>\n",
-       "      <th>T_ext</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2014-10-05 16:30:00</th>\n",
-       "      <td>10.994</td>\n",
-       "      <td>6.840</td>\n",
-       "      <td>16.92</td>\n",
-       "      <td>14.68</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014-10-05 16:35:00</th>\n",
-       "      <td>10.952</td>\n",
-       "      <td>6.012</td>\n",
-       "      <td>16.92</td>\n",
-       "      <td>14.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014-10-05 16:40:00</th>\n",
-       "      <td>10.882</td>\n",
-       "      <td>7.040</td>\n",
-       "      <td>16.93</td>\n",
-       "      <td>14.66</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014-10-05 16:45:00</th>\n",
-       "      <td>10.798</td>\n",
-       "      <td>8.880</td>\n",
-       "      <td>16.93</td>\n",
-       "      <td>14.59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014-10-05 16:50:00</th>\n",
-       "      <td>10.756</td>\n",
-       "      <td>10.491</td>\n",
-       "      <td>16.94</td>\n",
-       "      <td>14.50</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                       Q_in   Q_out  T_int  T_ext\n",
-       "2014-10-05 16:30:00  10.994   6.840  16.92  14.68\n",
-       "2014-10-05 16:35:00  10.952   6.012  16.92  14.69\n",
-       "2014-10-05 16:40:00  10.882   7.040  16.93  14.66\n",
-       "2014-10-05 16:45:00  10.798   8.880  16.93  14.59\n",
-       "2014-10-05 16:50:00  10.756  10.491  16.94  14.50"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = pd.read_csv('data/DataOWall.csv', parse_dates=[0], index_col=0, header=0, skiprows=[1,2])\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The index contains Pandas `Timestamp` objects, which is good for dealing with real-world dates and times, but not as good for running the simulations, so I'm going to convert to seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Timestamp('2014-10-05 16:30:00')"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "timestamp_0 = get_first_label(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Subtracting the first `Timestamp` yields `Timedelta` objects:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TimedeltaIndex(['0 days 00:00:00', '0 days 00:05:00', '0 days 00:10:00',\n",
-       "                '0 days 00:15:00', '0 days 00:20:00', '0 days 00:25:00',\n",
-       "                '0 days 00:30:00', '0 days 00:35:00', '0 days 00:40:00',\n",
-       "                '0 days 00:45:00',\n",
-       "                ...\n",
-       "                '2 days 23:10:00', '2 days 23:15:00', '2 days 23:20:00',\n",
-       "                '2 days 23:25:00', '2 days 23:30:00', '2 days 23:35:00',\n",
-       "                '2 days 23:40:00', '2 days 23:45:00', '2 days 23:50:00',\n",
-       "                '2 days 23:55:00'],\n",
-       "               dtype='timedelta64[ns]', length=864, freq=None)"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "time_deltas = data.index - timestamp_0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we can convert to seconds and replace the index."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Q_in</th>\n",
-       "      <th>Q_out</th>\n",
-       "      <th>T_int</th>\n",
-       "      <th>T_ext</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10.994</td>\n",
-       "      <td>6.840</td>\n",
-       "      <td>16.92</td>\n",
-       "      <td>14.68</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>300</th>\n",
-       "      <td>10.952</td>\n",
-       "      <td>6.012</td>\n",
-       "      <td>16.92</td>\n",
-       "      <td>14.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>600</th>\n",
-       "      <td>10.882</td>\n",
-       "      <td>7.040</td>\n",
-       "      <td>16.93</td>\n",
-       "      <td>14.66</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>900</th>\n",
-       "      <td>10.798</td>\n",
-       "      <td>8.880</td>\n",
-       "      <td>16.93</td>\n",
-       "      <td>14.59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1200</th>\n",
-       "      <td>10.756</td>\n",
-       "      <td>10.491</td>\n",
-       "      <td>16.94</td>\n",
-       "      <td>14.50</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Q_in   Q_out  T_int  T_ext\n",
-       "0     10.994   6.840  16.92  14.68\n",
-       "300   10.952   6.012  16.92  14.69\n",
-       "600   10.882   7.040  16.93  14.66\n",
-       "900   10.798   8.880  16.93  14.59\n",
-       "1200  10.756  10.491  16.94  14.50"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.index = time_deltas.days * 86400 + time_deltas.seconds\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The timesteps are all 5 minutes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.all(np.diff(data.index) == 300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Mark the columns of the `Dataframe` with units."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data.Q_in.units = W / m**2\n",
-    "data.Q_out.units = W / m**2\n",
-    "data.T_int.units = degC\n",
-    "data.T_ext.units = degC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the measured fluxes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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l5T+cybhh8sJmfVPPDPE1WuSePVoJlqG5A7lk1Dnqc8Wj6ix9Mwu5dsIV6vPRvU5iUJYc7pgzxGPsvzv0vVeWWSxodHeCnTd1INfMH8kL/3cGw/rLMf3WEAXZgUJ8QT0ot4HKSksCvD2ojoT4dlXJmY8j84di0hvb2Tswc4ZMZ3K/8Rj1RuYNmxlwn1G9TuKhM+4EoNXqfV13NBQZiuQkAz8+awT9C+R1vUAhvqfe2cwfX1zDN+t7Zt3UP9a9qj5WSi96KokkiThF8aAGFco3b0EQuGyux3Pq2yud4QOy2VFUTV1Tz/Sgmt31TulugVGFi0bOY+G+ZdgcNib0GdPl4587Yi6H6kpYUbyO0wZOYc6QaTRamslLzaGoppiSxjKe3/AGvdLyeP6CR7v1t3QHxQgN6pPJxe4EAGUhP1R32EAhvmAKEb4hvo81NUAdic4oqtZjNOoCnUUQBO6a8UvM9ja/71zLsJxBCIKgXh8K+nZEYTtDmrtWqtGnTXx1vZlV2+Q1y+93lnH+aUP83hvPLNq3DIt7bXd84aigGXw9hYSBilPK3AWX/Qv8BR4VstPl2XBPDfE1W+W/Md2U6rU93ZTGyxc9zobS7cwcNKVb57h5ytX8/JQrSHOfI8+9xjGmt0iJW3GiqqUGl8sVsTh6eyizeEW2ByDZnS7uW7+kRauvpxDIaIHH0AW6yf/nsx30zk1l7LDAqcaNbU2sO7IZkBfVu4Nepw9pnABMBhOF6b047PIeazg8KAXFgzp0rJEtUiUTRbn4dH+JJjHJnaPRYrZhdzjJcv/e4pXGtiZe3/qh+vyuGTfFcDThIRHii0McDicNzRYEAXIygv8ost2vLVh5kBVbAiuAxzOqgUryv2Glm9I4Y+gMjF0MJynodXrVOGm5bJS32kEgZYNooayDaNPLU5NlYxVKc/GJt/3X14Jp8jW7ZZACZXy2tNm594U1Qc+z5OBqbE47E/uMVVUEIo0iOqslnKUU2s961VZPlmeVpjB42/4qXvhoO1c/sJjrH/2WI+WdK2qONjU+GpTaNP+eSsJAxSENLVZcLnm9QB9i1pitMV5PvbPZT9E53mm2KB5U6Bl1JMhOyeKu0zwzzM/2fB31MSiY3QYqJdnjQRn0OvQ6AbvDFXCxfm9xrWp0tATat6SiiRq3YdKqKnQEm8OmtjA5K8i6UST4ydiLGJjpSZE26IWwe7i/uEAOHzebPWE+34SVxeuKcThdWG0OHnp1PT978KsOtTeJBVpV8VD1Tz2JhIGKMXaHky1SpVcoR+ldk5MZOqTQO9fbM6gKIQsTjwRbg4oWvgXAtih0CA1Eq8Ud4tMYKEEQSHGH/LQisI0tVh5/YwO//9eqgMcKlDihLVmYMKKASSM77gU9vfY/anq5Im0TDQRBYGQvz5qrk/BL6YwcLId76xo9IfIjFXJykjgwx2//ytpW6pst3PPc6pBlILFCq+J/vUaZvCeTMFAx5uNl+3nw3+t45v2tqgek9K7JyQjtovfN976x3/z4kh7VCynYGlQ0+cPM36iPq1piMzNWwngpSd5LwgPdCTLb93uKdResLGLtD8FrowJ5UMp1ddKAbIwGXcCbb8BjOWxeLb9zUyOjFuDL4bJG1u04hvZSduIIu95bbqb8+6ppbKO51UpZdQt73JqGF7fjaf4zDrsJKFmxl46aH1B7ryeSMFAxZukmuRhx9fZj/PhPC9lbXEute0bXngclCAJ3XeWJ1TucLr+spHhGMVAZSYGlfKLBxL7jGNdbzkwrbw4tzhoplGQHk4/w6YQRsqdzWLP20d70w9eDam61ql7BgN6ywRvar2Op+yUNHkN42ehzO1SYGw5+++RyHnt9I0WlmjUVwRX2CUSO20DVN7Xx51e+56bHl2C22MlINbZrxOOxOL7ZbaBiFZGIBAkDFWOSNeKeFquD73eWUeeua1JmeKGYM2kAnz1xkfq8J7W2booDDwqgd7psCLQN9KKJ0njQN8NO+f6132mv7JSQx/L1oG56fCkfLt0vH9+dZJDXzjEUFLmcyf3Gc+W4i9rZO/yUVjZpnrk4EObGeElGPWkpRuwOF5JGVqxXTip5WZ7f3qljCtVkpcmj5ALjmoY2XvtiV1jH012a1Anf8WOgekyauSiK84C/AicBlcATkiS9LIqiCXgOuAJwAE9LkvR47EbaOZJN3l+Bze6k1dKxEJ+CXicwYUQvtu2roqahjSF9u1bcGm1imSShpY/bQB0OoNkXDRTFct8sNeUmqc28ay+TTetBHa1q9lLAVxJutGtdoahslj2WggjL5QTDS1dQgF2V+zi9m2UHvgRq8pidLicnvfngORQdbWDkoBxKq5pZtfUoV88fyd/e2sSWvZV8suIA584YTGFefBgET8g8PsYTDnqEgRJFcQDwMfBz4HNgEvC1KIrFwBxABIYBWcBXoigelSTJvy9AHJLic7Oob7aoVf4d8aAU8rPkWXGwhnXxRnFdCRaHlTRjCsmG2NaXKOKyO2IkfaR6UD5iqMqsXVuI7WgnU1PrQR2tbPZ6zeA2bmnJHUvdP1BbDEBheq8O7R9utH+qIDg5XB/+Uoq++Wkcq/b+zfRxr+3mZCYz2f0bHDkoV1V8v/6CMWzZWwnAvz/bQa/sFG68eGy7vakiTZPl+POgekqIbzDwriRJn0qS5JQkaSOwAjgN2Wj9RZKkOkmSioEngZtjNdDOoqQYK+m/9U0WNasoO0QNlC8jBskx8x0Bmty5XC4+XrafLVJld4cbNna5mwae2v+UmBXIKihyMLXm+pgkmSgelKEDIb72DJTWgyqv9b7xejyo9g2U0+lUGxN2Rk0+YghOShqO4XSGVx/vtis9mZxD+2Zx2sl9uebc0Cnag/pkcvV8WXh24+4KFq0tZtOe2P+2lCy+nAi1vogFPcKDkiRpFaDm1YqimAvMBN4C+gC7NbvvBcZFdYDdQKnBGD0kj8++K+JIeRNGo3wj6YwHNXqIPLs7dNS/mHDz3kpeXyh/RF88dXF3hxwWyprkH/TAdtoBRAOj3ohJb8TqsGFxWKPu0SlGR+cTvstMT0InyKnlNrsTo0GHw+cGfcFpQ/hS055D60H51vQo4cFg4rBaNY3q1lpsDhs5yVmq+kYs0esFLA4rFS3VYS0WHj0kj7cfms/aHWXMmzqww2oVviUeR8obmT4udrp3DqeDurYGBISI9WaKBT3Fg1IRRTELWACsBza7N2tFu1qB2K66dwKlYdpJA7LJzkiivtmi3ljay+LT0jc/DZ0AFbUt2Oze6biHNW294yUN/WiTrFRdmB4dZYL2UNQm9lUfjOh53lq8hwUri7y2KQbK9+ao1wlku9chlcQZ32LsQp9SA63UUZtPb6j2tOy03plS+9ReK/BwE6zYPMkoz6UP1oVfwDUrPYlzpw/ulJSSr4E6WtUcZM/o8NqWD3C5XGQmZ3S5t1M80qMMlCiKI4DvgQrkpAglzUeblpQKxPZq6QTKIm16ipGTBnhmPplpJr8EilAYDXr65KfhdMn6YlpqNWrngRaFo43T6VS75Q7NGRDj0cikGeUbzqPfPas2Ugw3LWYbHyzZx38+3+mlJh4sSQI8k5R69zqU9gaem5mkrispaKWOfIVm27sBa70v1UBlRNdABSo0BkhLkj+HzUd3BHw92vgaKN/JQDRxOp18U7QSIK47R3eFHmOgRFGchew1fQZcIUlSmyRJdUA5cpKEwki8Q35xi9XmwGp3YtALJJn0XvUpXfF0FLHPHQe816HqNYvsNXGQhl7eXEmb3UJeag7ZXWynEW60t/nypsikm2sNQLHbq3U6XWoygG+IDzwJDcrEQuvlGPQ6v7boNptDvXZ8u+u2lwHolWDh7sUUbQMVrAdTikE2CFJNZD3cjuKbYRusk3E0qGr11IcNzx0cs3FEgh5hoERRHAZ8CTwgSdK9kiRpf5ZvAQ+KopgviuJg4G73trjH4z2ZEASBs6Z4OseOGx5YWToUirKEr7q5tqiwJg7aw5e56436ZcRPtbtVI3NU5M5eCzfa9aNqtyyVJ4MvsNac0hZCEZTVGii9XufVFj0lyYDTBSu2lPLix9vVBBzt/qHQdvaNVYgvmAdlNjtJMiRR1VLjJekTK3wnE8HanESDo40V6uNbTv15zMYRCXqEgQJuATKAx0VRbNb8+xvwALAT2AVsRE5Hfyl2Q+04ithnWoocyivMS+OVP83j8rnDuWZ+58UeFakcXwVsrQdV2xD7EMBRd5uLwozYpC8HQtslVgmXhButd6AYGsVoBfNulJolpTW5w6H1oASvkJ/y/T/97hYWrS1m235vT9A3S9CXrfs8+8cqxBfMgzJbHIxz96LaULotmkMKynO/n8uV8+QxWUL07Yo0x9zruecMnx2zmrVI0VOy+O4E7gyxyy3ufz0KrQel0Ds3lesu6FqTPtVAaWbOLpfLK005HjyodSVbABiZPzzGI/Fw46SfUtlSw5GGoxyqK6G0oYz+Ye5GqvV+FMOiGJxg3o0S4lN6Rmm9sNt+cgo7izzhnfZURAKFELUoYao2Wxt15gYMOkPUW4YHM1B3XjWRWmMqm479oNZnxZpBhZkIE+C9b6WYelCl7nBsv+NEf09LT/GgjktUDyq1ez2PFBQDpW0TXlVvpqnVE756+6u9HIthxpHL5aKk4RgAE/uMjdk4fMlNzebJ+feRnZwJwJ1fPRz2c2jDVx4PyhPiC4RSyN1ilr9TxbBde94oxEG5XiG+9mgvSUIZX2WLR0FCp4vuLWL55hK/bW8+eA6TR/VmcLacULO/5lDcZKMmuROZFOP+2he7eHXBzqiOb7e7pnBozsB29ux5JAxUDGl2y9Ckd7Cyvz1SAjS5U1LMtfI2976wOuhMNdI0tDViddjIMKWRauqYJlw0OWPoaRE7tjYFXCk4VTyiYMbD34PyVp3Qhvh+0Y7n7atU4Tc+HwPVKwbhov99I/ltMxrltOmhOQPJSsqgrKmS4gioSnSFJPfYLDYHbRY7n6w4wGffFbGnuDYq568111PeXEWKMZlhuYOics5okjBQMaQlQh6U1kBVu0N6p44pVNs31DZaqKxr9T9AFKhokTMMC9I6nwQSDS4ffa76ONz9obQhPodPiC9Y+C3Z/Z0qacxOH2HZIZrMz8vmDmeQ+zsORLs6fm4DWtUSWw0+X5S1M4PewMS+cg3+upLNod4SNZLcYs8Wq4NGje5haWV0ohQHaooBOXtPfxzVPykkDFQEaWyx8tQ7m9UeM740a2qgwoGvgTpW1cwLH20HoCA3lefunsvYYfJNZ8HKIv76xsaAXVkjSYW7pUVBenwaKKPeqLb/aOlgTUldUxvPvr+VxWsPhdzP5uVBeYf4giUwKDN0pW7KNyQ4ZVRv7r56Ei/feyYgF50Go70kCaWGqipGHlQwGSetxt2UficDsHjfcqz22LeWMWk8qCZNq5uK2lYOlEQ+21BZjzve0ssVEgYqQmzfX8XVDyxmxZZS/u+5VQH7x4TbQOVkJKPTCVTWttLQbGHTHk/6qZLKXpgrp6IvWlvMmh+O8cny/WE5d0epdHtQvTtooL5Zf5g7/rEiqv130t1Fuy3WjnmZC1cf4tsNR3jh4x+81MN90SY4eDwoJYsv8E9RnaEHMVCCIDB7Yn/65stG1bfpoRZtIsaYof7GJ9AaVDRxBEgxTzbpvTy/yf3GMyCzDxaHNS5qovQ6AaNBh8vlnaTywZJ93PHP75AORy7U53Q5VQ/qpLzBETtPLEkYqAhx30trvZ6/sdC/dlipVwo16+0MaSlGxgzJw+F0se9IHQ2aGd3U0XK6cIGfhlgT0aS6Rf7B5qfmdmj/f32wjQOlDXywZF8kh+WFInv0yfrNXPPgYrbtCy0Eql1v2H0weFM9uz1AiC+IDp+Cdo0DPDfxYMkLvgYqSdNvTHujv+dnk5k5wVsHURmfEoaNtgcVqAYqkMEdkS8LKx/T1P/EEuU7qg6QIfvd1si0cPn2wCqu++ROdlbKa3bDEh5Ugo6wZW8lt/9jhd/2QGs+9Urn3A72feoIhXnyzfWVz3eqfYR+ffnJ9O0lz7B753onJvgKikaaZrdXktnJLrqhPJNwk2SQ0/6/+cpOQ7OVlz8NLa9TpmlxUlUf/PO0az0o91iTKPkAACAASURBVNqTPYiSuToWo2eNA7QeVOBz+ArBao+apvHUczKTufY871o7u8OJ0+nkmJK2HOVCarvDP8QXyED1SpMnN1Wt0UlEaA9lElAXIM1fW4MYTv6z+V1VkisvNYecOFFkCTcJAxVmHvzPOopKG/y2B+oVowiAdkYUtj1y3U3ujlW3sGTjEUDW9VPolePtQZXVNEc1JbbFJhuotE520Y2m1tnNk6/2em53BD+3y+XyuglVhzJQ2jUol2+aeeCfosmtbK94UE7V4wq2v/d1dtp4TwGyoniv4HtOu8NJeUsVNqedvNScqGdZBvSgAjRXVLzv6ngxUO7PvLXNv1g3mHJ8OJk16NSInyNW9IhC3Z5CSYV3uOzUMYWs3yXPRn0vVJfLpYYEssMU4gPole1/489K8xy/t4+BMlscNJttZKSafN8WERQPShFn7SjR1DorzCjgx2Mv5I0N8hphZZ1ZbXfhS2ub3bvFRQgDpVWBUNajfLPyfFHqbKw+BipYRl5Wmvf3+OvLx3P+aUNINhn8JkkGg/cx1u8qZ9ZceZ+BGmWNaBHIQKUm+a/PqgaqJU4MlEkxUP4JR44AXmG4+cm4CyN+jliR8KDCiGKMFLTq5Cafm0NZTQstZhvZ6UmdakzYHjNO9lc/GKhJPVbaiGtpT4EgnCiJB+md7Prpq8wdbhxOFwvXHFI7El8x5jwEQb65OJ3w4ffr2Va2y+99vrqHza3BsyK1IT6n+8alGLdgBsc3xFfhDhUHM2jZPj3Ekox6ThqQw4De/unngWqv/rnwKwD6ZUa/t1FH16Dy0+LUg7L4X6O2EN53uNAJx+9t/Pj9y2KAbyuL2RP7q48NBoElG46olfILV8spySMH54S1o2xGqok3HjzHa5s2CSOQpE6g2HmkUA1UZz2oCEvJfLn6IC998gN3P7uSNduPsaOoGpfL8718uH4tj618zu+m2NjsvTYWqp2JPYAWn9K7yzc0p6AN8X39fTHS4ToguEEbownj3fHTiUHHEuwYQmselt2n0t84IuR7I4E9QPF4cpL/55Kbko0gCNSZG7A7YqeBp6B4UL7ivBBcuqm7KF7kX+f9ISLHjxcSIb4w0qJx8W++dByFeWkkm/S0WR0s3VjC0o2ycZo0sjcLVskpsiMHdSybrTPkZiZz9qmD2LCrnH/cMdvv9buumsjuQ7U0tVpZvf1Y1Dyo93Z8jtkunyvZGN2ute2xs0jOXGtotvLXNzf6ve5qcyvFmxu9MhBbLfJ3np+dQnW9We2QHAi7VovPvQZldd/AghkorZTOexqVBV2QSc3AwkyeuHUmuVnJFOSEngQECllaK+TQ3juflnHGmOg2pg5UB5UU4HMx6PQUpveirKmSQ/UlnJQ3JBrDC4rSTFEJ8Y0ZmscudzZnpAyU2V2jF68F7+GiywZKFEUdcu+lAsCB3JfpgE8rjBOKVrde2h0/ncgZk2XdsFt/PIEn3vauen/7qz3qY+0idji59ccTgr42Z9IA5kwawH8+k7PTGluikyH3ye6v1MfxFpZwtnMfcdnltR2709uTU77z3rmpbgPVQQ9KCfHZFAMVOm3cbLGToVEcMQYxaAAjB3ds0hNK+qgyytmdEPhmHshAAYwtEClrqmRHxd7YGyiTd4hv9JBcrj5nJH98cU1EDJTT5aRVneiFLwM4Hun0XUIUxVmiKH4M1CG3uVgGfAfsBapEUXxbFMUZ4R1mz0DxoNI0mUeBZsaL1xYD8kyrMK9zazHhJN2dGBFq3SRcNFs9qdi3T7+h0+8PXxA0MBZb6FCRyyZ/Vq0273IB5TvvlSNnvDW32oJmRXp10XUqHpQ7xBcgyxPkMJxyPWkzGZNNwQ1UR9HpBB66aTpXnHFSt48VDgKtQQXzLMf1HgnAF9ISdlZIMRWPVYyoouCi1+lU7zRQ2LK7NFrkzNt0U9px1d49EB02UKIoniSK4jLgv8BB4DKgH5CM3GZ9MHAdcAx4TxTF5e4W7ScMygWaqqk3CTYDBOiVHVuxVEXBIho1RssOyoXLYwpGMGPg5Iifr7MEyxJUUvQVD6rZR11CSS3OTk/CaNDhcLqCrpdZvAyUfONSjFaodGRlIqHVWEw2hSc6P1Es4Jxp8SEyGijjLdjnMrKX3KqlxdrKwyv+yZ6qAxEdWyg8WXxuA6UXMLjHHQkPqqZVXofMS80J+7Hjjc5c5W8DD0uStDDI6yXuf1+KongPcIn7PVO7N8Segyr+qlEn750XfB0g1GvRQAkZRUOPT2kJcMaQyKmFdwerLfCNpCAnRQ6B2oy4XNBqM1PfZMFmd9IrJ0Vdd0hNMpBs0mOzO7FYHQENiNYIKuniVlvoNSiA9FQjFbXeN7tweFAKhXlp5GYmUdsYPTmpQNgCeFDB/CKlLYrCobojjC6IjSeoelDua0GRP4LIZPGdSAaqMyG+aSGMkxeSJLkkSfoUOH4ryAKg3qw0Ib6++elcNGsoAL7r2gMKgitPR4NohviKag8DIOYP7fB7nF5JBWEfkhfBQnypyUb3zUYHLh3NlhZ+9uevuP7Rb2iz2tUQX2qK0a83kP85/EN8ShZfKA8qI8W/Ri0pjAYKQIxAsk5n2bLXX1IqVOhuUJZHqknpABwLlO9CuUa9DFQEPKhSpSN1evx0pI4UHTZQXUl+ONESJlrcLn6aj/jrLy8ex7uPnMtr95/N+JM8WTeD+3jPAqNNemp0QnwNbY00WJpIMSR3St/Nq8FfgNl1OAlmVAwGnccbcug5WuHJeKxvstDUIhuojFST6tUEq9nSnqOh2cJrX+xio1vQN5QHpaxvaQlXiE+hvVYc0eDzlUV+20ItLf1h1i3kpsi1hjsrJLXHVrTxDePrdDp1TTESBupgnawQczw2KPSlQwZKlDlTFMUMn+0XRGZYPQ+Xy6W6+KkBigszUk3kZaWQne7JuglUPBlNFPWISIf4jrg76A7I6tupmq+9GiXoQAvo4cQSJMRn1OtIcdfiuJwGahs8xry1zU5jixwWy0zTGqj2Pajvd5bzyYoDast2UwgPylfUFcIb4oP228FHk3OnD1Yfh/Kg8lJzeO6CR+Umhs2VLDm4Ogqj88fXm9XpiKgHVeYWye0fg2LqaNOugRJF8Rbgc+B3wC5RFC/VvBz+vtg9FLPFjtPlbg8QorX23Mn96ZWTwl1XT4r5TUFJkmiOsAd1pF5WdO6MfM7hskb+9KJHEd4WYcmYYIkNBoNObRqIQ0+rxfNZtbTZVO8zM83kCfEFONaugzWs2hZc2TqUBxXI0w51jXWFWHtQdocTZQi/vMRTf9Vecp5Bp+fq8fItafnBtaF3jhC+HpQ2iy/cBsrlcqntUDrasqYn05Gr/GZgkiRJFwOzgftEUbzT/Vr8TLtijJLBk9pO+/ZJI3vz3/vOZo5GZSJWKAaqxWzzWu8JN4oHNTDb3xMIxsFj3oK7kUjXVXA6XV4p4FqMeh0pbsPjchi8MulazTa1hkw2UN6yRFr+8Hzo2X2oNahwSmEFI9BkKZqp200tVpwuTzakgrMDY5gxYBKpxhSK6g6rk6Fo4utB6XWCOuEIdl11lQZLExaHlTRTaqcFl3siHTFQBkmSWgAkSToEzAHOEUXxaRIGSkWtgUrpOeIcer2OlCQDTldgHbFwcaSh8x6Ub5g0UhX54KlFCoTRoPPI7Tj1XutLTa02VcncO8Tn/Vl25EYfak1JEATOmuJZb7h41rB2j9dZAhXtBmp/ESkUw68UJp9/mlx8e+6Mwe2+12QwcZq7dOGDnV9GZoAh8P3u9DoBg16HXifgcLrCeu1WujtS9z7OFSQUOmKgykVRVGUJJElqAs4H8oHoaqHEMYqiQHseVLyhpppHMMx31N1fqH8nDJRvm4VIrkEF0lBTMOi1SRIGWts8n9P+kjqs7nTz1GSjul+bxdvgWTtwg2pvYjN+hCdja8zQ8GfcBfKgbCEMd7hp9TFQv7rsZD752wVqp+D2uHjUOeh1ejYc3cb1n97NikPrIjZWX/xCfG4h32SfbsjhQOlIfbxLHCl0xEBdiyxjpCJJkl2SpGuBWREZVQ/EoyLRswxUekpkU83bbG202S0Y9UYyTB1XzRB8nPNIGqjqhuCyPkaDTjWWelcSZo13tMitCDK8v5xJptxcFX0+kNPJv15X7HXMQFl5gdpKaMnTqJSHe/0JAq9BRdJr9aXNbaC04rCBeqgFoyAtj1MKxwCyasmrm98L7wBD4Lt+qPTq8oR8wxedUNaflKaNxzvtXumSJJVKklQe5LU14R9Sz0Qp0k0N0GAtnol0qnl9WyMgF1Z2JoPPd03M7nBGbE3k8Tf8xWEVDHqdmu2YacjF5fC/aSpZdspn2aIx9m8t2s1/Pt/ptf+Pz/QXWAnUmE+Ldh3KEEJDr6tYHP6CwZHOnNTiG+LrCjdO/imj3AoT0ZQAMvroKCrGPlBd3K6DNSxee6jL52q2yJJhWcmxzQCOFl26GkRRvBz4F7JQ7BFgK7BF+SdJUuyq5mJEq7oG1bM8qEinmmsNVGdw+Bgjl0s2WsH6IHWHUG3vjQadGqrpm9qf6ppiv30UySrFG23SfJYfL/eX4AlUZNvedZOuEYqNxGdQ11bvty2aHlQ4DFRuSja3TbuBX31xLyZ9dBpwQoA6KPdETO3lpQnxKckyQ/pldamTQbOtaw0/eypdnYo9gywSexHwNNAA/Aj4DFmL74SjpYNZfPFGeoTXoLpqoAJlFSpSOG8s3M39L60NW2xfyRqbr6m/0b6W4dbjS9dlg9PfuChec0fW8567e25AfcZAtXNatB2PI5ES3mRt9tsWXQMlf5fdMVAAqW5171Zb9NTYfTMwFc9TTZqx+F+nFTWtfts6gtJP7UTI4IOut9tIB+53Z/WpiKJoAsZ2e1Q9kNYASuY9AbUWKs48qEDpxXa7E0zw0bL9AFzxhy/59eUnc96M7rVbMBllDb3h/bP8XjPodaoklMtuQucy4nu7Ubwfj/iu/Fn6ql/MmdSfQX0y1a64gY4RDG3320g0b2yyNgHZXtuiaaCUguf2Qp3tkWRIQkDA4rDicDrQRyHU5zvhUJJLFE/5D8+v4uV7z6J3rseodDV8eqIZqK56UAuAU3w3SpJklSRpS/eG1DPZUyyrHuTHWKG8syg336YIJUlEwoPS8uLHP3RtYG5cLpe6QK/1UhSMBp2XqG6mURboNBg9Y1ESY5TPUlmPfO/bfV7HGuhWDvGtk9LWzXQEcwRKAhotjepjxRhGM4tvp7vBn5Jw0lUEQSDF7UWZbdFpxOnbm0vJ2lQSfZwu+OVjS3jg357Mwq5OCFUDlQjxheRu4LeiKIbuKX2C4HK51Fbcp44pjPFoOkek08w9BsrfOwlFIANlt3c/SaKmweyVbGF3OHE4XRj0QsDwklaLz2J1kGWUb6BOg8cLUmbKarjU3VX3vW89HXAvmzOci9z1S74yRXMmdaxo++ThcmrxuGHhTTFuaGvE4vQomSueYDQ9qOp6OSQXDn3KVKM8SWyxdS2M1lmSfJIkFIV6X694274q9XFDc9eU4xstcig2Iyl2feSiSVf96VOAQcBGURS3A2uBze5/uyRJit7UKw4wW+zY7E5SkvTqLLqnoKaZRzjE19mso4AhPrcxCYXL5QqaLbh9fxX3vbSWU8cUct/1stC+svaRbDIEVHOQ65s8ChHKPVubzaecTxviczpdmAw6rHYn5582hF9cOEbdf9LI3lwyexhbpEqOlDdx+dyOtYl45OYZWGyObq/T+FJrbgDB87maTJETOg2GEg41hCGFPispg+rWWhramugdBcVv3zErSZa/vGQsa34IvCTfGqL2Lhh2p4P6tkYEBHJSuudp9hS6eqW/DEjAX4ChwCTgCuSsvjbkBoYnDPXu2VBWeuQlacJNpNPMG8zhC/HZHc6Q0jHNZhu//utSTh/fl5svO9nv9dKKJgDW7yrH6XSh0wma+huD2mROy+A+mRjcWXMWm13VBEzqe4S2YpF5p3lugNr2JQdK67HaneRmJvMrn7HodAI3XDSWG5ClcDoa3tPpAnt53aXF2oK281JyDAyUoloRDgOVk5oNdYepNftnJkYC7YRIrxM4e6rcADIvK4XXHzib6x7+xu89ypp1Z6gz1+PCRU5y1nHfSVehq1d7PnCmJEle+viiKA4gwNrU8UytuZ7yWjmU0BMNVEaEe0KpIb6UMIT42jFQa7Yfo77ZwpdrDgU0UFrBWatdbiqoFFgnm/QYA9wc+xekU+du5GexOtR1sDMnDmVp9nIqM/vjdE5Dp9ORZNSrXtNdz6wEYNLIgpB/Z2fWniJFs7XVy4NSFv17qgeV677WomWgtDxy8wyPuDCykfrDtVP465vetXZdWUdUinTzT4BGhQpdvRq+BfziEpIklUiStKB7Q+o5tFrN/G7hA/xl6b8BsAr+qbrxjuJBKVlU4aTR0kyNWV6by07qfohP6VYbDO3a0gsfbefvb23y2qZd9FeOs2a7HIIZ2jfLK8R3zrRBvP3QfAx6nbrG1GZ1qO+7bNw80tJ07Ksp4gtpifo+bb0SdExLLtY0W1sRtAZK9aCiF6m3OxUPqvsp9Pmpcn1RhVu3Lpq4AvQAPm18X8SB3kaluKyRD5fuUz349nA6nXy252sAhpwAfaAUumqgPgceFkVxcBjH0uNIMpiYUDgGZ5sc0Txi3s/eKv/CzHgmL0teUK5ttLBV8u9o2lXq2xq58bPfA5BhSsNkaH9tzuVy8dbiPWzYXR44i8/uDJlirTVGi9cVs2rbUTWjTnm/gnKc5ZtLAJg/Y7CXtE5eVorqEWv7PCnvy0vP5LpTfgTAOz98yurD8gw5N8s7izPWTSk7QrNfiE/2AKIa4nOfKxwyToOzBwCexn7R4JQRvUhJ0jNiQGDvxnfiUl7TypuL9vDWV3s6dPw3t3/M9vLdAIzsFX6x4Hilq1fDq8BkYK8oiotEUbxPFMVzRVEMHc8IA6IoThVFsVLz3CSK4r9FUawVRbFKFMV7Iz0GBb1Oz12n3cTwNHkBXJfcwiMrnmHLsR3RGkK30RZ9Pvzq+rAd93B9qfr411Ov7dB7dh+q5YMl+3jk1fUoGeUpSQaGueuT7A4ni9cV+71PSZxQutNq0Ro0bXjQYnXw4dJ9VLpVJEYOyvHyoLQNBI0GHYKgyC3Jr+l1ArMHT+OikWcD8Orm/1FcV+qlmffPO2Z3Sk8uVjRbWwKG+PaX1AftDhxuHE4lxNd9D2pY7kAEBIpqD0ct1fyhm6bz7iPneYX3tARrTqpk9llsDn731HL+85n/vcPpcrL8kNzrSifomNxvfJhGHf901UBlI7fduAeoAn6M7FWViaJYEp6heSOKoiCK4o3AN4B2Ov4QIALDgCnAz0VR7NgdMQwIgkC6Tm5jPqJPX2xOO39b9SJrj2yO1hC6zTnTBrkfdS6N+4u9S9Swgy9V7nj5zEFTmdzPfz0oENqQkhKjn3VKP7LSZE/GanPwxaqDQd+3cXcAA6UJCWrrqOqbLLy5SJ696gRZmNToY5QUBEHwKsbMdHtWgiBw1biLmdJvPC02M39d9TzTJuRh0Ou4+dJxDOtmTU+0qG9rBJ3ns1FCfIvXFXP/S9FpAhjOJImMpHRG5A3B7rSrXkekEQQh5NiDeVaKl7pNquTQsUYWBLi+D9WVYLa1kWZK5dVLniDZ0PPWurtKl64GSZKaJElaKUnSM5Ik/VySpJOBDGA68EhYR+jhIeDXwKM+238O/EWSpDpJkoqBJ5GbLEYNZZZ55YTzOGf4bFy4eHHjW3yyezF2Z/xn3N986clq75qOzpidLidvbf+Yd3/4jCaL99rb/ppD/HvTuwD0yei4U601Jker5GPqND/8liCp8Da7kw27AuoZU99s4cOl+zhW3YxN09a9ptEzs1aiidobjG/xpVY/TyvcqtPpuH36DYzMH0atuZ4vyt/itYfmcMHpQ0P+rfFEQ1sT6DyfvdYY7z1cx4HSet75am+7Kf7dQUmSCJdSu+JlbDy6PSzH6y4DCgN7UEoAI1RLFsXITu8/8YRRkFDo8NUgiuJ+URSfF0XxElEU/QLrkiRZJEnaIEnSv8M7RJWXJEmaBGzSjCkb6ANop0l7iXKfKmW2n5Zs5PqJP2H24GlY7Bbe27GAqz78LY+vfI7qltpoDqlTGA06+vZKx+WC0sqOJXpY7Z609JIG71qPD3Z+oT4+pU/Hla9aNLUhyuKxTidgMMi/4mC1Wk2tVh75b+Dw5JuL9vDmoj3c+uQKrzWVukb/0E+wEB94ayxm+2RrGvVG7jztJtKMKZQ1VfLAsifc6zo9g/q2RgS957P3FbO94x/f8d63Eht2lUXk/A6nC6cLBCF8OoNT3F77lrKdOOJgktg331NYq5VDU2y+dnLmK4P0Q7ns6Y/vMzqCI4xPOjNdOR/ZEPwCKBZFcY0oig+KojhDFMXw6//7IElSoIo3pZuZtmS8lSjXYam1NCYDgiDwm6nXcseMG9XZztayXfzmyz+xp2p/NIfVKQa6Z3hHyhvb2VOmze7J+iuq9SxGu1wuDtbJUd5rxl/KsNxBfu8NhrY2RFk70uk8HlQwOSal7XogdrkldKw2h1fn3LJqfwPi5UH5GKjMNE9U2ddAgVzn9eDcO8gwpVHeXMUDS5/iQADl83jD6XLK4Vi9xoMKoLYu7xuZMajeUxjbiPTNLKRvRm9arK3siYPEJZNRz9XzR3LxrGH016xHKVEBrbKEtuTD5XJxqF7+PY3MP3GSIxQ6fEVIkrRPkqTnJUm6GOiFvP6kA54CKkVR/FQUxV+Lojg8QmMNhHKX0aZOpQJRzff2VWIWBIHpAybx8Bl3MSLPE+p5cNnTcbs2Ncj9oykqbeiQDIvWQL21/WM2lG4DYNnBNTRZmskwpXGhOK/D5y8ua+TfmgXidTvk2bo2xBdMjqmjNVxrNVX9gZIttF1lfdUotDp92hCflsE5A3j4zLvpnZZPaWMZDyx7ig92foHNEZkas3BwsPYIrTYzWamen1Cw9vPf7yiLSI8o5ZhGQ3hV2qf2lxuBrz4SvN9XNLlynsiNF4/1CqE2m224XC6v6IC2aL7WXI/Z1kZGUjpZnSx2Px7o6hqUQ5Kk1ZIkPShJ0nTkBIW3gJOBxeEcYDvjqEPu9itqNo/EO+QXcYL1shmQ1ZdHz/o9D8y5Xd323PrXkaq96pvjgoGF8sW/YNVBrnnwK45VhbbxWgMFsHDfUg7UFPP6to8AmNr/lE41KLz1yeUE6keo0wipNjQHMVAdlGmyOwK7APf+fEq77/XyoIIYKIB+mYU8Of9+ZgyYhN1p56Ndi3j2+9dUDbV448NdXwIwprdndh6oHQjAii2lfLBkX8DXuoOythVODwrg9IHy97r12M6INbvsCj8923O7cjpdmC12L+++rskTft50VBZDHnoC1T5p6cwa1HxRFAMqFEqS1CBJ0ieSJP1akqSOCYuFj7eAB0VRzHfXZd3t3hYVXC5PYoGvCKjC2N4ir1/2NEmGJOxOO/cvfZJn1r0aVz+ascPyvJ5/s/5wyP0VA6UkQeypOsAfl/wNi93CoOz+XD3+krCMSydAfpacul1a1RRwn2DJEx1h/vTBzDi5r992X9vaXohPS5LBxO+mXc9vT70Oo97I+tKt3LHoz3y1f0WXxxkJjjWWs7VsF0a9kcvGna1uT0sJLjDz7Ybw1xbZw6gioWVAVl9yU7Kpa2uIWjZfRxg7LJ+3H5pPrrskocVs90rgOVDiUcBYdkhuWj5nyLSIjMVmd1IbYD02XujMFbEQqHWvPT0iiuJcURTjId/xAWAnsAvYCHwMvBStk1tsDk9tTIgfWKoxhXtn3kJmkrxstubIJp5f/wZHGwNnn0WbrPQkrpk/Un1e3k5DtTZ3kkRBWh4zB01Vt5/cexQPn3EX6abwqC3rdAIF7j46R8oDGyhFPbwrBFOf9/X+cjX1TaE8KAWdTseswafylzN/z5iCETRZW/jvlvd5fOVz7Kv2TyWOBZvc9XozBkyid6ZHiipU083GLqpwh0JRqQ93p2BBEDhvxBkAvLdjQVxNCLPSkzRtXKxeHlRJhextW+1WiutK0Qs6pvSbEJFx3PGPFfz8oa85Vh2fHn5ntPjygdOQU8lPB+4AdKIofo/cXXcZsD7SSuaSJK1A01lNkqQ24Bb3v6ijhvc60GhtdMFJvHLJEywtWs3Lm95h5eH1rD+6jctHn8v84bNJNia3e4xI8pN5IkaDjte+3M2uQzU4nK6gWVVtdnnWlWRI4oZJV9I3o7d6QwhnnYZOJ6gt1YMpG7SYO15MesFpQ/hyjafP5sQgteW+f3WvHM8aTWFex43v4JwBPDDndlYcWsd/t7zP1rJdbC3bxfjC0dx66nVkdlLlPZwcqC0GYFSvk0hNNvKby08myWRQ+1sFIlQ6dFfxFOmGP9fqnOGz+VJawsG6I2w8ul1dl4oH0jTNQrXXtjLhKmuuxIWLgvRemPSR6dR92D3p23Ggmr756e3sHX06kyRRJ0nSl5Ik/UmSpLlAFjAbuc37GOBtoE4UxYWRGWp80qZp19BRzhx2OvfM/A0j8oZisVt494fPuPaTO7jnm8cobYxMKm9HuXTOcHrnplLfZGH97hLe/eEz9lUfpLbVW3iz0SJf2OmmNFKNKVw+5jwuG31ul4xTqJmtXhD8ZvS+jQWDJU+MHORfHDm0XxbTx/UB4NFfzfBKjNCi7X4K3l5FQU7nmlIKgsDcoTP41/kPM75QThXeXr6bm7+4l/uXPMGifcsingq99shmbv78D7y6+T1arK00tjWxtWwXAKN6yXlN584YwhmTB7Tb3TfceEJ84W9ln2QwcdnocwF4f8cCnM7oyTe1h9LqpsXHQDW12miyNPPnZU8D0C+zT8THEg+ixYHosna/JEkOURRLgTKgEjiC3G6j51QohgFl/amzbRAm9R3HxD5j+aFiD69s+h8VLdUcqivhzsUPM3fIDH40VwEgdgAAIABJREFU5nzy03IjMeSQCILAqWMLWbDyIF9s3UiRSVaLSDWm8MKFf1GbwZU3yRIthWHotxNK802nE/zSnsVBOWzSyBodC5AyDoHV5Y0GHX+8bqrabsOXf94xm2NVLX4qEGOH5jFqcC6jBud2KvlDS3ZKFn+afSvbynaxcN9StpfvQao5iFRzkIX7lnHrqb+ImM7ap7sXU9fWwNcHvqO4vlRN1BlfONqvmDq1nWhAqJ5bXcHhUEJ8kalWOXPoaSzY+y0ljWWsObKJmYOntv+mKKCs9TW3ehuoY3U13ODWsQSY1Ilawq5yXBgoURTTkSWOzgLmIWfMlQDLkfX5rgpSr3TcEiyDryMIgsD4wtE8etbv+Xj3YnURffmhtawr2czZw2cztd94RuRH1+YPKJDDTpU1VrkMGmi1mVl2cC3nDJ/FG1s/4psiuZ1E7/Tud3dtz0D5Jp/4PldqnXwJ9J0o2njBPKdh/bMDShSZjHr+fuvMoOPsDBP6jGFCnzGsPbKJbw6s5EBtMVUtNTyw7ElG5A3lt9Ou65Lhb7Q08/mer5nSbzwje3lXe1g1qe6KcUoyJHHT5Kv8jtOeB9XYYg1raxlbhJIkFIx6I1eMOY+XNr7N/3Z8zoQ+o8lIin04S+kfVtNg9tKJrGtuIwU5OnHxyLOZO2RGRM5/WFPzGCxzM9Z0+K4qiuIqYCpQB6wAngGWSZIU+yq4GGK2hM7g6whZyZlcP/EnXDpqPisOrePLfUtpsjSzYO83LNj7DacPmsqcwdMY13tkWGeuwRjaT14wb6pJxlXoyWh7c9tHvOlOIwfITclmXMHIQIfoFKFqa3Q+OngQvJC0T14aZTUeb6oxQOjPaIx4TXmHmTFwMjMGTsbqsPHoimfYW13EvpqD3LbwQQZk9eVHY8/n1P4da69W0VzFrQsfAGDV4Q08d/4jqoK83WGnsqUaAYGbp1zDa1vex+Kwctu06+mVlud3LG2Rsjgoh965qazcelTd1tBsCauBUpTMA3U0DhezB0/j2wOrKKo7zN9Wvcif596BQR/+5o+dIcM9EXj7q73kZWnWnx1Gfjb+ci4Qz4zo7/0RjTi0KY5+F1o68w2dBpQC/0VOiFgnSVL8ViBGCXUNKgydTnNSsrh09HwuGjmPNUc28e4Pn1Frrmf14Q2sPryBFEMyowtO4uYp13S6Q21nGN4/m6x0Ew3NVpKsKdw041IaLE0slJbQYjOTm5LNzVOuZlT+8LAkdoQ0UAFCfMHW+6aM7u0ltjlnYn+27PVuIRKoKWGsMemNPDDndvbVHOS/Wz7gSMNRjjQc5ak1/+aikfM4c+jpCIKAy+XCqDOQl5rjd+Pa6K6XAVm66JM9X3HluIsAKG+uwuFy0jstnzOGzuD0gZNBEIIuvGs/o1NGFPj15moL0ZOrKygZbKYIKr/rdXp+P/NX/GnJ39lXc5Bn17/GHdNvjMqELxh9enm8uJoGTaq3U89bb1oYcXMd4qDIhfkraj2ZuoJfWlB80Jm76lDk0N6ZyKKtaaIorgaWIhusLZIkxU8eZ5ToTogvGHqdnlmDT2X6gInsqNjLd8XrWVeyGbO9jc3HdnD7oj/zu2nXM7FvZGLTOp1An7w0GpqtuCzJZCVnMG/4TC4Uz6KsqZLC9PywZhwGK6BVxmLQ69DpBLVHVDBv1deQjRrs/+OO5Cy9Oxj0BkYXjODJ+fex5shG3tz2MXXmBhbs/ZYFe7/12nd47mBOGziZecNnqUZGUY/PSEqnydLMJ7sXU1R7GINOz2Z3Onn/LDle215vLm9FDTD43MTNbeFtwWGLggcFssd/y9RreXjFM3xfsoV1/bcwY+CkiJ4zGP9Y+wrbisqQtQ38MVvsvPzpDp6+fXZUxhOoQWg80OG7qlsp/BX3P0RRHA+cgWyw7gdsoiiuAJZKkvRC2Ecah7hcLt5xNxwLp4FSMOqNTOw7jol9x3Ft6+WsPryRL/ctpaGtkb+uep4ZAyZx+qApjO09slup3XaHXQ13lDaWUdtaTws1gA6XNUXVFEwymBic0z8cf5rP+eUbVJ+8NHQ6OFrlCdMlm/RquwtlMhAsXq41UClJeq/aJYV4XQzWctrAKcwYMJmNR7ezeP9ydlft98p0PFBbzIHaYt7Y9hFDsgdwzYTL+LZoFQC/nPRTShvL+HDXQr/i1AvFszp0fq1X4XS6/MKirV1oVx4Kqy06BgpgbO+R3DjpSl7Z/B4vbXyL3un5ndKLDAfHmipYV7IZlxF8DZROB0qiYSTuKcGIpxoxLd3J4tsObBdF8RlgErKI7M+AS4ATwkBV17dR7XbNtUoDkSAvNYeLR53N+SPO4N0dn7NQWsraks2sLZG1/eYNm8kF4lk0WppIMSTTL7OQVpuZdSWbWVeyhYK0fGYPnkatuZ4mSzNrSzbTajOTbkpjT9V+BEEg05ROgzt93GY9CRiG3pJN34zeEf3b1DRjg84vDVhJKU8yaQxUkBBfklG7XZZIuv+GU71i7fHqQfkiCAJT+09gav8JatZcrbmemtY6dlXu44u939JkbeFQfQmPrHgGkCc0w3IHMW3AROYMmc6ifctZc3gjYq9h/GryNaSaOpceD/LM2uAjQWQOs4GKxhqUlrOGzWTj0R/YXr6bv696kT/NvpWB2f2icm6Xy8U/174CyN6pcdBubIc9KuWZaUnUN8nF0MHWWsPF8P5ZHChtACInBNxdOm2gRFHsD5wKTHP/PxGwA2uRezWtCOP44hptOm6kDZSCQW/g2gmXM63/KawsXs+qwxsw29v4tmiVOosOxC72qV05A+FyuVTjlGFKo2BINjvLoI9rNDkpHpUBp9NFdb1ZVXcIB8oNyqAXsPgsb2S4P9cUk4F6Qv9wtdsVJ2Dq6ELGDstjZ1GN+xw9w0BpUTya3JRsclOyOSlvCJeMOodjjeV8uGshG49ux6AzcOeMX6pJD/mpuVw74XKunXB5t87tdLr80r/D3WXX5pC/9GgZKJ2g457Tf82Dy59mf80h7l/6JLdOuy4qnWqrW2spdneb/vPcO2mtzuAh9wQqNdlARqpRNVCNbu3JQ8caKK1sZuaE8BrRvKwU1UD1eA9KFMVPkLP4+gBNwBrgC2Ttu82RVpCIR7QGKtqtvUfkD2VE/lBumHQlH+9ezBd7v8VsbyMzKR2zrQ2b03MTGecOASrN2yYUjmZyv/H0zSigztyIUW+gX2YhFruV7ORM8tNyaTHbuHLtIg4da2bz3gomjZS9qFe/2MmClQf5/TWTmHVKeMJ9Wi22Nrwvo0y3B9U7N1XN0OsVpFA2aOhPs70nhPg6St/MQm6bfgM2hw2DzhCRBX+XCz+v9tMVcuLueTOGhOUcnhBf9L4bg97A/XNu4/GVz7Onaj9Prvk35500lwtGnkVuSuQ6Ide01gGQbEhidMFJ1CSZ1dfEgTm0atb3KurkJIbfPbUCkEPgwweEb2za8o44tU+d8qBMwD+RPaQtkiTFT0l2jNDeEBwxqlAXBIErxpzHZaPmY3FYSTKYEBCwOKzoEDDqjeo4q1pqyEvNQSe0P1PV1sL8+T/fs+DJixAEgQUr5Sy5j5cfCKOB8rT79p3JKdmR/Xuns22/XBw8anAu1543ipyMJJ55f5u6r9aD0nZ/1W5X9M+OJ4wRksEBOcTnu+ZUXtPKix//wDnTBntJYTmdLr7fWcbIwbkB1/+07Cyq5vOVRfzigjFRS5LwJdmQxP1zbuOVTe+y7NBavty3lDVHNnHJqHMYUzAiImG/WrOsyHJy4SjAW+NxYGEms07px93PrsTlgvomi5cQckVtawQNVHxaqM4YqGskSapvfzcPoijmuFtiHLecM20Qa7Yf47QAitjRRKfTkaLzXOyBkiYC1byE4oozTuKjZXKTxWazzUtiKJyFfVoP6vzThvDqAlmCRxyYQ75bhy8vy+M16XUCPzpzBC1mm7eB0ozJockM1E4kornwfDzgdLm8ZvVabDYHes3nuXTjEZ79YBsFOSm8et/ZAd8DUFLRxL0vyCrdDc1Wpo2VswtjsT5o0Om5acrVnDrgFD7ZtRip5iCvbf0AkBsETup7MtMHTqKgk78dLW12CzsrJJ5f/zotNtljUrw0QRB45ObpfL7yIJefMZycjGTeefhc7nluFSUVzV51fcGKy7uKVqA2Tu1TpwzUd6IovgO8LElSQ6gdRVHMB34F/IQot1+PNrdcMZ6bLz25xyy+d4afnz+aTXsqKC5rZMXmUi+ttOoGM4+9voGzpgxkahBF8I6y+1AtIK9BXTRzGOOG5TOoTyZ6naAaF21qufJZG3w+c62npA1LaQ1XLOteeiIul3enYy0Wm8Or/m+jW36qss4ccH8F36Jfmz26a1C+6AQdp/QZy7iCkawoXseG0m1sK9/N3uoi9lYX8c4Pn5KRlE5uSjYzB03l5N4jqTXXy9EJ9zGcLhdOlwudIGBz2mlsa2JX5T6ONpVTVOvdusakN3p1AJgwooAJIzxyUxmpJnIykimpaKaqzlOrZA+zUK/Wg+rxaebIhbqPAqWiKK4BvkJucVGNLP7cCxiPLCA7E3jD/Z7jGkEQwt4JNJ7o1yvdr9stQFWdmao6M+t2lPHc3XMZ1KdrhcPlNS28+/VeQDY4Op0QUGpIa6CUxna+CQ9aQ6TNSko/DsN60cLpdAVVN7fYvNcLO5rdp00oykg1aUJ8sV0fNOgNnDVsJmcNm8nB2sN8tvcbbA4bW8t20WRppsnSzGF3gkNnGZTVj/y0XKb2m8CU/uPbbUejRCsee93TDbglyEShq9i8PKgebqAkSWoGbhdF8a/AzcBVwCmAclXZgK3IfaN+eaJp8h2vXDZ3OGt+CP1VPvzq97xwz5ldCvsd09Q8hcqw0yY3KKEO31YgSSY9ep3gtf4EMH/aYBasPMisU6KTSnw84XS5uGreSGob28jPTuGz7zzdoK2+BqqDBbwZGgOVkmxQDZQpjqIQQ3MHceeMXwKyxmG9uYGjTeUs3recZmsrWT5tUposLVS0VFOYlk9mcjoGnRExfygDs/rRN6OAvpmdizIEmlQFC7V2FW8PKqyHDhudDshLklQOPAQ8JIqiDsgDnJIkBVbsTNCjGTEwhxsuGsurC3b6vda/IJ02i53KOjM7i6rVTL/OoC0CDWWgOpIenmTUo9fr/FpXDOidwdsPzVfFORN0HKfTRWaaiXuuncKOomofA+Udcmq1dH6Gn5lmotbd4jzabT46SmZSOplJ6QzM7sf0AdFRnkgP8FkEC7V2Fe33F68eVLemLJIkOSVJqkoYp+ObS2YP47MnLvL60SSb9Fw9fyRzJg0AUOuMOovWBwr1I/FdbwqE4kEFIis9KehrCYKj/UpSfAqkLT6afKFU6bU4NNqL2elJHDwqL2krIsUJ/HueQeAQn7Mbro/2va4uLm+9/uUubn58yf+3d+bxcpRV3v92992z7wmBkHANJ4SEfQuLCCqIijBGRRQHBARlHRQcEQdBRGVR8XUDlVcdfBkchgERFRHBkciIASEQIwcJSMCQkIQkJPfe5G79/lHVfaurq/r23lX3nu/ncz/pfmrp50l116/Oec5zTtXFM0N0bGoj0qSSCT77UWdi9/1vnc9/fuldHLnv7OxNxZu6vxS8NzX/E7mXYi2oWhS9G814Hxr8VaP9Lr5iF0F7cy/2DwyybpMTCLDr9MaXwIgK++6ZX27F7+Lb0dvPmV98gK/d/kRZn+ENjCg3SOKuh59n7cYulq2ozYyOCZRRNIs7p/KTq9/Bh48fKvsx1w2OeP7lLWU9zXlvcqkC4hKWhfx9x87Pvm5tTjFnptOfieOqVw5iNHKAOFFlR3vWuvlD9Hf2Fy9QK1dv5JpbH2PT1p6cNYPdO/vp7RugpSlpSwA8vGnXidx4UW79Mb+VsnL1JjZt3cHDT5QXuJFjQZV1huBzVRP7Rhgl4a8DtOv0sUyd2M7GLT28uHZrYAReIXo9FlQhF09YKqnFnVOza7VSqSSXfvhAbv/1s5x8dG0q044WrjzrUDZu3cEMT0orfxZ5v4uvkPWaWfeUujvBos6hNUWb33Dmn8aNabElAD78pTb8FlSloeFe67jUOSh/VeVahamXZUGJyHUikjeLJyJ7iMjDlXfLiAuJRIJ5uzhWy4Ythde/BOENdX3PUeGVg3efNZ7T3rGAT592UE67P4PH1IntXHTK/llLyiiPVCqZI06Qn6S3HBfflm076e8fupltdvPO1SuXZdy45twl2bySfgsqXaHV4j28FIG67Vd/5exrf8N2T0HQWllQ5br4TgX+7JbcAEBELgGewayyUceEMY5VtXV7b8lPYpl5p+MP252OkPU2GU55u3CUL1TcH1Ju1A5/kIlfoPxJZcNY9eJQQM2adU6CYhOoYPbbczrf+fSxAHT5LKiK3XLeOagSgiT+88HneG1zDw89/rLn+GgJ1CLgceAxEblWRB4FrgAuVtWjCh9qjDQmjHVuLt+68ynOv+GhHKtoOHorzCJgAtU4dvqCWoqpVjwwOMhjf1mX1z5rqgVIhJEJv9/e7bOgKnXxDZbv4gOi6+JT1TeAM4E7gcuBg4BTVfUHVeybERPGjxmal3p5/XZeeW170cdmLKhyy30fINOZMqGNtx8yp6zjjfLZ2Zf7RJ9M5SaODSIsFP2IfWZVr2MjjAljWmlKJdnW3csV3/1DVkwq1QTvJSrnOc9rUEcqSEJEDgBuBuYD5+NklLhPRL4N/JuqdhU63hhZ+MOD17/ezbxdilvT0ue6ifxVW4ulvbWJ//u546qeSNMYnkLLAvoHBmlJ5j90hAlUW0gRSsPJnJJJqPz08xvp2dlPR1tzxQJVSZAEMFR0jdp5Msp18f0JWAcsUtXvquo5wAnAScBfq9U5Ix4ctNeMHAtm/evdBfbOpc/94RXjHgrDxKkx+OegvJMiYULUGyZQFmJekIP2GsrSksl5WKlbzXt8OWfyipp3bVs1KfeucIaqvkdVs2mJVfUhnMzld1WlZ0ZsSCYTXHTK/pz2jgWAkwA2jE1be/j1H1/K3twyroFiJ9iNxnLuPw0VJ/CHmefesIKFKCwjtz+E3cjl7JMWZV9nw80r0IR0Op1jgRVjQf3t5c18564V2fdeUcp7WKkSZT22qOpPQtq7gUsq6pERW3ab4STQLGRBnX3tg/QPDPKtO5/iv687MesaSNoamFjw7iP3oK0lxTd++lReNnPvLS7Mggprt0W6hZk9bSydu05g9Stbq2JB+Q8tJmT9kzf9Pue9N2VVrX6+5c5BbaCAfqvq9LBtxsgls24mTKD6+gdynqyff3lLVqAKZZEwokUms3w5Lr6wCM9Ws6CGJSPiGYGqxKnmF7dyppC8804nLJlXQW/CKfex5dKA83QCZ+BE9RmjkBlTnBo361/vzltpDrDNFyY7mE4PufhsHik2ZMqq5FtQQzesMCEKEq5EoroVmkcqeQJVkQWVLvi+GDIW1JQJbUyb1D7M3uVRrovvx0HtIrIcuBinWKExyhjb3syY9ma6evrYsn0nk8a1sb27lxfXvsGizils86w8B2dlfNbFZwIVG7wW1NoN2/nD02s5+ejOHLdR2KR5ULRXW0uTpTkqgkICFfRAWAj/ZajEgio2SXA5VNvxuwI4tMrnNGLEzCkdrH5lK+s3dTNpXBufu+VRVr+ylc+ecXDOeimA7T192acws6Diw5BADXL1D/7I2o1dbHFTFmUoZbG2BUgUh1+gBjwPAYOD6ZLc5P45p3IsqIy7PnICJSILA5onAJ8Dnq+oR0asmTVlDKtf2crajdtZMHcyq19xav38ceU6lizOXYzZ1dOX9YWbQMWHrIuvd4C1G52IzQeXr6Fz9lCi4L7+QdLpNK9u6mLD6/k5GqdPaue1zU67hZgXR1ag3Ci+fo/IDKbTlCLz/jmo4QRqICAqc+t2xyNSy/nDcr8ZK3Hm6Px3lZdx5qGMUcrsac6i3a//x5Ms8GRjTqfTdPXkzkF19ZiLL45kbkg7+wZoa0mxo3eA7h39OXNQP33wOVau3hQabu5NPOsvhGgE05FnQZVfst2//3AGVF/AdXxw+RoAxtewUnW53wx/yEYa6AXWq2rdk6O5SWtvBvYBXgDOVNXl9e6HAYvfNJWfPvgcAE88+1q2fXAQduzMTY2zvacvGySRTNo6qLiQdfH1D+RYvt6b3FPPbSh4Dq9br63VXHzFkCkYmRWowVwXXyn4LabhQtYLLcSdNXVMSZ9dCkULVIhbz89kEUFVV1XQp5IQkRbgZ8BNwJuBpcADIrK7mzPQqCP7zp/G/ntO48nnNvDcms3Z9q4dffS4CzvHtjezvaePe/5ndXa7ufjiQ4ublqq3b6Bsy9e77snSHBVH/hyUx4IqUaD8+w9nQYUtsAY47YS9SvrsUijlmxHm1suQ2ZaGktyhlfIWoFlVb3Lf3yEiFwCnAN+vYz8Ml7cePIcnn9vA758cqvS5rbs3a0FNndjOdp+7z1x88cE7B+Wdfyhlor3VLKiSyQhUd5AFVWKQQ8lzUCH1OGZPG1PTUimlCNQngEeBqFklC8nP//csTtolowHMmOIs2PU+pG3r6qWnd0ig/v5q7tfILKj4kBGoHb0D7PCkOyrlHum1msyCKg6/BdU/UImLL/f9cIeHLbz2V9iuNqV8M74OiKq+LCIDwExVLexorg9jAX/qgm6gI2Bfow7MnJzvk97W3ZuNPpo6MX9Rn1lQ8aEaeRO9c1CWRaI4/FF8Xqum0jmo4SyosGCXKAnUq8DNIvInHFfeZSISWPhHVb9Qjc4VSRfgv+N1AMUXJTKqyoSxLbS2pHKSiW7r7uM3f3KifqZObMs7xiyo+FOKi88bWl5uLbDRRsF1UCW6+PwLpoc7fiAkSGJihATqdOBK4F0480xvB/oD9ksD9RSoVeQnqF0A/Hsd+2B4SCQSzJjckS3n7WfCmPwvtQlU/CnlFum1oMqtpjzaKBTFV2o9pjw9KiPMHCJkQanqMuA4ABF5EXibqm6qVcdK4GEgISKXAN/CieLbB7i7ob0a5RQSqKCwVHPxjQBKuEc2e6wmezgpjmpG8ZUeZh4sUBPH1i5AAsov+T4vIuKEqvbiFEtcCrwOXAGcHJH5sVHLzClDInTphw/M2dY5O7/absrWQcWK85buU9HxXqvJ8vAVR55AVRLFV2KYeZiLz5++rNqMiPAZVV0JHNnofhhDTBk/NM905H6zOXK/2Xznv1Ywc0pHYGobe4qOF0FuuXQJJpT3eLv0xdHanCKZcCLq+gcGc6yaktdBlRokERLFl1kTVytGhEAZ0cMrQhnxufAD+wHBPwZz8cWLoAShpTzE51hQdu2LIpFI0N7aRNeOfnp29ucliy2FksPMQ1x8zTUOcDG/ilETFs5z8vAF3ciCXDomUPGiKdCCKuH4lNeCsmtfLN5Q85ww85Jz8VWeLBZqH+BSbjbzh4D3quoWX/s04H5VPTD4SGO0MG+XCXz5vCOYEbAmKghz8cWL5qC1UCWYULlzUNXo0eigva0JtlKxBeXfv9xcfJERKBF5C07WBoCjgXMC1kHthVNZ1zBY1Dm16H3NgooXlVpQFiRRHt5AiUqCJPy73/v7F/jYSeHJd8JcfEHfg2pSigW1CafUe8L9uwDwViVL4yyO/VTVemeMGsyCiheVzkGZi688vPn4qhkkMRwZF98esyewcN5k7lv2IhAhC0pVnwH2ABCRh3FcfJsLH2UYxWEWVLwIvDGVGSRhKwyKp1oWVJCgFSobn3Hxdc6ewMELZ9ZNoMpdB3VMkDiJSIuILKm8W8Zow9ZBxYsgC6o/JON1EObiK4+cIIkQC+q1zd2hQQ0ZMkERMyYPpSxdtmItt967MlC8Bt1rm0wmssmCAZpTtY3iKzdI4jDgFpw5Kf83NV3ueY3RS4ulu4kVQU/Ow90Uwcnd1rWjL1t5GSxIohTCLKgNm3t4uWMb27p7+ddvLePQvWfyuTMPDTzHn1at40f3OSX7Jo5r5Y2uXnp29nP9bY8DsLhzKofsPZN0Ou1WTW7KilYqmchZ+xQZF5+PbwAbgA8CPwY+BuyGk8Xh7Op0zRgtLO6cyqTx+QlkjegSaEEVqLqa4ftXvI10Ordg4WS79kWT+X97o6s3pyDojf/viZz9HvvLutBzXHPrY9nXyYQjOD07h7Zv3ua8+d49z3Dfshf5zqePzYphMpnIVlSGiLr4cHLdfVJV7wKexCn1fj1wMfAv1eqcMXLJ3JT23mMK13z88Ab3xiiVVCrf7AnL1+alraUpe5P9wjlLWHrMmzh88S5V799IJZMw9o7faE4trnJJJPKXDGSuY2ae6We/X52d40olk7kuvohaUP0MFS58DtgPeAgncetNYQcZRoZrzl3C/X98ieMO3d0i+GJIUORdWL62KRPa2LR1R177/jKd/WV61fs2kmkPSBMWxsBgetjfVjKZoLk5dx7J76p9o6s3e22TyUSOKAVZ0tWk3LM/BpwnIklgBU6yVoBFQG81OmaMbObMHM85Jy9m7qzxje6KUQaBAhUSJFFqKQgjnI4SBKp7R1/29X3LXmD5qny3XzKRyJv/9btq3+jqzV7DVDKRE3Fb6+jbci2oy4FfAq8BtwL/KiIvADOA71epb4ZhRJSgyDsTotrT3tpc9L6PPPUP3nn4PNZu3M4tdz8DwM+/elLOPolEvpvO76rd1t3rcfElmDi2lQNkOuM6altqA8oPM18OzAV+7IabHwjcCJxJfvFAwzBGGEGrAsIE6qz3LGLKhDYuPmW/Gvdq5FOKi++7dz0NwI6d4XNVyUQiL+GrX6D8Lr5EIsHV5yzh0tNqn9Gu7HBwVe0Skd1F5EjgAeBXwN9V1R6jDGOEU8oc1O4zx/GjK4+vdZdGBaUIVAavheRPCptKJWlpzm3LE6jtO3MsqHpSlgUlIuNF5JfASuBOHNfe14GnRGTXKvbPMIwIEuTiGwyZg7JURtWjrbWyhbF+8WnMPF4HAAATpUlEQVRK5VtQfW7tp4wYDqaHAifqnfGl3CCJrwKtwK5Aj9t2EbAVi+IzjBFP0I0qbArK9Kl6dLQNzUEdvX9xtoDX9eoPTW9K5YaNe/f3LsjtdUUrFhYU8C7gMlVdm2lQ1TXAhcCx1eiYYRjRpZT7lOVZrB4zJndwwfv344vnHs6lpx3Im/efnbP9nutPzDvGGzbun49qSiWZND63bHvGyvJG8/X2OcfV+1qWOwc1liHLyUsKK4JoGCOeUvLnmYuvuhx/2O7Z117BGNfRTCpgXVKuBdWfs60plWTqhPactkx59z5PmffM67i4+O4HrhKRjL2ZdosV3gj8pio9MwwjspRyozILqnZ4xT8o4XL/wGBO8tedPhdfKpVgysRcgcoImne+KpNWKVXnh41yBepCnNx7m4AO4EFgDTABJ92RYRgjmFLuU5atvHakhlk029s3UNCCak4lGdOW70gbGEznCNsrrzm1aZM1zhzhpywXn6quBw4XkWNwMpo3AX8FfmNh5oYx8inFbWcuvtrhFaWg/Ii9fYM5GT56+3Kj+FKpZKDlFZaZvt7XspSS7wsDmte7fxn2EhFUdVXFPTMMI7KU5uKrYUdGObkuvhALyhPs4K/Z1ZRKBB4Xlvi33lF8pVhQK3FqPXl76LeWEm5bbatYGYbRUCxIIhrkWFABTwLLV63jZjfNEeRbRk2pZJ7llUjkBkh4CbLSakkpAjXP9z6BI1rvBF6qWo8Mw4g8pTxI2xxU7RjOxecVJ8hPBBvm4guzoCLr4lPVPBESkTTwStA2wzBGLolEgkQC0kXMOFsUX+0YzsXnJyiTRJ4FRSK0+GRcwswNwxjlhFlGZ5+0iOMODV6rY1SXXBff8P/PgS6+Euag6p2x3gTKMIyyCLsfHrHPLpy3dJ9h9zMqx/t/G+Sq89Pns4ycOajc4wrNQfX3V17FtxRMoAzDKIuw+YhEwqymejHcHJSffAsqJIovRKDChKtWlBJmfl7I8aeJyEZvo6p+p9KOGYYRbRIFRMjr/gtKv2NUh6BMEjdcdBSX/Z9HAvfPn4PKj+JLp8NdfH0h7bWilCi+ywLa1gEf9bWlARMowxjhDGckffm8I+jtG8zLlm1Uj6A5qAW7T+bIfXdh2Yq1efv7gx+SyUSeazBNOlSIwiyrWlFKFJ8/zNwwjFFMuIvPaV/UObWe3RmVeAWqyVOYsKkp2Gr1W0adu07Mc/Gl0+FCFHbeWmG2t2EYZREWxWezT/XD+5AwYWxL9nVziFvVL1BzZ43Pc/ENptOhLj5vdGY9MIEyDKMsQhfgmkLVDa8FNWlcW/Z1c4ils2bdtry2vOi/AnNQbS3lVmgqDxMowzDKImzdTcIUqm54LShv4UF/GfcM//vMq3lt/us4mE7T3x+NnN/1lcMqICKXAEer6smetjnArcBhwGvAhar6ywZ10TBGBaEGlOlT3fBaUBPGDAlUUwk584Ki+OodrRdGbCwoERkrIjcAXw3YfAfwNDAF+Bhwh4jsUc/+GcZow3LsNR6vd66tZchqCktVFEReFF86Xff1TmHERqCAX+AkrL3F2ygiewIHAVeqaq+qPgTcC5xV/y4axughbDGuCVf98Fa4bfGE8z+zemPQ7sHnCIricy2oEw6fW1kHKyQyLj4RaQEmB2xKuwUST1XVtSJyFTDLs30hsEZVuzxtzwKH1KyzhmGYiy8CeB8SvAJ14ILpvPCPrQWP3WP2hLxzgLMOKiNQzakkMmcSumYz83ebWK1uF01kBAo4HHg4oH0AaFLV/FVnDmOBbl9bN04pesMwaoSFmTceryi1elx8H3jrntz5278VPPaLHz88sN27DqopleQzpx/Mzx95gROPqv+sSWQESlV/R3nf7S6g3dfWAWyvtE+GYYQT+mM1E6pudLQN3cK9GTvaWgvf2jvamhjX0RK4bXAwna28m0olmDqxnY+euHcVels6cZqDCmMVMEdEvCK1wG03DKPOmDzVj47W5uzrlhJSSg1XeDBTGb7RSX9jL1CqqsAK4FoRaRWRY4CTgNsb2zPDGJ2YAVU/2j0WVEtz+O38Q8cvyHlfKIx8MJ1m0K37VO8Kun5iL1AuS4G9cNZA/QA4S1VXNrZLhjGyicZSztHNmPYhCyosKe+Y9mZOPU5y2nb2htd1SqcdkYLGW1CRmYMqFlW9KqDtZeCE+vfGMEYv6ZB67xZmXj86Wr0WVLBAlXo10mZBGYYRd0L0yeag6og3GKKpSnW30umhhw+zoAzDiCemUA2nvbWJ09+1sOD8U6lGUDqdZiAiFpQJlGEYZTEYpk/m4qsr7zt2flXPF6U5KHPxGYZRFqFzUHXuhzEcw18Rrw6l8cxBNVghTKAMwyiLsCg+M6CihTcUPcPJR3fmvPdavek0WYFKWZCEYRhxJPzWZQoVBWZPGwPACUvm5m3rdPPwZfAK1GA6nXXfNtrFZ3NQhmGUhSWLjTafP3sJq17cxLEH7Za3LZUX8TdkD3stKBMowzBiiSWLjTazpo5h1tQxgdv8BQ0HPREvAwOD9PU7C3ktis8wjFhiyWLji79IoTci89mXNvPsS5uBxltQNgdlGEZZJMIKFta5H0bpFPsMYQJlGEYsCXXxmUJFnmLXqjXaxWcCZRhGWYQ9XNtC3ZGDCZRhGLEkYc682FKs8JiLzzCMWBJ0jzPjKR4UPwdV234M+/mN/XjDMOJKkCvP9CkeFG1BmYvPMIw4EnjvMhMqHlgUn2EYIxmzoOJLa0twcUM/JlCGYcQSM5bii8yZVNR+5uIzDCOWBN28TLSiz6LOKXnW75iAjOdgFpRhGDElWIxMoaJO0PKA6y44ivm7TcxrN4EyDCOWBN3ozIKKPumASl67zxrPJacekNduLj7DMGJJIuDuYfoUX4K0KGUWlGEYcWRx59T8RjOhYkuQtdRoF5+V2zAMoyxOO2Evdpk2lrFtzVz/k8cB06c4kM738DkEXDtz8RmGEUtam1OcsGQuUya2ZdtMn+JLFC0oEyjDMCrCexPb0TvQwJ4YhThhyVwATj66M3B74MLrBj9xmIvPMIyKaLQbyCiOTyzdhw8dv4CJ41oDtwddxZ6d/bXt1DCYBWUYRkV4LaiwBZ9G40kkEqHilNnuZ/L4toA964d9mwzDqIgdnqfs6y88qoE9MSrBq0/fvPQYunr6mDllTOM6hAmUYRgVkvIUDZozc3wDe2JUglegZk8bQ3NTcQlla4kJlGEYFbFg7iQ+dJwwv8gEpEY08br4Uo2uVOhiAmUYRkUkEglOPX5Bo7thVEjas0Cq0eHlGaIhk4ZhGEZDGRgIW8HbOEygDMMwDAZDU0w0DnPxGYZhGEyf1MHCeZMbHrnnJTYCJSIXAxcDUwAFPqWqj7jb5gC3AocBrwEXquovG9VXwzCMuJFMJrjugmgtE4iFi09E3gt8Gng3MAn4LnCfiExzd7kDeBpHvD4G3CEiezSir4ZhGEZ1iIVAAbOAL6nqKlUdVNUfAgPAYhHZEzgIuFJVe1X1IeBe4KwG9tcwDMOokMi4+ESkBZgcsCmtqt/27ftmYCzwF2AJsEZVuzy7PAscUqu+GoZhGLUnShbU4cCrAX//8O4kIouAnwKfU9X1OELV7TtXN9BR6w4bhmEYtSMyFpSq/o5hysmIyLuB24CvqOr1bnMX0O7btQPYXu0+GoZhGPUjShZUQdwovv8AzlbV6zybVgFzRMQrUgvcdsMwDCOmxEKgROQDwJeAt6nqXd5tqqrACuBaEWkVkWOAk4Db699TwzAMo1pExsU3DJ8BWoHfioi3/YOqeh+wFPgezhqojcBZqrqyyHOnANatW1e93hqGYRhF4bn35qVPT6QjmN6inojIkcAjje6HYRjGKOcoVV3mbYiLBVVLlgNH4UQMDjS4L4ZhGKONFM5a1+X+DaPegjIMwzCiSSyCJAzDMIzRhwmUYRiGEUlMoAzDMIxIYgJlGIZhRBITKMMwDCOSmEAZhmEYkcQEyjAMw4gkJlCGYRhGJLFMEhUgIvsCNwP7AC8AZ6pq3mroKCIiZwK3ADs9zefjZIz/FvA+nMwaX1PVL3uOyyTunQX8D3CGqr7mbpsD3AochpMX8UJV/aW7LQFcA5wDtAA/BC5T1f4aDjOLiBwC3Keq0933LY0Yp5vM+BtAJ06S44+o6uo6jrsV2Ab0enZ7VFWPGwnjFpG3A18B5rt9vEFVbxnp17vAuGN9vc2CKhP3C/8znOKJE4FrgQdEZHxDO1Y8BwBfVdWxnr8fA1cDgvPFOhg4XUT+GUBEFuJ8Yc8ApgB/A+7wnPMO4Gl328eAO0RkD3fbOcB73c+d7577szUdodPnhIicDTyA80PKUPdxishU4B73sycCdwO/FpGq/w4LjHsx8LrvumduVrEet4jsBtwFfNH9nFOBL4vI8Yzg6z3MuGN9vU2gyuctQLOq3qSqfap6B04J+lMa262iORB4KqD9dOBaVd2sqn8HbgTOdbedBvxcVZep6g7gcuAIEZkvInsCBwFXqmqvqj4E3Auc5TnvTar6iqpuAK7ynLeWXA18AufH66UR43wv8BdVvcv9ztyAk6X/rdUfdui4w647xH/cc4HbVfVuVR10vRm/A45gZF/vQuOO9fU2gSqfhcBffW3P4jyxRBoRSeG4JT8iImtF5HkR+YyITMIx9b3FHr1jWujdpqrdwMvu9oXAGlXtKuZYd9suIjK5eiML5GZVPRB4PNMgIhNpzDj92wCU2nxn8sbtcgAwXUSeFpH1InKniMwO6nvcxq2qj6jqxzPv3c8+CniSEXy9hxl3rK+3CVT5jAW6fW3dOOXmo840nBvXj4F5OH75TwAXutu94/KOqdCYh/v/8G/PvK7p/5eqrg1oHuvrQ+Z1rcdZt+9MyLgBuoA/4DzNCtCD44JhmP7FYtwZRGQCzhP/Y8ATvj75P3+kjvtnxPx6W5BE+XQB7b62DmB7A/pSEqq6Djja0/SUiHwTOMF97x2Xd0yFxpwosC3o2MyXtRH/X5mnwnqPs4v8H2ldvzOq+knvexH5JLDBnccYEeN23VM/w3mK/7CnXyP6evvHraqDQKyvt1lQ5bMK54nEywLyTdvIISJ7i8jVvuYWYAewjtxxeceUM2YR6QDmuO2rgDki0l7Mse62V1V1S2WjKR1V3Uxjxtnw74yIfEFE9vI0ZQIodjACxi0ib8axHu4B3qeqO0bD9Q4at9se6+ttFlT5PAwkROQSnPDVpTjzOncXPCoabAE+JSKv4ETx7A9cBFyAE+jxeRF5GsdUvxQnXBTgdmCZiLwF+F/gy8CTqvocgIisAK4VkcuBw4GTgCXusbcBl4rIb3GesK5y2xrFbdR/nHcD17uhvXcDFwODOBPa9WIf4CAR+ZD7/hvAL1R1g4jEetwi0gncB1yhqt/0bR6x13uYccf6epsFVSaq2ovjElsKvA5cAZzsRrREGlX9B/AenKibN3BCVK9R1f8CrgRW4gjVcnfbze5xzwBnuu83AnsD7/eceimwF86aiR8AZ6nqSnfbzcCdwKM44ayr3M9qFHUfp7u+5EScaKnXceb+TnS/S/XiLGAz8Dzwd5z1MR9x+xf3cZ8PjMMJsd7u+buOkX29C4071tfbKuoahmEYkcQsKMMwDCOSmEAZhmEYkcQEyjAMw4gkJlCGYRhGJDGBMgzDMCKJCZRhGIYRSWyhrmHUCBH5EU7W5zCuxlm8+DAwTlXrkvLITRb8B+CfM4syQ/ZLAn/EqeWj9eibYXgxC8owasfFOFm0Z+GUZwE4xNN2I85Cx1kM5QesBxcBKwqJE4Cby+0LuAtaDaPe2EJdw6gDIrIIeAaY59YjalQ/2oA1wLGerADDHbMaJ4vA72rZN8PwYy4+w2ggbh60rItPRNI4FVEvx0m4+ThOYbnLcFLUvAFcrqq3ucePA76Kk04mDTwEXFyg3MYHgS1ecRKRf8OpkDoNp8bZZ1X1V55jMvnUfleFIRtG0ZiLzzCix1eAfwEOw8ku/WccYToY+G/gFhHJ1LT6Ho6QHY9TQiWNU1477OHzXcD9mTci8k/uZ52Gk3H6F8CdIjLec8z9wNsKnNMwaoIJlGFEj2+r6sOq+hROlurtOFaNAl/DqcMzT0T2wLGIPqSqy12r6CM4JcDfEXLug3ASpmaYC+wEXnJdj1/AKdnd59lnFU4G8AVVGZ1hFIk9ERlG9Hje87ob+LuqZiaLd7j/tgK7u69VJKf8TgeOVXVfwLln4GSuzvATnEjDF0TkCZxqrD9U1R7PPpvcf6eXOA7DqAizoAwjevT53g+G7Nfk7rs/sJ/nb0/ghyHHDOJUSwXALQ9zII7F9ShwBvC0G9SRIXOfGCh6BIZRBUygDCO+/BVoBsao6vOq+jzwKnADjkgFsQ4nGAIAEXkvcK6qPqCqF+NYXtuAd3qOmeY51jDqhrn4DCOmqKqKyL3Av4vI+cAG4Fqc4IpnQw57AtjX8z4F3CAi63EiBg8DZrqvM+zLUNE7w6gbZkEZRrw5HUdM7sGpFDsBeLuqbgnZ/xc40X4AqOqdwOdxrK7ngC8CF6jqQ55j3gzcr6rm4jPqii3UNYxRhIh04JT+foeq/rmI/ZPASziRgo/UuHuGkYNZUIYxilDVbhxr6fwiDzkJeMHEyWgEJlCGMfr4OrCP+GLT/bjW0xXAx+vSK8PwYS4+wzAMI5KYBWUYhmFEEhMowzAMI5KYQBmGYRiRxATKMAzDiCQmUIZhGEYk+f+gMfelqQBGMQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(data.Q_in, color='C2')\n",
-    "plot(data.Q_out, color='C0')\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Heat flux (W/$m^2$)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the measured temperatures."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(data.T_int, color='C2')\n",
-    "plot(data.T_ext, color='C0')\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Temperature (degC)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Params and System objects\n",
-    "\n",
-    "Here's a `Params` object with the [estimated parameters from the paper](https://www.sciencedirect.com/science/article/pii/S0378778816313056#tbl0005)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>R1</th>\n",
-       "      <td>0.076 kelvin * meter ** 2 / watt</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>R2</th>\n",
-       "      <td>0.272 kelvin * meter ** 2 / watt</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>R3</th>\n",
-       "      <td>0.078 kelvin * meter ** 2 / watt</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C1</th>\n",
-       "      <td>212900.0 joule / kelvin / meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C2</th>\n",
-       "      <td>113100.0 joule / kelvin / meter ** 2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "R1        0.076 kelvin * meter ** 2 / watt\n",
-       "R2        0.272 kelvin * meter ** 2 / watt\n",
-       "R3        0.078 kelvin * meter ** 2 / watt\n",
-       "C1    212900.0 joule / kelvin / meter ** 2\n",
-       "C2    113100.0 joule / kelvin / meter ** 2\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = Params(\n",
-    "    R1 = 0.076 * m**2 * K / W,\n",
-    "    R2 = 0.272 * m**2 * K / W,\n",
-    "    R3 = 0.078 * m**2 * K / W,\n",
-    "    C1 = 212900 * J / m**2 / K,\n",
-    "    C2 = 113100 * J / m**2 / K)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll pass the `Params` object `make_system`, which computes `init`, packs the parameters into `Series` objects, and computes the interpolation functions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params, data):\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    R1, R2, R3, C1, C2 = params\n",
-    "    \n",
-    "    init = State(T_C1 = Quantity(16.11, degC),\n",
-    "                 T_C2 = Quantity(15.27, degC))\n",
-    "    \n",
-    "    ts = data.index\n",
-    "    t_end = ts[-1] * s\n",
-    "    \n",
-    "    return System(init=init,\n",
-    "                  R=Series([R1, R2, R3]),\n",
-    "                  C=Series([C1, C2]),\n",
-    "                  T_int_func=interpolate(data.T_int),\n",
-    "                  T_ext_func=interpolate(data.T_ext),\n",
-    "                  unit_temp=degC,\n",
-    "                  t_end=t_end, ts=ts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Make a `System` object"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>T_C1    16.11 degC\n",
-       "T_C2    15.27 degC\n",
-       "dtype: o...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>R</th>\n",
-       "      <td>0    0.076 kelvin * meter ** 2 / watt\n",
-       "1    0.2...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C</th>\n",
-       "      <td>0    212900.0 joule / kelvin / meter ** 2\n",
-       "1   ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>T_int_func</th>\n",
-       "      <td>&lt;function interpolate.&lt;locals&gt;.wrapper at 0x7f...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>T_ext_func</th>\n",
-       "      <td>&lt;function interpolate.&lt;locals&gt;.wrapper at 0x7f...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>unit_temp</th>\n",
-       "      <td>degC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>258900 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ts</th>\n",
-       "      <td>Int64Index([     0,    300,    600,    900,   ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "init          T_C1    16.11 degC\n",
-       "T_C2    15.27 degC\n",
-       "dtype: o...\n",
-       "R             0    0.076 kelvin * meter ** 2 / watt\n",
-       "1    0.2...\n",
-       "C             0    212900.0 joule / kelvin / meter ** 2\n",
-       "1   ...\n",
-       "T_int_func    <function interpolate.<locals>.wrapper at 0x7f...\n",
-       "T_ext_func    <function interpolate.<locals>.wrapper at 0x7f...\n",
-       "unit_temp                                                  degC\n",
-       "t_end                                             258900 second\n",
-       "ts            Int64Index([     0,    300,    600,    900,   ...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test the interpolation function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(14.68, 14.684999999999999, 14.69)"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.T_ext_func(0), system.T_ext_func(150), system.T_ext_func(300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Implementing the model\n",
-    "\n",
-    "Next we need a slope function that takes instantaneous values of the two internal temperatures and computes their time rates of change.\n",
-    "\n",
-    "The slope function gets called two ways.\n",
-    "\n",
-    "* When we call it directly, `state` is a `State` object and the values it contains have units.\n",
-    "\n",
-    "* When `run_ode_solver` calls it, `state` is an array and the values it contains don't have units.\n",
-    "\n",
-    "In the second case, we have to apply the units before attempting the computation.  `require_units` applies units if necessary:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function computes the fluxes between the four zones."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_flux(state, t, system):\n",
-    "    \"\"\"Compute the fluxes between the walls surfaces and the internal masses.\n",
-    "    \n",
-    "    state: State with T_C1 and T_C2\n",
-    "    t: time in seconds\n",
-    "    system: System with interpolated measurements and the R Series\n",
-    "    \n",
-    "    returns: Series of fluxes\n",
-    "    \"\"\"    \n",
-    "    # unpack the temperatures\n",
-    "    T_C1, T_C2 = state\n",
-    "        \n",
-    "    # compute a series of temperatures from inside out\n",
-    "    T_int = system.T_int_func(t)\n",
-    "    T_ext = system.T_ext_func(t)\n",
-    "    \n",
-    "    T = [require_units(T_int, system.unit_temp),\n",
-    "         require_units(T_C1, system.unit_temp),\n",
-    "         require_units(T_C2, system.unit_temp),\n",
-    "         require_units(T_ext, system.unit_temp)]\n",
-    "    \n",
-    "    # compute differences of adjacent temperatures\n",
-    "    T_diff = np.diff(T)\n",
-    "\n",
-    "    # compute fluxes between adjacent compartments\n",
-    "    Q = T_diff / system.R\n",
-    "    return Q"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    -10.657894736842135 delta_degC * watt / kelvin...\n",
-       "1    -3.0882352941176463 delta_degC * watt / kelvin...\n",
-       "2    -7.564102564102562 delta_degC * watt / kelvin ...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_flux(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a slope function that computes derivatives of `T_C1` and `T_C2`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    Q = compute_flux(state, t, system)\n",
-    "\n",
-    "    # compute the net flux in each node\n",
-    "    Q_diff = np.diff(Q)\n",
-    "        \n",
-    "    # compute the rate of change of temperature\n",
-    "    dQdt = Q_diff / system.C\n",
-    "    return dQdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0     3.555499973097458e-05 delta_degC * watt / joule\n",
-       "1    -3.844086774341106e-05 delta_degC * watt / joule\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slopes = slope_func(system.init, system.ts[1], system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "16.11 degC 3.555499973097458e-05 delta_degC * second * watt / joule\n",
-      "15.27 degC -3.844086774341106e-05 delta_degC * second * watt / joule\n"
-     ]
-    }
-   ],
-   "source": [
-    "for y, slope in zip(system.init, slopes):\n",
-    "    print(y, slope*s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's run the simulation, generating estimates for the time steps in the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "success                                                 True\n",
-       "message    The solver successfully reached the end of the...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>T_C1</th>\n",
-       "      <th>T_C2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>16.11 degC</td>\n",
-       "      <td>15.27 degC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2589</th>\n",
-       "      <td>16.19153931824483 degC</td>\n",
-       "      <td>15.119152775835897 degC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5178</th>\n",
-       "      <td>16.2528855986394 degC</td>\n",
-       "      <td>14.860249399364765 degC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7767</th>\n",
-       "      <td>16.294553283278788 degC</td>\n",
-       "      <td>14.58496288165028 degC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10356</th>\n",
-       "      <td>16.317098239731408 degC</td>\n",
-       "      <td>14.348725465924332 degC</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                          T_C1                     T_C2\n",
-       "0                   16.11 degC               15.27 degC\n",
-       "2589    16.19153931824483 degC  15.119152775835897 degC\n",
-       "5178     16.2528855986394 degC  14.860249399364765 degC\n",
-       "7767   16.294553283278788 degC   14.58496288165028 degC\n",
-       "10356  16.317098239731408 degC  14.348725465924332 degC"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_results(results, data):\n",
-    "    plot(data.T_int, color='C2')\n",
-    "    plot(results.T_C1, color='C3')\n",
-    "    plot(results.T_C2, color='C1')\n",
-    "    plot(data.T_ext, color='C0')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Temperature (degC)')\n",
-    "    \n",
-    "plot_results(results, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These results are similar to what's in the paper:\n",
-    "\n",
-    "![Figure 5](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr5.jpg).  \n",
-    "\n",
-    "To get the estimated fluxes, we have to go through the results and basically do the flux calculation again."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Q_in</th>\n",
-       "      <th>Q_out</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10.6579</td>\n",
-       "      <td>7.5641</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2589</th>\n",
-       "      <td>9.93106</td>\n",
-       "      <td>12.6725</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5178</th>\n",
-       "      <td>9.30414</td>\n",
-       "      <td>17.6391</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7767</th>\n",
-       "      <td>8.90193</td>\n",
-       "      <td>17.1162</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10356</th>\n",
-       "      <td>8.79081</td>\n",
-       "      <td>16.4555</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Q_in    Q_out\n",
-       "0      10.6579   7.5641\n",
-       "2589   9.93106  12.6725\n",
-       "5178   9.30414  17.6391\n",
-       "7767   8.90193  17.1162\n",
-       "10356  8.79081  16.4555"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def recompute_fluxes(results, system):\n",
-    "    \"\"\"Compute fluxes between wall surfaces and internal masses.\n",
-    "    \n",
-    "    results: Timeframe with T_C1 and T_C2\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: Timeframe with Q_in and Q_out\n",
-    "    \"\"\"\n",
-    "    Q_frame = TimeFrame(index=results.index, columns=['Q_in', 'Q_out'])\n",
-    "    \n",
-    "    for t, row in results.iterrows():\n",
-    "        Q = compute_flux(row, t, system)\n",
-    "        \n",
-    "        Q_frame.row[t] = (-Q[0].magnitude, \n",
-    "                          -Q[2].magnitude)\n",
-    "        \n",
-    "    return Q_frame\n",
-    "            \n",
-    "Q_frame = recompute_fluxes(results, system)\n",
-    "Q_frame.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see how the estimates compare to the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_Q_in(frame, data):\n",
-    "    plot(frame.Q_in, color='gray')\n",
-    "    plot(data.Q_in, color='C2')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Heat flux (W/$m^2$)')\n",
-    "    \n",
-    "plot_Q_in(Q_frame, data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_Q_out(frame, data):\n",
-    "    plot(frame.Q_out, color='gray')\n",
-    "    plot(data.Q_out, color='C0')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Heat flux (W/$m^2$)')\n",
-    "    \n",
-    "plot_Q_out(Q_frame, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These results are also similar to what's in the paper (the bottom row):\n",
-    "\n",
-    "![Figure 3](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr3.jpg)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_error(frame, data):\n",
-    "    model_Q_in = interpolate(Q_frame.Q_in)(data.index)\n",
-    "    error_Q_in = model_Q_in - data.Q_in\n",
-    "    \n",
-    "    model_Q_out = interpolate(Q_frame.Q_out)(data.index)\n",
-    "    error_Q_out = model_Q_out - data.Q_out\n",
-    "    \n",
-    "    return np.hstack([error_Q_in, error_Q_out])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-0.33610526, -0.37832695, -0.39254863, ...,  2.26392988,\n",
-       "       -0.7752054 ,  1.99865932])"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "errors = compute_error(Q_frame, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the root mean squared error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.17995317290043\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.sqrt(np.mean(errors**2)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Estimating parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's see if we can do any better than the parameters in the paper.\n",
-    "\n",
-    "In order to work with `leastsq`, we need a version of `params` with no units."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0.076, 0.272, 0.078, 212900, 113100]"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = [0.076, 0.272, 0.078, 212900, 113100]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an error function that takes a hypothetical set of parameters, runs the simulation, and returns an array of errors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func(params, data):\n",
-    "    \"\"\"Run a simulation and return an array of errors.\n",
-    "    \n",
-    "    params: Params object or array\n",
-    "    data: DataFrame\n",
-    "    \n",
-    "    returns: array of float\n",
-    "    \"\"\"\n",
-    "    print(params)\n",
-    "    system = make_system(params, data)\n",
-    "    system = remove_units(system)\n",
-    "    \n",
-    "    results, details = run_ode_solver(system, slope_func)\n",
-    "    Q_frame = recompute_fluxes(results, system)\n",
-    "    errors = compute_error(Q_frame, data)\n",
-    "    print('RMSE', np.sqrt(np.mean(errors**2)))\n",
-    "    return errors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `error_func`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.076, 0.272, 0.078, 212900, 113100]\n",
-      "RMSE 2.17995317290043\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([-0.33610526, -0.37832695, -0.39254863, ...,  2.26392988,\n",
-       "       -0.7752054 ,  1.99865932])"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "errors = error_func(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pass `error_func` to `leastsq` to see if it can do any better."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[7.600e-02 2.720e-01 7.800e-02 2.129e+05 1.131e+05]\n",
-      "RMSE 2.17995317290043\n",
-      "[7.600e-02 2.720e-01 7.800e-02 2.129e+05 1.131e+05]\n",
-      "RMSE 2.17995317290043\n",
-      "[7.600e-02 2.720e-01 7.800e-02 2.129e+05 1.131e+05]\n",
-      "RMSE 2.17995317290043\n",
-      "[7.60000011e-02 2.72000000e-01 7.80000000e-02 2.12900000e+05\n",
-      " 1.13100000e+05]\n",
-      "RMSE 2.17995317290043\n",
-      "[7.60000000e-02 2.72000004e-01 7.80000000e-02 2.12900000e+05\n",
-      " 1.13100000e+05]\n",
-      "RMSE 2.17995317290043\n",
-      "[7.60000000e-02 2.72000000e-01 7.80000012e-02 2.12900000e+05\n",
-      " 1.13100000e+05]\n",
-      "RMSE 2.17995317290043\n",
-      "[7.60000000e-02 2.72000000e-01 7.80000000e-02 2.12900003e+05\n",
-      " 1.13100000e+05]\n",
-      "RMSE 2.17995317290043\n",
-      "[7.60000000e-02 2.72000000e-01 7.80000000e-02 2.12900000e+05\n",
-      " 1.13100002e+05]\n",
-      "RMSE 2.17995317290043\n"
-     ]
-    }
-   ],
-   "source": [
-    "best_params, details = leastsq(error_func, params, data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>fvec</th>\n",
-       "      <td>[-0.33610526315786693, -0.378326947375756, -0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nfev</th>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fjac</th>\n",
-       "      <td>[[-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ipvt</th>\n",
-       "      <td>[1, 2, 3, 4, 5]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>qtf</th>\n",
-       "      <td>[-0.33610526315786693, -0.378326947375756, -0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cov_x</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mesg</th>\n",
-       "      <td>The cosine of the angle between func(x) and an...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ier</th>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "fvec     [-0.33610526315786693, -0.378326947375756, -0....\n",
-       "nfev                                                     6\n",
-       "fjac     [[-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...\n",
-       "ipvt                                       [1, 2, 3, 4, 5]\n",
-       "qtf      [-0.33610526315786693, -0.378326947375756, -0....\n",
-       "cov_x                                                 None\n",
-       "mesg     The cosine of the angle between func(x) and an...\n",
-       "ier                                                      4\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The cosine of the angle between func(x) and any column of the\\n  Jacobian is at most 0.000000 in absolute value'"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "details.mesg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The best params are only slightly different from the starting place. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([7.600e-02, 2.720e-01, 7.800e-02, 2.129e+05, 1.131e+05])"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "best_params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like with the `best_params`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.17995317290043\n"
-     ]
-    }
-   ],
-   "source": [
-    "system = make_system(best_params, data)\n",
-    "system = remove_units(system)\n",
-    "\n",
-    "results, details = run_ode_solver(system, slope_func, t_eval=system.ts)\n",
-    "Q_frame = recompute_fluxes(results, system)\n",
-    "errors = compute_error(Q_frame, data)\n",
-    "print(np.sqrt(np.mean(errors**2)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The RMS error is only slightly smaller.\n",
-    "\n",
-    "And the results are visually similar."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_Q_in(Q_frame, data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_Q_out(Q_frame, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Try starting the model with a different set of parameters and see if it moves toward the parameters in the paper.\n",
-    "\n",
-    "I found that no matter where I start, `leastsq` doesn't move far, which suggests that it is not able to optimize the parameters effectively."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Notes\n",
-    "\n",
-    "Notes on working with degC.\n",
-    "\n",
-    "Usually I construct a `Quantity` object by multiplying a number and a unit.  With degC, that doesn't work; you get\n",
-    "\n",
-    "```\n",
-    "OffsetUnitCalculusError: Ambiguous operation with offset unit (degC).\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#16.11 * C "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The problem is that it doesn't know whether you want a temperature measurement or a temperature difference.\n",
-    "\n",
-    "You can create a temperature measurement like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "16.11 degC"
-      ],
-      "text/latex": [
-       "$16.11\\ \\mathrm{degC}$"
-      ],
-      "text/plain": [
-       "16.11 <Unit('degC')>"
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "T = Quantity(16.11, degC)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you convert to Kelvin, it does the right thing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "289.26 kelvin"
-      ],
-      "text/latex": [
-       "$289.26\\ \\mathrm{kelvin}$"
-      ],
-      "text/plain": [
-       "289.26 <Unit('kelvin')>"
-      ]
-     },
-     "execution_count": 113,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "T.to(K)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When you subtract temperatures, the results is a temperature difference, indicated by the units."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "16.11 delta_degC"
-      ],
-      "text/latex": [
-       "$16.11\\ \\mathrm{delta_degC}$"
-      ],
-      "text/plain": [
-       "16.11 <Unit('delta_degC')>"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diff = T - 273.15 * K"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you convert a temperature difference to Kelvin, it does the right thing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "16.11 kelvin"
-      ],
-      "text/latex": [
-       "$16.11\\ \\mathrm{kelvin}$"
-      ],
-      "text/plain": [
-       "16.11 <Unit('kelvin')>"
-      ]
-     },
-     "execution_count": 115,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diff.to(K)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/soln/yoyo_soln.ipynb b/soln/yoyo_soln.ipynb
deleted file mode 100644
index 2c0f1a30..00000000
--- a/soln/yoyo_soln.ipynb
+++ /dev/null
@@ -1,1168 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case study\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Yo-yo\n",
-    "\n",
-    "Suppose you are holding a yo-yo with a length of string wound around its axle, and you drop it while holding the end of the string stationary.  As gravity accelerates the yo-yo downward, tension in the string exerts a force upward.  Since this force acts on a point offset from the center of mass, it exerts a torque that causes the yo-yo to spin.\n",
-    "\n",
-    "![](diagrams/yoyo.png)\n",
-    "\n",
-    "This figure shows the forces on the yo-yo and the resulting torque.  The outer shaded area shows the body of the yo-yo.  The inner shaded area shows the rolled up string, the radius of which changes as the yo-yo unrolls.\n",
-    "\n",
-    "In this model, we can't figure out the linear and angular acceleration independently; we have to solve a system of equations:\n",
-    "\n",
-    "$\\sum F = m a $\n",
-    "\n",
-    "$\\sum \\tau = I \\alpha$\n",
-    "\n",
-    "where the summations indicate that we are adding up forces and torques.\n",
-    "\n",
-    "As in the previous examples, linear and angular velocity are related because of the way the string unrolls:\n",
-    "\n",
-    "$\\frac{dy}{dt} = -r \\frac{d \\theta}{dt} $\n",
-    "\n",
-    "In this example, the linear and angular accelerations have opposite sign.  As the yo-yo rotates counter-clockwise, $\\theta$ increases and $y$, which is the length of the rolled part of the string, decreases.\n",
-    "\n",
-    "Taking the derivative of both sides yields a similar relationship between linear and angular acceleration:\n",
-    "\n",
-    "$\\frac{d^2 y}{dt^2} = -r \\frac{d^2 \\theta}{dt^2} $\n",
-    "\n",
-    "Which we can write more concisely:\n",
-    "\n",
-    "$ a = -r \\alpha $\n",
-    "\n",
-    "This relationship is not a general law of nature; it is specific to scenarios like this where there is rolling without stretching or slipping.\n",
-    "\n",
-    "Because of the way we've set up the problem, $y$ actually has two meanings: it represents the length of the rolled string and the height of the yo-yo, which decreases as the yo-yo falls.  Similarly, $a$ represents acceleration in the length of the rolled string and the height of the yo-yo.\n",
-    "\n",
-    "We can compute the acceleration of the yo-yo by adding up the linear forces:\n",
-    "\n",
-    "$\\sum F = T - mg = ma $\n",
-    "\n",
-    "Where $T$ is positive because the tension force points up, and $mg$ is negative because gravity points down.\n",
-    "\n",
-    "Because gravity acts on the center of mass, it creates no torque, so the only torque is due to tension:\n",
-    "\n",
-    "$\\sum \\tau = T r = I \\alpha $\n",
-    "\n",
-    "Positive (upward) tension yields positive (counter-clockwise) angular acceleration.\n",
-    "\n",
-    "Now we have three equations in three unknowns, $T$, $a$, and $\\alpha$, with $I$, $m$, $g$, and $r$ as known parameters.  It is simple enough to solve these equations by hand, but we can also get SymPy to do it for us.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sympy import init_printing, symbols, Eq, solve\n",
-    "\n",
-    "init_printing()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "T, a, alpha, I, m, g, r = symbols('T a alpha I m g r')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAE4AAAAJCAYAAACc9xrJAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAB7klEQVRIDdWWi00CQRRFWUMBJlYgdkCkA+wAtQKwA40VGOgArcBPB2gFRDqQDgh0gOcMM7DAQoIx0X3Jm5n3G+a+vbNLNp/PK0myLKuxvkNn6DH6hd6jp+TpK7WArwuASQRxxvwKrvdNUOQ18dXRBtpGz6PNVKlQ03MIim3yFK3lfJ+LvEVO8hfN5Hko8w/RftFev+3jTBJCEjTT3qwlxhJvtFvGkW6cB6zFk/za0xCLCWmTjnZSkvroINllncFg00Iz8hjEhoaHx3yL2geZlhplnazMk6uuXSWgyBaLXjRyIgtt3p8J18ZzfRx4gDbgRtZQ7/ll3IP2hoyxvYbKCTUz8sfWxt+17iZEGfCvrjWGfim7xixsD2wwdNi8Mirn38KWcBCTMMYDcZLfGWmhLItxV2NnbVJ4QlZEkW1WbvpTfG1mH3881KwF9hs+3cv9KT+P7sGW31TsQ84xyztZX6A7saeras3QISfXrBPd7f6IzaV2oRDza/yvxGbQPM/ku2qXzMh7Kwhe4Xss8AfXkZuz8u56n4PwYx0WviRToxrkpfUiqTyj4GXPmkSM+mSc70LxBmFtL/Q/LzzbY0ZDLDLpCU2ss5E21OunT7atXow4yiTgE4eyZB54ApuI+fEQ6wRfzyR84aOI7X+9QvkGjRGdEcRjQZEAAAAASUVORK5CYII=\n",
-      "text/latex": [
-       "$\\displaystyle a = - \\alpha r$"
-      ],
-      "text/plain": [
-       "a = -α⋅r"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eq1 = Eq(a, -r * alpha)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle T - g m = a m$"
-      ],
-      "text/plain": [
-       "T - g⋅m = a⋅m"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eq2 = Eq(T - m * g, m * a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle T r = I \\alpha$"
-      ],
-      "text/plain": [
-       "T⋅r = I⋅α"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eq3 = Eq(T * r, I * alpha)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\left\\{ T : \\frac{I g m}{I + m r^{2}}, \\  a : - \\frac{g m r^{2}}{I + m r^{2}}, \\  \\alpha : \\frac{g m r}{I + m r^{2}}\\right\\}$"
-      ],
-      "text/plain": [
-       "⎧                      2              ⎫\n",
-       "⎪    I⋅g⋅m       -g⋅m⋅r        g⋅m⋅r  ⎪\n",
-       "⎨T: ────────, a: ────────, α: ────────⎬\n",
-       "⎪          2            2            2⎪\n",
-       "⎩   I + m⋅r      I + m⋅r      I + m⋅r ⎭"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "soln = solve([eq1, eq2, eq3], [T, a, alpha])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{I g m}{I + m r^{2}}$"
-      ],
-      "text/plain": [
-       " I⋅g⋅m  \n",
-       "────────\n",
-       "       2\n",
-       "I + m⋅r "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "soln[T]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle - \\frac{g m r^{2}}{I + m r^{2}}$"
-      ],
-      "text/plain": [
-       "      2 \n",
-       "-g⋅m⋅r  \n",
-       "────────\n",
-       "       2\n",
-       "I + m⋅r "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "soln[a]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{g m r}{I + m r^{2}}$"
-      ],
-      "text/plain": [
-       " g⋅m⋅r  \n",
-       "────────\n",
-       "       2\n",
-       "I + m⋅r "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "soln[alpha]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "The results are\n",
-    "\n",
-    "$T      = m g I / I^*   $\n",
-    "\n",
-    "$a      = -m g r^2 / I^* $\n",
-    "\n",
-    "$\\alpha = m g r / I^*    $\n",
-    "\n",
-    "where $I^*$ is the augmented moment of inertia, $I + m r^2$.\n",
-    "\n",
-    "You can also see [the derivation of these equations in this video](https://www.youtube.com/watch?v=chC7xVDKl4Q).\n",
-    "\n",
-    "To simulate the system, we don't really need $T$; we can plug $a$ and $\\alpha$ directly into the slope function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "newton"
-      ],
-      "text/latex": [
-       "$\\mathrm{newton}$"
-      ],
-      "text/plain": [
-       "<Unit('newton')>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "radian = UNITS.radian\n",
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Simulate the descent of a yo-yo.  How long does it take to reach the end of the string?\n",
-    "\n",
-    "I provide a `Params` object with the system parameters:\n",
-    "\n",
-    "* `Rmin` is the radius of the axle.  `Rmax` is the radius of the axle plus rolled string.\n",
-    "\n",
-    "* `Rout` is the radius of the yo-yo body.  `mass` is the total mass of the yo-yo, ignoring the string.  \n",
-    "\n",
-    "* `L` is the length of the string.\n",
-    "\n",
-    "* `g` is the acceleration of gravity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Rmin</th>\n",
-       "      <td>0.008 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rmax</th>\n",
-       "      <td>0.016 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rout</th>\n",
-       "      <td>0.035 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>0.05 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>L</th>\n",
-       "      <td>1 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>1 second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Rmin                 0.008 meter\n",
-       "Rmax                 0.016 meter\n",
-       "Rout                 0.035 meter\n",
-       "mass               0.05 kilogram\n",
-       "L                        1 meter\n",
-       "g        9.8 meter / second ** 2\n",
-       "t_end                   1 second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params = Params(Rmin = 8e-3 * m,\n",
-    "                Rmax = 16e-3 * m,\n",
-    "                Rout = 35e-3 * m,\n",
-    "                mass = 50e-3 * kg,\n",
-    "                L = 1 * m,\n",
-    "                g = 9.8 * m / s**2,\n",
-    "                t_end = 1 * s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a `make_system` function that computes `I` and `k` based on the system parameters.\n",
-    "\n",
-    "I estimated `I` by modeling the yo-yo as a solid cylinder with uniform density ([see here](https://en.wikipedia.org/wiki/List_of_moments_of_inertia)).\n",
-    "\n",
-    "In reality, the distribution of weight in a yo-yo is often designed to achieve desired effects.  But we'll keep it simple."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params with Rmin, Rmax, Rout, \n",
-    "                              mass, L, g, t_end\n",
-    "    \n",
-    "    returns: System with init, k, Rmin, Rmax, mass,\n",
-    "                         I, g, ts\n",
-    "    \"\"\"\n",
-    "    L, mass = params.L, params.mass\n",
-    "    Rout, Rmax, Rmin = params.Rout, params.Rmax, params.Rmin \n",
-    "    \n",
-    "    init = State(theta = 0 * radian,\n",
-    "                 omega = 0 * radian/s,\n",
-    "                 y = L,\n",
-    "                 v = 0 * m / s)\n",
-    "    \n",
-    "    I = mass * Rout**2 / 2\n",
-    "    k = (Rmax**2 - Rmin**2) / 2 / L / radian    \n",
-    "    \n",
-    "    return System(params, init=init, I=I, k=k)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `make_system`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Rmin</th>\n",
-       "      <td>0.008 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rmax</th>\n",
-       "      <td>0.016 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rout</th>\n",
-       "      <td>0.035 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mass</th>\n",
-       "      <td>0.05 kilogram</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>L</th>\n",
-       "      <td>1 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8 meter / second ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>1 second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>theta               0 radian\n",
-       "omega    0.0 radi...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>I</th>\n",
-       "      <td>3.0625000000000006e-05 kilogram * meter ** 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k</th>\n",
-       "      <td>9.6e-05 meter / radian</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Rmin                                           0.008 meter\n",
-       "Rmax                                           0.016 meter\n",
-       "Rout                                           0.035 meter\n",
-       "mass                                         0.05 kilogram\n",
-       "L                                                  1 meter\n",
-       "g                                  9.8 meter / second ** 2\n",
-       "t_end                                             1 second\n",
-       "init     theta               0 radian\n",
-       "omega    0.0 radi...\n",
-       "I             3.0625000000000006e-05 kilogram * meter ** 2\n",
-       "k                                   9.6e-05 meter / radian\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>theta</th>\n",
-       "      <td>0 radian</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>omega</th>\n",
-       "      <td>0.0 radian / second</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>1 meter</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v</th>\n",
-       "      <td>0.0 meter / second</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "theta               0 radian\n",
-       "omega    0.0 radian / second\n",
-       "y                    1 meter\n",
-       "v         0.0 meter / second\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write a slope function for this system, using these results from the book:\n",
-    "\n",
-    "$ r = \\sqrt{2 k y + R_{min}^2} $ \n",
-    "\n",
-    "$ T      = m g I / I^*  $\n",
-    "\n",
-    "$ a      = -m g r^2 / I^* $\n",
-    "\n",
-    "$ \\alpha  = m g r / I^*  $\n",
-    "\n",
-    "where $I^*$ is the augmented moment of inertia, $I + m r^2$.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object with theta, omega, y, v\n",
-    "    t: time\n",
-    "    system: System object with Rmin, k, I, mass\n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    theta, omega, y, v = state\n",
-    "    g, k, Rmin = system.g, system.k, system.Rmin\n",
-    "    I, mass = system.I, system.mass\n",
-    "    \n",
-    "    r = sqrt(2*k*y + Rmin**2)\n",
-    "    alpha = mass * g * r / (I + mass * r**2)\n",
-    "    a = -r * alpha\n",
-    "        \n",
-    "    return omega, alpha, v, a        "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your slope function with the initial paramss."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0 <Unit('radian / second')>,\n",
-       " 180.54116292458264 <Unit('1 / radian ** 0.5 / second ** 2')>,\n",
-       " 0.0 <Unit('meter / second')>,\n",
-       " -2.888658606793322 <Unit('meter / radian / second ** 2')>)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "slope_func(system.init, 0*s, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write an event function that will stop the simulation when `y` is 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Stops when y is 0.\n",
-    "    \n",
-    "    state: State object with theta, omega, y, v\n",
-    "    t: time\n",
-    "    system: System object with Rmin, k, I, mass\n",
-    "    \n",
-    "    returns: y\n",
-    "    \"\"\"\n",
-    "    theta, omega, y, v = state\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your event function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "1 meter"
-      ],
-      "text/latex": [
-       "$1\\ \\mathrm{meter}$"
-      ],
-      "text/plain": [
-       "1 <Unit('meter')>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "event_func(system.init, 0*s, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>sol</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_events</th>\n",
-       "      <td>[[0.879217870162702]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nfev</th>\n",
-       "      <td>134</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>njev</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nlu</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>status</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>A termination event occurred.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "sol                                  None\n",
-       "t_events            [[0.879217870162702]]\n",
-       "nfev                                  134\n",
-       "njev                                    0\n",
-       "nlu                                     0\n",
-       "status                                  1\n",
-       "message     A termination event occurred.\n",
-       "success                              True\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results, details = run_ode_solver(system, slope_func, events=event_func, max_step=0.05)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Check the final state.  If things have gone according to plan, the final value of `y` should be close to 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>theta</th>\n",
-       "      <th>omega</th>\n",
-       "      <th>y</th>\n",
-       "      <th>v</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.706147</th>\n",
-       "      <td>44.0182</td>\n",
-       "      <td>121.32</td>\n",
-       "      <td>0.327291</td>\n",
-       "      <td>-1.76573</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.756147</th>\n",
-       "      <td>50.268</td>\n",
-       "      <td>128.609</td>\n",
-       "      <td>0.236982</td>\n",
-       "      <td>-1.84499</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.806147</th>\n",
-       "      <td>56.8724</td>\n",
-       "      <td>135.496</td>\n",
-       "      <td>0.142962</td>\n",
-       "      <td>-1.91405</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.856147</th>\n",
-       "      <td>63.8093</td>\n",
-       "      <td>141.885</td>\n",
-       "      <td>0.0457628</td>\n",
-       "      <td>-1.97198</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.879218</th>\n",
-       "      <td>67.1147</td>\n",
-       "      <td>144.631</td>\n",
-       "      <td>9.02056e-17</td>\n",
-       "      <td>-1.99469</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            theta    omega            y        v\n",
-       "0.706147  44.0182   121.32     0.327291 -1.76573\n",
-       "0.756147   50.268  128.609     0.236982 -1.84499\n",
-       "0.806147  56.8724  135.496     0.142962 -1.91405\n",
-       "0.856147  63.8093  141.885    0.0457628 -1.97198\n",
-       "0.879218  67.1147  144.631  9.02056e-17 -1.99469"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`theta` should increase and accelerate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_theta(results):\n",
-    "    plot(results.theta, color='C0', label='theta')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angle (rad)')\n",
-    "plot_theta(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`y` should decrease and accelerate down."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_y(results):\n",
-    "    plot(results.y, color='C1', label='y')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Length (m)')\n",
-    "    \n",
-    "plot_y(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot velocity as a function of time; is the yo-yo accelerating?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "v = results.v # m / s\n",
-    "plot(v)\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use `gradient` to estimate the derivative of `v`.  How does the acceleration of the yo-yo compare to `g`?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "a = gradient(v)\n",
-    "plot(a)\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Acceleration (m/$s^2$)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 8ae5c94cc95cf45f9d42f567dc945a249073257f Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 27 Apr 2023 17:18:14 -0400
Subject: [PATCH 105/144] Updating examples

---
 examples/plague.ipynb | 56 +++++++++++++++++++++----------------------
 1 file changed, 28 insertions(+), 28 deletions(-)

diff --git a/examples/plague.ipynb b/examples/plague.ipynb
index a2b391c5..6c55ca6f 100644
--- a/examples/plague.ipynb
+++ b/examples/plague.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 1,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 2,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 3,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 4,
    "id": "undefined-miller",
    "metadata": {
     "tags": []
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 5,
    "id": "growing-sperm",
    "metadata": {
     "tags": []
@@ -124,19 +124,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 6,
    "id": "recent-cooper",
    "metadata": {},
    "outputs": [],
    "source": [
     "def add_immunization(system, fraction):\n",
-    "    system.init.S -= fraction\n",
-    "    system.init.R += fraction"
+    "    system.init.s -= fraction\n",
+    "    system.init.r += fraction"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 7,
    "id": "found-learning",
    "metadata": {},
    "outputs": [],
@@ -152,14 +152,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 8,
    "id": "synthetic-element",
    "metadata": {},
    "outputs": [],
    "source": [
     "def calc_total_infected(results, system):\n",
-    "    s_0 = results.S[0]\n",
-    "    s_end = results.S[system.t_end]\n",
+    "    s_0 = results.s[0]\n",
+    "    s_end = results.s[system.t_end]\n",
     "    return s_0 - s_end"
    ]
   },
@@ -224,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 9,
    "id": "blond-armstrong",
    "metadata": {},
    "outputs": [],
@@ -259,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 10,
    "id": "disturbed-amount",
    "metadata": {},
    "outputs": [],
@@ -283,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 11,
    "id": "otherwise-answer",
    "metadata": {},
    "outputs": [],
@@ -308,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 12,
    "id": "seventh-strike",
    "metadata": {},
    "outputs": [],
@@ -334,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 13,
    "id": "obvious-congress",
    "metadata": {},
    "outputs": [],
@@ -355,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 14,
    "id": "polish-multiple",
    "metadata": {},
    "outputs": [],
@@ -376,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 15,
    "id": "statistical-garden",
    "metadata": {},
    "outputs": [],
@@ -403,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 16,
    "id": "joined-graduation",
    "metadata": {},
    "outputs": [],
@@ -424,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 17,
    "id": "cheap-decision",
    "metadata": {},
    "outputs": [],
@@ -464,7 +464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 18,
    "id": "surrounded-copying",
    "metadata": {},
    "outputs": [],
@@ -489,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 19,
    "id": "shaped-utility",
    "metadata": {},
    "outputs": [],
@@ -509,7 +509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 20,
    "id": "recognized-release",
    "metadata": {},
    "outputs": [],
@@ -546,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 21,
    "id": "worth-mounting",
    "metadata": {},
    "outputs": [],
@@ -556,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 22,
    "id": "adjusted-highlight",
    "metadata": {},
    "outputs": [],
@@ -601,7 +601,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -615,7 +615,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,

From 3efd0f904764a9770bb67651f31ff05c0a212ce6 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 27 Apr 2023 17:18:23 -0400
Subject: [PATCH 106/144] Adding notes

---
 chapters/build.sh | 25 +++++++++++++++++++++++++
 examples/build.sh | 24 ++++++++++++++++++++++++
 notes.txt         | 19 +++++++++++++++++++
 3 files changed, 68 insertions(+)
 create mode 100644 chapters/build.sh
 create mode 100644 examples/build.sh
 create mode 100644 notes.txt

diff --git a/chapters/build.sh b/chapters/build.sh
new file mode 100644
index 00000000..b16f5a3c
--- /dev/null
+++ b/chapters/build.sh
@@ -0,0 +1,25 @@
+# copy the notebooks and remove the solutions
+cp ../ModSimPySolutions/chapters/chap*.ipynb .
+python remove_soln.py
+
+# run nbmake
+rm modsim.py chap*.py
+pytest --nbmake chap*.ipynb
+
+# add and commit
+git add chap*.ipynb chap*.py
+git commit -m "Updating chapters"
+
+# build the zip file
+cd ../..; zip -r ModSimPyNotebooks.zip \
+    ModSimPy/chapters/chap*.ipynb \
+    ModSimPy/examples/*.ipynb
+
+# add and commit it
+mv ModSimPyNotebooks.zip ModSimPy
+cd ModSimPy
+
+git add ModSimPyNotebooks.zip
+git commit -m "Updating the notebook zip file"
+
+git push
diff --git a/examples/build.sh b/examples/build.sh
new file mode 100644
index 00000000..56d540b8
--- /dev/null
+++ b/examples/build.sh
@@ -0,0 +1,24 @@
+# copy the notebooks and remove the solutions
+cp ../ModSimPySolutions/examples/*.ipynb .
+python remove_soln.py
+
+# run nbmake
+rm modsim.py chap*.py
+pytest --nbmake \
+bungee1.ipynb \
+bungee2.ipynb \
+glucose.ipynb \
+header.ipynb \
+insulin.ipynb \
+plague.ipynb \
+queue.ipynb \
+spiderman.ipynb \
+throwingaxe.ipynb \
+trees.ipynb \
+wall.ipynb \
+
+
+# add and commit
+git add *.ipynb
+git commit -m "Updating examples"
+git push
diff --git a/notes.txt b/notes.txt
new file mode 100644
index 00000000..be6a7006
--- /dev/null
+++ b/notes.txt
@@ -0,0 +1,19 @@
+How to do an update:
+
+There are three repos involved:
+
+* ModSim: contains shared resources between ModSimPy and ModSimMATLAB, mostly figures. Active branch is main.
+
+* ModSimPy: Public repo with notebooks and examples without solutions. Active branch is master.
+
+* ModSimPySolutions: Private repo where I do development and keep solutions.
+
+The active directories are chapters and examples. There are lots of leftover bits I should clean up some day!
+
+ModSimPySolutions is has actions on GitHub that run tests on push and once a month.
+
+In ModSimPy, the chapters and examples directories have build.sh files that copy the notebooks from ModSimPySolutions, removes solutions, runs tests, and pushes updates to GitHub.
+
+Not all notebooks can run without solutions, so those don't get tested.
+
+The canonical location of modsim.py is in the top directory of ModSimPy. Everything else is a copy.

From b981af5236396be59982b8f82d050ccf78c34834 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 2 May 2023 18:00:46 -0400
Subject: [PATCH 107/144] Update README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 5339bbf3..845d86c4 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
 # Modeling and Simulation in Python
 
-[Read the book and run the code](https://allendowney.github.io/ModSimPy/).
+Order the book from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) or [Amazon](https://amzn.to/3y9UxNb).
 
-[Pre-order the book from No Starch Press](https://nostarch.com/modeling-and-simulation-python).
+[Read the book and run the code](https://allendowney.github.io/ModSimPy/).
 
 *Modeling and Simulation in Python* is an introduction to physical modeling using a computational approach.  It is organized in three parts:
 

From a7153bec6d519e9c5438ab7d9fe0f7703cf84781 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Tue, 2 May 2023 18:01:31 -0400
Subject: [PATCH 108/144] Add files via upload

---
 modsimpy_cover.png | Bin 0 -> 241955 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 modsimpy_cover.png

diff --git a/modsimpy_cover.png b/modsimpy_cover.png
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literal 0
HcmV?d00001


From ab03b58e4b53e28910f98eabf371ecbdeecef067 Mon Sep 17 00:00:00 2001
From: rioj7 <38918937+rioj7@users.noreply.github.com>
Date: Mon, 22 May 2023 15:50:14 +0200
Subject: [PATCH 109/144] Updating trees.ipynb

---
 examples/trees.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/trees.ipynb b/examples/trees.ipynb
index 5dad9e80..b0be531f 100644
--- a/examples/trees.ipynb
+++ b/examples/trees.ipynb
@@ -197,7 +197,7 @@
     "\n",
     "$$ m_{n+1} = m_n + \\alpha A$$\n",
     "\n",
-    "where $m_n$ is the mass of the at time step $n$, $A$ is the area exposed to sunlight, and $\\alpha$ is an unknown growth parameter.\n",
+    "where $m_n$ is the mass of the tree at time step $n$, $A$ is the area exposed to sunlight, and $\\alpha$ is an unknown growth parameter.\n",
     "\n",
     "To get from $m$ to $A$, I'll make the additional assumption that mass is proportional to height raised to an unknown power:\n",
     "\n",
@@ -440,7 +440,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "I'll wrap the code from the previous section is a function that takes the parameters as inputs and makes a `System` object."
+    "I'll wrap the code from the previous section in a function that takes the parameters as inputs and makes a `System` object."
    ]
   },
   {
@@ -519,7 +519,7 @@
     "\n",
     "data.plot(label='data')\n",
     "decorate(xlabel='Time (years)',\n",
-    "             ylabel='Height (feet)')"
+    "         ylabel='Height (feet)')"
    ]
   },
   {
@@ -899,7 +899,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Taking the derivative of the last equation yields\n",
+    "Taking the derivative of (3) yields\n",
     "\n",
     "(4) $\\frac{dm}{dt} = D h^{D-1} \\frac{dh}{dt}$\n",
     "\n",

From 6d77b68687110e65b2e249c5396ccd2c9a230763 Mon Sep 17 00:00:00 2001
From: rioj7 <38918937+rioj7@users.noreply.github.com>
Date: Fri, 26 May 2023 10:12:04 +0200
Subject: [PATCH 110/144] find root, min, max of func: update doc string and
 refactor kwargs check

---
 modsim.py        | 101 +++++++++++++++++++++++++----------------------
 modsim/modsim.py | 101 +++++++++++++++++++++++++----------------------
 2 files changed, 108 insertions(+), 94 deletions(-)

diff --git a/modsim.py b/modsim.py
index ed012ec6..0cd9034f 100644
--- a/modsim.py
+++ b/modsim.py
@@ -105,38 +105,42 @@ def linrange(start, stop=None, step=1):
     return linspace(start, stop, n+1)
 
 
+def __check_kwargs(kwargs, param_name, param_len, func, func_name):
+    """Check if `kwargs` has a parameter that is a sequence of a particular length
+       param_len: sequence enumerating possible lengths
+    """
+    param_val = kwargs.get(param_name, None)
+    if param_val is None or len(param_val) not in param_len:
+        msg = ("To run `{}`, you have to provide a "
+               "`{}` keyword argument with a sequence of length {}.")
+        raise ValueError(msg.format(func_name, param_name, ' or '.join(map(str, param_len))))
+
+    try:
+        func(param_val[0])
+    except Exception as e:
+        msg = ("In `{}` I tried running the function you provided "
+               "with `{}[0]`, and I got the following error:")
+        logger.error(msg.format(func_name, param_name))
+        raise (e)
+
 def root_scalar(func, *args, **kwargs):
-    """Finds the input value that minimizes `min_func`.
+    """Find the input value that is a root of `func`.
 
     Wrapper for
     https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root_scalar.html
 
-    func: computes the function to be minimized
+    func: computes the function to find a root of
     bracket: sequence of two values, lower and upper bounds of the range to be searched
-    args: any additional positional arguments are passed to func
-    kwargs: any keyword arguments are passed to root_scalar
+    args: any additional positional arguments are passed to `func`
+    kwargs: any keyword arguments are passed to `root_scalar`
 
     returns: RootResults object
     """
-    bracket = kwargs.get('bracket', None)
-    if bracket is None or len(bracket) != 2:
-        msg = ("To run root_scalar, you have to provide a "
-               "`bracket` keyword argument with a sequence "
-               "of length 2.")
-        raise ValueError(msg)
-
-    try:
-        func(bracket[0], *args)
-    except Exception as e:
-        msg = ("Before running scipy.integrate.root_scalar "
-               "I tried running the function you provided "
-               "with `bracket[0]`, "
-               "and I got the following error:")
-        logger.error(msg)
-        raise (e)
 
     underride(kwargs, rtol=1e-4)
 
+    __check_kwargs(kwargs, 'bracket', [2], lambda x: func(x, *args), 'root_scalar')
+
     res = spo.root_scalar(func, *args, **kwargs)
 
     if not res.converged:
@@ -148,37 +152,36 @@ def root_scalar(func, *args, **kwargs):
 
 
 def minimize_scalar(func, *args, **kwargs):
-    """Finds the input value that minimizes `func`.
+    """Find the input value that minimizes `func`.
 
     Wrapper for
     https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
 
     func: computes the function to be minimized
-    args: any additional positional arguments are passed to func
-    kwargs: any keyword arguments are passed to minimize_scalar
+    bracket: (`method` is `brent` or `golden`) sequence of two or three values, the range to be searched
+    bounds: (`method` is `bounded`) sequence of two values, the range to be searched
+    args: any additional positional arguments are passed to `func`
+    kwargs: any keyword arguments are passed to `minimize_scalar`
 
     returns: OptimizeResult object
     """
-    bounds = kwargs.get('bounds', None)
 
-    if bounds is None or len(bounds) != 2:
-        msg = ("To run maximize_scalar or minimize_scalar, "
-               "you have to provide a `bounds` "
-               "keyword argument with a sequence "
-               "of length 2.")
-        raise ValueError(msg)
+    underride(kwargs, __func_name='minimize_scalar')
 
-    try:
-        func(bounds[0], *args)
-    except Exception as e:
-        msg = ("Before running scipy.integrate.minimize_scalar, "
-               "I tried running the function you provided "
-               "with the lower bound, "
-               "and I got the following error:")
-        logger.error(msg)
-        raise (e)
+    method = kwargs.get('method', None)
+    if method is None:
+        method = 'bounded' if kwargs.get('bounds', None) else 'brent'
+        kwargs['method'] = method
+
+    if method == 'bounded':
+        param_name = 'bounds'
+        param_len = [2]
+    else:
+        param_name = 'bracket'
+        param_len = [2, 3]
 
-    underride(kwargs, method='bounded')
+    func_name = kwargs.pop('__func_name')
+    __check_kwargs(kwargs, param_name, param_len, lambda x: func(x, *args), func_name)
 
     res = spo.minimize_scalar(func, args=args, **kwargs)
 
@@ -191,19 +194,23 @@ def minimize_scalar(func, *args, **kwargs):
     return res
 
 
-def maximize_scalar(max_func, *args, **kwargs):
-    """Finds the input value that maximizes `max_func`.
+def maximize_scalar(func, *args, **kwargs):
+    """Find the input value that maximizes `func`.
 
     Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
 
-    min_func: computes the function to be maximized
-    args: any additional positional arguments are passed to max_func
-    options: any keyword arguments are passed as options to minimize_scalar
+    func: computes the function to be maximized
+    bracket: (`method` is `brent` or `golden`) sequence of two or three values, the range to be searched
+    bounds: (`method` is `bounded`) sequence of two values, the range to be searched
+    args: any additional positional arguments are passed to `func`
+    kwargs: any keyword arguments are passed as options to `minimize_scalar`
 
-    returns: ModSimSeries object
+    returns: OptimizeResult object
     """
     def min_func(*args):
-        return -max_func(*args)
+        return -func(*args)
+
+    underride(kwargs, __func_name='maximize_scalar')
 
     res = minimize_scalar(min_func, *args, **kwargs)
 
diff --git a/modsim/modsim.py b/modsim/modsim.py
index ed012ec6..0cd9034f 100644
--- a/modsim/modsim.py
+++ b/modsim/modsim.py
@@ -105,38 +105,42 @@ def linrange(start, stop=None, step=1):
     return linspace(start, stop, n+1)
 
 
+def __check_kwargs(kwargs, param_name, param_len, func, func_name):
+    """Check if `kwargs` has a parameter that is a sequence of a particular length
+       param_len: sequence enumerating possible lengths
+    """
+    param_val = kwargs.get(param_name, None)
+    if param_val is None or len(param_val) not in param_len:
+        msg = ("To run `{}`, you have to provide a "
+               "`{}` keyword argument with a sequence of length {}.")
+        raise ValueError(msg.format(func_name, param_name, ' or '.join(map(str, param_len))))
+
+    try:
+        func(param_val[0])
+    except Exception as e:
+        msg = ("In `{}` I tried running the function you provided "
+               "with `{}[0]`, and I got the following error:")
+        logger.error(msg.format(func_name, param_name))
+        raise (e)
+
 def root_scalar(func, *args, **kwargs):
-    """Finds the input value that minimizes `min_func`.
+    """Find the input value that is a root of `func`.
 
     Wrapper for
     https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root_scalar.html
 
-    func: computes the function to be minimized
+    func: computes the function to find a root of
     bracket: sequence of two values, lower and upper bounds of the range to be searched
-    args: any additional positional arguments are passed to func
-    kwargs: any keyword arguments are passed to root_scalar
+    args: any additional positional arguments are passed to `func`
+    kwargs: any keyword arguments are passed to `root_scalar`
 
     returns: RootResults object
     """
-    bracket = kwargs.get('bracket', None)
-    if bracket is None or len(bracket) != 2:
-        msg = ("To run root_scalar, you have to provide a "
-               "`bracket` keyword argument with a sequence "
-               "of length 2.")
-        raise ValueError(msg)
-
-    try:
-        func(bracket[0], *args)
-    except Exception as e:
-        msg = ("Before running scipy.integrate.root_scalar "
-               "I tried running the function you provided "
-               "with `bracket[0]`, "
-               "and I got the following error:")
-        logger.error(msg)
-        raise (e)
 
     underride(kwargs, rtol=1e-4)
 
+    __check_kwargs(kwargs, 'bracket', [2], lambda x: func(x, *args), 'root_scalar')
+
     res = spo.root_scalar(func, *args, **kwargs)
 
     if not res.converged:
@@ -148,37 +152,36 @@ def root_scalar(func, *args, **kwargs):
 
 
 def minimize_scalar(func, *args, **kwargs):
-    """Finds the input value that minimizes `func`.
+    """Find the input value that minimizes `func`.
 
     Wrapper for
     https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
 
     func: computes the function to be minimized
-    args: any additional positional arguments are passed to func
-    kwargs: any keyword arguments are passed to minimize_scalar
+    bracket: (`method` is `brent` or `golden`) sequence of two or three values, the range to be searched
+    bounds: (`method` is `bounded`) sequence of two values, the range to be searched
+    args: any additional positional arguments are passed to `func`
+    kwargs: any keyword arguments are passed to `minimize_scalar`
 
     returns: OptimizeResult object
     """
-    bounds = kwargs.get('bounds', None)
 
-    if bounds is None or len(bounds) != 2:
-        msg = ("To run maximize_scalar or minimize_scalar, "
-               "you have to provide a `bounds` "
-               "keyword argument with a sequence "
-               "of length 2.")
-        raise ValueError(msg)
+    underride(kwargs, __func_name='minimize_scalar')
 
-    try:
-        func(bounds[0], *args)
-    except Exception as e:
-        msg = ("Before running scipy.integrate.minimize_scalar, "
-               "I tried running the function you provided "
-               "with the lower bound, "
-               "and I got the following error:")
-        logger.error(msg)
-        raise (e)
+    method = kwargs.get('method', None)
+    if method is None:
+        method = 'bounded' if kwargs.get('bounds', None) else 'brent'
+        kwargs['method'] = method
+
+    if method == 'bounded':
+        param_name = 'bounds'
+        param_len = [2]
+    else:
+        param_name = 'bracket'
+        param_len = [2, 3]
 
-    underride(kwargs, method='bounded')
+    func_name = kwargs.pop('__func_name')
+    __check_kwargs(kwargs, param_name, param_len, lambda x: func(x, *args), func_name)
 
     res = spo.minimize_scalar(func, args=args, **kwargs)
 
@@ -191,19 +194,23 @@ def minimize_scalar(func, *args, **kwargs):
     return res
 
 
-def maximize_scalar(max_func, *args, **kwargs):
-    """Finds the input value that maximizes `max_func`.
+def maximize_scalar(func, *args, **kwargs):
+    """Find the input value that maximizes `func`.
 
     Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
 
-    min_func: computes the function to be maximized
-    args: any additional positional arguments are passed to max_func
-    options: any keyword arguments are passed as options to minimize_scalar
+    func: computes the function to be maximized
+    bracket: (`method` is `brent` or `golden`) sequence of two or three values, the range to be searched
+    bounds: (`method` is `bounded`) sequence of two values, the range to be searched
+    args: any additional positional arguments are passed to `func`
+    kwargs: any keyword arguments are passed as options to `minimize_scalar`
 
-    returns: ModSimSeries object
+    returns: OptimizeResult object
     """
     def min_func(*args):
-        return -max_func(*args)
+        return -func(*args)
+
+    underride(kwargs, __func_name='maximize_scalar')
 
     res = minimize_scalar(min_func, *args, **kwargs)
 

From 60716525c9b2a5fd1632ed3a782f587be852cace Mon Sep 17 00:00:00 2001
From: bluthej <joffrey.bluthe@e.email>
Date: Sat, 2 Sep 2023 17:16:31 +0200
Subject: [PATCH 111/144] Fix 'the the' occurrences

---
 book/book.tex       | 4 ++--
 examples/yoyo.ipynb | 2 +-
 jb/preface.md       | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/book/book.tex b/book/book.tex
index 0b0274e3..ea1c9070 100644
--- a/book/book.tex
+++ b/book/book.tex
@@ -366,7 +366,7 @@ \section{How much programming do I need?}
 
 Some features in the ModSim library are like training wheels; at some point you will probably stop using them and start working with the underlying libraries directly.  Other features you might find useful the whole time you are working through the book, and later.
 
-I encourage you to read the the ModSim library code.  Most of it is not complicated, and I tried to make it readable.  Particularly if you have some programming experience, you might learn something by reverse engineering my design decisions.
+I encourage you to read the ModSim library code.  Most of it is not complicated, and I tried to make it readable.  Particularly if you have some programming experience, you might learn something by reverse engineering my design decisions.
 
 
 \section{How much math and science do I need?} 
@@ -5099,7 +5099,7 @@ \section{The glucose minimal model}
 insulin must first move from plasma to a remote compartment [...] to exert its action on glucose disposal.
 \end{quote}
 
-To paraphrase the second point, the effect of insulin on glucose disposal, as seen in the data, happens more slowly than we would expect if it depended primarily on the the concentration of insulin in the blood.  Bergman's group hypothesized that insulin must move relatively slowly from the blood to a ``remote compartment" where it has its effect.
+To paraphrase the second point, the effect of insulin on glucose disposal, as seen in the data, happens more slowly than we would expect if it depended primarily on the concentration of insulin in the blood.  Bergman's group hypothesized that insulin must move relatively slowly from the blood to a ``remote compartment" where it has its effect.
 
 \index{compartment model}
 
diff --git a/examples/yoyo.ipynb b/examples/yoyo.ipynb
index 316e3e0e..e732e41a 100644
--- a/examples/yoyo.ipynb
+++ b/examples/yoyo.ipynb
@@ -327,7 +327,7 @@
    "source": [
     "The state variables we'll use are angle, `theta`, angular velocity, `omega`, the length of the spooled string, `y`, and the linear velocity of the yo-yo, `v`.\n",
     "\n",
-    "Here is a `State` object with the the initial conditions."
+    "Here is a `State` object with the initial conditions."
    ]
   },
   {
diff --git a/jb/preface.md b/jb/preface.md
index cca775c2..f1aa6f5f 100644
--- a/jb/preface.md
+++ b/jb/preface.md
@@ -75,7 +75,7 @@ Some features in the ModSim library are like training wheels; at some
 point you will probably stop using them and start working with the
 underlying libraries directly. Other features you might find useful the whole time you are working through the book, and later.
 
-I encourage you to read the the ModSim library code. Most of it is not
+I encourage you to read the ModSim library code. Most of it is not
 complicated, and I tried to make it readable. Particularly if you have
 some programming experience, you might learn something by reverse
 engineering my design decisions.

From dbe6b0eef1fae6ebc431ee503d9b253c6237ab68 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sat, 2 Sep 2023 13:35:12 -0400
Subject: [PATCH 112/144] Updating chapters

---
 chapters/chap03.ipynb | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/chapters/chap03.ipynb b/chapters/chap03.ipynb
index dbb7f229..21c4f895 100644
--- a/chapters/chap03.ipynb
+++ b/chapters/chap03.ipynb
@@ -496,14 +496,14 @@
    "source": [
     "The equals operator is one of Python's *comparison operators*; the complete list is in the following table.\n",
     "\n",
-    "| Operation             \t| Symbol \t|\n",
-    "|-----------------------\t|--------\t|\n",
-    "| Less than             \t| `<`      \t|\n",
-    "| Greater than          \t| `>`      \t|\n",
-    "| Less than or equal    \t| `<=`     \t|\n",
-    "| Greater than or equal \t| `>=`     \t|\n",
-    "| Equal                 \t| `==`     \t|\n",
-    "| Not equal             \t| `!=`     \t|"
+    "| Operation             | Symbol |\n",
+    "|-----------------------|--------|\n",
+    "| Less than             | `<`    |\n",
+    "| Greater than          | `>`    |\n",
+    "| Less than or equal    | `<=`   |\n",
+    "| Greater than or equal | `>=`   |\n",
+    "| Equal                 | `==`   |\n",
+    "| Not equal             | `!=`   |"
    ]
   },
   {
@@ -848,7 +848,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,

From 9d023f950330b95e4c46969a2bec8ddd77a79608 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sat, 2 Sep 2023 13:35:12 -0400
Subject: [PATCH 113/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 240629 -> 240635 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index 9949a6f52dc14595d072564f21e27856a9192209..f8b5120dd3565e16707329b439a1303f8f54e51e 100644
GIT binary patch
delta 13034
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P#Xu^Af@J8C3*i3&_^pjZ


From bc47350e97db300324643c9787b471c8b8abe263 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sat, 2 Sep 2023 13:37:41 -0400
Subject: [PATCH 114/144] update installation

---
 jb/preface.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/jb/preface.md b/jb/preface.md
index f1aa6f5f..522750d9 100644
--- a/jb/preface.md
+++ b/jb/preface.md
@@ -156,7 +156,7 @@ typos):
 ```
 conda install jupyter pandas sympy
 conda install beautifulsoup4 lxml html5lib
-conda install -c unidata pint
+conda install pint
 ```
 
 That should be everything you need.

From 9de6579aab1b99bfb3f26b0bdf7e8ae0a8b3e658 Mon Sep 17 00:00:00 2001
From: mcsmart76 <mcsmart76@users.noreply.github.com>
Date: Fri, 8 Sep 2023 20:15:45 -0700
Subject: [PATCH 115/144] Fix incorrect population number.

---
 chapters/chap05.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/chapters/chap05.ipynb b/chapters/chap05.ipynb
index 04ddeaab..ac455abb 100644
--- a/chapters/chap05.ipynb
+++ b/chapters/chap05.ipynb
@@ -252,7 +252,7 @@
    "source": [
     "The first column, which is labeled `Year`, is special.  It is the *index* for this `DataFrame`, which means it contains the labels for the rows.\n",
     "\n",
-    "Some of the values use scientific notation; for example, `2.516000e+09` is shorthand for $2.516 \\cdot 10^9$ or 2.544 billion.\n",
+    "Some of the values use scientific notation; for example, `2.516000e+09` is shorthand for $2.516 \\cdot 10^9$ or 2.516 billion.\n",
     "\n",
     "`NaN` is a special value that indicates missing data."
    ]

From 4a6dc7ab08506a416dda080ce2e851d800156712 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sun, 10 Sep 2023 12:00:30 -0400
Subject: [PATCH 116/144] Updating examples

---
 examples/queue.ipynb  | 2 +-
 examples/salmon.ipynb | 6 +++---
 examples/trees.ipynb  | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/examples/queue.ipynb b/examples/queue.ipynb
index 743a2097..3980cf92 100644
--- a/examples/queue.ipynb
+++ b/examples/queue.ipynb
@@ -100,7 +100,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Write an update function that takes as parameters `x`, which is the total number of customer in the store, including the one checking out; `t`, which is the number of minutes that have elapsed in the simulation, and `system`, which is a `System` object.\n",
+    "Write an update function that takes as parameters `x`, which is the total number of customers in the store, including the one checking out; `t`, which is the number of minutes that have elapsed in the simulation, and `system`, which is a `System` object.\n",
     "\n",
     "If there's a customer checking out, it should use `flip` to decide whether they are done.  And it should use `flip` to decide if a new customer has arrived.\n",
     "\n",
diff --git a/examples/salmon.ipynb b/examples/salmon.ipynb
index b63b753c..8d052e80 100644
--- a/examples/salmon.ipynb
+++ b/examples/salmon.ipynb
@@ -336,7 +336,7 @@
    "source": [
     "Use `run_simulation` to run generate a prediction for the next 10 years.\n",
     "\n",
-    "The plot your prediction along with the original data.  Your prediction should pick up where the data leave off."
+    "Then plot your prediction along with the original data.  Your prediction should pick up where the data leave off."
    ]
   },
   {
@@ -409,7 +409,7 @@
    "source": [
     "## Distribution of net changes\n",
     "\n",
-    "To describe the distribution of net changes, write a function called `run_many_simulations` that runs many simulations, saves the final populations in a `ModSimSeries`, and returns the `ModSimSeries`.\n"
+    "To describe the distribution of net changes, write a function called `run_many_simulations` that runs many simulations, saves the final populations in a `SweepSeries`, and returns the `SweepSeries`."
    ]
   },
   {
@@ -689,7 +689,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/examples/trees.ipynb b/examples/trees.ipynb
index b0be531f..9faf211d 100644
--- a/examples/trees.ipynb
+++ b/examples/trees.ipynb
@@ -899,7 +899,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Taking the derivative of (3) yields\n",
+    "Taking the derivative of the previous equation (3) yields\n",
     "\n",
     "(4) $\\frac{dm}{dt} = D h^{D-1} \\frac{dh}{dt}$\n",
     "\n",

From 047bfb02948e332af03332a9a6a501771b491454 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sun, 10 Sep 2023 12:02:19 -0400
Subject: [PATCH 117/144] Updating chapters

---
 chapters/chap05.ipynb | 2 +-
 chapters/chap09.ipynb | 4 ++--
 chapters/chap10.ipynb | 3 ++-
 chapters/chap11.ipynb | 6 ++----
 4 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/chapters/chap05.ipynb b/chapters/chap05.ipynb
index ac455abb..4ca1367b 100644
--- a/chapters/chap05.ipynb
+++ b/chapters/chap05.ipynb
@@ -984,7 +984,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap09.ipynb b/chapters/chap09.ipynb
index 26e145d2..22896d73 100644
--- a/chapters/chap09.ipynb
+++ b/chapters/chap09.ipynb
@@ -904,7 +904,7 @@
     "tags": []
    },
    "source": [
-    "If you use SymPy to compute and expression, and then want to evaluate that expression in Python, SumPy provides a function called `pycode` that generates Python code:"
+    "If you use SymPy to compute an expression, and then want to evaluate that expression in Python, SymPy provides a function called `pycode` that generates Python code:"
    ]
   },
   {
@@ -1103,7 +1103,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap10.ipynb b/chapters/chap10.ipynb
index f35a6820..674ccb62 100644
--- a/chapters/chap10.ipynb
+++ b/chapters/chap10.ipynb
@@ -321,6 +321,7 @@
     "\n",
     "![One queue, one server (left), one queue, two servers (middle), two\n",
     "queues, two servers (right).](https://github.com/AllenDowney/ModSim/raw/main/figs/queue.png)\n",
+    "\n",
     "*One queue, one server (left), one queue, two servers (middle), two\n",
     "queues, two servers (right).*\n",
     "\n",
@@ -417,7 +418,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap11.ipynb b/chapters/chap11.ipynb
index cddab379..abab6411 100644
--- a/chapters/chap11.ipynb
+++ b/chapters/chap11.ipynb
@@ -171,9 +171,7 @@
     "\n",
     "-   The number of recoveries we expect per day is $\\gamma i N$; dividing by $N$ yields the fraction of the population that recovers in a day, which is $\\gamma i$.\n",
     "\n",
-    "-   The number of new infections we expect per day is $\\beta s i N$;\n",
-    "    dividing by $N$ yields the fraction of the population that gets\n",
-    "    infected in a day, which is $\\beta s i$.\n",
+    "-   The number of new infections we expect per day is $\\beta s i N$; dividing by $N$ yields the fraction of the population that gets infected in a day, which is $\\beta s i$.\n",
     "\n",
     "The KM model assumes that the population is closed; that is, no one\n",
     "arrives or departs, so the size of the population, $N$, is constant."
@@ -826,7 +824,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,

From 32f6b738f40966c47637917b713bda71d3f4e1b9 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sun, 10 Sep 2023 12:02:19 -0400
Subject: [PATCH 118/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 240635 -> 240612 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index f8b5120dd3565e16707329b439a1303f8f54e51e..c6a3aebcf92dcfc5791d28f235fad686bf39632d 100644
GIT binary patch
delta 36864
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From a6d698073f54753fb5fc3e639a2ff27c1d017418 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Wed, 15 May 2024 10:10:43 -0400
Subject: [PATCH 119/144] Test the examples directory

---
 .github/workflows/tests.yml | 36 ++++++++++++++++++++++++++++++++++++
 Makefile                    |  5 +++--
 2 files changed, 39 insertions(+), 2 deletions(-)
 create mode 100644 .github/workflows/tests.yml

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
new file mode 100644
index 00000000..975f3a75
--- /dev/null
+++ b/.github/workflows/tests.yml
@@ -0,0 +1,36 @@
+name: tests
+
+on:
+  push:
+    branches: [master]
+  pull_request:
+  schedule:
+    # Run on the first of the month
+    - cron: "0 0 1 * *"
+  workflow_dispatch:
+
+jobs:
+  tests:
+    name: Tests (${{ matrix.os }}, Python ${{ matrix.python-version }})
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-latest]
+        python-version: ["3.10"]
+
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements-dev.txt
+
+      - name: Run tests
+        run: |
+          make tests
diff --git a/Makefile b/Makefile
index 4d980441..6fcc56e7 100644
--- a/Makefile
+++ b/Makefile
@@ -1,5 +1,5 @@
 PROJECT_NAME = ModSimPy
-PYTHON_VERSION = 3.8
+PYTHON_VERSION = 3.10
 PYTHON_INTERPRETER = python
 
 
@@ -29,4 +29,5 @@ clean:
 
 
 tests:
-	cd chapters; pytest --nbmake chap01.ipynb
+	cd chapters; pytest --nbmake chap*.ipynb
+	cd examples; pytest --nbmake *.ipynb

From 75eff475dc737316fb114eedb79f88981a7b3562 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Wed, 31 Jul 2024 20:46:29 -0400
Subject: [PATCH 120/144] Update tests.yml

Don't run tests on macos
---
 .github/workflows/tests.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 975f3a75..d114f87b 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -15,7 +15,8 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ubuntu-latest, macos-latest, windows-latest]
+        # don't run on macos-latest; it can't install pytables
+        os: [ubuntu-latest, windows-latest]
         python-version: ["3.10"]
 
     steps:

From aa88df33f38512a32dfd018665c4642a55441f9e Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Wed, 31 Jul 2024 20:55:56 -0400
Subject: [PATCH 121/144] Update Makefile

Limiting tests
---
 Makefile | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 6fcc56e7..d2bdb316 100644
--- a/Makefile
+++ b/Makefile
@@ -30,4 +30,5 @@ clean:
 
 tests:
 	cd chapters; pytest --nbmake chap*.ipynb
-	cd examples; pytest --nbmake *.ipynb
+	# many of the examples don't pass tests without the solutions
+	# cd examples; pytest --nbmake *.ipynb

From b47506ba6e436f6329ced0e95dd0b3f947a97391 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 1 Aug 2024 13:50:37 -0400
Subject: [PATCH 122/144] Updating modsim.py

---
 chap11.py | 2 +-
 chap22.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/chap11.py b/chap11.py
index d8569569..859f4dc4 100644
--- a/chap11.py
+++ b/chap11.py
@@ -26,7 +26,7 @@ def update_func(t, state, system):
 def plot_results(S, I, R):
     S.plot(style='--', label='Susceptible')
     I.plot(style='-', label='Infected')
-    R.plot(style=':', label='Resistant')
+    R.plot(style=':', label='Recovered')
     decorate(xlabel='Time (days)',
              ylabel='Fraction of population')
 
diff --git a/chap22.py b/chap22.py
index 00fe5be2..a5b5f745 100644
--- a/chap22.py
+++ b/chap22.py
@@ -21,7 +21,7 @@
 
 def make_system(params):
     
-    # convert angle to degrees
+    # convert angle to radians
     theta = deg2rad(params.angle)
     
     # compute x and y components of velocity

From 0bc01a09c5ba606aa06cf7f1331d1403fb3fb007 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 1 Aug 2024 13:58:13 -0400
Subject: [PATCH 123/144] Updating chapters

---
 chapters/chap01.ipynb | 10 +++++++++-
 chapters/chap02.ipynb | 10 +++++++++-
 chapters/chap03.ipynb |  8 ++++++++
 chapters/chap04.ipynb |  8 ++++++++
 chapters/chap05.ipynb | 20 ++++++++++++++++++--
 chapters/chap06.ipynb |  8 ++++++++
 chapters/chap07.ipynb | 10 +++++++++-
 chapters/chap08.ipynb | 10 +++++++++-
 chapters/chap09.ipynb | 10 +++++++++-
 chapters/chap10.ipynb |  8 ++++++++
 chapters/chap11.ipynb |  8 ++++++++
 chapters/chap12.ipynb |  8 ++++++++
 chapters/chap13.ipynb |  8 ++++++++
 chapters/chap14.ipynb |  8 ++++++++
 chapters/chap15.ipynb |  8 ++++++++
 chapters/chap16.ipynb |  8 ++++++++
 chapters/chap17.ipynb |  8 ++++++++
 chapters/chap18.ipynb |  8 ++++++++
 chapters/chap19.ipynb |  8 ++++++++
 chapters/chap20.ipynb |  8 ++++++++
 chapters/chap21.ipynb |  8 ++++++++
 chapters/chap22.ipynb |  8 ++++++++
 chapters/chap23.ipynb |  8 ++++++++
 chapters/chap24.ipynb |  8 ++++++++
 chapters/chap25.ipynb |  8 ++++++++
 chapters/chap26.ipynb |  8 ++++++++
 chapters/chap27.ipynb |  8 ++++++++
 27 files changed, 231 insertions(+), 7 deletions(-)

diff --git a/chapters/chap01.ipynb b/chapters/chap01.ipynb
index 7bb182f1..c5c409ae 100644
--- a/chapters/chap01.ipynb
+++ b/chapters/chap01.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "affecting-malta",
@@ -1335,7 +1343,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap02.ipynb b/chapters/chap02.ipynb
index f1fc36d1..b02e008c 100644
--- a/chapters/chap02.ipynb
+++ b/chapters/chap02.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "victorian-latitude",
@@ -1222,7 +1230,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap03.ipynb b/chapters/chap03.ipynb
index 21c4f895..1716e158 100644
--- a/chapters/chap03.ipynb
+++ b/chapters/chap03.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "signal-format",
diff --git a/chapters/chap04.ipynb b/chapters/chap04.ipynb
index f233a1f8..239f8a03 100644
--- a/chapters/chap04.ipynb
+++ b/chapters/chap04.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "existing-guidance",
diff --git a/chapters/chap05.ipynb b/chapters/chap05.ipynb
index 4ca1367b..49786750 100644
--- a/chapters/chap05.ipynb
+++ b/chapters/chap05.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "wicked-mozambique",
@@ -958,13 +966,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "id": "political-loading",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Solution goes here"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89bab9ad",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -984,7 +1000,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap06.ipynb b/chapters/chap06.ipynb
index 058a6412..1656fe55 100644
--- a/chapters/chap06.ipynb
+++ b/chapters/chap06.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "creative-motel",
diff --git a/chapters/chap07.ipynb b/chapters/chap07.ipynb
index e463dcaf..7bf8dacf 100644
--- a/chapters/chap07.ipynb
+++ b/chapters/chap07.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "progressive-travel",
@@ -781,7 +789,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap08.ipynb b/chapters/chap08.ipynb
index d93ef6a4..2610feb4 100644
--- a/chapters/chap08.ipynb
+++ b/chapters/chap08.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "external-reward",
@@ -769,7 +777,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap09.ipynb b/chapters/chap09.ipynb
index 22896d73..217108dc 100644
--- a/chapters/chap09.ipynb
+++ b/chapters/chap09.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "asian-customer",
@@ -1103,7 +1111,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap10.ipynb b/chapters/chap10.ipynb
index 674ccb62..eb5a456f 100644
--- a/chapters/chap10.ipynb
+++ b/chapters/chap10.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "dense-storm",
diff --git a/chapters/chap11.ipynb b/chapters/chap11.ipynb
index abab6411..3752aeb7 100644
--- a/chapters/chap11.ipynb
+++ b/chapters/chap11.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "pressing-munich",
diff --git a/chapters/chap12.ipynb b/chapters/chap12.ipynb
index 2f576a70..2746d139 100644
--- a/chapters/chap12.ipynb
+++ b/chapters/chap12.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "combined-semiconductor",
diff --git a/chapters/chap13.ipynb b/chapters/chap13.ipynb
index 0f04b715..c6137eb9 100644
--- a/chapters/chap13.ipynb
+++ b/chapters/chap13.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "colonial-jacob",
diff --git a/chapters/chap14.ipynb b/chapters/chap14.ipynb
index f8283be9..3bd644a8 100644
--- a/chapters/chap14.ipynb
+++ b/chapters/chap14.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "infrared-shower",
diff --git a/chapters/chap15.ipynb b/chapters/chap15.ipynb
index 92d125d9..2636a53c 100644
--- a/chapters/chap15.ipynb
+++ b/chapters/chap15.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "romantic-speech",
diff --git a/chapters/chap16.ipynb b/chapters/chap16.ipynb
index 562342c3..3c0b142c 100644
--- a/chapters/chap16.ipynb
+++ b/chapters/chap16.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "medieval-johnston",
diff --git a/chapters/chap17.ipynb b/chapters/chap17.ipynb
index 46c74d6b..56ba08b8 100644
--- a/chapters/chap17.ipynb
+++ b/chapters/chap17.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "interracial-guitar",
diff --git a/chapters/chap18.ipynb b/chapters/chap18.ipynb
index b82e3a16..4cf5efab 100644
--- a/chapters/chap18.ipynb
+++ b/chapters/chap18.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "electric-netherlands",
diff --git a/chapters/chap19.ipynb b/chapters/chap19.ipynb
index 70050688..624e8a10 100644
--- a/chapters/chap19.ipynb
+++ b/chapters/chap19.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "mighty-israeli",
diff --git a/chapters/chap20.ipynb b/chapters/chap20.ipynb
index 0ce7dfe4..e17f53ef 100644
--- a/chapters/chap20.ipynb
+++ b/chapters/chap20.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "funded-utilization",
diff --git a/chapters/chap21.ipynb b/chapters/chap21.ipynb
index 240aa9a2..32bc62f1 100644
--- a/chapters/chap21.ipynb
+++ b/chapters/chap21.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "excited-advance",
diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
index 83941ba4..85d7a56c 100644
--- a/chapters/chap22.ipynb
+++ b/chapters/chap22.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "collaborative-people",
diff --git a/chapters/chap23.ipynb b/chapters/chap23.ipynb
index 6c1d23f8..6d94cd6c 100644
--- a/chapters/chap23.ipynb
+++ b/chapters/chap23.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "foreign-pepper",
diff --git a/chapters/chap24.ipynb b/chapters/chap24.ipynb
index fa25bd5a..0c4de2f8 100644
--- a/chapters/chap24.ipynb
+++ b/chapters/chap24.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "august-parks",
diff --git a/chapters/chap25.ipynb b/chapters/chap25.ipynb
index 4d0155ee..11b06002 100644
--- a/chapters/chap25.ipynb
+++ b/chapters/chap25.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "unauthorized-winter",
diff --git a/chapters/chap26.ipynb b/chapters/chap26.ipynb
index a5fcd00b..af4305b7 100644
--- a/chapters/chap26.ipynb
+++ b/chapters/chap26.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "early-drove",
diff --git a/chapters/chap27.ipynb b/chapters/chap27.ipynb
index 6f3a40f3..6daf5e6b 100644
--- a/chapters/chap27.ipynb
+++ b/chapters/chap27.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8b892c8",
+   "metadata": {},
+   "source": [
+    "Printed and electronic copies of *Modeling and Simulation in Python* are available from [No Starch Press](https://nostarch.com/modeling-and-simulation-python) and [Bookshop.org](https://bookshop.org/p/books/modeling-and-simulation-in-python-allen-b-downey/17836697?ean=9781718502161) and [Amazon](https://amzn.to/3y9UxNb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "early-drove",

From b17890138c2edfb43aa47dd1ff5b81649489b009 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 1 Aug 2024 13:58:13 -0400
Subject: [PATCH 124/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 240612 -> 244852 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
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From 9f1cd31a8b69999ffd61e02cfc3d0e78cf5fe4dd Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 2 Sep 2024 08:02:29 -0400
Subject: [PATCH 125/144] Updating examples

---
 examples/salmon.ipynb | 72 +++++++++++++++++++++----------------------
 1 file changed, 36 insertions(+), 36 deletions(-)

diff --git a/examples/salmon.ipynb b/examples/salmon.ipynb
index 8d052e80..58818287 100644
--- a/examples/salmon.ipynb
+++ b/examples/salmon.ipynb
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -124,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -161,16 +161,16 @@
    "source": [
     "## Modeling changes\n",
     "\n",
-    "To see how the population changes from year-to-year, I'll use `diff` to compute the absolute difference between each year and the next.\n"
+    "To see how the population changes from year-to-year, I'll use `diff` to compute the absolute difference between each year and the next and `shift` to align the changes with the year they happened."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "abs_diffs = pop_series.diff()\n",
+    "abs_diffs = pop_series.diff().shift(-1)\n",
     "abs_diffs"
    ]
   },
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -195,12 +195,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "These relative differences are observed annual net growth rates.  So let's drop the `0` and save them."
+    "These relative differences are observed annual net growth rates.\n",
+    "So let's drop the `NaN` and save them."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -237,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -276,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -292,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -308,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -341,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -357,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -370,8 +371,7 @@
     "    \"\"\"\n",
     "    for i in range(iters):\n",
     "        results = run_simulation(system, update_func)\n",
-    "        results.plot(color='gray', label='_nolegend', \n",
-    "                     linewidth=1, alpha=0.3)"
+    "        results.plot(color='gray', label='', linewidth=1, alpha=0.3)"
    ]
   },
   {
@@ -385,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -414,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -432,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -448,7 +448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -464,7 +464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -481,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -500,7 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -516,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -532,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -567,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -585,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -601,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -617,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -633,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -649,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -665,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -689,7 +689,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

From 0db87da09c5e47bff48e9bec1f190e26f9fdf598 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Mon, 2 Sep 2024 08:04:55 -0400
Subject: [PATCH 126/144] Updating build.sh

---
 examples/build.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/build.sh b/examples/build.sh
index 56d540b8..304386ff 100644
--- a/examples/build.sh
+++ b/examples/build.sh
@@ -12,6 +12,7 @@ header.ipynb \
 insulin.ipynb \
 plague.ipynb \
 queue.ipynb \
+# salmon.ipynb \ does not run without solutions
 spiderman.ipynb \
 throwingaxe.ipynb \
 trees.ipynb \

From 1801b17d9df914fc4ecadefe62152e56bb85aea4 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 22 May 2025 14:26:10 -0400
Subject: [PATCH 127/144] Cleanup

---
 jupyter/modsim.py | 1087 ---------------------------------------------
 1 file changed, 1087 deletions(-)
 delete mode 100644 jupyter/modsim.py

diff --git a/jupyter/modsim.py b/jupyter/modsim.py
deleted file mode 100644
index b59bd97c..00000000
--- a/jupyter/modsim.py
+++ /dev/null
@@ -1,1087 +0,0 @@
-"""
-Code from Modeling and Simulation in Python.
-
-Copyright 2020 Allen Downey
-
-MIT License: https://opensource.org/licenses/MIT
-"""
-
-import logging
-
-logger = logging.getLogger(name="modsim.py")
-
-# make sure we have Python 3.6 or better
-import sys
-
-if sys.version_info < (3, 6):
-    logger.warning("modsim.py depends on Python 3.6 features.")
-
-import inspect
-
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import scipy
-
-from scipy.interpolate import interp1d
-from scipy.interpolate import InterpolatedUnivariateSpline
-
-from scipy.integrate import odeint
-from scipy.integrate import solve_ivp
-
-from types import SimpleNamespace
-from copy import copy
-
-import pint
-
-units = pint.UnitRegistry()
-Quantity = units.Quantity
-
-
-def flip(p=0.5):
-    """Flips a coin with the given probability.
-
-    p: float 0-1
-
-    returns: boolean (True or False)
-    """
-    return np.random.random() < p
-
-
-def cart2pol(x, y, z=None):
-    """Convert Cartesian coordinates to polar.
-
-    x: number or sequence
-    y: number or sequence
-    z: number or sequence (optional)
-
-    returns: theta, rho OR theta, rho, z
-    """
-    x = np.asarray(x)
-    y = np.asarray(y)
-
-    rho = np.hypot(x, y)
-    theta = np.arctan2(y, x)
-
-    if z is None:
-        return theta, rho
-    else:
-        return theta, rho, z
-
-
-def pol2cart(theta, rho, z=None):
-    """Convert polar coordinates to Cartesian.
-
-    theta: number or sequence in radians
-    rho: number or sequence
-    z: number or sequence (optional)
-
-    returns: x, y OR x, y, z
-    """
-    x = rho * np.cos(theta)
-    y = rho * np.sin(theta)
-
-    if z is None:
-        return x, y
-    else:
-        return x, y, z
-
-from numpy import linspace
-
-def linrange(start, stop, step=1, **options):
-    """Make an array of equally spaced values.
-
-    start: first value
-    stop: last value (might be approximate)
-    step: difference between elements (should be consistent)
-
-    returns: NumPy array
-    """
-    n = int(round((stop-start) / step))
-    return linspace(start, stop, n+1, **options)
-
-
-def leastsq(error_func, x0, *args, **options):
-    """Find the parameters that yield the best fit for the data.
-
-    `x0` can be a sequence, array, Series, or Params
-
-    Positional arguments are passed along to `error_func`.
-
-    Keyword arguments are passed to `scipy.optimize.leastsq`
-
-    error_func: function that computes a sequence of errors
-    x0: initial guess for the best parameters
-    args: passed to error_func
-    options: passed to leastsq
-
-    :returns: Params object with best_params and ModSimSeries with details
-    """
-    # override `full_output` so we get a message if something goes wrong
-    options["full_output"] = True
-
-    # run leastsq
-    t = scipy.optimize.leastsq(error_func, x0=x0, args=args, **options)
-    best_params, cov_x, infodict, mesg, ier = t
-
-    # pack the results into a ModSimSeries object
-    details = ModSimSeries(infodict)
-    details.set(cov_x=cov_x, mesg=mesg, ier=ier)
-
-    # if we got a Params object, we should return a Params object
-    if isinstance(x0, Params):
-        best_params = Params(Series(best_params, x0.index))
-
-    # return the best parameters and details
-    return best_params, details
-
-
-def minimize_scalar(min_func, bounds, *args, **options):
-    """Finds the input value that minimizes `min_func`.
-
-    Wrapper for
-    https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
-
-    min_func: computes the function to be minimized
-    bounds: sequence of two values, lower and upper bounds of the range to be searched
-    args: any additional positional arguments are passed to min_func
-    options: any keyword arguments are passed as options to minimize_scalar
-
-    returns: ModSimSeries object
-    """
-    try:
-        min_func(bounds[0], *args)
-    except Exception as e:
-        msg = """Before running scipy.integrate.minimize_scalar, I tried
-                 running the function you provided with the
-                 lower bound, and I got the following error:"""
-        logger.error(msg)
-        raise (e)
-
-    underride(options, xatol=1e-3)
-
-    res = scipy.optimize.minimize_scalar(
-        min_func,
-        bracket=bounds,
-        bounds=bounds,
-        args=args,
-        method="bounded",
-        options=options,
-    )
-
-    if not res.success:
-        msg = (
-            """scipy.optimize.minimize_scalar did not succeed.
-                 The message it returned is %s"""
-            % res.message
-        )
-        raise Exception(msg)
-
-    return res
-
-
-def maximize_scalar(max_func, bounds, *args, **options):
-    """Finds the input value that maximizes `max_func`.
-
-    Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
-
-    min_func: computes the function to be maximized
-    bounds: sequence of two values, lower and upper bounds of the
-            range to be searched
-    args: any additional positional arguments are passed to max_func
-    options: any keyword arguments are passed as options to minimize_scalar
-
-    returns: ModSimSeries object
-    """
-    def min_func(*args):
-        return -max_func(*args)
-
-    res = minimize_scalar(min_func, bounds, *args, **options)
-
-    # we have to negate the function value before returning res
-    res.fun = -res.fun
-    return res
-
-
-def minimize_golden(min_func, bracket, *args, **options):
-    """Find the minimum of a function by golden section search.
-
-    Based on
-    https://en.wikipedia.org/wiki/Golden-section_search#Iterative_algorithm
-
-    :param min_func: function to be minimized
-    :param bracket: interval containing a minimum
-    :param args: arguments passes to min_func
-    :param options: rtol and maxiter
-
-    :return: ModSimSeries
-    """
-    maxiter = options.get("maxiter", 100)
-    rtol = options.get("rtol", 1e-3)
-
-    def success(**kwargs):
-        return ModSimSeries(dict(success=True, **kwargs))
-
-    def failure(**kwargs):
-        return ModSimSeries(dict(success=False, **kwargs))
-
-    a, b = bracket
-    ya = min_func(a, *args)
-    yb = min_func(b, *args)
-
-    phi = 2 / (np.sqrt(5) - 1)
-    h = b - a
-    c = b - h / phi
-    yc = min_func(c, *args)
-
-    d = a + h / phi
-    yd = min_func(d, *args)
-
-    if yc > ya or yc > yb:
-        return failure(message="The bracket is not well-formed.")
-
-    for i in range(maxiter):
-
-        # check for convergence
-        if abs(h / c) < rtol:
-            return success(x=c, fun=yc)
-
-        if yc < yd:
-            b, yb = d, yd
-            d, yd = c, yc
-            h = b - a
-            c = b - h / phi
-            yc = min_func(c, *args)
-        else:
-            a, ya = c, yc
-            c, yc = d, yd
-            h = b - a
-            d = a + h / phi
-            yd = min_func(d, *args)
-
-    # if we exited the loop, too many iterations
-    return failure(root=c, message="maximum iterations = %d exceeded" % maxiter)
-
-
-def maximize_golden(max_func, bracket, *args, **options):
-    """Find the maximum of a function by golden section search.
-
-    :param min_func: function to be maximized
-    :param bracket: interval containing a maximum
-    :param args: arguments passes to min_func
-    :param options: rtol and maxiter
-
-    :return: ModSimSeries
-    """
-
-    def min_func(*args):
-        return -max_func(*args)
-
-    res = minimize_golden(min_func, bracket, *args, **options)
-
-    # we have to negate the function value before returning res
-    res.fun = -res.fun
-    return res
-
-
-def minimize_powell(min_func, x0, *args, **options):
-    """Finds the input value that minimizes `min_func`.
-    Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
-    min_func: computes the function to be minimized
-    x0: initial guess
-    args: any additional positional arguments are passed to min_func
-    options: any keyword arguments are passed as options to minimize_scalar
-    returns: ModSimSeries object
-    """
-    underride(options, tol=1e-3)
-
-    res = scipy.optimize.minimize(min_func, x0, *args, **options)
-
-    return ModSimSeries(res)
-
-
-# make aliases for minimize and maximize
-minimize = minimize_golden
-maximize = maximize_golden
-
-
-def run_solve_ivp(system, slope_func, **options):
-    """Computes a numerical solution to a differential equation.
-
-    `system` must contain `init` with initial conditions,
-    `t_0` with the start time, and `t_end` with the end time.
-
-    It can contain any other parameters required by the slope function.
-
-    `options` can be any legal options of `scipy.integrate.solve_ivp`
-
-    system: System object
-    slope_func: function that computes slopes
-
-    returns: TimeFrame
-    """
-    system = remove_units(system)
-
-    # make sure `system` contains `init`
-    if not hasattr(system, "init"):
-        msg = """It looks like `system` does not contain `init`
-                 as a system variable.  `init` should be a State
-                 object that specifies the initial condition:"""
-        raise ValueError(msg)
-
-    # make sure `system` contains `t_end`
-    if not hasattr(system, "t_end"):
-        msg = """It looks like `system` does not contain `t_end`
-                 as a system variable.  `t_end` should be the
-                 final time:"""
-        raise ValueError(msg)
-
-    # the default value for t_0 is 0
-    t_0 = getattr(system, "t_0", 0)
-
-    # try running the slope function with the initial conditions
-    try:
-        slope_func(t_0, system.init, system)
-    except Exception as e:
-        msg = """Before running scipy.integrate.solve_ivp, I tried
-                 running the slope function you provided with the
-                 initial conditions in `system` and `t=t_0` and I got
-                 the following error:"""
-        logger.error(msg)
-        raise (e)
-
-    # get the list of event functions
-    events = options.get('events', [])
-
-    # if there's only one event function, put it in a list
-    try:
-        iter(events)
-    except TypeError:
-        events = [events]
-
-    for event_func in events:
-        # make events terminal unless otherwise specified
-        if not hasattr(event_func, 'terminal'):
-            event_func.terminal = True
-
-        # test the event function with the initial conditions
-        try:
-            event_func(t_0, system.init, system)
-        except Exception as e:
-            msg = """Before running scipy.integrate.solve_ivp, I tried
-                     running the event function you provided with the
-                     initial conditions in `system` and `t=t_0` and I got
-                     the following error:"""
-            logger.error(msg)
-            raise (e)
-
-    # get dense output unless otherwise specified
-    underride(options, dense_output=True)
-
-    # run the solver
-    bunch = solve_ivp(slope_func, [t_0, system.t_end], system.init,
-                      args=[system], **options)
-
-    # separate the results from the details
-    y = bunch.pop("y")
-    t = bunch.pop("t")
-
-    # get the column names from `init`, if possible
-    if hasattr(system.init, 'index'):
-        columns = system.init.index
-    else:
-        columns = range(len(system.init))
-
-    # evaluate the results at equally-spaced points
-    if options.get('dense_output', False):
-        try:
-            num = system.num
-        except AttributeError:
-            num = 51
-        t_final = t[-1]
-        t_array = linspace(t_0, t_final, num)
-        y_array = bunch.sol(t_array)
-
-        # pack the results into a TimeFrame
-        results = TimeFrame(y_array.T, index=t_array,
-                        columns=columns)
-    else:
-        results = TimeFrame(y.T, index=t,
-                        columns=columns)
-
-    return results, bunch
-
-
-def check_system(system, slope_func):
-    """Make sure the system object has the fields we need for run_ode_solver.
-
-    :param system:
-    :param slope_func:
-    :return:
-    """
-    # make sure `system` contains `init`
-    if not hasattr(system, "init"):
-        msg = """It looks like `system` does not contain `init`
-                 as a system variable.  `init` should be a State
-                 object that specifies the initial condition:"""
-        raise ValueError(msg)
-
-    # make sure `system` contains `t_end`
-    if not hasattr(system, "t_end"):
-        msg = """It looks like `system` does not contain `t_end`
-                 as a system variable.  `t_end` should be the
-                 final time:"""
-        raise ValueError(msg)
-
-    # the default value for t_0 is 0
-    t_0 = getattr(system, "t_0", 0)
-
-    # get the initial conditions
-    init = system.init
-
-    # get t_end
-    t_end = system.t_end
-
-    # if dt is not specified, take 100 steps
-    try:
-        dt = system.dt
-    except AttributeError:
-        dt = t_end / 100
-
-    return init, t_0, t_end, dt
-
-
-def run_euler(system, slope_func, **options):
-    """Computes a numerical solution to a differential equation.
-
-    `system` must contain `init` with initial conditions,
-    `t_end` with the end time, and `dt` with the time step.
-
-    `system` may contain `t_0` to override the default, 0
-
-    It can contain any other parameters required by the slope function.
-
-    `options` can be ...
-
-    system: System object
-    slope_func: function that computes slopes
-
-    returns: TimeFrame
-    """
-    # the default message if nothing changes
-    msg = "The solver successfully reached the end of the integration interval."
-
-    # get parameters from system
-    init, t_0, t_end, dt = check_system(system, slope_func)
-
-    # make the TimeFrame
-    frame = TimeFrame(columns=init.index)
-    frame.row[t_0] = init
-    ts = linrange(t_0, t_end, dt) * get_units(t_end)
-
-    # run the solver
-    for t1 in ts:
-        y1 = frame.row[t1]
-        slopes = slope_func(y1, t1, system)
-        y2 = [y + slope * dt for y, slope in zip(y1, slopes)]
-        t2 = t1 + dt
-        frame.row[t2] = y2
-
-    details = ModSimSeries(dict(message="Success"))
-    return frame, details
-
-
-def run_ralston(system, slope_func, **options):
-    """Computes a numerical solution to a differential equation.
-
-    `system` must contain `init` with initial conditions,
-     and `t_end` with the end time.
-
-     `system` may contain `t_0` to override the default, 0
-
-    It can contain any other parameters required by the slope function.
-
-    `options` can be ...
-
-    system: System object
-    slope_func: function that computes slopes
-
-    returns: TimeFrame
-    """
-    # the default message if nothing changes
-    msg = "The solver successfully reached the end of the integration interval."
-
-    # get parameters from system
-    init, t_0, t_end, dt = check_system(system, slope_func)
-
-    # make the TimeFrame
-    frame = TimeFrame(columns=init.index)
-    frame.row[t_0] = init
-    ts = linrange(t_0, t_end, dt) * get_units(t_end)
-
-    event_func = options.get("events", None)
-    z1 = np.nan
-
-    def project(y1, t1, slopes, dt):
-        t2 = t1 + dt
-        y2 = [y + slope * dt for y, slope in zip(y1, slopes)]
-        return y2, t2
-
-    # run the solver
-    for t1 in ts:
-        y1 = frame.row[t1]
-
-        # evaluate the slopes at the start of the time step
-        slopes1 = slope_func(y1, t1, system)
-
-        # evaluate the slopes at the two-thirds point
-        y_mid, t_mid = project(y1, t1, slopes1, 2 * dt / 3)
-        slopes2 = slope_func(y_mid, t_mid, system)
-
-        # compute the weighted sum of the slopes
-        slopes = [(k1 + 3 * k2) / 4 for k1, k2 in zip(slopes1, slopes2)]
-
-        # compute the next time stamp
-        y2, t2 = project(y1, t1, slopes, dt)
-
-        # check for a terminating event
-        if event_func:
-            z2 = event_func(y2, t2, system)
-            if z1 * z2 < 0:
-                scale = magnitude(z1 / (z1 - z2))
-                y2, t2 = project(y1, t1, slopes, scale * dt)
-                frame.row[t2] = y2
-                msg = "A termination event occurred."
-                break
-            else:
-                z1 = z2
-
-        # store the results
-        frame.row[t2] = y2
-
-    details = ModSimSeries(dict(success=True, message=msg))
-    return frame, details
-
-
-run_ode_solver = run_ralston
-
-# TODO: Implement leapfrog
-
-
-def fsolve(func, x0, *args, **options):
-    """Return the roots of the (non-linear) equations
-    defined by func(x) = 0 given a starting estimate.
-
-    Uses scipy.optimize.fsolve, with extra error-checking.
-
-    func: function to find the roots of
-    x0: scalar or array, initial guess
-    args: additional positional arguments are passed along to fsolve,
-          which passes them along to func
-
-    returns: solution as an array
-    """
-    # make sure we can run the given function with x0
-    try:
-        func(x0, *args)
-    except Exception as e:
-        msg = """Before running scipy.optimize.fsolve, I tried
-                 running the error function you provided with the x0
-                 you provided, and I got the following error:"""
-        logger.error(msg)
-        raise (e)
-
-    # make the tolerance more forgiving than the default
-    underride(options, xtol=1e-6)
-
-    # run fsolve
-    result = scipy.optimize.fsolve(func, x0, args=args, **options)
-
-    return result
-
-
-def crossings(series, value):
-    """Find the labels where the series passes through value.
-
-    The labels in series must be increasing numerical values.
-
-    series: Series
-    value: number
-
-    returns: sequence of labels
-    """
-    values = series.values - value
-    interp = InterpolatedUnivariateSpline(series.index, values)
-    return interp.roots()
-
-
-def has_nan(a):
-    """Checks whether the an array contains any NaNs.
-
-    :param a: NumPy array or Pandas Series
-    :return: boolean
-    """
-    return np.any(np.isnan(a))
-
-
-def is_strictly_increasing(a):
-    """Checks whether the elements of an array are strictly increasing.
-
-    :param a: NumPy array or Pandas Series
-    :return: boolean
-    """
-    return np.all(np.diff(a) > 0)
-
-
-def interpolate(series, **options):
-    """Creates an interpolation function.
-
-    series: Series object
-    options: any legal options to scipy.interpolate.interp1d
-
-    returns: function that maps from the index to the values
-    """
-    if has_nan(series.index):
-        msg = """The Series you passed to interpolate contains
-                 NaN values in the index, which would result in
-                 undefined behavior.  So I'm putting a stop to that."""
-        raise ValueError(msg)
-
-    if not is_strictly_increasing(series.index):
-        msg = """The Series you passed to interpolate has an index
-                 that is not strictly increasing, which would result in
-                 undefined behavior.  So I'm putting a stop to that."""
-        raise ValueError(msg)
-
-    # make the interpolate function extrapolate past the ends of
-    # the range, unless `options` already specifies a value for `fill_value`
-    underride(options, fill_value="extrapolate")
-
-    # call interp1d, which returns a new function object
-    x = series.index
-    y = series.values
-    interp_func = interp1d(x, y, **options)
-    return interp_func
-
-
-def interpolate_inverse(series, **options):
-    """Interpolate the inverse function of a Series.
-
-    series: Series object, represents a mapping from `a` to `b`
-    options: any legal options to scipy.interpolate.interp1d
-
-    returns: interpolation object, can be used as a function
-             from `b` to `a`
-    """
-    inverse = Series(series.index, index=series.values)
-    interp_func = interpolate(inverse, **options)
-    return interp_func
-
-
-def gradient(series, **options):
-    """Computes the numerical derivative of a series.
-
-    If the elements of series have units, they are dropped.
-
-    series: Series object
-    options: any legal options to np.gradient
-
-    returns: Series, same subclass as series
-    """
-    x = series.index
-    y = series.values
-
-    a = np.gradient(y, x, **options)
-    return series.__class__(a, series.index)
-
-
-def source_code(obj):
-    """Prints the source code for a given object.
-
-    obj: function or method object
-    """
-    print(inspect.getsource(obj))
-
-
-def underride(d, **options):
-    """Add key-value pairs to d only if key is not in d.
-
-    If d is None, create a new dictionary.
-
-    d: dictionary
-    options: keyword args to add to d
-    """
-    if d is None:
-        d = {}
-
-    for key, val in options.items():
-        d.setdefault(key, val)
-
-    return d
-
-
-def contour(df, **options):
-    """Makes a contour plot from a DataFrame.
-
-    Wrapper for plt.contour
-    https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.contour.html
-
-    Note: columns and index must be numerical
-
-    df: DataFrame
-    options: passed to plt.contour
-    """
-    fontsize = options.pop("fontsize", 12)
-    underride(options, cmap="viridis")
-    x = df.columns
-    y = df.index
-    X, Y = np.meshgrid(x, y)
-    cs = plt.contour(X, Y, df, **options)
-    plt.clabel(cs, inline=1, fontsize=fontsize)
-
-
-def savefig(filename, **options):
-    """Save the current figure.
-
-    Keyword arguments are passed along to plt.savefig
-
-    https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html
-
-    filename: string
-    """
-    print("Saving figure to file", filename)
-    plt.savefig(filename, **options)
-
-
-def decorate(**options):
-    """Decorate the current axes.
-
-    Call decorate with keyword arguments like
-    decorate(title='Title',
-             xlabel='x',
-             ylabel='y')
-
-    The keyword arguments can be any of the axis properties
-    https://matplotlib.org/api/axes_api.html
-    """
-    ax = plt.gca()
-    ax.set(**options)
-
-    handles, labels = ax.get_legend_handles_labels()
-    if handles:
-        ax.legend(handles, labels)
-
-    plt.tight_layout()
-
-
-def remove_from_legend(bad_labels):
-    """Removes some labels from the legend.
-
-    bad_labels: sequence of strings
-    """
-    ax = plt.gca()
-    handles, labels = ax.get_legend_handles_labels()
-    handle_list, label_list = [], []
-    for handle, label in zip(handles, labels):
-        if label not in bad_labels:
-            handle_list.append(handle)
-            label_list.append(label)
-    ax.legend(handle_list, label_list)
-
-
-class SettableNamespace(SimpleNamespace):
-    """Contains a collection of parameters.
-
-    Used to make a System object.
-
-    Takes keyword arguments and stores them as attributes.
-    """
-    def __init__(self, namespace=None, **kwargs):
-        super().__init__()
-        if namespace:
-            self.__dict__.update(namespace.__dict__)
-        self.__dict__.update(kwargs)
-
-    def get(self, name, default=None):
-        """Look up a variable.
-
-        name: string varname
-        default: value returned if `name` is not present
-        """
-        try:
-            return self.__getattribute__(name, default)
-        except AttributeError:
-            return default
-
-    def set(self, **variables):
-        """Make a copy and update the given variables.
-
-        returns: Params
-        """
-        new = copy(self)
-        new.__dict__.update(variables)
-        return new
-
-
-def magnitude(x):
-    """Returns the magnitude of a Quantity or number.
-
-    x: Quantity or number
-
-    returns: number
-    """
-    return x.magnitude if hasattr(x, 'magnitude') else x
-
-
-def remove_units(namespace):
-    """Removes units from the values in a Namespace.
-
-    Only removes units from top-level values;
-    does not traverse nested values.
-
-    returns: new Namespace object
-    """
-    res = copy(namespace)
-    for label, value in res.__dict__.items():
-        if isinstance(value, pd.Series):
-            value = remove_units_series(value)
-        res.__dict__[label] = magnitude(value)
-    return res
-
-
-def remove_units_series(series):
-    """Removes units from the values in a Series.
-
-    Only removes units from top-level values;
-    does not traverse nested values.
-
-    returns: new Series object
-    """
-    res = copy(series)
-    for label, value in res.iteritems():
-        res[label] = magnitude(value)
-    return res
-
-
-class System(SettableNamespace):
-    """Contains system parameters and their values.
-
-    Takes keyword arguments and stores them as attributes.
-    """
-    pass
-
-
-class Params(SettableNamespace):
-    """Contains system parameters and their values.
-
-    Takes keyword arguments and stores them as attributes.
-    """
-    pass
-
-
-def State(**variables):
-    """Contains the values of state variables."""
-    return pd.Series(variables)
-
-
-def TimeSeries(*args, **kwargs):
-    """
-    """
-    if args or kwargs:
-        series = pd.Series(*args, **kwargs)
-    else:
-        series = pd.Series([], dtype=np.float64)
-
-    series.index.name = 'Time'
-    if 'name' not in kwargs:
-        series.name = 'Quantity'
-    return series
-
-
-def SweepSeries(*args, **kwargs):
-    """
-    """
-    if args or kwargs:
-        series = pd.Series(*args, **kwargs)
-    else:
-        series = pd.Series([], dtype=np.float64)
-
-    series.index.name = 'Parameter'
-    if 'name' not in kwargs:
-        series.name = 'Metric'
-    return series
-
-
-def TimeFrame(*args, **kwargs):
-    """DataFrame that maps from time to State.
-    """
-    return pd.DataFrame(*args, **kwargs)
-
-
-def SweepFrame(*args, **kwargs):
-    """DataFrame that maps from parameter value to SweepSeries.
-    """
-    return pd.DataFrame(*args, **kwargs)
-
-
-def Vector(x, y, z=None, **options):
-    """
-    """
-    if z is None:
-        return pd.Series(dict(x=x, y=y), **options)
-    else:
-        return pd.Series(dict(x=x, y=y, z=z), **options)
-
-
-## Vector functions (should work with any sequence)
-
-def vector_mag(v):
-    """Vector magnitude."""
-    return np.sqrt(np.dot(v, v))
-
-
-def vector_mag2(v):
-    """Vector magnitude squared."""
-    return np.dot(v, v)
-
-
-def vector_angle(v):
-    """Angle between v and the positive x axis.
-
-    Only works with 2-D vectors.
-
-    returns: angle in radians
-    """
-    assert len(v) == 2
-    x, y = v
-    return np.arctan2(y, x)
-
-
-def vector_polar(v):
-    """Vector magnitude and angle.
-
-    returns: (number, angle in radians)
-    """
-    return vector_mag(v), vector_angle(v)
-
-
-def vector_hat(v):
-    """Unit vector in the direction of v.
-
-    returns: Vector or array
-    """
-    # check if the magnitude of the Quantity is 0
-    mag = vector_mag(v)
-    if mag == 0:
-        return v
-    else:
-        return v / mag
-
-
-def vector_perp(v):
-    """Perpendicular Vector (rotated left).
-
-    Only works with 2-D Vectors.
-
-    returns: Vector
-    """
-    assert len(v) == 2
-    x, y = v
-    return Vector(-y, x)
-
-
-def vector_dot(v, w):
-    """Dot product of v and w.
-
-    returns: number or Quantity
-    """
-    return np.dot(v, w)
-
-
-def vector_cross(v, w):
-    """Cross product of v and w.
-
-    returns: number or Quantity for 2-D, Vector for 3-D
-    """
-    res = np.cross(v, w)
-
-    if len(v) == 3:
-        return Vector(*res)
-    else:
-        return res
-
-
-def vector_proj(v, w):
-    """Projection of v onto w.
-
-    returns: array or Vector with direction of w and units of v.
-    """
-    w_hat = vector_hat(w)
-    return vector_dot(v, w_hat) * w_hat
-
-
-def scalar_proj(v, w):
-    """Returns the scalar projection of v onto w.
-
-    Which is the magnitude of the projection of v onto w.
-
-    returns: scalar with units of v.
-    """
-    return vector_dot(v, vector_hat(w))
-
-
-def vector_dist(v, w):
-    """Euclidean distance from v to w, with units."""
-    if isinstance(v, list):
-        v = np.asarray(v)
-    return vector_mag(v - w)
-
-
-def vector_diff_angle(v, w):
-    """Angular difference between two vectors, in radians.
-    """
-    if len(v) == 2:
-        return vector_angle(v) - vector_angle(w)
-    else:
-        # TODO: see http://www.euclideanspace.com/maths/algebra/
-        # vectors/angleBetween/
-        raise NotImplementedError()
-
-
-def plot_segment(A, B, **options):
-    """Plots a line segment between two Vectors.
-
-    For 3-D vectors, the z axis is ignored.
-
-    Additional options are passed along to plot().
-
-    A: Vector
-    B: Vector
-    """
-    xs = A.x, B.x
-    ys = A.y, B.y
-    plot(xs, ys, **options)
-
-
-from time import sleep
-from IPython.display import clear_output
-
-def animate(results, draw_func, *args, interval=None):
-    """Animate results from a simulation.
-
-    results: TimeFrame
-    draw_func: function that draws state
-    interval: time between frames in seconds
-    """
-    plt.figure()
-    try:
-        for t, state in results.iterrows():
-            draw_func(t, state, *args)
-            plt.show()
-            if interval:
-                sleep(interval)
-            clear_output(wait=True)
-        draw_func(t, state, *args)
-        plt.show()
-    except KeyboardInterrupt:
-        pass

From 7996c85a4d9090ce03c66b9f9323fb3426a11dbd Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 22 May 2025 14:35:21 -0400
Subject: [PATCH 128/144] Removing old stuff

---
 notebooks/chap01.ipynb                        |   538 -
 notebooks/chap02.ipynb                        |   917 -
 notebooks/chap03.ipynb                        |   595 -
 notebooks/chap04.ipynb                        |   667 -
 notebooks/chap05.ipynb                        |   594 -
 notebooks/chap06.ipynb                        |   527 -
 notebooks/chap07.ipynb                        |   523 -
 notebooks/chap08.ipynb                        |   572 -
 notebooks/chap09.ipynb                        |   624 -
 notebooks/chap09sympy.ipynb                   |   632 -
 notebooks/chap10.ipynb                        |   447 -
 notebooks/chap11.ipynb                        |   463 -
 notebooks/chap12.ipynb                        |   877 -
 notebooks/chap13.ipynb                        |   517 -
 notebooks/chap14.ipynb                        |   475 -
 notebooks/chap15.ipynb                        |   318 -
 notebooks/chap16.ipynb                        |   842 -
 notebooks/chap16sympy.ipynb                   |   464 -
 notebooks/chap17.ipynb                        |   273 -
 notebooks/chap18.ipynb                        |   533 -
 notebooks/chap20.ipynb                        |   643 -
 notebooks/chap21.ipynb                        |   514 -
 notebooks/chap22.ipynb                        |  1238 -
 notebooks/chap23.ipynb                        |   571 -
 notebooks/chap24.ipynb                        |   640 -
 notebooks/chap25.ipynb                        |   944 -
 notebooks/data/DataOWall.csv                  |   867 -
 ...C2018-Report-30-2017-Activities-Page11.pdf |   Bin 28967 -> 0 bytes
 .../USASAC2018-Report-30-2017-Activities.pdf  |   Bin 4817307 -> 0 bytes
 notebooks/data/USASAC_Report_2017_Page18.png  |   Bin 61252 -> 0 bytes
 notebooks/data/World_population_estimates.csv |    68 -
 .../data/World_population_estimates.html      |  2634 -
 .../data/World_population_estimates2.csv      |    68 -
 .../data/World_population_estimates3.csv      |    46 -
 notebooks/data/baseball_drag.csv              |    17 -
 notebooks/data/baseball_drag.png              |   Bin 250310 -> 0 bytes
 notebooks/data/glucose_insulin.csv            |    25 -
 notebooks/diagrams/RC_Divider.png             |   Bin 1752 -> 0 bytes
 notebooks/diagrams/RC_Divider.svg             |   193 -
 notebooks/diagrams/queue.png                  |   Bin 8874 -> 0 bytes
 notebooks/diagrams/throwingaxe1.png           |   Bin 27102 -> 0 bytes
 notebooks/diagrams/throwingaxe2.png           |   Bin 34525 -> 0 bytes
 notebooks/examples/bigbelly.ipynb             |  1104 -
 notebooks/examples/world_pop_transition.ipynb |   233 -
 notebooks/figs/README                         |     1 -
 notebooks/filter.ipynb                        |   900 -
 notebooks/glucose.ipynb                       |   719 -
 notebooks/hopper.ipynb                        |  1342 -
 notebooks/insulin.ipynb                       |   316 -
 notebooks/jump.ipynb                          |   674 -
 notebooks/jump2.ipynb                         |   924 -
 notebooks/kitten.ipynb                        |   399 -
 notebooks/lotka-volterra.ipynb                |   944 -
 notebooks/modsim.html                         | 58638 ----------------
 notebooks/oem.ipynb                           |  1039 -
 notebooks/pendulum.ipynb                      |  4548 --
 notebooks/pendulum2.ipynb                     |  4548 --
 notebooks/pendulum_sympy.ipynb                |   978 -
 notebooks/queue.ipynb                         |   553 -
 notebooks/rabbits.ipynb                       |   773 -
 notebooks/rabbits2.ipynb                      |   399 -
 notebooks/rabbits3.ipynb                      |   630 -
 notebooks/salmon.ipynb                        |   703 -
 notebooks/spiderman.ipynb                     |   838 -
 notebooks/test_install.py                     |    48 -
 notebooks/throwingaxe.ipynb                   |   487 -
 notebooks/trees.ipynb                         |   972 -
 notebooks/variables_r_strange.ipynb           |   490 -
 notebooks/wall.ipynb                          |   930 -
 notebooks/yoyo.ipynb                          |   519 -
 70 files changed, 104515 deletions(-)
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 delete mode 100644 notebooks/data/USASAC2018-Report-30-2017-Activities-Page11.pdf
 delete mode 100644 notebooks/data/USASAC2018-Report-30-2017-Activities.pdf
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diff --git a/notebooks/chap01.ipynb b/notebooks/chap01.ipynb
deleted file mode 100644
index 4d73a4a0..00000000
--- a/notebooks/chap01.ipynb
+++ /dev/null
@@ -1,538 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 1\n",
-    "\n",
-    "Copyright 2020 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Jupyter\n",
-    "\n",
-    "Welcome to *Modeling and Simulation*, welcome to Python, and welcome to Jupyter.\n",
-    "\n",
-    "This is a Jupyter notebook, which is a development environment where you can write and run Python code.  Each notebook is divided into cells.  Each cell contains either text (like this cell) or Python code.\n",
-    "\n",
-    "### Selecting and running cells\n",
-    "\n",
-    "To select a cell, click in the left margin next to the cell.  You should see a blue frame surrounding the selected cell.\n",
-    "\n",
-    "To edit a code cell, click inside the cell.  You should see a green frame around the selected cell, and you should see a cursor inside the cell.\n",
-    "\n",
-    "To edit a text cell, double-click inside the cell.  Again, you should see a green frame around the selected cell, and you should see a cursor inside the cell.\n",
-    "\n",
-    "To run a cell, hold down SHIFT and press ENTER.  \n",
-    "\n",
-    "* If you run a text cell, Jupyter formats the text and displays the result.\n",
-    "\n",
-    "* If you run a code cell, Jupyter runs the Python code in the cell and displays the result, if any.\n",
-    "\n",
-    "To try it out, edit this cell, change some of the text, and then press SHIFT-ENTER to format it."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Adding and removing cells\n",
-    "\n",
-    "You can add and remove cells from a notebook using the buttons in the toolbar and the items in the menu, both of which you should see at the top of this notebook.\n",
-    "\n",
-    "Try the following exercises:\n",
-    "\n",
-    "1.  From the Insert menu select \"Insert cell below\" to add a cell below this one.  By default, you get a code cell, as you can see in the pulldown menu that says \"Code\".\n",
-    "\n",
-    "2.  In the new cell, add a print statement like `print('Hello')`, and run it.\n",
-    "\n",
-    "3.  Add another cell, select the new cell, and then click on the pulldown menu that says \"Code\" and select \"Markdown\".  This makes the new cell a text cell.\n",
-    "\n",
-    "4.  In the new cell, type some text, and then run it.\n",
-    "\n",
-    "5.  Use the arrow buttons in the toolbar to move cells up and down.\n",
-    "\n",
-    "6.  Use the cut, copy, and paste buttons to delete, add, and move cells.\n",
-    "\n",
-    "7.  As you make changes, Jupyter saves your notebook automatically, but if you want to make sure, you can press the save button, which looks like a floppy disk from the 1990s.\n",
-    "\n",
-    "8.  Finally, when you are done with a notebook, select \"Close and Halt\" from the File menu."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using the notebooks\n",
-    "\n",
-    "The notebooks for each chapter contain the code from the chapter along with additional examples, explanatory text, and exercises.  I recommend you \n",
-    "\n",
-    "1. Read the chapter first to understand the concepts and vocabulary, \n",
-    "2. Run the notebook to review what you learned and see it in action, and then\n",
-    "3. Attempt the exercises.\n",
-    "\n",
-    "If you try to work through the notebooks without reading the book, you're gonna have a bad time.  The notebooks contain some explanatory text, but it is probably not enough to make sense if you have not read the book.  If you are working through a notebook and you get stuck, you might want to re-read (or read!) the corresponding section of the book."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installing modules\n",
-    "\n",
-    "These notebooks use standard Python modules like NumPy and SciPy.  I assume you already have them installed in your environment.\n",
-    "\n",
-    "They also use two less common modules: Pint, which provides units, and modsim, which contains code I wrote specifically for this book.\n",
-    "\n",
-    "The following cells check whether you have these modules already and tries to install them if you don't."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    import pint\n",
-    "except ImportError:\n",
-    "    !pip install pint\n",
-    "    import pint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    from modsim import *\n",
-    "except ImportError:\n",
-    "    !pip install modsimpy\n",
-    "    from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first time you run this on a new installation of Python, it might produce a warning message in pink.  That's probably ok, but if you get a message that says `modsim.py depends on Python 3.7 features`, that means you have an older version of Python, and some features in `modsim.py` won't work correctly.\n",
-    "\n",
-    "If you need a newer version of Python, I recommend installing Anaconda.  You'll find more information in the preface of the book.\n",
-    "\n",
-    "You can find out what version of Python and Jupyter you have by running the following cells."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python --version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!jupyter-notebook --version"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Configuring Jupyter\n",
-    "\n",
-    "The following cell:\n",
-    "\n",
-    "1.  Uses a Jupyter \"magic command\" to specify whether figures should appear in the notebook, or pop up in a new window.\n",
-    "\n",
-    "2.  Configures Jupyter to display some values that would otherwise be invisible.  \n",
-    "\n",
-    "Select the following cell and press SHIFT-ENTER to run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The penny myth\n",
-    "\n",
-    "The following cells contain code from the beginning of Chapter 1.\n",
-    "\n",
-    "`modsim` defines `UNITS`, which contains variables representing pretty much every unit you've ever heard of.  It uses [Pint](https://pint.readthedocs.io/en/latest/), which is a Python library that provides tools for computing with units.\n",
-    "\n",
-    "The following lines create new variables named `meter` and `second`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "meter = UNITS.meter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "second = UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To find out what other units are defined, type `UNITS.` (including the period) in the next cell and then press TAB.  You should see a pop-up menu with a list of units."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a variable named `a` and give it the value of acceleration due to gravity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = 9.8 * meter / second**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create `t` and give it the value 4 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = 4 * second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute the distance a penny would fall after `t` seconds with constant acceleration `a`.  Notice that the units of the result are correct."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a * t**2 / 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise**: Compute the velocity of the penny after `t` seconds.  Check that the units of the result are correct."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise**: Why would it be nonsensical to add `a` and `t`? What happens if you try?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The error messages you get from Python are big and scary, but if you read them carefully, they contain a lot of useful information.\n",
-    "\n",
-    "1.  Start from the bottom and read up.\n",
-    "2.  The last line usually tells you what type of error happened, and sometimes additional information.\n",
-    "3.  The previous lines are a \"traceback\" of what was happening when the error occurred.  The first section of the traceback shows the code you wrote.  The following sections are often from Python libraries.\n",
-    "\n",
-    "In this example, you should get a `DimensionalityError`, which is defined by Pint to indicate that you have violated a rules of dimensional analysis: you cannot add quantities with different dimensions.\n",
-    "\n",
-    "Before you go on, you might want to delete the erroneous code so the notebook can run without errors."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Falling pennies\n",
-    "\n",
-    "Now let's solve the falling penny problem.\n",
-    "\n",
-    "Set `h` to the height of the Empire State Building:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h = 381 * meter"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute the time it would take a penny to fall, assuming constant acceleration.\n",
-    "\n",
-    "$ a t^2 / 2 = h $\n",
-    "\n",
-    "$ t = \\sqrt{2 h / a}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = sqrt(2 * h / a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Given `t`, we can compute the velocity of the penny when it lands.\n",
-    "\n",
-    "$v = a t$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v = a * t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can convert from one set of units to another like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mile = UNITS.mile\n",
-    "hour = UNITS.hour"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v.to(mile/hour)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose you bring a 10 foot pole to the top of the Empire State Building and use it to drop the penny from `h` plus 10 feet.\n",
-    "\n",
-    "Define a variable named `foot` that contains the unit `foot` provided by `UNITS`.  Define a variable named `pole_height` and give it the value 10 feet.\n",
-    "\n",
-    "What happens if you add `h`, which is in units of meters, to `pole_height`, which is in units of feet?  What happens if you write the addition the other way around?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** In reality, air resistance limits the velocity of the penny.  At about 18 m/s, the force of air resistance equals the force of gravity and the penny stops accelerating.\n",
-    "\n",
-    "As a simplification, let's assume that the acceleration of the penny is `a` until the penny reaches 18 m/s, and then 0 afterwards.  What is the total time for the penny to fall 381 m?\n",
-    "\n",
-    "You can break this question into three parts:\n",
-    "\n",
-    "1. How long until the penny reaches 18 m/s with constant acceleration `a`.\n",
-    "2. How far would the penny fall during that time?\n",
-    "3. How long to fall the remaining distance with constant velocity 18 m/s?\n",
-    "\n",
-    "Suggestion: Assign each intermediate result to a variable with a meaningful name.  And assign units to all quantities!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Restart and run all\n",
-    "\n",
-    "When you change the contents of a cell, you have to run it again for those changes to have an effect.  If you forget to do that, the results can be confusing, because the code you are looking at is not the code you ran.\n",
-    "\n",
-    "If you ever lose track of which cells have run, and in what order, you should go to the Kernel menu and select \"Restart & Run All\".  Restarting the kernel means that all of your variables get deleted, and running all the cells means all of your code will run again, in the right order.\n",
-    "\n",
-    "**Exercise:**  Select \"Restart & Run All\" now and confirm that it does what you want."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap02.ipynb b/notebooks/chap02.ipynb
deleted file mode 100644
index 192035b5..00000000
--- a/notebooks/chap02.ipynb
+++ /dev/null
@@ -1,917 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 2\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim library\n",
-    "from modsim import *\n",
-    "\n",
-    "# set the random number generator\n",
-    "np.random.seed(7)\n",
-    "\n",
-    "# If this cell runs successfully, it produces no output."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Modeling a bikeshare system"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll start with a `State` object that represents the number of bikes at each station.\n",
-    "\n",
-    "When you display a `State` object, it lists the state variables and their values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can access the state variables using dot notation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare.olin"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "bikeshare.wellesley"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** What happens if you spell the name of a state variable wrong?  Edit the previous cell, change the spelling of `wellesley`, and run the cell again.\n",
-    "\n",
-    "The error message uses the word \"attribute\", which is another name for what we are calling a state variable. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Add a third attribute called `babson` with initial value 0, and display the state of `bikeshare` again."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Updating\n",
-    "\n",
-    "We can use the update operators `+=` and `-=` to change state variables."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare.olin -= 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we display `bikeshare`, we should see the change."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Of course, if we subtract a bike from `olin`, we should add it to `wellesley`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare.wellesley += 1\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Functions\n",
-    "\n",
-    "We can take the code we've written so far and encapsulate it in a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_wellesley():\n",
-    "    bikeshare.olin -= 1\n",
-    "    bikeshare.wellesley += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When you define a function, it doesn't run the statements inside the function, yet.  When you call the function, it runs the statements inside."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bike_to_wellesley()\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "One common error is to omit the parentheses, which has the effect of looking up the function, but not calling it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bike_to_wellesley"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The output indicates that `bike_to_wellesley` is a function defined in a \"namespace\" called `__main__`, but you don't have to understand what that means."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Define a function called `bike_to_olin` that moves a bike from Wellesley to Olin.  Call the new function and display `bikeshare`  to confirm that it works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Conditionals"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`modsim.py` provides `flip`, which takes a probability and returns either `True` or `False`, which are special values defined by Python.\n",
-    "\n",
-    "The Python function `help` looks up a function and displays its documentation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(flip)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following example, the probability is 0.7 or 70%.  If you run this cell several times, you should get `True` about 70% of the time and `False` about 30%."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "flip(0.7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following example, we use `flip` as part of an if statement.  If the result from `flip` is `True`, we print `heads`; otherwise we do nothing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if flip(0.7):\n",
-    "    print('heads')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With an else clause, we can print heads or tails depending on whether `flip` returns `True` or `False`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if flip(0.7):\n",
-    "    print('heads')\n",
-    "else:\n",
-    "    print('tails')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step\n",
-    "\n",
-    "Now let's get back to the bikeshare state.  Again let's start with a new `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Suppose that in any given minute, there is a 50% chance that a student picks up a bike at Olin and rides to Wellesley.  We can simulate that like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if flip(0.5):\n",
-    "    bike_to_wellesley()\n",
-    "    print('Moving a bike to Wellesley')\n",
-    "\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And maybe at the same time, there is also a 40% chance that a student at Wellesley rides to Olin."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if flip(0.4):\n",
-    "    bike_to_olin()\n",
-    "    print('Moving a bike to Olin')\n",
-    "\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can wrap that code in a function called `step` that simulates one time step.  In any given minute, a student might ride from Olin to Wellesley, from Wellesley to Olin, or both, or neither, depending on the results of `flip`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step():\n",
-    "    if flip(0.5):\n",
-    "        bike_to_wellesley()\n",
-    "        print('Moving a bike to Wellesley')\n",
-    "        \n",
-    "    if flip(0.4):\n",
-    "        bike_to_olin()\n",
-    "        print('Moving a bike to Olin')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since this function takes no parameters, we call it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "step()\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Parameters\n",
-    "\n",
-    "As defined in the previous section, `step` is not as useful as it could be, because the probabilities `0.5` and `0.4` are \"hard coded\".\n",
-    "\n",
-    "It would be better to generalize this function so it takes the probabilities `p1` and `p2` as parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step(p1, p2):\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley()\n",
-    "        print('Moving a bike to Wellesley')\n",
-    "        \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin()\n",
-    "        print('Moving a bike to Olin')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can call it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "step(0.5, 0.4)\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** At the beginning of `step`, add a print statement that displays the values of `p1` and `p2`.  Call it again with values `0.3`, and `0.2`, and confirm that the values of the parameters are what you expect. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## For loop"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before we go on, I'll redefine `step` without the print statements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step(p1, p2):\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley()\n",
-    "    \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And let's start again with a new `State` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use a `for` loop to move 4 bikes from Olin to Wellesley."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(4):\n",
-    "    bike_to_wellesley()\n",
-    "    \n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or we can simulate 4 random time steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(4):\n",
-    "    step(0.3, 0.2)\n",
-    "    \n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If each step corresponds to a minute, we can simulate an entire hour like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(60):\n",
-    "    step(0.3, 0.2)\n",
-    "\n",
-    "bikeshare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After 60 minutes, you might see that the number of bike at Olin is negative.  We'll fix that problem in the next notebook.\n",
-    "\n",
-    "But first, we want to plot the results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## TimeSeries\n",
-    "\n",
-    "`modsim.py` provides an object called a `TimeSeries` that can contain a sequence of values changing over time.\n",
-    "\n",
-    "We can create a new, empty `TimeSeries` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = TimeSeries()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can add a value to the `TimeSeries` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results[0] = bikeshare.olin\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `0` in brackets is an `index` that indicates that this value is associated with time step 0.\n",
-    "\n",
-    "Now we'll use a for loop to save the results of the simulation.  I'll start one more time with a new `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a for loop that runs 10 steps and stores the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(10):\n",
-    "    step(0.3, 0.2)\n",
-    "    results[i] = bikeshare.olin"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can display the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `TimeSeries` is a specialized version of a Pandas `Series`, so we can use any of the functions provided by `Series`, including several that compute summary statistics:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can read the documentation of `Series` [here](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting\n",
-    "\n",
-    "We can also plot the results like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results, label='Olin')\n",
-    "\n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Time step (min)', \n",
-    "         ylabel='Number of bikes')\n",
-    "\n",
-    "savefig('figs/chap02-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`decorate`, which is defined in the `modsim` library, adds a title and labels the axes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(decorate)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`savefig()` saves a figure in a file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(savefig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The suffix of the filename indicates the format you want.  This example saves the current figure in a PDF file."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Wrap the code from this section in a function named `run_simulation` that takes three parameters, named `p1`, `p2`, and `num_steps`.\n",
-    "\n",
-    "It should:\n",
-    "\n",
-    "1. Create a `TimeSeries` object to hold the results.\n",
-    "2. Use a for loop to run `step` the number of times specified by `num_steps`, passing along the specified values of `p1` and `p2`.\n",
-    "3. After each step, it should save the number of bikes at Olin in the `TimeSeries`.\n",
-    "4. After the for loop, it should plot the results and\n",
-    "5. Decorate the axes.\n",
-    "\n",
-    "To test your function:\n",
-    "\n",
-    "1. Create a `State` object with the initial state of the system.\n",
-    "2. Call `run_simulation` with appropriate parameters.\n",
-    "3. Save the resulting figure.\n",
-    "\n",
-    "Optional:\n",
-    "\n",
-    "1. Extend your solution so it creates two `TimeSeries` objects, keeps track of the number of bikes at Olin *and* at Wellesley, and plots both series at the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Opening the hood\n",
-    "\n",
-    "The functions in `modsim.py` are built on top of several widely-used Python libraries, especially NumPy, SciPy, and Pandas.  These libraries are powerful but can be hard to use.  The intent of `modsim.py` is to give you the power of these libraries while making it easy to get started.\n",
-    "\n",
-    "In the future, you might want to use these libraries directly, rather than using `modsim.py`.  So we will pause occasionally to open the hood and let you see how `modsim.py` works.\n",
-    "\n",
-    "You don't need to know anything in these sections, so if you are already feeling overwhelmed, you might want to skip them.  But if you are curious, read on."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pandas\n",
-    "\n",
-    "This chapter introduces two objects, `State` and `TimeSeries`.  Both are based on the `Series` object defined by Pandas, which is a library primarily used for data science.\n",
-    "\n",
-    "You can read the documentation of the `Series` object [here](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html)\n",
-    "\n",
-    "The primary differences between `TimeSeries` and `Series` are:\n",
-    "\n",
-    "1. I made it easier to create a new, empty `Series` while avoiding a [confusing inconsistency](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html).\n",
-    "\n",
-    "2. I provide a function so the `Series` looks good when displayed in Jupyter.\n",
-    "\n",
-    "3. I provide a function called `set` that we'll use later.\n",
-    "\n",
-    "`State` has all of those capabilities; in addition, it provides an easier way to initialize state variables, and it provides functions called `T` and `dt`, which will help us avoid a confusing error later."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pyplot\n",
-    "\n",
-    "The `plot` function in `modsim.py` is based on the `plot` function in Pyplot, which is part of Matplotlib.  You can read the documentation of `plot` [here](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html).\n",
-    "\n",
-    "`decorate` provides a convenient way to call the `pyplot` functions `title`, `xlabel`, and `ylabel`, and `legend`.  It also avoids an annoying warning message if you try to make a legend when you don't have any labelled lines."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(decorate)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### NumPy\n",
-    "\n",
-    "The `flip` function in `modsim.py` uses NumPy's `random` function to generate a random number between 0 and 1.\n",
-    "\n",
-    "You can get the source code for `flip` by running the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_code(flip)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap03.ipynb b/notebooks/chap03.ipynb
deleted file mode 100644
index 82a9d53a..00000000
--- a/notebooks/chap03.ipynb
+++ /dev/null
@@ -1,595 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 3\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim library\n",
-    "from modsim import *\n",
-    "\n",
-    "# set the random number generator\n",
-    "np.random.seed(7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## More than one State object\n",
-    "\n",
-    "Here's the code from the previous chapter, with two changes:\n",
-    "\n",
-    "1. I've added DocStrings that explain what each function does, and what parameters it takes.\n",
-    "\n",
-    "2. I've added a parameter named `state` to the functions so they work with whatever `State` object we give them, instead of always using `bikeshare`.  That makes it possible to work with more than one `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step(state, p1, p2):\n",
-    "    \"\"\"Simulate one minute of time.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    p1: probability of an Olin->Wellesley customer arrival\n",
-    "    p2: probability of a Wellesley->Olin customer arrival\n",
-    "    \"\"\"\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley(state)\n",
-    "    \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin(state)\n",
-    "        \n",
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1\n",
-    "    \n",
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1\n",
-    "    \n",
-    "def decorate_bikeshare():\n",
-    "    \"\"\"Add a title and label the axes.\"\"\"\n",
-    "    decorate(title='Olin-Wellesley Bikeshare',\n",
-    "             xlabel='Time step (min)', \n",
-    "             ylabel='Number of bikes')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's `run_simulation`, which is a solution to the exercise at the end of the previous notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(state, p1, p2, num_steps):\n",
-    "    \"\"\"Simulate the given number of time steps.\n",
-    "    \n",
-    "    state: State object\n",
-    "    p1: probability of an Olin->Wellesley customer arrival\n",
-    "    p2: probability of a Wellesley->Olin customer arrival\n",
-    "    num_steps: number of time steps\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()    \n",
-    "    for i in range(num_steps):\n",
-    "        step(state, p1, p2)\n",
-    "        results[i] = state.olin\n",
-    "        \n",
-    "    plot(results, label='Olin')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can create more than one `State` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare1 = State(olin=10, wellesley=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare2 = State(olin=2, wellesley=10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Whenever we call a function, we indicate which `State` object to work with:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bike_to_olin(bikeshare1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bike_to_wellesley(bikeshare2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And you can confirm that the different objects are getting updated independently:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Negative bikes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the code we have so far, the number of bikes at one of the locations can go negative, and the number of bikes at the other location can exceed the actual number of bikes in the system.\n",
-    "\n",
-    "If you run this simulation a few times, it happens often."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)\n",
-    "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
-    "decorate_bikeshare()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can fix this problem using the `return` statement to exit the function early if an update would cause negative bikes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.olin == 0:\n",
-    "        return\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1\n",
-    "    \n",
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.wellesley == 0:\n",
-    "        return\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now if you run the simulation again, it should behave."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)\n",
-    "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
-    "decorate_bikeshare()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Comparison operators"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `if` statements in the previous section used the comparison operator `==`.  The other comparison operators are listed in the book.\n",
-    "\n",
-    "It is easy to confuse the comparison operator `==` with the assignment operator `=`.\n",
-    "\n",
-    "Remember that `=` creates a variable or gives an existing variable a new value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Whereas `==` compares two values and returns `True` if they are equal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x == 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can use `==` in an `if` statement."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if x == 5:\n",
-    "    print('yes, x is 5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But if you use `=` in an `if` statement, you get an error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# If you remove the # from the if statement and run it, you'll get\n",
-    "# SyntaxError: invalid syntax\n",
-    "\n",
-    "#if x = 5:\n",
-    "#    print('yes, x is 5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Add an `else` clause to the `if` statement above, and print an appropriate message.\n",
-    "\n",
-    "Replace the `==` operator with one or two of the other comparison operators, and confirm they do what you expect."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have a working simulation, we'll use it to evaluate alternative designs and see how good or bad they are.  The metric we'll use is the number of customers who arrive and find no bikes available, which might indicate a design problem."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we'll make a new `State` object that creates and initializes additional state variables to keep track of the metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2, \n",
-    "                  olin_empty=0, wellesley_empty=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we need versions of `bike_to_wellesley` and `bike_to_olin` that update the metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.olin == 0:\n",
-    "        state.olin_empty += 1\n",
-    "        return\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1\n",
-    "    \n",
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.wellesley == 0:\n",
-    "        state.wellesley_empty += 1\n",
-    "        return\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now when we run a simulation, it keeps track of unhappy customers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
-    "decorate_bikeshare()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After the simulation, we can print the number of unhappy customers at each location."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare.wellesley_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:** As another metric, we might be interested in the time until the first customer arrives and doesn't find a bike.  To make that work, we have to add a \"clock\" to keep track of how many time steps have elapsed:\n",
-    "\n",
-    "1. Create a new `State` object with an additional state variable, `clock`, initialized to 0. \n",
-    "\n",
-    "2. Write a modified version of `step` that adds one to the clock each time it is invoked.\n",
-    "\n",
-    "Test your code by running the simulation and check the value of `clock` at the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2, \n",
-    "                  olin_empty=0, wellesley_empty=0,\n",
-    "                  clock=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Continuing the previous exercise, let's record the time when the first customer arrives and doesn't find a bike.\n",
-    "\n",
-    "1. Create a new `State` object with an additional state variable, `t_first_empty`, initialized to -1 as a special value to indicate that it has not been set. \n",
-    "\n",
-    "2. Write a modified version of `step` that checks whether`olin_empty` and `wellesley_empty` are 0.  If not, it should set `t_first_empty` to `clock` (but only if `t_first_empty` has not already been set).\n",
-    "\n",
-    "Test your code by running the simulation and printing the values of `olin_empty`, `wellesley_empty`, and `t_first_empty` at the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap04.ipynb b/notebooks/chap04.ipynb
deleted file mode 100644
index 1945f357..00000000
--- a/notebooks/chap04.ipynb
+++ /dev/null
@@ -1,667 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 4\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim library\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Returning values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a simple function that returns a value:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def add_five(x):\n",
-    "    return x + 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we call it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y = add_five(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you run a function on the last line of a cell, Jupyter displays the result:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "add_five(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But that can be a bad habit, because usually if you call a function and don't assign the result in a variable, the result gets discarded.\n",
-    "\n",
-    "In the following example, Jupyter shows the second result, but the first result just disappears."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "add_five(3)\n",
-    "add_five(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When you call a function that returns a variable, it is generally a good idea to assign the result to a variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y1 = add_five(3)\n",
-    "y2 = add_five(5)\n",
-    "\n",
-    "print(y1, y2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write a function called `make_state` that creates a `State` object with the state variables `olin=10` and `wellesley=2`, and then returns the new `State` object.\n",
-    "\n",
-    "Write a line of code that calls `make_state` and assigns the result to a variable named `init`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Running simulations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the code from the previous notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def step(state, p1, p2):\n",
-    "    \"\"\"Simulate one minute of time.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    p1: probability of an Olin->Wellesley customer arrival\n",
-    "    p2: probability of a Wellesley->Olin customer arrival\n",
-    "    \"\"\"\n",
-    "    if flip(p1):\n",
-    "        bike_to_wellesley(state)\n",
-    "    \n",
-    "    if flip(p2):\n",
-    "        bike_to_olin(state)\n",
-    "        \n",
-    "def bike_to_wellesley(state):\n",
-    "    \"\"\"Move one bike from Olin to Wellesley.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.olin == 0:\n",
-    "        state.olin_empty += 1\n",
-    "        return\n",
-    "    state.olin -= 1\n",
-    "    state.wellesley += 1\n",
-    "    \n",
-    "def bike_to_olin(state):\n",
-    "    \"\"\"Move one bike from Wellesley to Olin.\n",
-    "    \n",
-    "    state: bikeshare State object\n",
-    "    \"\"\"\n",
-    "    if state.wellesley == 0:\n",
-    "        state.wellesley_empty += 1\n",
-    "        return\n",
-    "    state.wellesley -= 1\n",
-    "    state.olin += 1\n",
-    "    \n",
-    "def decorate_bikeshare():\n",
-    "    \"\"\"Add a title and label the axes.\"\"\"\n",
-    "    decorate(title='Olin-Wellesley Bikeshare',\n",
-    "             xlabel='Time step (min)', \n",
-    "             ylabel='Number of bikes')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a modified version of `run_simulation` that creates a `State` object, runs the simulation, and returns the `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(p1, p2, num_steps):\n",
-    "    \"\"\"Simulate the given number of time steps.\n",
-    "    \n",
-    "    p1: probability of an Olin->Wellesley customer arrival\n",
-    "    p2: probability of a Wellesley->Olin customer arrival\n",
-    "    num_steps: number of time steps\n",
-    "    \"\"\"\n",
-    "    state = State(olin=10, wellesley=2, \n",
-    "                  olin_empty=0, wellesley_empty=0)\n",
-    "                    \n",
-    "    for i in range(num_steps):\n",
-    "        step(state, p1, p2)\n",
-    "        \n",
-    "    return state"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now `run_simulation` doesn't plot anything:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = run_simulation(0.4, 0.2, 60)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But after the simulation, we can read the metrics from the `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run simulations with different values for the parameters.  When `p1` is small, we probably don't run out of bikes at Olin."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = run_simulation(0.2, 0.2, 60)\n",
-    "state.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When `p1` is large, we probably do."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = run_simulation(0.6, 0.2, 60)\n",
-    "state.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## More for loops"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`linspace` creates a NumPy array of equally spaced numbers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p1_array = linspace(0, 1, 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use an array in a `for` loop, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for p1 in p1_array:\n",
-    "    print(p1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This will come in handy in the next section.\n",
-    "\n",
-    "`linspace` is defined in `modsim.py`.  You can get the documentation using `help`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(linspace)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`linspace` is based on a NumPy function with the same name.  [Click here](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html) to read more about how to use it."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** \n",
-    "Use `linspace` to make an array of 10 equally spaced numbers from 1 to 10 (including both)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** The `modsim` library provides a related function called `linrange`.  You can view the documentation by running the following cell:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(linrange)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use `linrange` to make an array of numbers from 1 to 11 with a step size of 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sweeping parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`p1_array` contains a range of values for `p1`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p2 = 0.2\n",
-    "num_steps = 60\n",
-    "p1_array = linspace(0, 1, 11)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following loop runs a simulation for each value of `p1` in `p1_array`; after each simulation, it prints the number of unhappy customers at the Olin station:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for p1 in p1_array:\n",
-    "    state = run_simulation(p1, p2, num_steps)\n",
-    "    print(p1, state.olin_empty)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can do the same thing, but storing the results in a `SweepSeries` instead of printing them.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sweep = SweepSeries()\n",
-    "\n",
-    "for p1 in p1_array:\n",
-    "    state = run_simulation(p1, p2, num_steps)\n",
-    "    sweep[p1] = state.olin_empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then we can plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(sweep, label='Olin')\n",
-    "\n",
-    "decorate(title='Olin-Wellesley Bikeshare',\n",
-    "         xlabel='Arrival rate at Olin (p1 in customers/min)', \n",
-    "         ylabel='Number of unhappy customers')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:** Wrap this code in a function named `sweep_p1` that takes an array called `p1_array` as a parameter.  It should create a new `SweepSeries`, run a simulation for each value of `p1` in `p1_array`, store the results in the `SweepSeries`, and return the `SweepSeries`.\n",
-    "\n",
-    "Use your function to plot the number of unhappy customers at Olin as a function of `p1`.  Label the axes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write a function called `sweep_p2` that runs simulations with `p1=0.5` and a range of values for `p2`.  It should store the results in a `SweepSeries` and return the `SweepSeries`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Optional Exercises\n",
-    "\n",
-    "The following two exercises are a little more challenging.  If you are comfortable with what you have learned so far, you should give them a try.  If you feel like you have your hands full, you might want to skip them for now.\n",
-    "\n",
-    "**Exercise:** Because our simulations are random, the results vary from one run to another, and the results of a parameter sweep tend to be noisy.  We can get a clearer picture of the relationship between a parameter and a metric by running multiple simulations with the same parameter and taking the average of the results.\n",
-    "\n",
-    "Write a function called `run_multiple_simulations` that takes as parameters `p1`, `p2`, `num_steps`, and `num_runs`.\n",
-    "\n",
-    "`num_runs` specifies how many times it should call `run_simulation`.\n",
-    "\n",
-    "After each run, it should store the total number of unhappy customers (at Olin or Wellesley) in a `TimeSeries`.  At the end, it should return the `TimeSeries`.\n",
-    "\n",
-    "Test your function with parameters\n",
-    "\n",
-    "```\n",
-    "p1 = 0.3\n",
-    "p2 = 0.3\n",
-    "num_steps = 60\n",
-    "num_runs = 10\n",
-    "```\n",
-    "\n",
-    "Display the resulting `TimeSeries` and use the `mean` function provided by the `TimeSeries` object to compute the average number of unhappy customers (see Section 2.7)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Continuting the previous exercise, use `run_multiple_simulations` to run simulations with a range of values for `p1` and\n",
-    "\n",
-    "```\n",
-    "p2 = 0.3\n",
-    "num_steps = 60\n",
-    "num_runs = 20\n",
-    "```\n",
-    "\n",
-    "Store the results in a `SweepSeries`, then plot the average number of unhappy customers as a function of `p1`.  Label the axes.\n",
-    "\n",
-    "What value of `p1` minimizes the average number of unhappy customers?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap05.ipynb b/notebooks/chap05.ipynb
deleted file mode 100644
index d624e63e..00000000
--- a/notebooks/chap05.ipynb
+++ /dev/null
@@ -1,594 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 5\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reading data\n",
-    "\n",
-    "Pandas is a library that provides tools for reading and processing data.  `read_html` reads a web page from a file or the Internet and creates one `DataFrame` for each table on the page."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas import read_html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The data directory contains a downloaded copy of https://en.wikipedia.org/wiki/World_population_estimates\n",
-    "\n",
-    "The arguments of `read_html` specify the file to read and how to interpret the tables in the file.  The result, `tables`, is a sequence of `DataFrame` objects; `len(tables)` reports the length of the sequence."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = 'data/World_population_estimates.html'\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "len(tables)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can select the `DataFrame` we want using the bracket operator.  The tables are numbered from 0, so `tables[2]` is actually the third table on the page.\n",
-    "\n",
-    "`head` selects the header and the first five rows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "table2 = tables[2]\n",
-    "table2.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`tail` selects the last five rows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table2.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Long column names are awkard to work with, but we can replace them with abbreviated names."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the DataFrame looks like now.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table2.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first column, which is labeled `Year`, is special.  It is the **index** for this `DataFrame`, which means it contains the labels for the rows.\n",
-    "\n",
-    "Some of the values use scientific notation; for example, `2.544000e+09` is shorthand for $2.544 \\cdot 10^9$ or 2.544 billion.\n",
-    "\n",
-    "`NaN` is a special value that indicates missing data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Series\n",
-    "\n",
-    "We can use dot notation to select a column from a `DataFrame`.  The result is a `Series`, which is like a `DataFrame` with a single column."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census = table2.census\n",
-    "census.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Like a `DataFrame`, a `Series` contains an index, which labels the rows.\n",
-    "\n",
-    "`1e9` is scientific notation for $1 \\cdot 10^9$ or 1 billion."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From here on, we will work in units of billions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "un.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census = table2.census / 1e9\n",
-    "census.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what these estimates look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "plot(census, ':', label='US Census')\n",
-    "plot(un, '--', label='UN DESA')\n",
-    "    \n",
-    "decorate(xlabel='Year',\n",
-    "         ylabel='World population (billion)')\n",
-    "\n",
-    "savefig('figs/chap05-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following expression computes the elementwise differences between the two series, then divides through by the UN value to produce [relative errors](https://en.wikipedia.org/wiki/Approximation_error), then finds the largest element.\n",
-    "\n",
-    "So the largest relative error between the estimates is about 1.3%."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max(abs(census - un) / un) * 100"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Break down that expression into smaller steps and display the intermediate results, to make sure you understand how it works.\n",
-    "\n",
-    "1.  Compute the elementwise differences, `census - un`\n",
-    "2.  Compute the absolute differences, `abs(census - un)`\n",
-    "3.  Compute the relative differences, `abs(census - un) / un`\n",
-    "4.  Compute the percent differences, `abs(census - un) / un * 100`\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`max` and `abs` are built-in functions provided by Python, but NumPy also provides version that are a little more general.  When you import `modsim`, you get the NumPy versions of these functions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Constant growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can select a value from a `Series` using bracket notation.  Here's the first element:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census[1950]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the last value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census[2016]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But rather than \"hard code\" those dates, we can get the first and last labels from the `Series`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = get_first_label(census)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_end = get_last_label(census)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "elapsed_time = t_end - t_0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can get the first and last values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_0 = get_first_value(census)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_end = get_last_value(census)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we can compute the average annual growth in billions of people per year."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "total_growth = p_end - p_0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "annual_growth = total_growth / elapsed_time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### TimeSeries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's create a `TimeSeries` to contain values generated by a linear growth model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = TimeSeries()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initially the `TimeSeries` is empty, but we can initialize it so the starting value, in 1950, is the 1950 population estimated by the US Census."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results[t_0] = census[t_0]\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After that, the population in the model grows by a constant amount each year."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for t in linrange(t_0, t_end):\n",
-    "    results[t+1] = results[t] + annual_growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results looks like, compared to the actual data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(census, ':', label='US Census')\n",
-    "plot(un, '--', label='UN DESA')\n",
-    "plot(results, color='gray', label='model')\n",
-    "\n",
-    "decorate(xlabel='Year', \n",
-    "         ylabel='World population (billion)',\n",
-    "         title='Constant growth')\n",
-    "\n",
-    "savefig('figs/chap05-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model fits the data pretty well after 1990, but not so well before."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Optional Exercise:**  Try fitting the model using data from 1970 to the present, and see if that does a better job.\n",
-    "\n",
-    "Hint: \n",
-    "\n",
-    "1. Copy the code from above and make a few changes.  Test your code after each small change.\n",
-    "\n",
-    "2. Make sure your `TimeSeries` starts in 1950, even though the estimated annual growth is based on later data.\n",
-    "\n",
-    "3. You might want to add a constant to the starting value to match the data better."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census.loc[1960:1970]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap06.ipynb b/notebooks/chap06.ipynb
deleted file mode 100644
index aee7cc16..00000000
--- a/notebooks/chap06.ipynb
+++ /dev/null
@@ -1,527 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 6\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *\n",
-    "\n",
-    "from pandas import read_html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = 'data/World_population_estimates.html'\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "table2 = tables[2]\n",
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "un.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census = table2.census / 1e9\n",
-    "census.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = get_first_label(census)\n",
-    "t_end = get_last_label(census)\n",
-    "elapsed_time = t_end - t_0\n",
-    "\n",
-    "p_0 = get_first_value(census)\n",
-    "p_end = get_last_value(census)\n",
-    "total_growth = p_end - p_0\n",
-    "\n",
-    "annual_growth = total_growth / elapsed_time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### System objects"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can rewrite the code from the previous chapter using system objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                annual_growth=annual_growth)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can encapsulate the code that runs the model in a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation1(system):\n",
-    "    \"\"\"Runs the constant growth model.\n",
-    "    \n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        results[t+1] = results[t] + system.annual_growth\n",
-    "    \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also encapsulate the code that plots the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(census, un, timeseries, title):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    census: TimeSeries of population estimates\n",
-    "    un: TimeSeries of population estimates\n",
-    "    timeseries: TimeSeries of simulation results\n",
-    "    title: string\n",
-    "    \"\"\"\n",
-    "    plot(census, ':', label='US Census')\n",
-    "    plot(un, '--', label='UN DESA')\n",
-    "    plot(timeseries, color='gray', label='model')\n",
-    "    \n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation1(system)\n",
-    "plot_results(census, un, results, 'Constant growth model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Proportional growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a more realistic model where the number of births and deaths is proportional to the current population."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation2(system):\n",
-    "    \"\"\"Run a model with proportional birth and death.\n",
-    "    \n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        births = system.birth_rate * results[t]\n",
-    "        deaths = system.death_rate * results[t]\n",
-    "        results[t+1] = results[t] + births - deaths\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I picked a death rate that seemed reasonable and then adjusted the birth rate to fit the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.death_rate = 0.01\n",
-    "system.birth_rate = 0.027"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation2(system)\n",
-    "plot_results(census, un, results, 'Proportional model')\n",
-    "savefig('figs/chap06-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model fits the data pretty well for the first 20 years, but not so well after that."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Factoring out the update function"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`run_simulation1` and `run_simulation2` are nearly identical except the body of the loop.  So we can factor that part out into a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func1(pop, t, system):\n",
-    "    \"\"\"Compute the population next year.\n",
-    "    \n",
-    "    pop: current population\n",
-    "    t: current year\n",
-    "    system: system object containing parameters of the model\n",
-    "    \n",
-    "    returns: population next year\n",
-    "    \"\"\"\n",
-    "    births = system.birth_rate * pop\n",
-    "    deaths = system.death_rate * pop\n",
-    "    return pop + births - deaths"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The name `update_func` refers to a function object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "update_func1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Which we can confirm by checking its type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(update_func1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`run_simulation` takes the update function as a parameter and calls it just like any other function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate the system using any update function.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that computes the population next year\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        results[t+1] = update_func(results[t], t, system)\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we use it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = get_first_label(census)\n",
-    "t_end = get_last_label(census)\n",
-    "p_0 = census[t_0]\n",
-    "\n",
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                birth_rate=0.027,\n",
-    "                death_rate=0.01)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func1)\n",
-    "plot_results(census, un, results, 'Proportional model, factored')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Remember not to put parentheses after `update_func1`.  What happens if you try?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** When you run `run_simulation`, it runs `update_func1` once for each year between `t_0` and `t_end`.  To see that for yourself, add a print statement at the beginning of `update_func1` that prints the values of `t` and `pop`, then run `run_simulation` again."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Combining birth and death"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since births and deaths get added up, we don't have to compute them separately.  We can combine the birth and death rates into a single net growth rate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func2(pop, t, system):\n",
-    "    \"\"\"Compute the population next year.\n",
-    "    \n",
-    "    pop: current population\n",
-    "    t: current year\n",
-    "    system: system object containing parameters of the model\n",
-    "    \n",
-    "    returns: population next year\n",
-    "    \"\"\"\n",
-    "    net_growth = system.alpha  * pop\n",
-    "    return pop + net_growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how it works:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.alpha = system.birth_rate - system.death_rate\n",
-    "\n",
-    "results = run_simulation(system, update_func2)\n",
-    "plot_results(census, un, results, 'Proportional model, combined birth and death')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Maybe the reason the proportional model doesn't work very well is that the growth rate, `alpha`, is changing over time.  So let's try a model with different growth rates before and after 1980 (as an arbitrary choice).\n",
-    "\n",
-    "Write an update function that takes `pop`, `t`, and `system` as parameters.  The system object, `system`, should contain two parameters: the growth rate before 1980, `alpha1`, and the growth rate after 1980, `alpha2`.  It should use `t` to determine which growth rate to use.  Note: Don't forget the `return` statement.\n",
-    "\n",
-    "Test your function by calling it directly, then pass it to `run_simulation`.  Plot the results.  Adjust the parameters `alpha1` and `alpha2` to fit the data as well as you can.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap07.ipynb b/notebooks/chap07.ipynb
deleted file mode 100644
index fb1ed01b..00000000
--- a/notebooks/chap07.ipynb
+++ /dev/null
@@ -1,523 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 7\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *\n",
-    "\n",
-    "from pandas import read_html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = 'data/World_population_estimates.html'\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "table2 = tables[2]\n",
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "un.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census = table2.census / 1e9\n",
-    "census.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(census, un, timeseries, title):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    census: TimeSeries of population estimates\n",
-    "    un: TimeSeries of population estimates\n",
-    "    timeseries: TimeSeries of simulation results\n",
-    "    title: string\n",
-    "    \"\"\"\n",
-    "    plot(census, ':', label='US Census')\n",
-    "    plot(un, '--', label='UN DESA')\n",
-    "    plot(timeseries, color='gray', label='model')\n",
-    "    \n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate the system using any update function.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that computes the population next year\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        results[t+1] = update_func(results[t], t, system)\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Quadratic growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the implementation of the quadratic growth model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func_quad(pop, t, system):\n",
-    "    \"\"\"Compute the population next year with a quadratic model.\n",
-    "    \n",
-    "    pop: current population\n",
-    "    t: current year\n",
-    "    system: system object containing parameters of the model\n",
-    "    \n",
-    "    returns: population next year\n",
-    "    \"\"\"\n",
-    "    net_growth = system.alpha * pop + system.beta * pop**2\n",
-    "    return pop + net_growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a `System` object with the parameters `alpha` and `beta`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = get_first_label(census)\n",
-    "t_end = get_last_label(census)\n",
-    "p_0 = census[t_0]\n",
-    "\n",
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                alpha=0.025,\n",
-    "                beta=-0.0018)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here are the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func_quad)\n",
-    "plot_results(census, un, results, 'Quadratic model')\n",
-    "savefig('figs/chap07-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Can you find values for the parameters that make the model fit better?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Equilibrium\n",
-    "\n",
-    "To understand the quadratic model better, let's plot net growth as a function of population."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pop_array = linspace(0, 15, 100)\n",
-    "net_growth_array = system.alpha * pop_array + system.beta * pop_array**2\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sns.set_style('whitegrid')\n",
-    "\n",
-    "plot(pop_array, net_growth_array)\n",
-    "decorate(xlabel='Population (billions)',\n",
-    "         ylabel='Net growth (billions)')\n",
-    "\n",
-    "sns.set_style('white')\n",
-    "\n",
-    "savefig('figs/chap07-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like.  Remember that the x axis is population now, not time."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It looks like the growth rate passes through 0 when the population is a little less than 14 billion.\n",
-    "\n",
-    "In the book we found that the net growth is 0 when the population is $-\\alpha/\\beta$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "-system.alpha / system.beta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is the equilibrium the population tends toward."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`sns` is a library called Seaborn which provides functions that control the appearance of plots.  In this case I want a grid to make it easier to estimate the population where the growth rate crosses through 0."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dysfunctions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When people first learn about functions, there are a few things they often find confusing.  In this section I present and explain some common problems with functions.\n",
-    "\n",
-    "As an example, suppose you want a function that takes a `System` object, with variables `alpha` and `beta`, as a parameter and computes the carrying capacity, `-alpha/beta`.  Here's a good solution:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def carrying_capacity(system):\n",
-    "    K = -system.alpha / system.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "pop = carrying_capacity(sys1)\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's see all the ways that can go wrong.\n",
-    "\n",
-    "**Dysfunction #1:** Not using parameters.  In the following version, the function doesn't take any parameters; when `sys1` appears inside the function, it refers to the object we created outside the function.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def carrying_capacity():\n",
-    "    K = -sys1.alpha / sys1.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "pop = carrying_capacity()\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This version actually works, but it is not as versatile as it could be.  If there are several `System` objects, this function can only work with one of them, and only if it is named `system`.\n",
-    "\n",
-    "**Dysfunction #2:** Clobbering the parameters.  When people first learn about parameters, they often write functions like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def carrying_capacity(system):\n",
-    "    system = System(alpha=0.025, beta=-0.0018)\n",
-    "    K = -system.alpha / system.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "pop = carrying_capacity(sys1)\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this example, we have a `System` object named `sys1` that gets passed as an argument to `carrying_capacity`.  But when the function runs, it ignores the argument and immediately replaces it with a new `System` object.  As a result, this function always returns the same value, no matter what argument is passed.\n",
-    "\n",
-    "When you write a function, you generally don't know what the values of the parameters will be.  Your job is to write a function that works for any valid values.  If you assign your own values to the parameters, you defeat the whole purpose of functions.\n",
-    "\n",
-    "\n",
-    "**Dysfunction #3:** No return value.  Here's a version that computes the value of `K` but doesn't return it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def carrying_capacity(system):\n",
-    "    K = -system.alpha / system.beta\n",
-    "    \n",
-    "sys1 = System(alpha=0.025, beta=-0.0018)\n",
-    "pop = carrying_capacity(sys1)\n",
-    "print(pop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A function that doesn't have a return statement always returns a special value called `None`, so in this example the value of `pop` is `None`.  If you are debugging a program and find that the value of a variable is `None` when it shouldn't be, a function without a return statement is a likely cause.\n",
-    "\n",
-    "**Dysfunction #4:** Ignoring the return value.  Finally, here's a version where the function is correct, but the way it's used is not."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def carrying_capacity(system):\n",
-    "    K = -system.alpha / system.beta\n",
-    "    return K\n",
-    "    \n",
-    "sys2 = System(alpha=0.025, beta=-0.0018)\n",
-    "carrying_capacity(sys2)\n",
-    "\n",
-    "# print(K)     This line won't work because K only exists inside the function."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this example, `carrying_capacity` runs and returns `K`, but the return value is dropped.\n",
-    "\n",
-    "When you call a function that returns a value, you should do something with the result.  Often you assign it to a variable, as in the previous examples, but you can also use it as part of an expression.\n",
-    "\n",
-    "For example, you could eliminate the temporary variable `pop` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(carrying_capacity(sys1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or if you had more than one system, you could compute the total carrying capacity like this:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "total = carrying_capacity(sys1) + carrying_capacity(sys2)\n",
-    "total"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:** In the book, I present a different way to parameterize the quadratic model:\n",
-    "\n",
-    "$ \\Delta p = r p (1 - p / K) $\n",
-    "\n",
-    "where $r=\\alpha$ and $K=-\\alpha/\\beta$.  Write a version of `update_func` that implements this version of the model.  Test it by computing the values of `r` and `K` that correspond to `alpha=0.025, beta=-0.0018`, and confirm that you get the same results. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap08.ipynb b/notebooks/chap08.ipynb
deleted file mode 100644
index f8690865..00000000
--- a/notebooks/chap08.ipynb
+++ /dev/null
@@ -1,572 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 8\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Functions from the previous chapter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(census, un, timeseries, title):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    census: TimeSeries of population estimates\n",
-    "    un: TimeSeries of population estimates\n",
-    "    timeseries: TimeSeries of simulation results\n",
-    "    title: string\n",
-    "    \"\"\"\n",
-    "    plot(census, ':', label='US Census')\n",
-    "    plot(un, '--', label='UN DESA')\n",
-    "    plot(timeseries, color='gray', label='model')\n",
-    "    \n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate the system using any update function.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that computes the population next year\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        results[t+1] = update_func(results[t], t, system)\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Reading the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def read_table2(filename = 'data/World_population_estimates.html'):\n",
-    "    tables = pd.read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "    table2 = tables[2]\n",
-    "    table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']\n",
-    "    return table2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#table2 = read_table2()\n",
-    "#table2.to_csv('data/World_population_estimates2.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table2 = pd.read_csv('data/World_population_estimates2.csv')\n",
-    "table2.index = table2.Year\n",
-    "table2.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "census = table2.census / 1e9\n",
-    "plot(census, ':', label='US Census')\n",
-    "plot(un, '--', label='UN DESA')\n",
-    "    \n",
-    "decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)',\n",
-    "             title='Estimated world population')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Running the quadratic model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the update function for the quadratic growth model with parameters `alpha` and `beta`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func_quad(pop, t, system):\n",
-    "    \"\"\"Update population based on a quadratic model.\n",
-    "    \n",
-    "    pop: current population in billions\n",
-    "    t: what year it is\n",
-    "    system: system object with model parameters\n",
-    "    \"\"\"\n",
-    "    net_growth = system.alpha * pop + system.beta * pop**2\n",
-    "    return pop + net_growth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Extract the starting time and population."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = get_first_label(census)\n",
-    "t_end = get_last_label(census)\n",
-    "p_0 = get_first_value(census)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initialize the system object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "system = System(t_0=t_0, \n",
-    "                t_end=t_end,\n",
-    "                p_0=p_0,\n",
-    "                alpha=0.025,\n",
-    "                beta=-0.0018)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the model and plot results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func_quad)\n",
-    "plot_results(census, un, results, 'Quadratic model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Generating projections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To generate projections, all we have to do is change `t_end`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "system.t_end = 2250\n",
-    "results = run_simulation(system, update_func_quad)\n",
-    "plot_results(census, un, results, 'World population projection')\n",
-    "savefig('figs/chap08-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The population in the model converges on the equilibrium population, `-alpha/beta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results[system.t_end]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "-system.alpha / system.beta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  What happens if we start with an initial population above the carrying capacity, like 20 billion?  Run the model with initial populations between 1 and 20 billion, and plot the results on the same axes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Comparing projections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can compare the projection from our model with projections produced by people who know what they are doing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def read_table3(filename = 'data/World_population_estimates.html'):\n",
-    "    tables = pd.read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "    table3 = tables[3]\n",
-    "    table3.columns = ['census', 'prb', 'un']\n",
-    "    return table3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#table3 = read_table3()\n",
-    "#table3.to_csv('data/World_population_estimates3.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table3 = pd.read_csv('data/World_population_estimates3.csv')\n",
-    "table3.index = table3.Year\n",
-    "table3.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`NaN` is a special value that represents missing data, in this case because some agencies did not publish projections for some years."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This function plots projections from the UN DESA and U.S. Census.  It uses `dropna` to remove the `NaN` values from each series before plotting it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_projections(table):\n",
-    "    \"\"\"Plot world population projections.\n",
-    "    \n",
-    "    table: DataFrame with columns 'un' and 'census'\n",
-    "    \"\"\"\n",
-    "    census_proj = table.census / 1e9\n",
-    "    un_proj = table.un / 1e9\n",
-    "    \n",
-    "    plot(census_proj.dropna(), ':', color='C0', label='US Census')\n",
-    "    plot(un_proj.dropna(), '--', color='C1', label='UN DESA')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the model until 2100, which is as far as the other projections go."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = System(t_0=t_0, \n",
-    "                t_end=2100,\n",
-    "                p_0=p_0,\n",
-    "                alpha=0.025,\n",
-    "                beta=-0.0018)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func_quad)\n",
-    "\n",
-    "plt.axvspan(1950, 2016, color='C0', alpha=0.05)\n",
-    "plot_results(census, un, results, 'World population projections')\n",
-    "plot_projections(table3)\n",
-    "savefig('figs/chap08-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "People who know what they are doing expect the growth rate to decline more sharply than our model projects."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:** The net growth rate of world population has been declining for several decades.  That observation suggests one more way to generate projections, by extrapolating observed changes in growth rate.\n",
-    "\n",
-    "The `modsim` library provides a function, `compute_rel_diff`, that computes relative differences of the elements in a sequence.\n",
-    "\n",
-    "Here's how we can use it to compute the relative differences in the `census` and `un` estimates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "alpha_census = compute_rel_diff(census)\n",
-    "plot(alpha_census, label='US Census')\n",
-    "\n",
-    "alpha_un = compute_rel_diff(un)\n",
-    "plot(alpha_un, label='UN DESA')\n",
-    "\n",
-    "decorate(xlabel='Year', label='Net growth rate')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Other than a bump around 1990, net growth rate has been declining roughly linearly since 1965.  As an exercise, you can use this data to make a projection of world population until 2100.\n",
-    "\n",
-    "1. Define a function, `alpha_func`, that takes `t` as a parameter and returns an estimate of the net growth rate at time `t`, based on a linear function `alpha = intercept + slope * t`.  Choose values of `slope` and `intercept` to fit the observed net growth rates since 1965.\n",
-    "\n",
-    "2. Call your function with a range of `ts` from 1960 to 2020 and plot the results.\n",
-    "\n",
-    "3. Create a `System` object that includes `alpha_func` as a system variable.\n",
-    "\n",
-    "4. Define an update function that uses `alpha_func` to compute the net growth rate at the given time `t`.\n",
-    "\n",
-    "5. Test your update function with `t_0 = 1960` and `p_0 = census[t_0]`.\n",
-    "\n",
-    "6. Run a simulation from 1960 to 2100 with your update function, and plot the results.\n",
-    "\n",
-    "7. Compare your projections with those from the US Census and UN."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Related viewing:** You might be interested in this [video by Hans Rosling about the demographic changes we expect in this century](https://www.youtube.com/watch?v=ezVk1ahRF78)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap09.ipynb b/notebooks/chap09.ipynb
deleted file mode 100644
index 183f2e33..00000000
--- a/notebooks/chap09.ipynb
+++ /dev/null
@@ -1,624 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 9\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import everything from SymPy.\n",
-    "from sympy import *\n",
-    "\n",
-    "# Set up Jupyter notebook to display math.\n",
-    "init_printing() "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following displays SymPy expressions and provides the option of showing results in LaTeX format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sympy.printing import latex\n",
-    "\n",
-    "def show(expr, show_latex=False):\n",
-    "    \"\"\"Display a SymPy expression.\n",
-    "    \n",
-    "    expr: SymPy expression\n",
-    "    show_latex: boolean\n",
-    "    \"\"\"\n",
-    "    if show_latex:\n",
-    "        print(latex(expr))\n",
-    "    return expr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis with SymPy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a symbol for time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = symbols('t')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you combine symbols and numbers, you get symbolic expressions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "expr = t + 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is an `Add` object, which just represents the sum without trying to compute it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(expr)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`subs` can be used to replace a symbol with a number, which allows the addition to proceed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "expr.subs(t, 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`f` is a special class of symbol that represents a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f = Function('f')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The type of `f` is `UndefinedFunction`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(f)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "SymPy understands that `f(t)` means `f` evaluated at `t`, but it doesn't try to evaluate it yet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f(t)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`diff` returns a `Derivative` object that represents the time derivative of `f`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dfdt = diff(f(t), t)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(dfdt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We need a symbol for `alpha`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "alpha = symbols('alpha')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can write the differential equation for proportional growth."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eq1 = Eq(dfdt, alpha*f(t))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And use `dsolve` to solve it.  The result is the general solution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "solution_eq = dsolve(eq1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can tell it's a general solution because it contains an unspecified constant, `C1`.\n",
-    "\n",
-    "In this example, finding the particular solution is easy: we just replace `C1` with `p_0`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "C1, p_0 = symbols('C1 p_0')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "particular = solution_eq.subs(C1, p_0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the next example, we have to work a little harder to find the particular solution."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Solving the quadratic growth equation \n",
-    "\n",
-    "We'll use the (r, K) parameterization, so we'll need two more symbols:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r, K = symbols('r K')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can write the differential equation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eq2 = Eq(diff(f(t), t), r * f(t) * (1 - f(t)/K))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And solve it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "solution_eq = dsolve(eq2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result, `solution_eq`, contains `rhs`, which is the right-hand side of the solution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "general = solution_eq.rhs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can evaluate the right-hand side at $t=0$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "at_0 = general.subs(t, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we want to find the value of `C1` that makes `f(0) = p_0`.\n",
-    "\n",
-    "So we'll create the equation `at_0 = p_0` and solve for `C1`.  Because this is just an algebraic identity, not a differential equation, we use `solve`, not `dsolve`.\n",
-    "\n",
-    "The result from `solve` is a list of solutions.  In this case, [we have reason to expect only one solution](https://en.wikipedia.org/wiki/Picard%E2%80%93Lindel%C3%B6f_theorem), but we still get a list, so we have to use the bracket operator, `[0]`, to select the first one."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "solutions = solve(Eq(at_0, p_0), C1)\n",
-    "type(solutions), len(solutions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "value_of_C1 = solutions[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now in the general solution, we want to replace `C1` with the value of `C1` we just figured out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "particular = general.subs(C1, value_of_C1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is complicated, but SymPy provides a method that tries to simplify it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "particular = simplify(particular)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Often simplicity is in the eye of the beholder, but that's about as simple as this expression gets.\n",
-    "\n",
-    "Just to double-check, we can evaluate it at `t=0` and confirm that we get `p_0`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "particular.subs(t, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This solution is called the [logistic function](https://en.wikipedia.org/wiki/Population_growth#Logistic_equation).\n",
-    "\n",
-    "In some places you'll see it written in a different form:\n",
-    "\n",
-    "$f(t) = \\frac{K}{1 + A e^{-rt}}$\n",
-    "\n",
-    "where $A = (K - p_0) / p_0$.\n",
-    "\n",
-    "We can use SymPy to confirm that these two forms are equivalent.  First we represent the alternative version of the logistic function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = (K - p_0) / p_0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "logistic = K / (1 + A * exp(-r*t))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To see whether two expressions are equivalent, we can check whether their difference simplifies to 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "simplify(particular - logistic)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This test only works one way: if SymPy says the difference reduces to 0, the expressions are definitely equivalent (and not just numerically close).\n",
-    "\n",
-    "But if SymPy can't find a way to simplify the result to 0, that doesn't necessarily mean there isn't one.  Testing whether two expressions are equivalent is a surprisingly hard problem; in fact, there is no algorithm that can solve it in general."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Solve the quadratic growth equation using the alternative parameterization\n",
-    "\n",
-    "$\\frac{df(t)}{dt} = \\alpha f(t) + \\beta f^2(t) $"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Use [WolframAlpha](https://www.wolframalpha.com/) to solve the quadratic growth model, using either or both forms of parameterization:\n",
-    "\n",
-    "    df(t) / dt = alpha f(t) + beta f(t)^2\n",
-    "\n",
-    "or\n",
-    "\n",
-    "    df(t) / dt = r f(t) (1 - f(t)/K)\n",
-    "\n",
-    "Find the general solution and also the particular solution where `f(0) = p_0`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap09sympy.ipynb b/notebooks/chap09sympy.ipynb
deleted file mode 100644
index ce3e1c0a..00000000
--- a/notebooks/chap09sympy.ipynb
+++ /dev/null
@@ -1,632 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 9\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import everything from SymPy.\n",
-    "from sympy import *\n",
-    "\n",
-    "# Set up Jupyter notebook to display math.\n",
-    "init_printing() "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following displays SymPy expressions and provides the option of showing results in LaTeX format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from sympy.printing import latex\n",
-    "\n",
-    "def show(expr, show_latex=False):\n",
-    "    \"\"\"Display a SymPy expression.\n",
-    "    \n",
-    "    expr: SymPy expression\n",
-    "    show_latex: boolean\n",
-    "    \"\"\"\n",
-    "    if show_latex:\n",
-    "        print(latex(expr))\n",
-    "    return expr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis with SymPy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a symbol for time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = symbols('t')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you combine symbols and numbers, you get symbolic expressions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "expr = t + 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is an `Add` object, which just represents the sum without trying to compute it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(expr)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`subs` can be used to replace a symbol with a number, which allows the addition to proceed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "expr.subs(t, 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`f` is a special class of symbol that represents a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f = Function('f')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The type of `f` is `UndefinedFunction`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(f)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "SymPy understands that `f(t)` means `f` evaluated at `t`, but it doesn't try to evaluate it yet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f(t)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`diff` returns a `Derivative` object that represents the time derivative of `f`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dfdt = diff(f(t), t)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(dfdt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We need a symbol for `alpha`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "alpha = symbols('alpha')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can write the differential equation for proportional growth."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eq1 = Eq(dfdt, alpha*f(t))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And use `dsolve` to solve it.  The result is the general solution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "solution_eq = dsolve(eq1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can tell it's a general solution because it contains an unspecified constant, `C1`.\n",
-    "\n",
-    "In this example, finding the particular solution is easy: we just replace `C1` with `p_0`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "C1, p_0 = symbols('C1 p_0')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "particular = solution_eq.subs(C1, p_0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the next example, we have to work a little harder to find the particular solution."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Solving the quadratic growth equation \n",
-    "\n",
-    "We'll use the (r, K) parameterization, so we'll need two more symbols:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "r, K = symbols('r K')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can write the differential equation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eq2 = Eq(diff(f(t), t), r * f(t) * (1 - f(t)/K))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And solve it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "solution_eq = dsolve(eq2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result, `solution_eq`, contains `rhs`, which is the right-hand side of the solution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "general = solution_eq.rhs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can evaluate the right-hand side at $t=0$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "at_0 = general.subs(t, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we want to find the value of `C1` that makes `f(0) = p_0`.\n",
-    "\n",
-    "So we'll create the equation `at_0 = p_0` and solve for `C1`.  Because this is just an algebraic identity, not a differential equation, we use `solve`, not `dsolve`.\n",
-    "\n",
-    "The result from `solve` is a list of solutions.  In this case, [we have reason to expect only one solution](https://en.wikipedia.org/wiki/Picard%E2%80%93Lindel%C3%B6f_theorem), but we still get a list, so we have to use the bracket operator, `[0]`, to select the first one."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "solutions = solve(Eq(at_0, p_0), C1)\n",
-    "type(solutions), len(solutions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "value_of_C1 = solutions[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now in the general solution, we want to replace `C1` with the value of `C1` we just figured out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "particular = general.subs(C1, value_of_C1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is complicated, but SymPy provides a method that tries to simplify it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "particular = simplify(particular)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Often simplicity is in the eye of the beholder, but that's about as simple as this expression gets.\n",
-    "\n",
-    "Just to double-check, we can evaluate it at `t=0` and confirm that we get `p_0`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "particular.subs(t, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This solution is called the [logistic function](https://en.wikipedia.org/wiki/Population_growth#Logistic_equation).\n",
-    "\n",
-    "In some places you'll see it written in a different form:\n",
-    "\n",
-    "$f(t) = \\frac{K}{1 + A e^{-rt}}$\n",
-    "\n",
-    "where $A = (K - p_0) / p_0$.\n",
-    "\n",
-    "We can use SymPy to confirm that these two forms are equivalent.  First we represent the alternative version of the logistic function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = (K - p_0) / p_0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "logistic = K / (1 + A * exp(-r*t))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To see whether two expressions are equivalent, we can check whether their difference simplifies to 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "simplify(particular - logistic)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This test only works one way: if SymPy says the difference reduces to 0, the expressions are definitely equivalent (and not just numerically close).\n",
-    "\n",
-    "But if SymPy can't find a way to simplify the result to 0, that doesn't necessarily mean there isn't one.  Testing whether two expressions are equivalent is a surprisingly hard problem; in fact, there is no algorithm that can solve it in general."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Solve the quadratic growth equation using the alternative parameterization\n",
-    "\n",
-    "$\\frac{df(t)}{dt} = \\alpha f(t) + \\beta f^2(t) $"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Use [WolframAlpha](https://www.wolframalpha.com/) to solve the quadratic growth model, using either or both forms of parameterization:\n",
-    "\n",
-    "    df(t) / dt = alpha f(t) + beta f(t)^2\n",
-    "\n",
-    "or\n",
-    "\n",
-    "    df(t) / dt = r f(t) (1 - f(t)/K)\n",
-    "\n",
-    "Find the general solution and also the particular solution where `f(0) = p_0`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap10.ipynb b/notebooks/chap10.ipynb
deleted file mode 100644
index e7167c43..00000000
--- a/notebooks/chap10.ipynb
+++ /dev/null
@@ -1,447 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 10\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *\n",
-    "\n",
-    "from pandas import read_html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "To get a `DataFrame` and a `Series`, I'll read the world population data and select a column.\n",
-    "\n",
-    "`DataFrame` and `Series` contain a variable called `shape` that indicates the number of rows and columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = 'data/World_population_estimates.html'\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "table2 = tables[2]\n",
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']\n",
-    "table2.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census = table2.census / 1e9\n",
-    "census.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "un.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `DataFrame` contains `index`, which labels the rows.  It is an `Int64Index`, which is similar to a NumPy array."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "table2.index"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And `columns`, which labels the columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "table2.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And `values`, which is an array of values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "table2.values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `Series` does not have `columns`, but it does have `name`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "census.name"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It contains `values`, which is an array."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census.values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And it contains `index`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "census.index"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you ever wonder what kind of object a variable refers to, you can use the `type` function.  The result indicates what type the object is, and the module where that type is defined.\n",
-    "\n",
-    "`DataFrame`, `Int64Index`, `Index`, and `Series` are defined by Pandas.\n",
-    "\n",
-    "`ndarray` is defined by NumPy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(table2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(table2.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(table2.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(table2.values)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(census)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(census.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "type(census.values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Optional exercise\n",
-    "\n",
-    "The following exercise provides a chance to practice what you have learned so far, and maybe develop a different growth model.  If you feel comfortable with what we have done so far, you might want to give it a try.\n",
-    "\n",
-    "**Optional Exercise:** On the Wikipedia page about world population estimates, the first table contains estimates for prehistoric populations.  The following cells process this table and plot some of the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = 'data/World_population_estimates.html'\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "len(tables)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Select `tables[1]`, which is the second table on the page."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table1 = tables[1]\n",
-    "table1.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Not all agencies and researchers provided estimates for the same dates.  Again `NaN` is the special value that indicates missing data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table1.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Again, we'll replace the long column names with more convenient abbreviations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table1.columns = ['PRB', 'UN', 'Maddison', 'HYDE', 'Tanton', \n",
-    "                  'Biraben', 'McEvedy & Jones', 'Thomlinson', 'Durand', 'Clark']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Some of the estimates are in a form Pandas doesn't recognize as numbers, but we can coerce them to be numeric."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in table1.columns:\n",
-    "    table1[col] = pd.to_numeric(table1[col], errors='coerce')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results.  Notice that we are working in millions now, not billions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "table1.plot()\n",
-    "decorate(xlim=[-10000, 2000], xlabel='Year', \n",
-    "         ylabel='World population (millions)',\n",
-    "         title='Prehistoric population estimates')\n",
-    "plt.legend(fontsize='small');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use `xlim` to zoom in on everything after Year 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table1.plot()\n",
-    "decorate(xlim=[0, 2000], xlabel='Year', \n",
-    "         ylabel='World population (millions)',\n",
-    "         title='CE population estimates')\n",
-    "plt.legend(fontsize='small');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "See if you can find a model that fits these data well from Year 0 to 1950.\n",
-    "\n",
-    "How well does your best model predict actual population growth from 1950 to the present?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap11.ipynb b/notebooks/chap11.ipynb
deleted file mode 100644
index e75bc7e8..00000000
--- a/notebooks/chap11.ipynb
+++ /dev/null
@@ -1,463 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 11\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### SIR implementation\n",
-    "\n",
-    "We'll use a `State` object to represent the number (or fraction) of people in each compartment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "init = State(S=89, I=1, R=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To convert from number of people to fractions, we divide through by the total."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "init /= sum(init)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system` creates a `System` object with the given parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an example with hypothetical values for `beta` and `gamma`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tc = 3      # time between contacts in days \n",
-    "tr = 4      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The update function takes the state during the current time step and returns the state during the next time step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State with variables S, I, R\n",
-    "    t: time step\n",
-    "    system: System with beta and gamma\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = system.beta * i * s    \n",
-    "    recovered = system.gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To run a single time step, we call it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = update_func(init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run a simulation by calling the update function for each time step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: State object for final state\n",
-    "    \"\"\"\n",
-    "    state = system.init\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        state = update_func(state, t, system)\n",
-    "        \n",
-    "    return state"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is the state of the system at `t_end`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "run_simulation(system, update_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise**  Suppose the time between contacts is 4 days and the recovery time is 5 days.  After 14 weeks, how many students, total, have been infected?\n",
-    "\n",
-    "Hint: what is the change in `S` between the beginning and the end of the simulation?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using TimeSeries objects"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we want to store the state of the system at each time step, we can use one `TimeSeries` object for each state variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "    \n",
-    "    Add three Series objects to the System: S, I, R\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \"\"\"\n",
-    "    S = TimeSeries()\n",
-    "    I = TimeSeries()\n",
-    "    R = TimeSeries()\n",
-    "\n",
-    "    state = system.init\n",
-    "    t0 = system.t0\n",
-    "    S[t0], I[t0], R[t0] = state\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        state = update_func(state, t, system)\n",
-    "        S[t+1], I[t+1], R[t+1] = state\n",
-    "    \n",
-    "    return S, I, R"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we call it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tc = 3      # time between contacts in days \n",
-    "tr = 4      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)\n",
-    "S, I, R = run_simulation(system, update_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then we can plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(S, I, R):\n",
-    "    \"\"\"Plot the results of a SIR model.\n",
-    "    \n",
-    "    S: TimeSeries\n",
-    "    I: TimeSeries\n",
-    "    R: TimeSeries\n",
-    "    \"\"\"\n",
-    "    plot(S, '--', label='Susceptible')\n",
-    "    plot(I, '-', label='Infected')\n",
-    "    plot(R, ':', label='Recovered')\n",
-    "    decorate(xlabel='Time (days)',\n",
-    "             ylabel='Fraction of population')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what they look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_results(S, I, R)\n",
-    "savefig('figs/chap11-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using a DataFrame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Instead of making three `TimeSeries` objects, we can use one `DataFrame`.\n",
-    "\n",
-    "We have to use `row` to selects rows, rather than columns.  But then Pandas does the right thing, matching up the state variables with the columns of the `DataFrame`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    frame = TimeFrame(columns=system.init.index)\n",
-    "    frame.row[system.t0] = system.init\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it, and what the result looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tc = 3      # time between contacts in days \n",
-    "tr = 4      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)\n",
-    "results = run_simulation(system, update_func)\n",
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can extract the results and plot them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_results(results.S, results.I, results.R)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise**  Suppose the time between contacts is 4 days and the recovery time is 5 days.  Simulate this scenario for 14 weeks and plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap12.ipynb b/notebooks/chap12.ipynb
deleted file mode 100644
index 50a9c232..00000000
--- a/notebooks/chap12.ipynb
+++ /dev/null
@@ -1,877 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 12\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code\n",
-    "\n",
-    "Here's the code from the previous notebook that we'll need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State with variables S, I, R\n",
-    "    t: time step\n",
-    "    system: System with beta and gamma\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = system.beta * i * s    \n",
-    "    recovered = system.gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    frame = TimeFrame(columns=system.init.index)\n",
-    "    frame.row[system.t0] = system.init\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Given the results, we can compute metrics that quantify whatever we are interested in, like the total number of sick students, for example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_total_infected(results):\n",
-    "    \"\"\"Fraction of population infected during the simulation.\n",
-    "    \n",
-    "    results: DataFrame with columns S, I, R\n",
-    "    \n",
-    "    returns: fraction of population\n",
-    "    \"\"\"\n",
-    "    return get_first_value(results.S) - get_last_value(results.S)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an example.|"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "beta = 0.333\n",
-    "gamma = 0.25\n",
-    "system = make_system(beta, gamma)\n",
-    "\n",
-    "results = run_simulation(system, update_func)\n",
-    "print(beta, gamma, calc_total_infected(results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write functions that take a `TimeFrame` object as a parameter and compute the other metrics mentioned in the book:\n",
-    "\n",
-    "1.  The fraction of students who are sick at the peak of the outbreak.\n",
-    "\n",
-    "2.  The day the outbreak peaks.\n",
-    "\n",
-    "3.  The fraction of students who are sick at the end of the semester.\n",
-    "\n",
-    "Note: Not all of these functions require the `System` object, but when you write a set of related functons, it is often convenient if they all take the same parameters.\n",
-    "\n",
-    "Hint:  If you have a `TimeSeries` called `I`, you can compute the largest value of the series like this:\n",
-    "\n",
-    "    I.max()\n",
-    "\n",
-    "And the index of the largest value like this:\n",
-    "\n",
-    "    I.idxmax()\n",
-    "\n",
-    "You can read about these functions in the `Series` [documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### What if?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use this model to evaluate \"what if\" scenarios.  For example, this function models the effect of immunization by moving some fraction of the population from S to R before the simulation starts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def add_immunization(system, fraction):\n",
-    "    \"\"\"Immunize a fraction of the population.\n",
-    "    \n",
-    "    Moves the given fraction from S to R.\n",
-    "    \n",
-    "    system: System object\n",
-    "    fraction: number from 0 to 1\n",
-    "    \"\"\"\n",
-    "    system.init.S -= fraction\n",
-    "    system.init.R += fraction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's start again with the system we used in the previous sections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tc = 3      # time between contacts in days \n",
-    "tr = 4      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "system = make_system(beta, gamma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And run the model without immunization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func)\n",
-    "calc_total_infected(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now with 10% immunization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system2 = make_system(beta, gamma)\n",
-    "add_immunization(system2, 0.1)\n",
-    "results2 = run_simulation(system2, update_func)\n",
-    "calc_total_infected(results2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "10% immunization leads to a drop in infections of 16 percentage points.\n",
-    "\n",
-    "Here's what the time series looks like for S, with and without immunization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results.S, '-', label='No immunization')\n",
-    "plot(results2.S, '--', label='10% immunization')\n",
-    "\n",
-    "decorate(xlabel='Time (days)',\n",
-    "         ylabel='Fraction susceptible')\n",
-    "\n",
-    "savefig('figs/chap12-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can sweep through a range of values for the fraction of the population who are immunized."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "immunize_array = linspace(0, 1, 11)\n",
-    "for fraction in immunize_array:\n",
-    "    system = make_system(beta, gamma)\n",
-    "    add_immunization(system, fraction)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    print(fraction, calc_total_infected(results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This function does the same thing and stores the results in a `Sweep` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_immunity(immunize_array):\n",
-    "    \"\"\"Sweeps a range of values for immunity.\n",
-    "    \n",
-    "    immunize_array: array of fraction immunized\n",
-    "    \n",
-    "    returns: Sweep object\n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for fraction in immunize_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        add_immunization(system, fraction)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[fraction] = calc_total_infected(results)\n",
-    "        \n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "immunize_array = linspace(0, 1, 21)\n",
-    "infected_sweep = sweep_immunity(immunize_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(infected_sweep)\n",
-    "\n",
-    "decorate(xlabel='Fraction immunized',\n",
-    "         ylabel='Total fraction infected',\n",
-    "         title='Fraction infected vs. immunization rate',\n",
-    "         legend=False)\n",
-    "\n",
-    "savefig('figs/chap12-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If 40% of the population is immunized, less than 4% of the population gets sick."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Logistic function"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To model the effect of a hand-washing campaign, I'll use a [generalized logistic function](https://en.wikipedia.org/wiki/Generalised_logistic_function) (GLF), which is a convenient function for modeling curves that have a generally sigmoid shape.  The parameters of the GLF correspond to various features of the curve in a way that makes it easy to find a function that has the shape you want, based on data or background information about the scenario."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def logistic(x, A=0, B=1, C=1, M=0, K=1, Q=1, nu=1):\n",
-    "    \"\"\"Computes the generalize logistic function.\n",
-    "    \n",
-    "    A: controls the lower bound\n",
-    "    B: controls the steepness of the transition \n",
-    "    C: not all that useful, AFAIK\n",
-    "    M: controls the location of the transition\n",
-    "    K: controls the upper bound\n",
-    "    Q: shift the transition left or right\n",
-    "    nu: affects the symmetry of the transition\n",
-    "    \n",
-    "    returns: float or array\n",
-    "    \"\"\"\n",
-    "    exponent = -B * (x - M)\n",
-    "    denom = C + Q * exp(exponent)\n",
-    "    return A + (K-A) / denom ** (1/nu)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following array represents the range of possible spending."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spending = linspace(0, 1200, 21)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`compute_factor` computes the reduction in `beta` for a given level of campaign spending.\n",
-    "\n",
-    "`M` is chosen so the transition happens around \\$500.\n",
-    "\n",
-    "`K` is the maximum reduction in `beta`, 20%.\n",
-    "\n",
-    "`B` is chosen by trial and error to yield a curve that seems feasible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_factor(spending):\n",
-    "    \"\"\"Reduction factor as a function of spending.\n",
-    "    \n",
-    "    spending: dollars from 0 to 1200\n",
-    "    \n",
-    "    returns: fractional reduction in beta\n",
-    "    \"\"\"\n",
-    "    return logistic(spending, M=500, K=0.2, B=0.01)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "percent_reduction = compute_factor(spending) * 100\n",
-    "\n",
-    "plot(spending, percent_reduction)\n",
-    "\n",
-    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
-    "         ylabel='Percent reduction in infection rate',\n",
-    "         title='Effect of hand washing on infection rate',\n",
-    "         legend=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Modify the parameters `M`, `K`, and `B`, and see what effect they have on the shape of the curve.  Read about the [generalized logistic function on Wikipedia](https://en.wikipedia.org/wiki/Generalised_logistic_function).  Modify the other parameters and see what effect they have."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Hand washing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can model the effect of a hand-washing campaign by modifying `beta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def add_hand_washing(system, spending):\n",
-    "    \"\"\"Modifies system to model the effect of hand washing.\n",
-    "    \n",
-    "    system: System object\n",
-    "    spending: campaign spending in USD\n",
-    "    \"\"\"\n",
-    "    factor = compute_factor(spending)\n",
-    "    system.beta *= (1 - factor)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's start with the same values of `beta` and `gamma` we've been using."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tc = 3      # time between contacts in days \n",
-    "tr = 4      # recovery time in days\n",
-    "\n",
-    "beta = 1 / tc      # contact rate in per day\n",
-    "gamma = 1 / tr     # recovery rate in per day\n",
-    "\n",
-    "beta, gamma"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can sweep different levels of campaign spending."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spending_array = linspace(0, 1200, 13)\n",
-    "\n",
-    "for spending in spending_array:\n",
-    "    system = make_system(beta, gamma)\n",
-    "    add_hand_washing(system, spending)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    print(spending, system.beta, calc_total_infected(results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a function that sweeps a range of spending and stores the results in a `SweepSeries`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_hand_washing(spending_array):\n",
-    "    \"\"\"Run simulations with a range of spending.\n",
-    "    \n",
-    "    spending_array: array of dollars from 0 to 1200\n",
-    "    \n",
-    "    returns: Sweep object\n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for spending in spending_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        add_hand_washing(system, spending)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[spending] = calc_total_infected(results)\n",
-    "        \n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spending_array = linspace(0, 1200, 20)\n",
-    "infected_sweep = sweep_hand_washing(spending_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's what it looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(infected_sweep)\n",
-    "\n",
-    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
-    "         ylabel='Total fraction infected',\n",
-    "         title='Effect of hand washing on total infections',\n",
-    "         legend=False)\n",
-    "\n",
-    "savefig('figs/chap12-fig03.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's put it all together to make some public health spending decisions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optimization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Suppose we have \\$1200 to spend on any combination of vaccines and a hand-washing campaign."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_students = 90\n",
-    "budget = 1200\n",
-    "price_per_dose = 100\n",
-    "max_doses = int(budget / price_per_dose)\n",
-    "dose_array = linrange(max_doses, endpoint=True)\n",
-    "max_doses"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can sweep through a range of doses from, 0 to `max_doses`, model the effects of immunization and the hand-washing campaign, and run simulations.\n",
-    "\n",
-    "For each scenario, we compute the fraction of students who get sick."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for doses in dose_array:\n",
-    "    fraction = doses / num_students\n",
-    "    spending = budget - doses * price_per_dose\n",
-    "    \n",
-    "    system = make_system(beta, gamma)\n",
-    "    add_immunization(system, fraction)\n",
-    "    add_hand_washing(system, spending)\n",
-    "    \n",
-    "    results = run_simulation(system, update_func)\n",
-    "    print(doses, system.init.S, system.beta, calc_total_infected(results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function wraps that loop and stores the results in a `Sweep` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_doses(dose_array):\n",
-    "    \"\"\"Runs simulations with different doses and campaign spending.\n",
-    "    \n",
-    "    dose_array: range of values for number of vaccinations\n",
-    "    \n",
-    "    return: Sweep object with total number of infections \n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    \n",
-    "    for doses in dose_array:\n",
-    "        fraction = doses / num_students\n",
-    "        spending = budget - doses * price_per_dose\n",
-    "        \n",
-    "        system = make_system(beta, gamma)\n",
-    "        add_immunization(system, fraction)\n",
-    "        add_hand_washing(system, spending)\n",
-    "        \n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[doses] = calc_total_infected(results)\n",
-    "\n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can compute the number of infected students for each possible allocation of the budget."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "infected_sweep = sweep_doses(dose_array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(infected_sweep)\n",
-    "\n",
-    "decorate(xlabel='Doses of vaccine',\n",
-    "         ylabel='Total fraction infected',\n",
-    "         title='Total infections vs. doses',\n",
-    "         legend=False)\n",
-    "\n",
-    "savefig('figs/chap12-fig04.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Suppose the price of the vaccine drops to $50 per dose.  How does that affect the optimal allocation of the spending?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose we have the option to quarantine infected students.  For example, a student who feels ill might be moved to an infirmary, or a private dorm room, until they are no longer infectious.\n",
-    "\n",
-    "How might you incorporate the effect of quarantine in the SIR model?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap13.ipynb b/notebooks/chap13.ipynb
deleted file mode 100644
index d368dd4c..00000000
--- a/notebooks/chap13.ipynb
+++ /dev/null
@@ -1,517 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 13\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from previous chapters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system`, `plot_results`, and `calc_total_infected` are unchanged."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= np.sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(S, I, R):\n",
-    "    \"\"\"Plot the results of a SIR model.\n",
-    "    \n",
-    "    S: TimeSeries\n",
-    "    I: TimeSeries\n",
-    "    R: TimeSeries\n",
-    "    \"\"\"\n",
-    "    plot(S, '--', label='Susceptible')\n",
-    "    plot(I, '-', label='Infected')\n",
-    "    plot(R, ':', label='Recovered')\n",
-    "    decorate(xlabel='Time (days)',\n",
-    "             ylabel='Fraction of population')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_total_infected(results):\n",
-    "    \"\"\"Fraction of population infected during the simulation.\n",
-    "    \n",
-    "    results: DataFrame with columns S, I, R\n",
-    "    \n",
-    "    returns: fraction of population\n",
-    "    \"\"\"\n",
-    "    return get_first_value(results.S) - get_last_value(results.S)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    init, t0, t_end = system.init, system.t0, system.t_end\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t0] = init\n",
-    "    \n",
-    "    for t in linrange(t0, t_end):\n",
-    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State (s, i, r)\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State (sir)\n",
-    "    \"\"\"\n",
-    "    beta, gamma = system.beta, system.gamma\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = beta * i * s    \n",
-    "    recovered = gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sweeping beta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Make a range of values for `beta`, with constant `gamma`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "beta_array = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 , 1.1]\n",
-    "gamma = 0.2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the simulation once for each value of `beta` and print total infections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for beta in beta_array:\n",
-    "    system = make_system(beta, gamma)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    print(system.beta, calc_total_infected(results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Wrap that loop in a function and return a `SweepSeries` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_beta(beta_array, gamma):\n",
-    "    \"\"\"Sweep a range of values for beta.\n",
-    "    \n",
-    "    beta_array: array of beta values\n",
-    "    gamma: recovery rate\n",
-    "    \n",
-    "    returns: SweepSeries that maps from beta to total infected\n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    for beta in beta_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[system.beta] = calc_total_infected(results)\n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sweep `beta` and plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "infected_sweep = sweep_beta(beta_array, gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "label = 'gamma = ' + str(gamma)\n",
-    "plot(infected_sweep, label=label)\n",
-    "\n",
-    "decorate(xlabel='Contact rate (beta)',\n",
-    "         ylabel='Fraction infected')\n",
-    "\n",
-    "savefig('figs/chap13-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sweeping gamma"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using the same array of values for `beta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "beta_array"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And now an array of values for `gamma`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gamma_array = [0.2, 0.4, 0.6, 0.8]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For each value of `gamma`, sweep `beta` and plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(7, 4))\n",
-    "\n",
-    "for gamma in gamma_array:\n",
-    "    infected_sweep = sweep_beta(beta_array, gamma)\n",
-    "    label = 'gamma = ' + str(gamma)\n",
-    "    plot(infected_sweep, label=label)\n",
-    "    \n",
-    "decorate(xlabel='Contact rate (beta)',\n",
-    "         ylabel='Fraction infected',\n",
-    "         loc='upper left')\n",
-    "\n",
-    "plt.legend(bbox_to_anchor=(1.02, 1.02))\n",
-    "plt.tight_layout()\n",
-    "savefig('figs/chap13-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Suppose the infectious period for the Freshman Plague is known to be 2 days on average, and suppose during one particularly bad year, 40% of the class is infected at some point.  Estimate the time between contacts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SweepFrame\n",
-    "\n",
-    "The following sweeps two parameters and stores the results in a `SweepFrame`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_parameters(beta_array, gamma_array):\n",
-    "    \"\"\"Sweep a range of values for beta and gamma.\n",
-    "    \n",
-    "    beta_array: array of infection rates\n",
-    "    gamma_array: array of recovery rates\n",
-    "    \n",
-    "    returns: SweepFrame with one row for each beta\n",
-    "             and one column for each gamma\n",
-    "    \"\"\"\n",
-    "    frame = SweepFrame(columns=gamma_array)\n",
-    "    for gamma in gamma_array:\n",
-    "        frame[gamma] = sweep_beta(beta_array, gamma)\n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the `SweepFrame` look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frame = sweep_parameters(beta_array, gamma_array)\n",
-    "frame.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we can plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for gamma in gamma_array:\n",
-    "    label = 'gamma = ' + str(gamma)\n",
-    "    plot(frame[gamma], label=label)\n",
-    "    \n",
-    "decorate(xlabel='Contact rate (beta)',\n",
-    "         ylabel='Fraction infected',\n",
-    "         title='',\n",
-    "         loc='upper left')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also plot one line for each value of `beta`, although there are a lot of them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(7, 4))\n",
-    "\n",
-    "\n",
-    "for beta in [1.1, 0.9, 0.7, 0.5, 0.3]:\n",
-    "    label = 'beta = ' + str(beta)\n",
-    "    plot(frame.row[beta], label=label)\n",
-    "    \n",
-    "decorate(xlabel='Recovery rate (gamma)',\n",
-    "         ylabel='Fraction infected')\n",
-    "\n",
-    "plt.legend(bbox_to_anchor=(1.02, 1.02))\n",
-    "plt.tight_layout()\n",
-    "savefig('figs/chap13-fig03.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's often useful to separate the code that generates results from the code that plots the results, so we can run the simulations once, save the results, and then use them for different analysis, visualization, etc.\n",
-    "\n",
-    "After running `sweep_parameters`, we have a `SweepFrame` with one row for each value of `beta` and one column for each value of `gamma`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "contour(frame)\n",
-    "\n",
-    "decorate(xlabel='Recovery rate (gamma)',\n",
-    "         ylabel='Contact rate (beta)',\n",
-    "         title='Fraction infected, contour plot')\n",
-    "\n",
-    "savefig('figs/chap13-fig04.pdf')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap14.ipynb b/notebooks/chap14.ipynb
deleted file mode 100644
index c9624be2..00000000
--- a/notebooks/chap14.ipynb
+++ /dev/null
@@ -1,475 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 14\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from previous chapters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(beta, gamma):\n",
-    "    \"\"\"Make a system object for the SIR model.\n",
-    "    \n",
-    "    beta: contact rate in days\n",
-    "    gamma: recovery rate in days\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(S=89, I=1, R=0)\n",
-    "    init /= np.sum(init)\n",
-    "\n",
-    "    t0 = 0\n",
-    "    t_end = 7 * 14\n",
-    "\n",
-    "    return System(init=init, t0=t0, t_end=t_end,\n",
-    "                  beta=beta, gamma=gamma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the SIR model.\n",
-    "    \n",
-    "    state: State (s, i, r)\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State (sir)\n",
-    "    \"\"\"\n",
-    "    s, i, r = state\n",
-    "\n",
-    "    infected = system.beta * i * s    \n",
-    "    recovered = system.gamma * i\n",
-    "    \n",
-    "    s -= infected\n",
-    "    i += infected - recovered\n",
-    "    r += recovered\n",
-    "    \n",
-    "    return State(S=s, I=i, R=r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    init, t0, t_end = system.init, system.t0, system.t_end\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t0] = init\n",
-    "    \n",
-    "    for t in linrange(t0, t_end):\n",
-    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_total_infected(results):\n",
-    "    \"\"\"Fraction of population infected during the simulation.\n",
-    "    \n",
-    "    results: DataFrame with columns S, I, R\n",
-    "    \n",
-    "    returns: fraction of population\n",
-    "    \"\"\"\n",
-    "    return get_first_value(results.S) - get_last_value(results.S)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_beta(beta_array, gamma):\n",
-    "    \"\"\"Sweep a range of values for beta.\n",
-    "    \n",
-    "    beta_array: array of beta values\n",
-    "    gamma: recovery rate\n",
-    "    \n",
-    "    returns: SweepSeries that maps from beta to total infected\n",
-    "    \"\"\"\n",
-    "    sweep = SweepSeries()\n",
-    "    for beta in beta_array:\n",
-    "        system = make_system(beta, gamma)\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        sweep[system.beta] = calc_total_infected(results)\n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_parameters(beta_array, gamma_array):\n",
-    "    \"\"\"Sweep a range of values for beta and gamma.\n",
-    "    \n",
-    "    beta_array: array of infection rates\n",
-    "    gamma_array: array of recovery rates\n",
-    "    \n",
-    "    returns: SweepFrame with one row for each beta\n",
-    "             and one column for each gamma\n",
-    "    \"\"\"\n",
-    "    frame = SweepFrame(columns=gamma_array)\n",
-    "    for gamma in gamma_array:\n",
-    "        frame[gamma] = sweep_beta(beta_array, gamma)\n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Contact number"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the `SweepFrame` from the previous chapter, with one row for each value of `beta` and one column for each value of `gamma`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "beta_array = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 , 1.1]\n",
-    "gamma_array = [0.2, 0.4, 0.6, 0.8]\n",
-    "frame = sweep_parameters(beta_array, gamma_array)\n",
-    "frame.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frame.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following loop shows how we can loop through the columns and rows of the `SweepFrame`.  With 11 rows and 4 columns, there are 44 elements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for gamma in frame.columns:\n",
-    "    column = frame[gamma]\n",
-    "    for beta in column.index:\n",
-    "        frac_infected = column[beta]\n",
-    "        print(beta, gamma, frac_infected)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can wrap that loop in a function and plot the results.  For each element of the `SweepFrame`, we have `beta`, `gamma`, and `frac_infected`, and we plot `beta/gamma` on the x-axis and `frac_infected` on the y-axis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_sweep_frame(frame):\n",
-    "    \"\"\"Plot the values from a SweepFrame.\n",
-    "    \n",
-    "    For each (beta, gamma), compute the contact number,\n",
-    "    beta/gamma\n",
-    "    \n",
-    "    frame: SweepFrame with one row per beta, one column per gamma\n",
-    "    \"\"\"\n",
-    "    for gamma in frame.columns:\n",
-    "        column = frame[gamma]\n",
-    "        for beta in column.index:\n",
-    "            frac_infected = column[beta]\n",
-    "            plot(beta/gamma, frac_infected, 'ro')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_sweep_frame(frame)\n",
-    "\n",
-    "decorate(xlabel='Contact number (beta/gamma)',\n",
-    "         ylabel='Fraction infected')\n",
-    "\n",
-    "savefig('figs/chap14-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It turns out that the ratio `beta/gamma`, called the \"contact number\" is sufficient to predict the total number of infections; we don't have to know `beta` and `gamma` separately.\n",
-    "\n",
-    "We can see that in the previous plot: when we plot the fraction infected versus the contact number, the results fall close to a curve."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the book we figured out the relationship between $c$ and $s_{\\infty}$ analytically.  Now we can compute it for a range of values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s_inf_array = linspace(0.0001, 0.9999, 101);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c_array = log(s_inf_array) / (s_inf_array - 1);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`total_infected` is the change in $s$ from the beginning to the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frac_infected = 1 - s_inf_array\n",
-    "frac_infected_series = Series(frac_infected, index=c_array);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can plot the analytic results and compare them to the simulations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_sweep_frame(frame)\n",
-    "plot(frac_infected_series, label='Analysis')\n",
-    "\n",
-    "decorate(xlabel='Contact number (c)',\n",
-    "         ylabel='Fraction infected')\n",
-    "\n",
-    "savefig('figs/chap14-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The agreement is generally good, except for values of `c` less than 1."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  If we didn't know about contact numbers, we might have explored other possibilities, like the difference between `beta` and `gamma`, rather than their ratio.\n",
-    "\n",
-    "Write a version of `plot_sweep_frame`, called `plot_sweep_frame_difference`, that plots the fraction infected versus the difference `beta-gamma`.\n",
-    "\n",
-    "What do the results look like, and what does that imply? "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose you run a survey at the end of the semester and find that 26% of students had the Freshman Plague at some point.\n",
-    "\n",
-    "What is your best estimate of `c`?\n",
-    "\n",
-    "Hint: if you print `frac_infected_series`, you can read off the answer. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap15.ipynb b/notebooks/chap15.ipynb
deleted file mode 100644
index 4e0f0cdb..00000000
--- a/notebooks/chap15.ipynb
+++ /dev/null
@@ -1,318 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 15\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The coffee cooling problem\n",
-    "\n",
-    "I'll use a `State` object to store the initial temperature.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "init = State(T=90)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And a `System` object to contain the system parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee = System(init=init,\n",
-    "                volume=300,\n",
-    "                r=0.01,\n",
-    "                T_env=22,\n",
-    "                t_0=0,\n",
-    "                t_end=30,\n",
-    "                dt=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The update function implements Newton's law of cooling."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the thermal transfer model.\n",
-    "    \n",
-    "    state: State (temp)\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State (temp)\n",
-    "    \"\"\"\n",
-    "    r, T_env, dt = system.r, system.T_env, system.dt\n",
-    "    \n",
-    "    T = state.T\n",
-    "    T += -r * (T - T_env) * dt\n",
-    "    \n",
-    "    return State(T=T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how it works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "update_func(init, 0, coffee)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a version of `run_simulation` that uses `linrange` to make an array of time steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "    \n",
-    "    Add a TimeFrame to the System: results\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \"\"\"\n",
-    "    init = system.init\n",
-    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t_0] = init\n",
-    "    ts = linrange(t_0, t_end, dt)\n",
-    "    \n",
-    "    for t in ts:\n",
-    "        frame.row[t+dt] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how it works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(coffee, update_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results.T, label='coffee')\n",
-    "decorate(xlabel='Time (minutes)',\n",
-    "         ylabel='Temperature (C)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the final temperature:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee.T_final = get_last_value(results.T)\n",
-    "T_final = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Encapsulation\n",
-    "\n",
-    "Before we go on, let's define a function to initialize `System` objects with relevant parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(T_init, r, volume, t_end):\n",
-    "    \"\"\"Makes a System object with the given parameters.\n",
-    "\n",
-    "    T_init: initial temperature in degC\n",
-    "    r: heat transfer rate, in 1/min\n",
-    "    volume: volume of liquid in mL\n",
-    "    t_end: end time of simulation\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(T=T_init)\n",
-    "                   \n",
-    "    return System(init=init,\n",
-    "                  r=r, \n",
-    "                  volume=volume,\n",
-    "                  temp=T_init,\n",
-    "                  t_0=0, \n",
-    "                  t_end=t_end, \n",
-    "                  dt=1,\n",
-    "                  T_env=22)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we use it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee = make_system(T_init=90, r=0.01, volume=300, t_end=30)\n",
-    "results = run_simulation(coffee, update_func)\n",
-    "T_final = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:**  Simulate the temperature of 50 mL of milk with a starting temperature of 5 degC, in a vessel with the same insulation, for 15 minutes, and plot the results.\n",
-    "\n",
-    "By trial and error, find a value for `r` that makes the final temperature close to 20 C."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results.T, label='milk')\n",
-    "decorate(xlabel='Time (minutes)',\n",
-    "         ylabel='Temperature (C)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap16.ipynb b/notebooks/chap16.ipynb
deleted file mode 100644
index b359b505..00000000
--- a/notebooks/chap16.ipynb
+++ /dev/null
@@ -1,842 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 16\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Code from previous notebooks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the thermal transfer model.\n",
-    "    \n",
-    "    state: State (temp)\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State (temp)\n",
-    "    \"\"\"\n",
-    "    r, T_env, dt = system.r, system.T_env, system.dt\n",
-    "    \n",
-    "    T = state.T\n",
-    "    T += -r * (T - T_env) * dt\n",
-    "    \n",
-    "    return State(T=T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "    \n",
-    "    Add a TimeFrame to the System: results\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \"\"\"\n",
-    "    init = system.init\n",
-    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t_0] = init\n",
-    "    ts = linrange(t_0, t_end, dt)\n",
-    "    \n",
-    "    for t in ts:\n",
-    "        frame.row[t+dt] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(T_init, r, volume, t_end):\n",
-    "    \"\"\"Makes a System object with the given parameters.\n",
-    "\n",
-    "    T_init: initial temperature in degC\n",
-    "    r: heat transfer rate, in 1/min\n",
-    "    volume: volume of liquid in mL\n",
-    "    t_end: end time of simulation\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(T=T_init)\n",
-    "                   \n",
-    "    return System(init=init,\n",
-    "                  r=r, \n",
-    "                  volume=volume,\n",
-    "                  temp=T_init,\n",
-    "                  t_0=0, \n",
-    "                  t_end=t_end, \n",
-    "                  dt=1,\n",
-    "                  T_env=22)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using `root_bisect`\n",
-    "\n",
-    "As a simple example, let's find the roots of this function; that is, the values of `x` that make the result 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def func(x):\n",
-    "    return (x-1) * (x-2) * (x-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`modsim.py` provides `root_bisect`, which searches for a root by bisection.  The first argument is the function whose roots we want.  The second argument is an interval that contains a root."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = root_bisect(func, [0.5, 1.5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is an object that contains the root that was found and other information."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we provide a different interval, we find a different root."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = root_bisect(func, [1.5, 2.5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If the interval doesn't contain a root, the results explain the error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = root_bisect(func, [4, 5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We want to find the value of `r` that makes the final temperature 70, so we define an \"error function\" that takes `r` as a parameter and returns the difference between the final temperature and the goal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func1(r):\n",
-    "    \"\"\"Runs a simulation and returns the `error`.\n",
-    "    \n",
-    "    r: heat transfer rate, in 1/min\n",
-    "    \n",
-    "    returns: difference between final temp and 70 C\n",
-    "    \"\"\"\n",
-    "    system = make_system(T_init=90, r=r, volume=300, t_end=30)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    T_final = get_last_value(results.T)\n",
-    "    return T_final - 70"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With `r=0.01`, we end up a little too warm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "error_func1(r=0.01)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With `r=0.02`, we end up too cold."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "error_func1(r=0.02)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The return value from `root_bisect` is an array with a single element, the estimated value of `r`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = root_bisect(error_func1, [0.01, 0.02])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r_coffee = res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we run the simulation with the estimated value of `r`, the final temperature is 70 C, as expected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=30)\n",
-    "results = run_simulation(coffee, update_func)\n",
-    "T_final = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  When you call `root_bisect`, it calls `error_func1` several times.  To see how this works, add a print statement to `error_func1` and run `root_bisect` again."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Repeat this process to estimate `r_milk`, given that it starts at 5 C and reaches 20 C after 15 minutes.  \n",
-    "\n",
-    "Before you use `root_bisect`, you might want to try a few values for `r_milk` and see how close you can get by trial and error.  Here's an initial guess to get you started:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r_milk = 0.1\n",
-    "milk = make_system(T_init=5, r=r_milk, volume=50, t_end=15)\n",
-    "results = run_simulation(milk, update_func)\n",
-    "T_final = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Mixing liquids"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function takes `System` objects that represent two liquids, computes the temperature of the mixture, and returns a new `System` object that represents the mixture."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mix(system1, system2):\n",
-    "    \"\"\"Simulates the mixture of two liquids.\n",
-    "    \n",
-    "    system1: System representing coffee\n",
-    "    system2: System representing milk\n",
-    "    \n",
-    "    returns: System representing the mixture\n",
-    "    \"\"\"\n",
-    "    assert system1.t_end == system2.t_end\n",
-    "    \n",
-    "    V1, V2 = system1.volume, system2.volume\n",
-    "    T1, T2 = system1.temp, system2.temp\n",
-    "    \n",
-    "    V_mix = V1 + V2\n",
-    "    T_mix = (V1 * T1 + V2 * T2) / V_mix\n",
-    "    \n",
-    "    return make_system(T_init=T_mix,\n",
-    "                       r=system1.r,\n",
-    "                       volume=V_mix,\n",
-    "                       t_end=30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`mix` requires the `System` objects to have `temp` as a system variable.  `make_system` initializes this variable;\n",
-    "the following function makes sure it gets updated when we run a simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_and_set(system):\n",
-    "    \"\"\"Run a simulation and set the final temperature.\n",
-    "    \n",
-    "    system: System\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    system.temp = get_last_value(results.T)\n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mixing immediately\n",
-    "\n",
-    "Next here's what we get if we add the milk immediately."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=30)\n",
-    "coffee.temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "milk = make_system(T_init=5, r=r_milk, volume=50, t_end=30)\n",
-    "milk.temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mix_first = mix(coffee, milk)\n",
-    "mix_first.temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mix_results = run_and_set(mix_first)\n",
-    "mix_first.temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mixing at the end\n",
-    "\n",
-    "First we'll see what happens if we add the milk at the end.  We'll simulate the coffee and the milk separately."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee_results = run_and_set(coffee)\n",
-    "coffee.temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "milk_results = run_and_set(milk)\n",
-    "milk.temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(coffee_results.T, label='coffee')\n",
-    "plot(milk_results.T, '--', label='milk')\n",
-    "\n",
-    "decorate(xlabel='Time (minutes)',\n",
-    "         ylabel='Temperature (C)',\n",
-    "         loc='center left')\n",
-    "\n",
-    "savefig('figs/chap16-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what happens when we mix them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mix_last = mix(coffee, milk)\n",
-    "mix_last.temp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mix_last.temp - mix_first.temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function takes `t_add`, which is the time when the milk is added, and returns the final temperature."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_and_mix(t_add, t_total):\n",
-    "    \"\"\"Simulates two liquids and them mixes them at t_add.\n",
-    "    \n",
-    "    t_add: time in minutes\n",
-    "    t_total: total time to simulate, min\n",
-    "    \n",
-    "    returns: final temperature\n",
-    "    \"\"\"\n",
-    "    coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=t_add)\n",
-    "    coffee_results = run_and_set(coffee)\n",
-    "    \n",
-    "    milk = make_system(T_init=5, r=r_milk, volume=50, t_end=t_add)\n",
-    "    milk_results = run_and_set(milk)\n",
-    "    \n",
-    "    mixture = mix(coffee, milk)\n",
-    "    mixture.t_end = t_total - t_add\n",
-    "    results = run_and_set(mixture)\n",
-    "\n",
-    "    return mixture.temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can try it out with a few values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_and_mix(t_add=0, t_total=30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_and_mix(t_add=15, t_total=30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_and_mix(t_add=30, t_total=30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then sweep a range of values for `t_add`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sweep = SweepSeries()\n",
-    "for t_add in linspace(0, 30, 11):\n",
-    "    sweep[t_add] = run_and_mix(t_add, 30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the result looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(sweep, label='final temp', color='C2')\n",
-    "decorate(xlabel='Time added (min)',\n",
-    "         ylabel='Final temperature (C)')\n",
-    "\n",
-    "savefig('figs/chap16-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can use the analytic result to compute temperature as a function of time.  The following function is similar to `run_simulation`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_analysis(system):\n",
-    "    \"\"\"Computes temperature using the analytic solution.\n",
-    "        \n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    T_env, r = system.T_env, system.r\n",
-    "    \n",
-    "    T_init = system.init.T    \n",
-    "    ts = linspace(0, system.t_end)\n",
-    "    \n",
-    "    T_array = T_env + (T_init - T_env) * exp(-r * ts)\n",
-    "    \n",
-    "    # to be consistent with run_simulation,\n",
-    "    # we put the array into a TimeFrame\n",
-    "    return TimeFrame(T_array, index=ts, columns=['T'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it.  From the analysis (see `chap16sympy.ipynb`), we have the computed value of `r_coffee2`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r_coffee2 = 0.011610223142273859\n",
-    "coffee2 = make_system(T_init=90, r=r_coffee2, volume=300, t_end=30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_analysis(coffee2)\n",
-    "T_final_analysis = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can compare to the results from simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=30)\n",
-    "results = run_simulation(coffee, update_func)\n",
-    "T_final_simulation = get_last_value(results.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "They are identical except for a small roundoff error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "T_final_analysis - T_final_simulation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:**  Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
-    "\n",
-    "Hint: Think about the simplest way to represent the behavior of a refrigerator in this model.  The change you make to test this variation of the problem should be very small!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap16sympy.ipynb b/notebooks/chap16sympy.ipynb
deleted file mode 100644
index c8323988..00000000
--- a/notebooks/chap16sympy.ipynb
+++ /dev/null
@@ -1,464 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "SymPy code for Chapter 16\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Mixing liquids"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can figure out the final temperature of a mixture by setting the total heat flow to zero and then solving for $T$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sympy import *\n",
-    "\n",
-    "init_printing() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle C_{1} \\left(T - T_{1}\\right) + C_{2} \\left(T - T_{2}\\right) = 0$"
-      ],
-      "text/plain": [
-       "C₁⋅(T - T₁) + C₂⋅(T - T₂) = 0"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "C1, C2, T1, T2, T = symbols('C1 C2 T1 T2 T')\n",
-    "\n",
-    "eq = Eq(C1 * (T - T1) + C2 * (T - T2), 0)\n",
-    "eq"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\left[ \\frac{C_{1} T_{1} + C_{2} T_{2}}{C_{1} + C_{2}}\\right]$"
-      ],
-      "text/plain": [
-       "⎡C₁⋅T₁ + C₂⋅T₂⎤\n",
-       "⎢─────────────⎥\n",
-       "⎣   C₁ + C₂   ⎦"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "solve(eq, T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use SymPy to solve the cooling differential equation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{d}{d t} T{\\left(t \\right)} = - r \\left(- T_{env} + T{\\left(t \\right)}\\right)$"
-      ],
-      "text/plain": [
-       "d                           \n",
-       "──(T(t)) = -r⋅(-Tₑₙᵥ + T(t))\n",
-       "dt                          "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "T_init, T_env, r, t = symbols('T_init T_env r t')\n",
-    "T = Function('T')\n",
-    "\n",
-    "eqn = Eq(diff(T(t), t), -r * (T(t) - T_env))\n",
-    "eqn"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the general solution:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle T{\\left(t \\right)} = C_{1} e^{- r t} + T_{env}$"
-      ],
-      "text/plain": [
-       "           -r⋅t       \n",
-       "T(t) = C₁⋅ℯ     + Tₑₙᵥ"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "solution_eq = dsolve(eqn)\n",
-    "solution_eq"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAH0AAAAWCAYAAADts5O8AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAE0ElEQVRoBe2Zi1EbMRBAbYYCgBKgAwgdkA4gHQQ6gKGCDHQAqSBAB4QKCHQAHQTcAXlPSJfznXycwT8MOyPrpF2t9qNdfdx9enrqzDt0u90bdPyOrrfzrmsb/brz7nQcvoQhHtGz28YgH4Fm4QMouYWO9x9Az9YqvutIJ4p30fQr5ZLyQFmlbFIOKIK1Tu9R/lBOPlN8pzMPTjeKdfpXHPqbhXDH957f1J24n/+gfWH7EwY4Pe6DKYqMEqNohXKJ8U7BG1EaNkUUzclClEG5gpzIYsTXADpPqmvgp5Lio5wuxJRtrIXt56rj4kx92nWdcjxO2y7GiYsKIfdpHFIOqoYEt005AfdNPGVkAF/TcBueO8jVS05knOncSK8BOA1Y0NYIJtOxxzQ1JyLbI/0PGRtrXxfJ+IBJOxZgiaLxnHAp9VdrcArlgNUqbhpt5NB467m56XcBn4sD1O9VMjPOqNzPzfFSH+PC/GU65aAolGeMYP9U02fm2krtcdQLTJDgio8vlA0mSukm4cr1OY1pR0+Qh2jReC7QQffvNfDXgZjsBd1EU3zMNLksZFYTcjgXp4fOsUFwOsIZvaZCHzCaHK4g7qNnfswA6PRwYBsgi3ptop8R/2sAzVi7sedpZoJ0/sjJftvCBxmW7bt8sNBwpvR7JjMyGgEDuhJXoB0qauK4Q8b+jRM419GwfOLYiVbIbnp3azgexcTwS/v5i/YexXxVHh7kPGgIRsWLgOJmgr5sgBJmCR2YVnAfH/CmM/l7rQqLhT4Xm3O3ObxBNh8Q9TZwppYtdXr56jCUZaOz06LRiTWARgU9B7h1JIfbp7NbLTTo5gma9vOJ6Gl6N9XohOUYxY0T68QqHX0hkumvpStwOly8TnYeoUc5q/IJmCn+IOsR0yenlCVR7hVKbktzW9wpEzd9R3sYaK3s3cTrLTj3c+Tuvzrk2tApbO16RJ+GuhswxqtJ7dqSo53VPuRX71dd2ao6wccgy9qqSjuu9iICeILcZQV6UMmtZNAFbELzmufMl/gWE8zzhzZGP7NG434OnTTlQ69RGQ6R4LzHb1D8u9irXcpMvjqmrdbnZ/s9a+mzkInoM5MFUBCjsXElg3dA9tGG/qZIN5N4yOt7hLANNM6ZGzONPuQcSaTDR4ep+PYgPcDpcB0abB3bRaakHR5vqM0Yu4lPbBePT7SDzamdL/FSj8vgCD5siCyYlJiJ88CVdbh0QJPTvSPfJH6RXsXkWQhZxs/aN3KOyuk+xhROyOkJ3jOQReeGkuhop0yhTfu2CNoF3xJdzS/gjhb50fEXhP6yHdQK1rMf8CHGVVakjdA7xA9jj+FputHJruAAb+GZeLyHGr3NkDrLYroVruh3y7vWPqHn/4+O8v+F2sMNfWGbZKz/N2jPALQd0wMf/Fai8wpdbMeR7jnSIaql3mH6YFxbUcOMn3Va9BtJpLfRk7mM2L4MSLsvy9I2tRcHar5dACmdl/vdWot3fL7DdrrAxyjAdD3PYNTlnlPHobNzpYxghtS2P9NEtEOKZwGV/28w6C7BuTirYLaWj+NCplisUgzTjoy8fzvpKm33olzKGobtzNFi4LTdTUI2T9puszopAPOX3wHsL1J2JDHSTeXu82Wc2/IhvK6pTf9h4f4D66qLhYPzB8MAAAAASUVORK5CYII=\n",
-      "text/latex": [
-       "$\\displaystyle C_{1} e^{- r t} + T_{env}$"
-      ],
-      "text/plain": [
-       "    -r⋅t       \n",
-       "C₁⋅ℯ     + Tₑₙᵥ"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "general = solution_eq.rhs\n",
-    "general"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use the initial condition to solve for $C_1$.  First we evaluate the general solution at $t=0$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle C_{1} + T_{env}$"
-      ],
-      "text/plain": [
-       "C₁ + Tₑₙᵥ"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "at0 = general.subs(t, 0)\n",
-    "at0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we set $T(0) = T_{init}$ and solve for $C_1$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle - T_{env} + T_{init}$"
-      ],
-      "text/plain": [
-       "-Tₑₙᵥ + Tᵢₙᵢₜ"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "solutions = solve(Eq(at0, T_init), C1)\n",
-    "value_of_C1 = solutions[0]\n",
-    "value_of_C1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we plug the result into the general solution to get the particular solution:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle T_{env} + \\left(- T_{env} + T_{init}\\right) e^{- r t}$"
-      ],
-      "text/plain": [
-       "                        -r⋅t\n",
-       "Tₑₙᵥ + (-Tₑₙᵥ + Tᵢₙᵢₜ)⋅ℯ    "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "particular = general.subs(C1, value_of_C1)\n",
-    "particular"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We use a similar process to estimate $r$ based on the observation $T(t_{end}) = T_{end}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_end, T_end = symbols('t_end T_end')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the particular solution evaluated at $t_{end}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQ0AAAAYCAYAAADgfqiUAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAHq0lEQVR4Ae2ci5ETORCGvdQGQHEZQAZwmwFkAHcRABlAbQQUZMBdBBxkwG4EPDKAi+BgM+C+Tyup5LHGHns8tsc7XaXVqPVq/Wq1Wprxnvz69Ws20YTAhMC4ETg5OfnCCJ6ynr8OPZKTyWgMDfHU/oTAsAhgMG7Tw0/W8smwPV23fmsXnUx9TAhMCAyKwENa/z5oD0Xjk6dRgDE9TggcGgJ4Ec+Q6RHhI+EH4S7hjPCSIBlrNK4Inwlvhz6iTJ4GKNeIybobJ6yWPfGOHAHm/rE6cCDDfIschisMwhvi+4S7PH8nPJdPeOUzYZA7DbB4S/hGP7PgaURwZCRrZSw9vo5mF8SJJ5AK/QYBk7WLxY4jAo/bjOQ949PCb40mnFG464U4Cl1D1vdM/kv0YGeuf6lsESu9i+BttOkj5XybcW9IOelDb+YJfTw/jUJqrRaMAAV/wv/RFBa+Vi9YnVj/2KJLBvRkgEFNOM9mY8LgKTqgLjzYhi7Ehddlo3Vx6lUEY0U9jyMeTxaIPDfwXHahwPYYbqC+oZklo6GrM7dIEEaPwh33Hws2yMp7sb4NORaSyK135Hh049Ym6mvVP6cJW7uB5RWOAueeGI8GA3TgirFeEF5sqk+lOtCGHrthXXKXf9VSybzQJnK6Xu8k3SWtLt8jhA0e/l886+3J1xBqiO4QJD2VOYMWy9mm5LoKnvcpGVqqmhVTGKmWZ0NeuhwjCdxWjyWCNOE8WgxcrP8SNtqEnPs+hN6EzZsF3XZXoVH4FPs4Tws/Lng9lqDLpD0dJPLIoyHxDiTlm85Gg/IaiWxISNt2cBRukTEjESyQzwWlhVOzjF+pc1WUPYpHgHGCxGMQL2rCeXy6FvX8O7qRNtFd67o6WVuDSQ6NwRnyvSB+l5jEHgO/wvdCV88iGwTG9IF0fgNDvo5DU+fP4QVDE/OzDK2vXCmY7jO0ZKMh5N74eEJdgde6CvhOaIw498G4BuqhY4B8r5H79iq9oJweuIvtvzhO185r6jUXZMweLkIWTwh6EqFvZeM5b/Skv5EOa5tn9d4838ako46exwPrxHySM52Fi1OfmkSh4BLBr91nNIsfU1rvyhvzndANxjnj2xWDqLgbG/Se9YMrn4WuPNC+nog78yMWVlqoriM3oLzL87wrCv0iV7i8jJ2mOw2NW3ncUV49j5Ksf059xy5pYILRqRoNMpMrVrvPsIFjJSfZ896u6KbiXOLbFQN3QN3qTalaPy6qVb/ZUCdcaFWiDfPcbGwnGQx5Govg4hPvlKIcVY+ZPBd/fvFBWlznsIUnXsHraAreZjSW3Wc029hLmonSZUwKV8rgZN0h/8+SGZ91vzJYlXxvkgW0SlE5LquZ7cxlCnnQOA+EcROpThgwb+XO2GxjZbpWP87n/Vpeo0ENgRtKG/0dM9Q7XX1JPfIbj1Z9CqVG+Kd6p8HAR3mfIf7I3udOw3F741y1sNue37Hi3AfjJob7xCCOwzuHpfd2lPOi8Avlqj8II9+Pqz6Qv2xDag59tOkFTwMAOt1nUM4dvbz08a1DeC1FXnoP7HnqMyF5BHNnUsrJd0LOEuDw9CBsax/nwKVuqHJtixjnTcY5wNgFg5qOWBl+Hx0Te/VL/fOizyPEst9s6IGGYwdxG63Kb6s3Pj6L0wWaAyNwMrScj0t++UyeBkOD4I0sUUj72XVoh7RtOCHu3M8Kvmk/7knltPKwQn+pLT2Fj6nMujF1re/HOHlMXZ/tl5Dl7Vpvk3L2Qxglzsi9McYlVh0xWNAR24h1N9KxJANtqMOtel6Uc670NKo6RZ6XhUHOZhn4G+lis51DSp8yqCYlF2uZi+4ZTsv6B1Y61GdQoR5prbhvXQTaT9DLb0A0NuGiMZZ7RezEe9cQzn7EH+CdwdsHeW7eyifDHYS/yTgneJZiUOiIRirrSOSvo2Nz9VPnxHq5Xe5K1Ill60FPZe4ODRnVdb3m4DkTHw0Fo8EAHZiL3SCQ0iV8DcMnFnI4dgTu9R8XevXsT9ngplHXhZ9vjklb56owDqmcF2H55jaW29dbG/vNMvO8VWJsNx7ndTAodMm3AHleCn5XHZur76Qih3quPgY9lLeEfifvaVs+bbyhPdtUxvyKE3717UVbO6PhM7Cqy7WMz+D0D/Mxw7JQOF6keqQ9ingznY4iAppczZKva/ewKNfLnaOtXq4z9ZVnbmxJtl3HyHGQOPfFeB0c6csdWxyMs97YBrRSx2K9hfrwfcsRjtSxTHXOY54fQq29To61zi1A2YR01ZJHooV1Qj2yBCKtx6IRKV0/PY2P5Lmom1QeWbpY/mb9Mq1s5ZGozOvyrDewj0vYmmyHinNfjGtjbeOpN+lnC/kIsIaOVevTpm9Myt9stOndOeWyl8PzjafqK9dVqEQj4eJyVw6EgchHGPKdKD9hTWdWDYuW/TeCVjsv6lQWvhOou5jzSO+FkMljSv4Edy9C0ClypHPxUeLcBdeIgRuSi9o3HGFxJ71ZpWNL6rvpaRDUuwvaKTc4WBn/S/J2dc8V+j30PxsZjUMfVF/5oqJt/Z/w9JVrqr9bBNCDg9g8djvq1b1NRqMFIxTGI5Z3LXv3fFpEnNgDIsD8+/ZPD6Tt2DJg74fd9P9VXbGAjnyP0QAAAABJRU5ErkJggg==\n",
-      "text/latex": [
-       "$\\displaystyle T_{env} + \\left(- T_{env} + T_{init}\\right) e^{- r t_{end}}$"
-      ],
-      "text/plain": [
-       "                        -r⋅t_end\n",
-       "Tₑₙᵥ + (-Tₑₙᵥ + Tᵢₙᵢₜ)⋅ℯ        "
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "at_end = particular.subs(t, t_end)\n",
-    "at_end"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we set $T(t_{end}) = T_{end}$ and solve for $r$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle \\frac{\\log{\\left(\\frac{- T_{env} + T_{init}}{T_{end} - T_{env}} \\right)}}{t_{end}}$"
-      ],
-      "text/plain": [
-       "   ⎛-Tₑₙᵥ + Tᵢₙᵢₜ⎞\n",
-       "log⎜─────────────⎟\n",
-       "   ⎝ T_end - Tₑₙᵥ⎠\n",
-       "──────────────────\n",
-       "      t_end       "
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "solutions = solve(Eq(at_end, T_end), r)\n",
-    "value_of_r = solutions[0]\n",
-    "value_of_r"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use `evalf` to plug in numbers for the symbols.  The result is a SymPy float, which we have to convert to a Python float."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "sympy.core.numbers.Float"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "subs = dict(t_end=30, T_end=70, T_init=90, T_env=22)\n",
-    "r_coffee2 = value_of_r.evalf(subs=subs)\n",
-    "type(r_coffee2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/latex": [
-       "$\\displaystyle 0.011610223142273859$"
-      ],
-      "text/plain": [
-       "0.011610223142273859"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r_coffee2 = float(r_coffee2)\n",
-    "r_coffee2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap17.ipynb b/notebooks/chap17.ipynb
deleted file mode 100644
index 834f0306..00000000
--- a/notebooks/chap17.ipynb
+++ /dev/null
@@ -1,273 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 17\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data\n",
-    "\n",
-    "We have data from Pacini and Bergman (1986), \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 23: 113-122.."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv('data/glucose_insulin.csv', index_col='time')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the glucose time series looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(data.glucose, 'bo', label='glucose')\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration (mg/dL)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the insulin time series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(data.insulin, 'go', label='insulin')\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the book, I put them in a single figure, using `subplot`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subplot(2, 1, 1)\n",
-    "plot(data.glucose, 'bo', label='glucose')\n",
-    "decorate(ylabel='Concentration (mg/dL)')\n",
-    "\n",
-    "subplot(2, 1, 2)\n",
-    "plot(data.insulin, 'go', label='insulin')\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')\n",
-    "\n",
-    "savefig('figs/chap17-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Interpolation\n",
-    "\n",
-    "We have measurements of insulin concentration at discrete points in time, but we need to estimate it at intervening points.  We'll use `interpolate`, which takes a `Series` and returns a function:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The return value from `interpolate` is a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "I = interpolate(data.insulin)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use the result, `I`, to estimate the insulin level at any point in time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "I(7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`I` can also take an array of time and return an array of estimates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = get_first_label(data)\n",
-    "t_end = get_last_label(data)\n",
-    "ts = linrange(t_0, t_end, endpoint=True)\n",
-    "I(ts)\n",
-    "type(ts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the interpolated values look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(data.insulin, 'go', label='insulin data')\n",
-    "plot(ts, I(ts), color='green', label='interpolated')\n",
-    "\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration ($\\mu$U/mL)')\n",
-    "\n",
-    "savefig('figs/chap17-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** [Read the documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html) of `scipy.interpolate.interp1d`.  Pass a keyword argument to `interpolate` to specify one of the other kinds of interpolation, and run the code again to see what it looks like. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Interpolate the glucose data and generate a plot, similar to the previous one, that shows the data points and the interpolated curve evaluated at the time values in `ts`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_code(interpolate)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap18.ipynb b/notebooks/chap18.ipynb
deleted file mode 100644
index e8e6df35..00000000
--- a/notebooks/chap18.ipynb
+++ /dev/null
@@ -1,533 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 18\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter\n",
-    "\n",
-    "Read the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv('data/glucose_insulin.csv', index_col='time');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Interpolate the insulin data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "I = interpolate(data.insulin)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The glucose minimal model\n",
-    "\n",
-    "I'll cheat by starting with parameters that fit the data roughly; then we'll see how to improve them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(G0 = 290,\n",
-    "                k1 = 0.03,\n",
-    "                k2 = 0.02,\n",
-    "                k3 = 1e-05)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a version of `make_system` that takes the parameters and data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params, data):\n",
-    "    \"\"\"Makes a System object with the given parameters.\n",
-    "    \n",
-    "    params: sequence of G0, k1, k2, k3\n",
-    "    data: DataFrame with `glucose` and `insulin`\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    G0, k1, k2, k3 = params\n",
-    "    \n",
-    "    Gb = data.glucose[0]\n",
-    "    Ib = data.insulin[0]\n",
-    "    I = interpolate(data.insulin)\n",
-    "    \n",
-    "    t_0 = get_first_label(data)\n",
-    "    t_end = get_last_label(data)\n",
-    "\n",
-    "    init = State(G=G0, X=0)\n",
-    "    \n",
-    "    return System(params,\n",
-    "                  init=init, Gb=Gb, Ib=Ib, I=I,\n",
-    "                  t_0=t_0, t_end=t_end, dt=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the update function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Updates the glucose minimal model.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time in min\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    G, X = state\n",
-    "    k1, k2, k3 = system.k1, system.k2, system.k3 \n",
-    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
-    "    dt = system.dt\n",
-    "        \n",
-    "    dGdt = -k1 * (G - Gb) - X*G\n",
-    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
-    "    \n",
-    "    G += dGdt * dt\n",
-    "    X += dXdt * dt\n",
-    "\n",
-    "    return State(G=G, X=X)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before running the simulation, it is always a good idea to test the update function using the initial conditions.  In this case we can veryify that the results are at least qualitatively correct."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "update_func(system.init, system.t_0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now `run_simulation` is pretty much the same as it always is."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    init = system.init\n",
-    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t_0] = init\n",
-    "    ts = linrange(t_0, t_end, dt)\n",
-    "    \n",
-    "    for t in ts:\n",
-    "        frame.row[t+dt] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The results are in a `TimeFrame` object with one column per state variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following plot shows the results of the simulation along with the actual glucose data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subplot(2, 1, 1)\n",
-    "\n",
-    "plot(results.G, 'b-', label='simulation')\n",
-    "plot(data.glucose, 'bo', label='glucose data')\n",
-    "decorate(ylabel='Concentration (mg/dL)')\n",
-    "\n",
-    "subplot(2, 1, 2)\n",
-    "\n",
-    "plot(results.X, 'C1', label='remote insulin')\n",
-    "\n",
-    "decorate(xlabel='Time (min)', \n",
-    "         ylabel='Concentration (arbitrary units)')\n",
-    "\n",
-    "savefig('figs/chap18-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Numerical solution\n",
-    "\n",
-    "Now let's solve the differential equation numerically using `run_ode_solver`, which is an implementation of Ralston's method.\n",
-    "\n",
-    "Instead of an update function, we provide a slope function that evaluates the right-hand side of the differential equations.\n",
-    "\n",
-    "We don't have to do the update part; the solver does it for us."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the glucose minimal model.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time in min\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: derivatives of G and X\n",
-    "    \"\"\"\n",
-    "    G, X = state\n",
-    "    k1, k2, k3 = system.k1, system.k2, system.k3 \n",
-    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
-    "    \n",
-    "    dGdt = -k1 * (G - Gb) - X*G\n",
-    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
-    "    \n",
-    "    return dGdt, dXdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run the ODE solver."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2, details = run_ode_solver(system, slope_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`details` is a `ModSimSeries` object with information about how the solver worked."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`results` is a `TimeFrame` with one row for each time step and one column for each state variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting the results from `run_simulation` and `run_ode_solver`, we can see that they are not very different."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results.G, 'C0', label='run_simulation')\n",
-    "plot(results2.G, 'C2--', label='run_ode_solver')\n",
-    "\n",
-    "decorate(xlabel='Time (min)', ylabel='Concentration (mg/dL)')\n",
-    "\n",
-    "savefig('figs/chap18-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The differences in `G` are less than 2%."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diff = results.G - results2.G\n",
-    "percent_diff = diff / results2.G * 100\n",
-    "percent_diff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max(abs(percent_diff))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:**  Our solution to the differential equations is only approximate because we used a finite step size, `dt=2` minutes.\n",
-    "\n",
-    "If we make the step size smaller, we expect the solution to be more accurate.  Run the simulation with `dt=1` and compare the results.  What is the largest relative error between the two solutions?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "Here's the source code for `run_ode_solver` if you'd like to know how it works.\n",
-    "\n",
-    "Notice that `run_ode_solver` is another name for `run_ralston`, which implements [Ralston's method](https://en.wikipedia.org/wiki/List_of_Runge–Kutta_methods)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_code(run_ode_solver)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Related reading:** You might be interested in this article about [people making a DIY artificial pancreas](https://www.bloomberg.com/news/features/2018-08-08/the-250-biohack-that-s-revolutionizing-life-with-diabetes)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap20.ipynb b/notebooks/chap20.ipynb
deleted file mode 100644
index 46fc858c..00000000
--- a/notebooks/chap20.ipynb
+++ /dev/null
@@ -1,643 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 20\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dropping pennies\n",
-    "\n",
-    "I'll start by getting the units we need from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And defining the initial state."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "init = State(y=381 * m, \n",
-    "             v=0 * m/s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Acceleration due to gravity is about 9.8 m / s$^2$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g = 9.8 * m/s**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll start with a duration of 10 seconds and step size 0.1 second."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_end = 10 * s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dt = 0.1 * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we make a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = System(init=init, g=g, t_end=t_end, dt=dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And define the slope function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object containing `g`\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    g = system.g    \n",
-    "\n",
-    "    dydt = v\n",
-    "    dvdt = -g\n",
-    "    \n",
-    "    return dydt, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's always a good idea to test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dydt, dvdt = slope_func(system.init, 0, system)\n",
-    "print(dydt)\n",
-    "print(dvdt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we're ready to call `run_ode_solver`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's position as a function of time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_position(results):\n",
-    "    plot(results.y, label='y')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "\n",
-    "plot_position(results)\n",
-    "savefig('figs/chap20-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Onto the sidewalk\n",
-    "\n",
-    "To figure out when the penny hit the sidewalk, we can use `crossings`, which finds the times where a `Series` passes through a given value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_crossings = crossings(results.y, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For this example there should be just one crossing, the time when the penny hits the sidewalk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_sidewalk = t_crossings[0] * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can compare that to the exact result.  Without air resistance, we have\n",
-    "\n",
-    "$v = -g t$\n",
-    "\n",
-    "and\n",
-    "\n",
-    "$y = 381 - g t^2 / 2$\n",
-    "\n",
-    "Setting $y=0$ and solving for $t$ yields\n",
-    "\n",
-    "$t = \\sqrt{\\frac{2 y_{init}}{g}}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sqrt(2 * init.y / g)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The estimate is accurate to about 9 decimal places."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Events\n",
-    "\n",
-    "Instead of running the simulation until the penny goes through the sidewalk, it would be better to detect the point where the penny hits the sidewalk and stop.  `run_ode_solver` provides exactly the tool we need, **event functions**.\n",
-    "\n",
-    "Here's an event function that returns the height of the penny above the sidewalk:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Return the height of the penny above the sidewalk.\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we pass it to `run_ode_solver`.  The solver should run until the event function returns 0, and then terminate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The message from the solver indicates the solver stopped because the event we wanted to detect happened.\n",
-    "\n",
-    "Here are the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the `events` option, the solver returns the actual time steps it computed, which are not necessarily equally spaced. \n",
-    "\n",
-    "The last time step is when the event occurred:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_sidewalk = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is accurate to about 4 decimal places.\n",
-    "\n",
-    "We can also check the velocity of the penny when it hits the sidewalk:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_sidewalk = get_last_value(results.v)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And convert to kilometers per hour."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "km = UNITS.kilometer\n",
-    "h = UNITS.hour\n",
-    "v_sidewalk.to(km / h)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If there were no air resistance, the penny would hit the sidewalk (or someone's head) at more than 300 km/h.\n",
-    "\n",
-    "So it's a good thing there is air resistance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Under the hood\n",
-    "\n",
-    "Here is the source code for `crossings` so you can see what's happening under the hood:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_code(crossings)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The [documentation of InterpolatedUnivariateSpline is here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.InterpolatedUnivariateSpline.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Here's a question from the web site [Ask an Astronomer](http://curious.astro.cornell.edu/about-us/39-our-solar-system/the-earth/other-catastrophes/57-how-long-would-it-take-the-earth-to-fall-into-the-sun-intermediate):\n",
-    "\n",
-    "\"If the Earth suddenly stopped orbiting the Sun, I know eventually it would be pulled in by the Sun's gravity and hit it. How long would it take the Earth to hit the Sun? I imagine it would go slowly at first and then pick up speed.\"\n",
-    "\n",
-    "Use `run_ode_solver` to answer this question.\n",
-    "\n",
-    "Here are some suggestions about how to proceed:\n",
-    "\n",
-    "1.  Look up the Law of Universal Gravitation and any constants you need.  I suggest you work entirely in SI units: meters, kilograms, and Newtons.\n",
-    "\n",
-    "2.  When the distance between the Earth and the Sun gets small, this system behaves badly, so you should use an event function to stop when the surface of Earth reaches the surface of the Sun.\n",
-    "\n",
-    "3. Express your answer in days, and plot the results as millions of kilometers versus days.\n",
-    "\n",
-    "If you read the reply by Dave Rothstein, you will see other ways to solve the problem, and a good discussion of the modeling decisions behind them.\n",
-    "\n",
-    "You might also be interested to know that [it's actually not that easy to get to the Sun](https://www.theatlantic.com/science/archive/2018/08/parker-solar-probe-launch-nasa/567197/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap21.ipynb b/notebooks/chap21.ipynb
deleted file mode 100644
index 54539d62..00000000
--- a/notebooks/chap21.ipynb
+++ /dev/null
@@ -1,514 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 21\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### With air resistance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we'll add air resistance using the [drag equation](https://en.wikipedia.org/wiki/Drag_equation)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll start by getting the units we'll need from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now I'll create a `Params` object to contain the quantities we need.  Using a Params object is convenient for grouping the system parameters in a way that's easy to read (and double-check)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(height = 381 * m,\n",
-    "                v_init = 0 * m / s,\n",
-    "                g = 9.8 * m/s**2,\n",
-    "                mass = 2.5e-3 * kg,\n",
-    "                diameter = 19e-3 * m,\n",
-    "                rho = 1.2 * kg/m**3,\n",
-    "                v_term = 18 * m / s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can pass the `Params` object `make_system` which computes some additional parameters and defines `init`.\n",
-    "\n",
-    "`make_system` uses the given radius to compute `area` and the given `v_term` to compute the drag coefficient `C_d`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    diameter, mass = params.diameter, params.mass\n",
-    "    g, rho = params.g, params.rho, \n",
-    "    v_init, v_term = params.v_init, params.v_term\n",
-    "    height = params.height\n",
-    "    \n",
-    "    area = np.pi * (diameter/2)**2\n",
-    "    C_d = 2 * mass * g / (rho * area * v_term**2)\n",
-    "    init = State(y=height, v=v_init)\n",
-    "    t_end = 30 * s\n",
-    "    dt = t_end / 100\n",
-    "    \n",
-    "    return System(params, area=area, C_d=C_d, \n",
-    "                  init=init, t_end=t_end, dt=dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the slope function, including acceleration due to gravity and drag."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    rho, C_d, g = system.rho, system.C_d, system.g\n",
-    "    area, mass = system.area, system.mass\n",
-    "    \n",
-    "    f_drag = rho * v**2 * C_d * area / 2\n",
-    "    a_drag = f_drag / mass\n",
-    "    \n",
-    "    dydt = v\n",
-    "    dvdt = -g + a_drag\n",
-    "    \n",
-    "    return dydt, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, let's test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use the same event function as in the previous chapter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Return the height of the penny above the sidewalk.\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final height is close to 0, as expected.\n",
-    "\n",
-    "Interestingly, the final velocity is not exactly terminal velocity, which suggests that there are some numerical errors.\n",
-    "\n",
-    "We can get the flight time from `results`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_sidewalk = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the plot of position as a function of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_position(results):\n",
-    "    plot(results.y)\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "    \n",
-    "plot_position(results)\n",
-    "savefig('figs/chap21-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And velocity as a function of time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_velocity(results):\n",
-    "    plot(results.v, color='C1', label='v')\n",
-    "        \n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')\n",
-    "    \n",
-    "plot_velocity(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From an initial velocity of 0, the penny accelerates downward until it reaches terminal velocity; after that, velocity is constant."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Run the simulation with an initial velocity, downward, that exceeds the penny's terminal velocity.  Hint: You can create a new `Params` object based on an existing one, like this:\n",
-    "\n",
-    "`params2 = Params(params, v_init=-30 * m/s)`\n",
-    "\n",
-    "What do you expect to happen?  Plot velocity and position as a function of time, and see if they are consistent with your prediction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Suppose we drop a quarter from the Empire State Building and find that its flight time is 19.1 seconds.  Use this measurement to estimate the terminal velocity.\n",
-    "\n",
-    "1. You can get the relevant dimensions of a quarter from https://en.wikipedia.org/wiki/Quarter_(United_States_coin).\n",
-    "\n",
-    "2. Create a `Params` object with the system parameters.  We don't know `v_term`, so we'll start with the inital guess `v_term = 18 * m / s`.\n",
-    "\n",
-    "3. Use `make_system` to create a `System` object.\n",
-    "\n",
-    "4. Call `run_ode_solver` to simulate the system.  How does the flight time of the simulation compare to the measurement?\n",
-    "\n",
-    "5. Try a few different values of `t_term` and see if you can get the simulated flight time close to 19.1 seconds.\n",
-    "\n",
-    "6. Optionally, write an error function and use `root_scalar` to improve your estimate.\n",
-    "\n",
-    "7. Use your best estimate of `v_term` to compute `C_d`.\n",
-    "\n",
-    "Note: I fabricated the observed flight time, so don't take the results of this exercise too seriously."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap22.ipynb b/notebooks/chap22.ipynb
deleted file mode 100644
index 5a41edee..00000000
--- a/notebooks/chap22.ipynb
+++ /dev/null
@@ -1,1238 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 22\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Vectors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `Vector` object represents a vector quantity.  In the context of mechanics, vector quantities include position, velocity, acceleration, and force, all of which might be in 2D or 3D.\n",
-    "\n",
-    "You can define a `Vector` object without units, but if it represents a physical quantity, you will often want to attach units to it.\n",
-    "\n",
-    "I'll start by grabbing the units we'll need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a two dimensional `Vector` in meters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = Vector(3, 4) * m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can access the elements by name."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A.x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A.y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The magnitude is the length of the vector."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A.mag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The angle is the number of radians between the vector and the positive x axis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A.angle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we make another `Vector` with the same units,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "B = Vector(1, 2) * m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can add `Vector` objects like this"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A + B"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And subtract like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A - B"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can compute the Euclidean distance between two Vectors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A.dist(B)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the difference in angle"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A.diff_angle(B)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we are given the magnitude and angle of a vector, what we have is the representation of the vector in polar coordinates."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mag = A.mag\n",
-    "angle = A.angle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use `pol2cart` to convert from polar to Cartesian coordinates, and then use the Cartesian coordinates to make a `Vector` object.\n",
-    "\n",
-    "In this example, the `Vector` we get should have the same components as `A`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x, y = pol2cart(angle, mag)\n",
-    "Vector(x, y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another way to represent the direction of `A` is a unit vector, which is a vector with magnitude 1 that points in the same direction as `A`.  You can compute a unit vector by dividing a vector by its magnitude:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A / A.mag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or by using the `hat` function, so named because unit vectors are conventionally decorated with a hat, like this: $\\hat{A}$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A.hat()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Create a `Vector` named `a_grav` that represents acceleration due to gravity, with x component 0 and y component $-9.8$ meters / second$^2$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Degrees and radians\n",
-    "\n",
-    "Pint provides units to represent degree and radians."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "degree = UNITS.degree\n",
-    "radian = UNITS.radian"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you have an angle in degrees,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "angle = 45 * degree\n",
-    "angle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can convert to radians."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "angle_rad = angle.to(radian)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If it's already in radians, `to` does the right thing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "angle_rad.to(radian)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also convert from radians to degrees."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "angle_rad.to(degree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As an alterative, you can use `np.deg2rad`, which works with Pint quantities, but it also works with simple numbers and NumPy arrays:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.deg2rad(angle)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Create a `Vector` named `a_force` that represents acceleration due to a force of 0.5 Newton applied to an object with mass 0.3 kilograms, in a direction 45 degrees up from the positive x-axis.\n",
-    "\n",
-    "Add `a_force` to `a_grav` from the previous exercise.  If that addition succeeds, that means that the units are compatible.  Confirm that the total acceleration seems to make sense."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Baseball"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a `Params` object that contains parameters for the flight of a baseball."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_end = 10 * s\n",
-    "dt = t_end / 100\n",
-    "\n",
-    "params = Params(x = 0 * m, \n",
-    "                y = 1 * m,\n",
-    "                g = 9.8 * m/s**2,\n",
-    "                mass = 145e-3 * kg,\n",
-    "                diameter = 73e-3 * m,\n",
-    "                rho = 1.2 * kg/m**3,\n",
-    "                C_d = 0.33,\n",
-    "                angle = 45 * degree,\n",
-    "                velocity = 40 * m / s,\n",
-    "                t_end=t_end, dt=dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the function that uses the `Params` object to make a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params object with angle, velocity, x, y,\n",
-    "               diameter, duration, g, mass, rho, and C_d\n",
-    "               \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    angle, velocity = params.angle, params.velocity\n",
-    "    \n",
-    "    # convert angle to degrees\n",
-    "    theta = np.deg2rad(angle)\n",
-    "    \n",
-    "    # compute x and y components of velocity\n",
-    "    vx, vy = pol2cart(theta, velocity)\n",
-    "    \n",
-    "    # make the initial state\n",
-    "    R = Vector(params.x, params.y)\n",
-    "    V = Vector(vx, vy)\n",
-    "    init = State(R=R, V=V)\n",
-    "    \n",
-    "    # compute area from diameter\n",
-    "    diameter = params.diameter\n",
-    "    area = np.pi * (diameter/2)**2\n",
-    "    \n",
-    "    return System(params, init=init, area=area)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we use it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a function that computes drag force using vectors:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(V, system):\n",
-    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
-    "    \n",
-    "    V: velocity Vector\n",
-    "    system: System object with rho, C_d, area\n",
-    "    \n",
-    "    returns: Vector drag force\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    mag = rho * V.mag**2 * C_d * area / 2\n",
-    "    direction = -V.hat()\n",
-    "    f_drag = direction * mag\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "V_test = Vector(10, 10) * m/s\n",
-    "drag_force(V_test, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the slope function that computes acceleration due to gravity and drag."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the state variables.\n",
-    "    \n",
-    "    state: State (x, y, x velocity, y velocity)\n",
-    "    t: time\n",
-    "    system: System object with g, rho, C_d, area, mass\n",
-    "    \n",
-    "    returns: sequence (vx, vy, ax, ay)\n",
-    "    \"\"\"\n",
-    "    R, V = state\n",
-    "    mass, g = system.mass, system.g\n",
-    "    \n",
-    "    a_drag = drag_force(V, system) / mass\n",
-    "    a_grav = Vector(0, -g)\n",
-    "    \n",
-    "    A = a_grav + a_drag\n",
-    "    \n",
-    "    return V, A"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Always test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use an event function to stop the simulation when the ball hits the ground:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Stop when the y coordinate is 0.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: y coordinate\n",
-    "    \"\"\"\n",
-    "    R, V = state\n",
-    "    return R.y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "event_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can call `run_ode_solver`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final label tells us the flight time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "flight_time = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final value of `x` tells us the how far the ball landed from home plate:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "R_final = get_last_value(results.R)\n",
-    "x_dist = R_final.x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing the results\n",
-    "\n",
-    "The simplest way to visualize the results is to plot x and y as functions of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xs = results.R.extract('x')\n",
-    "ys = results.R.extract('y')\n",
-    "\n",
-    "xs.plot()\n",
-    "ys.plot()\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Position (m)')\n",
-    "\n",
-    "savefig('figs/chap22-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot the velocities the same way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vx = results.V.extract('x')\n",
-    "vy = results.V.extract('y')\n",
-    "\n",
-    "vx.plot(label='vx')\n",
-    "vy.plot(label='vy')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The x velocity slows down due to drag.\n",
-    "\n",
-    "The y velocity drops quickly while drag and gravity are in the same direction, then more slowly after the ball starts to fall.\n",
-    "\n",
-    "Another way to visualize the results is to plot y versus x.  The result is the trajectory of the ball through its plane of motion."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_trajectory(results):\n",
-    "    xs = results.R.extract('x')\n",
-    "    ys = results.R.extract('y')\n",
-    "    plot(xs, ys, color='C2', label='trajectory')\n",
-    "\n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)')\n",
-    "\n",
-    "plot_trajectory(results)\n",
-    "savefig('figs/chap22-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Animation\n",
-    "\n",
-    "One of the best ways to visualize the results of a physical model is animation.  If there are problems with the model, animation can make them apparent.\n",
-    "\n",
-    "The ModSimPy library provides `animate`, which takes as parameters a `TimeSeries` and a draw function.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The draw function should take as parameters a `State` object and the time.  It should draw a single frame of the animation.\n",
-    "\n",
-    "Inside the draw function, you almost always have to call `set_xlim` and `set_ylim`.  Otherwise `matplotlib` auto-scales the axes, which is usually not what you want."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xs = results.R.extract('x')\n",
-    "ys = results.R.extract('y')\n",
-    "\n",
-    "def draw_func(state, t):\n",
-    "    set_xlim(xs)\n",
-    "    set_ylim(ys)\n",
-    "    x, y = state.R\n",
-    "    plot(x, y, 'bo')\n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "animate(results, draw_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Delete the lines that set the x and y axes (or [comment them out](https://en.wiktionary.org/wiki/comment_out)) and see what the animation does."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "`Vector` is a function that returns a `ModSimVector` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "V = Vector(3, 4)\n",
-    "type(V)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `ModSimVector` is a specialized kind of Pint `Quantity`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "isinstance(V, Quantity)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There's one gotcha you might run into with Vectors and Quantities.  If you multiply a `ModSimVector` and a `Quantity`, you get a `ModSimVector`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "V1 = V * m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(V1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But if you multiply a `Quantity` and a `Vector`, you get a `Quantity`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "V2 = m * V"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(V2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With a `ModSimVector` you can get the coordinates using dot notation, as well as `mag`, `mag2`, and `angle`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "V1.x, V1.y, V1.mag, V1.angle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With a `Quantity`, you can't.  But you can use indexing to get the coordinates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "V2[0], V2[1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And you can use vector functions to get the magnitude and angle."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vector_mag(V2), vector_angle(V2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And often you can avoid the whole issue by doing the multiplication with the `ModSimVector` on the left."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Run the simulation with and without air resistance.  How wrong would we be if we ignored drag?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Hint\n",
-    "\n",
-    "system_no_drag = System(system, C_d=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / meter$^3$.  How much farther would a ball hit with the same velocity and launch angle travel?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Hint\n",
-    "\n",
-    "system2 = System(system, rho=1.0*kg/m**3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** The model so far is based on the assumption that coefficient of drag does not depend on velocity, but in reality it does.  The following figure, from Adair, [*The Physics of Baseball*](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC), shows coefficient of drag as a function of velocity.\n",
-    "\n",
-    "<img src=\"data/baseball_drag.png\" width=\"400\">\n",
-    "\n",
-    "\n",
-    "I used [an online graph digitizer](https://automeris.io/WebPlotDigitizer/) to extract the data and save it in a CSV file.  Here's how we can read it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "baseball_drag = pd.read_csv('data/baseball_drag.csv')\n",
-    "mph = Quantity(baseball_drag['Velocity in mph'], UNITS.mph)\n",
-    "mps = mph.to(m/s)\n",
-    "baseball_drag.index = magnitude(mps)\n",
-    "baseball_drag.index.name = 'Velocity in meters per second'\n",
-    "baseball_drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Modify the model to include the dependence of `C_d` on velocity, and see how much it affects the results.  Hint: use `interpolate`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "C_d = drag_interp(43 * m / s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap23.ipynb b/notebooks/chap23.ipynb
deleted file mode 100644
index 0eca5394..00000000
--- a/notebooks/chap23.ipynb
+++ /dev/null
@@ -1,571 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 23\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "degree = UNITS.degree"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_end = 20 * s\n",
-    "dt = t_end / 100\n",
-    "\n",
-    "params = Params(x = 0 * m, \n",
-    "                y = 1 * m,\n",
-    "                g = 9.8 * m/s**2,\n",
-    "                mass = 145e-3 * kg,\n",
-    "                diameter = 73e-3 * m,\n",
-    "                rho = 1.2 * kg/m**3,\n",
-    "                C_d = 0.3,\n",
-    "                angle = 45 * degree,\n",
-    "                velocity = 40 * m / s,\n",
-    "                t_end=t_end,\n",
-    "                dt=dt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params object with angle, velocity, x, y,\n",
-    "               diameter, duration, g, mass, rho, and C_d\n",
-    "               \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    angle, velocity = params.angle, params.velocity\n",
-    "    \n",
-    "    # convert angle to degrees\n",
-    "    theta = np.deg2rad(angle)\n",
-    "    \n",
-    "    # compute x and y components of velocity\n",
-    "    vx, vy = pol2cart(theta, velocity)\n",
-    "    \n",
-    "    # make the initial state\n",
-    "    R = Vector(params.x, params.y)\n",
-    "    V = Vector(vx, vy)\n",
-    "    init = State(R=R, V=V)\n",
-    "    \n",
-    "    # compute area from diameter\n",
-    "    diameter = params.diameter\n",
-    "    area = np.pi * (diameter/2)**2\n",
-    "    \n",
-    "    return System(params, init=init, area=area)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(V, system):\n",
-    "    \"\"\"Computes drag force in the opposite direction of `V`.\n",
-    "    \n",
-    "    V: velocity Vector\n",
-    "    system: System object with rho, C_d, area\n",
-    "    \n",
-    "    returns: Vector drag force\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    mag = rho * V.mag**2 * C_d * area / 2\n",
-    "    direction = -V.hat()\n",
-    "    f_drag = direction * mag\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the state variables.\n",
-    "    \n",
-    "    state: State (x, y, x velocity, y velocity)\n",
-    "    t: time\n",
-    "    system: System object with g, rho, C_d, area, mass\n",
-    "    \n",
-    "    returns: sequence (vx, vy, ax, ay)\n",
-    "    \"\"\"\n",
-    "    R, V = state\n",
-    "    mass, g = system.mass, system.g\n",
-    "    \n",
-    "    a_drag = drag_force(V, system) / mass\n",
-    "    a_grav = Vector(0, -g)\n",
-    "    \n",
-    "    A = a_grav + a_drag\n",
-    "    \n",
-    "    return V, A"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Stop when the y coordinate is 0.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: y coordinate\n",
-    "    \"\"\"\n",
-    "    R, V = state\n",
-    "    return R.y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optimal launch angle\n",
-    "\n",
-    "To find the launch angle that maximizes distance from home plate, we need a function that takes launch angle and returns range."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def range_func(angle, params):  \n",
-    "    \"\"\"Computes range for a given launch angle.\n",
-    "    \n",
-    "    angle: launch angle in degrees\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: distance in meters\n",
-    "    \"\"\"\n",
-    "    params = Params(params, angle=angle)\n",
-    "    system = make_system(params)\n",
-    "    results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "    x_dist = get_last_value(results.R).x\n",
-    "    print(angle, x_dist)\n",
-    "    return x_dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's test `range_func`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "range_func(45, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And sweep through a range of angles."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "angles = linspace(20, 80, 21)\n",
-    "sweep = SweepSeries()\n",
-    "\n",
-    "for angle in angles:\n",
-    "    x_dist = range_func(angle, params)\n",
-    "    sweep[angle] = x_dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting the `Sweep` object, it looks like the peak is between 40 and 45 degrees."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(sweep, color='C2')\n",
-    "decorate(xlabel='Launch angle (degree)',\n",
-    "         ylabel='Range (m)',\n",
-    "         title='Range as a function of launch angle',\n",
-    "         legend=False)\n",
-    "\n",
-    "savefig('figs/chap23-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use `maximize` to search for the peak efficiently."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bounds = [0, 90] * degree\n",
-    "res = maximize(range_func, bounds, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`res` is an `ModSimSeries` object with detailed results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`x` is the optimal angle and `fun` the optional range."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimal_angle = res.x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_x_dist = res.fun"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "Read the source code for `maximize` and `minimize_scalar`, below.\n",
-    "\n",
-    "Add a print statement to `range_func` that prints `angle`.  Then run `maximize` again so you can see how many times it calls `range_func` and what the arguments are."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_code(maximize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_code(minimize_scalar)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The Manny Ramirez problem\n",
-    "\n",
-    "Finally, let's solve the Manny Ramirez problem:\n",
-    "\n",
-    "*What is the minimum effort required to hit a home run in Fenway Park?*\n",
-    "\n",
-    "Fenway Park is a baseball stadium in Boston, Massachusetts.  One of its most famous features is the \"Green Monster\", which is a wall in left field that is unusually close to home plate, only 310 feet along the left field line.  To compensate for the short distance, the wall is unusually high, at 37 feet.\n",
-    "\n",
-    "Although the problem asks for a minimum, it is not an optimization problem.  Rather, we want to solve for the initial velocity that just barely gets the ball to the top of the wall, given that it is launched at the optimal angle.\n",
-    "\n",
-    "And we have to be careful about what we mean by \"optimal\".  For this problem, we don't want the longest range, we want the maximum height at the point where it reaches the wall.\n",
-    "\n",
-    "If you are ready to solve the problem on your own, go ahead.  Otherwise I will walk you through the process with an outline and some starter code.\n",
-    "\n",
-    "As a first step, write a function called `height_func` that takes a launch angle and a params as parameters, simulates the flights of a baseball, and returns the height of the baseball when it reaches a point 94.5 meters (310 feet) from home plate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Always test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your function with a launch angle of 45 degrees:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now use `maximize` to find the optimal angle.  Is it higher or lower than the angle that maximizes range?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With initial velocity 40 m/s and an optimal launch angle, the ball clears the Green Monster with a little room to spare.\n",
-    "\n",
-    "Which means we can get over the wall with a lower initial velocity."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Finding the minimum velocity\n",
-    "\n",
-    "Even though we are finding the \"minimum\" velocity, we are not really solving a minimization problem.  Rather, we want to find the velocity that makes the height at the wall exactly 11 m, given given that it's launched at the optimal angle.  And that's a job for `root_bisect`.\n",
-    "\n",
-    "Write an error function that takes a velocity and a `Params` object as parameters.  It should use `maximize` to find the highest possible height of the ball at the wall, for the given velocity.  Then it should return the difference between that optimal height and 11 meters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your error function before you call `root_bisect`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then use `root_bisect` to find the answer to the problem, the minimum velocity that gets the ball out of the park."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And just to check, run `error_func` with the value you found."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap24.ipynb b/notebooks/chap24.ipynb
deleted file mode 100644
index 6d7e6b52..00000000
--- a/notebooks/chap24.ipynb
+++ /dev/null
@@ -1,640 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 24\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Rolling paper\n",
-    "\n",
-    "We'll start by loading the units we need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radian = UNITS.radian\n",
-    "m = UNITS.meter\n",
-    "s = UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And creating a `Params` object with the system parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(Rmin = 0.02 * m,\n",
-    "                Rmax = 0.055 * m,\n",
-    "                L = 47 * m,\n",
-    "                omega = 10 * radian / s,\n",
-    "                t_end = 130 * s,\n",
-    "                dt = 1*s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function estimates the parameter `k`, which is the increase in the radius of the roll for each radian of rotation. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def estimate_k(params):\n",
-    "    \"\"\"Estimates the parameter `k`.\n",
-    "    \n",
-    "    params: Params with Rmin, Rmax, and L\n",
-    "    \n",
-    "    returns: k in meters per radian\n",
-    "    \"\"\"\n",
-    "    Rmin, Rmax, L = params.Rmin, params.Rmax, params.L\n",
-    "    \n",
-    "    Ravg = (Rmax + Rmin) / 2\n",
-    "    Cavg = 2 * pi * Ravg\n",
-    "    revs = L / Cavg\n",
-    "    rads = 2 * pi * revs\n",
-    "    k = (Rmax - Rmin) / rads\n",
-    "    return k"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As usual, `make_system` takes a `Params` object and returns a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params with Rmin, Rmax, and L\n",
-    "    \n",
-    "    returns: System with init, k, and ts\n",
-    "    \"\"\"\n",
-    "    init = State(theta = 0 * radian,\n",
-    "                 y = 0 * m,\n",
-    "                 r = params.Rmin)\n",
-    "    \n",
-    "    k = estimate_k(params)\n",
-    "\n",
-    "    return System(params, init=init, k=k)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `make_system`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can write a slope function based on the differential equations\n",
-    "\n",
-    "$\\omega = \\frac{d\\theta}{dt} = 10$\n",
-    "\n",
-    "$\\frac{dy}{dt} = r \\frac{d\\theta}{dt}$\n",
-    "\n",
-    "$\\frac{dr}{dt} = k \\frac{d\\theta}{dt}$\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object with theta, y, r\n",
-    "    t: time\n",
-    "    system: System object with r, k\n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    theta, y, r = state\n",
-    "    k, omega = system.k, system.omega\n",
-    "    \n",
-    "    dydt = r * omega\n",
-    "    drdt = k * omega\n",
-    "    \n",
-    "    return omega, dydt, drdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `slope_func`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll use an event function to stop when `y=L`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Detects when we've rolled length `L`.\n",
-    "    \n",
-    "    state: State object with theta, y, r\n",
-    "    t: time\n",
-    "    system: System object with L\n",
-    "    \n",
-    "    returns: difference between `y` and `L`\n",
-    "    \"\"\"\n",
-    "    theta, y, r = state\n",
-    "    \n",
-    "    return y - system.L"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "event_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And look at the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final value of `y` is 47 meters, as expected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unrolled = get_last_value(results.y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final value of radius is `R_max`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radius = get_last_value(results.r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The total number of rotations is close to 200, which seems plausible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radians = get_last_value(results.theta) \n",
-    "rotations = magnitude(radians) / 2 / np.pi"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The elapsed time is about 2 minutes, which is also plausible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_final = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `theta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_theta(results):\n",
-    "    plot(results.theta, color='C0', label='theta')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angle (rad)')\n",
-    "    \n",
-    "plot_theta(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `y`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_y(results):\n",
-    "    plot(results.y, color='C1', label='y')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Length (m)')\n",
-    "    \n",
-    "plot_y(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `r`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_r(results):\n",
-    "    plot(results.r, color='C2', label='r')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Radius (m)')\n",
-    "    \n",
-    "plot_r(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also see the relationship between `y` and `r`, which I derive analytically in the book."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results.r, results.y, color='C3')\n",
-    "\n",
-    "decorate(xlabel='Radius (m)',\n",
-    "         ylabel='Length (m)',\n",
-    "         legend=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the figure from the book."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_three(results):\n",
-    "    subplot(3, 1, 1)\n",
-    "    plot_theta(results)\n",
-    "\n",
-    "    subplot(3, 1, 2)\n",
-    "    plot_y(results)\n",
-    "\n",
-    "    subplot(3, 1, 3)\n",
-    "    plot_r(results)\n",
-    "\n",
-    "plot_three(results)\n",
-    "savefig('figs/chap24-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Animation\n",
-    "\n",
-    "Here's a draw function that animates the results using `matplotlib` patches."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib.patches import Circle\n",
-    "from matplotlib.patches import Arrow\n",
-    "\n",
-    "def draw_func(state, t):\n",
-    "    # get radius in mm\n",
-    "    theta, y, r = state\n",
-    "    radius = r.magnitude * 1000\n",
-    "    \n",
-    "    # draw a circle with\n",
-    "    circle = Circle([0, 0], radius, fill=True)\n",
-    "    plt.gca().add_patch(circle)\n",
-    "    \n",
-    "    # draw an arrow to show rotation\n",
-    "    dx, dy = pol2cart(theta, radius)\n",
-    "    arrow = Arrow(0, 0, dx, dy)\n",
-    "    plt.gca().add_patch(arrow)\n",
-    "\n",
-    "    # make the aspect ratio 1\n",
-    "    plt.axis('equal')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "animate(results, draw_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Run the simulation again with a smaller step size to smooth out the animation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "**Exercise:** Since we keep `omega` constant, the linear velocity of the paper increases with radius.  Use `gradient` to estimate the derivative of `results.y`.  What is the peak linear velocity?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(dydt, label='dydt')\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Linear velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now suppose the peak velocity is the limit; that is, we can't move the paper any faster than that.\n",
-    "\n",
-    "Nevertheless, we might be able to speed up the process by keeping the linear velocity at the maximum all the time.\n",
-    "\n",
-    "Write a slope function that keeps the linear velocity, `dydt`, constant, and computes the angular velocity, `omega`, accordingly.\n",
-    "\n",
-    "Run the simulation and see how much faster we could finish rolling the paper."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/chap25.ipynb b/notebooks/chap25.ipynb
deleted file mode 100644
index 5c74611e..00000000
--- a/notebooks/chap25.ipynb
+++ /dev/null
@@ -1,944 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 25\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Teapots and Turntables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Tables in Chinese restaurants often have a rotating tray or turntable that makes it easy for customers to share dishes.  These turntables are supported by low-friction bearings that allow them to turn easily and glide.  However, they can be heavy, especially when they are loaded with food, so they have a high moment of inertia.\n",
-    "\n",
-    "Suppose I am sitting at a table with a pot of tea on the turntable directly in front of me, and the person sitting directly opposite asks me to pass the tea.  I push on the edge of the turntable with 1 Newton of force until it has turned 0.5 radians, then let go.  The turntable glides until it comes to a stop 1.5 radians from the starting position.  How much force should I apply for a second push so the teapot glides to a stop directly opposite me?\n",
-    "\n",
-    "The following figure shows the scenario, where `F` is the force I apply to the turntable at the perimeter, perpendicular to the moment arm, `r`, and `tau` is the resulting torque.  The blue circle near the bottom is the teapot.\n",
-    "\n",
-    "![](diagrams/teapot.png)\n",
-    "\n",
-    "We'll answer this question in these steps:\n",
-    "\n",
-    "1.  We'll use the results from the first push to estimate the coefficient of friction for the turntable.\n",
-    "\n",
-    "2.  We'll use that coefficient of friction to estimate the force needed to rotate the turntable through the remaining angle.\n",
-    "\n",
-    "Our simulation will use the following parameters:\n",
-    "\n",
-    "1.  The radius of the turntable is 0.5 meters, and its weight is 7 kg.\n",
-    "\n",
-    "2.  The teapot weights 0.3 kg, and it sits 0.4 meters from the center of the turntable.\n",
-    "\n",
-    "As usual, I'll get units from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radian = UNITS.radian\n",
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And store the parameters in a `Params` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(radius_disk=0.5*m,\n",
-    "                mass_disk=7*kg,\n",
-    "                radius_pot=0.4*m,\n",
-    "                mass_pot=0.3*kg,\n",
-    "                force=1*N,\n",
-    "                torque_friction=0.2*N*m,\n",
-    "                theta_end=0.5*radian,\n",
-    "                t_end=20*s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system` creates the initial state, `init`, and computes the total moment of inertia for the turntable and the teapot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    mass_disk, mass_pot = params.mass_disk, params.mass_pot\n",
-    "    radius_disk, radius_pot = params.radius_disk, params.radius_pot\n",
-    "    \n",
-    "    init = State(theta=0*radian, omega=0*radian/s)\n",
-    "    \n",
-    "    I_disk = mass_disk * radius_disk**2 / 2\n",
-    "    I_pot = mass_pot * radius_pot**2\n",
-    "    \n",
-    "    return System(params, init=init, I=I_disk+I_pot)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the `System` object we'll use for the first phase of the simulation, while I am pushing the turntable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system1 = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Simulation\n",
-    "\n",
-    "When I stop pushing on the turntable, the angular acceleration changes abruptly.  We could implement the slope function with an `if` statement that checks the value of `theta` and sets `force` accordingly.  And for a coarse model like this one, that might be fine.  But we will get more accurate results if we simulate the system in two phases:\n",
-    "\n",
-    "1.  During the first phase, force is constant, and we run until `theta` is 0.5 radians.\n",
-    "\n",
-    "2.  During the second phase, force is 0, and we run until `omega` is 0.\n",
-    "\n",
-    "Then we can combine the results of the two phases into a single `TimeFrame`.\n",
-    "\n",
-    "Here's the slope function we'll use:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    theta, omega = state\n",
-    "    radius_disk, force = system.radius_disk, system.force\n",
-    "    torque_friction, I = system.torque_friction, system.I\n",
-    "    \n",
-    "    torque = radius_disk * force - torque_friction\n",
-    "    alpha = torque / I\n",
-    "    \n",
-    "    return omega, alpha "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, we'll test the slope function before running the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system1.init, 0, system1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an event function that stops the simulation when `theta` reaches `theta_end`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func1(state, t, system):\n",
-    "    \"\"\"Stops when theta reaches theta_end.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: difference from target\n",
-    "    \"\"\"\n",
-    "    theta, omega = state\n",
-    "    return theta - system.theta_end "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "event_func1(system1.init, 0, system1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the first phase."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results1, details1 = run_ode_solver(system1, slope_func, events=event_func1)\n",
-    "details1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And look at the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results1.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Phase 2\n",
-    "\n",
-    "Before we run the second phase, we have to extract the final time and state of the first phase."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = results1.last_label() * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And make an initial `State` object for Phase 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "init2 = results1.last_row()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And a new `System` object with zero force."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system2 = System(system1, t_0=t_0, init=init2, force=0*N)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an event function that stops when angular velocity is 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func2(state, t, system):\n",
-    "    \"\"\"Stops when omega is 0.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: omega\n",
-    "    \"\"\"\n",
-    "    theta, omega = state\n",
-    "    return omega"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "event_func2(system2.init, 0, system2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the second phase."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system2.init, system2.t_0, system2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2, details2 = run_ode_solver(system2, slope_func, events=event_func2)\n",
-    "details2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And check the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pandas provides `combine_first`, which combines `results1` and `results2`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = results1.combine_first(results2)\n",
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can plot `theta` for both phases."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_theta(results):\n",
-    "    plot(results.theta, label='theta')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angle (rad)')\n",
-    "    \n",
-    "plot_theta(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And `omega`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_omega(results):\n",
-    "    plot(results.omega, label='omega', color='C1')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angular velocity (rad/s)')\n",
-    "    \n",
-    "plot_omega(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subplot(2, 1, 1)\n",
-    "plot_theta(results)\n",
-    "subplot(2, 1, 2)\n",
-    "plot_omega(results)\n",
-    "savefig('figs/chap25-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Estimating friction\n",
-    "\n",
-    "Let's take the code from the previous section and wrap it in a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_two_phases(force, torque_friction, params):\n",
-    "    \"\"\"Run both phases.\n",
-    "    \n",
-    "    force: force applied to the turntable\n",
-    "    torque_friction: friction due to torque\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: TimeFrame of simulation results\n",
-    "    \"\"\"\n",
-    "    # put the specified parameters into the Params object\n",
-    "    params = Params(params, force=force, torque_friction=torque_friction)\n",
-    "\n",
-    "    # run phase 1\n",
-    "    system1 = make_system(params)\n",
-    "    results1, details1 = run_ode_solver(system1, slope_func, \n",
-    "                                          events=event_func1)\n",
-    "\n",
-    "    # get the final state from phase 1\n",
-    "    t_0 = results1.last_label() * s\n",
-    "    init2 = results1.last_row()\n",
-    "    \n",
-    "    # run phase 2\n",
-    "    system2 = System(system1, t_0=t_0, init=init2, force=0*N)\n",
-    "    results2, details2 = run_ode_solver(system2, slope_func, \n",
-    "                                        events=event_func2)\n",
-    "    \n",
-    "    # combine and return the results\n",
-    "    results = results1.combine_first(results2)\n",
-    "    return TimeFrame(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's test it with the same parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "force = 1*N\n",
-    "torque_friction = 0.2*N*m\n",
-    "results = run_two_phases(force, torque_friction, params)\n",
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And check the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "theta_final = results.last_row().theta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the error function we'll use with `root_bisect`.\n",
-    "\n",
-    "It takes a hypothetical value for `torque_friction` and returns the difference between `theta_final` and the observed duration of the first push, 1.5 radian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func1(torque_friction, params):\n",
-    "    \"\"\"Error function for root_scalar.\n",
-    "    \n",
-    "    torque_friction: hypothetical value\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: offset from target value\n",
-    "    \"\"\"\n",
-    "    force = 1 * N\n",
-    "    results = run_two_phases(force, torque_friction, params)\n",
-    "    theta_final = results.last_row().theta\n",
-    "    print(torque_friction, theta_final)\n",
-    "    return theta_final - 1.5 * radian"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing the error function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "guess1 = 0.1*N*m\n",
-    "error_func1(guess1, params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "guess2 = 0.3*N*m\n",
-    "error_func1(guess2, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And running `root_scalar`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = root_bisect(error_func1, [guess1, guess2], params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is the coefficient of friction that yields a total rotation of 1.5 radian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torque_friction = res.root"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a test run with the estimated value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "force = 1 * N\n",
-    "results = run_two_phases(force, torque_friction, params)\n",
-    "theta_final = get_last_value(results.theta)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looks good."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Animation\n",
-    "\n",
-    "\n",
-    "Here's a draw function we can use to animate the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib.patches import Circle\n",
-    "from matplotlib.patches import Arrow\n",
-    "\n",
-    "def draw_func(state, t):\n",
-    "    theta, omega = state\n",
-    "    \n",
-    "    # draw a circle for the table\n",
-    "    radius_disk = magnitude(params.radius_disk)\n",
-    "    circle1 = Circle([0, 0], radius_disk)\n",
-    "    plt.gca().add_patch(circle1)\n",
-    "    \n",
-    "    # draw a circle for the teapot\n",
-    "    radius_pot = magnitude(params.radius_pot)\n",
-    "    center = pol2cart(theta, radius_pot)\n",
-    "    circle2 = Circle(center, 0.05, color='C1')\n",
-    "    plt.gca().add_patch(circle2)\n",
-    "\n",
-    "    # make the aspect ratio 1\n",
-    "    plt.axis('equal')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = results.first_row()\n",
-    "draw_func(state, 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "animate(results, draw_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "### Exercises\n",
-    "\n",
-    "Now finish off the example by estimating the force that delivers the teapot to the desired position.\n",
-    "\n",
-    "Write an error function that takes `force` and `params` and returns the offset from the desired angle."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test the error function with `force=1`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And run `root_bisect` to find the desired force."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "force = res.root\n",
-    "results = run_two_phases(force, torque_friction, params)\n",
-    "theta_final = get_last_value(results.theta)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "remaining_angle = np.pi - 1.5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Now suppose my friend pours 0.1 kg of tea and puts the teapot back on the turntable at distance 0.3 meters from the center.  If I ask for the tea back, how much force should they apply, over an arc of 0.5 radians, to make the teapot glide to a stop back in front of me?  You can assume that torque due to friction is proportional to the total mass of the teapot and the turntable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/data/DataOWall.csv b/notebooks/data/DataOWall.csv
deleted file mode 100644
index 040eabf1..00000000
--- a/notebooks/data/DataOWall.csv
+++ /dev/null
@@ -1,867 +0,0 @@
-,Q_in,Q_out,T_int,T_ext
-,W/m2,W/m2,Deg C,Deg C
-,Avg,Avg,Avg,Avg
-2014-10-05 16:30:00,10.994,6.84,16.92,14.68
-2014-10-05 16:35:00,10.952,6.012,16.92,14.69
-2014-10-05 16:40:00,10.882,7.04,16.93,14.66
-2014-10-05 16:45:00,10.798,8.88,16.93,14.59
-2014-10-05 16:50:00,10.756,10.491,16.94,14.5
-2014-10-05 16:55:00,10.924,11.058,16.93,14.43
-2014-10-05 17:00:00,10.728,12.025,16.93,14.33
-2014-10-05 17:05:00,10.616,12.224,16.94,14.28
-2014-10-05 17:10:00,10.616,13.497,16.94,14.2
-2014-10-05 17:15:00,10.518,15.046,16.95,14.09
-2014-10-05 17:20:00,10.392,16.472,16.96,13.96
-2014-10-05 17:25:00,10.35,16.564,16.96,13.89
-2014-10-05 17:30:00,10.322,16.917,16.95,13.79
-2014-10-05 17:35:00,10.196,16.089,16.96,13.77
-2014-10-05 17:40:00,10.098,16.196,16.95,13.7
-2014-10-05 17:45:00,10.014,16.396,16.95,13.64
-2014-10-05 17:50:00,9.972,17.055,16.96,13.58
-2014-10-05 17:55:00,9.958,17.04,16.96,13.5
-2014-10-05 18:00:00,9.888,17.423,16.96,13.44
-2014-10-05 18:05:00,9.846,17.04,16.95,13.39
-2014-10-05 18:10:00,9.776,16.917,16.96,13.38
-2014-10-05 18:15:00,9.65,17.577,16.97,13.32
-2014-10-05 18:20:00,9.734,16.104,16.98,13.37
-2014-10-05 18:25:00,9.776,17.347,16.98,13.28
-2014-10-05 18:30:00,9.65,15.153,16.98,13.29
-2014-10-05 18:35:00,9.482,14.985,16.98,13.33
-2014-10-05 18:40:00,9.538,15.752,16.97,13.24
-2014-10-05 18:45:00,9.44,14.923,16.98,13.25
-2014-10-05 18:50:00,9.426,14.908,16.99,13.24
-2014-10-05 18:55:00,9.356,15.598,16.99,13.19
-2014-10-05 19:00:00,9.062,15.997,16.97,13.12
-2014-10-05 19:05:00,9.02,15.537,16.98,13.11
-2014-10-05 19:10:00,8.978,15.552,16.96,13.07
-2014-10-05 19:15:00,9.02,14.969,16.97,13.06
-2014-10-05 19:20:00,8.908,14.294,16.98,13.06
-2014-10-05 19:25:00,8.922,14.371,16.99,13.07
-2014-10-05 19:30:00,8.796,14.678,16.98,13.04
-2014-10-05 19:35:00,8.697,15.199,16.98,13
-2014-10-05 19:40:00,8.599,15.936,16.97,12.95
-2014-10-05 19:45:00,8.585,15.859,16.97,12.91
-2014-10-05 19:50:00,8.599,16.273,16.97,12.88
-2014-10-05 19:55:00,8.515,16.595,16.98,12.84
-2014-10-05 20:00:00,8.459,17.485,16.97,12.77
-2014-10-05 20:05:00,8.403,18.957,16.97,12.73
-2014-10-05 20:10:00,8.375,20.046,16.96,12.64
-2014-10-05 20:15:00,8.333,21.288,16.96,12.57
-2014-10-05 20:20:00,8.291,20.537,16.97,12.52
-2014-10-05 20:25:00,8.179,20.506,16.98,12.47
-2014-10-05 20:30:00,8.151,20.736,16.96,12.42
-2014-10-05 20:35:00,8.109,20.859,16.96,12.38
-2014-10-05 20:40:00,8.221,20.997,16.97,12.36
-2014-10-05 20:45:00,8.109,20.061,16.96,12.31
-2014-10-05 20:50:00,7.969,19.724,16.96,12.32
-2014-10-05 20:55:00,7.969,19.279,16.96,12.31
-2014-10-05 21:00:00,7.997,19.156,16.95,12.29
-2014-10-05 21:05:00,7.927,19.172,16.96,12.28
-2014-10-05 21:10:00,7.857,19.064,16.95,12.24
-2014-10-05 21:15:00,7.871,19.724,16.95,12.2
-2014-10-05 21:20:00,7.773,20.629,16.95,12.15
-2014-10-05 21:25:00,7.717,21.488,16.96,12.11
-2014-10-05 21:30:00,7.717,22.285,16.94,12.03
-2014-10-05 21:35:00,7.703,22.807,16.94,11.97
-2014-10-05 21:40:00,7.703,23.236,16.94,11.93
-2014-10-05 21:45:00,7.549,23.543,16.93,11.85
-2014-10-05 21:50:00,7.577,23.42,16.93,11.81
-2014-10-05 21:55:00,7.535,23.267,16.93,11.77
-2014-10-05 22:00:00,7.465,23.512,16.92,11.72
-2014-10-05 22:05:00,7.465,23.62,16.92,11.69
-2014-10-05 22:10:00,7.409,23.221,16.91,11.65
-2014-10-05 22:15:00,7.423,23.236,16.91,11.62
-2014-10-05 22:20:00,7.339,23.359,16.92,11.6
-2014-10-05 22:25:00,7.297,23.451,16.92,11.58
-2014-10-05 22:30:00,7.283,22.975,16.9,11.53
-2014-10-05 22:35:00,7.353,22.837,16.9,11.51
-2014-10-05 22:40:00,7.255,23.175,16.89,11.48
-2014-10-05 22:45:00,7.185,23.42,16.9,11.44
-2014-10-05 22:50:00,7.185,23.865,16.89,11.4
-2014-10-05 22:55:00,7.087,24.463,16.88,11.34
-2014-10-05 23:00:00,7.003,24.049,16.87,11.32
-2014-10-05 23:05:00,6.989,23.451,16.87,11.3
-2014-10-05 23:10:00,6.933,23.252,16.87,11.29
-2014-10-05 23:15:00,6.835,22.914,16.86,11.28
-2014-10-05 23:20:00,6.821,23.313,16.85,11.25
-2014-10-05 23:25:00,6.807,23.083,16.85,11.23
-2014-10-05 23:30:00,6.751,23.451,16.84,11.2
-2014-10-05 23:35:00,6.765,22.469,16.84,11.19
-2014-10-05 23:40:00,6.709,21.61,16.82,11.19
-2014-10-05 23:45:00,6.723,21.273,16.82,11.2
-2014-10-05 23:50:00,6.695,21.242,16.83,11.2
-2014-10-05 23:55:00,6.555,21.81,16.81,11.18
-2014-10-06 00:00:00,6.569,21.304,16.8,11.16
-2014-10-06 00:05:00,6.513,21.104,16.8,11.16
-2014-10-06 00:10:00,6.457,21.181,16.79,11.15
-2014-10-06 00:15:00,6.527,20.399,16.78,11.16
-2014-10-06 00:20:00,6.457,20.092,16.77,11.16
-2014-10-06 00:25:00,6.485,19.555,16.78,11.19
-2014-10-06 00:30:00,6.499,19.248,16.77,11.2
-2014-10-06 00:35:00,6.555,17.592,16.76,11.23
-2014-10-06 00:40:00,6.527,17.362,16.76,11.26
-2014-10-06 00:45:00,6.471,16.595,16.75,11.29
-2014-10-06 00:50:00,6.485,16.503,16.73,11.29
-2014-10-06 00:55:00,6.457,16.871,16.73,11.31
-2014-10-06 01:00:00,6.485,16.35,16.73,11.32
-2014-10-06 01:05:00,6.499,16.074,16.73,11.34
-2014-10-06 01:10:00,6.457,16.549,16.72,11.36
-2014-10-06 01:15:00,6.513,16.227,16.69,11.33
-2014-10-06 01:20:00,6.513,16.58,16.71,11.35
-2014-10-06 01:25:00,6.471,17.607,16.7,11.32
-2014-10-06 01:30:00,6.555,17.546,16.7,11.31
-2014-10-06 01:35:00,6.653,17.408,16.67,11.28
-2014-10-06 01:40:00,6.625,18.206,16.66,11.26
-2014-10-06 01:45:00,6.639,18.62,16.67,11.23
-2014-10-06 01:50:00,6.611,19.571,16.66,11.19
-2014-10-06 01:55:00,6.541,20.015,16.66,11.16
-2014-10-06 02:00:00,6.513,19.172,16.65,11.17
-2014-10-06 02:05:00,6.429,18.436,16.63,11.16
-2014-10-06 02:10:00,6.513,18.067,16.62,11.16
-2014-10-06 02:15:00,6.569,18.16,16.63,11.17
-2014-10-06 02:20:00,6.527,18.19,16.61,11.15
-2014-10-06 02:25:00,6.513,18.113,16.59,11.14
-2014-10-06 02:30:00,6.555,17.408,16.61,11.17
-2014-10-06 02:35:00,6.583,16.549,16.6,11.19
-2014-10-06 02:40:00,6.611,16.12,16.61,11.23
-2014-10-06 02:45:00,6.611,16.549,16.6,11.22
-2014-10-06 02:50:00,6.681,17.285,16.58,11.19
-2014-10-06 02:55:00,6.667,17.592,16.57,11.16
-2014-10-06 03:00:00,6.639,17.301,16.56,11.14
-2014-10-06 03:05:00,6.653,16.702,16.55,11.14
-2014-10-06 03:10:00,6.625,15.859,16.56,11.19
-2014-10-06 03:15:00,6.611,14.678,16.54,11.22
-2014-10-06 03:20:00,6.639,13.252,16.52,11.25
-2014-10-06 03:25:00,6.723,12.117,16.52,11.31
-2014-10-06 03:30:00,6.639,11.028,16.52,11.38
-2014-10-06 03:35:00,6.667,10.506,16.52,11.44
-2014-10-06 03:40:00,6.667,10.66,16.53,11.5
-2014-10-06 03:45:00,6.653,10.199,16.53,11.54
-2014-10-06 03:50:00,6.681,9.709,16.5,11.56
-2014-10-06 03:55:00,6.639,9.187,16.5,11.61
-2014-10-06 04:00:00,6.653,9.018,16.5,11.65
-2014-10-06 04:05:00,6.709,9.08,16.5,11.68
-2014-10-06 04:10:00,6.765,8.819,16.48,11.69
-2014-10-06 04:15:00,6.709,9.202,16.46,11.71
-2014-10-06 04:20:00,6.737,8.834,16.46,11.73
-2014-10-06 04:25:00,6.765,8.497,16.46,11.77
-2014-10-06 04:30:00,6.779,8.298,16.46,11.8
-2014-10-06 04:35:00,6.821,7.776,16.45,11.83
-2014-10-06 04:40:00,6.779,7.531,16.44,11.87
-2014-10-06 04:45:00,6.849,6.825,16.44,11.92
-2014-10-06 04:50:00,6.863,6.534,16.43,11.96
-2014-10-06 04:55:00,6.905,6.028,16.43,12.02
-2014-10-06 05:00:00,8.179,6.212,16.45,12.03
-2014-10-06 05:05:00,7.801,6.104,16.44,12.04
-2014-10-06 05:10:00,7.885,5.475,16.44,12.08
-2014-10-06 05:15:00,8.039,5.368,16.44,12.11
-2014-10-06 05:20:00,8.095,5.261,16.45,12.15
-2014-10-06 05:25:00,7.773,5.123,16.43,12.17
-2014-10-06 05:30:00,7.689,5.322,16.41,12.17
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-2014-10-06 12:05:00,23.347,12.899,18.12,13.17
-2014-10-06 12:10:00,23.571,10.844,18.16,13.13
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diff --git a/notebooks/data/USASAC2018-Report-30-2017-Activities-Page11.pdf b/notebooks/data/USASAC2018-Report-30-2017-Activities-Page11.pdf
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diff --git a/notebooks/data/World_population_estimates.csv b/notebooks/data/World_population_estimates.csv
deleted file mode 100644
index ed7de057..00000000
--- a/notebooks/data/World_population_estimates.csv
+++ /dev/null
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-1992,5456136278,,5494899570.0,5435722000.0,,,,,,,
-1993,5538268316,,5578865109.0,5518127000.0,,,,,,,
-1994,5618682132,,5661086346.0,5599396000.0,,,,,,,
-1995,5699202985,5760000000.0,5741822412.0,5681575000.0,,,,,,,
-1996,5779440593,,5821016750.0,5762212000.0,,,,,,,
-1997,5857972543,5840000000.0,5898688337.0,5842122000.0,,,,,,,
-1998,5935213248,,5975303657.0,5921366000.0,,,,,,,
-1999,6012074922,,6051478010.0,5999622000.0,,,,,,,
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-2002,6242016348,6215000000.0,6280853817.0,6231704000.0,,,,,,,
-2003,6318590956,6314000000.0,6357991749.0,6308364000.0,,,,,,,
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-2007,6629913759,6625000000.0,6673105937.0,6616689000.0,,,,,,,
-2008,6709049780,6705000000.0,6753649228.0,6694832000.0,,,,,,,
-2009,6788214394,6809972000.0,6834721933.0,6764086000.0,,,,,,,
-2010,6858584755,6892319000.0,6916183482.0,,,,,,,,
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-2013,7092128094,7136796000.0,7162119434.0,,,,,,,,
-2014,7169968185,7238184000.0,7243784000.0,,,,,,,,
-2015,7247892788,7336435000.0,7349472000.0,,,,,,,,
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-<h1 id="firstHeading" class="firstHeading" lang="en">World population estimates</h1>			<div id="bodyContent" class="mw-body-content">
-				<div id="siteSub" class="noprint">From Wikipedia, the free encyclopedia</div>				<div id="contentSub"></div>
-				<div id="jump-to-nav"></div>				<a class="mw-jump-link" href="#mw-head">Jump to navigation</a>
-				<a class="mw-jump-link" href="#p-search">Jump to search</a>
-				<div id="mw-content-text" lang="en" dir="ltr" class="mw-content-ltr"><div class="mw-parser-output"><p>This article lists estimates of <a href="/wiki/World_population" title="World population">world population</a>, as well as projections of <a href="/wiki/Projections_of_population_growth" title="Projections of population growth">future developments</a>. In summary, estimates for the progression of world population since the late medieval period are in the following ranges:</p>
-<table class="wikitable">
-<tr>
-<td>year</td>
-<td>1400</td>
-<td>1500</td>
-<td>1600</td>
-<td>1700</td>
-<td>1800</td>
-<td>1900</td>
-<td>2000</td>
-<td>2100<sup id="cite_ref-UNProspect2017_1-0" class="reference"><a href="#cite_note-UNProspect2017-1">[1]</a></sup></td>
-</tr>
-<tr>
-<td>population<br />
-(in billions)</td>
-<td>0.35–0.40</td>
-<td>0.42–0.50</td>
-<td>0.50–0.58</td>
-<td>0.60–0.68</td>
-<td>0.89–0.98</td>
-<td>1.56–1.71</td>
-<td>6.06–6.15</td>
-<td>ca. 10–13</td>
-</tr>
-<tr>
-<td>growth p.a.<sup id="cite_ref-2" class="reference"><a href="#cite_note-2">[2]</a></sup></td>
-<td>&lt;0%</td>
-<td>&lt;0.12%</td>
-<td>0.15%–0.3%</td>
-<td>0.1%–0.15%</td>
-<td>0.3%–0.5%</td>
-<td>0.5%–0.6%</td>
-<td>1.3%–1.4%</td>
-<td>0.7%–0.8%</td>
-</tr>
-</table>
-<p>Estimates for pre-modern times are necessarily fraught with great uncertainties, and few of the published estimates have <a href="/wiki/Confidence_interval" title="Confidence interval">confidence intervals</a>; in the absence of a straightforward means to assess the error of such estimates, a rough idea of expert consensus can be gained by comparing the values given in independent publications. Population estimates cannot be considered accurate to more than two decimal digits; for example, world population for the year 2012 was estimated at 7.02, 7.06 and 7.08 billion by the <a href="/wiki/United_States_Census_Bureau" title="United States Census Bureau">United States Census Bureau</a>, the <a href="/wiki/Population_Reference_Bureau" title="Population Reference Bureau">Population Reference Bureau</a> and the <a href="/wiki/United_Nations_Department_of_Economic_and_Social_Affairs" title="United Nations Department of Economic and Social Affairs">United Nations Department of Economic and Social Affairs</a>, respectively, corresponding to a spread of estimates of the order of 0.8%.</p>
-<p></p>
-<div id="toc" class="toc">
-<div class="toctitle" lang="en" dir="ltr">
-<h2>Contents</h2>
-</div>
-<ul>
-<li class="toclevel-1 tocsection-1"><a href="#Deep_prehistory"><span class="tocnumber">1</span> <span class="toctext">Deep prehistory</span></a></li>
-<li class="toclevel-1 tocsection-2"><a href="#Historical_population"><span class="tocnumber">2</span> <span class="toctext">Historical population</span></a>
-<ul>
-<li class="toclevel-2 tocsection-3"><a href="#Before_1950"><span class="tocnumber">2.1</span> <span class="toctext">Before 1950</span></a></li>
-<li class="toclevel-2 tocsection-4"><a href="#1950_to_present"><span class="tocnumber">2.2</span> <span class="toctext">1950 to present</span></a></li>
-</ul>
-</li>
-<li class="toclevel-1 tocsection-5"><a href="#Projections"><span class="tocnumber">3</span> <span class="toctext">Projections</span></a></li>
-<li class="toclevel-1 tocsection-6"><a href="#By_world_region"><span class="tocnumber">4</span> <span class="toctext">By world region</span></a></li>
-<li class="toclevel-1"><a href="#See_also"><span class="tocnumber">5</span> <span class="toctext">See also</span></a></li>
-<li class="toclevel-1 tocsection-7"><a href="#References"><span class="tocnumber">6</span> <span class="toctext">References</span></a></li>
-<li class="toclevel-1 tocsection-8"><a href="#Further_reading"><span class="tocnumber">7</span> <span class="toctext">Further reading</span></a></li>
-<li class="toclevel-1 tocsection-9"><a href="#External_links"><span class="tocnumber">8</span> <span class="toctext">External links</span></a></li>
-</ul>
-</div>
-<p></p>
-<h2><span class="mw-headline" id="Deep_prehistory">Deep prehistory</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=1" title="Edit section: Deep prehistory">edit</a><span class="mw-editsection-bracket">]</span></span></h2>
-<div role="note" class="hatnote navigation-not-searchable">Further information: <a href="/wiki/Paleodemography" title="Paleodemography">Paleodemography</a></div>
-<div class="thumb tright">
-<div class="thumbinner" style="width:352px;"><a href="/wiki/File:Population_curve.svg" class="image"><img alt="" src="//upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Population_curve.svg/350px-Population_curve.svg.png" width="350" height="212" class="thumbimage" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Population_curve.svg/525px-Population_curve.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Population_curve.svg/700px-Population_curve.svg.png 2x" data-file-width="547" data-file-height="332" /></a>
-<div class="thumbcaption">
-<div class="magnify"><a href="/wiki/File:Population_curve.svg" class="internal" title="Enlarge"></a></div>
-Graph of world population over the past 12,000 years (<a href="/wiki/Holocene" title="Holocene">Holocene</a>).</div>
-</div>
-</div>
-<p>As a general rule, the confidence of estimates on historical world population decreases for the more distant past. Robust population data only exists for the last two or three centuries. Until the late 18th century, few governments had ever performed an accurate census. In many early attempts, such as in <a href="/wiki/Census_in_Egypt" class="mw-redirect" title="Census in Egypt">Ancient Egypt</a> and the <a href="/wiki/Achaemenid_Empire" title="Achaemenid Empire">Persian Empire</a>, the focus was on counting merely a subset of the population for purposes of taxation or military service.<sup id="cite_ref-3" class="reference"><a href="#cite_note-3">[3]</a></sup> Published estimates for the 1st century ("<a href="/wiki/AD_1" title="AD 1">AD 1</a>") suggest an uncertainty of the order of 50% (estimates range between 150 and 330 million). Some estimates extend their timeline into deep prehistory, to "<a href="/wiki/10,000_BC" class="mw-redirect" title="10,000 BC">10,000 BC</a>", i.e. the <a href="/wiki/Holocene" title="Holocene">early Holocene</a>, when world population estimates range roughly between one and ten million (with an uncertainty of up to an order of magnitude).<sup id="cite_ref-4" class="reference"><a href="#cite_note-4">[4]</a></sup><sup id="cite_ref-5" class="reference"><a href="#cite_note-5">[5]</a></sup></p>
-<p>Estimates for yet deeper prehistory, into the <a href="/wiki/Paleolithic" title="Paleolithic">Paleolithic</a>, are of a different nature. At this time human populations consisted entirely of non-sedentary <a href="/wiki/Hunter-gatherer" title="Hunter-gatherer">hunter-gatherer</a> populations, with <a href="/wiki/Anatomically_modern_humans" class="mw-redirect" title="Anatomically modern humans">anatomically modern humans</a> existing alongside <a href="/wiki/Archaic_humans" title="Archaic humans">archaic human varieties</a>, some of which are still ancestral to the modern human population due to <a href="/wiki/Archaic_human_admixture_with_modern_humans" title="Archaic human admixture with modern humans">interbreeding with modern humans</a> during the <a href="/wiki/Upper_Paleolithic" title="Upper Paleolithic">Upper Paleolithic</a>. Estimates of the size of these populations are a topic of <a href="/wiki/Paleoanthropology" title="Paleoanthropology">paleoanthropology</a>. A late human <a href="/wiki/Population_bottleneck" title="Population bottleneck">population bottleneck</a> is postulated by some scholars at approximately 70,000 years ago, during the <a href="/wiki/Toba_catastrophe_theory" title="Toba catastrophe theory">Toba catastrophe</a>, when <i><a href="/wiki/Homo_sapiens" title="Homo sapiens">Homo sapiens</a></i> population may have dropped to as low as between 1,000 and 10,000 individuals.<sup id="cite_ref-ambrose1998_6-0" class="reference"><a href="#cite_note-ambrose1998-6">[6]</a></sup><sup id="cite_ref-robock2009_7-0" class="reference"><a href="#cite_note-robock2009-7">[7]</a></sup> For the time of speciation of <i><a href="/wiki/Homo_sapiens" title="Homo sapiens">Homo sapiens</a></i>, some 200,000 years ago, an <a href="/wiki/Effective_population_size" title="Effective population size">effective population size</a> of the order of 10,000 to 30,000 individuals has been estimated, with an actual "census population" of early <i>Homo sapiens</i> of roughly 100,000 to 300,000 individuals.<sup id="cite_ref-8" class="reference"><a href="#cite_note-8">[8]</a></sup></p>
-<p><span id="Cumulative_population"></span><span id="Number_of_humans_who_have_ever_lived"></span> The question of "how many people have ever lived?" or "what percentage of people who have ever lived are alive today" can be traced to the 1970s.<sup id="cite_ref-9" class="reference"><a href="#cite_note-9">[9]</a></sup> The more dramatic phrasing of "the living outnumber the dead" also dates to the 1970s, a time of <a href="/wiki/Population_explosion" class="mw-redirect" title="Population explosion">population explosion</a> and growing fears of <a href="/wiki/Human_overpopulation" title="Human overpopulation">human overpopulation</a> in the wake of <a href="/wiki/Decolonization" title="Decolonization">decolonization</a> and before the adoption of China's <a href="/wiki/One-child_policy" title="One-child policy">One-child policy</a>. The claim that "the living outnumber the dead" was never accurate (although it may be roughly accurate if only <a href="/wiki/Effective_population_size" title="Effective population size">ancestral population</a> is considered). <a href="/wiki/Arthur_C._Clarke" title="Arthur C. Clarke">Arthur C. Clarke</a> in <i><a href="/wiki/2001:_A_Space_Odyssey" title="2001: A Space Odyssey">2001: A Space Odyssey</a></i> (1968) has the claim that "Behind every man now alive stand 30 ghosts, for that is the ratio by which the dead outnumber the living", which was roughly accurate at the time of writing.<sup id="cite_ref-10" class="reference"><a href="#cite_note-10">[10]</a></sup></p>
-<p>Estimates of the "total number of people who have ever lived" is 107.6 billion as of 2011.<sup id="cite_ref-11" class="reference"><a href="#cite_note-11">[11]</a></sup> The answer naturally depends on the definition of "people", i.e. is only <i><a href="/wiki/Homo_sapiens" title="Homo sapiens">Homo sapiens</a></i> to be counted, or all of genus <i><a href="/wiki/Homo" title="Homo">Homo</a></i>, but due to the small population sizes in the Lower Paleolithic, the order of magnitude of the estimate is not affected by the choice of cut-off date substantially more than by the uncertainty of estimates throughout the Neolithic to Iron Age.<sup id="cite_ref-12" class="reference"><a href="#cite_note-12">[12]</a></sup> The estimate is more crucially affected by the estimate of <a href="/wiki/Infant_mortality" title="Infant mortality">infant mortalitys</a> vs. <a href="/wiki/Stillborn" class="mw-redirect" title="Stillborn">stillborn</a> infants, due to the very high infant mortality throughout the pre-modern period. An estimate on the "total number of people who have ever lived" as of 1995 was calculated by Haub (1995) at "about 105 billion births since the dawn of the human race" with a cut-off date at 50,000 BC (beginning of the Upper Paleolithic), and an inclusion of a high infant mortality rate throughout pre-modern history.<sup id="cite_ref-13" class="reference"><a href="#cite_note-13">[13]</a></sup></p>
-<h2><span class="mw-headline" id="Historical_population">Historical population</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=2" title="Edit section: Historical population">edit</a><span class="mw-editsection-bracket">]</span></span></h2>
-<h3><span class="mw-headline" id="Before_1950">Before 1950</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=3" title="Edit section: Before 1950">edit</a><span class="mw-editsection-bracket">]</span></span></h3>
-<p>The following table uses <a href="/wiki/Astronomical_year_numbering" title="Astronomical year numbering">astronomical year numbering</a> for dates, negative numbers corresponding roughly to the corresponding year BC (i.e. -10000 = 10,001 BC, etc.). The table starts counting around the <a href="/wiki/Late_Glacial_Maximum" class="mw-redirect" title="Late Glacial Maximum">Late Glacial Maximum</a> period, in which ice retreated and humans started to spread into the northern hemisphere.</p>
-<p>From the beginning of the <a href="/wiki/Early_modern_period" title="Early modern period">early modern period</a> until the 20th century, world population has been characterized by a faster-than-exponential growth. For the period of <a href="/wiki/Classical_Antiquity" class="mw-redirect" title="Classical Antiquity">Classical Antiquity</a> to the <a href="/wiki/Middle_Ages" title="Middle Ages">Middle Ages</a>, roughly 500 BC to AD 1500, there has also been a general tendency of growth (estimated at roughly a factor 4 to 5 over the 2,000 year period), but not strictily <a href="/wiki/Monotonic_function" title="Monotonic function">monotonic</a>: A noticeable dip in world population is assumed due to the <a href="/wiki/Black_Death" title="Black Death">Black Death</a> in the mid-14th century.<sup id="cite_ref-Haub1650_14-0" class="reference"><a href="#cite_note-Haub1650-14">[14]</a></sup></p>
-<table class="wikitable" style="text-align:right;font-size:small;">
-<tr>
-<th>Year</th>
-<th><a href="/wiki/Population_Reference_Bureau" title="Population Reference Bureau">Population Reference Bureau</a>
-<p>(1973–2016)<sup id="cite_ref-Population_Reference_Bureau_15-0" class="reference"><a href="#cite_note-Population_Reference_Bureau-15">[15]</a></sup></p>
-</th>
-<th><a href="/wiki/United_Nations_Department_of_Economic_and_Social_Affairs" title="United Nations Department of Economic and Social Affairs">United Nations Department of Economic and Social Affairs</a>
-<p>(2015)<sup id="cite_ref-The_World_at_Six_Billion,_1999_16-0" class="reference"><a href="#cite_note-The_World_at_Six_Billion,_1999-16">[16]</a></sup></p>
-</th>
-<th><a href="/wiki/Angus_Maddison" title="Angus Maddison">Maddison</a>
-<p>(2008)<sup id="cite_ref-www.ggdc.net/maddison/Historical_Statistics_17-0" class="reference"><a href="#cite_note-www.ggdc.net/maddison/Historical_Statistics-17">[17]</a></sup></p>
-</th>
-<th>HYDE
-<p>(2010)<sup class="noprint Inline-Template Template-Fact" style="white-space:nowrap;">[<i><a href="/wiki/Wikipedia:Citation_needed" title="Wikipedia:Citation needed"><span title="This claim needs references to reliable sources. (January 2017)">citation needed</span></a></i>]</sup></p>
-</th>
-<th><a href="/wiki/John_Tanton" title="John Tanton">Tanton</a>
-<p>(1994)<sup id="cite_ref-thesocialcontract.com_18-0" class="reference"><a href="#cite_note-thesocialcontract.com-18">[18]</a></sup></p>
-</th>
-<th>Biraben
-<p>(1980)<sup id="cite_ref-French_1980,_pp._1_19-0" class="reference"><a href="#cite_note-French_1980,_pp._1-19">[19]</a></sup></p>
-</th>
-<th><a href="/wiki/Colin_McEvedy" title="Colin McEvedy">McEvedy</a> &amp;
-<p>Jones (1978)<sup id="cite_ref-Richard_Jones_1978_20-0" class="reference"><a href="#cite_note-Richard_Jones_1978-20">[20]</a></sup></p>
-</th>
-<th>Thomlinson
-<p>(1975)<sup id="cite_ref-ReferenceA_21-0" class="reference"><a href="#cite_note-ReferenceA-21">[21]</a></sup></p>
-</th>
-<th>Durand
-<p>(1974)<sup id="cite_ref-John_D._Durand_1974_22-0" class="reference"><a href="#cite_note-John_D._Durand_1974-22">[22]</a></sup></p>
-</th>
-<th><a href="/wiki/Colin_Clark_(economist)" title="Colin Clark (economist)">Clark</a>
-<p>(1967)<sup id="cite_ref-Colin_Clark_1967_23-0" class="reference"><a href="#cite_note-Colin_Clark_1967-23">[23]</a></sup></p>
-</th>
-</tr>
-<tr>
-<td>-10000</td>
-<td></td>
-<td></td>
-<td></td>
-<td>2M<sup id="cite_ref-mnp.nl_24-0" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
-<td></td>
-<td></td>
-<td>4M</td>
-<td>1–10M</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>-9000</td>
-<td></td>
-<td></td>
-<td></td>
-<td>4M</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>-8000</td>
-<td>5M</td>
-<td></td>
-<td></td>
-<td>5M</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td>5–10M</td>
-<td></td>
-</tr>
-<tr>
-<td>-7000</td>
-<td></td>
-<td></td>
-<td></td>
-<td>8M</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>-6000</td>
-<td></td>
-<td></td>
-<td></td>
-<td>11M</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>-5000</td>
-<td></td>
-<td></td>
-<td></td>
-<td>18M<sup id="cite_ref-mnp.nl_24-1" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
-<td></td>
-<td></td>
-<td>5M</td>
-<td>5–20M</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>-4000</td>
-<td></td>
-<td></td>
-<td></td>
-<td>28M</td>
-<td></td>
-<td></td>
-<td>7M</td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>-3000</td>
-<td></td>
-<td></td>
-<td></td>
-<td>45M</td>
-<td></td>
-<td></td>
-<td>14M</td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>-2000</td>
-<td></td>
-<td></td>
-<td></td>
-<td>72M</td>
-<td></td>
-<td></td>
-<td>27M</td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>-1000</td>
-<td></td>
-<td></td>
-<td></td>
-<td>115M</td>
-<td></td>
-<td></td>
-<td>50M</td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>-200</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td>150M</td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1</td>
-<td>300M<sup id="cite_ref-25" class="reference"><a href="#cite_note-25">[25]</a></sup></td>
-<td>300M</td>
-<td>231M<sup id="cite_ref-26" class="reference"><a href="#cite_note-26">[26]</a></sup></td>
-<td>188M<sup id="cite_ref-mnp.nl_24-2" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
-<td>150M</td>
-<td>255M</td>
-<td>170M</td>
-<td>200M</td>
-<td>270–330M</td>
-<td>256M<sup id="cite_ref-27" class="reference"><a href="#cite_note-27">[27]</a></sup></td>
-</tr>
-<tr>
-<td>100</td>
-<td></td>
-<td></td>
-<td></td>
-<td>195M</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>200</td>
-<td></td>
-<td></td>
-<td></td>
-<td>202M</td>
-<td></td>
-<td>256M</td>
-<td>190M</td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>300</td>
-<td></td>
-<td></td>
-<td></td>
-<td>205M</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>350</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td>254M</td>
-</tr>
-<tr>
-<td>400</td>
-<td></td>
-<td></td>
-<td></td>
-<td>209M</td>
-<td></td>
-<td>206M</td>
-<td>190M</td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>500</td>
-<td></td>
-<td></td>
-<td></td>
-<td>210M<sup id="cite_ref-mnp.nl_24-3" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
-<td></td>
-<td>206M</td>
-<td>190M</td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>600</td>
-<td></td>
-<td></td>
-<td></td>
-<td>213M</td>
-<td></td>
-<td>206M</td>
-<td>200M</td>
-<td></td>
-<td></td>
-<td>237M</td>
-</tr>
-<tr>
-<td>700</td>
-<td></td>
-<td></td>
-<td></td>
-<td>226M</td>
-<td></td>
-<td>207M</td>
-<td>210M</td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>800</td>
-<td></td>
-<td></td>
-<td></td>
-<td>240M</td>
-<td></td>
-<td>224M</td>
-<td>220M</td>
-<td></td>
-<td></td>
-<td>261M</td>
-</tr>
-<tr>
-<td>900</td>
-<td></td>
-<td></td>
-<td></td>
-<td>269M</td>
-<td></td>
-<td>226M</td>
-<td>240M</td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1000</td>
-<td></td>
-<td>310M</td>
-<td>267M</td>
-<td>295M<sup id="cite_ref-mnp.nl_24-4" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
-<td></td>
-<td>254M</td>
-<td>265M</td>
-<td></td>
-<td>275–345M</td>
-<td>280M</td>
-</tr>
-<tr>
-<td>1100</td>
-<td></td>
-<td></td>
-<td></td>
-<td>353M</td>
-<td></td>
-<td>301M</td>
-<td>320M</td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1200</td>
-<td></td>
-<td></td>
-<td></td>
-<td>393M</td>
-<td></td>
-<td>400M</td>
-<td>360M</td>
-<td></td>
-<td></td>
-<td>384M</td>
-</tr>
-<tr>
-<td>1250</td>
-<td></td>
-<td>400M</td>
-<td></td>
-<td></td>
-<td></td>
-<td>416M</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1300</td>
-<td></td>
-<td></td>
-<td></td>
-<td>392M</td>
-<td>300M</td>
-<td>432M</td>
-<td>360M</td>
-<td>400M</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1340</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td>443M</td>
-<td></td>
-<td></td>
-<td></td>
-<td>378M</td>
-</tr>
-<tr>
-<td>1400</td>
-<td></td>
-<td></td>
-<td></td>
-<td>390M</td>
-<td></td>
-<td>374M</td>
-<td>350M</td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1500</td>
-<td></td>
-<td>500M</td>
-<td>438M</td>
-<td>461M<sup id="cite_ref-mnp.nl_24-5" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
-<td>460M</td>
-<td>425M</td>
-<td></td>
-<td></td>
-<td>440–540M</td>
-<td>427M</td>
-</tr>
-<tr>
-<td>1600</td>
-<td></td>
-<td></td>
-<td>556M</td>
-<td>554M<sup id="cite_ref-mnp.nl_24-6" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
-<td></td>
-<td>579M</td>
-<td>545M</td>
-<td></td>
-<td></td>
-<td>498M</td>
-</tr>
-<tr>
-<td>1650</td>
-<td>(&lt;700M)<sup id="cite_ref-Haub1650_14-1" class="reference"><a href="#cite_note-Haub1650-14">[14]</a></sup></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td>545M</td>
-<td>500M</td>
-<td></td>
-<td>516M</td>
-</tr>
-<tr>
-<td>1700</td>
-<td></td>
-<td></td>
-<td>603M</td>
-<td>603M<sup id="cite_ref-mnp.nl_24-7" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
-<td>600M</td>
-<td>679M</td>
-<td>610M</td>
-<td>600M</td>
-<td></td>
-<td>641M</td>
-</tr>
-<tr>
-<td>1750</td>
-<td></td>
-<td>791M</td>
-<td></td>
-<td>814M</td>
-<td></td>
-<td>770M</td>
-<td>720M</td>
-<td>700M</td>
-<td>735–805M</td>
-<td>731M</td>
-</tr>
-<tr>
-<td>1800</td>
-<td>1,000M</td>
-<td>978M</td>
-<td></td>
-<td>989M<sup id="cite_ref-mnp.nl_24-8" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
-<td>900M</td>
-<td>954M</td>
-<td>900M</td>
-<td>900M</td>
-<td></td>
-<td>890M</td>
-</tr>
-<tr>
-<td>1820</td>
-<td></td>
-<td></td>
-<td>1,042M</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1850</td>
-<td>1,265M</td>
-<td>1,262M</td>
-<td></td>
-<td>1,263M</td>
-<td></td>
-<td>1,241M</td>
-<td>1,200M</td>
-<td>1,200M</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1870</td>
-<td></td>
-<td></td>
-<td>1,276M</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1875</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td>1,325M</td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1900</td>
-<td>1,656M</td>
-<td>1,650M</td>
-<td>1,563M</td>
-<td>1,654M<sup id="cite_ref-mnp.nl_24-9" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></td>
-<td>1,600M</td>
-<td>1,633M</td>
-<td>1,625M</td>
-<td>1,600M</td>
-<td>1,650–1,710M</td>
-<td>1,668M</td>
-</tr>
-<tr>
-<td>1910</td>
-<td></td>
-<td>1,750M</td>
-<td></td>
-<td>1,777M</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1913</td>
-<td></td>
-<td></td>
-<td>1,793M</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1920</td>
-<td></td>
-<td>1,860M</td>
-<td>1,863M</td>
-<td>1,912M</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td>1,968M</td>
-</tr>
-<tr>
-<td>1925</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td>2,000M</td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1930</td>
-<td></td>
-<td>2,070M</td>
-<td></td>
-<td>2,092M</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td>2,145M</td>
-</tr>
-<tr>
-<td>1940</td>
-<td></td>
-<td>2,300M</td>
-<td>2,299M</td>
-<td>2,307M</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td>2,340M</td>
-</tr>
-</table>
-<h3><span class="mw-headline" id="1950_to_present">1950 to present</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=4" title="Edit section: 1950 to present">edit</a><span class="mw-editsection-bracket">]</span></span></h3>
-<p>For times after <a href="/wiki/World_War_II" title="World War II">World War II</a>, demographic data of some accuracy becomes available for a significant number of countries, and population estimates are often given as grand totals of numbers (typically given by country) of widely diverging accuracies. Some sources give these numbers rounded to the nearest million or the nearest thousand, while others give them without any rounding.</p>
-<p>Taking these numbers at face value would be <a href="/wiki/False_precision" title="False precision">false precision</a>; in spite of being stated to four, seven or even ten digits, they should not be interpreted as accurate to more than three digits at best (estimates by the <a href="/wiki/United_States_Census_Bureau" title="United States Census Bureau">United States Census Bureau</a> and by the <a href="/wiki/United_Nations_Department_of_Economic_and_Social_Affairs" title="United Nations Department of Economic and Social Affairs">United Nations</a> differ by about 0.5%–1.5%).</p>
-<table class="wikitable" style="text-align:right;font-size:small;">
-<tr>
-<th>Year</th>
-<th><a href="/wiki/United_States_Census_Bureau" title="United States Census Bureau">United States Census Bureau</a>
-<p>(2017)<sup id="cite_ref-census.gov_28-0" class="reference"><a href="#cite_note-census.gov-28">[28]</a></sup></p>
-</th>
-<th><a href="/wiki/Population_Reference_Bureau" title="Population Reference Bureau">Population Reference Bureau</a>
-<p>(1973–2016)<sup id="cite_ref-Population_Reference_Bureau_15-1" class="reference"><a href="#cite_note-Population_Reference_Bureau-15">[15]</a></sup></p>
-</th>
-<th><a href="/wiki/United_Nations_Department_of_Economic_and_Social_Affairs" title="United Nations Department of Economic and Social Affairs">United Nations Department of Economic and Social Affairs</a>
-<p>(2015)<sup id="cite_ref-The_World_at_Six_Billion,_1999_16-1" class="reference"><a href="#cite_note-The_World_at_Six_Billion,_1999-16">[16]</a></sup></p>
-</th>
-<th><a href="/wiki/Angus_Maddison" title="Angus Maddison">Maddison</a>
-<p>(2008)<sup id="cite_ref-www.ggdc.net/maddison/Historical_Statistics_17-1" class="reference"><a href="#cite_note-www.ggdc.net/maddison/Historical_Statistics-17">[17]</a></sup></p>
-</th>
-<th>HYDE
-<p>(2007)<sup id="cite_ref-mnp.nl_24-10" class="reference"><a href="#cite_note-mnp.nl-24">[24]</a></sup></p>
-</th>
-<th><a href="/wiki/John_Tanton" title="John Tanton">Tanton</a>
-<p>(1994)<sup id="cite_ref-thesocialcontract.com_18-1" class="reference"><a href="#cite_note-thesocialcontract.com-18">[18]</a></sup></p>
-</th>
-<th>Biraben
-<p>(1980)<sup id="cite_ref-French_1980,_pp._1_19-1" class="reference"><a href="#cite_note-French_1980,_pp._1-19">[19]</a></sup></p>
-</th>
-<th><a href="/wiki/Colin_McEvedy" title="Colin McEvedy">McEvedy</a> &amp;
-<p>Jones (1978)<sup id="cite_ref-Richard_Jones_1978_20-1" class="reference"><a href="#cite_note-Richard_Jones_1978-20">[20]</a></sup></p>
-</th>
-<th>Thomlinson
-<p>(1975)<sup id="cite_ref-ReferenceA_21-1" class="reference"><a href="#cite_note-ReferenceA-21">[21]</a></sup></p>
-</th>
-<th>Durand
-<p>(1974)<sup id="cite_ref-John_D._Durand_1974_22-1" class="reference"><a href="#cite_note-John_D._Durand_1974-22">[22]</a></sup></p>
-</th>
-<th><a href="/wiki/Colin_Clark_(economist)" title="Colin Clark (economist)">Clark</a>
-<p>(1967)<sup id="cite_ref-Colin_Clark_1967_23-1" class="reference"><a href="#cite_note-Colin_Clark_1967-23">[23]</a></sup></p>
-</th>
-</tr>
-<tr>
-<td>1950</td>
-<td>2,557,628,654</td>
-<td>2,516,000,000</td>
-<td>2,525,149,000</td>
-<td>2,544,000,000</td>
-<td>2,527,960,000</td>
-<td>2,400,000,000</td>
-<td>2,527,000,000</td>
-<td>2,500,000,000</td>
-<td>2,400,000,000</td>
-<td></td>
-<td>2,486,000,000</td>
-</tr>
-<tr>
-<td>1951</td>
-<td>2,594,939,877</td>
-<td></td>
-<td>2,572,850,917</td>
-<td>2,571,663,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1952</td>
-<td>2,636,772,306</td>
-<td></td>
-<td>2,619,292,068</td>
-<td>2,617,949,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1953</td>
-<td>2,682,053,389</td>
-<td></td>
-<td>2,665,865,392</td>
-<td>2,665,959,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1954</td>
-<td>2,730,228,104</td>
-<td></td>
-<td>2,713,172,027</td>
-<td>2,716,927,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1955</td>
-<td>2,782,098,943</td>
-<td></td>
-<td>2,761,650,981</td>
-<td>2,769,074,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1956</td>
-<td>2,835,299,673</td>
-<td></td>
-<td>2,811,572,031</td>
-<td>2,822,502,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1957</td>
-<td>2,891,349,717</td>
-<td></td>
-<td>2,863,042,795</td>
-<td>2,879,934,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1958</td>
-<td>2,948,137,248</td>
-<td></td>
-<td>2,916,030,167</td>
-<td>2,939,254,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1959</td>
-<td>3,000,716,593</td>
-<td></td>
-<td>2,970,395,814</td>
-<td>2,995,909,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1960</td>
-<td>3,043,001,508</td>
-<td></td>
-<td>3,026,002,942</td>
-<td>3,041,507,000</td>
-<td>3,042,000,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1961</td>
-<td>3,083,966,929</td>
-<td></td>
-<td>3,082,830,266</td>
-<td>3,082,161,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1962</td>
-<td>3,140,093,217</td>
-<td></td>
-<td>3,141,071,531</td>
-<td>3,135,787,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td>3,036,000,000</td>
-</tr>
-<tr>
-<td>1963</td>
-<td>3,209,827,882</td>
-<td></td>
-<td>3,201,178,277</td>
-<td>3,201,354,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1964</td>
-<td>3,281,201,306</td>
-<td></td>
-<td>3,263,738,832</td>
-<td>3,266,477,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1965</td>
-<td>3,350,425,793</td>
-<td></td>
-<td>3,329,122,479</td>
-<td>3,333,138,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1966</td>
-<td>3,420,677,923</td>
-<td></td>
-<td>3,397,475,247</td>
-<td>3,402,224,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td>3,288,000,000</td>
-</tr>
-<tr>
-<td>1967</td>
-<td>3,490,333,715</td>
-<td></td>
-<td>3,468,521,724</td>
-<td>3,471,464,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1968</td>
-<td>3,562,313,822</td>
-<td></td>
-<td>3,541,674,891</td>
-<td>3,543,086,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1969</td>
-<td>3,637,159,050</td>
-<td></td>
-<td>3,616,108,749</td>
-<td>3,615,743,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1970</td>
-<td>3,712,697,742</td>
-<td></td>
-<td>3,691,172,616</td>
-<td>3,691,157,000</td>
-<td>3,710,000,000</td>
-<td></td>
-<td>3,637,000,000</td>
-<td></td>
-<td>3,600,000,000</td>
-<td>3,600,000,000– 3,700,000,000</td>
-<td>3,632,000,000</td>
-</tr>
-<tr>
-<td>1971</td>
-<td>3,790,326,948</td>
-<td></td>
-<td>3,766,754,345</td>
-<td>3,769,818,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1972</td>
-<td>3,866,568,653</td>
-<td></td>
-<td>3,842,873,611</td>
-<td>3,846,499,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1973</td>
-<td>3,942,096,442</td>
-<td></td>
-<td>3,919,182,332</td>
-<td>3,922,793,000</td>
-<td>3,923,000,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td>3,860,000,000</td>
-</tr>
-<tr>
-<td>1974</td>
-<td>4,016,608,813</td>
-<td></td>
-<td>3,995,304,922</td>
-<td>3,997,677,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1975</td>
-<td>4,089,083,233</td>
-<td></td>
-<td>4,071,020,434</td>
-<td>4,070,671,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td>3,900,000,000</td>
-<td>4,000,000,000</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1976</td>
-<td>4,160,185,010</td>
-<td></td>
-<td>4,146,135,850</td>
-<td>4,141,445,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1977</td>
-<td>4,232,084,578</td>
-<td></td>
-<td>4,220,816,737</td>
-<td>4,213,539,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1978</td>
-<td>4,304,105,753</td>
-<td></td>
-<td>4,295,664,825</td>
-<td>4,286,317,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1979</td>
-<td>4,379,013,942</td>
-<td></td>
-<td>4,371,527,871</td>
-<td>4,363,144,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1980</td>
-<td>4,451,362,735</td>
-<td></td>
-<td>4,449,048,798</td>
-<td>4,439,529,000</td>
-<td>4,461,000,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1981</td>
-<td>4,534,410,125</td>
-<td></td>
-<td>4,528,234,634</td>
-<td>4,514,838,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1982</td>
-<td>4,614,566,561</td>
-<td></td>
-<td>4,608,962,418</td>
-<td>4,587,307,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1983</td>
-<td>4,695,736,743</td>
-<td></td>
-<td>4,691,559,840</td>
-<td>4,676,388,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1984</td>
-<td>4,774,569,391</td>
-<td></td>
-<td>4,776,392,828</td>
-<td>4,756,521,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1985</td>
-<td>4,856,462,699</td>
-<td></td>
-<td>4,863,601,517</td>
-<td>4,837,719,000</td>
-<td></td>
-<td>5,000,000,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1986</td>
-<td>4,940,571,232</td>
-<td></td>
-<td>4,953,376,710</td>
-<td>4,920,968,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1987</td>
-<td>5,027,200,492</td>
-<td></td>
-<td>5,045,315,871</td>
-<td>5,006,672,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1988</td>
-<td>5,114,557,167</td>
-<td></td>
-<td>5,138,214,688</td>
-<td>5,093,306,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1989</td>
-<td>5,201,440,110</td>
-<td></td>
-<td>5,230,000,000</td>
-<td>5,180,540,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1990</td>
-<td>5,288,955,934</td>
-<td></td>
-<td>5,320,816,667</td>
-<td>5,269,029,000</td>
-<td>5,308,000,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1991</td>
-<td>5,371,585,922</td>
-<td></td>
-<td>5,408,908,724</td>
-<td>5,351,922,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1992</td>
-<td>5,456,136,278</td>
-<td></td>
-<td>5,494,899,570</td>
-<td>5,435,722,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1993</td>
-<td>5,538,268,316</td>
-<td></td>
-<td>5,578,865,109</td>
-<td>5,518,127,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1994</td>
-<td>5,618,682,132</td>
-<td></td>
-<td>5,661,086,346</td>
-<td>5,599,396,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1995</td>
-<td>5,699,202,985</td>
-<td>5,760,000,000</td>
-<td>5,741,822,412</td>
-<td>5,681,575,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1996</td>
-<td>5,779,440,593</td>
-<td></td>
-<td>5,821,016,750</td>
-<td>5,762,212,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1997</td>
-<td>5,857,972,543</td>
-<td>5,840,000,000</td>
-<td>5,898,688,337</td>
-<td>5,842,122,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1998</td>
-<td>5,935,213,248</td>
-<td></td>
-<td>5,975,303,657</td>
-<td>5,921,366,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>1999</td>
-<td>6,012,074,922</td>
-<td></td>
-<td>6,051,478,010</td>
-<td>5,999,622,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2000</td>
-<td>6,088,571,383</td>
-<td>6,067,000,000</td>
-<td>6,127,700,428</td>
-<td>6,076,558,000</td>
-<td>6,145,000,000</td>
-<td></td>
-<td></td>
-<td>5,750,000,000</td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2001</td>
-<td>6,165,219,247</td>
-<td>6,137,000,000</td>
-<td>6,204,147,026</td>
-<td>6,154,791,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2002</td>
-<td>6,242,016,348</td>
-<td>6,215,000,000</td>
-<td>6,280,853,817</td>
-<td>6,231,704,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2003</td>
-<td>6,318,590,956</td>
-<td>6,314,000,000</td>
-<td>6,357,991,749</td>
-<td>6,308,364,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2004</td>
-<td>6,395,699,509</td>
-<td>6,396,000,000</td>
-<td>6,435,705,595</td>
-<td>6,374,056,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2005</td>
-<td>6,473,044,732</td>
-<td>6,477,000,000</td>
-<td>6,514,094,605</td>
-<td>6,462,987,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2006</td>
-<td>6,551,263,534</td>
-<td>6,555,000,000</td>
-<td>6,593,227,977</td>
-<td>6,540,214,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2007</td>
-<td>6,629,913,759</td>
-<td>6,625,000,000</td>
-<td>6,673,105,937</td>
-<td>6,616,689,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2008</td>
-<td>6,709,049,780</td>
-<td>6,705,000,000</td>
-<td>6,753,649,228</td>
-<td>6,694,832,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2009</td>
-<td>6,788,214,394</td>
-<td>6,809,972,000</td>
-<td>6,834,721,933</td>
-<td>6,764,086,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2010</td>
-<td>6,858,584,755</td>
-<td>6,892,319,000</td>
-<td>6,916,183,482</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2011</td>
-<td>6,935,999,491</td>
-<td>6,986,951,000</td>
-<td>6,997,998,760</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2012</td>
-<td>7,013,871,313</td>
-<td>7,057,075,000</td>
-<td>7,080,072,417</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2013</td>
-<td>7,092,128,094</td>
-<td>7,136,796,000</td>
-<td>7,162,119,434</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2014</td>
-<td>7,169,968,185</td>
-<td>7,238,184,000</td>
-<td>7,243,784,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2015</td>
-<td>7,247,892,788</td>
-<td>7,336,435,000</td>
-<td>7,349,472,000</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td>2016</td>
-<td>7,325,996,709</td>
-<td>7,418,151,841</td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-</table>
-<h2><span class="mw-headline" id="Projections">Projections</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=5" title="Edit section: Projections">edit</a><span class="mw-editsection-bracket">]</span></span></h2>
-<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Projections_of_population_growth" title="Projections of population growth">Projections of population growth</a></div>
-<div class="thumb tright">
-<div class="thumbinner" style="width:302px;"><a href="/wiki/File:World-Population-1800-2100.svg" class="image"><img alt="" src="//upload.wikimedia.org/wikipedia/commons/thumb/5/56/World-Population-1800-2100.svg/300px-World-Population-1800-2100.svg.png" width="300" height="306" class="thumbimage" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/5/56/World-Population-1800-2100.svg/450px-World-Population-1800-2100.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/56/World-Population-1800-2100.svg/600px-World-Population-1800-2100.svg.png 2x" data-file-width="1003" data-file-height="1024" /></a>
-<div class="thumbcaption">
-<div class="magnify"><a href="/wiki/File:World-Population-1800-2100.svg" class="internal" title="Enlarge"></a></div>
-World population estimates from 1800 to 2100, based on "high", "medium" and "low" <a rel="nofollow" class="external text" href="http://esa.un.org/wpp/unpp/panel_population.htm">United Nations projections in 2010</a> (colored red, orange and green) and <a rel="nofollow" class="external text" href="https://www.census.gov/ipc/www/worldhis.html">US Census Bureau historical estimates</a> (in black). Actual recorded population figures (as of 2010) are colored in blue. According to the highest estimate, the world population may rise to 16 <a href="/wiki/Thousand_million" class="mw-redirect" title="Thousand million">billion</a> by 2100; according to the lowest estimate, it may decline to 6 billion.</div>
-</div>
-</div>
-<p>As of 2015<sup class="plainlinks noexcerpt noprint asof-tag update" style="display:none;"><a class="external text" href="//en.wikipedia.org/w/index.php?title=World_population_estimates&amp;action=edit">[update]</a></sup>, the population of the world is projected to reach 8 billion in 2025, and 9 billion by about 2040/42. Kapitza (1996) estimated an asymptotic limit of population growth of 14 billion, 90% of which (12.6 billion) expected to be reached by 2135.<sup id="cite_ref-29" class="reference"><a href="#cite_note-29">[29]</a></sup></p>
-<p>Reasonable predictions of population development are possible for the next 30 years or so, representing the period of fertility of the children alive today. Projections of population reaching more than one generation into the future are highly speculative: Thus, the United Nations Department of Economic and Social Affairs report of 2004 projected the world population to peak at 9.22 billion in 2075 and then stabilise at a value close to 9 billion;<sup id="cite_ref-un-esa1_30-0" class="reference"><a href="#cite_note-un-esa1-30">[30]</a></sup> By contrast, a 2014 projection by the United Nations Population Division predicts a population close to 11 billion by 2100 without any declining trend in the foreseeable future.<sup id="cite_ref-Science_31-0" class="reference"><a href="#cite_note-Science-31">[31]</a></sup> On the other hand, a conservative scenario published in 2012 assumes that a maximum of 8 billion will be reached before 2040.<sup id="cite_ref-32" class="reference"><a href="#cite_note-32">[32]</a></sup></p>
-<p>The following table shows projections of world population for the 21st century.</p>
-<table class="wikitable" style="text-align:right;font-size:small;">
-<tr>
-<th>Year</th>
-<th><a href="/wiki/United_States_Census_Bureau" title="United States Census Bureau">United States Census Bureau</a>
-<p>(2015)<sup id="cite_ref-census.gov_28-1" class="reference"><a href="#cite_note-census.gov-28">[28]</a></sup></p>
-</th>
-<th><a href="/wiki/Population_Reference_Bureau" title="Population Reference Bureau">Population Reference Bureau</a>
-<p>(1973-2015)<sup id="cite_ref-Population_Reference_Bureau_15-2" class="reference"><a href="#cite_note-Population_Reference_Bureau-15">[15]</a></sup></p>
-</th>
-<th><a href="/wiki/United_Nations_Department_of_Economic_and_Social_Affairs" title="United Nations Department of Economic and Social Affairs">United Nations Department of Economic and Social Affairs</a>
-<p>(2015)<sup id="cite_ref-The_World_at_Six_Billion,_1999_16-2" class="reference"><a href="#cite_note-The_World_at_Six_Billion,_1999-16">[16]</a></sup></p>
-</th>
-</tr>
-<tr>
-<td><span id="2016"></span>2016</td>
-<td>7,334,771,614</td>
-<td></td>
-<td>7,432,663,280</td>
-</tr>
-<tr>
-<td><span id="2017"></span>2017</td>
-<td>7,412,778,971</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2018"></span>2018</td>
-<td>7,490,427,640</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2019"></span>2019</td>
-<td>7,567,402,977</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2020"></span>2020</td>
-<td>7,643,402,123</td>
-<td></td>
-<td>7,758,157,000</td>
-</tr>
-<tr>
-<td><span id="2021"></span>2021</td>
-<td>7,718,256,830</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2022"></span>2022</td>
-<td>7,792,021,317</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2023"></span>2023</td>
-<td>7,864,725,370</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2024"></span>2024</td>
-<td>7,936,271,554</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2025"></span>2025</td>
-<td>8,006,580,553</td>
-<td>8,000,000,000</td>
-<td>8,141,661,000</td>
-</tr>
-<tr>
-<td><span id="2026"></span>2026</td>
-<td>8,075,716,000</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2027"></span>2027</td>
-<td>8,143,729,466</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2028"></span>2028</td>
-<td>8,210,559,895</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2029"></span>2029</td>
-<td>8,276,190,519</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2030"></span>2030</td>
-<td>8,340,606,590</td>
-<td>8,505,000,000</td>
-<td>8,500,766,000</td>
-</tr>
-<tr>
-<td><span id="2031"></span>2031</td>
-<td>8,403,880,343</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2032"></span>2032</td>
-<td>8,466,094,022</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2033"></span>2033</td>
-<td>8,527,246,205</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2034"></span>2034</td>
-<td>8,587,325,154</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2035"></span>2035</td>
-<td>8,646,304,704</td>
-<td></td>
-<td>8,838,908,000</td>
-</tr>
-<tr>
-<td><span id="2036"></span>2036</td>
-<td>8,704,239,274</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2037"></span>2037</td>
-<td>8,761,189,197</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2038"></span>2038</td>
-<td>8,817,138,785</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2039"></span>2039</td>
-<td>8,872,066,537</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2040"></span>2040</td>
-<td>8,925,949,679</td>
-<td></td>
-<td>9,157,234,000</td>
-</tr>
-<tr>
-<td><span id="2041"></span>2041</td>
-<td>8,978,822,945</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2042"></span>2042</td>
-<td>9,030,723,366</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2043"></span>2043</td>
-<td>9,081,617,002</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2044"></span>2044</td>
-<td>9,131,462,326</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2045"></span>2045</td>
-<td>9,180,225,214</td>
-<td></td>
-<td>9,453,892,000</td>
-</tr>
-<tr>
-<td><span id="2046"></span>2046</td>
-<td>9,227,935,007</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2047"></span>2047</td>
-<td>9,274,616,811</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2048"></span>2048</td>
-<td>9,320,232,984</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2049"></span>2049</td>
-<td>9,364,750,182</td>
-<td></td>
-<td></td>
-</tr>
-<tr>
-<td><span id="2050"></span>2050</td>
-<td>9,408,141,302</td>
-<td>9,804,000,000</td>
-<td>9,725,148,000</td>
-</tr>
-<tr>
-<td><span id="2055"></span>2055</td>
-<td></td>
-<td></td>
-<td>9,968,809,000</td>
-</tr>
-<tr>
-<td><span id="2060"></span>2060</td>
-<td></td>
-<td></td>
-<td>10,184,290,000</td>
-</tr>
-<tr>
-<td><span id="2065"></span>2065</td>
-<td></td>
-<td></td>
-<td>10,375,719,000</td>
-</tr>
-<tr>
-<td><span id="2070"></span>2070</td>
-<td></td>
-<td></td>
-<td>10,547,989,000</td>
-</tr>
-<tr>
-<td><span id="2075"></span>2075</td>
-<td></td>
-<td></td>
-<td>10,701,653,000</td>
-</tr>
-<tr>
-<td><span id="2080"></span>2080</td>
-<td></td>
-<td></td>
-<td>10,836,635,000</td>
-</tr>
-<tr>
-<td><span id="2085"></span>2085</td>
-<td></td>
-<td></td>
-<td>10,953,525,000</td>
-</tr>
-<tr>
-<td><span id="2090"></span>2090</td>
-<td></td>
-<td></td>
-<td>11,055,270,000</td>
-</tr>
-<tr>
-<td><span id="2095"></span>2095</td>
-<td></td>
-<td></td>
-<td>11,142,461,000</td>
-</tr>
-<tr>
-<td><span id="2100"></span>2100</td>
-<td></td>
-<td></td>
-<td>11,213,317,000</td>
-</tr>
-</table>
-<p>Other, historical projections include</p>
-<ul>
-<li><a href="/wiki/John_Tanton" title="John Tanton">Tanton</a> (1994):<sup id="cite_ref-thesocialcontract.com_18-2" class="reference"><a href="#cite_note-thesocialcontract.com-18">[18]</a></sup> 8 billion for the year 2020;</li>
-<li><a href="/wiki/Colin_McEvedy" title="Colin McEvedy">McEvedy</a> &amp; Jones (1978):<sup id="cite_ref-Richard_Jones_1978_20-2" class="reference"><a href="#cite_note-Richard_Jones_1978-20">[20]</a></sup> 5.75 billion for the year 2000, 8.25 billion for the year 2200.</li>
-</ul>
-<h2><span class="mw-headline" id="By_world_region">By world region</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=6" title="Edit section: By world region">edit</a><span class="mw-editsection-bracket">]</span></span></h2>
-<div class="thumb tright">
-<div class="thumbinner" style="width:252px;"><a href="/wiki/File:Pop_continents_2000_2050.png" class="image"><img alt="" src="//upload.wikimedia.org/wikipedia/commons/thumb/0/0c/Pop_continents_2000_2050.png/250px-Pop_continents_2000_2050.png" width="250" height="134" class="thumbimage" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/0/0c/Pop_continents_2000_2050.png/375px-Pop_continents_2000_2050.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/0/0c/Pop_continents_2000_2050.png/500px-Pop_continents_2000_2050.png 2x" data-file-width="825" data-file-height="443" /></a>
-<div class="thumbcaption">
-<div class="magnify"><a href="/wiki/File:Pop_continents_2000_2050.png" class="internal" title="Enlarge"></a></div>
-UN estimates (as of 2017) for world population by continent in 2000 and in 2050 (pie chart size to scale).<br />
-<span style="margin:0px; font-size:90%;"><span style="border:none; background-color:yellow; color:yellow;">&#160;&#160;&#160;&#160;</span>&#160;Asia</span> <span style="margin:0px; font-size:90%;"><span style="border:none; background-color:black; color:black;">&#160;&#160;&#160;&#160;</span>&#160;Africa</span> <span style="margin:0px; font-size:90%;"><span style="border:none; background-color:blue; color:blue;">&#160;&#160;&#160;&#160;</span>&#160;Europe</span> <span style="margin:0px; font-size:90%;"><span style="border:none; background-color:red; color:red;">&#160;&#160;&#160;&#160;</span>&#160;South/Central America</span> <span style="margin:0px; font-size:90%;"><span style="border:none; background-color:brown; color:brown;">&#160;&#160;&#160;&#160;</span>&#160;North America</span> <span style="margin:0px; font-size:90%;"><span style="border:none; background-color:green; color:green;">&#160;&#160;&#160;&#160;</span>&#160;Oceania</span></div>
-</div>
-</div>
-<p>Population estimates for world regions based on Maddison (2007),<sup id="cite_ref-33" class="reference"><a href="#cite_note-33">[33]</a></sup> in millions. The row showing total world population includes the average growth rate per year over the period separating each column from the preceding one.</p>
-<table class="wikitable">
-<tr>
-<th>year</th>
-<th>1</th>
-<th>1000</th>
-<th>1500</th>
-<th>1600</th>
-<th>1700</th>
-<th>1820</th>
-<th>1913</th>
-<th>2000</th>
-<th>2030</th>
-</tr>
-<tr>
-<td><a href="/wiki/East_Asia" title="East Asia">East</a>/<a href="/wiki/Southeast_Asia" title="Southeast Asia">Southeast Asia</a></td>
-<td>74 (33%)</td>
-<td>88 (33%)</td>
-<td>166 (38%)</td>
-<td>223 (40%)</td>
-<td>216 (36%)</td>
-<td>469 (45%)</td>
-<td>613 (34%)</td>
-<td>1,996 (33%)</td>
-<td>2,417 (30%)</td>
-</tr>
-<tr>
-<td><a href="/wiki/Ethnic_groups_of_South_Asia" class="mw-redirect" title="Ethnic groups of South Asia">South Asia</a></td>
-<td>75 (33%)</td>
-<td>75 (28%)</td>
-<td>110 (25%)</td>
-<td>135 (24%)</td>
-<td>165 (27%)</td>
-<td>216 (21%)</td>
-<td>326 (18%)</td>
-<td>1,372 (23%)</td>
-<td>2,003 (25%)</td>
-</tr>
-<tr>
-<td><a href="/wiki/Demographics_of_Europe" title="Demographics of Europe">Europe</a><sup id="cite_ref-34" class="reference"><a href="#cite_note-34">[34]</a></sup></td>
-<td>34 (15%)</td>
-<td>40 (15%)</td>
-<td>78 (18%)</td>
-<td>112 (20%)</td>
-<td>127 (21%)</td>
-<td>224 (21%)</td>
-<td>498 (28%)</td>
-<td>742 (13%)</td>
-<td>829 (11%)</td>
-</tr>
-<tr>
-<td><a href="/wiki/Western_Asia#Demographics" title="Western Asia">West Asia</a></td>
-<td>19 (8%)</td>
-<td>20 (7%)</td>
-<td>18 (3%)</td>
-<td>21 (3%)</td>
-<td>21 (3%)</td>
-<td>25 (2%)</td>
-<td>39 (2%)</td>
-<td>237 (4%)</td>
-<td>370 (5%)</td>
-</tr>
-<tr>
-<td><a href="/wiki/Demographics_of_Africa" title="Demographics of Africa">Africa</a></td>
-<td>17 (8%)</td>
-<td>32 (12%)</td>
-<td>47 (11%)</td>
-<td>55 (10%)</td>
-<td>61 (10%)</td>
-<td>74 (7%)</td>
-<td>125 (7%)</td>
-<td>798 (13%)</td>
-<td>1,449 (18%)</td>
-</tr>
-<tr>
-<td><a href="/wiki/Demographics_of_Latin_America" class="mw-redirect" title="Demographics of Latin America">South/Central America</a></td>
-<td>6 (3%)</td>
-<td>11 (4%)</td>
-<td>18 (4%)</td>
-<td>9 (2%)</td>
-<td>12 (2%)</td>
-<td>22 (2%)</td>
-<td>81 (5%)</td>
-<td>520 (9%)</td>
-<td>702 (9%)</td>
-</tr>
-<tr>
-<td><a href="/wiki/North_America" title="North America">North America</a></td>
-<td>1 (0%)</td>
-<td>1 (0%)</td>
-<td>2 (0%)</td>
-<td>2 (0%)</td>
-<td>1 (0%)</td>
-<td>11 (1%)</td>
-<td>105 (6%)</td>
-<td>314 (5%)</td>
-<td>413 (5%)</td>
-</tr>
-<tr>
-<th>World</th>
-<th>226</th>
-<th>267<br />
-<small>(+ 0.02% p.a.)</small></th>
-<th>438<br />
-<small>(+ 0.1% p.a.)</small></th>
-<th>556<br />
-<small>(+ 0.2% p.a.)</small></th>
-<th>603<br />
-<small>(+ 0.1% p.a.)</small></th>
-<th>1,041<br />
-<small>(+ 0.5% p.a.)</small></th>
-<th>1,791<br />
-<small>(+ 0.6% p.a.)</small></th>
-<th>6,062<br />
-<small>(+ 1.4% p.a.)</small></th>
-<th>8,175<br />
-<small>(+ 1.0% p.a.)</small></th>
-</tr>
-</table>
-<p>When considering population estimates by world region, it is worth noting that <a href="/wiki/Population_history_of_the_indigenous_peoples_of_the_Americas" class="mw-redirect" title="Population history of the indigenous peoples of the Americas">population history of the indigenous peoples of the Americas</a> before the 1492 voyage of Christopher Columbus has proven difficult to establish, with many historians arguing for an estimate of 50 million people throughout the Americas, and some estimating that populations may have reached 100 million people or more.<sup id="cite_ref-35" class="reference"><a href="#cite_note-35">[35]</a></sup> It is therefore estimated by some that populations in Mexico, Central, and South America could have reached 37 million by 1492.<sup id="cite_ref-36" class="reference"><a href="#cite_note-36">[36]</a></sup> Additionally, the population estimate of 2 million for North America for the same time period represents the low end of modern estimates, and some estimate the population to have been as high as 18 million.<sup id="cite_ref-37" class="reference"><a href="#cite_note-37">[37]</a></sup></p>
-<h2><span class="mw-headline" id="See_also">See also</span></h2>
-<ul>
-<li><a href="/wiki/Projections_of_population_growth" title="Projections of population growth">Projections of population growth</a></li>
-<li><a href="/wiki/Population_growth" title="Population growth">Population growth</a></li>
-</ul>
-<h2><span class="mw-headline" id="References">References</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=7" title="Edit section: References">edit</a><span class="mw-editsection-bracket">]</span></span></h2>
-<div class="reflist columns references-column-width" style="-moz-column-width: 30em; -webkit-column-width: 30em; column-width: 30em; list-style-type: decimal;">
-<ol class="references">
-<li id="cite_note-UNProspect2017-1"><span class="mw-cite-backlink"><b><a href="#cite_ref-UNProspect2017_1-0">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="https://esa.un.org/unpd/wpp/DataQuery/">UN World Population Prospect 2017</a> gives a "median" estimate of 11.2 billion for 2100, with a "95% confidence interval" between 10 and 13 billion.</span></li>
-<li id="cite_note-2"><span class="mw-cite-backlink"><b><a href="#cite_ref-2">^</a></b></span> <span class="reference-text">range of estimates for average growth rates over the preceding century according to the data cited under <a href="#Historical_population">#Historical_population</a>; The average growth rate for the 14th century is negative as a consequence of the <a href="/wiki/Black_Death" title="Black Death">Black Death</a>.</span></li>
-<li id="cite_note-3"><span class="mw-cite-backlink"><b><a href="#cite_ref-3">^</a></b></span> <span class="reference-text">Kuhrt, A. (1995). <i>The Ancient Near East, c. 3000–330 BCE</i>. Vol. 2. London: Routledge. p. 695.</span></li>
-<li id="cite_note-4"><span class="mw-cite-backlink"><b><a href="#cite_ref-4">^</a></b></span> <span class="reference-text">Thomlinson (1975): "one to ten million".</span></li>
-<li id="cite_note-5"><span class="mw-cite-backlink"><b><a href="#cite_ref-5">^</a></b></span> <span class="reference-text"><cite class="citation journal">Pala, M; Olivieri, A; Achilli, A; Accetturo, M; Metspalu, E; Reidla, M; Tamm, E; Karmin, M; Reisberg, T; Hooshiar Kashani, B; Perego, UA; Carossa, V; Gandini, F; Pereira, JB; Soares, P; Angerhofer, N; Rychkov, S; Al-Zahery, N; Carelli, V; Sanati, MH; Houshmand, M; Hatina, J; Macaulay, V; Pereira, L; Woodward, SR; Davies, W; Gamble, C; Baird, D; Semino, O; Villems, R; Torroni, A; Richards, MB (2012). <a rel="nofollow" class="external text" href="//www.ncbi.nlm.nih.gov/pmc/articles/PMC3376494">"Mitochondrial DNA signals of late glacial recolonization of Europe from near eastern refugia"</a>. <i>Am. J. Hum. Genet</i>. <b>90</b>: 915–24. <a href="/wiki/Digital_object_identifier" title="Digital object identifier">doi</a>:<a rel="nofollow" class="external text" href="//doi.org/10.1016%2Fj.ajhg.2012.04.003">10.1016/j.ajhg.2012.04.003</a>. <a href="/wiki/PubMed_Central" title="PubMed Central">PMC</a>&#160;<span class="plainlinks"><a rel="nofollow" class="external text" href="//www.ncbi.nlm.nih.gov/pmc/articles/PMC3376494">3376494</a> <img alt="Freely accessible" src="//upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/9px-Lock-green.svg.png" title="Freely accessible" width="9" height="14" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/14px-Lock-green.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/18px-Lock-green.svg.png 2x" data-file-width="512" data-file-height="813" /></span>. <a href="/wiki/PubMed_Identifier" class="mw-redirect" title="PubMed Identifier">PMID</a>&#160;<a rel="nofollow" class="external text" href="//www.ncbi.nlm.nih.gov/pubmed/22560092">22560092</a>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Am.+J.+Hum.+Genet.&amp;rft.atitle=Mitochondrial+DNA+signals+of+late+glacial+recolonization+of+Europe+from+near+eastern+refugia&amp;rft.volume=90&amp;rft.pages=915-24&amp;rft.date=2012&amp;rft_id=%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC3376494&amp;rft_id=info%3Apmid%2F22560092&amp;rft_id=info%3Adoi%2F10.1016%2Fj.ajhg.2012.04.003&amp;rft.aulast=Pala&amp;rft.aufirst=M&amp;rft.au=Olivieri%2C+A&amp;rft.au=Achilli%2C+A&amp;rft.au=Accetturo%2C+M&amp;rft.au=Metspalu%2C+E&amp;rft.au=Reidla%2C+M&amp;rft.au=Tamm%2C+E&amp;rft.au=Karmin%2C+M&amp;rft.au=Reisberg%2C+T&amp;rft.au=Hooshiar+Kashani%2C+B&amp;rft.au=Perego%2C+UA&amp;rft.au=Carossa%2C+V&amp;rft.au=Gandini%2C+F&amp;rft.au=Pereira%2C+JB&amp;rft.au=Soares%2C+P&amp;rft.au=Angerhofer%2C+N&amp;rft.au=Rychkov%2C+S&amp;rft.au=Al-Zahery%2C+N&amp;rft.au=Carelli%2C+V&amp;rft.au=Sanati%2C+MH&amp;rft.au=Houshmand%2C+M&amp;rft.au=Hatina%2C+J&amp;rft.au=Macaulay%2C+V&amp;rft.au=Pereira%2C+L&amp;rft.au=Woodward%2C+SR&amp;rft.au=Davies%2C+W&amp;rft.au=Gamble%2C+C&amp;rft.au=Baird%2C+D&amp;rft.au=Semino%2C+O&amp;rft.au=Villems%2C+R&amp;rft.au=Torroni%2C+A&amp;rft.au=Richards%2C+MB&amp;rft_id=%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC3376494&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span></span></li>
-<li id="cite_note-ambrose1998-6"><span class="mw-cite-backlink"><b><a href="#cite_ref-ambrose1998_6-0">^</a></b></span> <span class="reference-text"><cite class="citation journal">Stanley H. Ambrose (1998). "Late Pleistocene human population bottlenecks, volcanic winter, and differentiation of modern humans". <i><a href="/wiki/Journal_of_Human_Evolution" title="Journal of Human Evolution">Journal of Human Evolution</a></i>. <b>34</b> (6): 623–651. <a href="/wiki/Digital_object_identifier" title="Digital object identifier">doi</a>:<a rel="nofollow" class="external text" href="//doi.org/10.1006%2Fjhev.1998.0219">10.1006/jhev.1998.0219</a>. <a href="/wiki/PubMed_Identifier" class="mw-redirect" title="PubMed Identifier">PMID</a>&#160;<a rel="nofollow" class="external text" href="//www.ncbi.nlm.nih.gov/pubmed/9650103">9650103</a>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Journal+of+Human+Evolution&amp;rft.atitle=Late+Pleistocene+human+population+bottlenecks%2C+volcanic+winter%2C+and+differentiation+of+modern+humans&amp;rft.volume=34&amp;rft.issue=6&amp;rft.pages=623-651&amp;rft.date=1998&amp;rft_id=info%3Adoi%2F10.1006%2Fjhev.1998.0219&amp;rft_id=info%3Apmid%2F9650103&amp;rft.au=Stanley+H.+Ambrose&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span> <cite class="citation web">Ambrose, Stanley H. (2005). <a rel="nofollow" class="external text" href="http://www.bradshawfoundation.com/evolution/">"Volcanic Winter, and Differentiation of Modern Humans"</a>. <i>Bradshaw Foundation</i><span class="reference-accessdate">. Retrieved <span class="nowrap">2006-04-08</span></span>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=Bradshaw+Foundation&amp;rft.atitle=Volcanic+Winter%2C+and+Differentiation+of+Modern+Humans&amp;rft.date=2005&amp;rft.au=Ambrose%2C+Stanley+H.&amp;rft_id=http%3A%2F%2Fwww.bradshawfoundation.com%2Fevolution%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span></span></li>
-<li id="cite_note-robock2009-7"><span class="mw-cite-backlink"><b><a href="#cite_ref-robock2009_7-0">^</a></b></span> <span class="reference-text"><cite class="citation journal">Robock, A., C.M. Ammann, L. Oman, D. Shindell, S. Levis, and G. Stenchikov (2009). "Did the Toba volcanic eruption of ~74k BP produce widespread glaciation?". <i><a href="/wiki/Journal_of_Geophysical_Research" title="Journal of Geophysical Research">Journal of Geophysical Research</a></i>. <b>114</b> (D10): D10107. <a href="/wiki/Bibcode" title="Bibcode">Bibcode</a>:<a rel="nofollow" class="external text" href="http://adsabs.harvard.edu/abs/2009JGRD..11410107R">2009JGRD..11410107R</a>. <a href="/wiki/Digital_object_identifier" title="Digital object identifier">doi</a>:<a rel="nofollow" class="external text" href="//doi.org/10.1029%2F2008JD011652">10.1029/2008JD011652</a>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Journal+of+Geophysical+Research&amp;rft.atitle=Did+the+Toba+volcanic+eruption+of+~74k+BP+produce+widespread+glaciation%3F&amp;rft.volume=114&amp;rft.issue=D10&amp;rft.pages=D10107&amp;rft.date=2009&amp;rft_id=info%3Adoi%2F10.1029%2F2008JD011652&amp;rft_id=info%3Abibcode%2F2009JGRD..11410107R&amp;rft.au=Robock%2C+A.%2C+C.M.+Ammann%2C+L.+Oman%2C+D.+Shindell%2C+S.+Levis%2C+and+G.+Stenchikov&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span><span class="citation-comment" style="display:none; color:#33aa33; margin-left:0.3em">CS1 maint: Multiple names: authors list (<a href="/wiki/Category:CS1_maint:_Multiple_names:_authors_list" title="Category:CS1 maint: Multiple names: authors list">link</a>)</span></span></li>
-<li id="cite_note-8"><span class="mw-cite-backlink"><b><a href="#cite_ref-8">^</a></b></span> <span class="reference-text">Per Sjödin, Agnès E Sjöstrand, Mattias Jakobsson and Michael G B Blum, "Resequencing data provide no evidence for a human bottleneck in Africa during the penultimate glacial period" Mol <i>Biol Evol</i> (2012) doi: 10.1093/molbev/mss061. "A small human effective population size, on the order of 10,000 individuals, which is smaller than the effective population size of most great apes, has been interpreted as a result of a very long history, starting ∼ 2 mya, of a small population size, coined as the long-necked bottle model (Harpending et al. 1998; Hawks et al. 2000). Our findings are consistent with this hypothesis, but, depending on the mutation rate, we find either an effective population size of NA = 12,000 (95% C.I. = 9,000−15,500 when averaging over all three demographic models) using the mutation rate calibrated with the human-chimp divergence or an effective population size of NA = 32,500 individuals (95% C.I. = 27,500−34,500) using the mutation rate given by whole-genome trio analysis (The 1000 Genomes Project Consortium 2010) (supplementary figure 4 and table 6, Supplementary Material online). Not surprisingly, the estimated effective mutation rates θ = 4NAµ are comparable for the two mutation rates we considered, and are equal to 1.4 × 10<sup>−3</sup>/bp/generation (95% C.I. = (1.1−1.7) × 10<sup>−3</sup>). Relating the estimated effective population size to the census population size during the Pleistocene is a difficult task because there are many factors affecting the effective population size (Charlesworth 2009). Nevertheless, based on published estimates of the ratio between effective and census population size, a comprehensive value on the order of 10% has been found by Frankham (1995). This 10% rule roughly predicts that 120,000−325,[0]00 individuals (depending on the assumed mutation rate)"</span></li>
-<li id="cite_note-9"><span class="mw-cite-backlink"><b><a href="#cite_ref-9">^</a></b></span> <span class="reference-text">Haub (1995): "at some time back in the 1970s, some now-forgotten writer made the statement that 75 percent of the people who had ever been born were alive at that moment." Haub (1995) is the basis of a 2007 article in Scientific American, <cite class="citation web"><a rel="nofollow" class="external text" href="http://www.scientificamerican.com/article/fact-or-fiction-living-outnumber-dead/">"Fact or Fiction"</a>. <i>Scientificamerican.com</i>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=Scientificamerican.com&amp;rft.atitle=Fact+or+Fiction&amp;rft_id=http%3A%2F%2Fwww.scientificamerican.com%2Farticle%2Ffact-or-fiction-living-outnumber-dead%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span></span></li>
-<li id="cite_note-10"><span class="mw-cite-backlink"><b><a href="#cite_ref-10">^</a></b></span> <span class="reference-text">Wesley Stephenson, <a rel="nofollow" class="external text" href="http://www.bbc.com/news/magazine-16870579">Do the dead outnumber the living?</a>, BBC News 4 February 2012. Ciara Curtin, <a rel="nofollow" class="external text" href="https://www.scientificamerican.com/article/fact-or-fiction-living-outnumber-dead/">Fact or Fiction?: Living People Outnumber the Dead</a>, Scientific American, 1 March 2007.</span></li>
-<li id="cite_note-11"><span class="mw-cite-backlink"><b><a href="#cite_ref-11">^</a></b></span> <span class="reference-text">Kapitza, 'The phenomenological theory of world population growth', <i>Physics-Uspekhi</i> 39(1) 57-71 (1996) cites estimates ranging between 80 and 150 billion (Sergei P Kapitza, 'The phenomenological theory of world population growth', <i>Physics-Uspekhi</i> 39(1) 57-71, 1996), citing K. M. Weiss, <i>Human Biology</i> 56637, 1984, and N. Keyfitz, <i>Applied Mathematical Demography</i>, New York: Wiley, 1977). Haub (1995) cited 105 billion, updated to 107 billion as of 2011 in <cite class="citation web">Haub, Carl (October 2011). <a rel="nofollow" class="external text" href="http://www.prb.org/Articles/2002/HowManyPeopleHaveEverLivedonEarth.aspx">"How Many People Have Ever Lived on Earth?"</a>. <a href="/wiki/Population_Reference_Bureau" title="Population Reference Bureau">Population Reference Bureau</a><span class="reference-accessdate">. Retrieved <span class="nowrap">April 29,</span> 2013</span>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=unknown&amp;rft.btitle=How+Many+People+Have+Ever+Lived+on+Earth%3F&amp;rft.pub=Population+Reference+Bureau&amp;rft.date=2011-10&amp;rft.aulast=Haub&amp;rft.aufirst=Carl&amp;rft_id=http%3A%2F%2Fwww.prb.org%2FArticles%2F2002%2FHowManyPeopleHaveEverLivedonEarth.aspx&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span></span></li>
-<li id="cite_note-12"><span class="mw-cite-backlink"><b><a href="#cite_ref-12">^</a></b></span> <span class="reference-text">Haub (1995): "Clearly, the period 8000 B.C. to 1 A.D. is key to the magnitude of our number, but, unfortunately, little is known about that era. [...] of course, pushing the date of humanity's arrival on the planet before 50,000 B.C. would also raise the number, although perhaps not by terribly much."</span></li>
-<li id="cite_note-13"><span class="mw-cite-backlink"><b><a href="#cite_ref-13">^</a></b></span> <span class="reference-text">Haub (1995): "Life expectancy at birth probably averaged only about 10 years for most of human history. Estimates of average life expectancy in Iron Age France have been put at only 10 or 12 years. Under these conditions, the birth rate would have to be about 80 per 1,000 people just for the species to survive. [...] Our birth rate assumption will greatly affect the estimate of the number of persons ever born. Infant mortality in the human race's earliest days is thought to have been very high--perhaps 500 infant deaths per 1,000 births, or even higher. [...] Birth rates were set at 80 per 1,000 per year through 1 A.D. and at 60 per 1,000 from 2 A.D. to 1750. Rates then declined to the low 30s by the modern period. (For a brief bibliography of sources consulted in the course of this alchemy, see [Colin McEvedy and Richard Jones 1978])." "So, our estimate here is that about 5.5 percent of all people ever born are alive today." Using the UN estimates for birth rates (<cite class="citation web"><a rel="nofollow" class="external text" href="http://data.un.org/Data.aspx?q=world+population&amp;d=PopDiv&amp;f=variableID%3A53%3BcrID%3A900">"UNdata: Crude birth rate"</a>. United Nations. 25 August 2011.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=unknown&amp;rft.btitle=UNdata%3A+Crude+birth+rate&amp;rft.pub=United+Nations&amp;rft.date=2011-08-25&amp;rft_id=http%3A%2F%2Fdata.un.org%2FData.aspx%3Fq%3Dworld%2Bpopulation%26d%3DPopDiv%26f%3DvariableID%253A53%253BcrID%253A900&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span>) and world population (U.S. Census Bureau, International Data Base), there were an estimated 3.0 billion births during 1995–2016, so that based on the estimate by Haub (1995), the figures for 2017 would be "about 108 billion births" and "about 7 percent of all people ever born are alive today."</span></li>
-<li id="cite_note-Haub1650-14"><span class="mw-cite-backlink">^ <a href="#cite_ref-Haub1650_14-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-Haub1650_14-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text">Haub (1995): "The average annual rate of growth was actually lower from 1 A.D. to 1650 than the rate suggested above for the 8000 B.C. to 1 A.D. period. One reason for this abnormally slow growth was the Black Plague. This dreaded scourge was not limited to 14th century Europe. The epidemic may have begun about 542 A.D. in Western Asia, spreading from there. It is believed that half the Byzantine Empire was destroyed in the 6th century, a total of 100 million deaths."</span></li>
-<li id="cite_note-Population_Reference_Bureau-15"><span class="mw-cite-backlink">^ <a href="#cite_ref-Population_Reference_Bureau_15-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-Population_Reference_Bureau_15-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-Population_Reference_Bureau_15-2"><sup><i><b>c</b></i></sup></a></span> <span class="reference-text">Data from <a rel="nofollow" class="external text" href="http://www.prb.org/Home.aspx">Population Reference Bureau</a>.<br />
-2016 estimate: (a) <a rel="nofollow" class="external text" href="http://www.prb.org/pdf16/prb-wpds2016-web-2016.pdf">"2016 World Population Data Sheet"</a><br />
-2015 estimate: (b) Toshiko Kaneda, 2015, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf15/2015-world-population-data-sheet_eng.pdf">"2015 World Population Data Sheet"</a>.<br />
-2014 estimate: (c) Carl Haub, 2014, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf14/2014-world-population-data-sheet_eng.pdf">"2014 World Population Data Sheet"</a>.<br />
-2013 estimate: (d) Carl Haub, 2013, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf13/2013-population-data-sheet_eng.pdf">"2013 World Population Data Sheet"</a>.<br />
-2012 estimate: (e) Carl Haub, 2012, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf12/2012-population-data-sheet_eng.pdf">"2012 World Population Data Sheet"</a>.<br />
-2011 estimate: (f) Carl Haub, 2011, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf11/2011population-data-sheet_eng.pdf">"2011 World Population Data Sheet"</a>.<br />
-2010 estimate: (g) Carl Haub, 2010, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf10/10wpds_eng.pdf">"2010 World Population Data Sheet"</a>.<br />
-2009 estimate: (h) Carl Haub, 2009, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf09/09wpds_eng.pdf">"2009 World Population Data Sheet"</a>.<br />
-2008 estimate: (i) Carl Haub, 2008, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf08/08WPDS_Eng.pdf">"2008 World Population Data Sheet"</a>.<br />
-2007 estimate: (j) Carl Haub, 2007, <a rel="nofollow" class="external text" href="http://www.prb.org/Publications/Datasheets/2007/2007WorldPopulationDataSheet.aspx">"2007 World Population Data Sheet"</a>.<br />
-2006 estimate: (k) Carl Haub, 2006, <a rel="nofollow" class="external text" href="http://www.prb.org/Publications/Datasheets/2006/2006WorldPopulationDataSheet.aspx">"2006 World Population Data Sheet"</a>.<br />
-2005 estimate: (l) Carl Haub, 2005, <a rel="nofollow" class="external text" href="http://www.prb.org/Publications/Datasheets/2005/2005WorldPopulationDataSheet.aspx">"2005 World Population Data Sheet"</a>.<br />
-2004 estimate: (m) Carl Haub, 2004, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf04/04WorldDataSheet_ENG.pdf">"2004 World Population Data Sheet"</a>.<br />
-2003 estimate: (n) Carl Haub, 2003, <a rel="nofollow" class="external text" href="http://homepage.ntu.edu.tw/~psc/WorldPopulationDS03_Eng.pdf">"2003 World Population Data Sheet"</a>.<br />
-2002 estimate: (o) Carl Haub, 2002, <a rel="nofollow" class="external text" href="http://www.prb.org/pdf/worldpopulationds02_eng.pdf">"2002 World Population Data Sheet"</a>.<br />
-2001 estimate: (p) Carl Haub, 2001, <a rel="nofollow" class="external text" href="http://www.ined.fr/fichier/s_rubrique/18790/publi_pdf2_pop_and_soc_english_370.en.pdf">"2001 World Population Data Sheet"</a>.<br />
-2000 estimate: (q) 2000, <a rel="nofollow" class="external text" href="http://www.prb.org/Publications/Articles/2000/9BillionWorldPopulationby2050.aspx">"9 Billion World Population by 2050"</a>.<br />
-1997 estimate: (r) 1997, <a rel="nofollow" class="external text" href="http://www.epidemiolog.net/evolving/DemographyBasics.pdf">"Studying Populations"</a>.<br />
-Estimates for 1995 and prior: (s) Carl Haub, 1995, <a rel="nofollow" class="external text" href="https://www.un.org/popin/popis/journals/poptoday/today0295.html">"How Many People Have Ever Lived on Earth?"</a> <i>Population Today</i>, Vol. 23 (no. 2), pp. 5–6.</span></li>
-<li id="cite_note-The_World_at_Six_Billion,_1999-16"><span class="mw-cite-backlink">^ <a href="#cite_ref-The_World_at_Six_Billion,_1999_16-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-The_World_at_Six_Billion,_1999_16-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-The_World_at_Six_Billion,_1999_16-2"><sup><i><b>c</b></i></sup></a></span> <span class="reference-text">Data from <a rel="nofollow" class="external text" href="http://esa.un.org/unpd/wpp/unpp/panel_population.htm">United Nations Department of Economic and Social Affairs, Population Division.</a><br />
-1950–2100 estimates (only medium variants shown): (a) <a rel="nofollow" class="external text" href="http://esa.un.org/unpp/">World Population Prospects: The 2008 Revision.</a><br />
-Estimates prior to 1950: (b) <a rel="nofollow" class="external text" href="https://www.un.org/esa/population/publications/sixbillion/sixbillion.htm">"The World at Six Billion", 1999.</a><br />
-Estimates from 1950 to 2100: (c) <a rel="nofollow" class="external text" href="http://www.geohive.com/earth/his_history3.aspx">"Population of the entire world, yearly, 1950 - 2100", 2013.</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20161119132504/http://www.geohive.com/earth/his_history3.aspx">Archived</a> November 19, 2016, at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>.<br />
-2014: (d) <a rel="nofollow" class="external free" href="http://esa.un.org/unpd/wup/Highlights/WUP2014-Highlights.pdf">http://esa.un.org/unpd/wup/Highlights/WUP2014-Highlights.pdf</a> "2014 World Urbanization Prospects", 2014.]<br />
-2015: (e) <a rel="nofollow" class="external free" href="http://esa.un.org/unpd/wpp/Publications/Files/Key_Findings_WPP_2015.pdf">http://esa.un.org/unpd/wpp/Publications/Files/Key_Findings_WPP_2015.pdf</a> "2015 World Urbanization Prospects", 2015.] <a rel="nofollow" class="external text" href="https://web.archive.org/web/20140320035709/http://esa.un.org/unpd/wpp/unpp/panel_population.htm">Archived</a> March 20, 2014, at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>.</span></li>
-<li id="cite_note-www.ggdc.net/maddison/Historical_Statistics-17"><span class="mw-cite-backlink">^ <a href="#cite_ref-www.ggdc.net/maddison/Historical_Statistics_17-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-www.ggdc.net/maddison/Historical_Statistics_17-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><a rel="nofollow" class="external text" href="http://www.theworldeconomy.org/publications/worldeconomy/">Angus Maddison, 2003, <i>The World Economy: Historical Statistics</i>, Vol. 2, OECD, Paris</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20080513232041/http://www.theworldeconomy.org/publications/worldeconomy/">Archived</a> May 13, 2008, at the <a href="/wiki/Wayback_Machine" title="Wayback Machine">Wayback Machine</a>. <a href="/wiki/International_Standard_Book_Number" title="International Standard Book Number">ISBN</a>&#160;<a href="/wiki/Special:BookSources/92-64-10412-7" title="Special:BookSources/92-64-10412-7">92-64-10412-7</a>.<br />
-<a rel="nofollow" class="external text" href="http://www.ggdc.net/maddison/Historical_Statistics/horizontal-file_02-2010.xls">"Statistical Appendix"</a> (2008, ggdc.net) "The historical data were originally developed in three books: Monitoring the World Economy 1820-1992, OECD, Paris 1995; The World Economy: A Millennial Perspective, OECD Development Centre, Paris 2001; The World Economy: Historical Statistics, OECD Development Centre, Paris 2003. All these contain detailed source notes. Figures for 1820 onwards are annual, wherever possible. For earlier years, benchmark figures are shown for 1 AD, 1000 AD, 1500, 1600 and 1700." "OECD countries GDP revised and updated 1991-2003 from National Accounts for OECD Countries, vol. I, 2006. Norway 1820-1990 GDP from Ola Grytten (2004), “The Gross Domestic Product for Norway, 1830-2003” in Eitrheim, Klovland and Qvigstad (eds), Historical Monetary Statistics for Norway, 1819-2003, Norges Bank, Oslo. Latin American GDP 2000-2003 revised and updated from ECLAC, Statistical Yearbook 2004 and preliminary version of the 2005 Yearbook supplied by Andre Hofman. For Chile, GDP 1820-2003 from Rolf Lűders (1998), “The Comparative Economic Performance of Chile 1810-1995”, Estudios de Economia, vol. 25, no. 2, with revised population estimates from Diaz, J., R. Lűders, and G. Wagner (2005) Chili 1810-2000: la Republica en Cifras, mimeo, Instituto de Economia, Universidad Católica de Chile. For Peru, GDP 1896-1990 and population 1896-1949 from Bruno Seminario and Arlette Beltran, Crecimiento Economico en el Peru 1896-1995, Universidad del Pacifico, 1998. " "For Asia there are amendments to the GDP estimates for South and North Korea, 1911-74, to correct an error in Maddison (2003). Estimates for the Philippines, 1902-1940 were amended in line with Richard Hooley (2005), 'American Economic Policy in the Philippines, 1902-1940', Journal of Asian Economics, 16. 1820 estimates were amended for Hong Kong, the Philippines, Singapore, Sri Lanka, Taiwan and Thailand." "Asian countries GDP revised and updated 1998-2003 from Asian Development Bank, Key Indicators 2005, except for South Korea and Japan, where OECD sources were used for 1991-2003. GDP for African countries updated 2000-2003 from IMF, World Economic Outlook, April 2005. Population estimates for all countries except China and Indonesia revised and updated 1950-2008 and 2030 from International Data Base, International Programs Center, Population Division, US Bureau of the Census, April 2005 version. China’s population 1990-2003 from China Statistical Yearbook 2005, China Statistics Press, Beijing. Indonesian population 1950-2003 kindly supplied by Pierre van der Eng. The figures now include three countries previously omitted: Cook Islands, Nauru and Tuvalu."</span></li>
-<li id="cite_note-thesocialcontract.com-18"><span class="mw-cite-backlink">^ <a href="#cite_ref-thesocialcontract.com_18-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-thesocialcontract.com_18-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-thesocialcontract.com_18-2"><sup><i><b>c</b></i></sup></a></span> <span class="reference-text"><a rel="nofollow" class="external text" href="http://www.thesocialcontract.com/artman2/publish/tsc0403/article_329.shtml">John H. Tanton, 1994, "End of the Migration Epoch? Time For a New Paradigm", The Social Contract, Vol. 4 (no 3), pp. 162–173</a>.</span></li>
-<li id="cite_note-French_1980,_pp._1-19"><span class="mw-cite-backlink">^ <a href="#cite_ref-French_1980,_pp._1_19-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-French_1980,_pp._1_19-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text">Slightly updated data from original paper in French: (a) Jean-Noël Biraben, 1980, "An Essay Concerning Mankind's Evolution", Population, Selected Papers, Vol. 4, pp. 1–13. Original paper in French: (b) Jean-Noël Biraben, 1979, "Essai sur l'évolution du nombre des hommes", Population, Vol. 34 (no. 1), pp. 13–25.</span></li>
-<li id="cite_note-Richard_Jones_1978-20"><span class="mw-cite-backlink">^ <a href="#cite_ref-Richard_Jones_1978_20-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-Richard_Jones_1978_20-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-Richard_Jones_1978_20-2"><sup><i><b>c</b></i></sup></a></span> <span class="reference-text">Colin McEvedy and Richard Jones, 1978, <i>Atlas of World Population History</i>, Facts on File, New York, <a href="/wiki/International_Standard_Book_Number" title="International Standard Book Number">ISBN</a>&#160;<a href="/wiki/Special:BookSources/0-7139-1031-3" title="Special:BookSources/0-7139-1031-3">0-7139-1031-3</a>.</span></li>
-<li id="cite_note-ReferenceA-21"><span class="mw-cite-backlink">^ <a href="#cite_ref-ReferenceA_21-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-ReferenceA_21-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><a href="/wiki/Ralph_Thomlinson" title="Ralph Thomlinson">Ralph Thomlinson</a>, 1975, <i>Demographic Problems: Controversy over population control</i>, 2nd Ed., Dickenson Publishing Company, Ecino, CA, <a href="/wiki/International_Standard_Book_Number" title="International Standard Book Number">ISBN</a>&#160;<a href="/wiki/Special:BookSources/0-8221-0166-1" title="Special:BookSources/0-8221-0166-1">0-8221-0166-1</a>.</span></li>
-<li id="cite_note-John_D._Durand_1974-22"><span class="mw-cite-backlink">^ <a href="#cite_ref-John_D._Durand_1974_22-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-John_D._Durand_1974_22-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text">John D. Durand, 1974, "Historical Estimates of World Population: An Evaluation", University of Pennsylvania, Population Center, Analytical and Technical Reports, Number 10.</span></li>
-<li id="cite_note-Colin_Clark_1967-23"><span class="mw-cite-backlink">^ <a href="#cite_ref-Colin_Clark_1967_23-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-Colin_Clark_1967_23-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text">Colin Clark, 1967, <i>Population Growth and Land Use</i>, St. Martin's Press, New York, <a href="/wiki/International_Standard_Book_Number" title="International Standard Book Number">ISBN</a>&#160;<a href="/wiki/Special:BookSources/0-333-01126-0" title="Special:BookSources/0-333-01126-0">0-333-01126-0</a>.</span></li>
-<li id="cite_note-mnp.nl-24"><span class="mw-cite-backlink">^ <a href="#cite_ref-mnp.nl_24-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-2"><sup><i><b>c</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-3"><sup><i><b>d</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-4"><sup><i><b>e</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-5"><sup><i><b>f</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-6"><sup><i><b>g</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-7"><sup><i><b>h</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-8"><sup><i><b>i</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-9"><sup><i><b>j</b></i></sup></a> <a href="#cite_ref-mnp.nl_24-10"><sup><i><b>k</b></i></sup></a></span> <span class="reference-text"><a rel="nofollow" class="external text" href="http://journals.sagepub.com/doi/pdf/10.1177/0959683609356587">Data</a> from <a rel="nofollow" class="external text" href="http://themasites.pbl.nl/tridion/en/themasites/hyde/index.html">History Database of the Global Environment.</a> K. Klein Goldewijk, A. Beusen and P. Janssen, "HYDE 3.1: Long-term dynamic modeling of global population and built-up area in a spatially explicit way", from table on pg. 2, Netherlands Environmental Assessment Agency (MNP), Bilthoven, The Netherlands.</span></li>
-<li id="cite_note-25"><span class="mw-cite-backlink"><b><a href="#cite_ref-25">^</a></b></span> <span class="reference-text">Haub (1995): "By 1 A.D., the world may have held about 300 million people. One estimate of the population of the Roman Empire, from Spain to Asia Minor, in 14 A.D. is 45 million. However, other historians set the figure twice as high, suggesting how imprecise population estimates of early historical periods can be."</span></li>
-<li id="cite_note-26"><span class="mw-cite-backlink"><b><a href="#cite_ref-26">^</a></b></span> <span class="reference-text">"The present figures are a revision and update of those presented on this website in 2003. The most significant changes are in the entries for the year 1, where gaps in previous tables have been filled with the new estimates for the Roman Empire in Maddison (2007). The estimates are in fact for 14 AD"</span></li>
-<li id="cite_note-27"><span class="mw-cite-backlink"><b><a href="#cite_ref-27">^</a></b></span> <span class="reference-text">The estimates are in fact for 14 AD"</span></li>
-<li id="cite_note-census.gov-28"><span class="mw-cite-backlink">^ <a href="#cite_ref-census.gov_28-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-census.gov_28-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text">Data from <a rel="nofollow" class="external text" href="https://www.census.gov/population/international/data/worldpop/table_population.php">U.S. Census Bureau, International Data Base</a> Retrieved on 28 Oct, 2017</span></li>
-<li id="cite_note-29"><span class="mw-cite-backlink"><b><a href="#cite_ref-29">^</a></b></span> <span class="reference-text">"The expression for growth (6) indicates a limit for world population N=πK<sup>2</sup>=14×10<sup>9</sup>, in the foreseeable future. Of this asymptotic limit 90% will be reached for Model III by year 2135, or in 3T years after T1 = 2007." Sergei P. Kapitza, 'The phenomenological theory of world population growth', <i><a href="/wiki/Physics-Uspekhi" title="Physics-Uspekhi">Physics-Uspekhi</a></i> 39(1) 57-71 (1996).</span></li>
-<li id="cite_note-un-esa1-30"><span class="mw-cite-backlink"><b><a href="#cite_ref-un-esa1_30-0">^</a></b></span> <span class="reference-text">United Nations Department of Economic and Social Affairs. <i><a rel="nofollow" class="external text" href="https://www.un.org/esa/population/publications/longrange2/WorldPop2300final.pdf">World Population to 2300</a></i>. 2004. Executive Summary, Page 2.</span></li>
-<li id="cite_note-Science-31"><span class="mw-cite-backlink"><b><a href="#cite_ref-Science_31-0">^</a></b></span> <span class="reference-text"><cite class="citation journal">Gerland, P.; Raftery, A. E.; Ev Ikova, H.; Li, N.; Gu, D.; Spoorenberg, T.; Alkema, L.; Fosdick, B. K.; Chunn, J.; Lalic, N.; Bay, G.; Buettner, T.; Heilig, G. K.; Wilmoth, J. (September 14, 2014). <a rel="nofollow" class="external text" href="http://www.sciencemag.org/content/early/2014/09/17/science.1257469">"World population stabilization unlikely this century"</a>. <i>Science</i>. AAAS. <b>346</b> (6206): 234–7. <a href="/wiki/Digital_object_identifier" title="Digital object identifier">doi</a>:<a rel="nofollow" class="external text" href="//doi.org/10.1126%2Fscience.1257469">10.1126/science.1257469</a>. <a href="/wiki/International_Standard_Serial_Number" title="International Standard Serial Number">ISSN</a>&#160;<a rel="nofollow" class="external text" href="//www.worldcat.org/issn/1095-9203">1095-9203</a>. <a href="/wiki/PubMed_Central" title="PubMed Central">PMC</a>&#160;<span class="plainlinks"><a rel="nofollow" class="external text" href="//www.ncbi.nlm.nih.gov/pmc/articles/PMC4230924">4230924</a> <img alt="Freely accessible" src="//upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/9px-Lock-green.svg.png" title="Freely accessible" width="9" height="14" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/14px-Lock-green.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/18px-Lock-green.svg.png 2x" data-file-width="512" data-file-height="813" /></span>. <a href="/wiki/PubMed_Identifier" class="mw-redirect" title="PubMed Identifier">PMID</a>&#160;<a rel="nofollow" class="external text" href="//www.ncbi.nlm.nih.gov/pubmed/25301627">25301627</a><span class="reference-accessdate">. Retrieved <span class="nowrap">September 21,</span> 2014</span>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Science&amp;rft.atitle=World+population+stabilization+unlikely+this+century&amp;rft.volume=346&amp;rft.issue=6206&amp;rft.pages=234-7&amp;rft.date=2014-09-14&amp;rft_id=%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC4230924&amp;rft.issn=1095-9203&amp;rft_id=info%3Apmid%2F25301627&amp;rft_id=info%3Adoi%2F10.1126%2Fscience.1257469&amp;rft.aulast=Gerland&amp;rft.aufirst=P.&amp;rft.au=Raftery%2C+A.+E.&amp;rft.au=Ev+Ikova%2C+H.&amp;rft.au=Li%2C+N.&amp;rft.au=Gu%2C+D.&amp;rft.au=Spoorenberg%2C+T.&amp;rft.au=Alkema%2C+L.&amp;rft.au=Fosdick%2C+B.+K.&amp;rft.au=Chunn%2C+J.&amp;rft.au=Lalic%2C+N.&amp;rft.au=Bay%2C+G.&amp;rft.au=Buettner%2C+T.&amp;rft.au=Heilig%2C+G.+K.&amp;rft.au=Wilmoth%2C+J.&amp;rft_id=http%3A%2F%2Fwww.sciencemag.org%2Fcontent%2Fearly%2F2014%2F09%2F17%2Fscience.1257469&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span></span></li>
-<li id="cite_note-32"><span class="mw-cite-backlink"><b><a href="#cite_ref-32">^</a></b></span> <span class="reference-text">Randers, Jorgen (2012). 2052: A Global Forecast for the Next Forty Years. Vermont: Chelsea Green Publishing. p. 62.</span></li>
-<li id="cite_note-33"><span class="mw-cite-backlink"><b><a href="#cite_ref-33">^</a></b></span> <span class="reference-text">Angus Maddison, The World Economy: Historical Statistics, Statistical Appendix (2007, ggdc.net). Estimates cited are for the beginning of the 1st millennium ("year 0"), the beginning of the 2nd millennium ("year 1000"), and for the beginning each century since the 16th (years 1820 and 1913 are given for the 19th and 20th century, respectively, as Maddison presents detailed estimates for these years), and a projection for the year 2030.</span></li>
-<li id="cite_note-34"><span class="mw-cite-backlink"><b><a href="#cite_ref-34">^</a></b></span> <span class="reference-text">includes <a href="/wiki/Demographics_of_Central_Asia" title="Demographics of Central Asia">Central Asia</a> (listed under "<a href="/wiki/Former_USSR" class="mw-redirect" title="Former USSR">former USSR</a>")</span></li>
-<li id="cite_note-35"><span class="mw-cite-backlink"><b><a href="#cite_ref-35">^</a></b></span> <span class="reference-text"><cite class="citation book">Taylor, Alan (2002). <a rel="nofollow" class="external text" href="https://books.google.com/books?id=NPoAQRgkrOcC&amp;pg=PA40&amp;dq=pre-Columbian+population+million&amp;cd=6#v=onepage&amp;q=pre-Columbianpopulationmillion&amp;f=false"><i>American Colonies</i></a>. Penguin. <a href="/wiki/International_Standard_Book_Number" title="International Standard Book Number">ISBN</a>&#160;<a href="/wiki/Special:BookSources/9780142002100" title="Special:BookSources/9780142002100">9780142002100</a>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=book&amp;rft.btitle=American+Colonies&amp;rft.pub=Penguin&amp;rft.date=2002&amp;rft.isbn=9780142002100&amp;rft.aulast=Taylor&amp;rft.aufirst=Alan&amp;rft_id=https%3A%2F%2Fbooks.google.com%2Fbooks%3Fid%3DNPoAQRgkrOcC%26pg%3DPA40%26dq%3Dpre-Columbian%2Bpopulation%2Bmillion%26cd%3D6%23v%3Donepage%26q%3Dpre-Columbianpopulationmillion%26f%3Dfalse&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span></span></li>
-<li id="cite_note-36"><span class="mw-cite-backlink"><b><a href="#cite_ref-36">^</a></b></span> <span class="reference-text">"La catastrophe démographique" (The Demographic Catastrophe"), <i><a href="/wiki/L%27Histoire" title="L'Histoire">L'Histoire</a></i> n°322, July–August 2007, p. 17.</span></li>
-<li id="cite_note-37"><span class="mw-cite-backlink"><b><a href="#cite_ref-37">^</a></b></span> <span class="reference-text">Dobyns, Henry (1983). <i>Their Number Become Thinned: Native American Dynamics in Eastern North America</i>. Knoxville: University of Tennessee Press.</span></li>
-</ol>
-</div>
-<h2><span class="mw-headline" id="Further_reading">Further reading</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=8" title="Edit section: Further reading">edit</a><span class="mw-editsection-bracket">]</span></span></h2>
-<ul>
-<li><cite class="citation web">Haub, Carl (October 2011). <a rel="nofollow" class="external text" href="http://www.prb.org/Articles/2002/HowManyPeopleHaveEverLivedonEarth.aspx">"How Many People Have Ever Lived on Earth?"</a>. <a href="/wiki/Population_Reference_Bureau" title="Population Reference Bureau">Population Reference Bureau</a><span class="reference-accessdate">. Retrieved <span class="nowrap">21 July</span> 2013</span>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=unknown&amp;rft.btitle=How+Many+People+Have+Ever+Lived+on+Earth%3F&amp;rft.pub=Population+Reference+Bureau&amp;rft.date=2011-10&amp;rft.aulast=Haub&amp;rft.aufirst=Carl&amp;rft_id=http%3A%2F%2Fwww.prb.org%2FArticles%2F2002%2FHowManyPeopleHaveEverLivedonEarth.aspx&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AWorld+population+estimates" class="Z3988"><span style="display:none;">&#160;</span></span></li>
-</ul>
-<h2><span class="mw-headline" id="External_links">External links</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=World_population_estimates&amp;action=edit&amp;section=9" title="Edit section: External links">edit</a><span class="mw-editsection-bracket">]</span></span></h2>
-<ul>
-<li><a rel="nofollow" class="external text" href="https://www.census.gov/population/international/data/worldpop/table_history.php">Historical Estimates of World Population</a>—<a href="/wiki/United_States_Census_Bureau" title="United States Census Bureau">United States Census Bureau</a></li>
-<li><a rel="nofollow" class="external text" href="http://itbulk.org/population/world-population-history/">World Population Estimation Graps 10000 BC - 1950 AD</a> World population estimation graphs; estimations by HYDE, <a href="/wiki/Colin_McEvedy" title="Colin McEvedy">Colin McEvedy</a>, <a href="/wiki/Massimo_Livi_Bacci" title="Massimo Livi Bacci">Massimo Livi Bacci</a></li>
-</ul>
-<div role="navigation" class="navbox" aria-labelledby="Global_human_population" style="padding:3px">
-<table class="nowraplinks collapsible autocollapse navbox-inner" style="border-spacing:0;background:transparent;color:inherit">
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-<ul>
-<li class="nv-view"><a href="/wiki/Template:Population" title="Template:Population"><abbr title="View this template" style=";background:#e6e6e6;;background:none transparent;border:none;-moz-box-shadow:none;-webkit-box-shadow:none;box-shadow:none;">v</abbr></a></li>
-<li class="nv-talk"><a href="/wiki/Template_talk:Population" title="Template talk:Population"><abbr title="Discuss this template" style=";background:#e6e6e6;;background:none transparent;border:none;-moz-box-shadow:none;-webkit-box-shadow:none;box-shadow:none;">t</abbr></a></li>
-<li class="nv-edit"><a class="external text" href="//en.wikipedia.org/w/index.php?title=Template:Population&amp;action=edit"><abbr title="Edit this template" style=";background:#e6e6e6;;background:none transparent;border:none;-moz-box-shadow:none;-webkit-box-shadow:none;box-shadow:none;">e</abbr></a></li>
-</ul>
-</div>
-<div id="Global_human_population" style="font-size:114%;margin:0 4em"><a href="/wiki/World_population" title="World population">Global human population</a></div>
-</th>
-</tr>
-<tr>
-<th scope="row" class="navbox-group" style="width:1%;background:#eee;">Major topics</th>
-<td class="navbox-list navbox-odd hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px">
-<div style="padding:0em 0.25em">
-<ul>
-<li><a href="/wiki/Biocapacity" title="Biocapacity">Biocapacity</a></li>
-<li><a href="/wiki/Optimum_population" title="Optimum population">Optimum population</a></li>
-<li><a href="/wiki/Human_overpopulation" title="Human overpopulation">Overpopulation</a>
-<ul>
-<li><a href="/wiki/Malthusian_catastrophe" title="Malthusian catastrophe">Malthusian catastrophe</a></li>
-</ul>
-</li>
-<li><a href="/wiki/Population" title="Population">Population</a></li>
-<li><a href="/wiki/Population_ethics" title="Population ethics">Population ethics</a></li>
-<li><a href="/wiki/Population_momentum" title="Population momentum">Population momentum</a></li>
-<li><a href="/wiki/Sustainable_development" title="Sustainable development">Sustainable development</a></li>
-<li><a href="/wiki/Reproductive_rights" title="Reproductive rights">Women's reproductive rights</a></li>
-<li><a href="/wiki/Zero_population_growth" title="Zero population growth">Zero population growth</a></li>
-</ul>
-</div>
-</td>
-</tr>
-<tr>
-<th scope="row" class="navbox-group" style="width:1%;background:#eee;">Biological and<br />
-related topics</th>
-<td class="navbox-list navbox-even hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px">
-<div style="padding:0em 0.25em">
-<ul>
-<li><a href="/wiki/Family_planning" title="Family planning">Family planning</a>
-<ul>
-<li><a href="/wiki/Population_Matters#Pledge_two_or_fewer" title="Population Matters">Pledge two or fewer</a></li>
-</ul>
-</li>
-<li><a href="/wiki/Human_population_planning" title="Human population planning">Human population planning</a>
-<ul>
-<li><a href="/wiki/One-child_policy" title="One-child policy">One-child policy</a></li>
-<li><a href="/wiki/Two-child_policy" title="Two-child policy">Two-child policy</a></li>
-</ul>
-</li>
-<li><a href="/wiki/Population_biology" title="Population biology">Population biology</a></li>
-<li><a href="/wiki/Population_decline" title="Population decline">Population decline</a></li>
-<li><a href="/wiki/Population_density" title="Population density">Population density</a>
-<ul>
-<li><a href="/wiki/Physiological_density" title="Physiological density">Physiological density</a></li>
-</ul>
-</li>
-<li><a href="/wiki/Population_dynamics" title="Population dynamics">Population dynamics</a></li>
-<li><a href="/wiki/Population_growth" title="Population growth">Population growth</a></li>
-<li><a href="/wiki/Population_model" title="Population model">Population model</a></li>
-<li><a href="/wiki/Population_pyramid" title="Population pyramid">Population pyramid</a></li>
-<li><a href="/wiki/Projections_of_population_growth" title="Projections of population growth">Projections of population growth</a></li>
-</ul>
-</div>
-</td>
-</tr>
-<tr>
-<th scope="row" class="navbox-group" style="width:1%;background:#eee;"><a href="/wiki/Human_impact_on_the_environment" title="Human impact on the environment">Human impact on<br />
-the environment</a></th>
-<td class="navbox-list navbox-odd hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px">
-<div style="padding:0em 0.25em">
-<ul>
-<li><a href="/wiki/Deforestation" title="Deforestation">Deforestation</a></li>
-<li><a href="/wiki/Desalination" title="Desalination">Desalination</a></li>
-<li><a href="/wiki/Desertification" title="Desertification">Desertification</a></li>
-<li><a href="/wiki/Human_impact_on_the_environment" title="Human impact on the environment">Environmental impact</a>
-<ul>
-<li><a href="/wiki/Environmental_impact_of_agriculture" title="Environmental impact of agriculture">of agriculture</a></li>
-<li><a href="/wiki/Environmental_impact_of_aviation" title="Environmental impact of aviation">of aviation</a></li>
-<li><a href="/wiki/Environmental_impact_of_biodiesel" title="Environmental impact of biodiesel">of biodiesel</a></li>
-<li><a href="/wiki/Environmental_impact_of_concrete" title="Environmental impact of concrete">of concrete</a></li>
-<li><a href="/wiki/Environmental_impact_of_electricity_generation" title="Environmental impact of electricity generation">of electricity generation</a></li>
-<li><a href="/wiki/Environmental_impact_of_the_energy_industry" title="Environmental impact of the energy industry">of the energy industry</a></li>
-<li><a href="/wiki/Environmental_impact_of_fishing" title="Environmental impact of fishing">of fishing</a></li>
-<li><a href="/wiki/Environmental_impact_of_irrigation" title="Environmental impact of irrigation">of irrigation</a></li>
-<li><a href="/wiki/Environmental_impact_of_mining" title="Environmental impact of mining">of mining</a></li>
-<li><a href="/wiki/Off-roading#Environmental_impact" title="Off-roading">of off-roading</a></li>
-<li><a href="/wiki/Environmental_impact_of_the_oil_shale_industry" title="Environmental impact of the oil shale industry">of oil shale industry</a></li>
-<li><a href="/wiki/Social_and_environmental_impact_of_palm_oil" title="Social and environmental impact of palm oil">of palm oil</a></li>
-<li><a href="/wiki/Environmental_impact_of_paper" title="Environmental impact of paper">of paper</a></li>
-<li><a href="/wiki/Environmental_impact_of_the_petroleum_industry" title="Environmental impact of the petroleum industry">of the petroleum industry</a></li>
-<li><a href="/wiki/Environmental_impact_of_reservoirs" title="Environmental impact of reservoirs">of reservoirs</a></li>
-<li><a href="/wiki/Environmental_impact_of_shipping" title="Environmental impact of shipping">of shipping</a></li>
-<li><a href="/wiki/Environmental_impact_of_war" title="Environmental impact of war">of war</a></li>
-</ul>
-</li>
-<li><a href="/wiki/Industrialisation" title="Industrialisation">Industrialisation</a></li>
-<li><a href="/wiki/Land_degradation" title="Land degradation">Land degradation</a></li>
-<li><a href="/wiki/Land_reclamation" title="Land reclamation">Land reclamation</a></li>
-<li><a href="/wiki/Overconsumption" title="Overconsumption">Overconsumption</a></li>
-<li><a href="/wiki/Pollution" title="Pollution">Pollution</a></li>
-<li><a href="/wiki/Quarry" title="Quarry">Quarrying</a></li>
-<li><a href="/wiki/Urbanization" title="Urbanization">Urbanization</a>
-<ul>
-<li><a href="/wiki/Green_belt" title="Green belt">Loss of green belts</a></li>
-<li><a href="/wiki/Urban_sprawl" title="Urban sprawl">Urban sprawl</a></li>
-</ul>
-</li>
-<li><a href="/wiki/Waste" title="Waste">Waste</a></li>
-<li><a href="/wiki/Water_scarcity" title="Water scarcity">Water scarcity</a>
-<ul>
-<li><a href="/wiki/Overdrafting" title="Overdrafting">Overdrafting</a></li>
-</ul>
-</li>
-</ul>
-</div>
-</td>
-</tr>
-<tr>
-<th scope="row" class="navbox-group" style="width:1%;background:#eee;">
-<div style="padding:0.1em 0;line-height:1.2em;"><a href="/wiki/Population_ecology" title="Population ecology">Population<br />
-ecology</a></div>
-</th>
-<td class="navbox-list navbox-even hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px">
-<div style="padding:0em 0.25em">
-<ul>
-<li><a href="/wiki/Carrying_capacity#Humans" title="Carrying capacity">Carrying capacity</a></li>
-<li><a href="/wiki/Deep_ecology" title="Deep ecology">Deep ecology</a></li>
-<li><a href="/wiki/Earth%27s_energy_budget" title="Earth's energy budget">Earth's energy budget</a></li>
-<li><a href="/wiki/Food_security" title="Food security">Food security</a></li>
-<li><a href="/wiki/Habitat_destruction" title="Habitat destruction">Habitat destruction</a></li>
-<li><span class="nowrap"><a href="/wiki/I_%3D_PAT" title="I = PAT">I = P × A  × T</a></span></li>
-<li><a href="/wiki/Malthusian_growth_model" title="Malthusian growth model">Malthusian growth model</a></li>
-<li><a href="/wiki/Overshoot_(population)" title="Overshoot (population)">Overshoot (population)</a></li>
-<li><a href="/wiki/World_energy_consumption" title="World energy consumption">World energy consumption</a></li>
-<li><a href="/wiki/World_energy_resources" title="World energy resources">World energy resources</a></li>
-<li><a href="/wiki/World3" title="World3">World3 model</a></li>
-</ul>
-</div>
-</td>
-</tr>
-<tr>
-<th scope="row" class="navbox-group" style="width:1%;background:#eee;">Literature</th>
-<td class="navbox-list navbox-odd hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px">
-<div style="padding:0em 0.25em">
-<ul>
-<li><i><a href="/wiki/A_Modest_Proposal" title="A Modest Proposal">A Modest Proposal</a></i></li>
-<li><i><a href="/wiki/Observations_Concerning_the_Increase_of_Mankind,_Peopling_of_Countries,_etc." title="Observations Concerning the Increase of Mankind, Peopling of Countries, etc.">Observations Concerning the Increase of Mankind, Peopling of Countries, etc.</a></i></li>
-<li><i><a href="/wiki/An_Essay_on_the_Principle_of_Population" title="An Essay on the Principle of Population">An Essay on the Principle of Population</a></i></li>
-<li>"<a href="/wiki/How_Much_Land_Does_a_Man_Need%3F" title="How Much Land Does a Man Need?">How Much Land Does a Man Need?</a>"</li>
-<li><i><a href="/wiki/Operating_Manual_for_Spaceship_Earth" title="Operating Manual for Spaceship Earth">Operating Manual for Spaceship Earth</a></i></li>
-<li><i><a href="/wiki/Population_Control:_Real_Costs,_Illusory_Benefits" title="Population Control: Real Costs, Illusory Benefits">Population Control: Real Costs, Illusory Benefits</a></i></li>
-<li><i><a href="/wiki/The_Limits_to_Growth" title="The Limits to Growth">The Limits to Growth</a></i></li>
-<li><i><a href="/wiki/The_Population_Bomb" title="The Population Bomb">The Population Bomb</a></i></li>
-<li><i><a href="/wiki/The_Skeptical_Environmentalist" title="The Skeptical Environmentalist">The Skeptical Environmentalist</a></i></li>
-<li><i><a href="/wiki/The_Ultimate_Resource" title="The Ultimate Resource">The Ultimate Resource</a></i></li>
-</ul>
-</div>
-</td>
-</tr>
-<tr>
-<th scope="row" class="navbox-group" style="width:1%;background:#eee;">Publications</th>
-<td class="navbox-list navbox-even hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px;font-style:italic;">
-<div style="padding:0em 0.25em">
-<ul>
-<li><a href="/wiki/Population_and_Environment" title="Population and Environment">Population and Environment</a></li>
-<li><a href="/wiki/Population_and_Development_Review" title="Population and Development Review">Population and Development Review</a></li>
-</ul>
-</div>
-</td>
-</tr>
-<tr>
-<th scope="row" class="navbox-group" style="width:1%;background:#eee;">Lists</th>
-<td class="navbox-list navbox-odd hlist" style="text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px">
-<div style="padding:0em 0.25em">
-<ul>
-<li><a href="/wiki/Population_and_housing_censuses_by_country" title="Population and housing censuses by country">Population and housing censuses by country</a></li>
-<li><a href="/wiki/List_of_metropolitan_areas_by_population" title="List of metropolitan areas by population">Metropolitan areas by population</a></li>
-<li><a href="/wiki/World_population_milestones" title="World population milestones">Population milestone babies</a></li>
-</ul>
-</div>
-</td>
-</tr>
-<tr>
-<th scope="row" class="navbox-group" style="width:1%;background:#eee;">
-<div style="padding:0.1em 0;line-height:1.2em;">Events and<br />
-organizations</div>
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diff --git a/notebooks/data/World_population_estimates2.csv b/notebooks/data/World_population_estimates2.csv
deleted file mode 100644
index ed7de057..00000000
--- a/notebooks/data/World_population_estimates2.csv
+++ /dev/null
@@ -1,68 +0,0 @@
-Year,census,prb,un,maddison,hyde,tanton,biraben,mj,thomlinson,durand,clark
-1950,2557628654,2516000000.0,2525149000.0,2544000000.0,2527960000.0,2400000000.0,2527000000.0,2500000000.0,2400000000.0,,2486000000.0
-1951,2594939877,,2572850917.0,2571663000.0,,,,,,,
-1952,2636772306,,2619292068.0,2617949000.0,,,,,,,
-1953,2682053389,,2665865392.0,2665959000.0,,,,,,,
-1954,2730228104,,2713172027.0,2716927000.0,,,,,,,
-1955,2782098943,,2761650981.0,2769074000.0,,,,,,,
-1956,2835299673,,2811572031.0,2822502000.0,,,,,,,
-1957,2891349717,,2863042795.0,2879934000.0,,,,,,,
-1958,2948137248,,2916030167.0,2939254000.0,,,,,,,
-1959,3000716593,,2970395814.0,2995909000.0,,,,,,,
-1960,3043001508,,3026002942.0,3041507000.0,3042000000.0,,,,,,
-1961,3083966929,,3082830266.0,3082161000.0,,,,,,,
-1962,3140093217,,3141071531.0,3135787000.0,,,,,,,3036000000.0
-1963,3209827882,,3201178277.0,3201354000.0,,,,,,,
-1964,3281201306,,3263738832.0,3266477000.0,,,,,,,
-1965,3350425793,,3329122479.0,3333138000.0,,,,,,,
-1966,3420677923,,3397475247.0,3402224000.0,,,,,,,3288000000.0
-1967,3490333715,,3468521724.0,3471464000.0,,,,,,,
-1968,3562313822,,3541674891.0,3543086000.0,,,,,,,
-1969,3637159050,,3616108749.0,3615743000.0,,,,,,,
-1970,3712697742,,3691172616.0,3691157000.0,3710000000.0,,3637000000.0,,3600000000.0,"3,600,000,000– 3,700,000,000",3632000000.0
-1971,3790326948,,3766754345.0,3769818000.0,,,,,,,
-1972,3866568653,,3842873611.0,3846499000.0,,,,,,,
-1973,3942096442,,3919182332.0,3922793000.0,3923000000.0,,,,,,3860000000.0
-1974,4016608813,,3995304922.0,3997677000.0,,,,,,,
-1975,4089083233,,4071020434.0,4070671000.0,,,,3900000000.0,4000000000.0,,
-1976,4160185010,,4146135850.0,4141445000.0,,,,,,,
-1977,4232084578,,4220816737.0,4213539000.0,,,,,,,
-1978,4304105753,,4295664825.0,4286317000.0,,,,,,,
-1979,4379013942,,4371527871.0,4363144000.0,,,,,,,
-1980,4451362735,,4449048798.0,4439529000.0,4461000000.0,,,,,,
-1981,4534410125,,4528234634.0,4514838000.0,,,,,,,
-1982,4614566561,,4608962418.0,4587307000.0,,,,,,,
-1983,4695736743,,4691559840.0,4676388000.0,,,,,,,
-1984,4774569391,,4776392828.0,4756521000.0,,,,,,,
-1985,4856462699,,4863601517.0,4837719000.0,,5000000000.0,,,,,
-1986,4940571232,,4953376710.0,4920968000.0,,,,,,,
-1987,5027200492,,5045315871.0,5006672000.0,,,,,,,
-1988,5114557167,,5138214688.0,5093306000.0,,,,,,,
-1989,5201440110,,5230000000.0,5180540000.0,,,,,,,
-1990,5288955934,,5320816667.0,5269029000.0,5308000000.0,,,,,,
-1991,5371585922,,5408908724.0,5351922000.0,,,,,,,
-1992,5456136278,,5494899570.0,5435722000.0,,,,,,,
-1993,5538268316,,5578865109.0,5518127000.0,,,,,,,
-1994,5618682132,,5661086346.0,5599396000.0,,,,,,,
-1995,5699202985,5760000000.0,5741822412.0,5681575000.0,,,,,,,
-1996,5779440593,,5821016750.0,5762212000.0,,,,,,,
-1997,5857972543,5840000000.0,5898688337.0,5842122000.0,,,,,,,
-1998,5935213248,,5975303657.0,5921366000.0,,,,,,,
-1999,6012074922,,6051478010.0,5999622000.0,,,,,,,
-2000,6088571383,6067000000.0,6127700428.0,6076558000.0,6145000000.0,,,5750000000.0,,,
-2001,6165219247,6137000000.0,6204147026.0,6154791000.0,,,,,,,
-2002,6242016348,6215000000.0,6280853817.0,6231704000.0,,,,,,,
-2003,6318590956,6314000000.0,6357991749.0,6308364000.0,,,,,,,
-2004,6395699509,6396000000.0,6435705595.0,6374056000.0,,,,,,,
-2005,6473044732,6477000000.0,6514094605.0,6462987000.0,,,,,,,
-2006,6551263534,6555000000.0,6593227977.0,6540214000.0,,,,,,,
-2007,6629913759,6625000000.0,6673105937.0,6616689000.0,,,,,,,
-2008,6709049780,6705000000.0,6753649228.0,6694832000.0,,,,,,,
-2009,6788214394,6809972000.0,6834721933.0,6764086000.0,,,,,,,
-2010,6858584755,6892319000.0,6916183482.0,,,,,,,,
-2011,6935999491,6986951000.0,6997998760.0,,,,,,,,
-2012,7013871313,7057075000.0,7080072417.0,,,,,,,,
-2013,7092128094,7136796000.0,7162119434.0,,,,,,,,
-2014,7169968185,7238184000.0,7243784000.0,,,,,,,,
-2015,7247892788,7336435000.0,7349472000.0,,,,,,,,
-2016,7325996709,7418151841.0,,,,,,,,,
diff --git a/notebooks/data/World_population_estimates3.csv b/notebooks/data/World_population_estimates3.csv
deleted file mode 100644
index 2ab604fe..00000000
--- a/notebooks/data/World_population_estimates3.csv
+++ /dev/null
@@ -1,46 +0,0 @@
-Year,census,prb,un
-2016,7334771614.0,,7432663280.0
-2017,7412778971.0,,
-2018,7490427640.0,,
-2019,7567402977.0,,
-2020,7643402123.0,,7758157000.0
-2021,7718256830.0,,
-2022,7792021317.0,,
-2023,7864725370.0,,
-2024,7936271554.0,,
-2025,8006580553.0,8000000000.0,8141661000.0
-2026,8075716000.0,,
-2027,8143729466.0,,
-2028,8210559895.0,,
-2029,8276190519.0,,
-2030,8340606590.0,8505000000.0,8500766000.0
-2031,8403880343.0,,
-2032,8466094022.0,,
-2033,8527246205.0,,
-2034,8587325154.0,,
-2035,8646304704.0,,8838908000.0
-2036,8704239274.0,,
-2037,8761189197.0,,
-2038,8817138785.0,,
-2039,8872066537.0,,
-2040,8925949679.0,,9157234000.0
-2041,8978822945.0,,
-2042,9030723366.0,,
-2043,9081617002.0,,
-2044,9131462326.0,,
-2045,9180225214.0,,9453892000.0
-2046,9227935007.0,,
-2047,9274616811.0,,
-2048,9320232984.0,,
-2049,9364750182.0,,
-2050,9408141302.0,9804000000.0,9725148000.0
-2055,,,9968809000.0
-2060,,,10184290000.0
-2065,,,10375719000.0
-2070,,,10547989000.0
-2075,,,10701653000.0
-2080,,,10836635000.0
-2085,,,10953525000.0
-2090,,,11055270000.0
-2095,,,11142461000.0
-2100,,,11213317000.0
diff --git a/notebooks/data/baseball_drag.csv b/notebooks/data/baseball_drag.csv
deleted file mode 100644
index 7de5138a..00000000
--- a/notebooks/data/baseball_drag.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-Velocity in mph,Drag coefficient
-0.058486,0.49965
-19.845,0.49878
-39.476,0.49704
-50.181,0.48225
-60.134,0.45004
-68.533,0.40914
-73.769,0.38042
-77.408,0.36562
-83.879,0.34822
-90.507,0.33081
-97.29,0.31427
-104.54,0.30035
-113.83,0.28816
-120.9,0.28381
-127.34,0.28033
-134.41,0.28207
diff --git a/notebooks/data/baseball_drag.png b/notebooks/data/baseball_drag.png
deleted file mode 100644
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diff --git a/notebooks/data/glucose_insulin.csv b/notebooks/data/glucose_insulin.csv
deleted file mode 100644
index 91688b17..00000000
--- a/notebooks/data/glucose_insulin.csv
+++ /dev/null
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diff --git a/notebooks/examples/bigbelly.ipynb b/notebooks/examples/bigbelly.ipynb
deleted file mode 100644
index 26426d99..00000000
--- a/notebooks/examples/bigbelly.ipynb
+++ /dev/null
@@ -1,1104 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim library\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bigbelly"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "https://www.youtube.com/watch?v=frix_zTkPEs\n",
-    "\n",
-    "If the following import fails, open a terminal and run\n",
-    "\n",
-    "`conda install -c conda-forge pysolar`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pysolar.solar import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime, timedelta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime.datetime(2018, 10, 29, 14, 13, 30, 886072)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dt = datetime.now()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime.datetime(2018, 10, 29, 14, 13, 30, 886072, tzinfo=<StaticTzInfo 'EST'>)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from pytz import timezone\n",
-    "\n",
-    "dt = pytz.timezone('EST').localize(dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22.52017419635824"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "get_altitude(42.2931671, -71.263665, dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "781.1690621573555"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "latitude_deg = 42.3\n",
-    "longitude_deg = -71.3\n",
-    "altitude_deg = get_altitude(latitude_deg, longitude_deg, dt)\n",
-    "azimuth_deg = get_azimuth(latitude_deg, longitude_deg, dt)\n",
-    "radiation.get_radiation_direct(dt, altitude_deg)\n",
-    "\n",
-    "# result is in Watts per square meter"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>lat_deg</th>\n",
-       "      <td>42.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>lon_deg</th>\n",
-       "      <td>-71.3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "lat_deg    42.3\n",
-       "lon_deg   -71.3\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "location = State(lat_deg=42.3, lon_deg=-71.3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime.datetime(2017, 9, 15, 12, 30, tzinfo=<StaticTzInfo 'EST'>)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dt = datetime(year=2017, month=9, day=15, hour=12, minute=30)\n",
-    "dt = pytz.timezone('EST').localize(dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_irradiance(location, dt):\n",
-    "    degree = UNITS.degree\n",
-    "    watt = UNITS.watt\n",
-    "    meter = UNITS.meter\n",
-    "    \n",
-    "    sun = State(\n",
-    "        altitude_deg = get_altitude(location.lat_deg, location.lon_deg, dt),\n",
-    "        azimuth_deg = get_azimuth(location.lat_deg, location.lon_deg, dt)\n",
-    "    )\n",
-    "\n",
-    "    if sun.altitude_deg <= 0:\n",
-    "        irradiance = 0\n",
-    "    else:\n",
-    "        irradiance = radiation.get_radiation_direct(dt, sun.altitude_deg)\n",
-    "\n",
-    "    sun.set(irradiance = irradiance * watt / meter**2)\n",
-    "    return sun"
-   ]
-  },
-  {
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-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "dt = datetime(year=2017, month=9, day=15)\n",
-    "dt = pytz.timezone('EST').localize(dt)\n",
-    "\n",
-    "delta_t = timedelta(minutes=15)\n",
-    "\n",
-    "result = TimeSeries()\n",
-    "for i in range(24 * 4):\n",
-    "    dt += delta_t\n",
-    "    sun = compute_irradiance(location, dt)\n",
-    "    result[dt] = sun.irradiance.magnitude\n",
-    "\n",
-    "result"
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "result.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "joule"
-      ],
-      "text/latex": [
-       "$joule$"
-      ],
-      "text/plain": [
-       "<Unit('joule')>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cm = UNITS.centimeter\n",
-    "meter = UNITS.meter\n",
-    "watt = UNITS.watt\n",
-    "second = UNITS.second\n",
-    "joule = UNITS.joule"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "45 centimeter"
-      ],
-      "text/latex": [
-       "$45 centimeter$"
-      ],
-      "text/plain": [
-       "<Quantity(45, 'centimeter')>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "width = 45 * cm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "2025 centimeter<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$2025 centimeter^{2}$"
-      ],
-      "text/plain": [
-       "<Quantity(2025, 'centimeter ** 2')>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "area = width**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.0 centimeter<sup>2</sup> watt/meter<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$0.0 \\frac{centimeter^{2} \\cdot watt}{meter^{2}}$"
-      ],
-      "text/plain": [
-       "<Quantity(0.0, 'centimeter ** 2 * watt / meter ** 2')>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "power = sun.irradiance * area"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.2025 meter<sup>2</sup>"
-      ],
-      "text/latex": [
-       "$0.2025 meter^{2}$"
-      ],
-      "text/plain": [
-       "<Quantity(0.2025, 'meter ** 2')>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "area = area.to(meter**2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.0 watt"
-      ],
-      "text/latex": [
-       "$0.0 watt$"
-      ],
-      "text/plain": [
-       "<Quantity(0.0, 'watt')>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "power = sun.irradiance * area"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.0 second watt"
-      ],
-      "text/latex": [
-       "$0.0 second \\cdot watt$"
-      ],
-      "text/plain": [
-       "<Quantity(0.0, 'second * watt')>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "delta_t = 1 * second\n",
-    "energy = power * delta_t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "0.0 joule"
-      ],
-      "text/latex": [
-       "$0.0 joule$"
-      ],
-      "text/plain": [
-       "<Quantity(0.0, 'joule')>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "energy.to(joule)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/examples/world_pop_transition.ipynb b/notebooks/examples/world_pop_transition.ipynb
deleted file mode 100644
index 2c57f798..00000000
--- a/notebooks/examples/world_pop_transition.ipynb
+++ /dev/null
@@ -1,233 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Project 1 example\n",
-    "\n",
-    "Copyright 2018 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim library\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas import read_html\n",
-    "\n",
-    "filename = 'data/World_population_estimates.html'\n",
-    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
-    "table2 = tables[2]\n",
-    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
-    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
-    "                  'thomlinson', 'durand', 'clark']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(census, un, timeseries, title):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    census: TimeSeries of population estimates\n",
-    "    un: TimeSeries of population estimates\n",
-    "    timeseries: TimeSeries of simulation results\n",
-    "    title: string\n",
-    "    \"\"\"\n",
-    "    plot(census, ':', label='US Census')\n",
-    "    plot(un, '--', label='UN DESA')\n",
-    "    if len(timeseries):\n",
-    "        plot(timeseries, color='gray', label='model')\n",
-    "    \n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='World population (billion)',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un = table2.un / 1e9\n",
-    "census = table2.census / 1e9\n",
-    "empty = TimeSeries()\n",
-    "plot_results(census, un, empty, 'World population estimates')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "half = get_first_value(census) / 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "init = State(young=half, old=half)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = System(birth_rate1 = 1/18,\n",
-    "                mature_rate = 1/40,\n",
-    "                death_rate = 1/40,\n",
-    "                t_0 = 1950,\n",
-    "                t_end = 2016,\n",
-    "                init=init)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func1(state, t, system):\n",
-    "    births = system.birth_rate1 * state.young\n",
-    "        \n",
-    "    maturings = system.mature_rate * state.young\n",
-    "    deaths = system.death_rate * state.old\n",
-    "    \n",
-    "    young = state.young + births - maturings\n",
-    "    old = state.old + maturings - deaths\n",
-    "    \n",
-    "    return State(young=young, old=old)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = update_func1(init, system.t_0, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = update_func1(state, system.t_0, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate the system using any update function.\n",
-    "    \n",
-    "    init: initial State object\n",
-    "    system: System object\n",
-    "    update_func: function that computes the population next year\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    \n",
-    "    state = system.init\n",
-    "    results[system.t_0] = state.young + state.old\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        state = update_func(state, t, system)\n",
-    "        results[t+1] = state.young + state.old\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func1);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_results(census, un, results, 'World population estimates')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/figs/README b/notebooks/figs/README
deleted file mode 100644
index c6b3d34b..00000000
--- a/notebooks/figs/README
+++ /dev/null
@@ -1 +0,0 @@
-This is the directory where saved figs go when you run the notebooks.
\ No newline at end of file
diff --git a/notebooks/filter.ipynb b/notebooks/filter.ipynb
deleted file mode 100644
index a0b3a439..00000000
--- a/notebooks/filter.ipynb
+++ /dev/null
@@ -1,900 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Low pass filter\n",
-    "\n",
-    "The following circuit diagram (from [Wikipedia](https://en.wikipedia.org/wiki/File:RC_Divider.svg)) shows a low-pass filter built with one resistor and one capacitor.  \n",
-    "\n",
-    "![Low-pass filter circuit diagram](diagrams/RC_Divider.png)\n",
-    "\n",
-    "A \"filter\" is a circuit takes a signal, $V_{in}$, as input and produces a signal, $V_{out}$, as output.  In this context, a \"signal\" is a voltage that changes over time.\n",
-    "\n",
-    "A filter is \"low-pass\" if it allows low-frequency signals to pass from $V_{in}$ to $V_{out}$ unchanged, but it reduces the amplitude of high-frequency signals.\n",
-    "\n",
-    "By applying the laws of circuit analysis, we can derive a differential equation that describes the behavior of this system.  By solving the differential equation, we can predict the effect of this circuit on any input signal.\n",
-    "\n",
-    "Suppose we are given $V_{in}$ and $V_{out}$ at a particular instant in time.  By Ohm's law, which is a simple model of the behavior of resistors, the instantaneous current through the resistor is:\n",
-    "\n",
-    "$ I_R = (V_{in} - V_{out}) / R $\n",
-    "\n",
-    "where $R$ is resistance in ohms.\n",
-    "\n",
-    "Assuming that no current flows through the output of the circuit, Kirchhoff's current law implies that the current through the capacitor is:\n",
-    "\n",
-    "$ I_C = I_R $\n",
-    "\n",
-    "According to a simple model of the behavior of capacitors, current through the capacitor causes a change in the voltage across the capacitor:\n",
-    "\n",
-    "$ I_C = C \\frac{d V_{out}}{dt} $\n",
-    "\n",
-    "where $C$ is capacitance in farads (F).\n",
-    "\n",
-    "Combining these equations yields a differential equation for $V_{out}$:\n",
-    "\n",
-    "$ \\frac{d }{dt} V_{out} = \\frac{V_{in} - V_{out}}{R C} $\n",
-    "\n",
-    "Follow the instructions blow to simulate the low-pass filter for input signals like this:\n",
-    "\n",
-    "$ V_{in}(t) = A \\cos (2 \\pi f t) $\n",
-    "\n",
-    "where $A$ is the amplitude of the input signal, say 5 V, and $f$ is the frequency of the signal in Hz."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Params and System objects\n",
-    "\n",
-    "Here's a `Params` object to contain the quantities we need.  I've chosen values for `R1` and `C1` that might be typical for a circuit that works with audio signal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ohm = UNITS.ohm\n",
-    "farad = UNITS.farad\n",
-    "volt = UNITS.volt\n",
-    "Hz = UNITS.Hz\n",
-    "second = UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(\n",
-    "    R1 = 1e6 * ohm,\n",
-    "    C1 = 1e-9 * farad,\n",
-    "    A = 5 * volt,\n",
-    "    f = 1000 * Hz)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can pass the `Params` object `make_system` which computes some additional parameters and defines `init`.\n",
-    "\n",
-    "* `omega` is the frequency of the input signal in radians/second.\n",
-    "\n",
-    "* `tau` is the time constant for this circuit, which is the time it takes to get from an initial startup phase to \n",
-    "\n",
-    "* `cutoff` is the cutoff frequency for this circuit (in Hz), which marks the transition from low frequency signals, which pass through the filter unchanged, to high frequency signals, which are attenuated.\n",
-    "\n",
-    "* `t_end` is chosen so we run the simulation for 4 cycles of the input signal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    f, R1, C1 = params.f, params.R1, params.C1\n",
-    "    \n",
-    "    init = State(V_out = 0)\n",
-    "    omega = 2 * np.pi * f\n",
-    "    tau = R1 * C1\n",
-    "    cutoff = 1 / R1 / C1 / 2 / np.pi\n",
-    "    t_end = 4 / f\n",
-    "    dt = t_end / 4000\n",
-    "    \n",
-    "    return System(params, init=init, t_end=t_end, dt=dt,\n",
-    "                  omega=omega, tau=tau, cutoff=cutoff.to(Hz))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write a slope function that takes as an input a `State` object that contains `V_out`, and returns the derivative of `V_out`.\n",
-    "\n",
-    "Note: The ODE solver requires the return value from slope_func to be a sequence, even if there is only one element.  The simplest way to do that is to return a list with a single element:\n",
-    "\n",
-    "```\n",
-    "    return [dV_out_dt]\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system.init, 0*UNITS.s, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then run the simulation.  I suggest using `t_eval=ts` to make sure we have enough data points to plot and analyze the results. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a function you can use to plot `V_out` as a function of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(results):\n",
-    "    xs = results.V_out.index\n",
-    "    ys = results.V_out.values\n",
-    "\n",
-    "    t_end = get_last_label(results)\n",
-    "    if t_end < 10:\n",
-    "        xs *= 1000\n",
-    "        xlabel = 'Time (ms)'\n",
-    "    else:\n",
-    "        xlabel = 'Time (s)'\n",
-    "        \n",
-    "    plot(xs, ys)\n",
-    "    decorate(xlabel=xlabel,\n",
-    "             ylabel='$V_{out}$ (volt)',\n",
-    "             legend=False)\n",
-    "    \n",
-    "plot_results(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If things have gone according to plan, the amplitude of the output signal should be about 0.8 V.\n",
-    "\n",
-    "Also, you might notice that it takes a few cycles for the signal to get to the full amplitude. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sweeping frequency\n",
-    "\n",
-    "Plot `V_out` looks like for a range of frequencies:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fs = [1, 10, 100, 1000, 10000, 100000] * Hz\n",
-    "\n",
-    "for i, f in enumerate(fs):\n",
-    "    system = make_system(Params(params, f=f))\n",
-    "    results, details = run_ode_solver(system, slope_func)\n",
-    "    subplot(3, 2, i+1)\n",
-    "    plot_results(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At low frequencies, notice that there is an initial \"transient\" before the output gets to a steady-state sinusoidal output.  The duration of this transient is a small multiple of the time constant, `tau`, which is 1 ms."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Estimating the output ratio\n",
-    "\n",
-    "Let's compare the amplitudes of the input and output signals.  Below the cutoff frequency, we expect them to be about the same.  Above the cutoff, we expect the amplitude of the output signal to be smaller.\n",
-    "\n",
-    "We'll start with a signal at the cutoff frequency, `f=1000` Hz."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(Params(params, f=1000*Hz))\n",
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "V_out = results.V_out\n",
-    "plot_results(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function computes `V_in` as a `TimeSeries`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_vin(results, system):\n",
-    "    \"\"\"Computes V_in as a TimeSeries.\n",
-    "    \n",
-    "    results: TimeFrame with simulation results\n",
-    "    system: System object with A and omega\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    A, omega = system.A, system.omega\n",
-    "    \n",
-    "    ts = results.index.values * UNITS.second\n",
-    "    V_in = A * np.cos(omega * ts)\n",
-    "    return TimeSeries(V_in, results.index, name='V_in')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the input and output look like.  Notice that the output is not just smaller; it is also \"out of phase\"; that is, the peaks of the output are shifted to the right, relative to the peaks of the input."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "V_in = compute_vin(results, system)\n",
-    "\n",
-    "plot(V_out)\n",
-    "plot(V_in)\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='V (volt)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function estimates the amplitude of a signal by computing half the distance between the min and max."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def estimate_A(series):\n",
-    "    \"\"\"Estimate amplitude.\n",
-    "    \n",
-    "    series: TimeSeries\n",
-    "    \n",
-    "    returns: amplitude in volts\n",
-    "    \"\"\"\n",
-    "    return (series.max() - series.min()) / 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The amplitude of `V_in` should be near 5 (but not exact because we evaluated it at a finite number of points)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A_in = estimate_A(V_in)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The amplitude of `V_out` should be lower."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A_out = estimate_A(V_out)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the ratio between them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ratio = A_out / A_in"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ratio.to_base_units()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Encapsulate the code we have so far in a function that takes two TimeSeries objects and returns the ratio between their amplitudes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And test your function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "estimate_ratio(V_out, V_in)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Estimating phase offset\n",
-    "\n",
-    "The delay between the peak of the input and the peak of the output is call a \"phase shift\" or \"phase offset\", usually measured in fractions of a cycle, degrees, or radians.\n",
-    "\n",
-    "To estimate the phase offset between two signals, we can use cross-correlation.  Here's what the cross-correlation looks like between `V_out` and `V_in`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corr = correlate(V_out, V_in, mode='same')\n",
-    "corr = TimeSeries(corr, V_in.index)\n",
-    "plot(corr, color='C4')\n",
-    "decorate(xlabel='Lag (s)',\n",
-    "         ylabel='Correlation')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The location of the peak in the cross correlation is the estimated shift between the two signals, in seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "peak_time = corr.idxmax() * UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can express the phase offset as a multiple of the period of the input signal:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "period = 1 / system.f"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(peak_time / period).to_reduced_units()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We don't care about whole period offsets, only the fractional part, which we can get using `modf`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frac, whole = np.modf(peak_time / period)\n",
-    "frac = frac.to_reduced_units()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we can convert from a fraction of a cycle to degrees:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frac * 360 * UNITS.degree"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Encapsulate this code in a function that takes two `TimeSeries` objects and a `System` object, and returns the phase offset in degrees.\n",
-    "\n",
-    "Note: by convention, if the output is shifted to the right, the phase offset is negative."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "estimate_offset(V_out, V_in, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sweeping frequency again\n",
-    "\n",
-    "**Exercise:** Write a function that takes as parameters an array of input frequencies and a `Params` object.\n",
-    "\n",
-    "For each input frequency it should run a simulation and use the results to estimate the output ratio (dimensionless) and phase offset (in degrees).\n",
-    "\n",
-    "It should return two `SweepSeries` objects, one for the ratios and one for the offsets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run your function with these frequencies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fs = 10 ** linspace(0, 4, 9) * Hz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ratios, offsets = sweep_frequency(fs, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot output ratios like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(ratios, color='C2', label='output ratio')\n",
-    "decorate(xlabel='Frequency (Hz)',\n",
-    "         ylabel='$V_{out} / V_{in}$')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But it is useful and conventional to plot ratios on a log-log scale.  The vertical gray line shows the cutoff frequency."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_ratios(ratios, system):\n",
-    "    \"\"\"Plot output ratios.\n",
-    "    \"\"\"\n",
-    "    # axvline can't handle a Quantity with units\n",
-    "    cutoff = magnitude(system.cutoff)\n",
-    "    plt.axvline(cutoff, color='gray', alpha=0.4)\n",
-    "    \n",
-    "    plot(ratios, color='C2', label='output ratio')\n",
-    "    decorate(xlabel='Frequency (Hz)',\n",
-    "             ylabel='$V_{out} / V_{in}$',\n",
-    "             xscale='log', yscale='log')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_ratios(ratios, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This plot shows the cutoff behavior more clearly.  Below the cutoff, the output ratio is close to 1.  Above the cutoff, it drops off linearly, on a log scale, which indicates that output ratios for high frequencies are practically 0.\n",
-    "\n",
-    "Here's the plot for phase offset, on a log-x scale:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_offsets(offsets, system):\n",
-    "    \"\"\"Plot phase offsets.\n",
-    "    \"\"\"\n",
-    "    # axvline can't handle a Quantity with units\n",
-    "    cutoff = magnitude(system.cutoff)\n",
-    "    plt.axvline(cutoff, color='gray', alpha=0.4)\n",
-    "    \n",
-    "    plot(offsets, color='C9')\n",
-    "    decorate(xlabel='Frequency (Hz)',\n",
-    "             ylabel='Phase offset (degree)',\n",
-    "             xscale='log')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_offsets(offsets, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For low frequencies, the phase offset is near 0.  For high frequencies, it approaches 90 degrees."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis\n",
-    "\n",
-    "By analysis we can show that the output ratio for this signal is\n",
-    "\n",
-    "$A = \\frac{1}{\\sqrt{1 + (R C \\omega)^2}}$ \n",
-    "\n",
-    "where $\\omega = 2 \\pi f$, and the phase offset is\n",
-    "\n",
-    "$ \\phi = \\arctan (- R C \\omega)$ \n",
-    "\n",
-    "**Exercise:** Write functions that take an array of input frequencies and returns $A(f)$ and $\\phi(f)$ as `SweepSeries` objects.  Plot these objects and compare them with the results from the previous section.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = output_ratios(fs, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "phi = phase_offsets(fs, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the theoretical results along with the simulation results and see if they agree."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(A, ':', color='gray')\n",
-    "plot_ratios(ratios, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(phi, ':', color='gray')\n",
-    "plot_offsets(offsets, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the phase offsets, there are small differences between the theoretical results and our estimates, but that is probably because it is not easy to estimate phase offsets precisely from numerical results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Consider modifying this notebook to model a [first order high-pass filter](https://en.wikipedia.org/wiki/High-pass_filter#First-order_continuous-time_implementation), a [two-stage second-order low-pass filter](https://www.electronics-tutorials.ws/filter/filter_2.html), or a [passive band-pass filter](https://www.electronics-tutorials.ws/filter/filter_4.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/glucose.ipynb b/notebooks/glucose.ipynb
deleted file mode 100644
index 8bf68edc..00000000
--- a/notebooks/glucose.ipynb
+++ /dev/null
@@ -1,719 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 18\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Code from the previous chapter\n",
-    "\n",
-    "Read the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv('data/glucose_insulin.csv', index_col='time');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Interpolate the insulin data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "I = interpolate(data.insulin)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initialize the parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G0 = 290\n",
-    "k1 = 0.03\n",
-    "k2 = 0.02\n",
-    "k3 = 1e-05"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To estimate basal levels, we'll use the concentrations at `t=0`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Gb = data.glucose[0]\n",
-    "Ib = data.insulin[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create the initial condtions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "init = State(G=G0, X=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Make the `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = get_first_label(data)\n",
-    "t_end = get_last_label(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = System(G0=G0, k1=k1, k2=k2, k3=k3,\n",
-    "                init=init, Gb=Gb, Ib=Ib, I=I,\n",
-    "                t_0=t_0, t_end=t_end, dt=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Updates the glucose minimal model.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time in min\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State object\n",
-    "    \"\"\"\n",
-    "    G, X = state\n",
-    "    k1, k2, k3 = system.k1, system.k2, system.k3 \n",
-    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
-    "    dt = system.dt\n",
-    "    \n",
-    "    dGdt = -k1 * (G - Gb) - X*G\n",
-    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
-    "    \n",
-    "    G += dGdt * dt\n",
-    "    X += dXdt * dt\n",
-    "\n",
-    "    return State(G=G, X=X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    t_0, t_end, dt = system.t_0, system.t_end, system.dt\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t_0] = init\n",
-    "    ts = linrange(t_0, t_end, dt)\n",
-    "    \n",
-    "    for t in ts:\n",
-    "        frame.row[t+dt] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update_func);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Numerical solution\n",
-    "\n",
-    "In the previous chapter, we approximated the differential equations with difference equations, and solved them using `run_simulation`.\n",
-    "\n",
-    "In this chapter, we solve the differential equation numerically using `run_ode_solver`, which is a wrapper for the SciPy ODE solver.\n",
-    "\n",
-    "Instead of an update function, we provide a slope function that evaluates the right-hand side of the differential equations.  We don't have to do the update part; the solver does it for us."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the glucose minimal model.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time in min\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: derivatives of G and X\n",
-    "    \"\"\"\n",
-    "    G, X = state\n",
-    "    k1, k2, k3 = system.k1, system.k2, system.k3 \n",
-    "    I, Ib, Gb = system.I, system.Ib, system.Gb\n",
-    "    \n",
-    "    dGdt = -k1 * (G - Gb) - X*G\n",
-    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
-    "    \n",
-    "    return dGdt, dXdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run the ODE solver."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2, details = run_ode_solver(system, slope_func, t_eval=data.index);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`details` is a `ModSimSeries` object with information about how the solver worked."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`results` is a `TimeFrame` with one row for each time step and one column for each state variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting the results from `run_simulation` and `run_ode_solver`, we can see that they are not very different."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results.G, '-')\n",
-    "plot(results2.G, '-')\n",
-    "plot(data.glucose, 'bo')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The differences in `G` are less than 1%."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diff = results.G - results2.G\n",
-    "percent_diff = diff / results2.G * 100\n",
-    "percent_diff.dropna()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optimization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's find the parameters that yield the best fit for the data.  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll use these values as an initial estimate and iteratively improve them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(G0 = 290,\n",
-    "                k1 = 0.03,\n",
-    "                k2 = 0.02,\n",
-    "                k3 = 1e-05)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system` takes the parameters and actual data and returns a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params, data):\n",
-    "    \"\"\"Makes a System object with the given parameters.\n",
-    "    \n",
-    "    params: sequence of G0, k1, k2, k3\n",
-    "    data: DataFrame with `glucose` and `insulin`\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    # params might be a Params object or an array,\n",
-    "    # so we have to unpack it like this\n",
-    "    G0, k1, k2, k3 = params\n",
-    "    \n",
-    "    Gb = data.glucose[0]\n",
-    "    Ib = data.insulin[0]\n",
-    "    I = interpolate(data.insulin)\n",
-    "    \n",
-    "    t_0 = get_first_label(data)\n",
-    "    t_end = get_last_label(data)\n",
-    "\n",
-    "    init = State(G=G0, X=0)\n",
-    "    \n",
-    "    return System(G0=G0, k1=k1, k2=k2, k3=k3,\n",
-    "                  init=init, Gb=Gb, Ib=Ib, I=I,\n",
-    "                  t_0=t_0, t_end=t_end, dt=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`error_func` takes the parameters and actual data, makes a `System` object, and runs `odeint`, then compares the results to the data.  It returns an array of errors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params, data)\n",
-    "results, details = run_ode_solver(system, slope_func, t_eval=data.index)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func(params, data):\n",
-    "    \"\"\"Computes an array of errors to be minimized.\n",
-    "    \n",
-    "    params: sequence of parameters\n",
-    "    data: DataFrame of values to be matched\n",
-    "    \n",
-    "    returns: array of errors\n",
-    "    \"\"\"\n",
-    "    print(params)\n",
-    "    \n",
-    "    # make a System with the given parameters\n",
-    "    system = make_system(params, data)\n",
-    "    \n",
-    "    # solve the ODE\n",
-    "    results, details = run_ode_solver(system, slope_func, t_eval=data.index)\n",
-    "    \n",
-    "    # compute the difference between the model\n",
-    "    # results and actual data\n",
-    "    errors = results.G - data.glucose\n",
-    "    return errors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When we call `error_func`, we provide a sequence of parameters as a single object."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how that works:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "error_func(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`leastsq` is a wrapper for `scipy.optimize.leastsq`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we call it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_params, fit_details = leastsq(error_func, params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first return value is a `Params` object with the best parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The second return value is a `ModSimSeries` object with information about the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fit_details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have `best_params`, we can use it to make a `System` object and run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(best_params, data)\n",
-    "results, details = run_ode_solver(system, slope_func, t_eval=data.index)\n",
-    "details.message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results, along with the data.  The first few points of the model don't fit the data, but we don't expect them to."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results.G, label='simulation')\n",
-    "plot(data.glucose, 'bo', label='glucose data')\n",
-    "\n",
-    "decorate(xlabel='Time (min)',\n",
-    "         ylabel='Concentration (mg/dL)')\n",
-    "\n",
-    "savefig('figs/chap08-fig04.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Interpreting parameters\n",
-    "\n",
-    "Based on the parameters of the model, we can estimate glucose effectiveness and insulin sensitivity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def indices(params):\n",
-    "    \"\"\"Compute glucose effectiveness and insulin sensitivity.\n",
-    "    \n",
-    "    params: sequence of G0, k1, k2, k3\n",
-    "    data: DataFrame with `glucose` and `insulin`\n",
-    "    \n",
-    "    returns: State object containing S_G and S_I\n",
-    "    \"\"\"\n",
-    "    G0, k1, k2, k3 = params\n",
-    "    return State(S_G=k1, S_I=k3/k2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "indices(best_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "Here's the source code for `run_ode_solver` and `leastsq`, if you'd like to know how they work."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_code(run_ode_solver)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_code(leastsq)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:** Since we don't expect the first few points to agree, it's probably better not to make them part of the optimization process.  We can ignore them by leaving them out of the `Series` returned by `error_func`.  Modify the last line of `error_func` to return `errors.loc[8:]`, which includes only the elements of the `Series` from `t=8` and up.\n",
-    "\n",
-    "Does that improve the quality of the fit?  Does it change the best parameters by much?\n",
-    "\n",
-    "Note: You can read more about this use of `loc` [in the Pandas documentation](https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-integer)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** How sensitive are the results to the starting guess for the parameters?  If you try different values for the starting guess, do we get the same values for the best parameters?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Related reading:** You might be interested in this article about [people making a DIY artificial pancreas](https://www.bloomberg.com/news/features/2018-08-08/the-250-biohack-that-s-revolutionizing-life-with-diabetes)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/hopper.ipynb b/notebooks/hopper.ipynb
deleted file mode 100644
index b4d8bd83..00000000
--- a/notebooks/hopper.ipynb
+++ /dev/null
@@ -1,1342 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case study: Hopper optimization\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# If you want the figures to appear in the notebook, \n",
-    "# and you want to interact with them, use\n",
-    "# %matplotlib notebook\n",
-    "\n",
-    "# If you want the figures to appear in the notebook, \n",
-    "# and you don't want to interact with them, use\n",
-    "# %matplotlib inline\n",
-    "\n",
-    "# If you want the figures to appear in separate windows, use\n",
-    "# %matplotlib qt5\n",
-    "\n",
-    "# tempo switch from one to another, you have to select Kernel->Restart\n",
-    "\n",
-    "%matplotlib inline\n",
-    "\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "kg = UNITS.kilogram\n",
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "N = UNITS.newton"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 371,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "condition = Condition(mass = 0.03 * kg,\n",
-    "                      fraction = 1 / 3,\n",
-    "                      k = 9810.0 * N / m,\n",
-    "                      duration = 0.3 * s,\n",
-    "                      L = 0.05 * m,\n",
-    "                      d = 0.005 * m,\n",
-    "                      v1 = 0 * m / s,\n",
-    "                      v2 = 0 * m / s,\n",
-    "                      g = 9.8 * m / s**2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 384,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "condition = Condition(mass = 0.03,\n",
-    "                      fraction = 1 / 3,\n",
-    "                      k = 9810.0,\n",
-    "                      duration = 0.3,\n",
-    "                      L = 0.05,\n",
-    "                      d = 0.005,\n",
-    "                      v1 = 0,\n",
-    "                      v2 = 0,\n",
-    "                      g = 9.8)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 398,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def make_system(condition):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    condition: Condition with \n",
-    "    \n",
-    "    returns: System with init\n",
-    "    \"\"\"\n",
-    "    unpack(condition)\n",
-    "    \n",
-    "    x1 = L - d    # upper mass\n",
-    "    x2 = 0        # lower mass  \n",
-    "    \n",
-    "    init = State(x1=x1, x2=x2, v1=v1, v2=v2)\n",
-    "    \n",
-    "    m1, m2 = fraction*mass, (1-fraction)*mass\n",
-    "    ts = linspace(0, duration, 1001)\n",
-    "    \n",
-    "    return System(init=init, m1=m1, m2=m2, k=k, L=L, ts=ts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `make_system`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 399,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th>value</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>x1    0.045\n",
-       "x2    0.000\n",
-       "v1    0.000\n",
-       "v2    0.00...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>m1</th>\n",
-       "      <td>0.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>m2</th>\n",
-       "      <td>0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>k</th>\n",
-       "      <td>9810</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>L</th>\n",
-       "      <td>0.05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ts</th>\n",
-       "      <td>[0.0, 0.0003, 0.0006, 0.0009, 0.0012, 0.0015, ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "init    x1    0.045\n",
-       "x2    0.000\n",
-       "v1    0.000\n",
-       "v2    0.00...\n",
-       "m1                                                   0.01\n",
-       "m2                                                   0.02\n",
-       "k                                                    9810\n",
-       "L                                                    0.05\n",
-       "ts      [0.0, 0.0003, 0.0006, 0.0009, 0.0012, 0.0015, ...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 399,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(condition)\n",
-    "system"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 400,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>x1</th>\n",
-       "      <td>0.045</td>\n",
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-       "    <tr>\n",
-       "      <th>x2</th>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v1</th>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>v2</th>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "x1    0.045\n",
-       "x2    0.000\n",
-       "v1    0.000\n",
-       "v2    0.000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 400,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 401,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object with theta, y, r\n",
-    "    t: time\n",
-    "    system: System object with r, k\n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    x1, x2, v1, v2 = state\n",
-    "    unpack(system)\n",
-    "    \n",
-    "    dx = x1 - x2\n",
-    "    f_spring = k * (L - dx)\n",
-    "    \n",
-    "    a1 = f_spring/m1 - g\n",
-    "    a2 = -f_spring/m2 - g\n",
-    "    \n",
-    "    if t < 0.003 and a2 < 0:\n",
-    "        a2 = 0\n",
-    "    \n",
-    "    return v1, v2, a1, a2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `slope_func`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 402,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0, 0.0, 4895.199999999998, 0)"
-      ]
-     },
-     "execution_count": 402,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 403,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_odeint(system, slope_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 404,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>x1</th>\n",
-       "      <th>x2</th>\n",
-       "      <th>v1</th>\n",
-       "      <th>v2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.2988</th>\n",
-       "      <td>0.108655</td>\n",
-       "      <td>0.055882</td>\n",
-       "      <td>-3.678405</td>\n",
-       "      <td>-0.058611</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.2991</th>\n",
-       "      <td>0.107446</td>\n",
-       "      <td>0.055916</td>\n",
-       "      <td>-4.321706</td>\n",
-       "      <td>0.258630</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.2994</th>\n",
-       "      <td>0.106102</td>\n",
-       "      <td>0.056017</td>\n",
-       "      <td>-4.565052</td>\n",
-       "      <td>0.375893</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.2997</th>\n",
-       "      <td>0.104750</td>\n",
-       "      <td>0.056120</td>\n",
-       "      <td>-4.376955</td>\n",
-       "      <td>0.277434</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.3000</th>\n",
-       "      <td>0.103517</td>\n",
-       "      <td>0.056163</td>\n",
-       "      <td>-3.782438</td>\n",
-       "      <td>-0.024235</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              x1        x2        v1        v2\n",
-       "0.2988  0.108655  0.055882 -3.678405 -0.058611\n",
-       "0.2991  0.107446  0.055916 -4.321706  0.258630\n",
-       "0.2994  0.106102  0.056017 -4.565052  0.375893\n",
-       "0.2997  0.104750  0.056120 -4.376955  0.277434\n",
-       "0.3000  0.103517  0.056163 -3.782438 -0.024235"
-      ]
-     },
-     "execution_count": 404,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.results.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 405,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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GIvift64iMBSB3aLH57YugU6jVmx3c1gNKLQZ8M7ZTqk6lyAIWFSSHvCJ5jOz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd4bf413780>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(system.results.x1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 406,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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mqlFk1CIzNQldA6PI0CWx2htRmGXrkxEfJ4Pb44N91IXB4XGoEuNxvnUQggAs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\nQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd4bf445240>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(system.results.x2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 407,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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h16aYx1VTCn5GKROr/GvinVF9zFD5X4+OamFA3Vv2c/L5yALkngzVbTGYCa1s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\nZJCUf4IECRIkSNBk8P8BwAYqZsPJv20AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd4bf2f7208>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(system.results.x1 - system.results.x2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "plot(ys, color='green', label='y')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Length (m)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `r`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "plot(rs, color='red', label='r')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Radius (mm)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also see the relationship between `y` and `r`, which I derive analytically in the book."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "plot(rs, ys, color='purple')\n",
-    "\n",
-    "decorate(xlabel='Radius (mm)',\n",
-    "         ylabel='Length (m)',\n",
-    "         legend=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the figure from the book."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "subplot(3, 1, 1)\n",
-    "plot(thetas, label='theta')\n",
-    "decorate(ylabel='Angle (rad)')\n",
-    "\n",
-    "subplot(3, 1, 2)\n",
-    "plot(ys, color='green', label='y')\n",
-    "decorate(ylabel='Length (m)')\n",
-    "\n",
-    "subplot(3, 1, 3)\n",
-    "plot(rs, color='red', label='r')\n",
-    "\n",
-    "decorate(xlabel='Time(s)',\n",
-    "         ylabel='Radius (mm)')\n",
-    "\n",
-    "savefig('chap11-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use interpolation to find the time when `y` is 47 meters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "T = interp_inverse(ys, kind='cubic')\n",
-    "t_end = T(47)\n",
-    "t_end"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At that point `r` is 55 mm, which is `Rmax`, as expected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "R = interpolate(rs, kind='cubic')\n",
-    "R(t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The total amount of rotation is 1253 rad."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "THETA = interpolate(thetas, kind='cubic')\n",
-    "THETA(t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Unrolling"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For unrolling the paper, we need more units:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And a few more parameters in the `Condition` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "condition = Condition(Rmin = 0.02 * m,\n",
-    "                      Rmax = 0.055 * m,\n",
-    "                      Mcore = 15e-3 * kg,\n",
-    "                      Mroll = 215e-3 * kg,\n",
-    "                      L = 47 * m,\n",
-    "                      tension = 2e-4 * N,\n",
-    "                      duration = 180 * s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system` computes `rho_h`, which we'll need to compute moment of inertia, and `k`, which we'll use to compute `r`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def make_system(condition):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    condition: Condition with Rmin, Rmax, Mcore, Mroll,\n",
-    "                              L, tension, and duration\n",
-    "    \n",
-    "    returns: System with init, k, rho_h, Rmin, Rmax,\n",
-    "                         Mcore, Mroll, ts\n",
-    "    \"\"\"\n",
-    "    unpack(condition)\n",
-    "    \n",
-    "    init = State(theta = 0 * radian,\n",
-    "                 omega = 0 * radian/s,\n",
-    "                 y = L)\n",
-    "    \n",
-    "    area = pi * (Rmax**2 - Rmin**2)\n",
-    "    rho_h = Mroll / area\n",
-    "    k = (Rmax**2 - Rmin**2) / 2 / L / radian    \n",
-    "    ts = linspace(0, duration, 101)\n",
-    "    \n",
-    "    return System(init=init, k=k, rho_h=rho_h,\n",
-    "                  Rmin=Rmin, Rmax=Rmax,\n",
-    "                  Mcore=Mcore, Mroll=Mroll, \n",
-    "                  ts=ts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `make_system`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "system = make_system(condition)\n",
-    "system"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we compute `I` as a function of `r`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def moment_of_inertia(r, system):\n",
-    "    \"\"\"Moment of inertia for a roll of toilet paper.\n",
-    "    \n",
-    "    r: current radius of roll in meters\n",
-    "    system: System object with Mcore, rho, Rmin, Rmax\n",
-    "    \n",
-    "    returns: moment of inertia in kg m**2\n",
-    "    \"\"\"\n",
-    "    unpack(system)\n",
-    "    Icore = Mcore * Rmin**2   \n",
-    "    Iroll = pi * rho_h / 2 * (r**4 - Rmin**4)\n",
-    "    return Icore + Iroll"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When `r` is `Rmin`, `I` is small."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "moment_of_inertia(system.Rmin, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As `r` increases, so does `I`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "moment_of_inertia(system.Rmax, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the slope function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object with theta, omega, y\n",
-    "    t: time\n",
-    "    system: System object with Rmin, k, Mcore, rho_h, tension\n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    theta, omega, y = state\n",
-    "    unpack(system)\n",
-    "    \n",
-    "    r = sqrt(2*k*y + Rmin**2)\n",
-    "    I = moment_of_inertia(r, system)\n",
-    "    tau = r * tension\n",
-    "    alpha = tau / I\n",
-    "    dydt = -r * omega\n",
-    "    \n",
-    "    return omega, alpha, dydt      "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `slope_func`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "slope_func(system.init, 0*s, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "run_odeint(system, slope_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And look at the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "system.results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Extrating the time series"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "thetas = system.results.theta\n",
-    "omegas = system.results.omega\n",
-    "ys = system.results.y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `theta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "plot(thetas, label='theta')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Angle (rad)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `omega`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "plot(omegas, color='orange', label='omega')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Angular velocity (rad/s)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `y`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "plot(ys, color='green', label='y')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Length (m)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the figure from the book."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "subplot(3, 1, 1)\n",
-    "plot(thetas, label='theta')\n",
-    "decorate(ylabel='Angle (rad)')\n",
-    "\n",
-    "subplot(3, 1, 2)\n",
-    "plot(omegas, color='orange', label='omega')\n",
-    "decorate(ylabel='Angular velocity (rad/s)')\n",
-    "\n",
-    "subplot(3, 1, 3)\n",
-    "plot(ys, color='green', label='y')\n",
-    "\n",
-    "decorate(xlabel='Time(s)',\n",
-    "         ylabel='Length (m)')\n",
-    "\n",
-    "savefig('chap11-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Yo-yo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Simulate the descent of a yo-yo.  How long does it take to reach the end of the string.\n",
-    "\n",
-    "I provide a `Condition` object with the system parameters:\n",
-    "\n",
-    "* `Rmin` is the radius of the axle.  `Rmax` is the radius of the axle plus rolled string.\n",
-    "\n",
-    "* `Rout` is the radius of the yo-yo body.  `mass` is the total mass of the yo-yo, ignoring the string.  \n",
-    "\n",
-    "* `L` is the length of the string.\n",
-    "\n",
-    "* `g` is the acceleration of gravity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "condition = Condition(Rmin = 8e-3 * m,\n",
-    "                      Rmax = 16e-3 * m,\n",
-    "                      Rout = 35e-3 * m,\n",
-    "                      mass = 50e-3 * kg,\n",
-    "                      L = 1 * m,\n",
-    "                      g = 9.8 * m / s**2,\n",
-    "                      duration = 1 * s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a `make_system` function that computes `I` and `k` based on the system parameters.\n",
-    "\n",
-    "I estimated `I` by modeling the yo-yo as a solid cylinder with uniform density ([see here](https://en.wikipedia.org/wiki/List_of_moments_of_inertia)).  In reality, the distribution of weight in a yo-yo is often designed to achieve desired effects.  But we'll keep it simple."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def make_system(condition):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    condition: Condition with Rmin, Rmax, Rout, \n",
-    "                              mass, L, g, duration\n",
-    "    \n",
-    "    returns: System with init, k, Rmin, Rmax, mass,\n",
-    "                         I, g, ts\n",
-    "    \"\"\"\n",
-    "    unpack(condition)\n",
-    "    \n",
-    "    init = State(theta = 0 * radian,\n",
-    "                 omega = 0 * radian/s,\n",
-    "                 y = L,\n",
-    "                 v = 0 * m / s)\n",
-    "    \n",
-    "    I = mass * Rout**2 / 2\n",
-    "    k = (Rmax**2 - Rmin**2) / 2 / L / radian    \n",
-    "    ts = linspace(0, duration, 101)\n",
-    "    \n",
-    "    return System(init=init, k=k,\n",
-    "                  Rmin=Rmin, Rmax=Rmax,\n",
-    "                  mass=mass, I=I, g=g,\n",
-    "                  ts=ts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `make_system`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "system = make_system(condition)\n",
-    "system"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write a slope function for this system, using these results from the book:\n",
-    "\n",
-    "$ r = \\sqrt{2 k y + R_{min}^2} $ \n",
-    "\n",
-    "$ T      = m g I / I^*  $\n",
-    "\n",
-    "$ a      = -m g r^2 / I^* $\n",
-    "\n",
-    "$ \\alpha  = m g r / I^*  $\n",
-    "\n",
-    "where $I^*$ is the augmented moment of inertia, $I + m r^2$.\n",
-    "\n",
-    "Hint: If `y` is less than 0, it means you have reached the end of the string, so the equation for `r` is no longer valid.  In this case, the simplest thing to do it return the sequence of derivatives `0, 0, 0, 0`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "slope_func(system.init, 0*s, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "run_odeint(system, slope_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Check the final conditions.  If things have gone according to plan, the final value of `y` should be close to 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "system.results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "thetas = system.results.theta\n",
-    "ys = system.results.y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`theta` should increase and accelerate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "plot(thetas, label='theta')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Angle (rad)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`y` should decrease and accelerate down."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "plot(ys, color='green', label='y')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Length (m)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/insulin.ipynb b/notebooks/insulin.ipynb
deleted file mode 100644
index 2f6642c0..00000000
--- a/notebooks/insulin.ipynb
+++ /dev/null
@@ -1,316 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Insulin minimal model\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data\n",
-    "\n",
-    "We have data from Pacini and Bergman (1986), \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 23: 113-122.."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv('data/glucose_insulin.csv', index_col='time');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The insulin minimal model\n",
-    "\n",
-    "In addition to the glucose minimal mode, Pacini and Bergman present an insulin minimal model, in which the concentration of insulin, $I$, is governed by this differential equation:\n",
-    "\n",
-    "$ \\frac{dI}{dt} = -k I(t) + \\gamma (G(t) - G_T) t $"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Write a version of `make_system` that takes the parameters of this model, `I0`, `k`, `gamma`, and `G_T` as parameters, along with a `DataFrame` containing the measurements, and returns a `System` object suitable for use with `run_simulation` or `run_odeint`.\n",
-    "\n",
-    "Use it to make a `System` object with the following parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(I0 = 360,\n",
-    "                k = 0.25,\n",
-    "                gamma = 0.004,\n",
-    "                G_T = 80)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write a slope function that takes state, t, system as parameters and returns the derivative of `I` with respect to time.  Test your function with the initial condition $I(0)=360$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Run `run_ode_solver` with your `System` object and slope function, and plot the results, along with the measured insulin levels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Write an error function that takes a sequence of parameters as an argument, along with the `DataFrame` containing the measurements.  It should make a `System` object with the given parameters, run it, and compute the difference between the results of the simulation and the measured values.  Test your error function by calling it with the parameters from the previous exercise.\n",
-    "\n",
-    "Hint: As we did in a previous exercise, you might want to drop the errors for times prior to `t=8`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Use `leastsq` to find the parameters that best fit the data.  Make a `System` object with those parameters, run it, and plot the results along with the measurements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Using the best parameters, estimate the sensitivity to glucose of the first and second phase pancreatic responsivity:\n",
-    "\n",
-    "$ \\phi_1 = \\frac{I_{max} - I_b}{k (G_0 - G_b)} $\n",
-    "\n",
-    "$ \\phi_2 = \\gamma \\times 10^4 $\n",
-    "\n",
-    "For $G_0$, use the best estimate from the glucose model, 290.  For $G_b$ and $I_b$, use the inital measurements from the data.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/jump.ipynb b/notebooks/jump.ipynb
deleted file mode 100644
index b3dda60a..00000000
--- a/notebooks/jump.ipynb
+++ /dev/null
@@ -1,674 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Bungee jump case study\n",
-    "\n",
-    "Copyright 2018 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Bungee jumping"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Suppose you want to set the world record for the highest \"bungee dunk\", [as shown in this video](https://www.youtube.com/watch?v=UBf7WC19lpw).  Since the record is 70 m, let's design a jump for 80 m.\n",
-    "\n",
-    "We'll make the following modeling assumptions:\n",
-    "\n",
-    "1. Initially the bungee cord hangs from a crane with the attachment point 80 m above a cup of tea.\n",
-    "\n",
-    "2. Until the cord is fully extended, it applies no force to the jumper.  It turns out this might not be a good assumption; we will revisit it.\n",
-    "\n",
-    "3. After the cord is fully extended, it obeys [Hooke's Law](https://en.wikipedia.org/wiki/Hooke%27s_law); that is, it applies a force to the jumper proportional to the extension of the cord beyond its resting length.\n",
-    "\n",
-    "4. The jumper is subject to drag force proportional to the square of their velocity, in the opposite of their direction of motion.\n",
-    "\n",
-    "Our objective is to choose the length of the cord, `L`, and its spring constant, `k`, so that the jumper falls all the way to the tea cup, but no farther! \n",
-    "\n",
-    "First I'll create a `Param` object to contain the quantities we'll need:\n",
-    "\n",
-    "1. Let's assume that the jumper's mass is 75 kg.\n",
-    "\n",
-    "2. With a terminal velocity of 60 m/s.\n",
-    "\n",
-    "3. The length of the bungee cord is `L = 40 m`.\n",
-    "\n",
-    "4. The spring constant of the cord is `k = 20 N / m` when the cord is stretched, and 0 when it's compressed.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(y_attach = 80 * m,\n",
-    "                 v_init = 0 * m / s,\n",
-    "                 g = 9.8 * m/s**2,\n",
-    "                 mass = 75 * kg,\n",
-    "                 area = 1 * m**2,\n",
-    "                 rho = 1.2 * kg/m**3,\n",
-    "                 v_term = 60 * m / s,\n",
-    "                 L = 25 * m,\n",
-    "                 k = 40 * N / m,\n",
-    "                 zero_force = 0 * N)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now here's a version of `make_system` that takes a `Params` object as a parameter.\n",
-    "\n",
-    "`make_system` uses the given value of `v_term` to compute the drag coefficient `C_d`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Makes a System object for the given params.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    area, mass = params.area, params.mass\n",
-    "    g, rho = params.g, params.rho\n",
-    "    v_init, v_term = params.v_init, params.v_term\n",
-    "    y_attach = params.y_attach\n",
-    "    \n",
-    "    C_d = 2 * mass * g / (rho * area * v_term**2)\n",
-    "    init = State(y=y_attach, v=v_init)\n",
-    "    t_end = 20 * s\n",
-    "\n",
-    "    return System(params, C_d=C_d, \n",
-    "                  init=init, t_end=t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`spring_force` computes the force of the cord on the jumper.\n",
-    "\n",
-    "If the spring is not extended, it returns `zero_force`, which is either 0 Newtons or 0, depending on whether the `System` object has units.  I did that so the slope function works correctly with and without units."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def spring_force(y, system):\n",
-    "    \"\"\"Computes the force of the bungee cord on the jumper:\n",
-    "    \n",
-    "    y: height of the jumper\n",
-    "    \n",
-    "    Uses these variables from system|\n",
-    "    y_attach: height of the attachment point\n",
-    "    L: resting length of the cord\n",
-    "    k: spring constant of the cord\n",
-    "    \n",
-    "    returns: force in N\n",
-    "    \"\"\"\n",
-    "    y_attach, L, k = system.y_attach, system.L, system.k\n",
-    "    \n",
-    "    distance_fallen = y_attach - y\n",
-    "    if distance_fallen <= L:\n",
-    "        return system.zero_force\n",
-    "    \n",
-    "    extension = distance_fallen - L\n",
-    "    f_spring = k * extension\n",
-    "    return f_spring"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The spring force is 0 until the cord is fully extended.  When it is extended 1 m, the spring force is 40 N. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spring_force(80*m, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spring_force(55*m, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spring_force(54*m, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`drag_force` computes drag as a function of velocity:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(v, system):\n",
-    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
-    "    \n",
-    "    v: velocity\n",
-    "    system: System object\n",
-    "\n",
-    "    returns: drag force\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    f_drag = -np.sign(v) * rho * v**2 * C_d * area / 2\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the drag force at 60 meters per second."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v = -60 * m/s\n",
-    "f_drag = drag_force(v, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Acceleration due to drag at 60 m/s is approximately g, which confirms that 60 m/s is terminal velocity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a_drag = f_drag / system.mass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now here's the slope function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object containing g, rho,\n",
-    "            C_d, area, and mass\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    mass, g = system.mass, system.g\n",
-    "    \n",
-    "    a_drag = drag_force(v, system) / mass\n",
-    "    a_spring = spring_force(y, system) / mass\n",
-    "    \n",
-    "    dvdt = -g + a_drag + a_spring\n",
-    "    \n",
-    "    return v, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, let's test the slope function with the initial params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the plot of position as a function of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_position(results):\n",
-    "    plot(results.y)\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After reaching the lowest point, the jumper springs back almost to almost 70 m, and oscillates several times.  That looks like more osciallation that we expect from an actual jump, which suggests that there some dissipation of energy in the real world that is not captured in our model.  To improve the model, that might be a good thing to investigate.\n",
-    "\n",
-    "But since we are primarily interested in the initial descent, the model might be good enough for now.\n",
-    "\n",
-    "We can use `min` to find the lowest point:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "min(results.y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At the lowest point, the jumper is still too high, so we'll need to increase `L` or decrease `k`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's velocity as a function of time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_velocity(results):\n",
-    "    plot(results.v, color='C1', label='v')\n",
-    "        \n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "plot_velocity(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Although we compute acceleration inside the slope function, we don't get acceleration as a result from `run_ode_solver`.\n",
-    "\n",
-    "We can approximate it by computing the numerical derivative of `ys`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = gradient(results.v)\n",
-    "plot(a)\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Acceleration (m/$s^2$)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can compute the maximum acceleration the jumper experiences:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_acceleration = max(a) * m/s**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Relative to the acceleration of gravity, the jumper \"pulls\" about \"1.7 g's\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_acceleration / system.g"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Under the hood\n",
-    "\n",
-    "The gradient function in `modsim.py` adapts the NumPy function of the same name so it works with `Series` objects.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "source_code(gradient)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Solving for length\n",
-    "\n",
-    "Assuming that `k` is fixed, let's find the length `L` that makes the minimum altitude of the jumper exactly 0."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The metric we are interested in is the lowest point of the first oscillation.  For both efficiency and accuracy, it is better to stop the simulation when we reach this point, rather than run past it and the compute the minimum.\n",
-    "\n",
-    "Here's an event function that stops the simulation when velocity is 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Return velocity.\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    return v"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As usual, we should test it with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "event_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can test it and confirm that it stops at the bottom of the jump."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "min(results.y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Write an error function that takes `L` and `params` as arguments, simulates a bungee jump, and returns the lowest point.\n",
-    "\n",
-    "Test the error function with a guess of 25 m and confirm that the return value is about 5 meters.\n",
-    "\n",
-    "Use `root_scalar` with your error function to find the value of `L` that yields a perfect bungee dunk.  Hint: before calling `root_scalar`, make a version of `params` with no dimensions.\n",
-    "\n",
-    "Run a simulation with the result from `root_scalar` and confirm that it works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/jump2.ipynb b/notebooks/jump2.ipynb
deleted file mode 100644
index 5ce6a306..00000000
--- a/notebooks/jump2.ipynb
+++ /dev/null
@@ -1,924 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Bungee dunk example, taking into account the mass of the bungee cord\n",
-    "\n",
-    "Copyright 2019 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Bungee jumping"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the previous case study, we simulated a bungee jump with a model that took into account gravity, air resistance, and the spring force of the bungee cord, but we ignored the weight of the cord.\n",
-    "\n",
-    "It is tempting to say that the weight of the cord doesn't matter, because it falls along with the jumper.  But that intuition is incorrect, as explained by [Heck, Uylings, and Kędzierska](http://iopscience.iop.org/article/10.1088/0031-9120/45/1/007).  As the cord falls, it transfers energy to the jumper.   They derive a differential equation that relates the acceleration of the jumper to position and velocity:\n",
-    "\n",
-    "$a = g + \\frac{\\mu v^2/2}{\\mu(L+y) + 2L}$ \n",
-    "\n",
-    "where $a$ is the net acceleration of the number, $g$ is acceleration due to gravity, $v$ is the velocity of the jumper, $y$ is the position of the jumper relative to the starting point (usually negative), $L$ is the length of the cord, and $\\mu$ is the mass ratio of the cord and jumper.\n",
-    "\n",
-    "If you don't believe this model is correct, [this video might convince you](https://www.youtube.com/watch?v=X-QFAB0gEtE).\n",
-    "\n",
-    "Following the example in Chapter 21, we'll model the jump with the following modeling assumptions:\n",
-    "\n",
-    "1. Initially the bungee cord hangs from a crane with the attachment point 80 m above a cup of tea.\n",
-    "\n",
-    "2. Until the cord is fully extended, it applies a force to the jumper as explained above.\n",
-    "\n",
-    "3. After the cord is fully extended, it obeys [Hooke's Law](https://en.wikipedia.org/wiki/Hooke%27s_law); that is, it applies a force to the jumper proportional to the extension of the cord beyond its resting length.\n",
-    "\n",
-    "4. The jumper is subject to drag force proportional to the square of their velocity, in the opposite of their direction of motion.\n",
-    "\n",
-    "First I'll create a `Param` object to contain the quantities we'll need:\n",
-    "\n",
-    "1. Let's assume that the jumper's mass is 75 kg and the cord's mass is also 75 kg, so `mu=1`.\n",
-    "\n",
-    "2. The jumpers's frontal area is 1 square meter, and terminal velocity is 60 m/s.  I'll use these values to back out the coefficient of drag.\n",
-    "\n",
-    "3. The length of the bungee cord is `L = 25 m`.\n",
-    "\n",
-    "4. The spring constant of the cord is `k = 40 N / m` when the cord is stretched, and 0 when it's compressed.\n",
-    "\n",
-    "I adopt the coordinate system and most of the variable names from [Heck, Uylings, and Kędzierska](http://iopscience.iop.org/article/10.1088/0031-9120/45/1/007).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(v_init = 0 * m / s,\n",
-    "                g = 9.8 * m/s**2,\n",
-    "                M = 75 * kg,                # mass of jumper\n",
-    "                m_cord = 75 * kg,           # mass of cord\n",
-    "                area = 1 * m**2,            # frontal area of jumper\n",
-    "                rho = 1.2 * kg/m**3,    # density of air\n",
-    "                v_term = 60 * m / s,        # terminal velocity of jumper\n",
-    "                L = 25 * m,                 # length of cord\n",
-    "                k = 40 * N / m)             # spring constant of cord"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now here's a version of `make_system` that takes a `Params` object as a parameter.\n",
-    "\n",
-    "`make_system` uses the given value of `v_term` to compute the drag coefficient `C_d`.\n",
-    "\n",
-    "It also computes `mu` and the initial `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Makes a System object for the given params.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    M, m_cord = params.M, params.m_cord\n",
-    "    g, rho, area =  params.g, params.rho, params.area\n",
-    "    v_init, v_term = params.v_init, params.v_term\n",
-    "    \n",
-    "    # back out the coefficient of drag\n",
-    "    C_d = 2 * M * g / (rho * area * v_term**2)\n",
-    "    \n",
-    "    mu = m_cord / M\n",
-    "    init = State(y=0*m, v=v_init)\n",
-    "    t_end = 10 * s\n",
-    "\n",
-    "    return System(params, C_d=C_d, mu=mu,\n",
-    "                  init=init, t_end=t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`drag_force` computes drag as a function of velocity:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(v, system):\n",
-    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
-    "    \n",
-    "    v: velocity\n",
-    "    \n",
-    "    returns: drag force in N\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "\n",
-    "    f_drag = -np.sign(v) * rho * v**2 * C_d * area / 2\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's drag force at 20 m/s."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drag_force(20 * m/s, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function computes the acceleration of the jumper due to tension in the cord.\n",
-    "\n",
-    "$a_{cord} = \\frac{\\mu v^2/2}{\\mu(L+y) + 2L}$ "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cord_acc(y, v, system):\n",
-    "    \"\"\"Computes the force of the bungee cord on the jumper:\n",
-    "    \n",
-    "    y: height of the jumper\n",
-    "    v: velocity of the jumpter\n",
-    "    \n",
-    "    returns: acceleration in m/s\n",
-    "    \"\"\"\n",
-    "    L, mu = system.L, system.mu\n",
-    "    \n",
-    "    a_cord = -v**2 / 2 / (2*L/mu + (L+y))\n",
-    "    return a_cord"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's acceleration due to tension in the cord if we're going 20 m/s after falling 20 m."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y = -20 * m\n",
-    "v = -20 * m/s\n",
-    "cord_acc(y, v, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now here's the slope function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func1(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object containing g, rho,\n",
-    "            C_d, area, and mass\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    M, g = system.M, system.g\n",
-    "    \n",
-    "    a_drag = drag_force(v, system) / M\n",
-    "    a_cord = cord_acc(y, v, system)\n",
-    "    dvdt = -g + a_cord + a_drag\n",
-    "    \n",
-    "    return v, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, let's test the slope function with the initial params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func1(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll need an event function to stop the simulation when we get to the end of the cord."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Run until y=-L.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object containing g, rho,\n",
-    "            C_d, area, and mass\n",
-    "    \n",
-    "    returns: difference between y and -L\n",
-    "    \"\"\"\n",
-    "    y, v = state   \n",
-    "    return y + system.L"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "event_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func1, events=event_func)\n",
-    "details.message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how long it takes to drop 25 meters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_final = get_last_label(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the plot of position as a function of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_position(results, **options):\n",
-    "    plot(results.y, **options)\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "    \n",
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use `min` to find the lowest point:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "min(results.y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's velocity as a function of time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "def plot_velocity(results):\n",
-    "    plot(results.v, color='C1', label='v')\n",
-    "        \n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')\n",
-    "    \n",
-    "plot_velocity(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Velocity when we reach the end of the cord."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "min(results.v)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Although we compute acceleration inside the slope function, we don't get acceleration as a result from `run_ode_solver`.\n",
-    "\n",
-    "We can approximate it by computing the numerical derivative of `v`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = gradient(results.v)\n",
-    "plot(a)\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Acceleration (m/$s^2$)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The maximum downward acceleration, as a factor of `g`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_acceleration = max(abs(a)) * m/s**2 / params.g"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using Equation (1) from [Heck, Uylings, and Kędzierska](http://iopscience.iop.org/article/10.1088/0031-9120/45/1/007), we can compute the peak acceleration due to interaction with the cord, neglecting drag."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def max_acceleration(system):\n",
-    "    mu = system.mu\n",
-    "    return 1 + mu * (4+mu) / 8\n",
-    "\n",
-    "max_acceleration(system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you set `C_d=0`, the simulated acceleration approaches the theoretical result, although you might have to reduce `max_step` to get a good numerical estimate."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sweeping cord weight\n",
-    "\n",
-    "Now let's see how velocity at the crossover point depends on the weight of the cord."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sweep_m_cord(m_cord_array, params):\n",
-    "    sweep = SweepSeries()\n",
-    "\n",
-    "    for m_cord in m_cord_array:\n",
-    "        system = make_system(Params(params, m_cord=m_cord))\n",
-    "        results, details = run_ode_solver(system, slope_func1, events=event_func)\n",
-    "        min_velocity = min(results.v) * m/s\n",
-    "        sweep[m_cord.magnitude] = min_velocity\n",
-    "        \n",
-    "    return sweep"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m_cord_array = linspace(1, 201, 21) * kg\n",
-    "sweep = sweep_m_cord(m_cord_array, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like.  As expected, a heavier cord gets the jumper going faster.\n",
-    "\n",
-    "There's a hitch near 25 kg that seems to be due to numerical error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(sweep)\n",
-    "\n",
-    "decorate(xlabel='Mass of cord (kg)',\n",
-    "         ylabel='Fastest downward velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Phase 2\n",
-    "\n",
-    "Once the jumper falls past the length of the cord, acceleration due to energy transfer from the cord stops abruptly.  As the cord stretches, it starts to exert a spring force.  So let's simulate this second phase."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`spring_force` computes the force of the cord on the jumper:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def spring_force(y, system):\n",
-    "    \"\"\"Computes the force of the bungee cord on the jumper:\n",
-    "    \n",
-    "    y: height of the jumper\n",
-    "    \n",
-    "    Uses these variables from system:\n",
-    "    y_attach: height of the attachment point\n",
-    "    L: resting length of the cord\n",
-    "    k: spring constant of the cord\n",
-    "    \n",
-    "    returns: force in N\n",
-    "    \"\"\"\n",
-    "    L, k = system.L, system.k\n",
-    "    \n",
-    "    distance_fallen = -y\n",
-    "    extension = distance_fallen - L\n",
-    "    f_spring = k * extension\n",
-    "    return f_spring"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The spring force is 0 until the cord is fully extended.  When it is extended 1 m, the spring force is 40 N. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spring_force(-25*m, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spring_force(-26*m, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The slope function for Phase 2 includes the spring force, and drops the acceleration due to the cord."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func2(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object containing g, rho,\n",
-    "            C_d, area, and mass\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    y, v = state\n",
-    "    M, g = system.M, system.g\n",
-    "    \n",
-    "    a_drag = drag_force(v, system) / M\n",
-    "    a_spring = spring_force(y, system) / M\n",
-    "    dvdt = -g + a_drag + a_spring\n",
-    "    \n",
-    "    return v, dvdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll run Phase 1 again so we can get the final state."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system1 = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "event_func.direction=-1\n",
-    "results1, details1 = run_ode_solver(system1, slope_func1, events=event_func)\n",
-    "print(details1.message)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now I need the final time, position, and velocity from Phase 1."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_final = get_last_label(results1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "init2 = results1.row[t_final]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And that gives me the starting conditions for Phase 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system2 = System(system1, t_0=t_final, init=init2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run Phase 2, setting the direction of the event function so it doesn't stop the simulation immediately. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "event_func.direction=+1\n",
-    "results2, details2 = run_ode_solver(system2, slope_func2, events=event_func)\n",
-    "print(details2.message)\n",
-    "t_final = get_last_label(results2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot the results on the same axes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_position(results1, label='Phase 1')\n",
-    "plot_position(results2, label='Phase 2')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And get the lowest position from Phase 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "min(results2.y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To see how big the effect of the cord is, I'll collect the previous code in a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def simulate_system2(params):\n",
-    "    \n",
-    "    system1 = make_system(params)\n",
-    "    event_func.direction=-1\n",
-    "    results1, details1 = run_ode_solver(system1, slope_func1, events=event_func)\n",
-    "\n",
-    "    t_final = get_last_label(results1)\n",
-    "    init2 = results1.row[t_final]\n",
-    "    \n",
-    "    system2 = System(system1, t_0=t_final, init=init2)\n",
-    "    results2, details2 = run_ode_solver(system2, slope_func2, events=event_func)\n",
-    "    t_final = get_last_label(results2)\n",
-    "    return TimeFrame(pd.concat([results1, results2]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run both phases and get the results in a single `TimeFrame`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = simulate_system2(params);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params_no_cord = Params(params, m_cord=1*kg)\n",
-    "results_no_cord = simulate_system2(params_no_cord);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "plot_position(results, label='m_cord = 75 kg')\n",
-    "plot_position(results_no_cord, label='m_cord = 1 kg')\n",
-    "\n",
-    "savefig('figs/jump.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "min(results_no_cord.y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diff = min(results.y) - min(results_no_cord.y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The difference is more than 2 meters, which could certainly be the difference between a successful bungee dunk and a bad day."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/kitten.ipynb b/notebooks/kitten.ipynb
deleted file mode 100644
index b813f164..00000000
--- a/notebooks/kitten.ipynb
+++ /dev/null
@@ -1,399 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case study.\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Unrolling\n",
-    "\n",
-    "Let's simulate a kitten unrolling toilet paper.  As reference material, see [this video](http://modsimpy.com/kitten).\n",
-    "\n",
-    "The interactions of the kitten and the paper roll are complex.  To keep things simple, let's assume that the kitten pulls down on the free end of the roll with constant force.  Also, we will neglect the friction between the roll and the axle.  \n",
-    "\n",
-    "![](diagrams/kitten.png)\n",
-    "\n",
-    "This figure shows the paper roll with $r$, $F$, and $\\tau$.  As a vector quantity, the direction of $\\tau$ is into the page, but we only care about its magnitude for now."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll start by loading the units we need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radian = UNITS.radian\n",
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And a few more parameters in the `Params` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(Rmin = 0.02 * m,\n",
-    "                Rmax = 0.055 * m,\n",
-    "                Mcore = 15e-3 * kg,\n",
-    "                Mroll = 215e-3 * kg,\n",
-    "                L = 47 * m,\n",
-    "                tension = 2e-4 * N,\n",
-    "                t_end = 120 * s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system` computes `rho_h`, which we'll need to compute moment of inertia, and `k`, which we'll use to compute `r`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params with Rmin, Rmax, Mcore, Mroll,\n",
-    "                              L, tension, and t_end\n",
-    "    \n",
-    "    returns: System with init, k, rho_h, Rmin, Rmax,\n",
-    "                         Mcore, Mroll, ts\n",
-    "    \"\"\"\n",
-    "    L, Rmax, Rmin = params.L, params.Rmax, params.Rmin\n",
-    "    Mroll = params.Mroll\n",
-    "    \n",
-    "    init = State(theta = 0 * radian,\n",
-    "                 omega = 0 * radian/s,\n",
-    "                 y = L)\n",
-    "    \n",
-    "    area = pi * (Rmax**2 - Rmin**2)\n",
-    "    rho_h = Mroll / area\n",
-    "    k = (Rmax**2 - Rmin**2) / 2 / L / radian    \n",
-    "    \n",
-    "    return System(params, init=init, area=area, rho_h=rho_h, k=k)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `make_system`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we compute `I` as a function of `r`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def moment_of_inertia(r, system):\n",
-    "    \"\"\"Moment of inertia for a roll of toilet paper.\n",
-    "    \n",
-    "    r: current radius of roll in meters\n",
-    "    system: System object with Mcore, rho, Rmin, Rmax\n",
-    "    \n",
-    "    returns: moment of inertia in kg m**2\n",
-    "    \"\"\"\n",
-    "    Mcore, Rmin, rho_h = system.Mcore, system.Rmin, system.rho_h\n",
-    "    \n",
-    "    Icore = Mcore * Rmin**2   \n",
-    "    Iroll = pi * rho_h / 2 * (r**4 - Rmin**4)\n",
-    "    return Icore + Iroll"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When `r` is `Rmin`, `I` is small."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "moment_of_inertia(system.Rmin, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As `r` increases, so does `I`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "moment_of_inertia(system.Rmax, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "Write a slope function we can use to simulate this system.  Here are some suggestions and hints:\n",
-    "\n",
-    "* `r` is no longer part of the `State` object.  Instead, we compute `r` at each time step, based on the current value of `y`, using\n",
-    "\n",
-    "$y = \\frac{1}{2k} (r^2 - R_{min}^2)$\n",
-    "\n",
-    "* Angular velocity, `omega`, is no longer constant.  Instead, we compute torque, `tau`, and angular acceleration, `alpha`, at each time step.\n",
-    "\n",
-    "* I changed the definition of `theta` so positive values correspond to clockwise rotation, so `dydt = -r * omega`; that is, positive values of `omega` yield decreasing values of `y`, the amount of paper still on the roll.\n",
-    "\n",
-    "* Your slope function should return `omega`, `alpha`, and `dydt`, which are the derivatives of `theta`, `omega`, and `y`, respectively.\n",
-    "\n",
-    "* Because `r` changes over time, we have to compute moment of inertia, `I`, at each time step.\n",
-    "\n",
-    "That last point might be more of a problem than I have made it seem.  In the same way that $F = m a$ only applies when $m$ is constant, $\\tau = I \\alpha$ only applies when $I$ is constant.  When $I$ varies, we usually have to use a more general version of Newton's law.  However, I believe that in this example, mass and moment of inertia vary together in a way that makes the simple approach work out.  Not all of my collegues are convinced."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test `slope_func` with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And look at the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Check the results to see if they seem plausible:\n",
-    "\n",
-    "* The final value of `theta` should be about 220 radians.\n",
-    "\n",
-    "* The final value of `omega` should be near 4 radians/second, which is less one revolution per second, so that seems plausible.\n",
-    "\n",
-    "* The final value of `y` should be about 35 meters of paper left on the roll, which means the kitten pulls off 12 meters in two minutes.  That doesn't seem impossible, although it is based on a level of consistency and focus that is unlikely in a kitten.\n",
-    "\n",
-    "* Angular velocity, `omega`, should increase almost linearly at first, as constant force yields almost constant torque.  Then, as the radius decreases, the lever arm decreases, yielding lower torque, but moment of inertia decreases even more, yielding higher angular acceleration."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot `theta`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_theta(results):\n",
-    "    plot(results.theta, color='C0', label='theta')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angle (rad)')\n",
-    "    \n",
-    "plot_theta(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot `omega`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_omega(results):\n",
-    "    plot(results.omega, color='C2', label='omega')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angular velocity (rad/s)')\n",
-    "    \n",
-    "plot_omega(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot `y`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_y(results):\n",
-    "    plot(results.y, color='C1', label='y')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Length (m)')\n",
-    "    \n",
-    "plot_y(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/lotka-volterra.ipynb b/notebooks/lotka-volterra.ipynb
deleted file mode 100644
index f584ca2d..00000000
--- a/notebooks/lotka-volterra.ipynb
+++ /dev/null
@@ -1,944 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Implementation of Lotka-Volterra using Euler and `run_ode_solver`\n",
-    "\n",
-    "Copyright 2018 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Euler with implicit `dt=1`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "        \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \n",
-    "    returns: TimeFrame\n",
-    "    \"\"\"\n",
-    "    unpack(system)\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.row[t0] = init\n",
-    "    \n",
-    "    for t in linrange(t0, t_end):\n",
-    "        frame.row[t+1] = update_func(frame.row[t], t, system)\n",
-    "    \n",
-    "    return frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update_func(state, t, system):\n",
-    "    \"\"\"Update the Lotka-Volterra model.\n",
-    "    \n",
-    "    state: State(x, y)\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State(x, y)\n",
-    "    \"\"\"\n",
-    "    unpack(system)\n",
-    "    x, y = state\n",
-    "\n",
-    "    dxdt = alpha * x - beta * x * y\n",
-    "    dydt = delta * x * y - gamma * y\n",
-    "    \n",
-    "    x += dxdt\n",
-    "    y += dydt\n",
-    "    \n",
-    "    return State(x=x, y=y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>x</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "x    1\n",
-       "y    1\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init = State(x=1, y=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "</style>\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>alpha</th>\n",
-       "      <td>0.05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>beta</th>\n",
-       "      <td>0.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gamma</th>\n",
-       "      <td>0.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>delta</th>\n",
-       "      <td>0.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t0</th>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>200.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "alpha      0.05\n",
-       "beta       0.10\n",
-       "gamma      0.10\n",
-       "delta      0.10\n",
-       "t0         0.00\n",
-       "t_end    200.00\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = System(alpha=0.05,\n",
-    "                beta=0.1,\n",
-    "                gamma=0.1,\n",
-    "                delta=0.1,\n",
-    "                t0=0,\n",
-    "                t_end=200)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
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-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>x</th>\n",
-       "      <td>0.95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>1.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "x    0.95\n",
-       "y    1.00\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "update_func(init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.0</th>\n",
-       "      <td>0.95</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.0</th>\n",
-       "      <td>0.9025</td>\n",
-       "      <td>0.995</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.0</th>\n",
-       "      <td>0.857826</td>\n",
-       "      <td>0.985299</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.0</th>\n",
-       "      <td>0.816196</td>\n",
-       "      <td>0.97129</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            x         y\n",
-       "0.0         1         1\n",
-       "1.0      0.95         1\n",
-       "2.0    0.9025     0.995\n",
-       "3.0  0.857826  0.985299\n",
-       "4.0  0.816196   0.97129"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = run_simulation(system, update_func)\n",
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa73f3c7c88>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using the ODE solver"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute slopes for the Lotka-Volterra model.\n",
-    "    \n",
-    "    state: State(x, y)\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: pair of derivatives\n",
-    "    \"\"\"\n",
-    "    unpack(system)\n",
-    "    x, y = state\n",
-    "\n",
-    "    dxdt = alpha * x - beta * x * y\n",
-    "    dydt = delta * x * y - gamma * y\n",
-    "    \n",
-    "    return dxdt, dydt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.set(init=init, t_end=200)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>sol</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_events</th>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nfev</th>\n",
-       "      <td>134</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>njev</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nlu</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>status</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "sol                                                      None\n",
-       "t_events                                                   []\n",
-       "nfev                                                      134\n",
-       "njev                                                        0\n",
-       "nlu                                                         0\n",
-       "status                                                      0\n",
-       "message     The solver successfully reached the end of the...\n",
-       "success                                                  True\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa72c15b9b0>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>sol</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_events</th>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nfev</th>\n",
-       "      <td>608</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>njev</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nlu</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>status</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "sol                                                      None\n",
-       "t_events                                                   []\n",
-       "nfev                                                      608\n",
-       "njev                                                        0\n",
-       "nlu                                                         0\n",
-       "status                                                      0\n",
-       "message     The solver successfully reached the end of the...\n",
-       "success                                                  True\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.set(init=init, t_end=200)\n",
-    "results, details = run_ode_solver(system, slope_func, max_step=2)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa72c06cf60>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Euler, running for a longer duration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.0</th>\n",
-       "      <td>0.95</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.0</th>\n",
-       "      <td>0.9025</td>\n",
-       "      <td>0.995</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.0</th>\n",
-       "      <td>0.857826</td>\n",
-       "      <td>0.985299</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.0</th>\n",
-       "      <td>0.816196</td>\n",
-       "      <td>0.97129</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            x         y\n",
-       "0.0         1         1\n",
-       "1.0      0.95         1\n",
-       "2.0    0.9025     0.995\n",
-       "3.0  0.857826  0.985299\n",
-       "4.0  0.816196   0.97129"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.set(t_end=2000)\n",
-    "results = run_simulation(system, update_func)\n",
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa72c052240>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Running the ODE solver for a longer duration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>sol</th>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_events</th>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nfev</th>\n",
-       "      <td>1172</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>njev</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nlu</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>status</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>message</th>\n",
-       "      <td>The solver successfully reached the end of the...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>success</th>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "sol                                                      None\n",
-       "t_events                                                   []\n",
-       "nfev                                                     1172\n",
-       "njev                                                        0\n",
-       "nlu                                                         0\n",
-       "status                                                      0\n",
-       "message     The solver successfully reached the end of the...\n",
-       "success                                                  True\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa72bfd2630>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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diff --git a/notebooks/modsim.html b/notebooks/modsim.html
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-<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN">
-<html><head><title>Python: module modsim</title>
-<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
-</head><body bgcolor="#f0f0f8">
-
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="heading">
-<tr bgcolor="#7799ee">
-<td valign=bottom>&nbsp;<br>
-<font color="#ffffff" face="helvetica, arial">&nbsp;<br><big><big><strong>modsim</strong></big></big></font></td
-><td align=right valign=bottom
-><font color="#ffffff" face="helvetica, arial"><a href=".">index</a><br><a href="file:/home/downey/ModSimPy/code/modsim.py">/home/downey/ModSimPy/code/modsim.py</a></font></td></tr></table>
-    <p><tt>Code&nbsp;from&nbsp;Modeling&nbsp;and&nbsp;Simulation&nbsp;in&nbsp;Python.<br>
-&nbsp;<br>
-Copyright&nbsp;2017&nbsp;Allen&nbsp;Downey<br>
-&nbsp;<br>
-License:&nbsp;https://creativecommons.org/licenses/by/4.0)</tt></p>
-<p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#aa55cc">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#ffffff" face="helvetica, arial"><big><strong>Modules</strong></big></font></td></tr>
-    
-<tr><td bgcolor="#aa55cc"><tt>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</tt></td><td>&nbsp;</td>
-<td width="100%"><table width="100%" summary="list"><tr><td width="25%" valign=top><a href="inspect.html">inspect</a><br>
-<a href="logging.html">logging</a><br>
-<a href="numpy.html">numpy</a><br>
-</td><td width="25%" valign=top><a href="pandas.html">pandas</a><br>
-<a href="pint.html">pint</a><br>
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-</td><td width="25%" valign=top><a href="scipy.html">scipy</a><br>
-<a href="seaborn.html">seaborn</a><br>
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-<td colspan=3 valign=bottom>&nbsp;<br>
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-    
-<tr><td bgcolor="#ee77aa"><tt>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</tt></td><td>&nbsp;</td>
-<td width="100%"><dl>
-<dt><font face="helvetica, arial"><a href="builtins.html#object">builtins.object</a>
-</font></dt><dd>
-<dl>
-<dt><font face="helvetica, arial"><a href="modsim.html#FigureState">FigureState</a>
-</font></dt><dt><font face="helvetica, arial"><a href="modsim.html#Simplot">Simplot</a>
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-</font></dt></dl>
-</dd>
-<dt><font face="helvetica, arial"><a href="numpy.html#ndarray">numpy.ndarray</a>(<a href="builtins.html#object">builtins.object</a>)
-</font></dt><dd>
-<dl>
-<dt><font face="helvetica, arial"><a href="modsim.html#Array">Array</a>
-</font></dt></dl>
-</dd>
-<dt><font face="helvetica, arial"><a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a>(<a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>)
-</font></dt><dd>
-<dl>
-<dt><font face="helvetica, arial"><a href="modsim.html#MyDataFrame">MyDataFrame</a>
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-<dl>
-<dt><font face="helvetica, arial"><a href="modsim.html#SweepFrame">SweepFrame</a>
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-</font></dt></dl>
-</dd>
-<dt><font face="helvetica, arial"><a href="modsim.html#TimeSeries">TimeSeries</a>
-</font></dt></dl>
-</dd>
-</dl>
-</dd>
-</dl>
- <p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#ffc8d8">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#000000" face="helvetica, arial"><a name="Array">class <strong>Array</strong></a>(<a href="numpy.html#ndarray">numpy.ndarray</a>)</font></td></tr>
-    
-<tr bgcolor="#ffc8d8"><td rowspan=2><tt>&nbsp;&nbsp;&nbsp;</tt></td>
-<td colspan=2><tt><a href="numpy.html#ndarray">ndarray</a>(shape,&nbsp;dtype=float,&nbsp;buffer=None,&nbsp;offset=0,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;strides=None,&nbsp;order=None)<br>
-&nbsp;<br>
-An&nbsp;array&nbsp;<a href="builtins.html#object">object</a>&nbsp;represents&nbsp;a&nbsp;multidimensional,&nbsp;homogeneous&nbsp;array<br>
-of&nbsp;fixed-size&nbsp;items.&nbsp;&nbsp;An&nbsp;associated&nbsp;data-type&nbsp;<a href="builtins.html#object">object</a>&nbsp;describes&nbsp;the<br>
-format&nbsp;of&nbsp;each&nbsp;element&nbsp;in&nbsp;the&nbsp;array&nbsp;(its&nbsp;byte-order,&nbsp;how&nbsp;many&nbsp;bytes&nbsp;it<br>
-occupies&nbsp;in&nbsp;memory,&nbsp;whether&nbsp;it&nbsp;is&nbsp;an&nbsp;integer,&nbsp;a&nbsp;floating&nbsp;point&nbsp;number,<br>
-or&nbsp;something&nbsp;else,&nbsp;etc.)<br>
-&nbsp;<br>
-Arrays&nbsp;should&nbsp;be&nbsp;constructed&nbsp;using&nbsp;`array`,&nbsp;`zeros`&nbsp;or&nbsp;`empty`&nbsp;(refer<br>
-to&nbsp;the&nbsp;See&nbsp;Also&nbsp;section&nbsp;below).&nbsp;&nbsp;The&nbsp;parameters&nbsp;given&nbsp;here&nbsp;refer&nbsp;to<br>
-a&nbsp;low-level&nbsp;method&nbsp;(`<a href="numpy.html#ndarray">ndarray</a>(...)`)&nbsp;for&nbsp;instantiating&nbsp;an&nbsp;array.<br>
-&nbsp;<br>
-For&nbsp;more&nbsp;information,&nbsp;refer&nbsp;to&nbsp;the&nbsp;`numpy`&nbsp;module&nbsp;and&nbsp;examine&nbsp;the<br>
-methods&nbsp;and&nbsp;attributes&nbsp;of&nbsp;an&nbsp;array.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-(for&nbsp;the&nbsp;__new__&nbsp;method;&nbsp;see&nbsp;Notes&nbsp;below)<br>
-&nbsp;<br>
-shape&nbsp;:&nbsp;tuple&nbsp;of&nbsp;ints<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Shape&nbsp;of&nbsp;created&nbsp;array.<br>
-dtype&nbsp;:&nbsp;data-type,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Any&nbsp;<a href="builtins.html#object">object</a>&nbsp;that&nbsp;can&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;numpy&nbsp;data&nbsp;type.<br>
-buffer&nbsp;:&nbsp;<a href="builtins.html#object">object</a>&nbsp;exposing&nbsp;buffer&nbsp;interface,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Used&nbsp;to&nbsp;fill&nbsp;the&nbsp;array&nbsp;with&nbsp;data.<br>
-offset&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Offset&nbsp;of&nbsp;array&nbsp;data&nbsp;in&nbsp;buffer.<br>
-strides&nbsp;:&nbsp;tuple&nbsp;of&nbsp;ints,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Strides&nbsp;of&nbsp;data&nbsp;in&nbsp;memory.<br>
-order&nbsp;:&nbsp;{'C',&nbsp;'F'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Row-major&nbsp;(C-style)&nbsp;or&nbsp;column-major&nbsp;(Fortran-style)&nbsp;order.<br>
-&nbsp;<br>
-Attributes<br>
-----------<br>
-T&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Transpose&nbsp;of&nbsp;the&nbsp;array.<br>
-data&nbsp;:&nbsp;buffer<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;array's&nbsp;elements,&nbsp;in&nbsp;memory.<br>
-dtype&nbsp;:&nbsp;dtype&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Describes&nbsp;the&nbsp;format&nbsp;of&nbsp;the&nbsp;elements&nbsp;in&nbsp;the&nbsp;array.<br>
-flags&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Dictionary&nbsp;containing&nbsp;information&nbsp;related&nbsp;to&nbsp;memory&nbsp;use,&nbsp;e.g.,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'C_CONTIGUOUS',&nbsp;'OWNDATA',&nbsp;'WRITEABLE',&nbsp;etc.<br>
-flat&nbsp;:&nbsp;numpy.flatiter&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Flattened&nbsp;version&nbsp;of&nbsp;the&nbsp;array&nbsp;as&nbsp;an&nbsp;iterator.&nbsp;&nbsp;The&nbsp;iterator<br>
-&nbsp;&nbsp;&nbsp;&nbsp;allows&nbsp;assignments,&nbsp;e.g.,&nbsp;``x.flat&nbsp;=&nbsp;3``&nbsp;(See&nbsp;`<a href="numpy.html#ndarray">ndarray</a>.flat`&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;assignment&nbsp;examples;&nbsp;TODO).<br>
-imag&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Imaginary&nbsp;part&nbsp;of&nbsp;the&nbsp;array.<br>
-real&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Real&nbsp;part&nbsp;of&nbsp;the&nbsp;array.<br>
-size&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;elements&nbsp;in&nbsp;the&nbsp;array.<br>
-itemsize&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;memory&nbsp;use&nbsp;of&nbsp;each&nbsp;array&nbsp;element&nbsp;in&nbsp;bytes.<br>
-nbytes&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;total&nbsp;number&nbsp;of&nbsp;bytes&nbsp;required&nbsp;to&nbsp;store&nbsp;the&nbsp;array&nbsp;data,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;i.e.,&nbsp;``itemsize&nbsp;*&nbsp;size``.<br>
-ndim&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;array's&nbsp;number&nbsp;of&nbsp;dimensions.<br>
-shape&nbsp;:&nbsp;tuple&nbsp;of&nbsp;ints<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Shape&nbsp;of&nbsp;the&nbsp;array.<br>
-strides&nbsp;:&nbsp;tuple&nbsp;of&nbsp;ints<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;step-size&nbsp;required&nbsp;to&nbsp;move&nbsp;from&nbsp;one&nbsp;element&nbsp;to&nbsp;the&nbsp;next&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;memory.&nbsp;For&nbsp;example,&nbsp;a&nbsp;contiguous&nbsp;``(3,&nbsp;4)``&nbsp;array&nbsp;of&nbsp;type<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``int16``&nbsp;in&nbsp;C-order&nbsp;has&nbsp;strides&nbsp;``(8,&nbsp;2)``.&nbsp;&nbsp;This&nbsp;implies&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;move&nbsp;from&nbsp;element&nbsp;to&nbsp;element&nbsp;in&nbsp;memory&nbsp;requires&nbsp;jumps&nbsp;of&nbsp;2&nbsp;bytes.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;move&nbsp;from&nbsp;row-to-row,&nbsp;one&nbsp;needs&nbsp;to&nbsp;jump&nbsp;8&nbsp;bytes&nbsp;at&nbsp;a&nbsp;time<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(``2&nbsp;*&nbsp;4``).<br>
-ctypes&nbsp;:&nbsp;ctypes&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Class&nbsp;containing&nbsp;properties&nbsp;of&nbsp;the&nbsp;array&nbsp;needed&nbsp;for&nbsp;interaction<br>
-&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;ctypes.<br>
-base&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;array&nbsp;is&nbsp;a&nbsp;view&nbsp;into&nbsp;another&nbsp;array,&nbsp;that&nbsp;array&nbsp;is&nbsp;its&nbsp;`base`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(unless&nbsp;that&nbsp;array&nbsp;is&nbsp;also&nbsp;a&nbsp;view).&nbsp;&nbsp;The&nbsp;`base`&nbsp;array&nbsp;is&nbsp;where&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;array&nbsp;data&nbsp;is&nbsp;actually&nbsp;stored.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-array&nbsp;:&nbsp;Construct&nbsp;an&nbsp;array.<br>
-zeros&nbsp;:&nbsp;Create&nbsp;an&nbsp;array,&nbsp;each&nbsp;element&nbsp;of&nbsp;which&nbsp;is&nbsp;zero.<br>
-empty&nbsp;:&nbsp;Create&nbsp;an&nbsp;array,&nbsp;but&nbsp;leave&nbsp;its&nbsp;allocated&nbsp;memory&nbsp;unchanged&nbsp;(i.e.,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;it&nbsp;contains&nbsp;"garbage").<br>
-dtype&nbsp;:&nbsp;Create&nbsp;a&nbsp;data-type.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-There&nbsp;are&nbsp;two&nbsp;modes&nbsp;of&nbsp;creating&nbsp;an&nbsp;array&nbsp;using&nbsp;``__new__``:<br>
-&nbsp;<br>
-1.&nbsp;If&nbsp;`buffer`&nbsp;is&nbsp;None,&nbsp;then&nbsp;only&nbsp;`shape`,&nbsp;`dtype`,&nbsp;and&nbsp;`order`<br>
-&nbsp;&nbsp;&nbsp;are&nbsp;used.<br>
-2.&nbsp;If&nbsp;`buffer`&nbsp;is&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;exposing&nbsp;the&nbsp;buffer&nbsp;interface,&nbsp;then<br>
-&nbsp;&nbsp;&nbsp;all&nbsp;keywords&nbsp;are&nbsp;interpreted.<br>
-&nbsp;<br>
-No&nbsp;``__init__``&nbsp;method&nbsp;is&nbsp;needed&nbsp;because&nbsp;the&nbsp;array&nbsp;is&nbsp;fully&nbsp;initialized<br>
-after&nbsp;the&nbsp;``__new__``&nbsp;method.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-These&nbsp;examples&nbsp;illustrate&nbsp;the&nbsp;low-level&nbsp;`<a href="numpy.html#ndarray">ndarray</a>`&nbsp;constructor.&nbsp;&nbsp;Refer<br>
-to&nbsp;the&nbsp;`See&nbsp;Also`&nbsp;section&nbsp;above&nbsp;for&nbsp;easier&nbsp;ways&nbsp;of&nbsp;constructing&nbsp;an<br>
-<a href="numpy.html#ndarray">ndarray</a>.<br>
-&nbsp;<br>
-First&nbsp;mode,&nbsp;`buffer`&nbsp;is&nbsp;None:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;np.<a href="numpy.html#ndarray">ndarray</a>(shape=(2,2),&nbsp;dtype=float,&nbsp;order='F')<br>
-array([[&nbsp;-1.13698227e+002,&nbsp;&nbsp;&nbsp;4.25087011e-303],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;&nbsp;2.88528414e-306,&nbsp;&nbsp;&nbsp;3.27025015e-309]])&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#random<br>
-&nbsp;<br>
-Second&nbsp;mode:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;np.<a href="numpy.html#ndarray">ndarray</a>((2,),&nbsp;buffer=np.array([1,2,3]),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;offset=np.int_().itemsize,<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dtype=int)&nbsp;#&nbsp;offset&nbsp;=&nbsp;1*itemsize,&nbsp;i.e.&nbsp;skip&nbsp;first&nbsp;element<br>
-array([2,&nbsp;3])<br>&nbsp;</tt></td></tr>
-<tr><td>&nbsp;</td>
-<td width="100%"><dl><dt>Method resolution order:</dt>
-<dd><a href="modsim.html#Array">Array</a></dd>
-<dd><a href="numpy.html#ndarray">numpy.ndarray</a></dd>
-<dd><a href="builtins.html#object">builtins.object</a></dd>
-</dl>
-<hr>
-Data descriptors defined here:<br>
-<dl><dt><strong>__dict__</strong></dt>
-<dd><tt>dictionary&nbsp;for&nbsp;instance&nbsp;variables&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<hr>
-Methods inherited from <a href="numpy.html#ndarray">numpy.ndarray</a>:<br>
-<dl><dt><a name="Array-__abs__"><strong>__abs__</strong></a>(self, /)</dt><dd><tt><a href="#-abs">abs</a>(self)</tt></dd></dl>
-
-<dl><dt><a name="Array-__add__"><strong>__add__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self+value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__and__"><strong>__and__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self&amp;value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__array__"><strong>__array__</strong></a>(...)</dt><dd><tt>a.<a href="#Array-__array__">__array__</a>(|dtype)&nbsp;-&gt;&nbsp;reference&nbsp;if&nbsp;type&nbsp;unchanged,&nbsp;copy&nbsp;otherwise.<br>
-&nbsp;<br>
-Returns&nbsp;either&nbsp;a&nbsp;new&nbsp;reference&nbsp;to&nbsp;self&nbsp;if&nbsp;dtype&nbsp;is&nbsp;not&nbsp;given&nbsp;or&nbsp;a&nbsp;new&nbsp;array<br>
-of&nbsp;provided&nbsp;data&nbsp;type&nbsp;if&nbsp;dtype&nbsp;is&nbsp;different&nbsp;from&nbsp;the&nbsp;current&nbsp;dtype&nbsp;of&nbsp;the<br>
-array.</tt></dd></dl>
-
-<dl><dt><a name="Array-__array_prepare__"><strong>__array_prepare__</strong></a>(...)</dt><dd><tt>a.<a href="#Array-__array_prepare__">__array_prepare__</a>(obj)&nbsp;-&gt;&nbsp;Object&nbsp;of&nbsp;same&nbsp;type&nbsp;as&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;<a href="builtins.html#object">object</a>&nbsp;obj.</tt></dd></dl>
-
-<dl><dt><a name="Array-__array_wrap__"><strong>__array_wrap__</strong></a>(...)</dt><dd><tt>a.<a href="#Array-__array_wrap__">__array_wrap__</a>(obj)&nbsp;-&gt;&nbsp;Object&nbsp;of&nbsp;same&nbsp;type&nbsp;as&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;<a href="builtins.html#object">object</a>&nbsp;a.</tt></dd></dl>
-
-<dl><dt><a name="Array-__bool__"><strong>__bool__</strong></a>(self, /)</dt><dd><tt>self&nbsp;!=&nbsp;0</tt></dd></dl>
-
-<dl><dt><a name="Array-__complex__"><strong>__complex__</strong></a>(...)</dt></dl>
-
-<dl><dt><a name="Array-__contains__"><strong>__contains__</strong></a>(self, key, /)</dt><dd><tt>Return&nbsp;key&nbsp;in&nbsp;self.</tt></dd></dl>
-
-<dl><dt><a name="Array-__copy__"><strong>__copy__</strong></a>(...)</dt><dd><tt>a.<a href="#Array-__copy__">__copy__</a>([order])<br>
-&nbsp;<br>
-Return&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;array.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-order&nbsp;:&nbsp;{'C',&nbsp;'F',&nbsp;'A'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;order&nbsp;is&nbsp;'C'&nbsp;(False)&nbsp;then&nbsp;the&nbsp;result&nbsp;is&nbsp;contiguous&nbsp;(default).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;order&nbsp;is&nbsp;'Fortran'&nbsp;(True)&nbsp;then&nbsp;the&nbsp;result&nbsp;has&nbsp;fortran&nbsp;order.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;order&nbsp;is&nbsp;'Any'&nbsp;(None)&nbsp;then&nbsp;the&nbsp;result&nbsp;has&nbsp;fortran&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;only&nbsp;if&nbsp;the&nbsp;array&nbsp;already&nbsp;is&nbsp;in&nbsp;fortran&nbsp;order.</tt></dd></dl>
-
-<dl><dt><a name="Array-__deepcopy__"><strong>__deepcopy__</strong></a>(...)</dt><dd><tt>a.<a href="#Array-__deepcopy__">__deepcopy__</a>()&nbsp;-&gt;&nbsp;Deep&nbsp;copy&nbsp;of&nbsp;array.<br>
-&nbsp;<br>
-Used&nbsp;if&nbsp;copy.deepcopy&nbsp;is&nbsp;called&nbsp;on&nbsp;an&nbsp;array.</tt></dd></dl>
-
-<dl><dt><a name="Array-__delitem__"><strong>__delitem__</strong></a>(self, key, /)</dt><dd><tt>Delete&nbsp;self[key].</tt></dd></dl>
-
-<dl><dt><a name="Array-__divmod__"><strong>__divmod__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;divmod(self,&nbsp;value).</tt></dd></dl>
-
-<dl><dt><a name="Array-__eq__"><strong>__eq__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self==value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__float__"><strong>__float__</strong></a>(self, /)</dt><dd><tt>float(self)</tt></dd></dl>
-
-<dl><dt><a name="Array-__floordiv__"><strong>__floordiv__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self//value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__ge__"><strong>__ge__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self&gt;=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__getitem__"><strong>__getitem__</strong></a>(self, key, /)</dt><dd><tt>Return&nbsp;self[key].</tt></dd></dl>
-
-<dl><dt><a name="Array-__gt__"><strong>__gt__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self&gt;value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__iadd__"><strong>__iadd__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self+=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__iand__"><strong>__iand__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self&amp;=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__ifloordiv__"><strong>__ifloordiv__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self//=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__ilshift__"><strong>__ilshift__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self&lt;&lt;=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__imatmul__"><strong>__imatmul__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self@=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__imod__"><strong>__imod__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self%=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__imul__"><strong>__imul__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self*=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__index__"><strong>__index__</strong></a>(self, /)</dt><dd><tt>Return&nbsp;self&nbsp;converted&nbsp;to&nbsp;an&nbsp;integer,&nbsp;if&nbsp;self&nbsp;is&nbsp;suitable&nbsp;for&nbsp;use&nbsp;as&nbsp;an&nbsp;index&nbsp;into&nbsp;a&nbsp;list.</tt></dd></dl>
-
-<dl><dt><a name="Array-__int__"><strong>__int__</strong></a>(self, /)</dt><dd><tt>int(self)</tt></dd></dl>
-
-<dl><dt><a name="Array-__invert__"><strong>__invert__</strong></a>(self, /)</dt><dd><tt>~self</tt></dd></dl>
-
-<dl><dt><a name="Array-__ior__"><strong>__ior__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self|=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__ipow__"><strong>__ipow__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self**=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__irshift__"><strong>__irshift__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self&gt;&gt;=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__isub__"><strong>__isub__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self-=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__iter__"><strong>__iter__</strong></a>(self, /)</dt><dd><tt>Implement&nbsp;iter(self).</tt></dd></dl>
-
-<dl><dt><a name="Array-__itruediv__"><strong>__itruediv__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self/=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__ixor__"><strong>__ixor__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self^=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__le__"><strong>__le__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self&lt;=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__len__"><strong>__len__</strong></a>(self, /)</dt><dd><tt>Return&nbsp;len(self).</tt></dd></dl>
-
-<dl><dt><a name="Array-__lshift__"><strong>__lshift__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self&lt;&lt;value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__lt__"><strong>__lt__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self&lt;value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__matmul__"><strong>__matmul__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self@value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__mod__"><strong>__mod__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self%value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__mul__"><strong>__mul__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self*value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__ne__"><strong>__ne__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self!=value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__neg__"><strong>__neg__</strong></a>(self, /)</dt><dd><tt>-self</tt></dd></dl>
-
-<dl><dt><a name="Array-__new__"><strong>__new__</strong></a>(*args, **kwargs)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Create&nbsp;and&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>.&nbsp;&nbsp;See&nbsp;help(type)&nbsp;for&nbsp;accurate&nbsp;signature.</tt></dd></dl>
-
-<dl><dt><a name="Array-__or__"><strong>__or__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self|value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__pos__"><strong>__pos__</strong></a>(self, /)</dt><dd><tt>+self</tt></dd></dl>
-
-<dl><dt><a name="Array-__pow__"><strong>__pow__</strong></a>(self, value, mod=None, /)</dt><dd><tt>Return&nbsp;pow(self,&nbsp;value,&nbsp;mod).</tt></dd></dl>
-
-<dl><dt><a name="Array-__radd__"><strong>__radd__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;value+self.</tt></dd></dl>
-
-<dl><dt><a name="Array-__rand__"><strong>__rand__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;value&amp;self.</tt></dd></dl>
-
-<dl><dt><a name="Array-__rdivmod__"><strong>__rdivmod__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;divmod(value,&nbsp;self).</tt></dd></dl>
-
-<dl><dt><a name="Array-__reduce__"><strong>__reduce__</strong></a>(...)</dt><dd><tt>a.<a href="#Array-__reduce__">__reduce__</a>()<br>
-&nbsp;<br>
-For&nbsp;pickling.</tt></dd></dl>
-
-<dl><dt><a name="Array-__repr__"><strong>__repr__</strong></a>(self, /)</dt><dd><tt>Return&nbsp;repr(self).</tt></dd></dl>
-
-<dl><dt><a name="Array-__rfloordiv__"><strong>__rfloordiv__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;value//self.</tt></dd></dl>
-
-<dl><dt><a name="Array-__rlshift__"><strong>__rlshift__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;value&lt;&lt;self.</tt></dd></dl>
-
-<dl><dt><a name="Array-__rmatmul__"><strong>__rmatmul__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;value@self.</tt></dd></dl>
-
-<dl><dt><a name="Array-__rmod__"><strong>__rmod__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;value%self.</tt></dd></dl>
-
-<dl><dt><a name="Array-__rmul__"><strong>__rmul__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;value*self.</tt></dd></dl>
-
-<dl><dt><a name="Array-__ror__"><strong>__ror__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;value|self.</tt></dd></dl>
-
-<dl><dt><a name="Array-__rpow__"><strong>__rpow__</strong></a>(self, value, mod=None, /)</dt><dd><tt>Return&nbsp;pow(value,&nbsp;self,&nbsp;mod).</tt></dd></dl>
-
-<dl><dt><a name="Array-__rrshift__"><strong>__rrshift__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;value&gt;&gt;self.</tt></dd></dl>
-
-<dl><dt><a name="Array-__rshift__"><strong>__rshift__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self&gt;&gt;value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__rsub__"><strong>__rsub__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;value-self.</tt></dd></dl>
-
-<dl><dt><a name="Array-__rtruediv__"><strong>__rtruediv__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;value/self.</tt></dd></dl>
-
-<dl><dt><a name="Array-__rxor__"><strong>__rxor__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;value^self.</tt></dd></dl>
-
-<dl><dt><a name="Array-__setitem__"><strong>__setitem__</strong></a>(self, key, value, /)</dt><dd><tt>Set&nbsp;self[key]&nbsp;to&nbsp;value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__setstate__"><strong>__setstate__</strong></a>(...)</dt><dd><tt>a.<a href="#Array-__setstate__">__setstate__</a>(version,&nbsp;shape,&nbsp;dtype,&nbsp;isfortran,&nbsp;rawdata)<br>
-&nbsp;<br>
-For&nbsp;unpickling.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-version&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;optional&nbsp;pickle&nbsp;version.&nbsp;If&nbsp;omitted&nbsp;defaults&nbsp;to&nbsp;0.<br>
-shape&nbsp;:&nbsp;tuple<br>
-dtype&nbsp;:&nbsp;data-type<br>
-isFortran&nbsp;:&nbsp;bool<br>
-rawdata&nbsp;:&nbsp;string&nbsp;or&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;binary&nbsp;string&nbsp;with&nbsp;the&nbsp;data&nbsp;(or&nbsp;a&nbsp;list&nbsp;if&nbsp;'a'&nbsp;is&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;array)</tt></dd></dl>
-
-<dl><dt><a name="Array-__sizeof__"><strong>__sizeof__</strong></a>(...)</dt><dd><tt><a href="#Array-__sizeof__">__sizeof__</a>()&nbsp;-&gt;&nbsp;int<br>
-size&nbsp;of&nbsp;<a href="builtins.html#object">object</a>&nbsp;in&nbsp;memory,&nbsp;in&nbsp;bytes</tt></dd></dl>
-
-<dl><dt><a name="Array-__str__"><strong>__str__</strong></a>(self, /)</dt><dd><tt>Return&nbsp;str(self).</tt></dd></dl>
-
-<dl><dt><a name="Array-__sub__"><strong>__sub__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self-value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__truediv__"><strong>__truediv__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self/value.</tt></dd></dl>
-
-<dl><dt><a name="Array-__xor__"><strong>__xor__</strong></a>(self, value, /)</dt><dd><tt>Return&nbsp;self^value.</tt></dd></dl>
-
-<dl><dt><a name="Array-all"><strong>all</strong></a>(...)</dt><dd><tt>a.<a href="#Array-all">all</a>(axis=None,&nbsp;out=None,&nbsp;keepdims=False)<br>
-&nbsp;<br>
-Returns&nbsp;True&nbsp;if&nbsp;all&nbsp;elements&nbsp;evaluate&nbsp;to&nbsp;True.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.all`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.all&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-any"><strong>any</strong></a>(...)</dt><dd><tt>a.<a href="#Array-any">any</a>(axis=None,&nbsp;out=None,&nbsp;keepdims=False)<br>
-&nbsp;<br>
-Returns&nbsp;True&nbsp;if&nbsp;any&nbsp;of&nbsp;the&nbsp;elements&nbsp;of&nbsp;`a`&nbsp;evaluate&nbsp;to&nbsp;True.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.any`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.any&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-argmax"><strong>argmax</strong></a>(...)</dt><dd><tt>a.<a href="#Array-argmax">argmax</a>(axis=None,&nbsp;out=None)<br>
-&nbsp;<br>
-Return&nbsp;indices&nbsp;of&nbsp;the&nbsp;maximum&nbsp;values&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.argmax`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.argmax&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-argmin"><strong>argmin</strong></a>(...)</dt><dd><tt>a.<a href="#Array-argmin">argmin</a>(axis=None,&nbsp;out=None)<br>
-&nbsp;<br>
-Return&nbsp;indices&nbsp;of&nbsp;the&nbsp;minimum&nbsp;values&nbsp;along&nbsp;the&nbsp;given&nbsp;axis&nbsp;of&nbsp;`a`.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.argmin`&nbsp;for&nbsp;detailed&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.argmin&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-argpartition"><strong>argpartition</strong></a>(...)</dt><dd><tt>a.<a href="#Array-argpartition">argpartition</a>(kth,&nbsp;axis=-1,&nbsp;kind='introselect',&nbsp;order=None)<br>
-&nbsp;<br>
-Returns&nbsp;the&nbsp;indices&nbsp;that&nbsp;would&nbsp;partition&nbsp;this&nbsp;array.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.argpartition`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;1.8.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.argpartition&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-argsort"><strong>argsort</strong></a>(...)</dt><dd><tt>a.<a href="#Array-argsort">argsort</a>(axis=-1,&nbsp;kind='quicksort',&nbsp;order=None)<br>
-&nbsp;<br>
-Returns&nbsp;the&nbsp;indices&nbsp;that&nbsp;would&nbsp;sort&nbsp;this&nbsp;array.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.argsort`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.argsort&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-astype"><strong>astype</strong></a>(...)</dt><dd><tt>a.<a href="#Array-astype">astype</a>(dtype,&nbsp;order='K',&nbsp;casting='unsafe',&nbsp;subok=True,&nbsp;copy=True)<br>
-&nbsp;<br>
-Copy&nbsp;of&nbsp;the&nbsp;array,&nbsp;cast&nbsp;to&nbsp;a&nbsp;specified&nbsp;type.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dtype&nbsp;:&nbsp;str&nbsp;or&nbsp;dtype<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Typecode&nbsp;or&nbsp;data-type&nbsp;to&nbsp;which&nbsp;the&nbsp;array&nbsp;is&nbsp;cast.<br>
-order&nbsp;:&nbsp;{'C',&nbsp;'F',&nbsp;'A',&nbsp;'K'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Controls&nbsp;the&nbsp;memory&nbsp;layout&nbsp;order&nbsp;of&nbsp;the&nbsp;result.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;means&nbsp;C&nbsp;order,&nbsp;'F'&nbsp;means&nbsp;Fortran&nbsp;order,&nbsp;'A'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;means&nbsp;'F'&nbsp;order&nbsp;if&nbsp;all&nbsp;the&nbsp;arrays&nbsp;are&nbsp;Fortran&nbsp;contiguous,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;order&nbsp;otherwise,&nbsp;and&nbsp;'K'&nbsp;means&nbsp;as&nbsp;close&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;order&nbsp;the&nbsp;array&nbsp;elements&nbsp;appear&nbsp;in&nbsp;memory&nbsp;as&nbsp;possible.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;is&nbsp;'K'.<br>
-casting&nbsp;:&nbsp;{'no',&nbsp;'equiv',&nbsp;'safe',&nbsp;'same_kind',&nbsp;'unsafe'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Controls&nbsp;what&nbsp;kind&nbsp;of&nbsp;data&nbsp;casting&nbsp;may&nbsp;occur.&nbsp;Defaults&nbsp;to&nbsp;'unsafe'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;backwards&nbsp;compatibility.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'no'&nbsp;means&nbsp;the&nbsp;data&nbsp;types&nbsp;should&nbsp;not&nbsp;be&nbsp;cast&nbsp;at&nbsp;all.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'equiv'&nbsp;means&nbsp;only&nbsp;byte-order&nbsp;changes&nbsp;are&nbsp;allowed.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'safe'&nbsp;means&nbsp;only&nbsp;casts&nbsp;which&nbsp;can&nbsp;preserve&nbsp;values&nbsp;are&nbsp;allowed.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'same_kind'&nbsp;means&nbsp;only&nbsp;safe&nbsp;casts&nbsp;or&nbsp;casts&nbsp;within&nbsp;a&nbsp;kind,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;like&nbsp;float64&nbsp;to&nbsp;float32,&nbsp;are&nbsp;allowed.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'unsafe'&nbsp;means&nbsp;any&nbsp;data&nbsp;conversions&nbsp;may&nbsp;be&nbsp;done.<br>
-subok&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;then&nbsp;sub-classes&nbsp;will&nbsp;be&nbsp;passed-through&nbsp;(default),&nbsp;otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;returned&nbsp;array&nbsp;will&nbsp;be&nbsp;forced&nbsp;to&nbsp;be&nbsp;a&nbsp;base-class&nbsp;array.<br>
-copy&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;By&nbsp;default,&nbsp;astype&nbsp;always&nbsp;returns&nbsp;a&nbsp;newly&nbsp;allocated&nbsp;array.&nbsp;If&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;set&nbsp;to&nbsp;false,&nbsp;and&nbsp;the&nbsp;`dtype`,&nbsp;`order`,&nbsp;and&nbsp;`subok`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;requirements&nbsp;are&nbsp;satisfied,&nbsp;the&nbsp;input&nbsp;array&nbsp;is&nbsp;returned&nbsp;instead<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;a&nbsp;copy.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-arr_t&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Unless&nbsp;`copy`&nbsp;is&nbsp;False&nbsp;and&nbsp;the&nbsp;other&nbsp;conditions&nbsp;for&nbsp;returning&nbsp;the&nbsp;input<br>
-&nbsp;&nbsp;&nbsp;&nbsp;array&nbsp;are&nbsp;satisfied&nbsp;(see&nbsp;description&nbsp;for&nbsp;`copy`&nbsp;input&nbsp;parameter),&nbsp;`arr_t`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;a&nbsp;new&nbsp;array&nbsp;of&nbsp;the&nbsp;same&nbsp;shape&nbsp;as&nbsp;the&nbsp;input&nbsp;array,&nbsp;with&nbsp;dtype,&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;given&nbsp;by&nbsp;`dtype`,&nbsp;`order`.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Starting&nbsp;in&nbsp;NumPy&nbsp;1.9,&nbsp;astype&nbsp;method&nbsp;now&nbsp;returns&nbsp;an&nbsp;error&nbsp;if&nbsp;the&nbsp;string<br>
-dtype&nbsp;to&nbsp;cast&nbsp;to&nbsp;is&nbsp;not&nbsp;long&nbsp;enough&nbsp;in&nbsp;'safe'&nbsp;casting&nbsp;mode&nbsp;to&nbsp;hold&nbsp;the&nbsp;max<br>
-value&nbsp;of&nbsp;integer/float&nbsp;array&nbsp;that&nbsp;is&nbsp;being&nbsp;casted.&nbsp;Previously&nbsp;the&nbsp;casting<br>
-was&nbsp;allowed&nbsp;even&nbsp;if&nbsp;the&nbsp;result&nbsp;was&nbsp;truncated.<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-ComplexWarning<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;casting&nbsp;from&nbsp;complex&nbsp;to&nbsp;float&nbsp;or&nbsp;int.&nbsp;To&nbsp;avoid&nbsp;this,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;one&nbsp;should&nbsp;use&nbsp;``a.real.<a href="#Array-astype">astype</a>(t)``.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.array([1,&nbsp;2,&nbsp;2.5])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-array([&nbsp;1.&nbsp;,&nbsp;&nbsp;2.&nbsp;,&nbsp;&nbsp;2.5])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-astype">astype</a>(int)<br>
-array([1,&nbsp;2,&nbsp;2])</tt></dd></dl>
-
-<dl><dt><a name="Array-byteswap"><strong>byteswap</strong></a>(...)</dt><dd><tt>a.<a href="#Array-byteswap">byteswap</a>(inplace)<br>
-&nbsp;<br>
-Swap&nbsp;the&nbsp;bytes&nbsp;of&nbsp;the&nbsp;array&nbsp;elements<br>
-&nbsp;<br>
-Toggle&nbsp;between&nbsp;low-endian&nbsp;and&nbsp;big-endian&nbsp;data&nbsp;representation&nbsp;by<br>
-returning&nbsp;a&nbsp;byteswapped&nbsp;array,&nbsp;optionally&nbsp;swapped&nbsp;in-place.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;``True``,&nbsp;swap&nbsp;bytes&nbsp;in-place,&nbsp;default&nbsp;is&nbsp;``False``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-out&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;byteswapped&nbsp;array.&nbsp;If&nbsp;`inplace`&nbsp;is&nbsp;``True``,&nbsp;this&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;view&nbsp;to&nbsp;self.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;A&nbsp;=&nbsp;np.array([1,&nbsp;256,&nbsp;8755],&nbsp;dtype=np.int16)<br>
-&gt;&gt;&gt;&nbsp;map(hex,&nbsp;A)<br>
-['0x1',&nbsp;'0x100',&nbsp;'0x2233']<br>
-&gt;&gt;&gt;&nbsp;A.<a href="#Array-byteswap">byteswap</a>(True)<br>
-array([&nbsp;&nbsp;256,&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1,&nbsp;13090],&nbsp;dtype=int16)<br>
-&gt;&gt;&gt;&nbsp;map(hex,&nbsp;A)<br>
-['0x100',&nbsp;'0x1',&nbsp;'0x3322']<br>
-&nbsp;<br>
-Arrays&nbsp;of&nbsp;strings&nbsp;are&nbsp;not&nbsp;swapped<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;A&nbsp;=&nbsp;np.array(['ceg',&nbsp;'fac'])<br>
-&gt;&gt;&gt;&nbsp;A.<a href="#Array-byteswap">byteswap</a>()<br>
-array(['ceg',&nbsp;'fac'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dtype='|S3')</tt></dd></dl>
-
-<dl><dt><a name="Array-choose"><strong>choose</strong></a>(...)</dt><dd><tt>a.<a href="#Array-choose">choose</a>(choices,&nbsp;out=None,&nbsp;mode='raise')<br>
-&nbsp;<br>
-Use&nbsp;an&nbsp;index&nbsp;array&nbsp;to&nbsp;construct&nbsp;a&nbsp;new&nbsp;array&nbsp;from&nbsp;a&nbsp;set&nbsp;of&nbsp;choices.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.choose`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.choose&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-clip"><strong>clip</strong></a>(...)</dt><dd><tt>a.<a href="#Array-clip">clip</a>(min=None,&nbsp;max=None,&nbsp;out=None)<br>
-&nbsp;<br>
-Return&nbsp;an&nbsp;array&nbsp;whose&nbsp;values&nbsp;are&nbsp;limited&nbsp;to&nbsp;``[min,&nbsp;max]``.<br>
-One&nbsp;of&nbsp;max&nbsp;or&nbsp;min&nbsp;must&nbsp;be&nbsp;given.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.clip`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.clip&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-compress"><strong>compress</strong></a>(...)</dt><dd><tt>a.<a href="#Array-compress">compress</a>(condition,&nbsp;axis=None,&nbsp;out=None)<br>
-&nbsp;<br>
-Return&nbsp;selected&nbsp;slices&nbsp;of&nbsp;this&nbsp;array&nbsp;along&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.compress`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.compress&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-conj"><strong>conj</strong></a>(...)</dt><dd><tt>a.<a href="#Array-conj">conj</a>()<br>
-&nbsp;<br>
-Complex-conjugate&nbsp;all&nbsp;elements.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.conjugate`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.conjugate&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-conjugate"><strong>conjugate</strong></a>(...)</dt><dd><tt>a.<a href="#Array-conjugate">conjugate</a>()<br>
-&nbsp;<br>
-Return&nbsp;the&nbsp;complex&nbsp;conjugate,&nbsp;element-wise.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.conjugate`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.conjugate&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-copy"><strong>copy</strong></a>(...)</dt><dd><tt>a.<a href="#Array-copy">copy</a>(order='C')<br>
-&nbsp;<br>
-Return&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;array.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-order&nbsp;:&nbsp;{'C',&nbsp;'F',&nbsp;'A',&nbsp;'K'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Controls&nbsp;the&nbsp;memory&nbsp;layout&nbsp;of&nbsp;the&nbsp;copy.&nbsp;'C'&nbsp;means&nbsp;C-order,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'F'&nbsp;means&nbsp;F-order,&nbsp;'A'&nbsp;means&nbsp;'F'&nbsp;if&nbsp;`a`&nbsp;is&nbsp;Fortran&nbsp;contiguous,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;otherwise.&nbsp;'K'&nbsp;means&nbsp;match&nbsp;the&nbsp;layout&nbsp;of&nbsp;`a`&nbsp;as&nbsp;closely<br>
-&nbsp;&nbsp;&nbsp;&nbsp;as&nbsp;possible.&nbsp;(Note&nbsp;that&nbsp;this&nbsp;function&nbsp;and&nbsp;:func:numpy.copy&nbsp;are&nbsp;very<br>
-&nbsp;&nbsp;&nbsp;&nbsp;similar,&nbsp;but&nbsp;have&nbsp;different&nbsp;default&nbsp;values&nbsp;for&nbsp;their&nbsp;order=<br>
-&nbsp;&nbsp;&nbsp;&nbsp;arguments.)<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.copy<br>
-numpy.copyto<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.array([[1,2,3],[4,5,6]],&nbsp;order='F')<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;y&nbsp;=&nbsp;x.<a href="#Array-copy">copy</a>()<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-fill">fill</a>(0)<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x<br>
-array([[0,&nbsp;0,&nbsp;0],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[0,&nbsp;0,&nbsp;0]])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;y<br>
-array([[1,&nbsp;2,&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[4,&nbsp;5,&nbsp;6]])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;y.flags['C_CONTIGUOUS']<br>
-True</tt></dd></dl>
-
-<dl><dt><a name="Array-cumprod"><strong>cumprod</strong></a>(...)</dt><dd><tt>a.<a href="#Array-cumprod">cumprod</a>(axis=None,&nbsp;dtype=None,&nbsp;out=None)<br>
-&nbsp;<br>
-Return&nbsp;the&nbsp;cumulative&nbsp;product&nbsp;of&nbsp;the&nbsp;elements&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.cumprod`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.cumprod&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-cumsum"><strong>cumsum</strong></a>(...)</dt><dd><tt>a.<a href="#Array-cumsum">cumsum</a>(axis=None,&nbsp;dtype=None,&nbsp;out=None)<br>
-&nbsp;<br>
-Return&nbsp;the&nbsp;cumulative&nbsp;sum&nbsp;of&nbsp;the&nbsp;elements&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.cumsum`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.cumsum&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-diagonal"><strong>diagonal</strong></a>(...)</dt><dd><tt>a.<a href="#Array-diagonal">diagonal</a>(offset=0,&nbsp;axis1=0,&nbsp;axis2=1)<br>
-&nbsp;<br>
-Return&nbsp;specified&nbsp;diagonals.&nbsp;In&nbsp;NumPy&nbsp;1.9&nbsp;the&nbsp;returned&nbsp;array&nbsp;is&nbsp;a<br>
-read-only&nbsp;view&nbsp;instead&nbsp;of&nbsp;a&nbsp;copy&nbsp;as&nbsp;in&nbsp;previous&nbsp;NumPy&nbsp;versions.&nbsp;&nbsp;In<br>
-a&nbsp;future&nbsp;version&nbsp;the&nbsp;read-only&nbsp;restriction&nbsp;will&nbsp;be&nbsp;removed.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;:func:`numpy.diagonal`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.diagonal&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-dot"><strong>dot</strong></a>(...)</dt><dd><tt>a.<a href="#Array-dot">dot</a>(b,&nbsp;out=None)<br>
-&nbsp;<br>
-Dot&nbsp;product&nbsp;of&nbsp;two&nbsp;arrays.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.dot`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.dot&nbsp;:&nbsp;equivalent&nbsp;function<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;a&nbsp;=&nbsp;np.eye(2)<br>
-&gt;&gt;&gt;&nbsp;b&nbsp;=&nbsp;np.ones((2,&nbsp;2))&nbsp;*&nbsp;2<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-dot">dot</a>(b)<br>
-array([[&nbsp;2.,&nbsp;&nbsp;2.],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;2.,&nbsp;&nbsp;2.]])<br>
-&nbsp;<br>
-This&nbsp;array&nbsp;method&nbsp;can&nbsp;be&nbsp;conveniently&nbsp;chained:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-dot">dot</a>(b).<a href="#Array-dot">dot</a>(b)<br>
-array([[&nbsp;8.,&nbsp;&nbsp;8.],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;8.,&nbsp;&nbsp;8.]])</tt></dd></dl>
-
-<dl><dt><a name="Array-dump"><strong>dump</strong></a>(...)</dt><dd><tt>a.<a href="#Array-dump">dump</a>(file)<br>
-&nbsp;<br>
-Dump&nbsp;a&nbsp;pickle&nbsp;of&nbsp;the&nbsp;array&nbsp;to&nbsp;the&nbsp;specified&nbsp;file.<br>
-The&nbsp;array&nbsp;can&nbsp;be&nbsp;read&nbsp;back&nbsp;with&nbsp;pickle.load&nbsp;or&nbsp;numpy.load.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-file&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;string&nbsp;naming&nbsp;the&nbsp;dump&nbsp;file.</tt></dd></dl>
-
-<dl><dt><a name="Array-dumps"><strong>dumps</strong></a>(...)</dt><dd><tt>a.<a href="#Array-dumps">dumps</a>()<br>
-&nbsp;<br>
-Returns&nbsp;the&nbsp;pickle&nbsp;of&nbsp;the&nbsp;array&nbsp;as&nbsp;a&nbsp;string.<br>
-pickle.loads&nbsp;or&nbsp;numpy.loads&nbsp;will&nbsp;convert&nbsp;the&nbsp;string&nbsp;back&nbsp;to&nbsp;an&nbsp;array.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-None</tt></dd></dl>
-
-<dl><dt><a name="Array-fill"><strong>fill</strong></a>(...)</dt><dd><tt>a.<a href="#Array-fill">fill</a>(value)<br>
-&nbsp;<br>
-Fill&nbsp;the&nbsp;array&nbsp;with&nbsp;a&nbsp;scalar&nbsp;value.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;All&nbsp;elements&nbsp;of&nbsp;`a`&nbsp;will&nbsp;be&nbsp;assigned&nbsp;this&nbsp;value.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;a&nbsp;=&nbsp;np.array([1,&nbsp;2])<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-fill">fill</a>(0)<br>
-&gt;&gt;&gt;&nbsp;a<br>
-array([0,&nbsp;0])<br>
-&gt;&gt;&gt;&nbsp;a&nbsp;=&nbsp;np.empty(2)<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-fill">fill</a>(1)<br>
-&gt;&gt;&gt;&nbsp;a<br>
-array([&nbsp;1.,&nbsp;&nbsp;1.])</tt></dd></dl>
-
-<dl><dt><a name="Array-flatten"><strong>flatten</strong></a>(...)</dt><dd><tt>a.<a href="#Array-flatten">flatten</a>(order='C')<br>
-&nbsp;<br>
-Return&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;array&nbsp;collapsed&nbsp;into&nbsp;one&nbsp;dimension.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-order&nbsp;:&nbsp;{'C',&nbsp;'F',&nbsp;'A',&nbsp;'K'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;means&nbsp;to&nbsp;flatten&nbsp;in&nbsp;row-major&nbsp;(C-style)&nbsp;order.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'F'&nbsp;means&nbsp;to&nbsp;flatten&nbsp;in&nbsp;column-major&nbsp;(Fortran-<br>
-&nbsp;&nbsp;&nbsp;&nbsp;style)&nbsp;order.&nbsp;'A'&nbsp;means&nbsp;to&nbsp;flatten&nbsp;in&nbsp;column-major<br>
-&nbsp;&nbsp;&nbsp;&nbsp;order&nbsp;if&nbsp;`a`&nbsp;is&nbsp;Fortran&nbsp;*contiguous*&nbsp;in&nbsp;memory,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;row-major&nbsp;order&nbsp;otherwise.&nbsp;'K'&nbsp;means&nbsp;to&nbsp;flatten<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`a`&nbsp;in&nbsp;the&nbsp;order&nbsp;the&nbsp;elements&nbsp;occur&nbsp;in&nbsp;memory.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;default&nbsp;is&nbsp;'C'.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;copy&nbsp;of&nbsp;the&nbsp;input&nbsp;array,&nbsp;flattened&nbsp;to&nbsp;one&nbsp;dimension.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-ravel&nbsp;:&nbsp;Return&nbsp;a&nbsp;flattened&nbsp;array.<br>
-flat&nbsp;:&nbsp;A&nbsp;1-D&nbsp;flat&nbsp;iterator&nbsp;over&nbsp;the&nbsp;array.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;a&nbsp;=&nbsp;np.array([[1,2],&nbsp;[3,4]])<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-flatten">flatten</a>()<br>
-array([1,&nbsp;2,&nbsp;3,&nbsp;4])<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-flatten">flatten</a>('F')<br>
-array([1,&nbsp;3,&nbsp;2,&nbsp;4])</tt></dd></dl>
-
-<dl><dt><a name="Array-getfield"><strong>getfield</strong></a>(...)</dt><dd><tt>a.<a href="#Array-getfield">getfield</a>(dtype,&nbsp;offset=0)<br>
-&nbsp;<br>
-Returns&nbsp;a&nbsp;field&nbsp;of&nbsp;the&nbsp;given&nbsp;array&nbsp;as&nbsp;a&nbsp;certain&nbsp;type.<br>
-&nbsp;<br>
-A&nbsp;field&nbsp;is&nbsp;a&nbsp;view&nbsp;of&nbsp;the&nbsp;array&nbsp;data&nbsp;with&nbsp;a&nbsp;given&nbsp;data-type.&nbsp;The&nbsp;values&nbsp;in<br>
-the&nbsp;view&nbsp;are&nbsp;determined&nbsp;by&nbsp;the&nbsp;given&nbsp;type&nbsp;and&nbsp;the&nbsp;offset&nbsp;into&nbsp;the&nbsp;current<br>
-array&nbsp;in&nbsp;bytes.&nbsp;The&nbsp;offset&nbsp;needs&nbsp;to&nbsp;be&nbsp;such&nbsp;that&nbsp;the&nbsp;view&nbsp;dtype&nbsp;fits&nbsp;in&nbsp;the<br>
-array&nbsp;dtype;&nbsp;for&nbsp;example&nbsp;an&nbsp;array&nbsp;of&nbsp;dtype&nbsp;complex128&nbsp;has&nbsp;16-byte&nbsp;elements.<br>
-If&nbsp;taking&nbsp;a&nbsp;view&nbsp;with&nbsp;a&nbsp;32-bit&nbsp;integer&nbsp;(4&nbsp;bytes),&nbsp;the&nbsp;offset&nbsp;needs&nbsp;to&nbsp;be<br>
-between&nbsp;0&nbsp;and&nbsp;12&nbsp;bytes.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dtype&nbsp;:&nbsp;str&nbsp;or&nbsp;dtype<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;data&nbsp;type&nbsp;of&nbsp;the&nbsp;view.&nbsp;The&nbsp;dtype&nbsp;size&nbsp;of&nbsp;the&nbsp;view&nbsp;can&nbsp;not&nbsp;be&nbsp;larger<br>
-&nbsp;&nbsp;&nbsp;&nbsp;than&nbsp;that&nbsp;of&nbsp;the&nbsp;array&nbsp;itself.<br>
-offset&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;bytes&nbsp;to&nbsp;skip&nbsp;before&nbsp;beginning&nbsp;the&nbsp;element&nbsp;view.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.diag([1.+1.j]*2)<br>
-&gt;&gt;&gt;&nbsp;x[1,&nbsp;1]&nbsp;=&nbsp;2&nbsp;+&nbsp;4.j<br>
-&gt;&gt;&gt;&nbsp;x<br>
-array([[&nbsp;1.+1.j,&nbsp;&nbsp;0.+0.j],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;0.+0.j,&nbsp;&nbsp;2.+4.j]])<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-getfield">getfield</a>(np.float64)<br>
-array([[&nbsp;1.,&nbsp;&nbsp;0.],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;0.,&nbsp;&nbsp;2.]])<br>
-&nbsp;<br>
-By&nbsp;choosing&nbsp;an&nbsp;offset&nbsp;of&nbsp;8&nbsp;bytes&nbsp;we&nbsp;can&nbsp;select&nbsp;the&nbsp;complex&nbsp;part&nbsp;of&nbsp;the<br>
-array&nbsp;for&nbsp;our&nbsp;view:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-getfield">getfield</a>(np.float64,&nbsp;offset=8)<br>
-array([[&nbsp;1.,&nbsp;&nbsp;0.],<br>
-&nbsp;&nbsp;&nbsp;[&nbsp;0.,&nbsp;&nbsp;4.]])</tt></dd></dl>
-
-<dl><dt><a name="Array-item"><strong>item</strong></a>(...)</dt><dd><tt>a.<a href="#Array-item">item</a>(*args)<br>
-&nbsp;<br>
-Copy&nbsp;an&nbsp;element&nbsp;of&nbsp;an&nbsp;array&nbsp;to&nbsp;a&nbsp;standard&nbsp;Python&nbsp;scalar&nbsp;and&nbsp;return&nbsp;it.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-\*args&nbsp;:&nbsp;Arguments&nbsp;(variable&nbsp;number&nbsp;and&nbsp;type)<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;none:&nbsp;in&nbsp;this&nbsp;case,&nbsp;the&nbsp;method&nbsp;only&nbsp;works&nbsp;for&nbsp;arrays<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;one&nbsp;element&nbsp;(`a.size&nbsp;==&nbsp;1`),&nbsp;which&nbsp;element&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;copied&nbsp;into&nbsp;a&nbsp;standard&nbsp;Python&nbsp;scalar&nbsp;<a href="builtins.html#object">object</a>&nbsp;and&nbsp;returned.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;int_type:&nbsp;this&nbsp;argument&nbsp;is&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;flat&nbsp;index&nbsp;into<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;array,&nbsp;specifying&nbsp;which&nbsp;element&nbsp;to&nbsp;copy&nbsp;and&nbsp;return.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;tuple&nbsp;of&nbsp;int_types:&nbsp;functions&nbsp;as&nbsp;does&nbsp;a&nbsp;single&nbsp;int_type&nbsp;argument,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;except&nbsp;that&nbsp;the&nbsp;argument&nbsp;is&nbsp;interpreted&nbsp;as&nbsp;an&nbsp;nd-index&nbsp;into&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;array.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-z&nbsp;:&nbsp;Standard&nbsp;Python&nbsp;scalar&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;copy&nbsp;of&nbsp;the&nbsp;specified&nbsp;element&nbsp;of&nbsp;the&nbsp;array&nbsp;as&nbsp;a&nbsp;suitable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Python&nbsp;scalar<br>
-&nbsp;<br>
-Notes<br>
------<br>
-When&nbsp;the&nbsp;data&nbsp;type&nbsp;of&nbsp;`a`&nbsp;is&nbsp;longdouble&nbsp;or&nbsp;clongdouble,&nbsp;<a href="#Array-item">item</a>()&nbsp;returns<br>
-a&nbsp;scalar&nbsp;array&nbsp;<a href="builtins.html#object">object</a>&nbsp;because&nbsp;there&nbsp;is&nbsp;no&nbsp;available&nbsp;Python&nbsp;scalar&nbsp;that<br>
-would&nbsp;not&nbsp;lose&nbsp;information.&nbsp;Void&nbsp;arrays&nbsp;return&nbsp;a&nbsp;buffer&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;<a href="#Array-item">item</a>(),<br>
-unless&nbsp;fields&nbsp;are&nbsp;defined,&nbsp;in&nbsp;which&nbsp;case&nbsp;a&nbsp;tuple&nbsp;is&nbsp;returned.<br>
-&nbsp;<br>
-`item`&nbsp;is&nbsp;very&nbsp;similar&nbsp;to&nbsp;a[args],&nbsp;except,&nbsp;instead&nbsp;of&nbsp;an&nbsp;array&nbsp;scalar,<br>
-a&nbsp;standard&nbsp;Python&nbsp;scalar&nbsp;is&nbsp;returned.&nbsp;This&nbsp;can&nbsp;be&nbsp;useful&nbsp;for&nbsp;speeding&nbsp;up<br>
-access&nbsp;to&nbsp;elements&nbsp;of&nbsp;the&nbsp;array&nbsp;and&nbsp;doing&nbsp;arithmetic&nbsp;on&nbsp;elements&nbsp;of&nbsp;the<br>
-array&nbsp;using&nbsp;Python's&nbsp;optimized&nbsp;math.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.random.randint(9,&nbsp;size=(3,&nbsp;3))<br>
-&gt;&gt;&gt;&nbsp;x<br>
-array([[3,&nbsp;1,&nbsp;7],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[2,&nbsp;8,&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[8,&nbsp;5,&nbsp;3]])<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-item">item</a>(3)<br>
-2<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-item">item</a>(7)<br>
-5<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-item">item</a>((0,&nbsp;1))<br>
-1<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-item">item</a>((2,&nbsp;2))<br>
-3</tt></dd></dl>
-
-<dl><dt><a name="Array-itemset"><strong>itemset</strong></a>(...)</dt><dd><tt>a.<a href="#Array-itemset">itemset</a>(*args)<br>
-&nbsp;<br>
-Insert&nbsp;scalar&nbsp;into&nbsp;an&nbsp;array&nbsp;(scalar&nbsp;is&nbsp;cast&nbsp;to&nbsp;array's&nbsp;dtype,&nbsp;if&nbsp;possible)<br>
-&nbsp;<br>
-There&nbsp;must&nbsp;be&nbsp;at&nbsp;least&nbsp;1&nbsp;argument,&nbsp;and&nbsp;define&nbsp;the&nbsp;last&nbsp;argument<br>
-as&nbsp;*item*.&nbsp;&nbsp;Then,&nbsp;``a.<a href="#Array-itemset">itemset</a>(*args)``&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;but&nbsp;faster<br>
-than&nbsp;``a[args]&nbsp;=&nbsp;item``.&nbsp;&nbsp;The&nbsp;item&nbsp;should&nbsp;be&nbsp;a&nbsp;scalar&nbsp;value&nbsp;and&nbsp;`args`<br>
-must&nbsp;select&nbsp;a&nbsp;single&nbsp;item&nbsp;in&nbsp;the&nbsp;array&nbsp;`a`.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-\*args&nbsp;:&nbsp;Arguments<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;one&nbsp;argument:&nbsp;a&nbsp;scalar,&nbsp;only&nbsp;used&nbsp;in&nbsp;case&nbsp;`a`&nbsp;is&nbsp;of&nbsp;size&nbsp;1.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;two&nbsp;arguments:&nbsp;the&nbsp;last&nbsp;argument&nbsp;is&nbsp;the&nbsp;value&nbsp;to&nbsp;be&nbsp;set<br>
-&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;must&nbsp;be&nbsp;a&nbsp;scalar,&nbsp;the&nbsp;first&nbsp;argument&nbsp;specifies&nbsp;a&nbsp;single&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;element&nbsp;location.&nbsp;It&nbsp;is&nbsp;either&nbsp;an&nbsp;int&nbsp;or&nbsp;a&nbsp;tuple.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Compared&nbsp;to&nbsp;indexing&nbsp;syntax,&nbsp;`itemset`&nbsp;provides&nbsp;some&nbsp;speed&nbsp;increase<br>
-for&nbsp;placing&nbsp;a&nbsp;scalar&nbsp;into&nbsp;a&nbsp;particular&nbsp;location&nbsp;in&nbsp;an&nbsp;`<a href="numpy.html#ndarray">ndarray</a>`,<br>
-if&nbsp;you&nbsp;must&nbsp;do&nbsp;this.&nbsp;&nbsp;However,&nbsp;generally&nbsp;this&nbsp;is&nbsp;discouraged:<br>
-among&nbsp;other&nbsp;problems,&nbsp;it&nbsp;complicates&nbsp;the&nbsp;appearance&nbsp;of&nbsp;the&nbsp;code.<br>
-Also,&nbsp;when&nbsp;using&nbsp;`itemset`&nbsp;(and&nbsp;`item`)&nbsp;inside&nbsp;a&nbsp;loop,&nbsp;be&nbsp;sure<br>
-to&nbsp;assign&nbsp;the&nbsp;methods&nbsp;to&nbsp;a&nbsp;local&nbsp;variable&nbsp;to&nbsp;avoid&nbsp;the&nbsp;attribute<br>
-look-up&nbsp;at&nbsp;each&nbsp;loop&nbsp;iteration.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.random.randint(9,&nbsp;size=(3,&nbsp;3))<br>
-&gt;&gt;&gt;&nbsp;x<br>
-array([[3,&nbsp;1,&nbsp;7],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[2,&nbsp;8,&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[8,&nbsp;5,&nbsp;3]])<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-itemset">itemset</a>(4,&nbsp;0)<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-itemset">itemset</a>((2,&nbsp;2),&nbsp;9)<br>
-&gt;&gt;&gt;&nbsp;x<br>
-array([[3,&nbsp;1,&nbsp;7],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[2,&nbsp;0,&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[8,&nbsp;5,&nbsp;9]])</tt></dd></dl>
-
-<dl><dt><a name="Array-max"><strong>max</strong></a>(...)</dt><dd><tt>a.<a href="#Array-max">max</a>(axis=None,&nbsp;out=None)<br>
-&nbsp;<br>
-Return&nbsp;the&nbsp;maximum&nbsp;along&nbsp;a&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.amax`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.amax&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-mean"><strong>mean</strong></a>(...)</dt><dd><tt>a.<a href="#Array-mean">mean</a>(axis=None,&nbsp;dtype=None,&nbsp;out=None,&nbsp;keepdims=False)<br>
-&nbsp;<br>
-Returns&nbsp;the&nbsp;average&nbsp;of&nbsp;the&nbsp;array&nbsp;elements&nbsp;along&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.mean`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.mean&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-min"><strong>min</strong></a>(...)</dt><dd><tt>a.<a href="#Array-min">min</a>(axis=None,&nbsp;out=None,&nbsp;keepdims=False)<br>
-&nbsp;<br>
-Return&nbsp;the&nbsp;minimum&nbsp;along&nbsp;a&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.amin`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.amin&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-newbyteorder"><strong>newbyteorder</strong></a>(...)</dt><dd><tt>arr.<a href="#Array-newbyteorder">newbyteorder</a>(new_order='S')<br>
-&nbsp;<br>
-Return&nbsp;the&nbsp;array&nbsp;with&nbsp;the&nbsp;same&nbsp;data&nbsp;viewed&nbsp;with&nbsp;a&nbsp;different&nbsp;byte&nbsp;order.<br>
-&nbsp;<br>
-Equivalent&nbsp;to::<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;arr.<a href="#Array-view">view</a>(arr.dtype.newbytorder(new_order))<br>
-&nbsp;<br>
-Changes&nbsp;are&nbsp;also&nbsp;made&nbsp;in&nbsp;all&nbsp;fields&nbsp;and&nbsp;sub-arrays&nbsp;of&nbsp;the&nbsp;array&nbsp;data<br>
-type.<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-new_order&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Byte&nbsp;order&nbsp;to&nbsp;force;&nbsp;a&nbsp;value&nbsp;from&nbsp;the&nbsp;byte&nbsp;order&nbsp;specifications<br>
-&nbsp;&nbsp;&nbsp;&nbsp;below.&nbsp;`new_order`&nbsp;codes&nbsp;can&nbsp;be&nbsp;any&nbsp;of:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'S'&nbsp;-&nbsp;swap&nbsp;dtype&nbsp;from&nbsp;current&nbsp;to&nbsp;opposite&nbsp;endian<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;{'&lt;',&nbsp;'L'}&nbsp;-&nbsp;little&nbsp;endian<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;{'&gt;',&nbsp;'B'}&nbsp;-&nbsp;big&nbsp;endian<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;{'=',&nbsp;'N'}&nbsp;-&nbsp;native&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;{'|',&nbsp;'I'}&nbsp;-&nbsp;ignore&nbsp;(no&nbsp;change&nbsp;to&nbsp;byte&nbsp;order)<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;default&nbsp;value&nbsp;('S')&nbsp;results&nbsp;in&nbsp;swapping&nbsp;the&nbsp;current<br>
-&nbsp;&nbsp;&nbsp;&nbsp;byte&nbsp;order.&nbsp;The&nbsp;code&nbsp;does&nbsp;a&nbsp;case-insensitive&nbsp;check&nbsp;on&nbsp;the&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;letter&nbsp;of&nbsp;`new_order`&nbsp;for&nbsp;the&nbsp;alternatives&nbsp;above.&nbsp;&nbsp;For&nbsp;example,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;any&nbsp;of&nbsp;'B'&nbsp;or&nbsp;'b'&nbsp;or&nbsp;'biggish'&nbsp;are&nbsp;valid&nbsp;to&nbsp;specify&nbsp;big-endian.<br>
-&nbsp;<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-new_arr&nbsp;:&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;New&nbsp;array&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;the&nbsp;dtype&nbsp;reflecting&nbsp;given&nbsp;change&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;byte&nbsp;order.</tt></dd></dl>
-
-<dl><dt><a name="Array-nonzero"><strong>nonzero</strong></a>(...)</dt><dd><tt>a.<a href="#Array-nonzero">nonzero</a>()<br>
-&nbsp;<br>
-Return&nbsp;the&nbsp;indices&nbsp;of&nbsp;the&nbsp;elements&nbsp;that&nbsp;are&nbsp;non-zero.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.nonzero`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.nonzero&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-partition"><strong>partition</strong></a>(...)</dt><dd><tt>a.<a href="#Array-partition">partition</a>(kth,&nbsp;axis=-1,&nbsp;kind='introselect',&nbsp;order=None)<br>
-&nbsp;<br>
-Rearranges&nbsp;the&nbsp;elements&nbsp;in&nbsp;the&nbsp;array&nbsp;in&nbsp;such&nbsp;a&nbsp;way&nbsp;that&nbsp;value&nbsp;of&nbsp;the<br>
-element&nbsp;in&nbsp;kth&nbsp;position&nbsp;is&nbsp;in&nbsp;the&nbsp;position&nbsp;it&nbsp;would&nbsp;be&nbsp;in&nbsp;a&nbsp;sorted&nbsp;array.<br>
-All&nbsp;elements&nbsp;smaller&nbsp;than&nbsp;the&nbsp;kth&nbsp;element&nbsp;are&nbsp;moved&nbsp;before&nbsp;this&nbsp;element&nbsp;and<br>
-all&nbsp;equal&nbsp;or&nbsp;greater&nbsp;are&nbsp;moved&nbsp;behind&nbsp;it.&nbsp;The&nbsp;ordering&nbsp;of&nbsp;the&nbsp;elements&nbsp;in<br>
-the&nbsp;two&nbsp;partitions&nbsp;is&nbsp;undefined.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;1.8.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-kth&nbsp;:&nbsp;int&nbsp;or&nbsp;sequence&nbsp;of&nbsp;ints<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Element&nbsp;index&nbsp;to&nbsp;partition&nbsp;by.&nbsp;The&nbsp;kth&nbsp;element&nbsp;value&nbsp;will&nbsp;be&nbsp;in&nbsp;its<br>
-&nbsp;&nbsp;&nbsp;&nbsp;final&nbsp;sorted&nbsp;position&nbsp;and&nbsp;all&nbsp;smaller&nbsp;elements&nbsp;will&nbsp;be&nbsp;moved&nbsp;before&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;all&nbsp;equal&nbsp;or&nbsp;greater&nbsp;elements&nbsp;behind&nbsp;it.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;order&nbsp;all&nbsp;elements&nbsp;in&nbsp;the&nbsp;partitions&nbsp;is&nbsp;undefined.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;provided&nbsp;with&nbsp;a&nbsp;sequence&nbsp;of&nbsp;kth&nbsp;it&nbsp;will&nbsp;partition&nbsp;all&nbsp;elements<br>
-&nbsp;&nbsp;&nbsp;&nbsp;indexed&nbsp;by&nbsp;kth&nbsp;of&nbsp;them&nbsp;into&nbsp;their&nbsp;sorted&nbsp;position&nbsp;at&nbsp;once.<br>
-axis&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;along&nbsp;which&nbsp;to&nbsp;sort.&nbsp;Default&nbsp;is&nbsp;-1,&nbsp;which&nbsp;means&nbsp;sort&nbsp;along&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;last&nbsp;axis.<br>
-kind&nbsp;:&nbsp;{'introselect'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Selection&nbsp;algorithm.&nbsp;Default&nbsp;is&nbsp;'introselect'.<br>
-order&nbsp;:&nbsp;str&nbsp;or&nbsp;list&nbsp;of&nbsp;str,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;`a`&nbsp;is&nbsp;an&nbsp;array&nbsp;with&nbsp;fields&nbsp;defined,&nbsp;this&nbsp;argument&nbsp;specifies<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;fields&nbsp;to&nbsp;compare&nbsp;first,&nbsp;second,&nbsp;etc.&nbsp;&nbsp;A&nbsp;single&nbsp;field&nbsp;can<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;specified&nbsp;as&nbsp;a&nbsp;string,&nbsp;and&nbsp;not&nbsp;all&nbsp;fields&nbsp;need&nbsp;be&nbsp;specified,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;unspecified&nbsp;fields&nbsp;will&nbsp;still&nbsp;be&nbsp;used,&nbsp;in&nbsp;the&nbsp;order&nbsp;in&nbsp;which<br>
-&nbsp;&nbsp;&nbsp;&nbsp;they&nbsp;come&nbsp;up&nbsp;in&nbsp;the&nbsp;dtype,&nbsp;to&nbsp;break&nbsp;ties.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.partition&nbsp;:&nbsp;Return&nbsp;a&nbsp;parititioned&nbsp;copy&nbsp;of&nbsp;an&nbsp;array.<br>
-argpartition&nbsp;:&nbsp;Indirect&nbsp;partition.<br>
-sort&nbsp;:&nbsp;Full&nbsp;sort.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;``np.partition``&nbsp;for&nbsp;notes&nbsp;on&nbsp;the&nbsp;different&nbsp;algorithms.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;a&nbsp;=&nbsp;np.array([3,&nbsp;4,&nbsp;2,&nbsp;1])<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-partition">partition</a>(3)<br>
-&gt;&gt;&gt;&nbsp;a<br>
-array([2,&nbsp;1,&nbsp;3,&nbsp;4])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-partition">partition</a>((1,&nbsp;3))<br>
-array([1,&nbsp;2,&nbsp;3,&nbsp;4])</tt></dd></dl>
-
-<dl><dt><a name="Array-prod"><strong>prod</strong></a>(...)</dt><dd><tt>a.<a href="#Array-prod">prod</a>(axis=None,&nbsp;dtype=None,&nbsp;out=None,&nbsp;keepdims=False)<br>
-&nbsp;<br>
-Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;array&nbsp;elements&nbsp;over&nbsp;the&nbsp;given&nbsp;axis<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.prod`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.prod&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-ptp"><strong>ptp</strong></a>(...)</dt><dd><tt>a.<a href="#Array-ptp">ptp</a>(axis=None,&nbsp;out=None)<br>
-&nbsp;<br>
-Peak&nbsp;to&nbsp;peak&nbsp;(maximum&nbsp;-&nbsp;minimum)&nbsp;value&nbsp;along&nbsp;a&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.ptp`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.ptp&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-put"><strong>put</strong></a>(...)</dt><dd><tt>a.<a href="#Array-put">put</a>(indices,&nbsp;values,&nbsp;mode='raise')<br>
-&nbsp;<br>
-Set&nbsp;``a.flat[n]&nbsp;=&nbsp;values[n]``&nbsp;for&nbsp;all&nbsp;`n`&nbsp;in&nbsp;indices.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.put`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.put&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-ravel"><strong>ravel</strong></a>(...)</dt><dd><tt>a.<a href="#Array-ravel">ravel</a>([order])<br>
-&nbsp;<br>
-Return&nbsp;a&nbsp;flattened&nbsp;array.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.ravel`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.ravel&nbsp;:&nbsp;equivalent&nbsp;function<br>
-&nbsp;<br>
-<a href="numpy.html#ndarray">ndarray</a>.flat&nbsp;:&nbsp;a&nbsp;flat&nbsp;iterator&nbsp;on&nbsp;the&nbsp;array.</tt></dd></dl>
-
-<dl><dt><a name="Array-repeat"><strong>repeat</strong></a>(...)</dt><dd><tt>a.<a href="#Array-repeat">repeat</a>(repeats,&nbsp;axis=None)<br>
-&nbsp;<br>
-Repeat&nbsp;elements&nbsp;of&nbsp;an&nbsp;array.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.repeat`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.repeat&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-reshape"><strong>reshape</strong></a>(...)</dt><dd><tt>a.<a href="#Array-reshape">reshape</a>(shape,&nbsp;order='C')<br>
-&nbsp;<br>
-Returns&nbsp;an&nbsp;array&nbsp;containing&nbsp;the&nbsp;same&nbsp;data&nbsp;with&nbsp;a&nbsp;new&nbsp;shape.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.reshape`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.reshape&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-resize"><strong>resize</strong></a>(...)</dt><dd><tt>a.<a href="#Array-resize">resize</a>(new_shape,&nbsp;refcheck=True)<br>
-&nbsp;<br>
-Change&nbsp;shape&nbsp;and&nbsp;size&nbsp;of&nbsp;array&nbsp;in-place.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-new_shape&nbsp;:&nbsp;tuple&nbsp;of&nbsp;ints,&nbsp;or&nbsp;`n`&nbsp;ints<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Shape&nbsp;of&nbsp;resized&nbsp;array.<br>
-refcheck&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;reference&nbsp;count&nbsp;will&nbsp;not&nbsp;be&nbsp;checked.&nbsp;Default&nbsp;is&nbsp;True.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-None<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;`a`&nbsp;does&nbsp;not&nbsp;own&nbsp;its&nbsp;own&nbsp;data&nbsp;or&nbsp;references&nbsp;or&nbsp;views&nbsp;to&nbsp;it&nbsp;exist,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;the&nbsp;data&nbsp;memory&nbsp;must&nbsp;be&nbsp;changed.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;PyPy&nbsp;only:&nbsp;will&nbsp;always&nbsp;raise&nbsp;if&nbsp;the&nbsp;data&nbsp;memory&nbsp;must&nbsp;be&nbsp;changed,&nbsp;since<br>
-&nbsp;&nbsp;&nbsp;&nbsp;there&nbsp;is&nbsp;no&nbsp;reliable&nbsp;way&nbsp;to&nbsp;determine&nbsp;if&nbsp;references&nbsp;or&nbsp;views&nbsp;to&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;exist.<br>
-&nbsp;<br>
-SystemError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;`order`&nbsp;keyword&nbsp;argument&nbsp;is&nbsp;specified.&nbsp;This&nbsp;behaviour&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;bug&nbsp;in&nbsp;NumPy.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-resize&nbsp;:&nbsp;Return&nbsp;a&nbsp;new&nbsp;array&nbsp;with&nbsp;the&nbsp;specified&nbsp;shape.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;reallocates&nbsp;space&nbsp;for&nbsp;the&nbsp;data&nbsp;area&nbsp;if&nbsp;necessary.<br>
-&nbsp;<br>
-Only&nbsp;contiguous&nbsp;arrays&nbsp;(data&nbsp;elements&nbsp;consecutive&nbsp;in&nbsp;memory)&nbsp;can&nbsp;be<br>
-resized.<br>
-&nbsp;<br>
-The&nbsp;purpose&nbsp;of&nbsp;the&nbsp;reference&nbsp;count&nbsp;check&nbsp;is&nbsp;to&nbsp;make&nbsp;sure&nbsp;you<br>
-do&nbsp;not&nbsp;use&nbsp;this&nbsp;array&nbsp;as&nbsp;a&nbsp;buffer&nbsp;for&nbsp;another&nbsp;Python&nbsp;<a href="builtins.html#object">object</a>&nbsp;and&nbsp;then<br>
-reallocate&nbsp;the&nbsp;memory.&nbsp;However,&nbsp;reference&nbsp;counts&nbsp;can&nbsp;increase&nbsp;in<br>
-other&nbsp;ways&nbsp;so&nbsp;if&nbsp;you&nbsp;are&nbsp;sure&nbsp;that&nbsp;you&nbsp;have&nbsp;not&nbsp;shared&nbsp;the&nbsp;memory<br>
-for&nbsp;this&nbsp;array&nbsp;with&nbsp;another&nbsp;Python&nbsp;<a href="builtins.html#object">object</a>,&nbsp;then&nbsp;you&nbsp;may&nbsp;safely&nbsp;set<br>
-`refcheck`&nbsp;to&nbsp;False.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-Shrinking&nbsp;an&nbsp;array:&nbsp;array&nbsp;is&nbsp;flattened&nbsp;(in&nbsp;the&nbsp;order&nbsp;that&nbsp;the&nbsp;data&nbsp;are<br>
-stored&nbsp;in&nbsp;memory),&nbsp;resized,&nbsp;and&nbsp;reshaped:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;a&nbsp;=&nbsp;np.array([[0,&nbsp;1],&nbsp;[2,&nbsp;3]],&nbsp;order='C')<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-resize">resize</a>((2,&nbsp;1))<br>
-&gt;&gt;&gt;&nbsp;a<br>
-array([[0],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[1]])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;a&nbsp;=&nbsp;np.array([[0,&nbsp;1],&nbsp;[2,&nbsp;3]],&nbsp;order='F')<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-resize">resize</a>((2,&nbsp;1))<br>
-&gt;&gt;&gt;&nbsp;a<br>
-array([[0],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[2]])<br>
-&nbsp;<br>
-Enlarging&nbsp;an&nbsp;array:&nbsp;as&nbsp;above,&nbsp;but&nbsp;missing&nbsp;entries&nbsp;are&nbsp;filled&nbsp;with&nbsp;zeros:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;b&nbsp;=&nbsp;np.array([[0,&nbsp;1],&nbsp;[2,&nbsp;3]])<br>
-&gt;&gt;&gt;&nbsp;b.<a href="#Array-resize">resize</a>(2,&nbsp;3)&nbsp;#&nbsp;new_shape&nbsp;parameter&nbsp;doesn't&nbsp;have&nbsp;to&nbsp;be&nbsp;a&nbsp;tuple<br>
-&gt;&gt;&gt;&nbsp;b<br>
-array([[0,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[3,&nbsp;0,&nbsp;0]])<br>
-&nbsp;<br>
-Referencing&nbsp;an&nbsp;array&nbsp;prevents&nbsp;resizing...<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;c&nbsp;=&nbsp;a<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-resize">resize</a>((1,&nbsp;1))<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-ValueError:&nbsp;cannot&nbsp;resize&nbsp;an&nbsp;array&nbsp;that&nbsp;has&nbsp;been&nbsp;referenced&nbsp;...<br>
-&nbsp;<br>
-Unless&nbsp;`refcheck`&nbsp;is&nbsp;False:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-resize">resize</a>((1,&nbsp;1),&nbsp;refcheck=False)<br>
-&gt;&gt;&gt;&nbsp;a<br>
-array([[0]])<br>
-&gt;&gt;&gt;&nbsp;c<br>
-array([[0]])</tt></dd></dl>
-
-<dl><dt><a name="Array-round"><strong>round</strong></a>(...)</dt><dd><tt>a.<a href="#Array-round">round</a>(decimals=0,&nbsp;out=None)<br>
-&nbsp;<br>
-Return&nbsp;`a`&nbsp;with&nbsp;each&nbsp;element&nbsp;rounded&nbsp;to&nbsp;the&nbsp;given&nbsp;number&nbsp;of&nbsp;decimals.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.around`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.around&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-searchsorted"><strong>searchsorted</strong></a>(...)</dt><dd><tt>a.<a href="#Array-searchsorted">searchsorted</a>(v,&nbsp;side='left',&nbsp;sorter=None)<br>
-&nbsp;<br>
-Find&nbsp;indices&nbsp;where&nbsp;elements&nbsp;of&nbsp;v&nbsp;should&nbsp;be&nbsp;inserted&nbsp;in&nbsp;a&nbsp;to&nbsp;maintain&nbsp;order.<br>
-&nbsp;<br>
-For&nbsp;full&nbsp;documentation,&nbsp;see&nbsp;`numpy.searchsorted`<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.searchsorted&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-setfield"><strong>setfield</strong></a>(...)</dt><dd><tt>a.<a href="#Array-setfield">setfield</a>(val,&nbsp;dtype,&nbsp;offset=0)<br>
-&nbsp;<br>
-Put&nbsp;a&nbsp;value&nbsp;into&nbsp;a&nbsp;specified&nbsp;place&nbsp;in&nbsp;a&nbsp;field&nbsp;defined&nbsp;by&nbsp;a&nbsp;data-type.<br>
-&nbsp;<br>
-Place&nbsp;`val`&nbsp;into&nbsp;`a`'s&nbsp;field&nbsp;defined&nbsp;by&nbsp;`dtype`&nbsp;and&nbsp;beginning&nbsp;`offset`<br>
-bytes&nbsp;into&nbsp;the&nbsp;field.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-val&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;be&nbsp;placed&nbsp;in&nbsp;field.<br>
-dtype&nbsp;:&nbsp;dtype&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Data-type&nbsp;of&nbsp;the&nbsp;field&nbsp;in&nbsp;which&nbsp;to&nbsp;place&nbsp;`val`.<br>
-offset&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;number&nbsp;of&nbsp;bytes&nbsp;into&nbsp;the&nbsp;field&nbsp;at&nbsp;which&nbsp;to&nbsp;place&nbsp;`val`.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-None<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-getfield<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.eye(3)<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-getfield">getfield</a>(np.float64)<br>
-array([[&nbsp;1.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;0.,&nbsp;&nbsp;1.,&nbsp;&nbsp;0.],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;1.]])<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-setfield">setfield</a>(3,&nbsp;np.int32)<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-getfield">getfield</a>(np.int32)<br>
-array([[3,&nbsp;3,&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[3,&nbsp;3,&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[3,&nbsp;3,&nbsp;3]])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-array([[&nbsp;&nbsp;1.00000000e+000,&nbsp;&nbsp;&nbsp;1.48219694e-323,&nbsp;&nbsp;&nbsp;1.48219694e-323],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;&nbsp;1.48219694e-323,&nbsp;&nbsp;&nbsp;1.00000000e+000,&nbsp;&nbsp;&nbsp;1.48219694e-323],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;&nbsp;1.48219694e-323,&nbsp;&nbsp;&nbsp;1.48219694e-323,&nbsp;&nbsp;&nbsp;1.00000000e+000]])<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-setfield">setfield</a>(np.eye(3),&nbsp;np.int32)<br>
-&gt;&gt;&gt;&nbsp;x<br>
-array([[&nbsp;1.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;0.,&nbsp;&nbsp;1.,&nbsp;&nbsp;0.],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;1.]])</tt></dd></dl>
-
-<dl><dt><a name="Array-setflags"><strong>setflags</strong></a>(...)</dt><dd><tt>a.<a href="#Array-setflags">setflags</a>(write=None,&nbsp;align=None,&nbsp;uic=None)<br>
-&nbsp;<br>
-Set&nbsp;array&nbsp;flags&nbsp;WRITEABLE,&nbsp;ALIGNED,&nbsp;and&nbsp;UPDATEIFCOPY,&nbsp;respectively.<br>
-&nbsp;<br>
-These&nbsp;Boolean-valued&nbsp;flags&nbsp;affect&nbsp;how&nbsp;numpy&nbsp;interprets&nbsp;the&nbsp;memory<br>
-area&nbsp;used&nbsp;by&nbsp;`a`&nbsp;(see&nbsp;Notes&nbsp;below).&nbsp;The&nbsp;ALIGNED&nbsp;flag&nbsp;can&nbsp;only<br>
-be&nbsp;set&nbsp;to&nbsp;True&nbsp;if&nbsp;the&nbsp;data&nbsp;is&nbsp;actually&nbsp;aligned&nbsp;according&nbsp;to&nbsp;the&nbsp;type.<br>
-The&nbsp;UPDATEIFCOPY&nbsp;flag&nbsp;can&nbsp;never&nbsp;be&nbsp;set&nbsp;to&nbsp;True.&nbsp;The&nbsp;flag&nbsp;WRITEABLE<br>
-can&nbsp;only&nbsp;be&nbsp;set&nbsp;to&nbsp;True&nbsp;if&nbsp;the&nbsp;array&nbsp;owns&nbsp;its&nbsp;own&nbsp;memory,&nbsp;or&nbsp;the<br>
-ultimate&nbsp;owner&nbsp;of&nbsp;the&nbsp;memory&nbsp;exposes&nbsp;a&nbsp;writeable&nbsp;buffer&nbsp;interface,<br>
-or&nbsp;is&nbsp;a&nbsp;string.&nbsp;(The&nbsp;exception&nbsp;for&nbsp;string&nbsp;is&nbsp;made&nbsp;so&nbsp;that&nbsp;unpickling<br>
-can&nbsp;be&nbsp;done&nbsp;without&nbsp;copying&nbsp;memory.)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-write&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Describes&nbsp;whether&nbsp;or&nbsp;not&nbsp;`a`&nbsp;can&nbsp;be&nbsp;written&nbsp;to.<br>
-align&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Describes&nbsp;whether&nbsp;or&nbsp;not&nbsp;`a`&nbsp;is&nbsp;aligned&nbsp;properly&nbsp;for&nbsp;its&nbsp;type.<br>
-uic&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Describes&nbsp;whether&nbsp;or&nbsp;not&nbsp;`a`&nbsp;is&nbsp;a&nbsp;copy&nbsp;of&nbsp;another&nbsp;"base"&nbsp;array.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-<a href="#Array">Array</a>&nbsp;flags&nbsp;provide&nbsp;information&nbsp;about&nbsp;how&nbsp;the&nbsp;memory&nbsp;area&nbsp;used<br>
-for&nbsp;the&nbsp;array&nbsp;is&nbsp;to&nbsp;be&nbsp;interpreted.&nbsp;There&nbsp;are&nbsp;6&nbsp;Boolean&nbsp;flags<br>
-in&nbsp;use,&nbsp;only&nbsp;three&nbsp;of&nbsp;which&nbsp;can&nbsp;be&nbsp;changed&nbsp;by&nbsp;the&nbsp;user:<br>
-UPDATEIFCOPY,&nbsp;WRITEABLE,&nbsp;and&nbsp;ALIGNED.<br>
-&nbsp;<br>
-WRITEABLE&nbsp;(W)&nbsp;the&nbsp;data&nbsp;area&nbsp;can&nbsp;be&nbsp;written&nbsp;to;<br>
-&nbsp;<br>
-ALIGNED&nbsp;(A)&nbsp;the&nbsp;data&nbsp;and&nbsp;strides&nbsp;are&nbsp;aligned&nbsp;appropriately&nbsp;for&nbsp;the&nbsp;hardware<br>
-(as&nbsp;determined&nbsp;by&nbsp;the&nbsp;compiler);<br>
-&nbsp;<br>
-UPDATEIFCOPY&nbsp;(U)&nbsp;this&nbsp;array&nbsp;is&nbsp;a&nbsp;copy&nbsp;of&nbsp;some&nbsp;other&nbsp;array&nbsp;(referenced<br>
-by&nbsp;.base).&nbsp;When&nbsp;this&nbsp;array&nbsp;is&nbsp;deallocated,&nbsp;the&nbsp;base&nbsp;array&nbsp;will&nbsp;be<br>
-updated&nbsp;with&nbsp;the&nbsp;contents&nbsp;of&nbsp;this&nbsp;array.<br>
-&nbsp;<br>
-All&nbsp;flags&nbsp;can&nbsp;be&nbsp;accessed&nbsp;using&nbsp;their&nbsp;first&nbsp;(upper&nbsp;case)&nbsp;letter&nbsp;as&nbsp;well<br>
-as&nbsp;the&nbsp;full&nbsp;name.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;y<br>
-array([[3,&nbsp;1,&nbsp;7],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[2,&nbsp;0,&nbsp;0],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[8,&nbsp;5,&nbsp;9]])<br>
-&gt;&gt;&gt;&nbsp;y.flags<br>
-&nbsp;&nbsp;C_CONTIGUOUS&nbsp;:&nbsp;True<br>
-&nbsp;&nbsp;F_CONTIGUOUS&nbsp;:&nbsp;False<br>
-&nbsp;&nbsp;OWNDATA&nbsp;:&nbsp;True<br>
-&nbsp;&nbsp;WRITEABLE&nbsp;:&nbsp;True<br>
-&nbsp;&nbsp;ALIGNED&nbsp;:&nbsp;True<br>
-&nbsp;&nbsp;UPDATEIFCOPY&nbsp;:&nbsp;False<br>
-&gt;&gt;&gt;&nbsp;y.<a href="#Array-setflags">setflags</a>(write=0,&nbsp;align=0)<br>
-&gt;&gt;&gt;&nbsp;y.flags<br>
-&nbsp;&nbsp;C_CONTIGUOUS&nbsp;:&nbsp;True<br>
-&nbsp;&nbsp;F_CONTIGUOUS&nbsp;:&nbsp;False<br>
-&nbsp;&nbsp;OWNDATA&nbsp;:&nbsp;True<br>
-&nbsp;&nbsp;WRITEABLE&nbsp;:&nbsp;False<br>
-&nbsp;&nbsp;ALIGNED&nbsp;:&nbsp;False<br>
-&nbsp;&nbsp;UPDATEIFCOPY&nbsp;:&nbsp;False<br>
-&gt;&gt;&gt;&nbsp;y.<a href="#Array-setflags">setflags</a>(uic=1)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-&nbsp;&nbsp;File&nbsp;"&lt;stdin&gt;",&nbsp;line&nbsp;1,&nbsp;in&nbsp;&lt;module&gt;<br>
-ValueError:&nbsp;cannot&nbsp;set&nbsp;UPDATEIFCOPY&nbsp;flag&nbsp;to&nbsp;True</tt></dd></dl>
-
-<dl><dt><a name="Array-sort"><strong>sort</strong></a>(...)</dt><dd><tt>a.<a href="#Array-sort">sort</a>(axis=-1,&nbsp;kind='quicksort',&nbsp;order=None)<br>
-&nbsp;<br>
-Sort&nbsp;an&nbsp;array,&nbsp;in-place.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;along&nbsp;which&nbsp;to&nbsp;sort.&nbsp;Default&nbsp;is&nbsp;-1,&nbsp;which&nbsp;means&nbsp;sort&nbsp;along&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;last&nbsp;axis.<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sorting&nbsp;algorithm.&nbsp;Default&nbsp;is&nbsp;'quicksort'.<br>
-order&nbsp;:&nbsp;str&nbsp;or&nbsp;list&nbsp;of&nbsp;str,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;`a`&nbsp;is&nbsp;an&nbsp;array&nbsp;with&nbsp;fields&nbsp;defined,&nbsp;this&nbsp;argument&nbsp;specifies<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;fields&nbsp;to&nbsp;compare&nbsp;first,&nbsp;second,&nbsp;etc.&nbsp;&nbsp;A&nbsp;single&nbsp;field&nbsp;can<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;specified&nbsp;as&nbsp;a&nbsp;string,&nbsp;and&nbsp;not&nbsp;all&nbsp;fields&nbsp;need&nbsp;be&nbsp;specified,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;unspecified&nbsp;fields&nbsp;will&nbsp;still&nbsp;be&nbsp;used,&nbsp;in&nbsp;the&nbsp;order&nbsp;in&nbsp;which<br>
-&nbsp;&nbsp;&nbsp;&nbsp;they&nbsp;come&nbsp;up&nbsp;in&nbsp;the&nbsp;dtype,&nbsp;to&nbsp;break&nbsp;ties.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.sort&nbsp;:&nbsp;Return&nbsp;a&nbsp;sorted&nbsp;copy&nbsp;of&nbsp;an&nbsp;array.<br>
-argsort&nbsp;:&nbsp;Indirect&nbsp;sort.<br>
-lexsort&nbsp;:&nbsp;Indirect&nbsp;stable&nbsp;sort&nbsp;on&nbsp;multiple&nbsp;keys.<br>
-searchsorted&nbsp;:&nbsp;Find&nbsp;elements&nbsp;in&nbsp;sorted&nbsp;array.<br>
-partition:&nbsp;Partial&nbsp;sort.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;``sort``&nbsp;for&nbsp;notes&nbsp;on&nbsp;the&nbsp;different&nbsp;sorting&nbsp;algorithms.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;a&nbsp;=&nbsp;np.array([[1,4],&nbsp;[3,1]])<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-sort">sort</a>(axis=1)<br>
-&gt;&gt;&gt;&nbsp;a<br>
-array([[1,&nbsp;4],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[1,&nbsp;3]])<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-sort">sort</a>(axis=0)<br>
-&gt;&gt;&gt;&nbsp;a<br>
-array([[1,&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[1,&nbsp;4]])<br>
-&nbsp;<br>
-Use&nbsp;the&nbsp;`order`&nbsp;keyword&nbsp;to&nbsp;specify&nbsp;a&nbsp;field&nbsp;to&nbsp;use&nbsp;when&nbsp;sorting&nbsp;a<br>
-structured&nbsp;array:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;a&nbsp;=&nbsp;np.array([('a',&nbsp;2),&nbsp;('c',&nbsp;1)],&nbsp;dtype=[('x',&nbsp;'S1'),&nbsp;('y',&nbsp;int)])<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-sort">sort</a>(order='y')<br>
-&gt;&gt;&gt;&nbsp;a<br>
-array([('c',&nbsp;1),&nbsp;('a',&nbsp;2)],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dtype=[('x',&nbsp;'|S1'),&nbsp;('y',&nbsp;'&lt;i4')])</tt></dd></dl>
-
-<dl><dt><a name="Array-squeeze"><strong>squeeze</strong></a>(...)</dt><dd><tt>a.<a href="#Array-squeeze">squeeze</a>(axis=None)<br>
-&nbsp;<br>
-Remove&nbsp;single-dimensional&nbsp;entries&nbsp;from&nbsp;the&nbsp;shape&nbsp;of&nbsp;`a`.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.squeeze`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.squeeze&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-std"><strong>std</strong></a>(...)</dt><dd><tt>a.<a href="#Array-std">std</a>(axis=None,&nbsp;dtype=None,&nbsp;out=None,&nbsp;ddof=0,&nbsp;keepdims=False)<br>
-&nbsp;<br>
-Returns&nbsp;the&nbsp;standard&nbsp;deviation&nbsp;of&nbsp;the&nbsp;array&nbsp;elements&nbsp;along&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.std`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.std&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-sum"><strong>sum</strong></a>(...)</dt><dd><tt>a.<a href="#Array-sum">sum</a>(axis=None,&nbsp;dtype=None,&nbsp;out=None,&nbsp;keepdims=False)<br>
-&nbsp;<br>
-Return&nbsp;the&nbsp;sum&nbsp;of&nbsp;the&nbsp;array&nbsp;elements&nbsp;over&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.sum`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.sum&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-swapaxes"><strong>swapaxes</strong></a>(...)</dt><dd><tt>a.<a href="#Array-swapaxes">swapaxes</a>(axis1,&nbsp;axis2)<br>
-&nbsp;<br>
-Return&nbsp;a&nbsp;view&nbsp;of&nbsp;the&nbsp;array&nbsp;with&nbsp;`axis1`&nbsp;and&nbsp;`axis2`&nbsp;interchanged.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.swapaxes`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.swapaxes&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-take"><strong>take</strong></a>(...)</dt><dd><tt>a.<a href="#Array-take">take</a>(indices,&nbsp;axis=None,&nbsp;out=None,&nbsp;mode='raise')<br>
-&nbsp;<br>
-Return&nbsp;an&nbsp;array&nbsp;formed&nbsp;from&nbsp;the&nbsp;elements&nbsp;of&nbsp;`a`&nbsp;at&nbsp;the&nbsp;given&nbsp;indices.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.take`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.take&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-tobytes"><strong>tobytes</strong></a>(...)</dt><dd><tt>a.<a href="#Array-tobytes">tobytes</a>(order='C')<br>
-&nbsp;<br>
-Construct&nbsp;Python&nbsp;bytes&nbsp;containing&nbsp;the&nbsp;raw&nbsp;data&nbsp;bytes&nbsp;in&nbsp;the&nbsp;array.<br>
-&nbsp;<br>
-Constructs&nbsp;Python&nbsp;bytes&nbsp;showing&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;raw&nbsp;contents&nbsp;of<br>
-data&nbsp;memory.&nbsp;The&nbsp;bytes&nbsp;<a href="builtins.html#object">object</a>&nbsp;can&nbsp;be&nbsp;produced&nbsp;in&nbsp;either&nbsp;'C'&nbsp;or&nbsp;'Fortran',<br>
-or&nbsp;'Any'&nbsp;order&nbsp;(the&nbsp;default&nbsp;is&nbsp;'C'-order).&nbsp;'Any'&nbsp;order&nbsp;means&nbsp;C-order<br>
-unless&nbsp;the&nbsp;F_CONTIGUOUS&nbsp;flag&nbsp;in&nbsp;the&nbsp;array&nbsp;is&nbsp;set,&nbsp;in&nbsp;which&nbsp;case&nbsp;it<br>
-means&nbsp;'Fortran'&nbsp;order.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;1.9.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-order&nbsp;:&nbsp;{'C',&nbsp;'F',&nbsp;None},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Order&nbsp;of&nbsp;the&nbsp;data&nbsp;for&nbsp;multidimensional&nbsp;arrays:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C,&nbsp;Fortran,&nbsp;or&nbsp;the&nbsp;same&nbsp;as&nbsp;for&nbsp;the&nbsp;original&nbsp;array.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-s&nbsp;:&nbsp;bytes<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Python&nbsp;bytes&nbsp;exhibiting&nbsp;a&nbsp;copy&nbsp;of&nbsp;`a`'s&nbsp;raw&nbsp;data.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.array([[0,&nbsp;1],&nbsp;[2,&nbsp;3]])<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-tobytes">tobytes</a>()<br>
-b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-tobytes">tobytes</a>('C')&nbsp;==&nbsp;x.<a href="#Array-tobytes">tobytes</a>()<br>
-True<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-tobytes">tobytes</a>('F')<br>
-b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'</tt></dd></dl>
-
-<dl><dt><a name="Array-tofile"><strong>tofile</strong></a>(...)</dt><dd><tt>a.<a href="#Array-tofile">tofile</a>(fid,&nbsp;sep="",&nbsp;format="%s")<br>
-&nbsp;<br>
-Write&nbsp;array&nbsp;to&nbsp;a&nbsp;file&nbsp;as&nbsp;text&nbsp;or&nbsp;binary&nbsp;(default).<br>
-&nbsp;<br>
-Data&nbsp;is&nbsp;always&nbsp;written&nbsp;in&nbsp;'C'&nbsp;order,&nbsp;independent&nbsp;of&nbsp;the&nbsp;order&nbsp;of&nbsp;`a`.<br>
-The&nbsp;data&nbsp;produced&nbsp;by&nbsp;this&nbsp;method&nbsp;can&nbsp;be&nbsp;recovered&nbsp;using&nbsp;the&nbsp;function<br>
-fromfile().<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-fid&nbsp;:&nbsp;file&nbsp;or&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;An&nbsp;open&nbsp;file&nbsp;<a href="builtins.html#object">object</a>,&nbsp;or&nbsp;a&nbsp;string&nbsp;containing&nbsp;a&nbsp;filename.<br>
-sep&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Separator&nbsp;between&nbsp;array&nbsp;items&nbsp;for&nbsp;text&nbsp;output.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;""&nbsp;(empty),&nbsp;a&nbsp;binary&nbsp;file&nbsp;is&nbsp;written,&nbsp;equivalent&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``file.write(a.<a href="#Array-tobytes">tobytes</a>())``.<br>
-format&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;text&nbsp;file&nbsp;output.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Each&nbsp;entry&nbsp;in&nbsp;the&nbsp;array&nbsp;is&nbsp;formatted&nbsp;to&nbsp;text&nbsp;by&nbsp;first&nbsp;converting<br>
-&nbsp;&nbsp;&nbsp;&nbsp;it&nbsp;to&nbsp;the&nbsp;closest&nbsp;Python&nbsp;type,&nbsp;and&nbsp;then&nbsp;using&nbsp;"format"&nbsp;%&nbsp;item.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;is&nbsp;a&nbsp;convenience&nbsp;function&nbsp;for&nbsp;quick&nbsp;storage&nbsp;of&nbsp;array&nbsp;data.<br>
-Information&nbsp;on&nbsp;endianness&nbsp;and&nbsp;precision&nbsp;is&nbsp;lost,&nbsp;so&nbsp;this&nbsp;method&nbsp;is&nbsp;not&nbsp;a<br>
-good&nbsp;choice&nbsp;for&nbsp;files&nbsp;intended&nbsp;to&nbsp;archive&nbsp;data&nbsp;or&nbsp;transport&nbsp;data&nbsp;between<br>
-machines&nbsp;with&nbsp;different&nbsp;endianness.&nbsp;Some&nbsp;of&nbsp;these&nbsp;problems&nbsp;can&nbsp;be&nbsp;overcome<br>
-by&nbsp;outputting&nbsp;the&nbsp;data&nbsp;as&nbsp;text&nbsp;files,&nbsp;at&nbsp;the&nbsp;expense&nbsp;of&nbsp;speed&nbsp;and&nbsp;file<br>
-size.</tt></dd></dl>
-
-<dl><dt><a name="Array-tolist"><strong>tolist</strong></a>(...)</dt><dd><tt>a.<a href="#Array-tolist">tolist</a>()<br>
-&nbsp;<br>
-Return&nbsp;the&nbsp;array&nbsp;as&nbsp;a&nbsp;(possibly&nbsp;nested)&nbsp;list.<br>
-&nbsp;<br>
-Return&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;array&nbsp;data&nbsp;as&nbsp;a&nbsp;(nested)&nbsp;Python&nbsp;list.<br>
-Data&nbsp;items&nbsp;are&nbsp;converted&nbsp;to&nbsp;the&nbsp;nearest&nbsp;compatible&nbsp;Python&nbsp;type.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-none<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;possibly&nbsp;nested&nbsp;list&nbsp;of&nbsp;array&nbsp;elements.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;array&nbsp;may&nbsp;be&nbsp;recreated,&nbsp;``a&nbsp;=&nbsp;np.array(a.<a href="#Array-tolist">tolist</a>())``.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;a&nbsp;=&nbsp;np.array([1,&nbsp;2])<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-tolist">tolist</a>()<br>
-[1,&nbsp;2]<br>
-&gt;&gt;&gt;&nbsp;a&nbsp;=&nbsp;np.array([[1,&nbsp;2],&nbsp;[3,&nbsp;4]])<br>
-&gt;&gt;&gt;&nbsp;list(a)<br>
-[array([1,&nbsp;2]),&nbsp;array([3,&nbsp;4])]<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-tolist">tolist</a>()<br>
-[[1,&nbsp;2],&nbsp;[3,&nbsp;4]]</tt></dd></dl>
-
-<dl><dt><a name="Array-tostring"><strong>tostring</strong></a>(...)</dt><dd><tt>a.<a href="#Array-tostring">tostring</a>(order='C')<br>
-&nbsp;<br>
-Construct&nbsp;Python&nbsp;bytes&nbsp;containing&nbsp;the&nbsp;raw&nbsp;data&nbsp;bytes&nbsp;in&nbsp;the&nbsp;array.<br>
-&nbsp;<br>
-Constructs&nbsp;Python&nbsp;bytes&nbsp;showing&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;raw&nbsp;contents&nbsp;of<br>
-data&nbsp;memory.&nbsp;The&nbsp;bytes&nbsp;<a href="builtins.html#object">object</a>&nbsp;can&nbsp;be&nbsp;produced&nbsp;in&nbsp;either&nbsp;'C'&nbsp;or&nbsp;'Fortran',<br>
-or&nbsp;'Any'&nbsp;order&nbsp;(the&nbsp;default&nbsp;is&nbsp;'C'-order).&nbsp;'Any'&nbsp;order&nbsp;means&nbsp;C-order<br>
-unless&nbsp;the&nbsp;F_CONTIGUOUS&nbsp;flag&nbsp;in&nbsp;the&nbsp;array&nbsp;is&nbsp;set,&nbsp;in&nbsp;which&nbsp;case&nbsp;it<br>
-means&nbsp;'Fortran'&nbsp;order.<br>
-&nbsp;<br>
-This&nbsp;function&nbsp;is&nbsp;a&nbsp;compatibility&nbsp;alias&nbsp;for&nbsp;tobytes.&nbsp;Despite&nbsp;its&nbsp;name&nbsp;it&nbsp;returns&nbsp;bytes&nbsp;not&nbsp;strings.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-order&nbsp;:&nbsp;{'C',&nbsp;'F',&nbsp;None},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Order&nbsp;of&nbsp;the&nbsp;data&nbsp;for&nbsp;multidimensional&nbsp;arrays:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C,&nbsp;Fortran,&nbsp;or&nbsp;the&nbsp;same&nbsp;as&nbsp;for&nbsp;the&nbsp;original&nbsp;array.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-s&nbsp;:&nbsp;bytes<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Python&nbsp;bytes&nbsp;exhibiting&nbsp;a&nbsp;copy&nbsp;of&nbsp;`a`'s&nbsp;raw&nbsp;data.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.array([[0,&nbsp;1],&nbsp;[2,&nbsp;3]])<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-tobytes">tobytes</a>()<br>
-b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-tobytes">tobytes</a>('C')&nbsp;==&nbsp;x.<a href="#Array-tobytes">tobytes</a>()<br>
-True<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Array-tobytes">tobytes</a>('F')<br>
-b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'</tt></dd></dl>
-
-<dl><dt><a name="Array-trace"><strong>trace</strong></a>(...)</dt><dd><tt>a.<a href="#Array-trace">trace</a>(offset=0,&nbsp;axis1=0,&nbsp;axis2=1,&nbsp;dtype=None,&nbsp;out=None)<br>
-&nbsp;<br>
-Return&nbsp;the&nbsp;sum&nbsp;along&nbsp;diagonals&nbsp;of&nbsp;the&nbsp;array.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.trace`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.trace&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-transpose"><strong>transpose</strong></a>(...)</dt><dd><tt>a.<a href="#Array-transpose">transpose</a>(*axes)<br>
-&nbsp;<br>
-Returns&nbsp;a&nbsp;view&nbsp;of&nbsp;the&nbsp;array&nbsp;with&nbsp;axes&nbsp;transposed.<br>
-&nbsp;<br>
-For&nbsp;a&nbsp;1-D&nbsp;array,&nbsp;this&nbsp;has&nbsp;no&nbsp;effect.&nbsp;(To&nbsp;change&nbsp;between&nbsp;column&nbsp;and<br>
-row&nbsp;vectors,&nbsp;first&nbsp;cast&nbsp;the&nbsp;1-D&nbsp;array&nbsp;into&nbsp;a&nbsp;matrix&nbsp;<a href="builtins.html#object">object</a>.)<br>
-For&nbsp;a&nbsp;2-D&nbsp;array,&nbsp;this&nbsp;is&nbsp;the&nbsp;usual&nbsp;matrix&nbsp;transpose.<br>
-For&nbsp;an&nbsp;n-D&nbsp;array,&nbsp;if&nbsp;axes&nbsp;are&nbsp;given,&nbsp;their&nbsp;order&nbsp;indicates&nbsp;how&nbsp;the<br>
-axes&nbsp;are&nbsp;permuted&nbsp;(see&nbsp;Examples).&nbsp;If&nbsp;axes&nbsp;are&nbsp;not&nbsp;provided&nbsp;and<br>
-``a.shape&nbsp;=&nbsp;(i[0],&nbsp;i[1],&nbsp;...&nbsp;i[n-2],&nbsp;i[n-1])``,&nbsp;then<br>
-``a.<a href="#Array-transpose">transpose</a>().shape&nbsp;=&nbsp;(i[n-1],&nbsp;i[n-2],&nbsp;...&nbsp;i[1],&nbsp;i[0])``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axes&nbsp;:&nbsp;None,&nbsp;tuple&nbsp;of&nbsp;ints,&nbsp;or&nbsp;`n`&nbsp;ints<br>
-&nbsp;<br>
-&nbsp;*&nbsp;None&nbsp;or&nbsp;no&nbsp;argument:&nbsp;reverses&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;axes.<br>
-&nbsp;<br>
-&nbsp;*&nbsp;tuple&nbsp;of&nbsp;ints:&nbsp;`i`&nbsp;in&nbsp;the&nbsp;`j`-th&nbsp;place&nbsp;in&nbsp;the&nbsp;tuple&nbsp;means&nbsp;`a`'s<br>
-&nbsp;&nbsp;&nbsp;`i`-th&nbsp;axis&nbsp;becomes&nbsp;`a.<a href="#Array-transpose">transpose</a>()`'s&nbsp;`j`-th&nbsp;axis.<br>
-&nbsp;<br>
-&nbsp;*&nbsp;`n`&nbsp;ints:&nbsp;same&nbsp;as&nbsp;an&nbsp;n-tuple&nbsp;of&nbsp;the&nbsp;same&nbsp;ints&nbsp;(this&nbsp;form&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;intended&nbsp;simply&nbsp;as&nbsp;a&nbsp;"convenience"&nbsp;alternative&nbsp;to&nbsp;the&nbsp;tuple&nbsp;form)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-out&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;View&nbsp;of&nbsp;`a`,&nbsp;with&nbsp;axes&nbsp;suitably&nbsp;permuted.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="numpy.html#ndarray">ndarray</a>.T&nbsp;:&nbsp;<a href="#Array">Array</a>&nbsp;property&nbsp;returning&nbsp;the&nbsp;array&nbsp;transposed.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;a&nbsp;=&nbsp;np.array([[1,&nbsp;2],&nbsp;[3,&nbsp;4]])<br>
-&gt;&gt;&gt;&nbsp;a<br>
-array([[1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[3,&nbsp;4]])<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-transpose">transpose</a>()<br>
-array([[1,&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[2,&nbsp;4]])<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-transpose">transpose</a>((1,&nbsp;0))<br>
-array([[1,&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[2,&nbsp;4]])<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#Array-transpose">transpose</a>(1,&nbsp;0)<br>
-array([[1,&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[2,&nbsp;4]])</tt></dd></dl>
-
-<dl><dt><a name="Array-var"><strong>var</strong></a>(...)</dt><dd><tt>a.<a href="#Array-var">var</a>(axis=None,&nbsp;dtype=None,&nbsp;out=None,&nbsp;ddof=0,&nbsp;keepdims=False)<br>
-&nbsp;<br>
-Returns&nbsp;the&nbsp;variance&nbsp;of&nbsp;the&nbsp;array&nbsp;elements,&nbsp;along&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Refer&nbsp;to&nbsp;`numpy.var`&nbsp;for&nbsp;full&nbsp;documentation.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.var&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="Array-view"><strong>view</strong></a>(...)</dt><dd><tt>a.<a href="#Array-view">view</a>(dtype=None,&nbsp;type=None)<br>
-&nbsp;<br>
-New&nbsp;view&nbsp;of&nbsp;array&nbsp;with&nbsp;the&nbsp;same&nbsp;data.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dtype&nbsp;:&nbsp;data-type&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;sub-class,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Data-type&nbsp;descriptor&nbsp;of&nbsp;the&nbsp;returned&nbsp;view,&nbsp;e.g.,&nbsp;float32&nbsp;or&nbsp;int16.&nbsp;The<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default,&nbsp;None,&nbsp;results&nbsp;in&nbsp;the&nbsp;view&nbsp;having&nbsp;the&nbsp;same&nbsp;data-type&nbsp;as&nbsp;`a`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;argument&nbsp;can&nbsp;also&nbsp;be&nbsp;specified&nbsp;as&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;sub-class,&nbsp;which<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;specifies&nbsp;the&nbsp;type&nbsp;of&nbsp;the&nbsp;returned&nbsp;<a href="builtins.html#object">object</a>&nbsp;(this&nbsp;is&nbsp;equivalent&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;setting&nbsp;the&nbsp;``type``&nbsp;parameter).<br>
-type&nbsp;:&nbsp;Python&nbsp;type,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Type&nbsp;of&nbsp;the&nbsp;returned&nbsp;view,&nbsp;e.g.,&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;or&nbsp;matrix.&nbsp;&nbsp;Again,&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None&nbsp;results&nbsp;in&nbsp;type&nbsp;preservation.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-``a.<a href="#Array-view">view</a>()``&nbsp;is&nbsp;used&nbsp;two&nbsp;different&nbsp;ways:<br>
-&nbsp;<br>
-``a.<a href="#Array-view">view</a>(some_dtype)``&nbsp;or&nbsp;``a.<a href="#Array-view">view</a>(dtype=some_dtype)``&nbsp;constructs&nbsp;a&nbsp;view<br>
-of&nbsp;the&nbsp;array's&nbsp;memory&nbsp;with&nbsp;a&nbsp;different&nbsp;data-type.&nbsp;&nbsp;This&nbsp;can&nbsp;cause&nbsp;a<br>
-reinterpretation&nbsp;of&nbsp;the&nbsp;bytes&nbsp;of&nbsp;memory.<br>
-&nbsp;<br>
-``a.<a href="#Array-view">view</a>(ndarray_subclass)``&nbsp;or&nbsp;``a.<a href="#Array-view">view</a>(type=ndarray_subclass)``&nbsp;just<br>
-returns&nbsp;an&nbsp;instance&nbsp;of&nbsp;`ndarray_subclass`&nbsp;that&nbsp;looks&nbsp;at&nbsp;the&nbsp;same&nbsp;array<br>
-(same&nbsp;shape,&nbsp;dtype,&nbsp;etc.)&nbsp;&nbsp;This&nbsp;does&nbsp;not&nbsp;cause&nbsp;a&nbsp;reinterpretation&nbsp;of&nbsp;the<br>
-memory.<br>
-&nbsp;<br>
-For&nbsp;``a.<a href="#Array-view">view</a>(some_dtype)``,&nbsp;if&nbsp;``some_dtype``&nbsp;has&nbsp;a&nbsp;different&nbsp;number&nbsp;of<br>
-bytes&nbsp;per&nbsp;entry&nbsp;than&nbsp;the&nbsp;previous&nbsp;dtype&nbsp;(for&nbsp;example,&nbsp;converting&nbsp;a<br>
-regular&nbsp;array&nbsp;to&nbsp;a&nbsp;structured&nbsp;array),&nbsp;then&nbsp;the&nbsp;behavior&nbsp;of&nbsp;the&nbsp;view<br>
-cannot&nbsp;be&nbsp;predicted&nbsp;just&nbsp;from&nbsp;the&nbsp;superficial&nbsp;appearance&nbsp;of&nbsp;``a``&nbsp;(shown<br>
-by&nbsp;``print(a)``).&nbsp;It&nbsp;also&nbsp;depends&nbsp;on&nbsp;exactly&nbsp;how&nbsp;``a``&nbsp;is&nbsp;stored&nbsp;in<br>
-memory.&nbsp;Therefore&nbsp;if&nbsp;``a``&nbsp;is&nbsp;C-ordered&nbsp;versus&nbsp;fortran-ordered,&nbsp;versus<br>
-defined&nbsp;as&nbsp;a&nbsp;slice&nbsp;or&nbsp;transpose,&nbsp;etc.,&nbsp;the&nbsp;view&nbsp;may&nbsp;give&nbsp;different<br>
-results.<br>
-&nbsp;<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.array([(1,&nbsp;2)],&nbsp;dtype=[('a',&nbsp;np.int8),&nbsp;('b',&nbsp;np.int8)])<br>
-&nbsp;<br>
-Viewing&nbsp;array&nbsp;data&nbsp;using&nbsp;a&nbsp;different&nbsp;type&nbsp;and&nbsp;dtype:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;y&nbsp;=&nbsp;x.<a href="#Array-view">view</a>(dtype=np.int16,&nbsp;type=np.matrix)<br>
-&gt;&gt;&gt;&nbsp;y<br>
-matrix([[513]],&nbsp;dtype=int16)<br>
-&gt;&gt;&gt;&nbsp;print(type(y))<br>
-&lt;class&nbsp;'numpy.matrixlib.defmatrix.matrix'&gt;<br>
-&nbsp;<br>
-Creating&nbsp;a&nbsp;view&nbsp;on&nbsp;a&nbsp;structured&nbsp;array&nbsp;so&nbsp;it&nbsp;can&nbsp;be&nbsp;used&nbsp;in&nbsp;calculations<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.array([(1,&nbsp;2),(3,4)],&nbsp;dtype=[('a',&nbsp;np.int8),&nbsp;('b',&nbsp;np.int8)])<br>
-&gt;&gt;&gt;&nbsp;xv&nbsp;=&nbsp;x.<a href="#Array-view">view</a>(dtype=np.int8).<a href="#Array-reshape">reshape</a>(-1,2)<br>
-&gt;&gt;&gt;&nbsp;xv<br>
-array([[1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[3,&nbsp;4]],&nbsp;dtype=int8)<br>
-&gt;&gt;&gt;&nbsp;xv.<a href="#Array-mean">mean</a>(0)<br>
-array([&nbsp;2.,&nbsp;&nbsp;3.])<br>
-&nbsp;<br>
-Making&nbsp;changes&nbsp;to&nbsp;the&nbsp;view&nbsp;changes&nbsp;the&nbsp;underlying&nbsp;array<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;xv[0,1]&nbsp;=&nbsp;20<br>
-&gt;&gt;&gt;&nbsp;print(x)<br>
-[(1,&nbsp;20)&nbsp;(3,&nbsp;4)]<br>
-&nbsp;<br>
-Using&nbsp;a&nbsp;view&nbsp;to&nbsp;convert&nbsp;an&nbsp;array&nbsp;to&nbsp;a&nbsp;recarray:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;z&nbsp;=&nbsp;x.<a href="#Array-view">view</a>(np.recarray)<br>
-&gt;&gt;&gt;&nbsp;z.a<br>
-array([1],&nbsp;dtype=int8)<br>
-&nbsp;<br>
-Views&nbsp;share&nbsp;data:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x[0]&nbsp;=&nbsp;(9,&nbsp;10)<br>
-&gt;&gt;&gt;&nbsp;z[0]<br>
-(9,&nbsp;10)<br>
-&nbsp;<br>
-Views&nbsp;that&nbsp;change&nbsp;the&nbsp;dtype&nbsp;size&nbsp;(bytes&nbsp;per&nbsp;entry)&nbsp;should&nbsp;normally&nbsp;be<br>
-avoided&nbsp;on&nbsp;arrays&nbsp;defined&nbsp;by&nbsp;slices,&nbsp;transposes,&nbsp;fortran-ordering,&nbsp;etc.:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.array([[1,2,3],[4,5,6]],&nbsp;dtype=np.int16)<br>
-&gt;&gt;&gt;&nbsp;y&nbsp;=&nbsp;x[:,&nbsp;0:2]<br>
-&gt;&gt;&gt;&nbsp;y<br>
-array([[1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[4,&nbsp;5]],&nbsp;dtype=int16)<br>
-&gt;&gt;&gt;&nbsp;y.<a href="#Array-view">view</a>(dtype=[('width',&nbsp;np.int16),&nbsp;('length',&nbsp;np.int16)])<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-&nbsp;&nbsp;File&nbsp;"&lt;stdin&gt;",&nbsp;line&nbsp;1,&nbsp;in&nbsp;&lt;module&gt;<br>
-ValueError:&nbsp;new&nbsp;type&nbsp;not&nbsp;compatible&nbsp;with&nbsp;array.<br>
-&gt;&gt;&gt;&nbsp;z&nbsp;=&nbsp;y.<a href="#Array-copy">copy</a>()<br>
-&gt;&gt;&gt;&nbsp;z.<a href="#Array-view">view</a>(dtype=[('width',&nbsp;np.int16),&nbsp;('length',&nbsp;np.int16)])<br>
-array([[(1,&nbsp;2)],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[(4,&nbsp;5)]],&nbsp;dtype=[('width',&nbsp;'&lt;i2'),&nbsp;('length',&nbsp;'&lt;i2')])</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="numpy.html#ndarray">numpy.ndarray</a>:<br>
-<dl><dt><strong>T</strong></dt>
-<dd><tt>Same&nbsp;as&nbsp;self.transpose(),&nbsp;except&nbsp;that&nbsp;self&nbsp;is&nbsp;returned&nbsp;if<br>
-self.<strong>ndim</strong>&nbsp;&lt;&nbsp;2.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.array([[1.,2.],[3.,4.]])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-array([[&nbsp;1.,&nbsp;&nbsp;2.],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;3.,&nbsp;&nbsp;4.]])<br>
-&gt;&gt;&gt;&nbsp;x.T<br>
-array([[&nbsp;1.,&nbsp;&nbsp;3.],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;2.,&nbsp;&nbsp;4.]])<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.array([1.,2.,3.,4.])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-array([&nbsp;1.,&nbsp;&nbsp;2.,&nbsp;&nbsp;3.,&nbsp;&nbsp;4.])<br>
-&gt;&gt;&gt;&nbsp;x.T<br>
-array([&nbsp;1.,&nbsp;&nbsp;2.,&nbsp;&nbsp;3.,&nbsp;&nbsp;4.])</tt></dd>
-</dl>
-<dl><dt><strong>__array_finalize__</strong></dt>
-<dd><tt>None.</tt></dd>
-</dl>
-<dl><dt><strong>__array_interface__</strong></dt>
-<dd><tt>Array&nbsp;protocol:&nbsp;Python&nbsp;side.</tt></dd>
-</dl>
-<dl><dt><strong>__array_priority__</strong></dt>
-<dd><tt>Array&nbsp;priority.</tt></dd>
-</dl>
-<dl><dt><strong>__array_struct__</strong></dt>
-<dd><tt>Array&nbsp;protocol:&nbsp;C-struct&nbsp;side.</tt></dd>
-</dl>
-<dl><dt><strong>base</strong></dt>
-<dd><tt>Base&nbsp;object&nbsp;if&nbsp;memory&nbsp;is&nbsp;from&nbsp;some&nbsp;other&nbsp;object.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-The&nbsp;base&nbsp;of&nbsp;an&nbsp;array&nbsp;that&nbsp;owns&nbsp;its&nbsp;memory&nbsp;is&nbsp;None:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.array([1,2,3,4])<br>
-&gt;&gt;&gt;&nbsp;x.base&nbsp;is&nbsp;None<br>
-True<br>
-&nbsp;<br>
-Slicing&nbsp;creates&nbsp;a&nbsp;view,&nbsp;whose&nbsp;memory&nbsp;is&nbsp;shared&nbsp;with&nbsp;x:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;y&nbsp;=&nbsp;x[2:]<br>
-&gt;&gt;&gt;&nbsp;y.base&nbsp;is&nbsp;x<br>
-True</tt></dd>
-</dl>
-<dl><dt><strong>ctypes</strong></dt>
-<dd><tt>An&nbsp;object&nbsp;to&nbsp;simplify&nbsp;the&nbsp;interaction&nbsp;of&nbsp;the&nbsp;array&nbsp;with&nbsp;the&nbsp;ctypes<br>
-module.<br>
-&nbsp;<br>
-This&nbsp;attribute&nbsp;creates&nbsp;an&nbsp;object&nbsp;that&nbsp;makes&nbsp;it&nbsp;easier&nbsp;to&nbsp;use&nbsp;arrays<br>
-when&nbsp;calling&nbsp;shared&nbsp;libraries&nbsp;with&nbsp;the&nbsp;ctypes&nbsp;module.&nbsp;The&nbsp;returned<br>
-object&nbsp;has,&nbsp;among&nbsp;others,&nbsp;data,&nbsp;shape,&nbsp;and&nbsp;strides&nbsp;attributes&nbsp;(see<br>
-Notes&nbsp;below)&nbsp;which&nbsp;themselves&nbsp;return&nbsp;ctypes&nbsp;objects&nbsp;that&nbsp;can&nbsp;be&nbsp;used<br>
-as&nbsp;arguments&nbsp;to&nbsp;a&nbsp;shared&nbsp;library.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-None<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-c&nbsp;:&nbsp;Python&nbsp;object<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Possessing&nbsp;attributes&nbsp;data,&nbsp;shape,&nbsp;strides,&nbsp;etc.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.ctypeslib<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Below&nbsp;are&nbsp;the&nbsp;public&nbsp;attributes&nbsp;of&nbsp;this&nbsp;object&nbsp;which&nbsp;were&nbsp;documented<br>
-in&nbsp;"Guide&nbsp;to&nbsp;NumPy"&nbsp;(we&nbsp;have&nbsp;omitted&nbsp;undocumented&nbsp;public&nbsp;attributes,<br>
-as&nbsp;well&nbsp;as&nbsp;documented&nbsp;private&nbsp;attributes):<br>
-&nbsp;<br>
-*&nbsp;data:&nbsp;A&nbsp;pointer&nbsp;to&nbsp;the&nbsp;memory&nbsp;area&nbsp;of&nbsp;the&nbsp;array&nbsp;as&nbsp;a&nbsp;Python&nbsp;integer.<br>
-&nbsp;&nbsp;This&nbsp;memory&nbsp;area&nbsp;may&nbsp;contain&nbsp;data&nbsp;that&nbsp;is&nbsp;not&nbsp;aligned,&nbsp;or&nbsp;not&nbsp;in&nbsp;correct<br>
-&nbsp;&nbsp;byte-order.&nbsp;The&nbsp;memory&nbsp;area&nbsp;may&nbsp;not&nbsp;even&nbsp;be&nbsp;writeable.&nbsp;The&nbsp;array<br>
-&nbsp;&nbsp;flags&nbsp;and&nbsp;data-type&nbsp;of&nbsp;this&nbsp;array&nbsp;should&nbsp;be&nbsp;respected&nbsp;when&nbsp;passing&nbsp;this<br>
-&nbsp;&nbsp;attribute&nbsp;to&nbsp;arbitrary&nbsp;C-code&nbsp;to&nbsp;avoid&nbsp;trouble&nbsp;that&nbsp;can&nbsp;include&nbsp;Python<br>
-&nbsp;&nbsp;crashing.&nbsp;User&nbsp;Beware!&nbsp;The&nbsp;value&nbsp;of&nbsp;this&nbsp;attribute&nbsp;is&nbsp;exactly&nbsp;the&nbsp;same<br>
-&nbsp;&nbsp;as&nbsp;self.<strong>_array_interface_</strong>['data'][0].<br>
-&nbsp;<br>
-*&nbsp;shape&nbsp;(c_intp*self.<strong>ndim</strong>):&nbsp;A&nbsp;ctypes&nbsp;array&nbsp;of&nbsp;length&nbsp;self.<strong>ndim</strong>&nbsp;where<br>
-&nbsp;&nbsp;the&nbsp;basetype&nbsp;is&nbsp;the&nbsp;C-integer&nbsp;corresponding&nbsp;to&nbsp;dtype('p')&nbsp;on&nbsp;this<br>
-&nbsp;&nbsp;platform.&nbsp;This&nbsp;base-type&nbsp;could&nbsp;be&nbsp;c_int,&nbsp;c_long,&nbsp;or&nbsp;c_longlong<br>
-&nbsp;&nbsp;depending&nbsp;on&nbsp;the&nbsp;platform.&nbsp;The&nbsp;c_intp&nbsp;type&nbsp;is&nbsp;defined&nbsp;accordingly&nbsp;in<br>
-&nbsp;&nbsp;numpy.ctypeslib.&nbsp;The&nbsp;ctypes&nbsp;array&nbsp;contains&nbsp;the&nbsp;shape&nbsp;of&nbsp;the&nbsp;underlying<br>
-&nbsp;&nbsp;array.<br>
-&nbsp;<br>
-*&nbsp;strides&nbsp;(c_intp*self.<strong>ndim</strong>):&nbsp;A&nbsp;ctypes&nbsp;array&nbsp;of&nbsp;length&nbsp;self.<strong>ndim</strong>&nbsp;where<br>
-&nbsp;&nbsp;the&nbsp;basetype&nbsp;is&nbsp;the&nbsp;same&nbsp;as&nbsp;for&nbsp;the&nbsp;shape&nbsp;attribute.&nbsp;This&nbsp;ctypes&nbsp;array<br>
-&nbsp;&nbsp;contains&nbsp;the&nbsp;strides&nbsp;information&nbsp;from&nbsp;the&nbsp;underlying&nbsp;array.&nbsp;This&nbsp;strides<br>
-&nbsp;&nbsp;information&nbsp;is&nbsp;important&nbsp;for&nbsp;showing&nbsp;how&nbsp;many&nbsp;bytes&nbsp;must&nbsp;be&nbsp;jumped&nbsp;to<br>
-&nbsp;&nbsp;get&nbsp;to&nbsp;the&nbsp;next&nbsp;element&nbsp;in&nbsp;the&nbsp;array.<br>
-&nbsp;<br>
-*&nbsp;data_as(obj):&nbsp;Return&nbsp;the&nbsp;data&nbsp;pointer&nbsp;cast&nbsp;to&nbsp;a&nbsp;particular&nbsp;c-types&nbsp;object.<br>
-&nbsp;&nbsp;For&nbsp;example,&nbsp;calling&nbsp;self.<strong>_as_parameter_</strong>&nbsp;is&nbsp;equivalent&nbsp;to<br>
-&nbsp;&nbsp;self.data_as(ctypes.c_void_p).&nbsp;Perhaps&nbsp;you&nbsp;want&nbsp;to&nbsp;use&nbsp;the&nbsp;data&nbsp;as&nbsp;a<br>
-&nbsp;&nbsp;pointer&nbsp;to&nbsp;a&nbsp;ctypes&nbsp;array&nbsp;of&nbsp;floating-point&nbsp;data:<br>
-&nbsp;&nbsp;self.data_as(ctypes.POINTER(ctypes.c_double)).<br>
-&nbsp;<br>
-*&nbsp;shape_as(obj):&nbsp;Return&nbsp;the&nbsp;shape&nbsp;tuple&nbsp;as&nbsp;an&nbsp;array&nbsp;of&nbsp;some&nbsp;other&nbsp;c-types<br>
-&nbsp;&nbsp;type.&nbsp;For&nbsp;example:&nbsp;self.shape_as(ctypes.c_short).<br>
-&nbsp;<br>
-*&nbsp;strides_as(obj):&nbsp;Return&nbsp;the&nbsp;strides&nbsp;tuple&nbsp;as&nbsp;an&nbsp;array&nbsp;of&nbsp;some&nbsp;other<br>
-&nbsp;&nbsp;c-types&nbsp;type.&nbsp;For&nbsp;example:&nbsp;self.strides_as(ctypes.c_longlong).<br>
-&nbsp;<br>
-Be&nbsp;careful&nbsp;using&nbsp;the&nbsp;ctypes&nbsp;attribute&nbsp;-&nbsp;especially&nbsp;on&nbsp;temporary<br>
-arrays&nbsp;or&nbsp;arrays&nbsp;constructed&nbsp;on&nbsp;the&nbsp;fly.&nbsp;For&nbsp;example,&nbsp;calling<br>
-``(a+b).ctypes.data_as(ctypes.c_void_p)``&nbsp;returns&nbsp;a&nbsp;pointer&nbsp;to&nbsp;memory<br>
-that&nbsp;is&nbsp;invalid&nbsp;because&nbsp;the&nbsp;array&nbsp;created&nbsp;as&nbsp;(a+b)&nbsp;is&nbsp;deallocated<br>
-before&nbsp;the&nbsp;next&nbsp;Python&nbsp;statement.&nbsp;You&nbsp;can&nbsp;avoid&nbsp;this&nbsp;problem&nbsp;using<br>
-either&nbsp;``c=a+b``&nbsp;or&nbsp;``ct=(a+b).ctypes``.&nbsp;In&nbsp;the&nbsp;latter&nbsp;case,&nbsp;ct&nbsp;will<br>
-hold&nbsp;a&nbsp;reference&nbsp;to&nbsp;the&nbsp;array&nbsp;until&nbsp;ct&nbsp;is&nbsp;deleted&nbsp;or&nbsp;re-assigned.<br>
-&nbsp;<br>
-If&nbsp;the&nbsp;ctypes&nbsp;module&nbsp;is&nbsp;not&nbsp;available,&nbsp;then&nbsp;the&nbsp;ctypes&nbsp;attribute<br>
-of&nbsp;array&nbsp;objects&nbsp;still&nbsp;returns&nbsp;something&nbsp;useful,&nbsp;but&nbsp;ctypes&nbsp;objects<br>
-are&nbsp;not&nbsp;returned&nbsp;and&nbsp;errors&nbsp;may&nbsp;be&nbsp;raised&nbsp;instead.&nbsp;In&nbsp;particular,<br>
-the&nbsp;object&nbsp;will&nbsp;still&nbsp;have&nbsp;the&nbsp;as&nbsp;parameter&nbsp;attribute&nbsp;which&nbsp;will<br>
-return&nbsp;an&nbsp;integer&nbsp;equal&nbsp;to&nbsp;the&nbsp;data&nbsp;attribute.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;ctypes<br>
-&gt;&gt;&gt;&nbsp;x<br>
-array([[0,&nbsp;1],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[2,&nbsp;3]])<br>
-&gt;&gt;&gt;&nbsp;x.ctypes.data<br>
-30439712<br>
-&gt;&gt;&gt;&nbsp;x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))<br>
-&lt;ctypes.LP_c_long&nbsp;object&nbsp;at&nbsp;0x01F01300&gt;<br>
-&gt;&gt;&gt;&nbsp;x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents<br>
-c_long(0)<br>
-&gt;&gt;&gt;&nbsp;x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents<br>
-c_longlong(4294967296L)<br>
-&gt;&gt;&gt;&nbsp;x.ctypes.shape<br>
-&lt;numpy.core._internal.c_long_Array_2&nbsp;object&nbsp;at&nbsp;0x01FFD580&gt;<br>
-&gt;&gt;&gt;&nbsp;x.ctypes.shape_as(ctypes.c_long)<br>
-&lt;numpy.core._internal.c_long_Array_2&nbsp;object&nbsp;at&nbsp;0x01FCE620&gt;<br>
-&gt;&gt;&gt;&nbsp;x.ctypes.strides<br>
-&lt;numpy.core._internal.c_long_Array_2&nbsp;object&nbsp;at&nbsp;0x01FCE620&gt;<br>
-&gt;&gt;&gt;&nbsp;x.ctypes.strides_as(ctypes.c_longlong)<br>
-&lt;numpy.core._internal.c_longlong_Array_2&nbsp;object&nbsp;at&nbsp;0x01F01300&gt;</tt></dd>
-</dl>
-<dl><dt><strong>data</strong></dt>
-<dd><tt>Python&nbsp;buffer&nbsp;object&nbsp;pointing&nbsp;to&nbsp;the&nbsp;start&nbsp;of&nbsp;the&nbsp;array's&nbsp;data.</tt></dd>
-</dl>
-<dl><dt><strong>dtype</strong></dt>
-<dd><tt>Data-type&nbsp;of&nbsp;the&nbsp;array's&nbsp;elements.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-None<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-d&nbsp;:&nbsp;numpy&nbsp;dtype&nbsp;object<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.dtype<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x<br>
-array([[0,&nbsp;1],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[2,&nbsp;3]])<br>
-&gt;&gt;&gt;&nbsp;x.dtype<br>
-dtype('int32')<br>
-&gt;&gt;&gt;&nbsp;type(x.dtype)<br>
-&lt;type&nbsp;'numpy.dtype'&gt;</tt></dd>
-</dl>
-<dl><dt><strong>flags</strong></dt>
-<dd><tt>Information&nbsp;about&nbsp;the&nbsp;memory&nbsp;layout&nbsp;of&nbsp;the&nbsp;array.<br>
-&nbsp;<br>
-Attributes<br>
-----------<br>
-C_CONTIGUOUS&nbsp;(C)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;data&nbsp;is&nbsp;in&nbsp;a&nbsp;single,&nbsp;C-style&nbsp;contiguous&nbsp;segment.<br>
-F_CONTIGUOUS&nbsp;(F)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;data&nbsp;is&nbsp;in&nbsp;a&nbsp;single,&nbsp;Fortran-style&nbsp;contiguous&nbsp;segment.<br>
-OWNDATA&nbsp;(O)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;array&nbsp;owns&nbsp;the&nbsp;memory&nbsp;it&nbsp;uses&nbsp;or&nbsp;borrows&nbsp;it&nbsp;from&nbsp;another&nbsp;object.<br>
-WRITEABLE&nbsp;(W)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;data&nbsp;area&nbsp;can&nbsp;be&nbsp;written&nbsp;to.&nbsp;&nbsp;Setting&nbsp;this&nbsp;to&nbsp;False&nbsp;locks<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;data,&nbsp;making&nbsp;it&nbsp;read-only.&nbsp;&nbsp;A&nbsp;view&nbsp;(slice,&nbsp;etc.)&nbsp;inherits&nbsp;WRITEABLE<br>
-&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;its&nbsp;base&nbsp;array&nbsp;at&nbsp;creation&nbsp;time,&nbsp;but&nbsp;a&nbsp;view&nbsp;of&nbsp;a&nbsp;writeable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;array&nbsp;may&nbsp;be&nbsp;subsequently&nbsp;locked&nbsp;while&nbsp;the&nbsp;base&nbsp;array&nbsp;remains&nbsp;writeable.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(The&nbsp;opposite&nbsp;is&nbsp;not&nbsp;true,&nbsp;in&nbsp;that&nbsp;a&nbsp;view&nbsp;of&nbsp;a&nbsp;locked&nbsp;array&nbsp;may&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;made&nbsp;writeable.&nbsp;&nbsp;However,&nbsp;currently,&nbsp;locking&nbsp;a&nbsp;base&nbsp;object&nbsp;does&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;lock&nbsp;any&nbsp;views&nbsp;that&nbsp;already&nbsp;reference&nbsp;it,&nbsp;so&nbsp;under&nbsp;that&nbsp;circumstance&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;possible&nbsp;to&nbsp;alter&nbsp;the&nbsp;contents&nbsp;of&nbsp;a&nbsp;locked&nbsp;array&nbsp;via&nbsp;a&nbsp;previously<br>
-&nbsp;&nbsp;&nbsp;&nbsp;created&nbsp;writeable&nbsp;view&nbsp;onto&nbsp;it.)&nbsp;&nbsp;Attempting&nbsp;to&nbsp;change&nbsp;a&nbsp;non-writeable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;array&nbsp;raises&nbsp;a&nbsp;RuntimeError&nbsp;exception.<br>
-ALIGNED&nbsp;(A)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;data&nbsp;and&nbsp;all&nbsp;elements&nbsp;are&nbsp;aligned&nbsp;appropriately&nbsp;for&nbsp;the&nbsp;hardware.<br>
-UPDATEIFCOPY&nbsp;(U)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;array&nbsp;is&nbsp;a&nbsp;copy&nbsp;of&nbsp;some&nbsp;other&nbsp;array.&nbsp;When&nbsp;this&nbsp;array&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;deallocated,&nbsp;the&nbsp;base&nbsp;array&nbsp;will&nbsp;be&nbsp;updated&nbsp;with&nbsp;the&nbsp;contents&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;array.<br>
-FNC<br>
-&nbsp;&nbsp;&nbsp;&nbsp;F_CONTIGUOUS&nbsp;and&nbsp;not&nbsp;C_CONTIGUOUS.<br>
-FORC<br>
-&nbsp;&nbsp;&nbsp;&nbsp;F_CONTIGUOUS&nbsp;or&nbsp;C_CONTIGUOUS&nbsp;(one-segment&nbsp;test).<br>
-BEHAVED&nbsp;(B)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;ALIGNED&nbsp;and&nbsp;WRITEABLE.<br>
-CARRAY&nbsp;(CA)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;BEHAVED&nbsp;and&nbsp;C_CONTIGUOUS.<br>
-FARRAY&nbsp;(FA)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;BEHAVED&nbsp;and&nbsp;F_CONTIGUOUS&nbsp;and&nbsp;not&nbsp;C_CONTIGUOUS.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;`flags`&nbsp;object&nbsp;can&nbsp;be&nbsp;accessed&nbsp;dictionary-like&nbsp;(as&nbsp;in&nbsp;``a.flags['WRITEABLE']``),<br>
-or&nbsp;by&nbsp;using&nbsp;lowercased&nbsp;attribute&nbsp;names&nbsp;(as&nbsp;in&nbsp;``a.flags.writeable``).&nbsp;Short&nbsp;flag<br>
-names&nbsp;are&nbsp;only&nbsp;supported&nbsp;in&nbsp;dictionary&nbsp;access.<br>
-&nbsp;<br>
-Only&nbsp;the&nbsp;UPDATEIFCOPY,&nbsp;WRITEABLE,&nbsp;and&nbsp;ALIGNED&nbsp;flags&nbsp;can&nbsp;be&nbsp;changed&nbsp;by<br>
-the&nbsp;user,&nbsp;via&nbsp;direct&nbsp;assignment&nbsp;to&nbsp;the&nbsp;attribute&nbsp;or&nbsp;dictionary&nbsp;entry,<br>
-or&nbsp;by&nbsp;calling&nbsp;`ndarray.setflags`.<br>
-&nbsp;<br>
-The&nbsp;array&nbsp;flags&nbsp;cannot&nbsp;be&nbsp;set&nbsp;arbitrarily:<br>
-&nbsp;<br>
--&nbsp;UPDATEIFCOPY&nbsp;can&nbsp;only&nbsp;be&nbsp;set&nbsp;``False``.<br>
--&nbsp;ALIGNED&nbsp;can&nbsp;only&nbsp;be&nbsp;set&nbsp;``True``&nbsp;if&nbsp;the&nbsp;data&nbsp;is&nbsp;truly&nbsp;aligned.<br>
--&nbsp;WRITEABLE&nbsp;can&nbsp;only&nbsp;be&nbsp;set&nbsp;``True``&nbsp;if&nbsp;the&nbsp;array&nbsp;owns&nbsp;its&nbsp;own&nbsp;memory<br>
-&nbsp;&nbsp;or&nbsp;the&nbsp;ultimate&nbsp;owner&nbsp;of&nbsp;the&nbsp;memory&nbsp;exposes&nbsp;a&nbsp;writeable&nbsp;buffer<br>
-&nbsp;&nbsp;interface&nbsp;or&nbsp;is&nbsp;a&nbsp;string.<br>
-&nbsp;<br>
-Arrays&nbsp;can&nbsp;be&nbsp;both&nbsp;C-style&nbsp;and&nbsp;Fortran-style&nbsp;contiguous&nbsp;simultaneously.<br>
-This&nbsp;is&nbsp;clear&nbsp;for&nbsp;1-dimensional&nbsp;arrays,&nbsp;but&nbsp;can&nbsp;also&nbsp;be&nbsp;true&nbsp;for&nbsp;higher<br>
-dimensional&nbsp;arrays.<br>
-&nbsp;<br>
-Even&nbsp;for&nbsp;contiguous&nbsp;arrays&nbsp;a&nbsp;stride&nbsp;for&nbsp;a&nbsp;given&nbsp;dimension<br>
-``arr.strides[dim]``&nbsp;may&nbsp;be&nbsp;*arbitrary*&nbsp;if&nbsp;``arr.shape[dim]&nbsp;==&nbsp;1``<br>
-or&nbsp;the&nbsp;array&nbsp;has&nbsp;no&nbsp;elements.<br>
-It&nbsp;does&nbsp;*not*&nbsp;generally&nbsp;hold&nbsp;that&nbsp;``self.<strong>strides</strong>[-1]&nbsp;==&nbsp;self.<strong>itemsize</strong>``<br>
-for&nbsp;C-style&nbsp;contiguous&nbsp;arrays&nbsp;or&nbsp;``self.<strong>strides</strong>[0]&nbsp;==&nbsp;self.<strong>itemsize</strong>``&nbsp;for<br>
-Fortran-style&nbsp;contiguous&nbsp;arrays&nbsp;is&nbsp;true.</tt></dd>
-</dl>
-<dl><dt><strong>flat</strong></dt>
-<dd><tt>A&nbsp;1-D&nbsp;iterator&nbsp;over&nbsp;the&nbsp;array.<br>
-&nbsp;<br>
-This&nbsp;is&nbsp;a&nbsp;`numpy.flatiter`&nbsp;instance,&nbsp;which&nbsp;acts&nbsp;similarly&nbsp;to,&nbsp;but&nbsp;is&nbsp;not<br>
-a&nbsp;subclass&nbsp;of,&nbsp;Python's&nbsp;built-in&nbsp;iterator&nbsp;object.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-flatten&nbsp;:&nbsp;Return&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;array&nbsp;collapsed&nbsp;into&nbsp;one&nbsp;dimension.<br>
-&nbsp;<br>
-flatiter<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.arange(1,&nbsp;7).reshape(2,&nbsp;3)<br>
-&gt;&gt;&gt;&nbsp;x<br>
-array([[1,&nbsp;2,&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[4,&nbsp;5,&nbsp;6]])<br>
-&gt;&gt;&gt;&nbsp;x.flat[3]<br>
-4<br>
-&gt;&gt;&gt;&nbsp;x.T<br>
-array([[1,&nbsp;4],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[2,&nbsp;5],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[3,&nbsp;6]])<br>
-&gt;&gt;&gt;&nbsp;x.T.flat[3]<br>
-5<br>
-&gt;&gt;&gt;&nbsp;type(x.flat)<br>
-&lt;type&nbsp;'numpy.flatiter'&gt;<br>
-&nbsp;<br>
-An&nbsp;assignment&nbsp;example:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.flat&nbsp;=&nbsp;3;&nbsp;x<br>
-array([[3,&nbsp;3,&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[3,&nbsp;3,&nbsp;3]])<br>
-&gt;&gt;&gt;&nbsp;x.flat[[1,4]]&nbsp;=&nbsp;1;&nbsp;x<br>
-array([[3,&nbsp;1,&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[3,&nbsp;1,&nbsp;3]])</tt></dd>
-</dl>
-<dl><dt><strong>imag</strong></dt>
-<dd><tt>The&nbsp;imaginary&nbsp;part&nbsp;of&nbsp;the&nbsp;array.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.sqrt([1+0j,&nbsp;0+1j])<br>
-&gt;&gt;&gt;&nbsp;x.imag<br>
-array([&nbsp;0.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;,&nbsp;&nbsp;0.70710678])<br>
-&gt;&gt;&gt;&nbsp;x.imag.dtype<br>
-dtype('float64')</tt></dd>
-</dl>
-<dl><dt><strong>itemsize</strong></dt>
-<dd><tt>Length&nbsp;of&nbsp;one&nbsp;array&nbsp;element&nbsp;in&nbsp;bytes.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.array([1,2,3],&nbsp;dtype=np.float64)<br>
-&gt;&gt;&gt;&nbsp;x.itemsize<br>
-8<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.array([1,2,3],&nbsp;dtype=np.complex128)<br>
-&gt;&gt;&gt;&nbsp;x.itemsize<br>
-16</tt></dd>
-</dl>
-<dl><dt><strong>nbytes</strong></dt>
-<dd><tt>Total&nbsp;bytes&nbsp;consumed&nbsp;by&nbsp;the&nbsp;elements&nbsp;of&nbsp;the&nbsp;array.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Does&nbsp;not&nbsp;include&nbsp;memory&nbsp;consumed&nbsp;by&nbsp;non-element&nbsp;attributes&nbsp;of&nbsp;the<br>
-array&nbsp;object.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.zeros((3,5,2),&nbsp;dtype=np.complex128)<br>
-&gt;&gt;&gt;&nbsp;x.nbytes<br>
-480<br>
-&gt;&gt;&gt;&nbsp;np.prod(x.shape)&nbsp;*&nbsp;x.itemsize<br>
-480</tt></dd>
-</dl>
-<dl><dt><strong>ndim</strong></dt>
-<dd><tt>Number&nbsp;of&nbsp;array&nbsp;dimensions.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.array([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;x.ndim<br>
-1<br>
-&gt;&gt;&gt;&nbsp;y&nbsp;=&nbsp;np.zeros((2,&nbsp;3,&nbsp;4))<br>
-&gt;&gt;&gt;&nbsp;y.ndim<br>
-3</tt></dd>
-</dl>
-<dl><dt><strong>real</strong></dt>
-<dd><tt>The&nbsp;real&nbsp;part&nbsp;of&nbsp;the&nbsp;array.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.sqrt([1+0j,&nbsp;0+1j])<br>
-&gt;&gt;&gt;&nbsp;x.real<br>
-array([&nbsp;1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;,&nbsp;&nbsp;0.70710678])<br>
-&gt;&gt;&gt;&nbsp;x.real.dtype<br>
-dtype('float64')<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.real&nbsp;:&nbsp;equivalent&nbsp;function</tt></dd>
-</dl>
-<dl><dt><strong>shape</strong></dt>
-<dd><tt>Tuple&nbsp;of&nbsp;array&nbsp;dimensions.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-May&nbsp;be&nbsp;used&nbsp;to&nbsp;"reshape"&nbsp;the&nbsp;array,&nbsp;as&nbsp;long&nbsp;as&nbsp;this&nbsp;would&nbsp;not<br>
-require&nbsp;a&nbsp;change&nbsp;in&nbsp;the&nbsp;total&nbsp;number&nbsp;of&nbsp;elements<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.array([1,&nbsp;2,&nbsp;3,&nbsp;4])<br>
-&gt;&gt;&gt;&nbsp;x.shape<br>
-(4,)<br>
-&gt;&gt;&gt;&nbsp;y&nbsp;=&nbsp;np.zeros((2,&nbsp;3,&nbsp;4))<br>
-&gt;&gt;&gt;&nbsp;y.shape<br>
-(2,&nbsp;3,&nbsp;4)<br>
-&gt;&gt;&gt;&nbsp;y.shape&nbsp;=&nbsp;(3,&nbsp;8)<br>
-&gt;&gt;&gt;&nbsp;y<br>
-array([[&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.,&nbsp;&nbsp;0.]])<br>
-&gt;&gt;&gt;&nbsp;y.shape&nbsp;=&nbsp;(3,&nbsp;6)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-&nbsp;&nbsp;File&nbsp;"&lt;stdin&gt;",&nbsp;line&nbsp;1,&nbsp;in&nbsp;&lt;module&gt;<br>
-ValueError:&nbsp;total&nbsp;size&nbsp;of&nbsp;new&nbsp;array&nbsp;must&nbsp;be&nbsp;unchanged</tt></dd>
-</dl>
-<dl><dt><strong>size</strong></dt>
-<dd><tt>Number&nbsp;of&nbsp;elements&nbsp;in&nbsp;the&nbsp;array.<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``np.prod(a.shape)``,&nbsp;i.e.,&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;array's<br>
-dimensions.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.zeros((3,&nbsp;5,&nbsp;2),&nbsp;dtype=np.complex128)<br>
-&gt;&gt;&gt;&nbsp;x.size<br>
-30<br>
-&gt;&gt;&gt;&nbsp;np.prod(x.shape)<br>
-30</tt></dd>
-</dl>
-<dl><dt><strong>strides</strong></dt>
-<dd><tt>Tuple&nbsp;of&nbsp;bytes&nbsp;to&nbsp;step&nbsp;in&nbsp;each&nbsp;dimension&nbsp;when&nbsp;traversing&nbsp;an&nbsp;array.<br>
-&nbsp;<br>
-The&nbsp;byte&nbsp;offset&nbsp;of&nbsp;element&nbsp;``(i[0],&nbsp;i[1],&nbsp;...,&nbsp;i[n])``&nbsp;in&nbsp;an&nbsp;array&nbsp;`a`<br>
-is::<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;offset&nbsp;=&nbsp;sum(np.array(i)&nbsp;*&nbsp;a.strides)<br>
-&nbsp;<br>
-A&nbsp;more&nbsp;detailed&nbsp;explanation&nbsp;of&nbsp;strides&nbsp;can&nbsp;be&nbsp;found&nbsp;in&nbsp;the<br>
-"ndarray.rst"&nbsp;file&nbsp;in&nbsp;the&nbsp;NumPy&nbsp;reference&nbsp;guide.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Imagine&nbsp;an&nbsp;array&nbsp;of&nbsp;32-bit&nbsp;integers&nbsp;(each&nbsp;4&nbsp;bytes)::<br>
-&nbsp;<br>
-&nbsp;&nbsp;x&nbsp;=&nbsp;np.array([[0,&nbsp;1,&nbsp;2,&nbsp;3,&nbsp;4],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[5,&nbsp;6,&nbsp;7,&nbsp;8,&nbsp;9]],&nbsp;dtype=np.int32)<br>
-&nbsp;<br>
-This&nbsp;array&nbsp;is&nbsp;stored&nbsp;in&nbsp;memory&nbsp;as&nbsp;40&nbsp;bytes,&nbsp;one&nbsp;after&nbsp;the&nbsp;other<br>
-(known&nbsp;as&nbsp;a&nbsp;contiguous&nbsp;block&nbsp;of&nbsp;memory).&nbsp;&nbsp;The&nbsp;strides&nbsp;of&nbsp;an&nbsp;array&nbsp;tell<br>
-us&nbsp;how&nbsp;many&nbsp;bytes&nbsp;we&nbsp;have&nbsp;to&nbsp;skip&nbsp;in&nbsp;memory&nbsp;to&nbsp;move&nbsp;to&nbsp;the&nbsp;next&nbsp;position<br>
-along&nbsp;a&nbsp;certain&nbsp;axis.&nbsp;&nbsp;For&nbsp;example,&nbsp;we&nbsp;have&nbsp;to&nbsp;skip&nbsp;4&nbsp;bytes&nbsp;(1&nbsp;value)&nbsp;to<br>
-move&nbsp;to&nbsp;the&nbsp;next&nbsp;column,&nbsp;but&nbsp;20&nbsp;bytes&nbsp;(5&nbsp;values)&nbsp;to&nbsp;get&nbsp;to&nbsp;the&nbsp;same<br>
-position&nbsp;in&nbsp;the&nbsp;next&nbsp;row.&nbsp;&nbsp;As&nbsp;such,&nbsp;the&nbsp;strides&nbsp;for&nbsp;the&nbsp;array&nbsp;`x`&nbsp;will&nbsp;be<br>
-``(20,&nbsp;4)``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.lib.stride_tricks.as_strided<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;y&nbsp;=&nbsp;np.reshape(np.arange(2*3*4),&nbsp;(2,3,4))<br>
-&gt;&gt;&gt;&nbsp;y<br>
-array([[[&nbsp;0,&nbsp;&nbsp;1,&nbsp;&nbsp;2,&nbsp;&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;4,&nbsp;&nbsp;5,&nbsp;&nbsp;6,&nbsp;&nbsp;7],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;8,&nbsp;&nbsp;9,&nbsp;10,&nbsp;11]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[12,&nbsp;13,&nbsp;14,&nbsp;15],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[16,&nbsp;17,&nbsp;18,&nbsp;19],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[20,&nbsp;21,&nbsp;22,&nbsp;23]]])<br>
-&gt;&gt;&gt;&nbsp;y.strides<br>
-(48,&nbsp;16,&nbsp;4)<br>
-&gt;&gt;&gt;&nbsp;y[1,1,1]<br>
-17<br>
-&gt;&gt;&gt;&nbsp;offset=sum(y.strides&nbsp;*&nbsp;np.array((1,1,1)))<br>
-&gt;&gt;&gt;&nbsp;offset/y.itemsize<br>
-17<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;np.reshape(np.arange(5*6*7*8),&nbsp;(5,6,7,8)).transpose(2,3,1,0)<br>
-&gt;&gt;&gt;&nbsp;x.strides<br>
-(32,&nbsp;4,&nbsp;224,&nbsp;1344)<br>
-&gt;&gt;&gt;&nbsp;i&nbsp;=&nbsp;np.array([3,5,2,2])<br>
-&gt;&gt;&gt;&nbsp;offset&nbsp;=&nbsp;sum(i&nbsp;*&nbsp;x.strides)<br>
-&gt;&gt;&gt;&nbsp;x[3,5,2,2]<br>
-813<br>
-&gt;&gt;&gt;&nbsp;offset&nbsp;/&nbsp;x.itemsize<br>
-813</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="numpy.html#ndarray">numpy.ndarray</a>:<br>
-<dl><dt><strong>__hash__</strong> = None</dl>
-
-</td></tr></table> <p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#ffc8d8">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#000000" face="helvetica, arial"><a name="Condition">class <strong>Condition</strong></a>(<a href="modsim.html#System">System</a>)</font></td></tr>
-    
-<tr bgcolor="#ffc8d8"><td rowspan=2><tt>&nbsp;&nbsp;&nbsp;</tt></td>
-<td colspan=2><tt>One-dimensional&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;with&nbsp;axis&nbsp;labels&nbsp;(including&nbsp;time&nbsp;series).<br>
-&nbsp;<br>
-Labels&nbsp;need&nbsp;not&nbsp;be&nbsp;unique&nbsp;but&nbsp;must&nbsp;be&nbsp;a&nbsp;hashable&nbsp;type.&nbsp;The&nbsp;<a href="builtins.html#object">object</a><br>
-supports&nbsp;both&nbsp;integer-&nbsp;and&nbsp;label-based&nbsp;indexing&nbsp;and&nbsp;provides&nbsp;a&nbsp;host&nbsp;of<br>
-methods&nbsp;for&nbsp;performing&nbsp;operations&nbsp;involving&nbsp;the&nbsp;index.&nbsp;Statistical<br>
-methods&nbsp;from&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;have&nbsp;been&nbsp;overridden&nbsp;to&nbsp;automatically&nbsp;exclude<br>
-missing&nbsp;data&nbsp;(currently&nbsp;represented&nbsp;as&nbsp;NaN).<br>
-&nbsp;<br>
-Operations&nbsp;between&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(+,&nbsp;-,&nbsp;/,&nbsp;*,&nbsp;**)&nbsp;align&nbsp;values&nbsp;based&nbsp;on&nbsp;their<br>
-associated&nbsp;index&nbsp;values--&nbsp;they&nbsp;need&nbsp;not&nbsp;be&nbsp;the&nbsp;same&nbsp;length.&nbsp;The&nbsp;result<br>
-index&nbsp;will&nbsp;be&nbsp;the&nbsp;sorted&nbsp;union&nbsp;of&nbsp;the&nbsp;two&nbsp;indexes.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;array-like,&nbsp;dict,&nbsp;or&nbsp;scalar&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Contains&nbsp;data&nbsp;stored&nbsp;in&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-index&nbsp;:&nbsp;array-like&nbsp;or&nbsp;Index&nbsp;(1d)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Values&nbsp;must&nbsp;be&nbsp;hashable&nbsp;and&nbsp;have&nbsp;the&nbsp;same&nbsp;length&nbsp;as&nbsp;`data`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Non-unique&nbsp;index&nbsp;values&nbsp;are&nbsp;allowed.&nbsp;Will&nbsp;default&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;RangeIndex(len(data))&nbsp;if&nbsp;not&nbsp;provided.&nbsp;If&nbsp;both&nbsp;a&nbsp;dict&nbsp;and&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;are&nbsp;used,&nbsp;the&nbsp;index&nbsp;will&nbsp;override&nbsp;the&nbsp;keys&nbsp;found&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict.<br>
-dtype&nbsp;:&nbsp;numpy.dtype&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;None,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;inferred<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Copy&nbsp;input&nbsp;data<br>&nbsp;</tt></td></tr>
-<tr><td>&nbsp;</td>
-<td width="100%"><dl><dt>Method resolution order:</dt>
-<dd><a href="modsim.html#Condition">Condition</a></dd>
-<dd><a href="modsim.html#System">System</a></dd>
-<dd><a href="modsim.html#MySeries">MySeries</a></dd>
-<dd><a href="pandas.core.series.html#Series">pandas.core.series.Series</a></dd>
-<dd><a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a></dd>
-<dd><a href="pandas.core.strings.html#StringAccessorMixin">pandas.core.strings.StringAccessorMixin</a></dd>
-<dd><a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a></dd>
-<dd><a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a></dd>
-<dd><a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a></dd>
-<dd><a href="pandas.core.base.html#SelectionMixin">pandas.core.base.SelectionMixin</a></dd>
-<dd><a href="builtins.html#object">builtins.object</a></dd>
-</dl>
-<hr>
-Methods inherited from <a href="modsim.html#System">System</a>:<br>
-<dl><dt><a name="Condition-__init__"><strong>__init__</strong></a>(self, *args, **kwargs)</dt><dd><tt>Initialize&nbsp;the&nbsp;series.<br>
-&nbsp;<br>
-If&nbsp;there&nbsp;are&nbsp;no&nbsp;positional&nbsp;arguments,&nbsp;use&nbsp;kwargs.<br>
-&nbsp;<br>
-If&nbsp;there&nbsp;is&nbsp;one&nbsp;positional&nbsp;argument,&nbsp;copy&nbsp;it.<br>
-&nbsp;<br>
-More&nbsp;than&nbsp;one&nbsp;positional&nbsp;argument&nbsp;is&nbsp;an&nbsp;error.</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="modsim.html#System">System</a>:<br>
-<dl><dt><strong>T</strong></dt>
-<dd><tt>Intercept&nbsp;the&nbsp;Series&nbsp;accessor&nbsp;object&nbsp;so&nbsp;we&nbsp;can&nbsp;use&nbsp;`T`<br>
-as&nbsp;a&nbsp;row&nbsp;label&nbsp;and&nbsp;access&nbsp;it&nbsp;using&nbsp;dot&nbsp;notation.<br>
-&nbsp;<br>
-https://pandas.pydata.org/pandas-docs/stable/generated/<br>
-pandas.Series.T.html#pandas.Series.T</tt></dd>
-</dl>
-<dl><dt><strong>dt</strong></dt>
-<dd><tt>Intercept&nbsp;the&nbsp;Series&nbsp;accessor&nbsp;object&nbsp;so&nbsp;we&nbsp;can&nbsp;use&nbsp;`dt`<br>
-as&nbsp;a&nbsp;row&nbsp;label&nbsp;and&nbsp;access&nbsp;it&nbsp;using&nbsp;dot&nbsp;notation.<br>
-&nbsp;<br>
-https://pandas.pydata.org/pandas-docs/stable/generated/<br>
-pandas.Series.dt.html</tt></dd>
-</dl>
-<hr>
-Methods inherited from <a href="modsim.html#MySeries">MySeries</a>:<br>
-<dl><dt><a name="Condition-set"><strong>set</strong></a>(self, **kwargs)</dt><dd><tt>Uses&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;update&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place.<br>
-&nbsp;<br>
-Example:&nbsp;series.<a href="#Condition-update">update</a>(a=1,&nbsp;b=2)</tt></dd></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><a name="Condition-__add__"><strong>__add__</strong></a> = wrapper(left, right, name='__add__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f2f0&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__and__"><strong>__and__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="Condition-__array__"><strong>__array__</strong></a>(self, result=None)</dt><dd><tt>the&nbsp;array&nbsp;interface,&nbsp;return&nbsp;my&nbsp;values</tt></dd></dl>
-
-<dl><dt><a name="Condition-__array_prepare__"><strong>__array_prepare__</strong></a>(self, result, context=None)</dt><dd><tt>Gets&nbsp;called&nbsp;prior&nbsp;to&nbsp;a&nbsp;ufunc</tt></dd></dl>
-
-<dl><dt><a name="Condition-__array_wrap__"><strong>__array_wrap__</strong></a>(self, result, context=None)</dt><dd><tt>Gets&nbsp;called&nbsp;after&nbsp;a&nbsp;ufunc</tt></dd></dl>
-
-<dl><dt><a name="Condition-__div__"><strong>__div__</strong></a> = wrapper(left, right, name='__truediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f9d8&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__divmod__"><strong>__divmod__</strong></a> = wrapper(left, right, name='__divmod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca343bf8&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__eq__"><strong>__eq__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="Condition-__float__"><strong>__float__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="Condition-__floordiv__"><strong>__floordiv__</strong></a> = wrapper(left, right, name='__floordiv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fb70&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__ge__"><strong>__ge__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="Condition-__getitem__"><strong>__getitem__</strong></a>(self, key)</dt></dl>
-
-<dl><dt><a name="Condition-__gt__"><strong>__gt__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="Condition-__iadd__"><strong>__iadd__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="Condition-__imul__"><strong>__imul__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="Condition-__int__"><strong>__int__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="Condition-__ipow__"><strong>__ipow__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="Condition-__isub__"><strong>__isub__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="Condition-__iter__"><strong>__iter__</strong></a>(self)</dt><dd><tt>provide&nbsp;iteration&nbsp;over&nbsp;the&nbsp;values&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-box&nbsp;values&nbsp;if&nbsp;necessary</tt></dd></dl>
-
-<dl><dt><a name="Condition-__itruediv__"><strong>__itruediv__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="Condition-__le__"><strong>__le__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="Condition-__len__"><strong>__len__</strong></a>(self)</dt><dd><tt>return&nbsp;the&nbsp;length&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-__long__"><strong>__long__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="Condition-__lt__"><strong>__lt__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="Condition-__mod__"><strong>__mod__</strong></a> = wrapper(left, right, name='__mod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fd08&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__mul__"><strong>__mul__</strong></a> = wrapper(left, right, name='__mul__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f840&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__ne__"><strong>__ne__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="Condition-__or__"><strong>__or__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="Condition-__pow__"><strong>__pow__</strong></a> = wrapper(left, right, name='__pow__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fea0&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__radd__"><strong>__radd__</strong></a> = wrapper(left, right, name='__radd__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f510&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__rand__"><strong>__rand__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="Condition-__rdiv__"><strong>__rdiv__</strong></a> = wrapper(left, right, name='__rtruediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342510&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__rfloordiv__"><strong>__rfloordiv__</strong></a> = wrapper(left, right, name='__rfloordiv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342730&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__rmod__"><strong>__rmod__</strong></a> = wrapper(left, right, name='__rmod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342b70&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__rmul__"><strong>__rmul__</strong></a> = wrapper(left, right, name='__rmul__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca3420d0&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__ror__"><strong>__ror__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="Condition-__rpow__"><strong>__rpow__</strong></a> = wrapper(left, right, name='__rpow__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342950&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__rsub__"><strong>__rsub__</strong></a> = wrapper(left, right, name='__rsub__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca3422f0&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__rtruediv__"><strong>__rtruediv__</strong></a> = wrapper(left, right, name='__rtruediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342510&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__rxor__"><strong>__rxor__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="Condition-__setitem__"><strong>__setitem__</strong></a>(self, key, value)</dt></dl>
-
-<dl><dt><a name="Condition-__sub__"><strong>__sub__</strong></a> = wrapper(left, right, name='__sub__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f6a8&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__truediv__"><strong>__truediv__</strong></a> = wrapper(left, right, name='__truediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f9d8&gt;)</dt></dl>
-
-<dl><dt><a name="Condition-__unicode__"><strong>__unicode__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;unicode(df)&nbsp;in&nbsp;py2&nbsp;only.&nbsp;Yields&nbsp;a&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;both<br>
-py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="Condition-__xor__"><strong>__xor__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="Condition-add"><strong>add</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Addition&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`add`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;+&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.radd</tt></dd></dl>
-
-<dl><dt><a name="Condition-agg"><strong>agg</strong></a> = aggregate(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.series.html#Series">Series</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#Condition-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#Condition-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10))<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-agg">agg</a>('min')<br>
--1.3018049988556679<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-agg">agg</a>(['min',&nbsp;'max'])<br>
-min&nbsp;&nbsp;&nbsp;-1.301805<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;1.127688<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.apply<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.transform</tt></dd></dl>
-
-<dl><dt><a name="Condition-aggregate"><strong>aggregate</strong></a>(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.series.html#Series">Series</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#Condition-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#Condition-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10))<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-agg">agg</a>('min')<br>
--1.3018049988556679<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-agg">agg</a>(['min',&nbsp;'max'])<br>
-min&nbsp;&nbsp;&nbsp;-1.301805<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;1.127688<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.apply<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.transform</tt></dd></dl>
-
-<dl><dt><a name="Condition-align"><strong>align</strong></a>(self, other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)</dt><dd><tt>Align&nbsp;two&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;their&nbsp;axes&nbsp;with&nbsp;the<br>
-specified&nbsp;join&nbsp;method&nbsp;for&nbsp;each&nbsp;axis&nbsp;Index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-join&nbsp;:&nbsp;{'outer',&nbsp;'inner',&nbsp;'left',&nbsp;'right'},&nbsp;default&nbsp;'outer'<br>
-axis&nbsp;:&nbsp;allowed&nbsp;axis&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;on&nbsp;index&nbsp;(0),&nbsp;columns&nbsp;(1),&nbsp;or&nbsp;both&nbsp;(None)<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Always&nbsp;returns&nbsp;new&nbsp;objects.&nbsp;If&nbsp;copy=False&nbsp;and&nbsp;no&nbsp;reindexing&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;required&nbsp;then&nbsp;original&nbsp;objects&nbsp;are&nbsp;returned.<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-method&nbsp;:&nbsp;str,&nbsp;default&nbsp;None<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-fill_axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Filling&nbsp;axis,&nbsp;method&nbsp;and&nbsp;limit<br>
-broadcast_axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;values&nbsp;along&nbsp;this&nbsp;axis,&nbsp;if&nbsp;aligning&nbsp;two&nbsp;objects&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;different&nbsp;dimensions<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-(left,&nbsp;right)&nbsp;:&nbsp;(<a href="pandas.core.series.html#Series">Series</a>,&nbsp;type&nbsp;of&nbsp;other)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Aligned&nbsp;objects</tt></dd></dl>
-
-<dl><dt><a name="Condition-all"><strong>all</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;all&nbsp;elements&nbsp;are&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-all&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-any"><strong>any</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;any&nbsp;element&nbsp;is&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-any&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-append"><strong>append</strong></a>(self, to_append, ignore_index=False, verify_integrity=False)</dt><dd><tt>Concatenate&nbsp;two&nbsp;or&nbsp;more&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-to_append&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;list/tuple&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-ignore_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;not&nbsp;use&nbsp;the&nbsp;index&nbsp;labels.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.19.0<br>
-&nbsp;<br>
-verify_integrity&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;raise&nbsp;Exception&nbsp;on&nbsp;creating&nbsp;index&nbsp;with&nbsp;duplicates<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-appended&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s1&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s2&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([4,&nbsp;5,&nbsp;6])<br>
-&gt;&gt;&gt;&nbsp;s3&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([4,&nbsp;5,&nbsp;6],&nbsp;index=[3,4,5])<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#Condition-append">append</a>(s2)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#Condition-append">append</a>(s3)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-With&nbsp;`ignore_index`&nbsp;set&nbsp;to&nbsp;True:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#Condition-append">append</a>(s2,&nbsp;ignore_index=True)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-With&nbsp;`verify_integrity`&nbsp;set&nbsp;to&nbsp;True:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#Condition-append">append</a>(s2,&nbsp;verify_integrity=True)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-ValueError:&nbsp;Indexes&nbsp;have&nbsp;overlapping&nbsp;values:&nbsp;[0,&nbsp;1,&nbsp;2]</tt></dd></dl>
-
-<dl><dt><a name="Condition-apply"><strong>apply</strong></a>(self, func, convert_dtype=True, args=(), **kwds)</dt><dd><tt>Invoke&nbsp;function&nbsp;on&nbsp;values&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Can&nbsp;be&nbsp;ufunc&nbsp;(a&nbsp;NumPy&nbsp;function<br>
-that&nbsp;applies&nbsp;to&nbsp;the&nbsp;entire&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;or&nbsp;a&nbsp;Python&nbsp;function&nbsp;that&nbsp;only&nbsp;works<br>
-on&nbsp;single&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-convert_dtype&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Try&nbsp;to&nbsp;find&nbsp;better&nbsp;dtype&nbsp;for&nbsp;elementwise&nbsp;function&nbsp;results.&nbsp;If<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False,&nbsp;leave&nbsp;as&nbsp;dtype=<a href="builtins.html#object">object</a><br>
-args&nbsp;:&nbsp;tuple<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Positional&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;to&nbsp;function&nbsp;in&nbsp;addition&nbsp;to&nbsp;the&nbsp;value<br>
-Additional&nbsp;keyword&nbsp;arguments&nbsp;will&nbsp;be&nbsp;passed&nbsp;as&nbsp;keywords&nbsp;to&nbsp;the&nbsp;function<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;if&nbsp;func&nbsp;returns&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.map:&nbsp;For&nbsp;element-wise&nbsp;operations<br>
-<a href="pandas.core.series.html#Series">Series</a>.agg:&nbsp;only&nbsp;perform&nbsp;aggregating&nbsp;type&nbsp;operations<br>
-<a href="pandas.core.series.html#Series">Series</a>.transform:&nbsp;only&nbsp;perform&nbsp;transformating&nbsp;type&nbsp;operations<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;series&nbsp;with&nbsp;typical&nbsp;summer&nbsp;temperatures&nbsp;for&nbsp;each&nbsp;city.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([20,&nbsp;21,&nbsp;12],&nbsp;index=['London',<br>
-...&nbsp;'New&nbsp;York','Helsinki'])<br>
-&gt;&gt;&gt;&nbsp;series<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;20<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Square&nbsp;the&nbsp;values&nbsp;by&nbsp;defining&nbsp;a&nbsp;function&nbsp;and&nbsp;passing&nbsp;it&nbsp;as&nbsp;an<br>
-argument&nbsp;to&nbsp;``<a href="#Condition-apply">apply</a>()``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;square(x):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x**2<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#Condition-apply">apply</a>(square)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;400<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;441<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;144<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Square&nbsp;the&nbsp;values&nbsp;by&nbsp;passing&nbsp;an&nbsp;anonymous&nbsp;function&nbsp;as&nbsp;an<br>
-argument&nbsp;to&nbsp;``<a href="#Condition-apply">apply</a>()``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#Condition-apply">apply</a>(lambda&nbsp;x:&nbsp;x**2)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;400<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;441<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;144<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Define&nbsp;a&nbsp;custom&nbsp;function&nbsp;that&nbsp;needs&nbsp;additional&nbsp;positional<br>
-arguments&nbsp;and&nbsp;pass&nbsp;these&nbsp;additional&nbsp;arguments&nbsp;using&nbsp;the<br>
-``args``&nbsp;keyword.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;subtract_custom_value(x,&nbsp;custom_value):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x-custom_value<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#Condition-apply">apply</a>(subtract_custom_value,&nbsp;args=(5,))<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;16<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Define&nbsp;a&nbsp;custom&nbsp;function&nbsp;that&nbsp;takes&nbsp;keyword&nbsp;arguments<br>
-and&nbsp;pass&nbsp;these&nbsp;arguments&nbsp;to&nbsp;``apply``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;add_custom_values(x,&nbsp;**kwargs):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;month&nbsp;in&nbsp;kwargs:<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;x+=kwargs[month]<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#Condition-apply">apply</a>(add_custom_values,&nbsp;june=30,&nbsp;july=20,&nbsp;august=25)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;95<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;96<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;87<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Use&nbsp;a&nbsp;function&nbsp;from&nbsp;the&nbsp;Numpy&nbsp;library.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#Condition-apply">apply</a>(np.log)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.995732<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;3.044522<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;2.484907<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="Condition-argmax"><strong>argmax</strong></a> = idxmax(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmax&nbsp;:&nbsp;Index&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmax``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmax<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmax</tt></dd></dl>
-
-<dl><dt><a name="Condition-argmin"><strong>argmin</strong></a> = idxmin(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmin&nbsp;:&nbsp;Index&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmin``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmin<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmin</tt></dd></dl>
-
-<dl><dt><a name="Condition-argsort"><strong>argsort</strong></a>(self, axis=0, kind='quicksort', order=None)</dt><dd><tt>Overrides&nbsp;<a href="numpy.html#ndarray">ndarray</a>.argsort.&nbsp;Argsorts&nbsp;the&nbsp;value,&nbsp;omitting&nbsp;NA/null&nbsp;values,<br>
-and&nbsp;places&nbsp;the&nbsp;result&nbsp;in&nbsp;the&nbsp;same&nbsp;locations&nbsp;as&nbsp;the&nbsp;non-NA&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;int&nbsp;(can&nbsp;only&nbsp;be&nbsp;zero)<br>
-kind&nbsp;:&nbsp;{'mergesort',&nbsp;'quicksort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;'mergesort'&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm<br>
-order&nbsp;:&nbsp;ignored<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-argsorted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;with&nbsp;-1&nbsp;indicated&nbsp;where&nbsp;nan&nbsp;values&nbsp;are&nbsp;present<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argsort</tt></dd></dl>
-
-<dl><dt><a name="Condition-autocorr"><strong>autocorr</strong></a>(self, lag=1)</dt><dd><tt>Lag-N&nbsp;autocorrelation<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-lag&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;lags&nbsp;to&nbsp;apply&nbsp;before&nbsp;performing&nbsp;autocorrelation.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-autocorr&nbsp;:&nbsp;float</tt></dd></dl>
-
-<dl><dt><a name="Condition-between"><strong>between</strong></a>(self, left, right, inclusive=True)</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;equivalent&nbsp;to&nbsp;left&nbsp;&lt;=&nbsp;series&nbsp;&lt;=&nbsp;right.&nbsp;NA&nbsp;values<br>
-will&nbsp;be&nbsp;treated&nbsp;as&nbsp;False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-left&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Left&nbsp;boundary<br>
-right&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Right&nbsp;boundary<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_between&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-combine"><strong>combine</strong></a>(self, other, func, fill_value=nan)</dt><dd><tt>Perform&nbsp;elementwise&nbsp;binary&nbsp;operation&nbsp;on&nbsp;two&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;using&nbsp;given&nbsp;function<br>
-with&nbsp;optional&nbsp;fill&nbsp;value&nbsp;when&nbsp;an&nbsp;index&nbsp;is&nbsp;missing&nbsp;from&nbsp;one&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or<br>
-the&nbsp;other<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-func&nbsp;:&nbsp;function<br>
-fill_value&nbsp;:&nbsp;scalar&nbsp;value<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-combine_first"><strong>combine_first</strong></a>(self, other)</dt><dd><tt>Combine&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values,&nbsp;choosing&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.series.html#Series">Series</a>'s&nbsp;values<br>
-first.&nbsp;Result&nbsp;index&nbsp;will&nbsp;be&nbsp;the&nbsp;union&nbsp;of&nbsp;the&nbsp;two&nbsp;indexes<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-compound"><strong>compound</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;compound&nbsp;percentage&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-compounded&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-compress"><strong>compress</strong></a>(self, condition, *args, **kwargs)</dt><dd><tt>Return&nbsp;selected&nbsp;slices&nbsp;of&nbsp;an&nbsp;array&nbsp;along&nbsp;given&nbsp;axis&nbsp;as&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.compress</tt></dd></dl>
-
-<dl><dt><a name="Condition-corr"><strong>corr</strong></a>(self, other, method='pearson', min_periods=None)</dt><dd><tt>Compute&nbsp;correlation&nbsp;with&nbsp;`other`&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;excluding&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-method&nbsp;:&nbsp;{'pearson',&nbsp;'kendall',&nbsp;'spearman'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pearson&nbsp;:&nbsp;standard&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;kendall&nbsp;:&nbsp;Kendall&nbsp;Tau&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;spearman&nbsp;:&nbsp;Spearman&nbsp;rank&nbsp;correlation<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;needed&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result<br>
-&nbsp;<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-correlation&nbsp;:&nbsp;float</tt></dd></dl>
-
-<dl><dt><a name="Condition-count"><strong>count</strong></a>(self, level=None)</dt><dd><tt>Return&nbsp;number&nbsp;of&nbsp;non-NA/null&nbsp;observations&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;smaller&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-nobs&nbsp;:&nbsp;int&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-cov"><strong>cov</strong></a>(self, other, min_periods=None)</dt><dd><tt>Compute&nbsp;covariance&nbsp;with&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;excluding&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;needed&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-covariance&nbsp;:&nbsp;float<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;(unbiased&nbsp;estimator).</tt></dd></dl>
-
-<dl><dt><a name="Condition-cummax"><strong>cummax</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;max&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummax&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.max&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="Condition-cummin"><strong>cummin</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;minimum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummin&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.min&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="Condition-cumprod"><strong>cumprod</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;product&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumprod&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.prod&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="Condition-cumsum"><strong>cumsum</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;sum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumsum&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.sum&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="Condition-diff"><strong>diff</strong></a>(self, periods=1)</dt><dd><tt>1st&nbsp;discrete&nbsp;difference&nbsp;of&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;difference<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-diffed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-div"><strong>div</strong></a> = truediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="Condition-divide"><strong>divide</strong></a> = truediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="Condition-dot"><strong>dot</strong></a>(self, other)</dt><dd><tt>Matrix&nbsp;multiplication&nbsp;with&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;inner-product&nbsp;with&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-objects<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dot_product&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-drop_duplicates"><strong>drop_duplicates</strong></a>(self, keep='first', inplace=False)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;duplicate&nbsp;values&nbsp;removed<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-&nbsp;<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Drop&nbsp;all&nbsp;duplicates.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-If&nbsp;True,&nbsp;performs&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;returns&nbsp;None.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-deduplicated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-dropna"><strong>dropna</strong></a>(self, axis=0, inplace=False, **kwargs)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;without&nbsp;null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-valid&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;operation&nbsp;in&nbsp;place.</tt></dd></dl>
-
-<dl><dt><a name="Condition-duplicated"><strong>duplicated</strong></a>(self, keep='first')</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;denoting&nbsp;duplicate&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the&nbsp;last<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Mark&nbsp;all&nbsp;duplicates&nbsp;as&nbsp;``True``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-duplicated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-eq"><strong>eq</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`eq`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;==&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="Condition-ewm"><strong>ewm</strong></a>(self, com=None, span=None, halflife=None, alpha=None, min_periods=0, freq=None, adjust=True, ignore_na=False, axis=0)</dt><dd><tt>Provides&nbsp;exponential&nbsp;weighted&nbsp;functions<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-com&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;/&nbsp;(1&nbsp;+&nbsp;com),\text{&nbsp;for&nbsp;}&nbsp;com&nbsp;\geq&nbsp;0`<br>
-span&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;span,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;2&nbsp;/&nbsp;(span&nbsp;+&nbsp;1),\text{&nbsp;for&nbsp;}&nbsp;span&nbsp;\geq&nbsp;1`<br>
-halflife&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;half-life,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;-&nbsp;exp(log(0.5)&nbsp;/&nbsp;halflife),\text{&nbsp;for&nbsp;}&nbsp;halflife&nbsp;&gt;&nbsp;0`<br>
-alpha&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;smoothing&nbsp;factor&nbsp;:math:`\alpha`&nbsp;directly,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`0&nbsp;&lt;&nbsp;\alpha&nbsp;\leq&nbsp;1`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;None&nbsp;or&nbsp;string&nbsp;alias&nbsp;/&nbsp;date&nbsp;offset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default=None&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;to&nbsp;before&nbsp;computing&nbsp;statistic<br>
-adjust&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Divide&nbsp;by&nbsp;decaying&nbsp;adjustment&nbsp;factor&nbsp;in&nbsp;beginning&nbsp;periods&nbsp;to&nbsp;account<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;imbalance&nbsp;in&nbsp;relative&nbsp;weightings&nbsp;(viewing&nbsp;EWMA&nbsp;as&nbsp;a&nbsp;moving&nbsp;average)<br>
-ignore_na&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Ignore&nbsp;missing&nbsp;values&nbsp;when&nbsp;calculating&nbsp;weights;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specify&nbsp;True&nbsp;to&nbsp;reproduce&nbsp;pre-0.15.0&nbsp;behavior<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-ewm">ewm</a>(com=0.5).<a href="#Condition-mean">mean</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.000000<br>
-1&nbsp;&nbsp;0.750000<br>
-2&nbsp;&nbsp;1.615385<br>
-3&nbsp;&nbsp;1.615385<br>
-4&nbsp;&nbsp;3.670213<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Exactly&nbsp;one&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,&nbsp;span,&nbsp;half-life,&nbsp;and&nbsp;alpha&nbsp;must&nbsp;be&nbsp;provided.<br>
-Allowed&nbsp;values&nbsp;and&nbsp;relationship&nbsp;between&nbsp;the&nbsp;parameters&nbsp;are&nbsp;specified&nbsp;in&nbsp;the<br>
-parameter&nbsp;descriptions&nbsp;above;&nbsp;see&nbsp;the&nbsp;link&nbsp;at&nbsp;the&nbsp;end&nbsp;of&nbsp;this&nbsp;section&nbsp;for<br>
-a&nbsp;detailed&nbsp;explanation.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;True&nbsp;(default),&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;using<br>
-weights&nbsp;(1-alpha)**(n-1),&nbsp;(1-alpha)**(n-2),&nbsp;...,&nbsp;1-alpha,&nbsp;1.<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;False,&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;recursively&nbsp;as:<br>
-&nbsp;&nbsp;&nbsp;weighted_average[0]&nbsp;=&nbsp;arg[0];<br>
-&nbsp;&nbsp;&nbsp;weighted_average[i]&nbsp;=&nbsp;(1-alpha)*weighted_average[i-1]&nbsp;+&nbsp;alpha*arg[i].<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;False&nbsp;(default),&nbsp;weights&nbsp;are&nbsp;based&nbsp;on&nbsp;absolute&nbsp;positions.<br>
-For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in&nbsp;calculating&nbsp;the&nbsp;final&nbsp;weighted<br>
-average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;(1-alpha)**2&nbsp;and&nbsp;1&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and<br>
-(1-alpha)**2&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;True&nbsp;(reproducing&nbsp;pre-0.15.0&nbsp;behavior),&nbsp;weights&nbsp;are&nbsp;based<br>
-on&nbsp;relative&nbsp;positions.&nbsp;For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in<br>
-calculating&nbsp;the&nbsp;final&nbsp;weighted&nbsp;average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;1-alpha&nbsp;and&nbsp;1<br>
-(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and&nbsp;1-alpha&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-More&nbsp;details&nbsp;can&nbsp;be&nbsp;found&nbsp;at<br>
-<a href="http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows">http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-expanding"><strong>expanding</strong></a>(self, min_periods=1, freq=None, center=False, axis=0)</dt><dd><tt>Provides&nbsp;expanding&nbsp;transformations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-expanding">expanding</a>(2).<a href="#Condition-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;7.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).</tt></dd></dl>
-
-<dl><dt><a name="Condition-fillna"><strong>fillna</strong></a>(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)</dt><dd><tt>Fill&nbsp;NA/NaN&nbsp;values&nbsp;using&nbsp;the&nbsp;specified&nbsp;method<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;values&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;each&nbsp;index&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;or&nbsp;column&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).&nbsp;(values&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;This&nbsp;value&nbsp;cannot<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;list.<br>
-method&nbsp;:&nbsp;{'backfill',&nbsp;'bfill',&nbsp;'pad',&nbsp;'ffill',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'}<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;fill&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>,&nbsp;(e.g.&nbsp;a&nbsp;no-copy&nbsp;slice&nbsp;for&nbsp;a&nbsp;column&nbsp;in&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;method&nbsp;is&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the&nbsp;maximum&nbsp;number&nbsp;of&nbsp;consecutive<br>
-&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;values&nbsp;to&nbsp;forward/backward&nbsp;fill.&nbsp;In&nbsp;other&nbsp;words,&nbsp;if&nbsp;there&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;gap&nbsp;with&nbsp;more&nbsp;than&nbsp;this&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs,&nbsp;it&nbsp;will&nbsp;only<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;partially&nbsp;filled.&nbsp;If&nbsp;method&nbsp;is&nbsp;not&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;maximum&nbsp;number&nbsp;of&nbsp;entries&nbsp;along&nbsp;the&nbsp;entire&nbsp;axis&nbsp;where&nbsp;NaNs&nbsp;will&nbsp;be<br>
-&nbsp;&nbsp;&nbsp;&nbsp;filled.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0&nbsp;if&nbsp;not&nbsp;None.<br>
-downcast&nbsp;:&nbsp;dict,&nbsp;default&nbsp;is&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;dict&nbsp;of&nbsp;item-&gt;dtype&nbsp;of&nbsp;what&nbsp;to&nbsp;downcast&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;the&nbsp;string&nbsp;'infer'&nbsp;which&nbsp;will&nbsp;try&nbsp;to&nbsp;downcast&nbsp;to&nbsp;an&nbsp;appropriate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;equal&nbsp;type&nbsp;(e.g.&nbsp;float64&nbsp;to&nbsp;int64&nbsp;if&nbsp;possible)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;asfreq<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-first_valid_index"><strong>first_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;first&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="Condition-floordiv"><strong>floordiv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`floordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;//&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rfloordiv</tt></dd></dl>
-
-<dl><dt><a name="Condition-ge"><strong>ge</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Greater&nbsp;than&nbsp;or&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`ge`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&gt;=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="Condition-get_value"><strong>get_value</strong></a>(self, label, takeable=False)</dt><dd><tt>Quickly&nbsp;retrieve&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;index&nbsp;label<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;label<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;scalar&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="Condition-get_values"><strong>get_values</strong></a>(self)</dt><dd><tt>same&nbsp;as&nbsp;values&nbsp;(but&nbsp;handles&nbsp;sparseness&nbsp;conversions);&nbsp;is&nbsp;a&nbsp;view</tt></dd></dl>
-
-<dl><dt><a name="Condition-gt"><strong>gt</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Greater&nbsp;than&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`gt`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&gt;&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="Condition-hist"><strong>hist</strong></a> = hist_series(self, by=None, ax=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, figsize=None, bins=10, **kwds)</dt><dd><tt>Draw&nbsp;histogram&nbsp;of&nbsp;the&nbsp;input&nbsp;series&nbsp;using&nbsp;matplotlib<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed,&nbsp;then&nbsp;used&nbsp;to&nbsp;form&nbsp;histograms&nbsp;for&nbsp;separate&nbsp;groups<br>
-ax&nbsp;:&nbsp;matplotlib&nbsp;axis&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;not&nbsp;passed,&nbsp;uses&nbsp;gca()<br>
-grid&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;show&nbsp;axis&nbsp;grid&nbsp;lines<br>
-xlabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;x-axis&nbsp;label&nbsp;size<br>
-xrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;x&nbsp;axis&nbsp;labels<br>
-ylabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;y-axis&nbsp;label&nbsp;size<br>
-yrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;y&nbsp;axis&nbsp;labels<br>
-figsize&nbsp;:&nbsp;tuple,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;figure&nbsp;size&nbsp;in&nbsp;inches&nbsp;by&nbsp;default<br>
-bins:&nbsp;integer,&nbsp;default&nbsp;10<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;histogram&nbsp;bins&nbsp;to&nbsp;be&nbsp;used<br>
-kwds&nbsp;:&nbsp;keywords<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;passed&nbsp;to&nbsp;the&nbsp;actual&nbsp;plotting&nbsp;function<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;matplotlib&nbsp;documentation&nbsp;online&nbsp;for&nbsp;more&nbsp;on&nbsp;this</tt></dd></dl>
-
-<dl><dt><a name="Condition-idxmax"><strong>idxmax</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmax&nbsp;:&nbsp;Index&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmax``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmax<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmax</tt></dd></dl>
-
-<dl><dt><a name="Condition-idxmin"><strong>idxmin</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmin&nbsp;:&nbsp;Index&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmin``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmin<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmin</tt></dd></dl>
-
-<dl><dt><a name="Condition-isin"><strong>isin</strong></a>(self, values)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;:class:`~pandas.<a href="pandas.core.series.html#Series">Series</a>`&nbsp;showing&nbsp;whether&nbsp;each&nbsp;element<br>
-in&nbsp;the&nbsp;:class:`~pandas.<a href="pandas.core.series.html#Series">Series</a>`&nbsp;is&nbsp;exactly&nbsp;contained&nbsp;in&nbsp;the&nbsp;passed<br>
-sequence&nbsp;of&nbsp;``values``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-values&nbsp;:&nbsp;set&nbsp;or&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;sequence&nbsp;of&nbsp;values&nbsp;to&nbsp;test.&nbsp;Passing&nbsp;in&nbsp;a&nbsp;single&nbsp;string&nbsp;will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;raise&nbsp;a&nbsp;``TypeError``.&nbsp;Instead,&nbsp;turn&nbsp;a&nbsp;single&nbsp;string&nbsp;into&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``list``&nbsp;of&nbsp;one&nbsp;element.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Support&nbsp;for&nbsp;values&nbsp;as&nbsp;a&nbsp;set<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-isin&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(bool&nbsp;dtype)<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;*&nbsp;If&nbsp;``values``&nbsp;is&nbsp;a&nbsp;string<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.isin<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(list('abc'))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-isin">isin</a>(['a',&nbsp;'c',&nbsp;'e'])<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-dtype:&nbsp;bool<br>
-&nbsp;<br>
-Passing&nbsp;a&nbsp;single&nbsp;string&nbsp;as&nbsp;``s.<a href="#Condition-isin">isin</a>('a')``&nbsp;will&nbsp;raise&nbsp;an&nbsp;error.&nbsp;Use<br>
-a&nbsp;list&nbsp;of&nbsp;one&nbsp;element&nbsp;instead:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-isin">isin</a>(['a'])<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-dtype:&nbsp;bool</tt></dd></dl>
-
-<dl><dt><a name="Condition-items"><strong>items</strong></a> = iteritems(self)</dt><dd><tt>Lazily&nbsp;iterate&nbsp;over&nbsp;(index,&nbsp;value)&nbsp;tuples</tt></dd></dl>
-
-<dl><dt><a name="Condition-iteritems"><strong>iteritems</strong></a>(self)</dt><dd><tt>Lazily&nbsp;iterate&nbsp;over&nbsp;(index,&nbsp;value)&nbsp;tuples</tt></dd></dl>
-
-<dl><dt><a name="Condition-keys"><strong>keys</strong></a>(self)</dt><dd><tt>Alias&nbsp;for&nbsp;index</tt></dd></dl>
-
-<dl><dt><a name="Condition-kurt"><strong>kurt</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-kurtosis"><strong>kurtosis</strong></a> = kurt(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-last_valid_index"><strong>last_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;last&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="Condition-le"><strong>le</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Less&nbsp;than&nbsp;or&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`le`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&lt;=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="Condition-lt"><strong>lt</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Less&nbsp;than&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`lt`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&lt;&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="Condition-mad"><strong>mad</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;absolute&nbsp;deviation&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mad&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-map"><strong>map</strong></a>(self, arg, na_action=None)</dt><dd><tt>Map&nbsp;values&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;using&nbsp;input&nbsp;correspondence&nbsp;(which&nbsp;can&nbsp;be<br>
-a&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;function)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-arg&nbsp;:&nbsp;function,&nbsp;dict,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-na_action&nbsp;:&nbsp;{None,&nbsp;'ignore'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'ignore',&nbsp;propagate&nbsp;NA&nbsp;values,&nbsp;without&nbsp;passing&nbsp;them&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;mapping&nbsp;function<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;same&nbsp;index&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Map&nbsp;inputs&nbsp;to&nbsp;outputs&nbsp;(both&nbsp;of&nbsp;type&nbsp;`<a href="pandas.core.series.html#Series">Series</a>`)<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,2,3],&nbsp;index=['one',&nbsp;'two',&nbsp;'three'])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-one&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-two&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-three&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;y&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(['foo',&nbsp;'bar',&nbsp;'baz'],&nbsp;index=[1,2,3])<br>
-&gt;&gt;&gt;&nbsp;y<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;foo<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;bar<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;baz<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Condition-map">map</a>(y)<br>
-one&nbsp;&nbsp;&nbsp;foo<br>
-two&nbsp;&nbsp;&nbsp;bar<br>
-three&nbsp;baz<br>
-&nbsp;<br>
-If&nbsp;`arg`&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;values&nbsp;converted<br>
-according&nbsp;to&nbsp;the&nbsp;dictionary's&nbsp;mapping:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;z&nbsp;=&nbsp;{1:&nbsp;'A',&nbsp;2:&nbsp;'B',&nbsp;3:&nbsp;'C'}<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Condition-map">map</a>(z)<br>
-one&nbsp;&nbsp;&nbsp;A<br>
-two&nbsp;&nbsp;&nbsp;B<br>
-three&nbsp;C<br>
-&nbsp;<br>
-Use&nbsp;na_action&nbsp;to&nbsp;control&nbsp;whether&nbsp;NA&nbsp;values&nbsp;are&nbsp;affected&nbsp;by&nbsp;the&nbsp;mapping<br>
-function.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;np.nan])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s2&nbsp;=&nbsp;s.<a href="#Condition-map">map</a>('this&nbsp;is&nbsp;a&nbsp;string&nbsp;{}'.format,&nbsp;na_action=None)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;1.0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;2.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;3.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;nan<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s3&nbsp;=&nbsp;s.<a href="#Condition-map">map</a>('this&nbsp;is&nbsp;a&nbsp;string&nbsp;{}'.format,&nbsp;na_action='ignore')<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;1.0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;2.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;3.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.apply:&nbsp;For&nbsp;applying&nbsp;more&nbsp;complex&nbsp;functions&nbsp;on&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply:&nbsp;Apply&nbsp;a&nbsp;function&nbsp;row-/column-wise<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap:&nbsp;Apply&nbsp;a&nbsp;function&nbsp;elementwise&nbsp;on&nbsp;a&nbsp;whole&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-When&nbsp;`arg`&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;values&nbsp;in&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;that&nbsp;are&nbsp;not&nbsp;in&nbsp;the<br>
-dictionary&nbsp;(as&nbsp;keys)&nbsp;are&nbsp;converted&nbsp;to&nbsp;``NaN``.&nbsp;However,&nbsp;if&nbsp;the<br>
-dictionary&nbsp;is&nbsp;a&nbsp;``dict``&nbsp;subclass&nbsp;that&nbsp;defines&nbsp;``__missing__``&nbsp;(i.e.<br>
-provides&nbsp;a&nbsp;method&nbsp;for&nbsp;default&nbsp;values),&nbsp;then&nbsp;this&nbsp;default&nbsp;is&nbsp;used<br>
-rather&nbsp;than&nbsp;``NaN``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;collections&nbsp;import&nbsp;Counter<br>
-&gt;&gt;&gt;&nbsp;counter&nbsp;=&nbsp;Counter()<br>
-&gt;&gt;&gt;&nbsp;counter['bar']&nbsp;+=&nbsp;1<br>
-&gt;&gt;&gt;&nbsp;y.<a href="#Condition-map">map</a>(counter)<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-dtype:&nbsp;int64</tt></dd></dl>
-
-<dl><dt><a name="Condition-max"><strong>max</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;maximum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;maximum,&nbsp;use&nbsp;``idxmax``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmax``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-max&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-mean"><strong>mean</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mean&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-median"><strong>median</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;median&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-median&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-memory_usage"><strong>memory_usage</strong></a>(self, index=True, deep=False)</dt><dd><tt>Memory&nbsp;usage&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;whether&nbsp;to&nbsp;include&nbsp;memory&nbsp;usage&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index<br>
-deep&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Introspect&nbsp;the&nbsp;data&nbsp;deeply,&nbsp;interrogate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`<a href="builtins.html#object">object</a>`&nbsp;dtypes&nbsp;for&nbsp;system-level&nbsp;memory&nbsp;consumption<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-scalar&nbsp;bytes&nbsp;of&nbsp;memory&nbsp;consumed<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Memory&nbsp;usage&nbsp;does&nbsp;not&nbsp;include&nbsp;memory&nbsp;consumed&nbsp;by&nbsp;elements&nbsp;that<br>
-are&nbsp;not&nbsp;components&nbsp;of&nbsp;the&nbsp;array&nbsp;if&nbsp;deep=False<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.nbytes</tt></dd></dl>
-
-<dl><dt><a name="Condition-min"><strong>min</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;minimum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;minimum,&nbsp;use&nbsp;``idxmin``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmin``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-min&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-mod"><strong>mod</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Modulo&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;%&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmod</tt></dd></dl>
-
-<dl><dt><a name="Condition-mode"><strong>mode</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;<a href="#Condition-mode">mode</a>(s)&nbsp;of&nbsp;the&nbsp;dataset.<br>
-&nbsp;<br>
-Always&nbsp;returns&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;even&nbsp;if&nbsp;only&nbsp;one&nbsp;value&nbsp;is&nbsp;returned.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-modes&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(sorted)</tt></dd></dl>
-
-<dl><dt><a name="Condition-mul"><strong>mul</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="Condition-multiply"><strong>multiply</strong></a> = mul(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="Condition-ne"><strong>ne</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Not&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`ne`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;!=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="Condition-nlargest"><strong>nlargest</strong></a>(self, n=5, keep='first')</dt><dd><tt>Return&nbsp;the&nbsp;largest&nbsp;`n`&nbsp;elements.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;this&nbsp;many&nbsp;descending&nbsp;sorted&nbsp;values<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-top_n&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;n&nbsp;largest&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;in&nbsp;sorted&nbsp;order<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Faster&nbsp;than&nbsp;``.<a href="#Condition-sort_values">sort_values</a>(ascending=False).<a href="#Condition-head">head</a>(n)``&nbsp;for&nbsp;small&nbsp;`n`<br>
-relative&nbsp;to&nbsp;the&nbsp;size&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.nsmallest<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10**6))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-nlargest">nlargest</a>(10)&nbsp;&nbsp;#&nbsp;only&nbsp;sorts&nbsp;up&nbsp;to&nbsp;the&nbsp;N&nbsp;requested<br>
-219921&nbsp;&nbsp;&nbsp;&nbsp;4.644710<br>
-82124&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4.608745<br>
-421689&nbsp;&nbsp;&nbsp;&nbsp;4.564644<br>
-425277&nbsp;&nbsp;&nbsp;&nbsp;4.447014<br>
-718691&nbsp;&nbsp;&nbsp;&nbsp;4.414137<br>
-43154&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4.403520<br>
-283187&nbsp;&nbsp;&nbsp;&nbsp;4.313922<br>
-595519&nbsp;&nbsp;&nbsp;&nbsp;4.273635<br>
-503969&nbsp;&nbsp;&nbsp;&nbsp;4.250236<br>
-121637&nbsp;&nbsp;&nbsp;&nbsp;4.240952<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="Condition-nonzero"><strong>nonzero</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;indices&nbsp;of&nbsp;the&nbsp;elements&nbsp;that&nbsp;are&nbsp;non-zero<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;calling&nbsp;`numpy.nonzero`&nbsp;on&nbsp;the<br>
-series&nbsp;data.&nbsp;For&nbsp;compatability&nbsp;with&nbsp;NumPy,&nbsp;the&nbsp;return&nbsp;value&nbsp;is<br>
-the&nbsp;same&nbsp;(a&nbsp;tuple&nbsp;with&nbsp;an&nbsp;array&nbsp;of&nbsp;indices&nbsp;for&nbsp;each&nbsp;dimension),<br>
-but&nbsp;it&nbsp;will&nbsp;always&nbsp;be&nbsp;a&nbsp;one-item&nbsp;tuple&nbsp;because&nbsp;series&nbsp;only&nbsp;have<br>
-one&nbsp;dimension.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0,&nbsp;3,&nbsp;0,&nbsp;4])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-nonzero">nonzero</a>()<br>
-(array([1,&nbsp;3]),)<br>
-&gt;&gt;&gt;&nbsp;s.iloc[s.<a href="#Condition-nonzero">nonzero</a>()[0]]<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.nonzero</tt></dd></dl>
-
-<dl><dt><a name="Condition-nsmallest"><strong>nsmallest</strong></a>(self, n=5, keep='first')</dt><dd><tt>Return&nbsp;the&nbsp;smallest&nbsp;`n`&nbsp;elements.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;this&nbsp;many&nbsp;ascending&nbsp;sorted&nbsp;values<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-bottom_n&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;n&nbsp;smallest&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;in&nbsp;sorted&nbsp;order<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Faster&nbsp;than&nbsp;``.<a href="#Condition-sort_values">sort_values</a>().<a href="#Condition-head">head</a>(n)``&nbsp;for&nbsp;small&nbsp;`n`&nbsp;relative&nbsp;to<br>
-the&nbsp;size&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.nlargest<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10**6))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-nsmallest">nsmallest</a>(10)&nbsp;&nbsp;#&nbsp;only&nbsp;sorts&nbsp;up&nbsp;to&nbsp;the&nbsp;N&nbsp;requested<br>
-288532&nbsp;&nbsp;&nbsp;-4.954580<br>
-732345&nbsp;&nbsp;&nbsp;-4.835960<br>
-64803&nbsp;&nbsp;&nbsp;&nbsp;-4.812550<br>
-446457&nbsp;&nbsp;&nbsp;-4.609998<br>
-501225&nbsp;&nbsp;&nbsp;-4.483945<br>
-669476&nbsp;&nbsp;&nbsp;-4.472935<br>
-973615&nbsp;&nbsp;&nbsp;-4.401699<br>
-621279&nbsp;&nbsp;&nbsp;-4.355126<br>
-773916&nbsp;&nbsp;&nbsp;-4.347355<br>
-359919&nbsp;&nbsp;&nbsp;-4.331927<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="Condition-pow"><strong>pow</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`pow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;**&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rpow</tt></dd></dl>
-
-<dl><dt><a name="Condition-prod"><strong>prod</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-product"><strong>product</strong></a> = prod(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-ptp"><strong>ptp</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Returns&nbsp;the&nbsp;difference&nbsp;between&nbsp;the&nbsp;maximum&nbsp;value&nbsp;and&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;minimum&nbsp;value&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.&nbsp;This&nbsp;is&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``ptp``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ptp&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-put"><strong>put</strong></a>(self, *args, **kwargs)</dt><dd><tt>Applies&nbsp;the&nbsp;`put`&nbsp;method&nbsp;to&nbsp;its&nbsp;`values`&nbsp;attribute<br>
-if&nbsp;it&nbsp;has&nbsp;one.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.put</tt></dd></dl>
-
-<dl><dt><a name="Condition-quantile"><strong>quantile</strong></a>(self, q=0.5, interpolation='linear')</dt><dd><tt>Return&nbsp;value&nbsp;at&nbsp;the&nbsp;given&nbsp;quantile,&nbsp;a&nbsp;la&nbsp;numpy.percentile.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-q&nbsp;:&nbsp;float&nbsp;or&nbsp;array-like,&nbsp;default&nbsp;0.5&nbsp;(50%&nbsp;quantile)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&lt;=&nbsp;q&nbsp;&lt;=&nbsp;1,&nbsp;the&nbsp;<a href="#Condition-quantile">quantile</a>(s)&nbsp;to&nbsp;compute<br>
-interpolation&nbsp;:&nbsp;{'linear',&nbsp;'lower',&nbsp;'higher',&nbsp;'midpoint',&nbsp;'nearest'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;optional&nbsp;parameter&nbsp;specifies&nbsp;the&nbsp;interpolation&nbsp;method&nbsp;to&nbsp;use,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;the&nbsp;desired&nbsp;quantile&nbsp;lies&nbsp;between&nbsp;two&nbsp;data&nbsp;points&nbsp;`i`&nbsp;and&nbsp;`j`:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;linear:&nbsp;`i&nbsp;+&nbsp;(j&nbsp;-&nbsp;i)&nbsp;*&nbsp;fraction`,&nbsp;where&nbsp;`fraction`&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;fractional&nbsp;part&nbsp;of&nbsp;the&nbsp;index&nbsp;surrounded&nbsp;by&nbsp;`i`&nbsp;and&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;lower:&nbsp;`i`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;higher:&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;`i`&nbsp;or&nbsp;`j`&nbsp;whichever&nbsp;is&nbsp;nearest.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;midpoint:&nbsp;(`i`&nbsp;+&nbsp;`j`)&nbsp;/&nbsp;2.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-quantile&nbsp;:&nbsp;float&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;``q``&nbsp;is&nbsp;an&nbsp;array,&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;returned&nbsp;where&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;is&nbsp;``q``&nbsp;and&nbsp;the&nbsp;values&nbsp;are&nbsp;the&nbsp;quantiles.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;4])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-quantile">quantile</a>(.5)<br>
-2.5<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-quantile">quantile</a>([.25,&nbsp;.5,&nbsp;.75])<br>
-0.25&nbsp;&nbsp;&nbsp;&nbsp;1.75<br>
-0.50&nbsp;&nbsp;&nbsp;&nbsp;2.50<br>
-0.75&nbsp;&nbsp;&nbsp;&nbsp;3.25<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="Condition-radd"><strong>radd</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Addition&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`radd`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;+&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.add</tt></dd></dl>
-
-<dl><dt><a name="Condition-ravel"><strong>ravel</strong></a>(self, order='C')</dt><dd><tt>Return&nbsp;the&nbsp;flattened&nbsp;underlying&nbsp;data&nbsp;as&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.ravel</tt></dd></dl>
-
-<dl><dt><a name="Condition-rdiv"><strong>rdiv</strong></a> = rtruediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="Condition-reindex"><strong>reindex</strong></a>(self, index=None, **kwargs)</dt><dd><tt>Conform&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;new&nbsp;index&nbsp;with&nbsp;optional&nbsp;filling&nbsp;logic,&nbsp;placing<br>
-NA/NaN&nbsp;in&nbsp;locations&nbsp;having&nbsp;no&nbsp;value&nbsp;in&nbsp;the&nbsp;previous&nbsp;index.&nbsp;A&nbsp;new&nbsp;<a href="builtins.html#object">object</a><br>
-is&nbsp;produced&nbsp;unless&nbsp;the&nbsp;new&nbsp;index&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;the&nbsp;current&nbsp;one&nbsp;and<br>
-copy=False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;array-like,&nbsp;optional&nbsp;(can&nbsp;be&nbsp;specified&nbsp;in&nbsp;order,&nbsp;or&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;keywords)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;New&nbsp;labels&nbsp;/&nbsp;index&nbsp;to&nbsp;conform&nbsp;to.&nbsp;Preferably&nbsp;an&nbsp;Index&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;avoid&nbsp;duplicating&nbsp;data<br>
-method&nbsp;:&nbsp;{None,&nbsp;'backfill'/'bfill',&nbsp;'pad'/'ffill',&nbsp;'nearest'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Please&nbsp;note:&nbsp;this&nbsp;is&nbsp;only&nbsp;&nbsp;applicable&nbsp;to&nbsp;DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;monotonically&nbsp;increasing/decreasing&nbsp;index.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;default:&nbsp;don't&nbsp;fill&nbsp;gaps<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;next&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;use&nbsp;nearest&nbsp;valid&nbsp;observations&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>,&nbsp;even&nbsp;if&nbsp;the&nbsp;passed&nbsp;indexes&nbsp;are&nbsp;the&nbsp;same<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;elements&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;original&nbsp;and&nbsp;new&nbsp;labels&nbsp;for&nbsp;inexact<br>
-&nbsp;&nbsp;&nbsp;&nbsp;matches.&nbsp;The&nbsp;values&nbsp;of&nbsp;the&nbsp;index&nbsp;at&nbsp;the&nbsp;matching&nbsp;locations&nbsp;most<br>
-&nbsp;&nbsp;&nbsp;&nbsp;satisfy&nbsp;the&nbsp;equation&nbsp;``<a href="#Condition-abs">abs</a>(index[indexer]&nbsp;-&nbsp;target)&nbsp;&lt;=&nbsp;tolerance``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;some&nbsp;fictional&nbsp;data.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;['Firefox',&nbsp;'Chrome',&nbsp;'Safari',&nbsp;'IE10',&nbsp;'Konqueror']<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'http_status':&nbsp;[200,200,404,404,301],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'response_time':&nbsp;[0.04,&nbsp;0.02,&nbsp;0.07,&nbsp;0.08,&nbsp;1.0]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Firefox&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.04<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Konqueror&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;301&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.00<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;new&nbsp;index&nbsp;and&nbsp;reindex&nbsp;the&nbsp;dataframe.&nbsp;By&nbsp;default<br>
-values&nbsp;in&nbsp;the&nbsp;new&nbsp;index&nbsp;that&nbsp;do&nbsp;not&nbsp;have&nbsp;corresponding<br>
-records&nbsp;in&nbsp;the&nbsp;dataframe&nbsp;are&nbsp;assigned&nbsp;``NaN``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;new_index=&nbsp;['Safari',&nbsp;'Iceweasel',&nbsp;'Comodo&nbsp;Dragon',&nbsp;'IE10',<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Chrome']<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-reindex">reindex</a>(new_index)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-We&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;by&nbsp;passing&nbsp;a&nbsp;value&nbsp;to<br>
-the&nbsp;keyword&nbsp;``fill_value``.&nbsp;Because&nbsp;the&nbsp;index&nbsp;is&nbsp;not&nbsp;monotonically<br>
-increasing&nbsp;or&nbsp;decreasing,&nbsp;we&nbsp;cannot&nbsp;use&nbsp;arguments&nbsp;to&nbsp;the&nbsp;keyword<br>
-``method``&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``&nbsp;values.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-reindex">reindex</a>(new_index,&nbsp;fill_value=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-reindex">reindex</a>(new_index,&nbsp;fill_value='missing')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-To&nbsp;further&nbsp;illustrate&nbsp;the&nbsp;filling&nbsp;functionality&nbsp;in<br>
-``reindex``,&nbsp;we&nbsp;will&nbsp;create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;a<br>
-monotonically&nbsp;increasing&nbsp;index&nbsp;(for&nbsp;example,&nbsp;a&nbsp;sequence<br>
-of&nbsp;dates).<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index&nbsp;=&nbsp;pd.date_range('1/1/2010',&nbsp;periods=6,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"prices":&nbsp;[100,&nbsp;101,&nbsp;np.nan,&nbsp;100,&nbsp;89,&nbsp;88]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=date_index)<br>
-&gt;&gt;&gt;&nbsp;df2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-&nbsp;<br>
-Suppose&nbsp;we&nbsp;decide&nbsp;to&nbsp;expand&nbsp;the&nbsp;dataframe&nbsp;to&nbsp;cover&nbsp;a&nbsp;wider<br>
-date&nbsp;range.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index2&nbsp;=&nbsp;pd.date_range('12/29/2009',&nbsp;periods=10,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#Condition-reindex">reindex</a>(date_index2)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-The&nbsp;index&nbsp;entries&nbsp;that&nbsp;did&nbsp;not&nbsp;have&nbsp;a&nbsp;value&nbsp;in&nbsp;the&nbsp;original&nbsp;data&nbsp;frame<br>
-(for&nbsp;example,&nbsp;'2009-12-29')&nbsp;are&nbsp;by&nbsp;default&nbsp;filled&nbsp;with&nbsp;``NaN``.<br>
-If&nbsp;desired,&nbsp;we&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;using&nbsp;one&nbsp;of&nbsp;several<br>
-options.<br>
-&nbsp;<br>
-For&nbsp;example,&nbsp;to&nbsp;backpropagate&nbsp;the&nbsp;last&nbsp;valid&nbsp;value&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values,&nbsp;pass&nbsp;``bfill``&nbsp;as&nbsp;an&nbsp;argument&nbsp;to&nbsp;the&nbsp;``method``&nbsp;keyword.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#Condition-reindex">reindex</a>(date_index2,&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;the&nbsp;``NaN``&nbsp;value&nbsp;present&nbsp;in&nbsp;the&nbsp;original&nbsp;dataframe<br>
-(at&nbsp;index&nbsp;value&nbsp;2010-01-03)&nbsp;will&nbsp;not&nbsp;be&nbsp;filled&nbsp;by&nbsp;any&nbsp;of&nbsp;the<br>
-value&nbsp;propagation&nbsp;schemes.&nbsp;This&nbsp;is&nbsp;because&nbsp;filling&nbsp;while&nbsp;reindexing<br>
-does&nbsp;not&nbsp;look&nbsp;at&nbsp;dataframe&nbsp;values,&nbsp;but&nbsp;only&nbsp;compares&nbsp;the&nbsp;original&nbsp;and<br>
-desired&nbsp;indexes.&nbsp;If&nbsp;you&nbsp;do&nbsp;want&nbsp;to&nbsp;fill&nbsp;in&nbsp;the&nbsp;``NaN``&nbsp;values&nbsp;present<br>
-in&nbsp;the&nbsp;original&nbsp;dataframe,&nbsp;use&nbsp;the&nbsp;``<a href="#Condition-fillna">fillna</a>()``&nbsp;method.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-reindex_axis"><strong>reindex_axis</strong></a>(self, labels, axis=0, **kwargs)</dt><dd><tt>for&nbsp;compatibility&nbsp;with&nbsp;higher&nbsp;dims</tt></dd></dl>
-
-<dl><dt><a name="Condition-rename"><strong>rename</strong></a>(self, index=None, **kwargs)</dt><dd><tt>Alter&nbsp;axes&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.&nbsp;Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be<br>
-unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in&nbsp;a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left<br>
-as-is.&nbsp;Extra&nbsp;labels&nbsp;listed&nbsp;don't&nbsp;throw&nbsp;an&nbsp;error.&nbsp;Alternatively,&nbsp;change<br>
-``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;with&nbsp;a&nbsp;scalar&nbsp;value&nbsp;(<a href="pandas.core.series.html#Series">Series</a>&nbsp;only).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Scalar&nbsp;or&nbsp;list-like&nbsp;will&nbsp;alter&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;attribute,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;raise&nbsp;on&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;Panel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict-like&nbsp;or&nbsp;functions&nbsp;are&nbsp;transformations&nbsp;to&nbsp;apply&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;that&nbsp;axis'&nbsp;values<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;If&nbsp;True&nbsp;then&nbsp;value&nbsp;of&nbsp;copy&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;ignored.<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;MultiIndex,&nbsp;only&nbsp;rename&nbsp;labels&nbsp;in&nbsp;the&nbsp;specified<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename_axis<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-rename">rename</a>("my_name")&nbsp;#&nbsp;scalar,&nbsp;changes&nbsp;<a href="pandas.core.series.html#Series">Series</a>.name<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;my_name,&nbsp;dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-rename">rename</a>(lambda&nbsp;x:&nbsp;x&nbsp;**&nbsp;2)&nbsp;&nbsp;#&nbsp;function,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-rename">rename</a>({1:&nbsp;3,&nbsp;2:&nbsp;5})&nbsp;&nbsp;#&nbsp;mapping,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-rename">rename</a>(2)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-TypeError:&nbsp;'int'&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;not&nbsp;callable<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"B":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;c<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"C":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="Condition-reorder_levels"><strong>reorder_levels</strong></a>(self, order)</dt><dd><tt>Rearrange&nbsp;index&nbsp;levels&nbsp;using&nbsp;input&nbsp;order.&nbsp;May&nbsp;not&nbsp;drop&nbsp;or&nbsp;duplicate<br>
-levels<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-order&nbsp;:&nbsp;list&nbsp;of&nbsp;int&nbsp;representing&nbsp;new&nbsp;level&nbsp;order.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(reference&nbsp;level&nbsp;by&nbsp;number&nbsp;or&nbsp;key)<br>
-axis&nbsp;:&nbsp;where&nbsp;to&nbsp;reorder&nbsp;levels<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-type&nbsp;of&nbsp;caller&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)</tt></dd></dl>
-
-<dl><dt><a name="Condition-repeat"><strong>repeat</strong></a>(self, repeats, *args, **kwargs)</dt><dd><tt>Repeat&nbsp;elements&nbsp;of&nbsp;an&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Refer&nbsp;to&nbsp;`numpy.<a href="numpy.html#ndarray">ndarray</a>.repeat`<br>
-for&nbsp;more&nbsp;information&nbsp;about&nbsp;the&nbsp;`repeats`&nbsp;argument.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.repeat</tt></dd></dl>
-
-<dl><dt><a name="Condition-reset_index"><strong>reset_index</strong></a>(self, level=None, drop=False, name=None, inplace=False)</dt><dd><tt>Analogous&nbsp;to&nbsp;the&nbsp;:meth:`pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.reset_index`&nbsp;function,&nbsp;see<br>
-docstring&nbsp;there.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;tuple,&nbsp;or&nbsp;list,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Only&nbsp;remove&nbsp;the&nbsp;given&nbsp;levels&nbsp;from&nbsp;the&nbsp;index.&nbsp;Removes&nbsp;all&nbsp;levels&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-drop&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;not&nbsp;try&nbsp;to&nbsp;insert&nbsp;index&nbsp;into&nbsp;dataframe&nbsp;columns<br>
-name&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;name&nbsp;of&nbsp;the&nbsp;column&nbsp;corresponding&nbsp;to&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Modify&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place&nbsp;(do&nbsp;not&nbsp;create&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-Returns<br>
-----------<br>
-resetted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;if&nbsp;drop&nbsp;==&nbsp;True</tt></dd></dl>
-
-<dl><dt><a name="Condition-reshape"><strong>reshape</strong></a>(self, *args, **kwargs)</dt><dd><tt>DEPRECATED:&nbsp;calling&nbsp;this&nbsp;method&nbsp;will&nbsp;raise&nbsp;an&nbsp;error&nbsp;in&nbsp;a<br>
-future&nbsp;release.&nbsp;Please&nbsp;call&nbsp;``.values.<a href="#Condition-reshape">reshape</a>(...)``&nbsp;instead.<br>
-&nbsp;<br>
-return&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;with&nbsp;the&nbsp;values&nbsp;shape<br>
-if&nbsp;the&nbsp;specified&nbsp;shape&nbsp;matches&nbsp;exactly&nbsp;the&nbsp;current&nbsp;shape,&nbsp;then<br>
-return&nbsp;self&nbsp;(for&nbsp;compat)<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.reshape</tt></dd></dl>
-
-<dl><dt><a name="Condition-rfloordiv"><strong>rfloordiv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rfloordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;//&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.floordiv</tt></dd></dl>
-
-<dl><dt><a name="Condition-rmod"><strong>rmod</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Modulo&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;%&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.mod</tt></dd></dl>
-
-<dl><dt><a name="Condition-rmul"><strong>rmul</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;*&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.mul</tt></dd></dl>
-
-<dl><dt><a name="Condition-rolling"><strong>rolling</strong></a>(self, window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0, closed=None)</dt><dd><tt>Provides&nbsp;rolling&nbsp;window&nbsp;calculcations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-window&nbsp;:&nbsp;int,&nbsp;or&nbsp;offset<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Size&nbsp;of&nbsp;the&nbsp;moving&nbsp;window.&nbsp;This&nbsp;is&nbsp;the&nbsp;number&nbsp;of&nbsp;observations&nbsp;used&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;calculating&nbsp;the&nbsp;statistic.&nbsp;Each&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;fixed&nbsp;size.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;its&nbsp;an&nbsp;offset&nbsp;then&nbsp;this&nbsp;will&nbsp;be&nbsp;the&nbsp;time&nbsp;period&nbsp;of&nbsp;each&nbsp;window.&nbsp;Each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;variable&nbsp;sized&nbsp;based&nbsp;on&nbsp;the&nbsp;observations&nbsp;included&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;time-period.&nbsp;This&nbsp;is&nbsp;only&nbsp;valid&nbsp;for&nbsp;datetimelike&nbsp;indexes.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;new&nbsp;in&nbsp;0.19.0<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).&nbsp;For&nbsp;a&nbsp;window&nbsp;that&nbsp;is&nbsp;specified&nbsp;by&nbsp;an&nbsp;offset,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;will&nbsp;default&nbsp;to&nbsp;1.<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-win_type&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Provide&nbsp;a&nbsp;window&nbsp;type.&nbsp;See&nbsp;the&nbsp;notes&nbsp;below.<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;on&nbsp;which&nbsp;to&nbsp;calculate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;rolling&nbsp;window,&nbsp;rather&nbsp;than&nbsp;the&nbsp;index<br>
-closed&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;the&nbsp;interval&nbsp;closed&nbsp;on&nbsp;the&nbsp;'right',&nbsp;'left',&nbsp;'both'&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'neither'&nbsp;endpoints.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;offset-based&nbsp;windows,&nbsp;it&nbsp;defaults&nbsp;to&nbsp;'right'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;fixed&nbsp;windows,&nbsp;defaults&nbsp;to&nbsp;'both'.&nbsp;Remaining&nbsp;cases&nbsp;not&nbsp;implemented<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;fixed&nbsp;windows.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;or&nbsp;Rolling&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;using&nbsp;the&nbsp;'triang'<br>
-window&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-rolling">rolling</a>(2,&nbsp;win_type='triang').<a href="#Condition-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.5<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;min_periods&nbsp;defaults<br>
-to&nbsp;the&nbsp;window&nbsp;length.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-rolling">rolling</a>(2).<a href="#Condition-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Same&nbsp;as&nbsp;above,&nbsp;but&nbsp;explicity&nbsp;set&nbsp;the&nbsp;min_periods<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-rolling">rolling</a>(2,&nbsp;min_periods=1).<a href="#Condition-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;2.0<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-A&nbsp;ragged&nbsp;(meaning&nbsp;not-a-regular&nbsp;frequency),&nbsp;time-indexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]},<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;=&nbsp;[pd.Timestamp('20130101&nbsp;09:00:00'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:02'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:03'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:05'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:06')])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;2.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Contrasting&nbsp;to&nbsp;an&nbsp;integer&nbsp;rolling&nbsp;window,&nbsp;this&nbsp;will&nbsp;roll&nbsp;a&nbsp;variable<br>
-length&nbsp;window&nbsp;corresponding&nbsp;to&nbsp;the&nbsp;time&nbsp;period.<br>
-The&nbsp;default&nbsp;for&nbsp;min_periods&nbsp;is&nbsp;1.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-rolling">rolling</a>('2s').<a href="#Condition-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;3.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offsets&nbsp;&amp;&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-The&nbsp;recognized&nbsp;win_types&nbsp;are:<br>
-&nbsp;<br>
-*&nbsp;``boxcar``<br>
-*&nbsp;``triang``<br>
-*&nbsp;``blackman``<br>
-*&nbsp;``hamming``<br>
-*&nbsp;``bartlett``<br>
-*&nbsp;``parzen``<br>
-*&nbsp;``bohman``<br>
-*&nbsp;``blackmanharris``<br>
-*&nbsp;``nuttall``<br>
-*&nbsp;``barthann``<br>
-*&nbsp;``kaiser``&nbsp;(needs&nbsp;beta)<br>
-*&nbsp;``gaussian``&nbsp;(needs&nbsp;std)<br>
-*&nbsp;``general_gaussian``&nbsp;(needs&nbsp;power,&nbsp;width)<br>
-*&nbsp;``slepian``&nbsp;(needs&nbsp;width).</tt></dd></dl>
-
-<dl><dt><a name="Condition-round"><strong>round</strong></a>(self, decimals=0, *args, **kwargs)</dt><dd><tt>Round&nbsp;each&nbsp;value&nbsp;in&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;the&nbsp;given&nbsp;number&nbsp;of&nbsp;decimals.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-decimals&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;round&nbsp;to&nbsp;(default:&nbsp;0).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;decimals&nbsp;is&nbsp;negative,&nbsp;it&nbsp;specifies&nbsp;the&nbsp;number&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;positions&nbsp;to&nbsp;the&nbsp;left&nbsp;of&nbsp;the&nbsp;decimal&nbsp;point.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.series.html#Series">Series</a>&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.around<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.round</tt></dd></dl>
-
-<dl><dt><a name="Condition-rpow"><strong>rpow</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rpow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;**&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.pow</tt></dd></dl>
-
-<dl><dt><a name="Condition-rsub"><strong>rsub</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rsub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;-&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.sub</tt></dd></dl>
-
-<dl><dt><a name="Condition-rtruediv"><strong>rtruediv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="Condition-searchsorted"><strong>searchsorted</strong></a>(self, value, side='left', sorter=None)</dt><dd><tt>Find&nbsp;indices&nbsp;where&nbsp;elements&nbsp;should&nbsp;be&nbsp;inserted&nbsp;to&nbsp;maintain&nbsp;order.<br>
-&nbsp;<br>
-Find&nbsp;the&nbsp;indices&nbsp;into&nbsp;a&nbsp;sorted&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;`self`&nbsp;such&nbsp;that,&nbsp;if&nbsp;the<br>
-corresponding&nbsp;elements&nbsp;in&nbsp;`value`&nbsp;were&nbsp;inserted&nbsp;before&nbsp;the&nbsp;indices,<br>
-the&nbsp;order&nbsp;of&nbsp;`self`&nbsp;would&nbsp;be&nbsp;preserved.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;array_like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Values&nbsp;to&nbsp;insert&nbsp;into&nbsp;`self`.<br>
-side&nbsp;:&nbsp;{'left',&nbsp;'right'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'left',&nbsp;the&nbsp;index&nbsp;of&nbsp;the&nbsp;first&nbsp;suitable&nbsp;location&nbsp;found&nbsp;is&nbsp;given.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'right',&nbsp;return&nbsp;the&nbsp;last&nbsp;such&nbsp;index.&nbsp;&nbsp;If&nbsp;there&nbsp;is&nbsp;no&nbsp;suitable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index,&nbsp;return&nbsp;either&nbsp;0&nbsp;or&nbsp;N&nbsp;(where&nbsp;N&nbsp;is&nbsp;the&nbsp;length&nbsp;of&nbsp;`self`).<br>
-sorter&nbsp;:&nbsp;1-D&nbsp;array_like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Optional&nbsp;array&nbsp;of&nbsp;integer&nbsp;indices&nbsp;that&nbsp;sort&nbsp;`self`&nbsp;into&nbsp;ascending<br>
-&nbsp;&nbsp;&nbsp;&nbsp;order.&nbsp;They&nbsp;are&nbsp;typically&nbsp;the&nbsp;result&nbsp;of&nbsp;``np.argsort``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-indices&nbsp;:&nbsp;array&nbsp;of&nbsp;ints<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Array">Array</a>&nbsp;of&nbsp;insertion&nbsp;points&nbsp;with&nbsp;the&nbsp;same&nbsp;shape&nbsp;as&nbsp;`value`.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.searchsorted<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Binary&nbsp;search&nbsp;is&nbsp;used&nbsp;to&nbsp;find&nbsp;the&nbsp;required&nbsp;insertion&nbsp;points.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Condition-searchsorted">searchsorted</a>(4)<br>
-array([3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Condition-searchsorted">searchsorted</a>([0,&nbsp;4])<br>
-array([0,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Condition-searchsorted">searchsorted</a>([1,&nbsp;3],&nbsp;side='left')<br>
-array([0,&nbsp;2])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Condition-searchsorted">searchsorted</a>([1,&nbsp;3],&nbsp;side='right')<br>
-array([1,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.Categorical(['apple',&nbsp;'bread',&nbsp;'bread',&nbsp;'cheese',&nbsp;'milk'&nbsp;])<br>
-[apple,&nbsp;bread,&nbsp;bread,&nbsp;cheese,&nbsp;milk]<br>
-Categories&nbsp;(4,&nbsp;<a href="builtins.html#object">object</a>):&nbsp;[apple&nbsp;&lt;&nbsp;bread&nbsp;&lt;&nbsp;cheese&nbsp;&lt;&nbsp;milk]<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Condition-searchsorted">searchsorted</a>('bread')<br>
-array([1])&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;Note:&nbsp;an&nbsp;array,&nbsp;not&nbsp;a&nbsp;scalar<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Condition-searchsorted">searchsorted</a>(['bread'])<br>
-array([1])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Condition-searchsorted">searchsorted</a>(['bread',&nbsp;'eggs'])<br>
-array([1,&nbsp;4])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#Condition-searchsorted">searchsorted</a>(['bread',&nbsp;'eggs'],&nbsp;side='right')<br>
-array([3,&nbsp;4])&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;eggs&nbsp;before&nbsp;milk</tt></dd></dl>
-
-<dl><dt><a name="Condition-sem"><strong>sem</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;standard&nbsp;error&nbsp;of&nbsp;the&nbsp;mean&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sem&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-set_value"><strong>set_value</strong></a>(self, label, value, takeable=False)</dt><dd><tt>Quickly&nbsp;set&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;label.&nbsp;If&nbsp;label&nbsp;is&nbsp;not&nbsp;contained,&nbsp;a<br>
-new&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;created&nbsp;with&nbsp;the&nbsp;label&nbsp;placed&nbsp;at&nbsp;the&nbsp;end&nbsp;of&nbsp;the&nbsp;result<br>
-index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-label&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Partial&nbsp;indexing&nbsp;with&nbsp;MultiIndex&nbsp;not&nbsp;allowed<br>
-value&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Scalar&nbsp;value<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-series&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;label&nbsp;is&nbsp;contained,&nbsp;will&nbsp;be&nbsp;reference&nbsp;to&nbsp;calling&nbsp;<a href="pandas.core.series.html#Series">Series</a>,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-shift"><strong>shift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;index&nbsp;by&nbsp;desired&nbsp;number&nbsp;of&nbsp;periods&nbsp;with&nbsp;an&nbsp;optional&nbsp;time&nbsp;freq<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM').<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;Notes.<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'}<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;specified&nbsp;then&nbsp;the&nbsp;index&nbsp;values&nbsp;are&nbsp;shifted&nbsp;but&nbsp;the&nbsp;data<br>
-is&nbsp;not&nbsp;realigned.&nbsp;That&nbsp;is,&nbsp;use&nbsp;freq&nbsp;if&nbsp;you&nbsp;would&nbsp;like&nbsp;to&nbsp;extend&nbsp;the<br>
-index&nbsp;when&nbsp;shifting&nbsp;and&nbsp;preserve&nbsp;the&nbsp;original&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-skew"><strong>skew</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;skew&nbsp;over&nbsp;requested&nbsp;axis<br>
-Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-skew&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-sort_index"><strong>sort_index</strong></a>(self, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True)</dt><dd><tt>Sort&nbsp;<a href="builtins.html#object">object</a>&nbsp;by&nbsp;labels&nbsp;(along&nbsp;an&nbsp;axis)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;index&nbsp;to&nbsp;direct&nbsp;sorting<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name&nbsp;or&nbsp;list&nbsp;of&nbsp;ints&nbsp;or&nbsp;list&nbsp;of&nbsp;level&nbsp;names<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None,&nbsp;sort&nbsp;on&nbsp;values&nbsp;in&nbsp;specified&nbsp;index&nbsp;level(s)<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;MultiIndex.<br>
-sort_remaining&nbsp;:&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;true&nbsp;and&nbsp;sorting&nbsp;by&nbsp;level&nbsp;and&nbsp;index&nbsp;is&nbsp;multilevel,&nbsp;sort&nbsp;by&nbsp;other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;levels&nbsp;too&nbsp;(in&nbsp;order)&nbsp;after&nbsp;sorting&nbsp;by&nbsp;specified&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-sort_values"><strong>sort_values</strong></a>(self, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')</dt><dd><tt>Sort&nbsp;by&nbsp;the&nbsp;values&nbsp;along&nbsp;either&nbsp;axis<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;direct&nbsp;sorting<br>
-ascending&nbsp;:&nbsp;bool&nbsp;or&nbsp;list&nbsp;of&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending.&nbsp;Specify&nbsp;list&nbsp;for&nbsp;multiple&nbsp;sort<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;orders.&nbsp;&nbsp;If&nbsp;this&nbsp;is&nbsp;a&nbsp;list&nbsp;of&nbsp;bools,&nbsp;must&nbsp;match&nbsp;the&nbsp;length&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;by.<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-sortlevel"><strong>sortlevel</strong></a>(self, level=0, ascending=True, sort_remaining=True)</dt><dd><tt>DEPRECATED:&nbsp;use&nbsp;:meth:`<a href="pandas.core.series.html#Series">Series</a>.sort_index`<br>
-&nbsp;<br>
-Sort&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;MultiIndex&nbsp;by&nbsp;chosen&nbsp;level.&nbsp;Data&nbsp;will&nbsp;be<br>
-lexicographically&nbsp;sorted&nbsp;by&nbsp;the&nbsp;chosen&nbsp;level&nbsp;followed&nbsp;by&nbsp;the&nbsp;other<br>
-levels&nbsp;(in&nbsp;order)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-ascending&nbsp;:&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.<a href="#Condition-sort_index">sort_index</a>(level=...)</tt></dd></dl>
-
-<dl><dt><a name="Condition-std"><strong>std</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;sample&nbsp;standard&nbsp;deviation&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-std&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-sub"><strong>sub</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="Condition-subtract"><strong>subtract</strong></a> = sub(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="Condition-sum"><strong>sum</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;sum&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sum&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-swaplevel"><strong>swaplevel</strong></a>(self, i=-2, j=-1, copy=True)</dt><dd><tt>Swap&nbsp;levels&nbsp;i&nbsp;and&nbsp;j&nbsp;in&nbsp;a&nbsp;MultiIndex<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-i,&nbsp;j&nbsp;:&nbsp;int,&nbsp;string&nbsp;(can&nbsp;be&nbsp;mixed)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level&nbsp;of&nbsp;index&nbsp;to&nbsp;be&nbsp;swapped.&nbsp;Can&nbsp;pass&nbsp;level&nbsp;name&nbsp;as&nbsp;string.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-swapped&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-..&nbsp;versionchanged::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;The&nbsp;indexes&nbsp;``i``&nbsp;and&nbsp;``j``&nbsp;are&nbsp;now&nbsp;optional,&nbsp;and&nbsp;default&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;the&nbsp;two&nbsp;innermost&nbsp;levels&nbsp;of&nbsp;the&nbsp;index.</tt></dd></dl>
-
-<dl><dt><a name="Condition-take"><strong>take</strong></a>(self, indices, axis=0, convert=True, is_copy=False, **kwargs)</dt><dd><tt>return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;corresponding&nbsp;to&nbsp;requested&nbsp;indices<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-indices&nbsp;:&nbsp;list&nbsp;/&nbsp;array&nbsp;of&nbsp;ints<br>
-convert&nbsp;:&nbsp;translate&nbsp;negative&nbsp;to&nbsp;positive&nbsp;indices&nbsp;(default)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-taken&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.take</tt></dd></dl>
-
-<dl><dt><a name="Condition-to_csv"><strong>to_csv</strong></a>(self, path=None, index=True, sep=',', na_rep='', float_format=None, header=False, index_label=None, mode='w', encoding=None, date_format=None, decimal='.')</dt><dd><tt>Write&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;a&nbsp;comma-separated&nbsp;values&nbsp;(csv)&nbsp;file<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;or&nbsp;file&nbsp;handle,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;<a href="builtins.html#object">object</a>,&nbsp;if&nbsp;None&nbsp;is&nbsp;provided&nbsp;the&nbsp;result&nbsp;is&nbsp;returned&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string.<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-header&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;series&nbsp;name<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-mode&nbsp;:&nbsp;Python&nbsp;write&nbsp;mode,&nbsp;default&nbsp;'w'<br>
-sep&nbsp;:&nbsp;character,&nbsp;default&nbsp;","<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter&nbsp;for&nbsp;the&nbsp;output&nbsp;file.<br>
-encoding&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;if&nbsp;the&nbsp;contents&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;non-ascii,&nbsp;for&nbsp;python&nbsp;versions&nbsp;prior&nbsp;to&nbsp;3<br>
-date_format:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;datetime&nbsp;objects.<br>
-decimal:&nbsp;string,&nbsp;default&nbsp;'.'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Character&nbsp;recognized&nbsp;as&nbsp;decimal&nbsp;separator.&nbsp;E.g.&nbsp;use&nbsp;','&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;European&nbsp;data</tt></dd></dl>
-
-<dl><dt><a name="Condition-to_dict"><strong>to_dict</strong></a>(self)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;{label&nbsp;-&gt;&nbsp;value}&nbsp;dict<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value_dict&nbsp;:&nbsp;dict</tt></dd></dl>
-
-<dl><dt><a name="Condition-to_excel"><strong>to_excel</strong></a>(self, excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True)</dt><dd><tt>Write&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;an&nbsp;excel&nbsp;sheet<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel_writer&nbsp;:&nbsp;string&nbsp;or&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;existing&nbsp;ExcelWriter<br>
-sheet_name&nbsp;:&nbsp;string,&nbsp;default&nbsp;'Sheet1'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;sheet&nbsp;which&nbsp;will&nbsp;contain&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Columns&nbsp;to&nbsp;write<br>
-header&nbsp;:&nbsp;boolean&nbsp;or&nbsp;list&nbsp;of&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given&nbsp;it&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;assumed&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-startrow&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;row&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-startcol&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;column&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-engine&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;write&nbsp;engine&nbsp;to&nbsp;use&nbsp;-&nbsp;you&nbsp;can&nbsp;also&nbsp;set&nbsp;this&nbsp;via&nbsp;the&nbsp;options<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsx.writer``,&nbsp;``io.excel.xls.writer``,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsm.writer``.<br>
-merge_cells&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;MultiIndex&nbsp;and&nbsp;Hierarchical&nbsp;Rows&nbsp;as&nbsp;merged&nbsp;cells.<br>
-encoding:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;encoding&nbsp;of&nbsp;the&nbsp;resulting&nbsp;excel&nbsp;file.&nbsp;Only&nbsp;necessary&nbsp;for&nbsp;xlwt,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;writers&nbsp;support&nbsp;unicode&nbsp;natively.<br>
-inf_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;'inf'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Representation&nbsp;for&nbsp;infinity&nbsp;(there&nbsp;is&nbsp;no&nbsp;native&nbsp;representation&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;infinity&nbsp;in&nbsp;Excel)<br>
-freeze_panes&nbsp;:&nbsp;tuple&nbsp;of&nbsp;integer&nbsp;(length&nbsp;2),&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;the&nbsp;one-based&nbsp;bottommost&nbsp;row&nbsp;and&nbsp;rightmost&nbsp;column&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;to&nbsp;be&nbsp;frozen<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;passing&nbsp;an&nbsp;existing&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a>,&nbsp;then&nbsp;the&nbsp;sheet&nbsp;will&nbsp;be&nbsp;added<br>
-to&nbsp;the&nbsp;existing&nbsp;workbook.&nbsp;&nbsp;This&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;save&nbsp;different<br>
-DataFrames&nbsp;to&nbsp;one&nbsp;workbook:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;writer&nbsp;=&nbsp;pd.ExcelWriter('output.xlsx')<br>
-&gt;&gt;&gt;&nbsp;df1.<a href="#Condition-to_excel">to_excel</a>(writer,'Sheet1')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#Condition-to_excel">to_excel</a>(writer,'Sheet2')<br>
-&gt;&gt;&gt;&nbsp;writer.save()<br>
-&nbsp;<br>
-For&nbsp;compatibility&nbsp;with&nbsp;to_csv,&nbsp;to_excel&nbsp;serializes&nbsp;lists&nbsp;and&nbsp;dicts&nbsp;to<br>
-strings&nbsp;before&nbsp;writing.</tt></dd></dl>
-
-<dl><dt><a name="Condition-to_frame"><strong>to_frame</strong></a>(self, name=None)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-name&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;passed&nbsp;name&nbsp;should&nbsp;substitute&nbsp;for&nbsp;the&nbsp;series&nbsp;name&nbsp;(if&nbsp;it&nbsp;has<br>
-&nbsp;&nbsp;&nbsp;&nbsp;one).<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-data_frame&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-to_period"><strong>to_period</strong></a>(self, freq=None, copy=True)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;from&nbsp;DatetimeIndex&nbsp;to&nbsp;PeriodIndex&nbsp;with&nbsp;desired<br>
-frequency&nbsp;(inferred&nbsp;from&nbsp;index&nbsp;if&nbsp;not&nbsp;passed)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;PeriodIndex</tt></dd></dl>
-
-<dl><dt><a name="Condition-to_sparse"><strong>to_sparse</strong></a>(self, kind='block', fill_value=None)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;SparseSeries<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-kind&nbsp;:&nbsp;{'block',&nbsp;'integer'}<br>
-fill_value&nbsp;:&nbsp;float,&nbsp;defaults&nbsp;to&nbsp;NaN&nbsp;(missing)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sp&nbsp;:&nbsp;SparseSeries</tt></dd></dl>
-
-<dl><dt><a name="Condition-to_string"><strong>to_string</strong></a>(self, buf=None, na_rep='NaN', float_format=None, header=True, index=True, length=False, dtype=False, name=False, max_rows=None)</dt><dd><tt>Render&nbsp;a&nbsp;string&nbsp;representation&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-buf&nbsp;:&nbsp;StringIO-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;buffer&nbsp;to&nbsp;write&nbsp;to<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;representation&nbsp;of&nbsp;NAN&nbsp;to&nbsp;use,&nbsp;default&nbsp;'NaN'<br>
-float_format&nbsp;:&nbsp;one-parameter&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;if&nbsp;they&nbsp;are&nbsp;floats<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None<br>
-header:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;header&nbsp;(index&nbsp;name)<br>
-index&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;index&nbsp;(row)&nbsp;labels,&nbsp;default&nbsp;True<br>
-length&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;length<br>
-dtype&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;dtype<br>
-name&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;name&nbsp;if&nbsp;not&nbsp;None<br>
-max_rows&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;rows&nbsp;to&nbsp;show&nbsp;before&nbsp;truncating.&nbsp;If&nbsp;None,&nbsp;show<br>
-&nbsp;&nbsp;&nbsp;&nbsp;all.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-formatted&nbsp;:&nbsp;string&nbsp;(if&nbsp;not&nbsp;buffer&nbsp;passed)</tt></dd></dl>
-
-<dl><dt><a name="Condition-to_timestamp"><strong>to_timestamp</strong></a>(self, freq=None, how='start', copy=True)</dt><dd><tt>Cast&nbsp;to&nbsp;datetimeindex&nbsp;of&nbsp;timestamps,&nbsp;at&nbsp;*beginning*&nbsp;of&nbsp;period<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default&nbsp;frequency&nbsp;of&nbsp;PeriodIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Desired&nbsp;frequency<br>
-how&nbsp;:&nbsp;{'s',&nbsp;'e',&nbsp;'start',&nbsp;'end'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Convention&nbsp;for&nbsp;converting&nbsp;period&nbsp;to&nbsp;timestamp;&nbsp;start&nbsp;of&nbsp;period<br>
-&nbsp;&nbsp;&nbsp;&nbsp;vs.&nbsp;end<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;DatetimeIndex</tt></dd></dl>
-
-<dl><dt><a name="Condition-tolist"><strong>tolist</strong></a>(self)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;a&nbsp;nested&nbsp;list</tt></dd></dl>
-
-<dl><dt><a name="Condition-transform"><strong>transform</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Call&nbsp;function&nbsp;producing&nbsp;a&nbsp;like-indexed&nbsp;NDFrame<br>
-and&nbsp;return&nbsp;a&nbsp;NDFrame&nbsp;with&nbsp;the&nbsp;transformed&nbsp;values`<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;apply&nbsp;to&nbsp;column<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-transformed&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(10,&nbsp;3),&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.date_range('1/1/2000',&nbsp;periods=10))<br>
-df.iloc[3:7]&nbsp;=&nbsp;np.nan<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-transform">transform</a>(lambda&nbsp;x:&nbsp;(x&nbsp;-&nbsp;x.<a href="#Condition-mean">mean</a>())&nbsp;/&nbsp;x.<a href="#Condition-std">std</a>())<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-2000-01-01&nbsp;&nbsp;0.579457&nbsp;&nbsp;1.236184&nbsp;&nbsp;0.123424<br>
-2000-01-02&nbsp;&nbsp;0.370357&nbsp;-0.605875&nbsp;-1.231325<br>
-2000-01-03&nbsp;&nbsp;1.455756&nbsp;-0.277446&nbsp;&nbsp;0.288967<br>
-2000-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-08&nbsp;-0.498658&nbsp;&nbsp;1.274522&nbsp;&nbsp;1.642524<br>
-2000-01-09&nbsp;-0.540524&nbsp;-1.012676&nbsp;-0.828968<br>
-2000-01-10&nbsp;-1.366388&nbsp;-0.614710&nbsp;&nbsp;0.005378<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.NDFrame.aggregate<br>
-pandas.NDFrame.apply</tt></dd></dl>
-
-<dl><dt><a name="Condition-truediv"><strong>truediv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="Condition-unique"><strong>unique</strong></a>(self)</dt><dd><tt>Return&nbsp;unique&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.&nbsp;Uniques&nbsp;are&nbsp;returned&nbsp;in&nbsp;order<br>
-of&nbsp;appearance,&nbsp;this&nbsp;does&nbsp;NOT&nbsp;sort.&nbsp;Hash&nbsp;table-based&nbsp;unique.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-values&nbsp;:&nbsp;1d&nbsp;array-like<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-unique&nbsp;values.<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;an&nbsp;Index,&nbsp;the&nbsp;return&nbsp;is&nbsp;an&nbsp;Index<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;a&nbsp;Categorical&nbsp;dtype,&nbsp;the&nbsp;return&nbsp;is&nbsp;a&nbsp;Categorical<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>/<a href="numpy.html#ndarray">ndarray</a>,&nbsp;the&nbsp;return&nbsp;will&nbsp;be&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-unique<br>
-Index.unique<br>
-<a href="pandas.core.series.html#Series">Series</a>.unique</tt></dd></dl>
-
-<dl><dt><a name="Condition-unstack"><strong>unstack</strong></a>(self, level=-1, fill_value=None)</dt><dd><tt>Unstack,&nbsp;a.k.a.&nbsp;pivot,&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;MultiIndex&nbsp;to&nbsp;produce&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-The&nbsp;level&nbsp;involved&nbsp;will&nbsp;automatically&nbsp;get&nbsp;sorted.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;string,&nbsp;or&nbsp;list&nbsp;of&nbsp;these,&nbsp;default&nbsp;last&nbsp;level<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level(s)&nbsp;to&nbsp;unstack,&nbsp;can&nbsp;pass&nbsp;level&nbsp;name<br>
-fill_value&nbsp;:&nbsp;replace&nbsp;NaN&nbsp;with&nbsp;this&nbsp;value&nbsp;if&nbsp;the&nbsp;unstack&nbsp;produces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.18.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;4],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.MultiIndex.from_product([['one',&nbsp;'two'],&nbsp;['a',&nbsp;'b']]))<br>
-&gt;&gt;&gt;&nbsp;s<br>
-one&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-two&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-unstack">unstack</a>(level=-1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b<br>
-one&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-two&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-unstack">unstack</a>(level=0)<br>
-&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;two<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-unstacked&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-update"><strong>update</strong></a>(self, other)</dt><dd><tt>Modify&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place&nbsp;using&nbsp;non-NA&nbsp;values&nbsp;from&nbsp;passed<br>
-<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Aligns&nbsp;on&nbsp;index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><strong>valid</strong> <em>lambda</em> self, inplace=False, **kwargs</dt></dl>
-
-<dl><dt><a name="Condition-var"><strong>var</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;variance&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-var&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="Condition-view"><strong>view</strong></a>(self, dtype=None)</dt></dl>
-
-<hr>
-Class methods inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><a name="Condition-from_array"><strong>from_array</strong></a>(arr, index=None, name=None, dtype=None, copy=False, fastpath=False)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt></dl>
-
-<dl><dt><a name="Condition-from_csv"><strong>from_csv</strong></a>(path, sep=',', parse_dates=True, header=None, index_col=0, encoding=None, infer_datetime_format=False)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Read&nbsp;CSV&nbsp;file&nbsp;(DISCOURAGED,&nbsp;please&nbsp;use&nbsp;:func:`pandas.read_csv`<br>
-instead).<br>
-&nbsp;<br>
-It&nbsp;is&nbsp;preferable&nbsp;to&nbsp;use&nbsp;the&nbsp;more&nbsp;powerful&nbsp;:func:`pandas.read_csv`<br>
-for&nbsp;most&nbsp;general&nbsp;purposes,&nbsp;but&nbsp;``from_csv``&nbsp;makes&nbsp;for&nbsp;an&nbsp;easy<br>
-roundtrip&nbsp;to&nbsp;and&nbsp;from&nbsp;a&nbsp;file&nbsp;(the&nbsp;exact&nbsp;counterpart&nbsp;of<br>
-``to_csv``),&nbsp;especially&nbsp;with&nbsp;a&nbsp;time&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;only&nbsp;differs&nbsp;from&nbsp;:func:`pandas.read_csv`&nbsp;in&nbsp;some&nbsp;defaults:<br>
-&nbsp;<br>
--&nbsp;`index_col`&nbsp;is&nbsp;``0``&nbsp;instead&nbsp;of&nbsp;``None``&nbsp;(take&nbsp;first&nbsp;column&nbsp;as&nbsp;index<br>
-&nbsp;&nbsp;by&nbsp;default)<br>
--&nbsp;`header`&nbsp;is&nbsp;``None``&nbsp;instead&nbsp;of&nbsp;``0``&nbsp;(the&nbsp;first&nbsp;row&nbsp;is&nbsp;not&nbsp;used&nbsp;as<br>
-&nbsp;&nbsp;the&nbsp;column&nbsp;names)<br>
--&nbsp;`parse_dates`&nbsp;is&nbsp;``True``&nbsp;instead&nbsp;of&nbsp;``False``&nbsp;(try&nbsp;parsing&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;as&nbsp;datetime&nbsp;by&nbsp;default)<br>
-&nbsp;<br>
-With&nbsp;:func:`pandas.read_csv`,&nbsp;the&nbsp;option&nbsp;``squeeze=True``&nbsp;can&nbsp;be&nbsp;used<br>
-to&nbsp;return&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;like&nbsp;``from_csv``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;file&nbsp;path&nbsp;or&nbsp;file&nbsp;handle&nbsp;/&nbsp;StringIO<br>
-sep&nbsp;:&nbsp;string,&nbsp;default&nbsp;','<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter<br>
-parse_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Parse&nbsp;dates.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-header&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Row&nbsp;to&nbsp;use&nbsp;as&nbsp;header&nbsp;(skip&nbsp;prior&nbsp;rows)<br>
-index_col&nbsp;:&nbsp;int&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;to&nbsp;use&nbsp;for&nbsp;index.&nbsp;If&nbsp;a&nbsp;sequence&nbsp;is&nbsp;given,&nbsp;a&nbsp;MultiIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;used.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-encoding&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;if&nbsp;the&nbsp;contents&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;non-ascii,&nbsp;for&nbsp;python&nbsp;versions&nbsp;prior&nbsp;to&nbsp;3<br>
-infer_datetime_format:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;and&nbsp;`parse_dates`&nbsp;is&nbsp;True&nbsp;for&nbsp;a&nbsp;column,&nbsp;try&nbsp;to&nbsp;infer&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;datetime&nbsp;format&nbsp;based&nbsp;on&nbsp;the&nbsp;first&nbsp;datetime&nbsp;string.&nbsp;If&nbsp;the&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;be&nbsp;inferred,&nbsp;there&nbsp;often&nbsp;will&nbsp;be&nbsp;a&nbsp;large&nbsp;parsing&nbsp;speed-up.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.read_csv<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><strong>asobject</strong></dt>
-<dd><tt>return&nbsp;object&nbsp;Series&nbsp;which&nbsp;contains&nbsp;boxed&nbsp;values<br>
-&nbsp;<br>
-*this&nbsp;is&nbsp;an&nbsp;internal&nbsp;non-public&nbsp;method*</tt></dd>
-</dl>
-<dl><dt><strong>axes</strong></dt>
-<dd><tt>Return&nbsp;a&nbsp;list&nbsp;of&nbsp;the&nbsp;row&nbsp;axis&nbsp;labels</tt></dd>
-</dl>
-<dl><dt><strong>dtype</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;dtype&nbsp;object&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>dtypes</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;dtype&nbsp;object&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>ftype</strong></dt>
-<dd><tt>return&nbsp;if&nbsp;the&nbsp;data&nbsp;is&nbsp;sparse|dense</tt></dd>
-</dl>
-<dl><dt><strong>ftypes</strong></dt>
-<dd><tt>return&nbsp;if&nbsp;the&nbsp;data&nbsp;is&nbsp;sparse|dense</tt></dd>
-</dl>
-<dl><dt><strong>imag</strong></dt>
-</dl>
-<dl><dt><strong>index</strong></dt>
-</dl>
-<dl><dt><strong>name</strong></dt>
-</dl>
-<dl><dt><strong>real</strong></dt>
-</dl>
-<dl><dt><strong>values</strong></dt>
-<dd><tt>Return&nbsp;Series&nbsp;as&nbsp;ndarray&nbsp;or&nbsp;ndarray-like<br>
-depending&nbsp;on&nbsp;the&nbsp;dtype<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-arr&nbsp;:&nbsp;numpy.ndarray&nbsp;or&nbsp;ndarray-like<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;pd.Series([1,&nbsp;2,&nbsp;3]).values<br>
-array([1,&nbsp;2,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(list('aabc')).values<br>
-array(['a',&nbsp;'a',&nbsp;'b',&nbsp;'c'],&nbsp;dtype=object)<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(list('aabc')).astype('category').values<br>
-[a,&nbsp;a,&nbsp;b,&nbsp;c]<br>
-Categories&nbsp;(3,&nbsp;object):&nbsp;[a,&nbsp;b,&nbsp;c]<br>
-&nbsp;<br>
-Timezone&nbsp;aware&nbsp;datetime&nbsp;data&nbsp;is&nbsp;converted&nbsp;to&nbsp;UTC:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(pd.date_range('20130101',&nbsp;periods=3,<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;tz='US/Eastern')).values<br>
-array(['2013-01-01T05:00:00.000000000',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'2013-01-02T05:00:00.000000000',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'2013-01-03T05:00:00.000000000'],&nbsp;dtype='datetime64[ns]')</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><strong>cat</strong> = &lt;class 'pandas.core.categorical.CategoricalAccessor'&gt;<dd><tt>Accessor&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;categorical&nbsp;properties&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values.<br>
-&nbsp;<br>
-Be&nbsp;aware&nbsp;that&nbsp;assigning&nbsp;to&nbsp;`categories`&nbsp;is&nbsp;a&nbsp;inplace&nbsp;operation,&nbsp;while&nbsp;all<br>
-methods&nbsp;return&nbsp;new&nbsp;categorical&nbsp;data&nbsp;per&nbsp;default&nbsp;(but&nbsp;can&nbsp;be&nbsp;called&nbsp;with<br>
-`inplace=True`).<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.cat.categories<br>
-&gt;&gt;&gt;&nbsp;s.cat.categories&nbsp;=&nbsp;list('abc')<br>
-&gt;&gt;&gt;&nbsp;s.cat.rename_categories(list('cab'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.reorder_categories(list('cab'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.add_categories(['d','e'])<br>
-&gt;&gt;&gt;&nbsp;s.cat.remove_categories(['d'])<br>
-&gt;&gt;&gt;&nbsp;s.cat.remove_unused_categories()<br>
-&gt;&gt;&gt;&nbsp;s.cat.set_categories(list('abcde'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.as_ordered()<br>
-&gt;&gt;&gt;&nbsp;s.cat.as_unordered()</tt></dl>
-
-<dl><dt><strong>plot</strong> = &lt;class 'pandas.plotting._core.SeriesPlotMethods'&gt;<dd><tt><a href="pandas.core.series.html#Series">Series</a>&nbsp;plotting&nbsp;accessor&nbsp;and&nbsp;method<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.plot.line()<br>
-&gt;&gt;&gt;&nbsp;s.plot.bar()<br>
-&gt;&gt;&gt;&nbsp;s.plot.<a href="#Condition-hist">hist</a>()<br>
-&nbsp;<br>
-Plotting&nbsp;methods&nbsp;can&nbsp;also&nbsp;be&nbsp;accessed&nbsp;by&nbsp;calling&nbsp;the&nbsp;accessor&nbsp;as&nbsp;a&nbsp;method<br>
-with&nbsp;the&nbsp;``kind``&nbsp;argument:<br>
-``s.<a href="#Condition-plot">plot</a>(kind='line')``&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;``s.plot.line()``</tt></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><a name="Condition-factorize"><strong>factorize</strong></a>(self, sort=False, na_sentinel=-1)</dt><dd><tt>Encode&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;as&nbsp;an&nbsp;enumerated&nbsp;type&nbsp;or&nbsp;categorical&nbsp;variable<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;by&nbsp;values<br>
-na_sentinel:&nbsp;int,&nbsp;default&nbsp;-1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;mark&nbsp;"not&nbsp;found"<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-labels&nbsp;:&nbsp;the&nbsp;indexer&nbsp;to&nbsp;the&nbsp;original&nbsp;array<br>
-uniques&nbsp;:&nbsp;the&nbsp;unique&nbsp;Index</tt></dd></dl>
-
-<dl><dt><a name="Condition-item"><strong>item</strong></a>(self)</dt><dd><tt>return&nbsp;the&nbsp;first&nbsp;element&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data&nbsp;as&nbsp;a&nbsp;python<br>
-scalar</tt></dd></dl>
-
-<dl><dt><a name="Condition-nunique"><strong>nunique</strong></a>(self, dropna=True)</dt><dd><tt>Return&nbsp;number&nbsp;of&nbsp;unique&nbsp;elements&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Excludes&nbsp;NA&nbsp;values&nbsp;by&nbsp;default.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Don't&nbsp;include&nbsp;NaN&nbsp;in&nbsp;the&nbsp;count.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-nunique&nbsp;:&nbsp;int</tt></dd></dl>
-
-<dl><dt><a name="Condition-transpose"><strong>transpose</strong></a>(self, *args, **kwargs)</dt><dd><tt>return&nbsp;the&nbsp;transpose,&nbsp;which&nbsp;is&nbsp;by&nbsp;definition&nbsp;self</tt></dd></dl>
-
-<dl><dt><a name="Condition-value_counts"><strong>value_counts</strong></a>(self, normalize=False, sort=True, ascending=False, bins=None, dropna=True)</dt><dd><tt>Returns&nbsp;<a href="builtins.html#object">object</a>&nbsp;containing&nbsp;counts&nbsp;of&nbsp;unique&nbsp;values.<br>
-&nbsp;<br>
-The&nbsp;resulting&nbsp;<a href="builtins.html#object">object</a>&nbsp;will&nbsp;be&nbsp;in&nbsp;descending&nbsp;order&nbsp;so&nbsp;that&nbsp;the<br>
-first&nbsp;element&nbsp;is&nbsp;the&nbsp;most&nbsp;frequently-occurring&nbsp;element.<br>
-Excludes&nbsp;NA&nbsp;values&nbsp;by&nbsp;default.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-normalize&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;then&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;returned&nbsp;will&nbsp;contain&nbsp;the&nbsp;relative<br>
-&nbsp;&nbsp;&nbsp;&nbsp;frequencies&nbsp;of&nbsp;the&nbsp;unique&nbsp;values.<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;by&nbsp;values<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;in&nbsp;ascending&nbsp;order<br>
-bins&nbsp;:&nbsp;integer,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Rather&nbsp;than&nbsp;count&nbsp;values,&nbsp;group&nbsp;them&nbsp;into&nbsp;half-open&nbsp;bins,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;convenience&nbsp;for&nbsp;pd.cut,&nbsp;only&nbsp;works&nbsp;with&nbsp;numeric&nbsp;data<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Don't&nbsp;include&nbsp;counts&nbsp;of&nbsp;NaN.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-counts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><strong>__dict__</strong></dt>
-<dd><tt>dictionary&nbsp;for&nbsp;instance&nbsp;variables&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>__weakref__</strong></dt>
-<dd><tt>list&nbsp;of&nbsp;weak&nbsp;references&nbsp;to&nbsp;the&nbsp;object&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>base</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;base&nbsp;object&nbsp;if&nbsp;the&nbsp;memory&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data&nbsp;is<br>
-shared</tt></dd>
-</dl>
-<dl><dt><strong>data</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;data&nbsp;pointer&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>empty</strong></dt>
-</dl>
-<dl><dt><strong>flags</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;ndarray.flags&nbsp;for&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>hasnans</strong></dt>
-</dl>
-<dl><dt><strong>is_monotonic</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_increasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_monotonic_decreasing</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_decreasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic_decreasing&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_monotonic_increasing</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_increasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_unique</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are&nbsp;unique<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_unique&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>itemsize</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;size&nbsp;of&nbsp;the&nbsp;dtype&nbsp;of&nbsp;the&nbsp;item&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>nbytes</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;bytes&nbsp;in&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>ndim</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;dimensions&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data,<br>
-by&nbsp;definition&nbsp;1</tt></dd>
-</dl>
-<dl><dt><strong>shape</strong></dt>
-<dd><tt>return&nbsp;a&nbsp;tuple&nbsp;of&nbsp;the&nbsp;shape&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>size</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;elements&nbsp;in&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>strides</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;strides&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><strong>__array_priority__</strong> = 1000</dl>
-
-<hr>
-Data and other attributes inherited from <a href="pandas.core.strings.html#StringAccessorMixin">pandas.core.strings.StringAccessorMixin</a>:<br>
-<dl><dt><strong>str</strong> = &lt;class 'pandas.core.strings.StringMethods'&gt;<dd><tt>Vectorized&nbsp;string&nbsp;functions&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;and&nbsp;Index.&nbsp;NAs&nbsp;stay&nbsp;NA&nbsp;unless<br>
-handled&nbsp;otherwise&nbsp;by&nbsp;a&nbsp;particular&nbsp;method.&nbsp;Patterned&nbsp;after&nbsp;Python's&nbsp;string<br>
-methods,&nbsp;with&nbsp;some&nbsp;inspiration&nbsp;from&nbsp;R's&nbsp;stringr&nbsp;package.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.str.split('_')<br>
-&gt;&gt;&gt;&nbsp;s.str.<a href="#Condition-replace">replace</a>('_',&nbsp;'')</tt></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><a name="Condition-__abs__"><strong>__abs__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="Condition-__bool__"><strong>__bool__</strong></a> = __nonzero__(self)</dt></dl>
-
-<dl><dt><a name="Condition-__contains__"><strong>__contains__</strong></a>(self, key)</dt><dd><tt>True&nbsp;if&nbsp;the&nbsp;key&nbsp;is&nbsp;in&nbsp;the&nbsp;info&nbsp;axis</tt></dd></dl>
-
-<dl><dt><a name="Condition-__copy__"><strong>__copy__</strong></a>(self, deep=True)</dt></dl>
-
-<dl><dt><a name="Condition-__deepcopy__"><strong>__deepcopy__</strong></a>(self, memo=None)</dt></dl>
-
-<dl><dt><a name="Condition-__delitem__"><strong>__delitem__</strong></a>(self, key)</dt><dd><tt>Delete&nbsp;item</tt></dd></dl>
-
-<dl><dt><a name="Condition-__finalize__"><strong>__finalize__</strong></a>(self, other, method=None, **kwargs)</dt><dd><tt>Propagate&nbsp;metadata&nbsp;from&nbsp;other&nbsp;to&nbsp;self.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;which&nbsp;to&nbsp;get&nbsp;the&nbsp;attributes&nbsp;that&nbsp;we&nbsp;are&nbsp;going<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;propagate<br>
-method&nbsp;:&nbsp;optional,&nbsp;a&nbsp;passed&nbsp;method&nbsp;name&nbsp;;&nbsp;possibly&nbsp;to&nbsp;take&nbsp;different<br>
-&nbsp;&nbsp;&nbsp;&nbsp;types&nbsp;of&nbsp;propagation&nbsp;actions&nbsp;based&nbsp;on&nbsp;this</tt></dd></dl>
-
-<dl><dt><a name="Condition-__getattr__"><strong>__getattr__</strong></a>(self, name)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;looking&nbsp;up&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="Condition-__getstate__"><strong>__getstate__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="Condition-__hash__"><strong>__hash__</strong></a>(self)</dt><dd><tt>Return&nbsp;hash(self).</tt></dd></dl>
-
-<dl><dt><a name="Condition-__invert__"><strong>__invert__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="Condition-__neg__"><strong>__neg__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="Condition-__nonzero__"><strong>__nonzero__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="Condition-__round__"><strong>__round__</strong></a>(self, decimals=0)</dt></dl>
-
-<dl><dt><a name="Condition-__setattr__"><strong>__setattr__</strong></a>(self, name, value)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;setting&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="Condition-__setstate__"><strong>__setstate__</strong></a>(self, state)</dt></dl>
-
-<dl><dt><a name="Condition-abs"><strong>abs</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;absolute&nbsp;value&nbsp;taken--only&nbsp;applicable&nbsp;to&nbsp;objects<br>
-that&nbsp;are&nbsp;all&nbsp;numeric.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-abs:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="Condition-add_prefix"><strong>add_prefix</strong></a>(self, prefix)</dt><dd><tt>Concatenate&nbsp;prefix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-prefix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_prefix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="Condition-add_suffix"><strong>add_suffix</strong></a>(self, suffix)</dt><dd><tt>Concatenate&nbsp;suffix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-suffix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_suffix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="Condition-as_blocks"><strong>as_blocks</strong></a>(self, copy=True)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types&nbsp;that&nbsp;each&nbsp;has<br>
-a&nbsp;homogeneous&nbsp;dtype.<br>
-&nbsp;<br>
-NOTE:&nbsp;the&nbsp;dtypes&nbsp;of&nbsp;the&nbsp;blocks&nbsp;WILL&nbsp;BE&nbsp;PRESERVED&nbsp;HERE&nbsp;(unlike&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;as_matrix)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types</tt></dd></dl>
-
-<dl><dt><a name="Condition-as_matrix"><strong>as_matrix</strong></a>(self, columns=None)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;its&nbsp;Numpy-array&nbsp;representation.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-columns:&nbsp;list,&nbsp;optional,&nbsp;default:None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;None,&nbsp;return&nbsp;all&nbsp;columns,&nbsp;otherwise,&nbsp;returns&nbsp;specified&nbsp;columns.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;caller&nbsp;is&nbsp;heterogeneous&nbsp;and&nbsp;contains&nbsp;booleans&nbsp;or&nbsp;objects,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;of&nbsp;dtype=<a href="builtins.html#object">object</a>.&nbsp;See&nbsp;Notes.<br>
-&nbsp;<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Return&nbsp;is&nbsp;NOT&nbsp;a&nbsp;Numpy-matrix,&nbsp;rather,&nbsp;a&nbsp;Numpy-array.<br>
-&nbsp;<br>
-The&nbsp;dtype&nbsp;will&nbsp;be&nbsp;a&nbsp;lower-common-denominator&nbsp;dtype&nbsp;(implicit<br>
-upcasting);&nbsp;that&nbsp;is&nbsp;to&nbsp;say&nbsp;if&nbsp;the&nbsp;dtypes&nbsp;(even&nbsp;of&nbsp;numeric&nbsp;types)<br>
-are&nbsp;mixed,&nbsp;the&nbsp;one&nbsp;that&nbsp;accommodates&nbsp;all&nbsp;will&nbsp;be&nbsp;chosen.&nbsp;Use&nbsp;this<br>
-with&nbsp;care&nbsp;if&nbsp;you&nbsp;are&nbsp;not&nbsp;dealing&nbsp;with&nbsp;the&nbsp;blocks.<br>
-&nbsp;<br>
-e.g.&nbsp;If&nbsp;the&nbsp;dtypes&nbsp;are&nbsp;float16&nbsp;and&nbsp;float32,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcast&nbsp;to<br>
-float32.&nbsp;&nbsp;If&nbsp;dtypes&nbsp;are&nbsp;int32&nbsp;and&nbsp;uint8,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcase&nbsp;to<br>
-int32.&nbsp;By&nbsp;numpy.find_common_type&nbsp;convention,&nbsp;mixing&nbsp;int64&nbsp;and&nbsp;uint64<br>
-will&nbsp;result&nbsp;in&nbsp;a&nbsp;flot64&nbsp;dtype.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;is&nbsp;provided&nbsp;for&nbsp;backwards&nbsp;compatibility.&nbsp;Generally,<br>
-it&nbsp;is&nbsp;recommended&nbsp;to&nbsp;use&nbsp;'.values'.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.values</tt></dd></dl>
-
-<dl><dt><a name="Condition-asfreq"><strong>asfreq</strong></a>(self, freq, method=None, how=None, normalize=False, fill_value=None)</dt><dd><tt>Convert&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;specified&nbsp;frequency.<br>
-&nbsp;<br>
-Optionally&nbsp;provide&nbsp;filling&nbsp;method&nbsp;to&nbsp;pad/backfill&nbsp;missing&nbsp;values.<br>
-&nbsp;<br>
-Returns&nbsp;the&nbsp;original&nbsp;data&nbsp;conformed&nbsp;to&nbsp;a&nbsp;new&nbsp;index&nbsp;with&nbsp;the&nbsp;specified<br>
-frequency.&nbsp;``resample``&nbsp;is&nbsp;more&nbsp;appropriate&nbsp;if&nbsp;an&nbsp;operation,&nbsp;such&nbsp;as<br>
-summarization,&nbsp;is&nbsp;necessary&nbsp;to&nbsp;represent&nbsp;the&nbsp;data&nbsp;at&nbsp;the&nbsp;new&nbsp;frequency.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;or&nbsp;string<br>
-method&nbsp;:&nbsp;{'backfill'/'bfill',&nbsp;'pad'/'ffill'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(note&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present):<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'pad'&nbsp;/&nbsp;'ffill':&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'backfill'&nbsp;/&nbsp;'bfill':&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill<br>
-how&nbsp;:&nbsp;{'start',&nbsp;'end'},&nbsp;default&nbsp;end<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;PeriodIndex&nbsp;only,&nbsp;see&nbsp;PeriodIndex.asfreq<br>
-normalize&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;reset&nbsp;output&nbsp;index&nbsp;to&nbsp;midnight<br>
-fill_value:&nbsp;scalar,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values,&nbsp;applied&nbsp;during&nbsp;upsampling&nbsp;(note<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;4&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=4,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0.0,&nbsp;None,&nbsp;2.0,&nbsp;3.0],&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'s':series})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-asfreq">asfreq</a>(freq='30S')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``fill&nbsp;value``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-asfreq">asfreq</a>(freq='30S',&nbsp;fill_value=9.0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``method``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-asfreq">asfreq</a>(freq='30S',&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.</tt></dd></dl>
-
-<dl><dt><a name="Condition-asof"><strong>asof</strong></a>(self, where, subset=None)</dt><dd><tt>The&nbsp;last&nbsp;row&nbsp;without&nbsp;any&nbsp;NaN&nbsp;is&nbsp;taken&nbsp;(or&nbsp;the&nbsp;last&nbsp;row&nbsp;without<br>
-NaN&nbsp;considering&nbsp;only&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;in&nbsp;the&nbsp;case&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0&nbsp;For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-If&nbsp;there&nbsp;is&nbsp;no&nbsp;good&nbsp;value,&nbsp;NaN&nbsp;is&nbsp;returned&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;NaN&nbsp;values&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-where&nbsp;:&nbsp;date&nbsp;or&nbsp;array&nbsp;of&nbsp;dates<br>
-subset&nbsp;:&nbsp;string&nbsp;or&nbsp;list&nbsp;of&nbsp;strings,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None&nbsp;use&nbsp;these&nbsp;columns&nbsp;for&nbsp;NaN&nbsp;propagation<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Dates&nbsp;are&nbsp;assumed&nbsp;to&nbsp;be&nbsp;sorted<br>
-Raises&nbsp;if&nbsp;this&nbsp;is&nbsp;not&nbsp;the&nbsp;case<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-where&nbsp;is&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;&nbsp;-&nbsp;value&nbsp;or&nbsp;NaN&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;-&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-where&nbsp;is&nbsp;Index:&nbsp;same&nbsp;shape&nbsp;<a href="builtins.html#object">object</a>&nbsp;as&nbsp;input<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-merge_asof</tt></dd></dl>
-
-<dl><dt><a name="Condition-astype"><strong>astype</strong></a>(self, dtype, copy=True, errors='raise', **kwargs)</dt><dd><tt>Cast&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;numpy.dtype<br>
-Return&nbsp;a&nbsp;copy&nbsp;when&nbsp;copy&nbsp;=&nbsp;True&nbsp;(be&nbsp;really&nbsp;careful&nbsp;with&nbsp;this!)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dtype&nbsp;:&nbsp;data&nbsp;type,&nbsp;or&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;-&gt;&nbsp;data&nbsp;type<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;entire&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;same&nbsp;type.&nbsp;Alternatively,&nbsp;use&nbsp;{col:&nbsp;dtype,&nbsp;...},&nbsp;where&nbsp;col&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;label&nbsp;and&nbsp;dtype&nbsp;is&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;one<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;more&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;columns&nbsp;to&nbsp;column-specific&nbsp;types.<br>
-errors&nbsp;:&nbsp;{'raise',&nbsp;'ignore'},&nbsp;default&nbsp;'raise'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Control&nbsp;raising&nbsp;of&nbsp;exceptions&nbsp;on&nbsp;invalid&nbsp;data&nbsp;for&nbsp;provided&nbsp;dtype.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``raise``&nbsp;:&nbsp;allow&nbsp;exceptions&nbsp;to&nbsp;be&nbsp;raised<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``ignore``&nbsp;:&nbsp;suppress&nbsp;exceptions.&nbsp;On&nbsp;error&nbsp;return&nbsp;original&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-raise_on_error&nbsp;:&nbsp;DEPRECATED&nbsp;use&nbsp;``errors``&nbsp;instead<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;constructor<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-casted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="Condition-at_time"><strong>at_time</strong></a>(self, time, asof=False)</dt><dd><tt>Select&nbsp;values&nbsp;at&nbsp;particular&nbsp;time&nbsp;of&nbsp;day&nbsp;(e.g.&nbsp;9:30AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_at_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="Condition-between_time"><strong>between_time</strong></a>(self, start_time, end_time, include_start=True, include_end=True)</dt><dd><tt>Select&nbsp;values&nbsp;between&nbsp;particular&nbsp;times&nbsp;of&nbsp;the&nbsp;day&nbsp;(e.g.,&nbsp;9:00-9:30&nbsp;AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-start_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-end_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-include_start&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-include_end&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_between_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="Condition-bfill"><strong>bfill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#Condition-fillna">fillna</a>(method='bfill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="Condition-bool"><strong>bool</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;bool&nbsp;of&nbsp;a&nbsp;single&nbsp;element&nbsp;PandasObject.<br>
-&nbsp;<br>
-This&nbsp;must&nbsp;be&nbsp;a&nbsp;boolean&nbsp;scalar&nbsp;value,&nbsp;either&nbsp;True&nbsp;or&nbsp;False.&nbsp;&nbsp;Raise&nbsp;a<br>
-ValueError&nbsp;if&nbsp;the&nbsp;PandasObject&nbsp;does&nbsp;not&nbsp;have&nbsp;exactly&nbsp;1&nbsp;element,&nbsp;or&nbsp;that<br>
-element&nbsp;is&nbsp;not&nbsp;boolean</tt></dd></dl>
-
-<dl><dt><a name="Condition-clip"><strong>clip</strong></a>(self, lower=None, upper=None, axis=None, *args, **kwargs)</dt><dd><tt>Trim&nbsp;values&nbsp;at&nbsp;input&nbsp;threshold(s).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-lower&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-upper&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;lower&nbsp;and&nbsp;upper&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.256177<br>
-1&nbsp;-1.367855&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.176076<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;1.261967&nbsp;&nbsp;0.570967<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-clip">clip</a>(-1.0,&nbsp;0.5)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.000000<br>
-1&nbsp;-1.000000&nbsp;&nbsp;0.500000<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.000000<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;0.500000&nbsp;&nbsp;0.500000<br>
-&gt;&gt;&gt;&nbsp;t<br>
-0&nbsp;&nbsp;&nbsp;-0.3<br>
-1&nbsp;&nbsp;&nbsp;-0.2<br>
-2&nbsp;&nbsp;&nbsp;-0.1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;0.1<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-clip">clip</a>(t,&nbsp;t&nbsp;+&nbsp;1,&nbsp;axis=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-0.300000<br>
-1&nbsp;-0.200000&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-0.100000<br>
-3&nbsp;&nbsp;0.230930&nbsp;&nbsp;0.000000<br>
-4&nbsp;&nbsp;1.100000&nbsp;&nbsp;0.570967</tt></dd></dl>
-
-<dl><dt><a name="Condition-clip_lower"><strong>clip_lower</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;the&nbsp;input&nbsp;with&nbsp;values&nbsp;below&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="Condition-clip_upper"><strong>clip_upper</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;input&nbsp;with&nbsp;values&nbsp;above&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="Condition-consolidate"><strong>consolidate</strong></a>(self, inplace=False)</dt><dd><tt>DEPRECATED:&nbsp;consolidate&nbsp;will&nbsp;be&nbsp;an&nbsp;internal&nbsp;implementation&nbsp;only.</tt></dd></dl>
-
-<dl><dt><a name="Condition-convert_objects"><strong>convert_objects</strong></a>(self, convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True)</dt><dd><tt>Deprecated.<br>
-&nbsp;<br>
-Attempt&nbsp;to&nbsp;infer&nbsp;better&nbsp;dtype&nbsp;for&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-convert_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;date&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-convert_numeric&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;attempt&nbsp;to&nbsp;coerce&nbsp;to&nbsp;numbers&nbsp;(including&nbsp;strings),&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaN.<br>
-convert_timedeltas&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;timedelta&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;return&nbsp;a&nbsp;copy&nbsp;even&nbsp;if&nbsp;no&nbsp;copy&nbsp;is&nbsp;necessary&nbsp;(e.g.&nbsp;no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion&nbsp;was&nbsp;done).&nbsp;Note:&nbsp;This&nbsp;is&nbsp;meant&nbsp;for&nbsp;internal&nbsp;use,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;not&nbsp;be&nbsp;confused&nbsp;with&nbsp;inplace.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.to_datetime&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;datetime.<br>
-pandas.to_timedelta&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;timedelta.<br>
-pandas.to_numeric&nbsp;:&nbsp;Return&nbsp;a&nbsp;fixed&nbsp;frequency&nbsp;timedelta&nbsp;index,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;day&nbsp;as&nbsp;the&nbsp;default.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;same&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-copy"><strong>copy</strong></a>(self, deep=True)</dt><dd><tt>Make&nbsp;a&nbsp;copy&nbsp;of&nbsp;this&nbsp;objects&nbsp;data.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-deep&nbsp;:&nbsp;boolean&nbsp;or&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;a&nbsp;deep&nbsp;copy,&nbsp;including&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;data&nbsp;and&nbsp;the&nbsp;indices.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;With&nbsp;``deep=False``&nbsp;neither&nbsp;the&nbsp;indices&nbsp;or&nbsp;the&nbsp;data&nbsp;are&nbsp;copied.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;that&nbsp;when&nbsp;``deep=True``&nbsp;data&nbsp;is&nbsp;copied,&nbsp;actual&nbsp;python&nbsp;objects<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;not&nbsp;be&nbsp;copied&nbsp;recursively,&nbsp;only&nbsp;the&nbsp;reference&nbsp;to&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;is&nbsp;in&nbsp;contrast&nbsp;to&nbsp;``copy.deepcopy``&nbsp;in&nbsp;the&nbsp;Standard&nbsp;Library,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;recursively&nbsp;copies&nbsp;<a href="builtins.html#object">object</a>&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-copy&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="Condition-describe"><strong>describe</strong></a>(self, percentiles=None, include=None, exclude=None)</dt><dd><tt>Generates&nbsp;descriptive&nbsp;statistics&nbsp;that&nbsp;summarize&nbsp;the&nbsp;central&nbsp;tendency,<br>
-dispersion&nbsp;and&nbsp;shape&nbsp;of&nbsp;a&nbsp;dataset's&nbsp;distribution,&nbsp;excluding<br>
-``NaN``&nbsp;values.<br>
-&nbsp;<br>
-Analyzes&nbsp;both&nbsp;numeric&nbsp;and&nbsp;<a href="builtins.html#object">object</a>&nbsp;series,&nbsp;as&nbsp;well<br>
-as&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;column&nbsp;sets&nbsp;of&nbsp;mixed&nbsp;data&nbsp;types.&nbsp;The&nbsp;output<br>
-will&nbsp;vary&nbsp;depending&nbsp;on&nbsp;what&nbsp;is&nbsp;provided.&nbsp;Refer&nbsp;to&nbsp;the&nbsp;notes<br>
-below&nbsp;for&nbsp;more&nbsp;detail.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-percentiles&nbsp;:&nbsp;list-like&nbsp;of&nbsp;numbers,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;percentiles&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;output.&nbsp;All&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fall&nbsp;between&nbsp;0&nbsp;and&nbsp;1.&nbsp;The&nbsp;default&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``[.25,&nbsp;.5,&nbsp;.75]``,&nbsp;which&nbsp;returns&nbsp;the&nbsp;25th,&nbsp;50th,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;75th&nbsp;percentiles.<br>
-include&nbsp;:&nbsp;'all',&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;white&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'all'&nbsp;:&nbsp;All&nbsp;columns&nbsp;of&nbsp;the&nbsp;input&nbsp;will&nbsp;be&nbsp;included&nbsp;in&nbsp;the&nbsp;output.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Limits&nbsp;the&nbsp;results&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;provided&nbsp;data&nbsp;types.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;limit&nbsp;the&nbsp;result&nbsp;to&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;limit&nbsp;it&nbsp;instead&nbsp;to&nbsp;categorical<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;objects&nbsp;submit&nbsp;the&nbsp;``numpy.<a href="builtins.html#object">object</a>``&nbsp;data&nbsp;type.&nbsp;Strings<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#Condition-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;include&nbsp;all&nbsp;numeric&nbsp;columns.<br>
-exclude&nbsp;:&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;black&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;omit&nbsp;from&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Excludes&nbsp;the&nbsp;provided&nbsp;data&nbsp;types<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;the&nbsp;result.&nbsp;To&nbsp;select&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;select&nbsp;categorical&nbsp;objects&nbsp;submit&nbsp;the&nbsp;data<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;type&nbsp;``numpy.<a href="builtins.html#object">object</a>``.&nbsp;Strings&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#Condition-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;exclude&nbsp;nothing.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-summary:&nbsp;&nbsp;<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;summary&nbsp;statistics<br>
-&nbsp;<br>
-Notes<br>
------<br>
-For&nbsp;numeric&nbsp;data,&nbsp;the&nbsp;result's&nbsp;index&nbsp;will&nbsp;include&nbsp;``count``,<br>
-``mean``,&nbsp;``std``,&nbsp;``min``,&nbsp;``max``&nbsp;as&nbsp;well&nbsp;as&nbsp;lower,&nbsp;``50``&nbsp;and<br>
-upper&nbsp;percentiles.&nbsp;By&nbsp;default&nbsp;the&nbsp;lower&nbsp;percentile&nbsp;is&nbsp;``25``&nbsp;and&nbsp;the<br>
-upper&nbsp;percentile&nbsp;is&nbsp;``75``.&nbsp;The&nbsp;``50``&nbsp;percentile&nbsp;is&nbsp;the<br>
-same&nbsp;as&nbsp;the&nbsp;median.<br>
-&nbsp;<br>
-For&nbsp;<a href="builtins.html#object">object</a>&nbsp;data&nbsp;(e.g.&nbsp;strings&nbsp;or&nbsp;timestamps),&nbsp;the&nbsp;result's&nbsp;index<br>
-will&nbsp;include&nbsp;``count``,&nbsp;``unique``,&nbsp;``top``,&nbsp;and&nbsp;``freq``.&nbsp;The&nbsp;``top``<br>
-is&nbsp;the&nbsp;most&nbsp;common&nbsp;value.&nbsp;The&nbsp;``freq``&nbsp;is&nbsp;the&nbsp;most&nbsp;common&nbsp;value's<br>
-frequency.&nbsp;Timestamps&nbsp;also&nbsp;include&nbsp;the&nbsp;``first``&nbsp;and&nbsp;``last``&nbsp;items.<br>
-&nbsp;<br>
-If&nbsp;multiple&nbsp;<a href="builtins.html#object">object</a>&nbsp;values&nbsp;have&nbsp;the&nbsp;highest&nbsp;count,&nbsp;then&nbsp;the<br>
-``count``&nbsp;and&nbsp;``top``&nbsp;results&nbsp;will&nbsp;be&nbsp;arbitrarily&nbsp;chosen&nbsp;from<br>
-among&nbsp;those&nbsp;with&nbsp;the&nbsp;highest&nbsp;count.<br>
-&nbsp;<br>
-For&nbsp;mixed&nbsp;data&nbsp;types&nbsp;provided&nbsp;via&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``,&nbsp;the&nbsp;default&nbsp;is&nbsp;to<br>
-return&nbsp;only&nbsp;an&nbsp;analysis&nbsp;of&nbsp;numeric&nbsp;columns.&nbsp;If&nbsp;``include='all'``<br>
-is&nbsp;provided&nbsp;as&nbsp;an&nbsp;option,&nbsp;the&nbsp;result&nbsp;will&nbsp;include&nbsp;a&nbsp;union&nbsp;of<br>
-attributes&nbsp;of&nbsp;each&nbsp;type.<br>
-&nbsp;<br>
-The&nbsp;`include`&nbsp;and&nbsp;`exclude`&nbsp;parameters&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;limit<br>
-which&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;are&nbsp;analyzed&nbsp;for&nbsp;the&nbsp;output.<br>
-The&nbsp;parameters&nbsp;are&nbsp;ignored&nbsp;when&nbsp;analyzing&nbsp;a&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-Describing&nbsp;a&nbsp;numeric&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;categorical&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(['a',&nbsp;'a',&nbsp;'b',&nbsp;'c'])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;timestamp&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2000-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01")<br>
-...&nbsp;])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-first&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2000-01-01&nbsp;00:00:00<br>
-last&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``.&nbsp;By&nbsp;default&nbsp;only&nbsp;numeric&nbsp;fields<br>
-are&nbsp;returned.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[1,&nbsp;'a'],&nbsp;[2,&nbsp;'b'],&nbsp;[3,&nbsp;'c']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['numeric',&nbsp;'<a href="builtins.html#object">object</a>'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-describe">describe</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;all&nbsp;columns&nbsp;of&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;regardless&nbsp;of&nbsp;data&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-describe">describe</a>(include='all')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;column&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;by&nbsp;accessing&nbsp;it&nbsp;as<br>
-an&nbsp;attribute.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.numeric.<a href="#Condition-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-Name:&nbsp;numeric,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;numeric&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-describe">describe</a>(include=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;string&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-describe">describe</a>(include=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;numeric&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-describe">describe</a>(exclude=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-describe">describe</a>(exclude=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.count<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.max<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.min<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mean<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.std<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select_dtypes</tt></dd></dl>
-
-<dl><dt><a name="Condition-drop"><strong>drop</strong></a>(self, labels, axis=0, level=None, inplace=False, errors='raise')</dt><dd><tt>Return&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;labels&nbsp;in&nbsp;requested&nbsp;axis&nbsp;removed.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-labels&nbsp;:&nbsp;single&nbsp;label&nbsp;or&nbsp;list-like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;axis&nbsp;name<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;MultiIndex<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;return&nbsp;None.<br>
-errors&nbsp;:&nbsp;{'ignore',&nbsp;'raise'},&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'ignore',&nbsp;suppress&nbsp;error&nbsp;and&nbsp;existing&nbsp;labels&nbsp;are&nbsp;dropped.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dropped&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="Condition-equals"><strong>equals</strong></a>(self, other)</dt><dd><tt>Determines&nbsp;if&nbsp;two&nbsp;NDFrame&nbsp;objects&nbsp;contain&nbsp;the&nbsp;same&nbsp;elements.&nbsp;NaNs&nbsp;in<br>
-the&nbsp;same&nbsp;location&nbsp;are&nbsp;considered&nbsp;equal.</tt></dd></dl>
-
-<dl><dt><a name="Condition-ffill"><strong>ffill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#Condition-fillna">fillna</a>(method='ffill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="Condition-filter"><strong>filter</strong></a>(self, items=None, like=None, regex=None, axis=None)</dt><dd><tt>Subset&nbsp;rows&nbsp;or&nbsp;columns&nbsp;of&nbsp;dataframe&nbsp;according&nbsp;to&nbsp;labels&nbsp;in<br>
-the&nbsp;specified&nbsp;index.<br>
-&nbsp;<br>
-Note&nbsp;that&nbsp;this&nbsp;routine&nbsp;does&nbsp;not&nbsp;filter&nbsp;a&nbsp;dataframe&nbsp;on&nbsp;its<br>
-contents.&nbsp;The&nbsp;filter&nbsp;is&nbsp;applied&nbsp;to&nbsp;the&nbsp;labels&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-items&nbsp;:&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;info&nbsp;axis&nbsp;to&nbsp;restrict&nbsp;to&nbsp;(must&nbsp;not&nbsp;all&nbsp;be&nbsp;present)<br>
-like&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;where&nbsp;"arg&nbsp;in&nbsp;col&nbsp;==&nbsp;True"<br>
-regex&nbsp;:&nbsp;string&nbsp;(regular&nbsp;expression)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;with&nbsp;re.search(regex,&nbsp;col)&nbsp;==&nbsp;True<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;filter&nbsp;on.&nbsp;&nbsp;By&nbsp;default&nbsp;this&nbsp;is&nbsp;the&nbsp;info&nbsp;axis,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'index'&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;'columns'&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;name<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-filter">filter</a>(items=['one',&nbsp;'three'])<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;regular&nbsp;expression<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-filter">filter</a>(regex='e$',&nbsp;axis=1)<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;rows&nbsp;containing&nbsp;'bbi'<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-filter">filter</a>(like='bbi',&nbsp;axis=0)<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;``items``,&nbsp;``like``,&nbsp;and&nbsp;``regex``&nbsp;parameters&nbsp;are<br>
-enforced&nbsp;to&nbsp;be&nbsp;mutually&nbsp;exclusive.<br>
-&nbsp;<br>
-``axis``&nbsp;defaults&nbsp;to&nbsp;the&nbsp;info&nbsp;axis&nbsp;that&nbsp;is&nbsp;used&nbsp;when&nbsp;indexing<br>
-with&nbsp;``[]``.</tt></dd></dl>
-
-<dl><dt><a name="Condition-first"><strong>first</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;initial&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#Condition-first">first</a>('10D')&nbsp;-&gt;&nbsp;First&nbsp;10&nbsp;days<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="Condition-get"><strong>get</strong></a>(self, key, default=None)</dt><dd><tt>Get&nbsp;item&nbsp;from&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;given&nbsp;key&nbsp;(<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column,&nbsp;Panel&nbsp;slice,<br>
-etc.).&nbsp;Returns&nbsp;default&nbsp;value&nbsp;if&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;type&nbsp;of&nbsp;items&nbsp;contained&nbsp;in&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-get_dtype_counts"><strong>get_dtype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;dtypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="Condition-get_ftype_counts"><strong>get_ftype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;ftypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="Condition-groupby"><strong>groupby</strong></a>(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)</dt><dd><tt>Group&nbsp;series&nbsp;using&nbsp;mapper&nbsp;(dict&nbsp;or&nbsp;key&nbsp;function,&nbsp;apply&nbsp;given&nbsp;function<br>
-to&nbsp;group,&nbsp;return&nbsp;result&nbsp;as&nbsp;series)&nbsp;or&nbsp;by&nbsp;a&nbsp;series&nbsp;of&nbsp;columns.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;mapping,&nbsp;function,&nbsp;str,&nbsp;or&nbsp;iterable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;for&nbsp;the&nbsp;groupby.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;``by``&nbsp;is&nbsp;a&nbsp;function,&nbsp;it's&nbsp;called&nbsp;on&nbsp;each&nbsp;value&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>'s<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;If&nbsp;a&nbsp;dict&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;is&nbsp;passed,&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;dict&nbsp;VALUES<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;(the&nbsp;<a href="pandas.core.series.html#Series">Series</a>'&nbsp;values&nbsp;are&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aligned;&nbsp;see&nbsp;``.<a href="#Condition-align">align</a>()``&nbsp;method).&nbsp;If&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;is&nbsp;passed,&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;are&nbsp;used&nbsp;as-is&nbsp;determine&nbsp;the&nbsp;groups.&nbsp;A&nbsp;str&nbsp;or&nbsp;list&nbsp;of&nbsp;strs<br>
-&nbsp;&nbsp;&nbsp;&nbsp;may&nbsp;be&nbsp;passed&nbsp;to&nbsp;group&nbsp;by&nbsp;the&nbsp;columns&nbsp;in&nbsp;``self``<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-level&nbsp;:&nbsp;int,&nbsp;level&nbsp;name,&nbsp;or&nbsp;sequence&nbsp;of&nbsp;such,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;group&nbsp;by&nbsp;a&nbsp;particular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level&nbsp;or&nbsp;levels<br>
-as_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;aggregated&nbsp;output,&nbsp;return&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;group&nbsp;labels&nbsp;as&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;Only&nbsp;relevant&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;input.&nbsp;as_index=False&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;effectively&nbsp;"SQL-style"&nbsp;grouped&nbsp;output<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;group&nbsp;keys.&nbsp;Get&nbsp;better&nbsp;performance&nbsp;by&nbsp;turning&nbsp;this&nbsp;off.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;this&nbsp;does&nbsp;not&nbsp;influence&nbsp;the&nbsp;order&nbsp;of&nbsp;observations&nbsp;within&nbsp;each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;group.&nbsp;&nbsp;groupby&nbsp;preserves&nbsp;the&nbsp;order&nbsp;of&nbsp;rows&nbsp;within&nbsp;each&nbsp;group.<br>
-group_keys&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;calling&nbsp;apply,&nbsp;add&nbsp;group&nbsp;keys&nbsp;to&nbsp;index&nbsp;to&nbsp;identify&nbsp;pieces<br>
-squeeze&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;reduce&nbsp;the&nbsp;dimensionality&nbsp;of&nbsp;the&nbsp;return&nbsp;type&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;return&nbsp;a&nbsp;consistent&nbsp;type<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;results<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#Condition-groupby">groupby</a>(func,&nbsp;axis=0).<a href="#Condition-mean">mean</a>()<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#Condition-groupby">groupby</a>(['col1',&nbsp;'col2'])['col3'].<a href="#Condition-mean">mean</a>()<br>
-&nbsp;<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;hierarchical&nbsp;index<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#Condition-groupby">groupby</a>(['col1',&nbsp;'col2']).<a href="#Condition-mean">mean</a>()<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-GroupBy&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-head"><strong>head</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;first&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="Condition-interpolate"><strong>interpolate</strong></a>(self, method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', downcast=None, **kwargs)</dt><dd><tt>Interpolate&nbsp;values&nbsp;according&nbsp;to&nbsp;different&nbsp;methods.<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;only&nbsp;``method='linear'``&nbsp;is&nbsp;supported&nbsp;for<br>
-DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a&nbsp;MultiIndex.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-method&nbsp;:&nbsp;{'linear',&nbsp;'time',&nbsp;'index',&nbsp;'values',&nbsp;'nearest',&nbsp;'zero',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',&nbsp;'barycentric',&nbsp;'krogh',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'polynomial',&nbsp;'spline',&nbsp;'piecewise_polynomial',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'from_derivatives',&nbsp;'pchip',&nbsp;'akima'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'linear':&nbsp;ignore&nbsp;the&nbsp;index&nbsp;and&nbsp;treat&nbsp;the&nbsp;values&nbsp;as&nbsp;equally<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;spaced.&nbsp;This&nbsp;is&nbsp;the&nbsp;only&nbsp;method&nbsp;supported&nbsp;on&nbsp;MultiIndexes.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'time':&nbsp;interpolation&nbsp;works&nbsp;on&nbsp;daily&nbsp;and&nbsp;higher&nbsp;resolution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;data&nbsp;to&nbsp;interpolate&nbsp;given&nbsp;length&nbsp;of&nbsp;interval<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'index',&nbsp;'values':&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'nearest',&nbsp;'zero',&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'barycentric',&nbsp;'polynomial'&nbsp;is&nbsp;passed&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``scipy.interpolate.interp1d``.&nbsp;Both&nbsp;'polynomial'&nbsp;and&nbsp;'spline'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;require&nbsp;that&nbsp;you&nbsp;also&nbsp;specify&nbsp;an&nbsp;`order`&nbsp;(int),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;e.g.&nbsp;df.<a href="#Condition-interpolate">interpolate</a>(method='polynomial',&nbsp;order=4).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'krogh',&nbsp;'piecewise_polynomial',&nbsp;'spline',&nbsp;'pchip'&nbsp;and&nbsp;'akima'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;all&nbsp;wrappers&nbsp;around&nbsp;the&nbsp;scipy&nbsp;interpolation&nbsp;methods&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;similar&nbsp;names.&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;For&nbsp;more&nbsp;information&nbsp;on&nbsp;their&nbsp;behavior,&nbsp;see&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`scipy&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__">http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;`tutorial&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__">http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'from_derivatives'&nbsp;refers&nbsp;to&nbsp;BPoly.from_derivatives&nbsp;which<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;replaces&nbsp;'piecewise_polynomial'&nbsp;interpolation&nbsp;method&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;0.18<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;support&nbsp;for&nbsp;the&nbsp;'akima'&nbsp;method<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;interpolate&nbsp;method&nbsp;'from_derivatives'&nbsp;which&nbsp;replaces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'piecewise_polynomial'&nbsp;in&nbsp;scipy&nbsp;0.18;&nbsp;backwards-compatible&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;&lt;&nbsp;0.18<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;0:&nbsp;fill&nbsp;column-by-column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;1:&nbsp;fill&nbsp;row-by-row<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs&nbsp;to&nbsp;fill.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0.<br>
-limit_direction&nbsp;:&nbsp;{'forward',&nbsp;'backward',&nbsp;'both'},&nbsp;default&nbsp;'forward'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;limit&nbsp;is&nbsp;specified,&nbsp;consecutive&nbsp;NaNs&nbsp;will&nbsp;be&nbsp;filled&nbsp;in&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;direction.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Update&nbsp;the&nbsp;NDFrame&nbsp;in&nbsp;place&nbsp;if&nbsp;possible.<br>
-downcast&nbsp;:&nbsp;optional,&nbsp;'infer'&nbsp;or&nbsp;None,&nbsp;defaults&nbsp;to&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Downcast&nbsp;dtypes&nbsp;if&nbsp;possible.<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;interpolating&nbsp;function.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;interpolated&nbsp;at&nbsp;the&nbsp;NaNs<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;replace,&nbsp;fillna<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Filling&nbsp;in&nbsp;NaNs<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0,&nbsp;1,&nbsp;np.nan,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-interpolate">interpolate</a>()<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="Condition-isnull"><strong>isnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-notnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;isnull</tt></dd></dl>
-
-<dl><dt><a name="Condition-last"><strong>last</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;final&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#Condition-last">last</a>('5M')&nbsp;-&gt;&nbsp;Last&nbsp;5&nbsp;months<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="Condition-mask"><strong>mask</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;False&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;mask&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``False``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#Condition-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#Condition-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``mask``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).<a href="#Condition-reshape">reshape</a>(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#Condition-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#Condition-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`</tt></dd></dl>
-
-<dl><dt><a name="Condition-notnull"><strong>notnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are<br>
-not&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-isnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;notnull</tt></dd></dl>
-
-<dl><dt><a name="Condition-pct_change"><strong>pct_change</strong></a>(self, periods=1, fill_method='pad', limit=None, freq=None, **kwargs)</dt><dd><tt>Percent&nbsp;change&nbsp;over&nbsp;given&nbsp;number&nbsp;of&nbsp;periods.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;percent&nbsp;change<br>
-fill_method&nbsp;:&nbsp;str,&nbsp;default&nbsp;'pad'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;How&nbsp;to&nbsp;handle&nbsp;NAs&nbsp;before&nbsp;computing&nbsp;percent&nbsp;changes<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NAs&nbsp;to&nbsp;fill&nbsp;before&nbsp;stopping<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;offset&nbsp;alias&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;time&nbsp;series&nbsp;API&nbsp;(e.g.&nbsp;'M'&nbsp;or&nbsp;BDay())<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-chg&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-By&nbsp;default,&nbsp;the&nbsp;percentage&nbsp;change&nbsp;is&nbsp;calculated&nbsp;along&nbsp;the&nbsp;stat<br>
-axis:&nbsp;0,&nbsp;or&nbsp;``Index``,&nbsp;for&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;and&nbsp;1,&nbsp;or&nbsp;``minor``&nbsp;for<br>
-``Panel``.&nbsp;You&nbsp;can&nbsp;change&nbsp;this&nbsp;with&nbsp;the&nbsp;``axis``&nbsp;keyword&nbsp;argument.</tt></dd></dl>
-
-<dl><dt><a name="Condition-pipe"><strong>pipe</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Apply&nbsp;func(self,&nbsp;\*args,&nbsp;\*\*kwargs)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.2<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;the&nbsp;NDFrame.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``args``,&nbsp;and&nbsp;``kwargs``&nbsp;are&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Alternatively&nbsp;a&nbsp;``(callable,&nbsp;data_keyword)``&nbsp;tuple&nbsp;where<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``data_keyword``&nbsp;is&nbsp;a&nbsp;string&nbsp;indicating&nbsp;the&nbsp;keyword&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``callable``&nbsp;that&nbsp;expects&nbsp;the&nbsp;NDFrame.<br>
-args&nbsp;:&nbsp;positional&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-kwargs&nbsp;:&nbsp;a&nbsp;dictionary&nbsp;of&nbsp;keyword&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="builtins.html#object">object</a>&nbsp;:&nbsp;the&nbsp;return&nbsp;type&nbsp;of&nbsp;``func``.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-Use&nbsp;``.pipe``&nbsp;when&nbsp;chaining&nbsp;together&nbsp;functions&nbsp;that&nbsp;expect<br>
-on&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;DataFrames.&nbsp;Instead&nbsp;of&nbsp;writing<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;f(g(h(df),&nbsp;arg1=a),&nbsp;arg2=b,&nbsp;arg3=c)<br>
-&nbsp;<br>
-You&nbsp;can&nbsp;write<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#Condition-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#Condition-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#Condition-pipe">pipe</a>(f,&nbsp;arg2=b,&nbsp;arg3=c)<br>
-...&nbsp;)<br>
-&nbsp;<br>
-If&nbsp;you&nbsp;have&nbsp;a&nbsp;function&nbsp;that&nbsp;takes&nbsp;the&nbsp;data&nbsp;as&nbsp;(say)&nbsp;the&nbsp;second<br>
-argument,&nbsp;pass&nbsp;a&nbsp;tuple&nbsp;indicating&nbsp;which&nbsp;keyword&nbsp;expects&nbsp;the<br>
-data.&nbsp;For&nbsp;example,&nbsp;suppose&nbsp;``f``&nbsp;takes&nbsp;its&nbsp;data&nbsp;as&nbsp;``arg2``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#Condition-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#Condition-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#Condition-pipe">pipe</a>((f,&nbsp;'arg2'),&nbsp;arg1=a,&nbsp;arg3=c)<br>
-...&nbsp;&nbsp;)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.map</tt></dd></dl>
-
-<dl><dt><a name="Condition-pop"><strong>pop</strong></a>(self, item)</dt><dd><tt>Return&nbsp;item&nbsp;and&nbsp;drop&nbsp;from&nbsp;frame.&nbsp;Raise&nbsp;KeyError&nbsp;if&nbsp;not&nbsp;found.</tt></dd></dl>
-
-<dl><dt><a name="Condition-rank"><strong>rank</strong></a>(self, axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)</dt><dd><tt>Compute&nbsp;numerical&nbsp;data&nbsp;ranks&nbsp;(1&nbsp;through&nbsp;n)&nbsp;along&nbsp;axis.&nbsp;Equal&nbsp;values&nbsp;are<br>
-assigned&nbsp;a&nbsp;rank&nbsp;that&nbsp;is&nbsp;the&nbsp;average&nbsp;of&nbsp;the&nbsp;ranks&nbsp;of&nbsp;those&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;to&nbsp;direct&nbsp;ranking<br>
-method&nbsp;:&nbsp;{'average',&nbsp;'min',&nbsp;'max',&nbsp;'first',&nbsp;'dense'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;average:&nbsp;average&nbsp;rank&nbsp;of&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;min:&nbsp;lowest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;max:&nbsp;highest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;first:&nbsp;ranks&nbsp;assigned&nbsp;in&nbsp;order&nbsp;they&nbsp;appear&nbsp;in&nbsp;the&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dense:&nbsp;like&nbsp;'min',&nbsp;but&nbsp;rank&nbsp;always&nbsp;increases&nbsp;by&nbsp;1&nbsp;between&nbsp;groups<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;data.&nbsp;Valid&nbsp;only&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Panel&nbsp;objects<br>
-na_option&nbsp;:&nbsp;{'keep',&nbsp;'top',&nbsp;'bottom'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;keep:&nbsp;leave&nbsp;NA&nbsp;values&nbsp;where&nbsp;they&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;top:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;ascending<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;bottom:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;descending<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False&nbsp;for&nbsp;ranks&nbsp;by&nbsp;high&nbsp;(1)&nbsp;to&nbsp;low&nbsp;(N)<br>
-pct&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Computes&nbsp;percentage&nbsp;rank&nbsp;of&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ranks&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="Condition-reindex_like"><strong>reindex_like</strong></a>(self, other, method=None, copy=True, limit=None, tolerance=None)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;matching&nbsp;indices&nbsp;to&nbsp;myself.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;Object<br>
-method&nbsp;:&nbsp;string&nbsp;or&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;labels&nbsp;to&nbsp;fill&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;labels&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>&nbsp;and&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Like&nbsp;calling&nbsp;s.<a href="#Condition-reindex">reindex</a>(index=other.index,&nbsp;columns=other.columns,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;method=...)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="Condition-rename_axis"><strong>rename_axis</strong></a>(self, mapper, axis=0, copy=True, inplace=False)</dt><dd><tt>Alter&nbsp;index&nbsp;and&nbsp;/&nbsp;or&nbsp;columns&nbsp;using&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.<br>
-A&nbsp;scalar&nbsp;or&nbsp;list-like&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;``Index.name``<br>
-or&nbsp;``MultiIndex.names``&nbsp;attribute.<br>
-A&nbsp;function&nbsp;or&nbsp;dict&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;labels.<br>
-Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be&nbsp;unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in<br>
-a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left&nbsp;as-is.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-mapper&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename<br>
-pandas.Index.rename<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-rename_axis">rename_axis</a>("foo")&nbsp;&nbsp;#&nbsp;scalar,&nbsp;alters&nbsp;df.index.name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-foo<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-rename_axis">rename_axis</a>(lambda&nbsp;x:&nbsp;2&nbsp;*&nbsp;x)&nbsp;&nbsp;#&nbsp;function:&nbsp;alters&nbsp;labels<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-4&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-rename_axis">rename_axis</a>({"A":&nbsp;"ehh",&nbsp;"C":&nbsp;"see"},&nbsp;axis="columns")&nbsp;&nbsp;#&nbsp;mapping<br>
-&nbsp;&nbsp;&nbsp;ehh&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="Condition-replace"><strong>replace</strong></a>(self, to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad', axis=None)</dt><dd><tt>Replace&nbsp;values&nbsp;given&nbsp;in&nbsp;'to_replace'&nbsp;with&nbsp;'value'.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-to_replace&nbsp;:&nbsp;str,&nbsp;regex,&nbsp;list,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;numeric,&nbsp;or&nbsp;None<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;str&nbsp;or&nbsp;regex:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str:&nbsp;string&nbsp;exactly&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;`value`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;regex:&nbsp;regexs&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`value`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;list&nbsp;of&nbsp;str,&nbsp;regex,&nbsp;or&nbsp;numeric:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;First,&nbsp;if&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;both&nbsp;lists,&nbsp;they<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Second,&nbsp;if&nbsp;``regex=True``&nbsp;then&nbsp;all&nbsp;of&nbsp;the&nbsp;strings&nbsp;in&nbsp;**both**<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;regexs&nbsp;otherwise&nbsp;they&nbsp;will&nbsp;match<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;directly.&nbsp;This&nbsp;doesn't&nbsp;matter&nbsp;much&nbsp;for&nbsp;`value`&nbsp;since&nbsp;there<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;only&nbsp;a&nbsp;few&nbsp;possible&nbsp;substitution&nbsp;regexes&nbsp;you&nbsp;can&nbsp;use.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str&nbsp;and&nbsp;regex&nbsp;rules&nbsp;apply&nbsp;as&nbsp;above.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dict:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Nested&nbsp;dictionaries,&nbsp;e.g.,&nbsp;{'a':&nbsp;{'b':&nbsp;nan}},&nbsp;are&nbsp;read&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;follows:&nbsp;look&nbsp;in&nbsp;column&nbsp;'a'&nbsp;for&nbsp;the&nbsp;value&nbsp;'b'&nbsp;and&nbsp;replace&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;nan.&nbsp;You&nbsp;can&nbsp;nest&nbsp;regular&nbsp;expressions&nbsp;as&nbsp;well.&nbsp;Note&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;names&nbsp;(the&nbsp;top-level&nbsp;dictionary&nbsp;keys&nbsp;in&nbsp;a&nbsp;nested<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dictionary)&nbsp;**cannot**&nbsp;be&nbsp;regular&nbsp;expressions.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Keys&nbsp;map&nbsp;to&nbsp;column&nbsp;names&nbsp;and&nbsp;values&nbsp;map&nbsp;to&nbsp;substitution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;values.&nbsp;You&nbsp;can&nbsp;treat&nbsp;this&nbsp;as&nbsp;a&nbsp;special&nbsp;case&nbsp;of&nbsp;passing&nbsp;two<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;except&nbsp;that&nbsp;you&nbsp;are&nbsp;specifying&nbsp;the&nbsp;column&nbsp;to&nbsp;search&nbsp;in.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;None:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;This&nbsp;means&nbsp;that&nbsp;the&nbsp;``regex``&nbsp;argument&nbsp;must&nbsp;be&nbsp;a&nbsp;string,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;compiled&nbsp;regular&nbsp;expression,&nbsp;or&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;such&nbsp;elements.&nbsp;If&nbsp;`value`&nbsp;is&nbsp;also&nbsp;``None``&nbsp;then&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;a&nbsp;nested&nbsp;dictionary&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;examples&nbsp;section&nbsp;for&nbsp;examples&nbsp;of&nbsp;each&nbsp;of&nbsp;these.<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;list,&nbsp;str,&nbsp;regex,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a&nbsp;dict&nbsp;of&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for&nbsp;each&nbsp;column&nbsp;(columns&nbsp;not&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;Regular&nbsp;expressions,&nbsp;strings&nbsp;and&nbsp;lists&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dicts&nbsp;of&nbsp;such&nbsp;objects&nbsp;are&nbsp;also&nbsp;allowed.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>&nbsp;(e.g.&nbsp;a&nbsp;column&nbsp;form&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Returns&nbsp;the&nbsp;caller&nbsp;if&nbsp;this&nbsp;is&nbsp;True.<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;size&nbsp;gap&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-regex&nbsp;:&nbsp;bool&nbsp;or&nbsp;same&nbsp;types&nbsp;as&nbsp;`to_replace`,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;interpret&nbsp;`to_replace`&nbsp;and/or&nbsp;`value`&nbsp;as&nbsp;regular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;expressions.&nbsp;If&nbsp;this&nbsp;is&nbsp;``True``&nbsp;then&nbsp;`to_replace`&nbsp;*must*&nbsp;be&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string.&nbsp;Otherwise,&nbsp;`to_replace`&nbsp;must&nbsp;be&nbsp;``None``&nbsp;because&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;parameter&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;regular&nbsp;expression&nbsp;or&nbsp;a&nbsp;list,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict,&nbsp;or&nbsp;array&nbsp;of&nbsp;regular&nbsp;expressions.<br>
-method&nbsp;:&nbsp;string,&nbsp;optional,&nbsp;{'pad',&nbsp;'ffill',&nbsp;'bfill'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;method&nbsp;to&nbsp;use&nbsp;when&nbsp;for&nbsp;replacement,&nbsp;when&nbsp;``to_replace``&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``list``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-NDFrame.reindex<br>
-NDFrame.asfreq<br>
-NDFrame.fillna<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-AssertionError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;a&nbsp;``bool``&nbsp;and&nbsp;`to_replace`&nbsp;is&nbsp;not&nbsp;``None``.<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;a&nbsp;``dict``&nbsp;and&nbsp;`value`&nbsp;is&nbsp;not&nbsp;a&nbsp;``list``,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``dict``,&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``,&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;``None``&nbsp;and&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;compilable&nbsp;into&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;regular&nbsp;expression&nbsp;or&nbsp;is&nbsp;a&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;``list``&nbsp;s&nbsp;or&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``&nbsp;s,&nbsp;but<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;they&nbsp;are&nbsp;not&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-*&nbsp;Regex&nbsp;substitution&nbsp;is&nbsp;performed&nbsp;under&nbsp;the&nbsp;hood&nbsp;with&nbsp;``re.sub``.&nbsp;The<br>
-&nbsp;&nbsp;rules&nbsp;for&nbsp;substitution&nbsp;for&nbsp;``re.sub``&nbsp;are&nbsp;the&nbsp;same.<br>
-*&nbsp;Regular&nbsp;expressions&nbsp;will&nbsp;only&nbsp;substitute&nbsp;on&nbsp;strings,&nbsp;meaning&nbsp;you<br>
-&nbsp;&nbsp;cannot&nbsp;provide,&nbsp;for&nbsp;example,&nbsp;a&nbsp;regular&nbsp;expression&nbsp;matching&nbsp;floating<br>
-&nbsp;&nbsp;point&nbsp;numbers&nbsp;and&nbsp;expect&nbsp;the&nbsp;columns&nbsp;in&nbsp;your&nbsp;frame&nbsp;that&nbsp;have&nbsp;a<br>
-&nbsp;&nbsp;numeric&nbsp;dtype&nbsp;to&nbsp;be&nbsp;matched.&nbsp;However,&nbsp;if&nbsp;those&nbsp;floating&nbsp;point&nbsp;numbers<br>
-&nbsp;&nbsp;*are*&nbsp;strings,&nbsp;then&nbsp;you&nbsp;can&nbsp;do&nbsp;this.<br>
-*&nbsp;This&nbsp;method&nbsp;has&nbsp;*a&nbsp;lot*&nbsp;of&nbsp;options.&nbsp;You&nbsp;are&nbsp;encouraged&nbsp;to&nbsp;experiment<br>
-&nbsp;&nbsp;and&nbsp;play&nbsp;with&nbsp;this&nbsp;method&nbsp;to&nbsp;gain&nbsp;intuition&nbsp;about&nbsp;how&nbsp;it&nbsp;works.</tt></dd></dl>
-
-<dl><dt><a name="Condition-resample"><strong>resample</strong></a>(self, rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0, on=None, level=None)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;frequency&nbsp;conversion&nbsp;and&nbsp;resampling&nbsp;of&nbsp;time<br>
-series.&nbsp;&nbsp;Object&nbsp;must&nbsp;have&nbsp;a&nbsp;datetime-like&nbsp;index&nbsp;(DatetimeIndex,<br>
-PeriodIndex,&nbsp;or&nbsp;TimedeltaIndex),&nbsp;or&nbsp;pass&nbsp;datetime-like&nbsp;values<br>
-to&nbsp;the&nbsp;on&nbsp;or&nbsp;level&nbsp;keyword.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-rule&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;offset&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a>&nbsp;representing&nbsp;target&nbsp;conversion<br>
-axis&nbsp;:&nbsp;int,&nbsp;optional,&nbsp;default&nbsp;0<br>
-closed&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;side&nbsp;of&nbsp;bin&nbsp;interval&nbsp;is&nbsp;closed<br>
-label&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;bin&nbsp;edge&nbsp;label&nbsp;to&nbsp;label&nbsp;bucket&nbsp;with<br>
-convention&nbsp;:&nbsp;{'start',&nbsp;'end',&nbsp;'s',&nbsp;'e'}<br>
-loffset&nbsp;:&nbsp;timedelta<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Adjust&nbsp;the&nbsp;resampled&nbsp;time&nbsp;labels<br>
-base&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;frequencies&nbsp;that&nbsp;evenly&nbsp;subdivide&nbsp;1&nbsp;day,&nbsp;the&nbsp;"origin"&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aggregated&nbsp;intervals.&nbsp;For&nbsp;example,&nbsp;for&nbsp;'5min'&nbsp;frequency,&nbsp;base&nbsp;could<br>
-&nbsp;&nbsp;&nbsp;&nbsp;range&nbsp;from&nbsp;0&nbsp;through&nbsp;4.&nbsp;Defaults&nbsp;to&nbsp;0<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;to&nbsp;use&nbsp;instead&nbsp;of&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-level&nbsp;:&nbsp;string&nbsp;or&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;MultiIndex,&nbsp;level&nbsp;(name&nbsp;or&nbsp;number)&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;resampling.&nbsp;&nbsp;Level&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offset&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;9&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(9),&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;series<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:04:00&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-2000-01-01&nbsp;00:05:00&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:07:00&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-2000-01-01&nbsp;00:08:00&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-Freq:&nbsp;T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;and&nbsp;sum&nbsp;the&nbsp;values<br>
-of&nbsp;the&nbsp;timestamps&nbsp;falling&nbsp;into&nbsp;a&nbsp;bin.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#Condition-resample">resample</a>('3T').<a href="#Condition-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;label&nbsp;each<br>
-bin&nbsp;using&nbsp;the&nbsp;right&nbsp;edge&nbsp;instead&nbsp;of&nbsp;the&nbsp;left.&nbsp;Please&nbsp;note&nbsp;that&nbsp;the<br>
-value&nbsp;in&nbsp;the&nbsp;bucket&nbsp;used&nbsp;as&nbsp;the&nbsp;label&nbsp;is&nbsp;not&nbsp;included&nbsp;in&nbsp;the&nbsp;bucket,<br>
-which&nbsp;it&nbsp;labels.&nbsp;For&nbsp;example,&nbsp;in&nbsp;the&nbsp;original&nbsp;series&nbsp;the<br>
-bucket&nbsp;``2000-01-01&nbsp;00:03:00``&nbsp;contains&nbsp;the&nbsp;value&nbsp;3,&nbsp;but&nbsp;the&nbsp;summed<br>
-value&nbsp;in&nbsp;the&nbsp;resampled&nbsp;bucket&nbsp;with&nbsp;the&nbsp;label``2000-01-01&nbsp;00:03:00``<br>
-does&nbsp;not&nbsp;include&nbsp;3&nbsp;(if&nbsp;it&nbsp;did,&nbsp;the&nbsp;summed&nbsp;value&nbsp;would&nbsp;be&nbsp;6,&nbsp;not&nbsp;3).<br>
-To&nbsp;include&nbsp;this&nbsp;value&nbsp;close&nbsp;the&nbsp;right&nbsp;side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval&nbsp;as<br>
-illustrated&nbsp;in&nbsp;the&nbsp;example&nbsp;below&nbsp;this&nbsp;one.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#Condition-resample">resample</a>('3T',&nbsp;label='right').<a href="#Condition-sum">sum</a>()<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;close&nbsp;the&nbsp;right<br>
-side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#Condition-resample">resample</a>('3T',&nbsp;label='right',&nbsp;closed='right').<a href="#Condition-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#Condition-resample">resample</a>('30S').<a href="#Condition-asfreq">asfreq</a>()[0:5]&nbsp;#select&nbsp;first&nbsp;5&nbsp;rows<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;1.0<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;2.0<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values&nbsp;using&nbsp;the&nbsp;``pad``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#Condition-resample">resample</a>('30S').pad()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the<br>
-``NaN``&nbsp;values&nbsp;using&nbsp;the&nbsp;``bfill``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#Condition-resample">resample</a>('30S').<a href="#Condition-bfill">bfill</a>()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Pass&nbsp;a&nbsp;custom&nbsp;function&nbsp;via&nbsp;``apply``<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;custom_resampler(array_like):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;np.<a href="#Condition-sum">sum</a>(array_like)+5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#Condition-resample">resample</a>('3T').<a href="#Condition-apply">apply</a>(custom_resampler)<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;17<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;26<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects,&nbsp;the&nbsp;keyword&nbsp;``on``&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;specify&nbsp;the<br>
-column&nbsp;instead&nbsp;of&nbsp;the&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=9*[range(4)],&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'])<br>
-&gt;&gt;&gt;&nbsp;df['time']&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-resample">resample</a>('3T',&nbsp;on='time').<a href="#Condition-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;c&nbsp;&nbsp;d<br>
-time<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-&nbsp;<br>
-For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;MultiIndex,&nbsp;the&nbsp;keyword&nbsp;``level``&nbsp;can&nbsp;be&nbsp;used&nbsp;to<br>
-specify&nbsp;on&nbsp;level&nbsp;the&nbsp;resampling&nbsp;needs&nbsp;to&nbsp;take&nbsp;place.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;time&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=5,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=10*[range(4)],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.MultiIndex.from_product([time,&nbsp;[1,&nbsp;2]])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;)<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#Condition-resample">resample</a>('3T',&nbsp;level=0).<a href="#Condition-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;d<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;6&nbsp;&nbsp;12&nbsp;&nbsp;18<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;8&nbsp;&nbsp;12</tt></dd></dl>
-
-<dl><dt><a name="Condition-sample"><strong>sample</strong></a>(self, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None)</dt><dd><tt>Returns&nbsp;a&nbsp;random&nbsp;sample&nbsp;of&nbsp;items&nbsp;from&nbsp;an&nbsp;axis&nbsp;of&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;items&nbsp;from&nbsp;axis&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`frac`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;=&nbsp;1&nbsp;if&nbsp;`frac`&nbsp;=&nbsp;None.<br>
-frac&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fraction&nbsp;of&nbsp;axis&nbsp;items&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`n`.<br>
-replace&nbsp;:&nbsp;boolean,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sample&nbsp;with&nbsp;or&nbsp;without&nbsp;replacement.&nbsp;Default&nbsp;=&nbsp;False.<br>
-weights&nbsp;:&nbsp;str&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;'None'&nbsp;results&nbsp;in&nbsp;equal&nbsp;probability&nbsp;weighting.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;will&nbsp;align&nbsp;with&nbsp;target&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;index.&nbsp;Index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;in&nbsp;weights&nbsp;not&nbsp;found&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;will&nbsp;be&nbsp;ignored&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;values&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;not&nbsp;in&nbsp;weights&nbsp;will&nbsp;be&nbsp;assigned<br>
-&nbsp;&nbsp;&nbsp;&nbsp;weights&nbsp;of&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;called&nbsp;on&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;will&nbsp;accept&nbsp;the&nbsp;name&nbsp;of&nbsp;a&nbsp;column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;axis&nbsp;=&nbsp;0.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Unless&nbsp;weights&nbsp;are&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;weights&nbsp;must&nbsp;be&nbsp;same&nbsp;length&nbsp;as&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;being&nbsp;sampled.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;weights&nbsp;do&nbsp;not&nbsp;sum&nbsp;to&nbsp;1,&nbsp;they&nbsp;will&nbsp;be&nbsp;normalized&nbsp;to&nbsp;sum&nbsp;to&nbsp;1.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;values&nbsp;in&nbsp;the&nbsp;weights&nbsp;column&nbsp;will&nbsp;be&nbsp;treated&nbsp;as&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;inf&nbsp;and&nbsp;-inf&nbsp;values&nbsp;not&nbsp;allowed.<br>
-random_state&nbsp;:&nbsp;int&nbsp;or&nbsp;numpy.random.RandomState,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Seed&nbsp;for&nbsp;the&nbsp;random&nbsp;number&nbsp;generator&nbsp;(if&nbsp;int),&nbsp;or&nbsp;numpy&nbsp;RandomState<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;sample.&nbsp;Accepts&nbsp;axis&nbsp;number&nbsp;or&nbsp;name.&nbsp;Default&nbsp;is&nbsp;stat&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;given&nbsp;data&nbsp;type&nbsp;(0&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;and&nbsp;DataFrames,&nbsp;1&nbsp;for&nbsp;Panels).<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-A&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;type&nbsp;as&nbsp;caller.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Generate&nbsp;an&nbsp;example&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;and&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(50))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-head">head</a>()<br>
-0&nbsp;&nbsp;&nbsp;-0.038497<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.820773<br>
-2&nbsp;&nbsp;&nbsp;-0.972766<br>
-3&nbsp;&nbsp;&nbsp;-1.598270<br>
-4&nbsp;&nbsp;&nbsp;-1.095526<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(50,&nbsp;4),&nbsp;columns=list('ABCD'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-head">head</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;0.016443&nbsp;-2.318952&nbsp;-0.566372&nbsp;-1.028078<br>
-1&nbsp;-1.051921&nbsp;&nbsp;0.438836&nbsp;&nbsp;0.658280&nbsp;-0.175797<br>
-2&nbsp;-1.243569&nbsp;-0.364626&nbsp;-0.215065&nbsp;&nbsp;0.057736<br>
-3&nbsp;&nbsp;1.768216&nbsp;&nbsp;0.404512&nbsp;-0.385604&nbsp;-1.457834<br>
-4&nbsp;&nbsp;1.072446&nbsp;-1.137172&nbsp;&nbsp;0.314194&nbsp;-0.046661<br>
-&nbsp;<br>
-Next&nbsp;extract&nbsp;a&nbsp;random&nbsp;sample&nbsp;from&nbsp;both&nbsp;of&nbsp;these&nbsp;objects...<br>
-&nbsp;<br>
-3&nbsp;random&nbsp;elements&nbsp;from&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-sample">sample</a>(n=3)<br>
-27&nbsp;&nbsp;&nbsp;-0.994689<br>
-55&nbsp;&nbsp;&nbsp;-1.049016<br>
-67&nbsp;&nbsp;&nbsp;-0.224565<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-And&nbsp;a&nbsp;random&nbsp;10%&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;with&nbsp;replacement:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-sample">sample</a>(frac=0.1,&nbsp;replace=True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-35&nbsp;&nbsp;1.981780&nbsp;&nbsp;0.142106&nbsp;&nbsp;1.817165&nbsp;-0.290805<br>
-49&nbsp;-1.336199&nbsp;-0.448634&nbsp;-0.789640&nbsp;&nbsp;0.217116<br>
-40&nbsp;&nbsp;0.823173&nbsp;-0.078816&nbsp;&nbsp;1.009536&nbsp;&nbsp;1.015108<br>
-15&nbsp;&nbsp;1.421154&nbsp;-0.055301&nbsp;-1.922594&nbsp;-0.019696<br>
-6&nbsp;&nbsp;-0.148339&nbsp;&nbsp;0.832938&nbsp;&nbsp;1.787600&nbsp;-1.383767</tt></dd></dl>
-
-<dl><dt><a name="Condition-select"><strong>select</strong></a>(self, crit, axis=0)</dt><dd><tt>Return&nbsp;data&nbsp;corresponding&nbsp;to&nbsp;axis&nbsp;labels&nbsp;matching&nbsp;criteria<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-crit&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;called&nbsp;on&nbsp;each&nbsp;index&nbsp;(label).&nbsp;Should&nbsp;return&nbsp;True&nbsp;or&nbsp;False<br>
-axis&nbsp;:&nbsp;int<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-selection&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="Condition-set_axis"><strong>set_axis</strong></a>(self, axis, labels)</dt><dd><tt>public&nbsp;verson&nbsp;of&nbsp;axis&nbsp;assignment</tt></dd></dl>
-
-<dl><dt><a name="Condition-slice_shift"><strong>slice_shift</strong></a>(self, periods=1, axis=0)</dt><dd><tt>Equivalent&nbsp;to&nbsp;`shift`&nbsp;without&nbsp;copying&nbsp;data.&nbsp;The&nbsp;shifted&nbsp;data&nbsp;will<br>
-not&nbsp;include&nbsp;the&nbsp;dropped&nbsp;periods&nbsp;and&nbsp;the&nbsp;shifted&nbsp;axis&nbsp;will&nbsp;be&nbsp;smaller<br>
-than&nbsp;the&nbsp;original.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-&nbsp;<br>
-Notes<br>
------<br>
-While&nbsp;the&nbsp;`slice_shift`&nbsp;is&nbsp;faster&nbsp;than&nbsp;`shift`,&nbsp;you&nbsp;may&nbsp;pay&nbsp;for&nbsp;it<br>
-later&nbsp;during&nbsp;alignment.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="Condition-squeeze"><strong>squeeze</strong></a>(self, axis=None)</dt><dd><tt>Squeeze&nbsp;length&nbsp;1&nbsp;dimensions.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;None,&nbsp;integer&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;squeeze&nbsp;if&nbsp;1-sized.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-scalar&nbsp;if&nbsp;1-sized,&nbsp;else&nbsp;original&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-swapaxes"><strong>swapaxes</strong></a>(self, axis1, axis2, copy=True)</dt><dd><tt>Interchange&nbsp;axes&nbsp;and&nbsp;swap&nbsp;values&nbsp;axes&nbsp;appropriately<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="Condition-tail"><strong>tail</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;last&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="Condition-to_clipboard"><strong>to_clipboard</strong></a>(self, excel=None, sep=None, **kwargs)</dt><dd><tt>Attempt&nbsp;to&nbsp;write&nbsp;text&nbsp;representation&nbsp;of&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;the&nbsp;system&nbsp;clipboard<br>
-This&nbsp;can&nbsp;be&nbsp;pasted&nbsp;into&nbsp;Excel,&nbsp;for&nbsp;example.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel&nbsp;:&nbsp;boolean,&nbsp;defaults&nbsp;to&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;use&nbsp;the&nbsp;provided&nbsp;separator,&nbsp;writing&nbsp;in&nbsp;a&nbsp;csv<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;format&nbsp;for&nbsp;allowing&nbsp;easy&nbsp;pasting&nbsp;into&nbsp;excel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;False,&nbsp;write&nbsp;a&nbsp;string&nbsp;representation&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;the&nbsp;clipboard<br>
-sep&nbsp;:&nbsp;optional,&nbsp;defaults&nbsp;to&nbsp;tab<br>
-other&nbsp;keywords&nbsp;are&nbsp;passed&nbsp;to&nbsp;to_csv<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Requirements&nbsp;for&nbsp;your&nbsp;platform<br>
-&nbsp;&nbsp;-&nbsp;Linux:&nbsp;xclip,&nbsp;or&nbsp;xsel&nbsp;(with&nbsp;gtk&nbsp;or&nbsp;PyQt4&nbsp;modules)<br>
-&nbsp;&nbsp;-&nbsp;Windows:&nbsp;none<br>
-&nbsp;&nbsp;-&nbsp;OS&nbsp;X:&nbsp;none</tt></dd></dl>
-
-<dl><dt><a name="Condition-to_dense"><strong>to_dense</strong></a>(self)</dt><dd><tt>Return&nbsp;dense&nbsp;representation&nbsp;of&nbsp;NDFrame&nbsp;(as&nbsp;opposed&nbsp;to&nbsp;sparse)</tt></dd></dl>
-
-<dl><dt><a name="Condition-to_hdf"><strong>to_hdf</strong></a>(self, path_or_buf, key, **kwargs)</dt><dd><tt>Write&nbsp;the&nbsp;contained&nbsp;data&nbsp;to&nbsp;an&nbsp;HDF5&nbsp;file&nbsp;using&nbsp;HDFStore.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;(string)&nbsp;or&nbsp;HDFStore&nbsp;<a href="builtins.html#object">object</a><br>
-key&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;identifier&nbsp;for&nbsp;the&nbsp;group&nbsp;in&nbsp;the&nbsp;store<br>
-mode&nbsp;:&nbsp;optional,&nbsp;{'a',&nbsp;'w',&nbsp;'r+'},&nbsp;default&nbsp;'a'<br>
-&nbsp;<br>
-&nbsp;&nbsp;``'w'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write;&nbsp;a&nbsp;new&nbsp;file&nbsp;is&nbsp;created&nbsp;(an&nbsp;existing&nbsp;file&nbsp;with&nbsp;the&nbsp;same<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;name&nbsp;would&nbsp;be&nbsp;deleted).<br>
-&nbsp;&nbsp;``'a'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Append;&nbsp;an&nbsp;existing&nbsp;file&nbsp;is&nbsp;opened&nbsp;for&nbsp;reading&nbsp;and&nbsp;writing,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;if&nbsp;the&nbsp;file&nbsp;does&nbsp;not&nbsp;exist&nbsp;it&nbsp;is&nbsp;created.<br>
-&nbsp;&nbsp;``'r+'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;It&nbsp;is&nbsp;similar&nbsp;to&nbsp;``'a'``,&nbsp;but&nbsp;the&nbsp;file&nbsp;must&nbsp;already&nbsp;exist.<br>
-format&nbsp;:&nbsp;'fixed(f)|table(t)',&nbsp;default&nbsp;is&nbsp;'fixed'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fixed(f)&nbsp;:&nbsp;Fixed&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Fast&nbsp;writing/reading.&nbsp;Not-appendable,&nbsp;nor&nbsp;searchable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;table(t)&nbsp;:&nbsp;Table&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;as&nbsp;a&nbsp;PyTables&nbsp;Table&nbsp;structure&nbsp;which&nbsp;may&nbsp;perform<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;worse&nbsp;but&nbsp;allow&nbsp;more&nbsp;flexible&nbsp;operations&nbsp;like&nbsp;searching<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/&nbsp;selecting&nbsp;subsets&nbsp;of&nbsp;the&nbsp;data<br>
-append&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;Table&nbsp;formats,&nbsp;append&nbsp;the&nbsp;input&nbsp;data&nbsp;to&nbsp;the&nbsp;existing<br>
-data_columns&nbsp;:&nbsp;&nbsp;list&nbsp;of&nbsp;columns,&nbsp;or&nbsp;True,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;columns&nbsp;to&nbsp;create&nbsp;as&nbsp;indexed&nbsp;data&nbsp;columns&nbsp;for&nbsp;on-disk<br>
-&nbsp;&nbsp;&nbsp;&nbsp;queries,&nbsp;or&nbsp;True&nbsp;to&nbsp;use&nbsp;all&nbsp;columns.&nbsp;By&nbsp;default&nbsp;only&nbsp;the&nbsp;axes<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;are&nbsp;indexed.&nbsp;See&nbsp;`here<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__">http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__</a>.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Applicable&nbsp;only&nbsp;to&nbsp;format='table'.<br>
-complevel&nbsp;:&nbsp;int,&nbsp;1-9,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;a&nbsp;complib&nbsp;is&nbsp;specified&nbsp;compression&nbsp;will&nbsp;be&nbsp;applied<br>
-&nbsp;&nbsp;&nbsp;&nbsp;where&nbsp;possible<br>
-complib&nbsp;:&nbsp;{'zlib',&nbsp;'bzip2',&nbsp;'lzo',&nbsp;'blosc',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;complevel&nbsp;is&nbsp;&gt;&nbsp;0&nbsp;apply&nbsp;compression&nbsp;to&nbsp;objects&nbsp;written<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;store&nbsp;wherever&nbsp;possible<br>
-fletcher32&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;applying&nbsp;compression&nbsp;use&nbsp;the&nbsp;fletcher32&nbsp;checksum<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;true,&nbsp;ALL&nbsp;nan&nbsp;rows&nbsp;will&nbsp;not&nbsp;be&nbsp;written&nbsp;to&nbsp;store.</tt></dd></dl>
-
-<dl><dt><a name="Condition-to_json"><strong>to_json</strong></a>(self, path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False)</dt><dd><tt>Convert&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;a&nbsp;JSON&nbsp;string.<br>
-&nbsp;<br>
-Note&nbsp;NaN's&nbsp;and&nbsp;None&nbsp;will&nbsp;be&nbsp;converted&nbsp;to&nbsp;null&nbsp;and&nbsp;datetime&nbsp;objects<br>
-will&nbsp;be&nbsp;converted&nbsp;to&nbsp;UNIX&nbsp;timestamps.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;or&nbsp;buffer&nbsp;to&nbsp;write&nbsp;the&nbsp;result&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;this&nbsp;is&nbsp;None,&nbsp;return&nbsp;a&nbsp;StringIO&nbsp;of&nbsp;the&nbsp;converted&nbsp;string<br>
-orient&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'index'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:&nbsp;{'split','records','index'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{'split','records','index','columns','values'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;The&nbsp;format&nbsp;of&nbsp;the&nbsp;JSON&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;split&nbsp;:&nbsp;dict&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{index&nbsp;-&gt;&nbsp;[index],&nbsp;columns&nbsp;-&gt;&nbsp;[columns],&nbsp;data&nbsp;-&gt;&nbsp;[values]}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;records&nbsp;:&nbsp;list&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[{column&nbsp;-&gt;&nbsp;value},&nbsp;...&nbsp;,&nbsp;{column&nbsp;-&gt;&nbsp;value}]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;index&nbsp;:&nbsp;dict&nbsp;like&nbsp;{index&nbsp;-&gt;&nbsp;{column&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;columns&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;{index&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;values&nbsp;:&nbsp;just&nbsp;the&nbsp;values&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;table&nbsp;:&nbsp;dict&nbsp;like&nbsp;{'schema':&nbsp;{schema},&nbsp;'data':&nbsp;{data}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;describing&nbsp;the&nbsp;data,&nbsp;and&nbsp;the&nbsp;data&nbsp;component&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;like&nbsp;``orient='records'``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionchanged::&nbsp;0.20.0<br>
-&nbsp;<br>
-date_format&nbsp;:&nbsp;{None,&nbsp;'epoch',&nbsp;'iso'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Type&nbsp;of&nbsp;date&nbsp;conversion.&nbsp;`epoch`&nbsp;=&nbsp;epoch&nbsp;milliseconds,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`iso`&nbsp;=&nbsp;ISO8601.&nbsp;The&nbsp;default&nbsp;depends&nbsp;on&nbsp;the&nbsp;`orient`.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`orient='table'`,&nbsp;the&nbsp;default&nbsp;is&nbsp;`'iso'`.&nbsp;For&nbsp;all&nbsp;other&nbsp;orients,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;default&nbsp;is&nbsp;`'epoch'`.<br>
-double_precision&nbsp;:&nbsp;The&nbsp;number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;use&nbsp;when&nbsp;encoding<br>
-&nbsp;&nbsp;&nbsp;&nbsp;floating&nbsp;point&nbsp;values,&nbsp;default&nbsp;10.<br>
-force_ascii&nbsp;:&nbsp;force&nbsp;encoded&nbsp;string&nbsp;to&nbsp;be&nbsp;ASCII,&nbsp;default&nbsp;True.<br>
-date_unit&nbsp;:&nbsp;string,&nbsp;default&nbsp;'ms'&nbsp;(milliseconds)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;time&nbsp;unit&nbsp;to&nbsp;encode&nbsp;to,&nbsp;governs&nbsp;timestamp&nbsp;and&nbsp;ISO8601<br>
-&nbsp;&nbsp;&nbsp;&nbsp;precision.&nbsp;&nbsp;One&nbsp;of&nbsp;'s',&nbsp;'ms',&nbsp;'us',&nbsp;'ns'&nbsp;for&nbsp;second,&nbsp;millisecond,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;microsecond,&nbsp;and&nbsp;nanosecond&nbsp;respectively.<br>
-default_handler&nbsp;:&nbsp;callable,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Handler&nbsp;to&nbsp;call&nbsp;if&nbsp;<a href="builtins.html#object">object</a>&nbsp;cannot&nbsp;otherwise&nbsp;be&nbsp;converted&nbsp;to&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;suitable&nbsp;format&nbsp;for&nbsp;JSON.&nbsp;Should&nbsp;receive&nbsp;a&nbsp;single&nbsp;argument&nbsp;which&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;convert&nbsp;and&nbsp;return&nbsp;a&nbsp;serialisable&nbsp;<a href="builtins.html#object">object</a>.<br>
-lines&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'orient'&nbsp;is&nbsp;'records'&nbsp;write&nbsp;out&nbsp;line&nbsp;delimited&nbsp;json&nbsp;format.&nbsp;Will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;throw&nbsp;ValueError&nbsp;if&nbsp;incorrect&nbsp;'orient'&nbsp;since&nbsp;others&nbsp;are&nbsp;not&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;filtered&nbsp;info&nbsp;axis<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pd.read_json<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([['a',&nbsp;'b'],&nbsp;['c',&nbsp;'d']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=['row&nbsp;1',&nbsp;'row&nbsp;2'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['col&nbsp;1',&nbsp;'col&nbsp;2'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-to_json">to_json</a>(orient='split')<br>
-'{"columns":["col&nbsp;1","col&nbsp;2"],<br>
-&nbsp;&nbsp;"index":["row&nbsp;1","row&nbsp;2"],<br>
-&nbsp;&nbsp;"data":[["a","b"],["c","d"]]}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'index'``&nbsp;formatted&nbsp;JSON:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-to_json">to_json</a>(orient='index')<br>
-'{"row&nbsp;1":{"col&nbsp;1":"a","col&nbsp;2":"b"},"row&nbsp;2":{"col&nbsp;1":"c","col&nbsp;2":"d"}}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'records'``&nbsp;formatted&nbsp;JSON.<br>
-Note&nbsp;that&nbsp;index&nbsp;labels&nbsp;are&nbsp;not&nbsp;preserved&nbsp;with&nbsp;this&nbsp;encoding.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-to_json">to_json</a>(orient='records')<br>
-'[{"col&nbsp;1":"a","col&nbsp;2":"b"},{"col&nbsp;1":"c","col&nbsp;2":"d"}]'<br>
-&nbsp;<br>
-Encoding&nbsp;with&nbsp;Table&nbsp;Schema<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-to_json">to_json</a>(orient='table')<br>
-'{"schema":&nbsp;{"fields":&nbsp;[{"name":&nbsp;"index",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;1",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;2",&nbsp;"type":&nbsp;"string"}],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"primaryKey":&nbsp;"index",<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"pandas_version":&nbsp;"0.20.0"},<br>
-&nbsp;&nbsp;"data":&nbsp;[{"index":&nbsp;"row&nbsp;1",&nbsp;"col&nbsp;1":&nbsp;"a",&nbsp;"col&nbsp;2":&nbsp;"b"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"index":&nbsp;"row&nbsp;2",&nbsp;"col&nbsp;1":&nbsp;"c",&nbsp;"col&nbsp;2":&nbsp;"d"}]}'</tt></dd></dl>
-
-<dl><dt><a name="Condition-to_msgpack"><strong>to_msgpack</strong></a>(self, path_or_buf=None, encoding='utf-8', **kwargs)</dt><dd><tt>msgpack&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path<br>
-&nbsp;<br>
-THIS&nbsp;IS&nbsp;AN&nbsp;EXPERIMENTAL&nbsp;LIBRARY&nbsp;and&nbsp;the&nbsp;storage&nbsp;format<br>
-may&nbsp;not&nbsp;be&nbsp;stable&nbsp;until&nbsp;a&nbsp;future&nbsp;release.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;File&nbsp;path,&nbsp;buffer-like,&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;None,&nbsp;return&nbsp;generated&nbsp;string<br>
-append&nbsp;:&nbsp;boolean&nbsp;whether&nbsp;to&nbsp;append&nbsp;to&nbsp;an&nbsp;existing&nbsp;msgpack<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(default&nbsp;is&nbsp;False)<br>
-compress&nbsp;:&nbsp;type&nbsp;of&nbsp;compressor&nbsp;(zlib&nbsp;or&nbsp;blosc),&nbsp;default&nbsp;to&nbsp;None&nbsp;(no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;compression)</tt></dd></dl>
-
-<dl><dt><a name="Condition-to_pickle"><strong>to_pickle</strong></a>(self, path, compression='infer')</dt><dd><tt>Pickle&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path<br>
-compression&nbsp;:&nbsp;{'infer',&nbsp;'gzip',&nbsp;'bz2',&nbsp;'xz',&nbsp;None},&nbsp;default&nbsp;'infer'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;compression&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0</tt></dd></dl>
-
-<dl><dt><a name="Condition-to_sql"><strong>to_sql</strong></a>(self, name, con, flavor=None, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None)</dt><dd><tt>Write&nbsp;records&nbsp;stored&nbsp;in&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;SQL&nbsp;database.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-name&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;SQL&nbsp;table<br>
-con&nbsp;:&nbsp;SQLAlchemy&nbsp;engine&nbsp;or&nbsp;DBAPI2&nbsp;connection&nbsp;(legacy&nbsp;mode)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Using&nbsp;SQLAlchemy&nbsp;makes&nbsp;it&nbsp;possible&nbsp;to&nbsp;use&nbsp;any&nbsp;DB&nbsp;supported&nbsp;by&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;library.&nbsp;If&nbsp;a&nbsp;DBAPI2&nbsp;<a href="builtins.html#object">object</a>,&nbsp;only&nbsp;sqlite3&nbsp;is&nbsp;supported.<br>
-flavor&nbsp;:&nbsp;'sqlite',&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;DEPRECATED:&nbsp;this&nbsp;parameter&nbsp;will&nbsp;be&nbsp;removed&nbsp;in&nbsp;a&nbsp;future&nbsp;version,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;as&nbsp;'sqlite'&nbsp;is&nbsp;the&nbsp;only&nbsp;supported&nbsp;option&nbsp;if&nbsp;SQLAlchemy&nbsp;is&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;installed.<br>
-schema&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;the&nbsp;schema&nbsp;(if&nbsp;database&nbsp;flavor&nbsp;supports&nbsp;this).&nbsp;If&nbsp;None,&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;schema.<br>
-if_exists&nbsp;:&nbsp;{'fail',&nbsp;'replace',&nbsp;'append'},&nbsp;default&nbsp;'fail'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;fail:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;do&nbsp;nothing.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;replace:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;drop&nbsp;it,&nbsp;recreate&nbsp;it,&nbsp;and&nbsp;insert&nbsp;data.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;append:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;insert&nbsp;data.&nbsp;Create&nbsp;if&nbsp;does&nbsp;not&nbsp;exist.<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;index&nbsp;as&nbsp;a&nbsp;column.<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s).&nbsp;If&nbsp;None&nbsp;is&nbsp;given&nbsp;(default)&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`index`&nbsp;is&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-chunksize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;not&nbsp;None,&nbsp;then&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;in&nbsp;batches&nbsp;of&nbsp;this&nbsp;size&nbsp;at&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;time.&nbsp;&nbsp;If&nbsp;None,&nbsp;all&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;at&nbsp;once.<br>
-dtype&nbsp;:&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;to&nbsp;SQL&nbsp;type,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Optional&nbsp;specifying&nbsp;the&nbsp;datatype&nbsp;for&nbsp;columns.&nbsp;The&nbsp;SQL&nbsp;type&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;SQLAlchemy&nbsp;type,&nbsp;or&nbsp;a&nbsp;string&nbsp;for&nbsp;sqlite3&nbsp;fallback&nbsp;connection.</tt></dd></dl>
-
-<dl><dt><a name="Condition-to_xarray"><strong>to_xarray</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;xarray&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;the&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;DataArray&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;Dataset&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-a&nbsp;DataArray&nbsp;for&nbsp;higher&nbsp;dims<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;foo&nbsp;&nbsp;4.0<br>
-1&nbsp;&nbsp;1&nbsp;&nbsp;bar&nbsp;&nbsp;5.0<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;foo&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(index:&nbsp;3)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;index&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;0&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;1&nbsp;1&nbsp;2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'foo'&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;float64&nbsp;4.0&nbsp;5.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;).set_index(['B','A'])<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-B&nbsp;&nbsp;&nbsp;A<br>
-foo&nbsp;1&nbsp;&nbsp;4.0<br>
-bar&nbsp;1&nbsp;&nbsp;5.0<br>
-foo&nbsp;2&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(A:&nbsp;2,&nbsp;B:&nbsp;2)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;*&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(A)&nbsp;int64&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B,&nbsp;A)&nbsp;float64&nbsp;5.0&nbsp;nan&nbsp;4.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p&nbsp;=&nbsp;pd.Panel(np.arange(24).<a href="#Condition-reshape">reshape</a>(4,3,2),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;items=list('ABCD'),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;major_axis=pd.date_range('20130101',&nbsp;periods=3),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;minor_axis=['first',&nbsp;'second'])<br>
-&gt;&gt;&gt;&nbsp;p<br>
-&lt;class&nbsp;'pandas.core.panel.Panel'&gt;<br>
-Dimensions:&nbsp;4&nbsp;(items)&nbsp;x&nbsp;3&nbsp;(major_axis)&nbsp;x&nbsp;2&nbsp;(minor_axis)<br>
-Items&nbsp;axis:&nbsp;A&nbsp;to&nbsp;D<br>
-Major_axis&nbsp;axis:&nbsp;2013-01-01&nbsp;00:00:00&nbsp;to&nbsp;2013-01-03&nbsp;00:00:00<br>
-Minor_axis&nbsp;axis:&nbsp;first&nbsp;to&nbsp;second<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p.<a href="#Condition-to_xarray">to_xarray</a>()<br>
-&lt;xarray.DataArray&nbsp;(items:&nbsp;4,&nbsp;major_axis:&nbsp;3,&nbsp;minor_axis:&nbsp;2)&gt;<br>
-array([[[&nbsp;0,&nbsp;&nbsp;1],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;2,&nbsp;&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;4,&nbsp;&nbsp;5]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[&nbsp;6,&nbsp;&nbsp;7],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;8,&nbsp;&nbsp;9],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[10,&nbsp;11]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[12,&nbsp;13],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[14,&nbsp;15],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[16,&nbsp;17]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[18,&nbsp;19],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[20,&nbsp;21],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[22,&nbsp;23]]])<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;items&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(items)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'A'&nbsp;'B'&nbsp;'C'&nbsp;'D'<br>
-&nbsp;&nbsp;*&nbsp;major_axis&nbsp;&nbsp;(major_axis)&nbsp;datetime64[ns]&nbsp;2013-01-01&nbsp;2013-01-02&nbsp;2013-01-03&nbsp;&nbsp;#&nbsp;noqa<br>
-&nbsp;&nbsp;*&nbsp;minor_axis&nbsp;&nbsp;(minor_axis)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'first'&nbsp;'second'<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;the&nbsp;`xarray&nbsp;docs&nbsp;&lt;<a href="http://xarray.pydata.org/en/stable/&gt;`__">http://xarray.pydata.org/en/stable/&gt;`__</a></tt></dd></dl>
-
-<dl><dt><a name="Condition-truncate"><strong>truncate</strong></a>(self, before=None, after=None, axis=None, copy=True)</dt><dd><tt>Truncates&nbsp;a&nbsp;sorted&nbsp;NDFrame&nbsp;before&nbsp;and/or&nbsp;after&nbsp;some&nbsp;particular<br>
-index&nbsp;value.&nbsp;If&nbsp;the&nbsp;axis&nbsp;contains&nbsp;only&nbsp;datetime&nbsp;values,&nbsp;before/after<br>
-parameters&nbsp;are&nbsp;converted&nbsp;to&nbsp;datetime&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-before&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;before&nbsp;index&nbsp;value<br>
-after&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;after&nbsp;index&nbsp;value<br>
-axis&nbsp;:&nbsp;the&nbsp;truncation&nbsp;axis,&nbsp;defaults&nbsp;to&nbsp;the&nbsp;stat&nbsp;axis<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;is&nbsp;True,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;truncated&nbsp;section<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-truncated&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="Condition-tshift"><strong>tshift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;the&nbsp;time&nbsp;index,&nbsp;using&nbsp;the&nbsp;index's&nbsp;frequency&nbsp;if&nbsp;available.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM')<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;basestring<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Corresponds&nbsp;to&nbsp;the&nbsp;axis&nbsp;that&nbsp;contains&nbsp;the&nbsp;Index<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;not&nbsp;specified&nbsp;then&nbsp;tries&nbsp;to&nbsp;use&nbsp;the&nbsp;freq&nbsp;or&nbsp;inferred_freq<br>
-attributes&nbsp;of&nbsp;the&nbsp;index.&nbsp;If&nbsp;neither&nbsp;of&nbsp;those&nbsp;attributes&nbsp;exist,&nbsp;a<br>
-ValueError&nbsp;is&nbsp;thrown<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;NDFrame</tt></dd></dl>
-
-<dl><dt><a name="Condition-tz_convert"><strong>tz_convert</strong></a>(self, tz, axis=0, level=None, copy=True)</dt><dd><tt>Convert&nbsp;tz-aware&nbsp;axis&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;convert<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;convert&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;tz-naive.</tt></dd></dl>
-
-<dl><dt><a name="Condition-tz_localize"><strong>tz_localize</strong></a>(self, tz, axis=0, level=None, copy=True, ambiguous='raise')</dt><dd><tt>Localize&nbsp;tz-naive&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;localize<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;localize&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-ambiguous&nbsp;:&nbsp;'infer',&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>,&nbsp;'NaT',&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'infer'&nbsp;will&nbsp;attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>&nbsp;where&nbsp;True&nbsp;signifies&nbsp;a&nbsp;DST&nbsp;time,&nbsp;False&nbsp;designates<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;non-DST&nbsp;time&nbsp;(note&nbsp;that&nbsp;this&nbsp;flag&nbsp;is&nbsp;only&nbsp;applicable&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ambiguous&nbsp;times)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'NaT'&nbsp;will&nbsp;return&nbsp;NaT&nbsp;where&nbsp;there&nbsp;are&nbsp;ambiguous&nbsp;times<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'raise'&nbsp;will&nbsp;raise&nbsp;an&nbsp;AmbiguousTimeError&nbsp;if&nbsp;there&nbsp;are&nbsp;ambiguous<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;times<br>
-infer_dst&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on&nbsp;order<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;is&nbsp;tz-aware&nbsp;and&nbsp;tz&nbsp;is&nbsp;not&nbsp;None.</tt></dd></dl>
-
-<dl><dt><a name="Condition-where"><strong>where</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;True&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;where&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``True``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#Condition-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#Condition-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``where``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#Condition-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).<a href="#Condition-reshape">reshape</a>(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#Condition-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#Condition-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mask`</tt></dd></dl>
-
-<dl><dt><a name="Condition-xs"><strong>xs</strong></a>(self, key, axis=0, level=None, drop_level=True)</dt><dd><tt>Returns&nbsp;a&nbsp;cross-section&nbsp;(row(s)&nbsp;or&nbsp;column(s))&nbsp;from&nbsp;the<br>
-<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Defaults&nbsp;to&nbsp;cross-section&nbsp;on&nbsp;the&nbsp;rows&nbsp;(axis=0).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Some&nbsp;label&nbsp;contained&nbsp;in&nbsp;the&nbsp;index,&nbsp;or&nbsp;partially&nbsp;in&nbsp;a&nbsp;MultiIndex<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;retrieve&nbsp;cross-section&nbsp;on<br>
-level&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;defaults&nbsp;to&nbsp;first&nbsp;n&nbsp;levels&nbsp;(n=1&nbsp;or&nbsp;len(key))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;key&nbsp;partially&nbsp;contained&nbsp;in&nbsp;a&nbsp;MultiIndex,&nbsp;indicate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;levels&nbsp;are&nbsp;used.&nbsp;Levels&nbsp;can&nbsp;be&nbsp;referred&nbsp;by&nbsp;label&nbsp;or&nbsp;position.<br>
-drop_level&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;returns&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;same&nbsp;levels&nbsp;as&nbsp;self.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C<br>
-a&nbsp;&nbsp;4&nbsp;&nbsp;5&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;4&nbsp;&nbsp;0&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;9&nbsp;&nbsp;7&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-xs">xs</a>('a')<br>
-A&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-B&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-C&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Name:&nbsp;a<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-xs">xs</a>('C',&nbsp;axis=1)<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;C<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;second&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7&nbsp;&nbsp;5&nbsp;&nbsp;5&nbsp;&nbsp;0<br>
-baz&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;three&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-xs">xs</a>(('baz',&nbsp;'three'))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-third<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-xs">xs</a>('one',&nbsp;level=1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-baz&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#Condition-xs">xs</a>(('baz',&nbsp;2),&nbsp;level=[0,&nbsp;'third'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-second<br>
-three&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-xs&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-xs&nbsp;is&nbsp;only&nbsp;for&nbsp;getting,&nbsp;not&nbsp;setting&nbsp;values.<br>
-&nbsp;<br>
-MultiIndex&nbsp;Slicers&nbsp;is&nbsp;a&nbsp;generic&nbsp;way&nbsp;to&nbsp;get/set&nbsp;values&nbsp;on&nbsp;any&nbsp;level&nbsp;or<br>
-levels.&nbsp;&nbsp;It&nbsp;is&nbsp;a&nbsp;superset&nbsp;of&nbsp;xs&nbsp;functionality,&nbsp;see<br>
-:ref:`MultiIndex&nbsp;Slicers&nbsp;&lt;advanced.mi_slicers&gt;`</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>at</strong></dt>
-<dd><tt>Fast&nbsp;label-based&nbsp;scalar&nbsp;accessor<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``loc``,&nbsp;``at``&nbsp;provides&nbsp;**label**&nbsp;based&nbsp;scalar&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>blocks</strong></dt>
-<dd><tt>Internal&nbsp;property,&nbsp;property&nbsp;synonym&nbsp;for&nbsp;as_blocks()</tt></dd>
-</dl>
-<dl><dt><strong>iat</strong></dt>
-<dd><tt>Fast&nbsp;integer&nbsp;location&nbsp;scalar&nbsp;accessor.<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``iloc``,&nbsp;``iat``&nbsp;provides&nbsp;**integer**&nbsp;based&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>iloc</strong></dt>
-<dd><tt>Purely&nbsp;integer-location&nbsp;based&nbsp;indexing&nbsp;for&nbsp;selection&nbsp;by&nbsp;position.<br>
-&nbsp;<br>
-``.iloc[]``&nbsp;is&nbsp;primarily&nbsp;integer&nbsp;position&nbsp;based&nbsp;(from&nbsp;``0``&nbsp;to<br>
-``length-1``&nbsp;of&nbsp;the&nbsp;axis),&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a&nbsp;boolean<br>
-array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;An&nbsp;integer,&nbsp;e.g.&nbsp;``5``.<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;integers,&nbsp;e.g.&nbsp;``[4,&nbsp;3,&nbsp;0]``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;ints,&nbsp;e.g.&nbsp;``1:7``.<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.iloc``&nbsp;will&nbsp;raise&nbsp;``IndexError``&nbsp;if&nbsp;a&nbsp;requested&nbsp;indexer&nbsp;is<br>
-out-of-bounds,&nbsp;except&nbsp;*slice*&nbsp;indexers&nbsp;which&nbsp;allow&nbsp;out-of-bounds<br>
-indexing&nbsp;(this&nbsp;conforms&nbsp;with&nbsp;python/numpy&nbsp;*slice*&nbsp;semantics).<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Position&nbsp;&lt;indexing.integer&gt;`</tt></dd>
-</dl>
-<dl><dt><strong>ix</strong></dt>
-<dd><tt>A&nbsp;primarily&nbsp;label-location&nbsp;based&nbsp;indexer,&nbsp;with&nbsp;integer&nbsp;position<br>
-fallback.<br>
-&nbsp;<br>
-``.ix[]``&nbsp;supports&nbsp;mixed&nbsp;integer&nbsp;and&nbsp;label&nbsp;based&nbsp;access.&nbsp;It&nbsp;is<br>
-primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;will&nbsp;fall&nbsp;back&nbsp;to&nbsp;integer&nbsp;positional<br>
-access&nbsp;unless&nbsp;the&nbsp;corresponding&nbsp;axis&nbsp;is&nbsp;of&nbsp;integer&nbsp;type.<br>
-&nbsp;<br>
-``.ix``&nbsp;is&nbsp;the&nbsp;most&nbsp;general&nbsp;indexer&nbsp;and&nbsp;will&nbsp;support&nbsp;any&nbsp;of&nbsp;the<br>
-inputs&nbsp;in&nbsp;``.loc``&nbsp;and&nbsp;``.iloc``.&nbsp;``.ix``&nbsp;also&nbsp;supports&nbsp;floating<br>
-point&nbsp;label&nbsp;schemes.&nbsp;``.ix``&nbsp;is&nbsp;exceptionally&nbsp;useful&nbsp;when&nbsp;dealing<br>
-with&nbsp;mixed&nbsp;positional&nbsp;and&nbsp;label&nbsp;based&nbsp;hierachical&nbsp;indexes.<br>
-&nbsp;<br>
-However,&nbsp;when&nbsp;an&nbsp;axis&nbsp;is&nbsp;integer&nbsp;based,&nbsp;ONLY&nbsp;label&nbsp;based&nbsp;access<br>
-and&nbsp;not&nbsp;positional&nbsp;access&nbsp;is&nbsp;supported.&nbsp;Thus,&nbsp;in&nbsp;such&nbsp;cases,&nbsp;it's<br>
-usually&nbsp;better&nbsp;to&nbsp;be&nbsp;explicit&nbsp;and&nbsp;use&nbsp;``.iloc``&nbsp;or&nbsp;``.loc``.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Advanced&nbsp;Indexing&nbsp;&lt;advanced&gt;`.</tt></dd>
-</dl>
-<dl><dt><strong>loc</strong></dt>
-<dd><tt>Purely&nbsp;label-location&nbsp;based&nbsp;indexer&nbsp;for&nbsp;selection&nbsp;by&nbsp;label.<br>
-&nbsp;<br>
-``.loc[]``&nbsp;is&nbsp;primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a<br>
-boolean&nbsp;array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;A&nbsp;single&nbsp;label,&nbsp;e.g.&nbsp;``5``&nbsp;or&nbsp;``'a'``,&nbsp;(note&nbsp;that&nbsp;``5``&nbsp;is<br>
-&nbsp;&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;*label*&nbsp;of&nbsp;the&nbsp;index,&nbsp;and&nbsp;**never**&nbsp;as&nbsp;an<br>
-&nbsp;&nbsp;integer&nbsp;position&nbsp;along&nbsp;the&nbsp;index).<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;labels,&nbsp;e.g.&nbsp;``['a',&nbsp;'b',&nbsp;'c']``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;labels,&nbsp;e.g.&nbsp;``'a':'f'``&nbsp;(note&nbsp;that&nbsp;contrary<br>
-&nbsp;&nbsp;to&nbsp;usual&nbsp;python&nbsp;slices,&nbsp;**both**&nbsp;the&nbsp;start&nbsp;and&nbsp;the&nbsp;stop&nbsp;are&nbsp;included!).<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.loc``&nbsp;will&nbsp;raise&nbsp;a&nbsp;``KeyError``&nbsp;when&nbsp;the&nbsp;items&nbsp;are&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Label&nbsp;&lt;indexing.label&gt;`</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>is_copy</strong> = None</dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a>:<br>
-<dl><dt><a name="Condition-__dir__"><strong>__dir__</strong></a>(self)</dt><dd><tt>Provide&nbsp;method&nbsp;name&nbsp;lookup&nbsp;and&nbsp;completion<br>
-Only&nbsp;provide&nbsp;'public'&nbsp;methods</tt></dd></dl>
-
-<dl><dt><a name="Condition-__sizeof__"><strong>__sizeof__</strong></a>(self)</dt><dd><tt>Generates&nbsp;the&nbsp;total&nbsp;memory&nbsp;usage&nbsp;for&nbsp;a&nbsp;<a href="builtins.html#object">object</a>&nbsp;that&nbsp;returns<br>
-either&nbsp;a&nbsp;value&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;values</tt></dd></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a>:<br>
-<dl><dt><a name="Condition-__bytes__"><strong>__bytes__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;bytes(obj)&nbsp;in&nbsp;py3&nbsp;only.<br>
-Yields&nbsp;a&nbsp;bytestring&nbsp;in&nbsp;both&nbsp;py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="Condition-__repr__"><strong>__repr__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-<dl><dt><a name="Condition-__str__"><strong>__str__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;Object<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;<a href="#Condition-str">str</a>(df)&nbsp;in&nbsp;both&nbsp;py2/py3.<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-</td></tr></table> <p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#ffc8d8">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#000000" face="helvetica, arial"><a name="FigureState">class <strong>FigureState</strong></a>(<a href="builtins.html#object">builtins.object</a>)</font></td></tr>
-    
-<tr bgcolor="#ffc8d8"><td rowspan=2><tt>&nbsp;&nbsp;&nbsp;</tt></td>
-<td colspan=2><tt>Encapsulates&nbsp;information&nbsp;about&nbsp;the&nbsp;current&nbsp;figure.<br>&nbsp;</tt></td></tr>
-<tr><td>&nbsp;</td>
-<td width="100%">Methods defined here:<br>
-<dl><dt><a name="FigureState-__init__"><strong>__init__</strong></a>(self)</dt><dd><tt>Initialize&nbsp;self.&nbsp;&nbsp;See&nbsp;help(type(self))&nbsp;for&nbsp;accurate&nbsp;signature.</tt></dd></dl>
-
-<dl><dt><a name="FigureState-clear_lines"><strong>clear_lines</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="FigureState-get_line"><strong>get_line</strong></a>(self, style, kwargs)</dt><dd><tt>Gets&nbsp;the&nbsp;line&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;a&nbsp;given&nbsp;style&nbsp;tuple.<br>
-&nbsp;<br>
-style:&nbsp;Matplotlib&nbsp;style&nbsp;string<br>
-kwargs:&nbsp;dictionary&nbsp;of&nbsp;style&nbsp;options<br>
-&nbsp;<br>
-returns:&nbsp;maplotlib.lines.Lines2D</tt></dd></dl>
-
-<dl><dt><a name="FigureState-make_line"><strong>make_line</strong></a>(self, style, kwargs)</dt></dl>
-
-<hr>
-Data descriptors defined here:<br>
-<dl><dt><strong>__dict__</strong></dt>
-<dd><tt>dictionary&nbsp;for&nbsp;instance&nbsp;variables&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>__weakref__</strong></dt>
-<dd><tt>list&nbsp;of&nbsp;weak&nbsp;references&nbsp;to&nbsp;the&nbsp;object&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-</td></tr></table> <p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#ffc8d8">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#000000" face="helvetica, arial"><a name="MyDataFrame">class <strong>MyDataFrame</strong></a>(<a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a>)</font></td></tr>
-    
-<tr bgcolor="#ffc8d8"><td rowspan=2><tt>&nbsp;&nbsp;&nbsp;</tt></td>
-<td colspan=2><tt>MyTimeFrame&nbsp;is&nbsp;a&nbsp;modified&nbsp;version&nbsp;of&nbsp;a&nbsp;Pandas&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,<br>
-with&nbsp;a&nbsp;few&nbsp;changes&nbsp;to&nbsp;make&nbsp;it&nbsp;more&nbsp;suited&nbsp;to&nbsp;our&nbsp;purpose.<br>
-&nbsp;<br>
-In&nbsp;particular,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;provides&nbsp;two&nbsp;special&nbsp;variables&nbsp;called<br>
-`dt`&nbsp;and&nbsp;`T`&nbsp;that&nbsp;cause&nbsp;problems&nbsp;if&nbsp;we&nbsp;try&nbsp;to&nbsp;use&nbsp;those&nbsp;names<br>
-as&nbsp;state&nbsp;variables.<br>
-&nbsp;<br>
-So&nbsp;I&nbsp;added&nbsp;new&nbsp;definitions&nbsp;that&nbsp;override&nbsp;the&nbsp;special&nbsp;variables<br>
-and&nbsp;make&nbsp;these&nbsp;names&nbsp;useable&nbsp;as&nbsp;row&nbsp;labels.<br>&nbsp;</tt></td></tr>
-<tr><td>&nbsp;</td>
-<td width="100%"><dl><dt>Method resolution order:</dt>
-<dd><a href="modsim.html#MyDataFrame">MyDataFrame</a></dd>
-<dd><a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a></dd>
-<dd><a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a></dd>
-<dd><a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a></dd>
-<dd><a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a></dd>
-<dd><a href="pandas.core.base.html#SelectionMixin">pandas.core.base.SelectionMixin</a></dd>
-<dd><a href="builtins.html#object">builtins.object</a></dd>
-</dl>
-<hr>
-Methods defined here:<br>
-<dl><dt><a name="MyDataFrame-__init__"><strong>__init__</strong></a>(self, *args, **kwargs)</dt><dd><tt>Initialize&nbsp;self.&nbsp;&nbsp;See&nbsp;help(type(self))&nbsp;for&nbsp;accurate&nbsp;signature.</tt></dd></dl>
-
-<hr>
-Data descriptors defined here:<br>
-<dl><dt><strong>T</strong></dt>
-<dd><tt>Intercept&nbsp;the&nbsp;Series&nbsp;accessor&nbsp;object&nbsp;so&nbsp;we&nbsp;can&nbsp;use&nbsp;`T`<br>
-as&nbsp;a&nbsp;row&nbsp;label&nbsp;and&nbsp;access&nbsp;it&nbsp;using&nbsp;dot&nbsp;notation.<br>
-&nbsp;<br>
-https://pandas.pydata.org/pandas-docs/stable/generated/<br>
-pandas.DataFrame.T.html#pandas.DataFrame.T</tt></dd>
-</dl>
-<dl><dt><strong>dt</strong></dt>
-<dd><tt>Intercept&nbsp;the&nbsp;Series&nbsp;accessor&nbsp;object&nbsp;so&nbsp;we&nbsp;can&nbsp;use&nbsp;`dt`<br>
-as&nbsp;a&nbsp;row&nbsp;label&nbsp;and&nbsp;access&nbsp;it&nbsp;using&nbsp;dot&nbsp;notation.<br>
-&nbsp;<br>
-https://pandas.pydata.org/pandas-docs/stable/generated/<br>
-pandas.DataFrame.dt.html</tt></dd>
-</dl>
-<hr>
-Methods inherited from <a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a>:<br>
-<dl><dt><a name="MyDataFrame-__add__"><strong>__add__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__add__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__and__"><strong>__and__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__and__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__div__"><strong>__div__</strong></a> = __truediv__(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__truediv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__eq__"><strong>__eq__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__eq__</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__floordiv__"><strong>__floordiv__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__floordiv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__ge__"><strong>__ge__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__ge__</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__getitem__"><strong>__getitem__</strong></a>(self, key)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__gt__"><strong>__gt__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__gt__</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__iadd__"><strong>__iadd__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__imul__"><strong>__imul__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__ipow__"><strong>__ipow__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__isub__"><strong>__isub__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__itruediv__"><strong>__itruediv__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__le__"><strong>__le__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__le__</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__len__"><strong>__len__</strong></a>(self)</dt><dd><tt>Returns&nbsp;length&nbsp;of&nbsp;info&nbsp;axis,&nbsp;but&nbsp;here&nbsp;we&nbsp;use&nbsp;the&nbsp;index</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__lt__"><strong>__lt__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__lt__</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__mod__"><strong>__mod__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__mod__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__mul__"><strong>__mul__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__mul__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__ne__"><strong>__ne__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__ne__</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__or__"><strong>__or__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__or__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__pow__"><strong>__pow__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__pow__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__radd__"><strong>__radd__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__radd__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__rand__"><strong>__rand__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rand__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__rdiv__"><strong>__rdiv__</strong></a> = __rtruediv__(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rtruediv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__rfloordiv__"><strong>__rfloordiv__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rfloordiv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__rmod__"><strong>__rmod__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rmod__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__rmul__"><strong>__rmul__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rmul__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__ror__"><strong>__ror__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__ror__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__rpow__"><strong>__rpow__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rpow__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__rsub__"><strong>__rsub__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rsub__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__rtruediv__"><strong>__rtruediv__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rtruediv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__rxor__"><strong>__rxor__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rxor__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__setitem__"><strong>__setitem__</strong></a>(self, key, value)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__sub__"><strong>__sub__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__sub__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__truediv__"><strong>__truediv__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__truediv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__unicode__"><strong>__unicode__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;unicode(df)&nbsp;in&nbsp;py2&nbsp;only.&nbsp;Yields&nbsp;a&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;both<br>
-py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__xor__"><strong>__xor__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__xor__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-add"><strong>add</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Addition&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`add`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;+&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.radd</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-agg"><strong>agg</strong></a> = aggregate(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#MyDataFrame-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#MyDataFrame-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(10,&nbsp;3),&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.date_range('1/1/2000',&nbsp;periods=10))<br>
-&gt;&gt;&gt;&nbsp;df.iloc[3:7]&nbsp;=&nbsp;np.nan<br>
-&nbsp;<br>
-Aggregate&nbsp;these&nbsp;functions&nbsp;across&nbsp;all&nbsp;columns<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-agg">agg</a>(['sum',&nbsp;'min'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-sum&nbsp;-0.182253&nbsp;-0.614014&nbsp;-2.909534<br>
-min&nbsp;-1.916563&nbsp;-1.460076&nbsp;-1.568297<br>
-&nbsp;<br>
-Different&nbsp;aggregations&nbsp;per&nbsp;column<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-agg">agg</a>({'A'&nbsp;:&nbsp;['sum',&nbsp;'min'],&nbsp;'B'&nbsp;:&nbsp;['min',&nbsp;'max']})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;1.514318<br>
-min&nbsp;-1.916563&nbsp;-1.460076<br>
-sum&nbsp;-0.182253&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.transform<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.groupby.aggregate<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.resample.aggregate<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rolling.aggregate</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-aggregate"><strong>aggregate</strong></a>(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#MyDataFrame-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#MyDataFrame-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(10,&nbsp;3),&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.date_range('1/1/2000',&nbsp;periods=10))<br>
-&gt;&gt;&gt;&nbsp;df.iloc[3:7]&nbsp;=&nbsp;np.nan<br>
-&nbsp;<br>
-Aggregate&nbsp;these&nbsp;functions&nbsp;across&nbsp;all&nbsp;columns<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-agg">agg</a>(['sum',&nbsp;'min'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-sum&nbsp;-0.182253&nbsp;-0.614014&nbsp;-2.909534<br>
-min&nbsp;-1.916563&nbsp;-1.460076&nbsp;-1.568297<br>
-&nbsp;<br>
-Different&nbsp;aggregations&nbsp;per&nbsp;column<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-agg">agg</a>({'A'&nbsp;:&nbsp;['sum',&nbsp;'min'],&nbsp;'B'&nbsp;:&nbsp;['min',&nbsp;'max']})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;1.514318<br>
-min&nbsp;-1.916563&nbsp;-1.460076<br>
-sum&nbsp;-0.182253&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.transform<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.groupby.aggregate<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.resample.aggregate<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rolling.aggregate</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-align"><strong>align</strong></a>(self, other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)</dt><dd><tt>Align&nbsp;two&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;their&nbsp;axes&nbsp;with&nbsp;the<br>
-specified&nbsp;join&nbsp;method&nbsp;for&nbsp;each&nbsp;axis&nbsp;Index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-join&nbsp;:&nbsp;{'outer',&nbsp;'inner',&nbsp;'left',&nbsp;'right'},&nbsp;default&nbsp;'outer'<br>
-axis&nbsp;:&nbsp;allowed&nbsp;axis&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;on&nbsp;index&nbsp;(0),&nbsp;columns&nbsp;(1),&nbsp;or&nbsp;both&nbsp;(None)<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Always&nbsp;returns&nbsp;new&nbsp;objects.&nbsp;If&nbsp;copy=False&nbsp;and&nbsp;no&nbsp;reindexing&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;required&nbsp;then&nbsp;original&nbsp;objects&nbsp;are&nbsp;returned.<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-method&nbsp;:&nbsp;str,&nbsp;default&nbsp;None<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-fill_axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Filling&nbsp;axis,&nbsp;method&nbsp;and&nbsp;limit<br>
-broadcast_axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;values&nbsp;along&nbsp;this&nbsp;axis,&nbsp;if&nbsp;aligning&nbsp;two&nbsp;objects&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;different&nbsp;dimensions<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-(left,&nbsp;right)&nbsp;:&nbsp;(<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;type&nbsp;of&nbsp;other)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Aligned&nbsp;objects</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-all"><strong>all</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;all&nbsp;elements&nbsp;are&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-all&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-any"><strong>any</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;any&nbsp;element&nbsp;is&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-any&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-append"><strong>append</strong></a>(self, other, ignore_index=False, verify_integrity=False)</dt><dd><tt>Append&nbsp;rows&nbsp;of&nbsp;`other`&nbsp;to&nbsp;the&nbsp;end&nbsp;of&nbsp;this&nbsp;frame,&nbsp;returning&nbsp;a&nbsp;new<br>
-<a href="builtins.html#object">object</a>.&nbsp;Columns&nbsp;not&nbsp;in&nbsp;this&nbsp;frame&nbsp;are&nbsp;added&nbsp;as&nbsp;new&nbsp;columns.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>/dict-like&nbsp;<a href="builtins.html#object">object</a>,&nbsp;or&nbsp;list&nbsp;of&nbsp;these<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;data&nbsp;to&nbsp;append.<br>
-ignore_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;not&nbsp;use&nbsp;the&nbsp;index&nbsp;labels.<br>
-verify_integrity&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;raise&nbsp;ValueError&nbsp;on&nbsp;creating&nbsp;index&nbsp;with&nbsp;duplicates.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-appended&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;a&nbsp;list&nbsp;of&nbsp;dict/series&nbsp;is&nbsp;passed&nbsp;and&nbsp;the&nbsp;keys&nbsp;are&nbsp;all&nbsp;contained&nbsp;in<br>
-the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;index,&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;columns&nbsp;in&nbsp;the&nbsp;resulting<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;be&nbsp;unchanged.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.concat&nbsp;:&nbsp;General&nbsp;function&nbsp;to&nbsp;concatenate&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;Panel&nbsp;objects<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[1,&nbsp;2],&nbsp;[3,&nbsp;4]],&nbsp;columns=list('AB'))<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-1&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[5,&nbsp;6],&nbsp;[7,&nbsp;8]],&nbsp;columns=list('AB'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-append">append</a>(df2)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-1&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-0&nbsp;&nbsp;5&nbsp;&nbsp;6<br>
-1&nbsp;&nbsp;7&nbsp;&nbsp;8<br>
-&nbsp;<br>
-With&nbsp;`ignore_index`&nbsp;set&nbsp;to&nbsp;True:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-append">append</a>(df2,&nbsp;ignore_index=True)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-1&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;5&nbsp;&nbsp;6<br>
-3&nbsp;&nbsp;7&nbsp;&nbsp;8</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-apply"><strong>apply</strong></a>(self, func, axis=0, broadcast=False, raw=False, reduce=None, args=(), **kwds)</dt><dd><tt>Applies&nbsp;function&nbsp;along&nbsp;input&nbsp;axis&nbsp;of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-Objects&nbsp;passed&nbsp;to&nbsp;functions&nbsp;are&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;objects&nbsp;having&nbsp;index<br>
-either&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;index&nbsp;(axis=0)&nbsp;or&nbsp;the&nbsp;columns&nbsp;(axis=1).<br>
-Return&nbsp;type&nbsp;depends&nbsp;on&nbsp;whether&nbsp;passed&nbsp;function&nbsp;aggregates,&nbsp;or&nbsp;the<br>
-reduce&nbsp;argument&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;is&nbsp;empty.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;apply&nbsp;to&nbsp;each&nbsp;column/row<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;0&nbsp;or&nbsp;'index':&nbsp;apply&nbsp;function&nbsp;to&nbsp;each&nbsp;column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;1&nbsp;or&nbsp;'columns':&nbsp;apply&nbsp;function&nbsp;to&nbsp;each&nbsp;row<br>
-broadcast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;aggregation&nbsp;functions,&nbsp;return&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;size&nbsp;with&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;&nbsp;propagated<br>
-raw&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;convert&nbsp;each&nbsp;row&nbsp;or&nbsp;column&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;If&nbsp;raw=True&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;function&nbsp;will&nbsp;receive&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;objects&nbsp;instead.&nbsp;If&nbsp;you&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;just&nbsp;applying&nbsp;a&nbsp;NumPy&nbsp;reduction&nbsp;function&nbsp;this&nbsp;will&nbsp;achieve&nbsp;much<br>
-&nbsp;&nbsp;&nbsp;&nbsp;better&nbsp;performance<br>
-reduce&nbsp;:&nbsp;boolean&nbsp;or&nbsp;None,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Try&nbsp;to&nbsp;apply&nbsp;reduction&nbsp;procedures.&nbsp;If&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;is&nbsp;empty,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;apply&nbsp;will&nbsp;use&nbsp;reduce&nbsp;to&nbsp;determine&nbsp;whether&nbsp;the&nbsp;result&nbsp;should&nbsp;be&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;If&nbsp;reduce&nbsp;is&nbsp;None&nbsp;(the&nbsp;default),&nbsp;apply's<br>
-&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;value&nbsp;will&nbsp;be&nbsp;guessed&nbsp;by&nbsp;calling&nbsp;func&nbsp;an&nbsp;empty&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(note:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;while&nbsp;guessing,&nbsp;exceptions&nbsp;raised&nbsp;by&nbsp;func&nbsp;will&nbsp;be&nbsp;ignored).&nbsp;If<br>
-&nbsp;&nbsp;&nbsp;&nbsp;reduce&nbsp;is&nbsp;True&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;always&nbsp;be&nbsp;returned,&nbsp;and&nbsp;if&nbsp;False&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;always&nbsp;be&nbsp;returned.<br>
-args&nbsp;:&nbsp;tuple<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Positional&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;to&nbsp;function&nbsp;in&nbsp;addition&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;array/series<br>
-Additional&nbsp;keyword&nbsp;arguments&nbsp;will&nbsp;be&nbsp;passed&nbsp;as&nbsp;keywords&nbsp;to&nbsp;the&nbsp;function<br>
-&nbsp;<br>
-Notes<br>
------<br>
-In&nbsp;the&nbsp;current&nbsp;implementation&nbsp;apply&nbsp;calls&nbsp;func&nbsp;twice&nbsp;on&nbsp;the<br>
-first&nbsp;column/row&nbsp;to&nbsp;decide&nbsp;whether&nbsp;it&nbsp;can&nbsp;take&nbsp;a&nbsp;fast&nbsp;or&nbsp;slow<br>
-code&nbsp;path.&nbsp;This&nbsp;can&nbsp;lead&nbsp;to&nbsp;unexpected&nbsp;behavior&nbsp;if&nbsp;func&nbsp;has<br>
-side-effects,&nbsp;as&nbsp;they&nbsp;will&nbsp;take&nbsp;effect&nbsp;twice&nbsp;for&nbsp;the&nbsp;first<br>
-column/row.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-apply">apply</a>(numpy.sqrt)&nbsp;#&nbsp;returns&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-apply">apply</a>(numpy.sum,&nbsp;axis=0)&nbsp;#&nbsp;equiv&nbsp;to&nbsp;df.<a href="#MyDataFrame-sum">sum</a>(0)<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-apply">apply</a>(numpy.sum,&nbsp;axis=1)&nbsp;#&nbsp;equiv&nbsp;to&nbsp;df.<a href="#MyDataFrame-sum">sum</a>(1)<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap:&nbsp;For&nbsp;elementwise&nbsp;operations<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.aggregate:&nbsp;only&nbsp;perform&nbsp;aggregating&nbsp;type&nbsp;operations<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.transform:&nbsp;only&nbsp;perform&nbsp;transformating&nbsp;type&nbsp;operations<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-applied&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-applymap"><strong>applymap</strong></a>(self, func)</dt><dd><tt>Apply&nbsp;a&nbsp;function&nbsp;to&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;that&nbsp;is&nbsp;intended&nbsp;to&nbsp;operate<br>
-elementwise,&nbsp;i.e.&nbsp;like&nbsp;doing&nbsp;map(func,&nbsp;series)&nbsp;for&nbsp;each&nbsp;series&nbsp;in&nbsp;the<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Python&nbsp;function,&nbsp;returns&nbsp;a&nbsp;single&nbsp;value&nbsp;from&nbsp;a&nbsp;single&nbsp;value<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(3,&nbsp;3))<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-0&nbsp;&nbsp;-0.029638&nbsp;&nbsp;1.081563&nbsp;&nbsp;&nbsp;1.280300<br>
-1&nbsp;&nbsp;&nbsp;0.647747&nbsp;&nbsp;0.831136&nbsp;&nbsp;-1.549481<br>
-2&nbsp;&nbsp;&nbsp;0.513416&nbsp;-0.884417&nbsp;&nbsp;&nbsp;0.195343<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;df.<a href="#MyDataFrame-applymap">applymap</a>(lambda&nbsp;x:&nbsp;'%.2f'&nbsp;%&nbsp;x)<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-0&nbsp;&nbsp;-0.03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.08&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.28<br>
-1&nbsp;&nbsp;&nbsp;0.65&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.83&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-1.55<br>
-2&nbsp;&nbsp;&nbsp;0.51&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-0.88&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.20<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-applied&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply&nbsp;:&nbsp;For&nbsp;operations&nbsp;on&nbsp;rows/columns</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-assign"><strong>assign</strong></a>(self, **kwargs)</dt><dd><tt>Assign&nbsp;new&nbsp;columns&nbsp;to&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;returning&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a><br>
-(a&nbsp;copy)&nbsp;with&nbsp;all&nbsp;the&nbsp;original&nbsp;columns&nbsp;in&nbsp;addition&nbsp;to&nbsp;the&nbsp;new&nbsp;ones.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-kwargs&nbsp;:&nbsp;keyword,&nbsp;value&nbsp;pairs<br>
-&nbsp;&nbsp;&nbsp;&nbsp;keywords&nbsp;are&nbsp;the&nbsp;column&nbsp;names.&nbsp;If&nbsp;the&nbsp;values&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;callable,&nbsp;they&nbsp;are&nbsp;computed&nbsp;on&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;assigned&nbsp;to&nbsp;the&nbsp;new&nbsp;columns.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;values&nbsp;are&nbsp;not&nbsp;callable,&nbsp;(e.g.&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;scalar,&nbsp;or&nbsp;array),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;they&nbsp;are&nbsp;simply&nbsp;assigned.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-df&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;new&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;the&nbsp;new&nbsp;columns&nbsp;in&nbsp;addition&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;all&nbsp;the&nbsp;existing&nbsp;columns.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Since&nbsp;``kwargs``&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;the&nbsp;order&nbsp;of&nbsp;your<br>
-arguments&nbsp;may&nbsp;not&nbsp;be&nbsp;preserved.&nbsp;To&nbsp;make&nbsp;things&nbsp;predicatable,<br>
-the&nbsp;columns&nbsp;are&nbsp;inserted&nbsp;in&nbsp;alphabetical&nbsp;order,&nbsp;at&nbsp;the&nbsp;end&nbsp;of<br>
-your&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Assigning&nbsp;multiple&nbsp;columns&nbsp;within&nbsp;the&nbsp;same<br>
-``assign``&nbsp;is&nbsp;possible,&nbsp;but&nbsp;you&nbsp;cannot&nbsp;reference&nbsp;other&nbsp;columns<br>
-created&nbsp;within&nbsp;the&nbsp;same&nbsp;``assign``&nbsp;call.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;range(1,&nbsp;11),&nbsp;'B':&nbsp;np.random.randn(10)})<br>
-&nbsp;<br>
-Where&nbsp;the&nbsp;value&nbsp;is&nbsp;a&nbsp;callable,&nbsp;evaluated&nbsp;on&nbsp;`df`:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-assign">assign</a>(ln_A&nbsp;=&nbsp;lambda&nbsp;x:&nbsp;np.log(x.A))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ln_A<br>
-0&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;0.426905&nbsp;&nbsp;0.000000<br>
-1&nbsp;&nbsp;&nbsp;2&nbsp;-0.780949&nbsp;&nbsp;0.693147<br>
-2&nbsp;&nbsp;&nbsp;3&nbsp;-0.418711&nbsp;&nbsp;1.098612<br>
-3&nbsp;&nbsp;&nbsp;4&nbsp;-0.269708&nbsp;&nbsp;1.386294<br>
-4&nbsp;&nbsp;&nbsp;5&nbsp;-0.274002&nbsp;&nbsp;1.609438<br>
-5&nbsp;&nbsp;&nbsp;6&nbsp;-0.500792&nbsp;&nbsp;1.791759<br>
-6&nbsp;&nbsp;&nbsp;7&nbsp;&nbsp;1.649697&nbsp;&nbsp;1.945910<br>
-7&nbsp;&nbsp;&nbsp;8&nbsp;-1.495604&nbsp;&nbsp;2.079442<br>
-8&nbsp;&nbsp;&nbsp;9&nbsp;&nbsp;0.549296&nbsp;&nbsp;2.197225<br>
-9&nbsp;&nbsp;10&nbsp;-0.758542&nbsp;&nbsp;2.302585<br>
-&nbsp;<br>
-Where&nbsp;the&nbsp;value&nbsp;already&nbsp;exists&nbsp;and&nbsp;is&nbsp;inserted:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;newcol&nbsp;=&nbsp;np.log(df['A'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-assign">assign</a>(ln_A=newcol)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ln_A<br>
-0&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;0.426905&nbsp;&nbsp;0.000000<br>
-1&nbsp;&nbsp;&nbsp;2&nbsp;-0.780949&nbsp;&nbsp;0.693147<br>
-2&nbsp;&nbsp;&nbsp;3&nbsp;-0.418711&nbsp;&nbsp;1.098612<br>
-3&nbsp;&nbsp;&nbsp;4&nbsp;-0.269708&nbsp;&nbsp;1.386294<br>
-4&nbsp;&nbsp;&nbsp;5&nbsp;-0.274002&nbsp;&nbsp;1.609438<br>
-5&nbsp;&nbsp;&nbsp;6&nbsp;-0.500792&nbsp;&nbsp;1.791759<br>
-6&nbsp;&nbsp;&nbsp;7&nbsp;&nbsp;1.649697&nbsp;&nbsp;1.945910<br>
-7&nbsp;&nbsp;&nbsp;8&nbsp;-1.495604&nbsp;&nbsp;2.079442<br>
-8&nbsp;&nbsp;&nbsp;9&nbsp;&nbsp;0.549296&nbsp;&nbsp;2.197225<br>
-9&nbsp;&nbsp;10&nbsp;-0.758542&nbsp;&nbsp;2.302585</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-boxplot"><strong>boxplot</strong></a>(self, column=None, by=None, ax=None, fontsize=None, rot=0, grid=True, figsize=None, layout=None, return_type=None, **kwds)</dt><dd><tt>Make&nbsp;a&nbsp;box&nbsp;plot&nbsp;from&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;optionally&nbsp;grouped&nbsp;by&nbsp;some&nbsp;columns&nbsp;or<br>
-other&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;the&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>&nbsp;holding&nbsp;the&nbsp;data<br>
-column&nbsp;:&nbsp;column&nbsp;name&nbsp;or&nbsp;list&nbsp;of&nbsp;names,&nbsp;or&nbsp;vector<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Can&nbsp;be&nbsp;any&nbsp;valid&nbsp;input&nbsp;to&nbsp;groupby<br>
-by&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;group&nbsp;by<br>
-ax&nbsp;:&nbsp;Matplotlib&nbsp;axes&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-fontsize&nbsp;:&nbsp;int&nbsp;or&nbsp;string<br>
-rot&nbsp;:&nbsp;label&nbsp;rotation&nbsp;angle<br>
-figsize&nbsp;:&nbsp;A&nbsp;tuple&nbsp;(width,&nbsp;height)&nbsp;in&nbsp;inches<br>
-grid&nbsp;:&nbsp;Setting&nbsp;this&nbsp;to&nbsp;True&nbsp;will&nbsp;show&nbsp;the&nbsp;grid<br>
-layout&nbsp;:&nbsp;tuple&nbsp;(optional)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(rows,&nbsp;columns)&nbsp;for&nbsp;the&nbsp;layout&nbsp;of&nbsp;the&nbsp;plot<br>
-return_type&nbsp;:&nbsp;{None,&nbsp;'axes',&nbsp;'dict',&nbsp;'both'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;kind&nbsp;of&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;return.&nbsp;The&nbsp;default&nbsp;is&nbsp;``axes``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'axes'&nbsp;returns&nbsp;the&nbsp;matplotlib&nbsp;axes&nbsp;the&nbsp;boxplot&nbsp;is&nbsp;drawn&nbsp;on;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'dict'&nbsp;returns&nbsp;a&nbsp;dictionary&nbsp;&nbsp;whose&nbsp;values&nbsp;are&nbsp;the&nbsp;matplotlib<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Lines&nbsp;of&nbsp;the&nbsp;boxplot;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'both'&nbsp;returns&nbsp;a&nbsp;namedtuple&nbsp;with&nbsp;the&nbsp;axes&nbsp;and&nbsp;dict.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;grouping&nbsp;with&nbsp;``by``,&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;mapping&nbsp;columns&nbsp;to&nbsp;``return_type``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;returned,&nbsp;unless&nbsp;``return_type``&nbsp;is&nbsp;None,&nbsp;in&nbsp;which&nbsp;case&nbsp;a&nbsp;NumPy<br>
-&nbsp;&nbsp;&nbsp;&nbsp;array&nbsp;of&nbsp;axes&nbsp;is&nbsp;returned&nbsp;with&nbsp;the&nbsp;same&nbsp;shape&nbsp;as&nbsp;``layout``.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;prose&nbsp;documentation&nbsp;for&nbsp;more.<br>
-&nbsp;<br>
-kwds&nbsp;:&nbsp;other&nbsp;plotting&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;be&nbsp;passed&nbsp;to&nbsp;matplotlib&nbsp;boxplot<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;function<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-lines&nbsp;:&nbsp;dict<br>
-ax&nbsp;:&nbsp;matplotlib&nbsp;Axes<br>
-(ax,&nbsp;lines):&nbsp;namedtuple<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Use&nbsp;``return_type='dict'``&nbsp;when&nbsp;you&nbsp;want&nbsp;to&nbsp;tweak&nbsp;the&nbsp;appearance<br>
-of&nbsp;the&nbsp;lines&nbsp;after&nbsp;plotting.&nbsp;In&nbsp;this&nbsp;case&nbsp;a&nbsp;dict&nbsp;containing&nbsp;the&nbsp;Lines<br>
-making&nbsp;up&nbsp;the&nbsp;boxes,&nbsp;caps,&nbsp;fliers,&nbsp;medians,&nbsp;and&nbsp;whiskers&nbsp;is&nbsp;returned.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-combine"><strong>combine</strong></a>(self, other, func, fill_value=None, overwrite=True)</dt><dd><tt>Add&nbsp;two&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects&nbsp;and&nbsp;do&nbsp;not&nbsp;propagate&nbsp;NaN&nbsp;values,&nbsp;so&nbsp;if&nbsp;for&nbsp;a<br>
-(column,&nbsp;time)&nbsp;one&nbsp;frame&nbsp;is&nbsp;missing&nbsp;a&nbsp;value,&nbsp;it&nbsp;will&nbsp;default&nbsp;to&nbsp;the<br>
-other&nbsp;frame's&nbsp;value&nbsp;(which&nbsp;might&nbsp;be&nbsp;NaN&nbsp;as&nbsp;well)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-func&nbsp;:&nbsp;function<br>
-fill_value&nbsp;:&nbsp;scalar&nbsp;value<br>
-overwrite&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;then&nbsp;overwrite&nbsp;values&nbsp;for&nbsp;common&nbsp;keys&nbsp;in&nbsp;the&nbsp;calling&nbsp;frame<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-combine_first"><strong>combine_first</strong></a>(self, other)</dt><dd><tt>Combine&nbsp;two&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects&nbsp;and&nbsp;default&nbsp;to&nbsp;non-null&nbsp;values&nbsp;in&nbsp;frame<br>
-calling&nbsp;the&nbsp;method.&nbsp;Result&nbsp;index&nbsp;columns&nbsp;will&nbsp;be&nbsp;the&nbsp;union&nbsp;of&nbsp;the<br>
-respective&nbsp;indexes&nbsp;and&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-a's&nbsp;values&nbsp;prioritized,&nbsp;use&nbsp;values&nbsp;from&nbsp;b&nbsp;to&nbsp;fill&nbsp;holes:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#MyDataFrame-combine_first">combine_first</a>(b)<br>
-&nbsp;<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-combined&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-compound"><strong>compound</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;compound&nbsp;percentage&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-compounded&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-corr"><strong>corr</strong></a>(self, method='pearson', min_periods=1)</dt><dd><tt>Compute&nbsp;pairwise&nbsp;correlation&nbsp;of&nbsp;columns,&nbsp;excluding&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-method&nbsp;:&nbsp;{'pearson',&nbsp;'kendall',&nbsp;'spearman'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pearson&nbsp;:&nbsp;standard&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;kendall&nbsp;:&nbsp;Kendall&nbsp;Tau&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;spearman&nbsp;:&nbsp;Spearman&nbsp;rank&nbsp;correlation<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;required&nbsp;per&nbsp;pair&nbsp;of&nbsp;columns<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result.&nbsp;Currently&nbsp;only&nbsp;available&nbsp;for&nbsp;pearson<br>
-&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;spearman&nbsp;correlation<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-corrwith"><strong>corrwith</strong></a>(self, other, axis=0, drop=False)</dt><dd><tt>Compute&nbsp;pairwise&nbsp;correlation&nbsp;between&nbsp;rows&nbsp;or&nbsp;columns&nbsp;of&nbsp;two&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-objects.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;or&nbsp;'index'&nbsp;to&nbsp;compute&nbsp;column-wise,&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;for&nbsp;row-wise<br>
-drop&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Drop&nbsp;missing&nbsp;indices&nbsp;from&nbsp;result,&nbsp;default&nbsp;returns&nbsp;union&nbsp;of&nbsp;all<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-correls&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-count"><strong>count</strong></a>(self, axis=0, level=None, numeric_only=False)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;number&nbsp;of&nbsp;non-NA/null&nbsp;observations&nbsp;over&nbsp;requested<br>
-axis.&nbsp;Works&nbsp;with&nbsp;non-floating&nbsp;point&nbsp;data&nbsp;as&nbsp;well&nbsp;(detects&nbsp;NaN&nbsp;and&nbsp;None)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;or&nbsp;'index'&nbsp;for&nbsp;row-wise,&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;for&nbsp;column-wise<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-count&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-cov"><strong>cov</strong></a>(self, min_periods=None)</dt><dd><tt>Compute&nbsp;pairwise&nbsp;covariance&nbsp;of&nbsp;columns,&nbsp;excluding&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;required&nbsp;per&nbsp;pair&nbsp;of&nbsp;columns<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-`y`&nbsp;contains&nbsp;the&nbsp;covariance&nbsp;matrix&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;time&nbsp;series.<br>
-The&nbsp;covariance&nbsp;is&nbsp;normalized&nbsp;by&nbsp;N-1&nbsp;(unbiased&nbsp;estimator).</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-cummax"><strong>cummax</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;max&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummax&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.max&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-cummin"><strong>cummin</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;minimum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummin&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.min&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-cumprod"><strong>cumprod</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;product&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumprod&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.prod&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-cumsum"><strong>cumsum</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;sum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumsum&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.sum&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-diff"><strong>diff</strong></a>(self, periods=1, axis=0)</dt><dd><tt>1st&nbsp;discrete&nbsp;difference&nbsp;of&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;difference<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Take&nbsp;difference&nbsp;over&nbsp;rows&nbsp;(0)&nbsp;or&nbsp;columns&nbsp;(1).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-diffed&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-div"><strong>div</strong></a> = truediv(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-divide"><strong>divide</strong></a> = truediv(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-dot"><strong>dot</strong></a>(self, other)</dt><dd><tt>Matrix&nbsp;multiplication&nbsp;with&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;objects<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dot_product&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-drop_duplicates"><strong>drop_duplicates</strong></a>(self, subset=None, keep='first', inplace=False)</dt><dd><tt>Return&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;duplicate&nbsp;rows&nbsp;removed,&nbsp;optionally&nbsp;only<br>
-considering&nbsp;certain&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-subset&nbsp;:&nbsp;column&nbsp;label&nbsp;or&nbsp;sequence&nbsp;of&nbsp;labels,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Only&nbsp;consider&nbsp;certain&nbsp;columns&nbsp;for&nbsp;identifying&nbsp;duplicates,&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;use&nbsp;all&nbsp;of&nbsp;the&nbsp;columns<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Drop&nbsp;all&nbsp;duplicates.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;drop&nbsp;duplicates&nbsp;in&nbsp;place&nbsp;or&nbsp;to&nbsp;return&nbsp;a&nbsp;copy<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-deduplicated&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-dropna"><strong>dropna</strong></a>(self, axis=0, how='any', thresh=None, subset=None, inplace=False)</dt><dd><tt>Return&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;labels&nbsp;on&nbsp;given&nbsp;axis&nbsp;omitted&nbsp;where&nbsp;alternately&nbsp;any<br>
-or&nbsp;all&nbsp;of&nbsp;the&nbsp;data&nbsp;are&nbsp;missing<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;or&nbsp;tuple/list&nbsp;thereof<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Pass&nbsp;tuple&nbsp;or&nbsp;list&nbsp;to&nbsp;drop&nbsp;on&nbsp;multiple&nbsp;axes<br>
-how&nbsp;:&nbsp;{'any',&nbsp;'all'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;any&nbsp;:&nbsp;if&nbsp;any&nbsp;NA&nbsp;values&nbsp;are&nbsp;present,&nbsp;drop&nbsp;that&nbsp;label<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;all&nbsp;:&nbsp;if&nbsp;all&nbsp;values&nbsp;are&nbsp;NA,&nbsp;drop&nbsp;that&nbsp;label<br>
-thresh&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;int&nbsp;value&nbsp;:&nbsp;require&nbsp;that&nbsp;many&nbsp;non-NA&nbsp;values<br>
-subset&nbsp;:&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Labels&nbsp;along&nbsp;other&nbsp;axis&nbsp;to&nbsp;consider,&nbsp;e.g.&nbsp;if&nbsp;you&nbsp;are&nbsp;dropping&nbsp;rows<br>
-&nbsp;&nbsp;&nbsp;&nbsp;these&nbsp;would&nbsp;be&nbsp;a&nbsp;list&nbsp;of&nbsp;columns&nbsp;to&nbsp;include<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;return&nbsp;None.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dropped&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[np.nan,&nbsp;2,&nbsp;np.nan,&nbsp;0],&nbsp;[3,&nbsp;4,&nbsp;np.nan,&nbsp;1],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[np.nan,&nbsp;np.nan,&nbsp;np.nan,&nbsp;5]],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=list('ABCD'))<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;NaN&nbsp;&nbsp;2.0&nbsp;NaN&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;3.0&nbsp;&nbsp;4.0&nbsp;NaN&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;NaN&nbsp;&nbsp;NaN&nbsp;NaN&nbsp;&nbsp;5<br>
-&nbsp;<br>
-Drop&nbsp;the&nbsp;columns&nbsp;where&nbsp;all&nbsp;elements&nbsp;are&nbsp;nan:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-dropna">dropna</a>(axis=1,&nbsp;how='all')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;NaN&nbsp;&nbsp;2.0&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;3.0&nbsp;&nbsp;4.0&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;NaN&nbsp;&nbsp;NaN&nbsp;&nbsp;5<br>
-&nbsp;<br>
-Drop&nbsp;the&nbsp;columns&nbsp;where&nbsp;any&nbsp;of&nbsp;the&nbsp;elements&nbsp;is&nbsp;nan<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-dropna">dropna</a>(axis=1,&nbsp;how='any')<br>
-&nbsp;&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;5<br>
-&nbsp;<br>
-Drop&nbsp;the&nbsp;rows&nbsp;where&nbsp;all&nbsp;of&nbsp;the&nbsp;elements&nbsp;are&nbsp;nan<br>
-(there&nbsp;is&nbsp;no&nbsp;row&nbsp;to&nbsp;drop,&nbsp;so&nbsp;df&nbsp;stays&nbsp;the&nbsp;same):<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-dropna">dropna</a>(axis=0,&nbsp;how='all')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;NaN&nbsp;&nbsp;2.0&nbsp;NaN&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;3.0&nbsp;&nbsp;4.0&nbsp;NaN&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;NaN&nbsp;&nbsp;NaN&nbsp;NaN&nbsp;&nbsp;5<br>
-&nbsp;<br>
-Keep&nbsp;only&nbsp;the&nbsp;rows&nbsp;with&nbsp;at&nbsp;least&nbsp;2&nbsp;non-na&nbsp;values:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-dropna">dropna</a>(thresh=2)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;NaN&nbsp;&nbsp;2.0&nbsp;NaN&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;3.0&nbsp;&nbsp;4.0&nbsp;NaN&nbsp;&nbsp;1</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-duplicated"><strong>duplicated</strong></a>(self, subset=None, keep='first')</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;denoting&nbsp;duplicate&nbsp;rows,&nbsp;optionally&nbsp;only<br>
-considering&nbsp;certain&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-subset&nbsp;:&nbsp;column&nbsp;label&nbsp;or&nbsp;sequence&nbsp;of&nbsp;labels,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Only&nbsp;consider&nbsp;certain&nbsp;columns&nbsp;for&nbsp;identifying&nbsp;duplicates,&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;use&nbsp;all&nbsp;of&nbsp;the&nbsp;columns<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;last&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Mark&nbsp;all&nbsp;duplicates&nbsp;as&nbsp;``True``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-duplicated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-eq"><strong>eq</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;eq</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-eval"><strong>eval</strong></a>(self, expr, inplace=None, **kwargs)</dt><dd><tt>Evaluate&nbsp;an&nbsp;expression&nbsp;in&nbsp;the&nbsp;context&nbsp;of&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-instance.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-expr&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;expression&nbsp;string&nbsp;to&nbsp;evaluate.<br>
-inplace&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;expression&nbsp;contains&nbsp;an&nbsp;assignment,&nbsp;whether&nbsp;to&nbsp;return&nbsp;a&nbsp;new<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;mutate&nbsp;the&nbsp;existing.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;WARNING:&nbsp;inplace=None&nbsp;currently&nbsp;falls&nbsp;back&nbsp;to&nbsp;to&nbsp;True,&nbsp;but<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;a&nbsp;future&nbsp;version,&nbsp;will&nbsp;default&nbsp;to&nbsp;False.&nbsp;&nbsp;Use&nbsp;inplace=True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;explicitly&nbsp;rather&nbsp;than&nbsp;relying&nbsp;on&nbsp;the&nbsp;default.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-kwargs&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;documentation&nbsp;for&nbsp;:func:`~pandas.eval`&nbsp;for&nbsp;complete&nbsp;details<br>
-&nbsp;&nbsp;&nbsp;&nbsp;on&nbsp;the&nbsp;keyword&nbsp;arguments&nbsp;accepted&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:meth:`~pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.query`.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ret&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;scalar,&nbsp;or&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.query<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.assign<br>
-pandas.eval<br>
-&nbsp;<br>
-Notes<br>
------<br>
-For&nbsp;more&nbsp;details&nbsp;see&nbsp;the&nbsp;API&nbsp;documentation&nbsp;for&nbsp;:func:`~pandas.eval`.<br>
-For&nbsp;detailed&nbsp;examples&nbsp;see&nbsp;:ref:`enhancing&nbsp;performance&nbsp;with&nbsp;eval<br>
-&lt;enhancingperf.eval&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;numpy.random&nbsp;import&nbsp;randn<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;pandas&nbsp;import&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(randn(10,&nbsp;2),&nbsp;columns=list('ab'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-eval">eval</a>('a&nbsp;+&nbsp;b')<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-eval">eval</a>('c&nbsp;=&nbsp;a&nbsp;+&nbsp;b')</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-ewm"><strong>ewm</strong></a>(self, com=None, span=None, halflife=None, alpha=None, min_periods=0, freq=None, adjust=True, ignore_na=False, axis=0)</dt><dd><tt>Provides&nbsp;exponential&nbsp;weighted&nbsp;functions<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-com&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;/&nbsp;(1&nbsp;+&nbsp;com),\text{&nbsp;for&nbsp;}&nbsp;com&nbsp;\geq&nbsp;0`<br>
-span&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;span,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;2&nbsp;/&nbsp;(span&nbsp;+&nbsp;1),\text{&nbsp;for&nbsp;}&nbsp;span&nbsp;\geq&nbsp;1`<br>
-halflife&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;half-life,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;-&nbsp;exp(log(0.5)&nbsp;/&nbsp;halflife),\text{&nbsp;for&nbsp;}&nbsp;halflife&nbsp;&gt;&nbsp;0`<br>
-alpha&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;smoothing&nbsp;factor&nbsp;:math:`\alpha`&nbsp;directly,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`0&nbsp;&lt;&nbsp;\alpha&nbsp;\leq&nbsp;1`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;None&nbsp;or&nbsp;string&nbsp;alias&nbsp;/&nbsp;date&nbsp;offset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default=None&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;to&nbsp;before&nbsp;computing&nbsp;statistic<br>
-adjust&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Divide&nbsp;by&nbsp;decaying&nbsp;adjustment&nbsp;factor&nbsp;in&nbsp;beginning&nbsp;periods&nbsp;to&nbsp;account<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;imbalance&nbsp;in&nbsp;relative&nbsp;weightings&nbsp;(viewing&nbsp;EWMA&nbsp;as&nbsp;a&nbsp;moving&nbsp;average)<br>
-ignore_na&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Ignore&nbsp;missing&nbsp;values&nbsp;when&nbsp;calculating&nbsp;weights;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specify&nbsp;True&nbsp;to&nbsp;reproduce&nbsp;pre-0.15.0&nbsp;behavior<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-ewm">ewm</a>(com=0.5).<a href="#MyDataFrame-mean">mean</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.000000<br>
-1&nbsp;&nbsp;0.750000<br>
-2&nbsp;&nbsp;1.615385<br>
-3&nbsp;&nbsp;1.615385<br>
-4&nbsp;&nbsp;3.670213<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Exactly&nbsp;one&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,&nbsp;span,&nbsp;half-life,&nbsp;and&nbsp;alpha&nbsp;must&nbsp;be&nbsp;provided.<br>
-Allowed&nbsp;values&nbsp;and&nbsp;relationship&nbsp;between&nbsp;the&nbsp;parameters&nbsp;are&nbsp;specified&nbsp;in&nbsp;the<br>
-parameter&nbsp;descriptions&nbsp;above;&nbsp;see&nbsp;the&nbsp;link&nbsp;at&nbsp;the&nbsp;end&nbsp;of&nbsp;this&nbsp;section&nbsp;for<br>
-a&nbsp;detailed&nbsp;explanation.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;True&nbsp;(default),&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;using<br>
-weights&nbsp;(1-alpha)**(n-1),&nbsp;(1-alpha)**(n-2),&nbsp;...,&nbsp;1-alpha,&nbsp;1.<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;False,&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;recursively&nbsp;as:<br>
-&nbsp;&nbsp;&nbsp;weighted_average[0]&nbsp;=&nbsp;arg[0];<br>
-&nbsp;&nbsp;&nbsp;weighted_average[i]&nbsp;=&nbsp;(1-alpha)*weighted_average[i-1]&nbsp;+&nbsp;alpha*arg[i].<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;False&nbsp;(default),&nbsp;weights&nbsp;are&nbsp;based&nbsp;on&nbsp;absolute&nbsp;positions.<br>
-For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in&nbsp;calculating&nbsp;the&nbsp;final&nbsp;weighted<br>
-average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;(1-alpha)**2&nbsp;and&nbsp;1&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and<br>
-(1-alpha)**2&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;True&nbsp;(reproducing&nbsp;pre-0.15.0&nbsp;behavior),&nbsp;weights&nbsp;are&nbsp;based<br>
-on&nbsp;relative&nbsp;positions.&nbsp;For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in<br>
-calculating&nbsp;the&nbsp;final&nbsp;weighted&nbsp;average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;1-alpha&nbsp;and&nbsp;1<br>
-(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and&nbsp;1-alpha&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-More&nbsp;details&nbsp;can&nbsp;be&nbsp;found&nbsp;at<br>
-<a href="http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows">http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-expanding"><strong>expanding</strong></a>(self, min_periods=1, freq=None, center=False, axis=0)</dt><dd><tt>Provides&nbsp;expanding&nbsp;transformations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-expanding">expanding</a>(2).<a href="#MyDataFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;7.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-fillna"><strong>fillna</strong></a>(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)</dt><dd><tt>Fill&nbsp;NA/NaN&nbsp;values&nbsp;using&nbsp;the&nbsp;specified&nbsp;method<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;values&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;each&nbsp;index&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;or&nbsp;column&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).&nbsp;(values&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;This&nbsp;value&nbsp;cannot<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;list.<br>
-method&nbsp;:&nbsp;{'backfill',&nbsp;'bfill',&nbsp;'pad',&nbsp;'ffill',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'}<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;fill&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>,&nbsp;(e.g.&nbsp;a&nbsp;no-copy&nbsp;slice&nbsp;for&nbsp;a&nbsp;column&nbsp;in&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;method&nbsp;is&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the&nbsp;maximum&nbsp;number&nbsp;of&nbsp;consecutive<br>
-&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;values&nbsp;to&nbsp;forward/backward&nbsp;fill.&nbsp;In&nbsp;other&nbsp;words,&nbsp;if&nbsp;there&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;gap&nbsp;with&nbsp;more&nbsp;than&nbsp;this&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs,&nbsp;it&nbsp;will&nbsp;only<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;partially&nbsp;filled.&nbsp;If&nbsp;method&nbsp;is&nbsp;not&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;maximum&nbsp;number&nbsp;of&nbsp;entries&nbsp;along&nbsp;the&nbsp;entire&nbsp;axis&nbsp;where&nbsp;NaNs&nbsp;will&nbsp;be<br>
-&nbsp;&nbsp;&nbsp;&nbsp;filled.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0&nbsp;if&nbsp;not&nbsp;None.<br>
-downcast&nbsp;:&nbsp;dict,&nbsp;default&nbsp;is&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;dict&nbsp;of&nbsp;item-&gt;dtype&nbsp;of&nbsp;what&nbsp;to&nbsp;downcast&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;the&nbsp;string&nbsp;'infer'&nbsp;which&nbsp;will&nbsp;try&nbsp;to&nbsp;downcast&nbsp;to&nbsp;an&nbsp;appropriate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;equal&nbsp;type&nbsp;(e.g.&nbsp;float64&nbsp;to&nbsp;int64&nbsp;if&nbsp;possible)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;asfreq<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-first_valid_index"><strong>first_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;first&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-floordiv"><strong>floordiv</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`floordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;//&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rfloordiv</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-ge"><strong>ge</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;ge</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-get_value"><strong>get_value</strong></a>(self, index, col, takeable=False)</dt><dd><tt>Quickly&nbsp;retrieve&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;column&nbsp;and&nbsp;index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;row&nbsp;label<br>
-col&nbsp;:&nbsp;column&nbsp;label<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index/col&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;scalar&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-gt"><strong>gt</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;gt</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-hist"><strong>hist</strong></a> = hist_frame(data, column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, **kwds)</dt><dd><tt>Draw&nbsp;histogram&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;series&nbsp;using&nbsp;matplotlib&nbsp;/&nbsp;pylab.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-column&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed,&nbsp;will&nbsp;be&nbsp;used&nbsp;to&nbsp;limit&nbsp;data&nbsp;to&nbsp;a&nbsp;subset&nbsp;of&nbsp;columns<br>
-by&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed,&nbsp;then&nbsp;used&nbsp;to&nbsp;form&nbsp;histograms&nbsp;for&nbsp;separate&nbsp;groups<br>
-grid&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;show&nbsp;axis&nbsp;grid&nbsp;lines<br>
-xlabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;x-axis&nbsp;label&nbsp;size<br>
-xrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;x&nbsp;axis&nbsp;labels<br>
-ylabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;y-axis&nbsp;label&nbsp;size<br>
-yrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;y&nbsp;axis&nbsp;labels<br>
-ax&nbsp;:&nbsp;matplotlib&nbsp;axes&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-sharex&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True&nbsp;if&nbsp;ax&nbsp;is&nbsp;None&nbsp;else&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;subplots=True,&nbsp;share&nbsp;x&nbsp;axis&nbsp;and&nbsp;set&nbsp;some&nbsp;x&nbsp;axis&nbsp;labels&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;invisible;&nbsp;defaults&nbsp;to&nbsp;True&nbsp;if&nbsp;ax&nbsp;is&nbsp;None&nbsp;otherwise&nbsp;False&nbsp;if&nbsp;an&nbsp;ax<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;passed&nbsp;in;&nbsp;Be&nbsp;aware,&nbsp;that&nbsp;passing&nbsp;in&nbsp;both&nbsp;an&nbsp;ax&nbsp;and&nbsp;sharex=True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;alter&nbsp;all&nbsp;x&nbsp;axis&nbsp;labels&nbsp;for&nbsp;all&nbsp;subplots&nbsp;in&nbsp;a&nbsp;figure!<br>
-sharey&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;subplots=True,&nbsp;share&nbsp;y&nbsp;axis&nbsp;and&nbsp;set&nbsp;some&nbsp;y&nbsp;axis&nbsp;labels&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;invisible<br>
-figsize&nbsp;:&nbsp;tuple<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;size&nbsp;of&nbsp;the&nbsp;figure&nbsp;to&nbsp;create&nbsp;in&nbsp;inches&nbsp;by&nbsp;default<br>
-layout&nbsp;:&nbsp;tuple,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Tuple&nbsp;of&nbsp;(rows,&nbsp;columns)&nbsp;for&nbsp;the&nbsp;layout&nbsp;of&nbsp;the&nbsp;histograms<br>
-bins&nbsp;:&nbsp;integer,&nbsp;default&nbsp;10<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;histogram&nbsp;bins&nbsp;to&nbsp;be&nbsp;used<br>
-kwds&nbsp;:&nbsp;other&nbsp;plotting&nbsp;keyword&nbsp;arguments<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;passed&nbsp;to&nbsp;hist&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-idxmax"><strong>idxmax</strong></a>(self, axis=0, skipna=True)</dt><dd><tt>Return&nbsp;index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;maximum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-NA/null&nbsp;values&nbsp;are&nbsp;excluded.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;or&nbsp;'index'&nbsp;for&nbsp;row-wise,&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;for&nbsp;column-wise<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;first&nbsp;index.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmax&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmax``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.idxmax</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-idxmin"><strong>idxmin</strong></a>(self, axis=0, skipna=True)</dt><dd><tt>Return&nbsp;index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;minimum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-NA/null&nbsp;values&nbsp;are&nbsp;excluded.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;or&nbsp;'index'&nbsp;for&nbsp;row-wise,&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;for&nbsp;column-wise<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmin&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmin``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.idxmin</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-info"><strong>info</strong></a>(self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None)</dt><dd><tt>Concise&nbsp;summary&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-verbose&nbsp;:&nbsp;{None,&nbsp;True,&nbsp;False},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;print&nbsp;the&nbsp;full&nbsp;summary.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;None&nbsp;follows&nbsp;the&nbsp;`display.max_info_columns`&nbsp;setting.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;True&nbsp;or&nbsp;False&nbsp;overrides&nbsp;the&nbsp;`display.max_info_columns`&nbsp;setting.<br>
-buf&nbsp;:&nbsp;writable&nbsp;buffer,&nbsp;defaults&nbsp;to&nbsp;sys.stdout<br>
-max_cols&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Determines&nbsp;whether&nbsp;full&nbsp;summary&nbsp;or&nbsp;short&nbsp;summary&nbsp;is&nbsp;printed.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;None&nbsp;follows&nbsp;the&nbsp;`display.max_info_columns`&nbsp;setting.<br>
-memory_usage&nbsp;:&nbsp;boolean/string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;whether&nbsp;total&nbsp;memory&nbsp;usage&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;elements&nbsp;(including&nbsp;index)&nbsp;should&nbsp;be&nbsp;displayed.&nbsp;None&nbsp;follows<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;`display.memory_usage`&nbsp;setting.&nbsp;True&nbsp;or&nbsp;False&nbsp;overrides<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;`display.memory_usage`&nbsp;setting.&nbsp;A&nbsp;value&nbsp;of&nbsp;'deep'&nbsp;is&nbsp;equivalent<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;True,&nbsp;with&nbsp;deep&nbsp;introspection.&nbsp;Memory&nbsp;usage&nbsp;is&nbsp;shown&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;human-readable&nbsp;units&nbsp;(base-2&nbsp;representation).<br>
-null_counts&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;show&nbsp;the&nbsp;non-null&nbsp;counts<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;If&nbsp;None,&nbsp;then&nbsp;only&nbsp;show&nbsp;if&nbsp;the&nbsp;frame&nbsp;is&nbsp;smaller&nbsp;than<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;max_info_rows&nbsp;and&nbsp;max_info_columns.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;If&nbsp;True,&nbsp;always&nbsp;show&nbsp;counts.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;If&nbsp;False,&nbsp;never&nbsp;show&nbsp;counts.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-insert"><strong>insert</strong></a>(self, loc, column, value, allow_duplicates=False)</dt><dd><tt>Insert&nbsp;column&nbsp;into&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;at&nbsp;specified&nbsp;location.<br>
-&nbsp;<br>
-If&nbsp;`allow_duplicates`&nbsp;is&nbsp;False,&nbsp;raises&nbsp;Exception&nbsp;if&nbsp;column<br>
-is&nbsp;already&nbsp;contained&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-loc&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Must&nbsp;have&nbsp;0&nbsp;&lt;=&nbsp;loc&nbsp;&lt;=&nbsp;len(columns)<br>
-column&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-value&nbsp;:&nbsp;scalar,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;array-like</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-isin"><strong>isin</strong></a>(self, values)</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;showing&nbsp;whether&nbsp;each&nbsp;element&nbsp;in&nbsp;the<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;is&nbsp;contained&nbsp;in&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-values&nbsp;:&nbsp;iterable,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;dictionary<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;result&nbsp;will&nbsp;only&nbsp;be&nbsp;true&nbsp;at&nbsp;a&nbsp;location&nbsp;if&nbsp;all&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;labels&nbsp;match.&nbsp;If&nbsp;`values`&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;that's&nbsp;the&nbsp;index.&nbsp;If<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`values`&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;the&nbsp;keys&nbsp;must&nbsp;be&nbsp;the&nbsp;column&nbsp;names,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;must&nbsp;match.&nbsp;If&nbsp;`values`&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;both&nbsp;the&nbsp;index&nbsp;and&nbsp;column&nbsp;labels&nbsp;must&nbsp;match.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;booleans<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-When&nbsp;``values``&nbsp;is&nbsp;a&nbsp;list:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;'B':&nbsp;['a',&nbsp;'b',&nbsp;'f']})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-isin">isin</a>([1,&nbsp;3,&nbsp;12,&nbsp;'a'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;False&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;False<br>
-&nbsp;<br>
-When&nbsp;``values``&nbsp;is&nbsp;a&nbsp;dict:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;'B':&nbsp;[1,&nbsp;4,&nbsp;7]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-isin">isin</a>({'A':&nbsp;[1,&nbsp;3],&nbsp;'B':&nbsp;[4,&nbsp;7,&nbsp;12]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;False&nbsp;&nbsp;#&nbsp;Note&nbsp;that&nbsp;B&nbsp;didn't&nbsp;match&nbsp;the&nbsp;1&nbsp;here.<br>
-1&nbsp;&nbsp;False&nbsp;&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;&nbsp;True<br>
-&nbsp;<br>
-When&nbsp;``values``&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;'B':&nbsp;['a',&nbsp;'b',&nbsp;'f']})<br>
-&gt;&gt;&gt;&nbsp;other&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;3,&nbsp;3,&nbsp;2],&nbsp;'B':&nbsp;['e',&nbsp;'f',&nbsp;'f',&nbsp;'e']})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-isin">isin</a>(other)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;False<br>
-1&nbsp;&nbsp;False&nbsp;&nbsp;False&nbsp;&nbsp;#&nbsp;Column&nbsp;A&nbsp;in&nbsp;`other`&nbsp;has&nbsp;a&nbsp;3,&nbsp;but&nbsp;not&nbsp;at&nbsp;index&nbsp;1.<br>
-2&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;&nbsp;True</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-items"><strong>items</strong></a> = iteritems(self)</dt><dd><tt>Iterator&nbsp;over&nbsp;(column&nbsp;name,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-iterrows&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;(index,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-itertuples&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;namedtuples&nbsp;of&nbsp;the&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-iteritems"><strong>iteritems</strong></a>(self)</dt><dd><tt>Iterator&nbsp;over&nbsp;(column&nbsp;name,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-iterrows&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;(index,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-itertuples&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;namedtuples&nbsp;of&nbsp;the&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-iterrows"><strong>iterrows</strong></a>(self)</dt><dd><tt>Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;(index,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-1.&nbsp;Because&nbsp;``iterrows``&nbsp;returns&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;for&nbsp;each&nbsp;row,<br>
-&nbsp;&nbsp;&nbsp;it&nbsp;does&nbsp;**not**&nbsp;preserve&nbsp;dtypes&nbsp;across&nbsp;the&nbsp;rows&nbsp;(dtypes&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;preserved&nbsp;across&nbsp;columns&nbsp;for&nbsp;DataFrames).&nbsp;For&nbsp;example,<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[1,&nbsp;1.5]],&nbsp;columns=['int',&nbsp;'float'])<br>
-&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;row&nbsp;=&nbsp;next(df.<a href="#MyDataFrame-iterrows">iterrows</a>())[1]<br>
-&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;row<br>
-&nbsp;&nbsp;&nbsp;int&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-&nbsp;&nbsp;&nbsp;float&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-&nbsp;&nbsp;&nbsp;Name:&nbsp;0,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;print(row['int'].dtype)<br>
-&nbsp;&nbsp;&nbsp;float64<br>
-&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;print(df['int'].dtype)<br>
-&nbsp;&nbsp;&nbsp;int64<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;To&nbsp;preserve&nbsp;dtypes&nbsp;while&nbsp;iterating&nbsp;over&nbsp;the&nbsp;rows,&nbsp;it&nbsp;is&nbsp;better<br>
-&nbsp;&nbsp;&nbsp;to&nbsp;use&nbsp;:meth:`itertuples`&nbsp;which&nbsp;returns&nbsp;namedtuples&nbsp;of&nbsp;the&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;and&nbsp;which&nbsp;is&nbsp;generally&nbsp;faster&nbsp;than&nbsp;``iterrows``.<br>
-&nbsp;<br>
-2.&nbsp;You&nbsp;should&nbsp;**never&nbsp;modify**&nbsp;something&nbsp;you&nbsp;are&nbsp;iterating&nbsp;over.<br>
-&nbsp;&nbsp;&nbsp;This&nbsp;is&nbsp;not&nbsp;guaranteed&nbsp;to&nbsp;work&nbsp;in&nbsp;all&nbsp;cases.&nbsp;Depending&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;data&nbsp;types,&nbsp;the&nbsp;iterator&nbsp;returns&nbsp;a&nbsp;copy&nbsp;and&nbsp;not&nbsp;a&nbsp;view,&nbsp;and&nbsp;writing<br>
-&nbsp;&nbsp;&nbsp;to&nbsp;it&nbsp;will&nbsp;have&nbsp;no&nbsp;effect.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-it&nbsp;:&nbsp;generator<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;generator&nbsp;that&nbsp;iterates&nbsp;over&nbsp;the&nbsp;rows&nbsp;of&nbsp;the&nbsp;frame.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-itertuples&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;namedtuples&nbsp;of&nbsp;the&nbsp;values.<br>
-iteritems&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;(column&nbsp;name,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-itertuples"><strong>itertuples</strong></a>(self, index=True, name='Pandas')</dt><dd><tt>Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;namedtuples,&nbsp;with&nbsp;index&nbsp;value&nbsp;as&nbsp;first<br>
-element&nbsp;of&nbsp;the&nbsp;tuple.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;return&nbsp;the&nbsp;index&nbsp;as&nbsp;the&nbsp;first&nbsp;element&nbsp;of&nbsp;the&nbsp;tuple.<br>
-name&nbsp;:&nbsp;string,&nbsp;default&nbsp;"Pandas"<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;name&nbsp;of&nbsp;the&nbsp;returned&nbsp;namedtuples&nbsp;or&nbsp;None&nbsp;to&nbsp;return&nbsp;regular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;tuples.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;column&nbsp;names&nbsp;will&nbsp;be&nbsp;renamed&nbsp;to&nbsp;positional&nbsp;names&nbsp;if&nbsp;they&nbsp;are<br>
-invalid&nbsp;Python&nbsp;identifiers,&nbsp;repeated,&nbsp;or&nbsp;start&nbsp;with&nbsp;an&nbsp;underscore.<br>
-With&nbsp;a&nbsp;large&nbsp;number&nbsp;of&nbsp;columns&nbsp;(&gt;255),&nbsp;regular&nbsp;tuples&nbsp;are&nbsp;returned.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-iterrows&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;(index,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-iteritems&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;(column&nbsp;name,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'col1':&nbsp;[1,&nbsp;2],&nbsp;'col2':&nbsp;[0.1,&nbsp;0.2]},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=['a',&nbsp;'b'])<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;col1&nbsp;&nbsp;col2<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;0.1<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;0.2<br>
-&gt;&gt;&gt;&nbsp;for&nbsp;row&nbsp;in&nbsp;df.<a href="#MyDataFrame-itertuples">itertuples</a>():<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;print(row)<br>
-...<br>
-Pandas(Index='a',&nbsp;col1=1,&nbsp;col2=0.10000000000000001)<br>
-Pandas(Index='b',&nbsp;col1=2,&nbsp;col2=0.20000000000000001)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-join"><strong>join</strong></a>(self, other, on=None, how='left', lsuffix='', rsuffix='', sort=False)</dt><dd><tt>Join&nbsp;columns&nbsp;with&nbsp;other&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;either&nbsp;on&nbsp;index&nbsp;or&nbsp;on&nbsp;a&nbsp;key<br>
-column.&nbsp;Efficiently&nbsp;Join&nbsp;multiple&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects&nbsp;by&nbsp;index&nbsp;at&nbsp;once&nbsp;by<br>
-passing&nbsp;a&nbsp;list.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;name&nbsp;field&nbsp;set,&nbsp;or&nbsp;list&nbsp;of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Index&nbsp;should&nbsp;be&nbsp;similar&nbsp;to&nbsp;one&nbsp;of&nbsp;the&nbsp;columns&nbsp;in&nbsp;this&nbsp;one.&nbsp;If&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;is&nbsp;passed,&nbsp;its&nbsp;name&nbsp;attribute&nbsp;must&nbsp;be&nbsp;set,&nbsp;and&nbsp;that&nbsp;will&nbsp;be<br>
-&nbsp;&nbsp;&nbsp;&nbsp;used&nbsp;as&nbsp;the&nbsp;column&nbsp;name&nbsp;in&nbsp;the&nbsp;resulting&nbsp;joined&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-on&nbsp;:&nbsp;column&nbsp;name,&nbsp;tuple/list&nbsp;of&nbsp;column&nbsp;names,&nbsp;or&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column(s)&nbsp;in&nbsp;the&nbsp;caller&nbsp;to&nbsp;join&nbsp;on&nbsp;the&nbsp;index&nbsp;in&nbsp;other,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;joins&nbsp;index-on-index.&nbsp;If&nbsp;multiples<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns&nbsp;given,&nbsp;the&nbsp;passed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;must&nbsp;have&nbsp;a&nbsp;MultiIndex.&nbsp;Can<br>
-&nbsp;&nbsp;&nbsp;&nbsp;pass&nbsp;an&nbsp;array&nbsp;as&nbsp;the&nbsp;join&nbsp;key&nbsp;if&nbsp;not&nbsp;already&nbsp;contained&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Like&nbsp;an&nbsp;Excel&nbsp;VLOOKUP&nbsp;operation<br>
-how&nbsp;:&nbsp;{'left',&nbsp;'right',&nbsp;'outer',&nbsp;'inner'},&nbsp;default:&nbsp;'left'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;How&nbsp;to&nbsp;handle&nbsp;the&nbsp;operation&nbsp;of&nbsp;the&nbsp;two&nbsp;objects.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;left:&nbsp;use&nbsp;calling&nbsp;frame's&nbsp;index&nbsp;(or&nbsp;column&nbsp;if&nbsp;on&nbsp;is&nbsp;specified)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;right:&nbsp;use&nbsp;other&nbsp;frame's&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;outer:&nbsp;form&nbsp;union&nbsp;of&nbsp;calling&nbsp;frame's&nbsp;index&nbsp;(or&nbsp;column&nbsp;if&nbsp;on&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;specified)&nbsp;with&nbsp;other&nbsp;frame's&nbsp;index,&nbsp;and&nbsp;sort&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lexicographically<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;inner:&nbsp;form&nbsp;intersection&nbsp;of&nbsp;calling&nbsp;frame's&nbsp;index&nbsp;(or&nbsp;column&nbsp;if<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;on&nbsp;is&nbsp;specified)&nbsp;with&nbsp;other&nbsp;frame's&nbsp;index,&nbsp;preserving&nbsp;the&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;calling's&nbsp;one<br>
-lsuffix&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Suffix&nbsp;to&nbsp;use&nbsp;from&nbsp;left&nbsp;frame's&nbsp;overlapping&nbsp;columns<br>
-rsuffix&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Suffix&nbsp;to&nbsp;use&nbsp;from&nbsp;right&nbsp;frame's&nbsp;overlapping&nbsp;columns<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Order&nbsp;result&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;lexicographically&nbsp;by&nbsp;the&nbsp;join&nbsp;key.&nbsp;If&nbsp;False,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;join&nbsp;key&nbsp;depends&nbsp;on&nbsp;the&nbsp;join&nbsp;type&nbsp;(how&nbsp;keyword)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-on,&nbsp;lsuffix,&nbsp;and&nbsp;rsuffix&nbsp;options&nbsp;are&nbsp;not&nbsp;supported&nbsp;when&nbsp;passing&nbsp;a&nbsp;list<br>
-of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;caller&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'key':&nbsp;['K0',&nbsp;'K1',&nbsp;'K2',&nbsp;'K3',&nbsp;'K4',&nbsp;'K5'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'A':&nbsp;['A0',&nbsp;'A1',&nbsp;'A2',&nbsp;'A3',&nbsp;'A4',&nbsp;'A5']})<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;caller<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;key<br>
-0&nbsp;&nbsp;A0&nbsp;&nbsp;K0<br>
-1&nbsp;&nbsp;A1&nbsp;&nbsp;K1<br>
-2&nbsp;&nbsp;A2&nbsp;&nbsp;K2<br>
-3&nbsp;&nbsp;A3&nbsp;&nbsp;K3<br>
-4&nbsp;&nbsp;A4&nbsp;&nbsp;K4<br>
-5&nbsp;&nbsp;A5&nbsp;&nbsp;K5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;other&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'key':&nbsp;['K0',&nbsp;'K1',&nbsp;'K2'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B':&nbsp;['B0',&nbsp;'B1',&nbsp;'B2']})<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;key<br>
-0&nbsp;&nbsp;B0&nbsp;&nbsp;K0<br>
-1&nbsp;&nbsp;B1&nbsp;&nbsp;K1<br>
-2&nbsp;&nbsp;B2&nbsp;&nbsp;K2<br>
-&nbsp;<br>
-Join&nbsp;DataFrames&nbsp;using&nbsp;their&nbsp;indexes.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;caller.<a href="#MyDataFrame-join">join</a>(other,&nbsp;lsuffix='_caller',&nbsp;rsuffix='_other')<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;key_caller&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;key_other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;A0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K0&nbsp;&nbsp;&nbsp;B0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;A1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K1&nbsp;&nbsp;&nbsp;B1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;A2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K2&nbsp;&nbsp;&nbsp;B2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;A3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K3&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;A4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K4&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;A5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K5&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-&nbsp;<br>
-If&nbsp;we&nbsp;want&nbsp;to&nbsp;join&nbsp;using&nbsp;the&nbsp;key&nbsp;columns,&nbsp;we&nbsp;need&nbsp;to&nbsp;set&nbsp;key&nbsp;to&nbsp;be<br>
-the&nbsp;index&nbsp;in&nbsp;both&nbsp;caller&nbsp;and&nbsp;other.&nbsp;The&nbsp;joined&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;have<br>
-key&nbsp;as&nbsp;its&nbsp;index.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;caller.<a href="#MyDataFrame-set_index">set_index</a>('key').<a href="#MyDataFrame-join">join</a>(other.<a href="#MyDataFrame-set_index">set_index</a>('key'))<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-&nbsp;&nbsp;&nbsp;&nbsp;key<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K0&nbsp;&nbsp;&nbsp;A0&nbsp;&nbsp;&nbsp;B0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K1&nbsp;&nbsp;&nbsp;A1&nbsp;&nbsp;&nbsp;B1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K2&nbsp;&nbsp;&nbsp;A2&nbsp;&nbsp;&nbsp;B2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K3&nbsp;&nbsp;&nbsp;A3&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K4&nbsp;&nbsp;&nbsp;A4&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K5&nbsp;&nbsp;&nbsp;A5&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Another&nbsp;option&nbsp;to&nbsp;join&nbsp;using&nbsp;the&nbsp;key&nbsp;columns&nbsp;is&nbsp;to&nbsp;use&nbsp;the&nbsp;on<br>
-parameter.&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.join&nbsp;always&nbsp;uses&nbsp;other's&nbsp;index&nbsp;but&nbsp;we&nbsp;can&nbsp;use&nbsp;any<br>
-column&nbsp;in&nbsp;the&nbsp;caller.&nbsp;This&nbsp;method&nbsp;preserves&nbsp;the&nbsp;original&nbsp;caller's<br>
-index&nbsp;in&nbsp;the&nbsp;result.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;caller.<a href="#MyDataFrame-join">join</a>(other.<a href="#MyDataFrame-set_index">set_index</a>('key'),&nbsp;on='key')<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;key&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;A0&nbsp;&nbsp;K0&nbsp;&nbsp;&nbsp;B0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;A1&nbsp;&nbsp;K1&nbsp;&nbsp;&nbsp;B1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;A2&nbsp;&nbsp;K2&nbsp;&nbsp;&nbsp;B2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;A3&nbsp;&nbsp;K3&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;A4&nbsp;&nbsp;K4&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;A5&nbsp;&nbsp;K5&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.merge&nbsp;:&nbsp;For&nbsp;column(s)-on-<a href="#MyDataFrame-columns">columns</a>(s)&nbsp;operations<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-joined&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-kurt"><strong>kurt</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-kurtosis"><strong>kurtosis</strong></a> = kurt(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-last_valid_index"><strong>last_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;last&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-le"><strong>le</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;le</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-lookup"><strong>lookup</strong></a>(self, row_labels, col_labels)</dt><dd><tt>Label-based&nbsp;"fancy&nbsp;indexing"&nbsp;function&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-Given&nbsp;equal-length&nbsp;arrays&nbsp;of&nbsp;row&nbsp;and&nbsp;column&nbsp;labels,&nbsp;return&nbsp;an<br>
-array&nbsp;of&nbsp;the&nbsp;values&nbsp;corresponding&nbsp;to&nbsp;each&nbsp;(row,&nbsp;col)&nbsp;pair.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-row_labels&nbsp;:&nbsp;sequence<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;row&nbsp;labels&nbsp;to&nbsp;use&nbsp;for&nbsp;lookup<br>
-col_labels&nbsp;:&nbsp;sequence<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;column&nbsp;labels&nbsp;to&nbsp;use&nbsp;for&nbsp;lookup<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Akin&nbsp;to::<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;result&nbsp;=&nbsp;[]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;row,&nbsp;col&nbsp;in&nbsp;zip(row_labels,&nbsp;col_labels):<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;result.<a href="#MyDataFrame-append">append</a>(df.<a href="#MyDataFrame-get_value">get_value</a>(row,&nbsp;col))<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-values&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;found&nbsp;values</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-lt"><strong>lt</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;lt</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-mad"><strong>mad</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;absolute&nbsp;deviation&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mad&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-max"><strong>max</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;maximum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;maximum,&nbsp;use&nbsp;``idxmax``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmax``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-max&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-mean"><strong>mean</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mean&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-median"><strong>median</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;median&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-median&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-melt"><strong>melt</strong></a>(self, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)</dt><dd><tt>"Unpivots"&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;from&nbsp;wide&nbsp;format&nbsp;to&nbsp;long&nbsp;format,&nbsp;optionally<br>
-leaving&nbsp;identifier&nbsp;variables&nbsp;set.<br>
-&nbsp;<br>
-This&nbsp;function&nbsp;is&nbsp;useful&nbsp;to&nbsp;massage&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;into&nbsp;a&nbsp;format&nbsp;where&nbsp;one<br>
-or&nbsp;more&nbsp;columns&nbsp;are&nbsp;identifier&nbsp;variables&nbsp;(`id_vars`),&nbsp;while&nbsp;all&nbsp;other<br>
-columns,&nbsp;considered&nbsp;measured&nbsp;variables&nbsp;(`value_vars`),&nbsp;are&nbsp;"unpivoted"&nbsp;to<br>
-the&nbsp;row&nbsp;axis,&nbsp;leaving&nbsp;just&nbsp;two&nbsp;non-identifier&nbsp;columns,&nbsp;'variable'&nbsp;and<br>
-'value'.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-frame&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-id_vars&nbsp;:&nbsp;tuple,&nbsp;list,&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column(s)&nbsp;to&nbsp;use&nbsp;as&nbsp;identifier&nbsp;variables.<br>
-value_vars&nbsp;:&nbsp;tuple,&nbsp;list,&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column(s)&nbsp;to&nbsp;unpivot.&nbsp;If&nbsp;not&nbsp;specified,&nbsp;uses&nbsp;all&nbsp;columns&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;not&nbsp;set&nbsp;as&nbsp;`id_vars`.<br>
-var_name&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;to&nbsp;use&nbsp;for&nbsp;the&nbsp;'variable'&nbsp;column.&nbsp;If&nbsp;None&nbsp;it&nbsp;uses<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``frame.columns.name``&nbsp;or&nbsp;'variable'.<br>
-value_name&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;'value'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;to&nbsp;use&nbsp;for&nbsp;the&nbsp;'value'&nbsp;column.<br>
-col_level&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;columns&nbsp;are&nbsp;a&nbsp;MultiIndex&nbsp;then&nbsp;use&nbsp;this&nbsp;level&nbsp;to&nbsp;melt.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-melt<br>
-pivot_table<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.pivot<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;{0:&nbsp;'a',&nbsp;1:&nbsp;'b',&nbsp;2:&nbsp;'c'},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B':&nbsp;{0:&nbsp;1,&nbsp;1:&nbsp;3,&nbsp;2:&nbsp;5},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C':&nbsp;{0:&nbsp;2,&nbsp;1:&nbsp;4,&nbsp;2:&nbsp;6}})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;5&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-melt">melt</a>(id_vars=['A'],&nbsp;value_vars=['B'])<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;variable&nbsp;&nbsp;value<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-melt">melt</a>(id_vars=['A'],&nbsp;value_vars=['B',&nbsp;'C'])<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;variable&nbsp;&nbsp;value<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-3&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-4&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-5&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-The&nbsp;names&nbsp;of&nbsp;'variable'&nbsp;and&nbsp;'value'&nbsp;columns&nbsp;can&nbsp;be&nbsp;customized:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-melt">melt</a>(id_vars=['A'],&nbsp;value_vars=['B'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;var_name='myVarname',&nbsp;value_name='myValname')<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;myVarname&nbsp;&nbsp;myValname<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-&nbsp;<br>
-If&nbsp;you&nbsp;have&nbsp;multi-index&nbsp;columns:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.columns&nbsp;=&nbsp;[list('ABC'),&nbsp;list('DEF')]<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C<br>
-&nbsp;&nbsp;&nbsp;D&nbsp;&nbsp;E&nbsp;&nbsp;F<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;5&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-melt">melt</a>(col_level=0,&nbsp;id_vars=['A'],&nbsp;value_vars=['B'])<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;variable&nbsp;&nbsp;value<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-melt">melt</a>(id_vars=[('A',&nbsp;'D')],&nbsp;value_vars=[('B',&nbsp;'E')])<br>
-&nbsp;&nbsp;(A,&nbsp;D)&nbsp;variable_0&nbsp;variable_1&nbsp;&nbsp;value<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;E&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;E&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;E&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-memory_usage"><strong>memory_usage</strong></a>(self, index=True, deep=False)</dt><dd><tt>Memory&nbsp;usage&nbsp;of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;columns.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;whether&nbsp;to&nbsp;include&nbsp;memory&nbsp;usage&nbsp;of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;in&nbsp;returned&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;If&nbsp;`index=True`&nbsp;(default&nbsp;is&nbsp;False)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;first&nbsp;index&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;is&nbsp;`Index`.<br>
-deep&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Introspect&nbsp;the&nbsp;data&nbsp;deeply,&nbsp;interrogate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`<a href="builtins.html#object">object</a>`&nbsp;dtypes&nbsp;for&nbsp;system-level&nbsp;memory&nbsp;consumption<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sizes&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;series&nbsp;with&nbsp;column&nbsp;names&nbsp;as&nbsp;index&nbsp;and&nbsp;memory&nbsp;usage&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns&nbsp;with&nbsp;units&nbsp;of&nbsp;bytes.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Memory&nbsp;usage&nbsp;does&nbsp;not&nbsp;include&nbsp;memory&nbsp;consumed&nbsp;by&nbsp;elements&nbsp;that<br>
-are&nbsp;not&nbsp;components&nbsp;of&nbsp;the&nbsp;array&nbsp;if&nbsp;deep=False<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.nbytes</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-merge"><strong>merge</strong></a>(self, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False)</dt><dd><tt>Merge&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects&nbsp;by&nbsp;performing&nbsp;a&nbsp;database-style&nbsp;join&nbsp;operation&nbsp;by<br>
-columns&nbsp;or&nbsp;indexes.<br>
-&nbsp;<br>
-If&nbsp;joining&nbsp;columns&nbsp;on&nbsp;columns,&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;indexes&nbsp;*will&nbsp;be<br>
-ignored*.&nbsp;Otherwise&nbsp;if&nbsp;joining&nbsp;indexes&nbsp;on&nbsp;indexes&nbsp;or&nbsp;indexes&nbsp;on&nbsp;a&nbsp;column&nbsp;or<br>
-columns,&nbsp;the&nbsp;index&nbsp;will&nbsp;be&nbsp;passed&nbsp;on.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-right&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-how&nbsp;:&nbsp;{'left',&nbsp;'right',&nbsp;'outer',&nbsp;'inner'},&nbsp;default&nbsp;'inner'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;left:&nbsp;use&nbsp;only&nbsp;keys&nbsp;from&nbsp;left&nbsp;frame,&nbsp;similar&nbsp;to&nbsp;a&nbsp;SQL&nbsp;left&nbsp;outer&nbsp;join;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;preserve&nbsp;key&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;right:&nbsp;use&nbsp;only&nbsp;keys&nbsp;from&nbsp;right&nbsp;frame,&nbsp;similar&nbsp;to&nbsp;a&nbsp;SQL&nbsp;right&nbsp;outer&nbsp;join;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;preserve&nbsp;key&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;outer:&nbsp;use&nbsp;union&nbsp;of&nbsp;keys&nbsp;from&nbsp;both&nbsp;frames,&nbsp;similar&nbsp;to&nbsp;a&nbsp;SQL&nbsp;full&nbsp;outer<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;join;&nbsp;sort&nbsp;keys&nbsp;lexicographically<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;inner:&nbsp;use&nbsp;intersection&nbsp;of&nbsp;keys&nbsp;from&nbsp;both&nbsp;frames,&nbsp;similar&nbsp;to&nbsp;a&nbsp;SQL&nbsp;inner<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;join;&nbsp;preserve&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;left&nbsp;keys<br>
-on&nbsp;:&nbsp;label&nbsp;or&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;names&nbsp;to&nbsp;join&nbsp;on.&nbsp;Must&nbsp;be&nbsp;found&nbsp;in&nbsp;both&nbsp;DataFrames.&nbsp;If&nbsp;on&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;None&nbsp;and&nbsp;not&nbsp;merging&nbsp;on&nbsp;indexes,&nbsp;then&nbsp;it&nbsp;merges&nbsp;on&nbsp;the&nbsp;intersection&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;columns&nbsp;by&nbsp;default.<br>
-left_on&nbsp;:&nbsp;label&nbsp;or&nbsp;list,&nbsp;or&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;names&nbsp;to&nbsp;join&nbsp;on&nbsp;in&nbsp;left&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Can&nbsp;be&nbsp;a&nbsp;vector&nbsp;or&nbsp;list&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;vectors&nbsp;of&nbsp;the&nbsp;length&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;use&nbsp;a&nbsp;particular&nbsp;vector&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;join&nbsp;key&nbsp;instead&nbsp;of&nbsp;columns<br>
-right_on&nbsp;:&nbsp;label&nbsp;or&nbsp;list,&nbsp;or&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;names&nbsp;to&nbsp;join&nbsp;on&nbsp;in&nbsp;right&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;vector/list&nbsp;of&nbsp;vectors&nbsp;per<br>
-&nbsp;&nbsp;&nbsp;&nbsp;left_on&nbsp;docs<br>
-left_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;the&nbsp;index&nbsp;from&nbsp;the&nbsp;left&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;as&nbsp;the&nbsp;join&nbsp;key(s).&nbsp;If&nbsp;it&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;MultiIndex,&nbsp;the&nbsp;number&nbsp;of&nbsp;keys&nbsp;in&nbsp;the&nbsp;other&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(either&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;a&nbsp;number&nbsp;of&nbsp;columns)&nbsp;must&nbsp;match&nbsp;the&nbsp;number&nbsp;of&nbsp;levels<br>
-right_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;the&nbsp;index&nbsp;from&nbsp;the&nbsp;right&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;as&nbsp;the&nbsp;join&nbsp;key.&nbsp;Same&nbsp;caveats&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;left_index<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;the&nbsp;join&nbsp;keys&nbsp;lexicographically&nbsp;in&nbsp;the&nbsp;result&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;If&nbsp;False,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;join&nbsp;keys&nbsp;depends&nbsp;on&nbsp;the&nbsp;join&nbsp;type&nbsp;(how&nbsp;keyword)<br>
-suffixes&nbsp;:&nbsp;2-length&nbsp;sequence&nbsp;(tuple,&nbsp;list,&nbsp;...)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Suffix&nbsp;to&nbsp;apply&nbsp;to&nbsp;overlapping&nbsp;column&nbsp;names&nbsp;in&nbsp;the&nbsp;left&nbsp;and&nbsp;right<br>
-&nbsp;&nbsp;&nbsp;&nbsp;side,&nbsp;respectively<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;do&nbsp;not&nbsp;copy&nbsp;data&nbsp;unnecessarily<br>
-indicator&nbsp;:&nbsp;boolean&nbsp;or&nbsp;string,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;adds&nbsp;a&nbsp;column&nbsp;to&nbsp;output&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;called&nbsp;"_merge"&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;information&nbsp;on&nbsp;the&nbsp;source&nbsp;of&nbsp;each&nbsp;row.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;string,&nbsp;column&nbsp;with&nbsp;information&nbsp;on&nbsp;source&nbsp;of&nbsp;each&nbsp;row&nbsp;will&nbsp;be&nbsp;added&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;output&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;and&nbsp;column&nbsp;will&nbsp;be&nbsp;named&nbsp;value&nbsp;of&nbsp;string.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Information&nbsp;column&nbsp;is&nbsp;Categorical-type&nbsp;and&nbsp;takes&nbsp;on&nbsp;a&nbsp;value&nbsp;of&nbsp;"left_only"<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;observations&nbsp;whose&nbsp;merge&nbsp;key&nbsp;only&nbsp;appears&nbsp;in&nbsp;'left'&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"right_only"&nbsp;for&nbsp;observations&nbsp;whose&nbsp;merge&nbsp;key&nbsp;only&nbsp;appears&nbsp;in&nbsp;'right'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;and&nbsp;"both"&nbsp;if&nbsp;the&nbsp;observation's&nbsp;merge&nbsp;key&nbsp;is&nbsp;found&nbsp;in&nbsp;both.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;B<br>
-&nbsp;&nbsp;&nbsp;&nbsp;lkey&nbsp;value&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;rkey&nbsp;value<br>
-0&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;5<br>
-1&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;6<br>
-2&nbsp;&nbsp;&nbsp;baz&nbsp;&nbsp;3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;qux&nbsp;&nbsp;7<br>
-3&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;8<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;A.<a href="#MyDataFrame-merge">merge</a>(B,&nbsp;left_on='lkey',&nbsp;right_on='rkey',&nbsp;how='outer')<br>
-&nbsp;&nbsp;&nbsp;lkey&nbsp;&nbsp;value_x&nbsp;&nbsp;rkey&nbsp;&nbsp;value_y<br>
-0&nbsp;&nbsp;foo&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;&nbsp;5<br>
-1&nbsp;&nbsp;foo&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;bar&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;&nbsp;6<br>
-3&nbsp;&nbsp;bar&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;&nbsp;8<br>
-4&nbsp;&nbsp;baz&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;NaN<br>
-5&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;qux&nbsp;&nbsp;&nbsp;7<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-merged&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;output&nbsp;type&nbsp;will&nbsp;the&nbsp;be&nbsp;same&nbsp;as&nbsp;'left',&nbsp;if&nbsp;it&nbsp;is&nbsp;a&nbsp;subclass<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-merge_ordered<br>
-merge_asof</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-min"><strong>min</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;minimum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;minimum,&nbsp;use&nbsp;``idxmin``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmin``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-min&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-mod"><strong>mod</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Modulo&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;%&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rmod</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-mode"><strong>mode</strong></a>(self, axis=0, numeric_only=False)</dt><dd><tt>Gets&nbsp;the&nbsp;<a href="#MyDataFrame-mode">mode</a>(s)&nbsp;of&nbsp;each&nbsp;element&nbsp;along&nbsp;the&nbsp;axis&nbsp;selected.&nbsp;Adds&nbsp;a&nbsp;row<br>
-for&nbsp;each&nbsp;mode&nbsp;per&nbsp;label,&nbsp;fills&nbsp;in&nbsp;gaps&nbsp;with&nbsp;nan.<br>
-&nbsp;<br>
-Note&nbsp;that&nbsp;there&nbsp;could&nbsp;be&nbsp;multiple&nbsp;values&nbsp;returned&nbsp;for&nbsp;the&nbsp;selected<br>
-axis&nbsp;(when&nbsp;more&nbsp;than&nbsp;one&nbsp;item&nbsp;share&nbsp;the&nbsp;maximum&nbsp;frequency),&nbsp;which&nbsp;is<br>
-the&nbsp;reason&nbsp;why&nbsp;a&nbsp;dataframe&nbsp;is&nbsp;returned.&nbsp;If&nbsp;you&nbsp;want&nbsp;to&nbsp;impute&nbsp;missing<br>
-values&nbsp;with&nbsp;the&nbsp;mode&nbsp;in&nbsp;a&nbsp;dataframe&nbsp;``df``,&nbsp;you&nbsp;can&nbsp;just&nbsp;do&nbsp;this:<br>
-``df.<a href="#MyDataFrame-fillna">fillna</a>(df.<a href="#MyDataFrame-mode">mode</a>().iloc[0])``<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;0&nbsp;or&nbsp;'index'&nbsp;:&nbsp;get&nbsp;mode&nbsp;of&nbsp;each&nbsp;column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;:&nbsp;get&nbsp;mode&nbsp;of&nbsp;each&nbsp;row<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;only&nbsp;apply&nbsp;to&nbsp;numeric&nbsp;columns<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-modes&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(sorted)<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;2,&nbsp;1,&nbsp;2,&nbsp;1,&nbsp;2,&nbsp;3]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-mode">mode</a>()<br>
-&nbsp;&nbsp;&nbsp;A<br>
-0&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;2</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-mul"><strong>mul</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Multiplication&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-multiply"><strong>multiply</strong></a> = mul(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Multiplication&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-ne"><strong>ne</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;ne</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-nlargest"><strong>nlargest</strong></a>(self, n, columns, keep='first')</dt><dd><tt>Get&nbsp;the&nbsp;rows&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;sorted&nbsp;by&nbsp;the&nbsp;`n`&nbsp;largest<br>
-values&nbsp;of&nbsp;`columns`.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;items&nbsp;to&nbsp;retrieve<br>
-columns&nbsp;:&nbsp;list&nbsp;or&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;name&nbsp;or&nbsp;names&nbsp;to&nbsp;order&nbsp;by<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'a':&nbsp;[1,&nbsp;10,&nbsp;8,&nbsp;11,&nbsp;-1],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'b':&nbsp;list('abdce'),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'c':&nbsp;[1.0,&nbsp;2.0,&nbsp;np.nan,&nbsp;3.0,&nbsp;4.0]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-nlargest">nlargest</a>(3,&nbsp;'a')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;c<br>
-3&nbsp;&nbsp;11&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;3<br>
-1&nbsp;&nbsp;10&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;8&nbsp;&nbsp;d&nbsp;NaN</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-nsmallest"><strong>nsmallest</strong></a>(self, n, columns, keep='first')</dt><dd><tt>Get&nbsp;the&nbsp;rows&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;sorted&nbsp;by&nbsp;the&nbsp;`n`&nbsp;smallest<br>
-values&nbsp;of&nbsp;`columns`.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;items&nbsp;to&nbsp;retrieve<br>
-columns&nbsp;:&nbsp;list&nbsp;or&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;name&nbsp;or&nbsp;names&nbsp;to&nbsp;order&nbsp;by<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'a':&nbsp;[1,&nbsp;10,&nbsp;8,&nbsp;11,&nbsp;-1],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'b':&nbsp;list('abdce'),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'c':&nbsp;[1.0,&nbsp;2.0,&nbsp;np.nan,&nbsp;3.0,&nbsp;4.0]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-nsmallest">nsmallest</a>(3,&nbsp;'a')<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;c<br>
-4&nbsp;-1&nbsp;&nbsp;e&nbsp;&nbsp;&nbsp;4<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;8&nbsp;&nbsp;d&nbsp;NaN</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-nunique"><strong>nunique</strong></a>(self, axis=0, dropna=True)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;number&nbsp;of&nbsp;distinct&nbsp;observations&nbsp;over&nbsp;requested<br>
-axis.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Don't&nbsp;include&nbsp;NaN&nbsp;in&nbsp;the&nbsp;counts.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-nunique&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;'B':&nbsp;[1,&nbsp;1,&nbsp;1]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-nunique">nunique</a>()<br>
-A&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-B&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-nunique">nunique</a>(axis=1)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-pivot"><strong>pivot</strong></a>(self, index=None, columns=None, values=None)</dt><dd><tt>Reshape&nbsp;data&nbsp;(produce&nbsp;a&nbsp;"pivot"&nbsp;table)&nbsp;based&nbsp;on&nbsp;column&nbsp;values.&nbsp;Uses<br>
-unique&nbsp;values&nbsp;from&nbsp;index&nbsp;/&nbsp;columns&nbsp;to&nbsp;form&nbsp;axes&nbsp;of&nbsp;the&nbsp;resulting<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;name&nbsp;to&nbsp;use&nbsp;to&nbsp;make&nbsp;new&nbsp;frame's&nbsp;index.&nbsp;If&nbsp;None,&nbsp;uses<br>
-&nbsp;&nbsp;&nbsp;&nbsp;existing&nbsp;index.<br>
-columns&nbsp;:&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;name&nbsp;to&nbsp;use&nbsp;to&nbsp;make&nbsp;new&nbsp;frame's&nbsp;columns<br>
-values&nbsp;:&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;name&nbsp;to&nbsp;use&nbsp;for&nbsp;populating&nbsp;new&nbsp;frame's&nbsp;values.&nbsp;If&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specified,&nbsp;all&nbsp;remaining&nbsp;columns&nbsp;will&nbsp;be&nbsp;used&nbsp;and&nbsp;the&nbsp;result&nbsp;will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;have&nbsp;hierarchically&nbsp;indexed&nbsp;columns<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-pivoted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.pivot_table&nbsp;:&nbsp;generalization&nbsp;of&nbsp;pivot&nbsp;that&nbsp;can&nbsp;handle<br>
-&nbsp;&nbsp;&nbsp;&nbsp;duplicate&nbsp;values&nbsp;for&nbsp;one&nbsp;index/column&nbsp;pair<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.unstack&nbsp;:&nbsp;pivot&nbsp;based&nbsp;on&nbsp;the&nbsp;index&nbsp;values&nbsp;instead&nbsp;of&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;column<br>
-&nbsp;<br>
-Notes<br>
------<br>
-For&nbsp;finer-tuned&nbsp;control,&nbsp;see&nbsp;hierarchical&nbsp;indexing&nbsp;documentation&nbsp;along<br>
-with&nbsp;the&nbsp;related&nbsp;stack/unstack&nbsp;methods<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'foo':&nbsp;['one','one','one','two','two','two'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'bar':&nbsp;['A',&nbsp;'B',&nbsp;'C',&nbsp;'A',&nbsp;'B',&nbsp;'C'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'baz':&nbsp;[1,&nbsp;2,&nbsp;3,&nbsp;4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;baz<br>
-0&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-4&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-5&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-pivot">pivot</a>(index='foo',&nbsp;columns='bar',&nbsp;values='baz')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C<br>
-one&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;3<br>
-two&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-pivot">pivot</a>(index='foo',&nbsp;columns='bar')['baz']<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C<br>
-one&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;3<br>
-two&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-pivot_table"><strong>pivot_table</strong></a>(data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All')</dt><dd><tt>Create&nbsp;a&nbsp;spreadsheet-style&nbsp;pivot&nbsp;table&nbsp;as&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;The&nbsp;levels&nbsp;in&nbsp;the<br>
-pivot&nbsp;table&nbsp;will&nbsp;be&nbsp;stored&nbsp;in&nbsp;MultiIndex&nbsp;objects&nbsp;(hierarchical&nbsp;indexes)&nbsp;on<br>
-the&nbsp;index&nbsp;and&nbsp;columns&nbsp;of&nbsp;the&nbsp;result&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-values&nbsp;:&nbsp;column&nbsp;to&nbsp;aggregate,&nbsp;optional<br>
-index&nbsp;:&nbsp;column,&nbsp;Grouper,&nbsp;array,&nbsp;or&nbsp;list&nbsp;of&nbsp;the&nbsp;previous<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;an&nbsp;array&nbsp;is&nbsp;passed,&nbsp;it&nbsp;must&nbsp;be&nbsp;the&nbsp;same&nbsp;length&nbsp;as&nbsp;the&nbsp;data.&nbsp;The&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;contain&nbsp;any&nbsp;of&nbsp;the&nbsp;other&nbsp;types&nbsp;(except&nbsp;list).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keys&nbsp;to&nbsp;group&nbsp;by&nbsp;on&nbsp;the&nbsp;pivot&nbsp;table&nbsp;index.&nbsp;&nbsp;If&nbsp;an&nbsp;array&nbsp;is&nbsp;passed,&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;being&nbsp;used&nbsp;as&nbsp;the&nbsp;same&nbsp;manner&nbsp;as&nbsp;column&nbsp;values.<br>
-columns&nbsp;:&nbsp;column,&nbsp;Grouper,&nbsp;array,&nbsp;or&nbsp;list&nbsp;of&nbsp;the&nbsp;previous<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;an&nbsp;array&nbsp;is&nbsp;passed,&nbsp;it&nbsp;must&nbsp;be&nbsp;the&nbsp;same&nbsp;length&nbsp;as&nbsp;the&nbsp;data.&nbsp;The&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;contain&nbsp;any&nbsp;of&nbsp;the&nbsp;other&nbsp;types&nbsp;(except&nbsp;list).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keys&nbsp;to&nbsp;group&nbsp;by&nbsp;on&nbsp;the&nbsp;pivot&nbsp;table&nbsp;column.&nbsp;&nbsp;If&nbsp;an&nbsp;array&nbsp;is&nbsp;passed,&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;being&nbsp;used&nbsp;as&nbsp;the&nbsp;same&nbsp;manner&nbsp;as&nbsp;column&nbsp;values.<br>
-aggfunc&nbsp;:&nbsp;function&nbsp;or&nbsp;list&nbsp;of&nbsp;functions,&nbsp;default&nbsp;numpy.mean<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;list&nbsp;of&nbsp;functions&nbsp;passed,&nbsp;the&nbsp;resulting&nbsp;pivot&nbsp;table&nbsp;will&nbsp;have<br>
-&nbsp;&nbsp;&nbsp;&nbsp;hierarchical&nbsp;columns&nbsp;whose&nbsp;top&nbsp;level&nbsp;are&nbsp;the&nbsp;function&nbsp;names&nbsp;(inferred<br>
-&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;the&nbsp;function&nbsp;objects&nbsp;themselves)<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;replace&nbsp;missing&nbsp;values&nbsp;with<br>
-margins&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;all&nbsp;row&nbsp;/&nbsp;columns&nbsp;(e.g.&nbsp;for&nbsp;subtotal&nbsp;/&nbsp;grand&nbsp;totals)<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;not&nbsp;include&nbsp;columns&nbsp;whose&nbsp;entries&nbsp;are&nbsp;all&nbsp;NaN<br>
-margins_name&nbsp;:&nbsp;string,&nbsp;default&nbsp;'All'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;the&nbsp;row&nbsp;/&nbsp;column&nbsp;that&nbsp;will&nbsp;contain&nbsp;the&nbsp;totals<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;margins&nbsp;is&nbsp;True.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;foo&nbsp;one&nbsp;small&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;foo&nbsp;one&nbsp;large&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;foo&nbsp;one&nbsp;large&nbsp;&nbsp;2<br>
-3&nbsp;&nbsp;foo&nbsp;two&nbsp;small&nbsp;&nbsp;3<br>
-4&nbsp;&nbsp;foo&nbsp;two&nbsp;small&nbsp;&nbsp;3<br>
-5&nbsp;&nbsp;bar&nbsp;one&nbsp;large&nbsp;&nbsp;4<br>
-6&nbsp;&nbsp;bar&nbsp;one&nbsp;small&nbsp;&nbsp;5<br>
-7&nbsp;&nbsp;bar&nbsp;two&nbsp;small&nbsp;&nbsp;6<br>
-8&nbsp;&nbsp;bar&nbsp;two&nbsp;large&nbsp;&nbsp;7<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;table&nbsp;=&nbsp;<a href="#MyDataFrame-pivot_table">pivot_table</a>(df,&nbsp;values='D',&nbsp;index=['A',&nbsp;'B'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['C'],&nbsp;aggfunc=np.sum)<br>
-&gt;&gt;&gt;&nbsp;table<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;small&nbsp;&nbsp;large<br>
-foo&nbsp;&nbsp;one&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;6&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-bar&nbsp;&nbsp;one&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;6&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-table&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.pivot&nbsp;:&nbsp;pivot&nbsp;without&nbsp;aggregation&nbsp;that&nbsp;can&nbsp;handle<br>
-&nbsp;&nbsp;&nbsp;&nbsp;non-numeric&nbsp;data</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-pow"><strong>pow</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`pow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;**&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rpow</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-prod"><strong>prod</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-product"><strong>product</strong></a> = prod(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-quantile"><strong>quantile</strong></a>(self, q=0.5, axis=0, numeric_only=True, interpolation='linear')</dt><dd><tt>Return&nbsp;values&nbsp;at&nbsp;the&nbsp;given&nbsp;quantile&nbsp;over&nbsp;requested&nbsp;axis,&nbsp;a&nbsp;la<br>
-numpy.percentile.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-q&nbsp;:&nbsp;float&nbsp;or&nbsp;array-like,&nbsp;default&nbsp;0.5&nbsp;(50%&nbsp;quantile)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&lt;=&nbsp;q&nbsp;&lt;=&nbsp;1,&nbsp;the&nbsp;<a href="#MyDataFrame-quantile">quantile</a>(s)&nbsp;to&nbsp;compute<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}&nbsp;(default&nbsp;0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;or&nbsp;'index'&nbsp;for&nbsp;row-wise,&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;for&nbsp;column-wise<br>
-interpolation&nbsp;:&nbsp;{'linear',&nbsp;'lower',&nbsp;'higher',&nbsp;'midpoint',&nbsp;'nearest'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;optional&nbsp;parameter&nbsp;specifies&nbsp;the&nbsp;interpolation&nbsp;method&nbsp;to&nbsp;use,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;the&nbsp;desired&nbsp;quantile&nbsp;lies&nbsp;between&nbsp;two&nbsp;data&nbsp;points&nbsp;`i`&nbsp;and&nbsp;`j`:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;linear:&nbsp;`i&nbsp;+&nbsp;(j&nbsp;-&nbsp;i)&nbsp;*&nbsp;fraction`,&nbsp;where&nbsp;`fraction`&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;fractional&nbsp;part&nbsp;of&nbsp;the&nbsp;index&nbsp;surrounded&nbsp;by&nbsp;`i`&nbsp;and&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;lower:&nbsp;`i`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;higher:&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;`i`&nbsp;or&nbsp;`j`&nbsp;whichever&nbsp;is&nbsp;nearest.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;midpoint:&nbsp;(`i`&nbsp;+&nbsp;`j`)&nbsp;/&nbsp;2.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-quantiles&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;If&nbsp;``q``&nbsp;is&nbsp;an&nbsp;array,&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;be&nbsp;returned&nbsp;where&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;is&nbsp;``q``,&nbsp;the&nbsp;columns&nbsp;are&nbsp;the&nbsp;columns&nbsp;of&nbsp;self,&nbsp;and&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;are&nbsp;the&nbsp;quantiles.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;If&nbsp;``q``&nbsp;is&nbsp;a&nbsp;float,&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;returned&nbsp;where&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;is&nbsp;the&nbsp;columns&nbsp;of&nbsp;self&nbsp;and&nbsp;the&nbsp;values&nbsp;are&nbsp;the&nbsp;quantiles.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.array([[1,&nbsp;1],&nbsp;[2,&nbsp;10],&nbsp;[3,&nbsp;100],&nbsp;[4,&nbsp;100]]),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['a',&nbsp;'b'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-quantile">quantile</a>(.1)<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;1.3<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;3.7<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-quantile">quantile</a>([.1,&nbsp;.5])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-0.1&nbsp;&nbsp;1.3&nbsp;&nbsp;&nbsp;3.7<br>
-0.5&nbsp;&nbsp;2.5&nbsp;&nbsp;55.0</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-query"><strong>query</strong></a>(self, expr, inplace=False, **kwargs)</dt><dd><tt>Query&nbsp;the&nbsp;columns&nbsp;of&nbsp;a&nbsp;frame&nbsp;with&nbsp;a&nbsp;boolean&nbsp;expression.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.13<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-expr&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;query&nbsp;string&nbsp;to&nbsp;evaluate.&nbsp;&nbsp;You&nbsp;can&nbsp;refer&nbsp;to&nbsp;variables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;environment&nbsp;by&nbsp;prefixing&nbsp;them&nbsp;with&nbsp;an&nbsp;'@'&nbsp;character&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``@a&nbsp;+&nbsp;b``.<br>
-inplace&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;the&nbsp;query&nbsp;should&nbsp;modify&nbsp;the&nbsp;data&nbsp;in&nbsp;place&nbsp;or&nbsp;return<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;modified&nbsp;copy<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-kwargs&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;documentation&nbsp;for&nbsp;:func:`pandas.eval`&nbsp;for&nbsp;complete&nbsp;details<br>
-&nbsp;&nbsp;&nbsp;&nbsp;on&nbsp;the&nbsp;keyword&nbsp;arguments&nbsp;accepted&nbsp;by&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.query`.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-q&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;result&nbsp;of&nbsp;the&nbsp;evaluation&nbsp;of&nbsp;this&nbsp;expression&nbsp;is&nbsp;first&nbsp;passed&nbsp;to<br>
-:attr:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.loc`&nbsp;and&nbsp;if&nbsp;that&nbsp;fails&nbsp;because&nbsp;of&nbsp;a<br>
-multidimensional&nbsp;key&nbsp;(e.g.,&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>)&nbsp;then&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;passed<br>
-to&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.__getitem__`.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;uses&nbsp;the&nbsp;top-level&nbsp;:func:`pandas.eval`&nbsp;function&nbsp;to<br>
-evaluate&nbsp;the&nbsp;passed&nbsp;query.<br>
-&nbsp;<br>
-The&nbsp;:meth:`~pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.query`&nbsp;method&nbsp;uses&nbsp;a&nbsp;slightly<br>
-modified&nbsp;Python&nbsp;syntax&nbsp;by&nbsp;default.&nbsp;For&nbsp;example,&nbsp;the&nbsp;``&amp;``&nbsp;and&nbsp;``|``<br>
-(bitwise)&nbsp;operators&nbsp;have&nbsp;the&nbsp;precedence&nbsp;of&nbsp;their&nbsp;boolean&nbsp;cousins,<br>
-:keyword:`and`&nbsp;and&nbsp;:keyword:`or`.&nbsp;This&nbsp;*is*&nbsp;syntactically&nbsp;valid&nbsp;Python,<br>
-however&nbsp;the&nbsp;semantics&nbsp;are&nbsp;different.<br>
-&nbsp;<br>
-You&nbsp;can&nbsp;change&nbsp;the&nbsp;semantics&nbsp;of&nbsp;the&nbsp;expression&nbsp;by&nbsp;passing&nbsp;the&nbsp;keyword<br>
-argument&nbsp;``parser='python'``.&nbsp;This&nbsp;enforces&nbsp;the&nbsp;same&nbsp;semantics&nbsp;as<br>
-evaluation&nbsp;in&nbsp;Python&nbsp;space.&nbsp;Likewise,&nbsp;you&nbsp;can&nbsp;pass&nbsp;``engine='python'``<br>
-to&nbsp;evaluate&nbsp;an&nbsp;expression&nbsp;using&nbsp;Python&nbsp;itself&nbsp;as&nbsp;a&nbsp;backend.&nbsp;This&nbsp;is&nbsp;not<br>
-recommended&nbsp;as&nbsp;it&nbsp;is&nbsp;inefficient&nbsp;compared&nbsp;to&nbsp;using&nbsp;``numexpr``&nbsp;as&nbsp;the<br>
-engine.<br>
-&nbsp;<br>
-The&nbsp;:attr:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.index`&nbsp;and<br>
-:attr:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.columns`&nbsp;attributes&nbsp;of&nbsp;the<br>
-:class:`~pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>`&nbsp;instance&nbsp;are&nbsp;placed&nbsp;in&nbsp;the&nbsp;query&nbsp;namespace<br>
-by&nbsp;default,&nbsp;which&nbsp;allows&nbsp;you&nbsp;to&nbsp;treat&nbsp;both&nbsp;the&nbsp;index&nbsp;and&nbsp;columns&nbsp;of&nbsp;the<br>
-frame&nbsp;as&nbsp;a&nbsp;column&nbsp;in&nbsp;the&nbsp;frame.<br>
-The&nbsp;identifier&nbsp;``index``&nbsp;is&nbsp;used&nbsp;for&nbsp;the&nbsp;frame&nbsp;index;&nbsp;you&nbsp;can&nbsp;also<br>
-use&nbsp;the&nbsp;name&nbsp;of&nbsp;the&nbsp;index&nbsp;to&nbsp;identify&nbsp;it&nbsp;in&nbsp;a&nbsp;query.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``query``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.query&gt;`.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.eval<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.eval<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;numpy.random&nbsp;import&nbsp;randn<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;pandas&nbsp;import&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(randn(10,&nbsp;2),&nbsp;columns=list('ab'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-query">query</a>('a&nbsp;&gt;&nbsp;b')<br>
-&gt;&gt;&gt;&nbsp;df[df.a&nbsp;&gt;&nbsp;df.b]&nbsp;&nbsp;#&nbsp;same&nbsp;result&nbsp;as&nbsp;the&nbsp;previous&nbsp;expression</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-radd"><strong>radd</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Addition&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`radd`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;+&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.add</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-rdiv"><strong>rdiv</strong></a> = rtruediv(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-reindex"><strong>reindex</strong></a>(self, index=None, columns=None, **kwargs)</dt><dd><tt>Conform&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;new&nbsp;index&nbsp;with&nbsp;optional&nbsp;filling&nbsp;logic,&nbsp;placing<br>
-NA/NaN&nbsp;in&nbsp;locations&nbsp;having&nbsp;no&nbsp;value&nbsp;in&nbsp;the&nbsp;previous&nbsp;index.&nbsp;A&nbsp;new&nbsp;<a href="builtins.html#object">object</a><br>
-is&nbsp;produced&nbsp;unless&nbsp;the&nbsp;new&nbsp;index&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;the&nbsp;current&nbsp;one&nbsp;and<br>
-copy=False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index,&nbsp;columns&nbsp;:&nbsp;array-like,&nbsp;optional&nbsp;(can&nbsp;be&nbsp;specified&nbsp;in&nbsp;order,&nbsp;or&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;keywords)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;New&nbsp;labels&nbsp;/&nbsp;index&nbsp;to&nbsp;conform&nbsp;to.&nbsp;Preferably&nbsp;an&nbsp;Index&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;avoid&nbsp;duplicating&nbsp;data<br>
-method&nbsp;:&nbsp;{None,&nbsp;'backfill'/'bfill',&nbsp;'pad'/'ffill',&nbsp;'nearest'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Please&nbsp;note:&nbsp;this&nbsp;is&nbsp;only&nbsp;&nbsp;applicable&nbsp;to&nbsp;DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;monotonically&nbsp;increasing/decreasing&nbsp;index.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;default:&nbsp;don't&nbsp;fill&nbsp;gaps<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;next&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;use&nbsp;nearest&nbsp;valid&nbsp;observations&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>,&nbsp;even&nbsp;if&nbsp;the&nbsp;passed&nbsp;indexes&nbsp;are&nbsp;the&nbsp;same<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;elements&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;original&nbsp;and&nbsp;new&nbsp;labels&nbsp;for&nbsp;inexact<br>
-&nbsp;&nbsp;&nbsp;&nbsp;matches.&nbsp;The&nbsp;values&nbsp;of&nbsp;the&nbsp;index&nbsp;at&nbsp;the&nbsp;matching&nbsp;locations&nbsp;most<br>
-&nbsp;&nbsp;&nbsp;&nbsp;satisfy&nbsp;the&nbsp;equation&nbsp;``<a href="#MyDataFrame-abs">abs</a>(index[indexer]&nbsp;-&nbsp;target)&nbsp;&lt;=&nbsp;tolerance``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;some&nbsp;fictional&nbsp;data.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;['Firefox',&nbsp;'Chrome',&nbsp;'Safari',&nbsp;'IE10',&nbsp;'Konqueror']<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'http_status':&nbsp;[200,200,404,404,301],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'response_time':&nbsp;[0.04,&nbsp;0.02,&nbsp;0.07,&nbsp;0.08,&nbsp;1.0]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Firefox&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.04<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Konqueror&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;301&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.00<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;new&nbsp;index&nbsp;and&nbsp;reindex&nbsp;the&nbsp;dataframe.&nbsp;By&nbsp;default<br>
-values&nbsp;in&nbsp;the&nbsp;new&nbsp;index&nbsp;that&nbsp;do&nbsp;not&nbsp;have&nbsp;corresponding<br>
-records&nbsp;in&nbsp;the&nbsp;dataframe&nbsp;are&nbsp;assigned&nbsp;``NaN``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;new_index=&nbsp;['Safari',&nbsp;'Iceweasel',&nbsp;'Comodo&nbsp;Dragon',&nbsp;'IE10',<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Chrome']<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-reindex">reindex</a>(new_index)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-We&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;by&nbsp;passing&nbsp;a&nbsp;value&nbsp;to<br>
-the&nbsp;keyword&nbsp;``fill_value``.&nbsp;Because&nbsp;the&nbsp;index&nbsp;is&nbsp;not&nbsp;monotonically<br>
-increasing&nbsp;or&nbsp;decreasing,&nbsp;we&nbsp;cannot&nbsp;use&nbsp;arguments&nbsp;to&nbsp;the&nbsp;keyword<br>
-``method``&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``&nbsp;values.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-reindex">reindex</a>(new_index,&nbsp;fill_value=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-reindex">reindex</a>(new_index,&nbsp;fill_value='missing')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-To&nbsp;further&nbsp;illustrate&nbsp;the&nbsp;filling&nbsp;functionality&nbsp;in<br>
-``reindex``,&nbsp;we&nbsp;will&nbsp;create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;a<br>
-monotonically&nbsp;increasing&nbsp;index&nbsp;(for&nbsp;example,&nbsp;a&nbsp;sequence<br>
-of&nbsp;dates).<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index&nbsp;=&nbsp;pd.date_range('1/1/2010',&nbsp;periods=6,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"prices":&nbsp;[100,&nbsp;101,&nbsp;np.nan,&nbsp;100,&nbsp;89,&nbsp;88]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=date_index)<br>
-&gt;&gt;&gt;&nbsp;df2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-&nbsp;<br>
-Suppose&nbsp;we&nbsp;decide&nbsp;to&nbsp;expand&nbsp;the&nbsp;dataframe&nbsp;to&nbsp;cover&nbsp;a&nbsp;wider<br>
-date&nbsp;range.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index2&nbsp;=&nbsp;pd.date_range('12/29/2009',&nbsp;periods=10,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#MyDataFrame-reindex">reindex</a>(date_index2)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-The&nbsp;index&nbsp;entries&nbsp;that&nbsp;did&nbsp;not&nbsp;have&nbsp;a&nbsp;value&nbsp;in&nbsp;the&nbsp;original&nbsp;data&nbsp;frame<br>
-(for&nbsp;example,&nbsp;'2009-12-29')&nbsp;are&nbsp;by&nbsp;default&nbsp;filled&nbsp;with&nbsp;``NaN``.<br>
-If&nbsp;desired,&nbsp;we&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;using&nbsp;one&nbsp;of&nbsp;several<br>
-options.<br>
-&nbsp;<br>
-For&nbsp;example,&nbsp;to&nbsp;backpropagate&nbsp;the&nbsp;last&nbsp;valid&nbsp;value&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values,&nbsp;pass&nbsp;``bfill``&nbsp;as&nbsp;an&nbsp;argument&nbsp;to&nbsp;the&nbsp;``method``&nbsp;keyword.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#MyDataFrame-reindex">reindex</a>(date_index2,&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;the&nbsp;``NaN``&nbsp;value&nbsp;present&nbsp;in&nbsp;the&nbsp;original&nbsp;dataframe<br>
-(at&nbsp;index&nbsp;value&nbsp;2010-01-03)&nbsp;will&nbsp;not&nbsp;be&nbsp;filled&nbsp;by&nbsp;any&nbsp;of&nbsp;the<br>
-value&nbsp;propagation&nbsp;schemes.&nbsp;This&nbsp;is&nbsp;because&nbsp;filling&nbsp;while&nbsp;reindexing<br>
-does&nbsp;not&nbsp;look&nbsp;at&nbsp;dataframe&nbsp;values,&nbsp;but&nbsp;only&nbsp;compares&nbsp;the&nbsp;original&nbsp;and<br>
-desired&nbsp;indexes.&nbsp;If&nbsp;you&nbsp;do&nbsp;want&nbsp;to&nbsp;fill&nbsp;in&nbsp;the&nbsp;``NaN``&nbsp;values&nbsp;present<br>
-in&nbsp;the&nbsp;original&nbsp;dataframe,&nbsp;use&nbsp;the&nbsp;``<a href="#MyDataFrame-fillna">fillna</a>()``&nbsp;method.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-reindex_axis"><strong>reindex_axis</strong></a>(self, labels, axis=0, method=None, level=None, copy=True, limit=None, fill_value=nan)</dt><dd><tt>Conform&nbsp;input&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;new&nbsp;index&nbsp;with&nbsp;optional<br>
-filling&nbsp;logic,&nbsp;placing&nbsp;NA/NaN&nbsp;in&nbsp;locations&nbsp;having&nbsp;no&nbsp;value&nbsp;in&nbsp;the<br>
-previous&nbsp;index.&nbsp;A&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;produced&nbsp;unless&nbsp;the&nbsp;new&nbsp;index&nbsp;is<br>
-equivalent&nbsp;to&nbsp;the&nbsp;current&nbsp;one&nbsp;and&nbsp;copy=False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-labels&nbsp;:&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;New&nbsp;labels&nbsp;/&nbsp;index&nbsp;to&nbsp;conform&nbsp;to.&nbsp;Preferably&nbsp;an&nbsp;Index&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;avoid&nbsp;duplicating&nbsp;data<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'}<br>
-method&nbsp;:&nbsp;{None,&nbsp;'backfill'/'bfill',&nbsp;'pad'/'ffill',&nbsp;'nearest'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;default:&nbsp;don't&nbsp;fill&nbsp;gaps<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;next&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;use&nbsp;nearest&nbsp;valid&nbsp;observations&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>,&nbsp;even&nbsp;if&nbsp;the&nbsp;passed&nbsp;indexes&nbsp;are&nbsp;the&nbsp;same<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;elements&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;original&nbsp;and&nbsp;new&nbsp;labels&nbsp;for&nbsp;inexact<br>
-&nbsp;&nbsp;&nbsp;&nbsp;matches.&nbsp;The&nbsp;values&nbsp;of&nbsp;the&nbsp;index&nbsp;at&nbsp;the&nbsp;matching&nbsp;locations&nbsp;most<br>
-&nbsp;&nbsp;&nbsp;&nbsp;satisfy&nbsp;the&nbsp;equation&nbsp;``<a href="#MyDataFrame-abs">abs</a>(index[indexer]&nbsp;-&nbsp;target)&nbsp;&lt;=&nbsp;tolerance``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-reindex_axis">reindex_axis</a>(['A',&nbsp;'B',&nbsp;'C'],&nbsp;axis=1)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;reindex_like<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-rename"><strong>rename</strong></a>(self, index=None, columns=None, **kwargs)</dt><dd><tt>Alter&nbsp;axes&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.&nbsp;Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be<br>
-unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in&nbsp;a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left<br>
-as-is.&nbsp;Extra&nbsp;labels&nbsp;listed&nbsp;don't&nbsp;throw&nbsp;an&nbsp;error.&nbsp;Alternatively,&nbsp;change<br>
-``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;with&nbsp;a&nbsp;scalar&nbsp;value&nbsp;(<a href="pandas.core.series.html#Series">Series</a>&nbsp;only).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index,&nbsp;columns&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Scalar&nbsp;or&nbsp;list-like&nbsp;will&nbsp;alter&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;attribute,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;raise&nbsp;on&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;Panel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict-like&nbsp;or&nbsp;functions&nbsp;are&nbsp;transformations&nbsp;to&nbsp;apply&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;that&nbsp;axis'&nbsp;values<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;If&nbsp;True&nbsp;then&nbsp;value&nbsp;of&nbsp;copy&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;ignored.<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;MultiIndex,&nbsp;only&nbsp;rename&nbsp;labels&nbsp;in&nbsp;the&nbsp;specified<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename_axis<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MyDataFrame-rename">rename</a>("my_name")&nbsp;#&nbsp;scalar,&nbsp;changes&nbsp;<a href="pandas.core.series.html#Series">Series</a>.name<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;my_name,&nbsp;dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MyDataFrame-rename">rename</a>(lambda&nbsp;x:&nbsp;x&nbsp;**&nbsp;2)&nbsp;&nbsp;#&nbsp;function,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MyDataFrame-rename">rename</a>({1:&nbsp;3,&nbsp;2:&nbsp;5})&nbsp;&nbsp;#&nbsp;mapping,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-rename">rename</a>(2)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-TypeError:&nbsp;'int'&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;not&nbsp;callable<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"B":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;c<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"C":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-reorder_levels"><strong>reorder_levels</strong></a>(self, order, axis=0)</dt><dd><tt>Rearrange&nbsp;index&nbsp;levels&nbsp;using&nbsp;input&nbsp;order.<br>
-May&nbsp;not&nbsp;drop&nbsp;or&nbsp;duplicate&nbsp;levels<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-order&nbsp;:&nbsp;list&nbsp;of&nbsp;int&nbsp;or&nbsp;list&nbsp;of&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;representing&nbsp;new&nbsp;level&nbsp;order.&nbsp;Reference&nbsp;level&nbsp;by&nbsp;number<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(position)&nbsp;or&nbsp;by&nbsp;key&nbsp;(label).<br>
-axis&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;to&nbsp;reorder&nbsp;levels.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-type&nbsp;of&nbsp;caller&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-reset_index"><strong>reset_index</strong></a>(self, level=None, drop=False, inplace=False, col_level=0, col_fill='')</dt><dd><tt>For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;multi-level&nbsp;index,&nbsp;return&nbsp;new&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with<br>
-labeling&nbsp;information&nbsp;in&nbsp;the&nbsp;columns&nbsp;under&nbsp;the&nbsp;index&nbsp;names,&nbsp;defaulting<br>
-to&nbsp;'level_0',&nbsp;'level_1',&nbsp;etc.&nbsp;if&nbsp;any&nbsp;are&nbsp;None.&nbsp;For&nbsp;a&nbsp;standard&nbsp;index,<br>
-the&nbsp;index&nbsp;name&nbsp;will&nbsp;be&nbsp;used&nbsp;(if&nbsp;set),&nbsp;otherwise&nbsp;a&nbsp;default&nbsp;'index'&nbsp;or<br>
-'level_0'&nbsp;(if&nbsp;'index'&nbsp;is&nbsp;already&nbsp;taken)&nbsp;will&nbsp;be&nbsp;used.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;tuple,&nbsp;or&nbsp;list,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Only&nbsp;remove&nbsp;the&nbsp;given&nbsp;levels&nbsp;from&nbsp;the&nbsp;index.&nbsp;Removes&nbsp;all&nbsp;levels&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-drop&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;not&nbsp;try&nbsp;to&nbsp;insert&nbsp;index&nbsp;into&nbsp;dataframe&nbsp;columns.&nbsp;This&nbsp;resets<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;index&nbsp;to&nbsp;the&nbsp;default&nbsp;integer&nbsp;index.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Modify&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;in&nbsp;place&nbsp;(do&nbsp;not&nbsp;create&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>)<br>
-col_level&nbsp;:&nbsp;int&nbsp;or&nbsp;str,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;columns&nbsp;have&nbsp;multiple&nbsp;levels,&nbsp;determines&nbsp;which&nbsp;level&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;labels&nbsp;are&nbsp;inserted&nbsp;into.&nbsp;By&nbsp;default&nbsp;it&nbsp;is&nbsp;inserted&nbsp;into&nbsp;the&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level.<br>
-col_fill&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;columns&nbsp;have&nbsp;multiple&nbsp;levels,&nbsp;determines&nbsp;how&nbsp;the&nbsp;other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;levels&nbsp;are&nbsp;named.&nbsp;If&nbsp;None&nbsp;then&nbsp;the&nbsp;index&nbsp;name&nbsp;is&nbsp;repeated.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-resetted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-rfloordiv"><strong>rfloordiv</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rfloordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;//&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.floordiv</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-rmod"><strong>rmod</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Modulo&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;%&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mod</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-rmul"><strong>rmul</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Multiplication&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;*&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mul</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-rolling"><strong>rolling</strong></a>(self, window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0, closed=None)</dt><dd><tt>Provides&nbsp;rolling&nbsp;window&nbsp;calculcations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-window&nbsp;:&nbsp;int,&nbsp;or&nbsp;offset<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Size&nbsp;of&nbsp;the&nbsp;moving&nbsp;window.&nbsp;This&nbsp;is&nbsp;the&nbsp;number&nbsp;of&nbsp;observations&nbsp;used&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;calculating&nbsp;the&nbsp;statistic.&nbsp;Each&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;fixed&nbsp;size.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;its&nbsp;an&nbsp;offset&nbsp;then&nbsp;this&nbsp;will&nbsp;be&nbsp;the&nbsp;time&nbsp;period&nbsp;of&nbsp;each&nbsp;window.&nbsp;Each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;variable&nbsp;sized&nbsp;based&nbsp;on&nbsp;the&nbsp;observations&nbsp;included&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;time-period.&nbsp;This&nbsp;is&nbsp;only&nbsp;valid&nbsp;for&nbsp;datetimelike&nbsp;indexes.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;new&nbsp;in&nbsp;0.19.0<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).&nbsp;For&nbsp;a&nbsp;window&nbsp;that&nbsp;is&nbsp;specified&nbsp;by&nbsp;an&nbsp;offset,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;will&nbsp;default&nbsp;to&nbsp;1.<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-win_type&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Provide&nbsp;a&nbsp;window&nbsp;type.&nbsp;See&nbsp;the&nbsp;notes&nbsp;below.<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;on&nbsp;which&nbsp;to&nbsp;calculate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;rolling&nbsp;window,&nbsp;rather&nbsp;than&nbsp;the&nbsp;index<br>
-closed&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;the&nbsp;interval&nbsp;closed&nbsp;on&nbsp;the&nbsp;'right',&nbsp;'left',&nbsp;'both'&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'neither'&nbsp;endpoints.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;offset-based&nbsp;windows,&nbsp;it&nbsp;defaults&nbsp;to&nbsp;'right'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;fixed&nbsp;windows,&nbsp;defaults&nbsp;to&nbsp;'both'.&nbsp;Remaining&nbsp;cases&nbsp;not&nbsp;implemented<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;fixed&nbsp;windows.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;or&nbsp;Rolling&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;using&nbsp;the&nbsp;'triang'<br>
-window&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-rolling">rolling</a>(2,&nbsp;win_type='triang').<a href="#MyDataFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.5<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;min_periods&nbsp;defaults<br>
-to&nbsp;the&nbsp;window&nbsp;length.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-rolling">rolling</a>(2).<a href="#MyDataFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Same&nbsp;as&nbsp;above,&nbsp;but&nbsp;explicity&nbsp;set&nbsp;the&nbsp;min_periods<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-rolling">rolling</a>(2,&nbsp;min_periods=1).<a href="#MyDataFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;2.0<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-A&nbsp;ragged&nbsp;(meaning&nbsp;not-a-regular&nbsp;frequency),&nbsp;time-indexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]},<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;=&nbsp;[pd.Timestamp('20130101&nbsp;09:00:00'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:02'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:03'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:05'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:06')])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;2.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Contrasting&nbsp;to&nbsp;an&nbsp;integer&nbsp;rolling&nbsp;window,&nbsp;this&nbsp;will&nbsp;roll&nbsp;a&nbsp;variable<br>
-length&nbsp;window&nbsp;corresponding&nbsp;to&nbsp;the&nbsp;time&nbsp;period.<br>
-The&nbsp;default&nbsp;for&nbsp;min_periods&nbsp;is&nbsp;1.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-rolling">rolling</a>('2s').<a href="#MyDataFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;3.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offsets&nbsp;&amp;&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-The&nbsp;recognized&nbsp;win_types&nbsp;are:<br>
-&nbsp;<br>
-*&nbsp;``boxcar``<br>
-*&nbsp;``triang``<br>
-*&nbsp;``blackman``<br>
-*&nbsp;``hamming``<br>
-*&nbsp;``bartlett``<br>
-*&nbsp;``parzen``<br>
-*&nbsp;``bohman``<br>
-*&nbsp;``blackmanharris``<br>
-*&nbsp;``nuttall``<br>
-*&nbsp;``barthann``<br>
-*&nbsp;``kaiser``&nbsp;(needs&nbsp;beta)<br>
-*&nbsp;``gaussian``&nbsp;(needs&nbsp;std)<br>
-*&nbsp;``general_gaussian``&nbsp;(needs&nbsp;power,&nbsp;width)<br>
-*&nbsp;``slepian``&nbsp;(needs&nbsp;width).</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-round"><strong>round</strong></a>(self, decimals=0, *args, **kwargs)</dt><dd><tt>Round&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;variable&nbsp;number&nbsp;of&nbsp;decimal&nbsp;places.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-decimals&nbsp;:&nbsp;int,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;round&nbsp;each&nbsp;column&nbsp;to.&nbsp;If&nbsp;an&nbsp;int&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;given,&nbsp;round&nbsp;each&nbsp;column&nbsp;to&nbsp;the&nbsp;same&nbsp;number&nbsp;of&nbsp;places.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Otherwise&nbsp;dict&nbsp;and&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;round&nbsp;to&nbsp;variable&nbsp;numbers&nbsp;of&nbsp;places.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;names&nbsp;should&nbsp;be&nbsp;in&nbsp;the&nbsp;keys&nbsp;if&nbsp;`decimals`&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict-like,&nbsp;or&nbsp;in&nbsp;the&nbsp;index&nbsp;if&nbsp;`decimals`&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns&nbsp;not&nbsp;included&nbsp;in&nbsp;`decimals`&nbsp;will&nbsp;be&nbsp;left&nbsp;as&nbsp;is.&nbsp;Elements<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;`decimals`&nbsp;which&nbsp;are&nbsp;not&nbsp;columns&nbsp;of&nbsp;the&nbsp;input&nbsp;will&nbsp;be<br>
-&nbsp;&nbsp;&nbsp;&nbsp;ignored.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.random([3,&nbsp;3]),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],&nbsp;index=['first',&nbsp;'second',&nbsp;'third'])<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-first&nbsp;&nbsp;&nbsp;0.028208&nbsp;&nbsp;0.992815&nbsp;&nbsp;0.173891<br>
-second&nbsp;&nbsp;0.038683&nbsp;&nbsp;0.645646&nbsp;&nbsp;0.577595<br>
-third&nbsp;&nbsp;&nbsp;0.877076&nbsp;&nbsp;0.149370&nbsp;&nbsp;0.491027<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-round">round</a>(2)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-first&nbsp;&nbsp;&nbsp;0.03&nbsp;&nbsp;0.99&nbsp;&nbsp;0.17<br>
-second&nbsp;&nbsp;0.04&nbsp;&nbsp;0.65&nbsp;&nbsp;0.58<br>
-third&nbsp;&nbsp;&nbsp;0.88&nbsp;&nbsp;0.15&nbsp;&nbsp;0.49<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-round">round</a>({'A':&nbsp;1,&nbsp;'C':&nbsp;2})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-first&nbsp;&nbsp;&nbsp;0.0&nbsp;&nbsp;0.992815&nbsp;&nbsp;0.17<br>
-second&nbsp;&nbsp;0.0&nbsp;&nbsp;0.645646&nbsp;&nbsp;0.58<br>
-third&nbsp;&nbsp;&nbsp;0.9&nbsp;&nbsp;0.149370&nbsp;&nbsp;0.49<br>
-&gt;&gt;&gt;&nbsp;decimals&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;0,&nbsp;2],&nbsp;index=['A',&nbsp;'B',&nbsp;'C'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-round">round</a>(decimals)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-first&nbsp;&nbsp;&nbsp;0.0&nbsp;&nbsp;1&nbsp;&nbsp;0.17<br>
-second&nbsp;&nbsp;0.0&nbsp;&nbsp;1&nbsp;&nbsp;0.58<br>
-third&nbsp;&nbsp;&nbsp;0.9&nbsp;&nbsp;0&nbsp;&nbsp;0.49<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.around<br>
-<a href="pandas.core.series.html#Series">Series</a>.round</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-rpow"><strong>rpow</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rpow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;**&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.pow</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-rsub"><strong>rsub</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Subtraction&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rsub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;-&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.sub</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-rtruediv"><strong>rtruediv</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-select_dtypes"><strong>select_dtypes</strong></a>(self, include=None, exclude=None)</dt><dd><tt>Return&nbsp;a&nbsp;subset&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;including/excluding&nbsp;columns&nbsp;based&nbsp;on<br>
-their&nbsp;``dtype``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-include,&nbsp;exclude&nbsp;:&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;list&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;strings&nbsp;to&nbsp;be&nbsp;included/excluded.&nbsp;You&nbsp;must&nbsp;pass<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;a&nbsp;non-empty&nbsp;sequence&nbsp;for&nbsp;at&nbsp;least&nbsp;one&nbsp;of&nbsp;these.<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;both&nbsp;of&nbsp;``include``&nbsp;and&nbsp;``exclude``&nbsp;are&nbsp;empty<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;``include``&nbsp;and&nbsp;``exclude``&nbsp;have&nbsp;overlapping&nbsp;elements<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;any&nbsp;kind&nbsp;of&nbsp;string&nbsp;dtype&nbsp;is&nbsp;passed&nbsp;in.<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;either&nbsp;of&nbsp;``include``&nbsp;or&nbsp;``exclude``&nbsp;is&nbsp;not&nbsp;a&nbsp;sequence<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;subset&nbsp;of&nbsp;the&nbsp;frame&nbsp;including&nbsp;the&nbsp;dtypes&nbsp;in&nbsp;``include``&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;excluding&nbsp;the&nbsp;dtypes&nbsp;in&nbsp;``exclude``.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-*&nbsp;To&nbsp;select&nbsp;all&nbsp;*numeric*&nbsp;types&nbsp;use&nbsp;the&nbsp;numpy&nbsp;dtype&nbsp;``numpy.number``<br>
-*&nbsp;To&nbsp;select&nbsp;strings&nbsp;you&nbsp;must&nbsp;use&nbsp;the&nbsp;``<a href="builtins.html#object">object</a>``&nbsp;dtype,&nbsp;but&nbsp;note&nbsp;that<br>
-&nbsp;&nbsp;this&nbsp;will&nbsp;return&nbsp;*all*&nbsp;<a href="builtins.html#object">object</a>&nbsp;dtype&nbsp;columns<br>
-*&nbsp;See&nbsp;the&nbsp;`numpy&nbsp;dtype&nbsp;hierarchy<br>
-&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html&gt;`__">http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html&gt;`__</a><br>
-*&nbsp;To&nbsp;select&nbsp;datetimes,&nbsp;use&nbsp;np.datetime64,&nbsp;'datetime'&nbsp;or&nbsp;'datetime64'<br>
-*&nbsp;To&nbsp;select&nbsp;timedeltas,&nbsp;use&nbsp;np.timedelta64,&nbsp;'timedelta'&nbsp;or<br>
-&nbsp;&nbsp;'timedelta64'<br>
-*&nbsp;To&nbsp;select&nbsp;Pandas&nbsp;categorical&nbsp;dtypes,&nbsp;use&nbsp;'category'<br>
-*&nbsp;To&nbsp;select&nbsp;Pandas&nbsp;datetimetz&nbsp;dtypes,&nbsp;use&nbsp;'datetimetz'&nbsp;(new&nbsp;in&nbsp;0.20.0),<br>
-&nbsp;&nbsp;or&nbsp;a&nbsp;'datetime64[ns,&nbsp;tz]'&nbsp;string<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'a':&nbsp;np.random.randn(6).<a href="#MyDataFrame-astype">astype</a>('f4'),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'b':&nbsp;[True,&nbsp;False]&nbsp;*&nbsp;3,<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'c':&nbsp;[1.0,&nbsp;2.0]&nbsp;*&nbsp;3})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;c<br>
-0&nbsp;&nbsp;0.3962&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;0.1459&nbsp;&nbsp;False&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;0.2623&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;0.0764&nbsp;&nbsp;False&nbsp;&nbsp;2<br>
-4&nbsp;-0.9703&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;1<br>
-5&nbsp;-1.2094&nbsp;&nbsp;False&nbsp;&nbsp;2<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-select_dtypes">select_dtypes</a>(include=['float64'])<br>
-&nbsp;&nbsp;&nbsp;c<br>
-0&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;2<br>
-4&nbsp;&nbsp;1<br>
-5&nbsp;&nbsp;2<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-select_dtypes">select_dtypes</a>(exclude=['floating'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-0&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;False<br>
-4&nbsp;&nbsp;&nbsp;True<br>
-5&nbsp;&nbsp;False</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-sem"><strong>sem</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;standard&nbsp;error&nbsp;of&nbsp;the&nbsp;mean&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sem&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-set_index"><strong>set_index</strong></a>(self, keys, drop=True, append=False, inplace=False, verify_integrity=False)</dt><dd><tt>Set&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;index&nbsp;(row&nbsp;labels)&nbsp;using&nbsp;one&nbsp;or&nbsp;more&nbsp;existing<br>
-columns.&nbsp;By&nbsp;default&nbsp;yields&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-keys&nbsp;:&nbsp;column&nbsp;label&nbsp;or&nbsp;list&nbsp;of&nbsp;column&nbsp;labels&nbsp;/&nbsp;arrays<br>
-drop&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Delete&nbsp;columns&nbsp;to&nbsp;be&nbsp;used&nbsp;as&nbsp;the&nbsp;new&nbsp;index<br>
-append&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;append&nbsp;columns&nbsp;to&nbsp;existing&nbsp;index<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Modify&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;in&nbsp;place&nbsp;(do&nbsp;not&nbsp;create&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>)<br>
-verify_integrity&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Check&nbsp;the&nbsp;new&nbsp;index&nbsp;for&nbsp;duplicates.&nbsp;Otherwise&nbsp;defer&nbsp;the&nbsp;check&nbsp;until<br>
-&nbsp;&nbsp;&nbsp;&nbsp;necessary.&nbsp;Setting&nbsp;to&nbsp;False&nbsp;will&nbsp;improve&nbsp;the&nbsp;performance&nbsp;of&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;method<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;indexed_df&nbsp;=&nbsp;df.<a href="#MyDataFrame-set_index">set_index</a>(['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;indexed_df2&nbsp;=&nbsp;df.<a href="#MyDataFrame-set_index">set_index</a>(['A',&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;0,&nbsp;1,&nbsp;2]])<br>
-&gt;&gt;&gt;&nbsp;indexed_df3&nbsp;=&nbsp;df.<a href="#MyDataFrame-set_index">set_index</a>([[0,&nbsp;1,&nbsp;2,&nbsp;0,&nbsp;1,&nbsp;2]])<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dataframe&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-set_value"><strong>set_value</strong></a>(self, index, col, value, takeable=False)</dt><dd><tt>Put&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;column&nbsp;and&nbsp;index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;row&nbsp;label<br>
-col&nbsp;:&nbsp;column&nbsp;label<br>
-value&nbsp;:&nbsp;scalar&nbsp;value<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index/col&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-frame&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;label&nbsp;pair&nbsp;is&nbsp;contained,&nbsp;will&nbsp;be&nbsp;reference&nbsp;to&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-shift"><strong>shift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;index&nbsp;by&nbsp;desired&nbsp;number&nbsp;of&nbsp;periods&nbsp;with&nbsp;an&nbsp;optional&nbsp;time&nbsp;freq<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM').<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;Notes.<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'}<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;specified&nbsp;then&nbsp;the&nbsp;index&nbsp;values&nbsp;are&nbsp;shifted&nbsp;but&nbsp;the&nbsp;data<br>
-is&nbsp;not&nbsp;realigned.&nbsp;That&nbsp;is,&nbsp;use&nbsp;freq&nbsp;if&nbsp;you&nbsp;would&nbsp;like&nbsp;to&nbsp;extend&nbsp;the<br>
-index&nbsp;when&nbsp;shifting&nbsp;and&nbsp;preserve&nbsp;the&nbsp;original&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-skew"><strong>skew</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;skew&nbsp;over&nbsp;requested&nbsp;axis<br>
-Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-skew&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-sort_index"><strong>sort_index</strong></a>(self, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, by=None)</dt><dd><tt>Sort&nbsp;<a href="builtins.html#object">object</a>&nbsp;by&nbsp;labels&nbsp;(along&nbsp;an&nbsp;axis)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;index,&nbsp;columns&nbsp;to&nbsp;direct&nbsp;sorting<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name&nbsp;or&nbsp;list&nbsp;of&nbsp;ints&nbsp;or&nbsp;list&nbsp;of&nbsp;level&nbsp;names<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None,&nbsp;sort&nbsp;on&nbsp;values&nbsp;in&nbsp;specified&nbsp;index&nbsp;level(s)<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;MultiIndex.<br>
-sort_remaining&nbsp;:&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;true&nbsp;and&nbsp;sorting&nbsp;by&nbsp;level&nbsp;and&nbsp;index&nbsp;is&nbsp;multilevel,&nbsp;sort&nbsp;by&nbsp;other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;levels&nbsp;too&nbsp;(in&nbsp;order)&nbsp;after&nbsp;sorting&nbsp;by&nbsp;specified&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-sort_values"><strong>sort_values</strong></a>(self, by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')</dt><dd><tt>Sort&nbsp;by&nbsp;the&nbsp;values&nbsp;along&nbsp;either&nbsp;axis<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;str&nbsp;or&nbsp;list&nbsp;of&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;or&nbsp;list&nbsp;of&nbsp;names&nbsp;which&nbsp;refer&nbsp;to&nbsp;the&nbsp;axis&nbsp;items.<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;direct&nbsp;sorting<br>
-ascending&nbsp;:&nbsp;bool&nbsp;or&nbsp;list&nbsp;of&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending.&nbsp;Specify&nbsp;list&nbsp;for&nbsp;multiple&nbsp;sort<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;orders.&nbsp;&nbsp;If&nbsp;this&nbsp;is&nbsp;a&nbsp;list&nbsp;of&nbsp;bools,&nbsp;must&nbsp;match&nbsp;the&nbsp;length&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;by.<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-sortlevel"><strong>sortlevel</strong></a>(self, level=0, axis=0, ascending=True, inplace=False, sort_remaining=True)</dt><dd><tt>DEPRECATED:&nbsp;use&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.sort_index`<br>
-&nbsp;<br>
-Sort&nbsp;multilevel&nbsp;index&nbsp;by&nbsp;chosen&nbsp;axis&nbsp;and&nbsp;primary&nbsp;level.&nbsp;Data&nbsp;will&nbsp;be<br>
-lexicographically&nbsp;sorted&nbsp;by&nbsp;the&nbsp;chosen&nbsp;level&nbsp;followed&nbsp;by&nbsp;the&nbsp;other<br>
-levels&nbsp;(in&nbsp;order)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;without&nbsp;creating&nbsp;a&nbsp;new&nbsp;instance<br>
-sort_remaining&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;by&nbsp;the&nbsp;other&nbsp;levels&nbsp;too.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#MyDataFrame-sort_index">sort_index</a>(level=...)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-stack"><strong>stack</strong></a>(self, level=-1, dropna=True)</dt><dd><tt>Pivot&nbsp;a&nbsp;level&nbsp;of&nbsp;the&nbsp;(possibly&nbsp;hierarchical)&nbsp;column&nbsp;labels,&nbsp;returning&nbsp;a<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;the&nbsp;case&nbsp;of&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;a&nbsp;single&nbsp;level&nbsp;of<br>
-column&nbsp;labels)&nbsp;having&nbsp;a&nbsp;hierarchical&nbsp;index&nbsp;with&nbsp;a&nbsp;new&nbsp;inner-most&nbsp;level<br>
-of&nbsp;row&nbsp;labels.<br>
-The&nbsp;level&nbsp;involved&nbsp;will&nbsp;automatically&nbsp;get&nbsp;sorted.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;string,&nbsp;or&nbsp;list&nbsp;of&nbsp;these,&nbsp;default&nbsp;last&nbsp;level<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level(s)&nbsp;to&nbsp;stack,&nbsp;can&nbsp;pass&nbsp;level&nbsp;name<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;drop&nbsp;rows&nbsp;in&nbsp;the&nbsp;resulting&nbsp;Frame/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;no&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values<br>
-&nbsp;<br>
-Examples<br>
-----------<br>
-&gt;&gt;&gt;&nbsp;s<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;b<br>
-one&nbsp;&nbsp;1.&nbsp;&nbsp;2.<br>
-two&nbsp;&nbsp;3.&nbsp;&nbsp;4.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MyDataFrame-stack">stack</a>()<br>
-one&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-two&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-stacked&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-std"><strong>std</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;sample&nbsp;standard&nbsp;deviation&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-std&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-sub"><strong>sub</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Subtraction&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-subtract"><strong>subtract</strong></a> = sub(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Subtraction&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-sum"><strong>sum</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;sum&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sum&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-swaplevel"><strong>swaplevel</strong></a>(self, i=-2, j=-1, axis=0)</dt><dd><tt>Swap&nbsp;levels&nbsp;i&nbsp;and&nbsp;j&nbsp;in&nbsp;a&nbsp;MultiIndex&nbsp;on&nbsp;a&nbsp;particular&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-i,&nbsp;j&nbsp;:&nbsp;int,&nbsp;string&nbsp;(can&nbsp;be&nbsp;mixed)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level&nbsp;of&nbsp;index&nbsp;to&nbsp;be&nbsp;swapped.&nbsp;Can&nbsp;pass&nbsp;level&nbsp;name&nbsp;as&nbsp;string.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-swapped&nbsp;:&nbsp;type&nbsp;of&nbsp;caller&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-..&nbsp;versionchanged::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;The&nbsp;indexes&nbsp;``i``&nbsp;and&nbsp;``j``&nbsp;are&nbsp;now&nbsp;optional,&nbsp;and&nbsp;default&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;the&nbsp;two&nbsp;innermost&nbsp;levels&nbsp;of&nbsp;the&nbsp;index.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_csv"><strong>to_csv</strong></a>(self, path_or_buf=None, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression=None, quoting=None, quotechar='"', line_terminator='\n', chunksize=None, tupleize_cols=False, date_format=None, doublequote=True, escapechar=None, decimal='.')</dt><dd><tt>Write&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;comma-separated&nbsp;values&nbsp;(csv)&nbsp;file<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;string&nbsp;or&nbsp;file&nbsp;handle,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;<a href="builtins.html#object">object</a>,&nbsp;if&nbsp;None&nbsp;is&nbsp;provided&nbsp;the&nbsp;result&nbsp;is&nbsp;returned&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string.<br>
-sep&nbsp;:&nbsp;character,&nbsp;default&nbsp;','<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter&nbsp;for&nbsp;the&nbsp;output&nbsp;file.<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Columns&nbsp;to&nbsp;write<br>
-header&nbsp;:&nbsp;boolean&nbsp;or&nbsp;list&nbsp;of&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given&nbsp;it&nbsp;is&nbsp;assumed<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;or&nbsp;False,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.&nbsp;&nbsp;If<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False&nbsp;do&nbsp;not&nbsp;print&nbsp;fields&nbsp;for&nbsp;index&nbsp;names.&nbsp;Use&nbsp;index_label=False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;easier&nbsp;importing&nbsp;in&nbsp;R<br>
-mode&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Python&nbsp;write&nbsp;mode,&nbsp;default&nbsp;'w'<br>
-encoding&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;defaults&nbsp;to&nbsp;'ascii'&nbsp;on&nbsp;Python&nbsp;2&nbsp;and&nbsp;'utf-8'&nbsp;on&nbsp;Python&nbsp;3.<br>
-compression&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;compression&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;allowed&nbsp;values&nbsp;are&nbsp;'gzip',&nbsp;'bz2',&nbsp;'xz',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;only&nbsp;used&nbsp;when&nbsp;the&nbsp;first&nbsp;argument&nbsp;is&nbsp;a&nbsp;filename<br>
-line_terminator&nbsp;:&nbsp;string,&nbsp;default&nbsp;``'\n'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;newline&nbsp;character&nbsp;or&nbsp;character&nbsp;sequence&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output<br>
-&nbsp;&nbsp;&nbsp;&nbsp;file<br>
-quoting&nbsp;:&nbsp;optional&nbsp;constant&nbsp;from&nbsp;csv&nbsp;module<br>
-&nbsp;&nbsp;&nbsp;&nbsp;defaults&nbsp;to&nbsp;csv.QUOTE_MINIMAL.&nbsp;If&nbsp;you&nbsp;have&nbsp;set&nbsp;a&nbsp;`float_format`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;floats&nbsp;are&nbsp;converted&nbsp;to&nbsp;strings&nbsp;and&nbsp;thus&nbsp;csv.QUOTE_NONNUMERIC<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;treat&nbsp;them&nbsp;as&nbsp;non-numeric<br>
-quotechar&nbsp;:&nbsp;string&nbsp;(length&nbsp;1),&nbsp;default&nbsp;'\"'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;character&nbsp;used&nbsp;to&nbsp;quote&nbsp;fields<br>
-doublequote&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Control&nbsp;quoting&nbsp;of&nbsp;`quotechar`&nbsp;inside&nbsp;a&nbsp;field<br>
-escapechar&nbsp;:&nbsp;string&nbsp;(length&nbsp;1),&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;character&nbsp;used&nbsp;to&nbsp;escape&nbsp;`sep`&nbsp;and&nbsp;`quotechar`&nbsp;when&nbsp;appropriate<br>
-chunksize&nbsp;:&nbsp;int&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rows&nbsp;to&nbsp;write&nbsp;at&nbsp;a&nbsp;time<br>
-tupleize_cols&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;write&nbsp;multi_index&nbsp;columns&nbsp;as&nbsp;a&nbsp;list&nbsp;of&nbsp;tuples&nbsp;(if&nbsp;True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;new&nbsp;(expanded&nbsp;format)&nbsp;if&nbsp;False)<br>
-date_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;datetime&nbsp;objects<br>
-decimal:&nbsp;string,&nbsp;default&nbsp;'.'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Character&nbsp;recognized&nbsp;as&nbsp;decimal&nbsp;separator.&nbsp;E.g.&nbsp;use&nbsp;','&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;European&nbsp;data<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.16.0</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_dict"><strong>to_dict</strong></a>(self, orient='dict')</dt><dd><tt>Convert&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;dictionary.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-orient&nbsp;:&nbsp;str&nbsp;{'dict',&nbsp;'list',&nbsp;'series',&nbsp;'split',&nbsp;'records',&nbsp;'index'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Determines&nbsp;the&nbsp;type&nbsp;of&nbsp;the&nbsp;values&nbsp;of&nbsp;the&nbsp;dictionary.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;(default)&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;{index&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;[values]}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;series&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;<a href="pandas.core.series.html#Series">Series</a>(values)}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;split&nbsp;:&nbsp;dict&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{index&nbsp;-&gt;&nbsp;[index],&nbsp;columns&nbsp;-&gt;&nbsp;[columns],&nbsp;data&nbsp;-&gt;&nbsp;[values]}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;records&nbsp;:&nbsp;list&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[{column&nbsp;-&gt;&nbsp;value},&nbsp;...&nbsp;,&nbsp;{column&nbsp;-&gt;&nbsp;value}]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;index&nbsp;:&nbsp;dict&nbsp;like&nbsp;{index&nbsp;-&gt;&nbsp;{column&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Abbreviations&nbsp;are&nbsp;allowed.&nbsp;`s`&nbsp;indicates&nbsp;`series`&nbsp;and&nbsp;`sp`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;indicates&nbsp;`split`.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;{index&nbsp;-&gt;&nbsp;value}}</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_excel"><strong>to_excel</strong></a>(self, excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True, freeze_panes=None)</dt><dd><tt>Write&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;an&nbsp;excel&nbsp;sheet<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel_writer&nbsp;:&nbsp;string&nbsp;or&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;existing&nbsp;ExcelWriter<br>
-sheet_name&nbsp;:&nbsp;string,&nbsp;default&nbsp;'Sheet1'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;sheet&nbsp;which&nbsp;will&nbsp;contain&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Columns&nbsp;to&nbsp;write<br>
-header&nbsp;:&nbsp;boolean&nbsp;or&nbsp;list&nbsp;of&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given&nbsp;it&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;assumed&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-startrow&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;row&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-startcol&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;column&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-engine&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;write&nbsp;engine&nbsp;to&nbsp;use&nbsp;-&nbsp;you&nbsp;can&nbsp;also&nbsp;set&nbsp;this&nbsp;via&nbsp;the&nbsp;options<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsx.writer``,&nbsp;``io.excel.xls.writer``,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsm.writer``.<br>
-merge_cells&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;MultiIndex&nbsp;and&nbsp;Hierarchical&nbsp;Rows&nbsp;as&nbsp;merged&nbsp;cells.<br>
-encoding:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;encoding&nbsp;of&nbsp;the&nbsp;resulting&nbsp;excel&nbsp;file.&nbsp;Only&nbsp;necessary&nbsp;for&nbsp;xlwt,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;writers&nbsp;support&nbsp;unicode&nbsp;natively.<br>
-inf_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;'inf'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Representation&nbsp;for&nbsp;infinity&nbsp;(there&nbsp;is&nbsp;no&nbsp;native&nbsp;representation&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;infinity&nbsp;in&nbsp;Excel)<br>
-freeze_panes&nbsp;:&nbsp;tuple&nbsp;of&nbsp;integer&nbsp;(length&nbsp;2),&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;the&nbsp;one-based&nbsp;bottommost&nbsp;row&nbsp;and&nbsp;rightmost&nbsp;column&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;to&nbsp;be&nbsp;frozen<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;passing&nbsp;an&nbsp;existing&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a>,&nbsp;then&nbsp;the&nbsp;sheet&nbsp;will&nbsp;be&nbsp;added<br>
-to&nbsp;the&nbsp;existing&nbsp;workbook.&nbsp;&nbsp;This&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;save&nbsp;different<br>
-DataFrames&nbsp;to&nbsp;one&nbsp;workbook:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;writer&nbsp;=&nbsp;pd.ExcelWriter('output.xlsx')<br>
-&gt;&gt;&gt;&nbsp;df1.<a href="#MyDataFrame-to_excel">to_excel</a>(writer,'Sheet1')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#MyDataFrame-to_excel">to_excel</a>(writer,'Sheet2')<br>
-&gt;&gt;&gt;&nbsp;writer.save()<br>
-&nbsp;<br>
-For&nbsp;compatibility&nbsp;with&nbsp;to_csv,&nbsp;to_excel&nbsp;serializes&nbsp;lists&nbsp;and&nbsp;dicts&nbsp;to<br>
-strings&nbsp;before&nbsp;writing.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_feather"><strong>to_feather</strong></a>(self, fname)</dt><dd><tt>write&nbsp;out&nbsp;the&nbsp;binary&nbsp;feather-format&nbsp;for&nbsp;DataFrames<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-fname&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;file&nbsp;path</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_gbq"><strong>to_gbq</strong></a>(self, destination_table, project_id, chunksize=10000, verbose=True, reauth=False, if_exists='fail', private_key=None)</dt><dd><tt>Write&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;Google&nbsp;BigQuery&nbsp;table.<br>
-&nbsp;<br>
-The&nbsp;main&nbsp;method&nbsp;a&nbsp;user&nbsp;calls&nbsp;to&nbsp;export&nbsp;pandas&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;contents&nbsp;to<br>
-Google&nbsp;BigQuery&nbsp;table.<br>
-&nbsp;<br>
-Google&nbsp;BigQuery&nbsp;API&nbsp;Client&nbsp;Library&nbsp;v2&nbsp;for&nbsp;Python&nbsp;is&nbsp;used.<br>
-Documentation&nbsp;is&nbsp;available&nbsp;`here<br>
-&lt;https://developers.google.com/api-client-library/python/apis/bigquery/v2&gt;`__<br>
-&nbsp;<br>
-Authentication&nbsp;to&nbsp;the&nbsp;Google&nbsp;BigQuery&nbsp;service&nbsp;is&nbsp;via&nbsp;OAuth&nbsp;2.0.<br>
-&nbsp;<br>
--&nbsp;If&nbsp;"private_key"&nbsp;is&nbsp;not&nbsp;provided:<br>
-&nbsp;<br>
-&nbsp;&nbsp;By&nbsp;default&nbsp;"application&nbsp;default&nbsp;credentials"&nbsp;are&nbsp;used.<br>
-&nbsp;<br>
-&nbsp;&nbsp;If&nbsp;default&nbsp;application&nbsp;credentials&nbsp;are&nbsp;not&nbsp;found&nbsp;or&nbsp;are&nbsp;restrictive,<br>
-&nbsp;&nbsp;user&nbsp;account&nbsp;credentials&nbsp;are&nbsp;used.&nbsp;In&nbsp;this&nbsp;case,&nbsp;you&nbsp;will&nbsp;be&nbsp;asked&nbsp;to<br>
-&nbsp;&nbsp;grant&nbsp;permissions&nbsp;for&nbsp;product&nbsp;name&nbsp;'pandas&nbsp;GBQ'.<br>
-&nbsp;<br>
--&nbsp;If&nbsp;"private_key"&nbsp;is&nbsp;provided:<br>
-&nbsp;<br>
-&nbsp;&nbsp;Service&nbsp;account&nbsp;credentials&nbsp;will&nbsp;be&nbsp;used&nbsp;to&nbsp;authenticate.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dataframe&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;be&nbsp;written<br>
-destination_table&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;table&nbsp;to&nbsp;be&nbsp;written,&nbsp;in&nbsp;the&nbsp;form&nbsp;'dataset.tablename'<br>
-project_id&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Google&nbsp;BigQuery&nbsp;Account&nbsp;project&nbsp;ID.<br>
-chunksize&nbsp;:&nbsp;int&nbsp;(default&nbsp;10000)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;rows&nbsp;to&nbsp;be&nbsp;inserted&nbsp;in&nbsp;each&nbsp;chunk&nbsp;from&nbsp;the&nbsp;dataframe.<br>
-verbose&nbsp;:&nbsp;boolean&nbsp;(default&nbsp;True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Show&nbsp;percentage&nbsp;complete<br>
-reauth&nbsp;:&nbsp;boolean&nbsp;(default&nbsp;False)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Force&nbsp;Google&nbsp;BigQuery&nbsp;to&nbsp;reauthenticate&nbsp;the&nbsp;user.&nbsp;This&nbsp;is&nbsp;useful<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;multiple&nbsp;accounts&nbsp;are&nbsp;used.<br>
-if_exists&nbsp;:&nbsp;{'fail',&nbsp;'replace',&nbsp;'append'},&nbsp;default&nbsp;'fail'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'fail':&nbsp;If&nbsp;table&nbsp;exists,&nbsp;do&nbsp;nothing.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'replace':&nbsp;If&nbsp;table&nbsp;exists,&nbsp;drop&nbsp;it,&nbsp;recreate&nbsp;it,&nbsp;and&nbsp;insert&nbsp;data.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'append':&nbsp;If&nbsp;table&nbsp;exists,&nbsp;insert&nbsp;data.&nbsp;Create&nbsp;if&nbsp;does&nbsp;not&nbsp;exist.<br>
-private_key&nbsp;:&nbsp;str&nbsp;(optional)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Service&nbsp;account&nbsp;private&nbsp;key&nbsp;in&nbsp;JSON&nbsp;format.&nbsp;Can&nbsp;be&nbsp;file&nbsp;path<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;string&nbsp;contents.&nbsp;This&nbsp;is&nbsp;useful&nbsp;for&nbsp;remote&nbsp;server<br>
-&nbsp;&nbsp;&nbsp;&nbsp;authentication&nbsp;(eg.&nbsp;jupyter&nbsp;iPython&nbsp;notebook&nbsp;on&nbsp;remote&nbsp;host)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_html"><strong>to_html</strong></a>(self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, bold_rows=True, classes=None, escape=True, max_rows=None, max_cols=None, show_dimensions=False, notebook=False, decimal='.', border=None)</dt><dd><tt>Render&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;as&nbsp;an&nbsp;HTML&nbsp;table.<br>
-&nbsp;<br>
-`to_html`-specific&nbsp;options:<br>
-&nbsp;<br>
-bold_rows&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;the&nbsp;row&nbsp;labels&nbsp;bold&nbsp;in&nbsp;the&nbsp;output<br>
-classes&nbsp;:&nbsp;str&nbsp;or&nbsp;list&nbsp;or&nbsp;tuple,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;CSS&nbsp;class(es)&nbsp;to&nbsp;apply&nbsp;to&nbsp;the&nbsp;resulting&nbsp;html&nbsp;table<br>
-escape&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Convert&nbsp;the&nbsp;characters&nbsp;&lt;,&nbsp;&gt;,&nbsp;and&nbsp;&amp;&nbsp;to&nbsp;HTML-safe&nbsp;sequences.=<br>
-max_rows&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;rows&nbsp;to&nbsp;show&nbsp;before&nbsp;truncating.&nbsp;If&nbsp;None,&nbsp;show<br>
-&nbsp;&nbsp;&nbsp;&nbsp;all.<br>
-max_cols&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;columns&nbsp;to&nbsp;show&nbsp;before&nbsp;truncating.&nbsp;If&nbsp;None,&nbsp;show<br>
-&nbsp;&nbsp;&nbsp;&nbsp;all.<br>
-decimal&nbsp;:&nbsp;string,&nbsp;default&nbsp;'.'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Character&nbsp;recognized&nbsp;as&nbsp;decimal&nbsp;separator,&nbsp;e.g.&nbsp;','&nbsp;in&nbsp;Europe<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-border&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;``border=border``&nbsp;attribute&nbsp;is&nbsp;included&nbsp;in&nbsp;the&nbsp;opening<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`&lt;table&gt;`&nbsp;tag.&nbsp;Default&nbsp;``pd.options.html.border``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-buf&nbsp;:&nbsp;StringIO-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;buffer&nbsp;to&nbsp;write&nbsp;to<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;to&nbsp;write;&nbsp;default&nbsp;None&nbsp;writes&nbsp;all&nbsp;columns<br>
-col_space&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;minimum&nbsp;width&nbsp;of&nbsp;each&nbsp;column<br>
-header&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;whether&nbsp;to&nbsp;print&nbsp;column&nbsp;labels,&nbsp;default&nbsp;True<br>
-index&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;whether&nbsp;to&nbsp;print&nbsp;index&nbsp;(row)&nbsp;labels,&nbsp;default&nbsp;True<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;representation&nbsp;of&nbsp;NAN&nbsp;to&nbsp;use,&nbsp;default&nbsp;'NaN'<br>
-formatters&nbsp;:&nbsp;list&nbsp;or&nbsp;dict&nbsp;of&nbsp;one-parameter&nbsp;functions,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;functions&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;by&nbsp;position&nbsp;or&nbsp;name,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;each&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;must&nbsp;be&nbsp;of&nbsp;length&nbsp;equal&nbsp;to&nbsp;the&nbsp;number&nbsp;of&nbsp;columns.<br>
-float_format&nbsp;:&nbsp;one-parameter&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;if&nbsp;they&nbsp;are&nbsp;floats,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;this&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-sparsify&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;to&nbsp;False&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;a&nbsp;hierarchical&nbsp;index&nbsp;to&nbsp;print&nbsp;every<br>
-&nbsp;&nbsp;&nbsp;&nbsp;multiindex&nbsp;key&nbsp;at&nbsp;each&nbsp;row,&nbsp;default&nbsp;True<br>
-index_names&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Prints&nbsp;the&nbsp;names&nbsp;of&nbsp;the&nbsp;indexes,&nbsp;default&nbsp;True<br>
-line_width&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Width&nbsp;to&nbsp;wrap&nbsp;a&nbsp;line&nbsp;in&nbsp;characters,&nbsp;default&nbsp;no&nbsp;wrap<br>
-justify&nbsp;:&nbsp;{'left',&nbsp;'right'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Left&nbsp;or&nbsp;right-justify&nbsp;the&nbsp;column&nbsp;labels.&nbsp;If&nbsp;None&nbsp;uses&nbsp;the&nbsp;option&nbsp;from<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;print&nbsp;configuration&nbsp;(controlled&nbsp;by&nbsp;set_option),&nbsp;'right'&nbsp;out<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;box.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-formatted&nbsp;:&nbsp;string&nbsp;(or&nbsp;unicode,&nbsp;depending&nbsp;on&nbsp;data&nbsp;and&nbsp;options)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_latex"><strong>to_latex</strong></a>(self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=True, column_format=None, longtable=None, escape=None, encoding=None, decimal='.', multicolumn=None, multicolumn_format=None, multirow=None)</dt><dd><tt>Render&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;tabular&nbsp;environment&nbsp;table.&nbsp;You&nbsp;can&nbsp;splice<br>
-this&nbsp;into&nbsp;a&nbsp;LaTeX&nbsp;document.&nbsp;Requires&nbsp;\usepackage{booktabs}.<br>
-&nbsp;<br>
-`to_latex`-specific&nbsp;options:<br>
-&nbsp;<br>
-bold_rows&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;the&nbsp;row&nbsp;labels&nbsp;bold&nbsp;in&nbsp;the&nbsp;output<br>
-column_format&nbsp;:&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;columns&nbsp;format&nbsp;as&nbsp;specified&nbsp;in&nbsp;`LaTeX&nbsp;table&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&lt;https://en.wikibooks.org/wiki/LaTeX/Tables&gt;`__&nbsp;e.g&nbsp;'rcl'&nbsp;for&nbsp;3<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns<br>
-longtable&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;will&nbsp;be&nbsp;read&nbsp;from&nbsp;the&nbsp;pandas&nbsp;config&nbsp;module<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default:&nbsp;False.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;a&nbsp;longtable&nbsp;environment&nbsp;instead&nbsp;of&nbsp;tabular.&nbsp;Requires&nbsp;adding<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;\usepackage{longtable}&nbsp;to&nbsp;your&nbsp;LaTeX&nbsp;preamble.<br>
-escape&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;will&nbsp;be&nbsp;read&nbsp;from&nbsp;the&nbsp;pandas&nbsp;config&nbsp;module<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default:&nbsp;True.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;set&nbsp;to&nbsp;False&nbsp;prevents&nbsp;from&nbsp;escaping&nbsp;latex&nbsp;special<br>
-&nbsp;&nbsp;&nbsp;&nbsp;characters&nbsp;in&nbsp;column&nbsp;names.<br>
-encoding&nbsp;:&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;defaults&nbsp;to&nbsp;'ascii'&nbsp;on&nbsp;Python&nbsp;2&nbsp;and&nbsp;'utf-8'&nbsp;on&nbsp;Python&nbsp;3.<br>
-decimal&nbsp;:&nbsp;string,&nbsp;default&nbsp;'.'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Character&nbsp;recognized&nbsp;as&nbsp;decimal&nbsp;separator,&nbsp;e.g.&nbsp;','&nbsp;in&nbsp;Europe.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-multicolumn&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;\multicolumn&nbsp;to&nbsp;enhance&nbsp;MultiIndex&nbsp;columns.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;default&nbsp;will&nbsp;be&nbsp;read&nbsp;from&nbsp;the&nbsp;config&nbsp;module.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-multicolumn_format&nbsp;:&nbsp;str,&nbsp;default&nbsp;'l'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;alignment&nbsp;for&nbsp;multicolumns,&nbsp;similar&nbsp;to&nbsp;`column_format`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;default&nbsp;will&nbsp;be&nbsp;read&nbsp;from&nbsp;the&nbsp;config&nbsp;module.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-multirow&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;\multirow&nbsp;to&nbsp;enhance&nbsp;MultiIndex&nbsp;rows.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Requires&nbsp;adding&nbsp;a&nbsp;\usepackage{multirow}&nbsp;to&nbsp;your&nbsp;LaTeX&nbsp;preamble.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Will&nbsp;print&nbsp;centered&nbsp;labels&nbsp;(instead&nbsp;of&nbsp;top-aligned)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;across&nbsp;the&nbsp;contained&nbsp;rows,&nbsp;separating&nbsp;groups&nbsp;via&nbsp;clines.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;default&nbsp;will&nbsp;be&nbsp;read&nbsp;from&nbsp;the&nbsp;pandas&nbsp;config&nbsp;module.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-buf&nbsp;:&nbsp;StringIO-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;buffer&nbsp;to&nbsp;write&nbsp;to<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;to&nbsp;write;&nbsp;default&nbsp;None&nbsp;writes&nbsp;all&nbsp;columns<br>
-col_space&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;minimum&nbsp;width&nbsp;of&nbsp;each&nbsp;column<br>
-header&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given,&nbsp;it&nbsp;is&nbsp;assumed&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names.<br>
-index&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;whether&nbsp;to&nbsp;print&nbsp;index&nbsp;(row)&nbsp;labels,&nbsp;default&nbsp;True<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;representation&nbsp;of&nbsp;NAN&nbsp;to&nbsp;use,&nbsp;default&nbsp;'NaN'<br>
-formatters&nbsp;:&nbsp;list&nbsp;or&nbsp;dict&nbsp;of&nbsp;one-parameter&nbsp;functions,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;functions&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;by&nbsp;position&nbsp;or&nbsp;name,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;each&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;must&nbsp;be&nbsp;of&nbsp;length&nbsp;equal&nbsp;to&nbsp;the&nbsp;number&nbsp;of&nbsp;columns.<br>
-float_format&nbsp;:&nbsp;one-parameter&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;if&nbsp;they&nbsp;are&nbsp;floats,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;this&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-sparsify&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;to&nbsp;False&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;a&nbsp;hierarchical&nbsp;index&nbsp;to&nbsp;print&nbsp;every<br>
-&nbsp;&nbsp;&nbsp;&nbsp;multiindex&nbsp;key&nbsp;at&nbsp;each&nbsp;row,&nbsp;default&nbsp;True<br>
-index_names&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Prints&nbsp;the&nbsp;names&nbsp;of&nbsp;the&nbsp;indexes,&nbsp;default&nbsp;True<br>
-line_width&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Width&nbsp;to&nbsp;wrap&nbsp;a&nbsp;line&nbsp;in&nbsp;characters,&nbsp;default&nbsp;no&nbsp;wrap<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-formatted&nbsp;:&nbsp;string&nbsp;(or&nbsp;unicode,&nbsp;depending&nbsp;on&nbsp;data&nbsp;and&nbsp;options)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_panel"><strong>to_panel</strong></a>(self)</dt><dd><tt>Transform&nbsp;long&nbsp;(stacked)&nbsp;format&nbsp;(<a href="pandas.core.frame.html#DataFrame">DataFrame</a>)&nbsp;into&nbsp;wide&nbsp;(3D,&nbsp;Panel)<br>
-format.<br>
-&nbsp;<br>
-Currently&nbsp;the&nbsp;index&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;must&nbsp;be&nbsp;a&nbsp;2-level&nbsp;MultiIndex.&nbsp;This<br>
-may&nbsp;be&nbsp;generalized&nbsp;later<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-panel&nbsp;:&nbsp;Panel</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_period"><strong>to_period</strong></a>(self, freq=None, axis=0, copy=True)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;from&nbsp;DatetimeIndex&nbsp;to&nbsp;PeriodIndex&nbsp;with&nbsp;desired<br>
-frequency&nbsp;(inferred&nbsp;from&nbsp;index&nbsp;if&nbsp;not&nbsp;passed)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;convert&nbsp;(the&nbsp;index&nbsp;by&nbsp;default)<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False&nbsp;then&nbsp;underlying&nbsp;input&nbsp;data&nbsp;is&nbsp;not&nbsp;copied<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ts&nbsp;:&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;with&nbsp;PeriodIndex</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_records"><strong>to_records</strong></a>(self, index=True, convert_datetime64=True)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;record&nbsp;array.&nbsp;Index&nbsp;will&nbsp;be&nbsp;put&nbsp;in&nbsp;the<br>
-'index'&nbsp;field&nbsp;of&nbsp;the&nbsp;record&nbsp;array&nbsp;if&nbsp;requested<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;index&nbsp;in&nbsp;resulting&nbsp;record&nbsp;array,&nbsp;stored&nbsp;in&nbsp;'index'&nbsp;field<br>
-convert_datetime64&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;convert&nbsp;the&nbsp;index&nbsp;to&nbsp;datetime.datetime&nbsp;if&nbsp;it&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;DatetimeIndex<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;recarray</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_sparse"><strong>to_sparse</strong></a>(self, fill_value=None, kind='block')</dt><dd><tt>Convert&nbsp;to&nbsp;SparseDataFrame<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-fill_value&nbsp;:&nbsp;float,&nbsp;default&nbsp;NaN<br>
-kind&nbsp;:&nbsp;{'block',&nbsp;'integer'}<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;SparseDataFrame</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_stata"><strong>to_stata</strong></a>(self, fname, convert_dates=None, write_index=True, encoding='latin-1', byteorder=None, time_stamp=None, data_label=None, variable_labels=None)</dt><dd><tt>A&nbsp;class&nbsp;for&nbsp;writing&nbsp;Stata&nbsp;binary&nbsp;dta&nbsp;files&nbsp;from&nbsp;array-like&nbsp;objects<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-fname&nbsp;:&nbsp;str&nbsp;or&nbsp;buffer<br>
-&nbsp;&nbsp;&nbsp;&nbsp;String&nbsp;path&nbsp;of&nbsp;file-like&nbsp;<a href="builtins.html#object">object</a><br>
-convert_dates&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Dictionary&nbsp;mapping&nbsp;columns&nbsp;containing&nbsp;datetime&nbsp;types&nbsp;to&nbsp;stata<br>
-&nbsp;&nbsp;&nbsp;&nbsp;internal&nbsp;format&nbsp;to&nbsp;use&nbsp;when&nbsp;wirting&nbsp;the&nbsp;dates.&nbsp;Options&nbsp;are&nbsp;'tc',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'td',&nbsp;'tm',&nbsp;'tw',&nbsp;'th',&nbsp;'tq',&nbsp;'ty'.&nbsp;Column&nbsp;can&nbsp;be&nbsp;either&nbsp;an&nbsp;integer<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;a&nbsp;name.&nbsp;Datetime&nbsp;columns&nbsp;that&nbsp;do&nbsp;not&nbsp;have&nbsp;a&nbsp;conversion&nbsp;type<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specified&nbsp;will&nbsp;be&nbsp;converted&nbsp;to&nbsp;'tc'.&nbsp;Raises&nbsp;NotImplementedError&nbsp;if<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;datetime&nbsp;column&nbsp;has&nbsp;timezone&nbsp;information<br>
-write_index&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;the&nbsp;index&nbsp;to&nbsp;Stata&nbsp;dataset.<br>
-encoding&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;is&nbsp;latin-1.&nbsp;Unicode&nbsp;is&nbsp;not&nbsp;supported<br>
-byteorder&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Can&nbsp;be&nbsp;"&gt;",&nbsp;"&lt;",&nbsp;"little",&nbsp;or&nbsp;"big".&nbsp;default&nbsp;is&nbsp;`sys.byteorder`<br>
-time_stamp&nbsp;:&nbsp;datetime<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;datetime&nbsp;to&nbsp;use&nbsp;as&nbsp;file&nbsp;creation&nbsp;date.&nbsp;&nbsp;Default&nbsp;is&nbsp;the&nbsp;current<br>
-&nbsp;&nbsp;&nbsp;&nbsp;time.<br>
-dataset_label&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;label&nbsp;for&nbsp;the&nbsp;data&nbsp;set.&nbsp;&nbsp;Must&nbsp;be&nbsp;80&nbsp;characters&nbsp;or&nbsp;smaller.<br>
-variable_labels&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Dictionary&nbsp;containing&nbsp;columns&nbsp;as&nbsp;keys&nbsp;and&nbsp;variable&nbsp;labels&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values.&nbsp;Each&nbsp;label&nbsp;must&nbsp;be&nbsp;80&nbsp;characters&nbsp;or&nbsp;smaller.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-NotImplementedError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;datetimes&nbsp;contain&nbsp;timezone&nbsp;information<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;Column&nbsp;dtype&nbsp;is&nbsp;not&nbsp;representable&nbsp;in&nbsp;Stata<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;Columns&nbsp;listed&nbsp;in&nbsp;convert_dates&nbsp;are&nbsp;noth&nbsp;either&nbsp;datetime64[ns]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;datetime.datetime<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;Column&nbsp;listed&nbsp;in&nbsp;convert_dates&nbsp;is&nbsp;not&nbsp;in&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;Categorical&nbsp;label&nbsp;contains&nbsp;more&nbsp;than&nbsp;32,000&nbsp;characters<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;writer&nbsp;=&nbsp;StataWriter('./data_file.dta',&nbsp;data)<br>
-&gt;&gt;&gt;&nbsp;writer.write_file()<br>
-&nbsp;<br>
-Or&nbsp;with&nbsp;dates<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;writer&nbsp;=&nbsp;StataWriter('./date_data_file.dta',&nbsp;data,&nbsp;{2&nbsp;:&nbsp;'tw'})<br>
-&gt;&gt;&gt;&nbsp;writer.write_file()</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_string"><strong>to_string</strong></a>(self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, line_width=None, max_rows=None, max_cols=None, show_dimensions=False)</dt><dd><tt>Render&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;console-friendly&nbsp;tabular&nbsp;output.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-buf&nbsp;:&nbsp;StringIO-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;buffer&nbsp;to&nbsp;write&nbsp;to<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;to&nbsp;write;&nbsp;default&nbsp;None&nbsp;writes&nbsp;all&nbsp;columns<br>
-col_space&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;minimum&nbsp;width&nbsp;of&nbsp;each&nbsp;column<br>
-header&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given,&nbsp;it&nbsp;is&nbsp;assumed&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names<br>
-index&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;whether&nbsp;to&nbsp;print&nbsp;index&nbsp;(row)&nbsp;labels,&nbsp;default&nbsp;True<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;representation&nbsp;of&nbsp;NAN&nbsp;to&nbsp;use,&nbsp;default&nbsp;'NaN'<br>
-formatters&nbsp;:&nbsp;list&nbsp;or&nbsp;dict&nbsp;of&nbsp;one-parameter&nbsp;functions,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;functions&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;by&nbsp;position&nbsp;or&nbsp;name,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;each&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;must&nbsp;be&nbsp;of&nbsp;length&nbsp;equal&nbsp;to&nbsp;the&nbsp;number&nbsp;of&nbsp;columns.<br>
-float_format&nbsp;:&nbsp;one-parameter&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;if&nbsp;they&nbsp;are&nbsp;floats,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;this&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-sparsify&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;to&nbsp;False&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;a&nbsp;hierarchical&nbsp;index&nbsp;to&nbsp;print&nbsp;every<br>
-&nbsp;&nbsp;&nbsp;&nbsp;multiindex&nbsp;key&nbsp;at&nbsp;each&nbsp;row,&nbsp;default&nbsp;True<br>
-index_names&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Prints&nbsp;the&nbsp;names&nbsp;of&nbsp;the&nbsp;indexes,&nbsp;default&nbsp;True<br>
-line_width&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Width&nbsp;to&nbsp;wrap&nbsp;a&nbsp;line&nbsp;in&nbsp;characters,&nbsp;default&nbsp;no&nbsp;wrap<br>
-justify&nbsp;:&nbsp;{'left',&nbsp;'right'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Left&nbsp;or&nbsp;right-justify&nbsp;the&nbsp;column&nbsp;labels.&nbsp;If&nbsp;None&nbsp;uses&nbsp;the&nbsp;option&nbsp;from<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;print&nbsp;configuration&nbsp;(controlled&nbsp;by&nbsp;set_option),&nbsp;'right'&nbsp;out<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;box.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-formatted&nbsp;:&nbsp;string&nbsp;(or&nbsp;unicode,&nbsp;depending&nbsp;on&nbsp;data&nbsp;and&nbsp;options)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_timestamp"><strong>to_timestamp</strong></a>(self, freq=None, how='start', axis=0, copy=True)</dt><dd><tt>Cast&nbsp;to&nbsp;DatetimeIndex&nbsp;of&nbsp;timestamps,&nbsp;at&nbsp;*beginning*&nbsp;of&nbsp;period<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default&nbsp;frequency&nbsp;of&nbsp;PeriodIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Desired&nbsp;frequency<br>
-how&nbsp;:&nbsp;{'s',&nbsp;'e',&nbsp;'start',&nbsp;'end'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Convention&nbsp;for&nbsp;converting&nbsp;period&nbsp;to&nbsp;timestamp;&nbsp;start&nbsp;of&nbsp;period<br>
-&nbsp;&nbsp;&nbsp;&nbsp;vs.&nbsp;end<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;convert&nbsp;(the&nbsp;index&nbsp;by&nbsp;default)<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;false&nbsp;then&nbsp;underlying&nbsp;input&nbsp;data&nbsp;is&nbsp;not&nbsp;copied<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-df&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;DatetimeIndex</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-transform"><strong>transform</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Call&nbsp;function&nbsp;producing&nbsp;a&nbsp;like-indexed&nbsp;NDFrame<br>
-and&nbsp;return&nbsp;a&nbsp;NDFrame&nbsp;with&nbsp;the&nbsp;transformed&nbsp;values`<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;apply&nbsp;to&nbsp;column<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-transformed&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(10,&nbsp;3),&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.date_range('1/1/2000',&nbsp;periods=10))<br>
-df.iloc[3:7]&nbsp;=&nbsp;np.nan<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-transform">transform</a>(lambda&nbsp;x:&nbsp;(x&nbsp;-&nbsp;x.<a href="#MyDataFrame-mean">mean</a>())&nbsp;/&nbsp;x.<a href="#MyDataFrame-std">std</a>())<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-2000-01-01&nbsp;&nbsp;0.579457&nbsp;&nbsp;1.236184&nbsp;&nbsp;0.123424<br>
-2000-01-02&nbsp;&nbsp;0.370357&nbsp;-0.605875&nbsp;-1.231325<br>
-2000-01-03&nbsp;&nbsp;1.455756&nbsp;-0.277446&nbsp;&nbsp;0.288967<br>
-2000-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-08&nbsp;-0.498658&nbsp;&nbsp;1.274522&nbsp;&nbsp;1.642524<br>
-2000-01-09&nbsp;-0.540524&nbsp;-1.012676&nbsp;-0.828968<br>
-2000-01-10&nbsp;-1.366388&nbsp;-0.614710&nbsp;&nbsp;0.005378<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.NDFrame.aggregate<br>
-pandas.NDFrame.apply</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-transpose"><strong>transpose</strong></a>(self, *args, **kwargs)</dt><dd><tt>Transpose&nbsp;index&nbsp;and&nbsp;columns</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-truediv"><strong>truediv</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-unstack"><strong>unstack</strong></a>(self, level=-1, fill_value=None)</dt><dd><tt>Pivot&nbsp;a&nbsp;level&nbsp;of&nbsp;the&nbsp;(necessarily&nbsp;hierarchical)&nbsp;index&nbsp;labels,&nbsp;returning<br>
-a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;having&nbsp;a&nbsp;new&nbsp;level&nbsp;of&nbsp;column&nbsp;labels&nbsp;whose&nbsp;inner-most&nbsp;level<br>
-consists&nbsp;of&nbsp;the&nbsp;pivoted&nbsp;index&nbsp;labels.&nbsp;If&nbsp;the&nbsp;index&nbsp;is&nbsp;not&nbsp;a&nbsp;MultiIndex,<br>
-the&nbsp;output&nbsp;will&nbsp;be&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(the&nbsp;analogue&nbsp;of&nbsp;stack&nbsp;when&nbsp;the&nbsp;columns&nbsp;are<br>
-not&nbsp;a&nbsp;MultiIndex).<br>
-The&nbsp;level&nbsp;involved&nbsp;will&nbsp;automatically&nbsp;get&nbsp;sorted.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;string,&nbsp;or&nbsp;list&nbsp;of&nbsp;these,&nbsp;default&nbsp;-1&nbsp;(last&nbsp;level)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level(s)&nbsp;of&nbsp;index&nbsp;to&nbsp;unstack,&nbsp;can&nbsp;pass&nbsp;level&nbsp;name<br>
-fill_value&nbsp;:&nbsp;replace&nbsp;NaN&nbsp;with&nbsp;this&nbsp;value&nbsp;if&nbsp;the&nbsp;unstack&nbsp;produces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.18.0<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.pivot&nbsp;:&nbsp;Pivot&nbsp;a&nbsp;table&nbsp;based&nbsp;on&nbsp;column&nbsp;values.<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.stack&nbsp;:&nbsp;Pivot&nbsp;a&nbsp;level&nbsp;of&nbsp;the&nbsp;column&nbsp;labels&nbsp;(inverse&nbsp;operation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;`unstack`).<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.MultiIndex.from_tuples([('one',&nbsp;'a'),&nbsp;('one',&nbsp;'b'),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;('two',&nbsp;'a'),&nbsp;('two',&nbsp;'b')])<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.arange(1.0,&nbsp;5.0),&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;s<br>
-one&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;1.0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;2.0<br>
-two&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;4.0<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MyDataFrame-unstack">unstack</a>(level=-1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;b<br>
-one&nbsp;&nbsp;1.0&nbsp;&nbsp;2.0<br>
-two&nbsp;&nbsp;3.0&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MyDataFrame-unstack">unstack</a>(level=0)<br>
-&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;two<br>
-a&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;3.0<br>
-b&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;s.<a href="#MyDataFrame-unstack">unstack</a>(level=0)<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-unstack">unstack</a>()<br>
-one&nbsp;&nbsp;a&nbsp;&nbsp;1.0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;2.0<br>
-two&nbsp;&nbsp;a&nbsp;&nbsp;3.0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;4.0<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-unstacked&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-update"><strong>update</strong></a>(self, other, join='left', overwrite=True, filter_func=None, raise_conflict=False)</dt><dd><tt>Modify&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;in&nbsp;place&nbsp;using&nbsp;non-NA&nbsp;values&nbsp;from&nbsp;passed<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Aligns&nbsp;on&nbsp;indices<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;<a href="builtins.html#object">object</a>&nbsp;coercible&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-join&nbsp;:&nbsp;{'left'},&nbsp;default&nbsp;'left'<br>
-overwrite&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;then&nbsp;overwrite&nbsp;values&nbsp;for&nbsp;common&nbsp;keys&nbsp;in&nbsp;the&nbsp;calling&nbsp;frame<br>
-filter_func&nbsp;:&nbsp;callable(1d-array)&nbsp;-&gt;&nbsp;1d-array&lt;boolean&gt;,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Can&nbsp;choose&nbsp;to&nbsp;replace&nbsp;values&nbsp;other&nbsp;than&nbsp;NA.&nbsp;Return&nbsp;True&nbsp;for&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;&nbsp;that&nbsp;should&nbsp;be&nbsp;updated<br>
-raise_conflict&nbsp;:&nbsp;boolean<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;will&nbsp;raise&nbsp;an&nbsp;error&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;and&nbsp;other&nbsp;both<br>
-&nbsp;&nbsp;&nbsp;&nbsp;contain&nbsp;data&nbsp;in&nbsp;the&nbsp;same&nbsp;place.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-var"><strong>var</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;variance&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-var&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<hr>
-Class methods inherited from <a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a>:<br>
-<dl><dt><a name="MyDataFrame-from_csv"><strong>from_csv</strong></a>(path, header=0, sep=',', index_col=0, parse_dates=True, encoding=None, tupleize_cols=False, infer_datetime_format=False)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Read&nbsp;CSV&nbsp;file&nbsp;(DISCOURAGED,&nbsp;please&nbsp;use&nbsp;:func:`pandas.read_csv`<br>
-instead).<br>
-&nbsp;<br>
-It&nbsp;is&nbsp;preferable&nbsp;to&nbsp;use&nbsp;the&nbsp;more&nbsp;powerful&nbsp;:func:`pandas.read_csv`<br>
-for&nbsp;most&nbsp;general&nbsp;purposes,&nbsp;but&nbsp;``from_csv``&nbsp;makes&nbsp;for&nbsp;an&nbsp;easy<br>
-roundtrip&nbsp;to&nbsp;and&nbsp;from&nbsp;a&nbsp;file&nbsp;(the&nbsp;exact&nbsp;counterpart&nbsp;of<br>
-``to_csv``),&nbsp;especially&nbsp;with&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;time&nbsp;series&nbsp;data.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;only&nbsp;differs&nbsp;from&nbsp;the&nbsp;preferred&nbsp;:func:`pandas.read_csv`<br>
-in&nbsp;some&nbsp;defaults:<br>
-&nbsp;<br>
--&nbsp;`index_col`&nbsp;is&nbsp;``0``&nbsp;instead&nbsp;of&nbsp;``None``&nbsp;(take&nbsp;first&nbsp;column&nbsp;as&nbsp;index<br>
-&nbsp;&nbsp;by&nbsp;default)<br>
--&nbsp;`parse_dates`&nbsp;is&nbsp;``True``&nbsp;instead&nbsp;of&nbsp;``False``&nbsp;(try&nbsp;parsing&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;as&nbsp;datetime&nbsp;by&nbsp;default)<br>
-&nbsp;<br>
-So&nbsp;a&nbsp;``pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#MyDataFrame-from_csv">from_csv</a>(path)``&nbsp;can&nbsp;be&nbsp;replaced&nbsp;by<br>
-``pd.read_csv(path,&nbsp;index_col=0,&nbsp;parse_dates=True)``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;file&nbsp;path&nbsp;or&nbsp;file&nbsp;handle&nbsp;/&nbsp;StringIO<br>
-header&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Row&nbsp;to&nbsp;use&nbsp;as&nbsp;header&nbsp;(skip&nbsp;prior&nbsp;rows)<br>
-sep&nbsp;:&nbsp;string,&nbsp;default&nbsp;','<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter<br>
-index_col&nbsp;:&nbsp;int&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;to&nbsp;use&nbsp;for&nbsp;index.&nbsp;If&nbsp;a&nbsp;sequence&nbsp;is&nbsp;given,&nbsp;a&nbsp;MultiIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;used.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-parse_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Parse&nbsp;dates.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-tupleize_cols&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;write&nbsp;multi_index&nbsp;columns&nbsp;as&nbsp;a&nbsp;list&nbsp;of&nbsp;tuples&nbsp;(if&nbsp;True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;new&nbsp;(expanded&nbsp;format)&nbsp;if&nbsp;False)<br>
-infer_datetime_format:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;and&nbsp;`parse_dates`&nbsp;is&nbsp;True&nbsp;for&nbsp;a&nbsp;column,&nbsp;try&nbsp;to&nbsp;infer&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;datetime&nbsp;format&nbsp;based&nbsp;on&nbsp;the&nbsp;first&nbsp;datetime&nbsp;string.&nbsp;If&nbsp;the&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;be&nbsp;inferred,&nbsp;there&nbsp;often&nbsp;will&nbsp;be&nbsp;a&nbsp;large&nbsp;parsing&nbsp;speed-up.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.read_csv<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-from_dict"><strong>from_dict</strong></a>(data, orient='columns', dtype=None)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Construct&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;from&nbsp;dict&nbsp;of&nbsp;array-like&nbsp;or&nbsp;dicts<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;{field&nbsp;:&nbsp;array-like}&nbsp;or&nbsp;{field&nbsp;:&nbsp;dict}<br>
-orient&nbsp;:&nbsp;{'columns',&nbsp;'index'},&nbsp;default&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;"orientation"&nbsp;of&nbsp;the&nbsp;data.&nbsp;If&nbsp;the&nbsp;keys&nbsp;of&nbsp;the&nbsp;passed&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;be&nbsp;the&nbsp;columns&nbsp;of&nbsp;the&nbsp;resulting&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;pass&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(default).&nbsp;Otherwise&nbsp;if&nbsp;the&nbsp;keys&nbsp;should&nbsp;be&nbsp;rows,&nbsp;pass&nbsp;'index'.<br>
-dtype&nbsp;:&nbsp;dtype,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Data&nbsp;type&nbsp;to&nbsp;force,&nbsp;otherwise&nbsp;infer<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-from_items"><strong>from_items</strong></a>(items, columns=None, orient='columns')<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Convert&nbsp;(key,&nbsp;value)&nbsp;pairs&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;The&nbsp;keys&nbsp;will&nbsp;be&nbsp;the&nbsp;axis<br>
-index&nbsp;(usually&nbsp;the&nbsp;columns,&nbsp;but&nbsp;depends&nbsp;on&nbsp;the&nbsp;specified<br>
-orientation).&nbsp;The&nbsp;values&nbsp;should&nbsp;be&nbsp;arrays&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-items&nbsp;:&nbsp;sequence&nbsp;of&nbsp;(key,&nbsp;value)&nbsp;pairs<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Values&nbsp;should&nbsp;be&nbsp;arrays&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-columns&nbsp;:&nbsp;sequence&nbsp;of&nbsp;column&nbsp;labels,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Must&nbsp;be&nbsp;passed&nbsp;if&nbsp;orient='index'.<br>
-orient&nbsp;:&nbsp;{'columns',&nbsp;'index'},&nbsp;default&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;"orientation"&nbsp;of&nbsp;the&nbsp;data.&nbsp;If&nbsp;the&nbsp;keys&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;input&nbsp;correspond&nbsp;to&nbsp;column&nbsp;labels,&nbsp;pass&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(default).&nbsp;Otherwise&nbsp;if&nbsp;the&nbsp;keys&nbsp;correspond&nbsp;to&nbsp;the&nbsp;index,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;pass&nbsp;'index'.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-frame&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-from_records"><strong>from_records</strong></a>(data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Convert&nbsp;structured&nbsp;or&nbsp;record&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;(structured&nbsp;dtype),&nbsp;list&nbsp;of&nbsp;tuples,&nbsp;dict,&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-index&nbsp;:&nbsp;string,&nbsp;list&nbsp;of&nbsp;fields,&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;of&nbsp;array&nbsp;to&nbsp;use&nbsp;as&nbsp;the&nbsp;index,&nbsp;alternately&nbsp;a&nbsp;specific&nbsp;set&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;input&nbsp;labels&nbsp;to&nbsp;use<br>
-exclude&nbsp;:&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Columns&nbsp;or&nbsp;fields&nbsp;to&nbsp;exclude<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;names&nbsp;to&nbsp;use.&nbsp;If&nbsp;the&nbsp;passed&nbsp;data&nbsp;do&nbsp;not&nbsp;have&nbsp;names<br>
-&nbsp;&nbsp;&nbsp;&nbsp;associated&nbsp;with&nbsp;them,&nbsp;this&nbsp;argument&nbsp;provides&nbsp;names&nbsp;for&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns.&nbsp;Otherwise&nbsp;this&nbsp;argument&nbsp;indicates&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;columns<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;result&nbsp;(any&nbsp;names&nbsp;not&nbsp;found&nbsp;in&nbsp;the&nbsp;data&nbsp;will&nbsp;become&nbsp;all-NA<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns)<br>
-coerce_float&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Attempt&nbsp;to&nbsp;convert&nbsp;values&nbsp;of&nbsp;non-string,&nbsp;non-numeric&nbsp;objects&nbsp;(like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;decimal.Decimal)&nbsp;to&nbsp;floating&nbsp;point,&nbsp;useful&nbsp;for&nbsp;SQL&nbsp;result&nbsp;sets<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-df&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a>:<br>
-<dl><dt><strong>axes</strong></dt>
-<dd><tt>Return&nbsp;a&nbsp;list&nbsp;with&nbsp;the&nbsp;row&nbsp;axis&nbsp;labels&nbsp;and&nbsp;column&nbsp;axis&nbsp;labels&nbsp;as&nbsp;the<br>
-only&nbsp;members.&nbsp;They&nbsp;are&nbsp;returned&nbsp;in&nbsp;that&nbsp;order.</tt></dd>
-</dl>
-<dl><dt><strong>columns</strong></dt>
-</dl>
-<dl><dt><strong>index</strong></dt>
-</dl>
-<dl><dt><strong>shape</strong></dt>
-<dd><tt>Return&nbsp;a&nbsp;tuple&nbsp;representing&nbsp;the&nbsp;dimensionality&nbsp;of&nbsp;the&nbsp;DataFrame.</tt></dd>
-</dl>
-<dl><dt><strong>style</strong></dt>
-<dd><tt>Property&nbsp;returning&nbsp;a&nbsp;Styler&nbsp;object&nbsp;containing&nbsp;methods&nbsp;for<br>
-building&nbsp;a&nbsp;styled&nbsp;HTML&nbsp;representation&nbsp;fo&nbsp;the&nbsp;DataFrame.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.io.formats.style.Styler</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a>:<br>
-<dl><dt><strong>plot</strong> = &lt;class 'pandas.plotting._core.FramePlotMethods'&gt;<dd><tt><a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;plotting&nbsp;accessor&nbsp;and&nbsp;method<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df.plot.line()<br>
-&gt;&gt;&gt;&nbsp;df.plot.scatter('x',&nbsp;'y')<br>
-&gt;&gt;&gt;&nbsp;df.plot.hexbin()<br>
-&nbsp;<br>
-These&nbsp;plotting&nbsp;methods&nbsp;can&nbsp;also&nbsp;be&nbsp;accessed&nbsp;by&nbsp;calling&nbsp;the&nbsp;accessor&nbsp;as&nbsp;a<br>
-method&nbsp;with&nbsp;the&nbsp;``kind``&nbsp;argument:<br>
-``df.<a href="#MyDataFrame-plot">plot</a>(kind='line')``&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;``df.plot.line()``</tt></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><a name="MyDataFrame-__abs__"><strong>__abs__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__array__"><strong>__array__</strong></a>(self, dtype=None)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__array_wrap__"><strong>__array_wrap__</strong></a>(self, result, context=None)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__bool__"><strong>__bool__</strong></a> = __nonzero__(self)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__contains__"><strong>__contains__</strong></a>(self, key)</dt><dd><tt>True&nbsp;if&nbsp;the&nbsp;key&nbsp;is&nbsp;in&nbsp;the&nbsp;info&nbsp;axis</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__copy__"><strong>__copy__</strong></a>(self, deep=True)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__deepcopy__"><strong>__deepcopy__</strong></a>(self, memo=None)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__delitem__"><strong>__delitem__</strong></a>(self, key)</dt><dd><tt>Delete&nbsp;item</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__finalize__"><strong>__finalize__</strong></a>(self, other, method=None, **kwargs)</dt><dd><tt>Propagate&nbsp;metadata&nbsp;from&nbsp;other&nbsp;to&nbsp;self.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;which&nbsp;to&nbsp;get&nbsp;the&nbsp;attributes&nbsp;that&nbsp;we&nbsp;are&nbsp;going<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;propagate<br>
-method&nbsp;:&nbsp;optional,&nbsp;a&nbsp;passed&nbsp;method&nbsp;name&nbsp;;&nbsp;possibly&nbsp;to&nbsp;take&nbsp;different<br>
-&nbsp;&nbsp;&nbsp;&nbsp;types&nbsp;of&nbsp;propagation&nbsp;actions&nbsp;based&nbsp;on&nbsp;this</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__getattr__"><strong>__getattr__</strong></a>(self, name)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;looking&nbsp;up&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__getstate__"><strong>__getstate__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__hash__"><strong>__hash__</strong></a>(self)</dt><dd><tt>Return&nbsp;hash(self).</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__invert__"><strong>__invert__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__iter__"><strong>__iter__</strong></a>(self)</dt><dd><tt>Iterate&nbsp;over&nbsp;infor&nbsp;axis</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__neg__"><strong>__neg__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__nonzero__"><strong>__nonzero__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__round__"><strong>__round__</strong></a>(self, decimals=0)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-__setattr__"><strong>__setattr__</strong></a>(self, name, value)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;setting&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__setstate__"><strong>__setstate__</strong></a>(self, state)</dt></dl>
-
-<dl><dt><a name="MyDataFrame-abs"><strong>abs</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;absolute&nbsp;value&nbsp;taken--only&nbsp;applicable&nbsp;to&nbsp;objects<br>
-that&nbsp;are&nbsp;all&nbsp;numeric.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-abs:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-add_prefix"><strong>add_prefix</strong></a>(self, prefix)</dt><dd><tt>Concatenate&nbsp;prefix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-prefix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_prefix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-add_suffix"><strong>add_suffix</strong></a>(self, suffix)</dt><dd><tt>Concatenate&nbsp;suffix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-suffix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_suffix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-as_blocks"><strong>as_blocks</strong></a>(self, copy=True)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types&nbsp;that&nbsp;each&nbsp;has<br>
-a&nbsp;homogeneous&nbsp;dtype.<br>
-&nbsp;<br>
-NOTE:&nbsp;the&nbsp;dtypes&nbsp;of&nbsp;the&nbsp;blocks&nbsp;WILL&nbsp;BE&nbsp;PRESERVED&nbsp;HERE&nbsp;(unlike&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;as_matrix)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-as_matrix"><strong>as_matrix</strong></a>(self, columns=None)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;its&nbsp;Numpy-array&nbsp;representation.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-columns:&nbsp;list,&nbsp;optional,&nbsp;default:None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;None,&nbsp;return&nbsp;all&nbsp;columns,&nbsp;otherwise,&nbsp;returns&nbsp;specified&nbsp;columns.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;caller&nbsp;is&nbsp;heterogeneous&nbsp;and&nbsp;contains&nbsp;booleans&nbsp;or&nbsp;objects,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;of&nbsp;dtype=<a href="builtins.html#object">object</a>.&nbsp;See&nbsp;Notes.<br>
-&nbsp;<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Return&nbsp;is&nbsp;NOT&nbsp;a&nbsp;Numpy-matrix,&nbsp;rather,&nbsp;a&nbsp;Numpy-array.<br>
-&nbsp;<br>
-The&nbsp;dtype&nbsp;will&nbsp;be&nbsp;a&nbsp;lower-common-denominator&nbsp;dtype&nbsp;(implicit<br>
-upcasting);&nbsp;that&nbsp;is&nbsp;to&nbsp;say&nbsp;if&nbsp;the&nbsp;dtypes&nbsp;(even&nbsp;of&nbsp;numeric&nbsp;types)<br>
-are&nbsp;mixed,&nbsp;the&nbsp;one&nbsp;that&nbsp;accommodates&nbsp;all&nbsp;will&nbsp;be&nbsp;chosen.&nbsp;Use&nbsp;this<br>
-with&nbsp;care&nbsp;if&nbsp;you&nbsp;are&nbsp;not&nbsp;dealing&nbsp;with&nbsp;the&nbsp;blocks.<br>
-&nbsp;<br>
-e.g.&nbsp;If&nbsp;the&nbsp;dtypes&nbsp;are&nbsp;float16&nbsp;and&nbsp;float32,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcast&nbsp;to<br>
-float32.&nbsp;&nbsp;If&nbsp;dtypes&nbsp;are&nbsp;int32&nbsp;and&nbsp;uint8,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcase&nbsp;to<br>
-int32.&nbsp;By&nbsp;numpy.find_common_type&nbsp;convention,&nbsp;mixing&nbsp;int64&nbsp;and&nbsp;uint64<br>
-will&nbsp;result&nbsp;in&nbsp;a&nbsp;flot64&nbsp;dtype.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;is&nbsp;provided&nbsp;for&nbsp;backwards&nbsp;compatibility.&nbsp;Generally,<br>
-it&nbsp;is&nbsp;recommended&nbsp;to&nbsp;use&nbsp;'.values'.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.values</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-asfreq"><strong>asfreq</strong></a>(self, freq, method=None, how=None, normalize=False, fill_value=None)</dt><dd><tt>Convert&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;specified&nbsp;frequency.<br>
-&nbsp;<br>
-Optionally&nbsp;provide&nbsp;filling&nbsp;method&nbsp;to&nbsp;pad/backfill&nbsp;missing&nbsp;values.<br>
-&nbsp;<br>
-Returns&nbsp;the&nbsp;original&nbsp;data&nbsp;conformed&nbsp;to&nbsp;a&nbsp;new&nbsp;index&nbsp;with&nbsp;the&nbsp;specified<br>
-frequency.&nbsp;``resample``&nbsp;is&nbsp;more&nbsp;appropriate&nbsp;if&nbsp;an&nbsp;operation,&nbsp;such&nbsp;as<br>
-summarization,&nbsp;is&nbsp;necessary&nbsp;to&nbsp;represent&nbsp;the&nbsp;data&nbsp;at&nbsp;the&nbsp;new&nbsp;frequency.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;or&nbsp;string<br>
-method&nbsp;:&nbsp;{'backfill'/'bfill',&nbsp;'pad'/'ffill'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(note&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present):<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'pad'&nbsp;/&nbsp;'ffill':&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'backfill'&nbsp;/&nbsp;'bfill':&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill<br>
-how&nbsp;:&nbsp;{'start',&nbsp;'end'},&nbsp;default&nbsp;end<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;PeriodIndex&nbsp;only,&nbsp;see&nbsp;PeriodIndex.asfreq<br>
-normalize&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;reset&nbsp;output&nbsp;index&nbsp;to&nbsp;midnight<br>
-fill_value:&nbsp;scalar,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values,&nbsp;applied&nbsp;during&nbsp;upsampling&nbsp;(note<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;4&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=4,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0.0,&nbsp;None,&nbsp;2.0,&nbsp;3.0],&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'s':series})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-asfreq">asfreq</a>(freq='30S')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``fill&nbsp;value``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-asfreq">asfreq</a>(freq='30S',&nbsp;fill_value=9.0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``method``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-asfreq">asfreq</a>(freq='30S',&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-asof"><strong>asof</strong></a>(self, where, subset=None)</dt><dd><tt>The&nbsp;last&nbsp;row&nbsp;without&nbsp;any&nbsp;NaN&nbsp;is&nbsp;taken&nbsp;(or&nbsp;the&nbsp;last&nbsp;row&nbsp;without<br>
-NaN&nbsp;considering&nbsp;only&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;in&nbsp;the&nbsp;case&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0&nbsp;For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-If&nbsp;there&nbsp;is&nbsp;no&nbsp;good&nbsp;value,&nbsp;NaN&nbsp;is&nbsp;returned&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;NaN&nbsp;values&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-where&nbsp;:&nbsp;date&nbsp;or&nbsp;array&nbsp;of&nbsp;dates<br>
-subset&nbsp;:&nbsp;string&nbsp;or&nbsp;list&nbsp;of&nbsp;strings,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None&nbsp;use&nbsp;these&nbsp;columns&nbsp;for&nbsp;NaN&nbsp;propagation<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Dates&nbsp;are&nbsp;assumed&nbsp;to&nbsp;be&nbsp;sorted<br>
-Raises&nbsp;if&nbsp;this&nbsp;is&nbsp;not&nbsp;the&nbsp;case<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-where&nbsp;is&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;&nbsp;-&nbsp;value&nbsp;or&nbsp;NaN&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;-&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-where&nbsp;is&nbsp;Index:&nbsp;same&nbsp;shape&nbsp;<a href="builtins.html#object">object</a>&nbsp;as&nbsp;input<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-merge_asof</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-astype"><strong>astype</strong></a>(self, dtype, copy=True, errors='raise', **kwargs)</dt><dd><tt>Cast&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;numpy.dtype<br>
-Return&nbsp;a&nbsp;copy&nbsp;when&nbsp;copy&nbsp;=&nbsp;True&nbsp;(be&nbsp;really&nbsp;careful&nbsp;with&nbsp;this!)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dtype&nbsp;:&nbsp;data&nbsp;type,&nbsp;or&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;-&gt;&nbsp;data&nbsp;type<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;entire&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;same&nbsp;type.&nbsp;Alternatively,&nbsp;use&nbsp;{col:&nbsp;dtype,&nbsp;...},&nbsp;where&nbsp;col&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;label&nbsp;and&nbsp;dtype&nbsp;is&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;one<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;more&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;columns&nbsp;to&nbsp;column-specific&nbsp;types.<br>
-errors&nbsp;:&nbsp;{'raise',&nbsp;'ignore'},&nbsp;default&nbsp;'raise'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Control&nbsp;raising&nbsp;of&nbsp;exceptions&nbsp;on&nbsp;invalid&nbsp;data&nbsp;for&nbsp;provided&nbsp;dtype.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``raise``&nbsp;:&nbsp;allow&nbsp;exceptions&nbsp;to&nbsp;be&nbsp;raised<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``ignore``&nbsp;:&nbsp;suppress&nbsp;exceptions.&nbsp;On&nbsp;error&nbsp;return&nbsp;original&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-raise_on_error&nbsp;:&nbsp;DEPRECATED&nbsp;use&nbsp;``errors``&nbsp;instead<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;constructor<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-casted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-at_time"><strong>at_time</strong></a>(self, time, asof=False)</dt><dd><tt>Select&nbsp;values&nbsp;at&nbsp;particular&nbsp;time&nbsp;of&nbsp;day&nbsp;(e.g.&nbsp;9:30AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_at_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-between_time"><strong>between_time</strong></a>(self, start_time, end_time, include_start=True, include_end=True)</dt><dd><tt>Select&nbsp;values&nbsp;between&nbsp;particular&nbsp;times&nbsp;of&nbsp;the&nbsp;day&nbsp;(e.g.,&nbsp;9:00-9:30&nbsp;AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-start_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-end_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-include_start&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-include_end&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_between_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-bfill"><strong>bfill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#MyDataFrame-fillna">fillna</a>(method='bfill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-bool"><strong>bool</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;bool&nbsp;of&nbsp;a&nbsp;single&nbsp;element&nbsp;PandasObject.<br>
-&nbsp;<br>
-This&nbsp;must&nbsp;be&nbsp;a&nbsp;boolean&nbsp;scalar&nbsp;value,&nbsp;either&nbsp;True&nbsp;or&nbsp;False.&nbsp;&nbsp;Raise&nbsp;a<br>
-ValueError&nbsp;if&nbsp;the&nbsp;PandasObject&nbsp;does&nbsp;not&nbsp;have&nbsp;exactly&nbsp;1&nbsp;element,&nbsp;or&nbsp;that<br>
-element&nbsp;is&nbsp;not&nbsp;boolean</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-clip"><strong>clip</strong></a>(self, lower=None, upper=None, axis=None, *args, **kwargs)</dt><dd><tt>Trim&nbsp;values&nbsp;at&nbsp;input&nbsp;threshold(s).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-lower&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-upper&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;lower&nbsp;and&nbsp;upper&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.256177<br>
-1&nbsp;-1.367855&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.176076<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;1.261967&nbsp;&nbsp;0.570967<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-clip">clip</a>(-1.0,&nbsp;0.5)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.000000<br>
-1&nbsp;-1.000000&nbsp;&nbsp;0.500000<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.000000<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;0.500000&nbsp;&nbsp;0.500000<br>
-&gt;&gt;&gt;&nbsp;t<br>
-0&nbsp;&nbsp;&nbsp;-0.3<br>
-1&nbsp;&nbsp;&nbsp;-0.2<br>
-2&nbsp;&nbsp;&nbsp;-0.1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;0.1<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-clip">clip</a>(t,&nbsp;t&nbsp;+&nbsp;1,&nbsp;axis=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-0.300000<br>
-1&nbsp;-0.200000&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-0.100000<br>
-3&nbsp;&nbsp;0.230930&nbsp;&nbsp;0.000000<br>
-4&nbsp;&nbsp;1.100000&nbsp;&nbsp;0.570967</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-clip_lower"><strong>clip_lower</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;the&nbsp;input&nbsp;with&nbsp;values&nbsp;below&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-clip_upper"><strong>clip_upper</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;input&nbsp;with&nbsp;values&nbsp;above&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-consolidate"><strong>consolidate</strong></a>(self, inplace=False)</dt><dd><tt>DEPRECATED:&nbsp;consolidate&nbsp;will&nbsp;be&nbsp;an&nbsp;internal&nbsp;implementation&nbsp;only.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-convert_objects"><strong>convert_objects</strong></a>(self, convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True)</dt><dd><tt>Deprecated.<br>
-&nbsp;<br>
-Attempt&nbsp;to&nbsp;infer&nbsp;better&nbsp;dtype&nbsp;for&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-convert_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;date&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-convert_numeric&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;attempt&nbsp;to&nbsp;coerce&nbsp;to&nbsp;numbers&nbsp;(including&nbsp;strings),&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaN.<br>
-convert_timedeltas&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;timedelta&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;return&nbsp;a&nbsp;copy&nbsp;even&nbsp;if&nbsp;no&nbsp;copy&nbsp;is&nbsp;necessary&nbsp;(e.g.&nbsp;no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion&nbsp;was&nbsp;done).&nbsp;Note:&nbsp;This&nbsp;is&nbsp;meant&nbsp;for&nbsp;internal&nbsp;use,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;not&nbsp;be&nbsp;confused&nbsp;with&nbsp;inplace.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.to_datetime&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;datetime.<br>
-pandas.to_timedelta&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;timedelta.<br>
-pandas.to_numeric&nbsp;:&nbsp;Return&nbsp;a&nbsp;fixed&nbsp;frequency&nbsp;timedelta&nbsp;index,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;day&nbsp;as&nbsp;the&nbsp;default.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;same&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-copy"><strong>copy</strong></a>(self, deep=True)</dt><dd><tt>Make&nbsp;a&nbsp;copy&nbsp;of&nbsp;this&nbsp;objects&nbsp;data.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-deep&nbsp;:&nbsp;boolean&nbsp;or&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;a&nbsp;deep&nbsp;copy,&nbsp;including&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;data&nbsp;and&nbsp;the&nbsp;indices.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;With&nbsp;``deep=False``&nbsp;neither&nbsp;the&nbsp;indices&nbsp;or&nbsp;the&nbsp;data&nbsp;are&nbsp;copied.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;that&nbsp;when&nbsp;``deep=True``&nbsp;data&nbsp;is&nbsp;copied,&nbsp;actual&nbsp;python&nbsp;objects<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;not&nbsp;be&nbsp;copied&nbsp;recursively,&nbsp;only&nbsp;the&nbsp;reference&nbsp;to&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;is&nbsp;in&nbsp;contrast&nbsp;to&nbsp;``copy.deepcopy``&nbsp;in&nbsp;the&nbsp;Standard&nbsp;Library,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;recursively&nbsp;copies&nbsp;<a href="builtins.html#object">object</a>&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-copy&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-describe"><strong>describe</strong></a>(self, percentiles=None, include=None, exclude=None)</dt><dd><tt>Generates&nbsp;descriptive&nbsp;statistics&nbsp;that&nbsp;summarize&nbsp;the&nbsp;central&nbsp;tendency,<br>
-dispersion&nbsp;and&nbsp;shape&nbsp;of&nbsp;a&nbsp;dataset's&nbsp;distribution,&nbsp;excluding<br>
-``NaN``&nbsp;values.<br>
-&nbsp;<br>
-Analyzes&nbsp;both&nbsp;numeric&nbsp;and&nbsp;<a href="builtins.html#object">object</a>&nbsp;series,&nbsp;as&nbsp;well<br>
-as&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;column&nbsp;sets&nbsp;of&nbsp;mixed&nbsp;data&nbsp;types.&nbsp;The&nbsp;output<br>
-will&nbsp;vary&nbsp;depending&nbsp;on&nbsp;what&nbsp;is&nbsp;provided.&nbsp;Refer&nbsp;to&nbsp;the&nbsp;notes<br>
-below&nbsp;for&nbsp;more&nbsp;detail.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-percentiles&nbsp;:&nbsp;list-like&nbsp;of&nbsp;numbers,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;percentiles&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;output.&nbsp;All&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fall&nbsp;between&nbsp;0&nbsp;and&nbsp;1.&nbsp;The&nbsp;default&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``[.25,&nbsp;.5,&nbsp;.75]``,&nbsp;which&nbsp;returns&nbsp;the&nbsp;25th,&nbsp;50th,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;75th&nbsp;percentiles.<br>
-include&nbsp;:&nbsp;'all',&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;white&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'all'&nbsp;:&nbsp;All&nbsp;columns&nbsp;of&nbsp;the&nbsp;input&nbsp;will&nbsp;be&nbsp;included&nbsp;in&nbsp;the&nbsp;output.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Limits&nbsp;the&nbsp;results&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;provided&nbsp;data&nbsp;types.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;limit&nbsp;the&nbsp;result&nbsp;to&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;limit&nbsp;it&nbsp;instead&nbsp;to&nbsp;categorical<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;objects&nbsp;submit&nbsp;the&nbsp;``numpy.<a href="builtins.html#object">object</a>``&nbsp;data&nbsp;type.&nbsp;Strings<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#MyDataFrame-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;include&nbsp;all&nbsp;numeric&nbsp;columns.<br>
-exclude&nbsp;:&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;black&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;omit&nbsp;from&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Excludes&nbsp;the&nbsp;provided&nbsp;data&nbsp;types<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;the&nbsp;result.&nbsp;To&nbsp;select&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;select&nbsp;categorical&nbsp;objects&nbsp;submit&nbsp;the&nbsp;data<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;type&nbsp;``numpy.<a href="builtins.html#object">object</a>``.&nbsp;Strings&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#MyDataFrame-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;exclude&nbsp;nothing.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-summary:&nbsp;&nbsp;<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;summary&nbsp;statistics<br>
-&nbsp;<br>
-Notes<br>
------<br>
-For&nbsp;numeric&nbsp;data,&nbsp;the&nbsp;result's&nbsp;index&nbsp;will&nbsp;include&nbsp;``count``,<br>
-``mean``,&nbsp;``std``,&nbsp;``min``,&nbsp;``max``&nbsp;as&nbsp;well&nbsp;as&nbsp;lower,&nbsp;``50``&nbsp;and<br>
-upper&nbsp;percentiles.&nbsp;By&nbsp;default&nbsp;the&nbsp;lower&nbsp;percentile&nbsp;is&nbsp;``25``&nbsp;and&nbsp;the<br>
-upper&nbsp;percentile&nbsp;is&nbsp;``75``.&nbsp;The&nbsp;``50``&nbsp;percentile&nbsp;is&nbsp;the<br>
-same&nbsp;as&nbsp;the&nbsp;median.<br>
-&nbsp;<br>
-For&nbsp;<a href="builtins.html#object">object</a>&nbsp;data&nbsp;(e.g.&nbsp;strings&nbsp;or&nbsp;timestamps),&nbsp;the&nbsp;result's&nbsp;index<br>
-will&nbsp;include&nbsp;``count``,&nbsp;``unique``,&nbsp;``top``,&nbsp;and&nbsp;``freq``.&nbsp;The&nbsp;``top``<br>
-is&nbsp;the&nbsp;most&nbsp;common&nbsp;value.&nbsp;The&nbsp;``freq``&nbsp;is&nbsp;the&nbsp;most&nbsp;common&nbsp;value's<br>
-frequency.&nbsp;Timestamps&nbsp;also&nbsp;include&nbsp;the&nbsp;``first``&nbsp;and&nbsp;``last``&nbsp;items.<br>
-&nbsp;<br>
-If&nbsp;multiple&nbsp;<a href="builtins.html#object">object</a>&nbsp;values&nbsp;have&nbsp;the&nbsp;highest&nbsp;count,&nbsp;then&nbsp;the<br>
-``count``&nbsp;and&nbsp;``top``&nbsp;results&nbsp;will&nbsp;be&nbsp;arbitrarily&nbsp;chosen&nbsp;from<br>
-among&nbsp;those&nbsp;with&nbsp;the&nbsp;highest&nbsp;count.<br>
-&nbsp;<br>
-For&nbsp;mixed&nbsp;data&nbsp;types&nbsp;provided&nbsp;via&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``,&nbsp;the&nbsp;default&nbsp;is&nbsp;to<br>
-return&nbsp;only&nbsp;an&nbsp;analysis&nbsp;of&nbsp;numeric&nbsp;columns.&nbsp;If&nbsp;``include='all'``<br>
-is&nbsp;provided&nbsp;as&nbsp;an&nbsp;option,&nbsp;the&nbsp;result&nbsp;will&nbsp;include&nbsp;a&nbsp;union&nbsp;of<br>
-attributes&nbsp;of&nbsp;each&nbsp;type.<br>
-&nbsp;<br>
-The&nbsp;`include`&nbsp;and&nbsp;`exclude`&nbsp;parameters&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;limit<br>
-which&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;are&nbsp;analyzed&nbsp;for&nbsp;the&nbsp;output.<br>
-The&nbsp;parameters&nbsp;are&nbsp;ignored&nbsp;when&nbsp;analyzing&nbsp;a&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-Describing&nbsp;a&nbsp;numeric&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MyDataFrame-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;categorical&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(['a',&nbsp;'a',&nbsp;'b',&nbsp;'c'])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MyDataFrame-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;timestamp&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2000-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01")<br>
-...&nbsp;])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MyDataFrame-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-first&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2000-01-01&nbsp;00:00:00<br>
-last&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``.&nbsp;By&nbsp;default&nbsp;only&nbsp;numeric&nbsp;fields<br>
-are&nbsp;returned.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[1,&nbsp;'a'],&nbsp;[2,&nbsp;'b'],&nbsp;[3,&nbsp;'c']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['numeric',&nbsp;'<a href="builtins.html#object">object</a>'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-describe">describe</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;all&nbsp;columns&nbsp;of&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;regardless&nbsp;of&nbsp;data&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-describe">describe</a>(include='all')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;column&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;by&nbsp;accessing&nbsp;it&nbsp;as<br>
-an&nbsp;attribute.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.numeric.<a href="#MyDataFrame-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-Name:&nbsp;numeric,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;numeric&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-describe">describe</a>(include=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;string&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-describe">describe</a>(include=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;numeric&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-describe">describe</a>(exclude=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-describe">describe</a>(exclude=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.count<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.max<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.min<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mean<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.std<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select_dtypes</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-drop"><strong>drop</strong></a>(self, labels, axis=0, level=None, inplace=False, errors='raise')</dt><dd><tt>Return&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;labels&nbsp;in&nbsp;requested&nbsp;axis&nbsp;removed.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-labels&nbsp;:&nbsp;single&nbsp;label&nbsp;or&nbsp;list-like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;axis&nbsp;name<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;MultiIndex<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;return&nbsp;None.<br>
-errors&nbsp;:&nbsp;{'ignore',&nbsp;'raise'},&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'ignore',&nbsp;suppress&nbsp;error&nbsp;and&nbsp;existing&nbsp;labels&nbsp;are&nbsp;dropped.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dropped&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-equals"><strong>equals</strong></a>(self, other)</dt><dd><tt>Determines&nbsp;if&nbsp;two&nbsp;NDFrame&nbsp;objects&nbsp;contain&nbsp;the&nbsp;same&nbsp;elements.&nbsp;NaNs&nbsp;in<br>
-the&nbsp;same&nbsp;location&nbsp;are&nbsp;considered&nbsp;equal.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-ffill"><strong>ffill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#MyDataFrame-fillna">fillna</a>(method='ffill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-filter"><strong>filter</strong></a>(self, items=None, like=None, regex=None, axis=None)</dt><dd><tt>Subset&nbsp;rows&nbsp;or&nbsp;columns&nbsp;of&nbsp;dataframe&nbsp;according&nbsp;to&nbsp;labels&nbsp;in<br>
-the&nbsp;specified&nbsp;index.<br>
-&nbsp;<br>
-Note&nbsp;that&nbsp;this&nbsp;routine&nbsp;does&nbsp;not&nbsp;filter&nbsp;a&nbsp;dataframe&nbsp;on&nbsp;its<br>
-contents.&nbsp;The&nbsp;filter&nbsp;is&nbsp;applied&nbsp;to&nbsp;the&nbsp;labels&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-items&nbsp;:&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;info&nbsp;axis&nbsp;to&nbsp;restrict&nbsp;to&nbsp;(must&nbsp;not&nbsp;all&nbsp;be&nbsp;present)<br>
-like&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;where&nbsp;"arg&nbsp;in&nbsp;col&nbsp;==&nbsp;True"<br>
-regex&nbsp;:&nbsp;string&nbsp;(regular&nbsp;expression)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;with&nbsp;re.search(regex,&nbsp;col)&nbsp;==&nbsp;True<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;filter&nbsp;on.&nbsp;&nbsp;By&nbsp;default&nbsp;this&nbsp;is&nbsp;the&nbsp;info&nbsp;axis,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'index'&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;'columns'&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;name<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-filter">filter</a>(items=['one',&nbsp;'three'])<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;regular&nbsp;expression<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-filter">filter</a>(regex='e$',&nbsp;axis=1)<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;rows&nbsp;containing&nbsp;'bbi'<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-filter">filter</a>(like='bbi',&nbsp;axis=0)<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;``items``,&nbsp;``like``,&nbsp;and&nbsp;``regex``&nbsp;parameters&nbsp;are<br>
-enforced&nbsp;to&nbsp;be&nbsp;mutually&nbsp;exclusive.<br>
-&nbsp;<br>
-``axis``&nbsp;defaults&nbsp;to&nbsp;the&nbsp;info&nbsp;axis&nbsp;that&nbsp;is&nbsp;used&nbsp;when&nbsp;indexing<br>
-with&nbsp;``[]``.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-first"><strong>first</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;initial&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#MyDataFrame-first">first</a>('10D')&nbsp;-&gt;&nbsp;First&nbsp;10&nbsp;days<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-get"><strong>get</strong></a>(self, key, default=None)</dt><dd><tt>Get&nbsp;item&nbsp;from&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;given&nbsp;key&nbsp;(<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column,&nbsp;Panel&nbsp;slice,<br>
-etc.).&nbsp;Returns&nbsp;default&nbsp;value&nbsp;if&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;type&nbsp;of&nbsp;items&nbsp;contained&nbsp;in&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-get_dtype_counts"><strong>get_dtype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;dtypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-get_ftype_counts"><strong>get_ftype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;ftypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-get_values"><strong>get_values</strong></a>(self)</dt><dd><tt>same&nbsp;as&nbsp;values&nbsp;(but&nbsp;handles&nbsp;sparseness&nbsp;conversions)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-groupby"><strong>groupby</strong></a>(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)</dt><dd><tt>Group&nbsp;series&nbsp;using&nbsp;mapper&nbsp;(dict&nbsp;or&nbsp;key&nbsp;function,&nbsp;apply&nbsp;given&nbsp;function<br>
-to&nbsp;group,&nbsp;return&nbsp;result&nbsp;as&nbsp;series)&nbsp;or&nbsp;by&nbsp;a&nbsp;series&nbsp;of&nbsp;columns.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;mapping,&nbsp;function,&nbsp;str,&nbsp;or&nbsp;iterable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;for&nbsp;the&nbsp;groupby.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;``by``&nbsp;is&nbsp;a&nbsp;function,&nbsp;it's&nbsp;called&nbsp;on&nbsp;each&nbsp;value&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>'s<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;If&nbsp;a&nbsp;dict&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;is&nbsp;passed,&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;dict&nbsp;VALUES<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;(the&nbsp;<a href="pandas.core.series.html#Series">Series</a>'&nbsp;values&nbsp;are&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aligned;&nbsp;see&nbsp;``.<a href="#MyDataFrame-align">align</a>()``&nbsp;method).&nbsp;If&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;is&nbsp;passed,&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;are&nbsp;used&nbsp;as-is&nbsp;determine&nbsp;the&nbsp;groups.&nbsp;A&nbsp;str&nbsp;or&nbsp;list&nbsp;of&nbsp;strs<br>
-&nbsp;&nbsp;&nbsp;&nbsp;may&nbsp;be&nbsp;passed&nbsp;to&nbsp;group&nbsp;by&nbsp;the&nbsp;columns&nbsp;in&nbsp;``self``<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-level&nbsp;:&nbsp;int,&nbsp;level&nbsp;name,&nbsp;or&nbsp;sequence&nbsp;of&nbsp;such,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;group&nbsp;by&nbsp;a&nbsp;particular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level&nbsp;or&nbsp;levels<br>
-as_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;aggregated&nbsp;output,&nbsp;return&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;group&nbsp;labels&nbsp;as&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;Only&nbsp;relevant&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;input.&nbsp;as_index=False&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;effectively&nbsp;"SQL-style"&nbsp;grouped&nbsp;output<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;group&nbsp;keys.&nbsp;Get&nbsp;better&nbsp;performance&nbsp;by&nbsp;turning&nbsp;this&nbsp;off.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;this&nbsp;does&nbsp;not&nbsp;influence&nbsp;the&nbsp;order&nbsp;of&nbsp;observations&nbsp;within&nbsp;each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;group.&nbsp;&nbsp;groupby&nbsp;preserves&nbsp;the&nbsp;order&nbsp;of&nbsp;rows&nbsp;within&nbsp;each&nbsp;group.<br>
-group_keys&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;calling&nbsp;apply,&nbsp;add&nbsp;group&nbsp;keys&nbsp;to&nbsp;index&nbsp;to&nbsp;identify&nbsp;pieces<br>
-squeeze&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;reduce&nbsp;the&nbsp;dimensionality&nbsp;of&nbsp;the&nbsp;return&nbsp;type&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;return&nbsp;a&nbsp;consistent&nbsp;type<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;results<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#MyDataFrame-groupby">groupby</a>(func,&nbsp;axis=0).<a href="#MyDataFrame-mean">mean</a>()<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#MyDataFrame-groupby">groupby</a>(['col1',&nbsp;'col2'])['col3'].<a href="#MyDataFrame-mean">mean</a>()<br>
-&nbsp;<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;hierarchical&nbsp;index<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#MyDataFrame-groupby">groupby</a>(['col1',&nbsp;'col2']).<a href="#MyDataFrame-mean">mean</a>()<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-GroupBy&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-head"><strong>head</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;first&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-interpolate"><strong>interpolate</strong></a>(self, method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', downcast=None, **kwargs)</dt><dd><tt>Interpolate&nbsp;values&nbsp;according&nbsp;to&nbsp;different&nbsp;methods.<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;only&nbsp;``method='linear'``&nbsp;is&nbsp;supported&nbsp;for<br>
-DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a&nbsp;MultiIndex.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-method&nbsp;:&nbsp;{'linear',&nbsp;'time',&nbsp;'index',&nbsp;'values',&nbsp;'nearest',&nbsp;'zero',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',&nbsp;'barycentric',&nbsp;'krogh',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'polynomial',&nbsp;'spline',&nbsp;'piecewise_polynomial',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'from_derivatives',&nbsp;'pchip',&nbsp;'akima'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'linear':&nbsp;ignore&nbsp;the&nbsp;index&nbsp;and&nbsp;treat&nbsp;the&nbsp;values&nbsp;as&nbsp;equally<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;spaced.&nbsp;This&nbsp;is&nbsp;the&nbsp;only&nbsp;method&nbsp;supported&nbsp;on&nbsp;MultiIndexes.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'time':&nbsp;interpolation&nbsp;works&nbsp;on&nbsp;daily&nbsp;and&nbsp;higher&nbsp;resolution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;data&nbsp;to&nbsp;interpolate&nbsp;given&nbsp;length&nbsp;of&nbsp;interval<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'index',&nbsp;'values':&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'nearest',&nbsp;'zero',&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'barycentric',&nbsp;'polynomial'&nbsp;is&nbsp;passed&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``scipy.interpolate.interp1d``.&nbsp;Both&nbsp;'polynomial'&nbsp;and&nbsp;'spline'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;require&nbsp;that&nbsp;you&nbsp;also&nbsp;specify&nbsp;an&nbsp;`order`&nbsp;(int),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;e.g.&nbsp;df.<a href="#MyDataFrame-interpolate">interpolate</a>(method='polynomial',&nbsp;order=4).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'krogh',&nbsp;'piecewise_polynomial',&nbsp;'spline',&nbsp;'pchip'&nbsp;and&nbsp;'akima'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;all&nbsp;wrappers&nbsp;around&nbsp;the&nbsp;scipy&nbsp;interpolation&nbsp;methods&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;similar&nbsp;names.&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;For&nbsp;more&nbsp;information&nbsp;on&nbsp;their&nbsp;behavior,&nbsp;see&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`scipy&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__">http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;`tutorial&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__">http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'from_derivatives'&nbsp;refers&nbsp;to&nbsp;BPoly.from_derivatives&nbsp;which<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;replaces&nbsp;'piecewise_polynomial'&nbsp;interpolation&nbsp;method&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;0.18<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;support&nbsp;for&nbsp;the&nbsp;'akima'&nbsp;method<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;interpolate&nbsp;method&nbsp;'from_derivatives'&nbsp;which&nbsp;replaces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'piecewise_polynomial'&nbsp;in&nbsp;scipy&nbsp;0.18;&nbsp;backwards-compatible&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;&lt;&nbsp;0.18<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;0:&nbsp;fill&nbsp;column-by-column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;1:&nbsp;fill&nbsp;row-by-row<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs&nbsp;to&nbsp;fill.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0.<br>
-limit_direction&nbsp;:&nbsp;{'forward',&nbsp;'backward',&nbsp;'both'},&nbsp;default&nbsp;'forward'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;limit&nbsp;is&nbsp;specified,&nbsp;consecutive&nbsp;NaNs&nbsp;will&nbsp;be&nbsp;filled&nbsp;in&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;direction.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Update&nbsp;the&nbsp;NDFrame&nbsp;in&nbsp;place&nbsp;if&nbsp;possible.<br>
-downcast&nbsp;:&nbsp;optional,&nbsp;'infer'&nbsp;or&nbsp;None,&nbsp;defaults&nbsp;to&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Downcast&nbsp;dtypes&nbsp;if&nbsp;possible.<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;interpolating&nbsp;function.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;interpolated&nbsp;at&nbsp;the&nbsp;NaNs<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;replace,&nbsp;fillna<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Filling&nbsp;in&nbsp;NaNs<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0,&nbsp;1,&nbsp;np.nan,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MyDataFrame-interpolate">interpolate</a>()<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-isnull"><strong>isnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-notnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;isnull</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-keys"><strong>keys</strong></a>(self)</dt><dd><tt>Get&nbsp;the&nbsp;'info&nbsp;axis'&nbsp;(see&nbsp;Indexing&nbsp;for&nbsp;more)<br>
-&nbsp;<br>
-This&nbsp;is&nbsp;index&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;columns&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;and&nbsp;major_axis&nbsp;for<br>
-Panel.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-last"><strong>last</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;final&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#MyDataFrame-last">last</a>('5M')&nbsp;-&gt;&nbsp;Last&nbsp;5&nbsp;months<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-mask"><strong>mask</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;False&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;mask&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``False``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#MyDataFrame-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#MyDataFrame-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``mask``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MyDataFrame-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).reshape(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#MyDataFrame-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#MyDataFrame-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-notnull"><strong>notnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are<br>
-not&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-isnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;notnull</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-pct_change"><strong>pct_change</strong></a>(self, periods=1, fill_method='pad', limit=None, freq=None, **kwargs)</dt><dd><tt>Percent&nbsp;change&nbsp;over&nbsp;given&nbsp;number&nbsp;of&nbsp;periods.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;percent&nbsp;change<br>
-fill_method&nbsp;:&nbsp;str,&nbsp;default&nbsp;'pad'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;How&nbsp;to&nbsp;handle&nbsp;NAs&nbsp;before&nbsp;computing&nbsp;percent&nbsp;changes<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NAs&nbsp;to&nbsp;fill&nbsp;before&nbsp;stopping<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;offset&nbsp;alias&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;time&nbsp;series&nbsp;API&nbsp;(e.g.&nbsp;'M'&nbsp;or&nbsp;BDay())<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-chg&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-By&nbsp;default,&nbsp;the&nbsp;percentage&nbsp;change&nbsp;is&nbsp;calculated&nbsp;along&nbsp;the&nbsp;stat<br>
-axis:&nbsp;0,&nbsp;or&nbsp;``Index``,&nbsp;for&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;and&nbsp;1,&nbsp;or&nbsp;``minor``&nbsp;for<br>
-``Panel``.&nbsp;You&nbsp;can&nbsp;change&nbsp;this&nbsp;with&nbsp;the&nbsp;``axis``&nbsp;keyword&nbsp;argument.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-pipe"><strong>pipe</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Apply&nbsp;func(self,&nbsp;\*args,&nbsp;\*\*kwargs)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.2<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;the&nbsp;NDFrame.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``args``,&nbsp;and&nbsp;``kwargs``&nbsp;are&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Alternatively&nbsp;a&nbsp;``(callable,&nbsp;data_keyword)``&nbsp;tuple&nbsp;where<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``data_keyword``&nbsp;is&nbsp;a&nbsp;string&nbsp;indicating&nbsp;the&nbsp;keyword&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``callable``&nbsp;that&nbsp;expects&nbsp;the&nbsp;NDFrame.<br>
-args&nbsp;:&nbsp;positional&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-kwargs&nbsp;:&nbsp;a&nbsp;dictionary&nbsp;of&nbsp;keyword&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="builtins.html#object">object</a>&nbsp;:&nbsp;the&nbsp;return&nbsp;type&nbsp;of&nbsp;``func``.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-Use&nbsp;``.pipe``&nbsp;when&nbsp;chaining&nbsp;together&nbsp;functions&nbsp;that&nbsp;expect<br>
-on&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;DataFrames.&nbsp;Instead&nbsp;of&nbsp;writing<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;f(g(h(df),&nbsp;arg1=a),&nbsp;arg2=b,&nbsp;arg3=c)<br>
-&nbsp;<br>
-You&nbsp;can&nbsp;write<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#MyDataFrame-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#MyDataFrame-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#MyDataFrame-pipe">pipe</a>(f,&nbsp;arg2=b,&nbsp;arg3=c)<br>
-...&nbsp;)<br>
-&nbsp;<br>
-If&nbsp;you&nbsp;have&nbsp;a&nbsp;function&nbsp;that&nbsp;takes&nbsp;the&nbsp;data&nbsp;as&nbsp;(say)&nbsp;the&nbsp;second<br>
-argument,&nbsp;pass&nbsp;a&nbsp;tuple&nbsp;indicating&nbsp;which&nbsp;keyword&nbsp;expects&nbsp;the<br>
-data.&nbsp;For&nbsp;example,&nbsp;suppose&nbsp;``f``&nbsp;takes&nbsp;its&nbsp;data&nbsp;as&nbsp;``arg2``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#MyDataFrame-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#MyDataFrame-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#MyDataFrame-pipe">pipe</a>((f,&nbsp;'arg2'),&nbsp;arg1=a,&nbsp;arg3=c)<br>
-...&nbsp;&nbsp;)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.map</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-pop"><strong>pop</strong></a>(self, item)</dt><dd><tt>Return&nbsp;item&nbsp;and&nbsp;drop&nbsp;from&nbsp;frame.&nbsp;Raise&nbsp;KeyError&nbsp;if&nbsp;not&nbsp;found.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-rank"><strong>rank</strong></a>(self, axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)</dt><dd><tt>Compute&nbsp;numerical&nbsp;data&nbsp;ranks&nbsp;(1&nbsp;through&nbsp;n)&nbsp;along&nbsp;axis.&nbsp;Equal&nbsp;values&nbsp;are<br>
-assigned&nbsp;a&nbsp;rank&nbsp;that&nbsp;is&nbsp;the&nbsp;average&nbsp;of&nbsp;the&nbsp;ranks&nbsp;of&nbsp;those&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;to&nbsp;direct&nbsp;ranking<br>
-method&nbsp;:&nbsp;{'average',&nbsp;'min',&nbsp;'max',&nbsp;'first',&nbsp;'dense'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;average:&nbsp;average&nbsp;rank&nbsp;of&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;min:&nbsp;lowest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;max:&nbsp;highest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;first:&nbsp;ranks&nbsp;assigned&nbsp;in&nbsp;order&nbsp;they&nbsp;appear&nbsp;in&nbsp;the&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dense:&nbsp;like&nbsp;'min',&nbsp;but&nbsp;rank&nbsp;always&nbsp;increases&nbsp;by&nbsp;1&nbsp;between&nbsp;groups<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;data.&nbsp;Valid&nbsp;only&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Panel&nbsp;objects<br>
-na_option&nbsp;:&nbsp;{'keep',&nbsp;'top',&nbsp;'bottom'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;keep:&nbsp;leave&nbsp;NA&nbsp;values&nbsp;where&nbsp;they&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;top:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;ascending<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;bottom:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;descending<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False&nbsp;for&nbsp;ranks&nbsp;by&nbsp;high&nbsp;(1)&nbsp;to&nbsp;low&nbsp;(N)<br>
-pct&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Computes&nbsp;percentage&nbsp;rank&nbsp;of&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ranks&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-reindex_like"><strong>reindex_like</strong></a>(self, other, method=None, copy=True, limit=None, tolerance=None)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;matching&nbsp;indices&nbsp;to&nbsp;myself.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;Object<br>
-method&nbsp;:&nbsp;string&nbsp;or&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;labels&nbsp;to&nbsp;fill&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;labels&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>&nbsp;and&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Like&nbsp;calling&nbsp;s.<a href="#MyDataFrame-reindex">reindex</a>(index=other.index,&nbsp;columns=other.columns,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;method=...)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-rename_axis"><strong>rename_axis</strong></a>(self, mapper, axis=0, copy=True, inplace=False)</dt><dd><tt>Alter&nbsp;index&nbsp;and&nbsp;/&nbsp;or&nbsp;columns&nbsp;using&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.<br>
-A&nbsp;scalar&nbsp;or&nbsp;list-like&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;``Index.name``<br>
-or&nbsp;``MultiIndex.names``&nbsp;attribute.<br>
-A&nbsp;function&nbsp;or&nbsp;dict&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;labels.<br>
-Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be&nbsp;unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in<br>
-a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left&nbsp;as-is.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-mapper&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename<br>
-pandas.Index.rename<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-rename_axis">rename_axis</a>("foo")&nbsp;&nbsp;#&nbsp;scalar,&nbsp;alters&nbsp;df.index.name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-foo<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-rename_axis">rename_axis</a>(lambda&nbsp;x:&nbsp;2&nbsp;*&nbsp;x)&nbsp;&nbsp;#&nbsp;function:&nbsp;alters&nbsp;labels<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-4&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-rename_axis">rename_axis</a>({"A":&nbsp;"ehh",&nbsp;"C":&nbsp;"see"},&nbsp;axis="columns")&nbsp;&nbsp;#&nbsp;mapping<br>
-&nbsp;&nbsp;&nbsp;ehh&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-replace"><strong>replace</strong></a>(self, to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad', axis=None)</dt><dd><tt>Replace&nbsp;values&nbsp;given&nbsp;in&nbsp;'to_replace'&nbsp;with&nbsp;'value'.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-to_replace&nbsp;:&nbsp;str,&nbsp;regex,&nbsp;list,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;numeric,&nbsp;or&nbsp;None<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;str&nbsp;or&nbsp;regex:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str:&nbsp;string&nbsp;exactly&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;`value`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;regex:&nbsp;regexs&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`value`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;list&nbsp;of&nbsp;str,&nbsp;regex,&nbsp;or&nbsp;numeric:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;First,&nbsp;if&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;both&nbsp;lists,&nbsp;they<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Second,&nbsp;if&nbsp;``regex=True``&nbsp;then&nbsp;all&nbsp;of&nbsp;the&nbsp;strings&nbsp;in&nbsp;**both**<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;regexs&nbsp;otherwise&nbsp;they&nbsp;will&nbsp;match<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;directly.&nbsp;This&nbsp;doesn't&nbsp;matter&nbsp;much&nbsp;for&nbsp;`value`&nbsp;since&nbsp;there<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;only&nbsp;a&nbsp;few&nbsp;possible&nbsp;substitution&nbsp;regexes&nbsp;you&nbsp;can&nbsp;use.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str&nbsp;and&nbsp;regex&nbsp;rules&nbsp;apply&nbsp;as&nbsp;above.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dict:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Nested&nbsp;dictionaries,&nbsp;e.g.,&nbsp;{'a':&nbsp;{'b':&nbsp;nan}},&nbsp;are&nbsp;read&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;follows:&nbsp;look&nbsp;in&nbsp;column&nbsp;'a'&nbsp;for&nbsp;the&nbsp;value&nbsp;'b'&nbsp;and&nbsp;replace&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;nan.&nbsp;You&nbsp;can&nbsp;nest&nbsp;regular&nbsp;expressions&nbsp;as&nbsp;well.&nbsp;Note&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;names&nbsp;(the&nbsp;top-level&nbsp;dictionary&nbsp;keys&nbsp;in&nbsp;a&nbsp;nested<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dictionary)&nbsp;**cannot**&nbsp;be&nbsp;regular&nbsp;expressions.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Keys&nbsp;map&nbsp;to&nbsp;column&nbsp;names&nbsp;and&nbsp;values&nbsp;map&nbsp;to&nbsp;substitution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;values.&nbsp;You&nbsp;can&nbsp;treat&nbsp;this&nbsp;as&nbsp;a&nbsp;special&nbsp;case&nbsp;of&nbsp;passing&nbsp;two<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;except&nbsp;that&nbsp;you&nbsp;are&nbsp;specifying&nbsp;the&nbsp;column&nbsp;to&nbsp;search&nbsp;in.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;None:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;This&nbsp;means&nbsp;that&nbsp;the&nbsp;``regex``&nbsp;argument&nbsp;must&nbsp;be&nbsp;a&nbsp;string,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;compiled&nbsp;regular&nbsp;expression,&nbsp;or&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;such&nbsp;elements.&nbsp;If&nbsp;`value`&nbsp;is&nbsp;also&nbsp;``None``&nbsp;then&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;a&nbsp;nested&nbsp;dictionary&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;examples&nbsp;section&nbsp;for&nbsp;examples&nbsp;of&nbsp;each&nbsp;of&nbsp;these.<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;list,&nbsp;str,&nbsp;regex,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a&nbsp;dict&nbsp;of&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for&nbsp;each&nbsp;column&nbsp;(columns&nbsp;not&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;Regular&nbsp;expressions,&nbsp;strings&nbsp;and&nbsp;lists&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dicts&nbsp;of&nbsp;such&nbsp;objects&nbsp;are&nbsp;also&nbsp;allowed.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>&nbsp;(e.g.&nbsp;a&nbsp;column&nbsp;form&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Returns&nbsp;the&nbsp;caller&nbsp;if&nbsp;this&nbsp;is&nbsp;True.<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;size&nbsp;gap&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-regex&nbsp;:&nbsp;bool&nbsp;or&nbsp;same&nbsp;types&nbsp;as&nbsp;`to_replace`,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;interpret&nbsp;`to_replace`&nbsp;and/or&nbsp;`value`&nbsp;as&nbsp;regular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;expressions.&nbsp;If&nbsp;this&nbsp;is&nbsp;``True``&nbsp;then&nbsp;`to_replace`&nbsp;*must*&nbsp;be&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string.&nbsp;Otherwise,&nbsp;`to_replace`&nbsp;must&nbsp;be&nbsp;``None``&nbsp;because&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;parameter&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;regular&nbsp;expression&nbsp;or&nbsp;a&nbsp;list,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict,&nbsp;or&nbsp;array&nbsp;of&nbsp;regular&nbsp;expressions.<br>
-method&nbsp;:&nbsp;string,&nbsp;optional,&nbsp;{'pad',&nbsp;'ffill',&nbsp;'bfill'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;method&nbsp;to&nbsp;use&nbsp;when&nbsp;for&nbsp;replacement,&nbsp;when&nbsp;``to_replace``&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``list``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-NDFrame.reindex<br>
-NDFrame.asfreq<br>
-NDFrame.fillna<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-AssertionError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;a&nbsp;``bool``&nbsp;and&nbsp;`to_replace`&nbsp;is&nbsp;not&nbsp;``None``.<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;a&nbsp;``dict``&nbsp;and&nbsp;`value`&nbsp;is&nbsp;not&nbsp;a&nbsp;``list``,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``dict``,&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``,&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;``None``&nbsp;and&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;compilable&nbsp;into&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;regular&nbsp;expression&nbsp;or&nbsp;is&nbsp;a&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;``list``&nbsp;s&nbsp;or&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``&nbsp;s,&nbsp;but<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;they&nbsp;are&nbsp;not&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-*&nbsp;Regex&nbsp;substitution&nbsp;is&nbsp;performed&nbsp;under&nbsp;the&nbsp;hood&nbsp;with&nbsp;``re.sub``.&nbsp;The<br>
-&nbsp;&nbsp;rules&nbsp;for&nbsp;substitution&nbsp;for&nbsp;``re.sub``&nbsp;are&nbsp;the&nbsp;same.<br>
-*&nbsp;Regular&nbsp;expressions&nbsp;will&nbsp;only&nbsp;substitute&nbsp;on&nbsp;strings,&nbsp;meaning&nbsp;you<br>
-&nbsp;&nbsp;cannot&nbsp;provide,&nbsp;for&nbsp;example,&nbsp;a&nbsp;regular&nbsp;expression&nbsp;matching&nbsp;floating<br>
-&nbsp;&nbsp;point&nbsp;numbers&nbsp;and&nbsp;expect&nbsp;the&nbsp;columns&nbsp;in&nbsp;your&nbsp;frame&nbsp;that&nbsp;have&nbsp;a<br>
-&nbsp;&nbsp;numeric&nbsp;dtype&nbsp;to&nbsp;be&nbsp;matched.&nbsp;However,&nbsp;if&nbsp;those&nbsp;floating&nbsp;point&nbsp;numbers<br>
-&nbsp;&nbsp;*are*&nbsp;strings,&nbsp;then&nbsp;you&nbsp;can&nbsp;do&nbsp;this.<br>
-*&nbsp;This&nbsp;method&nbsp;has&nbsp;*a&nbsp;lot*&nbsp;of&nbsp;options.&nbsp;You&nbsp;are&nbsp;encouraged&nbsp;to&nbsp;experiment<br>
-&nbsp;&nbsp;and&nbsp;play&nbsp;with&nbsp;this&nbsp;method&nbsp;to&nbsp;gain&nbsp;intuition&nbsp;about&nbsp;how&nbsp;it&nbsp;works.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-resample"><strong>resample</strong></a>(self, rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0, on=None, level=None)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;frequency&nbsp;conversion&nbsp;and&nbsp;resampling&nbsp;of&nbsp;time<br>
-series.&nbsp;&nbsp;Object&nbsp;must&nbsp;have&nbsp;a&nbsp;datetime-like&nbsp;index&nbsp;(DatetimeIndex,<br>
-PeriodIndex,&nbsp;or&nbsp;TimedeltaIndex),&nbsp;or&nbsp;pass&nbsp;datetime-like&nbsp;values<br>
-to&nbsp;the&nbsp;on&nbsp;or&nbsp;level&nbsp;keyword.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-rule&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;offset&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a>&nbsp;representing&nbsp;target&nbsp;conversion<br>
-axis&nbsp;:&nbsp;int,&nbsp;optional,&nbsp;default&nbsp;0<br>
-closed&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;side&nbsp;of&nbsp;bin&nbsp;interval&nbsp;is&nbsp;closed<br>
-label&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;bin&nbsp;edge&nbsp;label&nbsp;to&nbsp;label&nbsp;bucket&nbsp;with<br>
-convention&nbsp;:&nbsp;{'start',&nbsp;'end',&nbsp;'s',&nbsp;'e'}<br>
-loffset&nbsp;:&nbsp;timedelta<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Adjust&nbsp;the&nbsp;resampled&nbsp;time&nbsp;labels<br>
-base&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;frequencies&nbsp;that&nbsp;evenly&nbsp;subdivide&nbsp;1&nbsp;day,&nbsp;the&nbsp;"origin"&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aggregated&nbsp;intervals.&nbsp;For&nbsp;example,&nbsp;for&nbsp;'5min'&nbsp;frequency,&nbsp;base&nbsp;could<br>
-&nbsp;&nbsp;&nbsp;&nbsp;range&nbsp;from&nbsp;0&nbsp;through&nbsp;4.&nbsp;Defaults&nbsp;to&nbsp;0<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;to&nbsp;use&nbsp;instead&nbsp;of&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-level&nbsp;:&nbsp;string&nbsp;or&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;MultiIndex,&nbsp;level&nbsp;(name&nbsp;or&nbsp;number)&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;resampling.&nbsp;&nbsp;Level&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offset&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;9&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(9),&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;series<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:04:00&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-2000-01-01&nbsp;00:05:00&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:07:00&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-2000-01-01&nbsp;00:08:00&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-Freq:&nbsp;T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;and&nbsp;sum&nbsp;the&nbsp;values<br>
-of&nbsp;the&nbsp;timestamps&nbsp;falling&nbsp;into&nbsp;a&nbsp;bin.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MyDataFrame-resample">resample</a>('3T').<a href="#MyDataFrame-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;label&nbsp;each<br>
-bin&nbsp;using&nbsp;the&nbsp;right&nbsp;edge&nbsp;instead&nbsp;of&nbsp;the&nbsp;left.&nbsp;Please&nbsp;note&nbsp;that&nbsp;the<br>
-value&nbsp;in&nbsp;the&nbsp;bucket&nbsp;used&nbsp;as&nbsp;the&nbsp;label&nbsp;is&nbsp;not&nbsp;included&nbsp;in&nbsp;the&nbsp;bucket,<br>
-which&nbsp;it&nbsp;labels.&nbsp;For&nbsp;example,&nbsp;in&nbsp;the&nbsp;original&nbsp;series&nbsp;the<br>
-bucket&nbsp;``2000-01-01&nbsp;00:03:00``&nbsp;contains&nbsp;the&nbsp;value&nbsp;3,&nbsp;but&nbsp;the&nbsp;summed<br>
-value&nbsp;in&nbsp;the&nbsp;resampled&nbsp;bucket&nbsp;with&nbsp;the&nbsp;label``2000-01-01&nbsp;00:03:00``<br>
-does&nbsp;not&nbsp;include&nbsp;3&nbsp;(if&nbsp;it&nbsp;did,&nbsp;the&nbsp;summed&nbsp;value&nbsp;would&nbsp;be&nbsp;6,&nbsp;not&nbsp;3).<br>
-To&nbsp;include&nbsp;this&nbsp;value&nbsp;close&nbsp;the&nbsp;right&nbsp;side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval&nbsp;as<br>
-illustrated&nbsp;in&nbsp;the&nbsp;example&nbsp;below&nbsp;this&nbsp;one.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MyDataFrame-resample">resample</a>('3T',&nbsp;label='right').<a href="#MyDataFrame-sum">sum</a>()<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;close&nbsp;the&nbsp;right<br>
-side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MyDataFrame-resample">resample</a>('3T',&nbsp;label='right',&nbsp;closed='right').<a href="#MyDataFrame-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MyDataFrame-resample">resample</a>('30S').<a href="#MyDataFrame-asfreq">asfreq</a>()[0:5]&nbsp;#select&nbsp;first&nbsp;5&nbsp;rows<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;1.0<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;2.0<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values&nbsp;using&nbsp;the&nbsp;``pad``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MyDataFrame-resample">resample</a>('30S').pad()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the<br>
-``NaN``&nbsp;values&nbsp;using&nbsp;the&nbsp;``bfill``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MyDataFrame-resample">resample</a>('30S').<a href="#MyDataFrame-bfill">bfill</a>()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Pass&nbsp;a&nbsp;custom&nbsp;function&nbsp;via&nbsp;``apply``<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;custom_resampler(array_like):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;np.<a href="#MyDataFrame-sum">sum</a>(array_like)+5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MyDataFrame-resample">resample</a>('3T').<a href="#MyDataFrame-apply">apply</a>(custom_resampler)<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;17<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;26<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects,&nbsp;the&nbsp;keyword&nbsp;``on``&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;specify&nbsp;the<br>
-column&nbsp;instead&nbsp;of&nbsp;the&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=9*[range(4)],&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'])<br>
-&gt;&gt;&gt;&nbsp;df['time']&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-resample">resample</a>('3T',&nbsp;on='time').<a href="#MyDataFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;c&nbsp;&nbsp;d<br>
-time<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-&nbsp;<br>
-For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;MultiIndex,&nbsp;the&nbsp;keyword&nbsp;``level``&nbsp;can&nbsp;be&nbsp;used&nbsp;to<br>
-specify&nbsp;on&nbsp;level&nbsp;the&nbsp;resampling&nbsp;needs&nbsp;to&nbsp;take&nbsp;place.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;time&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=5,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=10*[range(4)],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.MultiIndex.from_product([time,&nbsp;[1,&nbsp;2]])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;)<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#MyDataFrame-resample">resample</a>('3T',&nbsp;level=0).<a href="#MyDataFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;d<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;6&nbsp;&nbsp;12&nbsp;&nbsp;18<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;8&nbsp;&nbsp;12</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-sample"><strong>sample</strong></a>(self, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None)</dt><dd><tt>Returns&nbsp;a&nbsp;random&nbsp;sample&nbsp;of&nbsp;items&nbsp;from&nbsp;an&nbsp;axis&nbsp;of&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;items&nbsp;from&nbsp;axis&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`frac`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;=&nbsp;1&nbsp;if&nbsp;`frac`&nbsp;=&nbsp;None.<br>
-frac&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fraction&nbsp;of&nbsp;axis&nbsp;items&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`n`.<br>
-replace&nbsp;:&nbsp;boolean,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sample&nbsp;with&nbsp;or&nbsp;without&nbsp;replacement.&nbsp;Default&nbsp;=&nbsp;False.<br>
-weights&nbsp;:&nbsp;str&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;'None'&nbsp;results&nbsp;in&nbsp;equal&nbsp;probability&nbsp;weighting.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;will&nbsp;align&nbsp;with&nbsp;target&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;index.&nbsp;Index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;in&nbsp;weights&nbsp;not&nbsp;found&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;will&nbsp;be&nbsp;ignored&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;values&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;not&nbsp;in&nbsp;weights&nbsp;will&nbsp;be&nbsp;assigned<br>
-&nbsp;&nbsp;&nbsp;&nbsp;weights&nbsp;of&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;called&nbsp;on&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;will&nbsp;accept&nbsp;the&nbsp;name&nbsp;of&nbsp;a&nbsp;column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;axis&nbsp;=&nbsp;0.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Unless&nbsp;weights&nbsp;are&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;weights&nbsp;must&nbsp;be&nbsp;same&nbsp;length&nbsp;as&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;being&nbsp;sampled.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;weights&nbsp;do&nbsp;not&nbsp;sum&nbsp;to&nbsp;1,&nbsp;they&nbsp;will&nbsp;be&nbsp;normalized&nbsp;to&nbsp;sum&nbsp;to&nbsp;1.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;values&nbsp;in&nbsp;the&nbsp;weights&nbsp;column&nbsp;will&nbsp;be&nbsp;treated&nbsp;as&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;inf&nbsp;and&nbsp;-inf&nbsp;values&nbsp;not&nbsp;allowed.<br>
-random_state&nbsp;:&nbsp;int&nbsp;or&nbsp;numpy.random.RandomState,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Seed&nbsp;for&nbsp;the&nbsp;random&nbsp;number&nbsp;generator&nbsp;(if&nbsp;int),&nbsp;or&nbsp;numpy&nbsp;RandomState<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;sample.&nbsp;Accepts&nbsp;axis&nbsp;number&nbsp;or&nbsp;name.&nbsp;Default&nbsp;is&nbsp;stat&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;given&nbsp;data&nbsp;type&nbsp;(0&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;and&nbsp;DataFrames,&nbsp;1&nbsp;for&nbsp;Panels).<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-A&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;type&nbsp;as&nbsp;caller.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Generate&nbsp;an&nbsp;example&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;and&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(50))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MyDataFrame-head">head</a>()<br>
-0&nbsp;&nbsp;&nbsp;-0.038497<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.820773<br>
-2&nbsp;&nbsp;&nbsp;-0.972766<br>
-3&nbsp;&nbsp;&nbsp;-1.598270<br>
-4&nbsp;&nbsp;&nbsp;-1.095526<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(50,&nbsp;4),&nbsp;columns=list('ABCD'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-head">head</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;0.016443&nbsp;-2.318952&nbsp;-0.566372&nbsp;-1.028078<br>
-1&nbsp;-1.051921&nbsp;&nbsp;0.438836&nbsp;&nbsp;0.658280&nbsp;-0.175797<br>
-2&nbsp;-1.243569&nbsp;-0.364626&nbsp;-0.215065&nbsp;&nbsp;0.057736<br>
-3&nbsp;&nbsp;1.768216&nbsp;&nbsp;0.404512&nbsp;-0.385604&nbsp;-1.457834<br>
-4&nbsp;&nbsp;1.072446&nbsp;-1.137172&nbsp;&nbsp;0.314194&nbsp;-0.046661<br>
-&nbsp;<br>
-Next&nbsp;extract&nbsp;a&nbsp;random&nbsp;sample&nbsp;from&nbsp;both&nbsp;of&nbsp;these&nbsp;objects...<br>
-&nbsp;<br>
-3&nbsp;random&nbsp;elements&nbsp;from&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MyDataFrame-sample">sample</a>(n=3)<br>
-27&nbsp;&nbsp;&nbsp;-0.994689<br>
-55&nbsp;&nbsp;&nbsp;-1.049016<br>
-67&nbsp;&nbsp;&nbsp;-0.224565<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-And&nbsp;a&nbsp;random&nbsp;10%&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;with&nbsp;replacement:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-sample">sample</a>(frac=0.1,&nbsp;replace=True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-35&nbsp;&nbsp;1.981780&nbsp;&nbsp;0.142106&nbsp;&nbsp;1.817165&nbsp;-0.290805<br>
-49&nbsp;-1.336199&nbsp;-0.448634&nbsp;-0.789640&nbsp;&nbsp;0.217116<br>
-40&nbsp;&nbsp;0.823173&nbsp;-0.078816&nbsp;&nbsp;1.009536&nbsp;&nbsp;1.015108<br>
-15&nbsp;&nbsp;1.421154&nbsp;-0.055301&nbsp;-1.922594&nbsp;-0.019696<br>
-6&nbsp;&nbsp;-0.148339&nbsp;&nbsp;0.832938&nbsp;&nbsp;1.787600&nbsp;-1.383767</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-select"><strong>select</strong></a>(self, crit, axis=0)</dt><dd><tt>Return&nbsp;data&nbsp;corresponding&nbsp;to&nbsp;axis&nbsp;labels&nbsp;matching&nbsp;criteria<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-crit&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;called&nbsp;on&nbsp;each&nbsp;index&nbsp;(label).&nbsp;Should&nbsp;return&nbsp;True&nbsp;or&nbsp;False<br>
-axis&nbsp;:&nbsp;int<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-selection&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-set_axis"><strong>set_axis</strong></a>(self, axis, labels)</dt><dd><tt>public&nbsp;verson&nbsp;of&nbsp;axis&nbsp;assignment</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-slice_shift"><strong>slice_shift</strong></a>(self, periods=1, axis=0)</dt><dd><tt>Equivalent&nbsp;to&nbsp;`shift`&nbsp;without&nbsp;copying&nbsp;data.&nbsp;The&nbsp;shifted&nbsp;data&nbsp;will<br>
-not&nbsp;include&nbsp;the&nbsp;dropped&nbsp;periods&nbsp;and&nbsp;the&nbsp;shifted&nbsp;axis&nbsp;will&nbsp;be&nbsp;smaller<br>
-than&nbsp;the&nbsp;original.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-&nbsp;<br>
-Notes<br>
------<br>
-While&nbsp;the&nbsp;`slice_shift`&nbsp;is&nbsp;faster&nbsp;than&nbsp;`shift`,&nbsp;you&nbsp;may&nbsp;pay&nbsp;for&nbsp;it<br>
-later&nbsp;during&nbsp;alignment.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-squeeze"><strong>squeeze</strong></a>(self, axis=None)</dt><dd><tt>Squeeze&nbsp;length&nbsp;1&nbsp;dimensions.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;None,&nbsp;integer&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;squeeze&nbsp;if&nbsp;1-sized.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-scalar&nbsp;if&nbsp;1-sized,&nbsp;else&nbsp;original&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-swapaxes"><strong>swapaxes</strong></a>(self, axis1, axis2, copy=True)</dt><dd><tt>Interchange&nbsp;axes&nbsp;and&nbsp;swap&nbsp;values&nbsp;axes&nbsp;appropriately<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-tail"><strong>tail</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;last&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-take"><strong>take</strong></a>(self, indices, axis=0, convert=True, is_copy=True, **kwargs)</dt><dd><tt>Analogous&nbsp;to&nbsp;<a href="numpy.html#ndarray">ndarray</a>.take<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-indices&nbsp;:&nbsp;list&nbsp;/&nbsp;array&nbsp;of&nbsp;ints<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-convert&nbsp;:&nbsp;translate&nbsp;neg&nbsp;to&nbsp;pos&nbsp;indices&nbsp;(default)<br>
-is_copy&nbsp;:&nbsp;mark&nbsp;the&nbsp;returned&nbsp;frame&nbsp;as&nbsp;a&nbsp;copy<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-taken&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_clipboard"><strong>to_clipboard</strong></a>(self, excel=None, sep=None, **kwargs)</dt><dd><tt>Attempt&nbsp;to&nbsp;write&nbsp;text&nbsp;representation&nbsp;of&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;the&nbsp;system&nbsp;clipboard<br>
-This&nbsp;can&nbsp;be&nbsp;pasted&nbsp;into&nbsp;Excel,&nbsp;for&nbsp;example.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel&nbsp;:&nbsp;boolean,&nbsp;defaults&nbsp;to&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;use&nbsp;the&nbsp;provided&nbsp;separator,&nbsp;writing&nbsp;in&nbsp;a&nbsp;csv<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;format&nbsp;for&nbsp;allowing&nbsp;easy&nbsp;pasting&nbsp;into&nbsp;excel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;False,&nbsp;write&nbsp;a&nbsp;string&nbsp;representation&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;the&nbsp;clipboard<br>
-sep&nbsp;:&nbsp;optional,&nbsp;defaults&nbsp;to&nbsp;tab<br>
-other&nbsp;keywords&nbsp;are&nbsp;passed&nbsp;to&nbsp;to_csv<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Requirements&nbsp;for&nbsp;your&nbsp;platform<br>
-&nbsp;&nbsp;-&nbsp;Linux:&nbsp;xclip,&nbsp;or&nbsp;xsel&nbsp;(with&nbsp;gtk&nbsp;or&nbsp;PyQt4&nbsp;modules)<br>
-&nbsp;&nbsp;-&nbsp;Windows:&nbsp;none<br>
-&nbsp;&nbsp;-&nbsp;OS&nbsp;X:&nbsp;none</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_dense"><strong>to_dense</strong></a>(self)</dt><dd><tt>Return&nbsp;dense&nbsp;representation&nbsp;of&nbsp;NDFrame&nbsp;(as&nbsp;opposed&nbsp;to&nbsp;sparse)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_hdf"><strong>to_hdf</strong></a>(self, path_or_buf, key, **kwargs)</dt><dd><tt>Write&nbsp;the&nbsp;contained&nbsp;data&nbsp;to&nbsp;an&nbsp;HDF5&nbsp;file&nbsp;using&nbsp;HDFStore.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;(string)&nbsp;or&nbsp;HDFStore&nbsp;<a href="builtins.html#object">object</a><br>
-key&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;identifier&nbsp;for&nbsp;the&nbsp;group&nbsp;in&nbsp;the&nbsp;store<br>
-mode&nbsp;:&nbsp;optional,&nbsp;{'a',&nbsp;'w',&nbsp;'r+'},&nbsp;default&nbsp;'a'<br>
-&nbsp;<br>
-&nbsp;&nbsp;``'w'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write;&nbsp;a&nbsp;new&nbsp;file&nbsp;is&nbsp;created&nbsp;(an&nbsp;existing&nbsp;file&nbsp;with&nbsp;the&nbsp;same<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;name&nbsp;would&nbsp;be&nbsp;deleted).<br>
-&nbsp;&nbsp;``'a'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Append;&nbsp;an&nbsp;existing&nbsp;file&nbsp;is&nbsp;opened&nbsp;for&nbsp;reading&nbsp;and&nbsp;writing,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;if&nbsp;the&nbsp;file&nbsp;does&nbsp;not&nbsp;exist&nbsp;it&nbsp;is&nbsp;created.<br>
-&nbsp;&nbsp;``'r+'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;It&nbsp;is&nbsp;similar&nbsp;to&nbsp;``'a'``,&nbsp;but&nbsp;the&nbsp;file&nbsp;must&nbsp;already&nbsp;exist.<br>
-format&nbsp;:&nbsp;'fixed(f)|table(t)',&nbsp;default&nbsp;is&nbsp;'fixed'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fixed(f)&nbsp;:&nbsp;Fixed&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Fast&nbsp;writing/reading.&nbsp;Not-appendable,&nbsp;nor&nbsp;searchable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;table(t)&nbsp;:&nbsp;Table&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;as&nbsp;a&nbsp;PyTables&nbsp;Table&nbsp;structure&nbsp;which&nbsp;may&nbsp;perform<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;worse&nbsp;but&nbsp;allow&nbsp;more&nbsp;flexible&nbsp;operations&nbsp;like&nbsp;searching<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/&nbsp;selecting&nbsp;subsets&nbsp;of&nbsp;the&nbsp;data<br>
-append&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;Table&nbsp;formats,&nbsp;append&nbsp;the&nbsp;input&nbsp;data&nbsp;to&nbsp;the&nbsp;existing<br>
-data_columns&nbsp;:&nbsp;&nbsp;list&nbsp;of&nbsp;columns,&nbsp;or&nbsp;True,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;columns&nbsp;to&nbsp;create&nbsp;as&nbsp;indexed&nbsp;data&nbsp;columns&nbsp;for&nbsp;on-disk<br>
-&nbsp;&nbsp;&nbsp;&nbsp;queries,&nbsp;or&nbsp;True&nbsp;to&nbsp;use&nbsp;all&nbsp;columns.&nbsp;By&nbsp;default&nbsp;only&nbsp;the&nbsp;axes<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;are&nbsp;indexed.&nbsp;See&nbsp;`here<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__">http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__</a>.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Applicable&nbsp;only&nbsp;to&nbsp;format='table'.<br>
-complevel&nbsp;:&nbsp;int,&nbsp;1-9,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;a&nbsp;complib&nbsp;is&nbsp;specified&nbsp;compression&nbsp;will&nbsp;be&nbsp;applied<br>
-&nbsp;&nbsp;&nbsp;&nbsp;where&nbsp;possible<br>
-complib&nbsp;:&nbsp;{'zlib',&nbsp;'bzip2',&nbsp;'lzo',&nbsp;'blosc',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;complevel&nbsp;is&nbsp;&gt;&nbsp;0&nbsp;apply&nbsp;compression&nbsp;to&nbsp;objects&nbsp;written<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;store&nbsp;wherever&nbsp;possible<br>
-fletcher32&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;applying&nbsp;compression&nbsp;use&nbsp;the&nbsp;fletcher32&nbsp;checksum<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;true,&nbsp;ALL&nbsp;nan&nbsp;rows&nbsp;will&nbsp;not&nbsp;be&nbsp;written&nbsp;to&nbsp;store.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_json"><strong>to_json</strong></a>(self, path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False)</dt><dd><tt>Convert&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;a&nbsp;JSON&nbsp;string.<br>
-&nbsp;<br>
-Note&nbsp;NaN's&nbsp;and&nbsp;None&nbsp;will&nbsp;be&nbsp;converted&nbsp;to&nbsp;null&nbsp;and&nbsp;datetime&nbsp;objects<br>
-will&nbsp;be&nbsp;converted&nbsp;to&nbsp;UNIX&nbsp;timestamps.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;or&nbsp;buffer&nbsp;to&nbsp;write&nbsp;the&nbsp;result&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;this&nbsp;is&nbsp;None,&nbsp;return&nbsp;a&nbsp;StringIO&nbsp;of&nbsp;the&nbsp;converted&nbsp;string<br>
-orient&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'index'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:&nbsp;{'split','records','index'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{'split','records','index','columns','values'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;The&nbsp;format&nbsp;of&nbsp;the&nbsp;JSON&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;split&nbsp;:&nbsp;dict&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{index&nbsp;-&gt;&nbsp;[index],&nbsp;columns&nbsp;-&gt;&nbsp;[columns],&nbsp;data&nbsp;-&gt;&nbsp;[values]}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;records&nbsp;:&nbsp;list&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[{column&nbsp;-&gt;&nbsp;value},&nbsp;...&nbsp;,&nbsp;{column&nbsp;-&gt;&nbsp;value}]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;index&nbsp;:&nbsp;dict&nbsp;like&nbsp;{index&nbsp;-&gt;&nbsp;{column&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;columns&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;{index&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;values&nbsp;:&nbsp;just&nbsp;the&nbsp;values&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;table&nbsp;:&nbsp;dict&nbsp;like&nbsp;{'schema':&nbsp;{schema},&nbsp;'data':&nbsp;{data}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;describing&nbsp;the&nbsp;data,&nbsp;and&nbsp;the&nbsp;data&nbsp;component&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;like&nbsp;``orient='records'``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionchanged::&nbsp;0.20.0<br>
-&nbsp;<br>
-date_format&nbsp;:&nbsp;{None,&nbsp;'epoch',&nbsp;'iso'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Type&nbsp;of&nbsp;date&nbsp;conversion.&nbsp;`epoch`&nbsp;=&nbsp;epoch&nbsp;milliseconds,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`iso`&nbsp;=&nbsp;ISO8601.&nbsp;The&nbsp;default&nbsp;depends&nbsp;on&nbsp;the&nbsp;`orient`.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`orient='table'`,&nbsp;the&nbsp;default&nbsp;is&nbsp;`'iso'`.&nbsp;For&nbsp;all&nbsp;other&nbsp;orients,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;default&nbsp;is&nbsp;`'epoch'`.<br>
-double_precision&nbsp;:&nbsp;The&nbsp;number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;use&nbsp;when&nbsp;encoding<br>
-&nbsp;&nbsp;&nbsp;&nbsp;floating&nbsp;point&nbsp;values,&nbsp;default&nbsp;10.<br>
-force_ascii&nbsp;:&nbsp;force&nbsp;encoded&nbsp;string&nbsp;to&nbsp;be&nbsp;ASCII,&nbsp;default&nbsp;True.<br>
-date_unit&nbsp;:&nbsp;string,&nbsp;default&nbsp;'ms'&nbsp;(milliseconds)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;time&nbsp;unit&nbsp;to&nbsp;encode&nbsp;to,&nbsp;governs&nbsp;timestamp&nbsp;and&nbsp;ISO8601<br>
-&nbsp;&nbsp;&nbsp;&nbsp;precision.&nbsp;&nbsp;One&nbsp;of&nbsp;'s',&nbsp;'ms',&nbsp;'us',&nbsp;'ns'&nbsp;for&nbsp;second,&nbsp;millisecond,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;microsecond,&nbsp;and&nbsp;nanosecond&nbsp;respectively.<br>
-default_handler&nbsp;:&nbsp;callable,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Handler&nbsp;to&nbsp;call&nbsp;if&nbsp;<a href="builtins.html#object">object</a>&nbsp;cannot&nbsp;otherwise&nbsp;be&nbsp;converted&nbsp;to&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;suitable&nbsp;format&nbsp;for&nbsp;JSON.&nbsp;Should&nbsp;receive&nbsp;a&nbsp;single&nbsp;argument&nbsp;which&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;convert&nbsp;and&nbsp;return&nbsp;a&nbsp;serialisable&nbsp;<a href="builtins.html#object">object</a>.<br>
-lines&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'orient'&nbsp;is&nbsp;'records'&nbsp;write&nbsp;out&nbsp;line&nbsp;delimited&nbsp;json&nbsp;format.&nbsp;Will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;throw&nbsp;ValueError&nbsp;if&nbsp;incorrect&nbsp;'orient'&nbsp;since&nbsp;others&nbsp;are&nbsp;not&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;filtered&nbsp;info&nbsp;axis<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pd.read_json<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([['a',&nbsp;'b'],&nbsp;['c',&nbsp;'d']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=['row&nbsp;1',&nbsp;'row&nbsp;2'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['col&nbsp;1',&nbsp;'col&nbsp;2'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-to_json">to_json</a>(orient='split')<br>
-'{"columns":["col&nbsp;1","col&nbsp;2"],<br>
-&nbsp;&nbsp;"index":["row&nbsp;1","row&nbsp;2"],<br>
-&nbsp;&nbsp;"data":[["a","b"],["c","d"]]}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'index'``&nbsp;formatted&nbsp;JSON:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-to_json">to_json</a>(orient='index')<br>
-'{"row&nbsp;1":{"col&nbsp;1":"a","col&nbsp;2":"b"},"row&nbsp;2":{"col&nbsp;1":"c","col&nbsp;2":"d"}}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'records'``&nbsp;formatted&nbsp;JSON.<br>
-Note&nbsp;that&nbsp;index&nbsp;labels&nbsp;are&nbsp;not&nbsp;preserved&nbsp;with&nbsp;this&nbsp;encoding.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-to_json">to_json</a>(orient='records')<br>
-'[{"col&nbsp;1":"a","col&nbsp;2":"b"},{"col&nbsp;1":"c","col&nbsp;2":"d"}]'<br>
-&nbsp;<br>
-Encoding&nbsp;with&nbsp;Table&nbsp;Schema<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-to_json">to_json</a>(orient='table')<br>
-'{"schema":&nbsp;{"fields":&nbsp;[{"name":&nbsp;"index",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;1",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;2",&nbsp;"type":&nbsp;"string"}],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"primaryKey":&nbsp;"index",<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"pandas_version":&nbsp;"0.20.0"},<br>
-&nbsp;&nbsp;"data":&nbsp;[{"index":&nbsp;"row&nbsp;1",&nbsp;"col&nbsp;1":&nbsp;"a",&nbsp;"col&nbsp;2":&nbsp;"b"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"index":&nbsp;"row&nbsp;2",&nbsp;"col&nbsp;1":&nbsp;"c",&nbsp;"col&nbsp;2":&nbsp;"d"}]}'</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_msgpack"><strong>to_msgpack</strong></a>(self, path_or_buf=None, encoding='utf-8', **kwargs)</dt><dd><tt>msgpack&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path<br>
-&nbsp;<br>
-THIS&nbsp;IS&nbsp;AN&nbsp;EXPERIMENTAL&nbsp;LIBRARY&nbsp;and&nbsp;the&nbsp;storage&nbsp;format<br>
-may&nbsp;not&nbsp;be&nbsp;stable&nbsp;until&nbsp;a&nbsp;future&nbsp;release.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;File&nbsp;path,&nbsp;buffer-like,&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;None,&nbsp;return&nbsp;generated&nbsp;string<br>
-append&nbsp;:&nbsp;boolean&nbsp;whether&nbsp;to&nbsp;append&nbsp;to&nbsp;an&nbsp;existing&nbsp;msgpack<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(default&nbsp;is&nbsp;False)<br>
-compress&nbsp;:&nbsp;type&nbsp;of&nbsp;compressor&nbsp;(zlib&nbsp;or&nbsp;blosc),&nbsp;default&nbsp;to&nbsp;None&nbsp;(no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;compression)</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_pickle"><strong>to_pickle</strong></a>(self, path, compression='infer')</dt><dd><tt>Pickle&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path<br>
-compression&nbsp;:&nbsp;{'infer',&nbsp;'gzip',&nbsp;'bz2',&nbsp;'xz',&nbsp;None},&nbsp;default&nbsp;'infer'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;compression&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_sql"><strong>to_sql</strong></a>(self, name, con, flavor=None, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None)</dt><dd><tt>Write&nbsp;records&nbsp;stored&nbsp;in&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;SQL&nbsp;database.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-name&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;SQL&nbsp;table<br>
-con&nbsp;:&nbsp;SQLAlchemy&nbsp;engine&nbsp;or&nbsp;DBAPI2&nbsp;connection&nbsp;(legacy&nbsp;mode)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Using&nbsp;SQLAlchemy&nbsp;makes&nbsp;it&nbsp;possible&nbsp;to&nbsp;use&nbsp;any&nbsp;DB&nbsp;supported&nbsp;by&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;library.&nbsp;If&nbsp;a&nbsp;DBAPI2&nbsp;<a href="builtins.html#object">object</a>,&nbsp;only&nbsp;sqlite3&nbsp;is&nbsp;supported.<br>
-flavor&nbsp;:&nbsp;'sqlite',&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;DEPRECATED:&nbsp;this&nbsp;parameter&nbsp;will&nbsp;be&nbsp;removed&nbsp;in&nbsp;a&nbsp;future&nbsp;version,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;as&nbsp;'sqlite'&nbsp;is&nbsp;the&nbsp;only&nbsp;supported&nbsp;option&nbsp;if&nbsp;SQLAlchemy&nbsp;is&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;installed.<br>
-schema&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;the&nbsp;schema&nbsp;(if&nbsp;database&nbsp;flavor&nbsp;supports&nbsp;this).&nbsp;If&nbsp;None,&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;schema.<br>
-if_exists&nbsp;:&nbsp;{'fail',&nbsp;'replace',&nbsp;'append'},&nbsp;default&nbsp;'fail'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;fail:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;do&nbsp;nothing.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;replace:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;drop&nbsp;it,&nbsp;recreate&nbsp;it,&nbsp;and&nbsp;insert&nbsp;data.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;append:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;insert&nbsp;data.&nbsp;Create&nbsp;if&nbsp;does&nbsp;not&nbsp;exist.<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;index&nbsp;as&nbsp;a&nbsp;column.<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s).&nbsp;If&nbsp;None&nbsp;is&nbsp;given&nbsp;(default)&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`index`&nbsp;is&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-chunksize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;not&nbsp;None,&nbsp;then&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;in&nbsp;batches&nbsp;of&nbsp;this&nbsp;size&nbsp;at&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;time.&nbsp;&nbsp;If&nbsp;None,&nbsp;all&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;at&nbsp;once.<br>
-dtype&nbsp;:&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;to&nbsp;SQL&nbsp;type,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Optional&nbsp;specifying&nbsp;the&nbsp;datatype&nbsp;for&nbsp;columns.&nbsp;The&nbsp;SQL&nbsp;type&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;SQLAlchemy&nbsp;type,&nbsp;or&nbsp;a&nbsp;string&nbsp;for&nbsp;sqlite3&nbsp;fallback&nbsp;connection.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-to_xarray"><strong>to_xarray</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;xarray&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;the&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;DataArray&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;Dataset&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-a&nbsp;DataArray&nbsp;for&nbsp;higher&nbsp;dims<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;foo&nbsp;&nbsp;4.0<br>
-1&nbsp;&nbsp;1&nbsp;&nbsp;bar&nbsp;&nbsp;5.0<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;foo&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(index:&nbsp;3)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;index&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;0&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;1&nbsp;1&nbsp;2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'foo'&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;float64&nbsp;4.0&nbsp;5.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;).<a href="#MyDataFrame-set_index">set_index</a>(['B','A'])<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-B&nbsp;&nbsp;&nbsp;A<br>
-foo&nbsp;1&nbsp;&nbsp;4.0<br>
-bar&nbsp;1&nbsp;&nbsp;5.0<br>
-foo&nbsp;2&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(A:&nbsp;2,&nbsp;B:&nbsp;2)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;*&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(A)&nbsp;int64&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B,&nbsp;A)&nbsp;float64&nbsp;5.0&nbsp;nan&nbsp;4.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p&nbsp;=&nbsp;pd.Panel(np.arange(24).reshape(4,3,2),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;items=list('ABCD'),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;major_axis=pd.date_range('20130101',&nbsp;periods=3),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;minor_axis=['first',&nbsp;'second'])<br>
-&gt;&gt;&gt;&nbsp;p<br>
-&lt;class&nbsp;'pandas.core.panel.Panel'&gt;<br>
-Dimensions:&nbsp;4&nbsp;(items)&nbsp;x&nbsp;3&nbsp;(major_axis)&nbsp;x&nbsp;2&nbsp;(minor_axis)<br>
-Items&nbsp;axis:&nbsp;A&nbsp;to&nbsp;D<br>
-Major_axis&nbsp;axis:&nbsp;2013-01-01&nbsp;00:00:00&nbsp;to&nbsp;2013-01-03&nbsp;00:00:00<br>
-Minor_axis&nbsp;axis:&nbsp;first&nbsp;to&nbsp;second<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p.<a href="#MyDataFrame-to_xarray">to_xarray</a>()<br>
-&lt;xarray.DataArray&nbsp;(items:&nbsp;4,&nbsp;major_axis:&nbsp;3,&nbsp;minor_axis:&nbsp;2)&gt;<br>
-array([[[&nbsp;0,&nbsp;&nbsp;1],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;2,&nbsp;&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;4,&nbsp;&nbsp;5]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[&nbsp;6,&nbsp;&nbsp;7],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;8,&nbsp;&nbsp;9],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[10,&nbsp;11]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[12,&nbsp;13],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[14,&nbsp;15],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[16,&nbsp;17]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[18,&nbsp;19],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[20,&nbsp;21],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[22,&nbsp;23]]])<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;items&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(items)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'A'&nbsp;'B'&nbsp;'C'&nbsp;'D'<br>
-&nbsp;&nbsp;*&nbsp;major_axis&nbsp;&nbsp;(major_axis)&nbsp;datetime64[ns]&nbsp;2013-01-01&nbsp;2013-01-02&nbsp;2013-01-03&nbsp;&nbsp;#&nbsp;noqa<br>
-&nbsp;&nbsp;*&nbsp;minor_axis&nbsp;&nbsp;(minor_axis)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'first'&nbsp;'second'<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;the&nbsp;`xarray&nbsp;docs&nbsp;&lt;<a href="http://xarray.pydata.org/en/stable/&gt;`__">http://xarray.pydata.org/en/stable/&gt;`__</a></tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-truncate"><strong>truncate</strong></a>(self, before=None, after=None, axis=None, copy=True)</dt><dd><tt>Truncates&nbsp;a&nbsp;sorted&nbsp;NDFrame&nbsp;before&nbsp;and/or&nbsp;after&nbsp;some&nbsp;particular<br>
-index&nbsp;value.&nbsp;If&nbsp;the&nbsp;axis&nbsp;contains&nbsp;only&nbsp;datetime&nbsp;values,&nbsp;before/after<br>
-parameters&nbsp;are&nbsp;converted&nbsp;to&nbsp;datetime&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-before&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;before&nbsp;index&nbsp;value<br>
-after&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;after&nbsp;index&nbsp;value<br>
-axis&nbsp;:&nbsp;the&nbsp;truncation&nbsp;axis,&nbsp;defaults&nbsp;to&nbsp;the&nbsp;stat&nbsp;axis<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;is&nbsp;True,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;truncated&nbsp;section<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-truncated&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-tshift"><strong>tshift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;the&nbsp;time&nbsp;index,&nbsp;using&nbsp;the&nbsp;index's&nbsp;frequency&nbsp;if&nbsp;available.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM')<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;basestring<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Corresponds&nbsp;to&nbsp;the&nbsp;axis&nbsp;that&nbsp;contains&nbsp;the&nbsp;Index<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;not&nbsp;specified&nbsp;then&nbsp;tries&nbsp;to&nbsp;use&nbsp;the&nbsp;freq&nbsp;or&nbsp;inferred_freq<br>
-attributes&nbsp;of&nbsp;the&nbsp;index.&nbsp;If&nbsp;neither&nbsp;of&nbsp;those&nbsp;attributes&nbsp;exist,&nbsp;a<br>
-ValueError&nbsp;is&nbsp;thrown<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;NDFrame</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-tz_convert"><strong>tz_convert</strong></a>(self, tz, axis=0, level=None, copy=True)</dt><dd><tt>Convert&nbsp;tz-aware&nbsp;axis&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;convert<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;convert&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;tz-naive.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-tz_localize"><strong>tz_localize</strong></a>(self, tz, axis=0, level=None, copy=True, ambiguous='raise')</dt><dd><tt>Localize&nbsp;tz-naive&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;localize<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;localize&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-ambiguous&nbsp;:&nbsp;'infer',&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>,&nbsp;'NaT',&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'infer'&nbsp;will&nbsp;attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>&nbsp;where&nbsp;True&nbsp;signifies&nbsp;a&nbsp;DST&nbsp;time,&nbsp;False&nbsp;designates<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;non-DST&nbsp;time&nbsp;(note&nbsp;that&nbsp;this&nbsp;flag&nbsp;is&nbsp;only&nbsp;applicable&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ambiguous&nbsp;times)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'NaT'&nbsp;will&nbsp;return&nbsp;NaT&nbsp;where&nbsp;there&nbsp;are&nbsp;ambiguous&nbsp;times<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'raise'&nbsp;will&nbsp;raise&nbsp;an&nbsp;AmbiguousTimeError&nbsp;if&nbsp;there&nbsp;are&nbsp;ambiguous<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;times<br>
-infer_dst&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on&nbsp;order<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;is&nbsp;tz-aware&nbsp;and&nbsp;tz&nbsp;is&nbsp;not&nbsp;None.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-where"><strong>where</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;True&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;where&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``True``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#MyDataFrame-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#MyDataFrame-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``where``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MyDataFrame-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).reshape(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#MyDataFrame-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#MyDataFrame-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mask`</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-xs"><strong>xs</strong></a>(self, key, axis=0, level=None, drop_level=True)</dt><dd><tt>Returns&nbsp;a&nbsp;cross-section&nbsp;(row(s)&nbsp;or&nbsp;column(s))&nbsp;from&nbsp;the<br>
-<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Defaults&nbsp;to&nbsp;cross-section&nbsp;on&nbsp;the&nbsp;rows&nbsp;(axis=0).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Some&nbsp;label&nbsp;contained&nbsp;in&nbsp;the&nbsp;index,&nbsp;or&nbsp;partially&nbsp;in&nbsp;a&nbsp;MultiIndex<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;retrieve&nbsp;cross-section&nbsp;on<br>
-level&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;defaults&nbsp;to&nbsp;first&nbsp;n&nbsp;levels&nbsp;(n=1&nbsp;or&nbsp;len(key))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;key&nbsp;partially&nbsp;contained&nbsp;in&nbsp;a&nbsp;MultiIndex,&nbsp;indicate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;levels&nbsp;are&nbsp;used.&nbsp;Levels&nbsp;can&nbsp;be&nbsp;referred&nbsp;by&nbsp;label&nbsp;or&nbsp;position.<br>
-drop_level&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;returns&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;same&nbsp;levels&nbsp;as&nbsp;self.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C<br>
-a&nbsp;&nbsp;4&nbsp;&nbsp;5&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;4&nbsp;&nbsp;0&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;9&nbsp;&nbsp;7&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-xs">xs</a>('a')<br>
-A&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-B&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-C&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Name:&nbsp;a<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-xs">xs</a>('C',&nbsp;axis=1)<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;C<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;second&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7&nbsp;&nbsp;5&nbsp;&nbsp;5&nbsp;&nbsp;0<br>
-baz&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;three&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-xs">xs</a>(('baz',&nbsp;'three'))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-third<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-xs">xs</a>('one',&nbsp;level=1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-baz&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MyDataFrame-xs">xs</a>(('baz',&nbsp;2),&nbsp;level=[0,&nbsp;'third'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-second<br>
-three&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-xs&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-xs&nbsp;is&nbsp;only&nbsp;for&nbsp;getting,&nbsp;not&nbsp;setting&nbsp;values.<br>
-&nbsp;<br>
-MultiIndex&nbsp;Slicers&nbsp;is&nbsp;a&nbsp;generic&nbsp;way&nbsp;to&nbsp;get/set&nbsp;values&nbsp;on&nbsp;any&nbsp;level&nbsp;or<br>
-levels.&nbsp;&nbsp;It&nbsp;is&nbsp;a&nbsp;superset&nbsp;of&nbsp;xs&nbsp;functionality,&nbsp;see<br>
-:ref:`MultiIndex&nbsp;Slicers&nbsp;&lt;advanced.mi_slicers&gt;`</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>at</strong></dt>
-<dd><tt>Fast&nbsp;label-based&nbsp;scalar&nbsp;accessor<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``loc``,&nbsp;``at``&nbsp;provides&nbsp;**label**&nbsp;based&nbsp;scalar&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>blocks</strong></dt>
-<dd><tt>Internal&nbsp;property,&nbsp;property&nbsp;synonym&nbsp;for&nbsp;as_blocks()</tt></dd>
-</dl>
-<dl><dt><strong>dtypes</strong></dt>
-<dd><tt>Return&nbsp;the&nbsp;dtypes&nbsp;in&nbsp;this&nbsp;object.</tt></dd>
-</dl>
-<dl><dt><strong>empty</strong></dt>
-<dd><tt>True&nbsp;if&nbsp;NDFrame&nbsp;is&nbsp;entirely&nbsp;empty&nbsp;[no&nbsp;items],&nbsp;meaning&nbsp;any&nbsp;of&nbsp;the<br>
-axes&nbsp;are&nbsp;of&nbsp;length&nbsp;0.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;NDFrame&nbsp;contains&nbsp;only&nbsp;NaNs,&nbsp;it&nbsp;is&nbsp;still&nbsp;not&nbsp;considered&nbsp;empty.&nbsp;See<br>
-the&nbsp;example&nbsp;below.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-An&nbsp;example&nbsp;of&nbsp;an&nbsp;actual&nbsp;empty&nbsp;DataFrame.&nbsp;Notice&nbsp;the&nbsp;index&nbsp;is&nbsp;empty:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df_empty&nbsp;=&nbsp;pd.DataFrame({'A'&nbsp;:&nbsp;[]})<br>
-&gt;&gt;&gt;&nbsp;df_empty<br>
-Empty&nbsp;DataFrame<br>
-Columns:&nbsp;[A]<br>
-Index:&nbsp;[]<br>
-&gt;&gt;&gt;&nbsp;df_empty.empty<br>
-True<br>
-&nbsp;<br>
-If&nbsp;we&nbsp;only&nbsp;have&nbsp;NaNs&nbsp;in&nbsp;our&nbsp;DataFrame,&nbsp;it&nbsp;is&nbsp;not&nbsp;considered&nbsp;empty!&nbsp;We<br>
-will&nbsp;need&nbsp;to&nbsp;drop&nbsp;the&nbsp;NaNs&nbsp;to&nbsp;make&nbsp;the&nbsp;DataFrame&nbsp;empty:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.DataFrame({'A'&nbsp;:&nbsp;[np.nan]})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A<br>
-0&nbsp;NaN<br>
-&gt;&gt;&gt;&nbsp;df.empty<br>
-False<br>
-&gt;&gt;&gt;&nbsp;df.dropna().empty<br>
-True<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.Series.dropna<br>
-pandas.DataFrame.dropna</tt></dd>
-</dl>
-<dl><dt><strong>ftypes</strong></dt>
-<dd><tt>Return&nbsp;the&nbsp;ftypes&nbsp;(indication&nbsp;of&nbsp;sparse/dense&nbsp;and&nbsp;dtype)<br>
-in&nbsp;this&nbsp;object.</tt></dd>
-</dl>
-<dl><dt><strong>iat</strong></dt>
-<dd><tt>Fast&nbsp;integer&nbsp;location&nbsp;scalar&nbsp;accessor.<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``iloc``,&nbsp;``iat``&nbsp;provides&nbsp;**integer**&nbsp;based&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>iloc</strong></dt>
-<dd><tt>Purely&nbsp;integer-location&nbsp;based&nbsp;indexing&nbsp;for&nbsp;selection&nbsp;by&nbsp;position.<br>
-&nbsp;<br>
-``.iloc[]``&nbsp;is&nbsp;primarily&nbsp;integer&nbsp;position&nbsp;based&nbsp;(from&nbsp;``0``&nbsp;to<br>
-``length-1``&nbsp;of&nbsp;the&nbsp;axis),&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a&nbsp;boolean<br>
-array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;An&nbsp;integer,&nbsp;e.g.&nbsp;``5``.<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;integers,&nbsp;e.g.&nbsp;``[4,&nbsp;3,&nbsp;0]``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;ints,&nbsp;e.g.&nbsp;``1:7``.<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.iloc``&nbsp;will&nbsp;raise&nbsp;``IndexError``&nbsp;if&nbsp;a&nbsp;requested&nbsp;indexer&nbsp;is<br>
-out-of-bounds,&nbsp;except&nbsp;*slice*&nbsp;indexers&nbsp;which&nbsp;allow&nbsp;out-of-bounds<br>
-indexing&nbsp;(this&nbsp;conforms&nbsp;with&nbsp;python/numpy&nbsp;*slice*&nbsp;semantics).<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Position&nbsp;&lt;indexing.integer&gt;`</tt></dd>
-</dl>
-<dl><dt><strong>ix</strong></dt>
-<dd><tt>A&nbsp;primarily&nbsp;label-location&nbsp;based&nbsp;indexer,&nbsp;with&nbsp;integer&nbsp;position<br>
-fallback.<br>
-&nbsp;<br>
-``.ix[]``&nbsp;supports&nbsp;mixed&nbsp;integer&nbsp;and&nbsp;label&nbsp;based&nbsp;access.&nbsp;It&nbsp;is<br>
-primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;will&nbsp;fall&nbsp;back&nbsp;to&nbsp;integer&nbsp;positional<br>
-access&nbsp;unless&nbsp;the&nbsp;corresponding&nbsp;axis&nbsp;is&nbsp;of&nbsp;integer&nbsp;type.<br>
-&nbsp;<br>
-``.ix``&nbsp;is&nbsp;the&nbsp;most&nbsp;general&nbsp;indexer&nbsp;and&nbsp;will&nbsp;support&nbsp;any&nbsp;of&nbsp;the<br>
-inputs&nbsp;in&nbsp;``.loc``&nbsp;and&nbsp;``.iloc``.&nbsp;``.ix``&nbsp;also&nbsp;supports&nbsp;floating<br>
-point&nbsp;label&nbsp;schemes.&nbsp;``.ix``&nbsp;is&nbsp;exceptionally&nbsp;useful&nbsp;when&nbsp;dealing<br>
-with&nbsp;mixed&nbsp;positional&nbsp;and&nbsp;label&nbsp;based&nbsp;hierachical&nbsp;indexes.<br>
-&nbsp;<br>
-However,&nbsp;when&nbsp;an&nbsp;axis&nbsp;is&nbsp;integer&nbsp;based,&nbsp;ONLY&nbsp;label&nbsp;based&nbsp;access<br>
-and&nbsp;not&nbsp;positional&nbsp;access&nbsp;is&nbsp;supported.&nbsp;Thus,&nbsp;in&nbsp;such&nbsp;cases,&nbsp;it's<br>
-usually&nbsp;better&nbsp;to&nbsp;be&nbsp;explicit&nbsp;and&nbsp;use&nbsp;``.iloc``&nbsp;or&nbsp;``.loc``.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Advanced&nbsp;Indexing&nbsp;&lt;advanced&gt;`.</tt></dd>
-</dl>
-<dl><dt><strong>loc</strong></dt>
-<dd><tt>Purely&nbsp;label-location&nbsp;based&nbsp;indexer&nbsp;for&nbsp;selection&nbsp;by&nbsp;label.<br>
-&nbsp;<br>
-``.loc[]``&nbsp;is&nbsp;primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a<br>
-boolean&nbsp;array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;A&nbsp;single&nbsp;label,&nbsp;e.g.&nbsp;``5``&nbsp;or&nbsp;``'a'``,&nbsp;(note&nbsp;that&nbsp;``5``&nbsp;is<br>
-&nbsp;&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;*label*&nbsp;of&nbsp;the&nbsp;index,&nbsp;and&nbsp;**never**&nbsp;as&nbsp;an<br>
-&nbsp;&nbsp;integer&nbsp;position&nbsp;along&nbsp;the&nbsp;index).<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;labels,&nbsp;e.g.&nbsp;``['a',&nbsp;'b',&nbsp;'c']``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;labels,&nbsp;e.g.&nbsp;``'a':'f'``&nbsp;(note&nbsp;that&nbsp;contrary<br>
-&nbsp;&nbsp;to&nbsp;usual&nbsp;python&nbsp;slices,&nbsp;**both**&nbsp;the&nbsp;start&nbsp;and&nbsp;the&nbsp;stop&nbsp;are&nbsp;included!).<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.loc``&nbsp;will&nbsp;raise&nbsp;a&nbsp;``KeyError``&nbsp;when&nbsp;the&nbsp;items&nbsp;are&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Label&nbsp;&lt;indexing.label&gt;`</tt></dd>
-</dl>
-<dl><dt><strong>ndim</strong></dt>
-<dd><tt>Number&nbsp;of&nbsp;axes&nbsp;/&nbsp;array&nbsp;dimensions</tt></dd>
-</dl>
-<dl><dt><strong>size</strong></dt>
-<dd><tt>number&nbsp;of&nbsp;elements&nbsp;in&nbsp;the&nbsp;NDFrame</tt></dd>
-</dl>
-<dl><dt><strong>values</strong></dt>
-<dd><tt>Numpy&nbsp;representation&nbsp;of&nbsp;NDFrame<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;dtype&nbsp;will&nbsp;be&nbsp;a&nbsp;lower-common-denominator&nbsp;dtype&nbsp;(implicit<br>
-upcasting);&nbsp;that&nbsp;is&nbsp;to&nbsp;say&nbsp;if&nbsp;the&nbsp;dtypes&nbsp;(even&nbsp;of&nbsp;numeric&nbsp;types)<br>
-are&nbsp;mixed,&nbsp;the&nbsp;one&nbsp;that&nbsp;accommodates&nbsp;all&nbsp;will&nbsp;be&nbsp;chosen.&nbsp;Use&nbsp;this<br>
-with&nbsp;care&nbsp;if&nbsp;you&nbsp;are&nbsp;not&nbsp;dealing&nbsp;with&nbsp;the&nbsp;blocks.<br>
-&nbsp;<br>
-e.g.&nbsp;If&nbsp;the&nbsp;dtypes&nbsp;are&nbsp;float16&nbsp;and&nbsp;float32,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcast&nbsp;to<br>
-float32.&nbsp;&nbsp;If&nbsp;dtypes&nbsp;are&nbsp;int32&nbsp;and&nbsp;uint8,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcast&nbsp;to<br>
-int32.&nbsp;By&nbsp;numpy.find_common_type&nbsp;convention,&nbsp;mixing&nbsp;int64&nbsp;and&nbsp;uint64<br>
-will&nbsp;result&nbsp;in&nbsp;a&nbsp;flot64&nbsp;dtype.</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>is_copy</strong> = None</dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a>:<br>
-<dl><dt><a name="MyDataFrame-__dir__"><strong>__dir__</strong></a>(self)</dt><dd><tt>Provide&nbsp;method&nbsp;name&nbsp;lookup&nbsp;and&nbsp;completion<br>
-Only&nbsp;provide&nbsp;'public'&nbsp;methods</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__sizeof__"><strong>__sizeof__</strong></a>(self)</dt><dd><tt>Generates&nbsp;the&nbsp;total&nbsp;memory&nbsp;usage&nbsp;for&nbsp;a&nbsp;<a href="builtins.html#object">object</a>&nbsp;that&nbsp;returns<br>
-either&nbsp;a&nbsp;value&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;values</tt></dd></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a>:<br>
-<dl><dt><a name="MyDataFrame-__bytes__"><strong>__bytes__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;bytes(obj)&nbsp;in&nbsp;py3&nbsp;only.<br>
-Yields&nbsp;a&nbsp;bytestring&nbsp;in&nbsp;both&nbsp;py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__repr__"><strong>__repr__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-<dl><dt><a name="MyDataFrame-__str__"><strong>__str__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;Object<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;str(df)&nbsp;in&nbsp;both&nbsp;py2/py3.<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a>:<br>
-<dl><dt><strong>__dict__</strong></dt>
-<dd><tt>dictionary&nbsp;for&nbsp;instance&nbsp;variables&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>__weakref__</strong></dt>
-<dd><tt>list&nbsp;of&nbsp;weak&nbsp;references&nbsp;to&nbsp;the&nbsp;object&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-</td></tr></table> <p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#ffc8d8">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#000000" face="helvetica, arial"><a name="MySeries">class <strong>MySeries</strong></a>(<a href="pandas.core.series.html#Series">pandas.core.series.Series</a>)</font></td></tr>
-    
-<tr bgcolor="#ffc8d8"><td rowspan=2><tt>&nbsp;&nbsp;&nbsp;</tt></td>
-<td colspan=2><tt>One-dimensional&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;with&nbsp;axis&nbsp;labels&nbsp;(including&nbsp;time&nbsp;series).<br>
-&nbsp;<br>
-Labels&nbsp;need&nbsp;not&nbsp;be&nbsp;unique&nbsp;but&nbsp;must&nbsp;be&nbsp;a&nbsp;hashable&nbsp;type.&nbsp;The&nbsp;<a href="builtins.html#object">object</a><br>
-supports&nbsp;both&nbsp;integer-&nbsp;and&nbsp;label-based&nbsp;indexing&nbsp;and&nbsp;provides&nbsp;a&nbsp;host&nbsp;of<br>
-methods&nbsp;for&nbsp;performing&nbsp;operations&nbsp;involving&nbsp;the&nbsp;index.&nbsp;Statistical<br>
-methods&nbsp;from&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;have&nbsp;been&nbsp;overridden&nbsp;to&nbsp;automatically&nbsp;exclude<br>
-missing&nbsp;data&nbsp;(currently&nbsp;represented&nbsp;as&nbsp;NaN).<br>
-&nbsp;<br>
-Operations&nbsp;between&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(+,&nbsp;-,&nbsp;/,&nbsp;*,&nbsp;**)&nbsp;align&nbsp;values&nbsp;based&nbsp;on&nbsp;their<br>
-associated&nbsp;index&nbsp;values--&nbsp;they&nbsp;need&nbsp;not&nbsp;be&nbsp;the&nbsp;same&nbsp;length.&nbsp;The&nbsp;result<br>
-index&nbsp;will&nbsp;be&nbsp;the&nbsp;sorted&nbsp;union&nbsp;of&nbsp;the&nbsp;two&nbsp;indexes.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;array-like,&nbsp;dict,&nbsp;or&nbsp;scalar&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Contains&nbsp;data&nbsp;stored&nbsp;in&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-index&nbsp;:&nbsp;array-like&nbsp;or&nbsp;Index&nbsp;(1d)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Values&nbsp;must&nbsp;be&nbsp;hashable&nbsp;and&nbsp;have&nbsp;the&nbsp;same&nbsp;length&nbsp;as&nbsp;`data`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Non-unique&nbsp;index&nbsp;values&nbsp;are&nbsp;allowed.&nbsp;Will&nbsp;default&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;RangeIndex(len(data))&nbsp;if&nbsp;not&nbsp;provided.&nbsp;If&nbsp;both&nbsp;a&nbsp;dict&nbsp;and&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;are&nbsp;used,&nbsp;the&nbsp;index&nbsp;will&nbsp;override&nbsp;the&nbsp;keys&nbsp;found&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict.<br>
-dtype&nbsp;:&nbsp;numpy.dtype&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;None,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;inferred<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Copy&nbsp;input&nbsp;data<br>&nbsp;</tt></td></tr>
-<tr><td>&nbsp;</td>
-<td width="100%"><dl><dt>Method resolution order:</dt>
-<dd><a href="modsim.html#MySeries">MySeries</a></dd>
-<dd><a href="pandas.core.series.html#Series">pandas.core.series.Series</a></dd>
-<dd><a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a></dd>
-<dd><a href="pandas.core.strings.html#StringAccessorMixin">pandas.core.strings.StringAccessorMixin</a></dd>
-<dd><a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a></dd>
-<dd><a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a></dd>
-<dd><a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a></dd>
-<dd><a href="pandas.core.base.html#SelectionMixin">pandas.core.base.SelectionMixin</a></dd>
-<dd><a href="builtins.html#object">builtins.object</a></dd>
-</dl>
-<hr>
-Methods defined here:<br>
-<dl><dt><a name="MySeries-__init__"><strong>__init__</strong></a>(self, *args, **kwargs)</dt><dd><tt>Initialize&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Note:&nbsp;this&nbsp;cleans&nbsp;up&nbsp;a&nbsp;weird&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;behavior,&nbsp;which&nbsp;is<br>
-that&nbsp;<a href="pandas.core.series.html#Series">Series</a>()&nbsp;and&nbsp;<a href="pandas.core.series.html#Series">Series</a>([])&nbsp;yield&nbsp;different&nbsp;results.<br>
-See:&nbsp;https://github.com/pandas-dev/pandas/issues/16737</tt></dd></dl>
-
-<dl><dt><a name="MySeries-set"><strong>set</strong></a>(self, **kwargs)</dt><dd><tt>Uses&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;update&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place.<br>
-&nbsp;<br>
-Example:&nbsp;series.<a href="#MySeries-update">update</a>(a=1,&nbsp;b=2)</tt></dd></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><a name="MySeries-__add__"><strong>__add__</strong></a> = wrapper(left, right, name='__add__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f2f0&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__and__"><strong>__and__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="MySeries-__array__"><strong>__array__</strong></a>(self, result=None)</dt><dd><tt>the&nbsp;array&nbsp;interface,&nbsp;return&nbsp;my&nbsp;values</tt></dd></dl>
-
-<dl><dt><a name="MySeries-__array_prepare__"><strong>__array_prepare__</strong></a>(self, result, context=None)</dt><dd><tt>Gets&nbsp;called&nbsp;prior&nbsp;to&nbsp;a&nbsp;ufunc</tt></dd></dl>
-
-<dl><dt><a name="MySeries-__array_wrap__"><strong>__array_wrap__</strong></a>(self, result, context=None)</dt><dd><tt>Gets&nbsp;called&nbsp;after&nbsp;a&nbsp;ufunc</tt></dd></dl>
-
-<dl><dt><a name="MySeries-__div__"><strong>__div__</strong></a> = wrapper(left, right, name='__truediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f9d8&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__divmod__"><strong>__divmod__</strong></a> = wrapper(left, right, name='__divmod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca343bf8&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__eq__"><strong>__eq__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="MySeries-__float__"><strong>__float__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="MySeries-__floordiv__"><strong>__floordiv__</strong></a> = wrapper(left, right, name='__floordiv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fb70&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__ge__"><strong>__ge__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="MySeries-__getitem__"><strong>__getitem__</strong></a>(self, key)</dt></dl>
-
-<dl><dt><a name="MySeries-__gt__"><strong>__gt__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="MySeries-__iadd__"><strong>__iadd__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="MySeries-__imul__"><strong>__imul__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="MySeries-__int__"><strong>__int__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="MySeries-__ipow__"><strong>__ipow__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="MySeries-__isub__"><strong>__isub__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="MySeries-__iter__"><strong>__iter__</strong></a>(self)</dt><dd><tt>provide&nbsp;iteration&nbsp;over&nbsp;the&nbsp;values&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-box&nbsp;values&nbsp;if&nbsp;necessary</tt></dd></dl>
-
-<dl><dt><a name="MySeries-__itruediv__"><strong>__itruediv__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="MySeries-__le__"><strong>__le__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="MySeries-__len__"><strong>__len__</strong></a>(self)</dt><dd><tt>return&nbsp;the&nbsp;length&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-__long__"><strong>__long__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="MySeries-__lt__"><strong>__lt__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="MySeries-__mod__"><strong>__mod__</strong></a> = wrapper(left, right, name='__mod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fd08&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__mul__"><strong>__mul__</strong></a> = wrapper(left, right, name='__mul__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f840&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__ne__"><strong>__ne__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="MySeries-__or__"><strong>__or__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="MySeries-__pow__"><strong>__pow__</strong></a> = wrapper(left, right, name='__pow__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fea0&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__radd__"><strong>__radd__</strong></a> = wrapper(left, right, name='__radd__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f510&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__rand__"><strong>__rand__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="MySeries-__rdiv__"><strong>__rdiv__</strong></a> = wrapper(left, right, name='__rtruediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342510&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__rfloordiv__"><strong>__rfloordiv__</strong></a> = wrapper(left, right, name='__rfloordiv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342730&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__rmod__"><strong>__rmod__</strong></a> = wrapper(left, right, name='__rmod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342b70&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__rmul__"><strong>__rmul__</strong></a> = wrapper(left, right, name='__rmul__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca3420d0&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__ror__"><strong>__ror__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="MySeries-__rpow__"><strong>__rpow__</strong></a> = wrapper(left, right, name='__rpow__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342950&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__rsub__"><strong>__rsub__</strong></a> = wrapper(left, right, name='__rsub__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca3422f0&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__rtruediv__"><strong>__rtruediv__</strong></a> = wrapper(left, right, name='__rtruediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342510&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__rxor__"><strong>__rxor__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="MySeries-__setitem__"><strong>__setitem__</strong></a>(self, key, value)</dt></dl>
-
-<dl><dt><a name="MySeries-__sub__"><strong>__sub__</strong></a> = wrapper(left, right, name='__sub__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f6a8&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__truediv__"><strong>__truediv__</strong></a> = wrapper(left, right, name='__truediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f9d8&gt;)</dt></dl>
-
-<dl><dt><a name="MySeries-__unicode__"><strong>__unicode__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;unicode(df)&nbsp;in&nbsp;py2&nbsp;only.&nbsp;Yields&nbsp;a&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;both<br>
-py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-__xor__"><strong>__xor__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="MySeries-add"><strong>add</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Addition&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`add`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;+&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.radd</tt></dd></dl>
-
-<dl><dt><a name="MySeries-agg"><strong>agg</strong></a> = aggregate(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.series.html#Series">Series</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#MySeries-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#MySeries-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10))<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-agg">agg</a>('min')<br>
--1.3018049988556679<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-agg">agg</a>(['min',&nbsp;'max'])<br>
-min&nbsp;&nbsp;&nbsp;-1.301805<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;1.127688<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.apply<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.transform</tt></dd></dl>
-
-<dl><dt><a name="MySeries-aggregate"><strong>aggregate</strong></a>(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.series.html#Series">Series</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#MySeries-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#MySeries-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10))<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-agg">agg</a>('min')<br>
--1.3018049988556679<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-agg">agg</a>(['min',&nbsp;'max'])<br>
-min&nbsp;&nbsp;&nbsp;-1.301805<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;1.127688<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.apply<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.transform</tt></dd></dl>
-
-<dl><dt><a name="MySeries-align"><strong>align</strong></a>(self, other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)</dt><dd><tt>Align&nbsp;two&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;their&nbsp;axes&nbsp;with&nbsp;the<br>
-specified&nbsp;join&nbsp;method&nbsp;for&nbsp;each&nbsp;axis&nbsp;Index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-join&nbsp;:&nbsp;{'outer',&nbsp;'inner',&nbsp;'left',&nbsp;'right'},&nbsp;default&nbsp;'outer'<br>
-axis&nbsp;:&nbsp;allowed&nbsp;axis&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;on&nbsp;index&nbsp;(0),&nbsp;columns&nbsp;(1),&nbsp;or&nbsp;both&nbsp;(None)<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Always&nbsp;returns&nbsp;new&nbsp;objects.&nbsp;If&nbsp;copy=False&nbsp;and&nbsp;no&nbsp;reindexing&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;required&nbsp;then&nbsp;original&nbsp;objects&nbsp;are&nbsp;returned.<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-method&nbsp;:&nbsp;str,&nbsp;default&nbsp;None<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-fill_axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Filling&nbsp;axis,&nbsp;method&nbsp;and&nbsp;limit<br>
-broadcast_axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;values&nbsp;along&nbsp;this&nbsp;axis,&nbsp;if&nbsp;aligning&nbsp;two&nbsp;objects&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;different&nbsp;dimensions<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-(left,&nbsp;right)&nbsp;:&nbsp;(<a href="pandas.core.series.html#Series">Series</a>,&nbsp;type&nbsp;of&nbsp;other)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Aligned&nbsp;objects</tt></dd></dl>
-
-<dl><dt><a name="MySeries-all"><strong>all</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;all&nbsp;elements&nbsp;are&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-all&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-any"><strong>any</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;any&nbsp;element&nbsp;is&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-any&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-append"><strong>append</strong></a>(self, to_append, ignore_index=False, verify_integrity=False)</dt><dd><tt>Concatenate&nbsp;two&nbsp;or&nbsp;more&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-to_append&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;list/tuple&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-ignore_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;not&nbsp;use&nbsp;the&nbsp;index&nbsp;labels.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.19.0<br>
-&nbsp;<br>
-verify_integrity&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;raise&nbsp;Exception&nbsp;on&nbsp;creating&nbsp;index&nbsp;with&nbsp;duplicates<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-appended&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s1&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s2&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([4,&nbsp;5,&nbsp;6])<br>
-&gt;&gt;&gt;&nbsp;s3&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([4,&nbsp;5,&nbsp;6],&nbsp;index=[3,4,5])<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#MySeries-append">append</a>(s2)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#MySeries-append">append</a>(s3)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-With&nbsp;`ignore_index`&nbsp;set&nbsp;to&nbsp;True:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#MySeries-append">append</a>(s2,&nbsp;ignore_index=True)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-With&nbsp;`verify_integrity`&nbsp;set&nbsp;to&nbsp;True:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#MySeries-append">append</a>(s2,&nbsp;verify_integrity=True)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-ValueError:&nbsp;Indexes&nbsp;have&nbsp;overlapping&nbsp;values:&nbsp;[0,&nbsp;1,&nbsp;2]</tt></dd></dl>
-
-<dl><dt><a name="MySeries-apply"><strong>apply</strong></a>(self, func, convert_dtype=True, args=(), **kwds)</dt><dd><tt>Invoke&nbsp;function&nbsp;on&nbsp;values&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Can&nbsp;be&nbsp;ufunc&nbsp;(a&nbsp;NumPy&nbsp;function<br>
-that&nbsp;applies&nbsp;to&nbsp;the&nbsp;entire&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;or&nbsp;a&nbsp;Python&nbsp;function&nbsp;that&nbsp;only&nbsp;works<br>
-on&nbsp;single&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-convert_dtype&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Try&nbsp;to&nbsp;find&nbsp;better&nbsp;dtype&nbsp;for&nbsp;elementwise&nbsp;function&nbsp;results.&nbsp;If<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False,&nbsp;leave&nbsp;as&nbsp;dtype=<a href="builtins.html#object">object</a><br>
-args&nbsp;:&nbsp;tuple<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Positional&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;to&nbsp;function&nbsp;in&nbsp;addition&nbsp;to&nbsp;the&nbsp;value<br>
-Additional&nbsp;keyword&nbsp;arguments&nbsp;will&nbsp;be&nbsp;passed&nbsp;as&nbsp;keywords&nbsp;to&nbsp;the&nbsp;function<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;if&nbsp;func&nbsp;returns&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.map:&nbsp;For&nbsp;element-wise&nbsp;operations<br>
-<a href="pandas.core.series.html#Series">Series</a>.agg:&nbsp;only&nbsp;perform&nbsp;aggregating&nbsp;type&nbsp;operations<br>
-<a href="pandas.core.series.html#Series">Series</a>.transform:&nbsp;only&nbsp;perform&nbsp;transformating&nbsp;type&nbsp;operations<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;series&nbsp;with&nbsp;typical&nbsp;summer&nbsp;temperatures&nbsp;for&nbsp;each&nbsp;city.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([20,&nbsp;21,&nbsp;12],&nbsp;index=['London',<br>
-...&nbsp;'New&nbsp;York','Helsinki'])<br>
-&gt;&gt;&gt;&nbsp;series<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;20<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Square&nbsp;the&nbsp;values&nbsp;by&nbsp;defining&nbsp;a&nbsp;function&nbsp;and&nbsp;passing&nbsp;it&nbsp;as&nbsp;an<br>
-argument&nbsp;to&nbsp;``<a href="#MySeries-apply">apply</a>()``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;square(x):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x**2<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MySeries-apply">apply</a>(square)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;400<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;441<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;144<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Square&nbsp;the&nbsp;values&nbsp;by&nbsp;passing&nbsp;an&nbsp;anonymous&nbsp;function&nbsp;as&nbsp;an<br>
-argument&nbsp;to&nbsp;``<a href="#MySeries-apply">apply</a>()``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MySeries-apply">apply</a>(lambda&nbsp;x:&nbsp;x**2)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;400<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;441<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;144<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Define&nbsp;a&nbsp;custom&nbsp;function&nbsp;that&nbsp;needs&nbsp;additional&nbsp;positional<br>
-arguments&nbsp;and&nbsp;pass&nbsp;these&nbsp;additional&nbsp;arguments&nbsp;using&nbsp;the<br>
-``args``&nbsp;keyword.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;subtract_custom_value(x,&nbsp;custom_value):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x-custom_value<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MySeries-apply">apply</a>(subtract_custom_value,&nbsp;args=(5,))<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;16<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Define&nbsp;a&nbsp;custom&nbsp;function&nbsp;that&nbsp;takes&nbsp;keyword&nbsp;arguments<br>
-and&nbsp;pass&nbsp;these&nbsp;arguments&nbsp;to&nbsp;``apply``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;add_custom_values(x,&nbsp;**kwargs):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;month&nbsp;in&nbsp;kwargs:<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;x+=kwargs[month]<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MySeries-apply">apply</a>(add_custom_values,&nbsp;june=30,&nbsp;july=20,&nbsp;august=25)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;95<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;96<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;87<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Use&nbsp;a&nbsp;function&nbsp;from&nbsp;the&nbsp;Numpy&nbsp;library.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MySeries-apply">apply</a>(np.log)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.995732<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;3.044522<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;2.484907<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="MySeries-argmax"><strong>argmax</strong></a> = idxmax(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmax&nbsp;:&nbsp;Index&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmax``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmax<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmax</tt></dd></dl>
-
-<dl><dt><a name="MySeries-argmin"><strong>argmin</strong></a> = idxmin(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmin&nbsp;:&nbsp;Index&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmin``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmin<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmin</tt></dd></dl>
-
-<dl><dt><a name="MySeries-argsort"><strong>argsort</strong></a>(self, axis=0, kind='quicksort', order=None)</dt><dd><tt>Overrides&nbsp;<a href="numpy.html#ndarray">ndarray</a>.argsort.&nbsp;Argsorts&nbsp;the&nbsp;value,&nbsp;omitting&nbsp;NA/null&nbsp;values,<br>
-and&nbsp;places&nbsp;the&nbsp;result&nbsp;in&nbsp;the&nbsp;same&nbsp;locations&nbsp;as&nbsp;the&nbsp;non-NA&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;int&nbsp;(can&nbsp;only&nbsp;be&nbsp;zero)<br>
-kind&nbsp;:&nbsp;{'mergesort',&nbsp;'quicksort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;'mergesort'&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm<br>
-order&nbsp;:&nbsp;ignored<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-argsorted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;with&nbsp;-1&nbsp;indicated&nbsp;where&nbsp;nan&nbsp;values&nbsp;are&nbsp;present<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argsort</tt></dd></dl>
-
-<dl><dt><a name="MySeries-autocorr"><strong>autocorr</strong></a>(self, lag=1)</dt><dd><tt>Lag-N&nbsp;autocorrelation<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-lag&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;lags&nbsp;to&nbsp;apply&nbsp;before&nbsp;performing&nbsp;autocorrelation.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-autocorr&nbsp;:&nbsp;float</tt></dd></dl>
-
-<dl><dt><a name="MySeries-between"><strong>between</strong></a>(self, left, right, inclusive=True)</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;equivalent&nbsp;to&nbsp;left&nbsp;&lt;=&nbsp;series&nbsp;&lt;=&nbsp;right.&nbsp;NA&nbsp;values<br>
-will&nbsp;be&nbsp;treated&nbsp;as&nbsp;False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-left&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Left&nbsp;boundary<br>
-right&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Right&nbsp;boundary<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_between&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-combine"><strong>combine</strong></a>(self, other, func, fill_value=nan)</dt><dd><tt>Perform&nbsp;elementwise&nbsp;binary&nbsp;operation&nbsp;on&nbsp;two&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;using&nbsp;given&nbsp;function<br>
-with&nbsp;optional&nbsp;fill&nbsp;value&nbsp;when&nbsp;an&nbsp;index&nbsp;is&nbsp;missing&nbsp;from&nbsp;one&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or<br>
-the&nbsp;other<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-func&nbsp;:&nbsp;function<br>
-fill_value&nbsp;:&nbsp;scalar&nbsp;value<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-combine_first"><strong>combine_first</strong></a>(self, other)</dt><dd><tt>Combine&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values,&nbsp;choosing&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.series.html#Series">Series</a>'s&nbsp;values<br>
-first.&nbsp;Result&nbsp;index&nbsp;will&nbsp;be&nbsp;the&nbsp;union&nbsp;of&nbsp;the&nbsp;two&nbsp;indexes<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-compound"><strong>compound</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;compound&nbsp;percentage&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-compounded&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-compress"><strong>compress</strong></a>(self, condition, *args, **kwargs)</dt><dd><tt>Return&nbsp;selected&nbsp;slices&nbsp;of&nbsp;an&nbsp;array&nbsp;along&nbsp;given&nbsp;axis&nbsp;as&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.compress</tt></dd></dl>
-
-<dl><dt><a name="MySeries-corr"><strong>corr</strong></a>(self, other, method='pearson', min_periods=None)</dt><dd><tt>Compute&nbsp;correlation&nbsp;with&nbsp;`other`&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;excluding&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-method&nbsp;:&nbsp;{'pearson',&nbsp;'kendall',&nbsp;'spearman'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pearson&nbsp;:&nbsp;standard&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;kendall&nbsp;:&nbsp;Kendall&nbsp;Tau&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;spearman&nbsp;:&nbsp;Spearman&nbsp;rank&nbsp;correlation<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;needed&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result<br>
-&nbsp;<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-correlation&nbsp;:&nbsp;float</tt></dd></dl>
-
-<dl><dt><a name="MySeries-count"><strong>count</strong></a>(self, level=None)</dt><dd><tt>Return&nbsp;number&nbsp;of&nbsp;non-NA/null&nbsp;observations&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;smaller&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-nobs&nbsp;:&nbsp;int&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-cov"><strong>cov</strong></a>(self, other, min_periods=None)</dt><dd><tt>Compute&nbsp;covariance&nbsp;with&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;excluding&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;needed&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-covariance&nbsp;:&nbsp;float<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;(unbiased&nbsp;estimator).</tt></dd></dl>
-
-<dl><dt><a name="MySeries-cummax"><strong>cummax</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;max&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummax&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.max&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-cummin"><strong>cummin</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;minimum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummin&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.min&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-cumprod"><strong>cumprod</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;product&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumprod&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.prod&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-cumsum"><strong>cumsum</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;sum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumsum&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.sum&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-diff"><strong>diff</strong></a>(self, periods=1)</dt><dd><tt>1st&nbsp;discrete&nbsp;difference&nbsp;of&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;difference<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-diffed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-div"><strong>div</strong></a> = truediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="MySeries-divide"><strong>divide</strong></a> = truediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="MySeries-dot"><strong>dot</strong></a>(self, other)</dt><dd><tt>Matrix&nbsp;multiplication&nbsp;with&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;inner-product&nbsp;with&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-objects<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dot_product&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-drop_duplicates"><strong>drop_duplicates</strong></a>(self, keep='first', inplace=False)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;duplicate&nbsp;values&nbsp;removed<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-&nbsp;<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Drop&nbsp;all&nbsp;duplicates.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-If&nbsp;True,&nbsp;performs&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;returns&nbsp;None.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-deduplicated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-dropna"><strong>dropna</strong></a>(self, axis=0, inplace=False, **kwargs)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;without&nbsp;null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-valid&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;operation&nbsp;in&nbsp;place.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-duplicated"><strong>duplicated</strong></a>(self, keep='first')</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;denoting&nbsp;duplicate&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the&nbsp;last<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Mark&nbsp;all&nbsp;duplicates&nbsp;as&nbsp;``True``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-duplicated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-eq"><strong>eq</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`eq`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;==&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="MySeries-ewm"><strong>ewm</strong></a>(self, com=None, span=None, halflife=None, alpha=None, min_periods=0, freq=None, adjust=True, ignore_na=False, axis=0)</dt><dd><tt>Provides&nbsp;exponential&nbsp;weighted&nbsp;functions<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-com&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;/&nbsp;(1&nbsp;+&nbsp;com),\text{&nbsp;for&nbsp;}&nbsp;com&nbsp;\geq&nbsp;0`<br>
-span&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;span,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;2&nbsp;/&nbsp;(span&nbsp;+&nbsp;1),\text{&nbsp;for&nbsp;}&nbsp;span&nbsp;\geq&nbsp;1`<br>
-halflife&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;half-life,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;-&nbsp;exp(log(0.5)&nbsp;/&nbsp;halflife),\text{&nbsp;for&nbsp;}&nbsp;halflife&nbsp;&gt;&nbsp;0`<br>
-alpha&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;smoothing&nbsp;factor&nbsp;:math:`\alpha`&nbsp;directly,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`0&nbsp;&lt;&nbsp;\alpha&nbsp;\leq&nbsp;1`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;None&nbsp;or&nbsp;string&nbsp;alias&nbsp;/&nbsp;date&nbsp;offset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default=None&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;to&nbsp;before&nbsp;computing&nbsp;statistic<br>
-adjust&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Divide&nbsp;by&nbsp;decaying&nbsp;adjustment&nbsp;factor&nbsp;in&nbsp;beginning&nbsp;periods&nbsp;to&nbsp;account<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;imbalance&nbsp;in&nbsp;relative&nbsp;weightings&nbsp;(viewing&nbsp;EWMA&nbsp;as&nbsp;a&nbsp;moving&nbsp;average)<br>
-ignore_na&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Ignore&nbsp;missing&nbsp;values&nbsp;when&nbsp;calculating&nbsp;weights;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specify&nbsp;True&nbsp;to&nbsp;reproduce&nbsp;pre-0.15.0&nbsp;behavior<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-ewm">ewm</a>(com=0.5).<a href="#MySeries-mean">mean</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.000000<br>
-1&nbsp;&nbsp;0.750000<br>
-2&nbsp;&nbsp;1.615385<br>
-3&nbsp;&nbsp;1.615385<br>
-4&nbsp;&nbsp;3.670213<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Exactly&nbsp;one&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,&nbsp;span,&nbsp;half-life,&nbsp;and&nbsp;alpha&nbsp;must&nbsp;be&nbsp;provided.<br>
-Allowed&nbsp;values&nbsp;and&nbsp;relationship&nbsp;between&nbsp;the&nbsp;parameters&nbsp;are&nbsp;specified&nbsp;in&nbsp;the<br>
-parameter&nbsp;descriptions&nbsp;above;&nbsp;see&nbsp;the&nbsp;link&nbsp;at&nbsp;the&nbsp;end&nbsp;of&nbsp;this&nbsp;section&nbsp;for<br>
-a&nbsp;detailed&nbsp;explanation.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;True&nbsp;(default),&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;using<br>
-weights&nbsp;(1-alpha)**(n-1),&nbsp;(1-alpha)**(n-2),&nbsp;...,&nbsp;1-alpha,&nbsp;1.<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;False,&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;recursively&nbsp;as:<br>
-&nbsp;&nbsp;&nbsp;weighted_average[0]&nbsp;=&nbsp;arg[0];<br>
-&nbsp;&nbsp;&nbsp;weighted_average[i]&nbsp;=&nbsp;(1-alpha)*weighted_average[i-1]&nbsp;+&nbsp;alpha*arg[i].<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;False&nbsp;(default),&nbsp;weights&nbsp;are&nbsp;based&nbsp;on&nbsp;absolute&nbsp;positions.<br>
-For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in&nbsp;calculating&nbsp;the&nbsp;final&nbsp;weighted<br>
-average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;(1-alpha)**2&nbsp;and&nbsp;1&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and<br>
-(1-alpha)**2&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;True&nbsp;(reproducing&nbsp;pre-0.15.0&nbsp;behavior),&nbsp;weights&nbsp;are&nbsp;based<br>
-on&nbsp;relative&nbsp;positions.&nbsp;For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in<br>
-calculating&nbsp;the&nbsp;final&nbsp;weighted&nbsp;average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;1-alpha&nbsp;and&nbsp;1<br>
-(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and&nbsp;1-alpha&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-More&nbsp;details&nbsp;can&nbsp;be&nbsp;found&nbsp;at<br>
-<a href="http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows">http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-expanding"><strong>expanding</strong></a>(self, min_periods=1, freq=None, center=False, axis=0)</dt><dd><tt>Provides&nbsp;expanding&nbsp;transformations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-expanding">expanding</a>(2).<a href="#MySeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;7.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).</tt></dd></dl>
-
-<dl><dt><a name="MySeries-fillna"><strong>fillna</strong></a>(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)</dt><dd><tt>Fill&nbsp;NA/NaN&nbsp;values&nbsp;using&nbsp;the&nbsp;specified&nbsp;method<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;values&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;each&nbsp;index&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;or&nbsp;column&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).&nbsp;(values&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;This&nbsp;value&nbsp;cannot<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;list.<br>
-method&nbsp;:&nbsp;{'backfill',&nbsp;'bfill',&nbsp;'pad',&nbsp;'ffill',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'}<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;fill&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>,&nbsp;(e.g.&nbsp;a&nbsp;no-copy&nbsp;slice&nbsp;for&nbsp;a&nbsp;column&nbsp;in&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;method&nbsp;is&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the&nbsp;maximum&nbsp;number&nbsp;of&nbsp;consecutive<br>
-&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;values&nbsp;to&nbsp;forward/backward&nbsp;fill.&nbsp;In&nbsp;other&nbsp;words,&nbsp;if&nbsp;there&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;gap&nbsp;with&nbsp;more&nbsp;than&nbsp;this&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs,&nbsp;it&nbsp;will&nbsp;only<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;partially&nbsp;filled.&nbsp;If&nbsp;method&nbsp;is&nbsp;not&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;maximum&nbsp;number&nbsp;of&nbsp;entries&nbsp;along&nbsp;the&nbsp;entire&nbsp;axis&nbsp;where&nbsp;NaNs&nbsp;will&nbsp;be<br>
-&nbsp;&nbsp;&nbsp;&nbsp;filled.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0&nbsp;if&nbsp;not&nbsp;None.<br>
-downcast&nbsp;:&nbsp;dict,&nbsp;default&nbsp;is&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;dict&nbsp;of&nbsp;item-&gt;dtype&nbsp;of&nbsp;what&nbsp;to&nbsp;downcast&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;the&nbsp;string&nbsp;'infer'&nbsp;which&nbsp;will&nbsp;try&nbsp;to&nbsp;downcast&nbsp;to&nbsp;an&nbsp;appropriate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;equal&nbsp;type&nbsp;(e.g.&nbsp;float64&nbsp;to&nbsp;int64&nbsp;if&nbsp;possible)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;asfreq<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-first_valid_index"><strong>first_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;first&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="MySeries-floordiv"><strong>floordiv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`floordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;//&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rfloordiv</tt></dd></dl>
-
-<dl><dt><a name="MySeries-ge"><strong>ge</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Greater&nbsp;than&nbsp;or&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`ge`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&gt;=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="MySeries-get_value"><strong>get_value</strong></a>(self, label, takeable=False)</dt><dd><tt>Quickly&nbsp;retrieve&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;index&nbsp;label<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;label<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;scalar&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="MySeries-get_values"><strong>get_values</strong></a>(self)</dt><dd><tt>same&nbsp;as&nbsp;values&nbsp;(but&nbsp;handles&nbsp;sparseness&nbsp;conversions);&nbsp;is&nbsp;a&nbsp;view</tt></dd></dl>
-
-<dl><dt><a name="MySeries-gt"><strong>gt</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Greater&nbsp;than&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`gt`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&gt;&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="MySeries-hist"><strong>hist</strong></a> = hist_series(self, by=None, ax=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, figsize=None, bins=10, **kwds)</dt><dd><tt>Draw&nbsp;histogram&nbsp;of&nbsp;the&nbsp;input&nbsp;series&nbsp;using&nbsp;matplotlib<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed,&nbsp;then&nbsp;used&nbsp;to&nbsp;form&nbsp;histograms&nbsp;for&nbsp;separate&nbsp;groups<br>
-ax&nbsp;:&nbsp;matplotlib&nbsp;axis&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;not&nbsp;passed,&nbsp;uses&nbsp;gca()<br>
-grid&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;show&nbsp;axis&nbsp;grid&nbsp;lines<br>
-xlabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;x-axis&nbsp;label&nbsp;size<br>
-xrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;x&nbsp;axis&nbsp;labels<br>
-ylabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;y-axis&nbsp;label&nbsp;size<br>
-yrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;y&nbsp;axis&nbsp;labels<br>
-figsize&nbsp;:&nbsp;tuple,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;figure&nbsp;size&nbsp;in&nbsp;inches&nbsp;by&nbsp;default<br>
-bins:&nbsp;integer,&nbsp;default&nbsp;10<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;histogram&nbsp;bins&nbsp;to&nbsp;be&nbsp;used<br>
-kwds&nbsp;:&nbsp;keywords<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;passed&nbsp;to&nbsp;the&nbsp;actual&nbsp;plotting&nbsp;function<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;matplotlib&nbsp;documentation&nbsp;online&nbsp;for&nbsp;more&nbsp;on&nbsp;this</tt></dd></dl>
-
-<dl><dt><a name="MySeries-idxmax"><strong>idxmax</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmax&nbsp;:&nbsp;Index&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmax``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmax<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmax</tt></dd></dl>
-
-<dl><dt><a name="MySeries-idxmin"><strong>idxmin</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmin&nbsp;:&nbsp;Index&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmin``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmin<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmin</tt></dd></dl>
-
-<dl><dt><a name="MySeries-isin"><strong>isin</strong></a>(self, values)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;:class:`~pandas.<a href="pandas.core.series.html#Series">Series</a>`&nbsp;showing&nbsp;whether&nbsp;each&nbsp;element<br>
-in&nbsp;the&nbsp;:class:`~pandas.<a href="pandas.core.series.html#Series">Series</a>`&nbsp;is&nbsp;exactly&nbsp;contained&nbsp;in&nbsp;the&nbsp;passed<br>
-sequence&nbsp;of&nbsp;``values``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-values&nbsp;:&nbsp;set&nbsp;or&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;sequence&nbsp;of&nbsp;values&nbsp;to&nbsp;test.&nbsp;Passing&nbsp;in&nbsp;a&nbsp;single&nbsp;string&nbsp;will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;raise&nbsp;a&nbsp;``TypeError``.&nbsp;Instead,&nbsp;turn&nbsp;a&nbsp;single&nbsp;string&nbsp;into&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``list``&nbsp;of&nbsp;one&nbsp;element.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Support&nbsp;for&nbsp;values&nbsp;as&nbsp;a&nbsp;set<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-isin&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(bool&nbsp;dtype)<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;*&nbsp;If&nbsp;``values``&nbsp;is&nbsp;a&nbsp;string<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.isin<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(list('abc'))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-isin">isin</a>(['a',&nbsp;'c',&nbsp;'e'])<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-dtype:&nbsp;bool<br>
-&nbsp;<br>
-Passing&nbsp;a&nbsp;single&nbsp;string&nbsp;as&nbsp;``s.<a href="#MySeries-isin">isin</a>('a')``&nbsp;will&nbsp;raise&nbsp;an&nbsp;error.&nbsp;Use<br>
-a&nbsp;list&nbsp;of&nbsp;one&nbsp;element&nbsp;instead:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-isin">isin</a>(['a'])<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-dtype:&nbsp;bool</tt></dd></dl>
-
-<dl><dt><a name="MySeries-items"><strong>items</strong></a> = iteritems(self)</dt><dd><tt>Lazily&nbsp;iterate&nbsp;over&nbsp;(index,&nbsp;value)&nbsp;tuples</tt></dd></dl>
-
-<dl><dt><a name="MySeries-iteritems"><strong>iteritems</strong></a>(self)</dt><dd><tt>Lazily&nbsp;iterate&nbsp;over&nbsp;(index,&nbsp;value)&nbsp;tuples</tt></dd></dl>
-
-<dl><dt><a name="MySeries-keys"><strong>keys</strong></a>(self)</dt><dd><tt>Alias&nbsp;for&nbsp;index</tt></dd></dl>
-
-<dl><dt><a name="MySeries-kurt"><strong>kurt</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-kurtosis"><strong>kurtosis</strong></a> = kurt(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-last_valid_index"><strong>last_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;last&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="MySeries-le"><strong>le</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Less&nbsp;than&nbsp;or&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`le`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&lt;=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="MySeries-lt"><strong>lt</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Less&nbsp;than&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`lt`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&lt;&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="MySeries-mad"><strong>mad</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;absolute&nbsp;deviation&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mad&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-map"><strong>map</strong></a>(self, arg, na_action=None)</dt><dd><tt>Map&nbsp;values&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;using&nbsp;input&nbsp;correspondence&nbsp;(which&nbsp;can&nbsp;be<br>
-a&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;function)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-arg&nbsp;:&nbsp;function,&nbsp;dict,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-na_action&nbsp;:&nbsp;{None,&nbsp;'ignore'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'ignore',&nbsp;propagate&nbsp;NA&nbsp;values,&nbsp;without&nbsp;passing&nbsp;them&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;mapping&nbsp;function<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;same&nbsp;index&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Map&nbsp;inputs&nbsp;to&nbsp;outputs&nbsp;(both&nbsp;of&nbsp;type&nbsp;`<a href="pandas.core.series.html#Series">Series</a>`)<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,2,3],&nbsp;index=['one',&nbsp;'two',&nbsp;'three'])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-one&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-two&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-three&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;y&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(['foo',&nbsp;'bar',&nbsp;'baz'],&nbsp;index=[1,2,3])<br>
-&gt;&gt;&gt;&nbsp;y<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;foo<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;bar<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;baz<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#MySeries-map">map</a>(y)<br>
-one&nbsp;&nbsp;&nbsp;foo<br>
-two&nbsp;&nbsp;&nbsp;bar<br>
-three&nbsp;baz<br>
-&nbsp;<br>
-If&nbsp;`arg`&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;values&nbsp;converted<br>
-according&nbsp;to&nbsp;the&nbsp;dictionary's&nbsp;mapping:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;z&nbsp;=&nbsp;{1:&nbsp;'A',&nbsp;2:&nbsp;'B',&nbsp;3:&nbsp;'C'}<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#MySeries-map">map</a>(z)<br>
-one&nbsp;&nbsp;&nbsp;A<br>
-two&nbsp;&nbsp;&nbsp;B<br>
-three&nbsp;C<br>
-&nbsp;<br>
-Use&nbsp;na_action&nbsp;to&nbsp;control&nbsp;whether&nbsp;NA&nbsp;values&nbsp;are&nbsp;affected&nbsp;by&nbsp;the&nbsp;mapping<br>
-function.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;np.nan])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s2&nbsp;=&nbsp;s.<a href="#MySeries-map">map</a>('this&nbsp;is&nbsp;a&nbsp;string&nbsp;{}'.format,&nbsp;na_action=None)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;1.0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;2.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;3.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;nan<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s3&nbsp;=&nbsp;s.<a href="#MySeries-map">map</a>('this&nbsp;is&nbsp;a&nbsp;string&nbsp;{}'.format,&nbsp;na_action='ignore')<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;1.0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;2.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;3.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.apply:&nbsp;For&nbsp;applying&nbsp;more&nbsp;complex&nbsp;functions&nbsp;on&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply:&nbsp;Apply&nbsp;a&nbsp;function&nbsp;row-/column-wise<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap:&nbsp;Apply&nbsp;a&nbsp;function&nbsp;elementwise&nbsp;on&nbsp;a&nbsp;whole&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-When&nbsp;`arg`&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;values&nbsp;in&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;that&nbsp;are&nbsp;not&nbsp;in&nbsp;the<br>
-dictionary&nbsp;(as&nbsp;keys)&nbsp;are&nbsp;converted&nbsp;to&nbsp;``NaN``.&nbsp;However,&nbsp;if&nbsp;the<br>
-dictionary&nbsp;is&nbsp;a&nbsp;``dict``&nbsp;subclass&nbsp;that&nbsp;defines&nbsp;``__missing__``&nbsp;(i.e.<br>
-provides&nbsp;a&nbsp;method&nbsp;for&nbsp;default&nbsp;values),&nbsp;then&nbsp;this&nbsp;default&nbsp;is&nbsp;used<br>
-rather&nbsp;than&nbsp;``NaN``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;collections&nbsp;import&nbsp;Counter<br>
-&gt;&gt;&gt;&nbsp;counter&nbsp;=&nbsp;Counter()<br>
-&gt;&gt;&gt;&nbsp;counter['bar']&nbsp;+=&nbsp;1<br>
-&gt;&gt;&gt;&nbsp;y.<a href="#MySeries-map">map</a>(counter)<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-dtype:&nbsp;int64</tt></dd></dl>
-
-<dl><dt><a name="MySeries-max"><strong>max</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;maximum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;maximum,&nbsp;use&nbsp;``idxmax``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmax``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-max&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-mean"><strong>mean</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mean&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-median"><strong>median</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;median&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-median&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-memory_usage"><strong>memory_usage</strong></a>(self, index=True, deep=False)</dt><dd><tt>Memory&nbsp;usage&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;whether&nbsp;to&nbsp;include&nbsp;memory&nbsp;usage&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index<br>
-deep&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Introspect&nbsp;the&nbsp;data&nbsp;deeply,&nbsp;interrogate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`<a href="builtins.html#object">object</a>`&nbsp;dtypes&nbsp;for&nbsp;system-level&nbsp;memory&nbsp;consumption<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-scalar&nbsp;bytes&nbsp;of&nbsp;memory&nbsp;consumed<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Memory&nbsp;usage&nbsp;does&nbsp;not&nbsp;include&nbsp;memory&nbsp;consumed&nbsp;by&nbsp;elements&nbsp;that<br>
-are&nbsp;not&nbsp;components&nbsp;of&nbsp;the&nbsp;array&nbsp;if&nbsp;deep=False<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.nbytes</tt></dd></dl>
-
-<dl><dt><a name="MySeries-min"><strong>min</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;minimum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;minimum,&nbsp;use&nbsp;``idxmin``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmin``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-min&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-mod"><strong>mod</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Modulo&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;%&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmod</tt></dd></dl>
-
-<dl><dt><a name="MySeries-mode"><strong>mode</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;<a href="#MySeries-mode">mode</a>(s)&nbsp;of&nbsp;the&nbsp;dataset.<br>
-&nbsp;<br>
-Always&nbsp;returns&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;even&nbsp;if&nbsp;only&nbsp;one&nbsp;value&nbsp;is&nbsp;returned.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-modes&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(sorted)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-mul"><strong>mul</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="MySeries-multiply"><strong>multiply</strong></a> = mul(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="MySeries-ne"><strong>ne</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Not&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`ne`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;!=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="MySeries-nlargest"><strong>nlargest</strong></a>(self, n=5, keep='first')</dt><dd><tt>Return&nbsp;the&nbsp;largest&nbsp;`n`&nbsp;elements.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;this&nbsp;many&nbsp;descending&nbsp;sorted&nbsp;values<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-top_n&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;n&nbsp;largest&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;in&nbsp;sorted&nbsp;order<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Faster&nbsp;than&nbsp;``.<a href="#MySeries-sort_values">sort_values</a>(ascending=False).<a href="#MySeries-head">head</a>(n)``&nbsp;for&nbsp;small&nbsp;`n`<br>
-relative&nbsp;to&nbsp;the&nbsp;size&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.nsmallest<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10**6))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-nlargest">nlargest</a>(10)&nbsp;&nbsp;#&nbsp;only&nbsp;sorts&nbsp;up&nbsp;to&nbsp;the&nbsp;N&nbsp;requested<br>
-219921&nbsp;&nbsp;&nbsp;&nbsp;4.644710<br>
-82124&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4.608745<br>
-421689&nbsp;&nbsp;&nbsp;&nbsp;4.564644<br>
-425277&nbsp;&nbsp;&nbsp;&nbsp;4.447014<br>
-718691&nbsp;&nbsp;&nbsp;&nbsp;4.414137<br>
-43154&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4.403520<br>
-283187&nbsp;&nbsp;&nbsp;&nbsp;4.313922<br>
-595519&nbsp;&nbsp;&nbsp;&nbsp;4.273635<br>
-503969&nbsp;&nbsp;&nbsp;&nbsp;4.250236<br>
-121637&nbsp;&nbsp;&nbsp;&nbsp;4.240952<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="MySeries-nonzero"><strong>nonzero</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;indices&nbsp;of&nbsp;the&nbsp;elements&nbsp;that&nbsp;are&nbsp;non-zero<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;calling&nbsp;`numpy.nonzero`&nbsp;on&nbsp;the<br>
-series&nbsp;data.&nbsp;For&nbsp;compatability&nbsp;with&nbsp;NumPy,&nbsp;the&nbsp;return&nbsp;value&nbsp;is<br>
-the&nbsp;same&nbsp;(a&nbsp;tuple&nbsp;with&nbsp;an&nbsp;array&nbsp;of&nbsp;indices&nbsp;for&nbsp;each&nbsp;dimension),<br>
-but&nbsp;it&nbsp;will&nbsp;always&nbsp;be&nbsp;a&nbsp;one-item&nbsp;tuple&nbsp;because&nbsp;series&nbsp;only&nbsp;have<br>
-one&nbsp;dimension.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0,&nbsp;3,&nbsp;0,&nbsp;4])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-nonzero">nonzero</a>()<br>
-(array([1,&nbsp;3]),)<br>
-&gt;&gt;&gt;&nbsp;s.iloc[s.<a href="#MySeries-nonzero">nonzero</a>()[0]]<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.nonzero</tt></dd></dl>
-
-<dl><dt><a name="MySeries-nsmallest"><strong>nsmallest</strong></a>(self, n=5, keep='first')</dt><dd><tt>Return&nbsp;the&nbsp;smallest&nbsp;`n`&nbsp;elements.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;this&nbsp;many&nbsp;ascending&nbsp;sorted&nbsp;values<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-bottom_n&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;n&nbsp;smallest&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;in&nbsp;sorted&nbsp;order<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Faster&nbsp;than&nbsp;``.<a href="#MySeries-sort_values">sort_values</a>().<a href="#MySeries-head">head</a>(n)``&nbsp;for&nbsp;small&nbsp;`n`&nbsp;relative&nbsp;to<br>
-the&nbsp;size&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.nlargest<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10**6))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-nsmallest">nsmallest</a>(10)&nbsp;&nbsp;#&nbsp;only&nbsp;sorts&nbsp;up&nbsp;to&nbsp;the&nbsp;N&nbsp;requested<br>
-288532&nbsp;&nbsp;&nbsp;-4.954580<br>
-732345&nbsp;&nbsp;&nbsp;-4.835960<br>
-64803&nbsp;&nbsp;&nbsp;&nbsp;-4.812550<br>
-446457&nbsp;&nbsp;&nbsp;-4.609998<br>
-501225&nbsp;&nbsp;&nbsp;-4.483945<br>
-669476&nbsp;&nbsp;&nbsp;-4.472935<br>
-973615&nbsp;&nbsp;&nbsp;-4.401699<br>
-621279&nbsp;&nbsp;&nbsp;-4.355126<br>
-773916&nbsp;&nbsp;&nbsp;-4.347355<br>
-359919&nbsp;&nbsp;&nbsp;-4.331927<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="MySeries-pow"><strong>pow</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`pow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;**&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rpow</tt></dd></dl>
-
-<dl><dt><a name="MySeries-prod"><strong>prod</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-product"><strong>product</strong></a> = prod(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-ptp"><strong>ptp</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Returns&nbsp;the&nbsp;difference&nbsp;between&nbsp;the&nbsp;maximum&nbsp;value&nbsp;and&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;minimum&nbsp;value&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.&nbsp;This&nbsp;is&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``ptp``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ptp&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-put"><strong>put</strong></a>(self, *args, **kwargs)</dt><dd><tt>Applies&nbsp;the&nbsp;`put`&nbsp;method&nbsp;to&nbsp;its&nbsp;`values`&nbsp;attribute<br>
-if&nbsp;it&nbsp;has&nbsp;one.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.put</tt></dd></dl>
-
-<dl><dt><a name="MySeries-quantile"><strong>quantile</strong></a>(self, q=0.5, interpolation='linear')</dt><dd><tt>Return&nbsp;value&nbsp;at&nbsp;the&nbsp;given&nbsp;quantile,&nbsp;a&nbsp;la&nbsp;numpy.percentile.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-q&nbsp;:&nbsp;float&nbsp;or&nbsp;array-like,&nbsp;default&nbsp;0.5&nbsp;(50%&nbsp;quantile)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&lt;=&nbsp;q&nbsp;&lt;=&nbsp;1,&nbsp;the&nbsp;<a href="#MySeries-quantile">quantile</a>(s)&nbsp;to&nbsp;compute<br>
-interpolation&nbsp;:&nbsp;{'linear',&nbsp;'lower',&nbsp;'higher',&nbsp;'midpoint',&nbsp;'nearest'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;optional&nbsp;parameter&nbsp;specifies&nbsp;the&nbsp;interpolation&nbsp;method&nbsp;to&nbsp;use,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;the&nbsp;desired&nbsp;quantile&nbsp;lies&nbsp;between&nbsp;two&nbsp;data&nbsp;points&nbsp;`i`&nbsp;and&nbsp;`j`:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;linear:&nbsp;`i&nbsp;+&nbsp;(j&nbsp;-&nbsp;i)&nbsp;*&nbsp;fraction`,&nbsp;where&nbsp;`fraction`&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;fractional&nbsp;part&nbsp;of&nbsp;the&nbsp;index&nbsp;surrounded&nbsp;by&nbsp;`i`&nbsp;and&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;lower:&nbsp;`i`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;higher:&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;`i`&nbsp;or&nbsp;`j`&nbsp;whichever&nbsp;is&nbsp;nearest.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;midpoint:&nbsp;(`i`&nbsp;+&nbsp;`j`)&nbsp;/&nbsp;2.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-quantile&nbsp;:&nbsp;float&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;``q``&nbsp;is&nbsp;an&nbsp;array,&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;returned&nbsp;where&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;is&nbsp;``q``&nbsp;and&nbsp;the&nbsp;values&nbsp;are&nbsp;the&nbsp;quantiles.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;4])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-quantile">quantile</a>(.5)<br>
-2.5<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-quantile">quantile</a>([.25,&nbsp;.5,&nbsp;.75])<br>
-0.25&nbsp;&nbsp;&nbsp;&nbsp;1.75<br>
-0.50&nbsp;&nbsp;&nbsp;&nbsp;2.50<br>
-0.75&nbsp;&nbsp;&nbsp;&nbsp;3.25<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="MySeries-radd"><strong>radd</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Addition&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`radd`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;+&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.add</tt></dd></dl>
-
-<dl><dt><a name="MySeries-ravel"><strong>ravel</strong></a>(self, order='C')</dt><dd><tt>Return&nbsp;the&nbsp;flattened&nbsp;underlying&nbsp;data&nbsp;as&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.ravel</tt></dd></dl>
-
-<dl><dt><a name="MySeries-rdiv"><strong>rdiv</strong></a> = rtruediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="MySeries-reindex"><strong>reindex</strong></a>(self, index=None, **kwargs)</dt><dd><tt>Conform&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;new&nbsp;index&nbsp;with&nbsp;optional&nbsp;filling&nbsp;logic,&nbsp;placing<br>
-NA/NaN&nbsp;in&nbsp;locations&nbsp;having&nbsp;no&nbsp;value&nbsp;in&nbsp;the&nbsp;previous&nbsp;index.&nbsp;A&nbsp;new&nbsp;<a href="builtins.html#object">object</a><br>
-is&nbsp;produced&nbsp;unless&nbsp;the&nbsp;new&nbsp;index&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;the&nbsp;current&nbsp;one&nbsp;and<br>
-copy=False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;array-like,&nbsp;optional&nbsp;(can&nbsp;be&nbsp;specified&nbsp;in&nbsp;order,&nbsp;or&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;keywords)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;New&nbsp;labels&nbsp;/&nbsp;index&nbsp;to&nbsp;conform&nbsp;to.&nbsp;Preferably&nbsp;an&nbsp;Index&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;avoid&nbsp;duplicating&nbsp;data<br>
-method&nbsp;:&nbsp;{None,&nbsp;'backfill'/'bfill',&nbsp;'pad'/'ffill',&nbsp;'nearest'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Please&nbsp;note:&nbsp;this&nbsp;is&nbsp;only&nbsp;&nbsp;applicable&nbsp;to&nbsp;DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;monotonically&nbsp;increasing/decreasing&nbsp;index.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;default:&nbsp;don't&nbsp;fill&nbsp;gaps<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;next&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;use&nbsp;nearest&nbsp;valid&nbsp;observations&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>,&nbsp;even&nbsp;if&nbsp;the&nbsp;passed&nbsp;indexes&nbsp;are&nbsp;the&nbsp;same<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;elements&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;original&nbsp;and&nbsp;new&nbsp;labels&nbsp;for&nbsp;inexact<br>
-&nbsp;&nbsp;&nbsp;&nbsp;matches.&nbsp;The&nbsp;values&nbsp;of&nbsp;the&nbsp;index&nbsp;at&nbsp;the&nbsp;matching&nbsp;locations&nbsp;most<br>
-&nbsp;&nbsp;&nbsp;&nbsp;satisfy&nbsp;the&nbsp;equation&nbsp;``<a href="#MySeries-abs">abs</a>(index[indexer]&nbsp;-&nbsp;target)&nbsp;&lt;=&nbsp;tolerance``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;some&nbsp;fictional&nbsp;data.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;['Firefox',&nbsp;'Chrome',&nbsp;'Safari',&nbsp;'IE10',&nbsp;'Konqueror']<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'http_status':&nbsp;[200,200,404,404,301],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'response_time':&nbsp;[0.04,&nbsp;0.02,&nbsp;0.07,&nbsp;0.08,&nbsp;1.0]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Firefox&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.04<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Konqueror&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;301&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.00<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;new&nbsp;index&nbsp;and&nbsp;reindex&nbsp;the&nbsp;dataframe.&nbsp;By&nbsp;default<br>
-values&nbsp;in&nbsp;the&nbsp;new&nbsp;index&nbsp;that&nbsp;do&nbsp;not&nbsp;have&nbsp;corresponding<br>
-records&nbsp;in&nbsp;the&nbsp;dataframe&nbsp;are&nbsp;assigned&nbsp;``NaN``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;new_index=&nbsp;['Safari',&nbsp;'Iceweasel',&nbsp;'Comodo&nbsp;Dragon',&nbsp;'IE10',<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Chrome']<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-reindex">reindex</a>(new_index)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-We&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;by&nbsp;passing&nbsp;a&nbsp;value&nbsp;to<br>
-the&nbsp;keyword&nbsp;``fill_value``.&nbsp;Because&nbsp;the&nbsp;index&nbsp;is&nbsp;not&nbsp;monotonically<br>
-increasing&nbsp;or&nbsp;decreasing,&nbsp;we&nbsp;cannot&nbsp;use&nbsp;arguments&nbsp;to&nbsp;the&nbsp;keyword<br>
-``method``&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``&nbsp;values.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-reindex">reindex</a>(new_index,&nbsp;fill_value=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-reindex">reindex</a>(new_index,&nbsp;fill_value='missing')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-To&nbsp;further&nbsp;illustrate&nbsp;the&nbsp;filling&nbsp;functionality&nbsp;in<br>
-``reindex``,&nbsp;we&nbsp;will&nbsp;create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;a<br>
-monotonically&nbsp;increasing&nbsp;index&nbsp;(for&nbsp;example,&nbsp;a&nbsp;sequence<br>
-of&nbsp;dates).<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index&nbsp;=&nbsp;pd.date_range('1/1/2010',&nbsp;periods=6,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"prices":&nbsp;[100,&nbsp;101,&nbsp;np.nan,&nbsp;100,&nbsp;89,&nbsp;88]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=date_index)<br>
-&gt;&gt;&gt;&nbsp;df2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-&nbsp;<br>
-Suppose&nbsp;we&nbsp;decide&nbsp;to&nbsp;expand&nbsp;the&nbsp;dataframe&nbsp;to&nbsp;cover&nbsp;a&nbsp;wider<br>
-date&nbsp;range.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index2&nbsp;=&nbsp;pd.date_range('12/29/2009',&nbsp;periods=10,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#MySeries-reindex">reindex</a>(date_index2)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-The&nbsp;index&nbsp;entries&nbsp;that&nbsp;did&nbsp;not&nbsp;have&nbsp;a&nbsp;value&nbsp;in&nbsp;the&nbsp;original&nbsp;data&nbsp;frame<br>
-(for&nbsp;example,&nbsp;'2009-12-29')&nbsp;are&nbsp;by&nbsp;default&nbsp;filled&nbsp;with&nbsp;``NaN``.<br>
-If&nbsp;desired,&nbsp;we&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;using&nbsp;one&nbsp;of&nbsp;several<br>
-options.<br>
-&nbsp;<br>
-For&nbsp;example,&nbsp;to&nbsp;backpropagate&nbsp;the&nbsp;last&nbsp;valid&nbsp;value&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values,&nbsp;pass&nbsp;``bfill``&nbsp;as&nbsp;an&nbsp;argument&nbsp;to&nbsp;the&nbsp;``method``&nbsp;keyword.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#MySeries-reindex">reindex</a>(date_index2,&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;the&nbsp;``NaN``&nbsp;value&nbsp;present&nbsp;in&nbsp;the&nbsp;original&nbsp;dataframe<br>
-(at&nbsp;index&nbsp;value&nbsp;2010-01-03)&nbsp;will&nbsp;not&nbsp;be&nbsp;filled&nbsp;by&nbsp;any&nbsp;of&nbsp;the<br>
-value&nbsp;propagation&nbsp;schemes.&nbsp;This&nbsp;is&nbsp;because&nbsp;filling&nbsp;while&nbsp;reindexing<br>
-does&nbsp;not&nbsp;look&nbsp;at&nbsp;dataframe&nbsp;values,&nbsp;but&nbsp;only&nbsp;compares&nbsp;the&nbsp;original&nbsp;and<br>
-desired&nbsp;indexes.&nbsp;If&nbsp;you&nbsp;do&nbsp;want&nbsp;to&nbsp;fill&nbsp;in&nbsp;the&nbsp;``NaN``&nbsp;values&nbsp;present<br>
-in&nbsp;the&nbsp;original&nbsp;dataframe,&nbsp;use&nbsp;the&nbsp;``<a href="#MySeries-fillna">fillna</a>()``&nbsp;method.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-reindex_axis"><strong>reindex_axis</strong></a>(self, labels, axis=0, **kwargs)</dt><dd><tt>for&nbsp;compatibility&nbsp;with&nbsp;higher&nbsp;dims</tt></dd></dl>
-
-<dl><dt><a name="MySeries-rename"><strong>rename</strong></a>(self, index=None, **kwargs)</dt><dd><tt>Alter&nbsp;axes&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.&nbsp;Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be<br>
-unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in&nbsp;a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left<br>
-as-is.&nbsp;Extra&nbsp;labels&nbsp;listed&nbsp;don't&nbsp;throw&nbsp;an&nbsp;error.&nbsp;Alternatively,&nbsp;change<br>
-``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;with&nbsp;a&nbsp;scalar&nbsp;value&nbsp;(<a href="pandas.core.series.html#Series">Series</a>&nbsp;only).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Scalar&nbsp;or&nbsp;list-like&nbsp;will&nbsp;alter&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;attribute,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;raise&nbsp;on&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;Panel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict-like&nbsp;or&nbsp;functions&nbsp;are&nbsp;transformations&nbsp;to&nbsp;apply&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;that&nbsp;axis'&nbsp;values<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;If&nbsp;True&nbsp;then&nbsp;value&nbsp;of&nbsp;copy&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;ignored.<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;MultiIndex,&nbsp;only&nbsp;rename&nbsp;labels&nbsp;in&nbsp;the&nbsp;specified<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename_axis<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-rename">rename</a>("my_name")&nbsp;#&nbsp;scalar,&nbsp;changes&nbsp;<a href="pandas.core.series.html#Series">Series</a>.name<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;my_name,&nbsp;dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-rename">rename</a>(lambda&nbsp;x:&nbsp;x&nbsp;**&nbsp;2)&nbsp;&nbsp;#&nbsp;function,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-rename">rename</a>({1:&nbsp;3,&nbsp;2:&nbsp;5})&nbsp;&nbsp;#&nbsp;mapping,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-rename">rename</a>(2)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-TypeError:&nbsp;'int'&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;not&nbsp;callable<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"B":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;c<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"C":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="MySeries-reorder_levels"><strong>reorder_levels</strong></a>(self, order)</dt><dd><tt>Rearrange&nbsp;index&nbsp;levels&nbsp;using&nbsp;input&nbsp;order.&nbsp;May&nbsp;not&nbsp;drop&nbsp;or&nbsp;duplicate<br>
-levels<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-order&nbsp;:&nbsp;list&nbsp;of&nbsp;int&nbsp;representing&nbsp;new&nbsp;level&nbsp;order.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(reference&nbsp;level&nbsp;by&nbsp;number&nbsp;or&nbsp;key)<br>
-axis&nbsp;:&nbsp;where&nbsp;to&nbsp;reorder&nbsp;levels<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-type&nbsp;of&nbsp;caller&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-repeat"><strong>repeat</strong></a>(self, repeats, *args, **kwargs)</dt><dd><tt>Repeat&nbsp;elements&nbsp;of&nbsp;an&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Refer&nbsp;to&nbsp;`numpy.<a href="numpy.html#ndarray">ndarray</a>.repeat`<br>
-for&nbsp;more&nbsp;information&nbsp;about&nbsp;the&nbsp;`repeats`&nbsp;argument.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.repeat</tt></dd></dl>
-
-<dl><dt><a name="MySeries-reset_index"><strong>reset_index</strong></a>(self, level=None, drop=False, name=None, inplace=False)</dt><dd><tt>Analogous&nbsp;to&nbsp;the&nbsp;:meth:`pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.reset_index`&nbsp;function,&nbsp;see<br>
-docstring&nbsp;there.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;tuple,&nbsp;or&nbsp;list,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Only&nbsp;remove&nbsp;the&nbsp;given&nbsp;levels&nbsp;from&nbsp;the&nbsp;index.&nbsp;Removes&nbsp;all&nbsp;levels&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-drop&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;not&nbsp;try&nbsp;to&nbsp;insert&nbsp;index&nbsp;into&nbsp;dataframe&nbsp;columns<br>
-name&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;name&nbsp;of&nbsp;the&nbsp;column&nbsp;corresponding&nbsp;to&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Modify&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place&nbsp;(do&nbsp;not&nbsp;create&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-Returns<br>
-----------<br>
-resetted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;if&nbsp;drop&nbsp;==&nbsp;True</tt></dd></dl>
-
-<dl><dt><a name="MySeries-reshape"><strong>reshape</strong></a>(self, *args, **kwargs)</dt><dd><tt>DEPRECATED:&nbsp;calling&nbsp;this&nbsp;method&nbsp;will&nbsp;raise&nbsp;an&nbsp;error&nbsp;in&nbsp;a<br>
-future&nbsp;release.&nbsp;Please&nbsp;call&nbsp;``.values.<a href="#MySeries-reshape">reshape</a>(...)``&nbsp;instead.<br>
-&nbsp;<br>
-return&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;with&nbsp;the&nbsp;values&nbsp;shape<br>
-if&nbsp;the&nbsp;specified&nbsp;shape&nbsp;matches&nbsp;exactly&nbsp;the&nbsp;current&nbsp;shape,&nbsp;then<br>
-return&nbsp;self&nbsp;(for&nbsp;compat)<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.reshape</tt></dd></dl>
-
-<dl><dt><a name="MySeries-rfloordiv"><strong>rfloordiv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rfloordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;//&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.floordiv</tt></dd></dl>
-
-<dl><dt><a name="MySeries-rmod"><strong>rmod</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Modulo&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;%&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.mod</tt></dd></dl>
-
-<dl><dt><a name="MySeries-rmul"><strong>rmul</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;*&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.mul</tt></dd></dl>
-
-<dl><dt><a name="MySeries-rolling"><strong>rolling</strong></a>(self, window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0, closed=None)</dt><dd><tt>Provides&nbsp;rolling&nbsp;window&nbsp;calculcations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-window&nbsp;:&nbsp;int,&nbsp;or&nbsp;offset<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Size&nbsp;of&nbsp;the&nbsp;moving&nbsp;window.&nbsp;This&nbsp;is&nbsp;the&nbsp;number&nbsp;of&nbsp;observations&nbsp;used&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;calculating&nbsp;the&nbsp;statistic.&nbsp;Each&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;fixed&nbsp;size.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;its&nbsp;an&nbsp;offset&nbsp;then&nbsp;this&nbsp;will&nbsp;be&nbsp;the&nbsp;time&nbsp;period&nbsp;of&nbsp;each&nbsp;window.&nbsp;Each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;variable&nbsp;sized&nbsp;based&nbsp;on&nbsp;the&nbsp;observations&nbsp;included&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;time-period.&nbsp;This&nbsp;is&nbsp;only&nbsp;valid&nbsp;for&nbsp;datetimelike&nbsp;indexes.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;new&nbsp;in&nbsp;0.19.0<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).&nbsp;For&nbsp;a&nbsp;window&nbsp;that&nbsp;is&nbsp;specified&nbsp;by&nbsp;an&nbsp;offset,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;will&nbsp;default&nbsp;to&nbsp;1.<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-win_type&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Provide&nbsp;a&nbsp;window&nbsp;type.&nbsp;See&nbsp;the&nbsp;notes&nbsp;below.<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;on&nbsp;which&nbsp;to&nbsp;calculate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;rolling&nbsp;window,&nbsp;rather&nbsp;than&nbsp;the&nbsp;index<br>
-closed&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;the&nbsp;interval&nbsp;closed&nbsp;on&nbsp;the&nbsp;'right',&nbsp;'left',&nbsp;'both'&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'neither'&nbsp;endpoints.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;offset-based&nbsp;windows,&nbsp;it&nbsp;defaults&nbsp;to&nbsp;'right'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;fixed&nbsp;windows,&nbsp;defaults&nbsp;to&nbsp;'both'.&nbsp;Remaining&nbsp;cases&nbsp;not&nbsp;implemented<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;fixed&nbsp;windows.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;or&nbsp;Rolling&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;using&nbsp;the&nbsp;'triang'<br>
-window&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-rolling">rolling</a>(2,&nbsp;win_type='triang').<a href="#MySeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.5<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;min_periods&nbsp;defaults<br>
-to&nbsp;the&nbsp;window&nbsp;length.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-rolling">rolling</a>(2).<a href="#MySeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Same&nbsp;as&nbsp;above,&nbsp;but&nbsp;explicity&nbsp;set&nbsp;the&nbsp;min_periods<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-rolling">rolling</a>(2,&nbsp;min_periods=1).<a href="#MySeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;2.0<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-A&nbsp;ragged&nbsp;(meaning&nbsp;not-a-regular&nbsp;frequency),&nbsp;time-indexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]},<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;=&nbsp;[pd.Timestamp('20130101&nbsp;09:00:00'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:02'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:03'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:05'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:06')])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;2.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Contrasting&nbsp;to&nbsp;an&nbsp;integer&nbsp;rolling&nbsp;window,&nbsp;this&nbsp;will&nbsp;roll&nbsp;a&nbsp;variable<br>
-length&nbsp;window&nbsp;corresponding&nbsp;to&nbsp;the&nbsp;time&nbsp;period.<br>
-The&nbsp;default&nbsp;for&nbsp;min_periods&nbsp;is&nbsp;1.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-rolling">rolling</a>('2s').<a href="#MySeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;3.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offsets&nbsp;&amp;&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-The&nbsp;recognized&nbsp;win_types&nbsp;are:<br>
-&nbsp;<br>
-*&nbsp;``boxcar``<br>
-*&nbsp;``triang``<br>
-*&nbsp;``blackman``<br>
-*&nbsp;``hamming``<br>
-*&nbsp;``bartlett``<br>
-*&nbsp;``parzen``<br>
-*&nbsp;``bohman``<br>
-*&nbsp;``blackmanharris``<br>
-*&nbsp;``nuttall``<br>
-*&nbsp;``barthann``<br>
-*&nbsp;``kaiser``&nbsp;(needs&nbsp;beta)<br>
-*&nbsp;``gaussian``&nbsp;(needs&nbsp;std)<br>
-*&nbsp;``general_gaussian``&nbsp;(needs&nbsp;power,&nbsp;width)<br>
-*&nbsp;``slepian``&nbsp;(needs&nbsp;width).</tt></dd></dl>
-
-<dl><dt><a name="MySeries-round"><strong>round</strong></a>(self, decimals=0, *args, **kwargs)</dt><dd><tt>Round&nbsp;each&nbsp;value&nbsp;in&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;the&nbsp;given&nbsp;number&nbsp;of&nbsp;decimals.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-decimals&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;round&nbsp;to&nbsp;(default:&nbsp;0).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;decimals&nbsp;is&nbsp;negative,&nbsp;it&nbsp;specifies&nbsp;the&nbsp;number&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;positions&nbsp;to&nbsp;the&nbsp;left&nbsp;of&nbsp;the&nbsp;decimal&nbsp;point.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.series.html#Series">Series</a>&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.around<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.round</tt></dd></dl>
-
-<dl><dt><a name="MySeries-rpow"><strong>rpow</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rpow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;**&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.pow</tt></dd></dl>
-
-<dl><dt><a name="MySeries-rsub"><strong>rsub</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rsub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;-&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.sub</tt></dd></dl>
-
-<dl><dt><a name="MySeries-rtruediv"><strong>rtruediv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="MySeries-searchsorted"><strong>searchsorted</strong></a>(self, value, side='left', sorter=None)</dt><dd><tt>Find&nbsp;indices&nbsp;where&nbsp;elements&nbsp;should&nbsp;be&nbsp;inserted&nbsp;to&nbsp;maintain&nbsp;order.<br>
-&nbsp;<br>
-Find&nbsp;the&nbsp;indices&nbsp;into&nbsp;a&nbsp;sorted&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;`self`&nbsp;such&nbsp;that,&nbsp;if&nbsp;the<br>
-corresponding&nbsp;elements&nbsp;in&nbsp;`value`&nbsp;were&nbsp;inserted&nbsp;before&nbsp;the&nbsp;indices,<br>
-the&nbsp;order&nbsp;of&nbsp;`self`&nbsp;would&nbsp;be&nbsp;preserved.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;array_like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Values&nbsp;to&nbsp;insert&nbsp;into&nbsp;`self`.<br>
-side&nbsp;:&nbsp;{'left',&nbsp;'right'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'left',&nbsp;the&nbsp;index&nbsp;of&nbsp;the&nbsp;first&nbsp;suitable&nbsp;location&nbsp;found&nbsp;is&nbsp;given.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'right',&nbsp;return&nbsp;the&nbsp;last&nbsp;such&nbsp;index.&nbsp;&nbsp;If&nbsp;there&nbsp;is&nbsp;no&nbsp;suitable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index,&nbsp;return&nbsp;either&nbsp;0&nbsp;or&nbsp;N&nbsp;(where&nbsp;N&nbsp;is&nbsp;the&nbsp;length&nbsp;of&nbsp;`self`).<br>
-sorter&nbsp;:&nbsp;1-D&nbsp;array_like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Optional&nbsp;array&nbsp;of&nbsp;integer&nbsp;indices&nbsp;that&nbsp;sort&nbsp;`self`&nbsp;into&nbsp;ascending<br>
-&nbsp;&nbsp;&nbsp;&nbsp;order.&nbsp;They&nbsp;are&nbsp;typically&nbsp;the&nbsp;result&nbsp;of&nbsp;``np.argsort``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-indices&nbsp;:&nbsp;array&nbsp;of&nbsp;ints<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Array">Array</a>&nbsp;of&nbsp;insertion&nbsp;points&nbsp;with&nbsp;the&nbsp;same&nbsp;shape&nbsp;as&nbsp;`value`.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.searchsorted<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Binary&nbsp;search&nbsp;is&nbsp;used&nbsp;to&nbsp;find&nbsp;the&nbsp;required&nbsp;insertion&nbsp;points.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#MySeries-searchsorted">searchsorted</a>(4)<br>
-array([3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#MySeries-searchsorted">searchsorted</a>([0,&nbsp;4])<br>
-array([0,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#MySeries-searchsorted">searchsorted</a>([1,&nbsp;3],&nbsp;side='left')<br>
-array([0,&nbsp;2])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#MySeries-searchsorted">searchsorted</a>([1,&nbsp;3],&nbsp;side='right')<br>
-array([1,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.Categorical(['apple',&nbsp;'bread',&nbsp;'bread',&nbsp;'cheese',&nbsp;'milk'&nbsp;])<br>
-[apple,&nbsp;bread,&nbsp;bread,&nbsp;cheese,&nbsp;milk]<br>
-Categories&nbsp;(4,&nbsp;<a href="builtins.html#object">object</a>):&nbsp;[apple&nbsp;&lt;&nbsp;bread&nbsp;&lt;&nbsp;cheese&nbsp;&lt;&nbsp;milk]<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#MySeries-searchsorted">searchsorted</a>('bread')<br>
-array([1])&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;Note:&nbsp;an&nbsp;array,&nbsp;not&nbsp;a&nbsp;scalar<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#MySeries-searchsorted">searchsorted</a>(['bread'])<br>
-array([1])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#MySeries-searchsorted">searchsorted</a>(['bread',&nbsp;'eggs'])<br>
-array([1,&nbsp;4])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#MySeries-searchsorted">searchsorted</a>(['bread',&nbsp;'eggs'],&nbsp;side='right')<br>
-array([3,&nbsp;4])&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;eggs&nbsp;before&nbsp;milk</tt></dd></dl>
-
-<dl><dt><a name="MySeries-sem"><strong>sem</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;standard&nbsp;error&nbsp;of&nbsp;the&nbsp;mean&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sem&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-set_value"><strong>set_value</strong></a>(self, label, value, takeable=False)</dt><dd><tt>Quickly&nbsp;set&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;label.&nbsp;If&nbsp;label&nbsp;is&nbsp;not&nbsp;contained,&nbsp;a<br>
-new&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;created&nbsp;with&nbsp;the&nbsp;label&nbsp;placed&nbsp;at&nbsp;the&nbsp;end&nbsp;of&nbsp;the&nbsp;result<br>
-index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-label&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Partial&nbsp;indexing&nbsp;with&nbsp;MultiIndex&nbsp;not&nbsp;allowed<br>
-value&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Scalar&nbsp;value<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-series&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;label&nbsp;is&nbsp;contained,&nbsp;will&nbsp;be&nbsp;reference&nbsp;to&nbsp;calling&nbsp;<a href="pandas.core.series.html#Series">Series</a>,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-shift"><strong>shift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;index&nbsp;by&nbsp;desired&nbsp;number&nbsp;of&nbsp;periods&nbsp;with&nbsp;an&nbsp;optional&nbsp;time&nbsp;freq<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM').<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;Notes.<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'}<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;specified&nbsp;then&nbsp;the&nbsp;index&nbsp;values&nbsp;are&nbsp;shifted&nbsp;but&nbsp;the&nbsp;data<br>
-is&nbsp;not&nbsp;realigned.&nbsp;That&nbsp;is,&nbsp;use&nbsp;freq&nbsp;if&nbsp;you&nbsp;would&nbsp;like&nbsp;to&nbsp;extend&nbsp;the<br>
-index&nbsp;when&nbsp;shifting&nbsp;and&nbsp;preserve&nbsp;the&nbsp;original&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-skew"><strong>skew</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;skew&nbsp;over&nbsp;requested&nbsp;axis<br>
-Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-skew&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-sort_index"><strong>sort_index</strong></a>(self, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True)</dt><dd><tt>Sort&nbsp;<a href="builtins.html#object">object</a>&nbsp;by&nbsp;labels&nbsp;(along&nbsp;an&nbsp;axis)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;index&nbsp;to&nbsp;direct&nbsp;sorting<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name&nbsp;or&nbsp;list&nbsp;of&nbsp;ints&nbsp;or&nbsp;list&nbsp;of&nbsp;level&nbsp;names<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None,&nbsp;sort&nbsp;on&nbsp;values&nbsp;in&nbsp;specified&nbsp;index&nbsp;level(s)<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;MultiIndex.<br>
-sort_remaining&nbsp;:&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;true&nbsp;and&nbsp;sorting&nbsp;by&nbsp;level&nbsp;and&nbsp;index&nbsp;is&nbsp;multilevel,&nbsp;sort&nbsp;by&nbsp;other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;levels&nbsp;too&nbsp;(in&nbsp;order)&nbsp;after&nbsp;sorting&nbsp;by&nbsp;specified&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-sort_values"><strong>sort_values</strong></a>(self, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')</dt><dd><tt>Sort&nbsp;by&nbsp;the&nbsp;values&nbsp;along&nbsp;either&nbsp;axis<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;direct&nbsp;sorting<br>
-ascending&nbsp;:&nbsp;bool&nbsp;or&nbsp;list&nbsp;of&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending.&nbsp;Specify&nbsp;list&nbsp;for&nbsp;multiple&nbsp;sort<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;orders.&nbsp;&nbsp;If&nbsp;this&nbsp;is&nbsp;a&nbsp;list&nbsp;of&nbsp;bools,&nbsp;must&nbsp;match&nbsp;the&nbsp;length&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;by.<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-sortlevel"><strong>sortlevel</strong></a>(self, level=0, ascending=True, sort_remaining=True)</dt><dd><tt>DEPRECATED:&nbsp;use&nbsp;:meth:`<a href="pandas.core.series.html#Series">Series</a>.sort_index`<br>
-&nbsp;<br>
-Sort&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;MultiIndex&nbsp;by&nbsp;chosen&nbsp;level.&nbsp;Data&nbsp;will&nbsp;be<br>
-lexicographically&nbsp;sorted&nbsp;by&nbsp;the&nbsp;chosen&nbsp;level&nbsp;followed&nbsp;by&nbsp;the&nbsp;other<br>
-levels&nbsp;(in&nbsp;order)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-ascending&nbsp;:&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.<a href="#MySeries-sort_index">sort_index</a>(level=...)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-std"><strong>std</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;sample&nbsp;standard&nbsp;deviation&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-std&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-sub"><strong>sub</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="MySeries-subtract"><strong>subtract</strong></a> = sub(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="MySeries-sum"><strong>sum</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;sum&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sum&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-swaplevel"><strong>swaplevel</strong></a>(self, i=-2, j=-1, copy=True)</dt><dd><tt>Swap&nbsp;levels&nbsp;i&nbsp;and&nbsp;j&nbsp;in&nbsp;a&nbsp;MultiIndex<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-i,&nbsp;j&nbsp;:&nbsp;int,&nbsp;string&nbsp;(can&nbsp;be&nbsp;mixed)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level&nbsp;of&nbsp;index&nbsp;to&nbsp;be&nbsp;swapped.&nbsp;Can&nbsp;pass&nbsp;level&nbsp;name&nbsp;as&nbsp;string.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-swapped&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-..&nbsp;versionchanged::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;The&nbsp;indexes&nbsp;``i``&nbsp;and&nbsp;``j``&nbsp;are&nbsp;now&nbsp;optional,&nbsp;and&nbsp;default&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;the&nbsp;two&nbsp;innermost&nbsp;levels&nbsp;of&nbsp;the&nbsp;index.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-take"><strong>take</strong></a>(self, indices, axis=0, convert=True, is_copy=False, **kwargs)</dt><dd><tt>return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;corresponding&nbsp;to&nbsp;requested&nbsp;indices<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-indices&nbsp;:&nbsp;list&nbsp;/&nbsp;array&nbsp;of&nbsp;ints<br>
-convert&nbsp;:&nbsp;translate&nbsp;negative&nbsp;to&nbsp;positive&nbsp;indices&nbsp;(default)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-taken&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.take</tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_csv"><strong>to_csv</strong></a>(self, path=None, index=True, sep=',', na_rep='', float_format=None, header=False, index_label=None, mode='w', encoding=None, date_format=None, decimal='.')</dt><dd><tt>Write&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;a&nbsp;comma-separated&nbsp;values&nbsp;(csv)&nbsp;file<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;or&nbsp;file&nbsp;handle,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;<a href="builtins.html#object">object</a>,&nbsp;if&nbsp;None&nbsp;is&nbsp;provided&nbsp;the&nbsp;result&nbsp;is&nbsp;returned&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string.<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-header&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;series&nbsp;name<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-mode&nbsp;:&nbsp;Python&nbsp;write&nbsp;mode,&nbsp;default&nbsp;'w'<br>
-sep&nbsp;:&nbsp;character,&nbsp;default&nbsp;","<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter&nbsp;for&nbsp;the&nbsp;output&nbsp;file.<br>
-encoding&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;if&nbsp;the&nbsp;contents&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;non-ascii,&nbsp;for&nbsp;python&nbsp;versions&nbsp;prior&nbsp;to&nbsp;3<br>
-date_format:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;datetime&nbsp;objects.<br>
-decimal:&nbsp;string,&nbsp;default&nbsp;'.'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Character&nbsp;recognized&nbsp;as&nbsp;decimal&nbsp;separator.&nbsp;E.g.&nbsp;use&nbsp;','&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;European&nbsp;data</tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_dict"><strong>to_dict</strong></a>(self)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;{label&nbsp;-&gt;&nbsp;value}&nbsp;dict<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value_dict&nbsp;:&nbsp;dict</tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_excel"><strong>to_excel</strong></a>(self, excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True)</dt><dd><tt>Write&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;an&nbsp;excel&nbsp;sheet<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel_writer&nbsp;:&nbsp;string&nbsp;or&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;existing&nbsp;ExcelWriter<br>
-sheet_name&nbsp;:&nbsp;string,&nbsp;default&nbsp;'Sheet1'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;sheet&nbsp;which&nbsp;will&nbsp;contain&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Columns&nbsp;to&nbsp;write<br>
-header&nbsp;:&nbsp;boolean&nbsp;or&nbsp;list&nbsp;of&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given&nbsp;it&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;assumed&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-startrow&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;row&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-startcol&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;column&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-engine&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;write&nbsp;engine&nbsp;to&nbsp;use&nbsp;-&nbsp;you&nbsp;can&nbsp;also&nbsp;set&nbsp;this&nbsp;via&nbsp;the&nbsp;options<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsx.writer``,&nbsp;``io.excel.xls.writer``,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsm.writer``.<br>
-merge_cells&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;MultiIndex&nbsp;and&nbsp;Hierarchical&nbsp;Rows&nbsp;as&nbsp;merged&nbsp;cells.<br>
-encoding:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;encoding&nbsp;of&nbsp;the&nbsp;resulting&nbsp;excel&nbsp;file.&nbsp;Only&nbsp;necessary&nbsp;for&nbsp;xlwt,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;writers&nbsp;support&nbsp;unicode&nbsp;natively.<br>
-inf_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;'inf'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Representation&nbsp;for&nbsp;infinity&nbsp;(there&nbsp;is&nbsp;no&nbsp;native&nbsp;representation&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;infinity&nbsp;in&nbsp;Excel)<br>
-freeze_panes&nbsp;:&nbsp;tuple&nbsp;of&nbsp;integer&nbsp;(length&nbsp;2),&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;the&nbsp;one-based&nbsp;bottommost&nbsp;row&nbsp;and&nbsp;rightmost&nbsp;column&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;to&nbsp;be&nbsp;frozen<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;passing&nbsp;an&nbsp;existing&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a>,&nbsp;then&nbsp;the&nbsp;sheet&nbsp;will&nbsp;be&nbsp;added<br>
-to&nbsp;the&nbsp;existing&nbsp;workbook.&nbsp;&nbsp;This&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;save&nbsp;different<br>
-DataFrames&nbsp;to&nbsp;one&nbsp;workbook:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;writer&nbsp;=&nbsp;pd.ExcelWriter('output.xlsx')<br>
-&gt;&gt;&gt;&nbsp;df1.<a href="#MySeries-to_excel">to_excel</a>(writer,'Sheet1')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#MySeries-to_excel">to_excel</a>(writer,'Sheet2')<br>
-&gt;&gt;&gt;&nbsp;writer.save()<br>
-&nbsp;<br>
-For&nbsp;compatibility&nbsp;with&nbsp;to_csv,&nbsp;to_excel&nbsp;serializes&nbsp;lists&nbsp;and&nbsp;dicts&nbsp;to<br>
-strings&nbsp;before&nbsp;writing.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_frame"><strong>to_frame</strong></a>(self, name=None)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-name&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;passed&nbsp;name&nbsp;should&nbsp;substitute&nbsp;for&nbsp;the&nbsp;series&nbsp;name&nbsp;(if&nbsp;it&nbsp;has<br>
-&nbsp;&nbsp;&nbsp;&nbsp;one).<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-data_frame&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_period"><strong>to_period</strong></a>(self, freq=None, copy=True)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;from&nbsp;DatetimeIndex&nbsp;to&nbsp;PeriodIndex&nbsp;with&nbsp;desired<br>
-frequency&nbsp;(inferred&nbsp;from&nbsp;index&nbsp;if&nbsp;not&nbsp;passed)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;PeriodIndex</tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_sparse"><strong>to_sparse</strong></a>(self, kind='block', fill_value=None)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;SparseSeries<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-kind&nbsp;:&nbsp;{'block',&nbsp;'integer'}<br>
-fill_value&nbsp;:&nbsp;float,&nbsp;defaults&nbsp;to&nbsp;NaN&nbsp;(missing)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sp&nbsp;:&nbsp;SparseSeries</tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_string"><strong>to_string</strong></a>(self, buf=None, na_rep='NaN', float_format=None, header=True, index=True, length=False, dtype=False, name=False, max_rows=None)</dt><dd><tt>Render&nbsp;a&nbsp;string&nbsp;representation&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-buf&nbsp;:&nbsp;StringIO-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;buffer&nbsp;to&nbsp;write&nbsp;to<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;representation&nbsp;of&nbsp;NAN&nbsp;to&nbsp;use,&nbsp;default&nbsp;'NaN'<br>
-float_format&nbsp;:&nbsp;one-parameter&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;if&nbsp;they&nbsp;are&nbsp;floats<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None<br>
-header:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;header&nbsp;(index&nbsp;name)<br>
-index&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;index&nbsp;(row)&nbsp;labels,&nbsp;default&nbsp;True<br>
-length&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;length<br>
-dtype&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;dtype<br>
-name&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;name&nbsp;if&nbsp;not&nbsp;None<br>
-max_rows&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;rows&nbsp;to&nbsp;show&nbsp;before&nbsp;truncating.&nbsp;If&nbsp;None,&nbsp;show<br>
-&nbsp;&nbsp;&nbsp;&nbsp;all.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-formatted&nbsp;:&nbsp;string&nbsp;(if&nbsp;not&nbsp;buffer&nbsp;passed)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_timestamp"><strong>to_timestamp</strong></a>(self, freq=None, how='start', copy=True)</dt><dd><tt>Cast&nbsp;to&nbsp;datetimeindex&nbsp;of&nbsp;timestamps,&nbsp;at&nbsp;*beginning*&nbsp;of&nbsp;period<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default&nbsp;frequency&nbsp;of&nbsp;PeriodIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Desired&nbsp;frequency<br>
-how&nbsp;:&nbsp;{'s',&nbsp;'e',&nbsp;'start',&nbsp;'end'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Convention&nbsp;for&nbsp;converting&nbsp;period&nbsp;to&nbsp;timestamp;&nbsp;start&nbsp;of&nbsp;period<br>
-&nbsp;&nbsp;&nbsp;&nbsp;vs.&nbsp;end<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;DatetimeIndex</tt></dd></dl>
-
-<dl><dt><a name="MySeries-tolist"><strong>tolist</strong></a>(self)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;a&nbsp;nested&nbsp;list</tt></dd></dl>
-
-<dl><dt><a name="MySeries-transform"><strong>transform</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Call&nbsp;function&nbsp;producing&nbsp;a&nbsp;like-indexed&nbsp;NDFrame<br>
-and&nbsp;return&nbsp;a&nbsp;NDFrame&nbsp;with&nbsp;the&nbsp;transformed&nbsp;values`<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;apply&nbsp;to&nbsp;column<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-transformed&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(10,&nbsp;3),&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.date_range('1/1/2000',&nbsp;periods=10))<br>
-df.iloc[3:7]&nbsp;=&nbsp;np.nan<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-transform">transform</a>(lambda&nbsp;x:&nbsp;(x&nbsp;-&nbsp;x.<a href="#MySeries-mean">mean</a>())&nbsp;/&nbsp;x.<a href="#MySeries-std">std</a>())<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-2000-01-01&nbsp;&nbsp;0.579457&nbsp;&nbsp;1.236184&nbsp;&nbsp;0.123424<br>
-2000-01-02&nbsp;&nbsp;0.370357&nbsp;-0.605875&nbsp;-1.231325<br>
-2000-01-03&nbsp;&nbsp;1.455756&nbsp;-0.277446&nbsp;&nbsp;0.288967<br>
-2000-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-08&nbsp;-0.498658&nbsp;&nbsp;1.274522&nbsp;&nbsp;1.642524<br>
-2000-01-09&nbsp;-0.540524&nbsp;-1.012676&nbsp;-0.828968<br>
-2000-01-10&nbsp;-1.366388&nbsp;-0.614710&nbsp;&nbsp;0.005378<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.NDFrame.aggregate<br>
-pandas.NDFrame.apply</tt></dd></dl>
-
-<dl><dt><a name="MySeries-truediv"><strong>truediv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="MySeries-unique"><strong>unique</strong></a>(self)</dt><dd><tt>Return&nbsp;unique&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.&nbsp;Uniques&nbsp;are&nbsp;returned&nbsp;in&nbsp;order<br>
-of&nbsp;appearance,&nbsp;this&nbsp;does&nbsp;NOT&nbsp;sort.&nbsp;Hash&nbsp;table-based&nbsp;unique.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-values&nbsp;:&nbsp;1d&nbsp;array-like<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-unique&nbsp;values.<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;an&nbsp;Index,&nbsp;the&nbsp;return&nbsp;is&nbsp;an&nbsp;Index<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;a&nbsp;Categorical&nbsp;dtype,&nbsp;the&nbsp;return&nbsp;is&nbsp;a&nbsp;Categorical<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>/<a href="numpy.html#ndarray">ndarray</a>,&nbsp;the&nbsp;return&nbsp;will&nbsp;be&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-unique<br>
-Index.unique<br>
-<a href="pandas.core.series.html#Series">Series</a>.unique</tt></dd></dl>
-
-<dl><dt><a name="MySeries-unstack"><strong>unstack</strong></a>(self, level=-1, fill_value=None)</dt><dd><tt>Unstack,&nbsp;a.k.a.&nbsp;pivot,&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;MultiIndex&nbsp;to&nbsp;produce&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-The&nbsp;level&nbsp;involved&nbsp;will&nbsp;automatically&nbsp;get&nbsp;sorted.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;string,&nbsp;or&nbsp;list&nbsp;of&nbsp;these,&nbsp;default&nbsp;last&nbsp;level<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level(s)&nbsp;to&nbsp;unstack,&nbsp;can&nbsp;pass&nbsp;level&nbsp;name<br>
-fill_value&nbsp;:&nbsp;replace&nbsp;NaN&nbsp;with&nbsp;this&nbsp;value&nbsp;if&nbsp;the&nbsp;unstack&nbsp;produces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.18.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;4],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.MultiIndex.from_product([['one',&nbsp;'two'],&nbsp;['a',&nbsp;'b']]))<br>
-&gt;&gt;&gt;&nbsp;s<br>
-one&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-two&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-unstack">unstack</a>(level=-1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b<br>
-one&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-two&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-unstack">unstack</a>(level=0)<br>
-&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;two<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-unstacked&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-update"><strong>update</strong></a>(self, other)</dt><dd><tt>Modify&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place&nbsp;using&nbsp;non-NA&nbsp;values&nbsp;from&nbsp;passed<br>
-<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Aligns&nbsp;on&nbsp;index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><strong>valid</strong> <em>lambda</em> self, inplace=False, **kwargs</dt></dl>
-
-<dl><dt><a name="MySeries-var"><strong>var</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;variance&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-var&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-view"><strong>view</strong></a>(self, dtype=None)</dt></dl>
-
-<hr>
-Class methods inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><a name="MySeries-from_array"><strong>from_array</strong></a>(arr, index=None, name=None, dtype=None, copy=False, fastpath=False)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt></dl>
-
-<dl><dt><a name="MySeries-from_csv"><strong>from_csv</strong></a>(path, sep=',', parse_dates=True, header=None, index_col=0, encoding=None, infer_datetime_format=False)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Read&nbsp;CSV&nbsp;file&nbsp;(DISCOURAGED,&nbsp;please&nbsp;use&nbsp;:func:`pandas.read_csv`<br>
-instead).<br>
-&nbsp;<br>
-It&nbsp;is&nbsp;preferable&nbsp;to&nbsp;use&nbsp;the&nbsp;more&nbsp;powerful&nbsp;:func:`pandas.read_csv`<br>
-for&nbsp;most&nbsp;general&nbsp;purposes,&nbsp;but&nbsp;``from_csv``&nbsp;makes&nbsp;for&nbsp;an&nbsp;easy<br>
-roundtrip&nbsp;to&nbsp;and&nbsp;from&nbsp;a&nbsp;file&nbsp;(the&nbsp;exact&nbsp;counterpart&nbsp;of<br>
-``to_csv``),&nbsp;especially&nbsp;with&nbsp;a&nbsp;time&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;only&nbsp;differs&nbsp;from&nbsp;:func:`pandas.read_csv`&nbsp;in&nbsp;some&nbsp;defaults:<br>
-&nbsp;<br>
--&nbsp;`index_col`&nbsp;is&nbsp;``0``&nbsp;instead&nbsp;of&nbsp;``None``&nbsp;(take&nbsp;first&nbsp;column&nbsp;as&nbsp;index<br>
-&nbsp;&nbsp;by&nbsp;default)<br>
--&nbsp;`header`&nbsp;is&nbsp;``None``&nbsp;instead&nbsp;of&nbsp;``0``&nbsp;(the&nbsp;first&nbsp;row&nbsp;is&nbsp;not&nbsp;used&nbsp;as<br>
-&nbsp;&nbsp;the&nbsp;column&nbsp;names)<br>
--&nbsp;`parse_dates`&nbsp;is&nbsp;``True``&nbsp;instead&nbsp;of&nbsp;``False``&nbsp;(try&nbsp;parsing&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;as&nbsp;datetime&nbsp;by&nbsp;default)<br>
-&nbsp;<br>
-With&nbsp;:func:`pandas.read_csv`,&nbsp;the&nbsp;option&nbsp;``squeeze=True``&nbsp;can&nbsp;be&nbsp;used<br>
-to&nbsp;return&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;like&nbsp;``from_csv``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;file&nbsp;path&nbsp;or&nbsp;file&nbsp;handle&nbsp;/&nbsp;StringIO<br>
-sep&nbsp;:&nbsp;string,&nbsp;default&nbsp;','<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter<br>
-parse_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Parse&nbsp;dates.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-header&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Row&nbsp;to&nbsp;use&nbsp;as&nbsp;header&nbsp;(skip&nbsp;prior&nbsp;rows)<br>
-index_col&nbsp;:&nbsp;int&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;to&nbsp;use&nbsp;for&nbsp;index.&nbsp;If&nbsp;a&nbsp;sequence&nbsp;is&nbsp;given,&nbsp;a&nbsp;MultiIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;used.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-encoding&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;if&nbsp;the&nbsp;contents&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;non-ascii,&nbsp;for&nbsp;python&nbsp;versions&nbsp;prior&nbsp;to&nbsp;3<br>
-infer_datetime_format:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;and&nbsp;`parse_dates`&nbsp;is&nbsp;True&nbsp;for&nbsp;a&nbsp;column,&nbsp;try&nbsp;to&nbsp;infer&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;datetime&nbsp;format&nbsp;based&nbsp;on&nbsp;the&nbsp;first&nbsp;datetime&nbsp;string.&nbsp;If&nbsp;the&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;be&nbsp;inferred,&nbsp;there&nbsp;often&nbsp;will&nbsp;be&nbsp;a&nbsp;large&nbsp;parsing&nbsp;speed-up.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.read_csv<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><strong>asobject</strong></dt>
-<dd><tt>return&nbsp;object&nbsp;Series&nbsp;which&nbsp;contains&nbsp;boxed&nbsp;values<br>
-&nbsp;<br>
-*this&nbsp;is&nbsp;an&nbsp;internal&nbsp;non-public&nbsp;method*</tt></dd>
-</dl>
-<dl><dt><strong>axes</strong></dt>
-<dd><tt>Return&nbsp;a&nbsp;list&nbsp;of&nbsp;the&nbsp;row&nbsp;axis&nbsp;labels</tt></dd>
-</dl>
-<dl><dt><strong>dtype</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;dtype&nbsp;object&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>dtypes</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;dtype&nbsp;object&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>ftype</strong></dt>
-<dd><tt>return&nbsp;if&nbsp;the&nbsp;data&nbsp;is&nbsp;sparse|dense</tt></dd>
-</dl>
-<dl><dt><strong>ftypes</strong></dt>
-<dd><tt>return&nbsp;if&nbsp;the&nbsp;data&nbsp;is&nbsp;sparse|dense</tt></dd>
-</dl>
-<dl><dt><strong>imag</strong></dt>
-</dl>
-<dl><dt><strong>index</strong></dt>
-</dl>
-<dl><dt><strong>name</strong></dt>
-</dl>
-<dl><dt><strong>real</strong></dt>
-</dl>
-<dl><dt><strong>values</strong></dt>
-<dd><tt>Return&nbsp;Series&nbsp;as&nbsp;ndarray&nbsp;or&nbsp;ndarray-like<br>
-depending&nbsp;on&nbsp;the&nbsp;dtype<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-arr&nbsp;:&nbsp;numpy.ndarray&nbsp;or&nbsp;ndarray-like<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;pd.Series([1,&nbsp;2,&nbsp;3]).values<br>
-array([1,&nbsp;2,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(list('aabc')).values<br>
-array(['a',&nbsp;'a',&nbsp;'b',&nbsp;'c'],&nbsp;dtype=object)<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(list('aabc')).astype('category').values<br>
-[a,&nbsp;a,&nbsp;b,&nbsp;c]<br>
-Categories&nbsp;(3,&nbsp;object):&nbsp;[a,&nbsp;b,&nbsp;c]<br>
-&nbsp;<br>
-Timezone&nbsp;aware&nbsp;datetime&nbsp;data&nbsp;is&nbsp;converted&nbsp;to&nbsp;UTC:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(pd.date_range('20130101',&nbsp;periods=3,<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;tz='US/Eastern')).values<br>
-array(['2013-01-01T05:00:00.000000000',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'2013-01-02T05:00:00.000000000',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'2013-01-03T05:00:00.000000000'],&nbsp;dtype='datetime64[ns]')</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><strong>cat</strong> = &lt;class 'pandas.core.categorical.CategoricalAccessor'&gt;<dd><tt>Accessor&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;categorical&nbsp;properties&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values.<br>
-&nbsp;<br>
-Be&nbsp;aware&nbsp;that&nbsp;assigning&nbsp;to&nbsp;`categories`&nbsp;is&nbsp;a&nbsp;inplace&nbsp;operation,&nbsp;while&nbsp;all<br>
-methods&nbsp;return&nbsp;new&nbsp;categorical&nbsp;data&nbsp;per&nbsp;default&nbsp;(but&nbsp;can&nbsp;be&nbsp;called&nbsp;with<br>
-`inplace=True`).<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.cat.categories<br>
-&gt;&gt;&gt;&nbsp;s.cat.categories&nbsp;=&nbsp;list('abc')<br>
-&gt;&gt;&gt;&nbsp;s.cat.rename_categories(list('cab'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.reorder_categories(list('cab'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.add_categories(['d','e'])<br>
-&gt;&gt;&gt;&nbsp;s.cat.remove_categories(['d'])<br>
-&gt;&gt;&gt;&nbsp;s.cat.remove_unused_categories()<br>
-&gt;&gt;&gt;&nbsp;s.cat.set_categories(list('abcde'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.as_ordered()<br>
-&gt;&gt;&gt;&nbsp;s.cat.as_unordered()</tt></dl>
-
-<dl><dt><strong>dt</strong> = &lt;class 'pandas.core.indexes.accessors.CombinedDatetimelikeProperties'&gt;<dd><tt>Accessor&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;datetimelike&nbsp;properties&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.dt.hour<br>
-&gt;&gt;&gt;&nbsp;s.dt.second<br>
-&gt;&gt;&gt;&nbsp;s.dt.quarter<br>
-&nbsp;<br>
-Returns&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;indexed&nbsp;like&nbsp;the&nbsp;original&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-Raises&nbsp;TypeError&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;does&nbsp;not&nbsp;contain&nbsp;datetimelike&nbsp;values.</tt></dl>
-
-<dl><dt><strong>plot</strong> = &lt;class 'pandas.plotting._core.SeriesPlotMethods'&gt;<dd><tt><a href="pandas.core.series.html#Series">Series</a>&nbsp;plotting&nbsp;accessor&nbsp;and&nbsp;method<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.plot.line()<br>
-&gt;&gt;&gt;&nbsp;s.plot.bar()<br>
-&gt;&gt;&gt;&nbsp;s.plot.<a href="#MySeries-hist">hist</a>()<br>
-&nbsp;<br>
-Plotting&nbsp;methods&nbsp;can&nbsp;also&nbsp;be&nbsp;accessed&nbsp;by&nbsp;calling&nbsp;the&nbsp;accessor&nbsp;as&nbsp;a&nbsp;method<br>
-with&nbsp;the&nbsp;``kind``&nbsp;argument:<br>
-``s.<a href="#MySeries-plot">plot</a>(kind='line')``&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;``s.plot.line()``</tt></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><a name="MySeries-factorize"><strong>factorize</strong></a>(self, sort=False, na_sentinel=-1)</dt><dd><tt>Encode&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;as&nbsp;an&nbsp;enumerated&nbsp;type&nbsp;or&nbsp;categorical&nbsp;variable<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;by&nbsp;values<br>
-na_sentinel:&nbsp;int,&nbsp;default&nbsp;-1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;mark&nbsp;"not&nbsp;found"<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-labels&nbsp;:&nbsp;the&nbsp;indexer&nbsp;to&nbsp;the&nbsp;original&nbsp;array<br>
-uniques&nbsp;:&nbsp;the&nbsp;unique&nbsp;Index</tt></dd></dl>
-
-<dl><dt><a name="MySeries-item"><strong>item</strong></a>(self)</dt><dd><tt>return&nbsp;the&nbsp;first&nbsp;element&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data&nbsp;as&nbsp;a&nbsp;python<br>
-scalar</tt></dd></dl>
-
-<dl><dt><a name="MySeries-nunique"><strong>nunique</strong></a>(self, dropna=True)</dt><dd><tt>Return&nbsp;number&nbsp;of&nbsp;unique&nbsp;elements&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Excludes&nbsp;NA&nbsp;values&nbsp;by&nbsp;default.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Don't&nbsp;include&nbsp;NaN&nbsp;in&nbsp;the&nbsp;count.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-nunique&nbsp;:&nbsp;int</tt></dd></dl>
-
-<dl><dt><a name="MySeries-transpose"><strong>transpose</strong></a>(self, *args, **kwargs)</dt><dd><tt>return&nbsp;the&nbsp;transpose,&nbsp;which&nbsp;is&nbsp;by&nbsp;definition&nbsp;self</tt></dd></dl>
-
-<dl><dt><a name="MySeries-value_counts"><strong>value_counts</strong></a>(self, normalize=False, sort=True, ascending=False, bins=None, dropna=True)</dt><dd><tt>Returns&nbsp;<a href="builtins.html#object">object</a>&nbsp;containing&nbsp;counts&nbsp;of&nbsp;unique&nbsp;values.<br>
-&nbsp;<br>
-The&nbsp;resulting&nbsp;<a href="builtins.html#object">object</a>&nbsp;will&nbsp;be&nbsp;in&nbsp;descending&nbsp;order&nbsp;so&nbsp;that&nbsp;the<br>
-first&nbsp;element&nbsp;is&nbsp;the&nbsp;most&nbsp;frequently-occurring&nbsp;element.<br>
-Excludes&nbsp;NA&nbsp;values&nbsp;by&nbsp;default.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-normalize&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;then&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;returned&nbsp;will&nbsp;contain&nbsp;the&nbsp;relative<br>
-&nbsp;&nbsp;&nbsp;&nbsp;frequencies&nbsp;of&nbsp;the&nbsp;unique&nbsp;values.<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;by&nbsp;values<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;in&nbsp;ascending&nbsp;order<br>
-bins&nbsp;:&nbsp;integer,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Rather&nbsp;than&nbsp;count&nbsp;values,&nbsp;group&nbsp;them&nbsp;into&nbsp;half-open&nbsp;bins,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;convenience&nbsp;for&nbsp;pd.cut,&nbsp;only&nbsp;works&nbsp;with&nbsp;numeric&nbsp;data<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Don't&nbsp;include&nbsp;counts&nbsp;of&nbsp;NaN.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-counts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><strong>T</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;transpose,&nbsp;which&nbsp;is&nbsp;by&nbsp;definition&nbsp;self</tt></dd>
-</dl>
-<dl><dt><strong>__dict__</strong></dt>
-<dd><tt>dictionary&nbsp;for&nbsp;instance&nbsp;variables&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>__weakref__</strong></dt>
-<dd><tt>list&nbsp;of&nbsp;weak&nbsp;references&nbsp;to&nbsp;the&nbsp;object&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>base</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;base&nbsp;object&nbsp;if&nbsp;the&nbsp;memory&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data&nbsp;is<br>
-shared</tt></dd>
-</dl>
-<dl><dt><strong>data</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;data&nbsp;pointer&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>empty</strong></dt>
-</dl>
-<dl><dt><strong>flags</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;ndarray.flags&nbsp;for&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>hasnans</strong></dt>
-</dl>
-<dl><dt><strong>is_monotonic</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_increasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_monotonic_decreasing</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_decreasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic_decreasing&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_monotonic_increasing</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_increasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_unique</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are&nbsp;unique<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_unique&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>itemsize</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;size&nbsp;of&nbsp;the&nbsp;dtype&nbsp;of&nbsp;the&nbsp;item&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>nbytes</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;bytes&nbsp;in&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>ndim</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;dimensions&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data,<br>
-by&nbsp;definition&nbsp;1</tt></dd>
-</dl>
-<dl><dt><strong>shape</strong></dt>
-<dd><tt>return&nbsp;a&nbsp;tuple&nbsp;of&nbsp;the&nbsp;shape&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>size</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;elements&nbsp;in&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>strides</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;strides&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><strong>__array_priority__</strong> = 1000</dl>
-
-<hr>
-Data and other attributes inherited from <a href="pandas.core.strings.html#StringAccessorMixin">pandas.core.strings.StringAccessorMixin</a>:<br>
-<dl><dt><strong>str</strong> = &lt;class 'pandas.core.strings.StringMethods'&gt;<dd><tt>Vectorized&nbsp;string&nbsp;functions&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;and&nbsp;Index.&nbsp;NAs&nbsp;stay&nbsp;NA&nbsp;unless<br>
-handled&nbsp;otherwise&nbsp;by&nbsp;a&nbsp;particular&nbsp;method.&nbsp;Patterned&nbsp;after&nbsp;Python's&nbsp;string<br>
-methods,&nbsp;with&nbsp;some&nbsp;inspiration&nbsp;from&nbsp;R's&nbsp;stringr&nbsp;package.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.str.split('_')<br>
-&gt;&gt;&gt;&nbsp;s.str.<a href="#MySeries-replace">replace</a>('_',&nbsp;'')</tt></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><a name="MySeries-__abs__"><strong>__abs__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="MySeries-__bool__"><strong>__bool__</strong></a> = __nonzero__(self)</dt></dl>
-
-<dl><dt><a name="MySeries-__contains__"><strong>__contains__</strong></a>(self, key)</dt><dd><tt>True&nbsp;if&nbsp;the&nbsp;key&nbsp;is&nbsp;in&nbsp;the&nbsp;info&nbsp;axis</tt></dd></dl>
-
-<dl><dt><a name="MySeries-__copy__"><strong>__copy__</strong></a>(self, deep=True)</dt></dl>
-
-<dl><dt><a name="MySeries-__deepcopy__"><strong>__deepcopy__</strong></a>(self, memo=None)</dt></dl>
-
-<dl><dt><a name="MySeries-__delitem__"><strong>__delitem__</strong></a>(self, key)</dt><dd><tt>Delete&nbsp;item</tt></dd></dl>
-
-<dl><dt><a name="MySeries-__finalize__"><strong>__finalize__</strong></a>(self, other, method=None, **kwargs)</dt><dd><tt>Propagate&nbsp;metadata&nbsp;from&nbsp;other&nbsp;to&nbsp;self.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;which&nbsp;to&nbsp;get&nbsp;the&nbsp;attributes&nbsp;that&nbsp;we&nbsp;are&nbsp;going<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;propagate<br>
-method&nbsp;:&nbsp;optional,&nbsp;a&nbsp;passed&nbsp;method&nbsp;name&nbsp;;&nbsp;possibly&nbsp;to&nbsp;take&nbsp;different<br>
-&nbsp;&nbsp;&nbsp;&nbsp;types&nbsp;of&nbsp;propagation&nbsp;actions&nbsp;based&nbsp;on&nbsp;this</tt></dd></dl>
-
-<dl><dt><a name="MySeries-__getattr__"><strong>__getattr__</strong></a>(self, name)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;looking&nbsp;up&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-__getstate__"><strong>__getstate__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="MySeries-__hash__"><strong>__hash__</strong></a>(self)</dt><dd><tt>Return&nbsp;hash(self).</tt></dd></dl>
-
-<dl><dt><a name="MySeries-__invert__"><strong>__invert__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="MySeries-__neg__"><strong>__neg__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="MySeries-__nonzero__"><strong>__nonzero__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="MySeries-__round__"><strong>__round__</strong></a>(self, decimals=0)</dt></dl>
-
-<dl><dt><a name="MySeries-__setattr__"><strong>__setattr__</strong></a>(self, name, value)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;setting&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-__setstate__"><strong>__setstate__</strong></a>(self, state)</dt></dl>
-
-<dl><dt><a name="MySeries-abs"><strong>abs</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;absolute&nbsp;value&nbsp;taken--only&nbsp;applicable&nbsp;to&nbsp;objects<br>
-that&nbsp;are&nbsp;all&nbsp;numeric.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-abs:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MySeries-add_prefix"><strong>add_prefix</strong></a>(self, prefix)</dt><dd><tt>Concatenate&nbsp;prefix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-prefix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_prefix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MySeries-add_suffix"><strong>add_suffix</strong></a>(self, suffix)</dt><dd><tt>Concatenate&nbsp;suffix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-suffix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_suffix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MySeries-as_blocks"><strong>as_blocks</strong></a>(self, copy=True)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types&nbsp;that&nbsp;each&nbsp;has<br>
-a&nbsp;homogeneous&nbsp;dtype.<br>
-&nbsp;<br>
-NOTE:&nbsp;the&nbsp;dtypes&nbsp;of&nbsp;the&nbsp;blocks&nbsp;WILL&nbsp;BE&nbsp;PRESERVED&nbsp;HERE&nbsp;(unlike&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;as_matrix)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types</tt></dd></dl>
-
-<dl><dt><a name="MySeries-as_matrix"><strong>as_matrix</strong></a>(self, columns=None)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;its&nbsp;Numpy-array&nbsp;representation.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-columns:&nbsp;list,&nbsp;optional,&nbsp;default:None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;None,&nbsp;return&nbsp;all&nbsp;columns,&nbsp;otherwise,&nbsp;returns&nbsp;specified&nbsp;columns.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;caller&nbsp;is&nbsp;heterogeneous&nbsp;and&nbsp;contains&nbsp;booleans&nbsp;or&nbsp;objects,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;of&nbsp;dtype=<a href="builtins.html#object">object</a>.&nbsp;See&nbsp;Notes.<br>
-&nbsp;<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Return&nbsp;is&nbsp;NOT&nbsp;a&nbsp;Numpy-matrix,&nbsp;rather,&nbsp;a&nbsp;Numpy-array.<br>
-&nbsp;<br>
-The&nbsp;dtype&nbsp;will&nbsp;be&nbsp;a&nbsp;lower-common-denominator&nbsp;dtype&nbsp;(implicit<br>
-upcasting);&nbsp;that&nbsp;is&nbsp;to&nbsp;say&nbsp;if&nbsp;the&nbsp;dtypes&nbsp;(even&nbsp;of&nbsp;numeric&nbsp;types)<br>
-are&nbsp;mixed,&nbsp;the&nbsp;one&nbsp;that&nbsp;accommodates&nbsp;all&nbsp;will&nbsp;be&nbsp;chosen.&nbsp;Use&nbsp;this<br>
-with&nbsp;care&nbsp;if&nbsp;you&nbsp;are&nbsp;not&nbsp;dealing&nbsp;with&nbsp;the&nbsp;blocks.<br>
-&nbsp;<br>
-e.g.&nbsp;If&nbsp;the&nbsp;dtypes&nbsp;are&nbsp;float16&nbsp;and&nbsp;float32,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcast&nbsp;to<br>
-float32.&nbsp;&nbsp;If&nbsp;dtypes&nbsp;are&nbsp;int32&nbsp;and&nbsp;uint8,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcase&nbsp;to<br>
-int32.&nbsp;By&nbsp;numpy.find_common_type&nbsp;convention,&nbsp;mixing&nbsp;int64&nbsp;and&nbsp;uint64<br>
-will&nbsp;result&nbsp;in&nbsp;a&nbsp;flot64&nbsp;dtype.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;is&nbsp;provided&nbsp;for&nbsp;backwards&nbsp;compatibility.&nbsp;Generally,<br>
-it&nbsp;is&nbsp;recommended&nbsp;to&nbsp;use&nbsp;'.values'.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.values</tt></dd></dl>
-
-<dl><dt><a name="MySeries-asfreq"><strong>asfreq</strong></a>(self, freq, method=None, how=None, normalize=False, fill_value=None)</dt><dd><tt>Convert&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;specified&nbsp;frequency.<br>
-&nbsp;<br>
-Optionally&nbsp;provide&nbsp;filling&nbsp;method&nbsp;to&nbsp;pad/backfill&nbsp;missing&nbsp;values.<br>
-&nbsp;<br>
-Returns&nbsp;the&nbsp;original&nbsp;data&nbsp;conformed&nbsp;to&nbsp;a&nbsp;new&nbsp;index&nbsp;with&nbsp;the&nbsp;specified<br>
-frequency.&nbsp;``resample``&nbsp;is&nbsp;more&nbsp;appropriate&nbsp;if&nbsp;an&nbsp;operation,&nbsp;such&nbsp;as<br>
-summarization,&nbsp;is&nbsp;necessary&nbsp;to&nbsp;represent&nbsp;the&nbsp;data&nbsp;at&nbsp;the&nbsp;new&nbsp;frequency.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;or&nbsp;string<br>
-method&nbsp;:&nbsp;{'backfill'/'bfill',&nbsp;'pad'/'ffill'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(note&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present):<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'pad'&nbsp;/&nbsp;'ffill':&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'backfill'&nbsp;/&nbsp;'bfill':&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill<br>
-how&nbsp;:&nbsp;{'start',&nbsp;'end'},&nbsp;default&nbsp;end<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;PeriodIndex&nbsp;only,&nbsp;see&nbsp;PeriodIndex.asfreq<br>
-normalize&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;reset&nbsp;output&nbsp;index&nbsp;to&nbsp;midnight<br>
-fill_value:&nbsp;scalar,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values,&nbsp;applied&nbsp;during&nbsp;upsampling&nbsp;(note<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;4&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=4,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0.0,&nbsp;None,&nbsp;2.0,&nbsp;3.0],&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'s':series})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-asfreq">asfreq</a>(freq='30S')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``fill&nbsp;value``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-asfreq">asfreq</a>(freq='30S',&nbsp;fill_value=9.0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``method``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-asfreq">asfreq</a>(freq='30S',&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-asof"><strong>asof</strong></a>(self, where, subset=None)</dt><dd><tt>The&nbsp;last&nbsp;row&nbsp;without&nbsp;any&nbsp;NaN&nbsp;is&nbsp;taken&nbsp;(or&nbsp;the&nbsp;last&nbsp;row&nbsp;without<br>
-NaN&nbsp;considering&nbsp;only&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;in&nbsp;the&nbsp;case&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0&nbsp;For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-If&nbsp;there&nbsp;is&nbsp;no&nbsp;good&nbsp;value,&nbsp;NaN&nbsp;is&nbsp;returned&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;NaN&nbsp;values&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-where&nbsp;:&nbsp;date&nbsp;or&nbsp;array&nbsp;of&nbsp;dates<br>
-subset&nbsp;:&nbsp;string&nbsp;or&nbsp;list&nbsp;of&nbsp;strings,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None&nbsp;use&nbsp;these&nbsp;columns&nbsp;for&nbsp;NaN&nbsp;propagation<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Dates&nbsp;are&nbsp;assumed&nbsp;to&nbsp;be&nbsp;sorted<br>
-Raises&nbsp;if&nbsp;this&nbsp;is&nbsp;not&nbsp;the&nbsp;case<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-where&nbsp;is&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;&nbsp;-&nbsp;value&nbsp;or&nbsp;NaN&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;-&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-where&nbsp;is&nbsp;Index:&nbsp;same&nbsp;shape&nbsp;<a href="builtins.html#object">object</a>&nbsp;as&nbsp;input<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-merge_asof</tt></dd></dl>
-
-<dl><dt><a name="MySeries-astype"><strong>astype</strong></a>(self, dtype, copy=True, errors='raise', **kwargs)</dt><dd><tt>Cast&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;numpy.dtype<br>
-Return&nbsp;a&nbsp;copy&nbsp;when&nbsp;copy&nbsp;=&nbsp;True&nbsp;(be&nbsp;really&nbsp;careful&nbsp;with&nbsp;this!)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dtype&nbsp;:&nbsp;data&nbsp;type,&nbsp;or&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;-&gt;&nbsp;data&nbsp;type<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;entire&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;same&nbsp;type.&nbsp;Alternatively,&nbsp;use&nbsp;{col:&nbsp;dtype,&nbsp;...},&nbsp;where&nbsp;col&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;label&nbsp;and&nbsp;dtype&nbsp;is&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;one<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;more&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;columns&nbsp;to&nbsp;column-specific&nbsp;types.<br>
-errors&nbsp;:&nbsp;{'raise',&nbsp;'ignore'},&nbsp;default&nbsp;'raise'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Control&nbsp;raising&nbsp;of&nbsp;exceptions&nbsp;on&nbsp;invalid&nbsp;data&nbsp;for&nbsp;provided&nbsp;dtype.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``raise``&nbsp;:&nbsp;allow&nbsp;exceptions&nbsp;to&nbsp;be&nbsp;raised<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``ignore``&nbsp;:&nbsp;suppress&nbsp;exceptions.&nbsp;On&nbsp;error&nbsp;return&nbsp;original&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-raise_on_error&nbsp;:&nbsp;DEPRECATED&nbsp;use&nbsp;``errors``&nbsp;instead<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;constructor<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-casted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MySeries-at_time"><strong>at_time</strong></a>(self, time, asof=False)</dt><dd><tt>Select&nbsp;values&nbsp;at&nbsp;particular&nbsp;time&nbsp;of&nbsp;day&nbsp;(e.g.&nbsp;9:30AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_at_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MySeries-between_time"><strong>between_time</strong></a>(self, start_time, end_time, include_start=True, include_end=True)</dt><dd><tt>Select&nbsp;values&nbsp;between&nbsp;particular&nbsp;times&nbsp;of&nbsp;the&nbsp;day&nbsp;(e.g.,&nbsp;9:00-9:30&nbsp;AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-start_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-end_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-include_start&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-include_end&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_between_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MySeries-bfill"><strong>bfill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#MySeries-fillna">fillna</a>(method='bfill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="MySeries-bool"><strong>bool</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;bool&nbsp;of&nbsp;a&nbsp;single&nbsp;element&nbsp;PandasObject.<br>
-&nbsp;<br>
-This&nbsp;must&nbsp;be&nbsp;a&nbsp;boolean&nbsp;scalar&nbsp;value,&nbsp;either&nbsp;True&nbsp;or&nbsp;False.&nbsp;&nbsp;Raise&nbsp;a<br>
-ValueError&nbsp;if&nbsp;the&nbsp;PandasObject&nbsp;does&nbsp;not&nbsp;have&nbsp;exactly&nbsp;1&nbsp;element,&nbsp;or&nbsp;that<br>
-element&nbsp;is&nbsp;not&nbsp;boolean</tt></dd></dl>
-
-<dl><dt><a name="MySeries-clip"><strong>clip</strong></a>(self, lower=None, upper=None, axis=None, *args, **kwargs)</dt><dd><tt>Trim&nbsp;values&nbsp;at&nbsp;input&nbsp;threshold(s).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-lower&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-upper&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;lower&nbsp;and&nbsp;upper&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.256177<br>
-1&nbsp;-1.367855&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.176076<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;1.261967&nbsp;&nbsp;0.570967<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-clip">clip</a>(-1.0,&nbsp;0.5)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.000000<br>
-1&nbsp;-1.000000&nbsp;&nbsp;0.500000<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.000000<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;0.500000&nbsp;&nbsp;0.500000<br>
-&gt;&gt;&gt;&nbsp;t<br>
-0&nbsp;&nbsp;&nbsp;-0.3<br>
-1&nbsp;&nbsp;&nbsp;-0.2<br>
-2&nbsp;&nbsp;&nbsp;-0.1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;0.1<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-clip">clip</a>(t,&nbsp;t&nbsp;+&nbsp;1,&nbsp;axis=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-0.300000<br>
-1&nbsp;-0.200000&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-0.100000<br>
-3&nbsp;&nbsp;0.230930&nbsp;&nbsp;0.000000<br>
-4&nbsp;&nbsp;1.100000&nbsp;&nbsp;0.570967</tt></dd></dl>
-
-<dl><dt><a name="MySeries-clip_lower"><strong>clip_lower</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;the&nbsp;input&nbsp;with&nbsp;values&nbsp;below&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="MySeries-clip_upper"><strong>clip_upper</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;input&nbsp;with&nbsp;values&nbsp;above&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="MySeries-consolidate"><strong>consolidate</strong></a>(self, inplace=False)</dt><dd><tt>DEPRECATED:&nbsp;consolidate&nbsp;will&nbsp;be&nbsp;an&nbsp;internal&nbsp;implementation&nbsp;only.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-convert_objects"><strong>convert_objects</strong></a>(self, convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True)</dt><dd><tt>Deprecated.<br>
-&nbsp;<br>
-Attempt&nbsp;to&nbsp;infer&nbsp;better&nbsp;dtype&nbsp;for&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-convert_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;date&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-convert_numeric&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;attempt&nbsp;to&nbsp;coerce&nbsp;to&nbsp;numbers&nbsp;(including&nbsp;strings),&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaN.<br>
-convert_timedeltas&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;timedelta&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;return&nbsp;a&nbsp;copy&nbsp;even&nbsp;if&nbsp;no&nbsp;copy&nbsp;is&nbsp;necessary&nbsp;(e.g.&nbsp;no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion&nbsp;was&nbsp;done).&nbsp;Note:&nbsp;This&nbsp;is&nbsp;meant&nbsp;for&nbsp;internal&nbsp;use,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;not&nbsp;be&nbsp;confused&nbsp;with&nbsp;inplace.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.to_datetime&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;datetime.<br>
-pandas.to_timedelta&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;timedelta.<br>
-pandas.to_numeric&nbsp;:&nbsp;Return&nbsp;a&nbsp;fixed&nbsp;frequency&nbsp;timedelta&nbsp;index,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;day&nbsp;as&nbsp;the&nbsp;default.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;same&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-copy"><strong>copy</strong></a>(self, deep=True)</dt><dd><tt>Make&nbsp;a&nbsp;copy&nbsp;of&nbsp;this&nbsp;objects&nbsp;data.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-deep&nbsp;:&nbsp;boolean&nbsp;or&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;a&nbsp;deep&nbsp;copy,&nbsp;including&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;data&nbsp;and&nbsp;the&nbsp;indices.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;With&nbsp;``deep=False``&nbsp;neither&nbsp;the&nbsp;indices&nbsp;or&nbsp;the&nbsp;data&nbsp;are&nbsp;copied.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;that&nbsp;when&nbsp;``deep=True``&nbsp;data&nbsp;is&nbsp;copied,&nbsp;actual&nbsp;python&nbsp;objects<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;not&nbsp;be&nbsp;copied&nbsp;recursively,&nbsp;only&nbsp;the&nbsp;reference&nbsp;to&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;is&nbsp;in&nbsp;contrast&nbsp;to&nbsp;``copy.deepcopy``&nbsp;in&nbsp;the&nbsp;Standard&nbsp;Library,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;recursively&nbsp;copies&nbsp;<a href="builtins.html#object">object</a>&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-copy&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MySeries-describe"><strong>describe</strong></a>(self, percentiles=None, include=None, exclude=None)</dt><dd><tt>Generates&nbsp;descriptive&nbsp;statistics&nbsp;that&nbsp;summarize&nbsp;the&nbsp;central&nbsp;tendency,<br>
-dispersion&nbsp;and&nbsp;shape&nbsp;of&nbsp;a&nbsp;dataset's&nbsp;distribution,&nbsp;excluding<br>
-``NaN``&nbsp;values.<br>
-&nbsp;<br>
-Analyzes&nbsp;both&nbsp;numeric&nbsp;and&nbsp;<a href="builtins.html#object">object</a>&nbsp;series,&nbsp;as&nbsp;well<br>
-as&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;column&nbsp;sets&nbsp;of&nbsp;mixed&nbsp;data&nbsp;types.&nbsp;The&nbsp;output<br>
-will&nbsp;vary&nbsp;depending&nbsp;on&nbsp;what&nbsp;is&nbsp;provided.&nbsp;Refer&nbsp;to&nbsp;the&nbsp;notes<br>
-below&nbsp;for&nbsp;more&nbsp;detail.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-percentiles&nbsp;:&nbsp;list-like&nbsp;of&nbsp;numbers,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;percentiles&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;output.&nbsp;All&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fall&nbsp;between&nbsp;0&nbsp;and&nbsp;1.&nbsp;The&nbsp;default&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``[.25,&nbsp;.5,&nbsp;.75]``,&nbsp;which&nbsp;returns&nbsp;the&nbsp;25th,&nbsp;50th,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;75th&nbsp;percentiles.<br>
-include&nbsp;:&nbsp;'all',&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;white&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'all'&nbsp;:&nbsp;All&nbsp;columns&nbsp;of&nbsp;the&nbsp;input&nbsp;will&nbsp;be&nbsp;included&nbsp;in&nbsp;the&nbsp;output.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Limits&nbsp;the&nbsp;results&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;provided&nbsp;data&nbsp;types.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;limit&nbsp;the&nbsp;result&nbsp;to&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;limit&nbsp;it&nbsp;instead&nbsp;to&nbsp;categorical<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;objects&nbsp;submit&nbsp;the&nbsp;``numpy.<a href="builtins.html#object">object</a>``&nbsp;data&nbsp;type.&nbsp;Strings<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#MySeries-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;include&nbsp;all&nbsp;numeric&nbsp;columns.<br>
-exclude&nbsp;:&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;black&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;omit&nbsp;from&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Excludes&nbsp;the&nbsp;provided&nbsp;data&nbsp;types<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;the&nbsp;result.&nbsp;To&nbsp;select&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;select&nbsp;categorical&nbsp;objects&nbsp;submit&nbsp;the&nbsp;data<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;type&nbsp;``numpy.<a href="builtins.html#object">object</a>``.&nbsp;Strings&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#MySeries-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;exclude&nbsp;nothing.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-summary:&nbsp;&nbsp;<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;summary&nbsp;statistics<br>
-&nbsp;<br>
-Notes<br>
------<br>
-For&nbsp;numeric&nbsp;data,&nbsp;the&nbsp;result's&nbsp;index&nbsp;will&nbsp;include&nbsp;``count``,<br>
-``mean``,&nbsp;``std``,&nbsp;``min``,&nbsp;``max``&nbsp;as&nbsp;well&nbsp;as&nbsp;lower,&nbsp;``50``&nbsp;and<br>
-upper&nbsp;percentiles.&nbsp;By&nbsp;default&nbsp;the&nbsp;lower&nbsp;percentile&nbsp;is&nbsp;``25``&nbsp;and&nbsp;the<br>
-upper&nbsp;percentile&nbsp;is&nbsp;``75``.&nbsp;The&nbsp;``50``&nbsp;percentile&nbsp;is&nbsp;the<br>
-same&nbsp;as&nbsp;the&nbsp;median.<br>
-&nbsp;<br>
-For&nbsp;<a href="builtins.html#object">object</a>&nbsp;data&nbsp;(e.g.&nbsp;strings&nbsp;or&nbsp;timestamps),&nbsp;the&nbsp;result's&nbsp;index<br>
-will&nbsp;include&nbsp;``count``,&nbsp;``unique``,&nbsp;``top``,&nbsp;and&nbsp;``freq``.&nbsp;The&nbsp;``top``<br>
-is&nbsp;the&nbsp;most&nbsp;common&nbsp;value.&nbsp;The&nbsp;``freq``&nbsp;is&nbsp;the&nbsp;most&nbsp;common&nbsp;value's<br>
-frequency.&nbsp;Timestamps&nbsp;also&nbsp;include&nbsp;the&nbsp;``first``&nbsp;and&nbsp;``last``&nbsp;items.<br>
-&nbsp;<br>
-If&nbsp;multiple&nbsp;<a href="builtins.html#object">object</a>&nbsp;values&nbsp;have&nbsp;the&nbsp;highest&nbsp;count,&nbsp;then&nbsp;the<br>
-``count``&nbsp;and&nbsp;``top``&nbsp;results&nbsp;will&nbsp;be&nbsp;arbitrarily&nbsp;chosen&nbsp;from<br>
-among&nbsp;those&nbsp;with&nbsp;the&nbsp;highest&nbsp;count.<br>
-&nbsp;<br>
-For&nbsp;mixed&nbsp;data&nbsp;types&nbsp;provided&nbsp;via&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``,&nbsp;the&nbsp;default&nbsp;is&nbsp;to<br>
-return&nbsp;only&nbsp;an&nbsp;analysis&nbsp;of&nbsp;numeric&nbsp;columns.&nbsp;If&nbsp;``include='all'``<br>
-is&nbsp;provided&nbsp;as&nbsp;an&nbsp;option,&nbsp;the&nbsp;result&nbsp;will&nbsp;include&nbsp;a&nbsp;union&nbsp;of<br>
-attributes&nbsp;of&nbsp;each&nbsp;type.<br>
-&nbsp;<br>
-The&nbsp;`include`&nbsp;and&nbsp;`exclude`&nbsp;parameters&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;limit<br>
-which&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;are&nbsp;analyzed&nbsp;for&nbsp;the&nbsp;output.<br>
-The&nbsp;parameters&nbsp;are&nbsp;ignored&nbsp;when&nbsp;analyzing&nbsp;a&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-Describing&nbsp;a&nbsp;numeric&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;categorical&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(['a',&nbsp;'a',&nbsp;'b',&nbsp;'c'])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;timestamp&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2000-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01")<br>
-...&nbsp;])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-first&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2000-01-01&nbsp;00:00:00<br>
-last&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``.&nbsp;By&nbsp;default&nbsp;only&nbsp;numeric&nbsp;fields<br>
-are&nbsp;returned.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[1,&nbsp;'a'],&nbsp;[2,&nbsp;'b'],&nbsp;[3,&nbsp;'c']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['numeric',&nbsp;'<a href="builtins.html#object">object</a>'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-describe">describe</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;all&nbsp;columns&nbsp;of&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;regardless&nbsp;of&nbsp;data&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-describe">describe</a>(include='all')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;column&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;by&nbsp;accessing&nbsp;it&nbsp;as<br>
-an&nbsp;attribute.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.numeric.<a href="#MySeries-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-Name:&nbsp;numeric,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;numeric&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-describe">describe</a>(include=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;string&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-describe">describe</a>(include=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;numeric&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-describe">describe</a>(exclude=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-describe">describe</a>(exclude=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.count<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.max<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.min<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mean<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.std<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select_dtypes</tt></dd></dl>
-
-<dl><dt><a name="MySeries-drop"><strong>drop</strong></a>(self, labels, axis=0, level=None, inplace=False, errors='raise')</dt><dd><tt>Return&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;labels&nbsp;in&nbsp;requested&nbsp;axis&nbsp;removed.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-labels&nbsp;:&nbsp;single&nbsp;label&nbsp;or&nbsp;list-like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;axis&nbsp;name<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;MultiIndex<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;return&nbsp;None.<br>
-errors&nbsp;:&nbsp;{'ignore',&nbsp;'raise'},&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'ignore',&nbsp;suppress&nbsp;error&nbsp;and&nbsp;existing&nbsp;labels&nbsp;are&nbsp;dropped.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dropped&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MySeries-equals"><strong>equals</strong></a>(self, other)</dt><dd><tt>Determines&nbsp;if&nbsp;two&nbsp;NDFrame&nbsp;objects&nbsp;contain&nbsp;the&nbsp;same&nbsp;elements.&nbsp;NaNs&nbsp;in<br>
-the&nbsp;same&nbsp;location&nbsp;are&nbsp;considered&nbsp;equal.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-ffill"><strong>ffill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#MySeries-fillna">fillna</a>(method='ffill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="MySeries-filter"><strong>filter</strong></a>(self, items=None, like=None, regex=None, axis=None)</dt><dd><tt>Subset&nbsp;rows&nbsp;or&nbsp;columns&nbsp;of&nbsp;dataframe&nbsp;according&nbsp;to&nbsp;labels&nbsp;in<br>
-the&nbsp;specified&nbsp;index.<br>
-&nbsp;<br>
-Note&nbsp;that&nbsp;this&nbsp;routine&nbsp;does&nbsp;not&nbsp;filter&nbsp;a&nbsp;dataframe&nbsp;on&nbsp;its<br>
-contents.&nbsp;The&nbsp;filter&nbsp;is&nbsp;applied&nbsp;to&nbsp;the&nbsp;labels&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-items&nbsp;:&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;info&nbsp;axis&nbsp;to&nbsp;restrict&nbsp;to&nbsp;(must&nbsp;not&nbsp;all&nbsp;be&nbsp;present)<br>
-like&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;where&nbsp;"arg&nbsp;in&nbsp;col&nbsp;==&nbsp;True"<br>
-regex&nbsp;:&nbsp;string&nbsp;(regular&nbsp;expression)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;with&nbsp;re.search(regex,&nbsp;col)&nbsp;==&nbsp;True<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;filter&nbsp;on.&nbsp;&nbsp;By&nbsp;default&nbsp;this&nbsp;is&nbsp;the&nbsp;info&nbsp;axis,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'index'&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;'columns'&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;name<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-filter">filter</a>(items=['one',&nbsp;'three'])<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;regular&nbsp;expression<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-filter">filter</a>(regex='e$',&nbsp;axis=1)<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;rows&nbsp;containing&nbsp;'bbi'<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-filter">filter</a>(like='bbi',&nbsp;axis=0)<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;``items``,&nbsp;``like``,&nbsp;and&nbsp;``regex``&nbsp;parameters&nbsp;are<br>
-enforced&nbsp;to&nbsp;be&nbsp;mutually&nbsp;exclusive.<br>
-&nbsp;<br>
-``axis``&nbsp;defaults&nbsp;to&nbsp;the&nbsp;info&nbsp;axis&nbsp;that&nbsp;is&nbsp;used&nbsp;when&nbsp;indexing<br>
-with&nbsp;``[]``.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-first"><strong>first</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;initial&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#MySeries-first">first</a>('10D')&nbsp;-&gt;&nbsp;First&nbsp;10&nbsp;days<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MySeries-get"><strong>get</strong></a>(self, key, default=None)</dt><dd><tt>Get&nbsp;item&nbsp;from&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;given&nbsp;key&nbsp;(<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column,&nbsp;Panel&nbsp;slice,<br>
-etc.).&nbsp;Returns&nbsp;default&nbsp;value&nbsp;if&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;type&nbsp;of&nbsp;items&nbsp;contained&nbsp;in&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-get_dtype_counts"><strong>get_dtype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;dtypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-get_ftype_counts"><strong>get_ftype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;ftypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-groupby"><strong>groupby</strong></a>(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)</dt><dd><tt>Group&nbsp;series&nbsp;using&nbsp;mapper&nbsp;(dict&nbsp;or&nbsp;key&nbsp;function,&nbsp;apply&nbsp;given&nbsp;function<br>
-to&nbsp;group,&nbsp;return&nbsp;result&nbsp;as&nbsp;series)&nbsp;or&nbsp;by&nbsp;a&nbsp;series&nbsp;of&nbsp;columns.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;mapping,&nbsp;function,&nbsp;str,&nbsp;or&nbsp;iterable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;for&nbsp;the&nbsp;groupby.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;``by``&nbsp;is&nbsp;a&nbsp;function,&nbsp;it's&nbsp;called&nbsp;on&nbsp;each&nbsp;value&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>'s<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;If&nbsp;a&nbsp;dict&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;is&nbsp;passed,&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;dict&nbsp;VALUES<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;(the&nbsp;<a href="pandas.core.series.html#Series">Series</a>'&nbsp;values&nbsp;are&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aligned;&nbsp;see&nbsp;``.<a href="#MySeries-align">align</a>()``&nbsp;method).&nbsp;If&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;is&nbsp;passed,&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;are&nbsp;used&nbsp;as-is&nbsp;determine&nbsp;the&nbsp;groups.&nbsp;A&nbsp;str&nbsp;or&nbsp;list&nbsp;of&nbsp;strs<br>
-&nbsp;&nbsp;&nbsp;&nbsp;may&nbsp;be&nbsp;passed&nbsp;to&nbsp;group&nbsp;by&nbsp;the&nbsp;columns&nbsp;in&nbsp;``self``<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-level&nbsp;:&nbsp;int,&nbsp;level&nbsp;name,&nbsp;or&nbsp;sequence&nbsp;of&nbsp;such,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;group&nbsp;by&nbsp;a&nbsp;particular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level&nbsp;or&nbsp;levels<br>
-as_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;aggregated&nbsp;output,&nbsp;return&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;group&nbsp;labels&nbsp;as&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;Only&nbsp;relevant&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;input.&nbsp;as_index=False&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;effectively&nbsp;"SQL-style"&nbsp;grouped&nbsp;output<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;group&nbsp;keys.&nbsp;Get&nbsp;better&nbsp;performance&nbsp;by&nbsp;turning&nbsp;this&nbsp;off.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;this&nbsp;does&nbsp;not&nbsp;influence&nbsp;the&nbsp;order&nbsp;of&nbsp;observations&nbsp;within&nbsp;each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;group.&nbsp;&nbsp;groupby&nbsp;preserves&nbsp;the&nbsp;order&nbsp;of&nbsp;rows&nbsp;within&nbsp;each&nbsp;group.<br>
-group_keys&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;calling&nbsp;apply,&nbsp;add&nbsp;group&nbsp;keys&nbsp;to&nbsp;index&nbsp;to&nbsp;identify&nbsp;pieces<br>
-squeeze&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;reduce&nbsp;the&nbsp;dimensionality&nbsp;of&nbsp;the&nbsp;return&nbsp;type&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;return&nbsp;a&nbsp;consistent&nbsp;type<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;results<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#MySeries-groupby">groupby</a>(func,&nbsp;axis=0).<a href="#MySeries-mean">mean</a>()<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#MySeries-groupby">groupby</a>(['col1',&nbsp;'col2'])['col3'].<a href="#MySeries-mean">mean</a>()<br>
-&nbsp;<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;hierarchical&nbsp;index<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#MySeries-groupby">groupby</a>(['col1',&nbsp;'col2']).<a href="#MySeries-mean">mean</a>()<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-GroupBy&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-head"><strong>head</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;first&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="MySeries-interpolate"><strong>interpolate</strong></a>(self, method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', downcast=None, **kwargs)</dt><dd><tt>Interpolate&nbsp;values&nbsp;according&nbsp;to&nbsp;different&nbsp;methods.<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;only&nbsp;``method='linear'``&nbsp;is&nbsp;supported&nbsp;for<br>
-DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a&nbsp;MultiIndex.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-method&nbsp;:&nbsp;{'linear',&nbsp;'time',&nbsp;'index',&nbsp;'values',&nbsp;'nearest',&nbsp;'zero',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',&nbsp;'barycentric',&nbsp;'krogh',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'polynomial',&nbsp;'spline',&nbsp;'piecewise_polynomial',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'from_derivatives',&nbsp;'pchip',&nbsp;'akima'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'linear':&nbsp;ignore&nbsp;the&nbsp;index&nbsp;and&nbsp;treat&nbsp;the&nbsp;values&nbsp;as&nbsp;equally<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;spaced.&nbsp;This&nbsp;is&nbsp;the&nbsp;only&nbsp;method&nbsp;supported&nbsp;on&nbsp;MultiIndexes.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'time':&nbsp;interpolation&nbsp;works&nbsp;on&nbsp;daily&nbsp;and&nbsp;higher&nbsp;resolution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;data&nbsp;to&nbsp;interpolate&nbsp;given&nbsp;length&nbsp;of&nbsp;interval<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'index',&nbsp;'values':&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'nearest',&nbsp;'zero',&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'barycentric',&nbsp;'polynomial'&nbsp;is&nbsp;passed&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``scipy.interpolate.interp1d``.&nbsp;Both&nbsp;'polynomial'&nbsp;and&nbsp;'spline'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;require&nbsp;that&nbsp;you&nbsp;also&nbsp;specify&nbsp;an&nbsp;`order`&nbsp;(int),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;e.g.&nbsp;df.<a href="#MySeries-interpolate">interpolate</a>(method='polynomial',&nbsp;order=4).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'krogh',&nbsp;'piecewise_polynomial',&nbsp;'spline',&nbsp;'pchip'&nbsp;and&nbsp;'akima'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;all&nbsp;wrappers&nbsp;around&nbsp;the&nbsp;scipy&nbsp;interpolation&nbsp;methods&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;similar&nbsp;names.&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;For&nbsp;more&nbsp;information&nbsp;on&nbsp;their&nbsp;behavior,&nbsp;see&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`scipy&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__">http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;`tutorial&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__">http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'from_derivatives'&nbsp;refers&nbsp;to&nbsp;BPoly.from_derivatives&nbsp;which<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;replaces&nbsp;'piecewise_polynomial'&nbsp;interpolation&nbsp;method&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;0.18<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;support&nbsp;for&nbsp;the&nbsp;'akima'&nbsp;method<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;interpolate&nbsp;method&nbsp;'from_derivatives'&nbsp;which&nbsp;replaces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'piecewise_polynomial'&nbsp;in&nbsp;scipy&nbsp;0.18;&nbsp;backwards-compatible&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;&lt;&nbsp;0.18<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;0:&nbsp;fill&nbsp;column-by-column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;1:&nbsp;fill&nbsp;row-by-row<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs&nbsp;to&nbsp;fill.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0.<br>
-limit_direction&nbsp;:&nbsp;{'forward',&nbsp;'backward',&nbsp;'both'},&nbsp;default&nbsp;'forward'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;limit&nbsp;is&nbsp;specified,&nbsp;consecutive&nbsp;NaNs&nbsp;will&nbsp;be&nbsp;filled&nbsp;in&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;direction.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Update&nbsp;the&nbsp;NDFrame&nbsp;in&nbsp;place&nbsp;if&nbsp;possible.<br>
-downcast&nbsp;:&nbsp;optional,&nbsp;'infer'&nbsp;or&nbsp;None,&nbsp;defaults&nbsp;to&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Downcast&nbsp;dtypes&nbsp;if&nbsp;possible.<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;interpolating&nbsp;function.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;interpolated&nbsp;at&nbsp;the&nbsp;NaNs<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;replace,&nbsp;fillna<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Filling&nbsp;in&nbsp;NaNs<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0,&nbsp;1,&nbsp;np.nan,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-interpolate">interpolate</a>()<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="MySeries-isnull"><strong>isnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-notnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;isnull</tt></dd></dl>
-
-<dl><dt><a name="MySeries-last"><strong>last</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;final&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#MySeries-last">last</a>('5M')&nbsp;-&gt;&nbsp;Last&nbsp;5&nbsp;months<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MySeries-mask"><strong>mask</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;False&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;mask&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``False``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#MySeries-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#MySeries-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``mask``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).<a href="#MySeries-reshape">reshape</a>(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#MySeries-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#MySeries-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`</tt></dd></dl>
-
-<dl><dt><a name="MySeries-notnull"><strong>notnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are<br>
-not&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-isnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;notnull</tt></dd></dl>
-
-<dl><dt><a name="MySeries-pct_change"><strong>pct_change</strong></a>(self, periods=1, fill_method='pad', limit=None, freq=None, **kwargs)</dt><dd><tt>Percent&nbsp;change&nbsp;over&nbsp;given&nbsp;number&nbsp;of&nbsp;periods.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;percent&nbsp;change<br>
-fill_method&nbsp;:&nbsp;str,&nbsp;default&nbsp;'pad'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;How&nbsp;to&nbsp;handle&nbsp;NAs&nbsp;before&nbsp;computing&nbsp;percent&nbsp;changes<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NAs&nbsp;to&nbsp;fill&nbsp;before&nbsp;stopping<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;offset&nbsp;alias&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;time&nbsp;series&nbsp;API&nbsp;(e.g.&nbsp;'M'&nbsp;or&nbsp;BDay())<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-chg&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-By&nbsp;default,&nbsp;the&nbsp;percentage&nbsp;change&nbsp;is&nbsp;calculated&nbsp;along&nbsp;the&nbsp;stat<br>
-axis:&nbsp;0,&nbsp;or&nbsp;``Index``,&nbsp;for&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;and&nbsp;1,&nbsp;or&nbsp;``minor``&nbsp;for<br>
-``Panel``.&nbsp;You&nbsp;can&nbsp;change&nbsp;this&nbsp;with&nbsp;the&nbsp;``axis``&nbsp;keyword&nbsp;argument.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-pipe"><strong>pipe</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Apply&nbsp;func(self,&nbsp;\*args,&nbsp;\*\*kwargs)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.2<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;the&nbsp;NDFrame.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``args``,&nbsp;and&nbsp;``kwargs``&nbsp;are&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Alternatively&nbsp;a&nbsp;``(callable,&nbsp;data_keyword)``&nbsp;tuple&nbsp;where<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``data_keyword``&nbsp;is&nbsp;a&nbsp;string&nbsp;indicating&nbsp;the&nbsp;keyword&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``callable``&nbsp;that&nbsp;expects&nbsp;the&nbsp;NDFrame.<br>
-args&nbsp;:&nbsp;positional&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-kwargs&nbsp;:&nbsp;a&nbsp;dictionary&nbsp;of&nbsp;keyword&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="builtins.html#object">object</a>&nbsp;:&nbsp;the&nbsp;return&nbsp;type&nbsp;of&nbsp;``func``.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-Use&nbsp;``.pipe``&nbsp;when&nbsp;chaining&nbsp;together&nbsp;functions&nbsp;that&nbsp;expect<br>
-on&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;DataFrames.&nbsp;Instead&nbsp;of&nbsp;writing<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;f(g(h(df),&nbsp;arg1=a),&nbsp;arg2=b,&nbsp;arg3=c)<br>
-&nbsp;<br>
-You&nbsp;can&nbsp;write<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#MySeries-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#MySeries-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#MySeries-pipe">pipe</a>(f,&nbsp;arg2=b,&nbsp;arg3=c)<br>
-...&nbsp;)<br>
-&nbsp;<br>
-If&nbsp;you&nbsp;have&nbsp;a&nbsp;function&nbsp;that&nbsp;takes&nbsp;the&nbsp;data&nbsp;as&nbsp;(say)&nbsp;the&nbsp;second<br>
-argument,&nbsp;pass&nbsp;a&nbsp;tuple&nbsp;indicating&nbsp;which&nbsp;keyword&nbsp;expects&nbsp;the<br>
-data.&nbsp;For&nbsp;example,&nbsp;suppose&nbsp;``f``&nbsp;takes&nbsp;its&nbsp;data&nbsp;as&nbsp;``arg2``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#MySeries-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#MySeries-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#MySeries-pipe">pipe</a>((f,&nbsp;'arg2'),&nbsp;arg1=a,&nbsp;arg3=c)<br>
-...&nbsp;&nbsp;)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.map</tt></dd></dl>
-
-<dl><dt><a name="MySeries-pop"><strong>pop</strong></a>(self, item)</dt><dd><tt>Return&nbsp;item&nbsp;and&nbsp;drop&nbsp;from&nbsp;frame.&nbsp;Raise&nbsp;KeyError&nbsp;if&nbsp;not&nbsp;found.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-rank"><strong>rank</strong></a>(self, axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)</dt><dd><tt>Compute&nbsp;numerical&nbsp;data&nbsp;ranks&nbsp;(1&nbsp;through&nbsp;n)&nbsp;along&nbsp;axis.&nbsp;Equal&nbsp;values&nbsp;are<br>
-assigned&nbsp;a&nbsp;rank&nbsp;that&nbsp;is&nbsp;the&nbsp;average&nbsp;of&nbsp;the&nbsp;ranks&nbsp;of&nbsp;those&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;to&nbsp;direct&nbsp;ranking<br>
-method&nbsp;:&nbsp;{'average',&nbsp;'min',&nbsp;'max',&nbsp;'first',&nbsp;'dense'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;average:&nbsp;average&nbsp;rank&nbsp;of&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;min:&nbsp;lowest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;max:&nbsp;highest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;first:&nbsp;ranks&nbsp;assigned&nbsp;in&nbsp;order&nbsp;they&nbsp;appear&nbsp;in&nbsp;the&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dense:&nbsp;like&nbsp;'min',&nbsp;but&nbsp;rank&nbsp;always&nbsp;increases&nbsp;by&nbsp;1&nbsp;between&nbsp;groups<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;data.&nbsp;Valid&nbsp;only&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Panel&nbsp;objects<br>
-na_option&nbsp;:&nbsp;{'keep',&nbsp;'top',&nbsp;'bottom'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;keep:&nbsp;leave&nbsp;NA&nbsp;values&nbsp;where&nbsp;they&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;top:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;ascending<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;bottom:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;descending<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False&nbsp;for&nbsp;ranks&nbsp;by&nbsp;high&nbsp;(1)&nbsp;to&nbsp;low&nbsp;(N)<br>
-pct&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Computes&nbsp;percentage&nbsp;rank&nbsp;of&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ranks&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MySeries-reindex_like"><strong>reindex_like</strong></a>(self, other, method=None, copy=True, limit=None, tolerance=None)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;matching&nbsp;indices&nbsp;to&nbsp;myself.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;Object<br>
-method&nbsp;:&nbsp;string&nbsp;or&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;labels&nbsp;to&nbsp;fill&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;labels&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>&nbsp;and&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Like&nbsp;calling&nbsp;s.<a href="#MySeries-reindex">reindex</a>(index=other.index,&nbsp;columns=other.columns,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;method=...)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="MySeries-rename_axis"><strong>rename_axis</strong></a>(self, mapper, axis=0, copy=True, inplace=False)</dt><dd><tt>Alter&nbsp;index&nbsp;and&nbsp;/&nbsp;or&nbsp;columns&nbsp;using&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.<br>
-A&nbsp;scalar&nbsp;or&nbsp;list-like&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;``Index.name``<br>
-or&nbsp;``MultiIndex.names``&nbsp;attribute.<br>
-A&nbsp;function&nbsp;or&nbsp;dict&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;labels.<br>
-Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be&nbsp;unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in<br>
-a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left&nbsp;as-is.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-mapper&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename<br>
-pandas.Index.rename<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-rename_axis">rename_axis</a>("foo")&nbsp;&nbsp;#&nbsp;scalar,&nbsp;alters&nbsp;df.index.name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-foo<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-rename_axis">rename_axis</a>(lambda&nbsp;x:&nbsp;2&nbsp;*&nbsp;x)&nbsp;&nbsp;#&nbsp;function:&nbsp;alters&nbsp;labels<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-4&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-rename_axis">rename_axis</a>({"A":&nbsp;"ehh",&nbsp;"C":&nbsp;"see"},&nbsp;axis="columns")&nbsp;&nbsp;#&nbsp;mapping<br>
-&nbsp;&nbsp;&nbsp;ehh&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="MySeries-replace"><strong>replace</strong></a>(self, to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad', axis=None)</dt><dd><tt>Replace&nbsp;values&nbsp;given&nbsp;in&nbsp;'to_replace'&nbsp;with&nbsp;'value'.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-to_replace&nbsp;:&nbsp;str,&nbsp;regex,&nbsp;list,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;numeric,&nbsp;or&nbsp;None<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;str&nbsp;or&nbsp;regex:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str:&nbsp;string&nbsp;exactly&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;`value`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;regex:&nbsp;regexs&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`value`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;list&nbsp;of&nbsp;str,&nbsp;regex,&nbsp;or&nbsp;numeric:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;First,&nbsp;if&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;both&nbsp;lists,&nbsp;they<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Second,&nbsp;if&nbsp;``regex=True``&nbsp;then&nbsp;all&nbsp;of&nbsp;the&nbsp;strings&nbsp;in&nbsp;**both**<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;regexs&nbsp;otherwise&nbsp;they&nbsp;will&nbsp;match<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;directly.&nbsp;This&nbsp;doesn't&nbsp;matter&nbsp;much&nbsp;for&nbsp;`value`&nbsp;since&nbsp;there<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;only&nbsp;a&nbsp;few&nbsp;possible&nbsp;substitution&nbsp;regexes&nbsp;you&nbsp;can&nbsp;use.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str&nbsp;and&nbsp;regex&nbsp;rules&nbsp;apply&nbsp;as&nbsp;above.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dict:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Nested&nbsp;dictionaries,&nbsp;e.g.,&nbsp;{'a':&nbsp;{'b':&nbsp;nan}},&nbsp;are&nbsp;read&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;follows:&nbsp;look&nbsp;in&nbsp;column&nbsp;'a'&nbsp;for&nbsp;the&nbsp;value&nbsp;'b'&nbsp;and&nbsp;replace&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;nan.&nbsp;You&nbsp;can&nbsp;nest&nbsp;regular&nbsp;expressions&nbsp;as&nbsp;well.&nbsp;Note&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;names&nbsp;(the&nbsp;top-level&nbsp;dictionary&nbsp;keys&nbsp;in&nbsp;a&nbsp;nested<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dictionary)&nbsp;**cannot**&nbsp;be&nbsp;regular&nbsp;expressions.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Keys&nbsp;map&nbsp;to&nbsp;column&nbsp;names&nbsp;and&nbsp;values&nbsp;map&nbsp;to&nbsp;substitution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;values.&nbsp;You&nbsp;can&nbsp;treat&nbsp;this&nbsp;as&nbsp;a&nbsp;special&nbsp;case&nbsp;of&nbsp;passing&nbsp;two<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;except&nbsp;that&nbsp;you&nbsp;are&nbsp;specifying&nbsp;the&nbsp;column&nbsp;to&nbsp;search&nbsp;in.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;None:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;This&nbsp;means&nbsp;that&nbsp;the&nbsp;``regex``&nbsp;argument&nbsp;must&nbsp;be&nbsp;a&nbsp;string,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;compiled&nbsp;regular&nbsp;expression,&nbsp;or&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;such&nbsp;elements.&nbsp;If&nbsp;`value`&nbsp;is&nbsp;also&nbsp;``None``&nbsp;then&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;a&nbsp;nested&nbsp;dictionary&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;examples&nbsp;section&nbsp;for&nbsp;examples&nbsp;of&nbsp;each&nbsp;of&nbsp;these.<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;list,&nbsp;str,&nbsp;regex,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a&nbsp;dict&nbsp;of&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for&nbsp;each&nbsp;column&nbsp;(columns&nbsp;not&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;Regular&nbsp;expressions,&nbsp;strings&nbsp;and&nbsp;lists&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dicts&nbsp;of&nbsp;such&nbsp;objects&nbsp;are&nbsp;also&nbsp;allowed.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>&nbsp;(e.g.&nbsp;a&nbsp;column&nbsp;form&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Returns&nbsp;the&nbsp;caller&nbsp;if&nbsp;this&nbsp;is&nbsp;True.<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;size&nbsp;gap&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-regex&nbsp;:&nbsp;bool&nbsp;or&nbsp;same&nbsp;types&nbsp;as&nbsp;`to_replace`,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;interpret&nbsp;`to_replace`&nbsp;and/or&nbsp;`value`&nbsp;as&nbsp;regular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;expressions.&nbsp;If&nbsp;this&nbsp;is&nbsp;``True``&nbsp;then&nbsp;`to_replace`&nbsp;*must*&nbsp;be&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string.&nbsp;Otherwise,&nbsp;`to_replace`&nbsp;must&nbsp;be&nbsp;``None``&nbsp;because&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;parameter&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;regular&nbsp;expression&nbsp;or&nbsp;a&nbsp;list,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict,&nbsp;or&nbsp;array&nbsp;of&nbsp;regular&nbsp;expressions.<br>
-method&nbsp;:&nbsp;string,&nbsp;optional,&nbsp;{'pad',&nbsp;'ffill',&nbsp;'bfill'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;method&nbsp;to&nbsp;use&nbsp;when&nbsp;for&nbsp;replacement,&nbsp;when&nbsp;``to_replace``&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``list``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-NDFrame.reindex<br>
-NDFrame.asfreq<br>
-NDFrame.fillna<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-AssertionError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;a&nbsp;``bool``&nbsp;and&nbsp;`to_replace`&nbsp;is&nbsp;not&nbsp;``None``.<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;a&nbsp;``dict``&nbsp;and&nbsp;`value`&nbsp;is&nbsp;not&nbsp;a&nbsp;``list``,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``dict``,&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``,&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;``None``&nbsp;and&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;compilable&nbsp;into&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;regular&nbsp;expression&nbsp;or&nbsp;is&nbsp;a&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;``list``&nbsp;s&nbsp;or&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``&nbsp;s,&nbsp;but<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;they&nbsp;are&nbsp;not&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-*&nbsp;Regex&nbsp;substitution&nbsp;is&nbsp;performed&nbsp;under&nbsp;the&nbsp;hood&nbsp;with&nbsp;``re.sub``.&nbsp;The<br>
-&nbsp;&nbsp;rules&nbsp;for&nbsp;substitution&nbsp;for&nbsp;``re.sub``&nbsp;are&nbsp;the&nbsp;same.<br>
-*&nbsp;Regular&nbsp;expressions&nbsp;will&nbsp;only&nbsp;substitute&nbsp;on&nbsp;strings,&nbsp;meaning&nbsp;you<br>
-&nbsp;&nbsp;cannot&nbsp;provide,&nbsp;for&nbsp;example,&nbsp;a&nbsp;regular&nbsp;expression&nbsp;matching&nbsp;floating<br>
-&nbsp;&nbsp;point&nbsp;numbers&nbsp;and&nbsp;expect&nbsp;the&nbsp;columns&nbsp;in&nbsp;your&nbsp;frame&nbsp;that&nbsp;have&nbsp;a<br>
-&nbsp;&nbsp;numeric&nbsp;dtype&nbsp;to&nbsp;be&nbsp;matched.&nbsp;However,&nbsp;if&nbsp;those&nbsp;floating&nbsp;point&nbsp;numbers<br>
-&nbsp;&nbsp;*are*&nbsp;strings,&nbsp;then&nbsp;you&nbsp;can&nbsp;do&nbsp;this.<br>
-*&nbsp;This&nbsp;method&nbsp;has&nbsp;*a&nbsp;lot*&nbsp;of&nbsp;options.&nbsp;You&nbsp;are&nbsp;encouraged&nbsp;to&nbsp;experiment<br>
-&nbsp;&nbsp;and&nbsp;play&nbsp;with&nbsp;this&nbsp;method&nbsp;to&nbsp;gain&nbsp;intuition&nbsp;about&nbsp;how&nbsp;it&nbsp;works.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-resample"><strong>resample</strong></a>(self, rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0, on=None, level=None)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;frequency&nbsp;conversion&nbsp;and&nbsp;resampling&nbsp;of&nbsp;time<br>
-series.&nbsp;&nbsp;Object&nbsp;must&nbsp;have&nbsp;a&nbsp;datetime-like&nbsp;index&nbsp;(DatetimeIndex,<br>
-PeriodIndex,&nbsp;or&nbsp;TimedeltaIndex),&nbsp;or&nbsp;pass&nbsp;datetime-like&nbsp;values<br>
-to&nbsp;the&nbsp;on&nbsp;or&nbsp;level&nbsp;keyword.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-rule&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;offset&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a>&nbsp;representing&nbsp;target&nbsp;conversion<br>
-axis&nbsp;:&nbsp;int,&nbsp;optional,&nbsp;default&nbsp;0<br>
-closed&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;side&nbsp;of&nbsp;bin&nbsp;interval&nbsp;is&nbsp;closed<br>
-label&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;bin&nbsp;edge&nbsp;label&nbsp;to&nbsp;label&nbsp;bucket&nbsp;with<br>
-convention&nbsp;:&nbsp;{'start',&nbsp;'end',&nbsp;'s',&nbsp;'e'}<br>
-loffset&nbsp;:&nbsp;timedelta<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Adjust&nbsp;the&nbsp;resampled&nbsp;time&nbsp;labels<br>
-base&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;frequencies&nbsp;that&nbsp;evenly&nbsp;subdivide&nbsp;1&nbsp;day,&nbsp;the&nbsp;"origin"&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aggregated&nbsp;intervals.&nbsp;For&nbsp;example,&nbsp;for&nbsp;'5min'&nbsp;frequency,&nbsp;base&nbsp;could<br>
-&nbsp;&nbsp;&nbsp;&nbsp;range&nbsp;from&nbsp;0&nbsp;through&nbsp;4.&nbsp;Defaults&nbsp;to&nbsp;0<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;to&nbsp;use&nbsp;instead&nbsp;of&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-level&nbsp;:&nbsp;string&nbsp;or&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;MultiIndex,&nbsp;level&nbsp;(name&nbsp;or&nbsp;number)&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;resampling.&nbsp;&nbsp;Level&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offset&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;9&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(9),&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;series<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:04:00&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-2000-01-01&nbsp;00:05:00&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:07:00&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-2000-01-01&nbsp;00:08:00&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-Freq:&nbsp;T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;and&nbsp;sum&nbsp;the&nbsp;values<br>
-of&nbsp;the&nbsp;timestamps&nbsp;falling&nbsp;into&nbsp;a&nbsp;bin.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MySeries-resample">resample</a>('3T').<a href="#MySeries-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;label&nbsp;each<br>
-bin&nbsp;using&nbsp;the&nbsp;right&nbsp;edge&nbsp;instead&nbsp;of&nbsp;the&nbsp;left.&nbsp;Please&nbsp;note&nbsp;that&nbsp;the<br>
-value&nbsp;in&nbsp;the&nbsp;bucket&nbsp;used&nbsp;as&nbsp;the&nbsp;label&nbsp;is&nbsp;not&nbsp;included&nbsp;in&nbsp;the&nbsp;bucket,<br>
-which&nbsp;it&nbsp;labels.&nbsp;For&nbsp;example,&nbsp;in&nbsp;the&nbsp;original&nbsp;series&nbsp;the<br>
-bucket&nbsp;``2000-01-01&nbsp;00:03:00``&nbsp;contains&nbsp;the&nbsp;value&nbsp;3,&nbsp;but&nbsp;the&nbsp;summed<br>
-value&nbsp;in&nbsp;the&nbsp;resampled&nbsp;bucket&nbsp;with&nbsp;the&nbsp;label``2000-01-01&nbsp;00:03:00``<br>
-does&nbsp;not&nbsp;include&nbsp;3&nbsp;(if&nbsp;it&nbsp;did,&nbsp;the&nbsp;summed&nbsp;value&nbsp;would&nbsp;be&nbsp;6,&nbsp;not&nbsp;3).<br>
-To&nbsp;include&nbsp;this&nbsp;value&nbsp;close&nbsp;the&nbsp;right&nbsp;side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval&nbsp;as<br>
-illustrated&nbsp;in&nbsp;the&nbsp;example&nbsp;below&nbsp;this&nbsp;one.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MySeries-resample">resample</a>('3T',&nbsp;label='right').<a href="#MySeries-sum">sum</a>()<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;close&nbsp;the&nbsp;right<br>
-side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MySeries-resample">resample</a>('3T',&nbsp;label='right',&nbsp;closed='right').<a href="#MySeries-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MySeries-resample">resample</a>('30S').<a href="#MySeries-asfreq">asfreq</a>()[0:5]&nbsp;#select&nbsp;first&nbsp;5&nbsp;rows<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;1.0<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;2.0<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values&nbsp;using&nbsp;the&nbsp;``pad``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MySeries-resample">resample</a>('30S').pad()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the<br>
-``NaN``&nbsp;values&nbsp;using&nbsp;the&nbsp;``bfill``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MySeries-resample">resample</a>('30S').<a href="#MySeries-bfill">bfill</a>()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Pass&nbsp;a&nbsp;custom&nbsp;function&nbsp;via&nbsp;``apply``<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;custom_resampler(array_like):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;np.<a href="#MySeries-sum">sum</a>(array_like)+5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#MySeries-resample">resample</a>('3T').<a href="#MySeries-apply">apply</a>(custom_resampler)<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;17<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;26<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects,&nbsp;the&nbsp;keyword&nbsp;``on``&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;specify&nbsp;the<br>
-column&nbsp;instead&nbsp;of&nbsp;the&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=9*[range(4)],&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'])<br>
-&gt;&gt;&gt;&nbsp;df['time']&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-resample">resample</a>('3T',&nbsp;on='time').<a href="#MySeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;c&nbsp;&nbsp;d<br>
-time<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-&nbsp;<br>
-For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;MultiIndex,&nbsp;the&nbsp;keyword&nbsp;``level``&nbsp;can&nbsp;be&nbsp;used&nbsp;to<br>
-specify&nbsp;on&nbsp;level&nbsp;the&nbsp;resampling&nbsp;needs&nbsp;to&nbsp;take&nbsp;place.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;time&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=5,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=10*[range(4)],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.MultiIndex.from_product([time,&nbsp;[1,&nbsp;2]])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;)<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#MySeries-resample">resample</a>('3T',&nbsp;level=0).<a href="#MySeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;d<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;6&nbsp;&nbsp;12&nbsp;&nbsp;18<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;8&nbsp;&nbsp;12</tt></dd></dl>
-
-<dl><dt><a name="MySeries-sample"><strong>sample</strong></a>(self, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None)</dt><dd><tt>Returns&nbsp;a&nbsp;random&nbsp;sample&nbsp;of&nbsp;items&nbsp;from&nbsp;an&nbsp;axis&nbsp;of&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;items&nbsp;from&nbsp;axis&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`frac`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;=&nbsp;1&nbsp;if&nbsp;`frac`&nbsp;=&nbsp;None.<br>
-frac&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fraction&nbsp;of&nbsp;axis&nbsp;items&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`n`.<br>
-replace&nbsp;:&nbsp;boolean,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sample&nbsp;with&nbsp;or&nbsp;without&nbsp;replacement.&nbsp;Default&nbsp;=&nbsp;False.<br>
-weights&nbsp;:&nbsp;str&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;'None'&nbsp;results&nbsp;in&nbsp;equal&nbsp;probability&nbsp;weighting.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;will&nbsp;align&nbsp;with&nbsp;target&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;index.&nbsp;Index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;in&nbsp;weights&nbsp;not&nbsp;found&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;will&nbsp;be&nbsp;ignored&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;values&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;not&nbsp;in&nbsp;weights&nbsp;will&nbsp;be&nbsp;assigned<br>
-&nbsp;&nbsp;&nbsp;&nbsp;weights&nbsp;of&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;called&nbsp;on&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;will&nbsp;accept&nbsp;the&nbsp;name&nbsp;of&nbsp;a&nbsp;column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;axis&nbsp;=&nbsp;0.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Unless&nbsp;weights&nbsp;are&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;weights&nbsp;must&nbsp;be&nbsp;same&nbsp;length&nbsp;as&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;being&nbsp;sampled.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;weights&nbsp;do&nbsp;not&nbsp;sum&nbsp;to&nbsp;1,&nbsp;they&nbsp;will&nbsp;be&nbsp;normalized&nbsp;to&nbsp;sum&nbsp;to&nbsp;1.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;values&nbsp;in&nbsp;the&nbsp;weights&nbsp;column&nbsp;will&nbsp;be&nbsp;treated&nbsp;as&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;inf&nbsp;and&nbsp;-inf&nbsp;values&nbsp;not&nbsp;allowed.<br>
-random_state&nbsp;:&nbsp;int&nbsp;or&nbsp;numpy.random.RandomState,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Seed&nbsp;for&nbsp;the&nbsp;random&nbsp;number&nbsp;generator&nbsp;(if&nbsp;int),&nbsp;or&nbsp;numpy&nbsp;RandomState<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;sample.&nbsp;Accepts&nbsp;axis&nbsp;number&nbsp;or&nbsp;name.&nbsp;Default&nbsp;is&nbsp;stat&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;given&nbsp;data&nbsp;type&nbsp;(0&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;and&nbsp;DataFrames,&nbsp;1&nbsp;for&nbsp;Panels).<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-A&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;type&nbsp;as&nbsp;caller.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Generate&nbsp;an&nbsp;example&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;and&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(50))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-head">head</a>()<br>
-0&nbsp;&nbsp;&nbsp;-0.038497<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.820773<br>
-2&nbsp;&nbsp;&nbsp;-0.972766<br>
-3&nbsp;&nbsp;&nbsp;-1.598270<br>
-4&nbsp;&nbsp;&nbsp;-1.095526<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(50,&nbsp;4),&nbsp;columns=list('ABCD'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-head">head</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;0.016443&nbsp;-2.318952&nbsp;-0.566372&nbsp;-1.028078<br>
-1&nbsp;-1.051921&nbsp;&nbsp;0.438836&nbsp;&nbsp;0.658280&nbsp;-0.175797<br>
-2&nbsp;-1.243569&nbsp;-0.364626&nbsp;-0.215065&nbsp;&nbsp;0.057736<br>
-3&nbsp;&nbsp;1.768216&nbsp;&nbsp;0.404512&nbsp;-0.385604&nbsp;-1.457834<br>
-4&nbsp;&nbsp;1.072446&nbsp;-1.137172&nbsp;&nbsp;0.314194&nbsp;-0.046661<br>
-&nbsp;<br>
-Next&nbsp;extract&nbsp;a&nbsp;random&nbsp;sample&nbsp;from&nbsp;both&nbsp;of&nbsp;these&nbsp;objects...<br>
-&nbsp;<br>
-3&nbsp;random&nbsp;elements&nbsp;from&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-sample">sample</a>(n=3)<br>
-27&nbsp;&nbsp;&nbsp;-0.994689<br>
-55&nbsp;&nbsp;&nbsp;-1.049016<br>
-67&nbsp;&nbsp;&nbsp;-0.224565<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-And&nbsp;a&nbsp;random&nbsp;10%&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;with&nbsp;replacement:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-sample">sample</a>(frac=0.1,&nbsp;replace=True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-35&nbsp;&nbsp;1.981780&nbsp;&nbsp;0.142106&nbsp;&nbsp;1.817165&nbsp;-0.290805<br>
-49&nbsp;-1.336199&nbsp;-0.448634&nbsp;-0.789640&nbsp;&nbsp;0.217116<br>
-40&nbsp;&nbsp;0.823173&nbsp;-0.078816&nbsp;&nbsp;1.009536&nbsp;&nbsp;1.015108<br>
-15&nbsp;&nbsp;1.421154&nbsp;-0.055301&nbsp;-1.922594&nbsp;-0.019696<br>
-6&nbsp;&nbsp;-0.148339&nbsp;&nbsp;0.832938&nbsp;&nbsp;1.787600&nbsp;-1.383767</tt></dd></dl>
-
-<dl><dt><a name="MySeries-select"><strong>select</strong></a>(self, crit, axis=0)</dt><dd><tt>Return&nbsp;data&nbsp;corresponding&nbsp;to&nbsp;axis&nbsp;labels&nbsp;matching&nbsp;criteria<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-crit&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;called&nbsp;on&nbsp;each&nbsp;index&nbsp;(label).&nbsp;Should&nbsp;return&nbsp;True&nbsp;or&nbsp;False<br>
-axis&nbsp;:&nbsp;int<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-selection&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MySeries-set_axis"><strong>set_axis</strong></a>(self, axis, labels)</dt><dd><tt>public&nbsp;verson&nbsp;of&nbsp;axis&nbsp;assignment</tt></dd></dl>
-
-<dl><dt><a name="MySeries-slice_shift"><strong>slice_shift</strong></a>(self, periods=1, axis=0)</dt><dd><tt>Equivalent&nbsp;to&nbsp;`shift`&nbsp;without&nbsp;copying&nbsp;data.&nbsp;The&nbsp;shifted&nbsp;data&nbsp;will<br>
-not&nbsp;include&nbsp;the&nbsp;dropped&nbsp;periods&nbsp;and&nbsp;the&nbsp;shifted&nbsp;axis&nbsp;will&nbsp;be&nbsp;smaller<br>
-than&nbsp;the&nbsp;original.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-&nbsp;<br>
-Notes<br>
------<br>
-While&nbsp;the&nbsp;`slice_shift`&nbsp;is&nbsp;faster&nbsp;than&nbsp;`shift`,&nbsp;you&nbsp;may&nbsp;pay&nbsp;for&nbsp;it<br>
-later&nbsp;during&nbsp;alignment.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MySeries-squeeze"><strong>squeeze</strong></a>(self, axis=None)</dt><dd><tt>Squeeze&nbsp;length&nbsp;1&nbsp;dimensions.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;None,&nbsp;integer&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;squeeze&nbsp;if&nbsp;1-sized.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-scalar&nbsp;if&nbsp;1-sized,&nbsp;else&nbsp;original&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-swapaxes"><strong>swapaxes</strong></a>(self, axis1, axis2, copy=True)</dt><dd><tt>Interchange&nbsp;axes&nbsp;and&nbsp;swap&nbsp;values&nbsp;axes&nbsp;appropriately<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="MySeries-tail"><strong>tail</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;last&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_clipboard"><strong>to_clipboard</strong></a>(self, excel=None, sep=None, **kwargs)</dt><dd><tt>Attempt&nbsp;to&nbsp;write&nbsp;text&nbsp;representation&nbsp;of&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;the&nbsp;system&nbsp;clipboard<br>
-This&nbsp;can&nbsp;be&nbsp;pasted&nbsp;into&nbsp;Excel,&nbsp;for&nbsp;example.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel&nbsp;:&nbsp;boolean,&nbsp;defaults&nbsp;to&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;use&nbsp;the&nbsp;provided&nbsp;separator,&nbsp;writing&nbsp;in&nbsp;a&nbsp;csv<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;format&nbsp;for&nbsp;allowing&nbsp;easy&nbsp;pasting&nbsp;into&nbsp;excel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;False,&nbsp;write&nbsp;a&nbsp;string&nbsp;representation&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;the&nbsp;clipboard<br>
-sep&nbsp;:&nbsp;optional,&nbsp;defaults&nbsp;to&nbsp;tab<br>
-other&nbsp;keywords&nbsp;are&nbsp;passed&nbsp;to&nbsp;to_csv<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Requirements&nbsp;for&nbsp;your&nbsp;platform<br>
-&nbsp;&nbsp;-&nbsp;Linux:&nbsp;xclip,&nbsp;or&nbsp;xsel&nbsp;(with&nbsp;gtk&nbsp;or&nbsp;PyQt4&nbsp;modules)<br>
-&nbsp;&nbsp;-&nbsp;Windows:&nbsp;none<br>
-&nbsp;&nbsp;-&nbsp;OS&nbsp;X:&nbsp;none</tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_dense"><strong>to_dense</strong></a>(self)</dt><dd><tt>Return&nbsp;dense&nbsp;representation&nbsp;of&nbsp;NDFrame&nbsp;(as&nbsp;opposed&nbsp;to&nbsp;sparse)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_hdf"><strong>to_hdf</strong></a>(self, path_or_buf, key, **kwargs)</dt><dd><tt>Write&nbsp;the&nbsp;contained&nbsp;data&nbsp;to&nbsp;an&nbsp;HDF5&nbsp;file&nbsp;using&nbsp;HDFStore.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;(string)&nbsp;or&nbsp;HDFStore&nbsp;<a href="builtins.html#object">object</a><br>
-key&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;identifier&nbsp;for&nbsp;the&nbsp;group&nbsp;in&nbsp;the&nbsp;store<br>
-mode&nbsp;:&nbsp;optional,&nbsp;{'a',&nbsp;'w',&nbsp;'r+'},&nbsp;default&nbsp;'a'<br>
-&nbsp;<br>
-&nbsp;&nbsp;``'w'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write;&nbsp;a&nbsp;new&nbsp;file&nbsp;is&nbsp;created&nbsp;(an&nbsp;existing&nbsp;file&nbsp;with&nbsp;the&nbsp;same<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;name&nbsp;would&nbsp;be&nbsp;deleted).<br>
-&nbsp;&nbsp;``'a'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Append;&nbsp;an&nbsp;existing&nbsp;file&nbsp;is&nbsp;opened&nbsp;for&nbsp;reading&nbsp;and&nbsp;writing,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;if&nbsp;the&nbsp;file&nbsp;does&nbsp;not&nbsp;exist&nbsp;it&nbsp;is&nbsp;created.<br>
-&nbsp;&nbsp;``'r+'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;It&nbsp;is&nbsp;similar&nbsp;to&nbsp;``'a'``,&nbsp;but&nbsp;the&nbsp;file&nbsp;must&nbsp;already&nbsp;exist.<br>
-format&nbsp;:&nbsp;'fixed(f)|table(t)',&nbsp;default&nbsp;is&nbsp;'fixed'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fixed(f)&nbsp;:&nbsp;Fixed&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Fast&nbsp;writing/reading.&nbsp;Not-appendable,&nbsp;nor&nbsp;searchable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;table(t)&nbsp;:&nbsp;Table&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;as&nbsp;a&nbsp;PyTables&nbsp;Table&nbsp;structure&nbsp;which&nbsp;may&nbsp;perform<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;worse&nbsp;but&nbsp;allow&nbsp;more&nbsp;flexible&nbsp;operations&nbsp;like&nbsp;searching<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/&nbsp;selecting&nbsp;subsets&nbsp;of&nbsp;the&nbsp;data<br>
-append&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;Table&nbsp;formats,&nbsp;append&nbsp;the&nbsp;input&nbsp;data&nbsp;to&nbsp;the&nbsp;existing<br>
-data_columns&nbsp;:&nbsp;&nbsp;list&nbsp;of&nbsp;columns,&nbsp;or&nbsp;True,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;columns&nbsp;to&nbsp;create&nbsp;as&nbsp;indexed&nbsp;data&nbsp;columns&nbsp;for&nbsp;on-disk<br>
-&nbsp;&nbsp;&nbsp;&nbsp;queries,&nbsp;or&nbsp;True&nbsp;to&nbsp;use&nbsp;all&nbsp;columns.&nbsp;By&nbsp;default&nbsp;only&nbsp;the&nbsp;axes<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;are&nbsp;indexed.&nbsp;See&nbsp;`here<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__">http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__</a>.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Applicable&nbsp;only&nbsp;to&nbsp;format='table'.<br>
-complevel&nbsp;:&nbsp;int,&nbsp;1-9,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;a&nbsp;complib&nbsp;is&nbsp;specified&nbsp;compression&nbsp;will&nbsp;be&nbsp;applied<br>
-&nbsp;&nbsp;&nbsp;&nbsp;where&nbsp;possible<br>
-complib&nbsp;:&nbsp;{'zlib',&nbsp;'bzip2',&nbsp;'lzo',&nbsp;'blosc',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;complevel&nbsp;is&nbsp;&gt;&nbsp;0&nbsp;apply&nbsp;compression&nbsp;to&nbsp;objects&nbsp;written<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;store&nbsp;wherever&nbsp;possible<br>
-fletcher32&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;applying&nbsp;compression&nbsp;use&nbsp;the&nbsp;fletcher32&nbsp;checksum<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;true,&nbsp;ALL&nbsp;nan&nbsp;rows&nbsp;will&nbsp;not&nbsp;be&nbsp;written&nbsp;to&nbsp;store.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_json"><strong>to_json</strong></a>(self, path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False)</dt><dd><tt>Convert&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;a&nbsp;JSON&nbsp;string.<br>
-&nbsp;<br>
-Note&nbsp;NaN's&nbsp;and&nbsp;None&nbsp;will&nbsp;be&nbsp;converted&nbsp;to&nbsp;null&nbsp;and&nbsp;datetime&nbsp;objects<br>
-will&nbsp;be&nbsp;converted&nbsp;to&nbsp;UNIX&nbsp;timestamps.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;or&nbsp;buffer&nbsp;to&nbsp;write&nbsp;the&nbsp;result&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;this&nbsp;is&nbsp;None,&nbsp;return&nbsp;a&nbsp;StringIO&nbsp;of&nbsp;the&nbsp;converted&nbsp;string<br>
-orient&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'index'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:&nbsp;{'split','records','index'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{'split','records','index','columns','values'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;The&nbsp;format&nbsp;of&nbsp;the&nbsp;JSON&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;split&nbsp;:&nbsp;dict&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{index&nbsp;-&gt;&nbsp;[index],&nbsp;columns&nbsp;-&gt;&nbsp;[columns],&nbsp;data&nbsp;-&gt;&nbsp;[values]}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;records&nbsp;:&nbsp;list&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[{column&nbsp;-&gt;&nbsp;value},&nbsp;...&nbsp;,&nbsp;{column&nbsp;-&gt;&nbsp;value}]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;index&nbsp;:&nbsp;dict&nbsp;like&nbsp;{index&nbsp;-&gt;&nbsp;{column&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;columns&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;{index&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;values&nbsp;:&nbsp;just&nbsp;the&nbsp;values&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;table&nbsp;:&nbsp;dict&nbsp;like&nbsp;{'schema':&nbsp;{schema},&nbsp;'data':&nbsp;{data}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;describing&nbsp;the&nbsp;data,&nbsp;and&nbsp;the&nbsp;data&nbsp;component&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;like&nbsp;``orient='records'``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionchanged::&nbsp;0.20.0<br>
-&nbsp;<br>
-date_format&nbsp;:&nbsp;{None,&nbsp;'epoch',&nbsp;'iso'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Type&nbsp;of&nbsp;date&nbsp;conversion.&nbsp;`epoch`&nbsp;=&nbsp;epoch&nbsp;milliseconds,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`iso`&nbsp;=&nbsp;ISO8601.&nbsp;The&nbsp;default&nbsp;depends&nbsp;on&nbsp;the&nbsp;`orient`.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`orient='table'`,&nbsp;the&nbsp;default&nbsp;is&nbsp;`'iso'`.&nbsp;For&nbsp;all&nbsp;other&nbsp;orients,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;default&nbsp;is&nbsp;`'epoch'`.<br>
-double_precision&nbsp;:&nbsp;The&nbsp;number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;use&nbsp;when&nbsp;encoding<br>
-&nbsp;&nbsp;&nbsp;&nbsp;floating&nbsp;point&nbsp;values,&nbsp;default&nbsp;10.<br>
-force_ascii&nbsp;:&nbsp;force&nbsp;encoded&nbsp;string&nbsp;to&nbsp;be&nbsp;ASCII,&nbsp;default&nbsp;True.<br>
-date_unit&nbsp;:&nbsp;string,&nbsp;default&nbsp;'ms'&nbsp;(milliseconds)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;time&nbsp;unit&nbsp;to&nbsp;encode&nbsp;to,&nbsp;governs&nbsp;timestamp&nbsp;and&nbsp;ISO8601<br>
-&nbsp;&nbsp;&nbsp;&nbsp;precision.&nbsp;&nbsp;One&nbsp;of&nbsp;'s',&nbsp;'ms',&nbsp;'us',&nbsp;'ns'&nbsp;for&nbsp;second,&nbsp;millisecond,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;microsecond,&nbsp;and&nbsp;nanosecond&nbsp;respectively.<br>
-default_handler&nbsp;:&nbsp;callable,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Handler&nbsp;to&nbsp;call&nbsp;if&nbsp;<a href="builtins.html#object">object</a>&nbsp;cannot&nbsp;otherwise&nbsp;be&nbsp;converted&nbsp;to&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;suitable&nbsp;format&nbsp;for&nbsp;JSON.&nbsp;Should&nbsp;receive&nbsp;a&nbsp;single&nbsp;argument&nbsp;which&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;convert&nbsp;and&nbsp;return&nbsp;a&nbsp;serialisable&nbsp;<a href="builtins.html#object">object</a>.<br>
-lines&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'orient'&nbsp;is&nbsp;'records'&nbsp;write&nbsp;out&nbsp;line&nbsp;delimited&nbsp;json&nbsp;format.&nbsp;Will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;throw&nbsp;ValueError&nbsp;if&nbsp;incorrect&nbsp;'orient'&nbsp;since&nbsp;others&nbsp;are&nbsp;not&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;filtered&nbsp;info&nbsp;axis<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pd.read_json<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([['a',&nbsp;'b'],&nbsp;['c',&nbsp;'d']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=['row&nbsp;1',&nbsp;'row&nbsp;2'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['col&nbsp;1',&nbsp;'col&nbsp;2'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-to_json">to_json</a>(orient='split')<br>
-'{"columns":["col&nbsp;1","col&nbsp;2"],<br>
-&nbsp;&nbsp;"index":["row&nbsp;1","row&nbsp;2"],<br>
-&nbsp;&nbsp;"data":[["a","b"],["c","d"]]}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'index'``&nbsp;formatted&nbsp;JSON:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-to_json">to_json</a>(orient='index')<br>
-'{"row&nbsp;1":{"col&nbsp;1":"a","col&nbsp;2":"b"},"row&nbsp;2":{"col&nbsp;1":"c","col&nbsp;2":"d"}}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'records'``&nbsp;formatted&nbsp;JSON.<br>
-Note&nbsp;that&nbsp;index&nbsp;labels&nbsp;are&nbsp;not&nbsp;preserved&nbsp;with&nbsp;this&nbsp;encoding.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-to_json">to_json</a>(orient='records')<br>
-'[{"col&nbsp;1":"a","col&nbsp;2":"b"},{"col&nbsp;1":"c","col&nbsp;2":"d"}]'<br>
-&nbsp;<br>
-Encoding&nbsp;with&nbsp;Table&nbsp;Schema<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-to_json">to_json</a>(orient='table')<br>
-'{"schema":&nbsp;{"fields":&nbsp;[{"name":&nbsp;"index",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;1",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;2",&nbsp;"type":&nbsp;"string"}],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"primaryKey":&nbsp;"index",<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"pandas_version":&nbsp;"0.20.0"},<br>
-&nbsp;&nbsp;"data":&nbsp;[{"index":&nbsp;"row&nbsp;1",&nbsp;"col&nbsp;1":&nbsp;"a",&nbsp;"col&nbsp;2":&nbsp;"b"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"index":&nbsp;"row&nbsp;2",&nbsp;"col&nbsp;1":&nbsp;"c",&nbsp;"col&nbsp;2":&nbsp;"d"}]}'</tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_msgpack"><strong>to_msgpack</strong></a>(self, path_or_buf=None, encoding='utf-8', **kwargs)</dt><dd><tt>msgpack&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path<br>
-&nbsp;<br>
-THIS&nbsp;IS&nbsp;AN&nbsp;EXPERIMENTAL&nbsp;LIBRARY&nbsp;and&nbsp;the&nbsp;storage&nbsp;format<br>
-may&nbsp;not&nbsp;be&nbsp;stable&nbsp;until&nbsp;a&nbsp;future&nbsp;release.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;File&nbsp;path,&nbsp;buffer-like,&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;None,&nbsp;return&nbsp;generated&nbsp;string<br>
-append&nbsp;:&nbsp;boolean&nbsp;whether&nbsp;to&nbsp;append&nbsp;to&nbsp;an&nbsp;existing&nbsp;msgpack<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(default&nbsp;is&nbsp;False)<br>
-compress&nbsp;:&nbsp;type&nbsp;of&nbsp;compressor&nbsp;(zlib&nbsp;or&nbsp;blosc),&nbsp;default&nbsp;to&nbsp;None&nbsp;(no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;compression)</tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_pickle"><strong>to_pickle</strong></a>(self, path, compression='infer')</dt><dd><tt>Pickle&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path<br>
-compression&nbsp;:&nbsp;{'infer',&nbsp;'gzip',&nbsp;'bz2',&nbsp;'xz',&nbsp;None},&nbsp;default&nbsp;'infer'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;compression&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0</tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_sql"><strong>to_sql</strong></a>(self, name, con, flavor=None, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None)</dt><dd><tt>Write&nbsp;records&nbsp;stored&nbsp;in&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;SQL&nbsp;database.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-name&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;SQL&nbsp;table<br>
-con&nbsp;:&nbsp;SQLAlchemy&nbsp;engine&nbsp;or&nbsp;DBAPI2&nbsp;connection&nbsp;(legacy&nbsp;mode)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Using&nbsp;SQLAlchemy&nbsp;makes&nbsp;it&nbsp;possible&nbsp;to&nbsp;use&nbsp;any&nbsp;DB&nbsp;supported&nbsp;by&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;library.&nbsp;If&nbsp;a&nbsp;DBAPI2&nbsp;<a href="builtins.html#object">object</a>,&nbsp;only&nbsp;sqlite3&nbsp;is&nbsp;supported.<br>
-flavor&nbsp;:&nbsp;'sqlite',&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;DEPRECATED:&nbsp;this&nbsp;parameter&nbsp;will&nbsp;be&nbsp;removed&nbsp;in&nbsp;a&nbsp;future&nbsp;version,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;as&nbsp;'sqlite'&nbsp;is&nbsp;the&nbsp;only&nbsp;supported&nbsp;option&nbsp;if&nbsp;SQLAlchemy&nbsp;is&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;installed.<br>
-schema&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;the&nbsp;schema&nbsp;(if&nbsp;database&nbsp;flavor&nbsp;supports&nbsp;this).&nbsp;If&nbsp;None,&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;schema.<br>
-if_exists&nbsp;:&nbsp;{'fail',&nbsp;'replace',&nbsp;'append'},&nbsp;default&nbsp;'fail'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;fail:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;do&nbsp;nothing.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;replace:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;drop&nbsp;it,&nbsp;recreate&nbsp;it,&nbsp;and&nbsp;insert&nbsp;data.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;append:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;insert&nbsp;data.&nbsp;Create&nbsp;if&nbsp;does&nbsp;not&nbsp;exist.<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;index&nbsp;as&nbsp;a&nbsp;column.<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s).&nbsp;If&nbsp;None&nbsp;is&nbsp;given&nbsp;(default)&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`index`&nbsp;is&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-chunksize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;not&nbsp;None,&nbsp;then&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;in&nbsp;batches&nbsp;of&nbsp;this&nbsp;size&nbsp;at&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;time.&nbsp;&nbsp;If&nbsp;None,&nbsp;all&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;at&nbsp;once.<br>
-dtype&nbsp;:&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;to&nbsp;SQL&nbsp;type,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Optional&nbsp;specifying&nbsp;the&nbsp;datatype&nbsp;for&nbsp;columns.&nbsp;The&nbsp;SQL&nbsp;type&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;SQLAlchemy&nbsp;type,&nbsp;or&nbsp;a&nbsp;string&nbsp;for&nbsp;sqlite3&nbsp;fallback&nbsp;connection.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-to_xarray"><strong>to_xarray</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;xarray&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;the&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;DataArray&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;Dataset&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-a&nbsp;DataArray&nbsp;for&nbsp;higher&nbsp;dims<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;foo&nbsp;&nbsp;4.0<br>
-1&nbsp;&nbsp;1&nbsp;&nbsp;bar&nbsp;&nbsp;5.0<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;foo&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(index:&nbsp;3)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;index&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;0&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;1&nbsp;1&nbsp;2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'foo'&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;float64&nbsp;4.0&nbsp;5.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;).set_index(['B','A'])<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-B&nbsp;&nbsp;&nbsp;A<br>
-foo&nbsp;1&nbsp;&nbsp;4.0<br>
-bar&nbsp;1&nbsp;&nbsp;5.0<br>
-foo&nbsp;2&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(A:&nbsp;2,&nbsp;B:&nbsp;2)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;*&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(A)&nbsp;int64&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B,&nbsp;A)&nbsp;float64&nbsp;5.0&nbsp;nan&nbsp;4.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p&nbsp;=&nbsp;pd.Panel(np.arange(24).<a href="#MySeries-reshape">reshape</a>(4,3,2),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;items=list('ABCD'),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;major_axis=pd.date_range('20130101',&nbsp;periods=3),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;minor_axis=['first',&nbsp;'second'])<br>
-&gt;&gt;&gt;&nbsp;p<br>
-&lt;class&nbsp;'pandas.core.panel.Panel'&gt;<br>
-Dimensions:&nbsp;4&nbsp;(items)&nbsp;x&nbsp;3&nbsp;(major_axis)&nbsp;x&nbsp;2&nbsp;(minor_axis)<br>
-Items&nbsp;axis:&nbsp;A&nbsp;to&nbsp;D<br>
-Major_axis&nbsp;axis:&nbsp;2013-01-01&nbsp;00:00:00&nbsp;to&nbsp;2013-01-03&nbsp;00:00:00<br>
-Minor_axis&nbsp;axis:&nbsp;first&nbsp;to&nbsp;second<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p.<a href="#MySeries-to_xarray">to_xarray</a>()<br>
-&lt;xarray.DataArray&nbsp;(items:&nbsp;4,&nbsp;major_axis:&nbsp;3,&nbsp;minor_axis:&nbsp;2)&gt;<br>
-array([[[&nbsp;0,&nbsp;&nbsp;1],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;2,&nbsp;&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;4,&nbsp;&nbsp;5]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[&nbsp;6,&nbsp;&nbsp;7],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;8,&nbsp;&nbsp;9],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[10,&nbsp;11]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[12,&nbsp;13],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[14,&nbsp;15],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[16,&nbsp;17]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[18,&nbsp;19],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[20,&nbsp;21],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[22,&nbsp;23]]])<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;items&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(items)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'A'&nbsp;'B'&nbsp;'C'&nbsp;'D'<br>
-&nbsp;&nbsp;*&nbsp;major_axis&nbsp;&nbsp;(major_axis)&nbsp;datetime64[ns]&nbsp;2013-01-01&nbsp;2013-01-02&nbsp;2013-01-03&nbsp;&nbsp;#&nbsp;noqa<br>
-&nbsp;&nbsp;*&nbsp;minor_axis&nbsp;&nbsp;(minor_axis)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'first'&nbsp;'second'<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;the&nbsp;`xarray&nbsp;docs&nbsp;&lt;<a href="http://xarray.pydata.org/en/stable/&gt;`__">http://xarray.pydata.org/en/stable/&gt;`__</a></tt></dd></dl>
-
-<dl><dt><a name="MySeries-truncate"><strong>truncate</strong></a>(self, before=None, after=None, axis=None, copy=True)</dt><dd><tt>Truncates&nbsp;a&nbsp;sorted&nbsp;NDFrame&nbsp;before&nbsp;and/or&nbsp;after&nbsp;some&nbsp;particular<br>
-index&nbsp;value.&nbsp;If&nbsp;the&nbsp;axis&nbsp;contains&nbsp;only&nbsp;datetime&nbsp;values,&nbsp;before/after<br>
-parameters&nbsp;are&nbsp;converted&nbsp;to&nbsp;datetime&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-before&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;before&nbsp;index&nbsp;value<br>
-after&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;after&nbsp;index&nbsp;value<br>
-axis&nbsp;:&nbsp;the&nbsp;truncation&nbsp;axis,&nbsp;defaults&nbsp;to&nbsp;the&nbsp;stat&nbsp;axis<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;is&nbsp;True,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;truncated&nbsp;section<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-truncated&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="MySeries-tshift"><strong>tshift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;the&nbsp;time&nbsp;index,&nbsp;using&nbsp;the&nbsp;index's&nbsp;frequency&nbsp;if&nbsp;available.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM')<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;basestring<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Corresponds&nbsp;to&nbsp;the&nbsp;axis&nbsp;that&nbsp;contains&nbsp;the&nbsp;Index<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;not&nbsp;specified&nbsp;then&nbsp;tries&nbsp;to&nbsp;use&nbsp;the&nbsp;freq&nbsp;or&nbsp;inferred_freq<br>
-attributes&nbsp;of&nbsp;the&nbsp;index.&nbsp;If&nbsp;neither&nbsp;of&nbsp;those&nbsp;attributes&nbsp;exist,&nbsp;a<br>
-ValueError&nbsp;is&nbsp;thrown<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;NDFrame</tt></dd></dl>
-
-<dl><dt><a name="MySeries-tz_convert"><strong>tz_convert</strong></a>(self, tz, axis=0, level=None, copy=True)</dt><dd><tt>Convert&nbsp;tz-aware&nbsp;axis&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;convert<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;convert&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;tz-naive.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-tz_localize"><strong>tz_localize</strong></a>(self, tz, axis=0, level=None, copy=True, ambiguous='raise')</dt><dd><tt>Localize&nbsp;tz-naive&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;localize<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;localize&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-ambiguous&nbsp;:&nbsp;'infer',&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>,&nbsp;'NaT',&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'infer'&nbsp;will&nbsp;attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>&nbsp;where&nbsp;True&nbsp;signifies&nbsp;a&nbsp;DST&nbsp;time,&nbsp;False&nbsp;designates<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;non-DST&nbsp;time&nbsp;(note&nbsp;that&nbsp;this&nbsp;flag&nbsp;is&nbsp;only&nbsp;applicable&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ambiguous&nbsp;times)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'NaT'&nbsp;will&nbsp;return&nbsp;NaT&nbsp;where&nbsp;there&nbsp;are&nbsp;ambiguous&nbsp;times<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'raise'&nbsp;will&nbsp;raise&nbsp;an&nbsp;AmbiguousTimeError&nbsp;if&nbsp;there&nbsp;are&nbsp;ambiguous<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;times<br>
-infer_dst&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on&nbsp;order<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;is&nbsp;tz-aware&nbsp;and&nbsp;tz&nbsp;is&nbsp;not&nbsp;None.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-where"><strong>where</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;True&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;where&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``True``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#MySeries-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#MySeries-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``where``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#MySeries-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).<a href="#MySeries-reshape">reshape</a>(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#MySeries-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#MySeries-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mask`</tt></dd></dl>
-
-<dl><dt><a name="MySeries-xs"><strong>xs</strong></a>(self, key, axis=0, level=None, drop_level=True)</dt><dd><tt>Returns&nbsp;a&nbsp;cross-section&nbsp;(row(s)&nbsp;or&nbsp;column(s))&nbsp;from&nbsp;the<br>
-<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Defaults&nbsp;to&nbsp;cross-section&nbsp;on&nbsp;the&nbsp;rows&nbsp;(axis=0).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Some&nbsp;label&nbsp;contained&nbsp;in&nbsp;the&nbsp;index,&nbsp;or&nbsp;partially&nbsp;in&nbsp;a&nbsp;MultiIndex<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;retrieve&nbsp;cross-section&nbsp;on<br>
-level&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;defaults&nbsp;to&nbsp;first&nbsp;n&nbsp;levels&nbsp;(n=1&nbsp;or&nbsp;len(key))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;key&nbsp;partially&nbsp;contained&nbsp;in&nbsp;a&nbsp;MultiIndex,&nbsp;indicate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;levels&nbsp;are&nbsp;used.&nbsp;Levels&nbsp;can&nbsp;be&nbsp;referred&nbsp;by&nbsp;label&nbsp;or&nbsp;position.<br>
-drop_level&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;returns&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;same&nbsp;levels&nbsp;as&nbsp;self.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C<br>
-a&nbsp;&nbsp;4&nbsp;&nbsp;5&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;4&nbsp;&nbsp;0&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;9&nbsp;&nbsp;7&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-xs">xs</a>('a')<br>
-A&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-B&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-C&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Name:&nbsp;a<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-xs">xs</a>('C',&nbsp;axis=1)<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;C<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;second&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7&nbsp;&nbsp;5&nbsp;&nbsp;5&nbsp;&nbsp;0<br>
-baz&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;three&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-xs">xs</a>(('baz',&nbsp;'three'))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-third<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-xs">xs</a>('one',&nbsp;level=1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-baz&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#MySeries-xs">xs</a>(('baz',&nbsp;2),&nbsp;level=[0,&nbsp;'third'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-second<br>
-three&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-xs&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-xs&nbsp;is&nbsp;only&nbsp;for&nbsp;getting,&nbsp;not&nbsp;setting&nbsp;values.<br>
-&nbsp;<br>
-MultiIndex&nbsp;Slicers&nbsp;is&nbsp;a&nbsp;generic&nbsp;way&nbsp;to&nbsp;get/set&nbsp;values&nbsp;on&nbsp;any&nbsp;level&nbsp;or<br>
-levels.&nbsp;&nbsp;It&nbsp;is&nbsp;a&nbsp;superset&nbsp;of&nbsp;xs&nbsp;functionality,&nbsp;see<br>
-:ref:`MultiIndex&nbsp;Slicers&nbsp;&lt;advanced.mi_slicers&gt;`</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>at</strong></dt>
-<dd><tt>Fast&nbsp;label-based&nbsp;scalar&nbsp;accessor<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``loc``,&nbsp;``at``&nbsp;provides&nbsp;**label**&nbsp;based&nbsp;scalar&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>blocks</strong></dt>
-<dd><tt>Internal&nbsp;property,&nbsp;property&nbsp;synonym&nbsp;for&nbsp;as_blocks()</tt></dd>
-</dl>
-<dl><dt><strong>iat</strong></dt>
-<dd><tt>Fast&nbsp;integer&nbsp;location&nbsp;scalar&nbsp;accessor.<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``iloc``,&nbsp;``iat``&nbsp;provides&nbsp;**integer**&nbsp;based&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>iloc</strong></dt>
-<dd><tt>Purely&nbsp;integer-location&nbsp;based&nbsp;indexing&nbsp;for&nbsp;selection&nbsp;by&nbsp;position.<br>
-&nbsp;<br>
-``.iloc[]``&nbsp;is&nbsp;primarily&nbsp;integer&nbsp;position&nbsp;based&nbsp;(from&nbsp;``0``&nbsp;to<br>
-``length-1``&nbsp;of&nbsp;the&nbsp;axis),&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a&nbsp;boolean<br>
-array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;An&nbsp;integer,&nbsp;e.g.&nbsp;``5``.<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;integers,&nbsp;e.g.&nbsp;``[4,&nbsp;3,&nbsp;0]``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;ints,&nbsp;e.g.&nbsp;``1:7``.<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.iloc``&nbsp;will&nbsp;raise&nbsp;``IndexError``&nbsp;if&nbsp;a&nbsp;requested&nbsp;indexer&nbsp;is<br>
-out-of-bounds,&nbsp;except&nbsp;*slice*&nbsp;indexers&nbsp;which&nbsp;allow&nbsp;out-of-bounds<br>
-indexing&nbsp;(this&nbsp;conforms&nbsp;with&nbsp;python/numpy&nbsp;*slice*&nbsp;semantics).<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Position&nbsp;&lt;indexing.integer&gt;`</tt></dd>
-</dl>
-<dl><dt><strong>ix</strong></dt>
-<dd><tt>A&nbsp;primarily&nbsp;label-location&nbsp;based&nbsp;indexer,&nbsp;with&nbsp;integer&nbsp;position<br>
-fallback.<br>
-&nbsp;<br>
-``.ix[]``&nbsp;supports&nbsp;mixed&nbsp;integer&nbsp;and&nbsp;label&nbsp;based&nbsp;access.&nbsp;It&nbsp;is<br>
-primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;will&nbsp;fall&nbsp;back&nbsp;to&nbsp;integer&nbsp;positional<br>
-access&nbsp;unless&nbsp;the&nbsp;corresponding&nbsp;axis&nbsp;is&nbsp;of&nbsp;integer&nbsp;type.<br>
-&nbsp;<br>
-``.ix``&nbsp;is&nbsp;the&nbsp;most&nbsp;general&nbsp;indexer&nbsp;and&nbsp;will&nbsp;support&nbsp;any&nbsp;of&nbsp;the<br>
-inputs&nbsp;in&nbsp;``.loc``&nbsp;and&nbsp;``.iloc``.&nbsp;``.ix``&nbsp;also&nbsp;supports&nbsp;floating<br>
-point&nbsp;label&nbsp;schemes.&nbsp;``.ix``&nbsp;is&nbsp;exceptionally&nbsp;useful&nbsp;when&nbsp;dealing<br>
-with&nbsp;mixed&nbsp;positional&nbsp;and&nbsp;label&nbsp;based&nbsp;hierachical&nbsp;indexes.<br>
-&nbsp;<br>
-However,&nbsp;when&nbsp;an&nbsp;axis&nbsp;is&nbsp;integer&nbsp;based,&nbsp;ONLY&nbsp;label&nbsp;based&nbsp;access<br>
-and&nbsp;not&nbsp;positional&nbsp;access&nbsp;is&nbsp;supported.&nbsp;Thus,&nbsp;in&nbsp;such&nbsp;cases,&nbsp;it's<br>
-usually&nbsp;better&nbsp;to&nbsp;be&nbsp;explicit&nbsp;and&nbsp;use&nbsp;``.iloc``&nbsp;or&nbsp;``.loc``.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Advanced&nbsp;Indexing&nbsp;&lt;advanced&gt;`.</tt></dd>
-</dl>
-<dl><dt><strong>loc</strong></dt>
-<dd><tt>Purely&nbsp;label-location&nbsp;based&nbsp;indexer&nbsp;for&nbsp;selection&nbsp;by&nbsp;label.<br>
-&nbsp;<br>
-``.loc[]``&nbsp;is&nbsp;primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a<br>
-boolean&nbsp;array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;A&nbsp;single&nbsp;label,&nbsp;e.g.&nbsp;``5``&nbsp;or&nbsp;``'a'``,&nbsp;(note&nbsp;that&nbsp;``5``&nbsp;is<br>
-&nbsp;&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;*label*&nbsp;of&nbsp;the&nbsp;index,&nbsp;and&nbsp;**never**&nbsp;as&nbsp;an<br>
-&nbsp;&nbsp;integer&nbsp;position&nbsp;along&nbsp;the&nbsp;index).<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;labels,&nbsp;e.g.&nbsp;``['a',&nbsp;'b',&nbsp;'c']``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;labels,&nbsp;e.g.&nbsp;``'a':'f'``&nbsp;(note&nbsp;that&nbsp;contrary<br>
-&nbsp;&nbsp;to&nbsp;usual&nbsp;python&nbsp;slices,&nbsp;**both**&nbsp;the&nbsp;start&nbsp;and&nbsp;the&nbsp;stop&nbsp;are&nbsp;included!).<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.loc``&nbsp;will&nbsp;raise&nbsp;a&nbsp;``KeyError``&nbsp;when&nbsp;the&nbsp;items&nbsp;are&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Label&nbsp;&lt;indexing.label&gt;`</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>is_copy</strong> = None</dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a>:<br>
-<dl><dt><a name="MySeries-__dir__"><strong>__dir__</strong></a>(self)</dt><dd><tt>Provide&nbsp;method&nbsp;name&nbsp;lookup&nbsp;and&nbsp;completion<br>
-Only&nbsp;provide&nbsp;'public'&nbsp;methods</tt></dd></dl>
-
-<dl><dt><a name="MySeries-__sizeof__"><strong>__sizeof__</strong></a>(self)</dt><dd><tt>Generates&nbsp;the&nbsp;total&nbsp;memory&nbsp;usage&nbsp;for&nbsp;a&nbsp;<a href="builtins.html#object">object</a>&nbsp;that&nbsp;returns<br>
-either&nbsp;a&nbsp;value&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;values</tt></dd></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a>:<br>
-<dl><dt><a name="MySeries-__bytes__"><strong>__bytes__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;bytes(obj)&nbsp;in&nbsp;py3&nbsp;only.<br>
-Yields&nbsp;a&nbsp;bytestring&nbsp;in&nbsp;both&nbsp;py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-__repr__"><strong>__repr__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-<dl><dt><a name="MySeries-__str__"><strong>__str__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;Object<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;<a href="#MySeries-str">str</a>(df)&nbsp;in&nbsp;both&nbsp;py2/py3.<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-</td></tr></table> <p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#ffc8d8">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#000000" face="helvetica, arial"><a name="Simplot">class <strong>Simplot</strong></a>(<a href="builtins.html#object">builtins.object</a>)</font></td></tr>
-    
-<tr bgcolor="#ffc8d8"><td rowspan=2><tt>&nbsp;&nbsp;&nbsp;</tt></td>
-<td colspan=2><tt>Provides&nbsp;a&nbsp;simplified&nbsp;interface&nbsp;to&nbsp;matplotlib.<br>&nbsp;</tt></td></tr>
-<tr><td>&nbsp;</td>
-<td width="100%">Methods defined here:<br>
-<dl><dt><a name="Simplot-__init__"><strong>__init__</strong></a>(self)</dt><dd><tt>Initializes&nbsp;the&nbsp;instance&nbsp;variables.</tt></dd></dl>
-
-<dl><dt><a name="Simplot-get_figure_state"><strong>get_figure_state</strong></a>(self, figure=None)</dt><dd><tt>Gets&nbsp;the&nbsp;state&nbsp;of&nbsp;the&nbsp;current&nbsp;figure.<br>
-&nbsp;<br>
-figure:&nbsp;Figure<br>
-&nbsp;<br>
-returns:&nbsp;<a href="#FigureState">FigureState</a>&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<hr>
-Data descriptors defined here:<br>
-<dl><dt><strong>__dict__</strong></dt>
-<dd><tt>dictionary&nbsp;for&nbsp;instance&nbsp;variables&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>__weakref__</strong></dt>
-<dd><tt>list&nbsp;of&nbsp;weak&nbsp;references&nbsp;to&nbsp;the&nbsp;object&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-</td></tr></table> <p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#ffc8d8">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#000000" face="helvetica, arial"><a name="State">class <strong>State</strong></a>(<a href="modsim.html#System">System</a>)</font></td></tr>
-    
-<tr bgcolor="#ffc8d8"><td rowspan=2><tt>&nbsp;&nbsp;&nbsp;</tt></td>
-<td colspan=2><tt>One-dimensional&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;with&nbsp;axis&nbsp;labels&nbsp;(including&nbsp;time&nbsp;series).<br>
-&nbsp;<br>
-Labels&nbsp;need&nbsp;not&nbsp;be&nbsp;unique&nbsp;but&nbsp;must&nbsp;be&nbsp;a&nbsp;hashable&nbsp;type.&nbsp;The&nbsp;<a href="builtins.html#object">object</a><br>
-supports&nbsp;both&nbsp;integer-&nbsp;and&nbsp;label-based&nbsp;indexing&nbsp;and&nbsp;provides&nbsp;a&nbsp;host&nbsp;of<br>
-methods&nbsp;for&nbsp;performing&nbsp;operations&nbsp;involving&nbsp;the&nbsp;index.&nbsp;Statistical<br>
-methods&nbsp;from&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;have&nbsp;been&nbsp;overridden&nbsp;to&nbsp;automatically&nbsp;exclude<br>
-missing&nbsp;data&nbsp;(currently&nbsp;represented&nbsp;as&nbsp;NaN).<br>
-&nbsp;<br>
-Operations&nbsp;between&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(+,&nbsp;-,&nbsp;/,&nbsp;*,&nbsp;**)&nbsp;align&nbsp;values&nbsp;based&nbsp;on&nbsp;their<br>
-associated&nbsp;index&nbsp;values--&nbsp;they&nbsp;need&nbsp;not&nbsp;be&nbsp;the&nbsp;same&nbsp;length.&nbsp;The&nbsp;result<br>
-index&nbsp;will&nbsp;be&nbsp;the&nbsp;sorted&nbsp;union&nbsp;of&nbsp;the&nbsp;two&nbsp;indexes.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;array-like,&nbsp;dict,&nbsp;or&nbsp;scalar&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Contains&nbsp;data&nbsp;stored&nbsp;in&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-index&nbsp;:&nbsp;array-like&nbsp;or&nbsp;Index&nbsp;(1d)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Values&nbsp;must&nbsp;be&nbsp;hashable&nbsp;and&nbsp;have&nbsp;the&nbsp;same&nbsp;length&nbsp;as&nbsp;`data`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Non-unique&nbsp;index&nbsp;values&nbsp;are&nbsp;allowed.&nbsp;Will&nbsp;default&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;RangeIndex(len(data))&nbsp;if&nbsp;not&nbsp;provided.&nbsp;If&nbsp;both&nbsp;a&nbsp;dict&nbsp;and&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;are&nbsp;used,&nbsp;the&nbsp;index&nbsp;will&nbsp;override&nbsp;the&nbsp;keys&nbsp;found&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict.<br>
-dtype&nbsp;:&nbsp;numpy.dtype&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;None,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;inferred<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Copy&nbsp;input&nbsp;data<br>&nbsp;</tt></td></tr>
-<tr><td>&nbsp;</td>
-<td width="100%"><dl><dt>Method resolution order:</dt>
-<dd><a href="modsim.html#State">State</a></dd>
-<dd><a href="modsim.html#System">System</a></dd>
-<dd><a href="modsim.html#MySeries">MySeries</a></dd>
-<dd><a href="pandas.core.series.html#Series">pandas.core.series.Series</a></dd>
-<dd><a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a></dd>
-<dd><a href="pandas.core.strings.html#StringAccessorMixin">pandas.core.strings.StringAccessorMixin</a></dd>
-<dd><a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a></dd>
-<dd><a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a></dd>
-<dd><a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a></dd>
-<dd><a href="pandas.core.base.html#SelectionMixin">pandas.core.base.SelectionMixin</a></dd>
-<dd><a href="builtins.html#object">builtins.object</a></dd>
-</dl>
-<hr>
-Methods inherited from <a href="modsim.html#System">System</a>:<br>
-<dl><dt><a name="State-__init__"><strong>__init__</strong></a>(self, *args, **kwargs)</dt><dd><tt>Initialize&nbsp;the&nbsp;series.<br>
-&nbsp;<br>
-If&nbsp;there&nbsp;are&nbsp;no&nbsp;positional&nbsp;arguments,&nbsp;use&nbsp;kwargs.<br>
-&nbsp;<br>
-If&nbsp;there&nbsp;is&nbsp;one&nbsp;positional&nbsp;argument,&nbsp;copy&nbsp;it.<br>
-&nbsp;<br>
-More&nbsp;than&nbsp;one&nbsp;positional&nbsp;argument&nbsp;is&nbsp;an&nbsp;error.</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="modsim.html#System">System</a>:<br>
-<dl><dt><strong>T</strong></dt>
-<dd><tt>Intercept&nbsp;the&nbsp;Series&nbsp;accessor&nbsp;object&nbsp;so&nbsp;we&nbsp;can&nbsp;use&nbsp;`T`<br>
-as&nbsp;a&nbsp;row&nbsp;label&nbsp;and&nbsp;access&nbsp;it&nbsp;using&nbsp;dot&nbsp;notation.<br>
-&nbsp;<br>
-https://pandas.pydata.org/pandas-docs/stable/generated/<br>
-pandas.Series.T.html#pandas.Series.T</tt></dd>
-</dl>
-<dl><dt><strong>dt</strong></dt>
-<dd><tt>Intercept&nbsp;the&nbsp;Series&nbsp;accessor&nbsp;object&nbsp;so&nbsp;we&nbsp;can&nbsp;use&nbsp;`dt`<br>
-as&nbsp;a&nbsp;row&nbsp;label&nbsp;and&nbsp;access&nbsp;it&nbsp;using&nbsp;dot&nbsp;notation.<br>
-&nbsp;<br>
-https://pandas.pydata.org/pandas-docs/stable/generated/<br>
-pandas.Series.dt.html</tt></dd>
-</dl>
-<hr>
-Methods inherited from <a href="modsim.html#MySeries">MySeries</a>:<br>
-<dl><dt><a name="State-set"><strong>set</strong></a>(self, **kwargs)</dt><dd><tt>Uses&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;update&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place.<br>
-&nbsp;<br>
-Example:&nbsp;series.<a href="#State-update">update</a>(a=1,&nbsp;b=2)</tt></dd></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><a name="State-__add__"><strong>__add__</strong></a> = wrapper(left, right, name='__add__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f2f0&gt;)</dt></dl>
-
-<dl><dt><a name="State-__and__"><strong>__and__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="State-__array__"><strong>__array__</strong></a>(self, result=None)</dt><dd><tt>the&nbsp;array&nbsp;interface,&nbsp;return&nbsp;my&nbsp;values</tt></dd></dl>
-
-<dl><dt><a name="State-__array_prepare__"><strong>__array_prepare__</strong></a>(self, result, context=None)</dt><dd><tt>Gets&nbsp;called&nbsp;prior&nbsp;to&nbsp;a&nbsp;ufunc</tt></dd></dl>
-
-<dl><dt><a name="State-__array_wrap__"><strong>__array_wrap__</strong></a>(self, result, context=None)</dt><dd><tt>Gets&nbsp;called&nbsp;after&nbsp;a&nbsp;ufunc</tt></dd></dl>
-
-<dl><dt><a name="State-__div__"><strong>__div__</strong></a> = wrapper(left, right, name='__truediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f9d8&gt;)</dt></dl>
-
-<dl><dt><a name="State-__divmod__"><strong>__divmod__</strong></a> = wrapper(left, right, name='__divmod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca343bf8&gt;)</dt></dl>
-
-<dl><dt><a name="State-__eq__"><strong>__eq__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="State-__float__"><strong>__float__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="State-__floordiv__"><strong>__floordiv__</strong></a> = wrapper(left, right, name='__floordiv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fb70&gt;)</dt></dl>
-
-<dl><dt><a name="State-__ge__"><strong>__ge__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="State-__getitem__"><strong>__getitem__</strong></a>(self, key)</dt></dl>
-
-<dl><dt><a name="State-__gt__"><strong>__gt__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="State-__iadd__"><strong>__iadd__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="State-__imul__"><strong>__imul__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="State-__int__"><strong>__int__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="State-__ipow__"><strong>__ipow__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="State-__isub__"><strong>__isub__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="State-__iter__"><strong>__iter__</strong></a>(self)</dt><dd><tt>provide&nbsp;iteration&nbsp;over&nbsp;the&nbsp;values&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-box&nbsp;values&nbsp;if&nbsp;necessary</tt></dd></dl>
-
-<dl><dt><a name="State-__itruediv__"><strong>__itruediv__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="State-__le__"><strong>__le__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="State-__len__"><strong>__len__</strong></a>(self)</dt><dd><tt>return&nbsp;the&nbsp;length&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="State-__long__"><strong>__long__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="State-__lt__"><strong>__lt__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="State-__mod__"><strong>__mod__</strong></a> = wrapper(left, right, name='__mod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fd08&gt;)</dt></dl>
-
-<dl><dt><a name="State-__mul__"><strong>__mul__</strong></a> = wrapper(left, right, name='__mul__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f840&gt;)</dt></dl>
-
-<dl><dt><a name="State-__ne__"><strong>__ne__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="State-__or__"><strong>__or__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="State-__pow__"><strong>__pow__</strong></a> = wrapper(left, right, name='__pow__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fea0&gt;)</dt></dl>
-
-<dl><dt><a name="State-__radd__"><strong>__radd__</strong></a> = wrapper(left, right, name='__radd__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f510&gt;)</dt></dl>
-
-<dl><dt><a name="State-__rand__"><strong>__rand__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="State-__rdiv__"><strong>__rdiv__</strong></a> = wrapper(left, right, name='__rtruediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342510&gt;)</dt></dl>
-
-<dl><dt><a name="State-__rfloordiv__"><strong>__rfloordiv__</strong></a> = wrapper(left, right, name='__rfloordiv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342730&gt;)</dt></dl>
-
-<dl><dt><a name="State-__rmod__"><strong>__rmod__</strong></a> = wrapper(left, right, name='__rmod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342b70&gt;)</dt></dl>
-
-<dl><dt><a name="State-__rmul__"><strong>__rmul__</strong></a> = wrapper(left, right, name='__rmul__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca3420d0&gt;)</dt></dl>
-
-<dl><dt><a name="State-__ror__"><strong>__ror__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="State-__rpow__"><strong>__rpow__</strong></a> = wrapper(left, right, name='__rpow__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342950&gt;)</dt></dl>
-
-<dl><dt><a name="State-__rsub__"><strong>__rsub__</strong></a> = wrapper(left, right, name='__rsub__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca3422f0&gt;)</dt></dl>
-
-<dl><dt><a name="State-__rtruediv__"><strong>__rtruediv__</strong></a> = wrapper(left, right, name='__rtruediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342510&gt;)</dt></dl>
-
-<dl><dt><a name="State-__rxor__"><strong>__rxor__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="State-__setitem__"><strong>__setitem__</strong></a>(self, key, value)</dt></dl>
-
-<dl><dt><a name="State-__sub__"><strong>__sub__</strong></a> = wrapper(left, right, name='__sub__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f6a8&gt;)</dt></dl>
-
-<dl><dt><a name="State-__truediv__"><strong>__truediv__</strong></a> = wrapper(left, right, name='__truediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f9d8&gt;)</dt></dl>
-
-<dl><dt><a name="State-__unicode__"><strong>__unicode__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;unicode(df)&nbsp;in&nbsp;py2&nbsp;only.&nbsp;Yields&nbsp;a&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;both<br>
-py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="State-__xor__"><strong>__xor__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="State-add"><strong>add</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Addition&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`add`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;+&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.radd</tt></dd></dl>
-
-<dl><dt><a name="State-agg"><strong>agg</strong></a> = aggregate(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.series.html#Series">Series</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#State-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#State-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10))<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-agg">agg</a>('min')<br>
--1.3018049988556679<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-agg">agg</a>(['min',&nbsp;'max'])<br>
-min&nbsp;&nbsp;&nbsp;-1.301805<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;1.127688<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.apply<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.transform</tt></dd></dl>
-
-<dl><dt><a name="State-aggregate"><strong>aggregate</strong></a>(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.series.html#Series">Series</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#State-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#State-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10))<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-agg">agg</a>('min')<br>
--1.3018049988556679<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-agg">agg</a>(['min',&nbsp;'max'])<br>
-min&nbsp;&nbsp;&nbsp;-1.301805<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;1.127688<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.apply<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.transform</tt></dd></dl>
-
-<dl><dt><a name="State-align"><strong>align</strong></a>(self, other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)</dt><dd><tt>Align&nbsp;two&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;their&nbsp;axes&nbsp;with&nbsp;the<br>
-specified&nbsp;join&nbsp;method&nbsp;for&nbsp;each&nbsp;axis&nbsp;Index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-join&nbsp;:&nbsp;{'outer',&nbsp;'inner',&nbsp;'left',&nbsp;'right'},&nbsp;default&nbsp;'outer'<br>
-axis&nbsp;:&nbsp;allowed&nbsp;axis&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;on&nbsp;index&nbsp;(0),&nbsp;columns&nbsp;(1),&nbsp;or&nbsp;both&nbsp;(None)<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Always&nbsp;returns&nbsp;new&nbsp;objects.&nbsp;If&nbsp;copy=False&nbsp;and&nbsp;no&nbsp;reindexing&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;required&nbsp;then&nbsp;original&nbsp;objects&nbsp;are&nbsp;returned.<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-method&nbsp;:&nbsp;str,&nbsp;default&nbsp;None<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-fill_axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Filling&nbsp;axis,&nbsp;method&nbsp;and&nbsp;limit<br>
-broadcast_axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;values&nbsp;along&nbsp;this&nbsp;axis,&nbsp;if&nbsp;aligning&nbsp;two&nbsp;objects&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;different&nbsp;dimensions<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-(left,&nbsp;right)&nbsp;:&nbsp;(<a href="pandas.core.series.html#Series">Series</a>,&nbsp;type&nbsp;of&nbsp;other)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Aligned&nbsp;objects</tt></dd></dl>
-
-<dl><dt><a name="State-all"><strong>all</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;all&nbsp;elements&nbsp;are&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-all&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-any"><strong>any</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;any&nbsp;element&nbsp;is&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-any&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-append"><strong>append</strong></a>(self, to_append, ignore_index=False, verify_integrity=False)</dt><dd><tt>Concatenate&nbsp;two&nbsp;or&nbsp;more&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-to_append&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;list/tuple&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-ignore_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;not&nbsp;use&nbsp;the&nbsp;index&nbsp;labels.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.19.0<br>
-&nbsp;<br>
-verify_integrity&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;raise&nbsp;Exception&nbsp;on&nbsp;creating&nbsp;index&nbsp;with&nbsp;duplicates<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-appended&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s1&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s2&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([4,&nbsp;5,&nbsp;6])<br>
-&gt;&gt;&gt;&nbsp;s3&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([4,&nbsp;5,&nbsp;6],&nbsp;index=[3,4,5])<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#State-append">append</a>(s2)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#State-append">append</a>(s3)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-With&nbsp;`ignore_index`&nbsp;set&nbsp;to&nbsp;True:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#State-append">append</a>(s2,&nbsp;ignore_index=True)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-With&nbsp;`verify_integrity`&nbsp;set&nbsp;to&nbsp;True:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#State-append">append</a>(s2,&nbsp;verify_integrity=True)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-ValueError:&nbsp;Indexes&nbsp;have&nbsp;overlapping&nbsp;values:&nbsp;[0,&nbsp;1,&nbsp;2]</tt></dd></dl>
-
-<dl><dt><a name="State-apply"><strong>apply</strong></a>(self, func, convert_dtype=True, args=(), **kwds)</dt><dd><tt>Invoke&nbsp;function&nbsp;on&nbsp;values&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Can&nbsp;be&nbsp;ufunc&nbsp;(a&nbsp;NumPy&nbsp;function<br>
-that&nbsp;applies&nbsp;to&nbsp;the&nbsp;entire&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;or&nbsp;a&nbsp;Python&nbsp;function&nbsp;that&nbsp;only&nbsp;works<br>
-on&nbsp;single&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-convert_dtype&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Try&nbsp;to&nbsp;find&nbsp;better&nbsp;dtype&nbsp;for&nbsp;elementwise&nbsp;function&nbsp;results.&nbsp;If<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False,&nbsp;leave&nbsp;as&nbsp;dtype=<a href="builtins.html#object">object</a><br>
-args&nbsp;:&nbsp;tuple<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Positional&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;to&nbsp;function&nbsp;in&nbsp;addition&nbsp;to&nbsp;the&nbsp;value<br>
-Additional&nbsp;keyword&nbsp;arguments&nbsp;will&nbsp;be&nbsp;passed&nbsp;as&nbsp;keywords&nbsp;to&nbsp;the&nbsp;function<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;if&nbsp;func&nbsp;returns&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.map:&nbsp;For&nbsp;element-wise&nbsp;operations<br>
-<a href="pandas.core.series.html#Series">Series</a>.agg:&nbsp;only&nbsp;perform&nbsp;aggregating&nbsp;type&nbsp;operations<br>
-<a href="pandas.core.series.html#Series">Series</a>.transform:&nbsp;only&nbsp;perform&nbsp;transformating&nbsp;type&nbsp;operations<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;series&nbsp;with&nbsp;typical&nbsp;summer&nbsp;temperatures&nbsp;for&nbsp;each&nbsp;city.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([20,&nbsp;21,&nbsp;12],&nbsp;index=['London',<br>
-...&nbsp;'New&nbsp;York','Helsinki'])<br>
-&gt;&gt;&gt;&nbsp;series<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;20<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Square&nbsp;the&nbsp;values&nbsp;by&nbsp;defining&nbsp;a&nbsp;function&nbsp;and&nbsp;passing&nbsp;it&nbsp;as&nbsp;an<br>
-argument&nbsp;to&nbsp;``<a href="#State-apply">apply</a>()``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;square(x):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x**2<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#State-apply">apply</a>(square)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;400<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;441<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;144<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Square&nbsp;the&nbsp;values&nbsp;by&nbsp;passing&nbsp;an&nbsp;anonymous&nbsp;function&nbsp;as&nbsp;an<br>
-argument&nbsp;to&nbsp;``<a href="#State-apply">apply</a>()``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#State-apply">apply</a>(lambda&nbsp;x:&nbsp;x**2)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;400<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;441<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;144<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Define&nbsp;a&nbsp;custom&nbsp;function&nbsp;that&nbsp;needs&nbsp;additional&nbsp;positional<br>
-arguments&nbsp;and&nbsp;pass&nbsp;these&nbsp;additional&nbsp;arguments&nbsp;using&nbsp;the<br>
-``args``&nbsp;keyword.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;subtract_custom_value(x,&nbsp;custom_value):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x-custom_value<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#State-apply">apply</a>(subtract_custom_value,&nbsp;args=(5,))<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;16<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Define&nbsp;a&nbsp;custom&nbsp;function&nbsp;that&nbsp;takes&nbsp;keyword&nbsp;arguments<br>
-and&nbsp;pass&nbsp;these&nbsp;arguments&nbsp;to&nbsp;``apply``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;add_custom_values(x,&nbsp;**kwargs):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;month&nbsp;in&nbsp;kwargs:<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;x+=kwargs[month]<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#State-apply">apply</a>(add_custom_values,&nbsp;june=30,&nbsp;july=20,&nbsp;august=25)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;95<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;96<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;87<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Use&nbsp;a&nbsp;function&nbsp;from&nbsp;the&nbsp;Numpy&nbsp;library.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#State-apply">apply</a>(np.log)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.995732<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;3.044522<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;2.484907<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="State-argmax"><strong>argmax</strong></a> = idxmax(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmax&nbsp;:&nbsp;Index&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmax``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmax<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmax</tt></dd></dl>
-
-<dl><dt><a name="State-argmin"><strong>argmin</strong></a> = idxmin(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmin&nbsp;:&nbsp;Index&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmin``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmin<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmin</tt></dd></dl>
-
-<dl><dt><a name="State-argsort"><strong>argsort</strong></a>(self, axis=0, kind='quicksort', order=None)</dt><dd><tt>Overrides&nbsp;<a href="numpy.html#ndarray">ndarray</a>.argsort.&nbsp;Argsorts&nbsp;the&nbsp;value,&nbsp;omitting&nbsp;NA/null&nbsp;values,<br>
-and&nbsp;places&nbsp;the&nbsp;result&nbsp;in&nbsp;the&nbsp;same&nbsp;locations&nbsp;as&nbsp;the&nbsp;non-NA&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;int&nbsp;(can&nbsp;only&nbsp;be&nbsp;zero)<br>
-kind&nbsp;:&nbsp;{'mergesort',&nbsp;'quicksort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;'mergesort'&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm<br>
-order&nbsp;:&nbsp;ignored<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-argsorted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;with&nbsp;-1&nbsp;indicated&nbsp;where&nbsp;nan&nbsp;values&nbsp;are&nbsp;present<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argsort</tt></dd></dl>
-
-<dl><dt><a name="State-autocorr"><strong>autocorr</strong></a>(self, lag=1)</dt><dd><tt>Lag-N&nbsp;autocorrelation<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-lag&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;lags&nbsp;to&nbsp;apply&nbsp;before&nbsp;performing&nbsp;autocorrelation.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-autocorr&nbsp;:&nbsp;float</tt></dd></dl>
-
-<dl><dt><a name="State-between"><strong>between</strong></a>(self, left, right, inclusive=True)</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;equivalent&nbsp;to&nbsp;left&nbsp;&lt;=&nbsp;series&nbsp;&lt;=&nbsp;right.&nbsp;NA&nbsp;values<br>
-will&nbsp;be&nbsp;treated&nbsp;as&nbsp;False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-left&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Left&nbsp;boundary<br>
-right&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Right&nbsp;boundary<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_between&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="State-combine"><strong>combine</strong></a>(self, other, func, fill_value=nan)</dt><dd><tt>Perform&nbsp;elementwise&nbsp;binary&nbsp;operation&nbsp;on&nbsp;two&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;using&nbsp;given&nbsp;function<br>
-with&nbsp;optional&nbsp;fill&nbsp;value&nbsp;when&nbsp;an&nbsp;index&nbsp;is&nbsp;missing&nbsp;from&nbsp;one&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or<br>
-the&nbsp;other<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-func&nbsp;:&nbsp;function<br>
-fill_value&nbsp;:&nbsp;scalar&nbsp;value<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="State-combine_first"><strong>combine_first</strong></a>(self, other)</dt><dd><tt>Combine&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values,&nbsp;choosing&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.series.html#Series">Series</a>'s&nbsp;values<br>
-first.&nbsp;Result&nbsp;index&nbsp;will&nbsp;be&nbsp;the&nbsp;union&nbsp;of&nbsp;the&nbsp;two&nbsp;indexes<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="State-compound"><strong>compound</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;compound&nbsp;percentage&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-compounded&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-compress"><strong>compress</strong></a>(self, condition, *args, **kwargs)</dt><dd><tt>Return&nbsp;selected&nbsp;slices&nbsp;of&nbsp;an&nbsp;array&nbsp;along&nbsp;given&nbsp;axis&nbsp;as&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.compress</tt></dd></dl>
-
-<dl><dt><a name="State-corr"><strong>corr</strong></a>(self, other, method='pearson', min_periods=None)</dt><dd><tt>Compute&nbsp;correlation&nbsp;with&nbsp;`other`&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;excluding&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-method&nbsp;:&nbsp;{'pearson',&nbsp;'kendall',&nbsp;'spearman'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pearson&nbsp;:&nbsp;standard&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;kendall&nbsp;:&nbsp;Kendall&nbsp;Tau&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;spearman&nbsp;:&nbsp;Spearman&nbsp;rank&nbsp;correlation<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;needed&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result<br>
-&nbsp;<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-correlation&nbsp;:&nbsp;float</tt></dd></dl>
-
-<dl><dt><a name="State-count"><strong>count</strong></a>(self, level=None)</dt><dd><tt>Return&nbsp;number&nbsp;of&nbsp;non-NA/null&nbsp;observations&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;smaller&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-nobs&nbsp;:&nbsp;int&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-cov"><strong>cov</strong></a>(self, other, min_periods=None)</dt><dd><tt>Compute&nbsp;covariance&nbsp;with&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;excluding&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;needed&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-covariance&nbsp;:&nbsp;float<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;(unbiased&nbsp;estimator).</tt></dd></dl>
-
-<dl><dt><a name="State-cummax"><strong>cummax</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;max&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummax&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.max&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="State-cummin"><strong>cummin</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;minimum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummin&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.min&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="State-cumprod"><strong>cumprod</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;product&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumprod&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.prod&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="State-cumsum"><strong>cumsum</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;sum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumsum&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.sum&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="State-diff"><strong>diff</strong></a>(self, periods=1)</dt><dd><tt>1st&nbsp;discrete&nbsp;difference&nbsp;of&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;difference<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-diffed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="State-div"><strong>div</strong></a> = truediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="State-divide"><strong>divide</strong></a> = truediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="State-dot"><strong>dot</strong></a>(self, other)</dt><dd><tt>Matrix&nbsp;multiplication&nbsp;with&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;inner-product&nbsp;with&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-objects<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dot_product&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="State-drop_duplicates"><strong>drop_duplicates</strong></a>(self, keep='first', inplace=False)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;duplicate&nbsp;values&nbsp;removed<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-&nbsp;<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Drop&nbsp;all&nbsp;duplicates.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-If&nbsp;True,&nbsp;performs&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;returns&nbsp;None.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-deduplicated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="State-dropna"><strong>dropna</strong></a>(self, axis=0, inplace=False, **kwargs)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;without&nbsp;null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-valid&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;operation&nbsp;in&nbsp;place.</tt></dd></dl>
-
-<dl><dt><a name="State-duplicated"><strong>duplicated</strong></a>(self, keep='first')</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;denoting&nbsp;duplicate&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the&nbsp;last<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Mark&nbsp;all&nbsp;duplicates&nbsp;as&nbsp;``True``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-duplicated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="State-eq"><strong>eq</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`eq`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;==&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="State-ewm"><strong>ewm</strong></a>(self, com=None, span=None, halflife=None, alpha=None, min_periods=0, freq=None, adjust=True, ignore_na=False, axis=0)</dt><dd><tt>Provides&nbsp;exponential&nbsp;weighted&nbsp;functions<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-com&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;/&nbsp;(1&nbsp;+&nbsp;com),\text{&nbsp;for&nbsp;}&nbsp;com&nbsp;\geq&nbsp;0`<br>
-span&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;span,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;2&nbsp;/&nbsp;(span&nbsp;+&nbsp;1),\text{&nbsp;for&nbsp;}&nbsp;span&nbsp;\geq&nbsp;1`<br>
-halflife&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;half-life,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;-&nbsp;exp(log(0.5)&nbsp;/&nbsp;halflife),\text{&nbsp;for&nbsp;}&nbsp;halflife&nbsp;&gt;&nbsp;0`<br>
-alpha&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;smoothing&nbsp;factor&nbsp;:math:`\alpha`&nbsp;directly,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`0&nbsp;&lt;&nbsp;\alpha&nbsp;\leq&nbsp;1`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;None&nbsp;or&nbsp;string&nbsp;alias&nbsp;/&nbsp;date&nbsp;offset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default=None&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;to&nbsp;before&nbsp;computing&nbsp;statistic<br>
-adjust&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Divide&nbsp;by&nbsp;decaying&nbsp;adjustment&nbsp;factor&nbsp;in&nbsp;beginning&nbsp;periods&nbsp;to&nbsp;account<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;imbalance&nbsp;in&nbsp;relative&nbsp;weightings&nbsp;(viewing&nbsp;EWMA&nbsp;as&nbsp;a&nbsp;moving&nbsp;average)<br>
-ignore_na&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Ignore&nbsp;missing&nbsp;values&nbsp;when&nbsp;calculating&nbsp;weights;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specify&nbsp;True&nbsp;to&nbsp;reproduce&nbsp;pre-0.15.0&nbsp;behavior<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-ewm">ewm</a>(com=0.5).<a href="#State-mean">mean</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.000000<br>
-1&nbsp;&nbsp;0.750000<br>
-2&nbsp;&nbsp;1.615385<br>
-3&nbsp;&nbsp;1.615385<br>
-4&nbsp;&nbsp;3.670213<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Exactly&nbsp;one&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,&nbsp;span,&nbsp;half-life,&nbsp;and&nbsp;alpha&nbsp;must&nbsp;be&nbsp;provided.<br>
-Allowed&nbsp;values&nbsp;and&nbsp;relationship&nbsp;between&nbsp;the&nbsp;parameters&nbsp;are&nbsp;specified&nbsp;in&nbsp;the<br>
-parameter&nbsp;descriptions&nbsp;above;&nbsp;see&nbsp;the&nbsp;link&nbsp;at&nbsp;the&nbsp;end&nbsp;of&nbsp;this&nbsp;section&nbsp;for<br>
-a&nbsp;detailed&nbsp;explanation.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;True&nbsp;(default),&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;using<br>
-weights&nbsp;(1-alpha)**(n-1),&nbsp;(1-alpha)**(n-2),&nbsp;...,&nbsp;1-alpha,&nbsp;1.<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;False,&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;recursively&nbsp;as:<br>
-&nbsp;&nbsp;&nbsp;weighted_average[0]&nbsp;=&nbsp;arg[0];<br>
-&nbsp;&nbsp;&nbsp;weighted_average[i]&nbsp;=&nbsp;(1-alpha)*weighted_average[i-1]&nbsp;+&nbsp;alpha*arg[i].<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;False&nbsp;(default),&nbsp;weights&nbsp;are&nbsp;based&nbsp;on&nbsp;absolute&nbsp;positions.<br>
-For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in&nbsp;calculating&nbsp;the&nbsp;final&nbsp;weighted<br>
-average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;(1-alpha)**2&nbsp;and&nbsp;1&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and<br>
-(1-alpha)**2&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;True&nbsp;(reproducing&nbsp;pre-0.15.0&nbsp;behavior),&nbsp;weights&nbsp;are&nbsp;based<br>
-on&nbsp;relative&nbsp;positions.&nbsp;For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in<br>
-calculating&nbsp;the&nbsp;final&nbsp;weighted&nbsp;average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;1-alpha&nbsp;and&nbsp;1<br>
-(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and&nbsp;1-alpha&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-More&nbsp;details&nbsp;can&nbsp;be&nbsp;found&nbsp;at<br>
-<a href="http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows">http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows</a></tt></dd></dl>
-
-<dl><dt><a name="State-expanding"><strong>expanding</strong></a>(self, min_periods=1, freq=None, center=False, axis=0)</dt><dd><tt>Provides&nbsp;expanding&nbsp;transformations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-expanding">expanding</a>(2).<a href="#State-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;7.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).</tt></dd></dl>
-
-<dl><dt><a name="State-fillna"><strong>fillna</strong></a>(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)</dt><dd><tt>Fill&nbsp;NA/NaN&nbsp;values&nbsp;using&nbsp;the&nbsp;specified&nbsp;method<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;values&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;each&nbsp;index&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;or&nbsp;column&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).&nbsp;(values&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;This&nbsp;value&nbsp;cannot<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;list.<br>
-method&nbsp;:&nbsp;{'backfill',&nbsp;'bfill',&nbsp;'pad',&nbsp;'ffill',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'}<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;fill&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>,&nbsp;(e.g.&nbsp;a&nbsp;no-copy&nbsp;slice&nbsp;for&nbsp;a&nbsp;column&nbsp;in&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;method&nbsp;is&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the&nbsp;maximum&nbsp;number&nbsp;of&nbsp;consecutive<br>
-&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;values&nbsp;to&nbsp;forward/backward&nbsp;fill.&nbsp;In&nbsp;other&nbsp;words,&nbsp;if&nbsp;there&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;gap&nbsp;with&nbsp;more&nbsp;than&nbsp;this&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs,&nbsp;it&nbsp;will&nbsp;only<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;partially&nbsp;filled.&nbsp;If&nbsp;method&nbsp;is&nbsp;not&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;maximum&nbsp;number&nbsp;of&nbsp;entries&nbsp;along&nbsp;the&nbsp;entire&nbsp;axis&nbsp;where&nbsp;NaNs&nbsp;will&nbsp;be<br>
-&nbsp;&nbsp;&nbsp;&nbsp;filled.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0&nbsp;if&nbsp;not&nbsp;None.<br>
-downcast&nbsp;:&nbsp;dict,&nbsp;default&nbsp;is&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;dict&nbsp;of&nbsp;item-&gt;dtype&nbsp;of&nbsp;what&nbsp;to&nbsp;downcast&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;the&nbsp;string&nbsp;'infer'&nbsp;which&nbsp;will&nbsp;try&nbsp;to&nbsp;downcast&nbsp;to&nbsp;an&nbsp;appropriate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;equal&nbsp;type&nbsp;(e.g.&nbsp;float64&nbsp;to&nbsp;int64&nbsp;if&nbsp;possible)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;asfreq<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="State-first_valid_index"><strong>first_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;first&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="State-floordiv"><strong>floordiv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`floordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;//&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rfloordiv</tt></dd></dl>
-
-<dl><dt><a name="State-ge"><strong>ge</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Greater&nbsp;than&nbsp;or&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`ge`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&gt;=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="State-get_value"><strong>get_value</strong></a>(self, label, takeable=False)</dt><dd><tt>Quickly&nbsp;retrieve&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;index&nbsp;label<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;label<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;scalar&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="State-get_values"><strong>get_values</strong></a>(self)</dt><dd><tt>same&nbsp;as&nbsp;values&nbsp;(but&nbsp;handles&nbsp;sparseness&nbsp;conversions);&nbsp;is&nbsp;a&nbsp;view</tt></dd></dl>
-
-<dl><dt><a name="State-gt"><strong>gt</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Greater&nbsp;than&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`gt`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&gt;&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="State-hist"><strong>hist</strong></a> = hist_series(self, by=None, ax=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, figsize=None, bins=10, **kwds)</dt><dd><tt>Draw&nbsp;histogram&nbsp;of&nbsp;the&nbsp;input&nbsp;series&nbsp;using&nbsp;matplotlib<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed,&nbsp;then&nbsp;used&nbsp;to&nbsp;form&nbsp;histograms&nbsp;for&nbsp;separate&nbsp;groups<br>
-ax&nbsp;:&nbsp;matplotlib&nbsp;axis&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;not&nbsp;passed,&nbsp;uses&nbsp;gca()<br>
-grid&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;show&nbsp;axis&nbsp;grid&nbsp;lines<br>
-xlabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;x-axis&nbsp;label&nbsp;size<br>
-xrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;x&nbsp;axis&nbsp;labels<br>
-ylabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;y-axis&nbsp;label&nbsp;size<br>
-yrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;y&nbsp;axis&nbsp;labels<br>
-figsize&nbsp;:&nbsp;tuple,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;figure&nbsp;size&nbsp;in&nbsp;inches&nbsp;by&nbsp;default<br>
-bins:&nbsp;integer,&nbsp;default&nbsp;10<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;histogram&nbsp;bins&nbsp;to&nbsp;be&nbsp;used<br>
-kwds&nbsp;:&nbsp;keywords<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;passed&nbsp;to&nbsp;the&nbsp;actual&nbsp;plotting&nbsp;function<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;matplotlib&nbsp;documentation&nbsp;online&nbsp;for&nbsp;more&nbsp;on&nbsp;this</tt></dd></dl>
-
-<dl><dt><a name="State-idxmax"><strong>idxmax</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmax&nbsp;:&nbsp;Index&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmax``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmax<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmax</tt></dd></dl>
-
-<dl><dt><a name="State-idxmin"><strong>idxmin</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmin&nbsp;:&nbsp;Index&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmin``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmin<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmin</tt></dd></dl>
-
-<dl><dt><a name="State-isin"><strong>isin</strong></a>(self, values)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;:class:`~pandas.<a href="pandas.core.series.html#Series">Series</a>`&nbsp;showing&nbsp;whether&nbsp;each&nbsp;element<br>
-in&nbsp;the&nbsp;:class:`~pandas.<a href="pandas.core.series.html#Series">Series</a>`&nbsp;is&nbsp;exactly&nbsp;contained&nbsp;in&nbsp;the&nbsp;passed<br>
-sequence&nbsp;of&nbsp;``values``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-values&nbsp;:&nbsp;set&nbsp;or&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;sequence&nbsp;of&nbsp;values&nbsp;to&nbsp;test.&nbsp;Passing&nbsp;in&nbsp;a&nbsp;single&nbsp;string&nbsp;will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;raise&nbsp;a&nbsp;``TypeError``.&nbsp;Instead,&nbsp;turn&nbsp;a&nbsp;single&nbsp;string&nbsp;into&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``list``&nbsp;of&nbsp;one&nbsp;element.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Support&nbsp;for&nbsp;values&nbsp;as&nbsp;a&nbsp;set<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-isin&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(bool&nbsp;dtype)<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;*&nbsp;If&nbsp;``values``&nbsp;is&nbsp;a&nbsp;string<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.isin<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(list('abc'))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-isin">isin</a>(['a',&nbsp;'c',&nbsp;'e'])<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-dtype:&nbsp;bool<br>
-&nbsp;<br>
-Passing&nbsp;a&nbsp;single&nbsp;string&nbsp;as&nbsp;``s.<a href="#State-isin">isin</a>('a')``&nbsp;will&nbsp;raise&nbsp;an&nbsp;error.&nbsp;Use<br>
-a&nbsp;list&nbsp;of&nbsp;one&nbsp;element&nbsp;instead:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-isin">isin</a>(['a'])<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-dtype:&nbsp;bool</tt></dd></dl>
-
-<dl><dt><a name="State-items"><strong>items</strong></a> = iteritems(self)</dt><dd><tt>Lazily&nbsp;iterate&nbsp;over&nbsp;(index,&nbsp;value)&nbsp;tuples</tt></dd></dl>
-
-<dl><dt><a name="State-iteritems"><strong>iteritems</strong></a>(self)</dt><dd><tt>Lazily&nbsp;iterate&nbsp;over&nbsp;(index,&nbsp;value)&nbsp;tuples</tt></dd></dl>
-
-<dl><dt><a name="State-keys"><strong>keys</strong></a>(self)</dt><dd><tt>Alias&nbsp;for&nbsp;index</tt></dd></dl>
-
-<dl><dt><a name="State-kurt"><strong>kurt</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-kurtosis"><strong>kurtosis</strong></a> = kurt(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-last_valid_index"><strong>last_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;last&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="State-le"><strong>le</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Less&nbsp;than&nbsp;or&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`le`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&lt;=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="State-lt"><strong>lt</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Less&nbsp;than&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`lt`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&lt;&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="State-mad"><strong>mad</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;absolute&nbsp;deviation&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mad&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-map"><strong>map</strong></a>(self, arg, na_action=None)</dt><dd><tt>Map&nbsp;values&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;using&nbsp;input&nbsp;correspondence&nbsp;(which&nbsp;can&nbsp;be<br>
-a&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;function)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-arg&nbsp;:&nbsp;function,&nbsp;dict,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-na_action&nbsp;:&nbsp;{None,&nbsp;'ignore'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'ignore',&nbsp;propagate&nbsp;NA&nbsp;values,&nbsp;without&nbsp;passing&nbsp;them&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;mapping&nbsp;function<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;same&nbsp;index&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Map&nbsp;inputs&nbsp;to&nbsp;outputs&nbsp;(both&nbsp;of&nbsp;type&nbsp;`<a href="pandas.core.series.html#Series">Series</a>`)<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,2,3],&nbsp;index=['one',&nbsp;'two',&nbsp;'three'])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-one&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-two&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-three&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;y&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(['foo',&nbsp;'bar',&nbsp;'baz'],&nbsp;index=[1,2,3])<br>
-&gt;&gt;&gt;&nbsp;y<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;foo<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;bar<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;baz<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#State-map">map</a>(y)<br>
-one&nbsp;&nbsp;&nbsp;foo<br>
-two&nbsp;&nbsp;&nbsp;bar<br>
-three&nbsp;baz<br>
-&nbsp;<br>
-If&nbsp;`arg`&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;values&nbsp;converted<br>
-according&nbsp;to&nbsp;the&nbsp;dictionary's&nbsp;mapping:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;z&nbsp;=&nbsp;{1:&nbsp;'A',&nbsp;2:&nbsp;'B',&nbsp;3:&nbsp;'C'}<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#State-map">map</a>(z)<br>
-one&nbsp;&nbsp;&nbsp;A<br>
-two&nbsp;&nbsp;&nbsp;B<br>
-three&nbsp;C<br>
-&nbsp;<br>
-Use&nbsp;na_action&nbsp;to&nbsp;control&nbsp;whether&nbsp;NA&nbsp;values&nbsp;are&nbsp;affected&nbsp;by&nbsp;the&nbsp;mapping<br>
-function.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;np.nan])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s2&nbsp;=&nbsp;s.<a href="#State-map">map</a>('this&nbsp;is&nbsp;a&nbsp;string&nbsp;{}'.format,&nbsp;na_action=None)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;1.0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;2.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;3.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;nan<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s3&nbsp;=&nbsp;s.<a href="#State-map">map</a>('this&nbsp;is&nbsp;a&nbsp;string&nbsp;{}'.format,&nbsp;na_action='ignore')<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;1.0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;2.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;3.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.apply:&nbsp;For&nbsp;applying&nbsp;more&nbsp;complex&nbsp;functions&nbsp;on&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply:&nbsp;Apply&nbsp;a&nbsp;function&nbsp;row-/column-wise<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap:&nbsp;Apply&nbsp;a&nbsp;function&nbsp;elementwise&nbsp;on&nbsp;a&nbsp;whole&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-When&nbsp;`arg`&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;values&nbsp;in&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;that&nbsp;are&nbsp;not&nbsp;in&nbsp;the<br>
-dictionary&nbsp;(as&nbsp;keys)&nbsp;are&nbsp;converted&nbsp;to&nbsp;``NaN``.&nbsp;However,&nbsp;if&nbsp;the<br>
-dictionary&nbsp;is&nbsp;a&nbsp;``dict``&nbsp;subclass&nbsp;that&nbsp;defines&nbsp;``__missing__``&nbsp;(i.e.<br>
-provides&nbsp;a&nbsp;method&nbsp;for&nbsp;default&nbsp;values),&nbsp;then&nbsp;this&nbsp;default&nbsp;is&nbsp;used<br>
-rather&nbsp;than&nbsp;``NaN``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;collections&nbsp;import&nbsp;Counter<br>
-&gt;&gt;&gt;&nbsp;counter&nbsp;=&nbsp;Counter()<br>
-&gt;&gt;&gt;&nbsp;counter['bar']&nbsp;+=&nbsp;1<br>
-&gt;&gt;&gt;&nbsp;y.<a href="#State-map">map</a>(counter)<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-dtype:&nbsp;int64</tt></dd></dl>
-
-<dl><dt><a name="State-max"><strong>max</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;maximum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;maximum,&nbsp;use&nbsp;``idxmax``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmax``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-max&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-mean"><strong>mean</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mean&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-median"><strong>median</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;median&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-median&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-memory_usage"><strong>memory_usage</strong></a>(self, index=True, deep=False)</dt><dd><tt>Memory&nbsp;usage&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;whether&nbsp;to&nbsp;include&nbsp;memory&nbsp;usage&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index<br>
-deep&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Introspect&nbsp;the&nbsp;data&nbsp;deeply,&nbsp;interrogate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`<a href="builtins.html#object">object</a>`&nbsp;dtypes&nbsp;for&nbsp;system-level&nbsp;memory&nbsp;consumption<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-scalar&nbsp;bytes&nbsp;of&nbsp;memory&nbsp;consumed<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Memory&nbsp;usage&nbsp;does&nbsp;not&nbsp;include&nbsp;memory&nbsp;consumed&nbsp;by&nbsp;elements&nbsp;that<br>
-are&nbsp;not&nbsp;components&nbsp;of&nbsp;the&nbsp;array&nbsp;if&nbsp;deep=False<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.nbytes</tt></dd></dl>
-
-<dl><dt><a name="State-min"><strong>min</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;minimum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;minimum,&nbsp;use&nbsp;``idxmin``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmin``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-min&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-mod"><strong>mod</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Modulo&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;%&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmod</tt></dd></dl>
-
-<dl><dt><a name="State-mode"><strong>mode</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;<a href="#State-mode">mode</a>(s)&nbsp;of&nbsp;the&nbsp;dataset.<br>
-&nbsp;<br>
-Always&nbsp;returns&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;even&nbsp;if&nbsp;only&nbsp;one&nbsp;value&nbsp;is&nbsp;returned.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-modes&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(sorted)</tt></dd></dl>
-
-<dl><dt><a name="State-mul"><strong>mul</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="State-multiply"><strong>multiply</strong></a> = mul(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="State-ne"><strong>ne</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Not&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`ne`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;!=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="State-nlargest"><strong>nlargest</strong></a>(self, n=5, keep='first')</dt><dd><tt>Return&nbsp;the&nbsp;largest&nbsp;`n`&nbsp;elements.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;this&nbsp;many&nbsp;descending&nbsp;sorted&nbsp;values<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-top_n&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;n&nbsp;largest&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;in&nbsp;sorted&nbsp;order<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Faster&nbsp;than&nbsp;``.<a href="#State-sort_values">sort_values</a>(ascending=False).<a href="#State-head">head</a>(n)``&nbsp;for&nbsp;small&nbsp;`n`<br>
-relative&nbsp;to&nbsp;the&nbsp;size&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.nsmallest<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10**6))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-nlargest">nlargest</a>(10)&nbsp;&nbsp;#&nbsp;only&nbsp;sorts&nbsp;up&nbsp;to&nbsp;the&nbsp;N&nbsp;requested<br>
-219921&nbsp;&nbsp;&nbsp;&nbsp;4.644710<br>
-82124&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4.608745<br>
-421689&nbsp;&nbsp;&nbsp;&nbsp;4.564644<br>
-425277&nbsp;&nbsp;&nbsp;&nbsp;4.447014<br>
-718691&nbsp;&nbsp;&nbsp;&nbsp;4.414137<br>
-43154&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4.403520<br>
-283187&nbsp;&nbsp;&nbsp;&nbsp;4.313922<br>
-595519&nbsp;&nbsp;&nbsp;&nbsp;4.273635<br>
-503969&nbsp;&nbsp;&nbsp;&nbsp;4.250236<br>
-121637&nbsp;&nbsp;&nbsp;&nbsp;4.240952<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="State-nonzero"><strong>nonzero</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;indices&nbsp;of&nbsp;the&nbsp;elements&nbsp;that&nbsp;are&nbsp;non-zero<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;calling&nbsp;`numpy.nonzero`&nbsp;on&nbsp;the<br>
-series&nbsp;data.&nbsp;For&nbsp;compatability&nbsp;with&nbsp;NumPy,&nbsp;the&nbsp;return&nbsp;value&nbsp;is<br>
-the&nbsp;same&nbsp;(a&nbsp;tuple&nbsp;with&nbsp;an&nbsp;array&nbsp;of&nbsp;indices&nbsp;for&nbsp;each&nbsp;dimension),<br>
-but&nbsp;it&nbsp;will&nbsp;always&nbsp;be&nbsp;a&nbsp;one-item&nbsp;tuple&nbsp;because&nbsp;series&nbsp;only&nbsp;have<br>
-one&nbsp;dimension.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0,&nbsp;3,&nbsp;0,&nbsp;4])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-nonzero">nonzero</a>()<br>
-(array([1,&nbsp;3]),)<br>
-&gt;&gt;&gt;&nbsp;s.iloc[s.<a href="#State-nonzero">nonzero</a>()[0]]<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.nonzero</tt></dd></dl>
-
-<dl><dt><a name="State-nsmallest"><strong>nsmallest</strong></a>(self, n=5, keep='first')</dt><dd><tt>Return&nbsp;the&nbsp;smallest&nbsp;`n`&nbsp;elements.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;this&nbsp;many&nbsp;ascending&nbsp;sorted&nbsp;values<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-bottom_n&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;n&nbsp;smallest&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;in&nbsp;sorted&nbsp;order<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Faster&nbsp;than&nbsp;``.<a href="#State-sort_values">sort_values</a>().<a href="#State-head">head</a>(n)``&nbsp;for&nbsp;small&nbsp;`n`&nbsp;relative&nbsp;to<br>
-the&nbsp;size&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.nlargest<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10**6))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-nsmallest">nsmallest</a>(10)&nbsp;&nbsp;#&nbsp;only&nbsp;sorts&nbsp;up&nbsp;to&nbsp;the&nbsp;N&nbsp;requested<br>
-288532&nbsp;&nbsp;&nbsp;-4.954580<br>
-732345&nbsp;&nbsp;&nbsp;-4.835960<br>
-64803&nbsp;&nbsp;&nbsp;&nbsp;-4.812550<br>
-446457&nbsp;&nbsp;&nbsp;-4.609998<br>
-501225&nbsp;&nbsp;&nbsp;-4.483945<br>
-669476&nbsp;&nbsp;&nbsp;-4.472935<br>
-973615&nbsp;&nbsp;&nbsp;-4.401699<br>
-621279&nbsp;&nbsp;&nbsp;-4.355126<br>
-773916&nbsp;&nbsp;&nbsp;-4.347355<br>
-359919&nbsp;&nbsp;&nbsp;-4.331927<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="State-pow"><strong>pow</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`pow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;**&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rpow</tt></dd></dl>
-
-<dl><dt><a name="State-prod"><strong>prod</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-product"><strong>product</strong></a> = prod(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-ptp"><strong>ptp</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Returns&nbsp;the&nbsp;difference&nbsp;between&nbsp;the&nbsp;maximum&nbsp;value&nbsp;and&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;minimum&nbsp;value&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.&nbsp;This&nbsp;is&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``ptp``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ptp&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-put"><strong>put</strong></a>(self, *args, **kwargs)</dt><dd><tt>Applies&nbsp;the&nbsp;`put`&nbsp;method&nbsp;to&nbsp;its&nbsp;`values`&nbsp;attribute<br>
-if&nbsp;it&nbsp;has&nbsp;one.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.put</tt></dd></dl>
-
-<dl><dt><a name="State-quantile"><strong>quantile</strong></a>(self, q=0.5, interpolation='linear')</dt><dd><tt>Return&nbsp;value&nbsp;at&nbsp;the&nbsp;given&nbsp;quantile,&nbsp;a&nbsp;la&nbsp;numpy.percentile.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-q&nbsp;:&nbsp;float&nbsp;or&nbsp;array-like,&nbsp;default&nbsp;0.5&nbsp;(50%&nbsp;quantile)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&lt;=&nbsp;q&nbsp;&lt;=&nbsp;1,&nbsp;the&nbsp;<a href="#State-quantile">quantile</a>(s)&nbsp;to&nbsp;compute<br>
-interpolation&nbsp;:&nbsp;{'linear',&nbsp;'lower',&nbsp;'higher',&nbsp;'midpoint',&nbsp;'nearest'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;optional&nbsp;parameter&nbsp;specifies&nbsp;the&nbsp;interpolation&nbsp;method&nbsp;to&nbsp;use,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;the&nbsp;desired&nbsp;quantile&nbsp;lies&nbsp;between&nbsp;two&nbsp;data&nbsp;points&nbsp;`i`&nbsp;and&nbsp;`j`:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;linear:&nbsp;`i&nbsp;+&nbsp;(j&nbsp;-&nbsp;i)&nbsp;*&nbsp;fraction`,&nbsp;where&nbsp;`fraction`&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;fractional&nbsp;part&nbsp;of&nbsp;the&nbsp;index&nbsp;surrounded&nbsp;by&nbsp;`i`&nbsp;and&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;lower:&nbsp;`i`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;higher:&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;`i`&nbsp;or&nbsp;`j`&nbsp;whichever&nbsp;is&nbsp;nearest.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;midpoint:&nbsp;(`i`&nbsp;+&nbsp;`j`)&nbsp;/&nbsp;2.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-quantile&nbsp;:&nbsp;float&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;``q``&nbsp;is&nbsp;an&nbsp;array,&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;returned&nbsp;where&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;is&nbsp;``q``&nbsp;and&nbsp;the&nbsp;values&nbsp;are&nbsp;the&nbsp;quantiles.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;4])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-quantile">quantile</a>(.5)<br>
-2.5<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-quantile">quantile</a>([.25,&nbsp;.5,&nbsp;.75])<br>
-0.25&nbsp;&nbsp;&nbsp;&nbsp;1.75<br>
-0.50&nbsp;&nbsp;&nbsp;&nbsp;2.50<br>
-0.75&nbsp;&nbsp;&nbsp;&nbsp;3.25<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="State-radd"><strong>radd</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Addition&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`radd`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;+&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.add</tt></dd></dl>
-
-<dl><dt><a name="State-ravel"><strong>ravel</strong></a>(self, order='C')</dt><dd><tt>Return&nbsp;the&nbsp;flattened&nbsp;underlying&nbsp;data&nbsp;as&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.ravel</tt></dd></dl>
-
-<dl><dt><a name="State-rdiv"><strong>rdiv</strong></a> = rtruediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="State-reindex"><strong>reindex</strong></a>(self, index=None, **kwargs)</dt><dd><tt>Conform&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;new&nbsp;index&nbsp;with&nbsp;optional&nbsp;filling&nbsp;logic,&nbsp;placing<br>
-NA/NaN&nbsp;in&nbsp;locations&nbsp;having&nbsp;no&nbsp;value&nbsp;in&nbsp;the&nbsp;previous&nbsp;index.&nbsp;A&nbsp;new&nbsp;<a href="builtins.html#object">object</a><br>
-is&nbsp;produced&nbsp;unless&nbsp;the&nbsp;new&nbsp;index&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;the&nbsp;current&nbsp;one&nbsp;and<br>
-copy=False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;array-like,&nbsp;optional&nbsp;(can&nbsp;be&nbsp;specified&nbsp;in&nbsp;order,&nbsp;or&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;keywords)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;New&nbsp;labels&nbsp;/&nbsp;index&nbsp;to&nbsp;conform&nbsp;to.&nbsp;Preferably&nbsp;an&nbsp;Index&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;avoid&nbsp;duplicating&nbsp;data<br>
-method&nbsp;:&nbsp;{None,&nbsp;'backfill'/'bfill',&nbsp;'pad'/'ffill',&nbsp;'nearest'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Please&nbsp;note:&nbsp;this&nbsp;is&nbsp;only&nbsp;&nbsp;applicable&nbsp;to&nbsp;DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;monotonically&nbsp;increasing/decreasing&nbsp;index.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;default:&nbsp;don't&nbsp;fill&nbsp;gaps<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;next&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;use&nbsp;nearest&nbsp;valid&nbsp;observations&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>,&nbsp;even&nbsp;if&nbsp;the&nbsp;passed&nbsp;indexes&nbsp;are&nbsp;the&nbsp;same<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;elements&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;original&nbsp;and&nbsp;new&nbsp;labels&nbsp;for&nbsp;inexact<br>
-&nbsp;&nbsp;&nbsp;&nbsp;matches.&nbsp;The&nbsp;values&nbsp;of&nbsp;the&nbsp;index&nbsp;at&nbsp;the&nbsp;matching&nbsp;locations&nbsp;most<br>
-&nbsp;&nbsp;&nbsp;&nbsp;satisfy&nbsp;the&nbsp;equation&nbsp;``<a href="#State-abs">abs</a>(index[indexer]&nbsp;-&nbsp;target)&nbsp;&lt;=&nbsp;tolerance``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;some&nbsp;fictional&nbsp;data.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;['Firefox',&nbsp;'Chrome',&nbsp;'Safari',&nbsp;'IE10',&nbsp;'Konqueror']<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'http_status':&nbsp;[200,200,404,404,301],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'response_time':&nbsp;[0.04,&nbsp;0.02,&nbsp;0.07,&nbsp;0.08,&nbsp;1.0]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Firefox&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.04<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Konqueror&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;301&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.00<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;new&nbsp;index&nbsp;and&nbsp;reindex&nbsp;the&nbsp;dataframe.&nbsp;By&nbsp;default<br>
-values&nbsp;in&nbsp;the&nbsp;new&nbsp;index&nbsp;that&nbsp;do&nbsp;not&nbsp;have&nbsp;corresponding<br>
-records&nbsp;in&nbsp;the&nbsp;dataframe&nbsp;are&nbsp;assigned&nbsp;``NaN``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;new_index=&nbsp;['Safari',&nbsp;'Iceweasel',&nbsp;'Comodo&nbsp;Dragon',&nbsp;'IE10',<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Chrome']<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-reindex">reindex</a>(new_index)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-We&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;by&nbsp;passing&nbsp;a&nbsp;value&nbsp;to<br>
-the&nbsp;keyword&nbsp;``fill_value``.&nbsp;Because&nbsp;the&nbsp;index&nbsp;is&nbsp;not&nbsp;monotonically<br>
-increasing&nbsp;or&nbsp;decreasing,&nbsp;we&nbsp;cannot&nbsp;use&nbsp;arguments&nbsp;to&nbsp;the&nbsp;keyword<br>
-``method``&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``&nbsp;values.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-reindex">reindex</a>(new_index,&nbsp;fill_value=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-reindex">reindex</a>(new_index,&nbsp;fill_value='missing')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-To&nbsp;further&nbsp;illustrate&nbsp;the&nbsp;filling&nbsp;functionality&nbsp;in<br>
-``reindex``,&nbsp;we&nbsp;will&nbsp;create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;a<br>
-monotonically&nbsp;increasing&nbsp;index&nbsp;(for&nbsp;example,&nbsp;a&nbsp;sequence<br>
-of&nbsp;dates).<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index&nbsp;=&nbsp;pd.date_range('1/1/2010',&nbsp;periods=6,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"prices":&nbsp;[100,&nbsp;101,&nbsp;np.nan,&nbsp;100,&nbsp;89,&nbsp;88]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=date_index)<br>
-&gt;&gt;&gt;&nbsp;df2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-&nbsp;<br>
-Suppose&nbsp;we&nbsp;decide&nbsp;to&nbsp;expand&nbsp;the&nbsp;dataframe&nbsp;to&nbsp;cover&nbsp;a&nbsp;wider<br>
-date&nbsp;range.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index2&nbsp;=&nbsp;pd.date_range('12/29/2009',&nbsp;periods=10,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#State-reindex">reindex</a>(date_index2)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-The&nbsp;index&nbsp;entries&nbsp;that&nbsp;did&nbsp;not&nbsp;have&nbsp;a&nbsp;value&nbsp;in&nbsp;the&nbsp;original&nbsp;data&nbsp;frame<br>
-(for&nbsp;example,&nbsp;'2009-12-29')&nbsp;are&nbsp;by&nbsp;default&nbsp;filled&nbsp;with&nbsp;``NaN``.<br>
-If&nbsp;desired,&nbsp;we&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;using&nbsp;one&nbsp;of&nbsp;several<br>
-options.<br>
-&nbsp;<br>
-For&nbsp;example,&nbsp;to&nbsp;backpropagate&nbsp;the&nbsp;last&nbsp;valid&nbsp;value&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values,&nbsp;pass&nbsp;``bfill``&nbsp;as&nbsp;an&nbsp;argument&nbsp;to&nbsp;the&nbsp;``method``&nbsp;keyword.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#State-reindex">reindex</a>(date_index2,&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;the&nbsp;``NaN``&nbsp;value&nbsp;present&nbsp;in&nbsp;the&nbsp;original&nbsp;dataframe<br>
-(at&nbsp;index&nbsp;value&nbsp;2010-01-03)&nbsp;will&nbsp;not&nbsp;be&nbsp;filled&nbsp;by&nbsp;any&nbsp;of&nbsp;the<br>
-value&nbsp;propagation&nbsp;schemes.&nbsp;This&nbsp;is&nbsp;because&nbsp;filling&nbsp;while&nbsp;reindexing<br>
-does&nbsp;not&nbsp;look&nbsp;at&nbsp;dataframe&nbsp;values,&nbsp;but&nbsp;only&nbsp;compares&nbsp;the&nbsp;original&nbsp;and<br>
-desired&nbsp;indexes.&nbsp;If&nbsp;you&nbsp;do&nbsp;want&nbsp;to&nbsp;fill&nbsp;in&nbsp;the&nbsp;``NaN``&nbsp;values&nbsp;present<br>
-in&nbsp;the&nbsp;original&nbsp;dataframe,&nbsp;use&nbsp;the&nbsp;``<a href="#State-fillna">fillna</a>()``&nbsp;method.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="State-reindex_axis"><strong>reindex_axis</strong></a>(self, labels, axis=0, **kwargs)</dt><dd><tt>for&nbsp;compatibility&nbsp;with&nbsp;higher&nbsp;dims</tt></dd></dl>
-
-<dl><dt><a name="State-rename"><strong>rename</strong></a>(self, index=None, **kwargs)</dt><dd><tt>Alter&nbsp;axes&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.&nbsp;Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be<br>
-unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in&nbsp;a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left<br>
-as-is.&nbsp;Extra&nbsp;labels&nbsp;listed&nbsp;don't&nbsp;throw&nbsp;an&nbsp;error.&nbsp;Alternatively,&nbsp;change<br>
-``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;with&nbsp;a&nbsp;scalar&nbsp;value&nbsp;(<a href="pandas.core.series.html#Series">Series</a>&nbsp;only).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Scalar&nbsp;or&nbsp;list-like&nbsp;will&nbsp;alter&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;attribute,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;raise&nbsp;on&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;Panel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict-like&nbsp;or&nbsp;functions&nbsp;are&nbsp;transformations&nbsp;to&nbsp;apply&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;that&nbsp;axis'&nbsp;values<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;If&nbsp;True&nbsp;then&nbsp;value&nbsp;of&nbsp;copy&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;ignored.<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;MultiIndex,&nbsp;only&nbsp;rename&nbsp;labels&nbsp;in&nbsp;the&nbsp;specified<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename_axis<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-rename">rename</a>("my_name")&nbsp;#&nbsp;scalar,&nbsp;changes&nbsp;<a href="pandas.core.series.html#Series">Series</a>.name<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;my_name,&nbsp;dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-rename">rename</a>(lambda&nbsp;x:&nbsp;x&nbsp;**&nbsp;2)&nbsp;&nbsp;#&nbsp;function,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-rename">rename</a>({1:&nbsp;3,&nbsp;2:&nbsp;5})&nbsp;&nbsp;#&nbsp;mapping,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-rename">rename</a>(2)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-TypeError:&nbsp;'int'&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;not&nbsp;callable<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"B":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;c<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"C":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="State-reorder_levels"><strong>reorder_levels</strong></a>(self, order)</dt><dd><tt>Rearrange&nbsp;index&nbsp;levels&nbsp;using&nbsp;input&nbsp;order.&nbsp;May&nbsp;not&nbsp;drop&nbsp;or&nbsp;duplicate<br>
-levels<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-order&nbsp;:&nbsp;list&nbsp;of&nbsp;int&nbsp;representing&nbsp;new&nbsp;level&nbsp;order.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(reference&nbsp;level&nbsp;by&nbsp;number&nbsp;or&nbsp;key)<br>
-axis&nbsp;:&nbsp;where&nbsp;to&nbsp;reorder&nbsp;levels<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-type&nbsp;of&nbsp;caller&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)</tt></dd></dl>
-
-<dl><dt><a name="State-repeat"><strong>repeat</strong></a>(self, repeats, *args, **kwargs)</dt><dd><tt>Repeat&nbsp;elements&nbsp;of&nbsp;an&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Refer&nbsp;to&nbsp;`numpy.<a href="numpy.html#ndarray">ndarray</a>.repeat`<br>
-for&nbsp;more&nbsp;information&nbsp;about&nbsp;the&nbsp;`repeats`&nbsp;argument.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.repeat</tt></dd></dl>
-
-<dl><dt><a name="State-reset_index"><strong>reset_index</strong></a>(self, level=None, drop=False, name=None, inplace=False)</dt><dd><tt>Analogous&nbsp;to&nbsp;the&nbsp;:meth:`pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.reset_index`&nbsp;function,&nbsp;see<br>
-docstring&nbsp;there.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;tuple,&nbsp;or&nbsp;list,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Only&nbsp;remove&nbsp;the&nbsp;given&nbsp;levels&nbsp;from&nbsp;the&nbsp;index.&nbsp;Removes&nbsp;all&nbsp;levels&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-drop&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;not&nbsp;try&nbsp;to&nbsp;insert&nbsp;index&nbsp;into&nbsp;dataframe&nbsp;columns<br>
-name&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;name&nbsp;of&nbsp;the&nbsp;column&nbsp;corresponding&nbsp;to&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Modify&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place&nbsp;(do&nbsp;not&nbsp;create&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-Returns<br>
-----------<br>
-resetted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;if&nbsp;drop&nbsp;==&nbsp;True</tt></dd></dl>
-
-<dl><dt><a name="State-reshape"><strong>reshape</strong></a>(self, *args, **kwargs)</dt><dd><tt>DEPRECATED:&nbsp;calling&nbsp;this&nbsp;method&nbsp;will&nbsp;raise&nbsp;an&nbsp;error&nbsp;in&nbsp;a<br>
-future&nbsp;release.&nbsp;Please&nbsp;call&nbsp;``.values.<a href="#State-reshape">reshape</a>(...)``&nbsp;instead.<br>
-&nbsp;<br>
-return&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;with&nbsp;the&nbsp;values&nbsp;shape<br>
-if&nbsp;the&nbsp;specified&nbsp;shape&nbsp;matches&nbsp;exactly&nbsp;the&nbsp;current&nbsp;shape,&nbsp;then<br>
-return&nbsp;self&nbsp;(for&nbsp;compat)<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.reshape</tt></dd></dl>
-
-<dl><dt><a name="State-rfloordiv"><strong>rfloordiv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rfloordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;//&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.floordiv</tt></dd></dl>
-
-<dl><dt><a name="State-rmod"><strong>rmod</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Modulo&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;%&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.mod</tt></dd></dl>
-
-<dl><dt><a name="State-rmul"><strong>rmul</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;*&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.mul</tt></dd></dl>
-
-<dl><dt><a name="State-rolling"><strong>rolling</strong></a>(self, window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0, closed=None)</dt><dd><tt>Provides&nbsp;rolling&nbsp;window&nbsp;calculcations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-window&nbsp;:&nbsp;int,&nbsp;or&nbsp;offset<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Size&nbsp;of&nbsp;the&nbsp;moving&nbsp;window.&nbsp;This&nbsp;is&nbsp;the&nbsp;number&nbsp;of&nbsp;observations&nbsp;used&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;calculating&nbsp;the&nbsp;statistic.&nbsp;Each&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;fixed&nbsp;size.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;its&nbsp;an&nbsp;offset&nbsp;then&nbsp;this&nbsp;will&nbsp;be&nbsp;the&nbsp;time&nbsp;period&nbsp;of&nbsp;each&nbsp;window.&nbsp;Each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;variable&nbsp;sized&nbsp;based&nbsp;on&nbsp;the&nbsp;observations&nbsp;included&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;time-period.&nbsp;This&nbsp;is&nbsp;only&nbsp;valid&nbsp;for&nbsp;datetimelike&nbsp;indexes.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;new&nbsp;in&nbsp;0.19.0<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).&nbsp;For&nbsp;a&nbsp;window&nbsp;that&nbsp;is&nbsp;specified&nbsp;by&nbsp;an&nbsp;offset,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;will&nbsp;default&nbsp;to&nbsp;1.<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-win_type&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Provide&nbsp;a&nbsp;window&nbsp;type.&nbsp;See&nbsp;the&nbsp;notes&nbsp;below.<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;on&nbsp;which&nbsp;to&nbsp;calculate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;rolling&nbsp;window,&nbsp;rather&nbsp;than&nbsp;the&nbsp;index<br>
-closed&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;the&nbsp;interval&nbsp;closed&nbsp;on&nbsp;the&nbsp;'right',&nbsp;'left',&nbsp;'both'&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'neither'&nbsp;endpoints.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;offset-based&nbsp;windows,&nbsp;it&nbsp;defaults&nbsp;to&nbsp;'right'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;fixed&nbsp;windows,&nbsp;defaults&nbsp;to&nbsp;'both'.&nbsp;Remaining&nbsp;cases&nbsp;not&nbsp;implemented<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;fixed&nbsp;windows.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;or&nbsp;Rolling&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;using&nbsp;the&nbsp;'triang'<br>
-window&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-rolling">rolling</a>(2,&nbsp;win_type='triang').<a href="#State-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.5<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;min_periods&nbsp;defaults<br>
-to&nbsp;the&nbsp;window&nbsp;length.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-rolling">rolling</a>(2).<a href="#State-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Same&nbsp;as&nbsp;above,&nbsp;but&nbsp;explicity&nbsp;set&nbsp;the&nbsp;min_periods<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-rolling">rolling</a>(2,&nbsp;min_periods=1).<a href="#State-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;2.0<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-A&nbsp;ragged&nbsp;(meaning&nbsp;not-a-regular&nbsp;frequency),&nbsp;time-indexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]},<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;=&nbsp;[pd.Timestamp('20130101&nbsp;09:00:00'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:02'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:03'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:05'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:06')])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;2.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Contrasting&nbsp;to&nbsp;an&nbsp;integer&nbsp;rolling&nbsp;window,&nbsp;this&nbsp;will&nbsp;roll&nbsp;a&nbsp;variable<br>
-length&nbsp;window&nbsp;corresponding&nbsp;to&nbsp;the&nbsp;time&nbsp;period.<br>
-The&nbsp;default&nbsp;for&nbsp;min_periods&nbsp;is&nbsp;1.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-rolling">rolling</a>('2s').<a href="#State-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;3.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offsets&nbsp;&amp;&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-The&nbsp;recognized&nbsp;win_types&nbsp;are:<br>
-&nbsp;<br>
-*&nbsp;``boxcar``<br>
-*&nbsp;``triang``<br>
-*&nbsp;``blackman``<br>
-*&nbsp;``hamming``<br>
-*&nbsp;``bartlett``<br>
-*&nbsp;``parzen``<br>
-*&nbsp;``bohman``<br>
-*&nbsp;``blackmanharris``<br>
-*&nbsp;``nuttall``<br>
-*&nbsp;``barthann``<br>
-*&nbsp;``kaiser``&nbsp;(needs&nbsp;beta)<br>
-*&nbsp;``gaussian``&nbsp;(needs&nbsp;std)<br>
-*&nbsp;``general_gaussian``&nbsp;(needs&nbsp;power,&nbsp;width)<br>
-*&nbsp;``slepian``&nbsp;(needs&nbsp;width).</tt></dd></dl>
-
-<dl><dt><a name="State-round"><strong>round</strong></a>(self, decimals=0, *args, **kwargs)</dt><dd><tt>Round&nbsp;each&nbsp;value&nbsp;in&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;the&nbsp;given&nbsp;number&nbsp;of&nbsp;decimals.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-decimals&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;round&nbsp;to&nbsp;(default:&nbsp;0).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;decimals&nbsp;is&nbsp;negative,&nbsp;it&nbsp;specifies&nbsp;the&nbsp;number&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;positions&nbsp;to&nbsp;the&nbsp;left&nbsp;of&nbsp;the&nbsp;decimal&nbsp;point.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.series.html#Series">Series</a>&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.around<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.round</tt></dd></dl>
-
-<dl><dt><a name="State-rpow"><strong>rpow</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rpow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;**&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.pow</tt></dd></dl>
-
-<dl><dt><a name="State-rsub"><strong>rsub</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rsub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;-&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.sub</tt></dd></dl>
-
-<dl><dt><a name="State-rtruediv"><strong>rtruediv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="State-searchsorted"><strong>searchsorted</strong></a>(self, value, side='left', sorter=None)</dt><dd><tt>Find&nbsp;indices&nbsp;where&nbsp;elements&nbsp;should&nbsp;be&nbsp;inserted&nbsp;to&nbsp;maintain&nbsp;order.<br>
-&nbsp;<br>
-Find&nbsp;the&nbsp;indices&nbsp;into&nbsp;a&nbsp;sorted&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;`self`&nbsp;such&nbsp;that,&nbsp;if&nbsp;the<br>
-corresponding&nbsp;elements&nbsp;in&nbsp;`value`&nbsp;were&nbsp;inserted&nbsp;before&nbsp;the&nbsp;indices,<br>
-the&nbsp;order&nbsp;of&nbsp;`self`&nbsp;would&nbsp;be&nbsp;preserved.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;array_like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Values&nbsp;to&nbsp;insert&nbsp;into&nbsp;`self`.<br>
-side&nbsp;:&nbsp;{'left',&nbsp;'right'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'left',&nbsp;the&nbsp;index&nbsp;of&nbsp;the&nbsp;first&nbsp;suitable&nbsp;location&nbsp;found&nbsp;is&nbsp;given.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'right',&nbsp;return&nbsp;the&nbsp;last&nbsp;such&nbsp;index.&nbsp;&nbsp;If&nbsp;there&nbsp;is&nbsp;no&nbsp;suitable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index,&nbsp;return&nbsp;either&nbsp;0&nbsp;or&nbsp;N&nbsp;(where&nbsp;N&nbsp;is&nbsp;the&nbsp;length&nbsp;of&nbsp;`self`).<br>
-sorter&nbsp;:&nbsp;1-D&nbsp;array_like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Optional&nbsp;array&nbsp;of&nbsp;integer&nbsp;indices&nbsp;that&nbsp;sort&nbsp;`self`&nbsp;into&nbsp;ascending<br>
-&nbsp;&nbsp;&nbsp;&nbsp;order.&nbsp;They&nbsp;are&nbsp;typically&nbsp;the&nbsp;result&nbsp;of&nbsp;``np.argsort``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-indices&nbsp;:&nbsp;array&nbsp;of&nbsp;ints<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Array">Array</a>&nbsp;of&nbsp;insertion&nbsp;points&nbsp;with&nbsp;the&nbsp;same&nbsp;shape&nbsp;as&nbsp;`value`.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.searchsorted<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Binary&nbsp;search&nbsp;is&nbsp;used&nbsp;to&nbsp;find&nbsp;the&nbsp;required&nbsp;insertion&nbsp;points.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#State-searchsorted">searchsorted</a>(4)<br>
-array([3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#State-searchsorted">searchsorted</a>([0,&nbsp;4])<br>
-array([0,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#State-searchsorted">searchsorted</a>([1,&nbsp;3],&nbsp;side='left')<br>
-array([0,&nbsp;2])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#State-searchsorted">searchsorted</a>([1,&nbsp;3],&nbsp;side='right')<br>
-array([1,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.Categorical(['apple',&nbsp;'bread',&nbsp;'bread',&nbsp;'cheese',&nbsp;'milk'&nbsp;])<br>
-[apple,&nbsp;bread,&nbsp;bread,&nbsp;cheese,&nbsp;milk]<br>
-Categories&nbsp;(4,&nbsp;<a href="builtins.html#object">object</a>):&nbsp;[apple&nbsp;&lt;&nbsp;bread&nbsp;&lt;&nbsp;cheese&nbsp;&lt;&nbsp;milk]<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#State-searchsorted">searchsorted</a>('bread')<br>
-array([1])&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;Note:&nbsp;an&nbsp;array,&nbsp;not&nbsp;a&nbsp;scalar<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#State-searchsorted">searchsorted</a>(['bread'])<br>
-array([1])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#State-searchsorted">searchsorted</a>(['bread',&nbsp;'eggs'])<br>
-array([1,&nbsp;4])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#State-searchsorted">searchsorted</a>(['bread',&nbsp;'eggs'],&nbsp;side='right')<br>
-array([3,&nbsp;4])&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;eggs&nbsp;before&nbsp;milk</tt></dd></dl>
-
-<dl><dt><a name="State-sem"><strong>sem</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;standard&nbsp;error&nbsp;of&nbsp;the&nbsp;mean&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sem&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-set_value"><strong>set_value</strong></a>(self, label, value, takeable=False)</dt><dd><tt>Quickly&nbsp;set&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;label.&nbsp;If&nbsp;label&nbsp;is&nbsp;not&nbsp;contained,&nbsp;a<br>
-new&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;created&nbsp;with&nbsp;the&nbsp;label&nbsp;placed&nbsp;at&nbsp;the&nbsp;end&nbsp;of&nbsp;the&nbsp;result<br>
-index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-label&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Partial&nbsp;indexing&nbsp;with&nbsp;MultiIndex&nbsp;not&nbsp;allowed<br>
-value&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Scalar&nbsp;value<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-series&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;label&nbsp;is&nbsp;contained,&nbsp;will&nbsp;be&nbsp;reference&nbsp;to&nbsp;calling&nbsp;<a href="pandas.core.series.html#Series">Series</a>,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="State-shift"><strong>shift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;index&nbsp;by&nbsp;desired&nbsp;number&nbsp;of&nbsp;periods&nbsp;with&nbsp;an&nbsp;optional&nbsp;time&nbsp;freq<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM').<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;Notes.<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'}<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;specified&nbsp;then&nbsp;the&nbsp;index&nbsp;values&nbsp;are&nbsp;shifted&nbsp;but&nbsp;the&nbsp;data<br>
-is&nbsp;not&nbsp;realigned.&nbsp;That&nbsp;is,&nbsp;use&nbsp;freq&nbsp;if&nbsp;you&nbsp;would&nbsp;like&nbsp;to&nbsp;extend&nbsp;the<br>
-index&nbsp;when&nbsp;shifting&nbsp;and&nbsp;preserve&nbsp;the&nbsp;original&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="State-skew"><strong>skew</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;skew&nbsp;over&nbsp;requested&nbsp;axis<br>
-Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-skew&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-sort_index"><strong>sort_index</strong></a>(self, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True)</dt><dd><tt>Sort&nbsp;<a href="builtins.html#object">object</a>&nbsp;by&nbsp;labels&nbsp;(along&nbsp;an&nbsp;axis)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;index&nbsp;to&nbsp;direct&nbsp;sorting<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name&nbsp;or&nbsp;list&nbsp;of&nbsp;ints&nbsp;or&nbsp;list&nbsp;of&nbsp;level&nbsp;names<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None,&nbsp;sort&nbsp;on&nbsp;values&nbsp;in&nbsp;specified&nbsp;index&nbsp;level(s)<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;MultiIndex.<br>
-sort_remaining&nbsp;:&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;true&nbsp;and&nbsp;sorting&nbsp;by&nbsp;level&nbsp;and&nbsp;index&nbsp;is&nbsp;multilevel,&nbsp;sort&nbsp;by&nbsp;other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;levels&nbsp;too&nbsp;(in&nbsp;order)&nbsp;after&nbsp;sorting&nbsp;by&nbsp;specified&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="State-sort_values"><strong>sort_values</strong></a>(self, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')</dt><dd><tt>Sort&nbsp;by&nbsp;the&nbsp;values&nbsp;along&nbsp;either&nbsp;axis<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;direct&nbsp;sorting<br>
-ascending&nbsp;:&nbsp;bool&nbsp;or&nbsp;list&nbsp;of&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending.&nbsp;Specify&nbsp;list&nbsp;for&nbsp;multiple&nbsp;sort<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;orders.&nbsp;&nbsp;If&nbsp;this&nbsp;is&nbsp;a&nbsp;list&nbsp;of&nbsp;bools,&nbsp;must&nbsp;match&nbsp;the&nbsp;length&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;by.<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="State-sortlevel"><strong>sortlevel</strong></a>(self, level=0, ascending=True, sort_remaining=True)</dt><dd><tt>DEPRECATED:&nbsp;use&nbsp;:meth:`<a href="pandas.core.series.html#Series">Series</a>.sort_index`<br>
-&nbsp;<br>
-Sort&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;MultiIndex&nbsp;by&nbsp;chosen&nbsp;level.&nbsp;Data&nbsp;will&nbsp;be<br>
-lexicographically&nbsp;sorted&nbsp;by&nbsp;the&nbsp;chosen&nbsp;level&nbsp;followed&nbsp;by&nbsp;the&nbsp;other<br>
-levels&nbsp;(in&nbsp;order)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-ascending&nbsp;:&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.<a href="#State-sort_index">sort_index</a>(level=...)</tt></dd></dl>
-
-<dl><dt><a name="State-std"><strong>std</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;sample&nbsp;standard&nbsp;deviation&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-std&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-sub"><strong>sub</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="State-subtract"><strong>subtract</strong></a> = sub(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="State-sum"><strong>sum</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;sum&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sum&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-swaplevel"><strong>swaplevel</strong></a>(self, i=-2, j=-1, copy=True)</dt><dd><tt>Swap&nbsp;levels&nbsp;i&nbsp;and&nbsp;j&nbsp;in&nbsp;a&nbsp;MultiIndex<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-i,&nbsp;j&nbsp;:&nbsp;int,&nbsp;string&nbsp;(can&nbsp;be&nbsp;mixed)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level&nbsp;of&nbsp;index&nbsp;to&nbsp;be&nbsp;swapped.&nbsp;Can&nbsp;pass&nbsp;level&nbsp;name&nbsp;as&nbsp;string.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-swapped&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-..&nbsp;versionchanged::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;The&nbsp;indexes&nbsp;``i``&nbsp;and&nbsp;``j``&nbsp;are&nbsp;now&nbsp;optional,&nbsp;and&nbsp;default&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;the&nbsp;two&nbsp;innermost&nbsp;levels&nbsp;of&nbsp;the&nbsp;index.</tt></dd></dl>
-
-<dl><dt><a name="State-take"><strong>take</strong></a>(self, indices, axis=0, convert=True, is_copy=False, **kwargs)</dt><dd><tt>return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;corresponding&nbsp;to&nbsp;requested&nbsp;indices<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-indices&nbsp;:&nbsp;list&nbsp;/&nbsp;array&nbsp;of&nbsp;ints<br>
-convert&nbsp;:&nbsp;translate&nbsp;negative&nbsp;to&nbsp;positive&nbsp;indices&nbsp;(default)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-taken&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.take</tt></dd></dl>
-
-<dl><dt><a name="State-to_csv"><strong>to_csv</strong></a>(self, path=None, index=True, sep=',', na_rep='', float_format=None, header=False, index_label=None, mode='w', encoding=None, date_format=None, decimal='.')</dt><dd><tt>Write&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;a&nbsp;comma-separated&nbsp;values&nbsp;(csv)&nbsp;file<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;or&nbsp;file&nbsp;handle,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;<a href="builtins.html#object">object</a>,&nbsp;if&nbsp;None&nbsp;is&nbsp;provided&nbsp;the&nbsp;result&nbsp;is&nbsp;returned&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string.<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-header&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;series&nbsp;name<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-mode&nbsp;:&nbsp;Python&nbsp;write&nbsp;mode,&nbsp;default&nbsp;'w'<br>
-sep&nbsp;:&nbsp;character,&nbsp;default&nbsp;","<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter&nbsp;for&nbsp;the&nbsp;output&nbsp;file.<br>
-encoding&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;if&nbsp;the&nbsp;contents&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;non-ascii,&nbsp;for&nbsp;python&nbsp;versions&nbsp;prior&nbsp;to&nbsp;3<br>
-date_format:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;datetime&nbsp;objects.<br>
-decimal:&nbsp;string,&nbsp;default&nbsp;'.'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Character&nbsp;recognized&nbsp;as&nbsp;decimal&nbsp;separator.&nbsp;E.g.&nbsp;use&nbsp;','&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;European&nbsp;data</tt></dd></dl>
-
-<dl><dt><a name="State-to_dict"><strong>to_dict</strong></a>(self)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;{label&nbsp;-&gt;&nbsp;value}&nbsp;dict<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value_dict&nbsp;:&nbsp;dict</tt></dd></dl>
-
-<dl><dt><a name="State-to_excel"><strong>to_excel</strong></a>(self, excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True)</dt><dd><tt>Write&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;an&nbsp;excel&nbsp;sheet<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel_writer&nbsp;:&nbsp;string&nbsp;or&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;existing&nbsp;ExcelWriter<br>
-sheet_name&nbsp;:&nbsp;string,&nbsp;default&nbsp;'Sheet1'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;sheet&nbsp;which&nbsp;will&nbsp;contain&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Columns&nbsp;to&nbsp;write<br>
-header&nbsp;:&nbsp;boolean&nbsp;or&nbsp;list&nbsp;of&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given&nbsp;it&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;assumed&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-startrow&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;row&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-startcol&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;column&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-engine&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;write&nbsp;engine&nbsp;to&nbsp;use&nbsp;-&nbsp;you&nbsp;can&nbsp;also&nbsp;set&nbsp;this&nbsp;via&nbsp;the&nbsp;options<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsx.writer``,&nbsp;``io.excel.xls.writer``,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsm.writer``.<br>
-merge_cells&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;MultiIndex&nbsp;and&nbsp;Hierarchical&nbsp;Rows&nbsp;as&nbsp;merged&nbsp;cells.<br>
-encoding:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;encoding&nbsp;of&nbsp;the&nbsp;resulting&nbsp;excel&nbsp;file.&nbsp;Only&nbsp;necessary&nbsp;for&nbsp;xlwt,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;writers&nbsp;support&nbsp;unicode&nbsp;natively.<br>
-inf_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;'inf'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Representation&nbsp;for&nbsp;infinity&nbsp;(there&nbsp;is&nbsp;no&nbsp;native&nbsp;representation&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;infinity&nbsp;in&nbsp;Excel)<br>
-freeze_panes&nbsp;:&nbsp;tuple&nbsp;of&nbsp;integer&nbsp;(length&nbsp;2),&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;the&nbsp;one-based&nbsp;bottommost&nbsp;row&nbsp;and&nbsp;rightmost&nbsp;column&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;to&nbsp;be&nbsp;frozen<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;passing&nbsp;an&nbsp;existing&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a>,&nbsp;then&nbsp;the&nbsp;sheet&nbsp;will&nbsp;be&nbsp;added<br>
-to&nbsp;the&nbsp;existing&nbsp;workbook.&nbsp;&nbsp;This&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;save&nbsp;different<br>
-DataFrames&nbsp;to&nbsp;one&nbsp;workbook:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;writer&nbsp;=&nbsp;pd.ExcelWriter('output.xlsx')<br>
-&gt;&gt;&gt;&nbsp;df1.<a href="#State-to_excel">to_excel</a>(writer,'Sheet1')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#State-to_excel">to_excel</a>(writer,'Sheet2')<br>
-&gt;&gt;&gt;&nbsp;writer.save()<br>
-&nbsp;<br>
-For&nbsp;compatibility&nbsp;with&nbsp;to_csv,&nbsp;to_excel&nbsp;serializes&nbsp;lists&nbsp;and&nbsp;dicts&nbsp;to<br>
-strings&nbsp;before&nbsp;writing.</tt></dd></dl>
-
-<dl><dt><a name="State-to_frame"><strong>to_frame</strong></a>(self, name=None)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-name&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;passed&nbsp;name&nbsp;should&nbsp;substitute&nbsp;for&nbsp;the&nbsp;series&nbsp;name&nbsp;(if&nbsp;it&nbsp;has<br>
-&nbsp;&nbsp;&nbsp;&nbsp;one).<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-data_frame&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="State-to_period"><strong>to_period</strong></a>(self, freq=None, copy=True)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;from&nbsp;DatetimeIndex&nbsp;to&nbsp;PeriodIndex&nbsp;with&nbsp;desired<br>
-frequency&nbsp;(inferred&nbsp;from&nbsp;index&nbsp;if&nbsp;not&nbsp;passed)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;PeriodIndex</tt></dd></dl>
-
-<dl><dt><a name="State-to_sparse"><strong>to_sparse</strong></a>(self, kind='block', fill_value=None)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;SparseSeries<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-kind&nbsp;:&nbsp;{'block',&nbsp;'integer'}<br>
-fill_value&nbsp;:&nbsp;float,&nbsp;defaults&nbsp;to&nbsp;NaN&nbsp;(missing)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sp&nbsp;:&nbsp;SparseSeries</tt></dd></dl>
-
-<dl><dt><a name="State-to_string"><strong>to_string</strong></a>(self, buf=None, na_rep='NaN', float_format=None, header=True, index=True, length=False, dtype=False, name=False, max_rows=None)</dt><dd><tt>Render&nbsp;a&nbsp;string&nbsp;representation&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-buf&nbsp;:&nbsp;StringIO-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;buffer&nbsp;to&nbsp;write&nbsp;to<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;representation&nbsp;of&nbsp;NAN&nbsp;to&nbsp;use,&nbsp;default&nbsp;'NaN'<br>
-float_format&nbsp;:&nbsp;one-parameter&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;if&nbsp;they&nbsp;are&nbsp;floats<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None<br>
-header:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;header&nbsp;(index&nbsp;name)<br>
-index&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;index&nbsp;(row)&nbsp;labels,&nbsp;default&nbsp;True<br>
-length&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;length<br>
-dtype&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;dtype<br>
-name&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;name&nbsp;if&nbsp;not&nbsp;None<br>
-max_rows&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;rows&nbsp;to&nbsp;show&nbsp;before&nbsp;truncating.&nbsp;If&nbsp;None,&nbsp;show<br>
-&nbsp;&nbsp;&nbsp;&nbsp;all.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-formatted&nbsp;:&nbsp;string&nbsp;(if&nbsp;not&nbsp;buffer&nbsp;passed)</tt></dd></dl>
-
-<dl><dt><a name="State-to_timestamp"><strong>to_timestamp</strong></a>(self, freq=None, how='start', copy=True)</dt><dd><tt>Cast&nbsp;to&nbsp;datetimeindex&nbsp;of&nbsp;timestamps,&nbsp;at&nbsp;*beginning*&nbsp;of&nbsp;period<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default&nbsp;frequency&nbsp;of&nbsp;PeriodIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Desired&nbsp;frequency<br>
-how&nbsp;:&nbsp;{'s',&nbsp;'e',&nbsp;'start',&nbsp;'end'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Convention&nbsp;for&nbsp;converting&nbsp;period&nbsp;to&nbsp;timestamp;&nbsp;start&nbsp;of&nbsp;period<br>
-&nbsp;&nbsp;&nbsp;&nbsp;vs.&nbsp;end<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;DatetimeIndex</tt></dd></dl>
-
-<dl><dt><a name="State-tolist"><strong>tolist</strong></a>(self)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;a&nbsp;nested&nbsp;list</tt></dd></dl>
-
-<dl><dt><a name="State-transform"><strong>transform</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Call&nbsp;function&nbsp;producing&nbsp;a&nbsp;like-indexed&nbsp;NDFrame<br>
-and&nbsp;return&nbsp;a&nbsp;NDFrame&nbsp;with&nbsp;the&nbsp;transformed&nbsp;values`<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;apply&nbsp;to&nbsp;column<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-transformed&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(10,&nbsp;3),&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.date_range('1/1/2000',&nbsp;periods=10))<br>
-df.iloc[3:7]&nbsp;=&nbsp;np.nan<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-transform">transform</a>(lambda&nbsp;x:&nbsp;(x&nbsp;-&nbsp;x.<a href="#State-mean">mean</a>())&nbsp;/&nbsp;x.<a href="#State-std">std</a>())<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-2000-01-01&nbsp;&nbsp;0.579457&nbsp;&nbsp;1.236184&nbsp;&nbsp;0.123424<br>
-2000-01-02&nbsp;&nbsp;0.370357&nbsp;-0.605875&nbsp;-1.231325<br>
-2000-01-03&nbsp;&nbsp;1.455756&nbsp;-0.277446&nbsp;&nbsp;0.288967<br>
-2000-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-08&nbsp;-0.498658&nbsp;&nbsp;1.274522&nbsp;&nbsp;1.642524<br>
-2000-01-09&nbsp;-0.540524&nbsp;-1.012676&nbsp;-0.828968<br>
-2000-01-10&nbsp;-1.366388&nbsp;-0.614710&nbsp;&nbsp;0.005378<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.NDFrame.aggregate<br>
-pandas.NDFrame.apply</tt></dd></dl>
-
-<dl><dt><a name="State-truediv"><strong>truediv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="State-unique"><strong>unique</strong></a>(self)</dt><dd><tt>Return&nbsp;unique&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.&nbsp;Uniques&nbsp;are&nbsp;returned&nbsp;in&nbsp;order<br>
-of&nbsp;appearance,&nbsp;this&nbsp;does&nbsp;NOT&nbsp;sort.&nbsp;Hash&nbsp;table-based&nbsp;unique.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-values&nbsp;:&nbsp;1d&nbsp;array-like<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-unique&nbsp;values.<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;an&nbsp;Index,&nbsp;the&nbsp;return&nbsp;is&nbsp;an&nbsp;Index<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;a&nbsp;Categorical&nbsp;dtype,&nbsp;the&nbsp;return&nbsp;is&nbsp;a&nbsp;Categorical<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>/<a href="numpy.html#ndarray">ndarray</a>,&nbsp;the&nbsp;return&nbsp;will&nbsp;be&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-unique<br>
-Index.unique<br>
-<a href="pandas.core.series.html#Series">Series</a>.unique</tt></dd></dl>
-
-<dl><dt><a name="State-unstack"><strong>unstack</strong></a>(self, level=-1, fill_value=None)</dt><dd><tt>Unstack,&nbsp;a.k.a.&nbsp;pivot,&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;MultiIndex&nbsp;to&nbsp;produce&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-The&nbsp;level&nbsp;involved&nbsp;will&nbsp;automatically&nbsp;get&nbsp;sorted.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;string,&nbsp;or&nbsp;list&nbsp;of&nbsp;these,&nbsp;default&nbsp;last&nbsp;level<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level(s)&nbsp;to&nbsp;unstack,&nbsp;can&nbsp;pass&nbsp;level&nbsp;name<br>
-fill_value&nbsp;:&nbsp;replace&nbsp;NaN&nbsp;with&nbsp;this&nbsp;value&nbsp;if&nbsp;the&nbsp;unstack&nbsp;produces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.18.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;4],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.MultiIndex.from_product([['one',&nbsp;'two'],&nbsp;['a',&nbsp;'b']]))<br>
-&gt;&gt;&gt;&nbsp;s<br>
-one&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-two&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-unstack">unstack</a>(level=-1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b<br>
-one&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-two&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-unstack">unstack</a>(level=0)<br>
-&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;two<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-unstacked&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="State-update"><strong>update</strong></a>(self, other)</dt><dd><tt>Modify&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place&nbsp;using&nbsp;non-NA&nbsp;values&nbsp;from&nbsp;passed<br>
-<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Aligns&nbsp;on&nbsp;index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><strong>valid</strong> <em>lambda</em> self, inplace=False, **kwargs</dt></dl>
-
-<dl><dt><a name="State-var"><strong>var</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;variance&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-var&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="State-view"><strong>view</strong></a>(self, dtype=None)</dt></dl>
-
-<hr>
-Class methods inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><a name="State-from_array"><strong>from_array</strong></a>(arr, index=None, name=None, dtype=None, copy=False, fastpath=False)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt></dl>
-
-<dl><dt><a name="State-from_csv"><strong>from_csv</strong></a>(path, sep=',', parse_dates=True, header=None, index_col=0, encoding=None, infer_datetime_format=False)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Read&nbsp;CSV&nbsp;file&nbsp;(DISCOURAGED,&nbsp;please&nbsp;use&nbsp;:func:`pandas.read_csv`<br>
-instead).<br>
-&nbsp;<br>
-It&nbsp;is&nbsp;preferable&nbsp;to&nbsp;use&nbsp;the&nbsp;more&nbsp;powerful&nbsp;:func:`pandas.read_csv`<br>
-for&nbsp;most&nbsp;general&nbsp;purposes,&nbsp;but&nbsp;``from_csv``&nbsp;makes&nbsp;for&nbsp;an&nbsp;easy<br>
-roundtrip&nbsp;to&nbsp;and&nbsp;from&nbsp;a&nbsp;file&nbsp;(the&nbsp;exact&nbsp;counterpart&nbsp;of<br>
-``to_csv``),&nbsp;especially&nbsp;with&nbsp;a&nbsp;time&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;only&nbsp;differs&nbsp;from&nbsp;:func:`pandas.read_csv`&nbsp;in&nbsp;some&nbsp;defaults:<br>
-&nbsp;<br>
--&nbsp;`index_col`&nbsp;is&nbsp;``0``&nbsp;instead&nbsp;of&nbsp;``None``&nbsp;(take&nbsp;first&nbsp;column&nbsp;as&nbsp;index<br>
-&nbsp;&nbsp;by&nbsp;default)<br>
--&nbsp;`header`&nbsp;is&nbsp;``None``&nbsp;instead&nbsp;of&nbsp;``0``&nbsp;(the&nbsp;first&nbsp;row&nbsp;is&nbsp;not&nbsp;used&nbsp;as<br>
-&nbsp;&nbsp;the&nbsp;column&nbsp;names)<br>
--&nbsp;`parse_dates`&nbsp;is&nbsp;``True``&nbsp;instead&nbsp;of&nbsp;``False``&nbsp;(try&nbsp;parsing&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;as&nbsp;datetime&nbsp;by&nbsp;default)<br>
-&nbsp;<br>
-With&nbsp;:func:`pandas.read_csv`,&nbsp;the&nbsp;option&nbsp;``squeeze=True``&nbsp;can&nbsp;be&nbsp;used<br>
-to&nbsp;return&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;like&nbsp;``from_csv``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;file&nbsp;path&nbsp;or&nbsp;file&nbsp;handle&nbsp;/&nbsp;StringIO<br>
-sep&nbsp;:&nbsp;string,&nbsp;default&nbsp;','<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter<br>
-parse_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Parse&nbsp;dates.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-header&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Row&nbsp;to&nbsp;use&nbsp;as&nbsp;header&nbsp;(skip&nbsp;prior&nbsp;rows)<br>
-index_col&nbsp;:&nbsp;int&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;to&nbsp;use&nbsp;for&nbsp;index.&nbsp;If&nbsp;a&nbsp;sequence&nbsp;is&nbsp;given,&nbsp;a&nbsp;MultiIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;used.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-encoding&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;if&nbsp;the&nbsp;contents&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;non-ascii,&nbsp;for&nbsp;python&nbsp;versions&nbsp;prior&nbsp;to&nbsp;3<br>
-infer_datetime_format:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;and&nbsp;`parse_dates`&nbsp;is&nbsp;True&nbsp;for&nbsp;a&nbsp;column,&nbsp;try&nbsp;to&nbsp;infer&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;datetime&nbsp;format&nbsp;based&nbsp;on&nbsp;the&nbsp;first&nbsp;datetime&nbsp;string.&nbsp;If&nbsp;the&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;be&nbsp;inferred,&nbsp;there&nbsp;often&nbsp;will&nbsp;be&nbsp;a&nbsp;large&nbsp;parsing&nbsp;speed-up.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.read_csv<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><strong>asobject</strong></dt>
-<dd><tt>return&nbsp;object&nbsp;Series&nbsp;which&nbsp;contains&nbsp;boxed&nbsp;values<br>
-&nbsp;<br>
-*this&nbsp;is&nbsp;an&nbsp;internal&nbsp;non-public&nbsp;method*</tt></dd>
-</dl>
-<dl><dt><strong>axes</strong></dt>
-<dd><tt>Return&nbsp;a&nbsp;list&nbsp;of&nbsp;the&nbsp;row&nbsp;axis&nbsp;labels</tt></dd>
-</dl>
-<dl><dt><strong>dtype</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;dtype&nbsp;object&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>dtypes</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;dtype&nbsp;object&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>ftype</strong></dt>
-<dd><tt>return&nbsp;if&nbsp;the&nbsp;data&nbsp;is&nbsp;sparse|dense</tt></dd>
-</dl>
-<dl><dt><strong>ftypes</strong></dt>
-<dd><tt>return&nbsp;if&nbsp;the&nbsp;data&nbsp;is&nbsp;sparse|dense</tt></dd>
-</dl>
-<dl><dt><strong>imag</strong></dt>
-</dl>
-<dl><dt><strong>index</strong></dt>
-</dl>
-<dl><dt><strong>name</strong></dt>
-</dl>
-<dl><dt><strong>real</strong></dt>
-</dl>
-<dl><dt><strong>values</strong></dt>
-<dd><tt>Return&nbsp;Series&nbsp;as&nbsp;ndarray&nbsp;or&nbsp;ndarray-like<br>
-depending&nbsp;on&nbsp;the&nbsp;dtype<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-arr&nbsp;:&nbsp;numpy.ndarray&nbsp;or&nbsp;ndarray-like<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;pd.Series([1,&nbsp;2,&nbsp;3]).values<br>
-array([1,&nbsp;2,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(list('aabc')).values<br>
-array(['a',&nbsp;'a',&nbsp;'b',&nbsp;'c'],&nbsp;dtype=object)<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(list('aabc')).astype('category').values<br>
-[a,&nbsp;a,&nbsp;b,&nbsp;c]<br>
-Categories&nbsp;(3,&nbsp;object):&nbsp;[a,&nbsp;b,&nbsp;c]<br>
-&nbsp;<br>
-Timezone&nbsp;aware&nbsp;datetime&nbsp;data&nbsp;is&nbsp;converted&nbsp;to&nbsp;UTC:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(pd.date_range('20130101',&nbsp;periods=3,<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;tz='US/Eastern')).values<br>
-array(['2013-01-01T05:00:00.000000000',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'2013-01-02T05:00:00.000000000',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'2013-01-03T05:00:00.000000000'],&nbsp;dtype='datetime64[ns]')</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><strong>cat</strong> = &lt;class 'pandas.core.categorical.CategoricalAccessor'&gt;<dd><tt>Accessor&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;categorical&nbsp;properties&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values.<br>
-&nbsp;<br>
-Be&nbsp;aware&nbsp;that&nbsp;assigning&nbsp;to&nbsp;`categories`&nbsp;is&nbsp;a&nbsp;inplace&nbsp;operation,&nbsp;while&nbsp;all<br>
-methods&nbsp;return&nbsp;new&nbsp;categorical&nbsp;data&nbsp;per&nbsp;default&nbsp;(but&nbsp;can&nbsp;be&nbsp;called&nbsp;with<br>
-`inplace=True`).<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.cat.categories<br>
-&gt;&gt;&gt;&nbsp;s.cat.categories&nbsp;=&nbsp;list('abc')<br>
-&gt;&gt;&gt;&nbsp;s.cat.rename_categories(list('cab'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.reorder_categories(list('cab'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.add_categories(['d','e'])<br>
-&gt;&gt;&gt;&nbsp;s.cat.remove_categories(['d'])<br>
-&gt;&gt;&gt;&nbsp;s.cat.remove_unused_categories()<br>
-&gt;&gt;&gt;&nbsp;s.cat.set_categories(list('abcde'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.as_ordered()<br>
-&gt;&gt;&gt;&nbsp;s.cat.as_unordered()</tt></dl>
-
-<dl><dt><strong>plot</strong> = &lt;class 'pandas.plotting._core.SeriesPlotMethods'&gt;<dd><tt><a href="pandas.core.series.html#Series">Series</a>&nbsp;plotting&nbsp;accessor&nbsp;and&nbsp;method<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.plot.line()<br>
-&gt;&gt;&gt;&nbsp;s.plot.bar()<br>
-&gt;&gt;&gt;&nbsp;s.plot.<a href="#State-hist">hist</a>()<br>
-&nbsp;<br>
-Plotting&nbsp;methods&nbsp;can&nbsp;also&nbsp;be&nbsp;accessed&nbsp;by&nbsp;calling&nbsp;the&nbsp;accessor&nbsp;as&nbsp;a&nbsp;method<br>
-with&nbsp;the&nbsp;``kind``&nbsp;argument:<br>
-``s.<a href="#State-plot">plot</a>(kind='line')``&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;``s.plot.line()``</tt></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><a name="State-factorize"><strong>factorize</strong></a>(self, sort=False, na_sentinel=-1)</dt><dd><tt>Encode&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;as&nbsp;an&nbsp;enumerated&nbsp;type&nbsp;or&nbsp;categorical&nbsp;variable<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;by&nbsp;values<br>
-na_sentinel:&nbsp;int,&nbsp;default&nbsp;-1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;mark&nbsp;"not&nbsp;found"<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-labels&nbsp;:&nbsp;the&nbsp;indexer&nbsp;to&nbsp;the&nbsp;original&nbsp;array<br>
-uniques&nbsp;:&nbsp;the&nbsp;unique&nbsp;Index</tt></dd></dl>
-
-<dl><dt><a name="State-item"><strong>item</strong></a>(self)</dt><dd><tt>return&nbsp;the&nbsp;first&nbsp;element&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data&nbsp;as&nbsp;a&nbsp;python<br>
-scalar</tt></dd></dl>
-
-<dl><dt><a name="State-nunique"><strong>nunique</strong></a>(self, dropna=True)</dt><dd><tt>Return&nbsp;number&nbsp;of&nbsp;unique&nbsp;elements&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Excludes&nbsp;NA&nbsp;values&nbsp;by&nbsp;default.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Don't&nbsp;include&nbsp;NaN&nbsp;in&nbsp;the&nbsp;count.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-nunique&nbsp;:&nbsp;int</tt></dd></dl>
-
-<dl><dt><a name="State-transpose"><strong>transpose</strong></a>(self, *args, **kwargs)</dt><dd><tt>return&nbsp;the&nbsp;transpose,&nbsp;which&nbsp;is&nbsp;by&nbsp;definition&nbsp;self</tt></dd></dl>
-
-<dl><dt><a name="State-value_counts"><strong>value_counts</strong></a>(self, normalize=False, sort=True, ascending=False, bins=None, dropna=True)</dt><dd><tt>Returns&nbsp;<a href="builtins.html#object">object</a>&nbsp;containing&nbsp;counts&nbsp;of&nbsp;unique&nbsp;values.<br>
-&nbsp;<br>
-The&nbsp;resulting&nbsp;<a href="builtins.html#object">object</a>&nbsp;will&nbsp;be&nbsp;in&nbsp;descending&nbsp;order&nbsp;so&nbsp;that&nbsp;the<br>
-first&nbsp;element&nbsp;is&nbsp;the&nbsp;most&nbsp;frequently-occurring&nbsp;element.<br>
-Excludes&nbsp;NA&nbsp;values&nbsp;by&nbsp;default.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-normalize&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;then&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;returned&nbsp;will&nbsp;contain&nbsp;the&nbsp;relative<br>
-&nbsp;&nbsp;&nbsp;&nbsp;frequencies&nbsp;of&nbsp;the&nbsp;unique&nbsp;values.<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;by&nbsp;values<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;in&nbsp;ascending&nbsp;order<br>
-bins&nbsp;:&nbsp;integer,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Rather&nbsp;than&nbsp;count&nbsp;values,&nbsp;group&nbsp;them&nbsp;into&nbsp;half-open&nbsp;bins,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;convenience&nbsp;for&nbsp;pd.cut,&nbsp;only&nbsp;works&nbsp;with&nbsp;numeric&nbsp;data<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Don't&nbsp;include&nbsp;counts&nbsp;of&nbsp;NaN.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-counts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><strong>__dict__</strong></dt>
-<dd><tt>dictionary&nbsp;for&nbsp;instance&nbsp;variables&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>__weakref__</strong></dt>
-<dd><tt>list&nbsp;of&nbsp;weak&nbsp;references&nbsp;to&nbsp;the&nbsp;object&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>base</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;base&nbsp;object&nbsp;if&nbsp;the&nbsp;memory&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data&nbsp;is<br>
-shared</tt></dd>
-</dl>
-<dl><dt><strong>data</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;data&nbsp;pointer&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>empty</strong></dt>
-</dl>
-<dl><dt><strong>flags</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;ndarray.flags&nbsp;for&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>hasnans</strong></dt>
-</dl>
-<dl><dt><strong>is_monotonic</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_increasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_monotonic_decreasing</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_decreasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic_decreasing&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_monotonic_increasing</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_increasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_unique</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are&nbsp;unique<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_unique&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>itemsize</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;size&nbsp;of&nbsp;the&nbsp;dtype&nbsp;of&nbsp;the&nbsp;item&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>nbytes</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;bytes&nbsp;in&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>ndim</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;dimensions&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data,<br>
-by&nbsp;definition&nbsp;1</tt></dd>
-</dl>
-<dl><dt><strong>shape</strong></dt>
-<dd><tt>return&nbsp;a&nbsp;tuple&nbsp;of&nbsp;the&nbsp;shape&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>size</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;elements&nbsp;in&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>strides</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;strides&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><strong>__array_priority__</strong> = 1000</dl>
-
-<hr>
-Data and other attributes inherited from <a href="pandas.core.strings.html#StringAccessorMixin">pandas.core.strings.StringAccessorMixin</a>:<br>
-<dl><dt><strong>str</strong> = &lt;class 'pandas.core.strings.StringMethods'&gt;<dd><tt>Vectorized&nbsp;string&nbsp;functions&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;and&nbsp;Index.&nbsp;NAs&nbsp;stay&nbsp;NA&nbsp;unless<br>
-handled&nbsp;otherwise&nbsp;by&nbsp;a&nbsp;particular&nbsp;method.&nbsp;Patterned&nbsp;after&nbsp;Python's&nbsp;string<br>
-methods,&nbsp;with&nbsp;some&nbsp;inspiration&nbsp;from&nbsp;R's&nbsp;stringr&nbsp;package.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.str.split('_')<br>
-&gt;&gt;&gt;&nbsp;s.str.<a href="#State-replace">replace</a>('_',&nbsp;'')</tt></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><a name="State-__abs__"><strong>__abs__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="State-__bool__"><strong>__bool__</strong></a> = __nonzero__(self)</dt></dl>
-
-<dl><dt><a name="State-__contains__"><strong>__contains__</strong></a>(self, key)</dt><dd><tt>True&nbsp;if&nbsp;the&nbsp;key&nbsp;is&nbsp;in&nbsp;the&nbsp;info&nbsp;axis</tt></dd></dl>
-
-<dl><dt><a name="State-__copy__"><strong>__copy__</strong></a>(self, deep=True)</dt></dl>
-
-<dl><dt><a name="State-__deepcopy__"><strong>__deepcopy__</strong></a>(self, memo=None)</dt></dl>
-
-<dl><dt><a name="State-__delitem__"><strong>__delitem__</strong></a>(self, key)</dt><dd><tt>Delete&nbsp;item</tt></dd></dl>
-
-<dl><dt><a name="State-__finalize__"><strong>__finalize__</strong></a>(self, other, method=None, **kwargs)</dt><dd><tt>Propagate&nbsp;metadata&nbsp;from&nbsp;other&nbsp;to&nbsp;self.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;which&nbsp;to&nbsp;get&nbsp;the&nbsp;attributes&nbsp;that&nbsp;we&nbsp;are&nbsp;going<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;propagate<br>
-method&nbsp;:&nbsp;optional,&nbsp;a&nbsp;passed&nbsp;method&nbsp;name&nbsp;;&nbsp;possibly&nbsp;to&nbsp;take&nbsp;different<br>
-&nbsp;&nbsp;&nbsp;&nbsp;types&nbsp;of&nbsp;propagation&nbsp;actions&nbsp;based&nbsp;on&nbsp;this</tt></dd></dl>
-
-<dl><dt><a name="State-__getattr__"><strong>__getattr__</strong></a>(self, name)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;looking&nbsp;up&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="State-__getstate__"><strong>__getstate__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="State-__hash__"><strong>__hash__</strong></a>(self)</dt><dd><tt>Return&nbsp;hash(self).</tt></dd></dl>
-
-<dl><dt><a name="State-__invert__"><strong>__invert__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="State-__neg__"><strong>__neg__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="State-__nonzero__"><strong>__nonzero__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="State-__round__"><strong>__round__</strong></a>(self, decimals=0)</dt></dl>
-
-<dl><dt><a name="State-__setattr__"><strong>__setattr__</strong></a>(self, name, value)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;setting&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="State-__setstate__"><strong>__setstate__</strong></a>(self, state)</dt></dl>
-
-<dl><dt><a name="State-abs"><strong>abs</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;absolute&nbsp;value&nbsp;taken--only&nbsp;applicable&nbsp;to&nbsp;objects<br>
-that&nbsp;are&nbsp;all&nbsp;numeric.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-abs:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="State-add_prefix"><strong>add_prefix</strong></a>(self, prefix)</dt><dd><tt>Concatenate&nbsp;prefix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-prefix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_prefix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="State-add_suffix"><strong>add_suffix</strong></a>(self, suffix)</dt><dd><tt>Concatenate&nbsp;suffix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-suffix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_suffix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="State-as_blocks"><strong>as_blocks</strong></a>(self, copy=True)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types&nbsp;that&nbsp;each&nbsp;has<br>
-a&nbsp;homogeneous&nbsp;dtype.<br>
-&nbsp;<br>
-NOTE:&nbsp;the&nbsp;dtypes&nbsp;of&nbsp;the&nbsp;blocks&nbsp;WILL&nbsp;BE&nbsp;PRESERVED&nbsp;HERE&nbsp;(unlike&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;as_matrix)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types</tt></dd></dl>
-
-<dl><dt><a name="State-as_matrix"><strong>as_matrix</strong></a>(self, columns=None)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;its&nbsp;Numpy-array&nbsp;representation.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-columns:&nbsp;list,&nbsp;optional,&nbsp;default:None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;None,&nbsp;return&nbsp;all&nbsp;columns,&nbsp;otherwise,&nbsp;returns&nbsp;specified&nbsp;columns.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;caller&nbsp;is&nbsp;heterogeneous&nbsp;and&nbsp;contains&nbsp;booleans&nbsp;or&nbsp;objects,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;of&nbsp;dtype=<a href="builtins.html#object">object</a>.&nbsp;See&nbsp;Notes.<br>
-&nbsp;<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Return&nbsp;is&nbsp;NOT&nbsp;a&nbsp;Numpy-matrix,&nbsp;rather,&nbsp;a&nbsp;Numpy-array.<br>
-&nbsp;<br>
-The&nbsp;dtype&nbsp;will&nbsp;be&nbsp;a&nbsp;lower-common-denominator&nbsp;dtype&nbsp;(implicit<br>
-upcasting);&nbsp;that&nbsp;is&nbsp;to&nbsp;say&nbsp;if&nbsp;the&nbsp;dtypes&nbsp;(even&nbsp;of&nbsp;numeric&nbsp;types)<br>
-are&nbsp;mixed,&nbsp;the&nbsp;one&nbsp;that&nbsp;accommodates&nbsp;all&nbsp;will&nbsp;be&nbsp;chosen.&nbsp;Use&nbsp;this<br>
-with&nbsp;care&nbsp;if&nbsp;you&nbsp;are&nbsp;not&nbsp;dealing&nbsp;with&nbsp;the&nbsp;blocks.<br>
-&nbsp;<br>
-e.g.&nbsp;If&nbsp;the&nbsp;dtypes&nbsp;are&nbsp;float16&nbsp;and&nbsp;float32,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcast&nbsp;to<br>
-float32.&nbsp;&nbsp;If&nbsp;dtypes&nbsp;are&nbsp;int32&nbsp;and&nbsp;uint8,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcase&nbsp;to<br>
-int32.&nbsp;By&nbsp;numpy.find_common_type&nbsp;convention,&nbsp;mixing&nbsp;int64&nbsp;and&nbsp;uint64<br>
-will&nbsp;result&nbsp;in&nbsp;a&nbsp;flot64&nbsp;dtype.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;is&nbsp;provided&nbsp;for&nbsp;backwards&nbsp;compatibility.&nbsp;Generally,<br>
-it&nbsp;is&nbsp;recommended&nbsp;to&nbsp;use&nbsp;'.values'.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.values</tt></dd></dl>
-
-<dl><dt><a name="State-asfreq"><strong>asfreq</strong></a>(self, freq, method=None, how=None, normalize=False, fill_value=None)</dt><dd><tt>Convert&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;specified&nbsp;frequency.<br>
-&nbsp;<br>
-Optionally&nbsp;provide&nbsp;filling&nbsp;method&nbsp;to&nbsp;pad/backfill&nbsp;missing&nbsp;values.<br>
-&nbsp;<br>
-Returns&nbsp;the&nbsp;original&nbsp;data&nbsp;conformed&nbsp;to&nbsp;a&nbsp;new&nbsp;index&nbsp;with&nbsp;the&nbsp;specified<br>
-frequency.&nbsp;``resample``&nbsp;is&nbsp;more&nbsp;appropriate&nbsp;if&nbsp;an&nbsp;operation,&nbsp;such&nbsp;as<br>
-summarization,&nbsp;is&nbsp;necessary&nbsp;to&nbsp;represent&nbsp;the&nbsp;data&nbsp;at&nbsp;the&nbsp;new&nbsp;frequency.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;or&nbsp;string<br>
-method&nbsp;:&nbsp;{'backfill'/'bfill',&nbsp;'pad'/'ffill'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(note&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present):<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'pad'&nbsp;/&nbsp;'ffill':&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'backfill'&nbsp;/&nbsp;'bfill':&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill<br>
-how&nbsp;:&nbsp;{'start',&nbsp;'end'},&nbsp;default&nbsp;end<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;PeriodIndex&nbsp;only,&nbsp;see&nbsp;PeriodIndex.asfreq<br>
-normalize&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;reset&nbsp;output&nbsp;index&nbsp;to&nbsp;midnight<br>
-fill_value:&nbsp;scalar,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values,&nbsp;applied&nbsp;during&nbsp;upsampling&nbsp;(note<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;4&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=4,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0.0,&nbsp;None,&nbsp;2.0,&nbsp;3.0],&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'s':series})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-asfreq">asfreq</a>(freq='30S')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``fill&nbsp;value``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-asfreq">asfreq</a>(freq='30S',&nbsp;fill_value=9.0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``method``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-asfreq">asfreq</a>(freq='30S',&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.</tt></dd></dl>
-
-<dl><dt><a name="State-asof"><strong>asof</strong></a>(self, where, subset=None)</dt><dd><tt>The&nbsp;last&nbsp;row&nbsp;without&nbsp;any&nbsp;NaN&nbsp;is&nbsp;taken&nbsp;(or&nbsp;the&nbsp;last&nbsp;row&nbsp;without<br>
-NaN&nbsp;considering&nbsp;only&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;in&nbsp;the&nbsp;case&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0&nbsp;For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-If&nbsp;there&nbsp;is&nbsp;no&nbsp;good&nbsp;value,&nbsp;NaN&nbsp;is&nbsp;returned&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;NaN&nbsp;values&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-where&nbsp;:&nbsp;date&nbsp;or&nbsp;array&nbsp;of&nbsp;dates<br>
-subset&nbsp;:&nbsp;string&nbsp;or&nbsp;list&nbsp;of&nbsp;strings,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None&nbsp;use&nbsp;these&nbsp;columns&nbsp;for&nbsp;NaN&nbsp;propagation<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Dates&nbsp;are&nbsp;assumed&nbsp;to&nbsp;be&nbsp;sorted<br>
-Raises&nbsp;if&nbsp;this&nbsp;is&nbsp;not&nbsp;the&nbsp;case<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-where&nbsp;is&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;&nbsp;-&nbsp;value&nbsp;or&nbsp;NaN&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;-&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-where&nbsp;is&nbsp;Index:&nbsp;same&nbsp;shape&nbsp;<a href="builtins.html#object">object</a>&nbsp;as&nbsp;input<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-merge_asof</tt></dd></dl>
-
-<dl><dt><a name="State-astype"><strong>astype</strong></a>(self, dtype, copy=True, errors='raise', **kwargs)</dt><dd><tt>Cast&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;numpy.dtype<br>
-Return&nbsp;a&nbsp;copy&nbsp;when&nbsp;copy&nbsp;=&nbsp;True&nbsp;(be&nbsp;really&nbsp;careful&nbsp;with&nbsp;this!)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dtype&nbsp;:&nbsp;data&nbsp;type,&nbsp;or&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;-&gt;&nbsp;data&nbsp;type<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;entire&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;same&nbsp;type.&nbsp;Alternatively,&nbsp;use&nbsp;{col:&nbsp;dtype,&nbsp;...},&nbsp;where&nbsp;col&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;label&nbsp;and&nbsp;dtype&nbsp;is&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;one<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;more&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;columns&nbsp;to&nbsp;column-specific&nbsp;types.<br>
-errors&nbsp;:&nbsp;{'raise',&nbsp;'ignore'},&nbsp;default&nbsp;'raise'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Control&nbsp;raising&nbsp;of&nbsp;exceptions&nbsp;on&nbsp;invalid&nbsp;data&nbsp;for&nbsp;provided&nbsp;dtype.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``raise``&nbsp;:&nbsp;allow&nbsp;exceptions&nbsp;to&nbsp;be&nbsp;raised<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``ignore``&nbsp;:&nbsp;suppress&nbsp;exceptions.&nbsp;On&nbsp;error&nbsp;return&nbsp;original&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-raise_on_error&nbsp;:&nbsp;DEPRECATED&nbsp;use&nbsp;``errors``&nbsp;instead<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;constructor<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-casted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="State-at_time"><strong>at_time</strong></a>(self, time, asof=False)</dt><dd><tt>Select&nbsp;values&nbsp;at&nbsp;particular&nbsp;time&nbsp;of&nbsp;day&nbsp;(e.g.&nbsp;9:30AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_at_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="State-between_time"><strong>between_time</strong></a>(self, start_time, end_time, include_start=True, include_end=True)</dt><dd><tt>Select&nbsp;values&nbsp;between&nbsp;particular&nbsp;times&nbsp;of&nbsp;the&nbsp;day&nbsp;(e.g.,&nbsp;9:00-9:30&nbsp;AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-start_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-end_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-include_start&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-include_end&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_between_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="State-bfill"><strong>bfill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#State-fillna">fillna</a>(method='bfill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="State-bool"><strong>bool</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;bool&nbsp;of&nbsp;a&nbsp;single&nbsp;element&nbsp;PandasObject.<br>
-&nbsp;<br>
-This&nbsp;must&nbsp;be&nbsp;a&nbsp;boolean&nbsp;scalar&nbsp;value,&nbsp;either&nbsp;True&nbsp;or&nbsp;False.&nbsp;&nbsp;Raise&nbsp;a<br>
-ValueError&nbsp;if&nbsp;the&nbsp;PandasObject&nbsp;does&nbsp;not&nbsp;have&nbsp;exactly&nbsp;1&nbsp;element,&nbsp;or&nbsp;that<br>
-element&nbsp;is&nbsp;not&nbsp;boolean</tt></dd></dl>
-
-<dl><dt><a name="State-clip"><strong>clip</strong></a>(self, lower=None, upper=None, axis=None, *args, **kwargs)</dt><dd><tt>Trim&nbsp;values&nbsp;at&nbsp;input&nbsp;threshold(s).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-lower&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-upper&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;lower&nbsp;and&nbsp;upper&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.256177<br>
-1&nbsp;-1.367855&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.176076<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;1.261967&nbsp;&nbsp;0.570967<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-clip">clip</a>(-1.0,&nbsp;0.5)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.000000<br>
-1&nbsp;-1.000000&nbsp;&nbsp;0.500000<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.000000<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;0.500000&nbsp;&nbsp;0.500000<br>
-&gt;&gt;&gt;&nbsp;t<br>
-0&nbsp;&nbsp;&nbsp;-0.3<br>
-1&nbsp;&nbsp;&nbsp;-0.2<br>
-2&nbsp;&nbsp;&nbsp;-0.1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;0.1<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-clip">clip</a>(t,&nbsp;t&nbsp;+&nbsp;1,&nbsp;axis=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-0.300000<br>
-1&nbsp;-0.200000&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-0.100000<br>
-3&nbsp;&nbsp;0.230930&nbsp;&nbsp;0.000000<br>
-4&nbsp;&nbsp;1.100000&nbsp;&nbsp;0.570967</tt></dd></dl>
-
-<dl><dt><a name="State-clip_lower"><strong>clip_lower</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;the&nbsp;input&nbsp;with&nbsp;values&nbsp;below&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="State-clip_upper"><strong>clip_upper</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;input&nbsp;with&nbsp;values&nbsp;above&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="State-consolidate"><strong>consolidate</strong></a>(self, inplace=False)</dt><dd><tt>DEPRECATED:&nbsp;consolidate&nbsp;will&nbsp;be&nbsp;an&nbsp;internal&nbsp;implementation&nbsp;only.</tt></dd></dl>
-
-<dl><dt><a name="State-convert_objects"><strong>convert_objects</strong></a>(self, convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True)</dt><dd><tt>Deprecated.<br>
-&nbsp;<br>
-Attempt&nbsp;to&nbsp;infer&nbsp;better&nbsp;dtype&nbsp;for&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-convert_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;date&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-convert_numeric&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;attempt&nbsp;to&nbsp;coerce&nbsp;to&nbsp;numbers&nbsp;(including&nbsp;strings),&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaN.<br>
-convert_timedeltas&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;timedelta&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;return&nbsp;a&nbsp;copy&nbsp;even&nbsp;if&nbsp;no&nbsp;copy&nbsp;is&nbsp;necessary&nbsp;(e.g.&nbsp;no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion&nbsp;was&nbsp;done).&nbsp;Note:&nbsp;This&nbsp;is&nbsp;meant&nbsp;for&nbsp;internal&nbsp;use,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;not&nbsp;be&nbsp;confused&nbsp;with&nbsp;inplace.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.to_datetime&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;datetime.<br>
-pandas.to_timedelta&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;timedelta.<br>
-pandas.to_numeric&nbsp;:&nbsp;Return&nbsp;a&nbsp;fixed&nbsp;frequency&nbsp;timedelta&nbsp;index,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;day&nbsp;as&nbsp;the&nbsp;default.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;same&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="State-copy"><strong>copy</strong></a>(self, deep=True)</dt><dd><tt>Make&nbsp;a&nbsp;copy&nbsp;of&nbsp;this&nbsp;objects&nbsp;data.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-deep&nbsp;:&nbsp;boolean&nbsp;or&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;a&nbsp;deep&nbsp;copy,&nbsp;including&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;data&nbsp;and&nbsp;the&nbsp;indices.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;With&nbsp;``deep=False``&nbsp;neither&nbsp;the&nbsp;indices&nbsp;or&nbsp;the&nbsp;data&nbsp;are&nbsp;copied.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;that&nbsp;when&nbsp;``deep=True``&nbsp;data&nbsp;is&nbsp;copied,&nbsp;actual&nbsp;python&nbsp;objects<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;not&nbsp;be&nbsp;copied&nbsp;recursively,&nbsp;only&nbsp;the&nbsp;reference&nbsp;to&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;is&nbsp;in&nbsp;contrast&nbsp;to&nbsp;``copy.deepcopy``&nbsp;in&nbsp;the&nbsp;Standard&nbsp;Library,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;recursively&nbsp;copies&nbsp;<a href="builtins.html#object">object</a>&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-copy&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="State-describe"><strong>describe</strong></a>(self, percentiles=None, include=None, exclude=None)</dt><dd><tt>Generates&nbsp;descriptive&nbsp;statistics&nbsp;that&nbsp;summarize&nbsp;the&nbsp;central&nbsp;tendency,<br>
-dispersion&nbsp;and&nbsp;shape&nbsp;of&nbsp;a&nbsp;dataset's&nbsp;distribution,&nbsp;excluding<br>
-``NaN``&nbsp;values.<br>
-&nbsp;<br>
-Analyzes&nbsp;both&nbsp;numeric&nbsp;and&nbsp;<a href="builtins.html#object">object</a>&nbsp;series,&nbsp;as&nbsp;well<br>
-as&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;column&nbsp;sets&nbsp;of&nbsp;mixed&nbsp;data&nbsp;types.&nbsp;The&nbsp;output<br>
-will&nbsp;vary&nbsp;depending&nbsp;on&nbsp;what&nbsp;is&nbsp;provided.&nbsp;Refer&nbsp;to&nbsp;the&nbsp;notes<br>
-below&nbsp;for&nbsp;more&nbsp;detail.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-percentiles&nbsp;:&nbsp;list-like&nbsp;of&nbsp;numbers,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;percentiles&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;output.&nbsp;All&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fall&nbsp;between&nbsp;0&nbsp;and&nbsp;1.&nbsp;The&nbsp;default&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``[.25,&nbsp;.5,&nbsp;.75]``,&nbsp;which&nbsp;returns&nbsp;the&nbsp;25th,&nbsp;50th,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;75th&nbsp;percentiles.<br>
-include&nbsp;:&nbsp;'all',&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;white&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'all'&nbsp;:&nbsp;All&nbsp;columns&nbsp;of&nbsp;the&nbsp;input&nbsp;will&nbsp;be&nbsp;included&nbsp;in&nbsp;the&nbsp;output.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Limits&nbsp;the&nbsp;results&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;provided&nbsp;data&nbsp;types.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;limit&nbsp;the&nbsp;result&nbsp;to&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;limit&nbsp;it&nbsp;instead&nbsp;to&nbsp;categorical<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;objects&nbsp;submit&nbsp;the&nbsp;``numpy.<a href="builtins.html#object">object</a>``&nbsp;data&nbsp;type.&nbsp;Strings<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#State-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;include&nbsp;all&nbsp;numeric&nbsp;columns.<br>
-exclude&nbsp;:&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;black&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;omit&nbsp;from&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Excludes&nbsp;the&nbsp;provided&nbsp;data&nbsp;types<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;the&nbsp;result.&nbsp;To&nbsp;select&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;select&nbsp;categorical&nbsp;objects&nbsp;submit&nbsp;the&nbsp;data<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;type&nbsp;``numpy.<a href="builtins.html#object">object</a>``.&nbsp;Strings&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#State-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;exclude&nbsp;nothing.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-summary:&nbsp;&nbsp;<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;summary&nbsp;statistics<br>
-&nbsp;<br>
-Notes<br>
------<br>
-For&nbsp;numeric&nbsp;data,&nbsp;the&nbsp;result's&nbsp;index&nbsp;will&nbsp;include&nbsp;``count``,<br>
-``mean``,&nbsp;``std``,&nbsp;``min``,&nbsp;``max``&nbsp;as&nbsp;well&nbsp;as&nbsp;lower,&nbsp;``50``&nbsp;and<br>
-upper&nbsp;percentiles.&nbsp;By&nbsp;default&nbsp;the&nbsp;lower&nbsp;percentile&nbsp;is&nbsp;``25``&nbsp;and&nbsp;the<br>
-upper&nbsp;percentile&nbsp;is&nbsp;``75``.&nbsp;The&nbsp;``50``&nbsp;percentile&nbsp;is&nbsp;the<br>
-same&nbsp;as&nbsp;the&nbsp;median.<br>
-&nbsp;<br>
-For&nbsp;<a href="builtins.html#object">object</a>&nbsp;data&nbsp;(e.g.&nbsp;strings&nbsp;or&nbsp;timestamps),&nbsp;the&nbsp;result's&nbsp;index<br>
-will&nbsp;include&nbsp;``count``,&nbsp;``unique``,&nbsp;``top``,&nbsp;and&nbsp;``freq``.&nbsp;The&nbsp;``top``<br>
-is&nbsp;the&nbsp;most&nbsp;common&nbsp;value.&nbsp;The&nbsp;``freq``&nbsp;is&nbsp;the&nbsp;most&nbsp;common&nbsp;value's<br>
-frequency.&nbsp;Timestamps&nbsp;also&nbsp;include&nbsp;the&nbsp;``first``&nbsp;and&nbsp;``last``&nbsp;items.<br>
-&nbsp;<br>
-If&nbsp;multiple&nbsp;<a href="builtins.html#object">object</a>&nbsp;values&nbsp;have&nbsp;the&nbsp;highest&nbsp;count,&nbsp;then&nbsp;the<br>
-``count``&nbsp;and&nbsp;``top``&nbsp;results&nbsp;will&nbsp;be&nbsp;arbitrarily&nbsp;chosen&nbsp;from<br>
-among&nbsp;those&nbsp;with&nbsp;the&nbsp;highest&nbsp;count.<br>
-&nbsp;<br>
-For&nbsp;mixed&nbsp;data&nbsp;types&nbsp;provided&nbsp;via&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``,&nbsp;the&nbsp;default&nbsp;is&nbsp;to<br>
-return&nbsp;only&nbsp;an&nbsp;analysis&nbsp;of&nbsp;numeric&nbsp;columns.&nbsp;If&nbsp;``include='all'``<br>
-is&nbsp;provided&nbsp;as&nbsp;an&nbsp;option,&nbsp;the&nbsp;result&nbsp;will&nbsp;include&nbsp;a&nbsp;union&nbsp;of<br>
-attributes&nbsp;of&nbsp;each&nbsp;type.<br>
-&nbsp;<br>
-The&nbsp;`include`&nbsp;and&nbsp;`exclude`&nbsp;parameters&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;limit<br>
-which&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;are&nbsp;analyzed&nbsp;for&nbsp;the&nbsp;output.<br>
-The&nbsp;parameters&nbsp;are&nbsp;ignored&nbsp;when&nbsp;analyzing&nbsp;a&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-Describing&nbsp;a&nbsp;numeric&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;categorical&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(['a',&nbsp;'a',&nbsp;'b',&nbsp;'c'])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;timestamp&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2000-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01")<br>
-...&nbsp;])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-first&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2000-01-01&nbsp;00:00:00<br>
-last&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``.&nbsp;By&nbsp;default&nbsp;only&nbsp;numeric&nbsp;fields<br>
-are&nbsp;returned.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[1,&nbsp;'a'],&nbsp;[2,&nbsp;'b'],&nbsp;[3,&nbsp;'c']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['numeric',&nbsp;'<a href="builtins.html#object">object</a>'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-describe">describe</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;all&nbsp;columns&nbsp;of&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;regardless&nbsp;of&nbsp;data&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-describe">describe</a>(include='all')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;column&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;by&nbsp;accessing&nbsp;it&nbsp;as<br>
-an&nbsp;attribute.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.numeric.<a href="#State-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-Name:&nbsp;numeric,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;numeric&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-describe">describe</a>(include=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;string&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-describe">describe</a>(include=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;numeric&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-describe">describe</a>(exclude=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-describe">describe</a>(exclude=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.count<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.max<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.min<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mean<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.std<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select_dtypes</tt></dd></dl>
-
-<dl><dt><a name="State-drop"><strong>drop</strong></a>(self, labels, axis=0, level=None, inplace=False, errors='raise')</dt><dd><tt>Return&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;labels&nbsp;in&nbsp;requested&nbsp;axis&nbsp;removed.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-labels&nbsp;:&nbsp;single&nbsp;label&nbsp;or&nbsp;list-like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;axis&nbsp;name<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;MultiIndex<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;return&nbsp;None.<br>
-errors&nbsp;:&nbsp;{'ignore',&nbsp;'raise'},&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'ignore',&nbsp;suppress&nbsp;error&nbsp;and&nbsp;existing&nbsp;labels&nbsp;are&nbsp;dropped.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dropped&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="State-equals"><strong>equals</strong></a>(self, other)</dt><dd><tt>Determines&nbsp;if&nbsp;two&nbsp;NDFrame&nbsp;objects&nbsp;contain&nbsp;the&nbsp;same&nbsp;elements.&nbsp;NaNs&nbsp;in<br>
-the&nbsp;same&nbsp;location&nbsp;are&nbsp;considered&nbsp;equal.</tt></dd></dl>
-
-<dl><dt><a name="State-ffill"><strong>ffill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#State-fillna">fillna</a>(method='ffill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="State-filter"><strong>filter</strong></a>(self, items=None, like=None, regex=None, axis=None)</dt><dd><tt>Subset&nbsp;rows&nbsp;or&nbsp;columns&nbsp;of&nbsp;dataframe&nbsp;according&nbsp;to&nbsp;labels&nbsp;in<br>
-the&nbsp;specified&nbsp;index.<br>
-&nbsp;<br>
-Note&nbsp;that&nbsp;this&nbsp;routine&nbsp;does&nbsp;not&nbsp;filter&nbsp;a&nbsp;dataframe&nbsp;on&nbsp;its<br>
-contents.&nbsp;The&nbsp;filter&nbsp;is&nbsp;applied&nbsp;to&nbsp;the&nbsp;labels&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-items&nbsp;:&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;info&nbsp;axis&nbsp;to&nbsp;restrict&nbsp;to&nbsp;(must&nbsp;not&nbsp;all&nbsp;be&nbsp;present)<br>
-like&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;where&nbsp;"arg&nbsp;in&nbsp;col&nbsp;==&nbsp;True"<br>
-regex&nbsp;:&nbsp;string&nbsp;(regular&nbsp;expression)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;with&nbsp;re.search(regex,&nbsp;col)&nbsp;==&nbsp;True<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;filter&nbsp;on.&nbsp;&nbsp;By&nbsp;default&nbsp;this&nbsp;is&nbsp;the&nbsp;info&nbsp;axis,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'index'&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;'columns'&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;name<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-filter">filter</a>(items=['one',&nbsp;'three'])<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;regular&nbsp;expression<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-filter">filter</a>(regex='e$',&nbsp;axis=1)<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;rows&nbsp;containing&nbsp;'bbi'<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-filter">filter</a>(like='bbi',&nbsp;axis=0)<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;``items``,&nbsp;``like``,&nbsp;and&nbsp;``regex``&nbsp;parameters&nbsp;are<br>
-enforced&nbsp;to&nbsp;be&nbsp;mutually&nbsp;exclusive.<br>
-&nbsp;<br>
-``axis``&nbsp;defaults&nbsp;to&nbsp;the&nbsp;info&nbsp;axis&nbsp;that&nbsp;is&nbsp;used&nbsp;when&nbsp;indexing<br>
-with&nbsp;``[]``.</tt></dd></dl>
-
-<dl><dt><a name="State-first"><strong>first</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;initial&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#State-first">first</a>('10D')&nbsp;-&gt;&nbsp;First&nbsp;10&nbsp;days<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="State-get"><strong>get</strong></a>(self, key, default=None)</dt><dd><tt>Get&nbsp;item&nbsp;from&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;given&nbsp;key&nbsp;(<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column,&nbsp;Panel&nbsp;slice,<br>
-etc.).&nbsp;Returns&nbsp;default&nbsp;value&nbsp;if&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;type&nbsp;of&nbsp;items&nbsp;contained&nbsp;in&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="State-get_dtype_counts"><strong>get_dtype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;dtypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="State-get_ftype_counts"><strong>get_ftype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;ftypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="State-groupby"><strong>groupby</strong></a>(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)</dt><dd><tt>Group&nbsp;series&nbsp;using&nbsp;mapper&nbsp;(dict&nbsp;or&nbsp;key&nbsp;function,&nbsp;apply&nbsp;given&nbsp;function<br>
-to&nbsp;group,&nbsp;return&nbsp;result&nbsp;as&nbsp;series)&nbsp;or&nbsp;by&nbsp;a&nbsp;series&nbsp;of&nbsp;columns.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;mapping,&nbsp;function,&nbsp;str,&nbsp;or&nbsp;iterable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;for&nbsp;the&nbsp;groupby.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;``by``&nbsp;is&nbsp;a&nbsp;function,&nbsp;it's&nbsp;called&nbsp;on&nbsp;each&nbsp;value&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>'s<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;If&nbsp;a&nbsp;dict&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;is&nbsp;passed,&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;dict&nbsp;VALUES<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;(the&nbsp;<a href="pandas.core.series.html#Series">Series</a>'&nbsp;values&nbsp;are&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aligned;&nbsp;see&nbsp;``.<a href="#State-align">align</a>()``&nbsp;method).&nbsp;If&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;is&nbsp;passed,&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;are&nbsp;used&nbsp;as-is&nbsp;determine&nbsp;the&nbsp;groups.&nbsp;A&nbsp;str&nbsp;or&nbsp;list&nbsp;of&nbsp;strs<br>
-&nbsp;&nbsp;&nbsp;&nbsp;may&nbsp;be&nbsp;passed&nbsp;to&nbsp;group&nbsp;by&nbsp;the&nbsp;columns&nbsp;in&nbsp;``self``<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-level&nbsp;:&nbsp;int,&nbsp;level&nbsp;name,&nbsp;or&nbsp;sequence&nbsp;of&nbsp;such,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;group&nbsp;by&nbsp;a&nbsp;particular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level&nbsp;or&nbsp;levels<br>
-as_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;aggregated&nbsp;output,&nbsp;return&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;group&nbsp;labels&nbsp;as&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;Only&nbsp;relevant&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;input.&nbsp;as_index=False&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;effectively&nbsp;"SQL-style"&nbsp;grouped&nbsp;output<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;group&nbsp;keys.&nbsp;Get&nbsp;better&nbsp;performance&nbsp;by&nbsp;turning&nbsp;this&nbsp;off.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;this&nbsp;does&nbsp;not&nbsp;influence&nbsp;the&nbsp;order&nbsp;of&nbsp;observations&nbsp;within&nbsp;each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;group.&nbsp;&nbsp;groupby&nbsp;preserves&nbsp;the&nbsp;order&nbsp;of&nbsp;rows&nbsp;within&nbsp;each&nbsp;group.<br>
-group_keys&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;calling&nbsp;apply,&nbsp;add&nbsp;group&nbsp;keys&nbsp;to&nbsp;index&nbsp;to&nbsp;identify&nbsp;pieces<br>
-squeeze&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;reduce&nbsp;the&nbsp;dimensionality&nbsp;of&nbsp;the&nbsp;return&nbsp;type&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;return&nbsp;a&nbsp;consistent&nbsp;type<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;results<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#State-groupby">groupby</a>(func,&nbsp;axis=0).<a href="#State-mean">mean</a>()<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#State-groupby">groupby</a>(['col1',&nbsp;'col2'])['col3'].<a href="#State-mean">mean</a>()<br>
-&nbsp;<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;hierarchical&nbsp;index<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#State-groupby">groupby</a>(['col1',&nbsp;'col2']).<a href="#State-mean">mean</a>()<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-GroupBy&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="State-head"><strong>head</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;first&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="State-interpolate"><strong>interpolate</strong></a>(self, method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', downcast=None, **kwargs)</dt><dd><tt>Interpolate&nbsp;values&nbsp;according&nbsp;to&nbsp;different&nbsp;methods.<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;only&nbsp;``method='linear'``&nbsp;is&nbsp;supported&nbsp;for<br>
-DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a&nbsp;MultiIndex.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-method&nbsp;:&nbsp;{'linear',&nbsp;'time',&nbsp;'index',&nbsp;'values',&nbsp;'nearest',&nbsp;'zero',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',&nbsp;'barycentric',&nbsp;'krogh',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'polynomial',&nbsp;'spline',&nbsp;'piecewise_polynomial',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'from_derivatives',&nbsp;'pchip',&nbsp;'akima'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'linear':&nbsp;ignore&nbsp;the&nbsp;index&nbsp;and&nbsp;treat&nbsp;the&nbsp;values&nbsp;as&nbsp;equally<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;spaced.&nbsp;This&nbsp;is&nbsp;the&nbsp;only&nbsp;method&nbsp;supported&nbsp;on&nbsp;MultiIndexes.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'time':&nbsp;interpolation&nbsp;works&nbsp;on&nbsp;daily&nbsp;and&nbsp;higher&nbsp;resolution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;data&nbsp;to&nbsp;interpolate&nbsp;given&nbsp;length&nbsp;of&nbsp;interval<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'index',&nbsp;'values':&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'nearest',&nbsp;'zero',&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'barycentric',&nbsp;'polynomial'&nbsp;is&nbsp;passed&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``scipy.interpolate.interp1d``.&nbsp;Both&nbsp;'polynomial'&nbsp;and&nbsp;'spline'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;require&nbsp;that&nbsp;you&nbsp;also&nbsp;specify&nbsp;an&nbsp;`order`&nbsp;(int),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;e.g.&nbsp;df.<a href="#State-interpolate">interpolate</a>(method='polynomial',&nbsp;order=4).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'krogh',&nbsp;'piecewise_polynomial',&nbsp;'spline',&nbsp;'pchip'&nbsp;and&nbsp;'akima'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;all&nbsp;wrappers&nbsp;around&nbsp;the&nbsp;scipy&nbsp;interpolation&nbsp;methods&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;similar&nbsp;names.&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;For&nbsp;more&nbsp;information&nbsp;on&nbsp;their&nbsp;behavior,&nbsp;see&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`scipy&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__">http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;`tutorial&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__">http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'from_derivatives'&nbsp;refers&nbsp;to&nbsp;BPoly.from_derivatives&nbsp;which<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;replaces&nbsp;'piecewise_polynomial'&nbsp;interpolation&nbsp;method&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;0.18<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;support&nbsp;for&nbsp;the&nbsp;'akima'&nbsp;method<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;interpolate&nbsp;method&nbsp;'from_derivatives'&nbsp;which&nbsp;replaces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'piecewise_polynomial'&nbsp;in&nbsp;scipy&nbsp;0.18;&nbsp;backwards-compatible&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;&lt;&nbsp;0.18<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;0:&nbsp;fill&nbsp;column-by-column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;1:&nbsp;fill&nbsp;row-by-row<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs&nbsp;to&nbsp;fill.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0.<br>
-limit_direction&nbsp;:&nbsp;{'forward',&nbsp;'backward',&nbsp;'both'},&nbsp;default&nbsp;'forward'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;limit&nbsp;is&nbsp;specified,&nbsp;consecutive&nbsp;NaNs&nbsp;will&nbsp;be&nbsp;filled&nbsp;in&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;direction.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Update&nbsp;the&nbsp;NDFrame&nbsp;in&nbsp;place&nbsp;if&nbsp;possible.<br>
-downcast&nbsp;:&nbsp;optional,&nbsp;'infer'&nbsp;or&nbsp;None,&nbsp;defaults&nbsp;to&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Downcast&nbsp;dtypes&nbsp;if&nbsp;possible.<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;interpolating&nbsp;function.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;interpolated&nbsp;at&nbsp;the&nbsp;NaNs<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;replace,&nbsp;fillna<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Filling&nbsp;in&nbsp;NaNs<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0,&nbsp;1,&nbsp;np.nan,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-interpolate">interpolate</a>()<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="State-isnull"><strong>isnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-notnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;isnull</tt></dd></dl>
-
-<dl><dt><a name="State-last"><strong>last</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;final&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#State-last">last</a>('5M')&nbsp;-&gt;&nbsp;Last&nbsp;5&nbsp;months<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="State-mask"><strong>mask</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;False&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;mask&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``False``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#State-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#State-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``mask``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).<a href="#State-reshape">reshape</a>(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#State-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#State-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`</tt></dd></dl>
-
-<dl><dt><a name="State-notnull"><strong>notnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are<br>
-not&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-isnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;notnull</tt></dd></dl>
-
-<dl><dt><a name="State-pct_change"><strong>pct_change</strong></a>(self, periods=1, fill_method='pad', limit=None, freq=None, **kwargs)</dt><dd><tt>Percent&nbsp;change&nbsp;over&nbsp;given&nbsp;number&nbsp;of&nbsp;periods.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;percent&nbsp;change<br>
-fill_method&nbsp;:&nbsp;str,&nbsp;default&nbsp;'pad'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;How&nbsp;to&nbsp;handle&nbsp;NAs&nbsp;before&nbsp;computing&nbsp;percent&nbsp;changes<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NAs&nbsp;to&nbsp;fill&nbsp;before&nbsp;stopping<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;offset&nbsp;alias&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;time&nbsp;series&nbsp;API&nbsp;(e.g.&nbsp;'M'&nbsp;or&nbsp;BDay())<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-chg&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-By&nbsp;default,&nbsp;the&nbsp;percentage&nbsp;change&nbsp;is&nbsp;calculated&nbsp;along&nbsp;the&nbsp;stat<br>
-axis:&nbsp;0,&nbsp;or&nbsp;``Index``,&nbsp;for&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;and&nbsp;1,&nbsp;or&nbsp;``minor``&nbsp;for<br>
-``Panel``.&nbsp;You&nbsp;can&nbsp;change&nbsp;this&nbsp;with&nbsp;the&nbsp;``axis``&nbsp;keyword&nbsp;argument.</tt></dd></dl>
-
-<dl><dt><a name="State-pipe"><strong>pipe</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Apply&nbsp;func(self,&nbsp;\*args,&nbsp;\*\*kwargs)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.2<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;the&nbsp;NDFrame.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``args``,&nbsp;and&nbsp;``kwargs``&nbsp;are&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Alternatively&nbsp;a&nbsp;``(callable,&nbsp;data_keyword)``&nbsp;tuple&nbsp;where<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``data_keyword``&nbsp;is&nbsp;a&nbsp;string&nbsp;indicating&nbsp;the&nbsp;keyword&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``callable``&nbsp;that&nbsp;expects&nbsp;the&nbsp;NDFrame.<br>
-args&nbsp;:&nbsp;positional&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-kwargs&nbsp;:&nbsp;a&nbsp;dictionary&nbsp;of&nbsp;keyword&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="builtins.html#object">object</a>&nbsp;:&nbsp;the&nbsp;return&nbsp;type&nbsp;of&nbsp;``func``.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-Use&nbsp;``.pipe``&nbsp;when&nbsp;chaining&nbsp;together&nbsp;functions&nbsp;that&nbsp;expect<br>
-on&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;DataFrames.&nbsp;Instead&nbsp;of&nbsp;writing<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;f(g(h(df),&nbsp;arg1=a),&nbsp;arg2=b,&nbsp;arg3=c)<br>
-&nbsp;<br>
-You&nbsp;can&nbsp;write<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#State-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#State-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#State-pipe">pipe</a>(f,&nbsp;arg2=b,&nbsp;arg3=c)<br>
-...&nbsp;)<br>
-&nbsp;<br>
-If&nbsp;you&nbsp;have&nbsp;a&nbsp;function&nbsp;that&nbsp;takes&nbsp;the&nbsp;data&nbsp;as&nbsp;(say)&nbsp;the&nbsp;second<br>
-argument,&nbsp;pass&nbsp;a&nbsp;tuple&nbsp;indicating&nbsp;which&nbsp;keyword&nbsp;expects&nbsp;the<br>
-data.&nbsp;For&nbsp;example,&nbsp;suppose&nbsp;``f``&nbsp;takes&nbsp;its&nbsp;data&nbsp;as&nbsp;``arg2``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#State-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#State-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#State-pipe">pipe</a>((f,&nbsp;'arg2'),&nbsp;arg1=a,&nbsp;arg3=c)<br>
-...&nbsp;&nbsp;)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.map</tt></dd></dl>
-
-<dl><dt><a name="State-pop"><strong>pop</strong></a>(self, item)</dt><dd><tt>Return&nbsp;item&nbsp;and&nbsp;drop&nbsp;from&nbsp;frame.&nbsp;Raise&nbsp;KeyError&nbsp;if&nbsp;not&nbsp;found.</tt></dd></dl>
-
-<dl><dt><a name="State-rank"><strong>rank</strong></a>(self, axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)</dt><dd><tt>Compute&nbsp;numerical&nbsp;data&nbsp;ranks&nbsp;(1&nbsp;through&nbsp;n)&nbsp;along&nbsp;axis.&nbsp;Equal&nbsp;values&nbsp;are<br>
-assigned&nbsp;a&nbsp;rank&nbsp;that&nbsp;is&nbsp;the&nbsp;average&nbsp;of&nbsp;the&nbsp;ranks&nbsp;of&nbsp;those&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;to&nbsp;direct&nbsp;ranking<br>
-method&nbsp;:&nbsp;{'average',&nbsp;'min',&nbsp;'max',&nbsp;'first',&nbsp;'dense'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;average:&nbsp;average&nbsp;rank&nbsp;of&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;min:&nbsp;lowest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;max:&nbsp;highest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;first:&nbsp;ranks&nbsp;assigned&nbsp;in&nbsp;order&nbsp;they&nbsp;appear&nbsp;in&nbsp;the&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dense:&nbsp;like&nbsp;'min',&nbsp;but&nbsp;rank&nbsp;always&nbsp;increases&nbsp;by&nbsp;1&nbsp;between&nbsp;groups<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;data.&nbsp;Valid&nbsp;only&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Panel&nbsp;objects<br>
-na_option&nbsp;:&nbsp;{'keep',&nbsp;'top',&nbsp;'bottom'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;keep:&nbsp;leave&nbsp;NA&nbsp;values&nbsp;where&nbsp;they&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;top:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;ascending<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;bottom:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;descending<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False&nbsp;for&nbsp;ranks&nbsp;by&nbsp;high&nbsp;(1)&nbsp;to&nbsp;low&nbsp;(N)<br>
-pct&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Computes&nbsp;percentage&nbsp;rank&nbsp;of&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ranks&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="State-reindex_like"><strong>reindex_like</strong></a>(self, other, method=None, copy=True, limit=None, tolerance=None)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;matching&nbsp;indices&nbsp;to&nbsp;myself.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;Object<br>
-method&nbsp;:&nbsp;string&nbsp;or&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;labels&nbsp;to&nbsp;fill&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;labels&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>&nbsp;and&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Like&nbsp;calling&nbsp;s.<a href="#State-reindex">reindex</a>(index=other.index,&nbsp;columns=other.columns,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;method=...)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="State-rename_axis"><strong>rename_axis</strong></a>(self, mapper, axis=0, copy=True, inplace=False)</dt><dd><tt>Alter&nbsp;index&nbsp;and&nbsp;/&nbsp;or&nbsp;columns&nbsp;using&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.<br>
-A&nbsp;scalar&nbsp;or&nbsp;list-like&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;``Index.name``<br>
-or&nbsp;``MultiIndex.names``&nbsp;attribute.<br>
-A&nbsp;function&nbsp;or&nbsp;dict&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;labels.<br>
-Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be&nbsp;unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in<br>
-a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left&nbsp;as-is.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-mapper&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename<br>
-pandas.Index.rename<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-rename_axis">rename_axis</a>("foo")&nbsp;&nbsp;#&nbsp;scalar,&nbsp;alters&nbsp;df.index.name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-foo<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-rename_axis">rename_axis</a>(lambda&nbsp;x:&nbsp;2&nbsp;*&nbsp;x)&nbsp;&nbsp;#&nbsp;function:&nbsp;alters&nbsp;labels<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-4&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-rename_axis">rename_axis</a>({"A":&nbsp;"ehh",&nbsp;"C":&nbsp;"see"},&nbsp;axis="columns")&nbsp;&nbsp;#&nbsp;mapping<br>
-&nbsp;&nbsp;&nbsp;ehh&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="State-replace"><strong>replace</strong></a>(self, to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad', axis=None)</dt><dd><tt>Replace&nbsp;values&nbsp;given&nbsp;in&nbsp;'to_replace'&nbsp;with&nbsp;'value'.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-to_replace&nbsp;:&nbsp;str,&nbsp;regex,&nbsp;list,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;numeric,&nbsp;or&nbsp;None<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;str&nbsp;or&nbsp;regex:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str:&nbsp;string&nbsp;exactly&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;`value`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;regex:&nbsp;regexs&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`value`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;list&nbsp;of&nbsp;str,&nbsp;regex,&nbsp;or&nbsp;numeric:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;First,&nbsp;if&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;both&nbsp;lists,&nbsp;they<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Second,&nbsp;if&nbsp;``regex=True``&nbsp;then&nbsp;all&nbsp;of&nbsp;the&nbsp;strings&nbsp;in&nbsp;**both**<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;regexs&nbsp;otherwise&nbsp;they&nbsp;will&nbsp;match<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;directly.&nbsp;This&nbsp;doesn't&nbsp;matter&nbsp;much&nbsp;for&nbsp;`value`&nbsp;since&nbsp;there<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;only&nbsp;a&nbsp;few&nbsp;possible&nbsp;substitution&nbsp;regexes&nbsp;you&nbsp;can&nbsp;use.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str&nbsp;and&nbsp;regex&nbsp;rules&nbsp;apply&nbsp;as&nbsp;above.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dict:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Nested&nbsp;dictionaries,&nbsp;e.g.,&nbsp;{'a':&nbsp;{'b':&nbsp;nan}},&nbsp;are&nbsp;read&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;follows:&nbsp;look&nbsp;in&nbsp;column&nbsp;'a'&nbsp;for&nbsp;the&nbsp;value&nbsp;'b'&nbsp;and&nbsp;replace&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;nan.&nbsp;You&nbsp;can&nbsp;nest&nbsp;regular&nbsp;expressions&nbsp;as&nbsp;well.&nbsp;Note&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;names&nbsp;(the&nbsp;top-level&nbsp;dictionary&nbsp;keys&nbsp;in&nbsp;a&nbsp;nested<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dictionary)&nbsp;**cannot**&nbsp;be&nbsp;regular&nbsp;expressions.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Keys&nbsp;map&nbsp;to&nbsp;column&nbsp;names&nbsp;and&nbsp;values&nbsp;map&nbsp;to&nbsp;substitution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;values.&nbsp;You&nbsp;can&nbsp;treat&nbsp;this&nbsp;as&nbsp;a&nbsp;special&nbsp;case&nbsp;of&nbsp;passing&nbsp;two<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;except&nbsp;that&nbsp;you&nbsp;are&nbsp;specifying&nbsp;the&nbsp;column&nbsp;to&nbsp;search&nbsp;in.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;None:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;This&nbsp;means&nbsp;that&nbsp;the&nbsp;``regex``&nbsp;argument&nbsp;must&nbsp;be&nbsp;a&nbsp;string,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;compiled&nbsp;regular&nbsp;expression,&nbsp;or&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;such&nbsp;elements.&nbsp;If&nbsp;`value`&nbsp;is&nbsp;also&nbsp;``None``&nbsp;then&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;a&nbsp;nested&nbsp;dictionary&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;examples&nbsp;section&nbsp;for&nbsp;examples&nbsp;of&nbsp;each&nbsp;of&nbsp;these.<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;list,&nbsp;str,&nbsp;regex,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a&nbsp;dict&nbsp;of&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for&nbsp;each&nbsp;column&nbsp;(columns&nbsp;not&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;Regular&nbsp;expressions,&nbsp;strings&nbsp;and&nbsp;lists&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dicts&nbsp;of&nbsp;such&nbsp;objects&nbsp;are&nbsp;also&nbsp;allowed.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>&nbsp;(e.g.&nbsp;a&nbsp;column&nbsp;form&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Returns&nbsp;the&nbsp;caller&nbsp;if&nbsp;this&nbsp;is&nbsp;True.<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;size&nbsp;gap&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-regex&nbsp;:&nbsp;bool&nbsp;or&nbsp;same&nbsp;types&nbsp;as&nbsp;`to_replace`,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;interpret&nbsp;`to_replace`&nbsp;and/or&nbsp;`value`&nbsp;as&nbsp;regular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;expressions.&nbsp;If&nbsp;this&nbsp;is&nbsp;``True``&nbsp;then&nbsp;`to_replace`&nbsp;*must*&nbsp;be&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string.&nbsp;Otherwise,&nbsp;`to_replace`&nbsp;must&nbsp;be&nbsp;``None``&nbsp;because&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;parameter&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;regular&nbsp;expression&nbsp;or&nbsp;a&nbsp;list,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict,&nbsp;or&nbsp;array&nbsp;of&nbsp;regular&nbsp;expressions.<br>
-method&nbsp;:&nbsp;string,&nbsp;optional,&nbsp;{'pad',&nbsp;'ffill',&nbsp;'bfill'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;method&nbsp;to&nbsp;use&nbsp;when&nbsp;for&nbsp;replacement,&nbsp;when&nbsp;``to_replace``&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``list``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-NDFrame.reindex<br>
-NDFrame.asfreq<br>
-NDFrame.fillna<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-AssertionError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;a&nbsp;``bool``&nbsp;and&nbsp;`to_replace`&nbsp;is&nbsp;not&nbsp;``None``.<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;a&nbsp;``dict``&nbsp;and&nbsp;`value`&nbsp;is&nbsp;not&nbsp;a&nbsp;``list``,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``dict``,&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``,&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;``None``&nbsp;and&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;compilable&nbsp;into&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;regular&nbsp;expression&nbsp;or&nbsp;is&nbsp;a&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;``list``&nbsp;s&nbsp;or&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``&nbsp;s,&nbsp;but<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;they&nbsp;are&nbsp;not&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-*&nbsp;Regex&nbsp;substitution&nbsp;is&nbsp;performed&nbsp;under&nbsp;the&nbsp;hood&nbsp;with&nbsp;``re.sub``.&nbsp;The<br>
-&nbsp;&nbsp;rules&nbsp;for&nbsp;substitution&nbsp;for&nbsp;``re.sub``&nbsp;are&nbsp;the&nbsp;same.<br>
-*&nbsp;Regular&nbsp;expressions&nbsp;will&nbsp;only&nbsp;substitute&nbsp;on&nbsp;strings,&nbsp;meaning&nbsp;you<br>
-&nbsp;&nbsp;cannot&nbsp;provide,&nbsp;for&nbsp;example,&nbsp;a&nbsp;regular&nbsp;expression&nbsp;matching&nbsp;floating<br>
-&nbsp;&nbsp;point&nbsp;numbers&nbsp;and&nbsp;expect&nbsp;the&nbsp;columns&nbsp;in&nbsp;your&nbsp;frame&nbsp;that&nbsp;have&nbsp;a<br>
-&nbsp;&nbsp;numeric&nbsp;dtype&nbsp;to&nbsp;be&nbsp;matched.&nbsp;However,&nbsp;if&nbsp;those&nbsp;floating&nbsp;point&nbsp;numbers<br>
-&nbsp;&nbsp;*are*&nbsp;strings,&nbsp;then&nbsp;you&nbsp;can&nbsp;do&nbsp;this.<br>
-*&nbsp;This&nbsp;method&nbsp;has&nbsp;*a&nbsp;lot*&nbsp;of&nbsp;options.&nbsp;You&nbsp;are&nbsp;encouraged&nbsp;to&nbsp;experiment<br>
-&nbsp;&nbsp;and&nbsp;play&nbsp;with&nbsp;this&nbsp;method&nbsp;to&nbsp;gain&nbsp;intuition&nbsp;about&nbsp;how&nbsp;it&nbsp;works.</tt></dd></dl>
-
-<dl><dt><a name="State-resample"><strong>resample</strong></a>(self, rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0, on=None, level=None)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;frequency&nbsp;conversion&nbsp;and&nbsp;resampling&nbsp;of&nbsp;time<br>
-series.&nbsp;&nbsp;Object&nbsp;must&nbsp;have&nbsp;a&nbsp;datetime-like&nbsp;index&nbsp;(DatetimeIndex,<br>
-PeriodIndex,&nbsp;or&nbsp;TimedeltaIndex),&nbsp;or&nbsp;pass&nbsp;datetime-like&nbsp;values<br>
-to&nbsp;the&nbsp;on&nbsp;or&nbsp;level&nbsp;keyword.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-rule&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;offset&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a>&nbsp;representing&nbsp;target&nbsp;conversion<br>
-axis&nbsp;:&nbsp;int,&nbsp;optional,&nbsp;default&nbsp;0<br>
-closed&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;side&nbsp;of&nbsp;bin&nbsp;interval&nbsp;is&nbsp;closed<br>
-label&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;bin&nbsp;edge&nbsp;label&nbsp;to&nbsp;label&nbsp;bucket&nbsp;with<br>
-convention&nbsp;:&nbsp;{'start',&nbsp;'end',&nbsp;'s',&nbsp;'e'}<br>
-loffset&nbsp;:&nbsp;timedelta<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Adjust&nbsp;the&nbsp;resampled&nbsp;time&nbsp;labels<br>
-base&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;frequencies&nbsp;that&nbsp;evenly&nbsp;subdivide&nbsp;1&nbsp;day,&nbsp;the&nbsp;"origin"&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aggregated&nbsp;intervals.&nbsp;For&nbsp;example,&nbsp;for&nbsp;'5min'&nbsp;frequency,&nbsp;base&nbsp;could<br>
-&nbsp;&nbsp;&nbsp;&nbsp;range&nbsp;from&nbsp;0&nbsp;through&nbsp;4.&nbsp;Defaults&nbsp;to&nbsp;0<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;to&nbsp;use&nbsp;instead&nbsp;of&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-level&nbsp;:&nbsp;string&nbsp;or&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;MultiIndex,&nbsp;level&nbsp;(name&nbsp;or&nbsp;number)&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;resampling.&nbsp;&nbsp;Level&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offset&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;9&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(9),&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;series<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:04:00&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-2000-01-01&nbsp;00:05:00&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:07:00&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-2000-01-01&nbsp;00:08:00&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-Freq:&nbsp;T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;and&nbsp;sum&nbsp;the&nbsp;values<br>
-of&nbsp;the&nbsp;timestamps&nbsp;falling&nbsp;into&nbsp;a&nbsp;bin.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#State-resample">resample</a>('3T').<a href="#State-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;label&nbsp;each<br>
-bin&nbsp;using&nbsp;the&nbsp;right&nbsp;edge&nbsp;instead&nbsp;of&nbsp;the&nbsp;left.&nbsp;Please&nbsp;note&nbsp;that&nbsp;the<br>
-value&nbsp;in&nbsp;the&nbsp;bucket&nbsp;used&nbsp;as&nbsp;the&nbsp;label&nbsp;is&nbsp;not&nbsp;included&nbsp;in&nbsp;the&nbsp;bucket,<br>
-which&nbsp;it&nbsp;labels.&nbsp;For&nbsp;example,&nbsp;in&nbsp;the&nbsp;original&nbsp;series&nbsp;the<br>
-bucket&nbsp;``2000-01-01&nbsp;00:03:00``&nbsp;contains&nbsp;the&nbsp;value&nbsp;3,&nbsp;but&nbsp;the&nbsp;summed<br>
-value&nbsp;in&nbsp;the&nbsp;resampled&nbsp;bucket&nbsp;with&nbsp;the&nbsp;label``2000-01-01&nbsp;00:03:00``<br>
-does&nbsp;not&nbsp;include&nbsp;3&nbsp;(if&nbsp;it&nbsp;did,&nbsp;the&nbsp;summed&nbsp;value&nbsp;would&nbsp;be&nbsp;6,&nbsp;not&nbsp;3).<br>
-To&nbsp;include&nbsp;this&nbsp;value&nbsp;close&nbsp;the&nbsp;right&nbsp;side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval&nbsp;as<br>
-illustrated&nbsp;in&nbsp;the&nbsp;example&nbsp;below&nbsp;this&nbsp;one.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#State-resample">resample</a>('3T',&nbsp;label='right').<a href="#State-sum">sum</a>()<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;close&nbsp;the&nbsp;right<br>
-side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#State-resample">resample</a>('3T',&nbsp;label='right',&nbsp;closed='right').<a href="#State-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#State-resample">resample</a>('30S').<a href="#State-asfreq">asfreq</a>()[0:5]&nbsp;#select&nbsp;first&nbsp;5&nbsp;rows<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;1.0<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;2.0<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values&nbsp;using&nbsp;the&nbsp;``pad``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#State-resample">resample</a>('30S').pad()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the<br>
-``NaN``&nbsp;values&nbsp;using&nbsp;the&nbsp;``bfill``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#State-resample">resample</a>('30S').<a href="#State-bfill">bfill</a>()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Pass&nbsp;a&nbsp;custom&nbsp;function&nbsp;via&nbsp;``apply``<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;custom_resampler(array_like):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;np.<a href="#State-sum">sum</a>(array_like)+5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#State-resample">resample</a>('3T').<a href="#State-apply">apply</a>(custom_resampler)<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;17<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;26<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects,&nbsp;the&nbsp;keyword&nbsp;``on``&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;specify&nbsp;the<br>
-column&nbsp;instead&nbsp;of&nbsp;the&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=9*[range(4)],&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'])<br>
-&gt;&gt;&gt;&nbsp;df['time']&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-resample">resample</a>('3T',&nbsp;on='time').<a href="#State-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;c&nbsp;&nbsp;d<br>
-time<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-&nbsp;<br>
-For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;MultiIndex,&nbsp;the&nbsp;keyword&nbsp;``level``&nbsp;can&nbsp;be&nbsp;used&nbsp;to<br>
-specify&nbsp;on&nbsp;level&nbsp;the&nbsp;resampling&nbsp;needs&nbsp;to&nbsp;take&nbsp;place.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;time&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=5,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=10*[range(4)],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.MultiIndex.from_product([time,&nbsp;[1,&nbsp;2]])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;)<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#State-resample">resample</a>('3T',&nbsp;level=0).<a href="#State-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;d<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;6&nbsp;&nbsp;12&nbsp;&nbsp;18<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;8&nbsp;&nbsp;12</tt></dd></dl>
-
-<dl><dt><a name="State-sample"><strong>sample</strong></a>(self, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None)</dt><dd><tt>Returns&nbsp;a&nbsp;random&nbsp;sample&nbsp;of&nbsp;items&nbsp;from&nbsp;an&nbsp;axis&nbsp;of&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;items&nbsp;from&nbsp;axis&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`frac`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;=&nbsp;1&nbsp;if&nbsp;`frac`&nbsp;=&nbsp;None.<br>
-frac&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fraction&nbsp;of&nbsp;axis&nbsp;items&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`n`.<br>
-replace&nbsp;:&nbsp;boolean,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sample&nbsp;with&nbsp;or&nbsp;without&nbsp;replacement.&nbsp;Default&nbsp;=&nbsp;False.<br>
-weights&nbsp;:&nbsp;str&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;'None'&nbsp;results&nbsp;in&nbsp;equal&nbsp;probability&nbsp;weighting.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;will&nbsp;align&nbsp;with&nbsp;target&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;index.&nbsp;Index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;in&nbsp;weights&nbsp;not&nbsp;found&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;will&nbsp;be&nbsp;ignored&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;values&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;not&nbsp;in&nbsp;weights&nbsp;will&nbsp;be&nbsp;assigned<br>
-&nbsp;&nbsp;&nbsp;&nbsp;weights&nbsp;of&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;called&nbsp;on&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;will&nbsp;accept&nbsp;the&nbsp;name&nbsp;of&nbsp;a&nbsp;column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;axis&nbsp;=&nbsp;0.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Unless&nbsp;weights&nbsp;are&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;weights&nbsp;must&nbsp;be&nbsp;same&nbsp;length&nbsp;as&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;being&nbsp;sampled.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;weights&nbsp;do&nbsp;not&nbsp;sum&nbsp;to&nbsp;1,&nbsp;they&nbsp;will&nbsp;be&nbsp;normalized&nbsp;to&nbsp;sum&nbsp;to&nbsp;1.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;values&nbsp;in&nbsp;the&nbsp;weights&nbsp;column&nbsp;will&nbsp;be&nbsp;treated&nbsp;as&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;inf&nbsp;and&nbsp;-inf&nbsp;values&nbsp;not&nbsp;allowed.<br>
-random_state&nbsp;:&nbsp;int&nbsp;or&nbsp;numpy.random.RandomState,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Seed&nbsp;for&nbsp;the&nbsp;random&nbsp;number&nbsp;generator&nbsp;(if&nbsp;int),&nbsp;or&nbsp;numpy&nbsp;RandomState<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;sample.&nbsp;Accepts&nbsp;axis&nbsp;number&nbsp;or&nbsp;name.&nbsp;Default&nbsp;is&nbsp;stat&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;given&nbsp;data&nbsp;type&nbsp;(0&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;and&nbsp;DataFrames,&nbsp;1&nbsp;for&nbsp;Panels).<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-A&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;type&nbsp;as&nbsp;caller.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Generate&nbsp;an&nbsp;example&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;and&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(50))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-head">head</a>()<br>
-0&nbsp;&nbsp;&nbsp;-0.038497<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.820773<br>
-2&nbsp;&nbsp;&nbsp;-0.972766<br>
-3&nbsp;&nbsp;&nbsp;-1.598270<br>
-4&nbsp;&nbsp;&nbsp;-1.095526<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(50,&nbsp;4),&nbsp;columns=list('ABCD'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-head">head</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;0.016443&nbsp;-2.318952&nbsp;-0.566372&nbsp;-1.028078<br>
-1&nbsp;-1.051921&nbsp;&nbsp;0.438836&nbsp;&nbsp;0.658280&nbsp;-0.175797<br>
-2&nbsp;-1.243569&nbsp;-0.364626&nbsp;-0.215065&nbsp;&nbsp;0.057736<br>
-3&nbsp;&nbsp;1.768216&nbsp;&nbsp;0.404512&nbsp;-0.385604&nbsp;-1.457834<br>
-4&nbsp;&nbsp;1.072446&nbsp;-1.137172&nbsp;&nbsp;0.314194&nbsp;-0.046661<br>
-&nbsp;<br>
-Next&nbsp;extract&nbsp;a&nbsp;random&nbsp;sample&nbsp;from&nbsp;both&nbsp;of&nbsp;these&nbsp;objects...<br>
-&nbsp;<br>
-3&nbsp;random&nbsp;elements&nbsp;from&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-sample">sample</a>(n=3)<br>
-27&nbsp;&nbsp;&nbsp;-0.994689<br>
-55&nbsp;&nbsp;&nbsp;-1.049016<br>
-67&nbsp;&nbsp;&nbsp;-0.224565<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-And&nbsp;a&nbsp;random&nbsp;10%&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;with&nbsp;replacement:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-sample">sample</a>(frac=0.1,&nbsp;replace=True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-35&nbsp;&nbsp;1.981780&nbsp;&nbsp;0.142106&nbsp;&nbsp;1.817165&nbsp;-0.290805<br>
-49&nbsp;-1.336199&nbsp;-0.448634&nbsp;-0.789640&nbsp;&nbsp;0.217116<br>
-40&nbsp;&nbsp;0.823173&nbsp;-0.078816&nbsp;&nbsp;1.009536&nbsp;&nbsp;1.015108<br>
-15&nbsp;&nbsp;1.421154&nbsp;-0.055301&nbsp;-1.922594&nbsp;-0.019696<br>
-6&nbsp;&nbsp;-0.148339&nbsp;&nbsp;0.832938&nbsp;&nbsp;1.787600&nbsp;-1.383767</tt></dd></dl>
-
-<dl><dt><a name="State-select"><strong>select</strong></a>(self, crit, axis=0)</dt><dd><tt>Return&nbsp;data&nbsp;corresponding&nbsp;to&nbsp;axis&nbsp;labels&nbsp;matching&nbsp;criteria<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-crit&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;called&nbsp;on&nbsp;each&nbsp;index&nbsp;(label).&nbsp;Should&nbsp;return&nbsp;True&nbsp;or&nbsp;False<br>
-axis&nbsp;:&nbsp;int<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-selection&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="State-set_axis"><strong>set_axis</strong></a>(self, axis, labels)</dt><dd><tt>public&nbsp;verson&nbsp;of&nbsp;axis&nbsp;assignment</tt></dd></dl>
-
-<dl><dt><a name="State-slice_shift"><strong>slice_shift</strong></a>(self, periods=1, axis=0)</dt><dd><tt>Equivalent&nbsp;to&nbsp;`shift`&nbsp;without&nbsp;copying&nbsp;data.&nbsp;The&nbsp;shifted&nbsp;data&nbsp;will<br>
-not&nbsp;include&nbsp;the&nbsp;dropped&nbsp;periods&nbsp;and&nbsp;the&nbsp;shifted&nbsp;axis&nbsp;will&nbsp;be&nbsp;smaller<br>
-than&nbsp;the&nbsp;original.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-&nbsp;<br>
-Notes<br>
------<br>
-While&nbsp;the&nbsp;`slice_shift`&nbsp;is&nbsp;faster&nbsp;than&nbsp;`shift`,&nbsp;you&nbsp;may&nbsp;pay&nbsp;for&nbsp;it<br>
-later&nbsp;during&nbsp;alignment.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="State-squeeze"><strong>squeeze</strong></a>(self, axis=None)</dt><dd><tt>Squeeze&nbsp;length&nbsp;1&nbsp;dimensions.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;None,&nbsp;integer&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;squeeze&nbsp;if&nbsp;1-sized.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-scalar&nbsp;if&nbsp;1-sized,&nbsp;else&nbsp;original&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="State-swapaxes"><strong>swapaxes</strong></a>(self, axis1, axis2, copy=True)</dt><dd><tt>Interchange&nbsp;axes&nbsp;and&nbsp;swap&nbsp;values&nbsp;axes&nbsp;appropriately<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="State-tail"><strong>tail</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;last&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="State-to_clipboard"><strong>to_clipboard</strong></a>(self, excel=None, sep=None, **kwargs)</dt><dd><tt>Attempt&nbsp;to&nbsp;write&nbsp;text&nbsp;representation&nbsp;of&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;the&nbsp;system&nbsp;clipboard<br>
-This&nbsp;can&nbsp;be&nbsp;pasted&nbsp;into&nbsp;Excel,&nbsp;for&nbsp;example.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel&nbsp;:&nbsp;boolean,&nbsp;defaults&nbsp;to&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;use&nbsp;the&nbsp;provided&nbsp;separator,&nbsp;writing&nbsp;in&nbsp;a&nbsp;csv<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;format&nbsp;for&nbsp;allowing&nbsp;easy&nbsp;pasting&nbsp;into&nbsp;excel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;False,&nbsp;write&nbsp;a&nbsp;string&nbsp;representation&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;the&nbsp;clipboard<br>
-sep&nbsp;:&nbsp;optional,&nbsp;defaults&nbsp;to&nbsp;tab<br>
-other&nbsp;keywords&nbsp;are&nbsp;passed&nbsp;to&nbsp;to_csv<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Requirements&nbsp;for&nbsp;your&nbsp;platform<br>
-&nbsp;&nbsp;-&nbsp;Linux:&nbsp;xclip,&nbsp;or&nbsp;xsel&nbsp;(with&nbsp;gtk&nbsp;or&nbsp;PyQt4&nbsp;modules)<br>
-&nbsp;&nbsp;-&nbsp;Windows:&nbsp;none<br>
-&nbsp;&nbsp;-&nbsp;OS&nbsp;X:&nbsp;none</tt></dd></dl>
-
-<dl><dt><a name="State-to_dense"><strong>to_dense</strong></a>(self)</dt><dd><tt>Return&nbsp;dense&nbsp;representation&nbsp;of&nbsp;NDFrame&nbsp;(as&nbsp;opposed&nbsp;to&nbsp;sparse)</tt></dd></dl>
-
-<dl><dt><a name="State-to_hdf"><strong>to_hdf</strong></a>(self, path_or_buf, key, **kwargs)</dt><dd><tt>Write&nbsp;the&nbsp;contained&nbsp;data&nbsp;to&nbsp;an&nbsp;HDF5&nbsp;file&nbsp;using&nbsp;HDFStore.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;(string)&nbsp;or&nbsp;HDFStore&nbsp;<a href="builtins.html#object">object</a><br>
-key&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;identifier&nbsp;for&nbsp;the&nbsp;group&nbsp;in&nbsp;the&nbsp;store<br>
-mode&nbsp;:&nbsp;optional,&nbsp;{'a',&nbsp;'w',&nbsp;'r+'},&nbsp;default&nbsp;'a'<br>
-&nbsp;<br>
-&nbsp;&nbsp;``'w'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write;&nbsp;a&nbsp;new&nbsp;file&nbsp;is&nbsp;created&nbsp;(an&nbsp;existing&nbsp;file&nbsp;with&nbsp;the&nbsp;same<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;name&nbsp;would&nbsp;be&nbsp;deleted).<br>
-&nbsp;&nbsp;``'a'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Append;&nbsp;an&nbsp;existing&nbsp;file&nbsp;is&nbsp;opened&nbsp;for&nbsp;reading&nbsp;and&nbsp;writing,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;if&nbsp;the&nbsp;file&nbsp;does&nbsp;not&nbsp;exist&nbsp;it&nbsp;is&nbsp;created.<br>
-&nbsp;&nbsp;``'r+'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;It&nbsp;is&nbsp;similar&nbsp;to&nbsp;``'a'``,&nbsp;but&nbsp;the&nbsp;file&nbsp;must&nbsp;already&nbsp;exist.<br>
-format&nbsp;:&nbsp;'fixed(f)|table(t)',&nbsp;default&nbsp;is&nbsp;'fixed'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fixed(f)&nbsp;:&nbsp;Fixed&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Fast&nbsp;writing/reading.&nbsp;Not-appendable,&nbsp;nor&nbsp;searchable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;table(t)&nbsp;:&nbsp;Table&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;as&nbsp;a&nbsp;PyTables&nbsp;Table&nbsp;structure&nbsp;which&nbsp;may&nbsp;perform<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;worse&nbsp;but&nbsp;allow&nbsp;more&nbsp;flexible&nbsp;operations&nbsp;like&nbsp;searching<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/&nbsp;selecting&nbsp;subsets&nbsp;of&nbsp;the&nbsp;data<br>
-append&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;Table&nbsp;formats,&nbsp;append&nbsp;the&nbsp;input&nbsp;data&nbsp;to&nbsp;the&nbsp;existing<br>
-data_columns&nbsp;:&nbsp;&nbsp;list&nbsp;of&nbsp;columns,&nbsp;or&nbsp;True,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;columns&nbsp;to&nbsp;create&nbsp;as&nbsp;indexed&nbsp;data&nbsp;columns&nbsp;for&nbsp;on-disk<br>
-&nbsp;&nbsp;&nbsp;&nbsp;queries,&nbsp;or&nbsp;True&nbsp;to&nbsp;use&nbsp;all&nbsp;columns.&nbsp;By&nbsp;default&nbsp;only&nbsp;the&nbsp;axes<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;are&nbsp;indexed.&nbsp;See&nbsp;`here<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__">http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__</a>.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Applicable&nbsp;only&nbsp;to&nbsp;format='table'.<br>
-complevel&nbsp;:&nbsp;int,&nbsp;1-9,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;a&nbsp;complib&nbsp;is&nbsp;specified&nbsp;compression&nbsp;will&nbsp;be&nbsp;applied<br>
-&nbsp;&nbsp;&nbsp;&nbsp;where&nbsp;possible<br>
-complib&nbsp;:&nbsp;{'zlib',&nbsp;'bzip2',&nbsp;'lzo',&nbsp;'blosc',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;complevel&nbsp;is&nbsp;&gt;&nbsp;0&nbsp;apply&nbsp;compression&nbsp;to&nbsp;objects&nbsp;written<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;store&nbsp;wherever&nbsp;possible<br>
-fletcher32&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;applying&nbsp;compression&nbsp;use&nbsp;the&nbsp;fletcher32&nbsp;checksum<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;true,&nbsp;ALL&nbsp;nan&nbsp;rows&nbsp;will&nbsp;not&nbsp;be&nbsp;written&nbsp;to&nbsp;store.</tt></dd></dl>
-
-<dl><dt><a name="State-to_json"><strong>to_json</strong></a>(self, path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False)</dt><dd><tt>Convert&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;a&nbsp;JSON&nbsp;string.<br>
-&nbsp;<br>
-Note&nbsp;NaN's&nbsp;and&nbsp;None&nbsp;will&nbsp;be&nbsp;converted&nbsp;to&nbsp;null&nbsp;and&nbsp;datetime&nbsp;objects<br>
-will&nbsp;be&nbsp;converted&nbsp;to&nbsp;UNIX&nbsp;timestamps.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;or&nbsp;buffer&nbsp;to&nbsp;write&nbsp;the&nbsp;result&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;this&nbsp;is&nbsp;None,&nbsp;return&nbsp;a&nbsp;StringIO&nbsp;of&nbsp;the&nbsp;converted&nbsp;string<br>
-orient&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'index'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:&nbsp;{'split','records','index'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{'split','records','index','columns','values'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;The&nbsp;format&nbsp;of&nbsp;the&nbsp;JSON&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;split&nbsp;:&nbsp;dict&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{index&nbsp;-&gt;&nbsp;[index],&nbsp;columns&nbsp;-&gt;&nbsp;[columns],&nbsp;data&nbsp;-&gt;&nbsp;[values]}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;records&nbsp;:&nbsp;list&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[{column&nbsp;-&gt;&nbsp;value},&nbsp;...&nbsp;,&nbsp;{column&nbsp;-&gt;&nbsp;value}]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;index&nbsp;:&nbsp;dict&nbsp;like&nbsp;{index&nbsp;-&gt;&nbsp;{column&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;columns&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;{index&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;values&nbsp;:&nbsp;just&nbsp;the&nbsp;values&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;table&nbsp;:&nbsp;dict&nbsp;like&nbsp;{'schema':&nbsp;{schema},&nbsp;'data':&nbsp;{data}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;describing&nbsp;the&nbsp;data,&nbsp;and&nbsp;the&nbsp;data&nbsp;component&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;like&nbsp;``orient='records'``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionchanged::&nbsp;0.20.0<br>
-&nbsp;<br>
-date_format&nbsp;:&nbsp;{None,&nbsp;'epoch',&nbsp;'iso'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Type&nbsp;of&nbsp;date&nbsp;conversion.&nbsp;`epoch`&nbsp;=&nbsp;epoch&nbsp;milliseconds,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`iso`&nbsp;=&nbsp;ISO8601.&nbsp;The&nbsp;default&nbsp;depends&nbsp;on&nbsp;the&nbsp;`orient`.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`orient='table'`,&nbsp;the&nbsp;default&nbsp;is&nbsp;`'iso'`.&nbsp;For&nbsp;all&nbsp;other&nbsp;orients,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;default&nbsp;is&nbsp;`'epoch'`.<br>
-double_precision&nbsp;:&nbsp;The&nbsp;number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;use&nbsp;when&nbsp;encoding<br>
-&nbsp;&nbsp;&nbsp;&nbsp;floating&nbsp;point&nbsp;values,&nbsp;default&nbsp;10.<br>
-force_ascii&nbsp;:&nbsp;force&nbsp;encoded&nbsp;string&nbsp;to&nbsp;be&nbsp;ASCII,&nbsp;default&nbsp;True.<br>
-date_unit&nbsp;:&nbsp;string,&nbsp;default&nbsp;'ms'&nbsp;(milliseconds)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;time&nbsp;unit&nbsp;to&nbsp;encode&nbsp;to,&nbsp;governs&nbsp;timestamp&nbsp;and&nbsp;ISO8601<br>
-&nbsp;&nbsp;&nbsp;&nbsp;precision.&nbsp;&nbsp;One&nbsp;of&nbsp;'s',&nbsp;'ms',&nbsp;'us',&nbsp;'ns'&nbsp;for&nbsp;second,&nbsp;millisecond,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;microsecond,&nbsp;and&nbsp;nanosecond&nbsp;respectively.<br>
-default_handler&nbsp;:&nbsp;callable,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Handler&nbsp;to&nbsp;call&nbsp;if&nbsp;<a href="builtins.html#object">object</a>&nbsp;cannot&nbsp;otherwise&nbsp;be&nbsp;converted&nbsp;to&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;suitable&nbsp;format&nbsp;for&nbsp;JSON.&nbsp;Should&nbsp;receive&nbsp;a&nbsp;single&nbsp;argument&nbsp;which&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;convert&nbsp;and&nbsp;return&nbsp;a&nbsp;serialisable&nbsp;<a href="builtins.html#object">object</a>.<br>
-lines&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'orient'&nbsp;is&nbsp;'records'&nbsp;write&nbsp;out&nbsp;line&nbsp;delimited&nbsp;json&nbsp;format.&nbsp;Will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;throw&nbsp;ValueError&nbsp;if&nbsp;incorrect&nbsp;'orient'&nbsp;since&nbsp;others&nbsp;are&nbsp;not&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;filtered&nbsp;info&nbsp;axis<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pd.read_json<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([['a',&nbsp;'b'],&nbsp;['c',&nbsp;'d']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=['row&nbsp;1',&nbsp;'row&nbsp;2'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['col&nbsp;1',&nbsp;'col&nbsp;2'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-to_json">to_json</a>(orient='split')<br>
-'{"columns":["col&nbsp;1","col&nbsp;2"],<br>
-&nbsp;&nbsp;"index":["row&nbsp;1","row&nbsp;2"],<br>
-&nbsp;&nbsp;"data":[["a","b"],["c","d"]]}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'index'``&nbsp;formatted&nbsp;JSON:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-to_json">to_json</a>(orient='index')<br>
-'{"row&nbsp;1":{"col&nbsp;1":"a","col&nbsp;2":"b"},"row&nbsp;2":{"col&nbsp;1":"c","col&nbsp;2":"d"}}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'records'``&nbsp;formatted&nbsp;JSON.<br>
-Note&nbsp;that&nbsp;index&nbsp;labels&nbsp;are&nbsp;not&nbsp;preserved&nbsp;with&nbsp;this&nbsp;encoding.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-to_json">to_json</a>(orient='records')<br>
-'[{"col&nbsp;1":"a","col&nbsp;2":"b"},{"col&nbsp;1":"c","col&nbsp;2":"d"}]'<br>
-&nbsp;<br>
-Encoding&nbsp;with&nbsp;Table&nbsp;Schema<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-to_json">to_json</a>(orient='table')<br>
-'{"schema":&nbsp;{"fields":&nbsp;[{"name":&nbsp;"index",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;1",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;2",&nbsp;"type":&nbsp;"string"}],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"primaryKey":&nbsp;"index",<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"pandas_version":&nbsp;"0.20.0"},<br>
-&nbsp;&nbsp;"data":&nbsp;[{"index":&nbsp;"row&nbsp;1",&nbsp;"col&nbsp;1":&nbsp;"a",&nbsp;"col&nbsp;2":&nbsp;"b"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"index":&nbsp;"row&nbsp;2",&nbsp;"col&nbsp;1":&nbsp;"c",&nbsp;"col&nbsp;2":&nbsp;"d"}]}'</tt></dd></dl>
-
-<dl><dt><a name="State-to_msgpack"><strong>to_msgpack</strong></a>(self, path_or_buf=None, encoding='utf-8', **kwargs)</dt><dd><tt>msgpack&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path<br>
-&nbsp;<br>
-THIS&nbsp;IS&nbsp;AN&nbsp;EXPERIMENTAL&nbsp;LIBRARY&nbsp;and&nbsp;the&nbsp;storage&nbsp;format<br>
-may&nbsp;not&nbsp;be&nbsp;stable&nbsp;until&nbsp;a&nbsp;future&nbsp;release.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;File&nbsp;path,&nbsp;buffer-like,&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;None,&nbsp;return&nbsp;generated&nbsp;string<br>
-append&nbsp;:&nbsp;boolean&nbsp;whether&nbsp;to&nbsp;append&nbsp;to&nbsp;an&nbsp;existing&nbsp;msgpack<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(default&nbsp;is&nbsp;False)<br>
-compress&nbsp;:&nbsp;type&nbsp;of&nbsp;compressor&nbsp;(zlib&nbsp;or&nbsp;blosc),&nbsp;default&nbsp;to&nbsp;None&nbsp;(no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;compression)</tt></dd></dl>
-
-<dl><dt><a name="State-to_pickle"><strong>to_pickle</strong></a>(self, path, compression='infer')</dt><dd><tt>Pickle&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path<br>
-compression&nbsp;:&nbsp;{'infer',&nbsp;'gzip',&nbsp;'bz2',&nbsp;'xz',&nbsp;None},&nbsp;default&nbsp;'infer'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;compression&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0</tt></dd></dl>
-
-<dl><dt><a name="State-to_sql"><strong>to_sql</strong></a>(self, name, con, flavor=None, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None)</dt><dd><tt>Write&nbsp;records&nbsp;stored&nbsp;in&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;SQL&nbsp;database.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-name&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;SQL&nbsp;table<br>
-con&nbsp;:&nbsp;SQLAlchemy&nbsp;engine&nbsp;or&nbsp;DBAPI2&nbsp;connection&nbsp;(legacy&nbsp;mode)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Using&nbsp;SQLAlchemy&nbsp;makes&nbsp;it&nbsp;possible&nbsp;to&nbsp;use&nbsp;any&nbsp;DB&nbsp;supported&nbsp;by&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;library.&nbsp;If&nbsp;a&nbsp;DBAPI2&nbsp;<a href="builtins.html#object">object</a>,&nbsp;only&nbsp;sqlite3&nbsp;is&nbsp;supported.<br>
-flavor&nbsp;:&nbsp;'sqlite',&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;DEPRECATED:&nbsp;this&nbsp;parameter&nbsp;will&nbsp;be&nbsp;removed&nbsp;in&nbsp;a&nbsp;future&nbsp;version,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;as&nbsp;'sqlite'&nbsp;is&nbsp;the&nbsp;only&nbsp;supported&nbsp;option&nbsp;if&nbsp;SQLAlchemy&nbsp;is&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;installed.<br>
-schema&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;the&nbsp;schema&nbsp;(if&nbsp;database&nbsp;flavor&nbsp;supports&nbsp;this).&nbsp;If&nbsp;None,&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;schema.<br>
-if_exists&nbsp;:&nbsp;{'fail',&nbsp;'replace',&nbsp;'append'},&nbsp;default&nbsp;'fail'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;fail:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;do&nbsp;nothing.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;replace:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;drop&nbsp;it,&nbsp;recreate&nbsp;it,&nbsp;and&nbsp;insert&nbsp;data.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;append:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;insert&nbsp;data.&nbsp;Create&nbsp;if&nbsp;does&nbsp;not&nbsp;exist.<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;index&nbsp;as&nbsp;a&nbsp;column.<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s).&nbsp;If&nbsp;None&nbsp;is&nbsp;given&nbsp;(default)&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`index`&nbsp;is&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-chunksize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;not&nbsp;None,&nbsp;then&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;in&nbsp;batches&nbsp;of&nbsp;this&nbsp;size&nbsp;at&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;time.&nbsp;&nbsp;If&nbsp;None,&nbsp;all&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;at&nbsp;once.<br>
-dtype&nbsp;:&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;to&nbsp;SQL&nbsp;type,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Optional&nbsp;specifying&nbsp;the&nbsp;datatype&nbsp;for&nbsp;columns.&nbsp;The&nbsp;SQL&nbsp;type&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;SQLAlchemy&nbsp;type,&nbsp;or&nbsp;a&nbsp;string&nbsp;for&nbsp;sqlite3&nbsp;fallback&nbsp;connection.</tt></dd></dl>
-
-<dl><dt><a name="State-to_xarray"><strong>to_xarray</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;xarray&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;the&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;DataArray&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;Dataset&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-a&nbsp;DataArray&nbsp;for&nbsp;higher&nbsp;dims<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;foo&nbsp;&nbsp;4.0<br>
-1&nbsp;&nbsp;1&nbsp;&nbsp;bar&nbsp;&nbsp;5.0<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;foo&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(index:&nbsp;3)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;index&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;0&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;1&nbsp;1&nbsp;2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'foo'&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;float64&nbsp;4.0&nbsp;5.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;).set_index(['B','A'])<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-B&nbsp;&nbsp;&nbsp;A<br>
-foo&nbsp;1&nbsp;&nbsp;4.0<br>
-bar&nbsp;1&nbsp;&nbsp;5.0<br>
-foo&nbsp;2&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(A:&nbsp;2,&nbsp;B:&nbsp;2)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;*&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(A)&nbsp;int64&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B,&nbsp;A)&nbsp;float64&nbsp;5.0&nbsp;nan&nbsp;4.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p&nbsp;=&nbsp;pd.Panel(np.arange(24).<a href="#State-reshape">reshape</a>(4,3,2),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;items=list('ABCD'),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;major_axis=pd.date_range('20130101',&nbsp;periods=3),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;minor_axis=['first',&nbsp;'second'])<br>
-&gt;&gt;&gt;&nbsp;p<br>
-&lt;class&nbsp;'pandas.core.panel.Panel'&gt;<br>
-Dimensions:&nbsp;4&nbsp;(items)&nbsp;x&nbsp;3&nbsp;(major_axis)&nbsp;x&nbsp;2&nbsp;(minor_axis)<br>
-Items&nbsp;axis:&nbsp;A&nbsp;to&nbsp;D<br>
-Major_axis&nbsp;axis:&nbsp;2013-01-01&nbsp;00:00:00&nbsp;to&nbsp;2013-01-03&nbsp;00:00:00<br>
-Minor_axis&nbsp;axis:&nbsp;first&nbsp;to&nbsp;second<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p.<a href="#State-to_xarray">to_xarray</a>()<br>
-&lt;xarray.DataArray&nbsp;(items:&nbsp;4,&nbsp;major_axis:&nbsp;3,&nbsp;minor_axis:&nbsp;2)&gt;<br>
-array([[[&nbsp;0,&nbsp;&nbsp;1],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;2,&nbsp;&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;4,&nbsp;&nbsp;5]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[&nbsp;6,&nbsp;&nbsp;7],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;8,&nbsp;&nbsp;9],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[10,&nbsp;11]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[12,&nbsp;13],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[14,&nbsp;15],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[16,&nbsp;17]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[18,&nbsp;19],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[20,&nbsp;21],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[22,&nbsp;23]]])<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;items&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(items)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'A'&nbsp;'B'&nbsp;'C'&nbsp;'D'<br>
-&nbsp;&nbsp;*&nbsp;major_axis&nbsp;&nbsp;(major_axis)&nbsp;datetime64[ns]&nbsp;2013-01-01&nbsp;2013-01-02&nbsp;2013-01-03&nbsp;&nbsp;#&nbsp;noqa<br>
-&nbsp;&nbsp;*&nbsp;minor_axis&nbsp;&nbsp;(minor_axis)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'first'&nbsp;'second'<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;the&nbsp;`xarray&nbsp;docs&nbsp;&lt;<a href="http://xarray.pydata.org/en/stable/&gt;`__">http://xarray.pydata.org/en/stable/&gt;`__</a></tt></dd></dl>
-
-<dl><dt><a name="State-truncate"><strong>truncate</strong></a>(self, before=None, after=None, axis=None, copy=True)</dt><dd><tt>Truncates&nbsp;a&nbsp;sorted&nbsp;NDFrame&nbsp;before&nbsp;and/or&nbsp;after&nbsp;some&nbsp;particular<br>
-index&nbsp;value.&nbsp;If&nbsp;the&nbsp;axis&nbsp;contains&nbsp;only&nbsp;datetime&nbsp;values,&nbsp;before/after<br>
-parameters&nbsp;are&nbsp;converted&nbsp;to&nbsp;datetime&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-before&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;before&nbsp;index&nbsp;value<br>
-after&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;after&nbsp;index&nbsp;value<br>
-axis&nbsp;:&nbsp;the&nbsp;truncation&nbsp;axis,&nbsp;defaults&nbsp;to&nbsp;the&nbsp;stat&nbsp;axis<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;is&nbsp;True,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;truncated&nbsp;section<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-truncated&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="State-tshift"><strong>tshift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;the&nbsp;time&nbsp;index,&nbsp;using&nbsp;the&nbsp;index's&nbsp;frequency&nbsp;if&nbsp;available.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM')<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;basestring<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Corresponds&nbsp;to&nbsp;the&nbsp;axis&nbsp;that&nbsp;contains&nbsp;the&nbsp;Index<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;not&nbsp;specified&nbsp;then&nbsp;tries&nbsp;to&nbsp;use&nbsp;the&nbsp;freq&nbsp;or&nbsp;inferred_freq<br>
-attributes&nbsp;of&nbsp;the&nbsp;index.&nbsp;If&nbsp;neither&nbsp;of&nbsp;those&nbsp;attributes&nbsp;exist,&nbsp;a<br>
-ValueError&nbsp;is&nbsp;thrown<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;NDFrame</tt></dd></dl>
-
-<dl><dt><a name="State-tz_convert"><strong>tz_convert</strong></a>(self, tz, axis=0, level=None, copy=True)</dt><dd><tt>Convert&nbsp;tz-aware&nbsp;axis&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;convert<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;convert&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;tz-naive.</tt></dd></dl>
-
-<dl><dt><a name="State-tz_localize"><strong>tz_localize</strong></a>(self, tz, axis=0, level=None, copy=True, ambiguous='raise')</dt><dd><tt>Localize&nbsp;tz-naive&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;localize<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;localize&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-ambiguous&nbsp;:&nbsp;'infer',&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>,&nbsp;'NaT',&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'infer'&nbsp;will&nbsp;attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>&nbsp;where&nbsp;True&nbsp;signifies&nbsp;a&nbsp;DST&nbsp;time,&nbsp;False&nbsp;designates<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;non-DST&nbsp;time&nbsp;(note&nbsp;that&nbsp;this&nbsp;flag&nbsp;is&nbsp;only&nbsp;applicable&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ambiguous&nbsp;times)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'NaT'&nbsp;will&nbsp;return&nbsp;NaT&nbsp;where&nbsp;there&nbsp;are&nbsp;ambiguous&nbsp;times<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'raise'&nbsp;will&nbsp;raise&nbsp;an&nbsp;AmbiguousTimeError&nbsp;if&nbsp;there&nbsp;are&nbsp;ambiguous<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;times<br>
-infer_dst&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on&nbsp;order<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;is&nbsp;tz-aware&nbsp;and&nbsp;tz&nbsp;is&nbsp;not&nbsp;None.</tt></dd></dl>
-
-<dl><dt><a name="State-where"><strong>where</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;True&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;where&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``True``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#State-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#State-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``where``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#State-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).<a href="#State-reshape">reshape</a>(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#State-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#State-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mask`</tt></dd></dl>
-
-<dl><dt><a name="State-xs"><strong>xs</strong></a>(self, key, axis=0, level=None, drop_level=True)</dt><dd><tt>Returns&nbsp;a&nbsp;cross-section&nbsp;(row(s)&nbsp;or&nbsp;column(s))&nbsp;from&nbsp;the<br>
-<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Defaults&nbsp;to&nbsp;cross-section&nbsp;on&nbsp;the&nbsp;rows&nbsp;(axis=0).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Some&nbsp;label&nbsp;contained&nbsp;in&nbsp;the&nbsp;index,&nbsp;or&nbsp;partially&nbsp;in&nbsp;a&nbsp;MultiIndex<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;retrieve&nbsp;cross-section&nbsp;on<br>
-level&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;defaults&nbsp;to&nbsp;first&nbsp;n&nbsp;levels&nbsp;(n=1&nbsp;or&nbsp;len(key))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;key&nbsp;partially&nbsp;contained&nbsp;in&nbsp;a&nbsp;MultiIndex,&nbsp;indicate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;levels&nbsp;are&nbsp;used.&nbsp;Levels&nbsp;can&nbsp;be&nbsp;referred&nbsp;by&nbsp;label&nbsp;or&nbsp;position.<br>
-drop_level&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;returns&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;same&nbsp;levels&nbsp;as&nbsp;self.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C<br>
-a&nbsp;&nbsp;4&nbsp;&nbsp;5&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;4&nbsp;&nbsp;0&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;9&nbsp;&nbsp;7&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-xs">xs</a>('a')<br>
-A&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-B&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-C&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Name:&nbsp;a<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-xs">xs</a>('C',&nbsp;axis=1)<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;C<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;second&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7&nbsp;&nbsp;5&nbsp;&nbsp;5&nbsp;&nbsp;0<br>
-baz&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;three&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-xs">xs</a>(('baz',&nbsp;'three'))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-third<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-xs">xs</a>('one',&nbsp;level=1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-baz&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#State-xs">xs</a>(('baz',&nbsp;2),&nbsp;level=[0,&nbsp;'third'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-second<br>
-three&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-xs&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-xs&nbsp;is&nbsp;only&nbsp;for&nbsp;getting,&nbsp;not&nbsp;setting&nbsp;values.<br>
-&nbsp;<br>
-MultiIndex&nbsp;Slicers&nbsp;is&nbsp;a&nbsp;generic&nbsp;way&nbsp;to&nbsp;get/set&nbsp;values&nbsp;on&nbsp;any&nbsp;level&nbsp;or<br>
-levels.&nbsp;&nbsp;It&nbsp;is&nbsp;a&nbsp;superset&nbsp;of&nbsp;xs&nbsp;functionality,&nbsp;see<br>
-:ref:`MultiIndex&nbsp;Slicers&nbsp;&lt;advanced.mi_slicers&gt;`</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>at</strong></dt>
-<dd><tt>Fast&nbsp;label-based&nbsp;scalar&nbsp;accessor<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``loc``,&nbsp;``at``&nbsp;provides&nbsp;**label**&nbsp;based&nbsp;scalar&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>blocks</strong></dt>
-<dd><tt>Internal&nbsp;property,&nbsp;property&nbsp;synonym&nbsp;for&nbsp;as_blocks()</tt></dd>
-</dl>
-<dl><dt><strong>iat</strong></dt>
-<dd><tt>Fast&nbsp;integer&nbsp;location&nbsp;scalar&nbsp;accessor.<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``iloc``,&nbsp;``iat``&nbsp;provides&nbsp;**integer**&nbsp;based&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>iloc</strong></dt>
-<dd><tt>Purely&nbsp;integer-location&nbsp;based&nbsp;indexing&nbsp;for&nbsp;selection&nbsp;by&nbsp;position.<br>
-&nbsp;<br>
-``.iloc[]``&nbsp;is&nbsp;primarily&nbsp;integer&nbsp;position&nbsp;based&nbsp;(from&nbsp;``0``&nbsp;to<br>
-``length-1``&nbsp;of&nbsp;the&nbsp;axis),&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a&nbsp;boolean<br>
-array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;An&nbsp;integer,&nbsp;e.g.&nbsp;``5``.<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;integers,&nbsp;e.g.&nbsp;``[4,&nbsp;3,&nbsp;0]``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;ints,&nbsp;e.g.&nbsp;``1:7``.<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.iloc``&nbsp;will&nbsp;raise&nbsp;``IndexError``&nbsp;if&nbsp;a&nbsp;requested&nbsp;indexer&nbsp;is<br>
-out-of-bounds,&nbsp;except&nbsp;*slice*&nbsp;indexers&nbsp;which&nbsp;allow&nbsp;out-of-bounds<br>
-indexing&nbsp;(this&nbsp;conforms&nbsp;with&nbsp;python/numpy&nbsp;*slice*&nbsp;semantics).<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Position&nbsp;&lt;indexing.integer&gt;`</tt></dd>
-</dl>
-<dl><dt><strong>ix</strong></dt>
-<dd><tt>A&nbsp;primarily&nbsp;label-location&nbsp;based&nbsp;indexer,&nbsp;with&nbsp;integer&nbsp;position<br>
-fallback.<br>
-&nbsp;<br>
-``.ix[]``&nbsp;supports&nbsp;mixed&nbsp;integer&nbsp;and&nbsp;label&nbsp;based&nbsp;access.&nbsp;It&nbsp;is<br>
-primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;will&nbsp;fall&nbsp;back&nbsp;to&nbsp;integer&nbsp;positional<br>
-access&nbsp;unless&nbsp;the&nbsp;corresponding&nbsp;axis&nbsp;is&nbsp;of&nbsp;integer&nbsp;type.<br>
-&nbsp;<br>
-``.ix``&nbsp;is&nbsp;the&nbsp;most&nbsp;general&nbsp;indexer&nbsp;and&nbsp;will&nbsp;support&nbsp;any&nbsp;of&nbsp;the<br>
-inputs&nbsp;in&nbsp;``.loc``&nbsp;and&nbsp;``.iloc``.&nbsp;``.ix``&nbsp;also&nbsp;supports&nbsp;floating<br>
-point&nbsp;label&nbsp;schemes.&nbsp;``.ix``&nbsp;is&nbsp;exceptionally&nbsp;useful&nbsp;when&nbsp;dealing<br>
-with&nbsp;mixed&nbsp;positional&nbsp;and&nbsp;label&nbsp;based&nbsp;hierachical&nbsp;indexes.<br>
-&nbsp;<br>
-However,&nbsp;when&nbsp;an&nbsp;axis&nbsp;is&nbsp;integer&nbsp;based,&nbsp;ONLY&nbsp;label&nbsp;based&nbsp;access<br>
-and&nbsp;not&nbsp;positional&nbsp;access&nbsp;is&nbsp;supported.&nbsp;Thus,&nbsp;in&nbsp;such&nbsp;cases,&nbsp;it's<br>
-usually&nbsp;better&nbsp;to&nbsp;be&nbsp;explicit&nbsp;and&nbsp;use&nbsp;``.iloc``&nbsp;or&nbsp;``.loc``.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Advanced&nbsp;Indexing&nbsp;&lt;advanced&gt;`.</tt></dd>
-</dl>
-<dl><dt><strong>loc</strong></dt>
-<dd><tt>Purely&nbsp;label-location&nbsp;based&nbsp;indexer&nbsp;for&nbsp;selection&nbsp;by&nbsp;label.<br>
-&nbsp;<br>
-``.loc[]``&nbsp;is&nbsp;primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a<br>
-boolean&nbsp;array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;A&nbsp;single&nbsp;label,&nbsp;e.g.&nbsp;``5``&nbsp;or&nbsp;``'a'``,&nbsp;(note&nbsp;that&nbsp;``5``&nbsp;is<br>
-&nbsp;&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;*label*&nbsp;of&nbsp;the&nbsp;index,&nbsp;and&nbsp;**never**&nbsp;as&nbsp;an<br>
-&nbsp;&nbsp;integer&nbsp;position&nbsp;along&nbsp;the&nbsp;index).<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;labels,&nbsp;e.g.&nbsp;``['a',&nbsp;'b',&nbsp;'c']``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;labels,&nbsp;e.g.&nbsp;``'a':'f'``&nbsp;(note&nbsp;that&nbsp;contrary<br>
-&nbsp;&nbsp;to&nbsp;usual&nbsp;python&nbsp;slices,&nbsp;**both**&nbsp;the&nbsp;start&nbsp;and&nbsp;the&nbsp;stop&nbsp;are&nbsp;included!).<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.loc``&nbsp;will&nbsp;raise&nbsp;a&nbsp;``KeyError``&nbsp;when&nbsp;the&nbsp;items&nbsp;are&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Label&nbsp;&lt;indexing.label&gt;`</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>is_copy</strong> = None</dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a>:<br>
-<dl><dt><a name="State-__dir__"><strong>__dir__</strong></a>(self)</dt><dd><tt>Provide&nbsp;method&nbsp;name&nbsp;lookup&nbsp;and&nbsp;completion<br>
-Only&nbsp;provide&nbsp;'public'&nbsp;methods</tt></dd></dl>
-
-<dl><dt><a name="State-__sizeof__"><strong>__sizeof__</strong></a>(self)</dt><dd><tt>Generates&nbsp;the&nbsp;total&nbsp;memory&nbsp;usage&nbsp;for&nbsp;a&nbsp;<a href="builtins.html#object">object</a>&nbsp;that&nbsp;returns<br>
-either&nbsp;a&nbsp;value&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;values</tt></dd></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a>:<br>
-<dl><dt><a name="State-__bytes__"><strong>__bytes__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;bytes(obj)&nbsp;in&nbsp;py3&nbsp;only.<br>
-Yields&nbsp;a&nbsp;bytestring&nbsp;in&nbsp;both&nbsp;py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="State-__repr__"><strong>__repr__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-<dl><dt><a name="State-__str__"><strong>__str__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;Object<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;<a href="#State-str">str</a>(df)&nbsp;in&nbsp;both&nbsp;py2/py3.<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-</td></tr></table> <p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#ffc8d8">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#000000" face="helvetica, arial"><a name="SubPlots">class <strong>SubPlots</strong></a>(<a href="builtins.html#object">builtins.object</a>)</font></td></tr>
-    
-<tr><td bgcolor="#ffc8d8"><tt>&nbsp;&nbsp;&nbsp;</tt></td><td>&nbsp;</td>
-<td width="100%">Methods defined here:<br>
-<dl><dt><a name="SubPlots-__init__"><strong>__init__</strong></a>(self, fig, axes_seq)</dt><dd><tt>Initialize&nbsp;self.&nbsp;&nbsp;See&nbsp;help(type(self))&nbsp;for&nbsp;accurate&nbsp;signature.</tt></dd></dl>
-
-<dl><dt><a name="SubPlots-current_axes"><strong>current_axes</strong></a>()</dt></dl>
-
-<dl><dt><a name="SubPlots-next_axes"><strong>next_axes</strong></a>(self)</dt><dd><tt>#&nbsp;TODO:&nbsp;consider&nbsp;making&nbsp;<a href="#SubPlots">SubPlots</a>&nbsp;iterable</tt></dd></dl>
-
-<hr>
-Data descriptors defined here:<br>
-<dl><dt><strong>__dict__</strong></dt>
-<dd><tt>dictionary&nbsp;for&nbsp;instance&nbsp;variables&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>__weakref__</strong></dt>
-<dd><tt>list&nbsp;of&nbsp;weak&nbsp;references&nbsp;to&nbsp;the&nbsp;object&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-</td></tr></table> <p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#ffc8d8">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#000000" face="helvetica, arial"><a name="SweepFrame">class <strong>SweepFrame</strong></a>(<a href="modsim.html#MyDataFrame">MyDataFrame</a>)</font></td></tr>
-    
-<tr bgcolor="#ffc8d8"><td rowspan=2><tt>&nbsp;&nbsp;&nbsp;</tt></td>
-<td colspan=2><tt>MyTimeFrame&nbsp;is&nbsp;a&nbsp;modified&nbsp;version&nbsp;of&nbsp;a&nbsp;Pandas&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,<br>
-with&nbsp;a&nbsp;few&nbsp;changes&nbsp;to&nbsp;make&nbsp;it&nbsp;more&nbsp;suited&nbsp;to&nbsp;our&nbsp;purpose.<br>
-&nbsp;<br>
-In&nbsp;particular,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;provides&nbsp;two&nbsp;special&nbsp;variables&nbsp;called<br>
-`dt`&nbsp;and&nbsp;`T`&nbsp;that&nbsp;cause&nbsp;problems&nbsp;if&nbsp;we&nbsp;try&nbsp;to&nbsp;use&nbsp;those&nbsp;names<br>
-as&nbsp;state&nbsp;variables.<br>
-&nbsp;<br>
-So&nbsp;I&nbsp;added&nbsp;new&nbsp;definitions&nbsp;that&nbsp;override&nbsp;the&nbsp;special&nbsp;variables<br>
-and&nbsp;make&nbsp;these&nbsp;names&nbsp;useable&nbsp;as&nbsp;row&nbsp;labels.<br>&nbsp;</tt></td></tr>
-<tr><td>&nbsp;</td>
-<td width="100%"><dl><dt>Method resolution order:</dt>
-<dd><a href="modsim.html#SweepFrame">SweepFrame</a></dd>
-<dd><a href="modsim.html#MyDataFrame">MyDataFrame</a></dd>
-<dd><a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a></dd>
-<dd><a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a></dd>
-<dd><a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a></dd>
-<dd><a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a></dd>
-<dd><a href="pandas.core.base.html#SelectionMixin">pandas.core.base.SelectionMixin</a></dd>
-<dd><a href="builtins.html#object">builtins.object</a></dd>
-</dl>
-<hr>
-Methods inherited from <a href="modsim.html#MyDataFrame">MyDataFrame</a>:<br>
-<dl><dt><a name="SweepFrame-__init__"><strong>__init__</strong></a>(self, *args, **kwargs)</dt><dd><tt>Initialize&nbsp;self.&nbsp;&nbsp;See&nbsp;help(type(self))&nbsp;for&nbsp;accurate&nbsp;signature.</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="modsim.html#MyDataFrame">MyDataFrame</a>:<br>
-<dl><dt><strong>T</strong></dt>
-<dd><tt>Intercept&nbsp;the&nbsp;Series&nbsp;accessor&nbsp;object&nbsp;so&nbsp;we&nbsp;can&nbsp;use&nbsp;`T`<br>
-as&nbsp;a&nbsp;row&nbsp;label&nbsp;and&nbsp;access&nbsp;it&nbsp;using&nbsp;dot&nbsp;notation.<br>
-&nbsp;<br>
-https://pandas.pydata.org/pandas-docs/stable/generated/<br>
-pandas.DataFrame.T.html#pandas.DataFrame.T</tt></dd>
-</dl>
-<dl><dt><strong>dt</strong></dt>
-<dd><tt>Intercept&nbsp;the&nbsp;Series&nbsp;accessor&nbsp;object&nbsp;so&nbsp;we&nbsp;can&nbsp;use&nbsp;`dt`<br>
-as&nbsp;a&nbsp;row&nbsp;label&nbsp;and&nbsp;access&nbsp;it&nbsp;using&nbsp;dot&nbsp;notation.<br>
-&nbsp;<br>
-https://pandas.pydata.org/pandas-docs/stable/generated/<br>
-pandas.DataFrame.dt.html</tt></dd>
-</dl>
-<hr>
-Methods inherited from <a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a>:<br>
-<dl><dt><a name="SweepFrame-__add__"><strong>__add__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__add__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__and__"><strong>__and__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__and__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__div__"><strong>__div__</strong></a> = __truediv__(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__truediv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__eq__"><strong>__eq__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__eq__</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__floordiv__"><strong>__floordiv__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__floordiv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__ge__"><strong>__ge__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__ge__</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__getitem__"><strong>__getitem__</strong></a>(self, key)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__gt__"><strong>__gt__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__gt__</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__iadd__"><strong>__iadd__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__imul__"><strong>__imul__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__ipow__"><strong>__ipow__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__isub__"><strong>__isub__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__itruediv__"><strong>__itruediv__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__le__"><strong>__le__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__le__</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__len__"><strong>__len__</strong></a>(self)</dt><dd><tt>Returns&nbsp;length&nbsp;of&nbsp;info&nbsp;axis,&nbsp;but&nbsp;here&nbsp;we&nbsp;use&nbsp;the&nbsp;index</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__lt__"><strong>__lt__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__lt__</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__mod__"><strong>__mod__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__mod__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__mul__"><strong>__mul__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__mul__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__ne__"><strong>__ne__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__ne__</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__or__"><strong>__or__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__or__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__pow__"><strong>__pow__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__pow__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__radd__"><strong>__radd__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__radd__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__rand__"><strong>__rand__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rand__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__rdiv__"><strong>__rdiv__</strong></a> = __rtruediv__(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rtruediv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__rfloordiv__"><strong>__rfloordiv__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rfloordiv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__rmod__"><strong>__rmod__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rmod__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__rmul__"><strong>__rmul__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rmul__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__ror__"><strong>__ror__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__ror__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__rpow__"><strong>__rpow__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rpow__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__rsub__"><strong>__rsub__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rsub__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__rtruediv__"><strong>__rtruediv__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rtruediv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__rxor__"><strong>__rxor__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rxor__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__setitem__"><strong>__setitem__</strong></a>(self, key, value)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__sub__"><strong>__sub__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__sub__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__truediv__"><strong>__truediv__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__truediv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__unicode__"><strong>__unicode__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;unicode(df)&nbsp;in&nbsp;py2&nbsp;only.&nbsp;Yields&nbsp;a&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;both<br>
-py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__xor__"><strong>__xor__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__xor__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-add"><strong>add</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Addition&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`add`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;+&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.radd</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-agg"><strong>agg</strong></a> = aggregate(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#SweepFrame-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#SweepFrame-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(10,&nbsp;3),&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.date_range('1/1/2000',&nbsp;periods=10))<br>
-&gt;&gt;&gt;&nbsp;df.iloc[3:7]&nbsp;=&nbsp;np.nan<br>
-&nbsp;<br>
-Aggregate&nbsp;these&nbsp;functions&nbsp;across&nbsp;all&nbsp;columns<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-agg">agg</a>(['sum',&nbsp;'min'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-sum&nbsp;-0.182253&nbsp;-0.614014&nbsp;-2.909534<br>
-min&nbsp;-1.916563&nbsp;-1.460076&nbsp;-1.568297<br>
-&nbsp;<br>
-Different&nbsp;aggregations&nbsp;per&nbsp;column<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-agg">agg</a>({'A'&nbsp;:&nbsp;['sum',&nbsp;'min'],&nbsp;'B'&nbsp;:&nbsp;['min',&nbsp;'max']})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;1.514318<br>
-min&nbsp;-1.916563&nbsp;-1.460076<br>
-sum&nbsp;-0.182253&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.transform<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.groupby.aggregate<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.resample.aggregate<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rolling.aggregate</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-aggregate"><strong>aggregate</strong></a>(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#SweepFrame-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#SweepFrame-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(10,&nbsp;3),&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.date_range('1/1/2000',&nbsp;periods=10))<br>
-&gt;&gt;&gt;&nbsp;df.iloc[3:7]&nbsp;=&nbsp;np.nan<br>
-&nbsp;<br>
-Aggregate&nbsp;these&nbsp;functions&nbsp;across&nbsp;all&nbsp;columns<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-agg">agg</a>(['sum',&nbsp;'min'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-sum&nbsp;-0.182253&nbsp;-0.614014&nbsp;-2.909534<br>
-min&nbsp;-1.916563&nbsp;-1.460076&nbsp;-1.568297<br>
-&nbsp;<br>
-Different&nbsp;aggregations&nbsp;per&nbsp;column<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-agg">agg</a>({'A'&nbsp;:&nbsp;['sum',&nbsp;'min'],&nbsp;'B'&nbsp;:&nbsp;['min',&nbsp;'max']})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;1.514318<br>
-min&nbsp;-1.916563&nbsp;-1.460076<br>
-sum&nbsp;-0.182253&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.transform<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.groupby.aggregate<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.resample.aggregate<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rolling.aggregate</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-align"><strong>align</strong></a>(self, other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)</dt><dd><tt>Align&nbsp;two&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;their&nbsp;axes&nbsp;with&nbsp;the<br>
-specified&nbsp;join&nbsp;method&nbsp;for&nbsp;each&nbsp;axis&nbsp;Index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-join&nbsp;:&nbsp;{'outer',&nbsp;'inner',&nbsp;'left',&nbsp;'right'},&nbsp;default&nbsp;'outer'<br>
-axis&nbsp;:&nbsp;allowed&nbsp;axis&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;on&nbsp;index&nbsp;(0),&nbsp;columns&nbsp;(1),&nbsp;or&nbsp;both&nbsp;(None)<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Always&nbsp;returns&nbsp;new&nbsp;objects.&nbsp;If&nbsp;copy=False&nbsp;and&nbsp;no&nbsp;reindexing&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;required&nbsp;then&nbsp;original&nbsp;objects&nbsp;are&nbsp;returned.<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-method&nbsp;:&nbsp;str,&nbsp;default&nbsp;None<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-fill_axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Filling&nbsp;axis,&nbsp;method&nbsp;and&nbsp;limit<br>
-broadcast_axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;values&nbsp;along&nbsp;this&nbsp;axis,&nbsp;if&nbsp;aligning&nbsp;two&nbsp;objects&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;different&nbsp;dimensions<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-(left,&nbsp;right)&nbsp;:&nbsp;(<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;type&nbsp;of&nbsp;other)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Aligned&nbsp;objects</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-all"><strong>all</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;all&nbsp;elements&nbsp;are&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-all&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-any"><strong>any</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;any&nbsp;element&nbsp;is&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-any&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-append"><strong>append</strong></a>(self, other, ignore_index=False, verify_integrity=False)</dt><dd><tt>Append&nbsp;rows&nbsp;of&nbsp;`other`&nbsp;to&nbsp;the&nbsp;end&nbsp;of&nbsp;this&nbsp;frame,&nbsp;returning&nbsp;a&nbsp;new<br>
-<a href="builtins.html#object">object</a>.&nbsp;Columns&nbsp;not&nbsp;in&nbsp;this&nbsp;frame&nbsp;are&nbsp;added&nbsp;as&nbsp;new&nbsp;columns.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>/dict-like&nbsp;<a href="builtins.html#object">object</a>,&nbsp;or&nbsp;list&nbsp;of&nbsp;these<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;data&nbsp;to&nbsp;append.<br>
-ignore_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;not&nbsp;use&nbsp;the&nbsp;index&nbsp;labels.<br>
-verify_integrity&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;raise&nbsp;ValueError&nbsp;on&nbsp;creating&nbsp;index&nbsp;with&nbsp;duplicates.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-appended&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;a&nbsp;list&nbsp;of&nbsp;dict/series&nbsp;is&nbsp;passed&nbsp;and&nbsp;the&nbsp;keys&nbsp;are&nbsp;all&nbsp;contained&nbsp;in<br>
-the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;index,&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;columns&nbsp;in&nbsp;the&nbsp;resulting<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;be&nbsp;unchanged.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.concat&nbsp;:&nbsp;General&nbsp;function&nbsp;to&nbsp;concatenate&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;Panel&nbsp;objects<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[1,&nbsp;2],&nbsp;[3,&nbsp;4]],&nbsp;columns=list('AB'))<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-1&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[5,&nbsp;6],&nbsp;[7,&nbsp;8]],&nbsp;columns=list('AB'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-append">append</a>(df2)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-1&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-0&nbsp;&nbsp;5&nbsp;&nbsp;6<br>
-1&nbsp;&nbsp;7&nbsp;&nbsp;8<br>
-&nbsp;<br>
-With&nbsp;`ignore_index`&nbsp;set&nbsp;to&nbsp;True:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-append">append</a>(df2,&nbsp;ignore_index=True)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-1&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;5&nbsp;&nbsp;6<br>
-3&nbsp;&nbsp;7&nbsp;&nbsp;8</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-apply"><strong>apply</strong></a>(self, func, axis=0, broadcast=False, raw=False, reduce=None, args=(), **kwds)</dt><dd><tt>Applies&nbsp;function&nbsp;along&nbsp;input&nbsp;axis&nbsp;of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-Objects&nbsp;passed&nbsp;to&nbsp;functions&nbsp;are&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;objects&nbsp;having&nbsp;index<br>
-either&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;index&nbsp;(axis=0)&nbsp;or&nbsp;the&nbsp;columns&nbsp;(axis=1).<br>
-Return&nbsp;type&nbsp;depends&nbsp;on&nbsp;whether&nbsp;passed&nbsp;function&nbsp;aggregates,&nbsp;or&nbsp;the<br>
-reduce&nbsp;argument&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;is&nbsp;empty.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;apply&nbsp;to&nbsp;each&nbsp;column/row<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;0&nbsp;or&nbsp;'index':&nbsp;apply&nbsp;function&nbsp;to&nbsp;each&nbsp;column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;1&nbsp;or&nbsp;'columns':&nbsp;apply&nbsp;function&nbsp;to&nbsp;each&nbsp;row<br>
-broadcast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;aggregation&nbsp;functions,&nbsp;return&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;size&nbsp;with&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;&nbsp;propagated<br>
-raw&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;convert&nbsp;each&nbsp;row&nbsp;or&nbsp;column&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;If&nbsp;raw=True&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;function&nbsp;will&nbsp;receive&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;objects&nbsp;instead.&nbsp;If&nbsp;you&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;just&nbsp;applying&nbsp;a&nbsp;NumPy&nbsp;reduction&nbsp;function&nbsp;this&nbsp;will&nbsp;achieve&nbsp;much<br>
-&nbsp;&nbsp;&nbsp;&nbsp;better&nbsp;performance<br>
-reduce&nbsp;:&nbsp;boolean&nbsp;or&nbsp;None,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Try&nbsp;to&nbsp;apply&nbsp;reduction&nbsp;procedures.&nbsp;If&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;is&nbsp;empty,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;apply&nbsp;will&nbsp;use&nbsp;reduce&nbsp;to&nbsp;determine&nbsp;whether&nbsp;the&nbsp;result&nbsp;should&nbsp;be&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;If&nbsp;reduce&nbsp;is&nbsp;None&nbsp;(the&nbsp;default),&nbsp;apply's<br>
-&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;value&nbsp;will&nbsp;be&nbsp;guessed&nbsp;by&nbsp;calling&nbsp;func&nbsp;an&nbsp;empty&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(note:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;while&nbsp;guessing,&nbsp;exceptions&nbsp;raised&nbsp;by&nbsp;func&nbsp;will&nbsp;be&nbsp;ignored).&nbsp;If<br>
-&nbsp;&nbsp;&nbsp;&nbsp;reduce&nbsp;is&nbsp;True&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;always&nbsp;be&nbsp;returned,&nbsp;and&nbsp;if&nbsp;False&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;always&nbsp;be&nbsp;returned.<br>
-args&nbsp;:&nbsp;tuple<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Positional&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;to&nbsp;function&nbsp;in&nbsp;addition&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;array/series<br>
-Additional&nbsp;keyword&nbsp;arguments&nbsp;will&nbsp;be&nbsp;passed&nbsp;as&nbsp;keywords&nbsp;to&nbsp;the&nbsp;function<br>
-&nbsp;<br>
-Notes<br>
------<br>
-In&nbsp;the&nbsp;current&nbsp;implementation&nbsp;apply&nbsp;calls&nbsp;func&nbsp;twice&nbsp;on&nbsp;the<br>
-first&nbsp;column/row&nbsp;to&nbsp;decide&nbsp;whether&nbsp;it&nbsp;can&nbsp;take&nbsp;a&nbsp;fast&nbsp;or&nbsp;slow<br>
-code&nbsp;path.&nbsp;This&nbsp;can&nbsp;lead&nbsp;to&nbsp;unexpected&nbsp;behavior&nbsp;if&nbsp;func&nbsp;has<br>
-side-effects,&nbsp;as&nbsp;they&nbsp;will&nbsp;take&nbsp;effect&nbsp;twice&nbsp;for&nbsp;the&nbsp;first<br>
-column/row.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-apply">apply</a>(numpy.sqrt)&nbsp;#&nbsp;returns&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-apply">apply</a>(numpy.sum,&nbsp;axis=0)&nbsp;#&nbsp;equiv&nbsp;to&nbsp;df.<a href="#SweepFrame-sum">sum</a>(0)<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-apply">apply</a>(numpy.sum,&nbsp;axis=1)&nbsp;#&nbsp;equiv&nbsp;to&nbsp;df.<a href="#SweepFrame-sum">sum</a>(1)<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap:&nbsp;For&nbsp;elementwise&nbsp;operations<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.aggregate:&nbsp;only&nbsp;perform&nbsp;aggregating&nbsp;type&nbsp;operations<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.transform:&nbsp;only&nbsp;perform&nbsp;transformating&nbsp;type&nbsp;operations<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-applied&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-applymap"><strong>applymap</strong></a>(self, func)</dt><dd><tt>Apply&nbsp;a&nbsp;function&nbsp;to&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;that&nbsp;is&nbsp;intended&nbsp;to&nbsp;operate<br>
-elementwise,&nbsp;i.e.&nbsp;like&nbsp;doing&nbsp;map(func,&nbsp;series)&nbsp;for&nbsp;each&nbsp;series&nbsp;in&nbsp;the<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Python&nbsp;function,&nbsp;returns&nbsp;a&nbsp;single&nbsp;value&nbsp;from&nbsp;a&nbsp;single&nbsp;value<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(3,&nbsp;3))<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-0&nbsp;&nbsp;-0.029638&nbsp;&nbsp;1.081563&nbsp;&nbsp;&nbsp;1.280300<br>
-1&nbsp;&nbsp;&nbsp;0.647747&nbsp;&nbsp;0.831136&nbsp;&nbsp;-1.549481<br>
-2&nbsp;&nbsp;&nbsp;0.513416&nbsp;-0.884417&nbsp;&nbsp;&nbsp;0.195343<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;df.<a href="#SweepFrame-applymap">applymap</a>(lambda&nbsp;x:&nbsp;'%.2f'&nbsp;%&nbsp;x)<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-0&nbsp;&nbsp;-0.03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.08&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.28<br>
-1&nbsp;&nbsp;&nbsp;0.65&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.83&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-1.55<br>
-2&nbsp;&nbsp;&nbsp;0.51&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-0.88&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.20<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-applied&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply&nbsp;:&nbsp;For&nbsp;operations&nbsp;on&nbsp;rows/columns</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-assign"><strong>assign</strong></a>(self, **kwargs)</dt><dd><tt>Assign&nbsp;new&nbsp;columns&nbsp;to&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;returning&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a><br>
-(a&nbsp;copy)&nbsp;with&nbsp;all&nbsp;the&nbsp;original&nbsp;columns&nbsp;in&nbsp;addition&nbsp;to&nbsp;the&nbsp;new&nbsp;ones.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-kwargs&nbsp;:&nbsp;keyword,&nbsp;value&nbsp;pairs<br>
-&nbsp;&nbsp;&nbsp;&nbsp;keywords&nbsp;are&nbsp;the&nbsp;column&nbsp;names.&nbsp;If&nbsp;the&nbsp;values&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;callable,&nbsp;they&nbsp;are&nbsp;computed&nbsp;on&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;assigned&nbsp;to&nbsp;the&nbsp;new&nbsp;columns.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;values&nbsp;are&nbsp;not&nbsp;callable,&nbsp;(e.g.&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;scalar,&nbsp;or&nbsp;array),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;they&nbsp;are&nbsp;simply&nbsp;assigned.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-df&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;new&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;the&nbsp;new&nbsp;columns&nbsp;in&nbsp;addition&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;all&nbsp;the&nbsp;existing&nbsp;columns.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Since&nbsp;``kwargs``&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;the&nbsp;order&nbsp;of&nbsp;your<br>
-arguments&nbsp;may&nbsp;not&nbsp;be&nbsp;preserved.&nbsp;To&nbsp;make&nbsp;things&nbsp;predicatable,<br>
-the&nbsp;columns&nbsp;are&nbsp;inserted&nbsp;in&nbsp;alphabetical&nbsp;order,&nbsp;at&nbsp;the&nbsp;end&nbsp;of<br>
-your&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Assigning&nbsp;multiple&nbsp;columns&nbsp;within&nbsp;the&nbsp;same<br>
-``assign``&nbsp;is&nbsp;possible,&nbsp;but&nbsp;you&nbsp;cannot&nbsp;reference&nbsp;other&nbsp;columns<br>
-created&nbsp;within&nbsp;the&nbsp;same&nbsp;``assign``&nbsp;call.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;range(1,&nbsp;11),&nbsp;'B':&nbsp;np.random.randn(10)})<br>
-&nbsp;<br>
-Where&nbsp;the&nbsp;value&nbsp;is&nbsp;a&nbsp;callable,&nbsp;evaluated&nbsp;on&nbsp;`df`:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-assign">assign</a>(ln_A&nbsp;=&nbsp;lambda&nbsp;x:&nbsp;np.log(x.A))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ln_A<br>
-0&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;0.426905&nbsp;&nbsp;0.000000<br>
-1&nbsp;&nbsp;&nbsp;2&nbsp;-0.780949&nbsp;&nbsp;0.693147<br>
-2&nbsp;&nbsp;&nbsp;3&nbsp;-0.418711&nbsp;&nbsp;1.098612<br>
-3&nbsp;&nbsp;&nbsp;4&nbsp;-0.269708&nbsp;&nbsp;1.386294<br>
-4&nbsp;&nbsp;&nbsp;5&nbsp;-0.274002&nbsp;&nbsp;1.609438<br>
-5&nbsp;&nbsp;&nbsp;6&nbsp;-0.500792&nbsp;&nbsp;1.791759<br>
-6&nbsp;&nbsp;&nbsp;7&nbsp;&nbsp;1.649697&nbsp;&nbsp;1.945910<br>
-7&nbsp;&nbsp;&nbsp;8&nbsp;-1.495604&nbsp;&nbsp;2.079442<br>
-8&nbsp;&nbsp;&nbsp;9&nbsp;&nbsp;0.549296&nbsp;&nbsp;2.197225<br>
-9&nbsp;&nbsp;10&nbsp;-0.758542&nbsp;&nbsp;2.302585<br>
-&nbsp;<br>
-Where&nbsp;the&nbsp;value&nbsp;already&nbsp;exists&nbsp;and&nbsp;is&nbsp;inserted:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;newcol&nbsp;=&nbsp;np.log(df['A'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-assign">assign</a>(ln_A=newcol)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ln_A<br>
-0&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;0.426905&nbsp;&nbsp;0.000000<br>
-1&nbsp;&nbsp;&nbsp;2&nbsp;-0.780949&nbsp;&nbsp;0.693147<br>
-2&nbsp;&nbsp;&nbsp;3&nbsp;-0.418711&nbsp;&nbsp;1.098612<br>
-3&nbsp;&nbsp;&nbsp;4&nbsp;-0.269708&nbsp;&nbsp;1.386294<br>
-4&nbsp;&nbsp;&nbsp;5&nbsp;-0.274002&nbsp;&nbsp;1.609438<br>
-5&nbsp;&nbsp;&nbsp;6&nbsp;-0.500792&nbsp;&nbsp;1.791759<br>
-6&nbsp;&nbsp;&nbsp;7&nbsp;&nbsp;1.649697&nbsp;&nbsp;1.945910<br>
-7&nbsp;&nbsp;&nbsp;8&nbsp;-1.495604&nbsp;&nbsp;2.079442<br>
-8&nbsp;&nbsp;&nbsp;9&nbsp;&nbsp;0.549296&nbsp;&nbsp;2.197225<br>
-9&nbsp;&nbsp;10&nbsp;-0.758542&nbsp;&nbsp;2.302585</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-boxplot"><strong>boxplot</strong></a>(self, column=None, by=None, ax=None, fontsize=None, rot=0, grid=True, figsize=None, layout=None, return_type=None, **kwds)</dt><dd><tt>Make&nbsp;a&nbsp;box&nbsp;plot&nbsp;from&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;optionally&nbsp;grouped&nbsp;by&nbsp;some&nbsp;columns&nbsp;or<br>
-other&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;the&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>&nbsp;holding&nbsp;the&nbsp;data<br>
-column&nbsp;:&nbsp;column&nbsp;name&nbsp;or&nbsp;list&nbsp;of&nbsp;names,&nbsp;or&nbsp;vector<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Can&nbsp;be&nbsp;any&nbsp;valid&nbsp;input&nbsp;to&nbsp;groupby<br>
-by&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;group&nbsp;by<br>
-ax&nbsp;:&nbsp;Matplotlib&nbsp;axes&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-fontsize&nbsp;:&nbsp;int&nbsp;or&nbsp;string<br>
-rot&nbsp;:&nbsp;label&nbsp;rotation&nbsp;angle<br>
-figsize&nbsp;:&nbsp;A&nbsp;tuple&nbsp;(width,&nbsp;height)&nbsp;in&nbsp;inches<br>
-grid&nbsp;:&nbsp;Setting&nbsp;this&nbsp;to&nbsp;True&nbsp;will&nbsp;show&nbsp;the&nbsp;grid<br>
-layout&nbsp;:&nbsp;tuple&nbsp;(optional)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(rows,&nbsp;columns)&nbsp;for&nbsp;the&nbsp;layout&nbsp;of&nbsp;the&nbsp;plot<br>
-return_type&nbsp;:&nbsp;{None,&nbsp;'axes',&nbsp;'dict',&nbsp;'both'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;kind&nbsp;of&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;return.&nbsp;The&nbsp;default&nbsp;is&nbsp;``axes``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'axes'&nbsp;returns&nbsp;the&nbsp;matplotlib&nbsp;axes&nbsp;the&nbsp;boxplot&nbsp;is&nbsp;drawn&nbsp;on;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'dict'&nbsp;returns&nbsp;a&nbsp;dictionary&nbsp;&nbsp;whose&nbsp;values&nbsp;are&nbsp;the&nbsp;matplotlib<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Lines&nbsp;of&nbsp;the&nbsp;boxplot;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'both'&nbsp;returns&nbsp;a&nbsp;namedtuple&nbsp;with&nbsp;the&nbsp;axes&nbsp;and&nbsp;dict.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;grouping&nbsp;with&nbsp;``by``,&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;mapping&nbsp;columns&nbsp;to&nbsp;``return_type``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;returned,&nbsp;unless&nbsp;``return_type``&nbsp;is&nbsp;None,&nbsp;in&nbsp;which&nbsp;case&nbsp;a&nbsp;NumPy<br>
-&nbsp;&nbsp;&nbsp;&nbsp;array&nbsp;of&nbsp;axes&nbsp;is&nbsp;returned&nbsp;with&nbsp;the&nbsp;same&nbsp;shape&nbsp;as&nbsp;``layout``.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;prose&nbsp;documentation&nbsp;for&nbsp;more.<br>
-&nbsp;<br>
-kwds&nbsp;:&nbsp;other&nbsp;plotting&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;be&nbsp;passed&nbsp;to&nbsp;matplotlib&nbsp;boxplot<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;function<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-lines&nbsp;:&nbsp;dict<br>
-ax&nbsp;:&nbsp;matplotlib&nbsp;Axes<br>
-(ax,&nbsp;lines):&nbsp;namedtuple<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Use&nbsp;``return_type='dict'``&nbsp;when&nbsp;you&nbsp;want&nbsp;to&nbsp;tweak&nbsp;the&nbsp;appearance<br>
-of&nbsp;the&nbsp;lines&nbsp;after&nbsp;plotting.&nbsp;In&nbsp;this&nbsp;case&nbsp;a&nbsp;dict&nbsp;containing&nbsp;the&nbsp;Lines<br>
-making&nbsp;up&nbsp;the&nbsp;boxes,&nbsp;caps,&nbsp;fliers,&nbsp;medians,&nbsp;and&nbsp;whiskers&nbsp;is&nbsp;returned.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-combine"><strong>combine</strong></a>(self, other, func, fill_value=None, overwrite=True)</dt><dd><tt>Add&nbsp;two&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects&nbsp;and&nbsp;do&nbsp;not&nbsp;propagate&nbsp;NaN&nbsp;values,&nbsp;so&nbsp;if&nbsp;for&nbsp;a<br>
-(column,&nbsp;time)&nbsp;one&nbsp;frame&nbsp;is&nbsp;missing&nbsp;a&nbsp;value,&nbsp;it&nbsp;will&nbsp;default&nbsp;to&nbsp;the<br>
-other&nbsp;frame's&nbsp;value&nbsp;(which&nbsp;might&nbsp;be&nbsp;NaN&nbsp;as&nbsp;well)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-func&nbsp;:&nbsp;function<br>
-fill_value&nbsp;:&nbsp;scalar&nbsp;value<br>
-overwrite&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;then&nbsp;overwrite&nbsp;values&nbsp;for&nbsp;common&nbsp;keys&nbsp;in&nbsp;the&nbsp;calling&nbsp;frame<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-combine_first"><strong>combine_first</strong></a>(self, other)</dt><dd><tt>Combine&nbsp;two&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects&nbsp;and&nbsp;default&nbsp;to&nbsp;non-null&nbsp;values&nbsp;in&nbsp;frame<br>
-calling&nbsp;the&nbsp;method.&nbsp;Result&nbsp;index&nbsp;columns&nbsp;will&nbsp;be&nbsp;the&nbsp;union&nbsp;of&nbsp;the<br>
-respective&nbsp;indexes&nbsp;and&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-a's&nbsp;values&nbsp;prioritized,&nbsp;use&nbsp;values&nbsp;from&nbsp;b&nbsp;to&nbsp;fill&nbsp;holes:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#SweepFrame-combine_first">combine_first</a>(b)<br>
-&nbsp;<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-combined&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-compound"><strong>compound</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;compound&nbsp;percentage&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-compounded&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-corr"><strong>corr</strong></a>(self, method='pearson', min_periods=1)</dt><dd><tt>Compute&nbsp;pairwise&nbsp;correlation&nbsp;of&nbsp;columns,&nbsp;excluding&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-method&nbsp;:&nbsp;{'pearson',&nbsp;'kendall',&nbsp;'spearman'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pearson&nbsp;:&nbsp;standard&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;kendall&nbsp;:&nbsp;Kendall&nbsp;Tau&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;spearman&nbsp;:&nbsp;Spearman&nbsp;rank&nbsp;correlation<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;required&nbsp;per&nbsp;pair&nbsp;of&nbsp;columns<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result.&nbsp;Currently&nbsp;only&nbsp;available&nbsp;for&nbsp;pearson<br>
-&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;spearman&nbsp;correlation<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-corrwith"><strong>corrwith</strong></a>(self, other, axis=0, drop=False)</dt><dd><tt>Compute&nbsp;pairwise&nbsp;correlation&nbsp;between&nbsp;rows&nbsp;or&nbsp;columns&nbsp;of&nbsp;two&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-objects.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;or&nbsp;'index'&nbsp;to&nbsp;compute&nbsp;column-wise,&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;for&nbsp;row-wise<br>
-drop&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Drop&nbsp;missing&nbsp;indices&nbsp;from&nbsp;result,&nbsp;default&nbsp;returns&nbsp;union&nbsp;of&nbsp;all<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-correls&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-count"><strong>count</strong></a>(self, axis=0, level=None, numeric_only=False)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;number&nbsp;of&nbsp;non-NA/null&nbsp;observations&nbsp;over&nbsp;requested<br>
-axis.&nbsp;Works&nbsp;with&nbsp;non-floating&nbsp;point&nbsp;data&nbsp;as&nbsp;well&nbsp;(detects&nbsp;NaN&nbsp;and&nbsp;None)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;or&nbsp;'index'&nbsp;for&nbsp;row-wise,&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;for&nbsp;column-wise<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-count&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-cov"><strong>cov</strong></a>(self, min_periods=None)</dt><dd><tt>Compute&nbsp;pairwise&nbsp;covariance&nbsp;of&nbsp;columns,&nbsp;excluding&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;required&nbsp;per&nbsp;pair&nbsp;of&nbsp;columns<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-`y`&nbsp;contains&nbsp;the&nbsp;covariance&nbsp;matrix&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;time&nbsp;series.<br>
-The&nbsp;covariance&nbsp;is&nbsp;normalized&nbsp;by&nbsp;N-1&nbsp;(unbiased&nbsp;estimator).</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-cummax"><strong>cummax</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;max&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummax&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.max&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-cummin"><strong>cummin</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;minimum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummin&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.min&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-cumprod"><strong>cumprod</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;product&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumprod&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.prod&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-cumsum"><strong>cumsum</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;sum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumsum&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.sum&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-diff"><strong>diff</strong></a>(self, periods=1, axis=0)</dt><dd><tt>1st&nbsp;discrete&nbsp;difference&nbsp;of&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;difference<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Take&nbsp;difference&nbsp;over&nbsp;rows&nbsp;(0)&nbsp;or&nbsp;columns&nbsp;(1).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-diffed&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-div"><strong>div</strong></a> = truediv(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-divide"><strong>divide</strong></a> = truediv(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-dot"><strong>dot</strong></a>(self, other)</dt><dd><tt>Matrix&nbsp;multiplication&nbsp;with&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;objects<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dot_product&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-drop_duplicates"><strong>drop_duplicates</strong></a>(self, subset=None, keep='first', inplace=False)</dt><dd><tt>Return&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;duplicate&nbsp;rows&nbsp;removed,&nbsp;optionally&nbsp;only<br>
-considering&nbsp;certain&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-subset&nbsp;:&nbsp;column&nbsp;label&nbsp;or&nbsp;sequence&nbsp;of&nbsp;labels,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Only&nbsp;consider&nbsp;certain&nbsp;columns&nbsp;for&nbsp;identifying&nbsp;duplicates,&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;use&nbsp;all&nbsp;of&nbsp;the&nbsp;columns<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Drop&nbsp;all&nbsp;duplicates.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;drop&nbsp;duplicates&nbsp;in&nbsp;place&nbsp;or&nbsp;to&nbsp;return&nbsp;a&nbsp;copy<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-deduplicated&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-dropna"><strong>dropna</strong></a>(self, axis=0, how='any', thresh=None, subset=None, inplace=False)</dt><dd><tt>Return&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;labels&nbsp;on&nbsp;given&nbsp;axis&nbsp;omitted&nbsp;where&nbsp;alternately&nbsp;any<br>
-or&nbsp;all&nbsp;of&nbsp;the&nbsp;data&nbsp;are&nbsp;missing<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;or&nbsp;tuple/list&nbsp;thereof<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Pass&nbsp;tuple&nbsp;or&nbsp;list&nbsp;to&nbsp;drop&nbsp;on&nbsp;multiple&nbsp;axes<br>
-how&nbsp;:&nbsp;{'any',&nbsp;'all'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;any&nbsp;:&nbsp;if&nbsp;any&nbsp;NA&nbsp;values&nbsp;are&nbsp;present,&nbsp;drop&nbsp;that&nbsp;label<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;all&nbsp;:&nbsp;if&nbsp;all&nbsp;values&nbsp;are&nbsp;NA,&nbsp;drop&nbsp;that&nbsp;label<br>
-thresh&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;int&nbsp;value&nbsp;:&nbsp;require&nbsp;that&nbsp;many&nbsp;non-NA&nbsp;values<br>
-subset&nbsp;:&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Labels&nbsp;along&nbsp;other&nbsp;axis&nbsp;to&nbsp;consider,&nbsp;e.g.&nbsp;if&nbsp;you&nbsp;are&nbsp;dropping&nbsp;rows<br>
-&nbsp;&nbsp;&nbsp;&nbsp;these&nbsp;would&nbsp;be&nbsp;a&nbsp;list&nbsp;of&nbsp;columns&nbsp;to&nbsp;include<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;return&nbsp;None.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dropped&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[np.nan,&nbsp;2,&nbsp;np.nan,&nbsp;0],&nbsp;[3,&nbsp;4,&nbsp;np.nan,&nbsp;1],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[np.nan,&nbsp;np.nan,&nbsp;np.nan,&nbsp;5]],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=list('ABCD'))<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;NaN&nbsp;&nbsp;2.0&nbsp;NaN&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;3.0&nbsp;&nbsp;4.0&nbsp;NaN&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;NaN&nbsp;&nbsp;NaN&nbsp;NaN&nbsp;&nbsp;5<br>
-&nbsp;<br>
-Drop&nbsp;the&nbsp;columns&nbsp;where&nbsp;all&nbsp;elements&nbsp;are&nbsp;nan:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-dropna">dropna</a>(axis=1,&nbsp;how='all')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;NaN&nbsp;&nbsp;2.0&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;3.0&nbsp;&nbsp;4.0&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;NaN&nbsp;&nbsp;NaN&nbsp;&nbsp;5<br>
-&nbsp;<br>
-Drop&nbsp;the&nbsp;columns&nbsp;where&nbsp;any&nbsp;of&nbsp;the&nbsp;elements&nbsp;is&nbsp;nan<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-dropna">dropna</a>(axis=1,&nbsp;how='any')<br>
-&nbsp;&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;5<br>
-&nbsp;<br>
-Drop&nbsp;the&nbsp;rows&nbsp;where&nbsp;all&nbsp;of&nbsp;the&nbsp;elements&nbsp;are&nbsp;nan<br>
-(there&nbsp;is&nbsp;no&nbsp;row&nbsp;to&nbsp;drop,&nbsp;so&nbsp;df&nbsp;stays&nbsp;the&nbsp;same):<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-dropna">dropna</a>(axis=0,&nbsp;how='all')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;NaN&nbsp;&nbsp;2.0&nbsp;NaN&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;3.0&nbsp;&nbsp;4.0&nbsp;NaN&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;NaN&nbsp;&nbsp;NaN&nbsp;NaN&nbsp;&nbsp;5<br>
-&nbsp;<br>
-Keep&nbsp;only&nbsp;the&nbsp;rows&nbsp;with&nbsp;at&nbsp;least&nbsp;2&nbsp;non-na&nbsp;values:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-dropna">dropna</a>(thresh=2)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;NaN&nbsp;&nbsp;2.0&nbsp;NaN&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;3.0&nbsp;&nbsp;4.0&nbsp;NaN&nbsp;&nbsp;1</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-duplicated"><strong>duplicated</strong></a>(self, subset=None, keep='first')</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;denoting&nbsp;duplicate&nbsp;rows,&nbsp;optionally&nbsp;only<br>
-considering&nbsp;certain&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-subset&nbsp;:&nbsp;column&nbsp;label&nbsp;or&nbsp;sequence&nbsp;of&nbsp;labels,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Only&nbsp;consider&nbsp;certain&nbsp;columns&nbsp;for&nbsp;identifying&nbsp;duplicates,&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;use&nbsp;all&nbsp;of&nbsp;the&nbsp;columns<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;last&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Mark&nbsp;all&nbsp;duplicates&nbsp;as&nbsp;``True``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-duplicated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-eq"><strong>eq</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;eq</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-eval"><strong>eval</strong></a>(self, expr, inplace=None, **kwargs)</dt><dd><tt>Evaluate&nbsp;an&nbsp;expression&nbsp;in&nbsp;the&nbsp;context&nbsp;of&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-instance.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-expr&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;expression&nbsp;string&nbsp;to&nbsp;evaluate.<br>
-inplace&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;expression&nbsp;contains&nbsp;an&nbsp;assignment,&nbsp;whether&nbsp;to&nbsp;return&nbsp;a&nbsp;new<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;mutate&nbsp;the&nbsp;existing.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;WARNING:&nbsp;inplace=None&nbsp;currently&nbsp;falls&nbsp;back&nbsp;to&nbsp;to&nbsp;True,&nbsp;but<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;a&nbsp;future&nbsp;version,&nbsp;will&nbsp;default&nbsp;to&nbsp;False.&nbsp;&nbsp;Use&nbsp;inplace=True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;explicitly&nbsp;rather&nbsp;than&nbsp;relying&nbsp;on&nbsp;the&nbsp;default.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-kwargs&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;documentation&nbsp;for&nbsp;:func:`~pandas.eval`&nbsp;for&nbsp;complete&nbsp;details<br>
-&nbsp;&nbsp;&nbsp;&nbsp;on&nbsp;the&nbsp;keyword&nbsp;arguments&nbsp;accepted&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:meth:`~pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.query`.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ret&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;scalar,&nbsp;or&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.query<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.assign<br>
-pandas.eval<br>
-&nbsp;<br>
-Notes<br>
------<br>
-For&nbsp;more&nbsp;details&nbsp;see&nbsp;the&nbsp;API&nbsp;documentation&nbsp;for&nbsp;:func:`~pandas.eval`.<br>
-For&nbsp;detailed&nbsp;examples&nbsp;see&nbsp;:ref:`enhancing&nbsp;performance&nbsp;with&nbsp;eval<br>
-&lt;enhancingperf.eval&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;numpy.random&nbsp;import&nbsp;randn<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;pandas&nbsp;import&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(randn(10,&nbsp;2),&nbsp;columns=list('ab'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-eval">eval</a>('a&nbsp;+&nbsp;b')<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-eval">eval</a>('c&nbsp;=&nbsp;a&nbsp;+&nbsp;b')</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-ewm"><strong>ewm</strong></a>(self, com=None, span=None, halflife=None, alpha=None, min_periods=0, freq=None, adjust=True, ignore_na=False, axis=0)</dt><dd><tt>Provides&nbsp;exponential&nbsp;weighted&nbsp;functions<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-com&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;/&nbsp;(1&nbsp;+&nbsp;com),\text{&nbsp;for&nbsp;}&nbsp;com&nbsp;\geq&nbsp;0`<br>
-span&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;span,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;2&nbsp;/&nbsp;(span&nbsp;+&nbsp;1),\text{&nbsp;for&nbsp;}&nbsp;span&nbsp;\geq&nbsp;1`<br>
-halflife&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;half-life,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;-&nbsp;exp(log(0.5)&nbsp;/&nbsp;halflife),\text{&nbsp;for&nbsp;}&nbsp;halflife&nbsp;&gt;&nbsp;0`<br>
-alpha&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;smoothing&nbsp;factor&nbsp;:math:`\alpha`&nbsp;directly,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`0&nbsp;&lt;&nbsp;\alpha&nbsp;\leq&nbsp;1`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;None&nbsp;or&nbsp;string&nbsp;alias&nbsp;/&nbsp;date&nbsp;offset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default=None&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;to&nbsp;before&nbsp;computing&nbsp;statistic<br>
-adjust&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Divide&nbsp;by&nbsp;decaying&nbsp;adjustment&nbsp;factor&nbsp;in&nbsp;beginning&nbsp;periods&nbsp;to&nbsp;account<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;imbalance&nbsp;in&nbsp;relative&nbsp;weightings&nbsp;(viewing&nbsp;EWMA&nbsp;as&nbsp;a&nbsp;moving&nbsp;average)<br>
-ignore_na&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Ignore&nbsp;missing&nbsp;values&nbsp;when&nbsp;calculating&nbsp;weights;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specify&nbsp;True&nbsp;to&nbsp;reproduce&nbsp;pre-0.15.0&nbsp;behavior<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-ewm">ewm</a>(com=0.5).<a href="#SweepFrame-mean">mean</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.000000<br>
-1&nbsp;&nbsp;0.750000<br>
-2&nbsp;&nbsp;1.615385<br>
-3&nbsp;&nbsp;1.615385<br>
-4&nbsp;&nbsp;3.670213<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Exactly&nbsp;one&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,&nbsp;span,&nbsp;half-life,&nbsp;and&nbsp;alpha&nbsp;must&nbsp;be&nbsp;provided.<br>
-Allowed&nbsp;values&nbsp;and&nbsp;relationship&nbsp;between&nbsp;the&nbsp;parameters&nbsp;are&nbsp;specified&nbsp;in&nbsp;the<br>
-parameter&nbsp;descriptions&nbsp;above;&nbsp;see&nbsp;the&nbsp;link&nbsp;at&nbsp;the&nbsp;end&nbsp;of&nbsp;this&nbsp;section&nbsp;for<br>
-a&nbsp;detailed&nbsp;explanation.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;True&nbsp;(default),&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;using<br>
-weights&nbsp;(1-alpha)**(n-1),&nbsp;(1-alpha)**(n-2),&nbsp;...,&nbsp;1-alpha,&nbsp;1.<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;False,&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;recursively&nbsp;as:<br>
-&nbsp;&nbsp;&nbsp;weighted_average[0]&nbsp;=&nbsp;arg[0];<br>
-&nbsp;&nbsp;&nbsp;weighted_average[i]&nbsp;=&nbsp;(1-alpha)*weighted_average[i-1]&nbsp;+&nbsp;alpha*arg[i].<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;False&nbsp;(default),&nbsp;weights&nbsp;are&nbsp;based&nbsp;on&nbsp;absolute&nbsp;positions.<br>
-For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in&nbsp;calculating&nbsp;the&nbsp;final&nbsp;weighted<br>
-average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;(1-alpha)**2&nbsp;and&nbsp;1&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and<br>
-(1-alpha)**2&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;True&nbsp;(reproducing&nbsp;pre-0.15.0&nbsp;behavior),&nbsp;weights&nbsp;are&nbsp;based<br>
-on&nbsp;relative&nbsp;positions.&nbsp;For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in<br>
-calculating&nbsp;the&nbsp;final&nbsp;weighted&nbsp;average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;1-alpha&nbsp;and&nbsp;1<br>
-(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and&nbsp;1-alpha&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-More&nbsp;details&nbsp;can&nbsp;be&nbsp;found&nbsp;at<br>
-<a href="http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows">http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-expanding"><strong>expanding</strong></a>(self, min_periods=1, freq=None, center=False, axis=0)</dt><dd><tt>Provides&nbsp;expanding&nbsp;transformations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-expanding">expanding</a>(2).<a href="#SweepFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;7.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-fillna"><strong>fillna</strong></a>(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)</dt><dd><tt>Fill&nbsp;NA/NaN&nbsp;values&nbsp;using&nbsp;the&nbsp;specified&nbsp;method<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;values&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;each&nbsp;index&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;or&nbsp;column&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).&nbsp;(values&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;This&nbsp;value&nbsp;cannot<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;list.<br>
-method&nbsp;:&nbsp;{'backfill',&nbsp;'bfill',&nbsp;'pad',&nbsp;'ffill',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'}<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;fill&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>,&nbsp;(e.g.&nbsp;a&nbsp;no-copy&nbsp;slice&nbsp;for&nbsp;a&nbsp;column&nbsp;in&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;method&nbsp;is&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the&nbsp;maximum&nbsp;number&nbsp;of&nbsp;consecutive<br>
-&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;values&nbsp;to&nbsp;forward/backward&nbsp;fill.&nbsp;In&nbsp;other&nbsp;words,&nbsp;if&nbsp;there&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;gap&nbsp;with&nbsp;more&nbsp;than&nbsp;this&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs,&nbsp;it&nbsp;will&nbsp;only<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;partially&nbsp;filled.&nbsp;If&nbsp;method&nbsp;is&nbsp;not&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;maximum&nbsp;number&nbsp;of&nbsp;entries&nbsp;along&nbsp;the&nbsp;entire&nbsp;axis&nbsp;where&nbsp;NaNs&nbsp;will&nbsp;be<br>
-&nbsp;&nbsp;&nbsp;&nbsp;filled.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0&nbsp;if&nbsp;not&nbsp;None.<br>
-downcast&nbsp;:&nbsp;dict,&nbsp;default&nbsp;is&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;dict&nbsp;of&nbsp;item-&gt;dtype&nbsp;of&nbsp;what&nbsp;to&nbsp;downcast&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;the&nbsp;string&nbsp;'infer'&nbsp;which&nbsp;will&nbsp;try&nbsp;to&nbsp;downcast&nbsp;to&nbsp;an&nbsp;appropriate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;equal&nbsp;type&nbsp;(e.g.&nbsp;float64&nbsp;to&nbsp;int64&nbsp;if&nbsp;possible)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;asfreq<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-first_valid_index"><strong>first_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;first&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-floordiv"><strong>floordiv</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`floordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;//&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rfloordiv</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-ge"><strong>ge</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;ge</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-get_value"><strong>get_value</strong></a>(self, index, col, takeable=False)</dt><dd><tt>Quickly&nbsp;retrieve&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;column&nbsp;and&nbsp;index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;row&nbsp;label<br>
-col&nbsp;:&nbsp;column&nbsp;label<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index/col&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;scalar&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-gt"><strong>gt</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;gt</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-hist"><strong>hist</strong></a> = hist_frame(data, column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, **kwds)</dt><dd><tt>Draw&nbsp;histogram&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;series&nbsp;using&nbsp;matplotlib&nbsp;/&nbsp;pylab.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-column&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed,&nbsp;will&nbsp;be&nbsp;used&nbsp;to&nbsp;limit&nbsp;data&nbsp;to&nbsp;a&nbsp;subset&nbsp;of&nbsp;columns<br>
-by&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed,&nbsp;then&nbsp;used&nbsp;to&nbsp;form&nbsp;histograms&nbsp;for&nbsp;separate&nbsp;groups<br>
-grid&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;show&nbsp;axis&nbsp;grid&nbsp;lines<br>
-xlabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;x-axis&nbsp;label&nbsp;size<br>
-xrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;x&nbsp;axis&nbsp;labels<br>
-ylabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;y-axis&nbsp;label&nbsp;size<br>
-yrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;y&nbsp;axis&nbsp;labels<br>
-ax&nbsp;:&nbsp;matplotlib&nbsp;axes&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-sharex&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True&nbsp;if&nbsp;ax&nbsp;is&nbsp;None&nbsp;else&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;subplots=True,&nbsp;share&nbsp;x&nbsp;axis&nbsp;and&nbsp;set&nbsp;some&nbsp;x&nbsp;axis&nbsp;labels&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;invisible;&nbsp;defaults&nbsp;to&nbsp;True&nbsp;if&nbsp;ax&nbsp;is&nbsp;None&nbsp;otherwise&nbsp;False&nbsp;if&nbsp;an&nbsp;ax<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;passed&nbsp;in;&nbsp;Be&nbsp;aware,&nbsp;that&nbsp;passing&nbsp;in&nbsp;both&nbsp;an&nbsp;ax&nbsp;and&nbsp;sharex=True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;alter&nbsp;all&nbsp;x&nbsp;axis&nbsp;labels&nbsp;for&nbsp;all&nbsp;subplots&nbsp;in&nbsp;a&nbsp;figure!<br>
-sharey&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;subplots=True,&nbsp;share&nbsp;y&nbsp;axis&nbsp;and&nbsp;set&nbsp;some&nbsp;y&nbsp;axis&nbsp;labels&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;invisible<br>
-figsize&nbsp;:&nbsp;tuple<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;size&nbsp;of&nbsp;the&nbsp;figure&nbsp;to&nbsp;create&nbsp;in&nbsp;inches&nbsp;by&nbsp;default<br>
-layout&nbsp;:&nbsp;tuple,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Tuple&nbsp;of&nbsp;(rows,&nbsp;columns)&nbsp;for&nbsp;the&nbsp;layout&nbsp;of&nbsp;the&nbsp;histograms<br>
-bins&nbsp;:&nbsp;integer,&nbsp;default&nbsp;10<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;histogram&nbsp;bins&nbsp;to&nbsp;be&nbsp;used<br>
-kwds&nbsp;:&nbsp;other&nbsp;plotting&nbsp;keyword&nbsp;arguments<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;passed&nbsp;to&nbsp;hist&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-idxmax"><strong>idxmax</strong></a>(self, axis=0, skipna=True)</dt><dd><tt>Return&nbsp;index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;maximum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-NA/null&nbsp;values&nbsp;are&nbsp;excluded.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;or&nbsp;'index'&nbsp;for&nbsp;row-wise,&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;for&nbsp;column-wise<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;first&nbsp;index.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmax&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmax``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.idxmax</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-idxmin"><strong>idxmin</strong></a>(self, axis=0, skipna=True)</dt><dd><tt>Return&nbsp;index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;minimum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-NA/null&nbsp;values&nbsp;are&nbsp;excluded.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;or&nbsp;'index'&nbsp;for&nbsp;row-wise,&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;for&nbsp;column-wise<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmin&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmin``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.idxmin</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-info"><strong>info</strong></a>(self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None)</dt><dd><tt>Concise&nbsp;summary&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-verbose&nbsp;:&nbsp;{None,&nbsp;True,&nbsp;False},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;print&nbsp;the&nbsp;full&nbsp;summary.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;None&nbsp;follows&nbsp;the&nbsp;`display.max_info_columns`&nbsp;setting.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;True&nbsp;or&nbsp;False&nbsp;overrides&nbsp;the&nbsp;`display.max_info_columns`&nbsp;setting.<br>
-buf&nbsp;:&nbsp;writable&nbsp;buffer,&nbsp;defaults&nbsp;to&nbsp;sys.stdout<br>
-max_cols&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Determines&nbsp;whether&nbsp;full&nbsp;summary&nbsp;or&nbsp;short&nbsp;summary&nbsp;is&nbsp;printed.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;None&nbsp;follows&nbsp;the&nbsp;`display.max_info_columns`&nbsp;setting.<br>
-memory_usage&nbsp;:&nbsp;boolean/string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;whether&nbsp;total&nbsp;memory&nbsp;usage&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;elements&nbsp;(including&nbsp;index)&nbsp;should&nbsp;be&nbsp;displayed.&nbsp;None&nbsp;follows<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;`display.memory_usage`&nbsp;setting.&nbsp;True&nbsp;or&nbsp;False&nbsp;overrides<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;`display.memory_usage`&nbsp;setting.&nbsp;A&nbsp;value&nbsp;of&nbsp;'deep'&nbsp;is&nbsp;equivalent<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;True,&nbsp;with&nbsp;deep&nbsp;introspection.&nbsp;Memory&nbsp;usage&nbsp;is&nbsp;shown&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;human-readable&nbsp;units&nbsp;(base-2&nbsp;representation).<br>
-null_counts&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;show&nbsp;the&nbsp;non-null&nbsp;counts<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;If&nbsp;None,&nbsp;then&nbsp;only&nbsp;show&nbsp;if&nbsp;the&nbsp;frame&nbsp;is&nbsp;smaller&nbsp;than<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;max_info_rows&nbsp;and&nbsp;max_info_columns.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;If&nbsp;True,&nbsp;always&nbsp;show&nbsp;counts.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;If&nbsp;False,&nbsp;never&nbsp;show&nbsp;counts.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-insert"><strong>insert</strong></a>(self, loc, column, value, allow_duplicates=False)</dt><dd><tt>Insert&nbsp;column&nbsp;into&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;at&nbsp;specified&nbsp;location.<br>
-&nbsp;<br>
-If&nbsp;`allow_duplicates`&nbsp;is&nbsp;False,&nbsp;raises&nbsp;Exception&nbsp;if&nbsp;column<br>
-is&nbsp;already&nbsp;contained&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-loc&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Must&nbsp;have&nbsp;0&nbsp;&lt;=&nbsp;loc&nbsp;&lt;=&nbsp;len(columns)<br>
-column&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-value&nbsp;:&nbsp;scalar,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;array-like</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-isin"><strong>isin</strong></a>(self, values)</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;showing&nbsp;whether&nbsp;each&nbsp;element&nbsp;in&nbsp;the<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;is&nbsp;contained&nbsp;in&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-values&nbsp;:&nbsp;iterable,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;dictionary<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;result&nbsp;will&nbsp;only&nbsp;be&nbsp;true&nbsp;at&nbsp;a&nbsp;location&nbsp;if&nbsp;all&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;labels&nbsp;match.&nbsp;If&nbsp;`values`&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;that's&nbsp;the&nbsp;index.&nbsp;If<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`values`&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;the&nbsp;keys&nbsp;must&nbsp;be&nbsp;the&nbsp;column&nbsp;names,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;must&nbsp;match.&nbsp;If&nbsp;`values`&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;both&nbsp;the&nbsp;index&nbsp;and&nbsp;column&nbsp;labels&nbsp;must&nbsp;match.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;booleans<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-When&nbsp;``values``&nbsp;is&nbsp;a&nbsp;list:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;'B':&nbsp;['a',&nbsp;'b',&nbsp;'f']})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-isin">isin</a>([1,&nbsp;3,&nbsp;12,&nbsp;'a'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;False&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;False<br>
-&nbsp;<br>
-When&nbsp;``values``&nbsp;is&nbsp;a&nbsp;dict:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;'B':&nbsp;[1,&nbsp;4,&nbsp;7]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-isin">isin</a>({'A':&nbsp;[1,&nbsp;3],&nbsp;'B':&nbsp;[4,&nbsp;7,&nbsp;12]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;False&nbsp;&nbsp;#&nbsp;Note&nbsp;that&nbsp;B&nbsp;didn't&nbsp;match&nbsp;the&nbsp;1&nbsp;here.<br>
-1&nbsp;&nbsp;False&nbsp;&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;&nbsp;True<br>
-&nbsp;<br>
-When&nbsp;``values``&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;'B':&nbsp;['a',&nbsp;'b',&nbsp;'f']})<br>
-&gt;&gt;&gt;&nbsp;other&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;3,&nbsp;3,&nbsp;2],&nbsp;'B':&nbsp;['e',&nbsp;'f',&nbsp;'f',&nbsp;'e']})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-isin">isin</a>(other)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;False<br>
-1&nbsp;&nbsp;False&nbsp;&nbsp;False&nbsp;&nbsp;#&nbsp;Column&nbsp;A&nbsp;in&nbsp;`other`&nbsp;has&nbsp;a&nbsp;3,&nbsp;but&nbsp;not&nbsp;at&nbsp;index&nbsp;1.<br>
-2&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;&nbsp;True</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-items"><strong>items</strong></a> = iteritems(self)</dt><dd><tt>Iterator&nbsp;over&nbsp;(column&nbsp;name,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-iterrows&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;(index,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-itertuples&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;namedtuples&nbsp;of&nbsp;the&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-iteritems"><strong>iteritems</strong></a>(self)</dt><dd><tt>Iterator&nbsp;over&nbsp;(column&nbsp;name,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-iterrows&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;(index,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-itertuples&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;namedtuples&nbsp;of&nbsp;the&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-iterrows"><strong>iterrows</strong></a>(self)</dt><dd><tt>Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;(index,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-1.&nbsp;Because&nbsp;``iterrows``&nbsp;returns&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;for&nbsp;each&nbsp;row,<br>
-&nbsp;&nbsp;&nbsp;it&nbsp;does&nbsp;**not**&nbsp;preserve&nbsp;dtypes&nbsp;across&nbsp;the&nbsp;rows&nbsp;(dtypes&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;preserved&nbsp;across&nbsp;columns&nbsp;for&nbsp;DataFrames).&nbsp;For&nbsp;example,<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[1,&nbsp;1.5]],&nbsp;columns=['int',&nbsp;'float'])<br>
-&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;row&nbsp;=&nbsp;next(df.<a href="#SweepFrame-iterrows">iterrows</a>())[1]<br>
-&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;row<br>
-&nbsp;&nbsp;&nbsp;int&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-&nbsp;&nbsp;&nbsp;float&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-&nbsp;&nbsp;&nbsp;Name:&nbsp;0,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;print(row['int'].dtype)<br>
-&nbsp;&nbsp;&nbsp;float64<br>
-&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;print(df['int'].dtype)<br>
-&nbsp;&nbsp;&nbsp;int64<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;To&nbsp;preserve&nbsp;dtypes&nbsp;while&nbsp;iterating&nbsp;over&nbsp;the&nbsp;rows,&nbsp;it&nbsp;is&nbsp;better<br>
-&nbsp;&nbsp;&nbsp;to&nbsp;use&nbsp;:meth:`itertuples`&nbsp;which&nbsp;returns&nbsp;namedtuples&nbsp;of&nbsp;the&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;and&nbsp;which&nbsp;is&nbsp;generally&nbsp;faster&nbsp;than&nbsp;``iterrows``.<br>
-&nbsp;<br>
-2.&nbsp;You&nbsp;should&nbsp;**never&nbsp;modify**&nbsp;something&nbsp;you&nbsp;are&nbsp;iterating&nbsp;over.<br>
-&nbsp;&nbsp;&nbsp;This&nbsp;is&nbsp;not&nbsp;guaranteed&nbsp;to&nbsp;work&nbsp;in&nbsp;all&nbsp;cases.&nbsp;Depending&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;data&nbsp;types,&nbsp;the&nbsp;iterator&nbsp;returns&nbsp;a&nbsp;copy&nbsp;and&nbsp;not&nbsp;a&nbsp;view,&nbsp;and&nbsp;writing<br>
-&nbsp;&nbsp;&nbsp;to&nbsp;it&nbsp;will&nbsp;have&nbsp;no&nbsp;effect.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-it&nbsp;:&nbsp;generator<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;generator&nbsp;that&nbsp;iterates&nbsp;over&nbsp;the&nbsp;rows&nbsp;of&nbsp;the&nbsp;frame.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-itertuples&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;namedtuples&nbsp;of&nbsp;the&nbsp;values.<br>
-iteritems&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;(column&nbsp;name,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-itertuples"><strong>itertuples</strong></a>(self, index=True, name='Pandas')</dt><dd><tt>Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;namedtuples,&nbsp;with&nbsp;index&nbsp;value&nbsp;as&nbsp;first<br>
-element&nbsp;of&nbsp;the&nbsp;tuple.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;return&nbsp;the&nbsp;index&nbsp;as&nbsp;the&nbsp;first&nbsp;element&nbsp;of&nbsp;the&nbsp;tuple.<br>
-name&nbsp;:&nbsp;string,&nbsp;default&nbsp;"Pandas"<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;name&nbsp;of&nbsp;the&nbsp;returned&nbsp;namedtuples&nbsp;or&nbsp;None&nbsp;to&nbsp;return&nbsp;regular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;tuples.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;column&nbsp;names&nbsp;will&nbsp;be&nbsp;renamed&nbsp;to&nbsp;positional&nbsp;names&nbsp;if&nbsp;they&nbsp;are<br>
-invalid&nbsp;Python&nbsp;identifiers,&nbsp;repeated,&nbsp;or&nbsp;start&nbsp;with&nbsp;an&nbsp;underscore.<br>
-With&nbsp;a&nbsp;large&nbsp;number&nbsp;of&nbsp;columns&nbsp;(&gt;255),&nbsp;regular&nbsp;tuples&nbsp;are&nbsp;returned.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-iterrows&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;(index,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-iteritems&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;(column&nbsp;name,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'col1':&nbsp;[1,&nbsp;2],&nbsp;'col2':&nbsp;[0.1,&nbsp;0.2]},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=['a',&nbsp;'b'])<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;col1&nbsp;&nbsp;col2<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;0.1<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;0.2<br>
-&gt;&gt;&gt;&nbsp;for&nbsp;row&nbsp;in&nbsp;df.<a href="#SweepFrame-itertuples">itertuples</a>():<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;print(row)<br>
-...<br>
-Pandas(Index='a',&nbsp;col1=1,&nbsp;col2=0.10000000000000001)<br>
-Pandas(Index='b',&nbsp;col1=2,&nbsp;col2=0.20000000000000001)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-join"><strong>join</strong></a>(self, other, on=None, how='left', lsuffix='', rsuffix='', sort=False)</dt><dd><tt>Join&nbsp;columns&nbsp;with&nbsp;other&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;either&nbsp;on&nbsp;index&nbsp;or&nbsp;on&nbsp;a&nbsp;key<br>
-column.&nbsp;Efficiently&nbsp;Join&nbsp;multiple&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects&nbsp;by&nbsp;index&nbsp;at&nbsp;once&nbsp;by<br>
-passing&nbsp;a&nbsp;list.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;name&nbsp;field&nbsp;set,&nbsp;or&nbsp;list&nbsp;of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Index&nbsp;should&nbsp;be&nbsp;similar&nbsp;to&nbsp;one&nbsp;of&nbsp;the&nbsp;columns&nbsp;in&nbsp;this&nbsp;one.&nbsp;If&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;is&nbsp;passed,&nbsp;its&nbsp;name&nbsp;attribute&nbsp;must&nbsp;be&nbsp;set,&nbsp;and&nbsp;that&nbsp;will&nbsp;be<br>
-&nbsp;&nbsp;&nbsp;&nbsp;used&nbsp;as&nbsp;the&nbsp;column&nbsp;name&nbsp;in&nbsp;the&nbsp;resulting&nbsp;joined&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-on&nbsp;:&nbsp;column&nbsp;name,&nbsp;tuple/list&nbsp;of&nbsp;column&nbsp;names,&nbsp;or&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column(s)&nbsp;in&nbsp;the&nbsp;caller&nbsp;to&nbsp;join&nbsp;on&nbsp;the&nbsp;index&nbsp;in&nbsp;other,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;joins&nbsp;index-on-index.&nbsp;If&nbsp;multiples<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns&nbsp;given,&nbsp;the&nbsp;passed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;must&nbsp;have&nbsp;a&nbsp;MultiIndex.&nbsp;Can<br>
-&nbsp;&nbsp;&nbsp;&nbsp;pass&nbsp;an&nbsp;array&nbsp;as&nbsp;the&nbsp;join&nbsp;key&nbsp;if&nbsp;not&nbsp;already&nbsp;contained&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Like&nbsp;an&nbsp;Excel&nbsp;VLOOKUP&nbsp;operation<br>
-how&nbsp;:&nbsp;{'left',&nbsp;'right',&nbsp;'outer',&nbsp;'inner'},&nbsp;default:&nbsp;'left'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;How&nbsp;to&nbsp;handle&nbsp;the&nbsp;operation&nbsp;of&nbsp;the&nbsp;two&nbsp;objects.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;left:&nbsp;use&nbsp;calling&nbsp;frame's&nbsp;index&nbsp;(or&nbsp;column&nbsp;if&nbsp;on&nbsp;is&nbsp;specified)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;right:&nbsp;use&nbsp;other&nbsp;frame's&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;outer:&nbsp;form&nbsp;union&nbsp;of&nbsp;calling&nbsp;frame's&nbsp;index&nbsp;(or&nbsp;column&nbsp;if&nbsp;on&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;specified)&nbsp;with&nbsp;other&nbsp;frame's&nbsp;index,&nbsp;and&nbsp;sort&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lexicographically<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;inner:&nbsp;form&nbsp;intersection&nbsp;of&nbsp;calling&nbsp;frame's&nbsp;index&nbsp;(or&nbsp;column&nbsp;if<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;on&nbsp;is&nbsp;specified)&nbsp;with&nbsp;other&nbsp;frame's&nbsp;index,&nbsp;preserving&nbsp;the&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;calling's&nbsp;one<br>
-lsuffix&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Suffix&nbsp;to&nbsp;use&nbsp;from&nbsp;left&nbsp;frame's&nbsp;overlapping&nbsp;columns<br>
-rsuffix&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Suffix&nbsp;to&nbsp;use&nbsp;from&nbsp;right&nbsp;frame's&nbsp;overlapping&nbsp;columns<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Order&nbsp;result&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;lexicographically&nbsp;by&nbsp;the&nbsp;join&nbsp;key.&nbsp;If&nbsp;False,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;join&nbsp;key&nbsp;depends&nbsp;on&nbsp;the&nbsp;join&nbsp;type&nbsp;(how&nbsp;keyword)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-on,&nbsp;lsuffix,&nbsp;and&nbsp;rsuffix&nbsp;options&nbsp;are&nbsp;not&nbsp;supported&nbsp;when&nbsp;passing&nbsp;a&nbsp;list<br>
-of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;caller&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'key':&nbsp;['K0',&nbsp;'K1',&nbsp;'K2',&nbsp;'K3',&nbsp;'K4',&nbsp;'K5'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'A':&nbsp;['A0',&nbsp;'A1',&nbsp;'A2',&nbsp;'A3',&nbsp;'A4',&nbsp;'A5']})<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;caller<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;key<br>
-0&nbsp;&nbsp;A0&nbsp;&nbsp;K0<br>
-1&nbsp;&nbsp;A1&nbsp;&nbsp;K1<br>
-2&nbsp;&nbsp;A2&nbsp;&nbsp;K2<br>
-3&nbsp;&nbsp;A3&nbsp;&nbsp;K3<br>
-4&nbsp;&nbsp;A4&nbsp;&nbsp;K4<br>
-5&nbsp;&nbsp;A5&nbsp;&nbsp;K5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;other&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'key':&nbsp;['K0',&nbsp;'K1',&nbsp;'K2'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B':&nbsp;['B0',&nbsp;'B1',&nbsp;'B2']})<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;key<br>
-0&nbsp;&nbsp;B0&nbsp;&nbsp;K0<br>
-1&nbsp;&nbsp;B1&nbsp;&nbsp;K1<br>
-2&nbsp;&nbsp;B2&nbsp;&nbsp;K2<br>
-&nbsp;<br>
-Join&nbsp;DataFrames&nbsp;using&nbsp;their&nbsp;indexes.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;caller.<a href="#SweepFrame-join">join</a>(other,&nbsp;lsuffix='_caller',&nbsp;rsuffix='_other')<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;key_caller&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;key_other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;A0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K0&nbsp;&nbsp;&nbsp;B0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;A1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K1&nbsp;&nbsp;&nbsp;B1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;A2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K2&nbsp;&nbsp;&nbsp;B2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;A3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K3&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;A4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K4&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;A5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K5&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-&nbsp;<br>
-If&nbsp;we&nbsp;want&nbsp;to&nbsp;join&nbsp;using&nbsp;the&nbsp;key&nbsp;columns,&nbsp;we&nbsp;need&nbsp;to&nbsp;set&nbsp;key&nbsp;to&nbsp;be<br>
-the&nbsp;index&nbsp;in&nbsp;both&nbsp;caller&nbsp;and&nbsp;other.&nbsp;The&nbsp;joined&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;have<br>
-key&nbsp;as&nbsp;its&nbsp;index.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;caller.<a href="#SweepFrame-set_index">set_index</a>('key').<a href="#SweepFrame-join">join</a>(other.<a href="#SweepFrame-set_index">set_index</a>('key'))<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-&nbsp;&nbsp;&nbsp;&nbsp;key<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K0&nbsp;&nbsp;&nbsp;A0&nbsp;&nbsp;&nbsp;B0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K1&nbsp;&nbsp;&nbsp;A1&nbsp;&nbsp;&nbsp;B1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K2&nbsp;&nbsp;&nbsp;A2&nbsp;&nbsp;&nbsp;B2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K3&nbsp;&nbsp;&nbsp;A3&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K4&nbsp;&nbsp;&nbsp;A4&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K5&nbsp;&nbsp;&nbsp;A5&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Another&nbsp;option&nbsp;to&nbsp;join&nbsp;using&nbsp;the&nbsp;key&nbsp;columns&nbsp;is&nbsp;to&nbsp;use&nbsp;the&nbsp;on<br>
-parameter.&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.join&nbsp;always&nbsp;uses&nbsp;other's&nbsp;index&nbsp;but&nbsp;we&nbsp;can&nbsp;use&nbsp;any<br>
-column&nbsp;in&nbsp;the&nbsp;caller.&nbsp;This&nbsp;method&nbsp;preserves&nbsp;the&nbsp;original&nbsp;caller's<br>
-index&nbsp;in&nbsp;the&nbsp;result.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;caller.<a href="#SweepFrame-join">join</a>(other.<a href="#SweepFrame-set_index">set_index</a>('key'),&nbsp;on='key')<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;key&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;A0&nbsp;&nbsp;K0&nbsp;&nbsp;&nbsp;B0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;A1&nbsp;&nbsp;K1&nbsp;&nbsp;&nbsp;B1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;A2&nbsp;&nbsp;K2&nbsp;&nbsp;&nbsp;B2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;A3&nbsp;&nbsp;K3&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;A4&nbsp;&nbsp;K4&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;A5&nbsp;&nbsp;K5&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.merge&nbsp;:&nbsp;For&nbsp;column(s)-on-<a href="#SweepFrame-columns">columns</a>(s)&nbsp;operations<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-joined&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-kurt"><strong>kurt</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-kurtosis"><strong>kurtosis</strong></a> = kurt(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-last_valid_index"><strong>last_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;last&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-le"><strong>le</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;le</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-lookup"><strong>lookup</strong></a>(self, row_labels, col_labels)</dt><dd><tt>Label-based&nbsp;"fancy&nbsp;indexing"&nbsp;function&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-Given&nbsp;equal-length&nbsp;arrays&nbsp;of&nbsp;row&nbsp;and&nbsp;column&nbsp;labels,&nbsp;return&nbsp;an<br>
-array&nbsp;of&nbsp;the&nbsp;values&nbsp;corresponding&nbsp;to&nbsp;each&nbsp;(row,&nbsp;col)&nbsp;pair.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-row_labels&nbsp;:&nbsp;sequence<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;row&nbsp;labels&nbsp;to&nbsp;use&nbsp;for&nbsp;lookup<br>
-col_labels&nbsp;:&nbsp;sequence<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;column&nbsp;labels&nbsp;to&nbsp;use&nbsp;for&nbsp;lookup<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Akin&nbsp;to::<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;result&nbsp;=&nbsp;[]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;row,&nbsp;col&nbsp;in&nbsp;zip(row_labels,&nbsp;col_labels):<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;result.<a href="#SweepFrame-append">append</a>(df.<a href="#SweepFrame-get_value">get_value</a>(row,&nbsp;col))<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-values&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;found&nbsp;values</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-lt"><strong>lt</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;lt</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-mad"><strong>mad</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;absolute&nbsp;deviation&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mad&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-max"><strong>max</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;maximum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;maximum,&nbsp;use&nbsp;``idxmax``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmax``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-max&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-mean"><strong>mean</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mean&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-median"><strong>median</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;median&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-median&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-melt"><strong>melt</strong></a>(self, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)</dt><dd><tt>"Unpivots"&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;from&nbsp;wide&nbsp;format&nbsp;to&nbsp;long&nbsp;format,&nbsp;optionally<br>
-leaving&nbsp;identifier&nbsp;variables&nbsp;set.<br>
-&nbsp;<br>
-This&nbsp;function&nbsp;is&nbsp;useful&nbsp;to&nbsp;massage&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;into&nbsp;a&nbsp;format&nbsp;where&nbsp;one<br>
-or&nbsp;more&nbsp;columns&nbsp;are&nbsp;identifier&nbsp;variables&nbsp;(`id_vars`),&nbsp;while&nbsp;all&nbsp;other<br>
-columns,&nbsp;considered&nbsp;measured&nbsp;variables&nbsp;(`value_vars`),&nbsp;are&nbsp;"unpivoted"&nbsp;to<br>
-the&nbsp;row&nbsp;axis,&nbsp;leaving&nbsp;just&nbsp;two&nbsp;non-identifier&nbsp;columns,&nbsp;'variable'&nbsp;and<br>
-'value'.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-frame&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-id_vars&nbsp;:&nbsp;tuple,&nbsp;list,&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column(s)&nbsp;to&nbsp;use&nbsp;as&nbsp;identifier&nbsp;variables.<br>
-value_vars&nbsp;:&nbsp;tuple,&nbsp;list,&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column(s)&nbsp;to&nbsp;unpivot.&nbsp;If&nbsp;not&nbsp;specified,&nbsp;uses&nbsp;all&nbsp;columns&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;not&nbsp;set&nbsp;as&nbsp;`id_vars`.<br>
-var_name&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;to&nbsp;use&nbsp;for&nbsp;the&nbsp;'variable'&nbsp;column.&nbsp;If&nbsp;None&nbsp;it&nbsp;uses<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``frame.columns.name``&nbsp;or&nbsp;'variable'.<br>
-value_name&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;'value'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;to&nbsp;use&nbsp;for&nbsp;the&nbsp;'value'&nbsp;column.<br>
-col_level&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;columns&nbsp;are&nbsp;a&nbsp;MultiIndex&nbsp;then&nbsp;use&nbsp;this&nbsp;level&nbsp;to&nbsp;melt.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-melt<br>
-pivot_table<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.pivot<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;{0:&nbsp;'a',&nbsp;1:&nbsp;'b',&nbsp;2:&nbsp;'c'},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B':&nbsp;{0:&nbsp;1,&nbsp;1:&nbsp;3,&nbsp;2:&nbsp;5},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C':&nbsp;{0:&nbsp;2,&nbsp;1:&nbsp;4,&nbsp;2:&nbsp;6}})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;5&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-melt">melt</a>(id_vars=['A'],&nbsp;value_vars=['B'])<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;variable&nbsp;&nbsp;value<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-melt">melt</a>(id_vars=['A'],&nbsp;value_vars=['B',&nbsp;'C'])<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;variable&nbsp;&nbsp;value<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-3&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-4&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-5&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-The&nbsp;names&nbsp;of&nbsp;'variable'&nbsp;and&nbsp;'value'&nbsp;columns&nbsp;can&nbsp;be&nbsp;customized:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-melt">melt</a>(id_vars=['A'],&nbsp;value_vars=['B'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;var_name='myVarname',&nbsp;value_name='myValname')<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;myVarname&nbsp;&nbsp;myValname<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-&nbsp;<br>
-If&nbsp;you&nbsp;have&nbsp;multi-index&nbsp;columns:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.columns&nbsp;=&nbsp;[list('ABC'),&nbsp;list('DEF')]<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C<br>
-&nbsp;&nbsp;&nbsp;D&nbsp;&nbsp;E&nbsp;&nbsp;F<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;5&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-melt">melt</a>(col_level=0,&nbsp;id_vars=['A'],&nbsp;value_vars=['B'])<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;variable&nbsp;&nbsp;value<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-melt">melt</a>(id_vars=[('A',&nbsp;'D')],&nbsp;value_vars=[('B',&nbsp;'E')])<br>
-&nbsp;&nbsp;(A,&nbsp;D)&nbsp;variable_0&nbsp;variable_1&nbsp;&nbsp;value<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;E&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;E&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;E&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-memory_usage"><strong>memory_usage</strong></a>(self, index=True, deep=False)</dt><dd><tt>Memory&nbsp;usage&nbsp;of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;columns.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;whether&nbsp;to&nbsp;include&nbsp;memory&nbsp;usage&nbsp;of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;in&nbsp;returned&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;If&nbsp;`index=True`&nbsp;(default&nbsp;is&nbsp;False)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;first&nbsp;index&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;is&nbsp;`Index`.<br>
-deep&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Introspect&nbsp;the&nbsp;data&nbsp;deeply,&nbsp;interrogate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`<a href="builtins.html#object">object</a>`&nbsp;dtypes&nbsp;for&nbsp;system-level&nbsp;memory&nbsp;consumption<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sizes&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;series&nbsp;with&nbsp;column&nbsp;names&nbsp;as&nbsp;index&nbsp;and&nbsp;memory&nbsp;usage&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns&nbsp;with&nbsp;units&nbsp;of&nbsp;bytes.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Memory&nbsp;usage&nbsp;does&nbsp;not&nbsp;include&nbsp;memory&nbsp;consumed&nbsp;by&nbsp;elements&nbsp;that<br>
-are&nbsp;not&nbsp;components&nbsp;of&nbsp;the&nbsp;array&nbsp;if&nbsp;deep=False<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.nbytes</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-merge"><strong>merge</strong></a>(self, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False)</dt><dd><tt>Merge&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects&nbsp;by&nbsp;performing&nbsp;a&nbsp;database-style&nbsp;join&nbsp;operation&nbsp;by<br>
-columns&nbsp;or&nbsp;indexes.<br>
-&nbsp;<br>
-If&nbsp;joining&nbsp;columns&nbsp;on&nbsp;columns,&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;indexes&nbsp;*will&nbsp;be<br>
-ignored*.&nbsp;Otherwise&nbsp;if&nbsp;joining&nbsp;indexes&nbsp;on&nbsp;indexes&nbsp;or&nbsp;indexes&nbsp;on&nbsp;a&nbsp;column&nbsp;or<br>
-columns,&nbsp;the&nbsp;index&nbsp;will&nbsp;be&nbsp;passed&nbsp;on.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-right&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-how&nbsp;:&nbsp;{'left',&nbsp;'right',&nbsp;'outer',&nbsp;'inner'},&nbsp;default&nbsp;'inner'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;left:&nbsp;use&nbsp;only&nbsp;keys&nbsp;from&nbsp;left&nbsp;frame,&nbsp;similar&nbsp;to&nbsp;a&nbsp;SQL&nbsp;left&nbsp;outer&nbsp;join;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;preserve&nbsp;key&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;right:&nbsp;use&nbsp;only&nbsp;keys&nbsp;from&nbsp;right&nbsp;frame,&nbsp;similar&nbsp;to&nbsp;a&nbsp;SQL&nbsp;right&nbsp;outer&nbsp;join;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;preserve&nbsp;key&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;outer:&nbsp;use&nbsp;union&nbsp;of&nbsp;keys&nbsp;from&nbsp;both&nbsp;frames,&nbsp;similar&nbsp;to&nbsp;a&nbsp;SQL&nbsp;full&nbsp;outer<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;join;&nbsp;sort&nbsp;keys&nbsp;lexicographically<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;inner:&nbsp;use&nbsp;intersection&nbsp;of&nbsp;keys&nbsp;from&nbsp;both&nbsp;frames,&nbsp;similar&nbsp;to&nbsp;a&nbsp;SQL&nbsp;inner<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;join;&nbsp;preserve&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;left&nbsp;keys<br>
-on&nbsp;:&nbsp;label&nbsp;or&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;names&nbsp;to&nbsp;join&nbsp;on.&nbsp;Must&nbsp;be&nbsp;found&nbsp;in&nbsp;both&nbsp;DataFrames.&nbsp;If&nbsp;on&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;None&nbsp;and&nbsp;not&nbsp;merging&nbsp;on&nbsp;indexes,&nbsp;then&nbsp;it&nbsp;merges&nbsp;on&nbsp;the&nbsp;intersection&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;columns&nbsp;by&nbsp;default.<br>
-left_on&nbsp;:&nbsp;label&nbsp;or&nbsp;list,&nbsp;or&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;names&nbsp;to&nbsp;join&nbsp;on&nbsp;in&nbsp;left&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Can&nbsp;be&nbsp;a&nbsp;vector&nbsp;or&nbsp;list&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;vectors&nbsp;of&nbsp;the&nbsp;length&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;use&nbsp;a&nbsp;particular&nbsp;vector&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;join&nbsp;key&nbsp;instead&nbsp;of&nbsp;columns<br>
-right_on&nbsp;:&nbsp;label&nbsp;or&nbsp;list,&nbsp;or&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;names&nbsp;to&nbsp;join&nbsp;on&nbsp;in&nbsp;right&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;vector/list&nbsp;of&nbsp;vectors&nbsp;per<br>
-&nbsp;&nbsp;&nbsp;&nbsp;left_on&nbsp;docs<br>
-left_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;the&nbsp;index&nbsp;from&nbsp;the&nbsp;left&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;as&nbsp;the&nbsp;join&nbsp;key(s).&nbsp;If&nbsp;it&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;MultiIndex,&nbsp;the&nbsp;number&nbsp;of&nbsp;keys&nbsp;in&nbsp;the&nbsp;other&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(either&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;a&nbsp;number&nbsp;of&nbsp;columns)&nbsp;must&nbsp;match&nbsp;the&nbsp;number&nbsp;of&nbsp;levels<br>
-right_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;the&nbsp;index&nbsp;from&nbsp;the&nbsp;right&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;as&nbsp;the&nbsp;join&nbsp;key.&nbsp;Same&nbsp;caveats&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;left_index<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;the&nbsp;join&nbsp;keys&nbsp;lexicographically&nbsp;in&nbsp;the&nbsp;result&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;If&nbsp;False,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;join&nbsp;keys&nbsp;depends&nbsp;on&nbsp;the&nbsp;join&nbsp;type&nbsp;(how&nbsp;keyword)<br>
-suffixes&nbsp;:&nbsp;2-length&nbsp;sequence&nbsp;(tuple,&nbsp;list,&nbsp;...)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Suffix&nbsp;to&nbsp;apply&nbsp;to&nbsp;overlapping&nbsp;column&nbsp;names&nbsp;in&nbsp;the&nbsp;left&nbsp;and&nbsp;right<br>
-&nbsp;&nbsp;&nbsp;&nbsp;side,&nbsp;respectively<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;do&nbsp;not&nbsp;copy&nbsp;data&nbsp;unnecessarily<br>
-indicator&nbsp;:&nbsp;boolean&nbsp;or&nbsp;string,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;adds&nbsp;a&nbsp;column&nbsp;to&nbsp;output&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;called&nbsp;"_merge"&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;information&nbsp;on&nbsp;the&nbsp;source&nbsp;of&nbsp;each&nbsp;row.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;string,&nbsp;column&nbsp;with&nbsp;information&nbsp;on&nbsp;source&nbsp;of&nbsp;each&nbsp;row&nbsp;will&nbsp;be&nbsp;added&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;output&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;and&nbsp;column&nbsp;will&nbsp;be&nbsp;named&nbsp;value&nbsp;of&nbsp;string.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Information&nbsp;column&nbsp;is&nbsp;Categorical-type&nbsp;and&nbsp;takes&nbsp;on&nbsp;a&nbsp;value&nbsp;of&nbsp;"left_only"<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;observations&nbsp;whose&nbsp;merge&nbsp;key&nbsp;only&nbsp;appears&nbsp;in&nbsp;'left'&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"right_only"&nbsp;for&nbsp;observations&nbsp;whose&nbsp;merge&nbsp;key&nbsp;only&nbsp;appears&nbsp;in&nbsp;'right'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;and&nbsp;"both"&nbsp;if&nbsp;the&nbsp;observation's&nbsp;merge&nbsp;key&nbsp;is&nbsp;found&nbsp;in&nbsp;both.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;B<br>
-&nbsp;&nbsp;&nbsp;&nbsp;lkey&nbsp;value&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;rkey&nbsp;value<br>
-0&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;5<br>
-1&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;6<br>
-2&nbsp;&nbsp;&nbsp;baz&nbsp;&nbsp;3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;qux&nbsp;&nbsp;7<br>
-3&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;8<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;A.<a href="#SweepFrame-merge">merge</a>(B,&nbsp;left_on='lkey',&nbsp;right_on='rkey',&nbsp;how='outer')<br>
-&nbsp;&nbsp;&nbsp;lkey&nbsp;&nbsp;value_x&nbsp;&nbsp;rkey&nbsp;&nbsp;value_y<br>
-0&nbsp;&nbsp;foo&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;&nbsp;5<br>
-1&nbsp;&nbsp;foo&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;bar&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;&nbsp;6<br>
-3&nbsp;&nbsp;bar&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;&nbsp;8<br>
-4&nbsp;&nbsp;baz&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;NaN<br>
-5&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;qux&nbsp;&nbsp;&nbsp;7<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-merged&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;output&nbsp;type&nbsp;will&nbsp;the&nbsp;be&nbsp;same&nbsp;as&nbsp;'left',&nbsp;if&nbsp;it&nbsp;is&nbsp;a&nbsp;subclass<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-merge_ordered<br>
-merge_asof</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-min"><strong>min</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;minimum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;minimum,&nbsp;use&nbsp;``idxmin``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmin``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-min&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-mod"><strong>mod</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Modulo&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;%&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rmod</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-mode"><strong>mode</strong></a>(self, axis=0, numeric_only=False)</dt><dd><tt>Gets&nbsp;the&nbsp;<a href="#SweepFrame-mode">mode</a>(s)&nbsp;of&nbsp;each&nbsp;element&nbsp;along&nbsp;the&nbsp;axis&nbsp;selected.&nbsp;Adds&nbsp;a&nbsp;row<br>
-for&nbsp;each&nbsp;mode&nbsp;per&nbsp;label,&nbsp;fills&nbsp;in&nbsp;gaps&nbsp;with&nbsp;nan.<br>
-&nbsp;<br>
-Note&nbsp;that&nbsp;there&nbsp;could&nbsp;be&nbsp;multiple&nbsp;values&nbsp;returned&nbsp;for&nbsp;the&nbsp;selected<br>
-axis&nbsp;(when&nbsp;more&nbsp;than&nbsp;one&nbsp;item&nbsp;share&nbsp;the&nbsp;maximum&nbsp;frequency),&nbsp;which&nbsp;is<br>
-the&nbsp;reason&nbsp;why&nbsp;a&nbsp;dataframe&nbsp;is&nbsp;returned.&nbsp;If&nbsp;you&nbsp;want&nbsp;to&nbsp;impute&nbsp;missing<br>
-values&nbsp;with&nbsp;the&nbsp;mode&nbsp;in&nbsp;a&nbsp;dataframe&nbsp;``df``,&nbsp;you&nbsp;can&nbsp;just&nbsp;do&nbsp;this:<br>
-``df.<a href="#SweepFrame-fillna">fillna</a>(df.<a href="#SweepFrame-mode">mode</a>().iloc[0])``<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;0&nbsp;or&nbsp;'index'&nbsp;:&nbsp;get&nbsp;mode&nbsp;of&nbsp;each&nbsp;column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;:&nbsp;get&nbsp;mode&nbsp;of&nbsp;each&nbsp;row<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;only&nbsp;apply&nbsp;to&nbsp;numeric&nbsp;columns<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-modes&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(sorted)<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;2,&nbsp;1,&nbsp;2,&nbsp;1,&nbsp;2,&nbsp;3]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-mode">mode</a>()<br>
-&nbsp;&nbsp;&nbsp;A<br>
-0&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;2</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-mul"><strong>mul</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Multiplication&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-multiply"><strong>multiply</strong></a> = mul(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Multiplication&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-ne"><strong>ne</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;ne</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-nlargest"><strong>nlargest</strong></a>(self, n, columns, keep='first')</dt><dd><tt>Get&nbsp;the&nbsp;rows&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;sorted&nbsp;by&nbsp;the&nbsp;`n`&nbsp;largest<br>
-values&nbsp;of&nbsp;`columns`.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;items&nbsp;to&nbsp;retrieve<br>
-columns&nbsp;:&nbsp;list&nbsp;or&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;name&nbsp;or&nbsp;names&nbsp;to&nbsp;order&nbsp;by<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'a':&nbsp;[1,&nbsp;10,&nbsp;8,&nbsp;11,&nbsp;-1],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'b':&nbsp;list('abdce'),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'c':&nbsp;[1.0,&nbsp;2.0,&nbsp;np.nan,&nbsp;3.0,&nbsp;4.0]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-nlargest">nlargest</a>(3,&nbsp;'a')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;c<br>
-3&nbsp;&nbsp;11&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;3<br>
-1&nbsp;&nbsp;10&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;8&nbsp;&nbsp;d&nbsp;NaN</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-nsmallest"><strong>nsmallest</strong></a>(self, n, columns, keep='first')</dt><dd><tt>Get&nbsp;the&nbsp;rows&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;sorted&nbsp;by&nbsp;the&nbsp;`n`&nbsp;smallest<br>
-values&nbsp;of&nbsp;`columns`.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;items&nbsp;to&nbsp;retrieve<br>
-columns&nbsp;:&nbsp;list&nbsp;or&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;name&nbsp;or&nbsp;names&nbsp;to&nbsp;order&nbsp;by<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'a':&nbsp;[1,&nbsp;10,&nbsp;8,&nbsp;11,&nbsp;-1],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'b':&nbsp;list('abdce'),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'c':&nbsp;[1.0,&nbsp;2.0,&nbsp;np.nan,&nbsp;3.0,&nbsp;4.0]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-nsmallest">nsmallest</a>(3,&nbsp;'a')<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;c<br>
-4&nbsp;-1&nbsp;&nbsp;e&nbsp;&nbsp;&nbsp;4<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;8&nbsp;&nbsp;d&nbsp;NaN</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-nunique"><strong>nunique</strong></a>(self, axis=0, dropna=True)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;number&nbsp;of&nbsp;distinct&nbsp;observations&nbsp;over&nbsp;requested<br>
-axis.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Don't&nbsp;include&nbsp;NaN&nbsp;in&nbsp;the&nbsp;counts.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-nunique&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;'B':&nbsp;[1,&nbsp;1,&nbsp;1]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-nunique">nunique</a>()<br>
-A&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-B&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-nunique">nunique</a>(axis=1)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-pivot"><strong>pivot</strong></a>(self, index=None, columns=None, values=None)</dt><dd><tt>Reshape&nbsp;data&nbsp;(produce&nbsp;a&nbsp;"pivot"&nbsp;table)&nbsp;based&nbsp;on&nbsp;column&nbsp;values.&nbsp;Uses<br>
-unique&nbsp;values&nbsp;from&nbsp;index&nbsp;/&nbsp;columns&nbsp;to&nbsp;form&nbsp;axes&nbsp;of&nbsp;the&nbsp;resulting<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;name&nbsp;to&nbsp;use&nbsp;to&nbsp;make&nbsp;new&nbsp;frame's&nbsp;index.&nbsp;If&nbsp;None,&nbsp;uses<br>
-&nbsp;&nbsp;&nbsp;&nbsp;existing&nbsp;index.<br>
-columns&nbsp;:&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;name&nbsp;to&nbsp;use&nbsp;to&nbsp;make&nbsp;new&nbsp;frame's&nbsp;columns<br>
-values&nbsp;:&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;name&nbsp;to&nbsp;use&nbsp;for&nbsp;populating&nbsp;new&nbsp;frame's&nbsp;values.&nbsp;If&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specified,&nbsp;all&nbsp;remaining&nbsp;columns&nbsp;will&nbsp;be&nbsp;used&nbsp;and&nbsp;the&nbsp;result&nbsp;will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;have&nbsp;hierarchically&nbsp;indexed&nbsp;columns<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-pivoted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.pivot_table&nbsp;:&nbsp;generalization&nbsp;of&nbsp;pivot&nbsp;that&nbsp;can&nbsp;handle<br>
-&nbsp;&nbsp;&nbsp;&nbsp;duplicate&nbsp;values&nbsp;for&nbsp;one&nbsp;index/column&nbsp;pair<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.unstack&nbsp;:&nbsp;pivot&nbsp;based&nbsp;on&nbsp;the&nbsp;index&nbsp;values&nbsp;instead&nbsp;of&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;column<br>
-&nbsp;<br>
-Notes<br>
------<br>
-For&nbsp;finer-tuned&nbsp;control,&nbsp;see&nbsp;hierarchical&nbsp;indexing&nbsp;documentation&nbsp;along<br>
-with&nbsp;the&nbsp;related&nbsp;stack/unstack&nbsp;methods<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'foo':&nbsp;['one','one','one','two','two','two'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'bar':&nbsp;['A',&nbsp;'B',&nbsp;'C',&nbsp;'A',&nbsp;'B',&nbsp;'C'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'baz':&nbsp;[1,&nbsp;2,&nbsp;3,&nbsp;4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;baz<br>
-0&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-4&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-5&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-pivot">pivot</a>(index='foo',&nbsp;columns='bar',&nbsp;values='baz')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C<br>
-one&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;3<br>
-two&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-pivot">pivot</a>(index='foo',&nbsp;columns='bar')['baz']<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C<br>
-one&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;3<br>
-two&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-pivot_table"><strong>pivot_table</strong></a>(data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All')</dt><dd><tt>Create&nbsp;a&nbsp;spreadsheet-style&nbsp;pivot&nbsp;table&nbsp;as&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;The&nbsp;levels&nbsp;in&nbsp;the<br>
-pivot&nbsp;table&nbsp;will&nbsp;be&nbsp;stored&nbsp;in&nbsp;MultiIndex&nbsp;objects&nbsp;(hierarchical&nbsp;indexes)&nbsp;on<br>
-the&nbsp;index&nbsp;and&nbsp;columns&nbsp;of&nbsp;the&nbsp;result&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-values&nbsp;:&nbsp;column&nbsp;to&nbsp;aggregate,&nbsp;optional<br>
-index&nbsp;:&nbsp;column,&nbsp;Grouper,&nbsp;array,&nbsp;or&nbsp;list&nbsp;of&nbsp;the&nbsp;previous<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;an&nbsp;array&nbsp;is&nbsp;passed,&nbsp;it&nbsp;must&nbsp;be&nbsp;the&nbsp;same&nbsp;length&nbsp;as&nbsp;the&nbsp;data.&nbsp;The&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;contain&nbsp;any&nbsp;of&nbsp;the&nbsp;other&nbsp;types&nbsp;(except&nbsp;list).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keys&nbsp;to&nbsp;group&nbsp;by&nbsp;on&nbsp;the&nbsp;pivot&nbsp;table&nbsp;index.&nbsp;&nbsp;If&nbsp;an&nbsp;array&nbsp;is&nbsp;passed,&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;being&nbsp;used&nbsp;as&nbsp;the&nbsp;same&nbsp;manner&nbsp;as&nbsp;column&nbsp;values.<br>
-columns&nbsp;:&nbsp;column,&nbsp;Grouper,&nbsp;array,&nbsp;or&nbsp;list&nbsp;of&nbsp;the&nbsp;previous<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;an&nbsp;array&nbsp;is&nbsp;passed,&nbsp;it&nbsp;must&nbsp;be&nbsp;the&nbsp;same&nbsp;length&nbsp;as&nbsp;the&nbsp;data.&nbsp;The&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;contain&nbsp;any&nbsp;of&nbsp;the&nbsp;other&nbsp;types&nbsp;(except&nbsp;list).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keys&nbsp;to&nbsp;group&nbsp;by&nbsp;on&nbsp;the&nbsp;pivot&nbsp;table&nbsp;column.&nbsp;&nbsp;If&nbsp;an&nbsp;array&nbsp;is&nbsp;passed,&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;being&nbsp;used&nbsp;as&nbsp;the&nbsp;same&nbsp;manner&nbsp;as&nbsp;column&nbsp;values.<br>
-aggfunc&nbsp;:&nbsp;function&nbsp;or&nbsp;list&nbsp;of&nbsp;functions,&nbsp;default&nbsp;numpy.mean<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;list&nbsp;of&nbsp;functions&nbsp;passed,&nbsp;the&nbsp;resulting&nbsp;pivot&nbsp;table&nbsp;will&nbsp;have<br>
-&nbsp;&nbsp;&nbsp;&nbsp;hierarchical&nbsp;columns&nbsp;whose&nbsp;top&nbsp;level&nbsp;are&nbsp;the&nbsp;function&nbsp;names&nbsp;(inferred<br>
-&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;the&nbsp;function&nbsp;objects&nbsp;themselves)<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;replace&nbsp;missing&nbsp;values&nbsp;with<br>
-margins&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;all&nbsp;row&nbsp;/&nbsp;columns&nbsp;(e.g.&nbsp;for&nbsp;subtotal&nbsp;/&nbsp;grand&nbsp;totals)<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;not&nbsp;include&nbsp;columns&nbsp;whose&nbsp;entries&nbsp;are&nbsp;all&nbsp;NaN<br>
-margins_name&nbsp;:&nbsp;string,&nbsp;default&nbsp;'All'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;the&nbsp;row&nbsp;/&nbsp;column&nbsp;that&nbsp;will&nbsp;contain&nbsp;the&nbsp;totals<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;margins&nbsp;is&nbsp;True.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;foo&nbsp;one&nbsp;small&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;foo&nbsp;one&nbsp;large&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;foo&nbsp;one&nbsp;large&nbsp;&nbsp;2<br>
-3&nbsp;&nbsp;foo&nbsp;two&nbsp;small&nbsp;&nbsp;3<br>
-4&nbsp;&nbsp;foo&nbsp;two&nbsp;small&nbsp;&nbsp;3<br>
-5&nbsp;&nbsp;bar&nbsp;one&nbsp;large&nbsp;&nbsp;4<br>
-6&nbsp;&nbsp;bar&nbsp;one&nbsp;small&nbsp;&nbsp;5<br>
-7&nbsp;&nbsp;bar&nbsp;two&nbsp;small&nbsp;&nbsp;6<br>
-8&nbsp;&nbsp;bar&nbsp;two&nbsp;large&nbsp;&nbsp;7<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;table&nbsp;=&nbsp;<a href="#SweepFrame-pivot_table">pivot_table</a>(df,&nbsp;values='D',&nbsp;index=['A',&nbsp;'B'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['C'],&nbsp;aggfunc=np.sum)<br>
-&gt;&gt;&gt;&nbsp;table<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;small&nbsp;&nbsp;large<br>
-foo&nbsp;&nbsp;one&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;6&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-bar&nbsp;&nbsp;one&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;6&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-table&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.pivot&nbsp;:&nbsp;pivot&nbsp;without&nbsp;aggregation&nbsp;that&nbsp;can&nbsp;handle<br>
-&nbsp;&nbsp;&nbsp;&nbsp;non-numeric&nbsp;data</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-pow"><strong>pow</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`pow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;**&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rpow</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-prod"><strong>prod</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-product"><strong>product</strong></a> = prod(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-quantile"><strong>quantile</strong></a>(self, q=0.5, axis=0, numeric_only=True, interpolation='linear')</dt><dd><tt>Return&nbsp;values&nbsp;at&nbsp;the&nbsp;given&nbsp;quantile&nbsp;over&nbsp;requested&nbsp;axis,&nbsp;a&nbsp;la<br>
-numpy.percentile.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-q&nbsp;:&nbsp;float&nbsp;or&nbsp;array-like,&nbsp;default&nbsp;0.5&nbsp;(50%&nbsp;quantile)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&lt;=&nbsp;q&nbsp;&lt;=&nbsp;1,&nbsp;the&nbsp;<a href="#SweepFrame-quantile">quantile</a>(s)&nbsp;to&nbsp;compute<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}&nbsp;(default&nbsp;0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;or&nbsp;'index'&nbsp;for&nbsp;row-wise,&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;for&nbsp;column-wise<br>
-interpolation&nbsp;:&nbsp;{'linear',&nbsp;'lower',&nbsp;'higher',&nbsp;'midpoint',&nbsp;'nearest'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;optional&nbsp;parameter&nbsp;specifies&nbsp;the&nbsp;interpolation&nbsp;method&nbsp;to&nbsp;use,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;the&nbsp;desired&nbsp;quantile&nbsp;lies&nbsp;between&nbsp;two&nbsp;data&nbsp;points&nbsp;`i`&nbsp;and&nbsp;`j`:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;linear:&nbsp;`i&nbsp;+&nbsp;(j&nbsp;-&nbsp;i)&nbsp;*&nbsp;fraction`,&nbsp;where&nbsp;`fraction`&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;fractional&nbsp;part&nbsp;of&nbsp;the&nbsp;index&nbsp;surrounded&nbsp;by&nbsp;`i`&nbsp;and&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;lower:&nbsp;`i`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;higher:&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;`i`&nbsp;or&nbsp;`j`&nbsp;whichever&nbsp;is&nbsp;nearest.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;midpoint:&nbsp;(`i`&nbsp;+&nbsp;`j`)&nbsp;/&nbsp;2.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-quantiles&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;If&nbsp;``q``&nbsp;is&nbsp;an&nbsp;array,&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;be&nbsp;returned&nbsp;where&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;is&nbsp;``q``,&nbsp;the&nbsp;columns&nbsp;are&nbsp;the&nbsp;columns&nbsp;of&nbsp;self,&nbsp;and&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;are&nbsp;the&nbsp;quantiles.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;If&nbsp;``q``&nbsp;is&nbsp;a&nbsp;float,&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;returned&nbsp;where&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;is&nbsp;the&nbsp;columns&nbsp;of&nbsp;self&nbsp;and&nbsp;the&nbsp;values&nbsp;are&nbsp;the&nbsp;quantiles.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.array([[1,&nbsp;1],&nbsp;[2,&nbsp;10],&nbsp;[3,&nbsp;100],&nbsp;[4,&nbsp;100]]),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['a',&nbsp;'b'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-quantile">quantile</a>(.1)<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;1.3<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;3.7<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-quantile">quantile</a>([.1,&nbsp;.5])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-0.1&nbsp;&nbsp;1.3&nbsp;&nbsp;&nbsp;3.7<br>
-0.5&nbsp;&nbsp;2.5&nbsp;&nbsp;55.0</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-query"><strong>query</strong></a>(self, expr, inplace=False, **kwargs)</dt><dd><tt>Query&nbsp;the&nbsp;columns&nbsp;of&nbsp;a&nbsp;frame&nbsp;with&nbsp;a&nbsp;boolean&nbsp;expression.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.13<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-expr&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;query&nbsp;string&nbsp;to&nbsp;evaluate.&nbsp;&nbsp;You&nbsp;can&nbsp;refer&nbsp;to&nbsp;variables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;environment&nbsp;by&nbsp;prefixing&nbsp;them&nbsp;with&nbsp;an&nbsp;'@'&nbsp;character&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``@a&nbsp;+&nbsp;b``.<br>
-inplace&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;the&nbsp;query&nbsp;should&nbsp;modify&nbsp;the&nbsp;data&nbsp;in&nbsp;place&nbsp;or&nbsp;return<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;modified&nbsp;copy<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-kwargs&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;documentation&nbsp;for&nbsp;:func:`pandas.eval`&nbsp;for&nbsp;complete&nbsp;details<br>
-&nbsp;&nbsp;&nbsp;&nbsp;on&nbsp;the&nbsp;keyword&nbsp;arguments&nbsp;accepted&nbsp;by&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.query`.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-q&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;result&nbsp;of&nbsp;the&nbsp;evaluation&nbsp;of&nbsp;this&nbsp;expression&nbsp;is&nbsp;first&nbsp;passed&nbsp;to<br>
-:attr:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.loc`&nbsp;and&nbsp;if&nbsp;that&nbsp;fails&nbsp;because&nbsp;of&nbsp;a<br>
-multidimensional&nbsp;key&nbsp;(e.g.,&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>)&nbsp;then&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;passed<br>
-to&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.__getitem__`.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;uses&nbsp;the&nbsp;top-level&nbsp;:func:`pandas.eval`&nbsp;function&nbsp;to<br>
-evaluate&nbsp;the&nbsp;passed&nbsp;query.<br>
-&nbsp;<br>
-The&nbsp;:meth:`~pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.query`&nbsp;method&nbsp;uses&nbsp;a&nbsp;slightly<br>
-modified&nbsp;Python&nbsp;syntax&nbsp;by&nbsp;default.&nbsp;For&nbsp;example,&nbsp;the&nbsp;``&amp;``&nbsp;and&nbsp;``|``<br>
-(bitwise)&nbsp;operators&nbsp;have&nbsp;the&nbsp;precedence&nbsp;of&nbsp;their&nbsp;boolean&nbsp;cousins,<br>
-:keyword:`and`&nbsp;and&nbsp;:keyword:`or`.&nbsp;This&nbsp;*is*&nbsp;syntactically&nbsp;valid&nbsp;Python,<br>
-however&nbsp;the&nbsp;semantics&nbsp;are&nbsp;different.<br>
-&nbsp;<br>
-You&nbsp;can&nbsp;change&nbsp;the&nbsp;semantics&nbsp;of&nbsp;the&nbsp;expression&nbsp;by&nbsp;passing&nbsp;the&nbsp;keyword<br>
-argument&nbsp;``parser='python'``.&nbsp;This&nbsp;enforces&nbsp;the&nbsp;same&nbsp;semantics&nbsp;as<br>
-evaluation&nbsp;in&nbsp;Python&nbsp;space.&nbsp;Likewise,&nbsp;you&nbsp;can&nbsp;pass&nbsp;``engine='python'``<br>
-to&nbsp;evaluate&nbsp;an&nbsp;expression&nbsp;using&nbsp;Python&nbsp;itself&nbsp;as&nbsp;a&nbsp;backend.&nbsp;This&nbsp;is&nbsp;not<br>
-recommended&nbsp;as&nbsp;it&nbsp;is&nbsp;inefficient&nbsp;compared&nbsp;to&nbsp;using&nbsp;``numexpr``&nbsp;as&nbsp;the<br>
-engine.<br>
-&nbsp;<br>
-The&nbsp;:attr:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.index`&nbsp;and<br>
-:attr:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.columns`&nbsp;attributes&nbsp;of&nbsp;the<br>
-:class:`~pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>`&nbsp;instance&nbsp;are&nbsp;placed&nbsp;in&nbsp;the&nbsp;query&nbsp;namespace<br>
-by&nbsp;default,&nbsp;which&nbsp;allows&nbsp;you&nbsp;to&nbsp;treat&nbsp;both&nbsp;the&nbsp;index&nbsp;and&nbsp;columns&nbsp;of&nbsp;the<br>
-frame&nbsp;as&nbsp;a&nbsp;column&nbsp;in&nbsp;the&nbsp;frame.<br>
-The&nbsp;identifier&nbsp;``index``&nbsp;is&nbsp;used&nbsp;for&nbsp;the&nbsp;frame&nbsp;index;&nbsp;you&nbsp;can&nbsp;also<br>
-use&nbsp;the&nbsp;name&nbsp;of&nbsp;the&nbsp;index&nbsp;to&nbsp;identify&nbsp;it&nbsp;in&nbsp;a&nbsp;query.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``query``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.query&gt;`.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.eval<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.eval<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;numpy.random&nbsp;import&nbsp;randn<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;pandas&nbsp;import&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(randn(10,&nbsp;2),&nbsp;columns=list('ab'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-query">query</a>('a&nbsp;&gt;&nbsp;b')<br>
-&gt;&gt;&gt;&nbsp;df[df.a&nbsp;&gt;&nbsp;df.b]&nbsp;&nbsp;#&nbsp;same&nbsp;result&nbsp;as&nbsp;the&nbsp;previous&nbsp;expression</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-radd"><strong>radd</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Addition&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`radd`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;+&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.add</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-rdiv"><strong>rdiv</strong></a> = rtruediv(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-reindex"><strong>reindex</strong></a>(self, index=None, columns=None, **kwargs)</dt><dd><tt>Conform&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;new&nbsp;index&nbsp;with&nbsp;optional&nbsp;filling&nbsp;logic,&nbsp;placing<br>
-NA/NaN&nbsp;in&nbsp;locations&nbsp;having&nbsp;no&nbsp;value&nbsp;in&nbsp;the&nbsp;previous&nbsp;index.&nbsp;A&nbsp;new&nbsp;<a href="builtins.html#object">object</a><br>
-is&nbsp;produced&nbsp;unless&nbsp;the&nbsp;new&nbsp;index&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;the&nbsp;current&nbsp;one&nbsp;and<br>
-copy=False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index,&nbsp;columns&nbsp;:&nbsp;array-like,&nbsp;optional&nbsp;(can&nbsp;be&nbsp;specified&nbsp;in&nbsp;order,&nbsp;or&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;keywords)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;New&nbsp;labels&nbsp;/&nbsp;index&nbsp;to&nbsp;conform&nbsp;to.&nbsp;Preferably&nbsp;an&nbsp;Index&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;avoid&nbsp;duplicating&nbsp;data<br>
-method&nbsp;:&nbsp;{None,&nbsp;'backfill'/'bfill',&nbsp;'pad'/'ffill',&nbsp;'nearest'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Please&nbsp;note:&nbsp;this&nbsp;is&nbsp;only&nbsp;&nbsp;applicable&nbsp;to&nbsp;DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;monotonically&nbsp;increasing/decreasing&nbsp;index.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;default:&nbsp;don't&nbsp;fill&nbsp;gaps<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;next&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;use&nbsp;nearest&nbsp;valid&nbsp;observations&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>,&nbsp;even&nbsp;if&nbsp;the&nbsp;passed&nbsp;indexes&nbsp;are&nbsp;the&nbsp;same<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;elements&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;original&nbsp;and&nbsp;new&nbsp;labels&nbsp;for&nbsp;inexact<br>
-&nbsp;&nbsp;&nbsp;&nbsp;matches.&nbsp;The&nbsp;values&nbsp;of&nbsp;the&nbsp;index&nbsp;at&nbsp;the&nbsp;matching&nbsp;locations&nbsp;most<br>
-&nbsp;&nbsp;&nbsp;&nbsp;satisfy&nbsp;the&nbsp;equation&nbsp;``<a href="#SweepFrame-abs">abs</a>(index[indexer]&nbsp;-&nbsp;target)&nbsp;&lt;=&nbsp;tolerance``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;some&nbsp;fictional&nbsp;data.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;['Firefox',&nbsp;'Chrome',&nbsp;'Safari',&nbsp;'IE10',&nbsp;'Konqueror']<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'http_status':&nbsp;[200,200,404,404,301],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'response_time':&nbsp;[0.04,&nbsp;0.02,&nbsp;0.07,&nbsp;0.08,&nbsp;1.0]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Firefox&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.04<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Konqueror&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;301&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.00<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;new&nbsp;index&nbsp;and&nbsp;reindex&nbsp;the&nbsp;dataframe.&nbsp;By&nbsp;default<br>
-values&nbsp;in&nbsp;the&nbsp;new&nbsp;index&nbsp;that&nbsp;do&nbsp;not&nbsp;have&nbsp;corresponding<br>
-records&nbsp;in&nbsp;the&nbsp;dataframe&nbsp;are&nbsp;assigned&nbsp;``NaN``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;new_index=&nbsp;['Safari',&nbsp;'Iceweasel',&nbsp;'Comodo&nbsp;Dragon',&nbsp;'IE10',<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Chrome']<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-reindex">reindex</a>(new_index)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-We&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;by&nbsp;passing&nbsp;a&nbsp;value&nbsp;to<br>
-the&nbsp;keyword&nbsp;``fill_value``.&nbsp;Because&nbsp;the&nbsp;index&nbsp;is&nbsp;not&nbsp;monotonically<br>
-increasing&nbsp;or&nbsp;decreasing,&nbsp;we&nbsp;cannot&nbsp;use&nbsp;arguments&nbsp;to&nbsp;the&nbsp;keyword<br>
-``method``&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``&nbsp;values.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-reindex">reindex</a>(new_index,&nbsp;fill_value=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-reindex">reindex</a>(new_index,&nbsp;fill_value='missing')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-To&nbsp;further&nbsp;illustrate&nbsp;the&nbsp;filling&nbsp;functionality&nbsp;in<br>
-``reindex``,&nbsp;we&nbsp;will&nbsp;create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;a<br>
-monotonically&nbsp;increasing&nbsp;index&nbsp;(for&nbsp;example,&nbsp;a&nbsp;sequence<br>
-of&nbsp;dates).<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index&nbsp;=&nbsp;pd.date_range('1/1/2010',&nbsp;periods=6,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"prices":&nbsp;[100,&nbsp;101,&nbsp;np.nan,&nbsp;100,&nbsp;89,&nbsp;88]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=date_index)<br>
-&gt;&gt;&gt;&nbsp;df2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-&nbsp;<br>
-Suppose&nbsp;we&nbsp;decide&nbsp;to&nbsp;expand&nbsp;the&nbsp;dataframe&nbsp;to&nbsp;cover&nbsp;a&nbsp;wider<br>
-date&nbsp;range.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index2&nbsp;=&nbsp;pd.date_range('12/29/2009',&nbsp;periods=10,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#SweepFrame-reindex">reindex</a>(date_index2)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-The&nbsp;index&nbsp;entries&nbsp;that&nbsp;did&nbsp;not&nbsp;have&nbsp;a&nbsp;value&nbsp;in&nbsp;the&nbsp;original&nbsp;data&nbsp;frame<br>
-(for&nbsp;example,&nbsp;'2009-12-29')&nbsp;are&nbsp;by&nbsp;default&nbsp;filled&nbsp;with&nbsp;``NaN``.<br>
-If&nbsp;desired,&nbsp;we&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;using&nbsp;one&nbsp;of&nbsp;several<br>
-options.<br>
-&nbsp;<br>
-For&nbsp;example,&nbsp;to&nbsp;backpropagate&nbsp;the&nbsp;last&nbsp;valid&nbsp;value&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values,&nbsp;pass&nbsp;``bfill``&nbsp;as&nbsp;an&nbsp;argument&nbsp;to&nbsp;the&nbsp;``method``&nbsp;keyword.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#SweepFrame-reindex">reindex</a>(date_index2,&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;the&nbsp;``NaN``&nbsp;value&nbsp;present&nbsp;in&nbsp;the&nbsp;original&nbsp;dataframe<br>
-(at&nbsp;index&nbsp;value&nbsp;2010-01-03)&nbsp;will&nbsp;not&nbsp;be&nbsp;filled&nbsp;by&nbsp;any&nbsp;of&nbsp;the<br>
-value&nbsp;propagation&nbsp;schemes.&nbsp;This&nbsp;is&nbsp;because&nbsp;filling&nbsp;while&nbsp;reindexing<br>
-does&nbsp;not&nbsp;look&nbsp;at&nbsp;dataframe&nbsp;values,&nbsp;but&nbsp;only&nbsp;compares&nbsp;the&nbsp;original&nbsp;and<br>
-desired&nbsp;indexes.&nbsp;If&nbsp;you&nbsp;do&nbsp;want&nbsp;to&nbsp;fill&nbsp;in&nbsp;the&nbsp;``NaN``&nbsp;values&nbsp;present<br>
-in&nbsp;the&nbsp;original&nbsp;dataframe,&nbsp;use&nbsp;the&nbsp;``<a href="#SweepFrame-fillna">fillna</a>()``&nbsp;method.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-reindex_axis"><strong>reindex_axis</strong></a>(self, labels, axis=0, method=None, level=None, copy=True, limit=None, fill_value=nan)</dt><dd><tt>Conform&nbsp;input&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;new&nbsp;index&nbsp;with&nbsp;optional<br>
-filling&nbsp;logic,&nbsp;placing&nbsp;NA/NaN&nbsp;in&nbsp;locations&nbsp;having&nbsp;no&nbsp;value&nbsp;in&nbsp;the<br>
-previous&nbsp;index.&nbsp;A&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;produced&nbsp;unless&nbsp;the&nbsp;new&nbsp;index&nbsp;is<br>
-equivalent&nbsp;to&nbsp;the&nbsp;current&nbsp;one&nbsp;and&nbsp;copy=False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-labels&nbsp;:&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;New&nbsp;labels&nbsp;/&nbsp;index&nbsp;to&nbsp;conform&nbsp;to.&nbsp;Preferably&nbsp;an&nbsp;Index&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;avoid&nbsp;duplicating&nbsp;data<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'}<br>
-method&nbsp;:&nbsp;{None,&nbsp;'backfill'/'bfill',&nbsp;'pad'/'ffill',&nbsp;'nearest'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;default:&nbsp;don't&nbsp;fill&nbsp;gaps<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;next&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;use&nbsp;nearest&nbsp;valid&nbsp;observations&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>,&nbsp;even&nbsp;if&nbsp;the&nbsp;passed&nbsp;indexes&nbsp;are&nbsp;the&nbsp;same<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;elements&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;original&nbsp;and&nbsp;new&nbsp;labels&nbsp;for&nbsp;inexact<br>
-&nbsp;&nbsp;&nbsp;&nbsp;matches.&nbsp;The&nbsp;values&nbsp;of&nbsp;the&nbsp;index&nbsp;at&nbsp;the&nbsp;matching&nbsp;locations&nbsp;most<br>
-&nbsp;&nbsp;&nbsp;&nbsp;satisfy&nbsp;the&nbsp;equation&nbsp;``<a href="#SweepFrame-abs">abs</a>(index[indexer]&nbsp;-&nbsp;target)&nbsp;&lt;=&nbsp;tolerance``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-reindex_axis">reindex_axis</a>(['A',&nbsp;'B',&nbsp;'C'],&nbsp;axis=1)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;reindex_like<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-rename"><strong>rename</strong></a>(self, index=None, columns=None, **kwargs)</dt><dd><tt>Alter&nbsp;axes&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.&nbsp;Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be<br>
-unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in&nbsp;a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left<br>
-as-is.&nbsp;Extra&nbsp;labels&nbsp;listed&nbsp;don't&nbsp;throw&nbsp;an&nbsp;error.&nbsp;Alternatively,&nbsp;change<br>
-``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;with&nbsp;a&nbsp;scalar&nbsp;value&nbsp;(<a href="pandas.core.series.html#Series">Series</a>&nbsp;only).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index,&nbsp;columns&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Scalar&nbsp;or&nbsp;list-like&nbsp;will&nbsp;alter&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;attribute,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;raise&nbsp;on&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;Panel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict-like&nbsp;or&nbsp;functions&nbsp;are&nbsp;transformations&nbsp;to&nbsp;apply&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;that&nbsp;axis'&nbsp;values<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;If&nbsp;True&nbsp;then&nbsp;value&nbsp;of&nbsp;copy&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;ignored.<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;MultiIndex,&nbsp;only&nbsp;rename&nbsp;labels&nbsp;in&nbsp;the&nbsp;specified<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename_axis<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepFrame-rename">rename</a>("my_name")&nbsp;#&nbsp;scalar,&nbsp;changes&nbsp;<a href="pandas.core.series.html#Series">Series</a>.name<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;my_name,&nbsp;dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepFrame-rename">rename</a>(lambda&nbsp;x:&nbsp;x&nbsp;**&nbsp;2)&nbsp;&nbsp;#&nbsp;function,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepFrame-rename">rename</a>({1:&nbsp;3,&nbsp;2:&nbsp;5})&nbsp;&nbsp;#&nbsp;mapping,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-rename">rename</a>(2)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-TypeError:&nbsp;'int'&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;not&nbsp;callable<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"B":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;c<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"C":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-reorder_levels"><strong>reorder_levels</strong></a>(self, order, axis=0)</dt><dd><tt>Rearrange&nbsp;index&nbsp;levels&nbsp;using&nbsp;input&nbsp;order.<br>
-May&nbsp;not&nbsp;drop&nbsp;or&nbsp;duplicate&nbsp;levels<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-order&nbsp;:&nbsp;list&nbsp;of&nbsp;int&nbsp;or&nbsp;list&nbsp;of&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;representing&nbsp;new&nbsp;level&nbsp;order.&nbsp;Reference&nbsp;level&nbsp;by&nbsp;number<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(position)&nbsp;or&nbsp;by&nbsp;key&nbsp;(label).<br>
-axis&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;to&nbsp;reorder&nbsp;levels.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-type&nbsp;of&nbsp;caller&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-reset_index"><strong>reset_index</strong></a>(self, level=None, drop=False, inplace=False, col_level=0, col_fill='')</dt><dd><tt>For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;multi-level&nbsp;index,&nbsp;return&nbsp;new&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with<br>
-labeling&nbsp;information&nbsp;in&nbsp;the&nbsp;columns&nbsp;under&nbsp;the&nbsp;index&nbsp;names,&nbsp;defaulting<br>
-to&nbsp;'level_0',&nbsp;'level_1',&nbsp;etc.&nbsp;if&nbsp;any&nbsp;are&nbsp;None.&nbsp;For&nbsp;a&nbsp;standard&nbsp;index,<br>
-the&nbsp;index&nbsp;name&nbsp;will&nbsp;be&nbsp;used&nbsp;(if&nbsp;set),&nbsp;otherwise&nbsp;a&nbsp;default&nbsp;'index'&nbsp;or<br>
-'level_0'&nbsp;(if&nbsp;'index'&nbsp;is&nbsp;already&nbsp;taken)&nbsp;will&nbsp;be&nbsp;used.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;tuple,&nbsp;or&nbsp;list,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Only&nbsp;remove&nbsp;the&nbsp;given&nbsp;levels&nbsp;from&nbsp;the&nbsp;index.&nbsp;Removes&nbsp;all&nbsp;levels&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-drop&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;not&nbsp;try&nbsp;to&nbsp;insert&nbsp;index&nbsp;into&nbsp;dataframe&nbsp;columns.&nbsp;This&nbsp;resets<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;index&nbsp;to&nbsp;the&nbsp;default&nbsp;integer&nbsp;index.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Modify&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;in&nbsp;place&nbsp;(do&nbsp;not&nbsp;create&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>)<br>
-col_level&nbsp;:&nbsp;int&nbsp;or&nbsp;str,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;columns&nbsp;have&nbsp;multiple&nbsp;levels,&nbsp;determines&nbsp;which&nbsp;level&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;labels&nbsp;are&nbsp;inserted&nbsp;into.&nbsp;By&nbsp;default&nbsp;it&nbsp;is&nbsp;inserted&nbsp;into&nbsp;the&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level.<br>
-col_fill&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;columns&nbsp;have&nbsp;multiple&nbsp;levels,&nbsp;determines&nbsp;how&nbsp;the&nbsp;other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;levels&nbsp;are&nbsp;named.&nbsp;If&nbsp;None&nbsp;then&nbsp;the&nbsp;index&nbsp;name&nbsp;is&nbsp;repeated.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-resetted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-rfloordiv"><strong>rfloordiv</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rfloordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;//&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.floordiv</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-rmod"><strong>rmod</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Modulo&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;%&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mod</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-rmul"><strong>rmul</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Multiplication&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;*&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mul</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-rolling"><strong>rolling</strong></a>(self, window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0, closed=None)</dt><dd><tt>Provides&nbsp;rolling&nbsp;window&nbsp;calculcations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-window&nbsp;:&nbsp;int,&nbsp;or&nbsp;offset<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Size&nbsp;of&nbsp;the&nbsp;moving&nbsp;window.&nbsp;This&nbsp;is&nbsp;the&nbsp;number&nbsp;of&nbsp;observations&nbsp;used&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;calculating&nbsp;the&nbsp;statistic.&nbsp;Each&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;fixed&nbsp;size.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;its&nbsp;an&nbsp;offset&nbsp;then&nbsp;this&nbsp;will&nbsp;be&nbsp;the&nbsp;time&nbsp;period&nbsp;of&nbsp;each&nbsp;window.&nbsp;Each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;variable&nbsp;sized&nbsp;based&nbsp;on&nbsp;the&nbsp;observations&nbsp;included&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;time-period.&nbsp;This&nbsp;is&nbsp;only&nbsp;valid&nbsp;for&nbsp;datetimelike&nbsp;indexes.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;new&nbsp;in&nbsp;0.19.0<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).&nbsp;For&nbsp;a&nbsp;window&nbsp;that&nbsp;is&nbsp;specified&nbsp;by&nbsp;an&nbsp;offset,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;will&nbsp;default&nbsp;to&nbsp;1.<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-win_type&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Provide&nbsp;a&nbsp;window&nbsp;type.&nbsp;See&nbsp;the&nbsp;notes&nbsp;below.<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;on&nbsp;which&nbsp;to&nbsp;calculate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;rolling&nbsp;window,&nbsp;rather&nbsp;than&nbsp;the&nbsp;index<br>
-closed&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;the&nbsp;interval&nbsp;closed&nbsp;on&nbsp;the&nbsp;'right',&nbsp;'left',&nbsp;'both'&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'neither'&nbsp;endpoints.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;offset-based&nbsp;windows,&nbsp;it&nbsp;defaults&nbsp;to&nbsp;'right'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;fixed&nbsp;windows,&nbsp;defaults&nbsp;to&nbsp;'both'.&nbsp;Remaining&nbsp;cases&nbsp;not&nbsp;implemented<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;fixed&nbsp;windows.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;or&nbsp;Rolling&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;using&nbsp;the&nbsp;'triang'<br>
-window&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-rolling">rolling</a>(2,&nbsp;win_type='triang').<a href="#SweepFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.5<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;min_periods&nbsp;defaults<br>
-to&nbsp;the&nbsp;window&nbsp;length.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-rolling">rolling</a>(2).<a href="#SweepFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Same&nbsp;as&nbsp;above,&nbsp;but&nbsp;explicity&nbsp;set&nbsp;the&nbsp;min_periods<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-rolling">rolling</a>(2,&nbsp;min_periods=1).<a href="#SweepFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;2.0<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-A&nbsp;ragged&nbsp;(meaning&nbsp;not-a-regular&nbsp;frequency),&nbsp;time-indexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]},<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;=&nbsp;[pd.Timestamp('20130101&nbsp;09:00:00'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:02'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:03'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:05'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:06')])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;2.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Contrasting&nbsp;to&nbsp;an&nbsp;integer&nbsp;rolling&nbsp;window,&nbsp;this&nbsp;will&nbsp;roll&nbsp;a&nbsp;variable<br>
-length&nbsp;window&nbsp;corresponding&nbsp;to&nbsp;the&nbsp;time&nbsp;period.<br>
-The&nbsp;default&nbsp;for&nbsp;min_periods&nbsp;is&nbsp;1.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-rolling">rolling</a>('2s').<a href="#SweepFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;3.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offsets&nbsp;&amp;&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-The&nbsp;recognized&nbsp;win_types&nbsp;are:<br>
-&nbsp;<br>
-*&nbsp;``boxcar``<br>
-*&nbsp;``triang``<br>
-*&nbsp;``blackman``<br>
-*&nbsp;``hamming``<br>
-*&nbsp;``bartlett``<br>
-*&nbsp;``parzen``<br>
-*&nbsp;``bohman``<br>
-*&nbsp;``blackmanharris``<br>
-*&nbsp;``nuttall``<br>
-*&nbsp;``barthann``<br>
-*&nbsp;``kaiser``&nbsp;(needs&nbsp;beta)<br>
-*&nbsp;``gaussian``&nbsp;(needs&nbsp;std)<br>
-*&nbsp;``general_gaussian``&nbsp;(needs&nbsp;power,&nbsp;width)<br>
-*&nbsp;``slepian``&nbsp;(needs&nbsp;width).</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-round"><strong>round</strong></a>(self, decimals=0, *args, **kwargs)</dt><dd><tt>Round&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;variable&nbsp;number&nbsp;of&nbsp;decimal&nbsp;places.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-decimals&nbsp;:&nbsp;int,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;round&nbsp;each&nbsp;column&nbsp;to.&nbsp;If&nbsp;an&nbsp;int&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;given,&nbsp;round&nbsp;each&nbsp;column&nbsp;to&nbsp;the&nbsp;same&nbsp;number&nbsp;of&nbsp;places.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Otherwise&nbsp;dict&nbsp;and&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;round&nbsp;to&nbsp;variable&nbsp;numbers&nbsp;of&nbsp;places.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;names&nbsp;should&nbsp;be&nbsp;in&nbsp;the&nbsp;keys&nbsp;if&nbsp;`decimals`&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict-like,&nbsp;or&nbsp;in&nbsp;the&nbsp;index&nbsp;if&nbsp;`decimals`&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns&nbsp;not&nbsp;included&nbsp;in&nbsp;`decimals`&nbsp;will&nbsp;be&nbsp;left&nbsp;as&nbsp;is.&nbsp;Elements<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;`decimals`&nbsp;which&nbsp;are&nbsp;not&nbsp;columns&nbsp;of&nbsp;the&nbsp;input&nbsp;will&nbsp;be<br>
-&nbsp;&nbsp;&nbsp;&nbsp;ignored.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.random([3,&nbsp;3]),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],&nbsp;index=['first',&nbsp;'second',&nbsp;'third'])<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-first&nbsp;&nbsp;&nbsp;0.028208&nbsp;&nbsp;0.992815&nbsp;&nbsp;0.173891<br>
-second&nbsp;&nbsp;0.038683&nbsp;&nbsp;0.645646&nbsp;&nbsp;0.577595<br>
-third&nbsp;&nbsp;&nbsp;0.877076&nbsp;&nbsp;0.149370&nbsp;&nbsp;0.491027<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-round">round</a>(2)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-first&nbsp;&nbsp;&nbsp;0.03&nbsp;&nbsp;0.99&nbsp;&nbsp;0.17<br>
-second&nbsp;&nbsp;0.04&nbsp;&nbsp;0.65&nbsp;&nbsp;0.58<br>
-third&nbsp;&nbsp;&nbsp;0.88&nbsp;&nbsp;0.15&nbsp;&nbsp;0.49<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-round">round</a>({'A':&nbsp;1,&nbsp;'C':&nbsp;2})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-first&nbsp;&nbsp;&nbsp;0.0&nbsp;&nbsp;0.992815&nbsp;&nbsp;0.17<br>
-second&nbsp;&nbsp;0.0&nbsp;&nbsp;0.645646&nbsp;&nbsp;0.58<br>
-third&nbsp;&nbsp;&nbsp;0.9&nbsp;&nbsp;0.149370&nbsp;&nbsp;0.49<br>
-&gt;&gt;&gt;&nbsp;decimals&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;0,&nbsp;2],&nbsp;index=['A',&nbsp;'B',&nbsp;'C'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-round">round</a>(decimals)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-first&nbsp;&nbsp;&nbsp;0.0&nbsp;&nbsp;1&nbsp;&nbsp;0.17<br>
-second&nbsp;&nbsp;0.0&nbsp;&nbsp;1&nbsp;&nbsp;0.58<br>
-third&nbsp;&nbsp;&nbsp;0.9&nbsp;&nbsp;0&nbsp;&nbsp;0.49<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.around<br>
-<a href="pandas.core.series.html#Series">Series</a>.round</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-rpow"><strong>rpow</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rpow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;**&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.pow</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-rsub"><strong>rsub</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Subtraction&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rsub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;-&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.sub</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-rtruediv"><strong>rtruediv</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-select_dtypes"><strong>select_dtypes</strong></a>(self, include=None, exclude=None)</dt><dd><tt>Return&nbsp;a&nbsp;subset&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;including/excluding&nbsp;columns&nbsp;based&nbsp;on<br>
-their&nbsp;``dtype``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-include,&nbsp;exclude&nbsp;:&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;list&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;strings&nbsp;to&nbsp;be&nbsp;included/excluded.&nbsp;You&nbsp;must&nbsp;pass<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;a&nbsp;non-empty&nbsp;sequence&nbsp;for&nbsp;at&nbsp;least&nbsp;one&nbsp;of&nbsp;these.<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;both&nbsp;of&nbsp;``include``&nbsp;and&nbsp;``exclude``&nbsp;are&nbsp;empty<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;``include``&nbsp;and&nbsp;``exclude``&nbsp;have&nbsp;overlapping&nbsp;elements<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;any&nbsp;kind&nbsp;of&nbsp;string&nbsp;dtype&nbsp;is&nbsp;passed&nbsp;in.<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;either&nbsp;of&nbsp;``include``&nbsp;or&nbsp;``exclude``&nbsp;is&nbsp;not&nbsp;a&nbsp;sequence<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;subset&nbsp;of&nbsp;the&nbsp;frame&nbsp;including&nbsp;the&nbsp;dtypes&nbsp;in&nbsp;``include``&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;excluding&nbsp;the&nbsp;dtypes&nbsp;in&nbsp;``exclude``.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-*&nbsp;To&nbsp;select&nbsp;all&nbsp;*numeric*&nbsp;types&nbsp;use&nbsp;the&nbsp;numpy&nbsp;dtype&nbsp;``numpy.number``<br>
-*&nbsp;To&nbsp;select&nbsp;strings&nbsp;you&nbsp;must&nbsp;use&nbsp;the&nbsp;``<a href="builtins.html#object">object</a>``&nbsp;dtype,&nbsp;but&nbsp;note&nbsp;that<br>
-&nbsp;&nbsp;this&nbsp;will&nbsp;return&nbsp;*all*&nbsp;<a href="builtins.html#object">object</a>&nbsp;dtype&nbsp;columns<br>
-*&nbsp;See&nbsp;the&nbsp;`numpy&nbsp;dtype&nbsp;hierarchy<br>
-&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html&gt;`__">http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html&gt;`__</a><br>
-*&nbsp;To&nbsp;select&nbsp;datetimes,&nbsp;use&nbsp;np.datetime64,&nbsp;'datetime'&nbsp;or&nbsp;'datetime64'<br>
-*&nbsp;To&nbsp;select&nbsp;timedeltas,&nbsp;use&nbsp;np.timedelta64,&nbsp;'timedelta'&nbsp;or<br>
-&nbsp;&nbsp;'timedelta64'<br>
-*&nbsp;To&nbsp;select&nbsp;Pandas&nbsp;categorical&nbsp;dtypes,&nbsp;use&nbsp;'category'<br>
-*&nbsp;To&nbsp;select&nbsp;Pandas&nbsp;datetimetz&nbsp;dtypes,&nbsp;use&nbsp;'datetimetz'&nbsp;(new&nbsp;in&nbsp;0.20.0),<br>
-&nbsp;&nbsp;or&nbsp;a&nbsp;'datetime64[ns,&nbsp;tz]'&nbsp;string<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'a':&nbsp;np.random.randn(6).<a href="#SweepFrame-astype">astype</a>('f4'),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'b':&nbsp;[True,&nbsp;False]&nbsp;*&nbsp;3,<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'c':&nbsp;[1.0,&nbsp;2.0]&nbsp;*&nbsp;3})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;c<br>
-0&nbsp;&nbsp;0.3962&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;0.1459&nbsp;&nbsp;False&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;0.2623&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;0.0764&nbsp;&nbsp;False&nbsp;&nbsp;2<br>
-4&nbsp;-0.9703&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;1<br>
-5&nbsp;-1.2094&nbsp;&nbsp;False&nbsp;&nbsp;2<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-select_dtypes">select_dtypes</a>(include=['float64'])<br>
-&nbsp;&nbsp;&nbsp;c<br>
-0&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;2<br>
-4&nbsp;&nbsp;1<br>
-5&nbsp;&nbsp;2<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-select_dtypes">select_dtypes</a>(exclude=['floating'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-0&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;False<br>
-4&nbsp;&nbsp;&nbsp;True<br>
-5&nbsp;&nbsp;False</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-sem"><strong>sem</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;standard&nbsp;error&nbsp;of&nbsp;the&nbsp;mean&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sem&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-set_index"><strong>set_index</strong></a>(self, keys, drop=True, append=False, inplace=False, verify_integrity=False)</dt><dd><tt>Set&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;index&nbsp;(row&nbsp;labels)&nbsp;using&nbsp;one&nbsp;or&nbsp;more&nbsp;existing<br>
-columns.&nbsp;By&nbsp;default&nbsp;yields&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-keys&nbsp;:&nbsp;column&nbsp;label&nbsp;or&nbsp;list&nbsp;of&nbsp;column&nbsp;labels&nbsp;/&nbsp;arrays<br>
-drop&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Delete&nbsp;columns&nbsp;to&nbsp;be&nbsp;used&nbsp;as&nbsp;the&nbsp;new&nbsp;index<br>
-append&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;append&nbsp;columns&nbsp;to&nbsp;existing&nbsp;index<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Modify&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;in&nbsp;place&nbsp;(do&nbsp;not&nbsp;create&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>)<br>
-verify_integrity&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Check&nbsp;the&nbsp;new&nbsp;index&nbsp;for&nbsp;duplicates.&nbsp;Otherwise&nbsp;defer&nbsp;the&nbsp;check&nbsp;until<br>
-&nbsp;&nbsp;&nbsp;&nbsp;necessary.&nbsp;Setting&nbsp;to&nbsp;False&nbsp;will&nbsp;improve&nbsp;the&nbsp;performance&nbsp;of&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;method<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;indexed_df&nbsp;=&nbsp;df.<a href="#SweepFrame-set_index">set_index</a>(['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;indexed_df2&nbsp;=&nbsp;df.<a href="#SweepFrame-set_index">set_index</a>(['A',&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;0,&nbsp;1,&nbsp;2]])<br>
-&gt;&gt;&gt;&nbsp;indexed_df3&nbsp;=&nbsp;df.<a href="#SweepFrame-set_index">set_index</a>([[0,&nbsp;1,&nbsp;2,&nbsp;0,&nbsp;1,&nbsp;2]])<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dataframe&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-set_value"><strong>set_value</strong></a>(self, index, col, value, takeable=False)</dt><dd><tt>Put&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;column&nbsp;and&nbsp;index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;row&nbsp;label<br>
-col&nbsp;:&nbsp;column&nbsp;label<br>
-value&nbsp;:&nbsp;scalar&nbsp;value<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index/col&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-frame&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;label&nbsp;pair&nbsp;is&nbsp;contained,&nbsp;will&nbsp;be&nbsp;reference&nbsp;to&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-shift"><strong>shift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;index&nbsp;by&nbsp;desired&nbsp;number&nbsp;of&nbsp;periods&nbsp;with&nbsp;an&nbsp;optional&nbsp;time&nbsp;freq<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM').<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;Notes.<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'}<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;specified&nbsp;then&nbsp;the&nbsp;index&nbsp;values&nbsp;are&nbsp;shifted&nbsp;but&nbsp;the&nbsp;data<br>
-is&nbsp;not&nbsp;realigned.&nbsp;That&nbsp;is,&nbsp;use&nbsp;freq&nbsp;if&nbsp;you&nbsp;would&nbsp;like&nbsp;to&nbsp;extend&nbsp;the<br>
-index&nbsp;when&nbsp;shifting&nbsp;and&nbsp;preserve&nbsp;the&nbsp;original&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-skew"><strong>skew</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;skew&nbsp;over&nbsp;requested&nbsp;axis<br>
-Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-skew&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-sort_index"><strong>sort_index</strong></a>(self, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, by=None)</dt><dd><tt>Sort&nbsp;<a href="builtins.html#object">object</a>&nbsp;by&nbsp;labels&nbsp;(along&nbsp;an&nbsp;axis)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;index,&nbsp;columns&nbsp;to&nbsp;direct&nbsp;sorting<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name&nbsp;or&nbsp;list&nbsp;of&nbsp;ints&nbsp;or&nbsp;list&nbsp;of&nbsp;level&nbsp;names<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None,&nbsp;sort&nbsp;on&nbsp;values&nbsp;in&nbsp;specified&nbsp;index&nbsp;level(s)<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;MultiIndex.<br>
-sort_remaining&nbsp;:&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;true&nbsp;and&nbsp;sorting&nbsp;by&nbsp;level&nbsp;and&nbsp;index&nbsp;is&nbsp;multilevel,&nbsp;sort&nbsp;by&nbsp;other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;levels&nbsp;too&nbsp;(in&nbsp;order)&nbsp;after&nbsp;sorting&nbsp;by&nbsp;specified&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-sort_values"><strong>sort_values</strong></a>(self, by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')</dt><dd><tt>Sort&nbsp;by&nbsp;the&nbsp;values&nbsp;along&nbsp;either&nbsp;axis<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;str&nbsp;or&nbsp;list&nbsp;of&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;or&nbsp;list&nbsp;of&nbsp;names&nbsp;which&nbsp;refer&nbsp;to&nbsp;the&nbsp;axis&nbsp;items.<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;direct&nbsp;sorting<br>
-ascending&nbsp;:&nbsp;bool&nbsp;or&nbsp;list&nbsp;of&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending.&nbsp;Specify&nbsp;list&nbsp;for&nbsp;multiple&nbsp;sort<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;orders.&nbsp;&nbsp;If&nbsp;this&nbsp;is&nbsp;a&nbsp;list&nbsp;of&nbsp;bools,&nbsp;must&nbsp;match&nbsp;the&nbsp;length&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;by.<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-sortlevel"><strong>sortlevel</strong></a>(self, level=0, axis=0, ascending=True, inplace=False, sort_remaining=True)</dt><dd><tt>DEPRECATED:&nbsp;use&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.sort_index`<br>
-&nbsp;<br>
-Sort&nbsp;multilevel&nbsp;index&nbsp;by&nbsp;chosen&nbsp;axis&nbsp;and&nbsp;primary&nbsp;level.&nbsp;Data&nbsp;will&nbsp;be<br>
-lexicographically&nbsp;sorted&nbsp;by&nbsp;the&nbsp;chosen&nbsp;level&nbsp;followed&nbsp;by&nbsp;the&nbsp;other<br>
-levels&nbsp;(in&nbsp;order)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;without&nbsp;creating&nbsp;a&nbsp;new&nbsp;instance<br>
-sort_remaining&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;by&nbsp;the&nbsp;other&nbsp;levels&nbsp;too.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#SweepFrame-sort_index">sort_index</a>(level=...)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-stack"><strong>stack</strong></a>(self, level=-1, dropna=True)</dt><dd><tt>Pivot&nbsp;a&nbsp;level&nbsp;of&nbsp;the&nbsp;(possibly&nbsp;hierarchical)&nbsp;column&nbsp;labels,&nbsp;returning&nbsp;a<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;the&nbsp;case&nbsp;of&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;a&nbsp;single&nbsp;level&nbsp;of<br>
-column&nbsp;labels)&nbsp;having&nbsp;a&nbsp;hierarchical&nbsp;index&nbsp;with&nbsp;a&nbsp;new&nbsp;inner-most&nbsp;level<br>
-of&nbsp;row&nbsp;labels.<br>
-The&nbsp;level&nbsp;involved&nbsp;will&nbsp;automatically&nbsp;get&nbsp;sorted.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;string,&nbsp;or&nbsp;list&nbsp;of&nbsp;these,&nbsp;default&nbsp;last&nbsp;level<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level(s)&nbsp;to&nbsp;stack,&nbsp;can&nbsp;pass&nbsp;level&nbsp;name<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;drop&nbsp;rows&nbsp;in&nbsp;the&nbsp;resulting&nbsp;Frame/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;no&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values<br>
-&nbsp;<br>
-Examples<br>
-----------<br>
-&gt;&gt;&gt;&nbsp;s<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;b<br>
-one&nbsp;&nbsp;1.&nbsp;&nbsp;2.<br>
-two&nbsp;&nbsp;3.&nbsp;&nbsp;4.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepFrame-stack">stack</a>()<br>
-one&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-two&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-stacked&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-std"><strong>std</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;sample&nbsp;standard&nbsp;deviation&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-std&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-sub"><strong>sub</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Subtraction&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-subtract"><strong>subtract</strong></a> = sub(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Subtraction&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-sum"><strong>sum</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;sum&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sum&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-swaplevel"><strong>swaplevel</strong></a>(self, i=-2, j=-1, axis=0)</dt><dd><tt>Swap&nbsp;levels&nbsp;i&nbsp;and&nbsp;j&nbsp;in&nbsp;a&nbsp;MultiIndex&nbsp;on&nbsp;a&nbsp;particular&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-i,&nbsp;j&nbsp;:&nbsp;int,&nbsp;string&nbsp;(can&nbsp;be&nbsp;mixed)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level&nbsp;of&nbsp;index&nbsp;to&nbsp;be&nbsp;swapped.&nbsp;Can&nbsp;pass&nbsp;level&nbsp;name&nbsp;as&nbsp;string.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-swapped&nbsp;:&nbsp;type&nbsp;of&nbsp;caller&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-..&nbsp;versionchanged::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;The&nbsp;indexes&nbsp;``i``&nbsp;and&nbsp;``j``&nbsp;are&nbsp;now&nbsp;optional,&nbsp;and&nbsp;default&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;the&nbsp;two&nbsp;innermost&nbsp;levels&nbsp;of&nbsp;the&nbsp;index.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_csv"><strong>to_csv</strong></a>(self, path_or_buf=None, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression=None, quoting=None, quotechar='"', line_terminator='\n', chunksize=None, tupleize_cols=False, date_format=None, doublequote=True, escapechar=None, decimal='.')</dt><dd><tt>Write&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;comma-separated&nbsp;values&nbsp;(csv)&nbsp;file<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;string&nbsp;or&nbsp;file&nbsp;handle,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;<a href="builtins.html#object">object</a>,&nbsp;if&nbsp;None&nbsp;is&nbsp;provided&nbsp;the&nbsp;result&nbsp;is&nbsp;returned&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string.<br>
-sep&nbsp;:&nbsp;character,&nbsp;default&nbsp;','<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter&nbsp;for&nbsp;the&nbsp;output&nbsp;file.<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Columns&nbsp;to&nbsp;write<br>
-header&nbsp;:&nbsp;boolean&nbsp;or&nbsp;list&nbsp;of&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given&nbsp;it&nbsp;is&nbsp;assumed<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;or&nbsp;False,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.&nbsp;&nbsp;If<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False&nbsp;do&nbsp;not&nbsp;print&nbsp;fields&nbsp;for&nbsp;index&nbsp;names.&nbsp;Use&nbsp;index_label=False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;easier&nbsp;importing&nbsp;in&nbsp;R<br>
-mode&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Python&nbsp;write&nbsp;mode,&nbsp;default&nbsp;'w'<br>
-encoding&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;defaults&nbsp;to&nbsp;'ascii'&nbsp;on&nbsp;Python&nbsp;2&nbsp;and&nbsp;'utf-8'&nbsp;on&nbsp;Python&nbsp;3.<br>
-compression&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;compression&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;allowed&nbsp;values&nbsp;are&nbsp;'gzip',&nbsp;'bz2',&nbsp;'xz',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;only&nbsp;used&nbsp;when&nbsp;the&nbsp;first&nbsp;argument&nbsp;is&nbsp;a&nbsp;filename<br>
-line_terminator&nbsp;:&nbsp;string,&nbsp;default&nbsp;``'\n'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;newline&nbsp;character&nbsp;or&nbsp;character&nbsp;sequence&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output<br>
-&nbsp;&nbsp;&nbsp;&nbsp;file<br>
-quoting&nbsp;:&nbsp;optional&nbsp;constant&nbsp;from&nbsp;csv&nbsp;module<br>
-&nbsp;&nbsp;&nbsp;&nbsp;defaults&nbsp;to&nbsp;csv.QUOTE_MINIMAL.&nbsp;If&nbsp;you&nbsp;have&nbsp;set&nbsp;a&nbsp;`float_format`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;floats&nbsp;are&nbsp;converted&nbsp;to&nbsp;strings&nbsp;and&nbsp;thus&nbsp;csv.QUOTE_NONNUMERIC<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;treat&nbsp;them&nbsp;as&nbsp;non-numeric<br>
-quotechar&nbsp;:&nbsp;string&nbsp;(length&nbsp;1),&nbsp;default&nbsp;'\"'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;character&nbsp;used&nbsp;to&nbsp;quote&nbsp;fields<br>
-doublequote&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Control&nbsp;quoting&nbsp;of&nbsp;`quotechar`&nbsp;inside&nbsp;a&nbsp;field<br>
-escapechar&nbsp;:&nbsp;string&nbsp;(length&nbsp;1),&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;character&nbsp;used&nbsp;to&nbsp;escape&nbsp;`sep`&nbsp;and&nbsp;`quotechar`&nbsp;when&nbsp;appropriate<br>
-chunksize&nbsp;:&nbsp;int&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rows&nbsp;to&nbsp;write&nbsp;at&nbsp;a&nbsp;time<br>
-tupleize_cols&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;write&nbsp;multi_index&nbsp;columns&nbsp;as&nbsp;a&nbsp;list&nbsp;of&nbsp;tuples&nbsp;(if&nbsp;True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;new&nbsp;(expanded&nbsp;format)&nbsp;if&nbsp;False)<br>
-date_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;datetime&nbsp;objects<br>
-decimal:&nbsp;string,&nbsp;default&nbsp;'.'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Character&nbsp;recognized&nbsp;as&nbsp;decimal&nbsp;separator.&nbsp;E.g.&nbsp;use&nbsp;','&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;European&nbsp;data<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.16.0</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_dict"><strong>to_dict</strong></a>(self, orient='dict')</dt><dd><tt>Convert&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;dictionary.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-orient&nbsp;:&nbsp;str&nbsp;{'dict',&nbsp;'list',&nbsp;'series',&nbsp;'split',&nbsp;'records',&nbsp;'index'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Determines&nbsp;the&nbsp;type&nbsp;of&nbsp;the&nbsp;values&nbsp;of&nbsp;the&nbsp;dictionary.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;(default)&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;{index&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;[values]}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;series&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;<a href="pandas.core.series.html#Series">Series</a>(values)}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;split&nbsp;:&nbsp;dict&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{index&nbsp;-&gt;&nbsp;[index],&nbsp;columns&nbsp;-&gt;&nbsp;[columns],&nbsp;data&nbsp;-&gt;&nbsp;[values]}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;records&nbsp;:&nbsp;list&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[{column&nbsp;-&gt;&nbsp;value},&nbsp;...&nbsp;,&nbsp;{column&nbsp;-&gt;&nbsp;value}]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;index&nbsp;:&nbsp;dict&nbsp;like&nbsp;{index&nbsp;-&gt;&nbsp;{column&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Abbreviations&nbsp;are&nbsp;allowed.&nbsp;`s`&nbsp;indicates&nbsp;`series`&nbsp;and&nbsp;`sp`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;indicates&nbsp;`split`.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;{index&nbsp;-&gt;&nbsp;value}}</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_excel"><strong>to_excel</strong></a>(self, excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True, freeze_panes=None)</dt><dd><tt>Write&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;an&nbsp;excel&nbsp;sheet<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel_writer&nbsp;:&nbsp;string&nbsp;or&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;existing&nbsp;ExcelWriter<br>
-sheet_name&nbsp;:&nbsp;string,&nbsp;default&nbsp;'Sheet1'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;sheet&nbsp;which&nbsp;will&nbsp;contain&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Columns&nbsp;to&nbsp;write<br>
-header&nbsp;:&nbsp;boolean&nbsp;or&nbsp;list&nbsp;of&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given&nbsp;it&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;assumed&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-startrow&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;row&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-startcol&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;column&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-engine&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;write&nbsp;engine&nbsp;to&nbsp;use&nbsp;-&nbsp;you&nbsp;can&nbsp;also&nbsp;set&nbsp;this&nbsp;via&nbsp;the&nbsp;options<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsx.writer``,&nbsp;``io.excel.xls.writer``,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsm.writer``.<br>
-merge_cells&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;MultiIndex&nbsp;and&nbsp;Hierarchical&nbsp;Rows&nbsp;as&nbsp;merged&nbsp;cells.<br>
-encoding:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;encoding&nbsp;of&nbsp;the&nbsp;resulting&nbsp;excel&nbsp;file.&nbsp;Only&nbsp;necessary&nbsp;for&nbsp;xlwt,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;writers&nbsp;support&nbsp;unicode&nbsp;natively.<br>
-inf_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;'inf'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Representation&nbsp;for&nbsp;infinity&nbsp;(there&nbsp;is&nbsp;no&nbsp;native&nbsp;representation&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;infinity&nbsp;in&nbsp;Excel)<br>
-freeze_panes&nbsp;:&nbsp;tuple&nbsp;of&nbsp;integer&nbsp;(length&nbsp;2),&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;the&nbsp;one-based&nbsp;bottommost&nbsp;row&nbsp;and&nbsp;rightmost&nbsp;column&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;to&nbsp;be&nbsp;frozen<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;passing&nbsp;an&nbsp;existing&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a>,&nbsp;then&nbsp;the&nbsp;sheet&nbsp;will&nbsp;be&nbsp;added<br>
-to&nbsp;the&nbsp;existing&nbsp;workbook.&nbsp;&nbsp;This&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;save&nbsp;different<br>
-DataFrames&nbsp;to&nbsp;one&nbsp;workbook:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;writer&nbsp;=&nbsp;pd.ExcelWriter('output.xlsx')<br>
-&gt;&gt;&gt;&nbsp;df1.<a href="#SweepFrame-to_excel">to_excel</a>(writer,'Sheet1')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#SweepFrame-to_excel">to_excel</a>(writer,'Sheet2')<br>
-&gt;&gt;&gt;&nbsp;writer.save()<br>
-&nbsp;<br>
-For&nbsp;compatibility&nbsp;with&nbsp;to_csv,&nbsp;to_excel&nbsp;serializes&nbsp;lists&nbsp;and&nbsp;dicts&nbsp;to<br>
-strings&nbsp;before&nbsp;writing.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_feather"><strong>to_feather</strong></a>(self, fname)</dt><dd><tt>write&nbsp;out&nbsp;the&nbsp;binary&nbsp;feather-format&nbsp;for&nbsp;DataFrames<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-fname&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;file&nbsp;path</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_gbq"><strong>to_gbq</strong></a>(self, destination_table, project_id, chunksize=10000, verbose=True, reauth=False, if_exists='fail', private_key=None)</dt><dd><tt>Write&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;Google&nbsp;BigQuery&nbsp;table.<br>
-&nbsp;<br>
-The&nbsp;main&nbsp;method&nbsp;a&nbsp;user&nbsp;calls&nbsp;to&nbsp;export&nbsp;pandas&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;contents&nbsp;to<br>
-Google&nbsp;BigQuery&nbsp;table.<br>
-&nbsp;<br>
-Google&nbsp;BigQuery&nbsp;API&nbsp;Client&nbsp;Library&nbsp;v2&nbsp;for&nbsp;Python&nbsp;is&nbsp;used.<br>
-Documentation&nbsp;is&nbsp;available&nbsp;`here<br>
-&lt;https://developers.google.com/api-client-library/python/apis/bigquery/v2&gt;`__<br>
-&nbsp;<br>
-Authentication&nbsp;to&nbsp;the&nbsp;Google&nbsp;BigQuery&nbsp;service&nbsp;is&nbsp;via&nbsp;OAuth&nbsp;2.0.<br>
-&nbsp;<br>
--&nbsp;If&nbsp;"private_key"&nbsp;is&nbsp;not&nbsp;provided:<br>
-&nbsp;<br>
-&nbsp;&nbsp;By&nbsp;default&nbsp;"application&nbsp;default&nbsp;credentials"&nbsp;are&nbsp;used.<br>
-&nbsp;<br>
-&nbsp;&nbsp;If&nbsp;default&nbsp;application&nbsp;credentials&nbsp;are&nbsp;not&nbsp;found&nbsp;or&nbsp;are&nbsp;restrictive,<br>
-&nbsp;&nbsp;user&nbsp;account&nbsp;credentials&nbsp;are&nbsp;used.&nbsp;In&nbsp;this&nbsp;case,&nbsp;you&nbsp;will&nbsp;be&nbsp;asked&nbsp;to<br>
-&nbsp;&nbsp;grant&nbsp;permissions&nbsp;for&nbsp;product&nbsp;name&nbsp;'pandas&nbsp;GBQ'.<br>
-&nbsp;<br>
--&nbsp;If&nbsp;"private_key"&nbsp;is&nbsp;provided:<br>
-&nbsp;<br>
-&nbsp;&nbsp;Service&nbsp;account&nbsp;credentials&nbsp;will&nbsp;be&nbsp;used&nbsp;to&nbsp;authenticate.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dataframe&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;be&nbsp;written<br>
-destination_table&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;table&nbsp;to&nbsp;be&nbsp;written,&nbsp;in&nbsp;the&nbsp;form&nbsp;'dataset.tablename'<br>
-project_id&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Google&nbsp;BigQuery&nbsp;Account&nbsp;project&nbsp;ID.<br>
-chunksize&nbsp;:&nbsp;int&nbsp;(default&nbsp;10000)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;rows&nbsp;to&nbsp;be&nbsp;inserted&nbsp;in&nbsp;each&nbsp;chunk&nbsp;from&nbsp;the&nbsp;dataframe.<br>
-verbose&nbsp;:&nbsp;boolean&nbsp;(default&nbsp;True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Show&nbsp;percentage&nbsp;complete<br>
-reauth&nbsp;:&nbsp;boolean&nbsp;(default&nbsp;False)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Force&nbsp;Google&nbsp;BigQuery&nbsp;to&nbsp;reauthenticate&nbsp;the&nbsp;user.&nbsp;This&nbsp;is&nbsp;useful<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;multiple&nbsp;accounts&nbsp;are&nbsp;used.<br>
-if_exists&nbsp;:&nbsp;{'fail',&nbsp;'replace',&nbsp;'append'},&nbsp;default&nbsp;'fail'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'fail':&nbsp;If&nbsp;table&nbsp;exists,&nbsp;do&nbsp;nothing.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'replace':&nbsp;If&nbsp;table&nbsp;exists,&nbsp;drop&nbsp;it,&nbsp;recreate&nbsp;it,&nbsp;and&nbsp;insert&nbsp;data.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'append':&nbsp;If&nbsp;table&nbsp;exists,&nbsp;insert&nbsp;data.&nbsp;Create&nbsp;if&nbsp;does&nbsp;not&nbsp;exist.<br>
-private_key&nbsp;:&nbsp;str&nbsp;(optional)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Service&nbsp;account&nbsp;private&nbsp;key&nbsp;in&nbsp;JSON&nbsp;format.&nbsp;Can&nbsp;be&nbsp;file&nbsp;path<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;string&nbsp;contents.&nbsp;This&nbsp;is&nbsp;useful&nbsp;for&nbsp;remote&nbsp;server<br>
-&nbsp;&nbsp;&nbsp;&nbsp;authentication&nbsp;(eg.&nbsp;jupyter&nbsp;iPython&nbsp;notebook&nbsp;on&nbsp;remote&nbsp;host)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_html"><strong>to_html</strong></a>(self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, bold_rows=True, classes=None, escape=True, max_rows=None, max_cols=None, show_dimensions=False, notebook=False, decimal='.', border=None)</dt><dd><tt>Render&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;as&nbsp;an&nbsp;HTML&nbsp;table.<br>
-&nbsp;<br>
-`to_html`-specific&nbsp;options:<br>
-&nbsp;<br>
-bold_rows&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;the&nbsp;row&nbsp;labels&nbsp;bold&nbsp;in&nbsp;the&nbsp;output<br>
-classes&nbsp;:&nbsp;str&nbsp;or&nbsp;list&nbsp;or&nbsp;tuple,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;CSS&nbsp;class(es)&nbsp;to&nbsp;apply&nbsp;to&nbsp;the&nbsp;resulting&nbsp;html&nbsp;table<br>
-escape&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Convert&nbsp;the&nbsp;characters&nbsp;&lt;,&nbsp;&gt;,&nbsp;and&nbsp;&amp;&nbsp;to&nbsp;HTML-safe&nbsp;sequences.=<br>
-max_rows&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;rows&nbsp;to&nbsp;show&nbsp;before&nbsp;truncating.&nbsp;If&nbsp;None,&nbsp;show<br>
-&nbsp;&nbsp;&nbsp;&nbsp;all.<br>
-max_cols&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;columns&nbsp;to&nbsp;show&nbsp;before&nbsp;truncating.&nbsp;If&nbsp;None,&nbsp;show<br>
-&nbsp;&nbsp;&nbsp;&nbsp;all.<br>
-decimal&nbsp;:&nbsp;string,&nbsp;default&nbsp;'.'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Character&nbsp;recognized&nbsp;as&nbsp;decimal&nbsp;separator,&nbsp;e.g.&nbsp;','&nbsp;in&nbsp;Europe<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-border&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;``border=border``&nbsp;attribute&nbsp;is&nbsp;included&nbsp;in&nbsp;the&nbsp;opening<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`&lt;table&gt;`&nbsp;tag.&nbsp;Default&nbsp;``pd.options.html.border``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-buf&nbsp;:&nbsp;StringIO-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;buffer&nbsp;to&nbsp;write&nbsp;to<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;to&nbsp;write;&nbsp;default&nbsp;None&nbsp;writes&nbsp;all&nbsp;columns<br>
-col_space&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;minimum&nbsp;width&nbsp;of&nbsp;each&nbsp;column<br>
-header&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;whether&nbsp;to&nbsp;print&nbsp;column&nbsp;labels,&nbsp;default&nbsp;True<br>
-index&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;whether&nbsp;to&nbsp;print&nbsp;index&nbsp;(row)&nbsp;labels,&nbsp;default&nbsp;True<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;representation&nbsp;of&nbsp;NAN&nbsp;to&nbsp;use,&nbsp;default&nbsp;'NaN'<br>
-formatters&nbsp;:&nbsp;list&nbsp;or&nbsp;dict&nbsp;of&nbsp;one-parameter&nbsp;functions,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;functions&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;by&nbsp;position&nbsp;or&nbsp;name,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;each&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;must&nbsp;be&nbsp;of&nbsp;length&nbsp;equal&nbsp;to&nbsp;the&nbsp;number&nbsp;of&nbsp;columns.<br>
-float_format&nbsp;:&nbsp;one-parameter&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;if&nbsp;they&nbsp;are&nbsp;floats,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;this&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-sparsify&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;to&nbsp;False&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;a&nbsp;hierarchical&nbsp;index&nbsp;to&nbsp;print&nbsp;every<br>
-&nbsp;&nbsp;&nbsp;&nbsp;multiindex&nbsp;key&nbsp;at&nbsp;each&nbsp;row,&nbsp;default&nbsp;True<br>
-index_names&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Prints&nbsp;the&nbsp;names&nbsp;of&nbsp;the&nbsp;indexes,&nbsp;default&nbsp;True<br>
-line_width&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Width&nbsp;to&nbsp;wrap&nbsp;a&nbsp;line&nbsp;in&nbsp;characters,&nbsp;default&nbsp;no&nbsp;wrap<br>
-justify&nbsp;:&nbsp;{'left',&nbsp;'right'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Left&nbsp;or&nbsp;right-justify&nbsp;the&nbsp;column&nbsp;labels.&nbsp;If&nbsp;None&nbsp;uses&nbsp;the&nbsp;option&nbsp;from<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;print&nbsp;configuration&nbsp;(controlled&nbsp;by&nbsp;set_option),&nbsp;'right'&nbsp;out<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;box.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-formatted&nbsp;:&nbsp;string&nbsp;(or&nbsp;unicode,&nbsp;depending&nbsp;on&nbsp;data&nbsp;and&nbsp;options)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_latex"><strong>to_latex</strong></a>(self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=True, column_format=None, longtable=None, escape=None, encoding=None, decimal='.', multicolumn=None, multicolumn_format=None, multirow=None)</dt><dd><tt>Render&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;tabular&nbsp;environment&nbsp;table.&nbsp;You&nbsp;can&nbsp;splice<br>
-this&nbsp;into&nbsp;a&nbsp;LaTeX&nbsp;document.&nbsp;Requires&nbsp;\usepackage{booktabs}.<br>
-&nbsp;<br>
-`to_latex`-specific&nbsp;options:<br>
-&nbsp;<br>
-bold_rows&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;the&nbsp;row&nbsp;labels&nbsp;bold&nbsp;in&nbsp;the&nbsp;output<br>
-column_format&nbsp;:&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;columns&nbsp;format&nbsp;as&nbsp;specified&nbsp;in&nbsp;`LaTeX&nbsp;table&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&lt;https://en.wikibooks.org/wiki/LaTeX/Tables&gt;`__&nbsp;e.g&nbsp;'rcl'&nbsp;for&nbsp;3<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns<br>
-longtable&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;will&nbsp;be&nbsp;read&nbsp;from&nbsp;the&nbsp;pandas&nbsp;config&nbsp;module<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default:&nbsp;False.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;a&nbsp;longtable&nbsp;environment&nbsp;instead&nbsp;of&nbsp;tabular.&nbsp;Requires&nbsp;adding<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;\usepackage{longtable}&nbsp;to&nbsp;your&nbsp;LaTeX&nbsp;preamble.<br>
-escape&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;will&nbsp;be&nbsp;read&nbsp;from&nbsp;the&nbsp;pandas&nbsp;config&nbsp;module<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default:&nbsp;True.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;set&nbsp;to&nbsp;False&nbsp;prevents&nbsp;from&nbsp;escaping&nbsp;latex&nbsp;special<br>
-&nbsp;&nbsp;&nbsp;&nbsp;characters&nbsp;in&nbsp;column&nbsp;names.<br>
-encoding&nbsp;:&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;defaults&nbsp;to&nbsp;'ascii'&nbsp;on&nbsp;Python&nbsp;2&nbsp;and&nbsp;'utf-8'&nbsp;on&nbsp;Python&nbsp;3.<br>
-decimal&nbsp;:&nbsp;string,&nbsp;default&nbsp;'.'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Character&nbsp;recognized&nbsp;as&nbsp;decimal&nbsp;separator,&nbsp;e.g.&nbsp;','&nbsp;in&nbsp;Europe.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-multicolumn&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;\multicolumn&nbsp;to&nbsp;enhance&nbsp;MultiIndex&nbsp;columns.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;default&nbsp;will&nbsp;be&nbsp;read&nbsp;from&nbsp;the&nbsp;config&nbsp;module.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-multicolumn_format&nbsp;:&nbsp;str,&nbsp;default&nbsp;'l'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;alignment&nbsp;for&nbsp;multicolumns,&nbsp;similar&nbsp;to&nbsp;`column_format`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;default&nbsp;will&nbsp;be&nbsp;read&nbsp;from&nbsp;the&nbsp;config&nbsp;module.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-multirow&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;\multirow&nbsp;to&nbsp;enhance&nbsp;MultiIndex&nbsp;rows.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Requires&nbsp;adding&nbsp;a&nbsp;\usepackage{multirow}&nbsp;to&nbsp;your&nbsp;LaTeX&nbsp;preamble.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Will&nbsp;print&nbsp;centered&nbsp;labels&nbsp;(instead&nbsp;of&nbsp;top-aligned)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;across&nbsp;the&nbsp;contained&nbsp;rows,&nbsp;separating&nbsp;groups&nbsp;via&nbsp;clines.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;default&nbsp;will&nbsp;be&nbsp;read&nbsp;from&nbsp;the&nbsp;pandas&nbsp;config&nbsp;module.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-buf&nbsp;:&nbsp;StringIO-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;buffer&nbsp;to&nbsp;write&nbsp;to<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;to&nbsp;write;&nbsp;default&nbsp;None&nbsp;writes&nbsp;all&nbsp;columns<br>
-col_space&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;minimum&nbsp;width&nbsp;of&nbsp;each&nbsp;column<br>
-header&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given,&nbsp;it&nbsp;is&nbsp;assumed&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names.<br>
-index&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;whether&nbsp;to&nbsp;print&nbsp;index&nbsp;(row)&nbsp;labels,&nbsp;default&nbsp;True<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;representation&nbsp;of&nbsp;NAN&nbsp;to&nbsp;use,&nbsp;default&nbsp;'NaN'<br>
-formatters&nbsp;:&nbsp;list&nbsp;or&nbsp;dict&nbsp;of&nbsp;one-parameter&nbsp;functions,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;functions&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;by&nbsp;position&nbsp;or&nbsp;name,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;each&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;must&nbsp;be&nbsp;of&nbsp;length&nbsp;equal&nbsp;to&nbsp;the&nbsp;number&nbsp;of&nbsp;columns.<br>
-float_format&nbsp;:&nbsp;one-parameter&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;if&nbsp;they&nbsp;are&nbsp;floats,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;this&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-sparsify&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;to&nbsp;False&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;a&nbsp;hierarchical&nbsp;index&nbsp;to&nbsp;print&nbsp;every<br>
-&nbsp;&nbsp;&nbsp;&nbsp;multiindex&nbsp;key&nbsp;at&nbsp;each&nbsp;row,&nbsp;default&nbsp;True<br>
-index_names&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Prints&nbsp;the&nbsp;names&nbsp;of&nbsp;the&nbsp;indexes,&nbsp;default&nbsp;True<br>
-line_width&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Width&nbsp;to&nbsp;wrap&nbsp;a&nbsp;line&nbsp;in&nbsp;characters,&nbsp;default&nbsp;no&nbsp;wrap<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-formatted&nbsp;:&nbsp;string&nbsp;(or&nbsp;unicode,&nbsp;depending&nbsp;on&nbsp;data&nbsp;and&nbsp;options)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_panel"><strong>to_panel</strong></a>(self)</dt><dd><tt>Transform&nbsp;long&nbsp;(stacked)&nbsp;format&nbsp;(<a href="pandas.core.frame.html#DataFrame">DataFrame</a>)&nbsp;into&nbsp;wide&nbsp;(3D,&nbsp;Panel)<br>
-format.<br>
-&nbsp;<br>
-Currently&nbsp;the&nbsp;index&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;must&nbsp;be&nbsp;a&nbsp;2-level&nbsp;MultiIndex.&nbsp;This<br>
-may&nbsp;be&nbsp;generalized&nbsp;later<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-panel&nbsp;:&nbsp;Panel</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_period"><strong>to_period</strong></a>(self, freq=None, axis=0, copy=True)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;from&nbsp;DatetimeIndex&nbsp;to&nbsp;PeriodIndex&nbsp;with&nbsp;desired<br>
-frequency&nbsp;(inferred&nbsp;from&nbsp;index&nbsp;if&nbsp;not&nbsp;passed)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;convert&nbsp;(the&nbsp;index&nbsp;by&nbsp;default)<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False&nbsp;then&nbsp;underlying&nbsp;input&nbsp;data&nbsp;is&nbsp;not&nbsp;copied<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ts&nbsp;:&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;with&nbsp;PeriodIndex</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_records"><strong>to_records</strong></a>(self, index=True, convert_datetime64=True)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;record&nbsp;array.&nbsp;Index&nbsp;will&nbsp;be&nbsp;put&nbsp;in&nbsp;the<br>
-'index'&nbsp;field&nbsp;of&nbsp;the&nbsp;record&nbsp;array&nbsp;if&nbsp;requested<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;index&nbsp;in&nbsp;resulting&nbsp;record&nbsp;array,&nbsp;stored&nbsp;in&nbsp;'index'&nbsp;field<br>
-convert_datetime64&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;convert&nbsp;the&nbsp;index&nbsp;to&nbsp;datetime.datetime&nbsp;if&nbsp;it&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;DatetimeIndex<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;recarray</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_sparse"><strong>to_sparse</strong></a>(self, fill_value=None, kind='block')</dt><dd><tt>Convert&nbsp;to&nbsp;SparseDataFrame<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-fill_value&nbsp;:&nbsp;float,&nbsp;default&nbsp;NaN<br>
-kind&nbsp;:&nbsp;{'block',&nbsp;'integer'}<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;SparseDataFrame</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_stata"><strong>to_stata</strong></a>(self, fname, convert_dates=None, write_index=True, encoding='latin-1', byteorder=None, time_stamp=None, data_label=None, variable_labels=None)</dt><dd><tt>A&nbsp;class&nbsp;for&nbsp;writing&nbsp;Stata&nbsp;binary&nbsp;dta&nbsp;files&nbsp;from&nbsp;array-like&nbsp;objects<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-fname&nbsp;:&nbsp;str&nbsp;or&nbsp;buffer<br>
-&nbsp;&nbsp;&nbsp;&nbsp;String&nbsp;path&nbsp;of&nbsp;file-like&nbsp;<a href="builtins.html#object">object</a><br>
-convert_dates&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Dictionary&nbsp;mapping&nbsp;columns&nbsp;containing&nbsp;datetime&nbsp;types&nbsp;to&nbsp;stata<br>
-&nbsp;&nbsp;&nbsp;&nbsp;internal&nbsp;format&nbsp;to&nbsp;use&nbsp;when&nbsp;wirting&nbsp;the&nbsp;dates.&nbsp;Options&nbsp;are&nbsp;'tc',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'td',&nbsp;'tm',&nbsp;'tw',&nbsp;'th',&nbsp;'tq',&nbsp;'ty'.&nbsp;Column&nbsp;can&nbsp;be&nbsp;either&nbsp;an&nbsp;integer<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;a&nbsp;name.&nbsp;Datetime&nbsp;columns&nbsp;that&nbsp;do&nbsp;not&nbsp;have&nbsp;a&nbsp;conversion&nbsp;type<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specified&nbsp;will&nbsp;be&nbsp;converted&nbsp;to&nbsp;'tc'.&nbsp;Raises&nbsp;NotImplementedError&nbsp;if<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;datetime&nbsp;column&nbsp;has&nbsp;timezone&nbsp;information<br>
-write_index&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;the&nbsp;index&nbsp;to&nbsp;Stata&nbsp;dataset.<br>
-encoding&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;is&nbsp;latin-1.&nbsp;Unicode&nbsp;is&nbsp;not&nbsp;supported<br>
-byteorder&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Can&nbsp;be&nbsp;"&gt;",&nbsp;"&lt;",&nbsp;"little",&nbsp;or&nbsp;"big".&nbsp;default&nbsp;is&nbsp;`sys.byteorder`<br>
-time_stamp&nbsp;:&nbsp;datetime<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;datetime&nbsp;to&nbsp;use&nbsp;as&nbsp;file&nbsp;creation&nbsp;date.&nbsp;&nbsp;Default&nbsp;is&nbsp;the&nbsp;current<br>
-&nbsp;&nbsp;&nbsp;&nbsp;time.<br>
-dataset_label&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;label&nbsp;for&nbsp;the&nbsp;data&nbsp;set.&nbsp;&nbsp;Must&nbsp;be&nbsp;80&nbsp;characters&nbsp;or&nbsp;smaller.<br>
-variable_labels&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Dictionary&nbsp;containing&nbsp;columns&nbsp;as&nbsp;keys&nbsp;and&nbsp;variable&nbsp;labels&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values.&nbsp;Each&nbsp;label&nbsp;must&nbsp;be&nbsp;80&nbsp;characters&nbsp;or&nbsp;smaller.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-NotImplementedError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;datetimes&nbsp;contain&nbsp;timezone&nbsp;information<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;Column&nbsp;dtype&nbsp;is&nbsp;not&nbsp;representable&nbsp;in&nbsp;Stata<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;Columns&nbsp;listed&nbsp;in&nbsp;convert_dates&nbsp;are&nbsp;noth&nbsp;either&nbsp;datetime64[ns]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;datetime.datetime<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;Column&nbsp;listed&nbsp;in&nbsp;convert_dates&nbsp;is&nbsp;not&nbsp;in&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;Categorical&nbsp;label&nbsp;contains&nbsp;more&nbsp;than&nbsp;32,000&nbsp;characters<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;writer&nbsp;=&nbsp;StataWriter('./data_file.dta',&nbsp;data)<br>
-&gt;&gt;&gt;&nbsp;writer.write_file()<br>
-&nbsp;<br>
-Or&nbsp;with&nbsp;dates<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;writer&nbsp;=&nbsp;StataWriter('./date_data_file.dta',&nbsp;data,&nbsp;{2&nbsp;:&nbsp;'tw'})<br>
-&gt;&gt;&gt;&nbsp;writer.write_file()</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_string"><strong>to_string</strong></a>(self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, line_width=None, max_rows=None, max_cols=None, show_dimensions=False)</dt><dd><tt>Render&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;console-friendly&nbsp;tabular&nbsp;output.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-buf&nbsp;:&nbsp;StringIO-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;buffer&nbsp;to&nbsp;write&nbsp;to<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;to&nbsp;write;&nbsp;default&nbsp;None&nbsp;writes&nbsp;all&nbsp;columns<br>
-col_space&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;minimum&nbsp;width&nbsp;of&nbsp;each&nbsp;column<br>
-header&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given,&nbsp;it&nbsp;is&nbsp;assumed&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names<br>
-index&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;whether&nbsp;to&nbsp;print&nbsp;index&nbsp;(row)&nbsp;labels,&nbsp;default&nbsp;True<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;representation&nbsp;of&nbsp;NAN&nbsp;to&nbsp;use,&nbsp;default&nbsp;'NaN'<br>
-formatters&nbsp;:&nbsp;list&nbsp;or&nbsp;dict&nbsp;of&nbsp;one-parameter&nbsp;functions,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;functions&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;by&nbsp;position&nbsp;or&nbsp;name,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;each&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;must&nbsp;be&nbsp;of&nbsp;length&nbsp;equal&nbsp;to&nbsp;the&nbsp;number&nbsp;of&nbsp;columns.<br>
-float_format&nbsp;:&nbsp;one-parameter&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;if&nbsp;they&nbsp;are&nbsp;floats,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;this&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-sparsify&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;to&nbsp;False&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;a&nbsp;hierarchical&nbsp;index&nbsp;to&nbsp;print&nbsp;every<br>
-&nbsp;&nbsp;&nbsp;&nbsp;multiindex&nbsp;key&nbsp;at&nbsp;each&nbsp;row,&nbsp;default&nbsp;True<br>
-index_names&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Prints&nbsp;the&nbsp;names&nbsp;of&nbsp;the&nbsp;indexes,&nbsp;default&nbsp;True<br>
-line_width&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Width&nbsp;to&nbsp;wrap&nbsp;a&nbsp;line&nbsp;in&nbsp;characters,&nbsp;default&nbsp;no&nbsp;wrap<br>
-justify&nbsp;:&nbsp;{'left',&nbsp;'right'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Left&nbsp;or&nbsp;right-justify&nbsp;the&nbsp;column&nbsp;labels.&nbsp;If&nbsp;None&nbsp;uses&nbsp;the&nbsp;option&nbsp;from<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;print&nbsp;configuration&nbsp;(controlled&nbsp;by&nbsp;set_option),&nbsp;'right'&nbsp;out<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;box.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-formatted&nbsp;:&nbsp;string&nbsp;(or&nbsp;unicode,&nbsp;depending&nbsp;on&nbsp;data&nbsp;and&nbsp;options)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_timestamp"><strong>to_timestamp</strong></a>(self, freq=None, how='start', axis=0, copy=True)</dt><dd><tt>Cast&nbsp;to&nbsp;DatetimeIndex&nbsp;of&nbsp;timestamps,&nbsp;at&nbsp;*beginning*&nbsp;of&nbsp;period<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default&nbsp;frequency&nbsp;of&nbsp;PeriodIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Desired&nbsp;frequency<br>
-how&nbsp;:&nbsp;{'s',&nbsp;'e',&nbsp;'start',&nbsp;'end'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Convention&nbsp;for&nbsp;converting&nbsp;period&nbsp;to&nbsp;timestamp;&nbsp;start&nbsp;of&nbsp;period<br>
-&nbsp;&nbsp;&nbsp;&nbsp;vs.&nbsp;end<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;convert&nbsp;(the&nbsp;index&nbsp;by&nbsp;default)<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;false&nbsp;then&nbsp;underlying&nbsp;input&nbsp;data&nbsp;is&nbsp;not&nbsp;copied<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-df&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;DatetimeIndex</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-transform"><strong>transform</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Call&nbsp;function&nbsp;producing&nbsp;a&nbsp;like-indexed&nbsp;NDFrame<br>
-and&nbsp;return&nbsp;a&nbsp;NDFrame&nbsp;with&nbsp;the&nbsp;transformed&nbsp;values`<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;apply&nbsp;to&nbsp;column<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-transformed&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(10,&nbsp;3),&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.date_range('1/1/2000',&nbsp;periods=10))<br>
-df.iloc[3:7]&nbsp;=&nbsp;np.nan<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-transform">transform</a>(lambda&nbsp;x:&nbsp;(x&nbsp;-&nbsp;x.<a href="#SweepFrame-mean">mean</a>())&nbsp;/&nbsp;x.<a href="#SweepFrame-std">std</a>())<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-2000-01-01&nbsp;&nbsp;0.579457&nbsp;&nbsp;1.236184&nbsp;&nbsp;0.123424<br>
-2000-01-02&nbsp;&nbsp;0.370357&nbsp;-0.605875&nbsp;-1.231325<br>
-2000-01-03&nbsp;&nbsp;1.455756&nbsp;-0.277446&nbsp;&nbsp;0.288967<br>
-2000-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-08&nbsp;-0.498658&nbsp;&nbsp;1.274522&nbsp;&nbsp;1.642524<br>
-2000-01-09&nbsp;-0.540524&nbsp;-1.012676&nbsp;-0.828968<br>
-2000-01-10&nbsp;-1.366388&nbsp;-0.614710&nbsp;&nbsp;0.005378<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.NDFrame.aggregate<br>
-pandas.NDFrame.apply</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-transpose"><strong>transpose</strong></a>(self, *args, **kwargs)</dt><dd><tt>Transpose&nbsp;index&nbsp;and&nbsp;columns</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-truediv"><strong>truediv</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-unstack"><strong>unstack</strong></a>(self, level=-1, fill_value=None)</dt><dd><tt>Pivot&nbsp;a&nbsp;level&nbsp;of&nbsp;the&nbsp;(necessarily&nbsp;hierarchical)&nbsp;index&nbsp;labels,&nbsp;returning<br>
-a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;having&nbsp;a&nbsp;new&nbsp;level&nbsp;of&nbsp;column&nbsp;labels&nbsp;whose&nbsp;inner-most&nbsp;level<br>
-consists&nbsp;of&nbsp;the&nbsp;pivoted&nbsp;index&nbsp;labels.&nbsp;If&nbsp;the&nbsp;index&nbsp;is&nbsp;not&nbsp;a&nbsp;MultiIndex,<br>
-the&nbsp;output&nbsp;will&nbsp;be&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(the&nbsp;analogue&nbsp;of&nbsp;stack&nbsp;when&nbsp;the&nbsp;columns&nbsp;are<br>
-not&nbsp;a&nbsp;MultiIndex).<br>
-The&nbsp;level&nbsp;involved&nbsp;will&nbsp;automatically&nbsp;get&nbsp;sorted.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;string,&nbsp;or&nbsp;list&nbsp;of&nbsp;these,&nbsp;default&nbsp;-1&nbsp;(last&nbsp;level)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level(s)&nbsp;of&nbsp;index&nbsp;to&nbsp;unstack,&nbsp;can&nbsp;pass&nbsp;level&nbsp;name<br>
-fill_value&nbsp;:&nbsp;replace&nbsp;NaN&nbsp;with&nbsp;this&nbsp;value&nbsp;if&nbsp;the&nbsp;unstack&nbsp;produces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.18.0<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.pivot&nbsp;:&nbsp;Pivot&nbsp;a&nbsp;table&nbsp;based&nbsp;on&nbsp;column&nbsp;values.<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.stack&nbsp;:&nbsp;Pivot&nbsp;a&nbsp;level&nbsp;of&nbsp;the&nbsp;column&nbsp;labels&nbsp;(inverse&nbsp;operation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;`unstack`).<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.MultiIndex.from_tuples([('one',&nbsp;'a'),&nbsp;('one',&nbsp;'b'),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;('two',&nbsp;'a'),&nbsp;('two',&nbsp;'b')])<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.arange(1.0,&nbsp;5.0),&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;s<br>
-one&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;1.0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;2.0<br>
-two&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;4.0<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepFrame-unstack">unstack</a>(level=-1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;b<br>
-one&nbsp;&nbsp;1.0&nbsp;&nbsp;2.0<br>
-two&nbsp;&nbsp;3.0&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepFrame-unstack">unstack</a>(level=0)<br>
-&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;two<br>
-a&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;3.0<br>
-b&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;s.<a href="#SweepFrame-unstack">unstack</a>(level=0)<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-unstack">unstack</a>()<br>
-one&nbsp;&nbsp;a&nbsp;&nbsp;1.0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;2.0<br>
-two&nbsp;&nbsp;a&nbsp;&nbsp;3.0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;4.0<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-unstacked&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-update"><strong>update</strong></a>(self, other, join='left', overwrite=True, filter_func=None, raise_conflict=False)</dt><dd><tt>Modify&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;in&nbsp;place&nbsp;using&nbsp;non-NA&nbsp;values&nbsp;from&nbsp;passed<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Aligns&nbsp;on&nbsp;indices<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;<a href="builtins.html#object">object</a>&nbsp;coercible&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-join&nbsp;:&nbsp;{'left'},&nbsp;default&nbsp;'left'<br>
-overwrite&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;then&nbsp;overwrite&nbsp;values&nbsp;for&nbsp;common&nbsp;keys&nbsp;in&nbsp;the&nbsp;calling&nbsp;frame<br>
-filter_func&nbsp;:&nbsp;callable(1d-array)&nbsp;-&gt;&nbsp;1d-array&lt;boolean&gt;,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Can&nbsp;choose&nbsp;to&nbsp;replace&nbsp;values&nbsp;other&nbsp;than&nbsp;NA.&nbsp;Return&nbsp;True&nbsp;for&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;&nbsp;that&nbsp;should&nbsp;be&nbsp;updated<br>
-raise_conflict&nbsp;:&nbsp;boolean<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;will&nbsp;raise&nbsp;an&nbsp;error&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;and&nbsp;other&nbsp;both<br>
-&nbsp;&nbsp;&nbsp;&nbsp;contain&nbsp;data&nbsp;in&nbsp;the&nbsp;same&nbsp;place.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-var"><strong>var</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;variance&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-var&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<hr>
-Class methods inherited from <a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a>:<br>
-<dl><dt><a name="SweepFrame-from_csv"><strong>from_csv</strong></a>(path, header=0, sep=',', index_col=0, parse_dates=True, encoding=None, tupleize_cols=False, infer_datetime_format=False)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Read&nbsp;CSV&nbsp;file&nbsp;(DISCOURAGED,&nbsp;please&nbsp;use&nbsp;:func:`pandas.read_csv`<br>
-instead).<br>
-&nbsp;<br>
-It&nbsp;is&nbsp;preferable&nbsp;to&nbsp;use&nbsp;the&nbsp;more&nbsp;powerful&nbsp;:func:`pandas.read_csv`<br>
-for&nbsp;most&nbsp;general&nbsp;purposes,&nbsp;but&nbsp;``from_csv``&nbsp;makes&nbsp;for&nbsp;an&nbsp;easy<br>
-roundtrip&nbsp;to&nbsp;and&nbsp;from&nbsp;a&nbsp;file&nbsp;(the&nbsp;exact&nbsp;counterpart&nbsp;of<br>
-``to_csv``),&nbsp;especially&nbsp;with&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;time&nbsp;series&nbsp;data.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;only&nbsp;differs&nbsp;from&nbsp;the&nbsp;preferred&nbsp;:func:`pandas.read_csv`<br>
-in&nbsp;some&nbsp;defaults:<br>
-&nbsp;<br>
--&nbsp;`index_col`&nbsp;is&nbsp;``0``&nbsp;instead&nbsp;of&nbsp;``None``&nbsp;(take&nbsp;first&nbsp;column&nbsp;as&nbsp;index<br>
-&nbsp;&nbsp;by&nbsp;default)<br>
--&nbsp;`parse_dates`&nbsp;is&nbsp;``True``&nbsp;instead&nbsp;of&nbsp;``False``&nbsp;(try&nbsp;parsing&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;as&nbsp;datetime&nbsp;by&nbsp;default)<br>
-&nbsp;<br>
-So&nbsp;a&nbsp;``pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#SweepFrame-from_csv">from_csv</a>(path)``&nbsp;can&nbsp;be&nbsp;replaced&nbsp;by<br>
-``pd.read_csv(path,&nbsp;index_col=0,&nbsp;parse_dates=True)``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;file&nbsp;path&nbsp;or&nbsp;file&nbsp;handle&nbsp;/&nbsp;StringIO<br>
-header&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Row&nbsp;to&nbsp;use&nbsp;as&nbsp;header&nbsp;(skip&nbsp;prior&nbsp;rows)<br>
-sep&nbsp;:&nbsp;string,&nbsp;default&nbsp;','<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter<br>
-index_col&nbsp;:&nbsp;int&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;to&nbsp;use&nbsp;for&nbsp;index.&nbsp;If&nbsp;a&nbsp;sequence&nbsp;is&nbsp;given,&nbsp;a&nbsp;MultiIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;used.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-parse_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Parse&nbsp;dates.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-tupleize_cols&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;write&nbsp;multi_index&nbsp;columns&nbsp;as&nbsp;a&nbsp;list&nbsp;of&nbsp;tuples&nbsp;(if&nbsp;True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;new&nbsp;(expanded&nbsp;format)&nbsp;if&nbsp;False)<br>
-infer_datetime_format:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;and&nbsp;`parse_dates`&nbsp;is&nbsp;True&nbsp;for&nbsp;a&nbsp;column,&nbsp;try&nbsp;to&nbsp;infer&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;datetime&nbsp;format&nbsp;based&nbsp;on&nbsp;the&nbsp;first&nbsp;datetime&nbsp;string.&nbsp;If&nbsp;the&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;be&nbsp;inferred,&nbsp;there&nbsp;often&nbsp;will&nbsp;be&nbsp;a&nbsp;large&nbsp;parsing&nbsp;speed-up.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.read_csv<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-from_dict"><strong>from_dict</strong></a>(data, orient='columns', dtype=None)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Construct&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;from&nbsp;dict&nbsp;of&nbsp;array-like&nbsp;or&nbsp;dicts<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;{field&nbsp;:&nbsp;array-like}&nbsp;or&nbsp;{field&nbsp;:&nbsp;dict}<br>
-orient&nbsp;:&nbsp;{'columns',&nbsp;'index'},&nbsp;default&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;"orientation"&nbsp;of&nbsp;the&nbsp;data.&nbsp;If&nbsp;the&nbsp;keys&nbsp;of&nbsp;the&nbsp;passed&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;be&nbsp;the&nbsp;columns&nbsp;of&nbsp;the&nbsp;resulting&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;pass&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(default).&nbsp;Otherwise&nbsp;if&nbsp;the&nbsp;keys&nbsp;should&nbsp;be&nbsp;rows,&nbsp;pass&nbsp;'index'.<br>
-dtype&nbsp;:&nbsp;dtype,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Data&nbsp;type&nbsp;to&nbsp;force,&nbsp;otherwise&nbsp;infer<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-from_items"><strong>from_items</strong></a>(items, columns=None, orient='columns')<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Convert&nbsp;(key,&nbsp;value)&nbsp;pairs&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;The&nbsp;keys&nbsp;will&nbsp;be&nbsp;the&nbsp;axis<br>
-index&nbsp;(usually&nbsp;the&nbsp;columns,&nbsp;but&nbsp;depends&nbsp;on&nbsp;the&nbsp;specified<br>
-orientation).&nbsp;The&nbsp;values&nbsp;should&nbsp;be&nbsp;arrays&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-items&nbsp;:&nbsp;sequence&nbsp;of&nbsp;(key,&nbsp;value)&nbsp;pairs<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Values&nbsp;should&nbsp;be&nbsp;arrays&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-columns&nbsp;:&nbsp;sequence&nbsp;of&nbsp;column&nbsp;labels,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Must&nbsp;be&nbsp;passed&nbsp;if&nbsp;orient='index'.<br>
-orient&nbsp;:&nbsp;{'columns',&nbsp;'index'},&nbsp;default&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;"orientation"&nbsp;of&nbsp;the&nbsp;data.&nbsp;If&nbsp;the&nbsp;keys&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;input&nbsp;correspond&nbsp;to&nbsp;column&nbsp;labels,&nbsp;pass&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(default).&nbsp;Otherwise&nbsp;if&nbsp;the&nbsp;keys&nbsp;correspond&nbsp;to&nbsp;the&nbsp;index,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;pass&nbsp;'index'.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-frame&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-from_records"><strong>from_records</strong></a>(data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Convert&nbsp;structured&nbsp;or&nbsp;record&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;(structured&nbsp;dtype),&nbsp;list&nbsp;of&nbsp;tuples,&nbsp;dict,&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-index&nbsp;:&nbsp;string,&nbsp;list&nbsp;of&nbsp;fields,&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;of&nbsp;array&nbsp;to&nbsp;use&nbsp;as&nbsp;the&nbsp;index,&nbsp;alternately&nbsp;a&nbsp;specific&nbsp;set&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;input&nbsp;labels&nbsp;to&nbsp;use<br>
-exclude&nbsp;:&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Columns&nbsp;or&nbsp;fields&nbsp;to&nbsp;exclude<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;names&nbsp;to&nbsp;use.&nbsp;If&nbsp;the&nbsp;passed&nbsp;data&nbsp;do&nbsp;not&nbsp;have&nbsp;names<br>
-&nbsp;&nbsp;&nbsp;&nbsp;associated&nbsp;with&nbsp;them,&nbsp;this&nbsp;argument&nbsp;provides&nbsp;names&nbsp;for&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns.&nbsp;Otherwise&nbsp;this&nbsp;argument&nbsp;indicates&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;columns<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;result&nbsp;(any&nbsp;names&nbsp;not&nbsp;found&nbsp;in&nbsp;the&nbsp;data&nbsp;will&nbsp;become&nbsp;all-NA<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns)<br>
-coerce_float&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Attempt&nbsp;to&nbsp;convert&nbsp;values&nbsp;of&nbsp;non-string,&nbsp;non-numeric&nbsp;objects&nbsp;(like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;decimal.Decimal)&nbsp;to&nbsp;floating&nbsp;point,&nbsp;useful&nbsp;for&nbsp;SQL&nbsp;result&nbsp;sets<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-df&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a>:<br>
-<dl><dt><strong>axes</strong></dt>
-<dd><tt>Return&nbsp;a&nbsp;list&nbsp;with&nbsp;the&nbsp;row&nbsp;axis&nbsp;labels&nbsp;and&nbsp;column&nbsp;axis&nbsp;labels&nbsp;as&nbsp;the<br>
-only&nbsp;members.&nbsp;They&nbsp;are&nbsp;returned&nbsp;in&nbsp;that&nbsp;order.</tt></dd>
-</dl>
-<dl><dt><strong>columns</strong></dt>
-</dl>
-<dl><dt><strong>index</strong></dt>
-</dl>
-<dl><dt><strong>shape</strong></dt>
-<dd><tt>Return&nbsp;a&nbsp;tuple&nbsp;representing&nbsp;the&nbsp;dimensionality&nbsp;of&nbsp;the&nbsp;DataFrame.</tt></dd>
-</dl>
-<dl><dt><strong>style</strong></dt>
-<dd><tt>Property&nbsp;returning&nbsp;a&nbsp;Styler&nbsp;object&nbsp;containing&nbsp;methods&nbsp;for<br>
-building&nbsp;a&nbsp;styled&nbsp;HTML&nbsp;representation&nbsp;fo&nbsp;the&nbsp;DataFrame.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.io.formats.style.Styler</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a>:<br>
-<dl><dt><strong>plot</strong> = &lt;class 'pandas.plotting._core.FramePlotMethods'&gt;<dd><tt><a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;plotting&nbsp;accessor&nbsp;and&nbsp;method<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df.plot.line()<br>
-&gt;&gt;&gt;&nbsp;df.plot.scatter('x',&nbsp;'y')<br>
-&gt;&gt;&gt;&nbsp;df.plot.hexbin()<br>
-&nbsp;<br>
-These&nbsp;plotting&nbsp;methods&nbsp;can&nbsp;also&nbsp;be&nbsp;accessed&nbsp;by&nbsp;calling&nbsp;the&nbsp;accessor&nbsp;as&nbsp;a<br>
-method&nbsp;with&nbsp;the&nbsp;``kind``&nbsp;argument:<br>
-``df.<a href="#SweepFrame-plot">plot</a>(kind='line')``&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;``df.plot.line()``</tt></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><a name="SweepFrame-__abs__"><strong>__abs__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__array__"><strong>__array__</strong></a>(self, dtype=None)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__array_wrap__"><strong>__array_wrap__</strong></a>(self, result, context=None)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__bool__"><strong>__bool__</strong></a> = __nonzero__(self)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__contains__"><strong>__contains__</strong></a>(self, key)</dt><dd><tt>True&nbsp;if&nbsp;the&nbsp;key&nbsp;is&nbsp;in&nbsp;the&nbsp;info&nbsp;axis</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__copy__"><strong>__copy__</strong></a>(self, deep=True)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__deepcopy__"><strong>__deepcopy__</strong></a>(self, memo=None)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__delitem__"><strong>__delitem__</strong></a>(self, key)</dt><dd><tt>Delete&nbsp;item</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__finalize__"><strong>__finalize__</strong></a>(self, other, method=None, **kwargs)</dt><dd><tt>Propagate&nbsp;metadata&nbsp;from&nbsp;other&nbsp;to&nbsp;self.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;which&nbsp;to&nbsp;get&nbsp;the&nbsp;attributes&nbsp;that&nbsp;we&nbsp;are&nbsp;going<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;propagate<br>
-method&nbsp;:&nbsp;optional,&nbsp;a&nbsp;passed&nbsp;method&nbsp;name&nbsp;;&nbsp;possibly&nbsp;to&nbsp;take&nbsp;different<br>
-&nbsp;&nbsp;&nbsp;&nbsp;types&nbsp;of&nbsp;propagation&nbsp;actions&nbsp;based&nbsp;on&nbsp;this</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__getattr__"><strong>__getattr__</strong></a>(self, name)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;looking&nbsp;up&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__getstate__"><strong>__getstate__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__hash__"><strong>__hash__</strong></a>(self)</dt><dd><tt>Return&nbsp;hash(self).</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__invert__"><strong>__invert__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__iter__"><strong>__iter__</strong></a>(self)</dt><dd><tt>Iterate&nbsp;over&nbsp;infor&nbsp;axis</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__neg__"><strong>__neg__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__nonzero__"><strong>__nonzero__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__round__"><strong>__round__</strong></a>(self, decimals=0)</dt></dl>
-
-<dl><dt><a name="SweepFrame-__setattr__"><strong>__setattr__</strong></a>(self, name, value)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;setting&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__setstate__"><strong>__setstate__</strong></a>(self, state)</dt></dl>
-
-<dl><dt><a name="SweepFrame-abs"><strong>abs</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;absolute&nbsp;value&nbsp;taken--only&nbsp;applicable&nbsp;to&nbsp;objects<br>
-that&nbsp;are&nbsp;all&nbsp;numeric.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-abs:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-add_prefix"><strong>add_prefix</strong></a>(self, prefix)</dt><dd><tt>Concatenate&nbsp;prefix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-prefix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_prefix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-add_suffix"><strong>add_suffix</strong></a>(self, suffix)</dt><dd><tt>Concatenate&nbsp;suffix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-suffix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_suffix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-as_blocks"><strong>as_blocks</strong></a>(self, copy=True)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types&nbsp;that&nbsp;each&nbsp;has<br>
-a&nbsp;homogeneous&nbsp;dtype.<br>
-&nbsp;<br>
-NOTE:&nbsp;the&nbsp;dtypes&nbsp;of&nbsp;the&nbsp;blocks&nbsp;WILL&nbsp;BE&nbsp;PRESERVED&nbsp;HERE&nbsp;(unlike&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;as_matrix)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-as_matrix"><strong>as_matrix</strong></a>(self, columns=None)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;its&nbsp;Numpy-array&nbsp;representation.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-columns:&nbsp;list,&nbsp;optional,&nbsp;default:None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;None,&nbsp;return&nbsp;all&nbsp;columns,&nbsp;otherwise,&nbsp;returns&nbsp;specified&nbsp;columns.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;caller&nbsp;is&nbsp;heterogeneous&nbsp;and&nbsp;contains&nbsp;booleans&nbsp;or&nbsp;objects,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;of&nbsp;dtype=<a href="builtins.html#object">object</a>.&nbsp;See&nbsp;Notes.<br>
-&nbsp;<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Return&nbsp;is&nbsp;NOT&nbsp;a&nbsp;Numpy-matrix,&nbsp;rather,&nbsp;a&nbsp;Numpy-array.<br>
-&nbsp;<br>
-The&nbsp;dtype&nbsp;will&nbsp;be&nbsp;a&nbsp;lower-common-denominator&nbsp;dtype&nbsp;(implicit<br>
-upcasting);&nbsp;that&nbsp;is&nbsp;to&nbsp;say&nbsp;if&nbsp;the&nbsp;dtypes&nbsp;(even&nbsp;of&nbsp;numeric&nbsp;types)<br>
-are&nbsp;mixed,&nbsp;the&nbsp;one&nbsp;that&nbsp;accommodates&nbsp;all&nbsp;will&nbsp;be&nbsp;chosen.&nbsp;Use&nbsp;this<br>
-with&nbsp;care&nbsp;if&nbsp;you&nbsp;are&nbsp;not&nbsp;dealing&nbsp;with&nbsp;the&nbsp;blocks.<br>
-&nbsp;<br>
-e.g.&nbsp;If&nbsp;the&nbsp;dtypes&nbsp;are&nbsp;float16&nbsp;and&nbsp;float32,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcast&nbsp;to<br>
-float32.&nbsp;&nbsp;If&nbsp;dtypes&nbsp;are&nbsp;int32&nbsp;and&nbsp;uint8,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcase&nbsp;to<br>
-int32.&nbsp;By&nbsp;numpy.find_common_type&nbsp;convention,&nbsp;mixing&nbsp;int64&nbsp;and&nbsp;uint64<br>
-will&nbsp;result&nbsp;in&nbsp;a&nbsp;flot64&nbsp;dtype.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;is&nbsp;provided&nbsp;for&nbsp;backwards&nbsp;compatibility.&nbsp;Generally,<br>
-it&nbsp;is&nbsp;recommended&nbsp;to&nbsp;use&nbsp;'.values'.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.values</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-asfreq"><strong>asfreq</strong></a>(self, freq, method=None, how=None, normalize=False, fill_value=None)</dt><dd><tt>Convert&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;specified&nbsp;frequency.<br>
-&nbsp;<br>
-Optionally&nbsp;provide&nbsp;filling&nbsp;method&nbsp;to&nbsp;pad/backfill&nbsp;missing&nbsp;values.<br>
-&nbsp;<br>
-Returns&nbsp;the&nbsp;original&nbsp;data&nbsp;conformed&nbsp;to&nbsp;a&nbsp;new&nbsp;index&nbsp;with&nbsp;the&nbsp;specified<br>
-frequency.&nbsp;``resample``&nbsp;is&nbsp;more&nbsp;appropriate&nbsp;if&nbsp;an&nbsp;operation,&nbsp;such&nbsp;as<br>
-summarization,&nbsp;is&nbsp;necessary&nbsp;to&nbsp;represent&nbsp;the&nbsp;data&nbsp;at&nbsp;the&nbsp;new&nbsp;frequency.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;or&nbsp;string<br>
-method&nbsp;:&nbsp;{'backfill'/'bfill',&nbsp;'pad'/'ffill'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(note&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present):<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'pad'&nbsp;/&nbsp;'ffill':&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'backfill'&nbsp;/&nbsp;'bfill':&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill<br>
-how&nbsp;:&nbsp;{'start',&nbsp;'end'},&nbsp;default&nbsp;end<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;PeriodIndex&nbsp;only,&nbsp;see&nbsp;PeriodIndex.asfreq<br>
-normalize&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;reset&nbsp;output&nbsp;index&nbsp;to&nbsp;midnight<br>
-fill_value:&nbsp;scalar,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values,&nbsp;applied&nbsp;during&nbsp;upsampling&nbsp;(note<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;4&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=4,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0.0,&nbsp;None,&nbsp;2.0,&nbsp;3.0],&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'s':series})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-asfreq">asfreq</a>(freq='30S')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``fill&nbsp;value``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-asfreq">asfreq</a>(freq='30S',&nbsp;fill_value=9.0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``method``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-asfreq">asfreq</a>(freq='30S',&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-asof"><strong>asof</strong></a>(self, where, subset=None)</dt><dd><tt>The&nbsp;last&nbsp;row&nbsp;without&nbsp;any&nbsp;NaN&nbsp;is&nbsp;taken&nbsp;(or&nbsp;the&nbsp;last&nbsp;row&nbsp;without<br>
-NaN&nbsp;considering&nbsp;only&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;in&nbsp;the&nbsp;case&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0&nbsp;For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-If&nbsp;there&nbsp;is&nbsp;no&nbsp;good&nbsp;value,&nbsp;NaN&nbsp;is&nbsp;returned&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;NaN&nbsp;values&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-where&nbsp;:&nbsp;date&nbsp;or&nbsp;array&nbsp;of&nbsp;dates<br>
-subset&nbsp;:&nbsp;string&nbsp;or&nbsp;list&nbsp;of&nbsp;strings,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None&nbsp;use&nbsp;these&nbsp;columns&nbsp;for&nbsp;NaN&nbsp;propagation<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Dates&nbsp;are&nbsp;assumed&nbsp;to&nbsp;be&nbsp;sorted<br>
-Raises&nbsp;if&nbsp;this&nbsp;is&nbsp;not&nbsp;the&nbsp;case<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-where&nbsp;is&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;&nbsp;-&nbsp;value&nbsp;or&nbsp;NaN&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;-&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-where&nbsp;is&nbsp;Index:&nbsp;same&nbsp;shape&nbsp;<a href="builtins.html#object">object</a>&nbsp;as&nbsp;input<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-merge_asof</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-astype"><strong>astype</strong></a>(self, dtype, copy=True, errors='raise', **kwargs)</dt><dd><tt>Cast&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;numpy.dtype<br>
-Return&nbsp;a&nbsp;copy&nbsp;when&nbsp;copy&nbsp;=&nbsp;True&nbsp;(be&nbsp;really&nbsp;careful&nbsp;with&nbsp;this!)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dtype&nbsp;:&nbsp;data&nbsp;type,&nbsp;or&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;-&gt;&nbsp;data&nbsp;type<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;entire&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;same&nbsp;type.&nbsp;Alternatively,&nbsp;use&nbsp;{col:&nbsp;dtype,&nbsp;...},&nbsp;where&nbsp;col&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;label&nbsp;and&nbsp;dtype&nbsp;is&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;one<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;more&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;columns&nbsp;to&nbsp;column-specific&nbsp;types.<br>
-errors&nbsp;:&nbsp;{'raise',&nbsp;'ignore'},&nbsp;default&nbsp;'raise'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Control&nbsp;raising&nbsp;of&nbsp;exceptions&nbsp;on&nbsp;invalid&nbsp;data&nbsp;for&nbsp;provided&nbsp;dtype.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``raise``&nbsp;:&nbsp;allow&nbsp;exceptions&nbsp;to&nbsp;be&nbsp;raised<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``ignore``&nbsp;:&nbsp;suppress&nbsp;exceptions.&nbsp;On&nbsp;error&nbsp;return&nbsp;original&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-raise_on_error&nbsp;:&nbsp;DEPRECATED&nbsp;use&nbsp;``errors``&nbsp;instead<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;constructor<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-casted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-at_time"><strong>at_time</strong></a>(self, time, asof=False)</dt><dd><tt>Select&nbsp;values&nbsp;at&nbsp;particular&nbsp;time&nbsp;of&nbsp;day&nbsp;(e.g.&nbsp;9:30AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_at_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-between_time"><strong>between_time</strong></a>(self, start_time, end_time, include_start=True, include_end=True)</dt><dd><tt>Select&nbsp;values&nbsp;between&nbsp;particular&nbsp;times&nbsp;of&nbsp;the&nbsp;day&nbsp;(e.g.,&nbsp;9:00-9:30&nbsp;AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-start_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-end_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-include_start&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-include_end&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_between_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-bfill"><strong>bfill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#SweepFrame-fillna">fillna</a>(method='bfill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-bool"><strong>bool</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;bool&nbsp;of&nbsp;a&nbsp;single&nbsp;element&nbsp;PandasObject.<br>
-&nbsp;<br>
-This&nbsp;must&nbsp;be&nbsp;a&nbsp;boolean&nbsp;scalar&nbsp;value,&nbsp;either&nbsp;True&nbsp;or&nbsp;False.&nbsp;&nbsp;Raise&nbsp;a<br>
-ValueError&nbsp;if&nbsp;the&nbsp;PandasObject&nbsp;does&nbsp;not&nbsp;have&nbsp;exactly&nbsp;1&nbsp;element,&nbsp;or&nbsp;that<br>
-element&nbsp;is&nbsp;not&nbsp;boolean</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-clip"><strong>clip</strong></a>(self, lower=None, upper=None, axis=None, *args, **kwargs)</dt><dd><tt>Trim&nbsp;values&nbsp;at&nbsp;input&nbsp;threshold(s).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-lower&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-upper&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;lower&nbsp;and&nbsp;upper&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.256177<br>
-1&nbsp;-1.367855&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.176076<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;1.261967&nbsp;&nbsp;0.570967<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-clip">clip</a>(-1.0,&nbsp;0.5)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.000000<br>
-1&nbsp;-1.000000&nbsp;&nbsp;0.500000<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.000000<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;0.500000&nbsp;&nbsp;0.500000<br>
-&gt;&gt;&gt;&nbsp;t<br>
-0&nbsp;&nbsp;&nbsp;-0.3<br>
-1&nbsp;&nbsp;&nbsp;-0.2<br>
-2&nbsp;&nbsp;&nbsp;-0.1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;0.1<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-clip">clip</a>(t,&nbsp;t&nbsp;+&nbsp;1,&nbsp;axis=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-0.300000<br>
-1&nbsp;-0.200000&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-0.100000<br>
-3&nbsp;&nbsp;0.230930&nbsp;&nbsp;0.000000<br>
-4&nbsp;&nbsp;1.100000&nbsp;&nbsp;0.570967</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-clip_lower"><strong>clip_lower</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;the&nbsp;input&nbsp;with&nbsp;values&nbsp;below&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-clip_upper"><strong>clip_upper</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;input&nbsp;with&nbsp;values&nbsp;above&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-consolidate"><strong>consolidate</strong></a>(self, inplace=False)</dt><dd><tt>DEPRECATED:&nbsp;consolidate&nbsp;will&nbsp;be&nbsp;an&nbsp;internal&nbsp;implementation&nbsp;only.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-convert_objects"><strong>convert_objects</strong></a>(self, convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True)</dt><dd><tt>Deprecated.<br>
-&nbsp;<br>
-Attempt&nbsp;to&nbsp;infer&nbsp;better&nbsp;dtype&nbsp;for&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-convert_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;date&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-convert_numeric&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;attempt&nbsp;to&nbsp;coerce&nbsp;to&nbsp;numbers&nbsp;(including&nbsp;strings),&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaN.<br>
-convert_timedeltas&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;timedelta&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;return&nbsp;a&nbsp;copy&nbsp;even&nbsp;if&nbsp;no&nbsp;copy&nbsp;is&nbsp;necessary&nbsp;(e.g.&nbsp;no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion&nbsp;was&nbsp;done).&nbsp;Note:&nbsp;This&nbsp;is&nbsp;meant&nbsp;for&nbsp;internal&nbsp;use,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;not&nbsp;be&nbsp;confused&nbsp;with&nbsp;inplace.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.to_datetime&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;datetime.<br>
-pandas.to_timedelta&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;timedelta.<br>
-pandas.to_numeric&nbsp;:&nbsp;Return&nbsp;a&nbsp;fixed&nbsp;frequency&nbsp;timedelta&nbsp;index,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;day&nbsp;as&nbsp;the&nbsp;default.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;same&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-copy"><strong>copy</strong></a>(self, deep=True)</dt><dd><tt>Make&nbsp;a&nbsp;copy&nbsp;of&nbsp;this&nbsp;objects&nbsp;data.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-deep&nbsp;:&nbsp;boolean&nbsp;or&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;a&nbsp;deep&nbsp;copy,&nbsp;including&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;data&nbsp;and&nbsp;the&nbsp;indices.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;With&nbsp;``deep=False``&nbsp;neither&nbsp;the&nbsp;indices&nbsp;or&nbsp;the&nbsp;data&nbsp;are&nbsp;copied.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;that&nbsp;when&nbsp;``deep=True``&nbsp;data&nbsp;is&nbsp;copied,&nbsp;actual&nbsp;python&nbsp;objects<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;not&nbsp;be&nbsp;copied&nbsp;recursively,&nbsp;only&nbsp;the&nbsp;reference&nbsp;to&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;is&nbsp;in&nbsp;contrast&nbsp;to&nbsp;``copy.deepcopy``&nbsp;in&nbsp;the&nbsp;Standard&nbsp;Library,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;recursively&nbsp;copies&nbsp;<a href="builtins.html#object">object</a>&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-copy&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-describe"><strong>describe</strong></a>(self, percentiles=None, include=None, exclude=None)</dt><dd><tt>Generates&nbsp;descriptive&nbsp;statistics&nbsp;that&nbsp;summarize&nbsp;the&nbsp;central&nbsp;tendency,<br>
-dispersion&nbsp;and&nbsp;shape&nbsp;of&nbsp;a&nbsp;dataset's&nbsp;distribution,&nbsp;excluding<br>
-``NaN``&nbsp;values.<br>
-&nbsp;<br>
-Analyzes&nbsp;both&nbsp;numeric&nbsp;and&nbsp;<a href="builtins.html#object">object</a>&nbsp;series,&nbsp;as&nbsp;well<br>
-as&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;column&nbsp;sets&nbsp;of&nbsp;mixed&nbsp;data&nbsp;types.&nbsp;The&nbsp;output<br>
-will&nbsp;vary&nbsp;depending&nbsp;on&nbsp;what&nbsp;is&nbsp;provided.&nbsp;Refer&nbsp;to&nbsp;the&nbsp;notes<br>
-below&nbsp;for&nbsp;more&nbsp;detail.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-percentiles&nbsp;:&nbsp;list-like&nbsp;of&nbsp;numbers,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;percentiles&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;output.&nbsp;All&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fall&nbsp;between&nbsp;0&nbsp;and&nbsp;1.&nbsp;The&nbsp;default&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``[.25,&nbsp;.5,&nbsp;.75]``,&nbsp;which&nbsp;returns&nbsp;the&nbsp;25th,&nbsp;50th,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;75th&nbsp;percentiles.<br>
-include&nbsp;:&nbsp;'all',&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;white&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'all'&nbsp;:&nbsp;All&nbsp;columns&nbsp;of&nbsp;the&nbsp;input&nbsp;will&nbsp;be&nbsp;included&nbsp;in&nbsp;the&nbsp;output.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Limits&nbsp;the&nbsp;results&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;provided&nbsp;data&nbsp;types.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;limit&nbsp;the&nbsp;result&nbsp;to&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;limit&nbsp;it&nbsp;instead&nbsp;to&nbsp;categorical<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;objects&nbsp;submit&nbsp;the&nbsp;``numpy.<a href="builtins.html#object">object</a>``&nbsp;data&nbsp;type.&nbsp;Strings<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#SweepFrame-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;include&nbsp;all&nbsp;numeric&nbsp;columns.<br>
-exclude&nbsp;:&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;black&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;omit&nbsp;from&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Excludes&nbsp;the&nbsp;provided&nbsp;data&nbsp;types<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;the&nbsp;result.&nbsp;To&nbsp;select&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;select&nbsp;categorical&nbsp;objects&nbsp;submit&nbsp;the&nbsp;data<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;type&nbsp;``numpy.<a href="builtins.html#object">object</a>``.&nbsp;Strings&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#SweepFrame-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;exclude&nbsp;nothing.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-summary:&nbsp;&nbsp;<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;summary&nbsp;statistics<br>
-&nbsp;<br>
-Notes<br>
------<br>
-For&nbsp;numeric&nbsp;data,&nbsp;the&nbsp;result's&nbsp;index&nbsp;will&nbsp;include&nbsp;``count``,<br>
-``mean``,&nbsp;``std``,&nbsp;``min``,&nbsp;``max``&nbsp;as&nbsp;well&nbsp;as&nbsp;lower,&nbsp;``50``&nbsp;and<br>
-upper&nbsp;percentiles.&nbsp;By&nbsp;default&nbsp;the&nbsp;lower&nbsp;percentile&nbsp;is&nbsp;``25``&nbsp;and&nbsp;the<br>
-upper&nbsp;percentile&nbsp;is&nbsp;``75``.&nbsp;The&nbsp;``50``&nbsp;percentile&nbsp;is&nbsp;the<br>
-same&nbsp;as&nbsp;the&nbsp;median.<br>
-&nbsp;<br>
-For&nbsp;<a href="builtins.html#object">object</a>&nbsp;data&nbsp;(e.g.&nbsp;strings&nbsp;or&nbsp;timestamps),&nbsp;the&nbsp;result's&nbsp;index<br>
-will&nbsp;include&nbsp;``count``,&nbsp;``unique``,&nbsp;``top``,&nbsp;and&nbsp;``freq``.&nbsp;The&nbsp;``top``<br>
-is&nbsp;the&nbsp;most&nbsp;common&nbsp;value.&nbsp;The&nbsp;``freq``&nbsp;is&nbsp;the&nbsp;most&nbsp;common&nbsp;value's<br>
-frequency.&nbsp;Timestamps&nbsp;also&nbsp;include&nbsp;the&nbsp;``first``&nbsp;and&nbsp;``last``&nbsp;items.<br>
-&nbsp;<br>
-If&nbsp;multiple&nbsp;<a href="builtins.html#object">object</a>&nbsp;values&nbsp;have&nbsp;the&nbsp;highest&nbsp;count,&nbsp;then&nbsp;the<br>
-``count``&nbsp;and&nbsp;``top``&nbsp;results&nbsp;will&nbsp;be&nbsp;arbitrarily&nbsp;chosen&nbsp;from<br>
-among&nbsp;those&nbsp;with&nbsp;the&nbsp;highest&nbsp;count.<br>
-&nbsp;<br>
-For&nbsp;mixed&nbsp;data&nbsp;types&nbsp;provided&nbsp;via&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``,&nbsp;the&nbsp;default&nbsp;is&nbsp;to<br>
-return&nbsp;only&nbsp;an&nbsp;analysis&nbsp;of&nbsp;numeric&nbsp;columns.&nbsp;If&nbsp;``include='all'``<br>
-is&nbsp;provided&nbsp;as&nbsp;an&nbsp;option,&nbsp;the&nbsp;result&nbsp;will&nbsp;include&nbsp;a&nbsp;union&nbsp;of<br>
-attributes&nbsp;of&nbsp;each&nbsp;type.<br>
-&nbsp;<br>
-The&nbsp;`include`&nbsp;and&nbsp;`exclude`&nbsp;parameters&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;limit<br>
-which&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;are&nbsp;analyzed&nbsp;for&nbsp;the&nbsp;output.<br>
-The&nbsp;parameters&nbsp;are&nbsp;ignored&nbsp;when&nbsp;analyzing&nbsp;a&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-Describing&nbsp;a&nbsp;numeric&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepFrame-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;categorical&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(['a',&nbsp;'a',&nbsp;'b',&nbsp;'c'])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepFrame-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;timestamp&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2000-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01")<br>
-...&nbsp;])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepFrame-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-first&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2000-01-01&nbsp;00:00:00<br>
-last&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``.&nbsp;By&nbsp;default&nbsp;only&nbsp;numeric&nbsp;fields<br>
-are&nbsp;returned.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[1,&nbsp;'a'],&nbsp;[2,&nbsp;'b'],&nbsp;[3,&nbsp;'c']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['numeric',&nbsp;'<a href="builtins.html#object">object</a>'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-describe">describe</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;all&nbsp;columns&nbsp;of&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;regardless&nbsp;of&nbsp;data&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-describe">describe</a>(include='all')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;column&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;by&nbsp;accessing&nbsp;it&nbsp;as<br>
-an&nbsp;attribute.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.numeric.<a href="#SweepFrame-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-Name:&nbsp;numeric,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;numeric&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-describe">describe</a>(include=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;string&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-describe">describe</a>(include=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;numeric&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-describe">describe</a>(exclude=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-describe">describe</a>(exclude=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.count<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.max<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.min<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mean<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.std<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select_dtypes</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-drop"><strong>drop</strong></a>(self, labels, axis=0, level=None, inplace=False, errors='raise')</dt><dd><tt>Return&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;labels&nbsp;in&nbsp;requested&nbsp;axis&nbsp;removed.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-labels&nbsp;:&nbsp;single&nbsp;label&nbsp;or&nbsp;list-like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;axis&nbsp;name<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;MultiIndex<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;return&nbsp;None.<br>
-errors&nbsp;:&nbsp;{'ignore',&nbsp;'raise'},&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'ignore',&nbsp;suppress&nbsp;error&nbsp;and&nbsp;existing&nbsp;labels&nbsp;are&nbsp;dropped.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dropped&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-equals"><strong>equals</strong></a>(self, other)</dt><dd><tt>Determines&nbsp;if&nbsp;two&nbsp;NDFrame&nbsp;objects&nbsp;contain&nbsp;the&nbsp;same&nbsp;elements.&nbsp;NaNs&nbsp;in<br>
-the&nbsp;same&nbsp;location&nbsp;are&nbsp;considered&nbsp;equal.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-ffill"><strong>ffill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#SweepFrame-fillna">fillna</a>(method='ffill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-filter"><strong>filter</strong></a>(self, items=None, like=None, regex=None, axis=None)</dt><dd><tt>Subset&nbsp;rows&nbsp;or&nbsp;columns&nbsp;of&nbsp;dataframe&nbsp;according&nbsp;to&nbsp;labels&nbsp;in<br>
-the&nbsp;specified&nbsp;index.<br>
-&nbsp;<br>
-Note&nbsp;that&nbsp;this&nbsp;routine&nbsp;does&nbsp;not&nbsp;filter&nbsp;a&nbsp;dataframe&nbsp;on&nbsp;its<br>
-contents.&nbsp;The&nbsp;filter&nbsp;is&nbsp;applied&nbsp;to&nbsp;the&nbsp;labels&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-items&nbsp;:&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;info&nbsp;axis&nbsp;to&nbsp;restrict&nbsp;to&nbsp;(must&nbsp;not&nbsp;all&nbsp;be&nbsp;present)<br>
-like&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;where&nbsp;"arg&nbsp;in&nbsp;col&nbsp;==&nbsp;True"<br>
-regex&nbsp;:&nbsp;string&nbsp;(regular&nbsp;expression)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;with&nbsp;re.search(regex,&nbsp;col)&nbsp;==&nbsp;True<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;filter&nbsp;on.&nbsp;&nbsp;By&nbsp;default&nbsp;this&nbsp;is&nbsp;the&nbsp;info&nbsp;axis,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'index'&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;'columns'&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;name<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-filter">filter</a>(items=['one',&nbsp;'three'])<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;regular&nbsp;expression<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-filter">filter</a>(regex='e$',&nbsp;axis=1)<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;rows&nbsp;containing&nbsp;'bbi'<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-filter">filter</a>(like='bbi',&nbsp;axis=0)<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;``items``,&nbsp;``like``,&nbsp;and&nbsp;``regex``&nbsp;parameters&nbsp;are<br>
-enforced&nbsp;to&nbsp;be&nbsp;mutually&nbsp;exclusive.<br>
-&nbsp;<br>
-``axis``&nbsp;defaults&nbsp;to&nbsp;the&nbsp;info&nbsp;axis&nbsp;that&nbsp;is&nbsp;used&nbsp;when&nbsp;indexing<br>
-with&nbsp;``[]``.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-first"><strong>first</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;initial&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#SweepFrame-first">first</a>('10D')&nbsp;-&gt;&nbsp;First&nbsp;10&nbsp;days<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-get"><strong>get</strong></a>(self, key, default=None)</dt><dd><tt>Get&nbsp;item&nbsp;from&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;given&nbsp;key&nbsp;(<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column,&nbsp;Panel&nbsp;slice,<br>
-etc.).&nbsp;Returns&nbsp;default&nbsp;value&nbsp;if&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;type&nbsp;of&nbsp;items&nbsp;contained&nbsp;in&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-get_dtype_counts"><strong>get_dtype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;dtypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-get_ftype_counts"><strong>get_ftype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;ftypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-get_values"><strong>get_values</strong></a>(self)</dt><dd><tt>same&nbsp;as&nbsp;values&nbsp;(but&nbsp;handles&nbsp;sparseness&nbsp;conversions)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-groupby"><strong>groupby</strong></a>(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)</dt><dd><tt>Group&nbsp;series&nbsp;using&nbsp;mapper&nbsp;(dict&nbsp;or&nbsp;key&nbsp;function,&nbsp;apply&nbsp;given&nbsp;function<br>
-to&nbsp;group,&nbsp;return&nbsp;result&nbsp;as&nbsp;series)&nbsp;or&nbsp;by&nbsp;a&nbsp;series&nbsp;of&nbsp;columns.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;mapping,&nbsp;function,&nbsp;str,&nbsp;or&nbsp;iterable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;for&nbsp;the&nbsp;groupby.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;``by``&nbsp;is&nbsp;a&nbsp;function,&nbsp;it's&nbsp;called&nbsp;on&nbsp;each&nbsp;value&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>'s<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;If&nbsp;a&nbsp;dict&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;is&nbsp;passed,&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;dict&nbsp;VALUES<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;(the&nbsp;<a href="pandas.core.series.html#Series">Series</a>'&nbsp;values&nbsp;are&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aligned;&nbsp;see&nbsp;``.<a href="#SweepFrame-align">align</a>()``&nbsp;method).&nbsp;If&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;is&nbsp;passed,&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;are&nbsp;used&nbsp;as-is&nbsp;determine&nbsp;the&nbsp;groups.&nbsp;A&nbsp;str&nbsp;or&nbsp;list&nbsp;of&nbsp;strs<br>
-&nbsp;&nbsp;&nbsp;&nbsp;may&nbsp;be&nbsp;passed&nbsp;to&nbsp;group&nbsp;by&nbsp;the&nbsp;columns&nbsp;in&nbsp;``self``<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-level&nbsp;:&nbsp;int,&nbsp;level&nbsp;name,&nbsp;or&nbsp;sequence&nbsp;of&nbsp;such,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;group&nbsp;by&nbsp;a&nbsp;particular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level&nbsp;or&nbsp;levels<br>
-as_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;aggregated&nbsp;output,&nbsp;return&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;group&nbsp;labels&nbsp;as&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;Only&nbsp;relevant&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;input.&nbsp;as_index=False&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;effectively&nbsp;"SQL-style"&nbsp;grouped&nbsp;output<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;group&nbsp;keys.&nbsp;Get&nbsp;better&nbsp;performance&nbsp;by&nbsp;turning&nbsp;this&nbsp;off.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;this&nbsp;does&nbsp;not&nbsp;influence&nbsp;the&nbsp;order&nbsp;of&nbsp;observations&nbsp;within&nbsp;each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;group.&nbsp;&nbsp;groupby&nbsp;preserves&nbsp;the&nbsp;order&nbsp;of&nbsp;rows&nbsp;within&nbsp;each&nbsp;group.<br>
-group_keys&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;calling&nbsp;apply,&nbsp;add&nbsp;group&nbsp;keys&nbsp;to&nbsp;index&nbsp;to&nbsp;identify&nbsp;pieces<br>
-squeeze&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;reduce&nbsp;the&nbsp;dimensionality&nbsp;of&nbsp;the&nbsp;return&nbsp;type&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;return&nbsp;a&nbsp;consistent&nbsp;type<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;results<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#SweepFrame-groupby">groupby</a>(func,&nbsp;axis=0).<a href="#SweepFrame-mean">mean</a>()<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#SweepFrame-groupby">groupby</a>(['col1',&nbsp;'col2'])['col3'].<a href="#SweepFrame-mean">mean</a>()<br>
-&nbsp;<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;hierarchical&nbsp;index<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#SweepFrame-groupby">groupby</a>(['col1',&nbsp;'col2']).<a href="#SweepFrame-mean">mean</a>()<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-GroupBy&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-head"><strong>head</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;first&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-interpolate"><strong>interpolate</strong></a>(self, method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', downcast=None, **kwargs)</dt><dd><tt>Interpolate&nbsp;values&nbsp;according&nbsp;to&nbsp;different&nbsp;methods.<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;only&nbsp;``method='linear'``&nbsp;is&nbsp;supported&nbsp;for<br>
-DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a&nbsp;MultiIndex.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-method&nbsp;:&nbsp;{'linear',&nbsp;'time',&nbsp;'index',&nbsp;'values',&nbsp;'nearest',&nbsp;'zero',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',&nbsp;'barycentric',&nbsp;'krogh',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'polynomial',&nbsp;'spline',&nbsp;'piecewise_polynomial',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'from_derivatives',&nbsp;'pchip',&nbsp;'akima'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'linear':&nbsp;ignore&nbsp;the&nbsp;index&nbsp;and&nbsp;treat&nbsp;the&nbsp;values&nbsp;as&nbsp;equally<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;spaced.&nbsp;This&nbsp;is&nbsp;the&nbsp;only&nbsp;method&nbsp;supported&nbsp;on&nbsp;MultiIndexes.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'time':&nbsp;interpolation&nbsp;works&nbsp;on&nbsp;daily&nbsp;and&nbsp;higher&nbsp;resolution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;data&nbsp;to&nbsp;interpolate&nbsp;given&nbsp;length&nbsp;of&nbsp;interval<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'index',&nbsp;'values':&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'nearest',&nbsp;'zero',&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'barycentric',&nbsp;'polynomial'&nbsp;is&nbsp;passed&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``scipy.interpolate.interp1d``.&nbsp;Both&nbsp;'polynomial'&nbsp;and&nbsp;'spline'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;require&nbsp;that&nbsp;you&nbsp;also&nbsp;specify&nbsp;an&nbsp;`order`&nbsp;(int),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;e.g.&nbsp;df.<a href="#SweepFrame-interpolate">interpolate</a>(method='polynomial',&nbsp;order=4).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'krogh',&nbsp;'piecewise_polynomial',&nbsp;'spline',&nbsp;'pchip'&nbsp;and&nbsp;'akima'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;all&nbsp;wrappers&nbsp;around&nbsp;the&nbsp;scipy&nbsp;interpolation&nbsp;methods&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;similar&nbsp;names.&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;For&nbsp;more&nbsp;information&nbsp;on&nbsp;their&nbsp;behavior,&nbsp;see&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`scipy&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__">http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;`tutorial&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__">http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'from_derivatives'&nbsp;refers&nbsp;to&nbsp;BPoly.from_derivatives&nbsp;which<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;replaces&nbsp;'piecewise_polynomial'&nbsp;interpolation&nbsp;method&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;0.18<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;support&nbsp;for&nbsp;the&nbsp;'akima'&nbsp;method<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;interpolate&nbsp;method&nbsp;'from_derivatives'&nbsp;which&nbsp;replaces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'piecewise_polynomial'&nbsp;in&nbsp;scipy&nbsp;0.18;&nbsp;backwards-compatible&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;&lt;&nbsp;0.18<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;0:&nbsp;fill&nbsp;column-by-column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;1:&nbsp;fill&nbsp;row-by-row<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs&nbsp;to&nbsp;fill.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0.<br>
-limit_direction&nbsp;:&nbsp;{'forward',&nbsp;'backward',&nbsp;'both'},&nbsp;default&nbsp;'forward'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;limit&nbsp;is&nbsp;specified,&nbsp;consecutive&nbsp;NaNs&nbsp;will&nbsp;be&nbsp;filled&nbsp;in&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;direction.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Update&nbsp;the&nbsp;NDFrame&nbsp;in&nbsp;place&nbsp;if&nbsp;possible.<br>
-downcast&nbsp;:&nbsp;optional,&nbsp;'infer'&nbsp;or&nbsp;None,&nbsp;defaults&nbsp;to&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Downcast&nbsp;dtypes&nbsp;if&nbsp;possible.<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;interpolating&nbsp;function.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;interpolated&nbsp;at&nbsp;the&nbsp;NaNs<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;replace,&nbsp;fillna<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Filling&nbsp;in&nbsp;NaNs<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0,&nbsp;1,&nbsp;np.nan,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepFrame-interpolate">interpolate</a>()<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-isnull"><strong>isnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-notnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;isnull</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-keys"><strong>keys</strong></a>(self)</dt><dd><tt>Get&nbsp;the&nbsp;'info&nbsp;axis'&nbsp;(see&nbsp;Indexing&nbsp;for&nbsp;more)<br>
-&nbsp;<br>
-This&nbsp;is&nbsp;index&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;columns&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;and&nbsp;major_axis&nbsp;for<br>
-Panel.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-last"><strong>last</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;final&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#SweepFrame-last">last</a>('5M')&nbsp;-&gt;&nbsp;Last&nbsp;5&nbsp;months<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-mask"><strong>mask</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;False&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;mask&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``False``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#SweepFrame-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#SweepFrame-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``mask``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepFrame-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).reshape(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#SweepFrame-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#SweepFrame-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-notnull"><strong>notnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are<br>
-not&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-isnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;notnull</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-pct_change"><strong>pct_change</strong></a>(self, periods=1, fill_method='pad', limit=None, freq=None, **kwargs)</dt><dd><tt>Percent&nbsp;change&nbsp;over&nbsp;given&nbsp;number&nbsp;of&nbsp;periods.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;percent&nbsp;change<br>
-fill_method&nbsp;:&nbsp;str,&nbsp;default&nbsp;'pad'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;How&nbsp;to&nbsp;handle&nbsp;NAs&nbsp;before&nbsp;computing&nbsp;percent&nbsp;changes<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NAs&nbsp;to&nbsp;fill&nbsp;before&nbsp;stopping<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;offset&nbsp;alias&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;time&nbsp;series&nbsp;API&nbsp;(e.g.&nbsp;'M'&nbsp;or&nbsp;BDay())<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-chg&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-By&nbsp;default,&nbsp;the&nbsp;percentage&nbsp;change&nbsp;is&nbsp;calculated&nbsp;along&nbsp;the&nbsp;stat<br>
-axis:&nbsp;0,&nbsp;or&nbsp;``Index``,&nbsp;for&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;and&nbsp;1,&nbsp;or&nbsp;``minor``&nbsp;for<br>
-``Panel``.&nbsp;You&nbsp;can&nbsp;change&nbsp;this&nbsp;with&nbsp;the&nbsp;``axis``&nbsp;keyword&nbsp;argument.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-pipe"><strong>pipe</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Apply&nbsp;func(self,&nbsp;\*args,&nbsp;\*\*kwargs)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.2<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;the&nbsp;NDFrame.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``args``,&nbsp;and&nbsp;``kwargs``&nbsp;are&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Alternatively&nbsp;a&nbsp;``(callable,&nbsp;data_keyword)``&nbsp;tuple&nbsp;where<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``data_keyword``&nbsp;is&nbsp;a&nbsp;string&nbsp;indicating&nbsp;the&nbsp;keyword&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``callable``&nbsp;that&nbsp;expects&nbsp;the&nbsp;NDFrame.<br>
-args&nbsp;:&nbsp;positional&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-kwargs&nbsp;:&nbsp;a&nbsp;dictionary&nbsp;of&nbsp;keyword&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="builtins.html#object">object</a>&nbsp;:&nbsp;the&nbsp;return&nbsp;type&nbsp;of&nbsp;``func``.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-Use&nbsp;``.pipe``&nbsp;when&nbsp;chaining&nbsp;together&nbsp;functions&nbsp;that&nbsp;expect<br>
-on&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;DataFrames.&nbsp;Instead&nbsp;of&nbsp;writing<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;f(g(h(df),&nbsp;arg1=a),&nbsp;arg2=b,&nbsp;arg3=c)<br>
-&nbsp;<br>
-You&nbsp;can&nbsp;write<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#SweepFrame-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#SweepFrame-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#SweepFrame-pipe">pipe</a>(f,&nbsp;arg2=b,&nbsp;arg3=c)<br>
-...&nbsp;)<br>
-&nbsp;<br>
-If&nbsp;you&nbsp;have&nbsp;a&nbsp;function&nbsp;that&nbsp;takes&nbsp;the&nbsp;data&nbsp;as&nbsp;(say)&nbsp;the&nbsp;second<br>
-argument,&nbsp;pass&nbsp;a&nbsp;tuple&nbsp;indicating&nbsp;which&nbsp;keyword&nbsp;expects&nbsp;the<br>
-data.&nbsp;For&nbsp;example,&nbsp;suppose&nbsp;``f``&nbsp;takes&nbsp;its&nbsp;data&nbsp;as&nbsp;``arg2``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#SweepFrame-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#SweepFrame-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#SweepFrame-pipe">pipe</a>((f,&nbsp;'arg2'),&nbsp;arg1=a,&nbsp;arg3=c)<br>
-...&nbsp;&nbsp;)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.map</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-pop"><strong>pop</strong></a>(self, item)</dt><dd><tt>Return&nbsp;item&nbsp;and&nbsp;drop&nbsp;from&nbsp;frame.&nbsp;Raise&nbsp;KeyError&nbsp;if&nbsp;not&nbsp;found.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-rank"><strong>rank</strong></a>(self, axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)</dt><dd><tt>Compute&nbsp;numerical&nbsp;data&nbsp;ranks&nbsp;(1&nbsp;through&nbsp;n)&nbsp;along&nbsp;axis.&nbsp;Equal&nbsp;values&nbsp;are<br>
-assigned&nbsp;a&nbsp;rank&nbsp;that&nbsp;is&nbsp;the&nbsp;average&nbsp;of&nbsp;the&nbsp;ranks&nbsp;of&nbsp;those&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;to&nbsp;direct&nbsp;ranking<br>
-method&nbsp;:&nbsp;{'average',&nbsp;'min',&nbsp;'max',&nbsp;'first',&nbsp;'dense'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;average:&nbsp;average&nbsp;rank&nbsp;of&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;min:&nbsp;lowest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;max:&nbsp;highest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;first:&nbsp;ranks&nbsp;assigned&nbsp;in&nbsp;order&nbsp;they&nbsp;appear&nbsp;in&nbsp;the&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dense:&nbsp;like&nbsp;'min',&nbsp;but&nbsp;rank&nbsp;always&nbsp;increases&nbsp;by&nbsp;1&nbsp;between&nbsp;groups<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;data.&nbsp;Valid&nbsp;only&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Panel&nbsp;objects<br>
-na_option&nbsp;:&nbsp;{'keep',&nbsp;'top',&nbsp;'bottom'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;keep:&nbsp;leave&nbsp;NA&nbsp;values&nbsp;where&nbsp;they&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;top:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;ascending<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;bottom:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;descending<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False&nbsp;for&nbsp;ranks&nbsp;by&nbsp;high&nbsp;(1)&nbsp;to&nbsp;low&nbsp;(N)<br>
-pct&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Computes&nbsp;percentage&nbsp;rank&nbsp;of&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ranks&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-reindex_like"><strong>reindex_like</strong></a>(self, other, method=None, copy=True, limit=None, tolerance=None)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;matching&nbsp;indices&nbsp;to&nbsp;myself.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;Object<br>
-method&nbsp;:&nbsp;string&nbsp;or&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;labels&nbsp;to&nbsp;fill&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;labels&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>&nbsp;and&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Like&nbsp;calling&nbsp;s.<a href="#SweepFrame-reindex">reindex</a>(index=other.index,&nbsp;columns=other.columns,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;method=...)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-rename_axis"><strong>rename_axis</strong></a>(self, mapper, axis=0, copy=True, inplace=False)</dt><dd><tt>Alter&nbsp;index&nbsp;and&nbsp;/&nbsp;or&nbsp;columns&nbsp;using&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.<br>
-A&nbsp;scalar&nbsp;or&nbsp;list-like&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;``Index.name``<br>
-or&nbsp;``MultiIndex.names``&nbsp;attribute.<br>
-A&nbsp;function&nbsp;or&nbsp;dict&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;labels.<br>
-Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be&nbsp;unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in<br>
-a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left&nbsp;as-is.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-mapper&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename<br>
-pandas.Index.rename<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-rename_axis">rename_axis</a>("foo")&nbsp;&nbsp;#&nbsp;scalar,&nbsp;alters&nbsp;df.index.name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-foo<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-rename_axis">rename_axis</a>(lambda&nbsp;x:&nbsp;2&nbsp;*&nbsp;x)&nbsp;&nbsp;#&nbsp;function:&nbsp;alters&nbsp;labels<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-4&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-rename_axis">rename_axis</a>({"A":&nbsp;"ehh",&nbsp;"C":&nbsp;"see"},&nbsp;axis="columns")&nbsp;&nbsp;#&nbsp;mapping<br>
-&nbsp;&nbsp;&nbsp;ehh&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-replace"><strong>replace</strong></a>(self, to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad', axis=None)</dt><dd><tt>Replace&nbsp;values&nbsp;given&nbsp;in&nbsp;'to_replace'&nbsp;with&nbsp;'value'.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-to_replace&nbsp;:&nbsp;str,&nbsp;regex,&nbsp;list,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;numeric,&nbsp;or&nbsp;None<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;str&nbsp;or&nbsp;regex:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str:&nbsp;string&nbsp;exactly&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;`value`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;regex:&nbsp;regexs&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`value`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;list&nbsp;of&nbsp;str,&nbsp;regex,&nbsp;or&nbsp;numeric:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;First,&nbsp;if&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;both&nbsp;lists,&nbsp;they<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Second,&nbsp;if&nbsp;``regex=True``&nbsp;then&nbsp;all&nbsp;of&nbsp;the&nbsp;strings&nbsp;in&nbsp;**both**<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;regexs&nbsp;otherwise&nbsp;they&nbsp;will&nbsp;match<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;directly.&nbsp;This&nbsp;doesn't&nbsp;matter&nbsp;much&nbsp;for&nbsp;`value`&nbsp;since&nbsp;there<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;only&nbsp;a&nbsp;few&nbsp;possible&nbsp;substitution&nbsp;regexes&nbsp;you&nbsp;can&nbsp;use.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str&nbsp;and&nbsp;regex&nbsp;rules&nbsp;apply&nbsp;as&nbsp;above.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dict:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Nested&nbsp;dictionaries,&nbsp;e.g.,&nbsp;{'a':&nbsp;{'b':&nbsp;nan}},&nbsp;are&nbsp;read&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;follows:&nbsp;look&nbsp;in&nbsp;column&nbsp;'a'&nbsp;for&nbsp;the&nbsp;value&nbsp;'b'&nbsp;and&nbsp;replace&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;nan.&nbsp;You&nbsp;can&nbsp;nest&nbsp;regular&nbsp;expressions&nbsp;as&nbsp;well.&nbsp;Note&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;names&nbsp;(the&nbsp;top-level&nbsp;dictionary&nbsp;keys&nbsp;in&nbsp;a&nbsp;nested<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dictionary)&nbsp;**cannot**&nbsp;be&nbsp;regular&nbsp;expressions.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Keys&nbsp;map&nbsp;to&nbsp;column&nbsp;names&nbsp;and&nbsp;values&nbsp;map&nbsp;to&nbsp;substitution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;values.&nbsp;You&nbsp;can&nbsp;treat&nbsp;this&nbsp;as&nbsp;a&nbsp;special&nbsp;case&nbsp;of&nbsp;passing&nbsp;two<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;except&nbsp;that&nbsp;you&nbsp;are&nbsp;specifying&nbsp;the&nbsp;column&nbsp;to&nbsp;search&nbsp;in.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;None:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;This&nbsp;means&nbsp;that&nbsp;the&nbsp;``regex``&nbsp;argument&nbsp;must&nbsp;be&nbsp;a&nbsp;string,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;compiled&nbsp;regular&nbsp;expression,&nbsp;or&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;such&nbsp;elements.&nbsp;If&nbsp;`value`&nbsp;is&nbsp;also&nbsp;``None``&nbsp;then&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;a&nbsp;nested&nbsp;dictionary&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;examples&nbsp;section&nbsp;for&nbsp;examples&nbsp;of&nbsp;each&nbsp;of&nbsp;these.<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;list,&nbsp;str,&nbsp;regex,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a&nbsp;dict&nbsp;of&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for&nbsp;each&nbsp;column&nbsp;(columns&nbsp;not&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;Regular&nbsp;expressions,&nbsp;strings&nbsp;and&nbsp;lists&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dicts&nbsp;of&nbsp;such&nbsp;objects&nbsp;are&nbsp;also&nbsp;allowed.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>&nbsp;(e.g.&nbsp;a&nbsp;column&nbsp;form&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Returns&nbsp;the&nbsp;caller&nbsp;if&nbsp;this&nbsp;is&nbsp;True.<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;size&nbsp;gap&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-regex&nbsp;:&nbsp;bool&nbsp;or&nbsp;same&nbsp;types&nbsp;as&nbsp;`to_replace`,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;interpret&nbsp;`to_replace`&nbsp;and/or&nbsp;`value`&nbsp;as&nbsp;regular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;expressions.&nbsp;If&nbsp;this&nbsp;is&nbsp;``True``&nbsp;then&nbsp;`to_replace`&nbsp;*must*&nbsp;be&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string.&nbsp;Otherwise,&nbsp;`to_replace`&nbsp;must&nbsp;be&nbsp;``None``&nbsp;because&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;parameter&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;regular&nbsp;expression&nbsp;or&nbsp;a&nbsp;list,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict,&nbsp;or&nbsp;array&nbsp;of&nbsp;regular&nbsp;expressions.<br>
-method&nbsp;:&nbsp;string,&nbsp;optional,&nbsp;{'pad',&nbsp;'ffill',&nbsp;'bfill'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;method&nbsp;to&nbsp;use&nbsp;when&nbsp;for&nbsp;replacement,&nbsp;when&nbsp;``to_replace``&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``list``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-NDFrame.reindex<br>
-NDFrame.asfreq<br>
-NDFrame.fillna<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-AssertionError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;a&nbsp;``bool``&nbsp;and&nbsp;`to_replace`&nbsp;is&nbsp;not&nbsp;``None``.<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;a&nbsp;``dict``&nbsp;and&nbsp;`value`&nbsp;is&nbsp;not&nbsp;a&nbsp;``list``,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``dict``,&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``,&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;``None``&nbsp;and&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;compilable&nbsp;into&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;regular&nbsp;expression&nbsp;or&nbsp;is&nbsp;a&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;``list``&nbsp;s&nbsp;or&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``&nbsp;s,&nbsp;but<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;they&nbsp;are&nbsp;not&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-*&nbsp;Regex&nbsp;substitution&nbsp;is&nbsp;performed&nbsp;under&nbsp;the&nbsp;hood&nbsp;with&nbsp;``re.sub``.&nbsp;The<br>
-&nbsp;&nbsp;rules&nbsp;for&nbsp;substitution&nbsp;for&nbsp;``re.sub``&nbsp;are&nbsp;the&nbsp;same.<br>
-*&nbsp;Regular&nbsp;expressions&nbsp;will&nbsp;only&nbsp;substitute&nbsp;on&nbsp;strings,&nbsp;meaning&nbsp;you<br>
-&nbsp;&nbsp;cannot&nbsp;provide,&nbsp;for&nbsp;example,&nbsp;a&nbsp;regular&nbsp;expression&nbsp;matching&nbsp;floating<br>
-&nbsp;&nbsp;point&nbsp;numbers&nbsp;and&nbsp;expect&nbsp;the&nbsp;columns&nbsp;in&nbsp;your&nbsp;frame&nbsp;that&nbsp;have&nbsp;a<br>
-&nbsp;&nbsp;numeric&nbsp;dtype&nbsp;to&nbsp;be&nbsp;matched.&nbsp;However,&nbsp;if&nbsp;those&nbsp;floating&nbsp;point&nbsp;numbers<br>
-&nbsp;&nbsp;*are*&nbsp;strings,&nbsp;then&nbsp;you&nbsp;can&nbsp;do&nbsp;this.<br>
-*&nbsp;This&nbsp;method&nbsp;has&nbsp;*a&nbsp;lot*&nbsp;of&nbsp;options.&nbsp;You&nbsp;are&nbsp;encouraged&nbsp;to&nbsp;experiment<br>
-&nbsp;&nbsp;and&nbsp;play&nbsp;with&nbsp;this&nbsp;method&nbsp;to&nbsp;gain&nbsp;intuition&nbsp;about&nbsp;how&nbsp;it&nbsp;works.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-resample"><strong>resample</strong></a>(self, rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0, on=None, level=None)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;frequency&nbsp;conversion&nbsp;and&nbsp;resampling&nbsp;of&nbsp;time<br>
-series.&nbsp;&nbsp;Object&nbsp;must&nbsp;have&nbsp;a&nbsp;datetime-like&nbsp;index&nbsp;(DatetimeIndex,<br>
-PeriodIndex,&nbsp;or&nbsp;TimedeltaIndex),&nbsp;or&nbsp;pass&nbsp;datetime-like&nbsp;values<br>
-to&nbsp;the&nbsp;on&nbsp;or&nbsp;level&nbsp;keyword.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-rule&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;offset&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a>&nbsp;representing&nbsp;target&nbsp;conversion<br>
-axis&nbsp;:&nbsp;int,&nbsp;optional,&nbsp;default&nbsp;0<br>
-closed&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;side&nbsp;of&nbsp;bin&nbsp;interval&nbsp;is&nbsp;closed<br>
-label&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;bin&nbsp;edge&nbsp;label&nbsp;to&nbsp;label&nbsp;bucket&nbsp;with<br>
-convention&nbsp;:&nbsp;{'start',&nbsp;'end',&nbsp;'s',&nbsp;'e'}<br>
-loffset&nbsp;:&nbsp;timedelta<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Adjust&nbsp;the&nbsp;resampled&nbsp;time&nbsp;labels<br>
-base&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;frequencies&nbsp;that&nbsp;evenly&nbsp;subdivide&nbsp;1&nbsp;day,&nbsp;the&nbsp;"origin"&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aggregated&nbsp;intervals.&nbsp;For&nbsp;example,&nbsp;for&nbsp;'5min'&nbsp;frequency,&nbsp;base&nbsp;could<br>
-&nbsp;&nbsp;&nbsp;&nbsp;range&nbsp;from&nbsp;0&nbsp;through&nbsp;4.&nbsp;Defaults&nbsp;to&nbsp;0<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;to&nbsp;use&nbsp;instead&nbsp;of&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-level&nbsp;:&nbsp;string&nbsp;or&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;MultiIndex,&nbsp;level&nbsp;(name&nbsp;or&nbsp;number)&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;resampling.&nbsp;&nbsp;Level&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offset&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;9&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(9),&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;series<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:04:00&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-2000-01-01&nbsp;00:05:00&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:07:00&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-2000-01-01&nbsp;00:08:00&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-Freq:&nbsp;T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;and&nbsp;sum&nbsp;the&nbsp;values<br>
-of&nbsp;the&nbsp;timestamps&nbsp;falling&nbsp;into&nbsp;a&nbsp;bin.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepFrame-resample">resample</a>('3T').<a href="#SweepFrame-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;label&nbsp;each<br>
-bin&nbsp;using&nbsp;the&nbsp;right&nbsp;edge&nbsp;instead&nbsp;of&nbsp;the&nbsp;left.&nbsp;Please&nbsp;note&nbsp;that&nbsp;the<br>
-value&nbsp;in&nbsp;the&nbsp;bucket&nbsp;used&nbsp;as&nbsp;the&nbsp;label&nbsp;is&nbsp;not&nbsp;included&nbsp;in&nbsp;the&nbsp;bucket,<br>
-which&nbsp;it&nbsp;labels.&nbsp;For&nbsp;example,&nbsp;in&nbsp;the&nbsp;original&nbsp;series&nbsp;the<br>
-bucket&nbsp;``2000-01-01&nbsp;00:03:00``&nbsp;contains&nbsp;the&nbsp;value&nbsp;3,&nbsp;but&nbsp;the&nbsp;summed<br>
-value&nbsp;in&nbsp;the&nbsp;resampled&nbsp;bucket&nbsp;with&nbsp;the&nbsp;label``2000-01-01&nbsp;00:03:00``<br>
-does&nbsp;not&nbsp;include&nbsp;3&nbsp;(if&nbsp;it&nbsp;did,&nbsp;the&nbsp;summed&nbsp;value&nbsp;would&nbsp;be&nbsp;6,&nbsp;not&nbsp;3).<br>
-To&nbsp;include&nbsp;this&nbsp;value&nbsp;close&nbsp;the&nbsp;right&nbsp;side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval&nbsp;as<br>
-illustrated&nbsp;in&nbsp;the&nbsp;example&nbsp;below&nbsp;this&nbsp;one.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepFrame-resample">resample</a>('3T',&nbsp;label='right').<a href="#SweepFrame-sum">sum</a>()<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;close&nbsp;the&nbsp;right<br>
-side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepFrame-resample">resample</a>('3T',&nbsp;label='right',&nbsp;closed='right').<a href="#SweepFrame-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepFrame-resample">resample</a>('30S').<a href="#SweepFrame-asfreq">asfreq</a>()[0:5]&nbsp;#select&nbsp;first&nbsp;5&nbsp;rows<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;1.0<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;2.0<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values&nbsp;using&nbsp;the&nbsp;``pad``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepFrame-resample">resample</a>('30S').pad()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the<br>
-``NaN``&nbsp;values&nbsp;using&nbsp;the&nbsp;``bfill``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepFrame-resample">resample</a>('30S').<a href="#SweepFrame-bfill">bfill</a>()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Pass&nbsp;a&nbsp;custom&nbsp;function&nbsp;via&nbsp;``apply``<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;custom_resampler(array_like):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;np.<a href="#SweepFrame-sum">sum</a>(array_like)+5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepFrame-resample">resample</a>('3T').<a href="#SweepFrame-apply">apply</a>(custom_resampler)<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;17<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;26<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects,&nbsp;the&nbsp;keyword&nbsp;``on``&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;specify&nbsp;the<br>
-column&nbsp;instead&nbsp;of&nbsp;the&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=9*[range(4)],&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'])<br>
-&gt;&gt;&gt;&nbsp;df['time']&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-resample">resample</a>('3T',&nbsp;on='time').<a href="#SweepFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;c&nbsp;&nbsp;d<br>
-time<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-&nbsp;<br>
-For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;MultiIndex,&nbsp;the&nbsp;keyword&nbsp;``level``&nbsp;can&nbsp;be&nbsp;used&nbsp;to<br>
-specify&nbsp;on&nbsp;level&nbsp;the&nbsp;resampling&nbsp;needs&nbsp;to&nbsp;take&nbsp;place.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;time&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=5,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=10*[range(4)],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.MultiIndex.from_product([time,&nbsp;[1,&nbsp;2]])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;)<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#SweepFrame-resample">resample</a>('3T',&nbsp;level=0).<a href="#SweepFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;d<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;6&nbsp;&nbsp;12&nbsp;&nbsp;18<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;8&nbsp;&nbsp;12</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-sample"><strong>sample</strong></a>(self, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None)</dt><dd><tt>Returns&nbsp;a&nbsp;random&nbsp;sample&nbsp;of&nbsp;items&nbsp;from&nbsp;an&nbsp;axis&nbsp;of&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;items&nbsp;from&nbsp;axis&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`frac`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;=&nbsp;1&nbsp;if&nbsp;`frac`&nbsp;=&nbsp;None.<br>
-frac&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fraction&nbsp;of&nbsp;axis&nbsp;items&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`n`.<br>
-replace&nbsp;:&nbsp;boolean,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sample&nbsp;with&nbsp;or&nbsp;without&nbsp;replacement.&nbsp;Default&nbsp;=&nbsp;False.<br>
-weights&nbsp;:&nbsp;str&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;'None'&nbsp;results&nbsp;in&nbsp;equal&nbsp;probability&nbsp;weighting.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;will&nbsp;align&nbsp;with&nbsp;target&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;index.&nbsp;Index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;in&nbsp;weights&nbsp;not&nbsp;found&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;will&nbsp;be&nbsp;ignored&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;values&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;not&nbsp;in&nbsp;weights&nbsp;will&nbsp;be&nbsp;assigned<br>
-&nbsp;&nbsp;&nbsp;&nbsp;weights&nbsp;of&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;called&nbsp;on&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;will&nbsp;accept&nbsp;the&nbsp;name&nbsp;of&nbsp;a&nbsp;column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;axis&nbsp;=&nbsp;0.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Unless&nbsp;weights&nbsp;are&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;weights&nbsp;must&nbsp;be&nbsp;same&nbsp;length&nbsp;as&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;being&nbsp;sampled.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;weights&nbsp;do&nbsp;not&nbsp;sum&nbsp;to&nbsp;1,&nbsp;they&nbsp;will&nbsp;be&nbsp;normalized&nbsp;to&nbsp;sum&nbsp;to&nbsp;1.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;values&nbsp;in&nbsp;the&nbsp;weights&nbsp;column&nbsp;will&nbsp;be&nbsp;treated&nbsp;as&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;inf&nbsp;and&nbsp;-inf&nbsp;values&nbsp;not&nbsp;allowed.<br>
-random_state&nbsp;:&nbsp;int&nbsp;or&nbsp;numpy.random.RandomState,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Seed&nbsp;for&nbsp;the&nbsp;random&nbsp;number&nbsp;generator&nbsp;(if&nbsp;int),&nbsp;or&nbsp;numpy&nbsp;RandomState<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;sample.&nbsp;Accepts&nbsp;axis&nbsp;number&nbsp;or&nbsp;name.&nbsp;Default&nbsp;is&nbsp;stat&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;given&nbsp;data&nbsp;type&nbsp;(0&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;and&nbsp;DataFrames,&nbsp;1&nbsp;for&nbsp;Panels).<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-A&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;type&nbsp;as&nbsp;caller.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Generate&nbsp;an&nbsp;example&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;and&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(50))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepFrame-head">head</a>()<br>
-0&nbsp;&nbsp;&nbsp;-0.038497<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.820773<br>
-2&nbsp;&nbsp;&nbsp;-0.972766<br>
-3&nbsp;&nbsp;&nbsp;-1.598270<br>
-4&nbsp;&nbsp;&nbsp;-1.095526<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(50,&nbsp;4),&nbsp;columns=list('ABCD'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-head">head</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;0.016443&nbsp;-2.318952&nbsp;-0.566372&nbsp;-1.028078<br>
-1&nbsp;-1.051921&nbsp;&nbsp;0.438836&nbsp;&nbsp;0.658280&nbsp;-0.175797<br>
-2&nbsp;-1.243569&nbsp;-0.364626&nbsp;-0.215065&nbsp;&nbsp;0.057736<br>
-3&nbsp;&nbsp;1.768216&nbsp;&nbsp;0.404512&nbsp;-0.385604&nbsp;-1.457834<br>
-4&nbsp;&nbsp;1.072446&nbsp;-1.137172&nbsp;&nbsp;0.314194&nbsp;-0.046661<br>
-&nbsp;<br>
-Next&nbsp;extract&nbsp;a&nbsp;random&nbsp;sample&nbsp;from&nbsp;both&nbsp;of&nbsp;these&nbsp;objects...<br>
-&nbsp;<br>
-3&nbsp;random&nbsp;elements&nbsp;from&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepFrame-sample">sample</a>(n=3)<br>
-27&nbsp;&nbsp;&nbsp;-0.994689<br>
-55&nbsp;&nbsp;&nbsp;-1.049016<br>
-67&nbsp;&nbsp;&nbsp;-0.224565<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-And&nbsp;a&nbsp;random&nbsp;10%&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;with&nbsp;replacement:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-sample">sample</a>(frac=0.1,&nbsp;replace=True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-35&nbsp;&nbsp;1.981780&nbsp;&nbsp;0.142106&nbsp;&nbsp;1.817165&nbsp;-0.290805<br>
-49&nbsp;-1.336199&nbsp;-0.448634&nbsp;-0.789640&nbsp;&nbsp;0.217116<br>
-40&nbsp;&nbsp;0.823173&nbsp;-0.078816&nbsp;&nbsp;1.009536&nbsp;&nbsp;1.015108<br>
-15&nbsp;&nbsp;1.421154&nbsp;-0.055301&nbsp;-1.922594&nbsp;-0.019696<br>
-6&nbsp;&nbsp;-0.148339&nbsp;&nbsp;0.832938&nbsp;&nbsp;1.787600&nbsp;-1.383767</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-select"><strong>select</strong></a>(self, crit, axis=0)</dt><dd><tt>Return&nbsp;data&nbsp;corresponding&nbsp;to&nbsp;axis&nbsp;labels&nbsp;matching&nbsp;criteria<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-crit&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;called&nbsp;on&nbsp;each&nbsp;index&nbsp;(label).&nbsp;Should&nbsp;return&nbsp;True&nbsp;or&nbsp;False<br>
-axis&nbsp;:&nbsp;int<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-selection&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-set_axis"><strong>set_axis</strong></a>(self, axis, labels)</dt><dd><tt>public&nbsp;verson&nbsp;of&nbsp;axis&nbsp;assignment</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-slice_shift"><strong>slice_shift</strong></a>(self, periods=1, axis=0)</dt><dd><tt>Equivalent&nbsp;to&nbsp;`shift`&nbsp;without&nbsp;copying&nbsp;data.&nbsp;The&nbsp;shifted&nbsp;data&nbsp;will<br>
-not&nbsp;include&nbsp;the&nbsp;dropped&nbsp;periods&nbsp;and&nbsp;the&nbsp;shifted&nbsp;axis&nbsp;will&nbsp;be&nbsp;smaller<br>
-than&nbsp;the&nbsp;original.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-&nbsp;<br>
-Notes<br>
------<br>
-While&nbsp;the&nbsp;`slice_shift`&nbsp;is&nbsp;faster&nbsp;than&nbsp;`shift`,&nbsp;you&nbsp;may&nbsp;pay&nbsp;for&nbsp;it<br>
-later&nbsp;during&nbsp;alignment.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-squeeze"><strong>squeeze</strong></a>(self, axis=None)</dt><dd><tt>Squeeze&nbsp;length&nbsp;1&nbsp;dimensions.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;None,&nbsp;integer&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;squeeze&nbsp;if&nbsp;1-sized.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-scalar&nbsp;if&nbsp;1-sized,&nbsp;else&nbsp;original&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-swapaxes"><strong>swapaxes</strong></a>(self, axis1, axis2, copy=True)</dt><dd><tt>Interchange&nbsp;axes&nbsp;and&nbsp;swap&nbsp;values&nbsp;axes&nbsp;appropriately<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-tail"><strong>tail</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;last&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-take"><strong>take</strong></a>(self, indices, axis=0, convert=True, is_copy=True, **kwargs)</dt><dd><tt>Analogous&nbsp;to&nbsp;<a href="numpy.html#ndarray">ndarray</a>.take<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-indices&nbsp;:&nbsp;list&nbsp;/&nbsp;array&nbsp;of&nbsp;ints<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-convert&nbsp;:&nbsp;translate&nbsp;neg&nbsp;to&nbsp;pos&nbsp;indices&nbsp;(default)<br>
-is_copy&nbsp;:&nbsp;mark&nbsp;the&nbsp;returned&nbsp;frame&nbsp;as&nbsp;a&nbsp;copy<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-taken&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_clipboard"><strong>to_clipboard</strong></a>(self, excel=None, sep=None, **kwargs)</dt><dd><tt>Attempt&nbsp;to&nbsp;write&nbsp;text&nbsp;representation&nbsp;of&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;the&nbsp;system&nbsp;clipboard<br>
-This&nbsp;can&nbsp;be&nbsp;pasted&nbsp;into&nbsp;Excel,&nbsp;for&nbsp;example.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel&nbsp;:&nbsp;boolean,&nbsp;defaults&nbsp;to&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;use&nbsp;the&nbsp;provided&nbsp;separator,&nbsp;writing&nbsp;in&nbsp;a&nbsp;csv<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;format&nbsp;for&nbsp;allowing&nbsp;easy&nbsp;pasting&nbsp;into&nbsp;excel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;False,&nbsp;write&nbsp;a&nbsp;string&nbsp;representation&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;the&nbsp;clipboard<br>
-sep&nbsp;:&nbsp;optional,&nbsp;defaults&nbsp;to&nbsp;tab<br>
-other&nbsp;keywords&nbsp;are&nbsp;passed&nbsp;to&nbsp;to_csv<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Requirements&nbsp;for&nbsp;your&nbsp;platform<br>
-&nbsp;&nbsp;-&nbsp;Linux:&nbsp;xclip,&nbsp;or&nbsp;xsel&nbsp;(with&nbsp;gtk&nbsp;or&nbsp;PyQt4&nbsp;modules)<br>
-&nbsp;&nbsp;-&nbsp;Windows:&nbsp;none<br>
-&nbsp;&nbsp;-&nbsp;OS&nbsp;X:&nbsp;none</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_dense"><strong>to_dense</strong></a>(self)</dt><dd><tt>Return&nbsp;dense&nbsp;representation&nbsp;of&nbsp;NDFrame&nbsp;(as&nbsp;opposed&nbsp;to&nbsp;sparse)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_hdf"><strong>to_hdf</strong></a>(self, path_or_buf, key, **kwargs)</dt><dd><tt>Write&nbsp;the&nbsp;contained&nbsp;data&nbsp;to&nbsp;an&nbsp;HDF5&nbsp;file&nbsp;using&nbsp;HDFStore.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;(string)&nbsp;or&nbsp;HDFStore&nbsp;<a href="builtins.html#object">object</a><br>
-key&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;identifier&nbsp;for&nbsp;the&nbsp;group&nbsp;in&nbsp;the&nbsp;store<br>
-mode&nbsp;:&nbsp;optional,&nbsp;{'a',&nbsp;'w',&nbsp;'r+'},&nbsp;default&nbsp;'a'<br>
-&nbsp;<br>
-&nbsp;&nbsp;``'w'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write;&nbsp;a&nbsp;new&nbsp;file&nbsp;is&nbsp;created&nbsp;(an&nbsp;existing&nbsp;file&nbsp;with&nbsp;the&nbsp;same<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;name&nbsp;would&nbsp;be&nbsp;deleted).<br>
-&nbsp;&nbsp;``'a'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Append;&nbsp;an&nbsp;existing&nbsp;file&nbsp;is&nbsp;opened&nbsp;for&nbsp;reading&nbsp;and&nbsp;writing,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;if&nbsp;the&nbsp;file&nbsp;does&nbsp;not&nbsp;exist&nbsp;it&nbsp;is&nbsp;created.<br>
-&nbsp;&nbsp;``'r+'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;It&nbsp;is&nbsp;similar&nbsp;to&nbsp;``'a'``,&nbsp;but&nbsp;the&nbsp;file&nbsp;must&nbsp;already&nbsp;exist.<br>
-format&nbsp;:&nbsp;'fixed(f)|table(t)',&nbsp;default&nbsp;is&nbsp;'fixed'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fixed(f)&nbsp;:&nbsp;Fixed&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Fast&nbsp;writing/reading.&nbsp;Not-appendable,&nbsp;nor&nbsp;searchable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;table(t)&nbsp;:&nbsp;Table&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;as&nbsp;a&nbsp;PyTables&nbsp;Table&nbsp;structure&nbsp;which&nbsp;may&nbsp;perform<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;worse&nbsp;but&nbsp;allow&nbsp;more&nbsp;flexible&nbsp;operations&nbsp;like&nbsp;searching<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/&nbsp;selecting&nbsp;subsets&nbsp;of&nbsp;the&nbsp;data<br>
-append&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;Table&nbsp;formats,&nbsp;append&nbsp;the&nbsp;input&nbsp;data&nbsp;to&nbsp;the&nbsp;existing<br>
-data_columns&nbsp;:&nbsp;&nbsp;list&nbsp;of&nbsp;columns,&nbsp;or&nbsp;True,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;columns&nbsp;to&nbsp;create&nbsp;as&nbsp;indexed&nbsp;data&nbsp;columns&nbsp;for&nbsp;on-disk<br>
-&nbsp;&nbsp;&nbsp;&nbsp;queries,&nbsp;or&nbsp;True&nbsp;to&nbsp;use&nbsp;all&nbsp;columns.&nbsp;By&nbsp;default&nbsp;only&nbsp;the&nbsp;axes<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;are&nbsp;indexed.&nbsp;See&nbsp;`here<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__">http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__</a>.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Applicable&nbsp;only&nbsp;to&nbsp;format='table'.<br>
-complevel&nbsp;:&nbsp;int,&nbsp;1-9,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;a&nbsp;complib&nbsp;is&nbsp;specified&nbsp;compression&nbsp;will&nbsp;be&nbsp;applied<br>
-&nbsp;&nbsp;&nbsp;&nbsp;where&nbsp;possible<br>
-complib&nbsp;:&nbsp;{'zlib',&nbsp;'bzip2',&nbsp;'lzo',&nbsp;'blosc',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;complevel&nbsp;is&nbsp;&gt;&nbsp;0&nbsp;apply&nbsp;compression&nbsp;to&nbsp;objects&nbsp;written<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;store&nbsp;wherever&nbsp;possible<br>
-fletcher32&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;applying&nbsp;compression&nbsp;use&nbsp;the&nbsp;fletcher32&nbsp;checksum<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;true,&nbsp;ALL&nbsp;nan&nbsp;rows&nbsp;will&nbsp;not&nbsp;be&nbsp;written&nbsp;to&nbsp;store.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_json"><strong>to_json</strong></a>(self, path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False)</dt><dd><tt>Convert&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;a&nbsp;JSON&nbsp;string.<br>
-&nbsp;<br>
-Note&nbsp;NaN's&nbsp;and&nbsp;None&nbsp;will&nbsp;be&nbsp;converted&nbsp;to&nbsp;null&nbsp;and&nbsp;datetime&nbsp;objects<br>
-will&nbsp;be&nbsp;converted&nbsp;to&nbsp;UNIX&nbsp;timestamps.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;or&nbsp;buffer&nbsp;to&nbsp;write&nbsp;the&nbsp;result&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;this&nbsp;is&nbsp;None,&nbsp;return&nbsp;a&nbsp;StringIO&nbsp;of&nbsp;the&nbsp;converted&nbsp;string<br>
-orient&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'index'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:&nbsp;{'split','records','index'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{'split','records','index','columns','values'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;The&nbsp;format&nbsp;of&nbsp;the&nbsp;JSON&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;split&nbsp;:&nbsp;dict&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{index&nbsp;-&gt;&nbsp;[index],&nbsp;columns&nbsp;-&gt;&nbsp;[columns],&nbsp;data&nbsp;-&gt;&nbsp;[values]}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;records&nbsp;:&nbsp;list&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[{column&nbsp;-&gt;&nbsp;value},&nbsp;...&nbsp;,&nbsp;{column&nbsp;-&gt;&nbsp;value}]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;index&nbsp;:&nbsp;dict&nbsp;like&nbsp;{index&nbsp;-&gt;&nbsp;{column&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;columns&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;{index&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;values&nbsp;:&nbsp;just&nbsp;the&nbsp;values&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;table&nbsp;:&nbsp;dict&nbsp;like&nbsp;{'schema':&nbsp;{schema},&nbsp;'data':&nbsp;{data}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;describing&nbsp;the&nbsp;data,&nbsp;and&nbsp;the&nbsp;data&nbsp;component&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;like&nbsp;``orient='records'``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionchanged::&nbsp;0.20.0<br>
-&nbsp;<br>
-date_format&nbsp;:&nbsp;{None,&nbsp;'epoch',&nbsp;'iso'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Type&nbsp;of&nbsp;date&nbsp;conversion.&nbsp;`epoch`&nbsp;=&nbsp;epoch&nbsp;milliseconds,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`iso`&nbsp;=&nbsp;ISO8601.&nbsp;The&nbsp;default&nbsp;depends&nbsp;on&nbsp;the&nbsp;`orient`.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`orient='table'`,&nbsp;the&nbsp;default&nbsp;is&nbsp;`'iso'`.&nbsp;For&nbsp;all&nbsp;other&nbsp;orients,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;default&nbsp;is&nbsp;`'epoch'`.<br>
-double_precision&nbsp;:&nbsp;The&nbsp;number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;use&nbsp;when&nbsp;encoding<br>
-&nbsp;&nbsp;&nbsp;&nbsp;floating&nbsp;point&nbsp;values,&nbsp;default&nbsp;10.<br>
-force_ascii&nbsp;:&nbsp;force&nbsp;encoded&nbsp;string&nbsp;to&nbsp;be&nbsp;ASCII,&nbsp;default&nbsp;True.<br>
-date_unit&nbsp;:&nbsp;string,&nbsp;default&nbsp;'ms'&nbsp;(milliseconds)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;time&nbsp;unit&nbsp;to&nbsp;encode&nbsp;to,&nbsp;governs&nbsp;timestamp&nbsp;and&nbsp;ISO8601<br>
-&nbsp;&nbsp;&nbsp;&nbsp;precision.&nbsp;&nbsp;One&nbsp;of&nbsp;'s',&nbsp;'ms',&nbsp;'us',&nbsp;'ns'&nbsp;for&nbsp;second,&nbsp;millisecond,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;microsecond,&nbsp;and&nbsp;nanosecond&nbsp;respectively.<br>
-default_handler&nbsp;:&nbsp;callable,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Handler&nbsp;to&nbsp;call&nbsp;if&nbsp;<a href="builtins.html#object">object</a>&nbsp;cannot&nbsp;otherwise&nbsp;be&nbsp;converted&nbsp;to&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;suitable&nbsp;format&nbsp;for&nbsp;JSON.&nbsp;Should&nbsp;receive&nbsp;a&nbsp;single&nbsp;argument&nbsp;which&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;convert&nbsp;and&nbsp;return&nbsp;a&nbsp;serialisable&nbsp;<a href="builtins.html#object">object</a>.<br>
-lines&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'orient'&nbsp;is&nbsp;'records'&nbsp;write&nbsp;out&nbsp;line&nbsp;delimited&nbsp;json&nbsp;format.&nbsp;Will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;throw&nbsp;ValueError&nbsp;if&nbsp;incorrect&nbsp;'orient'&nbsp;since&nbsp;others&nbsp;are&nbsp;not&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;filtered&nbsp;info&nbsp;axis<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pd.read_json<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([['a',&nbsp;'b'],&nbsp;['c',&nbsp;'d']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=['row&nbsp;1',&nbsp;'row&nbsp;2'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['col&nbsp;1',&nbsp;'col&nbsp;2'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-to_json">to_json</a>(orient='split')<br>
-'{"columns":["col&nbsp;1","col&nbsp;2"],<br>
-&nbsp;&nbsp;"index":["row&nbsp;1","row&nbsp;2"],<br>
-&nbsp;&nbsp;"data":[["a","b"],["c","d"]]}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'index'``&nbsp;formatted&nbsp;JSON:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-to_json">to_json</a>(orient='index')<br>
-'{"row&nbsp;1":{"col&nbsp;1":"a","col&nbsp;2":"b"},"row&nbsp;2":{"col&nbsp;1":"c","col&nbsp;2":"d"}}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'records'``&nbsp;formatted&nbsp;JSON.<br>
-Note&nbsp;that&nbsp;index&nbsp;labels&nbsp;are&nbsp;not&nbsp;preserved&nbsp;with&nbsp;this&nbsp;encoding.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-to_json">to_json</a>(orient='records')<br>
-'[{"col&nbsp;1":"a","col&nbsp;2":"b"},{"col&nbsp;1":"c","col&nbsp;2":"d"}]'<br>
-&nbsp;<br>
-Encoding&nbsp;with&nbsp;Table&nbsp;Schema<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-to_json">to_json</a>(orient='table')<br>
-'{"schema":&nbsp;{"fields":&nbsp;[{"name":&nbsp;"index",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;1",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;2",&nbsp;"type":&nbsp;"string"}],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"primaryKey":&nbsp;"index",<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"pandas_version":&nbsp;"0.20.0"},<br>
-&nbsp;&nbsp;"data":&nbsp;[{"index":&nbsp;"row&nbsp;1",&nbsp;"col&nbsp;1":&nbsp;"a",&nbsp;"col&nbsp;2":&nbsp;"b"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"index":&nbsp;"row&nbsp;2",&nbsp;"col&nbsp;1":&nbsp;"c",&nbsp;"col&nbsp;2":&nbsp;"d"}]}'</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_msgpack"><strong>to_msgpack</strong></a>(self, path_or_buf=None, encoding='utf-8', **kwargs)</dt><dd><tt>msgpack&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path<br>
-&nbsp;<br>
-THIS&nbsp;IS&nbsp;AN&nbsp;EXPERIMENTAL&nbsp;LIBRARY&nbsp;and&nbsp;the&nbsp;storage&nbsp;format<br>
-may&nbsp;not&nbsp;be&nbsp;stable&nbsp;until&nbsp;a&nbsp;future&nbsp;release.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;File&nbsp;path,&nbsp;buffer-like,&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;None,&nbsp;return&nbsp;generated&nbsp;string<br>
-append&nbsp;:&nbsp;boolean&nbsp;whether&nbsp;to&nbsp;append&nbsp;to&nbsp;an&nbsp;existing&nbsp;msgpack<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(default&nbsp;is&nbsp;False)<br>
-compress&nbsp;:&nbsp;type&nbsp;of&nbsp;compressor&nbsp;(zlib&nbsp;or&nbsp;blosc),&nbsp;default&nbsp;to&nbsp;None&nbsp;(no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;compression)</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_pickle"><strong>to_pickle</strong></a>(self, path, compression='infer')</dt><dd><tt>Pickle&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path<br>
-compression&nbsp;:&nbsp;{'infer',&nbsp;'gzip',&nbsp;'bz2',&nbsp;'xz',&nbsp;None},&nbsp;default&nbsp;'infer'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;compression&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_sql"><strong>to_sql</strong></a>(self, name, con, flavor=None, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None)</dt><dd><tt>Write&nbsp;records&nbsp;stored&nbsp;in&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;SQL&nbsp;database.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-name&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;SQL&nbsp;table<br>
-con&nbsp;:&nbsp;SQLAlchemy&nbsp;engine&nbsp;or&nbsp;DBAPI2&nbsp;connection&nbsp;(legacy&nbsp;mode)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Using&nbsp;SQLAlchemy&nbsp;makes&nbsp;it&nbsp;possible&nbsp;to&nbsp;use&nbsp;any&nbsp;DB&nbsp;supported&nbsp;by&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;library.&nbsp;If&nbsp;a&nbsp;DBAPI2&nbsp;<a href="builtins.html#object">object</a>,&nbsp;only&nbsp;sqlite3&nbsp;is&nbsp;supported.<br>
-flavor&nbsp;:&nbsp;'sqlite',&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;DEPRECATED:&nbsp;this&nbsp;parameter&nbsp;will&nbsp;be&nbsp;removed&nbsp;in&nbsp;a&nbsp;future&nbsp;version,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;as&nbsp;'sqlite'&nbsp;is&nbsp;the&nbsp;only&nbsp;supported&nbsp;option&nbsp;if&nbsp;SQLAlchemy&nbsp;is&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;installed.<br>
-schema&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;the&nbsp;schema&nbsp;(if&nbsp;database&nbsp;flavor&nbsp;supports&nbsp;this).&nbsp;If&nbsp;None,&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;schema.<br>
-if_exists&nbsp;:&nbsp;{'fail',&nbsp;'replace',&nbsp;'append'},&nbsp;default&nbsp;'fail'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;fail:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;do&nbsp;nothing.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;replace:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;drop&nbsp;it,&nbsp;recreate&nbsp;it,&nbsp;and&nbsp;insert&nbsp;data.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;append:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;insert&nbsp;data.&nbsp;Create&nbsp;if&nbsp;does&nbsp;not&nbsp;exist.<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;index&nbsp;as&nbsp;a&nbsp;column.<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s).&nbsp;If&nbsp;None&nbsp;is&nbsp;given&nbsp;(default)&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`index`&nbsp;is&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-chunksize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;not&nbsp;None,&nbsp;then&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;in&nbsp;batches&nbsp;of&nbsp;this&nbsp;size&nbsp;at&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;time.&nbsp;&nbsp;If&nbsp;None,&nbsp;all&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;at&nbsp;once.<br>
-dtype&nbsp;:&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;to&nbsp;SQL&nbsp;type,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Optional&nbsp;specifying&nbsp;the&nbsp;datatype&nbsp;for&nbsp;columns.&nbsp;The&nbsp;SQL&nbsp;type&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;SQLAlchemy&nbsp;type,&nbsp;or&nbsp;a&nbsp;string&nbsp;for&nbsp;sqlite3&nbsp;fallback&nbsp;connection.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-to_xarray"><strong>to_xarray</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;xarray&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;the&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;DataArray&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;Dataset&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-a&nbsp;DataArray&nbsp;for&nbsp;higher&nbsp;dims<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;foo&nbsp;&nbsp;4.0<br>
-1&nbsp;&nbsp;1&nbsp;&nbsp;bar&nbsp;&nbsp;5.0<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;foo&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(index:&nbsp;3)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;index&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;0&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;1&nbsp;1&nbsp;2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'foo'&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;float64&nbsp;4.0&nbsp;5.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;).<a href="#SweepFrame-set_index">set_index</a>(['B','A'])<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-B&nbsp;&nbsp;&nbsp;A<br>
-foo&nbsp;1&nbsp;&nbsp;4.0<br>
-bar&nbsp;1&nbsp;&nbsp;5.0<br>
-foo&nbsp;2&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(A:&nbsp;2,&nbsp;B:&nbsp;2)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;*&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(A)&nbsp;int64&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B,&nbsp;A)&nbsp;float64&nbsp;5.0&nbsp;nan&nbsp;4.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p&nbsp;=&nbsp;pd.Panel(np.arange(24).reshape(4,3,2),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;items=list('ABCD'),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;major_axis=pd.date_range('20130101',&nbsp;periods=3),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;minor_axis=['first',&nbsp;'second'])<br>
-&gt;&gt;&gt;&nbsp;p<br>
-&lt;class&nbsp;'pandas.core.panel.Panel'&gt;<br>
-Dimensions:&nbsp;4&nbsp;(items)&nbsp;x&nbsp;3&nbsp;(major_axis)&nbsp;x&nbsp;2&nbsp;(minor_axis)<br>
-Items&nbsp;axis:&nbsp;A&nbsp;to&nbsp;D<br>
-Major_axis&nbsp;axis:&nbsp;2013-01-01&nbsp;00:00:00&nbsp;to&nbsp;2013-01-03&nbsp;00:00:00<br>
-Minor_axis&nbsp;axis:&nbsp;first&nbsp;to&nbsp;second<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p.<a href="#SweepFrame-to_xarray">to_xarray</a>()<br>
-&lt;xarray.DataArray&nbsp;(items:&nbsp;4,&nbsp;major_axis:&nbsp;3,&nbsp;minor_axis:&nbsp;2)&gt;<br>
-array([[[&nbsp;0,&nbsp;&nbsp;1],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;2,&nbsp;&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;4,&nbsp;&nbsp;5]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[&nbsp;6,&nbsp;&nbsp;7],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;8,&nbsp;&nbsp;9],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[10,&nbsp;11]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[12,&nbsp;13],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[14,&nbsp;15],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[16,&nbsp;17]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[18,&nbsp;19],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[20,&nbsp;21],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[22,&nbsp;23]]])<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;items&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(items)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'A'&nbsp;'B'&nbsp;'C'&nbsp;'D'<br>
-&nbsp;&nbsp;*&nbsp;major_axis&nbsp;&nbsp;(major_axis)&nbsp;datetime64[ns]&nbsp;2013-01-01&nbsp;2013-01-02&nbsp;2013-01-03&nbsp;&nbsp;#&nbsp;noqa<br>
-&nbsp;&nbsp;*&nbsp;minor_axis&nbsp;&nbsp;(minor_axis)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'first'&nbsp;'second'<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;the&nbsp;`xarray&nbsp;docs&nbsp;&lt;<a href="http://xarray.pydata.org/en/stable/&gt;`__">http://xarray.pydata.org/en/stable/&gt;`__</a></tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-truncate"><strong>truncate</strong></a>(self, before=None, after=None, axis=None, copy=True)</dt><dd><tt>Truncates&nbsp;a&nbsp;sorted&nbsp;NDFrame&nbsp;before&nbsp;and/or&nbsp;after&nbsp;some&nbsp;particular<br>
-index&nbsp;value.&nbsp;If&nbsp;the&nbsp;axis&nbsp;contains&nbsp;only&nbsp;datetime&nbsp;values,&nbsp;before/after<br>
-parameters&nbsp;are&nbsp;converted&nbsp;to&nbsp;datetime&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-before&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;before&nbsp;index&nbsp;value<br>
-after&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;after&nbsp;index&nbsp;value<br>
-axis&nbsp;:&nbsp;the&nbsp;truncation&nbsp;axis,&nbsp;defaults&nbsp;to&nbsp;the&nbsp;stat&nbsp;axis<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;is&nbsp;True,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;truncated&nbsp;section<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-truncated&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-tshift"><strong>tshift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;the&nbsp;time&nbsp;index,&nbsp;using&nbsp;the&nbsp;index's&nbsp;frequency&nbsp;if&nbsp;available.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM')<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;basestring<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Corresponds&nbsp;to&nbsp;the&nbsp;axis&nbsp;that&nbsp;contains&nbsp;the&nbsp;Index<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;not&nbsp;specified&nbsp;then&nbsp;tries&nbsp;to&nbsp;use&nbsp;the&nbsp;freq&nbsp;or&nbsp;inferred_freq<br>
-attributes&nbsp;of&nbsp;the&nbsp;index.&nbsp;If&nbsp;neither&nbsp;of&nbsp;those&nbsp;attributes&nbsp;exist,&nbsp;a<br>
-ValueError&nbsp;is&nbsp;thrown<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;NDFrame</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-tz_convert"><strong>tz_convert</strong></a>(self, tz, axis=0, level=None, copy=True)</dt><dd><tt>Convert&nbsp;tz-aware&nbsp;axis&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;convert<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;convert&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;tz-naive.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-tz_localize"><strong>tz_localize</strong></a>(self, tz, axis=0, level=None, copy=True, ambiguous='raise')</dt><dd><tt>Localize&nbsp;tz-naive&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;localize<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;localize&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-ambiguous&nbsp;:&nbsp;'infer',&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>,&nbsp;'NaT',&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'infer'&nbsp;will&nbsp;attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>&nbsp;where&nbsp;True&nbsp;signifies&nbsp;a&nbsp;DST&nbsp;time,&nbsp;False&nbsp;designates<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;non-DST&nbsp;time&nbsp;(note&nbsp;that&nbsp;this&nbsp;flag&nbsp;is&nbsp;only&nbsp;applicable&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ambiguous&nbsp;times)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'NaT'&nbsp;will&nbsp;return&nbsp;NaT&nbsp;where&nbsp;there&nbsp;are&nbsp;ambiguous&nbsp;times<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'raise'&nbsp;will&nbsp;raise&nbsp;an&nbsp;AmbiguousTimeError&nbsp;if&nbsp;there&nbsp;are&nbsp;ambiguous<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;times<br>
-infer_dst&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on&nbsp;order<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;is&nbsp;tz-aware&nbsp;and&nbsp;tz&nbsp;is&nbsp;not&nbsp;None.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-where"><strong>where</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;True&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;where&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``True``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#SweepFrame-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#SweepFrame-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``where``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepFrame-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).reshape(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#SweepFrame-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#SweepFrame-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mask`</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-xs"><strong>xs</strong></a>(self, key, axis=0, level=None, drop_level=True)</dt><dd><tt>Returns&nbsp;a&nbsp;cross-section&nbsp;(row(s)&nbsp;or&nbsp;column(s))&nbsp;from&nbsp;the<br>
-<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Defaults&nbsp;to&nbsp;cross-section&nbsp;on&nbsp;the&nbsp;rows&nbsp;(axis=0).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Some&nbsp;label&nbsp;contained&nbsp;in&nbsp;the&nbsp;index,&nbsp;or&nbsp;partially&nbsp;in&nbsp;a&nbsp;MultiIndex<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;retrieve&nbsp;cross-section&nbsp;on<br>
-level&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;defaults&nbsp;to&nbsp;first&nbsp;n&nbsp;levels&nbsp;(n=1&nbsp;or&nbsp;len(key))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;key&nbsp;partially&nbsp;contained&nbsp;in&nbsp;a&nbsp;MultiIndex,&nbsp;indicate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;levels&nbsp;are&nbsp;used.&nbsp;Levels&nbsp;can&nbsp;be&nbsp;referred&nbsp;by&nbsp;label&nbsp;or&nbsp;position.<br>
-drop_level&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;returns&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;same&nbsp;levels&nbsp;as&nbsp;self.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C<br>
-a&nbsp;&nbsp;4&nbsp;&nbsp;5&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;4&nbsp;&nbsp;0&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;9&nbsp;&nbsp;7&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-xs">xs</a>('a')<br>
-A&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-B&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-C&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Name:&nbsp;a<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-xs">xs</a>('C',&nbsp;axis=1)<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;C<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;second&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7&nbsp;&nbsp;5&nbsp;&nbsp;5&nbsp;&nbsp;0<br>
-baz&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;three&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-xs">xs</a>(('baz',&nbsp;'three'))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-third<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-xs">xs</a>('one',&nbsp;level=1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-baz&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepFrame-xs">xs</a>(('baz',&nbsp;2),&nbsp;level=[0,&nbsp;'third'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-second<br>
-three&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-xs&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-xs&nbsp;is&nbsp;only&nbsp;for&nbsp;getting,&nbsp;not&nbsp;setting&nbsp;values.<br>
-&nbsp;<br>
-MultiIndex&nbsp;Slicers&nbsp;is&nbsp;a&nbsp;generic&nbsp;way&nbsp;to&nbsp;get/set&nbsp;values&nbsp;on&nbsp;any&nbsp;level&nbsp;or<br>
-levels.&nbsp;&nbsp;It&nbsp;is&nbsp;a&nbsp;superset&nbsp;of&nbsp;xs&nbsp;functionality,&nbsp;see<br>
-:ref:`MultiIndex&nbsp;Slicers&nbsp;&lt;advanced.mi_slicers&gt;`</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>at</strong></dt>
-<dd><tt>Fast&nbsp;label-based&nbsp;scalar&nbsp;accessor<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``loc``,&nbsp;``at``&nbsp;provides&nbsp;**label**&nbsp;based&nbsp;scalar&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>blocks</strong></dt>
-<dd><tt>Internal&nbsp;property,&nbsp;property&nbsp;synonym&nbsp;for&nbsp;as_blocks()</tt></dd>
-</dl>
-<dl><dt><strong>dtypes</strong></dt>
-<dd><tt>Return&nbsp;the&nbsp;dtypes&nbsp;in&nbsp;this&nbsp;object.</tt></dd>
-</dl>
-<dl><dt><strong>empty</strong></dt>
-<dd><tt>True&nbsp;if&nbsp;NDFrame&nbsp;is&nbsp;entirely&nbsp;empty&nbsp;[no&nbsp;items],&nbsp;meaning&nbsp;any&nbsp;of&nbsp;the<br>
-axes&nbsp;are&nbsp;of&nbsp;length&nbsp;0.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;NDFrame&nbsp;contains&nbsp;only&nbsp;NaNs,&nbsp;it&nbsp;is&nbsp;still&nbsp;not&nbsp;considered&nbsp;empty.&nbsp;See<br>
-the&nbsp;example&nbsp;below.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-An&nbsp;example&nbsp;of&nbsp;an&nbsp;actual&nbsp;empty&nbsp;DataFrame.&nbsp;Notice&nbsp;the&nbsp;index&nbsp;is&nbsp;empty:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df_empty&nbsp;=&nbsp;pd.DataFrame({'A'&nbsp;:&nbsp;[]})<br>
-&gt;&gt;&gt;&nbsp;df_empty<br>
-Empty&nbsp;DataFrame<br>
-Columns:&nbsp;[A]<br>
-Index:&nbsp;[]<br>
-&gt;&gt;&gt;&nbsp;df_empty.empty<br>
-True<br>
-&nbsp;<br>
-If&nbsp;we&nbsp;only&nbsp;have&nbsp;NaNs&nbsp;in&nbsp;our&nbsp;DataFrame,&nbsp;it&nbsp;is&nbsp;not&nbsp;considered&nbsp;empty!&nbsp;We<br>
-will&nbsp;need&nbsp;to&nbsp;drop&nbsp;the&nbsp;NaNs&nbsp;to&nbsp;make&nbsp;the&nbsp;DataFrame&nbsp;empty:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.DataFrame({'A'&nbsp;:&nbsp;[np.nan]})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A<br>
-0&nbsp;NaN<br>
-&gt;&gt;&gt;&nbsp;df.empty<br>
-False<br>
-&gt;&gt;&gt;&nbsp;df.dropna().empty<br>
-True<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.Series.dropna<br>
-pandas.DataFrame.dropna</tt></dd>
-</dl>
-<dl><dt><strong>ftypes</strong></dt>
-<dd><tt>Return&nbsp;the&nbsp;ftypes&nbsp;(indication&nbsp;of&nbsp;sparse/dense&nbsp;and&nbsp;dtype)<br>
-in&nbsp;this&nbsp;object.</tt></dd>
-</dl>
-<dl><dt><strong>iat</strong></dt>
-<dd><tt>Fast&nbsp;integer&nbsp;location&nbsp;scalar&nbsp;accessor.<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``iloc``,&nbsp;``iat``&nbsp;provides&nbsp;**integer**&nbsp;based&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>iloc</strong></dt>
-<dd><tt>Purely&nbsp;integer-location&nbsp;based&nbsp;indexing&nbsp;for&nbsp;selection&nbsp;by&nbsp;position.<br>
-&nbsp;<br>
-``.iloc[]``&nbsp;is&nbsp;primarily&nbsp;integer&nbsp;position&nbsp;based&nbsp;(from&nbsp;``0``&nbsp;to<br>
-``length-1``&nbsp;of&nbsp;the&nbsp;axis),&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a&nbsp;boolean<br>
-array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;An&nbsp;integer,&nbsp;e.g.&nbsp;``5``.<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;integers,&nbsp;e.g.&nbsp;``[4,&nbsp;3,&nbsp;0]``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;ints,&nbsp;e.g.&nbsp;``1:7``.<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.iloc``&nbsp;will&nbsp;raise&nbsp;``IndexError``&nbsp;if&nbsp;a&nbsp;requested&nbsp;indexer&nbsp;is<br>
-out-of-bounds,&nbsp;except&nbsp;*slice*&nbsp;indexers&nbsp;which&nbsp;allow&nbsp;out-of-bounds<br>
-indexing&nbsp;(this&nbsp;conforms&nbsp;with&nbsp;python/numpy&nbsp;*slice*&nbsp;semantics).<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Position&nbsp;&lt;indexing.integer&gt;`</tt></dd>
-</dl>
-<dl><dt><strong>ix</strong></dt>
-<dd><tt>A&nbsp;primarily&nbsp;label-location&nbsp;based&nbsp;indexer,&nbsp;with&nbsp;integer&nbsp;position<br>
-fallback.<br>
-&nbsp;<br>
-``.ix[]``&nbsp;supports&nbsp;mixed&nbsp;integer&nbsp;and&nbsp;label&nbsp;based&nbsp;access.&nbsp;It&nbsp;is<br>
-primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;will&nbsp;fall&nbsp;back&nbsp;to&nbsp;integer&nbsp;positional<br>
-access&nbsp;unless&nbsp;the&nbsp;corresponding&nbsp;axis&nbsp;is&nbsp;of&nbsp;integer&nbsp;type.<br>
-&nbsp;<br>
-``.ix``&nbsp;is&nbsp;the&nbsp;most&nbsp;general&nbsp;indexer&nbsp;and&nbsp;will&nbsp;support&nbsp;any&nbsp;of&nbsp;the<br>
-inputs&nbsp;in&nbsp;``.loc``&nbsp;and&nbsp;``.iloc``.&nbsp;``.ix``&nbsp;also&nbsp;supports&nbsp;floating<br>
-point&nbsp;label&nbsp;schemes.&nbsp;``.ix``&nbsp;is&nbsp;exceptionally&nbsp;useful&nbsp;when&nbsp;dealing<br>
-with&nbsp;mixed&nbsp;positional&nbsp;and&nbsp;label&nbsp;based&nbsp;hierachical&nbsp;indexes.<br>
-&nbsp;<br>
-However,&nbsp;when&nbsp;an&nbsp;axis&nbsp;is&nbsp;integer&nbsp;based,&nbsp;ONLY&nbsp;label&nbsp;based&nbsp;access<br>
-and&nbsp;not&nbsp;positional&nbsp;access&nbsp;is&nbsp;supported.&nbsp;Thus,&nbsp;in&nbsp;such&nbsp;cases,&nbsp;it's<br>
-usually&nbsp;better&nbsp;to&nbsp;be&nbsp;explicit&nbsp;and&nbsp;use&nbsp;``.iloc``&nbsp;or&nbsp;``.loc``.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Advanced&nbsp;Indexing&nbsp;&lt;advanced&gt;`.</tt></dd>
-</dl>
-<dl><dt><strong>loc</strong></dt>
-<dd><tt>Purely&nbsp;label-location&nbsp;based&nbsp;indexer&nbsp;for&nbsp;selection&nbsp;by&nbsp;label.<br>
-&nbsp;<br>
-``.loc[]``&nbsp;is&nbsp;primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a<br>
-boolean&nbsp;array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;A&nbsp;single&nbsp;label,&nbsp;e.g.&nbsp;``5``&nbsp;or&nbsp;``'a'``,&nbsp;(note&nbsp;that&nbsp;``5``&nbsp;is<br>
-&nbsp;&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;*label*&nbsp;of&nbsp;the&nbsp;index,&nbsp;and&nbsp;**never**&nbsp;as&nbsp;an<br>
-&nbsp;&nbsp;integer&nbsp;position&nbsp;along&nbsp;the&nbsp;index).<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;labels,&nbsp;e.g.&nbsp;``['a',&nbsp;'b',&nbsp;'c']``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;labels,&nbsp;e.g.&nbsp;``'a':'f'``&nbsp;(note&nbsp;that&nbsp;contrary<br>
-&nbsp;&nbsp;to&nbsp;usual&nbsp;python&nbsp;slices,&nbsp;**both**&nbsp;the&nbsp;start&nbsp;and&nbsp;the&nbsp;stop&nbsp;are&nbsp;included!).<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.loc``&nbsp;will&nbsp;raise&nbsp;a&nbsp;``KeyError``&nbsp;when&nbsp;the&nbsp;items&nbsp;are&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Label&nbsp;&lt;indexing.label&gt;`</tt></dd>
-</dl>
-<dl><dt><strong>ndim</strong></dt>
-<dd><tt>Number&nbsp;of&nbsp;axes&nbsp;/&nbsp;array&nbsp;dimensions</tt></dd>
-</dl>
-<dl><dt><strong>size</strong></dt>
-<dd><tt>number&nbsp;of&nbsp;elements&nbsp;in&nbsp;the&nbsp;NDFrame</tt></dd>
-</dl>
-<dl><dt><strong>values</strong></dt>
-<dd><tt>Numpy&nbsp;representation&nbsp;of&nbsp;NDFrame<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;dtype&nbsp;will&nbsp;be&nbsp;a&nbsp;lower-common-denominator&nbsp;dtype&nbsp;(implicit<br>
-upcasting);&nbsp;that&nbsp;is&nbsp;to&nbsp;say&nbsp;if&nbsp;the&nbsp;dtypes&nbsp;(even&nbsp;of&nbsp;numeric&nbsp;types)<br>
-are&nbsp;mixed,&nbsp;the&nbsp;one&nbsp;that&nbsp;accommodates&nbsp;all&nbsp;will&nbsp;be&nbsp;chosen.&nbsp;Use&nbsp;this<br>
-with&nbsp;care&nbsp;if&nbsp;you&nbsp;are&nbsp;not&nbsp;dealing&nbsp;with&nbsp;the&nbsp;blocks.<br>
-&nbsp;<br>
-e.g.&nbsp;If&nbsp;the&nbsp;dtypes&nbsp;are&nbsp;float16&nbsp;and&nbsp;float32,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcast&nbsp;to<br>
-float32.&nbsp;&nbsp;If&nbsp;dtypes&nbsp;are&nbsp;int32&nbsp;and&nbsp;uint8,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcast&nbsp;to<br>
-int32.&nbsp;By&nbsp;numpy.find_common_type&nbsp;convention,&nbsp;mixing&nbsp;int64&nbsp;and&nbsp;uint64<br>
-will&nbsp;result&nbsp;in&nbsp;a&nbsp;flot64&nbsp;dtype.</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>is_copy</strong> = None</dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a>:<br>
-<dl><dt><a name="SweepFrame-__dir__"><strong>__dir__</strong></a>(self)</dt><dd><tt>Provide&nbsp;method&nbsp;name&nbsp;lookup&nbsp;and&nbsp;completion<br>
-Only&nbsp;provide&nbsp;'public'&nbsp;methods</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__sizeof__"><strong>__sizeof__</strong></a>(self)</dt><dd><tt>Generates&nbsp;the&nbsp;total&nbsp;memory&nbsp;usage&nbsp;for&nbsp;a&nbsp;<a href="builtins.html#object">object</a>&nbsp;that&nbsp;returns<br>
-either&nbsp;a&nbsp;value&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;values</tt></dd></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a>:<br>
-<dl><dt><a name="SweepFrame-__bytes__"><strong>__bytes__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;bytes(obj)&nbsp;in&nbsp;py3&nbsp;only.<br>
-Yields&nbsp;a&nbsp;bytestring&nbsp;in&nbsp;both&nbsp;py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__repr__"><strong>__repr__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-<dl><dt><a name="SweepFrame-__str__"><strong>__str__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;Object<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;str(df)&nbsp;in&nbsp;both&nbsp;py2/py3.<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a>:<br>
-<dl><dt><strong>__dict__</strong></dt>
-<dd><tt>dictionary&nbsp;for&nbsp;instance&nbsp;variables&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>__weakref__</strong></dt>
-<dd><tt>list&nbsp;of&nbsp;weak&nbsp;references&nbsp;to&nbsp;the&nbsp;object&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-</td></tr></table> <p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#ffc8d8">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#000000" face="helvetica, arial"><a name="SweepSeries">class <strong>SweepSeries</strong></a>(<a href="modsim.html#MySeries">MySeries</a>)</font></td></tr>
-    
-<tr bgcolor="#ffc8d8"><td rowspan=2><tt>&nbsp;&nbsp;&nbsp;</tt></td>
-<td colspan=2><tt>Represents&nbsp;a&nbsp;mapping&nbsp;from&nbsp;parameter&nbsp;values&nbsp;to&nbsp;metrics.<br>&nbsp;</tt></td></tr>
-<tr><td>&nbsp;</td>
-<td width="100%"><dl><dt>Method resolution order:</dt>
-<dd><a href="modsim.html#SweepSeries">SweepSeries</a></dd>
-<dd><a href="modsim.html#MySeries">MySeries</a></dd>
-<dd><a href="pandas.core.series.html#Series">pandas.core.series.Series</a></dd>
-<dd><a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a></dd>
-<dd><a href="pandas.core.strings.html#StringAccessorMixin">pandas.core.strings.StringAccessorMixin</a></dd>
-<dd><a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a></dd>
-<dd><a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a></dd>
-<dd><a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a></dd>
-<dd><a href="pandas.core.base.html#SelectionMixin">pandas.core.base.SelectionMixin</a></dd>
-<dd><a href="builtins.html#object">builtins.object</a></dd>
-</dl>
-<hr>
-Methods inherited from <a href="modsim.html#MySeries">MySeries</a>:<br>
-<dl><dt><a name="SweepSeries-__init__"><strong>__init__</strong></a>(self, *args, **kwargs)</dt><dd><tt>Initialize&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Note:&nbsp;this&nbsp;cleans&nbsp;up&nbsp;a&nbsp;weird&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;behavior,&nbsp;which&nbsp;is<br>
-that&nbsp;<a href="pandas.core.series.html#Series">Series</a>()&nbsp;and&nbsp;<a href="pandas.core.series.html#Series">Series</a>([])&nbsp;yield&nbsp;different&nbsp;results.<br>
-See:&nbsp;https://github.com/pandas-dev/pandas/issues/16737</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-set"><strong>set</strong></a>(self, **kwargs)</dt><dd><tt>Uses&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;update&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place.<br>
-&nbsp;<br>
-Example:&nbsp;series.<a href="#SweepSeries-update">update</a>(a=1,&nbsp;b=2)</tt></dd></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><a name="SweepSeries-__add__"><strong>__add__</strong></a> = wrapper(left, right, name='__add__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f2f0&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__and__"><strong>__and__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__array__"><strong>__array__</strong></a>(self, result=None)</dt><dd><tt>the&nbsp;array&nbsp;interface,&nbsp;return&nbsp;my&nbsp;values</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-__array_prepare__"><strong>__array_prepare__</strong></a>(self, result, context=None)</dt><dd><tt>Gets&nbsp;called&nbsp;prior&nbsp;to&nbsp;a&nbsp;ufunc</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-__array_wrap__"><strong>__array_wrap__</strong></a>(self, result, context=None)</dt><dd><tt>Gets&nbsp;called&nbsp;after&nbsp;a&nbsp;ufunc</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-__div__"><strong>__div__</strong></a> = wrapper(left, right, name='__truediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f9d8&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__divmod__"><strong>__divmod__</strong></a> = wrapper(left, right, name='__divmod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca343bf8&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__eq__"><strong>__eq__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__float__"><strong>__float__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__floordiv__"><strong>__floordiv__</strong></a> = wrapper(left, right, name='__floordiv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fb70&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__ge__"><strong>__ge__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__getitem__"><strong>__getitem__</strong></a>(self, key)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__gt__"><strong>__gt__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__iadd__"><strong>__iadd__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__imul__"><strong>__imul__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__int__"><strong>__int__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__ipow__"><strong>__ipow__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__isub__"><strong>__isub__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__iter__"><strong>__iter__</strong></a>(self)</dt><dd><tt>provide&nbsp;iteration&nbsp;over&nbsp;the&nbsp;values&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-box&nbsp;values&nbsp;if&nbsp;necessary</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-__itruediv__"><strong>__itruediv__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__le__"><strong>__le__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__len__"><strong>__len__</strong></a>(self)</dt><dd><tt>return&nbsp;the&nbsp;length&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-__long__"><strong>__long__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__lt__"><strong>__lt__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__mod__"><strong>__mod__</strong></a> = wrapper(left, right, name='__mod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fd08&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__mul__"><strong>__mul__</strong></a> = wrapper(left, right, name='__mul__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f840&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__ne__"><strong>__ne__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__or__"><strong>__or__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__pow__"><strong>__pow__</strong></a> = wrapper(left, right, name='__pow__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fea0&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__radd__"><strong>__radd__</strong></a> = wrapper(left, right, name='__radd__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f510&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__rand__"><strong>__rand__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__rdiv__"><strong>__rdiv__</strong></a> = wrapper(left, right, name='__rtruediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342510&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__rfloordiv__"><strong>__rfloordiv__</strong></a> = wrapper(left, right, name='__rfloordiv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342730&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__rmod__"><strong>__rmod__</strong></a> = wrapper(left, right, name='__rmod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342b70&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__rmul__"><strong>__rmul__</strong></a> = wrapper(left, right, name='__rmul__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca3420d0&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__ror__"><strong>__ror__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__rpow__"><strong>__rpow__</strong></a> = wrapper(left, right, name='__rpow__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342950&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__rsub__"><strong>__rsub__</strong></a> = wrapper(left, right, name='__rsub__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca3422f0&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__rtruediv__"><strong>__rtruediv__</strong></a> = wrapper(left, right, name='__rtruediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342510&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__rxor__"><strong>__rxor__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__setitem__"><strong>__setitem__</strong></a>(self, key, value)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__sub__"><strong>__sub__</strong></a> = wrapper(left, right, name='__sub__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f6a8&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__truediv__"><strong>__truediv__</strong></a> = wrapper(left, right, name='__truediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f9d8&gt;)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__unicode__"><strong>__unicode__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;unicode(df)&nbsp;in&nbsp;py2&nbsp;only.&nbsp;Yields&nbsp;a&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;both<br>
-py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-__xor__"><strong>__xor__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="SweepSeries-add"><strong>add</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Addition&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`add`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;+&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.radd</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-agg"><strong>agg</strong></a> = aggregate(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.series.html#Series">Series</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#SweepSeries-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#SweepSeries-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10))<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-agg">agg</a>('min')<br>
--1.3018049988556679<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-agg">agg</a>(['min',&nbsp;'max'])<br>
-min&nbsp;&nbsp;&nbsp;-1.301805<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;1.127688<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.apply<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.transform</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-aggregate"><strong>aggregate</strong></a>(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.series.html#Series">Series</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#SweepSeries-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#SweepSeries-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10))<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-agg">agg</a>('min')<br>
--1.3018049988556679<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-agg">agg</a>(['min',&nbsp;'max'])<br>
-min&nbsp;&nbsp;&nbsp;-1.301805<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;1.127688<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.apply<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.transform</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-align"><strong>align</strong></a>(self, other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)</dt><dd><tt>Align&nbsp;two&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;their&nbsp;axes&nbsp;with&nbsp;the<br>
-specified&nbsp;join&nbsp;method&nbsp;for&nbsp;each&nbsp;axis&nbsp;Index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-join&nbsp;:&nbsp;{'outer',&nbsp;'inner',&nbsp;'left',&nbsp;'right'},&nbsp;default&nbsp;'outer'<br>
-axis&nbsp;:&nbsp;allowed&nbsp;axis&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;on&nbsp;index&nbsp;(0),&nbsp;columns&nbsp;(1),&nbsp;or&nbsp;both&nbsp;(None)<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Always&nbsp;returns&nbsp;new&nbsp;objects.&nbsp;If&nbsp;copy=False&nbsp;and&nbsp;no&nbsp;reindexing&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;required&nbsp;then&nbsp;original&nbsp;objects&nbsp;are&nbsp;returned.<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-method&nbsp;:&nbsp;str,&nbsp;default&nbsp;None<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-fill_axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Filling&nbsp;axis,&nbsp;method&nbsp;and&nbsp;limit<br>
-broadcast_axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;values&nbsp;along&nbsp;this&nbsp;axis,&nbsp;if&nbsp;aligning&nbsp;two&nbsp;objects&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;different&nbsp;dimensions<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-(left,&nbsp;right)&nbsp;:&nbsp;(<a href="pandas.core.series.html#Series">Series</a>,&nbsp;type&nbsp;of&nbsp;other)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Aligned&nbsp;objects</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-all"><strong>all</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;all&nbsp;elements&nbsp;are&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-all&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-any"><strong>any</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;any&nbsp;element&nbsp;is&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-any&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-append"><strong>append</strong></a>(self, to_append, ignore_index=False, verify_integrity=False)</dt><dd><tt>Concatenate&nbsp;two&nbsp;or&nbsp;more&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-to_append&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;list/tuple&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-ignore_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;not&nbsp;use&nbsp;the&nbsp;index&nbsp;labels.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.19.0<br>
-&nbsp;<br>
-verify_integrity&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;raise&nbsp;Exception&nbsp;on&nbsp;creating&nbsp;index&nbsp;with&nbsp;duplicates<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-appended&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s1&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s2&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([4,&nbsp;5,&nbsp;6])<br>
-&gt;&gt;&gt;&nbsp;s3&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([4,&nbsp;5,&nbsp;6],&nbsp;index=[3,4,5])<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#SweepSeries-append">append</a>(s2)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#SweepSeries-append">append</a>(s3)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-With&nbsp;`ignore_index`&nbsp;set&nbsp;to&nbsp;True:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#SweepSeries-append">append</a>(s2,&nbsp;ignore_index=True)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-With&nbsp;`verify_integrity`&nbsp;set&nbsp;to&nbsp;True:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#SweepSeries-append">append</a>(s2,&nbsp;verify_integrity=True)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-ValueError:&nbsp;Indexes&nbsp;have&nbsp;overlapping&nbsp;values:&nbsp;[0,&nbsp;1,&nbsp;2]</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-apply"><strong>apply</strong></a>(self, func, convert_dtype=True, args=(), **kwds)</dt><dd><tt>Invoke&nbsp;function&nbsp;on&nbsp;values&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Can&nbsp;be&nbsp;ufunc&nbsp;(a&nbsp;NumPy&nbsp;function<br>
-that&nbsp;applies&nbsp;to&nbsp;the&nbsp;entire&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;or&nbsp;a&nbsp;Python&nbsp;function&nbsp;that&nbsp;only&nbsp;works<br>
-on&nbsp;single&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-convert_dtype&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Try&nbsp;to&nbsp;find&nbsp;better&nbsp;dtype&nbsp;for&nbsp;elementwise&nbsp;function&nbsp;results.&nbsp;If<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False,&nbsp;leave&nbsp;as&nbsp;dtype=<a href="builtins.html#object">object</a><br>
-args&nbsp;:&nbsp;tuple<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Positional&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;to&nbsp;function&nbsp;in&nbsp;addition&nbsp;to&nbsp;the&nbsp;value<br>
-Additional&nbsp;keyword&nbsp;arguments&nbsp;will&nbsp;be&nbsp;passed&nbsp;as&nbsp;keywords&nbsp;to&nbsp;the&nbsp;function<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;if&nbsp;func&nbsp;returns&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.map:&nbsp;For&nbsp;element-wise&nbsp;operations<br>
-<a href="pandas.core.series.html#Series">Series</a>.agg:&nbsp;only&nbsp;perform&nbsp;aggregating&nbsp;type&nbsp;operations<br>
-<a href="pandas.core.series.html#Series">Series</a>.transform:&nbsp;only&nbsp;perform&nbsp;transformating&nbsp;type&nbsp;operations<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;series&nbsp;with&nbsp;typical&nbsp;summer&nbsp;temperatures&nbsp;for&nbsp;each&nbsp;city.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([20,&nbsp;21,&nbsp;12],&nbsp;index=['London',<br>
-...&nbsp;'New&nbsp;York','Helsinki'])<br>
-&gt;&gt;&gt;&nbsp;series<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;20<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Square&nbsp;the&nbsp;values&nbsp;by&nbsp;defining&nbsp;a&nbsp;function&nbsp;and&nbsp;passing&nbsp;it&nbsp;as&nbsp;an<br>
-argument&nbsp;to&nbsp;``<a href="#SweepSeries-apply">apply</a>()``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;square(x):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x**2<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepSeries-apply">apply</a>(square)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;400<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;441<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;144<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Square&nbsp;the&nbsp;values&nbsp;by&nbsp;passing&nbsp;an&nbsp;anonymous&nbsp;function&nbsp;as&nbsp;an<br>
-argument&nbsp;to&nbsp;``<a href="#SweepSeries-apply">apply</a>()``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepSeries-apply">apply</a>(lambda&nbsp;x:&nbsp;x**2)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;400<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;441<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;144<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Define&nbsp;a&nbsp;custom&nbsp;function&nbsp;that&nbsp;needs&nbsp;additional&nbsp;positional<br>
-arguments&nbsp;and&nbsp;pass&nbsp;these&nbsp;additional&nbsp;arguments&nbsp;using&nbsp;the<br>
-``args``&nbsp;keyword.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;subtract_custom_value(x,&nbsp;custom_value):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x-custom_value<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepSeries-apply">apply</a>(subtract_custom_value,&nbsp;args=(5,))<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;16<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Define&nbsp;a&nbsp;custom&nbsp;function&nbsp;that&nbsp;takes&nbsp;keyword&nbsp;arguments<br>
-and&nbsp;pass&nbsp;these&nbsp;arguments&nbsp;to&nbsp;``apply``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;add_custom_values(x,&nbsp;**kwargs):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;month&nbsp;in&nbsp;kwargs:<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;x+=kwargs[month]<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepSeries-apply">apply</a>(add_custom_values,&nbsp;june=30,&nbsp;july=20,&nbsp;august=25)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;95<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;96<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;87<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Use&nbsp;a&nbsp;function&nbsp;from&nbsp;the&nbsp;Numpy&nbsp;library.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepSeries-apply">apply</a>(np.log)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.995732<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;3.044522<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;2.484907<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-argmax"><strong>argmax</strong></a> = idxmax(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmax&nbsp;:&nbsp;Index&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmax``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmax<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmax</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-argmin"><strong>argmin</strong></a> = idxmin(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmin&nbsp;:&nbsp;Index&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmin``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmin<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmin</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-argsort"><strong>argsort</strong></a>(self, axis=0, kind='quicksort', order=None)</dt><dd><tt>Overrides&nbsp;<a href="numpy.html#ndarray">ndarray</a>.argsort.&nbsp;Argsorts&nbsp;the&nbsp;value,&nbsp;omitting&nbsp;NA/null&nbsp;values,<br>
-and&nbsp;places&nbsp;the&nbsp;result&nbsp;in&nbsp;the&nbsp;same&nbsp;locations&nbsp;as&nbsp;the&nbsp;non-NA&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;int&nbsp;(can&nbsp;only&nbsp;be&nbsp;zero)<br>
-kind&nbsp;:&nbsp;{'mergesort',&nbsp;'quicksort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;'mergesort'&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm<br>
-order&nbsp;:&nbsp;ignored<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-argsorted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;with&nbsp;-1&nbsp;indicated&nbsp;where&nbsp;nan&nbsp;values&nbsp;are&nbsp;present<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argsort</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-autocorr"><strong>autocorr</strong></a>(self, lag=1)</dt><dd><tt>Lag-N&nbsp;autocorrelation<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-lag&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;lags&nbsp;to&nbsp;apply&nbsp;before&nbsp;performing&nbsp;autocorrelation.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-autocorr&nbsp;:&nbsp;float</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-between"><strong>between</strong></a>(self, left, right, inclusive=True)</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;equivalent&nbsp;to&nbsp;left&nbsp;&lt;=&nbsp;series&nbsp;&lt;=&nbsp;right.&nbsp;NA&nbsp;values<br>
-will&nbsp;be&nbsp;treated&nbsp;as&nbsp;False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-left&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Left&nbsp;boundary<br>
-right&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Right&nbsp;boundary<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_between&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-combine"><strong>combine</strong></a>(self, other, func, fill_value=nan)</dt><dd><tt>Perform&nbsp;elementwise&nbsp;binary&nbsp;operation&nbsp;on&nbsp;two&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;using&nbsp;given&nbsp;function<br>
-with&nbsp;optional&nbsp;fill&nbsp;value&nbsp;when&nbsp;an&nbsp;index&nbsp;is&nbsp;missing&nbsp;from&nbsp;one&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or<br>
-the&nbsp;other<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-func&nbsp;:&nbsp;function<br>
-fill_value&nbsp;:&nbsp;scalar&nbsp;value<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-combine_first"><strong>combine_first</strong></a>(self, other)</dt><dd><tt>Combine&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values,&nbsp;choosing&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.series.html#Series">Series</a>'s&nbsp;values<br>
-first.&nbsp;Result&nbsp;index&nbsp;will&nbsp;be&nbsp;the&nbsp;union&nbsp;of&nbsp;the&nbsp;two&nbsp;indexes<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-compound"><strong>compound</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;compound&nbsp;percentage&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-compounded&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-compress"><strong>compress</strong></a>(self, condition, *args, **kwargs)</dt><dd><tt>Return&nbsp;selected&nbsp;slices&nbsp;of&nbsp;an&nbsp;array&nbsp;along&nbsp;given&nbsp;axis&nbsp;as&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.compress</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-corr"><strong>corr</strong></a>(self, other, method='pearson', min_periods=None)</dt><dd><tt>Compute&nbsp;correlation&nbsp;with&nbsp;`other`&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;excluding&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-method&nbsp;:&nbsp;{'pearson',&nbsp;'kendall',&nbsp;'spearman'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pearson&nbsp;:&nbsp;standard&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;kendall&nbsp;:&nbsp;Kendall&nbsp;Tau&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;spearman&nbsp;:&nbsp;Spearman&nbsp;rank&nbsp;correlation<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;needed&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result<br>
-&nbsp;<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-correlation&nbsp;:&nbsp;float</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-count"><strong>count</strong></a>(self, level=None)</dt><dd><tt>Return&nbsp;number&nbsp;of&nbsp;non-NA/null&nbsp;observations&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;smaller&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-nobs&nbsp;:&nbsp;int&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-cov"><strong>cov</strong></a>(self, other, min_periods=None)</dt><dd><tt>Compute&nbsp;covariance&nbsp;with&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;excluding&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;needed&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-covariance&nbsp;:&nbsp;float<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;(unbiased&nbsp;estimator).</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-cummax"><strong>cummax</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;max&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummax&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.max&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-cummin"><strong>cummin</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;minimum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummin&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.min&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-cumprod"><strong>cumprod</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;product&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumprod&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.prod&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-cumsum"><strong>cumsum</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;sum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumsum&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.sum&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-diff"><strong>diff</strong></a>(self, periods=1)</dt><dd><tt>1st&nbsp;discrete&nbsp;difference&nbsp;of&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;difference<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-diffed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-div"><strong>div</strong></a> = truediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-divide"><strong>divide</strong></a> = truediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-dot"><strong>dot</strong></a>(self, other)</dt><dd><tt>Matrix&nbsp;multiplication&nbsp;with&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;inner-product&nbsp;with&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-objects<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dot_product&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-drop_duplicates"><strong>drop_duplicates</strong></a>(self, keep='first', inplace=False)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;duplicate&nbsp;values&nbsp;removed<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-&nbsp;<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Drop&nbsp;all&nbsp;duplicates.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-If&nbsp;True,&nbsp;performs&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;returns&nbsp;None.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-deduplicated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-dropna"><strong>dropna</strong></a>(self, axis=0, inplace=False, **kwargs)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;without&nbsp;null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-valid&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;operation&nbsp;in&nbsp;place.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-duplicated"><strong>duplicated</strong></a>(self, keep='first')</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;denoting&nbsp;duplicate&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the&nbsp;last<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Mark&nbsp;all&nbsp;duplicates&nbsp;as&nbsp;``True``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-duplicated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-eq"><strong>eq</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`eq`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;==&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-ewm"><strong>ewm</strong></a>(self, com=None, span=None, halflife=None, alpha=None, min_periods=0, freq=None, adjust=True, ignore_na=False, axis=0)</dt><dd><tt>Provides&nbsp;exponential&nbsp;weighted&nbsp;functions<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-com&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;/&nbsp;(1&nbsp;+&nbsp;com),\text{&nbsp;for&nbsp;}&nbsp;com&nbsp;\geq&nbsp;0`<br>
-span&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;span,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;2&nbsp;/&nbsp;(span&nbsp;+&nbsp;1),\text{&nbsp;for&nbsp;}&nbsp;span&nbsp;\geq&nbsp;1`<br>
-halflife&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;half-life,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;-&nbsp;exp(log(0.5)&nbsp;/&nbsp;halflife),\text{&nbsp;for&nbsp;}&nbsp;halflife&nbsp;&gt;&nbsp;0`<br>
-alpha&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;smoothing&nbsp;factor&nbsp;:math:`\alpha`&nbsp;directly,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`0&nbsp;&lt;&nbsp;\alpha&nbsp;\leq&nbsp;1`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;None&nbsp;or&nbsp;string&nbsp;alias&nbsp;/&nbsp;date&nbsp;offset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default=None&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;to&nbsp;before&nbsp;computing&nbsp;statistic<br>
-adjust&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Divide&nbsp;by&nbsp;decaying&nbsp;adjustment&nbsp;factor&nbsp;in&nbsp;beginning&nbsp;periods&nbsp;to&nbsp;account<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;imbalance&nbsp;in&nbsp;relative&nbsp;weightings&nbsp;(viewing&nbsp;EWMA&nbsp;as&nbsp;a&nbsp;moving&nbsp;average)<br>
-ignore_na&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Ignore&nbsp;missing&nbsp;values&nbsp;when&nbsp;calculating&nbsp;weights;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specify&nbsp;True&nbsp;to&nbsp;reproduce&nbsp;pre-0.15.0&nbsp;behavior<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-ewm">ewm</a>(com=0.5).<a href="#SweepSeries-mean">mean</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.000000<br>
-1&nbsp;&nbsp;0.750000<br>
-2&nbsp;&nbsp;1.615385<br>
-3&nbsp;&nbsp;1.615385<br>
-4&nbsp;&nbsp;3.670213<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Exactly&nbsp;one&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,&nbsp;span,&nbsp;half-life,&nbsp;and&nbsp;alpha&nbsp;must&nbsp;be&nbsp;provided.<br>
-Allowed&nbsp;values&nbsp;and&nbsp;relationship&nbsp;between&nbsp;the&nbsp;parameters&nbsp;are&nbsp;specified&nbsp;in&nbsp;the<br>
-parameter&nbsp;descriptions&nbsp;above;&nbsp;see&nbsp;the&nbsp;link&nbsp;at&nbsp;the&nbsp;end&nbsp;of&nbsp;this&nbsp;section&nbsp;for<br>
-a&nbsp;detailed&nbsp;explanation.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;True&nbsp;(default),&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;using<br>
-weights&nbsp;(1-alpha)**(n-1),&nbsp;(1-alpha)**(n-2),&nbsp;...,&nbsp;1-alpha,&nbsp;1.<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;False,&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;recursively&nbsp;as:<br>
-&nbsp;&nbsp;&nbsp;weighted_average[0]&nbsp;=&nbsp;arg[0];<br>
-&nbsp;&nbsp;&nbsp;weighted_average[i]&nbsp;=&nbsp;(1-alpha)*weighted_average[i-1]&nbsp;+&nbsp;alpha*arg[i].<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;False&nbsp;(default),&nbsp;weights&nbsp;are&nbsp;based&nbsp;on&nbsp;absolute&nbsp;positions.<br>
-For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in&nbsp;calculating&nbsp;the&nbsp;final&nbsp;weighted<br>
-average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;(1-alpha)**2&nbsp;and&nbsp;1&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and<br>
-(1-alpha)**2&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;True&nbsp;(reproducing&nbsp;pre-0.15.0&nbsp;behavior),&nbsp;weights&nbsp;are&nbsp;based<br>
-on&nbsp;relative&nbsp;positions.&nbsp;For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in<br>
-calculating&nbsp;the&nbsp;final&nbsp;weighted&nbsp;average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;1-alpha&nbsp;and&nbsp;1<br>
-(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and&nbsp;1-alpha&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-More&nbsp;details&nbsp;can&nbsp;be&nbsp;found&nbsp;at<br>
-<a href="http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows">http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-expanding"><strong>expanding</strong></a>(self, min_periods=1, freq=None, center=False, axis=0)</dt><dd><tt>Provides&nbsp;expanding&nbsp;transformations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-expanding">expanding</a>(2).<a href="#SweepSeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;7.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-fillna"><strong>fillna</strong></a>(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)</dt><dd><tt>Fill&nbsp;NA/NaN&nbsp;values&nbsp;using&nbsp;the&nbsp;specified&nbsp;method<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;values&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;each&nbsp;index&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;or&nbsp;column&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).&nbsp;(values&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;This&nbsp;value&nbsp;cannot<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;list.<br>
-method&nbsp;:&nbsp;{'backfill',&nbsp;'bfill',&nbsp;'pad',&nbsp;'ffill',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'}<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;fill&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>,&nbsp;(e.g.&nbsp;a&nbsp;no-copy&nbsp;slice&nbsp;for&nbsp;a&nbsp;column&nbsp;in&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;method&nbsp;is&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the&nbsp;maximum&nbsp;number&nbsp;of&nbsp;consecutive<br>
-&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;values&nbsp;to&nbsp;forward/backward&nbsp;fill.&nbsp;In&nbsp;other&nbsp;words,&nbsp;if&nbsp;there&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;gap&nbsp;with&nbsp;more&nbsp;than&nbsp;this&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs,&nbsp;it&nbsp;will&nbsp;only<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;partially&nbsp;filled.&nbsp;If&nbsp;method&nbsp;is&nbsp;not&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;maximum&nbsp;number&nbsp;of&nbsp;entries&nbsp;along&nbsp;the&nbsp;entire&nbsp;axis&nbsp;where&nbsp;NaNs&nbsp;will&nbsp;be<br>
-&nbsp;&nbsp;&nbsp;&nbsp;filled.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0&nbsp;if&nbsp;not&nbsp;None.<br>
-downcast&nbsp;:&nbsp;dict,&nbsp;default&nbsp;is&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;dict&nbsp;of&nbsp;item-&gt;dtype&nbsp;of&nbsp;what&nbsp;to&nbsp;downcast&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;the&nbsp;string&nbsp;'infer'&nbsp;which&nbsp;will&nbsp;try&nbsp;to&nbsp;downcast&nbsp;to&nbsp;an&nbsp;appropriate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;equal&nbsp;type&nbsp;(e.g.&nbsp;float64&nbsp;to&nbsp;int64&nbsp;if&nbsp;possible)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;asfreq<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-first_valid_index"><strong>first_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;first&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-floordiv"><strong>floordiv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`floordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;//&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rfloordiv</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-ge"><strong>ge</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Greater&nbsp;than&nbsp;or&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`ge`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&gt;=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-get_value"><strong>get_value</strong></a>(self, label, takeable=False)</dt><dd><tt>Quickly&nbsp;retrieve&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;index&nbsp;label<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;label<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;scalar&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-get_values"><strong>get_values</strong></a>(self)</dt><dd><tt>same&nbsp;as&nbsp;values&nbsp;(but&nbsp;handles&nbsp;sparseness&nbsp;conversions);&nbsp;is&nbsp;a&nbsp;view</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-gt"><strong>gt</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Greater&nbsp;than&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`gt`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&gt;&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-hist"><strong>hist</strong></a> = hist_series(self, by=None, ax=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, figsize=None, bins=10, **kwds)</dt><dd><tt>Draw&nbsp;histogram&nbsp;of&nbsp;the&nbsp;input&nbsp;series&nbsp;using&nbsp;matplotlib<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed,&nbsp;then&nbsp;used&nbsp;to&nbsp;form&nbsp;histograms&nbsp;for&nbsp;separate&nbsp;groups<br>
-ax&nbsp;:&nbsp;matplotlib&nbsp;axis&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;not&nbsp;passed,&nbsp;uses&nbsp;gca()<br>
-grid&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;show&nbsp;axis&nbsp;grid&nbsp;lines<br>
-xlabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;x-axis&nbsp;label&nbsp;size<br>
-xrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;x&nbsp;axis&nbsp;labels<br>
-ylabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;y-axis&nbsp;label&nbsp;size<br>
-yrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;y&nbsp;axis&nbsp;labels<br>
-figsize&nbsp;:&nbsp;tuple,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;figure&nbsp;size&nbsp;in&nbsp;inches&nbsp;by&nbsp;default<br>
-bins:&nbsp;integer,&nbsp;default&nbsp;10<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;histogram&nbsp;bins&nbsp;to&nbsp;be&nbsp;used<br>
-kwds&nbsp;:&nbsp;keywords<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;passed&nbsp;to&nbsp;the&nbsp;actual&nbsp;plotting&nbsp;function<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;matplotlib&nbsp;documentation&nbsp;online&nbsp;for&nbsp;more&nbsp;on&nbsp;this</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-idxmax"><strong>idxmax</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmax&nbsp;:&nbsp;Index&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmax``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmax<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmax</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-idxmin"><strong>idxmin</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmin&nbsp;:&nbsp;Index&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmin``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmin<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmin</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-isin"><strong>isin</strong></a>(self, values)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;:class:`~pandas.<a href="pandas.core.series.html#Series">Series</a>`&nbsp;showing&nbsp;whether&nbsp;each&nbsp;element<br>
-in&nbsp;the&nbsp;:class:`~pandas.<a href="pandas.core.series.html#Series">Series</a>`&nbsp;is&nbsp;exactly&nbsp;contained&nbsp;in&nbsp;the&nbsp;passed<br>
-sequence&nbsp;of&nbsp;``values``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-values&nbsp;:&nbsp;set&nbsp;or&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;sequence&nbsp;of&nbsp;values&nbsp;to&nbsp;test.&nbsp;Passing&nbsp;in&nbsp;a&nbsp;single&nbsp;string&nbsp;will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;raise&nbsp;a&nbsp;``TypeError``.&nbsp;Instead,&nbsp;turn&nbsp;a&nbsp;single&nbsp;string&nbsp;into&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``list``&nbsp;of&nbsp;one&nbsp;element.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Support&nbsp;for&nbsp;values&nbsp;as&nbsp;a&nbsp;set<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-isin&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(bool&nbsp;dtype)<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;*&nbsp;If&nbsp;``values``&nbsp;is&nbsp;a&nbsp;string<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.isin<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(list('abc'))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-isin">isin</a>(['a',&nbsp;'c',&nbsp;'e'])<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-dtype:&nbsp;bool<br>
-&nbsp;<br>
-Passing&nbsp;a&nbsp;single&nbsp;string&nbsp;as&nbsp;``s.<a href="#SweepSeries-isin">isin</a>('a')``&nbsp;will&nbsp;raise&nbsp;an&nbsp;error.&nbsp;Use<br>
-a&nbsp;list&nbsp;of&nbsp;one&nbsp;element&nbsp;instead:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-isin">isin</a>(['a'])<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-dtype:&nbsp;bool</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-items"><strong>items</strong></a> = iteritems(self)</dt><dd><tt>Lazily&nbsp;iterate&nbsp;over&nbsp;(index,&nbsp;value)&nbsp;tuples</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-iteritems"><strong>iteritems</strong></a>(self)</dt><dd><tt>Lazily&nbsp;iterate&nbsp;over&nbsp;(index,&nbsp;value)&nbsp;tuples</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-keys"><strong>keys</strong></a>(self)</dt><dd><tt>Alias&nbsp;for&nbsp;index</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-kurt"><strong>kurt</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-kurtosis"><strong>kurtosis</strong></a> = kurt(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-last_valid_index"><strong>last_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;last&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-le"><strong>le</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Less&nbsp;than&nbsp;or&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`le`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&lt;=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-lt"><strong>lt</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Less&nbsp;than&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`lt`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&lt;&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-mad"><strong>mad</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;absolute&nbsp;deviation&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mad&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-map"><strong>map</strong></a>(self, arg, na_action=None)</dt><dd><tt>Map&nbsp;values&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;using&nbsp;input&nbsp;correspondence&nbsp;(which&nbsp;can&nbsp;be<br>
-a&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;function)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-arg&nbsp;:&nbsp;function,&nbsp;dict,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-na_action&nbsp;:&nbsp;{None,&nbsp;'ignore'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'ignore',&nbsp;propagate&nbsp;NA&nbsp;values,&nbsp;without&nbsp;passing&nbsp;them&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;mapping&nbsp;function<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;same&nbsp;index&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Map&nbsp;inputs&nbsp;to&nbsp;outputs&nbsp;(both&nbsp;of&nbsp;type&nbsp;`<a href="pandas.core.series.html#Series">Series</a>`)<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,2,3],&nbsp;index=['one',&nbsp;'two',&nbsp;'three'])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-one&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-two&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-three&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;y&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(['foo',&nbsp;'bar',&nbsp;'baz'],&nbsp;index=[1,2,3])<br>
-&gt;&gt;&gt;&nbsp;y<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;foo<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;bar<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;baz<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#SweepSeries-map">map</a>(y)<br>
-one&nbsp;&nbsp;&nbsp;foo<br>
-two&nbsp;&nbsp;&nbsp;bar<br>
-three&nbsp;baz<br>
-&nbsp;<br>
-If&nbsp;`arg`&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;values&nbsp;converted<br>
-according&nbsp;to&nbsp;the&nbsp;dictionary's&nbsp;mapping:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;z&nbsp;=&nbsp;{1:&nbsp;'A',&nbsp;2:&nbsp;'B',&nbsp;3:&nbsp;'C'}<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#SweepSeries-map">map</a>(z)<br>
-one&nbsp;&nbsp;&nbsp;A<br>
-two&nbsp;&nbsp;&nbsp;B<br>
-three&nbsp;C<br>
-&nbsp;<br>
-Use&nbsp;na_action&nbsp;to&nbsp;control&nbsp;whether&nbsp;NA&nbsp;values&nbsp;are&nbsp;affected&nbsp;by&nbsp;the&nbsp;mapping<br>
-function.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;np.nan])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s2&nbsp;=&nbsp;s.<a href="#SweepSeries-map">map</a>('this&nbsp;is&nbsp;a&nbsp;string&nbsp;{}'.format,&nbsp;na_action=None)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;1.0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;2.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;3.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;nan<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s3&nbsp;=&nbsp;s.<a href="#SweepSeries-map">map</a>('this&nbsp;is&nbsp;a&nbsp;string&nbsp;{}'.format,&nbsp;na_action='ignore')<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;1.0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;2.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;3.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.apply:&nbsp;For&nbsp;applying&nbsp;more&nbsp;complex&nbsp;functions&nbsp;on&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply:&nbsp;Apply&nbsp;a&nbsp;function&nbsp;row-/column-wise<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap:&nbsp;Apply&nbsp;a&nbsp;function&nbsp;elementwise&nbsp;on&nbsp;a&nbsp;whole&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-When&nbsp;`arg`&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;values&nbsp;in&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;that&nbsp;are&nbsp;not&nbsp;in&nbsp;the<br>
-dictionary&nbsp;(as&nbsp;keys)&nbsp;are&nbsp;converted&nbsp;to&nbsp;``NaN``.&nbsp;However,&nbsp;if&nbsp;the<br>
-dictionary&nbsp;is&nbsp;a&nbsp;``dict``&nbsp;subclass&nbsp;that&nbsp;defines&nbsp;``__missing__``&nbsp;(i.e.<br>
-provides&nbsp;a&nbsp;method&nbsp;for&nbsp;default&nbsp;values),&nbsp;then&nbsp;this&nbsp;default&nbsp;is&nbsp;used<br>
-rather&nbsp;than&nbsp;``NaN``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;collections&nbsp;import&nbsp;Counter<br>
-&gt;&gt;&gt;&nbsp;counter&nbsp;=&nbsp;Counter()<br>
-&gt;&gt;&gt;&nbsp;counter['bar']&nbsp;+=&nbsp;1<br>
-&gt;&gt;&gt;&nbsp;y.<a href="#SweepSeries-map">map</a>(counter)<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-dtype:&nbsp;int64</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-max"><strong>max</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;maximum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;maximum,&nbsp;use&nbsp;``idxmax``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmax``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-max&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-mean"><strong>mean</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mean&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-median"><strong>median</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;median&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-median&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-memory_usage"><strong>memory_usage</strong></a>(self, index=True, deep=False)</dt><dd><tt>Memory&nbsp;usage&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;whether&nbsp;to&nbsp;include&nbsp;memory&nbsp;usage&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index<br>
-deep&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Introspect&nbsp;the&nbsp;data&nbsp;deeply,&nbsp;interrogate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`<a href="builtins.html#object">object</a>`&nbsp;dtypes&nbsp;for&nbsp;system-level&nbsp;memory&nbsp;consumption<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-scalar&nbsp;bytes&nbsp;of&nbsp;memory&nbsp;consumed<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Memory&nbsp;usage&nbsp;does&nbsp;not&nbsp;include&nbsp;memory&nbsp;consumed&nbsp;by&nbsp;elements&nbsp;that<br>
-are&nbsp;not&nbsp;components&nbsp;of&nbsp;the&nbsp;array&nbsp;if&nbsp;deep=False<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.nbytes</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-min"><strong>min</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;minimum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;minimum,&nbsp;use&nbsp;``idxmin``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmin``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-min&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-mod"><strong>mod</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Modulo&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;%&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmod</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-mode"><strong>mode</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;<a href="#SweepSeries-mode">mode</a>(s)&nbsp;of&nbsp;the&nbsp;dataset.<br>
-&nbsp;<br>
-Always&nbsp;returns&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;even&nbsp;if&nbsp;only&nbsp;one&nbsp;value&nbsp;is&nbsp;returned.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-modes&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(sorted)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-mul"><strong>mul</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-multiply"><strong>multiply</strong></a> = mul(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-ne"><strong>ne</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Not&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`ne`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;!=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-nlargest"><strong>nlargest</strong></a>(self, n=5, keep='first')</dt><dd><tt>Return&nbsp;the&nbsp;largest&nbsp;`n`&nbsp;elements.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;this&nbsp;many&nbsp;descending&nbsp;sorted&nbsp;values<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-top_n&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;n&nbsp;largest&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;in&nbsp;sorted&nbsp;order<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Faster&nbsp;than&nbsp;``.<a href="#SweepSeries-sort_values">sort_values</a>(ascending=False).<a href="#SweepSeries-head">head</a>(n)``&nbsp;for&nbsp;small&nbsp;`n`<br>
-relative&nbsp;to&nbsp;the&nbsp;size&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.nsmallest<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10**6))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-nlargest">nlargest</a>(10)&nbsp;&nbsp;#&nbsp;only&nbsp;sorts&nbsp;up&nbsp;to&nbsp;the&nbsp;N&nbsp;requested<br>
-219921&nbsp;&nbsp;&nbsp;&nbsp;4.644710<br>
-82124&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4.608745<br>
-421689&nbsp;&nbsp;&nbsp;&nbsp;4.564644<br>
-425277&nbsp;&nbsp;&nbsp;&nbsp;4.447014<br>
-718691&nbsp;&nbsp;&nbsp;&nbsp;4.414137<br>
-43154&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4.403520<br>
-283187&nbsp;&nbsp;&nbsp;&nbsp;4.313922<br>
-595519&nbsp;&nbsp;&nbsp;&nbsp;4.273635<br>
-503969&nbsp;&nbsp;&nbsp;&nbsp;4.250236<br>
-121637&nbsp;&nbsp;&nbsp;&nbsp;4.240952<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-nonzero"><strong>nonzero</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;indices&nbsp;of&nbsp;the&nbsp;elements&nbsp;that&nbsp;are&nbsp;non-zero<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;calling&nbsp;`numpy.nonzero`&nbsp;on&nbsp;the<br>
-series&nbsp;data.&nbsp;For&nbsp;compatability&nbsp;with&nbsp;NumPy,&nbsp;the&nbsp;return&nbsp;value&nbsp;is<br>
-the&nbsp;same&nbsp;(a&nbsp;tuple&nbsp;with&nbsp;an&nbsp;array&nbsp;of&nbsp;indices&nbsp;for&nbsp;each&nbsp;dimension),<br>
-but&nbsp;it&nbsp;will&nbsp;always&nbsp;be&nbsp;a&nbsp;one-item&nbsp;tuple&nbsp;because&nbsp;series&nbsp;only&nbsp;have<br>
-one&nbsp;dimension.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0,&nbsp;3,&nbsp;0,&nbsp;4])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-nonzero">nonzero</a>()<br>
-(array([1,&nbsp;3]),)<br>
-&gt;&gt;&gt;&nbsp;s.iloc[s.<a href="#SweepSeries-nonzero">nonzero</a>()[0]]<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.nonzero</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-nsmallest"><strong>nsmallest</strong></a>(self, n=5, keep='first')</dt><dd><tt>Return&nbsp;the&nbsp;smallest&nbsp;`n`&nbsp;elements.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;this&nbsp;many&nbsp;ascending&nbsp;sorted&nbsp;values<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-bottom_n&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;n&nbsp;smallest&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;in&nbsp;sorted&nbsp;order<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Faster&nbsp;than&nbsp;``.<a href="#SweepSeries-sort_values">sort_values</a>().<a href="#SweepSeries-head">head</a>(n)``&nbsp;for&nbsp;small&nbsp;`n`&nbsp;relative&nbsp;to<br>
-the&nbsp;size&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.nlargest<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10**6))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-nsmallest">nsmallest</a>(10)&nbsp;&nbsp;#&nbsp;only&nbsp;sorts&nbsp;up&nbsp;to&nbsp;the&nbsp;N&nbsp;requested<br>
-288532&nbsp;&nbsp;&nbsp;-4.954580<br>
-732345&nbsp;&nbsp;&nbsp;-4.835960<br>
-64803&nbsp;&nbsp;&nbsp;&nbsp;-4.812550<br>
-446457&nbsp;&nbsp;&nbsp;-4.609998<br>
-501225&nbsp;&nbsp;&nbsp;-4.483945<br>
-669476&nbsp;&nbsp;&nbsp;-4.472935<br>
-973615&nbsp;&nbsp;&nbsp;-4.401699<br>
-621279&nbsp;&nbsp;&nbsp;-4.355126<br>
-773916&nbsp;&nbsp;&nbsp;-4.347355<br>
-359919&nbsp;&nbsp;&nbsp;-4.331927<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-pow"><strong>pow</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`pow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;**&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rpow</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-prod"><strong>prod</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-product"><strong>product</strong></a> = prod(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-ptp"><strong>ptp</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Returns&nbsp;the&nbsp;difference&nbsp;between&nbsp;the&nbsp;maximum&nbsp;value&nbsp;and&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;minimum&nbsp;value&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.&nbsp;This&nbsp;is&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``ptp``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ptp&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-put"><strong>put</strong></a>(self, *args, **kwargs)</dt><dd><tt>Applies&nbsp;the&nbsp;`put`&nbsp;method&nbsp;to&nbsp;its&nbsp;`values`&nbsp;attribute<br>
-if&nbsp;it&nbsp;has&nbsp;one.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.put</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-quantile"><strong>quantile</strong></a>(self, q=0.5, interpolation='linear')</dt><dd><tt>Return&nbsp;value&nbsp;at&nbsp;the&nbsp;given&nbsp;quantile,&nbsp;a&nbsp;la&nbsp;numpy.percentile.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-q&nbsp;:&nbsp;float&nbsp;or&nbsp;array-like,&nbsp;default&nbsp;0.5&nbsp;(50%&nbsp;quantile)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&lt;=&nbsp;q&nbsp;&lt;=&nbsp;1,&nbsp;the&nbsp;<a href="#SweepSeries-quantile">quantile</a>(s)&nbsp;to&nbsp;compute<br>
-interpolation&nbsp;:&nbsp;{'linear',&nbsp;'lower',&nbsp;'higher',&nbsp;'midpoint',&nbsp;'nearest'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;optional&nbsp;parameter&nbsp;specifies&nbsp;the&nbsp;interpolation&nbsp;method&nbsp;to&nbsp;use,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;the&nbsp;desired&nbsp;quantile&nbsp;lies&nbsp;between&nbsp;two&nbsp;data&nbsp;points&nbsp;`i`&nbsp;and&nbsp;`j`:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;linear:&nbsp;`i&nbsp;+&nbsp;(j&nbsp;-&nbsp;i)&nbsp;*&nbsp;fraction`,&nbsp;where&nbsp;`fraction`&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;fractional&nbsp;part&nbsp;of&nbsp;the&nbsp;index&nbsp;surrounded&nbsp;by&nbsp;`i`&nbsp;and&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;lower:&nbsp;`i`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;higher:&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;`i`&nbsp;or&nbsp;`j`&nbsp;whichever&nbsp;is&nbsp;nearest.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;midpoint:&nbsp;(`i`&nbsp;+&nbsp;`j`)&nbsp;/&nbsp;2.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-quantile&nbsp;:&nbsp;float&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;``q``&nbsp;is&nbsp;an&nbsp;array,&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;returned&nbsp;where&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;is&nbsp;``q``&nbsp;and&nbsp;the&nbsp;values&nbsp;are&nbsp;the&nbsp;quantiles.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;4])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-quantile">quantile</a>(.5)<br>
-2.5<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-quantile">quantile</a>([.25,&nbsp;.5,&nbsp;.75])<br>
-0.25&nbsp;&nbsp;&nbsp;&nbsp;1.75<br>
-0.50&nbsp;&nbsp;&nbsp;&nbsp;2.50<br>
-0.75&nbsp;&nbsp;&nbsp;&nbsp;3.25<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-radd"><strong>radd</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Addition&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`radd`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;+&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.add</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-ravel"><strong>ravel</strong></a>(self, order='C')</dt><dd><tt>Return&nbsp;the&nbsp;flattened&nbsp;underlying&nbsp;data&nbsp;as&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.ravel</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-rdiv"><strong>rdiv</strong></a> = rtruediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-reindex"><strong>reindex</strong></a>(self, index=None, **kwargs)</dt><dd><tt>Conform&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;new&nbsp;index&nbsp;with&nbsp;optional&nbsp;filling&nbsp;logic,&nbsp;placing<br>
-NA/NaN&nbsp;in&nbsp;locations&nbsp;having&nbsp;no&nbsp;value&nbsp;in&nbsp;the&nbsp;previous&nbsp;index.&nbsp;A&nbsp;new&nbsp;<a href="builtins.html#object">object</a><br>
-is&nbsp;produced&nbsp;unless&nbsp;the&nbsp;new&nbsp;index&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;the&nbsp;current&nbsp;one&nbsp;and<br>
-copy=False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;array-like,&nbsp;optional&nbsp;(can&nbsp;be&nbsp;specified&nbsp;in&nbsp;order,&nbsp;or&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;keywords)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;New&nbsp;labels&nbsp;/&nbsp;index&nbsp;to&nbsp;conform&nbsp;to.&nbsp;Preferably&nbsp;an&nbsp;Index&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;avoid&nbsp;duplicating&nbsp;data<br>
-method&nbsp;:&nbsp;{None,&nbsp;'backfill'/'bfill',&nbsp;'pad'/'ffill',&nbsp;'nearest'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Please&nbsp;note:&nbsp;this&nbsp;is&nbsp;only&nbsp;&nbsp;applicable&nbsp;to&nbsp;DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;monotonically&nbsp;increasing/decreasing&nbsp;index.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;default:&nbsp;don't&nbsp;fill&nbsp;gaps<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;next&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;use&nbsp;nearest&nbsp;valid&nbsp;observations&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>,&nbsp;even&nbsp;if&nbsp;the&nbsp;passed&nbsp;indexes&nbsp;are&nbsp;the&nbsp;same<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;elements&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;original&nbsp;and&nbsp;new&nbsp;labels&nbsp;for&nbsp;inexact<br>
-&nbsp;&nbsp;&nbsp;&nbsp;matches.&nbsp;The&nbsp;values&nbsp;of&nbsp;the&nbsp;index&nbsp;at&nbsp;the&nbsp;matching&nbsp;locations&nbsp;most<br>
-&nbsp;&nbsp;&nbsp;&nbsp;satisfy&nbsp;the&nbsp;equation&nbsp;``<a href="#SweepSeries-abs">abs</a>(index[indexer]&nbsp;-&nbsp;target)&nbsp;&lt;=&nbsp;tolerance``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;some&nbsp;fictional&nbsp;data.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;['Firefox',&nbsp;'Chrome',&nbsp;'Safari',&nbsp;'IE10',&nbsp;'Konqueror']<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'http_status':&nbsp;[200,200,404,404,301],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'response_time':&nbsp;[0.04,&nbsp;0.02,&nbsp;0.07,&nbsp;0.08,&nbsp;1.0]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Firefox&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.04<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Konqueror&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;301&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.00<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;new&nbsp;index&nbsp;and&nbsp;reindex&nbsp;the&nbsp;dataframe.&nbsp;By&nbsp;default<br>
-values&nbsp;in&nbsp;the&nbsp;new&nbsp;index&nbsp;that&nbsp;do&nbsp;not&nbsp;have&nbsp;corresponding<br>
-records&nbsp;in&nbsp;the&nbsp;dataframe&nbsp;are&nbsp;assigned&nbsp;``NaN``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;new_index=&nbsp;['Safari',&nbsp;'Iceweasel',&nbsp;'Comodo&nbsp;Dragon',&nbsp;'IE10',<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Chrome']<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-reindex">reindex</a>(new_index)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-We&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;by&nbsp;passing&nbsp;a&nbsp;value&nbsp;to<br>
-the&nbsp;keyword&nbsp;``fill_value``.&nbsp;Because&nbsp;the&nbsp;index&nbsp;is&nbsp;not&nbsp;monotonically<br>
-increasing&nbsp;or&nbsp;decreasing,&nbsp;we&nbsp;cannot&nbsp;use&nbsp;arguments&nbsp;to&nbsp;the&nbsp;keyword<br>
-``method``&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``&nbsp;values.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-reindex">reindex</a>(new_index,&nbsp;fill_value=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-reindex">reindex</a>(new_index,&nbsp;fill_value='missing')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-To&nbsp;further&nbsp;illustrate&nbsp;the&nbsp;filling&nbsp;functionality&nbsp;in<br>
-``reindex``,&nbsp;we&nbsp;will&nbsp;create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;a<br>
-monotonically&nbsp;increasing&nbsp;index&nbsp;(for&nbsp;example,&nbsp;a&nbsp;sequence<br>
-of&nbsp;dates).<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index&nbsp;=&nbsp;pd.date_range('1/1/2010',&nbsp;periods=6,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"prices":&nbsp;[100,&nbsp;101,&nbsp;np.nan,&nbsp;100,&nbsp;89,&nbsp;88]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=date_index)<br>
-&gt;&gt;&gt;&nbsp;df2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-&nbsp;<br>
-Suppose&nbsp;we&nbsp;decide&nbsp;to&nbsp;expand&nbsp;the&nbsp;dataframe&nbsp;to&nbsp;cover&nbsp;a&nbsp;wider<br>
-date&nbsp;range.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index2&nbsp;=&nbsp;pd.date_range('12/29/2009',&nbsp;periods=10,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#SweepSeries-reindex">reindex</a>(date_index2)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-The&nbsp;index&nbsp;entries&nbsp;that&nbsp;did&nbsp;not&nbsp;have&nbsp;a&nbsp;value&nbsp;in&nbsp;the&nbsp;original&nbsp;data&nbsp;frame<br>
-(for&nbsp;example,&nbsp;'2009-12-29')&nbsp;are&nbsp;by&nbsp;default&nbsp;filled&nbsp;with&nbsp;``NaN``.<br>
-If&nbsp;desired,&nbsp;we&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;using&nbsp;one&nbsp;of&nbsp;several<br>
-options.<br>
-&nbsp;<br>
-For&nbsp;example,&nbsp;to&nbsp;backpropagate&nbsp;the&nbsp;last&nbsp;valid&nbsp;value&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values,&nbsp;pass&nbsp;``bfill``&nbsp;as&nbsp;an&nbsp;argument&nbsp;to&nbsp;the&nbsp;``method``&nbsp;keyword.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#SweepSeries-reindex">reindex</a>(date_index2,&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;the&nbsp;``NaN``&nbsp;value&nbsp;present&nbsp;in&nbsp;the&nbsp;original&nbsp;dataframe<br>
-(at&nbsp;index&nbsp;value&nbsp;2010-01-03)&nbsp;will&nbsp;not&nbsp;be&nbsp;filled&nbsp;by&nbsp;any&nbsp;of&nbsp;the<br>
-value&nbsp;propagation&nbsp;schemes.&nbsp;This&nbsp;is&nbsp;because&nbsp;filling&nbsp;while&nbsp;reindexing<br>
-does&nbsp;not&nbsp;look&nbsp;at&nbsp;dataframe&nbsp;values,&nbsp;but&nbsp;only&nbsp;compares&nbsp;the&nbsp;original&nbsp;and<br>
-desired&nbsp;indexes.&nbsp;If&nbsp;you&nbsp;do&nbsp;want&nbsp;to&nbsp;fill&nbsp;in&nbsp;the&nbsp;``NaN``&nbsp;values&nbsp;present<br>
-in&nbsp;the&nbsp;original&nbsp;dataframe,&nbsp;use&nbsp;the&nbsp;``<a href="#SweepSeries-fillna">fillna</a>()``&nbsp;method.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-reindex_axis"><strong>reindex_axis</strong></a>(self, labels, axis=0, **kwargs)</dt><dd><tt>for&nbsp;compatibility&nbsp;with&nbsp;higher&nbsp;dims</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-rename"><strong>rename</strong></a>(self, index=None, **kwargs)</dt><dd><tt>Alter&nbsp;axes&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.&nbsp;Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be<br>
-unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in&nbsp;a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left<br>
-as-is.&nbsp;Extra&nbsp;labels&nbsp;listed&nbsp;don't&nbsp;throw&nbsp;an&nbsp;error.&nbsp;Alternatively,&nbsp;change<br>
-``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;with&nbsp;a&nbsp;scalar&nbsp;value&nbsp;(<a href="pandas.core.series.html#Series">Series</a>&nbsp;only).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Scalar&nbsp;or&nbsp;list-like&nbsp;will&nbsp;alter&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;attribute,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;raise&nbsp;on&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;Panel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict-like&nbsp;or&nbsp;functions&nbsp;are&nbsp;transformations&nbsp;to&nbsp;apply&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;that&nbsp;axis'&nbsp;values<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;If&nbsp;True&nbsp;then&nbsp;value&nbsp;of&nbsp;copy&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;ignored.<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;MultiIndex,&nbsp;only&nbsp;rename&nbsp;labels&nbsp;in&nbsp;the&nbsp;specified<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename_axis<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-rename">rename</a>("my_name")&nbsp;#&nbsp;scalar,&nbsp;changes&nbsp;<a href="pandas.core.series.html#Series">Series</a>.name<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;my_name,&nbsp;dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-rename">rename</a>(lambda&nbsp;x:&nbsp;x&nbsp;**&nbsp;2)&nbsp;&nbsp;#&nbsp;function,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-rename">rename</a>({1:&nbsp;3,&nbsp;2:&nbsp;5})&nbsp;&nbsp;#&nbsp;mapping,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-rename">rename</a>(2)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-TypeError:&nbsp;'int'&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;not&nbsp;callable<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"B":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;c<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"C":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-reorder_levels"><strong>reorder_levels</strong></a>(self, order)</dt><dd><tt>Rearrange&nbsp;index&nbsp;levels&nbsp;using&nbsp;input&nbsp;order.&nbsp;May&nbsp;not&nbsp;drop&nbsp;or&nbsp;duplicate<br>
-levels<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-order&nbsp;:&nbsp;list&nbsp;of&nbsp;int&nbsp;representing&nbsp;new&nbsp;level&nbsp;order.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(reference&nbsp;level&nbsp;by&nbsp;number&nbsp;or&nbsp;key)<br>
-axis&nbsp;:&nbsp;where&nbsp;to&nbsp;reorder&nbsp;levels<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-type&nbsp;of&nbsp;caller&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-repeat"><strong>repeat</strong></a>(self, repeats, *args, **kwargs)</dt><dd><tt>Repeat&nbsp;elements&nbsp;of&nbsp;an&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Refer&nbsp;to&nbsp;`numpy.<a href="numpy.html#ndarray">ndarray</a>.repeat`<br>
-for&nbsp;more&nbsp;information&nbsp;about&nbsp;the&nbsp;`repeats`&nbsp;argument.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.repeat</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-reset_index"><strong>reset_index</strong></a>(self, level=None, drop=False, name=None, inplace=False)</dt><dd><tt>Analogous&nbsp;to&nbsp;the&nbsp;:meth:`pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.reset_index`&nbsp;function,&nbsp;see<br>
-docstring&nbsp;there.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;tuple,&nbsp;or&nbsp;list,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Only&nbsp;remove&nbsp;the&nbsp;given&nbsp;levels&nbsp;from&nbsp;the&nbsp;index.&nbsp;Removes&nbsp;all&nbsp;levels&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-drop&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;not&nbsp;try&nbsp;to&nbsp;insert&nbsp;index&nbsp;into&nbsp;dataframe&nbsp;columns<br>
-name&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;name&nbsp;of&nbsp;the&nbsp;column&nbsp;corresponding&nbsp;to&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Modify&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place&nbsp;(do&nbsp;not&nbsp;create&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-Returns<br>
-----------<br>
-resetted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;if&nbsp;drop&nbsp;==&nbsp;True</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-reshape"><strong>reshape</strong></a>(self, *args, **kwargs)</dt><dd><tt>DEPRECATED:&nbsp;calling&nbsp;this&nbsp;method&nbsp;will&nbsp;raise&nbsp;an&nbsp;error&nbsp;in&nbsp;a<br>
-future&nbsp;release.&nbsp;Please&nbsp;call&nbsp;``.values.<a href="#SweepSeries-reshape">reshape</a>(...)``&nbsp;instead.<br>
-&nbsp;<br>
-return&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;with&nbsp;the&nbsp;values&nbsp;shape<br>
-if&nbsp;the&nbsp;specified&nbsp;shape&nbsp;matches&nbsp;exactly&nbsp;the&nbsp;current&nbsp;shape,&nbsp;then<br>
-return&nbsp;self&nbsp;(for&nbsp;compat)<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.reshape</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-rfloordiv"><strong>rfloordiv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rfloordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;//&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.floordiv</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-rmod"><strong>rmod</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Modulo&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;%&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.mod</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-rmul"><strong>rmul</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;*&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.mul</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-rolling"><strong>rolling</strong></a>(self, window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0, closed=None)</dt><dd><tt>Provides&nbsp;rolling&nbsp;window&nbsp;calculcations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-window&nbsp;:&nbsp;int,&nbsp;or&nbsp;offset<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Size&nbsp;of&nbsp;the&nbsp;moving&nbsp;window.&nbsp;This&nbsp;is&nbsp;the&nbsp;number&nbsp;of&nbsp;observations&nbsp;used&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;calculating&nbsp;the&nbsp;statistic.&nbsp;Each&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;fixed&nbsp;size.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;its&nbsp;an&nbsp;offset&nbsp;then&nbsp;this&nbsp;will&nbsp;be&nbsp;the&nbsp;time&nbsp;period&nbsp;of&nbsp;each&nbsp;window.&nbsp;Each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;variable&nbsp;sized&nbsp;based&nbsp;on&nbsp;the&nbsp;observations&nbsp;included&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;time-period.&nbsp;This&nbsp;is&nbsp;only&nbsp;valid&nbsp;for&nbsp;datetimelike&nbsp;indexes.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;new&nbsp;in&nbsp;0.19.0<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).&nbsp;For&nbsp;a&nbsp;window&nbsp;that&nbsp;is&nbsp;specified&nbsp;by&nbsp;an&nbsp;offset,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;will&nbsp;default&nbsp;to&nbsp;1.<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-win_type&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Provide&nbsp;a&nbsp;window&nbsp;type.&nbsp;See&nbsp;the&nbsp;notes&nbsp;below.<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;on&nbsp;which&nbsp;to&nbsp;calculate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;rolling&nbsp;window,&nbsp;rather&nbsp;than&nbsp;the&nbsp;index<br>
-closed&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;the&nbsp;interval&nbsp;closed&nbsp;on&nbsp;the&nbsp;'right',&nbsp;'left',&nbsp;'both'&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'neither'&nbsp;endpoints.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;offset-based&nbsp;windows,&nbsp;it&nbsp;defaults&nbsp;to&nbsp;'right'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;fixed&nbsp;windows,&nbsp;defaults&nbsp;to&nbsp;'both'.&nbsp;Remaining&nbsp;cases&nbsp;not&nbsp;implemented<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;fixed&nbsp;windows.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;or&nbsp;Rolling&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;using&nbsp;the&nbsp;'triang'<br>
-window&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-rolling">rolling</a>(2,&nbsp;win_type='triang').<a href="#SweepSeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.5<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;min_periods&nbsp;defaults<br>
-to&nbsp;the&nbsp;window&nbsp;length.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-rolling">rolling</a>(2).<a href="#SweepSeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Same&nbsp;as&nbsp;above,&nbsp;but&nbsp;explicity&nbsp;set&nbsp;the&nbsp;min_periods<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-rolling">rolling</a>(2,&nbsp;min_periods=1).<a href="#SweepSeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;2.0<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-A&nbsp;ragged&nbsp;(meaning&nbsp;not-a-regular&nbsp;frequency),&nbsp;time-indexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]},<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;=&nbsp;[pd.Timestamp('20130101&nbsp;09:00:00'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:02'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:03'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:05'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:06')])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;2.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Contrasting&nbsp;to&nbsp;an&nbsp;integer&nbsp;rolling&nbsp;window,&nbsp;this&nbsp;will&nbsp;roll&nbsp;a&nbsp;variable<br>
-length&nbsp;window&nbsp;corresponding&nbsp;to&nbsp;the&nbsp;time&nbsp;period.<br>
-The&nbsp;default&nbsp;for&nbsp;min_periods&nbsp;is&nbsp;1.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-rolling">rolling</a>('2s').<a href="#SweepSeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;3.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offsets&nbsp;&amp;&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-The&nbsp;recognized&nbsp;win_types&nbsp;are:<br>
-&nbsp;<br>
-*&nbsp;``boxcar``<br>
-*&nbsp;``triang``<br>
-*&nbsp;``blackman``<br>
-*&nbsp;``hamming``<br>
-*&nbsp;``bartlett``<br>
-*&nbsp;``parzen``<br>
-*&nbsp;``bohman``<br>
-*&nbsp;``blackmanharris``<br>
-*&nbsp;``nuttall``<br>
-*&nbsp;``barthann``<br>
-*&nbsp;``kaiser``&nbsp;(needs&nbsp;beta)<br>
-*&nbsp;``gaussian``&nbsp;(needs&nbsp;std)<br>
-*&nbsp;``general_gaussian``&nbsp;(needs&nbsp;power,&nbsp;width)<br>
-*&nbsp;``slepian``&nbsp;(needs&nbsp;width).</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-round"><strong>round</strong></a>(self, decimals=0, *args, **kwargs)</dt><dd><tt>Round&nbsp;each&nbsp;value&nbsp;in&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;the&nbsp;given&nbsp;number&nbsp;of&nbsp;decimals.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-decimals&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;round&nbsp;to&nbsp;(default:&nbsp;0).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;decimals&nbsp;is&nbsp;negative,&nbsp;it&nbsp;specifies&nbsp;the&nbsp;number&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;positions&nbsp;to&nbsp;the&nbsp;left&nbsp;of&nbsp;the&nbsp;decimal&nbsp;point.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.series.html#Series">Series</a>&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.around<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.round</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-rpow"><strong>rpow</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rpow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;**&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.pow</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-rsub"><strong>rsub</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rsub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;-&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.sub</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-rtruediv"><strong>rtruediv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-searchsorted"><strong>searchsorted</strong></a>(self, value, side='left', sorter=None)</dt><dd><tt>Find&nbsp;indices&nbsp;where&nbsp;elements&nbsp;should&nbsp;be&nbsp;inserted&nbsp;to&nbsp;maintain&nbsp;order.<br>
-&nbsp;<br>
-Find&nbsp;the&nbsp;indices&nbsp;into&nbsp;a&nbsp;sorted&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;`self`&nbsp;such&nbsp;that,&nbsp;if&nbsp;the<br>
-corresponding&nbsp;elements&nbsp;in&nbsp;`value`&nbsp;were&nbsp;inserted&nbsp;before&nbsp;the&nbsp;indices,<br>
-the&nbsp;order&nbsp;of&nbsp;`self`&nbsp;would&nbsp;be&nbsp;preserved.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;array_like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Values&nbsp;to&nbsp;insert&nbsp;into&nbsp;`self`.<br>
-side&nbsp;:&nbsp;{'left',&nbsp;'right'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'left',&nbsp;the&nbsp;index&nbsp;of&nbsp;the&nbsp;first&nbsp;suitable&nbsp;location&nbsp;found&nbsp;is&nbsp;given.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'right',&nbsp;return&nbsp;the&nbsp;last&nbsp;such&nbsp;index.&nbsp;&nbsp;If&nbsp;there&nbsp;is&nbsp;no&nbsp;suitable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index,&nbsp;return&nbsp;either&nbsp;0&nbsp;or&nbsp;N&nbsp;(where&nbsp;N&nbsp;is&nbsp;the&nbsp;length&nbsp;of&nbsp;`self`).<br>
-sorter&nbsp;:&nbsp;1-D&nbsp;array_like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Optional&nbsp;array&nbsp;of&nbsp;integer&nbsp;indices&nbsp;that&nbsp;sort&nbsp;`self`&nbsp;into&nbsp;ascending<br>
-&nbsp;&nbsp;&nbsp;&nbsp;order.&nbsp;They&nbsp;are&nbsp;typically&nbsp;the&nbsp;result&nbsp;of&nbsp;``np.argsort``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-indices&nbsp;:&nbsp;array&nbsp;of&nbsp;ints<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Array">Array</a>&nbsp;of&nbsp;insertion&nbsp;points&nbsp;with&nbsp;the&nbsp;same&nbsp;shape&nbsp;as&nbsp;`value`.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.searchsorted<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Binary&nbsp;search&nbsp;is&nbsp;used&nbsp;to&nbsp;find&nbsp;the&nbsp;required&nbsp;insertion&nbsp;points.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#SweepSeries-searchsorted">searchsorted</a>(4)<br>
-array([3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#SweepSeries-searchsorted">searchsorted</a>([0,&nbsp;4])<br>
-array([0,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#SweepSeries-searchsorted">searchsorted</a>([1,&nbsp;3],&nbsp;side='left')<br>
-array([0,&nbsp;2])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#SweepSeries-searchsorted">searchsorted</a>([1,&nbsp;3],&nbsp;side='right')<br>
-array([1,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.Categorical(['apple',&nbsp;'bread',&nbsp;'bread',&nbsp;'cheese',&nbsp;'milk'&nbsp;])<br>
-[apple,&nbsp;bread,&nbsp;bread,&nbsp;cheese,&nbsp;milk]<br>
-Categories&nbsp;(4,&nbsp;<a href="builtins.html#object">object</a>):&nbsp;[apple&nbsp;&lt;&nbsp;bread&nbsp;&lt;&nbsp;cheese&nbsp;&lt;&nbsp;milk]<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#SweepSeries-searchsorted">searchsorted</a>('bread')<br>
-array([1])&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;Note:&nbsp;an&nbsp;array,&nbsp;not&nbsp;a&nbsp;scalar<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#SweepSeries-searchsorted">searchsorted</a>(['bread'])<br>
-array([1])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#SweepSeries-searchsorted">searchsorted</a>(['bread',&nbsp;'eggs'])<br>
-array([1,&nbsp;4])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#SweepSeries-searchsorted">searchsorted</a>(['bread',&nbsp;'eggs'],&nbsp;side='right')<br>
-array([3,&nbsp;4])&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;eggs&nbsp;before&nbsp;milk</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-sem"><strong>sem</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;standard&nbsp;error&nbsp;of&nbsp;the&nbsp;mean&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sem&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-set_value"><strong>set_value</strong></a>(self, label, value, takeable=False)</dt><dd><tt>Quickly&nbsp;set&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;label.&nbsp;If&nbsp;label&nbsp;is&nbsp;not&nbsp;contained,&nbsp;a<br>
-new&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;created&nbsp;with&nbsp;the&nbsp;label&nbsp;placed&nbsp;at&nbsp;the&nbsp;end&nbsp;of&nbsp;the&nbsp;result<br>
-index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-label&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Partial&nbsp;indexing&nbsp;with&nbsp;MultiIndex&nbsp;not&nbsp;allowed<br>
-value&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Scalar&nbsp;value<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-series&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;label&nbsp;is&nbsp;contained,&nbsp;will&nbsp;be&nbsp;reference&nbsp;to&nbsp;calling&nbsp;<a href="pandas.core.series.html#Series">Series</a>,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-shift"><strong>shift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;index&nbsp;by&nbsp;desired&nbsp;number&nbsp;of&nbsp;periods&nbsp;with&nbsp;an&nbsp;optional&nbsp;time&nbsp;freq<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM').<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;Notes.<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'}<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;specified&nbsp;then&nbsp;the&nbsp;index&nbsp;values&nbsp;are&nbsp;shifted&nbsp;but&nbsp;the&nbsp;data<br>
-is&nbsp;not&nbsp;realigned.&nbsp;That&nbsp;is,&nbsp;use&nbsp;freq&nbsp;if&nbsp;you&nbsp;would&nbsp;like&nbsp;to&nbsp;extend&nbsp;the<br>
-index&nbsp;when&nbsp;shifting&nbsp;and&nbsp;preserve&nbsp;the&nbsp;original&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-skew"><strong>skew</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;skew&nbsp;over&nbsp;requested&nbsp;axis<br>
-Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-skew&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-sort_index"><strong>sort_index</strong></a>(self, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True)</dt><dd><tt>Sort&nbsp;<a href="builtins.html#object">object</a>&nbsp;by&nbsp;labels&nbsp;(along&nbsp;an&nbsp;axis)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;index&nbsp;to&nbsp;direct&nbsp;sorting<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name&nbsp;or&nbsp;list&nbsp;of&nbsp;ints&nbsp;or&nbsp;list&nbsp;of&nbsp;level&nbsp;names<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None,&nbsp;sort&nbsp;on&nbsp;values&nbsp;in&nbsp;specified&nbsp;index&nbsp;level(s)<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;MultiIndex.<br>
-sort_remaining&nbsp;:&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;true&nbsp;and&nbsp;sorting&nbsp;by&nbsp;level&nbsp;and&nbsp;index&nbsp;is&nbsp;multilevel,&nbsp;sort&nbsp;by&nbsp;other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;levels&nbsp;too&nbsp;(in&nbsp;order)&nbsp;after&nbsp;sorting&nbsp;by&nbsp;specified&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-sort_values"><strong>sort_values</strong></a>(self, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')</dt><dd><tt>Sort&nbsp;by&nbsp;the&nbsp;values&nbsp;along&nbsp;either&nbsp;axis<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;direct&nbsp;sorting<br>
-ascending&nbsp;:&nbsp;bool&nbsp;or&nbsp;list&nbsp;of&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending.&nbsp;Specify&nbsp;list&nbsp;for&nbsp;multiple&nbsp;sort<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;orders.&nbsp;&nbsp;If&nbsp;this&nbsp;is&nbsp;a&nbsp;list&nbsp;of&nbsp;bools,&nbsp;must&nbsp;match&nbsp;the&nbsp;length&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;by.<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-sortlevel"><strong>sortlevel</strong></a>(self, level=0, ascending=True, sort_remaining=True)</dt><dd><tt>DEPRECATED:&nbsp;use&nbsp;:meth:`<a href="pandas.core.series.html#Series">Series</a>.sort_index`<br>
-&nbsp;<br>
-Sort&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;MultiIndex&nbsp;by&nbsp;chosen&nbsp;level.&nbsp;Data&nbsp;will&nbsp;be<br>
-lexicographically&nbsp;sorted&nbsp;by&nbsp;the&nbsp;chosen&nbsp;level&nbsp;followed&nbsp;by&nbsp;the&nbsp;other<br>
-levels&nbsp;(in&nbsp;order)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-ascending&nbsp;:&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.<a href="#SweepSeries-sort_index">sort_index</a>(level=...)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-std"><strong>std</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;sample&nbsp;standard&nbsp;deviation&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-std&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-sub"><strong>sub</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-subtract"><strong>subtract</strong></a> = sub(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-sum"><strong>sum</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;sum&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sum&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-swaplevel"><strong>swaplevel</strong></a>(self, i=-2, j=-1, copy=True)</dt><dd><tt>Swap&nbsp;levels&nbsp;i&nbsp;and&nbsp;j&nbsp;in&nbsp;a&nbsp;MultiIndex<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-i,&nbsp;j&nbsp;:&nbsp;int,&nbsp;string&nbsp;(can&nbsp;be&nbsp;mixed)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level&nbsp;of&nbsp;index&nbsp;to&nbsp;be&nbsp;swapped.&nbsp;Can&nbsp;pass&nbsp;level&nbsp;name&nbsp;as&nbsp;string.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-swapped&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-..&nbsp;versionchanged::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;The&nbsp;indexes&nbsp;``i``&nbsp;and&nbsp;``j``&nbsp;are&nbsp;now&nbsp;optional,&nbsp;and&nbsp;default&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;the&nbsp;two&nbsp;innermost&nbsp;levels&nbsp;of&nbsp;the&nbsp;index.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-take"><strong>take</strong></a>(self, indices, axis=0, convert=True, is_copy=False, **kwargs)</dt><dd><tt>return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;corresponding&nbsp;to&nbsp;requested&nbsp;indices<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-indices&nbsp;:&nbsp;list&nbsp;/&nbsp;array&nbsp;of&nbsp;ints<br>
-convert&nbsp;:&nbsp;translate&nbsp;negative&nbsp;to&nbsp;positive&nbsp;indices&nbsp;(default)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-taken&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.take</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_csv"><strong>to_csv</strong></a>(self, path=None, index=True, sep=',', na_rep='', float_format=None, header=False, index_label=None, mode='w', encoding=None, date_format=None, decimal='.')</dt><dd><tt>Write&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;a&nbsp;comma-separated&nbsp;values&nbsp;(csv)&nbsp;file<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;or&nbsp;file&nbsp;handle,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;<a href="builtins.html#object">object</a>,&nbsp;if&nbsp;None&nbsp;is&nbsp;provided&nbsp;the&nbsp;result&nbsp;is&nbsp;returned&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string.<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-header&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;series&nbsp;name<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-mode&nbsp;:&nbsp;Python&nbsp;write&nbsp;mode,&nbsp;default&nbsp;'w'<br>
-sep&nbsp;:&nbsp;character,&nbsp;default&nbsp;","<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter&nbsp;for&nbsp;the&nbsp;output&nbsp;file.<br>
-encoding&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;if&nbsp;the&nbsp;contents&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;non-ascii,&nbsp;for&nbsp;python&nbsp;versions&nbsp;prior&nbsp;to&nbsp;3<br>
-date_format:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;datetime&nbsp;objects.<br>
-decimal:&nbsp;string,&nbsp;default&nbsp;'.'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Character&nbsp;recognized&nbsp;as&nbsp;decimal&nbsp;separator.&nbsp;E.g.&nbsp;use&nbsp;','&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;European&nbsp;data</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_dict"><strong>to_dict</strong></a>(self)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;{label&nbsp;-&gt;&nbsp;value}&nbsp;dict<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value_dict&nbsp;:&nbsp;dict</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_excel"><strong>to_excel</strong></a>(self, excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True)</dt><dd><tt>Write&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;an&nbsp;excel&nbsp;sheet<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel_writer&nbsp;:&nbsp;string&nbsp;or&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;existing&nbsp;ExcelWriter<br>
-sheet_name&nbsp;:&nbsp;string,&nbsp;default&nbsp;'Sheet1'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;sheet&nbsp;which&nbsp;will&nbsp;contain&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Columns&nbsp;to&nbsp;write<br>
-header&nbsp;:&nbsp;boolean&nbsp;or&nbsp;list&nbsp;of&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given&nbsp;it&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;assumed&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-startrow&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;row&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-startcol&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;column&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-engine&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;write&nbsp;engine&nbsp;to&nbsp;use&nbsp;-&nbsp;you&nbsp;can&nbsp;also&nbsp;set&nbsp;this&nbsp;via&nbsp;the&nbsp;options<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsx.writer``,&nbsp;``io.excel.xls.writer``,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsm.writer``.<br>
-merge_cells&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;MultiIndex&nbsp;and&nbsp;Hierarchical&nbsp;Rows&nbsp;as&nbsp;merged&nbsp;cells.<br>
-encoding:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;encoding&nbsp;of&nbsp;the&nbsp;resulting&nbsp;excel&nbsp;file.&nbsp;Only&nbsp;necessary&nbsp;for&nbsp;xlwt,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;writers&nbsp;support&nbsp;unicode&nbsp;natively.<br>
-inf_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;'inf'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Representation&nbsp;for&nbsp;infinity&nbsp;(there&nbsp;is&nbsp;no&nbsp;native&nbsp;representation&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;infinity&nbsp;in&nbsp;Excel)<br>
-freeze_panes&nbsp;:&nbsp;tuple&nbsp;of&nbsp;integer&nbsp;(length&nbsp;2),&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;the&nbsp;one-based&nbsp;bottommost&nbsp;row&nbsp;and&nbsp;rightmost&nbsp;column&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;to&nbsp;be&nbsp;frozen<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;passing&nbsp;an&nbsp;existing&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a>,&nbsp;then&nbsp;the&nbsp;sheet&nbsp;will&nbsp;be&nbsp;added<br>
-to&nbsp;the&nbsp;existing&nbsp;workbook.&nbsp;&nbsp;This&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;save&nbsp;different<br>
-DataFrames&nbsp;to&nbsp;one&nbsp;workbook:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;writer&nbsp;=&nbsp;pd.ExcelWriter('output.xlsx')<br>
-&gt;&gt;&gt;&nbsp;df1.<a href="#SweepSeries-to_excel">to_excel</a>(writer,'Sheet1')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#SweepSeries-to_excel">to_excel</a>(writer,'Sheet2')<br>
-&gt;&gt;&gt;&nbsp;writer.save()<br>
-&nbsp;<br>
-For&nbsp;compatibility&nbsp;with&nbsp;to_csv,&nbsp;to_excel&nbsp;serializes&nbsp;lists&nbsp;and&nbsp;dicts&nbsp;to<br>
-strings&nbsp;before&nbsp;writing.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_frame"><strong>to_frame</strong></a>(self, name=None)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-name&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;passed&nbsp;name&nbsp;should&nbsp;substitute&nbsp;for&nbsp;the&nbsp;series&nbsp;name&nbsp;(if&nbsp;it&nbsp;has<br>
-&nbsp;&nbsp;&nbsp;&nbsp;one).<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-data_frame&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_period"><strong>to_period</strong></a>(self, freq=None, copy=True)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;from&nbsp;DatetimeIndex&nbsp;to&nbsp;PeriodIndex&nbsp;with&nbsp;desired<br>
-frequency&nbsp;(inferred&nbsp;from&nbsp;index&nbsp;if&nbsp;not&nbsp;passed)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;PeriodIndex</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_sparse"><strong>to_sparse</strong></a>(self, kind='block', fill_value=None)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;SparseSeries<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-kind&nbsp;:&nbsp;{'block',&nbsp;'integer'}<br>
-fill_value&nbsp;:&nbsp;float,&nbsp;defaults&nbsp;to&nbsp;NaN&nbsp;(missing)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sp&nbsp;:&nbsp;SparseSeries</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_string"><strong>to_string</strong></a>(self, buf=None, na_rep='NaN', float_format=None, header=True, index=True, length=False, dtype=False, name=False, max_rows=None)</dt><dd><tt>Render&nbsp;a&nbsp;string&nbsp;representation&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-buf&nbsp;:&nbsp;StringIO-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;buffer&nbsp;to&nbsp;write&nbsp;to<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;representation&nbsp;of&nbsp;NAN&nbsp;to&nbsp;use,&nbsp;default&nbsp;'NaN'<br>
-float_format&nbsp;:&nbsp;one-parameter&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;if&nbsp;they&nbsp;are&nbsp;floats<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None<br>
-header:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;header&nbsp;(index&nbsp;name)<br>
-index&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;index&nbsp;(row)&nbsp;labels,&nbsp;default&nbsp;True<br>
-length&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;length<br>
-dtype&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;dtype<br>
-name&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;name&nbsp;if&nbsp;not&nbsp;None<br>
-max_rows&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;rows&nbsp;to&nbsp;show&nbsp;before&nbsp;truncating.&nbsp;If&nbsp;None,&nbsp;show<br>
-&nbsp;&nbsp;&nbsp;&nbsp;all.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-formatted&nbsp;:&nbsp;string&nbsp;(if&nbsp;not&nbsp;buffer&nbsp;passed)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_timestamp"><strong>to_timestamp</strong></a>(self, freq=None, how='start', copy=True)</dt><dd><tt>Cast&nbsp;to&nbsp;datetimeindex&nbsp;of&nbsp;timestamps,&nbsp;at&nbsp;*beginning*&nbsp;of&nbsp;period<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default&nbsp;frequency&nbsp;of&nbsp;PeriodIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Desired&nbsp;frequency<br>
-how&nbsp;:&nbsp;{'s',&nbsp;'e',&nbsp;'start',&nbsp;'end'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Convention&nbsp;for&nbsp;converting&nbsp;period&nbsp;to&nbsp;timestamp;&nbsp;start&nbsp;of&nbsp;period<br>
-&nbsp;&nbsp;&nbsp;&nbsp;vs.&nbsp;end<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;DatetimeIndex</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-tolist"><strong>tolist</strong></a>(self)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;a&nbsp;nested&nbsp;list</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-transform"><strong>transform</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Call&nbsp;function&nbsp;producing&nbsp;a&nbsp;like-indexed&nbsp;NDFrame<br>
-and&nbsp;return&nbsp;a&nbsp;NDFrame&nbsp;with&nbsp;the&nbsp;transformed&nbsp;values`<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;apply&nbsp;to&nbsp;column<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-transformed&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(10,&nbsp;3),&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.date_range('1/1/2000',&nbsp;periods=10))<br>
-df.iloc[3:7]&nbsp;=&nbsp;np.nan<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-transform">transform</a>(lambda&nbsp;x:&nbsp;(x&nbsp;-&nbsp;x.<a href="#SweepSeries-mean">mean</a>())&nbsp;/&nbsp;x.<a href="#SweepSeries-std">std</a>())<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-2000-01-01&nbsp;&nbsp;0.579457&nbsp;&nbsp;1.236184&nbsp;&nbsp;0.123424<br>
-2000-01-02&nbsp;&nbsp;0.370357&nbsp;-0.605875&nbsp;-1.231325<br>
-2000-01-03&nbsp;&nbsp;1.455756&nbsp;-0.277446&nbsp;&nbsp;0.288967<br>
-2000-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-08&nbsp;-0.498658&nbsp;&nbsp;1.274522&nbsp;&nbsp;1.642524<br>
-2000-01-09&nbsp;-0.540524&nbsp;-1.012676&nbsp;-0.828968<br>
-2000-01-10&nbsp;-1.366388&nbsp;-0.614710&nbsp;&nbsp;0.005378<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.NDFrame.aggregate<br>
-pandas.NDFrame.apply</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-truediv"><strong>truediv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-unique"><strong>unique</strong></a>(self)</dt><dd><tt>Return&nbsp;unique&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.&nbsp;Uniques&nbsp;are&nbsp;returned&nbsp;in&nbsp;order<br>
-of&nbsp;appearance,&nbsp;this&nbsp;does&nbsp;NOT&nbsp;sort.&nbsp;Hash&nbsp;table-based&nbsp;unique.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-values&nbsp;:&nbsp;1d&nbsp;array-like<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-unique&nbsp;values.<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;an&nbsp;Index,&nbsp;the&nbsp;return&nbsp;is&nbsp;an&nbsp;Index<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;a&nbsp;Categorical&nbsp;dtype,&nbsp;the&nbsp;return&nbsp;is&nbsp;a&nbsp;Categorical<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>/<a href="numpy.html#ndarray">ndarray</a>,&nbsp;the&nbsp;return&nbsp;will&nbsp;be&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-unique<br>
-Index.unique<br>
-<a href="pandas.core.series.html#Series">Series</a>.unique</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-unstack"><strong>unstack</strong></a>(self, level=-1, fill_value=None)</dt><dd><tt>Unstack,&nbsp;a.k.a.&nbsp;pivot,&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;MultiIndex&nbsp;to&nbsp;produce&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-The&nbsp;level&nbsp;involved&nbsp;will&nbsp;automatically&nbsp;get&nbsp;sorted.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;string,&nbsp;or&nbsp;list&nbsp;of&nbsp;these,&nbsp;default&nbsp;last&nbsp;level<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level(s)&nbsp;to&nbsp;unstack,&nbsp;can&nbsp;pass&nbsp;level&nbsp;name<br>
-fill_value&nbsp;:&nbsp;replace&nbsp;NaN&nbsp;with&nbsp;this&nbsp;value&nbsp;if&nbsp;the&nbsp;unstack&nbsp;produces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.18.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;4],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.MultiIndex.from_product([['one',&nbsp;'two'],&nbsp;['a',&nbsp;'b']]))<br>
-&gt;&gt;&gt;&nbsp;s<br>
-one&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-two&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-unstack">unstack</a>(level=-1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b<br>
-one&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-two&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-unstack">unstack</a>(level=0)<br>
-&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;two<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-unstacked&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-update"><strong>update</strong></a>(self, other)</dt><dd><tt>Modify&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place&nbsp;using&nbsp;non-NA&nbsp;values&nbsp;from&nbsp;passed<br>
-<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Aligns&nbsp;on&nbsp;index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><strong>valid</strong> <em>lambda</em> self, inplace=False, **kwargs</dt></dl>
-
-<dl><dt><a name="SweepSeries-var"><strong>var</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;variance&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-var&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-view"><strong>view</strong></a>(self, dtype=None)</dt></dl>
-
-<hr>
-Class methods inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><a name="SweepSeries-from_array"><strong>from_array</strong></a>(arr, index=None, name=None, dtype=None, copy=False, fastpath=False)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt></dl>
-
-<dl><dt><a name="SweepSeries-from_csv"><strong>from_csv</strong></a>(path, sep=',', parse_dates=True, header=None, index_col=0, encoding=None, infer_datetime_format=False)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Read&nbsp;CSV&nbsp;file&nbsp;(DISCOURAGED,&nbsp;please&nbsp;use&nbsp;:func:`pandas.read_csv`<br>
-instead).<br>
-&nbsp;<br>
-It&nbsp;is&nbsp;preferable&nbsp;to&nbsp;use&nbsp;the&nbsp;more&nbsp;powerful&nbsp;:func:`pandas.read_csv`<br>
-for&nbsp;most&nbsp;general&nbsp;purposes,&nbsp;but&nbsp;``from_csv``&nbsp;makes&nbsp;for&nbsp;an&nbsp;easy<br>
-roundtrip&nbsp;to&nbsp;and&nbsp;from&nbsp;a&nbsp;file&nbsp;(the&nbsp;exact&nbsp;counterpart&nbsp;of<br>
-``to_csv``),&nbsp;especially&nbsp;with&nbsp;a&nbsp;time&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;only&nbsp;differs&nbsp;from&nbsp;:func:`pandas.read_csv`&nbsp;in&nbsp;some&nbsp;defaults:<br>
-&nbsp;<br>
--&nbsp;`index_col`&nbsp;is&nbsp;``0``&nbsp;instead&nbsp;of&nbsp;``None``&nbsp;(take&nbsp;first&nbsp;column&nbsp;as&nbsp;index<br>
-&nbsp;&nbsp;by&nbsp;default)<br>
--&nbsp;`header`&nbsp;is&nbsp;``None``&nbsp;instead&nbsp;of&nbsp;``0``&nbsp;(the&nbsp;first&nbsp;row&nbsp;is&nbsp;not&nbsp;used&nbsp;as<br>
-&nbsp;&nbsp;the&nbsp;column&nbsp;names)<br>
--&nbsp;`parse_dates`&nbsp;is&nbsp;``True``&nbsp;instead&nbsp;of&nbsp;``False``&nbsp;(try&nbsp;parsing&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;as&nbsp;datetime&nbsp;by&nbsp;default)<br>
-&nbsp;<br>
-With&nbsp;:func:`pandas.read_csv`,&nbsp;the&nbsp;option&nbsp;``squeeze=True``&nbsp;can&nbsp;be&nbsp;used<br>
-to&nbsp;return&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;like&nbsp;``from_csv``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;file&nbsp;path&nbsp;or&nbsp;file&nbsp;handle&nbsp;/&nbsp;StringIO<br>
-sep&nbsp;:&nbsp;string,&nbsp;default&nbsp;','<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter<br>
-parse_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Parse&nbsp;dates.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-header&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Row&nbsp;to&nbsp;use&nbsp;as&nbsp;header&nbsp;(skip&nbsp;prior&nbsp;rows)<br>
-index_col&nbsp;:&nbsp;int&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;to&nbsp;use&nbsp;for&nbsp;index.&nbsp;If&nbsp;a&nbsp;sequence&nbsp;is&nbsp;given,&nbsp;a&nbsp;MultiIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;used.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-encoding&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;if&nbsp;the&nbsp;contents&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;non-ascii,&nbsp;for&nbsp;python&nbsp;versions&nbsp;prior&nbsp;to&nbsp;3<br>
-infer_datetime_format:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;and&nbsp;`parse_dates`&nbsp;is&nbsp;True&nbsp;for&nbsp;a&nbsp;column,&nbsp;try&nbsp;to&nbsp;infer&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;datetime&nbsp;format&nbsp;based&nbsp;on&nbsp;the&nbsp;first&nbsp;datetime&nbsp;string.&nbsp;If&nbsp;the&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;be&nbsp;inferred,&nbsp;there&nbsp;often&nbsp;will&nbsp;be&nbsp;a&nbsp;large&nbsp;parsing&nbsp;speed-up.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.read_csv<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><strong>asobject</strong></dt>
-<dd><tt>return&nbsp;object&nbsp;Series&nbsp;which&nbsp;contains&nbsp;boxed&nbsp;values<br>
-&nbsp;<br>
-*this&nbsp;is&nbsp;an&nbsp;internal&nbsp;non-public&nbsp;method*</tt></dd>
-</dl>
-<dl><dt><strong>axes</strong></dt>
-<dd><tt>Return&nbsp;a&nbsp;list&nbsp;of&nbsp;the&nbsp;row&nbsp;axis&nbsp;labels</tt></dd>
-</dl>
-<dl><dt><strong>dtype</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;dtype&nbsp;object&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>dtypes</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;dtype&nbsp;object&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>ftype</strong></dt>
-<dd><tt>return&nbsp;if&nbsp;the&nbsp;data&nbsp;is&nbsp;sparse|dense</tt></dd>
-</dl>
-<dl><dt><strong>ftypes</strong></dt>
-<dd><tt>return&nbsp;if&nbsp;the&nbsp;data&nbsp;is&nbsp;sparse|dense</tt></dd>
-</dl>
-<dl><dt><strong>imag</strong></dt>
-</dl>
-<dl><dt><strong>index</strong></dt>
-</dl>
-<dl><dt><strong>name</strong></dt>
-</dl>
-<dl><dt><strong>real</strong></dt>
-</dl>
-<dl><dt><strong>values</strong></dt>
-<dd><tt>Return&nbsp;Series&nbsp;as&nbsp;ndarray&nbsp;or&nbsp;ndarray-like<br>
-depending&nbsp;on&nbsp;the&nbsp;dtype<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-arr&nbsp;:&nbsp;numpy.ndarray&nbsp;or&nbsp;ndarray-like<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;pd.Series([1,&nbsp;2,&nbsp;3]).values<br>
-array([1,&nbsp;2,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(list('aabc')).values<br>
-array(['a',&nbsp;'a',&nbsp;'b',&nbsp;'c'],&nbsp;dtype=object)<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(list('aabc')).astype('category').values<br>
-[a,&nbsp;a,&nbsp;b,&nbsp;c]<br>
-Categories&nbsp;(3,&nbsp;object):&nbsp;[a,&nbsp;b,&nbsp;c]<br>
-&nbsp;<br>
-Timezone&nbsp;aware&nbsp;datetime&nbsp;data&nbsp;is&nbsp;converted&nbsp;to&nbsp;UTC:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(pd.date_range('20130101',&nbsp;periods=3,<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;tz='US/Eastern')).values<br>
-array(['2013-01-01T05:00:00.000000000',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'2013-01-02T05:00:00.000000000',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'2013-01-03T05:00:00.000000000'],&nbsp;dtype='datetime64[ns]')</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><strong>cat</strong> = &lt;class 'pandas.core.categorical.CategoricalAccessor'&gt;<dd><tt>Accessor&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;categorical&nbsp;properties&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values.<br>
-&nbsp;<br>
-Be&nbsp;aware&nbsp;that&nbsp;assigning&nbsp;to&nbsp;`categories`&nbsp;is&nbsp;a&nbsp;inplace&nbsp;operation,&nbsp;while&nbsp;all<br>
-methods&nbsp;return&nbsp;new&nbsp;categorical&nbsp;data&nbsp;per&nbsp;default&nbsp;(but&nbsp;can&nbsp;be&nbsp;called&nbsp;with<br>
-`inplace=True`).<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.cat.categories<br>
-&gt;&gt;&gt;&nbsp;s.cat.categories&nbsp;=&nbsp;list('abc')<br>
-&gt;&gt;&gt;&nbsp;s.cat.rename_categories(list('cab'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.reorder_categories(list('cab'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.add_categories(['d','e'])<br>
-&gt;&gt;&gt;&nbsp;s.cat.remove_categories(['d'])<br>
-&gt;&gt;&gt;&nbsp;s.cat.remove_unused_categories()<br>
-&gt;&gt;&gt;&nbsp;s.cat.set_categories(list('abcde'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.as_ordered()<br>
-&gt;&gt;&gt;&nbsp;s.cat.as_unordered()</tt></dl>
-
-<dl><dt><strong>dt</strong> = &lt;class 'pandas.core.indexes.accessors.CombinedDatetimelikeProperties'&gt;<dd><tt>Accessor&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;datetimelike&nbsp;properties&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.dt.hour<br>
-&gt;&gt;&gt;&nbsp;s.dt.second<br>
-&gt;&gt;&gt;&nbsp;s.dt.quarter<br>
-&nbsp;<br>
-Returns&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;indexed&nbsp;like&nbsp;the&nbsp;original&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-Raises&nbsp;TypeError&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;does&nbsp;not&nbsp;contain&nbsp;datetimelike&nbsp;values.</tt></dl>
-
-<dl><dt><strong>plot</strong> = &lt;class 'pandas.plotting._core.SeriesPlotMethods'&gt;<dd><tt><a href="pandas.core.series.html#Series">Series</a>&nbsp;plotting&nbsp;accessor&nbsp;and&nbsp;method<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.plot.line()<br>
-&gt;&gt;&gt;&nbsp;s.plot.bar()<br>
-&gt;&gt;&gt;&nbsp;s.plot.<a href="#SweepSeries-hist">hist</a>()<br>
-&nbsp;<br>
-Plotting&nbsp;methods&nbsp;can&nbsp;also&nbsp;be&nbsp;accessed&nbsp;by&nbsp;calling&nbsp;the&nbsp;accessor&nbsp;as&nbsp;a&nbsp;method<br>
-with&nbsp;the&nbsp;``kind``&nbsp;argument:<br>
-``s.<a href="#SweepSeries-plot">plot</a>(kind='line')``&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;``s.plot.line()``</tt></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><a name="SweepSeries-factorize"><strong>factorize</strong></a>(self, sort=False, na_sentinel=-1)</dt><dd><tt>Encode&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;as&nbsp;an&nbsp;enumerated&nbsp;type&nbsp;or&nbsp;categorical&nbsp;variable<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;by&nbsp;values<br>
-na_sentinel:&nbsp;int,&nbsp;default&nbsp;-1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;mark&nbsp;"not&nbsp;found"<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-labels&nbsp;:&nbsp;the&nbsp;indexer&nbsp;to&nbsp;the&nbsp;original&nbsp;array<br>
-uniques&nbsp;:&nbsp;the&nbsp;unique&nbsp;Index</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-item"><strong>item</strong></a>(self)</dt><dd><tt>return&nbsp;the&nbsp;first&nbsp;element&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data&nbsp;as&nbsp;a&nbsp;python<br>
-scalar</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-nunique"><strong>nunique</strong></a>(self, dropna=True)</dt><dd><tt>Return&nbsp;number&nbsp;of&nbsp;unique&nbsp;elements&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Excludes&nbsp;NA&nbsp;values&nbsp;by&nbsp;default.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Don't&nbsp;include&nbsp;NaN&nbsp;in&nbsp;the&nbsp;count.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-nunique&nbsp;:&nbsp;int</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-transpose"><strong>transpose</strong></a>(self, *args, **kwargs)</dt><dd><tt>return&nbsp;the&nbsp;transpose,&nbsp;which&nbsp;is&nbsp;by&nbsp;definition&nbsp;self</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-value_counts"><strong>value_counts</strong></a>(self, normalize=False, sort=True, ascending=False, bins=None, dropna=True)</dt><dd><tt>Returns&nbsp;<a href="builtins.html#object">object</a>&nbsp;containing&nbsp;counts&nbsp;of&nbsp;unique&nbsp;values.<br>
-&nbsp;<br>
-The&nbsp;resulting&nbsp;<a href="builtins.html#object">object</a>&nbsp;will&nbsp;be&nbsp;in&nbsp;descending&nbsp;order&nbsp;so&nbsp;that&nbsp;the<br>
-first&nbsp;element&nbsp;is&nbsp;the&nbsp;most&nbsp;frequently-occurring&nbsp;element.<br>
-Excludes&nbsp;NA&nbsp;values&nbsp;by&nbsp;default.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-normalize&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;then&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;returned&nbsp;will&nbsp;contain&nbsp;the&nbsp;relative<br>
-&nbsp;&nbsp;&nbsp;&nbsp;frequencies&nbsp;of&nbsp;the&nbsp;unique&nbsp;values.<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;by&nbsp;values<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;in&nbsp;ascending&nbsp;order<br>
-bins&nbsp;:&nbsp;integer,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Rather&nbsp;than&nbsp;count&nbsp;values,&nbsp;group&nbsp;them&nbsp;into&nbsp;half-open&nbsp;bins,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;convenience&nbsp;for&nbsp;pd.cut,&nbsp;only&nbsp;works&nbsp;with&nbsp;numeric&nbsp;data<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Don't&nbsp;include&nbsp;counts&nbsp;of&nbsp;NaN.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-counts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><strong>T</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;transpose,&nbsp;which&nbsp;is&nbsp;by&nbsp;definition&nbsp;self</tt></dd>
-</dl>
-<dl><dt><strong>__dict__</strong></dt>
-<dd><tt>dictionary&nbsp;for&nbsp;instance&nbsp;variables&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>__weakref__</strong></dt>
-<dd><tt>list&nbsp;of&nbsp;weak&nbsp;references&nbsp;to&nbsp;the&nbsp;object&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>base</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;base&nbsp;object&nbsp;if&nbsp;the&nbsp;memory&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data&nbsp;is<br>
-shared</tt></dd>
-</dl>
-<dl><dt><strong>data</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;data&nbsp;pointer&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>empty</strong></dt>
-</dl>
-<dl><dt><strong>flags</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;ndarray.flags&nbsp;for&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>hasnans</strong></dt>
-</dl>
-<dl><dt><strong>is_monotonic</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_increasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_monotonic_decreasing</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_decreasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic_decreasing&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_monotonic_increasing</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_increasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_unique</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are&nbsp;unique<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_unique&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>itemsize</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;size&nbsp;of&nbsp;the&nbsp;dtype&nbsp;of&nbsp;the&nbsp;item&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>nbytes</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;bytes&nbsp;in&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>ndim</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;dimensions&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data,<br>
-by&nbsp;definition&nbsp;1</tt></dd>
-</dl>
-<dl><dt><strong>shape</strong></dt>
-<dd><tt>return&nbsp;a&nbsp;tuple&nbsp;of&nbsp;the&nbsp;shape&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>size</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;elements&nbsp;in&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>strides</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;strides&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><strong>__array_priority__</strong> = 1000</dl>
-
-<hr>
-Data and other attributes inherited from <a href="pandas.core.strings.html#StringAccessorMixin">pandas.core.strings.StringAccessorMixin</a>:<br>
-<dl><dt><strong>str</strong> = &lt;class 'pandas.core.strings.StringMethods'&gt;<dd><tt>Vectorized&nbsp;string&nbsp;functions&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;and&nbsp;Index.&nbsp;NAs&nbsp;stay&nbsp;NA&nbsp;unless<br>
-handled&nbsp;otherwise&nbsp;by&nbsp;a&nbsp;particular&nbsp;method.&nbsp;Patterned&nbsp;after&nbsp;Python's&nbsp;string<br>
-methods,&nbsp;with&nbsp;some&nbsp;inspiration&nbsp;from&nbsp;R's&nbsp;stringr&nbsp;package.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.str.split('_')<br>
-&gt;&gt;&gt;&nbsp;s.str.<a href="#SweepSeries-replace">replace</a>('_',&nbsp;'')</tt></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><a name="SweepSeries-__abs__"><strong>__abs__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__bool__"><strong>__bool__</strong></a> = __nonzero__(self)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__contains__"><strong>__contains__</strong></a>(self, key)</dt><dd><tt>True&nbsp;if&nbsp;the&nbsp;key&nbsp;is&nbsp;in&nbsp;the&nbsp;info&nbsp;axis</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-__copy__"><strong>__copy__</strong></a>(self, deep=True)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__deepcopy__"><strong>__deepcopy__</strong></a>(self, memo=None)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__delitem__"><strong>__delitem__</strong></a>(self, key)</dt><dd><tt>Delete&nbsp;item</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-__finalize__"><strong>__finalize__</strong></a>(self, other, method=None, **kwargs)</dt><dd><tt>Propagate&nbsp;metadata&nbsp;from&nbsp;other&nbsp;to&nbsp;self.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;which&nbsp;to&nbsp;get&nbsp;the&nbsp;attributes&nbsp;that&nbsp;we&nbsp;are&nbsp;going<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;propagate<br>
-method&nbsp;:&nbsp;optional,&nbsp;a&nbsp;passed&nbsp;method&nbsp;name&nbsp;;&nbsp;possibly&nbsp;to&nbsp;take&nbsp;different<br>
-&nbsp;&nbsp;&nbsp;&nbsp;types&nbsp;of&nbsp;propagation&nbsp;actions&nbsp;based&nbsp;on&nbsp;this</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-__getattr__"><strong>__getattr__</strong></a>(self, name)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;looking&nbsp;up&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-__getstate__"><strong>__getstate__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__hash__"><strong>__hash__</strong></a>(self)</dt><dd><tt>Return&nbsp;hash(self).</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-__invert__"><strong>__invert__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__neg__"><strong>__neg__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__nonzero__"><strong>__nonzero__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__round__"><strong>__round__</strong></a>(self, decimals=0)</dt></dl>
-
-<dl><dt><a name="SweepSeries-__setattr__"><strong>__setattr__</strong></a>(self, name, value)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;setting&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-__setstate__"><strong>__setstate__</strong></a>(self, state)</dt></dl>
-
-<dl><dt><a name="SweepSeries-abs"><strong>abs</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;absolute&nbsp;value&nbsp;taken--only&nbsp;applicable&nbsp;to&nbsp;objects<br>
-that&nbsp;are&nbsp;all&nbsp;numeric.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-abs:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-add_prefix"><strong>add_prefix</strong></a>(self, prefix)</dt><dd><tt>Concatenate&nbsp;prefix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-prefix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_prefix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-add_suffix"><strong>add_suffix</strong></a>(self, suffix)</dt><dd><tt>Concatenate&nbsp;suffix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-suffix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_suffix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-as_blocks"><strong>as_blocks</strong></a>(self, copy=True)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types&nbsp;that&nbsp;each&nbsp;has<br>
-a&nbsp;homogeneous&nbsp;dtype.<br>
-&nbsp;<br>
-NOTE:&nbsp;the&nbsp;dtypes&nbsp;of&nbsp;the&nbsp;blocks&nbsp;WILL&nbsp;BE&nbsp;PRESERVED&nbsp;HERE&nbsp;(unlike&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;as_matrix)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-as_matrix"><strong>as_matrix</strong></a>(self, columns=None)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;its&nbsp;Numpy-array&nbsp;representation.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-columns:&nbsp;list,&nbsp;optional,&nbsp;default:None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;None,&nbsp;return&nbsp;all&nbsp;columns,&nbsp;otherwise,&nbsp;returns&nbsp;specified&nbsp;columns.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;caller&nbsp;is&nbsp;heterogeneous&nbsp;and&nbsp;contains&nbsp;booleans&nbsp;or&nbsp;objects,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;of&nbsp;dtype=<a href="builtins.html#object">object</a>.&nbsp;See&nbsp;Notes.<br>
-&nbsp;<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Return&nbsp;is&nbsp;NOT&nbsp;a&nbsp;Numpy-matrix,&nbsp;rather,&nbsp;a&nbsp;Numpy-array.<br>
-&nbsp;<br>
-The&nbsp;dtype&nbsp;will&nbsp;be&nbsp;a&nbsp;lower-common-denominator&nbsp;dtype&nbsp;(implicit<br>
-upcasting);&nbsp;that&nbsp;is&nbsp;to&nbsp;say&nbsp;if&nbsp;the&nbsp;dtypes&nbsp;(even&nbsp;of&nbsp;numeric&nbsp;types)<br>
-are&nbsp;mixed,&nbsp;the&nbsp;one&nbsp;that&nbsp;accommodates&nbsp;all&nbsp;will&nbsp;be&nbsp;chosen.&nbsp;Use&nbsp;this<br>
-with&nbsp;care&nbsp;if&nbsp;you&nbsp;are&nbsp;not&nbsp;dealing&nbsp;with&nbsp;the&nbsp;blocks.<br>
-&nbsp;<br>
-e.g.&nbsp;If&nbsp;the&nbsp;dtypes&nbsp;are&nbsp;float16&nbsp;and&nbsp;float32,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcast&nbsp;to<br>
-float32.&nbsp;&nbsp;If&nbsp;dtypes&nbsp;are&nbsp;int32&nbsp;and&nbsp;uint8,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcase&nbsp;to<br>
-int32.&nbsp;By&nbsp;numpy.find_common_type&nbsp;convention,&nbsp;mixing&nbsp;int64&nbsp;and&nbsp;uint64<br>
-will&nbsp;result&nbsp;in&nbsp;a&nbsp;flot64&nbsp;dtype.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;is&nbsp;provided&nbsp;for&nbsp;backwards&nbsp;compatibility.&nbsp;Generally,<br>
-it&nbsp;is&nbsp;recommended&nbsp;to&nbsp;use&nbsp;'.values'.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.values</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-asfreq"><strong>asfreq</strong></a>(self, freq, method=None, how=None, normalize=False, fill_value=None)</dt><dd><tt>Convert&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;specified&nbsp;frequency.<br>
-&nbsp;<br>
-Optionally&nbsp;provide&nbsp;filling&nbsp;method&nbsp;to&nbsp;pad/backfill&nbsp;missing&nbsp;values.<br>
-&nbsp;<br>
-Returns&nbsp;the&nbsp;original&nbsp;data&nbsp;conformed&nbsp;to&nbsp;a&nbsp;new&nbsp;index&nbsp;with&nbsp;the&nbsp;specified<br>
-frequency.&nbsp;``resample``&nbsp;is&nbsp;more&nbsp;appropriate&nbsp;if&nbsp;an&nbsp;operation,&nbsp;such&nbsp;as<br>
-summarization,&nbsp;is&nbsp;necessary&nbsp;to&nbsp;represent&nbsp;the&nbsp;data&nbsp;at&nbsp;the&nbsp;new&nbsp;frequency.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;or&nbsp;string<br>
-method&nbsp;:&nbsp;{'backfill'/'bfill',&nbsp;'pad'/'ffill'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(note&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present):<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'pad'&nbsp;/&nbsp;'ffill':&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'backfill'&nbsp;/&nbsp;'bfill':&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill<br>
-how&nbsp;:&nbsp;{'start',&nbsp;'end'},&nbsp;default&nbsp;end<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;PeriodIndex&nbsp;only,&nbsp;see&nbsp;PeriodIndex.asfreq<br>
-normalize&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;reset&nbsp;output&nbsp;index&nbsp;to&nbsp;midnight<br>
-fill_value:&nbsp;scalar,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values,&nbsp;applied&nbsp;during&nbsp;upsampling&nbsp;(note<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;4&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=4,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0.0,&nbsp;None,&nbsp;2.0,&nbsp;3.0],&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'s':series})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-asfreq">asfreq</a>(freq='30S')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``fill&nbsp;value``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-asfreq">asfreq</a>(freq='30S',&nbsp;fill_value=9.0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``method``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-asfreq">asfreq</a>(freq='30S',&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-asof"><strong>asof</strong></a>(self, where, subset=None)</dt><dd><tt>The&nbsp;last&nbsp;row&nbsp;without&nbsp;any&nbsp;NaN&nbsp;is&nbsp;taken&nbsp;(or&nbsp;the&nbsp;last&nbsp;row&nbsp;without<br>
-NaN&nbsp;considering&nbsp;only&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;in&nbsp;the&nbsp;case&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0&nbsp;For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-If&nbsp;there&nbsp;is&nbsp;no&nbsp;good&nbsp;value,&nbsp;NaN&nbsp;is&nbsp;returned&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;NaN&nbsp;values&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-where&nbsp;:&nbsp;date&nbsp;or&nbsp;array&nbsp;of&nbsp;dates<br>
-subset&nbsp;:&nbsp;string&nbsp;or&nbsp;list&nbsp;of&nbsp;strings,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None&nbsp;use&nbsp;these&nbsp;columns&nbsp;for&nbsp;NaN&nbsp;propagation<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Dates&nbsp;are&nbsp;assumed&nbsp;to&nbsp;be&nbsp;sorted<br>
-Raises&nbsp;if&nbsp;this&nbsp;is&nbsp;not&nbsp;the&nbsp;case<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-where&nbsp;is&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;&nbsp;-&nbsp;value&nbsp;or&nbsp;NaN&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;-&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-where&nbsp;is&nbsp;Index:&nbsp;same&nbsp;shape&nbsp;<a href="builtins.html#object">object</a>&nbsp;as&nbsp;input<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-merge_asof</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-astype"><strong>astype</strong></a>(self, dtype, copy=True, errors='raise', **kwargs)</dt><dd><tt>Cast&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;numpy.dtype<br>
-Return&nbsp;a&nbsp;copy&nbsp;when&nbsp;copy&nbsp;=&nbsp;True&nbsp;(be&nbsp;really&nbsp;careful&nbsp;with&nbsp;this!)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dtype&nbsp;:&nbsp;data&nbsp;type,&nbsp;or&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;-&gt;&nbsp;data&nbsp;type<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;entire&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;same&nbsp;type.&nbsp;Alternatively,&nbsp;use&nbsp;{col:&nbsp;dtype,&nbsp;...},&nbsp;where&nbsp;col&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;label&nbsp;and&nbsp;dtype&nbsp;is&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;one<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;more&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;columns&nbsp;to&nbsp;column-specific&nbsp;types.<br>
-errors&nbsp;:&nbsp;{'raise',&nbsp;'ignore'},&nbsp;default&nbsp;'raise'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Control&nbsp;raising&nbsp;of&nbsp;exceptions&nbsp;on&nbsp;invalid&nbsp;data&nbsp;for&nbsp;provided&nbsp;dtype.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``raise``&nbsp;:&nbsp;allow&nbsp;exceptions&nbsp;to&nbsp;be&nbsp;raised<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``ignore``&nbsp;:&nbsp;suppress&nbsp;exceptions.&nbsp;On&nbsp;error&nbsp;return&nbsp;original&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-raise_on_error&nbsp;:&nbsp;DEPRECATED&nbsp;use&nbsp;``errors``&nbsp;instead<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;constructor<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-casted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-at_time"><strong>at_time</strong></a>(self, time, asof=False)</dt><dd><tt>Select&nbsp;values&nbsp;at&nbsp;particular&nbsp;time&nbsp;of&nbsp;day&nbsp;(e.g.&nbsp;9:30AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_at_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-between_time"><strong>between_time</strong></a>(self, start_time, end_time, include_start=True, include_end=True)</dt><dd><tt>Select&nbsp;values&nbsp;between&nbsp;particular&nbsp;times&nbsp;of&nbsp;the&nbsp;day&nbsp;(e.g.,&nbsp;9:00-9:30&nbsp;AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-start_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-end_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-include_start&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-include_end&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_between_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-bfill"><strong>bfill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#SweepSeries-fillna">fillna</a>(method='bfill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-bool"><strong>bool</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;bool&nbsp;of&nbsp;a&nbsp;single&nbsp;element&nbsp;PandasObject.<br>
-&nbsp;<br>
-This&nbsp;must&nbsp;be&nbsp;a&nbsp;boolean&nbsp;scalar&nbsp;value,&nbsp;either&nbsp;True&nbsp;or&nbsp;False.&nbsp;&nbsp;Raise&nbsp;a<br>
-ValueError&nbsp;if&nbsp;the&nbsp;PandasObject&nbsp;does&nbsp;not&nbsp;have&nbsp;exactly&nbsp;1&nbsp;element,&nbsp;or&nbsp;that<br>
-element&nbsp;is&nbsp;not&nbsp;boolean</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-clip"><strong>clip</strong></a>(self, lower=None, upper=None, axis=None, *args, **kwargs)</dt><dd><tt>Trim&nbsp;values&nbsp;at&nbsp;input&nbsp;threshold(s).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-lower&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-upper&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;lower&nbsp;and&nbsp;upper&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.256177<br>
-1&nbsp;-1.367855&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.176076<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;1.261967&nbsp;&nbsp;0.570967<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-clip">clip</a>(-1.0,&nbsp;0.5)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.000000<br>
-1&nbsp;-1.000000&nbsp;&nbsp;0.500000<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.000000<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;0.500000&nbsp;&nbsp;0.500000<br>
-&gt;&gt;&gt;&nbsp;t<br>
-0&nbsp;&nbsp;&nbsp;-0.3<br>
-1&nbsp;&nbsp;&nbsp;-0.2<br>
-2&nbsp;&nbsp;&nbsp;-0.1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;0.1<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-clip">clip</a>(t,&nbsp;t&nbsp;+&nbsp;1,&nbsp;axis=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-0.300000<br>
-1&nbsp;-0.200000&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-0.100000<br>
-3&nbsp;&nbsp;0.230930&nbsp;&nbsp;0.000000<br>
-4&nbsp;&nbsp;1.100000&nbsp;&nbsp;0.570967</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-clip_lower"><strong>clip_lower</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;the&nbsp;input&nbsp;with&nbsp;values&nbsp;below&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-clip_upper"><strong>clip_upper</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;input&nbsp;with&nbsp;values&nbsp;above&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-consolidate"><strong>consolidate</strong></a>(self, inplace=False)</dt><dd><tt>DEPRECATED:&nbsp;consolidate&nbsp;will&nbsp;be&nbsp;an&nbsp;internal&nbsp;implementation&nbsp;only.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-convert_objects"><strong>convert_objects</strong></a>(self, convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True)</dt><dd><tt>Deprecated.<br>
-&nbsp;<br>
-Attempt&nbsp;to&nbsp;infer&nbsp;better&nbsp;dtype&nbsp;for&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-convert_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;date&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-convert_numeric&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;attempt&nbsp;to&nbsp;coerce&nbsp;to&nbsp;numbers&nbsp;(including&nbsp;strings),&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaN.<br>
-convert_timedeltas&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;timedelta&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;return&nbsp;a&nbsp;copy&nbsp;even&nbsp;if&nbsp;no&nbsp;copy&nbsp;is&nbsp;necessary&nbsp;(e.g.&nbsp;no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion&nbsp;was&nbsp;done).&nbsp;Note:&nbsp;This&nbsp;is&nbsp;meant&nbsp;for&nbsp;internal&nbsp;use,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;not&nbsp;be&nbsp;confused&nbsp;with&nbsp;inplace.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.to_datetime&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;datetime.<br>
-pandas.to_timedelta&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;timedelta.<br>
-pandas.to_numeric&nbsp;:&nbsp;Return&nbsp;a&nbsp;fixed&nbsp;frequency&nbsp;timedelta&nbsp;index,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;day&nbsp;as&nbsp;the&nbsp;default.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;same&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-copy"><strong>copy</strong></a>(self, deep=True)</dt><dd><tt>Make&nbsp;a&nbsp;copy&nbsp;of&nbsp;this&nbsp;objects&nbsp;data.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-deep&nbsp;:&nbsp;boolean&nbsp;or&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;a&nbsp;deep&nbsp;copy,&nbsp;including&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;data&nbsp;and&nbsp;the&nbsp;indices.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;With&nbsp;``deep=False``&nbsp;neither&nbsp;the&nbsp;indices&nbsp;or&nbsp;the&nbsp;data&nbsp;are&nbsp;copied.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;that&nbsp;when&nbsp;``deep=True``&nbsp;data&nbsp;is&nbsp;copied,&nbsp;actual&nbsp;python&nbsp;objects<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;not&nbsp;be&nbsp;copied&nbsp;recursively,&nbsp;only&nbsp;the&nbsp;reference&nbsp;to&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;is&nbsp;in&nbsp;contrast&nbsp;to&nbsp;``copy.deepcopy``&nbsp;in&nbsp;the&nbsp;Standard&nbsp;Library,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;recursively&nbsp;copies&nbsp;<a href="builtins.html#object">object</a>&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-copy&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-describe"><strong>describe</strong></a>(self, percentiles=None, include=None, exclude=None)</dt><dd><tt>Generates&nbsp;descriptive&nbsp;statistics&nbsp;that&nbsp;summarize&nbsp;the&nbsp;central&nbsp;tendency,<br>
-dispersion&nbsp;and&nbsp;shape&nbsp;of&nbsp;a&nbsp;dataset's&nbsp;distribution,&nbsp;excluding<br>
-``NaN``&nbsp;values.<br>
-&nbsp;<br>
-Analyzes&nbsp;both&nbsp;numeric&nbsp;and&nbsp;<a href="builtins.html#object">object</a>&nbsp;series,&nbsp;as&nbsp;well<br>
-as&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;column&nbsp;sets&nbsp;of&nbsp;mixed&nbsp;data&nbsp;types.&nbsp;The&nbsp;output<br>
-will&nbsp;vary&nbsp;depending&nbsp;on&nbsp;what&nbsp;is&nbsp;provided.&nbsp;Refer&nbsp;to&nbsp;the&nbsp;notes<br>
-below&nbsp;for&nbsp;more&nbsp;detail.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-percentiles&nbsp;:&nbsp;list-like&nbsp;of&nbsp;numbers,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;percentiles&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;output.&nbsp;All&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fall&nbsp;between&nbsp;0&nbsp;and&nbsp;1.&nbsp;The&nbsp;default&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``[.25,&nbsp;.5,&nbsp;.75]``,&nbsp;which&nbsp;returns&nbsp;the&nbsp;25th,&nbsp;50th,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;75th&nbsp;percentiles.<br>
-include&nbsp;:&nbsp;'all',&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;white&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'all'&nbsp;:&nbsp;All&nbsp;columns&nbsp;of&nbsp;the&nbsp;input&nbsp;will&nbsp;be&nbsp;included&nbsp;in&nbsp;the&nbsp;output.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Limits&nbsp;the&nbsp;results&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;provided&nbsp;data&nbsp;types.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;limit&nbsp;the&nbsp;result&nbsp;to&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;limit&nbsp;it&nbsp;instead&nbsp;to&nbsp;categorical<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;objects&nbsp;submit&nbsp;the&nbsp;``numpy.<a href="builtins.html#object">object</a>``&nbsp;data&nbsp;type.&nbsp;Strings<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#SweepSeries-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;include&nbsp;all&nbsp;numeric&nbsp;columns.<br>
-exclude&nbsp;:&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;black&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;omit&nbsp;from&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Excludes&nbsp;the&nbsp;provided&nbsp;data&nbsp;types<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;the&nbsp;result.&nbsp;To&nbsp;select&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;select&nbsp;categorical&nbsp;objects&nbsp;submit&nbsp;the&nbsp;data<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;type&nbsp;``numpy.<a href="builtins.html#object">object</a>``.&nbsp;Strings&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#SweepSeries-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;exclude&nbsp;nothing.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-summary:&nbsp;&nbsp;<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;summary&nbsp;statistics<br>
-&nbsp;<br>
-Notes<br>
------<br>
-For&nbsp;numeric&nbsp;data,&nbsp;the&nbsp;result's&nbsp;index&nbsp;will&nbsp;include&nbsp;``count``,<br>
-``mean``,&nbsp;``std``,&nbsp;``min``,&nbsp;``max``&nbsp;as&nbsp;well&nbsp;as&nbsp;lower,&nbsp;``50``&nbsp;and<br>
-upper&nbsp;percentiles.&nbsp;By&nbsp;default&nbsp;the&nbsp;lower&nbsp;percentile&nbsp;is&nbsp;``25``&nbsp;and&nbsp;the<br>
-upper&nbsp;percentile&nbsp;is&nbsp;``75``.&nbsp;The&nbsp;``50``&nbsp;percentile&nbsp;is&nbsp;the<br>
-same&nbsp;as&nbsp;the&nbsp;median.<br>
-&nbsp;<br>
-For&nbsp;<a href="builtins.html#object">object</a>&nbsp;data&nbsp;(e.g.&nbsp;strings&nbsp;or&nbsp;timestamps),&nbsp;the&nbsp;result's&nbsp;index<br>
-will&nbsp;include&nbsp;``count``,&nbsp;``unique``,&nbsp;``top``,&nbsp;and&nbsp;``freq``.&nbsp;The&nbsp;``top``<br>
-is&nbsp;the&nbsp;most&nbsp;common&nbsp;value.&nbsp;The&nbsp;``freq``&nbsp;is&nbsp;the&nbsp;most&nbsp;common&nbsp;value's<br>
-frequency.&nbsp;Timestamps&nbsp;also&nbsp;include&nbsp;the&nbsp;``first``&nbsp;and&nbsp;``last``&nbsp;items.<br>
-&nbsp;<br>
-If&nbsp;multiple&nbsp;<a href="builtins.html#object">object</a>&nbsp;values&nbsp;have&nbsp;the&nbsp;highest&nbsp;count,&nbsp;then&nbsp;the<br>
-``count``&nbsp;and&nbsp;``top``&nbsp;results&nbsp;will&nbsp;be&nbsp;arbitrarily&nbsp;chosen&nbsp;from<br>
-among&nbsp;those&nbsp;with&nbsp;the&nbsp;highest&nbsp;count.<br>
-&nbsp;<br>
-For&nbsp;mixed&nbsp;data&nbsp;types&nbsp;provided&nbsp;via&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``,&nbsp;the&nbsp;default&nbsp;is&nbsp;to<br>
-return&nbsp;only&nbsp;an&nbsp;analysis&nbsp;of&nbsp;numeric&nbsp;columns.&nbsp;If&nbsp;``include='all'``<br>
-is&nbsp;provided&nbsp;as&nbsp;an&nbsp;option,&nbsp;the&nbsp;result&nbsp;will&nbsp;include&nbsp;a&nbsp;union&nbsp;of<br>
-attributes&nbsp;of&nbsp;each&nbsp;type.<br>
-&nbsp;<br>
-The&nbsp;`include`&nbsp;and&nbsp;`exclude`&nbsp;parameters&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;limit<br>
-which&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;are&nbsp;analyzed&nbsp;for&nbsp;the&nbsp;output.<br>
-The&nbsp;parameters&nbsp;are&nbsp;ignored&nbsp;when&nbsp;analyzing&nbsp;a&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-Describing&nbsp;a&nbsp;numeric&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;categorical&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(['a',&nbsp;'a',&nbsp;'b',&nbsp;'c'])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;timestamp&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2000-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01")<br>
-...&nbsp;])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-first&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2000-01-01&nbsp;00:00:00<br>
-last&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``.&nbsp;By&nbsp;default&nbsp;only&nbsp;numeric&nbsp;fields<br>
-are&nbsp;returned.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[1,&nbsp;'a'],&nbsp;[2,&nbsp;'b'],&nbsp;[3,&nbsp;'c']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['numeric',&nbsp;'<a href="builtins.html#object">object</a>'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-describe">describe</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;all&nbsp;columns&nbsp;of&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;regardless&nbsp;of&nbsp;data&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-describe">describe</a>(include='all')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;column&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;by&nbsp;accessing&nbsp;it&nbsp;as<br>
-an&nbsp;attribute.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.numeric.<a href="#SweepSeries-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-Name:&nbsp;numeric,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;numeric&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-describe">describe</a>(include=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;string&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-describe">describe</a>(include=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;numeric&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-describe">describe</a>(exclude=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-describe">describe</a>(exclude=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.count<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.max<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.min<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mean<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.std<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select_dtypes</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-drop"><strong>drop</strong></a>(self, labels, axis=0, level=None, inplace=False, errors='raise')</dt><dd><tt>Return&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;labels&nbsp;in&nbsp;requested&nbsp;axis&nbsp;removed.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-labels&nbsp;:&nbsp;single&nbsp;label&nbsp;or&nbsp;list-like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;axis&nbsp;name<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;MultiIndex<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;return&nbsp;None.<br>
-errors&nbsp;:&nbsp;{'ignore',&nbsp;'raise'},&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'ignore',&nbsp;suppress&nbsp;error&nbsp;and&nbsp;existing&nbsp;labels&nbsp;are&nbsp;dropped.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dropped&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-equals"><strong>equals</strong></a>(self, other)</dt><dd><tt>Determines&nbsp;if&nbsp;two&nbsp;NDFrame&nbsp;objects&nbsp;contain&nbsp;the&nbsp;same&nbsp;elements.&nbsp;NaNs&nbsp;in<br>
-the&nbsp;same&nbsp;location&nbsp;are&nbsp;considered&nbsp;equal.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-ffill"><strong>ffill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#SweepSeries-fillna">fillna</a>(method='ffill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-filter"><strong>filter</strong></a>(self, items=None, like=None, regex=None, axis=None)</dt><dd><tt>Subset&nbsp;rows&nbsp;or&nbsp;columns&nbsp;of&nbsp;dataframe&nbsp;according&nbsp;to&nbsp;labels&nbsp;in<br>
-the&nbsp;specified&nbsp;index.<br>
-&nbsp;<br>
-Note&nbsp;that&nbsp;this&nbsp;routine&nbsp;does&nbsp;not&nbsp;filter&nbsp;a&nbsp;dataframe&nbsp;on&nbsp;its<br>
-contents.&nbsp;The&nbsp;filter&nbsp;is&nbsp;applied&nbsp;to&nbsp;the&nbsp;labels&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-items&nbsp;:&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;info&nbsp;axis&nbsp;to&nbsp;restrict&nbsp;to&nbsp;(must&nbsp;not&nbsp;all&nbsp;be&nbsp;present)<br>
-like&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;where&nbsp;"arg&nbsp;in&nbsp;col&nbsp;==&nbsp;True"<br>
-regex&nbsp;:&nbsp;string&nbsp;(regular&nbsp;expression)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;with&nbsp;re.search(regex,&nbsp;col)&nbsp;==&nbsp;True<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;filter&nbsp;on.&nbsp;&nbsp;By&nbsp;default&nbsp;this&nbsp;is&nbsp;the&nbsp;info&nbsp;axis,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'index'&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;'columns'&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;name<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-filter">filter</a>(items=['one',&nbsp;'three'])<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;regular&nbsp;expression<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-filter">filter</a>(regex='e$',&nbsp;axis=1)<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;rows&nbsp;containing&nbsp;'bbi'<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-filter">filter</a>(like='bbi',&nbsp;axis=0)<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;``items``,&nbsp;``like``,&nbsp;and&nbsp;``regex``&nbsp;parameters&nbsp;are<br>
-enforced&nbsp;to&nbsp;be&nbsp;mutually&nbsp;exclusive.<br>
-&nbsp;<br>
-``axis``&nbsp;defaults&nbsp;to&nbsp;the&nbsp;info&nbsp;axis&nbsp;that&nbsp;is&nbsp;used&nbsp;when&nbsp;indexing<br>
-with&nbsp;``[]``.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-first"><strong>first</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;initial&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#SweepSeries-first">first</a>('10D')&nbsp;-&gt;&nbsp;First&nbsp;10&nbsp;days<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-get"><strong>get</strong></a>(self, key, default=None)</dt><dd><tt>Get&nbsp;item&nbsp;from&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;given&nbsp;key&nbsp;(<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column,&nbsp;Panel&nbsp;slice,<br>
-etc.).&nbsp;Returns&nbsp;default&nbsp;value&nbsp;if&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;type&nbsp;of&nbsp;items&nbsp;contained&nbsp;in&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-get_dtype_counts"><strong>get_dtype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;dtypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-get_ftype_counts"><strong>get_ftype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;ftypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-groupby"><strong>groupby</strong></a>(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)</dt><dd><tt>Group&nbsp;series&nbsp;using&nbsp;mapper&nbsp;(dict&nbsp;or&nbsp;key&nbsp;function,&nbsp;apply&nbsp;given&nbsp;function<br>
-to&nbsp;group,&nbsp;return&nbsp;result&nbsp;as&nbsp;series)&nbsp;or&nbsp;by&nbsp;a&nbsp;series&nbsp;of&nbsp;columns.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;mapping,&nbsp;function,&nbsp;str,&nbsp;or&nbsp;iterable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;for&nbsp;the&nbsp;groupby.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;``by``&nbsp;is&nbsp;a&nbsp;function,&nbsp;it's&nbsp;called&nbsp;on&nbsp;each&nbsp;value&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>'s<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;If&nbsp;a&nbsp;dict&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;is&nbsp;passed,&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;dict&nbsp;VALUES<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;(the&nbsp;<a href="pandas.core.series.html#Series">Series</a>'&nbsp;values&nbsp;are&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aligned;&nbsp;see&nbsp;``.<a href="#SweepSeries-align">align</a>()``&nbsp;method).&nbsp;If&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;is&nbsp;passed,&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;are&nbsp;used&nbsp;as-is&nbsp;determine&nbsp;the&nbsp;groups.&nbsp;A&nbsp;str&nbsp;or&nbsp;list&nbsp;of&nbsp;strs<br>
-&nbsp;&nbsp;&nbsp;&nbsp;may&nbsp;be&nbsp;passed&nbsp;to&nbsp;group&nbsp;by&nbsp;the&nbsp;columns&nbsp;in&nbsp;``self``<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-level&nbsp;:&nbsp;int,&nbsp;level&nbsp;name,&nbsp;or&nbsp;sequence&nbsp;of&nbsp;such,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;group&nbsp;by&nbsp;a&nbsp;particular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level&nbsp;or&nbsp;levels<br>
-as_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;aggregated&nbsp;output,&nbsp;return&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;group&nbsp;labels&nbsp;as&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;Only&nbsp;relevant&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;input.&nbsp;as_index=False&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;effectively&nbsp;"SQL-style"&nbsp;grouped&nbsp;output<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;group&nbsp;keys.&nbsp;Get&nbsp;better&nbsp;performance&nbsp;by&nbsp;turning&nbsp;this&nbsp;off.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;this&nbsp;does&nbsp;not&nbsp;influence&nbsp;the&nbsp;order&nbsp;of&nbsp;observations&nbsp;within&nbsp;each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;group.&nbsp;&nbsp;groupby&nbsp;preserves&nbsp;the&nbsp;order&nbsp;of&nbsp;rows&nbsp;within&nbsp;each&nbsp;group.<br>
-group_keys&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;calling&nbsp;apply,&nbsp;add&nbsp;group&nbsp;keys&nbsp;to&nbsp;index&nbsp;to&nbsp;identify&nbsp;pieces<br>
-squeeze&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;reduce&nbsp;the&nbsp;dimensionality&nbsp;of&nbsp;the&nbsp;return&nbsp;type&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;return&nbsp;a&nbsp;consistent&nbsp;type<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;results<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#SweepSeries-groupby">groupby</a>(func,&nbsp;axis=0).<a href="#SweepSeries-mean">mean</a>()<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#SweepSeries-groupby">groupby</a>(['col1',&nbsp;'col2'])['col3'].<a href="#SweepSeries-mean">mean</a>()<br>
-&nbsp;<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;hierarchical&nbsp;index<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#SweepSeries-groupby">groupby</a>(['col1',&nbsp;'col2']).<a href="#SweepSeries-mean">mean</a>()<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-GroupBy&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-head"><strong>head</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;first&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-interpolate"><strong>interpolate</strong></a>(self, method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', downcast=None, **kwargs)</dt><dd><tt>Interpolate&nbsp;values&nbsp;according&nbsp;to&nbsp;different&nbsp;methods.<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;only&nbsp;``method='linear'``&nbsp;is&nbsp;supported&nbsp;for<br>
-DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a&nbsp;MultiIndex.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-method&nbsp;:&nbsp;{'linear',&nbsp;'time',&nbsp;'index',&nbsp;'values',&nbsp;'nearest',&nbsp;'zero',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',&nbsp;'barycentric',&nbsp;'krogh',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'polynomial',&nbsp;'spline',&nbsp;'piecewise_polynomial',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'from_derivatives',&nbsp;'pchip',&nbsp;'akima'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'linear':&nbsp;ignore&nbsp;the&nbsp;index&nbsp;and&nbsp;treat&nbsp;the&nbsp;values&nbsp;as&nbsp;equally<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;spaced.&nbsp;This&nbsp;is&nbsp;the&nbsp;only&nbsp;method&nbsp;supported&nbsp;on&nbsp;MultiIndexes.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'time':&nbsp;interpolation&nbsp;works&nbsp;on&nbsp;daily&nbsp;and&nbsp;higher&nbsp;resolution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;data&nbsp;to&nbsp;interpolate&nbsp;given&nbsp;length&nbsp;of&nbsp;interval<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'index',&nbsp;'values':&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'nearest',&nbsp;'zero',&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'barycentric',&nbsp;'polynomial'&nbsp;is&nbsp;passed&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``scipy.interpolate.interp1d``.&nbsp;Both&nbsp;'polynomial'&nbsp;and&nbsp;'spline'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;require&nbsp;that&nbsp;you&nbsp;also&nbsp;specify&nbsp;an&nbsp;`order`&nbsp;(int),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;e.g.&nbsp;df.<a href="#SweepSeries-interpolate">interpolate</a>(method='polynomial',&nbsp;order=4).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'krogh',&nbsp;'piecewise_polynomial',&nbsp;'spline',&nbsp;'pchip'&nbsp;and&nbsp;'akima'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;all&nbsp;wrappers&nbsp;around&nbsp;the&nbsp;scipy&nbsp;interpolation&nbsp;methods&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;similar&nbsp;names.&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;For&nbsp;more&nbsp;information&nbsp;on&nbsp;their&nbsp;behavior,&nbsp;see&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`scipy&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__">http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;`tutorial&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__">http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'from_derivatives'&nbsp;refers&nbsp;to&nbsp;BPoly.from_derivatives&nbsp;which<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;replaces&nbsp;'piecewise_polynomial'&nbsp;interpolation&nbsp;method&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;0.18<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;support&nbsp;for&nbsp;the&nbsp;'akima'&nbsp;method<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;interpolate&nbsp;method&nbsp;'from_derivatives'&nbsp;which&nbsp;replaces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'piecewise_polynomial'&nbsp;in&nbsp;scipy&nbsp;0.18;&nbsp;backwards-compatible&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;&lt;&nbsp;0.18<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;0:&nbsp;fill&nbsp;column-by-column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;1:&nbsp;fill&nbsp;row-by-row<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs&nbsp;to&nbsp;fill.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0.<br>
-limit_direction&nbsp;:&nbsp;{'forward',&nbsp;'backward',&nbsp;'both'},&nbsp;default&nbsp;'forward'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;limit&nbsp;is&nbsp;specified,&nbsp;consecutive&nbsp;NaNs&nbsp;will&nbsp;be&nbsp;filled&nbsp;in&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;direction.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Update&nbsp;the&nbsp;NDFrame&nbsp;in&nbsp;place&nbsp;if&nbsp;possible.<br>
-downcast&nbsp;:&nbsp;optional,&nbsp;'infer'&nbsp;or&nbsp;None,&nbsp;defaults&nbsp;to&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Downcast&nbsp;dtypes&nbsp;if&nbsp;possible.<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;interpolating&nbsp;function.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;interpolated&nbsp;at&nbsp;the&nbsp;NaNs<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;replace,&nbsp;fillna<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Filling&nbsp;in&nbsp;NaNs<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0,&nbsp;1,&nbsp;np.nan,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-interpolate">interpolate</a>()<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-isnull"><strong>isnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-notnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;isnull</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-last"><strong>last</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;final&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#SweepSeries-last">last</a>('5M')&nbsp;-&gt;&nbsp;Last&nbsp;5&nbsp;months<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-mask"><strong>mask</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;False&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;mask&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``False``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#SweepSeries-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#SweepSeries-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``mask``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).<a href="#SweepSeries-reshape">reshape</a>(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#SweepSeries-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#SweepSeries-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-notnull"><strong>notnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are<br>
-not&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-isnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;notnull</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-pct_change"><strong>pct_change</strong></a>(self, periods=1, fill_method='pad', limit=None, freq=None, **kwargs)</dt><dd><tt>Percent&nbsp;change&nbsp;over&nbsp;given&nbsp;number&nbsp;of&nbsp;periods.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;percent&nbsp;change<br>
-fill_method&nbsp;:&nbsp;str,&nbsp;default&nbsp;'pad'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;How&nbsp;to&nbsp;handle&nbsp;NAs&nbsp;before&nbsp;computing&nbsp;percent&nbsp;changes<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NAs&nbsp;to&nbsp;fill&nbsp;before&nbsp;stopping<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;offset&nbsp;alias&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;time&nbsp;series&nbsp;API&nbsp;(e.g.&nbsp;'M'&nbsp;or&nbsp;BDay())<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-chg&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-By&nbsp;default,&nbsp;the&nbsp;percentage&nbsp;change&nbsp;is&nbsp;calculated&nbsp;along&nbsp;the&nbsp;stat<br>
-axis:&nbsp;0,&nbsp;or&nbsp;``Index``,&nbsp;for&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;and&nbsp;1,&nbsp;or&nbsp;``minor``&nbsp;for<br>
-``Panel``.&nbsp;You&nbsp;can&nbsp;change&nbsp;this&nbsp;with&nbsp;the&nbsp;``axis``&nbsp;keyword&nbsp;argument.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-pipe"><strong>pipe</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Apply&nbsp;func(self,&nbsp;\*args,&nbsp;\*\*kwargs)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.2<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;the&nbsp;NDFrame.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``args``,&nbsp;and&nbsp;``kwargs``&nbsp;are&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Alternatively&nbsp;a&nbsp;``(callable,&nbsp;data_keyword)``&nbsp;tuple&nbsp;where<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``data_keyword``&nbsp;is&nbsp;a&nbsp;string&nbsp;indicating&nbsp;the&nbsp;keyword&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``callable``&nbsp;that&nbsp;expects&nbsp;the&nbsp;NDFrame.<br>
-args&nbsp;:&nbsp;positional&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-kwargs&nbsp;:&nbsp;a&nbsp;dictionary&nbsp;of&nbsp;keyword&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="builtins.html#object">object</a>&nbsp;:&nbsp;the&nbsp;return&nbsp;type&nbsp;of&nbsp;``func``.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-Use&nbsp;``.pipe``&nbsp;when&nbsp;chaining&nbsp;together&nbsp;functions&nbsp;that&nbsp;expect<br>
-on&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;DataFrames.&nbsp;Instead&nbsp;of&nbsp;writing<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;f(g(h(df),&nbsp;arg1=a),&nbsp;arg2=b,&nbsp;arg3=c)<br>
-&nbsp;<br>
-You&nbsp;can&nbsp;write<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#SweepSeries-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#SweepSeries-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#SweepSeries-pipe">pipe</a>(f,&nbsp;arg2=b,&nbsp;arg3=c)<br>
-...&nbsp;)<br>
-&nbsp;<br>
-If&nbsp;you&nbsp;have&nbsp;a&nbsp;function&nbsp;that&nbsp;takes&nbsp;the&nbsp;data&nbsp;as&nbsp;(say)&nbsp;the&nbsp;second<br>
-argument,&nbsp;pass&nbsp;a&nbsp;tuple&nbsp;indicating&nbsp;which&nbsp;keyword&nbsp;expects&nbsp;the<br>
-data.&nbsp;For&nbsp;example,&nbsp;suppose&nbsp;``f``&nbsp;takes&nbsp;its&nbsp;data&nbsp;as&nbsp;``arg2``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#SweepSeries-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#SweepSeries-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#SweepSeries-pipe">pipe</a>((f,&nbsp;'arg2'),&nbsp;arg1=a,&nbsp;arg3=c)<br>
-...&nbsp;&nbsp;)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.map</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-pop"><strong>pop</strong></a>(self, item)</dt><dd><tt>Return&nbsp;item&nbsp;and&nbsp;drop&nbsp;from&nbsp;frame.&nbsp;Raise&nbsp;KeyError&nbsp;if&nbsp;not&nbsp;found.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-rank"><strong>rank</strong></a>(self, axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)</dt><dd><tt>Compute&nbsp;numerical&nbsp;data&nbsp;ranks&nbsp;(1&nbsp;through&nbsp;n)&nbsp;along&nbsp;axis.&nbsp;Equal&nbsp;values&nbsp;are<br>
-assigned&nbsp;a&nbsp;rank&nbsp;that&nbsp;is&nbsp;the&nbsp;average&nbsp;of&nbsp;the&nbsp;ranks&nbsp;of&nbsp;those&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;to&nbsp;direct&nbsp;ranking<br>
-method&nbsp;:&nbsp;{'average',&nbsp;'min',&nbsp;'max',&nbsp;'first',&nbsp;'dense'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;average:&nbsp;average&nbsp;rank&nbsp;of&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;min:&nbsp;lowest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;max:&nbsp;highest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;first:&nbsp;ranks&nbsp;assigned&nbsp;in&nbsp;order&nbsp;they&nbsp;appear&nbsp;in&nbsp;the&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dense:&nbsp;like&nbsp;'min',&nbsp;but&nbsp;rank&nbsp;always&nbsp;increases&nbsp;by&nbsp;1&nbsp;between&nbsp;groups<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;data.&nbsp;Valid&nbsp;only&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Panel&nbsp;objects<br>
-na_option&nbsp;:&nbsp;{'keep',&nbsp;'top',&nbsp;'bottom'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;keep:&nbsp;leave&nbsp;NA&nbsp;values&nbsp;where&nbsp;they&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;top:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;ascending<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;bottom:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;descending<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False&nbsp;for&nbsp;ranks&nbsp;by&nbsp;high&nbsp;(1)&nbsp;to&nbsp;low&nbsp;(N)<br>
-pct&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Computes&nbsp;percentage&nbsp;rank&nbsp;of&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ranks&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-reindex_like"><strong>reindex_like</strong></a>(self, other, method=None, copy=True, limit=None, tolerance=None)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;matching&nbsp;indices&nbsp;to&nbsp;myself.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;Object<br>
-method&nbsp;:&nbsp;string&nbsp;or&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;labels&nbsp;to&nbsp;fill&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;labels&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>&nbsp;and&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Like&nbsp;calling&nbsp;s.<a href="#SweepSeries-reindex">reindex</a>(index=other.index,&nbsp;columns=other.columns,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;method=...)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-rename_axis"><strong>rename_axis</strong></a>(self, mapper, axis=0, copy=True, inplace=False)</dt><dd><tt>Alter&nbsp;index&nbsp;and&nbsp;/&nbsp;or&nbsp;columns&nbsp;using&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.<br>
-A&nbsp;scalar&nbsp;or&nbsp;list-like&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;``Index.name``<br>
-or&nbsp;``MultiIndex.names``&nbsp;attribute.<br>
-A&nbsp;function&nbsp;or&nbsp;dict&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;labels.<br>
-Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be&nbsp;unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in<br>
-a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left&nbsp;as-is.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-mapper&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename<br>
-pandas.Index.rename<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-rename_axis">rename_axis</a>("foo")&nbsp;&nbsp;#&nbsp;scalar,&nbsp;alters&nbsp;df.index.name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-foo<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-rename_axis">rename_axis</a>(lambda&nbsp;x:&nbsp;2&nbsp;*&nbsp;x)&nbsp;&nbsp;#&nbsp;function:&nbsp;alters&nbsp;labels<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-4&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-rename_axis">rename_axis</a>({"A":&nbsp;"ehh",&nbsp;"C":&nbsp;"see"},&nbsp;axis="columns")&nbsp;&nbsp;#&nbsp;mapping<br>
-&nbsp;&nbsp;&nbsp;ehh&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-replace"><strong>replace</strong></a>(self, to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad', axis=None)</dt><dd><tt>Replace&nbsp;values&nbsp;given&nbsp;in&nbsp;'to_replace'&nbsp;with&nbsp;'value'.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-to_replace&nbsp;:&nbsp;str,&nbsp;regex,&nbsp;list,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;numeric,&nbsp;or&nbsp;None<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;str&nbsp;or&nbsp;regex:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str:&nbsp;string&nbsp;exactly&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;`value`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;regex:&nbsp;regexs&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`value`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;list&nbsp;of&nbsp;str,&nbsp;regex,&nbsp;or&nbsp;numeric:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;First,&nbsp;if&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;both&nbsp;lists,&nbsp;they<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Second,&nbsp;if&nbsp;``regex=True``&nbsp;then&nbsp;all&nbsp;of&nbsp;the&nbsp;strings&nbsp;in&nbsp;**both**<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;regexs&nbsp;otherwise&nbsp;they&nbsp;will&nbsp;match<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;directly.&nbsp;This&nbsp;doesn't&nbsp;matter&nbsp;much&nbsp;for&nbsp;`value`&nbsp;since&nbsp;there<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;only&nbsp;a&nbsp;few&nbsp;possible&nbsp;substitution&nbsp;regexes&nbsp;you&nbsp;can&nbsp;use.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str&nbsp;and&nbsp;regex&nbsp;rules&nbsp;apply&nbsp;as&nbsp;above.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dict:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Nested&nbsp;dictionaries,&nbsp;e.g.,&nbsp;{'a':&nbsp;{'b':&nbsp;nan}},&nbsp;are&nbsp;read&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;follows:&nbsp;look&nbsp;in&nbsp;column&nbsp;'a'&nbsp;for&nbsp;the&nbsp;value&nbsp;'b'&nbsp;and&nbsp;replace&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;nan.&nbsp;You&nbsp;can&nbsp;nest&nbsp;regular&nbsp;expressions&nbsp;as&nbsp;well.&nbsp;Note&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;names&nbsp;(the&nbsp;top-level&nbsp;dictionary&nbsp;keys&nbsp;in&nbsp;a&nbsp;nested<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dictionary)&nbsp;**cannot**&nbsp;be&nbsp;regular&nbsp;expressions.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Keys&nbsp;map&nbsp;to&nbsp;column&nbsp;names&nbsp;and&nbsp;values&nbsp;map&nbsp;to&nbsp;substitution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;values.&nbsp;You&nbsp;can&nbsp;treat&nbsp;this&nbsp;as&nbsp;a&nbsp;special&nbsp;case&nbsp;of&nbsp;passing&nbsp;two<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;except&nbsp;that&nbsp;you&nbsp;are&nbsp;specifying&nbsp;the&nbsp;column&nbsp;to&nbsp;search&nbsp;in.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;None:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;This&nbsp;means&nbsp;that&nbsp;the&nbsp;``regex``&nbsp;argument&nbsp;must&nbsp;be&nbsp;a&nbsp;string,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;compiled&nbsp;regular&nbsp;expression,&nbsp;or&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;such&nbsp;elements.&nbsp;If&nbsp;`value`&nbsp;is&nbsp;also&nbsp;``None``&nbsp;then&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;a&nbsp;nested&nbsp;dictionary&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;examples&nbsp;section&nbsp;for&nbsp;examples&nbsp;of&nbsp;each&nbsp;of&nbsp;these.<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;list,&nbsp;str,&nbsp;regex,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a&nbsp;dict&nbsp;of&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for&nbsp;each&nbsp;column&nbsp;(columns&nbsp;not&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;Regular&nbsp;expressions,&nbsp;strings&nbsp;and&nbsp;lists&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dicts&nbsp;of&nbsp;such&nbsp;objects&nbsp;are&nbsp;also&nbsp;allowed.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>&nbsp;(e.g.&nbsp;a&nbsp;column&nbsp;form&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Returns&nbsp;the&nbsp;caller&nbsp;if&nbsp;this&nbsp;is&nbsp;True.<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;size&nbsp;gap&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-regex&nbsp;:&nbsp;bool&nbsp;or&nbsp;same&nbsp;types&nbsp;as&nbsp;`to_replace`,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;interpret&nbsp;`to_replace`&nbsp;and/or&nbsp;`value`&nbsp;as&nbsp;regular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;expressions.&nbsp;If&nbsp;this&nbsp;is&nbsp;``True``&nbsp;then&nbsp;`to_replace`&nbsp;*must*&nbsp;be&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string.&nbsp;Otherwise,&nbsp;`to_replace`&nbsp;must&nbsp;be&nbsp;``None``&nbsp;because&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;parameter&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;regular&nbsp;expression&nbsp;or&nbsp;a&nbsp;list,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict,&nbsp;or&nbsp;array&nbsp;of&nbsp;regular&nbsp;expressions.<br>
-method&nbsp;:&nbsp;string,&nbsp;optional,&nbsp;{'pad',&nbsp;'ffill',&nbsp;'bfill'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;method&nbsp;to&nbsp;use&nbsp;when&nbsp;for&nbsp;replacement,&nbsp;when&nbsp;``to_replace``&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``list``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-NDFrame.reindex<br>
-NDFrame.asfreq<br>
-NDFrame.fillna<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-AssertionError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;a&nbsp;``bool``&nbsp;and&nbsp;`to_replace`&nbsp;is&nbsp;not&nbsp;``None``.<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;a&nbsp;``dict``&nbsp;and&nbsp;`value`&nbsp;is&nbsp;not&nbsp;a&nbsp;``list``,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``dict``,&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``,&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;``None``&nbsp;and&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;compilable&nbsp;into&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;regular&nbsp;expression&nbsp;or&nbsp;is&nbsp;a&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;``list``&nbsp;s&nbsp;or&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``&nbsp;s,&nbsp;but<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;they&nbsp;are&nbsp;not&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-*&nbsp;Regex&nbsp;substitution&nbsp;is&nbsp;performed&nbsp;under&nbsp;the&nbsp;hood&nbsp;with&nbsp;``re.sub``.&nbsp;The<br>
-&nbsp;&nbsp;rules&nbsp;for&nbsp;substitution&nbsp;for&nbsp;``re.sub``&nbsp;are&nbsp;the&nbsp;same.<br>
-*&nbsp;Regular&nbsp;expressions&nbsp;will&nbsp;only&nbsp;substitute&nbsp;on&nbsp;strings,&nbsp;meaning&nbsp;you<br>
-&nbsp;&nbsp;cannot&nbsp;provide,&nbsp;for&nbsp;example,&nbsp;a&nbsp;regular&nbsp;expression&nbsp;matching&nbsp;floating<br>
-&nbsp;&nbsp;point&nbsp;numbers&nbsp;and&nbsp;expect&nbsp;the&nbsp;columns&nbsp;in&nbsp;your&nbsp;frame&nbsp;that&nbsp;have&nbsp;a<br>
-&nbsp;&nbsp;numeric&nbsp;dtype&nbsp;to&nbsp;be&nbsp;matched.&nbsp;However,&nbsp;if&nbsp;those&nbsp;floating&nbsp;point&nbsp;numbers<br>
-&nbsp;&nbsp;*are*&nbsp;strings,&nbsp;then&nbsp;you&nbsp;can&nbsp;do&nbsp;this.<br>
-*&nbsp;This&nbsp;method&nbsp;has&nbsp;*a&nbsp;lot*&nbsp;of&nbsp;options.&nbsp;You&nbsp;are&nbsp;encouraged&nbsp;to&nbsp;experiment<br>
-&nbsp;&nbsp;and&nbsp;play&nbsp;with&nbsp;this&nbsp;method&nbsp;to&nbsp;gain&nbsp;intuition&nbsp;about&nbsp;how&nbsp;it&nbsp;works.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-resample"><strong>resample</strong></a>(self, rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0, on=None, level=None)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;frequency&nbsp;conversion&nbsp;and&nbsp;resampling&nbsp;of&nbsp;time<br>
-series.&nbsp;&nbsp;Object&nbsp;must&nbsp;have&nbsp;a&nbsp;datetime-like&nbsp;index&nbsp;(DatetimeIndex,<br>
-PeriodIndex,&nbsp;or&nbsp;TimedeltaIndex),&nbsp;or&nbsp;pass&nbsp;datetime-like&nbsp;values<br>
-to&nbsp;the&nbsp;on&nbsp;or&nbsp;level&nbsp;keyword.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-rule&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;offset&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a>&nbsp;representing&nbsp;target&nbsp;conversion<br>
-axis&nbsp;:&nbsp;int,&nbsp;optional,&nbsp;default&nbsp;0<br>
-closed&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;side&nbsp;of&nbsp;bin&nbsp;interval&nbsp;is&nbsp;closed<br>
-label&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;bin&nbsp;edge&nbsp;label&nbsp;to&nbsp;label&nbsp;bucket&nbsp;with<br>
-convention&nbsp;:&nbsp;{'start',&nbsp;'end',&nbsp;'s',&nbsp;'e'}<br>
-loffset&nbsp;:&nbsp;timedelta<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Adjust&nbsp;the&nbsp;resampled&nbsp;time&nbsp;labels<br>
-base&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;frequencies&nbsp;that&nbsp;evenly&nbsp;subdivide&nbsp;1&nbsp;day,&nbsp;the&nbsp;"origin"&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aggregated&nbsp;intervals.&nbsp;For&nbsp;example,&nbsp;for&nbsp;'5min'&nbsp;frequency,&nbsp;base&nbsp;could<br>
-&nbsp;&nbsp;&nbsp;&nbsp;range&nbsp;from&nbsp;0&nbsp;through&nbsp;4.&nbsp;Defaults&nbsp;to&nbsp;0<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;to&nbsp;use&nbsp;instead&nbsp;of&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-level&nbsp;:&nbsp;string&nbsp;or&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;MultiIndex,&nbsp;level&nbsp;(name&nbsp;or&nbsp;number)&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;resampling.&nbsp;&nbsp;Level&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offset&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;9&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(9),&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;series<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:04:00&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-2000-01-01&nbsp;00:05:00&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:07:00&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-2000-01-01&nbsp;00:08:00&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-Freq:&nbsp;T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;and&nbsp;sum&nbsp;the&nbsp;values<br>
-of&nbsp;the&nbsp;timestamps&nbsp;falling&nbsp;into&nbsp;a&nbsp;bin.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepSeries-resample">resample</a>('3T').<a href="#SweepSeries-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;label&nbsp;each<br>
-bin&nbsp;using&nbsp;the&nbsp;right&nbsp;edge&nbsp;instead&nbsp;of&nbsp;the&nbsp;left.&nbsp;Please&nbsp;note&nbsp;that&nbsp;the<br>
-value&nbsp;in&nbsp;the&nbsp;bucket&nbsp;used&nbsp;as&nbsp;the&nbsp;label&nbsp;is&nbsp;not&nbsp;included&nbsp;in&nbsp;the&nbsp;bucket,<br>
-which&nbsp;it&nbsp;labels.&nbsp;For&nbsp;example,&nbsp;in&nbsp;the&nbsp;original&nbsp;series&nbsp;the<br>
-bucket&nbsp;``2000-01-01&nbsp;00:03:00``&nbsp;contains&nbsp;the&nbsp;value&nbsp;3,&nbsp;but&nbsp;the&nbsp;summed<br>
-value&nbsp;in&nbsp;the&nbsp;resampled&nbsp;bucket&nbsp;with&nbsp;the&nbsp;label``2000-01-01&nbsp;00:03:00``<br>
-does&nbsp;not&nbsp;include&nbsp;3&nbsp;(if&nbsp;it&nbsp;did,&nbsp;the&nbsp;summed&nbsp;value&nbsp;would&nbsp;be&nbsp;6,&nbsp;not&nbsp;3).<br>
-To&nbsp;include&nbsp;this&nbsp;value&nbsp;close&nbsp;the&nbsp;right&nbsp;side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval&nbsp;as<br>
-illustrated&nbsp;in&nbsp;the&nbsp;example&nbsp;below&nbsp;this&nbsp;one.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepSeries-resample">resample</a>('3T',&nbsp;label='right').<a href="#SweepSeries-sum">sum</a>()<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;close&nbsp;the&nbsp;right<br>
-side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepSeries-resample">resample</a>('3T',&nbsp;label='right',&nbsp;closed='right').<a href="#SweepSeries-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepSeries-resample">resample</a>('30S').<a href="#SweepSeries-asfreq">asfreq</a>()[0:5]&nbsp;#select&nbsp;first&nbsp;5&nbsp;rows<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;1.0<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;2.0<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values&nbsp;using&nbsp;the&nbsp;``pad``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepSeries-resample">resample</a>('30S').pad()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the<br>
-``NaN``&nbsp;values&nbsp;using&nbsp;the&nbsp;``bfill``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepSeries-resample">resample</a>('30S').<a href="#SweepSeries-bfill">bfill</a>()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Pass&nbsp;a&nbsp;custom&nbsp;function&nbsp;via&nbsp;``apply``<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;custom_resampler(array_like):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;np.<a href="#SweepSeries-sum">sum</a>(array_like)+5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#SweepSeries-resample">resample</a>('3T').<a href="#SweepSeries-apply">apply</a>(custom_resampler)<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;17<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;26<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects,&nbsp;the&nbsp;keyword&nbsp;``on``&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;specify&nbsp;the<br>
-column&nbsp;instead&nbsp;of&nbsp;the&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=9*[range(4)],&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'])<br>
-&gt;&gt;&gt;&nbsp;df['time']&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-resample">resample</a>('3T',&nbsp;on='time').<a href="#SweepSeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;c&nbsp;&nbsp;d<br>
-time<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-&nbsp;<br>
-For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;MultiIndex,&nbsp;the&nbsp;keyword&nbsp;``level``&nbsp;can&nbsp;be&nbsp;used&nbsp;to<br>
-specify&nbsp;on&nbsp;level&nbsp;the&nbsp;resampling&nbsp;needs&nbsp;to&nbsp;take&nbsp;place.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;time&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=5,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=10*[range(4)],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.MultiIndex.from_product([time,&nbsp;[1,&nbsp;2]])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;)<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#SweepSeries-resample">resample</a>('3T',&nbsp;level=0).<a href="#SweepSeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;d<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;6&nbsp;&nbsp;12&nbsp;&nbsp;18<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;8&nbsp;&nbsp;12</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-sample"><strong>sample</strong></a>(self, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None)</dt><dd><tt>Returns&nbsp;a&nbsp;random&nbsp;sample&nbsp;of&nbsp;items&nbsp;from&nbsp;an&nbsp;axis&nbsp;of&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;items&nbsp;from&nbsp;axis&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`frac`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;=&nbsp;1&nbsp;if&nbsp;`frac`&nbsp;=&nbsp;None.<br>
-frac&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fraction&nbsp;of&nbsp;axis&nbsp;items&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`n`.<br>
-replace&nbsp;:&nbsp;boolean,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sample&nbsp;with&nbsp;or&nbsp;without&nbsp;replacement.&nbsp;Default&nbsp;=&nbsp;False.<br>
-weights&nbsp;:&nbsp;str&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;'None'&nbsp;results&nbsp;in&nbsp;equal&nbsp;probability&nbsp;weighting.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;will&nbsp;align&nbsp;with&nbsp;target&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;index.&nbsp;Index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;in&nbsp;weights&nbsp;not&nbsp;found&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;will&nbsp;be&nbsp;ignored&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;values&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;not&nbsp;in&nbsp;weights&nbsp;will&nbsp;be&nbsp;assigned<br>
-&nbsp;&nbsp;&nbsp;&nbsp;weights&nbsp;of&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;called&nbsp;on&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;will&nbsp;accept&nbsp;the&nbsp;name&nbsp;of&nbsp;a&nbsp;column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;axis&nbsp;=&nbsp;0.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Unless&nbsp;weights&nbsp;are&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;weights&nbsp;must&nbsp;be&nbsp;same&nbsp;length&nbsp;as&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;being&nbsp;sampled.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;weights&nbsp;do&nbsp;not&nbsp;sum&nbsp;to&nbsp;1,&nbsp;they&nbsp;will&nbsp;be&nbsp;normalized&nbsp;to&nbsp;sum&nbsp;to&nbsp;1.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;values&nbsp;in&nbsp;the&nbsp;weights&nbsp;column&nbsp;will&nbsp;be&nbsp;treated&nbsp;as&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;inf&nbsp;and&nbsp;-inf&nbsp;values&nbsp;not&nbsp;allowed.<br>
-random_state&nbsp;:&nbsp;int&nbsp;or&nbsp;numpy.random.RandomState,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Seed&nbsp;for&nbsp;the&nbsp;random&nbsp;number&nbsp;generator&nbsp;(if&nbsp;int),&nbsp;or&nbsp;numpy&nbsp;RandomState<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;sample.&nbsp;Accepts&nbsp;axis&nbsp;number&nbsp;or&nbsp;name.&nbsp;Default&nbsp;is&nbsp;stat&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;given&nbsp;data&nbsp;type&nbsp;(0&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;and&nbsp;DataFrames,&nbsp;1&nbsp;for&nbsp;Panels).<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-A&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;type&nbsp;as&nbsp;caller.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Generate&nbsp;an&nbsp;example&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;and&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(50))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-head">head</a>()<br>
-0&nbsp;&nbsp;&nbsp;-0.038497<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.820773<br>
-2&nbsp;&nbsp;&nbsp;-0.972766<br>
-3&nbsp;&nbsp;&nbsp;-1.598270<br>
-4&nbsp;&nbsp;&nbsp;-1.095526<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(50,&nbsp;4),&nbsp;columns=list('ABCD'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-head">head</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;0.016443&nbsp;-2.318952&nbsp;-0.566372&nbsp;-1.028078<br>
-1&nbsp;-1.051921&nbsp;&nbsp;0.438836&nbsp;&nbsp;0.658280&nbsp;-0.175797<br>
-2&nbsp;-1.243569&nbsp;-0.364626&nbsp;-0.215065&nbsp;&nbsp;0.057736<br>
-3&nbsp;&nbsp;1.768216&nbsp;&nbsp;0.404512&nbsp;-0.385604&nbsp;-1.457834<br>
-4&nbsp;&nbsp;1.072446&nbsp;-1.137172&nbsp;&nbsp;0.314194&nbsp;-0.046661<br>
-&nbsp;<br>
-Next&nbsp;extract&nbsp;a&nbsp;random&nbsp;sample&nbsp;from&nbsp;both&nbsp;of&nbsp;these&nbsp;objects...<br>
-&nbsp;<br>
-3&nbsp;random&nbsp;elements&nbsp;from&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-sample">sample</a>(n=3)<br>
-27&nbsp;&nbsp;&nbsp;-0.994689<br>
-55&nbsp;&nbsp;&nbsp;-1.049016<br>
-67&nbsp;&nbsp;&nbsp;-0.224565<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-And&nbsp;a&nbsp;random&nbsp;10%&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;with&nbsp;replacement:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-sample">sample</a>(frac=0.1,&nbsp;replace=True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-35&nbsp;&nbsp;1.981780&nbsp;&nbsp;0.142106&nbsp;&nbsp;1.817165&nbsp;-0.290805<br>
-49&nbsp;-1.336199&nbsp;-0.448634&nbsp;-0.789640&nbsp;&nbsp;0.217116<br>
-40&nbsp;&nbsp;0.823173&nbsp;-0.078816&nbsp;&nbsp;1.009536&nbsp;&nbsp;1.015108<br>
-15&nbsp;&nbsp;1.421154&nbsp;-0.055301&nbsp;-1.922594&nbsp;-0.019696<br>
-6&nbsp;&nbsp;-0.148339&nbsp;&nbsp;0.832938&nbsp;&nbsp;1.787600&nbsp;-1.383767</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-select"><strong>select</strong></a>(self, crit, axis=0)</dt><dd><tt>Return&nbsp;data&nbsp;corresponding&nbsp;to&nbsp;axis&nbsp;labels&nbsp;matching&nbsp;criteria<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-crit&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;called&nbsp;on&nbsp;each&nbsp;index&nbsp;(label).&nbsp;Should&nbsp;return&nbsp;True&nbsp;or&nbsp;False<br>
-axis&nbsp;:&nbsp;int<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-selection&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-set_axis"><strong>set_axis</strong></a>(self, axis, labels)</dt><dd><tt>public&nbsp;verson&nbsp;of&nbsp;axis&nbsp;assignment</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-slice_shift"><strong>slice_shift</strong></a>(self, periods=1, axis=0)</dt><dd><tt>Equivalent&nbsp;to&nbsp;`shift`&nbsp;without&nbsp;copying&nbsp;data.&nbsp;The&nbsp;shifted&nbsp;data&nbsp;will<br>
-not&nbsp;include&nbsp;the&nbsp;dropped&nbsp;periods&nbsp;and&nbsp;the&nbsp;shifted&nbsp;axis&nbsp;will&nbsp;be&nbsp;smaller<br>
-than&nbsp;the&nbsp;original.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-&nbsp;<br>
-Notes<br>
------<br>
-While&nbsp;the&nbsp;`slice_shift`&nbsp;is&nbsp;faster&nbsp;than&nbsp;`shift`,&nbsp;you&nbsp;may&nbsp;pay&nbsp;for&nbsp;it<br>
-later&nbsp;during&nbsp;alignment.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-squeeze"><strong>squeeze</strong></a>(self, axis=None)</dt><dd><tt>Squeeze&nbsp;length&nbsp;1&nbsp;dimensions.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;None,&nbsp;integer&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;squeeze&nbsp;if&nbsp;1-sized.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-scalar&nbsp;if&nbsp;1-sized,&nbsp;else&nbsp;original&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-swapaxes"><strong>swapaxes</strong></a>(self, axis1, axis2, copy=True)</dt><dd><tt>Interchange&nbsp;axes&nbsp;and&nbsp;swap&nbsp;values&nbsp;axes&nbsp;appropriately<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-tail"><strong>tail</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;last&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_clipboard"><strong>to_clipboard</strong></a>(self, excel=None, sep=None, **kwargs)</dt><dd><tt>Attempt&nbsp;to&nbsp;write&nbsp;text&nbsp;representation&nbsp;of&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;the&nbsp;system&nbsp;clipboard<br>
-This&nbsp;can&nbsp;be&nbsp;pasted&nbsp;into&nbsp;Excel,&nbsp;for&nbsp;example.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel&nbsp;:&nbsp;boolean,&nbsp;defaults&nbsp;to&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;use&nbsp;the&nbsp;provided&nbsp;separator,&nbsp;writing&nbsp;in&nbsp;a&nbsp;csv<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;format&nbsp;for&nbsp;allowing&nbsp;easy&nbsp;pasting&nbsp;into&nbsp;excel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;False,&nbsp;write&nbsp;a&nbsp;string&nbsp;representation&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;the&nbsp;clipboard<br>
-sep&nbsp;:&nbsp;optional,&nbsp;defaults&nbsp;to&nbsp;tab<br>
-other&nbsp;keywords&nbsp;are&nbsp;passed&nbsp;to&nbsp;to_csv<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Requirements&nbsp;for&nbsp;your&nbsp;platform<br>
-&nbsp;&nbsp;-&nbsp;Linux:&nbsp;xclip,&nbsp;or&nbsp;xsel&nbsp;(with&nbsp;gtk&nbsp;or&nbsp;PyQt4&nbsp;modules)<br>
-&nbsp;&nbsp;-&nbsp;Windows:&nbsp;none<br>
-&nbsp;&nbsp;-&nbsp;OS&nbsp;X:&nbsp;none</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_dense"><strong>to_dense</strong></a>(self)</dt><dd><tt>Return&nbsp;dense&nbsp;representation&nbsp;of&nbsp;NDFrame&nbsp;(as&nbsp;opposed&nbsp;to&nbsp;sparse)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_hdf"><strong>to_hdf</strong></a>(self, path_or_buf, key, **kwargs)</dt><dd><tt>Write&nbsp;the&nbsp;contained&nbsp;data&nbsp;to&nbsp;an&nbsp;HDF5&nbsp;file&nbsp;using&nbsp;HDFStore.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;(string)&nbsp;or&nbsp;HDFStore&nbsp;<a href="builtins.html#object">object</a><br>
-key&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;identifier&nbsp;for&nbsp;the&nbsp;group&nbsp;in&nbsp;the&nbsp;store<br>
-mode&nbsp;:&nbsp;optional,&nbsp;{'a',&nbsp;'w',&nbsp;'r+'},&nbsp;default&nbsp;'a'<br>
-&nbsp;<br>
-&nbsp;&nbsp;``'w'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write;&nbsp;a&nbsp;new&nbsp;file&nbsp;is&nbsp;created&nbsp;(an&nbsp;existing&nbsp;file&nbsp;with&nbsp;the&nbsp;same<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;name&nbsp;would&nbsp;be&nbsp;deleted).<br>
-&nbsp;&nbsp;``'a'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Append;&nbsp;an&nbsp;existing&nbsp;file&nbsp;is&nbsp;opened&nbsp;for&nbsp;reading&nbsp;and&nbsp;writing,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;if&nbsp;the&nbsp;file&nbsp;does&nbsp;not&nbsp;exist&nbsp;it&nbsp;is&nbsp;created.<br>
-&nbsp;&nbsp;``'r+'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;It&nbsp;is&nbsp;similar&nbsp;to&nbsp;``'a'``,&nbsp;but&nbsp;the&nbsp;file&nbsp;must&nbsp;already&nbsp;exist.<br>
-format&nbsp;:&nbsp;'fixed(f)|table(t)',&nbsp;default&nbsp;is&nbsp;'fixed'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fixed(f)&nbsp;:&nbsp;Fixed&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Fast&nbsp;writing/reading.&nbsp;Not-appendable,&nbsp;nor&nbsp;searchable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;table(t)&nbsp;:&nbsp;Table&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;as&nbsp;a&nbsp;PyTables&nbsp;Table&nbsp;structure&nbsp;which&nbsp;may&nbsp;perform<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;worse&nbsp;but&nbsp;allow&nbsp;more&nbsp;flexible&nbsp;operations&nbsp;like&nbsp;searching<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/&nbsp;selecting&nbsp;subsets&nbsp;of&nbsp;the&nbsp;data<br>
-append&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;Table&nbsp;formats,&nbsp;append&nbsp;the&nbsp;input&nbsp;data&nbsp;to&nbsp;the&nbsp;existing<br>
-data_columns&nbsp;:&nbsp;&nbsp;list&nbsp;of&nbsp;columns,&nbsp;or&nbsp;True,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;columns&nbsp;to&nbsp;create&nbsp;as&nbsp;indexed&nbsp;data&nbsp;columns&nbsp;for&nbsp;on-disk<br>
-&nbsp;&nbsp;&nbsp;&nbsp;queries,&nbsp;or&nbsp;True&nbsp;to&nbsp;use&nbsp;all&nbsp;columns.&nbsp;By&nbsp;default&nbsp;only&nbsp;the&nbsp;axes<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;are&nbsp;indexed.&nbsp;See&nbsp;`here<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__">http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__</a>.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Applicable&nbsp;only&nbsp;to&nbsp;format='table'.<br>
-complevel&nbsp;:&nbsp;int,&nbsp;1-9,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;a&nbsp;complib&nbsp;is&nbsp;specified&nbsp;compression&nbsp;will&nbsp;be&nbsp;applied<br>
-&nbsp;&nbsp;&nbsp;&nbsp;where&nbsp;possible<br>
-complib&nbsp;:&nbsp;{'zlib',&nbsp;'bzip2',&nbsp;'lzo',&nbsp;'blosc',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;complevel&nbsp;is&nbsp;&gt;&nbsp;0&nbsp;apply&nbsp;compression&nbsp;to&nbsp;objects&nbsp;written<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;store&nbsp;wherever&nbsp;possible<br>
-fletcher32&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;applying&nbsp;compression&nbsp;use&nbsp;the&nbsp;fletcher32&nbsp;checksum<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;true,&nbsp;ALL&nbsp;nan&nbsp;rows&nbsp;will&nbsp;not&nbsp;be&nbsp;written&nbsp;to&nbsp;store.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_json"><strong>to_json</strong></a>(self, path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False)</dt><dd><tt>Convert&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;a&nbsp;JSON&nbsp;string.<br>
-&nbsp;<br>
-Note&nbsp;NaN's&nbsp;and&nbsp;None&nbsp;will&nbsp;be&nbsp;converted&nbsp;to&nbsp;null&nbsp;and&nbsp;datetime&nbsp;objects<br>
-will&nbsp;be&nbsp;converted&nbsp;to&nbsp;UNIX&nbsp;timestamps.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;or&nbsp;buffer&nbsp;to&nbsp;write&nbsp;the&nbsp;result&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;this&nbsp;is&nbsp;None,&nbsp;return&nbsp;a&nbsp;StringIO&nbsp;of&nbsp;the&nbsp;converted&nbsp;string<br>
-orient&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'index'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:&nbsp;{'split','records','index'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{'split','records','index','columns','values'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;The&nbsp;format&nbsp;of&nbsp;the&nbsp;JSON&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;split&nbsp;:&nbsp;dict&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{index&nbsp;-&gt;&nbsp;[index],&nbsp;columns&nbsp;-&gt;&nbsp;[columns],&nbsp;data&nbsp;-&gt;&nbsp;[values]}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;records&nbsp;:&nbsp;list&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[{column&nbsp;-&gt;&nbsp;value},&nbsp;...&nbsp;,&nbsp;{column&nbsp;-&gt;&nbsp;value}]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;index&nbsp;:&nbsp;dict&nbsp;like&nbsp;{index&nbsp;-&gt;&nbsp;{column&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;columns&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;{index&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;values&nbsp;:&nbsp;just&nbsp;the&nbsp;values&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;table&nbsp;:&nbsp;dict&nbsp;like&nbsp;{'schema':&nbsp;{schema},&nbsp;'data':&nbsp;{data}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;describing&nbsp;the&nbsp;data,&nbsp;and&nbsp;the&nbsp;data&nbsp;component&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;like&nbsp;``orient='records'``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionchanged::&nbsp;0.20.0<br>
-&nbsp;<br>
-date_format&nbsp;:&nbsp;{None,&nbsp;'epoch',&nbsp;'iso'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Type&nbsp;of&nbsp;date&nbsp;conversion.&nbsp;`epoch`&nbsp;=&nbsp;epoch&nbsp;milliseconds,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`iso`&nbsp;=&nbsp;ISO8601.&nbsp;The&nbsp;default&nbsp;depends&nbsp;on&nbsp;the&nbsp;`orient`.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`orient='table'`,&nbsp;the&nbsp;default&nbsp;is&nbsp;`'iso'`.&nbsp;For&nbsp;all&nbsp;other&nbsp;orients,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;default&nbsp;is&nbsp;`'epoch'`.<br>
-double_precision&nbsp;:&nbsp;The&nbsp;number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;use&nbsp;when&nbsp;encoding<br>
-&nbsp;&nbsp;&nbsp;&nbsp;floating&nbsp;point&nbsp;values,&nbsp;default&nbsp;10.<br>
-force_ascii&nbsp;:&nbsp;force&nbsp;encoded&nbsp;string&nbsp;to&nbsp;be&nbsp;ASCII,&nbsp;default&nbsp;True.<br>
-date_unit&nbsp;:&nbsp;string,&nbsp;default&nbsp;'ms'&nbsp;(milliseconds)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;time&nbsp;unit&nbsp;to&nbsp;encode&nbsp;to,&nbsp;governs&nbsp;timestamp&nbsp;and&nbsp;ISO8601<br>
-&nbsp;&nbsp;&nbsp;&nbsp;precision.&nbsp;&nbsp;One&nbsp;of&nbsp;'s',&nbsp;'ms',&nbsp;'us',&nbsp;'ns'&nbsp;for&nbsp;second,&nbsp;millisecond,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;microsecond,&nbsp;and&nbsp;nanosecond&nbsp;respectively.<br>
-default_handler&nbsp;:&nbsp;callable,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Handler&nbsp;to&nbsp;call&nbsp;if&nbsp;<a href="builtins.html#object">object</a>&nbsp;cannot&nbsp;otherwise&nbsp;be&nbsp;converted&nbsp;to&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;suitable&nbsp;format&nbsp;for&nbsp;JSON.&nbsp;Should&nbsp;receive&nbsp;a&nbsp;single&nbsp;argument&nbsp;which&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;convert&nbsp;and&nbsp;return&nbsp;a&nbsp;serialisable&nbsp;<a href="builtins.html#object">object</a>.<br>
-lines&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'orient'&nbsp;is&nbsp;'records'&nbsp;write&nbsp;out&nbsp;line&nbsp;delimited&nbsp;json&nbsp;format.&nbsp;Will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;throw&nbsp;ValueError&nbsp;if&nbsp;incorrect&nbsp;'orient'&nbsp;since&nbsp;others&nbsp;are&nbsp;not&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;filtered&nbsp;info&nbsp;axis<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pd.read_json<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([['a',&nbsp;'b'],&nbsp;['c',&nbsp;'d']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=['row&nbsp;1',&nbsp;'row&nbsp;2'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['col&nbsp;1',&nbsp;'col&nbsp;2'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-to_json">to_json</a>(orient='split')<br>
-'{"columns":["col&nbsp;1","col&nbsp;2"],<br>
-&nbsp;&nbsp;"index":["row&nbsp;1","row&nbsp;2"],<br>
-&nbsp;&nbsp;"data":[["a","b"],["c","d"]]}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'index'``&nbsp;formatted&nbsp;JSON:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-to_json">to_json</a>(orient='index')<br>
-'{"row&nbsp;1":{"col&nbsp;1":"a","col&nbsp;2":"b"},"row&nbsp;2":{"col&nbsp;1":"c","col&nbsp;2":"d"}}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'records'``&nbsp;formatted&nbsp;JSON.<br>
-Note&nbsp;that&nbsp;index&nbsp;labels&nbsp;are&nbsp;not&nbsp;preserved&nbsp;with&nbsp;this&nbsp;encoding.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-to_json">to_json</a>(orient='records')<br>
-'[{"col&nbsp;1":"a","col&nbsp;2":"b"},{"col&nbsp;1":"c","col&nbsp;2":"d"}]'<br>
-&nbsp;<br>
-Encoding&nbsp;with&nbsp;Table&nbsp;Schema<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-to_json">to_json</a>(orient='table')<br>
-'{"schema":&nbsp;{"fields":&nbsp;[{"name":&nbsp;"index",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;1",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;2",&nbsp;"type":&nbsp;"string"}],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"primaryKey":&nbsp;"index",<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"pandas_version":&nbsp;"0.20.0"},<br>
-&nbsp;&nbsp;"data":&nbsp;[{"index":&nbsp;"row&nbsp;1",&nbsp;"col&nbsp;1":&nbsp;"a",&nbsp;"col&nbsp;2":&nbsp;"b"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"index":&nbsp;"row&nbsp;2",&nbsp;"col&nbsp;1":&nbsp;"c",&nbsp;"col&nbsp;2":&nbsp;"d"}]}'</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_msgpack"><strong>to_msgpack</strong></a>(self, path_or_buf=None, encoding='utf-8', **kwargs)</dt><dd><tt>msgpack&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path<br>
-&nbsp;<br>
-THIS&nbsp;IS&nbsp;AN&nbsp;EXPERIMENTAL&nbsp;LIBRARY&nbsp;and&nbsp;the&nbsp;storage&nbsp;format<br>
-may&nbsp;not&nbsp;be&nbsp;stable&nbsp;until&nbsp;a&nbsp;future&nbsp;release.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;File&nbsp;path,&nbsp;buffer-like,&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;None,&nbsp;return&nbsp;generated&nbsp;string<br>
-append&nbsp;:&nbsp;boolean&nbsp;whether&nbsp;to&nbsp;append&nbsp;to&nbsp;an&nbsp;existing&nbsp;msgpack<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(default&nbsp;is&nbsp;False)<br>
-compress&nbsp;:&nbsp;type&nbsp;of&nbsp;compressor&nbsp;(zlib&nbsp;or&nbsp;blosc),&nbsp;default&nbsp;to&nbsp;None&nbsp;(no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;compression)</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_pickle"><strong>to_pickle</strong></a>(self, path, compression='infer')</dt><dd><tt>Pickle&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path<br>
-compression&nbsp;:&nbsp;{'infer',&nbsp;'gzip',&nbsp;'bz2',&nbsp;'xz',&nbsp;None},&nbsp;default&nbsp;'infer'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;compression&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_sql"><strong>to_sql</strong></a>(self, name, con, flavor=None, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None)</dt><dd><tt>Write&nbsp;records&nbsp;stored&nbsp;in&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;SQL&nbsp;database.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-name&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;SQL&nbsp;table<br>
-con&nbsp;:&nbsp;SQLAlchemy&nbsp;engine&nbsp;or&nbsp;DBAPI2&nbsp;connection&nbsp;(legacy&nbsp;mode)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Using&nbsp;SQLAlchemy&nbsp;makes&nbsp;it&nbsp;possible&nbsp;to&nbsp;use&nbsp;any&nbsp;DB&nbsp;supported&nbsp;by&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;library.&nbsp;If&nbsp;a&nbsp;DBAPI2&nbsp;<a href="builtins.html#object">object</a>,&nbsp;only&nbsp;sqlite3&nbsp;is&nbsp;supported.<br>
-flavor&nbsp;:&nbsp;'sqlite',&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;DEPRECATED:&nbsp;this&nbsp;parameter&nbsp;will&nbsp;be&nbsp;removed&nbsp;in&nbsp;a&nbsp;future&nbsp;version,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;as&nbsp;'sqlite'&nbsp;is&nbsp;the&nbsp;only&nbsp;supported&nbsp;option&nbsp;if&nbsp;SQLAlchemy&nbsp;is&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;installed.<br>
-schema&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;the&nbsp;schema&nbsp;(if&nbsp;database&nbsp;flavor&nbsp;supports&nbsp;this).&nbsp;If&nbsp;None,&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;schema.<br>
-if_exists&nbsp;:&nbsp;{'fail',&nbsp;'replace',&nbsp;'append'},&nbsp;default&nbsp;'fail'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;fail:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;do&nbsp;nothing.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;replace:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;drop&nbsp;it,&nbsp;recreate&nbsp;it,&nbsp;and&nbsp;insert&nbsp;data.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;append:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;insert&nbsp;data.&nbsp;Create&nbsp;if&nbsp;does&nbsp;not&nbsp;exist.<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;index&nbsp;as&nbsp;a&nbsp;column.<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s).&nbsp;If&nbsp;None&nbsp;is&nbsp;given&nbsp;(default)&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`index`&nbsp;is&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-chunksize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;not&nbsp;None,&nbsp;then&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;in&nbsp;batches&nbsp;of&nbsp;this&nbsp;size&nbsp;at&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;time.&nbsp;&nbsp;If&nbsp;None,&nbsp;all&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;at&nbsp;once.<br>
-dtype&nbsp;:&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;to&nbsp;SQL&nbsp;type,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Optional&nbsp;specifying&nbsp;the&nbsp;datatype&nbsp;for&nbsp;columns.&nbsp;The&nbsp;SQL&nbsp;type&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;SQLAlchemy&nbsp;type,&nbsp;or&nbsp;a&nbsp;string&nbsp;for&nbsp;sqlite3&nbsp;fallback&nbsp;connection.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-to_xarray"><strong>to_xarray</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;xarray&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;the&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;DataArray&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;Dataset&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-a&nbsp;DataArray&nbsp;for&nbsp;higher&nbsp;dims<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;foo&nbsp;&nbsp;4.0<br>
-1&nbsp;&nbsp;1&nbsp;&nbsp;bar&nbsp;&nbsp;5.0<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;foo&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(index:&nbsp;3)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;index&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;0&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;1&nbsp;1&nbsp;2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'foo'&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;float64&nbsp;4.0&nbsp;5.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;).set_index(['B','A'])<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-B&nbsp;&nbsp;&nbsp;A<br>
-foo&nbsp;1&nbsp;&nbsp;4.0<br>
-bar&nbsp;1&nbsp;&nbsp;5.0<br>
-foo&nbsp;2&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(A:&nbsp;2,&nbsp;B:&nbsp;2)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;*&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(A)&nbsp;int64&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B,&nbsp;A)&nbsp;float64&nbsp;5.0&nbsp;nan&nbsp;4.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p&nbsp;=&nbsp;pd.Panel(np.arange(24).<a href="#SweepSeries-reshape">reshape</a>(4,3,2),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;items=list('ABCD'),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;major_axis=pd.date_range('20130101',&nbsp;periods=3),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;minor_axis=['first',&nbsp;'second'])<br>
-&gt;&gt;&gt;&nbsp;p<br>
-&lt;class&nbsp;'pandas.core.panel.Panel'&gt;<br>
-Dimensions:&nbsp;4&nbsp;(items)&nbsp;x&nbsp;3&nbsp;(major_axis)&nbsp;x&nbsp;2&nbsp;(minor_axis)<br>
-Items&nbsp;axis:&nbsp;A&nbsp;to&nbsp;D<br>
-Major_axis&nbsp;axis:&nbsp;2013-01-01&nbsp;00:00:00&nbsp;to&nbsp;2013-01-03&nbsp;00:00:00<br>
-Minor_axis&nbsp;axis:&nbsp;first&nbsp;to&nbsp;second<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p.<a href="#SweepSeries-to_xarray">to_xarray</a>()<br>
-&lt;xarray.DataArray&nbsp;(items:&nbsp;4,&nbsp;major_axis:&nbsp;3,&nbsp;minor_axis:&nbsp;2)&gt;<br>
-array([[[&nbsp;0,&nbsp;&nbsp;1],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;2,&nbsp;&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;4,&nbsp;&nbsp;5]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[&nbsp;6,&nbsp;&nbsp;7],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;8,&nbsp;&nbsp;9],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[10,&nbsp;11]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[12,&nbsp;13],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[14,&nbsp;15],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[16,&nbsp;17]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[18,&nbsp;19],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[20,&nbsp;21],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[22,&nbsp;23]]])<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;items&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(items)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'A'&nbsp;'B'&nbsp;'C'&nbsp;'D'<br>
-&nbsp;&nbsp;*&nbsp;major_axis&nbsp;&nbsp;(major_axis)&nbsp;datetime64[ns]&nbsp;2013-01-01&nbsp;2013-01-02&nbsp;2013-01-03&nbsp;&nbsp;#&nbsp;noqa<br>
-&nbsp;&nbsp;*&nbsp;minor_axis&nbsp;&nbsp;(minor_axis)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'first'&nbsp;'second'<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;the&nbsp;`xarray&nbsp;docs&nbsp;&lt;<a href="http://xarray.pydata.org/en/stable/&gt;`__">http://xarray.pydata.org/en/stable/&gt;`__</a></tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-truncate"><strong>truncate</strong></a>(self, before=None, after=None, axis=None, copy=True)</dt><dd><tt>Truncates&nbsp;a&nbsp;sorted&nbsp;NDFrame&nbsp;before&nbsp;and/or&nbsp;after&nbsp;some&nbsp;particular<br>
-index&nbsp;value.&nbsp;If&nbsp;the&nbsp;axis&nbsp;contains&nbsp;only&nbsp;datetime&nbsp;values,&nbsp;before/after<br>
-parameters&nbsp;are&nbsp;converted&nbsp;to&nbsp;datetime&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-before&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;before&nbsp;index&nbsp;value<br>
-after&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;after&nbsp;index&nbsp;value<br>
-axis&nbsp;:&nbsp;the&nbsp;truncation&nbsp;axis,&nbsp;defaults&nbsp;to&nbsp;the&nbsp;stat&nbsp;axis<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;is&nbsp;True,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;truncated&nbsp;section<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-truncated&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-tshift"><strong>tshift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;the&nbsp;time&nbsp;index,&nbsp;using&nbsp;the&nbsp;index's&nbsp;frequency&nbsp;if&nbsp;available.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM')<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;basestring<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Corresponds&nbsp;to&nbsp;the&nbsp;axis&nbsp;that&nbsp;contains&nbsp;the&nbsp;Index<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;not&nbsp;specified&nbsp;then&nbsp;tries&nbsp;to&nbsp;use&nbsp;the&nbsp;freq&nbsp;or&nbsp;inferred_freq<br>
-attributes&nbsp;of&nbsp;the&nbsp;index.&nbsp;If&nbsp;neither&nbsp;of&nbsp;those&nbsp;attributes&nbsp;exist,&nbsp;a<br>
-ValueError&nbsp;is&nbsp;thrown<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;NDFrame</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-tz_convert"><strong>tz_convert</strong></a>(self, tz, axis=0, level=None, copy=True)</dt><dd><tt>Convert&nbsp;tz-aware&nbsp;axis&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;convert<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;convert&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;tz-naive.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-tz_localize"><strong>tz_localize</strong></a>(self, tz, axis=0, level=None, copy=True, ambiguous='raise')</dt><dd><tt>Localize&nbsp;tz-naive&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;localize<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;localize&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-ambiguous&nbsp;:&nbsp;'infer',&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>,&nbsp;'NaT',&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'infer'&nbsp;will&nbsp;attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>&nbsp;where&nbsp;True&nbsp;signifies&nbsp;a&nbsp;DST&nbsp;time,&nbsp;False&nbsp;designates<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;non-DST&nbsp;time&nbsp;(note&nbsp;that&nbsp;this&nbsp;flag&nbsp;is&nbsp;only&nbsp;applicable&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ambiguous&nbsp;times)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'NaT'&nbsp;will&nbsp;return&nbsp;NaT&nbsp;where&nbsp;there&nbsp;are&nbsp;ambiguous&nbsp;times<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'raise'&nbsp;will&nbsp;raise&nbsp;an&nbsp;AmbiguousTimeError&nbsp;if&nbsp;there&nbsp;are&nbsp;ambiguous<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;times<br>
-infer_dst&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on&nbsp;order<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;is&nbsp;tz-aware&nbsp;and&nbsp;tz&nbsp;is&nbsp;not&nbsp;None.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-where"><strong>where</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;True&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;where&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``True``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#SweepSeries-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#SweepSeries-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``where``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#SweepSeries-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).<a href="#SweepSeries-reshape">reshape</a>(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#SweepSeries-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#SweepSeries-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mask`</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-xs"><strong>xs</strong></a>(self, key, axis=0, level=None, drop_level=True)</dt><dd><tt>Returns&nbsp;a&nbsp;cross-section&nbsp;(row(s)&nbsp;or&nbsp;column(s))&nbsp;from&nbsp;the<br>
-<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Defaults&nbsp;to&nbsp;cross-section&nbsp;on&nbsp;the&nbsp;rows&nbsp;(axis=0).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Some&nbsp;label&nbsp;contained&nbsp;in&nbsp;the&nbsp;index,&nbsp;or&nbsp;partially&nbsp;in&nbsp;a&nbsp;MultiIndex<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;retrieve&nbsp;cross-section&nbsp;on<br>
-level&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;defaults&nbsp;to&nbsp;first&nbsp;n&nbsp;levels&nbsp;(n=1&nbsp;or&nbsp;len(key))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;key&nbsp;partially&nbsp;contained&nbsp;in&nbsp;a&nbsp;MultiIndex,&nbsp;indicate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;levels&nbsp;are&nbsp;used.&nbsp;Levels&nbsp;can&nbsp;be&nbsp;referred&nbsp;by&nbsp;label&nbsp;or&nbsp;position.<br>
-drop_level&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;returns&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;same&nbsp;levels&nbsp;as&nbsp;self.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C<br>
-a&nbsp;&nbsp;4&nbsp;&nbsp;5&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;4&nbsp;&nbsp;0&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;9&nbsp;&nbsp;7&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-xs">xs</a>('a')<br>
-A&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-B&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-C&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Name:&nbsp;a<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-xs">xs</a>('C',&nbsp;axis=1)<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;C<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;second&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7&nbsp;&nbsp;5&nbsp;&nbsp;5&nbsp;&nbsp;0<br>
-baz&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;three&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-xs">xs</a>(('baz',&nbsp;'three'))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-third<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-xs">xs</a>('one',&nbsp;level=1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-baz&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#SweepSeries-xs">xs</a>(('baz',&nbsp;2),&nbsp;level=[0,&nbsp;'third'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-second<br>
-three&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-xs&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-xs&nbsp;is&nbsp;only&nbsp;for&nbsp;getting,&nbsp;not&nbsp;setting&nbsp;values.<br>
-&nbsp;<br>
-MultiIndex&nbsp;Slicers&nbsp;is&nbsp;a&nbsp;generic&nbsp;way&nbsp;to&nbsp;get/set&nbsp;values&nbsp;on&nbsp;any&nbsp;level&nbsp;or<br>
-levels.&nbsp;&nbsp;It&nbsp;is&nbsp;a&nbsp;superset&nbsp;of&nbsp;xs&nbsp;functionality,&nbsp;see<br>
-:ref:`MultiIndex&nbsp;Slicers&nbsp;&lt;advanced.mi_slicers&gt;`</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>at</strong></dt>
-<dd><tt>Fast&nbsp;label-based&nbsp;scalar&nbsp;accessor<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``loc``,&nbsp;``at``&nbsp;provides&nbsp;**label**&nbsp;based&nbsp;scalar&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>blocks</strong></dt>
-<dd><tt>Internal&nbsp;property,&nbsp;property&nbsp;synonym&nbsp;for&nbsp;as_blocks()</tt></dd>
-</dl>
-<dl><dt><strong>iat</strong></dt>
-<dd><tt>Fast&nbsp;integer&nbsp;location&nbsp;scalar&nbsp;accessor.<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``iloc``,&nbsp;``iat``&nbsp;provides&nbsp;**integer**&nbsp;based&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>iloc</strong></dt>
-<dd><tt>Purely&nbsp;integer-location&nbsp;based&nbsp;indexing&nbsp;for&nbsp;selection&nbsp;by&nbsp;position.<br>
-&nbsp;<br>
-``.iloc[]``&nbsp;is&nbsp;primarily&nbsp;integer&nbsp;position&nbsp;based&nbsp;(from&nbsp;``0``&nbsp;to<br>
-``length-1``&nbsp;of&nbsp;the&nbsp;axis),&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a&nbsp;boolean<br>
-array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;An&nbsp;integer,&nbsp;e.g.&nbsp;``5``.<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;integers,&nbsp;e.g.&nbsp;``[4,&nbsp;3,&nbsp;0]``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;ints,&nbsp;e.g.&nbsp;``1:7``.<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.iloc``&nbsp;will&nbsp;raise&nbsp;``IndexError``&nbsp;if&nbsp;a&nbsp;requested&nbsp;indexer&nbsp;is<br>
-out-of-bounds,&nbsp;except&nbsp;*slice*&nbsp;indexers&nbsp;which&nbsp;allow&nbsp;out-of-bounds<br>
-indexing&nbsp;(this&nbsp;conforms&nbsp;with&nbsp;python/numpy&nbsp;*slice*&nbsp;semantics).<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Position&nbsp;&lt;indexing.integer&gt;`</tt></dd>
-</dl>
-<dl><dt><strong>ix</strong></dt>
-<dd><tt>A&nbsp;primarily&nbsp;label-location&nbsp;based&nbsp;indexer,&nbsp;with&nbsp;integer&nbsp;position<br>
-fallback.<br>
-&nbsp;<br>
-``.ix[]``&nbsp;supports&nbsp;mixed&nbsp;integer&nbsp;and&nbsp;label&nbsp;based&nbsp;access.&nbsp;It&nbsp;is<br>
-primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;will&nbsp;fall&nbsp;back&nbsp;to&nbsp;integer&nbsp;positional<br>
-access&nbsp;unless&nbsp;the&nbsp;corresponding&nbsp;axis&nbsp;is&nbsp;of&nbsp;integer&nbsp;type.<br>
-&nbsp;<br>
-``.ix``&nbsp;is&nbsp;the&nbsp;most&nbsp;general&nbsp;indexer&nbsp;and&nbsp;will&nbsp;support&nbsp;any&nbsp;of&nbsp;the<br>
-inputs&nbsp;in&nbsp;``.loc``&nbsp;and&nbsp;``.iloc``.&nbsp;``.ix``&nbsp;also&nbsp;supports&nbsp;floating<br>
-point&nbsp;label&nbsp;schemes.&nbsp;``.ix``&nbsp;is&nbsp;exceptionally&nbsp;useful&nbsp;when&nbsp;dealing<br>
-with&nbsp;mixed&nbsp;positional&nbsp;and&nbsp;label&nbsp;based&nbsp;hierachical&nbsp;indexes.<br>
-&nbsp;<br>
-However,&nbsp;when&nbsp;an&nbsp;axis&nbsp;is&nbsp;integer&nbsp;based,&nbsp;ONLY&nbsp;label&nbsp;based&nbsp;access<br>
-and&nbsp;not&nbsp;positional&nbsp;access&nbsp;is&nbsp;supported.&nbsp;Thus,&nbsp;in&nbsp;such&nbsp;cases,&nbsp;it's<br>
-usually&nbsp;better&nbsp;to&nbsp;be&nbsp;explicit&nbsp;and&nbsp;use&nbsp;``.iloc``&nbsp;or&nbsp;``.loc``.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Advanced&nbsp;Indexing&nbsp;&lt;advanced&gt;`.</tt></dd>
-</dl>
-<dl><dt><strong>loc</strong></dt>
-<dd><tt>Purely&nbsp;label-location&nbsp;based&nbsp;indexer&nbsp;for&nbsp;selection&nbsp;by&nbsp;label.<br>
-&nbsp;<br>
-``.loc[]``&nbsp;is&nbsp;primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a<br>
-boolean&nbsp;array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;A&nbsp;single&nbsp;label,&nbsp;e.g.&nbsp;``5``&nbsp;or&nbsp;``'a'``,&nbsp;(note&nbsp;that&nbsp;``5``&nbsp;is<br>
-&nbsp;&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;*label*&nbsp;of&nbsp;the&nbsp;index,&nbsp;and&nbsp;**never**&nbsp;as&nbsp;an<br>
-&nbsp;&nbsp;integer&nbsp;position&nbsp;along&nbsp;the&nbsp;index).<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;labels,&nbsp;e.g.&nbsp;``['a',&nbsp;'b',&nbsp;'c']``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;labels,&nbsp;e.g.&nbsp;``'a':'f'``&nbsp;(note&nbsp;that&nbsp;contrary<br>
-&nbsp;&nbsp;to&nbsp;usual&nbsp;python&nbsp;slices,&nbsp;**both**&nbsp;the&nbsp;start&nbsp;and&nbsp;the&nbsp;stop&nbsp;are&nbsp;included!).<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.loc``&nbsp;will&nbsp;raise&nbsp;a&nbsp;``KeyError``&nbsp;when&nbsp;the&nbsp;items&nbsp;are&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Label&nbsp;&lt;indexing.label&gt;`</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>is_copy</strong> = None</dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a>:<br>
-<dl><dt><a name="SweepSeries-__dir__"><strong>__dir__</strong></a>(self)</dt><dd><tt>Provide&nbsp;method&nbsp;name&nbsp;lookup&nbsp;and&nbsp;completion<br>
-Only&nbsp;provide&nbsp;'public'&nbsp;methods</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-__sizeof__"><strong>__sizeof__</strong></a>(self)</dt><dd><tt>Generates&nbsp;the&nbsp;total&nbsp;memory&nbsp;usage&nbsp;for&nbsp;a&nbsp;<a href="builtins.html#object">object</a>&nbsp;that&nbsp;returns<br>
-either&nbsp;a&nbsp;value&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;values</tt></dd></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a>:<br>
-<dl><dt><a name="SweepSeries-__bytes__"><strong>__bytes__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;bytes(obj)&nbsp;in&nbsp;py3&nbsp;only.<br>
-Yields&nbsp;a&nbsp;bytestring&nbsp;in&nbsp;both&nbsp;py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-__repr__"><strong>__repr__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-<dl><dt><a name="SweepSeries-__str__"><strong>__str__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;Object<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;<a href="#SweepSeries-str">str</a>(df)&nbsp;in&nbsp;both&nbsp;py2/py3.<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-</td></tr></table> <p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#ffc8d8">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#000000" face="helvetica, arial"><a name="System">class <strong>System</strong></a>(<a href="modsim.html#MySeries">MySeries</a>)</font></td></tr>
-    
-<tr bgcolor="#ffc8d8"><td rowspan=2><tt>&nbsp;&nbsp;&nbsp;</tt></td>
-<td colspan=2><tt>One-dimensional&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;with&nbsp;axis&nbsp;labels&nbsp;(including&nbsp;time&nbsp;series).<br>
-&nbsp;<br>
-Labels&nbsp;need&nbsp;not&nbsp;be&nbsp;unique&nbsp;but&nbsp;must&nbsp;be&nbsp;a&nbsp;hashable&nbsp;type.&nbsp;The&nbsp;<a href="builtins.html#object">object</a><br>
-supports&nbsp;both&nbsp;integer-&nbsp;and&nbsp;label-based&nbsp;indexing&nbsp;and&nbsp;provides&nbsp;a&nbsp;host&nbsp;of<br>
-methods&nbsp;for&nbsp;performing&nbsp;operations&nbsp;involving&nbsp;the&nbsp;index.&nbsp;Statistical<br>
-methods&nbsp;from&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;have&nbsp;been&nbsp;overridden&nbsp;to&nbsp;automatically&nbsp;exclude<br>
-missing&nbsp;data&nbsp;(currently&nbsp;represented&nbsp;as&nbsp;NaN).<br>
-&nbsp;<br>
-Operations&nbsp;between&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(+,&nbsp;-,&nbsp;/,&nbsp;*,&nbsp;**)&nbsp;align&nbsp;values&nbsp;based&nbsp;on&nbsp;their<br>
-associated&nbsp;index&nbsp;values--&nbsp;they&nbsp;need&nbsp;not&nbsp;be&nbsp;the&nbsp;same&nbsp;length.&nbsp;The&nbsp;result<br>
-index&nbsp;will&nbsp;be&nbsp;the&nbsp;sorted&nbsp;union&nbsp;of&nbsp;the&nbsp;two&nbsp;indexes.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;array-like,&nbsp;dict,&nbsp;or&nbsp;scalar&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Contains&nbsp;data&nbsp;stored&nbsp;in&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-index&nbsp;:&nbsp;array-like&nbsp;or&nbsp;Index&nbsp;(1d)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Values&nbsp;must&nbsp;be&nbsp;hashable&nbsp;and&nbsp;have&nbsp;the&nbsp;same&nbsp;length&nbsp;as&nbsp;`data`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Non-unique&nbsp;index&nbsp;values&nbsp;are&nbsp;allowed.&nbsp;Will&nbsp;default&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;RangeIndex(len(data))&nbsp;if&nbsp;not&nbsp;provided.&nbsp;If&nbsp;both&nbsp;a&nbsp;dict&nbsp;and&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;are&nbsp;used,&nbsp;the&nbsp;index&nbsp;will&nbsp;override&nbsp;the&nbsp;keys&nbsp;found&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict.<br>
-dtype&nbsp;:&nbsp;numpy.dtype&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;None,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;inferred<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Copy&nbsp;input&nbsp;data<br>&nbsp;</tt></td></tr>
-<tr><td>&nbsp;</td>
-<td width="100%"><dl><dt>Method resolution order:</dt>
-<dd><a href="modsim.html#System">System</a></dd>
-<dd><a href="modsim.html#MySeries">MySeries</a></dd>
-<dd><a href="pandas.core.series.html#Series">pandas.core.series.Series</a></dd>
-<dd><a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a></dd>
-<dd><a href="pandas.core.strings.html#StringAccessorMixin">pandas.core.strings.StringAccessorMixin</a></dd>
-<dd><a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a></dd>
-<dd><a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a></dd>
-<dd><a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a></dd>
-<dd><a href="pandas.core.base.html#SelectionMixin">pandas.core.base.SelectionMixin</a></dd>
-<dd><a href="builtins.html#object">builtins.object</a></dd>
-</dl>
-<hr>
-Methods defined here:<br>
-<dl><dt><a name="System-__init__"><strong>__init__</strong></a>(self, *args, **kwargs)</dt><dd><tt>Initialize&nbsp;the&nbsp;series.<br>
-&nbsp;<br>
-If&nbsp;there&nbsp;are&nbsp;no&nbsp;positional&nbsp;arguments,&nbsp;use&nbsp;kwargs.<br>
-&nbsp;<br>
-If&nbsp;there&nbsp;is&nbsp;one&nbsp;positional&nbsp;argument,&nbsp;copy&nbsp;it.<br>
-&nbsp;<br>
-More&nbsp;than&nbsp;one&nbsp;positional&nbsp;argument&nbsp;is&nbsp;an&nbsp;error.</tt></dd></dl>
-
-<hr>
-Data descriptors defined here:<br>
-<dl><dt><strong>T</strong></dt>
-<dd><tt>Intercept&nbsp;the&nbsp;Series&nbsp;accessor&nbsp;object&nbsp;so&nbsp;we&nbsp;can&nbsp;use&nbsp;`T`<br>
-as&nbsp;a&nbsp;row&nbsp;label&nbsp;and&nbsp;access&nbsp;it&nbsp;using&nbsp;dot&nbsp;notation.<br>
-&nbsp;<br>
-https://pandas.pydata.org/pandas-docs/stable/generated/<br>
-pandas.Series.T.html#pandas.Series.T</tt></dd>
-</dl>
-<dl><dt><strong>dt</strong></dt>
-<dd><tt>Intercept&nbsp;the&nbsp;Series&nbsp;accessor&nbsp;object&nbsp;so&nbsp;we&nbsp;can&nbsp;use&nbsp;`dt`<br>
-as&nbsp;a&nbsp;row&nbsp;label&nbsp;and&nbsp;access&nbsp;it&nbsp;using&nbsp;dot&nbsp;notation.<br>
-&nbsp;<br>
-https://pandas.pydata.org/pandas-docs/stable/generated/<br>
-pandas.Series.dt.html</tt></dd>
-</dl>
-<hr>
-Methods inherited from <a href="modsim.html#MySeries">MySeries</a>:<br>
-<dl><dt><a name="System-set"><strong>set</strong></a>(self, **kwargs)</dt><dd><tt>Uses&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;update&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place.<br>
-&nbsp;<br>
-Example:&nbsp;series.<a href="#System-update">update</a>(a=1,&nbsp;b=2)</tt></dd></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><a name="System-__add__"><strong>__add__</strong></a> = wrapper(left, right, name='__add__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f2f0&gt;)</dt></dl>
-
-<dl><dt><a name="System-__and__"><strong>__and__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="System-__array__"><strong>__array__</strong></a>(self, result=None)</dt><dd><tt>the&nbsp;array&nbsp;interface,&nbsp;return&nbsp;my&nbsp;values</tt></dd></dl>
-
-<dl><dt><a name="System-__array_prepare__"><strong>__array_prepare__</strong></a>(self, result, context=None)</dt><dd><tt>Gets&nbsp;called&nbsp;prior&nbsp;to&nbsp;a&nbsp;ufunc</tt></dd></dl>
-
-<dl><dt><a name="System-__array_wrap__"><strong>__array_wrap__</strong></a>(self, result, context=None)</dt><dd><tt>Gets&nbsp;called&nbsp;after&nbsp;a&nbsp;ufunc</tt></dd></dl>
-
-<dl><dt><a name="System-__div__"><strong>__div__</strong></a> = wrapper(left, right, name='__truediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f9d8&gt;)</dt></dl>
-
-<dl><dt><a name="System-__divmod__"><strong>__divmod__</strong></a> = wrapper(left, right, name='__divmod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca343bf8&gt;)</dt></dl>
-
-<dl><dt><a name="System-__eq__"><strong>__eq__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="System-__float__"><strong>__float__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="System-__floordiv__"><strong>__floordiv__</strong></a> = wrapper(left, right, name='__floordiv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fb70&gt;)</dt></dl>
-
-<dl><dt><a name="System-__ge__"><strong>__ge__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="System-__getitem__"><strong>__getitem__</strong></a>(self, key)</dt></dl>
-
-<dl><dt><a name="System-__gt__"><strong>__gt__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="System-__iadd__"><strong>__iadd__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="System-__imul__"><strong>__imul__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="System-__int__"><strong>__int__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="System-__ipow__"><strong>__ipow__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="System-__isub__"><strong>__isub__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="System-__iter__"><strong>__iter__</strong></a>(self)</dt><dd><tt>provide&nbsp;iteration&nbsp;over&nbsp;the&nbsp;values&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-box&nbsp;values&nbsp;if&nbsp;necessary</tt></dd></dl>
-
-<dl><dt><a name="System-__itruediv__"><strong>__itruediv__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="System-__le__"><strong>__le__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="System-__len__"><strong>__len__</strong></a>(self)</dt><dd><tt>return&nbsp;the&nbsp;length&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="System-__long__"><strong>__long__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="System-__lt__"><strong>__lt__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="System-__mod__"><strong>__mod__</strong></a> = wrapper(left, right, name='__mod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fd08&gt;)</dt></dl>
-
-<dl><dt><a name="System-__mul__"><strong>__mul__</strong></a> = wrapper(left, right, name='__mul__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f840&gt;)</dt></dl>
-
-<dl><dt><a name="System-__ne__"><strong>__ne__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="System-__or__"><strong>__or__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="System-__pow__"><strong>__pow__</strong></a> = wrapper(left, right, name='__pow__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fea0&gt;)</dt></dl>
-
-<dl><dt><a name="System-__radd__"><strong>__radd__</strong></a> = wrapper(left, right, name='__radd__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f510&gt;)</dt></dl>
-
-<dl><dt><a name="System-__rand__"><strong>__rand__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="System-__rdiv__"><strong>__rdiv__</strong></a> = wrapper(left, right, name='__rtruediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342510&gt;)</dt></dl>
-
-<dl><dt><a name="System-__rfloordiv__"><strong>__rfloordiv__</strong></a> = wrapper(left, right, name='__rfloordiv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342730&gt;)</dt></dl>
-
-<dl><dt><a name="System-__rmod__"><strong>__rmod__</strong></a> = wrapper(left, right, name='__rmod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342b70&gt;)</dt></dl>
-
-<dl><dt><a name="System-__rmul__"><strong>__rmul__</strong></a> = wrapper(left, right, name='__rmul__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca3420d0&gt;)</dt></dl>
-
-<dl><dt><a name="System-__ror__"><strong>__ror__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="System-__rpow__"><strong>__rpow__</strong></a> = wrapper(left, right, name='__rpow__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342950&gt;)</dt></dl>
-
-<dl><dt><a name="System-__rsub__"><strong>__rsub__</strong></a> = wrapper(left, right, name='__rsub__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca3422f0&gt;)</dt></dl>
-
-<dl><dt><a name="System-__rtruediv__"><strong>__rtruediv__</strong></a> = wrapper(left, right, name='__rtruediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342510&gt;)</dt></dl>
-
-<dl><dt><a name="System-__rxor__"><strong>__rxor__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="System-__setitem__"><strong>__setitem__</strong></a>(self, key, value)</dt></dl>
-
-<dl><dt><a name="System-__sub__"><strong>__sub__</strong></a> = wrapper(left, right, name='__sub__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f6a8&gt;)</dt></dl>
-
-<dl><dt><a name="System-__truediv__"><strong>__truediv__</strong></a> = wrapper(left, right, name='__truediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f9d8&gt;)</dt></dl>
-
-<dl><dt><a name="System-__unicode__"><strong>__unicode__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;unicode(df)&nbsp;in&nbsp;py2&nbsp;only.&nbsp;Yields&nbsp;a&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;both<br>
-py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="System-__xor__"><strong>__xor__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="System-add"><strong>add</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Addition&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`add`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;+&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.radd</tt></dd></dl>
-
-<dl><dt><a name="System-agg"><strong>agg</strong></a> = aggregate(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.series.html#Series">Series</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#System-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#System-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10))<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-agg">agg</a>('min')<br>
--1.3018049988556679<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-agg">agg</a>(['min',&nbsp;'max'])<br>
-min&nbsp;&nbsp;&nbsp;-1.301805<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;1.127688<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.apply<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.transform</tt></dd></dl>
-
-<dl><dt><a name="System-aggregate"><strong>aggregate</strong></a>(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.series.html#Series">Series</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#System-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#System-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10))<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-agg">agg</a>('min')<br>
--1.3018049988556679<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-agg">agg</a>(['min',&nbsp;'max'])<br>
-min&nbsp;&nbsp;&nbsp;-1.301805<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;1.127688<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.apply<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.transform</tt></dd></dl>
-
-<dl><dt><a name="System-align"><strong>align</strong></a>(self, other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)</dt><dd><tt>Align&nbsp;two&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;their&nbsp;axes&nbsp;with&nbsp;the<br>
-specified&nbsp;join&nbsp;method&nbsp;for&nbsp;each&nbsp;axis&nbsp;Index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-join&nbsp;:&nbsp;{'outer',&nbsp;'inner',&nbsp;'left',&nbsp;'right'},&nbsp;default&nbsp;'outer'<br>
-axis&nbsp;:&nbsp;allowed&nbsp;axis&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;on&nbsp;index&nbsp;(0),&nbsp;columns&nbsp;(1),&nbsp;or&nbsp;both&nbsp;(None)<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Always&nbsp;returns&nbsp;new&nbsp;objects.&nbsp;If&nbsp;copy=False&nbsp;and&nbsp;no&nbsp;reindexing&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;required&nbsp;then&nbsp;original&nbsp;objects&nbsp;are&nbsp;returned.<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-method&nbsp;:&nbsp;str,&nbsp;default&nbsp;None<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-fill_axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Filling&nbsp;axis,&nbsp;method&nbsp;and&nbsp;limit<br>
-broadcast_axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;values&nbsp;along&nbsp;this&nbsp;axis,&nbsp;if&nbsp;aligning&nbsp;two&nbsp;objects&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;different&nbsp;dimensions<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-(left,&nbsp;right)&nbsp;:&nbsp;(<a href="pandas.core.series.html#Series">Series</a>,&nbsp;type&nbsp;of&nbsp;other)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Aligned&nbsp;objects</tt></dd></dl>
-
-<dl><dt><a name="System-all"><strong>all</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;all&nbsp;elements&nbsp;are&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-all&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-any"><strong>any</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;any&nbsp;element&nbsp;is&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-any&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-append"><strong>append</strong></a>(self, to_append, ignore_index=False, verify_integrity=False)</dt><dd><tt>Concatenate&nbsp;two&nbsp;or&nbsp;more&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-to_append&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;list/tuple&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-ignore_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;not&nbsp;use&nbsp;the&nbsp;index&nbsp;labels.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.19.0<br>
-&nbsp;<br>
-verify_integrity&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;raise&nbsp;Exception&nbsp;on&nbsp;creating&nbsp;index&nbsp;with&nbsp;duplicates<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-appended&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s1&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s2&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([4,&nbsp;5,&nbsp;6])<br>
-&gt;&gt;&gt;&nbsp;s3&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([4,&nbsp;5,&nbsp;6],&nbsp;index=[3,4,5])<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#System-append">append</a>(s2)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#System-append">append</a>(s3)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-With&nbsp;`ignore_index`&nbsp;set&nbsp;to&nbsp;True:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#System-append">append</a>(s2,&nbsp;ignore_index=True)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-With&nbsp;`verify_integrity`&nbsp;set&nbsp;to&nbsp;True:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#System-append">append</a>(s2,&nbsp;verify_integrity=True)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-ValueError:&nbsp;Indexes&nbsp;have&nbsp;overlapping&nbsp;values:&nbsp;[0,&nbsp;1,&nbsp;2]</tt></dd></dl>
-
-<dl><dt><a name="System-apply"><strong>apply</strong></a>(self, func, convert_dtype=True, args=(), **kwds)</dt><dd><tt>Invoke&nbsp;function&nbsp;on&nbsp;values&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Can&nbsp;be&nbsp;ufunc&nbsp;(a&nbsp;NumPy&nbsp;function<br>
-that&nbsp;applies&nbsp;to&nbsp;the&nbsp;entire&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;or&nbsp;a&nbsp;Python&nbsp;function&nbsp;that&nbsp;only&nbsp;works<br>
-on&nbsp;single&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-convert_dtype&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Try&nbsp;to&nbsp;find&nbsp;better&nbsp;dtype&nbsp;for&nbsp;elementwise&nbsp;function&nbsp;results.&nbsp;If<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False,&nbsp;leave&nbsp;as&nbsp;dtype=<a href="builtins.html#object">object</a><br>
-args&nbsp;:&nbsp;tuple<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Positional&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;to&nbsp;function&nbsp;in&nbsp;addition&nbsp;to&nbsp;the&nbsp;value<br>
-Additional&nbsp;keyword&nbsp;arguments&nbsp;will&nbsp;be&nbsp;passed&nbsp;as&nbsp;keywords&nbsp;to&nbsp;the&nbsp;function<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;if&nbsp;func&nbsp;returns&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.map:&nbsp;For&nbsp;element-wise&nbsp;operations<br>
-<a href="pandas.core.series.html#Series">Series</a>.agg:&nbsp;only&nbsp;perform&nbsp;aggregating&nbsp;type&nbsp;operations<br>
-<a href="pandas.core.series.html#Series">Series</a>.transform:&nbsp;only&nbsp;perform&nbsp;transformating&nbsp;type&nbsp;operations<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;series&nbsp;with&nbsp;typical&nbsp;summer&nbsp;temperatures&nbsp;for&nbsp;each&nbsp;city.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([20,&nbsp;21,&nbsp;12],&nbsp;index=['London',<br>
-...&nbsp;'New&nbsp;York','Helsinki'])<br>
-&gt;&gt;&gt;&nbsp;series<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;20<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Square&nbsp;the&nbsp;values&nbsp;by&nbsp;defining&nbsp;a&nbsp;function&nbsp;and&nbsp;passing&nbsp;it&nbsp;as&nbsp;an<br>
-argument&nbsp;to&nbsp;``<a href="#System-apply">apply</a>()``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;square(x):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x**2<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#System-apply">apply</a>(square)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;400<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;441<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;144<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Square&nbsp;the&nbsp;values&nbsp;by&nbsp;passing&nbsp;an&nbsp;anonymous&nbsp;function&nbsp;as&nbsp;an<br>
-argument&nbsp;to&nbsp;``<a href="#System-apply">apply</a>()``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#System-apply">apply</a>(lambda&nbsp;x:&nbsp;x**2)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;400<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;441<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;144<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Define&nbsp;a&nbsp;custom&nbsp;function&nbsp;that&nbsp;needs&nbsp;additional&nbsp;positional<br>
-arguments&nbsp;and&nbsp;pass&nbsp;these&nbsp;additional&nbsp;arguments&nbsp;using&nbsp;the<br>
-``args``&nbsp;keyword.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;subtract_custom_value(x,&nbsp;custom_value):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x-custom_value<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#System-apply">apply</a>(subtract_custom_value,&nbsp;args=(5,))<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;16<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Define&nbsp;a&nbsp;custom&nbsp;function&nbsp;that&nbsp;takes&nbsp;keyword&nbsp;arguments<br>
-and&nbsp;pass&nbsp;these&nbsp;arguments&nbsp;to&nbsp;``apply``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;add_custom_values(x,&nbsp;**kwargs):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;month&nbsp;in&nbsp;kwargs:<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;x+=kwargs[month]<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#System-apply">apply</a>(add_custom_values,&nbsp;june=30,&nbsp;july=20,&nbsp;august=25)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;95<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;96<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;87<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Use&nbsp;a&nbsp;function&nbsp;from&nbsp;the&nbsp;Numpy&nbsp;library.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#System-apply">apply</a>(np.log)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.995732<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;3.044522<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;2.484907<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="System-argmax"><strong>argmax</strong></a> = idxmax(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmax&nbsp;:&nbsp;Index&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmax``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmax<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmax</tt></dd></dl>
-
-<dl><dt><a name="System-argmin"><strong>argmin</strong></a> = idxmin(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmin&nbsp;:&nbsp;Index&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmin``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmin<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmin</tt></dd></dl>
-
-<dl><dt><a name="System-argsort"><strong>argsort</strong></a>(self, axis=0, kind='quicksort', order=None)</dt><dd><tt>Overrides&nbsp;<a href="numpy.html#ndarray">ndarray</a>.argsort.&nbsp;Argsorts&nbsp;the&nbsp;value,&nbsp;omitting&nbsp;NA/null&nbsp;values,<br>
-and&nbsp;places&nbsp;the&nbsp;result&nbsp;in&nbsp;the&nbsp;same&nbsp;locations&nbsp;as&nbsp;the&nbsp;non-NA&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;int&nbsp;(can&nbsp;only&nbsp;be&nbsp;zero)<br>
-kind&nbsp;:&nbsp;{'mergesort',&nbsp;'quicksort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;'mergesort'&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm<br>
-order&nbsp;:&nbsp;ignored<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-argsorted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;with&nbsp;-1&nbsp;indicated&nbsp;where&nbsp;nan&nbsp;values&nbsp;are&nbsp;present<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argsort</tt></dd></dl>
-
-<dl><dt><a name="System-autocorr"><strong>autocorr</strong></a>(self, lag=1)</dt><dd><tt>Lag-N&nbsp;autocorrelation<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-lag&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;lags&nbsp;to&nbsp;apply&nbsp;before&nbsp;performing&nbsp;autocorrelation.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-autocorr&nbsp;:&nbsp;float</tt></dd></dl>
-
-<dl><dt><a name="System-between"><strong>between</strong></a>(self, left, right, inclusive=True)</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;equivalent&nbsp;to&nbsp;left&nbsp;&lt;=&nbsp;series&nbsp;&lt;=&nbsp;right.&nbsp;NA&nbsp;values<br>
-will&nbsp;be&nbsp;treated&nbsp;as&nbsp;False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-left&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Left&nbsp;boundary<br>
-right&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Right&nbsp;boundary<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_between&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="System-combine"><strong>combine</strong></a>(self, other, func, fill_value=nan)</dt><dd><tt>Perform&nbsp;elementwise&nbsp;binary&nbsp;operation&nbsp;on&nbsp;two&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;using&nbsp;given&nbsp;function<br>
-with&nbsp;optional&nbsp;fill&nbsp;value&nbsp;when&nbsp;an&nbsp;index&nbsp;is&nbsp;missing&nbsp;from&nbsp;one&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or<br>
-the&nbsp;other<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-func&nbsp;:&nbsp;function<br>
-fill_value&nbsp;:&nbsp;scalar&nbsp;value<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="System-combine_first"><strong>combine_first</strong></a>(self, other)</dt><dd><tt>Combine&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values,&nbsp;choosing&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.series.html#Series">Series</a>'s&nbsp;values<br>
-first.&nbsp;Result&nbsp;index&nbsp;will&nbsp;be&nbsp;the&nbsp;union&nbsp;of&nbsp;the&nbsp;two&nbsp;indexes<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="System-compound"><strong>compound</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;compound&nbsp;percentage&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-compounded&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-compress"><strong>compress</strong></a>(self, condition, *args, **kwargs)</dt><dd><tt>Return&nbsp;selected&nbsp;slices&nbsp;of&nbsp;an&nbsp;array&nbsp;along&nbsp;given&nbsp;axis&nbsp;as&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.compress</tt></dd></dl>
-
-<dl><dt><a name="System-corr"><strong>corr</strong></a>(self, other, method='pearson', min_periods=None)</dt><dd><tt>Compute&nbsp;correlation&nbsp;with&nbsp;`other`&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;excluding&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-method&nbsp;:&nbsp;{'pearson',&nbsp;'kendall',&nbsp;'spearman'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pearson&nbsp;:&nbsp;standard&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;kendall&nbsp;:&nbsp;Kendall&nbsp;Tau&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;spearman&nbsp;:&nbsp;Spearman&nbsp;rank&nbsp;correlation<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;needed&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result<br>
-&nbsp;<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-correlation&nbsp;:&nbsp;float</tt></dd></dl>
-
-<dl><dt><a name="System-count"><strong>count</strong></a>(self, level=None)</dt><dd><tt>Return&nbsp;number&nbsp;of&nbsp;non-NA/null&nbsp;observations&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;smaller&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-nobs&nbsp;:&nbsp;int&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-cov"><strong>cov</strong></a>(self, other, min_periods=None)</dt><dd><tt>Compute&nbsp;covariance&nbsp;with&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;excluding&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;needed&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-covariance&nbsp;:&nbsp;float<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;(unbiased&nbsp;estimator).</tt></dd></dl>
-
-<dl><dt><a name="System-cummax"><strong>cummax</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;max&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummax&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.max&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="System-cummin"><strong>cummin</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;minimum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummin&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.min&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="System-cumprod"><strong>cumprod</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;product&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumprod&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.prod&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="System-cumsum"><strong>cumsum</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;sum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumsum&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.sum&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="System-diff"><strong>diff</strong></a>(self, periods=1)</dt><dd><tt>1st&nbsp;discrete&nbsp;difference&nbsp;of&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;difference<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-diffed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="System-div"><strong>div</strong></a> = truediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="System-divide"><strong>divide</strong></a> = truediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="System-dot"><strong>dot</strong></a>(self, other)</dt><dd><tt>Matrix&nbsp;multiplication&nbsp;with&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;inner-product&nbsp;with&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-objects<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dot_product&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="System-drop_duplicates"><strong>drop_duplicates</strong></a>(self, keep='first', inplace=False)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;duplicate&nbsp;values&nbsp;removed<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-&nbsp;<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Drop&nbsp;all&nbsp;duplicates.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-If&nbsp;True,&nbsp;performs&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;returns&nbsp;None.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-deduplicated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="System-dropna"><strong>dropna</strong></a>(self, axis=0, inplace=False, **kwargs)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;without&nbsp;null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-valid&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;operation&nbsp;in&nbsp;place.</tt></dd></dl>
-
-<dl><dt><a name="System-duplicated"><strong>duplicated</strong></a>(self, keep='first')</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;denoting&nbsp;duplicate&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the&nbsp;last<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Mark&nbsp;all&nbsp;duplicates&nbsp;as&nbsp;``True``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-duplicated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="System-eq"><strong>eq</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`eq`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;==&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="System-ewm"><strong>ewm</strong></a>(self, com=None, span=None, halflife=None, alpha=None, min_periods=0, freq=None, adjust=True, ignore_na=False, axis=0)</dt><dd><tt>Provides&nbsp;exponential&nbsp;weighted&nbsp;functions<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-com&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;/&nbsp;(1&nbsp;+&nbsp;com),\text{&nbsp;for&nbsp;}&nbsp;com&nbsp;\geq&nbsp;0`<br>
-span&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;span,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;2&nbsp;/&nbsp;(span&nbsp;+&nbsp;1),\text{&nbsp;for&nbsp;}&nbsp;span&nbsp;\geq&nbsp;1`<br>
-halflife&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;half-life,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;-&nbsp;exp(log(0.5)&nbsp;/&nbsp;halflife),\text{&nbsp;for&nbsp;}&nbsp;halflife&nbsp;&gt;&nbsp;0`<br>
-alpha&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;smoothing&nbsp;factor&nbsp;:math:`\alpha`&nbsp;directly,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`0&nbsp;&lt;&nbsp;\alpha&nbsp;\leq&nbsp;1`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;None&nbsp;or&nbsp;string&nbsp;alias&nbsp;/&nbsp;date&nbsp;offset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default=None&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;to&nbsp;before&nbsp;computing&nbsp;statistic<br>
-adjust&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Divide&nbsp;by&nbsp;decaying&nbsp;adjustment&nbsp;factor&nbsp;in&nbsp;beginning&nbsp;periods&nbsp;to&nbsp;account<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;imbalance&nbsp;in&nbsp;relative&nbsp;weightings&nbsp;(viewing&nbsp;EWMA&nbsp;as&nbsp;a&nbsp;moving&nbsp;average)<br>
-ignore_na&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Ignore&nbsp;missing&nbsp;values&nbsp;when&nbsp;calculating&nbsp;weights;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specify&nbsp;True&nbsp;to&nbsp;reproduce&nbsp;pre-0.15.0&nbsp;behavior<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-ewm">ewm</a>(com=0.5).<a href="#System-mean">mean</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.000000<br>
-1&nbsp;&nbsp;0.750000<br>
-2&nbsp;&nbsp;1.615385<br>
-3&nbsp;&nbsp;1.615385<br>
-4&nbsp;&nbsp;3.670213<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Exactly&nbsp;one&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,&nbsp;span,&nbsp;half-life,&nbsp;and&nbsp;alpha&nbsp;must&nbsp;be&nbsp;provided.<br>
-Allowed&nbsp;values&nbsp;and&nbsp;relationship&nbsp;between&nbsp;the&nbsp;parameters&nbsp;are&nbsp;specified&nbsp;in&nbsp;the<br>
-parameter&nbsp;descriptions&nbsp;above;&nbsp;see&nbsp;the&nbsp;link&nbsp;at&nbsp;the&nbsp;end&nbsp;of&nbsp;this&nbsp;section&nbsp;for<br>
-a&nbsp;detailed&nbsp;explanation.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;True&nbsp;(default),&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;using<br>
-weights&nbsp;(1-alpha)**(n-1),&nbsp;(1-alpha)**(n-2),&nbsp;...,&nbsp;1-alpha,&nbsp;1.<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;False,&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;recursively&nbsp;as:<br>
-&nbsp;&nbsp;&nbsp;weighted_average[0]&nbsp;=&nbsp;arg[0];<br>
-&nbsp;&nbsp;&nbsp;weighted_average[i]&nbsp;=&nbsp;(1-alpha)*weighted_average[i-1]&nbsp;+&nbsp;alpha*arg[i].<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;False&nbsp;(default),&nbsp;weights&nbsp;are&nbsp;based&nbsp;on&nbsp;absolute&nbsp;positions.<br>
-For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in&nbsp;calculating&nbsp;the&nbsp;final&nbsp;weighted<br>
-average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;(1-alpha)**2&nbsp;and&nbsp;1&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and<br>
-(1-alpha)**2&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;True&nbsp;(reproducing&nbsp;pre-0.15.0&nbsp;behavior),&nbsp;weights&nbsp;are&nbsp;based<br>
-on&nbsp;relative&nbsp;positions.&nbsp;For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in<br>
-calculating&nbsp;the&nbsp;final&nbsp;weighted&nbsp;average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;1-alpha&nbsp;and&nbsp;1<br>
-(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and&nbsp;1-alpha&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-More&nbsp;details&nbsp;can&nbsp;be&nbsp;found&nbsp;at<br>
-<a href="http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows">http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows</a></tt></dd></dl>
-
-<dl><dt><a name="System-expanding"><strong>expanding</strong></a>(self, min_periods=1, freq=None, center=False, axis=0)</dt><dd><tt>Provides&nbsp;expanding&nbsp;transformations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-expanding">expanding</a>(2).<a href="#System-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;7.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).</tt></dd></dl>
-
-<dl><dt><a name="System-fillna"><strong>fillna</strong></a>(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)</dt><dd><tt>Fill&nbsp;NA/NaN&nbsp;values&nbsp;using&nbsp;the&nbsp;specified&nbsp;method<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;values&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;each&nbsp;index&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;or&nbsp;column&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).&nbsp;(values&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;This&nbsp;value&nbsp;cannot<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;list.<br>
-method&nbsp;:&nbsp;{'backfill',&nbsp;'bfill',&nbsp;'pad',&nbsp;'ffill',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'}<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;fill&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>,&nbsp;(e.g.&nbsp;a&nbsp;no-copy&nbsp;slice&nbsp;for&nbsp;a&nbsp;column&nbsp;in&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;method&nbsp;is&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the&nbsp;maximum&nbsp;number&nbsp;of&nbsp;consecutive<br>
-&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;values&nbsp;to&nbsp;forward/backward&nbsp;fill.&nbsp;In&nbsp;other&nbsp;words,&nbsp;if&nbsp;there&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;gap&nbsp;with&nbsp;more&nbsp;than&nbsp;this&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs,&nbsp;it&nbsp;will&nbsp;only<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;partially&nbsp;filled.&nbsp;If&nbsp;method&nbsp;is&nbsp;not&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;maximum&nbsp;number&nbsp;of&nbsp;entries&nbsp;along&nbsp;the&nbsp;entire&nbsp;axis&nbsp;where&nbsp;NaNs&nbsp;will&nbsp;be<br>
-&nbsp;&nbsp;&nbsp;&nbsp;filled.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0&nbsp;if&nbsp;not&nbsp;None.<br>
-downcast&nbsp;:&nbsp;dict,&nbsp;default&nbsp;is&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;dict&nbsp;of&nbsp;item-&gt;dtype&nbsp;of&nbsp;what&nbsp;to&nbsp;downcast&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;the&nbsp;string&nbsp;'infer'&nbsp;which&nbsp;will&nbsp;try&nbsp;to&nbsp;downcast&nbsp;to&nbsp;an&nbsp;appropriate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;equal&nbsp;type&nbsp;(e.g.&nbsp;float64&nbsp;to&nbsp;int64&nbsp;if&nbsp;possible)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;asfreq<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="System-first_valid_index"><strong>first_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;first&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="System-floordiv"><strong>floordiv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`floordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;//&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rfloordiv</tt></dd></dl>
-
-<dl><dt><a name="System-ge"><strong>ge</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Greater&nbsp;than&nbsp;or&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`ge`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&gt;=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="System-get_value"><strong>get_value</strong></a>(self, label, takeable=False)</dt><dd><tt>Quickly&nbsp;retrieve&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;index&nbsp;label<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;label<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;scalar&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="System-get_values"><strong>get_values</strong></a>(self)</dt><dd><tt>same&nbsp;as&nbsp;values&nbsp;(but&nbsp;handles&nbsp;sparseness&nbsp;conversions);&nbsp;is&nbsp;a&nbsp;view</tt></dd></dl>
-
-<dl><dt><a name="System-gt"><strong>gt</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Greater&nbsp;than&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`gt`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&gt;&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="System-hist"><strong>hist</strong></a> = hist_series(self, by=None, ax=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, figsize=None, bins=10, **kwds)</dt><dd><tt>Draw&nbsp;histogram&nbsp;of&nbsp;the&nbsp;input&nbsp;series&nbsp;using&nbsp;matplotlib<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed,&nbsp;then&nbsp;used&nbsp;to&nbsp;form&nbsp;histograms&nbsp;for&nbsp;separate&nbsp;groups<br>
-ax&nbsp;:&nbsp;matplotlib&nbsp;axis&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;not&nbsp;passed,&nbsp;uses&nbsp;gca()<br>
-grid&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;show&nbsp;axis&nbsp;grid&nbsp;lines<br>
-xlabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;x-axis&nbsp;label&nbsp;size<br>
-xrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;x&nbsp;axis&nbsp;labels<br>
-ylabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;y-axis&nbsp;label&nbsp;size<br>
-yrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;y&nbsp;axis&nbsp;labels<br>
-figsize&nbsp;:&nbsp;tuple,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;figure&nbsp;size&nbsp;in&nbsp;inches&nbsp;by&nbsp;default<br>
-bins:&nbsp;integer,&nbsp;default&nbsp;10<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;histogram&nbsp;bins&nbsp;to&nbsp;be&nbsp;used<br>
-kwds&nbsp;:&nbsp;keywords<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;passed&nbsp;to&nbsp;the&nbsp;actual&nbsp;plotting&nbsp;function<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;matplotlib&nbsp;documentation&nbsp;online&nbsp;for&nbsp;more&nbsp;on&nbsp;this</tt></dd></dl>
-
-<dl><dt><a name="System-idxmax"><strong>idxmax</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmax&nbsp;:&nbsp;Index&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmax``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmax<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmax</tt></dd></dl>
-
-<dl><dt><a name="System-idxmin"><strong>idxmin</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmin&nbsp;:&nbsp;Index&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmin``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmin<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmin</tt></dd></dl>
-
-<dl><dt><a name="System-isin"><strong>isin</strong></a>(self, values)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;:class:`~pandas.<a href="pandas.core.series.html#Series">Series</a>`&nbsp;showing&nbsp;whether&nbsp;each&nbsp;element<br>
-in&nbsp;the&nbsp;:class:`~pandas.<a href="pandas.core.series.html#Series">Series</a>`&nbsp;is&nbsp;exactly&nbsp;contained&nbsp;in&nbsp;the&nbsp;passed<br>
-sequence&nbsp;of&nbsp;``values``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-values&nbsp;:&nbsp;set&nbsp;or&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;sequence&nbsp;of&nbsp;values&nbsp;to&nbsp;test.&nbsp;Passing&nbsp;in&nbsp;a&nbsp;single&nbsp;string&nbsp;will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;raise&nbsp;a&nbsp;``TypeError``.&nbsp;Instead,&nbsp;turn&nbsp;a&nbsp;single&nbsp;string&nbsp;into&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``list``&nbsp;of&nbsp;one&nbsp;element.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Support&nbsp;for&nbsp;values&nbsp;as&nbsp;a&nbsp;set<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-isin&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(bool&nbsp;dtype)<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;*&nbsp;If&nbsp;``values``&nbsp;is&nbsp;a&nbsp;string<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.isin<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(list('abc'))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-isin">isin</a>(['a',&nbsp;'c',&nbsp;'e'])<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-dtype:&nbsp;bool<br>
-&nbsp;<br>
-Passing&nbsp;a&nbsp;single&nbsp;string&nbsp;as&nbsp;``s.<a href="#System-isin">isin</a>('a')``&nbsp;will&nbsp;raise&nbsp;an&nbsp;error.&nbsp;Use<br>
-a&nbsp;list&nbsp;of&nbsp;one&nbsp;element&nbsp;instead:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-isin">isin</a>(['a'])<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-dtype:&nbsp;bool</tt></dd></dl>
-
-<dl><dt><a name="System-items"><strong>items</strong></a> = iteritems(self)</dt><dd><tt>Lazily&nbsp;iterate&nbsp;over&nbsp;(index,&nbsp;value)&nbsp;tuples</tt></dd></dl>
-
-<dl><dt><a name="System-iteritems"><strong>iteritems</strong></a>(self)</dt><dd><tt>Lazily&nbsp;iterate&nbsp;over&nbsp;(index,&nbsp;value)&nbsp;tuples</tt></dd></dl>
-
-<dl><dt><a name="System-keys"><strong>keys</strong></a>(self)</dt><dd><tt>Alias&nbsp;for&nbsp;index</tt></dd></dl>
-
-<dl><dt><a name="System-kurt"><strong>kurt</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-kurtosis"><strong>kurtosis</strong></a> = kurt(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-last_valid_index"><strong>last_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;last&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="System-le"><strong>le</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Less&nbsp;than&nbsp;or&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`le`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&lt;=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="System-lt"><strong>lt</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Less&nbsp;than&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`lt`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&lt;&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="System-mad"><strong>mad</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;absolute&nbsp;deviation&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mad&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-map"><strong>map</strong></a>(self, arg, na_action=None)</dt><dd><tt>Map&nbsp;values&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;using&nbsp;input&nbsp;correspondence&nbsp;(which&nbsp;can&nbsp;be<br>
-a&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;function)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-arg&nbsp;:&nbsp;function,&nbsp;dict,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-na_action&nbsp;:&nbsp;{None,&nbsp;'ignore'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'ignore',&nbsp;propagate&nbsp;NA&nbsp;values,&nbsp;without&nbsp;passing&nbsp;them&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;mapping&nbsp;function<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;same&nbsp;index&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Map&nbsp;inputs&nbsp;to&nbsp;outputs&nbsp;(both&nbsp;of&nbsp;type&nbsp;`<a href="pandas.core.series.html#Series">Series</a>`)<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,2,3],&nbsp;index=['one',&nbsp;'two',&nbsp;'three'])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-one&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-two&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-three&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;y&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(['foo',&nbsp;'bar',&nbsp;'baz'],&nbsp;index=[1,2,3])<br>
-&gt;&gt;&gt;&nbsp;y<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;foo<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;bar<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;baz<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#System-map">map</a>(y)<br>
-one&nbsp;&nbsp;&nbsp;foo<br>
-two&nbsp;&nbsp;&nbsp;bar<br>
-three&nbsp;baz<br>
-&nbsp;<br>
-If&nbsp;`arg`&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;values&nbsp;converted<br>
-according&nbsp;to&nbsp;the&nbsp;dictionary's&nbsp;mapping:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;z&nbsp;=&nbsp;{1:&nbsp;'A',&nbsp;2:&nbsp;'B',&nbsp;3:&nbsp;'C'}<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#System-map">map</a>(z)<br>
-one&nbsp;&nbsp;&nbsp;A<br>
-two&nbsp;&nbsp;&nbsp;B<br>
-three&nbsp;C<br>
-&nbsp;<br>
-Use&nbsp;na_action&nbsp;to&nbsp;control&nbsp;whether&nbsp;NA&nbsp;values&nbsp;are&nbsp;affected&nbsp;by&nbsp;the&nbsp;mapping<br>
-function.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;np.nan])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s2&nbsp;=&nbsp;s.<a href="#System-map">map</a>('this&nbsp;is&nbsp;a&nbsp;string&nbsp;{}'.format,&nbsp;na_action=None)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;1.0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;2.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;3.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;nan<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s3&nbsp;=&nbsp;s.<a href="#System-map">map</a>('this&nbsp;is&nbsp;a&nbsp;string&nbsp;{}'.format,&nbsp;na_action='ignore')<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;1.0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;2.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;3.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.apply:&nbsp;For&nbsp;applying&nbsp;more&nbsp;complex&nbsp;functions&nbsp;on&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply:&nbsp;Apply&nbsp;a&nbsp;function&nbsp;row-/column-wise<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap:&nbsp;Apply&nbsp;a&nbsp;function&nbsp;elementwise&nbsp;on&nbsp;a&nbsp;whole&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-When&nbsp;`arg`&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;values&nbsp;in&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;that&nbsp;are&nbsp;not&nbsp;in&nbsp;the<br>
-dictionary&nbsp;(as&nbsp;keys)&nbsp;are&nbsp;converted&nbsp;to&nbsp;``NaN``.&nbsp;However,&nbsp;if&nbsp;the<br>
-dictionary&nbsp;is&nbsp;a&nbsp;``dict``&nbsp;subclass&nbsp;that&nbsp;defines&nbsp;``__missing__``&nbsp;(i.e.<br>
-provides&nbsp;a&nbsp;method&nbsp;for&nbsp;default&nbsp;values),&nbsp;then&nbsp;this&nbsp;default&nbsp;is&nbsp;used<br>
-rather&nbsp;than&nbsp;``NaN``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;collections&nbsp;import&nbsp;Counter<br>
-&gt;&gt;&gt;&nbsp;counter&nbsp;=&nbsp;Counter()<br>
-&gt;&gt;&gt;&nbsp;counter['bar']&nbsp;+=&nbsp;1<br>
-&gt;&gt;&gt;&nbsp;y.<a href="#System-map">map</a>(counter)<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-dtype:&nbsp;int64</tt></dd></dl>
-
-<dl><dt><a name="System-max"><strong>max</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;maximum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;maximum,&nbsp;use&nbsp;``idxmax``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmax``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-max&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-mean"><strong>mean</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mean&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-median"><strong>median</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;median&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-median&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-memory_usage"><strong>memory_usage</strong></a>(self, index=True, deep=False)</dt><dd><tt>Memory&nbsp;usage&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;whether&nbsp;to&nbsp;include&nbsp;memory&nbsp;usage&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index<br>
-deep&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Introspect&nbsp;the&nbsp;data&nbsp;deeply,&nbsp;interrogate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`<a href="builtins.html#object">object</a>`&nbsp;dtypes&nbsp;for&nbsp;system-level&nbsp;memory&nbsp;consumption<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-scalar&nbsp;bytes&nbsp;of&nbsp;memory&nbsp;consumed<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Memory&nbsp;usage&nbsp;does&nbsp;not&nbsp;include&nbsp;memory&nbsp;consumed&nbsp;by&nbsp;elements&nbsp;that<br>
-are&nbsp;not&nbsp;components&nbsp;of&nbsp;the&nbsp;array&nbsp;if&nbsp;deep=False<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.nbytes</tt></dd></dl>
-
-<dl><dt><a name="System-min"><strong>min</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;minimum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;minimum,&nbsp;use&nbsp;``idxmin``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmin``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-min&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-mod"><strong>mod</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Modulo&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;%&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmod</tt></dd></dl>
-
-<dl><dt><a name="System-mode"><strong>mode</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;<a href="#System-mode">mode</a>(s)&nbsp;of&nbsp;the&nbsp;dataset.<br>
-&nbsp;<br>
-Always&nbsp;returns&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;even&nbsp;if&nbsp;only&nbsp;one&nbsp;value&nbsp;is&nbsp;returned.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-modes&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(sorted)</tt></dd></dl>
-
-<dl><dt><a name="System-mul"><strong>mul</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="System-multiply"><strong>multiply</strong></a> = mul(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="System-ne"><strong>ne</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Not&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`ne`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;!=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="System-nlargest"><strong>nlargest</strong></a>(self, n=5, keep='first')</dt><dd><tt>Return&nbsp;the&nbsp;largest&nbsp;`n`&nbsp;elements.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;this&nbsp;many&nbsp;descending&nbsp;sorted&nbsp;values<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-top_n&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;n&nbsp;largest&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;in&nbsp;sorted&nbsp;order<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Faster&nbsp;than&nbsp;``.<a href="#System-sort_values">sort_values</a>(ascending=False).<a href="#System-head">head</a>(n)``&nbsp;for&nbsp;small&nbsp;`n`<br>
-relative&nbsp;to&nbsp;the&nbsp;size&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.nsmallest<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10**6))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-nlargest">nlargest</a>(10)&nbsp;&nbsp;#&nbsp;only&nbsp;sorts&nbsp;up&nbsp;to&nbsp;the&nbsp;N&nbsp;requested<br>
-219921&nbsp;&nbsp;&nbsp;&nbsp;4.644710<br>
-82124&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4.608745<br>
-421689&nbsp;&nbsp;&nbsp;&nbsp;4.564644<br>
-425277&nbsp;&nbsp;&nbsp;&nbsp;4.447014<br>
-718691&nbsp;&nbsp;&nbsp;&nbsp;4.414137<br>
-43154&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4.403520<br>
-283187&nbsp;&nbsp;&nbsp;&nbsp;4.313922<br>
-595519&nbsp;&nbsp;&nbsp;&nbsp;4.273635<br>
-503969&nbsp;&nbsp;&nbsp;&nbsp;4.250236<br>
-121637&nbsp;&nbsp;&nbsp;&nbsp;4.240952<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="System-nonzero"><strong>nonzero</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;indices&nbsp;of&nbsp;the&nbsp;elements&nbsp;that&nbsp;are&nbsp;non-zero<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;calling&nbsp;`numpy.nonzero`&nbsp;on&nbsp;the<br>
-series&nbsp;data.&nbsp;For&nbsp;compatability&nbsp;with&nbsp;NumPy,&nbsp;the&nbsp;return&nbsp;value&nbsp;is<br>
-the&nbsp;same&nbsp;(a&nbsp;tuple&nbsp;with&nbsp;an&nbsp;array&nbsp;of&nbsp;indices&nbsp;for&nbsp;each&nbsp;dimension),<br>
-but&nbsp;it&nbsp;will&nbsp;always&nbsp;be&nbsp;a&nbsp;one-item&nbsp;tuple&nbsp;because&nbsp;series&nbsp;only&nbsp;have<br>
-one&nbsp;dimension.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0,&nbsp;3,&nbsp;0,&nbsp;4])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-nonzero">nonzero</a>()<br>
-(array([1,&nbsp;3]),)<br>
-&gt;&gt;&gt;&nbsp;s.iloc[s.<a href="#System-nonzero">nonzero</a>()[0]]<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.nonzero</tt></dd></dl>
-
-<dl><dt><a name="System-nsmallest"><strong>nsmallest</strong></a>(self, n=5, keep='first')</dt><dd><tt>Return&nbsp;the&nbsp;smallest&nbsp;`n`&nbsp;elements.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;this&nbsp;many&nbsp;ascending&nbsp;sorted&nbsp;values<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-bottom_n&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;n&nbsp;smallest&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;in&nbsp;sorted&nbsp;order<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Faster&nbsp;than&nbsp;``.<a href="#System-sort_values">sort_values</a>().<a href="#System-head">head</a>(n)``&nbsp;for&nbsp;small&nbsp;`n`&nbsp;relative&nbsp;to<br>
-the&nbsp;size&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.nlargest<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10**6))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-nsmallest">nsmallest</a>(10)&nbsp;&nbsp;#&nbsp;only&nbsp;sorts&nbsp;up&nbsp;to&nbsp;the&nbsp;N&nbsp;requested<br>
-288532&nbsp;&nbsp;&nbsp;-4.954580<br>
-732345&nbsp;&nbsp;&nbsp;-4.835960<br>
-64803&nbsp;&nbsp;&nbsp;&nbsp;-4.812550<br>
-446457&nbsp;&nbsp;&nbsp;-4.609998<br>
-501225&nbsp;&nbsp;&nbsp;-4.483945<br>
-669476&nbsp;&nbsp;&nbsp;-4.472935<br>
-973615&nbsp;&nbsp;&nbsp;-4.401699<br>
-621279&nbsp;&nbsp;&nbsp;-4.355126<br>
-773916&nbsp;&nbsp;&nbsp;-4.347355<br>
-359919&nbsp;&nbsp;&nbsp;-4.331927<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="System-pow"><strong>pow</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`pow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;**&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rpow</tt></dd></dl>
-
-<dl><dt><a name="System-prod"><strong>prod</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-product"><strong>product</strong></a> = prod(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-ptp"><strong>ptp</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Returns&nbsp;the&nbsp;difference&nbsp;between&nbsp;the&nbsp;maximum&nbsp;value&nbsp;and&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;minimum&nbsp;value&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.&nbsp;This&nbsp;is&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``ptp``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ptp&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-put"><strong>put</strong></a>(self, *args, **kwargs)</dt><dd><tt>Applies&nbsp;the&nbsp;`put`&nbsp;method&nbsp;to&nbsp;its&nbsp;`values`&nbsp;attribute<br>
-if&nbsp;it&nbsp;has&nbsp;one.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.put</tt></dd></dl>
-
-<dl><dt><a name="System-quantile"><strong>quantile</strong></a>(self, q=0.5, interpolation='linear')</dt><dd><tt>Return&nbsp;value&nbsp;at&nbsp;the&nbsp;given&nbsp;quantile,&nbsp;a&nbsp;la&nbsp;numpy.percentile.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-q&nbsp;:&nbsp;float&nbsp;or&nbsp;array-like,&nbsp;default&nbsp;0.5&nbsp;(50%&nbsp;quantile)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&lt;=&nbsp;q&nbsp;&lt;=&nbsp;1,&nbsp;the&nbsp;<a href="#System-quantile">quantile</a>(s)&nbsp;to&nbsp;compute<br>
-interpolation&nbsp;:&nbsp;{'linear',&nbsp;'lower',&nbsp;'higher',&nbsp;'midpoint',&nbsp;'nearest'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;optional&nbsp;parameter&nbsp;specifies&nbsp;the&nbsp;interpolation&nbsp;method&nbsp;to&nbsp;use,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;the&nbsp;desired&nbsp;quantile&nbsp;lies&nbsp;between&nbsp;two&nbsp;data&nbsp;points&nbsp;`i`&nbsp;and&nbsp;`j`:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;linear:&nbsp;`i&nbsp;+&nbsp;(j&nbsp;-&nbsp;i)&nbsp;*&nbsp;fraction`,&nbsp;where&nbsp;`fraction`&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;fractional&nbsp;part&nbsp;of&nbsp;the&nbsp;index&nbsp;surrounded&nbsp;by&nbsp;`i`&nbsp;and&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;lower:&nbsp;`i`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;higher:&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;`i`&nbsp;or&nbsp;`j`&nbsp;whichever&nbsp;is&nbsp;nearest.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;midpoint:&nbsp;(`i`&nbsp;+&nbsp;`j`)&nbsp;/&nbsp;2.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-quantile&nbsp;:&nbsp;float&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;``q``&nbsp;is&nbsp;an&nbsp;array,&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;returned&nbsp;where&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;is&nbsp;``q``&nbsp;and&nbsp;the&nbsp;values&nbsp;are&nbsp;the&nbsp;quantiles.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;4])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-quantile">quantile</a>(.5)<br>
-2.5<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-quantile">quantile</a>([.25,&nbsp;.5,&nbsp;.75])<br>
-0.25&nbsp;&nbsp;&nbsp;&nbsp;1.75<br>
-0.50&nbsp;&nbsp;&nbsp;&nbsp;2.50<br>
-0.75&nbsp;&nbsp;&nbsp;&nbsp;3.25<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="System-radd"><strong>radd</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Addition&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`radd`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;+&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.add</tt></dd></dl>
-
-<dl><dt><a name="System-ravel"><strong>ravel</strong></a>(self, order='C')</dt><dd><tt>Return&nbsp;the&nbsp;flattened&nbsp;underlying&nbsp;data&nbsp;as&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.ravel</tt></dd></dl>
-
-<dl><dt><a name="System-rdiv"><strong>rdiv</strong></a> = rtruediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="System-reindex"><strong>reindex</strong></a>(self, index=None, **kwargs)</dt><dd><tt>Conform&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;new&nbsp;index&nbsp;with&nbsp;optional&nbsp;filling&nbsp;logic,&nbsp;placing<br>
-NA/NaN&nbsp;in&nbsp;locations&nbsp;having&nbsp;no&nbsp;value&nbsp;in&nbsp;the&nbsp;previous&nbsp;index.&nbsp;A&nbsp;new&nbsp;<a href="builtins.html#object">object</a><br>
-is&nbsp;produced&nbsp;unless&nbsp;the&nbsp;new&nbsp;index&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;the&nbsp;current&nbsp;one&nbsp;and<br>
-copy=False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;array-like,&nbsp;optional&nbsp;(can&nbsp;be&nbsp;specified&nbsp;in&nbsp;order,&nbsp;or&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;keywords)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;New&nbsp;labels&nbsp;/&nbsp;index&nbsp;to&nbsp;conform&nbsp;to.&nbsp;Preferably&nbsp;an&nbsp;Index&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;avoid&nbsp;duplicating&nbsp;data<br>
-method&nbsp;:&nbsp;{None,&nbsp;'backfill'/'bfill',&nbsp;'pad'/'ffill',&nbsp;'nearest'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Please&nbsp;note:&nbsp;this&nbsp;is&nbsp;only&nbsp;&nbsp;applicable&nbsp;to&nbsp;DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;monotonically&nbsp;increasing/decreasing&nbsp;index.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;default:&nbsp;don't&nbsp;fill&nbsp;gaps<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;next&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;use&nbsp;nearest&nbsp;valid&nbsp;observations&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>,&nbsp;even&nbsp;if&nbsp;the&nbsp;passed&nbsp;indexes&nbsp;are&nbsp;the&nbsp;same<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;elements&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;original&nbsp;and&nbsp;new&nbsp;labels&nbsp;for&nbsp;inexact<br>
-&nbsp;&nbsp;&nbsp;&nbsp;matches.&nbsp;The&nbsp;values&nbsp;of&nbsp;the&nbsp;index&nbsp;at&nbsp;the&nbsp;matching&nbsp;locations&nbsp;most<br>
-&nbsp;&nbsp;&nbsp;&nbsp;satisfy&nbsp;the&nbsp;equation&nbsp;``<a href="#System-abs">abs</a>(index[indexer]&nbsp;-&nbsp;target)&nbsp;&lt;=&nbsp;tolerance``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;some&nbsp;fictional&nbsp;data.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;['Firefox',&nbsp;'Chrome',&nbsp;'Safari',&nbsp;'IE10',&nbsp;'Konqueror']<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'http_status':&nbsp;[200,200,404,404,301],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'response_time':&nbsp;[0.04,&nbsp;0.02,&nbsp;0.07,&nbsp;0.08,&nbsp;1.0]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Firefox&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.04<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Konqueror&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;301&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.00<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;new&nbsp;index&nbsp;and&nbsp;reindex&nbsp;the&nbsp;dataframe.&nbsp;By&nbsp;default<br>
-values&nbsp;in&nbsp;the&nbsp;new&nbsp;index&nbsp;that&nbsp;do&nbsp;not&nbsp;have&nbsp;corresponding<br>
-records&nbsp;in&nbsp;the&nbsp;dataframe&nbsp;are&nbsp;assigned&nbsp;``NaN``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;new_index=&nbsp;['Safari',&nbsp;'Iceweasel',&nbsp;'Comodo&nbsp;Dragon',&nbsp;'IE10',<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Chrome']<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-reindex">reindex</a>(new_index)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-We&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;by&nbsp;passing&nbsp;a&nbsp;value&nbsp;to<br>
-the&nbsp;keyword&nbsp;``fill_value``.&nbsp;Because&nbsp;the&nbsp;index&nbsp;is&nbsp;not&nbsp;monotonically<br>
-increasing&nbsp;or&nbsp;decreasing,&nbsp;we&nbsp;cannot&nbsp;use&nbsp;arguments&nbsp;to&nbsp;the&nbsp;keyword<br>
-``method``&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``&nbsp;values.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-reindex">reindex</a>(new_index,&nbsp;fill_value=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-reindex">reindex</a>(new_index,&nbsp;fill_value='missing')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-To&nbsp;further&nbsp;illustrate&nbsp;the&nbsp;filling&nbsp;functionality&nbsp;in<br>
-``reindex``,&nbsp;we&nbsp;will&nbsp;create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;a<br>
-monotonically&nbsp;increasing&nbsp;index&nbsp;(for&nbsp;example,&nbsp;a&nbsp;sequence<br>
-of&nbsp;dates).<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index&nbsp;=&nbsp;pd.date_range('1/1/2010',&nbsp;periods=6,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"prices":&nbsp;[100,&nbsp;101,&nbsp;np.nan,&nbsp;100,&nbsp;89,&nbsp;88]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=date_index)<br>
-&gt;&gt;&gt;&nbsp;df2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-&nbsp;<br>
-Suppose&nbsp;we&nbsp;decide&nbsp;to&nbsp;expand&nbsp;the&nbsp;dataframe&nbsp;to&nbsp;cover&nbsp;a&nbsp;wider<br>
-date&nbsp;range.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index2&nbsp;=&nbsp;pd.date_range('12/29/2009',&nbsp;periods=10,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#System-reindex">reindex</a>(date_index2)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-The&nbsp;index&nbsp;entries&nbsp;that&nbsp;did&nbsp;not&nbsp;have&nbsp;a&nbsp;value&nbsp;in&nbsp;the&nbsp;original&nbsp;data&nbsp;frame<br>
-(for&nbsp;example,&nbsp;'2009-12-29')&nbsp;are&nbsp;by&nbsp;default&nbsp;filled&nbsp;with&nbsp;``NaN``.<br>
-If&nbsp;desired,&nbsp;we&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;using&nbsp;one&nbsp;of&nbsp;several<br>
-options.<br>
-&nbsp;<br>
-For&nbsp;example,&nbsp;to&nbsp;backpropagate&nbsp;the&nbsp;last&nbsp;valid&nbsp;value&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values,&nbsp;pass&nbsp;``bfill``&nbsp;as&nbsp;an&nbsp;argument&nbsp;to&nbsp;the&nbsp;``method``&nbsp;keyword.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#System-reindex">reindex</a>(date_index2,&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;the&nbsp;``NaN``&nbsp;value&nbsp;present&nbsp;in&nbsp;the&nbsp;original&nbsp;dataframe<br>
-(at&nbsp;index&nbsp;value&nbsp;2010-01-03)&nbsp;will&nbsp;not&nbsp;be&nbsp;filled&nbsp;by&nbsp;any&nbsp;of&nbsp;the<br>
-value&nbsp;propagation&nbsp;schemes.&nbsp;This&nbsp;is&nbsp;because&nbsp;filling&nbsp;while&nbsp;reindexing<br>
-does&nbsp;not&nbsp;look&nbsp;at&nbsp;dataframe&nbsp;values,&nbsp;but&nbsp;only&nbsp;compares&nbsp;the&nbsp;original&nbsp;and<br>
-desired&nbsp;indexes.&nbsp;If&nbsp;you&nbsp;do&nbsp;want&nbsp;to&nbsp;fill&nbsp;in&nbsp;the&nbsp;``NaN``&nbsp;values&nbsp;present<br>
-in&nbsp;the&nbsp;original&nbsp;dataframe,&nbsp;use&nbsp;the&nbsp;``<a href="#System-fillna">fillna</a>()``&nbsp;method.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="System-reindex_axis"><strong>reindex_axis</strong></a>(self, labels, axis=0, **kwargs)</dt><dd><tt>for&nbsp;compatibility&nbsp;with&nbsp;higher&nbsp;dims</tt></dd></dl>
-
-<dl><dt><a name="System-rename"><strong>rename</strong></a>(self, index=None, **kwargs)</dt><dd><tt>Alter&nbsp;axes&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.&nbsp;Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be<br>
-unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in&nbsp;a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left<br>
-as-is.&nbsp;Extra&nbsp;labels&nbsp;listed&nbsp;don't&nbsp;throw&nbsp;an&nbsp;error.&nbsp;Alternatively,&nbsp;change<br>
-``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;with&nbsp;a&nbsp;scalar&nbsp;value&nbsp;(<a href="pandas.core.series.html#Series">Series</a>&nbsp;only).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Scalar&nbsp;or&nbsp;list-like&nbsp;will&nbsp;alter&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;attribute,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;raise&nbsp;on&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;Panel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict-like&nbsp;or&nbsp;functions&nbsp;are&nbsp;transformations&nbsp;to&nbsp;apply&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;that&nbsp;axis'&nbsp;values<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;If&nbsp;True&nbsp;then&nbsp;value&nbsp;of&nbsp;copy&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;ignored.<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;MultiIndex,&nbsp;only&nbsp;rename&nbsp;labels&nbsp;in&nbsp;the&nbsp;specified<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename_axis<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-rename">rename</a>("my_name")&nbsp;#&nbsp;scalar,&nbsp;changes&nbsp;<a href="pandas.core.series.html#Series">Series</a>.name<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;my_name,&nbsp;dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-rename">rename</a>(lambda&nbsp;x:&nbsp;x&nbsp;**&nbsp;2)&nbsp;&nbsp;#&nbsp;function,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-rename">rename</a>({1:&nbsp;3,&nbsp;2:&nbsp;5})&nbsp;&nbsp;#&nbsp;mapping,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-rename">rename</a>(2)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-TypeError:&nbsp;'int'&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;not&nbsp;callable<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"B":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;c<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"C":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="System-reorder_levels"><strong>reorder_levels</strong></a>(self, order)</dt><dd><tt>Rearrange&nbsp;index&nbsp;levels&nbsp;using&nbsp;input&nbsp;order.&nbsp;May&nbsp;not&nbsp;drop&nbsp;or&nbsp;duplicate<br>
-levels<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-order&nbsp;:&nbsp;list&nbsp;of&nbsp;int&nbsp;representing&nbsp;new&nbsp;level&nbsp;order.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(reference&nbsp;level&nbsp;by&nbsp;number&nbsp;or&nbsp;key)<br>
-axis&nbsp;:&nbsp;where&nbsp;to&nbsp;reorder&nbsp;levels<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-type&nbsp;of&nbsp;caller&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)</tt></dd></dl>
-
-<dl><dt><a name="System-repeat"><strong>repeat</strong></a>(self, repeats, *args, **kwargs)</dt><dd><tt>Repeat&nbsp;elements&nbsp;of&nbsp;an&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Refer&nbsp;to&nbsp;`numpy.<a href="numpy.html#ndarray">ndarray</a>.repeat`<br>
-for&nbsp;more&nbsp;information&nbsp;about&nbsp;the&nbsp;`repeats`&nbsp;argument.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.repeat</tt></dd></dl>
-
-<dl><dt><a name="System-reset_index"><strong>reset_index</strong></a>(self, level=None, drop=False, name=None, inplace=False)</dt><dd><tt>Analogous&nbsp;to&nbsp;the&nbsp;:meth:`pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.reset_index`&nbsp;function,&nbsp;see<br>
-docstring&nbsp;there.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;tuple,&nbsp;or&nbsp;list,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Only&nbsp;remove&nbsp;the&nbsp;given&nbsp;levels&nbsp;from&nbsp;the&nbsp;index.&nbsp;Removes&nbsp;all&nbsp;levels&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-drop&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;not&nbsp;try&nbsp;to&nbsp;insert&nbsp;index&nbsp;into&nbsp;dataframe&nbsp;columns<br>
-name&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;name&nbsp;of&nbsp;the&nbsp;column&nbsp;corresponding&nbsp;to&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Modify&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place&nbsp;(do&nbsp;not&nbsp;create&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-Returns<br>
-----------<br>
-resetted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;if&nbsp;drop&nbsp;==&nbsp;True</tt></dd></dl>
-
-<dl><dt><a name="System-reshape"><strong>reshape</strong></a>(self, *args, **kwargs)</dt><dd><tt>DEPRECATED:&nbsp;calling&nbsp;this&nbsp;method&nbsp;will&nbsp;raise&nbsp;an&nbsp;error&nbsp;in&nbsp;a<br>
-future&nbsp;release.&nbsp;Please&nbsp;call&nbsp;``.values.<a href="#System-reshape">reshape</a>(...)``&nbsp;instead.<br>
-&nbsp;<br>
-return&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;with&nbsp;the&nbsp;values&nbsp;shape<br>
-if&nbsp;the&nbsp;specified&nbsp;shape&nbsp;matches&nbsp;exactly&nbsp;the&nbsp;current&nbsp;shape,&nbsp;then<br>
-return&nbsp;self&nbsp;(for&nbsp;compat)<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.reshape</tt></dd></dl>
-
-<dl><dt><a name="System-rfloordiv"><strong>rfloordiv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rfloordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;//&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.floordiv</tt></dd></dl>
-
-<dl><dt><a name="System-rmod"><strong>rmod</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Modulo&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;%&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.mod</tt></dd></dl>
-
-<dl><dt><a name="System-rmul"><strong>rmul</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;*&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.mul</tt></dd></dl>
-
-<dl><dt><a name="System-rolling"><strong>rolling</strong></a>(self, window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0, closed=None)</dt><dd><tt>Provides&nbsp;rolling&nbsp;window&nbsp;calculcations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-window&nbsp;:&nbsp;int,&nbsp;or&nbsp;offset<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Size&nbsp;of&nbsp;the&nbsp;moving&nbsp;window.&nbsp;This&nbsp;is&nbsp;the&nbsp;number&nbsp;of&nbsp;observations&nbsp;used&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;calculating&nbsp;the&nbsp;statistic.&nbsp;Each&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;fixed&nbsp;size.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;its&nbsp;an&nbsp;offset&nbsp;then&nbsp;this&nbsp;will&nbsp;be&nbsp;the&nbsp;time&nbsp;period&nbsp;of&nbsp;each&nbsp;window.&nbsp;Each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;variable&nbsp;sized&nbsp;based&nbsp;on&nbsp;the&nbsp;observations&nbsp;included&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;time-period.&nbsp;This&nbsp;is&nbsp;only&nbsp;valid&nbsp;for&nbsp;datetimelike&nbsp;indexes.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;new&nbsp;in&nbsp;0.19.0<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).&nbsp;For&nbsp;a&nbsp;window&nbsp;that&nbsp;is&nbsp;specified&nbsp;by&nbsp;an&nbsp;offset,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;will&nbsp;default&nbsp;to&nbsp;1.<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-win_type&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Provide&nbsp;a&nbsp;window&nbsp;type.&nbsp;See&nbsp;the&nbsp;notes&nbsp;below.<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;on&nbsp;which&nbsp;to&nbsp;calculate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;rolling&nbsp;window,&nbsp;rather&nbsp;than&nbsp;the&nbsp;index<br>
-closed&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;the&nbsp;interval&nbsp;closed&nbsp;on&nbsp;the&nbsp;'right',&nbsp;'left',&nbsp;'both'&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'neither'&nbsp;endpoints.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;offset-based&nbsp;windows,&nbsp;it&nbsp;defaults&nbsp;to&nbsp;'right'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;fixed&nbsp;windows,&nbsp;defaults&nbsp;to&nbsp;'both'.&nbsp;Remaining&nbsp;cases&nbsp;not&nbsp;implemented<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;fixed&nbsp;windows.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;or&nbsp;Rolling&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;using&nbsp;the&nbsp;'triang'<br>
-window&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-rolling">rolling</a>(2,&nbsp;win_type='triang').<a href="#System-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.5<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;min_periods&nbsp;defaults<br>
-to&nbsp;the&nbsp;window&nbsp;length.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-rolling">rolling</a>(2).<a href="#System-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Same&nbsp;as&nbsp;above,&nbsp;but&nbsp;explicity&nbsp;set&nbsp;the&nbsp;min_periods<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-rolling">rolling</a>(2,&nbsp;min_periods=1).<a href="#System-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;2.0<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-A&nbsp;ragged&nbsp;(meaning&nbsp;not-a-regular&nbsp;frequency),&nbsp;time-indexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]},<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;=&nbsp;[pd.Timestamp('20130101&nbsp;09:00:00'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:02'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:03'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:05'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:06')])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;2.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Contrasting&nbsp;to&nbsp;an&nbsp;integer&nbsp;rolling&nbsp;window,&nbsp;this&nbsp;will&nbsp;roll&nbsp;a&nbsp;variable<br>
-length&nbsp;window&nbsp;corresponding&nbsp;to&nbsp;the&nbsp;time&nbsp;period.<br>
-The&nbsp;default&nbsp;for&nbsp;min_periods&nbsp;is&nbsp;1.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-rolling">rolling</a>('2s').<a href="#System-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;3.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offsets&nbsp;&amp;&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-The&nbsp;recognized&nbsp;win_types&nbsp;are:<br>
-&nbsp;<br>
-*&nbsp;``boxcar``<br>
-*&nbsp;``triang``<br>
-*&nbsp;``blackman``<br>
-*&nbsp;``hamming``<br>
-*&nbsp;``bartlett``<br>
-*&nbsp;``parzen``<br>
-*&nbsp;``bohman``<br>
-*&nbsp;``blackmanharris``<br>
-*&nbsp;``nuttall``<br>
-*&nbsp;``barthann``<br>
-*&nbsp;``kaiser``&nbsp;(needs&nbsp;beta)<br>
-*&nbsp;``gaussian``&nbsp;(needs&nbsp;std)<br>
-*&nbsp;``general_gaussian``&nbsp;(needs&nbsp;power,&nbsp;width)<br>
-*&nbsp;``slepian``&nbsp;(needs&nbsp;width).</tt></dd></dl>
-
-<dl><dt><a name="System-round"><strong>round</strong></a>(self, decimals=0, *args, **kwargs)</dt><dd><tt>Round&nbsp;each&nbsp;value&nbsp;in&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;the&nbsp;given&nbsp;number&nbsp;of&nbsp;decimals.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-decimals&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;round&nbsp;to&nbsp;(default:&nbsp;0).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;decimals&nbsp;is&nbsp;negative,&nbsp;it&nbsp;specifies&nbsp;the&nbsp;number&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;positions&nbsp;to&nbsp;the&nbsp;left&nbsp;of&nbsp;the&nbsp;decimal&nbsp;point.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.series.html#Series">Series</a>&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.around<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.round</tt></dd></dl>
-
-<dl><dt><a name="System-rpow"><strong>rpow</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rpow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;**&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.pow</tt></dd></dl>
-
-<dl><dt><a name="System-rsub"><strong>rsub</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rsub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;-&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.sub</tt></dd></dl>
-
-<dl><dt><a name="System-rtruediv"><strong>rtruediv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="System-searchsorted"><strong>searchsorted</strong></a>(self, value, side='left', sorter=None)</dt><dd><tt>Find&nbsp;indices&nbsp;where&nbsp;elements&nbsp;should&nbsp;be&nbsp;inserted&nbsp;to&nbsp;maintain&nbsp;order.<br>
-&nbsp;<br>
-Find&nbsp;the&nbsp;indices&nbsp;into&nbsp;a&nbsp;sorted&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;`self`&nbsp;such&nbsp;that,&nbsp;if&nbsp;the<br>
-corresponding&nbsp;elements&nbsp;in&nbsp;`value`&nbsp;were&nbsp;inserted&nbsp;before&nbsp;the&nbsp;indices,<br>
-the&nbsp;order&nbsp;of&nbsp;`self`&nbsp;would&nbsp;be&nbsp;preserved.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;array_like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Values&nbsp;to&nbsp;insert&nbsp;into&nbsp;`self`.<br>
-side&nbsp;:&nbsp;{'left',&nbsp;'right'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'left',&nbsp;the&nbsp;index&nbsp;of&nbsp;the&nbsp;first&nbsp;suitable&nbsp;location&nbsp;found&nbsp;is&nbsp;given.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'right',&nbsp;return&nbsp;the&nbsp;last&nbsp;such&nbsp;index.&nbsp;&nbsp;If&nbsp;there&nbsp;is&nbsp;no&nbsp;suitable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index,&nbsp;return&nbsp;either&nbsp;0&nbsp;or&nbsp;N&nbsp;(where&nbsp;N&nbsp;is&nbsp;the&nbsp;length&nbsp;of&nbsp;`self`).<br>
-sorter&nbsp;:&nbsp;1-D&nbsp;array_like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Optional&nbsp;array&nbsp;of&nbsp;integer&nbsp;indices&nbsp;that&nbsp;sort&nbsp;`self`&nbsp;into&nbsp;ascending<br>
-&nbsp;&nbsp;&nbsp;&nbsp;order.&nbsp;They&nbsp;are&nbsp;typically&nbsp;the&nbsp;result&nbsp;of&nbsp;``np.argsort``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-indices&nbsp;:&nbsp;array&nbsp;of&nbsp;ints<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Array">Array</a>&nbsp;of&nbsp;insertion&nbsp;points&nbsp;with&nbsp;the&nbsp;same&nbsp;shape&nbsp;as&nbsp;`value`.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.searchsorted<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Binary&nbsp;search&nbsp;is&nbsp;used&nbsp;to&nbsp;find&nbsp;the&nbsp;required&nbsp;insertion&nbsp;points.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#System-searchsorted">searchsorted</a>(4)<br>
-array([3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#System-searchsorted">searchsorted</a>([0,&nbsp;4])<br>
-array([0,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#System-searchsorted">searchsorted</a>([1,&nbsp;3],&nbsp;side='left')<br>
-array([0,&nbsp;2])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#System-searchsorted">searchsorted</a>([1,&nbsp;3],&nbsp;side='right')<br>
-array([1,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.Categorical(['apple',&nbsp;'bread',&nbsp;'bread',&nbsp;'cheese',&nbsp;'milk'&nbsp;])<br>
-[apple,&nbsp;bread,&nbsp;bread,&nbsp;cheese,&nbsp;milk]<br>
-Categories&nbsp;(4,&nbsp;<a href="builtins.html#object">object</a>):&nbsp;[apple&nbsp;&lt;&nbsp;bread&nbsp;&lt;&nbsp;cheese&nbsp;&lt;&nbsp;milk]<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#System-searchsorted">searchsorted</a>('bread')<br>
-array([1])&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;Note:&nbsp;an&nbsp;array,&nbsp;not&nbsp;a&nbsp;scalar<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#System-searchsorted">searchsorted</a>(['bread'])<br>
-array([1])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#System-searchsorted">searchsorted</a>(['bread',&nbsp;'eggs'])<br>
-array([1,&nbsp;4])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#System-searchsorted">searchsorted</a>(['bread',&nbsp;'eggs'],&nbsp;side='right')<br>
-array([3,&nbsp;4])&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;eggs&nbsp;before&nbsp;milk</tt></dd></dl>
-
-<dl><dt><a name="System-sem"><strong>sem</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;standard&nbsp;error&nbsp;of&nbsp;the&nbsp;mean&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sem&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-set_value"><strong>set_value</strong></a>(self, label, value, takeable=False)</dt><dd><tt>Quickly&nbsp;set&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;label.&nbsp;If&nbsp;label&nbsp;is&nbsp;not&nbsp;contained,&nbsp;a<br>
-new&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;created&nbsp;with&nbsp;the&nbsp;label&nbsp;placed&nbsp;at&nbsp;the&nbsp;end&nbsp;of&nbsp;the&nbsp;result<br>
-index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-label&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Partial&nbsp;indexing&nbsp;with&nbsp;MultiIndex&nbsp;not&nbsp;allowed<br>
-value&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Scalar&nbsp;value<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-series&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;label&nbsp;is&nbsp;contained,&nbsp;will&nbsp;be&nbsp;reference&nbsp;to&nbsp;calling&nbsp;<a href="pandas.core.series.html#Series">Series</a>,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="System-shift"><strong>shift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;index&nbsp;by&nbsp;desired&nbsp;number&nbsp;of&nbsp;periods&nbsp;with&nbsp;an&nbsp;optional&nbsp;time&nbsp;freq<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM').<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;Notes.<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'}<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;specified&nbsp;then&nbsp;the&nbsp;index&nbsp;values&nbsp;are&nbsp;shifted&nbsp;but&nbsp;the&nbsp;data<br>
-is&nbsp;not&nbsp;realigned.&nbsp;That&nbsp;is,&nbsp;use&nbsp;freq&nbsp;if&nbsp;you&nbsp;would&nbsp;like&nbsp;to&nbsp;extend&nbsp;the<br>
-index&nbsp;when&nbsp;shifting&nbsp;and&nbsp;preserve&nbsp;the&nbsp;original&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="System-skew"><strong>skew</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;skew&nbsp;over&nbsp;requested&nbsp;axis<br>
-Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-skew&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-sort_index"><strong>sort_index</strong></a>(self, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True)</dt><dd><tt>Sort&nbsp;<a href="builtins.html#object">object</a>&nbsp;by&nbsp;labels&nbsp;(along&nbsp;an&nbsp;axis)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;index&nbsp;to&nbsp;direct&nbsp;sorting<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name&nbsp;or&nbsp;list&nbsp;of&nbsp;ints&nbsp;or&nbsp;list&nbsp;of&nbsp;level&nbsp;names<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None,&nbsp;sort&nbsp;on&nbsp;values&nbsp;in&nbsp;specified&nbsp;index&nbsp;level(s)<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;MultiIndex.<br>
-sort_remaining&nbsp;:&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;true&nbsp;and&nbsp;sorting&nbsp;by&nbsp;level&nbsp;and&nbsp;index&nbsp;is&nbsp;multilevel,&nbsp;sort&nbsp;by&nbsp;other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;levels&nbsp;too&nbsp;(in&nbsp;order)&nbsp;after&nbsp;sorting&nbsp;by&nbsp;specified&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="System-sort_values"><strong>sort_values</strong></a>(self, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')</dt><dd><tt>Sort&nbsp;by&nbsp;the&nbsp;values&nbsp;along&nbsp;either&nbsp;axis<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;direct&nbsp;sorting<br>
-ascending&nbsp;:&nbsp;bool&nbsp;or&nbsp;list&nbsp;of&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending.&nbsp;Specify&nbsp;list&nbsp;for&nbsp;multiple&nbsp;sort<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;orders.&nbsp;&nbsp;If&nbsp;this&nbsp;is&nbsp;a&nbsp;list&nbsp;of&nbsp;bools,&nbsp;must&nbsp;match&nbsp;the&nbsp;length&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;by.<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="System-sortlevel"><strong>sortlevel</strong></a>(self, level=0, ascending=True, sort_remaining=True)</dt><dd><tt>DEPRECATED:&nbsp;use&nbsp;:meth:`<a href="pandas.core.series.html#Series">Series</a>.sort_index`<br>
-&nbsp;<br>
-Sort&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;MultiIndex&nbsp;by&nbsp;chosen&nbsp;level.&nbsp;Data&nbsp;will&nbsp;be<br>
-lexicographically&nbsp;sorted&nbsp;by&nbsp;the&nbsp;chosen&nbsp;level&nbsp;followed&nbsp;by&nbsp;the&nbsp;other<br>
-levels&nbsp;(in&nbsp;order)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-ascending&nbsp;:&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.<a href="#System-sort_index">sort_index</a>(level=...)</tt></dd></dl>
-
-<dl><dt><a name="System-std"><strong>std</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;sample&nbsp;standard&nbsp;deviation&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-std&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-sub"><strong>sub</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="System-subtract"><strong>subtract</strong></a> = sub(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="System-sum"><strong>sum</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;sum&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sum&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-swaplevel"><strong>swaplevel</strong></a>(self, i=-2, j=-1, copy=True)</dt><dd><tt>Swap&nbsp;levels&nbsp;i&nbsp;and&nbsp;j&nbsp;in&nbsp;a&nbsp;MultiIndex<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-i,&nbsp;j&nbsp;:&nbsp;int,&nbsp;string&nbsp;(can&nbsp;be&nbsp;mixed)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level&nbsp;of&nbsp;index&nbsp;to&nbsp;be&nbsp;swapped.&nbsp;Can&nbsp;pass&nbsp;level&nbsp;name&nbsp;as&nbsp;string.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-swapped&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-..&nbsp;versionchanged::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;The&nbsp;indexes&nbsp;``i``&nbsp;and&nbsp;``j``&nbsp;are&nbsp;now&nbsp;optional,&nbsp;and&nbsp;default&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;the&nbsp;two&nbsp;innermost&nbsp;levels&nbsp;of&nbsp;the&nbsp;index.</tt></dd></dl>
-
-<dl><dt><a name="System-take"><strong>take</strong></a>(self, indices, axis=0, convert=True, is_copy=False, **kwargs)</dt><dd><tt>return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;corresponding&nbsp;to&nbsp;requested&nbsp;indices<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-indices&nbsp;:&nbsp;list&nbsp;/&nbsp;array&nbsp;of&nbsp;ints<br>
-convert&nbsp;:&nbsp;translate&nbsp;negative&nbsp;to&nbsp;positive&nbsp;indices&nbsp;(default)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-taken&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.take</tt></dd></dl>
-
-<dl><dt><a name="System-to_csv"><strong>to_csv</strong></a>(self, path=None, index=True, sep=',', na_rep='', float_format=None, header=False, index_label=None, mode='w', encoding=None, date_format=None, decimal='.')</dt><dd><tt>Write&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;a&nbsp;comma-separated&nbsp;values&nbsp;(csv)&nbsp;file<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;or&nbsp;file&nbsp;handle,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;<a href="builtins.html#object">object</a>,&nbsp;if&nbsp;None&nbsp;is&nbsp;provided&nbsp;the&nbsp;result&nbsp;is&nbsp;returned&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string.<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-header&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;series&nbsp;name<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-mode&nbsp;:&nbsp;Python&nbsp;write&nbsp;mode,&nbsp;default&nbsp;'w'<br>
-sep&nbsp;:&nbsp;character,&nbsp;default&nbsp;","<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter&nbsp;for&nbsp;the&nbsp;output&nbsp;file.<br>
-encoding&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;if&nbsp;the&nbsp;contents&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;non-ascii,&nbsp;for&nbsp;python&nbsp;versions&nbsp;prior&nbsp;to&nbsp;3<br>
-date_format:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;datetime&nbsp;objects.<br>
-decimal:&nbsp;string,&nbsp;default&nbsp;'.'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Character&nbsp;recognized&nbsp;as&nbsp;decimal&nbsp;separator.&nbsp;E.g.&nbsp;use&nbsp;','&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;European&nbsp;data</tt></dd></dl>
-
-<dl><dt><a name="System-to_dict"><strong>to_dict</strong></a>(self)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;{label&nbsp;-&gt;&nbsp;value}&nbsp;dict<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value_dict&nbsp;:&nbsp;dict</tt></dd></dl>
-
-<dl><dt><a name="System-to_excel"><strong>to_excel</strong></a>(self, excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True)</dt><dd><tt>Write&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;an&nbsp;excel&nbsp;sheet<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel_writer&nbsp;:&nbsp;string&nbsp;or&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;existing&nbsp;ExcelWriter<br>
-sheet_name&nbsp;:&nbsp;string,&nbsp;default&nbsp;'Sheet1'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;sheet&nbsp;which&nbsp;will&nbsp;contain&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Columns&nbsp;to&nbsp;write<br>
-header&nbsp;:&nbsp;boolean&nbsp;or&nbsp;list&nbsp;of&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given&nbsp;it&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;assumed&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-startrow&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;row&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-startcol&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;column&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-engine&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;write&nbsp;engine&nbsp;to&nbsp;use&nbsp;-&nbsp;you&nbsp;can&nbsp;also&nbsp;set&nbsp;this&nbsp;via&nbsp;the&nbsp;options<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsx.writer``,&nbsp;``io.excel.xls.writer``,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsm.writer``.<br>
-merge_cells&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;MultiIndex&nbsp;and&nbsp;Hierarchical&nbsp;Rows&nbsp;as&nbsp;merged&nbsp;cells.<br>
-encoding:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;encoding&nbsp;of&nbsp;the&nbsp;resulting&nbsp;excel&nbsp;file.&nbsp;Only&nbsp;necessary&nbsp;for&nbsp;xlwt,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;writers&nbsp;support&nbsp;unicode&nbsp;natively.<br>
-inf_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;'inf'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Representation&nbsp;for&nbsp;infinity&nbsp;(there&nbsp;is&nbsp;no&nbsp;native&nbsp;representation&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;infinity&nbsp;in&nbsp;Excel)<br>
-freeze_panes&nbsp;:&nbsp;tuple&nbsp;of&nbsp;integer&nbsp;(length&nbsp;2),&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;the&nbsp;one-based&nbsp;bottommost&nbsp;row&nbsp;and&nbsp;rightmost&nbsp;column&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;to&nbsp;be&nbsp;frozen<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;passing&nbsp;an&nbsp;existing&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a>,&nbsp;then&nbsp;the&nbsp;sheet&nbsp;will&nbsp;be&nbsp;added<br>
-to&nbsp;the&nbsp;existing&nbsp;workbook.&nbsp;&nbsp;This&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;save&nbsp;different<br>
-DataFrames&nbsp;to&nbsp;one&nbsp;workbook:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;writer&nbsp;=&nbsp;pd.ExcelWriter('output.xlsx')<br>
-&gt;&gt;&gt;&nbsp;df1.<a href="#System-to_excel">to_excel</a>(writer,'Sheet1')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#System-to_excel">to_excel</a>(writer,'Sheet2')<br>
-&gt;&gt;&gt;&nbsp;writer.save()<br>
-&nbsp;<br>
-For&nbsp;compatibility&nbsp;with&nbsp;to_csv,&nbsp;to_excel&nbsp;serializes&nbsp;lists&nbsp;and&nbsp;dicts&nbsp;to<br>
-strings&nbsp;before&nbsp;writing.</tt></dd></dl>
-
-<dl><dt><a name="System-to_frame"><strong>to_frame</strong></a>(self, name=None)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-name&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;passed&nbsp;name&nbsp;should&nbsp;substitute&nbsp;for&nbsp;the&nbsp;series&nbsp;name&nbsp;(if&nbsp;it&nbsp;has<br>
-&nbsp;&nbsp;&nbsp;&nbsp;one).<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-data_frame&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="System-to_period"><strong>to_period</strong></a>(self, freq=None, copy=True)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;from&nbsp;DatetimeIndex&nbsp;to&nbsp;PeriodIndex&nbsp;with&nbsp;desired<br>
-frequency&nbsp;(inferred&nbsp;from&nbsp;index&nbsp;if&nbsp;not&nbsp;passed)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;PeriodIndex</tt></dd></dl>
-
-<dl><dt><a name="System-to_sparse"><strong>to_sparse</strong></a>(self, kind='block', fill_value=None)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;SparseSeries<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-kind&nbsp;:&nbsp;{'block',&nbsp;'integer'}<br>
-fill_value&nbsp;:&nbsp;float,&nbsp;defaults&nbsp;to&nbsp;NaN&nbsp;(missing)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sp&nbsp;:&nbsp;SparseSeries</tt></dd></dl>
-
-<dl><dt><a name="System-to_string"><strong>to_string</strong></a>(self, buf=None, na_rep='NaN', float_format=None, header=True, index=True, length=False, dtype=False, name=False, max_rows=None)</dt><dd><tt>Render&nbsp;a&nbsp;string&nbsp;representation&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-buf&nbsp;:&nbsp;StringIO-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;buffer&nbsp;to&nbsp;write&nbsp;to<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;representation&nbsp;of&nbsp;NAN&nbsp;to&nbsp;use,&nbsp;default&nbsp;'NaN'<br>
-float_format&nbsp;:&nbsp;one-parameter&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;if&nbsp;they&nbsp;are&nbsp;floats<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None<br>
-header:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;header&nbsp;(index&nbsp;name)<br>
-index&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;index&nbsp;(row)&nbsp;labels,&nbsp;default&nbsp;True<br>
-length&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;length<br>
-dtype&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;dtype<br>
-name&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;name&nbsp;if&nbsp;not&nbsp;None<br>
-max_rows&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;rows&nbsp;to&nbsp;show&nbsp;before&nbsp;truncating.&nbsp;If&nbsp;None,&nbsp;show<br>
-&nbsp;&nbsp;&nbsp;&nbsp;all.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-formatted&nbsp;:&nbsp;string&nbsp;(if&nbsp;not&nbsp;buffer&nbsp;passed)</tt></dd></dl>
-
-<dl><dt><a name="System-to_timestamp"><strong>to_timestamp</strong></a>(self, freq=None, how='start', copy=True)</dt><dd><tt>Cast&nbsp;to&nbsp;datetimeindex&nbsp;of&nbsp;timestamps,&nbsp;at&nbsp;*beginning*&nbsp;of&nbsp;period<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default&nbsp;frequency&nbsp;of&nbsp;PeriodIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Desired&nbsp;frequency<br>
-how&nbsp;:&nbsp;{'s',&nbsp;'e',&nbsp;'start',&nbsp;'end'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Convention&nbsp;for&nbsp;converting&nbsp;period&nbsp;to&nbsp;timestamp;&nbsp;start&nbsp;of&nbsp;period<br>
-&nbsp;&nbsp;&nbsp;&nbsp;vs.&nbsp;end<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;DatetimeIndex</tt></dd></dl>
-
-<dl><dt><a name="System-tolist"><strong>tolist</strong></a>(self)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;a&nbsp;nested&nbsp;list</tt></dd></dl>
-
-<dl><dt><a name="System-transform"><strong>transform</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Call&nbsp;function&nbsp;producing&nbsp;a&nbsp;like-indexed&nbsp;NDFrame<br>
-and&nbsp;return&nbsp;a&nbsp;NDFrame&nbsp;with&nbsp;the&nbsp;transformed&nbsp;values`<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;apply&nbsp;to&nbsp;column<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-transformed&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(10,&nbsp;3),&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.date_range('1/1/2000',&nbsp;periods=10))<br>
-df.iloc[3:7]&nbsp;=&nbsp;np.nan<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-transform">transform</a>(lambda&nbsp;x:&nbsp;(x&nbsp;-&nbsp;x.<a href="#System-mean">mean</a>())&nbsp;/&nbsp;x.<a href="#System-std">std</a>())<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-2000-01-01&nbsp;&nbsp;0.579457&nbsp;&nbsp;1.236184&nbsp;&nbsp;0.123424<br>
-2000-01-02&nbsp;&nbsp;0.370357&nbsp;-0.605875&nbsp;-1.231325<br>
-2000-01-03&nbsp;&nbsp;1.455756&nbsp;-0.277446&nbsp;&nbsp;0.288967<br>
-2000-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-08&nbsp;-0.498658&nbsp;&nbsp;1.274522&nbsp;&nbsp;1.642524<br>
-2000-01-09&nbsp;-0.540524&nbsp;-1.012676&nbsp;-0.828968<br>
-2000-01-10&nbsp;-1.366388&nbsp;-0.614710&nbsp;&nbsp;0.005378<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.NDFrame.aggregate<br>
-pandas.NDFrame.apply</tt></dd></dl>
-
-<dl><dt><a name="System-truediv"><strong>truediv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="System-unique"><strong>unique</strong></a>(self)</dt><dd><tt>Return&nbsp;unique&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.&nbsp;Uniques&nbsp;are&nbsp;returned&nbsp;in&nbsp;order<br>
-of&nbsp;appearance,&nbsp;this&nbsp;does&nbsp;NOT&nbsp;sort.&nbsp;Hash&nbsp;table-based&nbsp;unique.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-values&nbsp;:&nbsp;1d&nbsp;array-like<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-unique&nbsp;values.<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;an&nbsp;Index,&nbsp;the&nbsp;return&nbsp;is&nbsp;an&nbsp;Index<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;a&nbsp;Categorical&nbsp;dtype,&nbsp;the&nbsp;return&nbsp;is&nbsp;a&nbsp;Categorical<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>/<a href="numpy.html#ndarray">ndarray</a>,&nbsp;the&nbsp;return&nbsp;will&nbsp;be&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-unique<br>
-Index.unique<br>
-<a href="pandas.core.series.html#Series">Series</a>.unique</tt></dd></dl>
-
-<dl><dt><a name="System-unstack"><strong>unstack</strong></a>(self, level=-1, fill_value=None)</dt><dd><tt>Unstack,&nbsp;a.k.a.&nbsp;pivot,&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;MultiIndex&nbsp;to&nbsp;produce&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-The&nbsp;level&nbsp;involved&nbsp;will&nbsp;automatically&nbsp;get&nbsp;sorted.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;string,&nbsp;or&nbsp;list&nbsp;of&nbsp;these,&nbsp;default&nbsp;last&nbsp;level<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level(s)&nbsp;to&nbsp;unstack,&nbsp;can&nbsp;pass&nbsp;level&nbsp;name<br>
-fill_value&nbsp;:&nbsp;replace&nbsp;NaN&nbsp;with&nbsp;this&nbsp;value&nbsp;if&nbsp;the&nbsp;unstack&nbsp;produces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.18.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;4],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.MultiIndex.from_product([['one',&nbsp;'two'],&nbsp;['a',&nbsp;'b']]))<br>
-&gt;&gt;&gt;&nbsp;s<br>
-one&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-two&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-unstack">unstack</a>(level=-1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b<br>
-one&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-two&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-unstack">unstack</a>(level=0)<br>
-&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;two<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-unstacked&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="System-update"><strong>update</strong></a>(self, other)</dt><dd><tt>Modify&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place&nbsp;using&nbsp;non-NA&nbsp;values&nbsp;from&nbsp;passed<br>
-<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Aligns&nbsp;on&nbsp;index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><strong>valid</strong> <em>lambda</em> self, inplace=False, **kwargs</dt></dl>
-
-<dl><dt><a name="System-var"><strong>var</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;variance&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-var&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="System-view"><strong>view</strong></a>(self, dtype=None)</dt></dl>
-
-<hr>
-Class methods inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><a name="System-from_array"><strong>from_array</strong></a>(arr, index=None, name=None, dtype=None, copy=False, fastpath=False)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt></dl>
-
-<dl><dt><a name="System-from_csv"><strong>from_csv</strong></a>(path, sep=',', parse_dates=True, header=None, index_col=0, encoding=None, infer_datetime_format=False)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Read&nbsp;CSV&nbsp;file&nbsp;(DISCOURAGED,&nbsp;please&nbsp;use&nbsp;:func:`pandas.read_csv`<br>
-instead).<br>
-&nbsp;<br>
-It&nbsp;is&nbsp;preferable&nbsp;to&nbsp;use&nbsp;the&nbsp;more&nbsp;powerful&nbsp;:func:`pandas.read_csv`<br>
-for&nbsp;most&nbsp;general&nbsp;purposes,&nbsp;but&nbsp;``from_csv``&nbsp;makes&nbsp;for&nbsp;an&nbsp;easy<br>
-roundtrip&nbsp;to&nbsp;and&nbsp;from&nbsp;a&nbsp;file&nbsp;(the&nbsp;exact&nbsp;counterpart&nbsp;of<br>
-``to_csv``),&nbsp;especially&nbsp;with&nbsp;a&nbsp;time&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;only&nbsp;differs&nbsp;from&nbsp;:func:`pandas.read_csv`&nbsp;in&nbsp;some&nbsp;defaults:<br>
-&nbsp;<br>
--&nbsp;`index_col`&nbsp;is&nbsp;``0``&nbsp;instead&nbsp;of&nbsp;``None``&nbsp;(take&nbsp;first&nbsp;column&nbsp;as&nbsp;index<br>
-&nbsp;&nbsp;by&nbsp;default)<br>
--&nbsp;`header`&nbsp;is&nbsp;``None``&nbsp;instead&nbsp;of&nbsp;``0``&nbsp;(the&nbsp;first&nbsp;row&nbsp;is&nbsp;not&nbsp;used&nbsp;as<br>
-&nbsp;&nbsp;the&nbsp;column&nbsp;names)<br>
--&nbsp;`parse_dates`&nbsp;is&nbsp;``True``&nbsp;instead&nbsp;of&nbsp;``False``&nbsp;(try&nbsp;parsing&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;as&nbsp;datetime&nbsp;by&nbsp;default)<br>
-&nbsp;<br>
-With&nbsp;:func:`pandas.read_csv`,&nbsp;the&nbsp;option&nbsp;``squeeze=True``&nbsp;can&nbsp;be&nbsp;used<br>
-to&nbsp;return&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;like&nbsp;``from_csv``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;file&nbsp;path&nbsp;or&nbsp;file&nbsp;handle&nbsp;/&nbsp;StringIO<br>
-sep&nbsp;:&nbsp;string,&nbsp;default&nbsp;','<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter<br>
-parse_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Parse&nbsp;dates.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-header&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Row&nbsp;to&nbsp;use&nbsp;as&nbsp;header&nbsp;(skip&nbsp;prior&nbsp;rows)<br>
-index_col&nbsp;:&nbsp;int&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;to&nbsp;use&nbsp;for&nbsp;index.&nbsp;If&nbsp;a&nbsp;sequence&nbsp;is&nbsp;given,&nbsp;a&nbsp;MultiIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;used.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-encoding&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;if&nbsp;the&nbsp;contents&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;non-ascii,&nbsp;for&nbsp;python&nbsp;versions&nbsp;prior&nbsp;to&nbsp;3<br>
-infer_datetime_format:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;and&nbsp;`parse_dates`&nbsp;is&nbsp;True&nbsp;for&nbsp;a&nbsp;column,&nbsp;try&nbsp;to&nbsp;infer&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;datetime&nbsp;format&nbsp;based&nbsp;on&nbsp;the&nbsp;first&nbsp;datetime&nbsp;string.&nbsp;If&nbsp;the&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;be&nbsp;inferred,&nbsp;there&nbsp;often&nbsp;will&nbsp;be&nbsp;a&nbsp;large&nbsp;parsing&nbsp;speed-up.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.read_csv<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><strong>asobject</strong></dt>
-<dd><tt>return&nbsp;object&nbsp;Series&nbsp;which&nbsp;contains&nbsp;boxed&nbsp;values<br>
-&nbsp;<br>
-*this&nbsp;is&nbsp;an&nbsp;internal&nbsp;non-public&nbsp;method*</tt></dd>
-</dl>
-<dl><dt><strong>axes</strong></dt>
-<dd><tt>Return&nbsp;a&nbsp;list&nbsp;of&nbsp;the&nbsp;row&nbsp;axis&nbsp;labels</tt></dd>
-</dl>
-<dl><dt><strong>dtype</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;dtype&nbsp;object&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>dtypes</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;dtype&nbsp;object&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>ftype</strong></dt>
-<dd><tt>return&nbsp;if&nbsp;the&nbsp;data&nbsp;is&nbsp;sparse|dense</tt></dd>
-</dl>
-<dl><dt><strong>ftypes</strong></dt>
-<dd><tt>return&nbsp;if&nbsp;the&nbsp;data&nbsp;is&nbsp;sparse|dense</tt></dd>
-</dl>
-<dl><dt><strong>imag</strong></dt>
-</dl>
-<dl><dt><strong>index</strong></dt>
-</dl>
-<dl><dt><strong>name</strong></dt>
-</dl>
-<dl><dt><strong>real</strong></dt>
-</dl>
-<dl><dt><strong>values</strong></dt>
-<dd><tt>Return&nbsp;Series&nbsp;as&nbsp;ndarray&nbsp;or&nbsp;ndarray-like<br>
-depending&nbsp;on&nbsp;the&nbsp;dtype<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-arr&nbsp;:&nbsp;numpy.ndarray&nbsp;or&nbsp;ndarray-like<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;pd.Series([1,&nbsp;2,&nbsp;3]).values<br>
-array([1,&nbsp;2,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(list('aabc')).values<br>
-array(['a',&nbsp;'a',&nbsp;'b',&nbsp;'c'],&nbsp;dtype=object)<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(list('aabc')).astype('category').values<br>
-[a,&nbsp;a,&nbsp;b,&nbsp;c]<br>
-Categories&nbsp;(3,&nbsp;object):&nbsp;[a,&nbsp;b,&nbsp;c]<br>
-&nbsp;<br>
-Timezone&nbsp;aware&nbsp;datetime&nbsp;data&nbsp;is&nbsp;converted&nbsp;to&nbsp;UTC:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(pd.date_range('20130101',&nbsp;periods=3,<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;tz='US/Eastern')).values<br>
-array(['2013-01-01T05:00:00.000000000',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'2013-01-02T05:00:00.000000000',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'2013-01-03T05:00:00.000000000'],&nbsp;dtype='datetime64[ns]')</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><strong>cat</strong> = &lt;class 'pandas.core.categorical.CategoricalAccessor'&gt;<dd><tt>Accessor&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;categorical&nbsp;properties&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values.<br>
-&nbsp;<br>
-Be&nbsp;aware&nbsp;that&nbsp;assigning&nbsp;to&nbsp;`categories`&nbsp;is&nbsp;a&nbsp;inplace&nbsp;operation,&nbsp;while&nbsp;all<br>
-methods&nbsp;return&nbsp;new&nbsp;categorical&nbsp;data&nbsp;per&nbsp;default&nbsp;(but&nbsp;can&nbsp;be&nbsp;called&nbsp;with<br>
-`inplace=True`).<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.cat.categories<br>
-&gt;&gt;&gt;&nbsp;s.cat.categories&nbsp;=&nbsp;list('abc')<br>
-&gt;&gt;&gt;&nbsp;s.cat.rename_categories(list('cab'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.reorder_categories(list('cab'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.add_categories(['d','e'])<br>
-&gt;&gt;&gt;&nbsp;s.cat.remove_categories(['d'])<br>
-&gt;&gt;&gt;&nbsp;s.cat.remove_unused_categories()<br>
-&gt;&gt;&gt;&nbsp;s.cat.set_categories(list('abcde'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.as_ordered()<br>
-&gt;&gt;&gt;&nbsp;s.cat.as_unordered()</tt></dl>
-
-<dl><dt><strong>plot</strong> = &lt;class 'pandas.plotting._core.SeriesPlotMethods'&gt;<dd><tt><a href="pandas.core.series.html#Series">Series</a>&nbsp;plotting&nbsp;accessor&nbsp;and&nbsp;method<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.plot.line()<br>
-&gt;&gt;&gt;&nbsp;s.plot.bar()<br>
-&gt;&gt;&gt;&nbsp;s.plot.<a href="#System-hist">hist</a>()<br>
-&nbsp;<br>
-Plotting&nbsp;methods&nbsp;can&nbsp;also&nbsp;be&nbsp;accessed&nbsp;by&nbsp;calling&nbsp;the&nbsp;accessor&nbsp;as&nbsp;a&nbsp;method<br>
-with&nbsp;the&nbsp;``kind``&nbsp;argument:<br>
-``s.<a href="#System-plot">plot</a>(kind='line')``&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;``s.plot.line()``</tt></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><a name="System-factorize"><strong>factorize</strong></a>(self, sort=False, na_sentinel=-1)</dt><dd><tt>Encode&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;as&nbsp;an&nbsp;enumerated&nbsp;type&nbsp;or&nbsp;categorical&nbsp;variable<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;by&nbsp;values<br>
-na_sentinel:&nbsp;int,&nbsp;default&nbsp;-1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;mark&nbsp;"not&nbsp;found"<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-labels&nbsp;:&nbsp;the&nbsp;indexer&nbsp;to&nbsp;the&nbsp;original&nbsp;array<br>
-uniques&nbsp;:&nbsp;the&nbsp;unique&nbsp;Index</tt></dd></dl>
-
-<dl><dt><a name="System-item"><strong>item</strong></a>(self)</dt><dd><tt>return&nbsp;the&nbsp;first&nbsp;element&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data&nbsp;as&nbsp;a&nbsp;python<br>
-scalar</tt></dd></dl>
-
-<dl><dt><a name="System-nunique"><strong>nunique</strong></a>(self, dropna=True)</dt><dd><tt>Return&nbsp;number&nbsp;of&nbsp;unique&nbsp;elements&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Excludes&nbsp;NA&nbsp;values&nbsp;by&nbsp;default.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Don't&nbsp;include&nbsp;NaN&nbsp;in&nbsp;the&nbsp;count.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-nunique&nbsp;:&nbsp;int</tt></dd></dl>
-
-<dl><dt><a name="System-transpose"><strong>transpose</strong></a>(self, *args, **kwargs)</dt><dd><tt>return&nbsp;the&nbsp;transpose,&nbsp;which&nbsp;is&nbsp;by&nbsp;definition&nbsp;self</tt></dd></dl>
-
-<dl><dt><a name="System-value_counts"><strong>value_counts</strong></a>(self, normalize=False, sort=True, ascending=False, bins=None, dropna=True)</dt><dd><tt>Returns&nbsp;<a href="builtins.html#object">object</a>&nbsp;containing&nbsp;counts&nbsp;of&nbsp;unique&nbsp;values.<br>
-&nbsp;<br>
-The&nbsp;resulting&nbsp;<a href="builtins.html#object">object</a>&nbsp;will&nbsp;be&nbsp;in&nbsp;descending&nbsp;order&nbsp;so&nbsp;that&nbsp;the<br>
-first&nbsp;element&nbsp;is&nbsp;the&nbsp;most&nbsp;frequently-occurring&nbsp;element.<br>
-Excludes&nbsp;NA&nbsp;values&nbsp;by&nbsp;default.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-normalize&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;then&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;returned&nbsp;will&nbsp;contain&nbsp;the&nbsp;relative<br>
-&nbsp;&nbsp;&nbsp;&nbsp;frequencies&nbsp;of&nbsp;the&nbsp;unique&nbsp;values.<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;by&nbsp;values<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;in&nbsp;ascending&nbsp;order<br>
-bins&nbsp;:&nbsp;integer,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Rather&nbsp;than&nbsp;count&nbsp;values,&nbsp;group&nbsp;them&nbsp;into&nbsp;half-open&nbsp;bins,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;convenience&nbsp;for&nbsp;pd.cut,&nbsp;only&nbsp;works&nbsp;with&nbsp;numeric&nbsp;data<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Don't&nbsp;include&nbsp;counts&nbsp;of&nbsp;NaN.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-counts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><strong>__dict__</strong></dt>
-<dd><tt>dictionary&nbsp;for&nbsp;instance&nbsp;variables&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>__weakref__</strong></dt>
-<dd><tt>list&nbsp;of&nbsp;weak&nbsp;references&nbsp;to&nbsp;the&nbsp;object&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>base</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;base&nbsp;object&nbsp;if&nbsp;the&nbsp;memory&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data&nbsp;is<br>
-shared</tt></dd>
-</dl>
-<dl><dt><strong>data</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;data&nbsp;pointer&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>empty</strong></dt>
-</dl>
-<dl><dt><strong>flags</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;ndarray.flags&nbsp;for&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>hasnans</strong></dt>
-</dl>
-<dl><dt><strong>is_monotonic</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_increasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_monotonic_decreasing</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_decreasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic_decreasing&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_monotonic_increasing</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_increasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_unique</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are&nbsp;unique<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_unique&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>itemsize</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;size&nbsp;of&nbsp;the&nbsp;dtype&nbsp;of&nbsp;the&nbsp;item&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>nbytes</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;bytes&nbsp;in&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>ndim</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;dimensions&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data,<br>
-by&nbsp;definition&nbsp;1</tt></dd>
-</dl>
-<dl><dt><strong>shape</strong></dt>
-<dd><tt>return&nbsp;a&nbsp;tuple&nbsp;of&nbsp;the&nbsp;shape&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>size</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;elements&nbsp;in&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>strides</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;strides&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><strong>__array_priority__</strong> = 1000</dl>
-
-<hr>
-Data and other attributes inherited from <a href="pandas.core.strings.html#StringAccessorMixin">pandas.core.strings.StringAccessorMixin</a>:<br>
-<dl><dt><strong>str</strong> = &lt;class 'pandas.core.strings.StringMethods'&gt;<dd><tt>Vectorized&nbsp;string&nbsp;functions&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;and&nbsp;Index.&nbsp;NAs&nbsp;stay&nbsp;NA&nbsp;unless<br>
-handled&nbsp;otherwise&nbsp;by&nbsp;a&nbsp;particular&nbsp;method.&nbsp;Patterned&nbsp;after&nbsp;Python's&nbsp;string<br>
-methods,&nbsp;with&nbsp;some&nbsp;inspiration&nbsp;from&nbsp;R's&nbsp;stringr&nbsp;package.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.str.split('_')<br>
-&gt;&gt;&gt;&nbsp;s.str.<a href="#System-replace">replace</a>('_',&nbsp;'')</tt></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><a name="System-__abs__"><strong>__abs__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="System-__bool__"><strong>__bool__</strong></a> = __nonzero__(self)</dt></dl>
-
-<dl><dt><a name="System-__contains__"><strong>__contains__</strong></a>(self, key)</dt><dd><tt>True&nbsp;if&nbsp;the&nbsp;key&nbsp;is&nbsp;in&nbsp;the&nbsp;info&nbsp;axis</tt></dd></dl>
-
-<dl><dt><a name="System-__copy__"><strong>__copy__</strong></a>(self, deep=True)</dt></dl>
-
-<dl><dt><a name="System-__deepcopy__"><strong>__deepcopy__</strong></a>(self, memo=None)</dt></dl>
-
-<dl><dt><a name="System-__delitem__"><strong>__delitem__</strong></a>(self, key)</dt><dd><tt>Delete&nbsp;item</tt></dd></dl>
-
-<dl><dt><a name="System-__finalize__"><strong>__finalize__</strong></a>(self, other, method=None, **kwargs)</dt><dd><tt>Propagate&nbsp;metadata&nbsp;from&nbsp;other&nbsp;to&nbsp;self.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;which&nbsp;to&nbsp;get&nbsp;the&nbsp;attributes&nbsp;that&nbsp;we&nbsp;are&nbsp;going<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;propagate<br>
-method&nbsp;:&nbsp;optional,&nbsp;a&nbsp;passed&nbsp;method&nbsp;name&nbsp;;&nbsp;possibly&nbsp;to&nbsp;take&nbsp;different<br>
-&nbsp;&nbsp;&nbsp;&nbsp;types&nbsp;of&nbsp;propagation&nbsp;actions&nbsp;based&nbsp;on&nbsp;this</tt></dd></dl>
-
-<dl><dt><a name="System-__getattr__"><strong>__getattr__</strong></a>(self, name)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;looking&nbsp;up&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="System-__getstate__"><strong>__getstate__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="System-__hash__"><strong>__hash__</strong></a>(self)</dt><dd><tt>Return&nbsp;hash(self).</tt></dd></dl>
-
-<dl><dt><a name="System-__invert__"><strong>__invert__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="System-__neg__"><strong>__neg__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="System-__nonzero__"><strong>__nonzero__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="System-__round__"><strong>__round__</strong></a>(self, decimals=0)</dt></dl>
-
-<dl><dt><a name="System-__setattr__"><strong>__setattr__</strong></a>(self, name, value)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;setting&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="System-__setstate__"><strong>__setstate__</strong></a>(self, state)</dt></dl>
-
-<dl><dt><a name="System-abs"><strong>abs</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;absolute&nbsp;value&nbsp;taken--only&nbsp;applicable&nbsp;to&nbsp;objects<br>
-that&nbsp;are&nbsp;all&nbsp;numeric.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-abs:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="System-add_prefix"><strong>add_prefix</strong></a>(self, prefix)</dt><dd><tt>Concatenate&nbsp;prefix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-prefix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_prefix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="System-add_suffix"><strong>add_suffix</strong></a>(self, suffix)</dt><dd><tt>Concatenate&nbsp;suffix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-suffix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_suffix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="System-as_blocks"><strong>as_blocks</strong></a>(self, copy=True)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types&nbsp;that&nbsp;each&nbsp;has<br>
-a&nbsp;homogeneous&nbsp;dtype.<br>
-&nbsp;<br>
-NOTE:&nbsp;the&nbsp;dtypes&nbsp;of&nbsp;the&nbsp;blocks&nbsp;WILL&nbsp;BE&nbsp;PRESERVED&nbsp;HERE&nbsp;(unlike&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;as_matrix)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types</tt></dd></dl>
-
-<dl><dt><a name="System-as_matrix"><strong>as_matrix</strong></a>(self, columns=None)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;its&nbsp;Numpy-array&nbsp;representation.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-columns:&nbsp;list,&nbsp;optional,&nbsp;default:None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;None,&nbsp;return&nbsp;all&nbsp;columns,&nbsp;otherwise,&nbsp;returns&nbsp;specified&nbsp;columns.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;caller&nbsp;is&nbsp;heterogeneous&nbsp;and&nbsp;contains&nbsp;booleans&nbsp;or&nbsp;objects,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;of&nbsp;dtype=<a href="builtins.html#object">object</a>.&nbsp;See&nbsp;Notes.<br>
-&nbsp;<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Return&nbsp;is&nbsp;NOT&nbsp;a&nbsp;Numpy-matrix,&nbsp;rather,&nbsp;a&nbsp;Numpy-array.<br>
-&nbsp;<br>
-The&nbsp;dtype&nbsp;will&nbsp;be&nbsp;a&nbsp;lower-common-denominator&nbsp;dtype&nbsp;(implicit<br>
-upcasting);&nbsp;that&nbsp;is&nbsp;to&nbsp;say&nbsp;if&nbsp;the&nbsp;dtypes&nbsp;(even&nbsp;of&nbsp;numeric&nbsp;types)<br>
-are&nbsp;mixed,&nbsp;the&nbsp;one&nbsp;that&nbsp;accommodates&nbsp;all&nbsp;will&nbsp;be&nbsp;chosen.&nbsp;Use&nbsp;this<br>
-with&nbsp;care&nbsp;if&nbsp;you&nbsp;are&nbsp;not&nbsp;dealing&nbsp;with&nbsp;the&nbsp;blocks.<br>
-&nbsp;<br>
-e.g.&nbsp;If&nbsp;the&nbsp;dtypes&nbsp;are&nbsp;float16&nbsp;and&nbsp;float32,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcast&nbsp;to<br>
-float32.&nbsp;&nbsp;If&nbsp;dtypes&nbsp;are&nbsp;int32&nbsp;and&nbsp;uint8,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcase&nbsp;to<br>
-int32.&nbsp;By&nbsp;numpy.find_common_type&nbsp;convention,&nbsp;mixing&nbsp;int64&nbsp;and&nbsp;uint64<br>
-will&nbsp;result&nbsp;in&nbsp;a&nbsp;flot64&nbsp;dtype.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;is&nbsp;provided&nbsp;for&nbsp;backwards&nbsp;compatibility.&nbsp;Generally,<br>
-it&nbsp;is&nbsp;recommended&nbsp;to&nbsp;use&nbsp;'.values'.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.values</tt></dd></dl>
-
-<dl><dt><a name="System-asfreq"><strong>asfreq</strong></a>(self, freq, method=None, how=None, normalize=False, fill_value=None)</dt><dd><tt>Convert&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;specified&nbsp;frequency.<br>
-&nbsp;<br>
-Optionally&nbsp;provide&nbsp;filling&nbsp;method&nbsp;to&nbsp;pad/backfill&nbsp;missing&nbsp;values.<br>
-&nbsp;<br>
-Returns&nbsp;the&nbsp;original&nbsp;data&nbsp;conformed&nbsp;to&nbsp;a&nbsp;new&nbsp;index&nbsp;with&nbsp;the&nbsp;specified<br>
-frequency.&nbsp;``resample``&nbsp;is&nbsp;more&nbsp;appropriate&nbsp;if&nbsp;an&nbsp;operation,&nbsp;such&nbsp;as<br>
-summarization,&nbsp;is&nbsp;necessary&nbsp;to&nbsp;represent&nbsp;the&nbsp;data&nbsp;at&nbsp;the&nbsp;new&nbsp;frequency.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;or&nbsp;string<br>
-method&nbsp;:&nbsp;{'backfill'/'bfill',&nbsp;'pad'/'ffill'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(note&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present):<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'pad'&nbsp;/&nbsp;'ffill':&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'backfill'&nbsp;/&nbsp;'bfill':&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill<br>
-how&nbsp;:&nbsp;{'start',&nbsp;'end'},&nbsp;default&nbsp;end<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;PeriodIndex&nbsp;only,&nbsp;see&nbsp;PeriodIndex.asfreq<br>
-normalize&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;reset&nbsp;output&nbsp;index&nbsp;to&nbsp;midnight<br>
-fill_value:&nbsp;scalar,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values,&nbsp;applied&nbsp;during&nbsp;upsampling&nbsp;(note<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;4&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=4,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0.0,&nbsp;None,&nbsp;2.0,&nbsp;3.0],&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'s':series})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-asfreq">asfreq</a>(freq='30S')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``fill&nbsp;value``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-asfreq">asfreq</a>(freq='30S',&nbsp;fill_value=9.0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``method``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-asfreq">asfreq</a>(freq='30S',&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.</tt></dd></dl>
-
-<dl><dt><a name="System-asof"><strong>asof</strong></a>(self, where, subset=None)</dt><dd><tt>The&nbsp;last&nbsp;row&nbsp;without&nbsp;any&nbsp;NaN&nbsp;is&nbsp;taken&nbsp;(or&nbsp;the&nbsp;last&nbsp;row&nbsp;without<br>
-NaN&nbsp;considering&nbsp;only&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;in&nbsp;the&nbsp;case&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0&nbsp;For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-If&nbsp;there&nbsp;is&nbsp;no&nbsp;good&nbsp;value,&nbsp;NaN&nbsp;is&nbsp;returned&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;NaN&nbsp;values&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-where&nbsp;:&nbsp;date&nbsp;or&nbsp;array&nbsp;of&nbsp;dates<br>
-subset&nbsp;:&nbsp;string&nbsp;or&nbsp;list&nbsp;of&nbsp;strings,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None&nbsp;use&nbsp;these&nbsp;columns&nbsp;for&nbsp;NaN&nbsp;propagation<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Dates&nbsp;are&nbsp;assumed&nbsp;to&nbsp;be&nbsp;sorted<br>
-Raises&nbsp;if&nbsp;this&nbsp;is&nbsp;not&nbsp;the&nbsp;case<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-where&nbsp;is&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;&nbsp;-&nbsp;value&nbsp;or&nbsp;NaN&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;-&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-where&nbsp;is&nbsp;Index:&nbsp;same&nbsp;shape&nbsp;<a href="builtins.html#object">object</a>&nbsp;as&nbsp;input<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-merge_asof</tt></dd></dl>
-
-<dl><dt><a name="System-astype"><strong>astype</strong></a>(self, dtype, copy=True, errors='raise', **kwargs)</dt><dd><tt>Cast&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;numpy.dtype<br>
-Return&nbsp;a&nbsp;copy&nbsp;when&nbsp;copy&nbsp;=&nbsp;True&nbsp;(be&nbsp;really&nbsp;careful&nbsp;with&nbsp;this!)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dtype&nbsp;:&nbsp;data&nbsp;type,&nbsp;or&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;-&gt;&nbsp;data&nbsp;type<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;entire&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;same&nbsp;type.&nbsp;Alternatively,&nbsp;use&nbsp;{col:&nbsp;dtype,&nbsp;...},&nbsp;where&nbsp;col&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;label&nbsp;and&nbsp;dtype&nbsp;is&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;one<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;more&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;columns&nbsp;to&nbsp;column-specific&nbsp;types.<br>
-errors&nbsp;:&nbsp;{'raise',&nbsp;'ignore'},&nbsp;default&nbsp;'raise'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Control&nbsp;raising&nbsp;of&nbsp;exceptions&nbsp;on&nbsp;invalid&nbsp;data&nbsp;for&nbsp;provided&nbsp;dtype.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``raise``&nbsp;:&nbsp;allow&nbsp;exceptions&nbsp;to&nbsp;be&nbsp;raised<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``ignore``&nbsp;:&nbsp;suppress&nbsp;exceptions.&nbsp;On&nbsp;error&nbsp;return&nbsp;original&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-raise_on_error&nbsp;:&nbsp;DEPRECATED&nbsp;use&nbsp;``errors``&nbsp;instead<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;constructor<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-casted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="System-at_time"><strong>at_time</strong></a>(self, time, asof=False)</dt><dd><tt>Select&nbsp;values&nbsp;at&nbsp;particular&nbsp;time&nbsp;of&nbsp;day&nbsp;(e.g.&nbsp;9:30AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_at_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="System-between_time"><strong>between_time</strong></a>(self, start_time, end_time, include_start=True, include_end=True)</dt><dd><tt>Select&nbsp;values&nbsp;between&nbsp;particular&nbsp;times&nbsp;of&nbsp;the&nbsp;day&nbsp;(e.g.,&nbsp;9:00-9:30&nbsp;AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-start_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-end_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-include_start&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-include_end&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_between_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="System-bfill"><strong>bfill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#System-fillna">fillna</a>(method='bfill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="System-bool"><strong>bool</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;bool&nbsp;of&nbsp;a&nbsp;single&nbsp;element&nbsp;PandasObject.<br>
-&nbsp;<br>
-This&nbsp;must&nbsp;be&nbsp;a&nbsp;boolean&nbsp;scalar&nbsp;value,&nbsp;either&nbsp;True&nbsp;or&nbsp;False.&nbsp;&nbsp;Raise&nbsp;a<br>
-ValueError&nbsp;if&nbsp;the&nbsp;PandasObject&nbsp;does&nbsp;not&nbsp;have&nbsp;exactly&nbsp;1&nbsp;element,&nbsp;or&nbsp;that<br>
-element&nbsp;is&nbsp;not&nbsp;boolean</tt></dd></dl>
-
-<dl><dt><a name="System-clip"><strong>clip</strong></a>(self, lower=None, upper=None, axis=None, *args, **kwargs)</dt><dd><tt>Trim&nbsp;values&nbsp;at&nbsp;input&nbsp;threshold(s).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-lower&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-upper&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;lower&nbsp;and&nbsp;upper&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.256177<br>
-1&nbsp;-1.367855&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.176076<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;1.261967&nbsp;&nbsp;0.570967<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-clip">clip</a>(-1.0,&nbsp;0.5)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.000000<br>
-1&nbsp;-1.000000&nbsp;&nbsp;0.500000<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.000000<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;0.500000&nbsp;&nbsp;0.500000<br>
-&gt;&gt;&gt;&nbsp;t<br>
-0&nbsp;&nbsp;&nbsp;-0.3<br>
-1&nbsp;&nbsp;&nbsp;-0.2<br>
-2&nbsp;&nbsp;&nbsp;-0.1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;0.1<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-clip">clip</a>(t,&nbsp;t&nbsp;+&nbsp;1,&nbsp;axis=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-0.300000<br>
-1&nbsp;-0.200000&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-0.100000<br>
-3&nbsp;&nbsp;0.230930&nbsp;&nbsp;0.000000<br>
-4&nbsp;&nbsp;1.100000&nbsp;&nbsp;0.570967</tt></dd></dl>
-
-<dl><dt><a name="System-clip_lower"><strong>clip_lower</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;the&nbsp;input&nbsp;with&nbsp;values&nbsp;below&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="System-clip_upper"><strong>clip_upper</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;input&nbsp;with&nbsp;values&nbsp;above&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="System-consolidate"><strong>consolidate</strong></a>(self, inplace=False)</dt><dd><tt>DEPRECATED:&nbsp;consolidate&nbsp;will&nbsp;be&nbsp;an&nbsp;internal&nbsp;implementation&nbsp;only.</tt></dd></dl>
-
-<dl><dt><a name="System-convert_objects"><strong>convert_objects</strong></a>(self, convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True)</dt><dd><tt>Deprecated.<br>
-&nbsp;<br>
-Attempt&nbsp;to&nbsp;infer&nbsp;better&nbsp;dtype&nbsp;for&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-convert_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;date&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-convert_numeric&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;attempt&nbsp;to&nbsp;coerce&nbsp;to&nbsp;numbers&nbsp;(including&nbsp;strings),&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaN.<br>
-convert_timedeltas&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;timedelta&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;return&nbsp;a&nbsp;copy&nbsp;even&nbsp;if&nbsp;no&nbsp;copy&nbsp;is&nbsp;necessary&nbsp;(e.g.&nbsp;no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion&nbsp;was&nbsp;done).&nbsp;Note:&nbsp;This&nbsp;is&nbsp;meant&nbsp;for&nbsp;internal&nbsp;use,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;not&nbsp;be&nbsp;confused&nbsp;with&nbsp;inplace.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.to_datetime&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;datetime.<br>
-pandas.to_timedelta&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;timedelta.<br>
-pandas.to_numeric&nbsp;:&nbsp;Return&nbsp;a&nbsp;fixed&nbsp;frequency&nbsp;timedelta&nbsp;index,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;day&nbsp;as&nbsp;the&nbsp;default.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;same&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="System-copy"><strong>copy</strong></a>(self, deep=True)</dt><dd><tt>Make&nbsp;a&nbsp;copy&nbsp;of&nbsp;this&nbsp;objects&nbsp;data.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-deep&nbsp;:&nbsp;boolean&nbsp;or&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;a&nbsp;deep&nbsp;copy,&nbsp;including&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;data&nbsp;and&nbsp;the&nbsp;indices.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;With&nbsp;``deep=False``&nbsp;neither&nbsp;the&nbsp;indices&nbsp;or&nbsp;the&nbsp;data&nbsp;are&nbsp;copied.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;that&nbsp;when&nbsp;``deep=True``&nbsp;data&nbsp;is&nbsp;copied,&nbsp;actual&nbsp;python&nbsp;objects<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;not&nbsp;be&nbsp;copied&nbsp;recursively,&nbsp;only&nbsp;the&nbsp;reference&nbsp;to&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;is&nbsp;in&nbsp;contrast&nbsp;to&nbsp;``copy.deepcopy``&nbsp;in&nbsp;the&nbsp;Standard&nbsp;Library,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;recursively&nbsp;copies&nbsp;<a href="builtins.html#object">object</a>&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-copy&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="System-describe"><strong>describe</strong></a>(self, percentiles=None, include=None, exclude=None)</dt><dd><tt>Generates&nbsp;descriptive&nbsp;statistics&nbsp;that&nbsp;summarize&nbsp;the&nbsp;central&nbsp;tendency,<br>
-dispersion&nbsp;and&nbsp;shape&nbsp;of&nbsp;a&nbsp;dataset's&nbsp;distribution,&nbsp;excluding<br>
-``NaN``&nbsp;values.<br>
-&nbsp;<br>
-Analyzes&nbsp;both&nbsp;numeric&nbsp;and&nbsp;<a href="builtins.html#object">object</a>&nbsp;series,&nbsp;as&nbsp;well<br>
-as&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;column&nbsp;sets&nbsp;of&nbsp;mixed&nbsp;data&nbsp;types.&nbsp;The&nbsp;output<br>
-will&nbsp;vary&nbsp;depending&nbsp;on&nbsp;what&nbsp;is&nbsp;provided.&nbsp;Refer&nbsp;to&nbsp;the&nbsp;notes<br>
-below&nbsp;for&nbsp;more&nbsp;detail.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-percentiles&nbsp;:&nbsp;list-like&nbsp;of&nbsp;numbers,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;percentiles&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;output.&nbsp;All&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fall&nbsp;between&nbsp;0&nbsp;and&nbsp;1.&nbsp;The&nbsp;default&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``[.25,&nbsp;.5,&nbsp;.75]``,&nbsp;which&nbsp;returns&nbsp;the&nbsp;25th,&nbsp;50th,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;75th&nbsp;percentiles.<br>
-include&nbsp;:&nbsp;'all',&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;white&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'all'&nbsp;:&nbsp;All&nbsp;columns&nbsp;of&nbsp;the&nbsp;input&nbsp;will&nbsp;be&nbsp;included&nbsp;in&nbsp;the&nbsp;output.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Limits&nbsp;the&nbsp;results&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;provided&nbsp;data&nbsp;types.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;limit&nbsp;the&nbsp;result&nbsp;to&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;limit&nbsp;it&nbsp;instead&nbsp;to&nbsp;categorical<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;objects&nbsp;submit&nbsp;the&nbsp;``numpy.<a href="builtins.html#object">object</a>``&nbsp;data&nbsp;type.&nbsp;Strings<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#System-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;include&nbsp;all&nbsp;numeric&nbsp;columns.<br>
-exclude&nbsp;:&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;black&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;omit&nbsp;from&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Excludes&nbsp;the&nbsp;provided&nbsp;data&nbsp;types<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;the&nbsp;result.&nbsp;To&nbsp;select&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;select&nbsp;categorical&nbsp;objects&nbsp;submit&nbsp;the&nbsp;data<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;type&nbsp;``numpy.<a href="builtins.html#object">object</a>``.&nbsp;Strings&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#System-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;exclude&nbsp;nothing.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-summary:&nbsp;&nbsp;<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;summary&nbsp;statistics<br>
-&nbsp;<br>
-Notes<br>
------<br>
-For&nbsp;numeric&nbsp;data,&nbsp;the&nbsp;result's&nbsp;index&nbsp;will&nbsp;include&nbsp;``count``,<br>
-``mean``,&nbsp;``std``,&nbsp;``min``,&nbsp;``max``&nbsp;as&nbsp;well&nbsp;as&nbsp;lower,&nbsp;``50``&nbsp;and<br>
-upper&nbsp;percentiles.&nbsp;By&nbsp;default&nbsp;the&nbsp;lower&nbsp;percentile&nbsp;is&nbsp;``25``&nbsp;and&nbsp;the<br>
-upper&nbsp;percentile&nbsp;is&nbsp;``75``.&nbsp;The&nbsp;``50``&nbsp;percentile&nbsp;is&nbsp;the<br>
-same&nbsp;as&nbsp;the&nbsp;median.<br>
-&nbsp;<br>
-For&nbsp;<a href="builtins.html#object">object</a>&nbsp;data&nbsp;(e.g.&nbsp;strings&nbsp;or&nbsp;timestamps),&nbsp;the&nbsp;result's&nbsp;index<br>
-will&nbsp;include&nbsp;``count``,&nbsp;``unique``,&nbsp;``top``,&nbsp;and&nbsp;``freq``.&nbsp;The&nbsp;``top``<br>
-is&nbsp;the&nbsp;most&nbsp;common&nbsp;value.&nbsp;The&nbsp;``freq``&nbsp;is&nbsp;the&nbsp;most&nbsp;common&nbsp;value's<br>
-frequency.&nbsp;Timestamps&nbsp;also&nbsp;include&nbsp;the&nbsp;``first``&nbsp;and&nbsp;``last``&nbsp;items.<br>
-&nbsp;<br>
-If&nbsp;multiple&nbsp;<a href="builtins.html#object">object</a>&nbsp;values&nbsp;have&nbsp;the&nbsp;highest&nbsp;count,&nbsp;then&nbsp;the<br>
-``count``&nbsp;and&nbsp;``top``&nbsp;results&nbsp;will&nbsp;be&nbsp;arbitrarily&nbsp;chosen&nbsp;from<br>
-among&nbsp;those&nbsp;with&nbsp;the&nbsp;highest&nbsp;count.<br>
-&nbsp;<br>
-For&nbsp;mixed&nbsp;data&nbsp;types&nbsp;provided&nbsp;via&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``,&nbsp;the&nbsp;default&nbsp;is&nbsp;to<br>
-return&nbsp;only&nbsp;an&nbsp;analysis&nbsp;of&nbsp;numeric&nbsp;columns.&nbsp;If&nbsp;``include='all'``<br>
-is&nbsp;provided&nbsp;as&nbsp;an&nbsp;option,&nbsp;the&nbsp;result&nbsp;will&nbsp;include&nbsp;a&nbsp;union&nbsp;of<br>
-attributes&nbsp;of&nbsp;each&nbsp;type.<br>
-&nbsp;<br>
-The&nbsp;`include`&nbsp;and&nbsp;`exclude`&nbsp;parameters&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;limit<br>
-which&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;are&nbsp;analyzed&nbsp;for&nbsp;the&nbsp;output.<br>
-The&nbsp;parameters&nbsp;are&nbsp;ignored&nbsp;when&nbsp;analyzing&nbsp;a&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-Describing&nbsp;a&nbsp;numeric&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;categorical&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(['a',&nbsp;'a',&nbsp;'b',&nbsp;'c'])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;timestamp&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2000-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01")<br>
-...&nbsp;])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-first&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2000-01-01&nbsp;00:00:00<br>
-last&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``.&nbsp;By&nbsp;default&nbsp;only&nbsp;numeric&nbsp;fields<br>
-are&nbsp;returned.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[1,&nbsp;'a'],&nbsp;[2,&nbsp;'b'],&nbsp;[3,&nbsp;'c']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['numeric',&nbsp;'<a href="builtins.html#object">object</a>'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-describe">describe</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;all&nbsp;columns&nbsp;of&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;regardless&nbsp;of&nbsp;data&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-describe">describe</a>(include='all')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;column&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;by&nbsp;accessing&nbsp;it&nbsp;as<br>
-an&nbsp;attribute.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.numeric.<a href="#System-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-Name:&nbsp;numeric,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;numeric&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-describe">describe</a>(include=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;string&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-describe">describe</a>(include=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;numeric&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-describe">describe</a>(exclude=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-describe">describe</a>(exclude=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.count<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.max<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.min<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mean<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.std<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select_dtypes</tt></dd></dl>
-
-<dl><dt><a name="System-drop"><strong>drop</strong></a>(self, labels, axis=0, level=None, inplace=False, errors='raise')</dt><dd><tt>Return&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;labels&nbsp;in&nbsp;requested&nbsp;axis&nbsp;removed.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-labels&nbsp;:&nbsp;single&nbsp;label&nbsp;or&nbsp;list-like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;axis&nbsp;name<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;MultiIndex<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;return&nbsp;None.<br>
-errors&nbsp;:&nbsp;{'ignore',&nbsp;'raise'},&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'ignore',&nbsp;suppress&nbsp;error&nbsp;and&nbsp;existing&nbsp;labels&nbsp;are&nbsp;dropped.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dropped&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="System-equals"><strong>equals</strong></a>(self, other)</dt><dd><tt>Determines&nbsp;if&nbsp;two&nbsp;NDFrame&nbsp;objects&nbsp;contain&nbsp;the&nbsp;same&nbsp;elements.&nbsp;NaNs&nbsp;in<br>
-the&nbsp;same&nbsp;location&nbsp;are&nbsp;considered&nbsp;equal.</tt></dd></dl>
-
-<dl><dt><a name="System-ffill"><strong>ffill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#System-fillna">fillna</a>(method='ffill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="System-filter"><strong>filter</strong></a>(self, items=None, like=None, regex=None, axis=None)</dt><dd><tt>Subset&nbsp;rows&nbsp;or&nbsp;columns&nbsp;of&nbsp;dataframe&nbsp;according&nbsp;to&nbsp;labels&nbsp;in<br>
-the&nbsp;specified&nbsp;index.<br>
-&nbsp;<br>
-Note&nbsp;that&nbsp;this&nbsp;routine&nbsp;does&nbsp;not&nbsp;filter&nbsp;a&nbsp;dataframe&nbsp;on&nbsp;its<br>
-contents.&nbsp;The&nbsp;filter&nbsp;is&nbsp;applied&nbsp;to&nbsp;the&nbsp;labels&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-items&nbsp;:&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;info&nbsp;axis&nbsp;to&nbsp;restrict&nbsp;to&nbsp;(must&nbsp;not&nbsp;all&nbsp;be&nbsp;present)<br>
-like&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;where&nbsp;"arg&nbsp;in&nbsp;col&nbsp;==&nbsp;True"<br>
-regex&nbsp;:&nbsp;string&nbsp;(regular&nbsp;expression)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;with&nbsp;re.search(regex,&nbsp;col)&nbsp;==&nbsp;True<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;filter&nbsp;on.&nbsp;&nbsp;By&nbsp;default&nbsp;this&nbsp;is&nbsp;the&nbsp;info&nbsp;axis,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'index'&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;'columns'&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;name<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-filter">filter</a>(items=['one',&nbsp;'three'])<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;regular&nbsp;expression<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-filter">filter</a>(regex='e$',&nbsp;axis=1)<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;rows&nbsp;containing&nbsp;'bbi'<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-filter">filter</a>(like='bbi',&nbsp;axis=0)<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;``items``,&nbsp;``like``,&nbsp;and&nbsp;``regex``&nbsp;parameters&nbsp;are<br>
-enforced&nbsp;to&nbsp;be&nbsp;mutually&nbsp;exclusive.<br>
-&nbsp;<br>
-``axis``&nbsp;defaults&nbsp;to&nbsp;the&nbsp;info&nbsp;axis&nbsp;that&nbsp;is&nbsp;used&nbsp;when&nbsp;indexing<br>
-with&nbsp;``[]``.</tt></dd></dl>
-
-<dl><dt><a name="System-first"><strong>first</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;initial&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#System-first">first</a>('10D')&nbsp;-&gt;&nbsp;First&nbsp;10&nbsp;days<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="System-get"><strong>get</strong></a>(self, key, default=None)</dt><dd><tt>Get&nbsp;item&nbsp;from&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;given&nbsp;key&nbsp;(<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column,&nbsp;Panel&nbsp;slice,<br>
-etc.).&nbsp;Returns&nbsp;default&nbsp;value&nbsp;if&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;type&nbsp;of&nbsp;items&nbsp;contained&nbsp;in&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="System-get_dtype_counts"><strong>get_dtype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;dtypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="System-get_ftype_counts"><strong>get_ftype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;ftypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="System-groupby"><strong>groupby</strong></a>(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)</dt><dd><tt>Group&nbsp;series&nbsp;using&nbsp;mapper&nbsp;(dict&nbsp;or&nbsp;key&nbsp;function,&nbsp;apply&nbsp;given&nbsp;function<br>
-to&nbsp;group,&nbsp;return&nbsp;result&nbsp;as&nbsp;series)&nbsp;or&nbsp;by&nbsp;a&nbsp;series&nbsp;of&nbsp;columns.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;mapping,&nbsp;function,&nbsp;str,&nbsp;or&nbsp;iterable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;for&nbsp;the&nbsp;groupby.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;``by``&nbsp;is&nbsp;a&nbsp;function,&nbsp;it's&nbsp;called&nbsp;on&nbsp;each&nbsp;value&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>'s<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;If&nbsp;a&nbsp;dict&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;is&nbsp;passed,&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;dict&nbsp;VALUES<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;(the&nbsp;<a href="pandas.core.series.html#Series">Series</a>'&nbsp;values&nbsp;are&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aligned;&nbsp;see&nbsp;``.<a href="#System-align">align</a>()``&nbsp;method).&nbsp;If&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;is&nbsp;passed,&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;are&nbsp;used&nbsp;as-is&nbsp;determine&nbsp;the&nbsp;groups.&nbsp;A&nbsp;str&nbsp;or&nbsp;list&nbsp;of&nbsp;strs<br>
-&nbsp;&nbsp;&nbsp;&nbsp;may&nbsp;be&nbsp;passed&nbsp;to&nbsp;group&nbsp;by&nbsp;the&nbsp;columns&nbsp;in&nbsp;``self``<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-level&nbsp;:&nbsp;int,&nbsp;level&nbsp;name,&nbsp;or&nbsp;sequence&nbsp;of&nbsp;such,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;group&nbsp;by&nbsp;a&nbsp;particular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level&nbsp;or&nbsp;levels<br>
-as_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;aggregated&nbsp;output,&nbsp;return&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;group&nbsp;labels&nbsp;as&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;Only&nbsp;relevant&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;input.&nbsp;as_index=False&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;effectively&nbsp;"SQL-style"&nbsp;grouped&nbsp;output<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;group&nbsp;keys.&nbsp;Get&nbsp;better&nbsp;performance&nbsp;by&nbsp;turning&nbsp;this&nbsp;off.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;this&nbsp;does&nbsp;not&nbsp;influence&nbsp;the&nbsp;order&nbsp;of&nbsp;observations&nbsp;within&nbsp;each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;group.&nbsp;&nbsp;groupby&nbsp;preserves&nbsp;the&nbsp;order&nbsp;of&nbsp;rows&nbsp;within&nbsp;each&nbsp;group.<br>
-group_keys&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;calling&nbsp;apply,&nbsp;add&nbsp;group&nbsp;keys&nbsp;to&nbsp;index&nbsp;to&nbsp;identify&nbsp;pieces<br>
-squeeze&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;reduce&nbsp;the&nbsp;dimensionality&nbsp;of&nbsp;the&nbsp;return&nbsp;type&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;return&nbsp;a&nbsp;consistent&nbsp;type<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;results<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#System-groupby">groupby</a>(func,&nbsp;axis=0).<a href="#System-mean">mean</a>()<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#System-groupby">groupby</a>(['col1',&nbsp;'col2'])['col3'].<a href="#System-mean">mean</a>()<br>
-&nbsp;<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;hierarchical&nbsp;index<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#System-groupby">groupby</a>(['col1',&nbsp;'col2']).<a href="#System-mean">mean</a>()<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-GroupBy&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="System-head"><strong>head</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;first&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="System-interpolate"><strong>interpolate</strong></a>(self, method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', downcast=None, **kwargs)</dt><dd><tt>Interpolate&nbsp;values&nbsp;according&nbsp;to&nbsp;different&nbsp;methods.<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;only&nbsp;``method='linear'``&nbsp;is&nbsp;supported&nbsp;for<br>
-DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a&nbsp;MultiIndex.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-method&nbsp;:&nbsp;{'linear',&nbsp;'time',&nbsp;'index',&nbsp;'values',&nbsp;'nearest',&nbsp;'zero',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',&nbsp;'barycentric',&nbsp;'krogh',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'polynomial',&nbsp;'spline',&nbsp;'piecewise_polynomial',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'from_derivatives',&nbsp;'pchip',&nbsp;'akima'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'linear':&nbsp;ignore&nbsp;the&nbsp;index&nbsp;and&nbsp;treat&nbsp;the&nbsp;values&nbsp;as&nbsp;equally<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;spaced.&nbsp;This&nbsp;is&nbsp;the&nbsp;only&nbsp;method&nbsp;supported&nbsp;on&nbsp;MultiIndexes.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'time':&nbsp;interpolation&nbsp;works&nbsp;on&nbsp;daily&nbsp;and&nbsp;higher&nbsp;resolution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;data&nbsp;to&nbsp;interpolate&nbsp;given&nbsp;length&nbsp;of&nbsp;interval<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'index',&nbsp;'values':&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'nearest',&nbsp;'zero',&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'barycentric',&nbsp;'polynomial'&nbsp;is&nbsp;passed&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``scipy.interpolate.interp1d``.&nbsp;Both&nbsp;'polynomial'&nbsp;and&nbsp;'spline'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;require&nbsp;that&nbsp;you&nbsp;also&nbsp;specify&nbsp;an&nbsp;`order`&nbsp;(int),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;e.g.&nbsp;df.<a href="#System-interpolate">interpolate</a>(method='polynomial',&nbsp;order=4).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'krogh',&nbsp;'piecewise_polynomial',&nbsp;'spline',&nbsp;'pchip'&nbsp;and&nbsp;'akima'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;all&nbsp;wrappers&nbsp;around&nbsp;the&nbsp;scipy&nbsp;interpolation&nbsp;methods&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;similar&nbsp;names.&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;For&nbsp;more&nbsp;information&nbsp;on&nbsp;their&nbsp;behavior,&nbsp;see&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`scipy&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__">http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;`tutorial&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__">http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'from_derivatives'&nbsp;refers&nbsp;to&nbsp;BPoly.from_derivatives&nbsp;which<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;replaces&nbsp;'piecewise_polynomial'&nbsp;interpolation&nbsp;method&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;0.18<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;support&nbsp;for&nbsp;the&nbsp;'akima'&nbsp;method<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;interpolate&nbsp;method&nbsp;'from_derivatives'&nbsp;which&nbsp;replaces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'piecewise_polynomial'&nbsp;in&nbsp;scipy&nbsp;0.18;&nbsp;backwards-compatible&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;&lt;&nbsp;0.18<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;0:&nbsp;fill&nbsp;column-by-column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;1:&nbsp;fill&nbsp;row-by-row<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs&nbsp;to&nbsp;fill.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0.<br>
-limit_direction&nbsp;:&nbsp;{'forward',&nbsp;'backward',&nbsp;'both'},&nbsp;default&nbsp;'forward'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;limit&nbsp;is&nbsp;specified,&nbsp;consecutive&nbsp;NaNs&nbsp;will&nbsp;be&nbsp;filled&nbsp;in&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;direction.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Update&nbsp;the&nbsp;NDFrame&nbsp;in&nbsp;place&nbsp;if&nbsp;possible.<br>
-downcast&nbsp;:&nbsp;optional,&nbsp;'infer'&nbsp;or&nbsp;None,&nbsp;defaults&nbsp;to&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Downcast&nbsp;dtypes&nbsp;if&nbsp;possible.<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;interpolating&nbsp;function.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;interpolated&nbsp;at&nbsp;the&nbsp;NaNs<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;replace,&nbsp;fillna<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Filling&nbsp;in&nbsp;NaNs<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0,&nbsp;1,&nbsp;np.nan,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-interpolate">interpolate</a>()<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="System-isnull"><strong>isnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-notnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;isnull</tt></dd></dl>
-
-<dl><dt><a name="System-last"><strong>last</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;final&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#System-last">last</a>('5M')&nbsp;-&gt;&nbsp;Last&nbsp;5&nbsp;months<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="System-mask"><strong>mask</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;False&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;mask&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``False``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#System-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#System-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``mask``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).<a href="#System-reshape">reshape</a>(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#System-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#System-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`</tt></dd></dl>
-
-<dl><dt><a name="System-notnull"><strong>notnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are<br>
-not&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-isnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;notnull</tt></dd></dl>
-
-<dl><dt><a name="System-pct_change"><strong>pct_change</strong></a>(self, periods=1, fill_method='pad', limit=None, freq=None, **kwargs)</dt><dd><tt>Percent&nbsp;change&nbsp;over&nbsp;given&nbsp;number&nbsp;of&nbsp;periods.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;percent&nbsp;change<br>
-fill_method&nbsp;:&nbsp;str,&nbsp;default&nbsp;'pad'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;How&nbsp;to&nbsp;handle&nbsp;NAs&nbsp;before&nbsp;computing&nbsp;percent&nbsp;changes<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NAs&nbsp;to&nbsp;fill&nbsp;before&nbsp;stopping<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;offset&nbsp;alias&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;time&nbsp;series&nbsp;API&nbsp;(e.g.&nbsp;'M'&nbsp;or&nbsp;BDay())<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-chg&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-By&nbsp;default,&nbsp;the&nbsp;percentage&nbsp;change&nbsp;is&nbsp;calculated&nbsp;along&nbsp;the&nbsp;stat<br>
-axis:&nbsp;0,&nbsp;or&nbsp;``Index``,&nbsp;for&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;and&nbsp;1,&nbsp;or&nbsp;``minor``&nbsp;for<br>
-``Panel``.&nbsp;You&nbsp;can&nbsp;change&nbsp;this&nbsp;with&nbsp;the&nbsp;``axis``&nbsp;keyword&nbsp;argument.</tt></dd></dl>
-
-<dl><dt><a name="System-pipe"><strong>pipe</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Apply&nbsp;func(self,&nbsp;\*args,&nbsp;\*\*kwargs)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.2<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;the&nbsp;NDFrame.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``args``,&nbsp;and&nbsp;``kwargs``&nbsp;are&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Alternatively&nbsp;a&nbsp;``(callable,&nbsp;data_keyword)``&nbsp;tuple&nbsp;where<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``data_keyword``&nbsp;is&nbsp;a&nbsp;string&nbsp;indicating&nbsp;the&nbsp;keyword&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``callable``&nbsp;that&nbsp;expects&nbsp;the&nbsp;NDFrame.<br>
-args&nbsp;:&nbsp;positional&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-kwargs&nbsp;:&nbsp;a&nbsp;dictionary&nbsp;of&nbsp;keyword&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="builtins.html#object">object</a>&nbsp;:&nbsp;the&nbsp;return&nbsp;type&nbsp;of&nbsp;``func``.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-Use&nbsp;``.pipe``&nbsp;when&nbsp;chaining&nbsp;together&nbsp;functions&nbsp;that&nbsp;expect<br>
-on&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;DataFrames.&nbsp;Instead&nbsp;of&nbsp;writing<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;f(g(h(df),&nbsp;arg1=a),&nbsp;arg2=b,&nbsp;arg3=c)<br>
-&nbsp;<br>
-You&nbsp;can&nbsp;write<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#System-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#System-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#System-pipe">pipe</a>(f,&nbsp;arg2=b,&nbsp;arg3=c)<br>
-...&nbsp;)<br>
-&nbsp;<br>
-If&nbsp;you&nbsp;have&nbsp;a&nbsp;function&nbsp;that&nbsp;takes&nbsp;the&nbsp;data&nbsp;as&nbsp;(say)&nbsp;the&nbsp;second<br>
-argument,&nbsp;pass&nbsp;a&nbsp;tuple&nbsp;indicating&nbsp;which&nbsp;keyword&nbsp;expects&nbsp;the<br>
-data.&nbsp;For&nbsp;example,&nbsp;suppose&nbsp;``f``&nbsp;takes&nbsp;its&nbsp;data&nbsp;as&nbsp;``arg2``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#System-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#System-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#System-pipe">pipe</a>((f,&nbsp;'arg2'),&nbsp;arg1=a,&nbsp;arg3=c)<br>
-...&nbsp;&nbsp;)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.map</tt></dd></dl>
-
-<dl><dt><a name="System-pop"><strong>pop</strong></a>(self, item)</dt><dd><tt>Return&nbsp;item&nbsp;and&nbsp;drop&nbsp;from&nbsp;frame.&nbsp;Raise&nbsp;KeyError&nbsp;if&nbsp;not&nbsp;found.</tt></dd></dl>
-
-<dl><dt><a name="System-rank"><strong>rank</strong></a>(self, axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)</dt><dd><tt>Compute&nbsp;numerical&nbsp;data&nbsp;ranks&nbsp;(1&nbsp;through&nbsp;n)&nbsp;along&nbsp;axis.&nbsp;Equal&nbsp;values&nbsp;are<br>
-assigned&nbsp;a&nbsp;rank&nbsp;that&nbsp;is&nbsp;the&nbsp;average&nbsp;of&nbsp;the&nbsp;ranks&nbsp;of&nbsp;those&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;to&nbsp;direct&nbsp;ranking<br>
-method&nbsp;:&nbsp;{'average',&nbsp;'min',&nbsp;'max',&nbsp;'first',&nbsp;'dense'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;average:&nbsp;average&nbsp;rank&nbsp;of&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;min:&nbsp;lowest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;max:&nbsp;highest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;first:&nbsp;ranks&nbsp;assigned&nbsp;in&nbsp;order&nbsp;they&nbsp;appear&nbsp;in&nbsp;the&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dense:&nbsp;like&nbsp;'min',&nbsp;but&nbsp;rank&nbsp;always&nbsp;increases&nbsp;by&nbsp;1&nbsp;between&nbsp;groups<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;data.&nbsp;Valid&nbsp;only&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Panel&nbsp;objects<br>
-na_option&nbsp;:&nbsp;{'keep',&nbsp;'top',&nbsp;'bottom'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;keep:&nbsp;leave&nbsp;NA&nbsp;values&nbsp;where&nbsp;they&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;top:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;ascending<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;bottom:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;descending<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False&nbsp;for&nbsp;ranks&nbsp;by&nbsp;high&nbsp;(1)&nbsp;to&nbsp;low&nbsp;(N)<br>
-pct&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Computes&nbsp;percentage&nbsp;rank&nbsp;of&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ranks&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="System-reindex_like"><strong>reindex_like</strong></a>(self, other, method=None, copy=True, limit=None, tolerance=None)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;matching&nbsp;indices&nbsp;to&nbsp;myself.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;Object<br>
-method&nbsp;:&nbsp;string&nbsp;or&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;labels&nbsp;to&nbsp;fill&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;labels&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>&nbsp;and&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Like&nbsp;calling&nbsp;s.<a href="#System-reindex">reindex</a>(index=other.index,&nbsp;columns=other.columns,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;method=...)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="System-rename_axis"><strong>rename_axis</strong></a>(self, mapper, axis=0, copy=True, inplace=False)</dt><dd><tt>Alter&nbsp;index&nbsp;and&nbsp;/&nbsp;or&nbsp;columns&nbsp;using&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.<br>
-A&nbsp;scalar&nbsp;or&nbsp;list-like&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;``Index.name``<br>
-or&nbsp;``MultiIndex.names``&nbsp;attribute.<br>
-A&nbsp;function&nbsp;or&nbsp;dict&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;labels.<br>
-Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be&nbsp;unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in<br>
-a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left&nbsp;as-is.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-mapper&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename<br>
-pandas.Index.rename<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-rename_axis">rename_axis</a>("foo")&nbsp;&nbsp;#&nbsp;scalar,&nbsp;alters&nbsp;df.index.name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-foo<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-rename_axis">rename_axis</a>(lambda&nbsp;x:&nbsp;2&nbsp;*&nbsp;x)&nbsp;&nbsp;#&nbsp;function:&nbsp;alters&nbsp;labels<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-4&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-rename_axis">rename_axis</a>({"A":&nbsp;"ehh",&nbsp;"C":&nbsp;"see"},&nbsp;axis="columns")&nbsp;&nbsp;#&nbsp;mapping<br>
-&nbsp;&nbsp;&nbsp;ehh&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="System-replace"><strong>replace</strong></a>(self, to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad', axis=None)</dt><dd><tt>Replace&nbsp;values&nbsp;given&nbsp;in&nbsp;'to_replace'&nbsp;with&nbsp;'value'.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-to_replace&nbsp;:&nbsp;str,&nbsp;regex,&nbsp;list,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;numeric,&nbsp;or&nbsp;None<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;str&nbsp;or&nbsp;regex:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str:&nbsp;string&nbsp;exactly&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;`value`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;regex:&nbsp;regexs&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`value`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;list&nbsp;of&nbsp;str,&nbsp;regex,&nbsp;or&nbsp;numeric:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;First,&nbsp;if&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;both&nbsp;lists,&nbsp;they<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Second,&nbsp;if&nbsp;``regex=True``&nbsp;then&nbsp;all&nbsp;of&nbsp;the&nbsp;strings&nbsp;in&nbsp;**both**<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;regexs&nbsp;otherwise&nbsp;they&nbsp;will&nbsp;match<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;directly.&nbsp;This&nbsp;doesn't&nbsp;matter&nbsp;much&nbsp;for&nbsp;`value`&nbsp;since&nbsp;there<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;only&nbsp;a&nbsp;few&nbsp;possible&nbsp;substitution&nbsp;regexes&nbsp;you&nbsp;can&nbsp;use.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str&nbsp;and&nbsp;regex&nbsp;rules&nbsp;apply&nbsp;as&nbsp;above.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dict:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Nested&nbsp;dictionaries,&nbsp;e.g.,&nbsp;{'a':&nbsp;{'b':&nbsp;nan}},&nbsp;are&nbsp;read&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;follows:&nbsp;look&nbsp;in&nbsp;column&nbsp;'a'&nbsp;for&nbsp;the&nbsp;value&nbsp;'b'&nbsp;and&nbsp;replace&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;nan.&nbsp;You&nbsp;can&nbsp;nest&nbsp;regular&nbsp;expressions&nbsp;as&nbsp;well.&nbsp;Note&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;names&nbsp;(the&nbsp;top-level&nbsp;dictionary&nbsp;keys&nbsp;in&nbsp;a&nbsp;nested<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dictionary)&nbsp;**cannot**&nbsp;be&nbsp;regular&nbsp;expressions.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Keys&nbsp;map&nbsp;to&nbsp;column&nbsp;names&nbsp;and&nbsp;values&nbsp;map&nbsp;to&nbsp;substitution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;values.&nbsp;You&nbsp;can&nbsp;treat&nbsp;this&nbsp;as&nbsp;a&nbsp;special&nbsp;case&nbsp;of&nbsp;passing&nbsp;two<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;except&nbsp;that&nbsp;you&nbsp;are&nbsp;specifying&nbsp;the&nbsp;column&nbsp;to&nbsp;search&nbsp;in.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;None:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;This&nbsp;means&nbsp;that&nbsp;the&nbsp;``regex``&nbsp;argument&nbsp;must&nbsp;be&nbsp;a&nbsp;string,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;compiled&nbsp;regular&nbsp;expression,&nbsp;or&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;such&nbsp;elements.&nbsp;If&nbsp;`value`&nbsp;is&nbsp;also&nbsp;``None``&nbsp;then&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;a&nbsp;nested&nbsp;dictionary&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;examples&nbsp;section&nbsp;for&nbsp;examples&nbsp;of&nbsp;each&nbsp;of&nbsp;these.<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;list,&nbsp;str,&nbsp;regex,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a&nbsp;dict&nbsp;of&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for&nbsp;each&nbsp;column&nbsp;(columns&nbsp;not&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;Regular&nbsp;expressions,&nbsp;strings&nbsp;and&nbsp;lists&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dicts&nbsp;of&nbsp;such&nbsp;objects&nbsp;are&nbsp;also&nbsp;allowed.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>&nbsp;(e.g.&nbsp;a&nbsp;column&nbsp;form&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Returns&nbsp;the&nbsp;caller&nbsp;if&nbsp;this&nbsp;is&nbsp;True.<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;size&nbsp;gap&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-regex&nbsp;:&nbsp;bool&nbsp;or&nbsp;same&nbsp;types&nbsp;as&nbsp;`to_replace`,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;interpret&nbsp;`to_replace`&nbsp;and/or&nbsp;`value`&nbsp;as&nbsp;regular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;expressions.&nbsp;If&nbsp;this&nbsp;is&nbsp;``True``&nbsp;then&nbsp;`to_replace`&nbsp;*must*&nbsp;be&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string.&nbsp;Otherwise,&nbsp;`to_replace`&nbsp;must&nbsp;be&nbsp;``None``&nbsp;because&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;parameter&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;regular&nbsp;expression&nbsp;or&nbsp;a&nbsp;list,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict,&nbsp;or&nbsp;array&nbsp;of&nbsp;regular&nbsp;expressions.<br>
-method&nbsp;:&nbsp;string,&nbsp;optional,&nbsp;{'pad',&nbsp;'ffill',&nbsp;'bfill'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;method&nbsp;to&nbsp;use&nbsp;when&nbsp;for&nbsp;replacement,&nbsp;when&nbsp;``to_replace``&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``list``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-NDFrame.reindex<br>
-NDFrame.asfreq<br>
-NDFrame.fillna<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-AssertionError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;a&nbsp;``bool``&nbsp;and&nbsp;`to_replace`&nbsp;is&nbsp;not&nbsp;``None``.<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;a&nbsp;``dict``&nbsp;and&nbsp;`value`&nbsp;is&nbsp;not&nbsp;a&nbsp;``list``,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``dict``,&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``,&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;``None``&nbsp;and&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;compilable&nbsp;into&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;regular&nbsp;expression&nbsp;or&nbsp;is&nbsp;a&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;``list``&nbsp;s&nbsp;or&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``&nbsp;s,&nbsp;but<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;they&nbsp;are&nbsp;not&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-*&nbsp;Regex&nbsp;substitution&nbsp;is&nbsp;performed&nbsp;under&nbsp;the&nbsp;hood&nbsp;with&nbsp;``re.sub``.&nbsp;The<br>
-&nbsp;&nbsp;rules&nbsp;for&nbsp;substitution&nbsp;for&nbsp;``re.sub``&nbsp;are&nbsp;the&nbsp;same.<br>
-*&nbsp;Regular&nbsp;expressions&nbsp;will&nbsp;only&nbsp;substitute&nbsp;on&nbsp;strings,&nbsp;meaning&nbsp;you<br>
-&nbsp;&nbsp;cannot&nbsp;provide,&nbsp;for&nbsp;example,&nbsp;a&nbsp;regular&nbsp;expression&nbsp;matching&nbsp;floating<br>
-&nbsp;&nbsp;point&nbsp;numbers&nbsp;and&nbsp;expect&nbsp;the&nbsp;columns&nbsp;in&nbsp;your&nbsp;frame&nbsp;that&nbsp;have&nbsp;a<br>
-&nbsp;&nbsp;numeric&nbsp;dtype&nbsp;to&nbsp;be&nbsp;matched.&nbsp;However,&nbsp;if&nbsp;those&nbsp;floating&nbsp;point&nbsp;numbers<br>
-&nbsp;&nbsp;*are*&nbsp;strings,&nbsp;then&nbsp;you&nbsp;can&nbsp;do&nbsp;this.<br>
-*&nbsp;This&nbsp;method&nbsp;has&nbsp;*a&nbsp;lot*&nbsp;of&nbsp;options.&nbsp;You&nbsp;are&nbsp;encouraged&nbsp;to&nbsp;experiment<br>
-&nbsp;&nbsp;and&nbsp;play&nbsp;with&nbsp;this&nbsp;method&nbsp;to&nbsp;gain&nbsp;intuition&nbsp;about&nbsp;how&nbsp;it&nbsp;works.</tt></dd></dl>
-
-<dl><dt><a name="System-resample"><strong>resample</strong></a>(self, rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0, on=None, level=None)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;frequency&nbsp;conversion&nbsp;and&nbsp;resampling&nbsp;of&nbsp;time<br>
-series.&nbsp;&nbsp;Object&nbsp;must&nbsp;have&nbsp;a&nbsp;datetime-like&nbsp;index&nbsp;(DatetimeIndex,<br>
-PeriodIndex,&nbsp;or&nbsp;TimedeltaIndex),&nbsp;or&nbsp;pass&nbsp;datetime-like&nbsp;values<br>
-to&nbsp;the&nbsp;on&nbsp;or&nbsp;level&nbsp;keyword.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-rule&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;offset&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a>&nbsp;representing&nbsp;target&nbsp;conversion<br>
-axis&nbsp;:&nbsp;int,&nbsp;optional,&nbsp;default&nbsp;0<br>
-closed&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;side&nbsp;of&nbsp;bin&nbsp;interval&nbsp;is&nbsp;closed<br>
-label&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;bin&nbsp;edge&nbsp;label&nbsp;to&nbsp;label&nbsp;bucket&nbsp;with<br>
-convention&nbsp;:&nbsp;{'start',&nbsp;'end',&nbsp;'s',&nbsp;'e'}<br>
-loffset&nbsp;:&nbsp;timedelta<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Adjust&nbsp;the&nbsp;resampled&nbsp;time&nbsp;labels<br>
-base&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;frequencies&nbsp;that&nbsp;evenly&nbsp;subdivide&nbsp;1&nbsp;day,&nbsp;the&nbsp;"origin"&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aggregated&nbsp;intervals.&nbsp;For&nbsp;example,&nbsp;for&nbsp;'5min'&nbsp;frequency,&nbsp;base&nbsp;could<br>
-&nbsp;&nbsp;&nbsp;&nbsp;range&nbsp;from&nbsp;0&nbsp;through&nbsp;4.&nbsp;Defaults&nbsp;to&nbsp;0<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;to&nbsp;use&nbsp;instead&nbsp;of&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-level&nbsp;:&nbsp;string&nbsp;or&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;MultiIndex,&nbsp;level&nbsp;(name&nbsp;or&nbsp;number)&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;resampling.&nbsp;&nbsp;Level&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offset&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;9&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(9),&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;series<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:04:00&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-2000-01-01&nbsp;00:05:00&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:07:00&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-2000-01-01&nbsp;00:08:00&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-Freq:&nbsp;T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;and&nbsp;sum&nbsp;the&nbsp;values<br>
-of&nbsp;the&nbsp;timestamps&nbsp;falling&nbsp;into&nbsp;a&nbsp;bin.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#System-resample">resample</a>('3T').<a href="#System-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;label&nbsp;each<br>
-bin&nbsp;using&nbsp;the&nbsp;right&nbsp;edge&nbsp;instead&nbsp;of&nbsp;the&nbsp;left.&nbsp;Please&nbsp;note&nbsp;that&nbsp;the<br>
-value&nbsp;in&nbsp;the&nbsp;bucket&nbsp;used&nbsp;as&nbsp;the&nbsp;label&nbsp;is&nbsp;not&nbsp;included&nbsp;in&nbsp;the&nbsp;bucket,<br>
-which&nbsp;it&nbsp;labels.&nbsp;For&nbsp;example,&nbsp;in&nbsp;the&nbsp;original&nbsp;series&nbsp;the<br>
-bucket&nbsp;``2000-01-01&nbsp;00:03:00``&nbsp;contains&nbsp;the&nbsp;value&nbsp;3,&nbsp;but&nbsp;the&nbsp;summed<br>
-value&nbsp;in&nbsp;the&nbsp;resampled&nbsp;bucket&nbsp;with&nbsp;the&nbsp;label``2000-01-01&nbsp;00:03:00``<br>
-does&nbsp;not&nbsp;include&nbsp;3&nbsp;(if&nbsp;it&nbsp;did,&nbsp;the&nbsp;summed&nbsp;value&nbsp;would&nbsp;be&nbsp;6,&nbsp;not&nbsp;3).<br>
-To&nbsp;include&nbsp;this&nbsp;value&nbsp;close&nbsp;the&nbsp;right&nbsp;side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval&nbsp;as<br>
-illustrated&nbsp;in&nbsp;the&nbsp;example&nbsp;below&nbsp;this&nbsp;one.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#System-resample">resample</a>('3T',&nbsp;label='right').<a href="#System-sum">sum</a>()<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;close&nbsp;the&nbsp;right<br>
-side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#System-resample">resample</a>('3T',&nbsp;label='right',&nbsp;closed='right').<a href="#System-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#System-resample">resample</a>('30S').<a href="#System-asfreq">asfreq</a>()[0:5]&nbsp;#select&nbsp;first&nbsp;5&nbsp;rows<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;1.0<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;2.0<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values&nbsp;using&nbsp;the&nbsp;``pad``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#System-resample">resample</a>('30S').pad()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the<br>
-``NaN``&nbsp;values&nbsp;using&nbsp;the&nbsp;``bfill``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#System-resample">resample</a>('30S').<a href="#System-bfill">bfill</a>()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Pass&nbsp;a&nbsp;custom&nbsp;function&nbsp;via&nbsp;``apply``<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;custom_resampler(array_like):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;np.<a href="#System-sum">sum</a>(array_like)+5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#System-resample">resample</a>('3T').<a href="#System-apply">apply</a>(custom_resampler)<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;17<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;26<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects,&nbsp;the&nbsp;keyword&nbsp;``on``&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;specify&nbsp;the<br>
-column&nbsp;instead&nbsp;of&nbsp;the&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=9*[range(4)],&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'])<br>
-&gt;&gt;&gt;&nbsp;df['time']&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-resample">resample</a>('3T',&nbsp;on='time').<a href="#System-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;c&nbsp;&nbsp;d<br>
-time<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-&nbsp;<br>
-For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;MultiIndex,&nbsp;the&nbsp;keyword&nbsp;``level``&nbsp;can&nbsp;be&nbsp;used&nbsp;to<br>
-specify&nbsp;on&nbsp;level&nbsp;the&nbsp;resampling&nbsp;needs&nbsp;to&nbsp;take&nbsp;place.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;time&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=5,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=10*[range(4)],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.MultiIndex.from_product([time,&nbsp;[1,&nbsp;2]])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;)<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#System-resample">resample</a>('3T',&nbsp;level=0).<a href="#System-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;d<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;6&nbsp;&nbsp;12&nbsp;&nbsp;18<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;8&nbsp;&nbsp;12</tt></dd></dl>
-
-<dl><dt><a name="System-sample"><strong>sample</strong></a>(self, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None)</dt><dd><tt>Returns&nbsp;a&nbsp;random&nbsp;sample&nbsp;of&nbsp;items&nbsp;from&nbsp;an&nbsp;axis&nbsp;of&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;items&nbsp;from&nbsp;axis&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`frac`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;=&nbsp;1&nbsp;if&nbsp;`frac`&nbsp;=&nbsp;None.<br>
-frac&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fraction&nbsp;of&nbsp;axis&nbsp;items&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`n`.<br>
-replace&nbsp;:&nbsp;boolean,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sample&nbsp;with&nbsp;or&nbsp;without&nbsp;replacement.&nbsp;Default&nbsp;=&nbsp;False.<br>
-weights&nbsp;:&nbsp;str&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;'None'&nbsp;results&nbsp;in&nbsp;equal&nbsp;probability&nbsp;weighting.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;will&nbsp;align&nbsp;with&nbsp;target&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;index.&nbsp;Index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;in&nbsp;weights&nbsp;not&nbsp;found&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;will&nbsp;be&nbsp;ignored&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;values&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;not&nbsp;in&nbsp;weights&nbsp;will&nbsp;be&nbsp;assigned<br>
-&nbsp;&nbsp;&nbsp;&nbsp;weights&nbsp;of&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;called&nbsp;on&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;will&nbsp;accept&nbsp;the&nbsp;name&nbsp;of&nbsp;a&nbsp;column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;axis&nbsp;=&nbsp;0.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Unless&nbsp;weights&nbsp;are&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;weights&nbsp;must&nbsp;be&nbsp;same&nbsp;length&nbsp;as&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;being&nbsp;sampled.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;weights&nbsp;do&nbsp;not&nbsp;sum&nbsp;to&nbsp;1,&nbsp;they&nbsp;will&nbsp;be&nbsp;normalized&nbsp;to&nbsp;sum&nbsp;to&nbsp;1.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;values&nbsp;in&nbsp;the&nbsp;weights&nbsp;column&nbsp;will&nbsp;be&nbsp;treated&nbsp;as&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;inf&nbsp;and&nbsp;-inf&nbsp;values&nbsp;not&nbsp;allowed.<br>
-random_state&nbsp;:&nbsp;int&nbsp;or&nbsp;numpy.random.RandomState,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Seed&nbsp;for&nbsp;the&nbsp;random&nbsp;number&nbsp;generator&nbsp;(if&nbsp;int),&nbsp;or&nbsp;numpy&nbsp;RandomState<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;sample.&nbsp;Accepts&nbsp;axis&nbsp;number&nbsp;or&nbsp;name.&nbsp;Default&nbsp;is&nbsp;stat&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;given&nbsp;data&nbsp;type&nbsp;(0&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;and&nbsp;DataFrames,&nbsp;1&nbsp;for&nbsp;Panels).<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-A&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;type&nbsp;as&nbsp;caller.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Generate&nbsp;an&nbsp;example&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;and&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(50))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-head">head</a>()<br>
-0&nbsp;&nbsp;&nbsp;-0.038497<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.820773<br>
-2&nbsp;&nbsp;&nbsp;-0.972766<br>
-3&nbsp;&nbsp;&nbsp;-1.598270<br>
-4&nbsp;&nbsp;&nbsp;-1.095526<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(50,&nbsp;4),&nbsp;columns=list('ABCD'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-head">head</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;0.016443&nbsp;-2.318952&nbsp;-0.566372&nbsp;-1.028078<br>
-1&nbsp;-1.051921&nbsp;&nbsp;0.438836&nbsp;&nbsp;0.658280&nbsp;-0.175797<br>
-2&nbsp;-1.243569&nbsp;-0.364626&nbsp;-0.215065&nbsp;&nbsp;0.057736<br>
-3&nbsp;&nbsp;1.768216&nbsp;&nbsp;0.404512&nbsp;-0.385604&nbsp;-1.457834<br>
-4&nbsp;&nbsp;1.072446&nbsp;-1.137172&nbsp;&nbsp;0.314194&nbsp;-0.046661<br>
-&nbsp;<br>
-Next&nbsp;extract&nbsp;a&nbsp;random&nbsp;sample&nbsp;from&nbsp;both&nbsp;of&nbsp;these&nbsp;objects...<br>
-&nbsp;<br>
-3&nbsp;random&nbsp;elements&nbsp;from&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-sample">sample</a>(n=3)<br>
-27&nbsp;&nbsp;&nbsp;-0.994689<br>
-55&nbsp;&nbsp;&nbsp;-1.049016<br>
-67&nbsp;&nbsp;&nbsp;-0.224565<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-And&nbsp;a&nbsp;random&nbsp;10%&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;with&nbsp;replacement:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-sample">sample</a>(frac=0.1,&nbsp;replace=True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-35&nbsp;&nbsp;1.981780&nbsp;&nbsp;0.142106&nbsp;&nbsp;1.817165&nbsp;-0.290805<br>
-49&nbsp;-1.336199&nbsp;-0.448634&nbsp;-0.789640&nbsp;&nbsp;0.217116<br>
-40&nbsp;&nbsp;0.823173&nbsp;-0.078816&nbsp;&nbsp;1.009536&nbsp;&nbsp;1.015108<br>
-15&nbsp;&nbsp;1.421154&nbsp;-0.055301&nbsp;-1.922594&nbsp;-0.019696<br>
-6&nbsp;&nbsp;-0.148339&nbsp;&nbsp;0.832938&nbsp;&nbsp;1.787600&nbsp;-1.383767</tt></dd></dl>
-
-<dl><dt><a name="System-select"><strong>select</strong></a>(self, crit, axis=0)</dt><dd><tt>Return&nbsp;data&nbsp;corresponding&nbsp;to&nbsp;axis&nbsp;labels&nbsp;matching&nbsp;criteria<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-crit&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;called&nbsp;on&nbsp;each&nbsp;index&nbsp;(label).&nbsp;Should&nbsp;return&nbsp;True&nbsp;or&nbsp;False<br>
-axis&nbsp;:&nbsp;int<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-selection&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="System-set_axis"><strong>set_axis</strong></a>(self, axis, labels)</dt><dd><tt>public&nbsp;verson&nbsp;of&nbsp;axis&nbsp;assignment</tt></dd></dl>
-
-<dl><dt><a name="System-slice_shift"><strong>slice_shift</strong></a>(self, periods=1, axis=0)</dt><dd><tt>Equivalent&nbsp;to&nbsp;`shift`&nbsp;without&nbsp;copying&nbsp;data.&nbsp;The&nbsp;shifted&nbsp;data&nbsp;will<br>
-not&nbsp;include&nbsp;the&nbsp;dropped&nbsp;periods&nbsp;and&nbsp;the&nbsp;shifted&nbsp;axis&nbsp;will&nbsp;be&nbsp;smaller<br>
-than&nbsp;the&nbsp;original.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-&nbsp;<br>
-Notes<br>
------<br>
-While&nbsp;the&nbsp;`slice_shift`&nbsp;is&nbsp;faster&nbsp;than&nbsp;`shift`,&nbsp;you&nbsp;may&nbsp;pay&nbsp;for&nbsp;it<br>
-later&nbsp;during&nbsp;alignment.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="System-squeeze"><strong>squeeze</strong></a>(self, axis=None)</dt><dd><tt>Squeeze&nbsp;length&nbsp;1&nbsp;dimensions.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;None,&nbsp;integer&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;squeeze&nbsp;if&nbsp;1-sized.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-scalar&nbsp;if&nbsp;1-sized,&nbsp;else&nbsp;original&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="System-swapaxes"><strong>swapaxes</strong></a>(self, axis1, axis2, copy=True)</dt><dd><tt>Interchange&nbsp;axes&nbsp;and&nbsp;swap&nbsp;values&nbsp;axes&nbsp;appropriately<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="System-tail"><strong>tail</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;last&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="System-to_clipboard"><strong>to_clipboard</strong></a>(self, excel=None, sep=None, **kwargs)</dt><dd><tt>Attempt&nbsp;to&nbsp;write&nbsp;text&nbsp;representation&nbsp;of&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;the&nbsp;system&nbsp;clipboard<br>
-This&nbsp;can&nbsp;be&nbsp;pasted&nbsp;into&nbsp;Excel,&nbsp;for&nbsp;example.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel&nbsp;:&nbsp;boolean,&nbsp;defaults&nbsp;to&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;use&nbsp;the&nbsp;provided&nbsp;separator,&nbsp;writing&nbsp;in&nbsp;a&nbsp;csv<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;format&nbsp;for&nbsp;allowing&nbsp;easy&nbsp;pasting&nbsp;into&nbsp;excel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;False,&nbsp;write&nbsp;a&nbsp;string&nbsp;representation&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;the&nbsp;clipboard<br>
-sep&nbsp;:&nbsp;optional,&nbsp;defaults&nbsp;to&nbsp;tab<br>
-other&nbsp;keywords&nbsp;are&nbsp;passed&nbsp;to&nbsp;to_csv<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Requirements&nbsp;for&nbsp;your&nbsp;platform<br>
-&nbsp;&nbsp;-&nbsp;Linux:&nbsp;xclip,&nbsp;or&nbsp;xsel&nbsp;(with&nbsp;gtk&nbsp;or&nbsp;PyQt4&nbsp;modules)<br>
-&nbsp;&nbsp;-&nbsp;Windows:&nbsp;none<br>
-&nbsp;&nbsp;-&nbsp;OS&nbsp;X:&nbsp;none</tt></dd></dl>
-
-<dl><dt><a name="System-to_dense"><strong>to_dense</strong></a>(self)</dt><dd><tt>Return&nbsp;dense&nbsp;representation&nbsp;of&nbsp;NDFrame&nbsp;(as&nbsp;opposed&nbsp;to&nbsp;sparse)</tt></dd></dl>
-
-<dl><dt><a name="System-to_hdf"><strong>to_hdf</strong></a>(self, path_or_buf, key, **kwargs)</dt><dd><tt>Write&nbsp;the&nbsp;contained&nbsp;data&nbsp;to&nbsp;an&nbsp;HDF5&nbsp;file&nbsp;using&nbsp;HDFStore.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;(string)&nbsp;or&nbsp;HDFStore&nbsp;<a href="builtins.html#object">object</a><br>
-key&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;identifier&nbsp;for&nbsp;the&nbsp;group&nbsp;in&nbsp;the&nbsp;store<br>
-mode&nbsp;:&nbsp;optional,&nbsp;{'a',&nbsp;'w',&nbsp;'r+'},&nbsp;default&nbsp;'a'<br>
-&nbsp;<br>
-&nbsp;&nbsp;``'w'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write;&nbsp;a&nbsp;new&nbsp;file&nbsp;is&nbsp;created&nbsp;(an&nbsp;existing&nbsp;file&nbsp;with&nbsp;the&nbsp;same<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;name&nbsp;would&nbsp;be&nbsp;deleted).<br>
-&nbsp;&nbsp;``'a'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Append;&nbsp;an&nbsp;existing&nbsp;file&nbsp;is&nbsp;opened&nbsp;for&nbsp;reading&nbsp;and&nbsp;writing,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;if&nbsp;the&nbsp;file&nbsp;does&nbsp;not&nbsp;exist&nbsp;it&nbsp;is&nbsp;created.<br>
-&nbsp;&nbsp;``'r+'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;It&nbsp;is&nbsp;similar&nbsp;to&nbsp;``'a'``,&nbsp;but&nbsp;the&nbsp;file&nbsp;must&nbsp;already&nbsp;exist.<br>
-format&nbsp;:&nbsp;'fixed(f)|table(t)',&nbsp;default&nbsp;is&nbsp;'fixed'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fixed(f)&nbsp;:&nbsp;Fixed&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Fast&nbsp;writing/reading.&nbsp;Not-appendable,&nbsp;nor&nbsp;searchable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;table(t)&nbsp;:&nbsp;Table&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;as&nbsp;a&nbsp;PyTables&nbsp;Table&nbsp;structure&nbsp;which&nbsp;may&nbsp;perform<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;worse&nbsp;but&nbsp;allow&nbsp;more&nbsp;flexible&nbsp;operations&nbsp;like&nbsp;searching<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/&nbsp;selecting&nbsp;subsets&nbsp;of&nbsp;the&nbsp;data<br>
-append&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;Table&nbsp;formats,&nbsp;append&nbsp;the&nbsp;input&nbsp;data&nbsp;to&nbsp;the&nbsp;existing<br>
-data_columns&nbsp;:&nbsp;&nbsp;list&nbsp;of&nbsp;columns,&nbsp;or&nbsp;True,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;columns&nbsp;to&nbsp;create&nbsp;as&nbsp;indexed&nbsp;data&nbsp;columns&nbsp;for&nbsp;on-disk<br>
-&nbsp;&nbsp;&nbsp;&nbsp;queries,&nbsp;or&nbsp;True&nbsp;to&nbsp;use&nbsp;all&nbsp;columns.&nbsp;By&nbsp;default&nbsp;only&nbsp;the&nbsp;axes<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;are&nbsp;indexed.&nbsp;See&nbsp;`here<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__">http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__</a>.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Applicable&nbsp;only&nbsp;to&nbsp;format='table'.<br>
-complevel&nbsp;:&nbsp;int,&nbsp;1-9,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;a&nbsp;complib&nbsp;is&nbsp;specified&nbsp;compression&nbsp;will&nbsp;be&nbsp;applied<br>
-&nbsp;&nbsp;&nbsp;&nbsp;where&nbsp;possible<br>
-complib&nbsp;:&nbsp;{'zlib',&nbsp;'bzip2',&nbsp;'lzo',&nbsp;'blosc',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;complevel&nbsp;is&nbsp;&gt;&nbsp;0&nbsp;apply&nbsp;compression&nbsp;to&nbsp;objects&nbsp;written<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;store&nbsp;wherever&nbsp;possible<br>
-fletcher32&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;applying&nbsp;compression&nbsp;use&nbsp;the&nbsp;fletcher32&nbsp;checksum<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;true,&nbsp;ALL&nbsp;nan&nbsp;rows&nbsp;will&nbsp;not&nbsp;be&nbsp;written&nbsp;to&nbsp;store.</tt></dd></dl>
-
-<dl><dt><a name="System-to_json"><strong>to_json</strong></a>(self, path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False)</dt><dd><tt>Convert&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;a&nbsp;JSON&nbsp;string.<br>
-&nbsp;<br>
-Note&nbsp;NaN's&nbsp;and&nbsp;None&nbsp;will&nbsp;be&nbsp;converted&nbsp;to&nbsp;null&nbsp;and&nbsp;datetime&nbsp;objects<br>
-will&nbsp;be&nbsp;converted&nbsp;to&nbsp;UNIX&nbsp;timestamps.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;or&nbsp;buffer&nbsp;to&nbsp;write&nbsp;the&nbsp;result&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;this&nbsp;is&nbsp;None,&nbsp;return&nbsp;a&nbsp;StringIO&nbsp;of&nbsp;the&nbsp;converted&nbsp;string<br>
-orient&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'index'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:&nbsp;{'split','records','index'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{'split','records','index','columns','values'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;The&nbsp;format&nbsp;of&nbsp;the&nbsp;JSON&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;split&nbsp;:&nbsp;dict&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{index&nbsp;-&gt;&nbsp;[index],&nbsp;columns&nbsp;-&gt;&nbsp;[columns],&nbsp;data&nbsp;-&gt;&nbsp;[values]}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;records&nbsp;:&nbsp;list&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[{column&nbsp;-&gt;&nbsp;value},&nbsp;...&nbsp;,&nbsp;{column&nbsp;-&gt;&nbsp;value}]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;index&nbsp;:&nbsp;dict&nbsp;like&nbsp;{index&nbsp;-&gt;&nbsp;{column&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;columns&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;{index&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;values&nbsp;:&nbsp;just&nbsp;the&nbsp;values&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;table&nbsp;:&nbsp;dict&nbsp;like&nbsp;{'schema':&nbsp;{schema},&nbsp;'data':&nbsp;{data}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;describing&nbsp;the&nbsp;data,&nbsp;and&nbsp;the&nbsp;data&nbsp;component&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;like&nbsp;``orient='records'``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionchanged::&nbsp;0.20.0<br>
-&nbsp;<br>
-date_format&nbsp;:&nbsp;{None,&nbsp;'epoch',&nbsp;'iso'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Type&nbsp;of&nbsp;date&nbsp;conversion.&nbsp;`epoch`&nbsp;=&nbsp;epoch&nbsp;milliseconds,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`iso`&nbsp;=&nbsp;ISO8601.&nbsp;The&nbsp;default&nbsp;depends&nbsp;on&nbsp;the&nbsp;`orient`.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`orient='table'`,&nbsp;the&nbsp;default&nbsp;is&nbsp;`'iso'`.&nbsp;For&nbsp;all&nbsp;other&nbsp;orients,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;default&nbsp;is&nbsp;`'epoch'`.<br>
-double_precision&nbsp;:&nbsp;The&nbsp;number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;use&nbsp;when&nbsp;encoding<br>
-&nbsp;&nbsp;&nbsp;&nbsp;floating&nbsp;point&nbsp;values,&nbsp;default&nbsp;10.<br>
-force_ascii&nbsp;:&nbsp;force&nbsp;encoded&nbsp;string&nbsp;to&nbsp;be&nbsp;ASCII,&nbsp;default&nbsp;True.<br>
-date_unit&nbsp;:&nbsp;string,&nbsp;default&nbsp;'ms'&nbsp;(milliseconds)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;time&nbsp;unit&nbsp;to&nbsp;encode&nbsp;to,&nbsp;governs&nbsp;timestamp&nbsp;and&nbsp;ISO8601<br>
-&nbsp;&nbsp;&nbsp;&nbsp;precision.&nbsp;&nbsp;One&nbsp;of&nbsp;'s',&nbsp;'ms',&nbsp;'us',&nbsp;'ns'&nbsp;for&nbsp;second,&nbsp;millisecond,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;microsecond,&nbsp;and&nbsp;nanosecond&nbsp;respectively.<br>
-default_handler&nbsp;:&nbsp;callable,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Handler&nbsp;to&nbsp;call&nbsp;if&nbsp;<a href="builtins.html#object">object</a>&nbsp;cannot&nbsp;otherwise&nbsp;be&nbsp;converted&nbsp;to&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;suitable&nbsp;format&nbsp;for&nbsp;JSON.&nbsp;Should&nbsp;receive&nbsp;a&nbsp;single&nbsp;argument&nbsp;which&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;convert&nbsp;and&nbsp;return&nbsp;a&nbsp;serialisable&nbsp;<a href="builtins.html#object">object</a>.<br>
-lines&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'orient'&nbsp;is&nbsp;'records'&nbsp;write&nbsp;out&nbsp;line&nbsp;delimited&nbsp;json&nbsp;format.&nbsp;Will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;throw&nbsp;ValueError&nbsp;if&nbsp;incorrect&nbsp;'orient'&nbsp;since&nbsp;others&nbsp;are&nbsp;not&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;filtered&nbsp;info&nbsp;axis<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pd.read_json<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([['a',&nbsp;'b'],&nbsp;['c',&nbsp;'d']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=['row&nbsp;1',&nbsp;'row&nbsp;2'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['col&nbsp;1',&nbsp;'col&nbsp;2'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-to_json">to_json</a>(orient='split')<br>
-'{"columns":["col&nbsp;1","col&nbsp;2"],<br>
-&nbsp;&nbsp;"index":["row&nbsp;1","row&nbsp;2"],<br>
-&nbsp;&nbsp;"data":[["a","b"],["c","d"]]}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'index'``&nbsp;formatted&nbsp;JSON:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-to_json">to_json</a>(orient='index')<br>
-'{"row&nbsp;1":{"col&nbsp;1":"a","col&nbsp;2":"b"},"row&nbsp;2":{"col&nbsp;1":"c","col&nbsp;2":"d"}}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'records'``&nbsp;formatted&nbsp;JSON.<br>
-Note&nbsp;that&nbsp;index&nbsp;labels&nbsp;are&nbsp;not&nbsp;preserved&nbsp;with&nbsp;this&nbsp;encoding.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-to_json">to_json</a>(orient='records')<br>
-'[{"col&nbsp;1":"a","col&nbsp;2":"b"},{"col&nbsp;1":"c","col&nbsp;2":"d"}]'<br>
-&nbsp;<br>
-Encoding&nbsp;with&nbsp;Table&nbsp;Schema<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-to_json">to_json</a>(orient='table')<br>
-'{"schema":&nbsp;{"fields":&nbsp;[{"name":&nbsp;"index",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;1",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;2",&nbsp;"type":&nbsp;"string"}],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"primaryKey":&nbsp;"index",<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"pandas_version":&nbsp;"0.20.0"},<br>
-&nbsp;&nbsp;"data":&nbsp;[{"index":&nbsp;"row&nbsp;1",&nbsp;"col&nbsp;1":&nbsp;"a",&nbsp;"col&nbsp;2":&nbsp;"b"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"index":&nbsp;"row&nbsp;2",&nbsp;"col&nbsp;1":&nbsp;"c",&nbsp;"col&nbsp;2":&nbsp;"d"}]}'</tt></dd></dl>
-
-<dl><dt><a name="System-to_msgpack"><strong>to_msgpack</strong></a>(self, path_or_buf=None, encoding='utf-8', **kwargs)</dt><dd><tt>msgpack&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path<br>
-&nbsp;<br>
-THIS&nbsp;IS&nbsp;AN&nbsp;EXPERIMENTAL&nbsp;LIBRARY&nbsp;and&nbsp;the&nbsp;storage&nbsp;format<br>
-may&nbsp;not&nbsp;be&nbsp;stable&nbsp;until&nbsp;a&nbsp;future&nbsp;release.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;File&nbsp;path,&nbsp;buffer-like,&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;None,&nbsp;return&nbsp;generated&nbsp;string<br>
-append&nbsp;:&nbsp;boolean&nbsp;whether&nbsp;to&nbsp;append&nbsp;to&nbsp;an&nbsp;existing&nbsp;msgpack<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(default&nbsp;is&nbsp;False)<br>
-compress&nbsp;:&nbsp;type&nbsp;of&nbsp;compressor&nbsp;(zlib&nbsp;or&nbsp;blosc),&nbsp;default&nbsp;to&nbsp;None&nbsp;(no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;compression)</tt></dd></dl>
-
-<dl><dt><a name="System-to_pickle"><strong>to_pickle</strong></a>(self, path, compression='infer')</dt><dd><tt>Pickle&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path<br>
-compression&nbsp;:&nbsp;{'infer',&nbsp;'gzip',&nbsp;'bz2',&nbsp;'xz',&nbsp;None},&nbsp;default&nbsp;'infer'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;compression&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0</tt></dd></dl>
-
-<dl><dt><a name="System-to_sql"><strong>to_sql</strong></a>(self, name, con, flavor=None, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None)</dt><dd><tt>Write&nbsp;records&nbsp;stored&nbsp;in&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;SQL&nbsp;database.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-name&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;SQL&nbsp;table<br>
-con&nbsp;:&nbsp;SQLAlchemy&nbsp;engine&nbsp;or&nbsp;DBAPI2&nbsp;connection&nbsp;(legacy&nbsp;mode)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Using&nbsp;SQLAlchemy&nbsp;makes&nbsp;it&nbsp;possible&nbsp;to&nbsp;use&nbsp;any&nbsp;DB&nbsp;supported&nbsp;by&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;library.&nbsp;If&nbsp;a&nbsp;DBAPI2&nbsp;<a href="builtins.html#object">object</a>,&nbsp;only&nbsp;sqlite3&nbsp;is&nbsp;supported.<br>
-flavor&nbsp;:&nbsp;'sqlite',&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;DEPRECATED:&nbsp;this&nbsp;parameter&nbsp;will&nbsp;be&nbsp;removed&nbsp;in&nbsp;a&nbsp;future&nbsp;version,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;as&nbsp;'sqlite'&nbsp;is&nbsp;the&nbsp;only&nbsp;supported&nbsp;option&nbsp;if&nbsp;SQLAlchemy&nbsp;is&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;installed.<br>
-schema&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;the&nbsp;schema&nbsp;(if&nbsp;database&nbsp;flavor&nbsp;supports&nbsp;this).&nbsp;If&nbsp;None,&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;schema.<br>
-if_exists&nbsp;:&nbsp;{'fail',&nbsp;'replace',&nbsp;'append'},&nbsp;default&nbsp;'fail'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;fail:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;do&nbsp;nothing.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;replace:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;drop&nbsp;it,&nbsp;recreate&nbsp;it,&nbsp;and&nbsp;insert&nbsp;data.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;append:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;insert&nbsp;data.&nbsp;Create&nbsp;if&nbsp;does&nbsp;not&nbsp;exist.<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;index&nbsp;as&nbsp;a&nbsp;column.<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s).&nbsp;If&nbsp;None&nbsp;is&nbsp;given&nbsp;(default)&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`index`&nbsp;is&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-chunksize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;not&nbsp;None,&nbsp;then&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;in&nbsp;batches&nbsp;of&nbsp;this&nbsp;size&nbsp;at&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;time.&nbsp;&nbsp;If&nbsp;None,&nbsp;all&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;at&nbsp;once.<br>
-dtype&nbsp;:&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;to&nbsp;SQL&nbsp;type,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Optional&nbsp;specifying&nbsp;the&nbsp;datatype&nbsp;for&nbsp;columns.&nbsp;The&nbsp;SQL&nbsp;type&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;SQLAlchemy&nbsp;type,&nbsp;or&nbsp;a&nbsp;string&nbsp;for&nbsp;sqlite3&nbsp;fallback&nbsp;connection.</tt></dd></dl>
-
-<dl><dt><a name="System-to_xarray"><strong>to_xarray</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;xarray&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;the&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;DataArray&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;Dataset&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-a&nbsp;DataArray&nbsp;for&nbsp;higher&nbsp;dims<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;foo&nbsp;&nbsp;4.0<br>
-1&nbsp;&nbsp;1&nbsp;&nbsp;bar&nbsp;&nbsp;5.0<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;foo&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(index:&nbsp;3)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;index&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;0&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;1&nbsp;1&nbsp;2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'foo'&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;float64&nbsp;4.0&nbsp;5.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;).set_index(['B','A'])<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-B&nbsp;&nbsp;&nbsp;A<br>
-foo&nbsp;1&nbsp;&nbsp;4.0<br>
-bar&nbsp;1&nbsp;&nbsp;5.0<br>
-foo&nbsp;2&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(A:&nbsp;2,&nbsp;B:&nbsp;2)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;*&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(A)&nbsp;int64&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B,&nbsp;A)&nbsp;float64&nbsp;5.0&nbsp;nan&nbsp;4.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p&nbsp;=&nbsp;pd.Panel(np.arange(24).<a href="#System-reshape">reshape</a>(4,3,2),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;items=list('ABCD'),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;major_axis=pd.date_range('20130101',&nbsp;periods=3),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;minor_axis=['first',&nbsp;'second'])<br>
-&gt;&gt;&gt;&nbsp;p<br>
-&lt;class&nbsp;'pandas.core.panel.Panel'&gt;<br>
-Dimensions:&nbsp;4&nbsp;(items)&nbsp;x&nbsp;3&nbsp;(major_axis)&nbsp;x&nbsp;2&nbsp;(minor_axis)<br>
-Items&nbsp;axis:&nbsp;A&nbsp;to&nbsp;D<br>
-Major_axis&nbsp;axis:&nbsp;2013-01-01&nbsp;00:00:00&nbsp;to&nbsp;2013-01-03&nbsp;00:00:00<br>
-Minor_axis&nbsp;axis:&nbsp;first&nbsp;to&nbsp;second<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p.<a href="#System-to_xarray">to_xarray</a>()<br>
-&lt;xarray.DataArray&nbsp;(items:&nbsp;4,&nbsp;major_axis:&nbsp;3,&nbsp;minor_axis:&nbsp;2)&gt;<br>
-array([[[&nbsp;0,&nbsp;&nbsp;1],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;2,&nbsp;&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;4,&nbsp;&nbsp;5]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[&nbsp;6,&nbsp;&nbsp;7],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;8,&nbsp;&nbsp;9],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[10,&nbsp;11]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[12,&nbsp;13],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[14,&nbsp;15],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[16,&nbsp;17]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[18,&nbsp;19],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[20,&nbsp;21],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[22,&nbsp;23]]])<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;items&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(items)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'A'&nbsp;'B'&nbsp;'C'&nbsp;'D'<br>
-&nbsp;&nbsp;*&nbsp;major_axis&nbsp;&nbsp;(major_axis)&nbsp;datetime64[ns]&nbsp;2013-01-01&nbsp;2013-01-02&nbsp;2013-01-03&nbsp;&nbsp;#&nbsp;noqa<br>
-&nbsp;&nbsp;*&nbsp;minor_axis&nbsp;&nbsp;(minor_axis)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'first'&nbsp;'second'<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;the&nbsp;`xarray&nbsp;docs&nbsp;&lt;<a href="http://xarray.pydata.org/en/stable/&gt;`__">http://xarray.pydata.org/en/stable/&gt;`__</a></tt></dd></dl>
-
-<dl><dt><a name="System-truncate"><strong>truncate</strong></a>(self, before=None, after=None, axis=None, copy=True)</dt><dd><tt>Truncates&nbsp;a&nbsp;sorted&nbsp;NDFrame&nbsp;before&nbsp;and/or&nbsp;after&nbsp;some&nbsp;particular<br>
-index&nbsp;value.&nbsp;If&nbsp;the&nbsp;axis&nbsp;contains&nbsp;only&nbsp;datetime&nbsp;values,&nbsp;before/after<br>
-parameters&nbsp;are&nbsp;converted&nbsp;to&nbsp;datetime&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-before&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;before&nbsp;index&nbsp;value<br>
-after&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;after&nbsp;index&nbsp;value<br>
-axis&nbsp;:&nbsp;the&nbsp;truncation&nbsp;axis,&nbsp;defaults&nbsp;to&nbsp;the&nbsp;stat&nbsp;axis<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;is&nbsp;True,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;truncated&nbsp;section<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-truncated&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="System-tshift"><strong>tshift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;the&nbsp;time&nbsp;index,&nbsp;using&nbsp;the&nbsp;index's&nbsp;frequency&nbsp;if&nbsp;available.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM')<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;basestring<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Corresponds&nbsp;to&nbsp;the&nbsp;axis&nbsp;that&nbsp;contains&nbsp;the&nbsp;Index<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;not&nbsp;specified&nbsp;then&nbsp;tries&nbsp;to&nbsp;use&nbsp;the&nbsp;freq&nbsp;or&nbsp;inferred_freq<br>
-attributes&nbsp;of&nbsp;the&nbsp;index.&nbsp;If&nbsp;neither&nbsp;of&nbsp;those&nbsp;attributes&nbsp;exist,&nbsp;a<br>
-ValueError&nbsp;is&nbsp;thrown<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;NDFrame</tt></dd></dl>
-
-<dl><dt><a name="System-tz_convert"><strong>tz_convert</strong></a>(self, tz, axis=0, level=None, copy=True)</dt><dd><tt>Convert&nbsp;tz-aware&nbsp;axis&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;convert<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;convert&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;tz-naive.</tt></dd></dl>
-
-<dl><dt><a name="System-tz_localize"><strong>tz_localize</strong></a>(self, tz, axis=0, level=None, copy=True, ambiguous='raise')</dt><dd><tt>Localize&nbsp;tz-naive&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;localize<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;localize&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-ambiguous&nbsp;:&nbsp;'infer',&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>,&nbsp;'NaT',&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'infer'&nbsp;will&nbsp;attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>&nbsp;where&nbsp;True&nbsp;signifies&nbsp;a&nbsp;DST&nbsp;time,&nbsp;False&nbsp;designates<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;non-DST&nbsp;time&nbsp;(note&nbsp;that&nbsp;this&nbsp;flag&nbsp;is&nbsp;only&nbsp;applicable&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ambiguous&nbsp;times)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'NaT'&nbsp;will&nbsp;return&nbsp;NaT&nbsp;where&nbsp;there&nbsp;are&nbsp;ambiguous&nbsp;times<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'raise'&nbsp;will&nbsp;raise&nbsp;an&nbsp;AmbiguousTimeError&nbsp;if&nbsp;there&nbsp;are&nbsp;ambiguous<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;times<br>
-infer_dst&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on&nbsp;order<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;is&nbsp;tz-aware&nbsp;and&nbsp;tz&nbsp;is&nbsp;not&nbsp;None.</tt></dd></dl>
-
-<dl><dt><a name="System-where"><strong>where</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;True&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;where&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``True``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#System-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#System-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``where``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#System-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).<a href="#System-reshape">reshape</a>(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#System-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#System-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mask`</tt></dd></dl>
-
-<dl><dt><a name="System-xs"><strong>xs</strong></a>(self, key, axis=0, level=None, drop_level=True)</dt><dd><tt>Returns&nbsp;a&nbsp;cross-section&nbsp;(row(s)&nbsp;or&nbsp;column(s))&nbsp;from&nbsp;the<br>
-<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Defaults&nbsp;to&nbsp;cross-section&nbsp;on&nbsp;the&nbsp;rows&nbsp;(axis=0).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Some&nbsp;label&nbsp;contained&nbsp;in&nbsp;the&nbsp;index,&nbsp;or&nbsp;partially&nbsp;in&nbsp;a&nbsp;MultiIndex<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;retrieve&nbsp;cross-section&nbsp;on<br>
-level&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;defaults&nbsp;to&nbsp;first&nbsp;n&nbsp;levels&nbsp;(n=1&nbsp;or&nbsp;len(key))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;key&nbsp;partially&nbsp;contained&nbsp;in&nbsp;a&nbsp;MultiIndex,&nbsp;indicate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;levels&nbsp;are&nbsp;used.&nbsp;Levels&nbsp;can&nbsp;be&nbsp;referred&nbsp;by&nbsp;label&nbsp;or&nbsp;position.<br>
-drop_level&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;returns&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;same&nbsp;levels&nbsp;as&nbsp;self.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C<br>
-a&nbsp;&nbsp;4&nbsp;&nbsp;5&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;4&nbsp;&nbsp;0&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;9&nbsp;&nbsp;7&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-xs">xs</a>('a')<br>
-A&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-B&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-C&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Name:&nbsp;a<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-xs">xs</a>('C',&nbsp;axis=1)<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;C<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;second&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7&nbsp;&nbsp;5&nbsp;&nbsp;5&nbsp;&nbsp;0<br>
-baz&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;three&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-xs">xs</a>(('baz',&nbsp;'three'))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-third<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-xs">xs</a>('one',&nbsp;level=1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-baz&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#System-xs">xs</a>(('baz',&nbsp;2),&nbsp;level=[0,&nbsp;'third'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-second<br>
-three&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-xs&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-xs&nbsp;is&nbsp;only&nbsp;for&nbsp;getting,&nbsp;not&nbsp;setting&nbsp;values.<br>
-&nbsp;<br>
-MultiIndex&nbsp;Slicers&nbsp;is&nbsp;a&nbsp;generic&nbsp;way&nbsp;to&nbsp;get/set&nbsp;values&nbsp;on&nbsp;any&nbsp;level&nbsp;or<br>
-levels.&nbsp;&nbsp;It&nbsp;is&nbsp;a&nbsp;superset&nbsp;of&nbsp;xs&nbsp;functionality,&nbsp;see<br>
-:ref:`MultiIndex&nbsp;Slicers&nbsp;&lt;advanced.mi_slicers&gt;`</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>at</strong></dt>
-<dd><tt>Fast&nbsp;label-based&nbsp;scalar&nbsp;accessor<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``loc``,&nbsp;``at``&nbsp;provides&nbsp;**label**&nbsp;based&nbsp;scalar&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>blocks</strong></dt>
-<dd><tt>Internal&nbsp;property,&nbsp;property&nbsp;synonym&nbsp;for&nbsp;as_blocks()</tt></dd>
-</dl>
-<dl><dt><strong>iat</strong></dt>
-<dd><tt>Fast&nbsp;integer&nbsp;location&nbsp;scalar&nbsp;accessor.<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``iloc``,&nbsp;``iat``&nbsp;provides&nbsp;**integer**&nbsp;based&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>iloc</strong></dt>
-<dd><tt>Purely&nbsp;integer-location&nbsp;based&nbsp;indexing&nbsp;for&nbsp;selection&nbsp;by&nbsp;position.<br>
-&nbsp;<br>
-``.iloc[]``&nbsp;is&nbsp;primarily&nbsp;integer&nbsp;position&nbsp;based&nbsp;(from&nbsp;``0``&nbsp;to<br>
-``length-1``&nbsp;of&nbsp;the&nbsp;axis),&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a&nbsp;boolean<br>
-array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;An&nbsp;integer,&nbsp;e.g.&nbsp;``5``.<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;integers,&nbsp;e.g.&nbsp;``[4,&nbsp;3,&nbsp;0]``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;ints,&nbsp;e.g.&nbsp;``1:7``.<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.iloc``&nbsp;will&nbsp;raise&nbsp;``IndexError``&nbsp;if&nbsp;a&nbsp;requested&nbsp;indexer&nbsp;is<br>
-out-of-bounds,&nbsp;except&nbsp;*slice*&nbsp;indexers&nbsp;which&nbsp;allow&nbsp;out-of-bounds<br>
-indexing&nbsp;(this&nbsp;conforms&nbsp;with&nbsp;python/numpy&nbsp;*slice*&nbsp;semantics).<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Position&nbsp;&lt;indexing.integer&gt;`</tt></dd>
-</dl>
-<dl><dt><strong>ix</strong></dt>
-<dd><tt>A&nbsp;primarily&nbsp;label-location&nbsp;based&nbsp;indexer,&nbsp;with&nbsp;integer&nbsp;position<br>
-fallback.<br>
-&nbsp;<br>
-``.ix[]``&nbsp;supports&nbsp;mixed&nbsp;integer&nbsp;and&nbsp;label&nbsp;based&nbsp;access.&nbsp;It&nbsp;is<br>
-primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;will&nbsp;fall&nbsp;back&nbsp;to&nbsp;integer&nbsp;positional<br>
-access&nbsp;unless&nbsp;the&nbsp;corresponding&nbsp;axis&nbsp;is&nbsp;of&nbsp;integer&nbsp;type.<br>
-&nbsp;<br>
-``.ix``&nbsp;is&nbsp;the&nbsp;most&nbsp;general&nbsp;indexer&nbsp;and&nbsp;will&nbsp;support&nbsp;any&nbsp;of&nbsp;the<br>
-inputs&nbsp;in&nbsp;``.loc``&nbsp;and&nbsp;``.iloc``.&nbsp;``.ix``&nbsp;also&nbsp;supports&nbsp;floating<br>
-point&nbsp;label&nbsp;schemes.&nbsp;``.ix``&nbsp;is&nbsp;exceptionally&nbsp;useful&nbsp;when&nbsp;dealing<br>
-with&nbsp;mixed&nbsp;positional&nbsp;and&nbsp;label&nbsp;based&nbsp;hierachical&nbsp;indexes.<br>
-&nbsp;<br>
-However,&nbsp;when&nbsp;an&nbsp;axis&nbsp;is&nbsp;integer&nbsp;based,&nbsp;ONLY&nbsp;label&nbsp;based&nbsp;access<br>
-and&nbsp;not&nbsp;positional&nbsp;access&nbsp;is&nbsp;supported.&nbsp;Thus,&nbsp;in&nbsp;such&nbsp;cases,&nbsp;it's<br>
-usually&nbsp;better&nbsp;to&nbsp;be&nbsp;explicit&nbsp;and&nbsp;use&nbsp;``.iloc``&nbsp;or&nbsp;``.loc``.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Advanced&nbsp;Indexing&nbsp;&lt;advanced&gt;`.</tt></dd>
-</dl>
-<dl><dt><strong>loc</strong></dt>
-<dd><tt>Purely&nbsp;label-location&nbsp;based&nbsp;indexer&nbsp;for&nbsp;selection&nbsp;by&nbsp;label.<br>
-&nbsp;<br>
-``.loc[]``&nbsp;is&nbsp;primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a<br>
-boolean&nbsp;array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;A&nbsp;single&nbsp;label,&nbsp;e.g.&nbsp;``5``&nbsp;or&nbsp;``'a'``,&nbsp;(note&nbsp;that&nbsp;``5``&nbsp;is<br>
-&nbsp;&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;*label*&nbsp;of&nbsp;the&nbsp;index,&nbsp;and&nbsp;**never**&nbsp;as&nbsp;an<br>
-&nbsp;&nbsp;integer&nbsp;position&nbsp;along&nbsp;the&nbsp;index).<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;labels,&nbsp;e.g.&nbsp;``['a',&nbsp;'b',&nbsp;'c']``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;labels,&nbsp;e.g.&nbsp;``'a':'f'``&nbsp;(note&nbsp;that&nbsp;contrary<br>
-&nbsp;&nbsp;to&nbsp;usual&nbsp;python&nbsp;slices,&nbsp;**both**&nbsp;the&nbsp;start&nbsp;and&nbsp;the&nbsp;stop&nbsp;are&nbsp;included!).<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.loc``&nbsp;will&nbsp;raise&nbsp;a&nbsp;``KeyError``&nbsp;when&nbsp;the&nbsp;items&nbsp;are&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Label&nbsp;&lt;indexing.label&gt;`</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>is_copy</strong> = None</dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a>:<br>
-<dl><dt><a name="System-__dir__"><strong>__dir__</strong></a>(self)</dt><dd><tt>Provide&nbsp;method&nbsp;name&nbsp;lookup&nbsp;and&nbsp;completion<br>
-Only&nbsp;provide&nbsp;'public'&nbsp;methods</tt></dd></dl>
-
-<dl><dt><a name="System-__sizeof__"><strong>__sizeof__</strong></a>(self)</dt><dd><tt>Generates&nbsp;the&nbsp;total&nbsp;memory&nbsp;usage&nbsp;for&nbsp;a&nbsp;<a href="builtins.html#object">object</a>&nbsp;that&nbsp;returns<br>
-either&nbsp;a&nbsp;value&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;values</tt></dd></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a>:<br>
-<dl><dt><a name="System-__bytes__"><strong>__bytes__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;bytes(obj)&nbsp;in&nbsp;py3&nbsp;only.<br>
-Yields&nbsp;a&nbsp;bytestring&nbsp;in&nbsp;both&nbsp;py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="System-__repr__"><strong>__repr__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-<dl><dt><a name="System-__str__"><strong>__str__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;Object<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;<a href="#System-str">str</a>(df)&nbsp;in&nbsp;both&nbsp;py2/py3.<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-</td></tr></table> <p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#ffc8d8">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#000000" face="helvetica, arial"><a name="TimeFrame">class <strong>TimeFrame</strong></a>(<a href="modsim.html#MyDataFrame">MyDataFrame</a>)</font></td></tr>
-    
-<tr bgcolor="#ffc8d8"><td rowspan=2><tt>&nbsp;&nbsp;&nbsp;</tt></td>
-<td colspan=2><tt>MyTimeFrame&nbsp;is&nbsp;a&nbsp;modified&nbsp;version&nbsp;of&nbsp;a&nbsp;Pandas&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,<br>
-with&nbsp;a&nbsp;few&nbsp;changes&nbsp;to&nbsp;make&nbsp;it&nbsp;more&nbsp;suited&nbsp;to&nbsp;our&nbsp;purpose.<br>
-&nbsp;<br>
-In&nbsp;particular,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;provides&nbsp;two&nbsp;special&nbsp;variables&nbsp;called<br>
-`dt`&nbsp;and&nbsp;`T`&nbsp;that&nbsp;cause&nbsp;problems&nbsp;if&nbsp;we&nbsp;try&nbsp;to&nbsp;use&nbsp;those&nbsp;names<br>
-as&nbsp;state&nbsp;variables.<br>
-&nbsp;<br>
-So&nbsp;I&nbsp;added&nbsp;new&nbsp;definitions&nbsp;that&nbsp;override&nbsp;the&nbsp;special&nbsp;variables<br>
-and&nbsp;make&nbsp;these&nbsp;names&nbsp;useable&nbsp;as&nbsp;row&nbsp;labels.<br>&nbsp;</tt></td></tr>
-<tr><td>&nbsp;</td>
-<td width="100%"><dl><dt>Method resolution order:</dt>
-<dd><a href="modsim.html#TimeFrame">TimeFrame</a></dd>
-<dd><a href="modsim.html#MyDataFrame">MyDataFrame</a></dd>
-<dd><a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a></dd>
-<dd><a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a></dd>
-<dd><a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a></dd>
-<dd><a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a></dd>
-<dd><a href="pandas.core.base.html#SelectionMixin">pandas.core.base.SelectionMixin</a></dd>
-<dd><a href="builtins.html#object">builtins.object</a></dd>
-</dl>
-<hr>
-Methods inherited from <a href="modsim.html#MyDataFrame">MyDataFrame</a>:<br>
-<dl><dt><a name="TimeFrame-__init__"><strong>__init__</strong></a>(self, *args, **kwargs)</dt><dd><tt>Initialize&nbsp;self.&nbsp;&nbsp;See&nbsp;help(type(self))&nbsp;for&nbsp;accurate&nbsp;signature.</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="modsim.html#MyDataFrame">MyDataFrame</a>:<br>
-<dl><dt><strong>T</strong></dt>
-<dd><tt>Intercept&nbsp;the&nbsp;Series&nbsp;accessor&nbsp;object&nbsp;so&nbsp;we&nbsp;can&nbsp;use&nbsp;`T`<br>
-as&nbsp;a&nbsp;row&nbsp;label&nbsp;and&nbsp;access&nbsp;it&nbsp;using&nbsp;dot&nbsp;notation.<br>
-&nbsp;<br>
-https://pandas.pydata.org/pandas-docs/stable/generated/<br>
-pandas.DataFrame.T.html#pandas.DataFrame.T</tt></dd>
-</dl>
-<dl><dt><strong>dt</strong></dt>
-<dd><tt>Intercept&nbsp;the&nbsp;Series&nbsp;accessor&nbsp;object&nbsp;so&nbsp;we&nbsp;can&nbsp;use&nbsp;`dt`<br>
-as&nbsp;a&nbsp;row&nbsp;label&nbsp;and&nbsp;access&nbsp;it&nbsp;using&nbsp;dot&nbsp;notation.<br>
-&nbsp;<br>
-https://pandas.pydata.org/pandas-docs/stable/generated/<br>
-pandas.DataFrame.dt.html</tt></dd>
-</dl>
-<hr>
-Methods inherited from <a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a>:<br>
-<dl><dt><a name="TimeFrame-__add__"><strong>__add__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__add__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__and__"><strong>__and__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__and__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__div__"><strong>__div__</strong></a> = __truediv__(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__truediv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__eq__"><strong>__eq__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__eq__</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__floordiv__"><strong>__floordiv__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__floordiv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__ge__"><strong>__ge__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__ge__</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__getitem__"><strong>__getitem__</strong></a>(self, key)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__gt__"><strong>__gt__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__gt__</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__iadd__"><strong>__iadd__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__imul__"><strong>__imul__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__ipow__"><strong>__ipow__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__isub__"><strong>__isub__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__itruediv__"><strong>__itruediv__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__le__"><strong>__le__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__le__</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__len__"><strong>__len__</strong></a>(self)</dt><dd><tt>Returns&nbsp;length&nbsp;of&nbsp;info&nbsp;axis,&nbsp;but&nbsp;here&nbsp;we&nbsp;use&nbsp;the&nbsp;index</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__lt__"><strong>__lt__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__lt__</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__mod__"><strong>__mod__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__mod__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__mul__"><strong>__mul__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__mul__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__ne__"><strong>__ne__</strong></a>(self, other)</dt><dd><tt>Wrapper&nbsp;for&nbsp;comparison&nbsp;method&nbsp;__ne__</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__or__"><strong>__or__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__or__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__pow__"><strong>__pow__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__pow__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__radd__"><strong>__radd__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__radd__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__rand__"><strong>__rand__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rand__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__rdiv__"><strong>__rdiv__</strong></a> = __rtruediv__(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rtruediv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__rfloordiv__"><strong>__rfloordiv__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rfloordiv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__rmod__"><strong>__rmod__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rmod__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__rmul__"><strong>__rmul__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rmul__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__ror__"><strong>__ror__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__ror__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__rpow__"><strong>__rpow__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rpow__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__rsub__"><strong>__rsub__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rsub__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__rtruediv__"><strong>__rtruediv__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rtruediv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__rxor__"><strong>__rxor__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__rxor__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__setitem__"><strong>__setitem__</strong></a>(self, key, value)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__sub__"><strong>__sub__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__sub__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__truediv__"><strong>__truediv__</strong></a>(self, other, axis=None, level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__truediv__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__unicode__"><strong>__unicode__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;unicode(df)&nbsp;in&nbsp;py2&nbsp;only.&nbsp;Yields&nbsp;a&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;both<br>
-py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__xor__"><strong>__xor__</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Binary&nbsp;operator&nbsp;__xor__&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for&nbsp;missing&nbsp;data&nbsp;in<br>
-one&nbsp;of&nbsp;the&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;locations&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-add"><strong>add</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Addition&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`add`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;+&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.radd</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-agg"><strong>agg</strong></a> = aggregate(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#TimeFrame-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#TimeFrame-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(10,&nbsp;3),&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.date_range('1/1/2000',&nbsp;periods=10))<br>
-&gt;&gt;&gt;&nbsp;df.iloc[3:7]&nbsp;=&nbsp;np.nan<br>
-&nbsp;<br>
-Aggregate&nbsp;these&nbsp;functions&nbsp;across&nbsp;all&nbsp;columns<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-agg">agg</a>(['sum',&nbsp;'min'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-sum&nbsp;-0.182253&nbsp;-0.614014&nbsp;-2.909534<br>
-min&nbsp;-1.916563&nbsp;-1.460076&nbsp;-1.568297<br>
-&nbsp;<br>
-Different&nbsp;aggregations&nbsp;per&nbsp;column<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-agg">agg</a>({'A'&nbsp;:&nbsp;['sum',&nbsp;'min'],&nbsp;'B'&nbsp;:&nbsp;['min',&nbsp;'max']})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;1.514318<br>
-min&nbsp;-1.916563&nbsp;-1.460076<br>
-sum&nbsp;-0.182253&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.transform<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.groupby.aggregate<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.resample.aggregate<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rolling.aggregate</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-aggregate"><strong>aggregate</strong></a>(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#TimeFrame-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#TimeFrame-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(10,&nbsp;3),&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.date_range('1/1/2000',&nbsp;periods=10))<br>
-&gt;&gt;&gt;&nbsp;df.iloc[3:7]&nbsp;=&nbsp;np.nan<br>
-&nbsp;<br>
-Aggregate&nbsp;these&nbsp;functions&nbsp;across&nbsp;all&nbsp;columns<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-agg">agg</a>(['sum',&nbsp;'min'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-sum&nbsp;-0.182253&nbsp;-0.614014&nbsp;-2.909534<br>
-min&nbsp;-1.916563&nbsp;-1.460076&nbsp;-1.568297<br>
-&nbsp;<br>
-Different&nbsp;aggregations&nbsp;per&nbsp;column<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-agg">agg</a>({'A'&nbsp;:&nbsp;['sum',&nbsp;'min'],&nbsp;'B'&nbsp;:&nbsp;['min',&nbsp;'max']})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;1.514318<br>
-min&nbsp;-1.916563&nbsp;-1.460076<br>
-sum&nbsp;-0.182253&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.transform<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.groupby.aggregate<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.resample.aggregate<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rolling.aggregate</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-align"><strong>align</strong></a>(self, other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)</dt><dd><tt>Align&nbsp;two&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;their&nbsp;axes&nbsp;with&nbsp;the<br>
-specified&nbsp;join&nbsp;method&nbsp;for&nbsp;each&nbsp;axis&nbsp;Index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-join&nbsp;:&nbsp;{'outer',&nbsp;'inner',&nbsp;'left',&nbsp;'right'},&nbsp;default&nbsp;'outer'<br>
-axis&nbsp;:&nbsp;allowed&nbsp;axis&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;on&nbsp;index&nbsp;(0),&nbsp;columns&nbsp;(1),&nbsp;or&nbsp;both&nbsp;(None)<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Always&nbsp;returns&nbsp;new&nbsp;objects.&nbsp;If&nbsp;copy=False&nbsp;and&nbsp;no&nbsp;reindexing&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;required&nbsp;then&nbsp;original&nbsp;objects&nbsp;are&nbsp;returned.<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-method&nbsp;:&nbsp;str,&nbsp;default&nbsp;None<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-fill_axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Filling&nbsp;axis,&nbsp;method&nbsp;and&nbsp;limit<br>
-broadcast_axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;values&nbsp;along&nbsp;this&nbsp;axis,&nbsp;if&nbsp;aligning&nbsp;two&nbsp;objects&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;different&nbsp;dimensions<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-(left,&nbsp;right)&nbsp;:&nbsp;(<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;type&nbsp;of&nbsp;other)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Aligned&nbsp;objects</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-all"><strong>all</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;all&nbsp;elements&nbsp;are&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-all&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-any"><strong>any</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;any&nbsp;element&nbsp;is&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-any&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-append"><strong>append</strong></a>(self, other, ignore_index=False, verify_integrity=False)</dt><dd><tt>Append&nbsp;rows&nbsp;of&nbsp;`other`&nbsp;to&nbsp;the&nbsp;end&nbsp;of&nbsp;this&nbsp;frame,&nbsp;returning&nbsp;a&nbsp;new<br>
-<a href="builtins.html#object">object</a>.&nbsp;Columns&nbsp;not&nbsp;in&nbsp;this&nbsp;frame&nbsp;are&nbsp;added&nbsp;as&nbsp;new&nbsp;columns.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>/dict-like&nbsp;<a href="builtins.html#object">object</a>,&nbsp;or&nbsp;list&nbsp;of&nbsp;these<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;data&nbsp;to&nbsp;append.<br>
-ignore_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;not&nbsp;use&nbsp;the&nbsp;index&nbsp;labels.<br>
-verify_integrity&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;raise&nbsp;ValueError&nbsp;on&nbsp;creating&nbsp;index&nbsp;with&nbsp;duplicates.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-appended&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;a&nbsp;list&nbsp;of&nbsp;dict/series&nbsp;is&nbsp;passed&nbsp;and&nbsp;the&nbsp;keys&nbsp;are&nbsp;all&nbsp;contained&nbsp;in<br>
-the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;index,&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;columns&nbsp;in&nbsp;the&nbsp;resulting<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;be&nbsp;unchanged.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.concat&nbsp;:&nbsp;General&nbsp;function&nbsp;to&nbsp;concatenate&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;Panel&nbsp;objects<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[1,&nbsp;2],&nbsp;[3,&nbsp;4]],&nbsp;columns=list('AB'))<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-1&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[5,&nbsp;6],&nbsp;[7,&nbsp;8]],&nbsp;columns=list('AB'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-append">append</a>(df2)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-1&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-0&nbsp;&nbsp;5&nbsp;&nbsp;6<br>
-1&nbsp;&nbsp;7&nbsp;&nbsp;8<br>
-&nbsp;<br>
-With&nbsp;`ignore_index`&nbsp;set&nbsp;to&nbsp;True:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-append">append</a>(df2,&nbsp;ignore_index=True)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-1&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;5&nbsp;&nbsp;6<br>
-3&nbsp;&nbsp;7&nbsp;&nbsp;8</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-apply"><strong>apply</strong></a>(self, func, axis=0, broadcast=False, raw=False, reduce=None, args=(), **kwds)</dt><dd><tt>Applies&nbsp;function&nbsp;along&nbsp;input&nbsp;axis&nbsp;of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-Objects&nbsp;passed&nbsp;to&nbsp;functions&nbsp;are&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;objects&nbsp;having&nbsp;index<br>
-either&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;index&nbsp;(axis=0)&nbsp;or&nbsp;the&nbsp;columns&nbsp;(axis=1).<br>
-Return&nbsp;type&nbsp;depends&nbsp;on&nbsp;whether&nbsp;passed&nbsp;function&nbsp;aggregates,&nbsp;or&nbsp;the<br>
-reduce&nbsp;argument&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;is&nbsp;empty.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;apply&nbsp;to&nbsp;each&nbsp;column/row<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;0&nbsp;or&nbsp;'index':&nbsp;apply&nbsp;function&nbsp;to&nbsp;each&nbsp;column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;1&nbsp;or&nbsp;'columns':&nbsp;apply&nbsp;function&nbsp;to&nbsp;each&nbsp;row<br>
-broadcast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;aggregation&nbsp;functions,&nbsp;return&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;size&nbsp;with&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;&nbsp;propagated<br>
-raw&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;convert&nbsp;each&nbsp;row&nbsp;or&nbsp;column&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;If&nbsp;raw=True&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;function&nbsp;will&nbsp;receive&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;objects&nbsp;instead.&nbsp;If&nbsp;you&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;just&nbsp;applying&nbsp;a&nbsp;NumPy&nbsp;reduction&nbsp;function&nbsp;this&nbsp;will&nbsp;achieve&nbsp;much<br>
-&nbsp;&nbsp;&nbsp;&nbsp;better&nbsp;performance<br>
-reduce&nbsp;:&nbsp;boolean&nbsp;or&nbsp;None,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Try&nbsp;to&nbsp;apply&nbsp;reduction&nbsp;procedures.&nbsp;If&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;is&nbsp;empty,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;apply&nbsp;will&nbsp;use&nbsp;reduce&nbsp;to&nbsp;determine&nbsp;whether&nbsp;the&nbsp;result&nbsp;should&nbsp;be&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;If&nbsp;reduce&nbsp;is&nbsp;None&nbsp;(the&nbsp;default),&nbsp;apply's<br>
-&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;value&nbsp;will&nbsp;be&nbsp;guessed&nbsp;by&nbsp;calling&nbsp;func&nbsp;an&nbsp;empty&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(note:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;while&nbsp;guessing,&nbsp;exceptions&nbsp;raised&nbsp;by&nbsp;func&nbsp;will&nbsp;be&nbsp;ignored).&nbsp;If<br>
-&nbsp;&nbsp;&nbsp;&nbsp;reduce&nbsp;is&nbsp;True&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;always&nbsp;be&nbsp;returned,&nbsp;and&nbsp;if&nbsp;False&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;always&nbsp;be&nbsp;returned.<br>
-args&nbsp;:&nbsp;tuple<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Positional&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;to&nbsp;function&nbsp;in&nbsp;addition&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;array/series<br>
-Additional&nbsp;keyword&nbsp;arguments&nbsp;will&nbsp;be&nbsp;passed&nbsp;as&nbsp;keywords&nbsp;to&nbsp;the&nbsp;function<br>
-&nbsp;<br>
-Notes<br>
------<br>
-In&nbsp;the&nbsp;current&nbsp;implementation&nbsp;apply&nbsp;calls&nbsp;func&nbsp;twice&nbsp;on&nbsp;the<br>
-first&nbsp;column/row&nbsp;to&nbsp;decide&nbsp;whether&nbsp;it&nbsp;can&nbsp;take&nbsp;a&nbsp;fast&nbsp;or&nbsp;slow<br>
-code&nbsp;path.&nbsp;This&nbsp;can&nbsp;lead&nbsp;to&nbsp;unexpected&nbsp;behavior&nbsp;if&nbsp;func&nbsp;has<br>
-side-effects,&nbsp;as&nbsp;they&nbsp;will&nbsp;take&nbsp;effect&nbsp;twice&nbsp;for&nbsp;the&nbsp;first<br>
-column/row.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-apply">apply</a>(numpy.sqrt)&nbsp;#&nbsp;returns&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-apply">apply</a>(numpy.sum,&nbsp;axis=0)&nbsp;#&nbsp;equiv&nbsp;to&nbsp;df.<a href="#TimeFrame-sum">sum</a>(0)<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-apply">apply</a>(numpy.sum,&nbsp;axis=1)&nbsp;#&nbsp;equiv&nbsp;to&nbsp;df.<a href="#TimeFrame-sum">sum</a>(1)<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap:&nbsp;For&nbsp;elementwise&nbsp;operations<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.aggregate:&nbsp;only&nbsp;perform&nbsp;aggregating&nbsp;type&nbsp;operations<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.transform:&nbsp;only&nbsp;perform&nbsp;transformating&nbsp;type&nbsp;operations<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-applied&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-applymap"><strong>applymap</strong></a>(self, func)</dt><dd><tt>Apply&nbsp;a&nbsp;function&nbsp;to&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;that&nbsp;is&nbsp;intended&nbsp;to&nbsp;operate<br>
-elementwise,&nbsp;i.e.&nbsp;like&nbsp;doing&nbsp;map(func,&nbsp;series)&nbsp;for&nbsp;each&nbsp;series&nbsp;in&nbsp;the<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Python&nbsp;function,&nbsp;returns&nbsp;a&nbsp;single&nbsp;value&nbsp;from&nbsp;a&nbsp;single&nbsp;value<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(3,&nbsp;3))<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-0&nbsp;&nbsp;-0.029638&nbsp;&nbsp;1.081563&nbsp;&nbsp;&nbsp;1.280300<br>
-1&nbsp;&nbsp;&nbsp;0.647747&nbsp;&nbsp;0.831136&nbsp;&nbsp;-1.549481<br>
-2&nbsp;&nbsp;&nbsp;0.513416&nbsp;-0.884417&nbsp;&nbsp;&nbsp;0.195343<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;df.<a href="#TimeFrame-applymap">applymap</a>(lambda&nbsp;x:&nbsp;'%.2f'&nbsp;%&nbsp;x)<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-0&nbsp;&nbsp;-0.03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.08&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.28<br>
-1&nbsp;&nbsp;&nbsp;0.65&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.83&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-1.55<br>
-2&nbsp;&nbsp;&nbsp;0.51&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-0.88&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.20<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-applied&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply&nbsp;:&nbsp;For&nbsp;operations&nbsp;on&nbsp;rows/columns</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-assign"><strong>assign</strong></a>(self, **kwargs)</dt><dd><tt>Assign&nbsp;new&nbsp;columns&nbsp;to&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;returning&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a><br>
-(a&nbsp;copy)&nbsp;with&nbsp;all&nbsp;the&nbsp;original&nbsp;columns&nbsp;in&nbsp;addition&nbsp;to&nbsp;the&nbsp;new&nbsp;ones.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-kwargs&nbsp;:&nbsp;keyword,&nbsp;value&nbsp;pairs<br>
-&nbsp;&nbsp;&nbsp;&nbsp;keywords&nbsp;are&nbsp;the&nbsp;column&nbsp;names.&nbsp;If&nbsp;the&nbsp;values&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;callable,&nbsp;they&nbsp;are&nbsp;computed&nbsp;on&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;assigned&nbsp;to&nbsp;the&nbsp;new&nbsp;columns.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;values&nbsp;are&nbsp;not&nbsp;callable,&nbsp;(e.g.&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;scalar,&nbsp;or&nbsp;array),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;they&nbsp;are&nbsp;simply&nbsp;assigned.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-df&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;new&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;the&nbsp;new&nbsp;columns&nbsp;in&nbsp;addition&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;all&nbsp;the&nbsp;existing&nbsp;columns.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Since&nbsp;``kwargs``&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;the&nbsp;order&nbsp;of&nbsp;your<br>
-arguments&nbsp;may&nbsp;not&nbsp;be&nbsp;preserved.&nbsp;To&nbsp;make&nbsp;things&nbsp;predicatable,<br>
-the&nbsp;columns&nbsp;are&nbsp;inserted&nbsp;in&nbsp;alphabetical&nbsp;order,&nbsp;at&nbsp;the&nbsp;end&nbsp;of<br>
-your&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Assigning&nbsp;multiple&nbsp;columns&nbsp;within&nbsp;the&nbsp;same<br>
-``assign``&nbsp;is&nbsp;possible,&nbsp;but&nbsp;you&nbsp;cannot&nbsp;reference&nbsp;other&nbsp;columns<br>
-created&nbsp;within&nbsp;the&nbsp;same&nbsp;``assign``&nbsp;call.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;range(1,&nbsp;11),&nbsp;'B':&nbsp;np.random.randn(10)})<br>
-&nbsp;<br>
-Where&nbsp;the&nbsp;value&nbsp;is&nbsp;a&nbsp;callable,&nbsp;evaluated&nbsp;on&nbsp;`df`:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-assign">assign</a>(ln_A&nbsp;=&nbsp;lambda&nbsp;x:&nbsp;np.log(x.A))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ln_A<br>
-0&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;0.426905&nbsp;&nbsp;0.000000<br>
-1&nbsp;&nbsp;&nbsp;2&nbsp;-0.780949&nbsp;&nbsp;0.693147<br>
-2&nbsp;&nbsp;&nbsp;3&nbsp;-0.418711&nbsp;&nbsp;1.098612<br>
-3&nbsp;&nbsp;&nbsp;4&nbsp;-0.269708&nbsp;&nbsp;1.386294<br>
-4&nbsp;&nbsp;&nbsp;5&nbsp;-0.274002&nbsp;&nbsp;1.609438<br>
-5&nbsp;&nbsp;&nbsp;6&nbsp;-0.500792&nbsp;&nbsp;1.791759<br>
-6&nbsp;&nbsp;&nbsp;7&nbsp;&nbsp;1.649697&nbsp;&nbsp;1.945910<br>
-7&nbsp;&nbsp;&nbsp;8&nbsp;-1.495604&nbsp;&nbsp;2.079442<br>
-8&nbsp;&nbsp;&nbsp;9&nbsp;&nbsp;0.549296&nbsp;&nbsp;2.197225<br>
-9&nbsp;&nbsp;10&nbsp;-0.758542&nbsp;&nbsp;2.302585<br>
-&nbsp;<br>
-Where&nbsp;the&nbsp;value&nbsp;already&nbsp;exists&nbsp;and&nbsp;is&nbsp;inserted:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;newcol&nbsp;=&nbsp;np.log(df['A'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-assign">assign</a>(ln_A=newcol)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ln_A<br>
-0&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;0.426905&nbsp;&nbsp;0.000000<br>
-1&nbsp;&nbsp;&nbsp;2&nbsp;-0.780949&nbsp;&nbsp;0.693147<br>
-2&nbsp;&nbsp;&nbsp;3&nbsp;-0.418711&nbsp;&nbsp;1.098612<br>
-3&nbsp;&nbsp;&nbsp;4&nbsp;-0.269708&nbsp;&nbsp;1.386294<br>
-4&nbsp;&nbsp;&nbsp;5&nbsp;-0.274002&nbsp;&nbsp;1.609438<br>
-5&nbsp;&nbsp;&nbsp;6&nbsp;-0.500792&nbsp;&nbsp;1.791759<br>
-6&nbsp;&nbsp;&nbsp;7&nbsp;&nbsp;1.649697&nbsp;&nbsp;1.945910<br>
-7&nbsp;&nbsp;&nbsp;8&nbsp;-1.495604&nbsp;&nbsp;2.079442<br>
-8&nbsp;&nbsp;&nbsp;9&nbsp;&nbsp;0.549296&nbsp;&nbsp;2.197225<br>
-9&nbsp;&nbsp;10&nbsp;-0.758542&nbsp;&nbsp;2.302585</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-boxplot"><strong>boxplot</strong></a>(self, column=None, by=None, ax=None, fontsize=None, rot=0, grid=True, figsize=None, layout=None, return_type=None, **kwds)</dt><dd><tt>Make&nbsp;a&nbsp;box&nbsp;plot&nbsp;from&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;optionally&nbsp;grouped&nbsp;by&nbsp;some&nbsp;columns&nbsp;or<br>
-other&nbsp;inputs<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;the&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>&nbsp;holding&nbsp;the&nbsp;data<br>
-column&nbsp;:&nbsp;column&nbsp;name&nbsp;or&nbsp;list&nbsp;of&nbsp;names,&nbsp;or&nbsp;vector<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Can&nbsp;be&nbsp;any&nbsp;valid&nbsp;input&nbsp;to&nbsp;groupby<br>
-by&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;group&nbsp;by<br>
-ax&nbsp;:&nbsp;Matplotlib&nbsp;axes&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-fontsize&nbsp;:&nbsp;int&nbsp;or&nbsp;string<br>
-rot&nbsp;:&nbsp;label&nbsp;rotation&nbsp;angle<br>
-figsize&nbsp;:&nbsp;A&nbsp;tuple&nbsp;(width,&nbsp;height)&nbsp;in&nbsp;inches<br>
-grid&nbsp;:&nbsp;Setting&nbsp;this&nbsp;to&nbsp;True&nbsp;will&nbsp;show&nbsp;the&nbsp;grid<br>
-layout&nbsp;:&nbsp;tuple&nbsp;(optional)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(rows,&nbsp;columns)&nbsp;for&nbsp;the&nbsp;layout&nbsp;of&nbsp;the&nbsp;plot<br>
-return_type&nbsp;:&nbsp;{None,&nbsp;'axes',&nbsp;'dict',&nbsp;'both'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;kind&nbsp;of&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;return.&nbsp;The&nbsp;default&nbsp;is&nbsp;``axes``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'axes'&nbsp;returns&nbsp;the&nbsp;matplotlib&nbsp;axes&nbsp;the&nbsp;boxplot&nbsp;is&nbsp;drawn&nbsp;on;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'dict'&nbsp;returns&nbsp;a&nbsp;dictionary&nbsp;&nbsp;whose&nbsp;values&nbsp;are&nbsp;the&nbsp;matplotlib<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Lines&nbsp;of&nbsp;the&nbsp;boxplot;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'both'&nbsp;returns&nbsp;a&nbsp;namedtuple&nbsp;with&nbsp;the&nbsp;axes&nbsp;and&nbsp;dict.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;grouping&nbsp;with&nbsp;``by``,&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;mapping&nbsp;columns&nbsp;to&nbsp;``return_type``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;returned,&nbsp;unless&nbsp;``return_type``&nbsp;is&nbsp;None,&nbsp;in&nbsp;which&nbsp;case&nbsp;a&nbsp;NumPy<br>
-&nbsp;&nbsp;&nbsp;&nbsp;array&nbsp;of&nbsp;axes&nbsp;is&nbsp;returned&nbsp;with&nbsp;the&nbsp;same&nbsp;shape&nbsp;as&nbsp;``layout``.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;prose&nbsp;documentation&nbsp;for&nbsp;more.<br>
-&nbsp;<br>
-kwds&nbsp;:&nbsp;other&nbsp;plotting&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;be&nbsp;passed&nbsp;to&nbsp;matplotlib&nbsp;boxplot<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;function<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-lines&nbsp;:&nbsp;dict<br>
-ax&nbsp;:&nbsp;matplotlib&nbsp;Axes<br>
-(ax,&nbsp;lines):&nbsp;namedtuple<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Use&nbsp;``return_type='dict'``&nbsp;when&nbsp;you&nbsp;want&nbsp;to&nbsp;tweak&nbsp;the&nbsp;appearance<br>
-of&nbsp;the&nbsp;lines&nbsp;after&nbsp;plotting.&nbsp;In&nbsp;this&nbsp;case&nbsp;a&nbsp;dict&nbsp;containing&nbsp;the&nbsp;Lines<br>
-making&nbsp;up&nbsp;the&nbsp;boxes,&nbsp;caps,&nbsp;fliers,&nbsp;medians,&nbsp;and&nbsp;whiskers&nbsp;is&nbsp;returned.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-combine"><strong>combine</strong></a>(self, other, func, fill_value=None, overwrite=True)</dt><dd><tt>Add&nbsp;two&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects&nbsp;and&nbsp;do&nbsp;not&nbsp;propagate&nbsp;NaN&nbsp;values,&nbsp;so&nbsp;if&nbsp;for&nbsp;a<br>
-(column,&nbsp;time)&nbsp;one&nbsp;frame&nbsp;is&nbsp;missing&nbsp;a&nbsp;value,&nbsp;it&nbsp;will&nbsp;default&nbsp;to&nbsp;the<br>
-other&nbsp;frame's&nbsp;value&nbsp;(which&nbsp;might&nbsp;be&nbsp;NaN&nbsp;as&nbsp;well)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-func&nbsp;:&nbsp;function<br>
-fill_value&nbsp;:&nbsp;scalar&nbsp;value<br>
-overwrite&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;then&nbsp;overwrite&nbsp;values&nbsp;for&nbsp;common&nbsp;keys&nbsp;in&nbsp;the&nbsp;calling&nbsp;frame<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-combine_first"><strong>combine_first</strong></a>(self, other)</dt><dd><tt>Combine&nbsp;two&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects&nbsp;and&nbsp;default&nbsp;to&nbsp;non-null&nbsp;values&nbsp;in&nbsp;frame<br>
-calling&nbsp;the&nbsp;method.&nbsp;Result&nbsp;index&nbsp;columns&nbsp;will&nbsp;be&nbsp;the&nbsp;union&nbsp;of&nbsp;the<br>
-respective&nbsp;indexes&nbsp;and&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-a's&nbsp;values&nbsp;prioritized,&nbsp;use&nbsp;values&nbsp;from&nbsp;b&nbsp;to&nbsp;fill&nbsp;holes:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;a.<a href="#TimeFrame-combine_first">combine_first</a>(b)<br>
-&nbsp;<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-combined&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-compound"><strong>compound</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;compound&nbsp;percentage&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-compounded&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-corr"><strong>corr</strong></a>(self, method='pearson', min_periods=1)</dt><dd><tt>Compute&nbsp;pairwise&nbsp;correlation&nbsp;of&nbsp;columns,&nbsp;excluding&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-method&nbsp;:&nbsp;{'pearson',&nbsp;'kendall',&nbsp;'spearman'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pearson&nbsp;:&nbsp;standard&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;kendall&nbsp;:&nbsp;Kendall&nbsp;Tau&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;spearman&nbsp;:&nbsp;Spearman&nbsp;rank&nbsp;correlation<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;required&nbsp;per&nbsp;pair&nbsp;of&nbsp;columns<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result.&nbsp;Currently&nbsp;only&nbsp;available&nbsp;for&nbsp;pearson<br>
-&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;spearman&nbsp;correlation<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-corrwith"><strong>corrwith</strong></a>(self, other, axis=0, drop=False)</dt><dd><tt>Compute&nbsp;pairwise&nbsp;correlation&nbsp;between&nbsp;rows&nbsp;or&nbsp;columns&nbsp;of&nbsp;two&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-objects.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;or&nbsp;'index'&nbsp;to&nbsp;compute&nbsp;column-wise,&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;for&nbsp;row-wise<br>
-drop&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Drop&nbsp;missing&nbsp;indices&nbsp;from&nbsp;result,&nbsp;default&nbsp;returns&nbsp;union&nbsp;of&nbsp;all<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-correls&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-count"><strong>count</strong></a>(self, axis=0, level=None, numeric_only=False)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;number&nbsp;of&nbsp;non-NA/null&nbsp;observations&nbsp;over&nbsp;requested<br>
-axis.&nbsp;Works&nbsp;with&nbsp;non-floating&nbsp;point&nbsp;data&nbsp;as&nbsp;well&nbsp;(detects&nbsp;NaN&nbsp;and&nbsp;None)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;or&nbsp;'index'&nbsp;for&nbsp;row-wise,&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;for&nbsp;column-wise<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-count&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-cov"><strong>cov</strong></a>(self, min_periods=None)</dt><dd><tt>Compute&nbsp;pairwise&nbsp;covariance&nbsp;of&nbsp;columns,&nbsp;excluding&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;required&nbsp;per&nbsp;pair&nbsp;of&nbsp;columns<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-`y`&nbsp;contains&nbsp;the&nbsp;covariance&nbsp;matrix&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;time&nbsp;series.<br>
-The&nbsp;covariance&nbsp;is&nbsp;normalized&nbsp;by&nbsp;N-1&nbsp;(unbiased&nbsp;estimator).</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-cummax"><strong>cummax</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;max&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummax&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.max&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-cummin"><strong>cummin</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;minimum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummin&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.min&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-cumprod"><strong>cumprod</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;product&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumprod&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.prod&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-cumsum"><strong>cumsum</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;sum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumsum&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.sum&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-diff"><strong>diff</strong></a>(self, periods=1, axis=0)</dt><dd><tt>1st&nbsp;discrete&nbsp;difference&nbsp;of&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;difference<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Take&nbsp;difference&nbsp;over&nbsp;rows&nbsp;(0)&nbsp;or&nbsp;columns&nbsp;(1).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-diffed&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-div"><strong>div</strong></a> = truediv(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-divide"><strong>divide</strong></a> = truediv(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-dot"><strong>dot</strong></a>(self, other)</dt><dd><tt>Matrix&nbsp;multiplication&nbsp;with&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;objects<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dot_product&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-drop_duplicates"><strong>drop_duplicates</strong></a>(self, subset=None, keep='first', inplace=False)</dt><dd><tt>Return&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;duplicate&nbsp;rows&nbsp;removed,&nbsp;optionally&nbsp;only<br>
-considering&nbsp;certain&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-subset&nbsp;:&nbsp;column&nbsp;label&nbsp;or&nbsp;sequence&nbsp;of&nbsp;labels,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Only&nbsp;consider&nbsp;certain&nbsp;columns&nbsp;for&nbsp;identifying&nbsp;duplicates,&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;use&nbsp;all&nbsp;of&nbsp;the&nbsp;columns<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Drop&nbsp;all&nbsp;duplicates.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;drop&nbsp;duplicates&nbsp;in&nbsp;place&nbsp;or&nbsp;to&nbsp;return&nbsp;a&nbsp;copy<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-deduplicated&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-dropna"><strong>dropna</strong></a>(self, axis=0, how='any', thresh=None, subset=None, inplace=False)</dt><dd><tt>Return&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;labels&nbsp;on&nbsp;given&nbsp;axis&nbsp;omitted&nbsp;where&nbsp;alternately&nbsp;any<br>
-or&nbsp;all&nbsp;of&nbsp;the&nbsp;data&nbsp;are&nbsp;missing<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;or&nbsp;tuple/list&nbsp;thereof<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Pass&nbsp;tuple&nbsp;or&nbsp;list&nbsp;to&nbsp;drop&nbsp;on&nbsp;multiple&nbsp;axes<br>
-how&nbsp;:&nbsp;{'any',&nbsp;'all'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;any&nbsp;:&nbsp;if&nbsp;any&nbsp;NA&nbsp;values&nbsp;are&nbsp;present,&nbsp;drop&nbsp;that&nbsp;label<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;all&nbsp;:&nbsp;if&nbsp;all&nbsp;values&nbsp;are&nbsp;NA,&nbsp;drop&nbsp;that&nbsp;label<br>
-thresh&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;int&nbsp;value&nbsp;:&nbsp;require&nbsp;that&nbsp;many&nbsp;non-NA&nbsp;values<br>
-subset&nbsp;:&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Labels&nbsp;along&nbsp;other&nbsp;axis&nbsp;to&nbsp;consider,&nbsp;e.g.&nbsp;if&nbsp;you&nbsp;are&nbsp;dropping&nbsp;rows<br>
-&nbsp;&nbsp;&nbsp;&nbsp;these&nbsp;would&nbsp;be&nbsp;a&nbsp;list&nbsp;of&nbsp;columns&nbsp;to&nbsp;include<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;return&nbsp;None.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dropped&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[np.nan,&nbsp;2,&nbsp;np.nan,&nbsp;0],&nbsp;[3,&nbsp;4,&nbsp;np.nan,&nbsp;1],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[np.nan,&nbsp;np.nan,&nbsp;np.nan,&nbsp;5]],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=list('ABCD'))<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;NaN&nbsp;&nbsp;2.0&nbsp;NaN&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;3.0&nbsp;&nbsp;4.0&nbsp;NaN&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;NaN&nbsp;&nbsp;NaN&nbsp;NaN&nbsp;&nbsp;5<br>
-&nbsp;<br>
-Drop&nbsp;the&nbsp;columns&nbsp;where&nbsp;all&nbsp;elements&nbsp;are&nbsp;nan:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-dropna">dropna</a>(axis=1,&nbsp;how='all')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;NaN&nbsp;&nbsp;2.0&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;3.0&nbsp;&nbsp;4.0&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;NaN&nbsp;&nbsp;NaN&nbsp;&nbsp;5<br>
-&nbsp;<br>
-Drop&nbsp;the&nbsp;columns&nbsp;where&nbsp;any&nbsp;of&nbsp;the&nbsp;elements&nbsp;is&nbsp;nan<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-dropna">dropna</a>(axis=1,&nbsp;how='any')<br>
-&nbsp;&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;5<br>
-&nbsp;<br>
-Drop&nbsp;the&nbsp;rows&nbsp;where&nbsp;all&nbsp;of&nbsp;the&nbsp;elements&nbsp;are&nbsp;nan<br>
-(there&nbsp;is&nbsp;no&nbsp;row&nbsp;to&nbsp;drop,&nbsp;so&nbsp;df&nbsp;stays&nbsp;the&nbsp;same):<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-dropna">dropna</a>(axis=0,&nbsp;how='all')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;NaN&nbsp;&nbsp;2.0&nbsp;NaN&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;3.0&nbsp;&nbsp;4.0&nbsp;NaN&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;NaN&nbsp;&nbsp;NaN&nbsp;NaN&nbsp;&nbsp;5<br>
-&nbsp;<br>
-Keep&nbsp;only&nbsp;the&nbsp;rows&nbsp;with&nbsp;at&nbsp;least&nbsp;2&nbsp;non-na&nbsp;values:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-dropna">dropna</a>(thresh=2)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;NaN&nbsp;&nbsp;2.0&nbsp;NaN&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;3.0&nbsp;&nbsp;4.0&nbsp;NaN&nbsp;&nbsp;1</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-duplicated"><strong>duplicated</strong></a>(self, subset=None, keep='first')</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;denoting&nbsp;duplicate&nbsp;rows,&nbsp;optionally&nbsp;only<br>
-considering&nbsp;certain&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-subset&nbsp;:&nbsp;column&nbsp;label&nbsp;or&nbsp;sequence&nbsp;of&nbsp;labels,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Only&nbsp;consider&nbsp;certain&nbsp;columns&nbsp;for&nbsp;identifying&nbsp;duplicates,&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;use&nbsp;all&nbsp;of&nbsp;the&nbsp;columns<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;last&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Mark&nbsp;all&nbsp;duplicates&nbsp;as&nbsp;``True``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-duplicated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-eq"><strong>eq</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;eq</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-eval"><strong>eval</strong></a>(self, expr, inplace=None, **kwargs)</dt><dd><tt>Evaluate&nbsp;an&nbsp;expression&nbsp;in&nbsp;the&nbsp;context&nbsp;of&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-instance.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-expr&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;expression&nbsp;string&nbsp;to&nbsp;evaluate.<br>
-inplace&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;expression&nbsp;contains&nbsp;an&nbsp;assignment,&nbsp;whether&nbsp;to&nbsp;return&nbsp;a&nbsp;new<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;mutate&nbsp;the&nbsp;existing.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;WARNING:&nbsp;inplace=None&nbsp;currently&nbsp;falls&nbsp;back&nbsp;to&nbsp;to&nbsp;True,&nbsp;but<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;a&nbsp;future&nbsp;version,&nbsp;will&nbsp;default&nbsp;to&nbsp;False.&nbsp;&nbsp;Use&nbsp;inplace=True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;explicitly&nbsp;rather&nbsp;than&nbsp;relying&nbsp;on&nbsp;the&nbsp;default.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-kwargs&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;documentation&nbsp;for&nbsp;:func:`~pandas.eval`&nbsp;for&nbsp;complete&nbsp;details<br>
-&nbsp;&nbsp;&nbsp;&nbsp;on&nbsp;the&nbsp;keyword&nbsp;arguments&nbsp;accepted&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:meth:`~pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.query`.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ret&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;scalar,&nbsp;or&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.query<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.assign<br>
-pandas.eval<br>
-&nbsp;<br>
-Notes<br>
------<br>
-For&nbsp;more&nbsp;details&nbsp;see&nbsp;the&nbsp;API&nbsp;documentation&nbsp;for&nbsp;:func:`~pandas.eval`.<br>
-For&nbsp;detailed&nbsp;examples&nbsp;see&nbsp;:ref:`enhancing&nbsp;performance&nbsp;with&nbsp;eval<br>
-&lt;enhancingperf.eval&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;numpy.random&nbsp;import&nbsp;randn<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;pandas&nbsp;import&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(randn(10,&nbsp;2),&nbsp;columns=list('ab'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-eval">eval</a>('a&nbsp;+&nbsp;b')<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-eval">eval</a>('c&nbsp;=&nbsp;a&nbsp;+&nbsp;b')</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-ewm"><strong>ewm</strong></a>(self, com=None, span=None, halflife=None, alpha=None, min_periods=0, freq=None, adjust=True, ignore_na=False, axis=0)</dt><dd><tt>Provides&nbsp;exponential&nbsp;weighted&nbsp;functions<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-com&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;/&nbsp;(1&nbsp;+&nbsp;com),\text{&nbsp;for&nbsp;}&nbsp;com&nbsp;\geq&nbsp;0`<br>
-span&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;span,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;2&nbsp;/&nbsp;(span&nbsp;+&nbsp;1),\text{&nbsp;for&nbsp;}&nbsp;span&nbsp;\geq&nbsp;1`<br>
-halflife&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;half-life,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;-&nbsp;exp(log(0.5)&nbsp;/&nbsp;halflife),\text{&nbsp;for&nbsp;}&nbsp;halflife&nbsp;&gt;&nbsp;0`<br>
-alpha&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;smoothing&nbsp;factor&nbsp;:math:`\alpha`&nbsp;directly,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`0&nbsp;&lt;&nbsp;\alpha&nbsp;\leq&nbsp;1`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;None&nbsp;or&nbsp;string&nbsp;alias&nbsp;/&nbsp;date&nbsp;offset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default=None&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;to&nbsp;before&nbsp;computing&nbsp;statistic<br>
-adjust&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Divide&nbsp;by&nbsp;decaying&nbsp;adjustment&nbsp;factor&nbsp;in&nbsp;beginning&nbsp;periods&nbsp;to&nbsp;account<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;imbalance&nbsp;in&nbsp;relative&nbsp;weightings&nbsp;(viewing&nbsp;EWMA&nbsp;as&nbsp;a&nbsp;moving&nbsp;average)<br>
-ignore_na&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Ignore&nbsp;missing&nbsp;values&nbsp;when&nbsp;calculating&nbsp;weights;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specify&nbsp;True&nbsp;to&nbsp;reproduce&nbsp;pre-0.15.0&nbsp;behavior<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-ewm">ewm</a>(com=0.5).<a href="#TimeFrame-mean">mean</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.000000<br>
-1&nbsp;&nbsp;0.750000<br>
-2&nbsp;&nbsp;1.615385<br>
-3&nbsp;&nbsp;1.615385<br>
-4&nbsp;&nbsp;3.670213<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Exactly&nbsp;one&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,&nbsp;span,&nbsp;half-life,&nbsp;and&nbsp;alpha&nbsp;must&nbsp;be&nbsp;provided.<br>
-Allowed&nbsp;values&nbsp;and&nbsp;relationship&nbsp;between&nbsp;the&nbsp;parameters&nbsp;are&nbsp;specified&nbsp;in&nbsp;the<br>
-parameter&nbsp;descriptions&nbsp;above;&nbsp;see&nbsp;the&nbsp;link&nbsp;at&nbsp;the&nbsp;end&nbsp;of&nbsp;this&nbsp;section&nbsp;for<br>
-a&nbsp;detailed&nbsp;explanation.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;True&nbsp;(default),&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;using<br>
-weights&nbsp;(1-alpha)**(n-1),&nbsp;(1-alpha)**(n-2),&nbsp;...,&nbsp;1-alpha,&nbsp;1.<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;False,&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;recursively&nbsp;as:<br>
-&nbsp;&nbsp;&nbsp;weighted_average[0]&nbsp;=&nbsp;arg[0];<br>
-&nbsp;&nbsp;&nbsp;weighted_average[i]&nbsp;=&nbsp;(1-alpha)*weighted_average[i-1]&nbsp;+&nbsp;alpha*arg[i].<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;False&nbsp;(default),&nbsp;weights&nbsp;are&nbsp;based&nbsp;on&nbsp;absolute&nbsp;positions.<br>
-For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in&nbsp;calculating&nbsp;the&nbsp;final&nbsp;weighted<br>
-average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;(1-alpha)**2&nbsp;and&nbsp;1&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and<br>
-(1-alpha)**2&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;True&nbsp;(reproducing&nbsp;pre-0.15.0&nbsp;behavior),&nbsp;weights&nbsp;are&nbsp;based<br>
-on&nbsp;relative&nbsp;positions.&nbsp;For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in<br>
-calculating&nbsp;the&nbsp;final&nbsp;weighted&nbsp;average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;1-alpha&nbsp;and&nbsp;1<br>
-(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and&nbsp;1-alpha&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-More&nbsp;details&nbsp;can&nbsp;be&nbsp;found&nbsp;at<br>
-<a href="http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows">http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-expanding"><strong>expanding</strong></a>(self, min_periods=1, freq=None, center=False, axis=0)</dt><dd><tt>Provides&nbsp;expanding&nbsp;transformations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-expanding">expanding</a>(2).<a href="#TimeFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;7.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-fillna"><strong>fillna</strong></a>(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)</dt><dd><tt>Fill&nbsp;NA/NaN&nbsp;values&nbsp;using&nbsp;the&nbsp;specified&nbsp;method<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;values&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;each&nbsp;index&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;or&nbsp;column&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).&nbsp;(values&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;This&nbsp;value&nbsp;cannot<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;list.<br>
-method&nbsp;:&nbsp;{'backfill',&nbsp;'bfill',&nbsp;'pad',&nbsp;'ffill',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'}<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;fill&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>,&nbsp;(e.g.&nbsp;a&nbsp;no-copy&nbsp;slice&nbsp;for&nbsp;a&nbsp;column&nbsp;in&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;method&nbsp;is&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the&nbsp;maximum&nbsp;number&nbsp;of&nbsp;consecutive<br>
-&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;values&nbsp;to&nbsp;forward/backward&nbsp;fill.&nbsp;In&nbsp;other&nbsp;words,&nbsp;if&nbsp;there&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;gap&nbsp;with&nbsp;more&nbsp;than&nbsp;this&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs,&nbsp;it&nbsp;will&nbsp;only<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;partially&nbsp;filled.&nbsp;If&nbsp;method&nbsp;is&nbsp;not&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;maximum&nbsp;number&nbsp;of&nbsp;entries&nbsp;along&nbsp;the&nbsp;entire&nbsp;axis&nbsp;where&nbsp;NaNs&nbsp;will&nbsp;be<br>
-&nbsp;&nbsp;&nbsp;&nbsp;filled.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0&nbsp;if&nbsp;not&nbsp;None.<br>
-downcast&nbsp;:&nbsp;dict,&nbsp;default&nbsp;is&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;dict&nbsp;of&nbsp;item-&gt;dtype&nbsp;of&nbsp;what&nbsp;to&nbsp;downcast&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;the&nbsp;string&nbsp;'infer'&nbsp;which&nbsp;will&nbsp;try&nbsp;to&nbsp;downcast&nbsp;to&nbsp;an&nbsp;appropriate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;equal&nbsp;type&nbsp;(e.g.&nbsp;float64&nbsp;to&nbsp;int64&nbsp;if&nbsp;possible)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;asfreq<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-first_valid_index"><strong>first_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;first&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-floordiv"><strong>floordiv</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`floordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;//&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rfloordiv</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-ge"><strong>ge</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;ge</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-get_value"><strong>get_value</strong></a>(self, index, col, takeable=False)</dt><dd><tt>Quickly&nbsp;retrieve&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;column&nbsp;and&nbsp;index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;row&nbsp;label<br>
-col&nbsp;:&nbsp;column&nbsp;label<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index/col&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;scalar&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-gt"><strong>gt</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;gt</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-hist"><strong>hist</strong></a> = hist_frame(data, column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, **kwds)</dt><dd><tt>Draw&nbsp;histogram&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;series&nbsp;using&nbsp;matplotlib&nbsp;/&nbsp;pylab.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-column&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed,&nbsp;will&nbsp;be&nbsp;used&nbsp;to&nbsp;limit&nbsp;data&nbsp;to&nbsp;a&nbsp;subset&nbsp;of&nbsp;columns<br>
-by&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed,&nbsp;then&nbsp;used&nbsp;to&nbsp;form&nbsp;histograms&nbsp;for&nbsp;separate&nbsp;groups<br>
-grid&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;show&nbsp;axis&nbsp;grid&nbsp;lines<br>
-xlabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;x-axis&nbsp;label&nbsp;size<br>
-xrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;x&nbsp;axis&nbsp;labels<br>
-ylabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;y-axis&nbsp;label&nbsp;size<br>
-yrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;y&nbsp;axis&nbsp;labels<br>
-ax&nbsp;:&nbsp;matplotlib&nbsp;axes&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-sharex&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True&nbsp;if&nbsp;ax&nbsp;is&nbsp;None&nbsp;else&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;subplots=True,&nbsp;share&nbsp;x&nbsp;axis&nbsp;and&nbsp;set&nbsp;some&nbsp;x&nbsp;axis&nbsp;labels&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;invisible;&nbsp;defaults&nbsp;to&nbsp;True&nbsp;if&nbsp;ax&nbsp;is&nbsp;None&nbsp;otherwise&nbsp;False&nbsp;if&nbsp;an&nbsp;ax<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;passed&nbsp;in;&nbsp;Be&nbsp;aware,&nbsp;that&nbsp;passing&nbsp;in&nbsp;both&nbsp;an&nbsp;ax&nbsp;and&nbsp;sharex=True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;alter&nbsp;all&nbsp;x&nbsp;axis&nbsp;labels&nbsp;for&nbsp;all&nbsp;subplots&nbsp;in&nbsp;a&nbsp;figure!<br>
-sharey&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;subplots=True,&nbsp;share&nbsp;y&nbsp;axis&nbsp;and&nbsp;set&nbsp;some&nbsp;y&nbsp;axis&nbsp;labels&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;invisible<br>
-figsize&nbsp;:&nbsp;tuple<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;size&nbsp;of&nbsp;the&nbsp;figure&nbsp;to&nbsp;create&nbsp;in&nbsp;inches&nbsp;by&nbsp;default<br>
-layout&nbsp;:&nbsp;tuple,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Tuple&nbsp;of&nbsp;(rows,&nbsp;columns)&nbsp;for&nbsp;the&nbsp;layout&nbsp;of&nbsp;the&nbsp;histograms<br>
-bins&nbsp;:&nbsp;integer,&nbsp;default&nbsp;10<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;histogram&nbsp;bins&nbsp;to&nbsp;be&nbsp;used<br>
-kwds&nbsp;:&nbsp;other&nbsp;plotting&nbsp;keyword&nbsp;arguments<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;passed&nbsp;to&nbsp;hist&nbsp;function</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-idxmax"><strong>idxmax</strong></a>(self, axis=0, skipna=True)</dt><dd><tt>Return&nbsp;index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;maximum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-NA/null&nbsp;values&nbsp;are&nbsp;excluded.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;or&nbsp;'index'&nbsp;for&nbsp;row-wise,&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;for&nbsp;column-wise<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;first&nbsp;index.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmax&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmax``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.idxmax</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-idxmin"><strong>idxmin</strong></a>(self, axis=0, skipna=True)</dt><dd><tt>Return&nbsp;index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;minimum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-NA/null&nbsp;values&nbsp;are&nbsp;excluded.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;or&nbsp;'index'&nbsp;for&nbsp;row-wise,&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;for&nbsp;column-wise<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmin&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmin``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.idxmin</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-info"><strong>info</strong></a>(self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None)</dt><dd><tt>Concise&nbsp;summary&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-verbose&nbsp;:&nbsp;{None,&nbsp;True,&nbsp;False},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;print&nbsp;the&nbsp;full&nbsp;summary.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;None&nbsp;follows&nbsp;the&nbsp;`display.max_info_columns`&nbsp;setting.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;True&nbsp;or&nbsp;False&nbsp;overrides&nbsp;the&nbsp;`display.max_info_columns`&nbsp;setting.<br>
-buf&nbsp;:&nbsp;writable&nbsp;buffer,&nbsp;defaults&nbsp;to&nbsp;sys.stdout<br>
-max_cols&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Determines&nbsp;whether&nbsp;full&nbsp;summary&nbsp;or&nbsp;short&nbsp;summary&nbsp;is&nbsp;printed.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;None&nbsp;follows&nbsp;the&nbsp;`display.max_info_columns`&nbsp;setting.<br>
-memory_usage&nbsp;:&nbsp;boolean/string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;whether&nbsp;total&nbsp;memory&nbsp;usage&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;elements&nbsp;(including&nbsp;index)&nbsp;should&nbsp;be&nbsp;displayed.&nbsp;None&nbsp;follows<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;`display.memory_usage`&nbsp;setting.&nbsp;True&nbsp;or&nbsp;False&nbsp;overrides<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;`display.memory_usage`&nbsp;setting.&nbsp;A&nbsp;value&nbsp;of&nbsp;'deep'&nbsp;is&nbsp;equivalent<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;True,&nbsp;with&nbsp;deep&nbsp;introspection.&nbsp;Memory&nbsp;usage&nbsp;is&nbsp;shown&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;human-readable&nbsp;units&nbsp;(base-2&nbsp;representation).<br>
-null_counts&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;show&nbsp;the&nbsp;non-null&nbsp;counts<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;If&nbsp;None,&nbsp;then&nbsp;only&nbsp;show&nbsp;if&nbsp;the&nbsp;frame&nbsp;is&nbsp;smaller&nbsp;than<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;max_info_rows&nbsp;and&nbsp;max_info_columns.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;If&nbsp;True,&nbsp;always&nbsp;show&nbsp;counts.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;If&nbsp;False,&nbsp;never&nbsp;show&nbsp;counts.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-insert"><strong>insert</strong></a>(self, loc, column, value, allow_duplicates=False)</dt><dd><tt>Insert&nbsp;column&nbsp;into&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;at&nbsp;specified&nbsp;location.<br>
-&nbsp;<br>
-If&nbsp;`allow_duplicates`&nbsp;is&nbsp;False,&nbsp;raises&nbsp;Exception&nbsp;if&nbsp;column<br>
-is&nbsp;already&nbsp;contained&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-loc&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Must&nbsp;have&nbsp;0&nbsp;&lt;=&nbsp;loc&nbsp;&lt;=&nbsp;len(columns)<br>
-column&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-value&nbsp;:&nbsp;scalar,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;array-like</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-isin"><strong>isin</strong></a>(self, values)</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;showing&nbsp;whether&nbsp;each&nbsp;element&nbsp;in&nbsp;the<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;is&nbsp;contained&nbsp;in&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-values&nbsp;:&nbsp;iterable,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;dictionary<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;result&nbsp;will&nbsp;only&nbsp;be&nbsp;true&nbsp;at&nbsp;a&nbsp;location&nbsp;if&nbsp;all&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;labels&nbsp;match.&nbsp;If&nbsp;`values`&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;that's&nbsp;the&nbsp;index.&nbsp;If<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`values`&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;the&nbsp;keys&nbsp;must&nbsp;be&nbsp;the&nbsp;column&nbsp;names,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;must&nbsp;match.&nbsp;If&nbsp;`values`&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;both&nbsp;the&nbsp;index&nbsp;and&nbsp;column&nbsp;labels&nbsp;must&nbsp;match.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;booleans<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-When&nbsp;``values``&nbsp;is&nbsp;a&nbsp;list:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;'B':&nbsp;['a',&nbsp;'b',&nbsp;'f']})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-isin">isin</a>([1,&nbsp;3,&nbsp;12,&nbsp;'a'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;False&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;False<br>
-&nbsp;<br>
-When&nbsp;``values``&nbsp;is&nbsp;a&nbsp;dict:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;'B':&nbsp;[1,&nbsp;4,&nbsp;7]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-isin">isin</a>({'A':&nbsp;[1,&nbsp;3],&nbsp;'B':&nbsp;[4,&nbsp;7,&nbsp;12]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;False&nbsp;&nbsp;#&nbsp;Note&nbsp;that&nbsp;B&nbsp;didn't&nbsp;match&nbsp;the&nbsp;1&nbsp;here.<br>
-1&nbsp;&nbsp;False&nbsp;&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;&nbsp;True<br>
-&nbsp;<br>
-When&nbsp;``values``&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;'B':&nbsp;['a',&nbsp;'b',&nbsp;'f']})<br>
-&gt;&gt;&gt;&nbsp;other&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;3,&nbsp;3,&nbsp;2],&nbsp;'B':&nbsp;['e',&nbsp;'f',&nbsp;'f',&nbsp;'e']})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-isin">isin</a>(other)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;False<br>
-1&nbsp;&nbsp;False&nbsp;&nbsp;False&nbsp;&nbsp;#&nbsp;Column&nbsp;A&nbsp;in&nbsp;`other`&nbsp;has&nbsp;a&nbsp;3,&nbsp;but&nbsp;not&nbsp;at&nbsp;index&nbsp;1.<br>
-2&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;&nbsp;True</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-items"><strong>items</strong></a> = iteritems(self)</dt><dd><tt>Iterator&nbsp;over&nbsp;(column&nbsp;name,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-iterrows&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;(index,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-itertuples&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;namedtuples&nbsp;of&nbsp;the&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-iteritems"><strong>iteritems</strong></a>(self)</dt><dd><tt>Iterator&nbsp;over&nbsp;(column&nbsp;name,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-iterrows&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;(index,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-itertuples&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;namedtuples&nbsp;of&nbsp;the&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-iterrows"><strong>iterrows</strong></a>(self)</dt><dd><tt>Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;(index,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-1.&nbsp;Because&nbsp;``iterrows``&nbsp;returns&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;for&nbsp;each&nbsp;row,<br>
-&nbsp;&nbsp;&nbsp;it&nbsp;does&nbsp;**not**&nbsp;preserve&nbsp;dtypes&nbsp;across&nbsp;the&nbsp;rows&nbsp;(dtypes&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;preserved&nbsp;across&nbsp;columns&nbsp;for&nbsp;DataFrames).&nbsp;For&nbsp;example,<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[1,&nbsp;1.5]],&nbsp;columns=['int',&nbsp;'float'])<br>
-&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;row&nbsp;=&nbsp;next(df.<a href="#TimeFrame-iterrows">iterrows</a>())[1]<br>
-&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;row<br>
-&nbsp;&nbsp;&nbsp;int&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-&nbsp;&nbsp;&nbsp;float&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-&nbsp;&nbsp;&nbsp;Name:&nbsp;0,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;print(row['int'].dtype)<br>
-&nbsp;&nbsp;&nbsp;float64<br>
-&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;print(df['int'].dtype)<br>
-&nbsp;&nbsp;&nbsp;int64<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;To&nbsp;preserve&nbsp;dtypes&nbsp;while&nbsp;iterating&nbsp;over&nbsp;the&nbsp;rows,&nbsp;it&nbsp;is&nbsp;better<br>
-&nbsp;&nbsp;&nbsp;to&nbsp;use&nbsp;:meth:`itertuples`&nbsp;which&nbsp;returns&nbsp;namedtuples&nbsp;of&nbsp;the&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;and&nbsp;which&nbsp;is&nbsp;generally&nbsp;faster&nbsp;than&nbsp;``iterrows``.<br>
-&nbsp;<br>
-2.&nbsp;You&nbsp;should&nbsp;**never&nbsp;modify**&nbsp;something&nbsp;you&nbsp;are&nbsp;iterating&nbsp;over.<br>
-&nbsp;&nbsp;&nbsp;This&nbsp;is&nbsp;not&nbsp;guaranteed&nbsp;to&nbsp;work&nbsp;in&nbsp;all&nbsp;cases.&nbsp;Depending&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;data&nbsp;types,&nbsp;the&nbsp;iterator&nbsp;returns&nbsp;a&nbsp;copy&nbsp;and&nbsp;not&nbsp;a&nbsp;view,&nbsp;and&nbsp;writing<br>
-&nbsp;&nbsp;&nbsp;to&nbsp;it&nbsp;will&nbsp;have&nbsp;no&nbsp;effect.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-it&nbsp;:&nbsp;generator<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;generator&nbsp;that&nbsp;iterates&nbsp;over&nbsp;the&nbsp;rows&nbsp;of&nbsp;the&nbsp;frame.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-itertuples&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;namedtuples&nbsp;of&nbsp;the&nbsp;values.<br>
-iteritems&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;(column&nbsp;name,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-itertuples"><strong>itertuples</strong></a>(self, index=True, name='Pandas')</dt><dd><tt>Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;namedtuples,&nbsp;with&nbsp;index&nbsp;value&nbsp;as&nbsp;first<br>
-element&nbsp;of&nbsp;the&nbsp;tuple.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;return&nbsp;the&nbsp;index&nbsp;as&nbsp;the&nbsp;first&nbsp;element&nbsp;of&nbsp;the&nbsp;tuple.<br>
-name&nbsp;:&nbsp;string,&nbsp;default&nbsp;"Pandas"<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;name&nbsp;of&nbsp;the&nbsp;returned&nbsp;namedtuples&nbsp;or&nbsp;None&nbsp;to&nbsp;return&nbsp;regular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;tuples.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;column&nbsp;names&nbsp;will&nbsp;be&nbsp;renamed&nbsp;to&nbsp;positional&nbsp;names&nbsp;if&nbsp;they&nbsp;are<br>
-invalid&nbsp;Python&nbsp;identifiers,&nbsp;repeated,&nbsp;or&nbsp;start&nbsp;with&nbsp;an&nbsp;underscore.<br>
-With&nbsp;a&nbsp;large&nbsp;number&nbsp;of&nbsp;columns&nbsp;(&gt;255),&nbsp;regular&nbsp;tuples&nbsp;are&nbsp;returned.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-iterrows&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;rows&nbsp;as&nbsp;(index,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-iteritems&nbsp;:&nbsp;Iterate&nbsp;over&nbsp;(column&nbsp;name,&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;pairs.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'col1':&nbsp;[1,&nbsp;2],&nbsp;'col2':&nbsp;[0.1,&nbsp;0.2]},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=['a',&nbsp;'b'])<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;col1&nbsp;&nbsp;col2<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;0.1<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;0.2<br>
-&gt;&gt;&gt;&nbsp;for&nbsp;row&nbsp;in&nbsp;df.<a href="#TimeFrame-itertuples">itertuples</a>():<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;print(row)<br>
-...<br>
-Pandas(Index='a',&nbsp;col1=1,&nbsp;col2=0.10000000000000001)<br>
-Pandas(Index='b',&nbsp;col1=2,&nbsp;col2=0.20000000000000001)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-join"><strong>join</strong></a>(self, other, on=None, how='left', lsuffix='', rsuffix='', sort=False)</dt><dd><tt>Join&nbsp;columns&nbsp;with&nbsp;other&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;either&nbsp;on&nbsp;index&nbsp;or&nbsp;on&nbsp;a&nbsp;key<br>
-column.&nbsp;Efficiently&nbsp;Join&nbsp;multiple&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects&nbsp;by&nbsp;index&nbsp;at&nbsp;once&nbsp;by<br>
-passing&nbsp;a&nbsp;list.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;name&nbsp;field&nbsp;set,&nbsp;or&nbsp;list&nbsp;of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Index&nbsp;should&nbsp;be&nbsp;similar&nbsp;to&nbsp;one&nbsp;of&nbsp;the&nbsp;columns&nbsp;in&nbsp;this&nbsp;one.&nbsp;If&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;is&nbsp;passed,&nbsp;its&nbsp;name&nbsp;attribute&nbsp;must&nbsp;be&nbsp;set,&nbsp;and&nbsp;that&nbsp;will&nbsp;be<br>
-&nbsp;&nbsp;&nbsp;&nbsp;used&nbsp;as&nbsp;the&nbsp;column&nbsp;name&nbsp;in&nbsp;the&nbsp;resulting&nbsp;joined&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-on&nbsp;:&nbsp;column&nbsp;name,&nbsp;tuple/list&nbsp;of&nbsp;column&nbsp;names,&nbsp;or&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column(s)&nbsp;in&nbsp;the&nbsp;caller&nbsp;to&nbsp;join&nbsp;on&nbsp;the&nbsp;index&nbsp;in&nbsp;other,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;joins&nbsp;index-on-index.&nbsp;If&nbsp;multiples<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns&nbsp;given,&nbsp;the&nbsp;passed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;must&nbsp;have&nbsp;a&nbsp;MultiIndex.&nbsp;Can<br>
-&nbsp;&nbsp;&nbsp;&nbsp;pass&nbsp;an&nbsp;array&nbsp;as&nbsp;the&nbsp;join&nbsp;key&nbsp;if&nbsp;not&nbsp;already&nbsp;contained&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Like&nbsp;an&nbsp;Excel&nbsp;VLOOKUP&nbsp;operation<br>
-how&nbsp;:&nbsp;{'left',&nbsp;'right',&nbsp;'outer',&nbsp;'inner'},&nbsp;default:&nbsp;'left'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;How&nbsp;to&nbsp;handle&nbsp;the&nbsp;operation&nbsp;of&nbsp;the&nbsp;two&nbsp;objects.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;left:&nbsp;use&nbsp;calling&nbsp;frame's&nbsp;index&nbsp;(or&nbsp;column&nbsp;if&nbsp;on&nbsp;is&nbsp;specified)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;right:&nbsp;use&nbsp;other&nbsp;frame's&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;outer:&nbsp;form&nbsp;union&nbsp;of&nbsp;calling&nbsp;frame's&nbsp;index&nbsp;(or&nbsp;column&nbsp;if&nbsp;on&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;specified)&nbsp;with&nbsp;other&nbsp;frame's&nbsp;index,&nbsp;and&nbsp;sort&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lexicographically<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;inner:&nbsp;form&nbsp;intersection&nbsp;of&nbsp;calling&nbsp;frame's&nbsp;index&nbsp;(or&nbsp;column&nbsp;if<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;on&nbsp;is&nbsp;specified)&nbsp;with&nbsp;other&nbsp;frame's&nbsp;index,&nbsp;preserving&nbsp;the&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;calling's&nbsp;one<br>
-lsuffix&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Suffix&nbsp;to&nbsp;use&nbsp;from&nbsp;left&nbsp;frame's&nbsp;overlapping&nbsp;columns<br>
-rsuffix&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Suffix&nbsp;to&nbsp;use&nbsp;from&nbsp;right&nbsp;frame's&nbsp;overlapping&nbsp;columns<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Order&nbsp;result&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;lexicographically&nbsp;by&nbsp;the&nbsp;join&nbsp;key.&nbsp;If&nbsp;False,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;join&nbsp;key&nbsp;depends&nbsp;on&nbsp;the&nbsp;join&nbsp;type&nbsp;(how&nbsp;keyword)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-on,&nbsp;lsuffix,&nbsp;and&nbsp;rsuffix&nbsp;options&nbsp;are&nbsp;not&nbsp;supported&nbsp;when&nbsp;passing&nbsp;a&nbsp;list<br>
-of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;caller&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'key':&nbsp;['K0',&nbsp;'K1',&nbsp;'K2',&nbsp;'K3',&nbsp;'K4',&nbsp;'K5'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'A':&nbsp;['A0',&nbsp;'A1',&nbsp;'A2',&nbsp;'A3',&nbsp;'A4',&nbsp;'A5']})<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;caller<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;key<br>
-0&nbsp;&nbsp;A0&nbsp;&nbsp;K0<br>
-1&nbsp;&nbsp;A1&nbsp;&nbsp;K1<br>
-2&nbsp;&nbsp;A2&nbsp;&nbsp;K2<br>
-3&nbsp;&nbsp;A3&nbsp;&nbsp;K3<br>
-4&nbsp;&nbsp;A4&nbsp;&nbsp;K4<br>
-5&nbsp;&nbsp;A5&nbsp;&nbsp;K5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;other&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'key':&nbsp;['K0',&nbsp;'K1',&nbsp;'K2'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B':&nbsp;['B0',&nbsp;'B1',&nbsp;'B2']})<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;key<br>
-0&nbsp;&nbsp;B0&nbsp;&nbsp;K0<br>
-1&nbsp;&nbsp;B1&nbsp;&nbsp;K1<br>
-2&nbsp;&nbsp;B2&nbsp;&nbsp;K2<br>
-&nbsp;<br>
-Join&nbsp;DataFrames&nbsp;using&nbsp;their&nbsp;indexes.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;caller.<a href="#TimeFrame-join">join</a>(other,&nbsp;lsuffix='_caller',&nbsp;rsuffix='_other')<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;key_caller&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;key_other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;A0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K0&nbsp;&nbsp;&nbsp;B0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;A1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K1&nbsp;&nbsp;&nbsp;B1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;A2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K2&nbsp;&nbsp;&nbsp;B2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;A3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K3&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;A4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K4&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;A5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;K5&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-&nbsp;<br>
-If&nbsp;we&nbsp;want&nbsp;to&nbsp;join&nbsp;using&nbsp;the&nbsp;key&nbsp;columns,&nbsp;we&nbsp;need&nbsp;to&nbsp;set&nbsp;key&nbsp;to&nbsp;be<br>
-the&nbsp;index&nbsp;in&nbsp;both&nbsp;caller&nbsp;and&nbsp;other.&nbsp;The&nbsp;joined&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;have<br>
-key&nbsp;as&nbsp;its&nbsp;index.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;caller.<a href="#TimeFrame-set_index">set_index</a>('key').<a href="#TimeFrame-join">join</a>(other.<a href="#TimeFrame-set_index">set_index</a>('key'))<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-&nbsp;&nbsp;&nbsp;&nbsp;key<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K0&nbsp;&nbsp;&nbsp;A0&nbsp;&nbsp;&nbsp;B0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K1&nbsp;&nbsp;&nbsp;A1&nbsp;&nbsp;&nbsp;B1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K2&nbsp;&nbsp;&nbsp;A2&nbsp;&nbsp;&nbsp;B2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K3&nbsp;&nbsp;&nbsp;A3&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K4&nbsp;&nbsp;&nbsp;A4&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;K5&nbsp;&nbsp;&nbsp;A5&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Another&nbsp;option&nbsp;to&nbsp;join&nbsp;using&nbsp;the&nbsp;key&nbsp;columns&nbsp;is&nbsp;to&nbsp;use&nbsp;the&nbsp;on<br>
-parameter.&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.join&nbsp;always&nbsp;uses&nbsp;other's&nbsp;index&nbsp;but&nbsp;we&nbsp;can&nbsp;use&nbsp;any<br>
-column&nbsp;in&nbsp;the&nbsp;caller.&nbsp;This&nbsp;method&nbsp;preserves&nbsp;the&nbsp;original&nbsp;caller's<br>
-index&nbsp;in&nbsp;the&nbsp;result.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;caller.<a href="#TimeFrame-join">join</a>(other.<a href="#TimeFrame-set_index">set_index</a>('key'),&nbsp;on='key')<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;key&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;A0&nbsp;&nbsp;K0&nbsp;&nbsp;&nbsp;B0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;A1&nbsp;&nbsp;K1&nbsp;&nbsp;&nbsp;B1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;A2&nbsp;&nbsp;K2&nbsp;&nbsp;&nbsp;B2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;A3&nbsp;&nbsp;K3&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;A4&nbsp;&nbsp;K4&nbsp;&nbsp;NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;A5&nbsp;&nbsp;K5&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.merge&nbsp;:&nbsp;For&nbsp;column(s)-on-<a href="#TimeFrame-columns">columns</a>(s)&nbsp;operations<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-joined&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-kurt"><strong>kurt</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-kurtosis"><strong>kurtosis</strong></a> = kurt(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-last_valid_index"><strong>last_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;last&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-le"><strong>le</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;le</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-lookup"><strong>lookup</strong></a>(self, row_labels, col_labels)</dt><dd><tt>Label-based&nbsp;"fancy&nbsp;indexing"&nbsp;function&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-Given&nbsp;equal-length&nbsp;arrays&nbsp;of&nbsp;row&nbsp;and&nbsp;column&nbsp;labels,&nbsp;return&nbsp;an<br>
-array&nbsp;of&nbsp;the&nbsp;values&nbsp;corresponding&nbsp;to&nbsp;each&nbsp;(row,&nbsp;col)&nbsp;pair.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-row_labels&nbsp;:&nbsp;sequence<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;row&nbsp;labels&nbsp;to&nbsp;use&nbsp;for&nbsp;lookup<br>
-col_labels&nbsp;:&nbsp;sequence<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;column&nbsp;labels&nbsp;to&nbsp;use&nbsp;for&nbsp;lookup<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Akin&nbsp;to::<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;result&nbsp;=&nbsp;[]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;row,&nbsp;col&nbsp;in&nbsp;zip(row_labels,&nbsp;col_labels):<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;result.<a href="#TimeFrame-append">append</a>(df.<a href="#TimeFrame-get_value">get_value</a>(row,&nbsp;col))<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-values&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;found&nbsp;values</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-lt"><strong>lt</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;lt</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-mad"><strong>mad</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;absolute&nbsp;deviation&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mad&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-max"><strong>max</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;maximum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;maximum,&nbsp;use&nbsp;``idxmax``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmax``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-max&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-mean"><strong>mean</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mean&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-median"><strong>median</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;median&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-median&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-melt"><strong>melt</strong></a>(self, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)</dt><dd><tt>"Unpivots"&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;from&nbsp;wide&nbsp;format&nbsp;to&nbsp;long&nbsp;format,&nbsp;optionally<br>
-leaving&nbsp;identifier&nbsp;variables&nbsp;set.<br>
-&nbsp;<br>
-This&nbsp;function&nbsp;is&nbsp;useful&nbsp;to&nbsp;massage&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;into&nbsp;a&nbsp;format&nbsp;where&nbsp;one<br>
-or&nbsp;more&nbsp;columns&nbsp;are&nbsp;identifier&nbsp;variables&nbsp;(`id_vars`),&nbsp;while&nbsp;all&nbsp;other<br>
-columns,&nbsp;considered&nbsp;measured&nbsp;variables&nbsp;(`value_vars`),&nbsp;are&nbsp;"unpivoted"&nbsp;to<br>
-the&nbsp;row&nbsp;axis,&nbsp;leaving&nbsp;just&nbsp;two&nbsp;non-identifier&nbsp;columns,&nbsp;'variable'&nbsp;and<br>
-'value'.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-frame&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-id_vars&nbsp;:&nbsp;tuple,&nbsp;list,&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column(s)&nbsp;to&nbsp;use&nbsp;as&nbsp;identifier&nbsp;variables.<br>
-value_vars&nbsp;:&nbsp;tuple,&nbsp;list,&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column(s)&nbsp;to&nbsp;unpivot.&nbsp;If&nbsp;not&nbsp;specified,&nbsp;uses&nbsp;all&nbsp;columns&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;not&nbsp;set&nbsp;as&nbsp;`id_vars`.<br>
-var_name&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;to&nbsp;use&nbsp;for&nbsp;the&nbsp;'variable'&nbsp;column.&nbsp;If&nbsp;None&nbsp;it&nbsp;uses<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``frame.columns.name``&nbsp;or&nbsp;'variable'.<br>
-value_name&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;'value'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;to&nbsp;use&nbsp;for&nbsp;the&nbsp;'value'&nbsp;column.<br>
-col_level&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;columns&nbsp;are&nbsp;a&nbsp;MultiIndex&nbsp;then&nbsp;use&nbsp;this&nbsp;level&nbsp;to&nbsp;melt.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-melt<br>
-pivot_table<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.pivot<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;{0:&nbsp;'a',&nbsp;1:&nbsp;'b',&nbsp;2:&nbsp;'c'},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B':&nbsp;{0:&nbsp;1,&nbsp;1:&nbsp;3,&nbsp;2:&nbsp;5},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C':&nbsp;{0:&nbsp;2,&nbsp;1:&nbsp;4,&nbsp;2:&nbsp;6}})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;5&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-melt">melt</a>(id_vars=['A'],&nbsp;value_vars=['B'])<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;variable&nbsp;&nbsp;value<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-melt">melt</a>(id_vars=['A'],&nbsp;value_vars=['B',&nbsp;'C'])<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;variable&nbsp;&nbsp;value<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-3&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-4&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-5&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-The&nbsp;names&nbsp;of&nbsp;'variable'&nbsp;and&nbsp;'value'&nbsp;columns&nbsp;can&nbsp;be&nbsp;customized:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-melt">melt</a>(id_vars=['A'],&nbsp;value_vars=['B'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;var_name='myVarname',&nbsp;value_name='myValname')<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;myVarname&nbsp;&nbsp;myValname<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-&nbsp;<br>
-If&nbsp;you&nbsp;have&nbsp;multi-index&nbsp;columns:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.columns&nbsp;=&nbsp;[list('ABC'),&nbsp;list('DEF')]<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C<br>
-&nbsp;&nbsp;&nbsp;D&nbsp;&nbsp;E&nbsp;&nbsp;F<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;5&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-melt">melt</a>(col_level=0,&nbsp;id_vars=['A'],&nbsp;value_vars=['B'])<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;variable&nbsp;&nbsp;value<br>
-0&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-melt">melt</a>(id_vars=[('A',&nbsp;'D')],&nbsp;value_vars=[('B',&nbsp;'E')])<br>
-&nbsp;&nbsp;(A,&nbsp;D)&nbsp;variable_0&nbsp;variable_1&nbsp;&nbsp;value<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;E&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;E&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;E&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-memory_usage"><strong>memory_usage</strong></a>(self, index=True, deep=False)</dt><dd><tt>Memory&nbsp;usage&nbsp;of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;columns.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;whether&nbsp;to&nbsp;include&nbsp;memory&nbsp;usage&nbsp;of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;in&nbsp;returned&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;If&nbsp;`index=True`&nbsp;(default&nbsp;is&nbsp;False)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;first&nbsp;index&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;is&nbsp;`Index`.<br>
-deep&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Introspect&nbsp;the&nbsp;data&nbsp;deeply,&nbsp;interrogate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`<a href="builtins.html#object">object</a>`&nbsp;dtypes&nbsp;for&nbsp;system-level&nbsp;memory&nbsp;consumption<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sizes&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;series&nbsp;with&nbsp;column&nbsp;names&nbsp;as&nbsp;index&nbsp;and&nbsp;memory&nbsp;usage&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns&nbsp;with&nbsp;units&nbsp;of&nbsp;bytes.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Memory&nbsp;usage&nbsp;does&nbsp;not&nbsp;include&nbsp;memory&nbsp;consumed&nbsp;by&nbsp;elements&nbsp;that<br>
-are&nbsp;not&nbsp;components&nbsp;of&nbsp;the&nbsp;array&nbsp;if&nbsp;deep=False<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.nbytes</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-merge"><strong>merge</strong></a>(self, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False)</dt><dd><tt>Merge&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects&nbsp;by&nbsp;performing&nbsp;a&nbsp;database-style&nbsp;join&nbsp;operation&nbsp;by<br>
-columns&nbsp;or&nbsp;indexes.<br>
-&nbsp;<br>
-If&nbsp;joining&nbsp;columns&nbsp;on&nbsp;columns,&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;indexes&nbsp;*will&nbsp;be<br>
-ignored*.&nbsp;Otherwise&nbsp;if&nbsp;joining&nbsp;indexes&nbsp;on&nbsp;indexes&nbsp;or&nbsp;indexes&nbsp;on&nbsp;a&nbsp;column&nbsp;or<br>
-columns,&nbsp;the&nbsp;index&nbsp;will&nbsp;be&nbsp;passed&nbsp;on.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-right&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-how&nbsp;:&nbsp;{'left',&nbsp;'right',&nbsp;'outer',&nbsp;'inner'},&nbsp;default&nbsp;'inner'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;left:&nbsp;use&nbsp;only&nbsp;keys&nbsp;from&nbsp;left&nbsp;frame,&nbsp;similar&nbsp;to&nbsp;a&nbsp;SQL&nbsp;left&nbsp;outer&nbsp;join;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;preserve&nbsp;key&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;right:&nbsp;use&nbsp;only&nbsp;keys&nbsp;from&nbsp;right&nbsp;frame,&nbsp;similar&nbsp;to&nbsp;a&nbsp;SQL&nbsp;right&nbsp;outer&nbsp;join;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;preserve&nbsp;key&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;outer:&nbsp;use&nbsp;union&nbsp;of&nbsp;keys&nbsp;from&nbsp;both&nbsp;frames,&nbsp;similar&nbsp;to&nbsp;a&nbsp;SQL&nbsp;full&nbsp;outer<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;join;&nbsp;sort&nbsp;keys&nbsp;lexicographically<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;inner:&nbsp;use&nbsp;intersection&nbsp;of&nbsp;keys&nbsp;from&nbsp;both&nbsp;frames,&nbsp;similar&nbsp;to&nbsp;a&nbsp;SQL&nbsp;inner<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;join;&nbsp;preserve&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;left&nbsp;keys<br>
-on&nbsp;:&nbsp;label&nbsp;or&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;names&nbsp;to&nbsp;join&nbsp;on.&nbsp;Must&nbsp;be&nbsp;found&nbsp;in&nbsp;both&nbsp;DataFrames.&nbsp;If&nbsp;on&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;None&nbsp;and&nbsp;not&nbsp;merging&nbsp;on&nbsp;indexes,&nbsp;then&nbsp;it&nbsp;merges&nbsp;on&nbsp;the&nbsp;intersection&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;columns&nbsp;by&nbsp;default.<br>
-left_on&nbsp;:&nbsp;label&nbsp;or&nbsp;list,&nbsp;or&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;names&nbsp;to&nbsp;join&nbsp;on&nbsp;in&nbsp;left&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Can&nbsp;be&nbsp;a&nbsp;vector&nbsp;or&nbsp;list&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;vectors&nbsp;of&nbsp;the&nbsp;length&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;use&nbsp;a&nbsp;particular&nbsp;vector&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;join&nbsp;key&nbsp;instead&nbsp;of&nbsp;columns<br>
-right_on&nbsp;:&nbsp;label&nbsp;or&nbsp;list,&nbsp;or&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;names&nbsp;to&nbsp;join&nbsp;on&nbsp;in&nbsp;right&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;vector/list&nbsp;of&nbsp;vectors&nbsp;per<br>
-&nbsp;&nbsp;&nbsp;&nbsp;left_on&nbsp;docs<br>
-left_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;the&nbsp;index&nbsp;from&nbsp;the&nbsp;left&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;as&nbsp;the&nbsp;join&nbsp;key(s).&nbsp;If&nbsp;it&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;MultiIndex,&nbsp;the&nbsp;number&nbsp;of&nbsp;keys&nbsp;in&nbsp;the&nbsp;other&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(either&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;a&nbsp;number&nbsp;of&nbsp;columns)&nbsp;must&nbsp;match&nbsp;the&nbsp;number&nbsp;of&nbsp;levels<br>
-right_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;the&nbsp;index&nbsp;from&nbsp;the&nbsp;right&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;as&nbsp;the&nbsp;join&nbsp;key.&nbsp;Same&nbsp;caveats&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;left_index<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;the&nbsp;join&nbsp;keys&nbsp;lexicographically&nbsp;in&nbsp;the&nbsp;result&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;If&nbsp;False,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;join&nbsp;keys&nbsp;depends&nbsp;on&nbsp;the&nbsp;join&nbsp;type&nbsp;(how&nbsp;keyword)<br>
-suffixes&nbsp;:&nbsp;2-length&nbsp;sequence&nbsp;(tuple,&nbsp;list,&nbsp;...)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Suffix&nbsp;to&nbsp;apply&nbsp;to&nbsp;overlapping&nbsp;column&nbsp;names&nbsp;in&nbsp;the&nbsp;left&nbsp;and&nbsp;right<br>
-&nbsp;&nbsp;&nbsp;&nbsp;side,&nbsp;respectively<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;do&nbsp;not&nbsp;copy&nbsp;data&nbsp;unnecessarily<br>
-indicator&nbsp;:&nbsp;boolean&nbsp;or&nbsp;string,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;adds&nbsp;a&nbsp;column&nbsp;to&nbsp;output&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;called&nbsp;"_merge"&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;information&nbsp;on&nbsp;the&nbsp;source&nbsp;of&nbsp;each&nbsp;row.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;string,&nbsp;column&nbsp;with&nbsp;information&nbsp;on&nbsp;source&nbsp;of&nbsp;each&nbsp;row&nbsp;will&nbsp;be&nbsp;added&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;output&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;and&nbsp;column&nbsp;will&nbsp;be&nbsp;named&nbsp;value&nbsp;of&nbsp;string.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Information&nbsp;column&nbsp;is&nbsp;Categorical-type&nbsp;and&nbsp;takes&nbsp;on&nbsp;a&nbsp;value&nbsp;of&nbsp;"left_only"<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;observations&nbsp;whose&nbsp;merge&nbsp;key&nbsp;only&nbsp;appears&nbsp;in&nbsp;'left'&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"right_only"&nbsp;for&nbsp;observations&nbsp;whose&nbsp;merge&nbsp;key&nbsp;only&nbsp;appears&nbsp;in&nbsp;'right'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;and&nbsp;"both"&nbsp;if&nbsp;the&nbsp;observation's&nbsp;merge&nbsp;key&nbsp;is&nbsp;found&nbsp;in&nbsp;both.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&gt;&gt;&gt;&nbsp;B<br>
-&nbsp;&nbsp;&nbsp;&nbsp;lkey&nbsp;value&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;rkey&nbsp;value<br>
-0&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;5<br>
-1&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;6<br>
-2&nbsp;&nbsp;&nbsp;baz&nbsp;&nbsp;3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;qux&nbsp;&nbsp;7<br>
-3&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;8<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;A.<a href="#TimeFrame-merge">merge</a>(B,&nbsp;left_on='lkey',&nbsp;right_on='rkey',&nbsp;how='outer')<br>
-&nbsp;&nbsp;&nbsp;lkey&nbsp;&nbsp;value_x&nbsp;&nbsp;rkey&nbsp;&nbsp;value_y<br>
-0&nbsp;&nbsp;foo&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;&nbsp;5<br>
-1&nbsp;&nbsp;foo&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;bar&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;&nbsp;6<br>
-3&nbsp;&nbsp;bar&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;&nbsp;8<br>
-4&nbsp;&nbsp;baz&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;NaN<br>
-5&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;qux&nbsp;&nbsp;&nbsp;7<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-merged&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;output&nbsp;type&nbsp;will&nbsp;the&nbsp;be&nbsp;same&nbsp;as&nbsp;'left',&nbsp;if&nbsp;it&nbsp;is&nbsp;a&nbsp;subclass<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-merge_ordered<br>
-merge_asof</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-min"><strong>min</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;minimum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;minimum,&nbsp;use&nbsp;``idxmin``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmin``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-min&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-mod"><strong>mod</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Modulo&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;%&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rmod</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-mode"><strong>mode</strong></a>(self, axis=0, numeric_only=False)</dt><dd><tt>Gets&nbsp;the&nbsp;<a href="#TimeFrame-mode">mode</a>(s)&nbsp;of&nbsp;each&nbsp;element&nbsp;along&nbsp;the&nbsp;axis&nbsp;selected.&nbsp;Adds&nbsp;a&nbsp;row<br>
-for&nbsp;each&nbsp;mode&nbsp;per&nbsp;label,&nbsp;fills&nbsp;in&nbsp;gaps&nbsp;with&nbsp;nan.<br>
-&nbsp;<br>
-Note&nbsp;that&nbsp;there&nbsp;could&nbsp;be&nbsp;multiple&nbsp;values&nbsp;returned&nbsp;for&nbsp;the&nbsp;selected<br>
-axis&nbsp;(when&nbsp;more&nbsp;than&nbsp;one&nbsp;item&nbsp;share&nbsp;the&nbsp;maximum&nbsp;frequency),&nbsp;which&nbsp;is<br>
-the&nbsp;reason&nbsp;why&nbsp;a&nbsp;dataframe&nbsp;is&nbsp;returned.&nbsp;If&nbsp;you&nbsp;want&nbsp;to&nbsp;impute&nbsp;missing<br>
-values&nbsp;with&nbsp;the&nbsp;mode&nbsp;in&nbsp;a&nbsp;dataframe&nbsp;``df``,&nbsp;you&nbsp;can&nbsp;just&nbsp;do&nbsp;this:<br>
-``df.<a href="#TimeFrame-fillna">fillna</a>(df.<a href="#TimeFrame-mode">mode</a>().iloc[0])``<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;0&nbsp;or&nbsp;'index'&nbsp;:&nbsp;get&nbsp;mode&nbsp;of&nbsp;each&nbsp;column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;:&nbsp;get&nbsp;mode&nbsp;of&nbsp;each&nbsp;row<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;only&nbsp;apply&nbsp;to&nbsp;numeric&nbsp;columns<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-modes&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(sorted)<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;2,&nbsp;1,&nbsp;2,&nbsp;1,&nbsp;2,&nbsp;3]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-mode">mode</a>()<br>
-&nbsp;&nbsp;&nbsp;A<br>
-0&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;2</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-mul"><strong>mul</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Multiplication&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-multiply"><strong>multiply</strong></a> = mul(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Multiplication&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-ne"><strong>ne</strong></a>(self, other, axis='columns', level=None)</dt><dd><tt>Wrapper&nbsp;for&nbsp;flexible&nbsp;comparison&nbsp;methods&nbsp;ne</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-nlargest"><strong>nlargest</strong></a>(self, n, columns, keep='first')</dt><dd><tt>Get&nbsp;the&nbsp;rows&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;sorted&nbsp;by&nbsp;the&nbsp;`n`&nbsp;largest<br>
-values&nbsp;of&nbsp;`columns`.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;items&nbsp;to&nbsp;retrieve<br>
-columns&nbsp;:&nbsp;list&nbsp;or&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;name&nbsp;or&nbsp;names&nbsp;to&nbsp;order&nbsp;by<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'a':&nbsp;[1,&nbsp;10,&nbsp;8,&nbsp;11,&nbsp;-1],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'b':&nbsp;list('abdce'),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'c':&nbsp;[1.0,&nbsp;2.0,&nbsp;np.nan,&nbsp;3.0,&nbsp;4.0]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-nlargest">nlargest</a>(3,&nbsp;'a')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;c<br>
-3&nbsp;&nbsp;11&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;3<br>
-1&nbsp;&nbsp;10&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;8&nbsp;&nbsp;d&nbsp;NaN</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-nsmallest"><strong>nsmallest</strong></a>(self, n, columns, keep='first')</dt><dd><tt>Get&nbsp;the&nbsp;rows&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;sorted&nbsp;by&nbsp;the&nbsp;`n`&nbsp;smallest<br>
-values&nbsp;of&nbsp;`columns`.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;items&nbsp;to&nbsp;retrieve<br>
-columns&nbsp;:&nbsp;list&nbsp;or&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;name&nbsp;or&nbsp;names&nbsp;to&nbsp;order&nbsp;by<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'a':&nbsp;[1,&nbsp;10,&nbsp;8,&nbsp;11,&nbsp;-1],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'b':&nbsp;list('abdce'),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'c':&nbsp;[1.0,&nbsp;2.0,&nbsp;np.nan,&nbsp;3.0,&nbsp;4.0]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-nsmallest">nsmallest</a>(3,&nbsp;'a')<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;c<br>
-4&nbsp;-1&nbsp;&nbsp;e&nbsp;&nbsp;&nbsp;4<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;8&nbsp;&nbsp;d&nbsp;NaN</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-nunique"><strong>nunique</strong></a>(self, axis=0, dropna=True)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;number&nbsp;of&nbsp;distinct&nbsp;observations&nbsp;over&nbsp;requested<br>
-axis.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Don't&nbsp;include&nbsp;NaN&nbsp;in&nbsp;the&nbsp;counts.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-nunique&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A':&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;'B':&nbsp;[1,&nbsp;1,&nbsp;1]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-nunique">nunique</a>()<br>
-A&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-B&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-nunique">nunique</a>(axis=1)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-pivot"><strong>pivot</strong></a>(self, index=None, columns=None, values=None)</dt><dd><tt>Reshape&nbsp;data&nbsp;(produce&nbsp;a&nbsp;"pivot"&nbsp;table)&nbsp;based&nbsp;on&nbsp;column&nbsp;values.&nbsp;Uses<br>
-unique&nbsp;values&nbsp;from&nbsp;index&nbsp;/&nbsp;columns&nbsp;to&nbsp;form&nbsp;axes&nbsp;of&nbsp;the&nbsp;resulting<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;name&nbsp;to&nbsp;use&nbsp;to&nbsp;make&nbsp;new&nbsp;frame's&nbsp;index.&nbsp;If&nbsp;None,&nbsp;uses<br>
-&nbsp;&nbsp;&nbsp;&nbsp;existing&nbsp;index.<br>
-columns&nbsp;:&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;name&nbsp;to&nbsp;use&nbsp;to&nbsp;make&nbsp;new&nbsp;frame's&nbsp;columns<br>
-values&nbsp;:&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;name&nbsp;to&nbsp;use&nbsp;for&nbsp;populating&nbsp;new&nbsp;frame's&nbsp;values.&nbsp;If&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specified,&nbsp;all&nbsp;remaining&nbsp;columns&nbsp;will&nbsp;be&nbsp;used&nbsp;and&nbsp;the&nbsp;result&nbsp;will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;have&nbsp;hierarchically&nbsp;indexed&nbsp;columns<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-pivoted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.pivot_table&nbsp;:&nbsp;generalization&nbsp;of&nbsp;pivot&nbsp;that&nbsp;can&nbsp;handle<br>
-&nbsp;&nbsp;&nbsp;&nbsp;duplicate&nbsp;values&nbsp;for&nbsp;one&nbsp;index/column&nbsp;pair<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.unstack&nbsp;:&nbsp;pivot&nbsp;based&nbsp;on&nbsp;the&nbsp;index&nbsp;values&nbsp;instead&nbsp;of&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;column<br>
-&nbsp;<br>
-Notes<br>
------<br>
-For&nbsp;finer-tuned&nbsp;control,&nbsp;see&nbsp;hierarchical&nbsp;indexing&nbsp;documentation&nbsp;along<br>
-with&nbsp;the&nbsp;related&nbsp;stack/unstack&nbsp;methods<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'foo':&nbsp;['one','one','one','two','two','two'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'bar':&nbsp;['A',&nbsp;'B',&nbsp;'C',&nbsp;'A',&nbsp;'B',&nbsp;'C'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'baz':&nbsp;[1,&nbsp;2,&nbsp;3,&nbsp;4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;foo&nbsp;&nbsp;&nbsp;bar&nbsp;&nbsp;baz<br>
-0&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-4&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-5&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-pivot">pivot</a>(index='foo',&nbsp;columns='bar',&nbsp;values='baz')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C<br>
-one&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;3<br>
-two&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-pivot">pivot</a>(index='foo',&nbsp;columns='bar')['baz']<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C<br>
-one&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;3<br>
-two&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-pivot_table"><strong>pivot_table</strong></a>(data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All')</dt><dd><tt>Create&nbsp;a&nbsp;spreadsheet-style&nbsp;pivot&nbsp;table&nbsp;as&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;The&nbsp;levels&nbsp;in&nbsp;the<br>
-pivot&nbsp;table&nbsp;will&nbsp;be&nbsp;stored&nbsp;in&nbsp;MultiIndex&nbsp;objects&nbsp;(hierarchical&nbsp;indexes)&nbsp;on<br>
-the&nbsp;index&nbsp;and&nbsp;columns&nbsp;of&nbsp;the&nbsp;result&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-values&nbsp;:&nbsp;column&nbsp;to&nbsp;aggregate,&nbsp;optional<br>
-index&nbsp;:&nbsp;column,&nbsp;Grouper,&nbsp;array,&nbsp;or&nbsp;list&nbsp;of&nbsp;the&nbsp;previous<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;an&nbsp;array&nbsp;is&nbsp;passed,&nbsp;it&nbsp;must&nbsp;be&nbsp;the&nbsp;same&nbsp;length&nbsp;as&nbsp;the&nbsp;data.&nbsp;The&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;contain&nbsp;any&nbsp;of&nbsp;the&nbsp;other&nbsp;types&nbsp;(except&nbsp;list).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keys&nbsp;to&nbsp;group&nbsp;by&nbsp;on&nbsp;the&nbsp;pivot&nbsp;table&nbsp;index.&nbsp;&nbsp;If&nbsp;an&nbsp;array&nbsp;is&nbsp;passed,&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;being&nbsp;used&nbsp;as&nbsp;the&nbsp;same&nbsp;manner&nbsp;as&nbsp;column&nbsp;values.<br>
-columns&nbsp;:&nbsp;column,&nbsp;Grouper,&nbsp;array,&nbsp;or&nbsp;list&nbsp;of&nbsp;the&nbsp;previous<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;an&nbsp;array&nbsp;is&nbsp;passed,&nbsp;it&nbsp;must&nbsp;be&nbsp;the&nbsp;same&nbsp;length&nbsp;as&nbsp;the&nbsp;data.&nbsp;The&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;contain&nbsp;any&nbsp;of&nbsp;the&nbsp;other&nbsp;types&nbsp;(except&nbsp;list).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keys&nbsp;to&nbsp;group&nbsp;by&nbsp;on&nbsp;the&nbsp;pivot&nbsp;table&nbsp;column.&nbsp;&nbsp;If&nbsp;an&nbsp;array&nbsp;is&nbsp;passed,&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;being&nbsp;used&nbsp;as&nbsp;the&nbsp;same&nbsp;manner&nbsp;as&nbsp;column&nbsp;values.<br>
-aggfunc&nbsp;:&nbsp;function&nbsp;or&nbsp;list&nbsp;of&nbsp;functions,&nbsp;default&nbsp;numpy.mean<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;list&nbsp;of&nbsp;functions&nbsp;passed,&nbsp;the&nbsp;resulting&nbsp;pivot&nbsp;table&nbsp;will&nbsp;have<br>
-&nbsp;&nbsp;&nbsp;&nbsp;hierarchical&nbsp;columns&nbsp;whose&nbsp;top&nbsp;level&nbsp;are&nbsp;the&nbsp;function&nbsp;names&nbsp;(inferred<br>
-&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;the&nbsp;function&nbsp;objects&nbsp;themselves)<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;replace&nbsp;missing&nbsp;values&nbsp;with<br>
-margins&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;all&nbsp;row&nbsp;/&nbsp;columns&nbsp;(e.g.&nbsp;for&nbsp;subtotal&nbsp;/&nbsp;grand&nbsp;totals)<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;not&nbsp;include&nbsp;columns&nbsp;whose&nbsp;entries&nbsp;are&nbsp;all&nbsp;NaN<br>
-margins_name&nbsp;:&nbsp;string,&nbsp;default&nbsp;'All'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;the&nbsp;row&nbsp;/&nbsp;column&nbsp;that&nbsp;will&nbsp;contain&nbsp;the&nbsp;totals<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;margins&nbsp;is&nbsp;True.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;foo&nbsp;one&nbsp;small&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;foo&nbsp;one&nbsp;large&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;foo&nbsp;one&nbsp;large&nbsp;&nbsp;2<br>
-3&nbsp;&nbsp;foo&nbsp;two&nbsp;small&nbsp;&nbsp;3<br>
-4&nbsp;&nbsp;foo&nbsp;two&nbsp;small&nbsp;&nbsp;3<br>
-5&nbsp;&nbsp;bar&nbsp;one&nbsp;large&nbsp;&nbsp;4<br>
-6&nbsp;&nbsp;bar&nbsp;one&nbsp;small&nbsp;&nbsp;5<br>
-7&nbsp;&nbsp;bar&nbsp;two&nbsp;small&nbsp;&nbsp;6<br>
-8&nbsp;&nbsp;bar&nbsp;two&nbsp;large&nbsp;&nbsp;7<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;table&nbsp;=&nbsp;<a href="#TimeFrame-pivot_table">pivot_table</a>(df,&nbsp;values='D',&nbsp;index=['A',&nbsp;'B'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['C'],&nbsp;aggfunc=np.sum)<br>
-&gt;&gt;&gt;&nbsp;table<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;small&nbsp;&nbsp;large<br>
-foo&nbsp;&nbsp;one&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;6&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-bar&nbsp;&nbsp;one&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;6&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-table&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.pivot&nbsp;:&nbsp;pivot&nbsp;without&nbsp;aggregation&nbsp;that&nbsp;can&nbsp;handle<br>
-&nbsp;&nbsp;&nbsp;&nbsp;non-numeric&nbsp;data</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-pow"><strong>pow</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`pow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;**&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rpow</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-prod"><strong>prod</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-product"><strong>product</strong></a> = prod(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-quantile"><strong>quantile</strong></a>(self, q=0.5, axis=0, numeric_only=True, interpolation='linear')</dt><dd><tt>Return&nbsp;values&nbsp;at&nbsp;the&nbsp;given&nbsp;quantile&nbsp;over&nbsp;requested&nbsp;axis,&nbsp;a&nbsp;la<br>
-numpy.percentile.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-q&nbsp;:&nbsp;float&nbsp;or&nbsp;array-like,&nbsp;default&nbsp;0.5&nbsp;(50%&nbsp;quantile)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&lt;=&nbsp;q&nbsp;&lt;=&nbsp;1,&nbsp;the&nbsp;<a href="#TimeFrame-quantile">quantile</a>(s)&nbsp;to&nbsp;compute<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}&nbsp;(default&nbsp;0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;or&nbsp;'index'&nbsp;for&nbsp;row-wise,&nbsp;1&nbsp;or&nbsp;'columns'&nbsp;for&nbsp;column-wise<br>
-interpolation&nbsp;:&nbsp;{'linear',&nbsp;'lower',&nbsp;'higher',&nbsp;'midpoint',&nbsp;'nearest'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;optional&nbsp;parameter&nbsp;specifies&nbsp;the&nbsp;interpolation&nbsp;method&nbsp;to&nbsp;use,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;the&nbsp;desired&nbsp;quantile&nbsp;lies&nbsp;between&nbsp;two&nbsp;data&nbsp;points&nbsp;`i`&nbsp;and&nbsp;`j`:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;linear:&nbsp;`i&nbsp;+&nbsp;(j&nbsp;-&nbsp;i)&nbsp;*&nbsp;fraction`,&nbsp;where&nbsp;`fraction`&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;fractional&nbsp;part&nbsp;of&nbsp;the&nbsp;index&nbsp;surrounded&nbsp;by&nbsp;`i`&nbsp;and&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;lower:&nbsp;`i`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;higher:&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;`i`&nbsp;or&nbsp;`j`&nbsp;whichever&nbsp;is&nbsp;nearest.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;midpoint:&nbsp;(`i`&nbsp;+&nbsp;`j`)&nbsp;/&nbsp;2.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-quantiles&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;If&nbsp;``q``&nbsp;is&nbsp;an&nbsp;array,&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;be&nbsp;returned&nbsp;where&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;is&nbsp;``q``,&nbsp;the&nbsp;columns&nbsp;are&nbsp;the&nbsp;columns&nbsp;of&nbsp;self,&nbsp;and&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;are&nbsp;the&nbsp;quantiles.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;If&nbsp;``q``&nbsp;is&nbsp;a&nbsp;float,&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;returned&nbsp;where&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;is&nbsp;the&nbsp;columns&nbsp;of&nbsp;self&nbsp;and&nbsp;the&nbsp;values&nbsp;are&nbsp;the&nbsp;quantiles.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.array([[1,&nbsp;1],&nbsp;[2,&nbsp;10],&nbsp;[3,&nbsp;100],&nbsp;[4,&nbsp;100]]),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['a',&nbsp;'b'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-quantile">quantile</a>(.1)<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;1.3<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;3.7<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-quantile">quantile</a>([.1,&nbsp;.5])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-0.1&nbsp;&nbsp;1.3&nbsp;&nbsp;&nbsp;3.7<br>
-0.5&nbsp;&nbsp;2.5&nbsp;&nbsp;55.0</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-query"><strong>query</strong></a>(self, expr, inplace=False, **kwargs)</dt><dd><tt>Query&nbsp;the&nbsp;columns&nbsp;of&nbsp;a&nbsp;frame&nbsp;with&nbsp;a&nbsp;boolean&nbsp;expression.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.13<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-expr&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;query&nbsp;string&nbsp;to&nbsp;evaluate.&nbsp;&nbsp;You&nbsp;can&nbsp;refer&nbsp;to&nbsp;variables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;environment&nbsp;by&nbsp;prefixing&nbsp;them&nbsp;with&nbsp;an&nbsp;'@'&nbsp;character&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``@a&nbsp;+&nbsp;b``.<br>
-inplace&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;the&nbsp;query&nbsp;should&nbsp;modify&nbsp;the&nbsp;data&nbsp;in&nbsp;place&nbsp;or&nbsp;return<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;modified&nbsp;copy<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-kwargs&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;documentation&nbsp;for&nbsp;:func:`pandas.eval`&nbsp;for&nbsp;complete&nbsp;details<br>
-&nbsp;&nbsp;&nbsp;&nbsp;on&nbsp;the&nbsp;keyword&nbsp;arguments&nbsp;accepted&nbsp;by&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.query`.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-q&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;result&nbsp;of&nbsp;the&nbsp;evaluation&nbsp;of&nbsp;this&nbsp;expression&nbsp;is&nbsp;first&nbsp;passed&nbsp;to<br>
-:attr:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.loc`&nbsp;and&nbsp;if&nbsp;that&nbsp;fails&nbsp;because&nbsp;of&nbsp;a<br>
-multidimensional&nbsp;key&nbsp;(e.g.,&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>)&nbsp;then&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;passed<br>
-to&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.__getitem__`.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;uses&nbsp;the&nbsp;top-level&nbsp;:func:`pandas.eval`&nbsp;function&nbsp;to<br>
-evaluate&nbsp;the&nbsp;passed&nbsp;query.<br>
-&nbsp;<br>
-The&nbsp;:meth:`~pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.query`&nbsp;method&nbsp;uses&nbsp;a&nbsp;slightly<br>
-modified&nbsp;Python&nbsp;syntax&nbsp;by&nbsp;default.&nbsp;For&nbsp;example,&nbsp;the&nbsp;``&amp;``&nbsp;and&nbsp;``|``<br>
-(bitwise)&nbsp;operators&nbsp;have&nbsp;the&nbsp;precedence&nbsp;of&nbsp;their&nbsp;boolean&nbsp;cousins,<br>
-:keyword:`and`&nbsp;and&nbsp;:keyword:`or`.&nbsp;This&nbsp;*is*&nbsp;syntactically&nbsp;valid&nbsp;Python,<br>
-however&nbsp;the&nbsp;semantics&nbsp;are&nbsp;different.<br>
-&nbsp;<br>
-You&nbsp;can&nbsp;change&nbsp;the&nbsp;semantics&nbsp;of&nbsp;the&nbsp;expression&nbsp;by&nbsp;passing&nbsp;the&nbsp;keyword<br>
-argument&nbsp;``parser='python'``.&nbsp;This&nbsp;enforces&nbsp;the&nbsp;same&nbsp;semantics&nbsp;as<br>
-evaluation&nbsp;in&nbsp;Python&nbsp;space.&nbsp;Likewise,&nbsp;you&nbsp;can&nbsp;pass&nbsp;``engine='python'``<br>
-to&nbsp;evaluate&nbsp;an&nbsp;expression&nbsp;using&nbsp;Python&nbsp;itself&nbsp;as&nbsp;a&nbsp;backend.&nbsp;This&nbsp;is&nbsp;not<br>
-recommended&nbsp;as&nbsp;it&nbsp;is&nbsp;inefficient&nbsp;compared&nbsp;to&nbsp;using&nbsp;``numexpr``&nbsp;as&nbsp;the<br>
-engine.<br>
-&nbsp;<br>
-The&nbsp;:attr:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.index`&nbsp;and<br>
-:attr:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.columns`&nbsp;attributes&nbsp;of&nbsp;the<br>
-:class:`~pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>`&nbsp;instance&nbsp;are&nbsp;placed&nbsp;in&nbsp;the&nbsp;query&nbsp;namespace<br>
-by&nbsp;default,&nbsp;which&nbsp;allows&nbsp;you&nbsp;to&nbsp;treat&nbsp;both&nbsp;the&nbsp;index&nbsp;and&nbsp;columns&nbsp;of&nbsp;the<br>
-frame&nbsp;as&nbsp;a&nbsp;column&nbsp;in&nbsp;the&nbsp;frame.<br>
-The&nbsp;identifier&nbsp;``index``&nbsp;is&nbsp;used&nbsp;for&nbsp;the&nbsp;frame&nbsp;index;&nbsp;you&nbsp;can&nbsp;also<br>
-use&nbsp;the&nbsp;name&nbsp;of&nbsp;the&nbsp;index&nbsp;to&nbsp;identify&nbsp;it&nbsp;in&nbsp;a&nbsp;query.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``query``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.query&gt;`.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.eval<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.eval<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;numpy.random&nbsp;import&nbsp;randn<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;pandas&nbsp;import&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(randn(10,&nbsp;2),&nbsp;columns=list('ab'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-query">query</a>('a&nbsp;&gt;&nbsp;b')<br>
-&gt;&gt;&gt;&nbsp;df[df.a&nbsp;&gt;&nbsp;df.b]&nbsp;&nbsp;#&nbsp;same&nbsp;result&nbsp;as&nbsp;the&nbsp;previous&nbsp;expression</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-radd"><strong>radd</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Addition&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`radd`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;+&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.add</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-rdiv"><strong>rdiv</strong></a> = rtruediv(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-reindex"><strong>reindex</strong></a>(self, index=None, columns=None, **kwargs)</dt><dd><tt>Conform&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;new&nbsp;index&nbsp;with&nbsp;optional&nbsp;filling&nbsp;logic,&nbsp;placing<br>
-NA/NaN&nbsp;in&nbsp;locations&nbsp;having&nbsp;no&nbsp;value&nbsp;in&nbsp;the&nbsp;previous&nbsp;index.&nbsp;A&nbsp;new&nbsp;<a href="builtins.html#object">object</a><br>
-is&nbsp;produced&nbsp;unless&nbsp;the&nbsp;new&nbsp;index&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;the&nbsp;current&nbsp;one&nbsp;and<br>
-copy=False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index,&nbsp;columns&nbsp;:&nbsp;array-like,&nbsp;optional&nbsp;(can&nbsp;be&nbsp;specified&nbsp;in&nbsp;order,&nbsp;or&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;keywords)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;New&nbsp;labels&nbsp;/&nbsp;index&nbsp;to&nbsp;conform&nbsp;to.&nbsp;Preferably&nbsp;an&nbsp;Index&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;avoid&nbsp;duplicating&nbsp;data<br>
-method&nbsp;:&nbsp;{None,&nbsp;'backfill'/'bfill',&nbsp;'pad'/'ffill',&nbsp;'nearest'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Please&nbsp;note:&nbsp;this&nbsp;is&nbsp;only&nbsp;&nbsp;applicable&nbsp;to&nbsp;DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;monotonically&nbsp;increasing/decreasing&nbsp;index.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;default:&nbsp;don't&nbsp;fill&nbsp;gaps<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;next&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;use&nbsp;nearest&nbsp;valid&nbsp;observations&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>,&nbsp;even&nbsp;if&nbsp;the&nbsp;passed&nbsp;indexes&nbsp;are&nbsp;the&nbsp;same<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;elements&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;original&nbsp;and&nbsp;new&nbsp;labels&nbsp;for&nbsp;inexact<br>
-&nbsp;&nbsp;&nbsp;&nbsp;matches.&nbsp;The&nbsp;values&nbsp;of&nbsp;the&nbsp;index&nbsp;at&nbsp;the&nbsp;matching&nbsp;locations&nbsp;most<br>
-&nbsp;&nbsp;&nbsp;&nbsp;satisfy&nbsp;the&nbsp;equation&nbsp;``<a href="#TimeFrame-abs">abs</a>(index[indexer]&nbsp;-&nbsp;target)&nbsp;&lt;=&nbsp;tolerance``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;some&nbsp;fictional&nbsp;data.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;['Firefox',&nbsp;'Chrome',&nbsp;'Safari',&nbsp;'IE10',&nbsp;'Konqueror']<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'http_status':&nbsp;[200,200,404,404,301],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'response_time':&nbsp;[0.04,&nbsp;0.02,&nbsp;0.07,&nbsp;0.08,&nbsp;1.0]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Firefox&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.04<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Konqueror&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;301&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.00<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;new&nbsp;index&nbsp;and&nbsp;reindex&nbsp;the&nbsp;dataframe.&nbsp;By&nbsp;default<br>
-values&nbsp;in&nbsp;the&nbsp;new&nbsp;index&nbsp;that&nbsp;do&nbsp;not&nbsp;have&nbsp;corresponding<br>
-records&nbsp;in&nbsp;the&nbsp;dataframe&nbsp;are&nbsp;assigned&nbsp;``NaN``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;new_index=&nbsp;['Safari',&nbsp;'Iceweasel',&nbsp;'Comodo&nbsp;Dragon',&nbsp;'IE10',<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Chrome']<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-reindex">reindex</a>(new_index)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-We&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;by&nbsp;passing&nbsp;a&nbsp;value&nbsp;to<br>
-the&nbsp;keyword&nbsp;``fill_value``.&nbsp;Because&nbsp;the&nbsp;index&nbsp;is&nbsp;not&nbsp;monotonically<br>
-increasing&nbsp;or&nbsp;decreasing,&nbsp;we&nbsp;cannot&nbsp;use&nbsp;arguments&nbsp;to&nbsp;the&nbsp;keyword<br>
-``method``&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``&nbsp;values.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-reindex">reindex</a>(new_index,&nbsp;fill_value=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-reindex">reindex</a>(new_index,&nbsp;fill_value='missing')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-To&nbsp;further&nbsp;illustrate&nbsp;the&nbsp;filling&nbsp;functionality&nbsp;in<br>
-``reindex``,&nbsp;we&nbsp;will&nbsp;create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;a<br>
-monotonically&nbsp;increasing&nbsp;index&nbsp;(for&nbsp;example,&nbsp;a&nbsp;sequence<br>
-of&nbsp;dates).<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index&nbsp;=&nbsp;pd.date_range('1/1/2010',&nbsp;periods=6,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"prices":&nbsp;[100,&nbsp;101,&nbsp;np.nan,&nbsp;100,&nbsp;89,&nbsp;88]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=date_index)<br>
-&gt;&gt;&gt;&nbsp;df2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-&nbsp;<br>
-Suppose&nbsp;we&nbsp;decide&nbsp;to&nbsp;expand&nbsp;the&nbsp;dataframe&nbsp;to&nbsp;cover&nbsp;a&nbsp;wider<br>
-date&nbsp;range.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index2&nbsp;=&nbsp;pd.date_range('12/29/2009',&nbsp;periods=10,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#TimeFrame-reindex">reindex</a>(date_index2)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-The&nbsp;index&nbsp;entries&nbsp;that&nbsp;did&nbsp;not&nbsp;have&nbsp;a&nbsp;value&nbsp;in&nbsp;the&nbsp;original&nbsp;data&nbsp;frame<br>
-(for&nbsp;example,&nbsp;'2009-12-29')&nbsp;are&nbsp;by&nbsp;default&nbsp;filled&nbsp;with&nbsp;``NaN``.<br>
-If&nbsp;desired,&nbsp;we&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;using&nbsp;one&nbsp;of&nbsp;several<br>
-options.<br>
-&nbsp;<br>
-For&nbsp;example,&nbsp;to&nbsp;backpropagate&nbsp;the&nbsp;last&nbsp;valid&nbsp;value&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values,&nbsp;pass&nbsp;``bfill``&nbsp;as&nbsp;an&nbsp;argument&nbsp;to&nbsp;the&nbsp;``method``&nbsp;keyword.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#TimeFrame-reindex">reindex</a>(date_index2,&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;the&nbsp;``NaN``&nbsp;value&nbsp;present&nbsp;in&nbsp;the&nbsp;original&nbsp;dataframe<br>
-(at&nbsp;index&nbsp;value&nbsp;2010-01-03)&nbsp;will&nbsp;not&nbsp;be&nbsp;filled&nbsp;by&nbsp;any&nbsp;of&nbsp;the<br>
-value&nbsp;propagation&nbsp;schemes.&nbsp;This&nbsp;is&nbsp;because&nbsp;filling&nbsp;while&nbsp;reindexing<br>
-does&nbsp;not&nbsp;look&nbsp;at&nbsp;dataframe&nbsp;values,&nbsp;but&nbsp;only&nbsp;compares&nbsp;the&nbsp;original&nbsp;and<br>
-desired&nbsp;indexes.&nbsp;If&nbsp;you&nbsp;do&nbsp;want&nbsp;to&nbsp;fill&nbsp;in&nbsp;the&nbsp;``NaN``&nbsp;values&nbsp;present<br>
-in&nbsp;the&nbsp;original&nbsp;dataframe,&nbsp;use&nbsp;the&nbsp;``<a href="#TimeFrame-fillna">fillna</a>()``&nbsp;method.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-reindex_axis"><strong>reindex_axis</strong></a>(self, labels, axis=0, method=None, level=None, copy=True, limit=None, fill_value=nan)</dt><dd><tt>Conform&nbsp;input&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;new&nbsp;index&nbsp;with&nbsp;optional<br>
-filling&nbsp;logic,&nbsp;placing&nbsp;NA/NaN&nbsp;in&nbsp;locations&nbsp;having&nbsp;no&nbsp;value&nbsp;in&nbsp;the<br>
-previous&nbsp;index.&nbsp;A&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;produced&nbsp;unless&nbsp;the&nbsp;new&nbsp;index&nbsp;is<br>
-equivalent&nbsp;to&nbsp;the&nbsp;current&nbsp;one&nbsp;and&nbsp;copy=False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-labels&nbsp;:&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;New&nbsp;labels&nbsp;/&nbsp;index&nbsp;to&nbsp;conform&nbsp;to.&nbsp;Preferably&nbsp;an&nbsp;Index&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;avoid&nbsp;duplicating&nbsp;data<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'}<br>
-method&nbsp;:&nbsp;{None,&nbsp;'backfill'/'bfill',&nbsp;'pad'/'ffill',&nbsp;'nearest'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;default:&nbsp;don't&nbsp;fill&nbsp;gaps<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;next&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;use&nbsp;nearest&nbsp;valid&nbsp;observations&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>,&nbsp;even&nbsp;if&nbsp;the&nbsp;passed&nbsp;indexes&nbsp;are&nbsp;the&nbsp;same<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;elements&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;original&nbsp;and&nbsp;new&nbsp;labels&nbsp;for&nbsp;inexact<br>
-&nbsp;&nbsp;&nbsp;&nbsp;matches.&nbsp;The&nbsp;values&nbsp;of&nbsp;the&nbsp;index&nbsp;at&nbsp;the&nbsp;matching&nbsp;locations&nbsp;most<br>
-&nbsp;&nbsp;&nbsp;&nbsp;satisfy&nbsp;the&nbsp;equation&nbsp;``<a href="#TimeFrame-abs">abs</a>(index[indexer]&nbsp;-&nbsp;target)&nbsp;&lt;=&nbsp;tolerance``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-reindex_axis">reindex_axis</a>(['A',&nbsp;'B',&nbsp;'C'],&nbsp;axis=1)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;reindex_like<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-rename"><strong>rename</strong></a>(self, index=None, columns=None, **kwargs)</dt><dd><tt>Alter&nbsp;axes&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.&nbsp;Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be<br>
-unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in&nbsp;a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left<br>
-as-is.&nbsp;Extra&nbsp;labels&nbsp;listed&nbsp;don't&nbsp;throw&nbsp;an&nbsp;error.&nbsp;Alternatively,&nbsp;change<br>
-``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;with&nbsp;a&nbsp;scalar&nbsp;value&nbsp;(<a href="pandas.core.series.html#Series">Series</a>&nbsp;only).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index,&nbsp;columns&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Scalar&nbsp;or&nbsp;list-like&nbsp;will&nbsp;alter&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;attribute,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;raise&nbsp;on&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;Panel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict-like&nbsp;or&nbsp;functions&nbsp;are&nbsp;transformations&nbsp;to&nbsp;apply&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;that&nbsp;axis'&nbsp;values<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;If&nbsp;True&nbsp;then&nbsp;value&nbsp;of&nbsp;copy&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;ignored.<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;MultiIndex,&nbsp;only&nbsp;rename&nbsp;labels&nbsp;in&nbsp;the&nbsp;specified<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename_axis<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeFrame-rename">rename</a>("my_name")&nbsp;#&nbsp;scalar,&nbsp;changes&nbsp;<a href="pandas.core.series.html#Series">Series</a>.name<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;my_name,&nbsp;dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeFrame-rename">rename</a>(lambda&nbsp;x:&nbsp;x&nbsp;**&nbsp;2)&nbsp;&nbsp;#&nbsp;function,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeFrame-rename">rename</a>({1:&nbsp;3,&nbsp;2:&nbsp;5})&nbsp;&nbsp;#&nbsp;mapping,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-rename">rename</a>(2)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-TypeError:&nbsp;'int'&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;not&nbsp;callable<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"B":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;c<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"C":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-reorder_levels"><strong>reorder_levels</strong></a>(self, order, axis=0)</dt><dd><tt>Rearrange&nbsp;index&nbsp;levels&nbsp;using&nbsp;input&nbsp;order.<br>
-May&nbsp;not&nbsp;drop&nbsp;or&nbsp;duplicate&nbsp;levels<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-order&nbsp;:&nbsp;list&nbsp;of&nbsp;int&nbsp;or&nbsp;list&nbsp;of&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;representing&nbsp;new&nbsp;level&nbsp;order.&nbsp;Reference&nbsp;level&nbsp;by&nbsp;number<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(position)&nbsp;or&nbsp;by&nbsp;key&nbsp;(label).<br>
-axis&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;to&nbsp;reorder&nbsp;levels.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-type&nbsp;of&nbsp;caller&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-reset_index"><strong>reset_index</strong></a>(self, level=None, drop=False, inplace=False, col_level=0, col_fill='')</dt><dd><tt>For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;multi-level&nbsp;index,&nbsp;return&nbsp;new&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with<br>
-labeling&nbsp;information&nbsp;in&nbsp;the&nbsp;columns&nbsp;under&nbsp;the&nbsp;index&nbsp;names,&nbsp;defaulting<br>
-to&nbsp;'level_0',&nbsp;'level_1',&nbsp;etc.&nbsp;if&nbsp;any&nbsp;are&nbsp;None.&nbsp;For&nbsp;a&nbsp;standard&nbsp;index,<br>
-the&nbsp;index&nbsp;name&nbsp;will&nbsp;be&nbsp;used&nbsp;(if&nbsp;set),&nbsp;otherwise&nbsp;a&nbsp;default&nbsp;'index'&nbsp;or<br>
-'level_0'&nbsp;(if&nbsp;'index'&nbsp;is&nbsp;already&nbsp;taken)&nbsp;will&nbsp;be&nbsp;used.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;tuple,&nbsp;or&nbsp;list,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Only&nbsp;remove&nbsp;the&nbsp;given&nbsp;levels&nbsp;from&nbsp;the&nbsp;index.&nbsp;Removes&nbsp;all&nbsp;levels&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-drop&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;not&nbsp;try&nbsp;to&nbsp;insert&nbsp;index&nbsp;into&nbsp;dataframe&nbsp;columns.&nbsp;This&nbsp;resets<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;index&nbsp;to&nbsp;the&nbsp;default&nbsp;integer&nbsp;index.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Modify&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;in&nbsp;place&nbsp;(do&nbsp;not&nbsp;create&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>)<br>
-col_level&nbsp;:&nbsp;int&nbsp;or&nbsp;str,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;columns&nbsp;have&nbsp;multiple&nbsp;levels,&nbsp;determines&nbsp;which&nbsp;level&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;labels&nbsp;are&nbsp;inserted&nbsp;into.&nbsp;By&nbsp;default&nbsp;it&nbsp;is&nbsp;inserted&nbsp;into&nbsp;the&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level.<br>
-col_fill&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;columns&nbsp;have&nbsp;multiple&nbsp;levels,&nbsp;determines&nbsp;how&nbsp;the&nbsp;other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;levels&nbsp;are&nbsp;named.&nbsp;If&nbsp;None&nbsp;then&nbsp;the&nbsp;index&nbsp;name&nbsp;is&nbsp;repeated.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-resetted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-rfloordiv"><strong>rfloordiv</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rfloordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;//&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.floordiv</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-rmod"><strong>rmod</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Modulo&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;%&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mod</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-rmul"><strong>rmul</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Multiplication&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;*&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mul</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-rolling"><strong>rolling</strong></a>(self, window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0, closed=None)</dt><dd><tt>Provides&nbsp;rolling&nbsp;window&nbsp;calculcations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-window&nbsp;:&nbsp;int,&nbsp;or&nbsp;offset<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Size&nbsp;of&nbsp;the&nbsp;moving&nbsp;window.&nbsp;This&nbsp;is&nbsp;the&nbsp;number&nbsp;of&nbsp;observations&nbsp;used&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;calculating&nbsp;the&nbsp;statistic.&nbsp;Each&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;fixed&nbsp;size.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;its&nbsp;an&nbsp;offset&nbsp;then&nbsp;this&nbsp;will&nbsp;be&nbsp;the&nbsp;time&nbsp;period&nbsp;of&nbsp;each&nbsp;window.&nbsp;Each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;variable&nbsp;sized&nbsp;based&nbsp;on&nbsp;the&nbsp;observations&nbsp;included&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;time-period.&nbsp;This&nbsp;is&nbsp;only&nbsp;valid&nbsp;for&nbsp;datetimelike&nbsp;indexes.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;new&nbsp;in&nbsp;0.19.0<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).&nbsp;For&nbsp;a&nbsp;window&nbsp;that&nbsp;is&nbsp;specified&nbsp;by&nbsp;an&nbsp;offset,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;will&nbsp;default&nbsp;to&nbsp;1.<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-win_type&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Provide&nbsp;a&nbsp;window&nbsp;type.&nbsp;See&nbsp;the&nbsp;notes&nbsp;below.<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;on&nbsp;which&nbsp;to&nbsp;calculate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;rolling&nbsp;window,&nbsp;rather&nbsp;than&nbsp;the&nbsp;index<br>
-closed&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;the&nbsp;interval&nbsp;closed&nbsp;on&nbsp;the&nbsp;'right',&nbsp;'left',&nbsp;'both'&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'neither'&nbsp;endpoints.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;offset-based&nbsp;windows,&nbsp;it&nbsp;defaults&nbsp;to&nbsp;'right'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;fixed&nbsp;windows,&nbsp;defaults&nbsp;to&nbsp;'both'.&nbsp;Remaining&nbsp;cases&nbsp;not&nbsp;implemented<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;fixed&nbsp;windows.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;or&nbsp;Rolling&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;using&nbsp;the&nbsp;'triang'<br>
-window&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-rolling">rolling</a>(2,&nbsp;win_type='triang').<a href="#TimeFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.5<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;min_periods&nbsp;defaults<br>
-to&nbsp;the&nbsp;window&nbsp;length.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-rolling">rolling</a>(2).<a href="#TimeFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Same&nbsp;as&nbsp;above,&nbsp;but&nbsp;explicity&nbsp;set&nbsp;the&nbsp;min_periods<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-rolling">rolling</a>(2,&nbsp;min_periods=1).<a href="#TimeFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;2.0<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-A&nbsp;ragged&nbsp;(meaning&nbsp;not-a-regular&nbsp;frequency),&nbsp;time-indexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]},<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;=&nbsp;[pd.Timestamp('20130101&nbsp;09:00:00'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:02'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:03'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:05'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:06')])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;2.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Contrasting&nbsp;to&nbsp;an&nbsp;integer&nbsp;rolling&nbsp;window,&nbsp;this&nbsp;will&nbsp;roll&nbsp;a&nbsp;variable<br>
-length&nbsp;window&nbsp;corresponding&nbsp;to&nbsp;the&nbsp;time&nbsp;period.<br>
-The&nbsp;default&nbsp;for&nbsp;min_periods&nbsp;is&nbsp;1.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-rolling">rolling</a>('2s').<a href="#TimeFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;3.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offsets&nbsp;&amp;&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-The&nbsp;recognized&nbsp;win_types&nbsp;are:<br>
-&nbsp;<br>
-*&nbsp;``boxcar``<br>
-*&nbsp;``triang``<br>
-*&nbsp;``blackman``<br>
-*&nbsp;``hamming``<br>
-*&nbsp;``bartlett``<br>
-*&nbsp;``parzen``<br>
-*&nbsp;``bohman``<br>
-*&nbsp;``blackmanharris``<br>
-*&nbsp;``nuttall``<br>
-*&nbsp;``barthann``<br>
-*&nbsp;``kaiser``&nbsp;(needs&nbsp;beta)<br>
-*&nbsp;``gaussian``&nbsp;(needs&nbsp;std)<br>
-*&nbsp;``general_gaussian``&nbsp;(needs&nbsp;power,&nbsp;width)<br>
-*&nbsp;``slepian``&nbsp;(needs&nbsp;width).</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-round"><strong>round</strong></a>(self, decimals=0, *args, **kwargs)</dt><dd><tt>Round&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;variable&nbsp;number&nbsp;of&nbsp;decimal&nbsp;places.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-decimals&nbsp;:&nbsp;int,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;round&nbsp;each&nbsp;column&nbsp;to.&nbsp;If&nbsp;an&nbsp;int&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;given,&nbsp;round&nbsp;each&nbsp;column&nbsp;to&nbsp;the&nbsp;same&nbsp;number&nbsp;of&nbsp;places.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Otherwise&nbsp;dict&nbsp;and&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;round&nbsp;to&nbsp;variable&nbsp;numbers&nbsp;of&nbsp;places.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;names&nbsp;should&nbsp;be&nbsp;in&nbsp;the&nbsp;keys&nbsp;if&nbsp;`decimals`&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict-like,&nbsp;or&nbsp;in&nbsp;the&nbsp;index&nbsp;if&nbsp;`decimals`&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns&nbsp;not&nbsp;included&nbsp;in&nbsp;`decimals`&nbsp;will&nbsp;be&nbsp;left&nbsp;as&nbsp;is.&nbsp;Elements<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;`decimals`&nbsp;which&nbsp;are&nbsp;not&nbsp;columns&nbsp;of&nbsp;the&nbsp;input&nbsp;will&nbsp;be<br>
-&nbsp;&nbsp;&nbsp;&nbsp;ignored.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.random([3,&nbsp;3]),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],&nbsp;index=['first',&nbsp;'second',&nbsp;'third'])<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-first&nbsp;&nbsp;&nbsp;0.028208&nbsp;&nbsp;0.992815&nbsp;&nbsp;0.173891<br>
-second&nbsp;&nbsp;0.038683&nbsp;&nbsp;0.645646&nbsp;&nbsp;0.577595<br>
-third&nbsp;&nbsp;&nbsp;0.877076&nbsp;&nbsp;0.149370&nbsp;&nbsp;0.491027<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-round">round</a>(2)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-first&nbsp;&nbsp;&nbsp;0.03&nbsp;&nbsp;0.99&nbsp;&nbsp;0.17<br>
-second&nbsp;&nbsp;0.04&nbsp;&nbsp;0.65&nbsp;&nbsp;0.58<br>
-third&nbsp;&nbsp;&nbsp;0.88&nbsp;&nbsp;0.15&nbsp;&nbsp;0.49<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-round">round</a>({'A':&nbsp;1,&nbsp;'C':&nbsp;2})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-first&nbsp;&nbsp;&nbsp;0.0&nbsp;&nbsp;0.992815&nbsp;&nbsp;0.17<br>
-second&nbsp;&nbsp;0.0&nbsp;&nbsp;0.645646&nbsp;&nbsp;0.58<br>
-third&nbsp;&nbsp;&nbsp;0.9&nbsp;&nbsp;0.149370&nbsp;&nbsp;0.49<br>
-&gt;&gt;&gt;&nbsp;decimals&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;0,&nbsp;2],&nbsp;index=['A',&nbsp;'B',&nbsp;'C'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-round">round</a>(decimals)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-first&nbsp;&nbsp;&nbsp;0.0&nbsp;&nbsp;1&nbsp;&nbsp;0.17<br>
-second&nbsp;&nbsp;0.0&nbsp;&nbsp;1&nbsp;&nbsp;0.58<br>
-third&nbsp;&nbsp;&nbsp;0.9&nbsp;&nbsp;0&nbsp;&nbsp;0.49<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.around<br>
-<a href="pandas.core.series.html#Series">Series</a>.round</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-rpow"><strong>rpow</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rpow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;**&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.pow</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-rsub"><strong>rsub</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Subtraction&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rsub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;-&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.sub</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-rtruediv"><strong>rtruediv</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;dataframe``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-select_dtypes"><strong>select_dtypes</strong></a>(self, include=None, exclude=None)</dt><dd><tt>Return&nbsp;a&nbsp;subset&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;including/excluding&nbsp;columns&nbsp;based&nbsp;on<br>
-their&nbsp;``dtype``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-include,&nbsp;exclude&nbsp;:&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;list&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;strings&nbsp;to&nbsp;be&nbsp;included/excluded.&nbsp;You&nbsp;must&nbsp;pass<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;a&nbsp;non-empty&nbsp;sequence&nbsp;for&nbsp;at&nbsp;least&nbsp;one&nbsp;of&nbsp;these.<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;both&nbsp;of&nbsp;``include``&nbsp;and&nbsp;``exclude``&nbsp;are&nbsp;empty<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;``include``&nbsp;and&nbsp;``exclude``&nbsp;have&nbsp;overlapping&nbsp;elements<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;any&nbsp;kind&nbsp;of&nbsp;string&nbsp;dtype&nbsp;is&nbsp;passed&nbsp;in.<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;either&nbsp;of&nbsp;``include``&nbsp;or&nbsp;``exclude``&nbsp;is&nbsp;not&nbsp;a&nbsp;sequence<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;subset&nbsp;of&nbsp;the&nbsp;frame&nbsp;including&nbsp;the&nbsp;dtypes&nbsp;in&nbsp;``include``&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;excluding&nbsp;the&nbsp;dtypes&nbsp;in&nbsp;``exclude``.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-*&nbsp;To&nbsp;select&nbsp;all&nbsp;*numeric*&nbsp;types&nbsp;use&nbsp;the&nbsp;numpy&nbsp;dtype&nbsp;``numpy.number``<br>
-*&nbsp;To&nbsp;select&nbsp;strings&nbsp;you&nbsp;must&nbsp;use&nbsp;the&nbsp;``<a href="builtins.html#object">object</a>``&nbsp;dtype,&nbsp;but&nbsp;note&nbsp;that<br>
-&nbsp;&nbsp;this&nbsp;will&nbsp;return&nbsp;*all*&nbsp;<a href="builtins.html#object">object</a>&nbsp;dtype&nbsp;columns<br>
-*&nbsp;See&nbsp;the&nbsp;`numpy&nbsp;dtype&nbsp;hierarchy<br>
-&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html&gt;`__">http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html&gt;`__</a><br>
-*&nbsp;To&nbsp;select&nbsp;datetimes,&nbsp;use&nbsp;np.datetime64,&nbsp;'datetime'&nbsp;or&nbsp;'datetime64'<br>
-*&nbsp;To&nbsp;select&nbsp;timedeltas,&nbsp;use&nbsp;np.timedelta64,&nbsp;'timedelta'&nbsp;or<br>
-&nbsp;&nbsp;'timedelta64'<br>
-*&nbsp;To&nbsp;select&nbsp;Pandas&nbsp;categorical&nbsp;dtypes,&nbsp;use&nbsp;'category'<br>
-*&nbsp;To&nbsp;select&nbsp;Pandas&nbsp;datetimetz&nbsp;dtypes,&nbsp;use&nbsp;'datetimetz'&nbsp;(new&nbsp;in&nbsp;0.20.0),<br>
-&nbsp;&nbsp;or&nbsp;a&nbsp;'datetime64[ns,&nbsp;tz]'&nbsp;string<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'a':&nbsp;np.random.randn(6).<a href="#TimeFrame-astype">astype</a>('f4'),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'b':&nbsp;[True,&nbsp;False]&nbsp;*&nbsp;3,<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'c':&nbsp;[1.0,&nbsp;2.0]&nbsp;*&nbsp;3})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;c<br>
-0&nbsp;&nbsp;0.3962&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;0.1459&nbsp;&nbsp;False&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;0.2623&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;0.0764&nbsp;&nbsp;False&nbsp;&nbsp;2<br>
-4&nbsp;-0.9703&nbsp;&nbsp;&nbsp;True&nbsp;&nbsp;1<br>
-5&nbsp;-1.2094&nbsp;&nbsp;False&nbsp;&nbsp;2<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-select_dtypes">select_dtypes</a>(include=['float64'])<br>
-&nbsp;&nbsp;&nbsp;c<br>
-0&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;2<br>
-4&nbsp;&nbsp;1<br>
-5&nbsp;&nbsp;2<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-select_dtypes">select_dtypes</a>(exclude=['floating'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-0&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;False<br>
-4&nbsp;&nbsp;&nbsp;True<br>
-5&nbsp;&nbsp;False</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-sem"><strong>sem</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;standard&nbsp;error&nbsp;of&nbsp;the&nbsp;mean&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sem&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-set_index"><strong>set_index</strong></a>(self, keys, drop=True, append=False, inplace=False, verify_integrity=False)</dt><dd><tt>Set&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;index&nbsp;(row&nbsp;labels)&nbsp;using&nbsp;one&nbsp;or&nbsp;more&nbsp;existing<br>
-columns.&nbsp;By&nbsp;default&nbsp;yields&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-keys&nbsp;:&nbsp;column&nbsp;label&nbsp;or&nbsp;list&nbsp;of&nbsp;column&nbsp;labels&nbsp;/&nbsp;arrays<br>
-drop&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Delete&nbsp;columns&nbsp;to&nbsp;be&nbsp;used&nbsp;as&nbsp;the&nbsp;new&nbsp;index<br>
-append&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;append&nbsp;columns&nbsp;to&nbsp;existing&nbsp;index<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Modify&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;in&nbsp;place&nbsp;(do&nbsp;not&nbsp;create&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>)<br>
-verify_integrity&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Check&nbsp;the&nbsp;new&nbsp;index&nbsp;for&nbsp;duplicates.&nbsp;Otherwise&nbsp;defer&nbsp;the&nbsp;check&nbsp;until<br>
-&nbsp;&nbsp;&nbsp;&nbsp;necessary.&nbsp;Setting&nbsp;to&nbsp;False&nbsp;will&nbsp;improve&nbsp;the&nbsp;performance&nbsp;of&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;method<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;indexed_df&nbsp;=&nbsp;df.<a href="#TimeFrame-set_index">set_index</a>(['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;indexed_df2&nbsp;=&nbsp;df.<a href="#TimeFrame-set_index">set_index</a>(['A',&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;0,&nbsp;1,&nbsp;2]])<br>
-&gt;&gt;&gt;&nbsp;indexed_df3&nbsp;=&nbsp;df.<a href="#TimeFrame-set_index">set_index</a>([[0,&nbsp;1,&nbsp;2,&nbsp;0,&nbsp;1,&nbsp;2]])<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dataframe&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-set_value"><strong>set_value</strong></a>(self, index, col, value, takeable=False)</dt><dd><tt>Put&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;column&nbsp;and&nbsp;index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;row&nbsp;label<br>
-col&nbsp;:&nbsp;column&nbsp;label<br>
-value&nbsp;:&nbsp;scalar&nbsp;value<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index/col&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-frame&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;label&nbsp;pair&nbsp;is&nbsp;contained,&nbsp;will&nbsp;be&nbsp;reference&nbsp;to&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-shift"><strong>shift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;index&nbsp;by&nbsp;desired&nbsp;number&nbsp;of&nbsp;periods&nbsp;with&nbsp;an&nbsp;optional&nbsp;time&nbsp;freq<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM').<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;Notes.<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'}<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;specified&nbsp;then&nbsp;the&nbsp;index&nbsp;values&nbsp;are&nbsp;shifted&nbsp;but&nbsp;the&nbsp;data<br>
-is&nbsp;not&nbsp;realigned.&nbsp;That&nbsp;is,&nbsp;use&nbsp;freq&nbsp;if&nbsp;you&nbsp;would&nbsp;like&nbsp;to&nbsp;extend&nbsp;the<br>
-index&nbsp;when&nbsp;shifting&nbsp;and&nbsp;preserve&nbsp;the&nbsp;original&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-skew"><strong>skew</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;skew&nbsp;over&nbsp;requested&nbsp;axis<br>
-Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-skew&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-sort_index"><strong>sort_index</strong></a>(self, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, by=None)</dt><dd><tt>Sort&nbsp;<a href="builtins.html#object">object</a>&nbsp;by&nbsp;labels&nbsp;(along&nbsp;an&nbsp;axis)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;index,&nbsp;columns&nbsp;to&nbsp;direct&nbsp;sorting<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name&nbsp;or&nbsp;list&nbsp;of&nbsp;ints&nbsp;or&nbsp;list&nbsp;of&nbsp;level&nbsp;names<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None,&nbsp;sort&nbsp;on&nbsp;values&nbsp;in&nbsp;specified&nbsp;index&nbsp;level(s)<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;MultiIndex.<br>
-sort_remaining&nbsp;:&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;true&nbsp;and&nbsp;sorting&nbsp;by&nbsp;level&nbsp;and&nbsp;index&nbsp;is&nbsp;multilevel,&nbsp;sort&nbsp;by&nbsp;other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;levels&nbsp;too&nbsp;(in&nbsp;order)&nbsp;after&nbsp;sorting&nbsp;by&nbsp;specified&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-sort_values"><strong>sort_values</strong></a>(self, by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')</dt><dd><tt>Sort&nbsp;by&nbsp;the&nbsp;values&nbsp;along&nbsp;either&nbsp;axis<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;str&nbsp;or&nbsp;list&nbsp;of&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;or&nbsp;list&nbsp;of&nbsp;names&nbsp;which&nbsp;refer&nbsp;to&nbsp;the&nbsp;axis&nbsp;items.<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;direct&nbsp;sorting<br>
-ascending&nbsp;:&nbsp;bool&nbsp;or&nbsp;list&nbsp;of&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending.&nbsp;Specify&nbsp;list&nbsp;for&nbsp;multiple&nbsp;sort<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;orders.&nbsp;&nbsp;If&nbsp;this&nbsp;is&nbsp;a&nbsp;list&nbsp;of&nbsp;bools,&nbsp;must&nbsp;match&nbsp;the&nbsp;length&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;by.<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-sortlevel"><strong>sortlevel</strong></a>(self, level=0, axis=0, ascending=True, inplace=False, sort_remaining=True)</dt><dd><tt>DEPRECATED:&nbsp;use&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.sort_index`<br>
-&nbsp;<br>
-Sort&nbsp;multilevel&nbsp;index&nbsp;by&nbsp;chosen&nbsp;axis&nbsp;and&nbsp;primary&nbsp;level.&nbsp;Data&nbsp;will&nbsp;be<br>
-lexicographically&nbsp;sorted&nbsp;by&nbsp;the&nbsp;chosen&nbsp;level&nbsp;followed&nbsp;by&nbsp;the&nbsp;other<br>
-levels&nbsp;(in&nbsp;order)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;without&nbsp;creating&nbsp;a&nbsp;new&nbsp;instance<br>
-sort_remaining&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;by&nbsp;the&nbsp;other&nbsp;levels&nbsp;too.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#TimeFrame-sort_index">sort_index</a>(level=...)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-stack"><strong>stack</strong></a>(self, level=-1, dropna=True)</dt><dd><tt>Pivot&nbsp;a&nbsp;level&nbsp;of&nbsp;the&nbsp;(possibly&nbsp;hierarchical)&nbsp;column&nbsp;labels,&nbsp;returning&nbsp;a<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;the&nbsp;case&nbsp;of&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;a&nbsp;single&nbsp;level&nbsp;of<br>
-column&nbsp;labels)&nbsp;having&nbsp;a&nbsp;hierarchical&nbsp;index&nbsp;with&nbsp;a&nbsp;new&nbsp;inner-most&nbsp;level<br>
-of&nbsp;row&nbsp;labels.<br>
-The&nbsp;level&nbsp;involved&nbsp;will&nbsp;automatically&nbsp;get&nbsp;sorted.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;string,&nbsp;or&nbsp;list&nbsp;of&nbsp;these,&nbsp;default&nbsp;last&nbsp;level<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level(s)&nbsp;to&nbsp;stack,&nbsp;can&nbsp;pass&nbsp;level&nbsp;name<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;drop&nbsp;rows&nbsp;in&nbsp;the&nbsp;resulting&nbsp;Frame/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;no&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values<br>
-&nbsp;<br>
-Examples<br>
-----------<br>
-&gt;&gt;&gt;&nbsp;s<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;b<br>
-one&nbsp;&nbsp;1.&nbsp;&nbsp;2.<br>
-two&nbsp;&nbsp;3.&nbsp;&nbsp;4.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeFrame-stack">stack</a>()<br>
-one&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-two&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-stacked&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-std"><strong>std</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;sample&nbsp;standard&nbsp;deviation&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-std&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-sub"><strong>sub</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Subtraction&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-subtract"><strong>subtract</strong></a> = sub(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Subtraction&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-sum"><strong>sum</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;sum&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sum&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-swaplevel"><strong>swaplevel</strong></a>(self, i=-2, j=-1, axis=0)</dt><dd><tt>Swap&nbsp;levels&nbsp;i&nbsp;and&nbsp;j&nbsp;in&nbsp;a&nbsp;MultiIndex&nbsp;on&nbsp;a&nbsp;particular&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-i,&nbsp;j&nbsp;:&nbsp;int,&nbsp;string&nbsp;(can&nbsp;be&nbsp;mixed)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level&nbsp;of&nbsp;index&nbsp;to&nbsp;be&nbsp;swapped.&nbsp;Can&nbsp;pass&nbsp;level&nbsp;name&nbsp;as&nbsp;string.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-swapped&nbsp;:&nbsp;type&nbsp;of&nbsp;caller&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-..&nbsp;versionchanged::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;The&nbsp;indexes&nbsp;``i``&nbsp;and&nbsp;``j``&nbsp;are&nbsp;now&nbsp;optional,&nbsp;and&nbsp;default&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;the&nbsp;two&nbsp;innermost&nbsp;levels&nbsp;of&nbsp;the&nbsp;index.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_csv"><strong>to_csv</strong></a>(self, path_or_buf=None, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression=None, quoting=None, quotechar='"', line_terminator='\n', chunksize=None, tupleize_cols=False, date_format=None, doublequote=True, escapechar=None, decimal='.')</dt><dd><tt>Write&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;comma-separated&nbsp;values&nbsp;(csv)&nbsp;file<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;string&nbsp;or&nbsp;file&nbsp;handle,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;<a href="builtins.html#object">object</a>,&nbsp;if&nbsp;None&nbsp;is&nbsp;provided&nbsp;the&nbsp;result&nbsp;is&nbsp;returned&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string.<br>
-sep&nbsp;:&nbsp;character,&nbsp;default&nbsp;','<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter&nbsp;for&nbsp;the&nbsp;output&nbsp;file.<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Columns&nbsp;to&nbsp;write<br>
-header&nbsp;:&nbsp;boolean&nbsp;or&nbsp;list&nbsp;of&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given&nbsp;it&nbsp;is&nbsp;assumed<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;or&nbsp;False,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.&nbsp;&nbsp;If<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False&nbsp;do&nbsp;not&nbsp;print&nbsp;fields&nbsp;for&nbsp;index&nbsp;names.&nbsp;Use&nbsp;index_label=False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;easier&nbsp;importing&nbsp;in&nbsp;R<br>
-mode&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Python&nbsp;write&nbsp;mode,&nbsp;default&nbsp;'w'<br>
-encoding&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;defaults&nbsp;to&nbsp;'ascii'&nbsp;on&nbsp;Python&nbsp;2&nbsp;and&nbsp;'utf-8'&nbsp;on&nbsp;Python&nbsp;3.<br>
-compression&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;compression&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;allowed&nbsp;values&nbsp;are&nbsp;'gzip',&nbsp;'bz2',&nbsp;'xz',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;only&nbsp;used&nbsp;when&nbsp;the&nbsp;first&nbsp;argument&nbsp;is&nbsp;a&nbsp;filename<br>
-line_terminator&nbsp;:&nbsp;string,&nbsp;default&nbsp;``'\n'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;newline&nbsp;character&nbsp;or&nbsp;character&nbsp;sequence&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output<br>
-&nbsp;&nbsp;&nbsp;&nbsp;file<br>
-quoting&nbsp;:&nbsp;optional&nbsp;constant&nbsp;from&nbsp;csv&nbsp;module<br>
-&nbsp;&nbsp;&nbsp;&nbsp;defaults&nbsp;to&nbsp;csv.QUOTE_MINIMAL.&nbsp;If&nbsp;you&nbsp;have&nbsp;set&nbsp;a&nbsp;`float_format`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;floats&nbsp;are&nbsp;converted&nbsp;to&nbsp;strings&nbsp;and&nbsp;thus&nbsp;csv.QUOTE_NONNUMERIC<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;treat&nbsp;them&nbsp;as&nbsp;non-numeric<br>
-quotechar&nbsp;:&nbsp;string&nbsp;(length&nbsp;1),&nbsp;default&nbsp;'\"'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;character&nbsp;used&nbsp;to&nbsp;quote&nbsp;fields<br>
-doublequote&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Control&nbsp;quoting&nbsp;of&nbsp;`quotechar`&nbsp;inside&nbsp;a&nbsp;field<br>
-escapechar&nbsp;:&nbsp;string&nbsp;(length&nbsp;1),&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;character&nbsp;used&nbsp;to&nbsp;escape&nbsp;`sep`&nbsp;and&nbsp;`quotechar`&nbsp;when&nbsp;appropriate<br>
-chunksize&nbsp;:&nbsp;int&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rows&nbsp;to&nbsp;write&nbsp;at&nbsp;a&nbsp;time<br>
-tupleize_cols&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;write&nbsp;multi_index&nbsp;columns&nbsp;as&nbsp;a&nbsp;list&nbsp;of&nbsp;tuples&nbsp;(if&nbsp;True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;new&nbsp;(expanded&nbsp;format)&nbsp;if&nbsp;False)<br>
-date_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;datetime&nbsp;objects<br>
-decimal:&nbsp;string,&nbsp;default&nbsp;'.'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Character&nbsp;recognized&nbsp;as&nbsp;decimal&nbsp;separator.&nbsp;E.g.&nbsp;use&nbsp;','&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;European&nbsp;data<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.16.0</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_dict"><strong>to_dict</strong></a>(self, orient='dict')</dt><dd><tt>Convert&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;dictionary.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-orient&nbsp;:&nbsp;str&nbsp;{'dict',&nbsp;'list',&nbsp;'series',&nbsp;'split',&nbsp;'records',&nbsp;'index'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Determines&nbsp;the&nbsp;type&nbsp;of&nbsp;the&nbsp;values&nbsp;of&nbsp;the&nbsp;dictionary.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;(default)&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;{index&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;[values]}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;series&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;<a href="pandas.core.series.html#Series">Series</a>(values)}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;split&nbsp;:&nbsp;dict&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{index&nbsp;-&gt;&nbsp;[index],&nbsp;columns&nbsp;-&gt;&nbsp;[columns],&nbsp;data&nbsp;-&gt;&nbsp;[values]}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;records&nbsp;:&nbsp;list&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[{column&nbsp;-&gt;&nbsp;value},&nbsp;...&nbsp;,&nbsp;{column&nbsp;-&gt;&nbsp;value}]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;index&nbsp;:&nbsp;dict&nbsp;like&nbsp;{index&nbsp;-&gt;&nbsp;{column&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Abbreviations&nbsp;are&nbsp;allowed.&nbsp;`s`&nbsp;indicates&nbsp;`series`&nbsp;and&nbsp;`sp`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;indicates&nbsp;`split`.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;{index&nbsp;-&gt;&nbsp;value}}</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_excel"><strong>to_excel</strong></a>(self, excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True, freeze_panes=None)</dt><dd><tt>Write&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;an&nbsp;excel&nbsp;sheet<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel_writer&nbsp;:&nbsp;string&nbsp;or&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;existing&nbsp;ExcelWriter<br>
-sheet_name&nbsp;:&nbsp;string,&nbsp;default&nbsp;'Sheet1'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;sheet&nbsp;which&nbsp;will&nbsp;contain&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Columns&nbsp;to&nbsp;write<br>
-header&nbsp;:&nbsp;boolean&nbsp;or&nbsp;list&nbsp;of&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given&nbsp;it&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;assumed&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-startrow&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;row&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-startcol&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;column&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-engine&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;write&nbsp;engine&nbsp;to&nbsp;use&nbsp;-&nbsp;you&nbsp;can&nbsp;also&nbsp;set&nbsp;this&nbsp;via&nbsp;the&nbsp;options<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsx.writer``,&nbsp;``io.excel.xls.writer``,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsm.writer``.<br>
-merge_cells&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;MultiIndex&nbsp;and&nbsp;Hierarchical&nbsp;Rows&nbsp;as&nbsp;merged&nbsp;cells.<br>
-encoding:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;encoding&nbsp;of&nbsp;the&nbsp;resulting&nbsp;excel&nbsp;file.&nbsp;Only&nbsp;necessary&nbsp;for&nbsp;xlwt,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;writers&nbsp;support&nbsp;unicode&nbsp;natively.<br>
-inf_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;'inf'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Representation&nbsp;for&nbsp;infinity&nbsp;(there&nbsp;is&nbsp;no&nbsp;native&nbsp;representation&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;infinity&nbsp;in&nbsp;Excel)<br>
-freeze_panes&nbsp;:&nbsp;tuple&nbsp;of&nbsp;integer&nbsp;(length&nbsp;2),&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;the&nbsp;one-based&nbsp;bottommost&nbsp;row&nbsp;and&nbsp;rightmost&nbsp;column&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;to&nbsp;be&nbsp;frozen<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;passing&nbsp;an&nbsp;existing&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a>,&nbsp;then&nbsp;the&nbsp;sheet&nbsp;will&nbsp;be&nbsp;added<br>
-to&nbsp;the&nbsp;existing&nbsp;workbook.&nbsp;&nbsp;This&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;save&nbsp;different<br>
-DataFrames&nbsp;to&nbsp;one&nbsp;workbook:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;writer&nbsp;=&nbsp;pd.ExcelWriter('output.xlsx')<br>
-&gt;&gt;&gt;&nbsp;df1.<a href="#TimeFrame-to_excel">to_excel</a>(writer,'Sheet1')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#TimeFrame-to_excel">to_excel</a>(writer,'Sheet2')<br>
-&gt;&gt;&gt;&nbsp;writer.save()<br>
-&nbsp;<br>
-For&nbsp;compatibility&nbsp;with&nbsp;to_csv,&nbsp;to_excel&nbsp;serializes&nbsp;lists&nbsp;and&nbsp;dicts&nbsp;to<br>
-strings&nbsp;before&nbsp;writing.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_feather"><strong>to_feather</strong></a>(self, fname)</dt><dd><tt>write&nbsp;out&nbsp;the&nbsp;binary&nbsp;feather-format&nbsp;for&nbsp;DataFrames<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-fname&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;file&nbsp;path</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_gbq"><strong>to_gbq</strong></a>(self, destination_table, project_id, chunksize=10000, verbose=True, reauth=False, if_exists='fail', private_key=None)</dt><dd><tt>Write&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;Google&nbsp;BigQuery&nbsp;table.<br>
-&nbsp;<br>
-The&nbsp;main&nbsp;method&nbsp;a&nbsp;user&nbsp;calls&nbsp;to&nbsp;export&nbsp;pandas&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;contents&nbsp;to<br>
-Google&nbsp;BigQuery&nbsp;table.<br>
-&nbsp;<br>
-Google&nbsp;BigQuery&nbsp;API&nbsp;Client&nbsp;Library&nbsp;v2&nbsp;for&nbsp;Python&nbsp;is&nbsp;used.<br>
-Documentation&nbsp;is&nbsp;available&nbsp;`here<br>
-&lt;https://developers.google.com/api-client-library/python/apis/bigquery/v2&gt;`__<br>
-&nbsp;<br>
-Authentication&nbsp;to&nbsp;the&nbsp;Google&nbsp;BigQuery&nbsp;service&nbsp;is&nbsp;via&nbsp;OAuth&nbsp;2.0.<br>
-&nbsp;<br>
--&nbsp;If&nbsp;"private_key"&nbsp;is&nbsp;not&nbsp;provided:<br>
-&nbsp;<br>
-&nbsp;&nbsp;By&nbsp;default&nbsp;"application&nbsp;default&nbsp;credentials"&nbsp;are&nbsp;used.<br>
-&nbsp;<br>
-&nbsp;&nbsp;If&nbsp;default&nbsp;application&nbsp;credentials&nbsp;are&nbsp;not&nbsp;found&nbsp;or&nbsp;are&nbsp;restrictive,<br>
-&nbsp;&nbsp;user&nbsp;account&nbsp;credentials&nbsp;are&nbsp;used.&nbsp;In&nbsp;this&nbsp;case,&nbsp;you&nbsp;will&nbsp;be&nbsp;asked&nbsp;to<br>
-&nbsp;&nbsp;grant&nbsp;permissions&nbsp;for&nbsp;product&nbsp;name&nbsp;'pandas&nbsp;GBQ'.<br>
-&nbsp;<br>
--&nbsp;If&nbsp;"private_key"&nbsp;is&nbsp;provided:<br>
-&nbsp;<br>
-&nbsp;&nbsp;Service&nbsp;account&nbsp;credentials&nbsp;will&nbsp;be&nbsp;used&nbsp;to&nbsp;authenticate.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dataframe&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;be&nbsp;written<br>
-destination_table&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;table&nbsp;to&nbsp;be&nbsp;written,&nbsp;in&nbsp;the&nbsp;form&nbsp;'dataset.tablename'<br>
-project_id&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Google&nbsp;BigQuery&nbsp;Account&nbsp;project&nbsp;ID.<br>
-chunksize&nbsp;:&nbsp;int&nbsp;(default&nbsp;10000)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;rows&nbsp;to&nbsp;be&nbsp;inserted&nbsp;in&nbsp;each&nbsp;chunk&nbsp;from&nbsp;the&nbsp;dataframe.<br>
-verbose&nbsp;:&nbsp;boolean&nbsp;(default&nbsp;True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Show&nbsp;percentage&nbsp;complete<br>
-reauth&nbsp;:&nbsp;boolean&nbsp;(default&nbsp;False)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Force&nbsp;Google&nbsp;BigQuery&nbsp;to&nbsp;reauthenticate&nbsp;the&nbsp;user.&nbsp;This&nbsp;is&nbsp;useful<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;multiple&nbsp;accounts&nbsp;are&nbsp;used.<br>
-if_exists&nbsp;:&nbsp;{'fail',&nbsp;'replace',&nbsp;'append'},&nbsp;default&nbsp;'fail'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'fail':&nbsp;If&nbsp;table&nbsp;exists,&nbsp;do&nbsp;nothing.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'replace':&nbsp;If&nbsp;table&nbsp;exists,&nbsp;drop&nbsp;it,&nbsp;recreate&nbsp;it,&nbsp;and&nbsp;insert&nbsp;data.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'append':&nbsp;If&nbsp;table&nbsp;exists,&nbsp;insert&nbsp;data.&nbsp;Create&nbsp;if&nbsp;does&nbsp;not&nbsp;exist.<br>
-private_key&nbsp;:&nbsp;str&nbsp;(optional)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Service&nbsp;account&nbsp;private&nbsp;key&nbsp;in&nbsp;JSON&nbsp;format.&nbsp;Can&nbsp;be&nbsp;file&nbsp;path<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;string&nbsp;contents.&nbsp;This&nbsp;is&nbsp;useful&nbsp;for&nbsp;remote&nbsp;server<br>
-&nbsp;&nbsp;&nbsp;&nbsp;authentication&nbsp;(eg.&nbsp;jupyter&nbsp;iPython&nbsp;notebook&nbsp;on&nbsp;remote&nbsp;host)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_html"><strong>to_html</strong></a>(self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, bold_rows=True, classes=None, escape=True, max_rows=None, max_cols=None, show_dimensions=False, notebook=False, decimal='.', border=None)</dt><dd><tt>Render&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;as&nbsp;an&nbsp;HTML&nbsp;table.<br>
-&nbsp;<br>
-`to_html`-specific&nbsp;options:<br>
-&nbsp;<br>
-bold_rows&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;the&nbsp;row&nbsp;labels&nbsp;bold&nbsp;in&nbsp;the&nbsp;output<br>
-classes&nbsp;:&nbsp;str&nbsp;or&nbsp;list&nbsp;or&nbsp;tuple,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;CSS&nbsp;class(es)&nbsp;to&nbsp;apply&nbsp;to&nbsp;the&nbsp;resulting&nbsp;html&nbsp;table<br>
-escape&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Convert&nbsp;the&nbsp;characters&nbsp;&lt;,&nbsp;&gt;,&nbsp;and&nbsp;&amp;&nbsp;to&nbsp;HTML-safe&nbsp;sequences.=<br>
-max_rows&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;rows&nbsp;to&nbsp;show&nbsp;before&nbsp;truncating.&nbsp;If&nbsp;None,&nbsp;show<br>
-&nbsp;&nbsp;&nbsp;&nbsp;all.<br>
-max_cols&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;columns&nbsp;to&nbsp;show&nbsp;before&nbsp;truncating.&nbsp;If&nbsp;None,&nbsp;show<br>
-&nbsp;&nbsp;&nbsp;&nbsp;all.<br>
-decimal&nbsp;:&nbsp;string,&nbsp;default&nbsp;'.'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Character&nbsp;recognized&nbsp;as&nbsp;decimal&nbsp;separator,&nbsp;e.g.&nbsp;','&nbsp;in&nbsp;Europe<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-border&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;``border=border``&nbsp;attribute&nbsp;is&nbsp;included&nbsp;in&nbsp;the&nbsp;opening<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`&lt;table&gt;`&nbsp;tag.&nbsp;Default&nbsp;``pd.options.html.border``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-buf&nbsp;:&nbsp;StringIO-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;buffer&nbsp;to&nbsp;write&nbsp;to<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;to&nbsp;write;&nbsp;default&nbsp;None&nbsp;writes&nbsp;all&nbsp;columns<br>
-col_space&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;minimum&nbsp;width&nbsp;of&nbsp;each&nbsp;column<br>
-header&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;whether&nbsp;to&nbsp;print&nbsp;column&nbsp;labels,&nbsp;default&nbsp;True<br>
-index&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;whether&nbsp;to&nbsp;print&nbsp;index&nbsp;(row)&nbsp;labels,&nbsp;default&nbsp;True<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;representation&nbsp;of&nbsp;NAN&nbsp;to&nbsp;use,&nbsp;default&nbsp;'NaN'<br>
-formatters&nbsp;:&nbsp;list&nbsp;or&nbsp;dict&nbsp;of&nbsp;one-parameter&nbsp;functions,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;functions&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;by&nbsp;position&nbsp;or&nbsp;name,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;each&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;must&nbsp;be&nbsp;of&nbsp;length&nbsp;equal&nbsp;to&nbsp;the&nbsp;number&nbsp;of&nbsp;columns.<br>
-float_format&nbsp;:&nbsp;one-parameter&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;if&nbsp;they&nbsp;are&nbsp;floats,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;this&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-sparsify&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;to&nbsp;False&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;a&nbsp;hierarchical&nbsp;index&nbsp;to&nbsp;print&nbsp;every<br>
-&nbsp;&nbsp;&nbsp;&nbsp;multiindex&nbsp;key&nbsp;at&nbsp;each&nbsp;row,&nbsp;default&nbsp;True<br>
-index_names&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Prints&nbsp;the&nbsp;names&nbsp;of&nbsp;the&nbsp;indexes,&nbsp;default&nbsp;True<br>
-line_width&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Width&nbsp;to&nbsp;wrap&nbsp;a&nbsp;line&nbsp;in&nbsp;characters,&nbsp;default&nbsp;no&nbsp;wrap<br>
-justify&nbsp;:&nbsp;{'left',&nbsp;'right'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Left&nbsp;or&nbsp;right-justify&nbsp;the&nbsp;column&nbsp;labels.&nbsp;If&nbsp;None&nbsp;uses&nbsp;the&nbsp;option&nbsp;from<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;print&nbsp;configuration&nbsp;(controlled&nbsp;by&nbsp;set_option),&nbsp;'right'&nbsp;out<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;box.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-formatted&nbsp;:&nbsp;string&nbsp;(or&nbsp;unicode,&nbsp;depending&nbsp;on&nbsp;data&nbsp;and&nbsp;options)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_latex"><strong>to_latex</strong></a>(self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=True, column_format=None, longtable=None, escape=None, encoding=None, decimal='.', multicolumn=None, multicolumn_format=None, multirow=None)</dt><dd><tt>Render&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;tabular&nbsp;environment&nbsp;table.&nbsp;You&nbsp;can&nbsp;splice<br>
-this&nbsp;into&nbsp;a&nbsp;LaTeX&nbsp;document.&nbsp;Requires&nbsp;\usepackage{booktabs}.<br>
-&nbsp;<br>
-`to_latex`-specific&nbsp;options:<br>
-&nbsp;<br>
-bold_rows&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;the&nbsp;row&nbsp;labels&nbsp;bold&nbsp;in&nbsp;the&nbsp;output<br>
-column_format&nbsp;:&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;columns&nbsp;format&nbsp;as&nbsp;specified&nbsp;in&nbsp;`LaTeX&nbsp;table&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&lt;https://en.wikibooks.org/wiki/LaTeX/Tables&gt;`__&nbsp;e.g&nbsp;'rcl'&nbsp;for&nbsp;3<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns<br>
-longtable&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;will&nbsp;be&nbsp;read&nbsp;from&nbsp;the&nbsp;pandas&nbsp;config&nbsp;module<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default:&nbsp;False.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;a&nbsp;longtable&nbsp;environment&nbsp;instead&nbsp;of&nbsp;tabular.&nbsp;Requires&nbsp;adding<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;\usepackage{longtable}&nbsp;to&nbsp;your&nbsp;LaTeX&nbsp;preamble.<br>
-escape&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;will&nbsp;be&nbsp;read&nbsp;from&nbsp;the&nbsp;pandas&nbsp;config&nbsp;module<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default:&nbsp;True.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;set&nbsp;to&nbsp;False&nbsp;prevents&nbsp;from&nbsp;escaping&nbsp;latex&nbsp;special<br>
-&nbsp;&nbsp;&nbsp;&nbsp;characters&nbsp;in&nbsp;column&nbsp;names.<br>
-encoding&nbsp;:&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;defaults&nbsp;to&nbsp;'ascii'&nbsp;on&nbsp;Python&nbsp;2&nbsp;and&nbsp;'utf-8'&nbsp;on&nbsp;Python&nbsp;3.<br>
-decimal&nbsp;:&nbsp;string,&nbsp;default&nbsp;'.'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Character&nbsp;recognized&nbsp;as&nbsp;decimal&nbsp;separator,&nbsp;e.g.&nbsp;','&nbsp;in&nbsp;Europe.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-multicolumn&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;\multicolumn&nbsp;to&nbsp;enhance&nbsp;MultiIndex&nbsp;columns.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;default&nbsp;will&nbsp;be&nbsp;read&nbsp;from&nbsp;the&nbsp;config&nbsp;module.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-multicolumn_format&nbsp;:&nbsp;str,&nbsp;default&nbsp;'l'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;alignment&nbsp;for&nbsp;multicolumns,&nbsp;similar&nbsp;to&nbsp;`column_format`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;default&nbsp;will&nbsp;be&nbsp;read&nbsp;from&nbsp;the&nbsp;config&nbsp;module.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-multirow&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;\multirow&nbsp;to&nbsp;enhance&nbsp;MultiIndex&nbsp;rows.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Requires&nbsp;adding&nbsp;a&nbsp;\usepackage{multirow}&nbsp;to&nbsp;your&nbsp;LaTeX&nbsp;preamble.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Will&nbsp;print&nbsp;centered&nbsp;labels&nbsp;(instead&nbsp;of&nbsp;top-aligned)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;across&nbsp;the&nbsp;contained&nbsp;rows,&nbsp;separating&nbsp;groups&nbsp;via&nbsp;clines.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;default&nbsp;will&nbsp;be&nbsp;read&nbsp;from&nbsp;the&nbsp;pandas&nbsp;config&nbsp;module.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-buf&nbsp;:&nbsp;StringIO-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;buffer&nbsp;to&nbsp;write&nbsp;to<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;to&nbsp;write;&nbsp;default&nbsp;None&nbsp;writes&nbsp;all&nbsp;columns<br>
-col_space&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;minimum&nbsp;width&nbsp;of&nbsp;each&nbsp;column<br>
-header&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given,&nbsp;it&nbsp;is&nbsp;assumed&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names.<br>
-index&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;whether&nbsp;to&nbsp;print&nbsp;index&nbsp;(row)&nbsp;labels,&nbsp;default&nbsp;True<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;representation&nbsp;of&nbsp;NAN&nbsp;to&nbsp;use,&nbsp;default&nbsp;'NaN'<br>
-formatters&nbsp;:&nbsp;list&nbsp;or&nbsp;dict&nbsp;of&nbsp;one-parameter&nbsp;functions,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;functions&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;by&nbsp;position&nbsp;or&nbsp;name,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;each&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;must&nbsp;be&nbsp;of&nbsp;length&nbsp;equal&nbsp;to&nbsp;the&nbsp;number&nbsp;of&nbsp;columns.<br>
-float_format&nbsp;:&nbsp;one-parameter&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;if&nbsp;they&nbsp;are&nbsp;floats,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;this&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-sparsify&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;to&nbsp;False&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;a&nbsp;hierarchical&nbsp;index&nbsp;to&nbsp;print&nbsp;every<br>
-&nbsp;&nbsp;&nbsp;&nbsp;multiindex&nbsp;key&nbsp;at&nbsp;each&nbsp;row,&nbsp;default&nbsp;True<br>
-index_names&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Prints&nbsp;the&nbsp;names&nbsp;of&nbsp;the&nbsp;indexes,&nbsp;default&nbsp;True<br>
-line_width&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Width&nbsp;to&nbsp;wrap&nbsp;a&nbsp;line&nbsp;in&nbsp;characters,&nbsp;default&nbsp;no&nbsp;wrap<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-formatted&nbsp;:&nbsp;string&nbsp;(or&nbsp;unicode,&nbsp;depending&nbsp;on&nbsp;data&nbsp;and&nbsp;options)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_panel"><strong>to_panel</strong></a>(self)</dt><dd><tt>Transform&nbsp;long&nbsp;(stacked)&nbsp;format&nbsp;(<a href="pandas.core.frame.html#DataFrame">DataFrame</a>)&nbsp;into&nbsp;wide&nbsp;(3D,&nbsp;Panel)<br>
-format.<br>
-&nbsp;<br>
-Currently&nbsp;the&nbsp;index&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;must&nbsp;be&nbsp;a&nbsp;2-level&nbsp;MultiIndex.&nbsp;This<br>
-may&nbsp;be&nbsp;generalized&nbsp;later<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-panel&nbsp;:&nbsp;Panel</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_period"><strong>to_period</strong></a>(self, freq=None, axis=0, copy=True)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;from&nbsp;DatetimeIndex&nbsp;to&nbsp;PeriodIndex&nbsp;with&nbsp;desired<br>
-frequency&nbsp;(inferred&nbsp;from&nbsp;index&nbsp;if&nbsp;not&nbsp;passed)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;convert&nbsp;(the&nbsp;index&nbsp;by&nbsp;default)<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False&nbsp;then&nbsp;underlying&nbsp;input&nbsp;data&nbsp;is&nbsp;not&nbsp;copied<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ts&nbsp;:&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;with&nbsp;PeriodIndex</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_records"><strong>to_records</strong></a>(self, index=True, convert_datetime64=True)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;record&nbsp;array.&nbsp;Index&nbsp;will&nbsp;be&nbsp;put&nbsp;in&nbsp;the<br>
-'index'&nbsp;field&nbsp;of&nbsp;the&nbsp;record&nbsp;array&nbsp;if&nbsp;requested<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;index&nbsp;in&nbsp;resulting&nbsp;record&nbsp;array,&nbsp;stored&nbsp;in&nbsp;'index'&nbsp;field<br>
-convert_datetime64&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;convert&nbsp;the&nbsp;index&nbsp;to&nbsp;datetime.datetime&nbsp;if&nbsp;it&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;DatetimeIndex<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;recarray</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_sparse"><strong>to_sparse</strong></a>(self, fill_value=None, kind='block')</dt><dd><tt>Convert&nbsp;to&nbsp;SparseDataFrame<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-fill_value&nbsp;:&nbsp;float,&nbsp;default&nbsp;NaN<br>
-kind&nbsp;:&nbsp;{'block',&nbsp;'integer'}<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;SparseDataFrame</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_stata"><strong>to_stata</strong></a>(self, fname, convert_dates=None, write_index=True, encoding='latin-1', byteorder=None, time_stamp=None, data_label=None, variable_labels=None)</dt><dd><tt>A&nbsp;class&nbsp;for&nbsp;writing&nbsp;Stata&nbsp;binary&nbsp;dta&nbsp;files&nbsp;from&nbsp;array-like&nbsp;objects<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-fname&nbsp;:&nbsp;str&nbsp;or&nbsp;buffer<br>
-&nbsp;&nbsp;&nbsp;&nbsp;String&nbsp;path&nbsp;of&nbsp;file-like&nbsp;<a href="builtins.html#object">object</a><br>
-convert_dates&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Dictionary&nbsp;mapping&nbsp;columns&nbsp;containing&nbsp;datetime&nbsp;types&nbsp;to&nbsp;stata<br>
-&nbsp;&nbsp;&nbsp;&nbsp;internal&nbsp;format&nbsp;to&nbsp;use&nbsp;when&nbsp;wirting&nbsp;the&nbsp;dates.&nbsp;Options&nbsp;are&nbsp;'tc',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'td',&nbsp;'tm',&nbsp;'tw',&nbsp;'th',&nbsp;'tq',&nbsp;'ty'.&nbsp;Column&nbsp;can&nbsp;be&nbsp;either&nbsp;an&nbsp;integer<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;a&nbsp;name.&nbsp;Datetime&nbsp;columns&nbsp;that&nbsp;do&nbsp;not&nbsp;have&nbsp;a&nbsp;conversion&nbsp;type<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specified&nbsp;will&nbsp;be&nbsp;converted&nbsp;to&nbsp;'tc'.&nbsp;Raises&nbsp;NotImplementedError&nbsp;if<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;datetime&nbsp;column&nbsp;has&nbsp;timezone&nbsp;information<br>
-write_index&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;the&nbsp;index&nbsp;to&nbsp;Stata&nbsp;dataset.<br>
-encoding&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;is&nbsp;latin-1.&nbsp;Unicode&nbsp;is&nbsp;not&nbsp;supported<br>
-byteorder&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Can&nbsp;be&nbsp;"&gt;",&nbsp;"&lt;",&nbsp;"little",&nbsp;or&nbsp;"big".&nbsp;default&nbsp;is&nbsp;`sys.byteorder`<br>
-time_stamp&nbsp;:&nbsp;datetime<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;datetime&nbsp;to&nbsp;use&nbsp;as&nbsp;file&nbsp;creation&nbsp;date.&nbsp;&nbsp;Default&nbsp;is&nbsp;the&nbsp;current<br>
-&nbsp;&nbsp;&nbsp;&nbsp;time.<br>
-dataset_label&nbsp;:&nbsp;str<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;label&nbsp;for&nbsp;the&nbsp;data&nbsp;set.&nbsp;&nbsp;Must&nbsp;be&nbsp;80&nbsp;characters&nbsp;or&nbsp;smaller.<br>
-variable_labels&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Dictionary&nbsp;containing&nbsp;columns&nbsp;as&nbsp;keys&nbsp;and&nbsp;variable&nbsp;labels&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values.&nbsp;Each&nbsp;label&nbsp;must&nbsp;be&nbsp;80&nbsp;characters&nbsp;or&nbsp;smaller.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-NotImplementedError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;datetimes&nbsp;contain&nbsp;timezone&nbsp;information<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;Column&nbsp;dtype&nbsp;is&nbsp;not&nbsp;representable&nbsp;in&nbsp;Stata<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;Columns&nbsp;listed&nbsp;in&nbsp;convert_dates&nbsp;are&nbsp;noth&nbsp;either&nbsp;datetime64[ns]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;datetime.datetime<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;Column&nbsp;listed&nbsp;in&nbsp;convert_dates&nbsp;is&nbsp;not&nbsp;in&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;Categorical&nbsp;label&nbsp;contains&nbsp;more&nbsp;than&nbsp;32,000&nbsp;characters<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;writer&nbsp;=&nbsp;StataWriter('./data_file.dta',&nbsp;data)<br>
-&gt;&gt;&gt;&nbsp;writer.write_file()<br>
-&nbsp;<br>
-Or&nbsp;with&nbsp;dates<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;writer&nbsp;=&nbsp;StataWriter('./date_data_file.dta',&nbsp;data,&nbsp;{2&nbsp;:&nbsp;'tw'})<br>
-&gt;&gt;&gt;&nbsp;writer.write_file()</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_string"><strong>to_string</strong></a>(self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, line_width=None, max_rows=None, max_cols=None, show_dimensions=False)</dt><dd><tt>Render&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;console-friendly&nbsp;tabular&nbsp;output.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-buf&nbsp;:&nbsp;StringIO-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;buffer&nbsp;to&nbsp;write&nbsp;to<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;to&nbsp;write;&nbsp;default&nbsp;None&nbsp;writes&nbsp;all&nbsp;columns<br>
-col_space&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;minimum&nbsp;width&nbsp;of&nbsp;each&nbsp;column<br>
-header&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given,&nbsp;it&nbsp;is&nbsp;assumed&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names<br>
-index&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;whether&nbsp;to&nbsp;print&nbsp;index&nbsp;(row)&nbsp;labels,&nbsp;default&nbsp;True<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;representation&nbsp;of&nbsp;NAN&nbsp;to&nbsp;use,&nbsp;default&nbsp;'NaN'<br>
-formatters&nbsp;:&nbsp;list&nbsp;or&nbsp;dict&nbsp;of&nbsp;one-parameter&nbsp;functions,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;functions&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;by&nbsp;position&nbsp;or&nbsp;name,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;each&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;must&nbsp;be&nbsp;of&nbsp;length&nbsp;equal&nbsp;to&nbsp;the&nbsp;number&nbsp;of&nbsp;columns.<br>
-float_format&nbsp;:&nbsp;one-parameter&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;if&nbsp;they&nbsp;are&nbsp;floats,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None.&nbsp;The&nbsp;result&nbsp;of&nbsp;this&nbsp;function&nbsp;must&nbsp;be&nbsp;a&nbsp;unicode&nbsp;string.<br>
-sparsify&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;to&nbsp;False&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;a&nbsp;hierarchical&nbsp;index&nbsp;to&nbsp;print&nbsp;every<br>
-&nbsp;&nbsp;&nbsp;&nbsp;multiindex&nbsp;key&nbsp;at&nbsp;each&nbsp;row,&nbsp;default&nbsp;True<br>
-index_names&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Prints&nbsp;the&nbsp;names&nbsp;of&nbsp;the&nbsp;indexes,&nbsp;default&nbsp;True<br>
-line_width&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Width&nbsp;to&nbsp;wrap&nbsp;a&nbsp;line&nbsp;in&nbsp;characters,&nbsp;default&nbsp;no&nbsp;wrap<br>
-justify&nbsp;:&nbsp;{'left',&nbsp;'right'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Left&nbsp;or&nbsp;right-justify&nbsp;the&nbsp;column&nbsp;labels.&nbsp;If&nbsp;None&nbsp;uses&nbsp;the&nbsp;option&nbsp;from<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;print&nbsp;configuration&nbsp;(controlled&nbsp;by&nbsp;set_option),&nbsp;'right'&nbsp;out<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;box.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-formatted&nbsp;:&nbsp;string&nbsp;(or&nbsp;unicode,&nbsp;depending&nbsp;on&nbsp;data&nbsp;and&nbsp;options)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_timestamp"><strong>to_timestamp</strong></a>(self, freq=None, how='start', axis=0, copy=True)</dt><dd><tt>Cast&nbsp;to&nbsp;DatetimeIndex&nbsp;of&nbsp;timestamps,&nbsp;at&nbsp;*beginning*&nbsp;of&nbsp;period<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default&nbsp;frequency&nbsp;of&nbsp;PeriodIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Desired&nbsp;frequency<br>
-how&nbsp;:&nbsp;{'s',&nbsp;'e',&nbsp;'start',&nbsp;'end'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Convention&nbsp;for&nbsp;converting&nbsp;period&nbsp;to&nbsp;timestamp;&nbsp;start&nbsp;of&nbsp;period<br>
-&nbsp;&nbsp;&nbsp;&nbsp;vs.&nbsp;end<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;convert&nbsp;(the&nbsp;index&nbsp;by&nbsp;default)<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;false&nbsp;then&nbsp;underlying&nbsp;input&nbsp;data&nbsp;is&nbsp;not&nbsp;copied<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-df&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;DatetimeIndex</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-transform"><strong>transform</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Call&nbsp;function&nbsp;producing&nbsp;a&nbsp;like-indexed&nbsp;NDFrame<br>
-and&nbsp;return&nbsp;a&nbsp;NDFrame&nbsp;with&nbsp;the&nbsp;transformed&nbsp;values`<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;apply&nbsp;to&nbsp;column<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-transformed&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(10,&nbsp;3),&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.date_range('1/1/2000',&nbsp;periods=10))<br>
-df.iloc[3:7]&nbsp;=&nbsp;np.nan<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-transform">transform</a>(lambda&nbsp;x:&nbsp;(x&nbsp;-&nbsp;x.<a href="#TimeFrame-mean">mean</a>())&nbsp;/&nbsp;x.<a href="#TimeFrame-std">std</a>())<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-2000-01-01&nbsp;&nbsp;0.579457&nbsp;&nbsp;1.236184&nbsp;&nbsp;0.123424<br>
-2000-01-02&nbsp;&nbsp;0.370357&nbsp;-0.605875&nbsp;-1.231325<br>
-2000-01-03&nbsp;&nbsp;1.455756&nbsp;-0.277446&nbsp;&nbsp;0.288967<br>
-2000-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-08&nbsp;-0.498658&nbsp;&nbsp;1.274522&nbsp;&nbsp;1.642524<br>
-2000-01-09&nbsp;-0.540524&nbsp;-1.012676&nbsp;-0.828968<br>
-2000-01-10&nbsp;-1.366388&nbsp;-0.614710&nbsp;&nbsp;0.005378<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.NDFrame.aggregate<br>
-pandas.NDFrame.apply</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-transpose"><strong>transpose</strong></a>(self, *args, **kwargs)</dt><dd><tt>Transpose&nbsp;index&nbsp;and&nbsp;columns</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-truediv"><strong>truediv</strong></a>(self, other, axis='columns', level=None, fill_value=None)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;dataframe&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``dataframe&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;constant<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1,&nbsp;'index',&nbsp;'columns'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;input,&nbsp;axis&nbsp;to&nbsp;match&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index&nbsp;on<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;locations&nbsp;are&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Mismatched&nbsp;indices&nbsp;will&nbsp;be&nbsp;unioned&nbsp;together<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-unstack"><strong>unstack</strong></a>(self, level=-1, fill_value=None)</dt><dd><tt>Pivot&nbsp;a&nbsp;level&nbsp;of&nbsp;the&nbsp;(necessarily&nbsp;hierarchical)&nbsp;index&nbsp;labels,&nbsp;returning<br>
-a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;having&nbsp;a&nbsp;new&nbsp;level&nbsp;of&nbsp;column&nbsp;labels&nbsp;whose&nbsp;inner-most&nbsp;level<br>
-consists&nbsp;of&nbsp;the&nbsp;pivoted&nbsp;index&nbsp;labels.&nbsp;If&nbsp;the&nbsp;index&nbsp;is&nbsp;not&nbsp;a&nbsp;MultiIndex,<br>
-the&nbsp;output&nbsp;will&nbsp;be&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(the&nbsp;analogue&nbsp;of&nbsp;stack&nbsp;when&nbsp;the&nbsp;columns&nbsp;are<br>
-not&nbsp;a&nbsp;MultiIndex).<br>
-The&nbsp;level&nbsp;involved&nbsp;will&nbsp;automatically&nbsp;get&nbsp;sorted.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;string,&nbsp;or&nbsp;list&nbsp;of&nbsp;these,&nbsp;default&nbsp;-1&nbsp;(last&nbsp;level)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level(s)&nbsp;of&nbsp;index&nbsp;to&nbsp;unstack,&nbsp;can&nbsp;pass&nbsp;level&nbsp;name<br>
-fill_value&nbsp;:&nbsp;replace&nbsp;NaN&nbsp;with&nbsp;this&nbsp;value&nbsp;if&nbsp;the&nbsp;unstack&nbsp;produces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.18.0<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.pivot&nbsp;:&nbsp;Pivot&nbsp;a&nbsp;table&nbsp;based&nbsp;on&nbsp;column&nbsp;values.<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.stack&nbsp;:&nbsp;Pivot&nbsp;a&nbsp;level&nbsp;of&nbsp;the&nbsp;column&nbsp;labels&nbsp;(inverse&nbsp;operation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;`unstack`).<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.MultiIndex.from_tuples([('one',&nbsp;'a'),&nbsp;('one',&nbsp;'b'),<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;('two',&nbsp;'a'),&nbsp;('two',&nbsp;'b')])<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.arange(1.0,&nbsp;5.0),&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;s<br>
-one&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;1.0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;2.0<br>
-two&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;4.0<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeFrame-unstack">unstack</a>(level=-1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;b<br>
-one&nbsp;&nbsp;1.0&nbsp;&nbsp;2.0<br>
-two&nbsp;&nbsp;3.0&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeFrame-unstack">unstack</a>(level=0)<br>
-&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;two<br>
-a&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;3.0<br>
-b&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;s.<a href="#TimeFrame-unstack">unstack</a>(level=0)<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-unstack">unstack</a>()<br>
-one&nbsp;&nbsp;a&nbsp;&nbsp;1.0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;2.0<br>
-two&nbsp;&nbsp;a&nbsp;&nbsp;3.0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;4.0<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-unstacked&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-update"><strong>update</strong></a>(self, other, join='left', overwrite=True, filter_func=None, raise_conflict=False)</dt><dd><tt>Modify&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;in&nbsp;place&nbsp;using&nbsp;non-NA&nbsp;values&nbsp;from&nbsp;passed<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Aligns&nbsp;on&nbsp;indices<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;<a href="builtins.html#object">object</a>&nbsp;coercible&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-join&nbsp;:&nbsp;{'left'},&nbsp;default&nbsp;'left'<br>
-overwrite&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;then&nbsp;overwrite&nbsp;values&nbsp;for&nbsp;common&nbsp;keys&nbsp;in&nbsp;the&nbsp;calling&nbsp;frame<br>
-filter_func&nbsp;:&nbsp;callable(1d-array)&nbsp;-&gt;&nbsp;1d-array&lt;boolean&gt;,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Can&nbsp;choose&nbsp;to&nbsp;replace&nbsp;values&nbsp;other&nbsp;than&nbsp;NA.&nbsp;Return&nbsp;True&nbsp;for&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;&nbsp;that&nbsp;should&nbsp;be&nbsp;updated<br>
-raise_conflict&nbsp;:&nbsp;boolean<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;will&nbsp;raise&nbsp;an&nbsp;error&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;and&nbsp;other&nbsp;both<br>
-&nbsp;&nbsp;&nbsp;&nbsp;contain&nbsp;data&nbsp;in&nbsp;the&nbsp;same&nbsp;place.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-var"><strong>var</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;variance&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0),&nbsp;columns&nbsp;(1)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-var&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<hr>
-Class methods inherited from <a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a>:<br>
-<dl><dt><a name="TimeFrame-from_csv"><strong>from_csv</strong></a>(path, header=0, sep=',', index_col=0, parse_dates=True, encoding=None, tupleize_cols=False, infer_datetime_format=False)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Read&nbsp;CSV&nbsp;file&nbsp;(DISCOURAGED,&nbsp;please&nbsp;use&nbsp;:func:`pandas.read_csv`<br>
-instead).<br>
-&nbsp;<br>
-It&nbsp;is&nbsp;preferable&nbsp;to&nbsp;use&nbsp;the&nbsp;more&nbsp;powerful&nbsp;:func:`pandas.read_csv`<br>
-for&nbsp;most&nbsp;general&nbsp;purposes,&nbsp;but&nbsp;``from_csv``&nbsp;makes&nbsp;for&nbsp;an&nbsp;easy<br>
-roundtrip&nbsp;to&nbsp;and&nbsp;from&nbsp;a&nbsp;file&nbsp;(the&nbsp;exact&nbsp;counterpart&nbsp;of<br>
-``to_csv``),&nbsp;especially&nbsp;with&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;time&nbsp;series&nbsp;data.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;only&nbsp;differs&nbsp;from&nbsp;the&nbsp;preferred&nbsp;:func:`pandas.read_csv`<br>
-in&nbsp;some&nbsp;defaults:<br>
-&nbsp;<br>
--&nbsp;`index_col`&nbsp;is&nbsp;``0``&nbsp;instead&nbsp;of&nbsp;``None``&nbsp;(take&nbsp;first&nbsp;column&nbsp;as&nbsp;index<br>
-&nbsp;&nbsp;by&nbsp;default)<br>
--&nbsp;`parse_dates`&nbsp;is&nbsp;``True``&nbsp;instead&nbsp;of&nbsp;``False``&nbsp;(try&nbsp;parsing&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;as&nbsp;datetime&nbsp;by&nbsp;default)<br>
-&nbsp;<br>
-So&nbsp;a&nbsp;``pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#TimeFrame-from_csv">from_csv</a>(path)``&nbsp;can&nbsp;be&nbsp;replaced&nbsp;by<br>
-``pd.read_csv(path,&nbsp;index_col=0,&nbsp;parse_dates=True)``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;file&nbsp;path&nbsp;or&nbsp;file&nbsp;handle&nbsp;/&nbsp;StringIO<br>
-header&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Row&nbsp;to&nbsp;use&nbsp;as&nbsp;header&nbsp;(skip&nbsp;prior&nbsp;rows)<br>
-sep&nbsp;:&nbsp;string,&nbsp;default&nbsp;','<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter<br>
-index_col&nbsp;:&nbsp;int&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;to&nbsp;use&nbsp;for&nbsp;index.&nbsp;If&nbsp;a&nbsp;sequence&nbsp;is&nbsp;given,&nbsp;a&nbsp;MultiIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;used.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-parse_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Parse&nbsp;dates.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-tupleize_cols&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;write&nbsp;multi_index&nbsp;columns&nbsp;as&nbsp;a&nbsp;list&nbsp;of&nbsp;tuples&nbsp;(if&nbsp;True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;new&nbsp;(expanded&nbsp;format)&nbsp;if&nbsp;False)<br>
-infer_datetime_format:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;and&nbsp;`parse_dates`&nbsp;is&nbsp;True&nbsp;for&nbsp;a&nbsp;column,&nbsp;try&nbsp;to&nbsp;infer&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;datetime&nbsp;format&nbsp;based&nbsp;on&nbsp;the&nbsp;first&nbsp;datetime&nbsp;string.&nbsp;If&nbsp;the&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;be&nbsp;inferred,&nbsp;there&nbsp;often&nbsp;will&nbsp;be&nbsp;a&nbsp;large&nbsp;parsing&nbsp;speed-up.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.read_csv<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-from_dict"><strong>from_dict</strong></a>(data, orient='columns', dtype=None)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Construct&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;from&nbsp;dict&nbsp;of&nbsp;array-like&nbsp;or&nbsp;dicts<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;{field&nbsp;:&nbsp;array-like}&nbsp;or&nbsp;{field&nbsp;:&nbsp;dict}<br>
-orient&nbsp;:&nbsp;{'columns',&nbsp;'index'},&nbsp;default&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;"orientation"&nbsp;of&nbsp;the&nbsp;data.&nbsp;If&nbsp;the&nbsp;keys&nbsp;of&nbsp;the&nbsp;passed&nbsp;dict<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;be&nbsp;the&nbsp;columns&nbsp;of&nbsp;the&nbsp;resulting&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;pass&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(default).&nbsp;Otherwise&nbsp;if&nbsp;the&nbsp;keys&nbsp;should&nbsp;be&nbsp;rows,&nbsp;pass&nbsp;'index'.<br>
-dtype&nbsp;:&nbsp;dtype,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Data&nbsp;type&nbsp;to&nbsp;force,&nbsp;otherwise&nbsp;infer<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-from_items"><strong>from_items</strong></a>(items, columns=None, orient='columns')<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Convert&nbsp;(key,&nbsp;value)&nbsp;pairs&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;The&nbsp;keys&nbsp;will&nbsp;be&nbsp;the&nbsp;axis<br>
-index&nbsp;(usually&nbsp;the&nbsp;columns,&nbsp;but&nbsp;depends&nbsp;on&nbsp;the&nbsp;specified<br>
-orientation).&nbsp;The&nbsp;values&nbsp;should&nbsp;be&nbsp;arrays&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-items&nbsp;:&nbsp;sequence&nbsp;of&nbsp;(key,&nbsp;value)&nbsp;pairs<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Values&nbsp;should&nbsp;be&nbsp;arrays&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-columns&nbsp;:&nbsp;sequence&nbsp;of&nbsp;column&nbsp;labels,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Must&nbsp;be&nbsp;passed&nbsp;if&nbsp;orient='index'.<br>
-orient&nbsp;:&nbsp;{'columns',&nbsp;'index'},&nbsp;default&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;"orientation"&nbsp;of&nbsp;the&nbsp;data.&nbsp;If&nbsp;the&nbsp;keys&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;input&nbsp;correspond&nbsp;to&nbsp;column&nbsp;labels,&nbsp;pass&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(default).&nbsp;Otherwise&nbsp;if&nbsp;the&nbsp;keys&nbsp;correspond&nbsp;to&nbsp;the&nbsp;index,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;pass&nbsp;'index'.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-frame&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-from_records"><strong>from_records</strong></a>(data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Convert&nbsp;structured&nbsp;or&nbsp;record&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;(structured&nbsp;dtype),&nbsp;list&nbsp;of&nbsp;tuples,&nbsp;dict,&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-index&nbsp;:&nbsp;string,&nbsp;list&nbsp;of&nbsp;fields,&nbsp;array-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;of&nbsp;array&nbsp;to&nbsp;use&nbsp;as&nbsp;the&nbsp;index,&nbsp;alternately&nbsp;a&nbsp;specific&nbsp;set&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;input&nbsp;labels&nbsp;to&nbsp;use<br>
-exclude&nbsp;:&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Columns&nbsp;or&nbsp;fields&nbsp;to&nbsp;exclude<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;names&nbsp;to&nbsp;use.&nbsp;If&nbsp;the&nbsp;passed&nbsp;data&nbsp;do&nbsp;not&nbsp;have&nbsp;names<br>
-&nbsp;&nbsp;&nbsp;&nbsp;associated&nbsp;with&nbsp;them,&nbsp;this&nbsp;argument&nbsp;provides&nbsp;names&nbsp;for&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns.&nbsp;Otherwise&nbsp;this&nbsp;argument&nbsp;indicates&nbsp;the&nbsp;order&nbsp;of&nbsp;the&nbsp;columns<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;result&nbsp;(any&nbsp;names&nbsp;not&nbsp;found&nbsp;in&nbsp;the&nbsp;data&nbsp;will&nbsp;become&nbsp;all-NA<br>
-&nbsp;&nbsp;&nbsp;&nbsp;columns)<br>
-coerce_float&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Attempt&nbsp;to&nbsp;convert&nbsp;values&nbsp;of&nbsp;non-string,&nbsp;non-numeric&nbsp;objects&nbsp;(like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;decimal.Decimal)&nbsp;to&nbsp;floating&nbsp;point,&nbsp;useful&nbsp;for&nbsp;SQL&nbsp;result&nbsp;sets<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-df&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a>:<br>
-<dl><dt><strong>axes</strong></dt>
-<dd><tt>Return&nbsp;a&nbsp;list&nbsp;with&nbsp;the&nbsp;row&nbsp;axis&nbsp;labels&nbsp;and&nbsp;column&nbsp;axis&nbsp;labels&nbsp;as&nbsp;the<br>
-only&nbsp;members.&nbsp;They&nbsp;are&nbsp;returned&nbsp;in&nbsp;that&nbsp;order.</tt></dd>
-</dl>
-<dl><dt><strong>columns</strong></dt>
-</dl>
-<dl><dt><strong>index</strong></dt>
-</dl>
-<dl><dt><strong>shape</strong></dt>
-<dd><tt>Return&nbsp;a&nbsp;tuple&nbsp;representing&nbsp;the&nbsp;dimensionality&nbsp;of&nbsp;the&nbsp;DataFrame.</tt></dd>
-</dl>
-<dl><dt><strong>style</strong></dt>
-<dd><tt>Property&nbsp;returning&nbsp;a&nbsp;Styler&nbsp;object&nbsp;containing&nbsp;methods&nbsp;for<br>
-building&nbsp;a&nbsp;styled&nbsp;HTML&nbsp;representation&nbsp;fo&nbsp;the&nbsp;DataFrame.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.io.formats.style.Styler</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.frame.html#DataFrame">pandas.core.frame.DataFrame</a>:<br>
-<dl><dt><strong>plot</strong> = &lt;class 'pandas.plotting._core.FramePlotMethods'&gt;<dd><tt><a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;plotting&nbsp;accessor&nbsp;and&nbsp;method<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df.plot.line()<br>
-&gt;&gt;&gt;&nbsp;df.plot.scatter('x',&nbsp;'y')<br>
-&gt;&gt;&gt;&nbsp;df.plot.hexbin()<br>
-&nbsp;<br>
-These&nbsp;plotting&nbsp;methods&nbsp;can&nbsp;also&nbsp;be&nbsp;accessed&nbsp;by&nbsp;calling&nbsp;the&nbsp;accessor&nbsp;as&nbsp;a<br>
-method&nbsp;with&nbsp;the&nbsp;``kind``&nbsp;argument:<br>
-``df.<a href="#TimeFrame-plot">plot</a>(kind='line')``&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;``df.plot.line()``</tt></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><a name="TimeFrame-__abs__"><strong>__abs__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__array__"><strong>__array__</strong></a>(self, dtype=None)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__array_wrap__"><strong>__array_wrap__</strong></a>(self, result, context=None)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__bool__"><strong>__bool__</strong></a> = __nonzero__(self)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__contains__"><strong>__contains__</strong></a>(self, key)</dt><dd><tt>True&nbsp;if&nbsp;the&nbsp;key&nbsp;is&nbsp;in&nbsp;the&nbsp;info&nbsp;axis</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__copy__"><strong>__copy__</strong></a>(self, deep=True)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__deepcopy__"><strong>__deepcopy__</strong></a>(self, memo=None)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__delitem__"><strong>__delitem__</strong></a>(self, key)</dt><dd><tt>Delete&nbsp;item</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__finalize__"><strong>__finalize__</strong></a>(self, other, method=None, **kwargs)</dt><dd><tt>Propagate&nbsp;metadata&nbsp;from&nbsp;other&nbsp;to&nbsp;self.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;which&nbsp;to&nbsp;get&nbsp;the&nbsp;attributes&nbsp;that&nbsp;we&nbsp;are&nbsp;going<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;propagate<br>
-method&nbsp;:&nbsp;optional,&nbsp;a&nbsp;passed&nbsp;method&nbsp;name&nbsp;;&nbsp;possibly&nbsp;to&nbsp;take&nbsp;different<br>
-&nbsp;&nbsp;&nbsp;&nbsp;types&nbsp;of&nbsp;propagation&nbsp;actions&nbsp;based&nbsp;on&nbsp;this</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__getattr__"><strong>__getattr__</strong></a>(self, name)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;looking&nbsp;up&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__getstate__"><strong>__getstate__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__hash__"><strong>__hash__</strong></a>(self)</dt><dd><tt>Return&nbsp;hash(self).</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__invert__"><strong>__invert__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__iter__"><strong>__iter__</strong></a>(self)</dt><dd><tt>Iterate&nbsp;over&nbsp;infor&nbsp;axis</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__neg__"><strong>__neg__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__nonzero__"><strong>__nonzero__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__round__"><strong>__round__</strong></a>(self, decimals=0)</dt></dl>
-
-<dl><dt><a name="TimeFrame-__setattr__"><strong>__setattr__</strong></a>(self, name, value)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;setting&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__setstate__"><strong>__setstate__</strong></a>(self, state)</dt></dl>
-
-<dl><dt><a name="TimeFrame-abs"><strong>abs</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;absolute&nbsp;value&nbsp;taken--only&nbsp;applicable&nbsp;to&nbsp;objects<br>
-that&nbsp;are&nbsp;all&nbsp;numeric.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-abs:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-add_prefix"><strong>add_prefix</strong></a>(self, prefix)</dt><dd><tt>Concatenate&nbsp;prefix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-prefix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_prefix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-add_suffix"><strong>add_suffix</strong></a>(self, suffix)</dt><dd><tt>Concatenate&nbsp;suffix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-suffix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_suffix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-as_blocks"><strong>as_blocks</strong></a>(self, copy=True)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types&nbsp;that&nbsp;each&nbsp;has<br>
-a&nbsp;homogeneous&nbsp;dtype.<br>
-&nbsp;<br>
-NOTE:&nbsp;the&nbsp;dtypes&nbsp;of&nbsp;the&nbsp;blocks&nbsp;WILL&nbsp;BE&nbsp;PRESERVED&nbsp;HERE&nbsp;(unlike&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;as_matrix)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-as_matrix"><strong>as_matrix</strong></a>(self, columns=None)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;its&nbsp;Numpy-array&nbsp;representation.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-columns:&nbsp;list,&nbsp;optional,&nbsp;default:None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;None,&nbsp;return&nbsp;all&nbsp;columns,&nbsp;otherwise,&nbsp;returns&nbsp;specified&nbsp;columns.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;caller&nbsp;is&nbsp;heterogeneous&nbsp;and&nbsp;contains&nbsp;booleans&nbsp;or&nbsp;objects,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;of&nbsp;dtype=<a href="builtins.html#object">object</a>.&nbsp;See&nbsp;Notes.<br>
-&nbsp;<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Return&nbsp;is&nbsp;NOT&nbsp;a&nbsp;Numpy-matrix,&nbsp;rather,&nbsp;a&nbsp;Numpy-array.<br>
-&nbsp;<br>
-The&nbsp;dtype&nbsp;will&nbsp;be&nbsp;a&nbsp;lower-common-denominator&nbsp;dtype&nbsp;(implicit<br>
-upcasting);&nbsp;that&nbsp;is&nbsp;to&nbsp;say&nbsp;if&nbsp;the&nbsp;dtypes&nbsp;(even&nbsp;of&nbsp;numeric&nbsp;types)<br>
-are&nbsp;mixed,&nbsp;the&nbsp;one&nbsp;that&nbsp;accommodates&nbsp;all&nbsp;will&nbsp;be&nbsp;chosen.&nbsp;Use&nbsp;this<br>
-with&nbsp;care&nbsp;if&nbsp;you&nbsp;are&nbsp;not&nbsp;dealing&nbsp;with&nbsp;the&nbsp;blocks.<br>
-&nbsp;<br>
-e.g.&nbsp;If&nbsp;the&nbsp;dtypes&nbsp;are&nbsp;float16&nbsp;and&nbsp;float32,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcast&nbsp;to<br>
-float32.&nbsp;&nbsp;If&nbsp;dtypes&nbsp;are&nbsp;int32&nbsp;and&nbsp;uint8,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcase&nbsp;to<br>
-int32.&nbsp;By&nbsp;numpy.find_common_type&nbsp;convention,&nbsp;mixing&nbsp;int64&nbsp;and&nbsp;uint64<br>
-will&nbsp;result&nbsp;in&nbsp;a&nbsp;flot64&nbsp;dtype.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;is&nbsp;provided&nbsp;for&nbsp;backwards&nbsp;compatibility.&nbsp;Generally,<br>
-it&nbsp;is&nbsp;recommended&nbsp;to&nbsp;use&nbsp;'.values'.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.values</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-asfreq"><strong>asfreq</strong></a>(self, freq, method=None, how=None, normalize=False, fill_value=None)</dt><dd><tt>Convert&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;specified&nbsp;frequency.<br>
-&nbsp;<br>
-Optionally&nbsp;provide&nbsp;filling&nbsp;method&nbsp;to&nbsp;pad/backfill&nbsp;missing&nbsp;values.<br>
-&nbsp;<br>
-Returns&nbsp;the&nbsp;original&nbsp;data&nbsp;conformed&nbsp;to&nbsp;a&nbsp;new&nbsp;index&nbsp;with&nbsp;the&nbsp;specified<br>
-frequency.&nbsp;``resample``&nbsp;is&nbsp;more&nbsp;appropriate&nbsp;if&nbsp;an&nbsp;operation,&nbsp;such&nbsp;as<br>
-summarization,&nbsp;is&nbsp;necessary&nbsp;to&nbsp;represent&nbsp;the&nbsp;data&nbsp;at&nbsp;the&nbsp;new&nbsp;frequency.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;or&nbsp;string<br>
-method&nbsp;:&nbsp;{'backfill'/'bfill',&nbsp;'pad'/'ffill'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(note&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present):<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'pad'&nbsp;/&nbsp;'ffill':&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'backfill'&nbsp;/&nbsp;'bfill':&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill<br>
-how&nbsp;:&nbsp;{'start',&nbsp;'end'},&nbsp;default&nbsp;end<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;PeriodIndex&nbsp;only,&nbsp;see&nbsp;PeriodIndex.asfreq<br>
-normalize&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;reset&nbsp;output&nbsp;index&nbsp;to&nbsp;midnight<br>
-fill_value:&nbsp;scalar,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values,&nbsp;applied&nbsp;during&nbsp;upsampling&nbsp;(note<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;4&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=4,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0.0,&nbsp;None,&nbsp;2.0,&nbsp;3.0],&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'s':series})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-asfreq">asfreq</a>(freq='30S')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``fill&nbsp;value``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-asfreq">asfreq</a>(freq='30S',&nbsp;fill_value=9.0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``method``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-asfreq">asfreq</a>(freq='30S',&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-asof"><strong>asof</strong></a>(self, where, subset=None)</dt><dd><tt>The&nbsp;last&nbsp;row&nbsp;without&nbsp;any&nbsp;NaN&nbsp;is&nbsp;taken&nbsp;(or&nbsp;the&nbsp;last&nbsp;row&nbsp;without<br>
-NaN&nbsp;considering&nbsp;only&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;in&nbsp;the&nbsp;case&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0&nbsp;For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-If&nbsp;there&nbsp;is&nbsp;no&nbsp;good&nbsp;value,&nbsp;NaN&nbsp;is&nbsp;returned&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;NaN&nbsp;values&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-where&nbsp;:&nbsp;date&nbsp;or&nbsp;array&nbsp;of&nbsp;dates<br>
-subset&nbsp;:&nbsp;string&nbsp;or&nbsp;list&nbsp;of&nbsp;strings,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None&nbsp;use&nbsp;these&nbsp;columns&nbsp;for&nbsp;NaN&nbsp;propagation<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Dates&nbsp;are&nbsp;assumed&nbsp;to&nbsp;be&nbsp;sorted<br>
-Raises&nbsp;if&nbsp;this&nbsp;is&nbsp;not&nbsp;the&nbsp;case<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-where&nbsp;is&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;&nbsp;-&nbsp;value&nbsp;or&nbsp;NaN&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;-&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-where&nbsp;is&nbsp;Index:&nbsp;same&nbsp;shape&nbsp;<a href="builtins.html#object">object</a>&nbsp;as&nbsp;input<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-merge_asof</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-astype"><strong>astype</strong></a>(self, dtype, copy=True, errors='raise', **kwargs)</dt><dd><tt>Cast&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;numpy.dtype<br>
-Return&nbsp;a&nbsp;copy&nbsp;when&nbsp;copy&nbsp;=&nbsp;True&nbsp;(be&nbsp;really&nbsp;careful&nbsp;with&nbsp;this!)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dtype&nbsp;:&nbsp;data&nbsp;type,&nbsp;or&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;-&gt;&nbsp;data&nbsp;type<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;entire&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;same&nbsp;type.&nbsp;Alternatively,&nbsp;use&nbsp;{col:&nbsp;dtype,&nbsp;...},&nbsp;where&nbsp;col&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;label&nbsp;and&nbsp;dtype&nbsp;is&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;one<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;more&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;columns&nbsp;to&nbsp;column-specific&nbsp;types.<br>
-errors&nbsp;:&nbsp;{'raise',&nbsp;'ignore'},&nbsp;default&nbsp;'raise'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Control&nbsp;raising&nbsp;of&nbsp;exceptions&nbsp;on&nbsp;invalid&nbsp;data&nbsp;for&nbsp;provided&nbsp;dtype.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``raise``&nbsp;:&nbsp;allow&nbsp;exceptions&nbsp;to&nbsp;be&nbsp;raised<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``ignore``&nbsp;:&nbsp;suppress&nbsp;exceptions.&nbsp;On&nbsp;error&nbsp;return&nbsp;original&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-raise_on_error&nbsp;:&nbsp;DEPRECATED&nbsp;use&nbsp;``errors``&nbsp;instead<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;constructor<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-casted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-at_time"><strong>at_time</strong></a>(self, time, asof=False)</dt><dd><tt>Select&nbsp;values&nbsp;at&nbsp;particular&nbsp;time&nbsp;of&nbsp;day&nbsp;(e.g.&nbsp;9:30AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_at_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-between_time"><strong>between_time</strong></a>(self, start_time, end_time, include_start=True, include_end=True)</dt><dd><tt>Select&nbsp;values&nbsp;between&nbsp;particular&nbsp;times&nbsp;of&nbsp;the&nbsp;day&nbsp;(e.g.,&nbsp;9:00-9:30&nbsp;AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-start_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-end_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-include_start&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-include_end&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_between_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-bfill"><strong>bfill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#TimeFrame-fillna">fillna</a>(method='bfill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-bool"><strong>bool</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;bool&nbsp;of&nbsp;a&nbsp;single&nbsp;element&nbsp;PandasObject.<br>
-&nbsp;<br>
-This&nbsp;must&nbsp;be&nbsp;a&nbsp;boolean&nbsp;scalar&nbsp;value,&nbsp;either&nbsp;True&nbsp;or&nbsp;False.&nbsp;&nbsp;Raise&nbsp;a<br>
-ValueError&nbsp;if&nbsp;the&nbsp;PandasObject&nbsp;does&nbsp;not&nbsp;have&nbsp;exactly&nbsp;1&nbsp;element,&nbsp;or&nbsp;that<br>
-element&nbsp;is&nbsp;not&nbsp;boolean</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-clip"><strong>clip</strong></a>(self, lower=None, upper=None, axis=None, *args, **kwargs)</dt><dd><tt>Trim&nbsp;values&nbsp;at&nbsp;input&nbsp;threshold(s).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-lower&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-upper&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;lower&nbsp;and&nbsp;upper&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.256177<br>
-1&nbsp;-1.367855&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.176076<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;1.261967&nbsp;&nbsp;0.570967<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-clip">clip</a>(-1.0,&nbsp;0.5)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.000000<br>
-1&nbsp;-1.000000&nbsp;&nbsp;0.500000<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.000000<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;0.500000&nbsp;&nbsp;0.500000<br>
-&gt;&gt;&gt;&nbsp;t<br>
-0&nbsp;&nbsp;&nbsp;-0.3<br>
-1&nbsp;&nbsp;&nbsp;-0.2<br>
-2&nbsp;&nbsp;&nbsp;-0.1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;0.1<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-clip">clip</a>(t,&nbsp;t&nbsp;+&nbsp;1,&nbsp;axis=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-0.300000<br>
-1&nbsp;-0.200000&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-0.100000<br>
-3&nbsp;&nbsp;0.230930&nbsp;&nbsp;0.000000<br>
-4&nbsp;&nbsp;1.100000&nbsp;&nbsp;0.570967</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-clip_lower"><strong>clip_lower</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;the&nbsp;input&nbsp;with&nbsp;values&nbsp;below&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-clip_upper"><strong>clip_upper</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;input&nbsp;with&nbsp;values&nbsp;above&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-consolidate"><strong>consolidate</strong></a>(self, inplace=False)</dt><dd><tt>DEPRECATED:&nbsp;consolidate&nbsp;will&nbsp;be&nbsp;an&nbsp;internal&nbsp;implementation&nbsp;only.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-convert_objects"><strong>convert_objects</strong></a>(self, convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True)</dt><dd><tt>Deprecated.<br>
-&nbsp;<br>
-Attempt&nbsp;to&nbsp;infer&nbsp;better&nbsp;dtype&nbsp;for&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-convert_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;date&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-convert_numeric&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;attempt&nbsp;to&nbsp;coerce&nbsp;to&nbsp;numbers&nbsp;(including&nbsp;strings),&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaN.<br>
-convert_timedeltas&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;timedelta&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;return&nbsp;a&nbsp;copy&nbsp;even&nbsp;if&nbsp;no&nbsp;copy&nbsp;is&nbsp;necessary&nbsp;(e.g.&nbsp;no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion&nbsp;was&nbsp;done).&nbsp;Note:&nbsp;This&nbsp;is&nbsp;meant&nbsp;for&nbsp;internal&nbsp;use,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;not&nbsp;be&nbsp;confused&nbsp;with&nbsp;inplace.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.to_datetime&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;datetime.<br>
-pandas.to_timedelta&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;timedelta.<br>
-pandas.to_numeric&nbsp;:&nbsp;Return&nbsp;a&nbsp;fixed&nbsp;frequency&nbsp;timedelta&nbsp;index,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;day&nbsp;as&nbsp;the&nbsp;default.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;same&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-copy"><strong>copy</strong></a>(self, deep=True)</dt><dd><tt>Make&nbsp;a&nbsp;copy&nbsp;of&nbsp;this&nbsp;objects&nbsp;data.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-deep&nbsp;:&nbsp;boolean&nbsp;or&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;a&nbsp;deep&nbsp;copy,&nbsp;including&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;data&nbsp;and&nbsp;the&nbsp;indices.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;With&nbsp;``deep=False``&nbsp;neither&nbsp;the&nbsp;indices&nbsp;or&nbsp;the&nbsp;data&nbsp;are&nbsp;copied.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;that&nbsp;when&nbsp;``deep=True``&nbsp;data&nbsp;is&nbsp;copied,&nbsp;actual&nbsp;python&nbsp;objects<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;not&nbsp;be&nbsp;copied&nbsp;recursively,&nbsp;only&nbsp;the&nbsp;reference&nbsp;to&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;is&nbsp;in&nbsp;contrast&nbsp;to&nbsp;``copy.deepcopy``&nbsp;in&nbsp;the&nbsp;Standard&nbsp;Library,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;recursively&nbsp;copies&nbsp;<a href="builtins.html#object">object</a>&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-copy&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-describe"><strong>describe</strong></a>(self, percentiles=None, include=None, exclude=None)</dt><dd><tt>Generates&nbsp;descriptive&nbsp;statistics&nbsp;that&nbsp;summarize&nbsp;the&nbsp;central&nbsp;tendency,<br>
-dispersion&nbsp;and&nbsp;shape&nbsp;of&nbsp;a&nbsp;dataset's&nbsp;distribution,&nbsp;excluding<br>
-``NaN``&nbsp;values.<br>
-&nbsp;<br>
-Analyzes&nbsp;both&nbsp;numeric&nbsp;and&nbsp;<a href="builtins.html#object">object</a>&nbsp;series,&nbsp;as&nbsp;well<br>
-as&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;column&nbsp;sets&nbsp;of&nbsp;mixed&nbsp;data&nbsp;types.&nbsp;The&nbsp;output<br>
-will&nbsp;vary&nbsp;depending&nbsp;on&nbsp;what&nbsp;is&nbsp;provided.&nbsp;Refer&nbsp;to&nbsp;the&nbsp;notes<br>
-below&nbsp;for&nbsp;more&nbsp;detail.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-percentiles&nbsp;:&nbsp;list-like&nbsp;of&nbsp;numbers,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;percentiles&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;output.&nbsp;All&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fall&nbsp;between&nbsp;0&nbsp;and&nbsp;1.&nbsp;The&nbsp;default&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``[.25,&nbsp;.5,&nbsp;.75]``,&nbsp;which&nbsp;returns&nbsp;the&nbsp;25th,&nbsp;50th,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;75th&nbsp;percentiles.<br>
-include&nbsp;:&nbsp;'all',&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;white&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'all'&nbsp;:&nbsp;All&nbsp;columns&nbsp;of&nbsp;the&nbsp;input&nbsp;will&nbsp;be&nbsp;included&nbsp;in&nbsp;the&nbsp;output.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Limits&nbsp;the&nbsp;results&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;provided&nbsp;data&nbsp;types.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;limit&nbsp;the&nbsp;result&nbsp;to&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;limit&nbsp;it&nbsp;instead&nbsp;to&nbsp;categorical<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;objects&nbsp;submit&nbsp;the&nbsp;``numpy.<a href="builtins.html#object">object</a>``&nbsp;data&nbsp;type.&nbsp;Strings<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#TimeFrame-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;include&nbsp;all&nbsp;numeric&nbsp;columns.<br>
-exclude&nbsp;:&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;black&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;omit&nbsp;from&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Excludes&nbsp;the&nbsp;provided&nbsp;data&nbsp;types<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;the&nbsp;result.&nbsp;To&nbsp;select&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;select&nbsp;categorical&nbsp;objects&nbsp;submit&nbsp;the&nbsp;data<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;type&nbsp;``numpy.<a href="builtins.html#object">object</a>``.&nbsp;Strings&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#TimeFrame-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;exclude&nbsp;nothing.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-summary:&nbsp;&nbsp;<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;summary&nbsp;statistics<br>
-&nbsp;<br>
-Notes<br>
------<br>
-For&nbsp;numeric&nbsp;data,&nbsp;the&nbsp;result's&nbsp;index&nbsp;will&nbsp;include&nbsp;``count``,<br>
-``mean``,&nbsp;``std``,&nbsp;``min``,&nbsp;``max``&nbsp;as&nbsp;well&nbsp;as&nbsp;lower,&nbsp;``50``&nbsp;and<br>
-upper&nbsp;percentiles.&nbsp;By&nbsp;default&nbsp;the&nbsp;lower&nbsp;percentile&nbsp;is&nbsp;``25``&nbsp;and&nbsp;the<br>
-upper&nbsp;percentile&nbsp;is&nbsp;``75``.&nbsp;The&nbsp;``50``&nbsp;percentile&nbsp;is&nbsp;the<br>
-same&nbsp;as&nbsp;the&nbsp;median.<br>
-&nbsp;<br>
-For&nbsp;<a href="builtins.html#object">object</a>&nbsp;data&nbsp;(e.g.&nbsp;strings&nbsp;or&nbsp;timestamps),&nbsp;the&nbsp;result's&nbsp;index<br>
-will&nbsp;include&nbsp;``count``,&nbsp;``unique``,&nbsp;``top``,&nbsp;and&nbsp;``freq``.&nbsp;The&nbsp;``top``<br>
-is&nbsp;the&nbsp;most&nbsp;common&nbsp;value.&nbsp;The&nbsp;``freq``&nbsp;is&nbsp;the&nbsp;most&nbsp;common&nbsp;value's<br>
-frequency.&nbsp;Timestamps&nbsp;also&nbsp;include&nbsp;the&nbsp;``first``&nbsp;and&nbsp;``last``&nbsp;items.<br>
-&nbsp;<br>
-If&nbsp;multiple&nbsp;<a href="builtins.html#object">object</a>&nbsp;values&nbsp;have&nbsp;the&nbsp;highest&nbsp;count,&nbsp;then&nbsp;the<br>
-``count``&nbsp;and&nbsp;``top``&nbsp;results&nbsp;will&nbsp;be&nbsp;arbitrarily&nbsp;chosen&nbsp;from<br>
-among&nbsp;those&nbsp;with&nbsp;the&nbsp;highest&nbsp;count.<br>
-&nbsp;<br>
-For&nbsp;mixed&nbsp;data&nbsp;types&nbsp;provided&nbsp;via&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``,&nbsp;the&nbsp;default&nbsp;is&nbsp;to<br>
-return&nbsp;only&nbsp;an&nbsp;analysis&nbsp;of&nbsp;numeric&nbsp;columns.&nbsp;If&nbsp;``include='all'``<br>
-is&nbsp;provided&nbsp;as&nbsp;an&nbsp;option,&nbsp;the&nbsp;result&nbsp;will&nbsp;include&nbsp;a&nbsp;union&nbsp;of<br>
-attributes&nbsp;of&nbsp;each&nbsp;type.<br>
-&nbsp;<br>
-The&nbsp;`include`&nbsp;and&nbsp;`exclude`&nbsp;parameters&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;limit<br>
-which&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;are&nbsp;analyzed&nbsp;for&nbsp;the&nbsp;output.<br>
-The&nbsp;parameters&nbsp;are&nbsp;ignored&nbsp;when&nbsp;analyzing&nbsp;a&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-Describing&nbsp;a&nbsp;numeric&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeFrame-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;categorical&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(['a',&nbsp;'a',&nbsp;'b',&nbsp;'c'])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeFrame-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;timestamp&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2000-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01")<br>
-...&nbsp;])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeFrame-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-first&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2000-01-01&nbsp;00:00:00<br>
-last&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``.&nbsp;By&nbsp;default&nbsp;only&nbsp;numeric&nbsp;fields<br>
-are&nbsp;returned.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[1,&nbsp;'a'],&nbsp;[2,&nbsp;'b'],&nbsp;[3,&nbsp;'c']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['numeric',&nbsp;'<a href="builtins.html#object">object</a>'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-describe">describe</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;all&nbsp;columns&nbsp;of&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;regardless&nbsp;of&nbsp;data&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-describe">describe</a>(include='all')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;column&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;by&nbsp;accessing&nbsp;it&nbsp;as<br>
-an&nbsp;attribute.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.numeric.<a href="#TimeFrame-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-Name:&nbsp;numeric,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;numeric&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-describe">describe</a>(include=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;string&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-describe">describe</a>(include=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;numeric&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-describe">describe</a>(exclude=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-describe">describe</a>(exclude=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.count<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.max<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.min<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mean<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.std<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select_dtypes</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-drop"><strong>drop</strong></a>(self, labels, axis=0, level=None, inplace=False, errors='raise')</dt><dd><tt>Return&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;labels&nbsp;in&nbsp;requested&nbsp;axis&nbsp;removed.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-labels&nbsp;:&nbsp;single&nbsp;label&nbsp;or&nbsp;list-like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;axis&nbsp;name<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;MultiIndex<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;return&nbsp;None.<br>
-errors&nbsp;:&nbsp;{'ignore',&nbsp;'raise'},&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'ignore',&nbsp;suppress&nbsp;error&nbsp;and&nbsp;existing&nbsp;labels&nbsp;are&nbsp;dropped.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dropped&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-equals"><strong>equals</strong></a>(self, other)</dt><dd><tt>Determines&nbsp;if&nbsp;two&nbsp;NDFrame&nbsp;objects&nbsp;contain&nbsp;the&nbsp;same&nbsp;elements.&nbsp;NaNs&nbsp;in<br>
-the&nbsp;same&nbsp;location&nbsp;are&nbsp;considered&nbsp;equal.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-ffill"><strong>ffill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#TimeFrame-fillna">fillna</a>(method='ffill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-filter"><strong>filter</strong></a>(self, items=None, like=None, regex=None, axis=None)</dt><dd><tt>Subset&nbsp;rows&nbsp;or&nbsp;columns&nbsp;of&nbsp;dataframe&nbsp;according&nbsp;to&nbsp;labels&nbsp;in<br>
-the&nbsp;specified&nbsp;index.<br>
-&nbsp;<br>
-Note&nbsp;that&nbsp;this&nbsp;routine&nbsp;does&nbsp;not&nbsp;filter&nbsp;a&nbsp;dataframe&nbsp;on&nbsp;its<br>
-contents.&nbsp;The&nbsp;filter&nbsp;is&nbsp;applied&nbsp;to&nbsp;the&nbsp;labels&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-items&nbsp;:&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;info&nbsp;axis&nbsp;to&nbsp;restrict&nbsp;to&nbsp;(must&nbsp;not&nbsp;all&nbsp;be&nbsp;present)<br>
-like&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;where&nbsp;"arg&nbsp;in&nbsp;col&nbsp;==&nbsp;True"<br>
-regex&nbsp;:&nbsp;string&nbsp;(regular&nbsp;expression)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;with&nbsp;re.search(regex,&nbsp;col)&nbsp;==&nbsp;True<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;filter&nbsp;on.&nbsp;&nbsp;By&nbsp;default&nbsp;this&nbsp;is&nbsp;the&nbsp;info&nbsp;axis,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'index'&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;'columns'&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;name<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-filter">filter</a>(items=['one',&nbsp;'three'])<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;regular&nbsp;expression<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-filter">filter</a>(regex='e$',&nbsp;axis=1)<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;rows&nbsp;containing&nbsp;'bbi'<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-filter">filter</a>(like='bbi',&nbsp;axis=0)<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;``items``,&nbsp;``like``,&nbsp;and&nbsp;``regex``&nbsp;parameters&nbsp;are<br>
-enforced&nbsp;to&nbsp;be&nbsp;mutually&nbsp;exclusive.<br>
-&nbsp;<br>
-``axis``&nbsp;defaults&nbsp;to&nbsp;the&nbsp;info&nbsp;axis&nbsp;that&nbsp;is&nbsp;used&nbsp;when&nbsp;indexing<br>
-with&nbsp;``[]``.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-first"><strong>first</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;initial&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#TimeFrame-first">first</a>('10D')&nbsp;-&gt;&nbsp;First&nbsp;10&nbsp;days<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-get"><strong>get</strong></a>(self, key, default=None)</dt><dd><tt>Get&nbsp;item&nbsp;from&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;given&nbsp;key&nbsp;(<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column,&nbsp;Panel&nbsp;slice,<br>
-etc.).&nbsp;Returns&nbsp;default&nbsp;value&nbsp;if&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;type&nbsp;of&nbsp;items&nbsp;contained&nbsp;in&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-get_dtype_counts"><strong>get_dtype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;dtypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-get_ftype_counts"><strong>get_ftype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;ftypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-get_values"><strong>get_values</strong></a>(self)</dt><dd><tt>same&nbsp;as&nbsp;values&nbsp;(but&nbsp;handles&nbsp;sparseness&nbsp;conversions)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-groupby"><strong>groupby</strong></a>(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)</dt><dd><tt>Group&nbsp;series&nbsp;using&nbsp;mapper&nbsp;(dict&nbsp;or&nbsp;key&nbsp;function,&nbsp;apply&nbsp;given&nbsp;function<br>
-to&nbsp;group,&nbsp;return&nbsp;result&nbsp;as&nbsp;series)&nbsp;or&nbsp;by&nbsp;a&nbsp;series&nbsp;of&nbsp;columns.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;mapping,&nbsp;function,&nbsp;str,&nbsp;or&nbsp;iterable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;for&nbsp;the&nbsp;groupby.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;``by``&nbsp;is&nbsp;a&nbsp;function,&nbsp;it's&nbsp;called&nbsp;on&nbsp;each&nbsp;value&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>'s<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;If&nbsp;a&nbsp;dict&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;is&nbsp;passed,&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;dict&nbsp;VALUES<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;(the&nbsp;<a href="pandas.core.series.html#Series">Series</a>'&nbsp;values&nbsp;are&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aligned;&nbsp;see&nbsp;``.<a href="#TimeFrame-align">align</a>()``&nbsp;method).&nbsp;If&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;is&nbsp;passed,&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;are&nbsp;used&nbsp;as-is&nbsp;determine&nbsp;the&nbsp;groups.&nbsp;A&nbsp;str&nbsp;or&nbsp;list&nbsp;of&nbsp;strs<br>
-&nbsp;&nbsp;&nbsp;&nbsp;may&nbsp;be&nbsp;passed&nbsp;to&nbsp;group&nbsp;by&nbsp;the&nbsp;columns&nbsp;in&nbsp;``self``<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-level&nbsp;:&nbsp;int,&nbsp;level&nbsp;name,&nbsp;or&nbsp;sequence&nbsp;of&nbsp;such,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;group&nbsp;by&nbsp;a&nbsp;particular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level&nbsp;or&nbsp;levels<br>
-as_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;aggregated&nbsp;output,&nbsp;return&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;group&nbsp;labels&nbsp;as&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;Only&nbsp;relevant&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;input.&nbsp;as_index=False&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;effectively&nbsp;"SQL-style"&nbsp;grouped&nbsp;output<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;group&nbsp;keys.&nbsp;Get&nbsp;better&nbsp;performance&nbsp;by&nbsp;turning&nbsp;this&nbsp;off.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;this&nbsp;does&nbsp;not&nbsp;influence&nbsp;the&nbsp;order&nbsp;of&nbsp;observations&nbsp;within&nbsp;each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;group.&nbsp;&nbsp;groupby&nbsp;preserves&nbsp;the&nbsp;order&nbsp;of&nbsp;rows&nbsp;within&nbsp;each&nbsp;group.<br>
-group_keys&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;calling&nbsp;apply,&nbsp;add&nbsp;group&nbsp;keys&nbsp;to&nbsp;index&nbsp;to&nbsp;identify&nbsp;pieces<br>
-squeeze&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;reduce&nbsp;the&nbsp;dimensionality&nbsp;of&nbsp;the&nbsp;return&nbsp;type&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;return&nbsp;a&nbsp;consistent&nbsp;type<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;results<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#TimeFrame-groupby">groupby</a>(func,&nbsp;axis=0).<a href="#TimeFrame-mean">mean</a>()<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#TimeFrame-groupby">groupby</a>(['col1',&nbsp;'col2'])['col3'].<a href="#TimeFrame-mean">mean</a>()<br>
-&nbsp;<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;hierarchical&nbsp;index<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#TimeFrame-groupby">groupby</a>(['col1',&nbsp;'col2']).<a href="#TimeFrame-mean">mean</a>()<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-GroupBy&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-head"><strong>head</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;first&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-interpolate"><strong>interpolate</strong></a>(self, method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', downcast=None, **kwargs)</dt><dd><tt>Interpolate&nbsp;values&nbsp;according&nbsp;to&nbsp;different&nbsp;methods.<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;only&nbsp;``method='linear'``&nbsp;is&nbsp;supported&nbsp;for<br>
-DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a&nbsp;MultiIndex.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-method&nbsp;:&nbsp;{'linear',&nbsp;'time',&nbsp;'index',&nbsp;'values',&nbsp;'nearest',&nbsp;'zero',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',&nbsp;'barycentric',&nbsp;'krogh',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'polynomial',&nbsp;'spline',&nbsp;'piecewise_polynomial',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'from_derivatives',&nbsp;'pchip',&nbsp;'akima'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'linear':&nbsp;ignore&nbsp;the&nbsp;index&nbsp;and&nbsp;treat&nbsp;the&nbsp;values&nbsp;as&nbsp;equally<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;spaced.&nbsp;This&nbsp;is&nbsp;the&nbsp;only&nbsp;method&nbsp;supported&nbsp;on&nbsp;MultiIndexes.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'time':&nbsp;interpolation&nbsp;works&nbsp;on&nbsp;daily&nbsp;and&nbsp;higher&nbsp;resolution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;data&nbsp;to&nbsp;interpolate&nbsp;given&nbsp;length&nbsp;of&nbsp;interval<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'index',&nbsp;'values':&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'nearest',&nbsp;'zero',&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'barycentric',&nbsp;'polynomial'&nbsp;is&nbsp;passed&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``scipy.interpolate.interp1d``.&nbsp;Both&nbsp;'polynomial'&nbsp;and&nbsp;'spline'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;require&nbsp;that&nbsp;you&nbsp;also&nbsp;specify&nbsp;an&nbsp;`order`&nbsp;(int),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;e.g.&nbsp;df.<a href="#TimeFrame-interpolate">interpolate</a>(method='polynomial',&nbsp;order=4).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'krogh',&nbsp;'piecewise_polynomial',&nbsp;'spline',&nbsp;'pchip'&nbsp;and&nbsp;'akima'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;all&nbsp;wrappers&nbsp;around&nbsp;the&nbsp;scipy&nbsp;interpolation&nbsp;methods&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;similar&nbsp;names.&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;For&nbsp;more&nbsp;information&nbsp;on&nbsp;their&nbsp;behavior,&nbsp;see&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`scipy&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__">http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;`tutorial&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__">http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'from_derivatives'&nbsp;refers&nbsp;to&nbsp;BPoly.from_derivatives&nbsp;which<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;replaces&nbsp;'piecewise_polynomial'&nbsp;interpolation&nbsp;method&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;0.18<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;support&nbsp;for&nbsp;the&nbsp;'akima'&nbsp;method<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;interpolate&nbsp;method&nbsp;'from_derivatives'&nbsp;which&nbsp;replaces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'piecewise_polynomial'&nbsp;in&nbsp;scipy&nbsp;0.18;&nbsp;backwards-compatible&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;&lt;&nbsp;0.18<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;0:&nbsp;fill&nbsp;column-by-column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;1:&nbsp;fill&nbsp;row-by-row<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs&nbsp;to&nbsp;fill.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0.<br>
-limit_direction&nbsp;:&nbsp;{'forward',&nbsp;'backward',&nbsp;'both'},&nbsp;default&nbsp;'forward'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;limit&nbsp;is&nbsp;specified,&nbsp;consecutive&nbsp;NaNs&nbsp;will&nbsp;be&nbsp;filled&nbsp;in&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;direction.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Update&nbsp;the&nbsp;NDFrame&nbsp;in&nbsp;place&nbsp;if&nbsp;possible.<br>
-downcast&nbsp;:&nbsp;optional,&nbsp;'infer'&nbsp;or&nbsp;None,&nbsp;defaults&nbsp;to&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Downcast&nbsp;dtypes&nbsp;if&nbsp;possible.<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;interpolating&nbsp;function.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;interpolated&nbsp;at&nbsp;the&nbsp;NaNs<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;replace,&nbsp;fillna<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Filling&nbsp;in&nbsp;NaNs<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0,&nbsp;1,&nbsp;np.nan,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeFrame-interpolate">interpolate</a>()<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-isnull"><strong>isnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-notnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;isnull</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-keys"><strong>keys</strong></a>(self)</dt><dd><tt>Get&nbsp;the&nbsp;'info&nbsp;axis'&nbsp;(see&nbsp;Indexing&nbsp;for&nbsp;more)<br>
-&nbsp;<br>
-This&nbsp;is&nbsp;index&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;columns&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;and&nbsp;major_axis&nbsp;for<br>
-Panel.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-last"><strong>last</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;final&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#TimeFrame-last">last</a>('5M')&nbsp;-&gt;&nbsp;Last&nbsp;5&nbsp;months<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-mask"><strong>mask</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;False&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;mask&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``False``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#TimeFrame-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#TimeFrame-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``mask``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeFrame-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).reshape(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#TimeFrame-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#TimeFrame-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-notnull"><strong>notnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are<br>
-not&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-isnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;notnull</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-pct_change"><strong>pct_change</strong></a>(self, periods=1, fill_method='pad', limit=None, freq=None, **kwargs)</dt><dd><tt>Percent&nbsp;change&nbsp;over&nbsp;given&nbsp;number&nbsp;of&nbsp;periods.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;percent&nbsp;change<br>
-fill_method&nbsp;:&nbsp;str,&nbsp;default&nbsp;'pad'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;How&nbsp;to&nbsp;handle&nbsp;NAs&nbsp;before&nbsp;computing&nbsp;percent&nbsp;changes<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NAs&nbsp;to&nbsp;fill&nbsp;before&nbsp;stopping<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;offset&nbsp;alias&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;time&nbsp;series&nbsp;API&nbsp;(e.g.&nbsp;'M'&nbsp;or&nbsp;BDay())<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-chg&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-By&nbsp;default,&nbsp;the&nbsp;percentage&nbsp;change&nbsp;is&nbsp;calculated&nbsp;along&nbsp;the&nbsp;stat<br>
-axis:&nbsp;0,&nbsp;or&nbsp;``Index``,&nbsp;for&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;and&nbsp;1,&nbsp;or&nbsp;``minor``&nbsp;for<br>
-``Panel``.&nbsp;You&nbsp;can&nbsp;change&nbsp;this&nbsp;with&nbsp;the&nbsp;``axis``&nbsp;keyword&nbsp;argument.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-pipe"><strong>pipe</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Apply&nbsp;func(self,&nbsp;\*args,&nbsp;\*\*kwargs)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.2<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;the&nbsp;NDFrame.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``args``,&nbsp;and&nbsp;``kwargs``&nbsp;are&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Alternatively&nbsp;a&nbsp;``(callable,&nbsp;data_keyword)``&nbsp;tuple&nbsp;where<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``data_keyword``&nbsp;is&nbsp;a&nbsp;string&nbsp;indicating&nbsp;the&nbsp;keyword&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``callable``&nbsp;that&nbsp;expects&nbsp;the&nbsp;NDFrame.<br>
-args&nbsp;:&nbsp;positional&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-kwargs&nbsp;:&nbsp;a&nbsp;dictionary&nbsp;of&nbsp;keyword&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="builtins.html#object">object</a>&nbsp;:&nbsp;the&nbsp;return&nbsp;type&nbsp;of&nbsp;``func``.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-Use&nbsp;``.pipe``&nbsp;when&nbsp;chaining&nbsp;together&nbsp;functions&nbsp;that&nbsp;expect<br>
-on&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;DataFrames.&nbsp;Instead&nbsp;of&nbsp;writing<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;f(g(h(df),&nbsp;arg1=a),&nbsp;arg2=b,&nbsp;arg3=c)<br>
-&nbsp;<br>
-You&nbsp;can&nbsp;write<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#TimeFrame-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#TimeFrame-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#TimeFrame-pipe">pipe</a>(f,&nbsp;arg2=b,&nbsp;arg3=c)<br>
-...&nbsp;)<br>
-&nbsp;<br>
-If&nbsp;you&nbsp;have&nbsp;a&nbsp;function&nbsp;that&nbsp;takes&nbsp;the&nbsp;data&nbsp;as&nbsp;(say)&nbsp;the&nbsp;second<br>
-argument,&nbsp;pass&nbsp;a&nbsp;tuple&nbsp;indicating&nbsp;which&nbsp;keyword&nbsp;expects&nbsp;the<br>
-data.&nbsp;For&nbsp;example,&nbsp;suppose&nbsp;``f``&nbsp;takes&nbsp;its&nbsp;data&nbsp;as&nbsp;``arg2``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#TimeFrame-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#TimeFrame-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#TimeFrame-pipe">pipe</a>((f,&nbsp;'arg2'),&nbsp;arg1=a,&nbsp;arg3=c)<br>
-...&nbsp;&nbsp;)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.map</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-pop"><strong>pop</strong></a>(self, item)</dt><dd><tt>Return&nbsp;item&nbsp;and&nbsp;drop&nbsp;from&nbsp;frame.&nbsp;Raise&nbsp;KeyError&nbsp;if&nbsp;not&nbsp;found.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-rank"><strong>rank</strong></a>(self, axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)</dt><dd><tt>Compute&nbsp;numerical&nbsp;data&nbsp;ranks&nbsp;(1&nbsp;through&nbsp;n)&nbsp;along&nbsp;axis.&nbsp;Equal&nbsp;values&nbsp;are<br>
-assigned&nbsp;a&nbsp;rank&nbsp;that&nbsp;is&nbsp;the&nbsp;average&nbsp;of&nbsp;the&nbsp;ranks&nbsp;of&nbsp;those&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;to&nbsp;direct&nbsp;ranking<br>
-method&nbsp;:&nbsp;{'average',&nbsp;'min',&nbsp;'max',&nbsp;'first',&nbsp;'dense'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;average:&nbsp;average&nbsp;rank&nbsp;of&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;min:&nbsp;lowest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;max:&nbsp;highest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;first:&nbsp;ranks&nbsp;assigned&nbsp;in&nbsp;order&nbsp;they&nbsp;appear&nbsp;in&nbsp;the&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dense:&nbsp;like&nbsp;'min',&nbsp;but&nbsp;rank&nbsp;always&nbsp;increases&nbsp;by&nbsp;1&nbsp;between&nbsp;groups<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;data.&nbsp;Valid&nbsp;only&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Panel&nbsp;objects<br>
-na_option&nbsp;:&nbsp;{'keep',&nbsp;'top',&nbsp;'bottom'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;keep:&nbsp;leave&nbsp;NA&nbsp;values&nbsp;where&nbsp;they&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;top:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;ascending<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;bottom:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;descending<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False&nbsp;for&nbsp;ranks&nbsp;by&nbsp;high&nbsp;(1)&nbsp;to&nbsp;low&nbsp;(N)<br>
-pct&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Computes&nbsp;percentage&nbsp;rank&nbsp;of&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ranks&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-reindex_like"><strong>reindex_like</strong></a>(self, other, method=None, copy=True, limit=None, tolerance=None)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;matching&nbsp;indices&nbsp;to&nbsp;myself.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;Object<br>
-method&nbsp;:&nbsp;string&nbsp;or&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;labels&nbsp;to&nbsp;fill&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;labels&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>&nbsp;and&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Like&nbsp;calling&nbsp;s.<a href="#TimeFrame-reindex">reindex</a>(index=other.index,&nbsp;columns=other.columns,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;method=...)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-rename_axis"><strong>rename_axis</strong></a>(self, mapper, axis=0, copy=True, inplace=False)</dt><dd><tt>Alter&nbsp;index&nbsp;and&nbsp;/&nbsp;or&nbsp;columns&nbsp;using&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.<br>
-A&nbsp;scalar&nbsp;or&nbsp;list-like&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;``Index.name``<br>
-or&nbsp;``MultiIndex.names``&nbsp;attribute.<br>
-A&nbsp;function&nbsp;or&nbsp;dict&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;labels.<br>
-Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be&nbsp;unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in<br>
-a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left&nbsp;as-is.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-mapper&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename<br>
-pandas.Index.rename<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-rename_axis">rename_axis</a>("foo")&nbsp;&nbsp;#&nbsp;scalar,&nbsp;alters&nbsp;df.index.name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-foo<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-rename_axis">rename_axis</a>(lambda&nbsp;x:&nbsp;2&nbsp;*&nbsp;x)&nbsp;&nbsp;#&nbsp;function:&nbsp;alters&nbsp;labels<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-4&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-rename_axis">rename_axis</a>({"A":&nbsp;"ehh",&nbsp;"C":&nbsp;"see"},&nbsp;axis="columns")&nbsp;&nbsp;#&nbsp;mapping<br>
-&nbsp;&nbsp;&nbsp;ehh&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-replace"><strong>replace</strong></a>(self, to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad', axis=None)</dt><dd><tt>Replace&nbsp;values&nbsp;given&nbsp;in&nbsp;'to_replace'&nbsp;with&nbsp;'value'.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-to_replace&nbsp;:&nbsp;str,&nbsp;regex,&nbsp;list,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;numeric,&nbsp;or&nbsp;None<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;str&nbsp;or&nbsp;regex:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str:&nbsp;string&nbsp;exactly&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;`value`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;regex:&nbsp;regexs&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`value`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;list&nbsp;of&nbsp;str,&nbsp;regex,&nbsp;or&nbsp;numeric:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;First,&nbsp;if&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;both&nbsp;lists,&nbsp;they<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Second,&nbsp;if&nbsp;``regex=True``&nbsp;then&nbsp;all&nbsp;of&nbsp;the&nbsp;strings&nbsp;in&nbsp;**both**<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;regexs&nbsp;otherwise&nbsp;they&nbsp;will&nbsp;match<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;directly.&nbsp;This&nbsp;doesn't&nbsp;matter&nbsp;much&nbsp;for&nbsp;`value`&nbsp;since&nbsp;there<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;only&nbsp;a&nbsp;few&nbsp;possible&nbsp;substitution&nbsp;regexes&nbsp;you&nbsp;can&nbsp;use.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str&nbsp;and&nbsp;regex&nbsp;rules&nbsp;apply&nbsp;as&nbsp;above.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dict:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Nested&nbsp;dictionaries,&nbsp;e.g.,&nbsp;{'a':&nbsp;{'b':&nbsp;nan}},&nbsp;are&nbsp;read&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;follows:&nbsp;look&nbsp;in&nbsp;column&nbsp;'a'&nbsp;for&nbsp;the&nbsp;value&nbsp;'b'&nbsp;and&nbsp;replace&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;nan.&nbsp;You&nbsp;can&nbsp;nest&nbsp;regular&nbsp;expressions&nbsp;as&nbsp;well.&nbsp;Note&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;names&nbsp;(the&nbsp;top-level&nbsp;dictionary&nbsp;keys&nbsp;in&nbsp;a&nbsp;nested<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dictionary)&nbsp;**cannot**&nbsp;be&nbsp;regular&nbsp;expressions.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Keys&nbsp;map&nbsp;to&nbsp;column&nbsp;names&nbsp;and&nbsp;values&nbsp;map&nbsp;to&nbsp;substitution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;values.&nbsp;You&nbsp;can&nbsp;treat&nbsp;this&nbsp;as&nbsp;a&nbsp;special&nbsp;case&nbsp;of&nbsp;passing&nbsp;two<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;except&nbsp;that&nbsp;you&nbsp;are&nbsp;specifying&nbsp;the&nbsp;column&nbsp;to&nbsp;search&nbsp;in.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;None:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;This&nbsp;means&nbsp;that&nbsp;the&nbsp;``regex``&nbsp;argument&nbsp;must&nbsp;be&nbsp;a&nbsp;string,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;compiled&nbsp;regular&nbsp;expression,&nbsp;or&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;such&nbsp;elements.&nbsp;If&nbsp;`value`&nbsp;is&nbsp;also&nbsp;``None``&nbsp;then&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;a&nbsp;nested&nbsp;dictionary&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;examples&nbsp;section&nbsp;for&nbsp;examples&nbsp;of&nbsp;each&nbsp;of&nbsp;these.<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;list,&nbsp;str,&nbsp;regex,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a&nbsp;dict&nbsp;of&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for&nbsp;each&nbsp;column&nbsp;(columns&nbsp;not&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;Regular&nbsp;expressions,&nbsp;strings&nbsp;and&nbsp;lists&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dicts&nbsp;of&nbsp;such&nbsp;objects&nbsp;are&nbsp;also&nbsp;allowed.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>&nbsp;(e.g.&nbsp;a&nbsp;column&nbsp;form&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Returns&nbsp;the&nbsp;caller&nbsp;if&nbsp;this&nbsp;is&nbsp;True.<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;size&nbsp;gap&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-regex&nbsp;:&nbsp;bool&nbsp;or&nbsp;same&nbsp;types&nbsp;as&nbsp;`to_replace`,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;interpret&nbsp;`to_replace`&nbsp;and/or&nbsp;`value`&nbsp;as&nbsp;regular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;expressions.&nbsp;If&nbsp;this&nbsp;is&nbsp;``True``&nbsp;then&nbsp;`to_replace`&nbsp;*must*&nbsp;be&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string.&nbsp;Otherwise,&nbsp;`to_replace`&nbsp;must&nbsp;be&nbsp;``None``&nbsp;because&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;parameter&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;regular&nbsp;expression&nbsp;or&nbsp;a&nbsp;list,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict,&nbsp;or&nbsp;array&nbsp;of&nbsp;regular&nbsp;expressions.<br>
-method&nbsp;:&nbsp;string,&nbsp;optional,&nbsp;{'pad',&nbsp;'ffill',&nbsp;'bfill'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;method&nbsp;to&nbsp;use&nbsp;when&nbsp;for&nbsp;replacement,&nbsp;when&nbsp;``to_replace``&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``list``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-NDFrame.reindex<br>
-NDFrame.asfreq<br>
-NDFrame.fillna<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-AssertionError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;a&nbsp;``bool``&nbsp;and&nbsp;`to_replace`&nbsp;is&nbsp;not&nbsp;``None``.<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;a&nbsp;``dict``&nbsp;and&nbsp;`value`&nbsp;is&nbsp;not&nbsp;a&nbsp;``list``,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``dict``,&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``,&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;``None``&nbsp;and&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;compilable&nbsp;into&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;regular&nbsp;expression&nbsp;or&nbsp;is&nbsp;a&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;``list``&nbsp;s&nbsp;or&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``&nbsp;s,&nbsp;but<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;they&nbsp;are&nbsp;not&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-*&nbsp;Regex&nbsp;substitution&nbsp;is&nbsp;performed&nbsp;under&nbsp;the&nbsp;hood&nbsp;with&nbsp;``re.sub``.&nbsp;The<br>
-&nbsp;&nbsp;rules&nbsp;for&nbsp;substitution&nbsp;for&nbsp;``re.sub``&nbsp;are&nbsp;the&nbsp;same.<br>
-*&nbsp;Regular&nbsp;expressions&nbsp;will&nbsp;only&nbsp;substitute&nbsp;on&nbsp;strings,&nbsp;meaning&nbsp;you<br>
-&nbsp;&nbsp;cannot&nbsp;provide,&nbsp;for&nbsp;example,&nbsp;a&nbsp;regular&nbsp;expression&nbsp;matching&nbsp;floating<br>
-&nbsp;&nbsp;point&nbsp;numbers&nbsp;and&nbsp;expect&nbsp;the&nbsp;columns&nbsp;in&nbsp;your&nbsp;frame&nbsp;that&nbsp;have&nbsp;a<br>
-&nbsp;&nbsp;numeric&nbsp;dtype&nbsp;to&nbsp;be&nbsp;matched.&nbsp;However,&nbsp;if&nbsp;those&nbsp;floating&nbsp;point&nbsp;numbers<br>
-&nbsp;&nbsp;*are*&nbsp;strings,&nbsp;then&nbsp;you&nbsp;can&nbsp;do&nbsp;this.<br>
-*&nbsp;This&nbsp;method&nbsp;has&nbsp;*a&nbsp;lot*&nbsp;of&nbsp;options.&nbsp;You&nbsp;are&nbsp;encouraged&nbsp;to&nbsp;experiment<br>
-&nbsp;&nbsp;and&nbsp;play&nbsp;with&nbsp;this&nbsp;method&nbsp;to&nbsp;gain&nbsp;intuition&nbsp;about&nbsp;how&nbsp;it&nbsp;works.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-resample"><strong>resample</strong></a>(self, rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0, on=None, level=None)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;frequency&nbsp;conversion&nbsp;and&nbsp;resampling&nbsp;of&nbsp;time<br>
-series.&nbsp;&nbsp;Object&nbsp;must&nbsp;have&nbsp;a&nbsp;datetime-like&nbsp;index&nbsp;(DatetimeIndex,<br>
-PeriodIndex,&nbsp;or&nbsp;TimedeltaIndex),&nbsp;or&nbsp;pass&nbsp;datetime-like&nbsp;values<br>
-to&nbsp;the&nbsp;on&nbsp;or&nbsp;level&nbsp;keyword.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-rule&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;offset&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a>&nbsp;representing&nbsp;target&nbsp;conversion<br>
-axis&nbsp;:&nbsp;int,&nbsp;optional,&nbsp;default&nbsp;0<br>
-closed&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;side&nbsp;of&nbsp;bin&nbsp;interval&nbsp;is&nbsp;closed<br>
-label&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;bin&nbsp;edge&nbsp;label&nbsp;to&nbsp;label&nbsp;bucket&nbsp;with<br>
-convention&nbsp;:&nbsp;{'start',&nbsp;'end',&nbsp;'s',&nbsp;'e'}<br>
-loffset&nbsp;:&nbsp;timedelta<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Adjust&nbsp;the&nbsp;resampled&nbsp;time&nbsp;labels<br>
-base&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;frequencies&nbsp;that&nbsp;evenly&nbsp;subdivide&nbsp;1&nbsp;day,&nbsp;the&nbsp;"origin"&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aggregated&nbsp;intervals.&nbsp;For&nbsp;example,&nbsp;for&nbsp;'5min'&nbsp;frequency,&nbsp;base&nbsp;could<br>
-&nbsp;&nbsp;&nbsp;&nbsp;range&nbsp;from&nbsp;0&nbsp;through&nbsp;4.&nbsp;Defaults&nbsp;to&nbsp;0<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;to&nbsp;use&nbsp;instead&nbsp;of&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-level&nbsp;:&nbsp;string&nbsp;or&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;MultiIndex,&nbsp;level&nbsp;(name&nbsp;or&nbsp;number)&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;resampling.&nbsp;&nbsp;Level&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offset&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;9&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(9),&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;series<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:04:00&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-2000-01-01&nbsp;00:05:00&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:07:00&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-2000-01-01&nbsp;00:08:00&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-Freq:&nbsp;T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;and&nbsp;sum&nbsp;the&nbsp;values<br>
-of&nbsp;the&nbsp;timestamps&nbsp;falling&nbsp;into&nbsp;a&nbsp;bin.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeFrame-resample">resample</a>('3T').<a href="#TimeFrame-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;label&nbsp;each<br>
-bin&nbsp;using&nbsp;the&nbsp;right&nbsp;edge&nbsp;instead&nbsp;of&nbsp;the&nbsp;left.&nbsp;Please&nbsp;note&nbsp;that&nbsp;the<br>
-value&nbsp;in&nbsp;the&nbsp;bucket&nbsp;used&nbsp;as&nbsp;the&nbsp;label&nbsp;is&nbsp;not&nbsp;included&nbsp;in&nbsp;the&nbsp;bucket,<br>
-which&nbsp;it&nbsp;labels.&nbsp;For&nbsp;example,&nbsp;in&nbsp;the&nbsp;original&nbsp;series&nbsp;the<br>
-bucket&nbsp;``2000-01-01&nbsp;00:03:00``&nbsp;contains&nbsp;the&nbsp;value&nbsp;3,&nbsp;but&nbsp;the&nbsp;summed<br>
-value&nbsp;in&nbsp;the&nbsp;resampled&nbsp;bucket&nbsp;with&nbsp;the&nbsp;label``2000-01-01&nbsp;00:03:00``<br>
-does&nbsp;not&nbsp;include&nbsp;3&nbsp;(if&nbsp;it&nbsp;did,&nbsp;the&nbsp;summed&nbsp;value&nbsp;would&nbsp;be&nbsp;6,&nbsp;not&nbsp;3).<br>
-To&nbsp;include&nbsp;this&nbsp;value&nbsp;close&nbsp;the&nbsp;right&nbsp;side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval&nbsp;as<br>
-illustrated&nbsp;in&nbsp;the&nbsp;example&nbsp;below&nbsp;this&nbsp;one.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeFrame-resample">resample</a>('3T',&nbsp;label='right').<a href="#TimeFrame-sum">sum</a>()<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;close&nbsp;the&nbsp;right<br>
-side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeFrame-resample">resample</a>('3T',&nbsp;label='right',&nbsp;closed='right').<a href="#TimeFrame-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeFrame-resample">resample</a>('30S').<a href="#TimeFrame-asfreq">asfreq</a>()[0:5]&nbsp;#select&nbsp;first&nbsp;5&nbsp;rows<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;1.0<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;2.0<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values&nbsp;using&nbsp;the&nbsp;``pad``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeFrame-resample">resample</a>('30S').pad()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the<br>
-``NaN``&nbsp;values&nbsp;using&nbsp;the&nbsp;``bfill``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeFrame-resample">resample</a>('30S').<a href="#TimeFrame-bfill">bfill</a>()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Pass&nbsp;a&nbsp;custom&nbsp;function&nbsp;via&nbsp;``apply``<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;custom_resampler(array_like):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;np.<a href="#TimeFrame-sum">sum</a>(array_like)+5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeFrame-resample">resample</a>('3T').<a href="#TimeFrame-apply">apply</a>(custom_resampler)<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;17<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;26<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects,&nbsp;the&nbsp;keyword&nbsp;``on``&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;specify&nbsp;the<br>
-column&nbsp;instead&nbsp;of&nbsp;the&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=9*[range(4)],&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'])<br>
-&gt;&gt;&gt;&nbsp;df['time']&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-resample">resample</a>('3T',&nbsp;on='time').<a href="#TimeFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;c&nbsp;&nbsp;d<br>
-time<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-&nbsp;<br>
-For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;MultiIndex,&nbsp;the&nbsp;keyword&nbsp;``level``&nbsp;can&nbsp;be&nbsp;used&nbsp;to<br>
-specify&nbsp;on&nbsp;level&nbsp;the&nbsp;resampling&nbsp;needs&nbsp;to&nbsp;take&nbsp;place.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;time&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=5,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=10*[range(4)],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.MultiIndex.from_product([time,&nbsp;[1,&nbsp;2]])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;)<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#TimeFrame-resample">resample</a>('3T',&nbsp;level=0).<a href="#TimeFrame-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;d<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;6&nbsp;&nbsp;12&nbsp;&nbsp;18<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;8&nbsp;&nbsp;12</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-sample"><strong>sample</strong></a>(self, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None)</dt><dd><tt>Returns&nbsp;a&nbsp;random&nbsp;sample&nbsp;of&nbsp;items&nbsp;from&nbsp;an&nbsp;axis&nbsp;of&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;items&nbsp;from&nbsp;axis&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`frac`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;=&nbsp;1&nbsp;if&nbsp;`frac`&nbsp;=&nbsp;None.<br>
-frac&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fraction&nbsp;of&nbsp;axis&nbsp;items&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`n`.<br>
-replace&nbsp;:&nbsp;boolean,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sample&nbsp;with&nbsp;or&nbsp;without&nbsp;replacement.&nbsp;Default&nbsp;=&nbsp;False.<br>
-weights&nbsp;:&nbsp;str&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;'None'&nbsp;results&nbsp;in&nbsp;equal&nbsp;probability&nbsp;weighting.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;will&nbsp;align&nbsp;with&nbsp;target&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;index.&nbsp;Index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;in&nbsp;weights&nbsp;not&nbsp;found&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;will&nbsp;be&nbsp;ignored&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;values&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;not&nbsp;in&nbsp;weights&nbsp;will&nbsp;be&nbsp;assigned<br>
-&nbsp;&nbsp;&nbsp;&nbsp;weights&nbsp;of&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;called&nbsp;on&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;will&nbsp;accept&nbsp;the&nbsp;name&nbsp;of&nbsp;a&nbsp;column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;axis&nbsp;=&nbsp;0.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Unless&nbsp;weights&nbsp;are&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;weights&nbsp;must&nbsp;be&nbsp;same&nbsp;length&nbsp;as&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;being&nbsp;sampled.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;weights&nbsp;do&nbsp;not&nbsp;sum&nbsp;to&nbsp;1,&nbsp;they&nbsp;will&nbsp;be&nbsp;normalized&nbsp;to&nbsp;sum&nbsp;to&nbsp;1.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;values&nbsp;in&nbsp;the&nbsp;weights&nbsp;column&nbsp;will&nbsp;be&nbsp;treated&nbsp;as&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;inf&nbsp;and&nbsp;-inf&nbsp;values&nbsp;not&nbsp;allowed.<br>
-random_state&nbsp;:&nbsp;int&nbsp;or&nbsp;numpy.random.RandomState,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Seed&nbsp;for&nbsp;the&nbsp;random&nbsp;number&nbsp;generator&nbsp;(if&nbsp;int),&nbsp;or&nbsp;numpy&nbsp;RandomState<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;sample.&nbsp;Accepts&nbsp;axis&nbsp;number&nbsp;or&nbsp;name.&nbsp;Default&nbsp;is&nbsp;stat&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;given&nbsp;data&nbsp;type&nbsp;(0&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;and&nbsp;DataFrames,&nbsp;1&nbsp;for&nbsp;Panels).<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-A&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;type&nbsp;as&nbsp;caller.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Generate&nbsp;an&nbsp;example&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;and&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(50))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeFrame-head">head</a>()<br>
-0&nbsp;&nbsp;&nbsp;-0.038497<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.820773<br>
-2&nbsp;&nbsp;&nbsp;-0.972766<br>
-3&nbsp;&nbsp;&nbsp;-1.598270<br>
-4&nbsp;&nbsp;&nbsp;-1.095526<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(50,&nbsp;4),&nbsp;columns=list('ABCD'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-head">head</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;0.016443&nbsp;-2.318952&nbsp;-0.566372&nbsp;-1.028078<br>
-1&nbsp;-1.051921&nbsp;&nbsp;0.438836&nbsp;&nbsp;0.658280&nbsp;-0.175797<br>
-2&nbsp;-1.243569&nbsp;-0.364626&nbsp;-0.215065&nbsp;&nbsp;0.057736<br>
-3&nbsp;&nbsp;1.768216&nbsp;&nbsp;0.404512&nbsp;-0.385604&nbsp;-1.457834<br>
-4&nbsp;&nbsp;1.072446&nbsp;-1.137172&nbsp;&nbsp;0.314194&nbsp;-0.046661<br>
-&nbsp;<br>
-Next&nbsp;extract&nbsp;a&nbsp;random&nbsp;sample&nbsp;from&nbsp;both&nbsp;of&nbsp;these&nbsp;objects...<br>
-&nbsp;<br>
-3&nbsp;random&nbsp;elements&nbsp;from&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeFrame-sample">sample</a>(n=3)<br>
-27&nbsp;&nbsp;&nbsp;-0.994689<br>
-55&nbsp;&nbsp;&nbsp;-1.049016<br>
-67&nbsp;&nbsp;&nbsp;-0.224565<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-And&nbsp;a&nbsp;random&nbsp;10%&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;with&nbsp;replacement:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-sample">sample</a>(frac=0.1,&nbsp;replace=True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-35&nbsp;&nbsp;1.981780&nbsp;&nbsp;0.142106&nbsp;&nbsp;1.817165&nbsp;-0.290805<br>
-49&nbsp;-1.336199&nbsp;-0.448634&nbsp;-0.789640&nbsp;&nbsp;0.217116<br>
-40&nbsp;&nbsp;0.823173&nbsp;-0.078816&nbsp;&nbsp;1.009536&nbsp;&nbsp;1.015108<br>
-15&nbsp;&nbsp;1.421154&nbsp;-0.055301&nbsp;-1.922594&nbsp;-0.019696<br>
-6&nbsp;&nbsp;-0.148339&nbsp;&nbsp;0.832938&nbsp;&nbsp;1.787600&nbsp;-1.383767</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-select"><strong>select</strong></a>(self, crit, axis=0)</dt><dd><tt>Return&nbsp;data&nbsp;corresponding&nbsp;to&nbsp;axis&nbsp;labels&nbsp;matching&nbsp;criteria<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-crit&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;called&nbsp;on&nbsp;each&nbsp;index&nbsp;(label).&nbsp;Should&nbsp;return&nbsp;True&nbsp;or&nbsp;False<br>
-axis&nbsp;:&nbsp;int<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-selection&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-set_axis"><strong>set_axis</strong></a>(self, axis, labels)</dt><dd><tt>public&nbsp;verson&nbsp;of&nbsp;axis&nbsp;assignment</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-slice_shift"><strong>slice_shift</strong></a>(self, periods=1, axis=0)</dt><dd><tt>Equivalent&nbsp;to&nbsp;`shift`&nbsp;without&nbsp;copying&nbsp;data.&nbsp;The&nbsp;shifted&nbsp;data&nbsp;will<br>
-not&nbsp;include&nbsp;the&nbsp;dropped&nbsp;periods&nbsp;and&nbsp;the&nbsp;shifted&nbsp;axis&nbsp;will&nbsp;be&nbsp;smaller<br>
-than&nbsp;the&nbsp;original.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-&nbsp;<br>
-Notes<br>
------<br>
-While&nbsp;the&nbsp;`slice_shift`&nbsp;is&nbsp;faster&nbsp;than&nbsp;`shift`,&nbsp;you&nbsp;may&nbsp;pay&nbsp;for&nbsp;it<br>
-later&nbsp;during&nbsp;alignment.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-squeeze"><strong>squeeze</strong></a>(self, axis=None)</dt><dd><tt>Squeeze&nbsp;length&nbsp;1&nbsp;dimensions.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;None,&nbsp;integer&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;squeeze&nbsp;if&nbsp;1-sized.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-scalar&nbsp;if&nbsp;1-sized,&nbsp;else&nbsp;original&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-swapaxes"><strong>swapaxes</strong></a>(self, axis1, axis2, copy=True)</dt><dd><tt>Interchange&nbsp;axes&nbsp;and&nbsp;swap&nbsp;values&nbsp;axes&nbsp;appropriately<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-tail"><strong>tail</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;last&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-take"><strong>take</strong></a>(self, indices, axis=0, convert=True, is_copy=True, **kwargs)</dt><dd><tt>Analogous&nbsp;to&nbsp;<a href="numpy.html#ndarray">ndarray</a>.take<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-indices&nbsp;:&nbsp;list&nbsp;/&nbsp;array&nbsp;of&nbsp;ints<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-convert&nbsp;:&nbsp;translate&nbsp;neg&nbsp;to&nbsp;pos&nbsp;indices&nbsp;(default)<br>
-is_copy&nbsp;:&nbsp;mark&nbsp;the&nbsp;returned&nbsp;frame&nbsp;as&nbsp;a&nbsp;copy<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-taken&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_clipboard"><strong>to_clipboard</strong></a>(self, excel=None, sep=None, **kwargs)</dt><dd><tt>Attempt&nbsp;to&nbsp;write&nbsp;text&nbsp;representation&nbsp;of&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;the&nbsp;system&nbsp;clipboard<br>
-This&nbsp;can&nbsp;be&nbsp;pasted&nbsp;into&nbsp;Excel,&nbsp;for&nbsp;example.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel&nbsp;:&nbsp;boolean,&nbsp;defaults&nbsp;to&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;use&nbsp;the&nbsp;provided&nbsp;separator,&nbsp;writing&nbsp;in&nbsp;a&nbsp;csv<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;format&nbsp;for&nbsp;allowing&nbsp;easy&nbsp;pasting&nbsp;into&nbsp;excel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;False,&nbsp;write&nbsp;a&nbsp;string&nbsp;representation&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;the&nbsp;clipboard<br>
-sep&nbsp;:&nbsp;optional,&nbsp;defaults&nbsp;to&nbsp;tab<br>
-other&nbsp;keywords&nbsp;are&nbsp;passed&nbsp;to&nbsp;to_csv<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Requirements&nbsp;for&nbsp;your&nbsp;platform<br>
-&nbsp;&nbsp;-&nbsp;Linux:&nbsp;xclip,&nbsp;or&nbsp;xsel&nbsp;(with&nbsp;gtk&nbsp;or&nbsp;PyQt4&nbsp;modules)<br>
-&nbsp;&nbsp;-&nbsp;Windows:&nbsp;none<br>
-&nbsp;&nbsp;-&nbsp;OS&nbsp;X:&nbsp;none</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_dense"><strong>to_dense</strong></a>(self)</dt><dd><tt>Return&nbsp;dense&nbsp;representation&nbsp;of&nbsp;NDFrame&nbsp;(as&nbsp;opposed&nbsp;to&nbsp;sparse)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_hdf"><strong>to_hdf</strong></a>(self, path_or_buf, key, **kwargs)</dt><dd><tt>Write&nbsp;the&nbsp;contained&nbsp;data&nbsp;to&nbsp;an&nbsp;HDF5&nbsp;file&nbsp;using&nbsp;HDFStore.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;(string)&nbsp;or&nbsp;HDFStore&nbsp;<a href="builtins.html#object">object</a><br>
-key&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;identifier&nbsp;for&nbsp;the&nbsp;group&nbsp;in&nbsp;the&nbsp;store<br>
-mode&nbsp;:&nbsp;optional,&nbsp;{'a',&nbsp;'w',&nbsp;'r+'},&nbsp;default&nbsp;'a'<br>
-&nbsp;<br>
-&nbsp;&nbsp;``'w'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write;&nbsp;a&nbsp;new&nbsp;file&nbsp;is&nbsp;created&nbsp;(an&nbsp;existing&nbsp;file&nbsp;with&nbsp;the&nbsp;same<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;name&nbsp;would&nbsp;be&nbsp;deleted).<br>
-&nbsp;&nbsp;``'a'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Append;&nbsp;an&nbsp;existing&nbsp;file&nbsp;is&nbsp;opened&nbsp;for&nbsp;reading&nbsp;and&nbsp;writing,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;if&nbsp;the&nbsp;file&nbsp;does&nbsp;not&nbsp;exist&nbsp;it&nbsp;is&nbsp;created.<br>
-&nbsp;&nbsp;``'r+'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;It&nbsp;is&nbsp;similar&nbsp;to&nbsp;``'a'``,&nbsp;but&nbsp;the&nbsp;file&nbsp;must&nbsp;already&nbsp;exist.<br>
-format&nbsp;:&nbsp;'fixed(f)|table(t)',&nbsp;default&nbsp;is&nbsp;'fixed'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fixed(f)&nbsp;:&nbsp;Fixed&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Fast&nbsp;writing/reading.&nbsp;Not-appendable,&nbsp;nor&nbsp;searchable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;table(t)&nbsp;:&nbsp;Table&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;as&nbsp;a&nbsp;PyTables&nbsp;Table&nbsp;structure&nbsp;which&nbsp;may&nbsp;perform<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;worse&nbsp;but&nbsp;allow&nbsp;more&nbsp;flexible&nbsp;operations&nbsp;like&nbsp;searching<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/&nbsp;selecting&nbsp;subsets&nbsp;of&nbsp;the&nbsp;data<br>
-append&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;Table&nbsp;formats,&nbsp;append&nbsp;the&nbsp;input&nbsp;data&nbsp;to&nbsp;the&nbsp;existing<br>
-data_columns&nbsp;:&nbsp;&nbsp;list&nbsp;of&nbsp;columns,&nbsp;or&nbsp;True,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;columns&nbsp;to&nbsp;create&nbsp;as&nbsp;indexed&nbsp;data&nbsp;columns&nbsp;for&nbsp;on-disk<br>
-&nbsp;&nbsp;&nbsp;&nbsp;queries,&nbsp;or&nbsp;True&nbsp;to&nbsp;use&nbsp;all&nbsp;columns.&nbsp;By&nbsp;default&nbsp;only&nbsp;the&nbsp;axes<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;are&nbsp;indexed.&nbsp;See&nbsp;`here<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__">http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__</a>.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Applicable&nbsp;only&nbsp;to&nbsp;format='table'.<br>
-complevel&nbsp;:&nbsp;int,&nbsp;1-9,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;a&nbsp;complib&nbsp;is&nbsp;specified&nbsp;compression&nbsp;will&nbsp;be&nbsp;applied<br>
-&nbsp;&nbsp;&nbsp;&nbsp;where&nbsp;possible<br>
-complib&nbsp;:&nbsp;{'zlib',&nbsp;'bzip2',&nbsp;'lzo',&nbsp;'blosc',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;complevel&nbsp;is&nbsp;&gt;&nbsp;0&nbsp;apply&nbsp;compression&nbsp;to&nbsp;objects&nbsp;written<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;store&nbsp;wherever&nbsp;possible<br>
-fletcher32&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;applying&nbsp;compression&nbsp;use&nbsp;the&nbsp;fletcher32&nbsp;checksum<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;true,&nbsp;ALL&nbsp;nan&nbsp;rows&nbsp;will&nbsp;not&nbsp;be&nbsp;written&nbsp;to&nbsp;store.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_json"><strong>to_json</strong></a>(self, path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False)</dt><dd><tt>Convert&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;a&nbsp;JSON&nbsp;string.<br>
-&nbsp;<br>
-Note&nbsp;NaN's&nbsp;and&nbsp;None&nbsp;will&nbsp;be&nbsp;converted&nbsp;to&nbsp;null&nbsp;and&nbsp;datetime&nbsp;objects<br>
-will&nbsp;be&nbsp;converted&nbsp;to&nbsp;UNIX&nbsp;timestamps.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;or&nbsp;buffer&nbsp;to&nbsp;write&nbsp;the&nbsp;result&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;this&nbsp;is&nbsp;None,&nbsp;return&nbsp;a&nbsp;StringIO&nbsp;of&nbsp;the&nbsp;converted&nbsp;string<br>
-orient&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'index'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:&nbsp;{'split','records','index'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{'split','records','index','columns','values'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;The&nbsp;format&nbsp;of&nbsp;the&nbsp;JSON&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;split&nbsp;:&nbsp;dict&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{index&nbsp;-&gt;&nbsp;[index],&nbsp;columns&nbsp;-&gt;&nbsp;[columns],&nbsp;data&nbsp;-&gt;&nbsp;[values]}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;records&nbsp;:&nbsp;list&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[{column&nbsp;-&gt;&nbsp;value},&nbsp;...&nbsp;,&nbsp;{column&nbsp;-&gt;&nbsp;value}]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;index&nbsp;:&nbsp;dict&nbsp;like&nbsp;{index&nbsp;-&gt;&nbsp;{column&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;columns&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;{index&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;values&nbsp;:&nbsp;just&nbsp;the&nbsp;values&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;table&nbsp;:&nbsp;dict&nbsp;like&nbsp;{'schema':&nbsp;{schema},&nbsp;'data':&nbsp;{data}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;describing&nbsp;the&nbsp;data,&nbsp;and&nbsp;the&nbsp;data&nbsp;component&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;like&nbsp;``orient='records'``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionchanged::&nbsp;0.20.0<br>
-&nbsp;<br>
-date_format&nbsp;:&nbsp;{None,&nbsp;'epoch',&nbsp;'iso'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Type&nbsp;of&nbsp;date&nbsp;conversion.&nbsp;`epoch`&nbsp;=&nbsp;epoch&nbsp;milliseconds,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`iso`&nbsp;=&nbsp;ISO8601.&nbsp;The&nbsp;default&nbsp;depends&nbsp;on&nbsp;the&nbsp;`orient`.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`orient='table'`,&nbsp;the&nbsp;default&nbsp;is&nbsp;`'iso'`.&nbsp;For&nbsp;all&nbsp;other&nbsp;orients,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;default&nbsp;is&nbsp;`'epoch'`.<br>
-double_precision&nbsp;:&nbsp;The&nbsp;number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;use&nbsp;when&nbsp;encoding<br>
-&nbsp;&nbsp;&nbsp;&nbsp;floating&nbsp;point&nbsp;values,&nbsp;default&nbsp;10.<br>
-force_ascii&nbsp;:&nbsp;force&nbsp;encoded&nbsp;string&nbsp;to&nbsp;be&nbsp;ASCII,&nbsp;default&nbsp;True.<br>
-date_unit&nbsp;:&nbsp;string,&nbsp;default&nbsp;'ms'&nbsp;(milliseconds)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;time&nbsp;unit&nbsp;to&nbsp;encode&nbsp;to,&nbsp;governs&nbsp;timestamp&nbsp;and&nbsp;ISO8601<br>
-&nbsp;&nbsp;&nbsp;&nbsp;precision.&nbsp;&nbsp;One&nbsp;of&nbsp;'s',&nbsp;'ms',&nbsp;'us',&nbsp;'ns'&nbsp;for&nbsp;second,&nbsp;millisecond,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;microsecond,&nbsp;and&nbsp;nanosecond&nbsp;respectively.<br>
-default_handler&nbsp;:&nbsp;callable,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Handler&nbsp;to&nbsp;call&nbsp;if&nbsp;<a href="builtins.html#object">object</a>&nbsp;cannot&nbsp;otherwise&nbsp;be&nbsp;converted&nbsp;to&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;suitable&nbsp;format&nbsp;for&nbsp;JSON.&nbsp;Should&nbsp;receive&nbsp;a&nbsp;single&nbsp;argument&nbsp;which&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;convert&nbsp;and&nbsp;return&nbsp;a&nbsp;serialisable&nbsp;<a href="builtins.html#object">object</a>.<br>
-lines&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'orient'&nbsp;is&nbsp;'records'&nbsp;write&nbsp;out&nbsp;line&nbsp;delimited&nbsp;json&nbsp;format.&nbsp;Will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;throw&nbsp;ValueError&nbsp;if&nbsp;incorrect&nbsp;'orient'&nbsp;since&nbsp;others&nbsp;are&nbsp;not&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;filtered&nbsp;info&nbsp;axis<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pd.read_json<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([['a',&nbsp;'b'],&nbsp;['c',&nbsp;'d']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=['row&nbsp;1',&nbsp;'row&nbsp;2'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['col&nbsp;1',&nbsp;'col&nbsp;2'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-to_json">to_json</a>(orient='split')<br>
-'{"columns":["col&nbsp;1","col&nbsp;2"],<br>
-&nbsp;&nbsp;"index":["row&nbsp;1","row&nbsp;2"],<br>
-&nbsp;&nbsp;"data":[["a","b"],["c","d"]]}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'index'``&nbsp;formatted&nbsp;JSON:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-to_json">to_json</a>(orient='index')<br>
-'{"row&nbsp;1":{"col&nbsp;1":"a","col&nbsp;2":"b"},"row&nbsp;2":{"col&nbsp;1":"c","col&nbsp;2":"d"}}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'records'``&nbsp;formatted&nbsp;JSON.<br>
-Note&nbsp;that&nbsp;index&nbsp;labels&nbsp;are&nbsp;not&nbsp;preserved&nbsp;with&nbsp;this&nbsp;encoding.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-to_json">to_json</a>(orient='records')<br>
-'[{"col&nbsp;1":"a","col&nbsp;2":"b"},{"col&nbsp;1":"c","col&nbsp;2":"d"}]'<br>
-&nbsp;<br>
-Encoding&nbsp;with&nbsp;Table&nbsp;Schema<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-to_json">to_json</a>(orient='table')<br>
-'{"schema":&nbsp;{"fields":&nbsp;[{"name":&nbsp;"index",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;1",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;2",&nbsp;"type":&nbsp;"string"}],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"primaryKey":&nbsp;"index",<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"pandas_version":&nbsp;"0.20.0"},<br>
-&nbsp;&nbsp;"data":&nbsp;[{"index":&nbsp;"row&nbsp;1",&nbsp;"col&nbsp;1":&nbsp;"a",&nbsp;"col&nbsp;2":&nbsp;"b"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"index":&nbsp;"row&nbsp;2",&nbsp;"col&nbsp;1":&nbsp;"c",&nbsp;"col&nbsp;2":&nbsp;"d"}]}'</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_msgpack"><strong>to_msgpack</strong></a>(self, path_or_buf=None, encoding='utf-8', **kwargs)</dt><dd><tt>msgpack&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path<br>
-&nbsp;<br>
-THIS&nbsp;IS&nbsp;AN&nbsp;EXPERIMENTAL&nbsp;LIBRARY&nbsp;and&nbsp;the&nbsp;storage&nbsp;format<br>
-may&nbsp;not&nbsp;be&nbsp;stable&nbsp;until&nbsp;a&nbsp;future&nbsp;release.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;File&nbsp;path,&nbsp;buffer-like,&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;None,&nbsp;return&nbsp;generated&nbsp;string<br>
-append&nbsp;:&nbsp;boolean&nbsp;whether&nbsp;to&nbsp;append&nbsp;to&nbsp;an&nbsp;existing&nbsp;msgpack<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(default&nbsp;is&nbsp;False)<br>
-compress&nbsp;:&nbsp;type&nbsp;of&nbsp;compressor&nbsp;(zlib&nbsp;or&nbsp;blosc),&nbsp;default&nbsp;to&nbsp;None&nbsp;(no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;compression)</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_pickle"><strong>to_pickle</strong></a>(self, path, compression='infer')</dt><dd><tt>Pickle&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path<br>
-compression&nbsp;:&nbsp;{'infer',&nbsp;'gzip',&nbsp;'bz2',&nbsp;'xz',&nbsp;None},&nbsp;default&nbsp;'infer'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;compression&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_sql"><strong>to_sql</strong></a>(self, name, con, flavor=None, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None)</dt><dd><tt>Write&nbsp;records&nbsp;stored&nbsp;in&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;SQL&nbsp;database.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-name&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;SQL&nbsp;table<br>
-con&nbsp;:&nbsp;SQLAlchemy&nbsp;engine&nbsp;or&nbsp;DBAPI2&nbsp;connection&nbsp;(legacy&nbsp;mode)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Using&nbsp;SQLAlchemy&nbsp;makes&nbsp;it&nbsp;possible&nbsp;to&nbsp;use&nbsp;any&nbsp;DB&nbsp;supported&nbsp;by&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;library.&nbsp;If&nbsp;a&nbsp;DBAPI2&nbsp;<a href="builtins.html#object">object</a>,&nbsp;only&nbsp;sqlite3&nbsp;is&nbsp;supported.<br>
-flavor&nbsp;:&nbsp;'sqlite',&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;DEPRECATED:&nbsp;this&nbsp;parameter&nbsp;will&nbsp;be&nbsp;removed&nbsp;in&nbsp;a&nbsp;future&nbsp;version,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;as&nbsp;'sqlite'&nbsp;is&nbsp;the&nbsp;only&nbsp;supported&nbsp;option&nbsp;if&nbsp;SQLAlchemy&nbsp;is&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;installed.<br>
-schema&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;the&nbsp;schema&nbsp;(if&nbsp;database&nbsp;flavor&nbsp;supports&nbsp;this).&nbsp;If&nbsp;None,&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;schema.<br>
-if_exists&nbsp;:&nbsp;{'fail',&nbsp;'replace',&nbsp;'append'},&nbsp;default&nbsp;'fail'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;fail:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;do&nbsp;nothing.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;replace:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;drop&nbsp;it,&nbsp;recreate&nbsp;it,&nbsp;and&nbsp;insert&nbsp;data.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;append:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;insert&nbsp;data.&nbsp;Create&nbsp;if&nbsp;does&nbsp;not&nbsp;exist.<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;index&nbsp;as&nbsp;a&nbsp;column.<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s).&nbsp;If&nbsp;None&nbsp;is&nbsp;given&nbsp;(default)&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`index`&nbsp;is&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-chunksize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;not&nbsp;None,&nbsp;then&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;in&nbsp;batches&nbsp;of&nbsp;this&nbsp;size&nbsp;at&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;time.&nbsp;&nbsp;If&nbsp;None,&nbsp;all&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;at&nbsp;once.<br>
-dtype&nbsp;:&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;to&nbsp;SQL&nbsp;type,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Optional&nbsp;specifying&nbsp;the&nbsp;datatype&nbsp;for&nbsp;columns.&nbsp;The&nbsp;SQL&nbsp;type&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;SQLAlchemy&nbsp;type,&nbsp;or&nbsp;a&nbsp;string&nbsp;for&nbsp;sqlite3&nbsp;fallback&nbsp;connection.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-to_xarray"><strong>to_xarray</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;xarray&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;the&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;DataArray&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;Dataset&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-a&nbsp;DataArray&nbsp;for&nbsp;higher&nbsp;dims<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;foo&nbsp;&nbsp;4.0<br>
-1&nbsp;&nbsp;1&nbsp;&nbsp;bar&nbsp;&nbsp;5.0<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;foo&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(index:&nbsp;3)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;index&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;0&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;1&nbsp;1&nbsp;2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'foo'&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;float64&nbsp;4.0&nbsp;5.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;).<a href="#TimeFrame-set_index">set_index</a>(['B','A'])<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-B&nbsp;&nbsp;&nbsp;A<br>
-foo&nbsp;1&nbsp;&nbsp;4.0<br>
-bar&nbsp;1&nbsp;&nbsp;5.0<br>
-foo&nbsp;2&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(A:&nbsp;2,&nbsp;B:&nbsp;2)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;*&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(A)&nbsp;int64&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B,&nbsp;A)&nbsp;float64&nbsp;5.0&nbsp;nan&nbsp;4.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p&nbsp;=&nbsp;pd.Panel(np.arange(24).reshape(4,3,2),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;items=list('ABCD'),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;major_axis=pd.date_range('20130101',&nbsp;periods=3),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;minor_axis=['first',&nbsp;'second'])<br>
-&gt;&gt;&gt;&nbsp;p<br>
-&lt;class&nbsp;'pandas.core.panel.Panel'&gt;<br>
-Dimensions:&nbsp;4&nbsp;(items)&nbsp;x&nbsp;3&nbsp;(major_axis)&nbsp;x&nbsp;2&nbsp;(minor_axis)<br>
-Items&nbsp;axis:&nbsp;A&nbsp;to&nbsp;D<br>
-Major_axis&nbsp;axis:&nbsp;2013-01-01&nbsp;00:00:00&nbsp;to&nbsp;2013-01-03&nbsp;00:00:00<br>
-Minor_axis&nbsp;axis:&nbsp;first&nbsp;to&nbsp;second<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p.<a href="#TimeFrame-to_xarray">to_xarray</a>()<br>
-&lt;xarray.DataArray&nbsp;(items:&nbsp;4,&nbsp;major_axis:&nbsp;3,&nbsp;minor_axis:&nbsp;2)&gt;<br>
-array([[[&nbsp;0,&nbsp;&nbsp;1],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;2,&nbsp;&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;4,&nbsp;&nbsp;5]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[&nbsp;6,&nbsp;&nbsp;7],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;8,&nbsp;&nbsp;9],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[10,&nbsp;11]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[12,&nbsp;13],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[14,&nbsp;15],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[16,&nbsp;17]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[18,&nbsp;19],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[20,&nbsp;21],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[22,&nbsp;23]]])<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;items&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(items)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'A'&nbsp;'B'&nbsp;'C'&nbsp;'D'<br>
-&nbsp;&nbsp;*&nbsp;major_axis&nbsp;&nbsp;(major_axis)&nbsp;datetime64[ns]&nbsp;2013-01-01&nbsp;2013-01-02&nbsp;2013-01-03&nbsp;&nbsp;#&nbsp;noqa<br>
-&nbsp;&nbsp;*&nbsp;minor_axis&nbsp;&nbsp;(minor_axis)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'first'&nbsp;'second'<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;the&nbsp;`xarray&nbsp;docs&nbsp;&lt;<a href="http://xarray.pydata.org/en/stable/&gt;`__">http://xarray.pydata.org/en/stable/&gt;`__</a></tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-truncate"><strong>truncate</strong></a>(self, before=None, after=None, axis=None, copy=True)</dt><dd><tt>Truncates&nbsp;a&nbsp;sorted&nbsp;NDFrame&nbsp;before&nbsp;and/or&nbsp;after&nbsp;some&nbsp;particular<br>
-index&nbsp;value.&nbsp;If&nbsp;the&nbsp;axis&nbsp;contains&nbsp;only&nbsp;datetime&nbsp;values,&nbsp;before/after<br>
-parameters&nbsp;are&nbsp;converted&nbsp;to&nbsp;datetime&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-before&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;before&nbsp;index&nbsp;value<br>
-after&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;after&nbsp;index&nbsp;value<br>
-axis&nbsp;:&nbsp;the&nbsp;truncation&nbsp;axis,&nbsp;defaults&nbsp;to&nbsp;the&nbsp;stat&nbsp;axis<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;is&nbsp;True,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;truncated&nbsp;section<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-truncated&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-tshift"><strong>tshift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;the&nbsp;time&nbsp;index,&nbsp;using&nbsp;the&nbsp;index's&nbsp;frequency&nbsp;if&nbsp;available.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM')<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;basestring<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Corresponds&nbsp;to&nbsp;the&nbsp;axis&nbsp;that&nbsp;contains&nbsp;the&nbsp;Index<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;not&nbsp;specified&nbsp;then&nbsp;tries&nbsp;to&nbsp;use&nbsp;the&nbsp;freq&nbsp;or&nbsp;inferred_freq<br>
-attributes&nbsp;of&nbsp;the&nbsp;index.&nbsp;If&nbsp;neither&nbsp;of&nbsp;those&nbsp;attributes&nbsp;exist,&nbsp;a<br>
-ValueError&nbsp;is&nbsp;thrown<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;NDFrame</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-tz_convert"><strong>tz_convert</strong></a>(self, tz, axis=0, level=None, copy=True)</dt><dd><tt>Convert&nbsp;tz-aware&nbsp;axis&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;convert<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;convert&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;tz-naive.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-tz_localize"><strong>tz_localize</strong></a>(self, tz, axis=0, level=None, copy=True, ambiguous='raise')</dt><dd><tt>Localize&nbsp;tz-naive&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;localize<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;localize&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-ambiguous&nbsp;:&nbsp;'infer',&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>,&nbsp;'NaT',&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'infer'&nbsp;will&nbsp;attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>&nbsp;where&nbsp;True&nbsp;signifies&nbsp;a&nbsp;DST&nbsp;time,&nbsp;False&nbsp;designates<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;non-DST&nbsp;time&nbsp;(note&nbsp;that&nbsp;this&nbsp;flag&nbsp;is&nbsp;only&nbsp;applicable&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ambiguous&nbsp;times)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'NaT'&nbsp;will&nbsp;return&nbsp;NaT&nbsp;where&nbsp;there&nbsp;are&nbsp;ambiguous&nbsp;times<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'raise'&nbsp;will&nbsp;raise&nbsp;an&nbsp;AmbiguousTimeError&nbsp;if&nbsp;there&nbsp;are&nbsp;ambiguous<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;times<br>
-infer_dst&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on&nbsp;order<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;is&nbsp;tz-aware&nbsp;and&nbsp;tz&nbsp;is&nbsp;not&nbsp;None.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-where"><strong>where</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;True&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;where&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``True``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#TimeFrame-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#TimeFrame-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``where``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeFrame-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).reshape(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#TimeFrame-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#TimeFrame-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mask`</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-xs"><strong>xs</strong></a>(self, key, axis=0, level=None, drop_level=True)</dt><dd><tt>Returns&nbsp;a&nbsp;cross-section&nbsp;(row(s)&nbsp;or&nbsp;column(s))&nbsp;from&nbsp;the<br>
-<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Defaults&nbsp;to&nbsp;cross-section&nbsp;on&nbsp;the&nbsp;rows&nbsp;(axis=0).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Some&nbsp;label&nbsp;contained&nbsp;in&nbsp;the&nbsp;index,&nbsp;or&nbsp;partially&nbsp;in&nbsp;a&nbsp;MultiIndex<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;retrieve&nbsp;cross-section&nbsp;on<br>
-level&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;defaults&nbsp;to&nbsp;first&nbsp;n&nbsp;levels&nbsp;(n=1&nbsp;or&nbsp;len(key))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;key&nbsp;partially&nbsp;contained&nbsp;in&nbsp;a&nbsp;MultiIndex,&nbsp;indicate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;levels&nbsp;are&nbsp;used.&nbsp;Levels&nbsp;can&nbsp;be&nbsp;referred&nbsp;by&nbsp;label&nbsp;or&nbsp;position.<br>
-drop_level&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;returns&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;same&nbsp;levels&nbsp;as&nbsp;self.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C<br>
-a&nbsp;&nbsp;4&nbsp;&nbsp;5&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;4&nbsp;&nbsp;0&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;9&nbsp;&nbsp;7&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-xs">xs</a>('a')<br>
-A&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-B&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-C&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Name:&nbsp;a<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-xs">xs</a>('C',&nbsp;axis=1)<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;C<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;second&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7&nbsp;&nbsp;5&nbsp;&nbsp;5&nbsp;&nbsp;0<br>
-baz&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;three&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-xs">xs</a>(('baz',&nbsp;'three'))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-third<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-xs">xs</a>('one',&nbsp;level=1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-baz&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeFrame-xs">xs</a>(('baz',&nbsp;2),&nbsp;level=[0,&nbsp;'third'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-second<br>
-three&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-xs&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-xs&nbsp;is&nbsp;only&nbsp;for&nbsp;getting,&nbsp;not&nbsp;setting&nbsp;values.<br>
-&nbsp;<br>
-MultiIndex&nbsp;Slicers&nbsp;is&nbsp;a&nbsp;generic&nbsp;way&nbsp;to&nbsp;get/set&nbsp;values&nbsp;on&nbsp;any&nbsp;level&nbsp;or<br>
-levels.&nbsp;&nbsp;It&nbsp;is&nbsp;a&nbsp;superset&nbsp;of&nbsp;xs&nbsp;functionality,&nbsp;see<br>
-:ref:`MultiIndex&nbsp;Slicers&nbsp;&lt;advanced.mi_slicers&gt;`</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>at</strong></dt>
-<dd><tt>Fast&nbsp;label-based&nbsp;scalar&nbsp;accessor<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``loc``,&nbsp;``at``&nbsp;provides&nbsp;**label**&nbsp;based&nbsp;scalar&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>blocks</strong></dt>
-<dd><tt>Internal&nbsp;property,&nbsp;property&nbsp;synonym&nbsp;for&nbsp;as_blocks()</tt></dd>
-</dl>
-<dl><dt><strong>dtypes</strong></dt>
-<dd><tt>Return&nbsp;the&nbsp;dtypes&nbsp;in&nbsp;this&nbsp;object.</tt></dd>
-</dl>
-<dl><dt><strong>empty</strong></dt>
-<dd><tt>True&nbsp;if&nbsp;NDFrame&nbsp;is&nbsp;entirely&nbsp;empty&nbsp;[no&nbsp;items],&nbsp;meaning&nbsp;any&nbsp;of&nbsp;the<br>
-axes&nbsp;are&nbsp;of&nbsp;length&nbsp;0.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;NDFrame&nbsp;contains&nbsp;only&nbsp;NaNs,&nbsp;it&nbsp;is&nbsp;still&nbsp;not&nbsp;considered&nbsp;empty.&nbsp;See<br>
-the&nbsp;example&nbsp;below.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-An&nbsp;example&nbsp;of&nbsp;an&nbsp;actual&nbsp;empty&nbsp;DataFrame.&nbsp;Notice&nbsp;the&nbsp;index&nbsp;is&nbsp;empty:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df_empty&nbsp;=&nbsp;pd.DataFrame({'A'&nbsp;:&nbsp;[]})<br>
-&gt;&gt;&gt;&nbsp;df_empty<br>
-Empty&nbsp;DataFrame<br>
-Columns:&nbsp;[A]<br>
-Index:&nbsp;[]<br>
-&gt;&gt;&gt;&nbsp;df_empty.empty<br>
-True<br>
-&nbsp;<br>
-If&nbsp;we&nbsp;only&nbsp;have&nbsp;NaNs&nbsp;in&nbsp;our&nbsp;DataFrame,&nbsp;it&nbsp;is&nbsp;not&nbsp;considered&nbsp;empty!&nbsp;We<br>
-will&nbsp;need&nbsp;to&nbsp;drop&nbsp;the&nbsp;NaNs&nbsp;to&nbsp;make&nbsp;the&nbsp;DataFrame&nbsp;empty:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.DataFrame({'A'&nbsp;:&nbsp;[np.nan]})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A<br>
-0&nbsp;NaN<br>
-&gt;&gt;&gt;&nbsp;df.empty<br>
-False<br>
-&gt;&gt;&gt;&nbsp;df.dropna().empty<br>
-True<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.Series.dropna<br>
-pandas.DataFrame.dropna</tt></dd>
-</dl>
-<dl><dt><strong>ftypes</strong></dt>
-<dd><tt>Return&nbsp;the&nbsp;ftypes&nbsp;(indication&nbsp;of&nbsp;sparse/dense&nbsp;and&nbsp;dtype)<br>
-in&nbsp;this&nbsp;object.</tt></dd>
-</dl>
-<dl><dt><strong>iat</strong></dt>
-<dd><tt>Fast&nbsp;integer&nbsp;location&nbsp;scalar&nbsp;accessor.<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``iloc``,&nbsp;``iat``&nbsp;provides&nbsp;**integer**&nbsp;based&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>iloc</strong></dt>
-<dd><tt>Purely&nbsp;integer-location&nbsp;based&nbsp;indexing&nbsp;for&nbsp;selection&nbsp;by&nbsp;position.<br>
-&nbsp;<br>
-``.iloc[]``&nbsp;is&nbsp;primarily&nbsp;integer&nbsp;position&nbsp;based&nbsp;(from&nbsp;``0``&nbsp;to<br>
-``length-1``&nbsp;of&nbsp;the&nbsp;axis),&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a&nbsp;boolean<br>
-array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;An&nbsp;integer,&nbsp;e.g.&nbsp;``5``.<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;integers,&nbsp;e.g.&nbsp;``[4,&nbsp;3,&nbsp;0]``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;ints,&nbsp;e.g.&nbsp;``1:7``.<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.iloc``&nbsp;will&nbsp;raise&nbsp;``IndexError``&nbsp;if&nbsp;a&nbsp;requested&nbsp;indexer&nbsp;is<br>
-out-of-bounds,&nbsp;except&nbsp;*slice*&nbsp;indexers&nbsp;which&nbsp;allow&nbsp;out-of-bounds<br>
-indexing&nbsp;(this&nbsp;conforms&nbsp;with&nbsp;python/numpy&nbsp;*slice*&nbsp;semantics).<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Position&nbsp;&lt;indexing.integer&gt;`</tt></dd>
-</dl>
-<dl><dt><strong>ix</strong></dt>
-<dd><tt>A&nbsp;primarily&nbsp;label-location&nbsp;based&nbsp;indexer,&nbsp;with&nbsp;integer&nbsp;position<br>
-fallback.<br>
-&nbsp;<br>
-``.ix[]``&nbsp;supports&nbsp;mixed&nbsp;integer&nbsp;and&nbsp;label&nbsp;based&nbsp;access.&nbsp;It&nbsp;is<br>
-primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;will&nbsp;fall&nbsp;back&nbsp;to&nbsp;integer&nbsp;positional<br>
-access&nbsp;unless&nbsp;the&nbsp;corresponding&nbsp;axis&nbsp;is&nbsp;of&nbsp;integer&nbsp;type.<br>
-&nbsp;<br>
-``.ix``&nbsp;is&nbsp;the&nbsp;most&nbsp;general&nbsp;indexer&nbsp;and&nbsp;will&nbsp;support&nbsp;any&nbsp;of&nbsp;the<br>
-inputs&nbsp;in&nbsp;``.loc``&nbsp;and&nbsp;``.iloc``.&nbsp;``.ix``&nbsp;also&nbsp;supports&nbsp;floating<br>
-point&nbsp;label&nbsp;schemes.&nbsp;``.ix``&nbsp;is&nbsp;exceptionally&nbsp;useful&nbsp;when&nbsp;dealing<br>
-with&nbsp;mixed&nbsp;positional&nbsp;and&nbsp;label&nbsp;based&nbsp;hierachical&nbsp;indexes.<br>
-&nbsp;<br>
-However,&nbsp;when&nbsp;an&nbsp;axis&nbsp;is&nbsp;integer&nbsp;based,&nbsp;ONLY&nbsp;label&nbsp;based&nbsp;access<br>
-and&nbsp;not&nbsp;positional&nbsp;access&nbsp;is&nbsp;supported.&nbsp;Thus,&nbsp;in&nbsp;such&nbsp;cases,&nbsp;it's<br>
-usually&nbsp;better&nbsp;to&nbsp;be&nbsp;explicit&nbsp;and&nbsp;use&nbsp;``.iloc``&nbsp;or&nbsp;``.loc``.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Advanced&nbsp;Indexing&nbsp;&lt;advanced&gt;`.</tt></dd>
-</dl>
-<dl><dt><strong>loc</strong></dt>
-<dd><tt>Purely&nbsp;label-location&nbsp;based&nbsp;indexer&nbsp;for&nbsp;selection&nbsp;by&nbsp;label.<br>
-&nbsp;<br>
-``.loc[]``&nbsp;is&nbsp;primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a<br>
-boolean&nbsp;array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;A&nbsp;single&nbsp;label,&nbsp;e.g.&nbsp;``5``&nbsp;or&nbsp;``'a'``,&nbsp;(note&nbsp;that&nbsp;``5``&nbsp;is<br>
-&nbsp;&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;*label*&nbsp;of&nbsp;the&nbsp;index,&nbsp;and&nbsp;**never**&nbsp;as&nbsp;an<br>
-&nbsp;&nbsp;integer&nbsp;position&nbsp;along&nbsp;the&nbsp;index).<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;labels,&nbsp;e.g.&nbsp;``['a',&nbsp;'b',&nbsp;'c']``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;labels,&nbsp;e.g.&nbsp;``'a':'f'``&nbsp;(note&nbsp;that&nbsp;contrary<br>
-&nbsp;&nbsp;to&nbsp;usual&nbsp;python&nbsp;slices,&nbsp;**both**&nbsp;the&nbsp;start&nbsp;and&nbsp;the&nbsp;stop&nbsp;are&nbsp;included!).<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.loc``&nbsp;will&nbsp;raise&nbsp;a&nbsp;``KeyError``&nbsp;when&nbsp;the&nbsp;items&nbsp;are&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Label&nbsp;&lt;indexing.label&gt;`</tt></dd>
-</dl>
-<dl><dt><strong>ndim</strong></dt>
-<dd><tt>Number&nbsp;of&nbsp;axes&nbsp;/&nbsp;array&nbsp;dimensions</tt></dd>
-</dl>
-<dl><dt><strong>size</strong></dt>
-<dd><tt>number&nbsp;of&nbsp;elements&nbsp;in&nbsp;the&nbsp;NDFrame</tt></dd>
-</dl>
-<dl><dt><strong>values</strong></dt>
-<dd><tt>Numpy&nbsp;representation&nbsp;of&nbsp;NDFrame<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;dtype&nbsp;will&nbsp;be&nbsp;a&nbsp;lower-common-denominator&nbsp;dtype&nbsp;(implicit<br>
-upcasting);&nbsp;that&nbsp;is&nbsp;to&nbsp;say&nbsp;if&nbsp;the&nbsp;dtypes&nbsp;(even&nbsp;of&nbsp;numeric&nbsp;types)<br>
-are&nbsp;mixed,&nbsp;the&nbsp;one&nbsp;that&nbsp;accommodates&nbsp;all&nbsp;will&nbsp;be&nbsp;chosen.&nbsp;Use&nbsp;this<br>
-with&nbsp;care&nbsp;if&nbsp;you&nbsp;are&nbsp;not&nbsp;dealing&nbsp;with&nbsp;the&nbsp;blocks.<br>
-&nbsp;<br>
-e.g.&nbsp;If&nbsp;the&nbsp;dtypes&nbsp;are&nbsp;float16&nbsp;and&nbsp;float32,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcast&nbsp;to<br>
-float32.&nbsp;&nbsp;If&nbsp;dtypes&nbsp;are&nbsp;int32&nbsp;and&nbsp;uint8,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcast&nbsp;to<br>
-int32.&nbsp;By&nbsp;numpy.find_common_type&nbsp;convention,&nbsp;mixing&nbsp;int64&nbsp;and&nbsp;uint64<br>
-will&nbsp;result&nbsp;in&nbsp;a&nbsp;flot64&nbsp;dtype.</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>is_copy</strong> = None</dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a>:<br>
-<dl><dt><a name="TimeFrame-__dir__"><strong>__dir__</strong></a>(self)</dt><dd><tt>Provide&nbsp;method&nbsp;name&nbsp;lookup&nbsp;and&nbsp;completion<br>
-Only&nbsp;provide&nbsp;'public'&nbsp;methods</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__sizeof__"><strong>__sizeof__</strong></a>(self)</dt><dd><tt>Generates&nbsp;the&nbsp;total&nbsp;memory&nbsp;usage&nbsp;for&nbsp;a&nbsp;<a href="builtins.html#object">object</a>&nbsp;that&nbsp;returns<br>
-either&nbsp;a&nbsp;value&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;values</tt></dd></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a>:<br>
-<dl><dt><a name="TimeFrame-__bytes__"><strong>__bytes__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;bytes(obj)&nbsp;in&nbsp;py3&nbsp;only.<br>
-Yields&nbsp;a&nbsp;bytestring&nbsp;in&nbsp;both&nbsp;py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__repr__"><strong>__repr__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-<dl><dt><a name="TimeFrame-__str__"><strong>__str__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;Object<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;str(df)&nbsp;in&nbsp;both&nbsp;py2/py3.<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a>:<br>
-<dl><dt><strong>__dict__</strong></dt>
-<dd><tt>dictionary&nbsp;for&nbsp;instance&nbsp;variables&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>__weakref__</strong></dt>
-<dd><tt>list&nbsp;of&nbsp;weak&nbsp;references&nbsp;to&nbsp;the&nbsp;object&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-</td></tr></table> <p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#ffc8d8">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#000000" face="helvetica, arial"><a name="TimeSeries">class <strong>TimeSeries</strong></a>(<a href="modsim.html#MySeries">MySeries</a>)</font></td></tr>
-    
-<tr bgcolor="#ffc8d8"><td rowspan=2><tt>&nbsp;&nbsp;&nbsp;</tt></td>
-<td colspan=2><tt>One-dimensional&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;with&nbsp;axis&nbsp;labels&nbsp;(including&nbsp;time&nbsp;series).<br>
-&nbsp;<br>
-Labels&nbsp;need&nbsp;not&nbsp;be&nbsp;unique&nbsp;but&nbsp;must&nbsp;be&nbsp;a&nbsp;hashable&nbsp;type.&nbsp;The&nbsp;<a href="builtins.html#object">object</a><br>
-supports&nbsp;both&nbsp;integer-&nbsp;and&nbsp;label-based&nbsp;indexing&nbsp;and&nbsp;provides&nbsp;a&nbsp;host&nbsp;of<br>
-methods&nbsp;for&nbsp;performing&nbsp;operations&nbsp;involving&nbsp;the&nbsp;index.&nbsp;Statistical<br>
-methods&nbsp;from&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;have&nbsp;been&nbsp;overridden&nbsp;to&nbsp;automatically&nbsp;exclude<br>
-missing&nbsp;data&nbsp;(currently&nbsp;represented&nbsp;as&nbsp;NaN).<br>
-&nbsp;<br>
-Operations&nbsp;between&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(+,&nbsp;-,&nbsp;/,&nbsp;*,&nbsp;**)&nbsp;align&nbsp;values&nbsp;based&nbsp;on&nbsp;their<br>
-associated&nbsp;index&nbsp;values--&nbsp;they&nbsp;need&nbsp;not&nbsp;be&nbsp;the&nbsp;same&nbsp;length.&nbsp;The&nbsp;result<br>
-index&nbsp;will&nbsp;be&nbsp;the&nbsp;sorted&nbsp;union&nbsp;of&nbsp;the&nbsp;two&nbsp;indexes.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-data&nbsp;:&nbsp;array-like,&nbsp;dict,&nbsp;or&nbsp;scalar&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Contains&nbsp;data&nbsp;stored&nbsp;in&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-index&nbsp;:&nbsp;array-like&nbsp;or&nbsp;Index&nbsp;(1d)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Values&nbsp;must&nbsp;be&nbsp;hashable&nbsp;and&nbsp;have&nbsp;the&nbsp;same&nbsp;length&nbsp;as&nbsp;`data`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Non-unique&nbsp;index&nbsp;values&nbsp;are&nbsp;allowed.&nbsp;Will&nbsp;default&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;RangeIndex(len(data))&nbsp;if&nbsp;not&nbsp;provided.&nbsp;If&nbsp;both&nbsp;a&nbsp;dict&nbsp;and&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;are&nbsp;used,&nbsp;the&nbsp;index&nbsp;will&nbsp;override&nbsp;the&nbsp;keys&nbsp;found&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict.<br>
-dtype&nbsp;:&nbsp;numpy.dtype&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;None,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;inferred<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Copy&nbsp;input&nbsp;data<br>&nbsp;</tt></td></tr>
-<tr><td>&nbsp;</td>
-<td width="100%"><dl><dt>Method resolution order:</dt>
-<dd><a href="modsim.html#TimeSeries">TimeSeries</a></dd>
-<dd><a href="modsim.html#MySeries">MySeries</a></dd>
-<dd><a href="pandas.core.series.html#Series">pandas.core.series.Series</a></dd>
-<dd><a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a></dd>
-<dd><a href="pandas.core.strings.html#StringAccessorMixin">pandas.core.strings.StringAccessorMixin</a></dd>
-<dd><a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a></dd>
-<dd><a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a></dd>
-<dd><a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a></dd>
-<dd><a href="pandas.core.base.html#SelectionMixin">pandas.core.base.SelectionMixin</a></dd>
-<dd><a href="builtins.html#object">builtins.object</a></dd>
-</dl>
-<hr>
-Methods inherited from <a href="modsim.html#MySeries">MySeries</a>:<br>
-<dl><dt><a name="TimeSeries-__init__"><strong>__init__</strong></a>(self, *args, **kwargs)</dt><dd><tt>Initialize&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Note:&nbsp;this&nbsp;cleans&nbsp;up&nbsp;a&nbsp;weird&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;behavior,&nbsp;which&nbsp;is<br>
-that&nbsp;<a href="pandas.core.series.html#Series">Series</a>()&nbsp;and&nbsp;<a href="pandas.core.series.html#Series">Series</a>([])&nbsp;yield&nbsp;different&nbsp;results.<br>
-See:&nbsp;https://github.com/pandas-dev/pandas/issues/16737</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-set"><strong>set</strong></a>(self, **kwargs)</dt><dd><tt>Uses&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;update&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place.<br>
-&nbsp;<br>
-Example:&nbsp;series.<a href="#TimeSeries-update">update</a>(a=1,&nbsp;b=2)</tt></dd></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><a name="TimeSeries-__add__"><strong>__add__</strong></a> = wrapper(left, right, name='__add__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f2f0&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__and__"><strong>__and__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__array__"><strong>__array__</strong></a>(self, result=None)</dt><dd><tt>the&nbsp;array&nbsp;interface,&nbsp;return&nbsp;my&nbsp;values</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-__array_prepare__"><strong>__array_prepare__</strong></a>(self, result, context=None)</dt><dd><tt>Gets&nbsp;called&nbsp;prior&nbsp;to&nbsp;a&nbsp;ufunc</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-__array_wrap__"><strong>__array_wrap__</strong></a>(self, result, context=None)</dt><dd><tt>Gets&nbsp;called&nbsp;after&nbsp;a&nbsp;ufunc</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-__div__"><strong>__div__</strong></a> = wrapper(left, right, name='__truediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f9d8&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__divmod__"><strong>__divmod__</strong></a> = wrapper(left, right, name='__divmod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca343bf8&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__eq__"><strong>__eq__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__float__"><strong>__float__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__floordiv__"><strong>__floordiv__</strong></a> = wrapper(left, right, name='__floordiv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fb70&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__ge__"><strong>__ge__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__getitem__"><strong>__getitem__</strong></a>(self, key)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__gt__"><strong>__gt__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__iadd__"><strong>__iadd__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__imul__"><strong>__imul__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__int__"><strong>__int__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__ipow__"><strong>__ipow__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__isub__"><strong>__isub__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__iter__"><strong>__iter__</strong></a>(self)</dt><dd><tt>provide&nbsp;iteration&nbsp;over&nbsp;the&nbsp;values&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-box&nbsp;values&nbsp;if&nbsp;necessary</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-__itruediv__"><strong>__itruediv__</strong></a> = f(self, other)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__le__"><strong>__le__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__len__"><strong>__len__</strong></a>(self)</dt><dd><tt>return&nbsp;the&nbsp;length&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-__long__"><strong>__long__</strong></a> = wrapper(self)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__lt__"><strong>__lt__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__mod__"><strong>__mod__</strong></a> = wrapper(left, right, name='__mod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fd08&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__mul__"><strong>__mul__</strong></a> = wrapper(left, right, name='__mul__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f840&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__ne__"><strong>__ne__</strong></a> = wrapper(self, other, axis=None)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__or__"><strong>__or__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__pow__"><strong>__pow__</strong></a> = wrapper(left, right, name='__pow__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33fea0&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__radd__"><strong>__radd__</strong></a> = wrapper(left, right, name='__radd__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f510&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__rand__"><strong>__rand__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__rdiv__"><strong>__rdiv__</strong></a> = wrapper(left, right, name='__rtruediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342510&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__rfloordiv__"><strong>__rfloordiv__</strong></a> = wrapper(left, right, name='__rfloordiv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342730&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__rmod__"><strong>__rmod__</strong></a> = wrapper(left, right, name='__rmod__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342b70&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__rmul__"><strong>__rmul__</strong></a> = wrapper(left, right, name='__rmul__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca3420d0&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__ror__"><strong>__ror__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__rpow__"><strong>__rpow__</strong></a> = wrapper(left, right, name='__rpow__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342950&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__rsub__"><strong>__rsub__</strong></a> = wrapper(left, right, name='__rsub__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca3422f0&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__rtruediv__"><strong>__rtruediv__</strong></a> = wrapper(left, right, name='__rtruediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca342510&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__rxor__"><strong>__rxor__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__setitem__"><strong>__setitem__</strong></a>(self, key, value)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__sub__"><strong>__sub__</strong></a> = wrapper(left, right, name='__sub__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f6a8&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__truediv__"><strong>__truediv__</strong></a> = wrapper(left, right, name='__truediv__', na_op=&lt;function _arith_method_SERIES.&lt;locals&gt;.na_op at 0x7f6aca33f9d8&gt;)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__unicode__"><strong>__unicode__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;unicode(df)&nbsp;in&nbsp;py2&nbsp;only.&nbsp;Yields&nbsp;a&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;both<br>
-py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-__xor__"><strong>__xor__</strong></a> = wrapper(self, other)</dt></dl>
-
-<dl><dt><a name="TimeSeries-add"><strong>add</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Addition&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`add`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;+&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.radd</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-agg"><strong>agg</strong></a> = aggregate(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.series.html#Series">Series</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#TimeSeries-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#TimeSeries-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10))<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-agg">agg</a>('min')<br>
--1.3018049988556679<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-agg">agg</a>(['min',&nbsp;'max'])<br>
-min&nbsp;&nbsp;&nbsp;-1.301805<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;1.127688<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.apply<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.transform</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-aggregate"><strong>aggregate</strong></a>(self, func, axis=0, *args, **kwargs)</dt><dd><tt>Aggregate&nbsp;using&nbsp;callable,&nbsp;string,&nbsp;dict,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Function&nbsp;to&nbsp;use&nbsp;for&nbsp;aggregating&nbsp;the&nbsp;data.&nbsp;If&nbsp;a&nbsp;function,&nbsp;must&nbsp;either<br>
-&nbsp;&nbsp;&nbsp;&nbsp;work&nbsp;when&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;when&nbsp;passed&nbsp;to&nbsp;<a href="pandas.core.series.html#Series">Series</a>.apply.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;can&nbsp;pass&nbsp;a&nbsp;dict,&nbsp;if&nbsp;the&nbsp;keys&nbsp;are&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column&nbsp;names.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Numpy&nbsp;functions&nbsp;mean/median/prod/sum/std/var&nbsp;are&nbsp;special&nbsp;cased&nbsp;so&nbsp;the<br>
-default&nbsp;behavior&nbsp;is&nbsp;applying&nbsp;the&nbsp;function&nbsp;along&nbsp;axis=0<br>
-(e.g.,&nbsp;np.<a href="#TimeSeries-mean">mean</a>(arr_2d,&nbsp;axis=0))&nbsp;as&nbsp;opposed&nbsp;to<br>
-mimicking&nbsp;the&nbsp;default&nbsp;Numpy&nbsp;behavior&nbsp;(e.g.,&nbsp;np.<a href="#TimeSeries-mean">mean</a>(arr_2d)).<br>
-&nbsp;<br>
-agg&nbsp;is&nbsp;an&nbsp;alias&nbsp;for&nbsp;aggregate.&nbsp;Use&nbsp;it.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-aggregated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10))<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-agg">agg</a>('min')<br>
--1.3018049988556679<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-agg">agg</a>(['min',&nbsp;'max'])<br>
-min&nbsp;&nbsp;&nbsp;-1.301805<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;1.127688<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.apply<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.transform</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-align"><strong>align</strong></a>(self, other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)</dt><dd><tt>Align&nbsp;two&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;their&nbsp;axes&nbsp;with&nbsp;the<br>
-specified&nbsp;join&nbsp;method&nbsp;for&nbsp;each&nbsp;axis&nbsp;Index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-join&nbsp;:&nbsp;{'outer',&nbsp;'inner',&nbsp;'left',&nbsp;'right'},&nbsp;default&nbsp;'outer'<br>
-axis&nbsp;:&nbsp;allowed&nbsp;axis&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;on&nbsp;index&nbsp;(0),&nbsp;columns&nbsp;(1),&nbsp;or&nbsp;both&nbsp;(None)<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Always&nbsp;returns&nbsp;new&nbsp;objects.&nbsp;If&nbsp;copy=False&nbsp;and&nbsp;no&nbsp;reindexing&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;required&nbsp;then&nbsp;original&nbsp;objects&nbsp;are&nbsp;returned.<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-method&nbsp;:&nbsp;str,&nbsp;default&nbsp;None<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-fill_axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Filling&nbsp;axis,&nbsp;method&nbsp;and&nbsp;limit<br>
-broadcast_axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;values&nbsp;along&nbsp;this&nbsp;axis,&nbsp;if&nbsp;aligning&nbsp;two&nbsp;objects&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;different&nbsp;dimensions<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-(left,&nbsp;right)&nbsp;:&nbsp;(<a href="pandas.core.series.html#Series">Series</a>,&nbsp;type&nbsp;of&nbsp;other)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Aligned&nbsp;objects</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-all"><strong>all</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;all&nbsp;elements&nbsp;are&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-all&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-any"><strong>any</strong></a>(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs)</dt><dd><tt>Return&nbsp;whether&nbsp;any&nbsp;element&nbsp;is&nbsp;True&nbsp;over&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-bool_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use&nbsp;everything,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;then&nbsp;use&nbsp;only&nbsp;boolean&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-any&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-append"><strong>append</strong></a>(self, to_append, ignore_index=False, verify_integrity=False)</dt><dd><tt>Concatenate&nbsp;two&nbsp;or&nbsp;more&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-to_append&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;list/tuple&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-ignore_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;not&nbsp;use&nbsp;the&nbsp;index&nbsp;labels.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.19.0<br>
-&nbsp;<br>
-verify_integrity&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;raise&nbsp;Exception&nbsp;on&nbsp;creating&nbsp;index&nbsp;with&nbsp;duplicates<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-appended&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s1&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s2&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([4,&nbsp;5,&nbsp;6])<br>
-&gt;&gt;&gt;&nbsp;s3&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([4,&nbsp;5,&nbsp;6],&nbsp;index=[3,4,5])<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#TimeSeries-append">append</a>(s2)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#TimeSeries-append">append</a>(s3)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-With&nbsp;`ignore_index`&nbsp;set&nbsp;to&nbsp;True:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#TimeSeries-append">append</a>(s2,&nbsp;ignore_index=True)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-With&nbsp;`verify_integrity`&nbsp;set&nbsp;to&nbsp;True:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s1.<a href="#TimeSeries-append">append</a>(s2,&nbsp;verify_integrity=True)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-ValueError:&nbsp;Indexes&nbsp;have&nbsp;overlapping&nbsp;values:&nbsp;[0,&nbsp;1,&nbsp;2]</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-apply"><strong>apply</strong></a>(self, func, convert_dtype=True, args=(), **kwds)</dt><dd><tt>Invoke&nbsp;function&nbsp;on&nbsp;values&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Can&nbsp;be&nbsp;ufunc&nbsp;(a&nbsp;NumPy&nbsp;function<br>
-that&nbsp;applies&nbsp;to&nbsp;the&nbsp;entire&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;or&nbsp;a&nbsp;Python&nbsp;function&nbsp;that&nbsp;only&nbsp;works<br>
-on&nbsp;single&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-convert_dtype&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Try&nbsp;to&nbsp;find&nbsp;better&nbsp;dtype&nbsp;for&nbsp;elementwise&nbsp;function&nbsp;results.&nbsp;If<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False,&nbsp;leave&nbsp;as&nbsp;dtype=<a href="builtins.html#object">object</a><br>
-args&nbsp;:&nbsp;tuple<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Positional&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;to&nbsp;function&nbsp;in&nbsp;addition&nbsp;to&nbsp;the&nbsp;value<br>
-Additional&nbsp;keyword&nbsp;arguments&nbsp;will&nbsp;be&nbsp;passed&nbsp;as&nbsp;keywords&nbsp;to&nbsp;the&nbsp;function<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;if&nbsp;func&nbsp;returns&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.map:&nbsp;For&nbsp;element-wise&nbsp;operations<br>
-<a href="pandas.core.series.html#Series">Series</a>.agg:&nbsp;only&nbsp;perform&nbsp;aggregating&nbsp;type&nbsp;operations<br>
-<a href="pandas.core.series.html#Series">Series</a>.transform:&nbsp;only&nbsp;perform&nbsp;transformating&nbsp;type&nbsp;operations<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;series&nbsp;with&nbsp;typical&nbsp;summer&nbsp;temperatures&nbsp;for&nbsp;each&nbsp;city.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([20,&nbsp;21,&nbsp;12],&nbsp;index=['London',<br>
-...&nbsp;'New&nbsp;York','Helsinki'])<br>
-&gt;&gt;&gt;&nbsp;series<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;20<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Square&nbsp;the&nbsp;values&nbsp;by&nbsp;defining&nbsp;a&nbsp;function&nbsp;and&nbsp;passing&nbsp;it&nbsp;as&nbsp;an<br>
-argument&nbsp;to&nbsp;``<a href="#TimeSeries-apply">apply</a>()``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;square(x):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x**2<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeSeries-apply">apply</a>(square)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;400<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;441<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;144<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Square&nbsp;the&nbsp;values&nbsp;by&nbsp;passing&nbsp;an&nbsp;anonymous&nbsp;function&nbsp;as&nbsp;an<br>
-argument&nbsp;to&nbsp;``<a href="#TimeSeries-apply">apply</a>()``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeSeries-apply">apply</a>(lambda&nbsp;x:&nbsp;x**2)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;400<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;441<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;144<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Define&nbsp;a&nbsp;custom&nbsp;function&nbsp;that&nbsp;needs&nbsp;additional&nbsp;positional<br>
-arguments&nbsp;and&nbsp;pass&nbsp;these&nbsp;additional&nbsp;arguments&nbsp;using&nbsp;the<br>
-``args``&nbsp;keyword.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;subtract_custom_value(x,&nbsp;custom_value):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x-custom_value<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeSeries-apply">apply</a>(subtract_custom_value,&nbsp;args=(5,))<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;16<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Define&nbsp;a&nbsp;custom&nbsp;function&nbsp;that&nbsp;takes&nbsp;keyword&nbsp;arguments<br>
-and&nbsp;pass&nbsp;these&nbsp;arguments&nbsp;to&nbsp;``apply``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;add_custom_values(x,&nbsp;**kwargs):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;month&nbsp;in&nbsp;kwargs:<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;x+=kwargs[month]<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;x<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeSeries-apply">apply</a>(add_custom_values,&nbsp;june=30,&nbsp;july=20,&nbsp;august=25)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;95<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;96<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;87<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-Use&nbsp;a&nbsp;function&nbsp;from&nbsp;the&nbsp;Numpy&nbsp;library.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeSeries-apply">apply</a>(np.log)<br>
-London&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.995732<br>
-New&nbsp;York&nbsp;&nbsp;&nbsp;&nbsp;3.044522<br>
-Helsinki&nbsp;&nbsp;&nbsp;&nbsp;2.484907<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-argmax"><strong>argmax</strong></a> = idxmax(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmax&nbsp;:&nbsp;Index&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmax``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmax<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmax</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-argmin"><strong>argmin</strong></a> = idxmin(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmin&nbsp;:&nbsp;Index&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmin``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmin<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmin</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-argsort"><strong>argsort</strong></a>(self, axis=0, kind='quicksort', order=None)</dt><dd><tt>Overrides&nbsp;<a href="numpy.html#ndarray">ndarray</a>.argsort.&nbsp;Argsorts&nbsp;the&nbsp;value,&nbsp;omitting&nbsp;NA/null&nbsp;values,<br>
-and&nbsp;places&nbsp;the&nbsp;result&nbsp;in&nbsp;the&nbsp;same&nbsp;locations&nbsp;as&nbsp;the&nbsp;non-NA&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;int&nbsp;(can&nbsp;only&nbsp;be&nbsp;zero)<br>
-kind&nbsp;:&nbsp;{'mergesort',&nbsp;'quicksort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;'mergesort'&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm<br>
-order&nbsp;:&nbsp;ignored<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-argsorted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;with&nbsp;-1&nbsp;indicated&nbsp;where&nbsp;nan&nbsp;values&nbsp;are&nbsp;present<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argsort</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-autocorr"><strong>autocorr</strong></a>(self, lag=1)</dt><dd><tt>Lag-N&nbsp;autocorrelation<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-lag&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;lags&nbsp;to&nbsp;apply&nbsp;before&nbsp;performing&nbsp;autocorrelation.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-autocorr&nbsp;:&nbsp;float</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-between"><strong>between</strong></a>(self, left, right, inclusive=True)</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;equivalent&nbsp;to&nbsp;left&nbsp;&lt;=&nbsp;series&nbsp;&lt;=&nbsp;right.&nbsp;NA&nbsp;values<br>
-will&nbsp;be&nbsp;treated&nbsp;as&nbsp;False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-left&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Left&nbsp;boundary<br>
-right&nbsp;:&nbsp;scalar<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Right&nbsp;boundary<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_between&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-combine"><strong>combine</strong></a>(self, other, func, fill_value=nan)</dt><dd><tt>Perform&nbsp;elementwise&nbsp;binary&nbsp;operation&nbsp;on&nbsp;two&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;using&nbsp;given&nbsp;function<br>
-with&nbsp;optional&nbsp;fill&nbsp;value&nbsp;when&nbsp;an&nbsp;index&nbsp;is&nbsp;missing&nbsp;from&nbsp;one&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or<br>
-the&nbsp;other<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-func&nbsp;:&nbsp;function<br>
-fill_value&nbsp;:&nbsp;scalar&nbsp;value<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-combine_first"><strong>combine_first</strong></a>(self, other)</dt><dd><tt>Combine&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values,&nbsp;choosing&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.series.html#Series">Series</a>'s&nbsp;values<br>
-first.&nbsp;Result&nbsp;index&nbsp;will&nbsp;be&nbsp;the&nbsp;union&nbsp;of&nbsp;the&nbsp;two&nbsp;indexes<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-compound"><strong>compound</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;compound&nbsp;percentage&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-compounded&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-compress"><strong>compress</strong></a>(self, condition, *args, **kwargs)</dt><dd><tt>Return&nbsp;selected&nbsp;slices&nbsp;of&nbsp;an&nbsp;array&nbsp;along&nbsp;given&nbsp;axis&nbsp;as&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.compress</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-corr"><strong>corr</strong></a>(self, other, method='pearson', min_periods=None)</dt><dd><tt>Compute&nbsp;correlation&nbsp;with&nbsp;`other`&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;excluding&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-method&nbsp;:&nbsp;{'pearson',&nbsp;'kendall',&nbsp;'spearman'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pearson&nbsp;:&nbsp;standard&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;kendall&nbsp;:&nbsp;Kendall&nbsp;Tau&nbsp;correlation&nbsp;coefficient<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;spearman&nbsp;:&nbsp;Spearman&nbsp;rank&nbsp;correlation<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;needed&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result<br>
-&nbsp;<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-correlation&nbsp;:&nbsp;float</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-count"><strong>count</strong></a>(self, level=None)</dt><dd><tt>Return&nbsp;number&nbsp;of&nbsp;non-NA/null&nbsp;observations&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;smaller&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-nobs&nbsp;:&nbsp;int&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-cov"><strong>cov</strong></a>(self, other, min_periods=None)</dt><dd><tt>Compute&nbsp;covariance&nbsp;with&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;excluding&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-min_periods&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;needed&nbsp;to&nbsp;have&nbsp;a&nbsp;valid&nbsp;result<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-covariance&nbsp;:&nbsp;float<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;(unbiased&nbsp;estimator).</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-cummax"><strong>cummax</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;max&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummax&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.max&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-cummin"><strong>cummin</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;minimum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cummin&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.min&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-cumprod"><strong>cumprod</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;product&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumprod&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.prod&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-cumsum"><strong>cumsum</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Return&nbsp;cumulative&nbsp;sum&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-cumsum&nbsp;:&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.core.window.Expanding.sum&nbsp;:&nbsp;Similar&nbsp;functionality<br>
-&nbsp;&nbsp;&nbsp;&nbsp;but&nbsp;ignores&nbsp;``NaN``&nbsp;values.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-diff"><strong>diff</strong></a>(self, periods=1)</dt><dd><tt>1st&nbsp;discrete&nbsp;difference&nbsp;of&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;difference<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-diffed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-div"><strong>div</strong></a> = truediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-divide"><strong>divide</strong></a> = truediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-dot"><strong>dot</strong></a>(self, other)</dt><dd><tt>Matrix&nbsp;multiplication&nbsp;with&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;inner-product&nbsp;with&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-objects<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dot_product&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-drop_duplicates"><strong>drop_duplicates</strong></a>(self, keep='first', inplace=False)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;duplicate&nbsp;values&nbsp;removed<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-&nbsp;<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Drop&nbsp;duplicates&nbsp;except&nbsp;for&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Drop&nbsp;all&nbsp;duplicates.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-If&nbsp;True,&nbsp;performs&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;returns&nbsp;None.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-deduplicated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-dropna"><strong>dropna</strong></a>(self, axis=0, inplace=False, **kwargs)</dt><dd><tt>Return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;without&nbsp;null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-valid&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;operation&nbsp;in&nbsp;place.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-duplicated"><strong>duplicated</strong></a>(self, keep='first')</dt><dd><tt>Return&nbsp;boolean&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;denoting&nbsp;duplicate&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;Mark&nbsp;duplicates&nbsp;as&nbsp;``True``&nbsp;except&nbsp;for&nbsp;the&nbsp;last<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;False&nbsp;:&nbsp;Mark&nbsp;all&nbsp;duplicates&nbsp;as&nbsp;``True``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-duplicated&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-eq"><strong>eq</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`eq`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;==&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-ewm"><strong>ewm</strong></a>(self, com=None, span=None, halflife=None, alpha=None, min_periods=0, freq=None, adjust=True, ignore_na=False, axis=0)</dt><dd><tt>Provides&nbsp;exponential&nbsp;weighted&nbsp;functions<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-com&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;/&nbsp;(1&nbsp;+&nbsp;com),\text{&nbsp;for&nbsp;}&nbsp;com&nbsp;\geq&nbsp;0`<br>
-span&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;span,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;2&nbsp;/&nbsp;(span&nbsp;+&nbsp;1),\text{&nbsp;for&nbsp;}&nbsp;span&nbsp;\geq&nbsp;1`<br>
-halflife&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;decay&nbsp;in&nbsp;terms&nbsp;of&nbsp;half-life,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`\alpha&nbsp;=&nbsp;1&nbsp;-&nbsp;exp(log(0.5)&nbsp;/&nbsp;halflife),\text{&nbsp;for&nbsp;}&nbsp;halflife&nbsp;&gt;&nbsp;0`<br>
-alpha&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;smoothing&nbsp;factor&nbsp;:math:`\alpha`&nbsp;directly,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;:math:`0&nbsp;&lt;&nbsp;\alpha&nbsp;\leq&nbsp;1`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;None&nbsp;or&nbsp;string&nbsp;alias&nbsp;/&nbsp;date&nbsp;offset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default=None&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;to&nbsp;before&nbsp;computing&nbsp;statistic<br>
-adjust&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Divide&nbsp;by&nbsp;decaying&nbsp;adjustment&nbsp;factor&nbsp;in&nbsp;beginning&nbsp;periods&nbsp;to&nbsp;account<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;imbalance&nbsp;in&nbsp;relative&nbsp;weightings&nbsp;(viewing&nbsp;EWMA&nbsp;as&nbsp;a&nbsp;moving&nbsp;average)<br>
-ignore_na&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Ignore&nbsp;missing&nbsp;values&nbsp;when&nbsp;calculating&nbsp;weights;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specify&nbsp;True&nbsp;to&nbsp;reproduce&nbsp;pre-0.15.0&nbsp;behavior<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-ewm">ewm</a>(com=0.5).<a href="#TimeSeries-mean">mean</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.000000<br>
-1&nbsp;&nbsp;0.750000<br>
-2&nbsp;&nbsp;1.615385<br>
-3&nbsp;&nbsp;1.615385<br>
-4&nbsp;&nbsp;3.670213<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Exactly&nbsp;one&nbsp;of&nbsp;center&nbsp;of&nbsp;mass,&nbsp;span,&nbsp;half-life,&nbsp;and&nbsp;alpha&nbsp;must&nbsp;be&nbsp;provided.<br>
-Allowed&nbsp;values&nbsp;and&nbsp;relationship&nbsp;between&nbsp;the&nbsp;parameters&nbsp;are&nbsp;specified&nbsp;in&nbsp;the<br>
-parameter&nbsp;descriptions&nbsp;above;&nbsp;see&nbsp;the&nbsp;link&nbsp;at&nbsp;the&nbsp;end&nbsp;of&nbsp;this&nbsp;section&nbsp;for<br>
-a&nbsp;detailed&nbsp;explanation.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;True&nbsp;(default),&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;using<br>
-weights&nbsp;(1-alpha)**(n-1),&nbsp;(1-alpha)**(n-2),&nbsp;...,&nbsp;1-alpha,&nbsp;1.<br>
-&nbsp;<br>
-When&nbsp;adjust&nbsp;is&nbsp;False,&nbsp;weighted&nbsp;averages&nbsp;are&nbsp;calculated&nbsp;recursively&nbsp;as:<br>
-&nbsp;&nbsp;&nbsp;weighted_average[0]&nbsp;=&nbsp;arg[0];<br>
-&nbsp;&nbsp;&nbsp;weighted_average[i]&nbsp;=&nbsp;(1-alpha)*weighted_average[i-1]&nbsp;+&nbsp;alpha*arg[i].<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;False&nbsp;(default),&nbsp;weights&nbsp;are&nbsp;based&nbsp;on&nbsp;absolute&nbsp;positions.<br>
-For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in&nbsp;calculating&nbsp;the&nbsp;final&nbsp;weighted<br>
-average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;(1-alpha)**2&nbsp;and&nbsp;1&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and<br>
-(1-alpha)**2&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-When&nbsp;ignore_na&nbsp;is&nbsp;True&nbsp;(reproducing&nbsp;pre-0.15.0&nbsp;behavior),&nbsp;weights&nbsp;are&nbsp;based<br>
-on&nbsp;relative&nbsp;positions.&nbsp;For&nbsp;example,&nbsp;the&nbsp;weights&nbsp;of&nbsp;x&nbsp;and&nbsp;y&nbsp;used&nbsp;in<br>
-calculating&nbsp;the&nbsp;final&nbsp;weighted&nbsp;average&nbsp;of&nbsp;[x,&nbsp;None,&nbsp;y]&nbsp;are&nbsp;1-alpha&nbsp;and&nbsp;1<br>
-(if&nbsp;adjust&nbsp;is&nbsp;True),&nbsp;and&nbsp;1-alpha&nbsp;and&nbsp;alpha&nbsp;(if&nbsp;adjust&nbsp;is&nbsp;False).<br>
-&nbsp;<br>
-More&nbsp;details&nbsp;can&nbsp;be&nbsp;found&nbsp;at<br>
-<a href="http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows">http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-expanding"><strong>expanding</strong></a>(self, min_periods=1, freq=None, center=False, axis=0)</dt><dd><tt>Provides&nbsp;expanding&nbsp;transformations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-expanding">expanding</a>(2).<a href="#TimeSeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;7.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-fillna"><strong>fillna</strong></a>(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)</dt><dd><tt>Fill&nbsp;NA/NaN&nbsp;values&nbsp;using&nbsp;the&nbsp;specified&nbsp;method<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;values&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;each&nbsp;index&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>)&nbsp;or&nbsp;column&nbsp;(for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).&nbsp;(values&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;dict/<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;This&nbsp;value&nbsp;cannot<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;list.<br>
-method&nbsp;:&nbsp;{'backfill',&nbsp;'bfill',&nbsp;'pad',&nbsp;'ffill',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'}<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;fill&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>,&nbsp;(e.g.&nbsp;a&nbsp;no-copy&nbsp;slice&nbsp;for&nbsp;a&nbsp;column&nbsp;in&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;method&nbsp;is&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the&nbsp;maximum&nbsp;number&nbsp;of&nbsp;consecutive<br>
-&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;values&nbsp;to&nbsp;forward/backward&nbsp;fill.&nbsp;In&nbsp;other&nbsp;words,&nbsp;if&nbsp;there&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;gap&nbsp;with&nbsp;more&nbsp;than&nbsp;this&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs,&nbsp;it&nbsp;will&nbsp;only<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;partially&nbsp;filled.&nbsp;If&nbsp;method&nbsp;is&nbsp;not&nbsp;specified,&nbsp;this&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;maximum&nbsp;number&nbsp;of&nbsp;entries&nbsp;along&nbsp;the&nbsp;entire&nbsp;axis&nbsp;where&nbsp;NaNs&nbsp;will&nbsp;be<br>
-&nbsp;&nbsp;&nbsp;&nbsp;filled.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0&nbsp;if&nbsp;not&nbsp;None.<br>
-downcast&nbsp;:&nbsp;dict,&nbsp;default&nbsp;is&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;dict&nbsp;of&nbsp;item-&gt;dtype&nbsp;of&nbsp;what&nbsp;to&nbsp;downcast&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;the&nbsp;string&nbsp;'infer'&nbsp;which&nbsp;will&nbsp;try&nbsp;to&nbsp;downcast&nbsp;to&nbsp;an&nbsp;appropriate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;equal&nbsp;type&nbsp;(e.g.&nbsp;float64&nbsp;to&nbsp;int64&nbsp;if&nbsp;possible)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;asfreq<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-first_valid_index"><strong>first_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;first&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-floordiv"><strong>floordiv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`floordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;//&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rfloordiv</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-ge"><strong>ge</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Greater&nbsp;than&nbsp;or&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`ge`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&gt;=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-get_value"><strong>get_value</strong></a>(self, label, takeable=False)</dt><dd><tt>Quickly&nbsp;retrieve&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;index&nbsp;label<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;label<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;scalar&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-get_values"><strong>get_values</strong></a>(self)</dt><dd><tt>same&nbsp;as&nbsp;values&nbsp;(but&nbsp;handles&nbsp;sparseness&nbsp;conversions);&nbsp;is&nbsp;a&nbsp;view</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-gt"><strong>gt</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Greater&nbsp;than&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`gt`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&gt;&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-hist"><strong>hist</strong></a> = hist_series(self, by=None, ax=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, figsize=None, bins=10, **kwds)</dt><dd><tt>Draw&nbsp;histogram&nbsp;of&nbsp;the&nbsp;input&nbsp;series&nbsp;using&nbsp;matplotlib<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed,&nbsp;then&nbsp;used&nbsp;to&nbsp;form&nbsp;histograms&nbsp;for&nbsp;separate&nbsp;groups<br>
-ax&nbsp;:&nbsp;matplotlib&nbsp;axis&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;not&nbsp;passed,&nbsp;uses&nbsp;gca()<br>
-grid&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;show&nbsp;axis&nbsp;grid&nbsp;lines<br>
-xlabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;x-axis&nbsp;label&nbsp;size<br>
-xrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;x&nbsp;axis&nbsp;labels<br>
-ylabelsize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;specified&nbsp;changes&nbsp;the&nbsp;y-axis&nbsp;label&nbsp;size<br>
-yrot&nbsp;:&nbsp;float,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;rotation&nbsp;of&nbsp;y&nbsp;axis&nbsp;labels<br>
-figsize&nbsp;:&nbsp;tuple,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;figure&nbsp;size&nbsp;in&nbsp;inches&nbsp;by&nbsp;default<br>
-bins:&nbsp;integer,&nbsp;default&nbsp;10<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;histogram&nbsp;bins&nbsp;to&nbsp;be&nbsp;used<br>
-kwds&nbsp;:&nbsp;keywords<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;passed&nbsp;to&nbsp;the&nbsp;actual&nbsp;plotting&nbsp;function<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;matplotlib&nbsp;documentation&nbsp;online&nbsp;for&nbsp;more&nbsp;on&nbsp;this</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-idxmax"><strong>idxmax</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmax&nbsp;:&nbsp;Index&nbsp;of&nbsp;maximum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmax``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmax<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmax</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-idxmin"><strong>idxmin</strong></a>(self, axis=None, skipna=True, *args, **kwargs)</dt><dd><tt>Index&nbsp;of&nbsp;first&nbsp;occurrence&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-idxmin&nbsp;:&nbsp;Index&nbsp;of&nbsp;minimum&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Notes<br>
------<br>
-This&nbsp;method&nbsp;is&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;version&nbsp;of&nbsp;``<a href="numpy.html#ndarray">ndarray</a>.argmin``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.idxmin<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.argmin</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-isin"><strong>isin</strong></a>(self, values)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;:class:`~pandas.<a href="pandas.core.series.html#Series">Series</a>`&nbsp;showing&nbsp;whether&nbsp;each&nbsp;element<br>
-in&nbsp;the&nbsp;:class:`~pandas.<a href="pandas.core.series.html#Series">Series</a>`&nbsp;is&nbsp;exactly&nbsp;contained&nbsp;in&nbsp;the&nbsp;passed<br>
-sequence&nbsp;of&nbsp;``values``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-values&nbsp;:&nbsp;set&nbsp;or&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;sequence&nbsp;of&nbsp;values&nbsp;to&nbsp;test.&nbsp;Passing&nbsp;in&nbsp;a&nbsp;single&nbsp;string&nbsp;will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;raise&nbsp;a&nbsp;``TypeError``.&nbsp;Instead,&nbsp;turn&nbsp;a&nbsp;single&nbsp;string&nbsp;into&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``list``&nbsp;of&nbsp;one&nbsp;element.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Support&nbsp;for&nbsp;values&nbsp;as&nbsp;a&nbsp;set<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-isin&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(bool&nbsp;dtype)<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;*&nbsp;If&nbsp;``values``&nbsp;is&nbsp;a&nbsp;string<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.isin<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(list('abc'))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-isin">isin</a>(['a',&nbsp;'c',&nbsp;'e'])<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-dtype:&nbsp;bool<br>
-&nbsp;<br>
-Passing&nbsp;a&nbsp;single&nbsp;string&nbsp;as&nbsp;``s.<a href="#TimeSeries-isin">isin</a>('a')``&nbsp;will&nbsp;raise&nbsp;an&nbsp;error.&nbsp;Use<br>
-a&nbsp;list&nbsp;of&nbsp;one&nbsp;element&nbsp;instead:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-isin">isin</a>(['a'])<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;False<br>
-dtype:&nbsp;bool</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-items"><strong>items</strong></a> = iteritems(self)</dt><dd><tt>Lazily&nbsp;iterate&nbsp;over&nbsp;(index,&nbsp;value)&nbsp;tuples</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-iteritems"><strong>iteritems</strong></a>(self)</dt><dd><tt>Lazily&nbsp;iterate&nbsp;over&nbsp;(index,&nbsp;value)&nbsp;tuples</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-keys"><strong>keys</strong></a>(self)</dt><dd><tt>Alias&nbsp;for&nbsp;index</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-kurt"><strong>kurt</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-kurtosis"><strong>kurtosis</strong></a> = kurt(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;kurtosis&nbsp;over&nbsp;requested&nbsp;axis&nbsp;using&nbsp;Fisher's&nbsp;definition&nbsp;of<br>
-kurtosis&nbsp;(kurtosis&nbsp;of&nbsp;normal&nbsp;==&nbsp;0.0).&nbsp;Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-kurt&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-last_valid_index"><strong>last_valid_index</strong></a>(self)</dt><dd><tt>Return&nbsp;label&nbsp;for&nbsp;last&nbsp;non-NA/null&nbsp;value</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-le"><strong>le</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Less&nbsp;than&nbsp;or&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`le`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&lt;=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-lt"><strong>lt</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Less&nbsp;than&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`lt`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;&lt;&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-mad"><strong>mad</strong></a>(self, axis=None, skipna=None, level=None)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;absolute&nbsp;deviation&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mad&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-map"><strong>map</strong></a>(self, arg, na_action=None)</dt><dd><tt>Map&nbsp;values&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;using&nbsp;input&nbsp;correspondence&nbsp;(which&nbsp;can&nbsp;be<br>
-a&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;or&nbsp;function)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-arg&nbsp;:&nbsp;function,&nbsp;dict,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-na_action&nbsp;:&nbsp;{None,&nbsp;'ignore'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'ignore',&nbsp;propagate&nbsp;NA&nbsp;values,&nbsp;without&nbsp;passing&nbsp;them&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;mapping&nbsp;function<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;same&nbsp;index&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Map&nbsp;inputs&nbsp;to&nbsp;outputs&nbsp;(both&nbsp;of&nbsp;type&nbsp;`<a href="pandas.core.series.html#Series">Series</a>`)<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,2,3],&nbsp;index=['one',&nbsp;'two',&nbsp;'three'])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-one&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-two&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-three&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;y&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(['foo',&nbsp;'bar',&nbsp;'baz'],&nbsp;index=[1,2,3])<br>
-&gt;&gt;&gt;&nbsp;y<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;foo<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;bar<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;baz<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#TimeSeries-map">map</a>(y)<br>
-one&nbsp;&nbsp;&nbsp;foo<br>
-two&nbsp;&nbsp;&nbsp;bar<br>
-three&nbsp;baz<br>
-&nbsp;<br>
-If&nbsp;`arg`&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;values&nbsp;converted<br>
-according&nbsp;to&nbsp;the&nbsp;dictionary's&nbsp;mapping:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;z&nbsp;=&nbsp;{1:&nbsp;'A',&nbsp;2:&nbsp;'B',&nbsp;3:&nbsp;'C'}<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#TimeSeries-map">map</a>(z)<br>
-one&nbsp;&nbsp;&nbsp;A<br>
-two&nbsp;&nbsp;&nbsp;B<br>
-three&nbsp;C<br>
-&nbsp;<br>
-Use&nbsp;na_action&nbsp;to&nbsp;control&nbsp;whether&nbsp;NA&nbsp;values&nbsp;are&nbsp;affected&nbsp;by&nbsp;the&nbsp;mapping<br>
-function.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;np.nan])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s2&nbsp;=&nbsp;s.<a href="#TimeSeries-map">map</a>('this&nbsp;is&nbsp;a&nbsp;string&nbsp;{}'.format,&nbsp;na_action=None)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;1.0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;2.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;3.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;nan<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s3&nbsp;=&nbsp;s.<a href="#TimeSeries-map">map</a>('this&nbsp;is&nbsp;a&nbsp;string&nbsp;{}'.format,&nbsp;na_action='ignore')<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;1.0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;2.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;is&nbsp;a&nbsp;string&nbsp;3.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.apply:&nbsp;For&nbsp;applying&nbsp;more&nbsp;complex&nbsp;functions&nbsp;on&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply:&nbsp;Apply&nbsp;a&nbsp;function&nbsp;row-/column-wise<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap:&nbsp;Apply&nbsp;a&nbsp;function&nbsp;elementwise&nbsp;on&nbsp;a&nbsp;whole&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-When&nbsp;`arg`&nbsp;is&nbsp;a&nbsp;dictionary,&nbsp;values&nbsp;in&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;that&nbsp;are&nbsp;not&nbsp;in&nbsp;the<br>
-dictionary&nbsp;(as&nbsp;keys)&nbsp;are&nbsp;converted&nbsp;to&nbsp;``NaN``.&nbsp;However,&nbsp;if&nbsp;the<br>
-dictionary&nbsp;is&nbsp;a&nbsp;``dict``&nbsp;subclass&nbsp;that&nbsp;defines&nbsp;``__missing__``&nbsp;(i.e.<br>
-provides&nbsp;a&nbsp;method&nbsp;for&nbsp;default&nbsp;values),&nbsp;then&nbsp;this&nbsp;default&nbsp;is&nbsp;used<br>
-rather&nbsp;than&nbsp;``NaN``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;from&nbsp;collections&nbsp;import&nbsp;Counter<br>
-&gt;&gt;&gt;&nbsp;counter&nbsp;=&nbsp;Counter()<br>
-&gt;&gt;&gt;&nbsp;counter['bar']&nbsp;+=&nbsp;1<br>
-&gt;&gt;&gt;&nbsp;y.<a href="#TimeSeries-map">map</a>(counter)<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-dtype:&nbsp;int64</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-max"><strong>max</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;maximum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;maximum,&nbsp;use&nbsp;``idxmax``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmax``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-max&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-mean"><strong>mean</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-mean&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-median"><strong>median</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;median&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-median&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-memory_usage"><strong>memory_usage</strong></a>(self, index=True, deep=False)</dt><dd><tt>Memory&nbsp;usage&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;whether&nbsp;to&nbsp;include&nbsp;memory&nbsp;usage&nbsp;of&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;index<br>
-deep&nbsp;:&nbsp;bool<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Introspect&nbsp;the&nbsp;data&nbsp;deeply,&nbsp;interrogate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`<a href="builtins.html#object">object</a>`&nbsp;dtypes&nbsp;for&nbsp;system-level&nbsp;memory&nbsp;consumption<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-scalar&nbsp;bytes&nbsp;of&nbsp;memory&nbsp;consumed<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Memory&nbsp;usage&nbsp;does&nbsp;not&nbsp;include&nbsp;memory&nbsp;consumed&nbsp;by&nbsp;elements&nbsp;that<br>
-are&nbsp;not&nbsp;components&nbsp;of&nbsp;the&nbsp;array&nbsp;if&nbsp;deep=False<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.nbytes</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-min"><strong>min</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>This&nbsp;method&nbsp;returns&nbsp;the&nbsp;minimum&nbsp;of&nbsp;the&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;you&nbsp;want&nbsp;the&nbsp;*index*&nbsp;of&nbsp;the&nbsp;minimum,&nbsp;use&nbsp;``idxmin``.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``argmin``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-min&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-mod"><strong>mod</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Modulo&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;%&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmod</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-mode"><strong>mode</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;<a href="#TimeSeries-mode">mode</a>(s)&nbsp;of&nbsp;the&nbsp;dataset.<br>
-&nbsp;<br>
-Always&nbsp;returns&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;even&nbsp;if&nbsp;only&nbsp;one&nbsp;value&nbsp;is&nbsp;returned.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-modes&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(sorted)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-mul"><strong>mul</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-multiply"><strong>multiply</strong></a> = mul(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`mul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;*&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rmul</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-ne"><strong>ne</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Not&nbsp;equal&nbsp;to&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`ne`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;!=&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.None</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-nlargest"><strong>nlargest</strong></a>(self, n=5, keep='first')</dt><dd><tt>Return&nbsp;the&nbsp;largest&nbsp;`n`&nbsp;elements.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;this&nbsp;many&nbsp;descending&nbsp;sorted&nbsp;values<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-top_n&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;n&nbsp;largest&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;in&nbsp;sorted&nbsp;order<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Faster&nbsp;than&nbsp;``.<a href="#TimeSeries-sort_values">sort_values</a>(ascending=False).<a href="#TimeSeries-head">head</a>(n)``&nbsp;for&nbsp;small&nbsp;`n`<br>
-relative&nbsp;to&nbsp;the&nbsp;size&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.nsmallest<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10**6))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-nlargest">nlargest</a>(10)&nbsp;&nbsp;#&nbsp;only&nbsp;sorts&nbsp;up&nbsp;to&nbsp;the&nbsp;N&nbsp;requested<br>
-219921&nbsp;&nbsp;&nbsp;&nbsp;4.644710<br>
-82124&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4.608745<br>
-421689&nbsp;&nbsp;&nbsp;&nbsp;4.564644<br>
-425277&nbsp;&nbsp;&nbsp;&nbsp;4.447014<br>
-718691&nbsp;&nbsp;&nbsp;&nbsp;4.414137<br>
-43154&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4.403520<br>
-283187&nbsp;&nbsp;&nbsp;&nbsp;4.313922<br>
-595519&nbsp;&nbsp;&nbsp;&nbsp;4.273635<br>
-503969&nbsp;&nbsp;&nbsp;&nbsp;4.250236<br>
-121637&nbsp;&nbsp;&nbsp;&nbsp;4.240952<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-nonzero"><strong>nonzero</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;indices&nbsp;of&nbsp;the&nbsp;elements&nbsp;that&nbsp;are&nbsp;non-zero<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;calling&nbsp;`numpy.nonzero`&nbsp;on&nbsp;the<br>
-series&nbsp;data.&nbsp;For&nbsp;compatability&nbsp;with&nbsp;NumPy,&nbsp;the&nbsp;return&nbsp;value&nbsp;is<br>
-the&nbsp;same&nbsp;(a&nbsp;tuple&nbsp;with&nbsp;an&nbsp;array&nbsp;of&nbsp;indices&nbsp;for&nbsp;each&nbsp;dimension),<br>
-but&nbsp;it&nbsp;will&nbsp;always&nbsp;be&nbsp;a&nbsp;one-item&nbsp;tuple&nbsp;because&nbsp;series&nbsp;only&nbsp;have<br>
-one&nbsp;dimension.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0,&nbsp;3,&nbsp;0,&nbsp;4])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-nonzero">nonzero</a>()<br>
-(array([1,&nbsp;3]),)<br>
-&gt;&gt;&gt;&nbsp;s.iloc[s.<a href="#TimeSeries-nonzero">nonzero</a>()[0]]<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.nonzero</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-nsmallest"><strong>nsmallest</strong></a>(self, n=5, keep='first')</dt><dd><tt>Return&nbsp;the&nbsp;smallest&nbsp;`n`&nbsp;elements.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;this&nbsp;many&nbsp;ascending&nbsp;sorted&nbsp;values<br>
-keep&nbsp;:&nbsp;{'first',&nbsp;'last',&nbsp;False},&nbsp;default&nbsp;'first'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Where&nbsp;there&nbsp;are&nbsp;duplicate&nbsp;values:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``first``&nbsp;:&nbsp;take&nbsp;the&nbsp;first&nbsp;occurrence.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``last``&nbsp;:&nbsp;take&nbsp;the&nbsp;last&nbsp;occurrence.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-bottom_n&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;n&nbsp;smallest&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;in&nbsp;sorted&nbsp;order<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Faster&nbsp;than&nbsp;``.<a href="#TimeSeries-sort_values">sort_values</a>().<a href="#TimeSeries-head">head</a>(n)``&nbsp;for&nbsp;small&nbsp;`n`&nbsp;relative&nbsp;to<br>
-the&nbsp;size&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.nlargest<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;pandas&nbsp;as&nbsp;pd<br>
-&gt;&gt;&gt;&nbsp;import&nbsp;numpy&nbsp;as&nbsp;np<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(10**6))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-nsmallest">nsmallest</a>(10)&nbsp;&nbsp;#&nbsp;only&nbsp;sorts&nbsp;up&nbsp;to&nbsp;the&nbsp;N&nbsp;requested<br>
-288532&nbsp;&nbsp;&nbsp;-4.954580<br>
-732345&nbsp;&nbsp;&nbsp;-4.835960<br>
-64803&nbsp;&nbsp;&nbsp;&nbsp;-4.812550<br>
-446457&nbsp;&nbsp;&nbsp;-4.609998<br>
-501225&nbsp;&nbsp;&nbsp;-4.483945<br>
-669476&nbsp;&nbsp;&nbsp;-4.472935<br>
-973615&nbsp;&nbsp;&nbsp;-4.401699<br>
-621279&nbsp;&nbsp;&nbsp;-4.355126<br>
-773916&nbsp;&nbsp;&nbsp;-4.347355<br>
-359919&nbsp;&nbsp;&nbsp;-4.331927<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-pow"><strong>pow</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`pow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;**&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rpow</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-prod"><strong>prod</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-product"><strong>product</strong></a> = prod(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;product&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-prod&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-ptp"><strong>ptp</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Returns&nbsp;the&nbsp;difference&nbsp;between&nbsp;the&nbsp;maximum&nbsp;value&nbsp;and&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;minimum&nbsp;value&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.&nbsp;This&nbsp;is&nbsp;the&nbsp;equivalent&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.<a href="numpy.html#ndarray">ndarray</a>``&nbsp;method&nbsp;``ptp``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ptp&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-put"><strong>put</strong></a>(self, *args, **kwargs)</dt><dd><tt>Applies&nbsp;the&nbsp;`put`&nbsp;method&nbsp;to&nbsp;its&nbsp;`values`&nbsp;attribute<br>
-if&nbsp;it&nbsp;has&nbsp;one.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.put</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-quantile"><strong>quantile</strong></a>(self, q=0.5, interpolation='linear')</dt><dd><tt>Return&nbsp;value&nbsp;at&nbsp;the&nbsp;given&nbsp;quantile,&nbsp;a&nbsp;la&nbsp;numpy.percentile.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-q&nbsp;:&nbsp;float&nbsp;or&nbsp;array-like,&nbsp;default&nbsp;0.5&nbsp;(50%&nbsp;quantile)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&lt;=&nbsp;q&nbsp;&lt;=&nbsp;1,&nbsp;the&nbsp;<a href="#TimeSeries-quantile">quantile</a>(s)&nbsp;to&nbsp;compute<br>
-interpolation&nbsp;:&nbsp;{'linear',&nbsp;'lower',&nbsp;'higher',&nbsp;'midpoint',&nbsp;'nearest'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;optional&nbsp;parameter&nbsp;specifies&nbsp;the&nbsp;interpolation&nbsp;method&nbsp;to&nbsp;use,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;the&nbsp;desired&nbsp;quantile&nbsp;lies&nbsp;between&nbsp;two&nbsp;data&nbsp;points&nbsp;`i`&nbsp;and&nbsp;`j`:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;linear:&nbsp;`i&nbsp;+&nbsp;(j&nbsp;-&nbsp;i)&nbsp;*&nbsp;fraction`,&nbsp;where&nbsp;`fraction`&nbsp;is&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;fractional&nbsp;part&nbsp;of&nbsp;the&nbsp;index&nbsp;surrounded&nbsp;by&nbsp;`i`&nbsp;and&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;lower:&nbsp;`i`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;higher:&nbsp;`j`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;`i`&nbsp;or&nbsp;`j`&nbsp;whichever&nbsp;is&nbsp;nearest.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;midpoint:&nbsp;(`i`&nbsp;+&nbsp;`j`)&nbsp;/&nbsp;2.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-quantile&nbsp;:&nbsp;float&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;``q``&nbsp;is&nbsp;an&nbsp;array,&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;returned&nbsp;where&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;is&nbsp;``q``&nbsp;and&nbsp;the&nbsp;values&nbsp;are&nbsp;the&nbsp;quantiles.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;4])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-quantile">quantile</a>(.5)<br>
-2.5<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-quantile">quantile</a>([.25,&nbsp;.5,&nbsp;.75])<br>
-0.25&nbsp;&nbsp;&nbsp;&nbsp;1.75<br>
-0.50&nbsp;&nbsp;&nbsp;&nbsp;2.50<br>
-0.75&nbsp;&nbsp;&nbsp;&nbsp;3.25<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-radd"><strong>radd</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Addition&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`radd`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;+&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.add</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-ravel"><strong>ravel</strong></a>(self, order='C')</dt><dd><tt>Return&nbsp;the&nbsp;flattened&nbsp;underlying&nbsp;data&nbsp;as&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.ravel</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-rdiv"><strong>rdiv</strong></a> = rtruediv(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-reindex"><strong>reindex</strong></a>(self, index=None, **kwargs)</dt><dd><tt>Conform&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;new&nbsp;index&nbsp;with&nbsp;optional&nbsp;filling&nbsp;logic,&nbsp;placing<br>
-NA/NaN&nbsp;in&nbsp;locations&nbsp;having&nbsp;no&nbsp;value&nbsp;in&nbsp;the&nbsp;previous&nbsp;index.&nbsp;A&nbsp;new&nbsp;<a href="builtins.html#object">object</a><br>
-is&nbsp;produced&nbsp;unless&nbsp;the&nbsp;new&nbsp;index&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;the&nbsp;current&nbsp;one&nbsp;and<br>
-copy=False<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;array-like,&nbsp;optional&nbsp;(can&nbsp;be&nbsp;specified&nbsp;in&nbsp;order,&nbsp;or&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;keywords)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;New&nbsp;labels&nbsp;/&nbsp;index&nbsp;to&nbsp;conform&nbsp;to.&nbsp;Preferably&nbsp;an&nbsp;Index&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;avoid&nbsp;duplicating&nbsp;data<br>
-method&nbsp;:&nbsp;{None,&nbsp;'backfill'/'bfill',&nbsp;'pad'/'ffill',&nbsp;'nearest'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Please&nbsp;note:&nbsp;this&nbsp;is&nbsp;only&nbsp;&nbsp;applicable&nbsp;to&nbsp;DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;monotonically&nbsp;increasing/decreasing&nbsp;index.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;default:&nbsp;don't&nbsp;fill&nbsp;gaps<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;pad&nbsp;/&nbsp;ffill:&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;backfill&nbsp;/&nbsp;bfill:&nbsp;use&nbsp;next&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;nearest:&nbsp;use&nbsp;nearest&nbsp;valid&nbsp;observations&nbsp;to&nbsp;fill&nbsp;gap<br>
-&nbsp;<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Return&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>,&nbsp;even&nbsp;if&nbsp;the&nbsp;passed&nbsp;indexes&nbsp;are&nbsp;the&nbsp;same<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-fill_value&nbsp;:&nbsp;scalar,&nbsp;default&nbsp;np.NaN<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values.&nbsp;Defaults&nbsp;to&nbsp;NaN,&nbsp;but&nbsp;can&nbsp;be&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;"compatible"&nbsp;value<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;elements&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;original&nbsp;and&nbsp;new&nbsp;labels&nbsp;for&nbsp;inexact<br>
-&nbsp;&nbsp;&nbsp;&nbsp;matches.&nbsp;The&nbsp;values&nbsp;of&nbsp;the&nbsp;index&nbsp;at&nbsp;the&nbsp;matching&nbsp;locations&nbsp;most<br>
-&nbsp;&nbsp;&nbsp;&nbsp;satisfy&nbsp;the&nbsp;equation&nbsp;``<a href="#TimeSeries-abs">abs</a>(index[indexer]&nbsp;-&nbsp;target)&nbsp;&lt;=&nbsp;tolerance``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;some&nbsp;fictional&nbsp;data.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;['Firefox',&nbsp;'Chrome',&nbsp;'Safari',&nbsp;'IE10',&nbsp;'Konqueror']<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'http_status':&nbsp;[200,200,404,404,301],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'response_time':&nbsp;[0.04,&nbsp;0.02,&nbsp;0.07,&nbsp;0.08,&nbsp;1.0]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Firefox&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.04<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Konqueror&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;301&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.00<br>
-&nbsp;<br>
-Create&nbsp;a&nbsp;new&nbsp;index&nbsp;and&nbsp;reindex&nbsp;the&nbsp;dataframe.&nbsp;By&nbsp;default<br>
-values&nbsp;in&nbsp;the&nbsp;new&nbsp;index&nbsp;that&nbsp;do&nbsp;not&nbsp;have&nbsp;corresponding<br>
-records&nbsp;in&nbsp;the&nbsp;dataframe&nbsp;are&nbsp;assigned&nbsp;``NaN``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;new_index=&nbsp;['Safari',&nbsp;'Iceweasel',&nbsp;'Comodo&nbsp;Dragon',&nbsp;'IE10',<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Chrome']<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-reindex">reindex</a>(new_index)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-We&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;by&nbsp;passing&nbsp;a&nbsp;value&nbsp;to<br>
-the&nbsp;keyword&nbsp;``fill_value``.&nbsp;Because&nbsp;the&nbsp;index&nbsp;is&nbsp;not&nbsp;monotonically<br>
-increasing&nbsp;or&nbsp;decreasing,&nbsp;we&nbsp;cannot&nbsp;use&nbsp;arguments&nbsp;to&nbsp;the&nbsp;keyword<br>
-``method``&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``&nbsp;values.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-reindex">reindex</a>(new_index,&nbsp;fill_value=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.00<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-reindex">reindex</a>(new_index,&nbsp;fill_value='missing')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;http_status&nbsp;response_time<br>
-Safari&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.07<br>
-Iceweasel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-Comodo&nbsp;Dragon&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;missing<br>
-IE10&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;404&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.08<br>
-Chrome&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;200&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0.02<br>
-&nbsp;<br>
-To&nbsp;further&nbsp;illustrate&nbsp;the&nbsp;filling&nbsp;functionality&nbsp;in<br>
-``reindex``,&nbsp;we&nbsp;will&nbsp;create&nbsp;a&nbsp;dataframe&nbsp;with&nbsp;a<br>
-monotonically&nbsp;increasing&nbsp;index&nbsp;(for&nbsp;example,&nbsp;a&nbsp;sequence<br>
-of&nbsp;dates).<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index&nbsp;=&nbsp;pd.date_range('1/1/2010',&nbsp;periods=6,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"prices":&nbsp;[100,&nbsp;101,&nbsp;np.nan,&nbsp;100,&nbsp;89,&nbsp;88]},<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=date_index)<br>
-&gt;&gt;&gt;&nbsp;df2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-&nbsp;<br>
-Suppose&nbsp;we&nbsp;decide&nbsp;to&nbsp;expand&nbsp;the&nbsp;dataframe&nbsp;to&nbsp;cover&nbsp;a&nbsp;wider<br>
-date&nbsp;range.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;date_index2&nbsp;=&nbsp;pd.date_range('12/29/2009',&nbsp;periods=10,&nbsp;freq='D')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#TimeSeries-reindex">reindex</a>(date_index2)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-The&nbsp;index&nbsp;entries&nbsp;that&nbsp;did&nbsp;not&nbsp;have&nbsp;a&nbsp;value&nbsp;in&nbsp;the&nbsp;original&nbsp;data&nbsp;frame<br>
-(for&nbsp;example,&nbsp;'2009-12-29')&nbsp;are&nbsp;by&nbsp;default&nbsp;filled&nbsp;with&nbsp;``NaN``.<br>
-If&nbsp;desired,&nbsp;we&nbsp;can&nbsp;fill&nbsp;in&nbsp;the&nbsp;missing&nbsp;values&nbsp;using&nbsp;one&nbsp;of&nbsp;several<br>
-options.<br>
-&nbsp;<br>
-For&nbsp;example,&nbsp;to&nbsp;backpropagate&nbsp;the&nbsp;last&nbsp;valid&nbsp;value&nbsp;to&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values,&nbsp;pass&nbsp;``bfill``&nbsp;as&nbsp;an&nbsp;argument&nbsp;to&nbsp;the&nbsp;``method``&nbsp;keyword.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#TimeSeries-reindex">reindex</a>(date_index2,&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;prices<br>
-2009-12-29&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-30&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2009-12-31&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-01&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-02&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;101<br>
-2010-01-03&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2010-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;100<br>
-2010-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;89<br>
-2010-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;88<br>
-2010-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;the&nbsp;``NaN``&nbsp;value&nbsp;present&nbsp;in&nbsp;the&nbsp;original&nbsp;dataframe<br>
-(at&nbsp;index&nbsp;value&nbsp;2010-01-03)&nbsp;will&nbsp;not&nbsp;be&nbsp;filled&nbsp;by&nbsp;any&nbsp;of&nbsp;the<br>
-value&nbsp;propagation&nbsp;schemes.&nbsp;This&nbsp;is&nbsp;because&nbsp;filling&nbsp;while&nbsp;reindexing<br>
-does&nbsp;not&nbsp;look&nbsp;at&nbsp;dataframe&nbsp;values,&nbsp;but&nbsp;only&nbsp;compares&nbsp;the&nbsp;original&nbsp;and<br>
-desired&nbsp;indexes.&nbsp;If&nbsp;you&nbsp;do&nbsp;want&nbsp;to&nbsp;fill&nbsp;in&nbsp;the&nbsp;``NaN``&nbsp;values&nbsp;present<br>
-in&nbsp;the&nbsp;original&nbsp;dataframe,&nbsp;use&nbsp;the&nbsp;``<a href="#TimeSeries-fillna">fillna</a>()``&nbsp;method.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-reindex_axis"><strong>reindex_axis</strong></a>(self, labels, axis=0, **kwargs)</dt><dd><tt>for&nbsp;compatibility&nbsp;with&nbsp;higher&nbsp;dims</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-rename"><strong>rename</strong></a>(self, index=None, **kwargs)</dt><dd><tt>Alter&nbsp;axes&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.&nbsp;Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be<br>
-unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in&nbsp;a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left<br>
-as-is.&nbsp;Extra&nbsp;labels&nbsp;listed&nbsp;don't&nbsp;throw&nbsp;an&nbsp;error.&nbsp;Alternatively,&nbsp;change<br>
-``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;with&nbsp;a&nbsp;scalar&nbsp;value&nbsp;(<a href="pandas.core.series.html#Series">Series</a>&nbsp;only).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-index&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Scalar&nbsp;or&nbsp;list-like&nbsp;will&nbsp;alter&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>.name``&nbsp;attribute,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;raise&nbsp;on&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or&nbsp;Panel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict-like&nbsp;or&nbsp;functions&nbsp;are&nbsp;transformations&nbsp;to&nbsp;apply&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;that&nbsp;axis'&nbsp;values<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;return&nbsp;a&nbsp;new&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;If&nbsp;True&nbsp;then&nbsp;value&nbsp;of&nbsp;copy&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;ignored.<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;MultiIndex,&nbsp;only&nbsp;rename&nbsp;labels&nbsp;in&nbsp;the&nbsp;specified<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename_axis<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-rename">rename</a>("my_name")&nbsp;#&nbsp;scalar,&nbsp;changes&nbsp;<a href="pandas.core.series.html#Series">Series</a>.name<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;my_name,&nbsp;dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-rename">rename</a>(lambda&nbsp;x:&nbsp;x&nbsp;**&nbsp;2)&nbsp;&nbsp;#&nbsp;function,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-rename">rename</a>({1:&nbsp;3,&nbsp;2:&nbsp;5})&nbsp;&nbsp;#&nbsp;mapping,&nbsp;changes&nbsp;labels<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-5&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-rename">rename</a>(2)<br>
-Traceback&nbsp;(most&nbsp;recent&nbsp;call&nbsp;last):<br>
-...<br>
-TypeError:&nbsp;'int'&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;not&nbsp;callable<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"B":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;c<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-rename">rename</a>(index=str,&nbsp;columns={"A":&nbsp;"a",&nbsp;"C":&nbsp;"c"})<br>
-&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-reorder_levels"><strong>reorder_levels</strong></a>(self, order)</dt><dd><tt>Rearrange&nbsp;index&nbsp;levels&nbsp;using&nbsp;input&nbsp;order.&nbsp;May&nbsp;not&nbsp;drop&nbsp;or&nbsp;duplicate<br>
-levels<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-order&nbsp;:&nbsp;list&nbsp;of&nbsp;int&nbsp;representing&nbsp;new&nbsp;level&nbsp;order.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(reference&nbsp;level&nbsp;by&nbsp;number&nbsp;or&nbsp;key)<br>
-axis&nbsp;:&nbsp;where&nbsp;to&nbsp;reorder&nbsp;levels<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-type&nbsp;of&nbsp;caller&nbsp;(new&nbsp;<a href="builtins.html#object">object</a>)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-repeat"><strong>repeat</strong></a>(self, repeats, *args, **kwargs)</dt><dd><tt>Repeat&nbsp;elements&nbsp;of&nbsp;an&nbsp;<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Refer&nbsp;to&nbsp;`numpy.<a href="numpy.html#ndarray">ndarray</a>.repeat`<br>
-for&nbsp;more&nbsp;information&nbsp;about&nbsp;the&nbsp;`repeats`&nbsp;argument.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.repeat</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-reset_index"><strong>reset_index</strong></a>(self, level=None, drop=False, name=None, inplace=False)</dt><dd><tt>Analogous&nbsp;to&nbsp;the&nbsp;:meth:`pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.reset_index`&nbsp;function,&nbsp;see<br>
-docstring&nbsp;there.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;tuple,&nbsp;or&nbsp;list,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Only&nbsp;remove&nbsp;the&nbsp;given&nbsp;levels&nbsp;from&nbsp;the&nbsp;index.&nbsp;Removes&nbsp;all&nbsp;levels&nbsp;by<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-drop&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Do&nbsp;not&nbsp;try&nbsp;to&nbsp;insert&nbsp;index&nbsp;into&nbsp;dataframe&nbsp;columns<br>
-name&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;name&nbsp;of&nbsp;the&nbsp;column&nbsp;corresponding&nbsp;to&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Modify&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place&nbsp;(do&nbsp;not&nbsp;create&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a>)<br>
-&nbsp;<br>
-Returns<br>
-----------<br>
-resetted&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;if&nbsp;drop&nbsp;==&nbsp;True</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-reshape"><strong>reshape</strong></a>(self, *args, **kwargs)</dt><dd><tt>DEPRECATED:&nbsp;calling&nbsp;this&nbsp;method&nbsp;will&nbsp;raise&nbsp;an&nbsp;error&nbsp;in&nbsp;a<br>
-future&nbsp;release.&nbsp;Please&nbsp;call&nbsp;``.values.<a href="#TimeSeries-reshape">reshape</a>(...)``&nbsp;instead.<br>
-&nbsp;<br>
-return&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;with&nbsp;the&nbsp;values&nbsp;shape<br>
-if&nbsp;the&nbsp;specified&nbsp;shape&nbsp;matches&nbsp;exactly&nbsp;the&nbsp;current&nbsp;shape,&nbsp;then<br>
-return&nbsp;self&nbsp;(for&nbsp;compat)<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.reshape</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-rfloordiv"><strong>rfloordiv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Integer&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rfloordiv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;//&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.floordiv</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-rmod"><strong>rmod</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Modulo&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmod`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;%&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.mod</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-rmul"><strong>rmul</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Multiplication&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rmul`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;*&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.mul</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-rolling"><strong>rolling</strong></a>(self, window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0, closed=None)</dt><dd><tt>Provides&nbsp;rolling&nbsp;window&nbsp;calculcations.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.18.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-window&nbsp;:&nbsp;int,&nbsp;or&nbsp;offset<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Size&nbsp;of&nbsp;the&nbsp;moving&nbsp;window.&nbsp;This&nbsp;is&nbsp;the&nbsp;number&nbsp;of&nbsp;observations&nbsp;used&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;calculating&nbsp;the&nbsp;statistic.&nbsp;Each&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;fixed&nbsp;size.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;its&nbsp;an&nbsp;offset&nbsp;then&nbsp;this&nbsp;will&nbsp;be&nbsp;the&nbsp;time&nbsp;period&nbsp;of&nbsp;each&nbsp;window.&nbsp;Each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;window&nbsp;will&nbsp;be&nbsp;a&nbsp;variable&nbsp;sized&nbsp;based&nbsp;on&nbsp;the&nbsp;observations&nbsp;included&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;time-period.&nbsp;This&nbsp;is&nbsp;only&nbsp;valid&nbsp;for&nbsp;datetimelike&nbsp;indexes.&nbsp;This&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;new&nbsp;in&nbsp;0.19.0<br>
-min_periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Minimum&nbsp;number&nbsp;of&nbsp;observations&nbsp;in&nbsp;window&nbsp;required&nbsp;to&nbsp;have&nbsp;a&nbsp;value<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(otherwise&nbsp;result&nbsp;is&nbsp;NA).&nbsp;For&nbsp;a&nbsp;window&nbsp;that&nbsp;is&nbsp;specified&nbsp;by&nbsp;an&nbsp;offset,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;will&nbsp;default&nbsp;to&nbsp;1.<br>
-freq&nbsp;:&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;optional&nbsp;(default&nbsp;None)&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Frequency&nbsp;to&nbsp;conform&nbsp;the&nbsp;data&nbsp;to&nbsp;before&nbsp;computing&nbsp;the&nbsp;statistic.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specified&nbsp;as&nbsp;a&nbsp;frequency&nbsp;string&nbsp;or&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>.<br>
-center&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Set&nbsp;the&nbsp;labels&nbsp;at&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window.<br>
-win_type&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Provide&nbsp;a&nbsp;window&nbsp;type.&nbsp;See&nbsp;the&nbsp;notes&nbsp;below.<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;on&nbsp;which&nbsp;to&nbsp;calculate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;rolling&nbsp;window,&nbsp;rather&nbsp;than&nbsp;the&nbsp;index<br>
-closed&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;the&nbsp;interval&nbsp;closed&nbsp;on&nbsp;the&nbsp;'right',&nbsp;'left',&nbsp;'both'&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'neither'&nbsp;endpoints.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;offset-based&nbsp;windows,&nbsp;it&nbsp;defaults&nbsp;to&nbsp;'right'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;fixed&nbsp;windows,&nbsp;defaults&nbsp;to&nbsp;'both'.&nbsp;Remaining&nbsp;cases&nbsp;not&nbsp;implemented<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;fixed&nbsp;windows.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;Window&nbsp;or&nbsp;Rolling&nbsp;sub-classed&nbsp;for&nbsp;the&nbsp;particular&nbsp;operation<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;using&nbsp;the&nbsp;'triang'<br>
-window&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-rolling">rolling</a>(2,&nbsp;win_type='triang').<a href="#TimeSeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;2.5<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Rolling&nbsp;sum&nbsp;with&nbsp;a&nbsp;window&nbsp;length&nbsp;of&nbsp;2,&nbsp;min_periods&nbsp;defaults<br>
-to&nbsp;the&nbsp;window&nbsp;length.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-rolling">rolling</a>(2).<a href="#TimeSeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;NaN<br>
-4&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Same&nbsp;as&nbsp;above,&nbsp;but&nbsp;explicity&nbsp;set&nbsp;the&nbsp;min_periods<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-rolling">rolling</a>(2,&nbsp;min_periods=1).<a href="#TimeSeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0.0<br>
-1&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;3.0<br>
-3&nbsp;&nbsp;2.0<br>
-4&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-A&nbsp;ragged&nbsp;(meaning&nbsp;not-a-regular&nbsp;frequency),&nbsp;time-indexed&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'B':&nbsp;[0,&nbsp;1,&nbsp;2,&nbsp;np.nan,&nbsp;4]},<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;=&nbsp;[pd.Timestamp('20130101&nbsp;09:00:00'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:02'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:03'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:05'),<br>
-....:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pd.Timestamp('20130101&nbsp;09:00:06')])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;2.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Contrasting&nbsp;to&nbsp;an&nbsp;integer&nbsp;rolling&nbsp;window,&nbsp;this&nbsp;will&nbsp;roll&nbsp;a&nbsp;variable<br>
-length&nbsp;window&nbsp;corresponding&nbsp;to&nbsp;the&nbsp;time&nbsp;period.<br>
-The&nbsp;default&nbsp;for&nbsp;min_periods&nbsp;is&nbsp;1.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-rolling">rolling</a>('2s').<a href="#TimeSeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-2013-01-01&nbsp;09:00:00&nbsp;&nbsp;0.0<br>
-2013-01-01&nbsp;09:00:02&nbsp;&nbsp;1.0<br>
-2013-01-01&nbsp;09:00:03&nbsp;&nbsp;3.0<br>
-2013-01-01&nbsp;09:00:05&nbsp;&nbsp;NaN<br>
-2013-01-01&nbsp;09:00:06&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-By&nbsp;default,&nbsp;the&nbsp;result&nbsp;is&nbsp;set&nbsp;to&nbsp;the&nbsp;right&nbsp;edge&nbsp;of&nbsp;the&nbsp;window.&nbsp;This&nbsp;can&nbsp;be<br>
-changed&nbsp;to&nbsp;the&nbsp;center&nbsp;of&nbsp;the&nbsp;window&nbsp;by&nbsp;setting&nbsp;``center=True``.<br>
-&nbsp;<br>
-The&nbsp;`freq`&nbsp;keyword&nbsp;is&nbsp;used&nbsp;to&nbsp;conform&nbsp;time&nbsp;series&nbsp;data&nbsp;to&nbsp;a&nbsp;specified<br>
-frequency&nbsp;by&nbsp;resampling&nbsp;the&nbsp;data.&nbsp;This&nbsp;is&nbsp;done&nbsp;with&nbsp;the&nbsp;default&nbsp;parameters<br>
-of&nbsp;:meth:`~pandas.<a href="pandas.core.series.html#Series">Series</a>.resample`&nbsp;(i.e.&nbsp;using&nbsp;the&nbsp;`mean`).<br>
-&nbsp;<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offsets&nbsp;&amp;&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-The&nbsp;recognized&nbsp;win_types&nbsp;are:<br>
-&nbsp;<br>
-*&nbsp;``boxcar``<br>
-*&nbsp;``triang``<br>
-*&nbsp;``blackman``<br>
-*&nbsp;``hamming``<br>
-*&nbsp;``bartlett``<br>
-*&nbsp;``parzen``<br>
-*&nbsp;``bohman``<br>
-*&nbsp;``blackmanharris``<br>
-*&nbsp;``nuttall``<br>
-*&nbsp;``barthann``<br>
-*&nbsp;``kaiser``&nbsp;(needs&nbsp;beta)<br>
-*&nbsp;``gaussian``&nbsp;(needs&nbsp;std)<br>
-*&nbsp;``general_gaussian``&nbsp;(needs&nbsp;power,&nbsp;width)<br>
-*&nbsp;``slepian``&nbsp;(needs&nbsp;width).</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-round"><strong>round</strong></a>(self, decimals=0, *args, **kwargs)</dt><dd><tt>Round&nbsp;each&nbsp;value&nbsp;in&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;the&nbsp;given&nbsp;number&nbsp;of&nbsp;decimals.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-decimals&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;round&nbsp;to&nbsp;(default:&nbsp;0).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;decimals&nbsp;is&nbsp;negative,&nbsp;it&nbsp;specifies&nbsp;the&nbsp;number&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;positions&nbsp;to&nbsp;the&nbsp;left&nbsp;of&nbsp;the&nbsp;decimal&nbsp;point.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.series.html#Series">Series</a>&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.around<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.round</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-rpow"><strong>rpow</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Exponential&nbsp;power&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rpow`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;**&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.pow</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-rsub"><strong>rsub</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rsub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;-&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.sub</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-rtruediv"><strong>rtruediv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`rtruediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``other&nbsp;/&nbsp;series``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.truediv</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-searchsorted"><strong>searchsorted</strong></a>(self, value, side='left', sorter=None)</dt><dd><tt>Find&nbsp;indices&nbsp;where&nbsp;elements&nbsp;should&nbsp;be&nbsp;inserted&nbsp;to&nbsp;maintain&nbsp;order.<br>
-&nbsp;<br>
-Find&nbsp;the&nbsp;indices&nbsp;into&nbsp;a&nbsp;sorted&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;`self`&nbsp;such&nbsp;that,&nbsp;if&nbsp;the<br>
-corresponding&nbsp;elements&nbsp;in&nbsp;`value`&nbsp;were&nbsp;inserted&nbsp;before&nbsp;the&nbsp;indices,<br>
-the&nbsp;order&nbsp;of&nbsp;`self`&nbsp;would&nbsp;be&nbsp;preserved.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-value&nbsp;:&nbsp;array_like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Values&nbsp;to&nbsp;insert&nbsp;into&nbsp;`self`.<br>
-side&nbsp;:&nbsp;{'left',&nbsp;'right'},&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'left',&nbsp;the&nbsp;index&nbsp;of&nbsp;the&nbsp;first&nbsp;suitable&nbsp;location&nbsp;found&nbsp;is&nbsp;given.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'right',&nbsp;return&nbsp;the&nbsp;last&nbsp;such&nbsp;index.&nbsp;&nbsp;If&nbsp;there&nbsp;is&nbsp;no&nbsp;suitable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index,&nbsp;return&nbsp;either&nbsp;0&nbsp;or&nbsp;N&nbsp;(where&nbsp;N&nbsp;is&nbsp;the&nbsp;length&nbsp;of&nbsp;`self`).<br>
-sorter&nbsp;:&nbsp;1-D&nbsp;array_like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Optional&nbsp;array&nbsp;of&nbsp;integer&nbsp;indices&nbsp;that&nbsp;sort&nbsp;`self`&nbsp;into&nbsp;ascending<br>
-&nbsp;&nbsp;&nbsp;&nbsp;order.&nbsp;They&nbsp;are&nbsp;typically&nbsp;the&nbsp;result&nbsp;of&nbsp;``np.argsort``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-indices&nbsp;:&nbsp;array&nbsp;of&nbsp;ints<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Array">Array</a>&nbsp;of&nbsp;insertion&nbsp;points&nbsp;with&nbsp;the&nbsp;same&nbsp;shape&nbsp;as&nbsp;`value`.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-numpy.searchsorted<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Binary&nbsp;search&nbsp;is&nbsp;used&nbsp;to&nbsp;find&nbsp;the&nbsp;required&nbsp;insertion&nbsp;points.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;x<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#TimeSeries-searchsorted">searchsorted</a>(4)<br>
-array([3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#TimeSeries-searchsorted">searchsorted</a>([0,&nbsp;4])<br>
-array([0,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#TimeSeries-searchsorted">searchsorted</a>([1,&nbsp;3],&nbsp;side='left')<br>
-array([0,&nbsp;2])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#TimeSeries-searchsorted">searchsorted</a>([1,&nbsp;3],&nbsp;side='right')<br>
-array([1,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x&nbsp;=&nbsp;pd.Categorical(['apple',&nbsp;'bread',&nbsp;'bread',&nbsp;'cheese',&nbsp;'milk'&nbsp;])<br>
-[apple,&nbsp;bread,&nbsp;bread,&nbsp;cheese,&nbsp;milk]<br>
-Categories&nbsp;(4,&nbsp;<a href="builtins.html#object">object</a>):&nbsp;[apple&nbsp;&lt;&nbsp;bread&nbsp;&lt;&nbsp;cheese&nbsp;&lt;&nbsp;milk]<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#TimeSeries-searchsorted">searchsorted</a>('bread')<br>
-array([1])&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;Note:&nbsp;an&nbsp;array,&nbsp;not&nbsp;a&nbsp;scalar<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#TimeSeries-searchsorted">searchsorted</a>(['bread'])<br>
-array([1])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#TimeSeries-searchsorted">searchsorted</a>(['bread',&nbsp;'eggs'])<br>
-array([1,&nbsp;4])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;x.<a href="#TimeSeries-searchsorted">searchsorted</a>(['bread',&nbsp;'eggs'],&nbsp;side='right')<br>
-array([3,&nbsp;4])&nbsp;&nbsp;&nbsp;&nbsp;#&nbsp;eggs&nbsp;before&nbsp;milk</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-sem"><strong>sem</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;standard&nbsp;error&nbsp;of&nbsp;the&nbsp;mean&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sem&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-set_value"><strong>set_value</strong></a>(self, label, value, takeable=False)</dt><dd><tt>Quickly&nbsp;set&nbsp;single&nbsp;value&nbsp;at&nbsp;passed&nbsp;label.&nbsp;If&nbsp;label&nbsp;is&nbsp;not&nbsp;contained,&nbsp;a<br>
-new&nbsp;<a href="builtins.html#object">object</a>&nbsp;is&nbsp;created&nbsp;with&nbsp;the&nbsp;label&nbsp;placed&nbsp;at&nbsp;the&nbsp;end&nbsp;of&nbsp;the&nbsp;result<br>
-index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-label&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Partial&nbsp;indexing&nbsp;with&nbsp;MultiIndex&nbsp;not&nbsp;allowed<br>
-value&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Scalar&nbsp;value<br>
-takeable&nbsp;:&nbsp;interpret&nbsp;the&nbsp;index&nbsp;as&nbsp;indexers,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-series&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;label&nbsp;is&nbsp;contained,&nbsp;will&nbsp;be&nbsp;reference&nbsp;to&nbsp;calling&nbsp;<a href="pandas.core.series.html#Series">Series</a>,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;a&nbsp;new&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-shift"><strong>shift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;index&nbsp;by&nbsp;desired&nbsp;number&nbsp;of&nbsp;periods&nbsp;with&nbsp;an&nbsp;optional&nbsp;time&nbsp;freq<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM').<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;Notes.<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'}<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;specified&nbsp;then&nbsp;the&nbsp;index&nbsp;values&nbsp;are&nbsp;shifted&nbsp;but&nbsp;the&nbsp;data<br>
-is&nbsp;not&nbsp;realigned.&nbsp;That&nbsp;is,&nbsp;use&nbsp;freq&nbsp;if&nbsp;you&nbsp;would&nbsp;like&nbsp;to&nbsp;extend&nbsp;the<br>
-index&nbsp;when&nbsp;shifting&nbsp;and&nbsp;preserve&nbsp;the&nbsp;original&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-skew"><strong>skew</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;skew&nbsp;over&nbsp;requested&nbsp;axis<br>
-Normalized&nbsp;by&nbsp;N-1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-skew&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-sort_index"><strong>sort_index</strong></a>(self, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True)</dt><dd><tt>Sort&nbsp;<a href="builtins.html#object">object</a>&nbsp;by&nbsp;labels&nbsp;(along&nbsp;an&nbsp;axis)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;index&nbsp;to&nbsp;direct&nbsp;sorting<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name&nbsp;or&nbsp;list&nbsp;of&nbsp;ints&nbsp;or&nbsp;list&nbsp;of&nbsp;level&nbsp;names<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None,&nbsp;sort&nbsp;on&nbsp;values&nbsp;in&nbsp;specified&nbsp;index&nbsp;level(s)<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;MultiIndex.<br>
-sort_remaining&nbsp;:&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;true&nbsp;and&nbsp;sorting&nbsp;by&nbsp;level&nbsp;and&nbsp;index&nbsp;is&nbsp;multilevel,&nbsp;sort&nbsp;by&nbsp;other<br>
-&nbsp;&nbsp;&nbsp;&nbsp;levels&nbsp;too&nbsp;(in&nbsp;order)&nbsp;after&nbsp;sorting&nbsp;by&nbsp;specified&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-sort_values"><strong>sort_values</strong></a>(self, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')</dt><dd><tt>Sort&nbsp;by&nbsp;the&nbsp;values&nbsp;along&nbsp;either&nbsp;axis<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0,&nbsp;'index'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;direct&nbsp;sorting<br>
-ascending&nbsp;:&nbsp;bool&nbsp;or&nbsp;list&nbsp;of&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;ascending&nbsp;vs.&nbsp;descending.&nbsp;Specify&nbsp;list&nbsp;for&nbsp;multiple&nbsp;sort<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;orders.&nbsp;&nbsp;If&nbsp;this&nbsp;is&nbsp;a&nbsp;list&nbsp;of&nbsp;bools,&nbsp;must&nbsp;match&nbsp;the&nbsp;length&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;by.<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;perform&nbsp;operation&nbsp;in-place<br>
-kind&nbsp;:&nbsp;{'quicksort',&nbsp;'mergesort',&nbsp;'heapsort'},&nbsp;default&nbsp;'quicksort'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Choice&nbsp;of&nbsp;sorting&nbsp;algorithm.&nbsp;See&nbsp;also&nbsp;<a href="numpy.html#ndarray">ndarray</a>.np.sort&nbsp;for&nbsp;more<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;information.&nbsp;&nbsp;`mergesort`&nbsp;is&nbsp;the&nbsp;only&nbsp;stable&nbsp;algorithm.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DataFrames,&nbsp;this&nbsp;option&nbsp;is&nbsp;only&nbsp;applied&nbsp;when&nbsp;sorting&nbsp;on&nbsp;a&nbsp;single<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;or&nbsp;label.<br>
-na_position&nbsp;:&nbsp;{'first',&nbsp;'last'},&nbsp;default&nbsp;'last'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`first`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;beginning,&nbsp;`last`&nbsp;puts&nbsp;NaNs&nbsp;at&nbsp;the&nbsp;end<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted_obj&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-sortlevel"><strong>sortlevel</strong></a>(self, level=0, ascending=True, sort_remaining=True)</dt><dd><tt>DEPRECATED:&nbsp;use&nbsp;:meth:`<a href="pandas.core.series.html#Series">Series</a>.sort_index`<br>
-&nbsp;<br>
-Sort&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;MultiIndex&nbsp;by&nbsp;chosen&nbsp;level.&nbsp;Data&nbsp;will&nbsp;be<br>
-lexicographically&nbsp;sorted&nbsp;by&nbsp;the&nbsp;chosen&nbsp;level&nbsp;followed&nbsp;by&nbsp;the&nbsp;other<br>
-levels&nbsp;(in&nbsp;order)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-ascending&nbsp;:&nbsp;bool,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sorted&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.<a href="#TimeSeries-sort_index">sort_index</a>(level=...)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-std"><strong>std</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;sample&nbsp;standard&nbsp;deviation&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-std&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-sub"><strong>sub</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-subtract"><strong>subtract</strong></a> = sub(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Subtraction&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`sub`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;-&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rsub</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-sum"><strong>sum</strong></a>(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;sum&nbsp;of&nbsp;the&nbsp;values&nbsp;for&nbsp;the&nbsp;requested&nbsp;axis<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sum&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-swaplevel"><strong>swaplevel</strong></a>(self, i=-2, j=-1, copy=True)</dt><dd><tt>Swap&nbsp;levels&nbsp;i&nbsp;and&nbsp;j&nbsp;in&nbsp;a&nbsp;MultiIndex<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-i,&nbsp;j&nbsp;:&nbsp;int,&nbsp;string&nbsp;(can&nbsp;be&nbsp;mixed)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level&nbsp;of&nbsp;index&nbsp;to&nbsp;be&nbsp;swapped.&nbsp;Can&nbsp;pass&nbsp;level&nbsp;name&nbsp;as&nbsp;string.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-swapped&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-..&nbsp;versionchanged::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;The&nbsp;indexes&nbsp;``i``&nbsp;and&nbsp;``j``&nbsp;are&nbsp;now&nbsp;optional,&nbsp;and&nbsp;default&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;the&nbsp;two&nbsp;innermost&nbsp;levels&nbsp;of&nbsp;the&nbsp;index.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-take"><strong>take</strong></a>(self, indices, axis=0, convert=True, is_copy=False, **kwargs)</dt><dd><tt>return&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;corresponding&nbsp;to&nbsp;requested&nbsp;indices<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-indices&nbsp;:&nbsp;list&nbsp;/&nbsp;array&nbsp;of&nbsp;ints<br>
-convert&nbsp;:&nbsp;translate&nbsp;negative&nbsp;to&nbsp;positive&nbsp;indices&nbsp;(default)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-taken&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-numpy.<a href="numpy.html#ndarray">ndarray</a>.take</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_csv"><strong>to_csv</strong></a>(self, path=None, index=True, sep=',', na_rep='', float_format=None, header=False, index_label=None, mode='w', encoding=None, date_format=None, decimal='.')</dt><dd><tt>Write&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;a&nbsp;comma-separated&nbsp;values&nbsp;(csv)&nbsp;file<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;or&nbsp;file&nbsp;handle,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;<a href="builtins.html#object">object</a>,&nbsp;if&nbsp;None&nbsp;is&nbsp;provided&nbsp;the&nbsp;result&nbsp;is&nbsp;returned&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string.<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-header&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;series&nbsp;name<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-mode&nbsp;:&nbsp;Python&nbsp;write&nbsp;mode,&nbsp;default&nbsp;'w'<br>
-sep&nbsp;:&nbsp;character,&nbsp;default&nbsp;","<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter&nbsp;for&nbsp;the&nbsp;output&nbsp;file.<br>
-encoding&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;if&nbsp;the&nbsp;contents&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;non-ascii,&nbsp;for&nbsp;python&nbsp;versions&nbsp;prior&nbsp;to&nbsp;3<br>
-date_format:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;datetime&nbsp;objects.<br>
-decimal:&nbsp;string,&nbsp;default&nbsp;'.'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Character&nbsp;recognized&nbsp;as&nbsp;decimal&nbsp;separator.&nbsp;E.g.&nbsp;use&nbsp;','&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;European&nbsp;data</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_dict"><strong>to_dict</strong></a>(self)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;{label&nbsp;-&gt;&nbsp;value}&nbsp;dict<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value_dict&nbsp;:&nbsp;dict</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_excel"><strong>to_excel</strong></a>(self, excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True)</dt><dd><tt>Write&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;an&nbsp;excel&nbsp;sheet<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel_writer&nbsp;:&nbsp;string&nbsp;or&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path&nbsp;or&nbsp;existing&nbsp;ExcelWriter<br>
-sheet_name&nbsp;:&nbsp;string,&nbsp;default&nbsp;'Sheet1'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;sheet&nbsp;which&nbsp;will&nbsp;contain&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-na_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;''<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;data&nbsp;representation<br>
-float_format&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Format&nbsp;string&nbsp;for&nbsp;floating&nbsp;point&nbsp;numbers<br>
-columns&nbsp;:&nbsp;sequence,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Columns&nbsp;to&nbsp;write<br>
-header&nbsp;:&nbsp;boolean&nbsp;or&nbsp;list&nbsp;of&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;out&nbsp;column&nbsp;names.&nbsp;If&nbsp;a&nbsp;list&nbsp;of&nbsp;string&nbsp;is&nbsp;given&nbsp;it&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;assumed&nbsp;to&nbsp;be&nbsp;aliases&nbsp;for&nbsp;the&nbsp;column&nbsp;names<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;row&nbsp;names&nbsp;(index)<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s)&nbsp;if&nbsp;desired.&nbsp;If&nbsp;None&nbsp;is&nbsp;given,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`header`&nbsp;and&nbsp;`index`&nbsp;are&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.&nbsp;A<br>
-&nbsp;&nbsp;&nbsp;&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-startrow&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;row&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-startcol&nbsp;:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;upper&nbsp;left&nbsp;cell&nbsp;column&nbsp;to&nbsp;dump&nbsp;data&nbsp;frame<br>
-engine&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;write&nbsp;engine&nbsp;to&nbsp;use&nbsp;-&nbsp;you&nbsp;can&nbsp;also&nbsp;set&nbsp;this&nbsp;via&nbsp;the&nbsp;options<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsx.writer``,&nbsp;``io.excel.xls.writer``,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``io.excel.xlsm.writer``.<br>
-merge_cells&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;MultiIndex&nbsp;and&nbsp;Hierarchical&nbsp;Rows&nbsp;as&nbsp;merged&nbsp;cells.<br>
-encoding:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;encoding&nbsp;of&nbsp;the&nbsp;resulting&nbsp;excel&nbsp;file.&nbsp;Only&nbsp;necessary&nbsp;for&nbsp;xlwt,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;writers&nbsp;support&nbsp;unicode&nbsp;natively.<br>
-inf_rep&nbsp;:&nbsp;string,&nbsp;default&nbsp;'inf'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Representation&nbsp;for&nbsp;infinity&nbsp;(there&nbsp;is&nbsp;no&nbsp;native&nbsp;representation&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;infinity&nbsp;in&nbsp;Excel)<br>
-freeze_panes&nbsp;:&nbsp;tuple&nbsp;of&nbsp;integer&nbsp;(length&nbsp;2),&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specifies&nbsp;the&nbsp;one-based&nbsp;bottommost&nbsp;row&nbsp;and&nbsp;rightmost&nbsp;column&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;to&nbsp;be&nbsp;frozen<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;passing&nbsp;an&nbsp;existing&nbsp;ExcelWriter&nbsp;<a href="builtins.html#object">object</a>,&nbsp;then&nbsp;the&nbsp;sheet&nbsp;will&nbsp;be&nbsp;added<br>
-to&nbsp;the&nbsp;existing&nbsp;workbook.&nbsp;&nbsp;This&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;save&nbsp;different<br>
-DataFrames&nbsp;to&nbsp;one&nbsp;workbook:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;writer&nbsp;=&nbsp;pd.ExcelWriter('output.xlsx')<br>
-&gt;&gt;&gt;&nbsp;df1.<a href="#TimeSeries-to_excel">to_excel</a>(writer,'Sheet1')<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#TimeSeries-to_excel">to_excel</a>(writer,'Sheet2')<br>
-&gt;&gt;&gt;&nbsp;writer.save()<br>
-&nbsp;<br>
-For&nbsp;compatibility&nbsp;with&nbsp;to_csv,&nbsp;to_excel&nbsp;serializes&nbsp;lists&nbsp;and&nbsp;dicts&nbsp;to<br>
-strings&nbsp;before&nbsp;writing.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_frame"><strong>to_frame</strong></a>(self, name=None)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-name&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;passed&nbsp;name&nbsp;should&nbsp;substitute&nbsp;for&nbsp;the&nbsp;series&nbsp;name&nbsp;(if&nbsp;it&nbsp;has<br>
-&nbsp;&nbsp;&nbsp;&nbsp;one).<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-data_frame&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_period"><strong>to_period</strong></a>(self, freq=None, copy=True)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;from&nbsp;DatetimeIndex&nbsp;to&nbsp;PeriodIndex&nbsp;with&nbsp;desired<br>
-frequency&nbsp;(inferred&nbsp;from&nbsp;index&nbsp;if&nbsp;not&nbsp;passed)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;PeriodIndex</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_sparse"><strong>to_sparse</strong></a>(self, kind='block', fill_value=None)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;SparseSeries<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-kind&nbsp;:&nbsp;{'block',&nbsp;'integer'}<br>
-fill_value&nbsp;:&nbsp;float,&nbsp;defaults&nbsp;to&nbsp;NaN&nbsp;(missing)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-sp&nbsp;:&nbsp;SparseSeries</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_string"><strong>to_string</strong></a>(self, buf=None, na_rep='NaN', float_format=None, header=True, index=True, length=False, dtype=False, name=False, max_rows=None)</dt><dd><tt>Render&nbsp;a&nbsp;string&nbsp;representation&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-buf&nbsp;:&nbsp;StringIO-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;buffer&nbsp;to&nbsp;write&nbsp;to<br>
-na_rep&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string&nbsp;representation&nbsp;of&nbsp;NAN&nbsp;to&nbsp;use,&nbsp;default&nbsp;'NaN'<br>
-float_format&nbsp;:&nbsp;one-parameter&nbsp;function,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;formatter&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;columns'&nbsp;elements&nbsp;if&nbsp;they&nbsp;are&nbsp;floats<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;None<br>
-header:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;header&nbsp;(index&nbsp;name)<br>
-index&nbsp;:&nbsp;bool,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;index&nbsp;(row)&nbsp;labels,&nbsp;default&nbsp;True<br>
-length&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;length<br>
-dtype&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;dtype<br>
-name&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Add&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;name&nbsp;if&nbsp;not&nbsp;None<br>
-max_rows&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;rows&nbsp;to&nbsp;show&nbsp;before&nbsp;truncating.&nbsp;If&nbsp;None,&nbsp;show<br>
-&nbsp;&nbsp;&nbsp;&nbsp;all.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-formatted&nbsp;:&nbsp;string&nbsp;(if&nbsp;not&nbsp;buffer&nbsp;passed)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_timestamp"><strong>to_timestamp</strong></a>(self, freq=None, how='start', copy=True)</dt><dd><tt>Cast&nbsp;to&nbsp;datetimeindex&nbsp;of&nbsp;timestamps,&nbsp;at&nbsp;*beginning*&nbsp;of&nbsp;period<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;string,&nbsp;default&nbsp;frequency&nbsp;of&nbsp;PeriodIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Desired&nbsp;frequency<br>
-how&nbsp;:&nbsp;{'s',&nbsp;'e',&nbsp;'start',&nbsp;'end'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Convention&nbsp;for&nbsp;converting&nbsp;period&nbsp;to&nbsp;timestamp;&nbsp;start&nbsp;of&nbsp;period<br>
-&nbsp;&nbsp;&nbsp;&nbsp;vs.&nbsp;end<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;DatetimeIndex</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-tolist"><strong>tolist</strong></a>(self)</dt><dd><tt>Convert&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;to&nbsp;a&nbsp;nested&nbsp;list</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-transform"><strong>transform</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Call&nbsp;function&nbsp;producing&nbsp;a&nbsp;like-indexed&nbsp;NDFrame<br>
-and&nbsp;return&nbsp;a&nbsp;NDFrame&nbsp;with&nbsp;the&nbsp;transformed&nbsp;values`<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;callable,&nbsp;string,&nbsp;dictionary,&nbsp;or&nbsp;list&nbsp;of&nbsp;string/callables<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;apply&nbsp;to&nbsp;column<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Accepted&nbsp;Combinations&nbsp;are:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;string&nbsp;function&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;list&nbsp;of&nbsp;functions<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;dict&nbsp;of&nbsp;column&nbsp;names&nbsp;-&gt;&nbsp;functions&nbsp;(or&nbsp;list&nbsp;of&nbsp;functions)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-transformed&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(10,&nbsp;3),&nbsp;columns=['A',&nbsp;'B',&nbsp;'C'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.date_range('1/1/2000',&nbsp;periods=10))<br>
-df.iloc[3:7]&nbsp;=&nbsp;np.nan<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-transform">transform</a>(lambda&nbsp;x:&nbsp;(x&nbsp;-&nbsp;x.<a href="#TimeSeries-mean">mean</a>())&nbsp;/&nbsp;x.<a href="#TimeSeries-std">std</a>())<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-2000-01-01&nbsp;&nbsp;0.579457&nbsp;&nbsp;1.236184&nbsp;&nbsp;0.123424<br>
-2000-01-02&nbsp;&nbsp;0.370357&nbsp;-0.605875&nbsp;-1.231325<br>
-2000-01-03&nbsp;&nbsp;1.455756&nbsp;-0.277446&nbsp;&nbsp;0.288967<br>
-2000-01-04&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-05&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-06&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-07&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-08&nbsp;-0.498658&nbsp;&nbsp;1.274522&nbsp;&nbsp;1.642524<br>
-2000-01-09&nbsp;-0.540524&nbsp;-1.012676&nbsp;-0.828968<br>
-2000-01-10&nbsp;-1.366388&nbsp;-0.614710&nbsp;&nbsp;0.005378<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.NDFrame.aggregate<br>
-pandas.NDFrame.apply</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-truediv"><strong>truediv</strong></a>(self, other, level=None, fill_value=None, axis=0)</dt><dd><tt>Floating&nbsp;division&nbsp;of&nbsp;series&nbsp;and&nbsp;other,&nbsp;element-wise&nbsp;(binary&nbsp;operator&nbsp;`truediv`).<br>
-&nbsp;<br>
-Equivalent&nbsp;to&nbsp;``series&nbsp;/&nbsp;other``,&nbsp;but&nbsp;with&nbsp;support&nbsp;to&nbsp;substitute&nbsp;a&nbsp;fill_value&nbsp;for<br>
-missing&nbsp;data&nbsp;in&nbsp;one&nbsp;of&nbsp;the&nbsp;inputs.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;scalar&nbsp;value<br>
-fill_value&nbsp;:&nbsp;None&nbsp;or&nbsp;float&nbsp;value,&nbsp;default&nbsp;None&nbsp;(NaN)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fill&nbsp;missing&nbsp;(NaN)&nbsp;values&nbsp;with&nbsp;this&nbsp;value.&nbsp;If&nbsp;both&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing,&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;missing<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Broadcast&nbsp;across&nbsp;a&nbsp;level,&nbsp;matching&nbsp;Index&nbsp;values&nbsp;on&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;passed&nbsp;MultiIndex&nbsp;level<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-result&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-<a href="pandas.core.series.html#Series">Series</a>.rtruediv</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-unique"><strong>unique</strong></a>(self)</dt><dd><tt>Return&nbsp;unique&nbsp;values&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.&nbsp;Uniques&nbsp;are&nbsp;returned&nbsp;in&nbsp;order<br>
-of&nbsp;appearance,&nbsp;this&nbsp;does&nbsp;NOT&nbsp;sort.&nbsp;Hash&nbsp;table-based&nbsp;unique.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-values&nbsp;:&nbsp;1d&nbsp;array-like<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-unique&nbsp;values.<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;an&nbsp;Index,&nbsp;the&nbsp;return&nbsp;is&nbsp;an&nbsp;Index<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;a&nbsp;Categorical&nbsp;dtype,&nbsp;the&nbsp;return&nbsp;is&nbsp;a&nbsp;Categorical<br>
-&nbsp;&nbsp;-&nbsp;If&nbsp;the&nbsp;input&nbsp;is&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>/<a href="numpy.html#ndarray">ndarray</a>,&nbsp;the&nbsp;return&nbsp;will&nbsp;be&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-unique<br>
-Index.unique<br>
-<a href="pandas.core.series.html#Series">Series</a>.unique</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-unstack"><strong>unstack</strong></a>(self, level=-1, fill_value=None)</dt><dd><tt>Unstack,&nbsp;a.k.a.&nbsp;pivot,&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;MultiIndex&nbsp;to&nbsp;produce&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-The&nbsp;level&nbsp;involved&nbsp;will&nbsp;automatically&nbsp;get&nbsp;sorted.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-level&nbsp;:&nbsp;int,&nbsp;string,&nbsp;or&nbsp;list&nbsp;of&nbsp;these,&nbsp;default&nbsp;last&nbsp;level<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Level(s)&nbsp;to&nbsp;unstack,&nbsp;can&nbsp;pass&nbsp;level&nbsp;name<br>
-fill_value&nbsp;:&nbsp;replace&nbsp;NaN&nbsp;with&nbsp;this&nbsp;value&nbsp;if&nbsp;the&nbsp;unstack&nbsp;produces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;missing&nbsp;values<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.18.0<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3,&nbsp;4],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.MultiIndex.from_product([['one',&nbsp;'two'],&nbsp;['a',&nbsp;'b']]))<br>
-&gt;&gt;&gt;&nbsp;s<br>
-one&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-two&nbsp;&nbsp;a&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-dtype:&nbsp;int64<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-unstack">unstack</a>(level=-1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b<br>
-one&nbsp;&nbsp;1&nbsp;&nbsp;2<br>
-two&nbsp;&nbsp;3&nbsp;&nbsp;4<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-unstack">unstack</a>(level=0)<br>
-&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;two<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-unstacked&nbsp;:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-update"><strong>update</strong></a>(self, other)</dt><dd><tt>Modify&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;in&nbsp;place&nbsp;using&nbsp;non-NA&nbsp;values&nbsp;from&nbsp;passed<br>
-<a href="pandas.core.series.html#Series">Series</a>.&nbsp;Aligns&nbsp;on&nbsp;index<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<dl><dt><strong>valid</strong> <em>lambda</em> self, inplace=False, **kwargs</dt></dl>
-
-<dl><dt><a name="TimeSeries-var"><strong>var</strong></a>(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)</dt><dd><tt>Return&nbsp;unbiased&nbsp;variance&nbsp;over&nbsp;requested&nbsp;axis.<br>
-&nbsp;<br>
-Normalized&nbsp;by&nbsp;N-1&nbsp;by&nbsp;default.&nbsp;This&nbsp;can&nbsp;be&nbsp;changed&nbsp;using&nbsp;the&nbsp;ddof&nbsp;argument<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{index&nbsp;(0)}<br>
-skipna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Exclude&nbsp;NA/null&nbsp;values.&nbsp;If&nbsp;an&nbsp;entire&nbsp;row/column&nbsp;is&nbsp;NA,&nbsp;the&nbsp;result<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;NA<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;count&nbsp;along&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;particular&nbsp;level,&nbsp;collapsing&nbsp;into&nbsp;a&nbsp;scalar<br>
-ddof&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;degrees&nbsp;of&nbsp;freedom<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;columns.&nbsp;If&nbsp;None,&nbsp;will&nbsp;attempt&nbsp;to&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;everything,&nbsp;then&nbsp;use&nbsp;only&nbsp;numeric&nbsp;data.&nbsp;Not&nbsp;implemented&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-var&nbsp;:&nbsp;scalar&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(if&nbsp;level&nbsp;specified)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-view"><strong>view</strong></a>(self, dtype=None)</dt></dl>
-
-<hr>
-Class methods inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><a name="TimeSeries-from_array"><strong>from_array</strong></a>(arr, index=None, name=None, dtype=None, copy=False, fastpath=False)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt></dl>
-
-<dl><dt><a name="TimeSeries-from_csv"><strong>from_csv</strong></a>(path, sep=',', parse_dates=True, header=None, index_col=0, encoding=None, infer_datetime_format=False)<font color="#909090"><font face="helvetica, arial"> from <a href="builtins.html#type">builtins.type</a></font></font></dt><dd><tt>Read&nbsp;CSV&nbsp;file&nbsp;(DISCOURAGED,&nbsp;please&nbsp;use&nbsp;:func:`pandas.read_csv`<br>
-instead).<br>
-&nbsp;<br>
-It&nbsp;is&nbsp;preferable&nbsp;to&nbsp;use&nbsp;the&nbsp;more&nbsp;powerful&nbsp;:func:`pandas.read_csv`<br>
-for&nbsp;most&nbsp;general&nbsp;purposes,&nbsp;but&nbsp;``from_csv``&nbsp;makes&nbsp;for&nbsp;an&nbsp;easy<br>
-roundtrip&nbsp;to&nbsp;and&nbsp;from&nbsp;a&nbsp;file&nbsp;(the&nbsp;exact&nbsp;counterpart&nbsp;of<br>
-``to_csv``),&nbsp;especially&nbsp;with&nbsp;a&nbsp;time&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;only&nbsp;differs&nbsp;from&nbsp;:func:`pandas.read_csv`&nbsp;in&nbsp;some&nbsp;defaults:<br>
-&nbsp;<br>
--&nbsp;`index_col`&nbsp;is&nbsp;``0``&nbsp;instead&nbsp;of&nbsp;``None``&nbsp;(take&nbsp;first&nbsp;column&nbsp;as&nbsp;index<br>
-&nbsp;&nbsp;by&nbsp;default)<br>
--&nbsp;`header`&nbsp;is&nbsp;``None``&nbsp;instead&nbsp;of&nbsp;``0``&nbsp;(the&nbsp;first&nbsp;row&nbsp;is&nbsp;not&nbsp;used&nbsp;as<br>
-&nbsp;&nbsp;the&nbsp;column&nbsp;names)<br>
--&nbsp;`parse_dates`&nbsp;is&nbsp;``True``&nbsp;instead&nbsp;of&nbsp;``False``&nbsp;(try&nbsp;parsing&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;as&nbsp;datetime&nbsp;by&nbsp;default)<br>
-&nbsp;<br>
-With&nbsp;:func:`pandas.read_csv`,&nbsp;the&nbsp;option&nbsp;``squeeze=True``&nbsp;can&nbsp;be&nbsp;used<br>
-to&nbsp;return&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;like&nbsp;``from_csv``.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;file&nbsp;path&nbsp;or&nbsp;file&nbsp;handle&nbsp;/&nbsp;StringIO<br>
-sep&nbsp;:&nbsp;string,&nbsp;default&nbsp;','<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Field&nbsp;delimiter<br>
-parse_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Parse&nbsp;dates.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-header&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Row&nbsp;to&nbsp;use&nbsp;as&nbsp;header&nbsp;(skip&nbsp;prior&nbsp;rows)<br>
-index_col&nbsp;:&nbsp;int&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;to&nbsp;use&nbsp;for&nbsp;index.&nbsp;If&nbsp;a&nbsp;sequence&nbsp;is&nbsp;given,&nbsp;a&nbsp;MultiIndex<br>
-&nbsp;&nbsp;&nbsp;&nbsp;is&nbsp;used.&nbsp;Different&nbsp;default&nbsp;from&nbsp;read_table<br>
-encoding&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;encoding&nbsp;to&nbsp;use&nbsp;if&nbsp;the&nbsp;contents&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;non-ascii,&nbsp;for&nbsp;python&nbsp;versions&nbsp;prior&nbsp;to&nbsp;3<br>
-infer_datetime_format:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;and&nbsp;`parse_dates`&nbsp;is&nbsp;True&nbsp;for&nbsp;a&nbsp;column,&nbsp;try&nbsp;to&nbsp;infer&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;datetime&nbsp;format&nbsp;based&nbsp;on&nbsp;the&nbsp;first&nbsp;datetime&nbsp;string.&nbsp;If&nbsp;the&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;be&nbsp;inferred,&nbsp;there&nbsp;often&nbsp;will&nbsp;be&nbsp;a&nbsp;large&nbsp;parsing&nbsp;speed-up.<br>
-&nbsp;<br>
-See&nbsp;also<br>
---------<br>
-pandas.read_csv<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><strong>asobject</strong></dt>
-<dd><tt>return&nbsp;object&nbsp;Series&nbsp;which&nbsp;contains&nbsp;boxed&nbsp;values<br>
-&nbsp;<br>
-*this&nbsp;is&nbsp;an&nbsp;internal&nbsp;non-public&nbsp;method*</tt></dd>
-</dl>
-<dl><dt><strong>axes</strong></dt>
-<dd><tt>Return&nbsp;a&nbsp;list&nbsp;of&nbsp;the&nbsp;row&nbsp;axis&nbsp;labels</tt></dd>
-</dl>
-<dl><dt><strong>dtype</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;dtype&nbsp;object&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>dtypes</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;dtype&nbsp;object&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>ftype</strong></dt>
-<dd><tt>return&nbsp;if&nbsp;the&nbsp;data&nbsp;is&nbsp;sparse|dense</tt></dd>
-</dl>
-<dl><dt><strong>ftypes</strong></dt>
-<dd><tt>return&nbsp;if&nbsp;the&nbsp;data&nbsp;is&nbsp;sparse|dense</tt></dd>
-</dl>
-<dl><dt><strong>imag</strong></dt>
-</dl>
-<dl><dt><strong>index</strong></dt>
-</dl>
-<dl><dt><strong>name</strong></dt>
-</dl>
-<dl><dt><strong>real</strong></dt>
-</dl>
-<dl><dt><strong>values</strong></dt>
-<dd><tt>Return&nbsp;Series&nbsp;as&nbsp;ndarray&nbsp;or&nbsp;ndarray-like<br>
-depending&nbsp;on&nbsp;the&nbsp;dtype<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-arr&nbsp;:&nbsp;numpy.ndarray&nbsp;or&nbsp;ndarray-like<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;pd.Series([1,&nbsp;2,&nbsp;3]).values<br>
-array([1,&nbsp;2,&nbsp;3])<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(list('aabc')).values<br>
-array(['a',&nbsp;'a',&nbsp;'b',&nbsp;'c'],&nbsp;dtype=object)<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(list('aabc')).astype('category').values<br>
-[a,&nbsp;a,&nbsp;b,&nbsp;c]<br>
-Categories&nbsp;(3,&nbsp;object):&nbsp;[a,&nbsp;b,&nbsp;c]<br>
-&nbsp;<br>
-Timezone&nbsp;aware&nbsp;datetime&nbsp;data&nbsp;is&nbsp;converted&nbsp;to&nbsp;UTC:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;pd.Series(pd.date_range('20130101',&nbsp;periods=3,<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;tz='US/Eastern')).values<br>
-array(['2013-01-01T05:00:00.000000000',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'2013-01-02T05:00:00.000000000',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'2013-01-03T05:00:00.000000000'],&nbsp;dtype='datetime64[ns]')</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.series.html#Series">pandas.core.series.Series</a>:<br>
-<dl><dt><strong>cat</strong> = &lt;class 'pandas.core.categorical.CategoricalAccessor'&gt;<dd><tt>Accessor&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;categorical&nbsp;properties&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values.<br>
-&nbsp;<br>
-Be&nbsp;aware&nbsp;that&nbsp;assigning&nbsp;to&nbsp;`categories`&nbsp;is&nbsp;a&nbsp;inplace&nbsp;operation,&nbsp;while&nbsp;all<br>
-methods&nbsp;return&nbsp;new&nbsp;categorical&nbsp;data&nbsp;per&nbsp;default&nbsp;(but&nbsp;can&nbsp;be&nbsp;called&nbsp;with<br>
-`inplace=True`).<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.cat.categories<br>
-&gt;&gt;&gt;&nbsp;s.cat.categories&nbsp;=&nbsp;list('abc')<br>
-&gt;&gt;&gt;&nbsp;s.cat.rename_categories(list('cab'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.reorder_categories(list('cab'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.add_categories(['d','e'])<br>
-&gt;&gt;&gt;&nbsp;s.cat.remove_categories(['d'])<br>
-&gt;&gt;&gt;&nbsp;s.cat.remove_unused_categories()<br>
-&gt;&gt;&gt;&nbsp;s.cat.set_categories(list('abcde'))<br>
-&gt;&gt;&gt;&nbsp;s.cat.as_ordered()<br>
-&gt;&gt;&gt;&nbsp;s.cat.as_unordered()</tt></dl>
-
-<dl><dt><strong>dt</strong> = &lt;class 'pandas.core.indexes.accessors.CombinedDatetimelikeProperties'&gt;<dd><tt>Accessor&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;datetimelike&nbsp;properties&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;values.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.dt.hour<br>
-&gt;&gt;&gt;&nbsp;s.dt.second<br>
-&gt;&gt;&gt;&nbsp;s.dt.quarter<br>
-&nbsp;<br>
-Returns&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;indexed&nbsp;like&nbsp;the&nbsp;original&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-Raises&nbsp;TypeError&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;does&nbsp;not&nbsp;contain&nbsp;datetimelike&nbsp;values.</tt></dl>
-
-<dl><dt><strong>plot</strong> = &lt;class 'pandas.plotting._core.SeriesPlotMethods'&gt;<dd><tt><a href="pandas.core.series.html#Series">Series</a>&nbsp;plotting&nbsp;accessor&nbsp;and&nbsp;method<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.plot.line()<br>
-&gt;&gt;&gt;&nbsp;s.plot.bar()<br>
-&gt;&gt;&gt;&nbsp;s.plot.<a href="#TimeSeries-hist">hist</a>()<br>
-&nbsp;<br>
-Plotting&nbsp;methods&nbsp;can&nbsp;also&nbsp;be&nbsp;accessed&nbsp;by&nbsp;calling&nbsp;the&nbsp;accessor&nbsp;as&nbsp;a&nbsp;method<br>
-with&nbsp;the&nbsp;``kind``&nbsp;argument:<br>
-``s.<a href="#TimeSeries-plot">plot</a>(kind='line')``&nbsp;is&nbsp;equivalent&nbsp;to&nbsp;``s.plot.line()``</tt></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><a name="TimeSeries-factorize"><strong>factorize</strong></a>(self, sort=False, na_sentinel=-1)</dt><dd><tt>Encode&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;as&nbsp;an&nbsp;enumerated&nbsp;type&nbsp;or&nbsp;categorical&nbsp;variable<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;by&nbsp;values<br>
-na_sentinel:&nbsp;int,&nbsp;default&nbsp;-1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;mark&nbsp;"not&nbsp;found"<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-labels&nbsp;:&nbsp;the&nbsp;indexer&nbsp;to&nbsp;the&nbsp;original&nbsp;array<br>
-uniques&nbsp;:&nbsp;the&nbsp;unique&nbsp;Index</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-item"><strong>item</strong></a>(self)</dt><dd><tt>return&nbsp;the&nbsp;first&nbsp;element&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data&nbsp;as&nbsp;a&nbsp;python<br>
-scalar</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-nunique"><strong>nunique</strong></a>(self, dropna=True)</dt><dd><tt>Return&nbsp;number&nbsp;of&nbsp;unique&nbsp;elements&nbsp;in&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Excludes&nbsp;NA&nbsp;values&nbsp;by&nbsp;default.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Don't&nbsp;include&nbsp;NaN&nbsp;in&nbsp;the&nbsp;count.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-nunique&nbsp;:&nbsp;int</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-transpose"><strong>transpose</strong></a>(self, *args, **kwargs)</dt><dd><tt>return&nbsp;the&nbsp;transpose,&nbsp;which&nbsp;is&nbsp;by&nbsp;definition&nbsp;self</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-value_counts"><strong>value_counts</strong></a>(self, normalize=False, sort=True, ascending=False, bins=None, dropna=True)</dt><dd><tt>Returns&nbsp;<a href="builtins.html#object">object</a>&nbsp;containing&nbsp;counts&nbsp;of&nbsp;unique&nbsp;values.<br>
-&nbsp;<br>
-The&nbsp;resulting&nbsp;<a href="builtins.html#object">object</a>&nbsp;will&nbsp;be&nbsp;in&nbsp;descending&nbsp;order&nbsp;so&nbsp;that&nbsp;the<br>
-first&nbsp;element&nbsp;is&nbsp;the&nbsp;most&nbsp;frequently-occurring&nbsp;element.<br>
-Excludes&nbsp;NA&nbsp;values&nbsp;by&nbsp;default.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-normalize&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True&nbsp;then&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;returned&nbsp;will&nbsp;contain&nbsp;the&nbsp;relative<br>
-&nbsp;&nbsp;&nbsp;&nbsp;frequencies&nbsp;of&nbsp;the&nbsp;unique&nbsp;values.<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;by&nbsp;values<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;in&nbsp;ascending&nbsp;order<br>
-bins&nbsp;:&nbsp;integer,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Rather&nbsp;than&nbsp;count&nbsp;values,&nbsp;group&nbsp;them&nbsp;into&nbsp;half-open&nbsp;bins,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;convenience&nbsp;for&nbsp;pd.cut,&nbsp;only&nbsp;works&nbsp;with&nbsp;numeric&nbsp;data<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Don't&nbsp;include&nbsp;counts&nbsp;of&nbsp;NaN.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-counts&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a></tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><strong>T</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;transpose,&nbsp;which&nbsp;is&nbsp;by&nbsp;definition&nbsp;self</tt></dd>
-</dl>
-<dl><dt><strong>__dict__</strong></dt>
-<dd><tt>dictionary&nbsp;for&nbsp;instance&nbsp;variables&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>__weakref__</strong></dt>
-<dd><tt>list&nbsp;of&nbsp;weak&nbsp;references&nbsp;to&nbsp;the&nbsp;object&nbsp;(if&nbsp;defined)</tt></dd>
-</dl>
-<dl><dt><strong>base</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;base&nbsp;object&nbsp;if&nbsp;the&nbsp;memory&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data&nbsp;is<br>
-shared</tt></dd>
-</dl>
-<dl><dt><strong>data</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;data&nbsp;pointer&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>empty</strong></dt>
-</dl>
-<dl><dt><strong>flags</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;ndarray.flags&nbsp;for&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>hasnans</strong></dt>
-</dl>
-<dl><dt><strong>is_monotonic</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_increasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_monotonic_decreasing</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_decreasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic_decreasing&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_monotonic_increasing</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are<br>
-monotonic_increasing<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_monotonic&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>is_unique</strong></dt>
-<dd><tt>Return&nbsp;boolean&nbsp;if&nbsp;values&nbsp;in&nbsp;the&nbsp;object&nbsp;are&nbsp;unique<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-is_unique&nbsp;:&nbsp;boolean</tt></dd>
-</dl>
-<dl><dt><strong>itemsize</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;size&nbsp;of&nbsp;the&nbsp;dtype&nbsp;of&nbsp;the&nbsp;item&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>nbytes</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;bytes&nbsp;in&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>ndim</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;dimensions&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data,<br>
-by&nbsp;definition&nbsp;1</tt></dd>
-</dl>
-<dl><dt><strong>shape</strong></dt>
-<dd><tt>return&nbsp;a&nbsp;tuple&nbsp;of&nbsp;the&nbsp;shape&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>size</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;number&nbsp;of&nbsp;elements&nbsp;in&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<dl><dt><strong>strides</strong></dt>
-<dd><tt>return&nbsp;the&nbsp;strides&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.base.html#IndexOpsMixin">pandas.core.base.IndexOpsMixin</a>:<br>
-<dl><dt><strong>__array_priority__</strong> = 1000</dl>
-
-<hr>
-Data and other attributes inherited from <a href="pandas.core.strings.html#StringAccessorMixin">pandas.core.strings.StringAccessorMixin</a>:<br>
-<dl><dt><strong>str</strong> = &lt;class 'pandas.core.strings.StringMethods'&gt;<dd><tt>Vectorized&nbsp;string&nbsp;functions&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;and&nbsp;Index.&nbsp;NAs&nbsp;stay&nbsp;NA&nbsp;unless<br>
-handled&nbsp;otherwise&nbsp;by&nbsp;a&nbsp;particular&nbsp;method.&nbsp;Patterned&nbsp;after&nbsp;Python's&nbsp;string<br>
-methods,&nbsp;with&nbsp;some&nbsp;inspiration&nbsp;from&nbsp;R's&nbsp;stringr&nbsp;package.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s.str.split('_')<br>
-&gt;&gt;&gt;&nbsp;s.str.<a href="#TimeSeries-replace">replace</a>('_',&nbsp;'')</tt></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><a name="TimeSeries-__abs__"><strong>__abs__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__bool__"><strong>__bool__</strong></a> = __nonzero__(self)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__contains__"><strong>__contains__</strong></a>(self, key)</dt><dd><tt>True&nbsp;if&nbsp;the&nbsp;key&nbsp;is&nbsp;in&nbsp;the&nbsp;info&nbsp;axis</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-__copy__"><strong>__copy__</strong></a>(self, deep=True)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__deepcopy__"><strong>__deepcopy__</strong></a>(self, memo=None)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__delitem__"><strong>__delitem__</strong></a>(self, key)</dt><dd><tt>Delete&nbsp;item</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-__finalize__"><strong>__finalize__</strong></a>(self, other, method=None, **kwargs)</dt><dd><tt>Propagate&nbsp;metadata&nbsp;from&nbsp;other&nbsp;to&nbsp;self.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;which&nbsp;to&nbsp;get&nbsp;the&nbsp;attributes&nbsp;that&nbsp;we&nbsp;are&nbsp;going<br>
-&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;propagate<br>
-method&nbsp;:&nbsp;optional,&nbsp;a&nbsp;passed&nbsp;method&nbsp;name&nbsp;;&nbsp;possibly&nbsp;to&nbsp;take&nbsp;different<br>
-&nbsp;&nbsp;&nbsp;&nbsp;types&nbsp;of&nbsp;propagation&nbsp;actions&nbsp;based&nbsp;on&nbsp;this</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-__getattr__"><strong>__getattr__</strong></a>(self, name)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;looking&nbsp;up&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-__getstate__"><strong>__getstate__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__hash__"><strong>__hash__</strong></a>(self)</dt><dd><tt>Return&nbsp;hash(self).</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-__invert__"><strong>__invert__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__neg__"><strong>__neg__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__nonzero__"><strong>__nonzero__</strong></a>(self)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__round__"><strong>__round__</strong></a>(self, decimals=0)</dt></dl>
-
-<dl><dt><a name="TimeSeries-__setattr__"><strong>__setattr__</strong></a>(self, name, value)</dt><dd><tt>After&nbsp;regular&nbsp;attribute&nbsp;access,&nbsp;try&nbsp;setting&nbsp;the&nbsp;name<br>
-This&nbsp;allows&nbsp;simpler&nbsp;access&nbsp;to&nbsp;columns&nbsp;for&nbsp;interactive&nbsp;use.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-__setstate__"><strong>__setstate__</strong></a>(self, state)</dt></dl>
-
-<dl><dt><a name="TimeSeries-abs"><strong>abs</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;absolute&nbsp;value&nbsp;taken--only&nbsp;applicable&nbsp;to&nbsp;objects<br>
-that&nbsp;are&nbsp;all&nbsp;numeric.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-abs:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-add_prefix"><strong>add_prefix</strong></a>(self, prefix)</dt><dd><tt>Concatenate&nbsp;prefix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-prefix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_prefix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-add_suffix"><strong>add_suffix</strong></a>(self, suffix)</dt><dd><tt>Concatenate&nbsp;suffix&nbsp;string&nbsp;with&nbsp;panel&nbsp;items&nbsp;names.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-suffix&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-with_suffix&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-as_blocks"><strong>as_blocks</strong></a>(self, copy=True)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types&nbsp;that&nbsp;each&nbsp;has<br>
-a&nbsp;homogeneous&nbsp;dtype.<br>
-&nbsp;<br>
-NOTE:&nbsp;the&nbsp;dtypes&nbsp;of&nbsp;the&nbsp;blocks&nbsp;WILL&nbsp;BE&nbsp;PRESERVED&nbsp;HERE&nbsp;(unlike&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;as_matrix)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded:&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;a&nbsp;dict&nbsp;of&nbsp;dtype&nbsp;-&gt;&nbsp;Constructor&nbsp;Types</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-as_matrix"><strong>as_matrix</strong></a>(self, columns=None)</dt><dd><tt>Convert&nbsp;the&nbsp;frame&nbsp;to&nbsp;its&nbsp;Numpy-array&nbsp;representation.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-columns:&nbsp;list,&nbsp;optional,&nbsp;default:None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;None,&nbsp;return&nbsp;all&nbsp;columns,&nbsp;otherwise,&nbsp;returns&nbsp;specified&nbsp;columns.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values&nbsp;:&nbsp;<a href="numpy.html#ndarray">ndarray</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;caller&nbsp;is&nbsp;heterogeneous&nbsp;and&nbsp;contains&nbsp;booleans&nbsp;or&nbsp;objects,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;result&nbsp;will&nbsp;be&nbsp;of&nbsp;dtype=<a href="builtins.html#object">object</a>.&nbsp;See&nbsp;Notes.<br>
-&nbsp;<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Return&nbsp;is&nbsp;NOT&nbsp;a&nbsp;Numpy-matrix,&nbsp;rather,&nbsp;a&nbsp;Numpy-array.<br>
-&nbsp;<br>
-The&nbsp;dtype&nbsp;will&nbsp;be&nbsp;a&nbsp;lower-common-denominator&nbsp;dtype&nbsp;(implicit<br>
-upcasting);&nbsp;that&nbsp;is&nbsp;to&nbsp;say&nbsp;if&nbsp;the&nbsp;dtypes&nbsp;(even&nbsp;of&nbsp;numeric&nbsp;types)<br>
-are&nbsp;mixed,&nbsp;the&nbsp;one&nbsp;that&nbsp;accommodates&nbsp;all&nbsp;will&nbsp;be&nbsp;chosen.&nbsp;Use&nbsp;this<br>
-with&nbsp;care&nbsp;if&nbsp;you&nbsp;are&nbsp;not&nbsp;dealing&nbsp;with&nbsp;the&nbsp;blocks.<br>
-&nbsp;<br>
-e.g.&nbsp;If&nbsp;the&nbsp;dtypes&nbsp;are&nbsp;float16&nbsp;and&nbsp;float32,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcast&nbsp;to<br>
-float32.&nbsp;&nbsp;If&nbsp;dtypes&nbsp;are&nbsp;int32&nbsp;and&nbsp;uint8,&nbsp;dtype&nbsp;will&nbsp;be&nbsp;upcase&nbsp;to<br>
-int32.&nbsp;By&nbsp;numpy.find_common_type&nbsp;convention,&nbsp;mixing&nbsp;int64&nbsp;and&nbsp;uint64<br>
-will&nbsp;result&nbsp;in&nbsp;a&nbsp;flot64&nbsp;dtype.<br>
-&nbsp;<br>
-This&nbsp;method&nbsp;is&nbsp;provided&nbsp;for&nbsp;backwards&nbsp;compatibility.&nbsp;Generally,<br>
-it&nbsp;is&nbsp;recommended&nbsp;to&nbsp;use&nbsp;'.values'.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.values</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-asfreq"><strong>asfreq</strong></a>(self, freq, method=None, how=None, normalize=False, fill_value=None)</dt><dd><tt>Convert&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;specified&nbsp;frequency.<br>
-&nbsp;<br>
-Optionally&nbsp;provide&nbsp;filling&nbsp;method&nbsp;to&nbsp;pad/backfill&nbsp;missing&nbsp;values.<br>
-&nbsp;<br>
-Returns&nbsp;the&nbsp;original&nbsp;data&nbsp;conformed&nbsp;to&nbsp;a&nbsp;new&nbsp;index&nbsp;with&nbsp;the&nbsp;specified<br>
-frequency.&nbsp;``resample``&nbsp;is&nbsp;more&nbsp;appropriate&nbsp;if&nbsp;an&nbsp;operation,&nbsp;such&nbsp;as<br>
-summarization,&nbsp;is&nbsp;necessary&nbsp;to&nbsp;represent&nbsp;the&nbsp;data&nbsp;at&nbsp;the&nbsp;new&nbsp;frequency.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-freq&nbsp;:&nbsp;DateOffset&nbsp;<a href="builtins.html#object">object</a>,&nbsp;or&nbsp;string<br>
-method&nbsp;:&nbsp;{'backfill'/'bfill',&nbsp;'pad'/'ffill'},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Method&nbsp;to&nbsp;use&nbsp;for&nbsp;filling&nbsp;holes&nbsp;in&nbsp;reindexed&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;(note&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present):<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'pad'&nbsp;/&nbsp;'ffill':&nbsp;propagate&nbsp;last&nbsp;valid&nbsp;observation&nbsp;forward&nbsp;to&nbsp;next<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;valid<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'backfill'&nbsp;/&nbsp;'bfill':&nbsp;use&nbsp;NEXT&nbsp;valid&nbsp;observation&nbsp;to&nbsp;fill<br>
-how&nbsp;:&nbsp;{'start',&nbsp;'end'},&nbsp;default&nbsp;end<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;PeriodIndex&nbsp;only,&nbsp;see&nbsp;PeriodIndex.asfreq<br>
-normalize&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;reset&nbsp;output&nbsp;index&nbsp;to&nbsp;midnight<br>
-fill_value:&nbsp;scalar,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;for&nbsp;missing&nbsp;values,&nbsp;applied&nbsp;during&nbsp;upsampling&nbsp;(note<br>
-&nbsp;&nbsp;&nbsp;&nbsp;this&nbsp;does&nbsp;not&nbsp;fill&nbsp;NaNs&nbsp;that&nbsp;already&nbsp;were&nbsp;present).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;4&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=4,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0.0,&nbsp;None,&nbsp;2.0,&nbsp;3.0],&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'s':series})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-asfreq">asfreq</a>(freq='30S')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``fill&nbsp;value``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-asfreq">asfreq</a>(freq='30S',&nbsp;fill_value=9.0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;9.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Upsample&nbsp;again,&nbsp;providing&nbsp;a&nbsp;``method``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-asfreq">asfreq</a>(freq='30S',&nbsp;method='bfill')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;s<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-2000-01-01&nbsp;00:02:30&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;frequency&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-asof"><strong>asof</strong></a>(self, where, subset=None)</dt><dd><tt>The&nbsp;last&nbsp;row&nbsp;without&nbsp;any&nbsp;NaN&nbsp;is&nbsp;taken&nbsp;(or&nbsp;the&nbsp;last&nbsp;row&nbsp;without<br>
-NaN&nbsp;considering&nbsp;only&nbsp;the&nbsp;subset&nbsp;of&nbsp;columns&nbsp;in&nbsp;the&nbsp;case&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.19.0&nbsp;For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-If&nbsp;there&nbsp;is&nbsp;no&nbsp;good&nbsp;value,&nbsp;NaN&nbsp;is&nbsp;returned&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;NaN&nbsp;values&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-where&nbsp;:&nbsp;date&nbsp;or&nbsp;array&nbsp;of&nbsp;dates<br>
-subset&nbsp;:&nbsp;string&nbsp;or&nbsp;list&nbsp;of&nbsp;strings,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;if&nbsp;not&nbsp;None&nbsp;use&nbsp;these&nbsp;columns&nbsp;for&nbsp;NaN&nbsp;propagation<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Dates&nbsp;are&nbsp;assumed&nbsp;to&nbsp;be&nbsp;sorted<br>
-Raises&nbsp;if&nbsp;this&nbsp;is&nbsp;not&nbsp;the&nbsp;case<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-where&nbsp;is&nbsp;scalar<br>
-&nbsp;<br>
-&nbsp;&nbsp;-&nbsp;value&nbsp;or&nbsp;NaN&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;-&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;if&nbsp;input&nbsp;is&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-where&nbsp;is&nbsp;Index:&nbsp;same&nbsp;shape&nbsp;<a href="builtins.html#object">object</a>&nbsp;as&nbsp;input<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-merge_asof</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-astype"><strong>astype</strong></a>(self, dtype, copy=True, errors='raise', **kwargs)</dt><dd><tt>Cast&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;numpy.dtype<br>
-Return&nbsp;a&nbsp;copy&nbsp;when&nbsp;copy&nbsp;=&nbsp;True&nbsp;(be&nbsp;really&nbsp;careful&nbsp;with&nbsp;this!)<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-dtype&nbsp;:&nbsp;data&nbsp;type,&nbsp;or&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;-&gt;&nbsp;data&nbsp;type<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Use&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;entire&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;same&nbsp;type.&nbsp;Alternatively,&nbsp;use&nbsp;{col:&nbsp;dtype,&nbsp;...},&nbsp;where&nbsp;col&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;label&nbsp;and&nbsp;dtype&nbsp;is&nbsp;a&nbsp;numpy.dtype&nbsp;or&nbsp;Python&nbsp;type&nbsp;to&nbsp;cast&nbsp;one<br>
-&nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;more&nbsp;of&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>'s&nbsp;columns&nbsp;to&nbsp;column-specific&nbsp;types.<br>
-errors&nbsp;:&nbsp;{'raise',&nbsp;'ignore'},&nbsp;default&nbsp;'raise'.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Control&nbsp;raising&nbsp;of&nbsp;exceptions&nbsp;on&nbsp;invalid&nbsp;data&nbsp;for&nbsp;provided&nbsp;dtype.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``raise``&nbsp;:&nbsp;allow&nbsp;exceptions&nbsp;to&nbsp;be&nbsp;raised<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;``ignore``&nbsp;:&nbsp;suppress&nbsp;exceptions.&nbsp;On&nbsp;error&nbsp;return&nbsp;original&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-raise_on_error&nbsp;:&nbsp;DEPRECATED&nbsp;use&nbsp;``errors``&nbsp;instead<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;constructor<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-casted&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-at_time"><strong>at_time</strong></a>(self, time, asof=False)</dt><dd><tt>Select&nbsp;values&nbsp;at&nbsp;particular&nbsp;time&nbsp;of&nbsp;day&nbsp;(e.g.&nbsp;9:30AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_at_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-between_time"><strong>between_time</strong></a>(self, start_time, end_time, include_start=True, include_end=True)</dt><dd><tt>Select&nbsp;values&nbsp;between&nbsp;particular&nbsp;times&nbsp;of&nbsp;the&nbsp;day&nbsp;(e.g.,&nbsp;9:00-9:30&nbsp;AM).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-start_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-end_time&nbsp;:&nbsp;datetime.time&nbsp;or&nbsp;string<br>
-include_start&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-include_end&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-values_between_time&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-bfill"><strong>bfill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#TimeSeries-fillna">fillna</a>(method='bfill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-bool"><strong>bool</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;bool&nbsp;of&nbsp;a&nbsp;single&nbsp;element&nbsp;PandasObject.<br>
-&nbsp;<br>
-This&nbsp;must&nbsp;be&nbsp;a&nbsp;boolean&nbsp;scalar&nbsp;value,&nbsp;either&nbsp;True&nbsp;or&nbsp;False.&nbsp;&nbsp;Raise&nbsp;a<br>
-ValueError&nbsp;if&nbsp;the&nbsp;PandasObject&nbsp;does&nbsp;not&nbsp;have&nbsp;exactly&nbsp;1&nbsp;element,&nbsp;or&nbsp;that<br>
-element&nbsp;is&nbsp;not&nbsp;boolean</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-clip"><strong>clip</strong></a>(self, lower=None, upper=None, axis=None, *args, **kwargs)</dt><dd><tt>Trim&nbsp;values&nbsp;at&nbsp;input&nbsp;threshold(s).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-lower&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-upper&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like,&nbsp;default&nbsp;None<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;lower&nbsp;and&nbsp;upper&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.256177<br>
-1&nbsp;-1.367855&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.176076<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;1.261967&nbsp;&nbsp;0.570967<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-clip">clip</a>(-1.0,&nbsp;0.5)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-1.000000<br>
-1&nbsp;-1.000000&nbsp;&nbsp;0.500000<br>
-2&nbsp;&nbsp;0.027753&nbsp;-1.000000<br>
-3&nbsp;&nbsp;0.230930&nbsp;-0.679613<br>
-4&nbsp;&nbsp;0.500000&nbsp;&nbsp;0.500000<br>
-&gt;&gt;&gt;&nbsp;t<br>
-0&nbsp;&nbsp;&nbsp;-0.3<br>
-1&nbsp;&nbsp;&nbsp;-0.2<br>
-2&nbsp;&nbsp;&nbsp;-0.1<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;0.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;0.1<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-clip">clip</a>(t,&nbsp;t&nbsp;+&nbsp;1,&nbsp;axis=0)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-0&nbsp;&nbsp;0.335232&nbsp;-0.300000<br>
-1&nbsp;-0.200000&nbsp;&nbsp;0.746646<br>
-2&nbsp;&nbsp;0.027753&nbsp;-0.100000<br>
-3&nbsp;&nbsp;0.230930&nbsp;&nbsp;0.000000<br>
-4&nbsp;&nbsp;1.100000&nbsp;&nbsp;0.570967</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-clip_lower"><strong>clip_lower</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;the&nbsp;input&nbsp;with&nbsp;values&nbsp;below&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-clip_upper"><strong>clip_upper</strong></a>(self, threshold, axis=None)</dt><dd><tt>Return&nbsp;copy&nbsp;of&nbsp;input&nbsp;with&nbsp;values&nbsp;above&nbsp;given&nbsp;value(s)&nbsp;truncated.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-threshold&nbsp;:&nbsp;float&nbsp;or&nbsp;array_like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Align&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;threshold&nbsp;along&nbsp;the&nbsp;given&nbsp;axis.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-clip<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-clipped&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-consolidate"><strong>consolidate</strong></a>(self, inplace=False)</dt><dd><tt>DEPRECATED:&nbsp;consolidate&nbsp;will&nbsp;be&nbsp;an&nbsp;internal&nbsp;implementation&nbsp;only.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-convert_objects"><strong>convert_objects</strong></a>(self, convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True)</dt><dd><tt>Deprecated.<br>
-&nbsp;<br>
-Attempt&nbsp;to&nbsp;infer&nbsp;better&nbsp;dtype&nbsp;for&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-convert_dates&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;date&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-convert_numeric&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;attempt&nbsp;to&nbsp;coerce&nbsp;to&nbsp;numbers&nbsp;(including&nbsp;strings),&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaN.<br>
-convert_timedeltas&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;convert&nbsp;to&nbsp;timedelta&nbsp;where&nbsp;possible.&nbsp;If&nbsp;'coerce',&nbsp;force<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion,&nbsp;with&nbsp;unconvertible&nbsp;values&nbsp;becoming&nbsp;NaT.<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;return&nbsp;a&nbsp;copy&nbsp;even&nbsp;if&nbsp;no&nbsp;copy&nbsp;is&nbsp;necessary&nbsp;(e.g.&nbsp;no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;conversion&nbsp;was&nbsp;done).&nbsp;Note:&nbsp;This&nbsp;is&nbsp;meant&nbsp;for&nbsp;internal&nbsp;use,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;not&nbsp;be&nbsp;confused&nbsp;with&nbsp;inplace.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.to_datetime&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;datetime.<br>
-pandas.to_timedelta&nbsp;:&nbsp;Convert&nbsp;argument&nbsp;to&nbsp;timedelta.<br>
-pandas.to_numeric&nbsp;:&nbsp;Return&nbsp;a&nbsp;fixed&nbsp;frequency&nbsp;timedelta&nbsp;index,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;day&nbsp;as&nbsp;the&nbsp;default.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-converted&nbsp;:&nbsp;same&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-copy"><strong>copy</strong></a>(self, deep=True)</dt><dd><tt>Make&nbsp;a&nbsp;copy&nbsp;of&nbsp;this&nbsp;objects&nbsp;data.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-deep&nbsp;:&nbsp;boolean&nbsp;or&nbsp;string,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Make&nbsp;a&nbsp;deep&nbsp;copy,&nbsp;including&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;data&nbsp;and&nbsp;the&nbsp;indices.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;With&nbsp;``deep=False``&nbsp;neither&nbsp;the&nbsp;indices&nbsp;or&nbsp;the&nbsp;data&nbsp;are&nbsp;copied.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;that&nbsp;when&nbsp;``deep=True``&nbsp;data&nbsp;is&nbsp;copied,&nbsp;actual&nbsp;python&nbsp;objects<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;not&nbsp;be&nbsp;copied&nbsp;recursively,&nbsp;only&nbsp;the&nbsp;reference&nbsp;to&nbsp;the&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;This&nbsp;is&nbsp;in&nbsp;contrast&nbsp;to&nbsp;``copy.deepcopy``&nbsp;in&nbsp;the&nbsp;Standard&nbsp;Library,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;recursively&nbsp;copies&nbsp;<a href="builtins.html#object">object</a>&nbsp;data.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-copy&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-describe"><strong>describe</strong></a>(self, percentiles=None, include=None, exclude=None)</dt><dd><tt>Generates&nbsp;descriptive&nbsp;statistics&nbsp;that&nbsp;summarize&nbsp;the&nbsp;central&nbsp;tendency,<br>
-dispersion&nbsp;and&nbsp;shape&nbsp;of&nbsp;a&nbsp;dataset's&nbsp;distribution,&nbsp;excluding<br>
-``NaN``&nbsp;values.<br>
-&nbsp;<br>
-Analyzes&nbsp;both&nbsp;numeric&nbsp;and&nbsp;<a href="builtins.html#object">object</a>&nbsp;series,&nbsp;as&nbsp;well<br>
-as&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;column&nbsp;sets&nbsp;of&nbsp;mixed&nbsp;data&nbsp;types.&nbsp;The&nbsp;output<br>
-will&nbsp;vary&nbsp;depending&nbsp;on&nbsp;what&nbsp;is&nbsp;provided.&nbsp;Refer&nbsp;to&nbsp;the&nbsp;notes<br>
-below&nbsp;for&nbsp;more&nbsp;detail.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-percentiles&nbsp;:&nbsp;list-like&nbsp;of&nbsp;numbers,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;percentiles&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;output.&nbsp;All&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fall&nbsp;between&nbsp;0&nbsp;and&nbsp;1.&nbsp;The&nbsp;default&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``[.25,&nbsp;.5,&nbsp;.75]``,&nbsp;which&nbsp;returns&nbsp;the&nbsp;25th,&nbsp;50th,&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;75th&nbsp;percentiles.<br>
-include&nbsp;:&nbsp;'all',&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;white&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;include&nbsp;in&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'all'&nbsp;:&nbsp;All&nbsp;columns&nbsp;of&nbsp;the&nbsp;input&nbsp;will&nbsp;be&nbsp;included&nbsp;in&nbsp;the&nbsp;output.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Limits&nbsp;the&nbsp;results&nbsp;to&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;provided&nbsp;data&nbsp;types.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;limit&nbsp;the&nbsp;result&nbsp;to&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;limit&nbsp;it&nbsp;instead&nbsp;to&nbsp;categorical<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;objects&nbsp;submit&nbsp;the&nbsp;``numpy.<a href="builtins.html#object">object</a>``&nbsp;data&nbsp;type.&nbsp;Strings<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#TimeSeries-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;include&nbsp;all&nbsp;numeric&nbsp;columns.<br>
-exclude&nbsp;:&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;or&nbsp;None&nbsp;(default),&nbsp;optional,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;black&nbsp;list&nbsp;of&nbsp;data&nbsp;types&nbsp;to&nbsp;omit&nbsp;from&nbsp;the&nbsp;result.&nbsp;Ignored<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.&nbsp;Here&nbsp;are&nbsp;the&nbsp;options:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;A&nbsp;list-like&nbsp;of&nbsp;dtypes&nbsp;:&nbsp;Excludes&nbsp;the&nbsp;provided&nbsp;data&nbsp;types<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;the&nbsp;result.&nbsp;To&nbsp;select&nbsp;numeric&nbsp;types&nbsp;submit<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``numpy.number``.&nbsp;To&nbsp;select&nbsp;categorical&nbsp;objects&nbsp;submit&nbsp;the&nbsp;data<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;type&nbsp;``numpy.<a href="builtins.html#object">object</a>``.&nbsp;Strings&nbsp;can&nbsp;also&nbsp;be&nbsp;used&nbsp;in&nbsp;the&nbsp;style&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``select_dtypes``&nbsp;(e.g.&nbsp;``df.<a href="#TimeSeries-describe">describe</a>(include=['O'])``)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;None&nbsp;(default)&nbsp;:&nbsp;The&nbsp;result&nbsp;will&nbsp;exclude&nbsp;nothing.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-summary:&nbsp;&nbsp;<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;summary&nbsp;statistics<br>
-&nbsp;<br>
-Notes<br>
------<br>
-For&nbsp;numeric&nbsp;data,&nbsp;the&nbsp;result's&nbsp;index&nbsp;will&nbsp;include&nbsp;``count``,<br>
-``mean``,&nbsp;``std``,&nbsp;``min``,&nbsp;``max``&nbsp;as&nbsp;well&nbsp;as&nbsp;lower,&nbsp;``50``&nbsp;and<br>
-upper&nbsp;percentiles.&nbsp;By&nbsp;default&nbsp;the&nbsp;lower&nbsp;percentile&nbsp;is&nbsp;``25``&nbsp;and&nbsp;the<br>
-upper&nbsp;percentile&nbsp;is&nbsp;``75``.&nbsp;The&nbsp;``50``&nbsp;percentile&nbsp;is&nbsp;the<br>
-same&nbsp;as&nbsp;the&nbsp;median.<br>
-&nbsp;<br>
-For&nbsp;<a href="builtins.html#object">object</a>&nbsp;data&nbsp;(e.g.&nbsp;strings&nbsp;or&nbsp;timestamps),&nbsp;the&nbsp;result's&nbsp;index<br>
-will&nbsp;include&nbsp;``count``,&nbsp;``unique``,&nbsp;``top``,&nbsp;and&nbsp;``freq``.&nbsp;The&nbsp;``top``<br>
-is&nbsp;the&nbsp;most&nbsp;common&nbsp;value.&nbsp;The&nbsp;``freq``&nbsp;is&nbsp;the&nbsp;most&nbsp;common&nbsp;value's<br>
-frequency.&nbsp;Timestamps&nbsp;also&nbsp;include&nbsp;the&nbsp;``first``&nbsp;and&nbsp;``last``&nbsp;items.<br>
-&nbsp;<br>
-If&nbsp;multiple&nbsp;<a href="builtins.html#object">object</a>&nbsp;values&nbsp;have&nbsp;the&nbsp;highest&nbsp;count,&nbsp;then&nbsp;the<br>
-``count``&nbsp;and&nbsp;``top``&nbsp;results&nbsp;will&nbsp;be&nbsp;arbitrarily&nbsp;chosen&nbsp;from<br>
-among&nbsp;those&nbsp;with&nbsp;the&nbsp;highest&nbsp;count.<br>
-&nbsp;<br>
-For&nbsp;mixed&nbsp;data&nbsp;types&nbsp;provided&nbsp;via&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``,&nbsp;the&nbsp;default&nbsp;is&nbsp;to<br>
-return&nbsp;only&nbsp;an&nbsp;analysis&nbsp;of&nbsp;numeric&nbsp;columns.&nbsp;If&nbsp;``include='all'``<br>
-is&nbsp;provided&nbsp;as&nbsp;an&nbsp;option,&nbsp;the&nbsp;result&nbsp;will&nbsp;include&nbsp;a&nbsp;union&nbsp;of<br>
-attributes&nbsp;of&nbsp;each&nbsp;type.<br>
-&nbsp;<br>
-The&nbsp;`include`&nbsp;and&nbsp;`exclude`&nbsp;parameters&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;limit<br>
-which&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;are&nbsp;analyzed&nbsp;for&nbsp;the&nbsp;output.<br>
-The&nbsp;parameters&nbsp;are&nbsp;ignored&nbsp;when&nbsp;analyzing&nbsp;a&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-Describing&nbsp;a&nbsp;numeric&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([1,&nbsp;2,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;categorical&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(['a',&nbsp;'a',&nbsp;'b',&nbsp;'c'])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;timestamp&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2000-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01"),<br>
-...&nbsp;&nbsp;&nbsp;np.datetime64("2010-01-01")<br>
-...&nbsp;])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-first&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2000-01-01&nbsp;00:00:00<br>
-last&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2010-01-01&nbsp;00:00:00<br>
-dtype:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``.&nbsp;By&nbsp;default&nbsp;only&nbsp;numeric&nbsp;fields<br>
-are&nbsp;returned.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([[1,&nbsp;'a'],&nbsp;[2,&nbsp;'b'],&nbsp;[3,&nbsp;'c']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['numeric',&nbsp;'<a href="builtins.html#object">object</a>'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-describe">describe</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Describing&nbsp;all&nbsp;columns&nbsp;of&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;regardless&nbsp;of&nbsp;data&nbsp;type.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-describe">describe</a>(include='all')<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NaN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-&nbsp;<br>
-Describing&nbsp;a&nbsp;column&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;by&nbsp;accessing&nbsp;it&nbsp;as<br>
-an&nbsp;attribute.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.numeric.<a href="#TimeSeries-describe">describe</a>()<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-Name:&nbsp;numeric,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;numeric&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-describe">describe</a>(include=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-Including&nbsp;only&nbsp;string&nbsp;columns&nbsp;in&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-describe">describe</a>(include=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;numeric&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-describe">describe</a>(exclude=[np.number])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a><br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-unique&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-top&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b<br>
-freq&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-&nbsp;<br>
-Excluding&nbsp;<a href="builtins.html#object">object</a>&nbsp;columns&nbsp;from&nbsp;a&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;description.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-describe">describe</a>(exclude=[np.<a href="builtins.html#object">object</a>])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numeric<br>
-count&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-mean&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-std&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-min&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-25%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1.5<br>
-50%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-75%&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2.5<br>
-max&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.count<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.max<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.min<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mean<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.std<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select_dtypes</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-drop"><strong>drop</strong></a>(self, labels, axis=0, level=None, inplace=False, errors='raise')</dt><dd><tt>Return&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;labels&nbsp;in&nbsp;requested&nbsp;axis&nbsp;removed.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-labels&nbsp;:&nbsp;single&nbsp;label&nbsp;or&nbsp;list-like<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;axis&nbsp;name<br>
-level&nbsp;:&nbsp;int&nbsp;or&nbsp;level&nbsp;name,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;MultiIndex<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;do&nbsp;operation&nbsp;inplace&nbsp;and&nbsp;return&nbsp;None.<br>
-errors&nbsp;:&nbsp;{'ignore',&nbsp;'raise'},&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'ignore',&nbsp;suppress&nbsp;error&nbsp;and&nbsp;existing&nbsp;labels&nbsp;are&nbsp;dropped.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-dropped&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-equals"><strong>equals</strong></a>(self, other)</dt><dd><tt>Determines&nbsp;if&nbsp;two&nbsp;NDFrame&nbsp;objects&nbsp;contain&nbsp;the&nbsp;same&nbsp;elements.&nbsp;NaNs&nbsp;in<br>
-the&nbsp;same&nbsp;location&nbsp;are&nbsp;considered&nbsp;equal.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-ffill"><strong>ffill</strong></a>(self, axis=None, inplace=False, limit=None, downcast=None)</dt><dd><tt>Synonym&nbsp;for&nbsp;:meth:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<a href="#TimeSeries-fillna">fillna</a>(method='ffill')&nbsp;&lt;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.fillna&gt;`</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-filter"><strong>filter</strong></a>(self, items=None, like=None, regex=None, axis=None)</dt><dd><tt>Subset&nbsp;rows&nbsp;or&nbsp;columns&nbsp;of&nbsp;dataframe&nbsp;according&nbsp;to&nbsp;labels&nbsp;in<br>
-the&nbsp;specified&nbsp;index.<br>
-&nbsp;<br>
-Note&nbsp;that&nbsp;this&nbsp;routine&nbsp;does&nbsp;not&nbsp;filter&nbsp;a&nbsp;dataframe&nbsp;on&nbsp;its<br>
-contents.&nbsp;The&nbsp;filter&nbsp;is&nbsp;applied&nbsp;to&nbsp;the&nbsp;labels&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-items&nbsp;:&nbsp;list-like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;info&nbsp;axis&nbsp;to&nbsp;restrict&nbsp;to&nbsp;(must&nbsp;not&nbsp;all&nbsp;be&nbsp;present)<br>
-like&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;where&nbsp;"arg&nbsp;in&nbsp;col&nbsp;==&nbsp;True"<br>
-regex&nbsp;:&nbsp;string&nbsp;(regular&nbsp;expression)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Keep&nbsp;info&nbsp;axis&nbsp;with&nbsp;re.search(regex,&nbsp;col)&nbsp;==&nbsp;True<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string&nbsp;axis&nbsp;name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;filter&nbsp;on.&nbsp;&nbsp;By&nbsp;default&nbsp;this&nbsp;is&nbsp;the&nbsp;info&nbsp;axis,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;'index'&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;'columns'&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;name<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-filter">filter</a>(items=['one',&nbsp;'three'])<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;columns&nbsp;by&nbsp;regular&nbsp;expression<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-filter">filter</a>(regex='e$',&nbsp;axis=1)<br>
-one&nbsp;&nbsp;three<br>
-mouse&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;#&nbsp;select&nbsp;rows&nbsp;containing&nbsp;'bbi'<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-filter">filter</a>(like='bbi',&nbsp;axis=0)<br>
-one&nbsp;&nbsp;two&nbsp;&nbsp;three<br>
-rabbit&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.select<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;``items``,&nbsp;``like``,&nbsp;and&nbsp;``regex``&nbsp;parameters&nbsp;are<br>
-enforced&nbsp;to&nbsp;be&nbsp;mutually&nbsp;exclusive.<br>
-&nbsp;<br>
-``axis``&nbsp;defaults&nbsp;to&nbsp;the&nbsp;info&nbsp;axis&nbsp;that&nbsp;is&nbsp;used&nbsp;when&nbsp;indexing<br>
-with&nbsp;``[]``.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-first"><strong>first</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;initial&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#TimeSeries-first">first</a>('10D')&nbsp;-&gt;&nbsp;First&nbsp;10&nbsp;days<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-get"><strong>get</strong></a>(self, key, default=None)</dt><dd><tt>Get&nbsp;item&nbsp;from&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;given&nbsp;key&nbsp;(<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;column,&nbsp;Panel&nbsp;slice,<br>
-etc.).&nbsp;Returns&nbsp;default&nbsp;value&nbsp;if&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;<br>
-Returns<br>
--------<br>
-value&nbsp;:&nbsp;type&nbsp;of&nbsp;items&nbsp;contained&nbsp;in&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-get_dtype_counts"><strong>get_dtype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;dtypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-get_ftype_counts"><strong>get_ftype_counts</strong></a>(self)</dt><dd><tt>Return&nbsp;the&nbsp;counts&nbsp;of&nbsp;ftypes&nbsp;in&nbsp;this&nbsp;<a href="builtins.html#object">object</a>.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-groupby"><strong>groupby</strong></a>(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)</dt><dd><tt>Group&nbsp;series&nbsp;using&nbsp;mapper&nbsp;(dict&nbsp;or&nbsp;key&nbsp;function,&nbsp;apply&nbsp;given&nbsp;function<br>
-to&nbsp;group,&nbsp;return&nbsp;result&nbsp;as&nbsp;series)&nbsp;or&nbsp;by&nbsp;a&nbsp;series&nbsp;of&nbsp;columns.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-by&nbsp;:&nbsp;mapping,&nbsp;function,&nbsp;str,&nbsp;or&nbsp;iterable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;for&nbsp;the&nbsp;groupby.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;``by``&nbsp;is&nbsp;a&nbsp;function,&nbsp;it's&nbsp;called&nbsp;on&nbsp;each&nbsp;value&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>'s<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;If&nbsp;a&nbsp;dict&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;is&nbsp;passed,&nbsp;the&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;dict&nbsp;VALUES<br>
-&nbsp;&nbsp;&nbsp;&nbsp;will&nbsp;be&nbsp;used&nbsp;to&nbsp;determine&nbsp;the&nbsp;groups&nbsp;(the&nbsp;<a href="pandas.core.series.html#Series">Series</a>'&nbsp;values&nbsp;are&nbsp;first<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aligned;&nbsp;see&nbsp;``.<a href="#TimeSeries-align">align</a>()``&nbsp;method).&nbsp;If&nbsp;an&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;is&nbsp;passed,&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;are&nbsp;used&nbsp;as-is&nbsp;determine&nbsp;the&nbsp;groups.&nbsp;A&nbsp;str&nbsp;or&nbsp;list&nbsp;of&nbsp;strs<br>
-&nbsp;&nbsp;&nbsp;&nbsp;may&nbsp;be&nbsp;passed&nbsp;to&nbsp;group&nbsp;by&nbsp;the&nbsp;columns&nbsp;in&nbsp;``self``<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-level&nbsp;:&nbsp;int,&nbsp;level&nbsp;name,&nbsp;or&nbsp;sequence&nbsp;of&nbsp;such,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;a&nbsp;MultiIndex&nbsp;(hierarchical),&nbsp;group&nbsp;by&nbsp;a&nbsp;particular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;level&nbsp;or&nbsp;levels<br>
-as_index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;aggregated&nbsp;output,&nbsp;return&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;group&nbsp;labels&nbsp;as&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;Only&nbsp;relevant&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;input.&nbsp;as_index=False&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;effectively&nbsp;"SQL-style"&nbsp;grouped&nbsp;output<br>
-sort&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sort&nbsp;group&nbsp;keys.&nbsp;Get&nbsp;better&nbsp;performance&nbsp;by&nbsp;turning&nbsp;this&nbsp;off.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Note&nbsp;this&nbsp;does&nbsp;not&nbsp;influence&nbsp;the&nbsp;order&nbsp;of&nbsp;observations&nbsp;within&nbsp;each<br>
-&nbsp;&nbsp;&nbsp;&nbsp;group.&nbsp;&nbsp;groupby&nbsp;preserves&nbsp;the&nbsp;order&nbsp;of&nbsp;rows&nbsp;within&nbsp;each&nbsp;group.<br>
-group_keys&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;When&nbsp;calling&nbsp;apply,&nbsp;add&nbsp;group&nbsp;keys&nbsp;to&nbsp;index&nbsp;to&nbsp;identify&nbsp;pieces<br>
-squeeze&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;reduce&nbsp;the&nbsp;dimensionality&nbsp;of&nbsp;the&nbsp;return&nbsp;type&nbsp;if&nbsp;possible,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;otherwise&nbsp;return&nbsp;a&nbsp;consistent&nbsp;type<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;results<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#TimeSeries-groupby">groupby</a>(func,&nbsp;axis=0).<a href="#TimeSeries-mean">mean</a>()<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#TimeSeries-groupby">groupby</a>(['col1',&nbsp;'col2'])['col3'].<a href="#TimeSeries-mean">mean</a>()<br>
-&nbsp;<br>
-<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;hierarchical&nbsp;index<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;data.<a href="#TimeSeries-groupby">groupby</a>(['col1',&nbsp;'col2']).<a href="#TimeSeries-mean">mean</a>()<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-GroupBy&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-head"><strong>head</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;first&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-interpolate"><strong>interpolate</strong></a>(self, method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', downcast=None, **kwargs)</dt><dd><tt>Interpolate&nbsp;values&nbsp;according&nbsp;to&nbsp;different&nbsp;methods.<br>
-&nbsp;<br>
-Please&nbsp;note&nbsp;that&nbsp;only&nbsp;``method='linear'``&nbsp;is&nbsp;supported&nbsp;for<br>
-DataFrames/<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;a&nbsp;MultiIndex.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-method&nbsp;:&nbsp;{'linear',&nbsp;'time',&nbsp;'index',&nbsp;'values',&nbsp;'nearest',&nbsp;'zero',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',&nbsp;'barycentric',&nbsp;'krogh',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'polynomial',&nbsp;'spline',&nbsp;'piecewise_polynomial',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'from_derivatives',&nbsp;'pchip',&nbsp;'akima'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'linear':&nbsp;ignore&nbsp;the&nbsp;index&nbsp;and&nbsp;treat&nbsp;the&nbsp;values&nbsp;as&nbsp;equally<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;spaced.&nbsp;This&nbsp;is&nbsp;the&nbsp;only&nbsp;method&nbsp;supported&nbsp;on&nbsp;MultiIndexes.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;default<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'time':&nbsp;interpolation&nbsp;works&nbsp;on&nbsp;daily&nbsp;and&nbsp;higher&nbsp;resolution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;data&nbsp;to&nbsp;interpolate&nbsp;given&nbsp;length&nbsp;of&nbsp;interval<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'index',&nbsp;'values':&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'nearest',&nbsp;'zero',&nbsp;'slinear',&nbsp;'quadratic',&nbsp;'cubic',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'barycentric',&nbsp;'polynomial'&nbsp;is&nbsp;passed&nbsp;to<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``scipy.interpolate.interp1d``.&nbsp;Both&nbsp;'polynomial'&nbsp;and&nbsp;'spline'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;require&nbsp;that&nbsp;you&nbsp;also&nbsp;specify&nbsp;an&nbsp;`order`&nbsp;(int),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;e.g.&nbsp;df.<a href="#TimeSeries-interpolate">interpolate</a>(method='polynomial',&nbsp;order=4).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the&nbsp;index.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'krogh',&nbsp;'piecewise_polynomial',&nbsp;'spline',&nbsp;'pchip'&nbsp;and&nbsp;'akima'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;all&nbsp;wrappers&nbsp;around&nbsp;the&nbsp;scipy&nbsp;interpolation&nbsp;methods&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;similar&nbsp;names.&nbsp;These&nbsp;use&nbsp;the&nbsp;actual&nbsp;numerical&nbsp;values&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index.&nbsp;For&nbsp;more&nbsp;information&nbsp;on&nbsp;their&nbsp;behavior,&nbsp;see&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`scipy&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__">http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;`tutorial&nbsp;documentation<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__">http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html&gt;`__</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;'from_derivatives'&nbsp;refers&nbsp;to&nbsp;BPoly.from_derivatives&nbsp;which<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;replaces&nbsp;'piecewise_polynomial'&nbsp;interpolation&nbsp;method&nbsp;in<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;0.18<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;support&nbsp;for&nbsp;the&nbsp;'akima'&nbsp;method<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Added&nbsp;interpolate&nbsp;method&nbsp;'from_derivatives'&nbsp;which&nbsp;replaces<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'piecewise_polynomial'&nbsp;in&nbsp;scipy&nbsp;0.18;&nbsp;backwards-compatible&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;scipy&nbsp;&lt;&nbsp;0.18<br>
-&nbsp;<br>
-axis&nbsp;:&nbsp;{0,&nbsp;1},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;0:&nbsp;fill&nbsp;column-by-column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;1:&nbsp;fill&nbsp;row-by-row<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NaNs&nbsp;to&nbsp;fill.&nbsp;Must&nbsp;be&nbsp;greater&nbsp;than&nbsp;0.<br>
-limit_direction&nbsp;:&nbsp;{'forward',&nbsp;'backward',&nbsp;'both'},&nbsp;default&nbsp;'forward'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;limit&nbsp;is&nbsp;specified,&nbsp;consecutive&nbsp;NaNs&nbsp;will&nbsp;be&nbsp;filled&nbsp;in&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;direction.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Update&nbsp;the&nbsp;NDFrame&nbsp;in&nbsp;place&nbsp;if&nbsp;possible.<br>
-downcast&nbsp;:&nbsp;optional,&nbsp;'infer'&nbsp;or&nbsp;None,&nbsp;defaults&nbsp;to&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Downcast&nbsp;dtypes&nbsp;if&nbsp;possible.<br>
-kwargs&nbsp;:&nbsp;keyword&nbsp;arguments&nbsp;to&nbsp;pass&nbsp;on&nbsp;to&nbsp;the&nbsp;interpolating&nbsp;function.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;interpolated&nbsp;at&nbsp;the&nbsp;NaNs<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-reindex,&nbsp;replace,&nbsp;fillna<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Filling&nbsp;in&nbsp;NaNs<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>([0,&nbsp;1,&nbsp;np.nan,&nbsp;3])<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-interpolate">interpolate</a>()<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-dtype:&nbsp;float64</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-isnull"><strong>isnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-notnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;isnull</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-last"><strong>last</strong></a>(self, offset)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;subsetting&nbsp;final&nbsp;periods&nbsp;of&nbsp;time&nbsp;series&nbsp;data<br>
-based&nbsp;on&nbsp;a&nbsp;date&nbsp;offset.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-offset&nbsp;:&nbsp;string,&nbsp;DateOffset,&nbsp;dateutil.relativedelta<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-ts.<a href="#TimeSeries-last">last</a>('5M')&nbsp;-&gt;&nbsp;Last&nbsp;5&nbsp;months<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-subset&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-mask"><strong>mask</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;False&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;mask&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``False``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#TimeSeries-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#TimeSeries-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``mask``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).<a href="#TimeSeries-reshape">reshape</a>(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#TimeSeries-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#TimeSeries-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-notnull"><strong>notnull</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;boolean&nbsp;same-sized&nbsp;<a href="builtins.html#object">object</a>&nbsp;indicating&nbsp;if&nbsp;the&nbsp;values&nbsp;are<br>
-not&nbsp;null.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-isnull&nbsp;:&nbsp;boolean&nbsp;inverse&nbsp;of&nbsp;notnull</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-pct_change"><strong>pct_change</strong></a>(self, periods=1, fill_method='pad', limit=None, freq=None, **kwargs)</dt><dd><tt>Percent&nbsp;change&nbsp;over&nbsp;given&nbsp;number&nbsp;of&nbsp;periods.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int,&nbsp;default&nbsp;1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Periods&nbsp;to&nbsp;shift&nbsp;for&nbsp;forming&nbsp;percent&nbsp;change<br>
-fill_method&nbsp;:&nbsp;str,&nbsp;default&nbsp;'pad'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;How&nbsp;to&nbsp;handle&nbsp;NAs&nbsp;before&nbsp;computing&nbsp;percent&nbsp;changes<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;NAs&nbsp;to&nbsp;fill&nbsp;before&nbsp;stopping<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;offset&nbsp;alias&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;time&nbsp;series&nbsp;API&nbsp;(e.g.&nbsp;'M'&nbsp;or&nbsp;BDay())<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-chg&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-By&nbsp;default,&nbsp;the&nbsp;percentage&nbsp;change&nbsp;is&nbsp;calculated&nbsp;along&nbsp;the&nbsp;stat<br>
-axis:&nbsp;0,&nbsp;or&nbsp;``Index``,&nbsp;for&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;and&nbsp;1,&nbsp;or&nbsp;``minor``&nbsp;for<br>
-``Panel``.&nbsp;You&nbsp;can&nbsp;change&nbsp;this&nbsp;with&nbsp;the&nbsp;``axis``&nbsp;keyword&nbsp;argument.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-pipe"><strong>pipe</strong></a>(self, func, *args, **kwargs)</dt><dd><tt>Apply&nbsp;func(self,&nbsp;\*args,&nbsp;\*\*kwargs)<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.2<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-func&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;function&nbsp;to&nbsp;apply&nbsp;to&nbsp;the&nbsp;NDFrame.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``args``,&nbsp;and&nbsp;``kwargs``&nbsp;are&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Alternatively&nbsp;a&nbsp;``(callable,&nbsp;data_keyword)``&nbsp;tuple&nbsp;where<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``data_keyword``&nbsp;is&nbsp;a&nbsp;string&nbsp;indicating&nbsp;the&nbsp;keyword&nbsp;of<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``callable``&nbsp;that&nbsp;expects&nbsp;the&nbsp;NDFrame.<br>
-args&nbsp;:&nbsp;positional&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-kwargs&nbsp;:&nbsp;a&nbsp;dictionary&nbsp;of&nbsp;keyword&nbsp;arguments&nbsp;passed&nbsp;into&nbsp;``func``.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-<a href="builtins.html#object">object</a>&nbsp;:&nbsp;the&nbsp;return&nbsp;type&nbsp;of&nbsp;``func``.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-&nbsp;<br>
-Use&nbsp;``.pipe``&nbsp;when&nbsp;chaining&nbsp;together&nbsp;functions&nbsp;that&nbsp;expect<br>
-on&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;DataFrames.&nbsp;Instead&nbsp;of&nbsp;writing<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;f(g(h(df),&nbsp;arg1=a),&nbsp;arg2=b,&nbsp;arg3=c)<br>
-&nbsp;<br>
-You&nbsp;can&nbsp;write<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#TimeSeries-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#TimeSeries-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#TimeSeries-pipe">pipe</a>(f,&nbsp;arg2=b,&nbsp;arg3=c)<br>
-...&nbsp;)<br>
-&nbsp;<br>
-If&nbsp;you&nbsp;have&nbsp;a&nbsp;function&nbsp;that&nbsp;takes&nbsp;the&nbsp;data&nbsp;as&nbsp;(say)&nbsp;the&nbsp;second<br>
-argument,&nbsp;pass&nbsp;a&nbsp;tuple&nbsp;indicating&nbsp;which&nbsp;keyword&nbsp;expects&nbsp;the<br>
-data.&nbsp;For&nbsp;example,&nbsp;suppose&nbsp;``f``&nbsp;takes&nbsp;its&nbsp;data&nbsp;as&nbsp;``arg2``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;(df.<a href="#TimeSeries-pipe">pipe</a>(h)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#TimeSeries-pipe">pipe</a>(g,&nbsp;arg1=a)<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;.<a href="#TimeSeries-pipe">pipe</a>((f,&nbsp;'arg2'),&nbsp;arg1=a,&nbsp;arg3=c)<br>
-...&nbsp;&nbsp;)<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.apply<br>
-pandas.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.applymap<br>
-pandas.<a href="pandas.core.series.html#Series">Series</a>.map</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-pop"><strong>pop</strong></a>(self, item)</dt><dd><tt>Return&nbsp;item&nbsp;and&nbsp;drop&nbsp;from&nbsp;frame.&nbsp;Raise&nbsp;KeyError&nbsp;if&nbsp;not&nbsp;found.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-rank"><strong>rank</strong></a>(self, axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)</dt><dd><tt>Compute&nbsp;numerical&nbsp;data&nbsp;ranks&nbsp;(1&nbsp;through&nbsp;n)&nbsp;along&nbsp;axis.&nbsp;Equal&nbsp;values&nbsp;are<br>
-assigned&nbsp;a&nbsp;rank&nbsp;that&nbsp;is&nbsp;the&nbsp;average&nbsp;of&nbsp;the&nbsp;ranks&nbsp;of&nbsp;those&nbsp;values<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;{0&nbsp;or&nbsp;'index',&nbsp;1&nbsp;or&nbsp;'columns'},&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;to&nbsp;direct&nbsp;ranking<br>
-method&nbsp;:&nbsp;{'average',&nbsp;'min',&nbsp;'max',&nbsp;'first',&nbsp;'dense'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;average:&nbsp;average&nbsp;rank&nbsp;of&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;min:&nbsp;lowest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;max:&nbsp;highest&nbsp;rank&nbsp;in&nbsp;group<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;first:&nbsp;ranks&nbsp;assigned&nbsp;in&nbsp;order&nbsp;they&nbsp;appear&nbsp;in&nbsp;the&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dense:&nbsp;like&nbsp;'min',&nbsp;but&nbsp;rank&nbsp;always&nbsp;increases&nbsp;by&nbsp;1&nbsp;between&nbsp;groups<br>
-numeric_only&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Include&nbsp;only&nbsp;float,&nbsp;int,&nbsp;boolean&nbsp;data.&nbsp;Valid&nbsp;only&nbsp;for&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Panel&nbsp;objects<br>
-na_option&nbsp;:&nbsp;{'keep',&nbsp;'top',&nbsp;'bottom'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;keep:&nbsp;leave&nbsp;NA&nbsp;values&nbsp;where&nbsp;they&nbsp;are<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;top:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;ascending<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;bottom:&nbsp;smallest&nbsp;rank&nbsp;if&nbsp;descending<br>
-ascending&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;False&nbsp;for&nbsp;ranks&nbsp;by&nbsp;high&nbsp;(1)&nbsp;to&nbsp;low&nbsp;(N)<br>
-pct&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Computes&nbsp;percentage&nbsp;rank&nbsp;of&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-ranks&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-reindex_like"><strong>reindex_like</strong></a>(self, other, method=None, copy=True, limit=None, tolerance=None)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;matching&nbsp;indices&nbsp;to&nbsp;myself.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-other&nbsp;:&nbsp;Object<br>
-method&nbsp;:&nbsp;string&nbsp;or&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;number&nbsp;of&nbsp;consecutive&nbsp;labels&nbsp;to&nbsp;fill&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-tolerance&nbsp;:&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;distance&nbsp;between&nbsp;labels&nbsp;of&nbsp;the&nbsp;other&nbsp;<a href="builtins.html#object">object</a>&nbsp;and&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>&nbsp;for&nbsp;inexact&nbsp;matches.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.17.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Like&nbsp;calling&nbsp;s.<a href="#TimeSeries-reindex">reindex</a>(index=other.index,&nbsp;columns=other.columns,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;method=...)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-reindexed&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-rename_axis"><strong>rename_axis</strong></a>(self, mapper, axis=0, copy=True, inplace=False)</dt><dd><tt>Alter&nbsp;index&nbsp;and&nbsp;/&nbsp;or&nbsp;columns&nbsp;using&nbsp;input&nbsp;function&nbsp;or&nbsp;functions.<br>
-A&nbsp;scalar&nbsp;or&nbsp;list-like&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;``Index.name``<br>
-or&nbsp;``MultiIndex.names``&nbsp;attribute.<br>
-A&nbsp;function&nbsp;or&nbsp;dict&nbsp;for&nbsp;``mapper``&nbsp;will&nbsp;alter&nbsp;the&nbsp;labels.<br>
-Function&nbsp;/&nbsp;dict&nbsp;values&nbsp;must&nbsp;be&nbsp;unique&nbsp;(1-to-1).&nbsp;Labels&nbsp;not&nbsp;contained&nbsp;in<br>
-a&nbsp;dict&nbsp;/&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;will&nbsp;be&nbsp;left&nbsp;as-is.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-mapper&nbsp;:&nbsp;scalar,&nbsp;list-like,&nbsp;dict-like&nbsp;or&nbsp;function,&nbsp;optional<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;default&nbsp;0<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;copy&nbsp;underlying&nbsp;data<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-renamed&nbsp;:&nbsp;type&nbsp;of&nbsp;caller<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pandas.NDFrame.rename<br>
-pandas.Index.rename<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({"A":&nbsp;[1,&nbsp;2,&nbsp;3],&nbsp;"B":&nbsp;[4,&nbsp;5,&nbsp;6]})<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-rename_axis">rename_axis</a>("foo")&nbsp;&nbsp;#&nbsp;scalar,&nbsp;alters&nbsp;df.index.name<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-foo<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-rename_axis">rename_axis</a>(lambda&nbsp;x:&nbsp;2&nbsp;*&nbsp;x)&nbsp;&nbsp;#&nbsp;function:&nbsp;alters&nbsp;labels<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-4&nbsp;&nbsp;3&nbsp;&nbsp;6<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-rename_axis">rename_axis</a>({"A":&nbsp;"ehh",&nbsp;"C":&nbsp;"see"},&nbsp;axis="columns")&nbsp;&nbsp;#&nbsp;mapping<br>
-&nbsp;&nbsp;&nbsp;ehh&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;4<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;5<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;3&nbsp;&nbsp;6</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-replace"><strong>replace</strong></a>(self, to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad', axis=None)</dt><dd><tt>Replace&nbsp;values&nbsp;given&nbsp;in&nbsp;'to_replace'&nbsp;with&nbsp;'value'.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-to_replace&nbsp;:&nbsp;str,&nbsp;regex,&nbsp;list,&nbsp;dict,&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;numeric,&nbsp;or&nbsp;None<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;str&nbsp;or&nbsp;regex:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str:&nbsp;string&nbsp;exactly&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;`value`<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;regex:&nbsp;regexs&nbsp;matching&nbsp;`to_replace`&nbsp;will&nbsp;be&nbsp;replaced&nbsp;with<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`value`<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;list&nbsp;of&nbsp;str,&nbsp;regex,&nbsp;or&nbsp;numeric:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;First,&nbsp;if&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;both&nbsp;lists,&nbsp;they<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Second,&nbsp;if&nbsp;``regex=True``&nbsp;then&nbsp;all&nbsp;of&nbsp;the&nbsp;strings&nbsp;in&nbsp;**both**<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;regexs&nbsp;otherwise&nbsp;they&nbsp;will&nbsp;match<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;directly.&nbsp;This&nbsp;doesn't&nbsp;matter&nbsp;much&nbsp;for&nbsp;`value`&nbsp;since&nbsp;there<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;are&nbsp;only&nbsp;a&nbsp;few&nbsp;possible&nbsp;substitution&nbsp;regexes&nbsp;you&nbsp;can&nbsp;use.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;str&nbsp;and&nbsp;regex&nbsp;rules&nbsp;apply&nbsp;as&nbsp;above.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;dict:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Nested&nbsp;dictionaries,&nbsp;e.g.,&nbsp;{'a':&nbsp;{'b':&nbsp;nan}},&nbsp;are&nbsp;read&nbsp;as<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;follows:&nbsp;look&nbsp;in&nbsp;column&nbsp;'a'&nbsp;for&nbsp;the&nbsp;value&nbsp;'b'&nbsp;and&nbsp;replace&nbsp;it<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;nan.&nbsp;You&nbsp;can&nbsp;nest&nbsp;regular&nbsp;expressions&nbsp;as&nbsp;well.&nbsp;Note&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;column&nbsp;names&nbsp;(the&nbsp;top-level&nbsp;dictionary&nbsp;keys&nbsp;in&nbsp;a&nbsp;nested<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dictionary)&nbsp;**cannot**&nbsp;be&nbsp;regular&nbsp;expressions.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;Keys&nbsp;map&nbsp;to&nbsp;column&nbsp;names&nbsp;and&nbsp;values&nbsp;map&nbsp;to&nbsp;substitution<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;values.&nbsp;You&nbsp;can&nbsp;treat&nbsp;this&nbsp;as&nbsp;a&nbsp;special&nbsp;case&nbsp;of&nbsp;passing&nbsp;two<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lists&nbsp;except&nbsp;that&nbsp;you&nbsp;are&nbsp;specifying&nbsp;the&nbsp;column&nbsp;to&nbsp;search&nbsp;in.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;None:<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;This&nbsp;means&nbsp;that&nbsp;the&nbsp;``regex``&nbsp;argument&nbsp;must&nbsp;be&nbsp;a&nbsp;string,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;compiled&nbsp;regular&nbsp;expression,&nbsp;or&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;such&nbsp;elements.&nbsp;If&nbsp;`value`&nbsp;is&nbsp;also&nbsp;``None``&nbsp;then&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**must**&nbsp;be&nbsp;a&nbsp;nested&nbsp;dictionary&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;See&nbsp;the&nbsp;examples&nbsp;section&nbsp;for&nbsp;examples&nbsp;of&nbsp;each&nbsp;of&nbsp;these.<br>
-value&nbsp;:&nbsp;scalar,&nbsp;dict,&nbsp;list,&nbsp;str,&nbsp;regex,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Value&nbsp;to&nbsp;use&nbsp;to&nbsp;fill&nbsp;holes&nbsp;(e.g.&nbsp;0),&nbsp;alternately&nbsp;a&nbsp;dict&nbsp;of&nbsp;values<br>
-&nbsp;&nbsp;&nbsp;&nbsp;specifying&nbsp;which&nbsp;value&nbsp;to&nbsp;use&nbsp;for&nbsp;each&nbsp;column&nbsp;(columns&nbsp;not&nbsp;in&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict&nbsp;will&nbsp;not&nbsp;be&nbsp;filled).&nbsp;Regular&nbsp;expressions,&nbsp;strings&nbsp;and&nbsp;lists&nbsp;or<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dicts&nbsp;of&nbsp;such&nbsp;objects&nbsp;are&nbsp;also&nbsp;allowed.<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;True,&nbsp;in&nbsp;place.&nbsp;Note:&nbsp;this&nbsp;will&nbsp;modify&nbsp;any<br>
-&nbsp;&nbsp;&nbsp;&nbsp;other&nbsp;views&nbsp;on&nbsp;this&nbsp;<a href="builtins.html#object">object</a>&nbsp;(e.g.&nbsp;a&nbsp;column&nbsp;form&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>).<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Returns&nbsp;the&nbsp;caller&nbsp;if&nbsp;this&nbsp;is&nbsp;True.<br>
-limit&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Maximum&nbsp;size&nbsp;gap&nbsp;to&nbsp;forward&nbsp;or&nbsp;backward&nbsp;fill<br>
-regex&nbsp;:&nbsp;bool&nbsp;or&nbsp;same&nbsp;types&nbsp;as&nbsp;`to_replace`,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;interpret&nbsp;`to_replace`&nbsp;and/or&nbsp;`value`&nbsp;as&nbsp;regular<br>
-&nbsp;&nbsp;&nbsp;&nbsp;expressions.&nbsp;If&nbsp;this&nbsp;is&nbsp;``True``&nbsp;then&nbsp;`to_replace`&nbsp;*must*&nbsp;be&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;string.&nbsp;Otherwise,&nbsp;`to_replace`&nbsp;must&nbsp;be&nbsp;``None``&nbsp;because&nbsp;this<br>
-&nbsp;&nbsp;&nbsp;&nbsp;parameter&nbsp;will&nbsp;be&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;regular&nbsp;expression&nbsp;or&nbsp;a&nbsp;list,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;dict,&nbsp;or&nbsp;array&nbsp;of&nbsp;regular&nbsp;expressions.<br>
-method&nbsp;:&nbsp;string,&nbsp;optional,&nbsp;{'pad',&nbsp;'ffill',&nbsp;'bfill'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;method&nbsp;to&nbsp;use&nbsp;when&nbsp;for&nbsp;replacement,&nbsp;when&nbsp;``to_replace``&nbsp;is&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;``list``.<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-NDFrame.reindex<br>
-NDFrame.asfreq<br>
-NDFrame.fillna<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-filled&nbsp;:&nbsp;NDFrame<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-AssertionError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;a&nbsp;``bool``&nbsp;and&nbsp;`to_replace`&nbsp;is&nbsp;not&nbsp;``None``.<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;a&nbsp;``dict``&nbsp;and&nbsp;`value`&nbsp;is&nbsp;not&nbsp;a&nbsp;``list``,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;``dict``,&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``,&nbsp;or&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;is&nbsp;``None``&nbsp;and&nbsp;`regex`&nbsp;is&nbsp;not&nbsp;compilable&nbsp;into&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;regular&nbsp;expression&nbsp;or&nbsp;is&nbsp;a&nbsp;list,&nbsp;dict,&nbsp;<a href="numpy.html#ndarray">ndarray</a>,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-ValueError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;If&nbsp;`to_replace`&nbsp;and&nbsp;`value`&nbsp;are&nbsp;``list``&nbsp;s&nbsp;or&nbsp;``<a href="numpy.html#ndarray">ndarray</a>``&nbsp;s,&nbsp;but<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;they&nbsp;are&nbsp;not&nbsp;the&nbsp;same&nbsp;length.<br>
-&nbsp;<br>
-Notes<br>
------<br>
-*&nbsp;Regex&nbsp;substitution&nbsp;is&nbsp;performed&nbsp;under&nbsp;the&nbsp;hood&nbsp;with&nbsp;``re.sub``.&nbsp;The<br>
-&nbsp;&nbsp;rules&nbsp;for&nbsp;substitution&nbsp;for&nbsp;``re.sub``&nbsp;are&nbsp;the&nbsp;same.<br>
-*&nbsp;Regular&nbsp;expressions&nbsp;will&nbsp;only&nbsp;substitute&nbsp;on&nbsp;strings,&nbsp;meaning&nbsp;you<br>
-&nbsp;&nbsp;cannot&nbsp;provide,&nbsp;for&nbsp;example,&nbsp;a&nbsp;regular&nbsp;expression&nbsp;matching&nbsp;floating<br>
-&nbsp;&nbsp;point&nbsp;numbers&nbsp;and&nbsp;expect&nbsp;the&nbsp;columns&nbsp;in&nbsp;your&nbsp;frame&nbsp;that&nbsp;have&nbsp;a<br>
-&nbsp;&nbsp;numeric&nbsp;dtype&nbsp;to&nbsp;be&nbsp;matched.&nbsp;However,&nbsp;if&nbsp;those&nbsp;floating&nbsp;point&nbsp;numbers<br>
-&nbsp;&nbsp;*are*&nbsp;strings,&nbsp;then&nbsp;you&nbsp;can&nbsp;do&nbsp;this.<br>
-*&nbsp;This&nbsp;method&nbsp;has&nbsp;*a&nbsp;lot*&nbsp;of&nbsp;options.&nbsp;You&nbsp;are&nbsp;encouraged&nbsp;to&nbsp;experiment<br>
-&nbsp;&nbsp;and&nbsp;play&nbsp;with&nbsp;this&nbsp;method&nbsp;to&nbsp;gain&nbsp;intuition&nbsp;about&nbsp;how&nbsp;it&nbsp;works.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-resample"><strong>resample</strong></a>(self, rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0, on=None, level=None)</dt><dd><tt>Convenience&nbsp;method&nbsp;for&nbsp;frequency&nbsp;conversion&nbsp;and&nbsp;resampling&nbsp;of&nbsp;time<br>
-series.&nbsp;&nbsp;Object&nbsp;must&nbsp;have&nbsp;a&nbsp;datetime-like&nbsp;index&nbsp;(DatetimeIndex,<br>
-PeriodIndex,&nbsp;or&nbsp;TimedeltaIndex),&nbsp;or&nbsp;pass&nbsp;datetime-like&nbsp;values<br>
-to&nbsp;the&nbsp;on&nbsp;or&nbsp;level&nbsp;keyword.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-rule&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;offset&nbsp;string&nbsp;or&nbsp;<a href="builtins.html#object">object</a>&nbsp;representing&nbsp;target&nbsp;conversion<br>
-axis&nbsp;:&nbsp;int,&nbsp;optional,&nbsp;default&nbsp;0<br>
-closed&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;side&nbsp;of&nbsp;bin&nbsp;interval&nbsp;is&nbsp;closed<br>
-label&nbsp;:&nbsp;{'right',&nbsp;'left'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Which&nbsp;bin&nbsp;edge&nbsp;label&nbsp;to&nbsp;label&nbsp;bucket&nbsp;with<br>
-convention&nbsp;:&nbsp;{'start',&nbsp;'end',&nbsp;'s',&nbsp;'e'}<br>
-loffset&nbsp;:&nbsp;timedelta<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Adjust&nbsp;the&nbsp;resampled&nbsp;time&nbsp;labels<br>
-base&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;frequencies&nbsp;that&nbsp;evenly&nbsp;subdivide&nbsp;1&nbsp;day,&nbsp;the&nbsp;"origin"&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;aggregated&nbsp;intervals.&nbsp;For&nbsp;example,&nbsp;for&nbsp;'5min'&nbsp;frequency,&nbsp;base&nbsp;could<br>
-&nbsp;&nbsp;&nbsp;&nbsp;range&nbsp;from&nbsp;0&nbsp;through&nbsp;4.&nbsp;Defaults&nbsp;to&nbsp;0<br>
-on&nbsp;:&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;column&nbsp;to&nbsp;use&nbsp;instead&nbsp;of&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-level&nbsp;:&nbsp;string&nbsp;or&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;a&nbsp;MultiIndex,&nbsp;level&nbsp;(name&nbsp;or&nbsp;number)&nbsp;to&nbsp;use&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;resampling.&nbsp;&nbsp;Level&nbsp;must&nbsp;be&nbsp;datetime-like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Notes<br>
------<br>
-To&nbsp;learn&nbsp;more&nbsp;about&nbsp;the&nbsp;offset&nbsp;strings,&nbsp;please&nbsp;see&nbsp;`this&nbsp;link<br>
-&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases&gt;`__</a>.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Start&nbsp;by&nbsp;creating&nbsp;a&nbsp;series&nbsp;with&nbsp;9&nbsp;one&nbsp;minute&nbsp;timestamps.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;index&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;series&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(9),&nbsp;index=index)<br>
-&gt;&gt;&gt;&nbsp;series<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:04:00&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-2000-01-01&nbsp;00:05:00&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:07:00&nbsp;&nbsp;&nbsp;&nbsp;7<br>
-2000-01-01&nbsp;00:08:00&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-Freq:&nbsp;T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;and&nbsp;sum&nbsp;the&nbsp;values<br>
-of&nbsp;the&nbsp;timestamps&nbsp;falling&nbsp;into&nbsp;a&nbsp;bin.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeSeries-resample">resample</a>('3T').<a href="#TimeSeries-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;label&nbsp;each<br>
-bin&nbsp;using&nbsp;the&nbsp;right&nbsp;edge&nbsp;instead&nbsp;of&nbsp;the&nbsp;left.&nbsp;Please&nbsp;note&nbsp;that&nbsp;the<br>
-value&nbsp;in&nbsp;the&nbsp;bucket&nbsp;used&nbsp;as&nbsp;the&nbsp;label&nbsp;is&nbsp;not&nbsp;included&nbsp;in&nbsp;the&nbsp;bucket,<br>
-which&nbsp;it&nbsp;labels.&nbsp;For&nbsp;example,&nbsp;in&nbsp;the&nbsp;original&nbsp;series&nbsp;the<br>
-bucket&nbsp;``2000-01-01&nbsp;00:03:00``&nbsp;contains&nbsp;the&nbsp;value&nbsp;3,&nbsp;but&nbsp;the&nbsp;summed<br>
-value&nbsp;in&nbsp;the&nbsp;resampled&nbsp;bucket&nbsp;with&nbsp;the&nbsp;label``2000-01-01&nbsp;00:03:00``<br>
-does&nbsp;not&nbsp;include&nbsp;3&nbsp;(if&nbsp;it&nbsp;did,&nbsp;the&nbsp;summed&nbsp;value&nbsp;would&nbsp;be&nbsp;6,&nbsp;not&nbsp;3).<br>
-To&nbsp;include&nbsp;this&nbsp;value&nbsp;close&nbsp;the&nbsp;right&nbsp;side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval&nbsp;as<br>
-illustrated&nbsp;in&nbsp;the&nbsp;example&nbsp;below&nbsp;this&nbsp;one.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeSeries-resample">resample</a>('3T',&nbsp;label='right').<a href="#TimeSeries-sum">sum</a>()<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;12<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;21<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Downsample&nbsp;the&nbsp;series&nbsp;into&nbsp;3&nbsp;minute&nbsp;bins&nbsp;as&nbsp;above,&nbsp;but&nbsp;close&nbsp;the&nbsp;right<br>
-side&nbsp;of&nbsp;the&nbsp;bin&nbsp;interval.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeSeries-resample">resample</a>('3T',&nbsp;label='right',&nbsp;closed='right').<a href="#TimeSeries-sum">sum</a>()<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-2000-01-01&nbsp;00:09:00&nbsp;&nbsp;&nbsp;&nbsp;15<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeSeries-resample">resample</a>('30S').<a href="#TimeSeries-asfreq">asfreq</a>()[0:5]&nbsp;#select&nbsp;first&nbsp;5&nbsp;rows<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;0.0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;1.0<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;NaN<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;2.0<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;float64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the&nbsp;``NaN``<br>
-values&nbsp;using&nbsp;the&nbsp;``pad``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeSeries-resample">resample</a>('30S').pad()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Upsample&nbsp;the&nbsp;series&nbsp;into&nbsp;30&nbsp;second&nbsp;bins&nbsp;and&nbsp;fill&nbsp;the<br>
-``NaN``&nbsp;values&nbsp;using&nbsp;the&nbsp;``bfill``&nbsp;method.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeSeries-resample">resample</a>('30S').<a href="#TimeSeries-bfill">bfill</a>()[0:5]<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;0<br>
-2000-01-01&nbsp;00:00:30&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:00&nbsp;&nbsp;&nbsp;&nbsp;1<br>
-2000-01-01&nbsp;00:01:30&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-2000-01-01&nbsp;00:02:00&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Freq:&nbsp;30S,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-Pass&nbsp;a&nbsp;custom&nbsp;function&nbsp;via&nbsp;``apply``<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;def&nbsp;custom_resampler(array_like):<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;np.<a href="#TimeSeries-sum">sum</a>(array_like)+5<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;series.<a href="#TimeSeries-resample">resample</a>('3T').<a href="#TimeSeries-apply">apply</a>(custom_resampler)<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;8<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;&nbsp;&nbsp;17<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;&nbsp;&nbsp;26<br>
-Freq:&nbsp;3T,&nbsp;dtype:&nbsp;int64<br>
-&nbsp;<br>
-For&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;objects,&nbsp;the&nbsp;keyword&nbsp;``on``&nbsp;can&nbsp;be&nbsp;used&nbsp;to&nbsp;specify&nbsp;the<br>
-column&nbsp;instead&nbsp;of&nbsp;the&nbsp;index&nbsp;for&nbsp;resampling.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=9*[range(4)],&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'])<br>
-&gt;&gt;&gt;&nbsp;df['time']&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=9,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-resample">resample</a>('3T',&nbsp;on='time').<a href="#TimeSeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;c&nbsp;&nbsp;d<br>
-time<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-2000-01-01&nbsp;00:06:00&nbsp;&nbsp;0&nbsp;&nbsp;3&nbsp;&nbsp;6&nbsp;&nbsp;9<br>
-&nbsp;<br>
-For&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;with&nbsp;MultiIndex,&nbsp;the&nbsp;keyword&nbsp;``level``&nbsp;can&nbsp;be&nbsp;used&nbsp;to<br>
-specify&nbsp;on&nbsp;level&nbsp;the&nbsp;resampling&nbsp;needs&nbsp;to&nbsp;take&nbsp;place.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;time&nbsp;=&nbsp;pd.date_range('1/1/2000',&nbsp;periods=5,&nbsp;freq='T')<br>
-&gt;&gt;&gt;&nbsp;df2&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(data=10*[range(4)],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['a',&nbsp;'b',&nbsp;'c',&nbsp;'d'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=pd.MultiIndex.from_product([time,&nbsp;[1,&nbsp;2]])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;)<br>
-&gt;&gt;&gt;&nbsp;df2.<a href="#TimeSeries-resample">resample</a>('3T',&nbsp;level=0).<a href="#TimeSeries-sum">sum</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;&nbsp;b&nbsp;&nbsp;&nbsp;c&nbsp;&nbsp;&nbsp;d<br>
-2000-01-01&nbsp;00:00:00&nbsp;&nbsp;0&nbsp;&nbsp;6&nbsp;&nbsp;12&nbsp;&nbsp;18<br>
-2000-01-01&nbsp;00:03:00&nbsp;&nbsp;0&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;8&nbsp;&nbsp;12</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-sample"><strong>sample</strong></a>(self, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None)</dt><dd><tt>Returns&nbsp;a&nbsp;random&nbsp;sample&nbsp;of&nbsp;items&nbsp;from&nbsp;an&nbsp;axis&nbsp;of&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-..&nbsp;versionadded::&nbsp;0.16.1<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-n&nbsp;:&nbsp;int,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;items&nbsp;from&nbsp;axis&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`frac`.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;=&nbsp;1&nbsp;if&nbsp;`frac`&nbsp;=&nbsp;None.<br>
-frac&nbsp;:&nbsp;float,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Fraction&nbsp;of&nbsp;axis&nbsp;items&nbsp;to&nbsp;return.&nbsp;Cannot&nbsp;be&nbsp;used&nbsp;with&nbsp;`n`.<br>
-replace&nbsp;:&nbsp;boolean,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Sample&nbsp;with&nbsp;or&nbsp;without&nbsp;replacement.&nbsp;Default&nbsp;=&nbsp;False.<br>
-weights&nbsp;:&nbsp;str&nbsp;or&nbsp;<a href="numpy.html#ndarray">ndarray</a>-like,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Default&nbsp;'None'&nbsp;results&nbsp;in&nbsp;equal&nbsp;probability&nbsp;weighting.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;passed&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;will&nbsp;align&nbsp;with&nbsp;target&nbsp;<a href="builtins.html#object">object</a>&nbsp;on&nbsp;index.&nbsp;Index<br>
-&nbsp;&nbsp;&nbsp;&nbsp;values&nbsp;in&nbsp;weights&nbsp;not&nbsp;found&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;will&nbsp;be&nbsp;ignored&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;index&nbsp;values&nbsp;in&nbsp;sampled&nbsp;<a href="builtins.html#object">object</a>&nbsp;not&nbsp;in&nbsp;weights&nbsp;will&nbsp;be&nbsp;assigned<br>
-&nbsp;&nbsp;&nbsp;&nbsp;weights&nbsp;of&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;called&nbsp;on&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;will&nbsp;accept&nbsp;the&nbsp;name&nbsp;of&nbsp;a&nbsp;column<br>
-&nbsp;&nbsp;&nbsp;&nbsp;when&nbsp;axis&nbsp;=&nbsp;0.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Unless&nbsp;weights&nbsp;are&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>,&nbsp;weights&nbsp;must&nbsp;be&nbsp;same&nbsp;length&nbsp;as&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;being&nbsp;sampled.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;weights&nbsp;do&nbsp;not&nbsp;sum&nbsp;to&nbsp;1,&nbsp;they&nbsp;will&nbsp;be&nbsp;normalized&nbsp;to&nbsp;sum&nbsp;to&nbsp;1.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Missing&nbsp;values&nbsp;in&nbsp;the&nbsp;weights&nbsp;column&nbsp;will&nbsp;be&nbsp;treated&nbsp;as&nbsp;zero.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;inf&nbsp;and&nbsp;-inf&nbsp;values&nbsp;not&nbsp;allowed.<br>
-random_state&nbsp;:&nbsp;int&nbsp;or&nbsp;numpy.random.RandomState,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Seed&nbsp;for&nbsp;the&nbsp;random&nbsp;number&nbsp;generator&nbsp;(if&nbsp;int),&nbsp;or&nbsp;numpy&nbsp;RandomState<br>
-&nbsp;&nbsp;&nbsp;&nbsp;<a href="builtins.html#object">object</a>.<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;string,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;sample.&nbsp;Accepts&nbsp;axis&nbsp;number&nbsp;or&nbsp;name.&nbsp;Default&nbsp;is&nbsp;stat&nbsp;axis<br>
-&nbsp;&nbsp;&nbsp;&nbsp;for&nbsp;given&nbsp;data&nbsp;type&nbsp;(0&nbsp;for&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;and&nbsp;DataFrames,&nbsp;1&nbsp;for&nbsp;Panels).<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-A&nbsp;new&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;type&nbsp;as&nbsp;caller.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-Generate&nbsp;an&nbsp;example&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``&nbsp;and&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(np.random.randn(50))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-head">head</a>()<br>
-0&nbsp;&nbsp;&nbsp;-0.038497<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.820773<br>
-2&nbsp;&nbsp;&nbsp;-0.972766<br>
-3&nbsp;&nbsp;&nbsp;-1.598270<br>
-4&nbsp;&nbsp;&nbsp;-1.095526<br>
-dtype:&nbsp;float64<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.random.randn(50,&nbsp;4),&nbsp;columns=list('ABCD'))<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-head">head</a>()<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-0&nbsp;&nbsp;0.016443&nbsp;-2.318952&nbsp;-0.566372&nbsp;-1.028078<br>
-1&nbsp;-1.051921&nbsp;&nbsp;0.438836&nbsp;&nbsp;0.658280&nbsp;-0.175797<br>
-2&nbsp;-1.243569&nbsp;-0.364626&nbsp;-0.215065&nbsp;&nbsp;0.057736<br>
-3&nbsp;&nbsp;1.768216&nbsp;&nbsp;0.404512&nbsp;-0.385604&nbsp;-1.457834<br>
-4&nbsp;&nbsp;1.072446&nbsp;-1.137172&nbsp;&nbsp;0.314194&nbsp;-0.046661<br>
-&nbsp;<br>
-Next&nbsp;extract&nbsp;a&nbsp;random&nbsp;sample&nbsp;from&nbsp;both&nbsp;of&nbsp;these&nbsp;objects...<br>
-&nbsp;<br>
-3&nbsp;random&nbsp;elements&nbsp;from&nbsp;the&nbsp;``<a href="pandas.core.series.html#Series">Series</a>``:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-sample">sample</a>(n=3)<br>
-27&nbsp;&nbsp;&nbsp;-0.994689<br>
-55&nbsp;&nbsp;&nbsp;-1.049016<br>
-67&nbsp;&nbsp;&nbsp;-0.224565<br>
-dtype:&nbsp;float64<br>
-&nbsp;<br>
-And&nbsp;a&nbsp;random&nbsp;10%&nbsp;of&nbsp;the&nbsp;``<a href="pandas.core.frame.html#DataFrame">DataFrame</a>``&nbsp;with&nbsp;replacement:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-sample">sample</a>(frac=0.1,&nbsp;replace=True)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;D<br>
-35&nbsp;&nbsp;1.981780&nbsp;&nbsp;0.142106&nbsp;&nbsp;1.817165&nbsp;-0.290805<br>
-49&nbsp;-1.336199&nbsp;-0.448634&nbsp;-0.789640&nbsp;&nbsp;0.217116<br>
-40&nbsp;&nbsp;0.823173&nbsp;-0.078816&nbsp;&nbsp;1.009536&nbsp;&nbsp;1.015108<br>
-15&nbsp;&nbsp;1.421154&nbsp;-0.055301&nbsp;-1.922594&nbsp;-0.019696<br>
-6&nbsp;&nbsp;-0.148339&nbsp;&nbsp;0.832938&nbsp;&nbsp;1.787600&nbsp;-1.383767</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-select"><strong>select</strong></a>(self, crit, axis=0)</dt><dd><tt>Return&nbsp;data&nbsp;corresponding&nbsp;to&nbsp;axis&nbsp;labels&nbsp;matching&nbsp;criteria<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-crit&nbsp;:&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;To&nbsp;be&nbsp;called&nbsp;on&nbsp;each&nbsp;index&nbsp;(label).&nbsp;Should&nbsp;return&nbsp;True&nbsp;or&nbsp;False<br>
-axis&nbsp;:&nbsp;int<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-selection&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-set_axis"><strong>set_axis</strong></a>(self, axis, labels)</dt><dd><tt>public&nbsp;verson&nbsp;of&nbsp;axis&nbsp;assignment</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-slice_shift"><strong>slice_shift</strong></a>(self, periods=1, axis=0)</dt><dd><tt>Equivalent&nbsp;to&nbsp;`shift`&nbsp;without&nbsp;copying&nbsp;data.&nbsp;The&nbsp;shifted&nbsp;data&nbsp;will<br>
-not&nbsp;include&nbsp;the&nbsp;dropped&nbsp;periods&nbsp;and&nbsp;the&nbsp;shifted&nbsp;axis&nbsp;will&nbsp;be&nbsp;smaller<br>
-than&nbsp;the&nbsp;original.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-&nbsp;<br>
-Notes<br>
------<br>
-While&nbsp;the&nbsp;`slice_shift`&nbsp;is&nbsp;faster&nbsp;than&nbsp;`shift`,&nbsp;you&nbsp;may&nbsp;pay&nbsp;for&nbsp;it<br>
-later&nbsp;during&nbsp;alignment.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-squeeze"><strong>squeeze</strong></a>(self, axis=None)</dt><dd><tt>Squeeze&nbsp;length&nbsp;1&nbsp;dimensions.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-axis&nbsp;:&nbsp;None,&nbsp;integer&nbsp;or&nbsp;string&nbsp;axis&nbsp;name,&nbsp;optional<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;axis&nbsp;to&nbsp;squeeze&nbsp;if&nbsp;1-sized.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-scalar&nbsp;if&nbsp;1-sized,&nbsp;else&nbsp;original&nbsp;<a href="builtins.html#object">object</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-swapaxes"><strong>swapaxes</strong></a>(self, axis1, axis2, copy=True)</dt><dd><tt>Interchange&nbsp;axes&nbsp;and&nbsp;swap&nbsp;values&nbsp;axes&nbsp;appropriately<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-y&nbsp;:&nbsp;same&nbsp;as&nbsp;input</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-tail"><strong>tail</strong></a>(self, n=5)</dt><dd><tt>Returns&nbsp;last&nbsp;n&nbsp;rows</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_clipboard"><strong>to_clipboard</strong></a>(self, excel=None, sep=None, **kwargs)</dt><dd><tt>Attempt&nbsp;to&nbsp;write&nbsp;text&nbsp;representation&nbsp;of&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;the&nbsp;system&nbsp;clipboard<br>
-This&nbsp;can&nbsp;be&nbsp;pasted&nbsp;into&nbsp;Excel,&nbsp;for&nbsp;example.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-excel&nbsp;:&nbsp;boolean,&nbsp;defaults&nbsp;to&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;True,&nbsp;use&nbsp;the&nbsp;provided&nbsp;separator,&nbsp;writing&nbsp;in&nbsp;a&nbsp;csv<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;format&nbsp;for&nbsp;allowing&nbsp;easy&nbsp;pasting&nbsp;into&nbsp;excel.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;False,&nbsp;write&nbsp;a&nbsp;string&nbsp;representation&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;to&nbsp;the&nbsp;clipboard<br>
-sep&nbsp;:&nbsp;optional,&nbsp;defaults&nbsp;to&nbsp;tab<br>
-other&nbsp;keywords&nbsp;are&nbsp;passed&nbsp;to&nbsp;to_csv<br>
-&nbsp;<br>
-Notes<br>
------<br>
-Requirements&nbsp;for&nbsp;your&nbsp;platform<br>
-&nbsp;&nbsp;-&nbsp;Linux:&nbsp;xclip,&nbsp;or&nbsp;xsel&nbsp;(with&nbsp;gtk&nbsp;or&nbsp;PyQt4&nbsp;modules)<br>
-&nbsp;&nbsp;-&nbsp;Windows:&nbsp;none<br>
-&nbsp;&nbsp;-&nbsp;OS&nbsp;X:&nbsp;none</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_dense"><strong>to_dense</strong></a>(self)</dt><dd><tt>Return&nbsp;dense&nbsp;representation&nbsp;of&nbsp;NDFrame&nbsp;(as&nbsp;opposed&nbsp;to&nbsp;sparse)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_hdf"><strong>to_hdf</strong></a>(self, path_or_buf, key, **kwargs)</dt><dd><tt>Write&nbsp;the&nbsp;contained&nbsp;data&nbsp;to&nbsp;an&nbsp;HDF5&nbsp;file&nbsp;using&nbsp;HDFStore.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;(string)&nbsp;or&nbsp;HDFStore&nbsp;<a href="builtins.html#object">object</a><br>
-key&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;identifier&nbsp;for&nbsp;the&nbsp;group&nbsp;in&nbsp;the&nbsp;store<br>
-mode&nbsp;:&nbsp;optional,&nbsp;{'a',&nbsp;'w',&nbsp;'r+'},&nbsp;default&nbsp;'a'<br>
-&nbsp;<br>
-&nbsp;&nbsp;``'w'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write;&nbsp;a&nbsp;new&nbsp;file&nbsp;is&nbsp;created&nbsp;(an&nbsp;existing&nbsp;file&nbsp;with&nbsp;the&nbsp;same<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;name&nbsp;would&nbsp;be&nbsp;deleted).<br>
-&nbsp;&nbsp;``'a'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Append;&nbsp;an&nbsp;existing&nbsp;file&nbsp;is&nbsp;opened&nbsp;for&nbsp;reading&nbsp;and&nbsp;writing,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and&nbsp;if&nbsp;the&nbsp;file&nbsp;does&nbsp;not&nbsp;exist&nbsp;it&nbsp;is&nbsp;created.<br>
-&nbsp;&nbsp;``'r+'``<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;It&nbsp;is&nbsp;similar&nbsp;to&nbsp;``'a'``,&nbsp;but&nbsp;the&nbsp;file&nbsp;must&nbsp;already&nbsp;exist.<br>
-format&nbsp;:&nbsp;'fixed(f)|table(t)',&nbsp;default&nbsp;is&nbsp;'fixed'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;fixed(f)&nbsp;:&nbsp;Fixed&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Fast&nbsp;writing/reading.&nbsp;Not-appendable,&nbsp;nor&nbsp;searchable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;table(t)&nbsp;:&nbsp;Table&nbsp;format<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;as&nbsp;a&nbsp;PyTables&nbsp;Table&nbsp;structure&nbsp;which&nbsp;may&nbsp;perform<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;worse&nbsp;but&nbsp;allow&nbsp;more&nbsp;flexible&nbsp;operations&nbsp;like&nbsp;searching<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/&nbsp;selecting&nbsp;subsets&nbsp;of&nbsp;the&nbsp;data<br>
-append&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;For&nbsp;Table&nbsp;formats,&nbsp;append&nbsp;the&nbsp;input&nbsp;data&nbsp;to&nbsp;the&nbsp;existing<br>
-data_columns&nbsp;:&nbsp;&nbsp;list&nbsp;of&nbsp;columns,&nbsp;or&nbsp;True,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;List&nbsp;of&nbsp;columns&nbsp;to&nbsp;create&nbsp;as&nbsp;indexed&nbsp;data&nbsp;columns&nbsp;for&nbsp;on-disk<br>
-&nbsp;&nbsp;&nbsp;&nbsp;queries,&nbsp;or&nbsp;True&nbsp;to&nbsp;use&nbsp;all&nbsp;columns.&nbsp;By&nbsp;default&nbsp;only&nbsp;the&nbsp;axes<br>
-&nbsp;&nbsp;&nbsp;&nbsp;of&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;are&nbsp;indexed.&nbsp;See&nbsp;`here<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&lt;<a href="http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__">http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns&gt;`__</a>.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Applicable&nbsp;only&nbsp;to&nbsp;format='table'.<br>
-complevel&nbsp;:&nbsp;int,&nbsp;1-9,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;a&nbsp;complib&nbsp;is&nbsp;specified&nbsp;compression&nbsp;will&nbsp;be&nbsp;applied<br>
-&nbsp;&nbsp;&nbsp;&nbsp;where&nbsp;possible<br>
-complib&nbsp;:&nbsp;{'zlib',&nbsp;'bzip2',&nbsp;'lzo',&nbsp;'blosc',&nbsp;None},&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;complevel&nbsp;is&nbsp;&gt;&nbsp;0&nbsp;apply&nbsp;compression&nbsp;to&nbsp;objects&nbsp;written<br>
-&nbsp;&nbsp;&nbsp;&nbsp;in&nbsp;the&nbsp;store&nbsp;wherever&nbsp;possible<br>
-fletcher32&nbsp;:&nbsp;bool,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;applying&nbsp;compression&nbsp;use&nbsp;the&nbsp;fletcher32&nbsp;checksum<br>
-dropna&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;true,&nbsp;ALL&nbsp;nan&nbsp;rows&nbsp;will&nbsp;not&nbsp;be&nbsp;written&nbsp;to&nbsp;store.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_json"><strong>to_json</strong></a>(self, path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False)</dt><dd><tt>Convert&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;a&nbsp;JSON&nbsp;string.<br>
-&nbsp;<br>
-Note&nbsp;NaN's&nbsp;and&nbsp;None&nbsp;will&nbsp;be&nbsp;converted&nbsp;to&nbsp;null&nbsp;and&nbsp;datetime&nbsp;objects<br>
-will&nbsp;be&nbsp;converted&nbsp;to&nbsp;UNIX&nbsp;timestamps.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path_or_buf&nbsp;:&nbsp;the&nbsp;path&nbsp;or&nbsp;buffer&nbsp;to&nbsp;write&nbsp;the&nbsp;result&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;this&nbsp;is&nbsp;None,&nbsp;return&nbsp;a&nbsp;StringIO&nbsp;of&nbsp;the&nbsp;converted&nbsp;string<br>
-orient&nbsp;:&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'index'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:&nbsp;{'split','records','index'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;default&nbsp;is&nbsp;'columns'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;allowed&nbsp;values&nbsp;are:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{'split','records','index','columns','values'}<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;*&nbsp;The&nbsp;format&nbsp;of&nbsp;the&nbsp;JSON&nbsp;string<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;split&nbsp;:&nbsp;dict&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{index&nbsp;-&gt;&nbsp;[index],&nbsp;columns&nbsp;-&gt;&nbsp;[columns],&nbsp;data&nbsp;-&gt;&nbsp;[values]}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;records&nbsp;:&nbsp;list&nbsp;like<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[{column&nbsp;-&gt;&nbsp;value},&nbsp;...&nbsp;,&nbsp;{column&nbsp;-&gt;&nbsp;value}]<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;index&nbsp;:&nbsp;dict&nbsp;like&nbsp;{index&nbsp;-&gt;&nbsp;{column&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;columns&nbsp;:&nbsp;dict&nbsp;like&nbsp;{column&nbsp;-&gt;&nbsp;{index&nbsp;-&gt;&nbsp;value}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;values&nbsp;:&nbsp;just&nbsp;the&nbsp;values&nbsp;array<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;table&nbsp;:&nbsp;dict&nbsp;like&nbsp;{'schema':&nbsp;{schema},&nbsp;'data':&nbsp;{data}}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;describing&nbsp;the&nbsp;data,&nbsp;and&nbsp;the&nbsp;data&nbsp;component&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;like&nbsp;``orient='records'``.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionchanged::&nbsp;0.20.0<br>
-&nbsp;<br>
-date_format&nbsp;:&nbsp;{None,&nbsp;'epoch',&nbsp;'iso'}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Type&nbsp;of&nbsp;date&nbsp;conversion.&nbsp;`epoch`&nbsp;=&nbsp;epoch&nbsp;milliseconds,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`iso`&nbsp;=&nbsp;ISO8601.&nbsp;The&nbsp;default&nbsp;depends&nbsp;on&nbsp;the&nbsp;`orient`.&nbsp;For<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`orient='table'`,&nbsp;the&nbsp;default&nbsp;is&nbsp;`'iso'`.&nbsp;For&nbsp;all&nbsp;other&nbsp;orients,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;default&nbsp;is&nbsp;`'epoch'`.<br>
-double_precision&nbsp;:&nbsp;The&nbsp;number&nbsp;of&nbsp;decimal&nbsp;places&nbsp;to&nbsp;use&nbsp;when&nbsp;encoding<br>
-&nbsp;&nbsp;&nbsp;&nbsp;floating&nbsp;point&nbsp;values,&nbsp;default&nbsp;10.<br>
-force_ascii&nbsp;:&nbsp;force&nbsp;encoded&nbsp;string&nbsp;to&nbsp;be&nbsp;ASCII,&nbsp;default&nbsp;True.<br>
-date_unit&nbsp;:&nbsp;string,&nbsp;default&nbsp;'ms'&nbsp;(milliseconds)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;The&nbsp;time&nbsp;unit&nbsp;to&nbsp;encode&nbsp;to,&nbsp;governs&nbsp;timestamp&nbsp;and&nbsp;ISO8601<br>
-&nbsp;&nbsp;&nbsp;&nbsp;precision.&nbsp;&nbsp;One&nbsp;of&nbsp;'s',&nbsp;'ms',&nbsp;'us',&nbsp;'ns'&nbsp;for&nbsp;second,&nbsp;millisecond,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;microsecond,&nbsp;and&nbsp;nanosecond&nbsp;respectively.<br>
-default_handler&nbsp;:&nbsp;callable,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Handler&nbsp;to&nbsp;call&nbsp;if&nbsp;<a href="builtins.html#object">object</a>&nbsp;cannot&nbsp;otherwise&nbsp;be&nbsp;converted&nbsp;to&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;suitable&nbsp;format&nbsp;for&nbsp;JSON.&nbsp;Should&nbsp;receive&nbsp;a&nbsp;single&nbsp;argument&nbsp;which&nbsp;is<br>
-&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;convert&nbsp;and&nbsp;return&nbsp;a&nbsp;serialisable&nbsp;<a href="builtins.html#object">object</a>.<br>
-lines&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;'orient'&nbsp;is&nbsp;'records'&nbsp;write&nbsp;out&nbsp;line&nbsp;delimited&nbsp;json&nbsp;format.&nbsp;Will<br>
-&nbsp;&nbsp;&nbsp;&nbsp;throw&nbsp;ValueError&nbsp;if&nbsp;incorrect&nbsp;'orient'&nbsp;since&nbsp;others&nbsp;are&nbsp;not&nbsp;list<br>
-&nbsp;&nbsp;&nbsp;&nbsp;like.<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.19.0<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-same&nbsp;type&nbsp;as&nbsp;input&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;filtered&nbsp;info&nbsp;axis<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-pd.read_json<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>([['a',&nbsp;'b'],&nbsp;['c',&nbsp;'d']],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;index=['row&nbsp;1',&nbsp;'row&nbsp;2'],<br>
-...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;columns=['col&nbsp;1',&nbsp;'col&nbsp;2'])<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-to_json">to_json</a>(orient='split')<br>
-'{"columns":["col&nbsp;1","col&nbsp;2"],<br>
-&nbsp;&nbsp;"index":["row&nbsp;1","row&nbsp;2"],<br>
-&nbsp;&nbsp;"data":[["a","b"],["c","d"]]}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'index'``&nbsp;formatted&nbsp;JSON:<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-to_json">to_json</a>(orient='index')<br>
-'{"row&nbsp;1":{"col&nbsp;1":"a","col&nbsp;2":"b"},"row&nbsp;2":{"col&nbsp;1":"c","col&nbsp;2":"d"}}'<br>
-&nbsp;<br>
-Encoding/decoding&nbsp;a&nbsp;Dataframe&nbsp;using&nbsp;``'records'``&nbsp;formatted&nbsp;JSON.<br>
-Note&nbsp;that&nbsp;index&nbsp;labels&nbsp;are&nbsp;not&nbsp;preserved&nbsp;with&nbsp;this&nbsp;encoding.<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-to_json">to_json</a>(orient='records')<br>
-'[{"col&nbsp;1":"a","col&nbsp;2":"b"},{"col&nbsp;1":"c","col&nbsp;2":"d"}]'<br>
-&nbsp;<br>
-Encoding&nbsp;with&nbsp;Table&nbsp;Schema<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-to_json">to_json</a>(orient='table')<br>
-'{"schema":&nbsp;{"fields":&nbsp;[{"name":&nbsp;"index",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;1",&nbsp;"type":&nbsp;"string"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"name":&nbsp;"col&nbsp;2",&nbsp;"type":&nbsp;"string"}],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"primaryKey":&nbsp;"index",<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"pandas_version":&nbsp;"0.20.0"},<br>
-&nbsp;&nbsp;"data":&nbsp;[{"index":&nbsp;"row&nbsp;1",&nbsp;"col&nbsp;1":&nbsp;"a",&nbsp;"col&nbsp;2":&nbsp;"b"},<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{"index":&nbsp;"row&nbsp;2",&nbsp;"col&nbsp;1":&nbsp;"c",&nbsp;"col&nbsp;2":&nbsp;"d"}]}'</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_msgpack"><strong>to_msgpack</strong></a>(self, path_or_buf=None, encoding='utf-8', **kwargs)</dt><dd><tt>msgpack&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path<br>
-&nbsp;<br>
-THIS&nbsp;IS&nbsp;AN&nbsp;EXPERIMENTAL&nbsp;LIBRARY&nbsp;and&nbsp;the&nbsp;storage&nbsp;format<br>
-may&nbsp;not&nbsp;be&nbsp;stable&nbsp;until&nbsp;a&nbsp;future&nbsp;release.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string&nbsp;File&nbsp;path,&nbsp;buffer-like,&nbsp;or&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;if&nbsp;None,&nbsp;return&nbsp;generated&nbsp;string<br>
-append&nbsp;:&nbsp;boolean&nbsp;whether&nbsp;to&nbsp;append&nbsp;to&nbsp;an&nbsp;existing&nbsp;msgpack<br>
-&nbsp;&nbsp;&nbsp;&nbsp;(default&nbsp;is&nbsp;False)<br>
-compress&nbsp;:&nbsp;type&nbsp;of&nbsp;compressor&nbsp;(zlib&nbsp;or&nbsp;blosc),&nbsp;default&nbsp;to&nbsp;None&nbsp;(no<br>
-&nbsp;&nbsp;&nbsp;&nbsp;compression)</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_pickle"><strong>to_pickle</strong></a>(self, path, compression='infer')</dt><dd><tt>Pickle&nbsp;(serialize)&nbsp;<a href="builtins.html#object">object</a>&nbsp;to&nbsp;input&nbsp;file&nbsp;path.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-path&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;File&nbsp;path<br>
-compression&nbsp;:&nbsp;{'infer',&nbsp;'gzip',&nbsp;'bz2',&nbsp;'xz',&nbsp;None},&nbsp;default&nbsp;'infer'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;string&nbsp;representing&nbsp;the&nbsp;compression&nbsp;to&nbsp;use&nbsp;in&nbsp;the&nbsp;output&nbsp;file<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.20.0</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_sql"><strong>to_sql</strong></a>(self, name, con, flavor=None, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None)</dt><dd><tt>Write&nbsp;records&nbsp;stored&nbsp;in&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;a&nbsp;SQL&nbsp;database.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-name&nbsp;:&nbsp;string<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;of&nbsp;SQL&nbsp;table<br>
-con&nbsp;:&nbsp;SQLAlchemy&nbsp;engine&nbsp;or&nbsp;DBAPI2&nbsp;connection&nbsp;(legacy&nbsp;mode)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Using&nbsp;SQLAlchemy&nbsp;makes&nbsp;it&nbsp;possible&nbsp;to&nbsp;use&nbsp;any&nbsp;DB&nbsp;supported&nbsp;by&nbsp;that<br>
-&nbsp;&nbsp;&nbsp;&nbsp;library.&nbsp;If&nbsp;a&nbsp;DBAPI2&nbsp;<a href="builtins.html#object">object</a>,&nbsp;only&nbsp;sqlite3&nbsp;is&nbsp;supported.<br>
-flavor&nbsp;:&nbsp;'sqlite',&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;DEPRECATED:&nbsp;this&nbsp;parameter&nbsp;will&nbsp;be&nbsp;removed&nbsp;in&nbsp;a&nbsp;future&nbsp;version,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;as&nbsp;'sqlite'&nbsp;is&nbsp;the&nbsp;only&nbsp;supported&nbsp;option&nbsp;if&nbsp;SQLAlchemy&nbsp;is&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;installed.<br>
-schema&nbsp;:&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Specify&nbsp;the&nbsp;schema&nbsp;(if&nbsp;database&nbsp;flavor&nbsp;supports&nbsp;this).&nbsp;If&nbsp;None,&nbsp;use<br>
-&nbsp;&nbsp;&nbsp;&nbsp;default&nbsp;schema.<br>
-if_exists&nbsp;:&nbsp;{'fail',&nbsp;'replace',&nbsp;'append'},&nbsp;default&nbsp;'fail'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;fail:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;do&nbsp;nothing.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;replace:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;drop&nbsp;it,&nbsp;recreate&nbsp;it,&nbsp;and&nbsp;insert&nbsp;data.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;append:&nbsp;If&nbsp;table&nbsp;exists,&nbsp;insert&nbsp;data.&nbsp;Create&nbsp;if&nbsp;does&nbsp;not&nbsp;exist.<br>
-index&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Write&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;index&nbsp;as&nbsp;a&nbsp;column.<br>
-index_label&nbsp;:&nbsp;string&nbsp;or&nbsp;sequence,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Column&nbsp;label&nbsp;for&nbsp;index&nbsp;column(s).&nbsp;If&nbsp;None&nbsp;is&nbsp;given&nbsp;(default)&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;`index`&nbsp;is&nbsp;True,&nbsp;then&nbsp;the&nbsp;index&nbsp;names&nbsp;are&nbsp;used.<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;sequence&nbsp;should&nbsp;be&nbsp;given&nbsp;if&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;uses&nbsp;MultiIndex.<br>
-chunksize&nbsp;:&nbsp;int,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;not&nbsp;None,&nbsp;then&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;in&nbsp;batches&nbsp;of&nbsp;this&nbsp;size&nbsp;at&nbsp;a<br>
-&nbsp;&nbsp;&nbsp;&nbsp;time.&nbsp;&nbsp;If&nbsp;None,&nbsp;all&nbsp;rows&nbsp;will&nbsp;be&nbsp;written&nbsp;at&nbsp;once.<br>
-dtype&nbsp;:&nbsp;dict&nbsp;of&nbsp;column&nbsp;name&nbsp;to&nbsp;SQL&nbsp;type,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Optional&nbsp;specifying&nbsp;the&nbsp;datatype&nbsp;for&nbsp;columns.&nbsp;The&nbsp;SQL&nbsp;type&nbsp;should<br>
-&nbsp;&nbsp;&nbsp;&nbsp;be&nbsp;a&nbsp;SQLAlchemy&nbsp;type,&nbsp;or&nbsp;a&nbsp;string&nbsp;for&nbsp;sqlite3&nbsp;fallback&nbsp;connection.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-to_xarray"><strong>to_xarray</strong></a>(self)</dt><dd><tt>Return&nbsp;an&nbsp;xarray&nbsp;<a href="builtins.html#object">object</a>&nbsp;from&nbsp;the&nbsp;pandas&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-a&nbsp;DataArray&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-a&nbsp;Dataset&nbsp;for&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-a&nbsp;DataArray&nbsp;for&nbsp;higher&nbsp;dims<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)})<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-0&nbsp;&nbsp;1&nbsp;&nbsp;foo&nbsp;&nbsp;4.0<br>
-1&nbsp;&nbsp;1&nbsp;&nbsp;bar&nbsp;&nbsp;5.0<br>
-2&nbsp;&nbsp;2&nbsp;&nbsp;foo&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(index:&nbsp;3)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;index&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;0&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;int64&nbsp;1&nbsp;1&nbsp;2<br>
-&nbsp;&nbsp;&nbsp;&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'foo'&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(index)&nbsp;float64&nbsp;4.0&nbsp;5.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>({'A'&nbsp;:&nbsp;[1,&nbsp;1,&nbsp;2],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'B'&nbsp;:&nbsp;['foo',&nbsp;'bar',&nbsp;'foo'],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'C'&nbsp;:&nbsp;np.arange(4.,7)}<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;).set_index(['B','A'])<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;C<br>
-B&nbsp;&nbsp;&nbsp;A<br>
-foo&nbsp;1&nbsp;&nbsp;4.0<br>
-bar&nbsp;1&nbsp;&nbsp;5.0<br>
-foo&nbsp;2&nbsp;&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-to_xarray">to_xarray</a>()<br>
-&lt;xarray.Dataset&gt;<br>
-Dimensions:&nbsp;&nbsp;(A:&nbsp;2,&nbsp;B:&nbsp;2)<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;B&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'bar'&nbsp;'foo'<br>
-&nbsp;&nbsp;*&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(A)&nbsp;int64&nbsp;1&nbsp;2<br>
-Data&nbsp;variables:<br>
-&nbsp;&nbsp;&nbsp;&nbsp;C&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(B,&nbsp;A)&nbsp;float64&nbsp;5.0&nbsp;nan&nbsp;4.0&nbsp;6.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p&nbsp;=&nbsp;pd.Panel(np.arange(24).<a href="#TimeSeries-reshape">reshape</a>(4,3,2),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;items=list('ABCD'),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;major_axis=pd.date_range('20130101',&nbsp;periods=3),<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;minor_axis=['first',&nbsp;'second'])<br>
-&gt;&gt;&gt;&nbsp;p<br>
-&lt;class&nbsp;'pandas.core.panel.Panel'&gt;<br>
-Dimensions:&nbsp;4&nbsp;(items)&nbsp;x&nbsp;3&nbsp;(major_axis)&nbsp;x&nbsp;2&nbsp;(minor_axis)<br>
-Items&nbsp;axis:&nbsp;A&nbsp;to&nbsp;D<br>
-Major_axis&nbsp;axis:&nbsp;2013-01-01&nbsp;00:00:00&nbsp;to&nbsp;2013-01-03&nbsp;00:00:00<br>
-Minor_axis&nbsp;axis:&nbsp;first&nbsp;to&nbsp;second<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;p.<a href="#TimeSeries-to_xarray">to_xarray</a>()<br>
-&lt;xarray.DataArray&nbsp;(items:&nbsp;4,&nbsp;major_axis:&nbsp;3,&nbsp;minor_axis:&nbsp;2)&gt;<br>
-array([[[&nbsp;0,&nbsp;&nbsp;1],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;2,&nbsp;&nbsp;3],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;4,&nbsp;&nbsp;5]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[&nbsp;6,&nbsp;&nbsp;7],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&nbsp;8,&nbsp;&nbsp;9],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[10,&nbsp;11]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[12,&nbsp;13],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[14,&nbsp;15],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[16,&nbsp;17]],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[[18,&nbsp;19],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[20,&nbsp;21],<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[22,&nbsp;23]]])<br>
-Coordinates:<br>
-&nbsp;&nbsp;*&nbsp;items&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(items)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'A'&nbsp;'B'&nbsp;'C'&nbsp;'D'<br>
-&nbsp;&nbsp;*&nbsp;major_axis&nbsp;&nbsp;(major_axis)&nbsp;datetime64[ns]&nbsp;2013-01-01&nbsp;2013-01-02&nbsp;2013-01-03&nbsp;&nbsp;#&nbsp;noqa<br>
-&nbsp;&nbsp;*&nbsp;minor_axis&nbsp;&nbsp;(minor_axis)&nbsp;<a href="builtins.html#object">object</a>&nbsp;'first'&nbsp;'second'<br>
-&nbsp;<br>
-Notes<br>
------<br>
-See&nbsp;the&nbsp;`xarray&nbsp;docs&nbsp;&lt;<a href="http://xarray.pydata.org/en/stable/&gt;`__">http://xarray.pydata.org/en/stable/&gt;`__</a></tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-truncate"><strong>truncate</strong></a>(self, before=None, after=None, axis=None, copy=True)</dt><dd><tt>Truncates&nbsp;a&nbsp;sorted&nbsp;NDFrame&nbsp;before&nbsp;and/or&nbsp;after&nbsp;some&nbsp;particular<br>
-index&nbsp;value.&nbsp;If&nbsp;the&nbsp;axis&nbsp;contains&nbsp;only&nbsp;datetime&nbsp;values,&nbsp;before/after<br>
-parameters&nbsp;are&nbsp;converted&nbsp;to&nbsp;datetime&nbsp;values.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-before&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;before&nbsp;index&nbsp;value<br>
-after&nbsp;:&nbsp;date<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Truncate&nbsp;after&nbsp;index&nbsp;value<br>
-axis&nbsp;:&nbsp;the&nbsp;truncation&nbsp;axis,&nbsp;defaults&nbsp;to&nbsp;the&nbsp;stat&nbsp;axis<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;is&nbsp;True,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;return&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;truncated&nbsp;section<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-truncated&nbsp;:&nbsp;type&nbsp;of&nbsp;caller</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-tshift"><strong>tshift</strong></a>(self, periods=1, freq=None, axis=0)</dt><dd><tt>Shift&nbsp;the&nbsp;time&nbsp;index,&nbsp;using&nbsp;the&nbsp;index's&nbsp;frequency&nbsp;if&nbsp;available.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-periods&nbsp;:&nbsp;int<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Number&nbsp;of&nbsp;periods&nbsp;to&nbsp;move,&nbsp;can&nbsp;be&nbsp;positive&nbsp;or&nbsp;negative<br>
-freq&nbsp;:&nbsp;DateOffset,&nbsp;timedelta,&nbsp;or&nbsp;time&nbsp;rule&nbsp;string,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Increment&nbsp;to&nbsp;use&nbsp;from&nbsp;the&nbsp;tseries&nbsp;module&nbsp;or&nbsp;time&nbsp;rule&nbsp;(e.g.&nbsp;'EOM')<br>
-axis&nbsp;:&nbsp;int&nbsp;or&nbsp;basestring<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Corresponds&nbsp;to&nbsp;the&nbsp;axis&nbsp;that&nbsp;contains&nbsp;the&nbsp;Index<br>
-&nbsp;<br>
-Notes<br>
------<br>
-If&nbsp;freq&nbsp;is&nbsp;not&nbsp;specified&nbsp;then&nbsp;tries&nbsp;to&nbsp;use&nbsp;the&nbsp;freq&nbsp;or&nbsp;inferred_freq<br>
-attributes&nbsp;of&nbsp;the&nbsp;index.&nbsp;If&nbsp;neither&nbsp;of&nbsp;those&nbsp;attributes&nbsp;exist,&nbsp;a<br>
-ValueError&nbsp;is&nbsp;thrown<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-shifted&nbsp;:&nbsp;NDFrame</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-tz_convert"><strong>tz_convert</strong></a>(self, tz, axis=0, level=None, copy=True)</dt><dd><tt>Convert&nbsp;tz-aware&nbsp;axis&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;convert<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;convert&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;axis&nbsp;is&nbsp;tz-naive.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-tz_localize"><strong>tz_localize</strong></a>(self, tz, axis=0, level=None, copy=True, ambiguous='raise')</dt><dd><tt>Localize&nbsp;tz-naive&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;to&nbsp;target&nbsp;time&nbsp;zone.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-tz&nbsp;:&nbsp;string&nbsp;or&nbsp;pytz.timezone&nbsp;<a href="builtins.html#object">object</a><br>
-axis&nbsp;:&nbsp;the&nbsp;axis&nbsp;to&nbsp;localize<br>
-level&nbsp;:&nbsp;int,&nbsp;str,&nbsp;default&nbsp;None<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;axis&nbsp;ia&nbsp;a&nbsp;MultiIndex,&nbsp;localize&nbsp;a&nbsp;specific&nbsp;level.&nbsp;Otherwise<br>
-&nbsp;&nbsp;&nbsp;&nbsp;must&nbsp;be&nbsp;None<br>
-copy&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Also&nbsp;make&nbsp;a&nbsp;copy&nbsp;of&nbsp;the&nbsp;underlying&nbsp;data<br>
-ambiguous&nbsp;:&nbsp;'infer',&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>,&nbsp;'NaT',&nbsp;default&nbsp;'raise'<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'infer'&nbsp;will&nbsp;attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;order<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;bool-<a href="numpy.html#ndarray">ndarray</a>&nbsp;where&nbsp;True&nbsp;signifies&nbsp;a&nbsp;DST&nbsp;time,&nbsp;False&nbsp;designates<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a&nbsp;non-DST&nbsp;time&nbsp;(note&nbsp;that&nbsp;this&nbsp;flag&nbsp;is&nbsp;only&nbsp;applicable&nbsp;for<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ambiguous&nbsp;times)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'NaT'&nbsp;will&nbsp;return&nbsp;NaT&nbsp;where&nbsp;there&nbsp;are&nbsp;ambiguous&nbsp;times<br>
-&nbsp;&nbsp;&nbsp;&nbsp;-&nbsp;'raise'&nbsp;will&nbsp;raise&nbsp;an&nbsp;AmbiguousTimeError&nbsp;if&nbsp;there&nbsp;are&nbsp;ambiguous<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;times<br>
-infer_dst&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False&nbsp;(DEPRECATED)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Attempt&nbsp;to&nbsp;infer&nbsp;fall&nbsp;dst-transition&nbsp;hours&nbsp;based&nbsp;on&nbsp;order<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-&nbsp;<br>
-Raises<br>
-------<br>
-TypeError<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;the&nbsp;<a href="#TimeSeries">TimeSeries</a>&nbsp;is&nbsp;tz-aware&nbsp;and&nbsp;tz&nbsp;is&nbsp;not&nbsp;None.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-where"><strong>where</strong></a>(self, cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)</dt><dd><tt>Return&nbsp;an&nbsp;<a href="builtins.html#object">object</a>&nbsp;of&nbsp;same&nbsp;shape&nbsp;as&nbsp;self&nbsp;and&nbsp;whose&nbsp;corresponding<br>
-entries&nbsp;are&nbsp;from&nbsp;self&nbsp;where&nbsp;cond&nbsp;is&nbsp;True&nbsp;and&nbsp;otherwise&nbsp;are&nbsp;from<br>
-other.<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-cond&nbsp;:&nbsp;boolean&nbsp;NDFrame,&nbsp;array-like,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;cond&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;boolean&nbsp;NDFrame&nbsp;or&nbsp;array.&nbsp;The&nbsp;callable&nbsp;must<br>
-&nbsp;&nbsp;&nbsp;&nbsp;not&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;cond.<br>
-&nbsp;<br>
-other&nbsp;:&nbsp;scalar,&nbsp;NDFrame,&nbsp;or&nbsp;callable<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;other&nbsp;is&nbsp;callable,&nbsp;it&nbsp;is&nbsp;computed&nbsp;on&nbsp;the&nbsp;NDFrame&nbsp;and<br>
-&nbsp;&nbsp;&nbsp;&nbsp;should&nbsp;return&nbsp;scalar&nbsp;or&nbsp;NDFrame.&nbsp;The&nbsp;callable&nbsp;must&nbsp;not<br>
-&nbsp;&nbsp;&nbsp;&nbsp;change&nbsp;input&nbsp;NDFrame&nbsp;(though&nbsp;pandas&nbsp;doesn't&nbsp;check&nbsp;it).<br>
-&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;..&nbsp;versionadded::&nbsp;0.18.1<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;callable&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;other.<br>
-&nbsp;<br>
-inplace&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;perform&nbsp;the&nbsp;operation&nbsp;in&nbsp;place&nbsp;on&nbsp;the&nbsp;data<br>
-axis&nbsp;:&nbsp;alignment&nbsp;axis&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-level&nbsp;:&nbsp;alignment&nbsp;level&nbsp;if&nbsp;needed,&nbsp;default&nbsp;None<br>
-try_cast&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;False<br>
-&nbsp;&nbsp;&nbsp;&nbsp;try&nbsp;to&nbsp;cast&nbsp;the&nbsp;result&nbsp;back&nbsp;to&nbsp;the&nbsp;input&nbsp;type&nbsp;(if&nbsp;possible),<br>
-raise_on_error&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Whether&nbsp;to&nbsp;raise&nbsp;on&nbsp;invalid&nbsp;data&nbsp;types&nbsp;(e.g.&nbsp;trying&nbsp;to&nbsp;where&nbsp;on<br>
-&nbsp;&nbsp;&nbsp;&nbsp;strings)<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-wh&nbsp;:&nbsp;same&nbsp;type&nbsp;as&nbsp;caller<br>
-&nbsp;<br>
-Notes<br>
------<br>
-The&nbsp;where&nbsp;method&nbsp;is&nbsp;an&nbsp;application&nbsp;of&nbsp;the&nbsp;if-then&nbsp;idiom.&nbsp;For&nbsp;each<br>
-element&nbsp;in&nbsp;the&nbsp;calling&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>,&nbsp;if&nbsp;``cond``&nbsp;is&nbsp;``True``&nbsp;the<br>
-element&nbsp;is&nbsp;used;&nbsp;otherwise&nbsp;the&nbsp;corresponding&nbsp;element&nbsp;from&nbsp;the&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-``other``&nbsp;is&nbsp;used.<br>
-&nbsp;<br>
-The&nbsp;signature&nbsp;for&nbsp;:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.where`&nbsp;differs&nbsp;from<br>
-:func:`numpy.where`.&nbsp;Roughly&nbsp;``df1.<a href="#TimeSeries-where">where</a>(m,&nbsp;df2)``&nbsp;is&nbsp;equivalent&nbsp;to<br>
-``np.<a href="#TimeSeries-where">where</a>(m,&nbsp;df1,&nbsp;df2)``.<br>
-&nbsp;<br>
-For&nbsp;further&nbsp;details&nbsp;and&nbsp;examples&nbsp;see&nbsp;the&nbsp;``where``&nbsp;documentation&nbsp;in<br>
-:ref:`indexing&nbsp;&lt;indexing.where_mask&gt;`.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;s&nbsp;=&nbsp;pd.<a href="pandas.core.series.html#Series">Series</a>(range(5))<br>
-&gt;&gt;&gt;&nbsp;s.<a href="#TimeSeries-where">where</a>(s&nbsp;&gt;&nbsp;0)<br>
-0&nbsp;&nbsp;&nbsp;&nbsp;NaN<br>
-1&nbsp;&nbsp;&nbsp;&nbsp;1.0<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;2.0<br>
-3&nbsp;&nbsp;&nbsp;&nbsp;3.0<br>
-4&nbsp;&nbsp;&nbsp;&nbsp;4.0<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df&nbsp;=&nbsp;pd.<a href="pandas.core.frame.html#DataFrame">DataFrame</a>(np.arange(10).<a href="#TimeSeries-reshape">reshape</a>(-1,&nbsp;2),&nbsp;columns=['A',&nbsp;'B'])<br>
-&gt;&gt;&gt;&nbsp;m&nbsp;=&nbsp;df&nbsp;%&nbsp;3&nbsp;==&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-where">where</a>(m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;0&nbsp;-1<br>
-1&nbsp;-2&nbsp;&nbsp;3<br>
-2&nbsp;-4&nbsp;-5<br>
-3&nbsp;&nbsp;6&nbsp;-7<br>
-4&nbsp;-8&nbsp;&nbsp;9<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;np.<a href="#TimeSeries-where">where</a>(m,&nbsp;df,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-where">where</a>(m,&nbsp;-df)&nbsp;==&nbsp;df.<a href="#TimeSeries-mask">mask</a>(~m,&nbsp;-df)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;B<br>
-0&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-1&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-2&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-3&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-4&nbsp;&nbsp;True&nbsp;&nbsp;True<br>
-&nbsp;<br>
-See&nbsp;Also<br>
---------<br>
-:func:`<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.mask`</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-xs"><strong>xs</strong></a>(self, key, axis=0, level=None, drop_level=True)</dt><dd><tt>Returns&nbsp;a&nbsp;cross-section&nbsp;(row(s)&nbsp;or&nbsp;column(s))&nbsp;from&nbsp;the<br>
-<a href="pandas.core.series.html#Series">Series</a>/<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.&nbsp;Defaults&nbsp;to&nbsp;cross-section&nbsp;on&nbsp;the&nbsp;rows&nbsp;(axis=0).<br>
-&nbsp;<br>
-Parameters<br>
-----------<br>
-key&nbsp;:&nbsp;<a href="builtins.html#object">object</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;Some&nbsp;label&nbsp;contained&nbsp;in&nbsp;the&nbsp;index,&nbsp;or&nbsp;partially&nbsp;in&nbsp;a&nbsp;MultiIndex<br>
-axis&nbsp;:&nbsp;int,&nbsp;default&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;Axis&nbsp;to&nbsp;retrieve&nbsp;cross-section&nbsp;on<br>
-level&nbsp;:&nbsp;<a href="builtins.html#object">object</a>,&nbsp;defaults&nbsp;to&nbsp;first&nbsp;n&nbsp;levels&nbsp;(n=1&nbsp;or&nbsp;len(key))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;In&nbsp;case&nbsp;of&nbsp;a&nbsp;key&nbsp;partially&nbsp;contained&nbsp;in&nbsp;a&nbsp;MultiIndex,&nbsp;indicate<br>
-&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;levels&nbsp;are&nbsp;used.&nbsp;Levels&nbsp;can&nbsp;be&nbsp;referred&nbsp;by&nbsp;label&nbsp;or&nbsp;position.<br>
-drop_level&nbsp;:&nbsp;boolean,&nbsp;default&nbsp;True<br>
-&nbsp;&nbsp;&nbsp;&nbsp;If&nbsp;False,&nbsp;returns&nbsp;<a href="builtins.html#object">object</a>&nbsp;with&nbsp;same&nbsp;levels&nbsp;as&nbsp;self.<br>
-&nbsp;<br>
-Examples<br>
---------<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C<br>
-a&nbsp;&nbsp;4&nbsp;&nbsp;5&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;4&nbsp;&nbsp;0&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;9&nbsp;&nbsp;7&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-xs">xs</a>('a')<br>
-A&nbsp;&nbsp;&nbsp;&nbsp;4<br>
-B&nbsp;&nbsp;&nbsp;&nbsp;5<br>
-C&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-Name:&nbsp;a<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-xs">xs</a>('C',&nbsp;axis=1)<br>
-a&nbsp;&nbsp;&nbsp;&nbsp;2<br>
-b&nbsp;&nbsp;&nbsp;&nbsp;9<br>
-c&nbsp;&nbsp;&nbsp;&nbsp;3<br>
-Name:&nbsp;C<br>
-&nbsp;<br>
-&gt;&gt;&gt;&nbsp;df<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;second&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;two&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;7&nbsp;&nbsp;5&nbsp;&nbsp;5&nbsp;&nbsp;0<br>
-baz&nbsp;&nbsp;&nbsp;one&nbsp;&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;three&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-xs">xs</a>(('baz',&nbsp;'three'))<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-third<br>
-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-xs">xs</a>('one',&nbsp;level=1)<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-first&nbsp;third<br>
-bar&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;1&nbsp;&nbsp;8&nbsp;&nbsp;9<br>
-baz&nbsp;&nbsp;&nbsp;1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;6&nbsp;&nbsp;6&nbsp;&nbsp;8&nbsp;&nbsp;0<br>
-&gt;&gt;&gt;&nbsp;df.<a href="#TimeSeries-xs">xs</a>(('baz',&nbsp;2),&nbsp;level=[0,&nbsp;'third'])<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A&nbsp;&nbsp;B&nbsp;&nbsp;C&nbsp;&nbsp;D<br>
-second<br>
-three&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;3&nbsp;&nbsp;5&nbsp;&nbsp;3<br>
-&nbsp;<br>
-Returns<br>
--------<br>
-xs&nbsp;:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;or&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a><br>
-&nbsp;<br>
-Notes<br>
------<br>
-xs&nbsp;is&nbsp;only&nbsp;for&nbsp;getting,&nbsp;not&nbsp;setting&nbsp;values.<br>
-&nbsp;<br>
-MultiIndex&nbsp;Slicers&nbsp;is&nbsp;a&nbsp;generic&nbsp;way&nbsp;to&nbsp;get/set&nbsp;values&nbsp;on&nbsp;any&nbsp;level&nbsp;or<br>
-levels.&nbsp;&nbsp;It&nbsp;is&nbsp;a&nbsp;superset&nbsp;of&nbsp;xs&nbsp;functionality,&nbsp;see<br>
-:ref:`MultiIndex&nbsp;Slicers&nbsp;&lt;advanced.mi_slicers&gt;`</tt></dd></dl>
-
-<hr>
-Data descriptors inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>at</strong></dt>
-<dd><tt>Fast&nbsp;label-based&nbsp;scalar&nbsp;accessor<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``loc``,&nbsp;``at``&nbsp;provides&nbsp;**label**&nbsp;based&nbsp;scalar&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>blocks</strong></dt>
-<dd><tt>Internal&nbsp;property,&nbsp;property&nbsp;synonym&nbsp;for&nbsp;as_blocks()</tt></dd>
-</dl>
-<dl><dt><strong>iat</strong></dt>
-<dd><tt>Fast&nbsp;integer&nbsp;location&nbsp;scalar&nbsp;accessor.<br>
-&nbsp;<br>
-Similarly&nbsp;to&nbsp;``iloc``,&nbsp;``iat``&nbsp;provides&nbsp;**integer**&nbsp;based&nbsp;lookups.<br>
-You&nbsp;can&nbsp;also&nbsp;set&nbsp;using&nbsp;these&nbsp;indexers.</tt></dd>
-</dl>
-<dl><dt><strong>iloc</strong></dt>
-<dd><tt>Purely&nbsp;integer-location&nbsp;based&nbsp;indexing&nbsp;for&nbsp;selection&nbsp;by&nbsp;position.<br>
-&nbsp;<br>
-``.iloc[]``&nbsp;is&nbsp;primarily&nbsp;integer&nbsp;position&nbsp;based&nbsp;(from&nbsp;``0``&nbsp;to<br>
-``length-1``&nbsp;of&nbsp;the&nbsp;axis),&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a&nbsp;boolean<br>
-array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;An&nbsp;integer,&nbsp;e.g.&nbsp;``5``.<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;integers,&nbsp;e.g.&nbsp;``[4,&nbsp;3,&nbsp;0]``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;ints,&nbsp;e.g.&nbsp;``1:7``.<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.iloc``&nbsp;will&nbsp;raise&nbsp;``IndexError``&nbsp;if&nbsp;a&nbsp;requested&nbsp;indexer&nbsp;is<br>
-out-of-bounds,&nbsp;except&nbsp;*slice*&nbsp;indexers&nbsp;which&nbsp;allow&nbsp;out-of-bounds<br>
-indexing&nbsp;(this&nbsp;conforms&nbsp;with&nbsp;python/numpy&nbsp;*slice*&nbsp;semantics).<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Position&nbsp;&lt;indexing.integer&gt;`</tt></dd>
-</dl>
-<dl><dt><strong>ix</strong></dt>
-<dd><tt>A&nbsp;primarily&nbsp;label-location&nbsp;based&nbsp;indexer,&nbsp;with&nbsp;integer&nbsp;position<br>
-fallback.<br>
-&nbsp;<br>
-``.ix[]``&nbsp;supports&nbsp;mixed&nbsp;integer&nbsp;and&nbsp;label&nbsp;based&nbsp;access.&nbsp;It&nbsp;is<br>
-primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;will&nbsp;fall&nbsp;back&nbsp;to&nbsp;integer&nbsp;positional<br>
-access&nbsp;unless&nbsp;the&nbsp;corresponding&nbsp;axis&nbsp;is&nbsp;of&nbsp;integer&nbsp;type.<br>
-&nbsp;<br>
-``.ix``&nbsp;is&nbsp;the&nbsp;most&nbsp;general&nbsp;indexer&nbsp;and&nbsp;will&nbsp;support&nbsp;any&nbsp;of&nbsp;the<br>
-inputs&nbsp;in&nbsp;``.loc``&nbsp;and&nbsp;``.iloc``.&nbsp;``.ix``&nbsp;also&nbsp;supports&nbsp;floating<br>
-point&nbsp;label&nbsp;schemes.&nbsp;``.ix``&nbsp;is&nbsp;exceptionally&nbsp;useful&nbsp;when&nbsp;dealing<br>
-with&nbsp;mixed&nbsp;positional&nbsp;and&nbsp;label&nbsp;based&nbsp;hierachical&nbsp;indexes.<br>
-&nbsp;<br>
-However,&nbsp;when&nbsp;an&nbsp;axis&nbsp;is&nbsp;integer&nbsp;based,&nbsp;ONLY&nbsp;label&nbsp;based&nbsp;access<br>
-and&nbsp;not&nbsp;positional&nbsp;access&nbsp;is&nbsp;supported.&nbsp;Thus,&nbsp;in&nbsp;such&nbsp;cases,&nbsp;it's<br>
-usually&nbsp;better&nbsp;to&nbsp;be&nbsp;explicit&nbsp;and&nbsp;use&nbsp;``.iloc``&nbsp;or&nbsp;``.loc``.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Advanced&nbsp;Indexing&nbsp;&lt;advanced&gt;`.</tt></dd>
-</dl>
-<dl><dt><strong>loc</strong></dt>
-<dd><tt>Purely&nbsp;label-location&nbsp;based&nbsp;indexer&nbsp;for&nbsp;selection&nbsp;by&nbsp;label.<br>
-&nbsp;<br>
-``.loc[]``&nbsp;is&nbsp;primarily&nbsp;label&nbsp;based,&nbsp;but&nbsp;may&nbsp;also&nbsp;be&nbsp;used&nbsp;with&nbsp;a<br>
-boolean&nbsp;array.<br>
-&nbsp;<br>
-Allowed&nbsp;inputs&nbsp;are:<br>
-&nbsp;<br>
--&nbsp;A&nbsp;single&nbsp;label,&nbsp;e.g.&nbsp;``5``&nbsp;or&nbsp;``'a'``,&nbsp;(note&nbsp;that&nbsp;``5``&nbsp;is<br>
-&nbsp;&nbsp;interpreted&nbsp;as&nbsp;a&nbsp;*label*&nbsp;of&nbsp;the&nbsp;index,&nbsp;and&nbsp;**never**&nbsp;as&nbsp;an<br>
-&nbsp;&nbsp;integer&nbsp;position&nbsp;along&nbsp;the&nbsp;index).<br>
--&nbsp;A&nbsp;list&nbsp;or&nbsp;array&nbsp;of&nbsp;labels,&nbsp;e.g.&nbsp;``['a',&nbsp;'b',&nbsp;'c']``.<br>
--&nbsp;A&nbsp;slice&nbsp;object&nbsp;with&nbsp;labels,&nbsp;e.g.&nbsp;``'a':'f'``&nbsp;(note&nbsp;that&nbsp;contrary<br>
-&nbsp;&nbsp;to&nbsp;usual&nbsp;python&nbsp;slices,&nbsp;**both**&nbsp;the&nbsp;start&nbsp;and&nbsp;the&nbsp;stop&nbsp;are&nbsp;included!).<br>
--&nbsp;A&nbsp;boolean&nbsp;array.<br>
--&nbsp;A&nbsp;``callable``&nbsp;function&nbsp;with&nbsp;one&nbsp;argument&nbsp;(the&nbsp;calling&nbsp;Series,&nbsp;DataFrame<br>
-&nbsp;&nbsp;or&nbsp;Panel)&nbsp;and&nbsp;that&nbsp;returns&nbsp;valid&nbsp;output&nbsp;for&nbsp;indexing&nbsp;(one&nbsp;of&nbsp;the&nbsp;above)<br>
-&nbsp;<br>
-``.loc``&nbsp;will&nbsp;raise&nbsp;a&nbsp;``KeyError``&nbsp;when&nbsp;the&nbsp;items&nbsp;are&nbsp;not&nbsp;found.<br>
-&nbsp;<br>
-See&nbsp;more&nbsp;at&nbsp;:ref:`Selection&nbsp;by&nbsp;Label&nbsp;&lt;indexing.label&gt;`</tt></dd>
-</dl>
-<hr>
-Data and other attributes inherited from <a href="pandas.core.generic.html#NDFrame">pandas.core.generic.NDFrame</a>:<br>
-<dl><dt><strong>is_copy</strong> = None</dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#PandasObject">pandas.core.base.PandasObject</a>:<br>
-<dl><dt><a name="TimeSeries-__dir__"><strong>__dir__</strong></a>(self)</dt><dd><tt>Provide&nbsp;method&nbsp;name&nbsp;lookup&nbsp;and&nbsp;completion<br>
-Only&nbsp;provide&nbsp;'public'&nbsp;methods</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-__sizeof__"><strong>__sizeof__</strong></a>(self)</dt><dd><tt>Generates&nbsp;the&nbsp;total&nbsp;memory&nbsp;usage&nbsp;for&nbsp;a&nbsp;<a href="builtins.html#object">object</a>&nbsp;that&nbsp;returns<br>
-either&nbsp;a&nbsp;value&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;of&nbsp;values</tt></dd></dl>
-
-<hr>
-Methods inherited from <a href="pandas.core.base.html#StringMixin">pandas.core.base.StringMixin</a>:<br>
-<dl><dt><a name="TimeSeries-__bytes__"><strong>__bytes__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;bytes(obj)&nbsp;in&nbsp;py3&nbsp;only.<br>
-Yields&nbsp;a&nbsp;bytestring&nbsp;in&nbsp;both&nbsp;py2/py3.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-__repr__"><strong>__repr__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;<a href="builtins.html#object">object</a>.<br>
-&nbsp;<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-<dl><dt><a name="TimeSeries-__str__"><strong>__str__</strong></a>(self)</dt><dd><tt>Return&nbsp;a&nbsp;string&nbsp;representation&nbsp;for&nbsp;a&nbsp;particular&nbsp;Object<br>
-&nbsp;<br>
-Invoked&nbsp;by&nbsp;<a href="#TimeSeries-str">str</a>(df)&nbsp;in&nbsp;both&nbsp;py2/py3.<br>
-Yields&nbsp;Bytestring&nbsp;in&nbsp;Py2,&nbsp;Unicode&nbsp;String&nbsp;in&nbsp;py3.</tt></dd></dl>
-
-</td></tr></table></td></tr></table><p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#eeaa77">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#ffffff" face="helvetica, arial"><big><strong>Functions</strong></big></font></td></tr>
-    
-<tr><td bgcolor="#eeaa77"><tt>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</tt></td><td>&nbsp;</td>
-<td width="100%"><dl><dt><a name="-Vector"><strong>Vector</strong></a>(*args, units=None)</dt></dl>
- <dl><dt><a name="-abs"><strong>abs</strong></a>(*args)</dt></dl>
- <dl><dt><a name="-cart2pol"><strong>cart2pol</strong></a>(x, y, z=None)</dt></dl>
- <dl><dt><a name="-contour"><strong>contour</strong></a>(df, **options)</dt><dd><tt>Makes&nbsp;a&nbsp;contour&nbsp;plot&nbsp;from&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>.<br>
-&nbsp;<br>
-Note:&nbsp;columns&nbsp;and&nbsp;index&nbsp;must&nbsp;be&nbsp;numerical<br>
-&nbsp;<br>
-df:&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a></tt></dd></dl>
- <dl><dt><a name="-decorate"><strong>decorate</strong></a>(**kwargs)</dt><dd><tt>Decorate&nbsp;the&nbsp;current&nbsp;axes.<br>
-&nbsp;<br>
-Call&nbsp;decorate&nbsp;with&nbsp;keyword&nbsp;arguments&nbsp;like<br>
-&nbsp;<br>
-<a href="#-decorate">decorate</a>(title='Title',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;xlabel='x',<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ylabel='y')<br>
-&nbsp;<br>
-The&nbsp;keyword&nbsp;arguments&nbsp;can&nbsp;be&nbsp;any&nbsp;of&nbsp;the&nbsp;axis&nbsp;properties<br>
-defined&nbsp;by&nbsp;Matplotlib.&nbsp;&nbsp;To&nbsp;see&nbsp;the&nbsp;list,&nbsp;run&nbsp;plt.getp(plt.gca())<br>
-&nbsp;<br>
-In&nbsp;addition,&nbsp;you&nbsp;can&nbsp;use&nbsp;`legend=False`&nbsp;to&nbsp;suppress&nbsp;the&nbsp;legend.<br>
-&nbsp;<br>
-And&nbsp;you&nbsp;can&nbsp;use&nbsp;`loc`&nbsp;to&nbsp;indicate&nbsp;the&nbsp;location&nbsp;of&nbsp;the&nbsp;legend<br>
-(the&nbsp;default&nbsp;value&nbsp;is&nbsp;'best')</tt></dd></dl>
- <dl><dt><a name="-fit_leastsq"><strong>fit_leastsq</strong></a>(error_func, params, data, **kwargs)</dt><dd><tt>Find&nbsp;the&nbsp;parameters&nbsp;that&nbsp;yield&nbsp;the&nbsp;best&nbsp;fit&nbsp;for&nbsp;the&nbsp;data.<br>
-&nbsp;<br>
-`params`&nbsp;can&nbsp;be&nbsp;a&nbsp;sequence,&nbsp;array,&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a><br>
-&nbsp;<br>
-error_func:&nbsp;function&nbsp;that&nbsp;computes&nbsp;a&nbsp;sequence&nbsp;of&nbsp;errors<br>
-params:&nbsp;initial&nbsp;guess&nbsp;for&nbsp;the&nbsp;best&nbsp;parameters<br>
-data:&nbsp;the&nbsp;data&nbsp;to&nbsp;be&nbsp;fit;&nbsp;will&nbsp;be&nbsp;passed&nbsp;to&nbsp;min_fun<br>
-kwargs:&nbsp;any&nbsp;other&nbsp;arguments&nbsp;are&nbsp;passed&nbsp;to&nbsp;leastsq</tt></dd></dl>
- <dl><dt><a name="-flip"><strong>flip</strong></a>(p=0.5)</dt></dl>
- <dl><dt><a name="-fsolve"><strong>fsolve</strong></a>(func, x0, *args, **kwargs)</dt><dd><tt>Return&nbsp;the&nbsp;roots&nbsp;of&nbsp;the&nbsp;(non-linear)&nbsp;equations<br>
-defined&nbsp;by&nbsp;func(x)&nbsp;=&nbsp;0&nbsp;given&nbsp;a&nbsp;starting&nbsp;estimate.<br>
-&nbsp;<br>
-Uses&nbsp;scipy.optimize.fsolve,&nbsp;with&nbsp;extra&nbsp;error-checking.<br>
-&nbsp;<br>
-func:&nbsp;function&nbsp;to&nbsp;find&nbsp;the&nbsp;roots&nbsp;of<br>
-x0:&nbsp;scalar&nbsp;or&nbsp;array,&nbsp;initial&nbsp;guess<br>
-args:&nbsp;additional&nbsp;positional&nbsp;arguments&nbsp;are&nbsp;passed&nbsp;along&nbsp;to&nbsp;fsolve,<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;which&nbsp;passes&nbsp;them&nbsp;along&nbsp;to&nbsp;func<br>
-&nbsp;<br>
-returns:&nbsp;solution&nbsp;as&nbsp;an&nbsp;array</tt></dd></dl>
- <dl><dt><a name="-interp_inverse"><strong>interp_inverse</strong></a>(series, **options)</dt><dd><tt>Interpolate&nbsp;the&nbsp;inverse&nbsp;function&nbsp;of&nbsp;a&nbsp;<a href="pandas.core.series.html#Series">Series</a>.<br>
-&nbsp;<br>
-series:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;<a href="builtins.html#object">object</a>,&nbsp;represents&nbsp;a&nbsp;mapping&nbsp;from&nbsp;`a`&nbsp;to&nbsp;`b`<br>
-kind:&nbsp;string,&nbsp;which&nbsp;kind&nbsp;of&nbsp;iterpolation<br>
-options:&nbsp;keyword&nbsp;arguments&nbsp;passed&nbsp;to&nbsp;interpolate<br>
-&nbsp;<br>
-returns:&nbsp;interpolation&nbsp;<a href="builtins.html#object">object</a>,&nbsp;can&nbsp;be&nbsp;used&nbsp;as&nbsp;a&nbsp;function<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;from&nbsp;`b`&nbsp;to&nbsp;`a`</tt></dd></dl>
- <dl><dt><a name="-interpolate"><strong>interpolate</strong></a>(series, **options)</dt><dd><tt>Creates&nbsp;an&nbsp;interpolation&nbsp;function.<br>
-&nbsp;<br>
-series:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;<a href="builtins.html#object">object</a><br>
-options:&nbsp;any&nbsp;legal&nbsp;options&nbsp;to&nbsp;scipy.interpolate.interp1d<br>
-&nbsp;<br>
-returns:&nbsp;function&nbsp;that&nbsp;maps&nbsp;from&nbsp;the&nbsp;index&nbsp;of&nbsp;the&nbsp;series&nbsp;to&nbsp;values</tt></dd></dl>
- <dl><dt><a name="-label_axes"><strong>label_axes</strong></a>(xlabel=None, ylabel=None, title=None, **kwargs)</dt><dd><tt>Puts&nbsp;labels&nbsp;and&nbsp;title&nbsp;on&nbsp;the&nbsp;axes.<br>
-&nbsp;<br>
-xlabel:&nbsp;string<br>
-ylabel:&nbsp;string<br>
-title:&nbsp;string<br>
-&nbsp;<br>
-kwargs:&nbsp;options&nbsp;passed&nbsp;to&nbsp;pyplot</tt></dd></dl>
- <dl><dt><a name="-legend"><strong>legend</strong></a>(**kwargs)</dt></dl>
- <dl><dt><a name="-linrange"><strong>linrange</strong></a>(start=0, stop=None, step=1, **kwargs)</dt><dd><tt>Returns&nbsp;an&nbsp;array&nbsp;of&nbsp;evenly-spaced&nbsp;values&nbsp;in&nbsp;the&nbsp;interval&nbsp;[start,&nbsp;stop].<br>
-&nbsp;<br>
-This&nbsp;function&nbsp;works&nbsp;best&nbsp;if&nbsp;the&nbsp;space&nbsp;between&nbsp;start&nbsp;and&nbsp;stop<br>
-is&nbsp;divisible&nbsp;by&nbsp;step;&nbsp;otherwise&nbsp;the&nbsp;results&nbsp;might&nbsp;be&nbsp;surprising.<br>
-&nbsp;<br>
-By&nbsp;default,&nbsp;the&nbsp;last&nbsp;value&nbsp;in&nbsp;the&nbsp;array&nbsp;is&nbsp;`stop`&nbsp;(at&nbsp;least&nbsp;approximately).<br>
-If&nbsp;you&nbsp;provide&nbsp;the&nbsp;keyword&nbsp;argument&nbsp;`endpoint=False`,&nbsp;the&nbsp;last&nbsp;value<br>
-in&nbsp;the&nbsp;array&nbsp;is&nbsp;`stop-step`.&nbsp;<br>
-&nbsp;<br>
-start:&nbsp;first&nbsp;value<br>
-stop:&nbsp;last&nbsp;value<br>
-step:&nbsp;space&nbsp;between&nbsp;values<br>
-&nbsp;<br>
-Also&nbsp;accepts&nbsp;the&nbsp;same&nbsp;keyword&nbsp;arguments&nbsp;as&nbsp;np.linspace.&nbsp;&nbsp;See<br>
-https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html<br>
-&nbsp;<br>
-returns:&nbsp;array&nbsp;or&nbsp;Quantity</tt></dd></dl>
- <dl><dt><a name="-linspace"><strong>linspace</strong></a>(start, stop, num=50, **kwargs)</dt><dd><tt>Returns&nbsp;an&nbsp;array&nbsp;of&nbsp;evenly-spaced&nbsp;values&nbsp;in&nbsp;the&nbsp;interval&nbsp;[start,&nbsp;stop].<br>
-&nbsp;<br>
-start:&nbsp;first&nbsp;value<br>
-stop:&nbsp;last&nbsp;value<br>
-num:&nbsp;number&nbsp;of&nbsp;values<br>
-&nbsp;<br>
-Also&nbsp;accepts&nbsp;the&nbsp;same&nbsp;keyword&nbsp;arguments&nbsp;as&nbsp;np.linspace.&nbsp;&nbsp;See<br>
-https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html<br>
-&nbsp;<br>
-returns:&nbsp;array&nbsp;or&nbsp;Quantity</tt></dd></dl>
- <dl><dt><a name="-max"><strong>max</strong></a>(*args)</dt></dl>
- <dl><dt><a name="-max_bounded"><strong>max_bounded</strong></a>(max_func, bounds, *args, **options)</dt><dd><tt>Finds&nbsp;the&nbsp;input&nbsp;value&nbsp;that&nbsp;maximizes&nbsp;`max_func`.<br>
-&nbsp;<br>
-min_func:&nbsp;computes&nbsp;the&nbsp;function&nbsp;to&nbsp;be&nbsp;maximized<br>
-bounds:&nbsp;sequence&nbsp;of&nbsp;two&nbsp;values,&nbsp;lower&nbsp;and&nbsp;upper&nbsp;bounds&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;range&nbsp;to&nbsp;be&nbsp;searched<br>
-args:&nbsp;any&nbsp;additional&nbsp;positional&nbsp;arguments&nbsp;are&nbsp;passed&nbsp;to&nbsp;max_func<br>
-options:&nbsp;any&nbsp;keyword&nbsp;arguments&nbsp;are&nbsp;passed&nbsp;as&nbsp;options&nbsp;to&nbsp;minimize_scalar<br>
-&nbsp;<br>
-returns:&nbsp;OptimizeResult&nbsp;<a href="builtins.html#object">object</a>&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(see&nbsp;https://docs.scipy.org/doc/scipy/<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;reference/generated/scipy.optimize.minimize_scalar.html)</tt></dd></dl>
- <dl><dt><a name="-min"><strong>min</strong></a>(*args)</dt></dl>
- <dl><dt><a name="-min_bounded"><strong>min_bounded</strong></a>(min_func, bounds, *args, **options)</dt><dd><tt>Finds&nbsp;the&nbsp;input&nbsp;value&nbsp;that&nbsp;minimizes&nbsp;`min_func`.<br>
-&nbsp;<br>
-min_func:&nbsp;computes&nbsp;the&nbsp;function&nbsp;to&nbsp;be&nbsp;minimized<br>
-bounds:&nbsp;sequence&nbsp;of&nbsp;two&nbsp;values,&nbsp;lower&nbsp;and&nbsp;upper&nbsp;bounds&nbsp;of&nbsp;the<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;range&nbsp;to&nbsp;be&nbsp;searched<br>
-args:&nbsp;any&nbsp;additional&nbsp;positional&nbsp;arguments&nbsp;are&nbsp;passed&nbsp;to&nbsp;min_func<br>
-options:&nbsp;any&nbsp;keyword&nbsp;arguments&nbsp;are&nbsp;passed&nbsp;as&nbsp;options&nbsp;to&nbsp;minimize_scalar<br>
-&nbsp;<br>
-returns:&nbsp;OptimizeResult&nbsp;<a href="builtins.html#object">object</a>&nbsp;<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(see&nbsp;https://docs.scipy.org/doc/scipy/<br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;reference/generated/scipy.optimize.minimize_scalar.html)</tt></dd></dl>
- <dl><dt><a name="-newfig"><strong>newfig</strong></a>(**kwargs)</dt><dd><tt>Creates&nbsp;a&nbsp;new&nbsp;figure.</tt></dd></dl>
- <dl><dt><a name="-nolegend"><strong>nolegend</strong></a>()</dt></dl>
- <dl><dt><a name="-plot"><strong>plot</strong></a>(*args, **kwargs)</dt><dd><tt>Makes&nbsp;line&nbsp;plots.<br>
-&nbsp;<br>
-args&nbsp;can&nbsp;be:<br>
-&nbsp;&nbsp;<a href="#-plot">plot</a>(y)<br>
-&nbsp;&nbsp;<a href="#-plot">plot</a>(y,&nbsp;style_string)<br>
-&nbsp;&nbsp;<a href="#-plot">plot</a>(x,&nbsp;y)<br>
-&nbsp;&nbsp;<a href="#-plot">plot</a>(x,&nbsp;y,&nbsp;style_string)<br>
-&nbsp;<br>
-kwargs&nbsp;are&nbsp;the&nbsp;same&nbsp;as&nbsp;for&nbsp;pyplot.plot<br>
-&nbsp;<br>
-If&nbsp;x&nbsp;or&nbsp;y&nbsp;have&nbsp;attributes&nbsp;label&nbsp;and/or&nbsp;units,<br>
-label&nbsp;the&nbsp;axes&nbsp;accordingly.</tt></dd></dl>
- <dl><dt><a name="-pol2cart"><strong>pol2cart</strong></a>(theta, rho, z=None)</dt></dl>
- <dl><dt><a name="-remove_from_legend"><strong>remove_from_legend</strong></a>(bad_labels)</dt><dd><tt>Removes&nbsp;some&nbsp;labels&nbsp;from&nbsp;the&nbsp;legend.<br>
-&nbsp;<br>
-bad_labels:&nbsp;sequence&nbsp;of&nbsp;strings</tt></dd></dl>
- <dl><dt><a name="-round"><strong>round</strong></a>(*args)</dt></dl>
- <dl><dt><a name="-run_odeint"><strong>run_odeint</strong></a>(system, slope_func, **kwargs)</dt><dd><tt>Runs&nbsp;a&nbsp;simulation&nbsp;of&nbsp;the&nbsp;system.<br>
-&nbsp;<br>
-`system`&nbsp;should&nbsp;contain&nbsp;system&nbsp;parameters&nbsp;and&nbsp;`ts`,&nbsp;which<br>
-is&nbsp;an&nbsp;array&nbsp;or&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;that&nbsp;specifies&nbsp;the&nbsp;time&nbsp;when&nbsp;the<br>
-solution&nbsp;will&nbsp;be&nbsp;computed.<br>
-&nbsp;<br>
-Adds&nbsp;a&nbsp;<a href="pandas.core.frame.html#DataFrame">DataFrame</a>&nbsp;to&nbsp;the&nbsp;<a href="#System">System</a>:&nbsp;results<br>
-&nbsp;<br>
-system:&nbsp;<a href="#System">System</a>&nbsp;<a href="builtins.html#object">object</a><br>
-slope_func:&nbsp;function&nbsp;that&nbsp;computes&nbsp;slopes</tt></dd></dl>
- <dl><dt><a name="-savefig"><strong>savefig</strong></a>(filename, *args, **kwargs)</dt><dd><tt>Save&nbsp;the&nbsp;current&nbsp;figure.<br>
-&nbsp;<br>
-filename:&nbsp;string</tt></dd></dl>
- <dl><dt><a name="-sleep"><strong>sleep</strong></a>(...)</dt><dd><tt><a href="#-sleep">sleep</a>(seconds)<br>
-&nbsp;<br>
-Delay&nbsp;execution&nbsp;for&nbsp;a&nbsp;given&nbsp;number&nbsp;of&nbsp;seconds.&nbsp;&nbsp;The&nbsp;argument&nbsp;may&nbsp;be<br>
-a&nbsp;floating&nbsp;point&nbsp;number&nbsp;for&nbsp;subsecond&nbsp;precision.</tt></dd></dl>
- <dl><dt><a name="-subplot"><strong>subplot</strong></a>(nrows, ncols, plot_number, **kwargs)</dt></dl>
- <dl><dt><a name="-subplots"><strong>subplots</strong></a>(*args, **kwargs)</dt></dl>
- <dl><dt><a name="-sum"><strong>sum</strong></a>(*args)</dt></dl>
- <dl><dt><a name="-underride"><strong>underride</strong></a>(d, **options)</dt><dd><tt>Add&nbsp;key-value&nbsp;pairs&nbsp;to&nbsp;d&nbsp;only&nbsp;if&nbsp;key&nbsp;is&nbsp;not&nbsp;in&nbsp;d.<br>
-&nbsp;<br>
-If&nbsp;d&nbsp;is&nbsp;None,&nbsp;create&nbsp;a&nbsp;new&nbsp;dictionary.<br>
-&nbsp;<br>
-d:&nbsp;dictionary<br>
-options:&nbsp;keyword&nbsp;args&nbsp;to&nbsp;add&nbsp;to&nbsp;d</tt></dd></dl>
- <dl><dt><a name="-units_off"><strong>units_off</strong></a>()</dt><dd><tt>Make&nbsp;all&nbsp;quantities&nbsp;behave&nbsp;as&nbsp;if&nbsp;they&nbsp;were&nbsp;dimensionless.</tt></dd></dl>
- <dl><dt><a name="-units_on"><strong>units_on</strong></a>()</dt><dd><tt>Restore&nbsp;the&nbsp;saved&nbsp;behavior&nbsp;of&nbsp;quantities.</tt></dd></dl>
- <dl><dt><a name="-unpack"><strong>unpack</strong></a>(series)</dt><dd><tt>Make&nbsp;the&nbsp;names&nbsp;in&nbsp;`series`&nbsp;available&nbsp;as&nbsp;globals.<br>
-&nbsp;<br>
-series:&nbsp;<a href="pandas.core.series.html#Series">Series</a>&nbsp;with&nbsp;variables&nbsp;names&nbsp;in&nbsp;the&nbsp;index</tt></dd></dl>
-</td></tr></table><p>
-<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
-<tr bgcolor="#55aa55">
-<td colspan=3 valign=bottom>&nbsp;<br>
-<font color="#ffffff" face="helvetica, arial"><big><strong>Data</strong></big></font></td></tr>
-    
-<tr><td bgcolor="#55aa55"><tt>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</tt></td><td>&nbsp;</td>
-<td width="100%"><strong>SIMPLOT</strong> = &lt;modsim.Simplot object&gt;<br>
-<strong>UNITS</strong> = &lt;pint.unit.UnitRegistry object&gt;<br>
-<dl><dt><strong>dimensionality</strong></dt>
-<dd><tt>Unit's&nbsp;dimensionality&nbsp;(e.g.&nbsp;{length:&nbsp;1,&nbsp;time:&nbsp;-1})<br>
-&nbsp;<br>
-This&nbsp;is&nbsp;a&nbsp;simplified&nbsp;version&nbsp;of&nbsp;this&nbsp;method&nbsp;that&nbsp;does&nbsp;no&nbsp;caching.<br>
-&nbsp;<br>
-returns:&nbsp;dimensionality</tt></dd>
-</dl>
-<br>
-<strong>exp</strong> = &lt;ufunc 'exp'&gt;<br>
-<strong>log</strong> = &lt;ufunc 'log'&gt;<br>
-<strong>logger</strong> = &lt;Logger modsim.py (WARNING)&gt;<br>
-<strong>pi</strong> = 3.141592653589793<br>
-<strong>sqrt</strong> = &lt;ufunc 'sqrt'&gt;</td></tr></table>
-</body></html>
\ No newline at end of file
diff --git a/notebooks/oem.ipynb b/notebooks/oem.ipynb
deleted file mode 100644
index f9ca5cb8..00000000
--- a/notebooks/oem.ipynb
+++ /dev/null
@@ -1,1039 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case study.\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Electric car"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Olin Electric Motorsports](https://www.olinelectricmotorsports.com/) is a club at Olin College that designs and builds electric cars, and participates in the [Formula SAE Electric](https://www.sae.org/attend/student-events/formula-sae-electric) competition.\n",
-    "\n",
-    "The goal of this case study is to use simulation to guide the design of a car intended to accelerate from standing to 100 kph as quickly as possible.  The [world record for this event](https://www.youtube.com/watch?annotation_id=annotation_2297602723&feature=iv&src_vid=I-NCH8ct24U&v=n2XiCYA3C9s), using a car that meets the competition requirements, is 1.513 seconds.\n",
-    "\n",
-    "We'll start with a simple model that takes into account the characteristics of the motor and vehicle:\n",
-    "\n",
-    "* The motor is an [Emrax 228 high voltage axial flux synchronous permanent magnet motor](http://emrax.com/products/emrax-228/); according to the [data sheet](http://emrax.com/wp-content/uploads/2017/01/emrax_228_technical_data_4.5.pdf), its maximum torque is 240 Nm, at 0 rpm.  But maximum torque decreases with motor speed; at 5000 rpm, maximum torque is 216 Nm.\n",
-    "\n",
-    "* The motor is connected to the drive axle with a chain drive with speed ratio 13:60 or 1:4.6; that is, the axle rotates once for each 4.6 rotations of the motor.\n",
-    "\n",
-    "* The radius of the tires is 0.26 meters.\n",
-    "\n",
-    "* The weight of the vehicle, including driver, is 300 kg.\n",
-    "\n",
-    "To start, we will assume no slipping between the tires and the road surface, no air resistance, and no rolling resistance.  Then we will relax these assumptions one at a time.\n",
-    "\n",
-    "* First we'll add drag, assuming that the frontal area of the vehicle is 0.6 square meters, with coefficient of drag 0.6.\n",
-    "\n",
-    "* Next we'll add rolling resistance, assuming a coefficient of 0.2.\n",
-    "\n",
-    "* Finally we'll compute the peak acceleration to see if the \"no slip\" assumption is credible.\n",
-    "\n",
-    "We'll use this model to estimate the potential benefit of possible design improvements, including decreasing drag and rolling resistance, or increasing the speed ratio.\n",
-    "\n",
-    "I'll start by loading the units we need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radian = UNITS.radian\n",
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "minute = UNITS.minute\n",
-    "hour = UNITS.hour\n",
-    "km = UNITS.kilometer\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton\n",
-    "rpm = UNITS.rpm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And store the parameters in a `Params` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(r_wheel=0.26 * m,\n",
-    "                speed_ratio=13/60,\n",
-    "                C_rr=0.2,\n",
-    "                C_d=0.5,\n",
-    "                area=0.6*m**2,\n",
-    "                rho=1.2*kg/m**3,\n",
-    "                mass=300*kg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`make_system` creates the initial state, `init`, and constructs an `interp1d` object that represents torque as a function of motor speed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(x=0*m, v=0*m/s)\n",
-    "    \n",
-    "    rpms = [0, 2000, 5000]\n",
-    "    torques = [240, 240, 216]\n",
-    "    interpolate_torque = interpolate(Series(torques, rpms))\n",
-    "    \n",
-    "    return System(params, init=init,\n",
-    "                  interpolate_torque=interpolate_torque,\n",
-    "                  t_end=3*s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `make_system`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Torque and speed\n",
-    "\n",
-    "The relationship between torque and motor speed is taken from the [Emrax 228 data sheet](http://emrax.com/wp-content/uploads/2017/01/emrax_228_technical_data_4.5.pdf).  The following functions reproduce the red dotted line that represents peak torque, which can only be sustained for a few seconds before the motor overheats."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_torque(omega, system):\n",
-    "    \"\"\"Maximum peak torque as a function of motor speed.\n",
-    "    \n",
-    "    omega: motor speed in radian/s\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: torque in Nm\n",
-    "    \"\"\"\n",
-    "    factor = (1 * radian / s).to(rpm)\n",
-    "    x = magnitude(omega * factor)\n",
-    "    return system.interpolate_torque(x) * N * m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compute_torque(0*radian/s, system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "omega = (5000 * rpm).to(radian/s)\n",
-    "compute_torque(omega, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the whole curve."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xs = linspace(0, 525, 21) * radian / s\n",
-    "taus = [compute_torque(x, system) for x in xs]\n",
-    "plot(xs, taus)\n",
-    "decorate(xlabel='Motor speed (rpm)',\n",
-    "         ylabel='Available torque (N m)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Simulation\n",
-    "\n",
-    "Here's the slope function that computes the maximum possible acceleration of the car as a function of it current speed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    x, v = state\n",
-    "    r_wheel, speed_ratio = system.r_wheel, system.speed_ratio\n",
-    "    mass = system.mass\n",
-    "    \n",
-    "    # use velocity, v, to compute angular velocity of the wheel\n",
-    "    omega2 = v / r_wheel\n",
-    "    \n",
-    "    # use the speed ratio to compute motor speed\n",
-    "    omega1 = omega2 / speed_ratio\n",
-    "    \n",
-    "    # look up motor speed to get maximum torque at the motor\n",
-    "    tau1 = compute_torque(omega1, system)\n",
-    "    \n",
-    "    # compute the corresponding torque at the axle\n",
-    "    tau2 = tau1 / speed_ratio\n",
-    "    \n",
-    "    # compute the force of the wheel on the ground\n",
-    "    F = tau2 / r_wheel\n",
-    "    \n",
-    "    # compute acceleration\n",
-    "    a = F/mass\n",
-    "\n",
-    "    return v, a      "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `slope_func` at linear velocity 10 m/s."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_state = State(x=0*m, v=10*m/s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(test_state, 0*s, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And look at the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After 3 seconds, the vehicle could be at 40 meters per second, in theory, which is 144 kph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_final = get_last_value(results.v)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_final.to(km/hour)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `x`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_position(results):\n",
-    "    plot(results.x, label='x')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "    \n",
-    "plot_position(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plotting `v`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_velocity(results):\n",
-    "    plot(results.v, label='v')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')\n",
-    "    \n",
-    "plot_velocity(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Stopping at 100 kph\n",
-    "\n",
-    "We'll use an event function to stop the simulation when we reach 100 kph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Stops when we get to 100 km/hour.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: difference from 100 km/hour\n",
-    "    \"\"\"\n",
-    "    x, v = state\n",
-    "    \n",
-    "    # convert to km/hour\n",
-    "    factor = (1 * m/s).to(km/hour)\n",
-    "    v = magnitude(v * factor)\n",
-    "    \n",
-    "    return v - 100     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subplot(2, 1, 1)\n",
-    "plot_position(results)\n",
-    "\n",
-    "subplot(2, 1, 2)\n",
-    "plot_velocity(results)\n",
-    "\n",
-    "savefig('figs/chap11-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "According to this model, we should be able to make this run in just over 2 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_final = get_last_label(results) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At the end of the run, the car has gone about 28 meters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "state = results.last_row()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we send the final state back to the slope function, we can see that the final acceleration is about 13 $m/s^2$, which is about 1.3 times the acceleration of gravity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v, a = slope_func(state, 0, system)\n",
-    "v.to(km/hour)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g = 9.8 * m/s**2\n",
-    "(a / g).to(UNITS.dimensionless)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's not easy for a vehicle to accelerate faster than `g`, because that implies a coefficient of friction between the wheels and the road surface that's greater than 1.  But racing tires on dry asphalt can do that; the OEM team at Olin has tested their tires and found a peak coefficient near 1.5.\n",
-    "\n",
-    "So it's possible that our no slip assumption is valid, but only under ideal conditions, where weight is distributed equally on four tires, and all tires are driving."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** How much time do we lose because maximum torque decreases as motor speed increases?  Run the model again with no drop off in torque and see how much time it saves."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section we'll see how much effect drag has on the results.\n",
-    "\n",
-    "Here's a function to compute drag force, as we saw in Chapter 21."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(v, system):\n",
-    "    \"\"\"Computes drag force in the opposite direction of `v`.\n",
-    "    \n",
-    "    v: velocity\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: drag force\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    f_drag = -np.sign(v) * rho * v**2 * C_d * area / 2\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it with a velocity of 20 m/s."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drag_force(20 * m/s, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the resulting acceleration of the vehicle due to drag.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drag_force(20 * m/s, system) / system.mass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the effect of drag is not huge, compared to the acceleration we computed in the previous section, but it is not negligible.\n",
-    "\n",
-    "Here's a modified slope function that takes drag into account."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func2(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    x, v = state\n",
-    "    r_wheel, speed_ratio = system.r_wheel, system.speed_ratio\n",
-    "    mass = system.mass\n",
-    "    \n",
-    "    omega2 = v / r_wheel * radian\n",
-    "    omega1 = omega2 / speed_ratio\n",
-    "    tau1 = compute_torque(omega1, system)\n",
-    "    tau2 = tau1 / speed_ratio\n",
-    "    F = tau2 / r_wheel\n",
-    "    a_motor = F / mass\n",
-    "    a_drag = drag_force(v, system) / mass\n",
-    "    \n",
-    "    a = a_motor + a_drag\n",
-    "    return v, a "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the next run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2, details = run_ode_solver(system, slope_func2, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The time to reach 100 kph is a bit higher."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_final2 = get_last_label(results2) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But the total effect of drag is only about 2/100 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_final2 - t_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That's not huge, which suggests we might not be able to save much time by decreasing the frontal area, or coefficient of drag, of the car."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Rolling resistance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we'll consider [rolling resistance](https://en.wikipedia.org/wiki/Rolling_resistance), which the force that resists the motion of the car as it rolls on tires.  The cofficient of rolling resistance, `C_rr`, is the ratio of rolling resistance to the normal force between the car and the ground (in that way it is similar to a coefficient of friction).\n",
-    "\n",
-    "The following function computes rolling resistance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.set(unit_rr = 1 * N / kg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def rolling_resistance(system):\n",
-    "    \"\"\"Computes force due to rolling resistance.\n",
-    "    \n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: force\n",
-    "    \"\"\"\n",
-    "    return -system.C_rr * system.mass * system.unit_rr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The acceleration due to rolling resistance is 0.2 (it is not a coincidence that it equals `C_rr`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rolling_resistance(system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rolling_resistance(system) / system.mass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a modified slope function that includes drag and rolling resistance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func3(state, t, system):\n",
-    "    \"\"\"Computes the derivatives of the state variables.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object \n",
-    "    \n",
-    "    returns: sequence of derivatives\n",
-    "    \"\"\"\n",
-    "    x, v = state\n",
-    "    r_wheel, speed_ratio = system.r_wheel, system.speed_ratio\n",
-    "    mass = system.mass\n",
-    "    \n",
-    "    omega2 = v / r_wheel * radian\n",
-    "    omega1 = omega2 / speed_ratio\n",
-    "    tau1 = compute_torque(omega1, system)\n",
-    "    tau2 = tau1 / speed_ratio\n",
-    "    F = tau2 / r_wheel\n",
-    "    a_motor = F / mass\n",
-    "    a_drag = drag_force(v, system) / mass\n",
-    "    a_roll = rolling_resistance(system) / mass\n",
-    "    \n",
-    "    a = a_motor + a_drag + a_roll\n",
-    "    return v, a "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results3, details = run_ode_solver(system, slope_func3, events=event_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final time is a little higher, but the total cost of rolling resistance is only 3/100 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_final3 = get_last_label(results3) * s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_final3 - t_final2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So, again, there is probably not much to be gained by decreasing rolling resistance.\n",
-    "\n",
-    "In fact, it is hard to decrease rolling resistance without also decreasing traction, so that might not help at all."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optimal gear ratio"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The gear ratio 13:60 is intended to maximize the acceleration of the car without causing the tires to slip.  In this section, we'll consider other gear ratios and estimate their effects on acceleration and time to reach 100 kph.\n",
-    "\n",
-    "Here's a function that takes a speed ratio as a parameter and returns time to reach 100 kph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def time_to_speed(speed_ratio, params):\n",
-    "    \"\"\"Computes times to reach 100 kph.\n",
-    "    \n",
-    "    speed_ratio: ratio of wheel speed to motor speed\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: time to reach 100 kph, in seconds\n",
-    "    \"\"\"\n",
-    "    params = Params(params, speed_ratio=speed_ratio)\n",
-    "    system = make_system(params)\n",
-    "    system.set(unit_rr = 1 * N / kg)\n",
-    "    \n",
-    "    results, details = run_ode_solver(system, slope_func3, events=event_func)\n",
-    "    t_final = get_last_label(results)\n",
-    "    a_initial = slope_func(system.init, 0, system)\n",
-    "    return t_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it with the default ratio:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "time_to_speed(13/60, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can try it with different numbers of teeth on the motor gear (assuming that the axle gear has 60 teeth):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for teeth in linrange(8, 18):\n",
-    "    print(teeth, time_to_speed(teeth/60, params))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Wow!  The speed ratio has a big effect on the results.  At first glance, it looks like we could break the world record (1.513 seconds) just by decreasing the number of teeth.\n",
-    "\n",
-    "But before we try it, let's see what effect that has on peak acceleration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def initial_acceleration(speed_ratio, params):\n",
-    "    \"\"\"Maximum acceleration as a function of speed ratio.\n",
-    "    \n",
-    "    speed_ratio: ratio of wheel speed to motor speed\n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: peak acceleration, in m/s^2\n",
-    "    \"\"\"\n",
-    "    params = Params(params, speed_ratio=speed_ratio)\n",
-    "    system = make_system(params)\n",
-    "    a_initial = slope_func(system.init, 0, system)[1] * m/s**2\n",
-    "    return a_initial"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for teeth in linrange(8, 18):\n",
-    "    print(teeth, initial_acceleration(teeth/60, params))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we decrease the speed ratio, the peak acceleration increases.  With 8 teeth on the motor gear, we could break the world record, but only if we can accelerate at 2.3 times the acceleration of gravity, which is impossible without very sticky ties and a vehicle that generates a lot of downforce."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "23.07 / 9.8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These results suggest that the most promising way to improve the performance of the car (for this event) would be to improve traction."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/pendulum.ipynb b/notebooks/pendulum.ipynb
deleted file mode 100644
index 5cc315d6..00000000
--- a/notebooks/pendulum.ipynb
+++ /dev/null
@@ -1,4548 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 10 Example: Springy Pendulum\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# If you want the figures to appear in the notebook, \n",
-    "# and you want to interact with them, use\n",
-    "# %matplotlib notebook\n",
-    "\n",
-    "# If you want the figures to appear in the notebook, \n",
-    "# and you don't want to interact with them, use\n",
-    "# %matplotlib inline\n",
-    "\n",
-    "# If you want the figures to appear in separate windows, use\n",
-    "# %matplotlib qt5\n",
-    "\n",
-    "# to switch from one to another, you have to select Kernel->Restart\n",
-    "\n",
-    "%matplotlib notebook\n",
-    "\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pendulum"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook solves the Spider-Man problem from spiderman.ipynb, demonstrating a different development process for physical simulations.\n",
-    "\n",
-    "In pendulum_sympy, we derive the equations of motion for a springy pendulum without drag, yielding:\n",
-    "\n",
-    "$ \\ddot{x} = \\frac{k length_{0} x}{m \\sqrt{x^{2} + y^{2}}} - \\frac{k x}{m} $\n",
-    "\n",
-    "$ \\ddot{y} = - g + \\frac{k length_{0} y}{m \\sqrt{x^{2} + y^{2}}} - \\frac{k y}{m} $\n",
-    "\n",
-    "We'll use the same conditions we saw in `spiderman.ipynb`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "condition = Condition(g = 9.8,\n",
-    "                      m = 75,\n",
-    "                      area = 1,\n",
-    "                      rho = 1.2,\n",
-    "                      v_term = 60,\n",
-    "                      duration = 30,\n",
-    "                      length0 = 100,\n",
-    "                      angle = (270 - 45),\n",
-    "                      k = 20)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "Now here's a version of `make_system` that takes a `Condition` object as a parameter.\n",
-    "\n",
-    "`make_system` uses the given value of `v_term` to compute the drag coefficient `C_d`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def make_system(condition):\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    condition: Condition with height, g, m, diameter, \n",
-    "               rho, v_term, and duration\n",
-    "    \n",
-    "    returns: System with init, g, m, rho, C_d, area, and ts\n",
-    "    \"\"\"\n",
-    "    unpack(condition)\n",
-    "        \n",
-    "    theta = np.deg2rad(angle)\n",
-    "    x, y = pol2cart(theta, length0)\n",
-    "    P = Vector(x, y)\n",
-    "    V = Vector(0, 0)\n",
-    "    \n",
-    "    init = State(x=P.x, y=P.y, vx=V.x, vy=V.y)\n",
-    "    C_d = 2 * m * g / (rho * area * v_term**2)\n",
-    "    ts = linspace(0, duration, 501)\n",
-    "    \n",
-    "    \n",
-    "    return System(init=init, g=g, m=m, rho=rho,\n",
-    "                  C_d=C_d, area=area, length0=length0,\n",
-    "                  k=k, ts=ts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
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-       "\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>x     -70.71067811865477 dimensionless\n",
-       "y     -...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>m</th>\n",
-       "      <td>75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.340278</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
-       "      <td>1</td>\n",
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-       "      <td>100</td>\n",
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-       "      <th>k</th>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ts</th>\n",
-       "      <td>[0.0, 0.06, 0.12, 0.18, 0.24, 0.3, 0.36, 0.42,...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "init       x     -70.71067811865477 dimensionless\n",
-       "y     -...\n",
-       "g                                                        9.8\n",
-       "m                                                         75\n",
-       "rho                                                      1.2\n",
-       "C_d                                                 0.340278\n",
-       "area                                                       1\n",
-       "length0                                                  100\n",
-       "k                                                         20\n",
-       "ts         [0.0, 0.06, 0.12, 0.18, 0.24, 0.3, 0.36, 0.42,...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(condition)\n",
-    "system"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>x</th>\n",
-       "      <td>-70.71067811865477 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>-70.71067811865474 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>vx</th>\n",
-       "      <td>0 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>vy</th>\n",
-       "      <td>0 dimensionless</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "x     -70.71067811865477 dimensionless\n",
-       "y     -70.71067811865474 dimensionless\n",
-       "vx                     0 dimensionless\n",
-       "vy                     0 dimensionless\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "To write the slope function, we can get the expressions for `ax` and `ay` directly from SymPy and plug them in."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the state variables.\n",
-    "    \n",
-    "    state: State (x, y, x velocity, y velocity)\n",
-    "    t: time\n",
-    "    system: System object with length0, m, k\n",
-    "    \n",
-    "    returns: sequence (vx, vy, ax, ay)\n",
-    "    \"\"\"\n",
-    "    x, y, vx, vy = state\n",
-    "    unpack(system)\n",
-    "\n",
-    "    ax = k*length0*x/(m*sqrt(x**2 + y**2)) - k*x/m\n",
-    "    ay = -g + k*length0*y/(m*sqrt(x**2 + y**2)) - k*y/m\n",
-    "\n",
-    "    return vx, vy, ax, ay"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "As always, let's test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<Quantity(0, 'dimensionless')>,\n",
-       " <Quantity(0, 'dimensionless')>,\n",
-       " <Quantity(0.0, 'dimensionless')>,\n",
-       " <Quantity(-9.8, 'dimensionless')>)"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "And then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 84 ms, sys: 0 ns, total: 84 ms\n",
-      "Wall time: 83.6 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time run_odeint(system, slope_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing the results\n",
-    "\n",
-    "We can extract the x and y components as `Series` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "xs = system.results.x\n",
-    "ys = system.results.y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The simplest way to visualize the results is to plot x and y as functions of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" width=\"799.9999826604674\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "newfig()\n",
-    "plot(xs, label='x')\n",
-    "plot(ys, label='y')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Position (m)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot the velocities the same way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "vxs = system.results.vx\n",
-    "vys = system.results.vy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\" width=\"799.9999826604674\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "newfig()\n",
-    "plot(vxs, label='vx')\n",
-    "plot(vys, label='vy')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another way to visualize the results is to plot y versus x.  The result is the trajectory through the plane of motion."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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CKEGuOaWNvxmY5HhFzkrYzBWPmR+ZiGrD+aL4os4XHTR0O2rTxZC1h1howAFKEABClCAAhQIXkCWfJLwdrmu+0t76cXsnbR3d3pxtb6pa2CZpVLu2plpDHBmlMLbhgEuPL+o7B1qgAu0fMDC6WMwJSctKschg35Zehmfnao1jC8TqqQmJ0StJg5MAQpQgAIUoAAFYllA1tiVtXb928CkeLywPM/UaypbDlai6sLNrt0HJMbh+6smmSJhgDPFFNZGDHBh8UVn51ADnDyqKI8s+rdnl6gY2WP62Ege1VfaZRw8YQxwLy7Px5DUxEiWwbEoQAEKUIACFKBAnxBoaW3Hb7eVfGPiknvNOhnooHd/UYOSM9e7fuR2KfjJM1NN+TDAmWIKayMGuLD4orNzqAFuy6dVqKq9YfhllAlDzK7rYcfRBloY/LvL8iAv2LJRgAIUoAAFKEABCgQnEGgZgLEjU7F64UTTS0YFWubJ7CzhDHDBna9QtmaAC0UtyvuEGuDWbi5CQ1NrV/WycOO3l5h7ntmuQy6qvIa9x84aug9mrRG76mK/FKAABShAAQpQINYEZAZJufsma/Z2NkVR8J1HVQwfbG4dN9nvSNFFHCkObY4CBjj7rxoGOPuNLR8hlAAnE5j8+qPThlomTRyORTPGWV5fMB2WVF3H7qM1hl0kVEq4ZKMABShAAQpQgAIUMC8g773J+2/+LX/CMCyZlWm+E5mjwHsZstSTf/ve4/kYnNL7Ky4McEFRh7QxA1xIbNHdKZQAd/Ha7Y7ZiPxbtCcwkVq0mjp8fLjaUNeaxTnIGJESXWSOTgEKUIACFKAABWJIoP7WHfx+Ryl8ut5Vtby79uLj+UFPDhfOHAUMcPZfNAxw9htbPkIoAa646hr2HDU+qrh6YTbGjUq1vL5gOiw/V4/th84Ydnnq4WzIs9psFKAABShAAQpQgALmBHZ8Xo2ys3WGjaepaZg/1dwC3P47nii7AnkPzr+9sCwPQ03MUcAAZ+58hbMVA1w4elHaN5QA9+nJWhz3XjZU/MoThUgZEN311mRSFZlcxb89uWAixqcPipIuh6UABShAAQpQgAKxJXD9ZjPe+dhrWOs3Id6Nlx7PR1JiXNAHc6r8KvYdNy5D8PxjHlPv0THABc0d9A4McEGTRX+HUALc1s+qUHm+ewZK+aX+wepJpmcjsuuoqy/cxKaDlYbuZWFxWWCcjQIUoAAFKEABClCgd4Gdh6vhrTHefXtw0mjMzB/V+84BtghnlnAGuJDIg9qJAS4oLmdsHEqAe2+nF1fqm7oOYMSQAfjOo56oH1CgxcWXz52AnLFDol4bC6AABShAAQpQgAJOF6i71Yzf7zDefUtKiMNLK/JNLdod6PjkqS15esu/cRIT51wJDHDOORemKwklwMkMlDITZWfLGj0IK+dPND2mXRvWXmnA+k/KDd0Hs9CkXXWxXwpQgAIUoAAFKBALAruO1KC0unvRbak5nLtvsv/Rkkv4/PQFw+G/srIAKckJvZLwDlyvRGFvwAAXNmHkOwg2wLW2teP1D08ZCp2SMwILp4+NfPE9Rgw0O+aSmZnIzxoW9dpYAAUoQAEKUIACFHCywI2GO/jdduPMk4ny7tvKAsh/h9q4DlyocpHZjwEuMs6WjhJsgJMXW2VaWf82b0oGHvCMtLSuUDq7dqOp46Vb//bwA2MxOXtEKN1xHwpQgAIUoAAFKNBvBGSGcZlp3L/NLkjH7ML0sAxkDThZC86//eCpyaZCIe/AhUVvamcGOFNM3Rvl5ORku1yu/wjgUQAZABoBnADwhqZpbwPoXnzj7m7Z2dk5brf7rwAsBZAGQFZY/BjAzzVNM87gYaKeYANcoPfMlj04HrnjhpoYzd5N5Juj324rMQzy0JQMTHdAuLT3yNk7BShAAQpQgAIUCF2gobEFb28tMaz7Fs7Mk/6VfHLsLE5XGoPhn35rqqnJ7xjgQj+nZvdkgDMrBSAnJ+dBl8slwUsWKTsuC9UrijJA1/Un7/7be5qmPe8f4jwez0xd13cDkJWpdyuKUqHrusweshhAPYBFmqZJADTdgg1wgRbLfnpRDsakRX+x7NtNrXhzc5Hh2OcUpmNWQXjfHJnG5IYUoAAFKEABClAgBgUOnjiPr7Qrhspn5I3C3Mmjwz6anmvKSTD84VOTTfXLAGeKKayNGOCC4FNVVW4V5em6/vOysrL/2rlrdnb2OLfbfQRAuqIoz3m93vfv/sytqqqEswIAazRN29C5j6qqPwXwLwC+0jRtBgCf2VKCDXAntCs4cMK4GON3l+VhmInFGM3WFOp2d1rb8asNxvfzZuSNxNzJcnOTjQIUoAAFKEABClCgp4BMTPfWlmK0tnV/fIxzu/DyygIMCGHdt579bzpQieqLN7v+WdYNlvWDzTQGODNK4W3DAGfST1XVPAAS4Fri4+NTi4qKWvx39Xg8/03X9f8CYJ2mad+Tn3k8nuW6rm/TdX1jWVnZ6p5Dqar6FYCp8milpmlyl85UCzbAHTp1AcdKLxn6/v6qSZb8gpsq+D4btft0/NsHxhuQU3PSsGD6mHC75v4UoAAFKEABClCgTwoEmiXSygnqPthThgvXbnfZDR+UhOeXyUfh3hsDXO9G4W7BAGdSUFXV2QAOAzinadq4nrvl5ub+SFGUXyiKssnr9a6Sn+fm5v6roig/AfCKpmlvBdjnZ4qi/DWA/6Np2r83WQqCDXB7jtaguKp7ellFUfDTZ6aYeo7ZbE3hbPevfzxheH67IGs4Hpn5DeJwhuC+FKAABShAAQpQoE8ItLX7Ot59a2xu7Toe+Wz34vI8DE5JtOQY120vQf2tO119ZYxIwZrFOab6ZoAzxRTWRgxwJvny8vKG+3w+uY3VHh8fP7yoqKjBf1ePx/NzXddlopJ/1DTtL+RnHo/nM13X5yqKMtXr9Z7sOZSqqk8DWA9gv6ZpD5ssJegAt+XTKlTV3ujqXm6tyx04p7RfbjiFltb2rnLUzKGQteDYKEABClCAAhSgAAWMAkWV17D32FnDP+aOG4JlD06wjEpeb5HXXDpbztghWD7XXP8McJadhnt2xAAXhLGqqr8F8CKAf7ob0jpmnMzKyhoVHx9/DMAoAJM1TeuYs19VVVkBMd3lco0oLS01TuXz9c9nAZB35wLe1btXacHegfvwk3Kcv9KdN4ekJuLF5flBHLm9m76xqcjwLdLEMYOxYl6WvYOydwpQgAIUoAAFKBBjArqu43c7Sg13x+QQnl2iYuSwZEuORu7w/WK98b5DMI9nMsBZchru2wkDXBDGU6ZMGdjU1PR7RVHkEcljuq5L+BqkKMoKmXlSUZQfe73eP3R26fF4buu6nuzz+ZLKy8u770Pf3SA3NzdfUZRiADc0TRtitpRgA9x7u7y4UtfU1f3Iocl4dqlqdjjbt3t7azFu3u5+pTBzVCpWLcy2fVwOQAEKUIACFKAABWJJQJ6okier/NvYkal46mHrPjfJZzL5bObfHpw0GjPz5T5F740BrnejcLfotwEuNzf3n+6uydarodvt/llpaakmG3o8nmdlFkq5gea3o67r+i/cbvffl5aWnun8d1VV5d6zS9M0V6D14VRVnQigAsAdTdOSei3k7gbBBrh120pQ39CdH63+RTdb9722e2dHKa7dbO768ejhA/HMI7nhdsv9KUABClCAAhSgQJ8SWL+3DLVXuycXkYN7csFEjE8fZNlxXrx2G3/cU2boT+YmkDkKzDQGODNK4W3TbwOcqqoStMy+aLVA07SDqqr+M4B/pyjKvvb29v8nMTGxqL29PbW9vf1JRVH+UU6Fz+dbUl5eLmvEdbyrBmBgS0vLgDNnznQnlLvnzO8OXJ2macPMnspgA1zPRxSzMgZj5UPOeUTxD7s1XLou66F/3dKGDMBzj8pSeWwUoAAFKEABClCAAiJw+Xoj3t/dcT+hq42Qz0xLVUsnpgt0l++J+RMxYbS5kMgAZ//12m8DXLC0Ho/nIV3XDwI409LSkt8zkKmqKksHvA3ggKZpC+8GuFoAo30+38jy8nLjSotfz1I5R1GUzwFUa5pm7s3Qu8EwOTl54PHjHTmx1ybPMcvzzJ0tb/xQLJ1tNrv22n3YGzj9Hb2wD5AdUIACFKAABShAgTAFdh2pRml1naGXpbMzkTfe9D0AUxWcrriKT748Z9g2mHfsGOBMMYe1EQOcST5VVf8BgMwuGXDK/4kTJw6Oi4url+58Pt/g8vLym6qq7gewQFGUWV6v92jPoVRVfQ7AuwB2apr2mMlSgpqFUl52/Zc/GtdZm5Q9AoseGGt2ONu3C2exSNuL4wAUoAAFKEABClAgygK3m1rx1tZi+Hwd8+d1tOSkeLy8Ih9ut7ypY107UnQRR4ovGjp8ZWUBUpITTA3CAGeKKayNGOBM8qmq+ksAP9B1/e/Lysr+suduMsFJc3Nzx1SP8fHxo4uKii7m5ub+L0VR/lzX9R+XlZW9HiDAdYbCv9M07T+ZLCWoABdoJqFpahrmT3XOQtnbDp1BxbmO7NvRkhLi8Npq5yxzYPa8cDsKUIACFKAABShgh0CgUDW7MB2zC9ItH27XkRqUVnevH+xSFPxozRS4XeZiAwOc5afkGx2aOxP21+H4EVRV/c8A/ruiKB97vd5lPQvOycmZ63K5PgPQcHdGyfbc3NyF8r4cgF2apj3aYx+XqqrlsgrBve7Q3QslmHfgZA0PWcvDvz3gGYl5UzIcY97zkYA4tws/XjPFMfWxEApQgAIUoAAFKBAtAfky/q0txWi609ZVgoSpl1cWdNyFs7qt31uO2qvdy08NGpiAl1YUmB6GAc40VcgbMsCZpMvLy1N9Pp/MqepWFOU5r9f7fueuhYWFCa2trZsBSEhbq2nan9z9maKq6iEAc3Rd/25ZWdk7nft4PJ6/1nX9ZwB2a5q21GQZHZsFE+Aam1shk5j4N/m2Rr61cUqT56zleWv/9tNnpsJl8psepxwH66AABShAAQpQgAJWC5RUXcfuozWGbuW9N3n/zY62dnMRGppau7oOdvZyBjg7zoqxTwa4IIw9Ho88DinLDyi6rn9ydw03WTVxCYBxALwA5mua1pVGVFXNk4lNAAxXFGUngEpd1+X20jwA510u13z/pQfMlBNMgGtobMHaLaGv5WGmnnC3+exkLb70XjZ0I49QyqOUbBSgAAUoQAEKUKC/CshcBu/v0nClvns9X7F4bqkHaUMHWM7SLot4f3gKMm5nK8gahkdmmg+LDHCWn5ZvdMgAF6RxXl7ePJ/P9+cAHgIwUtZwk+Cm6/qHLpfrn71e762eXebk5Ix1uVx/A+BxmSVfUZSLuq5vbG9v/3lFRYUxuZioJ5gAd6PhDn67rcTQqzw+KY9ROqUdLbmEz09fMJQjjwWkmnxZ1inHwTooQAEKUIACFKCAlQLnrzRAZuv2bxkjUrBmcY6Vw3T1VX/rDtZtN35uDGYRb+mIAc6WU2PolAHOfmPLRwgmwNXdbMbvdpQaalgwbQym5qZZXleoHZ4qv4p9x43T1T7/mAfDB1v/zVKoNXI/ClCAAhSgAAUoEGmBbZ9VoeL8DcOwj8+dgOyxQ2wppebiTWw8UGno+7E546FmDjU9HgOcaaqQN2SAC5kuejuGG+AWTh+DKTnOCXDe6uvYecT4bPczi3MxesTA6CFzZApQgAIUoAAFKBBFAXmKat32UsPjjDKhyIvL822bJyDQGnDfeiQX6cPNfyZjgLP/omGAs9/Y8hH6WoCrqr2BLZ9WGZyenD8R40cPstyOHVKAAhSgAAUoQIFYEDh44jy+0q4YSn1oSgam2/gaTKB5CV59sjCo2S4Z4Oy/uhjg7De2fIS+FuACPd8d7O16y5HZIQUoQAEKUIACFIiSQGtbO9ZuLoYsB9XZ4uNcHUsH2DnJW8+1eePdLvzw6clQFPORgQHO/ovG/NmwvxaOYFKgrwW4K3VNeG+XTODZ3RbPGIfCicNNinAzClCAAhSgAAUo0HcEiiqvYe+xs4YDmpw9Ahniy2EAACAASURBVA8/MNbWg/z9jlJcv9ncNUbakAF47lFPUGMywAXFFdLGDHAhsUV3p74W4GJhpszonnGOTgEKUIACFKBAfxG419IBLyzLw9BBSbYxyBICr394Cj6/JQRk8hJ5KiqYxgAXjFZo2zLAheYW1b36WoALtNj4zPxRkGlr2ShAAQpQgAIUoEB/Erh47Tb+uKfMcMjBLqYdipfceZM7cP4t2CUEZF8GuFD0g9uHAS44L0dsHVSAu9WM3203/jLOn5qBaapz1oGTb3z+bf1Jg+3UnDQsmD7GEd4sggIUoAAFKEABCkRKYOfhanhr6gzDLZ87ATk2LR3QOVD5uXpsP3TGMG4oSxYwwNl/pTDA2W9s+QjBBLibt1vw9tZiQw3zJmfggTznBDgp7t8+OIF2n95VZ974YVg6O9NyO3ZIAQpQgAIUoAAFnCogTyW9taXY8JkoZUA8vreiAG6XvR/bvyi+iMNFFw00oTy2yQBn/9Vl75Vgf/39coRgAtztpla8ubnI4DSnMB2zCtIdZfebjafRdKetq6bsMYPx+LwsR9XIYihAAQpQgAIUoICdAl+WXsZnp2oNQ8wuTMfsCHxu2/F5NcrOdt/5c7kU/OjpKUEHRwY4O6+Qr/tmgLPf2PIRgglwzXfa8OuNpw01OPH9snXbSlDfcKerzkg86235iWGHFKAABShAAQpQIEQBn0/Huu0lkKenOptLUfDSygLIXTi727s7vbha39Q1zPBBSXh+WV7QwzLABU0W9A4McEGTRX+HYAKcrCMiMwr5N1kAUhaCdFKTZQRkOYHONnJoMp5dqjqpRNZCAQpQgAIUoAAFbBOovnATmw5WGvrPHjsE8h6a3U3C4+sfnjQ8uinv3Mm7d8E2BrhgxYLfngEueLOo7xFMgJP3yuT9Mv/mxAlCNuyrwLnLt7rKHJKSiBcfz4+6NQugAAUoQAEKUIACkRDYfLASZy7cNAy1emE2xo1KtX34+lt3Ou7++Td5bFMe3wy2McAFKxb89gxwwZtFfY9gApysJfIvfzQGuEkTh2PRjHFRPw7/ArYdOoOKc/Vd/5SUEIfXVk9yVI0shgIUoAAFKEABCtghIGvirtteCvnc1tmGpCZCJhFRFPs/rgeagXLZg+ORO25o0IfLABc0WdA72H9FBF0Sd+hNIJgAJ339Yv1JtLX7urrNGz8US2cHtyhjbzWF+/O9x86iqPJaVzfyx+ona6ZAXqBlowAFKEABClCAAn1Z4LOTtfjSe9lwiAumjcHU3LSIHPahUxdwrPSSYawXludhaGrwC4czwNl/yvjp2H5jy0cINsDFwgyPh07V4lip8Q/Xa6smISkxznI/dkgBClCAAhSgAAWcIiDr4a7dUmyYjTve7cLLTxRAnkiKRNt4oAI1F7tfZYmPc+GHT00O6e4fA5z9Z4wBzn5jy0cINsDJOnD+MxrJs9TyTLWTmnzrJN8++bcXl+dDHh9gowAFKEABClCAAn1VwFt9HTuP1BgOr3DicCyO0Osu8tjmG5uKDAEyY0QK1izOCYmcAS4ktqB2YoALissZGwcb4HpOC5s+fCC+9UiuMw7mbhXFVdew5+hZQ01So9TKRgEKUIACFKAABfqqwAd7ynDh2m3D4T231IO0oQMicsgNTa1Y22PNYHl0Ux7hDKUxwIWiFtw+DHDBeTli62ADXM8/DMMHD8Dzj3kccSydRVSev4Gtn1UZanpy/kSMHz3IUXWyGApQgAIUoAAFKGCVgCyhJEsp+bdIf9FeVXsDWz41fgZbOjsTeeOHhXSYDHAhsQW1EwNcUFzO2DjYANfzueZBAxPw0ooCZxzM3SpqrzRg/SflhpoenZ0JT4h/PBx1cCyGAhSgAAUoQAEKBBDoOYmbbBJOeAoF+UjxRRwpumjYVb7oly/8Q2kMcKGoBbcPA1xwXo7YOtgA13OK/gGJcfj+KmdN0X/tRhPe+dj4DdTC6WMwJScysy854sSyCApQgAIUoAAF+o3AndZ2rN1UhFa/mcJl0pJXnihAnNsVMQe5+yZ34TqbjC0TmIQ6EzgDnP2njgHOfmPLRwg2wO3+ogYlZ6531eF2KfjxmikhzSxk+cHc7fB2Uyve7PH8dagLSNpVI/ulAAUoQAEKUIACVgmcLL+C/cfPG7p7wDMS86ZkWDWEqX7k/Td5D66zjRqWjG8vUU3tG2gjBriQ6UzvyABnmso5GwYb4A6eOI+vtCuGA/jR05MRH+d2zEHJOnWyXp1/m5IzAgunj3VMjSyEAhSgAAUoQAEKWCEgMz/Kk0fXbzZ3dSdr4L64PA+DUyI3A3djc2vHDJT+bVL2CCx6IPTPXwxwVlwh9++DAc5+Y8tHCDbAHS25hM9PXzDU8crKAqQkJ1heWzgdvr7+pOExAk/mUDw6x1kLjodzfNyXAhSgAAUoQAEKiMD5Kw34sMe7/+PTB+HJBRMjClR94SY2Haw0jCnLF8gyBqE2BrhQ5czvxwBn3soxWwYb4E5VXMW+L88Z6v/Oox6MGBLay6l2QfS8hZ+ZnopVC5y1Xp1dx85+KUABClCAAhToPwLbD51B+bl6wwGvfCgLWRmDI4pw+PQFfFFyyTBmuEsYMMDZfwoZ4Ow3tnyEYANc2dk67Pi82lCHLOQtC3o7qck0ujKdbmeT9U/kjwgbBShAAQpQgAIU6CsC8r7Z21uK4dP1rkOSGcJfXJ4f8sQhodps2FeBc5dvde0eH+fCD1aHPoGJdMQAF+rZML8fA5x5K8dsGWyAO3vpFj7aX2Gof/ncCcgZO8QxxySF9FzuIGVAPF55otBRNbIYClCAAhSgAAUoEI5AoGn7504ejRl5o8LpNuh9fT4dv/roFFrbfF37jh2ZiqceDu/pJwa4oE9F0DswwAVNFv0dgg1wl+sa8f4uzVC4vJwqL6k6qe06Uo3S6rqukpw4W6aTvFgLBShAAQpQgAKxJSCh6e2txYZZH+XzzssrC5CcFB/Rg7la34R3dxqXcJqZPwoPThodVh0McGHxmdqZAc4Uk7M2CjbA3bzd0vHHwr/JL6f8kjqpfXqyFse9lw0lvbZ6EmRNFDYKUIACFKAABSgQ6wIV5+oh6/P6t2hN2na64io+6TFHwpPzJ2L86EFhMTPAhcVnamcGOFNMztoo2ADX0tqOX244ZTiIaWoa5k8d46gD+9J7GZ+drDXU9MLyPAxNTXJUnSyGAhSgAAUoQAEKhCIgr7TIqy3+7ZnFuRg9YmAo3YW1T88nn6Sz11ZNQlJieF+cM8CFdVpM7cwAZ4rJWRsFG+BkrZHXPzwFWWuts+WOG4plDzpriv7S6uvYdaTGgL1mcQ4yRqQ46wSwGgpQgAIUoAAFKBCkQN2tZvxue6lhL5kR/LmlKmQNuEi3ddtKUN9wp2tY+cJcvjgPtzHAhSvY+/6Rv1p6r4lb9CIQbICT7uQRSnmUsrONHZmCpx7OcZR19cWb2HTAuBbJ43MnINthk604Co3FUIACFKAABSgQEwIHvjqPE2VXDLVGa06CQAt4508YhiWzMsO2ZIALm7DXDhjgeiVy3gahBLgP9pThwrXbXQczbFASvrss/G9ZrNSJlclWrDxm9kUBClCAAhSgQN8XkJke124pwp2W9q6DTYh340+eKEB8nDviAFW1N7Dl0yrDuOEu4N3ZGQOc/aeTAc5+Y8tHCCXAbfusChXnb3TVkpjg7ljnw0mtobEFa7cYJ1uZXZiO2QXpTiqTtVCAAhSgAAUoQIGgBIqrrmHP0bOGfabkjMDC6WOD6seqjWXOAZl7wL89/5gHwwcPCHsIBriwCXvtgAGuVyLnbRBKgNv35TmcqrhqOJifrJkCt9vlmANsb/fh39afNNQzOXsEHn4gOn/cHAPDQihAAQpQgAIUiFkBmYvg/d0artQ12RKYQoFZv7cMtVe7n8xKjHdDZv624l08BrhQzkhw+zDABefliK1DCXBfFF/E4aKLhvplzZHU5ARHHFNnEb/acAp3WrsfL5DFxmXRcTYKUIACFKAABSgQiwKXrjfiD7uN6/GOSUvB04uiMxeBTGonn7fafXoXZ2Z6KlYtCG8B787OGODsv0oZ4Ow3tnyEUAJcUeU17D1mvHX/7SUqRg1Ltry+cDrsOSNSNP/AhXMc3JcCFKAABShAAQqIgMywLTNt+zeZCVxmBI9GO3f5FjbsqzAMPXfyaMzIs2Z9YAY4+88qA5z9xpaPEEqAC/SyqhNneOw52YpVU9pafhLYIQUoQAEKUIACFOhFoPlOG97cXGS425WcFI+XV+RH7TWWI0UXcaTY+FTWtx7JRfpwa9aiY4Cz/9eCAc5+Y8tHCCXAyXPX7+3yGmpZMG0MpuamWV5fOB1uO3QGFefqu7qQGZp++JSzJlsJ5/i4LwUoQAEKUIAC/UfguPcyPj1ZazjgWfmjMGfS6KghrN9bjtqrDV3jx8e58NrqyXC7rIkFDHD2n1przpT9dXIEP4FQApx8A/TrjacNjtM9I/HQlAxH2e4/fg4ny42Trfzo6clRmWLXUTAshgIUoAAFKECBmBKQyUvWbS/FDb/Fsl2KgpdW5CMlSnMQBHz/bVQqVi205v03OUEMcPZfpgxw9htbPkIoAU7+iPzyw1Nobfd11ZM7bgiWPeisCUK+LL2Mz04Zv6l6cXk+hqQmWu7IDilAAQpQgAIUoIBdAtUXb2LTgUpD9xPHDMaKeVl2Ddlrv+evNODDT8oN2z04aTRm5lvz/hsDXK+nwJINGOAsYYxsJ6EEOKnwd9tLUXeruatYedZZnnl2UpOXfOVlX/8mszTJZCZsFKAABShAAQpQIFYEZKFsmYPAv61emI1xo1Kjdgh2v//GABeZU8sAFxlnS0cJNcBt3F+Bmku3umpJGRCPV54otLS2cDs7e+kWPtpvnBnpsTnjoWZGZ6amcI+H+1OAAhSgAAUo0P8Ebt5uwW+3lUCegOpsQ1IS8cLyPEvWWgtV9Bvvv7ldeO0p695/Y4AL9cwEtx8DXHBejtg61AC352gNiqu6p7GVxRp/LIt5W/TSqhU4dTeb8bsdpYau5k3JwAOekVZ0zz4oQAEKUIACFKCA7QKHTl3AsdJLhnHmT83ANDV6n2da29rxq49Ow+e3/pvcDZS7glY2vgNnpWbgvhjg7De2fIRQA5xMGSu3zv3bSysKMGigcxbzbmltxy83nDLUOE1Nw/ypYyx3ZIcUoAAFKEABClDAaoH2dh/WbilG0522rq7j3C688kQBkhLirB7OdH9nLtzE5oPGd/Ls+JKcAc70KQl5Qwa4kOmit2OoAa6k6jp2HzW+X7ZmUQ4yHPR+WcdkKxtOobXN2ZOtRO/sc2QKUIACFKAABZwsoNXU4ePD1YYSC7KG4ZGZmVEt+8BX53Gi7IqhhueWepA2dICldTHAWcoZsDMGOPuNLR8h1AB37vItbNhnfL9sycxM5GcNs7zGcDpct60E9X5T7maMSMGaxTnhdMl9KUABClCAAhSgQEQE1u8tQ+3V24axnl2iYuSw5IiMf69B3tlRims3uyezG5AYh1efLLT8nTwGOPtPMwOc/caWjxBqgLvV2IK3thQb6pFpY2X6WCc1md5WprntbPKIpzzqyUYBClCAAhSgAAWcLHC1vgnv7vQaShw1LBnfXqJGteyGplas3VxkqCF33FAse3C85XUxwFlO+o0OGeDsN7Z8hFADnLy0+vqHJ9Hu9/KqXb+84Ry0PHYgjx90NnluXBbzlklX2ChAAQpQgAIUoIBTBT45dhanK68Zyls6KxN5E6L7tFOgZZoemTkOBVnDLadkgLOclAHOflL7Rwg1wEllPdeCc8K3Qj3FPj1Zi+Pey4Z/fm31pKi++Gv/WeUIFKAABShAAQrEsoBMxPbm5iLDe/wyaYlMXiJfRkez7TpSjdLq7i/HpZZXVhYgJdn6iewY4Ow/07ylYb+x5SOEE+Bk9iGZhaizyR8WCUdOaie0Kzhw4ryhpOcf82D4YGtfsnXSMbMWClCAAhSgAAViW+BU+VXsO37OcBDTPSPx0JSMqB6YTBD35uZiNDa3dtUxJDURLy7Pt6UuBjhbWA2dMsDZb2z5COEEuAPHz+NEuXEGIqfd3So7W4cdnxtnb1q1YCIy0wdZbskOKUABClCAAhSgQLgCEpLe+diL636ThMirHy8uz8PglMRwuw9r/yt1TXhvl/G9vCk5I7Bw+tiw+r3XzgxwtrAywNnPau8I4QQ4mT5WppH1b06YGcm/ngtXb+ODvWWGGu16TtveM8XeKUABClCAAhToDwIy+ZpMwubfxqcPwpMLJkb98I+WXMLnpy8Y6nhy/kSMH23PF+MMcPafct6Bs9/Y8hHCCXDVF25iU49FHGUGIpnMxCmtobGlYwFM/zYrfxTmOGy2TKd4sQ4KUIACFKAABaIrsP3QGZSfqzcUsfKhLGRlDI5uYQA+2FOGC9e6lzVwuxS8tnoy4uPseS+PAc7+U84AZ7+x5SOEE+DqbjV3TGTi32QZAVlOwClNZsv8xfqT8Ol6V0l544di6Wzrp7p1yjGzDgpQgAIUoAAFYlNApuh/e0ux4XOLLIEk75i5XNH9qN18pw2/2VQEecSzs2Wmp2LVgmzbsBngbKPt6ji6V5X9x9cnRwgnwLW3+/D6h6cMf2Q8mUPx6BxnhaO3txbj5u2WrvM3Ji0FTy/iYt598oLmQVGAAhSgAAViWOBI8UUcKbpoOIK5k0djRl70vxyXZZlkeSb/tnD6GEzJSbNNnAHONloGOPtp7RshnAAnVa3bXoL6W3e6CkwbOgDPLfXYV3AIPXMx7xDQuAsFKEABClCAAhEVkLV1f7u1GHIXrrPJI4ovryxAclJ8RGsJNFig5QPkzqDMQmlXY4CzS7a7X96Bs9/Y8hHCDXBbP6tC5fkbXXXFu134ocMWyu75B8elKPjxmilRfxTB8pPJDilAAQpQgAIUiFkBee9N3n/zb055skkem3xjUxGa7rR1lTckJREvPm7P8gGdgzDA2X85M8DZb2z5COEGuEOnLuBY6SVDXfJNUaoNizmGevCHT1/AFyXGGu1acDLUGrkfBShAAQpQgAL9W2DDvgqcu3zLgPCtR3KRPnxg1GEuXW/EH3Zrhjqm5qZhwbQxttbGAGcrb0fnDHD2G1s+QrgBrrT6OnYdqTHUJdPcynS3TmlFldew99hZQznPLM7F6BHR/4PoFCPWQQEKUIACFKBA9ARkzbff7zBODJc2ZACeXapC1oCLdgv0ZXgk1tVlgLP/zEf/6rL/GPvcCOEGuEDfyCyYOgZTVfteaA32JJy9dAsf7a8w7PbYnPFQM52z3EGwx8TtKUABClCAAhToOwIHjp/HifIrhgNaPGMcCicOd8RBvrvTi6v1TV21yLIB3181CXFue5YP6ByIAc7+088AZ7+x5SOEG+BaWtvxyw2nDHXJHxv5o+OUFgvLHTjFinVQgAIUoAAFKBBZgda2dry5uRjymaqzJca78coTBYiPc0e2mACjyUzeMqO3f8seMxiPz8uyvTYGONuJ+Qil/cTWjxBugJOK1m4uMsyYlDEiBWsWO2ea/rZ2X8dacP5t0sThWOSgkGn9mWWPFKAABShAAQrEgkCgVz0i8X6ZWZsTZVdw4Kvzhs2Xzs5E3vhhZrsIeTsGuJDpTO/IO3CmqZyzoRUBTh5PlMcUO1tSQhy+v6rQEc9sd9b0m42nDTMnyTt68q4eGwUoQAEKUIACFIiWgMzu+N4uzfB4otTywvI8DE1NilZZhnF7Tq4i7+R9/8lCJCXG2V4fA5ztxLwDZz+x9SNYEeDkWxn5dsa/vfJEIVIGRH/Nks6aZOYkeV+vsw0blITvLsuzHpQ9UoACFKAABShAAZMCF67exgd7ywxbjx2ZiqcezjbZg72bNbe04Y2NRfDpetdAkXzSigHO3vMrvfMOnP3Glo9gRYArqbqO3UedPRPltkNnUHGuvstPXr794VOTHXWX0PKTyw4pQAEKUIACFHC0wM7D1fDW1BlqfHzuBGSPHeKIurWaOnx8uNpQy/ypGZimjoxIfQxw9jMzwAFQVfUHAP4ZQJLL5coqLS01rsjodx6ys7Nz3G73XwFYCkCmbbwK4GMAP9c0rTLQKVNVdQmAvwAwB4DMg39GUZQ/APh7r9drXDzExDm3IsBdrmvE+7uMa4PMm5yBB/Ii88tt4jDx6YlaHNcuGzZ9bdWkiNz+N1Mft6EABShAAQpQoH8JNDa34q0txWj3dd/dkqeXXlpRAJfLGR+rd3x+BmVnu78AlzP0vcfzMTglMSIniwHOfmZnXGn2H2fAEaZMmTKwqanpdUVRXujc4H4BzuPxzNR1fTeAFAC7FUWp0HXdA2AxAPlNWaRp2gn/wVRV/SGA1wHcBrBVUZTruq4vBJAP4Ku2trZFlZWVN4IhsCLAySQhv/zwlOH2ukzRL1P1O6WdLL+C/ceNL+B+e4mKUcOSnVIi66AABShAAQpQoB8JHC25hM9PXzAc8ZzCdMwqSHeEQnu7D7/ZVGSYHXP4oCQ8H8FXUBjg7L8U+m2Ay8/Pz21vb//obpD6FYCVADLuE+DcqqpKOCsAsEbTtA2dp0dV1Z8C+BcJZJqmzQDgk5/l5eVN8Pl8JQAafD7fg+Xl5Z0Lm0lf6wB8B8D/0TTt3wdzqq0IcDKeLD4pi1B2tkj/gvd2zNUXbmLTQeNNTa4F15saf04BClCAAhSggB0CPp/eMTV/Q1NrV/cuRcHLKwsw0CFzCAT67DQjbxTmTh5tB0nAPhng7KfutwFOVdU/A/APiqL8qdfr/Y2qqvLY5Ph7BTiPx7Nc1/Vtuq5vLCsrW93z1Kiq+hWAqfJopaZpcpdOHs38OwB/CeD/1TTtn/z3yc7OHul2u88CaI+Pjx9ZVFTUYPZ0WxXgdnxejbKz3c9wyx+hHz09GW6bF3g0e5z1t+5g3XbJv93twUmjMTN/lNkuuB0FKEABClCAAhSwRKD8XD22HzK+ZZM7bgiWPTjBkv6t6GT3FzUoOXPd0FWkn15igLPiTN6/j34b4Dwez2pd12s1Tfvibti6b4DLzc39V0VRfgLgFU3T3urJmpub+zNFUf7a/46aqqqygmK+2+2eUFJSYnyb9OuAtx/AAl3XnykrK1tv9nRbFeCOlV7CoVPGxwCeW+pB2tABZkuxdTt5DOD1Ho95yvolso4JGwUoQAEKUIACFIikwIeflOP8FeP37bKGrszw6IQm7+W9sek07rR0Ly4+aGBCx/tvsoxApBoDnP3SkTub9h9LWCOYuAP3ma7rcxVFmer1eo0rTH8dxp4GICFsv6ZpD0+YMCEpISFB3nu7oWlawFUTPR7P/9Z1XR6f/JmmaX9j9gCsCnCBbrMvnZWJvAn2L/Jo9ljlUYWbt1u6Nh89fCCeeSTX7O7cjgIUoAAFKEABCoQtcLW+Ce/u9Br6SRsyAM8uVSMaju53IDUXb2LjAeOrJ9PUNMyfOibs4w+mAwa4YLRC25YB7q5bbwFOVVW5VZXucrlGlJaWXuvJrarqLABHAJzTNG2cx+Px6Lpequv66bKyssmBTk9ubu7/pyjK3wNYp2na98yeQqsCXENjC9ZukZuE3S0av+j3O+6eC44nJ8Xj1ScLzVJxOwpQgAIUoAAFKBC2wN5jZ1FUafz498jMcSjIGh5231Z1EKjGbz2Si/ThMgF65BoDnP3WDHAmA5zH47mt63qyz+dLKi8vv9Pz1OTm5uYriiJpSO64DfELdJ9rmjY30Kn0eDw/0XX9XwF8pGnaU2ZPt1UBTtd1/GZjEWTBx84WyYUezRzvJ1+ew+kKWamhu8lacAnxbjO7cxsKUIACFKAABSgQlkDznbaOL7xlBu/OlpQQh1eeKECcQ+YNkAlW3txchKY73Z/pZHkDmWAlko9Pig8DXFiXm6md+0SAy83NlQlCZE22Xpvb7f5ZaWmpcQG0rx+BvO87cKqqygPFLk3TXAC6F//oDoATAcgsk3c0TUtSVXWBPE7Z+UjlPQLcq7qu/0bX9R1lZWXLey2+e6yG5OTkgcePHze7yz2323igAjUXu5eik8Wyf7B6smPWMvlKu4yDJ2oN9X/nUQ9GDHHGe3phnwB2QAEKUIACFKCAowW+9F7GZyeNn0Vm5I3E3MkZjqn73OVb2LCvc7Lzr8uampOGBdMj+/gkA1xkLok+EeA6w5dJsgWaph3sua2JACdvrQ5saWkZcObMme659+925HcHrk7eebu7ZpxMkHJY07QH7xHgOu/Afahp2hqT9UvYtCzAyVomsqaJf/vusjwMG5Rkthxbt6uqvYEtn1YZxlg+dwJyxg6xdVx2TgEKUIACFKAABeTOlsyI7f8+vtzRemlFPlKTExwDtO/LczjV44mlNYtykJEW+QlWeAfO/suiTwQ4K5hMBDj56mW0z+cbWV5efqXnmLm5uXMURfkcQLWmaRPy8vJUn88nb7sWa5oW8KUtj8fzl7quy1IDb2ma9orZ47AywFWev4GtnxkDkszyKLM9OqHJOnWyXp1/mzc5Aw/kjXRCeayBAhSgAAUoQIE+LBDoi+TsMYPx+Lwsxxy1hEx5xLOxuXt9Opkz4JWVBVF5oooBzv5LgwHurrGJANcx5b+iKLO8Xu/RnqdGVdXnALwLYKemaY8VFhYmtLa2yiyUzZqmpQY6lR6P5990Xf8xgP+iadr/MHu6rQxwgSYyidYt90DHL8+b/2K9cdJPeWFYXhxmowAFKEABClCAAnYKyGOJ8niif3t6UQ7GROHO1r2O8+ylW5BJ3/zb5OwRePiBsXbS3LNvBjj72RngTAa43Nzc/6Uoyp9L4CorK3s9QID7BwB/AeDvNE37T/LzzsW9FUXJ83q9xrlnv/65BMEZLpdreWlp6Q6zp9vKACcTmby52fitjdOm6l+7uQgNTd3fKo0dmYKnHs4xy8XtKEABClCAAhSgQNACgZ4CGj4oCd95zBPxiUHuV/yeo2dRXGWcITOaIZMBLuhLSCdxjAAAIABJREFULegdGODMB7iFiqLsA7BL07RHe0i7VFUtB5Dlf4dOVdX/CuBvdV3/q7Kysv/uv4+qqjLpiexTHx8fn15UVNS92Fkvp9HKACdDbT5YiTMXbnaNKjMqyUyPLpczLo+eC2fKrEqvPMGlBIL+becOFKAABShAAQqYFgg0E/biGeNQONE5Swe0t/vwxuYiw+Ld8jnppRXReXxScBngTF9iIW/ojE/oIZdv3Y69PUIJQFFV9RCAObquf7esrOydztE9Hs9f67r+MwC7NU1b2vnvWVlZo+Lj48sAyLyzD2qa1vEy193HKzcAeFzX9b8uKyv7b8EcidUB7kjRRRwpvmgowUkzPQb6ZunHa6Y4ZureYM4dt6UABShAAQpQwPkCd1rbIU8AtbZ1Lx2QmODueK8sPs45SxkFekcv2mv6MsDZf3332wAnd8d0Xe+aqUNRlFcBpCqK8qbP5+u6HVVWVvYfOk+Dqqp5AA4AGK4oyk4AlbquTwEwD8B5l8s1v7S0VJYj6Gp33437PYAmAFtknTgAjwDIlsDn8/lWBlpX7n6n3uoAJ3ff5C6cf3PS4pRfll7GZ6eM0/c6aaZM+39NOQIFKEABClCAApEUOKFdwYET5w1DTldH4qGpzlk6QIrb8Xk1ys7WGer89hIVo4YlR5LL6DR9OhobG29rmhb5KTCjdtSRHbg/B7iOdd9649Y0zWCUk5Mz1uVy/Y3cPZO15xRFuajr+sb29vafV1RUXA7UX05OzlyXy/Wf7wa9gbquy125txMSEv53MI9O+gVJy5YRkD5l1qI3NhUZSpfHA+QxASe08nP12H7IkIux8qEsZGUMdkJ5rIECFKAABShAgT4kIPMD/G57Keob7nQdlSwd8OLyPAxOSXTMkba2teONjUVo9VtgXOqTOiO9eLc/Cu/A2X+J9NsAZz+tfSNYfQdOKn17a7FhjRN5Sff5ZXLDMfrtan0T3t1pnAOGSwlE/7ywAgpQgAIUoEBfFKi+cBObejyZlDV6EFbOl+kLnNO0mjp8fLjaUNDM/FF4cNLoqBbJAGc/PwOc/caWj2BHgOt5C16+uXlt9SQkxkf/OW9ZSuD1D09BvhHrbPkThmHJrEzLbdkhBShAAQpQgAL9W2DjgQrUXDQuHbB6YTbGjQq4KlTUsLYcrESV3yR0UogTXjFhgLP/kmCAs9/Y8hHsCHAny69g/3Hjs96rFkxEZvogy+sPpcOedwjThw/Etx7JDaUr7kMBClCAAhSgAAUCCtTdau54fNK/DU1NwneXOWvpgOaWto7XX2QR7842YsgAyCR00W4McPafAQY4+40tH8GOAHf5eiPe360Zap1dkI7ZhemW1x9Kh5sOVKL6YvdSBzIT1GurJkX1Ge9QjoP7UIACFKAABSjgXIH9x8/hZPlVQ4EPTx+LyTkjHFX06YqrkGUO/NvcyaMxI29U1OtkgLP/FDDA2W9s+Qh2BLh2n45fbzhleBFWHhWQRwac0A6eOI+vtCuGUl59shDJSfFOKI81UIACFKAABSgQ4wItsnTAlmLIf3e2hPivlw6Q/3ZS+2BPGS5cu20oSdZ+GzQwIeplMsDZfwoY4Ow3tnwEOwKcFNlzwWz5YyV3uZywoHdR5TXsPXbWYPn0ohyMSeMMtZZfYOyQAhSgAAUo0A8FAi0dMDU3DQumjXGURv2tO1i3vcRQk3weks9FTmgMcPafBQY4+40tH8GuAHfo1AUcK71kqNcpC3rXXmnA+k/KDbUtemAsJmU765EGy082O6QABShAAQpQwHYBeZdMQtHN2y1dY8mEbi8sy8OQVOcsHSDFHT59AV+UGD+vLZmZifysruWNbfe63wAMcPbzM8DZb2z5CHYFuEALejslJAVaq86J34pZfrLZIQUoQAEKUIACtgtUnKvHth5rzsp6s7LurJOazMj9223GoBnndkFeK3HKY54McPZfMQxw9htbPoJdAa75Tht+vfG0oV5P5lA8OqfX9c4tP8aeHcofrN9sLILMutTZMtNTsWqBM97Rsx2AA1CAAhSgAAUoYJvA+r3lqL3aYOj/qYezMXaks5YOCPREkpo5FI854LNaJx4DnG2XaVfHDHD2G1s+gl0BTgqVxwfk2erOJi/DykuxTmg9X9h1Um1O8GENFKAABShAAQoEL3C5rhHv7zLOxC1T8j+3VHXcbNd7jtaguOq64SCdtOyTFMYAF/w1GOweDHDBijlgezsDnEwUIhOG+DenzGoU6I/Wj56ejPg4Z80M5YBLhCVQgAIUoAAFKGBSYNeRapRW1xm2dtI7ZZ2Ftbb58ObmIsMsmQOT4vHyygJHTDjXWScDnMkLL4zNGODCwIvWrnYGOK2mDh8frnbkH7GvtMs4eKLWUNu3l6gYNSw5WqeC41KAAhSgAAUoEMMCt5ta8dbWYsOC2AMS4zpCkbxb5qQW6DPadM9IPDQlw0ll8g5cBM4GA1wEkK0ews4A19DUirWbiwwl540fhqWzM60+jKD7q7l4ExsPVDoyXAZ9MNyBAhSgAAUoQIGoC3x++gKO9pjRcXZhOmYXpEe9tp4FbNxfgZpLtwz//PxjHgwfPMBRtfIOnP2ngwHOfmPLR7AzwEmx67aVoL7Bee/BBQqX09Q0zJ/qrPVZLD/h7JACFKAABShAAcsF2tp9eGtLMZrudE+Q5nYpHXffkpPiLR8vnA5leQOZfVImdetsaUPlPT1PON3asi8DnC2shk4Z4Ow3tnwEuwPcnqNnUVzlvPfg5I+WzJJ5p6W9yzRzVCpWLeRMlJZfZOyQAhSgAAUo0McF5J1/efffv+VPGIYls6L/1FFP+iNFF3Gk+KLhnx+ePhaTc5y3Hi4DnP2/OAxw9htbPoLdAc5bfR07j9QY6l46KxN5E6K/QGTPaX5TBsTjlScKLTdmhxSgAAUoQAEK9F0B+VL43Y+9uHaz2XCQ33nUA5mB0klNFhl/e2sx5Emkzibv573yRAGSEuKcVGpHLQxw9p8SBjj7jS0fwe4A19DYgrVbih35jdQnX57D6YqrhtpeWzUJSYnO+wNm+YlnhxSgAAUoQAEKWCJw9tItfLS/wtCXrPkma785rVVfvIlNPeYAcMo6vYGsGODsv4IY4Ow3tnwEuwOcFCzPWd9w4Htwp8qvYt/xcwbTNYtykJGWYrkzO6QABShAAQpQoG8KSCCSYOTfVj6UhayMwY474G2HzqDiXL2hrqcX5WCMQz/7MMDZfwkxwNlvbPkIkQhwgdZcc8J6cLVXGrD+k3KD6cMPjMXkbOc9A275iWeHFKAABShAAQqELVB3sxm/21Fq6GdISiJeWJ7nuIW7G5tldvBi+PwmL3FqrZ2gDHBhX6K9dhCxAKeq6ghFUTLa29sHSlVut/u2ruu1mqYZn4frtWRuEIkAF+g9uEdmjkNB1vConoDmO20dE5n4NwlvEuLYKEABClCAAhSgQG8Cnxw7i9OVxsnaFk4fgyk5ab3tGvGff+m9jM9OGtfAnTt5NGbkjYp4LWYHZIAzKxX6drYFOI/HkwXgWV3XHwMwA0DqPcqUBS2OKYqyA8AfvF5vVeiH0z/2jESAk4Ut3+yxHlzuuCFY9uCEqCO/uakIt5u7X+TNGJGCNYtzol4XC6AABShAAQpQwNkCckfr7a0lkCUEOltivLtjQpD4OLejipeJVuROYf2t7qWdXMrXyxwMHOCsZQ784Rjg7L+MLA9wOTk5091u99/qur4CgPRvdgxZ2EL+sxnA32iadsL+w4/NESIR4ETmHZmd6UZTF5LMdPTqk4VwucyeUnt85aVjefm46w9vghsykYmiRLcue46WvVKAAhSgAAUoYJWATMUvU/IbAodnJB6akmHVEJb1c+HqbXywt8zQn7yjJ+/qObkxwNl/diz7xJuRkZGcmpr6j7quvwbAdTeMHVUU5eP29vYjiqKU67p+fuzYsY1yWDU1NQPdbneGrus5brd79t07dTPvBj6foii/unXr1l/U1tZ2bM/WLRCpAPfpiVoc1y4b6L+9RMWoYclRPR2B6pJvo1KTE6JaFwenAAUoQAEKUMC5AoEW7pY7Wi+tyEeKAz9D7DxcDW9NnQHUqROtGALx9OlobGy8rWkaZ5iz6dfBkgDn8Xg8Pp9vk6Io8hybTOnzr21tbb+orKw0LibWy0FMnDgxMy4u7icA5D+DAGgul2tVaWmpZtPxx2S3kQpwgaatfXDSaMzMj+5z14Hez4uFP2gxebGxaApQgAIUoEAfEQi0cLdTp+NvutOGtZuL0O6Th9O+brL2rUwoF+0noXq7HHgHrjeh8H9uSYBTVVXmNh2kKMrriqL8VWlpqfHN0CDrzMvLG+7z+f4HgB8AqNc0LforSAd5DHZuHqkA19rmw68/OmX44yFT1srUtdFs8linPN7p32YXpmN2QXo0y+LYFKAABShAAQo4VCDQ+2RS6nNLPUgb6qyFu6WuL0sv47NTxslL5HOOfN5xemOAs/8MWRXgruq6/nxZWdlOK0v2eDzLdV1fp2ka54j3g41UgJMhe75vJt/6yPtmCfHRe9FXvo365YcnDcEye8xgPD7P2c+EW/m7wb4oQAEKUIACFDAvUFV7A1s+Nc6TN25UKlYvdN7C3RI2ZT3em7dbug6w41HPlQUdd+Gc3hjg7D9DlgQ4mXHSrtkj7ezbfl57RohkgAs0fa0THld8f5eGy3Xdr0cOGpjQ8VgBGwUoQAEKUIACFOgpsH5vOWqvNhj+edWCichMlzd2nNUCvcISS19UM8DZfz1ZEuDsL5Mj+AtEMsBdrW/CuzuNjytOyRmBhdOju+5aoIXGX1s9CTJTJhsFKEABClCAAhToFLh47Tb+uMc4m+PwwQPwnUdVR85gveVgJaouyJQS3U3uFModw1hoDHD2nyVbA1xWVtaouLi4lxRFeUBRlGG6rvd231fXNG2J/Ycd2yNEMsDJbfw3NhVBXqbtbENSE/Hi8vyoIp4sv4L9x88balizKAcZaZzwKKonhoNTgAIUoAAFHCaw/dAZlJ+T6Rq629LZmcgb77wpFuSxSXl8Uj5/+X/uemFZniPDZqBTzQBn/y+AbQHO4/HM1HVd3omTe9Nmx5EAF72Xq+z3tmSESAY4KTjQNLbfezwfg1MSLTmeUDqRxyDkcQj/tmDaGEzNTQulO+5DAQpQgAIUoEAfFLjRcAfrtpcaApG8RyafY9xuWfXKWe3QqQs4VnrJ+Plm6hhMVWPn8w0DnP3XlNlgFXQlqqruBrAYwA1FUbbqul4NoHsp+Xv0qGna3wY9WD/bIdIBLtC0/dEOSy2t7fjlhlOGM58/YRiWzMrsZ1cDD5cCFKAABShAgXsJ7D9+DifLrxp+PG9yBh7IG+k4tPZ2H9ZuKTY89RTnduGVJwpi6hURBjj7Ly07A9wtAO2KohR4vV7jPKj2H1efHiHSAa75Tht+s6nI8O2VE2ZuWretBPUN3d8JpA0ZgOce9fTpc8+DowAFKEABClDAnIB8fnlrSzFa231dO8THufDySmcGIq2mDh8flvsd3S0Wv5xmgDN3fYazlZ0BTh42PsZ32sI5PYH3jXSAkyrW7y1D7dXbXQU5YTmBbYfOoMLvmXap6UdPTXbkIxHWXwXskQIUoAAFKECB+wkcLbmEz09fMGwyTU3D/KljHAn3wZ4yXLjW/VlLinx2iYqRw5IdWe+9imKAs/902Rng9iuKEuf1eufZfxj9a4RoBLhAC0o+PncCsscOiRp+oD/Mzy5VMXJobP2hixogB6YABShAAQr0UYG2dh/e3lqCxubW7i+fFQXfW5GP1OQExx31peuN+MNuzVCXfJ6RzzWx1hjg7D9jtgW43NzcJxRF+VDX9SVlZWX77T+U/jNCNALctRtNeOdj43IC0b6tH2idlMUzxqFw4vD+czHwSClAAQpQgAIU+IZAcdU17Dl61vDvauZQPDZnvCO1Ak0Y9+jsTHgcOFNmb4AMcL0Jhf9z2wKclObxeF7Vdf1/AlinKMrm9vb2Gp/P1/1VSID6Kysra8I/rL7dQzQCnExnK9PayvS2nW1AYhxefbIwatPaytIGv9l42nCyC7KG45GZ4/r2BcCjowAFKEABClDgngLymUW+dL5+s9mwjVOf0mloasXbW4vh83UvHZCcFI+XVzhzpszeLj0GuN6Ewv+5rQFOVdUfKIryV7qum131WZYR4ErMvZzXaAQ4KSnQTE7fXqJiVBSfzZY/eP6hkhOZhP9HgT1QgAIUoAAFYlmgqvYGtnxaZTiEsSNT8dTD2Y48LHlPT14L8W9zCtMxqyDdkfX2VhQDXG9C4f/ctgCXm5v7Y0VR/uVuiabH0TTNeYtyhO9saQ/RCnCBHlmclT8KcyaNtvT4gunsGxOZKAp++PRkyLS7bBSgAAUoQAEK9C8Bufv2xz1lkHfK/NuT8ydi/GhZmthZTd7Vk5ky5amizuZ2KR0zZcpduFhsDHD2nzXTwSrYUnJzc08pilII4EOfz/d6XFzcGUVRel0HrqSkxDh/arAD94PtoxXg5I+MPLLY2tY9HW+073gFmlzlW4/kIn34wH5wJfAQKUABClCAAhTwFzh/pQEfflJuQBkhywwtVaP2ysf9zlBJ1XXsPmp8eyjacwyEe0UxwIUr2Pv+tgU4j8dzW9f1S5qmTey9DG4RjEC0ApzUuPWzKlSev2EoV74litaMTmcv3cJH+ysM9SycPgZTctKCIeW2FKAABShAAQr0AYGNBypQc1GWIu5uMnGJTGDitCZ3C9/bpeFqfZOhtOeWepA2dIDTyjVdDwOcaaqQN7QtwKmqKot3n9A07fGQq+OOAQWiGeACfVO0YOoYTFWjE5iaW9rw64+ME5nkjR+GpbMzefVQgAIUoAAFKNCPBC7XNeL9Xcap+AcNTMCLy/Mha8U6rQW6WzgmLQVPL8pxWqlB1cMAFxRXSBvbdjWrqvougBmapuWGVBl3uqdANAOcPKP95qYi+PTumZIyRqRgzeLo/bFZt70E9be6n84dNigJ312WxyuIAhSgAAUoQIF+JLDj8zMoO1tvOOJFD4zFpOwRjlQI9FRTtNfYtQKKAc4Kxfv3YVuAy8nJyXa5XJ/quv7LsrKy/2r/ofSfEaIZ4ER5w74KnLvc/XiCoij4kyei97Ltx4erodXUdV0AUs8Pn5qE+Dh3/7koeKQUoAAFKECBfiwgX+T+bkcp5LHEziaTgLy0It+RE5vV3WrG73d4DfU6+W5hMJcWA1wwWqFta1uAy8vLm9fe3j5BUZS/A3ATwEcAZE7X7oXEAtSsadrboR1K/9kr2gHuVPlV7Dt+zjHfcH2lXcbBE/LEbndbsygHGWkp/eei4JFSgAIUoAAF+rHA3mNnUVR5zSAwb3IGHsgb6UiVQPXOn5qBaaoz6w0GkQEuGK3QtrUtwKmqKlMVdn4NIuN0fyVy71q5DpyJ8xjtACcLTq7dXGSodNyoVKxeGJ31VWqvNmD9XuOMUw9NycB0T+z/ETRxOXATClCAAhSgQL8WkM8lv91ajHa/hbAT490dU/EnxDvvaZzG5taOpQNipd5gLy4GuGDFgt/ezgB3xmRoM1StaVpW8IfRv/aIdoAT7Q/2lOHCtdtd8C5FwatPFiIpMfLrsLe2teNXG04b3svLHjsE8hw5GwUoQAEKUIACfVvg05O1OO69bDjIGXmjMHdy9NapvZ94oIW7H/CMxLwpGX3iRDHA2X8abQtw9pfef0dwQoCTP5TyB9O/LZ2VibwJw6JyYt7b6cUVv2l4UwbEd3zzJu/DsVGAAhSgAAUo0DcFZDZquZvlv0ZtnNvV8e6bExfCli+d124pxp2W9q4TIgt3f29FAeSzS19oDHD2n0VLPt3OmDEj/tixY612lGtn33bUG4k+nRDgbjTcwW+3lRgONytjMFY+FJ0bqPu+PIdTFVcN9URzfbpIXAccgwIUoAAFKNDfBY6WXILc0fJvk7NH4OEHxjqS5kTZFRz46ryhtoKsYXhkZt9Z/ogBzv5Lz5IA5/F4PmtpaXm6qqrqkpUlezweuZf8R6/XO8/KfmO9LycEODHseddLvkH6/qpJUXne3Ft9HTuP1BhO7bIHxyN3nPMW7oz164/1U4ACFKAABZwgIHfd3t5aDFniqLPJKx0vLM/D4JREJ5RoqMHn0yFLH928bZzPT5Y+kiWQ+kpjgLP/TFoS4O5OWHJFUZQ/83q9f7CibFVVnwPwfwEM0zTNeW+gWnGQIfbhlAAX6Fuvx+aMh5oZ+dAk0wfLH0X/NjU3DQumjQlRmbtRgAIUoAAFKOBkgZPlV7D/uPFulidzKB6dM96RZcuSR7L0kX/LGj0IK+dPdGS9oRbFABeqnPn9LAlwHo/nBV3XfwVAvu44qCjK33q93j3my+jeUlXVJQD+BsBDAO7ouv79srKyd0Lpq6/u45QAd/2mrGFSavxDFKXHKGXdlzc2FRm+hRs1LBnfXqL21cuAx0UBClCAAhTotwIyg+O6bSW41Wi8m/X8Yx4MHzzAcS7yOeX9XZrhfX0psi8ue8QAZ//lZ0mAkzJzc3OnKYqyDkDB3dknNQDrXS7XTrfbfbSoqKgh0OEUFhamtLe3z/T5fI/KdQxAPnEruq4XuVyuF7xe70n7GWJrBKcEOFF752Mvrt1o6gKUxyj/RGajTIj8bJRbDlai6oIsOfh1c7lkQe/JjlzAM7auOFZLAQpQgAIUcJZAafV17Orx6oST72advXQLH+2vMCDKF83feiS3z024xgBn/++KZQFOSpUJR27duvXvAPxHACN6LCNwWVGUWl3XO+aeVxRloK7r8o6b/2JdUo/MRPE/U1NT/69dE6PYz2rvCE4KcIEeo3xk5jgUZA23FyFA74FqeWZxLkaPGBjxWjggBShAAQpQgAL2CMi7ZPIFct2tZsMATv7//A37ynHusvFehix3JMse9bXGAGf/GbU0wHWWO2HChKTExMTvAnhO1/WHAST0cigtiqLs8/l877a2tr5z5swZ42+k/Q4xNYKTAlyg2Sijtaj3+SsN+PAT44Le86dmYJrKBb1j6gJnsRSgAAUoQIH7CJSdrcOOz43vkmWMGIg1i3Md6Xbh6m18sLfMUNuQlETI5CXytFBfawxw9p9R26+awsLChNbW1mm6rme7XK5Ruq4ny2EpitLo8/kuKYpSER8f/1VRUZHxIWb7jz1mR3BSgBPEP+4pw0W/Rb1l7bU/eaIg4uuvyNoqv9xwGvKceWfjgt4xe5mzcApQgAIUoMA3BOT/49+V1zduGr/rf3LBRIxPH+RIsU0HKlF9sfsVDykyWk8rRQKIAc5+ZdsDnP2H0P9GcFqAC7SmycLpYzAlJy3iJyfQgt6vPFEY8To4IAUoQAEKUIAC1gtUnKvHtkNnDB07+V2yK3VNeG+X11CvLNj9vcfz4Xa7rAdyQI8McPafBAY4+40tH8FpAa6xuRVvbi423PlKHz6w48XcSLdPvjyH0z0W9H5pRQEGDeztKd5IV8rxKEABClCAAhQIRuBeMzmufCgLWRmDg+kqYttK2JTQ6d+i9SV3pA6aAc5+aQY4+40tH8FpAU4OcMO+Cpy7fMtwrPLtUqQX0gy0xsrS2ZnIGz/M8vPADilAAQpQgAIUiJxAVe0NbPm0yjBg2pABeHap6siZHAMtt5ScFI+XVuT36RmyGeDs/51ggLPf2PIRnBjgiquuYc/Rs4ZjfXDSaMzMH2X58d+vw4bGFqzdUmzYRGbElGfN2ShAAQpQgAIUiE0Bufsm79xfut5oOAAnz+S483A1vDV1hnrnTcnAA56+PbkaA5z9v2MMcPYbWz6CEwNcc0tbx0LaMrVvZ5OFNGVBzUi3t7cW4+bt7jlxhqQm4sXl+ZEug+NRgAIUoAAFKGCRgEwCIpOB+Lfhg5Lwncc8jrz7JrN0r9teani9RNbIfXllPuLj3BapOLMbBjj7zwsDnP3Glo/gxAAnBymPNcjjDf7tuaUepA0dYLnB/TrcdaQapdXGb7xefbIw4rNiRvSgORgFKEABClCgjwrI3bf1e8txwW/GaznUZQ+OR+64oY486r3HzqKo8pqhtjmF6ZhVkO7Ieq0sigHOSs3AfTHA2W9s+QhODXCB1mX5/9l7D+i4rvPe939m0EgQIEEA7CTqnAN2sYi9d4miZImqlmwrvomf/ZKbuhyv1Pvie5OV5yQvcYoTJ1ZkWV20uth7pygWUSQIzJ4BQIAgWMBOkCDa7Le+EUDOwQyIKWdPwXx7rSw5xD57f/u3z8yZ/9lfmezIx/yHRlrO4EED0hcmfXH6tlWzC1HaB4tlRhUsT8YEmAATYAJMIAYEzl68hY/3VJlmzsnK8Hr5xGMdNQrn+NXGCpNXUlqq3Rv7Rqdwfb2xgFO/wyzg1DO2fIZ4FXDtHR68+mk5Wto67q25X3oKXl49Lqqpcq/duos3N1WauE8uzcf8KdEVkpZvPA/IBJgAE2ACTCAJCXy4y41zjU2mlS+fMQZGnCYo232sHie7ZcSeVjYUsycOT4rdYwGnfptZwKlnbPkM8SrgaKG7jp7FqW4uA9FO70uuFlTWgMobdDXKUvXc8ujH41m++TwgE2ACTIAJMIEkItDQ2IQPdrlNKx40IB3fXFkWl6dvt+604vVup28pdpv39I0yUCZDYwGnfpejIuBKSkpKNU3L0zSt12JcLpdrj/plJ/YM8SzgLly57c0S5duKRw7Eo3OKogq9e90VTdPwm09MQHpq3w4cjipknowJMAEmwASYgGIC5DpJLpS+ben0MRhbFJ/lgQLFvk0xhmDupBGKScXP8Czg1O+FUgFnGMYfSyn/CEBekEuRQoi+7xwcJIyeusWzgKPTr7c2O0FujF3Npml4+bFxUX3z9JW7EXuOnzMhXDOvGAXDsyOkz5czASbABJgAE2AC0SAQ6KVwdmYaXlw1Fnab0p+wYS2PMk9SCIdH3s/InZpiA9XFTZbTNwLHAi6s2yeki5Td/Q6H4/uapv1JHZ1OAAAgAElEQVQsJGsACCFsoV4TaX9d138LwD8DyLDZbEWVlZVnHiCeZkgpf6hp2nwA9PqH0i4eAvBPQojtga7TdX0pABKyMwFkAjijado6AD9xOp3m10pBLCaeBRyZf6zyEg6cbDCtZP7kkZis5wexOmu6XL7ejHe2Ok2DUd0Vqr/CjQkwASbABJgAE4h/AlQ2gMoH+LbF00ZjfHFuXBq//Ys6VJy5arJtWtkQzJ6YXL89WMCpvz2VCThd178EMFHTtNc6Ojr+AYDb7Xa3qF9S8DNMmjQps7m5+eeapr3YddWDBJzD4fgNTdP+CwD54ZGrJymEUgCLAUhN037b6XT+u68Fuq5/D8DPAdwGsEHTtKtSygUAqDDZl+3t7Yuqq6vNufd7WUK8C7im5ja8tv60qfZJtGPQqB7dK5+cMiVUGZabiaeXOIK/QbgnE2ACTIAJMAEmEBMCl67ewXvbhWnuAf1SvadZdnvU3/X3yuD6rRa8tdn/9O07j45DRnpyOZexgOv1dom4g0oBdwdAgxCCBE7ctbFjxzo6Ojo+7hRSJMpWAxjRk4ArKysr9Hg8lNowTUr5tMvl+qBrUQ6H45uapr2padodKWWBEOIy/a3zmgoATR6PZ5bb7e7KgWvXdf0NAM8D+BchxO+GAijeBRyt5ZO9Vai7YD5cjHZNuM/2VePM+ftv7siV87e+MaHPF9AM5V7ivkyACTABJsAE4pFAoNqyC6eMwsTSYKNyoruqQDVop48dilkTkiPzpC9tFnDq7z2VAo4qKe8TQqxRv4zQZ9B1/XcA/H3nqdkruq6T22RBTwLOMIy/kFL+WNO0951O59PdZ9R1/Ri5/UopX3S5XG/R33Vd/1sAPwLwh0KIf/S9pqSkZIjdbqdiZR2pqalDysvLzflxH7CkRBBw8VAT7rjzEvZ/ZXbl5Di40D8rfAUTYAJMgAkwgWgSCHT6lpmRim89OhaU0THe2rWbd/HWFqfJ84iSppG9yVD3rft+sIBTf4cqE3CGYRyQUtqFEBT3FXfNMIwnpJR0QvhFp9h6oIArKyub5vF4pno8npNut5ti3kxN1/V3ATwrpfxjl8v1d51jnqYTPrvdXlhRUVEb4Bpyw5wvpVzre6LXG6xEEHDxUBOu8Voz3t1mjoObog/B3MnJ5Yve2/3Ef2cCTIAJMAEmEE8EunvQkG3RjqUPhcfmQ7WgF9e+bcb4YZgxblgow/SZvizg1G+lMgGn6/q3Abxqs9lmVFZWHlW/lMhm6O0ErrfRdV3fC2Cepmm/6XQ6XyksLMxIS0ujuLcbQoiAuW4Nw/iplJLcJ38shPhfvc3R9fdEEHBka6CacCtmFkAfkxPsUiPqRxkxX/mkHHdb2++NE+1YvIgWwBczASbABJgAE0gyAoEyT1IGR6qjFo+nb1duUNI0YT59S7Pj24+OS9rSRSzg1H9olQk4Mr3ThfA7VEogOzt73dGjR+9XVla/tpBmiETAlZWV6R6Ph2LdaH0lQohzhmEYUspKKeUpl8s1MZAxDoeDsln+BMAbQohvBWtwogi4QC4QI/MH4MlF0QuL7F4Pjhj/j8cnoF+SBRQHe29xPybABJgAE2ACsSQQKIZ+wZSRmFQavUzWoax/44EaVJ0z56KjuDeKf0vWxgJO/c4rE3C6rncl+XiYkoMAaAVQB+BBmSipDtxk9cv2nyFcATd+/Pi09vb2XVLK2VLKf3K5XH/QKV5p3YepxIAQYnagNRmG8QMpJZVa+FgI8Y1g150oAo7W8942gUvXKJ/N/fbiqjLkZGUEu9yI+p2suozdx+pNY6yaXYjSUYMiGpcvZgJMgAkwASbABKwlcP7ybby/02UalDJPvvRIfJ6+BTotpBfElCkzLZUSlidnYwGnft9VCjgPpdYHEMocJOBCvuMdDgclCAnq1Yzdbv9xZWWlOS/t16eFD4yBC7QVpaWl6Xa7fZ2Uco2macdu3749r76+vrlTwFGdOIpx2yOEWNiDgPuulPIVKeVml8u1KtjtTiQBV159BTuPUq6W++0hPR/zJo8MdrkR9aOC4lRU07dNKM7FommjIxqXL2YCTIAJMAEmwASsJfDR7irUXzJnsF40dRQmlMRf5kkK0yB7zzWac9BRvVmqO5vMjQWc+t0PRVyFZI2u60HHdPkOLIT4q5Am8hFfQV43Xwixr3vfUAXc+PHjB7S1tX0EgIp0U6aMpeQ62TWuYRjTpZSUIOVzIcSsHgRc1wnch0KIp4K0n8RmU//+/TOPHz8e7CUx69fW3oFXPzuN1raOezZQRqaXHxsXFV92+oKlmnRUm66rDRqQ7n2bx40JMAEmwASYABOIDwIkhD7c5TYZk9U/DS+tKovLum9UYJwKjfu2eD4tjOYus4BTT1uZgFNvurUzhCLgysrKcj0ezwYAM6gYd1tb26qampqLvhZ1xsWRsDsthBjfg4D7kZSSSg28JoR4OdgVJZKAozXtOlaPU1Xe0nj32vIZY2AUBMztEiyGoPsFqs3y8upxGNA/LegxuCMTYAJMgAkwASaghgC9bP1wVxUaLptPsxZPG43xxblqJo1gVLL33W0Cl697na7utSXTR2NcUfzZG8FSw7qUBVxY2EK6iAVcJ65gBVxxcfHAlJQUyjhJiUm2aZr2lNPpNJ/3A6DYuLa2NspCeVcIkdWDgPt3KeX3AfyZEOJvgt25RBNw9AX3zlZzOv8ReZl4arEj2CVH1K+y9iq2Habwy/tt2cNjUFYYHQEZkfF8MRNgAkyACTCBPk7g7MVb+HhPlWmV2ZlpeHHVWNht8fdTVdRdw5bPzdWhBmdn4PnlBmxxaG+0bx8WcOqJR+VT4XA4HtI0jVwNy6SUgzRN82iado0yNHZ0dGyqqqoyn5mrX7ffDMEIuGnTpqXeunVrE4AlAD7yeDzPu93uHpOy6Lr+JYDJmqaVOZ1Os4L52vXzCIBpNpttVWVl5eZgl51oAo7WtW67wMWr5mQmL6wwkDuwX7DLDrtf051W/HI9leS738oKcrBsRkHYY/KFTIAJMAEmwASYQOQE6DTr/Z1uUEIQ3xavL1o7Ojx4c3Mlbt6m3Hz326NzilA8cmDkQPrACCzg1G+iUgFXXFw8xm63v6Zp2gKfpXTNSQlOutq69PT0H5w8edJcBVH9+u/NEIyA03X9/wNAWSZ3pqamriovLzd/ervZq+v6XwL4Kynln7tcrr/2/bOu68UASLheT01NHdbbWN2uTZgYuC67K2quYvsR8ynY5NJ8zJ8SnWQmb2yqwPVb97U2+al/Z/U4aJrSj0AU72CeigkwASbABJhA4hEIFEtGserfXFkWl6dZX7kbsef4vZQHXuDDcjOxdnEp/6bovP1YwKn/HCr79drpakhZNuiYg+ahLI+npZTXbTabJqWkas4TAIzqzFZ5JDU1dX4oQsZKPL0JuLKysokej4dO1G7YbDZHZWXlld7mLyoqGpqamkr5cCkj5ywhhDcdYqd7JSVAeURK+Rcul+v/9DZWogu4tnYPfvlZOVp8kpmkp9nx8urxSE2xhbL8sPoGisOL1glgWAbzRUyACTABJsAE+jgBOn1bt93lV24omnHyoSCmhGyvb6xAc0u76bKnFpViRP6AUIbq031ZwKnfXmUCrjMLJWWiPObxeH7L7XYHTJlYWlo622az/RcASgv4e0KIf1W/bK/74l9KKe8FQWma9l0AWZqmverxeG522eByuX6f/reu678CQMW2T0opdzzAxsMul+utrr/ruv4cAPr/KdJ1PQnAThfMEgDbPR7P6ge5YQaaJxFdKGkde47X4yu3OZlJtAKUq8/dwIYDNSaccyeNwJQkT/Ubjc8az8EEmAATYAJMIBABd/11bDpI7/fvt3iOJTt8+gIOl18w2VswLBtr5pNTFbcuAizg1N8LygScYRhHKeato6Oj0O12Nz5oKeRqmZKSQidVXwgh5qlf9v26b73NJYTwMjIMg4p1B6zn1m0Mv4ySnSL1TwHMAZAppaS1/iotLe2n4Zw4JqqAu3KjGW9vMYcCUgzc88t15W4H9NbsFx+fgkfe99wdPTQLTywgHc2NCTABJsAEmAATiCYBj0d6fxNQvVbftmpWIUpHD4qmKUHNdedum/f0jTyKuhqFYTy3TEfeIPXx/EEZGSedWMCp3whlAk7X9euapn3pdDoXBbMMXde97pZCCE4N2AuwRBVwtKyPdrtRf8mcJvgbC0swakjARJ3B3DpB96H6Mr4FNymz1W8+MQGpKSHXjg96Tu7IBJgAE2ACTIAJ+BMIFBufn9MPzy5V/1I3nP3YdfQsTlWbo2eMMTlYPpMTonXnyQIunDsstGtUCjh6pbJdCLE6GJMMw9hMJ1xCiIxg+idzn0QWcDUNN7B+v9mVsWjEQKyeW6R8S49WXsTBk+dN8zw2rxiFw7OVz80TMAEmwASYABNgAl8T6CmTI7kikktivDXyIHpnqwDF7HU1Khfw4soyDByQHm/mxtweFnDqt0ClgDtLn1EhRGEwyzAMo1ZKaRNCjA6mfzL3SWQBRy4TlBHSN/0uuSC8tEr9l2DjtWa8u83swjmpNA8LplAeHW5MgAkwASbABJhANAgEyuQ4Im8AnlxUojykIpz1fbK3CnUXzCV/KYaeYum5+RNgAaf+rlAp4N4F8LSmab/jdDr//UFLcTgc39c07WcA3hVCvKB+2Yk9QyILOCL/pbiEfScaTJvwkJ6PeZPVlhSgN2evfnYa5Mfe1ShV8UuPUP4cbkyACTABJsAEmIBqAm3tHfjVBv9MjmsXOzA8L1P19CGPH6jMQUZaCl56pAz0X24s4GJxDygTcGVlZXM8Hs8+KhGgadqHHo/nbZvNdqq1tfU6LTQ1NTVH07SJUsoXAayhflLKuS6X6/NYgEikORNdwFEpASop4BsInJ5qx8uPjVMej7btcB0qa6+atvulVWMxKItdIBLpM8C2MgEmwASYQGISOFJxEYdOmcMZKJSBQhrirZHX0Dtbnbh605xoZeGUUZhYmhdv5saNPXwCp34rlAk4Mt0wjB9JKf+ml2WQDR4p5R+6XK5/Vr/kxJ8h0QUc7cDuY/U4WWUuKRCNL0RRdw1bPq813QTRmDfx7zpeARNgAkyACTCByAjcbW3H6xsqTDVhacTnlxtxmcnxVNVlUB1Z35aTlYHnVxigRGjcAhNgAaf+zlB+95WWli602Ww/BLAUgOmYQ9O0ZinlVo/H87dut/uQ+uX2jRn6goCjtMFvbvLWNb/X6BSMAoIpJk5Vu9vSjlc+LTcFIhcNz8bqOHzzp4oBj8sEmAATYAJMIBYEDp5swNHKS6apHaNzsHJW/GVyJG+hNwIU7V4zrxgFnPzsgbcPCzj1ny51v5S72b5o0aKU8+fPl3R0dJDrpLTb7dcqKyurKNGJ+mX2rRn6goCjHfl0bzXIt9y3RSMD1brtAhev3rk3bard5i0nYLfb+taNwqthAkyACTABJhAnBCj+nGLf2jvu11GzaRq+ubIsLsMYDnzVgGNOs9ik+rGPzy9W+qI5TrYrIjNYwEWEL6iLoybggrKGOwVFoK8IuECBwWOG0Zej2uLah8sv4PDpCybWVNCbvpi5MQEmwASYABNgAtYTCBQ6Mb44F4unxV/y8RtNLXhrcyU6PPfLBpB30PPLdeQO5KLdvd0dLOB6IxT531nARc4w6iP0FQFHWSHf2uwEuVP6NtW+8Beu3Mavd7hMc0525GP+Q2qzYEb9RuEJmQATYAJMgAnEAYHrt74WRB6fOmoUQ/atR8ZiQP+0OLDQbMLmQ2fgOuvNuXevxavYjDt4AFjAqd8VSwScruvkBvmREGJtl8md/xbqCqQQgnOy9kKtrwg4WmagAGFjTA6Wz1TnD09ZpV79rBzNLe33SGdnpnkfJCrj70L9MHB/JsAEmAATYAJ9gcDGg2dQVW8WRFP0IZg7Of7qqJ1rbMKHu9wm7KkpNu9vhP4ZqX1hO5SvgQWccsSwSsCRQzMJuKd8BNx9J+fg10ECzh589+Ts2ZcEHPnCv7b+tElMkU881WYjUaWqbf+iDhVnzOUEXlhhsGuEKuA8LhNgAkyACSQlgUBeL1Q6iARRRnp8vbOnF7zvbnXiSreyAbMmDMf0sUOTcv/CWTQLuHCohXaNJQKOMk0CuOx2u8u7pu/8t9CsAeB2u3eHfFGSXdCXBBxtXaCaMJNL8zF/ijqXRnoTSG8EfRt/QSfZB4mXywSYABNgAkoJUKjEh7uq0HC5yTTPnIkjMLVsiNK5wxn8hKsRe788Z7qUXiZTopUUTnQWNFIWcEGjCrujJQIu7Nn5wrAI9DUBR6n9X9tw2lTYmzJDfmf1OGVv59raO/CLj0+ZApSH5Wbi6SWOsPaEL2ICTIAJMAEmwATMBGoabmD9/hrTPw7ol+r1sok3QURZMt/YVInWNnNy9NVzi1A0YiBvbQgEWMCFACvMrsoEnGEY/y2lPCaE+NdgbNN1fSuABiHEd4Lpn8x9+pqAo72kN1705su3zRg/DDPGDVO21d3LGFD82288No593JUR54GZABNgAkwgWQiQO+I7W5242s0dcen0MRhbNDjuMAQKrSgYlo3H5hVxfHyIu8UCLkRgYXRXJuB0XfeLi3uQfbqunwYwVAiRG8Y6kuqSvijgmu60euvD+GaoykhLwXdWj0VqipqwyEAJVJZMH41xRXwLJtUHihfLBJgAE2AClhM4XXMFO46cNY1LKfifW6bDZlP28zOsdQSK06MsmS+siM8adWEtMooXsYBTD9vST1BxcfGYLpNTUlLOSCm3dHR0fO9By0hJSUnRNG2alPJNAO1CiP7ql53YM/RFAUc7su1wLSprr5k2Z8GUkZhUmq9kw0g0/nI9vTe438hNgtwluDEBJsAEmAATYALhEWhr9+DNTRVoam4zDbBmXjEKhmeHN6iiq+ikkEoLXbp2xzTDtLKhmD1xuKJZ+/awLODU76+lAq7z1O1+1cPQ7Nc0TTvkdDrnhHZZ8vXuqwLuyo1mvL3FadpQCh5+adVYZW/r3tsmTF/a5JP/m09MiDvf/OS7y3nFTIAJMAEmkKgEAiUnG5k/AN9YWBJ37ojl1Vew86j5pJDi9F5cVabMAyhR9zVYu1nABUsq/H6WCjiHw/HnmqbNBED/R35ooYx/wm63f6uiouJU+MtJjiv7qoCj3ftsXzXOnL9p2sgVMwugj8lRsrmHT1/A4fILprE5YFkJah6UCTABJsAEkoAA1Vh9fWOFXzKQZ5bqGDo4vpysKIkaJS6523q/Lixt0cpZBXCMVvO7IwluAS7kHYVNDkVghWQOncaRC2VKSsr/9aALW1paZL9+/a6Wl5ebc8yGNFtyde7LAq6hsQkfdCugST7zzy/Xlby1a7zWjHe3mU/9xhfnYvG00cl1U/FqmQATYAJMgAlYQCBQUjLH6EFYOavQgtGtHWL3sXqcrLpsGjReTwqtXbna0fgETi1fGl2lgHsVwNFgs1CqX2rfmaEvCziqGfP+TjcooNi3PTK7ECWjBlm+iTQfFRL39dPvn5HqzUZJWSm5MQEmwASYABNgAsERuH6rBW9tqQTFlXU1m6Z566gNykoPbpAo9bp09Q7W7XCBfgf42vrcch304phb+ARYwIXPLtgr+RdqsKTiqF9fFnCEOVDdmLxBX2euUiGqAr2BW7vYgeF5mXG062wKE2ACTIAJMIH4JrDxQA2qzt0wGTmpNA8LpoyKK8NJYK7bIUBeOL5tsiMf8x8aGVe2JqIxLODU75olAs7hcCyQUl52u933UvrRv4Vjvsvl2hPOdcl0TV8XcPQ2jJKLNF43f7E+OqcIxSOtL6Z59uItfLynynQLPaTnY95k/hJPps8Vr5UJMAEmwATCJ3CusQkfdguBSEu146VVZXFXX/WEaMTeE+dMiyXvm2+uNEAljLhFRoAFXGT8grnaEgGn6zqVrf9ICLG2a9IwM1JKIQR/cnrZub4u4Gj5gU7h8gf1w7MKTuE6PBKvflpuCmLO6p+Gbz86VsmJXzAfTO7DBJgAE2ACTCBRCHhfvG73P9GaM2kEphpD4moZVELozc2VoFIHvk1lwrS4AhAFY1jAqYdslYDzK9rdKeBCXoEQwhbyRUl2QTIIuJ5O4VRliNxxpA6na66a7qR4zJiVZLc6L5cJMAEmwAQSgEBl7VVsO1xnspTKAFHsG5Xniae24UANqru5eY4ZlgWqUaciTCOe1h4tW1jAqSdtiYBTbybP4EsgGQQcrbeq/jo2Hjxj2vz8nH54dqn1sXC152/i033VprnorSG9PeTGBJgAE2ACTIAJBCbQ1t6BNzdV+hXtXjWrEKWjrU8+Fsk+BPLuIYH5wgoDAwfEV5KVSNYZ62tZwKnfARZw6hlbPkOyCDg6hXt3m8DlbrFwKk7hOjo8+O9Py9HSRt7AXzd6e/itR9iN0vIbmAdkAkyACTCBPkMgUD3V4bmZeGpxaVydaLW2deCtzf5Cc9aE4Zg+dmif2Y94WAgLOPW7EBUBt2jRopRdu3aZqiROnDgxp7W1daaUMgPAPiGEuRCH+rUn7AzJIuBog9z117Gp2ynckJz+eGapw/IHw7bDtaisvWa6L55bZoBO/bgxASbABJgAE2ACZgJUgufNjRVo6zDHk8VjCMK+E+fwpWg0LSA3O8MbW2+PMzfPRL/PWMCp30GlAo4yUWqa9lNN0/7V6XS+0rUcwzCekFL+kg45Ov+tBcDvCSH+S/2SE3+GZBJwdAr3zhYnrty8a9q4x+YVo3B41+1jzZ4Gcq2gt3L0do4bE2ACTIAJMAEmYCaw/Ys6VJwxx48bY3KwfGZBXKG6dO0O1m0313wjA7lkkJptYgGnhqvvqMoEnMPhGKtp2mEA/TVN+3+cTuf/polLSkpG2+12JwA6eaMcrhcATOk0arYQ4gv1y07sGZJJwNFOBTqFGzq4P55eYu0pXHuHB698csqUmYoKj764sszy077EvgPZeibABJgAE0h2AoFEEcWTvbiqDJTJOV4a1Xz79Q4XyF7fNr44F4unjY4XM/uUHSzg1G+nMgGn6/q/AfiBlPLvXC7XnwLwBhfpuv4PAP5A07RDKSkpi8rLy1sdDsdaTdPWAfiVEOJl9ctO7BmSTcD1dAqnIhZu86FauM6a3SgpuDl3ILtRJvanhq1nAkyACTABqwjQc/mj3VWg2m++LR69VgLVfOuXnuIVmlzzzao7wjwOCzg1XH1HVSngKgAMFkKQ/9k952jDMGqllKNsNtujlZWVm7uM0XXdBcAmhChRv+zEniHZBBztFokqEle+bXB2Bp5fbsBms+42DnTaN2P8MMwYNyyxbxq2ngkwASbABJiARQQCZYmmQthUtJuKd8dLu9HUgre3OEEeNr6Na76p3SEWcGr50ujW/fLtZquu6zcAfC6EWNH1J8MwDCklCbsbQohcX2HncDg2aZq2UAjBRx297HsyCrieMlIue3gMygoHW/ZJoXTIr3xSbvqypyDnF1aWWTYHD8QEmAATYAJMIFEJkBiibI43b7ealrBk+miMK6KfdvHR6HfDx3uqUH/JfEo4emgWHp/PNd9U7hILOJV0vx5bpYC7K6Xc5XK5VnUtw+FwfF/TtJ8B+FAIsdZ3eYZhbJZSzhVCDFC/7MSeIRkFHO1YoFpt5GdPb/yszCBFtefo7aJvYzfKxP7MsPVMgAkwASZgDYEjFRdx6NR502B5g76u0WqlR0yk1p6quoxdx+pNw6TabXiea75FirbX61nA9Yoo4g4qBRxVYO7wdYnUdX0HgIWapn3PNyslrULXdUpskiaEKIp4VX18gGQVcPQ27cNdVWi4bH6bNv+hkZjsyLds191nr2PTIXMB8WllQzB7Ihf1tgwyD8QEmAATYAIJR6DpTqu3aHf3sgFPLirFyPz4ef9+606r13WSar/5tgVTRmJSqXW/FxJuA6NkMAs49aBVCri3ATwrpfz95ubmX2RmZr4gpfwFgDs2m62gsrLyStfyDMOYK6XcK6X81OVyPaF+2Yk9Q7IKONq185dv4/2dFC55v1EwMhXctsrvntxDqKi37xc/F/VO7M8MW88EmAATYAKREwiU6MsxehBWziqMfHCLRqCXvZ/uq0bdhVumEUfkZYKEpqYp++lr0QoSfxgWcOr3UNldXFpaOstms+3r5qZJ8/2/Qog/8RFvL0kpKTNlHoAXhRDvqF92Ys+QzAKOdm79/hpQzTbfZnWikW2H61BZa65t89TiUozIi583jIl9F7P1TIAJMAEmkEgEGhqb8MEut8nkeCwbUFFzFduP1PnZ+dxyHTlZVMGKm2oCLOBUE1YYA0em67r+PAASZ5SJks6x/1MI8du+y9J1/VcASMS973K5nlG/5MSfIdkF3JUbzXhnqwC9ZetqdPpGsXCUBcuKVnfhJj7ZW20aakJJHhZNHWXF8DwGE2ACTIAJMIGEIUC11N7bLnD5erPJ5pnjh+HhOMrS3NTchre3VKKl1ew6OWfSCEw1hiQM70Q3lAWc+h1UdgLna3pRUdHQlpaWWw0NDeYqigDKyspWSimznE7nr9Uvt2/MkOwCjnZx2+FaVNaa67U9pOdj3uSRlmwyPaxe/awczS3t98ajejG/sWY87BaWLbDEWB6ECTABJsAEmIBCAoESglBowTdXloFO4eKh0UvdDQfO+HnoDB3cH2sXO+IqwUo88FJpAws4lXS/HjsqAk79MpJrBhZwANV2eXNzJUhodTUSVi89MhaUmdKKtud4Pb5yXzYNtWZeMQqGZ1sxPI/BBJgAE2ACTCDuCdxtaccbmypxt/X+C00y+pHZhSgZNShu7Bd117Dlc3O9WMqK+dwyHbkDuUJVNDeKBZx62lERcLqu50kpF9KBm81mG+TxeDyapl2TUp6y2Wy7nU6nOdJU/boTegYWcF9vXyCBVVaQg2UzCizZ3wtXbuPXO8wJU4wxOVg+05rxLTGSB2ECTIAJMAEmoJBAoGftqOxLaM0AACAASURBVCFZeGJB/NRSu3OXXCedJq8ZQjJrwnBMHztUIR0eOhABFnDq7wulAq6wsHBQWlraPwL4JoCUHpZDbpX/lpqa+pfl5eXmqpDq15+QM7CA+3rb6Av79Y0VaGv3mPaRatEMGdw/4r0ldwwa37dYaWqKDd9dMx6pKfaIx+cBmAATYAJMgAnEM4FAMec2TQMlBImXU62eXCfzB/XD00t1DnuIwQ3GAk49dGUCrrS0NN1ms+0HMKXTVbMFQBUAqpBM8+YAKAFAWSfID26jEGJN5/9Wv/IEnoEF3P3NO1x+AYdPXzDtppWpgqlYKRUt9W0rZhZAH0O3LzcmwASYABNgAn2TAAmjj3ZX4Vyjufbq5NJ8zJ9iTby5FeRO11zBjiNnTUORyHxmqY78HHadtIJxqGOwgAuVWOj9lQk4Xdf/oDMDJTkk/2FWVtZnR48ebfM1kUSepmlPaZr2d52ZKl8WQrwe+jKS6woWcPf3u629A29srMTtu6ZbC6tmF6LUAt/8qzfv4q3NlaYbrGh4NlbPK06um45XywSYABNgAklFIFBMGdVdfXFVGSipVzw0iod/Z6vTzxNnxrhhoPJC3GJDgAWceu7KBJxhGAeklNM8Hs84t9tNJ289trKysokej+c4gB1CiBXql53YM7CAM+9f5Zmr2PaFueaLldmx3t3qRKNP6mR6s/fyY+MsK1mQ2HcjW88EmAATYAJ9jUBrWwfe3OT/cnTxtNEYX5wbF8ulJGZ0Qthw2XxCOCSnP9YucbDrZAx3iQWcevjKBJyu61cAVAoh5gazDF3XTwEYKoTID6Z/MvdhAWfefXLzWLfdhUvXzFUq5kwcgallkdd9Oea8hANfNZgmnT95JCbrfKsm8+eQ184EmAAT6KsE9n55DidcjablkTB6ekn8pOM/VnkJB06an81U0oCyTuZkc8HuWN6bLODU01cp4CjmbUtnXFuvK3E4HJs0TVsshEjvtXOSd2AB538DNDQ24YNdbtMfrCruTYVBX1t/2lQ4PG9QPzy/3EjyO5GXzwSYABNgAn2NABXrfnebMD3zNE3zijeqqRYPjWxct12gw6eUENm1YMpITCrll6ux3iMWcOp3QKWAO08JS4QQY4NZhq7rTgDZQojhwfRP5j4s4ALv/saDZ1BVTzly7jdy9SCXj0jbp3urUXvhpmmY55YZHCAdKVi+ngkwASbABOKGAHm0fLDTjfNXbptsmlCci0UWPEutWGhHhwfvbXeBMmT6ttFDs/D4/PgpbWDFWhN1DBZw6ndOmYAzDOMTKeVqAGuFEB89aCmGYTwupfxISvmpy+V6Qv2yE3sGFnCB94+CmSnhiO8bOXprSO4UdGIWSXOfvY5Nh86YhphUmocFU0ZFMixfywSYABNgAkwgbggEyugYb4lLKKSBQht8W3qaHS8sNzCgf1rcsExmQ1jAqd99ZQLO4XA8pmnaJwDIlfLfbDbb2+3t7afcbjf9/ygsLMxIS0ubKKV8UdO071M5AU3TVjudzk3ql53YM7CA63n/9n/VgOPdvtitKDhKb/xe/ew07ra235ucsnD9xmPjYLfbEvuGYuuZABNgAkwg6QncbWnHG5sqTc85grJ0+hiMLRocF3woXOLD3VUm904yjMv7xMX23DOCBZz6/VAm4Mh0h8Pxs05xRnXeulpXsW7f1ySapmn/4HQ6f6h+yYk/Awu4nvewpY3KClSgueW+0KLeq+cWoWjEwIg2f8/xenzlvmwaw6pyBREZxhczASbABJgAE4iQwM6jZ1FeTfnn7jcr66pGaB4oMyaVDLh5u+tn5NcjOkYP8go48rjhFh8EWMCp3wfld7uu698G8McAxvWwnK80Tftrp9O5Tv1y+8YMLOAevI+nqi5j17F6UycrygpQlsv3tgnTuAXDsrFmPteE6xufLF4FE2ACTCA5CVy4chvv73SbTraoZM5zy3XkDowsBMEKohSbt+1wHZx110zDDeiX6k0olpEeH3XprFhrXxiDBZz6XVQu4LqWUFpaOiolJWW8x+PJASA9Hs81u91+yul0mnPAql9zws/AAu7BW0i1YSiDVvcA54fHDsXMCeHnyKEHCI1L2a+6Gr3x+87qcaCHCDcmwASYABNgAolGgJ6Z63YINF4zJwV5SM/HvMkj42I5geq9kmH0ApVepHKLLwIs4NTvR9QEnPqlJM8MLOB63+tz5CffrayA3abhhRVlGJQVfqWKE6IRe0+cMxkwe+JwTCsb2rtR3IMJMAEmwASYQJwROOm+jN3HzV4r9FLymyvLQOV4Yt2u3brr9X5pa/eYTJlYkoeFUzmRWKz3J9D8LODU70pUBJyu62VSylmaphUCyNI0zePxeG4AqLbb7QcqKyvN6f3UrzuhZ2ABF9z2bf281s/dYszQLO8bu3B95e/cbcMvPzsNj7wf1kmC8MWVZWGPGdxquBcTYAJMgAkwAWsJ3G5u82Zvpvhx37ZyVgEco8lhKraNEoj9eocLjT6eL2QRuXU+s9QBKtzNLf4IsIBTvydKBVxZWdk0j8fzHwCm9rKU3Zqm/cDpdFItOG69EGABF9wtQmKLMmpR4LNvizTxyMYDNag6R+8f7re1ix0YnpcZnGHciwkwASbABJhAHBAIVD81nuqp7f3yHE64Gk2kSLQ9u0zH4OyMOCDIJgQiwAJO/X2hTMCVlpaOs9vtX0gp6RNG8zQBqAFAv3xtUsqBnSdyXb96KTL1YSFEtfplJ/YMLOCC3z/64qcHgG8j15AXV5UhNSU815CahhtYv59u5fttXNFgLJk+JnjDuCcTYAJMgAkwgRgSCPQso1CD51cYyMmKvTg6c/4mPtvn/5Nw8bTRGF+cG0NyPHVvBFjA9UYo8r8rE3C6rr8H4GlN045LKX9PCLGfkpd0M9lmGMZCKeU/AaCacG+5XK6XIl9W3x6BBVzw++sNzt4u/NwvphhDMHfSiOAH8ulJY/5y/WnQCV9XozeCLz82DlQbjhsTYAJMgAkwgXgmQJ4p5DrZ1Hz/OUb2zhw/DA+PGxZz08mud7c6/UoCccmAmG9NUAawgAsKU0SdVAq4iwDSW1tbC8+cOXP9QVaWlpbm22y2KgB3hBCx/+aICKn6i1nAhcaY0iOTD71vizQ98sGT53G0km7x+23BlJGYVJofmnHcmwkwASbABJhAlAnsPX4OJ9xm10RySXxumQ57jOPK6CXpJ3urUH+JHLfuNyoH9NxyA+lxkFglytuVcNOxgFO/ZSoF3F1N03Y7nc6VwSxD1/UtAOYJIfoH0z+Z+7CAC333dxypw+maq6YLR+QNwJOLSsJKPkKFRF/fWGGqmUMPvxdWGGGNF/qK+AomwASYABNgAqETuHj1jvelJpXG8W1PLS4FPRdj3Y5UXMShU+dNZtBLV7JvWC7Hmsd6f4KZnwVcMJQi66NSwNVpmlbvdDrnBGOiruvbABQKIUqD6Z/MfVjAhb77zS3teHNTJe62tpsuXj5jDIyCwaEPCODTvdWovXDT/ABcVIoR+bF/AIa1IL6ICTABJsAE+jSBjs6wAt96prTgCcW5WDRtdMzXfv7ybW8JIN9Mz2TUrAnDMX0sl+uJ+QYFaQALuCBBRdBNmYAzDOM/pJTf9Hg8o9xut/lXbjeDDcPIklKelVK+6nK5/iCC9STFpSzgwtvm8uor2Hn0rOnifukp3lo39N9QW6AAcEq7TOmXuTEBJsAEmAATiDcCxyov4cDJBpNZ/TOo5psR8xhuiiunem/d4/JGDcnCEwvCL/8Tb3uQDPawgFO/y8oEnK7reQAOSinrOzo6vlNdXV0XaDklJSVD7Hb7a3T61t7ePqu6utqcn109g4SbgQVceFtG7iLkNkLuI77NGJOD5TNDF13kp/+rDadNDxubTcPLq8eBHojcmAATYAJMgAnEC4EbTS14e4sT7R3mgtiRltaxYn30PP10XzXOXrzl95L1+eUGMvvxM9UKztEagwWcetIqBdz/DWAIgN8DQD5lh6SUJzVNuyal7LDZbAOllOMo7o2SlwD4r87/+q1aCPFj9SgSZwYWcOHv1aVrd/Dr7S4/94w184pRMDw75IED+erPmTgCU8vo1ufGBJgAE2ACTCD2BOgF5id7/QVS0fBsPDq3KOax24fLL+Dw6Qt+oMJ9NseeeHJbwAJO/f6rFHD0iociZH3n6F5G4EF/u7d6IUR4BbvU84vJDCzgIsN+4KsGHHNeMg1CteHIlTItxOxWt5vb8Nr60yZBSJmyvvXI2Jg/ECOjxFczASbABJhAXyHgrL2KrYfNjlCpKTa8uLIMA/qnxXSZFEv+2b4av6QqFPNGsW/cEo8ACzj1e6ZMwBmGsctDZ+IWNJfLtdiCYfrMECzgIttKch95Z4sT15taTANNLMnDwqmjQh5848EzqKo3V8pYM78YBcNCP9ELeXK+gAkwASbABJjAAwhQbBm5TlIyL982/6GRmOyIbekbyuhMcW/dE4xR3Nvj84tBYQncEo8ACzj1e8afDAC6rv8WgH8GkGGz2YoqKyvPBIte1/W/BvCnAGqFEIWBrtN1fSmAP6IamQAoB+4ZTdPWAfiJ0+k0O3wHMTELuCAg9dKlobEJH+xy+/UKJ40y+ex/vIfKGN5vRSMGYvXcosgN5RGYABNgAkyACURAYPOhM3CdNb9kHDq4P9YudsRUIHV0eLzP4e5x6eQR8+wynWPJI9jzWF/KAk79DiS1gJs0aVJmc3PzzzVNe7ELdSgCbuzYsRM6OjqOAaDo2oACTtf17wH4OYDbADZomnZVSrkAwFgAX7a3ty8KNXELCzhrPhi7jp7FqeorpsEGZaWDAqZTQihkSrEFVKLA90RP0zR859GxMXdNsYYUj8IEmAATYAKJSIC8Q8hLxLdRTTUSSHmD+sV0SbuP1eNk1WU/255cVIrheVzvLaabE+HkLOAiBBjE5Ukr4MaOHevo6Oj4uFNIUQKV1QBGhCDgNMMw9kspdQC5gQRcWVlZocfjqQDQ5PF4Zrnd7q5jGruu628AeB7AvwghfjeIvbrXhQVcKLR67tva1oG3Nlf6pSyeVjYUsyeG5nd/3HkJ+78yp2YOZxxrVsajMAEmwASYQLITuNvSjre2OEEulL4tHp5Nou4atnxe67dF8yePxGQ9tm6dyX7fWLF+FnBWUHzwGEkr4HRd/x0Af69p2m87nc5XdF2nV1QFwQo4Xdcpy+a/dbpG/kMgAafr+t8C+BGAPxRC/KPvVnSWT6CiZB2pqalDysvLm4LdbhZwwZLqvd+Z8xQ8Xe33BvCZpTryc4J/O0kPylc/KwcVSe1qGWkp+M7qcaBAcW5MgAkwASbABKJJYOvntXDWXTNNOTg7A88t02EPwcvEapuv3ryLddsE2rqVMygdNchbR5U8WLglNgEWcOr3L2k/JYZhPCGlbBBCfEGYQxFwhmGMkFJWSCnrsrOzp966dau1BwF3mk747HZ7YUVFhd+rJl3X9wCYL6Vc63K5Pgh2u1nABUsquH6bD9XCddb8kCPx9swSPaT4gB1H6nC65qpp0sXTRmN8MR3QcmMCTIAJMAEmEB0CNQ03sH5/jWkyEkZPL3GA4t9i1cjzZd12F67dumsygcIXnl2qh5wJOlbr4HkfTIAFnPo7JGkFXHe0oQg4XdffB/Ckx+OZ63a7D+q6Tscuphi4wsLCjLS0NIp7uyGEGBxoKw3D+KmUktwnfyyE+F/BbjcLuGBJBdeP3Eve2uz0y4JF6YspjXGw7cqNZm+mL99GbztfWGHwG8VgIXI/JsAEmAATiIgAZXSkTMtNzWbXySnGEMydNCKisSO5mOLFKR6v+twN0zCpdhueXupA7sDgvV4isYOvVU+ABZx6xizgOhkHK+AMw3hcSvmxpmn/4XQ6f0CXBxJwhmEYUspKKeUpl8s1MdBWOhyOH2qa9hMAbwghvhXsdrOAC5ZU8P0C+eNToDe9rRwSwttKykZJWSl9G5cUCH4fuCcTYAJMgAlERiCQN0g4Cbois8L/6p6KdS+fMQZGQcD33FabwONFiQALOPWgWcCFIOAMw8iSUpJbpL29vX1sV/bIQAJO1/WHARwGcEgIMTvQVhqG8QMp5c8AfCyE+Eaw280CLlhSwfejN4Mb9teg5vxN00V0gkbZuoLNShkopm7MMKpnUxK8MdyTCTABJsAEmEAYBKgo9qd7zXHd5Dr5VIwzO7rrr2NTt2yYtLwJJXlYFEb91TDQ8CVRJMACTj1sZQLO4XD8z7S0tDfLy8vNQUEK1uRwOChBSFBpi+x2+48rKytFdzOCOYHTdZ1qxf1PTdOedTqdVMfN23oQcPMBUIzbHiHEwh4E3HellK9IKTe7XK5VwaJhARcsqdD69eRKSYVOqeBpMM1bUmBzJa7fMhcJ/+bKMpAY5MYEmAATYAJMQAWBnjIrh/IMU2HX5evNeH+nC23tHtPwI/IG4IkFxTFNqKJivTwmwAJO/V2gTMDpuk6f1FZN0z7zeDy/dLlcGynjoooldYmvIMeeL4TYF6qA03V9BoCDADYKIR7zvb4HF8rpUkpKkPK5EGJWDwKu6wTuQyHEU0HaT4KxqX///pnHjx8P9hLuFySBnt4SPrGgBKOHZgU1ykn3Zew+Xm/qO6E4F4umjQ7qeu7EBJgAE2ACTCBUAoFqmw4c8HVt01hlQ25uace67QI3b1Out/uNi3WHuruJ1Z8FnPr9UibgDMOolVJ2/WKlJB+Nmqa9brPZXquoqDilfmmhzfCgE7hFixalNDQ0HAFQYrfbJ3TPKBlIwJWVlekej4cyWpwWQozvQcD9SEpJpQZeE0K8HKzFLOCCJRVev22Ha1FZa85KSQ+b51cYoNIAvbW29g78cv1ptLTef19BLpgvrx6HjPTer+9tfP47E2ACTIAJMAFfAhR7TTHY3RsVxR6ZPyAmsKiszqd7q1B/yVwliZ6HTy0uxZCc2GXDjAmQJJqUBZz6zVYm4Mj0zlOrZwGspRprncshMXcMwC9TU1PfjoaLZTAYHyTgDMN4Rkr5HoAaTdPIdlOjMgCapt2h0zn6Q0dHxw/S09NvtLW1URbKu0KIgEc3hmH8u5Ty+wD+TAjxN8HY2cmVT+CChRVGv5a2DrwdoMC3MSYHy2d23cYPHpiKelNxb99GxcGpgCo3JsAEmAATYAJWEejpmTWxJA8LYxhftvf4OZxwN/otc8XMAuhjcqxaPo8ThwRYwKnfFKUCztd8SupBsWMkdgAUAiAhRzluP5FSvtbpYml2kFa//nszPEjAORyOlzVNezVYc7qKgeu6/iWAyZqmlTmdTnN++a8FLp3qTbPZbKsqKys3Bzs+n8AFSyr8fvWXbuGj3f5vM1fNLgQVG+2t3brTitc3VMAj7xf2plO8bz06DnZb1D52vZnJf2cCTIAJMIEEJxDIayQ7M81bwiY1xR6T1Z2uuYIdR876zT2tbAhmT4xdKYOYwEjCSVnAqd/0mPySNAyD4sOeB/ACgOGdYu6ilPL1lJSUX1RUVLjUL908QzBJTHqyKZALJfXVdf0vAfyVlPLPXS7XX3cTtMUA3ACup6amDisvLzc7iD8AAAu46Nwd+06cw5fC/PaQXCjpoZjZL7VXIzYfOgPX2eumfvzmsVds3IEJMAEmwASCJFBVf91bW617CyVuO8ipgu524cptfLjLDXKh9G0Fw7Kxem4RbPwSM2iWidqRBZz6nYuJgOtaVmds2Z8D+BMAXcFB9In/1G63/0U0Y+VUCLiioqKhqampJEbpZHGWEKKS1j5+/Pi0tra2jwA8IqX8C5fL9X9C2WoWcKHQCr9ve4cH720TuHrzrt9D6LF5Rb0W56aH2K93mN9F5A3qh+eW6b1eG77VfCUTYAJMgAkkAwHKnPz2FicoUYhvi2XWyaY7rXhvuwtkm2+jOnTPLNWRnhqbE8FkuB/iaY0s4NTvRkwEnMPhWEDulJ2xcUMAkB0tmqZtlVLO7CwJ0Cal/KHL5aLU/ZY3Oh2TUt6rHKlp2ncBZJGrpMfjuVcMzOVy/X5vk/d0AkfX6br+HIC3ADQDWA/gBoAllBAFwHaPx7Pa7Xabc873MiELuN52xLq/N15r9mbQ8nWFpNHnTx6JyXrvlStIwJGQ822PzStG4fBs64zkkZgAE2ACTCCpCFhVu9RKaFTG4INdblDZAN+WlmrHM0sdyMniUjpW8o7nsVjAqd+dqAm4kpKSUpvN9m1N017qTGjSNfdJKeUvMjIy3jh58uS1UaNG9cvMzPxdKeWPO0/lXhRCvGM1imBLDwghemX0IAFHdpeWls622Wx/CmAOgEwpJR3L/CotLe2nobhOdjFgAWf13fDg8Y5UXMShU+dNncgF5OnFDgwZ/OAsWjUNN7B+f43p2hF5mXhqsSO6i+DZmAATYAJMoM8QCBRjZtM0PL2k9+eSCggej/Q+66iQuG+jIuKPzS1CAb+0VIE9bsdkAad+a3oVJ5GYUFxcPDAlJYVOoL4NYHbnWDTnTU3T3gbwitPppEQefs0wjMellORm+JUQ4qFI7Ohr17KAi+6O0oOJ/PnPdztJo/o65A5Jbxd7avSWlFxcurthUgplKmLKjQkwASbABJhAKARuNLXgna1Ov8LYM8YPw4xxw0IZypK+9JzbffwcTlVd9htvzsQRmFpGjlbckokACzj1u61MwOm6/i6ANQDSO10kaTV7SbTduXNnXX19vfmMPbCIOyqlnCCEoDG4dRJgARf9W4GKkL67zWmq7UZWOEbnYMXMMQ+MaausvYpth+tMRlMw95r5lMeGGxNgAkyACTCB4AjQC0XKkNxw2Vxbbejg/li72BGTBCFfikvYd6LBbwHji3OxaOoojvkObmv7VC8WcOq3U6WA6yoJcJEKVdvt9ldCzS5pGAaVGFgmhOBqjz73Ags49R+MQDNUn7uBDQfM7pDUb/G00aAHVU+NMnG9uakCJAJ92/PLDVBSE25MgAkwASbABIIhcMx5CQe+MoslKoz93HI9JjFm9FykLJh0CufbRg/NAsV7c9mcYHa17/VhAad+T5UJOIfD8bHNZiMXSUrc0RHOUkpKSobY7fZUIcS5cK7vq9ewgIvdzu45Xo+v3GY3EXp4UoB27sCexdhX7kbsOW6+jen0buWs4AqDx27FPDMTYAJMgAnEA4ErN5q9mZG7p+dfMGUkJpX2nlTL6jVcunrHm7SEMjb7ttzsDDy1xMEZJ60GnkDjsYBTv1nKBJx605N3BhZwsdv7jg6PtzRAY7csW4OzM7wirqeiqW3tHvxqw2lTumcK7n5xZRkovTI3JsAEmAATYAI9EaBnz7odLr8Mj3TS9fj84qi7Kd6604p1AcoF9M9I9SZSoULi3JKXAAs49XvPAk49Y8tnYAFnOdKQBrx+q8UbD0eizLeNKxqMJdPH9DhWoGyW5HpJLpjcmAATYAJMgAn0RGDfiXP4UjSa/pyeZscLyw0M6B9dsUTlAt7f4cKVbjVSyRvlyUWloHg8bslNgAWc+v1nAaeeseUzsICzHGnIA4q6a9jyea3fdStmFkAfkxNwvLut7fjVhgrQw6+rUXzAtx4dhwH9UkO2gS9gAkyACTCBvk+AUvN/urc6pOeNKirkvrl+XzXqLt4yTUEeJatmFaBk1CBVU/O4CUSABZz6zWIBp56x5TOwgLMcaVgD7jhSh9M1V03XpqbY8OyynoPJKficgtB92xRjCOZOGhGWDXwRE2ACTIAJ9F0Cd+62eUvRNLe0mxbpGD0I9MKQhFO0GiUq2XHkLCrOmJ97ND89w+hZxo0JEAEWcOrvg+h98tWvJWlmYAEXH1vd1t7hjQHoXuPtQfFw9DB+bf1pUxA6ib5vPTIWFDvAjQkwASbABJgAESDB9Cmddl0wn3ZRfBm9KMxIS4kqqIMnG3C00vwCkgyYUJyLhVwuIKp7Ee+TsYBTv0Ms4NQztnwGFnCWIw17QMoKRiKuexYuciMhd5JAb0d3HT2LU9VXTHPyKVzYW8AXMgEmwAT6JIETohF7T5izF9Mz5alFpRielxnVNQeyhQwYMywLj80tjkn9uagC4MlCIsACLiRcYXVmARcWtthexAIutvy7z3665orXraR7mzNpBKYGcCmhenBvbKoAFWTtahT8/e1H+RQuvnaWrWECTIAJxIZA47Vm/HqHf8mAGeOHYca4YVE1qqeY7/ycfnhyYSnSUu1RtYcni38CLODU7xELOPWMLZ+BBZzlSCMecOfRsyjvdqpGb0opvTOlee7edh2rx6kqcz25yY58zH9oZMS28ABMgAkwASaQuATIPf/dbQKU8di3jcjLxDcWlkb1tIsSqKzfVwNPt0LdAwekY+3iUnb9T9zbTKnlLOCU4vUOzgJOPWPLZ2ABZznSiAekGj1U0PTi1TumsShGgWIVutfEabrTitc3Vphi4TgjZcTbwAMwASbABBKeAHl0kGeHb6OSAc8vN5AVxZIB9Dz7aLfbr2QOxWuTeCMRx40JBCLAAk79fcECTj1jy2dgAWc5UksGJFFGb027ZwsjN5O1ix0gN0nftvf4OZxwm+v6TCrNw4IpoyyxhwdhAkyACTCBxCLgrr+OTQfP+Bm9anYhSqOYov/arbt4f4cbVP7Gt5G7JLlN0nONGxPoiQALOPX3Bgs49Ywtn4EFnOVILRvwXGMTPt5d5eduUlYwGEsfHm1KanK7uc17CuebAMV7CvfI2KgXZrUMAA/EBJgAE2ACYRG4dacV72x1oqX1fq1QGmhcUS6WTB8d1pjhXNTU3OYt1E32+DZ6Pj2+oAQj8weEMyxfk0QEWMCp32wWcOoZWz4DCzjLkVo6YE/ZuhZOGYWJpXmmufadOIcvhfkUjlIyL5oWvYe1pYvnwZgAE2ACTCBkApTUitwVGy7fNl2bk5WBZ5c5kJoSnUQhdOL24U43rty8a7KDC3WHvKVJfQELOPXbzwJOPWPLZ2ABZzlSSwek2j1bD9eBMnf5Npum4clu6Z+pLtzrF8cThwAAIABJREFUGyrQ1uG519Vm0/DSqrF+cXOWGsmDMQEmwASYQNwQOHjyPI5WXjTZQydeTy/Ro+auSMlTPtlTjfNXzCKSjFo0dRQmlJhfQMYNPDYk7giwgFO/JSzg1DO2fAYWcJYjtXzAtnYP3t/pwuXrzaaxKfj7maUOUyD6ga8acMxpLo4abZcZywHwgEyACTABJhAUgdrzN70Fu7u3eZNH4CF9SFBjRNqJXPk/21eD+kvmouE0bixKF0S6Hr4+tgRYwKnnzwJOPWPLZ2ABZzlSJQPeaGrBe9uFXzxD7kBKanK/dg4lPfnVhtOmTF90WvfiqjLO8qVkZ3hQJsAEmEB8EKA4s3e3Cr9kIQXDsvHYvCJT3LQqiymL8oYDZ0AlA7q3iSWUWGtkVOxQtT4eN/oEWMCpZ84CTj1jy2dgAWc5UmUD0gOR3mqSW6VvKxyejUfnFN2r5xPIfUYfk4MVMwuU2cYDMwEmwASYQOwIkHD6cHcVLnRzWRzQL9VbMiAjPUW5cRR7t/nQGVSdu+E3l2P0ICyfURDVunPKF8wTRIUACzj1mFnAqWds+Qws4CxHqnRAco8kN8nuzbdw9106hdtYgdY2c/axZ5fqGDK4v1L7eHAmwASYABOIPoFASazI++KpxaUYlpup3CASbxSv7TprjtemiYtGDASVLqA4PG5MIFQCLOBCJRZ6f/5khs4s5lewgIv5FoRkAJ2+7Txa71eYlQZZOHUUyEWF2henL+Dz8gumsUcNGYAnFpSw+0pIxLkzE2ACTCC+CVTVX8fGAPXe5k8eicl6vnLj6blEBcMrzlz1m2vM0Cw8OrfIr3apcqN4gj5DgAWc+q1kAaeeseUzsICzHKnyATs8Ep/urfYLEKe3ravnFYHiHSgD2OsbK0GZKX3bmnnFKBierdxGnoAJMAEmwATUE/DGR28TaOnmcVEy8utTL0rZr7KReNtz/BxOVl32m4ZqvD02rxipKTaVJvDYfZwACzj1G6z2W0K9/Uk5Awu4xNx2qq/z6x0uXL/VYlpAWqrdm9SEkpuUV1/BzqNnTX/Pzc7Ac8sNjkNIzG1nq5kAE2AC9whQtkfKUNx4zZyhODszDc8u05GRpjbujcTbga/O47gwZz4mA8lt8/H5xaBnEjcmEAkBFnCR0AvuWhZwwXGKq14s4OJqO0IyhsQbiTgSc76NHt5PL3F4H97vbHXiarciqkumjwaVFuDGBJgAE2ACiUtg17F6nOp28kVxZmuXODAkR32886FT53Gkwlxvjmjm5/TzuuurFpCJu3NseSgEWMCFQiu8vizgwuMW06tYwMUUf8STNzQ24aM9VaAAct9Gbz+/sbAEZy/ewvr9Naa/UVayF1eNZbeWiOnzAEyACTCB2BAQddew5fNav8l9Y6FVWkbCjQRc90beH08uLIlK1kuV6+Ox44cACzj1e8ECTj1jy2dgAWc50qgPWFl7FdsO1/nNS2mbqXTAR7urcK6xyfT3WROGY/rYoVG3lSdkAkyACTCByAiQV8W67cJU75NGdIymcjFjlMa9kdvkF6cv4vBpc5Ismn9QVjqeWlSK/hmpkS2Qr2YCPgRYwKm/HVjAqWds+Qws4CxHGpMBA9V+I0Mml+ZDL8jxPux9G8UlvLSqjB+0MdktnpQJMAEmEB4BSlaybpvA9SZz/DOJJyoVozLmjMTboVMXcLTS322SXPefWuwAeXhwYwJWEmABZyXNwGOxgFPP2PIZWMBZjjQmA9KDddOhWlA66e6NTtuu3LjrV59nUmkeFkwZFRN7eVImwASYABMIjQB9z2/YX4Oa8zdNF6bYbXhmqcObvEpV8yYsOXkex53+CUuy+qfhyUWlIBHHjQlYTYAFnNVE/cdjAaeeseUzsICzHGnMBqSMZOQueeHKbT8bppUN9WYK842Vo7ID31xZ5nV74cYEmAATYALxTeBw+YWArovLZoxBWcFgZcaTeNv3ZQNOuBv95iDR9o2FLN6UweeBwQJO/U3AAk49Y8tnYAFnOdKYDni3pR0f7HL7ZZ6kWkBZ/VNx83aryb6i4dlYPa84pjbz5EyACTABJvBgAjUNN/wSUtEV5CY/f8pIZfhIvO0+fs4v2yVNOHBAujdhyYD+fPKmbAN4YBZwUbgHWMBFAbLVU7CAs5po7MdrutPqLS/Q1Gwu4t2TZVzcO/Z7xhYwASbABHoicI2SluxwobVbse4ReQPwxMISUOkAFY3E286j9Thdc8VvePLcoJM3jnlTQZ7H9CXAJ3Dq7wc13yDq7U7qGVjA9c3tpyxlH+x0+9WIC7TaQQPS8cIKA3a7rW/C4FUxASbABBKUAIm2ddtduHbrrmkFJJyoWLeqjI/kbr/jyFlQluPubXB2hrdMjaq5E3Sr2GxFBFjAKQLrMywLOPWMLZ+BBZzlSONmQIqFo5g4io3rrc2ZOAJTy4b01o3/zgSYABNgAlEiQCdgGw+eQfW5G6YZ6cSNMj4OHaymWDeJt21f1IFqzXVvlCjliQXFLN6idA/wNGAXyijcBCzgogDZ6ilYwFlNNL7Gqz1/0xs34ZHmQt/drUxNsXmLe7M7THztH1vDBJhA8hLoqVj20uljMLZITdKSjg4PthyuC5jROH8QiTcu0p28d2RsVs4ncOq5s4BTz9jyGVjAWY407gbsqdB3d0P1MVQEtiDu7GeDmAATYALJRoBevn22vwZ0CufbJpTkYdFUNeVfyF1zw4EzqL90yw/3kJz+eHxBMTLSUpJtK3i9MSbAAk79BrCAU8/Y8hlYwFmONC4HpNo9+79q6NW2pxaVYkT+gF77cQcmwASYABNQQ+D6rRas2y5ARbt92/DcTG/smYp45Tt32/DZvhpcunbHb1HDcjOxZn4x0lPtahbMozKBBxBgAaf+9mABp56x5TOwgLMcadwOSAIuUBFWX4PzBvXDs0t12BRlNYtbOGwYE2ACTCAOCNxtbfdmESYR59syM75OWpLZL9VyK2/dacXHe6r85qSJKNPlY/OKkMbizXLuPGBwBFjABccpkl4s4CKhF6NrWcDFCHwMpiVXHMoqVnHGP6uYrzkLp4zCxNK8GFjIUzIBJsAEkpdAh0fi073Vfi6M9EKNvCPoJMzqRhmLP9lTFbDsDNUJXTm7ECmcodhq7DxeCARYwIUAK8yuLODCBBfLy1jAxZJ+9Oem7GLbv6iDM0B2sS5r0tPseGnVWPRL51iH6O8Qz8gEmEAyEvAWzD5Wj1PV/jXXFk8bjfHFuZZjoUzF5DZJp37dW1lBDhZPH6Osxpzli+EB+ywBFnDqt5YFnHrGls/AAs5ypHE/IIm4rYdr4Tp7vUdb6ccC/WjgxgSYABNgAuoJnHA1Yu+X5/wmmuzIx/yHRlpuQN2Fm94SBW3t/mVmpuhDMGfScGga/6yzHDwPGDIBFnAhIwv5Av6kh4ws9hewgIv9HsTCAnLV2XLoDKq61RfytYUTmsRiZ3hOJsAEko1A7YWb3pOw7hknC4ZlY/XcIstjkl1nr2Hr4TrQy7zujWqCTjHyWbwl200Yx+tlAad+c1jAqWds+Qws4CxHmjADUr2fTQfPoOb8zYA2DxyQjhdWGBz/kDA7yoYyASaQaAQoBo2SllAKf9+Wm52BtUsclicPOVl1GXuOn/MTi3TaRuUJVLhqJtqesL3xRYAFnPr9YAGnnrHlM7CAsxxpQg1IIm79gRrUXfCv+0MLmVY2FLMnDk+oNbGxTIAJMIFEIECp+0m83bzdajKX4o+fXuIAvUSzqtHp3sGT53HMeclvSLtN89YALRk1yKrpeBwmYBkBFnCWoexxIBZw6hlbPgMLOMuRJtyA7R0er/tOoOKttJjnlhnIz+mXcOtig5kAE2AC8UqAXp5R6v6Gy7dNJpKY+sbCUgzPsy7jJH3Hk8tkVb1/3HNqig2PzinC6KFZ8YqK7UpyAizg1N8ALODUM7Z8BhZwliNNyAEpkJ3SVzdcbgpo//efmsSulAm5s2w0E2AC8UaATsO2f3EWlbX+JV2WzRiDsoLBlplMp3wbDpwBZZzs3uikb828YgwZ3N+y+XggJmA1ARZwVhP1H48FnHrGls/AAs5ypAk7IMVgvLdN4HqTuYBs14JYxCXs1rLhTIAJxBGBY5WXcOBkg59FVrusX7t5F5/uq/Zz0aSJBw1Ix+p5RcjJyogjMmwKE/AnwAJO/V3BAk49Y8tnYAFnOdKEHpBE3H9+dDLgGgZlpeO5ZTpSU+wJvUY2ngkwASYQKwKi7hq2fF7rN33JyIFYNbvQsuyP5xqbsOFADVpazclRaOLhuZl4dG4R1/qM1U3A84ZEgAVcSLjC6swCLixssb2IBVxs+cfj7G3tHfj5h4FFHD346a1tRhoX+Y7HvWObmAATiF8CFGf8yd5qv/T9FGNMZVusejlGrpk7jpwNWCbAMToHSx8ezS7x8XubsGXdCLCAU39LsIBTz9jyGVjAWY60Twx45UYz3t7iDLiW/EH9sGZ+MfpnpPaJtfIimAATYAKqCdB36vs73X7lAjIzUvHMUgcG9E+L2ASKrfvi9EUcPn0h4FjkojlrwjDLTvkiNpgHYAJBEGABFwSkCLuwgIsQYCwuZwEXC+qJMSf9CDhcHviHALlTPrGgBFkW/OhIDBpsJRNgAkwgPAJNd1q95QKamttMA6Sl2r0nb3mDIs/yS1kt6dTNWXfNz0gb1XibNgrjinLDWwBfxQRiSIAFnHr4LODUM7Z8BhZwliPtMwN2eCTe3eoEFZoN1Ab0S8UTC0s4CL7P7DgvhAkwAasJ3G1tx4c73bjS7XuURBV5MliRvp8yTW48cAbnA2SaJJH4yOxCS+axmg2PxwSCIcACLhhKkfVhARcZv5hczQIuJtgTZtKLV+9g3XbRo72UhvqxecUYymmoE2ZP2VAmwASiQ4BOxSjmjRKKdG9WlQug7+iNB2r8TvdovuzMNKyeW4TcgZGf8EWHGM/CBPwJsIBTf1ewgFPP2PIZWMBZjrTPDbj/qwYcd17qcV1UCJaypxUMy+5za+cFMQEmwATCIUDxaFs+r4PrrL9L46wJwzF97NBwhjVdU1FzFbuOnQV5S3Rv9FKNxBvHKkeMmQeIMQEWcOo3gAWcesaWz8ACznKkfW7A9g6Ptz5cT66UtGByB1ry8GhLC9D2OZC8ICbABJKGQE8vviaU5GHhlJERJRIhwbb/xDl85b4ckCeVJFg2owD0co0bE0h0Aizg1O8gCzj1jC2fgQWc5Uj75IDkpvP+Dhc80v9Nr++C50wcgSlGfkQ/TvokQF4UE2ACSUPghKsRe78857feohEDvfFoNlv4P5co3m3zodqAbpk0IZ3uTSsbwt/BSXO39f2FsoBTv8fhfyOpt41n6IEACzi+NYIlcOjUeRypuNhr90mleZg3eWREP1J6nYQ7MAEmwATikID77HVs/rwW5ELp24blZnoz90ZyKtZ4rdlbnPvWnVa/lVOykuUzxoBEIjcm0JcIsIBTv5ss4NQztnwGFnCWI+2zA1JA/rodLly+3tzrGouGZ2PFLHLhsffalzswASbABPoCgdoLN7F+f41fAe1BA9KxdokDlPQp3CbqrnnLBJBLe/dGZV1WzylCTnZGuMPzdUwgbgmwgFO/NSzg1DO2fAYWcJYj7dMDknijrJSBgua7Lzw/px8em1uMzH5c8LtP3xS8OCbABNDQ2OTNONldYJFoe3qJAwMHpIdFyeOROHjqfI+JpAqHZ2P5zAKkp/LLsrAA80VxT4AFnPotYgGnnrHlM7CAsxxpnx/wmPMSDnzVENQ6qVYc1TriNNZB4eJOTIAJJCCBS9fu4KPdVWht6zBZT+6STy4sxZAwy6xQ4e+tn/cc70aZLGeOH8bxbgl4z7DJwRNgARc8q3B7soALl1wMr2MBF0P4CTo1xXZ8vKcK9Zf8axsFWhLFZqyYWQB6U8yNCTABJtCXCFB23g93udHc0m5alt32daHuUUOywlru2Yu3sOXzWr9xaTAShksfHoPSUYPCGpsvYgKJRIAFnPrdYgGnnrHlM7CAsxxpUgzYdKcVb29xoqXbG+eeFq9pGuZMHI6HdM5QmRQ3CC+SCSQBgZu3W/HBTpdfEW0qq/LInMKwEoqQy+QXpy/gSOUlv0QohJSLcyfBjcVLNBFgAaf+hmABp56x5TOwgLMcadIMSEH19Ia4e6PYN8qWFqiNLRyMRVNHwW7n+kRJc6PwQplAHyRwu7kNH+xy40ZTi2l19LKKskHqY3JCXjWNSd+p5xoDezeMGZaFFTMKkBFBMpSQjeILmECMCbCAU78BLODUM7Z8BhZwliNNqgEpPsNZd8205vQ0O6YaQ3C4/ELAZCcj8jKxanYh+mdwcpOkull4sUygjxC429LudZu8cvOu34roBRUV6w61Pchlkk70Zk4Y5v1eJYHIjQkkEwEWcOp3m79V1DO2fAYWcJYjTaoBKWj/vW0C17u9hR6em4nZk4Zj08FaUOHZ7o2Smzw6pyjs4P6kgsyLZQJMIG4I0HcexQBfvHrHz6Y5E0dgatmQkGwll0mqr/lFxcWALpP0XUkxxCPyB4Q0LndmAn2FAAs49TvJAk49Y8tnYAFnOdKkG/DS1Tv49U6XX+0jypA2vjgXG/bXoDFA7TgK8l88fTTKCgYnHTNeMBNgAolHgEoEfLavOmACp2llQzF74vCQFkUvt8hlsqeEUOQyuezhMeytEBJV7tzXCLCAU7+jLODUM7Z8BhZwliNNygG/FJew74R/aYE184oxIj8T2w7XoercjYBsJpXmYe7kkSBBx40JMAEmEI8ESLxtPHAGVKy7e5tYkocFU0aG5N5Yd+Emtn1xNqCHArlJUnmAaWXsMhmP9wLbFF0CLODU8+ZfXwB0Xf8tAP8MIMNmsxVVVlaeeRB6wzBeklL+NoAJAMjXrFzTtH9xOp3vBbpO1/WlAP4IwEwAmQDOaJq2DsBPnE7nrVC3mQVcqMS4fyACVFpg/f4anDlv/nGTkZaC55brIDegL05fxOHTFwICHJE3AKtmF/CbZr69mAATiDsCDxJvZQU53pT+wcam0VgHT57HCVdjwHWyy2TcbT8bFGMCLODUb0BSC7hJkyZlNjc3/1zTtBe7UPcm4HRd/08AJPjOAdikadogKeVjANIB/FgI8b98t03X9e8B+DmA2wA2aJp2VUq5AMBYAF+2t7cvqq6uDnzM0cP+s4BT/8FIlhmoDtK7W51+KbWHDu6PpxaVejNPVp+7ga2Ha9HW7vHDQklNVs3iWI9kuV94nUwgEQg8SLwVjxyIVbMKYQvSe+DKjWZs+bwO9N9AbczQLCybwS6TiXBfsI3RI8ACTj3rpBVwY8eOdXR0dHzcKaT+C8BqACMeJOB0Xf82gNcA7GhqalrT0NDgjYguLS2darPZ9lOtTjrQE0JU07+XlZUVejyeCgBNHo9nltvtrurcUruu628AeB7AvwghfjeUrWYBFwot7tsbgQtXbuODnW54pDR1nezIx/yHRnr/jQrfbjhQg+u3zOm36W+UbW3WhOGYYnC9uN5Y89+ZABNQS+BB4o3i0ygRU0oQJVHIQ+Fk1WXsP9EQMDMvu0yq3UcePbEJsIBTv39JK+B0Xf8dAH+vadpvO53OV3RdJ7fJgl4EnIv6eDyeYrfbXe+7PeSGKaW0p6SkfFxRUXGe/qbr+t8C+BGAPxRC/KNv/5KSkiF2u/0sgI7U1NQh5eXlgYvIBLgHWMCp/2Ak2ww9xcNRJrWu2khUAHzb57Wo6eZy2cWqaMRALH14NMgFkxsTYAJMINoEOjo82NBDzFso4q3pTit2HDmLuouBIxyoMDclKuEsk9HeYZ4vUQiwgFO/U0kr4AzDeEJK2SCE+KJTbD1QwHWesh0F8JkQYk0wW6Pr+mk64bPb7YUVFRV+1ZN1Xd8DYL6Ucq3L5fogmDE7bW3q379/5vHjx4O9hPsxgQcSoLfNmw6e8Utakmq3Ye0SB/IG9fNeT/0offbh04HTZ9MPm5WzCkEumNyYABNgAtEiYIV4o+83UXcNe46fA72wCtTohdbCqaOQnmqP1tJ4HiaQcARYwKnfsqQVcN3R9nYCZxjG70kp/wnAnwgh6GTtga2wsDAjLS2N4t5uCCEC5lw3DOOnUkpyn/SLnXvQ4HwC1xt9/ns4BOgHy7oA9eFIlD27TDedrPVWwHbWxOGYorNLZTj7wNcwASYQGgErxBuVB9h9rL7HzLtpqXYsnDISBpdQCW1zuHdSEmABp37bWcB1Mu5NwOm6TolIvqdp2rNSSnKR/DPKKqlpWrqU8pSU8t9dLtcvu7bMMAxDSllJf3O5XBMDbaXD4fihpmk/AfCGEOJbwW43C7hgSXG/UAlQoP6vt7vQ1mFOWFIwLBur5xaZAv+bmtuw+eAZnL9C7yn8G9dDCpU+92cCTCBUAiTeNh4845dNl8YJ1m2SEjXtPHoWlNQpUBuem+lNVDJwAOUq48YEmEBvBFjA9UYo8r+zgAtewH0EgNwuf6ppGsXPHQNAPoyU5WEVADv9zeVy/T4Nqev6wwAOAzgkhJgdaKsMw/iBlPJnAD4WQnwj2O1kARcsKe4XDgFyIaJCtd1boKK3HR6JQyfP47i4FHAqylK5fMYYjB6aFY4pfA0TYAJMoEcCkYo3OnXb++U5uM5eDzgH1bmcOX44HtLzg85aydvFBJgAwAJO/V3QJwScw+GgBCH5weCy2+0/rqysFN37BnECtxXAMgAeTdO+7XQ63+waw+FwLNA0bQeJOAALhBB7dV2fD4Bi3PYIIRb2IOC+K6V8RUq52eVykQgMqrGACwoTd4qAAGVeCyTKVs4qgGN0jt/INQ03sP2Ls7jbGvgNNv0AokyVwWR/i8BsvpQJMIEkIUBlTTYerEHdBf9EI72dvFGsm7v+ujfWradTt/xB/bynbrkDv47/5cYEmEDwBFjABc8q3J59QsB1ia8gIcwXQuwLVcAZhrFZSrmCarkJIajkgKnpuk7lBb4tpfxvl8v1PwzDmC6lpAQpnwshZvUg4LpO4D4UQjwVpP10usdJTIKFxf3CIuDxSHyytxr1l8w/jkiAPbmoNGCSEsrcRvWSGi4HTqhKP4RWzOQfRGFtCF/EBJjAPQIUr7t+XzUaLvu7b1Ndtkfn9lwq4HZzG3Yf///bOw/4Ns76jT/vybIdx86ejjOcSHdO3KQjHelOWyiUQqFll73LhrJXgRZK2Zu27Bb4s6FQoC200L3TkdSJ75WcOMvZ046T2Na9/89zkRzZlmw50dmW/Hs/GLvR3Tu+70m6535rk1/fMlNjeYClddNw2sLpfh1MaUJACAyegAi4wTMb7BlFIeAGu+hMxw9kgYtGo39USr1CKXWN67rXZRBwbwLAGLgVWutT6+rqbM/zXACrtdb1WQTcJ4wxTIhyi9b6zbmuQwRcrqTkuOMhwCfTf7xHY/+Bjh7djC0P45XPs1E5hmUPezYKvycbt+GJLFkq6ZJ05uKZYI053ihJEwJCQAgMhgDdHm9/YC127O1bWLs/8Uar2+p1u/HwypasGSYnjSvHRafNkSy6g9kQOVYIZCAgAi74y0LuoJKMcxBwX1VKfRzAV7TWn+69NY7jvNAYcweANVrrRfX19aWdnZ18PHhIa50xAMhxnBuNMVcxIYrW+vpct1sEXK6k5LjjJbBz70H8+X8x0F0pvU2bWIErLohkdYncvKMN/3lsPZjoJFObNbUSF546W5ICHO8GyflCYBQRoJX/tvubsLf1cJ9VM9HSJWfNy/iZtHv/Idy7YmNGix07spTCKWJ1G0VXkiw1aAIi4IImDIiAy1HA2bb9agC/A/AHrTX/7tFs2347gJ8AeEBrfR5ftG37GQAnKqXqXNelNa73OU8CWGpZ1gsbGxvvynW7RcDlSkqOywcBxrexOC6fYKe3SM0EMCYumyWN8XCMMWFSlEwtXGLh7CXVqJ8/Waxx+dgo6UMIFDEBira/3d+E1vaeHgFccnT2BL+wdm+XRyY5WdG43fcKoHdApsYalxedOgdTJ0qsWxFfPrK0ISYgAi544CLgchRw8+fPH19SUrJNKZUoKSmZ3dDQsDt9e2zbprh7tVLqG67rfiwp4K4B8EVjzGdjsdiXex0/H0AcwN5wODyjoaGh77dSlv0XARf8G0NG6ElgReM2PLKK1TN6tlOcaThrSXW/uCjgWF8pW2FcZqi8YOlssN6cNCEgBIRAbwL0BGBMLt0ne7dFtZOx/JSaPlkiWauSsW6ZrHXsg+7cpy6cjlPqpvt/SxMCQiB/BETA5Y9ltp7kUytHAcfDmO1SKfUhY8xvZs2a9eZ7773XT7kXjUYvVUr9nRkqASzWWjfy32tra6eHw+FY8t+Xpf496V7JsgSXGGM+F4vFvjSYrRYBNxhacmw+CND6dvfjG+BmsKZRfNGK1l/jU3OeT9fKTI1Fcs9aPFOscfnYLOlDCBQRga27DuD2B9ficEeiz6qY3ZZW/HQvALptP/Rs9tIA7KR6SiWWL60BY96kCQEhkH8CIuDyz7R3j6NWwNm2fY0xZlIKiFLqrQCqlFK/8Dxvf+rfU3Xd/A/96uqKysrKewAwq+QapdR9nudVK6VeTDd6Y8wHYrHY99MhJ10v/w8AI67/CYCpry4EsADAPZ7nXRqPx/s69Pez9yLggn9jyAh9CXQlPPztvqY+hbsZP3LpObVgDEp/jSJwZXynb8ljX5kaY+N4YzWxSm6s5BoUAqOdAK1o/3p4XZ8YXHJhWRJmi0yJN9akXBXfgccatmY8nueU8UHRkmosqp0kbtuj/eKS9QdKQARcoHj9zkezgGsGMHcgxFrrHowikUhZKBT6uDHmSgDzKMyMMY8rpb6utaa469MikciZlmUx8clZAMYaY2iVu7W0tPS7g3GdTHUsAm6gXZPXgyLAzJR//m8Me9t6PnNgPNsVy6M5xZHQpemeJzb0EYKpOdOd6bRFM3CyM01cm4LaSOlXCIxwAkzzf9f3/qoIAAAgAElEQVSjzaAw693OPWmWn8k21WjZZ7ztrn19M1OmjmGcHM+rKO+bPXeEo5DpCYGCIyACLvgtG7UCLni0wY0gAi44ttLzwAQowP7031ifot28MXr5BZGcMksyocDK+A48+tzWrNY4Jhege+b0SRUDT0qOEAJCoGgIrIrvxP3PbO6TOInWtotOnY26eUecZ1ji5KGVLWjatDfr2sdXluG8k2Zh7sz+PQSKBp4sRAiMAAIi4ILfBBFwwTPO+wgi4PKOVDocJAEW66Y7Ze+n4xMqy/zyArk+5d7Tegj/e3JT1uLfvGGrr52EZYtnory0ZJCzlMOFgBAoJAJ0s3541RY87W7vM21a5i8+Yy4W1ExAZ1cCTzVux9N6R9YHQDx+KZOUONOyljspJDYyVyFQSAREwAW/WyLggmec9xFEwOUdqXR4DARiG/fgrkfX9zmTNeJedv4CMDFJLo03bQ1rd/k3bh2dfRMVsI8xZSU4+8RqOHMmSuxKLlDlGCFQYAQYF0vX6tjGvta0cMjCi86uRc20Sv91FuPOVmOSy2Y87nknz8rJG6DAMMl0hUBBEBABF/w2iYALnnHeRxABl3ek0uExEnhW78ADz27uczZLA7z47No+dZn6G4Y3ZCw3wLpz2Rqzx51/yixMHi81m45xy+Q0ITDiCBw63OXXmqRlv3fjw5tLz671rf0PPduC7Xvas86fpUjOOXEWaqvHyYOeEbfLMqHRREAEXPC7LQIueMZ5H0EEXN6RSofHQYBPw5/K4PJEV6cXnDG3T32m/oaiNa5p8z488PRmHMhQ84nnMuvlCQsm4/RFM1BeJm6Vx7F1cqoQGHYC+9oO+2UCMtVro0s2E488t3ZXvw92mERpad10sKxAScga9jXJBITAaCcgAi74K0AEXPCM8z6CCLi8I5UOj4MARdc9T2xE4/oete39HhfOm4QLT5096KfhdKVkOnCWHWD/mRpj4s44YQbqaycPSiQex1LlVCEgBPJIYPvudvzjoXUZC3TTmsYERk2b9sHL8hnAqdTNnYhli6tROUayS+Zxa6QrIXBcBETAHRe+nE4WAZcTppF1kAi4kbUfMhv47k13PLwOzVu6Syh2Y1kSmeI/RU8vtpsrsx17DuK+pzeBxXyzNWarPOfEatRMq8q1WzlOCAiBYSZAV+l/P7oenVlqQvLzItvDG059xuSx/vuev6UJASEwsgiIgAt+P0TABc847yOIgMs7UukwDwQ6uzzc/sDajHEsdG86c/HMYxqFN3Gr1+32C4Af6ujK2kftzHE4c0k1Jo2TIuDHBFpOEgJDROC5pp247+m+ZQJyGZ6WubMWV2NBzfhjeiiUyxhyjBAQAsdHQATc8fHL5WwRcLlQGmHHiIAbYRsi0+kmQNfH2+5rypho4PT6GX7c2rE2ircnGrZhVdPOrC5VjI9bVDsJHCvXUgbHOh85TwgIgcERYP3Hh1e14Bm9Y3AnAigrDeG0hdOxeMGUQSVHGvRAcoIQEALHTUAE3HEjHLADEXADIhp5B4iAG3l7IjM6SoAZ5f56bxy79h/qg2XZCTNx6sLpx4Vr176DePDZFmzc1pq1HyY1ONmZhpOiU3MuZ3Bck5KThYAQ6JfA4c4E7nq0GRu2Zn/fZuqA9dwWR6bg1LrpkrRIrjEhUCAERMAFv1Ei4IJnnPcRRMDlHal0mGcCBw524i/3xsEMc71bPkQc3SrXtezHQytbMo6RGpMpyJfWTcMJC6ZIdro877F0JwRyJcAMk/96eB12Z3iok60PxsAxCdLpi6ajsqI016HkOCEgBEYAARFwwW+CCLjgGed9BBFweUcqHQZAYP+BDtx2Xxz83bsxhuWUumnHPWoi4eG5pl14Ys22fuPjmKHutEUzUDdvEvhEX5oQEAJDQ2DT9lbc+cj6ft+fvWfCEiTL6mdgosSzDs0myShCIM8ERMDlGWiG7uROJnjGeR9BBFzekUqHARGgeKM7ZWt7XxGXD0tcatqMj1vRuB0rYzv8jJjZGutK0YXTnjNRSg8EtOfSrRAgAVrJWQbkgWc25wxk7oxxOKN+BqZNqsj5HDlQCAiBkUdABFzweyICLnjGeR9BBFzekUqHARKgGyVFXNvBzj6jMDvlshNm5C2bHAXjY89tgd64t98U5OMp5Oqmw547USxyAe69dD06CXQlPNzxcDPWb+1bViQTkTkzqvwER1ISYHReL7Lq4iMgAi74PRUBFzzjvI8gAi7vSKXDgAn0J+JOjE716zkdS524bNNmohMWAl+7eV+/K2NKclrknDkTJbNdwNeAdD86CDC50N/ub8ppsbOnV/kWNxFuOeGSg4RAwRAQARf8VomAC55x3kcQAZd3pNLhEBCgiGOJgUzulCfMn4zzT6nJq4jjklgAnEKuv4yVPI4xcifZU1E/fzLCJaEhoCFDCIHiIsD32u0PrsXhjsSAC6OrJB+czJwiRbgHhCUHCIECJCACLvhNEwEXPOO8jyACLu9IpcMhIkAXRz6dz5SdMjp7Ip532uxALGFMpPB4w7aMRcbTl85aU0sWTPHTlksduSG6KGSYgiXAeNO1m/fi2dhO/2HJQI3JSei6PHXimIEOldeFgBAoYAIi4ILfPBFwwTPO+wgi4PKOVDocQgKMhfvbfU3Y09q3ThxjYS45c15gVrDNO9rweMNW8Hd/rSRk+SnMl0SnYGJV+RDSkaGEwMgn0H6oE6vX7cZzTTszxrb2XkF09gQ/xk2ySo78vZUZCoF8EBABlw+K/fchAi54xnkfQQRc3pFKh0NMgDeAf39gLXbuPdhn5OmTKvCSc+YHWrSXAu6J1dtAy9xAbd7McWCcXs20yry7eA40trwuBEYSge2727GqaSf0hj39ZntNzdlSCm980UKp4zaSNlHmIgSGgIAIuOAhi4ALnnHeRxABl3ek0uEwEDh0uAv/eGhdRterSePK8ZJz56Mq4AK+dPt6yt0+YLIT4pkyYQyWRKaArp7hEmsYiMmQQmDoCXR2JaA37MVza3dix56+D1yyzehFZ9Vi/qzxQz9hGVEICIFhJyACLvgtEAEXPOO8jyACLu9IpcNhIsCbwzseacaGrX0tYUwscunZ84ckXmb3/kN42t0Od/0eeCZ7HTliYpwc3SuZ8ETcK4fpwpFhAydA6/hza3f51raOzoETk6QmRAv6JWfV+omBpAkBITA6CYiAC37fRcAFzzjvI4iAyztS6XAYCSQSHu5+YiNiG/f0mQUtXS88cx6YtW4oWlt7h5+QoWHdrpxuWpkGnRk051WPl3pyQ7FBMkagBGgV1xv3YE3z7kFZ21KTOtmehmWLZ8p7IdBdks6FwMgnIAIu+D0SARc847yPIAIu70ilw2EmYIzBA89sxsr4zj4zYRwNSwzQ4jVUjRYH3sQ+G9sBZs4cqDFjpTN3IhbNmySJGgaCJa+PKAKeZ7Bxeysam3f7rsTMLDnYVhoO4aJTZ4NZJqUJASEgBETABX8NiIALnnHeRxABl3ek0uEIIEAR97S7Aw+vask4m1OcaVh2wkxY1tB9bPHmdv3W/XhG7xgwc2Vq0jMnj8XC2klg5j2pKTcCLiyZQh8CfK/t2HvQd4+MbdiLA4c6j5kSSwK84Ix5mFBVdsx9yIlCQAgUFwERcMHv59DdCQW/llEzggi4UbPVo3KhdKW8+/ENGS0BtTPH4flnzAWf+A9127XvIJ5r2gU3x5igcMjykzjYcydi9rSqIRWeQ81GxisMAqy/GNu414/1zFTGY7CrOMmeijNPmBlI7cbBzkWOFwJCYOQQEAEX/F6IgAuecd5HEAGXd6TS4Qgj0LKjDf98eB0Od/RNnjB5XDkuPWc+xo0tHZZZ072SlotVTbtAUZdLG1NWAnvORP9n2sQxUo4gF2hyTF4IULQ1bdqH+Ka92L6nPS99lpeW4KLTZqO2WrJM5gWodCIEioyACLjgN1QEXPCM8z6CCLi8I5UORyABWghuf2Btxhg0CiIW/K6eWjlsM6cb2rbd7X5BY1oNO7u8nOZC4RmpmeD/0P1MKfkYzgmcHJQzgWMVbbw2mVmV9RGzxcJVT6nExWfMkdpuOe+GHCgERh8BEXDB77ncOQTPOO8jiIDLO1LpcIQSYFY8lhlg4e3ejclNzl5SjSXRKcMuglgOIb5xH1av24Utuw7kTJM3zEz8sGDWeDD9uoi5nNHJgWkE+DBh+56DfhKS5pZ92LX/UM58WBZjwSw+UBiPjdvb/HhP9pfp/XZ6/QwwFnUo41BzXogcKASEwIghIAIu+K0QARc847yPIAIu70ilwxFMgGUG7n9mMxrW7so4SxbWvvDUmhGTMISWQ8YY0c0ylwyWqUUxk2Vt9TjfLa1mWiVKQlIsfARflsM+NT402LS9Dc1b9qO5Zf+gEpGELOWXvrBnT8C8meOwa98h3P3EBrAeYqbGBCUXnz4X0yZVDPu6ZQJCQAiMfAIi4ILfIxFwwTPO+wgi4PKOVDoc4QRoEVjVtBMPPNOS0TrAuDgWDx5JmfA456272uGu343Ypr0Z4/myYWcClNkzqvz6d3NmVKGqYnji/Ub4ZTGqpsfriQKLRe83bGsF40QHk/KfVjMm06Hrbu2scWAcGx+OPLFmG55q3J61gD3rHJ59YvWIeUAyqjZdFisECpSACLjgN04EXPCM8z6CCLi8I5UOC4TAxm2tuOvR9TjU0dVnxsxMeeHS2YjMHnm1qHijzJvupk17sbZlf05FwtMXOGlcuS/k5kyv8uP+xDpXIBfscU6z7WAnNm9v9S1tvPb534NpFG28Zija5lUfEW2ptnXXAfzvyY1Z3S1pEb5gaY0kKhkMcDlWCAgBn4AIuOAvBBFwwTPO+wgi4PKOVDosIAJ0S7zzkeasGfVOWDAF55xYPWJFzvGKObq/zZg8FrOmVaJmaqUfOxcSd8sCuoKzT7X9UCdadh7A5u1tvmg7llT/fJAxd0aV7xo5d2ZP0caR6Xr56KqtWNm0M6M1m8cwW+p5J81CedlRwVcUgGURQkAIDAkBEXDBYxYBFzzjvI8gAi7vSKXDAiPQxbi4pzf5GSAztSkTxuAFy+b6GfVGcqOY27SjzY9hWteyb9AWFq6N7pYzpozFTP5MHosZkyvE3W0kb3pybnSJ3Nt2GFt2Huj+4X8fS6scE/Zj2hhDSVGfTdCzKP19T23KGpvJ7K7nn1LjW+ykCQEhIASOlYAIuGMll/t5IuByZzVijhQBN2K2QiYyzASY2IRCLlMsULjEwnkn1aBu3sSCyO7IG/odew92izn+fSyN2TknTyhPirmxmDaxAuMrSwuCwbGst1DOOXi4C9t3t2PbnnZs29XuW5D5b8fSuMcU7KkYycnjy/vd3wMHO/Hgsy1+uYtsjdlQzz95Fug6KU0IjBYCb3jDG/D444/j1ltvxRlnnDFalh34OkXABY4YIuCCZ5z3EUTA5R2pdFjABHbsOYi7Hm32rRmZGlP0X7B0dsG5g9GdjnFz67e0+vFPmeL+ct02poqfOqHCLyLOTIJTJ4zxC6FL2YJcCeZ+HIX4gUNd2LX3oC/Id+496Iu1wWQkzTTahMoy322Woo1ZSukqOVDzPIPn1u7Eo89tzRp3ObY8jPNOnuWXs5AmBEYbgV//+tdYv349Xv/612Pu3LmjbfmBrVcEXGBouzsWARc847yPIAIu70ilwwIn0NGZwL1PbfJT92dqvEm96LTZmDNjXEGulDfiFAMUcoyNYgIKupEeT6OFcvL4MaD1ZkryN7N40o1OhF1uZFmnkJkh+bOn9bCfjn/XvoPHbFlLH5VukbOmVqJmWpUv3Ci4B9No7eN7guIxW1tUOxlnLZnZI7nJYMaQY4VAkATe//73Y+/evfjVr34V5DDD0ncxr41ARcAFf1mJgAuecd5HEAGXd6TSYREQoOVjTfNu3P/05qziZvGCKf4Na7hkYOvFSEbC2Lltu9v9+LlN29r8m/TjFXSp9dJax9jBiVVlflkGWn7GjS3DuMpSlOVg9RnJ3I5lbnw4sK+tA/vaDvtW3v0HDmNva4efYORYXSAzzYOc/TjG5A//+1iENC23jzVs9eNDMxXk5tjjK8v8DJMUh9KEwEglcOGFF2LWrFlFKeCKeW0i4IbmHSUCbmg453UUEXB5xSmdFRkBWkP+8/h60LUyU6Mlgy6Vs6cXz80rBR0tdLTM+UkxdrWDN/L5bhR35DeuohRVY0v9eClaivjDv8eOCYOWvUJoFDeHOxO+CGs/1AXGibW1d6K1vcNPJtPW3oHW9s7jcl3NxoFlIOjGOn1yhZ9RtHrK2OOOPWMc6HPxnXh89VZ/XZkaM5gurZuOU+qmjdgsrYVw7cgcgyewZ88eLFu2DKeffnrRCbhiXlvqyhALXPDvERFwwTPO+wgi4PKOVDosMgIUNI+v3oan3O1ZrRAsUHzWkuqcYokKDQ/FCWOuaJnbvucgdiR/05oUdKOAoxsmf1h3bExZyI8/ZMxWaYmV/B1CadjyLaGhkPLFBMUFsyeWWAqsX6aS304pKxT/0wCgO6n/Y478pnCh9bErYfwU+Z1d/Nvzfx/uSPhihr8P+X93+X9TsFHgDqYQ9rFy41pYx4/xh9MnHUkqM2l8ub/efDW61j7wzGbflTNbo7Xt/FNmjfjMrPliIv0ULoFPfvKT+Otf/9pnAffccw8uuugizJkzB7/97W/xmc98xk9A8q53vQtXXXWVf3xnZ6f/2j//+U/E43EcOnQIEyZMwNKlS/Hud78bCxcu7NFvtiQm/Az985//jL/85S9obGxEV1cXZs6c6Y/PscaN6+uO39raiptuugl33303WlpaUFFR4YvQD37wg5g/f74/bra1ua7bPa/Nmzfj5ptvxoMPPojt27ejrKwMkUgEl112GV796lejpORoeQ/O71Of+hRe8YpX4FWvehW+8IUv+Ov+xS9+gXe84x1ob2/35zN79uw+PDdu3IjnPe95/loeeughlJYOzk27vytMBFzw77/8fYMEP1cZIUlABJxcCkIgNwItO9pw9xMbsiaQoOWIadNrq8fn1mEBH5VKW0/LZCpWiwk2BlscuoARBD51ClY/pnDCGN/Cxt90RQ2qTh8F28MrW9C8ZX/WtdEyyrqI0dkTjsklM3BoMoAQ6EXg9ttvx3/+8x/cddddmD59Ol7wghf4R7zpTW/qFnCO4/jJRyjMzj77bDz/+c/3j3nf+97nnzt+/Hj/36uqqnxBs2LFCl+g/OY3v8GSJUu6R8wk4PhZ+aEPfQh33nmnL27YD899+umnsWHDBtTU1OB3v/sdpk6d2t3P7t27fQFFUXTSSSehrq4OFGIUYRRct9xyiz/XbGujGGWjWHzjG9+Iffv2gWusr6/3/37yySf931zn97///e73ckrAUdw98sgjsG3bT8byute9zheBf//738F4O3Lp3X7605/i61//uj/v6667Lq/XoQi4vOLM2JkIuOAZ530EEXB5RyodFjEBWp0eWtkClhzI1pip8tyTZqGyIn9PIAsFKbNb7t53NBHH3tbDfnwX3QezxVAVytqCmCctgmPLSzChqhyTxpVh4jj+PhIzOFQp+HOJc6Pl78ToVJy2cHpRWpmD2Fvpc+QQeOyxx3whk+5CuWnTJl/AVVZW+kKFoijdavTcc8/h5S9/uS/a/vGPf2DGjBndC/rBD37gC59zzz0XFC6plknA/fGPf8RnP/tZ32rGLJWTJ0/2D+/o6MA111zjWwcvvfRSfOtb3+ruh4Lvjjvu8K18/DvVKKA+9rGP+aKPljB+fmRaW+r4l770pb6Io5Xvwx/+cHc/O3fu9IUWReFXv/pVvOxlL/NfSwk4CtYrrrjCt/Cl2sMPP4y3vOUtmDdvni+Gezf29+yzz/prPO200/K6+SLg8opTBFzwOIdmBBFwQ8NZRikuAnQz+9+KjVmtcXT9W1Y/E4sjU3wXvtHe6IbIxB10xdzf1oH97R1H/vZ/DvsuisXaaEmrqjgS28dYv/HJJC5M/sEYQLp8Dkeji+jTegeedrf3y5+lBs45qVrcJYdjk45xTLoDP6N3+NbUfCUkOsapHNNpfE/Mqx6Hk+2pebH09ifgOMGvfe1roNhJb7R+PfrooxgzZgxe/OIX93ht27ZtOO+883xxR2tWqmUScC95yUugtfaFHgVfemtra/P/7fDhw77b4cSJE7Fjxw6/77Fjx4KiqbcrIoUoXTs5Z7oyZhNwzzzzjO8iScvevffe28NVknOg1e/zn/98D1GbEnB8nefQzTPVPM/DBRdcgK1bt4KiNN3yuGXLFv+16upq0DX1WBIm9XehiIA7prfRoE6Su5RB4RoZB4uAGxn7ILMoPAK0xj2yagtWNe3MOnm6vdEaxxTu0rITIEu6XzL5x4FDRxKA0DJ08DDjzbrAFPtMEMKfoYg1G2ivGIPHLJpMxFJRVoKK8hKMKQ/7sXr8m6UmKpOibaRlKeUNfUPTLjzZuK3fzJfMXHnOSbMwb2ZhlssYaA+L+XXG69IdttAb44pPcaYd9zIGEnD/+9//fPGRa6M3Ad0a2dLjzXoLuFSCkXA4DAqq9Hiz1Fhve9vbfNfIG2+8EcwmSVdLxrmdddZZfuzZQC2bgPv5z3/uW9coPr/5zW/26YYuoxdffLEfW/fUU0/5oisl4GjhoxDr3djPj3/8Y7/O3ec+97nul2m9vP766/34wauvvnqgKQ/6dRFwg0Y26BNEwA0a2fCfIAJu+PdAZlDYBBgbxxpZ/SV9iNRM8JOcDLb+VmGTyf/seePUnVSkM4HOTs9PLEILXkdXAokEE5F4yd9HjqU1gs3/f8P/GRgDP7GJpY4kOfF/+LdSKCk5kgiFVtTUb4owJko5ItpK8po0JP+UMvdI4dvYvBtPrN7ab6wiLYanLZoOJuYJKt5uqNY8Wse54+F1aNq8r+CXT3f0S86qPe51DCTgVq5c6Sf36N1obaIQoiWOVjfWkevd+hNwq1evxuWXX+73TWtYpsZYs1gshk9/+tN+XN7PfvYz37rGODTGlA3Usgk4xqHRnfGd73wnPvKRj/Tphi6cixcv9v+dfTA5S0rAnXjiifjDH/7Q55ympia86EUvwqRJk/DAAw90C1LGyNESyWQvTJCS7yYCLt9E+/YnAi54xnkfQQRc3pFKh6OQADNV0h3tyTXbsrosUQzwafLJztSCrx03Cre4YJdMAcui9Lw2WXsuW2MmS8a5sSwARZy0wiUgFriee9efgAuFQqDQ6t0oVq688kpftFHcMAMkhUvKinbrrbf6p/Qn4ChqKG5yabS6vec978EPf/hDfO973/Nj0L7yla8MeGo2AcdEJn/605+yJh1hx7Qi8qHYfffd58f4pQTcmWeeiV/+8pcZx2aGylWrVvlJTZYvX97t8sm+MmX7HHABORwgAi4HSMd5iAi44wQ4HKeLgBsO6jJmsRJgnNd9T2/Chq2tWZfI5BSnL5qOhbWTC9KSU6x7V2zr4kOFxvV7sKJxW9ZYTa6ZrlP27Ak444SZYiEukouAN+V8oNTcIjFw3NJjEXBMIPLf//7XF250G0y30CUSCSxatGhAAbdmzRo/QQgTlzCeLZdGt8kbbrjBzxDJZCkDtWwC7ktf+pJf8y6bBY5xd6k4tieeeMLPkJmLgKNVj9a9lGsmM3Fee+21fsITJjkJoomAC4Jqzz5FwAXPOO8jiIDLO1LpcJQT4M3Tupb9ePDZzf3eOE+oKsOZJ8zE/Fnj8x70Pcq3YFQvn26ja5p346nG7X4h8f4aS14sO2EGJo8fM6qZyeKLm8CxCDhmUty/f3+fhB0klXIl5N/9WeBYyy2VkZFumrnURmPZAqbpp7hispCBWjYBRwsaLXi9M1ym+kutgYlYKODSY+D6s8Axro+JVyho6f7JGD6WVWDSk2nTjj9eMdN6RcANdBUc/+si4I6f4ZD3IAJuyJHLgKOEAG+kmQluxZpt6Exkz7I4fVIFTq+fgTnTq0TIjZJrI4hlMtnLc027sDK+008A01+bPb0KZ9TPwIzJY4OYivQpBEYUgZTIoZiiBYktVUYgmwtlUjT4SUVqa3vG4aWSebAfpulPZV3MlIWSFjha4nhO72yWPJ/ui6y1xvT8bLt27cI555zj/33//ff3qA/Hf6NrZUNDg5/ghIlOMq2Nx9HNke6OtP5xDCZSSW90Af3yl7+M888/37cwsuVigeNx733ve/0yBrS8sdg3BR9jBYNqIuCCInu0XxFwwTPO+wgi4PKOVDoUAj0IMLvio6ta4G7Y228tNN5M07WSN9f5TsMsW1K8BGhl44OC1et2DViOoXpKpZ+ghNeYNCEwWgikarrNmjWrO839QAIuJZRSyUVSrG677TZf8FBoMT6OIosFwtkyCbg///nPfoISijS6NKaOTQk0xr0xxo5ZH1Numh/4wAf8WmsUYHSFTH0f0KWTx7M0AP+mKMu0ttRcX/nKV4KWv9514Jicha9t3769O5ZtMAKO4o0ijtY7WhnTa8kFcU2JgAuCas8+RcAFzzjvI4iAyztS6VAIZCSwY89BPLKqBRu2ZY+P44kzJ4/FqQunY84MEXJyKWUmQDfdrbvasTK+A02b9sFjWs1+Ws20Kl+4STkLuaJGI4FUvbX29nYsXLgQLFTNItrMqJjNAnf77bfjox/9KCzL8mucTZkyxbdqNTc3+9YvFvJm+v8TTjgBl1xyCd7+9rdnFHB8r7IfFgNn0XBavPibLoxMckK3SpYQSFnduD/MePna177WL7TNWDtmi2StNY7H+TLRCfthy7a2BQsWIB6P+yn/6fbIddfX12P37t14/PHH/fOYYIUcUi1XCxzr0HG+FLCsk8cadqxbF1QTARcU2aP9ioALnnHeRxABl3ek0qEQ6JcAi4CzRtOOvQf7PY5xSac4UxGdPVGKgcs15RNgYpLYpr1YGduJ7XvaB6TChwCnLZyBmVOCu7kacBJygBAYAQQooJiWn5YzZlykywO8DC4AACAASURBVB8ThWQTcJwyLWZ0uaSQopXslFNO8S1gzLhIyxeFGS15FHDsO5MFjv2wCDYtd8wKyZi5Q4cO+RktTz/9dF/4UVz1bhRHFHa0zNFixnptp556qm/5ohBLb5nWNmfOHP8QCj/2Q/FHi1t5ebk/3mte8xo/Pi695SrgeE6qTEG2OnP53HIRcPmkmbkvEXDBM877CCLg8o5UOhQCAxLgU9n4pr14YvW2fuvHsSPWjjvJnoq6uZPAAtLSRh8BZjddvW637ybJYub9Nbpbse4gy1VMm1gx+mDJioXAMBHIJuCGaTqBDku3ULqHMlkKY+CCbCLggqR7pG8RcMEzzvsIIuDyjlQ6FAI5E2CNLgq5x1dvxd7W7DW62CHF28J5k7B4wRQwg6W04iZAa9u6LfvRsHYXaLUdqIVDFhbWTvJruY2vlOtjIF7yuhDINwG6PT711FNgan1ay4q10ZJ30UUXoaamBnfccUfgyxQBFzhiEXDBI87/CCLg8s9UehQCgyVAIRfbyJpd2we0yNHCMndGlS/kmIzCsuTZ2WB5j+Tjd+076Ndva2zePaC1jeuoHBPGCQum4IT5k1FeJgW4R/LeytyKmwDj0uju+K9//QuMQSvGxvpxTIrCunasV3f55ZcHvkwRcIEjFgEXPOL8jyACLv9MpUchcKwE6FrZvGW/L+S27jowYDe8eV9UO9m3vFRVlA54vBwwMgkw7b/esAfu+j0DxkamVlA9ZSwWR6Zg/qwJUhB+ZG6rzGqUELjpppv8dP4UNcwyyZpoTH5STI1rYuZLrnHjxo1Yvnw5uO6hyJgsAi74K0keAwfPOO8jiIDLO1LpUAgcNwEKuS07D/hCbv3W/QP2xy/R2dMr/Tg5FmcOlxTXzcOAAArwgEOHu7C2ZZ/vQrtpW9uAmSS5RLpJRudM8C1uEt9WgJsuUy5KAkzJzwyVTBxy/fXXF6X7JEsnsJbdxIkTcdlll+Hqq6/2E6IMRRMBFzxlEXDBM877CCLg8o5UOhQCeSWwp/UQVsV3Yk3z7gHrfPk3+SUWFswaD3vORDB9vLhY5nU7jqszFttet3m/L9oY1zZQ+v/UYFMmjEH9/Mn+npZJIpvj2gM5WQgIgcIiIAIu+P0SARc847yPIAIu70ilQyEQCIGOzgQa1+/2U8jvbes/4UlqAhXlYcyvHocFNRNQPbVSXO0C2Zn+O2UGyeaW/X5CkpYduVna2COT1jCbJIXbtIljhsRVaRjwyJBCQAgIgX4JiIAL/gIRARc847yPIAIu70ilQyEQKAG6V7bsPIDVa3f5lpyE138R59RkykpDqJ05HvNnjUfNtEopSRDQLjF75Nbd7diwtdWPZ2RSklyb5bvCVqFu3kTfFbYkJK6wubKT44SAEChOAiLggt9XEXDBM877CCLg8o5UOhQCQ0aAcVR64x6/RtjOAQqDp0+KbpXVUyrBQs/MaDlpXLlYeI5x1yioWQJi4/ZWbNzaik072nJydU0fbvqkCkRnT/BdJGk1lSYEhIAQEAJHCIiAC/5KEAEXPOO8jyACLu9IpUMhMCwEaOnRG/b62Qxb2zsGNQdms5w1tdJ3s+Tv8ZWlIuiyEKRg27XvEFp2tmHzjgO+W+RAxbUzdcUkJJHZE3w3SRZrlyYEhIAQEAJ9CYiAC/6qEAEXPOO8jyACLu9IpUMhMKwEUhksYxv3Yu3mfThwqHPQ86GgmzllLGZMGovpkyvAJBqj1Z2PKf637W73f7bz9552HO5IDJopM4Uy9X9t9TjfPVKKbQ8aoZwgBITAKCQgAi74TRcBFzzjvI8gAi7vSKVDITBiCFDMUXg0bd6Hpk17sf/A4CxzqYXQ5XLK+DF+Mo3JE8b4f08eX15UcXQsps6EIzv3HfQtbLv2HvRrsrUdHLwATnFjIpI506t80TZ3xjgptD1i3hkyESEgBAqFgAi44HdKBFzwjPM+ggi4vCOVDoXAiCRAMbd7/yGs39qKDVuZEfFAzmnssy2Irn8Tq8oxoaoMEyrL/N+0LNGCNxLLF5DBgUNdaD3Q4cetMZun/9N62BdvXQnvuPaOVjbGs1G0MRnJtEkVkvnzuIjKyUJACIx2AiLggr8CRMAFzzjvI4iAyztS6VAIFAQBliXYtL0NG7a1+nFcFHf5asymWFFegqqKUlRWhFFZUYqKshKMSf6Ul5WgvDTk1zRj3TqKPYqfwTYKMmbh5Fo6uzzftZHxaPxpT/0+2InW9k60HezwrWm0tOWrcc5Txpf7CWFmTh2LmqmVYmXLF1zpRwgIASEgSUyG5BoY/LfvkExLBumPgAg4uT6EgBAgAcZ60Sq3eUfbEUHXehgUSEPRKPjCYQvh0BExx//2RR3g/+YsKLz8H3Pkd2fCQ2end9xWxMGsj2KTyUdoZWPClxmTK1BeWjKYLuRYISAEhIAQGAQBscANAtYxHioCDoBt2+8A8D0A5ZZl1TY2NjZn41lXV7c8kUh8WCl1FoDxAPYAeBDAd7TWD2Q6z7btiwB8BMAZAMYCaFZK/RHA11zXbR3s3omAGywxOV4IjA4CtGpt33MkeUcqgcfxxIMVGjWKyUnjy/0ELhRs/KG76Eh0DS00tjJfISAEhECuBETA5Urq2I8b1QJuyZIlYw8ePHizUup1KYT9CTjbtt+XFHp8uPxfpVTcGDMbwCWA/+D5A1rrH6Rvh23b7wRwM4ADAP6llNptjDkPwEIAz3R1dS1fu3btvsFsoQi4wdCSY4XA6CbAunO79h/ya86xbAGTfTCG7FiyMo4UksyumYrdmzyu3E/OMnn8GCmlMFI2SOYhBITAqCYgAi747R+1Am7hwoXRRCLxt6SQ+gmASwFUZxNw8+fPn1NSUhIDwIqtF2ut705tTyQSudiyrDsBdHqetyAej2/ia3V1dfM8z1sDoM3zvGXxeLwpeU7Itu1fA3gNgO9rrT8wmK0WATcYWnKsEBACmQhQ2KUSguxrPezHmnXHnbV3HndykOOhHrIUxo4J+/F4Vcl4PP59JAFLmf/ascTfHc+c5FwhIASEgBDIjYAIuNw4Hc9Ro1bAJa1p31BKvdd13Z/Ztk23ybnZBFw0Gr1KKXWjUuo+13WX94Zu2zbdKM9WSr2d/fF127ZvAPAJAFdrrb+dfs6CBQumhUKhjQAS4XB4WkNDQ1uuGykCLldScpwQEALHQoBxdH5ykY4uHDqcwKGOLrQf6vJ/M/HIkR8mITH+7yNxbvDj71IxbxRYqbg4ywJCSqGkxPIToIRLQijl7/CRpCipRCn8XV525N9EoB3Lzsk5QkAICIHhJyACLvg9GLUCznGclxpjWrTWTyTFVr8CznGcjxtjvgrg11rrN2QQcL9NWtQ+orX+VrLP1bTwhUKheWvWrFmf4Zz7AZxrjHl5LBb7S67bLQIuV1JynBAQAkJACAgBISAEhMBQEhABFzztUSvgMoipgQQcBd9tSqlHXNdlApMezbZtJjA5x/O8y+Lx+O3z5s0rLy0tZdzbPq31pExb6TjOd40xdJ+8Vmv9+Vy3WwRcrqTkOCEgBISAEBACQkAICIGhJCACLnjaIuCSjAdyoVy6dGm4tbXVBVAL4HKt9W2p7bFt+xwAtKY1V1VVOStWrOh0HMcxxjQaY56LxWKLM21lNBr9mFLqa9msetm2XwRc8G8MGUEICAEhIASEgBAQAkJg8AREwA2e2WDPEAGXo4DjYXV1dbbneX9NJj5h0pJ1SqnZxhhmoVxrjHlZLBZj0hLGv50G4HEAj2qtz8y0MY7jvNsY8yMAf9NavyzXzRMBlyspOU4ICAEhIASEgBAQAkJgKAmIgAuedlEIuGg0ygQhU3PBFQqFrm1sbNS9jx3IAsfj6RYZDoffr5T6HICqtD5Yy+368vLy769cuZJukxRw5yatcvdrrc/PIuDeaoz5mTHmrlgs9sJc5p/su62iomLs008/nespcpwQEAJCQAgIASEgBISAEAicgAi4wBH7tcsKvqXEV44LOVdrzYyRPdpAAi4Z00Y3ydOMMTeWlJR8taKioqW9vb3a87y3G2M+bYxZbVnWWSzO7TjOqcYYJkh5TGu9LIuAS1ng/qq1viLH+VMceqw7V1FRkespcpwQEAJCQAgIASEgBISAEAicQHt7O8cwWmsr8MFG6QBFIeDysXcDCbhoNPoZpdSXAPxea836bT1aNBr9mVKKFrXrYrHYNUl3S8bMrdZa12cRcJ8wxrDUwC1a6zfnug7bthPJwuH+O0SaEBACQkAICAEhIASEgBAYIQRoYaCAC42Q+RTdNETAJbd0IAFn2/aTAJZmS/mfLEvALJVPua67tL6+vrSzs5PulIe01unult0XkeM4NxpjrgLwGa319UV3dcmChIAQEAJCQAgIASEgBISAEMgrARFwOQq4aDSqlVJRAC/SWt/Rexei0eilSql/AHC11nV83bbtZwCcqJSqc12X1rgeLSUKLct6YWNj41153VnpTAgIASEgBISAEBACQkAICIGiIyACLkcBZ9v2vwE8H8CntdZfySDGPsVEJgDu1FozKyUF3DUAvmiM+WwsFvty+jm2bc8HEAewNxwOz2hoaOgouqtLFiQEhIAQEAJCQAgIASEgBIRAXgmIgMtRwDmO42eMBLDT87zz4/H46tRO2LY9CwATlswE8Gat9S18rba2dno4HI4BYNKRZVrrRv570r2SdeQuMcZ8LhaLMbZOmhAQAkJACAgBISAEhIAQEAJCoF8Co1bA0TpmjJmUosMEJCwNoJT6hed5+1P/HovFPpT824pGo79XSr0CwGEA/wSwBcA0ACwBwDi3PyYTnFCw+c227VcD+D8AB5Pn7ANwIYAFAO7xPO/SeDzO/qQJASEgBISAEBACQkAICAEhIAREwGUikGvpAa11ushVjuNcaYx5C4CTAYwHQEHGWDdmkvwVs+70Hi8SiZxpWdanAZwFYKwxhla5W0tLS78rrpPyDhUCQkAICAEhIASEgBAQAkIgVwKj1gKXKyA5TggIASEgBISAEBACQkAICAEhMFIIiIAbKTsh8xACQkAICAEhIASEgBAQAkJACAxAQAScXCJCQAgIASEgBISAEBACQkAICIECISACrkA2SqYpBISAEBACQkAICAEhIASEgBAQASfXgBAQAkJACAgBISAEhIAQEAJCoEAIiIArkI06nmlGIpEFlmV9MlmIvBpAO4BnAfxca31rpsyZCxYsiIRCoc8CeB6Aqax/B4DFzK/TWq89nvkU4rnMJBoKhT5ljDlLKTXGGLNOKXXr4cOHv9fc3Hyo95qEX+Zdjkajz1dK8TqCZVm1jY2NzcIu+zsiEonUWJb1CQAvAlDDEiZKqeeMMb/UWv80WWOyRwdy7fXkGYlETgmFQp82xpwLYAKAFgC3d3Z2fnndunXbCvHzKN9zjkQiF1uW9UEAZySzK+8A8IBS6uuu6z7ZezzHcV4J4L3GmJMAlAHQxphflZaWfk8yKwOO4/zGGHOlUuo+13WXC79+r1iWaHqvUuptrLwEoE0p9bQx5uta67uFXXZ2tm1fDuA9AJYCqASwHcB/lVLfcF13ZaYz5b2b70/P4etPBNzwsR+SkSORyDLLsnjDzDp1TwN4KilAXpL8t99rrV+bLuIcxznVGHNP8gPhHqVUkzHGAXABgL0AlmutKQBHRbNt+w0AfgGAQu0fSql2Y8yLk8L2f1rr5wNIpGAIv8yXRU1NzZiKiornAMzPJuCE3VF20Wj0JKUU34esV7nGGPOIUop/s+5kOQC+d1+TTlv49bz2otHopUqpv/LzzRhzp2VZLcYY3uycppTa0NXVdU5TU9PGUfFBlmWRrIkK4IvJhwH8rtgEYAmA0wF0eZ53RTwevz11um3b1wP4FIDdAP6V/FzkZ+BcPpyprKx88YoVKzpHK9O6uroXeJ53J9efScAJv6NXxvLly0taWlr+DuASPgQAcB8APmTmf1sUda7r/lyuvb7vJsdxvm6M+WiyLvHdSqmNafdpHQBeo7XmZ193k2uvuD6VRMAV1372WY1t22sA1BljrovFYvyi9tuCBQtmh0KhxwHMUEq92nXdPyRfCtm2TXG2CMAVWuvb0j48+aTnh6x7p7XmTVB3wfJixZi0ZjQkLZDnpqyPixcvnnj48OFHAFDYvllrfYvw6/8qsG37BgC0Ju0CMDmDBU6uvaMILdu2KXYXAviW1vpjqfeb4ziOMeaJ5AOYS7XWvIlmE35pl2AkEhlnWRZrbo4zxiyPxWKPpV52HOdrxpiPKaVud133smL9/BpoXckapQ8DOOh53kXxeJyfaX6zbZsijWKtpaqqah5FWdrx2vO8c+LxOC11SD6coWg5D8BHtNbfGmjsYny9urq6orKyku/bcfyM6y3ghF/PXU97ePDr6urqt9x7771dyWuPHgf/BLBfKVXjum6rsDvKLvlgnu/Vg5ZlndHY2Lgq7X37DgA/5vdse3v77E2bNh3ka8Kv+D5xRMAV3552r8i27To+uQfQEQ6Hq3q7tjiO8yVjzGcA/FprTSsTXT9eaIy5wxjz91gs9tLeeGzbZtHyE+laqbWmdaCom23bdFN7Wy+Rm7rBucgYc7JS6j8pi6Twy3w5OI6zxBizQil1R/KG+vzeAk7YHWVn2zbd/e4HsE1rPSvdwpu8weEN8ocB/ERr/U557/a97qLR6FVKqRuVUt9zXZfugd2tvr6+tLOzk5a3aZ7nReLxeFNRf5BlWVw0Gv2ZUuqtAL6ptebT/PTGhwh0yeLDlrMbGxsftm37dwBenXy41+PpftJi/DQtm67rzsvkml/sjFMPBihiybS3gBN+R6+AJUuWjD106BDdmTvKy8vnrVy58kD69RGNRj9qWdbuw4cP/6W5uXmvsOvx/ZB6GHqL1vrNGe7TaEXn90b3fZrwK75PHxFwxben6QKOLjB86rxJaz2791Kj0ei7lFI3pT+FjkajP1JKvbuXVan71Gg0eq1S6nMAvq+1/kAR40PSvYM3MJbWenLvm+hMaxd+Ga8I3gjyaeFiACcopX5ujOkj4ITdUXZJC/mFxpi2WCz2595UHcd5tzHmR3Rh01pfyteFX09Ktm3TMnmJ53nL4/E4XbN6NNu2Gf/LB1ej1mIUjUZpMaO3xX2xWIwP+3oz4vfH6UqpV7mu+2fbtvcBCHueNz4ejx/OcPwGALM9z1saj8efKubvhwzfp3R5foLhB6FQ6CrP8xgnnR4Dx89B4ZcEF41Gr1BK8bPtB1rr9w9wrQi7NECpz3ql1Jdc1+X9WO/3Lb9vlxljXh6Lxf7Cexi59orv00gEXPHtafeK6urqJnuexyD9RDgcntzQ0NCWvlzHca4zxjBRCV20+MSQFriHjTFnKqVOzBQEmwya5QfC/Vrr84sYH12IaGl8xhhzVywWY9zRgE349UUUjUbfTysI42a01jc4jnNvJgEn7Aa8vLoPSHvvplvP5b2bhtC2bT7dn5kUG/sz3OTQgklL5q1a6zflTn/0HGnbti/I+CTfsqyNnue5jKXWWp+SiUI0Gv2bUuoyY8xbY7EY44ZHS+MN8qPJB1T1SinTW8DV1dXZwu/o5RCNRr+tlPoQgNdqrWnZzdqEXU80juN80BjzHQC/1Vpf2Ruc4zjrjTFzLMtaQvdK4VecH0Mi4IpzX7tXZdv2rwC8HsC3kyLN8MXa2trp4XB4BYDptIxorRv577Ztb2FcnGVZUxobGxmr1KPZtn0aAMbOZbTqFRPOaDT6WqXU/wH4USKRuCEUCl0H4AVMKmGMWc/X2travtbS0sKsnn4Tfj2vgGQWxdUAmquqqpYyjiabgBN2ub17ku5/tJbMN8a8ImWhE35H+c2bN6+8tLSUsR9tWmsmcOrTmI3NGMPY3we11nRZlZZGwLZtZiD+D4Ad7e3tcysrK89LJuf4h9aaSbD6NNu2GSP9nmyWgWIFnLqhVkp90nXdr9bV1c3LIOBSyU2E35GHxXcZYy5OWnhnJhNyMKupUkqt8DzvG7FY7B+8ZtISwwg7APX19ZM6OzuZDbycD0N7xfe+zhjz6/TPNeFXnJ88IuCKc1+7V0U/84MHD/4fn4oCWGGMofgap5RikLBRSl3luu4fUyc4jnPAGFPheV55JheZaDS6UCnFG/J9Wmum5C7a5jjOh4wx3wZAEUwLHH30702mzSa/8XRR7ejoWJ4qJSD8el4Otm0zCc5LPM87Ox6P8wk1v7izWeDk2svh3WTb9jcBXJ20hDCZkP9QRq69o/AikchUy7Lo/rxVaz0zi9hgpju6WT6rteaNo7QkgWSSJn5XRGglcV33u2mC93fJzMV9eEWj0a8qpT5ujPluLBajdaXoW9Ldmd+J66qrq09hIo5MAk749fluSMXT+/G8SinG/DYaY1hKgBmv2a7WWn9b2PV9GyWTkvw++RCeQndrsgzDRQCeDIVCL12zZg0fyPO7IfWwSt67RfSJJAKugDaTLgfJ1PUDzjoUCl3b2NjItLx8876KWSiTb+7UuUyrfVMoFPpaei0u27aZDp8xX1amIHTbtpkCngH/h7XWTGVeMG2w/BKJxCv5JDnJ4UdVVVUfTqXHXrhw4cxEIkE/c6bOvsZ1XfKlBa4o+Q2WHa+9tBiHH2mt35u6UPqxwBUlO677WPhlER2fBvBlPkCxLOv01Hu8mK+9Y/mASd5Ub0gm1JibqQ/HcRhjyERMrtaaCZ+k0R3jSIZdJhtiTTjGWNLa5iXLqTBuMKvLaSpGWil1s+u6V40GoI7j/N0Yc2n6Q6pMAk749RFwzBDLBwQsy3NZelK0aDTKGnq/YRkLpZRtjDmH151ce0cZ8n3a0dHxEWPMxxmXmkZ3q1Lq2pkzZ/4kLasnY32FX5F9IImAK6ANtW2bRY8z3oxkWAZT3j9o2zZjjxiDdF8ikfhwWVlZQyKRqEokEi9RSvmpnpPpo1kjjgKEcXJjOzo6xmQqUJ1mgdujtWZNqoJpg+UHgG5VTKO92/O86t4WyWg0+hYm5ACwVmu9oJj5DZad53krLcuim5/yPK8uHo93xyD1I+Dk2jvybvLfu73fWLZtfwXAJ+kWaFnWSxobG2kN7m7F/N4d7IeMbdtT6PqXzOI5I4sYTlngssZ0DXbcQj8+6Vp/VzLT8MOe512Seu86jvMKYwy9NfrUH0ytO2WBS7rs00pc1C2NSY+HVFkscMKv5+cV4ylpbevBLnWIbdtMPHQeH5Am62DKtZeEs2DBgmmhUIjlP+YaY75SUlJyY0VFxc4DBw7UGmOuNsa8iwW9tdZ0UU3Ie7c4P4ZEwBXnvvqrchznbGMMbwSbOzo6FvYWZGlPBB/QWjMbGQVcKvB/WqrGTzqiaDR6hlKKrnDrtdZMFV20zbZtWo1+kPwgpFtCj7Zw4cK5iUSCohrJMg1twu8IolQsTHqMVgpePwJOrr3M7yYmSLgRAMsF8GHCi9PrdaXd8Ai/JIxIJFJmWdYhPt13XXdsJqy2bTMd/u8yFVsu2g+1fhbmOA5v/ljIO8LYt/Ly8svTU7tHIpGLLcuiuOvOfNq7O8dxbjTG0PL2Ra31F4qZ4/z588eXlJTwIZXned6i9IdUmQSc8Ot5Ndi2zVqWpwJ4o9aaYQo9mm3bnwfwBWaqTCQSP5Zr7yge27Z/AuDtAL6qteZDvd7s6FlwYaoQulx7xflJJAKuOPc1dRP9jWQ9mowp/5NfQHt5cCpTm23b9EM/Vyl1muu6T2b4YPBvevgFn3y6U7QEbdv2n9ArpR5xXfes3gtNS5SARCIxvampabvw606QQzHRrpTiDV+PxqBrALSQ3MEbbKXUD2hNEnaZ30q2bf8SwJvoDmiMeUEq4VCG96a8d9OgpGVQnKq13plBbHzCGMN6St219Ir2w2yAhSVdTukSztpRv66qqnpryl08dWqa+3yD1vqETF3atk3XS9YSfV0sFmMCqKJt0WiUheC/BqBBKeUnAUs1xpGzhAWAnXxAwH9Purox/ED4HXnIx/itVwF4l9aahad7tFQdR35PAHhfMnRD2B1hx88zZhnPWK4jLUvlX7TWL5f3bnF+DImAK8599Vdl2zY/FN9hjPlaLBb7RO+lJgtp+qUFwuHwzIaGhq3RaPQ7SimmqL0qFovdnOEmMSUKb9Baf6qI8THT04zOzk4Kke1a6z5uWAsWLIiEQiH68bNMQwULpQs/P2OYn4Et12vDGPOWWCz2S2HXl1hauQCdSCSe19TUxOLTGZvw64klmUDnpZZlvbCxsbHPgwTHcf7EOknZPutyvX4L/Tg+yAuFQg8qpSjK6M7Gm2U/MU6vpmzb3g2gsry8fELvwsvJ7xy6rU5RStW5rksXuaJttm3TwkgrUU6NceXC7ygqx3H8ByjZ7k8cx/mSMeYzTCLGMh/C7ig727Y7eNtmjFmUpX6j7z2UVgJJ3rs5vUsL6yARcIW1X4OarW3bfsIDpdS/Xddl+vseLZnFiH7UTLXNjJIJFnZNPjG8W2v9/F6n8AsozioE2Sx0g5pgARycLEC9TCl1ieu6d6ZPOe0J4ZNaa5ZXYLIK4TfAvmZzoRR2PcE5jsN6Wn8DsKWrq2vZ2rVrWZMraxN+PdHYtv1GALcA+KnW+h3przqOU2WM4cOZMZ2dnbPWrVvHepmjstm2zbqel9PyprVmsoOszXGcn/OBi1Lq9a7rMslEd6urq1vued7/+rMwjRbAmVwouXbhd/QKsG2biYPogrqpurq6NpVwI3VEsq4eE+m8T2v9Q2F3lF00GtVKqagx5spYLPbb3u8r27b58P2dSqmbXNd9t1x7xfnJIwKuOPfVX1WyeCPTG4eUUq92XZc1j/yWrCXF1LMUab/UWr8l+RKf1NCV5ozeHw6O43zOGHMtgHu01qwRVPTNcZyXGmOYCr8BwPKUK1bS+sablRql1Ntd1/2Z8Mvtcsgm4JjwRK69IwyTMVx8WMLr60LXdXmtDdSEXxohujiXlZW5xhjWmHpeLBajiykbOdE7WT3JXAAAEzhJREFUgTEkP9Na8/eobNFo9FKlFL8H4uFwuJ5eBP2BqKurW+x5HhNebQqHw8votcHjk+74vEZPVkq9wXVd1qEatS2bgBN+PS8J27b/CuBlAHp49KQ9HN0fDodrGxoadgu7o+xS2V4B6GQSuk2pVyORSL1lWYwvHON53vJ4PO678Aq/4vs4EgFXfHvaY0VJX2iWH1DGmHuTNdzon8+kHLOZQhvAOekxIsknYw/Qx1opxUKua40xSwAwDmyzZVnnpJceKHKEdEX1M3kyqx1vdowxTNnL1NrjjTF/isVi9OPvdjkSfv1fEf0IOLLmU9lRf+05jvNWYwwfCuwxxjD9c8amlNqtteZDFb8Jvz43iMwkS/fJkmQ86xZjDD/H+Hm2qqys7PxVq1btKfbPsGzrS8WdAnjIGNMn5jl1nmVZd6Y8EKLR6EeVUl9nQh0A/zTGdNJDAQDr7fnubllcMEcN5mwCjgCE39HLIJlN8aFk4pwnjTFP0LIEgA+IO40xr4nFYrQQ+03YHeFQU1MzpqKigvdmZwNghmfGCe40xsxWStHbqgzAt7TWH0l/0wm/4voIEgFXXPuZcTV1dXVneZ73weSbfRpruFG4GWP+alnW91zXbe19YiQSqbEsi/79/GKeqpTaaoz5eyKRuI7JOkYBth5LdBzndQDea4w5QSkVMsasNsb8NBaLMRuUJ/xyvyL6E3DsRa49X4jlGl/TJxus8Ot5LUYikUWWZTEVOYsDTzDGrFdK/VEpdUOmz77cr+TCP3IQ5UF6ZJVMWu5YJuBUpVSJMYYeCj/WWvOhQ6b4ucKHNYgV9CfgkkKElk/hd9R6+zkAVyST6FCQ3G9Z1vWNjY0remOXa+8IkaVLl4ZbW1uZmfi1ABi/ymy7fKjyhOd5N8fj8dszXbLCbxBv5BF+qAi4Eb5BMj0hIASEgBAQAkJACAgBISAEhECKgAg4uRaEgBAQAkJACAgBISAEhIAQEAIFQkAEXIFslExTCAgBISAEhIAQEAJCQAgIASEgAk6uASEgBISAEBACQkAICAEhIASEQIEQEAFXIBsl0xQCQkAICAEhIASEgBAQAkJACIiAk2tACAgBISAEhIAQEAJCQAgIASFQIAREwBXIRsk0hYAQEAJCQAgIASEgBISAEBACIuDkGhACQkAICAEhIASEgBAQAkJACBQIARFwBbJRMk0hIASEgBAQAkJACAgBISAEhIAIOLkGhIAQEAJCQAgIASEgBISAEBACBUJABFyBbJRMUwgIASEgBISAEBACQkAICAEhIAJOrgEhIASEgBAoGgK2bRsuxrKsCxobG+8daGF1dXXLPc/7H4/TWhfEd6LjOK8zxvwawGOe550fj8cPD7TOwbxu2/Z8AE8AKDHGLIvFYmsGc74cKwSEgBAQAsESKIgvq2ARSO9CQAgIASFQLARs276NawmFQp9ds2bNcwOta+HChSckEokvJQXcywY6frhfj0ajJymlHgFwQCm1xHXdliDmVFdX9wLP8+4E0BQOh09qaGhoC2Ic6VMICAEhIAQGT0AE3OCZyRlCQAgIASFQYASi0ehnlVLXWZZV29jY2Fxg0/enW19fX9rZ2fkU/wTwWq3174Jch+M4Nxlj3gXgJ1rrdwY5lvQtBISAEBACuRMQAZc7KzlSCAgBISAECpSAbdv/AHBpIQs427avBvBNAA9prc8Jeits255CCxyAKsuyTmtsbFwR9JjSvxAQAkJACAxMQATcwIzkCCEgBISAEChwArZt7wAwpVAFXE1NzZiKiooNXIMx5uJYLPafodgS27a/AuCTAP6ptX7xUIwpYwgBISAEhED/BETAyRUiBISAEChCAslYqccBhJVSF7mu+9/ey7Rt+58AXgTgHq318wH4CUCyNcdx7jXGnA/g/Z7n/T4UCl1njLkUwDQA+4wx9xljvhCPxxsy9RGNRl9sWdY7jTGnA5gEgHFVLoA/dnR0/Ki5uflQ7/MikciZlmV9QCl1ljFmBgAm7GgxxvzXGPODeDy+Ov2c3klMbNv+JYA3ZZjPLVrrNw+UxMRxnLONMR8AcDaAqQA4R1ql/t7R0fGd5ubmvb3G/wKAzwP4m9b6ZbZtvxHAuwEsBFDGXClKqZtc171xMJed4zhvNcb87EiuFe1k2Eu6hc4FcG4ikdgaCoUY18e9Gg9grVLqh6kxuQ9KqU8AWMJwQQC8Tj6htWbikh5t4cKFcxOJxDr+o+d50Xg8zrVLEwJCQAgIgWEkIAJuGOHL0EJACAiBIAnYtv1FANcAaAyHwyc2NDR0pMaLRqNXKKX+DKA1FAotXrNmzfqB5pIm4K4F8DoAFFRMFNIK4Ay62gE4SOHQWwxEo9EfKaUoZNg0gHhSxJ3KbIdKqadKSkqe39DQsDs1D9u2XwPgN0wqCYACJZb8+0RaopjIw7KsF6dnm+wt4KLRKMUfLUcUqGy0XLVTAMZise/1J+Acx/mEMeaG5HkbAVCYjgVwGoByzimZ7bI7ps627W4BZ4xxlVIfA0DXw+1JEVeb7O9TWutU3wOhh23b/06u4Qat9ad6n2Dbti/glFKvN8Z8i+JYKRU3xlDsUdixvUsp1WWM+TGAJ40xB5VSS5P7tj8UCtWtWbNmS4a+KfC45k9rrWmRkyYEhIAQEALDSEAE3DDCl6GFgBAQAkESWLp0abi1tfXJpKXlM1rr6znekiVLxh46dIip4WcbY66KxWI35zKPNAG3D8DKsrKyl65atWoPz62vr5/U2dl5N4CTKcZc16Uw8FvSCnULgA5jzJWxWIzC0W8LFy6MJhIJiiqKjFu11ilrmbJtexOAaqXUh13X/U7qnOXLl5ds2bLlK8aYj3IeWmsKutRYfcoI1NXVzfM8z7ci9XahzCbgIpHI+ZZlsbwAvyffr7X+YcpCWVtbO720tPRfxphTANyvtaalKzV+SsBtpvUTwEu01hRAbFzTzwG8GcBOrfV0GrYGYh+JRMosy9oPoLQfa2rKAkdm5PjZ5Hwt27b/BOByABShY5RSr3Jd1y+dEIlEaizLoggfr5T6pOu6X+09H8dxvmSM+Ywx5t5YLHbBQPOV14WAEBACQiBYAiLgguUrvQsBISAEhpVAJBI52bKsx5VSHUqpemZgjEajX1VKfRzA3UnXyZzmmCbgPKVUxHVdXxSliZdzKWj4357n1afcG23bpkCoN8Z8JxaLfbj3YGnWwK5EIjGrqalpe319/YzOzk7fGlReXl65cuXKA+nnUcS1tLR8hu6U4XD4lpR1MVMduGMRcKmkJwBu01pT/PRokUjkFMuy/KQenuctjcfjzA5JsZoScPzPN2qtf5V+YrJswSr+m2VZTmNjI62R/bZoNHqGUurR5Fjj4/E4xVyPlrLAAVijtT4hXRimlQSAMea7sVjsQ7327f+Y1RLAH7TWr+7dt+M4lxlj/kZLq9Z63EDzldeFgBAQAkIgWAIi4ILlK70LASEgBIadQDQavVYp9Tml1O2e531CKfUsY7lydZ1MLSBNwD2stWZMWO9Gaw+tc5VKqbe5rvvzBQsWzA6FQky+wXZ6pjirZHp8ipKypHXoj0krIfsKKaXe7rou478GbPkQcEnLJd1Cu+eTaWDbtilg5xljPh6Lxb7OY9IEXIfneeN6F9meN29eeWlpKd1MKeDObmxsfHigRUWj0SuVUnQl3Z602vU5JU3A9XGxdBzHMcY0Jk96ntb6nvQO0hKVZBT0kUhkkWVZflxjIpGYToE90JzldSEgBISAEAiOgAi44NhKz0JACAiBEUEgKUhoLVoMgLFudFd8l9aasVA5t5SAU0rd7LruVVlEzUqOY4y5LhaLXeM4zguNMXfw2Pb29opNmzb54qV3s22bAmGRUuoa13Wv4+vpcXN037Ms6w/GmP9prVNiJFM/x+1Cadt2HS1Z7NwYsygWi/l/Z5iznwTGGPPzWCz2Nr6eJuDiWutolvP6zLG/TbBtm1ZLxrU9o7U+OUufKRfKPvuaboEMhUL2mjVrGEvY3VJzVkrd57ru8t79z58/f3xJSYmfrMUYc3IsFnsm54tGDhQCQkAICIG8ExABl3ek0qEQEAJCYOQRcBznVGNMKsvgg1prujsOqqVZ4DIm0kgKmAcAnJNyl4xGo69VStFFr0NrzSyMGZtt2w8BOItCRWv9keRBVjQa/YpS6v2M3Uo7kbFcf+rq6vrO2rVrU9Y9/+V8WOCSmS99y1goFKrOlNiDrzmO8xvG9AH4i9b65cnxUy6UDUlXxj7rzTTH/jYiGo1+RinFrJJZ67+lLHDGmLfEYjFm3uxu/bmQps85m4BLPgDwE+Aopc5xXZd7JU0ICAEhIASGiYAIuGECL8MKASEgBIaSgG3b7wPw/eSYu8PhcH1DQ8PWwcwhzQL3Zdd1mSQjkzjpIcSSmSR/O5CAcxznYWPMmUqpb7iuy8yN3S0SiUy1LOtlyZIHFwJIxWExpf+VWuu/pg7Ok4BbZlnWI+yzPwFn27YfO2aM+VMsFntluhhixkoRcIO5uuRYISAEhIAQyJWACLhcSclxQkAICIECJeA4Tq0xhokzSliQGcAVqTplg1lSmgXuB1prWsUyCTg/YQlroWmtr01PoNHW1ja2paWlPct5dFOk62J3tsxMxyWTl7AkAGutsXTBfsuy5jc2Nu5KCqjjdqFMjxlLT8bSez62bdM19IUAfqK1fmdQAm4wLpRBWODmzZs3obS01M82Ki6Ug3nHyLFCQAgIgWAIiIALhqv0KgSEgBAYKQSYup5JK5j+/fPhcPhbnZ2dFEs1rBnmui6TY+TU0gTcf7XWF2U4KZRMYjJWKfUG13V/bdv2LABMbc+b/2WxWOyx3uclE3swiQmLjr/Udd2/DzShZGp9FpVm/5dqrf+VLwGXFImcD902X6O1/n0W0UlXzhpjzAdZUy4oATeYJCZBCDhJYjLQ1SivCwEhIASGloAIuKHlLaMJASEgBIaUQDQavUopdWN6MW/btumOSLfDQblSpgm4jnA4PLe3C6bjOBewQHZygQu01muTooZZL5fQhVNr/YHeAFJxckqp9s7Ozuq1a9fuSyY/YbHwf/dOxZ8637Zt1rhbaox5eSwW+0s2Abdw4cK5iUTCL7adSCSiTU1NLCLut2x14KLR6N+UUpcxc6frupf1nnN6nFx6OYC0JCb5dKHMuYxAEAJOyggM6VtWBhMCQkAIDEhABNyAiOQAISAEhEBhEpg/f/6ckpISujRWGmOWx2Ixv0ZbUujcBuClg3GlTBNwe40xjxljXpWqSZaMU2Mhbwq1HsWtHcd5lTGGVqxOpdQrXddlTbGUgFrsed6dyYLd3fFvjuO80hjzBxa8ZhFqrfWD6bsQjUZfrpTi652JRGJOKrV9phg4x3GqjDGp2mk9LGr9FPI+07Isjmkppd7jui5FsN+SXGnxY2277vi3JNe8JzEZTCHvgAScFPIuzI8AmbUQEAJFSkAEXJFurCxLCAgBIeA4zl3GmIsB/Exr/fZ0IpFIpMayLLpSsmZbTq6UaUlMvm6MofibAYBWMA/AMvYFYJ/neWelininCcZvAria/22MiVmWtc4YU5OMe7MA3NnR0XF5c3MzE5Ow0fXzT8l4Pf8cpVSzUsoYYxYA4A//fk8sFrspbZyMKfpt22bq+xMBdAGgBa6FbqDZBBz7cxzng8aYb3MuAOguyfIF01jugO6eAFZYlvWCVPxdUAIu2e+/ATD2L2MG0CCzUNq2/TiA0wB8Smt9g7yzhIAQEAJCYHgJiIAbXv4yuhAQAkIgEAKO47zNGPNTADvC4XBdQ0PD7t4DOY7zoaRAycmVMs0C98WysrLvdnR0fMEY8xJaz5hMBMA9iUTic+kuiulj2rb9IgDvZkFvAJOS5zxjjPlVLBb7FT0ce82RZQReo5RiKYIlxpipTAxJ8QXgQWPMD3rH1GVL0e84Ds9n3buTABw0xvwzFou9vj8Bx7kkXSU/xNIIADh+u1JqNYDfHz58+OY0welPPQgXyqSYfKsxhsXMtdba6b2XQQm4pPspC5bD87xoPB5n3KE0ISAEhIAQGEYCIuCGEb4MLQSEgBAoJALpAk5rTVdBaUNEoKamZkxFRQVr3k2hVTUWi/1nKIa2bfsrAD4J4B9aa4p1aUJACAgBITDMBETADfMGyPBCQAgIgUIhIAJueHfKtm26oNIVNWtB73zO0LbtKQBocavyPO/UeDz+VD77l76EgBAQAkLg2AiIgDs2bnKWEBACQmDUERABN7xbXl9fX9rZ2UkRxeQpV8ZiMRZID6zZtn0zANa3665zF9hg0rEQEAJCQAjkTEAEXM6o5EAhIASEwOgmIAJu+Pfftm0mYnmUsXjM+Km13hzErJJlHFioPB4Oh09uaGhoC2Ic6VMICAEhIAQGT0AE3OCZyRlCQAgIgVFJQATcyNj2tMLej3med348Hj+cz5nZtj0fwBMASpLF15mtVJoQEAJCQAiMEAIi4EbIRsg0hIAQEAJCQAgIASEgBISAEBACAxEQATcQIXldCAgBISAEhIAQEAJCQAgIASEwQgiIgBshGyHTEAJCQAgIASEgBISAEBACQkAIDETg/wHQeuYqlUtsfAAAAABJRU5ErkJggg==\" width=\"799.9999826604674\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "newfig()\n",
-    "plot(xs, ys, label='trajectory')\n",
-    "\n",
-    "decorate(xlabel='x position (m)',\n",
-    "         ylabel='y position (m)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also animate the trajectory.  If there's an error in the simulation, we can sometimes spot it by looking at animations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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eEql9/4wSeF3dD9sNBpPmZqauu/hJ1U/nMBVlaIdAggggAACCCCAQJ0CBLj02gS4vTDOsuxUSa/w3j/BGNP03t/svf9wURQfklTOHXJ8fPzgRqNxjqTwIJRHGWP+y3v/6Xa7fV542EmvUyDA9SpGewQQQAABBBBAAIE6BAhw6ZUJcOmNo1cgwEUnZUAEEEAAAQQQQACBCAIEuAiIXYYgwKU3jl6BABedlAERQAABBBBAAAEEIggQ4CIgEuDSI9ZdgQBXtzj1EEAAAQQQQAABBKoIEOCqKPXXhhO4/vwWpDcBbkHYKYoAAggggAACCCDQRYAAl/4SIcClN45egQAXnZQBEUAAAQQQQAABBCIIEOAiIHYZggCX3jh6BQJcdFIGRAABBBBAAAEEEIggQICLgEiAS49YdwUCXN3i1EMAAQQQQAABBBCoIkCAq6LUXxtO4PrzW5DeBLgFYacoAggggAACCCCAQBcBAlz6S4QAl944egUCXHRSBkQAAQQQQAABBBCIIECAi4DYZQgCXHrj6BUIcNFJGRABBBBAAAEEEEAgggABLgIiAS49Yt0VCHB1i1MPAQQQQAABBBBAoIoAAa6KUn9tOIHrz29BehPgFoSdoggggAACCCCAAAJdBAhw6S8RAlx64+gVCHDRSRkQAQQQQAABBBBAIIIAAS4CYpchCHDpjaNXIMBFJ2VABBBAAAEEEEAAgQgCBLgIiAS49Ih1VyDA1S1OPQQQQAABBBBAAIEqAgS4Kkr9teEErj+/BelNgFsQdooigAACCCCAAAIIdBEgwKW/RAhw6Y2jVyDARSdlQAQQQAABBBBAAIEIAgS4CIhdhiDApTeOXoEAF52UARFAAAEEEEAAAQQiCBDgIiAS4NIj1l2BAFe3OPUQQAABBBBAAAEEqggQ4Koo9deGE7j+/BakNwFuQdgpigACCCCAAAIIINBFgACX/hIhwKU3jl6BABedlAERQAABBBBAAAEEIggQ4CIgdhmCAJfeOHoFAlx0UgZEAAEEEEAAAQQQiCBAgIuASIBLj1h3BQJc3eLUQwABBBBAAAEEEKgiQICrotRfG07g+vNbkN4EuAVhpygCCCCAAAIIIIBAFwECXPpLhACX3jh6BQJcdFIGRAABBBBAAAEEEIggQICLgNhlCAJceuPoFQhw0UkZEAEEEEAAAQQQQCCCAAEuAiIBLj1i3RUIcHWLUw8BBBBAAAEEEECgigABropSf204gevPb0F6E+D6Z9+5U7r2Wumaa6QtW6Tly6VjjpGOOkoaHe1/fEZAAAEEEEAAAQSGUYAAl37XCXDpjaNXIMD1R/qTn0jvepf0s589cJz995de/WrpwAP7q0FvBBBAAAEEEEBgGAUIcOl3nQCX3jh6BQLc3pOGk7dzzpk/vM2MesAB0l/8BSdxe69MTwQQQAABBBAYVgECXPqdJ8ClN45egQC396Rf/7p0ySXd+7/kJdKTn9y9HS0QQAABBBBAAAEE/leAAJf+aiDApTeOXoEAt/ek73iH5Fz3/lkmvfa13dvRAgEEEEAAAQQQQIAAV+c1QICrUztSLQLc3kOGWyN/9KPu/Q866L9vteSDAAIIIIAAAgggUF2AE7jqVnvbkgC3t3IL2I8At/f4nMDtvR09EUAAAQQQQACBbgIEuG5C/X9PgOvfsPYRCHB7T85v4Pbejp4IIIAAAggggEA3AQJcN6H+vyfA9W9Y+wgEuL0n5ymUe29HTwQQQAABBBBAoJsAAa6bUP/fE+D6N6x9BAJcf+ThPXDvfrd0++0PHCe8QuDMM3kPXH/C9EYAAQQQQACBYRUgwKXfeQJceuPoFQhw/ZOGk7hrr5W+9S1pyxZp+XLp6KOlo47i/W/96zICAggggAACCAyrAAEu/c4T4NIbR69AgItOyoAIIIAAAggggAACEQQIcBEQuwxBgEtvHL0CAS46KQMigAACCCCAAAIIRBAgwEVAJMClR6y7AgGubnHqIYAAAggggAACCFQRIMBVUeqvDSdw/fktSG8C3IKwUxQBBBBAAAEEEECgiwABLv0lQoBLbxy9AgEuOikDIoAAAggggAACCEQQIMBFQOwyBAEuvXH0CgS46KQMiAACCCCAAAIIIBBBgAAXAZEAlx6x7goEuLrFqYcAAggggAACCCBQRYAAV0WpvzacwPXoNz4+PtZoNP6vpKdLOkjSPZK+K+nvnXOXSPJzhxwbGxtvNptnSXqapEdJCq+Q/oKk85xzG3ucgghwvYrRHgEEEEAAAQQQQKAOAQJcemUCXA/G4+PjxzQajRC8lku6XtJ3jDEP8d4/u/O3f3bOvXB2iMuy7Ajv/VWSHirpKmPMBu99Jumpku6UtNY5FwJg5Q8BrjIVDRFAAAEEEEAAAQRqFCDApccmwPVgbK1dL2nCe39eURRvnuk6Njb2uGazea2kxxhjTsnz/BOd75rW2hDOHi/puc65y2f6WGv/RNL7Jd3gnDtcUll1KgS4qlK0QwABBBBAAAEEEKhTgACXXpsAV9HYWjshKQS4HaOjo8snJyd3zO6aZdlfeu//XNKlzrkXh++yLDvZe/857/2ni6J4ztxS1tobJD0x3FrpnAundJU+BLhKTDRCAAEEEEAAAQQQqFmAAJcenABX0dhae5Skb0m6zTn3uLndWq3W/zHGfNAY8295nv9W+L7Val1gjDlD0mnOuYvn6XOuMeZsSe91zr2q4lT4DVxVKNohgAACCCCAAAII1CpAgEvPTYCraDwxMbF/WZY/ltQeHR3df3JycuvsrlmWnee9Dw8q+Vvn3OvCd1mWfcN7f6wx5ol5nn9vbilr7e9I+qSkq51zJ1acCgGuKhTtEEAAAQQQQAABBGoVIMCl5ybA9WBsrf1HSb8v6Z2dkHbfEydXrlz56NHR0XWSHi3pUOfcVPi7tfZH4XdxjUbjgKmpqZ/NE+COlBR+Ozfvqd7upsYtlD1sGk0RQAABBBBAAAEEahMgwKWnJsD1YHzYYYct27Zt20eNMeEWyXXe+xC+HmaM+Y3w5EljzOl5nl82M2SWZXd775eWZbnv9PT09rmlWq3WamPMzZLucs49oupUCHBVpWiHAAIIIIAAAgggUKcAAS699tAGuFar9c7OO9m6KjebzXOnpqZcaJhl2fPDUyjDAdusjt57/8Fms3n+1NTUppm/W2vbkhrOucZ874ez1q6StEHSdufcvl0n0mlAgKsqRTsEEEAAAQQQQACBOgUIcOm1hzbAWWtD0DqkIvHxzrmvWWvfI+lPjTFfabfbr9lnn30m2+328na7/WxjzN+GscqyPGl6ejq8I+6+36pJWrZjx46HbNq06d65tWadwN3hnNuv4lz4DVxVKNohgAACCCCAAAII1CpAgEvPPbQBrlfaLMue7L3/mqRNO3bsWD03kFlrw6sDLpH0VefcCZ0At1nSY8uyPHB6evqn8wS4o40x10i61Tm3ouqcOIGrKkU7BBBAAAEEEEAAgToFCHDptQlwFY2ttW+XFJ4uOe8j/1etWvXwkZGRO8NwZVk+fHp6+hfW2qslHW+MOTLP8+vmlrLWniLp45K+6Jx7RsWpcAJXFYp2CCCAAAIIIIAAArUKEODScxPgKhpba/9O0su89+cXRfGGud3CA07uvffe+14tMDo6+tjJycn/arVa7zLGnOm9P70oigvnCXAzofBtzrk3VpwKAa4qFO0QQAABBBBAAAEEahUgwKXnJsBVNLbWvknSW40xX8jz/NfndhsfHz+20Wh8Q9LWzhMl261W64TwezlJ/+Gce/qcPg1r7XR4C8HuTuh2NzVuoay4aTRDAAEEEEAAAQQQqFWAAJeemwBX0XhiYsKWZRke+d80xpyS5/knZrquWbNmyc6dOz8jKYS0f3DO/WHnO2Ot/aako733LyqK4mMzfbIsO9t7f66kq5xzT6s4jfuaEeB60aItAggggAACCCCAQF0CBLj00gS4HoyzLAu3Q4bXDxjv/Zc773BbKukkSY+TlEt6inPu9plhrbUT4cEmkvY3xnxR0kbv/WGSjpP0w0aj8ZTZrx6oMh0CXBUl2iCAAAIIIIAAAgjULUCASy9OgOvReGJi4riyLM+U9GRJB4Z3uIXg5r3/VKPReE+e51vmDjk+Pn5wo9E4R9Izw7vnjDH/5b3/dLvdPm/Dhg0/6XEKnMD1CkZ7BBBAAAEEEEAAgVoECHDpmQlw6Y2jV+AELjopAyKAAAIIIIAAAghEECDARUDsMgQBLr1x9AoEuOikDIgAAggggAACCCAQQYAAFwGRAJcese4KBLi6xamHAAIIIIAAAgggUEWAAFdFqb82nMD157cgvQlwC8JOUQQQQAABBBBAAIEuAgS49JcIAS69cfQKBLjopAyIAAIIIIAAAgggEEGAABcBscsQBLj0xtErEOCikzIgAggggAACCCCAQAQBAlwERAJcesS6KxDg6hanHgIIIIAAAggggEAVAQJcFaX+2nAC15/fgvQmwC0IO0URQAABBBBAAAEEuggQ4NJfIgS49MbRKxDgopMyIAIIIIAAAggggEAEAQJcBMQuQxDg0htHr0CAi07KgAgggAACCCCAAAIRBAhwERAJcOkR665AgKtbnHoIIIAAAggggAACVQQIcFWU+mvDCVx/fgvSmwC3IOwURQABBBBAAAEEEOgiQIBLf4kQ4NIbR69AgItOyoAIIIAAAggggAACEQQIcBEQuwxBgEtvHL0CAS46KQMigAACCCCAAAIIRBAgwEVAJMClR6y7AgGubnHqIYAAAggggAACCFQRIMBVUeqvDSdw/fktSG8C3IKwUxQBBBBAAAEEEECgiwABLv0lQoBLbxy9AgEuOikDIoAAAggggAACCEQQIMBFQOwyBAEuvXH0CgS46KQMiAACCCCAAAIIIBBBgAAXAZEAlx6x7goEuLrFqYcAAggggAACCCBQRYAAV0WpvzacwPXntyC9CXALwk5RBBBAAAEEEEAAgS4CBLj0lwgBLr1x9AoEuOikDIgAAggggAACCCAQQYAAFwGxyxAEuPTG0SsQ4KKTMiACCCCAAAIIIIBABAECXAREAlx6xLorEODqFqceAggggAACCCCAQBUBAlwVpf7acALXn9+C9CbALQg7RRFAAAEEEEAAAQS6CBDg0l8iBLj0xtErEOCikzIgAggggAACCCCAQAQBAlwExC5DEODSG0evQICLTsqACCCAAAIIIIAAAhEECHAREAlw6RHrrkCAq1uceggggAACCCCAAAJVBAhwVZT6a8MJXH9+C9KbALcg7BRFAAEEEEAAAQQQ6CJAgEt/iRDg0htHr0CAi07KgAgggAACCCCAAAIRBAhwERC7DEGAS28cvQIBLjopAyKAAAIIIIAAAghEECDARUAkwKVHrLsCAa5uceohgAACCCCAAAIIVBEgwFVR6q8NJ3D9+S1IbwLcgrBTFAEEEEAAAQQQQKCLAAEu/SVCgEtvHL0CAS46KQMigAACCCCAAAIIRBAgwEVA7DIEAS69cfQKBLjopAyIAAIIIIAAAgggEEGAABcBkQCXHrHuCgS4usWphwACCCCAAAIIIFBFgABXRam/NpzA9ee3IL0JcAvCTlEEEEAAAQQQQACBLgIEuPSXCAEuvXH0CgS46KQMiAACCCCAAAIIIBBBgAAXAbHLEAS49MbRKxDgopMyIAIIIIAAAggggEAEAQJcBEQCXHrEuisQ4OoWpx4CCCCAAAIIIIBAFQECXBWl/tpwAtef34L0JsAtCDtFEUAAAQQQQAABBLoIEODSXyIEuPTG0SsQ4KKTMiACCCCAAAIIIIBABAECXATELkMQ4NIbR69AgItOyoAIIIAAAggggAACEQQIcBEQCXDpEeuuQICrW5x6CCCAAAIIIIAAAlUECHBVlPprwwlcf34L0psAtyDsFEUAAQQQQAABBBDoIkCAS3+JEODSG0evQICLTsqACCCAAAIIIIAAAhEECHARELsMQYCTZK19maT3SNq30WisnJqa2rQ7t7GxsfFms3mWpHr5XwkAACAASURBVKdJepSk2yV9QdJ5zrmN8/Wz1p4k6XWSjpa0TNImY8xlks7P83xLr9tMgOtVjPYIIIAAAggggAACdQgQ4NIrD3WAO+yww5Zt27btQmPMqTPUewpwWZYd4b2/StJDJV1ljNngvc8kPVXSnZLWOue+O3vbrLUvl3ShpLslfdYY83Pv/QmSVku6YdeuXWs3btx4Vy9bTYDrRYu2CCCAAAIIIIAAAnUJEODSSw9tgFu9enWr3W5f0QlSH5L0LEkH7SHANa21IZw9XtJznXOXz2yPtfZPJL0/BDLn3OGSyvDdxMTEirIs10vaWpblMdPT0xs6fcJYl0p6gaT3Oude1ctWE+B60aItAggggAACCCCAQF0CBLj00kMb4Ky1r5T0dmPMK/I8v8haG26bPGR3AS7LspO995/z3n+6KIrnzN0aa+0Nkp4Ybq10zoVTunBr5tskvUHSa51z75zdZ2xs7MBms/kDSe3R0dEDJycnt1bdbgJcVSnaIYAAAggggAACCNQpQIBLrz20AS7Lsud47zc7577dCVt7DHCtVusCY8wZkk5zzl08d2tarda5xpizZ5+oWWtvDid8zWZzxfr162+dJ/RdLel47/3ziqL4ZNXtJsBVlaIdAggggAACCCCAQJ0CBLj02kMb4OYJU91O4L7hvT/WGPPEPM+/N0//35EUQtjVzrkTV6xYse+SJUvC797ucs7tN99WZln2bu99uH3yXOfcOVW3mwBXVYp2CCCAAAIIIIAAAnUKEODSaxPgOsbdbqG01v5I0mMajcYBU1NTP5snwB0p6VpJtznnHpdlWea9n/Le31QUxaHzbWWr1Xq9MeZ8SZc6515cdbsJcFWlaIcAAggggAACCCBQpwABLr02Aa5igMuy7G7v/dKyLPednp7ePndrWq3WamNMuGUynLg9wlo7E+iucc4du5sTuDO89xdIusI599tVt5sAV1WKdggggAACCCCAAAJ1ChDg0msvigDXarXCA0LCO9m6fprN5rlTU1NubsMKJ3BtSQ3nXEOSn6f/KknhKZPbnXP7WmuPD7dTztxSuZsA91Lv/UXe+yuLoji56+T/N2xuXbp06bLrr7++ahfaIYAAAggggAACCCCQXIAAl5xYiyLAzYSvilzHO+e+thcBLjwlctmOHTsesmnTpnvn9p91AndH+M1b551x4QEp33LOHbObADdzAvcp59xzK84/PN2SAFcVi3YIIIAAAggggAACtQkQ4NJTL4oAF4OpwgncZkmPLcvywOnp6Z/OE+CONsZcI+lW59yKiYkJW5ZlLulm59ya3QS4N3jvw6sGLnbOnVZ1HQS4qlK0QwABBBBAAAEEEKhTgACXXru2AGetPcAYc1C73V4WltVsNsNvysJj/G9Pv8zuFSoEuPse+W+MOTLP8+vmjmitPUXSxyV90Tn3jDVr1izZuXNneArlvc655bsJcB/w3p8u6c+dc3/VfZb/3YIAV1WKdggggAACCCCAAAJ1ChDg0msnC3BZlq2U9Hzv/TMkHS5p3hAjaYukdcaYKyVdluf5LemX/cAK3QJcq9V6lzHmzBC4iqK4cJ4A93ZJr5P0NufcGztB676XextjJvI8D6dx9/tYa0MQPLzRaJw8NTUV1l/pQ4CrxEQjBBBAAAEEEEAAgZoFCHDpwaMHuPHx8Sc1m823eO9/Q7rvN3ZVa4QHg4R/PiPpHOfcd9Mv/38rVAhwJxhjviLpP5xzT58zt4a1dlrSytkndNbaN0sKFmcVRfHW2X2steGhJ6HPnaOjo4+ZnJzcUXW9BLiqUrRDAAEEEEAAAQQQqFOAAJdeu2q46jqTgw46aOny5cv/1nv/x+FpjZ0wdp0x5gvtdvtaY8y09/6HBx988D1hsO9///vLms3mQd778WazeVTnpO6ITuArjTEf2rJly+s2b958X/vUn24BLszLWvtNSUd7719UFMXHZuaUZdnZ3vtzJV3lnHvazN9Xrlz56NHR0UJSKekY59xU+K5ze+Xlkp7pvT+7KIq/7GV9BLhetGiLAAIIIIAAAgggUJcAAS69dJQAF15aXZblvxljxiX9QtIFu3bt+uDGjRu/38sSVq1a9csjIyNnSAr/PEySazQavzXfY/97GXe+tuF0zHu/38x3xpiXhts8jTEfKcsyrOG+T1EUr575d2vthKSvStrfGPNFSRu994dJOk7SDxuNxlOmpqY2za7X+W3cRyVtk/Tv4T1xkn5N0lgIfGVZPmu+98rtaX0EuH53n/4IIIAAAggggAACKQQIcClU7z9mlABnrb0zBC5jzIXGmLOmpqZ+1s/UJyYm9i/LMjzU42XhFsPwWP5+xttNgAtB65Bu4zrn7mc0Pj5+cKPROCecnoV3zxlj/st7/+l2u33ehg0bfjLfeOPj48c2Go03dYLeMu99OJW7ZMmSJe/u5dbJWUGS1wh02zi+RwABBBBAAAEEEKhdgACXnjxWgLvde//CoijCqVS0T5ZlJ3vvL3XOHRBt0EUwECdwi2ATWQICCCCAAAIIILAIBQhw6Tc1SoALT5xM9fTIlGOn501TgQCXxpVREUAAAQQQQAABBPoTIMD151eld5QAV6UQbeIJEODiWTISAggggAACCCCAQDwBAlw8y92NlDTAhacwjoyMvMQY86vGmP2896NdluSdcyelX/ZgVyDADfb+MXsEEEAAAQQQQGCxChDg0u9ssgCXZdkR3vvwm7jwNMmqdUKAa6Zf9mBXIMAN9v4xewQQQAABBBBAYLEKEODS72zVYNXzTKy1V0l6anhsvjHms977WyVt7zaQc+4t3doM+/cEuGG/Alg/AggggAACCCDw4BQgwKXfl5QBbouktjHm8Xmeb06/lOGpQIAbnr1mpQgggAACCCCAwCAJEODS71bKABfeDbeO37TF30QCXHxTRkQAAQQQQAABBBDoX4AA179htxFSBrirjTEjeZ4f120SfN+bAAGuNy9aI4AAAggggAACCNQjQIBL75wswLVard80xnzKe39SURRXp1/K8FQgwA3PXrNSBBBAAAEEEEBgkAQIcOl3K1mAC1PPsuyl3vu/lnSpMeYz7Xb7+2VZ7tzTsjZu3Pj99Mse7AoEuMHeP2aPAAIIIIAAAggsVgECXPqdTRrgrLUvM8ac5b0/uOJSwmsERiq2HdpmBLih3XoWjgACCCCAAAIIPKgFCHDptydZgGu1WqcbY97fWULlOs65RvplD3YFAtxg7x+zRwABBBBAAAEEFqsAAS79zlYOVr1OpdVq3WiMWSPpU2VZXjgyMrLJGNP1PXDr168P74vjswcBAhyXBwIIIIAAAggggMCDUYAAl35XkgW4LMvu9t7/2Dm3Kv0yhqsCAW649pvVIoAAAggggAACgyJAgEu/U8kCnLU2vLz7u865Z6ZfxnBVIMAN136zWgQQQAABBBBAYFAECHDpdyplgPu4pMOdc630yxiuCgS44dpvVosAAggggAACCAyKAAEu/U4lC3Dj4+NjjUbj6977vyuK4s3plzI8FQhww7PXrBQBBBBAAAEEEBgkAQJc+t1KFuAmJiaOa7fbK4wxb5P0C0lXSLpF0o49Lcs5d0n6ZQ92BQLcYO8fs0cAAQQQQAABBBarAAEu/c4mC3DW2lKS7ywh1Jn59z2tivfAVdhzAlwFJJoggAACCCCAAAII1C5AgEtPnjLAbaoY2u63SufcyvTLHuwKBLjB3j9mjwACCCCAAAIILFYBAlz6nU0W4NJPfXgrEOCGd+9ZOQIIIIAAAggg8GAWIMCl350oAe7www8fXbdu3c4U0005dor51jEmAa4OZWoggAACCCCAAAII9CpAgOtVrPf2UQJclmXf2LFjx+/ccsstP+59CrvvkWXZQZL+Jc/z42KOO+hjEeAGfQeZPwIIIIAAAgggsDgFCHDp9zVKgOs8sOSnxphX5nl+WYxpW2tPkfQ+Sfs555oxxlwsYxDgFstOsg4EEEAAAQQQQGBxCRDg0u9nlACXZdmp3vsPSdpH0teMMW/J8/xLezN9a+1Jks6R9GRJ2733f1QUxcf2ZqzF2ocAt1h3lnUhgAACCCCAAAKDLUCAS79/UQJcmGar1foVY8ylkh7fefqkk/TJRqPxxWazed3k5OTW+ZazZs2ah7bb7SPKsny6pOdKspKM936y0Wicmuf599IzDFYFAtxg7RezRQABBBBAAAEEhkWAAJd+p6MFuDDV8MCRLVu2/Kmk/yvpgDmvEfiJMWaz9/7u0NYYs8x7H37jduCsZYb53C7pr5cvX/6+VA9GSc+atgIBLq0voyOAAAIIIIAAAgjsnQABbu/ceukVNcDNFF6xYsW+++yzz4skneK9P1HSki6T2mGM+UpZlh/fuXPnxzZt2nRvL4sYtrYEuGHbcdaLAAIIIIAAAggMhgABLv0+JQlws6e9Zs2aJTt37vwV7/1Yo9F4tPd+afjeGHNPWZY/NsZsGB0dvWFycnJH+uUujgoEuMWxj6wCAQQQQAABBBBYbAIEuPQ7mjzApV/C8FUgwA3fnrNiBBBAAAEEEEBgEAQIcOl3iQCX3jh6BQJcdFIGRAABBBBAAAEEEIggQICLgNhlCAJceuPoFQhw0UkZEAEEEEAAAQQQQCCCAAEuAiIBLj1i3RUIcHWLUw8BBBBAAAEEEECgigABropSf204gevPb0F6E+AWhJ2iCCCAAAIIIIAAAl0ECHDpLxECXHrj6BUIcNFJGRABBBBAAAEEEEAgggABLgJilyEIcOmNo1cgwEUnZUAEEEAAAQQQQACBCAIEuAiIBLj0iHVXIMDVLU49BBBAAAEEEEAAgSoCBLgqSv214QSuP78F6U2AWxB2iiKAAAIIIIAAAgh0ESDApb9EaglwY2Nj48aYA4wxS7otqSiKq7u1GfbvCXDDfgWwfgQQQAABBBBA4MEpQIBLvy9JA1yWZX/mvX+dpAMqLsU750Yqth3aZgS4od16Fo4AAggggAACCDyoBQhw6bcnWYBrtVqnG2Mu6HUJzrlGr32GrT0Bbth2nPUigAACCCCAAAKDIUCAS79PyQKctfYGSYcaYy5ut9vvkDQ9PT29Pf2SFn8FAtzi32NWiAACCCCAAAIIDKIAAS79rqUMcPdI2uycG0+/jOGqQIAbrv1mtQgggAACCCCAwKAIEODS71TKAHeHpK85556dfhnDVYEAN1z7zWoRQAABBBBAAIFBESDApd+pZAEuy7JveO+bzrmj0y+jvwrW2pdJeo+kfRuNxsqpqalNuxvRWnuU9/71xpjjJe0n6S5J10h6l3Puqvn6WWtPkhQe5hIslknaZIy5TNL5eZ5v6XX2BLhexWiPAAIIIIAAAgggUIcAAS69crIAZ619iaSPNBqNo6amptalX0rvFQ477LBl27Ztu9AYc+pM7z0FuFar9YfGmA9JakoKrzvIJYVbRJ8qyRtjXpHn+Qdmz8Ra+3JJF0q6W9JnjTE/996fIGm1pBt27dq1duPGjSEEVv4Q4CpT0RABBBBAAAEEEECgRgECXHrsZAEuTN1a+zZJfxBeJfCwhz3ssnXr1u1Mv6RqFVavXt1qt9tXdIJUCGXPknTQ7gLcxMTEirIspyQt8d7/blEUn5yp1Gq1XmSM+SdjzD3e+0Occ7eH7zp91kvaWpblMdPT0xs6fZrW2kslvUDSe51zr6o26/9uRYDrRYu2CCCAAAIIIIAAAnUJEODSSycLcNbamYBzZAhGknZI+r6kPT2JMrwH7onpl31fCHqlpLd3Ts0ustaG2yYP2V2Ay7LsbO/9ucaYf83z/HfnztFa+x1JT/Len1oUxUdnBdg3SHqtc+6ds/uMjY0d2Gw2fyCpPTo6euDk5OTWqusmwFWVoh0CCCCAAAIIIIBAnQIEuPTaKQNcGW4rlNRLjRDgwu2JyT9Zlj3Hex+ekvntTtjaY4CbmJg4vCzLXy3L8sbp6enwm7f7fay1/yzp+d77PyuK4m86Y94cTviazeaK9evX3zpPn3Ab5vHe++fNPtHrtngCXDchvkcAAQQQQAABBBBYCAECXHr1XsJVT7Ox1p7TU4dOY+fcW/amX799up3AdRvfWvtVSU8xxvxxnucXrVixYt8lS5aE373d5ZwLDzt5wCfLsnd778Ptk+c65yp7EeC67QbfI4AAAggggAACCCyEAAEuvXqyAJd+6nEr9BPgJiYmbFmW4bdu4Td+Y865H2ZZlnnvp7z3NxVFceh8s221WuFpludLutQ59+KqKyLAVZWiHQIIIIAAAggggECdAgS49NoEuI7x3ga4NWvWLNm1a9eXvffHeu/fVRTFa8KQ1trw279rwysGnHPHzreVWZad4b2/QNIVzrnfrrrdBLiqUrRDAAEEEEAAAQQQqFOAAJdeu5YA12q1fsUYE96FNuG9f4QxpjTG3BFOp9rt9uc3bNgw3c9SW61WeEDIo6qM0Ww2z52amnJz2+5NgBsfH9+n2Wxe5r1/tjHmO3ffffdTbrvttm2dABfeExd+43a1c+7E3QS4l3rvL/LeX1kUxclV5t8Ze+vSpUuXXX/99VW70A4BBBBAAAEEEEAAgeQCBLjkxD09YKTn2axateqXm83mxcaY8N6zmc9MaAwPOJn5XLbPPvucceONN97Rc5H/Pu267wEkFfse75z7Wr8Bbs2aNQ/duXPn5ZJCMA3vgzsp3Do5M26WZUd478MDUr7lnDtmNwFu5gTuU86551acP68RqApFOwQQQAABBBBAAIFaBQhw6bmTncCtWrXq4SMjI+GIKASrUCeErJu993c2Gg3jvX+kpCdIOrjztMrrRkdHj5+cnAyvG6j908sJ3MTExP5lWX5W0lHhZdw7d+48+ZZbbvnx7El3fhcXgt3Nzrk1uwlwb/Deh3flXeycO63qormFsqoU7RBAAAEEEEAAAQTqFCDApddOFuA6T6EMT1b8TlmWL5uenp73fr/x8fFjG41GeJH2aklnOufel37ZD6xQNcB1gml44mR4MMl/GGOem+f5lrkjht/G7dy5MzyF8l7n3PLdBLgPeO9Pl/Tnzrm/qrpuAlxVKdohgAACCCCAAAII1ClAgEuvnSzAZVm2Lvzmrd1ur5ienv7pnpYSbrUcGRkpJH3bOfeU9MveuwB3+OGHj27ZsuXzkn5N0uVlWb5genp6ty8mt9beIOmJxpiJPM/Dadz9Ptba6yQd3mg0Tp6amrqy6roJcFWlaIcAAggggAACCCBQpwABLr12sgBnrb3TGHNDnudrqyzDWnvf7Za7e2dalTH6aVPlBM5a+7eSwlMm/3N0dPTkbrd7WmvfLOkt3vuziqJ46+z5WWtXSQoPb7lzdHT0Md3GmtOXh5j0s9n0RQABBBBAAAEEEEgiQIBLwnq/QVMGuHslXeWce1aVZWRZdqX3/kTn3L5V2sdu0y3ATUxMHFqWZThRu6vRaLSmpqZ+1m0OK1eufPTo6Gg4WSwlHeOcmwp9OrdXhgegPNN7f3ZRFH/ZbSwCXC9CtEUAAQQQQAABBBBYCAECXHr1lAHuB5LazrkVVZaRZdmt3vuGc+5xVdr32yacjnnv95sZxxjzUknLjTEfKcvyFzN/L4ri1eHfrbWXSAov277Re/+lPdS/tiiKj858b609RVL47/B6gX8PAbBzC+ZYCLhlWT5rT7dhzleHWyj73X36I4AAAggggAACCKQQIMClUL3/mCkD3D9L+l1jzCvzPP/AnpbSarVON8aEF1r/s3PuhemXXf3VA865+4yyLAsv6573fW5z5vuAJ0p2HtTyJknHSVrmvQ+ncpcsWbLk3b3cOjkrFHILZR0XCTUQQAABBBBAAAEEehIgwPXEtVeNkwW4iYmJ48qyDO9b88aYT5Vl+bFGo3HTjh077gwzHR0dfaQx5lDv/amSnh3aee+fXBTFt/ZqJUPUiRO4IdpslooAAggggAACCAyQAAEu/WYlC3CdU6vwnrNuj8cPcyi9968tiuI96Zc8+BUIcIO/h6wAAQQQQAABBBBYjAIEuPS7mjTAhemPj4+f2Gg0Xi/pJEn7zF6SMWab9/6LZVm+bXp6+pr0y10cFQhwi2MfWQUCCCCAAAIIILDYBAhw6Xc0eYCbWcLatWtHfvSjH4212+1w66RvNpt3TE1NbQgPOkm/zMVVgQC3uPaT1SCAAAIIIIAAAotFgACXfidrC3DplzI8FQhww7PXrBQBBBBAAAEEEBgkAQJc+t0iwKU3jl6BABedlAERQAABBBBAAAEEIggQ4CIgdhkiSoCz1obbIC93zj1vpl7nb72uwDvnRnrtNGztCXDDtuOsFwEEEEAAAQQQGAwBAlz6fYoV4MpOgHvurAAX/tbrJwS4Zq+dhq09AW7Ydpz1IoAAAggggAACgyFAgEu/T1ECXHjSpKTbp6enJ2em3PlbzyuYnp7+Ss+dhqwDAW7INpzlIoAAAggggAACAyJAgEu/UVECXPppUmG2AAGO6wEBBBBAAAEEEEDgwShAgEu/K8kCXJZlf++9/45z7n1VlmGt/aKkzc65P6jSfpjbEOCGefdZOwIIIIAAAggg8OAVIMCl35tkAc5a+4Dfxe1pOdbamyU92jm3f/plD3YFAtxg7x+zRwABBBBAAAEEFqsAAS79zkYNcKtWrfrlmSmPjIxs8t5/od1uv3xPyxgZGRkxxhzuvf8nSbucc0vTL3uwKxDgBnv/mD0CCCCAAAIIILBYBQhw6Xc2aoDrnLr5vZy2McZck+f5cXvZf2i6EeCGZqtZKAIIIIAAAgggMFACBLj02xU1wLVarbOMMUdLCv+EWyF7Gf+7zWbzxevXr78p/bIHuwIBbrD3j9kjgAACCCCAAAKLVYAAl35newlYPc0mnMaFWyhHRkb+z546bt++3T/kIQ/5+eTk5NaeCgxxYwLcEG8+S0cAAQQQQAABBB7EAgS49JuTMsB9RNK6qk+hTL/UxVOBALd49pKVIIAAAggggAACi0mAAJd+N5MFuPRTH94KBLjh3XtWjgACCCCAAAIIPJgFCHDpdydKgGu1Wid472+fnp4OrwK47xP+tjfTL4ri6r3pN0x9CHDDtNusFQEEEEAAAQQQGBwBAlz6vYoS4Ky1bUmXO+eeNzPlvXwipXfOjaRf9mBXIMAN9v4xewQQQAABBBBAYLEKEODS72ysAPeAl3Z3AlzPK3DONXruNGQdCHBDtuEsFwEEEEAAAQQQGBABAlz6jYoS4NJPkwqzBQhwXA8IIIAAAggggAACD0YBAlz6XSHApTeOXoEAF52UARFAAAEEEEAAAQQiCBDgIiB2GaKWALd27dqRL3/5y7tmz+XQQw995I4dO4723u8r6WvOudvTL3dxVCDALY59ZBUIIIAAAggggMBiEyDApd/RpAEuPInSGPNuY8z78jy/aGY5WZY9x3v/D5Ie1vnbdklnOuc+lH7Jg1+BADf4e8gKEEAAAQQQQACBxShAgEu/q8kCXKvVWm2MuVbSUmPMX+R5fl5YztjY2OOazWYuKZy8/VDSf0l6Umepxzrnvp1+2YNdgQA32PvH7BFAAAEEEEAAgcUqQIBLv7PJApy19v2SzvDe/01RFG+SFF41IGvtOyS9xhhzzcjIyNrJyckdrVbrecaYyyRd4pw7Lf2yB7sCAW6w94/ZI4AAAggggAACi1WAAJd+Z1MGuPWS9nPOPVZSeM3AfZ8sy2713h/caDR+Y2pq6sqZv1trC0kN59xY+mUPdgUC3GDvH7NHAAEEEEAAAQQWqwABLv3Opgxwd0n6lnPuGbPCW+a9D8HuLufc/rODXavV+rwx5kTn3EPSL3uwKxDgBnv/mD0CCCCAAAIIILBYBQhw6Xc2ZYC713v/5aIoTp5ZRqvVOt0Yc4GkTznnnjd7eVmWXem9f7Jz7qHplz3YFQhwg71/zB4BBBBAAAEEEFisAgS49DubMsBtCr97m31LpLX2S5JONMa8fPZTKcMyrbXhwSZLnHMr0y97sCsQ4AZ7/5g9AggggAACCCCwWAUIcOl3NmWA+5ik53vvX71t27YPL1u27IXe+w9LuqfRaBwyNTX1s5nlZVn2ZO/9V733/1YUxXPSL3uwKxDgBnv/mD0CCCCAAAIIILBYBQhw6Xc2WYAbHx8/ptFofE3S7Brh3/+fc+6Ns8Lb73vvw5MpD5B0qnPu4+mXPdgVCHCDvX/MHgEEEEAAAQQQWKwCBLj0O5sswIWpW2tfICmEs/AkyvAagb9zzr1i9rKstZdICiHuX4ui+L30Sx78CgS4wd9DVoAAAggggAACCCxGAQJc+l1NGuBmpr9y5cpHb9++fcvmzZvvmbukiYmJX/feL8/z/F/SL3dxVCDALY59ZBUIIIAAAggggMBiEyDApd/RWgJc+mUMVwUC3HDtN6tFAAEEEEAAAQQGRYAAl36naglw1toDvPcnSppoNBqPKMuyNMbc4b2/qdFofCXP8y3pl7p4KhDgFs9eshIEEEAAAQQQQGAxCRDg0u9m0gC3YsWKRyxZsuSdkl4kaWQ3ywm3Vb5/dHT0zZOTkzvSL3nwKxDgBn8PWQECCCCAAAIIILAYBQhw6Xc1WYAbHx/fp9FofF3SkzpPotwuaYOkOzv//UhJY5JGJXlJn3POPbvz7+lXPsAVCHADvHlMHQEEEEAAAQQQWMQCBLj0m5sswFlrX9N5AuWtkl67fPnyz6xbt27n7CWFkGeMea4x5m86T6o8zTn3j+mXPdgVCHCDvX/MHgEEEEAAAQQQWKwCBLj0O5sswGVZ9g3v/eFlWT5+eno6nLzt9jMxMXFoWZbXS/qSc+4Z6Zc92BUIcIO9f8weAQQQQAABBBBYrAIEuPQ7myzAWWt/JmnKOffkKsuw1t4k6dHOuUdVaT/MbQhww7z7rB0BBBBAAAEEEHjwChDg0u9NygAXfvP2hc7v2rqupNVqfd4Y81Tn3D5dGw95AwLckF8ALB8BBBBAAAEEEHiQChDg0m9MygD3o/DA8zGuNAAAIABJREFUEufc6irLsNbmkh7mnHtslfbD3IYAN8y7z9oRQAABBBBAAIEHrwABLv3eJAtwWZZ92nv/LEnPc85dvqelZFn2W977y733/1YUxXPSL3uwKxDgBnv/mD0CCCCAAAIIILBYBQhw6Xc2WYBrtVq/aYz5tKRwK+X7G43Gx3bt2nXT9PR0+G+tWLFi3yVLlhzqvT/VGHN6eJ2AMeZZeZ5/Pv2y71/BWvsySe+RtG+j0Vg5NTW1qeocrLVvlfQmSbc651bM189ae5Kk10k6WtIySZuMMZdJOn9vXmJOgKu6O7RDAAEEEEAAAQQQqFOAAJdeO1mAC1NvtVoXdMJZeM/bzGfmZd1LZv3NGGPekef569Mv+X8rHHbYYcu2bdt2oTHm1Jm/9hLgVq9e/YR2u/2dzrvs5g1w1tqXS7pQ0t2SPmuM+bn3/gRJ4dbSG3bt2rV248aNd/WybgJcL1q0RQABBBBAAAEEEKhLgACXXjppgAvTt9a+RNKfSXr8bpbzPWPMW/M8DydStX1Wr17darfbV3SC1Ickhds9D+ohwJksy77uvbeS9p/vBG5iYmJFWZbrJW0ty/KYWa9TaFprL5X0Aknvdc69qpeFE+B60aItAggggAACCCCAQF0CBLj00skD3MwSxsfHDx4ZGVlTluUjJfmyLO9oNps35Xm+Of0yH1jBWvtKSW83xrwiz/OLrLXhtslDqgY4a+2fhFtDO7dGvmO+AGetfZukN4QXmTvn3jl7FmNjYwc2m80fSGqPjo4eODk5ubWqAwGuqhTtEEAAAQQQQAABBOoUIMCl164twKVfSm8Vsix7jvd+s3Pu26FnLwEuy7KDvPfrvffff9jDHvarW7ZsCbeFPuAWSmvtzeGEr9lsrli/fv2tc2dorb1a0vHe++cVRfHJqisgwFWVoh0CCCCAAAIIIIBAnQIEuPTatQQ4a+2E9/4YY0x4yMdyY0xZlmX43dfGZrP5jV4eGpKKpJcAZ639V0m/U5blk6enp79prQ2/8btfgOs8pCX87u0u59x+8807y7J3e+/D7ZPnOufOqbo2AlxVKdohgAACCCCAAAII1ClAgEuvnTTATUxMHF6W5Qcl/WqXpXzFGHNGnufhXXAL8qka4DqvPLjCGPPBPM/PCJOdL8BlWZZ576e89zcVRXHofItqtVqvN8acL+lS59yLqy6cAFdVinYIIIAAAggggAACdQoQ4NJrJwtw4+Pjj282m9/23u8rKdQJv/G6JZxISWp47x/eOZELj9UPnzskHemc25h+2Q+sUCXAZVm23Hsfbots7tq1a/XM0yPnC3DW2iMlXSvpGufcsfOtKcuyM7z3F0i6wjn321XXTYCrKkU7BBBAAAEEEEAAgToFCHDptZMFOGvtJyT9rjHmeu/9mc65r4eHl8xZUiPLshO99++SFN4J99GiKH6/12W3Wq3wgJBHVenXbDbPnZqacnPbVglw1trwrrg/NcY8f/ZTM3cT4I6XFH7jdrVz7sTdBLiXeu8v8t5fWRTFyVXmH9oQ4KpK0Q4BBBBAAAEEEECgTgECXHrtlAHux5L22bFjx4pNmzbduaeljI+PP6rRaGyQdI9z7jG9LnsmfFXsd7xz7mu9Bjhr7VGSvinpc86535zdfze3UB7hvQ8PSPmWc+6Y3QS4mRO4Tznnnltx/gS4qlC0QwABBBBAAAEEEKhVgACXnjtlgLvXGPOVPM9/vcoyrLVfkPQU59zSKu1jt9nTCdzatWtHNm/efJ2ksWaz+YS5T5ScL8BNTEzYsizDb/puds6t2U2Ae4P3Prxq4GLn3GlV18QJXFUp2iGAAAIIIIAAAgjUKUCAS6+dMsB93xhzW57nx1VZhrX2PyStcM6NV2kfu82eAlyWZb/nvQ+3hN5ijPnO3NrhNQDGmHvC6Vz4rt1un7HPPvvctXPnzvAUynudc8t3E+A+4L0/XdKfO+f+quqaCHBVpWiHAAIIIIAAAgggUKcAAS69drIAl2XZB733LyrL8uDp6elf7GkpnYeD/MB7/5GiKF6TftkPrLCnANdqtU4zxnyk6rxmXgZurb1B0hONMRPzPWHTWhtO9Q5vNBonT01NXVl1fAJcVSnaIYAAAggggAACCNQpQIBLr50swFlrDwi/GfPe39Zut/9g48aN359vOWNjYwc2m82Lw+nbrl27jpl5smP6pd+/QpWHmOxuTvPdQhnaWmvfLOkt3vuziqJ46+z+1tpVkqYl3Tk6OvqYycnJ8DLwSh8CXCUmGiGAAAIIIIAAAgjULECASw+eMsD9iaQDJZ0p6aHhcfre+xuNMXd479uNRuPh3vvHh9+9hYeXSPpQ538fsGrn3LmpKVIEuJUrVz56dHS0kFRKOsY5NxXWsWbNmiU7d+68XNIzvfdnF0Xxl72sjwDXixZtEUAAAQQQQAABBOoSIMCll04Z4EJoCa8NmF1j7msE9vTd/6zeOdeMTRFOx7z3+82Ma4x5qaTl4VbJsiz/55bPoihe3a327k7gQj9r7SmSPippm6R/77wH79fCA1EkXVWW5bOmp6e3d6sx+3sCXC9atEUAAQQQQAABBBCoS4AAl146WYDLsuzLZVnODWx7taKiKJ66Vx330Knqqwecc12N9hTgwhTGx8ePbTQab5IUHuiyzHsfTuUuWbJkybt7uXVyZjkEuNhXA+MhgAACCCCAAAIIxBAgwMVQ3PMYXcNJ+ilQoVcBAlyvYrRHAAEEEEAAAQQQqEOAAJdemQCX3jh6BQJcdFIGRAABBBBAAAEEEIggQICLgNhlCAJceuPoFQhw0UkZEAEEEEAAAQQQQCCCAAEuAiIBLj1i3RUIcHWLUw8BBBBAAAEEEECgigABropSf204gevPb0F6E+AWhJ2iCCCAAAIIIIAAAl0ECHDpLxECXHrj6BUIcNFJGRABBBBAAAEEEEAgggABLgJilyEIcOmNo1cgwEUnZUAEEEAAAQQQQACBCAIEuAiIBLj0iHVXIMDVLU49BBBAAAEEEEAAgSoCBLgqSv21SXYC12q1/nTJkiX/NDk5+fP+pkjvuQIEOK4JBBBAAAEEEEAAgQejAAEu/a4kC3DW2lLSDmPMZ8qy/IeiKD4nqZ1+SYu/AgFu8e8xK0QAAQQQQAABBAZRgACXfteSBbgsy2713j+uswQv6afGmH9sNBoXr1+//qb0S1u8FQhwi3dvWRkCCCCAAAIIIDDIAgS49LuXLMCFqVtrj5L0fEnPk3TIrDD3HUn/MDo6+jFusex9kwlwvZvRAwEEEEAAAQQQQCC9AAEuvXHSADd7+tbaI40xz/fehzC3QlI4ldsp6dPe+4s7t1iG2y75dBEgwHGJIIAAAggggAACCDwYBQhw6XeltgA3eylZlh3hvX+BpBdKemwnzP3Ye/+PIyMjH16/fn2RfumDW4EAN7h7x8wRQAABBBBAAIHFLECAS7+7CxLgZpa1du3akc2bN58l6Y2SRjp/Dydz/9ZsNs/mt3LzXwAEuPT/x6ACAggggAACCCCAQO8CBLjezXrtsSABrtVqnRBup+z8Nu5ASWEe240xX/TeHy3pUeH2Su/964uieE+vi1rs7Qlwi32HWR8CCCCAAAIIIDCYAgS49PtWW4AbGxsbbzQaLzHG/H7ngSYztW/03n943333vfTGG2+84+CDD37IsmXLXuW9P7dzKneqc+7j6SkGpwIBbnD2ipkigAACCCCAAALDJECAS7/bSQPcqlWrHj4yMnKKpJdIOraznFDzF8aYj0m6KM/z6+ZbZpZlv+W9v1zS95xzv5KeYnAqEOAGZ6+YKQIIIIAAAgggMEwCBLj0u50swFlr/1nSsyXt07lFMqzmqyG03XPPPZfddttt27otL8uydd77Jzjnwhh8OgIEOC4FBBBAAAEEEEAAgQejAAEu/a6kDHAzrwT4saSLm83mRb0+XTLLsvCKgac555ampxicCgS4wdkrZooAAggggAACCAyTAAEu/W4nC3CtVuuKRqMRbpH8d0ntvVnK2NjYgc1mc9Q598O96b9Y+xDgFuvOsi4EEEAAAQQQQGCwBQhw6fcvWYBLP/XhrUCAG969Z+UIIIAAAggggMCDWYAAl353CHDpjaNXIMBFJ2VABBBAAAEEEEAAgQgCBLgIiF2GIMClN45egQAXnZQBEUAAAQQQQAABBCIIEOAiIBLg0iPWXYEAV7c49RBAAAEEEEAAAQSqCBDgqij114YTuP78FqQ3AW5B2CmKAAIIIIAAAggg0EWAAJf+EiHApTeOXoEAF52UARFAAAEEEEAAAQQiCBDgIiB2GYIAl944egUCXHRSBkQAAQQQQAABBBCIIECAi4BIgEuPWHcFAlzd4tRDAAEEEEAAAQQQqCJAgKui1F8bTuD681uQ3gS4BWGnKAIIIIAAAggggEAXAQJc+kuEAJfeOHoFAlx0UgZEAAEEEEAAAQQQiCBAgIuA2GUIAlx64+gVCHDRSRkQAQQQQAABBBBAIIIAAS4CIgEuPWLdFQhwdYtTDwEEEEAAAQQQQKCKAAGuilJ/bTiB689vQXoT4BaEnaIIIIAAAggggAACXQQIcOkvEQJceuPoFQhw0UkZEAEEEEAAAQQQQCCCAAEuAmKXIQhw6Y2jVyDARSdlQAQQQAABBBBAAIEIAgS4CIgEuPSIdVcgwNUtTj0EEEAAAQQQQACBKgIEuCpK/bXhBK4/vwXpTYBbEHaKIoAAAggggAACCHQRIMClv0QIcOmNo1cgwEUnZUAEEEAAAQQQQACBCAIEuAiIXYYgwKU3jl6BABedlAERQAABBBBAAAEEIggQ4CIgEuDSI9ZdgQBXtzj1EEAAAQQQQAABBKoIEOCqKPXXhhO4/vwWpDcBbkHYKYoAAggggAACCCDQRYAAl/4SIcClN45egQAXnZQBEUAAAQQQQAABBCIIEOAiIHYZggCX3jh6BQJcdFIGRAABBBBAAAEEEIggQICLgEiAS49YdwUCXN3i1EMAAQQQQAABBBCoIkCAq6LUXxtO4CRZa18m6T2S9m00GiunpqY27Yk1y7Lf996/QtITJO2UNGmMeW+e55+Yr5+19iRJr5N0tKRlkjYZYy6TdH6e51t63UICXK9itEcAAQQQQAABBBCoQ4AAl155qAPcYYcdtmzbtm0XGmNOnaHuFuCstX8nKQS+H0r6vDHmEd7735S0j6RznXPnzN42a+3LJV0o6W5JnzXG/Nx7f4Kk1ZJu2LVr19qNGzfe1ctWE+B60aItAggggAACCCCAQF0CBLj00kMb4FavXt1qt9tXdILUhyQ9S9JBewpw1tqXSLpY0pe2bt367M2bN98Ttmh8fPxXG43G1yWNhgM959zG8PeJiYkVZVmul7S1LMtjpqenN3S2tGmtvVTSCyS91zn3ql62mgDXixZtEUAAAQQQQAABBOoSIMCllx7aAGetfaWktxtjXpHn+UXW2nDb5CFdAlwR2pRluWp6evq2OSdtL/PeN0dGRq5Yv379j8J31tq3SXqDpNc65945u/3Y2NiBzWbzB5Lao6OjB05OTm6tut0EuKpStEMAAQQQQAABBBCoU4AAl157aANclmXP8d5vds59uxO29hjgOqds6yR9xjn37CpbY629OZzwNZvNFevXr791bh9r7dWSjvfeP68oik9WGbMz161Lly5ddv3111ftQjsEEEAAAQQQQAABBJILEOCSE2toA9w8YWqPAS7LsjO99++S9EbnXDhZ2+NnxYoV+y5ZsiT87u0u59x+8zXOsuzd3vtw++QDfju3p8E5geumz/cIIIAAAggggAACCyFAgEuvToDrGHe7hdJaGx5E8nJjzPO99+EWyT8PT5U0xuzjvb/Je/+Boij+YWbLsizLvPdT4buiKA6dbytbrdbrjTHnS7rUOffiqttNgKsqRTsEEEAAAQQQQACBOgUIcOm1CXDVA9zlksJtl+82xoTfz31HUriH8ZcknSypGb4riuLVYUhr7ZGSrpV0jXPu2N2cwJ3hvb9A0hXOud+uut0EuKpStEMAAQQQQAABBBCoU4AAl157UQS4VqsVHhDyqCpczWbz3KmpKTe3bYUTuC9Kepqk0hjzkjzP/2lmjFardYIx5kshxEk6wTn3VWvt8ZLCb9yuds6duJsA91Lv/UXe+yuLogghsNKHAFeJiUYIIIAAAggggAACNQsQ4NKDL4oANxO+KnId75z7Wq8BLsuyK733zwjvcnPOhVcO3O9jrQ2vF3iJ9/7vi6L4oyzLjvDehwekfMs5d8xuAtzMCdynnHPPrTj/cLrHQ0yqYtEOAQQQQAABBBBAoDYBAlx66kUR4GIwdTuBa7ValxljftcY8+Y8z8+bJ8D9gaTwG7h1zrkjJiYmbFmWuaSbnXNrdhPg3uC9Dw9Eudg5d1rVdRDgqkrRDgEEEEAAAQQQQKBOAQJcem0CXMe4QoD7f8aYP5P01865N83dmizLTvbef07Seufc49esWbNk586d4SmU9zrnlu8mwH3Ae396eCCKc+6vqm43Aa6qFO0QQAABBBBAAAEE6hQgwKXXJsBVDHDW2lMkfVzSJ5xz4d/v97HW/rGkD0n6qnPuhPCltfYGSU80xkzkeR5O4+b2uU7S4Y1G4+Spqakrq243Aa6qFO0QQAABBBBAAAEE6hQgwKXXJsBVDHCrVq16+MjIyI+NMe2RkZHHTU5O/nz29lhrQ7g7xRjz9jzPX98JcG+W9Bbv/VlFUbx1TvtVkqYl3Tk6OvqYycnJHVW3mwBXVYp2CCCAAAIIIIAAAnUKEODSaxPgKga40Cw87dIY82rv/T/90i/90mn/v717AZOkKs8H/p7q7dllYYU/4SYuYXen69SsK6CuyIIgKyqCoCAaoxgTQowBjXiL4g0v4P2OFwgxqCgxKkbFK14weAmKBiXgMFNf9S6rwcUkBkWWhd3Zru//fGP1pre3Z7p6pqpnZ/qt58kT3Kk6p/pXZ3rqrXPqnBtuuGFH9u+nOee+ZDNUAjhCRMbt31euXHlwtVpNsn9f1/z3bHilLUtwqqpelCTJm3u51AxwvWhxXwpQgAIUoAAFKECBfgkwwJUvPbABznv/elXdv0nsnDsXwDLn3MfSNP1989+b67rZ/z700EOX7rPPPtcDsFklx5xz303T9FDn3OkAAlW9IEmSD7Zetmzo5acA3A/gqwDuAXASgGEA16dpelq9Xt/Wy6VmgOtFi/tSgAIUoAAFKEABCvRLgAGufOlBDnCbABzejVhEdjGq1WqLK5XKK1X1bAArLJip6o+dc+8SEQt3u221Wu3YIAhs4pPjAOytqtYr94mhoaFLexk62SyYAa7bVePPKUABClCAAhSgAAXmQoABrnz1gQ1w5dOWVwMDXHm2LJkCFKAABShAAQpQYOYCDHAzt8t7JANcXqk9aD8GuD3oYvBUKEABClCAAhSgAAV2CjDAld8YGODKNy68Bga4wklZIAUoQAEKUIACFKBAAQIMcAUgdimCAa5848JrYIArnJQFUoACFKAABShAAQoUIMAAVwAiA1z5iP2ugQGu3+KsjwIUoAAFKEABClAgjwADXB6l2e3DHrjZ+c3J0Qxwc8LOSilAAQpQgAIUoAAFuggwwJXfRBjgyjcuvAYGuMJJWSAFKEABClCAAhSgQAECDHAFIHYpggGufOPCa2CAK5yUBVKAAhSgAAUoQAEKFCDAAFcAIgNc+Yj9roEBrt/irI8CFKAABShAAQpQII8AA1wepdntwx642fnNydEMcHPCzkopQAEKUIACFKAABboIMMCV30QY4Mo3LrwGBrjCSVkgBShAAQpQgAIUoEABAgxwBSB2KYIBrnzjwmtggCuclAVSgAIUoAAFKEABChQgwABXACIDXPmI/a6BAa7f4qyPAhSgAAUoQAEKUCCPAANcHqXZ7cMeuNn5zcnRDHBzws5KKUABClCAAhSgAAW6CDDAld9EGODKNy68Bga4wklZIAUoQAEKUIACFKBAAQIMcAUgdimCAa5848JrYIArnJQFUoACFKAABShAAQoUIMAAVwAiA1z5iP2ugQGu3+KsjwIUoAAFKEABClAgjwADXB6l2e3DHrjZ+c3J0Qxwc8LOSilAAQpQgAIUoAAFuggwwJXfRBjgyjcuvAYGuMJJWSAFKEABClCAAhSgQAECDHAFIHYpggGufOPCa2CAK5yUBVKAAhSgAAUoQAEKFCDAAFcAIgNc+Yj9roEBrt/irI8CFKAABShAAQpQII8AA1wepdntwx642fnNydEMcHPCzkopQAEKUIACFKAABboIMMCV30QY4Mo3LrwGBrjCSVkgBShAAQpQgAIUoEABAgxwBSB2KYIBrnzjwmtggCuclAVSgAIUoAAFKEABChQgwABXACIDXPmI/a6BAa7f4qyPAhSgAAUoQAEKUCCPAANcHqXZ7cMeuNn5zcnRDHBzws5KKUABClCAAhSgAAW6CDDAld9EGODKNy68Bga4wklZIAUoQAEKUIACFKBAAQIMcAUgdimCAa5848JrYIArnJQFUoACFKAABShAAQoUIMAAVwAiA1z5iP2ugQGu3+KsjwIUoAAFKEABClAgjwADXB6l2e3DHrjZ+c3J0Qxwc8LOSilAAQpQgAIUoAAFuggwwJXfRBjgyjcuvAYGuMJJWSAFKEABClCAAhSgQAECDHAFIHYpggGufOPCa2CAK5yUBVKAAhSgAAUoQAEKFCDAAFcAIgNc+Yj9roEBrt/irI8CFKAABShAAQpQII8AA1wepdntwx642fnNydEMcHPCzkopQAEKUIACFKAABboIMMCV30QY4Mo3LrwGBrjCSVkgBShAAQpQgAIUoEABAgxwBSB2KYIBrnzjwmtggCuclAVSgAIUoAAFKEABChQgwABXACIDXPmI/a6BAa7f4qyPAhSgAAUoQAEKUCCPAANcHqXZ7cMeuNn5zcnRDHBzws5KKUABClCAAhSgAAW6CDDAld9EGODKNy68Bga4wklZIAUoQAEKUIACFKBAAQIMcAUgdimCAa5848JrYIArnJQFUoACFKAABShAAQoUIMAAVwAiA1z5iP2ugQGu3+KsjwIUoAAFKEABClAgjwADXB6l2e3DHrjZ+c3J0Qxwc8LOSilAAQpQgAIUoAAFuggwwJXfRBjgyjcuvAYGuMJJWSAFKEABClCAAhSgQAECDHAFIHYpggGufOPCa2CAK5yUBVKAAhSgAAUoQAEKFCDAAFcAIgNc+Yj9roEBrt/irI8CFKAABShAAQpQII8AA1wepdntwx44AN77vwbwAQBLgiBYOT4+vmkq1pGRkfWNRuOlzrnjAOwL4LcAfgDg/SLy/U7Hee8fD+DlAI4BsDeATc65awC8M47je3u9hAxwvYpxfwpQgAIUoAAFKECBfggwwJWvPNAB7sgjj9z7/vvvv8I595wm9XQBznv/t1nQUwDfcc7VVfUwAKcCMMsLRORDrZfNe/98AFcAuA/A15xzd6vqYwGsBnDLjh071m/cuPGeXi41A1wvWtyXAhSgAAUoQAEKUKBfAgxw5UsPbIBbvXp12Gg0rs2C1EcAnAbg0KkC3KpVq/540aJFCYAqgJNF5NvNy1Or1U4OguA6ABNpmg7X6/U77WcjIyMr0jQdA7AlTdN19Xp9Q3ZMxXt/NYBnAfigiFzQy6VmgOtFi/tSgAIUoAAFKEABCvRLgAGufOmBDXBZb9q7nXMvjOP4Su+9DZs8fKoAF4bhec65y51z343jeH37pfHe2zDKxzjnnmfl2c+9928HcCGAl4nI+1qPGR4ePqhSqfwngEa1Wj1odHR0S97LzQCXV4r7UYACFKAABShAAQr0U4ABrnztgQ1wURSdoaqbReQnWdiaNsBFUfRKVX0HgKtF5LkdAtw/Zz1qLxeR92Zl3m49fJVKZcXY2NgvOhzzPQAnqOrTkyT5fN7LzQCXV4r7UYACFKAABShAAQr0U4ABrnztgQ1wHcJUtwBnge+LzrkfxnFsE5jssnnvbQKT49M0fWq9Xv/yihUrlgwNDdl7b/eIyP6dLmUURZeqqg2fvFhE3pD3cjPA5ZXifhSgAAUoQAEKUIAC/RRggCtfmwEuM+42hHLt2rXVe++9NwawEsDTROSLzcvjvT8egPWmbVq2bFl08803T0RRFKnquKr+PEmSIzpdyjAMX+Gce+dUvXpTXX4GuPJ/MVgDBShAAQpQgAIUoEDvAgxwvZv1egQDXM4AZ7uNjIz4NE2/kE18YpOW3OGcO0xVbRbKjap6ZpIkNmmJvf92NIAfA/iRiBzb6cJEUXS+ql4G4FoROTPvxWOAyyvF/ShAAQpQgAIUoAAF+inAAFe+9oIIcGEY2gQhB+bhqlQqF4+Pj0v7vt164Gx/GxZZrVZf5Jy7CMCyljJsLbe3Llmy5IO33nqrDZu0AHdC1iv3PRE5cYoAd66qXqmq30iS5JQ855+VvWXp0qV7/+xnP8t7CPejAAUoQAEKUIACFKBA6QIMcKUTT65dNu+3ZvjK+UFOEBGbMXKXrVuAy95ps2GSR6vq5YsWLXrH0qVLN2/duvXQNE2fp6qvUdXbgyA4zhbnjqLoUapqE6TcJCLrpghwzR64L4jIWTnP38IhA1xeLO5HAQpQgAIUoAAFKNA3AQa48qkXRIArgqlbgAvD8LXOuTcD+IyI2Pptu2xhGF7pnLMetUuSJHl9NtzS3pm7XUTWTBHgLlRVW2rgKhE5J+/nYIDLK8X9KEABClCAAhSgAAX6KcAAV742A1xm3C3Aee//HcDaqab8z5YlsFkqfxrH8do1a9YMTUxM2HDKB0SkdbjlzqsaRdHlqnoegNeKyFvzXm4GuLxS3I8CFKAABShAAQpQoJ8CDHDlazPA5QxwYRiKcy4E8GQR+Xr7pQnD8DTn3FcAxCIyYj/33t8C4Cjn3Egcx9Ybt8vWDIVBEJwyPj7+jbyXmwEurxT3owAFKEABClCAAhTopwBk3rSUAAAgAElEQVQDXPnaDHA5A5z3/psAngjgNSLytg5h7NU2kQmA60TEZqW0APd6AG9S1dclSfKW1mO896sA1AH8rlqtHjI6Oro97+VmgMsrxf0oQAEKUIACFKAABfopwABXvjYDXM4AF0XR5IyRAH6TpumJ9Xr99ubl8d4/BIBNWPJgAOeIyFX2s5UrVx5crVYTACmAdSIybv+eDa+0deROVdWLkiSxd+tybwxwuam4IwUoQAEKUIACFKBAHwUY4MrHHtgAZ71jqrp/k9gmILGlAZxzH0vT9PfNf0+S5CXZfwdhGH7GOfcMANsAfBXAXQAOAmBLANh7btdkE5xYYJvcvPd/CuBTAO7PjrkHwEkAhgFcn6bpafV63crLvTHA5abijhSgAAUoQAEKUIACfRRggCsfe5AD3CYAh3cjFpFWIxdF0dmq+pcAHgFgXwAWyOxdN5tJ8pMAtL3MWq12bBAErwFwHIC9VdV65T4xNDR0aS9DJ1tCIZcR6Hbh+HMKUIACFKAABShAgb4LMMCVTz6wAa582vJqYA9cebYsmQIUoAAFKEABClBg5gIMcDO3y3skA1xeqT1oPwa4Pehi8FQoQAEKUIACFKAABXYKMMCV3xgY4Mo3LrwGBrjCSVkgBShAAQpQgAIUoEABAgxwBSB2KYIBrnzjwmtggCuclAVSgAIUoAAFKEABChQgwABXACIDXPmI/a6BAa7f4qyPAhSgAAUoQAEKUCCPAANcHqXZ7cMeuNn5zcnRDHBzws5KKUABClCAAhSgAAW6CDDAld9EGODKNy68Bga4wklZIAUoQAEKUIACFKBAAQIMcAUgdimCAa5848JrYIArnJQFUoACFKAABShAAQoUIMAAVwAiA1z5iP2ugQGu3+KsjwIUoAAFKEABClAgjwADXB6l2e3DHrjZ+c3J0Qxwc8LOSilAAQpQgAIUoAAFuggwwJXfRBjgyjcuvAYGuMJJWSAFKEABClCAAhSgQAECDHAFIHYpggGufOPCa2CAK5yUBVKAAhSgAAUoQAEKFCDAAFcAIgNc+Yj9roEBrt/irI8CFKAABShAAQpQII8AA1wepdntwx642fnNydEMcHPCzkopQAEKUIACFKAABboIMMCV30QY4Mo3LrwGBrjCSVkgBShAAQpQgAIUoEABAgxwBSB2KYIBrnzjwmtggCuclAVSgAIUoAAFKEABChQgwABXACIDXPmI/a6BAa7f4qyPAhSgAAUoQAEKUCCPAANcHqXZ7cMeuNn5zcnRDHBzws5KKUABClCAAhSgAAW6CDDAld9EGODKNy68Bga4wklZIAUoQAEKUIACFKBAAQIMcAUgdimCAa5848JrYIArnJQFUoACFKAABShAAQoUIMAAVwAiA1z5iP2ugQGu3+KsjwIUoAAFKEABClAgjwADXB6l2e3DHrjZ+c3J0Qxwc8LOSilAAQpQgAIUoAAFuggwwJXfRBjgyjcuvAYGuMJJWSAFKEABClCAAhSgQAECDHAFIHYpggGufOPCa2CAK5yUBVKAAhSgAAUoQAEKFCDAAFcAIgNc+Yj9roEBrt/irI8CFKAABShAAQpQII8AA1wepdntwx642fnNydEMcHPCzkopQAEKUIACFKAABboIMMCV30QY4Mo3LrwGBrjCSVkgBShAAQpQgAIUoEABAgxwBSB2KYIBrnzjwmtggCuclAVSgAIUoAAFKEABChQgwABXACIDXPmI/a6BAa7f4qyPAhSgAAUoQAEKUCCPAANcHqXZ7cMeuNn5zcnRDHBzws5KKUABClCAAhSgAAW6CDDAld9EGODKNy68Bga4wklZIAUoQAEKUIACFKBAAQIMcAUgdimCAa5848JrYIArnJQFUoACFKAABShAAQoUIMAAVwAiA1z5iP2ugQGu3+KsjwIUoAAFKEABClAgjwADXB6l2e3DHrjZ+c3J0Qxwc8LOSilAAQpQgAIUoAAFuggwwJXfRBjgyjcuvAYGuMJJWSAFKEABClCAAhSgQAECDHAFIHYpggGufOPCa2CAK5yUBVKAAhSgAAUoQAEKFCDAAFcAIgNc+Yj9roEBrt/irI8CFKAABShAAQpQII8AA1wepdntwx642fnNydEMcHPCzkopQAEKUIACFKAABboIMMCV30QY4Mo3LrwGBrjCSVkgBShAAQpQgAIUoEABAgxwBSB2KYIBrnzjwmtggCuclAVSgAIUoAAFKEABChQgwABXACIDXPmI/a6BAa7f4qyPAhSgAAUoQAEKUCCPAANcHqXZ7cMeuNn5zcnRDHBzws5KKUABClCAAhSgAAW6CDDAld9EGODKNy68Bga4wklZIAUoQAEKUIACFKBAAQIMcAUgdimCAa5848JrYIArnJQFUoACFKAABShAAQoUIMAAVwAiA1z5iP2ugQGu3+KsjwIUoAAFKEABClAgjwADXB6l2e0z0D1wtVpteRAEFwJ4MoDlALY5536uqh8XkX8EkLbzDg8P1yqVyusAPAHAgQB+A+CbAC4RkY2dLof3/vEAXg7gGAB7A9jknLsGwDvjOL6310vIANerGPenAAUoQAEKUIACFOiHAANc+coDG+DCMHy4c+56APsDGFPVHzrn7L9PAbAEwGdE5FmtlyCKokepqh2zD4DrnXMbVDUC8DgAvwOwXkT+o/UY7/3zAVwB4D4AX3PO3a2qjwWwGsAtO3bsWL9x48Z7ernUDHC9aHFfClCAAhSgAAUoQIF+CTDAlS89qAEu8N7/PAtR7xWRVzR726IoilT1JwCWAThNRL6WXYaK997C2UMBnCUiX2xeHu/9CwB82AKZiKxtljUyMrIiTdMxAFvSNF1Xr9c3tJR1NQALiB8UkQt6udQMcL1ocV8KUIACFKAABShAgX4JMMCVLz2QAc57fwKA7wH4LxF5CIBGW6/ZewG8FMBHRMR60BBF0Smq+nVV/VKSJGe0Xxrv/S0AjrKhlSJivXTw3r8dgA3RfJmIvK/1mOHh4YMqlcp/Wt3VavWg0dHRLXkvNwNcXinuRwEKUIACFKAABSjQTwEGuPK1BzLADQ8PH1apVE5S1S1JkvxLO3MUReer6mU25FFETrOfh2F4mXPufADniMhV7ceEYXixc+6i1h417/3t1stXqVRWjI2N/aJD6LMQeYKqPj1Jks/nvdwMcHmluB8FKEABClCAAhSgQD8FGODK1x7IANeNNYqiS1TVJiq5WkSea/tHUXSjqh7rnDsqjuNbO4SxpwGwEPY9ETlxxYoVS4aGhuy9t3tExN6t222LouhSVbXhkxeLyBu6nVfz5wxweaW4HwUoQAEKUIACFKBAPwUY4MrXZoBrM16zZs3QxMSEvbe2SlWf0eyh897fBeCQIAgOGB8f/98OAe5oAD8GcKeIHJa9Szeuqj9PkuSITpcyDMNXOOfe2RoU81xyBrg8StyHAhSgAAUoQAEKUKDfAgxw5YszwLUZe+/fY++sAfhZNiGJ2i5RFN2nqkvTNF1Sr9e3tV+aMAxXO+dsyKT1uO3nvW8Guh+JyLGdLmXLUM1rReTMvJfbe2/LG7ilS5fmPYT7UYACFKAABShAAQpQoHSBrVu3Wh0qIkHplQ1oBQsiwIVhaBOE2JpsXbdKpXLx+Pi4dNrRe/8aAG+xEBYEwaNb9/Pe20QnQdYYJ0Nd6+a9XwXAZpncJiJLWiZKmRxSOUWAO1dVr1TVbyRJYssX5Nqyc7FrN/kbwo0CFKAABShAAQpQgAJ7iID1MFiAq+wh57PgTmNBBDjv/SYAh+e8OieIyA86BLC3AXiVTfkfBMFTxsfHb2gLaDZL5N7bt2/fa9OmTQ+0H9/SA/dbe+ctWzPOliO4SUTWTRHgmpOlfEFEzsp5/tyNAhSgAAUoQAEKUIACFBhQgQUR4GZ57WxNuMsB2HIBd6dpenq9Xv9hh4C3GcCD0zQ9qF6v/0+HAHeMc+5HAH4hIitGRkZ8mqYxgNtFZM0UAe5CVbWlBq4SkXNm+Tl4OAUoQAEKUIACFKAABSiwwAUGPsB57z8O4C+cc79U1SeJyHina+69n5zy3zl3dBzH/94h4P0pgE8D+JaInJxNhmKzUD4gIrYo+G5bFEWXq+p5AF4rIm9d4G2NH48CFKAABShAAQpQgAIUmKXAQAe4luUCpNFoPGHDhg22sHbHLQzD9zvnXmyBK0mSKzoEuHcDeDmAt4vIq+3nzcW9nXMjcRxbb9wum/feguDaIAhOGR8f/8YsryUPpwAFKEABClCAAhSgAAUWuMDABrgoip6qqtcCuGvHjh3rNm7c+MvprnUYho91zn0XwLdF5Ilt+9owzDqAla09dN771wN4k60plySJTY6yc8smPbFjfletVg8ZHR3dvsDbGj8eBShAAQpQgAIUoAAFKDBLgYEMcLVabXEQBBaeljvnTorj+F9zODrvvb0bd4yqnp0kyT83j4mi6CJVvRjA9SLyhOa/r1y58uBqtZoAsGn/1zWHZ2bDK78I4FRVvShJkjfnqJ+7UIACFKAABShAAQpQgAIDLjCQAS6Kosnp+wH8VlU/MVUbcM7dLSIWzCY37/0IgO8D+CPn3LcAbFTVIwEcB+BXQRAcPz4+bjNi7ty89/Zu3KcA3A/gq7ZEAYCTAAxb4EvT9LRO68oNeLvkx6cABShAAQpQgAIUoAAFOggMZIDz3r8RwBtytIjJGSVb96vVasuDILBjT7W155xzv1bVLzUajUs2bNjw353KrNVqxwZBYGvMWdDbW1WtV+4TQ0NDl3LoZI6rwF0oQAEKUIACFKAABShAgUmBgQxwvPYUoAAFKEABClCAAhSgAAXmowAD3Hy8ajxnClCAAhSgAAUoQAEKUGAgBRjgBvKy80NTgAIUoAAFKEABClCAAvNRgAFuD7xq3vu/BvABAEuCIFjZPjFK6ykPDw/XKpXK6wDY7JcHAvgNgG8CuERENnb6eN77x2dr1h1j7+QB2OScuwbAO+M4vncPJCn0lGbyDuTIyMj6NE27zVZ6n4jsU+jJzqPCvPfa7XRbl9lo7juTNtytnoX08+y92wsBPNlmzgWwzTn3c1X9uIj8YzbL7eRHZjvt/crXarVHViqV16jqCQD2A7AZwJcnJibecscdd/xX7yUOxhG1Wu3kIAhebDMzA9gXwP/YJF/OuXfFcWxrnE5u3vuPA/iLLiofFpG/HQy5zp8yDMNznHMf62IwKiIPa90niqI/AfBCVX04gMUARFU/OTQ09IFBfsfee28Tyh2eo029SUTeyHY6tdSqVav2XbRo0UcBnGXLacVxvH6qvXttjzYrfKVSsTWWz7avCwATzrmfpWn6wSRJ/iXH9RvYXRjg9qBLf+SRR+59//33X+Gce07ztKYLcFEUPUpVrwdgoeF659wGVY0APM7WlwOwXkT+o/Ujeu+fD8AWIr8PwNdspk1VfSyA1QBu2bFjx/qNGzfaTJkLdoui6JQ0TU+Z6gMGQXBg9mVys4g8qu3G+Feq+rkpjt2WJIndaA/k1gxwqnrpVABpmr5nw4YN/9n8+Uza8CDhhmH4cOec/Y7vD2BMVX/onLP/tva7BMBnRORZTZOWAMd2mqOhhGF4mnPuCwBUVa8LgmCzqq4FcLRz7pc7duw4vrW95ihyIHZprnGaPTywB4Z3ArAZmR8NYEeapmfV6/UvtwW4b6nq7VMAfX/Qb9ZaApz9npvpblsQBHfFcfyO5g+8928F8GoAd9vfcwAPALB1ag93zn1zn332Of3mm2+eGIhG2fYhrY2qqn1XdtyccycCsND7IhH5UEuAYzttEbMHXEEQ2AP+VfbP0wW4XttjtqTX1+2e1Sb3c87dkN3PngbgQTbZYOtM8IPYjqf7zAxwe0iLWL16ddhoNGxhcQtSHwFgDfjQaQJcxXtv4eyh9lRERGxducnNe/8CAB+2QCYidjNi69DZ0/kVaZqOAdiSpum6er2+ITvEyroagN0IflBELthDWObkNLz31qvxV6r6lCRJvpLZTfbAdXv6NCcnvIdU2gxwIpL3e6XnNryHfNR+nUbgvf959p3wXhF5RfN3OYqiSFV/AmCZfVeIiN287eyBYzvtfolqtdqDgiCwGYEfpKrrkyS5qXlUFEXvVNVXOOe+HMfxU7uXNjh7ZLMq32hL46Rp+vh6vW7rozb/9liYsFCxedmyZSssPDRvjFX1L5Mksd44bh0EWgLcVSJyTjeklusgaZoeX6/XrQcUy5cv32vp0qXXAbAHsy8Xkfd2K2vQfr5ixYr9hoaG7Hf//q1bt0Z33nnn/Wynu7eCKIrsPsjuJbcBsPuil031t2Um7dF7/yoAbwNwzaGHHnr2DTfcsKPlXvXHtmRXEASPHh8fv3nQ2miez5v3RitPWdxnFgLeexs+8m7n3AvjOL6y2f0/VYCzXiRV/botYZAkyRntVXvvbwFwlA2tFBF7gm/B7u0ArIfoZSLyvtZjhoeHD6pUKtYz0qhWqweNjo5umcXHmbeHjoyMrE3T1L44bFH2k5sfpNmzwRvjqS9trwFuJm143jasGZy4996G9H0PwH+JyEPsd7O1GO+93Zi91B74iIj1rDPA9eAchuF5zrnLnXMfiOPYhgLu3NasWTM0MTFh34cHpWlaa3nY1UMNC3PXMAyvdM6dC+A9IvJ3bZ/SHjrYcjp24/WY8fHxG3ljnK8d9BrgvPefBmDrzNoDXOtF3rllPfc/s17kOI5tKaSuw9vzneXC2CuKoktV9QJVfU6SJLZO786hvnzQ8H/X2HtvQ6H3CoLgadahMN1D7Bm0R/uusJ77AyYmJg5rH64eRdFLVNXuU68WkecujJZX7KdggCvWc8alRVF0hqpuFhF7qm5fJpPjt6cKcGEYXuacOx/AOSJyVXvFYRhe7Jy7qLVHzXtvw1dWVyqVFWNjY79oP8Z7bzeLJ6jq05Mk+fyMP8z8PdB5739g73RUKpWHj42NWe/H5MYA1/2i9hrgZtKGu5/FwtljeHj4sEqlcpKqbuk0vCyKovNV9TIbOiUi1mPPdtrD5ffeW6/lqWmarq/X69/t8H34CQB248BejF3DgfXs2MiP7yZJYiM6dtm899aT+Wjn3DPjOL6GAS5fo+wxwNnNr73qUE3TdN96vW49JO3X4ZcADkvTdG29Xv9pvrNY+HvVarU1QRDYA257ReLYZrhlO9392nvvX12tVj9oD/S73AP13B699zbc+iZVvSFJEnvtp7392pBNGyV2j4j8Pz6E2P36MMDtod9X3QJcFEU3quqxzrmj4ji+tUPjtycmFsK+JyInrlixYsnQ0JC992a/DB3HhTefSgG4WETyLHS+h+rN7LTCMHy6c87eb7tSRJ7XWgoDXHfTXgNcr224+xkM1h5RFF2iqjaB0c4nlGyn+duA994mK3lwdgP8+w7foda7ab2cnxCRbpNw5K94ge/pvZ8MDs3RH7wxznfBewlwIyMjPk3TGMDPROSRnWoIw/Ba59xTVfXcJEm6TY6S7yQXwF7ee3s383R7WC0i9sB2cmM7nf7iTve3ZSbtsWU+hveJyMs61Z49pHiQc25VHMd3LIDmV+hHYIArlLO4wroFOO/9XQAOCYLggPHx8f/tcPNxNAAbCniniByWvTMzrqo/T5LkiCm+8O2dj3cOape19956P49yzkXtXxatX17Ouaer6pmqGjrnJtI0/dnQ0NBXBnnGr+wP4OQwnWq1unj79u32B9Im4Kg65+qqeq2I2AypO7de23Bxv13zv6RsiJ/1fqxS1Wc0e+jYTvNd2+yB1v32PrCI2HuEu202m5qqfhbAD0TEhrNy6yLgvbfZkL9lM1Ju3br18PZ3i1T11kqlYvscYDMmNxqNb7N36A+orQGu0WhcVKlUnqKqNhnJVufcTXEc28QmzffZn5Smqb3n9hURecoUN7/27tILnHNvjuPYRuMM/LZ69eqHNRoNe+D9HRGxdtj692hytlQbQsl2untT6RLgem6P3nt7983egfs7EXnPFG14NOvt3/kq0MA34hYABrg9tDV0C3BRFN2nqkvTNF3SafhEGIarnXM2ZNJ63Pbz3jcD3Y+yYQOdbliaQ7LsZvvMPZSmlNNqufHoON66ZXY/CyF7ZcsvtJ7LpiAInjHIL9u2LCNgw3Pbp2+2dyr/tnW4b69tuJQLP08L9d7bHzx7amlP4G2iosnwzHaa74LWarUDgyCwd7V+LSIPnuLm4dRsZr//EBGbrY7bNAJHHHHE/9u2bZs9NKw5514Sx/HkbLQts/t1+l6wXT6zZcuWczdv3rx1kIFbApz1DNuSQNU2D5u0zN5329jycOHTIvLsTm5hGL7DOfdKmxU4SZKXDLJt87OHYXh1Nsv3boGA7XT6FjJdgJtJe/TeTz5gUNXzkiSxmdF325rDsQE8rXWiPrblPwgwwJXQEsIwtBcv7Qu461apVC4eHx+X9h27BTjvvU1oEIhI0GlssPe+OX54m4gsaZkQYXJIZacTi6LoXFW9UlW/kSTJlNPsd/1Qfd6hIO9vA3i8qq5rnY2u+VFaboy3A3hLpVK5yqZ03rFjx0j2ou1J9tS50Wg8bMOGDXZjOK+3mZi2BDj7I/muRqMRq6pNjvNKW6coA3liy6Q6PbXheQ36hyfss/5eyG6IX2Nt0B7OZDN07fz+GLR2OtM2kb1f+MtskoeOa0VFUWTvH9oEULGIjMy0rkE4LgtvNh24rQln72Rar9Bkb1HLjbG10wvTNP3O4sWL0+3btz/FJpDJeuMGfphqS4D7vapeNDQ09Nlt27b9dtGiRTaxlt3s2kMEm3HyyCAInmlDe6cb3tt8D945d0Ucx+cNQjuc7jNms3DbzJOJiNg7nLtsbKfTt5DpApz33t4V7qk9NidDmm6Ib3NeBgDPFhGbtIdbiwADXAnNoYcFJK32XcZhN08nR4CzHo29t2/fvtemTZts7ZddtpYeuN/aO2/Zels2RPAmEVnX6WO3TIrwBRE5qwSaUoqcrXe2hIPdXIx1+mK3k46iyIZZRc6537QvrL5+/fpFmzdvtpfEbWjqgnh/cCam1sacc9vGx8dv69Aem5Pu7HyA4L3vqQ2X0nj6WOhMTDvcZDSHnWwJguAp4+Pjtm7Ozm3Q2ulML5/33obw2bTrNsPnIZ3K8d43e+CmfM9opvUvpONWrlx5cLVa/UY26/GNaZqeWq/Xd75TaDfOqmrecRzH97Z+9lqtti4IAluSAGmahoM82+fIyMgfqepKW4IhjmPrhdu5ZQspj9trE3bDGwTBvapqa3Ptsg5k6zHNHjgAU75jtJDaYbfP4r23h16vcc5dGMexvSqyy8Z2Or1glx64Z/TaHr33H8oWoD8/SZK/n+I7uDkh0hlxHH+p2zUetJ8zwO2hVzxHgGu+gH9Qc/2Xti/vY5xzPwLwCxFZ0fKS6e0isqbTx46i6EJVtaUGcq1Ds4fS9XxaLYvSvklE3thzAX/oXZmckhzAjSLymJmUsZCP8d5bD4a9s9XYunXrsuzdmJ7a8EL2yfHZbJYva1+2XMDdaZqe3rr+Vo7jJ3dhO/2DVLaA7AP2flEcx3tPcfNgU7R/mkuHTN26oihamS06XbN335YsWfK0W2+91SbLyr157+3vlPXcPV9EbA1Ubh0EmssAOec+1Wg0bASIheadM9C2HxJF0eU2PA3AjP+uLaQL4b23GQ1XzXRCjEFvp9MFuFqtdnKv7bG56LeqvjJJkndN8R1s9wwjQRA8rv1h5UJqmzP9LAxwM5Ur+bgcAW5yyn/n3NFxHNtaHbts3vvJmw/7o2rrmWWTHtgf1gemeWm/+YX/WhGxxVgHYssmL3lUmqbH1ut1u5noeWu+Q6eqSZIkvucCFvgBWS/lhH1M59xD7Alzc3hE3ja8wImm/XjN4T025E9VnyQi9jS+543t9P/IWmZLPLB9gh3bq+WB1s519noGX8AHZMNQbRFvW6Pw6mXLlp1rC3f3+pFb3ksaqL87vTp5721mZAu4NkmMBTMLJKMi8rApbn5tSKutF7tzrbNe61wo+2dLB9iyQDMeDj3o7bTLEMrmKzu522PzlR0AHxYRWwe5fbNlneyeda/mPcNCaY9FfQ4GuKIkCy6nW4ALw/D9zrkXT/UCqPf+3bZ+EYC3i8ir7fSai3s750ZsLEv7KWeLNq4NguCU8fFxe7q34Lfs3Q2bmOT+Qw89dL8bbrhhxzQf2n5fOi6I2vIS75RDVBc85h8+oL2TOfnuS+vWMmmEbtmyZR+bsGAmbXhADHf5mC3LBUij0XjChg0bbIHp6Ta20xwNxXv/RQBnTPV9F0XR52xNzOless9RzYLcxYb0VSqVHzjnLDxclt2ATbdYdMfvhezv0lcBPFlVpxxKtSARO3+oKZ3CMGzOEm3DJp/tvb8bwD5LlizZr1Ovp/fehggfMNXf+wEytYcxdq9k90zd3gdkO52iYXRZosbCVk/tsbnYPICOE+tlM4ba6xhTTjQ1SG2402dlgNtDW0COAPdYG9oD4Nsi8sS2j2HDreoAVrb2bjSHCtraUUmS2HjwnVs26Ykd87tqtXrIoEyJ3zL75E9ExBaW7Lh57+1ppi02eVpzEo42P+vt/FP7I5Ekia0fNVBbFEUXqardYFzaacrqlqF7t4jIIwwnDMOe2/BAof6hF8jWcboWgE2Ys27jxo22xtaUG9tp/hbivf9zGy4O4B9F5K9bj7R3CVXVhvjuNTEx8ZA77rjjv/KXvPD39N7bGqO21mjHWXubArVa7aFBENgEUTaZ1nD7w53swY71JNk7xg8XEZtpcRC3ivf+ZpsyvdFoPHTDhg32t3iXzXtvvZ3rmjN8RlH0UZvy3jn3Z3Ec/1Przi2TGU3ZIzJIyC29Zy8UkcvaPzvbaffW0G2N0Zm0R+/9RpuxeseOHSvb/7Z57+11FluPeKoeuu4nvcD3YIDbQy9wtwBnI9GyL/RjVPXsJEn+uflRspvpiwFc37rWSfayuc3CZD0k65rDsLLhlfY0+vsvi/cAABSVSURBVFSb/SpJkjfvoSyFn1YYhi+ymdDsvYI4jp8zVQUtpqMTExOPb72hi6LoDFX9AoAdzrkjOvVuFn7ie1iBLbOc2pCHk0TEphOf3LJJYr4P4OC2Gad6bsN72Mcu9XSy97TsRm65c+6kOI7/tVuFbKfdhP7v57YW3OLFi22m1Aer6hOSJLFh6bZZu/wHADZk7UoRsf/PLRMIw/A059xXANSr1eqaLg/7zNL+5lh4e4eI2Ayqkz30a9eurd57772ftAdfAKacHXlQ4Juz8tkaZc65M1snfAnD8AJ7OJY9YB0eHR29e2Rk5Ahbg9TWeq1Wq+tGR0d/bVbZhCf2XfEI59xz4zi+elAMp/n7fbOqPlJVT06SxIagtm9sp10aSbcAN5P26L1/gQU0W89w2bJlZzWHYGdDXu2BxVCapmsGeXKj6S4LA9we8s1mvWOqun/zdJxz59pTSefcx9I03TmjV+t6LtnEEHZj/EfOOftS2qiqRwI4DsCvgiA4vn3GxOzduE/ZkEEANnTlHrvhzv7AXp+m6Wmd1pXbQ5gKP42WF8M/EMfxi6eqYPny5XstXbrUniSbrV2P76jqXc45mxDmsRbeVPX5SZJ8rPCTnCcF2jT59nQ4u0Gzm5ANqnqwPRgAsLjTzfBM2vA84Zj1aba8I/BbVbUpmjtuzrm7RcQe2IDttDf27MGDDRdfZBNCOOfuUlX7Hbfv0dsWL1584m233fbb3kpd2Hu3TO39b6q62/vXzU8fBMF1cRxfF0XRY2xpmmztTBu6f5NzzpYQsWVb/tj+blUqlZPGxsZsnbiB3YaHh23JFft7bu9Q21I033XO/a+q2hquttbjfc65Z5hpEykMw7+zJVtsYiP7e66qE845+761tQ0/KSJ/MdWw/0GC9t5buD04TdNH1ut1C727bWynu5IMDw/XgiDY+W5aEATLbUi53Vuq6uda9v5xkiR2T2mjanptj9bz/C82lN0eCNl9FYD9nHOn2zrHnNho+t9SBrg95Fss7xTjIrLLNavVasuDILBuZvvSPtA592tV/VKj0bhkqvXIarXasUEQ2JNQu1HZ2ybesDU8hoaGLh2UoZPNy95cTBLAW0XktdM1h6yn0r7QbOFUm1XRQokNrbohTdP3TvWHYQ9pYn05De+9Das6H8AjAeybPSCw5Sv+QUSsl3K3bSZtuC8fZo4raRlC0u1MJmeabe7EdtqNa9efZ8OnXp8Nkd5PVX/hnLvGOff29mnveyt5Ye6d929V6+yHtVptOAiCCwE8wSY9cc7ZAy/7u/P5HTt2XLpx40Z7kDjwWzZ0195dP8s5N6yq9mDhV865bzYajXd16onIekRfBsCWcVmkqqPZ9+2VDG9/aFJRFN1ngaBSqfixsTFrdx03ttP/Y2kZhtvt93KXWct7bY82wdldd931QlU9xy6VDbe29+KCIHgHZ55kgOvW+PhzClCAAhSgAAUoQAEKUIAC80KAPXDz4jLxJClAAQpQgAIUoAAFKEABCgAMcGwFFKAABShAAQpQgAIUoAAF5okAA9w8uVA8TQpQgAIUoAAFKEABClCAAgxwbAMUoAAFKEABClCAAhSgAAXmiQAD3Dy5UDxNClCAAhSgAAUoQAEKUIACDHBsAxSgAAUoQAEKUIACFKAABeaJAAPcPLlQPE0KUIACFKAABShAAQpQgAIMcGwDFKAABShAAQpQgAIUoAAF5okAA9w8uVA8TQpQgAIUoAAFKEABClCAAgxwbAMUoAAFKEABClCAAhSgAAXmiQAD3Dy5UDxNClCAAhToLuC9V9srCILHjY+P39DtiJGRkfVpmv6r7Sci8+JvYhRFz1HVqwHclKbpifV6fVu3z9nLz733qwD8BMAiVV2XJMlYL8dzXwpQgAIUKFdgXvyxKpeApVOAAhSgwEIR8N5/0T5LpVJ53djY2M+7fa7Vq1c/rNFovDkLcGd223+ufx6G4cOdcz8EcJ9z7sg4jjeXcU4jIyNPStP0OgAbqtXqw0dHR7eUUQ/LpAAFKECB3gUY4Ho34xEUoAAFKDDPBMIwfJ1z7pIgCFaOj49vmmenP3m6a9asGZqYmPip/SeAZ4vIp8v8HFEU/b2q/g2Aj4jI88usi2VTgAIUoEB+AQa4/FbckwIUoAAF5qmA9/4rAE6bzwHOe/8yAO8B8G8icnzZl8J7f4D1wAFYFgTB0ePj4zeXXSfLpwAFKECB7gIMcN2NuAcFKEABCsxzAe/9/wA4YL4GuOXLl++1dOnSX9pnUNWTkyT5Vj8uiff+bQBeBeCrInJ6P+pkHRSgAAUoML0AAxxbCAUoQIEFKJC9K/VjAFXn3OPjOP5O+8f03n8VwJMBXC8iTwQwOQHIVFsURTeo6okAXpSm6WcqlcolqnoagIMA3KOq31XVN9br9dFOZYRheHoQBM9X1UcD2B+AvVcVA7hm+/btl23atOmB9uNqtdqxQRBc4Jw7TlUPAWATdmxW1e+o6ofq9frtrce0T2Livf84gL/ocD5Xicg53SYxiaLoMap6AYDHADgQgJ2j9Up9afv27e/ftGnT79rqfyOANwC4VkTO9N7/OYDzAawGsNjmSnHO/X0cx5f30uyiKDpXVa/8w1wrEnW4ljYs9HAAJzQajV9XKhV7r8+u1b4ANjrnPtys066Dc+5CAEfa64IArJ1cKCI2ccku2+rVqw9vNBp32D+maRrW63X77NwoQAEKUGAOBRjg5hCfVVOAAhQoU8B7/yYArwcwXq1WjxodHd3erC8Mw7Occ/8C4N5KpXLE2NjYL7qdS0uAuxjAcwBYoLKJQu4FcIwNtQNwvwWH9jAQhuFlzjkLMrYJgHoW4h5lsx065366aNGiJ46Ojt7dPA/v/bMA/JNNKgnAAkqS/fdR1hNlE3kEQXB662yT7QEuDEMLf9ZzZAHVNuu52moBMEmSD0wX4KIoulBV354d958ALJjuDeBoAEvsnLLZLne+U+e93xngVDV2zr0CgA09/O8sxK3Mynu1iDTL7kYP7/03s8/wdhF5dfsB3vvJAOec+zNVfa+FY+dcXVUt7Fmws+1vnHM7VPUfAPy7qt7vnFubXbffVyqVkbGxsbs6lG0Bzz7za0TEeuS4UYACFKDAHAowwM0hPqumAAUoUKbA2rVrq/fee++/Zz0trxWRt1p9Rx555N4PPPCATQ1/mKqelyTJFXnOoyXA3QPg1sWLF59x2223/daOXbNmzf4TExPfBvAIC2NxHFswmNyyXqirAGxX1bOTJLHgOLmtXr06bDQaFqosZHxCRJq9Zc57fyeAQ51zL43j+P3NY9avX7/orrvuepuq/p2dh4hYoGvWtdsyAiMjIyvSNJ3sRWofQjlVgKvVaicGQWDLC9jfyReJyIebPZQrV648eGho6Guq+kgA3xMR6+lq1t8McL+y3k8ATxERC0C22Wf6KIBzAPxGRA62jq1u9rVabXEQBL8HMDRNb2qzB87MzPF12fkG3vvPAXgaAAuheznnnhnH8eTSCbVabXkQBBbC93XOvSqO43e0n08URW9W1deq6g1Jkjyu2/ny5xSgAAUoUK4AA1y5viydAhSgwJwK1Gq1RwRB8GPn3Hbn3BqbgTEMw3c4514J4NvZ0Mlc59gS4FLnXC2O48lQ1BJeTrBAY/87TdM1zeGN3nsLCGtU9f1Jkry0vbKW3sAdjUbjIRs2bPjvNWvWHDIxMTHZG7RkyZJ9br311vtaj7MQt3nz5tfacMpqtXpVs3ex0zpwMwlwzUlPAHxRRCz87LLVarVHBkEwOalHmqZr6/W6zQ5pYbUZ4Ox//rmIfLL1wGzZgtvs34IgiMbHx603ctotDMNjnHM/yurat16vW5jbZWv2wAEYE5GHtQbDliUBoKqXJknykrbr9imb1RLAZ0XkT9vLjqLoqap6rfW0isiDup0vf04BClCAAuUKMMCV68vSKUABCsy5QBiGFzvnLnLOfTlN0wudc/9h73LlHTrZ/AAtAe5GEbF3wto36+2x3rl9nHN/FcfxR4eHhw+rVCo2+YZtj+70nlU2Pb6FksVZ79A1WS+hlVVxzj0vjmN7/6vrVkSAy3oubVjozvPpVLH33gLsClV9ZZIk77J9WgLc9jRNH9S+yPaKFSuWDA0N2TBTC3CPGR8fv7HbhwrD8GznnA0l/e+s1263Q1oC3G5DLKMoilR1PDvoCSJyfWsBLROVdAz0tVrtoUEQTL7X2Gg0DraA3e2c+XMKUIACFChPgAGuPFuWTAEKUGCPEMgCifUWHQHA3nWz4Yp/IyL2LlTurRngnHNXxHF83hSh5larR1UvSZLk9VEUnaKqX7d9t27duvTOO++cDC/tm/feAsJDnXOvj+P4Evt563tzNnwvCILPquq/ikgzjHQqZ9ZDKL33I9aTZYWr6kOTJJn87w7nPDkJjKp+NEmSv7KftwS4uoiEUxy32zlOdxG899Zrae+13SIij5iizOYQyt2ua2sPZKVS8WNjY/Yu4c6tec7Oue/Gcby+vfxVq1btu2jRosnJWlT1EUmS3JK70XBHClCAAhQoXIABrnBSFkgBClBgzxOIouhRqtqcZfAHImLDHXvaWnrgOk6kkQWY7wM4vjlcMgzDZzvnbIjedhGxWRg7bt77fwNwnAUVEXl5tlMQhuHbnHMvsne3Wg60d7k+t2PHjvdv3Lix2bs3+eMieuCymS8ne8YqlcqhnSb2sJ9FUfRP9k4fgM+LyNOz+ptDKEezoYy7fd5O5zjdhQjD8LXOOZtVcsr135o9cKr6l0mS2MybO7fphpC2nvNUAS57ADA5AY5z7vg4ju1acaMABShAgTkSYICbI3hWSwEKUKCfAt77vwXwwazOu6vV6prR0dFf93IOLT1wb4nj2CbJ6BROdgli2UyS/9wtwEVRdKOqHuuce3ccxzZz486tVqsdGATBmdmSBycBaL6HZVP6ny0iX2juXFCAWxcEwQ+tzOkCnPd+8t0xVf1ckiR/0hqGbMZKBrheWhf3pQAFKECBvAIMcHmluB8FKECBeSoQRdFKVbWJMxbZgswAzmquU9bLR2rpgfuQiFivWKcANzlhia2FJiIXt06gsWXLlr03b968dYrjbJiiDV3cOVtmp/2yyUtsSQBba82WLvh9EASrxsfH/zcLULMeQtn6zljrZCzt5+O9t6GhpwD4iIg8v6wA18sQyjJ64FasWLHf0NDQ5GyjHELZy28M96UABShQjgADXDmuLJUCFKDAniJgU9fbpBU2/fsbqtXqeycmJiwsLbc1w+I4tskxcm0tAe47IvL4DgdVsklM9nbOPTeO46u99w8BYFPb283/uiRJbmo/LpvYwyYxsUXHz4jj+EvdTiibWt8WlbbyTxORrxUV4LKQaOdjwzafJSKfmSJ02lDO5ar6YltTrqwA18skJmUEOE5i0q018ucUoAAF+ivAANdfb9ZGAQpQoK8CYRie55y7vHUxb++9DUe0YYc9DaVsCXDbq9Xq4e1DMKMoepwtkJ19wGER2ZiFGpv18kgbwikiF7QDNN+Tc85tnZiYOHTjxo33ZJOf2GLh32yfir95vPfe1rhbq6pPT5Lk81MFuNWrVx/eaDQmF9tuNBrhhg0bbBHxyW2qdeDCMLzWOfdUm7kzjuOntp9z63tyrcsBtExiUuQQytzLCJQR4LiMQF9/ZVkZBShAga4CDHBdibgDBShAgfkpsGrVqj9etGiRDWncR1XXJ0kyuUZbFnS+COCMXoZStgS436nqTar6zOaaZNl7araQtwW1XRa3jqLomapqvVgTzrk/iePY1hRrBqgj0jS9Lluwe+f7b1EU/YmqftYWvLZFqEXkB61XIQzDpzvn7OcTjUbjj5tT23d6By6KomWq2lw7bZcetWkW8j42CAKrM3DOvSCOYwvBk1vmaj1+trbdzvffMtfCJzHpZSHvkgIcF/Ken18BPGsKUGCBCjDALdALy49FAQpQIIqib6jqyQCuFJHntYrUarXlQRDYUEpbsy3XUMqWSUzepaoW/g4BYL1gKYB1VhaAe9I0Pa65iHdLYHwPgJfZ/1bVJAiCO1R1efbeWwDguu3btz9t06ZNNjGJbTb083PZ+3qTxzjnNjnnVFWHAdj/2X+/IEmSv2+pp+MU/d57m/r+KAA7AFgP3GYbBjpVgLPyoih6saq+z84FgA2XtOULDrLlDmy4J4CbgyB4UvP9u7ICXFbuNwHYu38dZwAtcxZK7/2PARwN4NUi8nb+ZlGAAhSgwNwKMMDNrT9rpwAFKFCKQBRFf6Wq/wjgf6rV6sjo6Ojd7RVFUfSSLKDkGkrZ0gP3psWLF1+6ffv2N6rqU6z3zCYTAXB9o9G4qHWIYmud3vsnAzjfFvQGsH92zC2q+skkST5pIxzbztGWEXiWc86WIjhSVQ+0iSEtfAH4gap+qP2duqmm6I+iyI63de8eDuB+Vf1qkiR/Nl2As3PJhkq+xJZGAGD1b3XO3Q7gM9u2bbuiJXBOnnoZQyizMHmuqtpi5iIiUfu1LCvAZcNPbcFypGka1ut1e++QGwUoQAEKzKEAA9wc4rNqClCAAvNJoDXAiYgNFeTWJ4Hly5fvtXTpUlvz7gDrVU2S5Fv9qNp7/zYArwLwFRGxsM6NAhSgAAXmWIABbo4vAKunAAUoMF8EGODm9kp5720Iqg1FnXJB7yLP0Ht/AADrcVuWpumj6vX6T4ssn2VRgAIUoMDMBBjgZubGoyhAAQoMnAAD3Nxe8jVr1gxNTExYiLLJU85OksQWSC9t895fAcDWt9u5zl1plbFgClCAAhTILcAAl5uKO1KAAhQYbAEGuLm//t57m4jlR/Yuns34KSK/KuOssmUcbKHyerVafcTo6OiWMuphmRSgAAUo0LsAA1zvZjyCAhSgwEAKMMDtGZe9ZWHvm9I0PbFer28r8sy896sA/ATAomzxdZutlBsFKEABCuwhAgxwe8iF4GlQgAIUoAAFKEABClCAAhToJsAA102IP6cABShAAQpQgAIUoAAFKLCHCDDA7SEXgqdBAQpQgAIUoAAFKEABClCgm8D/B4h8FR6eYBPIAAAAAElFTkSuQmCC\" width=\"799.9999826604674\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "newfig()\n",
-    "decorate(xlabel='x position (m)',\n",
-    "         ylabel='y position (m)',\n",
-    "         xlim=[-100, 100],\n",
-    "         ylim=[-200, -50],\n",
-    "         legend=False)\n",
-    "\n",
-    "for x, y in zip(xs, ys):\n",
-    "    plot(x, y, 'bo', update=True)\n",
-    "    sleep(0.01)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a function that encapsulates that code and runs the animation in (approximately) real time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def animate2d(xs, ys, speedup=1):\n",
-    "    \"\"\"Animate the results of a projectile simulation.\n",
-    "    \n",
-    "    xs: x position as a function of time\n",
-    "    ys: y position as a function of time\n",
-    "    \n",
-    "    speedup: how much to divide `dt` by\n",
-    "    \"\"\"\n",
-    "    # get the time intervals between elements\n",
-    "    ts = xs.index\n",
-    "    dts = np.diff(ts)\n",
-    "    dts = np.append(dts, 0)\n",
-    "\n",
-    "    # decorate the plot\n",
-    "    newfig()\n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)',\n",
-    "             xlim=[xs.min(), xs.max()],\n",
-    "             ylim=[ys.min(), ys.max()],\n",
-    "             legend=False)\n",
-    "\n",
-    "    # loop through the values\n",
-    "    for x, y, dt in zip(xs, ys, dts):\n",
-    "        plot(x, y, 'bo', update=True)\n",
-    "        sleep(dt / speedup)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" width=\"799.9999826604674\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "animate2d(system.results.x, system.results.y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/pendulum2.ipynb b/notebooks/pendulum2.ipynb
deleted file mode 100644
index d31a1f36..00000000
--- a/notebooks/pendulum2.ipynb
+++ /dev/null
@@ -1,4548 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 10 Example: Rigid Pendulum\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# If you want the figures to appear in the notebook, \n",
-    "# and you want to interact with them, use\n",
-    "# %matplotlib notebook\n",
-    "\n",
-    "# If you want the figures to appear in the notebook, \n",
-    "# and you don't want to interact with them, use\n",
-    "# %matplotlib inline\n",
-    "\n",
-    "# If you want the figures to appear in separate windows, use\n",
-    "# %matplotlib qt5\n",
-    "\n",
-    "# tempo switch from one to another, you have to select Kernel->Restart\n",
-    "\n",
-    "%matplotlib notebook\n",
-    "\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pendulum"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook solves the Spider-Man problem from spiderman.ipynb, demonstrating a different development process for physical simulations.\n",
-    "\n",
-    "In `pendulum_sympy`, we derive the equations of motion for a rigid pendulum without drag, yielding:\n",
-    "\n",
-    "$ \\ddot{x} = \\frac{x}{x^{2} + y^{2}} \\left(g y - vx^{2} - vy^{2}\\right) $\n",
-    "\n",
-    "$ \\ddot{y} = - \\frac{1}{x^{2} + y^{2}} \\left(g x^{2} + y \\left(vx^{2} + vy^{2}\\right)\\right) $\n",
-    "\n",
-    "We'll use the same conditions we saw in `spiderman.ipynb`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "condition = Condition(g = 9.8,\n",
-    "                      m = 75,\n",
-    "                      area = 1,\n",
-    "                      rho = 1.2,\n",
-    "                      v_term = 60,\n",
-    "                      duration = 30,\n",
-    "                      length0 = 100,\n",
-    "                      angle = (270 - 45),\n",
-    "                      k = 20)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "Now here's a version of `make_system` that takes a `Condition` object as a parameter.\n",
-    "\n",
-    "`make_system` uses the given value of `v_term` to compute the drag coefficient `C_d`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def make_system(condition):\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    condition: Condition with height, g, m, diameter, \n",
-    "               rho, v_term, and duration\n",
-    "    \n",
-    "    returns: System with init, g, m, rho, C_d, area, and ts\n",
-    "    \"\"\"\n",
-    "    unpack(condition)\n",
-    "        \n",
-    "    theta = np.deg2rad(angle)\n",
-    "    x, y = pol2cart(theta, length0)\n",
-    "    P = Vector(x, y)\n",
-    "    V = Vector(0, 0)\n",
-    "    \n",
-    "    init = State(x=P.x, y=P.y, vx=V.x, vy=V.y)\n",
-    "    C_d = 2 * m * g / (rho * area * v_term**2)\n",
-    "    ts = linspace(0, duration, 501)\n",
-    "    \n",
-    "    \n",
-    "    return System(init=init, g=g, m=m, rho=rho,\n",
-    "                  C_d=C_d, area=area, length0=length0,\n",
-    "                  k=k, ts=ts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "    .dataframe thead tr:only-child th {\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>x     -70.71067811865477 dimensionless\n",
-       "y     -...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>g</th>\n",
-       "      <td>9.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>m</th>\n",
-       "      <td>75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rho</th>\n",
-       "      <td>1.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C_d</th>\n",
-       "      <td>0.340278</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>area</th>\n",
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-       "      <th>k</th>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ts</th>\n",
-       "      <td>[0.0, 0.06, 0.12, 0.18, 0.24, 0.3, 0.36, 0.42,...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "init       x     -70.71067811865477 dimensionless\n",
-       "y     -...\n",
-       "g                                                        9.8\n",
-       "m                                                         75\n",
-       "rho                                                      1.2\n",
-       "C_d                                                 0.340278\n",
-       "area                                                       1\n",
-       "length0                                                  100\n",
-       "k                                                         20\n",
-       "ts         [0.0, 0.06, 0.12, 0.18, 0.24, 0.3, 0.36, 0.42,...\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system(condition)\n",
-    "system"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
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-       "  <tbody>\n",
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-       "      <th>x</th>\n",
-       "      <td>-70.71067811865477 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>y</th>\n",
-       "      <td>-70.71067811865474 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>vx</th>\n",
-       "      <td>0 dimensionless</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>vy</th>\n",
-       "      <td>0 dimensionless</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "x     -70.71067811865477 dimensionless\n",
-       "y     -70.71067811865474 dimensionless\n",
-       "vx                     0 dimensionless\n",
-       "vy                     0 dimensionless\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "To write the slope function, we can get the expressions for `ax` and `ay` directly from SymPy and plug them in."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the state variables.\n",
-    "    \n",
-    "    state: State (x, y, x velocity, y velocity)\n",
-    "    t: time\n",
-    "    system: System object with length0, m, k\n",
-    "    \n",
-    "    returns: sequence (vx, vy, ax, ay)\n",
-    "    \"\"\"\n",
-    "    x, y, vx, vy = state\n",
-    "    unpack(system)\n",
-    "\n",
-    "    ax = x*(g*y - vx**2 - vy**2)/(x**2 + y**2)\n",
-    "    ay = -(g*x**2 + y*(vx**2 + vy**2))/(x**2 + y**2)\n",
-    "\n",
-    "    return vx, vy, ax, ay"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "As always, let's test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<Quantity(0, 'dimensionless')>,\n",
-       " <Quantity(0, 'dimensionless')>,\n",
-       " <Quantity(4.9, 'dimensionless')>,\n",
-       " <Quantity(-4.900000000000003, 'dimensionless')>)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "And then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 132 ms, sys: 0 ns, total: 132 ms\n",
-      "Wall time: 130 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time run_odeint(system, slope_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing the results\n",
-    "\n",
-    "We can extract the x and y components as `Series` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "xs = system.results.x\n",
-    "ys = system.results.y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The simplest way to visualize the results is to plot x and y as functions of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
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-       "\n",
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-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\" width=\"799.9999826604674\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "newfig()\n",
-    "plot(xs, label='x')\n",
-    "plot(ys, label='y')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Position (m)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot the velocities the same way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "vxs = system.results.vx\n",
-    "vys = system.results.vy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\" width=\"799.9999826604674\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "newfig()\n",
-    "plot(vxs, label='vx')\n",
-    "plot(vys, label='vy')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Velocity (m/s)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another way to visualize the results is to plot y versus x.  The result is the trajectory through the plane of motion."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\" width=\"799.9999826604674\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "newfig()\n",
-    "plot(xs, ys, label='trajectory')\n",
-    "\n",
-    "decorate(xlabel='x position (m)',\n",
-    "         ylabel='y position (m)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also animate the trajectory.  If there's an error in the simulation, we can sometimes spot it by looking at animations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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zNXhKZXe+8Mkhd/R/ajRaDxtYmLigYefVP1wAldVinYIIIAAAggggAACdQoQ4NJrE+D2wjjLstMlvdJ7/yRjTNN7f6v3/qNFUXxEUjl9yNHR0YMbjcZbJYUHoTzGGPNf3vvPdjqd88LDTvqdAgGuXzHaI4AAAggggAACCNQhQIBLr0yAS28cvQIBLjopAyKAAAIIIIAAAghEECDARUDsMQQBLr1x9AoEuOikDIgAAggggAACCCAQQYAAFwGRAJcese4KBLi6xamHAAIIIIAAAgggUEWAAFdFaXZtOIGbnd+c9CbAzQk7RRFAAAEEEEAAAQR6CBDg0l8iBLj0xtErEOCikzIgAggggAACCCCAQAQBAlwExB5DEODSG0evQICLTsqACCCAAAIIIIAAAhEECHAREAlw6RHrrkCAq1uceggggAACCCCAAAJVBAhwVZRm14YTuNn5zUlvAtycsFMUAQQQQAABBBBAoIcAAS79JUKAS28cvQIBLjopAyKAAAIIIIAAAghEECDARUDsMQQBLr1x9AoEuOikDIgAAggggAACCCAQQYAAFwGRAJcese4KBLi6xamHAAIIIIAAAgggUEWAAFdFaXZtOIGbnd+c9CbAzQk7RRFAAAEEEEAAAQR6CBDg0l8iBLj0xtErEOCikzIgAggggAACCCCAQAQBAlwExB5DEODSG0evQICLTsqACCCAAAIIIIAAAhEECHAREAlw6RHrrkCAq1uceggggAACCCCAAAJVBAhwVZRm14YTuNn5zUlvAtycsFMUAQQQQAABBBBAoIcAAS79JUKAS28cvQIBLjopAyKAAAIIIIAAAghEECDARUDsMQQBLr1x9AoEuOikDIgAAggggAACCCAQQYAAFwGRAJcese4KBLi6xamHAAIIIIAAAgggUEWAAFdFaXZtOIGbnd+c9CbAzQk7RRFAAAEEEEAAAQR6CBDg0l8iBLj0xtErEOCikzIgAggggAACCCCAQAQBAlwExB5DEODSG0evQICLTsqACCCAAAIIIIAAAhEECHAREAlw6RHrrkCAq1uceggggAACCCCAAAJVBAhwVZRm14YTuNn5zUlvAtycsFMUAQQQQAABBBBAoIcAAS79JUKAS28cvQIBLjopAyKAAAIIIIAAAghEECDARUDsMQQBLr1x9AoEuOikDIgAAggggAACCCAQQYAAFwGRAJcese4KBLi6xamHAAIIIIAAAgggUEWAAFdFaXZtOIGbnd+c9CbAzQk7RRFAAAEEEEAAAQR6CBDg0l8iBLj0xtErEOCikzIgAggggAACCCCAQAQBAlwExB5DEODSG0evQICLTsqACCCAAAIIIIAAAhEECHAREAlw6RHrrkCAq1uceggggAACCCCAAAJVBAhwVZRm14YTuNn5zUlvAtycsFMUAQQQQAABBBII7NwpXX+9dN110pYt0vLl0jHHSEcdJQ0PJyjIkEkFCHBJeR8YnACX3jh6BQJcdFIGRAABBBBAAIE5EPjpT6X3vlf6+c8fWnz//aXXvEY68MA5mBgl91qAALfXdJU7EuAqU+07DQlw+85eMBMEEEAAAQQQ2DuBcPL21rfOHN4mRzzgAOkv/5KTuL0TnpteBLj07gS49MbRKxDgopMyIAIIIIAAAgjULPCNb0iXXtq76EteIj31qb3b0WLfECDApd8HAlx64+gVCHDRSRkQAQQQQAABBGoWePe7Jed6F80y6XWv692OFvuGAAEu/T4Q4NIbR69AgItOyoAIIIAAAgggULNAuDXyxz/uXfSgg/77Vks+gyFAgEu/TwS49MbRKxDgopMyIAIIIIAAAgjULMAJXM3gNZUjwKWHJsClN45egQAXnZQBEUAAAQQQQKBmAX4DVzN4TeUIcOmhCXDpjaNXIMBFJ2VABBBAAAEEEKhZgKdQ1gxeUzkCXHpoAlx64+gVCHDRSRkQAQQQQAABBOZAILwH7oILpDvvfGjx8AqBs87iPXBzsC2zKkmAmxVfpc4EuEpM+1YjAty+tR/MBgEEEEAAAQT2XiCcxF1/vfTtb0tbtkjLl0tHHy0ddRTvf9t71bnrSYBLb0+AS28cvQIBLjopAyKAAAIIIIAAAghEECDARUDsMQQBLr1x9AoEuOikDIgAAggggAACCCAQQYAAFwGRAJcese4KBLi6xamHAAIIIIAAAgggUEWAAFdFaXZtOIHr0290dHSk0Wj8X0nPlHSQpPskfU/S3zvnLpXkpw85MjIy2mw2z5b0DEmPkRR+qvslSec55zb2OQUR4PoVoz0CCCCAAAIIIIBAHQIEuPTKBLg+jEdHR49pNBoheC2XdKOk7xpjHua9f273b//snHvh1BCXZdkR3vtrJD1c0jXGmA3e+0zS0yXdLWmtcy4EwMofAlxlKhoigAACCCCAAAII1ChAgEuPTYDrw9hau17SmPf+vKIo3jLZdWRk5AnNZvN6SY8zxpyW5/mnut81rbUhnD1R0qnOuSsm+1hr/1TSByXd5Jw7XFJZdSoEuKpStEMAAQQQQAABBBCoU4AAl16bAFfR2Fo7JikEuB3Dw8PLx8fHd0ztmmXZ2733fyHpMufci8N3WZad7L3/gvf+s0VRnDK9lLX2JklPDrdWOufCKV2lDwGuEhONEEAAAQQQQAABBGoWIMClByfAVTS21h4l6duS7nDOPWF6t1ar9X+MMR82xvxbnue/Hb5vtVoXGmPOlPRS59wlM/Q51xhzjqT3O+deXXEq/AauKhTtEEAAAQQQQAABBGoVIMCl5ybAVTQeGxvbvyzLn0jqDA8P7z8+Pr51atcsy87z3ocHlfytc+714bssy77pvT/WGPPkPM+/P72UtfZ3JX1a0rXOuRMrToUAVxWKdggggAACCCCAAAK1ChDg0nMT4Powttb+o6Q/kPSebkh74ImTK1eufOzw8PA6SY+VdKhzbiL83Vr74/C7uEajccDExMTPZwhwR0oKv52b8VRvd1PjFso+No2mCCCAAAIIIIAAArUJEODSUxPg+jA+7LDDlm3btu3jxphwi+Q6730IX48wxvxmePKkMeaMPM8vnxwyy7J7vfdLy7Jc0m63t08v1Wq1VhtjbpV0j3PuUVWnQoCrKkU7BBBAAAEEEEAAgToFCHDptRdsgGu1Wu/pvpOtp3Kz2Tx3YmLChYZZlj0/PIUyHLBN6ei99x9uNpvnT0xMbJr8u7W2I6nhnGvM9H44a+0qSRskbXfOLek5kW4DAlxVKdohgAACCCCAAAII1ClAgEuvvWADnLU2BK1DKhIf75z7urX2fZL+zBjz1U6n89rFixePdzqd5Z1O57nGmL8NY5VleVK73Q7viHvgt2qSlu3YseNhmzZtun96rSkncHc55/arOBd+A1cVinYIIIAAAggggAACtQoQ4NJzL9gA1y9tlmVP9d5/XdKmHTt2rJ4eyKy14dUBl0r6mnPuhG6A2yzp8WVZHthut382Q4A72hhznaTbnXMrqs6JE7iqUrRDAAEEEEAAAQQQqFOAAJdemwBX0dha+y5J4emSMz7yf9WqVY8cGhq6OwxXluUj2+32L62110o63hhzZJ7nN0wvZa09TdInJV3tnHtWxalwAlcVinYIIIAAAggggAACtQoQ4NJzE+AqGltr/07Sy7335xdF8cbp3cIDTu6///4HXi0wPDz8+PHx8f9qtVrvNcac5b0/oyiKi2YIcJOh8J3OuTdVnAoBrioU7RBAAAEEEEAAAQRqFSDApecmwFU0tta+WdI7jDFfyvP8N6Z3Gx0dPbbRaHxT0tbuEyU7rVbrhPB7OUn/4Zx75rQ+DWttO7yFYHcndLubGrdQVtw0miGAAAIIIIAAAgjUKkCAS89NgKtoPDY2ZsuyDI/8bxpjTsvz/FOTXdesWbNo586dn5MUQto/OOf+qPudsdZ+S9LR3vsXFUXxick+WZad470/V9I1zrlnVJzGA80IcP1o0RYBBBBAAAEEEECgLgECXHppAlwfxlmWhdshw+sHjPf+K913uC2VdJKkJ0jKJT3NOXfn5LDW2rHwYBNJ+xtjrpa00Xt/mKTjJP2o0Wg8beqrB6pMhwBXRYk2CCCAAAIIIIAAAnULEODSixPg+jQeGxs7rizLsyQ9VdKB4R1uIbh57z/TaDTel+f5lulDjo6OHtxoNN4q6dnh3XPGmP/y3n+20+mct2HDhp/2OQVO4PoFoz0CCCCAAAIIIIBALQIEuPTMBLj0xtErcAIXnZQBEUAAAQQQQAABBCIIEOAiIPYYggCX3jh6BQJcdFIGRAABBBBAAAEEEIggQICLgEiAS49YdwUCXN3i1EMAAQQQQAABBBCoIkCAq6I0uzacwM3Ob056E+DmhJ2iCCCAAAIIIIAAAj0ECHDpLxECXHrj6BUIcNFJGRABBBBAAAEEEEAgggABLgJijyEIcOmNo1cgwEUnZUAEEEAAAQQQQACBCAIEuAiIBLj0iHVXIMDVLU49BBBAAAEEEEAAgSoCBLgqSrNrwwnc7PzmpDcBbk7YKYoAAggggAACCCDQQ4AAl/4SIcClN45egQAXnZQBEUAAAQQQQAABBCIIEOAiIPYYggCX3jh6BQJcdFIGRAABBBBAAAEEEIggQICLgEiAS49YdwUCXN3i1EMAAQQQQAABBBCoIkCAq6I0uzacwM3Ob056E+DmhJ2iCCCAAAIIIIAAAj0ECHDpLxECXHrj6BUIcNFJGRABBBBAAAEEEEAgggABLgJijyEIcOmNo1cgwEUnZUAEEEAAAQQQQACBCAIEuAiIBLj0iHVXIMDVLU49BBBAAAEEEEAAgSoCBLgqSrNrwwnc7PzmpDcBbk7YKYoAAggggAACCCDQQ4AAl/4SIcClN45egQAXnZQBEUAAAQQQQAABBCIIEOAiIPYYggCX3jh6BQJcdFIGRAABBBBAAAEEEIggQICLgEiAS49YdwUCXN3i1EMAAQQQQAABBBCoIkCAq6I0uzacwM3Ob056E+DmhJ2iCCCAAAIIIIAAAj0ECHDpLxECXHrj6BUIcNFJGRABBBBAAAEEEEAgggABLgJijyEIcOmNo1cgwEUnZUAEEEAAAQQQQACBCAIEuAiIBLj0iHVXIMDVLU49BBBAAAEEEEAAgSoCBLgqSrNrwwnc7PzmpDcBbk7YKYoAAggggAACCCDQQ4AAl/4SIcClN45egQAXnZQBEUAAAQQQQAABBCIIEOAiIPYYggCX3jh6BQJcdFIGRAABBBBAAAEEEIggQICLgEiAS49YdwUCXN3i1EMAAQQQQAABBBCoIkCAq6I0uzacwM3Ob056E+DmhJ2iCCCAAAIIIIAAAj0ECHDpLxECXHrj6BUIcNFJGRABBBBAAAEEEEAgggABLgJijyEIcOmNo1cgwEUnZUAEEEAAAQQQQACBCAIEuAiIBLj0iHVXIMDVLU49BBBAAAEEEEAAgSoCBLgqSrNrwwnc7PzmpDcBbk7YKYoAAggggAACCCDQQ4AAl/4SIcClN45egQAXnZQBEUAAAQQQQAABBCIIEOAiIPYYggCX3jh6BQJcdFIGRAABBBBAAAEEEIggQICLgEiAS49YdwUCXN3i1EMAAQQQQAABBBCoIkCAq6I0uzacwM3Ob056E+DmhJ2iCCCAAAIIIIAAAj0ECHDpLxECXHrj6BUIcNFJGRABBBBAAAEEEEAgggABLgJijyEIcOmNo1cgwEUnZUAEEEAAAQQQQACBCAIEuAiIBLj0iHVXIMDVLU49BBBAAAEEEEAAgSoCBLgqSrNrwwnc7PzmpDcBbk7YKYoAAggggAACCCDQQ4AAl/4SIcClN45egQAXnZQBEUAAAQQQQAABBCIIEOAiIPYYggCX3jh6BQJcdFIGRAABBBBAAAEEEIggQICLgEiAS49YdwUCXN3i1EMAAQQQQAABBBCoIkCAq6I0uzacwM3Ob056E+DmhJ2iCCCAAAIIIIAAAj0ECHDpLxECXHrj6BUIcNFJGRABBBBAAAEEEEAgggABLgJijyEIcJKstS+X9F6pmwYAACAASURBVD5JSxqNxsqJiYlNu3MbGRkZbTabZ0t6hqTHSLpT0pckneec2zhTP2vtSZJeL+loScskbTLGXC7p/DzPt/S7zQS4fsVojwACCCCAAAIIIFCHAAEuvfKCDnCHHXbYsm3btl1kjDl9knpPAS7LsiO899dIerika4wxG7z3maSnS7pb0lrn3Pembpu19hWSLpJ0r6TPG2N+4b0/QdJqSTft2rVr7caNG+/pZ6sJcP1o0RYBBBBAAAEEEECgLgECXHrpBRvgVq9e3ep0Old2g9RHJD1H0kF7CHBNa20IZ0+UdKpz7orJ7bHW/qmkD4ZA5pw7XFIZvhsbG1tRluV6SVvLsjym3W5v6PYJY10m6QWS3u+ce3U/W02A60eLtggggAACCCCAAAJ1CRDg0ksv2ABnrX2VpHcZY16Z5/nF1tpw2+QhuwtwWZad7L3/gvf+s0VRnDJ9a6y1N0l6cri10jkXTunCrZnvlPRGSa9zzr1nap+RkZEDm83mDyV1hoeHDxwfH99adbsJcFWlaIcAAggggAACCCBQpwABLr32gg1wWZad4r3f7Jz7Tjds7THAtVqtC40xZ0p6qXPukulb02q1zjXGnDP1RM1ae2s44Ws2myvWr19/+wyh71pJx3vvn1cUxaerbjcBrqoU7RBAAAEEEEAAAQTqFCDApddesAFuhjDV6wTum977Y40xT87z/Psz9P9dSSGEXeucO3HFihVLFi1aFH73do9zbr+ZtjLLsgu89+H2yXOdc2+tut0EuKpStEMAAQQQQAABBBCoU4AAl16bANc17nULpbX2x5Ie12g0DpiYmPj5DAHuSEnXS7rDOfeELMsy7/2E9/6WoigOnWkrW63WG4wx50u6zDn34qrbTYCrKkU7BBBAAAEEEEAAgToFCHDptQlwFQNclmX3eu+XlmW5pN1ub5++Na1Wa7UxJtwyGU7cHmWtnQx01znnjt3NCdyZ3vsLJV3pnPudqttNgKsqRTsEEEAAAQQQQACBOgUIcOm150WAa7Va4QEh4Z1sPT/NZvPciYkJN71hhRO4jqSGc64hyc/Qf5Wk8JTJ7c65Jdba48PtlJO3VO4mwL3Me3+x9/6qoihO7jn5/w2bW5cuXbrsxhtvrNqFdggggAACCCCAAAIIJBcgwCUn1rwIcJPhqyLX8c65r+9FgAtPiVy2Y8eOh23atOn+6f2nnMDdFX7z1n1nXHhAyredc8fsJsBNnsB9xjl3asX5h6dbEuCqYtEOAQQQQAABBBBAoDYBAlx66nkR4GIwVTiB2yzp8WVZHthut382Q4A72hhznaTbnXMrxsbGbFmWuaRbnXNrdhPg3ui9D68auMQ599Kq6yDAVZWiHQIIIIAAAggggECdAgS49Nq1BThr7QHGmIM6nc6ysKxmsxl+UxYe439n+mX2rlAhwD3wyH9jzJF5nt8wfURr7WmSPinpaufcs9asWbNo586d4SmU9zvnlu8mwH3Ie3+GpL9wzv1V71n+dwsCXFUp2iGAAAIIIIAAAgjUKUCAS6+dLMBlWbZS0vO998+SdLikGUOMpC2S1hljrpJ0eZ7nt6Vf9kMr9ApwrVbrvcaYs0LgKoriohkC3LskvV7SO51zb+oGrQde7m2MGcvzPJzGPehjrQ1B8PBGo3HyxMREWH+lDwGuEhONEEAAAQQQQAABBGoWIMClB48e4EZHR5/SbDbf5r3/TemB39hVrREeDBL++Zyktzrnvpd++f9boUKAO8EY81VJ/+Gce+a0uTWstW1JK6ee0Flr3yIpWJxdFMU7pvax1oaHnoQ+dw8PDz9ufHx8R9X1EuCqStEOAQQQQAABBBBAoE4BAlx67arhqudMDjrooKXLly//W+/9n4SnNXbD2A3GmC91Op3rjTFt7/2PDj744PvCYD/4wQ+WNZvNg7z3o81m86juSd0R3cBXGmM+smXLltdv3rz5gfapP70CXJiXtfZbko723r+oKIpPTM4py7JzvPfnSrrGOfeMyb+vXLnyscPDw4WkUtIxzrmJ8F339sorJD3be39OURRv72d9BLh+tGiLAAIIIIAAAgggUJcAAS69dJQAF15aXZblvxljRiX9UtKFu3bt+vDGjRt/0M8SVq1a9StDQ0NnSgr/PEKSazQavz3TY//7GXemtuF0zHu/3+R3xpiXhds8jTEfK8syrOGBT1EUr5n8d2vtmKSvSdrfGHO1pI3e+8MkHSfpR41G42kTExObptbr/jbu45K2Sfr38J44Sb8uaSQEvrIsnzPTe+X2tD4C3Gx3n/4IIIAAAggggAACKQQIcClUHzxmlABnrb07BC5jzEXGmLMnJiZ+Ppupj42N7V+WZXiox8vDLYbhsfyzGW83AS4ErUN6jeuce5DR6OjowY1G463h9Cy8e84Y81/e+892Op3zNmzY8NOZxhsdHT220Wi8uRv0lnnvw6ncpYsWLbqgn1snpwRJXiPQa+P4HgEEEEAAAQQQQKB2AQJcevJYAe5O7/0Li6IIp1LRPlmWney9v8w5d0C0QefBQJzAzYNNZAkIIIAAAggggMA8FCDApd/UKAEuPHEy1dMjU46dnjdNBQJcGldGRQABBBBAAAEEEJidAAFudn5VekcJcFUK0SaeAAEuniUjIYAAAggggAACCMQTIMDFs9zdSEkDXHgK49DQ0EuMMb9mjNnPez/cY0neOXdS+mUPdgUC3GDvH7NHAAEEEEAAAQTmqwABLv3OJgtwWZYd4b0Pv4kLT5OsWicEuGb6ZQ92BQLcYO8fs0cAAQQQQAABBOarAAEu/c5WDVZ9z8Rae42kp4fH5htjPu+9v13S9l4DOefe1qvNQv+eALfQrwDWjwACCCCAAAII7JsCBLj0+5IywG2R1DHGPDHP883pl7JwKhDgFs5es1IEEEAAAQQQQGCQBAhw6XcrZYAL74Zbx2/a4m8iAS6+KSMigAACCCCAAAIIzF6AADd7w14jpAxw1xpjhvI8P67XJPi+PwECXH9etEYAAQQQQAABBBCoR4AAl945WYBrtVq/ZYz5jPf+pKIork2/lIVTgQC3cPaalSKAAAIIIIAAAoMkQIBLv1vJAlyYepZlL/Pe/7Wky4wxn+t0Oj8oy3Lnnpa1cePGH6Rf9mBXIMAN9v4xewQQQAABBBBAYL4KEODS72zSAGetfbkx5mzv/cEVlxJeIzBUse2CbUaAW7Bbz8IRQAABBBBAAIF9WoAAl357kgW4Vqt1hjHmg90lVK7jnGukX/ZgVyDADfb+MXsEEEAAAQQQQGC+ChDg0u9s5WDV71RardbNxpg1kj5TluVFQ0NDm4wxPd8Dt379+vC+OD57ECDAcXkggAACCCCAAAII7IsCBLj0u5IswGVZdq/3/ifOuVXpl7GwKhDgFtZ+s1oEEEAAAQQQQGBQBAhw6XcqWYCz1oaXd3/POffs9MtYWBUIcAtrv1ktAggggAACCCAwKAIEuPQ7lTLAfVLS4c65VvplLKwKBLiFtd+sFgEEEEAAAQQQGBQBAlz6nUoW4EZHR0cajcY3vPd/VxTFW9IvZeFUIMAtnL1mpQgggAACCCCAwCAJEODS71ayADc2NnZcp9NZYYx5p6RfSrpS0m2SduxpWc65S9Mve7ArEOAGe/+YPQIIIIAAAgggMF8FCHDpdzZZgLPWlpJ8dwmhzuS/72lVvAeuwp4T4Cog0QQBBBBAAAEEEECgdgECXHrylAFuU8XQ9qBVOudWpl/2YFcgwA32/jF7BBBAAAEEEEBgvgoQ4NLvbLIAl37qC7cCAW7h7j0rRwABBBBAAAEE9mUBAlz63YkS4A4//PDhdevW7Uwx3ZRjp5hvHWMS4OpQpgYCCCCAAAIIIIBAvwIEuH7F+m8fJcBlWfbNHTt2/O5tt932k/6nsPseWZYdJOlf8jw/Lua4gz4WAW7Qd5D5I4AAAggggAAC81OAAJd+X6MEuO4DS35mjHlVnueXx5i2tfY0SR+QtJ9zrhljzPkyBgFuvuwk60AAAQQQQAABBOaXAAEu/X5GCXBZlp3uvf+IpMWSvm6MeVue51/em+lba0+S9FZJT5W03Xv/x0VRfGJvxpqvfQhw83VnWRcCCCCAAAIIIDDYAgS49PsXJcCFabZarV81xlwm6Yndp086SZ9uNBpXN5vNG8bHx7fOtJw1a9Y8vNPpHFGW5TMlnSrJSjLe+/FGo3F6nuffT88wWBUIcIO1X8wWAQQQQAABBBBYKAIEuPQ7HS3AhamGB45s2bLlzyT9X0kHTHuNwE+NMZu99/eGtsaYZd778Bu3A6csM8znTkl/vXz58g+kejBKeta0FQhwaX0ZHQEEEEAAAQQQQGDvBAhwe+fWT6+oAW6y8IoVK5YsXrz4RZJO896fKGlRj0ntMMZ8tSzLT+7cufMTmzZtur+fRSy0tgS4hbbjrBcBBBBAAAEEEBgMAQJc+n1KEuCmTnvNmjWLdu7c+ave+5FGo/FY7/3S8L0x5r6yLH9ijNkwPDx80/j4+I70y50fFQhw82MfWQUCCCCAAAIIIDDfBAhw6Xc0eYBLv4SFV4EAt/D2nBUjgAACCCCAAAKDIECAS79LBLj0xtErEOCikzIgAggggAACCCCAQAQBAlwExB5DEODSG0evQICLTsqACCCAAAIIIIAAAhEECHAREAlw6RHrrkCAq1uceggggAACCCCAAAJVBAhwVZRm14YTuNn5zUlvAtycsFMUAQQQQAABBBBAoIcAAS79JUKAS28cvQIBLjopAyKAAAIIIIAAAghEECDARUDsMQQBLr1x9AoEuOikDIgAAggggAACCCAQQYAAFwGRAJcese4KBLi6xamHAAIIIIAAAgggUEWAAFdFaXZtOIGbnd+c9CbAzQk7RRFAAAEEEEAAAQR6CBDg0l8itQS4kZGRUWPMAcaYRb2WVBTFtb3aLPTvCXAL/Qpg/QgggAACCCCAwL4pQIBLvy9JA1yWZX/uvX+9pAMqLsU754Yqtl2wzQhwC3brWTgCCCCAAAIIILBPCxDg0m9PsgDXarXOMMZc2O8SnHONfvsstPYEuIW246wXAQQQQAABBBAYDAECXPp9ShbgrLU3STrUGHNJp9N5t6R2u93enn5J878CAW7+7zErRAABBBBAAAEEBlGAAJd+11IGuPskbXbOjaZfxsKqQIBbWPvNahFAAAEEEEAAgUERIMCl36mUAe4uSV93zj03/TIWVgUC3MLab1aLAAIIIIAAAggMigABLv1OJQtwWZZ903vfdM4dnX4Zs6tgrX25pPdJWtJoNFZOTExs2t2I1tqjvPdvMMYcL2k/SfdIuk7Se51z18zUz1p7kqTwMJdgsUzSJmPM5ZLOz/N8S7+zJ8D1K0Z7BBBAAAEEEEAAgToECHDplZMFOGvtSyR9rNFoHDUxMbEu/VL6r3DYYYct27Zt20XGmNMne+8pwLVarT8yxnxEUlNSeN1BLincIvp0Sd4Y88o8zz80dSbW2ldIukjSvZI+b4z5hff+BEmrJd20a9eutRs3bgwhsPKHAFeZioYIIIAAAggggAACNQoQ4NJjJwtwYerW2ndK+sPwKoFHPOIRl69bt25n+iVVq7B69epWp9O5shukQih7jqSDdhfgxsbGVpRlOSFpkff+94qi+PRkpVar9SJjzD8ZY+7z3h/inLszfNfts17S1rIsj2m32xu6fZrW2sskvUDS+51zr6426/9uRYDrR4u2CCCAAAIIIIAAAnUJEODSSycLcNbayYBzZAhGknZI+oGkPT2JMrwH7snpl/1ACHqVpHd1T80uttaG2yYP2V2Ay7LsHO/9ucaYf83z/Pemz9Fa+11JT/Hen14UxcenBNg3Snqdc+49U/uMjIwc2Gw2fyipMzw8fOD4+PjWqusmwFWVoh0CCCCAAAIIIIBAnQIEuPTaKQNcGW4rlNRPjRDgwu2JyT9Zlp3ivQ9PyfxON2ztMcCNjY0dXpblr5VleXO73Q6/eXvQx1r7z5Ke773/86Io/qY75q3hhK/ZbK5Yv3797TP0CbdhHu+9f97UE71eiyfA9RLiewQQQAABBBBAAIG5ECDApVfvJ1z1NRtr7Vv76tBt7Jx72970m22fXidwvca31n5N0tOMMX+S5/nFK1asWLJo0aLwu7d7nHPhYScP+WRZdoH3Ptw+ea5zrrIXAa7XbvA9AggggAACCCCAwFwIEODSqycLcOmnHrfCbALc2NiYLcsy/NYt/MZvxDn3oyzLMu/9hPf+lqIoDp1ptq1WKzzN8nxJlznnXlx1RQS4qlK0QwABBBBAAAEEEKhTgACXXpsA1zXe2wC3Zs2aRbt27fqK9/5Y7/17i6J4bRjSWht++3d9eMWAc+7YmbYyy7IzvfcXSrrSOfc7VbebAFdVinYIIIAAAggggAACdQoQ4NJr1xLgWq3WrxpjwrvQxrz3jzLGlMaYu8LpVKfT+eKGDRvas1lqq9UKDwh5TJUxms3muRMTE256270JcKOjo4ubzebl3vvnGmO+e++99z7tjjvu2NYNcOE9ceE3btc6507cTYB7mff+Yu/9VUVRnFxl/t2xty5dunTZjTfeWLUL7RBAAAEEEEAAAQQQSC5AgEtO3NcDRvqezapVq36l2WxeYowJ7z2b/EyGxvCAk8nP5YsXLz7z5ptvvqvvIv992vXAA0gq9j3eOff12Qa4NWvWPHznzp1XSArBNLwP7qRw6+TkuFmWHeG9Dw9I+bZz7pjdBLjJE7jPOOdOrTh/XiNQFYp2CCCAAAIIIIAAArUKEODScyc7gVu1atUjh4aGwhFRCFahTghZt3rv7240GsZ7/2hJT5J0cPdplTcMDw8fPz4+Hl43UPunnxO4sbGx/cuy/Lyko8LLuHfu3Hnybbfd9pOpk+7+Li4Eu1udc2t2E+De6L0P78q7xDn30qqL5hbKqlK0QwABBBBAAAEEEKhTgACXXjtZgOs+hTI8WfG7ZVm+vN1uz3i/3+jo6LGNRiO8SHu1pLOccx9Iv+yHVqga4LrBNDxxMjyY5D+MMafmeb5l+ojht3E7d+4MT6G83zm3fDcB7kPe+zMk/YVz7q+qrpsAV1WKdggggAACCCCAAAJ1ChDg0msnC3BZlq0Lv3nrdDor2u32z/a0lHCr5dDQUCHpO865p6Vf9t4FuMMPP3x4y5YtX5T065KuKMvyBe12e7cvJrfW3iTpycaYsTzPw2ncgz7W2hskHd5oNE6emJi4quq6CXBVpWiHAAIIIIAAAgggUKcAAS69drIAZ6292xhzU57na6ssw1r7wO2Wu3tnWpUxZtOmygmctfZvJYWnTP7n8PDwyb1u97TWvkXS27z3ZxdF8Y6p87PWrpIUHt5y9/Dw8ON6jTWtLw8xmc1m0xcBBBBAAAEEEEAgiQABLgnrgwZNGeDul3SNc+45VZaRZdlV3vsTnXNLqrSP3aZXgBsbGzu0LMtwonZPo9FoTUxM/LzXHFauXPnY4eHhcLJYSjrGOTcR+nRvrwwPQHm29/6coije3mssAlw/QrRFAAEEEEAAAQQQmAsBAlx69ZQB7oeSOs65FVWWkWXZ7d77hnPuCVXaz7ZNOB3z3u83OY4x5mWSlhtjPlaW5S8n/14UxWvCv1trL5UUXrZ9s/f+y3uof31RFB+f/N5ae5qk8N/h9QL/HgJg9xbMkRBwy7J8zp5uw5ypDrdQznb36Y8AAggggAACCCCQQoAAl0L1wWOmDHD/LOn3jDGvyvP8Q3taSqvVOsMYE15o/c/OuRemX3b1Vw845x4wyrIsvKx7xve5TZvvQ54o2X1Qy5slHSdpmfc+nMpdumjRogv6uXVySijkFso6LhJqIIAAAggggAACCPQlQIDri2uvGicLcGNjY8eVZRnet+aNMZ8py/ITjUbjlh07dtwdZjo8PPxoY8yh3vvTJT03tPPeP7Uoim/v1UoWUCdO4BbQZrNUBBBAAAEEEEBggAQIcOk3K1mA655ahfec9Xo8fphD6b1/XVEU70u/5MGvQIAb/D1kBQgggAACCCCAwHwUIMCl39WkAS5Mf3R09MRGo/EGSSdJWjx1ScaYbd77q8uyfGe73b4u/XLnRwUC3PzYR1aBAAIIIIAAAgjMNwECXPodTR7gJpewdu3aoR//+McjnU4n3Drpm83mXRMTExvCg07SL3N+VSDAza/9ZDUIIIAAAggggMB8ESDApd/J2gJc+qUsnAoEuIWz16wUAQQQQAABBBAYJAECXPrdIsClN45egQAXnZQBEUAAAQQQQAABBCIIEOAiIPYYIkqAs9aG2yCvcM49b7Je92/9rsA754b67bTQ2hPgFtqOs14EEEAAAQQQQGAwBAhw6fcpVoAruwHu1CkBLvyt308IcM1+Oy209gS4hbbjrBcBBBBAAAEEEBgMAQJc+n2KEuDCkyYl3dlut8cnp9z9W98raLfbX+270wLrQIBbYBvOchFAAAEEEEAAgQERIMCl36goAS79NKkwVYAAx/WAAAIIIIAAAgggsC8KEODS70qyAJdl2d9777/rnPtAlWVYa6+WtNk594dV2i/kNgS4hbz7rB0BBBBAAAEEENh3BQhw6fcmWYCz1j7kd3F7Wo619lZJj3XO7Z9+2YNdgQA32PvH7BFAAAEEEEAAgfkqQIBLv7NRA9yqVat+ZXLKQ0NDm7z3X+p0Oq/Y0zKGhoaGjDGHe+//SdIu59zS9Mse7AoEuMHeP2aPAAIIIIAAAgjMVwECXPqdjRrguqdufi+nbYwx1+V5ftxe9l8w3QhwC2arWSgCCCCAAAIIIDBQAgS49NsVNcC1Wq2zjTFHSwr/hFsh+xn/e81m88Xr16+/Jf2yB7sCAW6w94/ZI4AAAggggAAC81WAAJd+Z/sJWH3NJpzGhVsoh4aG/s+eOm7fvt0/7GEP+8X4+PjWvgos4MYEuAW8+SwdAQQQQAABBBDYhwUIcOk3J2WA+5ikdVWfQpl+qfOnAgFu/uwlK0EAAQQQQAABBOaTAAEu/W4mC3Dpp75wKxDgFu7es3IEEEAAAQQQQGBfFiDApd+dKAGu1Wqd4L2/s91uh1cBPPAJf9ub6RdFce3e9FtIfQhwC2m3WSsCCCCAAAIIIDA4AgS49HsVJcBZazuSrnDOPW9yynv5RErvnBtKv+zBrkCAG+z9Y/YIIIAAAggggMB8FSDApd/ZWAHuIS/t7ga4vlfgnGv03WmBdSDALbANZ7kIIIAAAggggMCACBDg0m9UlACXfppUmCpAgON6QAABBBBAAAEEENgXBQhw6XeFAJfeOHoFAlx0UgZEAAEEEEAAAQQQiCBAgIuA2GOIWgLc2rVrh77yla/smjqXQw899NE7duw42nu/RNLXnXN3pl/u/KhAgJsf+8gqEEAAAQQQQACB+SZAgEu/o0kDXHgSpTHmAmPMB/I8v3hyOVmWneK9/wdJj+j+bbuks5xzH0m/5MGvQIAb/D1kBQgggAACCCCAwHwUIMCl39VkAa7Vaq02xlwvaakx5i/zPD8vLGdkZOQJzWYzlxRO3n4k6b8kPaW71GOdc99Jv+zBrkCAG+z9Y/YIIIAAAggggMB8FSDApd/ZZAHOWvtBSWd67/+mKIo3SwqvGpC19t2SXmuMuW5oaGjt+Pj4jlar9TxjzOWSLnXOvTT9sge7AgFusPeP2SOAAAIIIIAAAvNVgACXfmdTBrj1kvZzzj1eUnjNwAOfLMtu994f3Gg0fnNiYuKqyb9bawtJDefcSPplD3YFAtxg7x+zRwABBBBAAAEE5qsAAS79zqYMcPdI+rZz7llTwlvmvQ/B7h7n3P5Tg12r1fqiMeZE59zD0i97sCsQ4AZ7/5g9AggggAACCCAwXwUIcOl3NmWAu997/5WiKE6eXEar1TrDGHOhpM845543dXlZll3lvX+qc+7h6Zc92BUIcIO9f8weAQQQQAABBBCYrwIEuPQ7mzLAbQq/e5t6S6S19suSTjTGvGLqUynDMq214cEmi5xzK9Mve7ArEOAGe/+YPQIIIIAAAgggMF8FCHDpdzZlgPuEpOd771+zbdu2jy5btuyF3vuPSrqv0WgcMjEx8fPJ5WVZ9lTv/de89/9WFMUp6Zc92BUIcIO9f8weAQQQQAABBBCYrwIEuPQ7myzAjY6OHtNoNL4uaWqN8O//zzn3pinh7Q+89+HJlAdIOt0598n0yx7sCgS4wd4/Zo8AAggggAACCMxXAQJc+p1NFuDC1K21L5AUwll4EmV4jcDfOedeOXVZ1tpLJYUQ969FUfx++iUPfgUC3ODvIStAAAEEEEAAAQTmowABLv2uJg1wk9NfuXLlY7dv375l8+bN901f0tjY2G9475fnef4v6Zc7PyoQ4ObHPrIKBBBAAAEEEEBgvgkQ4NLvaC0BLv0yFlYFAtzC2m9WiwACCCCAAAIIDIoAAS79TtUS4Ky1B3jvT5Q01mg0HlWWZWmMuct7f0uj0fhqnudb0i91/lQgwM2fvWQlCCCAAAIIIIDAfBIgwKXfzaQBbsWKFY9atGjReyS9SNLQbpYTbqv84PDw8FvGx8d3pF/y4FcgwA3+HrICBBBAAAEEEEBgPgoQ4NLvarIANzo6urjRaHxD0lO6T6LcLmmDpLu7//1oSSOShiV5SV9wzj23++/pVz7AFQhwA7x5TB0BBBBAAAEEEJjHAgS49JubLMBZa1/bfQLl7ZJet3z58s+tW7du59QlhZBnjDnVGPM33SdVvtQ594/plz3YFQhwg71/zB4BBBBAAAEEEJivAgS49DubLMBlWfZN7/3hZVk+sd1uh5O33X7GxsYOLcvyRklfds49K/2yB7sCAW6w94/ZI4AAAggggAAC81WAAJd+Z5MFOGvtzyVNOOeeWmUZ1tpbJD3WOfeYKu0XchsC3ELefdaOAAIIIIAAAgjsuwIEuPR7kzLAhd+8fan7u7aeK2m1Wl80xjzdObe4Z+MF3oAAt8AvAJaPEH4aLgAAIABJREFUAAIIIIAAAgjsowIEuPQbkzLA/Tg8sMQ5t7rKMqy1uaRHOOceX6X9Qm5DgFvIu8/aEUAAAQQQQACBfVeAAJd+b5IFuCzLPuu9f46k5znnrtjTUrIs+23v/RXe+38riuKU9Mse7AoEuMHeP2aPAAIIIIAAAgjMVwECXPqdTRbgWq3WbxljPisp3Er5wUaj8Yldu3bd0m63w39rxYoVSxYtWnSo9/50Y8wZ4XUCxpjn5Hn+xfTLfnAFa+3LJb1P0pJGo7FyYmJiU9U5WGvfIenNkm53zq2YqZ+19iRJr5d0tKRlkjYZYy6XdP7evMScAFd1d2iHAAIIIIAAAgggUKcAAS69drIAF6bearUu7Iaz8J63yc/ky7oXTfmbMca8O8/zN6Rf8v9WOOyww5Zt27btImPM6ZN/7SfArV69+kmdTue73XfZzRjgrLWvkHSRpHslfd4Y8wvv/QmSwq2lN+3atWvtxo0b7+ln3QS4frRoiwACCCCAAAIIIFCXAAEuvXTSABemb619iaQ/l/TE3Szn+8aYd+R5Hk6kavusXr261el0ruwGqY9ICrd7HtRHgDNZln3De28l7T/TCdzY2NiKsizXS9paluUxU16n0LTWXibpBZLe75x7dT8LJ8D1o0VbBBBAAAEEEEAAgboECHDppZMHuMkljI6OHjw0NLSmLMtHS/JlWd7VbDZvyfN8c/plPrSCtfZVkt5ljHllnucXW2vDbZOHVA1w1to/DbeGdm+NfPdMAc5a+05JbwwvMnfOvWfqLEZGRg5sNps/lNQZHh4+cHx8fGtVBwJcVSnaIYAAAggggAACCNQpQIBLr11bgEu/lP4qZFl2ivd+s3PuO6FnPwEuy7KDvPfrvfc/eMQjHvFrW7ZsCbeFPuQWSmvtreGEr9lsrli/fv3t02dorb1W0vHe++cVRfHpqisgwFWVoh0CCCCAAAIIIIBAnQIEuPTatQQ4a+2Y9/4YY0x4yMdyY0xZlmX43dfGZrP5zX4eGpKKpJ8AZ639V0m/W5blU9vt9resteE3fg8KcN2HtITfvd3jnNtvpnlnWXaB9z7cPnmuc+6tVddGgKsqRTsEEEAAAQQQQACBOgUIcOm1kwa4sbGxw8uy/LCkX+uxlK8aY87M8zy8C25OPlUDXPeVB1caYz6c5/mZYbIzBbgsyzLv/YT3/paiKA6daVGtVusNxpjzJV3mnHtx1YUT4KpK0Q4BBBBAAAEEEECgTgECXHrtZAFudHT0ic1m8zve+yWSQp3wG6/bwomUpIb3/pHdE7nwWP3wuUvSkc65jemX/dAKVQJclmXLvffhtsjmrl27Vk8+PXKmAGetPVLS9ZKuc84dO9Oasiw703t/oaQrnXO/U3XdBLiqUrRDAAEEEEAAAQQQqFOAAJdeO1mAs9Z+StLvGWNu9N6f5Zz7Rnh4ybQlNbIsO9F7/15J4Z1wHy+K4g/6XXar1QoPCHlMlX7NZvPciYkJN71tlQBnrQ3vivszY8zzpz41czcB7nhJ4Tdu1zrnTtxNgHuZ9/5i7/1VRVGcXGX+oQ0BrqoU7RBAAAEEEEAAAQTqFCDApddOGeB+Imnxjh07VmzatOnuPS1ldHT0MY1GY4Ok+5xzj+t32ZPhq2K/451zX+83wFlrj5L0LUlfcM791tT+u7mF8gjvfXhAyredc8fsJsBNnsB9xjl3asX5E+CqQtEOAQQQQAABBBBAoFYBAlx67pQB7n5jzFfzPP+NKsuw1n5J0tOcc0urtI/dZk8ncGvXrh3avHnzDZJGms3mk6Y/UXKmADc2NmbLsgy/6bvVObdmNwHujd778KqBS5xzL626Jk7gqkrRDgEEEEAAAQQQQKBOAQJceu2UAe4Hxpg78jw/rsoyrLX/IWmFc260SvvYbfYU4LIs+33vfbgl9DZjzHen1w6vATDG3BdO58J3nU7nzMWLF9+zc+fO8BTK+51zy3cT4D7kvT9D0l845/6q6poIcFWlaIcAAggggAACCCBQpwABLr12sgCXZdmHvfcvKsvy4Ha7/cs9LaX7cJAfeu8/VhTFa9Mv+6EV9hTgWq3WS40xH6s6r8mXgVtrb5L0ZGPM2ExP2LTWhlO9wxuNxskTExNXVR2fAFdVinYIIIAAAggggAACdQoQ4NJrJwtw1toDwm/GvPd3dDqdP9y4ceMPZlrOyMjIgc1m85Jw+rZr165jJp/smH7pD65Q5SEmu5vTTLdQhrbW2rdIepv3/uyiKN4xtb+1dpWktqS7h4eHHzc+Ph5eBl7pQ4CrxEQjBBBAAAEEEEAAgZoFCHDpwVMGuD+VdKCksyQ9PDxO33t/szHmLu99p9FoPNJ7/8Twu7fw8BJJH+n+70NW7Zw7NzVFigC3cuXKxw4PDxeSSknHOOcmwjrWrFmzaOfOnVdIerb3/pyiKN7ez/oIcP1o0RYBBBBAAAEEEECgLgECXHrplAEuhJbw2oCpNaa/RmBP3/3P6p1zzdgU4XTMe7/f5LjGmJdJWh5ulSzL8n9u+SyK4jW9au/uBC70s9aeJunjkrZJ+vfue/B+PTwQRdI1ZVk+p91ub+9VY+r3BLh+tGiLAAIIIIAAAgggUJcAAS69dLIAl2XZV8qynB7Y9mpFRVE8fa867qFT1VcPOOd6Gu0pwIUpjI6OHttoNN4sKTzQZZn3PpzKXbpo0aIL+rl1cnI5BLjYVwPjIYAAAggggAACCMQQIMDFUNzzGD3DSfopUKFfAQJcv2K0RwABBBBAAAEEEKhDgACXXpkAl944egUCXHRSBkQAAQQQQAABBBCIIECAi4DYYwgCXHrj6BUIcNFJGRABBBBAAAEEEEAgggABLgIiAS49Yt0VCHB1i1MPAQQQQAABBBBAoIoAAa6K0uzacAI3O7856U2AmxN2iiKAAAIIIIAAAgj0ECDApb9ECHDpjaNXIMBFJ2VABBBAAAEEEEAAgQgCBLgIiD2GIMClN45egQAXnZQBEUAAAQQQQAABBCIIEOAiIBLg0iPWXYEAV7c49RBAAAEEEEAAAQSqCBDgqijNrk2yE7hWq/VnixYt+qfx8fFfzG6K9J4uQIDjmkAAAQQQQAABBBDYFwUIcOl3JVmAs9aWknYYYz5XluU/FEXxBUmd9Eua/xUIcPN/j1khAggggAACCCAwiAIEuPS7lizAZVl2u/f+Cd0leEk/M8b8Y6PRuGT9+vW3pF/a/K1AgJu/e8vKEEAAAQQQQACBQRYgwKXfvWQBLkzdWnuUpOdLep6kQ6aEue9K+ofh4eFPcItl/5tMgOvfjB4IIIAAAggggAAC6QUIcOmNkwa4qdO31h5pjHm+9z6EuRWSwqncTkmf9d5f0r3FMtx2yaeHAAGOSwQBBBBAAAEEEEBgXxQgwKXfldoC3NSlZFl2hPf+BZJeKOnx3TD3E+/9Pw4NDX10/fr1RfqlD24FAtzg7h0zRwABBBBAAAEE5rMAAS797s5JgJtc1tq1a4c2b958tqQ3SRrq/j2czP1bs9k8h9/KzXwBEODS/x+DCggggAACCCCAAAL9CxDg+jfrt8ecBLhWq3VCuJ2y+9u4AyWFeWw3xlztvT9a0mPC7ZXe+zcURfG+fhc139sT4Ob7DrM+BBBAAAEEEEBgMAUIcOn3rbYANzIyMtpoNF5ijPmD7gNNJmvf7L3/6JIlSy67+eab7zr44IMftmzZsld778/tnsqd7pz7ZHqKwalAgBucvWKmCCCAAAIIIIDAQhIgwKXf7aQBbtWqVY8cGho6TdJLJB3bXU6o+UtjzCckXZzn+Q0zLTPLst/23l8h6fvOuV9NTzE4FQhwg7NXzBQBBBBAAAEEEFhIAgS49LudLMBZa/9Z0nMlLe7eIhlW87UQ2u67777L77jjjm29lpdl2Trv/ZOcc2EMPl0BAhyXAgIIIIAAAggggMC+KECAS78rKQPc5CsBfiLpkmazeXG/T5fMsiy8YuAZzrml6SkGpwIBbnD2ipkigAACCCCAAAILSYAAl363kwW4Vqt1ZaPRCLdI/rukzt4sZWRk5MBmsznsnPvR3vSfr30IcPN1Z1kXAggggAACCCAw2AIEuPT7lyzApZ/6wq1AgFu4e8/KEUAAAQQQQACBfVmAAJd+dwhw6Y2jVyDARSdlQAQQQAABBBBAAIEIAgS4CIg9hiDApTeOXoEAF52UARFAAAEEEEAAAQQiCBDgIiAS4NIj1l2BAFe3OPUQQAABBBBAAAEEqggQ4Kooza4NJ3Cz85uT3gS4OWGnKAIIIIAAAggggEAPAQJc+kuEAJfeOHoFAlx0UgZEAAEEEEAAAQQQiCBAgIuA2GMIAlx64+gVCHDRSRkQAQQQQAABBBBAIIIAAS4CIgEuPWLdFQhwdYtTDwEEEEAAAQQQQKCKAAGuitLs2nACNzu/OelNgJsTdooigAACCCCAAAII9BAgwKW/RAhw6Y2jVyDARSdlQAQQQAABBBBAAIEIAgS4CIg9hiDApTeOXoEAF52UARFAAAEEEEAAAQQiCBDgIiAS4NIj1l2BAFe3OPUQQAABBBBAAAEEqggQ4Kooza4NJ3Cz85uT3gS4OWGnKAIIIIAAAggggEAPAQJc+kuEAJfeOHoFAlx0UgZEAAEEEEAAAQQQiCBAgIuA2GMIAlx64+gVCHDRSRkQAQQQQAABBBBAIIIAAS4CIgEuPWLdFQhwdYtTDwEEEEAAAQQQQKCKAAGuitLs2nACNzu/OelNgJsTdooigAACCCCAAAII9BAgwKW/RAhw6Y2jVyDARSdlQAQQQAABBBBAAIEIAgS4CIg9hiDApTeOXoEAF52UARFAAAEEEEAAAQQiCBDgIiAS4NIj1l2BAFe3OPUQQAABBBBAAAEEqggQ4Kooza4NJ3Cz85uT3gS4OWGnKAIIIIAAAggggEAPAQJc+kuEAJfeOHoFAlx0UgZEAAEEEEAAAQQQiCBAgIuA2GMIAlx64+gVCHDRSRkQAQQQQAABBBBAIIIAAS4CIgEuPWLdFQhwdYtTDwEEEEAAAQQQQKCKAAGuitLs2nACJ8la+3JJ75O0pNForJyYmNi0J9Ysy/7Ae/9KSU+StFPSuDHm/Xmef2qmftbakyS9XtLRkpZJ2mSMuVzS+Xmeb+l3Cwlw/YrRHgEEEEAAAQQQQKAOAQJceuUFHeAOO+ywZdu2bbvIGHP6JHWvAGet/TtJIfD9SNIXjTGP8t7/lqTFks51zr116rZZa18h6SJJ90r6vDHmF977EyStlnTTrl271m7cuPGefraaANePFm0RQAABBBBAAAEE6hIgwKWXXrABbvXq1a1Op3NlN0h9RNJzJB20pwBnrX2JpEskfXnr1q3P3bx5831hi0ZHR3+t0Wh8Q9JwONBzzm0Mfx8bG1tRluV6SVvLsjym3W5v6G5p01p7maQXSHq/c+7V/Ww1Aa4fLdoigAACCCCAAAII1CVAgEsvvWADnLX2VZLeZYx5ZZ7nF1trw22Th/QIcEVoU5blqna7fce0k7aXe++bQ0NDV65fv/7H4Ttr7TslvVHS65xz75nafmRk5MBms/lDSZ3h4eEDx8fHt1bdbgJcVSnaIYAAAggggAACCNQpQIBLr71gA1yWZad47zc7577TDVt7DHDdU7Z1kj7nnHtula2x1t4aTviazeaK9evX3z69j7X2WknHe++fVxTFp6uM2Z3r1qVLly678cYbq3ahHQIIIIAAAggggAACyQUIcMmJtWAD3Axhao8BLsuys7z375X0JudcOFnb42fFihVLFi1aFH73do9zbr+ZGmdZdoH3Ptw++ZDfzu1pcE7geunzPQIIIIAAAggggMBcCBDg0qsT4LrGvW6htNaGB5G8whjzfO99uEXyL8JTJY0xi733t3jvP1QUxT9MblmWZZn3fiJ8VxTFoTNtZavVeoMx5nxJlznnXlx1uwlwVaVohwACCCCAAAIIIFCnAAEuvTYBrnqAu0JSuO3yAmNM+P3cdyWFexj/P0knS2qG74qieE0Y0lp7pKTrJV3nnDt2NydwZ3rvL5R0pXPud6puNwGuqhTtEEAAAQQQQAABBOoUIMCl154XAa7VaoUHhDymClez2Tx3YmLCTW9b4QTuaknPkFQaY16S5/k/TY7RarVOMMZ8OYQ4SSc4575mrT1eUviN27XOuRN3E+Be5r2/2Ht/VVEUIQRW+hDgKjHRCAEEEEAAAQQQQKBmAQJcevB5EeAmw1dFruOdc1/vN8BlWXaV9/5Z4V1uzrnwyoEHfay14fUCL/He/31RFH+cZdkR3vvwgJRvO+eO2U2AmzyB+4xz7tSK8w+nezzEpCoW7RBAAAEEEEAAAQRqEyDApaeeFwEuBlOvE7hWq3W5Meb3jDFvyfP8vBkC3B9KCr+BW+ecO2JsbMyWZZlLutU5t2Y3Ae6N3vvwQJRLnHMvrboOAlxVKdohgAACCCCAAAII1ClAgEuvTYDrGlcIcP/PGPPnkv7aOffm6VuTZdnJ3vsvSFrvnHvimjVrFu3cuTM8hfJ+59zy3QS4D3nvzwgPRHHO/VXV7SbAVZWiHQIIIIAAAggggECdAgS49NoEuIoBzlp7mqRPSvqUcy78+4M+1to/kfQRSV9zzp0QvrTW3iTpycaYsTzPw2nc9D43SDq80WicPDExcVXV7SbAVZWiHQIIIIAAAggggECdAgS49NoEuIoBbtWqVY8cGhr6iTGmMzQ09ITx8fFfTN0ea20Id6cZY96V5/kbugHuLZLe5r0/uyiKd0xrv0pSW9Ldw8PDjxsfH99RdbsJcFWlaIcAAggggAACCCBQpwABLr02Aa5igAvNwtMujTGv8d7//+3dC5gkVXk+8PdUb88uCyv8CTdhCbs7XadmXQF1RRYEWVERBAXRGMWYEGIMaMRbFBXxAl7wLl4gxKCixKgYFa94weAlKBqUgMN0fdW7rAYXkxgUWRZ2Z7u+//MN1Zve3p7p7pmqnku/9Tx5gjtV51T/6kxPvXVOnfNPhxxyyNk33njjjuzfT3XOfdlmqARwuIhU7d9Xrlx5YLlcTrJ/X9f492x4pS1LcIqqXpQkyVt7udQMcL1ocV8KUIACFKAABShAgX4JMMAVLz2wAc57/0ZV3bdB7Jw7B8Ay59zH0zT9Q+PfG+u62f8++OCDl+611143ALBZJcecc99L0/Rg59xpAAJVPT9Jkg81X7Zs6OWnATwA4GsA7gVwIoBhADekaXpqrVbb1sulZoDrRYv7UoACFKAABShAAQr0S4ABrnjpQQ5wmwAc1olYRHYxqlQqi0ul0mtU9SwAKyyYqepPnHPvFhELd7ttlUrlmCAIbOKTYwHsqarWK/fJoaGhy3oZOtkomAGu01XjzylAAQpQgAIUoAAFZkOAAa549YENcMXTFlcDA1xxtiyZAhSgAAUoQAEKUGD6Agxw07fr9kgGuG6l5tB+DHBz6GLwVChAAQpQgAIUoAAFdgowwBXfGBjgijfOvQYGuNxJWSAFKEABClCAAhSgQA4CDHA5IHYoggGueOPca2CAy52UBVKAAhSgAAUoQAEK5CDAAJcDIgNc8Yj9roEBrt/irI8CFKAABShAAQpQoBsBBrhulGa2D3vgZuY3K0czwM0KOyulAAUoQAEKUIACFOggwABXfBNhgCveOPcaGOByJ2WBFKAABShAAQpQgAI5CDDA5YDYoQgGuOKNc6+BAS53UhZIAQpQgAIUoAAFKJCDAANcDogMcMUj9rsGBrh+i7M+ClCAAhSgAAUoQIFuBBjgulGa2T7sgZuZ36wczQA3K+yslAIUoAAFKEABClCggwADXPFNhAGueOPca2CAy52UBVKAAhSgAAUoQAEK5CDAAJcDYociGOCKN869Bga43ElZIAUoQAEKUIACFKBADgIMcDkgMsAVj9jvGhjg+i3O+ihAAQpQgAIUoAAFuhFggOtGaWb7sAduZn6zcjQD3Kyws1IKUIACFKAABShAgQ4CDHDFNxEGuOKNc6+BAS53UhZIAQpQgAIUoAAFKJCDAANcDogdimCAK9449xoY4HInZYEUoAAFKEABClCAAjkIMMDlgMgAVzxiv2tggOu3OOujAAUoQAEKUIACFOhGgAGuG6WZ7cMeuJn5zcrRDHCzws5KKUABClCAAhSgAAU6CDDAFd9EGOCKN869Bga43ElZIAUoQAEKUIACFKBADgIMcDkgdiiCAa5449xrYIDLnZQFUoACFKAABShAAQrkIMAAlwMiA1zxiP2ugQGu3+KsjwIUoAAFKEABClCgGwEGuG6UZrYPe+Bm5jcrRzPAzQo7K6UABShAAQpQgAIU6CDAAFd8E2GAK9449xoY4HInZYEUoAAFKEABClCAAjkIMMDlgNihCAa44o1zr4EBLndSFkgBClCAAhSgAAUokIMAA1wOiAxwxSP2uwYGuH6Lsz4KUIACFKAABShAgW4EGOC6UZrZPuyBm5nfrBzNADcr7KyUAhSgAAUoQAEKUKCDAANc8U2EAa5449xrYIDLnZQFUoACFKAABShAAQrkIMAAlwNihyIY4Io3zr0GBrjcSVkgBShAAQpQgAIUoEAOAgxwOSAywBWP2O8aGOD6Lc76KEABClCAAhSgAAW6EWCA60ZpZvuwB25mfrNyNAPcrLCzUgpQgAIUoAAFKECBDgIMcMU3EQa44o1zr4EBLndSFkgBClCAAhSgAAUokIMAA1wOiB2KYIAr3jj3GhjgcidlgRSgAAUoQAEKUIACOQgwwOWAyABXPGK/a2CA67c466MABShAAQpQgAIU6EaAAa4bpZntwx64mfnNytEMcLPCzkopQAEKUIACFKAABToIMMAV30QY4Io3zr0GBrjcSVkgBShAAQpQgAIUoEAOAgxwOSB2KIIBrnjj3GtggMudlAVSgAIUoAAFKEABCuQgwACXAyIDXPGI/a6BAa7f4qyPAhSgAAUoQAEKUKAbAQa4bpRmtg974GbmNytHM8DNCjsrpQAFKEABClCAAhToIMAAV3wTYYAr3jj3GhjgcidlgRSgAAUoQAEKUIACOQgwwOWA2KEIBrjijXOvgQEud1IWSAEKUIACFKAABSiQgwADXA6IDHDFI/a7Bga4fouzPgpQgAIUoAAFKECBbgQY4LpRmtk+7IGbmd+sHM0ANyvsrJQCFKAABShAAQpQoIMAA1zxTYQBrnjj3GtggMudlAVSgAIUoAAFKEABCuQgwACXA2KHIhjgijfOvQYGuNxJWSAFKEABClCAAhSgQA4CDHA5IDLAFY/Y7xoY4PotzvooQAEKUIACFKAABboRYIDrRmlm+7AHDoD3/q8BfBDAkiAIVlar1U2TsY6MjKyv1+uvcM4dC2BvAL8D8EMAHxCRH7Q7znv/JACvAnA0gD0BbHLOXQvgXXEc39frJWSA61WM+1OAAhSgAAUoQAEK9EOAAa545YEOcEccccSeDzzwwJXOuec3qKcKcN77v82CngL4rnOupqqHAjgFgFmeLyIfbr5s3vsXAbgSwP0Avu6cu0dVnwBgNYBbd+zYsX7jxo339nKpGeB60eK+FKAABShAAQpQgAL9EmCAK156YAPc6tWrw3q9fl0WpD4K4FQAB08W4FatWvXHixYtSgCUAZwkIt9pXJ5KpXJSEATXAxhP03S4VqvdZT8bGRlZkabpGIAtaZquq9VqG7JjSt77awA8F8CHROT8Xi41A1wvWtyXAhSgAAUoQAEKUKBfAgxwxUsPbIDLetPe45x7SRzHV3nvbdjkYZMFuDAMz3XOXeGc+14cx+tbL4333oZRPt4590Irz37uvb8UwAUAXiki728+Znh4+IBSqfSfAOrlcvmA0dHRLd1ebga4bqW4HwUoQAEKUIACFKBAPwUY4IrXHtgAF0XR6aq6WUR+moWtKQNcFEWvUdV3ArhGRF7QJsD9c9aj9ioReV9W5h3Ww1cqlVaMjY39ss0x3wdwvKo+K0mSL3R7uRngupXifhSgAAUoQAEKUIAC/RRggCtee2ADXJsw1SnAWeD7knPuR3Ec2wQmu2zee5vA5Lg0TZ9Rq9W+smLFiiVDQ0P23tu9IrJvu0sZRdFlqmrDJy8WkTd1e7kZ4LqV4n4UoAAFKEABClCAAv0UYIArXpsBLjPuNIRy7dq15fvuuy8GsBLAM0XkS43L470/DoD1pm1atmxZdMstt4xHURSpalVVf5EkyeHtLmUYhq92zr1rsl69yS4/A1zxvxisgQIUoAAFKEABClCgdwEGuN7Nej2CAa7LAGe7jYyM+DRNv5hNfGKTltzpnDtUVW0Wyo2qekaSJDZpib3/dhSAnwD4sYgc0+7CRFF0nqpeDuA6ETmj24vHANetFPejAAUoQAEKUIACFOinAANc8doLIsCFYWgThOzfDVepVLq4Wq1K676deuBsfxsWWS6XX+qcuwjAsqYybC23ty9ZsuRDt912mw2btAB3fNYr930ROWGSAHeOql6lqt9MkuTkbs4/K3vL0qVL9/z5z3/e7SHcjwIUoAAFKEABClCAAoULMMAVTjyxdtm83xrhq8sPcryI2IyRu2ydAlz2TpsNkzxKVa9YtGjRO5cuXbp569atB6dp+kJVfb2q3hEEwbG2OHcURY9VVZsg5WYRWTdJgGv0wH1RRM7s8vwtHDLAdYvF/ShAAQpQgAIUoAAF+ibAAFc89YIIcHkwdQpwYRhe6Jx7K4DPioit37bLFobhVc4561G7JEmSN2bDLe2duTtEZM0kAe4CVbWlBq4WkbO7/RwMcN1KcT8KUIACFKAABShAgX4KMMAVr80Alxl3CnDe+38HsHayKf+zZQlslsqfxXG8ds2aNUPj4+M2nPJBEWkebrnzqkZRdIWqngvgQhF5e7eXmwGuWynuRwEKUIACFKAABSjQTwEGuOK1GeC6DHBhGIpzLgTwNBH5RuulCcPwVOfcVwHEIjJiP/fe3wrgSOfcSBzH1hu3y9YIhUEQnFytVr/Pfj2PAAAgAElEQVTZ7eVmgOtWivtRgAIUoAAFKEABCvRTgAGueG0GuC4DnPf+WwCeAuD1IvKONmHsdTaRCYDrRcRmpbQA90YAb1HVNyRJ8rbmY7z3qwDUAPy+XC4fNDo6ur3by80A160U96MABShAAQpQgAIU6KcAA1zx2gxwXQa4KIomZowE8Ns0TU+o1Wp3NC6P9/4QADZhycMBnC0iV9vPVq5ceWC5XE4ApADWiUjV/j0bXmnryJ2iqhclSWLv1nW9McB1TcUdKUABClCAAhSgAAX6KMAAVzz2wAY46x1T1X0bxDYBiS0N4Jz7eJqmf2j8e5IkL8/+OwjD8LPOuWcD2AbgawDuBnAAAFsCwN5zuzab4MQC28Tmvf9TAJ8G8EB2zL0ATgQwDOCGNE1PrdVqVl7XGwNc11TckQIUoAAFKEABClCgjwIMcMVjD3KA2wTgsE7EItJs5KIoOktV/xLAowHsDcACmb3rZjNJfgqAtpZZqVSOCYLg9QCOBbCnqlqv3CeHhoYu62XoZFMo5DICnS4cf04BClCAAhSgAAUo0HcBBrjiyQc2wBVPW1wN7IErzpYlU4ACFKAABShAAQpMX4ABbvp23R7JANet1BzajwFuDl0MngoFKEABClCAAhSgwE4BBrjiGwMDXPHGudfAAJc7KQukAAUoQAEKUIACFMhBgAEuB8QORTDAFW+cew0McLmTskAKUIACFKAABShAgRwEGOByQGSAKx6x3zUwwPVbnPVRgAIUoAAFKEABCnQjwADXjdLM9mEP3Mz8ZuVoBrhZYWelFKAABShAAQpQgAIdBBjgim8iDHDFG+deAwNc7qQskAIUoAAFKEABClAgBwEGuBwQOxTBAFe8ce41MMDlTsoCKUABClCAAhSgAAVyEGCAywGRAa54xH7XwADXb3HWRwEKUIACFKAABSjQjQADXDdKM9uHPXAz85uVoxngZoWdlVKAAhSgAAUoQAEKdBBggCu+iTDAFW+cew0McLmTskAKUIACFKAABShAgRwEGOByQOxQBANc8ca518AAlzspC6QABShAAQpQgAIUyEGAAS4HRAa44hH7XQMDXL/FWR8FKEABClCAAhSgQDcCDHDdKM1sH/bAzcxvVo5mgJsVdlZKAQpQgAIUoAAFKNBBgAGu+CbCAFe8ce41MMDlTsoCKUABClCAAhSgAAVyEGCAywGxQxEMcMUb514DA1zupCyQAhSgAAUoQAEKUCAHAQa4HBAZ4IpH7HcNDHD9Fmd9FKAABShAAQpQgALdCDDAdaM0s33YAzczv1k5mgFuVthZKQUoQAEKUIACFKBABwEGuOKbCANc8ca518AAlzspC6QABShAAQpQgAIUyEGAAS4HxA5FMMAVb5x7DQxwuZOyQApQgAIUoAAFKECBHAQY4HJAZIArHrHfNTDA9Vuc9VGAAhSgAAUoQAEKdCPAANeN0sz2YQ/czPxm5WgGuFlhZ6UUoAAFKEABClCAAh0EGOCKbyIMcMUb514DA1zupCyQAhSgAAUoQAEKUCAHAQa4HBA7FMEAV7xx7jUwwOVOygIpQAEKUIACFKAABXIQYIDLAZEBrnjEftfAANdvcdZHAQpQgAIUoAAFKNCNAANcN0oz24c9cDPzm5WjGeBmhZ2VUoACFKAABShAAQp0EGCAK76JMMAVb5x7DQxwuZOyQApQgAIUoAAFKECBHAQY4HJA7FAEA1zxxrnXwACXOykLpAAFKEABClCAAhTIQYABLgdEBrjiEftdAwNcv8VZHwUoQAEKUIACFKBANwIMcN0ozWwf9sDNzG9WjmaAmxV2VkoBClCAAhSgAAUo0EGAAa74JsIAV7xx7jUwwOVOygIpQAEKUIACFKAABXIQYIDLAbFDEQxwxRvnXgMDXO6kLJACFKAABShAAQpQIAcBBrgcEBngikfsdw0McP0WZ30UoAAFKEABClCAAt0IMMB1ozSzfdgDNzO/WTmaAW5W2FkpBShAAQpQgAIUoEAHAQa44psIA1zxxrnXwACXOykLpAAFKEABClCAAhTIQYABLgfEDkUwwBVvnHsNDHC5k7JAClCAAhSgAAUoQIEcBBjgckBkgCsesd81MMD1W5z1UYACFKAABShAAQp0I8AA143SzPZhD9zM/GblaAa4WWFnpRSgAAUoQAEKUIACHQQY4IpvIgxwxRvnXgMDXO6kLJACFKAABShAAQpQIAcBBrgcEDsUwQBXvHHuNTDA5U7KAilAAQpQgAIUoAAFchBggMsBkQGueMR+18AA129x1kcBClCAAhSgAAUo0I0AA1w3SjPbhz1wM/OblaMZ4GaFnZVSgAIUoAAFKEABCnQQYIArvokwwBVvnHsNDHC5k7JAClCAAhSgAAUoQIEcBBjgckDsUAQDXPHGudfAAJc7KQukAAUoQAEKUIACFMhBgAEuB0QGuOIR+10DA1y/xVkfBShAAQpQgAIUoEA3Agxw3SjNbJ+B7oGrVCrLgyC4AMDTACwHsM059wtV/YSI/COAtJV3eHi4UiqV3gDgyQD2B/BbAN8CcImIbGx3Obz3TwLwKgBHA9gTwCbn3LUA3hXH8X29XkIGuF7FuD8FKEABClCAAhSgQD8EGOCKVx7YABeG4aOcczcA2BfAmKr+yDln/30ygCUAPisiz22+BFEUPVZV7Zi9ANzgnNugqhGAJwL4PYD1IvIfzcd4718E4EoA9wP4unPuHlV9AoDVAG7dsWPH+o0bN97by6VmgOtFi/tSgAIUoAAFKEABCvRLgAGueOlBDXCB9/4XWYh6n4i8utHbFkVRpKo/BbAMwKki8vXsMpS89xbOHgHgTBH5UuPyeO9fDOAjFshEZG2jrJGRkRVpmo4B2JKm6bparbahqaxrAFhA/JCInN/LpWaA60WL+1KAAhSgAAUoQAEK9EuAAa546YEMcN774wF8H8B/icghAOotvWbvA/AKAB8VEetBQxRFJ6vqN1T1y0mSnN56abz3twI40oZWioj10sF7fykAG6L5ShF5f/Mxw8PDB5RKpf+0usvl8gGjo6Nbur3cDHDdSnE/ClCAAhSgAAUoQIF+CjDAFa89kAFueHj40FKpdKKqbkmS5F9amaMoOk9VL7chjyJyqv08DMPLnXPnAThbRK5uPSYMw4udcxc196h57++wXr5SqbRibGzsl21Cn4XI41X1WUmSfKHby80A160U96MABShAAQpQgAIU6KcAA1zx2gMZ4DqxRlF0iaraRCXXiMgLbP8oim5S1WOcc0fGcXxbmzD2TAAWwr4vIiesWLFiydDQkL33dq+I2Lt1u21RFF2mqjZ88mIReVOn82r8nAGuWynuRwEKUIACFKAABSjQTwEGuOK1GeBajNesWTM0Pj5u762tUtVnN3rovPd3AzgoCIL9qtXq/7YJcEcB+AmAu0Tk0Oxduqqq/iJJksPbXcowDF/tnHtXc1Ds5pIzwHWjxH0oQAEKUIACFKAABfotwABXvDgDXIux9/699s4agJ9nE5Ko7RJF0f2qujRN0yW1Wm1b66UJw3C1c86GTFqP2z7e+0ag+7GIHNPuUjYN1bxORM7o9nJ77215A7d06dJuD+F+FKAABShAAQpQgAIUKFxg69atVoeKSFB4ZQNawYIIcGEY2gQhtiZbx61UKl1crVal3Y7e+9cDeJuFsCAIHte8n/feJjoJssY4EeqaN+/9KgA2y+Q2EVnSNFHKxJDKSQLcOap6lap+M0kSW76gqy07F7t2E78h3ChAAQpQgAIUoAAFKDBHBKyHwQJcaY6cz4I7jQUR4Lz3mwAc1uXVOV5EftgmgL0DwGttyv8gCJ5erVZvbAloNkvkntu3b99j06ZND7Ye39QD9zt75y1bM86WI7hZRNZNEuAak6V8UUTO7PL8uRsFKEABClCAAhSgAAUoMKACCyLAzfDa2ZpwVwCw5QLuSdP0tFqt9qM2AW8zgIenaXpArVb7nzYB7mjn3I8B/FJEVoyMjPg0TWMAd4jImkkC3AWqaksNXC0iZ8/wc/BwClCAAhSgAAUoQAEKUGCBCwx8gPPefwLAXzjnfqWqTxWRartr7r2fmPLfOXdUHMf/3ibg/SmAzwD4toiclE2GYrNQPigitij4blsURVeo6rkALhSRty/wtsaPRwEKUIACFKAABShAAQrMUGCgA1zTcgFSr9efvGHDBltYu+0WhuEHnHMvs8CVJMmVbQLcewC8CsClIvI6+3ljcW/n3Egcx9Ybt8vmvbcguDYIgpOr1eo3Z3gteTgFKEABClCAAhSgAAUosMAFBjbARVH0DFW9DsDdO3bsWLdx48ZfTXWtwzB8gnPuewC+IyJPadnXhmHWAKxs7qHz3r8RwFtsTbkkSWxylJ1bNumJHfP7crl80Ojo6PYF3tb48ShAAQpQgAIUoAAFKECBGQoMZICrVCqLgyCw8LTcOXdiHMf/2oWj897bu3FHq+pZSZL8c+OYKIouUtWLAdwgIk9u/PvKlSsPLJfLCQCb9n9dY3hmNrzySwBOUdWLkiR5axf1cxcKUIACFKAABShAAQpQYMAFBjLARVE0MX0/gN+p6icnawPOuXtExILZxOa9HwHwAwB/5Jz7NoCNqnoEgGMB/DoIguOq1arNiLlz897bu3GfBvAAgK/ZEgUATgQwbIEvTdNT260rN+Dtkh+fAhSgAAUoQAEKUIACFGgjMJABznv/ZgBv6qJFTMwo2bxfpVJZHgSBHXuKrT3nnPuNqn65Xq9fsmHDhv9uV2alUjkmCAJbY86C3p6qar1ynxwaGrqMQye7uArchQIUoAAFKEABClCAAhSYEBjIAMdrTwEKUIACFKAABShAAQpQYD4KMMDNx6vGc6YABShAAQpQgAIUoAAFBlKAAW4gLzs/NAUoQAEKUIACFKAABSgwHwUY4ObgVfPe/zWADwJYEgTBytaJUZpPeXh4uFIqld4AwGa/3B/AbwF8C8AlIrKx3cfz3j8pW7PuaHsnD8Am59y1AN4Vx/F9c5Ak11OazjuQIyMj69M07TRb6f0isleuJzuPCvPea6fTbV5mo7HvdNpwp3oW0s+z924vAPA0mzkXwDbn3C9U9RMi8o/ZLLcTH5nttPcrX6lUHlMqlV6vqscD2AfAZgBfGR8ff9udd975X72XOBhHVCqVk4IgeJnNzAxgbwD/Y5N8OefeHcexrXE6sXnvPwHgLzqofERE/nYw5Np/yjAMz3bOfbyDwaiIPLJ5nyiK/gTAS1T1UQAWAxBV/dTQ0NAHB/kde++9TSh3WBdt6i0i8ma208mlVq1atfeiRYs+BuBMW04rjuP1k+3da3u0WeFLpZKtsXyWfV0AGHfO/TxN0w8lSfIvXVy/gd2FAW4OXfojjjhizwceeOBK59zzG6c1VYCLouixqnoDAAsNNzjnNqhqBOCJtr4cgPUi8h/NH9F7/yIAthD5/QC+bjNtquoTAKwGcOuOHTvWb9y40WbKXLBbFEUnp2l68mQfMAiC/bMvk1tE5LEtN8a/VtXPT3LstiRJ7EZ7ILdGgFPVyyYDSNP0vRs2bPjPxs+n04YHCTcMw0c55+x3fF8AY6r6I+ec/be13yUAPisiz22YNAU4ttMuGkoYhqc6574IQFX1+iAINqvqWgBHOed+tWPHjuOa22sXRQ7ELo01TrOHB/bA8C4ANiPz4wDsSNP0zFqt9pWWAPdtVb1jEqAfDPrNWlOAs99zM91tC4Lg7jiO39n4gff+7QBeB+Ae+3sO4EEAtk7tYc65b+21116n3XLLLeMD0ShbPqS1UVW178q2m3PuBAAWel8qIh9uCnBsp01i9oArCAJ7wL/K/nmqANdre8yW9PqG3bPa5H7OuRuz+9lTATzMJhtsngl+ENvxVJ+ZAW6OtIjVq1eH9XrdFha3IPVRANaAD54iwJW89xbOHmFPRUTE1pWb2Lz3LwbwEQtkImI3I7YOnT2dX5Gm6RiALWmarqvVahuyQ6ysawDYjeCHROT8OcIyK6fhvbdejb9S1acnSfLVzG6iB67T06dZOeE5UmkjwIlIt98rPbfhOfJR+3Uagff+F9l3wvtE5NWN3+UoiiJV/SmAZfZdISJ287azB47ttPMlqlQqDwuCwGYEfpiqrk+S5ObGUVEUvUtVX+2c+0ocx8/oXNrg7JHNqnyTLY2TpumTarWarY/a+NtjYcJCxeZly5atsPDQuDFW1b9MksR647i1EWgKcFeLyNmdkJqug6RpelytVrMeUCxfvnyPpUuXXg/AHsy+SkTe16msQfv5ihUr9hkaGrLf/Qe2bt0a3XXXXQ+wne7eCqIosvsgu5fcBsDui1452d+W6bRH7/1rAbwDwLUHH3zwWTfeeOOOpnvVn9iSXUEQPK5ard4yaG20m8/b7Y1WN2VxnxkIeO9t+Mh7nHMvieP4qkb3/2QBznqRVPUbtoRBkiSnt1btvb8VwJE2tFJE7Am+BbtLAVgP0StF5P3NxwwPDx9QKpWsZ6ReLpcPGB0d3TKDjzNvDx0ZGVmbpql9cdii7Cc1PkijZ4M3xpNf2l4D3HTa8LxtWNM4ce+9Den7PoD/EpFD7HezuRjvvd2YvcIe+IiI9awzwPXgHIbhuc65K5xzH4zj2IYC7tzWrFkzND4+bt+HB6RpWml62NVDDQtz1zAMr3LOnQPgvSLydy2f0h462HI6duP1+Gq1ehNvjLtrB70GOO/9ZwDYOrP2ANd6kXduWc/9z60XOY5jWwqp4/D27s5yYewVRdFlqnq+qj4/SRJbp3fnUF8+aPi/a+y9t6HQewRB8EzrUJjqIfY02qN9V1jP/X7j4+OHtg5Xj6Lo5apq96nXiMgLFkbLy/dTMMDl6znt0qIoOl1VN4uIPVW3L5OJ8duTBbgwDC93zp0H4GwRubq14jAML3bOXdTco+a9t+Erq0ul0oqxsbFfth7jvbebxeNV9VlJknxh2h9m/h7ovPc/tHc6SqXSo8bGxqz3Y2JjgOt8UXsNcNNpw53PYuHsMTw8fGipVDpRVbe0G14WRdF5qnq5DZ0SEeuxZzvt4fJ7763X8pQ0TdfXarXvtfk+/CQAu3FgL8au4cB6dmzkx/eSJLERHbts3nvryXycc+45cRxfywDXXaPsMcDZza+96lBO03TvWq1mPSSt1+FXAA5N03RtrVb7WXdnsfD3qlQqa4IgsAfc9orEMY1wy3a6+7X33r+uXC5/yB7od7gH6rk9eu9tuPXNqnpjkiT22k9r+7UhmzZK7F4R+X98CLH79WGAm6PfV50CXBRFN6nqMc65I+M4vq1N47cnJhbCvi8iJ6xYsWLJ0NCQvfdmvwxtx4U3nkoBuFhEulnofI7qTe+0wjB8lnPO3m+7SkRe2FwKA1xn014DXK9tuPMZDNYeURRdoqo2gdHOJ5Rsp923Ae+9TVby8OwG+A9tvkOtd9N6OT8pIp0m4ei+4gW+p/d+Ijg0Rn/wxri7C95LgBsZGfFpmsYAfi4ij2lXQxiG1znnnqGq5yRJ0mlylO5OcgHs5b23dzNPs4fVImIPbCc2ttOpL+5Uf1um0x6b5mN4v4i8sl3t2UOKhznnVsVxfOcCaH65fgQGuFw58yusU4Dz3t8N4KAgCParVqv/2+bm4ygANhTwLhE5NHtnpqqqv0iS5PBJvvDtnY93DWqXtffeej+PdM5FrV8WzV9ezrlnqeoZqho658bTNP350NDQVwd5xq/sD+DEMJ1yubx4+/bt9gfSJuAoO+dqqnqdiNgMqTu3Xttwfr9d87+kbIif9X6sUtVnN3ro2E67u7bZA60H7H1gEbH3CHfbbDY1Vf0cgB+KiA1n5dZBwHtvsyF/22ak3Lp162Gt7xap6m2lUsn22c9mTK7X699h79BDqM0Brl6vX1QqlZ6uqjYZyVbn3M1xHNvEJo332Z+apqm95/ZVEXn6JDe/9u7Si51zb43j2EbjDPy2evXqR9brdXvg/V0RsXbY/PdoYrZUG0LJdrp7U+kQ4Hpuj957e/fN3oH7OxF57yRteDTr7d/5KtDAN+ImAAa4OdoaOgW4KIruV9WlaZouaTd8IgzD1c45GzJpPW77eO8bge7H2bCBdjcsjSFZdrN9xhylKeS0mm482o63bprdz0LIHtnyC83nsikIgmcP8su2TcsI2PDc1umb7Z3Kv20e7ttrGy7kws/TQr339gfPnlraE3ibqGgiPLOddndBK5XK/kEQ2LtavxGRh09y83BKNrPff4iIzVbHbQqBww8//P9t27bNHhpWnHMvj+N4Yjbaptn92n0v2C6f3bJlyzmbN2/eOsjATQHOeoZtSaByi4dNWmbvu21serjwGRF5Xju3MAzf6Zx7jc0KnCTJywfZtvHZwzC8Jpvle7dAwHY6dQuZKsBNpz167yceMKjquUmS2Mzou22N4dgAntk8UR/b8kMCDHAFtIQwDO3FS/sC7riVSqWLq9WqtO7YKcB5721Cg0BEgnZjg733jfHD20RkSdOECBNDKtudWBRF56jqVar6zSRJJp1mv+OH6vMOOXl/B8CTVHVd82x0jY/SdGO8HcDbSqXS1Tal844dO0ayF21PtKfO9Xr9kRs2bLAbw3m9Tce0KcDZH8l31+v1WFVtcpzX2DpFGchTmibV6akNz2vQh56wz/h7Ibshfr21QXs4k83QtfP7Y9Da6XTbRPZ+4a+ySR7arhUVRZG9f2gTQMUiMjLdugbhuCy82XTgtiacvZNpvUITvUVNN8bWTi9I0/S7ixcvTrdv3/50m0Am640b+GGqTQHuD6p60dDQ0Oe2bdv2u0WLFtnEWnazaw8RbMbJI4IgeI4N7Z1qeG/jPXjn3JVxHJ87CO1wqs+YzcJtM08mImLvcO6ysZ1O3UKmCnDee3tXuKf22JgMaaohvo15GQA8T0Rs0h5uTQIMcAU0hx4WkLTadxmH3TidLgKc9WjsuX379j02bdpka7/ssjX1wP3O3nnL1tuyIYI3i8i6dh+7aVKEL4rImQXQFFLkTL2zJRzs5mKs3Re7nXQURTbMKnLO/bZ1YfX169cv2rx5s70kbkNTF8T7g9MxtTbmnNtWrVZvb9MeG5Pu7HyA4L3vqQ0X0nj6WOh0TNvcZDSGnWwJguDp1WrV1s3ZuQ1aO53u5fPe2xA+m3bdZvg8qF053vtGD9yk7xlNt/6FdNzKlSsPLJfL38xmPb4pTdNTarXazncK7cZZVc07juP4vubPXqlU1gVBYEsSIE3TcJBn+xwZGfkjVV1pSzDEcWy9cDu3bCHlqr02YTe8QRDcp6q2Ntcu60A2H9PogQMw6TtGC6kddvos3nt76PV659wFcRzbqyK7bGynUwt26IF7dq/t0Xv/4WwB+vOSJPn7Sb6DGxMinR7H8Zc7XeNB+zkD3By94l0EuMYL+Ac01n9p+fI+2jn3YwC/FJEVTS+Z3iEia9p97CiKLlBVW2qgq3Vo5ihdz6fVtCjtW0TkzT0X8FDvysSU5ABuEpHHT6eMhXyM9956MOydrfrWrVuXZe/G9NSGF7JPF5/NZvmy9mXLBdyTpulpzetvdXH8xC5spw9JZQvIPmjvF8VxvOckNw82RftnuHTI5K0riqKV2aLTFXv3bcmSJc+87bbbbLKsrjfvvf2dsp67F4mIrYHKrY1AYxkg59yn6/W6jQCx0LxzBtrWQ6IousKGpwGY9t+1hXQhvPc2o+Gq6U6IMejtdKoAV6lUTuq1PTYW/VbV1yRJ8u5JvoPtnmEkCIIntj6sXEhtc7qfhQFuunIFH9dFgJuY8t85d1Qcx7ZWxy6b937i5sP+qNp6ZtmkB/aH9cEpXtpvfOFfKCK2GOtAbNnkJY9N0/SYWq1mNxM9b4136FQ1SZLE91zAAj8g66Uct4/pnDvEnjA3hkd024YXONGUH68xvMeG/KnqU0XEnsb3vLGd/h9Z02yJ+7dOsGN7NT3Q2rnOXs/gC/iAbBiqLeJtaxRes2zZsnNs4e5eP3LTe0kD9XenVyfvvc2MbAHXJomxYGaBZFREHjnJza8NabX1YneuddZrnQtl/2zpAFsWaNrDoQe9nXYYQtl4Zafr9th4ZQfAR0TE1kFu3WxZJ7tn3aNxz7BQ2mNen4MBLi/JnMvpFODCMPyAc+5lk70A6r1/j61fBOBSEXmdnV5jcW/n3IiNZWk95WzRxrVBEJxcrVbt6d6C37J3N2xikgcOPvjgfW688cYdU3xo+31puyBq00u8kw5RXfCYD31Aeydz4t2X5q1p0gjdsmXLXjZhwXTa8IAY7vIxm5YLkHq9/uQNGzbYAtNTbWynXTQU7/2XAJw+2fddFEWftzUxp3rJvotqFuQuNqSvVCr90Dln4eHy7AZsqsWi234vZH+Xvgbgaao66VCqBYnY/kNN6hSGYWOWaBs2+Tzv/T0A9lqyZMk+7Xo9vfc2RHi/yf7eD5CpPYyxeyW7Z+r0PiDb6SQNo8MSNRa2emqPjcXmAbSdWC+bMdRex5h0oqlBasPtPisD3BxtAV0EuCfY0B4A3xGRp7R8DBtuVQOwsrl3ozFU0NaOSpLExoPv3LJJT+yY35fL5YMGZUr8ptknfyoitrBk2817b08zbbHJUxuTcLT4WW/nn9ofiSRJbP2ogdqiKLpIVe0G47J2U1Y3Dd27VUQebThhGPbchgcK9aFeIFvH6ToANmHOuo0bN9oaW5NubKfdtxDv/Z/bcHEA/ygif918pL1LqKo2xHeP8fHxQ+68887/6r7khb+n997WGLW1RtvO2tsQqFQqjwiCwCaIssm0hlsf7mQPdqwnyd4xfpSI2EyLg7iVvPe32JTp9Xr9ERs2bLC/xbts3nvr7VzXmOEziqKP2ZT3zrk/i+P4n5p3bprMaNIekUFCbuo9e4mIXN762dlOO7eGTmuMTqc9eu832ozVO3bsWNn6t817b6+z2HrEk/XQdT7pBb4HA9wcvcCdApyNRMu+0I9W1bOSJPnnxkfJbqYvBifzg04AABSoSURBVHBD81on2cvmNguT9ZCsawzDyoZX2tPoU2z2qyRJ3jpHWXI/rTAMX2ozodl7BXEcP3+yCppMR8fHx5/UfEMXRdHpqvpFADucc4e3693M/cTnWIFNs5zakIcTRcSmE5/YsklifgDgwJYZp3puw3PsYxd6Otl7WnYjt9w5d2Icx//aqUK2005C//dzWwtu8eLFNlPqw1X1yUmS2LB026xd/gMAG7J2lYjY/+eWCYRheKpz7qsAauVyeU2Hh31maX9zLLy9U0RsBtWJHvq1a9eW77vvvk/Zgy8Ak86OPCjwjVn5bI0y59wZzRO+hGF4vj0cyx6wDo+Ojt4zMjJyuK1Bamu9lsvldaOjo78xq2zCE/uueLRz7gVxHF8zKIZT/P2+RVUfo6onJUliQ1BbN7bTDo2kU4CbTnv03r/YApqtZ7hs2bIzG0OwsyGv9sBiKE3TNYM8udFUl4UBbo58s1nvmKru2zgd59w59lTSOffxNE13zujVvJ5LNjGE3Rj/kXPOvpQ2quoRAI4F8OsgCI5rnTExezfu0zZkEIANXbnXbrizP7A3pGl6art15eYIU+6n0fRi+AfjOH7ZZBUsX758j6VLl9qTZLO16/FdVb3bOWcTwjzBwpuqvihJko/nfpLzpECbJt+eDmc3aHYTskFVD7QHAwAWt7sZnk4bniccMz7NpncEfqeqNkVz2805d4+I2AMbsJ32xp49eLDh4otsQgjn3N2qar/j9j16++LFi0+4/fbbf9dbqQt776apvf9NVXd7/7rx6YMguD6O4+ujKHq8LU2TrZ1pQ/dvds7ZEiK2bMsf29+tUql04tjYmK0TN7Db8PCwLblif8/tHWpbiuZ7zrn/VVVbw9XWerzfOfdsM20ghWH4d7Zki01sZH/PVXXcOWfft7a24adE5C8mG/Y/SNDeewu3B6Zp+pharWahd7eN7XRXkuHh4UoQBDvfTQuCYLkNKbd7S1X9fNPeP0mSxO4pbVRNr+3Rep7/xYay2wMhu68CsI9z7jRb55gTG039W8oAN0e+xbqdYlxEdrlmlUpleRAE1s1sX9r7O+d+o6pfrtfrl0y2HlmlUjkmCAJ7Emo3KnvaxBu2hsfQ0NBlgzJ0snHZG4tJAni7iFw4VXPIeirtC80WTrVZFS2U2NCqG9M0fd9kfxjmSBPry2l4721Y1XkAHgNg7+wBgS1f8Q8iYr2Uu23TacN9+TCzXEnTEJJOZzIx02xjJ7bTTly7/jwbPvXGbIj0Pqr6S+fctc65S1unve+t5IW5d7d/q5pnP6xUKsNBEFwA4Mk26Ylzzh542d+dL+zYseOyjRs32oPEgd+yobv27vqZzrlhVbUHC792zn2rXq+/u11PRNYj+koAtozLIlUdzb5vr2J4e6hJRVF0vwWCUqnkx8bGrN213dhO/4+laRhup9/LXWYt77U92gRnd99990tU9Wy7VDbc2t6LC4LgnZx5kgGuU+PjzylAAQpQgAIUoAAFKEABCswLAfbAzYvLxJOkAAUoQAEKUIACFKAABSgAMMCxFVCAAhSgAAUoQAEKUIACFJgnAgxw8+RC8TQpQAEKUIACFKAABShAAQowwLENUIACFKAABShAAQpQgAIUmCcCDHDz5ELxNClAAQpQgAIUoAAFKEABCjDAsQ1QgAIUoAAFKEABClCAAhSYJwIMcPPkQvE0KUABClCAAhSgAAUoQAEKMMCxDVCAAhSgAAUoQAEKUIACFJgnAgxw8+RC8TQpQAEKUIACFKAABShAAQowwLENUIACFKAABShAAQpQgAIUmCcCDHDz5ELxNClAAQpQoLOA915tryAInlitVm/sdMTIyMj6NE3/1fYTkXnxNzGKouer6jUAbk7T9IRarbat0+fs5efe+1UAfgpgkaquS5JkrJfjuS8FKEABChQrMC/+WBVLwNIpQAEKUGChCHjvv2SfpVQqvWFsbOwXnT7X6tWrH1mv19+aBbgzOu0/2z8Pw/BRzrkfAbjfOXdEHMebizinkZGRp6Zpej2ADeVy+VGjo6NbiqiHZVKAAhSgQO8CDHC9m/EIClCAAhSYZwJhGL7BOXdJEAQrq9Xqpnl2+hOnu2bNmqHx8fGf2X8CeJ6IfKbIzxFF0d+r6t8A+KiIvKjIulg2BShAAQp0L8AA170V96QABShAgXkq4L3/KoBT53OA896/EsB7AfybiBxX9KXw3u9nPXAAlgVBcFS1Wr2l6DpZPgUoQAEKdBZggOtsxD0oQAEKUGCeC3jv/wfAfvM1wC1fvnyPpUuX/so+g6qelCTJt/txSbz37wDwWgBfE5HT+lEn66AABShAgakFGODYQihAAQosQIHsXamfACg7554Ux/F3Wz+m9/5rAJ4G4AYReQqAiQlAJtuiKLpRVU8A8NI0TT9bKpUuUdVTARwA4F5V/Z6qvrlWq422KyMMw9OCIHiRqj4OwL4A7L2qGMC127dvv3zTpk0Pth5XqVSOCYLgfOfcsap6EACbsGOzqn5XVT9cq9XuaD6mdRIT7/0nAPxFm/O5WkTO7jSJSRRFj1fV8wE8HsD+AOwcrVfqy9u3b//Apk2bft9S/5sBvAnAdSJyhvf+zwGcB2A1gMU2V4pz7u/jOL6il2YXRdE5qnrVQ3OtSNTmWtqw0MMAHF+v139TKpXsvT67VnsD2Oic+0ijTrsOzrkLABxhrwsCsHZygYjYxCW7bKtXrz6sXq/faf+YpmlYq9Xss3OjAAUoQIFZFGCAm0V8Vk0BClCgSAHv/VsAvBFAtVwuHzk6Orq9UV8Yhmc65/4FwH2lUunwsbGxX3Y6l6YAdzGA5wOwQGUThdwH4GgbagfgAQsOrWEgDMPLnXMWZGwTALUsxD3WZjt0zv1s0aJFTxkdHb2ncR7e++cC+CebVBKABZQk++8jrSfKJvIIguC05tkmWwNcGIYW/qznyAKqbdZztdUCYJIkH5wqwEVRdIGqXpod958ALJjuCeAoAEvsnLLZLne+U+e93xngVDV2zr0agA09/O8sxK3MynudiDTK7kQP7/23ss9wqYi8rvUA7/1EgHPO/Zmqvs/CsXOupqoW9izY2fY3zrkdqvoPAP5dVR9wzq3NrtsfSqXSyNjY2N1tyraAZ5/59SJiPXLcKEABClBgFgUY4GYRn1VTgAIUKFJg7dq15fvuu+/fs56WC0Xk7VbfEUccseeDDz5oU8MfqqrnJklyZTfn0RTg7gVw2+LFi0+//fbbf2fHrlmzZt/x8fHvAHi0hbE4ji0YTGxZL9TVALar6llJklhwnNhWr14d1ut1C1UWMj4pIo3eMue9vwvAwc65V8Rx/IHGMevXr1909913v0NV/87OQ0Qs0DXq2m0ZgZGRkRVpmk70IrUOoZwswFUqlROCILDlBezv5EtF5CONHsqVK1ceODQ09HVVfQyA74uI9XQ16m8EuF9b7yeAp4uIBSDb7DN9DMDZAH4rIgdax1Yn+0qlsjgIgj8AGJqiN7XRA2dm5viG7HwD7/3nATwTgIXQPZxzz4njeGLphEqlsjwIAgvhezvnXhvH8TtbzyeKoreq6oWqemOSJE/sdL78OQUoQAEKFCvAAFesL0unAAUoMKsClUrl0UEQ/MQ5t905t8ZmYAzD8J3OudcA+E42dLKrc2wKcKlzrhLH8UQoagovx1ugsf+dpumaxvBG770FhDWq+oEkSV7RWllTb+COer1+yIYNG/57zZo1B42Pj0/0Bi1ZsmSv22677f7m4yzEbd68+UIbTlkul69u9C62WwduOgGuMekJgC+JiIWfXbZKpfKYIAgmJvVI03RtrVaz2SEtrDYCnP3PPxeRTzUfmC1bcLv9WxAEUbVatd7IKbcwDI92zv04q2vvWq1mYW6XrdEDB2BMRB7ZHAyblgSAql6WJMnLW67bp21WSwCfE5E/bS07iqJnqOp11tMqIg/rdL78OQUoQAEKFCvAAFesL0unAAUoMOsCYRhe7Jy7yDn3lTRNL3DO/Ye9y9Xt0MnGB2gKcDeJiL0T1rpZb4/1zu3lnPurOI4/Njw8fGipVLLJN2x7XLv3rLLp8S2ULM56h67NegmtrJJz7oVxHNv7Xx23PAJc1nNpw0J3nk+7ir33FmBXqOprkiR5t+3TFOC2p2n6sNZFtlesWLFkaGjIhplagHt8tVq9qdOHCsPwLOecDSX976zXbrdDmgLcbkMsoyiKVLWaHfRkEbmhuYCmiUraBvpKpfKIIAgm3mus1+sHWsDudM78OQUoQAEKFCfAAFecLUumAAUoMCcEskBivUWHA7B33Wy44t+IiL0L1fXWCHDOuSvjOD53klBzm9WjqpckSfLGKIpOVtVv2L5bt25detddd02El9bNe28B4RHOuTfGcXyJ/bz5vTkbvhcEwedU9V9FpBFG2pUz4yGU3vsR68mywlX1EUmSTPx3m3OemARGVT+WJMlf2c+bAlxNRMJJjtvtHKe6CN5767W099puFZFHT1JmYwjlbte1uQeyVCr5sbExe5dw59Y4Z+fc9+I4Xt9a/qpVq/ZetGjRxGQtqvroJElu7brRcEcKUIACFMhdgAEud1IWSAEKUGDuCURR9FhVbcwy+EMRseGOPW1NPXBtJ9LIAswPABzXGC4ZhuHznHM2RG+7iNgsjG037/2/ATjWgoqIvCrbKQjD8B3OuZfau1tNB9q7XJ/fsWPHBzZu3Njo3Zv4cR49cNnMlxM9Y6VS6eB2E3vYz6Io+id7pw/AF0TkWVn9jSGUo9lQxt0+b7tznOpChGF4oXPOZpWcdP23Rg+cqv5lkiQ28+bObaohpM3nPFmAyx4ATEyA45w7Lo5ju1bcKEABClBglgQY4GYJntVSgAIU6KeA9/5vAXwoq/Oecrm8ZnR09De9nENTD9zb4ji2STLahZNdglg2k+Q/dwpwURTdpKrHOOfeE8exzdy4c6tUKvsHQXBGtuTBiQAa72HZlP5nicgXGzvnFODWBUHwIytzqgDnvZ94d0xVP58kyZ80hyGbsZIBrpfWxX0pQAEKUKBbAQa4bqW4HwUoQIF5KhBF0UpVtYkzFtmCzADObKxT1stHauqB+7CIWK9YuwA3MWGJrYUmIhc3T6CxZcuWPTdv3rx1kuNsmKINXdw5W2a7/bLJS2xJAFtrzZYu+EMQBKuq1er/ZgFqxkMom98Za56MpfV8vPc2NPRkAB8VkRcVFeB6GUJZRA/cihUr9hkaGpqYbZRDKHv5jeG+FKAABYoRYIArxpWlUoACFJgrAjZ1vU1aYdO/v6lcLr9vfHzcwtJyWzMsjmObHKOrrSnAfVdEntTmoFI2icmezrkXxHF8jff+EAA2tb3d/K9LkuTm1uOyiT1sEhNbdPz0OI6/3OmEsqn1bVFpK/9UEfl6XgEuC4l2PjZs87ki8tlJQqcN5Vyuqi+zNeWKCnC9TGJSRIDjJCadWiN/TgEKUKC/Agxw/fVmbRSgAAX6KhCG4bnOuSuaF/P23ttwRBt22NNQyqYAt71cLh/WOgQziqIn2gLZ2QccFpGNWaixWS+PsCGcInJ+K0DjPTnn3Nbx8fGDN27ceG82+YktFv6t1qn4G8d7722Nu7Wq+qwkSb4wWYBbvXr1YfV6fWKx7Xq9Hm7YsMEWEZ/YJlsHLgzD65xzz7CZO+M4fkbrOTe/J9e8HEDTJCZ5DqHsehmBIgIclxHo668sK6MABSjQUYABriMRd6AABSgwPwVWrVr1x4sWLbIhjXup6vokSSbWaMuCzpcAnN7LUMqmAPd7Vb1ZVZ/TWJMse0/NFvK2oLbL4tZRFD1HVa0Xa9w59ydxHNuaYo0AdXiaptdnC3bvfP8tiqI/UdXP2YLXtgi1iPyw+SqEYfgs55z9fLxer/9xY2r7du/ARVG0TFUba6ft0qM2xULexwRBYHUGzrkXx3FsIXhiy1ytx8/Wttv5/lvmmvskJr0s5F1QgONC3vPzK4BnTQEKLFABBrgFemH5sShAAQpEUfRNVT0JwFUi8sJmkUqlsjwIAhtKaWu2dTWUsmkSk3erqoW/gwBYL1gKYJ2VBeDeNE2PbSzi3RQY3wvglfa/VTUJguBOVV2evfcWALh++/btz9y0aZNNTGKbDf38fPa+3sQxzrlNzjlV1WEA9n/23y9OkuTvm+ppO0W/996mvj8SwA4A1gO32YaBThbgrLwoil6mqu+3cwFgwyVt+YIDbLkDG+4J4JYgCJ7aeP+uqACXlfstAPbuX9sZQIuchdJ7/xMARwF4nYhcyt8sClCAAhSYXQEGuNn1Z+0UoAAFChGIouivVPUfAfxPuVweGR0dvae1oiiKXp4FlK6GUjb1wL1l8eLFl23fvv3Nqvp06z2zyUQA3FCv1y9qHqLYXKf3/mkAzrMFvQHsmx1zq6p+KkmST9kIx5ZztGUEnuucs6UIjlDV/W1iSAtfAH6oqh9ufadusin6oyiy423du0cBeEBVv5YkyZ9NFeDsXLKhki+3pREAWP1bnXN3APjstm3brmwKnBOnXsQQyixMnqOqtpi5iEjUei2LCnDZ8FNbsBxpmoa1Ws3eO+RGAQpQgAKzKMAAN4v4rJoCFKDAfBJoDnAiYkMFufVJYPny5XssXbrU1rzbz3pVkyT5dj+q9t6/A8BrAXxVRCysc6MABShAgVkWYICb5QvA6ilAAQrMFwEGuNm9Ut57G4JqQ1EnXdA7zzP03u8HwHrclqVp+tharfazPMtnWRSgAAUoMD0BBrjpufEoClCAAgMnwAA3u5d8zZo1Q+Pj4xaibPKUs5IksQXSC9u891cCsPXtdq5zV1hlLJgCFKAABboWYIDrmoo7UoACFBhsAQa42b/+3nubiOXH9i6ezfgpIr8u4qyyZRxsofJauVx+9Ojo6JYi6mGZFKAABSjQuwADXO9mPIICFKDAQAowwM2Ny960sPfNaZqeUKvVtuV5Zt77VQB+CmBRtvi6zVbKjQIUoAAF5ogAA9wcuRA8DQpQgAIUoAAFKEABClCAAp0EGOA6CfHnFKAABShAAQpQgAIUoAAF5ogAA9wcuRA8DQpQgAIUoAAFKEABClCAAp0E/j8MaBUeO6RDhwAAAABJRU5ErkJggg==\" 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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "newfig()\n",
-    "decorate(xlabel='x position (m)',\n",
-    "         ylabel='y position (m)',\n",
-    "         xlim=[-100, 100],\n",
-    "         ylim=[-200, -50],\n",
-    "         legend=False)\n",
-    "\n",
-    "for x, y in zip(xs, ys):\n",
-    "    plot(x, y, 'bo', update=True)\n",
-    "    sleep(0.01)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a function that encapsulates that code and runs the animation in (approximately) real time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def animate2d(xs, ys, speedup=1):\n",
-    "    \"\"\"Animate the results of a projectile simulation.\n",
-    "    \n",
-    "    xs: x position as a function of time\n",
-    "    ys: y position as a function of time\n",
-    "    \n",
-    "    speedup: how much to divide `dt` by\n",
-    "    \"\"\"\n",
-    "    # get the time intervals between elements\n",
-    "    ts = xs.index\n",
-    "    dts = np.diff(ts)\n",
-    "    dts = np.append(dts, 0)\n",
-    "\n",
-    "    # decorate the plot\n",
-    "    newfig()\n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)',\n",
-    "             xlim=[xs.min(), xs.max()],\n",
-    "             ylim=[ys.min(), ys.max()],\n",
-    "             legend=False)\n",
-    "\n",
-    "    # loop through the values\n",
-    "    for x, y, dt in zip(xs, ys, dts):\n",
-    "        plot(x, y, 'bo', update=True)\n",
-    "        sleep(dt / speedup)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" width=\"799.9999826604674\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "animate2d(system.results.x, system.results.y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/pendulum_sympy.ipynb b/notebooks/pendulum_sympy.ipynb
deleted file mode 100644
index 2c029682..00000000
--- a/notebooks/pendulum_sympy.ipynb
+++ /dev/null
@@ -1,978 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "from sympy import *\n",
-    "\n",
-    "init_printing()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook uses SymPy to derive the equations of motion for a springy pendulum and a rigid pendulum.\n",
-    "\n",
-    "Here are the symbols we need:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x, y, vx, vy, ax, ay = symbols('x, y, vx, vy, ax, ay')\n",
-    "length0, k, m, g, R, t = symbols('length0, k, m, g, R, t')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll use vectors P, V, and A to represent position, velocity and acceleration.  The Vector class in modsim.py doesn't play nicely with SymPy, so I'll use NumPy arrays instead:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([x, y], dtype=object)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "P = np.array([x, y])\n",
-    "P"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([vx, vy], dtype=object)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "V = np.array([vx, vy])\n",
-    "V"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ax, ay], dtype=object)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A = np.array([ax, ay])\n",
-    "A"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The only vector operations we need are `mag` and `hat`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def mag(P):\n",
-    "    return sqrt(P[0]**2 + P[1]**2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFYAAAAaBAMAAADf4rbTAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nZq+qjTLQ/AHXdX0I04TwoAAAAABJRU5ErkJggg==\n",
-      "text/latex": [
-       "$$\\sqrt{x^{2} + y^{2}}$$"
-      ],
-      "text/plain": [
-       "   _________\n",
-       "  ╱  2    2 \n",
-       "╲╱  x  + y  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mag(P)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def hat(P):\n",
-    "    return P / mag(P)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([x/sqrt(x**2 + y**2), y/sqrt(x**2 + y**2)], dtype=object)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hat(P)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For convenience, I'll define intermediate variables like length:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFYAAAAaBAMAAADf4rbTAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nZq+qjTLQ/AHXdX0I04TwoAAAAABJRU5ErkJggg==\n",
-      "text/latex": [
-       "$$\\sqrt{x^{2} + y^{2}}$$"
-      ],
-      "text/plain": [
-       "   _________\n",
-       "  ╱  2    2 \n",
-       "╲╱  x  + y  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "length = mag(P)\n",
-    "length"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`f_spring` is the force on the particle due to the spring"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-k*x*(-length0 + sqrt(x**2 + y**2))/sqrt(x**2 + y**2),\n",
-       "       -k*y*(-length0 + sqrt(x**2 + y**2))/sqrt(x**2 + y**2)], dtype=object)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f_spring = -k * (length - length0) * hat(P)\n",
-    "f_spring"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "xhat and yhat are unit vectors along the x and y axes: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "xhat = np.array([1, 0])\n",
-    "yhat = np.array([0, 1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now I can write force due to gravity as a Vector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, -g*m], dtype=object)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f_grav = -m * g * yhat\n",
-    "f_grav"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To write $\\sum F = ma$, I'll define the left-hand side and right-hand sides of the equations separately."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lhs = f_spring + f_grav"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rhs = m * A"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now I can make two equations, one for each component of F and A:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAA1BAMAAACZwnArAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\ngX8Bc50nOQluB+sAAAAASUVORK5CYII=\n",
-      "text/latex": [
-       "$$- \\frac{k x}{\\sqrt{x^{2} + y^{2}}} \\left(- length_{0} + \\sqrt{x^{2} + y^{2}}\\right) = ax m$$"
-      ],
-      "text/plain": [
-       "     ⎛              _________⎞        \n",
-       "     ⎜             ╱  2    2 ⎟        \n",
-       "-k⋅x⋅⎝-length₀ + ╲╱  x  + y  ⎠        \n",
-       "─────────────────────────────── = ax⋅m\n",
-       "             _________                \n",
-       "            ╱  2    2                 \n",
-       "          ╲╱  x  + y                  "
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eq1 = Eq(lhs[0], rhs[0])\n",
-    "eq1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAccAAAA1BAMAAADG/z0nAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
-      "text/latex": [
-       "$$- g m - \\frac{k y}{\\sqrt{x^{2} + y^{2}}} \\left(- length_{0} + \\sqrt{x^{2} + y^{2}}\\right) = ay m$$"
-      ],
-      "text/plain": [
-       "           ⎛              _________⎞       \n",
-       "           ⎜             ╱  2    2 ⎟       \n",
-       "       k⋅y⋅⎝-length₀ + ╲╱  x  + y  ⎠       \n",
-       "-g⋅m - ───────────────────────────── = ay⋅m\n",
-       "                   _________               \n",
-       "                  ╱  2    2                \n",
-       "                ╲╱  x  + y                 "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eq2 = Eq(lhs[1], rhs[1])\n",
-    "eq2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now I want equations that are explicit in ax and ay.  In this case I can get them easily by dividing through by m.  But for the rigid pendulum we will need to use solve, so I want to demonstrate that here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "soln = solve([eq1, eq2], [ax, ay])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can extract the expressions for ax and ay"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKAAAAA1BAMAAAA0Sv3uAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAELvv3c2ZVESJZjJ2\nIqu2f7MxAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAEpElEQVRYCe1XXWgcZRQ9s7vZmdnszyAKBmN3\nrVCk1LQPPjQouKAFxQSjkmoRYa0GlLZk1YeiaLOiL6nSTNUErFH3QQKKkH0RX4RMU02trOmiSFXU\nLBUflBQ2QknqJo3nm5/d2ZkhZu1DH/TCztx7zv3ufn8z3xmAJt2c5TV+f0kEXrNIL9oaX3Nba4yO\nnACmPShDqR826efcyF53QD+VF8CQB2Uo1xzSz7mRZXdAf0wTwB0elGF0wCH9nAtRllyBcMfFJVEX\n11aL6MBbrVBQpPJ/W2wfpm5HrAZ0lQ9h9Naf5g2OdvJ05tjOHgN7X5itWNlS+cLrky0N7aBDjz1R\nQvf892fYkCatdF2/G8kMlH78gNxWQ1oD3sBCDiMkL1aSVcQn2dcufGDsMVt4LumXj79SkPRYX8Tq\nqlw3mBE28G1eOtCZ3yNGLy+JpeI6yXWEMvgQR4DjeF97yVPLDEc+403V1CXZGkv0YoHATAm/fvyV\nJkkrYvThDGYquNdcF/7VfUhp0PCg2d53GX6EkISOjM1Evv6N3ingsgC4VZIDmCnic84FwHVZKHEO\nwnlyqyLBb32/mENNGTaVLqXzFfRIuCQAbpVUUZuuYBuUGrR0AW/ihiVEikBnnf3wm1SXlhOEOUbN\nZKeRLuj4S0UvH0CEdYxoOpdjGeqAXOLz0xsTBQ08E6/hpL8eZ0haUSFrZxG3Cp5HqqRjpRtfQDqE\nhQLGY6V0JbmGaLUbnI0VtZNDNpTlUC1RDCjI/VFXMZbfiR8tdjtiB4E5HfGDHwGvAt+chrL4dhaJ\nHQa2AxO6VEeqIC2ePEPeb6EipgxsKXefKPrJJhLKNX3cI1b5iqzEeXUVmBP78EpMXUKPu7369OPu\nsH1f/mMu336r/1tc5Rm47m5hj/p6sf5vzFPFXcJD/VfDYfecBPrtzUwi017+P2YnC1aKuj/wfTCx\nrd332Hf2X74rXtY+Uys8wdqzp+z0fnGO+ixS4XHQlklOB3aL08Vn4UKCZ2s7Fs03ske0hutyqCna\nsmub2X22e2MTosfz1GejFyiwRmcLPoLAiQao5mz3xQYknImWyAp0CqzVgrwrgJKyDXDK8VoKxnIO\n3LybAuuSKQihvPdzeVGZvMVh40XKHlMHKrpSslC7oDRBfaaN4jUnt3EXAotTywOfIiy0A+kv8ZxD\nRuhYOvCTw8/ai2IXVDmks50PH77JyW3eKbCiWSEIgU+pERd08wtAEgll/iwdeOf6ugBodsFzlFFb\nw+vrNQt1X8lQpY1V2AOhrrg9zlNjUwwCWf68OtAuqLEHqyLHbxRYrDOUyJOizHrS/AI4+hCjmG5m\nu9qNDw7eNThofoiMadRzgcZHgCptnyrIIVCnKmsc70iRm4yd9ulAu4dMje8StN8osOaBI+8Iplfo\n1OjAOYrsLPC8gLw60CnYg1BV8Bsan0wq6g6dQ43zIX2AyT4d6BTcj7HihsUEqWQRr0L+vcTt9yfk\nHCGfDnQKbpkcrog2m7bhSkdgA6dg4OfgRtUX9GOB9FELTQ6Ir4J2LFR7bKP0iBEyNuL9nHK56geb\niFI+0Aw25wV/02+ubWDWbCB6dcG/AVt5k8Zzd9rPAAAAAElFTkSuQmCC\n",
-      "text/latex": [
-       "$$\\frac{k length_{0} x}{m \\sqrt{x^{2} + y^{2}}} - \\frac{k x}{m}$$"
-      ],
-      "text/plain": [
-       " k⋅length₀⋅x     k⋅x\n",
-       "────────────── - ───\n",
-       "     _________    m \n",
-       "    ╱  2    2       \n",
-       "m⋅╲╱  x  + y        "
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "soln[ax]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAANUAAAA1BAMAAAAkKrMyAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nTkSuQmCC\n",
-      "text/latex": [
-       "$$- g + \\frac{k length_{0} y}{m \\sqrt{x^{2} + y^{2}}} - \\frac{k y}{m}$$"
-      ],
-      "text/plain": [
-       "      k⋅length₀⋅y     k⋅y\n",
-       "-g + ────────────── - ───\n",
-       "          _________    m \n",
-       "         ╱  2    2       \n",
-       "     m⋅╲╱  x  + y        "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "soln[ay]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can get SymPy to format the result for LaTeX:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\frac{k length_{0} x}{m \\sqrt{x^{2} + y^{2}}} - \\frac{k x}{m}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(latex(soln[ax]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "- g + \\frac{k length_{0} y}{m \\sqrt{x^{2} + y^{2}}} - \\frac{k y}{m}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(latex(soln[ay]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or generate Python code we can paste into a slope function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "k = Symbol('k')\n",
-      "length0 = Symbol('length0')\n",
-      "x = Symbol('x')\n",
-      "m = Symbol('m')\n",
-      "y = Symbol('y')\n",
-      "e = k*length0*x/(m*sqrt(x**2 + y**2)) - k*x/m\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(python(soln[ax]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "g = Symbol('g')\n",
-      "k = Symbol('k')\n",
-      "length0 = Symbol('length0')\n",
-      "y = Symbol('y')\n",
-      "m = Symbol('m')\n",
-      "x = Symbol('x')\n",
-      "e = -g + k*length0*y/(m*sqrt(x**2 + y**2)) - k*y/m\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(python(soln[ay]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To see these equations run, see pendulum.ipynb"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Rigid pendulum"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Solving the rigid pendulum is almost the same, except we need a third equation to represent the geometric constraint.  The simplest form of the constraint is:\n",
-    "\n",
-    "$ x^2 + y ^2 = length$\n",
-    "\n",
-    "But this equation doesn't involve vx, vy, ax, and ay, so it's not much help.  However, if we take the time derivative of both sides, we get\n",
-    "\n",
-    "$ 2 x vx + 2 y vy = 0 $\n",
-    "\n",
-    "And if we take the time derivative one more time (and divide through by 2), we get\n",
-    "\n",
-    "$ x ax + y ay + vx^2 + vy^2 = 0 $\n",
-    "\n",
-    "And that's just what we need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAAYBAMAAADQRaYKAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nh94aeBqs/VsslHGtfwB1oelRKPxU1AAAAABJRU5ErkJggg==\n",
-      "text/latex": [
-       "$$ax x + ay y + vx^{2} + vy^{2} = 0$$"
-      ],
-      "text/plain": [
-       "                2     2    \n",
-       "ax⋅x + ay⋅y + vx  + vy  = 0"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eq3 = Eq(x*ax + y*ay + vx**2 + vy**2, 0)\n",
-    "eq3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can represent the force due to tension as a vector with unknown magnitude, R, and direction opposite P. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "f_tension = -R * hat(P)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Again, we can write $\\sum F = ma$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "lhs = f_grav + f_tension"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "rhs = m * A"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And make one equation for each dimension:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAALAAAAA0CAMAAAAdSEKiAAAAM1BMVEX///8AAAAAAAAAAAAAAAAA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\n34zpH3T4A9v4IFjKd628AAAAAElFTkSuQmCC\n",
-      "text/latex": [
-       "$$- \\frac{R x}{\\sqrt{x^{2} + y^{2}}} = ax m$$"
-      ],
-      "text/plain": [
-       "   -R⋅x            \n",
-       "──────────── = ax⋅m\n",
-       "   _________       \n",
-       "  ╱  2    2        \n",
-       "╲╱  x  + y         "
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eq4 = Eq(lhs[0], rhs[0])\n",
-    "eq4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOYAAAA0BAMAAABsslmsAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
-      "text/latex": [
-       "$$- \\frac{R y}{\\sqrt{x^{2} + y^{2}}} - g m = ay m$$"
-      ],
-      "text/plain": [
-       "      R⋅y                  \n",
-       "- ──────────── - g⋅m = ay⋅m\n",
-       "     _________             \n",
-       "    ╱  2    2              \n",
-       "  ╲╱  x  + y               "
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eq5 = Eq(lhs[1], rhs[1])\n",
-    "eq5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we have three equations in three unknowns:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "soln = solve([eq3, eq4, eq5], [ax, ay, R])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can get explicit expressions for ax and ay"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOEAAAApBAMAAAAxJlOQAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nrkJggg==\n",
-      "text/latex": [
-       "$$\\frac{x}{x^{2} + y^{2}} \\left(g y - vx^{2} - vy^{2}\\right)$$"
-      ],
-      "text/plain": [
-       "  ⎛        2     2⎞\n",
-       "x⋅⎝g⋅y - vx  - vy ⎠\n",
-       "───────────────────\n",
-       "       2    2      \n",
-       "      x  + y       "
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "soln[ax]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAR4AAAAuBAMAAAAM6mv8AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEM3dMiK7mat272ZE\niVTFV83YAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAExUlEQVRYCe1XTWhcVRQ+LzNv/t/royK0i9Jx\nFHUl9Q8b3QQXLkTIrFyoNOOmiaumPxS7UENBwUXt6ypGCEldCEKxAy7828zCRYtoxoqE4sIRtP5A\nYwqGIpWM55z77n333nlvXjadZJELc9455/vu3C/33bnnBCBtjAVpyLbk3WM7So8zfX5H6QFY2tUz\n9GDu7s/Q7dk9P8O3Z2v7450+nvE1Ebw6nf5zHYbF355ynvOdmALwI5zVQ91/QQ+8Zqmux7rPWEgZ\nNjqk+yl6ntM58CRMNo2ECvIt5aKTa+Y39Fj3GbtCGTY6pPspeu7TOXAJpjpGQgVm+at03P8UZDmM\neW3MsrFQGb5++TBR7FG09/TvwKaI+IyV9m9ZCS0k7FGK2WhAtjtlLz+ePMe1l8/Zf4g2j7B5itlo\nQLa7alG8WSsRhQN5e6I+jbApSrDRkUz/E4vxWRQ7TROo9My4KnXbRKQxlmujx8acODzKW6+h1q1N\n8Aw/NCdOBmb8LXwtEjYRs4wVW+ixEbyt2eqyyXt27YZY2F7mS5OXf2TtVZGxiQACK9M3szFnDo+K\ndcJvLtwAZ+Htq9fhcr8vJshlDpwB9yKmTuAH/XvasP/9VxbDSr+/bhCd1dB5hhjuRYHVaOfZCN7W\nbKGFPK/l3oYD8Gn4WDxJ6un+BWVa+Sh+0M/NOd3qeK4+QPT8ZThMDGYjnP9Hmpib7Y3NIQevnE34\nHn4OfosnRHrc3glgzQ8DuL2T4He9wLvlNweIR4qH4KZiI8z3JZq9j9N4AKCfNejPzs2i+Qjw8g/g\nwXgRgEiPAx/DnhCBpwDIr/UcGGslEINKCN8pNhLcTWl0dpZP+5O/A+VDSPxXkb1G4/4PGw3Siidg\ncgKfuD/kl/HB+vCJQyOuBFg+JRshuT/M27IhPdUNKODS+MKdeJ48P8U6fEBZ0oOvxMMH6gsoJYYk\nnsTzBZKNENfbhKKb3IzIPqfUwlezDnt68AsexAvRGviQyxTmYIay7+AH/V/BD5agnKBnBvwJYjAb\nyfzTYkPT1UhpVGSfQxeEswHnm7XNwrrbVtOUnmKrdoeytEle3enCSu8YvBzzFPEa/kLx/hNsxPlm\ns6+31EZF9jn8B1xZ/AGcdy8sfpWwjHNunhsdPB4A55Cxb2H/T5pupadEWclGbsr9nNKoqD7nEmuY\n1pQIV74v/OI6ZSodsgkjJjIo2OiOhdJYk1IaFbx0aLyBJw+cwd6qFu3BWTjYIV6xSzZhSCJDko3B\nQfp+NtYk2ajca+ajPud5wHsQj2HaGIfXGMovpzH0vGRj7nPKs9EJ6MtG5U8j782KsNSDP75404CM\n4Or8hIjfM9IpgWIDPEEUNiZXNSqmHtXn1E16arQ3FUkEqvR+2SiYC7FoRigX6cFCDC8Fqs+B3xV/\nuONHGzqcpdAXyWMjU6IQx41KpIcK8RKoPgf8npyQ8VzLwE34OIVsZF4U4rhRifQcwV/k0bjPkey7\n/7QLcaSHCnFcOe++DG0FrRBfazSebjQeInAl4FZJ443KNQtxtD/YeVLRGv2wC7HUM0NVeBuGXYil\nntOw0t4GOQOFWOrZd/2t5nbosdeUesT/LjY6+vgbsWSpnlDNR69GrZgLC6EKdoBTWzg1ShX/AzW7\nVy7awE/eAAAAAElFTkSuQmCC\n",
-      "text/latex": [
-       "$$- \\frac{1}{x^{2} + y^{2}} \\left(g x^{2} + y \\left(vx^{2} + vy^{2}\\right)\\right)$$"
-      ],
-      "text/plain": [
-       " ⎛   2     ⎛  2     2⎞⎞ \n",
-       "-⎝g⋅x  + y⋅⎝vx  + vy ⎠⎠ \n",
-       "────────────────────────\n",
-       "         2    2         \n",
-       "        x  + y          "
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "soln[ay]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Again, we can get the results in LaTeX or Python."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\frac{x}{x^{2} + y^{2}} \\left(g y - vx^{2} - vy^{2}\\right)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(latex(soln[ax]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "- \\frac{1}{x^{2} + y^{2}} \\left(g x^{2} + y \\left(vx^{2} + vy^{2}\\right)\\right)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(latex(soln[ay]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To see these equations run, see pendulum2.ipynb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x = Symbol('x')\n",
-      "g = Symbol('g')\n",
-      "y = Symbol('y')\n",
-      "vx = Symbol('vx')\n",
-      "vy = Symbol('vy')\n",
-      "e = x*(g*y - vx**2 - vy**2)/(x**2 + y**2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(python(soln[ax]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "g = Symbol('g')\n",
-      "x = Symbol('x')\n",
-      "y = Symbol('y')\n",
-      "vx = Symbol('vx')\n",
-      "vy = Symbol('vy')\n",
-      "e = -(g*x**2 + y*(vx**2 + vy**2))/(x**2 + y**2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(python(soln[ay]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/queue.ipynb b/notebooks/queue.ipynb
deleted file mode 100644
index be871ebf..00000000
--- a/notebooks/queue.ipynb
+++ /dev/null
@@ -1,553 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case Study: Queueing theory\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *\n",
-    "\n",
-    "# set the random number generator\n",
-    "np.random.seed(7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## One queue or two?\n",
-    "\n",
-    "This notebook presents a solution to an exercise from *Modeling and Simulation in Python*.  It uses features from the first four chapters to answer a question related to queueing theory, which is the study of systems that involve waiting in lines, also known as \"queues\".\n",
-    "\n",
-    "Suppose you are designing the checkout area for a new store.  There is room for two checkout counters and a waiting area for customers.  You can make two lines, one for each counter, or one line that serves both counters.\n",
-    "\n",
-    "In theory, you might expect a single line to be better, but it has some practical drawbacks: in order to maintain a single line, you would have to install rope barriers, and customers might be put off by what seems to be a longer line, even if it moves faster.\n",
-    "\n",
-    "So you'd like to check whether the single line is really better and by how much.  Simulation can help answer this question.\n",
-    "\n",
-    "As we did in the bikeshare model, we'll assume that a customer is equally likely to arrive during any timestep.  I'll denote this probability using the Greek letter lambda, $\\lambda$, or the variable name `lam`.  The value of $\\lambda$ probably varies from day to day, so we'll have to consider a range of possibilities.\n",
-    "\n",
-    "Based on data from other stores, you know that it takes 5 minutes for a customer to check out, on average.  But checkout times are highly variable: most customers take less than 5 minutes, but some take substantially more.  A simple way to model this variability is to assume that when a customer is checking out, they have the same probability of finishing up during each time step.  I'll denote this probability using the Greek letter mu, $\\mu$, or the variable name `mu`.\n",
-    "\n",
-    "If we choose $\\mu=1/5$, the average number of time steps for each checkout will be 5 minutes, which is consistent with the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### One server, one queue\n",
-    "\n",
-    "Write a function called `make_system` that takes `lam` and `mu` as parameters and returns a `System` object with variables `lam`, `mu`, and `duration`.  Set `duration`, which is the number of time steps to simulate, to 10 hours, expressed in minutes. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test this function by creating a `System` object with `lam=1/8` and `mu=1/5`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write an update function that takes as parameters `x`, which is the total number of customer in the store, including the one checking out; `t`, which is the number of minutes that have elapsed in the simulation, and `system`, which is a `System` object.\n",
-    "\n",
-    "If there's a customer checking out, it should use `flip` to decide whether they are done.  And it should use `flip` to decide if a new customer has arrived.\n",
-    "\n",
-    "It should return the total number of customers at the end of the time step.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your function by calling it with `x=1`, `t=0`, and the `System` object you created.  If you run it a few times, you should see different results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the simulation.  Here's a version of `run_simulation` that creates a `TimeSeries` with the total number of customers in the store, including the one checking out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate a queueing system.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function object\n",
-    "    \"\"\"\n",
-    "    x = 0\n",
-    "    results = TimeSeries()\n",
-    "    results[0] = x\n",
-    "    \n",
-    "    for t in linrange(0, system.duration):\n",
-    "        x = update_func(x, t, system)\n",
-    "        results[t+1] = x\n",
-    "\n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Call `run_simulation` with your update function and plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After the simulation, we can compute `L`, which is the average number of customers in the system, and `W`, which is the average time customers spend in the store.  `L` and `W` are related by Little's Law:\n",
-    "\n",
-    "$L = \\lambda W$\n",
-    "\n",
-    "Where $\\lambda$ is the arrival rate.  Here's a function that computes them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_metrics(results, system):\n",
-    "    \"\"\"Compute average number of customers and wait time.\n",
-    "    \n",
-    "    results: TimeSeries of queue lengths\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: L, W\n",
-    "    \"\"\"\n",
-    "    L = results.mean()\n",
-    "    W = L / system.lam\n",
-    "    return L, W"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Call `compute_metrics` with the results from your simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Parameter sweep\n",
-    "\n",
-    "Since we don't know the actual value of $\\lambda$, we can sweep through a range of possibilities, from 10% to 80% of the completion rate, $\\mu$.  (If customers arrive faster than the completion rate, the queue grows without bound.  In that case the metrics `L` and `W` just depend on how long the store is open.)\n",
-    "\n",
-    "Create an array of values for `lam`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write a function that takes an array of values for `lam`, a single value for `mu`, and an update function.\n",
-    "\n",
-    "For each value of `lam`, it should run a simulation, compute `L` and `W`, and store the value of `W` in a `SweepSeries`.\n",
-    "\n",
-    "It should return the `SweepSeries`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Call your function to generate a `SweepSeries`, and plot it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we imagine that this range of values represents arrival rates on different days, we can use the average value of `W`, for a range of values of `lam`, to compare different queueing strategies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# W_avg = sweep.mean()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis\n",
-    "\n",
-    "The model I chose for this system is a common model in queueing theory, in part because many of its properties can be derived analytically.\n",
-    "\n",
-    "In particular, we can derive the average time in the store as a function of $\\mu$ and $\\lambda$:\n",
-    "\n",
-    "$W = 1 / (\\mu - \\lambda)$\n",
-    "\n",
-    "The following function plots the theoretical value of $W$ as a function of $\\lambda$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_W(lam_array, mu):\n",
-    "    \"\"\"Plot the theoretical mean wait time.\n",
-    "    \n",
-    "    lam_array: array of values for `lam`\n",
-    "    mu: probability of finishing a checkout\n",
-    "    \"\"\"\n",
-    "    W = 1 / (mu - lam_array)\n",
-    "    plot(lam_array, W, 'g-')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use this function to plot the theoretical results, then plot your simulation results again on the same graph.  How do they compare?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multiple servers\n",
-    "\n",
-    "Now let's try the other two queueing strategies:\n",
-    "\n",
-    "1.  One queue with two checkout counters.\n",
-    "2.  Two queues, one for each counter.\n",
-    "\n",
-    "The following figure shows the three scenarios:\n",
-    "\n",
-    "![](diagrams/queue.png)\n",
-    "\n",
-    "Write an update function for one queue with two servers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use this update function to simulate the system, plot the results, and print the metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since we have two checkout counters now, we can consider values for $\\lambda$ that exceed $\\mu$.\n",
-    "\n",
-    "Create a new array of values for `lam` from 10% to 160% of `mu`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use your sweep function to simulate the two server, one queue scenario with a range of values for `lam`.\n",
-    "\n",
-    "Plot the results and print the average value of `W` across all values of `lam`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multiple queues\n",
-    "\n",
-    "To simulate the scenario with two separate queues, we need two state variables to keep track of customers in each queue.\n",
-    "\n",
-    "Write an update function that takes `x1`, `x2`, `t`, and `system` as parameters and returns `x1` and `x2` as return values.  f you are not sure how to return more than one return value, see `compute_metrics`.\n",
-    "\n",
-    "When a customer arrives, which queue do they join?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write a version of `run_simulation` that works with this update function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your functions by running a simulation with a single value of `lam`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sweep a range of values for `lam`, plot the results, and print the average wait time across all values of `lam`.\n",
-    "\n",
-    "How do the results compare to the scenario with two servers and one queue."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/rabbits.ipynb b/notebooks/rabbits.ipynb
deleted file mode 100644
index e8ff3cab..00000000
--- a/notebooks/rabbits.ipynb
+++ /dev/null
@@ -1,773 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Rabbit example\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# If you want the figures to appear in the notebook, \n",
-    "# and you want to interact with them, use\n",
-    "# %matplotlib notebook\n",
-    "\n",
-    "# If you want the figures to appear in the notebook, \n",
-    "# and you don't want to interact with them, use\n",
-    "# %matplotlib inline\n",
-    "\n",
-    "# If you want the figures to appear in separate windows, use\n",
-    "# %matplotlib qt5\n",
-    "\n",
-    "# To switch from one to another, you have to select Kernel->Restart\n",
-    "\n",
-    "%matplotlib inline\n",
-    "\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook develops a simple growth model, like the ones in Chapter 3, and uses it to demonstrate a parameter sweep.\n",
-    "\n",
-    "The system we'll model is a rabbit farm.  Suppose you start with an initial population of rabbits and let them breed.  For simplicity, we'll assume that all rabbits are on the same breeding cycle, and we'll measure time in \"seasons\", where a season is the reproductive time of a rabbit.\n",
-    "\n",
-    "If we provide all the food, space and other resources a rabbit might need, we expect the number of new rabbits each season to be proportional to the current population, controlled by a parameter, `birth_rate`, that represents the number of new rabbits per existing rabbit, per season.  As a starting place, I'll assume `birth_rate = 0.9`.\n",
-    "\n",
-    "Sadly, during each season, some proportion of the rabbits die.  In a detailed model, we might keep track of each rabbit's age, because the chance of dying is probably highest for young and old rabbits, and lowest in between.  But for simplicity, we'll model the death process with a single parameter, `death_rate`, that represent the number\n",
-    "of deaths per rabbit per season.   As a starting place, I'll assume `death_rate = 0.5`.\n",
-    "\n",
-    "Here's a system object that contains these parameters as well as:\n",
-    "\n",
-    "* The initial population, `p0`,\n",
-    "* The initial time, `t0`, and\n",
-    "* The duration of the simulation, `t_end`, measured in seasons."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>t0</th>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>10.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p0</th>\n",
-       "      <td>10.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>birth_rate</th>\n",
-       "      <td>0.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>death_rate</th>\n",
-       "      <td>0.5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "t0             0.0\n",
-       "t_end         10.0\n",
-       "p0            10.0\n",
-       "birth_rate     0.9\n",
-       "death_rate     0.5\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = System(t0 = 0, \n",
-    "                t_end = 10,\n",
-    "                p0 = 10,\n",
-    "                birth_rate = 0.9,\n",
-    "                death_rate = 0.5)\n",
-    "\n",
-    "system"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a version of run_simulation, similar to the one in Chapter 3, with both births and deaths proportional to the current population."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def run_simulation(system):\n",
-    "    \"\"\"Runs a proportional growth model.\n",
-    "    \n",
-    "    Adds TimeSeries to `system` as `results`.\n",
-    "    \n",
-    "    system: System object with t0, t_end, p0,\n",
-    "            birth_rate and death_rate\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t0] = system.p0\n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        births = system.birth_rate * results[t]\n",
-    "        deaths = system.death_rate * results[t]\n",
-    "        results[t+1] = results[t] + births - deaths\n",
-    "    system.results = results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run the simulation and display the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>14.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>19.600000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>27.440000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>38.416000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>53.782400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>75.295360</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>105.413504</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>147.578906</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>206.610468</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>289.254655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>404.956517</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "0      10.000000\n",
-       "1      14.000000\n",
-       "2      19.600000\n",
-       "3      27.440000\n",
-       "4      38.416000\n",
-       "5      53.782400\n",
-       "6      75.295360\n",
-       "7     105.413504\n",
-       "8     147.578906\n",
-       "9     206.610468\n",
-       "10    289.254655\n",
-       "11    404.956517\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run_simulation(system)\n",
-    "system.results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice that the simulation actually runs one season past `t_end`.  That's an off-by-one error that I'll fix later, but for now we don't really care.\n",
-    "\n",
-    "The following function plots the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def plot_results(system, title=None):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    system: System object with `results`\n",
-    "    \"\"\"\n",
-    "    newfig()\n",
-    "    plot(system.results, 'bo', label='rabbits')\n",
-    "    decorate(xlabel='Season', \n",
-    "             ylabel='Rabbit population',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we call it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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F4CfSIM72JyKVaNOmDU6cOIHc3FxMmTIFzZo1w44dO3Dz5k357YSJSDM47E9URxz2V96Z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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb4b7d3b208>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_results(system, title='Proportional growth model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's suppose our goal is to maximize the number of rabbits, so the metric we care about is the final population.  We can extract it from the results like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def final_population(system):\n",
-    "    t_end = system.results.index[-1]\n",
-    "    return system.results[t_end]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And call it like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "404.95651696640027"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "final_population(system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To explore the effect of the parameters on the results, we'll define `make_system`, which takes the system parameters as function parameters(!) and returns a `System` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def make_system(birth_rate=0.9, death_rate=0.5):\n",
-    "    \n",
-    "    system = System(t0 = 0, \n",
-    "                    t_end = 10,\n",
-    "                    p0 = 10,\n",
-    "                    birth_rate = birth_rate,\n",
-    "                    death_rate = death_rate)\n",
-    "    return system"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can make a `System`, run a simulation, and extract a metric:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "404.95651696640027"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = make_system()\n",
-    "run_simulation(system)\n",
-    "final_population(system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To see the relationship between `birth_rate` and final population, we'll define `sweep_birth_rate`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def sweep_birth_rate(birth_rates, death_rate=0.5):\n",
-    "    \n",
-    "    for birth_rate in birth_rates:\n",
-    "        system = make_system(birth_rate=birth_rate,\n",
-    "                             death_rate=death_rate)\n",
-    "        run_simulation(system)\n",
-    "        p_end = final_population(system)\n",
-    "        plot(birth_rate, p_end, 'gs', label='rabbits')\n",
-    "        \n",
-    "    decorate(xlabel='Births per rabbit per season',\n",
-    "             ylabel='Final population')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first parameter of `sweep_birth_rate` is supposed to be an array; we can use `linspace` to make one."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.  ,  0.05,  0.1 ,  0.15,  0.2 ,  0.25,  0.3 ,  0.35,  0.4 ,\n",
-       "        0.45,  0.5 ,  0.55,  0.6 ,  0.65,  0.7 ,  0.75,  0.8 ,  0.85,\n",
-       "        0.9 ,  0.95,  1.  ])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "birth_rates = linspace(0, 1, 21)\n",
-    "birth_rates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can call `sweep_birth_rate`.\n",
-    "\n",
-    "The resulting figure shows the final population for a range of values of `birth_rate`.\n",
-    "\n",
-    "Confusingly, the results from a parameter sweep sometimes resemble a time series.  It is very important to remember the difference.  One way to avoid confusion: LABEL THE AXES.\n",
-    "\n",
-    "In the following figure, the x-axis is `birth_rate`, NOT TIME."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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JjUkXBubn58PLy+ulN1azZk0AMJrOBwAXFxdkZmZCLpcXOt2QnZ0NAFAoFHBycirydER2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\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb4b556b908>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "birth_rates = linspace(0, 1, 21)\n",
-    "sweep_birth_rate(birth_rates)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The code to sweep the death rate is similar."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def sweep_death_rate(death_rates, birth_rate=0.9):\n",
-    "    \n",
-    "    for death_rate in death_rates:\n",
-    "        system = make_system(birth_rate=birth_rate,\n",
-    "                             death_rate=death_rate)\n",
-    "        run_simulation(system)\n",
-    "        p_end = final_population(system)\n",
-    "        plot(death_rate, p_end, 'r^', label='rabbits')\n",
-    "        \n",
-    "    decorate(xlabel='Deaths per rabbit per season',\n",
-    "             ylabel='Final population')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here are the results.  Again, the x-axis is `death_rate`, NOT TIME."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Q1RAREWlgUDC048eB0tLyn/fuBfLzDVsPERHRXzAoGFJJSXlQqJCfXx4WiIiI6ggGBUNK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\nAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb4b547b828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "death_rates = linspace(0.1, 1, 20)\n",
-    "sweep_death_rate(death_rates)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the previous sweeps, we hold one parameter constant and sweep the other.\n",
-    "\n",
-    "You can also sweep more than one variable at a time, and plot multiple lines on a single axis.\n",
-    "\n",
-    "To keep the figure from getting too cluttered, I'll reduce the number of values in `birth_rates`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.4,  0.6,  0.8,  1. ])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "birth_rates = linspace(0.4, 1, 4)\n",
-    "birth_rates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By putting one for loop inside another, we can enumerate all pairs of values.\n",
-    "\n",
-    "The results show 4 lines, one for each value of `birth_rate`.\n",
-    "\n",
-    "(I did not plot the lines between the data points because of a limitation in `plot`.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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8jh8/jhkzZiA8PBwXLlx4oLZlMtkDrSciIqLKTBqRGDduHN5//32kpqaic+fOcHZ2rrRN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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb4b53cb7b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for birth_rate in birth_rates:\n",
-    "    for death_rate in death_rates:\n",
-    "        system = make_system(birth_rate=birth_rate,\n",
-    "                             death_rate=death_rate)\n",
-    "        run_simulation(system)\n",
-    "        p_end = final_population(system)\n",
-    "        plot(death_rate, p_end, 'c^', label='rabbits')\n",
-    "        \n",
-    "decorate(xlabel='Deaths per rabbit per season',\n",
-    "         ylabel='Final population')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you suspect that the results depend on the difference between `birth_rate` and `death_rate`, you could run the same loop, plotting the \"net birth rate\" on the x axis.\n",
-    "\n",
-    "If you are right, the results will fall on a single curve, which means that knowing the difference is sufficient to predict the outcome; you don't actually have to know the two parameters separately."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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x3NzcoNFo4OzsrHN8WbKg0WiQlZVVaX/ZMZmZmdBoNACg9xxl+4iIiKhmBnVh9OnTB4cP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\nv37g+OqCKepYJi8vD9OnT8eJEyewZs0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3Zf2ot7fX44mT6OhoiouLCQgI4NmzZ1PmHX/O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\nQgjhM+lACCGEEMJn/wEDHeDjKnY/1AAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb4b52fe710>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for birth_rate in birth_rates:\n",
-    "    for death_rate in death_rates:\n",
-    "        system = make_system(birth_rate=birth_rate,\n",
-    "                             death_rate=death_rate)\n",
-    "        run_simulation(system)\n",
-    "        p_end = final_population(system)\n",
-    "        net_birth_rate = birth_rate - death_rate\n",
-    "        plot(net_birth_rate, p_end, 'mv', label='rabbits')\n",
-    "        \n",
-    "decorate(xlabel='Net births per rabbit per season',\n",
-    "         ylabel='Final population')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "On the other hand, if you guess that the results depend on the ratio of the parameters, rather than the difference, you could check by plotting the ratio on the x axis.\n",
-    "\n",
-    "If the results don't fall on a single curve, that suggests that the ratio alone is not sufficient to predict the outcome. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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o9XpERUVZuxYAwMqVK5GUlISBAwdCkiR4e3tj+fLl8PHxgVqthqenp8n+dWFBrVajtLS0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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb4b5390860>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for birth_rate in birth_rates:\n",
-    "    for death_rate in death_rates:\n",
-    "        system = make_system(birth_rate=birth_rate,\n",
-    "                             death_rate=death_rate)\n",
-    "        run_simulation(system)\n",
-    "        p_end = final_population(system)\n",
-    "        birth_ratio = birth_rate / death_rate\n",
-    "        plot(birth_ratio, p_end, 'y>', label='rabbits')\n",
-    "        \n",
-    "decorate(xlabel='Ratio of births to deaths',\n",
-    "         ylabel='Final population')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/rabbits2.ipynb b/notebooks/rabbits2.ipynb
deleted file mode 100644
index f77cb8cf..00000000
--- a/notebooks/rabbits2.ipynb
+++ /dev/null
@@ -1,399 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Rabbit example\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Rabbit Redux\n",
-    "\n",
-    "This notebook starts with a version of the rabbit population growth model and walks through some steps for extending it.\n",
-    "\n",
-    "In the original model, we treat all rabbits as adults; that is, we assume that a rabbit is able to breed in the season after it is born.  In this notebook, we extend the model to include both juvenile and adult rabbits.\n",
-    "\n",
-    "As an example, let's assume that rabbits take 3 seasons to mature.  We could model that process explicitly by counting the number of rabbits that are 1, 2, or 3 seasons old.  As an alternative, we can model just two stages, juvenile and adult.  In the simpler model, the maturation rate is 1/3 of the juveniles per season.\n",
-    "\n",
-    "To implement this model, make these changes in the System object:\n",
-    "\n",
-    "0.  Before you make any changes, run all cells and confirm your understand them.\n",
-    "\n",
-    "1.  Then, add a second initial populations: `juvenile_pop0`, with value `0`.\n",
-    "\n",
-    "2.  Add an additional variable, `mature_rate`, with the value `0.33`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>t0</th>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>10.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>adult_pop0</th>\n",
-       "      <td>10.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>birth_rate</th>\n",
-       "      <td>0.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>death_rate</th>\n",
-       "      <td>0.5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "t0             0.0\n",
-       "t_end         10.0\n",
-       "adult_pop0    10.0\n",
-       "birth_rate     0.9\n",
-       "death_rate     0.5\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = System(t0 = 0, \n",
-    "                t_end = 10,\n",
-    "                adult_pop0 = 10,\n",
-    "                birth_rate = 0.9,\n",
-    "                death_rate = 0.5)\n",
-    "\n",
-    "system"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now update `run_simulation` with the following changes:\n",
-    "\n",
-    "1.  Add a second TimeSeries, named `juveniles`, to keep track of the juvenile population, and initialize it with `juvenile_pop0`.\n",
-    "\n",
-    "2.  Inside the for loop, compute the number of juveniles that mature during each time step.\n",
-    "\n",
-    "3.  Also inside the for loop, add a line that stores the number of juveniles in the new `TimeSeries`.  For simplicity, let's assume that only adult rabbits die.\n",
-    "\n",
-    "4.  During each time step, subtract the number of maturations from the juvenile population and add it to the adult population.\n",
-    "\n",
-    "5.  After the for loop, store the `juveniles` `TimeSeries` as a variable in `System`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def run_simulation(system):\n",
-    "    \"\"\"Runs a proportional growth model.\n",
-    "    \n",
-    "    Adds TimeSeries to `system` as `results`.\n",
-    "    \n",
-    "    system: System object with t0, t_end, p0,\n",
-    "            birth_rate and death_rate\n",
-    "    \"\"\"\n",
-    "    adults = TimeSeries()\n",
-    "    adults[system.t0] = system.adult_pop0\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        births = system.birth_rate * adults[t]\n",
-    "        deaths = system.death_rate * adults[t]\n",
-    "        \n",
-    "        adults[t+1] = adults[t] + births - deaths\n",
-    "        \n",
-    "    system.adults = adults"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your changes in `run_simulation`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>14.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>19.600000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>27.440000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>38.416000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>53.782400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>75.295360</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>105.413504</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>147.578906</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>206.610468</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>289.254655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>404.956517</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "0      10.000000\n",
-       "1      14.000000\n",
-       "2      19.600000\n",
-       "3      27.440000\n",
-       "4      38.416000\n",
-       "5      53.782400\n",
-       "6      75.295360\n",
-       "7     105.413504\n",
-       "8     147.578906\n",
-       "9     206.610468\n",
-       "10    289.254655\n",
-       "11    404.956517\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run_simulation(system)\n",
-    "system.adults"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, update `plot_results` to plot both the adult and juvenile `TimeSeries`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def plot_results(system, title=None):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    system: System object with `results`\n",
-    "    \"\"\"\n",
-    "    newfig()\n",
-    "    plot(system.adults, 'bo-', label='adults')\n",
-    "    decorate(xlabel='Season', \n",
-    "             ylabel='Rabbit population',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And test your updated version of `plot_results`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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hHoJGoz9tb48eSi3/6kySvxBCCPEQfv8dLlxQlq2soHt3k4ZjEEn+QgghxEPY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\nkcIw/ImIiBSG4U9ERKQwDH8iIiKF+T/pMNrVl3T6wAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f615e3c12e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_results(system, title='Proportional growth model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook demonstrates the steps we recommend for starting your project:\n",
-    "\n",
-    "1.  Start with one of the examples from the book, either by copying a notebook or pasting code into a new notebook.  Get the code working before you make any changes.\n",
-    "\n",
-    "2.  Make one small change, and run the code again.\n",
-    "\n",
-    "3.  Repeat step 2 until you have a basic implementation of your model.\n",
-    "\n",
-    "If you start with working code that you understand and make small changes, you can avoid spending a lot of time debugging.\n",
-    "\n",
-    "One you have a basic model working, you can think about what metrics to measure, what parameters to sweep, and how to use the model to predict, explain, or design."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Bonus question\n",
-    "\n",
-    "Suppose you only have room for 30 adult rabbits.  Whenever the adult population exceeds 30, you take any excess rabbits to market (as pets for kind children, of course).  Modify `run_simulation` to model this strategy.  What effect does it have on the behavior of the system?  You might have to run for more than 10 seasons to see what happens."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/rabbits3.ipynb b/notebooks/rabbits3.ipynb
deleted file mode 100644
index 1aab7049..00000000
--- a/notebooks/rabbits3.ipynb
+++ /dev/null
@@ -1,630 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Rabbit example\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Rabbit is Rich\n",
-    "\n",
-    "This notebook starts with a version of the rabbit population growth model.  You will modify it using some of the tools in Chapter 5.  Before you attempt this diagnostic, you should have a good understanding of State objects, as presented in Section 5.4.  And you should understand the version of `run_simulation` in Section 5.7.\n",
-    "\n",
-    "### Separating the `State` from the `System`\n",
-    "\n",
-    "Here's the `System` object from the previous diagnostic.  Notice that it includes system parameters, which don't change while the simulation is running, and population variables, which do.  We're going to improve that by pulling the population variables into a `State` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>t0</th>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>20.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>juvenile_pop0</th>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>adult_pop0</th>\n",
-       "      <td>10.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>birth_rate</th>\n",
-       "      <td>0.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mature_rate</th>\n",
-       "      <td>0.33</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>death_rate</th>\n",
-       "      <td>0.50</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "t0                0.00\n",
-       "t_end            20.00\n",
-       "juvenile_pop0     0.00\n",
-       "adult_pop0       10.00\n",
-       "birth_rate        0.90\n",
-       "mature_rate       0.33\n",
-       "death_rate        0.50\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "system = System(t0 = 0, \n",
-    "                t_end = 20,\n",
-    "                juvenile_pop0 = 0,\n",
-    "                adult_pop0 = 10,\n",
-    "                birth_rate = 0.9,\n",
-    "                mature_rate = 0.33,\n",
-    "                death_rate = 0.5)\n",
-    "\n",
-    "system"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following cells, define a `State` object named `init` that contains two state variables, `juveniles` and `adults`, with initial values `0` and `10`.  Make a version of the `System` object that does NOT contain `juvenile_pop0` and `adult_pop0`, but DOES contain `init`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Updating `run_simulation`\n",
-    "\n",
-    "Here's the version of `run_simulation` from last time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def run_simulation(system):\n",
-    "    \"\"\"Runs a proportional growth model.\n",
-    "    \n",
-    "    Adds TimeSeries to `system` as `results`.\n",
-    "    \n",
-    "    system: System object\n",
-    "    \"\"\"\n",
-    "    juveniles = TimeSeries()\n",
-    "    juveniles[system.t0] = system.juvenile_pop0\n",
-    "    \n",
-    "    adults = TimeSeries()\n",
-    "    adults[system.t0] = system.adult_pop0\n",
-    "    \n",
-    "    for t in linrange(system.t0, system.t_end):\n",
-    "        maturations = system.mature_rate * juveniles[t]\n",
-    "        births = system.birth_rate * adults[t]\n",
-    "        deaths = system.death_rate * adults[t]\n",
-    "        \n",
-    "        if adults[t] > 30:\n",
-    "            market = adults[t] - 30\n",
-    "        else:\n",
-    "            market = 0\n",
-    "            \n",
-    "        juveniles[t+1] = juveniles[t] + births - maturations\n",
-    "        adults[t+1] = adults[t] + maturations - deaths - market\n",
-    "        \n",
-    "    system.adults = adults\n",
-    "    system.juveniles = juveniles"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the cell below, write a version of `run_simulation` that works with the new `System` object (the one that contains a `State` object named `init`).\n",
-    "\n",
-    "Hint: you only have to change two lines."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your changes in `run_simulation`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>5.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5.470000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>6.209900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.057723</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>8.021560</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>9.117031</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>10.362107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>11.777219</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>13.385586</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>15.213601</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>17.291261</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>19.652658</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>22.336542</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>25.386953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>28.853947</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>32.794414</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>34.478600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>36.487431</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>37.893339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>39.401924</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>40.546917</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "0     10.000000\n",
-       "1      5.000000\n",
-       "2      5.470000\n",
-       "3      6.209900\n",
-       "4      7.057723\n",
-       "5      8.021560\n",
-       "6      9.117031\n",
-       "7     10.362107\n",
-       "8     11.777219\n",
-       "9     13.385586\n",
-       "10    15.213601\n",
-       "11    17.291261\n",
-       "12    19.652658\n",
-       "13    22.336542\n",
-       "14    25.386953\n",
-       "15    28.853947\n",
-       "16    32.794414\n",
-       "17    34.478600\n",
-       "18    36.487431\n",
-       "19    37.893339\n",
-       "20    39.401924\n",
-       "21    40.546917\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run_simulation(system)\n",
-    "system.adults"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting the results\n",
-    "\n",
-    "Here's a version of `plot_results` that plots both the adult and juvenile `TimeSeries`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def plot_results(system, title=None):\n",
-    "    \"\"\"Plot the estimates and the model.\n",
-    "    \n",
-    "    system: System object with `results`\n",
-    "    \"\"\"\n",
-    "    newfig()\n",
-    "    plot(system.adults, 'bo-', label='adults')\n",
-    "    plot(system.juveniles, 'gs-', label='juveniles')\n",
-    "    decorate(xlabel='Season', \n",
-    "             ylabel='Rabbit population',\n",
-    "             title=title)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If your changes in the previous section were successful, you should be able to run this new version of `plot_results`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Q/yF0btTZhFWZltxzrwQ2btxIv379aNu2LR07Vo1JDYQQojTczr7Nlwe+VINd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\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f44d1c87588>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_results(system, title='Proportional growth model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That's the end of the diagnostic.  If you were able to get it done quickly, and you would like a challenge, here are two bonus questions:\n",
-    "\n",
-    "\n",
-    "### Bonus question #1\n",
-    "\n",
-    "Write a version of `run_simulation` that puts the results into a single `TimeFrame` named `results`, rather than two `TimeSeries` objects.\n",
-    "\n",
-    "Write a version of `plot_results` that can plot the results in this form.\n",
-    "\n",
-    "WARNING: This question is substantially harder, and requires you to have a good understanding of everything in Chapter 5.  We don't expect most people to be able to do this exercise at this point."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "run_simulation(system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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pohJYdOUeFRXF6tWr6dGjB+np6Rw5coSMjAx69erF6tWradeuXVXXKYQQopIV\nG4o5evWoKdgdbR1p4dNCBtDVARbPc2/evDlvv/12VdYihBCimhgxciL9BPnF+QDYqGy42/du7NR2\nVq5MVIZyw33Hjh3cc8892NrasmPHjn98Idk4Rgghagej0cip9FOkF6Sb2pp7N8fFzsWKVYnKVG64\njx07lj///BNvb2/Gjh1r2iSmLLJxjBBC1B5rT6zlct5l0+NGbo3wdfa1YkWispUb7suXL8fd3d30\nsxBCiNrvt7O/mS0tG6AJIMQjxIoViapQbrjfuFnMpUuXeOihh7C3ty913uXLl/nll19kcxkhhKjh\n4pLjWHVklemxt5M3d3ndhQrZwrWusWhA3csvv0y3bt3w8vIqdezq1avMnj2bkSNHVnZtQgghKsmx\nq8dYemCp6fGotqN4/p7nsbcpfdEmar9bhvvw4cNNfe2TJk3Czs58FKXRaCQxMRE3N7cqLVIIIcTt\nS8pKYuHehegNegAauDZgUvQkCfY67JaTGQcMGEBIiNIXo9frKS4uNvvS6/Xcfffd/Pe//63Qm65Z\ns4YHH3yQiIgIYmJiWLZsmenY+vXrGTBgAJGRkfTq1YvZs2ej1+sr/smEEEKQmpfKvD3zTJvBeDp5\n8lzH53Cxl5Hxddktr9wHDhzIwIEDSUxMZMGCBZVyhb5hwwY+/PBDYmNjiY6OZv/+/bzxxhtERUWR\nn5/P9OnTmTlzJjExMZw7d44JEyZgZ2fH5MmT7/i9hRCiPrlWeI05u+aQo80BwMXehec6Poenk6eV\nKxNVzaJliL766qtyg/3SpUv06dPH4jdcsGABY8eO5d5778Xe3p6OHTuyadMmwsPDWbFiBd26daNP\nnz7Y29sTFhbGyJEj+eqrrzAYDBa/hxBC1HcFugLm7p5LWn4aAHY2dkzuMJkGrg2sXJmoDhavULdl\nyxa2b99OVlaWqc1oNHL69GmuXr1q0WukpqZy5swZnJ2dGTJkCCdOnCAoKIhnnnmGfv36ceDAAYYO\nHWr2nNatW5OVlUViYiKhoaGWliuEEPWWTq/jk7hPuJh9EQC1Ss349uMJ9ZR/Q+sLi8J91apVvPba\na/j4+JCRkYGvry/Xrl2jsLCQtm3bWrws7eXLyqIJ3333HTNnzqRhw4b88MMPvPjiizRo0ICMjAzT\n3PoSnp7K7aOMjAwJdyGE+AcGo4Ev9n/ByfSTpran2zxNhH+EFasS1c2i2/LLly/nP//5Dzt27MDB\nwYEVK1aZIXdIAAAgAElEQVSwf/9+PvroI9RqNVFRURa9WckKd8OHDycsLAxnZ2eefvppwsPDWbNm\nze1/CiGEEBiNRr5J+IZ9KftMbQNbDqRTw05WrEpYg0XhfuHCBR544AFAWWpWr9ejUql4+OGHeeyx\nx3jjjTcsejM/Pz/g+tV4iUaNGnHlyhV8fHzMbvsDZGZmAuDrK0sjCiHErWw4tYFtSdtMj3uE9qBX\n015WrEhYi0XhbmtrS2FhIQDu7u6m2+sA99xzD7t377bozfz8/PDw8CAhIcGsPSkpiaCgICIjIzl4\n8KDZsfj4eHx9fWnUqJFF7yGEEPXRtqRtrDuxzvS4Q1AHBrUahEolq8/VRxaFe9u2bYmNjSUnJ4ew\nsDA+//xzU9j/+uuvODg4WPRmNjY2jBo1ihUrVrBz506KiopYuXIlx44dY8iQIYwYMYIdO3awceNG\nioqKSEhIYOnSpYwaNUr+gAohRDn2p+zn64SvTY9b+bZiRNsR8u9mPWbRgLopU6YwduxYMjIyGDly\nJGPGjKFDhw7Y29uTl5fHiBEjLH7D8ePHU1xczMsvv0x6ejpNmjTh888/p2XLlgDExsYyd+5cXnrp\nJXx8fBg+fDijR4++vU8nhBB13Mn0kyzet9g0pqmxR2MmRE3AVm3xZChRB6mM5e3jepPc3FwcHR2x\ntbUlISGBDRs2UFxcTNu2benbt69Vf0O8ePEiMTEx/PbbbwQHB1utDiGEqE4Xsy8y88+ZFBYrd1L9\nXPx46d6XcHVwtXJloqr9U+5Z/KudRqMx/RwREUFEhEyrEEIIa0nLT2POrjmmYHd3dOf5e56XYBfA\nLcI9NjbW4hdRqVRMmzatUgoSQghxaznaHObsmkO2NhsAR1tHpnacirezt5UrEzVFueG+aNEii19E\nwl0IIapHYXEh8/bMIzUvFQBbtS2TOkwi2E26JMV15Yb78ePHq7MOIYQQ5Ri/bjwABgwcST1CZqGy\n/ocKFSsHrqS5d3NrlidqIIumwgkhhLAuI0ZOpp00BTtAM69mRDaItGJVoqayaEDd008//Y/nLF++\n/I6LEUIIUZoRIyfTT5Kan2pqC3EPoYFGdngTZbMo3HU6Xampbnl5eSQmJhIQEECLFi2qpDghhKjv\n9AY9J9JOmAV7A00DGrnLqp2ifBaF+zfffFNme2ZmJv/+97/p3bt3pRYlhBACig3FLN63uFSwN/Nq\nhgpZfU6U74763D09PXn++eeZO3duZdUjhBACZU/2T/d+yv6U/aa2QE2gBLuwyB2vT2hnZ0dKSkpl\n1CKEEAIo0hexMG4hR68eNbUFuwbTxLOJBLuwiEXhvmPHjlJtRqORa9eusXLlSgIDAyu9MCGEqI+0\nxVrm75nPyfSTprb3Y97nkbBHZCMYYTGLwn3s2LGoVCrKWobezc2N//73v5VemBBC1DeFxYXM3T2X\nMxlnTG39w/rTt3lfK1YlaiOLwr2saW4qlQpXV1dCQkJwcnKq9MKEEKI+ydflM2fXHBKzEk1tA1sO\npHczGbAsKs6icO/QoUNV1yGEEPVWXlEes3fN5sK1C6a2wXcPJiY0xopVidrM4gF1mzdvZt26dVy4\ncIFr167h4eFB06ZNGThwIJ06darKGoUQos7K1mbz8a6PSc5ONrUNaz2MbiHdrFiVqO0smgq3ZMkS\npkyZwuHDhwkMDKR9+/YEBAQQFxfH6NGj+fLLL6u6TiGEqHOyCrOYtXOWKdhVKhVPt3lagl3cMYv7\n3MeNG8e//vWvUsc+/PBDvvjiC0aMGFHpxQkhRF2VWZBJ7F+xpt3dVCoVo9qOomNwRytXJuoCi67c\ns7KyGDRoUJnHBg8eTFZWVqUWJYQQdVlafhozd840BbtapWZcu3ES7KLSWBTuYWFhXL58ucxjly9f\npmXLlpValBBC1FWpeal8tPMj0vPTAbBR2zAhagLtA9tbuTJRl1h0W/6tt97i3XffJScnh7Zt2+Lq\n6kp+fj579+5l2bJlTJ8+naKiItP59vb2VVawEELUVik5KczeNZtrhdcAsFXb8mz0s4T7hVu5MlHX\nWBTuTzzxBFqtlr1795Y6ZjQaGTJkiOmxSqXi6NGjpc4TQoj6LDk7mdm7ZpOjzQHAzsaOSdGTaOkr\ndz5F5avQCnVCCCEq7vy183y862PyivIAcLB1YEqHKdzlfZeVKxN1lUXhPmXKlKquQwgh6qTErETm\n7JpDvi4fAEdbR5675zlCPUOtXJmoyyxexCY3N5dNmzZx7Ngx8vLycHV1pXXr1vTu3RsHB4eqrFEI\nIWqV8evGA8oCNYdTD1NsLAaUPvb1Q9YT4hFizfJEPWBRuJ85c4YRI0aQlpaGq6srLi4u5ObmsmLF\nChYsWMDy5cvx9/ev6lqFEKLWyCrM4sjVI+iNegDs1HZE+EVIsItqYdFUuFmzZhEUFMSmTZuIi4tj\ny5Yt7N27l59//hknJyfZFU4IIW5wJe8KCakJpmC3V9vT2r81GnuNlSsT9YVF4b53715eeeUVmjRp\nYtbevHlzXn311TL3exdCiPrGaDTy47EfOZF+AiPKFtkONg60CWiDi52LlasT9YlFt+ULCgpwc3Mr\n85ifnx/5+fmVWpQQQtQ2Or2OpQeWEn8p3tSmsdNwt9/dONjIuCRRvSy6cg8JCWHTpk1lHtuwYQMh\nIdKHJISov7K12cz6a5ZZsHs7edMmoI0Eu7AKi67cn376aV577TUSEhKIjIxEo9GQk5PDvn372Lp1\nK++8805V1ymEEDXSpZxLzN8z37ScLECQaxChnqGokPVBhHVYFO6DBw8GlK1ff//9d1N748aNeffd\ndxk4cGDVVCeEEDXY0atH+WzvZxQWFwLKCp1P3P0EDzR5wMqVifrO4nnugwcPZvDgweTm5pKXl4eL\niwsajYz8FELUT9uStvFNwjcYjAZAWXVuXLtxRPhHWLkyISoQ7gAnT57kwoULZGdn4+HhQbNmzWjY\nsGFV1SaEEDWOwWhgzbE1bD6z2dTm6eTJ5A6TCXYLtmJlQlxnUbhfuHCBKVOmcOLECYxGo6ldpVIR\nGRnJzJkzCQoKqrIihRCiJtAWa/li/xccuHzA1NbIvRGTOkzCw9HDipUJYc6icH/ttdfIzs7mnXfe\n4e6778bZ2Zm8vDwOHz7MJ598wmuvvcaSJUuqulYhhLCarMIsPon7hKSsJFNbm4A2jIkcg4OtjIgX\nNYtF4b5v3z4WL15MdHS0WXvLli1p2LAhEyZMqJLihBCiJriYfZH5e+aTWZBpauvZtCcDWw5ErbJo\nRrEQ1cqicNdoNPj6+pZ5zN/fHxcXWXlJCFE3HU49zKL4RWiLtQCoVWqGRAyhW0g3K1cmRPks+pVz\n4MCBrF69usxjP/zwA4899lilFiWEEDXBH+f+YP6e+aZgd7R1ZErHKRLsosaz6Mrd1dWVb7/9lq1b\ntxIZGYmrqysFBQXExcVx7do1+vXrR2xsLKAMsps2bVqVFi2EEFXJYDSw6sgq/jj3h6nN29mbyR0m\nE+gaaMXKhLCMReFeEtygTIe72eLFi00/S7gLIWqzwuJCFu9bTMKVBFNbY4/GTOowCTeHsvfYEKKm\nsSjcjx8/XtV1CCGEVYxfN970s1av5UjqEXJ1uQB0a9SNdg3aMSpyFPY29tYqUYgKq9AiNkIIUVfl\nFOVw5OoRivRFprYHmz3Ioy0eRaWSNeJF7SLhLoSo14wYSc1L5VTGKdNSsipU3OV1FwNaDrBydULc\nHgl3IUS9pS3WcjL9JFfyrpjabNW2tPJpJSvOiVpNwl0IUS9dyrnEovhFZsHubOdMK99WONs6W7Ey\nIe7cHYe7VqslKysLf3//yqhHCCGq3M4LO/k64Wt0ep2pzd/Fn2ZezbBR2VixMiEqh0WL2LRs2ZL0\n9PQyj507d45HHnmkUosSQoiqoC3W8uWBL/nywJemYFer1IR5hxHmHSbBLuqMW165//TTTwAYjUY2\nbdpUav92o9HInj170Gq1VVehEEJUgpScFD6L/4yUnBRTW4AmgM3DN8vCNKLOuWW4r169msOHD6NS\nqXjnnXfKPW/48OG39ebx8fE89dRTTJw4kSlTpgCwfv16lixZQmJiIr6+vvTp04epU6diYyO/UQsh\nbs+ui7tYeWil2TS3jsEdGRYxTHZ0EzVOXBxs2gQpKdCgAfTpAzft2/aPbhnuX331FcXFxYSHh/Pd\nd9/h6elZ6hw3Nzc8PCo+qrSwsJAZM2aYbTqzZ88epk+fzsyZM4mJieHcuXNMmDABOzs7Jk+eXOH3\nEELUb0X6Ir49/C1/nv/T1GZnY8eQ8CF0bthZ5q+LGsFohJwcJcx//x1++AEKC8HTEwwGKFkEtiIB\n/48D6mxtbfntt98IDAys1L8IsbGxNGnSBD8/P1PbihUr6NatG3369AEgLCyMkSNH8sknnzBx4kTU\natlaUQhhmcu5l/ls72dcyrlkavPX+DO+/XiC3IKsWJmor0pC/NIlJchv/J6Xp5wTH3/954wM8PIC\nJyf45ZdKCvfY2FieffZZnJyc+O677275IhVdT37v3r2sXbuWn3/+mRdffNHUfuDAAYYOHWp2buvW\nrcnKyiIxMZHQ0FCL30MIUX/tvriblQkrTbu5gdyGF1WjrFvoUVGlQ7zk55LgLk9+/vWfHR3B4e8/\nrpculX1+ecoN90WLFjFixAicnJxYtGjRLV+kIuFeUFDAjBkz+Pe//11q+lxGRgbu7u5mbSVdARkZ\nGRLuQohb0ul1fHv4W3ac32Fqs7Ox48nwJ7m34b1yG15Uqrg4WLRICfLcXDhxAjZuhCZNwK2Ceww5\nOCi/HFy9ClotODuDuzuU3LAOrOCYz3LD/cbNYipz45jY2FgaN27MwIEDK+01hRDicu5lFsUvIjk7\n2dTmr/HnmfbPEOwWbMXKRF1hNCrhe+4cnD0LS5YoV9RGo/l5p09Du3Zlv0ZJiAcGXv8eGKj0r6tU\nyi8MN2y0avLggxWrtcKL2KSlpVFQUICLiwteXl4Vem7J7fh169aVedzHx4esrCyztszMTAB8fX0r\nWqoQop7Yk7yHFYdWmN2Gjw6K5qnWT+Fo62jFykRtVlgIiYlKkJd83XhbvaxgB+UcR0clvG8O8pIQ\nL09Jv/ovvyivHxioBHuljpYvodVqmTlzJuvWrSM7O9vU7unpyYABA3j++eexs7P7x9dZvXo1+fn5\n9O/f39SWm5vLoUOH+P3334mMjOTgwYNmz4mPj8fX15dGjRpZ+pmEEPWETq/juyPfsT1pu6nNVm3L\nk+FP0qVRF7kNLyxmNCp94iVX5WfPKo/LCu8Szs5KkDs7g6sruLgoX82awbvv3jrEbyU6uuJhfjOL\nwv31119n48aNPPLII4SFheHk5ER+fj5Hjhxh+fLl5OTk8NZbb/3j60yfPp3nnnvOrO25556jbdu2\njB07luTkZJ566ik2btxIjx49OHHiBEuXLmX06NHyl1QIAVzff72guIBjV4+Z9l4HGNRyEOOjxstt\neFGukgFw588rt8ibNAFbWyXUCwv/+fnOzspzQkPhgQdg82bl+TcaMOD2g72yWBTuv/76K++8847Z\nFXeJ6OhoPvjgA4vC3d3dvdSAOXt7ezQaDb6+vvj6+hIbG8vcuXN56aWX8PHxYfjw4YwePdrCjyOE\nqOuMGLmSe4UzmWfQG/Wmdj9nP17p9orchhelFBbChQvwv//BqlXKALiCguvHW7SAG2Zlm6hUEBx8\nPcxDQ5Xzbgzupk3v/BZ6VbAo3A0GA23bti3zWIcOHdDr9WUes8RXX31l9rhXr1706tXrtl9PCFF3\nZRRkcDj1MJmFmaY2NWqaejUlQBMgwS4oKoKLFyEpSekvT0qCy5eV2+s3ziG/0cWLSmi7uV0P8SZN\nICTk+lS08lTGLfSqYFG433ffffz1119l9nvv2bOHLl26VHphQghRwmg0sv38dlYfXW0W7E62TrT0\naYnGXnOLZ4u6qrgYkpPNg/zSJWVVt7LcOIdcrQaNRukr9/CA995TFoyx9u30ylJuuO/YcX2eaI8e\nPZg7dy6nT58mMjISjUZDQUEBcXFxbN++nZdffrlaihVC1D/p+eksP7ic42nXp+SqUBHkGkSIR4js\n5FaH3bhATEAAtG+vjDYvCfLkZCXg/4lKpdwyb9FCOV+jUQa+lcwhDw4Gb+8q/SjVrtxwHzt2LCqV\nCqPRaPr+1VdflbqNDvDss89y7NixKi1UCFG/GI1GtiZtZc2xNWZT3JxtnWnu3Rw3hwquEiJqld27\n4eOPlSVYs7KURWK+/bb8/vESKhX4+yu31ENCoHFjaNgQ7O0rbw55bVBuuC9fvrw66xBCCJOreVdZ\nfnA5J9NPmtpUKhW9mvbCYDSgVsk+E3VRXh4cOQIJCcoCMZmZpc8p6R8v4eOjBHhJkDdqpMwxL0tl\nzSGvDcoN9w4dOlRnHUIIgdFo5Pdzv/Pj8R/R6XWm9gauDRjRZgRNPJswsKWsbllXGI1KWB8+rAT6\n2bPX55XftJ4ZoAxuc3KCRx+9HuQ3bCxqkZo6AK6yWbxC3ebNm1m7di1nzpwxrVDXrFkzBg4cyH33\n3VeVNQoh6oEruVf48uCXnMk4Y2pTq9T0btabh5s/jK26wgtqihpIq4Vjx64HelkhDsp8cp1OGeTm\n5aWMZLe3V/rH/944VNyCRX9bPv/8c2bNmkVISIjZIjaHDx/mf//7HzNmzGD48OFVXasQog4yGA38\ndvY31p5Ya3a1HugayMi2IwnxCLFidaIypKYqQZ6QAKdOlT8ITqVSpqBFRMDDD8O6daVHr9fF/vGq\nYFG4L1++nGeeeYYXXnih1LEPPviAxYsXS7gLISosJSeF5QeXczbzrKlNrVLz0F0P0eeuPnK1XsuU\njG5PTlaushs0UBaLSU0t/znOznD33Uqgt2qlTE0rERBQP/rHq4JFf3OuXbvGY489VuaxJ598km++\n+aZSixJC1G0Go4HNZzbz84mfKTZcv4xr6N6QEW1G0NC9oRWrExVlMMD69cr2p1lZylfJ2mZljW4P\nDobwcCXQQ0OvT0m7WX3pH68KFoV7q1atSEpKIiSk9O2xlJQUWrRoUemFCSHqpks5l/jywJckZiWa\n2mzUNvS9qy8PNnsQG7XMW6/p9HplnvnJk8pt9tOn4c8/y1/9LThYCfmICOXL07P6a65vyg33oqIi\n088zZszgvffeo6ioiLZt2+Lq6kp+fj579+5l2bJlvPnmm9VSrBCidinZ5AWUNeEvZF/gfNZ5DBjo\n1qgbACEeIYxoM4IgtyBrlSn+gU6nbKxy6pQS6GfPKsu83ujG1d9AmY7m7a18xcaCBRuHikpUbri3\nbt3abCc2o9HIlClTSp1nNBp5/PHHSUhIqJoKhRC1Xm5RLiczTpJbdH0HN1u1LQ83f5jezXrLvPUa\nRqtVArzkyvzcuX9eCc7LC2xswN1dWc7V0fH6xisS7NWv3HCfNGmSbLMqhLgjOoOOpKwkUnJTMHJ9\nY2xXe1de7fYqDVwbWLE6UTIA7sIFZQBcSIgyzzwxsfz12Uv4+EDz5nDXXcr3s2eVhWduJqPbraPc\ncC/rKr0shYWFHDx4sNIKEkLUfgajga2JW4m7FGc2YE6NmsYejQlyC5Jgt7Ldu5XNUlJSlKVdSxaP\nKW95V39/8zC/ud/cx0e5UpfR7TVDheeZFN3U0RIXF8fUqVPZv39/pRUlhKi9jqcd57vD33Ep55JZ\nsHs6etLMqxlOtk5WrE6Acrt9xgwlhG9WsrxrYKB5mLtZsJS/jG6vOSwK96ysLF577TV27NhBwY07\n3P+tadOmlV6YEKJ2Sc9P5/uj37M/xfwXfUdbR5p6NsXLyQsV0tVnTWlpsHo17NunXLGXUKmUZVw9\nPJSv2NiKL+sqahaLwn3mzJkcPXqUYcOGsXTpUp588kmKiorYvHkzPXv2ZNq0aVVdpxCihtIWa/nl\n9C/878z/zK7UHWwdaOLRhCDXIBkwZ2WFhUrf+q+/Xh8Y5+ysLDDTsCEEBYHt32kQHCzBXhdYFO47\nduxg1qxZREVFsWLFCkaMGEHDhg156aWXGDNmDAcPHuT++++v4lKFEDWJ0Whk76W9rD62mswC8+27\nOgZ3ZGDLgXg4elipOgHKoLi//oKffoLsbPNjffooA+kcHMzbZQBc3WBRuKenp9OwobJilK2tLVqt\nsreyRqNh+vTpvP766xLuQtQjF65d4NvD33I647RZe4hHCE+GP0moZ6iVKhMlTp2C775TAvxGTZrA\n4MHKynBxcTIArq6yKNw9PT05d+4c/v7++Pj4cOTIEZo1a2Y6dv78+SotUghRM+Roc1h7Yi07zu/A\naLxhapuDKwNaDKBzw84yhdbKbuxXv5GHBwwcCB06XN+MRQbA1V0WhXtJv/r3339P165def/999Hp\ndHh4eLBy5UqCgmRlKSHqMr1Bz5bELaw7uY4C3fVBtWqVmpjQGPre1RcnOxkFb02FhcpV+ObN5gvO\n2NlB797Qq1fpW/Ci7rIo3F988UUKCgpwdHRk/Pjx7N69m1dffRUAd3d3Zs2aVaVFCiGs5+jVo6w6\nsoqUnBSz9nC/cAbfPRh/jb+VKhOgzE/fubPsfvUOHZSrdVnLvf6xKNydnZ15//33TY/Xrl3LyZMn\n0el0hIaG4uQkv7ELURfcuBZ8QXEBZzPPkl6QDmBaC97PxY8nwp8g3C/cKjWK606dglWr4Oae0caN\n4YknlH51UT/d9mbJzZs3N/1cVFSEvb19pRQkhLCuYkMxF7IvkJydjIHra5A62jrSt3lfujfpLvus\nW0HJUrEpKcr67Wo1pKebn1NWv7qon275N/TEiROsXLmSlJQUAgMDGTJkSKntXffu3ct//vMfNm3a\nVKWFCiGqVmFxIeezz3Mx+6LZfHWAAJcA3u7+Nm4OFixTJipdXBwsXqxstXrhgrKKnMFwfalY6VcX\nNys33A8dOsTw4cOxs7OjUaNGHDx4kDVr1rBo0SI6depEbm4uM2fOZNWqVaaR80KI2kdbrGVL4hb+\n78z/me2xDuBm70ZTr6a42rtKsFvR+vWQnKwE+40rgF+8CH37KlfrXl7Wq0/UPOWG+4IFC4iKimLe\nvHk4OztTWFjIK6+8QmxsLM8++yxvvPEGOTk5TJs2jdGjR1dnzUKISqDT69iWtI1fTv9CttZ8JJaT\nrRONPRrj4+wjS8ZakU4H27bB2rXKNqw3cnVV1n0fO9Y6tYmardxw379/PwsXLsTZ2RkAR0dHpk+f\nTteuXZk0aRL3338/r776qkyDE6KWKTYU8+f5P9l4aiNZhVlmxxxtHQlxD8HPxU9C3Yp0Oti+XZna\ndu2asjRsSbg7OCgD5vz8lKVjhShLueGenZ1tWpWuhK+vL46Ojrz55ps88sgjVV6cEKLy6A16dl3c\nxYZTG0jPNx+J5enkSd+7+mLAgBpZB95adDrYsUMJ9awbfu9q2BDOnVO+BwQog+lAlooV5bvlgDob\nG5tSbSqVinbt2lVZQUKIymUwGtiTvIf1J9dzNe+q2TE3BzceuushujTqgp2NHV1DulqpyvqtuFgJ\n9U2bzEMdlJHxTz4Jjo7Kxi+yVKywhMxnEaKOMhqNxKfEs+7EOi7nXjY7prHX8GCzB7mv8X3Y28g0\nVmspLlYWoNm4ETLN997BzU0J8G7dlNHwAJ06VX+NonYqN9xVKpWsES1ELWQ0Gjlw+QDrTq4jOTvZ\n7JiznTO9mvaie5PuONjKnClrKS5WdmvbuBEyMsyPubkp09ruu+96qAtRUeWGu9FopF+/fqUCvrCw\nkCeeeAK1+nq/nEqlYvv27VVXpRCilBtXkwMwYiSzIJPEa4m0CzDvOnO0daRn057ENImRNeCtSK9X\nrtQ3bSq9AI2r6/UrdVkTTNypcsN9wIAB1VmHEOI2GTGSVZhFUlYS2UXmU9ocbB3o3qQ7PUN74mLv\nYqUKhV4Pu3bBhg1lh3qvXsqVuixAIypLueF+41ryQoiax4iR9Px0LmZfLBXqdjZ2PND4AXo17YWr\ng6uVKqzf4uKUMD94UAl0b29l+loJjUYJ9fvvl1AXlU8G1AlRy2iLtey8sJO4S3EUFheaHVOjpoFr\nA97t/i7uju5WqlDs3AkffKCsIFfw9w65V/+eqNCkidKnLqEuqpKEuxC1RFZhFn+c+4NtSdvI1+Wb\nBbsaNf4afxq5N8LBxkGC3UqysmDLFvjvf0uPfrezU67W33tPmdYmRFWScBeihkvOTmbz2c3sSd6D\n3qA3O2antqOBpgGBboHYq2UUlrVcuKDMQY+LU/rXb5yrbmsLwcEQFKQEvAS7qA4S7kLUQEajkWNp\nx9h8ZjNHrx4tddzXxZdmXs3wd/HHRlV6sSlR9YxGOHRICfWTJ82POTsrIR8UpKwoV7IeWGBg9dcp\n6icJdyFqkGJDMXHJcWw+u7nUHHWApl5N6RnakzYBbVCrZJlYa9BqlT7133+H1NTSx5s2ha5dlQ1f\nbl4qRJaLFdVFwl2IGiBfl8/WxK38kfgH1wqvmR1TqVREBkTSs2lPQj1DrVShyMyEP/5QNnTJzzc/\nplZD+/bQo4eyqQtAWJiyRrwsFyusQcJdCCtKy0/j17O/svPCTrTF5nt6Otg60LlhZ3qE9sDH2cdK\nFYrEROXWe3w8GAzmx5ydlav0Bx4AT0/zY9HREubCeiTchagmN64ol12UTXJ2Mmn5aRgx0q1RN9Mx\nd0d3Hmj8AN1CusnCM1ZiMCjz03/9FU6fLn3czw+6d4fOnWU6m6iZJNyFqCZ6o560/DRSclJKLToD\nEOgaSK+mvYgOisZWLX81q1NcnLIk7MWLUFSk9JWXtQRs8+bKrfeIiOvbrgpRE8m/IEJUsYvZF9me\ntJ3dybspNhSXOu7p6Mlz9zxHS5+WslmTFcTFwZw5cPkyXLmibOoC0KKFcoWuViu313v0gEaNrFur\nEJaScBeiCmiLtey9tJdtSdtIzEoEMAt2NWp8XXwJdgvGxc6FVr6trFRp/aXVwt698MYbyhX7za5c\ngbncCU8AACAASURBVBEjlJXkPDyquzoh7oyEuxCVKCkrie3nt7MneU+pAXIATrZOBGgC8Nf4y6Iz\nVmA0wrlzsGOHEuxaLSTfNOPQ2fn6/PRHH7VOnULcKQl3Ie5QYXEhe5L3sC1pGxeuXSh13FZtS2SD\nSLIKs3B3dEeF3Hqvbrm5sHu3EuqXLpkfc3ZW1n/39lYC3cND6XMPDrZOrUJUBgl3IW6D0WgkMSuR\n7ee3E5ccR5G+qNQ5/hp/uoV0457ge9DYa4hLjrNCpfWX0QjHjyuBfuDA9b70GzVoAKNGKdPc7OzM\nj8mCM6I2q/ZwT09P56OPPmL79u3k5+fTrFkzpk2bRqdOnQBYv349S5YsITExEV9fX/r06cPUqVOx\nsZElNoX15evy2X1xN9vPby9zBTlbtS3tA9vTtVFXmnk1Mxsg91m/z6qz1HorMxP+/FNZRe7mvdNB\nGQUfHQ1duig7tKlUyqA6WXBG1CXVHu4TJ05Eo9Hw448/4ubmxvz585k4cSK//PILSUlJTJ8+nZkz\nZxITE8O5c+eYMGECdnZ2TJ48ubpLFfVcybx0I0aytdlczr3M1fyrGIwGs3npoExj6xrSlY5BHWVu\nuhUUF0NCgnKVfuSIctV+syZNlECPiiq9eYssOCPqmmoN95ycHJo2bcqYMWPw9fUFYNy4cSxatIhD\nhw6xbt06unXrRp8+fQAICwtj5MiRfPLJJ0ycOBG1TCwV1aiwuJCr+Ve5kneFfF1+qeP2NvZEBUbR\nNaQrTTyayDS2alQyL/3MGWVQnK0tuJTxO5WLC9xzjxLqsmmLqE+qNdxdXV157733zNouXFAGIAUE\nBHDgwAGGDh1qdrx169ZkZWWRmJhIaKisqy2qVrY2m/hL8exJ3sOeS3vKPEdjr2FoxFA6BHXAyc6p\nmisUW7ZAbKwyVS37hrWASualA7RsqQR627ZK8AtR31j1j31ubi4vv/wyMTExREREkJGRgbu7u9k5\nnn8v2JyRkSHhLqpEvi6fA5cPsCd5D8fTjmMs456ujcoGPxc/AjQBaOw13Nf4PitUWn/l58P+/coV\n+8qVyuj3m6WlKYPjOncGH1mKX9RzVgv35ORkJkyYgI+PDx999JG1yhD1VJG+iENXDhGXHMfh1MNl\nrhynQoWnoye+Lr74OPvIvunVrLBQ2S89Lk7pR9frlfa8vOvnqFTXp7B5e0P//tapVYiaxirhfujQ\nISZMmECvXr145ZVXsPt7DoqPjw9ZWVlm52ZmZgKY+uiFuF16g55jacfYk7yHA5cPlLnIDMBd3nfR\nIagDOoMOO7VdmeeIqqHTKQPj4uKU7zpd6XOcnZVb7b6+ylfJFLagoOqtVYiarNrD/eTJk4wbN47/\n396dh0dV3Y8ff0/2dbIOCWtIAoFAAEMCgj7SghupICqCiGAoKtSW+lNLEKiVqmAFEVtjqyC7LGoR\nFFq0iK3i8hVZQ2QNkGASAgnZ9/X+/jjOJJOZhDXJZPJ5Pc99wtw593JuTuZ+5p71ySefZNq0aWbv\nRUdHk5SUZLbvwIEDGAwGesikzuIaaJrG6bzT/JD5AweyDlBaVWo1XQ+fHgzpOoQhXYbg566agjYe\n2diaWe2wamrg2DE1Y9zhw6qDnDU9e6oe7RMnwocfWr4v49KFqNeqwb22tpa5c+cyYcIEi8AOEB8f\nz5QpU9i5cyd33HEHJ0+eZM2aNUyfPl16IotmNVxOVUOjtKqU7LJsckpzuLnrzVaPCfIKYkiXIQzt\nOpQgryCL92Vcesupq4OTJ9UT+qFDqk3dmm7d1NC12Fj1lG6k18u4dCGa06rB/dChQxw9epRTp06x\nbt06s/fGjRvHwoULWbZsGW+++SZz5swhMDCQqVOnMn369NbMpmiHNDRKqkrIK88jpzSHshrr0cLX\nzZehXYcypOsQuuu7y5fGVmActnb+vFr73GCAggIoLraePihIBerYWDWDnDUyLl2I5rVqcI+NjeXk\nyZPNprnrrru46667WilHoj2rqavh5KWTJF1MUgu11Fqvz/V08SSmcwxDuw61mDVOtKz/+z944w3I\ny1O92Y1V7g2HrYHqDBcbqwJ2t26qo5wQ4trJCFDRrpRWlfJj9o8cvnCYozlHTZ3iGgd2R50jgR6B\nGDwNvHbnazg6SE/31lJaqjrDJSXBmjVQWGiZJiMDeveur3I3TgMrhLgxJLgLm3ep7BJJF5I4fOEw\np/NOU6fVWU3n7OCMv7s/Ae4B+Ln7mYauSWBvWZqmJpRJSlJD186cqZ/+teEkM6B6tgcGqqr3V18F\nmXRSiJYhwV3YHE3TOFd4zhTQzxefbzJtoEcgNwXfRGFlIXpXvSyn2kpqa+H0aRXMjxyB7Gzr6Tw8\nVNqAALX5+NQvpyqBXYiWI8FdtImGvdsB6rQ6CioLyC3LJbZLLIUVVupyfxbqF8qgoEEMCh5EZ6/O\n6HQ6dp/d3dJZ7vDKytRkMklJ6mdTPdx1OggLg4EDYdw4+Phjyyp3GbYmRMuS4C7aTFVtFfkV+eSW\n55Jfnk+tpqYgaxzYnRyciDREMihoEAODBuLj5mNxLhm2duMYe7dnZakhZz16qI5wKSlqCJs1rq7Q\nr58K6AMGgLd3/XsGgwxbE6K1SXAXraaqtoqU3BSO5RxTE8pUW59QBlQP94FBAxkUNIh+hn64Orm2\nYk47ru++g7/+VQ1Vy82tfzpv3LsdwM8PBg1SAT0ion6muMZk2JoQrU+Cu2gxdVod6YXpHMs5xvFL\nxzmTd8Y0h7u1wO7u5E6gRyCzb5lNuH84DjpplG1pdXXw009w/Lja3n/f+vjzjAwV3Hv2VMF84EAZ\nsiaELZPgLm6o3LJcUzA/celEk9O9AjjggN5Vj5+7HwEeAbg7uaNDR++A3q2Y445F01Tnt+PH4cQJ\nNUtcw7bzxqutOTioJ/TAQFiyRHWIE0LYPgnu4rqUV5dzMvckx3OOcyznGNmlTXSb/lkX7y5EGiLJ\nq8jDx9VHVlprBUVFKpAbn85/XovJKg8P9QXAzw98fdXm6Kie0iWwC9F+SHAXl9V43vaiyiIKKgrI\nL89ncOfBTY47B9C76ok0RNLP0I++gX3xdfMF4IuzX7R4vjuChp3fOneGuDjVoS0lpT6gZ2Y2fw5f\nX9WmHhkJDz0EH3xgmUZ6twvRvkhwF82qqauhqLKIwspCCisKKaosokarX/u8cWB3dnQmIiCCfoZ+\nRAZG0sW7i9XpXqV3+/Xbtw9WrlTt5iUlkJoKO3eqpU8DA5s+zs0N+vRRwbxvX7UWesMi8vaW3u1C\ntHcS3IWZ8upyzuafJSUvhdN5p0nNT+XwxcNNptfpdPTw6UFkYCSRhkh6+ffCyUH+rFpSWRmcPQvL\nlkFamqp2bzhErbbWPLg7Oqpx55GRauvZs/kJZKR3uxDtn9yFO7iiyiJSclNMwTyjKAPNOHdoE9yc\n3PBz88PXzZfX73odTxfPVsptx5SXp6Z0PX1abZmZql08Kal+mteGSktVG7mxqr13bzUOXQjRcUhw\n70A0TSOnLIeUXBXIU/JSyCnNuexx7k7u+Lj54OPqg95Vj5uTm2maVwnsN1ZdnaoONwby06eb7gDn\n4aECOaiqdl9f1RGuXz/4059aL89CCNsjwd0OGTvAaWiUVpVSWKnaygsrChnWbVizx+p0Orrru9PL\nvxe9A3rTy78XCbsSWiPbHULjDnB33KGq0I2B/MwZqKho/hw6HXTvDiEhsH+/mkWu4ZP5uHEtew1C\nCNsnwd1OaJpGfkU+aQVppBakUlxZTHFVsWlK16Y4OTgR6hdKb38VyMP9w3FzcmulXHcsP/wA//iH\nmiSmqAgOHlQ90yMiLGd/a8jVVS2J2quX2kJD1ZM6qC8L0vlNCNGYBPd2qrSqlHOF51Qwz08lrSCN\nokq1vmZ6UXqTx3k4exDuH24K5iG+IZftACc9269NcbHq8HbunPq5ebNqP2/MOPubkV5fH8h79VJP\n6U11gJPOb0IIayS4twPVtdWkF6WbgnhaQdplJ4sxcnV0Re+qN7WZL7t7mdWhaeL6lJWpIG4M5Glp\nlm3lTbWdaxrcdlt9MA8IkGldhRDXR4J7G2u89KmGRll1GcWVxUzsP5HUglQyizKbnSjGyM3JjRDf\nEFILUvF28cbb1RsXRxezNc4lsF8da5PEDByo5mM3BvJz55pez7whDw+1upqXlxpLrterLTQUpkxp\n8UsRQnQgHSK4W7tB20pVZlVdFcWVxRRVFlm0k+85t6fJ4xwdHOmm70aobyg9fXvS07cnwV7B6HQ6\nTl462VrZt2v79sHy5eqpvLhYzfi2fbtaAtVguPzxzs4qbUiIGlt+//2wdausbS6EaHl2H9wbzuLl\n4KDGCK9cqd5r7QBfW1dLRlEGZ/PPcib/DKn5qXyf8f0VHRvkFWQK5KF+oXT17oqzYxNrbIqrpmmq\nPTwzU7WBZ2TApk3qibzxWPL0dMvgbpx/3RjIQ0JUB7fGbeUBAdIBTgjR8uw+uH/6qao6TU9XC19E\nRKiexp991vI31aLKIs7mnzVtaQVpVNdWX/Y4F0cX9C567ut7Hz19exLiG4KHs8cV/7/SAa55lZUq\niDcM5BkZlkPQrAV2UE/yXbrUB/GePdWUr02tZ96QdIATQrQGuw/uWVmQk6Nu0gUFavhRRETz0282\np3EbuZGGxh9v+6Ppqfxs/llyy3Ivez4HnQNeLl74uPqY2sldHdWg5bjecdeWyQ6qcfPL6NFq2tWG\nATwzs/7v4XI8PFQgd3evbyf38lLzsi9Y0PLXI4QQ18rug3vnzurp6uRJdUOvqYFjx1S7Z1UVuLhc\n23mr66opqiwybcVVxbzy9SuXPS7AI4AwvzDTtujrRThwjd80BKDK9X//q28fLy1VX+I+/FBNvdrc\nGPKGPDxU1bpxu+ce1cbu2GhV2jFjbvw1CCHEjWT3wT0uTj2tubmpDlHGqtfaWnjlFXj8cXUjvxr5\nFfkcv3ScmrqaZtM5OzoT4hNiFsx93MwXxZbAfuXq6tRT94ULasvKUtuFC/Dtt/VTsTbUeAw5qFqb\noCDzQN61q5q+tXFnt86dpY1cCNH+2H1wN96IP/tMValmZ6ube6dOKjD85S/w4IPwy19e2djiytpK\nTlw6YTWwN34q76bvJhPENKOpUQyVlXDxYn0AN/7MzlZfyqwpK7O+v6pKLaDSMJAHB19Z+zhIG7kQ\non2y++AO5jdoTYPvvoP331c3/poa9e9jxyA+Xn0BaEpNXQ3Hco5RXac6xbk4uBDkFYS3izd6Vz2v\n3H75anmh/PADvP02lJerwJySAv/5z7WvYObjo57sPTzU5umpttBQeOaZG59/IYSwZR0iuDek08Gt\nt0J4uBoSl/7zTK1HjsDLL8P06arDlDVbjm2huKpYnQcd/Qz90LvqWynn7U9dnZqVLSdHbdnZ9T//\n/W81v3pjR4/C4MFNn9PXVz15d+5s/vPkSVi1yjJ9nPRJFEJ0QB0uuBsFB8PcuWpSkS++UPsKCuCN\nN1S76tix5h2p9p/fz/9S/2d6HeYX1qECe1NV6LW1kJtrHryN/750SdWMWFNcbH1/aalqNjEYrAdx\ntybWtBk6VH1xk/ZxIYTowMEdwMkJJk6EyEhYuxZKSlS1/aefqifBxx5Ty3FeKLnA+qT1AIzoMYLo\nztHMjJnZIaZy1TT45ht45x3VFl5RAadOqSDat6+qQq+7/My4FoxTsbq7q81YnR4erjo6Ol3DX6a0\njwshhNKhg7vRgAHwwguwerXqUQ9w9qyqpn9ociWfly+nsqYSAIOngfhB8XYT2DVNfanJy1NP4Na2\npnqiJyc3X4UOamx4p07qSdxgUP/u1EnNzb5xo2X6CROuLbALIYSoJ7fRn/n4wNNPw65d8PHH6mm0\nvEJj/uZN6HqcJzwc3JydmRkzE3dn97bO7mUZq9HPn1dTng4ZonqKWwveVVXNn6upnujGgO/raz2A\nGwxNV6P37Kme2KUaXQghbjwJ7g3odHD33apD3bvvQnLRN1x0+R4uQFEh/HHsw3T36d7W2TSpq1Nt\n1/n5qr+A8efBg7B7t6r2rqxU6bZuVdXoVzqhS0Pe3qrt3M1NVcMbq9JDQ2HRomufCEiq0YUQomVI\ncLeiZ0+I/38/8Zv178PPS3nqC2/hizW34lMIo0bd2PW2rXVWu+kmKCy0DNwNfxYWWm/vPnDgyid0\nARW0AwLqN39/1dfA31+9Pn7cek/0iROvPbALIYRoORLcrSirLmNd8nJ69alB7wcXTnUjvPxhalBT\nmn76qXqCzcu7siVk6+rqp0Vt/PPwYTUsrKZGbfv3w5YtqmPZtTxlg2U1upOTCuDu7nD77eYBPCBA\n7W/uy4r0RBdCiPZFgnsjmqax9vBaLpVdQgeEdHFj0e0z+WSTC+fOqSFee/aoJ9aQEDW17fffqxnu\nunSxHsAbrzbW0NU+ZTfm6Ql+fqrd2/iztlaNIXdxUUHdOKSvWzf1tH0tpApdCCHaDwnujXx+9nOS\nLiSZXsffFE//zp3oMwc++URNVwuqE1pKSv1xOTmX7zluTVOd1crKVLA2BuyGwbvhPmvTqAYF1a9Z\n39Do0VefPyGEEO2PBPcGUnJT2HZ8m+n1HWF3MLizithOTjB+vKqWP3HCsoe5tadvI51OVX17etZP\njWoc111UpDYnJxWoXVzUFhamhuddi4bz6Us1uhBCdDwS3H9WVFnEuwffpU5TPdTC/cN5IPIBi3T9\n+6ugnJmp5kU3BuWgIHjkEfPgbfzZXJt2RIT1p+zrnTZVqtGFEKLjkuAO1Gl1rDy4ksKKQgC8XLx4\nYvATODo4WqSNi1PBODTUfP/jj19bMJWnbCGEEDeaBHdgx8kdnLx0EgCdTsdjgx/Dz93PatqWCMby\nlC2EEOJG6vDBPfliMjtTdppej4kYQz9Dv2aPkWAshBDCljm0dQbaUm5ZLqsPrTa97mfox696/6oN\ncySEEEJcvw4b3GvqalhxYAVl1Wosmp+7H9Ojp+Og67C/EiGEEHaiw0ayfx79J2kFaQA46ByYETMD\nb1fvts2UEEIIcQN0yOC+L3MfX6Z9aXr9YL8HCfMLa7sMCSGEEDdQhwvuWcVZvHfkPdPrwZ0HMyp0\nVBvmSAghhLixOlRwr6ypZPmB5VTWVALQybMT8TfFo7uRS7wJIYQQbczmgnt5eTl//vOfGTVqFDEx\nMTz00EN8++23131eTdPYmLyRrOIsAJwdnflN7G9wc3K77nMLIYQQtsTmxrm/9NJLHDt2jFWrVtGl\nSxe2bdvGb37zGz755BPCwq6+XXzmjpkAZJVkkZJXv9JLn4A+dNV3vWH5FkIIIWyFTT25FxYWsmPH\nDn7/+98TGhqKq6srkyZNIjw8nPfff/+az1tcVcyZvDOm18FewQR5Bt2ILAshhBA2x6aC+9GjR6mu\nrmbAgAFm+wcOHEhSUlITR11ean4qdagFYbycvejl1+u68imEEELYMpsK7nl5eQD4+vqa7ffz8yM3\nN/eaz1ur1QLgpHMi0hApE9UIIYSwazbX5t6U6+nRHhkYSW55LgHuAdKBTgghhN2zqUfYgIAAAAoK\nCsz25+fnExgYeM3ndXNyo6t3VwnsQgghOgSbCu5RUVG4uLhw+PBhs/0HDx4kNja2jXIlhBBCtC82\nVS3v7e3N+PHjSUxMJCIiguDgYDZt2kRmZiaTJk26pnMuH7v8BudSCCGEsG02FdwB5s+fz5IlS5g8\neTKlpaVERkaycuVKunZtekx6ba3qMHfhwoXWyqYQQgjRZozxzhj/GtNpmqa1ZoZawv79+3nkkUfa\nOhtCCCFEq9q4caPVZmu7CO4VFRX8+OOPGAwGHB0d2zo7QgghRIuqra0lJyeHqKgo3NwsO4vbRXAX\nQgghRD2b6i0vhBBCiOsnwV0IIYSwMxLchRBCCDsjwV0IIYSwMxLchRBCCDtjc5PYXKvy8nIWL17M\nnj17KCwspFevXjz11FPceuutVtN/++23JCYmcvr0aby9vbntttuYN28e7u7urZxzS7m5uSxdupSv\nv/6asrIyevXqxTPPPMPw4cMt0m7dupV58+bh4uJitj8uLo4lS5a0VpabNGrUKC5evIiDg/n3yO3b\ntxMaGmqR3lbLZd++fUyfPt1if01NDffddx9/+ctfzPbbWrmkp6czf/58fvjhB7744gu6detmeu9f\n//oXq1atIi0tDYPBQFxcHE899VSTw0rz8vJYtGgR+/bto7y8nMjISObMmUNUVFRrXU6z17Nx40Y2\nbtxIVlYWfn5+3HfffcyaNcvib9CoT58+ODs7WyxOdeDAAYvyawlNXUtiYiJ///vfcXZ2Nkv/2GOP\n8fTTT1s9V1uXTVPXcvfdd3P+/HmztJqmUV1dzcmTJ62eqy3L5XL34HbxmdHsxNy5c7V7771XO3v2\nrFZRUaFt3rxZi4qK0s6cOWORNjU1VYuKitLWr1+vlZWVaT/99JN2//33a3Pnzm2DnFuaOHGiNn36\ndC07O1urqKjQli5dqt10003ahQsXLNJ+9NFH2siRI9sgl1dm5MiR2kcffXRFaW29XBrLzs7Whg4d\nqu3du9fiPVsql127dmnDhw/X5syZo0VERGjp6emm9/bu3av1799f27lzp1ZZWamdOHFC++Uvf6kl\nJiY2eb6pU6dq06ZN07KysrSSkhLtjTfe0IYOHarl5eW1xuU0ez2bN2/WYmJitL1792o1NTXa/v37\ntejoaG3t2rVNni8iIkL7/vvvWyPrFpq7ljfffFObMmXKVZ2vLcumuWux5plnnmn2s92W5dLcPbi9\nfGbsolq+sLCQHTt28Pvf/57Q0FBcXV2ZNGkS4eHhvP/++xbpP/jgA8LCwpg6dSru7u50796d3/72\nt2zfvt20pnxbKS4uJjw8nPnz52MwGHB1deWJJ56grKyMI0eOtGneWpotl4s1CxYsIC4ujqFDh7Z1\nVppVUFDAxo0bGTdunMV7GzZsYMSIEcTFxeHi4kKfPn2YNm0a7733HnV1dRbpT506xd69e5kzZw7B\nwcF4enoya9YsdDod27dvb43LafZ6qqqqSEhIYOjQoTg6OhITE8OwYcP4/vvvWyVvV6u5a7labV02\nV3Mtu3fvZt++fcybN6/F83W1LncPbi+fGbsI7kePHqW6upoBAwaY7R84cCBJSUkW6Q8fPszAgQMt\n0tbU1HD06NEWzevleHt788orrxAeHm7al56eDkBwcLDVY0pLS/nd737H8OHDue2225g/f77Fsrlt\n6dNPP+VXv/oVMTExPPDAA+zevdtqOlsul8b++9//cvDgQWbPnt1kGlsplwkTJlhtAoGmf+cFBQWk\npaVZpE9KSsLZ2Zm+ffua9jk5OdG/f3+rn7WW0Nz1PProozz00EOm15qmkZmZSefOnZs953vvvced\nd95JbGwsDz/8MPv377+heW5Kc9cCav7wX//619x8882MGjWKxYsXU1FRYTVtW5fN5a7FqKKigpde\neonnnnsOvV7fbNq2KJfL3YPby2fGLoK78anO19fXbL+fnx+5ublW0/v4+FikBaymb0slJSXMmzeP\n22+/3eLLC6h8h4eHM2XKFL7++mtWrFjBoUOHSEhIaIPcWoqIiCAsLIwNGzbw1VdfceeddzJr1iyL\nZX2h/ZRLXV0dy5YtY8aMGXh5eVlNY+vlYtTc79xabYkxfeN2UF9fX5sqI6O///3vnD9/3mp/CaP+\n/fvTv39/tm3bxueff06fPn147LHHyMjIaMWcWurUqRM9evTg2Wef5ZtvvmHx4sXs2LHDon+HUXsp\nm/Xr1+Pr68s999zTbDpbKZfG9+D28pmxi+DenMa/0BudviVlZmby8MMPExAQwNKlS62mGTlyJJs2\nbWL48OE4OTkRGRnJ7Nmz2bNnD1lZWa2cY0vvvPMO8+bNw9/fHy8vL5588kkiIyP58MMPr+o8tlQu\nu3bt4uLFi80uVmTr5dISbKmMamtrWbRoEe+99x4rVqww63DX2NatW3nyySfx8vLCz8+P559/Hk9P\nTz755JNWzLGlhx56iFWrVjFgwACcnZ0ZMmQIM2bMYOvWrdTU1FzVuWylbKqqqli1ahUzZ868bJ5s\noVyu5B58PVqyXOwiuAcEBABYVHnm5+cTGBhokT4wMNBqWgCDwdBCubw6R44cYcKECcTExLBixQo8\nPDyu+NiQkBAALl682FLZuy49evSwmrf2UC6gevqPGjUKV1fXqzrOFsvlan/nAQEBFBYWojVakqKg\noMDqZ60tVFRU8OSTT/Ltt9/ywQcfEB0dfVXHOzk50aVLF5sqJ6OQkBCqqqpMZdRQeyibPXv2UFFR\nwciRI6/62NYul6buwe3lM2MXwT0qKgoXFxeLqt6DBw9aXQovOjraoq3DOLzCWtV3azt16hRPPPEE\nM2bM4M9//rPFUJiGNm/ezMcff2y278yZM4AKom0pPT2dF198kaKiIrP9Z8+eNQW6hmy9XEBV0e3Z\ns4c77rij2XS2XC4NNfU7NxgMVvMZHR1NdXW1WR+IqqoqkpOTrX7WWlttbS2zZs2ivLycDz74gJ49\nezab/ujRoyxcuNCsI1RVVRXp6elW/0Zb09tvv82XX35ptu/MmTN4eHhYDQq2Xjag+t/ccsstl31Y\naetyae4e3F4+M3YR3L29vRk/fjyJiYmkpqZSXl7OqlWryMzMZNKkSRw5coTRo0ebxllOmjSJ9PR0\n1q5dS0VFBWfPniUxMZEJEybg7e3dptdSW1vL3LlzmTBhAtOmTbN4v/G1VFdX89JLL/Hdd99RU1PD\niRMnWLZsGffddx/+/v6tnHtzgYGBfPHFF7z44ovk5+dTVlbGW2+9RWpqKlOmTGlX5WJ0/Phxqqur\niYyMNNvfnsqlofj4eL755ht27txpuuGsWbOGX//616Yqw/j4eNatWwdAeHg4I0aMYPHixVy8eJGS\nkhKWLl2Kq6srY8aMactLAVQHrHPnzvHOO+80+TfT8HoCAgLYunUrS5YsoaSkhMLCQhYuXAjAc18L\n6gAAB7hJREFU/fff32r5tqagoIAXXniB5ORkampq2LdvHytXrmy3ZQOqA2e/fv2svmcr5XK5e3B7\n+czYzSQ28+fPZ8mSJUyePJnS0lIiIyNZuXIlXbt2JSMjg9TUVKqrqwHo1q0b7777LkuWLOH1119H\nr9czZswY/vCHP7TxVcChQ4c4evQop06dMv1xGI0bN46xY8eaXcujjz5KTU0NL774IllZWej1eu6/\n/35+97vftUX2zbi7u7NmzRpee+014uLiKC8vp1+/fmzYsIGwsDD27t3bbsrFKDs7G6hvCjIqLy+3\n2XIxTiBirBYcPXo0Op2OcePGsXDhQpYtW8abb77JnDlzCAwMZOrUqWYd0NLT0806Cr3++ussXLiQ\nMWPGUF1dTXR0NGvWrGmyc2FrXs/evXvJzMxk2LBhFsclJydbXE9wcDCrV69m2bJljBo1iurqamJi\nYti0aVOrfAlr7lpeeOEF3NzcePrpp8nOzsZgMPD4448THx9vOt6WyuZyf2egPj9N/V5tpVwudw9u\nL58ZWc9dCCGEsDN2US0vhBBCiHoS3IUQQgg7I8FdCCGEsDMS3IUQQgg7I8FdCCGEsDMS3IUQQgg7\nYzfj3IUQlg4fPszatWtJSkoiJyfHtETl5MmTGTt2bFtnTwjRQuTJXQg7tXfvXiZPnoyjoyN/+9vf\n2L17N+vWraN3797Mnj2bjRs3tnUWhRAtRJ7chbBTmzdvJigoiKVLl5qmxQwODmbAgAGUl5fz448/\ntnEOhRAtRYK7EHaqoqKC2tpaqqurcXFxMXvvtddeM/1b0zTWrl3Ltm3b+Omnn/Dy8mL06NE8++yz\nZgt8rFmzhg8//JD09HQ8PT2JiooiISGBvn37ms6zfPlytm3bRlZWFh4eHsTGxvLcc8/RvXt3ACor\nK/nrX//Kp59+yqVLl/Dz82PUqFHMnj3bNBf81KlT8fPzIy4ujsTERDIyMujevTuzZ8++ptXEhOiI\npFpeCDs1YsQILl68yJQpU9i1axclJSVW07399tssWbKEe++9l+3bt/PSSy/xn//8hzlz5pjSbNu2\njVdffZX4+Hh27drFunXrcHBwYMaMGVRUVACwZcsWli9fTkJCAp999hkrVqygqKiImTNnms4zf/58\ntmzZwrPPPsvOnTtZsGABu3fv5umnnzbL04kTJ/joo4947bXX2LJlC3q9noSEBEpLS1vgNyWEHdKE\nEHaprq5OS0xM1AYOHKhFRERokZGR2vjx47Vly5ZpaWlpmqZpWlVVlTZ48GDtueeeMzv2448/1iIi\nIrSUlBRN0zStsLBQO3XqlFmar776SouIiNCSkpI0TdO0BQsWaHFxcWZpcnNzteTkZK22tla7cOGC\n1qdPH23VqlVmaTZv3qxFRERoqampmqZp2pQpU7SoqCgtNzfXlObf//63FhERoR05cuT6fzFCdADy\n5C6EndLpdMyaNYtvvvmG119/nQcffJCSkhLeeecd4uLi+Oc//8mZM2coKSnhlltuMTt2+PDhAKY1\nqN3d3fnqq6944IEHGDZsGNHR0cyaNQtQS5MCjBw5krS0NKZNm2aqmvf39ycqKgoHBwd+/PFHNE1j\n8ODBZv/XoEGDADh27JhpX0hIiNnqX8Z/G/8vIUTzpM1dCDvn7e3NmDFjTGtHJycnk5CQwMsvv8zq\n1asBeP7551mwYIHFsTk5OQAsXryYDRs2MGvWLEaOHImXlxdJSUkkJCSY0v7iF79g/fr1rF+/nkWL\nFlFcXMygQYN47rnniImJMTULNF7m0tPTE8Csyr1hWz9g6hCoySKWQlwRCe5C2KnKykoAXF1dzfYP\nGDCAZ555hqeeeoq6ujoAEhISGDFihMU5fHx8ANixYwf33HOP6Wkd6tdHbyg2NpbY2Fhqamo4cOAA\nb731Fk888QRffvmlqcNccXGx2THG13q9/lovVQjRiFTLC2GHsrOziY2N5e2337b6fkZGBgA9evRA\nr9dz/vx5QkJCTFvnzp2pq6vD19cXgKqqKvz8/MzOsW3bNqD+afrrr78mJSUFACcnJ26++WbmzZtH\naWkpqamp9O/fHwcHBw4cOGB2nkOHDqHT6YiKirpxvwAhOjh5chfCDnXq1IlHHnmE5cuXU1lZyd13\n343BYKC4uJg9e/bw1ltvMXHiRIKDg3n88cf5xz/+Qffu3bn11lspKSlhxYoV7N27l88++wxfX1+i\no6PZtWsXY8eOxdPTk3fffZdu3boBkJSURHR0NFu3buXYsWP86U9/IiwsjJKSEtasWUNgYCDh4eF4\neXlx7733snz5crp06cKAAQNITk4mMTGRe+65h65du7bxb00I+yHBXQg7NXfuXPr378+WLVvYsWMH\n+fn5uLu707t3b55//nkefPBBAGbOnIm7uzvr16/nlVdewdXVlWHDhrFhwwbTk/uCBQt4/vnniY+P\nx8fHh4cffpiZM2eSn5/PihUrcHJy4uWXX2bp0qX88Y9/JDc3F71ez6BBg1i9erWpnf3ll1/G39+f\nV199ldzcXAIDAxk/frzFUDghxPXRadJDRQghhLAr0uYuhBBC2BkJ7kIIIYSdkeAuhBBC2BkJ7kII\nIYSdkeAuhBBC2BkJ7kIIIYSdkeAuhBBC2BkJ7kIIIYSdkeAuhBBC2Jn/DxrtaKJPbPsCAAAAAElF\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f44cf56abe0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_results(system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Bonus question #2\n",
-    "\n",
-    "Factor out the update function.\n",
-    "\n",
-    "1.  Write a function called `update` that takes a `State` object and a `System` object and returns a new `State` object that represents the state of the system after one time step.\n",
-    "\n",
-    "2.  Write a version of `run_simulation` that takes an update function as a parameter and uses it to compute the update.\n",
-    "\n",
-    "3.  Run your new version of `run_simulation` and plot the results.\n",
-    "\n",
-    "WARNING: This question is substantially harder, and requires you to have a good understanding of everything in Chapter 5.  We don't expect most people to be able to do this exercise at this point."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'update' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-15-5d0666d579e1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_simulation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'update' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "run_simulation(system, update)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "plot_results(system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/salmon.ipynb b/notebooks/salmon.ipynb
deleted file mode 100644
index f4d73559..00000000
--- a/notebooks/salmon.ipynb
+++ /dev/null
@@ -1,703 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case Study: Predicting salmon returns\n",
-    "\n",
-    "This case study is based on a ModSim student project by Josh Deng and Erika Lu.\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Can we predict salmon populations?\n",
-    "\n",
-    "Each year the [U.S. Atlantic Salmon Assessment Committee](https://www.nefsc.noaa.gov/USASAC/Reports/USASAC2018-Report-30-2017-Activities.pdf) reports estimates of salmon populations in oceans and rivers in the northeastern United States.  The reports are useful for monitoring changes in these populations, but they generally do not include predictions.\n",
-    "\n",
-    "The goal of this case study is to model year-to-year changes in population, evaluate how predictable these changes are, and estimate the probability that a particular population will increase or decrease in the next 10 years.\n",
-    "\n",
-    "As an example, I'll use data from page 18 of the 2017 report, which provides population estimates for the Narraguagus and Sheepscot Rivers in Maine.\n",
-    "\n",
-    "![USASAC_Report_2017_Page18](data/USASAC_Report_2017_Page18.png)\n",
-    "\n",
-    "At the end of this notebook, I make some suggestions for extracting data from a PDF document automatically, but for this example I will keep it simple and type it in.\n",
-    "\n",
-    "Here are the population estimates for the Narraguagus River:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pops = [2749, 2845, 4247, 1843, 2562, 1774, 1201, 1284, 1287, 2339, 1177, 962, 1176, 2149, 1404, 969, 1237, 1615, 1201];"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get this data into a Pandas Series, I'll also make a range of years to use as an index."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "years = range(1997, 2016)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pop_series = TimeSeries(pops, index=years, dtype=np.float64)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what it looks like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_population(series):\n",
-    "    plot(series, label='Estimated population')\n",
-    "    decorate(xlabel='Year', \n",
-    "             ylabel='Population estimate', \n",
-    "             title='Narraguacus River',\n",
-    "             ylim=[0, 5000])\n",
-    "    \n",
-    "plot_population(pop_series)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Modeling changes\n",
-    "\n",
-    "To see how the population changes from year-to-year, I'll use `ediff1d` to compute the absolute difference between each year and the next.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abs_diffs = np.ediff1d(pop_series, to_end=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can compute relative differences by dividing by the original series elementwise."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rel_diffs = abs_diffs / pop_series"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or we can use the `modsim` function `compute_rel_diff`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rel_diffs = compute_rel_diff(pop_series)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These relative differences are observed annual net growth rates.  So let's drop the `0` and save them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rates = rel_diffs.drop(2015)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A simple way to model this system is to draw a random value from this series of observed rates each year.  We can use the NumPy function `choice` to make a random choice from a series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.random.choice(rates)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simulation\n",
-    "\n",
-    "Now we can simulate the system by drawing random growth rates from the series of observed rates.\n",
-    "\n",
-    "I'll start the simulation in 2015."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_0 = 2015\n",
-    "p_0 = pop_series[t_0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a `System` object with variables `t_0`, `p_0`, `rates`, and `duration=10` years. \n",
-    "\n",
-    "The series of observed rates is one big parameter of the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = System(t_0=t_0,\n",
-    "                p_0=p_0,\n",
-    "                duration=10,\n",
-    "                rates=rates)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write an update functon that takes as parameters `pop`, `t`, and `system`.\n",
-    "It should choose a random growth rate, compute the change in population, and return the new population."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your update function and run it a few times"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "update_func1(p_0, t_0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a version of `run_simulation` that stores the results in a `TimeSeries` and returns it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate a queueing system.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function object\n",
-    "    \"\"\"\n",
-    "    t_0 = system.t_0\n",
-    "    t_end = t_0 + system.duration\n",
-    "    \n",
-    "    results = TimeSeries()\n",
-    "    results[t_0] = system.p_0\n",
-    "    \n",
-    "    for t in linrange(t_0, t_end):\n",
-    "        results[t+1] = update_func(results[t], t, system)\n",
-    "\n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use `run_simulation` to run generate a prediction for the next 10 years.\n",
-    "\n",
-    "The plot your prediction along with the original data.  Your prediction should pick up where the data leave off."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get a sense of how much the results vary, we can run the model several times and plot all of the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_many_simulations(system, update_func, iters):\n",
-    "    \"\"\"Runs simulations and plots the results.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function object\n",
-    "    iters: number of simulations to run\n",
-    "    \"\"\"\n",
-    "    for i in range(iters):\n",
-    "        results = run_simulation(system, update_func)\n",
-    "        plot(results, color='gray', linewidth=5, alpha=0.1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The plot option `alpha=0.1` makes the lines semi-transparent, so they are darker where they overlap.\n",
-    "\n",
-    "Run `plot_many_simulations` with your update function and `iters=30`.  Also plot the original data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The results are highly variable: according to this model, the population might continue to decline over the next 10 years, or it might recover and grow rapidly!\n",
-    "\n",
-    "It's hard to say how seriously we should take this model.  There are many factors that influence salmon populations that are not included in the model.  For example, if the population starts to grow quickly, it might be limited by resource limits, predators, or fishing.  If the population starts to fall, humans might restrict fishing and stock the river with farmed fish.\n",
-    "\n",
-    "So these results should probably not be considered useful predictions.  However, there might be something useful we can do, which is to estimate the probability that the population will increase or decrease in the next 10 years.  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Distribution of net changes\n",
-    "\n",
-    "To describe the distribution of net changes, write a function called `run_many_simulations` that runs many simulations, saves the final populations in a `ModSimSeries`, and returns the `ModSimSeries`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_many_simulations(system, update_func, iters):\n",
-    "    \"\"\"Runs simulations and report final populations.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function object\n",
-    "    iters: number of simulations to run\n",
-    "    \n",
-    "    returns: series of final populations\n",
-    "    \"\"\"\n",
-    "    # FILL THIS IN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your function by running it with `iters=5`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_many_simulations(system, update_func1, 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run 1000 simulations and describe the distribution of the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "last_pops = run_many_simulations(system, update_func1, 1000)\n",
-    "last_pops.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we substract off the initial population, we get the distribution of changes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "net_changes = last_pops - p_0\n",
-    "net_changes.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The median is negative, which indicates that the population decreases more often than it increases.\n",
-    "\n",
-    "We can be more specific by counting the number of runs where `net_changes` is positive."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.sum(net_changes > 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or we can use `mean` to compute the fraction of runs where `net_changes` is positive."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.mean(net_changes > 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the fraction where it's negative."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.mean(net_changes < 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So, based on observed past changes, this model predicts that the population is more likely to decrease than increase over the next 10 years, by about 2:1."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## A refined model\n",
-    "\n",
-    "There are a few ways we could improve the model.\n",
-    "\n",
-    "1.  It looks like there might be cyclic behavior in the past data, with a period of 4-5 years.  We could extend the model to include this effect.\n",
-    "\n",
-    "2.  Older data might not be as relevant for prediction as newer data, so we could give more weight to newer data.\n",
-    "\n",
-    "The second option is easier to implement, so let's try it.\n",
-    "\n",
-    "I'll use `linspace` to create an array of \"weights\" for the observed rates.  The probability that I choose each rate will be proportional to these weights.\n",
-    "\n",
-    "The weights have to add up to 1, so I divide through by the total."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "weights = linspace(0, 1, len(rates))\n",
-    "weights /= sum(weights)\n",
-    "plot(weights)\n",
-    "decorate(xlabel='Index into the rates array',\n",
-    "         ylabel='Weight')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll add the weights to the `System` object, since they are parameters of the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.weights = weights"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can pass these weights as a parameter to `np.random.choice` (see the [documentation](https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.choice.html))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.random.choice(system.rates, p=system.weights)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write an update function that takes the weights into account."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use `plot_many_simulations` to plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use `run_many_simulations` to collect the results and `describe` to summarize the distribution of net changes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Does the refined model have much effect on the probability of population decline?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extracting data from a PDF document\n",
-    "\n",
-    "The following section uses `tabula-py` to get data from a PDF document.\n",
-    "\n",
-    "If you don't already have it installed, and you are using Anaconda, you can install it by running the following command in a Terminal or Git Bash:\n",
-    "\n",
-    "```\n",
-    "conda install -c conda-forge tabula-py\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tabula import read_pdf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = read_pdf('data/USASAC2018-Report-30-2017-Activities-Page11.pdf')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/spiderman.ipynb b/notebooks/spiderman.ipynb
deleted file mode 100644
index 325af4a4..00000000
--- a/notebooks/spiderman.ipynb
+++ /dev/null
@@ -1,838 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case study: Spider-Man\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "I'll start by getting the units we'll need from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton\n",
-    "degree = UNITS.degree\n",
-    "radian = UNITS.radian"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Spider-Man"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this case study we'll develop a model of Spider-Man swinging from a springy cable of webbing attached to the top of the Empire State Building.  Initially, Spider-Man is at the top of a nearby building, as shown in this diagram.\n",
-    "\n",
-    "![](diagrams/spiderman.png)\n",
-    "\n",
-    "The origin, `O⃗`, is at the base of the Empire State Building.  The vector `H⃗` represents the position where the webbing is attached to the building, relative to `O⃗`.  The vector `P⃗` is the position of Spider-Man relative to `O⃗`.  And `L⃗` is the vector from the attachment point to Spider-Man.\n",
-    "\n",
-    "By following the arrows from `O⃗`, along `H⃗`, and along `L⃗`, we can see that \n",
-    "\n",
-    "`H⃗ + L⃗ = P⃗`\n",
-    "\n",
-    "So we can compute `L⃗` like this:\n",
-    "\n",
-    "`L⃗ = P⃗ - H⃗`\n",
-    "\n",
-    "The goals of this case study are:\n",
-    "\n",
-    "1. Implement a model of this scenario to predict Spider-Man's trajectory.\n",
-    "\n",
-    "2. Choose the right time for Spider-Man to let go of the webbing in order to maximize the distance he travels before landing.\n",
-    "\n",
-    "3. Choose the best angle for Spider-Man to jump off the building, and let go of the webbing, to maximize range."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll create a `Params` object to contain the quantities we'll need:\n",
-    "\n",
-    "1. According to [the Spider-Man Wiki](http://spiderman.wikia.com/wiki/Peter_Parker_%28Earth-616%29), Spider-Man weighs 76 kg.\n",
-    "\n",
-    "2. Let's assume his terminal velocity is 60 m/s.\n",
-    "\n",
-    "3. The length of the web is 100 m.\n",
-    "\n",
-    "4. The initial angle of the web is 45 degrees to the left of straight down.\n",
-    "\n",
-    "5. The spring constant of the web is 40 N / m when the cord is stretched, and 0 when it's compressed.\n",
-    "\n",
-    "Here's a `Params` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(height = 381 * m,\n",
-    "                g = 9.8 * m/s**2,\n",
-    "                mass = 75 * kg,\n",
-    "                area = 1 * m**2,\n",
-    "                rho = 1.2 * kg/m**3,\n",
-    "                v_term = 60 * m / s,\n",
-    "                length = 100 * m,\n",
-    "                angle = (270 - 45) * degree,\n",
-    "                k = 40 * N / m,\n",
-    "                t_0 = 0 * s,\n",
-    "                t_end = 30 * s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute the initial position"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def initial_condition(params):\n",
-    "    \"\"\"Compute the initial position and velocity.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \"\"\"\n",
-    "    height, length, angle = params.height, params.length, params.angle\n",
-    "    \n",
-    "    H⃗ = Vector(0, height)\n",
-    "    theta = angle.to(radian)\n",
-    "    x, y = pol2cart(theta, length)\n",
-    "    L⃗ = Vector(x, y)\n",
-    "    P⃗ = H⃗ + L⃗\n",
-    "    V⃗ = Vector(0, 0) * m/s\n",
-    "    \n",
-    "    return State(P⃗=P⃗, V⃗=V⃗)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "initial_condition(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now here's a version of `make_system` that takes a `Params` object as a parameter.\n",
-    "\n",
-    "`make_system` uses the given value of `v_term` to compute the drag coefficient `C_d`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = initial_condition(params)\n",
-    "    \n",
-    "    mass, g = params.mass, params.g\n",
-    "    rho, area, v_term = params.rho, params.area, params.v_term\n",
-    "    C_d = 2 * mass * g / (rho * area * v_term**2)\n",
-    "    \n",
-    "    return System(params, init=init, C_d=C_d)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Drag and spring forces\n",
-    "\n",
-    "Here's drag force, as we saw in Chapter 22."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def drag_force(V⃗, system):\n",
-    "    \"\"\"Compute drag force.\n",
-    "    \n",
-    "    V⃗: velocity Vector\n",
-    "    system: `System` object\n",
-    "    \n",
-    "    returns: force Vector\n",
-    "    \"\"\"\n",
-    "    rho, C_d, area = system.rho, system.C_d, system.area\n",
-    "    \n",
-    "    mag = rho * V⃗.mag**2 * C_d * area / 2\n",
-    "    direction = -V⃗.hat()\n",
-    "    f_drag = direction * mag\n",
-    "    return f_drag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "V⃗_test = Vector(10, 10) * m/s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drag_force(V⃗_test, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the 2-D version of spring force.  We saw the 1-D version in Chapter 21."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def spring_force(L⃗, system):\n",
-    "    \"\"\"Compute drag force.\n",
-    "    \n",
-    "    L⃗: Vector representing the webbing\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: force Vector\n",
-    "    \"\"\"\n",
-    "    extension = L⃗.mag - system.length\n",
-    "    if magnitude(extension) < 0:\n",
-    "        mag = 0\n",
-    "    else:\n",
-    "        mag = system.k * extension\n",
-    "        \n",
-    "    direction = -L⃗.hat()\n",
-    "    f_spring = direction * mag\n",
-    "    return f_spring"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "L⃗_test = Vector(0, -system.length-1*m)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f_spring = spring_force(L⃗_test, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the slope function, including acceleration due to gravity, drag, and the spring force of the webbing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the state variables.\n",
-    "    \n",
-    "    state: State (x, y, x velocity, y velocity)\n",
-    "    t: time\n",
-    "    system: System object with g, rho, C_d, area, mass\n",
-    "    \n",
-    "    returns: sequence (vx, vy, ax, ay)\n",
-    "    \"\"\"\n",
-    "    P⃗, V⃗ = state\n",
-    "    g, mass = system.g, system.mass\n",
-    "    \n",
-    "    H⃗ = Vector(0, system.height)\n",
-    "    L⃗ = P⃗ - H⃗\n",
-    "    \n",
-    "    a_grav = Vector(0, -g)\n",
-    "    a_spring = spring_force(L⃗, system) / mass\n",
-    "    a_drag = drag_force(V⃗, system) / mass\n",
-    "    \n",
-    "    A⃗ = a_grav + a_drag + a_spring\n",
-    "    \n",
-    "    return V⃗, A⃗"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, let's test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing the results\n",
-    "\n",
-    "We can extract the x and y components as `Series` objects."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The simplest way to visualize the results is to plot x and y as functions of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_position(P⃗):\n",
-    "    x = P⃗.extract('x')\n",
-    "    y = P⃗.extract('y')\n",
-    "    plot(x, label='x')\n",
-    "    plot(y, label='y')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "    \n",
-    "plot_position(results.P⃗)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot the velocities the same way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_velocity(V⃗):\n",
-    "    vx = V⃗.extract('x')\n",
-    "    vy = V⃗.extract('y')\n",
-    "    plot(vx, label='vx')\n",
-    "    plot(vy, label='vy')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')\n",
-    "    \n",
-    "plot_velocity(results.V⃗)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another way to visualize the results is to plot y versus x.  The result is the trajectory through the plane of motion."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_trajectory(P⃗, **options):\n",
-    "    x = P⃗.extract('x')\n",
-    "    y = P⃗.extract('y')\n",
-    "    plot(x, y, **options)\n",
-    "    \n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)')\n",
-    "    \n",
-    "plot_trajectory(results.P⃗, label='trajectory')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Letting go\n",
-    "\n",
-    "Now let's find the optimal time for Spider-Man to let go.  We have to run the simulation in two phases because the spring force changes abruptly when Spider-Man lets go, so we can't integrate through it.\n",
-    "\n",
-    "Here are the parameters for Phase 1, running for 9 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params1 = Params(params, t_end=9*s)\n",
-    "system1 = make_system(params1)\n",
-    "results1, details1 = run_ode_solver(system1, slope_func)\n",
-    "plot_trajectory(results1.P⃗, label='Phase 1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final conditions from Phase 1 are the initial conditions for Phase 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_final = get_last_label(results1) * s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the position Vector."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "init = results1.last_row()\n",
-    "init.P⃗"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the velocity Vector."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "init.V⃗"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here is the `System` for Phase 2.  We can turn off the spring force by setting `k=0`, so we don't have to write a new slope function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system2 = System(system1, t_0=t_final, t_end=t_final+10*s, init=init, k=0*N/m)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an event function that stops the simulation when Spider-Man reaches the ground."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def event_func(state, t, system):\n",
-    "    \"\"\"Stops when y=0.\n",
-    "    \n",
-    "    state: State object\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: height\n",
-    "    \"\"\"\n",
-    "    P⃗, V⃗ = state\n",
-    "    return P⃗.y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run Phase 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results2, details2 = run_ode_solver(system2, slope_func, events=event_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_trajectory(results1.P⃗, label='Phase 1')\n",
-    "plot_trajectory(results2.P⃗, label='Phase 2')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can gather all that into a function that takes `t_release` and `V_0`, runs both phases, and returns the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_two_phase(t_release, V⃗_0, params):\n",
-    "    \"\"\"Run both phases.\n",
-    "    \n",
-    "    t_release: time when Spider-Man lets go of the webbing\n",
-    "    V_0: initial velocity\n",
-    "    \"\"\"\n",
-    "    params1 = Params(params, t_end=t_release, V⃗_0=V⃗_0)\n",
-    "    system1 = make_system(params1)\n",
-    "    results1, details1 = run_ode_solver(system1, slope_func)\n",
-    "\n",
-    "    t_0 = get_last_label(results1) * s\n",
-    "    t_end = t_0 + 10 * s\n",
-    "    init = results1.last_row()\n",
-    "\n",
-    "    system2 = System(system1, t_0=t_0, t_end=t_end, init=init, k=0*N/m)\n",
-    "    results2, details2 = run_ode_solver(system2, slope_func, events=event_func)\n",
-    "\n",
-    "    results = results1.combine_first(results2)\n",
-    "    return TimeFrame(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's a test run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_release = 9 * s\n",
-    "V⃗_0 = Vector(0, 0) * m/s\n",
-    "\n",
-    "results = run_two_phase(t_release, V⃗_0, params)\n",
-    "plot_trajectory(results.P⃗)\n",
-    "x_final = results.P⃗.last_value().x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Animation\n",
-    "\n",
-    "Here's a draw function we can use to animate the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xs = results.P⃗.extract('x')\n",
-    "ys = results.P⃗.extract('y')\n",
-    "\n",
-    "def draw_func(state, t):\n",
-    "    set_xlim(xs)\n",
-    "    set_ylim(ys)\n",
-    "    x, y = state.P⃗\n",
-    "    plot(x, y, 'bo')\n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "animate(results, draw_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Maximizing range\n",
-    "\n",
-    "To find the best value of `t_release`, we need a function that takes possible values, runs the simulation, and returns the range."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def range_func(t_release, params):\n",
-    "    \"\"\"Compute the final value of x.\n",
-    "    \n",
-    "    t_release: time to release web\n",
-    "    params: Params object\n",
-    "    \"\"\"\n",
-    "    V_0 = Vector(0, 0) * m/s\n",
-    "    results = run_two_phase(t_release, V_0, params)\n",
-    "    x_final = results.P⃗.last_value().x\n",
-    "    print(t_release, x_final)\n",
-    "    return x_final"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "range_func(9*s, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And run it for a few values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for t_release in linrange(3, 15, 3) * s:\n",
-    "    range_func(t_release, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can use `maximize_scalar` to find the optimum."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bounds = [6, 12] * s\n",
-    "res = maximize_golden(range_func, bounds, params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we can run the simulation with the optimal value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_time = res.x\n",
-    "V⃗_0 = Vector(0, 0) * m/s\n",
-    "results = run_two_phase(best_time, V⃗_0, params)\n",
-    "plot_trajectory(results.P⃗)\n",
-    "x_final = results.P⃗.last_value().x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/test_install.py b/notebooks/test_install.py
deleted file mode 100644
index 64aa3040..00000000
--- a/notebooks/test_install.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""
-Code from Modeling and Simulation in Python.
-
-Copyright 2017 Allen Downey
-
-License: https://creativecommons.org/licenses/by/4.0)
-"""
-
-import logging
-logger = logging.getLogger(name='modsim.py')
-
-#TODO: Make this Python 3.7 when conda is ready
-
-# make sure we have Python 3.6 or better
-import sys
-if sys.version_info < (3, 6):
-    logger.warning('modsim.py depends on Python 3.6 features.')
-
-import inspect
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import scipy
-import sympy
-
-import seaborn as sns
-sns.set(style='white', font_scale=1.2)
-
-import pint
-UNITS = pint.UnitRegistry()
-Quantity = UNITS.Quantity
-
-# expose some names so we can use them without dot notation
-from copy import copy
-from numpy import sqrt, log, exp, pi
-from pandas import DataFrame, Series
-from time import sleep
-
-from scipy.interpolate import interp1d
-from scipy.interpolate import InterpolatedUnivariateSpline
-from scipy.integrate import odeint
-from scipy.integrate import solve_ivp
-from scipy.optimize import leastsq
-from scipy.optimize import minimize_scalar
-
-import scipy.optimize
-
-print("All imports were successful.")
diff --git a/notebooks/throwingaxe.ipynb b/notebooks/throwingaxe.ipynb
deleted file mode 100644
index 327be0dc..00000000
--- a/notebooks/throwingaxe.ipynb
+++ /dev/null
@@ -1,487 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case study: Throwing Axe\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Throwing axe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our favorite event at Lumberjack Competitions is axe throwing.  The axes used for this event typically weigh 1.5 to 2 kg, with handles roughly 0.7 m long.  They are thrown overhead at a target typically 6 m away and 1.5 m off the ground.  Normally, the axe makes one full rotation in the air to hit the target blade first, with the handle close to vertical.\n",
-    "\n",
-    "![Diagram of throwing axe](diagrams/throwingaxe1.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a version of `make_system` that sets the initial conditions.\n",
-    "\n",
-    "The state variables are x, y, theta, vx, vy, omega, where theta is the orientation (angle) of the axe in radians and omega is the angular velocity in radians per second.\n",
-    "\n",
-    "I chose initial conditions based on videos of axe throwing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "radian = UNITS.radian\n",
-    "\n",
-    "def make_system():\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    returns: System with init, ...\n",
-    "    \"\"\"\n",
-    "    P = Vector(0, 2) * m\n",
-    "    V = Vector(8, 4) * m/s\n",
-    "    theta = 2 * radian\n",
-    "    omega = -7 * radian/s\n",
-    "    \n",
-    "    init = State(P=P, V=V, theta=theta, omega=omega)\n",
-    "\n",
-    "    t_end = 1.0 * s\n",
-    "    \n",
-    "    return System(init=init, t_end=t_end,\n",
-    "                  g = 9.8 * m/s**2,\n",
-    "                  mass = 1.5 * kg,\n",
-    "                  length = 0.7 * m)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a `System`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As a simple starting place, I ignore drag, so `vx` and `omega` are constant, and `ay` is just `-g`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Computes derivatives of the state variables.\n",
-    "    \n",
-    "    state: State (x, y, x velocity, y velocity)\n",
-    "    t: time\n",
-    "    system: System object with length0, m, k\n",
-    "    \n",
-    "    returns: sequence (vx, vy, ax, ay)\n",
-    "    \"\"\"\n",
-    "    P, V, theta, omega = state\n",
-    "\n",
-    "    A = Vector(0, -system.g)\n",
-    "    alpha = 0 * radian / s**2\n",
-    "\n",
-    "    return V, A, omega, alpha"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, let's test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope_func(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing the results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The simplest way to visualize the results is to plot the state variables as a function of time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_position(P):\n",
-    "    x = P.extract('x')\n",
-    "    y = P.extract('y')\n",
-    "    plot(x, label='x')\n",
-    "    plot(y, label='y')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Position (m)')\n",
-    "    \n",
-    "plot_position(results.P)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot the velocities the same way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_velocity(V):\n",
-    "    vx = V.extract('x')\n",
-    "    vy = V.extract('y')\n",
-    "    plot(vx, label='vx')\n",
-    "    plot(vy, label='vy')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Velocity (m/s)')\n",
-    "    \n",
-    "plot_velocity(results.V)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results.theta, label='theta', color='C2')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Angle (radian)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(results.omega, label='omega', color='C2')\n",
-    "\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Angular velocity (rad/s)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another way to visualize the results is to plot y versus x.  The result is the trajectory through the plane of motion."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_trajectory(P, **options):\n",
-    "    x = P.extract('x')\n",
-    "    y = P.extract('y')\n",
-    "    plot(x, y, **options)\n",
-    "    \n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)')\n",
-    "    \n",
-    "plot_trajectory(results.P, label='trajectory')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Animation\n",
-    "\n",
-    "Animating this system is a little more complicated, if we want to show the shape and orientation of the axe.\n",
-    "\n",
-    "It is useful to construct a frame with $\\hat{r}$ along the handle of the axe and $\\hat{\\theta}$ perpendicular."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we're ready to animate the results.  The following figure shows the frame and the labeled points A, B, C, and D.\n",
-    "\n",
-    "![Diagram of the axe with reference frame](diagrams/throwingaxe2.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_frame(theta):\n",
-    "    rhat = Vector(pol2cart(theta, 1))\n",
-    "    that = rhat.perp()\n",
-    "    return rhat, that"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P, V, theta, omega = results.first_row()\n",
-    "rhat, that = make_frame(theta)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rhat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "that"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.dot(rhat, that)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "O = Vector(0, 0)\n",
-    "plot_segment(O, rhat)\n",
-    "plot_segment(O, that)\n",
-    "plt.axis('equal')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xs = results.P.extract('x')\n",
-    "ys = results.P.extract('y')\n",
-    "\n",
-    "l1 = 0.6 * m\n",
-    "l2 = 0.1 * m\n",
-    "\n",
-    "def draw_func(state, t):\n",
-    "    plt.axis('equal')\n",
-    "    set_xlim([0,8])\n",
-    "    set_ylim([0,6])\n",
-    "    \n",
-    "    P, V, theta, omega = state\n",
-    "    rhat, that = make_frame(theta)\n",
-    "    \n",
-    "    # plot the handle\n",
-    "    A = P - l1 * rhat\n",
-    "    B = P + l2 * rhat\n",
-    "    plot_segment(A, B, color='red')\n",
-    "\n",
-    "    # plot the axe head\n",
-    "    C = B + l2 * that\n",
-    "    D = B - l2 * that\n",
-    "    plot_segment(C, D, color='black', linewidth=10)\n",
-    "\n",
-    "    # plot the COG\n",
-    "    x, y = P\n",
-    "    plot(x, y, 'bo')\n",
-    "\n",
-    "    decorate(xlabel='x position (m)',\n",
-    "             ylabel='y position (m)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "During the animation, the parts of the axe seem to slide around relative to each other.  I think that's because the lines and circles get rounded off to the nearest pixel.\n",
-    "\n",
-    "Here's the final state of the axe at the point of impact (assuming the target is 8 m away)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "state = results.first_row()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "draw_func(state, 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "animate(results, draw_func)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "**Exercise:**  Find the starting conditions that make the final height of the COG as close as possible to 1.5 m.  Ideally, the final angle should be a little past vertical."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/trees.ipynb b/notebooks/trees.ipynb
deleted file mode 100644
index 857e251f..00000000
--- a/notebooks/trees.ipynb
+++ /dev/null
@@ -1,972 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Copyright 2018 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Modeling tree growth\n",
-    "\n",
-    "This case study is based on \"[Height-Age Curves for Planted Stands of Douglas Fir, with Adjustments for Density](http://www.cfr.washington.edu/research.smc/working_papers/smc_working_paper_1.pdf)\", a working paper by Flewelling, Collier, Gonyea, Marshall, and Turnblom.\n",
-    "\n",
-    "It provides \"site index curves\", which are curves that show the expected height of the tallest tree in a stand of Douglas firs as a function of age, for a stand where the trees are the same age.\n",
-    "\n",
-    "Depending on the quality of the site, the trees might grow more quickly or slowing.  So each curve is identified by a \"site index\" that indicates the quality of the site.\n",
-    "\n",
-    "I'll start with some of the data from their Table 1.  Here's the sequence of ages."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "years = [2, 3, 4, 5, 6, 8, 10, 15, 20, 25, 30,\n",
-    "         35, 40, 45, 50, 55, 60, 65, 70]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the series of heights for a site with index 45, indicating that height at 30 years is 45 feet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "site45 = TimeSeries([1.4, 1.49, 1.75, 2.18, 2.78, 4.45, 6.74,\n",
-    "                    14.86, 25.39, 35.60, 45.00, 53.65, 61.60,\n",
-    "                    68.92, 75.66, 81.85, 87.56, 92.8, 97.63],\n",
-    "                    index=years)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the series for site index 65."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "site65 = TimeSeries([1.4, 1.56, 2.01, 2.76, 3.79, 6.64, 10.44, \n",
-    "                    23.26, 37.65, 51.66, 65.00, 77.50, 89.07, \n",
-    "                    99.66, 109.28, 117.96, 125.74, 132.68, 138.84],\n",
-    "                    index=years)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And for site index 85."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "site85 = Series([1.4, 1.8, 2.71, 4.09, 5.92, 10.73, 16.81, \n",
-    "                 34.03, 51.26, 68.54, 85, 100.34, 114.33,\n",
-    "                 126.91, 138.06, 147.86, 156.39, 163.76, 170.10],\n",
-    "               index=years)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the curves look like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(site85, label='SI 85')\n",
-    "plot(site65, label='SI 65')\n",
-    "plot(site45, label='SI 45')\n",
-    "decorate(xlabel='Time (years)',\n",
-    "         ylabel='Height (feet)')\n",
-    "\n",
-    "savefig('figs/trees-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For my examples I'll work with the SI 65 data; as an exercise, you can run the notebook again with either of the other curves."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = site65;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Model 1\n",
-    "\n",
-    "As a starting place, let's assume that the ability of the tree to gain mass is limited by the area it exposes to sunlight, and that the growth rate (in mass) is proportional to that area.  In that case we can write:\n",
-    "\n",
-    "$ m_{n+1} = m_n + \\alpha A$\n",
-    "\n",
-    "where $m_n$ is the mass of the at time step $n$, $A$ is the area exposed to sunlight, and $\\alpha$ is an unknown growth parameter.\n",
-    "\n",
-    "To get from $m$ to $A$, I'll make the additional assumption that mass is proportional to height raised to an unknown power:\n",
-    "\n",
-    "$ m = \\beta h^D $\n",
-    "\n",
-    "where $h$ is height, $\\beta$ is an unknown constant of proportionality, and $D$ is the dimension that relates height and mass.  \n",
-    "\n",
-    "We'll start by assuming $D=3$, but we'll revisit that assumption.\n",
-    "\n",
-    "Finally, we'll assume that area is proportional to height squared:\n",
-    "\n",
-    "$ A = \\gamma h^2$\n",
-    "\n",
-    "I'll specify height in feet, and choose units for mass and area so that $\\beta=1$ and $\\gamma=1$.\n",
-    "\n",
-    "Putting all that together, we can write a difference equation for height:\n",
-    "\n",
-    "$ h_{n+1}^D = h_n^D + \\alpha h_n^2 $\n",
-    "\n",
-    "Now let's solve it.  Here's a system object with the parameters and initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "alpha = 7\n",
-    "dim = 3\n",
-    "\n",
-    "t_0 = get_first_label(data)\n",
-    "t_end = get_last_label(data)\n",
-    "\n",
-    "h_0 = get_first_value(data)\n",
-    "\n",
-    "system = System(alpha=alpha, dim=dim, \n",
-    "                h_0=h_0, t_0=t_0, t_end=t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's an update function that takes the current height as a parameter and returns the height during the next time step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update(height, t, system):\n",
-    "    \"\"\"Update height based on geometric model.\n",
-    "    \n",
-    "    height: current height in feet\n",
-    "    t: what year it is\n",
-    "    system: system object with model parameters\n",
-    "    \"\"\"\n",
-    "    area = height**2\n",
-    "    mass = height**system.dim\n",
-    "    mass += system.alpha * area\n",
-    "    return mass**(1/system.dim)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test the update function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "update(h_0, t_0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's our usual version of `run_simulation`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Simulate the system using any update function.\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that computes the population next year\n",
-    "    \n",
-    "    returns: TimeSeries\n",
-    "    \"\"\"\n",
-    "    results = TimeSeries()\n",
-    "    results[system.t_0] = system.h_0\n",
-    "    \n",
-    "    for t in linrange(system.t_0, system.t_end):\n",
-    "        results[t+1] = update_func(results[t], t, system)\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how we run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = run_simulation(system, update)\n",
-    "results.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(results, data):\n",
-    "    plot(results, ':', label='model', color='gray')\n",
-    "    plot(data, label='data')\n",
-    "    decorate(xlabel='Time (years)',\n",
-    "             ylabel='Height (feet)')\n",
-    "    \n",
-    "plot_results(results, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result converges to a straight line.\n",
-    "\n",
-    "I chose the value of `alpha` to fit the data as well as I could, but it is clear that the data have curvature that's not captured by the model.\n",
-    "\n",
-    "Here are the errors:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "errors = results - data\n",
-    "errors.dropna()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the mean absolute error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mean_abs_error(results, data):\n",
-    "    return np.mean(np.abs(results-data))\n",
-    "\n",
-    "mean_abs_error(results, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This model might explain why the height of a tree grows roughly linearly:\n",
-    "\n",
-    "1. If area is proportional to $h^2$ and mass is proportional to $h^3$, and\n",
-    "\n",
-    "2. Change in mass is proportional to area, and\n",
-    "\n",
-    "3. Height grows linearly, then\n",
-    "\n",
-    "4. Area grows in proportion to $h^2$, and\n",
-    "\n",
-    "5. Mass grows in proportion to $h^3$.\n",
-    "\n",
-    "If the goal is to explain (approximate) linear growth, we might stop there.  But this model does not fit the data particularly well, and it implies that trees could keep growing forever.\n",
-    "\n",
-    "So we might want to do better."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Model 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As a second attempt, let's suppose that we don't know $D$.  In fact, we don't, because trees are not like simple solids; they are more like fractals, which have [fractal dimension](https://en.wikipedia.org/wiki/Fractal_dimension).\n",
-    "\n",
-    "I would expect the fractal dimension of a tree to be between 2 and 3, so I'll guess 2.5."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "alpha = 7\n",
-    "dim = 2.8\n",
-    "\n",
-    "params = alpha, dim"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll wrap the code from the previous section is a function that takes the parameters as inputs and makes a `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params, data):\n",
-    "    \"\"\"Makes a System object.\n",
-    "    \n",
-    "    params: sequence of alpha, dim\n",
-    "    data: Series\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    alpha, dim = params\n",
-    "    \n",
-    "    t_0 = get_first_label(data)\n",
-    "    t_end = get_last_label(data)\n",
-    "\n",
-    "    h_0 = get_first_value(data)\n",
-    "\n",
-    "    return System(alpha=alpha, dim=dim, \n",
-    "                  h_0=h_0, t_0=t_0, t_end=t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we use it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With different values for the parameters, we get curves with different behavior.  Here are a few that I chose by hand."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_and_plot(alpha, dim, data):\n",
-    "    params = alpha, dim\n",
-    "    system = make_system(params, data)\n",
-    "    results = run_simulation(system, update)\n",
-    "    plot(results, ':', color='gray')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_and_plot(0.145, 2, data)\n",
-    "run_and_plot(0.58, 2.4, data)\n",
-    "run_and_plot(2.8, 2.8, data)\n",
-    "run_and_plot(6.6, 3, data)\n",
-    "run_and_plot(15.5, 3.2, data)\n",
-    "run_and_plot(38, 3.4, data)\n",
-    "\n",
-    "plot(data, label='data')\n",
-    "decorate(xlabel='Time (years)',\n",
-    "             ylabel='Height (feet)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To find the parameters that best fit the data, I'll use `leastsq`.\n",
-    "\n",
-    "We need an error function that takes parameters and returns errors:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func(params, data, update_func):\n",
-    "    \"\"\"Runs the model and returns errors.\n",
-    "    \n",
-    "    params: sequence of alpha, dim\n",
-    "    data: Series\n",
-    "    update_func: function object\n",
-    "    \n",
-    "    returns: Series of errors\n",
-    "    \"\"\"\n",
-    "    print(params)\n",
-    "    system = make_system(params, data)\n",
-    "    results = run_simulation(system, update_func)\n",
-    "    return (results - data).dropna()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we use it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "errors = error_func(params, data, update)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can pass `error_func` to `leastsq`, which finds the parameters that minimize the squares of the errors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_params, details = leastsq(error_func, params, data, update)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using the best parameters we found, we can run the model and plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(best_params, data)\n",
-    "results = run_simulation(system, update)\n",
-    "\n",
-    "plot_results(results, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The mean absolute error is better than for Model 1, but that doesn't mean much.  The model still doesn't fit the data well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean_abs_error(results, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the estimated fractal dimension is 3.11, which doesn't seem likely.\n",
-    "\n",
-    "Let's try one more thing."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Model 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Models 1 and 2 imply that trees can grow forever, but we know that's not true.  As trees get taller, it gets harder for them to move water and nutrients against the force of gravity, and their growth slows.\n",
-    "\n",
-    "We can model this effect by adding a term to the model similar to what we saw in the logistic model of population growth.  Instead of assuming:\n",
-    "\n",
-    "$ m_{n+1} = m_n + \\alpha A $ \n",
-    "\n",
-    "Let's assume\n",
-    "\n",
-    "$ m_{n+1} = m_n + \\alpha A (1 - h / K) $\n",
-    "\n",
-    "where $K$ is similar to the carrying capacity of the logistic model.  As $h$ approaches $K$, the factor $(1 - h/K)$ goes to 0, causing growth to level off.\n",
-    "\n",
-    "Here's what the implementation of this model looks like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "alpha = 2.0\n",
-    "dim = 2.5\n",
-    "K = 150\n",
-    "\n",
-    "params = [alpha, dim, K]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an updated version of `make_system`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params, data):\n",
-    "    \"\"\"Makes a System object.\n",
-    "    \n",
-    "    params: sequence of alpha, dim, K\n",
-    "    data: Series\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    alpha, dim, K = params\n",
-    "    \n",
-    "    t_0 = get_first_label(data)\n",
-    "    t_end = get_last_label(data)\n",
-    "\n",
-    "    h_0 = get_first_value(data)\n",
-    "\n",
-    "    return System(alpha=alpha, dim=dim, K=K, \n",
-    "                  h_0=h_0, t_0=t_0, t_end=t_end)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the new `System` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's the new update function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update3(height, t, system):\n",
-    "    \"\"\"Update height based on geometric model with growth limiting term.\n",
-    "    \n",
-    "    height: current height in feet\n",
-    "    t: what year it is\n",
-    "    system: system object with model parameters\n",
-    "    \"\"\"\n",
-    "    area = height**2\n",
-    "    mass = height**system.dim\n",
-    "    mass += system.alpha * area * (1 - height/system.K)\n",
-    "    return mass**(1/system.dim)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As always, we'll test the update function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "update3(h_0, t_0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can test the error function with the new update function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "error_func(params, data, update3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And search for the best parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_params, details = leastsq(error_func, params, data, update3)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With these parameters, we can fit the data much better."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(best_params, data)\n",
-    "results = run_simulation(system, update3)\n",
-    "\n",
-    "plot_results(results, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the mean absolute error is substantually smaller."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean_abs_error(results, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The estimated fractal dimension is about 2.6, which is plausible for a tree.\n",
-    "\n",
-    "Basically, it suggests that if you double the height of the tree, the mass grows by a factor of $2^{2.6}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "2**2.6"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In other words, the mass of the tree scales faster than area, but not as fast as it would for a solid 3-D object.\n",
-    "\n",
-    "What is this model good for?\n",
-    "\n",
-    "1) It offers a possible explanation for the shape of tree growth curves.\n",
-    "\n",
-    "2) It provides a way to estimate the fractal dimension of a tree based on a growth curve (probably with different values for different species).\n",
-    "\n",
-    "3) It might provide a way to predict future growth of a tree, based on measurements of past growth.  As with the logistic population model, this would probably only work if we have observed the part of the curve where the growth rate starts to decline."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis\n",
-    "\n",
-    "With some help from my colleague, John Geddes, we can do some analysis.\n",
-    "\n",
-    "Starting with the difference equation in terms of mass:\n",
-    " \n",
-    "$m_{n+1} = m_n + \\alpha A (1 - h / K) $\n",
-    "\n",
-    "We can write the corresponding differential equation:\n",
-    "\n",
-    "(1) $ \\frac{dm}{dt} = \\alpha A (1 - h / K) $\n",
-    "\n",
-    "With\n",
-    "\n",
-    "(2) $A = h^2$\n",
-    "\n",
-    "and\n",
-    "\n",
-    "(3) $m = h^D$\n",
-    "\n",
-    "Taking the derivative of the last equation yields\n",
-    "\n",
-    "(4) $\\frac{dm}{dt} = D h^{D-1} \\frac{dh}{dt}$\n",
-    "\n",
-    "Combining (1), (2), and (4), we can write a differential equation for $h$:\n",
-    "\n",
-    "(5) $\\frac{dh}{dt} = \\frac{\\alpha}{D} h^{3-D} (1 - h/K)$\n",
-    "\n",
-    "Now let's consider two cases:\n",
-    "\n",
-    "* With infinite $K$, the factor $(1 - h/K)$ approaches 1, so we have Model 2.\n",
-    "* With finite $K$, we have Model 3.\n",
-    "\n",
-    "#### Model 2\n",
-    "\n",
-    "Within Model 2, we'll consider two special cases, with $D=2$ and $D=3$.\n",
-    "\n",
-    "With $D=2$, we have\n",
-    "\n",
-    "$\\frac{dh}{dt} = \\frac{\\alpha}{2} h$\n",
-    "\n",
-    "which yields exponential growth with parameter $\\alpha/2$.\n",
-    "\n",
-    "With $D=3$, we have Model 1, with this equation:\n",
-    "\n",
-    "$\\frac{dh}{dt} = \\frac{\\alpha}{3}$\n",
-    "\n",
-    "which yields linear growth with parameter $\\alpha/3$.\n",
-    "\n",
-    "This result explains why Model 1 is linear.\n",
-    "\n",
-    "\n",
-    "\n",
-    "#### Model 3\n",
-    "\n",
-    "Within Model 3, we'll consider two special cases, with $D=2$ and $D=3$.\n",
-    "\n",
-    "With $D=2$, we have\n",
-    "\n",
-    "$\\frac{dh}{dt} = \\frac{\\alpha}{2} h (1 - h/K)$\n",
-    "\n",
-    "which yields logisitic growth with parameters $r = \\alpha/2$ and $K$.\n",
-    "\n",
-    "With $D=3$, we have\n",
-    "\n",
-    "$\\frac{dh}{dt} = \\frac{\\alpha}{3} (1 - h/K)$\n",
-    "\n",
-    "which yields a first order step response; that is, it converges to $K$ like a negative exponential:\n",
-    "\n",
-    "$ h(t) = c \\exp(-\\frac{\\alpha}{3K} t) + K $\n",
-    "\n",
-    "where $c$ is a constant that depends on the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "alpha = 10\n",
-    "D = 3\n",
-    "K = 200\n",
-    "params = alpha, D, K\n",
-    "system = make_system(params, data)\n",
-    "results = run_simulation(system, update3);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = results.index\n",
-    "a = alpha/3\n",
-    "h = (-220) * exp(-a * t / K) + K\n",
-    "plot(t, h)\n",
-    "plot(results)\n",
-    "decorate(xlabel='Time (years)',\n",
-    "         ylabel='Height (feet)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Additional resources:\n",
-    "\n",
-    "Garcia, [A stochastic differential equation model for the\n",
-    "height growth of forest stands](http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=664FED1E46ABCBF6E16741C294B79976?doi=10.1.1.608.81&rep=rep1&type=pdf)\n",
-    "\n",
-    "[EasySDE software and data](http://forestgrowth.unbc.ca/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/variables_r_strange.ipynb b/notebooks/variables_r_strange.ipynb
deleted file mode 100644
index bd2531f0..00000000
--- a/notebooks/variables_r_strange.ipynb
+++ /dev/null
@@ -1,490 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hey friends. #\n",
-    "\n",
-    "Variables r weird. Shift+Enter if you agree.\n",
-    "\n",
-    "by Jane Sieving"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from modsim import System\n",
-    "# If this doesn't work, move this file into your /code folder.\n",
-    "# It needs to be in the same folder as modsim.py."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's write a function, like they do in ModSim notebooks all the time. We'll give it some parameters just to make it feel important."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def func1(input1, input2):\n",
-    "    print(\"Input 1 = \", input1)\n",
-    "    print(\"Input 2 = \", input2)\n",
-    "    output = input1 + input2\n",
-    "    print(\"Output = \", output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "func1(1, 2.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "All it does is add two numbers and print a lot of stuff. Printing is fun, let's do it some more."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(output)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Well, shucks. That didn't work. `output` isn't defined? But we defined it in that super technical function!\n",
-    "You can try printing `input1` or `input2` as well, and you'll probably see the same error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = 5\n",
-    "print(output)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So, output was defined in the function, but doesn't exist outside of the function executing. What happens if you run the function again and print output?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "func1(1, 2.5) # will print input1, input2, and output\n",
-    "print(\"Output also =\", output)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What? There are 2 versions of `output`? Well ya see, this is where we come to **global** and **local** variables. Local variables only exist within a function or class* that is using them (they are temporary for a function/specific to a class). Global variables can be accessed by any function or class.\n",
-    "\n",
-    "When you create `output` in `func1` (and when you pass in values for `input1` and `input2`) those are local variables that only `func1` \"knows\" about. When you assign a value to output outside of `func1`, that is a global variable that you can access any time.\n",
-    "\n",
-    "\\**Classes are pre-defined or user-defined objects that have different methods and parmeters. TimeSeries, Series, State and System are all classes defined in the modsim.py library.*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def func2(input1, input2):\n",
-    "    print(\"Input 1 = \", input1)\n",
-    "    print(\"Input 2 = \", input2)\n",
-    "    print(\"Output = \", output) # this will be the global value from earlier, since it's not locally computed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "func1(1, 2.5)\n",
-    "func2(1, 2.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we're running two similar functions one after another, and they print their results one after another.\n",
-    "The only difference between the functions is that one computes a value for `output` locally, and the other just prints `output`.\n",
-    "\n",
-    "`func2` accesses the global version of `output` because it has no other option.* `func1` accesses the local variable `output` because local variables override global values with the same name.\n",
-    "\n",
-    "\\**This isn't universally true, in some languages/cases you have to declare a variable as global for it to be accessed anywhere.*\n",
-    "\n",
-    "Let's look at another example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def forgetful_func(input1, input2):\n",
-    "    print(input1, input2)\n",
-    "    \n",
-    "forgetful_func(1, 6)\n",
-    "\n",
-    "print(\"Now we try to print those:\")\n",
-    "print(input1, input2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Why isn't the second `print` command (outside of the function) working? Because `input1` and `input2` still aren't global. We getting this? Good.\n",
-    "\n",
-    "Now let's do what the book does all the time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def useful_func(input1, input2):\n",
-    "    print(input1 + input2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "useful_func(2, 3) # This should make sense. You pass in these parameters, they get used, they aren't stored after printing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "input1 = 1.5\n",
-    "input2 = 4\n",
-    "useful_func(input1, input2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, I dare you to do something w_i_l_d: change those variable names in the cell above. Change them to be all different. Change them to be all the same. How does the function care about your variable names?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cat = 5\n",
-    "dog = 10\n",
-    "useful_func(cat, dog)\n",
-    "useful_func(dog, dog)\n",
-    "useful_func(input2, dog)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Cool, so that's inputs (mostly). What about outputs? That poor little small-town `output` variable wants to be a global star.\n",
-    "This, friends, is why `return` is so gosh darn important."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def will_you_remember_me(thing1, thing2):\n",
-    "    new_thing = thing1 + thing2\n",
-    "    return new_thing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "will_you_remember_me(3, 8)\n",
-    "print(new_thing)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Foiled again! But we used `return` and everything! Well guess what, a return value that doesn't get assigned to anything is like a letter without an address (cue joke about The Twitter and snail mail implying that I'm old or something).\n",
-    "To store a return value, you HAVE TO assign it to a variable OUTSIDE of the function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "I_will_remember_you = will_you_remember_me(6, 7)\n",
-    "print(I_will_remember_you)\n",
-    "# Please, never name things like this. Please."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you're lazy you can do this, but your variable will not be saved so be careful.\n",
-    "It just feeds the output (return value) of your function to the input of print()."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(will_you_remember_me(5, 6))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can do the above as a shortcut, but remember that your return value will be LOST FOREVER after that.\n",
-    "\n",
-    "Like Jack at the end of *Titanic*.\n",
-    "\n",
-    "Think about the choices that you make."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Keyword arguments ##\n",
-    "Okay, I guess I should talk about keyword arguments and that whole `System(this=this, that=that)` nonsense you're seeing.\n",
-    "\n",
-    "So basically, a class (object) has a bunch of properties (attributes). Usually a given class will have specific attributes, but classes like `State` and `System` are set up to pretty much take any parameters you give them and accept them as attributes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "solar_system = System(planets=8, central_mass=\"Sun\")\n",
-    "\n",
-    "print(solar_system.planets, solar_system.central_mass)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "other_system = System(planets=5, central_mass=\"Me\")\n",
-    "\n",
-    "print(other_system.planets, other_system.central_mass)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "friends = 1000\n",
-    "center_of_universe = \" \" # Type your name here?\n",
-    "\n",
-    "personal_universe = System(friends=friends, \n",
-    "                           center_of_universe=center_of_universe)\n",
-    "\n",
-    "print(personal_universe.friends, \n",
-    "      personal_universe.center_of_universe)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So, you've created two systems by directly setting parameters, and one by passing in premade variables. In the book those variables usually have the same name as their target, which obscures what's actually happening."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "apples = 1\n",
-    "bananas = 1\n",
-    "cherries = 7\n",
-    "grapes = 13\n",
-    "\n",
-    "fruit_salad = System(a=apples, b=bananas, c=cherries, d=grapes)\n",
-    "\n",
-    "print(fruit_salad.a, fruit_salad.b, fruit_salad.c, fruit_salad.d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(fruit_salad.apples)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Oh dear, did that last one fail? Of course it did, apples isn't an attribute of fruit_salad. You *can* print plain old apples, because it's global:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(apples)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You *can't* print plain old a, because that's an attribute of fruit_salad and isn't defined outside of that."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can, however, do this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "global_a = fruit_salad.a # This is a name I chose, it doesn't 'make' the variable global.\n",
-    "print(global_a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And you could just as well call `global_a` something else, like `a`, and there would be a global variable `a` as well as a property `a`, and if you changed one the other would not be affected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b = fruit_salad.b\n",
-    "b += 10\n",
-    "fruit_salad.b += 100\n",
-    "print(b, fruit_salad.b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So, when you type `bikeshare = System(this=this, that=that)`,\n",
-    "the *left* parts are specific attributes of the System object, *local* to the system.\n",
-    "The *right* parts are values you are *passing in*, which happen to already be defined as global variables with the same names.\n",
-    "\n",
-    "You're just telling a function or class to use the global value on the right, calling it by the name on the left."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/wall.ipynb b/notebooks/wall.ipynb
deleted file mode 100644
index f96dbb55..00000000
--- a/notebooks/wall.ipynb
+++ /dev/null
@@ -1,930 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "\n",
-    "Copyright 2018 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Inferring thermal resistance and thermal mass of a wall\n",
-    "\n",
-    "This case study is based on Gori, Marincioni, Biddulph, Elwell, \"Inferring the thermal resistance and effective thermal mass distribution of a wall from in situ measurements to characterise heat transfer at both the interior and exterior surfaces\", *Energy and Buildings*, Volume 135, 15 January 2017, Pages 398-409, [which I downloaded here](https://www.sciencedirect.com/science/article/pii/S0378778816313056).\n",
-    "    \n",
-    "The authors put their paper under a Creative Commons license, and [make their data available here](http://discovery.ucl.ac.uk/1526521).  I thank them for their commitment to open, reproducible science, which made this case study possible.\n",
-    "\n",
-    "The goal of their paper is to model the thermal behavior of a wall as a step toward understanding the \"performance gap between the expected energy use of buildings and their measured energy use\".  The wall they study is identified as the exterior wall of an office building in central London, [not unlike this one](https://www.google.com/maps/@51.5269375,-0.1303666,3a,75y,90h,88.17t/data=!3m6!1e1!3m4!1sAoAXzN0mbGF9acaVEgUdDA!2e0!7i13312!8i6656).\n",
-    "\n",
-    "The following figure shows the scenario and their model:\n",
-    "\n",
-    "![Figure 2](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr2.jpg)\n",
-    "\n",
-    "On the interior and exterior surfaces of the wall, they measure temperature and heat flux over a period of three days.  They model the wall using two thermal masses connected to the surfaces, and to each other, by thermal resistors.\n",
-    "\n",
-    "The primary methodology of the paper is a Bayesian method for inferring the parameters of the system (two thermal masses and three thermal resistances).\n",
-    "\n",
-    "The primary result is a comparison of two models: the one shown here with two thermal masses, and a simpler model with only one thermal mass.  They find that the two-mass model is able to reproduce the measured fluxes substantially better.\n",
-    "\n",
-    "Tempting as it is, I will not replicate their method for estimating the parameters.  Rather, I will\n",
-    "\n",
-    "1. Implement their model and run it with their estimated parameters, to replicate the results, and \n",
-    "\n",
-    "2. Use SciPy's `leastsq` to see if I can find parameters that yield lower errors (root mean square).\n",
-    "\n",
-    "`leastsq` is a wrapper for some venerable FORTRAN code that runs [\"a modification of the Levenberg-Marquardt algorithm\"](https://www.math.utah.edu/software/minpack/minpack/lmdif.html), which is one of my favorites ([really](http://allendowney.com/research/model)).\n",
-    "\n",
-    "Implementing their model in the ModSimPy framework turns out to be straightforward.  The simulations run fast enough even when we carry units through the computation.  And the results are visually similar to the ones in the original paper.\n",
-    "\n",
-    "I find that `leastsq` is not able to find parameters that yield substantially better results, which suggest that the estimates in the paper are at least locally optimal.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Loading the data\n",
-    "\n",
-    "First I'll load the units we need from Pint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = UNITS.meter\n",
-    "K = UNITS.kelvin\n",
-    "W = UNITS.watt\n",
-    "J = UNITS.joule\n",
-    "degC = UNITS.celsius\n",
-    "s = UNITS.second"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Read the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv('data/DataOWall.csv', parse_dates=[0], index_col=0, header=0, skiprows=[1,2])\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The index contains Pandas `Timestamp` objects, which is good for dealing with real-world dates and times, but not as good for running the simulations, so I'm going to convert to seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "timestamp_0 = get_first_label(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Subtracting the first `Timestamp` yields `Timedelta` objects:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "time_deltas = data.index - timestamp_0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we can convert to seconds and replace the index."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data.index = time_deltas.days * 86400 + time_deltas.seconds\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The timesteps are all 5 minutes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.all(np.diff(data.index) == 300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Mark the columns of the `Dataframe` with units."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data.Q_in.units = W / m**2\n",
-    "data.Q_out.units = W / m**2\n",
-    "data.T_int.units = degC\n",
-    "data.T_ext.units = degC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the measured fluxes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(data.Q_in, color='C2')\n",
-    "plot(data.Q_out, color='C0')\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Heat flux (W/$m^2$)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the measured temperatures."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(data.T_int, color='C2')\n",
-    "plot(data.T_ext, color='C0')\n",
-    "decorate(xlabel='Time (s)',\n",
-    "         ylabel='Temperature (degC)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Params and System objects\n",
-    "\n",
-    "Here's a `Params` object with the [estimated parameters from the paper](https://www.sciencedirect.com/science/article/pii/S0378778816313056#tbl0005)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(\n",
-    "    R1 = 0.076 * m**2 * K / W,\n",
-    "    R2 = 0.272 * m**2 * K / W,\n",
-    "    R3 = 0.078 * m**2 * K / W,\n",
-    "    C1 = 212900 * J / m**2 / K,\n",
-    "    C2 = 113100 * J / m**2 / K)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I'll pass the `Params` object `make_system`, which computes `init`, packs the parameters into `Series` objects, and computes the interpolation functions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params, data):\n",
-    "    \"\"\"Makes a System object for the given conditions.\n",
-    "    \n",
-    "    params: Params object\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    R1, R2, R3, C1, C2 = params\n",
-    "    \n",
-    "    init = State(T_C1 = Quantity(16.11, degC),\n",
-    "                 T_C2 = Quantity(15.27, degC))\n",
-    "    \n",
-    "    ts = data.index\n",
-    "    t_end = ts[-1] * s\n",
-    "    \n",
-    "    return System(init=init,\n",
-    "                  R=Series([R1, R2, R3]),\n",
-    "                  C=Series([C1, C2]),\n",
-    "                  T_int_func=interpolate(data.T_int),\n",
-    "                  T_ext_func=interpolate(data.T_ext),\n",
-    "                  unit_temp=degC,\n",
-    "                  t_end=t_end, ts=ts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Make a `System` object"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test the interpolation function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.T_ext_func(0), system.T_ext_func(150), system.T_ext_func(300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Implementing the model\n",
-    "\n",
-    "Next we need a slope function that takes instantaneous values of the two internal temperatures and computes their time rates of change.\n",
-    "\n",
-    "The slope function gets called two ways.\n",
-    "\n",
-    "* When we call it directly, `state` is a `State` object and the values it contains have units.\n",
-    "\n",
-    "* When `run_ode_solver` calls it, `state` is an array and the values it contains don't have units.\n",
-    "\n",
-    "In the second case, we have to apply the units before attempting the computation.  `require_units` applies units if necessary:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function computes the fluxes between the four zones."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_flux(state, t, system):\n",
-    "    \"\"\"Compute the fluxes between the walls surfaces and the internal masses.\n",
-    "    \n",
-    "    state: State with T_C1 and T_C2\n",
-    "    t: time in seconds\n",
-    "    system: System with interpolated measurements and the R Series\n",
-    "    \n",
-    "    returns: Series of fluxes\n",
-    "    \"\"\"    \n",
-    "    # unpack the temperatures\n",
-    "    T_C1, T_C2 = state\n",
-    "        \n",
-    "    # compute a series of temperatures from inside out\n",
-    "    T_int = system.T_int_func(t)\n",
-    "    T_ext = system.T_ext_func(t)\n",
-    "    \n",
-    "    T = [require_units(T_int, system.unit_temp),\n",
-    "         require_units(T_C1, system.unit_temp),\n",
-    "         require_units(T_C2, system.unit_temp),\n",
-    "         require_units(T_ext, system.unit_temp)]\n",
-    "    \n",
-    "    # compute differences of adjacent temperatures\n",
-    "    T_diff = np.diff(T)\n",
-    "\n",
-    "    # compute fluxes between adjacent compartments\n",
-    "    Q = T_diff / system.R\n",
-    "    return Q"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test it like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compute_flux(system.init, 0, system)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a slope function that computes derivatives of `T_C1` and `T_C2`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def slope_func(state, t, system):\n",
-    "    \"\"\"Compute derivatives of the state.\n",
-    "    \n",
-    "    state: position, velocity\n",
-    "    t: time\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: derivatives of y and v\n",
-    "    \"\"\"\n",
-    "    Q = compute_flux(state, t, system)\n",
-    "\n",
-    "    # compute the net flux in each node\n",
-    "    Q_diff = np.diff(Q)\n",
-    "        \n",
-    "    # compute the rate of change of temperature\n",
-    "    dQdt = Q_diff / system.C\n",
-    "    return dQdt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test the slope function with the initial conditions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slopes = slope_func(system.init, system.ts[1], system)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for y, slope in zip(system.init, slopes):\n",
-    "    print(y, slope*s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's run the simulation, generating estimates for the time steps in the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results, details = run_ode_solver(system, slope_func)\n",
-    "details"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_results(results, data):\n",
-    "    plot(data.T_int, color='C2')\n",
-    "    plot(results.T_C1, color='C3')\n",
-    "    plot(results.T_C2, color='C1')\n",
-    "    plot(data.T_ext, color='C0')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Temperature (degC)')\n",
-    "    \n",
-    "plot_results(results, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These results are similar to what's in the paper:\n",
-    "\n",
-    "![Figure 5](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr5.jpg).  \n",
-    "\n",
-    "To get the estimated fluxes, we have to go through the results and basically do the flux calculation again."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def recompute_fluxes(results, system):\n",
-    "    \"\"\"Compute fluxes between wall surfaces and internal masses.\n",
-    "    \n",
-    "    results: Timeframe with T_C1 and T_C2\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: Timeframe with Q_in and Q_out\n",
-    "    \"\"\"\n",
-    "    Q_frame = TimeFrame(index=results.index, columns=['Q_in', 'Q_out'])\n",
-    "    \n",
-    "    for t, row in results.iterrows():\n",
-    "        Q = compute_flux(row, t, system)\n",
-    "        \n",
-    "        Q_frame.row[t] = (-Q[0].magnitude, \n",
-    "                          -Q[2].magnitude)\n",
-    "        \n",
-    "    return Q_frame\n",
-    "            \n",
-    "Q_frame = recompute_fluxes(results, system)\n",
-    "Q_frame.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see how the estimates compare to the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_Q_in(frame, data):\n",
-    "    plot(frame.Q_in, color='gray')\n",
-    "    plot(data.Q_in, color='C2')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Heat flux (W/$m^2$)')\n",
-    "    \n",
-    "plot_Q_in(Q_frame, data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_Q_out(frame, data):\n",
-    "    plot(frame.Q_out, color='gray')\n",
-    "    plot(data.Q_out, color='C0')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Heat flux (W/$m^2$)')\n",
-    "    \n",
-    "plot_Q_out(Q_frame, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These results are also similar to what's in the paper (the bottom row):\n",
-    "\n",
-    "![Figure 3](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr3.jpg)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_error(frame, data):\n",
-    "    model_Q_in = interpolate(Q_frame.Q_in)(data.index)\n",
-    "    error_Q_in = model_Q_in - data.Q_in\n",
-    "    \n",
-    "    model_Q_out = interpolate(Q_frame.Q_out)(data.index)\n",
-    "    error_Q_out = model_Q_out - data.Q_out\n",
-    "    \n",
-    "    return np.hstack([error_Q_in, error_Q_out])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "errors = compute_error(Q_frame, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's the root mean squared error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(np.sqrt(np.mean(errors**2)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Estimating parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's see if we can do any better than the parameters in the paper.\n",
-    "\n",
-    "In order to work with `leastsq`, we need a version of `params` with no units."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = [0.076, 0.272, 0.078, 212900, 113100]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's an error function that takes a hypothetical set of parameters, runs the simulation, and returns an array of errors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error_func(params, data):\n",
-    "    \"\"\"Run a simulation and return an array of errors.\n",
-    "    \n",
-    "    params: Params object or array\n",
-    "    data: DataFrame\n",
-    "    \n",
-    "    returns: array of float\n",
-    "    \"\"\"\n",
-    "    print(params)\n",
-    "    system = make_system(params, data)\n",
-    "    system = remove_units(system)\n",
-    "    \n",
-    "    results, details = run_ode_solver(system, slope_func)\n",
-    "    Q_frame = recompute_fluxes(results, system)\n",
-    "    errors = compute_error(Q_frame, data)\n",
-    "    print('RMSE', np.sqrt(np.mean(errors**2)))\n",
-    "    return errors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `error_func`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "errors = error_func(params, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pass `error_func` to `leastsq` to see if it can do any better."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_params, details = leastsq(error_func, params, data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "details.mesg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The best params are only slightly different from the starting place. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like with the `best_params`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(best_params, data)\n",
-    "system = remove_units(system)\n",
-    "\n",
-    "results, details = run_ode_solver(system, slope_func, t_eval=system.ts)\n",
-    "Q_frame = recompute_fluxes(results, system)\n",
-    "errors = compute_error(Q_frame, data)\n",
-    "print(np.sqrt(np.mean(errors**2)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The RMS error is only slightly smaller.\n",
-    "\n",
-    "And the results are visually similar."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_Q_in(Q_frame, data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_Q_out(Q_frame, data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Try starting the model with a different set of parameters and see if it moves toward the parameters in the paper.\n",
-    "\n",
-    "I found that no matter where I start, `leastsq` doesn't move far, which suggests that it is not able to optimize the parameters effectively."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Notes\n",
-    "\n",
-    "Notes on working with degC.\n",
-    "\n",
-    "Usually I construct a `Quantity` object by multiplying a number and a unit.  With degC, that doesn't work; you get\n",
-    "\n",
-    "```\n",
-    "OffsetUnitCalculusError: Ambiguous operation with offset unit (degC).\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#16.11 * C "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The problem is that it doesn't know whether you want a temperature measurement or a temperature difference.\n",
-    "\n",
-    "You can create a temperature measurement like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "T = Quantity(16.11, degC)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you convert to Kelvin, it does the right thing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "T.to(K)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When you subtract temperatures, the results is a temperature difference, indicated by the units."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diff = T - 273.15 * K"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you convert a temperature difference to Kelvin, it does the right thing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diff.to(K)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/yoyo.ipynb b/notebooks/yoyo.ipynb
deleted file mode 100644
index d9f2b8bf..00000000
--- a/notebooks/yoyo.ipynb
+++ /dev/null
@@ -1,519 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Case study\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure Jupyter so figures appear in the notebook\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Configure Jupyter to display the assigned value after an assignment\n",
-    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
-    "\n",
-    "# import functions from the modsim.py module\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Yo-yo\n",
-    "\n",
-    "Suppose you are holding a yo-yo with a length of string wound around its axle, and you drop it while holding the end of the string stationary.  As gravity accelerates the yo-yo downward, tension in the string exerts a force upward.  Since this force acts on a point offset from the center of mass, it exerts a torque that causes the yo-yo to spin.\n",
-    "\n",
-    "![](diagrams/yoyo.png)\n",
-    "\n",
-    "This figure shows the forces on the yo-yo and the resulting torque.  The outer shaded area shows the body of the yo-yo.  The inner shaded area shows the rolled up string, the radius of which changes as the yo-yo unrolls.\n",
-    "\n",
-    "In this model, we can't figure out the linear and angular acceleration independently; we have to solve a system of equations:\n",
-    "\n",
-    "$\\sum F = m a $\n",
-    "\n",
-    "$\\sum \\tau = I \\alpha$\n",
-    "\n",
-    "where the summations indicate that we are adding up forces and torques.\n",
-    "\n",
-    "As in the previous examples, linear and angular velocity are related because of the way the string unrolls:\n",
-    "\n",
-    "$\\frac{dy}{dt} = -r \\frac{d \\theta}{dt} $\n",
-    "\n",
-    "In this example, the linear and angular accelerations have opposite sign.  As the yo-yo rotates counter-clockwise, $\\theta$ increases and $y$, which is the length of the rolled part of the string, decreases.\n",
-    "\n",
-    "Taking the derivative of both sides yields a similar relationship between linear and angular acceleration:\n",
-    "\n",
-    "$\\frac{d^2 y}{dt^2} = -r \\frac{d^2 \\theta}{dt^2} $\n",
-    "\n",
-    "Which we can write more concisely:\n",
-    "\n",
-    "$ a = -r \\alpha $\n",
-    "\n",
-    "This relationship is not a general law of nature; it is specific to scenarios like this where there is rolling without stretching or slipping.\n",
-    "\n",
-    "Because of the way we've set up the problem, $y$ actually has two meanings: it represents the length of the rolled string and the height of the yo-yo, which decreases as the yo-yo falls.  Similarly, $a$ represents acceleration in the length of the rolled string and the height of the yo-yo.\n",
-    "\n",
-    "We can compute the acceleration of the yo-yo by adding up the linear forces:\n",
-    "\n",
-    "$\\sum F = T - mg = ma $\n",
-    "\n",
-    "Where $T$ is positive because the tension force points up, and $mg$ is negative because gravity points down.\n",
-    "\n",
-    "Because gravity acts on the center of mass, it creates no torque, so the only torque is due to tension:\n",
-    "\n",
-    "$\\sum \\tau = T r = I \\alpha $\n",
-    "\n",
-    "Positive (upward) tension yields positive (counter-clockwise) angular acceleration.\n",
-    "\n",
-    "Now we have three equations in three unknowns, $T$, $a$, and $\\alpha$, with $I$, $m$, $g$, and $r$ as known parameters.  It is simple enough to solve these equations by hand, but we can also get SymPy to do it for us.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sympy import init_printing, symbols, Eq, solve\n",
-    "\n",
-    "init_printing()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "T, a, alpha, I, m, g, r = symbols('T a alpha I m g r')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eq1 = Eq(a, -r * alpha)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eq2 = Eq(T - m * g, m * a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eq3 = Eq(T * r, I * alpha)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "soln = solve([eq1, eq2, eq3], [T, a, alpha])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "soln[T]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "soln[a]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "soln[alpha]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "The results are\n",
-    "\n",
-    "$T      = m g I / I^*   $\n",
-    "\n",
-    "$a      = -m g r^2 / I^* $\n",
-    "\n",
-    "$\\alpha = m g r / I^*    $\n",
-    "\n",
-    "where $I^*$ is the augmented moment of inertia, $I + m r^2$.\n",
-    "\n",
-    "You can also see [the derivation of these equations in this video](https://www.youtube.com/watch?v=chC7xVDKl4Q).\n",
-    "\n",
-    "To simulate the system, we don't really need $T$; we can plug $a$ and $\\alpha$ directly into the slope function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radian = UNITS.radian\n",
-    "m = UNITS.meter\n",
-    "s = UNITS.second\n",
-    "kg = UNITS.kilogram\n",
-    "N = UNITS.newton"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Simulate the descent of a yo-yo.  How long does it take to reach the end of the string?\n",
-    "\n",
-    "I provide a `Params` object with the system parameters:\n",
-    "\n",
-    "* `Rmin` is the radius of the axle.  `Rmax` is the radius of the axle plus rolled string.\n",
-    "\n",
-    "* `Rout` is the radius of the yo-yo body.  `mass` is the total mass of the yo-yo, ignoring the string.  \n",
-    "\n",
-    "* `L` is the length of the string.\n",
-    "\n",
-    "* `g` is the acceleration of gravity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = Params(Rmin = 8e-3 * m,\n",
-    "                Rmax = 16e-3 * m,\n",
-    "                Rout = 35e-3 * m,\n",
-    "                mass = 50e-3 * kg,\n",
-    "                L = 1 * m,\n",
-    "                g = 9.8 * m / s**2,\n",
-    "                t_end = 1 * s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's a `make_system` function that computes `I` and `k` based on the system parameters.\n",
-    "\n",
-    "I estimated `I` by modeling the yo-yo as a solid cylinder with uniform density ([see here](https://en.wikipedia.org/wiki/List_of_moments_of_inertia)).\n",
-    "\n",
-    "In reality, the distribution of weight in a yo-yo is often designed to achieve desired effects.  But we'll keep it simple."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_system(params):\n",
-    "    \"\"\"Make a system object.\n",
-    "    \n",
-    "    params: Params with Rmin, Rmax, Rout, \n",
-    "                              mass, L, g, t_end\n",
-    "    \n",
-    "    returns: System with init, k, Rmin, Rmax, mass,\n",
-    "                         I, g, ts\n",
-    "    \"\"\"\n",
-    "    L, mass = params.L, params.mass\n",
-    "    Rout, Rmax, Rmin = params.Rout, params.Rmax, params.Rmin \n",
-    "    \n",
-    "    init = State(theta = 0 * radian,\n",
-    "                 omega = 0 * radian/s,\n",
-    "                 y = L,\n",
-    "                 v = 0 * m / s)\n",
-    "    \n",
-    "    I = mass * Rout**2 / 2\n",
-    "    k = (Rmax**2 - Rmin**2) / 2 / L / radian    \n",
-    "    \n",
-    "    return System(params, init=init, I=I, k=k)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Testing `make_system`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = make_system(params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system.init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write a slope function for this system, using these results from the book:\n",
-    "\n",
-    "$ r = \\sqrt{2 k y + R_{min}^2} $ \n",
-    "\n",
-    "$ T      = m g I / I^*  $\n",
-    "\n",
-    "$ a      = -m g r^2 / I^* $\n",
-    "\n",
-    "$ \\alpha  = m g r / I^*  $\n",
-    "\n",
-    "where $I^*$ is the augmented moment of inertia, $I + m r^2$.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your slope function with the initial paramss."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write an event function that will stop the simulation when `y` is 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your event function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Check the final state.  If things have gone according to plan, the final value of `y` should be close to 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`theta` should increase and accelerate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_theta(results):\n",
-    "    plot(results.theta, color='C0', label='theta')\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Angle (rad)')\n",
-    "plot_theta(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`y` should decrease and accelerate down."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_y(results):\n",
-    "    plot(results.y, color='C1', label='y')\n",
-    "\n",
-    "    decorate(xlabel='Time (s)',\n",
-    "             ylabel='Length (m)')\n",
-    "    \n",
-    "plot_y(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot velocity as a function of time; is the yo-yo accelerating?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use `gradient` to estimate the derivative of `v`.  How does the acceleration of the yo-yo compare to `g`?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 7c4ecf73d76af0b2a57f69e939ea269914d976df Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 22 May 2025 14:38:59 -0400
Subject: [PATCH 129/144] Removing old stuff

---
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diff --git a/book/Makefile b/book/Makefile
deleted file mode 100644
index d31fc5a4..00000000
--- a/book/Makefile
+++ /dev/null
@@ -1,87 +0,0 @@
-LATEX = latex
-
-DVIPS = dvips
-
-PDFFLAGS = -dCompatibilityLevel=1.4 -dPDFSETTINGS=/prepress \
-           -dEmbedAllFonts=true -dSubsetFonts=true -dMaxSubsetPct=100
-#           -dCompressPages=true -dUseFlateCompression=true  \
-
-
-%.dvi: %.tex
-	$(LATEX) $<
-
-%.ps: %.dvi
-	$(DVIPS) -o $@ $<
-
-%.pdf: %.ps
-	ps2pdf $<
-
-# this book uses siunitx, which you can install with apt
-# sudo apt-get install texlive-science 
-
-all:	book.tex
-	pdflatex --shell-escape book
-	makeindex book.idx
-	evince book.pdf
-
-ps:
-	pdf2ps book.pdf
-	# and then upload to https://www.ps2pdf.com/convert-ps-to-pdf
-
-hevea:
-	#sed 's/\(figs\/[^.]*\).\(pdf\|png\)/\1.eps/' 
-	cp book.tex ModSimPy.tex
-	rm -rf html
-	mkdir html
-	hevea -O -e latexonly htmlonly ModSimPy
-# the following greps are a kludge to prevent imagen from seeing
-# the definitions in latexonly, and to avoid headers on the images
-	grep -v latexonly ModSimPy.image.tex > a; mv a ModSimPy.image.tex
-	grep -v fancyhdr ModSimPy.image.tex > a; mv a ModSimPy.image.tex
-	imagen -png ModSimPy
-	hacha ModSimPy.html
-	cp up.png next.png back.png html
-	mv index.html ModSimPy.css ModSimPy*.html  html
-	mv ModSimPy*.png *motif.gif html
-
-DEST = /home/downey/public_html/greent/ModSimPy/
-PDF = ModSimPy3.pdf
-
-distrib:
-	cp book.pdf $(PDF)
-	rsync -a $(PDF) $(DEST)
-	#chmod -R o+r $(DEST)/*
-	cd /home/downey/public_html/greent; sh back
-
-plastex:
-	# Before running plastex, we need the current directory in PYTHONPATH
-	# export PYTHONPATH=$PYTHONPATH:.
-	# rm -rf /home/downey/ModSimPy/book/.svn
-	plastex --renderer=DocBook --theme=book --image-resolution=300 --filename=book.xml book.tex
-	# rm -rf /home/downey/ModSimPy/trunk/book/.svn
-
-xxe:
-	xmlcopyeditor ~/ModSimPy/book/book/book.xml &
-	#~/Downloads/xxe-perso-4_8_0/bin/xxe book/book.xml
-
-lint:
-	xmllint -noout book/book.xml
-
-OREILLY = atlas
-
-oreilly:
-	rsync -a book/ $(OREILLY)
-	rsync -a figs/* $(OREILLY)/figs/
-	cd $(OREILLY); git add .
-	cd $(OREILLY); git commit -m "Automated check in."
-	cd $(OREILLY); git push
-
-clean:
-	rm -f *~ *.aux *.log *.dvi *.idx *.ilg *.ind *.toc
-
-split:
-	cp book.pdf ModSimPy.pdf
-	rm -f ModSimPyChapters/*
-	~/Downloads/sejda-console-3.2.51/bin/sejda-console splitbybookmarks -f ModSimPy.pdf -l 1 -o ModSimPyChapters
-
-
diff --git a/book/back.png b/book/back.png
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diff --git a/book/book.tex b/book/book.tex
deleted file mode 100644
index ea1c9070..00000000
--- a/book/book.tex
+++ /dev/null
@@ -1,7700 +0,0 @@
-% LaTeX source for ``Modeling and Simulation in Python''
-% Copyright 2017  Allen B. Downey.
-
-% License: Creative Commons Attribution-NonCommercial 4.0 Unported License.
-% https://creativecommons.org/licenses/by-nc/4.0/
-%
-
-\documentclass[12pt]{book}
-
-\title{Modeling and Simulation in Python}
-\author{Allen B. Downey}
-
-\newcommand{\thetitle}{Modeling and Simulation in Python}
-\newcommand{\thesubtitle}{}
-\newcommand{\theauthors}{Allen B. Downey}
-\newcommand{\theversion}{3.4.3}
-
-
-%%%% Both LATEX and PLASTEX
-
-\usepackage{graphicx}
-\usepackage{hevea}
-\usepackage{makeidx}
-\usepackage{setspace}
-\usepackage{upquote}
-\usepackage{xcolor}
-\usepackage[listings]{tcolorbox}
-
-
-% to get siunitx
-% sudo apt-get install texlive-science
-\usepackage{siunitx}
-\sisetup{per-mode=symbol}
-
-\definecolor{light-gray}{gray}{0.95}
-
-\newtcblisting{python}{
-  skin=standard,
-  boxrule=0.4pt,
-  %colback=light-gray,
-  listing only,
-  top=0pt,
-  bottom=0pt,
-  left=0pt,
-  right=0pt,
-  boxsep=2pt,
-  listing options={
-    basicstyle=\ttfamily,
-    language=python,
-    showstringspaces=false,
-  },
-}
- 
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-{\Large
-\theauthors \\
-}
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-\vspace{0.5in}
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-{\Large Green Tea Press}
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-{\small Needham, Massachusetts}
-
-%\includegraphics[width=1in]{figs/logo1.eps}
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-
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-%--copyright--------------------------------------------------
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-Copyright \copyright ~2017 \theauthors.
-
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-\vspace{0.2in}
-
-\begin{flushleft}
-Green Tea Press       \\
-9 Washburn Ave \\
-Needham MA 02492
-\end{flushleft}
-
-Permission is granted to copy, distribute, transmit and adapt this work under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License: \url{http://modsimpy.com/license}.
-
-% TODO: get the shortened URLs working with https
-
-If you are interested in distributing a commercial version of this
-work, please contact the author.
-
-The \LaTeX\ source and code for this book is available from
-
-\begin{code}
-https://github.com/AllenDowney/ModSimPy
-\end{code}
-
-
-Cover design by Tim Sauder.
-
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-Copyright \copyright ~2017 \theauthors.
-
-Permission is granted to copy, distribute, and/or modify this work
-under the terms of the Creative Commons
-Attribution-NonCommercial-ShareAlike 4.0 International License, which is
-available at \url{http://modsimpy.com/license}.
-
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-\fi
-
-\chapter{Preface}
-\label{preface}
-
-
-\section{Can modeling be taught?}
-
-The essential skills of modeling --- abstraction, analysis, simulation, and validation --- are central in engineering, natural sciences, social sciences, medicine, and many other fields.  Some students learn these skills implicitly, but in most schools they are not taught explicitly, and students get little practice.  That's the problem this book is meant to address.
-
-At Olin College, we use this book in a class called Modeling and Simulation, which all students take in their first semester.  My colleagues, John Geddes and Mark Somerville, and I developed this class and taught it for the first time in 2009.
-
-It is based on our belief that modeling should be taught explicitly, early, and throughout the curriculum.  It is also based on our conviction that computation is an essential part of this process.
-
-If students are limited to the mathematical analysis they can do by hand, they are restricted to a small number of simple physical systems, like a projectile moving in a vacuum or a block on a frictionless plane.
-
-And they will only work with bad models; that is, models that are too simple for their intended purpose.  In nearly every mechanical system, air resistance and friction are essential features; if we ignore them, our predictions will be wrong and our designs won't work.
-
-In most freshman physics classes, students don't make modeling decisions; sometimes they are not even aware of the decisions that have been made for them.  Our goal is to teach the entire modeling process and give students a chance to practice it.
-
-
-\section{How much programming do I need?}
-
-If you have never programmed before, you should be able to read this book, understand it, and do the exercises.  I will do my best to explain everything you need to know; in particular, I have chosen carefully the vocabulary I introduce, and I try to define each term the first time it is used.  If you find that I have used a term without defining it, let me know.
-
-If you have programmed before, you will have an easier time getting started, but you might be uncomfortable in some places.  I take an approach to programming you have probably not seen before.
-
-Most programming classes\footnote{Including many I have taught.} have two big problems:
-
-\begin{enumerate}
-
-\item They go ``bottom up", starting with basic language features and gradually adding more powerful tools.  As a result, it takes a long time before students can do anything more interesting than convert Fahrenheit to Celsius.
-
-\index{bottom up}
-
-\item They have no context.  Students learn to program with no particular goal in mind, so the exercises span an incoherent collection of topics, and the exercises tend to be unmotivated.
-
-\end{enumerate}
-
-In this book, you learn to program with an immediate goal in mind: writing simulations of physical systems.  And we proceed ``top down", by which I mean we use professional-strength data structures and language features right away.  In particular, we use the following Python {\bf libraries}:
-
-\index{top down}
-
-\begin{itemize}
-
-\item NumPy for basic numerical computation (see \url{https://www.numpy.org/}).
-
-\index{NumPy}
-
-\item SciPy for scientific computation (see \url{https://www.scipy.org/}).
-
-\index{SciPy}
-
-\item Matplotlib for visualization (see \url{https://matplotlib.org/}).
-
-\index{Matplotlib}
-
-\item Pandas for working with data (see \url{https://pandas.pydata.org/}).
-
-\index{Pandas}
-
-\item SymPy for symbolic computation, (see \url{https://www.sympy.org}).
-
-\index{SymPy}
-
-\item Pint for units like kilograms and meters (see \url{https://pint.readthedocs.io}).
-
-\index{Pint}
-
-\item Jupyter for reading, running, and developing code (see \url{https://jupyter.org}).
-
-\index{Jupyter}
-
-\end{itemize}
-
-These tools let you work on more interesting programs sooner, but there are some drawbacks: they can be hard to use, and it can be challenging to keep track of which library does what and how they interact.
-
-I have tried to mitigate these problems by providing a library, called \py{modsim}, that makes it easier to get started with these tools, and provides some additional capabilities.
-
-\index{ModSim library}
-
-Some features in the ModSim library are like training wheels; at some point you will probably stop using them and start working with the underlying libraries directly.  Other features you might find useful the whole time you are working through the book, and later.
-
-I encourage you to read the ModSim library code.  Most of it is not complicated, and I tried to make it readable.  Particularly if you have some programming experience, you might learn something by reverse engineering my design decisions.
-
-
-\section{How much math and science do I need?} 
-
-I assume that you know what derivatives and integrals are, but that's about all.  In particular, you don't need to know (or remember) much about finding derivatives or integrals of functions analytically.  If you know the derivative of $x^2$ and you can integrate $2x~dx$, that will do it\footnote{And if you noticed that those two questions answer each other, even better.}.  More importantly, you should understand what those concepts {\em mean}; but if you don't, this book might help you figure it out.
-
-\index{calculus}
-
-You don't have to know anything about differential equations.
-
-As for science, we will cover topics from a variety of fields, including demography, epidemiology, medicine, thermodynamics, and mechanics. For the most part, I don't assume you know anything about these topics.  In fact, one of the skills you need to do modeling is the ability to learn enough about new fields to develop models and simulations.
-
-When we get to mechanics, I assume you understand the relationship between position, velocity, and acceleration, and that you are familiar with Newton's laws of motion, especially the second law, which is often expressed as $F = ma$ (force equals mass times acceleration).
-
-\index{science}
-\index{mechanics}
-
-I think that's everything you need, but if you find that I left something out, please let me know.
-
-
-\section{Getting started}
-\label{code}
-
-To run the examples and work on the exercises in this book, you have to:
-
-\begin{enumerate}
-
-\item Install Python on your computer, along with the libraries we will use.
-
-\item Copy my files onto your computer.
-
-\item Run Jupyter, which is a tool for running and writing programs, and load a {\bf notebook}, which is a file that contains code and text.
-
-\end{enumerate}
-
-The next three sections provide details for these steps.  I wish there were an easier way to get started; it's regrettable that you have to do so much work before you write your first program.  Be persistent!
-
-
-\section{Installing Python}
-
-You might already have Python installed on your computer, but you might not have the latest version.  To use the code in this book, you need Python 3.6 or later.  Even if you have the latest version, you probably don't have all of the libraries we need.
-
-\index{installing Python}
-
-You could update Python and install these libraries, but I strongly recommend that you don't go down that road.  I think you will find it easier to use {\bf Anaconda}, which is a free Python distribution that includes all the libraries you need for this book (and more).
-
-\index{Anaconda}
-
-Anaconda is available for Linux, macOS, and Windows.  By default, it puts all files in your home directory, so you don't need administrator (root) permission to install it, and if you have a version of Python already, Anaconda will not remove or modify it.
-
-Start at \url{https://www.anaconda.com/download}.  Download the installer for your system and run it.  You don't need administrative privileges to install Anaconda, so I recommend you run the installer as a normal user, not as administrator or root.
-
-I suggest you accept the recommended options.
-On Windows you have the option to install Visual Studio Code, which is an interactive environment for writing programs.  You won't need it for this book, but you might want it for other projects.
-
-By default, Anaconda installs most of the packages you need, but there are a few more you have to add.  Once the installation is complete, open a command window.  On macOS or Linux, you can use Terminal.  On Windows, open the Anaconda Prompt that should be in your Start menu.
-
-Run the following command (copy and paste it if you can, to avoid typos):
-
-\begin{code}
-conda install jupyterlab pandas seaborn sympy 
-conda install beautifulsoup4 lxml html5lib pytables
-\end{code}
-
-Some of these packages might already be installed.  To install Pint, run this command:
-
-\begin{code}
-conda install -c conda-forge pint
-\end{code}
-
-And to install the ModSim library, run this command:
-
-\begin{code}
-pip install modsimpy
-\end{code}
-
-That should be everything you need.
-
-
-\section{Copying my files}
-
-The simplest way to get the files for this book is to download a Zip archive from \url{https://github.com/AllenDowney/ModSimPy/archive/master.zip}.  You will need a program like WinZip or gzip to unpack the Zip file.  Make a note of the location of the files you unpack.
-
-If you download the Zip file, you can skip the rest of this section, which explains how to use Git.
-
-The code for this book is available from
-\url{https://github.com/AllenDowney/ModSimPy}, which is a {\bf Git repository}.  Git is a software tool that helps you keep track of the programs and other files that make up a project.  A collection of files under Git's control is called a repository (the cool kids call it a ``repo").  GitHub is a hosting service that provides storage for Git repositories and a convenient web interface.
-
-\index{repository}
-\index{Git}
-\index{GitHub}
-
-Before you download these files, I suggest you copy my repository on GitHub, which is called {\bf forking}.  If you don't already have a GitHub account, you'll need to create one.  
-
-Use a browser to view the homepage of my repository at \url{https://github.com/AllenDowney/ModSimPy}.   You should see a gray button in the upper right that says {\sf Fork}.  If you press it, GitHub will create a copy of my repository that belongs to you.
-
-Now, the best way to download the files is to use a {\bf Git client}, which is a program that manages git repositories.  You can get installation instructions for Windows, macOS, and Linux at \url{http://modsimpy.com/getgit}. 
-
-In Windows, I suggest you accept the options recommended by the installer, with two exceptions:
-
-\begin{itemize}
-
-\item As the default editor, choose \py{nano} instead of \py{vim}.
-
-\item For ``Configuring line ending conversions", select ``Check out as is, commit as is".
-
-\end{itemize}
-
-For macOS and Linux, I suggest you accept the recommended options.
-
-Once the installation is complete, open a command window.  On Windows, open Git Bash, which should be in your Start menu.  On macOS or Linux, you can use Terminal.  
-
-To find out what directory you are in, type \py{pwd}, which stands for ``print working directory".  On Windows, most likely you are in \py{Users\\yourusername}.  On MacOS or Linux, you are probably in your home directory, \py{/home/yourusername}.  
-
-The next step is to copy files from your repository on GitHub to your computer; in Git vocabulary, this process is called {\bf cloning}.  Run this command:
-
-\begin{python}
-git clone https://github.com/YourGitHubUserName/ModSimPy
-\end{python}
-
-Of course, you should replace \py{YourGitHubUserName} with your GitHub user name.  After cloning, you should have a new directory called \py{ModSimPy}.
-
-\section{Running Jupyter}
-
-The code for each chapter, and starter code for the exercises, is in
-Jupyter notebooks.  If you have not used Jupyter before, you can read
-about it at \url{https://jupyter.org}.
-
-\index{Jupyter}
-
-To start Jupyter on macOS or Linux, open a Terminal; on Windows, open Git Bash.  Use \py{cd} to ``change directory" into the directory in the repository that contains the notebooks.  If you downloaded the Zip file, it's probably:
-
-\begin{code}
-cd ModSimPy-master/notebooks
-\end{code}
-
-If you cloned it with Git, it's probably:
-
-\begin{code}
-cd ModSimPy/notebooks
-\end{code}
-
-Then launch the Jupyter notebook server:
-
-\begin{code}
-jupyter notebook
-\end{code}
-
-Jupyter should open a window in a browser, and you should see the list of notebooks in my repository.  Click on the first notebook, \py{chap01.ipynb} and follow the instructions to run the first few ``cells".  The first time you run a notebook, it might take several seconds to start while some Python files get initialized.  After that, it should run faster.
-
-Feel free to read through the notebook, but it might not make sense until you read Chapter~\ref{chap01}.
-
-You can also launch Jupyter from the Start menu on Windows, the Dock on macOS, or the Anaconda Navigator on any system.  If you do that, Jupyter might start in your home directory or somewhere else in your file system, so you might have to navigate to find the \py{ModSimPy} directory.
-
-
-\section*{Contributor List}
-
-If you have a suggestion or correction, send it to 
-{\tt downey@allendowney.com}.  Or if you are a Git user, send me a pull request!
-
-If I make a change based on your feedback, I will add you to the contributor list, unless you ask to be omitted.
-\index{contributors}
-
-If you include at least part of the sentence the error appears in, that makes it easy for me to search.  Page and section numbers are fine, too, but not as easy to work with.  Thanks!
-
-\begin{itemize}
-
-\item I am grateful to John Geddes and Mark Somerville for their early collaboration with me to create Modeling and Simulation, the class at Olin College this book is based on.
-
-\item My early work on this book benefited from conversations with
-my amazing colleagues at Olin College, including John Geddes, Alison
-Wood, Chris Lee, and Jason Woodard.
-
-\item I am grateful to Lisa Downey and Jason Woodard for their thoughtful and careful copy editing.
-
-\item Thanks to Alessandra Ferzoco, Erhardt Graeff, Emily Tow,
-Kelsey Houston-Edwards, Linda Vanasupa, Matt Neal, Joanne Pratt, and Steve Matsumoto for their helpful suggestions.
-
-\item Special thanks to Tim Sauder for the cover design.
-
-% ENDCONTRIB
-
-\end{itemize}
-
-
-
-\normalsize
-
-\cleardoublepage
-
-% TABLE OF CONTENTS
-\begin{latexonly}
-
-% \tableofcontents
-
-\cleardoublepage
-
-\end{latexonly}
-
-% START THE BOOK
-\mainmatter
-
-
-\chapter{Modeling}
-\label{chap01}
-
-This book is about modeling and simulation of physical systems.  
-The following diagram shows what I mean by ``modeling":
-
-\index{modeling}
-
-\vspace{0.2in}
-\centerline{\includegraphics[height=3in]{figs/modeling_framework.pdf}}
-
-Starting in the lower left, the {\bf system} is something in the real world we are interested in.  Often, it is something complicated, so we have to decide which details can be left out; removing details is called {\bf abstraction}.
-
-\index{system}
-
-The result of abstraction is a {\bf model}, which is a description of the system that includes only the features we think are essential.  A model can be represented in the form of diagrams and equations, which can be used for mathematical {\bf analysis}.  It can also be implemented in the form of a computer program, which can run {\bf simulations}.
-
-\index{model}
-\index{abstraction}
-\index{analysis}
-
-The result of analysis and simulation might be a {\bf prediction} about what the system will do, an {\bf explanation} of why it behaves the way it does, or a {\bf design} intended to achieve a purpose.
-
-\index{prediction}
-\index{explanation}
-\index{design}
-
-We can {\bf validate} predictions and test designs by taking {\bf measurements} from the real world and comparing the {\bf data} we get with the results from analysis and simulation. 
-
-\index{validation}
-\index{data}
-
-For any physical system, there are many possible models, each one including and excluding different features, or including different levels of detail.  The goal of the modeling process is to find the model best suited to its purpose (prediction, explanation, or design).
-
-\index{iterative modeling}
-
-Sometimes the best model is the most detailed.  If we include more features, the model is more realistic, and we expect its predictions to be more accurate.
-
-\index{realism}
-
-But often a simpler model is better.  If we include only the essential features and leave out the rest, we get models that are easier to work with, and the explanations they provide can be clearer and more compelling.
-
-\index{simplicity}
-
-As an example, suppose someone asks you why the orbit of the Earth is elliptical.  If you model the Earth and Sun as point masses (ignoring their actual size), compute the gravitational force between them using Newton's law of universal gravitation, and compute the resulting orbit using Newton's laws of motion, you can show that the result is an ellipse.
-
-\index{orbit}
-\index{ellipse}
-
-Of course, the actual orbit of Earth is not a perfect ellipse, because of the gravitational forces of the Moon, Jupiter, and other objects in the solar system, and because Newton's laws of motion are only approximately true (they don't take into account relativistic effects).
-
-\index{Newton}
-\index{relativity}
-
-But adding these features to the model would not improve the explanation; more detail would only be a distraction from the fundamental cause.  However, if the goal is to predict the position of the Earth with great precision, including more details might be necessary.  
-
-Choosing the best model depends on what the model is for.  It is usually a good idea to start with a simple model, even if it is likely to be too simple, and test whether it is good enough for its purpose.  Then you can add features gradually, starting with the ones you expect to be most essential.  This process is called {\bf iterative modeling}.
-
-Comparing results of successive models provides a form of {\bf internal validation}, so you can catch conceptual, mathematical, and software errors.  And by adding and removing features, you can tell which ones have the biggest effect on the results, and which can be ignored.
-
-\index{internal validation}
-\index{validation!internal}
-\index{external validation}
-\index{validation!external}
-
-Comparing results to data from the real world provides {\bf external validation}, which is generally the strongest test.
-
-
-\section{The falling penny myth}
-\label{penny}
-
-Let's see an example of how models are used.  You might have heard that a penny dropped from the top of the Empire State Building would be going so fast when it hit the pavement that it would be embedded in the concrete; or if it hit a person, it would break their skull.
-
-\index{Empire State Building}
-\index{penny}
-\index{myth}
-
-We can test this myth by making and analyzing a model.  To get started, we'll assume that the effect of air resistance is small.  This will turn out to be a bad assumption, but bear with me.
-
-If air resistance is negligible, the primary force acting on the penny is gravity, which causes the penny to accelerate downward.
-\index{air resistance}
-
-If the initial velocity is 0, the velocity after $t$ seconds is $a t$, and the distance the penny has dropped is
-%
-\[ h = a t^2 / 2 \]
-%
-Using algebra, we can solve for $t$:
-%
-\[ t = \sqrt{ 2 h / a} \]
-%
-Plugging in the acceleration of gravity, $a = \SI{9.8}{\meter\per\second\squared}$, and the height of the Empire State Building, $h=\SI{381}{\meter}$, we get $t = \SI{8.8}{\second}$.  Then computing $v = a t$ we get a velocity on impact of $\SI{86}{\meter\per\second}$, which is about 190 miles per hour.  That sounds like it could hurt.
-
-Of course, these results are not exact because the model is based on simplifications.  For example, we assume that gravity is constant.  In fact, the force of gravity is different on different parts of the globe, and gets weaker as you move away from the surface.  But these differences are small, so ignoring them is probably a good choice for this scenario.
-\index{gravity}
-
-On the other hand, ignoring air resistance is not a good choice.  Once the penny gets to about \SI{18}{\meter\per\second}, the upward force of air resistance equals the downward force of gravity, so the penny stops accelerating.  After that, it doesn't matter how far the penny falls; it hits the sidewalk (or your head) at about \SI{18}{\meter\per\second}, much less than \SI{86}{\meter\per\second}, as the simple model predicts.
-
-The statistician George Box famously said ``All models are wrong, but some are useful."  He was talking about statistical models, but his wise words apply to all kinds of models.  Our first model, which ignores air resistance, is very wrong, and probably not useful.  In the notebook for this chapter, you will see another model, which assumes that acceleration is constant until the penny reaches terminal velocity.  This model is also wrong, but it's better, and it's good enough to refute the myth.
-
-\index{Box, George}
-
-The television show {\it Mythbusters} has tested the myth of the falling penny more carefully; you can view the results at \url{http://modsimpy.com/myth}.  Their work is based on a mathematical model of motion, measurements to determine the force of air resistance on a penny, and a physical model of a human head.
-
-\index{Mythbusters}
-
-
-\section{Computation}
-\label{computation}
-
-There are (at least) two ways to work with mathematical models, {\bf analysis} and {\bf simulation}.  Analysis often involves algebra and other kinds of symbolic manipulation.  Simulation often involves computers.
-\index{analysis}
-\index{simulation}
-
-In this book we do some analysis and a lot of simulation; along the way, I discuss the pros and cons of each.  The primary tools we use for simulation are the Python programming language and Jupyter, which is an environment for writing and running programs.
-
-As a first example, I'll show you how I computed the results from the previous section using Python.
-
-First I create a {\bf variable} to represent acceleration.
-
-\index{variable}
-\index{value}
-
-\begin{python}
-a = 9.8 * meter / second**2
-\end{python}
-
-A variable is a name that corresponds to a value.  In this example, the name is \py{a} and the value is the number \py{9.8} multiplied by the units \py{meter / second**2}.  This example demonstrates some of the symbols Python uses to perform mathematical operations:
-\index{operator!mathematical}
-
-\begin{tabular}{l|c}
-{\bf Operation} & {\bf Symbol} \\ 
-\hline 
-Addition & \py{+} \\ 
-Subtraction & \py{-} \\ 
-Multiplication & \py{*} \\ 
-Division & \py{/} \\ 
-Exponentiation & \py{**}  \\ 
-\end{tabular} 
-
-Next, we compute the time it takes for the penny to drop \SI{381}{\meter}, the height of the Empire State Building.
-
-\begin{python}
-h = 381 * meter
-t = sqrt(2 * h / a)
-\end{python}
-
-These lines create two more variables: \py{h} gets the height of the building in meters; \py{t} gets the time, in seconds, for the penny to fall to the sidewalk.  \py{sqrt} is a {\bf function} that computes square roots.  Python keeps track of units, so the result, \py{t}, has the correct units, seconds.
-\index{unit}
-\index{function}
-\index{sqrt}
-
-Finally, we compute the velocity of the penny after $t$ seconds:
-
-\begin{python}
-v = a * t
-\end{python}
-
-The result is about \SI{86}{\meter\per\second}, again with the correct units.
-
-This example demonstrates analysis and computation using Python.  In the next chapter, we'll see an example of simulation.  
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap01.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-\chapter{Bike share}
-\label{chap02}
-
-This chapter presents a simple model of a bike share system and demonstrates the features of Python we'll use to develop simulations of real-world systems.  
-
-Along the way, we'll make decisions about how to model the system.  In the next chapter we'll review these decisions and gradually improve the model.
-
-
-\section{Modeling}
-\label{modeling}
-
-Imagine a bike share system for students traveling between Olin College and Wellesley College, which are about 3 miles apart in eastern Massachusetts.
-
-\index{Wellesley College}
-\index{Olin College}
-
-Suppose the system contains 12 bikes and two bike racks, one at Olin and one at Wellesley, each with the capacity to hold 12 bikes.
-
-\index{bike share system}
-
-As students arrive, check out a bike, and ride to the other campus, the number of bikes in each location changes.  In the simulation, we'll need to keep track of where the bikes are.  To do that, I'll create a \py{State} object, which is defined in the ModSim library.  
-
-\index{State object}
-
-Before we can use the library, we have to \py{import} it:
-
-\begin{python}
-from modsim import *
-\end{python}
-
-This line of code is an {\bf import statement} that tells Python
-to read the file {\tt modsim.py} and make the functions it defines available.
-
-\index{import statement}
-
-Functions in the \py{modsim.py} library include \py{sqrt}, which we used in the previous section, and \py{State}, which we are using now.  \py{State} creates a \py{State} object, which is a collection of {\bf state variables}.  
-
-\index{state variable}
-
-\begin{python}
-bikeshare = State(olin=10, wellesley=2)
-\end{python}
-
-The state variables, \py{olin} and \py{wellesley}, represent the number of bikes at each location.  The initial values are 10 and 2, indicating that there are 10 bikes at Olin and 2 at Wellesley.  The \py{State} object created by \py{State} is assigned to a new variable named \py{bikeshare}.
-
-\index{dot operator}
-\index{operator!dot}
-
-We can read the variables inside a \py{State} object using the {\bf dot operator}, like this:
-
-\begin{python}
-bikeshare.olin
-\end{python}
-
-The result is the value 10.  Similarly, for:
-
-\begin{python}
-bikeshare.wellesley
-\end{python}
-
-The result is 2.  If you forget what variables a state object has, you can just type the name:
-
-\begin{python}
-bikeshare
-\end{python}
-
-The result looks like a table with the variable names and their values:
-
-\begin{tabular}{lr}
- & {\bf \sf value} \\ 
-\hline 
-{\bf \sf olin} & 10 \\ 
-{\bf \sf wellesley} & 2 \\ 
-\end{tabular} 
-
-The state variables and their values make up the {\bf state} of the system.  We can update the state by assigning new values to the variables.  For example, if a student moves a bike from Olin to Wellesley, we can figure out the new values and assign them:
-
-\index{state}
-
-\begin{python}
-bikeshare.olin = 9
-bikeshare.wellesley = 3
-\end{python}
-
-Or we can use {\bf update operators}, \py{-=} and \py{+=}, to subtract 1 from \py{olin} and add 1 to \py{wellesley}:
-
-\index{update operator}
-\index{operator!update}
-
-\begin{python}
-bikeshare.olin -= 1
-bikeshare.wellesley += 1
-\end{python}
-
-The result is the same either way, but the second version is more versatile.
-
-
-\section{Defining functions}
-
-So far we have used functions defined in ModSim and other libraries.  Now we're going to define our own functions.
-
-\index{function}
-\index{defining functions}
-
-When you are developing code in Jupyter, it is often efficient to
-write a few lines of code, test them to confirm they do what
-you intend, and then use them to define a new function.  For
-example, these lines move a bike from Olin to Wellesley:
-
-\begin{python}
-bikeshare.olin -= 1
-bikeshare.wellesley += 1
-\end{python}
-
-Rather than repeat them every time a bike moves, we can define a
-new function:
-
-\begin{python}
-def bike_to_wellesley():
-    bikeshare.olin -= 1
-    bikeshare.wellesley += 1
-\end{python}
-
-\py{def} is a special word in Python that indicates we are defining a new
-function.  The name of the function is \py{bike_to_wellesley}.  The empty parentheses indicate that this function requires no additional information when it runs.  The colon indicates the beginning of an indented
-{\bf code block}.
-\index{def}
-\index{code block}
-\index{body}
-\index{indentation}
-
-The next two lines are the {\bf body} of the function.  They have to be indented; by convention, the indentation is 4 spaces.
-
-When you define a function, it has no immediate effect.  The body
-of the function doesn't run until you {\bf call} the function.
-Here's how to call this function:
-\index{call}
-
-\begin{python}
-bike_to_wellesley()
-\end{python}
-
-When you call the function, it runs the statements in the body, which update the variables of the {\tt bikeshare} object; you can check by displaying the new state.
-
-When you call a function, you have to include the parentheses.  If you leave them out, like this:
-\index{parentheses}
-
-\begin{python}
-bike_to_wellesley
-\end{python}
-
-Python looks up the name of the function and displays:
-
-\begin{python}
-<function __main__.bike_to_wellesley>
-\end{python}
-
-This result indicates that \py{bike_to_wellesley} is a function.  You don't have to know what \py{__main__} means, but if you see something like this, it probably means that you looked up a function but you didn't
-actually call it.  So don't forget the parentheses.
-
-Just like \py{bike_to_wellesley}, we can define a function that moves a bike from Wellesley to Olin:
-
-\begin{python}
-def bike_to_olin():
-    bikeshare.wellesley -= 1
-    bikeshare.olin += 1
-\end{python}
-
-And call it like this:
-
-\begin{python}
-bike_to_olin()
-\end{python}
-
-One benefit of defining functions is that you avoid repeating chunks
-of code, which makes programs smaller.  Another benefit is that the
-name you give the function documents what it does, which makes programs
-more readable.
-
-
-\section{Print statements}
-
-As you write more complicated programs, it is easy to lose track of what is going on.  One of the most useful tools for debugging is the {\bf print statement}, which displays text in the Jupyter notebook.
-\index{print statement}
-\index{statement!print}
-
-Normally when Jupyter runs the code in a cell, it displays the value of the last line of code.  For example, if you run:
-
-\begin{python}
-bikeshare.olin
-bikeshare.wellesley
-\end{python}
-
-Jupyter runs both lines of code, but it only displays the value of the second line.  If you want to display more than one value, you can use print statements:
-
-\begin{python}
-print(bikeshare.olin)
-print(bikeshare.wellesley)
-\end{python}
-
-When you call the \py{print} function, you can put a variable name in parentheses, as in the previous example, or you can provide a sequence of variables separated by commas, like this:
-
-\begin{python}
-print(bikeshare.olin, bikeshare.wellesley)
-\end{python}
-
-Python looks up the values of the variables and displays them; in this example, it displays two values on the same line, with a space between them.
-
-Print statements are useful for debugging functions.  For example, we can add a print statement to \py{move_bike}, like this:
-
-\begin{python}
-def bike_to_wellesley():
-    print('Moving a bike to Wellesley')
-    bikeshare.olin -= 1
-    bikeshare.wellesley += 1
-\end{python}
-
-Each time we call this version of the function, it displays a message, which can help us keep track of what the program is doing.
-
-The message in this example is a {\bf string}, which is a sequence of letters and other symbols in quotes.
-\index{string}
-
-
-\section{If statements}
-
-The ModSim library provides a function called \py{flip}; when you call it, you provide a value between 0 and 1; in this example, it's \py{0.7}:
-
-\begin{python}
-flip(0.7)
-\end{python}
-
-The result is one of two values: \py{True} with probability 0.7 or \py{False} with probability 0.3.  If you run \py{flip} like this 100 times, you should get \py{True} about 70 times and \py{False} about 30 times.  But the results are random, so they might differ from these expectations.
-\index{flip}
-\index{True}
-\index{False}
-
-\py{True} and \py{False} are special values defined by Python.  Note
-that they are not strings.  There is a difference between \py{True},
-which is a special value, and \py{'True'}, which is a string.
-\index{string}
-\index{boolean}
-
-\py{True} and \py{False} are called {\bf boolean} values because
-they are related to Boolean algebra (\url{http://modsimpy.com/boolean}).
-
-We can use boolean values to control the behavior of the program, using
-an {\bf if statement}:
-\index{if statement}
-\index{statement!if}
-
-\begin{python}
-if flip(0.5):
-    print('heads')
-\end{python}
-
-If the result from \py{flip} is \py{True}, the program displays the string \py{'heads'}.  Otherwise it does nothing.
-
-The punctuation for \py{if} statements is similar to the punctuation for function definitions: the first line has to end with a colon, and the lines inside the \py{if} statement have to be indented.
-\index{indentation}
-\index{else clause}
-
-Optionally, you can add an {\bf else clause} to indicate what should happen if the result is \py{False}:
-
-\begin{python}
-if flip(0.5):
-    print('heads')
-else:
-    print('tails')    
-\end{python}
-
-Now we can use \py{flip} to simulate the arrival of students who want to borrow a bike.  Suppose students arrive at the Olin station every 2 minutes, on average.  In that case, the chance of an arrival during any one-minute period is 50\%, and we can simulate it like this:
-
-\begin{python}
-if flip(0.5):
-    bike_to_wellesley()
-\end{python}
-
-If students arrive at the Wellesley station every 3 minutes, on average, the chance of an arrival during any one-minute period is 33\%, and we can simulate it like this:
-
-\begin{python}
-if flip(0.33):
-    bike_to_olin()
-\end{python}
-
-We can combine these snippets into a function that simulates a {\bf time step}, which is an interval of time, in this case one minute:
-
-\index{time step}
-
-\begin{python}
-def step():
-    if flip(0.5):
-        bike_to_wellesley()
-    
-    if flip(0.33):
-        bike_to_olin()
-\end{python}
-
-Then we can simulate a time step like this:
-
-\begin{python}
-step()
-\end{python}
-
-Even though there are no values in parentheses, we have to include them.
-
-
-\section{Parameters}
-
-The previous version of \py{step} is fine if the arrival probabilities never change, but in reality, these probabilities vary over time.
-
-So instead of putting the constant values 0.5 and 0.33 in \py{step} we can replace them with {\bf parameters}.  Parameters are variables whose values are set when a function is called.
-
-Here's a version of \py{step} that takes two parameters, \py{p1} and \py{p2}:
-
-\index{probability}
-
-\begin{python}
-def step(p1, p2):
-    if flip(p1):
-        bike_to_wellesley()
-    
-    if flip(p2):
-        bike_to_olin()
-\end{python}
-
-The values of \py{p1} and \py{p2} are not set inside this function; instead, they are provided when the function is called, like this:
-
-\begin{python}
-step(0.5, 0.33)
-\end{python}
-
-The values you provide when you call the function are called {\bf arguments}.
-The arguments, \py{0.5} and \py{0.33} in this example, get assigned to the parameters, \py{p1} and \py{p2}, in order.  So running this function has the same effect as:
-
-\begin{python}
-p1 = 0.5
-p2 = 0.33
-
-if flip(p1):
-    bike_to_wellesley()
-    
-if flip(p2):
-    bike_to_olin()
-\end{python}
-
-The advantage of using parameters is that you can call the same function many times, providing different arguments each time.  
-
-Adding parameters to a function is called {\bf generalization}, because it makes the function more general, that is, less specialized.
-
-\index{generalization}
-
-
-\section{For loops}
-\label{forloop}
-
-At some point you will get sick of running cells over and over. Fortunately, there is an easy way to repeat a chunk of code, the {\bf for loop}.  Here's an example:
-\index{for loop}
-\index{loop}
-
-\begin{python}
-for i in range(4):
-    bike_to_wellesley()
-\end{python}
-
-The punctuation here should look familiar; the first line ends with a colon, and the lines inside the \py{for} loop are indented.  The other elements of the loop are:
-\index{range}
-
-\begin{itemize}
-
-\item The words \py{for} and \py{in} are special words we have to use in a for loop.  
-
-\item \py{range} is a Python function we're using here to control the number of times the loop runs.
-\index{range}
-
-\item \py{i} is a {\bf loop variable} that gets created when the for loop runs. 
-\index{loop variable}
-
-\end{itemize}
-
-In this example we don't actually use \py{i}; we will see examples later where we use the loop variable inside the loop.
-
-When this loop runs, it runs the statements inside the loop four times, 
-which moves one bike at a time from Olin to Wellesley.
-
-
-\section{TimeSeries}
-\label{timeseries}
-
-When we run a simulation, we usually want to save the results for later analysis.  The ModSim library provides a \py{TimeSeries} object for this purpose.  A \py{TimeSeries} contains a sequence of time stamps and a corresponding sequence of values.  In this example, the time stamps are integers representing minutes, and the values are the number of bikes at one location.
-
-%TODO: index modsim library functions
-\index{ModSim library}
-\index{TimeSeries}
-
-We can create a new, empty \py{TimeSeries} like this:
-
-\begin{python}
-results = TimeSeries()
-\end{python}
-
-And we can add a value to a \py{TimeSeries} like this:
-
-\begin{python}
-results[0] = bikeshare.olin
-\end{python}
-
-The number in brackets is the time stamp, also called a {\bf label}.
-\index{label}
-
-We can use a \py{TimeSeries} inside a for loop to store the results of the simulation:
-
-\begin{python}
-for i in range(10):
-    step(0.3, 0.2)
-    results[i] = bikeshare.olin
-\end{python}
-
-Each time through the loop, we call \py{step}, which updates \py{bikeshare}.  Then we store the number of bikes at Olin in \py{results}.  We use the loop variable, \py{i}, as the time stamp.
-
-\index{loop}
-\index{loop variable}
-\index{time stamp}
-
-When the loop exits, \py{results} contains 10 time stamps, from 0 through 9, and the number of bikes at Olin at the end of each time step.
-\index{loop variable}
-
-\py{TimeSeries} is a specialized version of \py{Series}, which is defined by Pandas, one of the libraries we'll be using extensively.  The \py{Series} object provides many functions; one example is \py{mean}, which we can call like this:
-
-\begin{python}
-results.mean()
-\end{python}
-
-You can read the documentation of \py{Series} at \url{http://modsimpy.com/series}.
-
-\index{Pandas}
-\index{Series}
-\index{TimeSeries}
-\index{mean}
-
-
-\section{Plotting}
-\label{plotting}
-
-The ModSim library provides a function called \py{plot} we can use to plot \py{results}:
-
-\begin{python}
-plot(results)
-\end{python}
-
-\py{plot} can take an additional argument that gives the line a label; this label will appear in the legend of the plot, if we create one.
-
-\begin{python}
-plot(results, label='Olin')
-\end{python}
-
-\py{label} is an example of a {\bf keyword argument}, so called because we provide a ``keyword'', which is \py{label} in this case, along with its value.  Arguments without keywords are called {\bf positional arguments} because they are assigned to parameters according to their position.  It is good to know these terms because they appear in Python error messages.
-
-\index{keyword argument}
-\index{positional argument}
-\index{argument}
-
-Whenever you make a figure, you should label the axes.  The ModSim library provides \py{decorate}, which labels the axes and gives the figure a title and legend:
-
-\begin{python}
-decorate(title='Olin-Wellesley Bikeshare',
-         xlabel='Time step (min)', 
-         ylabel='Number of bikes')
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap02-fig01.pdf}}
-\caption{Simulation of a bikeshare system showing number of bikes at Olin over time.}
-\label{chap02-fig01}
-\end{figure}
-
-Figure~\ref{chap02-fig01} shows the result.
-
-\py{plot} and \py{decorate} are based on Pyplot, which is a Python library for generating figures.  You can read more about \py{plot} and the arguments it takes at \url{http://modsimpy.com/plot}.
-
-\index{Pyplot}
-\index{plot}
-\index{decorate}
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap02.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-
-\chapter{Iterative modeling}
-\label{chap03}
-
-To paraphrase two Georges, ``All models are wrong, but some models are more wrong than others."  In this chapter, I demonstrate the process we use to make models less wrong.
-
-\index{Box, George}
-\index{Orwell, George}
-
-As an example, we'll review the bikeshare model from the previous chapter, consider its strengths and weaknesses, and gradually improve it.  We'll also see ways to use the model to understand the behavior of the system and evaluate designs intended to make it work better.
-
-\index{bikeshare}
-
-
-\section{Iterative modeling}
-
-The model we have so far is simple, but it is based on unrealistic assumptions.  Before you go on, take a minute to review the model from the previous chapters.  What assumptions is it based on?  Make a list of ways this model might be unrealistic; that is, what are the differences between the model and the real world?
-
-Here are some of the differences on my list:
-
-\begin{itemize}
-
-\item In the model, a student is equally likely to arrive during any one-minute period.  In reality, this probability varies depending on time of day, day of the week, etc.
-
-\index{probability}
-
-\item The model does not account for travel time from one bike station to another.
-
-\item The model does not check whether a bike is available, so it's possible for the number of bikes to be negative (as you might have noticed in some of your simulations).
-
-\end{itemize}
-
-Some of these modeling decisions are better than others.  For example, the first assumption might be reasonable if we simulate the system for a short period of time, like one hour.
-
-The second assumption is not very realistic, but it might not affect the results very much, depending on what we use the model for.
-
-\index{realism}
-
-On the other hand, the third assumption seems problematic, and it is relatively easy to fix.  In Section~\ref{negativebikes}, we will.
-
-This process, starting with a simple model, identifying the most important problems, and making gradual improvements, is called {\bf iterative modeling}.
-
-\index{iterative modeling}
-
-For any physical system, there are many possible models, based on different assumptions and simplifications.  It often takes several iterations to develop a model that is good enough for the intended purpose, but no more complicated than necessary.
-
-
-\section{More than one State object}
-
-Before we go on, I want to make a few changes to the code from the previous chapter.  First I'll generalize the functions we wrote so they take a \py{State} object as a parameter.  Then, I'll make the code more readable by adding documentation.
-
-\index{parameter}
-
-Here is one of the functions from the previous chapter, \py{bike_to_wellesley}:
-
-\begin{python}
-def bike_to_wellesley():
-    bikeshare.olin -= 1
-    bikeshare.wellesley += 1
-\end{python}
-
-When this function is called, it modifies \py{bikeshare}.  As long as there is only one \py{State} object, that's fine, but what if there is more than one bike share system in the world?  Or what if we want to run more than one simulation?
-
-This function would be more flexible if it took a \py{State} object as a parameter.  Here's what that looks like:
-
-\index{State object}
-
-\begin{python}
-def bike_to_wellesley(state):
-    state.olin -= 1
-    state.wellesley += 1
-\end{python}
-
-The name of the parameter is \py{state} rather than \py{bikeshare} as a reminder that the value of \py{state} could be any \py{State} object, not just \py{bikeshare}.
-
-This version of \py{bike_to_wellesley} requires a \py{State} object as a parameter, so we have to provide one when we call it:
-
-\begin{python}
-bike_to_wellesley(bikeshare)
-\end{python}
-
-Again, the argument we provide gets assigned to the parameter, so this function call has the same effect as:
-
-\begin{code}
-state = bikeshare
-state.olin -= 1
-state.wellesley += 1
-\end{code}
-
-Now we can create as many \py{State} objects as we want:
-
-\begin{python}
-bikeshare1 = State(olin=10, wellesley=2)
-bikeshare2 = State(olin=2, wellesley=10)
-\end{python}
-
-And update them independently:
-
-\begin{python}
-bike_to_wellesley(bikeshare1)
-bike_to_wellesley(bikeshare2)
-\end{python}
-
-Changes in \py{bikeshare1} do not affect \py{bikeshare2}, and vice versa.  So we can simulate different bike share systems, or run multiple simulations of the same system.
-
-
-\section{Documentation}
-\label{documentation}
-
-Another problem with the code we have so far is that it contains no {\bf documentation}.  Documentation is text we add to a program to help other programmers read and understand it.  It has no effect on the program when it runs.
-
-\index{documentation}
-\index{docstring}
-\index{comment}
-
-There are two forms of documentation, {\bf docstrings} and {\bf comments}.   
- A docstring is a string in triple-quotes that appears at the beginning of a function, like this:
-
-\begin{python}
-def run_simulation(state, p1, p2, num_steps):
-    """Simulate the given number of time steps.
-    
-    state: State object
-    p1: probability of an Olin->Wellesley customer arrival
-    p2: probability of a Wellesley->Olin customer arrival
-    num_steps: number of time steps
-    """
-    results = TimeSeries()    
-    for i in range(num_steps):
-        step(state, p1, p2)
-        results[i] = state.olin
-        
-    plot(results, label='Olin')
-\end{python}
-
-Docstrings follow a conventional format:
-
-\begin{itemize}
-
-\item The first line is a single sentence that describes what the function does.
-
-\item The following lines explain what each of the parameters are.
-
-\end{itemize}
-
-A function's docstring should include the information someone needs to know to {\em use} the function; it should not include details about how the function works.  That's what comments are for.
-
-A comment is a line of text that begins with a hash symbol, \py{#}.  It usually appears inside a function to explain something that would not be obvious to someone reading the program.
-
-\index{comment}
-\index{hash symbol}
-
-For example, here is a version of \py{bike_to_olin} with a docstring and a comment.
-
-\begin{python}
-def bike_to_olin(state):
-	"""Move one bike from Wellesley to Olin.
-	
-	state: State object	
-	"""
-	# We decrease one state variable and increase the
-	# other, so the total number of bikes is unchanged.
-    state.wellesley -= 1
-    state.olin += 1
-\end{python}
-
-At this point we have more documentation than code, which is not unusual for short functions.
-
-
-\section{Negative bikes}
-\label{negativebikes}
-
-The changes we've made so far improve the quality of the code, but we haven't done anything to improve the quality of the model yet.   Let's do that now.
-
-\index{code quality}
-
-Currently the simulation does not check whether a bike is available when a customer arrives, so the number of bikes at a location can be negative.  That's not very realistic.  Here's an updated version of \py{bike_to_olin} that fixes the problem:
-
-\begin{python}
-def bike_to_olin(state):
-    if state.wellesley == 0:
-        return
-    state.wellesley -= 1
-    state.olin += 1
-\end{python}
-
-The first line checks whether the number of bikes at Wellesley is zero.  If so, it uses a {\bf return statement}, which causes the function to end immediately, without running the rest of the statements.  So if there are no bikes at Wellesley, we ``return" from \py{bike_to_olin} without changing the state.
-
-\index{return statement}
-\index{statement!return}
-
-We can update \py{bike_to_wellesley} the same way.
-
-
-\section{Comparison operators}
-
-The version of \py{bike_to_olin} in the previous section uses the equals operator, \py{==}, which compares two values and returns \py{True} if they are equal and \py{False} otherwise.
-
-It is easy to confuse the equals operators with the assignment operator, \py{=}, which assigns a value to a variable.  For example, the following statement creates a variable, \py{x}, if it doesn't already exist, and gives it the value \py{5}.
-
-\index{equality}
-\index{assignment operator}
-\index{operator!assignment}
-
-\begin{python}
-x = 5
-\end{python}
-
-On the other hand, the following statement checks whether \py{x} is \py{5} and returns \py{True} or \py{False}.  It does not create \py{x} or change its value.  
-
-\begin{python}
-x == 5
-\end{python}
-
-You can use the equals operator in an \py{if} statement, like this:
-
-\index{if statement}
-\index{statement!if}
-
-\begin{python}
-if x == 5:
-    print('yes, x is 5')
-\end{python}
-
-If you make a mistake and use \py{=} in an \py{if} statement, like this:
-
-\begin{python}
-if x = 5:
-    print('yes, x is 5')
-\end{python}
-
-That's a {\bf syntax error}, which means that the structure of the program is invalid.  Python will print an error message and the program won't run.
-
-\index{syntax error}
-\index{error!syntax}
-
-The equals operator is one of the {\bf comparison operators}. The others are:
-
-\index{comparison operator}
-\index{operator!comparison}
-
-\begin{tabular}{l|c}
-{\bf Operation} & {\bf Symbol} \\ 
-\hline 
-Less than & \py{<} \\ 
-Greater than & \py{>} \\
-Less than or equal & \py{<=}  \\ 
-Greater than or equal & \py{>=} \\ 
-Equal & \py{==} \\ 
-Not equal & \py{!=} \\ 
-\end{tabular} 
-
-
-\section{Metrics}
-\label{metrics}
-
-Getting back to the bike share system, at this point we have the ability to simulate the behavior of the system.  Since the arrival of customers is random, the state of the system is different each time we run a simulation.  Models like this are called random or {\bf stochastic}; models that do the same thing every time they run are {\bf deterministic}.
-
-\index{stochastic}
-\index{deterministic}
-
-Suppose we want to use our model to predict how well the bike share system will work, or to design a system that works better.  First, we have to decide what we mean by ``how well" and ``better".
-
-From the customer's point of view, we might like to know the probability of finding an available bike.  From the system-owner's point of view, we might want to minimize the number of customers who don't get a bike when they want one, or maximize the number of bikes in use.  Statistics like these that quantify how well the system works are called {\bf metrics}.
-
-\index{metric}
-
-As a simple example, let's measure the number of unhappy customers.  Here's a version of \py{bike_to_olin} that keeps track of the number of customers who arrive at a station with no bikes:
-
-\begin{python}
-def bike_to_olin(state):
-    if state.wellesley == 0:
-        state.wellesley_empty += 1
-        return
-    state.wellesley -= 1
-    state.olin += 1
-\end{python}
-
-If a customer arrives at the Wellesley station and finds no bike available, \py{bike_to_olin} updates \py{wellesley_empty} which counts the number of unhappy customers.
-
-This function only works if we initialize \py{wellesley_empty} when we create the \py{State} object, like this:
-
-\begin{python}
-bikeshare = State(olin=10, wellesley=2, 
-                  olin_empty=0, wellesley_empty=0)
-\end{python}
-
-Assuming we update \py{move_to_wellesley} the same way, we can run the simulation like this (see Section~\ref{documentation}):
-
-\begin{python}
-run_simulation(bikeshare, 0.4, 0.2, 60)
-\end{python}
-
-Then we can check the metrics:
-
-\begin{python}
-print(bikeshare.olin_empty, bikeshare.wellesley_empty)
-\end{python}
-
-Because the simulation is stochastic, the results are different each time it runs.
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap03.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-\chapter{Sweeping parameters}
-\label{chap04}
-
-In the previous chapter we defined metrics that quantify the performance of bike sharing this system.  In this chapter we see how those metrics depend on the parameters of the system, like the arrival rate of customers at bike stations.
-
-We also discuss a program development strategy, called incremental development, that might help you write programs faster and spend less time debugging.
-
-
-\section{Functions that return values}
-
-We have seen several functions that return values; for example, when you run \py{sqrt}, it returns a number you can assign to a variable.
-
-\index{return value}
-
-\begin{python}
-t = sqrt(2 * h / a)
-\end{python}
-
-When you run \py{State}, it returns a new \py{State} object:
- 
-\begin{python}
-bikeshare = State(olin=10, wellesley=2)
-\end{python}
-
-Not all functions have return values.  For example, when you run \py{step}, it updates a \py{State} object, but it doesn't return a value.
-
-To write functions that return values, we can use a second form of the \py{return} statement, like this:
-
-\index{return statement}
-\index{statement!return}
-
-\begin{python}
-def add_five(x):
-    return x + 5
-\end{python}
-
-\py{add_five} takes a parameter, \py{x}, which could be any number.  It computes \py{x + 5} and returns the result.  So if we run it like this, the result is \py{8}:
-
-\begin{python}
-add_five(3)
-\end{python}
-
-As a more useful example, here's a version of \py{run_simulation} that creates a \py{State} object, runs a simulation, and then returns the \py{State} object as a result:
-
-\begin{python}
-def run_simulation():
-    p1 = 0.4
-    p2 = 0.2
-    num_steps = 60
-    
-    state = State(olin=10, wellesley=2, 
-                    olin_empty=0, wellesley_empty=0)
-                    
-    for i in range(num_steps):
-        step(state, p1, p2)
-        
-    return state
-\end{python}
-
-If we call \py{run_simulation} like this:
-
-\begin{python}
-state = run_simulation()
-\end{python}
-
-It assigns the \py{State} object from \py{run_simulation} to \py{state}, which contains the metrics we are interested in:
-
-\begin{python}
-print(state.olin_empty, state.wellesley_empty)
-\end{python}
-
-
-\section{Two kinds of parameters}
-
-This version of \py{run_simulation} always starts with the same initial condition, 10 bikes at Olin and 2 bikes at Wellesley, and the same values of \py{p1}, \py{p2}, and \py{num_steps}.  Taken together, these five values are the {\bf parameters of the model}, which are values that determine the behavior of the system.
-
-\index{parameter!of a model}
-\index{parameter!of a function}
-
-It is easy to get the parameters of a model confused with the parameters of a function.  They are closely related ideas; in fact, it is common for the parameters of the model to appear as parameters in functions.  For example, we can write a more general version of \py{run_simulation} that takes \py{p1} and \py{p2} as function parameters:
-
-\begin{python}
-def run_simulation(p1, p2, num_steps):
-    state = State(olin=10, wellesley=2, 
-                    olin_empty=0, wellesley_empty=0)
-                    
-    for i in range(num_steps):
-        step(state, p1, p2)
-        
-    return state
-\end{python}
-
-Now we can run it with different arrival rates, like this:
-
-\begin{python}
-state = run_simulation(0.6, 0.3, 60)
-\end{python}
-
-In this example, \py{0.6} gets assigned to \py{p1}, \py{0.3} gets assigned to \py{p2}, and \py{60} gets assigned to \py{num_steps}.
-
-Now we can call \py{run_simulation} with different parameters and see how the metrics, like the number of unhappy customers, depend on the parameters.  But before we do that, we need a new version of a for loop.
-
-\index{metric}
-
-
-\section{Loops and arrays}
-\label{array}
-
-In Section~\ref{forloop}, we saw a loop like this:
-
-\begin{python}
-for i in range(4):
-    bike_to_wellesley()
-\end{python}
-
-\py{range(4)} creates a sequence of numbers from 0 to 3.  Each time through the loop, the next number in the sequence gets assigned to the loop variable, \py{i}.
-
-\index{loop}
-\index{loop variable}
-\index{variable!loop}
-
-\py{range} only works with integers; to get a sequence of non-integer values, we can use \py{linspace}, which is defined in the ModSim library:
-
-\begin{python}
-p1_array = linspace(0, 1, 5)
-\end{python}
-
-The arguments indicate where the sequence should start and stop, and how many elements it should contain.  In this example, the sequence contains \py{5} equally-spaced numbers, starting at \py{0} and ending at \py{1}. 
-
-\index{linspace}
-\index{NumPy}
-\index{array}
-
-The result is a NumPy {\bf array}, which is a new kind of object we have not seen before.  An array is a container for a sequence of numbers.
-
-We can use an array in a \py{for} loop like this:
-
-\begin{python}
-for p1 in p1_array:
-    print(p1)
-\end{python}
-
-When this loop runs, it
-
-\begin{enumerate}
-
-\item Gets the first value from the array and assigns it to \py{p1}.
-
-\item Runs the body of the loop, which prints \py{p1}.
-
-\item Gets the next value from the array and assigns it to \py{p1}.
-
-\item Runs the body of the loop, which prints \py{p1}.
-
-\end{enumerate}
-
-And so on, until it gets to the end of the array.  The result is:
-
-\begin{result}
-0.0
-0.25
-0.5
-0.75
-1.0
-\end{result}
-
-This will come in handy in the next section.
-
-
-\section{Sweeping parameters}
-
-If we know the actual values of parameters like \py{p1} and \py{p2}, we can use them to make specific predictions, like how many bikes will be at Olin after one hour.
-
-\index{prediction}
-\index{explanation}
-
-But prediction is not the only goal; models like this are also used to explain why systems behave as they do and to evaluate alternative designs.  For example, if we observe the system and notice that we often run out of bikes at a particular time, we could use the model to figure out why that happens.  And if we are considering adding more bikes, or another station, we could evaluate the effect of various ``what if" scenarios.
-\index{what if scenario}
-
-As an example, suppose we have enough data to estimate that \py{p2} is about \py{0.2}, but we don't have any information about \py{p1}.  We could run simulations with a range of values for \py{p1} and see how the results vary.  This process is called {\bf sweeping} a parameter, in the sense that the value of the parameter ``sweeps" through a range of possible values.
-
-\index{sweep}
-\index{parameter sweep}
-
-Now that we know about loops and arrays, we can use them like this:
-
-\begin{python}
-p1_array = linspace(0, 1, 11)
-p2 = 0.2
-num_steps = 60
-
-for p1 in p1_array:
-    state = run_simulation(p1, p2, num_steps)
-    print(p1, state.olin_empty)
-\end{python}
-
-Each time through the loop, we run a simulation with a different value of \py{p1} and the same value of \py{p2}, \py{0.2}.  Then we print \py{p1} and the number of unhappy customers at Olin.
-
-To save and plot the results, we can use a \py{SweepSeries} object, which is similar to a \py{TimeSeries}; the difference is that the labels in a \py{SweepSeries} are parameter values rather than time values. 
-
-We can create an empty \py{SweepSeries} like this:
-
-\begin{code}
-sweep = SweepSeries()
-\end{code}
-
-And add values like this:
-
-\begin{python}
-for p1 in p1_array:
-    state = run_simulation(p1, p2, num_steps)
-    sweep[p1] = state.olin_empty
-\end{python}
-
-The result is a \py{SweepSeries} that maps from each value of \py{p1} to the resulting number of unhappy customers.  Then we can plot the results:
-
-\begin{code}
-plot(sweep, label='Olin')
-\end{code}
-
-
-
-
-\section{Incremental development}
-
-When you start writing programs that are more than a few lines, you
-might find yourself spending more and more time debugging.  The more
-code you write before you start debugging, the harder it is to find
-the problem.
-
-\index{debugging}
-\index{incremental development}
-
-{\bf Incremental development} is a way of programming that tries
-to minimize the pain of debugging.  The fundamental steps are:
-
-\begin{enumerate}
-
-\item Always start with a working program.  If you have an
-example from a book, or a program you wrote that is similar to
-what you are working on, start with that.  Otherwise, start with
-something you {\em know} is correct, like {\tt x=5}.  Run the program
-and confirm that it does what you expect.
-
-\item Make one small, testable change at a time.  A ``testable''
-change is one that displays something or has some
-other effect you can check.  Ideally, you should know what
-the correct answer is, or be able to check it by performing another
-computation. 
-
-\index{testable change}
-
-\item Run the program and see if the change worked.  If so, go back
-to Step 2.  If not, you will have to do some debugging, but if the
-change you made was small, it shouldn't take long to find the problem.
-
-\end{enumerate}
-
-When this process works, your changes usually work the first time, or if they don't, the problem is obvious.  In practice, there are two problems with incremental development:
-
-\begin{itemize}
-
-\item Sometimes you have to write extra code to generate visible output that you can check.  This extra code is called {\bf scaffolding} because you use it to build the program and then remove it when you are done.  That might seem like a waste, but time you spend on scaffolding is almost always time you save on debugging.
-
-\index{scaffolding}
-
-\item When you are getting started, it might not be obvious how to
-choose the steps that get from {\tt x=5} to the program you are trying
-to write.  You will see more examples of this process as we go along, and you will get better with experience.
-
-\end{itemize}
-
-If you find yourself writing more than a few lines of code before you start testing, and you are spending a lot of time debugging, try incremental development.
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap04.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-%\part{Modeling population growth}
-
-\chapter{World population}
-\label{chap05}
-
-In 1968 Paul Erlich published {\it The Population Bomb}, in which he predicted that world population would grow quickly during the 1970s, that agricultural production could not keep up, and that mass starvation in the next two decades was inevitable (see \url{http://modsimpy.com/popbomb}).  As someone who grew up during those decades, I am happy to report that those predictions were wrong.
-  
-\index{Erlich, Paul}
-\index{Population Bomb}
-
-But world population growth is still a topic of concern, and it is an open question how many people the earth can sustain while maintaining and improving our quality of life.
-
-\index{world population}
-\index{population}
-
-In this chapter and the next, we use tools from the previous chapters to explain world population growth since 1950 and generate predictions for the next 50--100 years.
-
-\index{prediction}
-
-For background on world population growth, watch this video from the American Museum of Natural History \url{http://modsimpy.com/human}.
-
-\index{American Museum of Natural History}
-
-
-\section{World Population Data}
-\label{worldpopdata}
-
-The Wikipedia article on world population contains tables with estimates of world population from prehistory to the present, and projections for the future (\url{http://modsimpy.com/worldpop}).
-
-\index{Wikipedia}
-\index{Pandas}
-
-To read this data, we will use Pandas, which provides functions for working with data.  The function we'll use is \py{read_html}, which can read a web page and extract data from any tables it contains.  Before we can use it, we have to import it.  You have already seen this import statement:
-
-\index{\py{read_html}}
-\index{import statement}
-\index{statement!import}
-
-\begin{python}
-from modsim import *
-\end{python}
-
-which imports all functions from the ModSim library.  To import \py{read_html}, the statement we need is:
-
-\begin{python}
-from pandas import read_html
-\end{python}
-
-Now we can use it like this:
-
-\begin{python}
-filename = 'data/World_population_estimates.html'
-tables = read_html(filename,
-                   header=0, 
-                   index_col=0,
-                   decimal='M')
-\end{python}
-
-The arguments are:
-\index{argument}
-
-\begin{itemize}
-
-\item \py{filename}: The name of the file (including the directory it's in) as a string.  This argument can also be a URL starting with \py{http}.
-
-\item \py{header}: Indicates which row of each table should be considered the header, that is, the set of labels that identify the columns.  In this case it is the first row (numbered 0).
-
-\item \py{index_col}: Indicates which column of each table should be considered the {\bf index}, that is, the set of labels that identify the rows.  In this case it is the first column, which contains the years.
-
-\item \py{decimal}: Normally this argument is used to indicate which character should be considered a decimal point, because some conventions use a period and some use a comma.  In this case I am abusing the feature by treating \py{M} as a decimal point, which allows some of the estimates, which are expressed in millions, to be read as numbers.
-
-\end{itemize}
-
-The result, which is assigned to \py{tables}, is a sequence that contains one \py{DataFrame} for each table.  A \py{DataFrame} is an object, defined by Pandas, that represents tabular data.
-
-\index{DataFrame}
-\index{sequence}
-
-To select a \py{DataFrame} from \py{tables}, we can use the bracket operator like this:
-
-\begin{python}
-table2 = tables[2]
-\end{python}
-
-This line selects the third table (numbered 2), which contains population estimates from 1950 to 2016.
-
-\index{bracket operator}
-\index{operator!bracket}
-
-We can display the first few lines like this:
-
-\begin{python}
-table2.head()
-\end{python}
-
-The column labels are long strings, which makes them hard to work with.  We can replace them with shorter strings like this:
-
-\index{string}
-\index{columns}
-
-\begin{python}
-table2.columns = ['census', 'prb', 'un', 'maddison', 
-                  'hyde', 'tanton', 'biraben', 'mj', 
-                  'thomlinson', 'durand', 'clark']
-\end{python}
-
-Now we can select a column from the \py{DataFrame} using the dot operator, like selecting a state variable from a \py{State} object:
-
-\index{dot operator}
-\index{operator!dot}
-
-\begin{python}
-census = table2.census / 1e9
-un = table2.un / 1e9
-\end{python}
-
-These lines select the estimates generated by the United Nations Department of Economic and Social Affairs (UN DESA) and the United States Census Bureau.
-
-\index{United Nations}
-\index{United States Census Bureau}
-
-Each result is a Pandas \py{Series}, which is like a \py{DataFrame} with just one column.
-
-\index{Series}
-
-The number \py{1e9} is a shorter, less error-prone way to write \py{1000000000} or one billion.  When we divide a \py{Series} by a number, it divides all of the elements of the \py{Series}.  From here on, we'll express population estimates in terms of billions.
-
-
-\section{Plotting}
-
-Now we can plot the estimates like this:
-
-\index{plot}
-
-\begin{python}
-plot(census, ':', label='US Census')
-plot(un, '--', label='UN DESA')
-\end{python}
-
-
-The next two lines plot the \py{Series} objects.  The {\bf format strings} \py{':'} and \py{'--'} indicate dotted and dashed lines.  For more about format strings in Pyplot, see \url{http://modsimpy.com/plot}.
-
-\index{format string}
-\index{Pyplot}
-
-The \py{label} argument provides the string that appears in the legend.
-
-\index{label}
-\index{legend}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap05-fig01.pdf}}
-\caption{Estimates of world population, 1950--2016.}
-\label{chap05-fig01}
-\end{figure}
-
-Figure~\ref{chap05-fig01} shows the result.  The lines overlap almost completely; for most dates the difference between the two estimates is less than 1\%.
-
-
-\section{Constant growth model}
-
-Suppose we want to predict world population growth over the next 50 or 100 years.  We can do that by developing a model that describes how populations grow, fitting the model to the data we have so far, and then using the model to generate predictions.
-
-\index{constant growth}
-
-In the next few sections I demonstrate this process starting with simple models and gradually improving them.
-
-\index{iterative modeling}
-
-Although there is some curvature in the plotted estimates, it looks like world population growth has been close to linear since 1960 or so.  So we'll start with a model that has constant growth.
-
-To fit the model to the data, we'll compute the average annual growth from 1950 to 2016.  Since the UN and Census data are so close, we'll use the Census data.
-
-We can select a value from a \py{Series} using the bracket operator:
-\index{bracket operator}
-\index{operator!bracket}
-
-\begin{python}
-census[1950]
-\end{python}
-
-So we can get the total growth during the interval like this:
-
-\begin{python}
-total_growth = census[2016] - census[1950]
-\end{python}
-
-The numbers in brackets are called {\bf labels}, because they label the rows of the \py{Series} (not to be confused with the labels we saw in the previous section, which label lines in a graph).
-
-\index{label}
-
-In this example, the labels 2016 and 1950 are part of the data, so it would be better not to make them part of the program.  Putting values like these in the program is called {\bf hard coding}; it is considered bad practice because if the data change in the future, we have to modify the program (see \url{http://modsimpy.com/hardcode}).
-
-\index{hard coding}
-
-It would be better to get the first and last labels from the \py{Series} like this:
-
-\begin{python}
-t_0 = get_first_label(census)
-t_end = get_last_label(census)
-elapsed_time = t_end - t_0
-\end{python}
-
-\py{get_first_label} and \py{get_last_label} are defined in \py{modsim.py}; as you might have guessed, they select the first and last labels from \py{census}.
-The difference between them is the elapsed time.
-
-The ModSim library also defines \py{get_first_value} and \py{get_last_value}, which we can use to compute \py{total_growth}:
-
-\begin{python}
-p_0 = get_first_value(census)
-p_end = get_last_value(census)
-total_growth = p_end - p_0
-\end{python}
-
-Finally, we can compute average annual growth.
-
-\begin{python}
-annual_growth = total_growth / elapsed_time
-\end{python}
-
-The next step is to use this estimate to simulate population growth since 1950.
-
-
-\section{Simulation}
-
-Our simulation will start with the observed population in 1950, \py{p_0}, and add \py{annual_growth} each year.  To store the results, we'll use a \py{TimeSeries} object:
-
-\index{TimeSeries}
-
-\begin{python}
-results = TimeSeries()
-\end{python}
-
-We can set the first value in the new \py{TimeSeries} by copying the first value from \py{census}:
-
-\begin{python}
-results[t_0] = census[p_0]
-\end{python}
-
-Then we set the rest of the values by simulating annual growth:
-
-\begin{python}
-for t in linrange(t_0, t_end):
-    results[t+1] = results[t] + annual_growth
-\end{python}
-
-\py{linrange} is defined in the ModSim library.  In this example it returns a NumPy array of integers from \py{t_0} to \py{t_end}, including the first but not the last.
-
-\index{linrange}
-\index{NumPy}
-\index{array}
-
-Each time through the loop, the loop variable \py{t} gets the next value from the array.  Inside the loop, we compute the population for each year by adding the population for the previous year and \py{annual_growth}.  The last time through the loop, the value of \py{t} is 2015, so the last label in \py{results} is 2016, which is what we want.
-
-\index{loop}
-\index{loop variable}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap05-fig02.pdf}}
-\caption{Estimates of world population, 1950--2016, and a constant growth model.}
-\label{chap05-fig02}
-\end{figure}
-
-Figure~\ref{chap05-fig02} shows the result. The model does not fit the data particularly well from 1950 to 1990, but after that, it's pretty good.  Nevertheless, there are problems:
-
-\begin{itemize}
-
-\item There is no obvious mechanism that could cause population growth to be constant from year to year.  Changes in population are determined by the fraction of people who die and the fraction of people who give birth, so we expect them to depend on the current population.
-
-\item According to this model, we would expect the population to keep growing at the same rate forever, and that does not seem reasonable.
-
-\end{itemize}
-
-We'll try out some different models in the next few sections, but first let's clean up the code.
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap05.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-\chapter{Modeling growth}
-\label{chap06}
-In the previous chapter we simulated a model of world population with constant growth.  In this chapter we see if we can make a better model with growth proportional to the population.
-
-But first, we can improve the code from the previous chapter by encapsulating it in a function and using \py{System} objects.
-
-\section{System objects}
-\label{nowwithsystem}
-
-Like a \py{State} object, a \py{System} object contains variables and their values.  The difference is:
-
-\begin{itemize}
-
-\item \py{State} objects contain state variables, which represent the state of the system, which get updated in the course of a simulation.
-
-\item \py{System} objects contain {\bf system variables}, which represent parameters of the system, which usually don't get updated over the course of a simulation.
-
-\end{itemize}  
-
-For example, in the bike share model, state variables include the number of bikes at each location, which get updated whenever a customer moves a bike.  System variables include the number of locations, total number of bikes, and arrival rates at each location.
-
-In the population model, the only state variable is the population.  System variables include the annual growth rate, the initial time and population, and the end time.
-
-Suppose we have the following variables, as computed in the previous chapter (assuming that \py{census} is a \py{Series} object):
-
-\begin{python}
-t_0 = get_first_label(census)
-t_end = get_last_label(census)
-elapsed_time = t_end - t_0
-
-p_0 = get_first_value(census)
-p_end = get_last_value(census)
-total_growth = p_end - p_0
-
-annual_growth = total_growth / elapsed_time
-\end{python}
-
-Some of these are parameters we need to simulate the system; others are temporary values we can discard.  We can put the parameters we need into a \py{System} object like this:
-
-\index{System object}
-
-\begin{python}
-system = System(t_0=t_0, 
-                t_end=t_end,
-                p_0=p_0,
-                annual_growth=annual_growth)
-\end{python}
-
-\py{t0} and \py{t_end} are the first and last years; \py{p_0} is the initial population, and \py{annual_growth} is the estimated annual growth.
-
-Next we'll wrap the code from the previous chapter in a function:
-
-\begin{python}
-def run_simulation1(system):
-    results = TimeSeries()
-    results[system.t_0] = system.p_0
-    
-    for t in linrange(system.t_0, system.t_end):
-        results[t+1] = results[t] + system.annual_growth
-    
-    return results
-\end{python}
-
-When \py{run_simulation1} runs, it stores the results in a \py{TimeSeries} and returns it.
- 
-\index{TimeSeries object}
-
-The following function plots the results along with the estimates \py{census} and \py{un}:
-
-\begin{python}
-def plot_results(census, un, timeseries, title):
-    plot(census, ':', label='US Census')
-    plot(un, '--', label='UN DESA')
-    plot(timeseries, color='gray', label='model')
-    
-    decorate(xlabel='Year', 
-             ylabel='World population (billion)',
-             title=title)
-\end{python}
-
-\index{plot}
-\index{decorate}
-
-The \py{color} argument specifies the color of the line.  For details on color specification in Pyplot, see \url{http://modsimpy.com/color}.
-
-\index{Pyplot}
-\index{color}
-
-Finally, we can run the simulation like this.
-
-\begin{python}
-results = run_simulation1(system)
-plot_results(census, un, results, 'Constant growth model')
-\end{python}
-
-The results are the same as Figure~\ref{chap05-fig02}.
-
-It might not be obvious that using functions and \py{System} objects is a big improvement, and for a simple model that we run only once, maybe it's not.  But as we work with more complex models, and when we run many simulations with different parameters, we'll see that the organization of the code makes a big difference.
-
-Now let's see if we can improve the model.
-
-
-\section{Proportional growth model}
-
-The biggest problem with the constant growth model is that it doesn't make any sense.  It is hard to imagine how people all over the world could conspire to keep population growth constant from year to year.
-
-\index{proportional growth}
-
-On the other hand, if some fraction of the population dies each year, and some fraction gives birth, we can compute the net change in the population like this:
-
-\begin{python}
-def run_simulation2(system):
-    results = TimeSeries()
-    results[system.t_0] = system.p_0
-    
-    for t in linrange(system.t_0, system.t_end):
-        births = system.birth_rate * results[t]
-        deaths = system.death_rate * results[t]
-        results[t+1] = results[t] + births - deaths
-        
-    return results
-\end{python}
-
-Now we can choose the values of \py{birth_rate} and \py{death_rate} that best fit the data.  Without trying too hard, I chose:
-
-\begin{python}
-system.death_rate = 0.01
-system.birth_rate = 0.027
-\end{python}
-
-Then I ran the simulation and plotted the results:
-
-\begin{python}
-results = run_simulation2(system)
-plot_results(census, un, results, 'Proportional model')
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap06-fig01.pdf}}
-\caption{Estimates of world population, 1950--2016, and a proportional model.}
-\label{chap06-fig01}
-\end{figure}
-
-Figure~\ref{chap06-fig01} shows the results.  The proportional model fits the data well from 1950 to 1965, but not so well after that.  Overall, the {\bf quality of fit} is not as good as the constant growth model, which is surprising, because it seems like the proportional model is more realistic.
-
-In the next chapter we'll try one more time to find a model that makes sense and fits the data.  But first, I want to make a few more improvements to the code.
-
-
-\section{Factoring out the update function}
-
-\py{run_simulation1} and \py{run_simulation2} are nearly identical except for the body of the \py{for} loop, where we compute the population for the next year.
-
-\index{update function}
-\index{function!update}
-
-Rather than repeat identical code, we can separate the things that change from the things that don't.  First, I'll pull out the update code from \py{run_simulation2} and make it a function:
-
-\begin{python}
-def update_func1(pop, t, system):
-    births = system.birth_rate * pop
-    deaths = system.death_rate * pop
-    return pop + births - deaths
-\end{python}
-
-This function takes as arguments the current population, current year, and a \py{System} object; it returns the computed population for the next year.
-
-This update function does not use \py{t}, so we could leave it out.  But we will see other functions that need it, and it is convenient if they all take the same parameters, used or not.
-
-Now we can write a function that runs any model:
-
-\begin{python}
-def run_simulation(system, update_func):
-    results = TimeSeries()
-    results[system.t_0] = system.p_0
-    
-    for t in linrange(system.t_0, system.t_end):
-        results[t+1] = update_func(results[t], t, system)
-        
-    return results
-\end{python}
-
-This function demonstrates a feature we have not seen before: it takes a function as a parameter!  When we call \py{run_simulation}, the second parameter is a function, like \py{update_func1}, that computes the population for the next year.
-
-\index{function!as parameter}
-
-Here's how we call it:
-
-\begin{python}
-results = run_simulation(system, update_func1)
-\end{python}
-
-Passing a function as an argument is the same as passing any other value.  The argument, which is \py{update_func1} in this example, gets assigned to the parameter, which is called \py{update_func}.  Inside \py{run_simulation}, we can run \py{update_func} just like any other function.
-
-The loop in \py{run_simulation} calls \py{update_func1} once for each year between \py{t_0} and \py{t_end-1}.  The result is the same as Figure~\ref{chap06-fig01}.
-
-
-\section{Combining birth and death}
-
-While we are at it, we can also simplify the code by combining births and deaths to compute the net growth rate.  Instead of two parameters, \py{birth_rate} and \py{death_rate}, we can write the update function in terms of a single parameter that represents the difference:
-
-\begin{python}
-system.alpha = system.birth_rate - system.death_rate
-\end{python}
-
-The name of this parameter, \py{alpha}, is the conventional name for a proportional growth rate.
-
-Here's the modified version of \py{update_func1}:
-
-\begin{python}
-def update_func2(pop, t, system):
-    net_growth = system.alpha  * pop
-    return pop + net_growth
-\end{python}
-
-And here's how we run it:
-
-\begin{python}
-results = run_simulation(system, update_func2)
-\end{python}
-
-Again, the result is the same as Figure~\ref{chap06-fig01}.
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap06.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-
-\chapter{Quadratic growth}
-\label{chap07}
-
-In the previous chapter we developed a population model where net growth during each time step is proportional to the current population.  This model seems more realistic than the constant growth model, but it does not fit the data as well.
-
-There are a few things we could try to improve the model:
-
-\begin{itemize}
-
-\item Maybe the net growth rate varies over time.
-
-\item Maybe net growth depends on the current population, but the relationship is quadratic, not linear.
-
-\end{itemize}
-
-In the notebook for this chapter, you will have a chance to try the first option.  In this chapter, we explore the second.
-
-
-\section{Quadratic growth}
-\label{quadratic}
-
-It makes sense that net growth should depend on the current population, but maybe it's not a linear relationship, like this:
-
-\begin{python}
-    net_growth = system.alpha * pop
-\end{python}
-
-Maybe it's a quadratic relationship, like this:
-
-\index{quadratic growth}
-
-\begin{python}
-    net_growth = system.alpha * pop + system.beta * pop**2
-\end{python}
-
-We can test that conjecture with a new update function:
-
-\begin{python}
-def update_func_quad(pop, t, system):
-    net_growth = system.alpha * pop + system.beta * pop**2
-    return pop + net_growth
-\end{python}
-
-Now we need two parameters.  I chose the following values by trial and error; we will see better ways to do it later.
-
-\index{parameter}
-
-\begin{python}
-system.alpha = 0.025
-system.beta = -0.0018
-\end{python}
-
-And here's how we run it:
-
-\begin{python}
-results = run_simulation(system, update_func_quad)
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap07-fig01.pdf}}
-\caption{Estimates of world population, 1950--2016, and a quadratic model.}
-\label{chap07-fig01}
-\end{figure}
-
-Figure~\ref{chap07-fig01} shows the result.  The model fits the data well over the whole range, with just a bit of space between them in the 1960s.
-
-Of course, we should expect the quadratic model to fit better than the constant and proportional models because it has two parameters we can choose, where the other models have only one.  In general, the more parameters you have to play with, the better you should expect the model to fit.
-
-\index{quality of fit}
-\index{data}
-\index{fitting data}
-
-But fitting the data is not the only reason to think the quadratic model might be a good choice.  It also makes sense; that is, there is a legitimate reason to expect the relationship between growth and population to have this form.
-
-To understand it, let's look at net growth as a function of population.  Here's how we compute it:
-
-\begin{python}
-pop_array = linspace(0, 15, 100)
-net_growth_array = (system.alpha * pop_array + 
-                    system.beta * pop_array**2)
-\end{python}
-
-\py{pop_array} contains 100 equally spaced values from 0 to 15.  \py{net_growth_array} contains the corresponding 100 values of net growth.  We can plot the results like this: 
-
-\begin{python}
-plot(pop_array, net_growth_array)
-\end{python}
-
-Previously we have used \py{plot} with \py{Series} objects.  In this example, we use two NumPy arrays, corresponding to the x and y axes.
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap07-fig02.pdf}}
-\caption{Net growth as a function of population.}
-\label{chap07-fig02}
-\end{figure}
-
-Figure~\ref{chap07-fig02} shows the result.  Note that the x-axis is not time, as in the previous figures, but population.  We can divide this curve into four regimes of behavior:
-\index{regime}
-
-\begin{itemize}
-
-\item When the population is less than 3-4 billion, net growth is proportional to population, as in the proportional model.  In this regime,  the population grows slowly because the population is small.
-
-\item Between 4 billion and 10 billion, the population grows quickly because there are a lot of people.
-
-\item Above 10 billion, population grows more slowly; this behavior models the effect of resource limitations that lower birth rates or increase death rates.
-
-\item Above 14 billion, resources are so limited that the death rate exceeds the birth rate and net growth becomes negative.
-
-\end{itemize}
-
-Just below 14 billion, there is a point where net growth is 0, which means that the population does not change.  At this point, the birth and death rates are equal, so the population is in {\bf equilibrium}.
-
-\index{equilibrium}
-
-
-\section{Equilibrium}
-\label{equilibrium}
-
-To find the equilibrium point, we can find the roots, or zeros, of this equation:
-%
-\[ \Delta p = \alpha p + \beta p^2 \]
-%
-where $\Delta p$ is net population growth, $p$ is current population, and $\alpha$ and $\beta$ are the parameters of the model.  We can rewrite the right hand side like this:
-%
-\[ \Delta p = p (\alpha + \beta p) \]
-%
-which is $0$ when $p=0$ or $p=-\alpha/\beta$.  In this example, $\alpha = 0.025$ and $\beta = -0.0018$, so $-\alpha/\beta = 13.9$.
-
-In the context of population modeling, the quadratic model is more conventionally written like this:
-%
-\[ \Delta p = r p (1 - p / K) \]
-%
-This is the same model; it's just a different way to {\bf parameterize} it.  Given $\alpha$ and $\beta$, we can compute $r=\alpha$ and $K=-\alpha/\beta$.
-
-\index{parameterize}
-
-In this version, it is easier to interpret the parameters: $r$ is the maximum growth rate, observed when $p$ is small, and $K$ is the equilibrium point.  $K$ is also called the {\bf carrying capacity}, since it indicates the maximum population the environment can sustain.
-
-\index{carrying capacity}
-
-In the next chapter we use the models we have developed to generate predictions.
-
-\section{Dysfunctions}
-
-When people learn about functions, there are a few things they often find confusing.  In this section I present and explain some common problems.
-
-As an example, suppose you want a function that takes as a parameter \py{System} object with variables \py{alpha} and \py{beta}, and computes the carrying capacity, \py{-alpha/beta}.  Here's a good solution: 
-
-\begin{python}
-def carrying_capacity(system):
-    K = -system.alpha / system.beta
-    return K
-    
-sys1 = System(alpha=0.025, beta=-0.0018)
-pop = carrying_capacity(sys1)
-print(pop)
-\end{python}
-
-Now let's see all the ways that can go wrong.
-
-Dysfunction \#1: Not using parameters.  In the following version, the function doesn't take any parameters; when \py{sys1} appears inside the function, it refers to the object we create outside the function.
-
-\begin{python}
-def carrying_capacity():
-    K = -sys1.alpha / sys1.beta
-    return K
-    
-sys1 = System(alpha=0.025, beta=-0.0018)
-pop = carrying_capacity()
-print(pop)
-\end{python}
-
-This version actually works, but it is not as versatile as it could be.  If there are several \py{System} objects, this function can only work with one of them, and only if it is named \py{sys1}.
-
-Dysfunction \#2: Clobbering the parameters.  When people first learn about parameters, they often write functions like this:
-
-\begin{python}
-# WRONG
-def carrying_capacity(system):
-    system = System(alpha=0.025, beta=-0.0018)
-    K = -system.alpha / system.beta
-    return K
-    
-sys1 = System(alpha=0.03, beta=-0.002)
-pop = carrying_capacity(sys1)
-print(pop)
-\end{python}
-
-In this example, we have a \py{System} object named \py{sys1} that gets passed as an argument to \py{carrying_capacity}.  But when the function runs, it ignores the argument and immediately replaces it with a new \py{System} object.  As a result, this function always returns the same value, no matter what argument is passed.
-
-When you write a function, you generally don't know what the values of the parameters will be.  Your job is to write a function that works for any valid values.  If you assign your own values to the parameters, you defeat the whole purpose of functions.
-
-
-Dysfunction \#3: No return value.  Here's a version that computes the value of \py{K} but doesn't return it.
-
-\begin{python}
-# WRONG
-def carrying_capacity(system):
-    K = -system.alpha / system.beta
-    
-sys1 = System(alpha=0.025, beta=-0.0018)
-pop = carrying_capacity(sys1)
-print(pop)
-\end{python}
-
-A function that doesn't have a return statement always returns a special value called \py{None}, so in this example the value of \py{pop} is \py{None}.  If you are debugging a program and find that the value of a variable is \py{None} when it shouldn't be, a function without a return statement is a likely cause.
-\index{None}
-
-Dysfunction \#4: Ignoring the return value.  Finally, here's a version where the function is correct, but the way it's used is not.
-
-\begin{python}
-# WRONG
-def carrying_capacity(system):
-    K = -system.alpha / system.beta
-    return K
-    
-sys1 = System(alpha=0.025, beta=-0.0018)
-carrying_capacity(sys1)
-print(K)
-\end{python}
-
-In this example, \py{carrying_capacity} runs and returns \py{K}, but the return value is dropped.
-
-When you call a function that returns a value, you should do something with the result.  Often you assign it to a variable, as in the previous examples, but you can also use it as part of an expression.  For example, you could eliminate the temporary variable \py{pop} like this:
-
-\begin{python}
-print(carrying_capacity(sys1))
-\end{python}
-
-Or if you had more than one system, you could compute the total carrying capacity like this:
-
-\begin{python}
-total = carrying_capacity(sys1) + carrying_capacity(sys2)
-\end{python}
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap07.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-
-
-\chapter{Prediction}
-\label{chap08}
-
-In the previous chapter we developed a quadratic model of world population growth from 1950 to 2016.  It is a simple model, but it fits the data well and the mechanisms it's based on are plausible.  
-
-In this chapter we'll use the quadratic model to generate projections of future growth, and compare our results to projections from actual demographers.  Also, we'll represent the models from the previous chapters as differential equations and solve them analytically.
-
-\index{prediction}
-\index{projection}
-
-
-\section{Generating projections}
-
-We'll start with the quadratic model from Section~\ref{quadratic}, which is based on this update function:
-\index{quadratic growth}
-
-\begin{python}
-def update_func_quad(pop, t, system):
-    net_growth = system.alpha * pop + system.beta * pop**2
-    return pop + net_growth
-\end{python}
-
-As we saw in the previous chapter, we can get the start date, end date, and initial population from \py{census}, which is a series that contains world population estimates generated by the U.S. Census:
-
-\begin{python}
-t_0 = get_first_label(census)
-t_end = get_last_label(census)
-p_0 = census[t_0]
-\end{python}
-
-Now we can create a \py{System} object:
-\index{System object}
-
-\begin{python}
-system = System(t_0=t_0, 
-                t_end=t_end,
-                p_0=p_0,
-                alpha=0.025,
-                beta=-0.0018)
-\end{python}
-
-And run the model:
-
-\begin{python}
-results = run_simulation(system, update_func_quad)
-\end{python}
-
-We have already seen the results in Figure~\ref{chap07-fig01}.  Now, to generate a projection, the only thing we have to change is \py{t_end}:
-
-\begin{python}
-system.t_end = 2250
-results = run_simulation(system, update_func_quad)
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap08-fig01.pdf}}
-\caption{Quadratic model of world population growth, with projection from 2016 to 2250.}
-\label{chap08-fig01}
-\end{figure}
-
-Figure~\ref{chap08-fig01} shows the result, with a projection until 2250.  According to this model, population growth will continue almost linearly for the next 50--100 years, then slow over the following 100 years, approaching 13.9 billion by 2250.
-
-I am using the word ``projection" deliberately, rather than ``prediction", with the following distinction: ``prediction" implies something like ``this is what we should reasonably expect to happen, at least approximately"; ``projection" implies something like ``if this model is actually a good description of what is happening in this system, and if nothing in the future causes the parameters of the model to change, this is what would happen."
-
-Using ``projection" leaves open the possibility that there are important things in the real world that are not captured in the model.  It also suggests that, even if the model is good, the parameters we estimate based on the past might be different in the future.
-
-The quadratic model we've been working with is based on the assumption that population growth is limited by the availability of resources; in that scenario, as the population approaches carrying capacity, birth rates fall and death rates rise because resources become scarce.
-
-\index{carrying capacity}
-
-If that assumption is valid, we might be able to use actual population growth to estimate carrying capacity, especially if we observe the transition into the regime where the growth rate starts to fall.
-
-But in the case of world population growth, those conditions don't apply.  Over the last 50 years, the net growth rate has leveled off, but not yet started to fall, so we don't have enough data to make a credible estimate of carrying capacity.  And resource limitations are probably {\em not} the primary reason growth has slowed.  As evidence, consider:
-
-\begin{itemize}
-
-\item First, the death rate is not increasing; rather, it has declined from 1.9\% in 1950 to 0.8\% now (see \url{http://modsimpy.com/mortality}).  So the decrease in net growth is due entirely to declining birth rates.
-
-\index{mortality rate}
-
-\item Second, the relationship between resources and birth rate is the opposite of what the model assumes; as nations develop and people become more wealthy, birth rates tend to fall.  
-
-\index{birth rate}
-
-\end{itemize} 
-
-We should not take too seriously the idea that this model can estimate carrying capacity.  But the predictions of a model can be credible even if the assumptions of the model are not strictly true.  For example, population growth might behave {\em as if} it is resource limited, even if the actual mechanism is something else.
-
-In fact, demographers who study population growth often use models similar to ours.  In the next section, we'll compare our projections to theirs.
-
-
-\section{Comparing projections}
-
-Table 3 from \url{http://modsimpy.com/worldpop} contains projections from the U.S. Census and the United Nations DESA:
-
-\begin{python}
-table3 = tables[3]
-\end{python}
-
-For some years, one agency or the other has not published a projection, so some elements of \py{table3} contain the special value \py{NaN}, which stands for ``not a number".  \py{NaN} is often used to indicate missing data.
-
-\index{not a number}
-\index{NaN}
-\index{missing data}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap08-fig02.pdf}}
-\caption{Projections of world population generated by the U.S. Census Bureau, the United Nations, and our quadratic model.}
-\label{chap08-fig02}
-\end{figure}
-
-Pandas provides functions that deal with missing data, including \py{dropna}, which removes any elements in a series that contain \py{NaN}.  Using \py{dropna}, we can plot the projections like this:
-
-\index{Pandas}
-\index{dropna}
-
-\begin{python}
-def plot_projections(table):
-    census_proj = table.census / 1e9
-    un_proj = table.un / 1e9
-    
-    plot(census_proj.dropna(), 'b:', label='US Census')
-    plot(un_proj.dropna(), 'g--', label='UN DESA')
-\end{python}
-
-The format string \py{'b:'} indicates a blue dotted line; \py{g--} indicates a green dashed line.
-
-\index{format string}
-\index{plot}
-
-We can run our model over the same interval:
-
-\begin{python}
-system.t_end = 2100
-results = run_simulation(system, update_func_quad)
-\end{python}
-
-And compare our projections to theirs.  Figure~\ref{chap08-fig02} shows the results.  Real demographers expect world population to grow more slowly than our model projects, probably because their models are broken down by region and country, where conditions are different, and they take into account expected economic development.
-
-\index{demography}
-
-Nevertheless, their projections are qualitatively similar to ours, and theirs differ from each other almost as much as they differ from ours.  So the results from this model, simple as it is, are not entirely crazy.
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap08.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-\chapter{Analysis}
-\label{chap09}
-
-In this chapter we express the models from previous chapters as difference equations and differential equations, solve the equations, and derive the functional forms of the solutions.  We also discuss the complementary roles of mathematical analysis and simulation.
-
-
-\section{Recurrence relations}
-
-The population models in the previous chapter and this one are simple enough that we didn't really need to run simulations.  We could have solved them mathematically.  For example, we wrote the constant growth model like this:
-
-\begin{python}
-model[t+1] = model[t] + annual_growth
-\end{python}
-
-In mathematical notation, we would write the same model like this:
-%
-\[ x_{n+1} = x_n + c \]
-%
-where $x_n$ is the population during year $n$, $x_0$ is a given initial population, and $c$ is constant annual growth.  This way of representing the model is a {\bf recurrence relation}; see \url{http://modsimpy.com/recur}.
-
-\index{recurrence relation}
-
-Sometimes it is possible to solve a recurrence relation by writing an equation that computes $x_n$, for a given value of $n$, directly; that is, without computing the intervening values from $x_1$ through $x_{n-1}$.
-
-In the case of constant growth we can see that $x_1 = x_0 + c$, and $x_2 = x_1 + c$.  Combining these, we get $x_2 = x_0 + 2c$, then $x_3 = x_0 + 3c$, and it is not hard to conclude that in general
-%
-\[ x_n = x_0 + nc \]
-%
-So if we want to know $x_{100}$ and we don't care about the other values, we can compute it with one multiplication and one addition.
-
-We can also write the proportional model as a recurrence relation:
-%
-\[ x_{n+1} = x_n + \alpha x_n \]
-%
-Or more conventionally as:
-%
-\[ x_{n+1} = x_n (1 + \alpha) \]
-%
-Now we can see that $x_1 = x_0 (1 + \alpha)$, and $x_2 = x_0 (1 + \alpha)^2$, and in general
-%
-\[ x_n = x_0 (1 + \alpha)^n \]
-%
-This result is a {\bf geometric progression}; see \url{http://modsimpy.com/geom}.  When $\alpha$ is positive, the factor $1+\alpha$ is greater than 1, so the elements of the sequence grow without bound.
-
-\index{geometric progression}
-\index{quadratic growth}
-
-Finally, we can write the quadratic model like this:
-%
-\[ x_{n+1} = x_n + \alpha x_n + \beta x_n^2 \]
-%
-or with the more conventional parameterization like this:
-%
-\[ x_{n+1} = x_n + r x_n (1 - x_n / K) \]
-%
-There is no analytic solution to this equation, but we can approximate it with a differential equation and solve that, which is what we'll do in the next section.
-
-
-\section{Differential equations}
-\label{diffeq}
-
-Starting again with the constant growth model
-%
-\[ x_{n+1} = x_n + c \]
-%
-If we define $\Delta x$ to be the change in $x$ from one time step to the next, we can write:
-%
-\[ \Delta x = x_{n+1} - x_n = c \]
-%
-If we define $\Delta t$ to be the time step, which is one year in the example, we can write the rate of change per unit of time like this:
-%
-\[ \frac{\Delta x}{\Delta t} = c \]
-%
-This model is {\bf discrete}, which means it is only defined at integer values of $n$ and not in between.  But in reality, people are born and die all the time, not once a year, so a {\bf continuous} model might be more realistic.
-
-\index{discrete}
-\index{continuous}
-\index{time step}
-
-We can make this model continuous by writing the rate of change in the form of a derivative:
-%
-\[ \frac{dx}{dt} = c \]
-%
-This way of representing the model is a {\bf differential equation}; see \url{http://modsimpy.com/diffeq}.
-
-\index{differential equation}
-
-We can solve this differential equation if we multiply both sides by $dt$:
-%
-\[ dx = c dt \]
-%
-And then integrate both sides:
-%
-\[ x(t) = c t + x_0 \]
-%
-Similarly, we can write the proportional growth model like this:
-%
-\[ \frac{\Delta x}{\Delta t} = \alpha x \]
-%
-And as a differential equation like this:
-%
-\[ \frac{dx}{dt} = \alpha x \]
-%
-If we multiply both sides by $dt$ and divide by $x$, we get
-%
-\[ \frac{1}{x}~dx = \alpha~dt \] 
-%
-Now we integrate both sides, yielding:
-%
-\[ \ln x = \alpha t + K \]
-%
-where $\ln$ is the natural logarithm and $K$ is the constant of integration.  Exponentiating both sides\footnote{The exponential function can be written $\exp(x)$ or $e^x$.  In this book I use the first form because it resembles the Python code. }, we have
-%
-\[ \exp(\ln(x)) = \exp(\alpha t + K) \]
-%
-which we can rewrite
-%
-\[ x = \exp(\alpha t) \exp(K) \]
-%
-Since $K$ is an arbitrary constant, $\exp(K)$ is also an arbitrary constant, so we can write
-%
-\[ x = C \exp(\alpha t) \]
-%
-where $C = \exp(K)$.  There are many solutions to this differential equation, with different values of $C$.  The particular solution we want is the one that has the value $x_0$ when $t=0$. 
-
-When $t=0$, $x(t) = C$, so $C = x_0$ and the solution we want is
-%
-\[ x(t) = x_0 \exp(\alpha t) \]
-%
-If you would like to see this derivation done more carefully, you might like this video: \url{http://modsimpy.com/khan1}.
-
-\index{logarithm}
-\index{exponentiation}
-\index{integration}
-\index{constant of integration}
-
-
-\section{Analysis and simulation}
-
-Once you have designed a model, there are generally two ways to proceed: simulation and analysis.  Simulation often comes in the form of a computer program that models changes in a system over time, like births and deaths, or bikes moving from place to place.  Analysis often comes in the form of algebra; that is, symbolic manipulation using mathematical notation.
-
-\index{analysis}
-\index{algebra}
-\index{symbolic manipulation}
-
-Analysis and simulation have different capabilities and limitations.  Simulation is generally more versatile; it is easy to add and remove parts of a program and test many versions of a model, as we have done in the previous examples.
-
-But there are several things we can do with analysis that are harder or impossible with simulations:
-
-\begin{itemize}
-
-\item With analysis we can sometimes compute, exactly and efficiently, a value that we could only approximate, less efficiently, with simulation.  For example, in Figure~\ref{chap07-fig02}, we can see that net growth goes to zero near 14 billion, and we could estimate carrying capacity using a numerical search algorithm (more about that later).  But with the analysis in Section~\ref{equilibrium}, we get the general result that $K=-\alpha/\beta$.
-
-\item Analysis often provides ``computational shortcuts", that is, the ability to jump forward in time to compute the state of a system many time steps in the future without computing the intervening states.
-
-\index{time step}
-
-\item We can use analysis to state and prove generalizations about models; for example, we might prove that certain results will always or never occur.  With simulations, we can show examples and sometimes find counterexamples, but it is hard to write proofs.
-
-\index{proof}
-
-\item Analysis can provide insight into models and the systems they describe; for example, sometimes we can identify regimes of qualitatively different behavior and key parameters that control those behaviors.
-
-\index{regime}
-
-\end{itemize}
-
-When people see what analysis can do, they sometimes get drunk with power, and imagine that it gives them a special ability to see past the veil of the material world and discern the laws of mathematics that govern the universe.  When they analyze a model of a physical system, they talk about ``the math behind it" as if our world is the mere shadow of a world of ideal mathematical entities\footnote{I am not making this up; see \url{http://modsimpy.com/plato}.}.
-
-\index{Plato}
-
-This is, of course, nonsense.  Mathematical notation is a language designed by humans for a purpose, specifically to facilitate symbolic manipulations like algebra.  Similarly, programming languages are designed  for a purpose, specifically to represent computational ideas and run programs.
-
-\index{math notation}
-\index{programming languages}
-
-Each of these languages is good for the purposes it was designed for and less good for other purposes.  But they are often complementary, and one of the goals of this book is to show how they can be used together.
-
-
-\section{Analysis with WolframAlpha}
-
-Until recently, most analysis was done by rubbing graphite on wood pulp\footnote{Or ``rubbing the white rock on the black rock", a line I got from Woodie Flowers, who got it from Stephen Jacobsen.}, a process that is laborious and error-prone.  A useful alternative is symbolic computation.  If you have used services like WolframAlpha, you have used symbolic computation.
-
-\index{symbolic computation}
-\index{WolframAlpha}
-
-For example, if you go to \url{https://www.wolframalpha.com/} and type
-
-\begin{python}
-df(t) / dt = alpha f(t)
-\end{python}
-
-WolframAlpha infers that \py{f(t)} is a function of \py{t} and \py{alpha} is a parameter; it classifies the query as a ``first-order linear ordinary differential equation", and reports the general solution:
-%
-\[ f(t) = c_1 \exp(\alpha t) \]
-%
-If you add a second equation to specify the initial condition:
-
-\begin{python}
-df(t) / dt = alpha f(t),  f(0) = p_0
-\end{python}
-
-WolframAlpha reports the particular solution:
-
-\[ f(t) = p_0 \exp(\alpha t) \]
-
-WolframAlpha is based on Mathematica, a powerful programming language designed specifically for symbolic computation.
-
-\index{Mathematica}
-
-
-\section{Analysis with SymPy}
-
-Python has a library called SymPy that provides symbolic computation tools similar to Mathematica.  They are not as easy to use as WolframAlpha, but they have some other advantages.
-
-\index{SymPy}
-
-Before we can use SymPy, we have to import it:
-
-\index{import statement}
-\index{statement!import}
-
-\begin{python}
-from sympy import *
-\end{python}
-
-SymPy defines many functions, and some of them conflict with functions defined in \py{modsim} and the other libraries we're using.  To avoid these conflicts, I suggest that you do symbolic computation with SymPy in a separate notebook. 
-
-SymPy defines a \py{Symbol} object that represents symbolic variable names, functions, and other mathematical entities.
-
-\index{Symbol object}
-
-The \py{symbols} function takes a string and returns \py{Symbol} objects.  So if we run this assignment:
-
-\begin{python}
-t = symbols('t')
-\end{python}
-
-Python understands that \py{t} is a symbol, not a numerical value.  If we now run
-
-\begin{python}
-expr = t + 1
-\end{python}
-
-Python doesn't try to perform numerical addition; rather, it creates a new \py{Symbol} that represents the sum of \py{t} and \py{1}.  We can evaluate the sum using \py{subs}, which substitutes a value for a symbol.  This example substitutes 2 for \py{t}:
-
-\begin{python}
-expr.subs(t, 2)
-\end{python}
-
-The result is 3.
-
-Functions in SymPy are represented by a special kind of \py{Symbol}:
-
-\begin{python}
-f = Function('f')
-\end{python}
-
-Now if we write \py{f(t)}, we get an object that represents the evaluation of a function, $f$, at a value, $t$.  But again SymPy doesn't actually try to evaluate it.
-
-
-\section{Differential equations in SymPy}
-
-SymPy provides a function, \py{diff}, that can differentiate a function.  We can apply it to \py{f(t)} like this:
-
-\index{differential equation}
-\index{SymPy}
-
-\begin{python}
-dfdt = diff(f(t), t)
-\end{python}
-
-The result is a \py{Symbol} that represents the derivative of \py{f} with respect to \py{t}.  But again, SymPy doesn't try to compute the derivative yet.
-
-\index{Symbol object}
-
-To represent a differential equation, we use \py{Eq}:
-
-\begin{python}
-alpha = symbols('alpha')
-eq1 = Eq(dfdt, alpha*f(t))
-\end{python}
-
-The result is an object that represents an equation, which is displayed like this:
-%
-\[ \frac{d}{d t} f{\left (t \right )} = \alpha f{\left (t \right )} \]
-%
-Now we can use \py{dsolve} to solve this differential equation:
-
-\begin{python}
-solution_eq = dsolve(eq1)
-\end{python}
-
-The result is the equation
-%
-\[ f{\left (t \right )} = C_{1} \exp(\alpha t) \]
-%
-This is the {\bf general solution}, which still contains an unspecified constant, $C_1$.  To get the {\bf particular solution} where $f(0) = p_0$, we substitute \py{p_0} for \py{C1}.  First, we have to create two more symbols:
-
-\index{general solution}
-\index{particular solution}
-
-\begin{python}
-C1, p_0 = symbols('C1 p_0')
-\end{python}
-
-Now we can perform the substitution:
-
-\begin{python}
-particular = solution_eq.subs(C1, p_0)
-\end{python}
-
-The result is 
-%
-\[ f{\left (t \right )} = p_{0} \exp(\alpha t) \]
-%
-This function is called the {\bf exponential growth curve}; see \url{http://modsimpy.com/expo}.
-
-\index{exponential growth}
-
-
-\section{Solving the quadratic growth model}
-
-In the notebook for this chapter, you will see how to use the same tools to solve the quadratic growth model with parameters $r$ and $K$.  The general solution is
-%
-\[ f{\left (t \right )} = \frac{K \exp(C_{1} K + r t)}{\exp(C_{1} K + r t) - 1} \]
-%
-To get the particular solution where $f(0) = p_0$, we evaluate the general solution at $t=0$, which yields:
-%
-\[ f(0) = \frac{K \exp(C_{1} K)}{\exp(C_{1} K) - 1} \]
-%
-Then we set this expression equal to $p_0$ and solve for $C_1$.  The result is:
-%
-\[ C_1 = \frac{1}{K} \ln{\left (- \frac{p_{0}}{K - p_{0}} \right )} \]
-%
-Finally, we substitute this value of $C_1$ into the general solution, which yields:
-%
-\[ f(t) = \frac{K p_{0} \exp(r t)}{K + p_{0} \exp(r t) - p_{0}} \]
-%
-This function is called the {\bf logistic growth curve}; see \url{http://modsimpy.com/logistic}.  In the context of growth models, the logistic function is often written, equivalently,
-%
-\[ f(t) = \frac{K}{1 + A \exp(-rt)} \]
-%
-where $A = (K - p_0) / p_0$.
-
-If you would like to see this differential equation solved by hand, you might like this video: \url{http://modsimpy.com/khan2}
-\index{quadratic growth}
-\index{logistic function}
-
-
-\section{Summary}
-
-The following tables summarize the results so far:
-
-\begin{tabular}{l|l} 
-\hline
-Growth type         & Discrete (difference equation) \\ 
-\hline 
-Constant & linear: $x_n = p_0 + \alpha n$  \\ 
- 
-Proportional & geometric: $x_n = p_0(1+\alpha)^n$  \\ 
-
-\end{tabular} 
-
-\begin{tabular}{l|l} 
-\hline
-        & Continuous (differential equation) \\ 
-\hline 
-Constant & linear: $x(t) = p_0 + \alpha t$ \\ 
- 
-Proportional & exponential: $x(t) = p_0 \exp(\alpha t)$ \\ 
- 
-Quadratic & logistic: $x(t) = K / (1 + A\exp(-rt))$ \\ 
-\end{tabular} 
-
-What I've been calling the constant growth model is more commonly called ``linear growth" because the solution is a line.  Similarly, what I've called proportional is commonly called ``exponential", and what I've called quadratic is commonly called ``logistic".  I avoided these terms until now because they are based on results we had not derived yet.
-
-\index{linear growth}
-\index{exponential growth}
-\index{logistic growth}
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap09sympy.ipynb}.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-\chapter{Case studies}
-\label{chap10}
-
-This chapter reviews the computational patterns we have seen so far and presents exercises where you can apply them.
-
-\section{Computational tools}
-
-In Chapter~\ref{chap01} we used Pint to define units and perform calculations with units:
-
-\begin{code}
-meter = UNITS.meter
-second = UNITS.second
-a = 9.8 * meter / second**2
-\end{code}
-
-In Chapter~\ref{chap02} we defined a \py{State} object that contains variables that represent the state of a system, usually changing over time:
-
-\begin{code}
-bikeshare = State(olin=10, wellesley=2)
-\end{code}
-
-We used update operators like \py{+=} and \py{-=} to change state variables.  We used \py{print} statements to display the values of variables.
-
-We used the \py{flip} function to simulate random arrivals, and used \py{if} statements to check the results.
-
-We learned to define new functions that take parameters:
-
-\begin{code}
-def step(p1, p2):
-    if flip(p1):
-        bike_to_wellesley()
-        
-    if flip(p2):
-        bike_to_olin()
-\end{code}
-
-We used a \py{for} loop with the \py{range} function to execute the body of the loop a specified number of times.
-
-\begin{code}
-for i in range(4):
-    step(p1, p2)
-\end{code}
-
-We learned to create a \py{TimeSeries} object and use it to store the value of a state variable as it changes over time:
-
-\begin{code}
-results = TimeSeries()
-
-for i in range(10):
-    step(0.3, 0.2)
-    results[i] = bikeshare.olin
-\end{code}
-
-We used \py{plot} to plot the results, \py{decorate} to label the axes, and \py{savefig} to save the figure.
-
-\begin{code}
-plot(results, label='Olin')
-decorate(xlabel='Time step (min)', 
-         ylabel='Number of bikes')
-savefig('chap01-fig01.pdf)
-\end{code}
-
-In Chapter~\ref{chap03} we used comparison operators to check for certain conditions and the \py{return} statement to end the execution of a function.
-
-\begin{code}
-def bike_to_olin(state):
-    if state.wellesley == 0:
-        state.wellesley_empty += 1
-        return
-    state.wellesley -= 1
-    state.olin += 1
-\end{code}
-
-In Chapter~\ref{chap04} we wrote a version of \py{run_simulation} that uses a \py{return} statement to return a value:
-
-\begin{code}
-def run_simulation(p1, p2, num_steps):
-    state = State(olin=10, wellesley=2, 
-                  olin_empty=0, wellesley_empty=0)
-                    
-    for i in range(num_steps):
-        step(state, p1, p2)
-        
-    return state
-\end{code}
-
-This version of \py{run_simulation} returns the final value of \py{state}, which contains metrics we can use to measure the performance of the system.
-
-We used \py{linspace} to create a NumPy array of equally spaced values, and a \py{for} loop to loop through the array.  We used a \py{SweepSeries} to store results from a series of simulations, mapping from the value of a parameter to the value of a resulting metric.
-
-\begin{code}
-p1_array = linspace(0, 1, 11)
-sweep = SweepSeries()
-
-for p1 in p1_array:
-    state = run_simulation(p1, p2, num_steps)
-    sweep[p1] = state.olin_empty
-\end{code}
-
-In Chapter~\ref{chap05} we used Pandas to read data from a web page and store the results in a \py{DataFrame}.  We selected a column from the \py{DataFrame} to get a \py{Series}.
-
-In Chapter~\ref{chap06} we created a \py{System} object to contain the parameters of the model, and defined another version of \py{run_simulation}:
-
-\begin{code}
-def run_simulation(system, update_func):
-    results = TimeSeries()
-    results[system.t_0] = system.p_0
-    
-    for t in linrange(system.t_0, system.t_end):
-        results[t+1] = update_func(results[t], t, system)
-        
-    return results
-\end{code}
-
-This version takes a \py{System} object as a parameter, and an update function.  Instead of returning the final state of the system, it returns a \py{TimeSeries} that contains the state as it changes over time.
-
-The update function takes the current state of the system, the time, and the \py{System} object as parameters, and returns the new state.  For example, here's the update function for the quadratic growth model.
-
-\begin{code}
-def update_func_quad(pop, t, system):
-    net_growth = system.alpha * pop + system.beta * pop**2
-    return pop + net_growth
-\end{code}
-
-In this example, the state of the system is a single number, \py{pop}.  Later we'll see examples where state is represented by a \py{State} object with more than one variable.
-
-Chapter~\ref{chap07} introduces the quadratic growth model and 
-Chapter~\ref{chap08} uses the model to generate predictions, but neither chapter introduces new computational tools.
-
-Chapter~\ref{chap09} introduces SymPy, which we can use to create \py{Symbol} objects:
-
-\begin{code}
-t, alpha = symbols('t alpha')
-f = Function('f')
-\end{code}
-
-Write differential equations:
-
-\begin{code}
-dfdt = diff(f(t), t)
-eq1 = Eq(dfdt, alpha*f(t))
-\end{code}
-
-And solve them:
-
-\begin{code}
-solution_eq = dsolve(eq1)
-\end{code}
-
-That's a brief summary of the computational tools we have seen so far.
-
-
-\section{Under the hood}
-\label{dataframe}
-
-So far we've been using \py{DataFrame} and \py{Series} objects without really understanding how they work.  In this section we'll review what we know so far and get into a little more detail.
-
-Each \py{DataFrame} contains three objects: \py{index} is a sequence of labels for the rows, \py{columns} is a sequence of labels for the columns, and \py{values} is a NumPy array that contains the data.
-
-In the \py{DataFrame} objects in this chapter, \py{index} contains years from 1950 to 2016, \py{columns} contains names of agencies and people that produce population estimates, and \py{values} is an array of estimates.
-
-\begin{figure}
-\centerline{\includegraphics[height=2.5in]{figs/dataframe.pdf}}
-\caption{The elements that make up a \py{DataFrame} and a \py{Series}.}
-\label{fig-dataframe}
-\end{figure}
-
-A \py{Series} is like a \py{DataFrame} with one column: it contains a string \py{name} that is like a column label, an index, and an array of values.
-
-Figure~\ref{fig-dataframe} shows the elements of a \py{DataFrame} and \py{Series} graphically.
-
-\index{type function}
-
-To determine the types of these elements, we can use the Python function \py{type}:
-
-\begin{code}
-type(table2)
-type(table2.index)
-type(table2.columns)
-type(table2.values)
-\end{code}
-
-The type of \py{table2} is \py{DataFrame}.  The type of \py{table2.index} is \py{Int64Index}, which is similar to a \py{Series}.
-
-The type of \py{table2.columns} is \py{Index}, which might seem strange, because ``the" index is the sequence of row labels.  But the sequence of column labels is also a kind of index.
-
-The type of \py{table2.values} is \py{ndarray}, which is the primary array type provided by NumPy; the name indicates that the array is ``n-dimensional"; that is, it can have an arbitrary number of dimensions.
-
-In \py{census} or \py{un}, the index is an \py{Int64Index} object and the values are stored in an \py{ndarray}.  
-
-In the ModSim library, the functions \py{get_first_label} and \py{get_last_label} provide a simple way to access the index of a \py{DataFrame} or \py{Series}:
-
-\begin{code}
-def get_first_label(series):
-    return series.index[0]
-
-def get_last_label(series):
-    return series.index[-1]
-\end{code}
-
-In brackets, the number \py{0} selects the first label; the number \py{-1} selects the last label.
-
-Several of the objects defined in \py{modsim} are modified versions of \py{Series} objects.  \py{State} and \py{System} objects are \py{Series} where the labels are variable names.  A \py{TimeSeries} is a \py{Series} where the labels are times, and a \py{SweepSeries} is a \py{Series} where the labels are parameter values.
-
-Defining these objects wasn't necessary; we could do all the same things using \py{Series} objects.  But giving them different names makes the code easier to read and understand, and helps avoid certain kinds of errors (like getting two \py{Series} objects mixed up).
-
-If you write simulations in Python in the future, you can continue using the objects in \py{modsim}, if you find them useful, or you can use Pandas objects directly.
-
-\section{One queue or two?}
-
-This chapter presents two cases studies that let you practice what you have learned so far.  The first case study is related to {\bf queueing theory}, which is the study of systems that involve waiting in lines, also known as ``queues".  
-
-Suppose you are designing the checkout area for a new store.  There is enough room in the store for two checkout counters and a waiting area for customers.  You can make two lines, one for each counter, or one line that feeds both counters.
-
-In theory, you might expect a single line to be better, but it has some practical drawbacks: in order to maintain a single line, you might have to install barriers, and customers might be put off by what seems to be a longer line, even if it moves faster.
-
-So you'd like to check whether the single line is really better and by how much.  Simulation can help answer this question.
-
-\begin{figure}
-\centerline{\includegraphics[width=4.5in]{figs/queue.pdf}}
-\caption{One queue, one server (left), one queue, two servers (middle), two queues, two servers (right).}
-\label{fig-queue}
-\end{figure}
-
-Figure~\ref{fig-queue} shows the three scenarios we'll consider.  As we did in the bike share model, we'll assume that a customer is equally likely to arrive during any time step.  I'll denote this probability using the Greek letter lambda, $\lambda$, or the variable name \py{lam}.  The value of $\lambda$ probably varies from day to day, so we'll have to consider a range of possibilities.
-
-Based on data from other stores, you know that it takes 5 minutes for a customer to check out, on average.  But checkout times are variable: most customers take less than 5 minutes, but some take substantially more.  A simple way to model this variability is to assume that when a customer is checking out, they always have the same probability of finishing during the next time step, regardless of how long they have been checking out.  I'll denote this probability using the Greek letter mu, $\mu$, or the variable name \py{mu}.
-
-If we choose $\mu=1/5$ per minute, the average time for each checkout will be 5 minutes, which is consistent with the data.  Most people takes less than 5 minutes, but a few take substantially longer, which is probably not a bad model of the distribution in real stores.
-
-Now we're ready to get started.  In the repository for this book, you'll find a notebook called \py{queue.ipynb} that contains some code to get you started and instructions.
-
-As always, you should practice incremental development: write no more than one or two lines of code a time, and test as you go!
-
-
-
-
-\section{Predicting salmon populations}
-
-Each year the U.S. Atlantic Salmon Assessment Committee reports estimates of salmon populations in oceans and rivers in the northeastern United States.  The reports are useful for monitoring changes in these populations, but they generally do not include predictions.
-
-The goal of this case study is to model year-to-year changes in population, evaluate how predictable these changes are, and estimate the probability that a particular population will increase or decrease in the next 10 years.
-
-As an example, I use data from page 18 of the 2017 report, which provides population estimates for the Narraguagus and Sheepscot Rivers in Maine.
-
-In the repository for this book, you'll find a notebook called \py{salmon.ipynb} that contains some code to get you started and instructions.
-
-You should take my instructions as suggestions; if you want to try something different, please do!
-
-
-\section{Tree growth}
-
-This case study is based on ``Height-Age Curves for Planted Stands of Douglas Fir, with Adjustments for Density", a working paper by Flewelling, Collier, Gonyea, Marshall, and Turnblom.
-
-% TODO: Add paper to GitHub \url{http://modsimpy.com/trees}
-
-It provides ``site index curves", which are curves that show the expected height of the tallest tree in a stand of Douglas firs as a function of age, for a stand where the trees are the same age.
-
-Depending on the quality of the site, the trees might grow more quickly or slowing.  So each curve is identified by a ``site index" that indicates the quality of the site.
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/trees-fig01.pdf}}
-\caption{Site index curves for tree growth.}
-\label{trees-fig01}
-\end{figure}
-
-Figure~\ref{trees-fig01} shows site curves for three different site indices.
-The goal of this case study is to explain the shape of these curves, that is, why trees grow the way they do.
-
-As a starting place, let's assume that the ability of the tree to gain mass is limited by the area it exposes to sunlight, and that the growth rate (in mass) is proportional to that area.  In that case we can write:
-%
-$ m_{n+1} = m_n + \alpha A$
-%
-where $m_n$ is the mass of the at time step $n$, $A$ is the area exposed to sunlight, and $\alpha$ is an unknown growth parameter.
-
-To get from $m$ to $A$, I'll make the additional assumption that mass is proportional to height raised to an unknown power:
-%
-$ m = \beta h^D $
-%
-where $h$ is height, $\beta$ is an unknown constant of proportionality, and $D$ is the dimension that relates height and mass.  I start by assuming $D=3$, but then revisit that assumption.
-
-Finally, we'll assume that area is proportional to height squared:
-
-$ A = \gamma h^2$
-
-I specify height in feet, and choose units for mass and area so that $\beta=1$ and $\gamma=1$.  Putting all that together, we can write a difference equation for height:
-
-$ h_{n+1}^D = h_n^D + \alpha h_n^2 $
-
-With $D=3$, the solution to this equation is close to a straight line, which is not a bad model for the growth curves.  But the model implies that trees can grow forever, and we know that's not true.  As trees get taller, it gets harder for them to move water and nutrients against the force of gravity, and their growth slows.
-
-We can model this effect by adding a factor to the model similar to what we saw in the logistic model of population growth.  Instead of assuming:
-
-$ m_{n+1} = m_n + \alpha A $ 
-
-Let's assume
-
-$ m_{n+1} = m_n + \alpha A (1 - h / K) $
-
-where $K$ is similar to the carrying capacity of the logistic model.  As $h$ approaches $K$, the factor $(1 - h/K)$ goes to 0, causing growth to level off.
-
-In the repository for this book, you'll find a notebook called \py{trees.ipynb} that implements both models and uses them to fit the data.  There are no exercises in this case study; it is mostly meant as an example of what you can do with the tools we have so far, and a preview of what we will be able to do with the tools in the next few chapters.
-
-
-
-\chapter{Epidemiology}
-\label{chap11}
-
-In this chapter, we develop a model of an epidemic as it spreads in a susceptible population, and use it to evaluate the effectiveness of possible interventions.
-
-\index{epidemic}
-
-My presentation of the SIR model in the next few chapters is based on an excellent article by David Smith and Lang Moore\footnote{Smith and Moore, ``The SIR Model for Spread of Disease," Journal of Online Mathematics and its Applications, December 2001, at \url{http://modsimpy.com/sir}.}.
-
-\index{SIR model}
-
-
-\section{The Freshman Plague}
-
-Every year at Olin College, about 90 new students come to campus from around the country and the world.  Most of them arrive healthy and happy, but usually at least one brings with them some kind of infectious disease.  A few weeks later, predictably, some fraction of the incoming class comes down with what we call ``The Freshman Plague".
-
-\index{Olin College}
-\index{Freshman Plague}
-\index{Kermack-McKendrick}
-
-In this chapter we introduce a well-known model of infectious disease, the Kermack-McKendrick model, and use it to explain the progression of the disease over the course of the semester, predict the effect of possible interventions (like immunization) and design the most effective intervention campaign.
-
-\index{disease}
-\index{infection}
-\index{design}
-
-So far we have done our own modeling; that is, we've chosen physical systems, identified factors that seem important, and made decisions about how to represent them.  In this chapter we start with an existing model and reverse-engineer it.  Along the way, we consider the modeling decisions that went into it and identify its capabilities and limitations.
-
-
-\section{The SIR model}
-\label{sirmodel}
-
-The Kermack-McKendrick model is a simple version of an {\bf SIR model}, so-named because it considers three categories of people:
-
-\begin{itemize}
-
-\item {\bf S}: People who are ``susceptible", that is, capable of contracting the disease if they come into contact with someone who is infected.
-
-\item {\bf I}: People who are ``infectious", that is, capable of passing along the disease if they come into contact with someone susceptible.
-
-\item {\bf R}: People who are ``recovered".  In the basic version of the model, people who have recovered are considered to be immune to reinfection.  That is a reasonable model for some diseases, but not for others, so it should be on the list of assumptions to reconsider later.
-
-\end{itemize}
-
-Let's think about how the number of people in each category changes over time.  Suppose we know that people with the disease are infectious for a period of 4 days, on average.  If 100 people are infectious at a particular point in time, and we ignore the particular time each one became infected, we expect about 1 out of 4 to recover on any particular day.
-
-Putting that a different way, if the time between recoveries is 4 days, the recovery rate is about 0.25 recoveries per day, which we'll denote with the Greek letter gamma, $\gamma$, or the variable name \py{gamma}.
-
-If the total number of people in the population is $N$, and the fraction currently infectious is $i$, the total number of recoveries we expect per day is $\gamma i N$.
-
-\index{recovery rate}
-
-Now let's think about the number of new infections.  Suppose we know that each susceptible person comes into contact with 1 person every 3 days, on average, in a way that would cause them to become infected if the other person is infected.  We'll denote this contact rate with the Greek letter beta, $\beta$.
-
-\index{infection rate}
-
-It's probably not reasonable to assume that we know $\beta$ ahead of time, but later we'll see how to estimate it based on data from previous outbreaks.
-
-If $s$ is the fraction of the population that's susceptible, $s N$ is the number of susceptible people, $\beta s N$ is the number of contacts per day, and $\beta s i N$ is the number of those contacts where the other person is infectious.
-
-\index{susceptible}
-
-In summary:
-
-\begin{itemize}
-
-\item The number of recoveries we expect per day is $\gamma i N$; dividing by $N$ yields the fraction of the population that recovers in a day, which is $\gamma i$.
-
-\item The number of new infections we expect per day is $\beta s i N$; dividing by $N$ yields the fraction of the population that gets infected in a day, which is $\beta s i$.
-
-\end{itemize}
-
-This model assumes that the population is closed; that is, no one arrives or departs, so the size of the population, $N$, is constant.
-
-
-\section{The SIR equations}
-\label{sireqn}
-
-If we treat time as a continuous quantity, we can write differential equations that describe the rates of change for $s$, $i$, and $r$ (where $r$ is the fraction of the population that has recovered):
-%
-\begin{align*}
-\frac{ds}{dt} &= -\beta s i \\
-\frac{di}{dt} &= \beta s i - \gamma i\\
-\frac{dr}{dt} &= \gamma i
-\end{align*}
-%
-To avoid cluttering the equations, I leave it implied that $s$ is a function of time, $s(t)$, and likewise for $i$ and $r$.
-\index{differential equation}
-
-SIR models are examples of {\bf compartment models}, so-called because they divide the world into discrete categories, or compartments, and describe transitions from one compartment to another.  Compartments are also called {\bf stocks} and transitions between them are called {\bf flows}.
-
-\index{compartment model}
-\index{stock}
-\index{flow}
-\index{stock and flow diagram}
-
-In this example, there are three stocks---susceptible, infectious, and recovered---and two flows---new infections and recoveries.  Compartment models are often represented visually using stock and flow diagrams (see \url{http://modsimpy.com/stock}).
-Figure~\ref{stock_flow1} shows the stock and flow diagram for an SIR model.
-
-\begin{figure}
-\centerline{\includegraphics[width=4in]{figs/stock_flow1.pdf}}
-\caption{Stock and flow diagram for an SIR model.}
-\label{stock_flow1}
-\end{figure}
-
-Stocks are represented by rectangles, flows by arrows.  The widget in the middle of the arrows represents a valve that controls the rate of flow; the diagram shows the parameters that control the valves.
-
-
-\section{Implementation}
-
-For a given physical system, there are many possible models, and for a given model, there are many ways to represent it.  For example, we can represent an SIR model as a stock-and-flow diagram, as a set of differential equations, or as a Python program.  The process of representing a model in these forms is called {\bf implementation}.  In this section, we implement the SIR model in Python.
-
-\index{implementation}
-
-I'll represent the initial state of the system using a \py{State} object with state variables \py{S}, \py{I}, and \py{R}; they represent the fraction of the population in each compartment.
-
-\index{System object}
-\index{State object}
-\index{state variable}
-
-We can initialize the \py{State} object with the {\em number} of people in each compartment, assuming there is one infected student in a class of 90:
-
-\begin{python}
-init = State(S=89, I=1, R=0)
-\end{python}
-
-And then convert the numbers to fractions by dividing by the total:
-
-\begin{python}
-init /= sum(init)
-\end{python}
-
-For now, let's assume we know the time between contacts and time between recoveries:
-
-\begin{python}
-tc = 3             # time between contacts in days 
-tr = 4             # recovery time in days
-\end{python}
-
-We can use them to compute the parameters of the model:
-
-\begin{python}
-beta = 1 / tc      # contact rate in per day
-gamma = 1 / tr     # recovery rate in per day
-\end{python}
-
-Now we need a \py{System} object to store the parameters and initial conditions.  The following function takes the system parameters as function parameters and returns a new \py{System} object:
-
-\index{\py{make_system}}
-
-\begin{python}
-def make_system(beta, gamma):
-    init = State(S=89, I=1, R=0)
-    init /= sum(init)
-
-    t0 = 0
-    t_end = 7 * 14
-
-    return System(init=init, t0=t0, t_end=t_end,
-                  beta=beta, gamma=gamma)
-\end{python}
-
-The default value for \py{t_end} is 14 weeks, about the length of a semester.
-
-
-\section{The update function}
-
-At any point in time, the state of the system is represented by a \py{State} object with three variables, \py{S}, \py{I} and \py{R}.  So I'll define an update function that takes as parameters a \py{State} object, the current time, and a \py{System} object:
-
-\index{update function}
-\index{function!update}
-\index{time step}
-
-\begin{python}
-def update_func(state, t, system):
-    s, i, r = state
-
-    infected = system.beta * i * s    
-    recovered = system.gamma * i
-    
-    s -= infected
-    i += infected - recovered
-    r += recovered
-    
-    return State(S=s, I=i, R=r)
-\end{python}
-
-The first line uses a feature we have not seen before, {\bf multiple assignment}.  The value on the right side is a \py{State} object that contains three values.  The left side is a sequence of three variable names.  The assignment does just what we want: it assigns the three values from the \py{State} object to the three variables, in order.
-
-The local variables, \py{s}, \py{i} and \py{r}, are lowercase to distinguish them from the state variables, \py{S}, \py{I} and \py{R}.
-
-\index{State object}
-\index{state variable}
-\index{local variable}
-
-The update function computes \py{infected} and \py{recovered} as a fraction of the population, then updates \py{s}, \py{i} and \py{r}.  The return value is a \py{State} that contains the updated values.
-
-\index{return value}
-
-When we call \py{update_func} like this:
-
-\begin{python}
-state = update_func(init, 0, system)
-\end{python}
-
-The result is a \py{State} object with these values:
-
-\begin{tabular}{lr}
- & {\bf \sf value} \\ 
-\hline 
-{\bf \sf S} & 0.985388 \\ 
-{\bf \sf I} & 0.011865 \\ 
-{\bf \sf R} & 0.002747 \\ 
-\end{tabular} 
-
-You might notice that this version of \py{update_func} does not use one of its parameters, \py{t}.  I include it anyway because update functions sometimes depend on time, and it is convenient if they all take the same parameters, whether they need them or not.
-
-%TODO: figure out when to talk about integers and floats (or never)
-
-
-\section{Running the simulation}
-
-Now we can simulate the model over a sequence of time steps:
-
-\index{time step}
-
-\begin{python}
-def run_simulation(system, update_func):
-    state = system.init
-
-    for t in linrange(system.t0, system.t_end):
-        state = update_func(state, t, system)
-
-    return state
-\end{python}
-
-The parameters of \py{run_simulation} are the \py{System} object and the update function.  The \py{System} object contains the parameters, initial conditions, and values of \py{t0} and \py{t_end}.
-
-\index{\py{run_simulation}}
-
-The outline of this function should look familiar; it is similar to the function we used for the population model in Section~\ref{nowwithsystem}.
-
-We can call \py{run_simulation} like this:
-
-\begin{python}
-system = make_system(beta, gamma)
-final_state = run_simulation(system, update_func)
-\end{python}
-
-The result is the final state of the system:
-
-\begin{tabular}{lr}
- & {\bf \sf value} \\ 
-\hline 
-{\bf \sf S} & 0.520819 \\ 
-{\bf \sf I} & 0.000676 \\ 
-{\bf \sf R} & 0.478505 \\ 
-\end{tabular} 
-
-This result indicates that after 14 weeks (98 days), about 52\% of the population is still susceptible, which means they were never infected, less than 1\% are actively infected, and 48\% have recovered, which means they were infected at some point.
-
-
-\section{Collecting the results}
-
-The previous version of \py{run_simulation} only returns the final state, but we might want to see how the state changes over time.  We'll consider two ways to do that: first, using three \py{TimeSeries} objects, then using a new object called a \py{TimeFrame}.
-
-\index{TimeFrame object}
-\index{TimeSeries object}
-
-Here's the first version:
-
-\begin{python}
-def run_simulation(system, update_func):
-    S = TimeSeries()
-    I = TimeSeries()
-    R = TimeSeries()
-
-    state = system.init
-    t0 = system.t0
-    S[t0], I[t0], R[t0] = state
-    
-    for t in linrange(system.t0, system.t_end):
-        state = update_func(state, t, system)
-        S[t+1], I[t+1], R[t+1] = state
-    
-    return S, I, R
-\end{python}
-
-First, we create \py{TimeSeries} objects to store the results.  Notice that the variables \py{S}, \py{I}, and \py{R} are \py{TimeSeries} objects now.
-
-Next we initialize \py{state}, \py{t0}, and the first elements of \py{S}, \py{I} and \py{R}.  
-
-Inside the loop, we use \py{update_func} to compute the state of the system at the next time step, then use multiple assignment to unpack the elements of \py{state}, assigning each to the corresponding \py{TimeSeries}.
-
-\index{time step}
-
-At the end of the function, we return the values \py{S}, \py{I}, and \py{R}.  This is the first example we have seen where a function returns more than one value.
-
-Now we can run the function like this:
-
-\begin{python}
-system = make_system(beta, gamma)
-S, I, R = run_simulation(system, update_func)
-\end{python}
-
-We'll use the following function to plot the results:
-
-\begin{python}
-def plot_results(S, I, R):
-    plot(S, '--', label='Susceptible')
-    plot(I, '-', label='Infected')
-    plot(R, ':', label='Resistant')
-    decorate(xlabel='Time (days)',
-             ylabel='Fraction of population')
-\end{python}
-
-\index{plot}
-\index{decorate}
-
-And run it like this:
-
-\begin{python}
-plot_results(S, I, R)
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap11-fig01.pdf}}
-\caption{Time series for \py{S}, \py{I}, and \py{R} over the course of 98 days.}
-\label{chap11-fig01}
-\end{figure}
-
-Figure~\ref{chap11-fig01} shows the result.  Notice that it takes about three weeks (21 days) for the outbreak to get going, and about six weeks (42 days) before it peaks.  The fraction of the population that's infected is never very high, but it adds up.  In total, almost half the population gets sick. 
-
-
-\section{Now with a TimeFrame}
-\label{timeframe}
-
-If the number of state variables is small, storing them as separate \py{TimeSeries} objects might not be so bad.  But a better alternative is to use a \py{TimeFrame}, which is another object defined in the ModSim library.
-
-\index{TimeFrame object}
-\index{DataFrame object}
-
-A \py{TimeFrame} is almost identical to a \py{DataFrame}, which we used in Section~\ref{worldpopdata}, with just a few changes I made to adapt it for our purposes.
-
-Here's a more concise version of \py{run_simulation} using a \py{TimeFrame}:
-
-\begin{python}
-def run_simulation(system, update_func):
-    frame = TimeFrame(columns=system.init.index)
-    frame.row[system.t0] = system.init
-    
-    for t in linrange(system.t0, system.t_end):
-        frame.row[t+1] = update_func(frame.row[t], system)
-    
-    return frame
-\end{python}
-
-The first line creates an empty \py{TimeFrame} with one column for each state variable.  Then, before the loop starts, we store the initial conditions in the \py{TimeFrame} at \py{t0}.  Based on the way we've been using \py{TimeSeries} objects, it is tempting to write:
-
-\begin{python}
-frame[system.t0] = system.init
-\end{python}
-
-But when you use the bracket operator with a \py{TimeFrame} or \py{DataFrame}, it selects a column, not a row.  For example, to select a column, we could write:
-
-\index{bracket operator}
-\index{operator~bracket}
-
-\begin{python}
-frame['S']
-\end{python}
-
-To select a row, we have to use \py{row}, like this:
-
-\index{row}
-
-\begin{python}
-frame.row[system.t0] = system.init
-\end{python}
-
-Since the value on the right side is a \py{State}, the assignment matches up the index of the \py{State} with the columns of the \py{TimeFrame}; that is, it assigns the \py{S} value from \py{system.init} to the \py{S} column of \py{frame}, and likewise with \py{I} and \py{R}.
-
-\index{assignment}
-
-We can use the same feature to write the loop more concisely, assigning the \py{State} we get from \py{update_func} directly to the next row of \py{frame}.
-  
-\index{system variable}
-
-Finally, we return \py{frame}.  We can call this version of \py{run_simulation} like this:
-
-\begin{python}
-results = run_simulation(system, update_func)
-\end{python}
-
-And plot the results like this:
-
-\begin{python}
-plot_results(results.S, results.I, results.R)
-\end{python}
-
-As with a \py{DataFrame}, we can use the dot operator to select columns from a \py{TimeFrame}.
-
-\index{dot operator}
-\index{operator!dot}
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap11.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-\chapter{Optimization}
-\label{chap12}
-
-In the previous chapter I presented the SIR model of infectious disease and used it to model the Freshman Plague at Olin.  In this chapter we'll consider metrics intended to quantify the effects of the disease and interventions intended to reduce those effects.
-
-\section{Metrics}
-\label{metrics2}
-
-When we plot a time series, we get a view of everything that happened when the model ran, but often we want to boil it down to a few numbers that summarize the outcome.  These summary statistics are called {\bf metrics}, as we saw in Section~\ref{metrics}.
-
-\index{metric}
-
-In the SIR model, we might want to know the time until the peak of the outbreak, the number of people who are sick at the peak, the number of students who will still be sick at the end of the semester, or the total number of students who get sick at any point.
-
-As an example, I will focus on the last one --- the total number of sick students --- and we will consider interventions intended to minimize it.
-
-When a person gets infected, they move from \py{S} to \py{I}, so we can get the total number of infections by computing the difference in \py{S} at the beginning and the end:
-
-\begin{python}
-def calc_total_infected(results, system):
-    return results.S[system.t0] - results.S[system.t_end]
-\end{python}
-
-In the notebook that accompanies this chapter, you will have a chance to write functions that compute other metrics.  Two functions you might find useful are \py{max} and \py{idxmax}.
-
-\index{max}
-\index{idxmax}
- 
-If you have a \py{Series} called \py{S}, you can compute the largest value of the series like this:
-
-\begin{python}
-largest_value = S.max()
-\end{python}
-
-And the label of the largest value like this:
-
-\begin{python}
-time_of_largest_value = S.idxmax()
-\end{python}
-
-If the \py{Series} is a \py{TimeSeries}, the label you get from \py{idxmax} is a time or date.  You can read more about these functions in the \py{Series} documentation at \url{http://modsimpy.com/series}.
-
-\index{Series}
-
-
-\section{Immunization}
-
-Models like this are useful for testing ``what if?" scenarios.  As an example, we'll consider the effect of immunization.
-
-\index{immunization}
-\index{vaccine}
-\index{Freshman Plague}
-
-Suppose there is a vaccine that causes a student to become immune to the Freshman Plague without being infected.  How might you modify the model to capture this effect?
-
-One option is to treat immunization as a shortcut from susceptible to recovered without going through infectious.  We can implement this feature like this:
-
-\begin{python}
-def add_immunization(system, fraction):
-    system.init.S -= fraction
-    system.init.R += fraction
-\end{python}
-
-\py{add_immunization} moves the given fraction of the population from \py{S} to \py{R}.  If we assume that 10\% of students are vaccinated at the beginning of the semester, and the vaccine is 100\% effective, we can simulate the effect like this:
-
-\begin{python}
-system2 = make_system(beta, gamma)
-add_immunization(system2, 0.1)
-results2 = run_simulation(system2, update_func)
-\end{python}
-
-For comparison, we can run the same model without immunization and plot the results.  Figure~\ref{chap12-fig01} shows \py{S} as a function of time, with and without immunization.  
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap12-fig01.pdf}}
-\caption{Time series for \py{S}, with and without immunization.}
-\label{chap12-fig01}
-\end{figure}
-
-Without immunization, almost 47\% of the population gets infected at some point.  With 10\% immunization, only 31\% gets infected.  That's pretty good.
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap12-fig02.pdf}}
-\caption{Fraction of the population infected as a function of immunization rate.}
-\label{chap12-fig02}
-\end{figure} 
-
-Now let's see what happens if we administer more vaccines.  This following function sweeps a range of immunization rates:
-
-\index{sweep}
-
-\begin{python}
-def sweep_immunity(immunize_array):
-    sweep = SweepSeries()
-
-    for fraction in immunize_array:
-        sir = make_system(beta, gamma)
-        add_immunization(sir, fraction)
-        results = run_simulation(sir, update_func)
-        sweep[fraction] = calc_total_infected(results, sir)
-
-    return sweep
-\end{python}
-
-The parameter of \py{sweep_immunity} is an array of immunization rates.  The result is a \py{SweepSeries} object that maps from each immunization rate to the resulting fraction of students ever infected.
-  
-\index{SweepSeries object}
-\index{parameter sweep}
-
-Figure~\ref{chap12-fig02} shows a plot of the \py{SweepSeries}.  Notice that the x-axis is the immunization rate, not time.
-
-As the immunization rate increases, the number of infections drops steeply.  If 40\% of the students are immunized, fewer than 4\% get sick.  That's because immunization has two effects: it protects the people who get immunized (of course) but it also protects the rest of the population. 
-
-Reducing the number of ``susceptibles" and increasing the number of ``resistants" makes it harder for the disease to spread, because some fraction of contacts are wasted on people who cannot be infected.  This phenomenon is called {\bf herd immunity}, and it is an important element of public health (see \url{http://modsimpy.com/herd}).
-
-\index{herd immunity}
-
-The steepness of the curve in Figure~\ref{chap12-fig02} is a blessing and a curse.  It's a blessing because it means we don't have to immunize everyone, and vaccines can protect the ``herd" even if they are not 100\% effective.
-
-But it's a curse because a small decrease in immunization can cause a big increase in infections.  In this example, if we drop from 80\% immunization to 60\%, that might not be too bad.  But if we drop from 40\% to 20\%, that would trigger a major outbreak, affecting more than 15\% of the population.  For a serious disease like measles, just to name one, that would be a public health catastrophe.
-
-\index{measles}
-
-One use of models like this is to demonstrate phenomena like herd immunity and to predict the effect of interventions like vaccination.  Another use is to evaluate alternatives and guide decision making.  We'll see an example in the next section.
-
-
-
-
-
-\section{Hand washing}
-
-Suppose you are the Dean of Student Life, and you have a budget of just \$1200 to combat the Freshman Plague.  You have two options for spending this money:
-
-\begin{enumerate}
-
-\item You can pay for vaccinations, at a rate of \$100 per dose.
-
-\item You can spend money on a campaign to remind students to wash hands frequently.
-
-\end{enumerate}
-
-We have already seen how we can model the effect of vaccination.  Now let's think about the hand-washing campaign.  We'll have to answer two questions:
-
-\begin{enumerate}
-
-\item How should we incorporate the effect of hand washing in the model?
-
-\item How should we quantify the effect of the money we spend on a hand-washing campaign?
-
-\end{enumerate}
-
-For the sake of simplicity, let's assume that we have data from a similar campaign at another school showing that a well-funded campaign can change student behavior enough to reduce the infection rate by 20\%.  
-
-In terms of the model, hand washing has the effect of reducing \py{beta}.  That's not the only way we could incorporate the effect, but it seems reasonable and it's easy to implement.
-
-Now we have to model the relationship between the money we spend and the effectiveness of the campaign.  Again, let's suppose we have data from another school that suggests:
-
-\begin{itemize}
-
-\item If we spend \$500 on posters, materials, and staff time, we can change student behavior in a way that decreases the effective value of \py{beta} by 10\%.
-
-\item If we spend \$1000, the total decrease in \py{beta} is almost 20\%.
-
-\item Above \$1000, additional spending has little additional benefit.
-
-\end{itemize}
-
-In the notebook for this chapter you will see how I used a logistic curve to fit this data.  The result is the following function, which takes spending as a parameter and returns \py{factor}, which is the factor by which \py{beta} is reduced:
-
-\index{logistic curve}
-
-\begin{python}
-def compute_factor(spending):
-    return logistic(spending, M=500, K=0.2, B=0.01)
-\end{python}
-
-I use \py{compute_factor} to write \py{add_hand_washing}, which takes a \py{System} object and a budget, and modifies \py{system.beta} to model the effect of hand washing:
-
-\begin{python}
-def add_hand_washing(system, spending):
-    factor = compute_factor(spending)
-    system.beta *= (1 - factor)
-\end{python}
-
-Now we can sweep a range of values for \py{spending} and use the simulation to compute the effect:
-
-\begin{python}
-def sweep_hand_washing(spending_array):
-    sweep = SweepSeries()
-    
-    for spending in spending_array:
-        sir = make_system(beta, gamma)
-        add_hand_washing(sir, spending)
-        results, run_simulation(sir, update_func)
-        sweep[spending] = calc_total_infected(results, sir)
-        
-    return sweep
-\end{python}
-
-Here's how we run it:
-
-\begin{python}
-spending_array = linspace(0, 1200, 20)
-infected_sweep = sweep_hand_washing(spending_array)
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap12-fig03.pdf}}
-\caption{Fraction of the population infected as a function of hand-washing campaign spending.}
-\label{chap12-fig03}
-\end{figure} 
-
-Figure~\ref{chap12-fig03} shows the result.  Below \$200, the campaign has little effect.  At \$800 it has a substantial effect, reducing total infections from 46\% to 20\%.  Above \$800, the additional benefit is small.
-
-
-\section{Optimization} 
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap12-fig04.pdf}}
-\caption{Fraction of the population infected as a function of the number of doses.}
-\label{chap12-fig04}
-\end{figure} 
-
-Let's put it all together.  With a fixed budget of \$1200, we have to decide how many doses of vaccine to buy and how much to spend on the hand-washing campaign.
-
-\index{optimization}
-
-Here are the parameters:
-
-\begin{python}
-num_students = 90
-budget = 1200
-price_per_dose = 100
-max_doses = int(budget / price_per_dose)
-\end{python}
-
-The fraction \py{budget/price_per_dose} might not be an integer.  \py{int} is a built-in function that converts numbers to integers, rounding down.
-
-We'll sweep the range of possible doses:
-
-\begin{python}
-dose_array = linrange(max_doses, endpoint=True)
-\end{python}
-
-In this example we call \py{linrange} with only one argument; it returns a NumPy array with the integers from 0 to \py{max_doses}.  With the argument \py{endpoint=True}, the result includes both endpoints.
-
-\index{linrange}
-\index{NumPy}
- \index{array}
-
-Then we run the simulation for each element of \py{dose_array}:
-
-\begin{python}
-def sweep_doses(dose_array):
-    sweep = SweepSeries()
-    
-    for doses in dose_array:
-        fraction = doses / num_students
-        spending = budget - doses * price_per_dose
-        
-        sir = make_system(beta, gamma)
-        add_immunization(sir, fraction)
-        add_hand_washing(sir, spending)
-        
-        run_simulation(sir, update_func)
-        sweep[doses] = calc_total_infected(sir)
-
-    return sweep
-\end{python}
-
-For each number of doses, we compute the fraction of students we can immunize, \py{fraction} and the remaining budget we can spend on the campaign, \py{spending}.  Then we run the simulation with those quantities and store the number of infections.
-
-Figure~\ref{chap12-fig04} shows the result.  If we buy no doses of vaccine and spend the entire budget on the campaign, the fraction infected is around 19\%.  At 4 doses, we have \$800 left for the campaign, and this is the optimal point that minimizes the number of students who get sick.
-
-As we increase the number of doses, we have to cut campaign spending, which turns out to make things worse.  But interestingly, when we get above 10 doses, the effect of herd immunity starts to kick in, and the number of sick students goes down again.
-
-\index{herd immunity}
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap12.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-
-\chapter{Sweeping two parameters}
-\label{chap13}
-
-In the previous chapters I presented an SIR model of infectious disease, specifically the Kermack-McKendrick model.  We extended the model to include vaccination and the effect of a hand-washing campaign, and used the extended model to allocate a limited budget optimally, that is, to minimize the number of infections.
-
-\index{Kermack-McKendrick model}
-\index{SIR model}
-
-But we assumed that the parameters of the model, contact rate and recovery rate, were known.  In this chapter, we explore the behavior of the model as we vary these parameters, use analysis to understand these relationships better, and propose a method for using data to estimate parameters.
-
-\section{Sweeping beta}
-
-Recall that $\beta$ is the contact rate, which captures both the frequency of interaction between people and the fraction of those interactions that result in a new infection.  If $N$ is the size of the population and $s$ is the fraction that's susceptible, $s N$ is the number of susceptibles, $\beta s N$ is the number of contacts per day between susceptibles and other people, and $\beta s i N$ is the number of those contacts where the other person is infectious.
-\index{parameter sweep}
-
-As $\beta$ increases, we expect the total number of infections to increase.  To quantify that relationship, I'll create a range of values for $\beta$:
-
-\begin{python}
-beta_array = linspace(0.1, 1.1, 11)
-\end{python}
-
-Then run the simulation for each value and print the results.
-
-\begin{python}
-for beta in beta_array:
-    sir = make_system(beta, gamma)
-    run_simulation(sir, update1)
-    print(sir.beta, calc_total_infected(sir))
-\end{python}
-
-We can wrap that code in a function and store the results in a \py{SweepSeries} object:
-\index{SweepSeries object}
-
-\begin{python}
-def sweep_beta(beta_array, gamma):
-    sweep = SweepSeries()
-    for beta in beta_array:
-        system = make_system(beta, gamma)
-        run_simulation(system, update1)
-        sweep[system.beta] = calc_total_infected(system)
-    return sweep
-\end{python}
-
-Now we can run \py{sweep_beta} like this:
-
-\begin{python}
-infected_sweep = sweep_beta(beta_array, gamma)
-\end{python}
-
-And plot the results:
-
-\begin{python}
-label = 'gamma = ' + str(gamma)
-plot(infected_sweep, label=label)
-\end{python}
-
-The first line uses string operations to assemble a label for the plotted line:
-\index{string}
-
-\begin{itemize}
-
-\item When the \py{+} operator is applied to strings, it joins them end-to-end, which is called {\bf concatenation}. 
- 
-\index{concatenation}
-
-\item The function \py{str} converts any type of object to a String representation.  In this case, \py{gamma} is a number, so we have to convert it to a string before trying to concatenate it.
-
-\index{str function}
-
-\end{itemize}
-
-% TODO: Make the gamma symbol appear in the line below and in the figures.
-
-If the value of \py{gamma} is \py{0.25}, the value of \py{label} is the string \py{'gamma = 0.25'}.
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap13-fig01.pdf}}
-\caption{Fraction of students infected as a function of the parameter \py{beta}, with \py{gamma = 0.25}.}
-\label{chap13-fig01}
-\end{figure}
-
-Figure~\ref{chap13-fig01} shows the results.  Remember that this figure is a parameter sweep, not a time series, so the x-axis is the parameter \py{beta}, not time.  
-
-When \py{beta} is small, the contact rate is low and the outbreak never really takes off; the total number of infected students is near zero.  As \py{beta} increases, it reaches a threshold near 0.3 where the fraction of infected students increases quickly.  When \py{beta} exceeds 0.5, more than 80\% of the population gets sick.
-
-
-\section{Sweeping gamma}
-
-Let's see what that looks like for a few different values of \py{gamma}.  Again, we'll use \py{linspace} to make an array of values:
-
-\index{linspace}
-
-\begin{python}
-gamma_array = linspace(0.1, 0.7, 4)
-\end{python}
-
-And run \py{sweep_beta} for each value of \py{gamma}:
-
-\begin{python}
-for gamma in gamma_array:
-    infected_sweep = sweep_beta(beta_array, gamma)
-    label = 'gamma = ' + str(gamma)
-    plot(infected_sweep, label=label)
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap13-fig02.pdf}}
-\caption{Fraction of students infected as a function of the parameter \py{beta}, for several values of \py{gamma}.}
-\label{chap13-fig02}
-\end{figure}
-
-Figure~\ref{chap13-fig02} shows the results.  When \py{gamma} is low, the recovery rate is low, which means people are infectious longer.  In that case, even a low contact rate (\py{beta}) results in an epidemic.
-
-When \py{gamma} is high, \py{beta} has to be even higher to get things going.
-
-
-\section{SweepFrame}
-\label{sweepframe}
-
-In the previous section, we swept a range of values for \py{gamma}, and for each value, we swept a range of values for \py{beta}.  This process is a {\bf two-dimensional sweep}.  
-
-If we want to store the results, rather than plot them, we can use a \py{SweepFrame}, which is a kind of {\tt DataFrame} where the rows sweep one parameter, the columns sweep another parameter, and the values contain  metrics from a simulation.
-
-\index{SweepFrame object}
-\index{DataFrame object}
-
-This function shows how it works:
-
-\begin{python}
-def sweep_parameters(beta_array, gamma_array):
-    frame = SweepFrame(columns=gamma_array)
-    for gamma in gamma_array:
-        frame[gamma] = sweep_beta(beta_array, gamma)
-    return frame
-\end{python}
-
-\py{sweep_parameters} takes as parameters an array of values for \py{beta} and an array of values for \py{gamma}.
-
-It creates a \py{SweepFrame} to store the results, with one column for each value of \py{gamma} and one row for each value of \py{beta}.
-
-Each time through the loop, we run \py{sweep_beta}.  The result is a \py{SweepSeries} object with one element for each value of \py{beta}.  The assignment inside the loop stores the \py{SweepSeries} as a new column in the \py{SweepFrame}, corresponding to the current value of \py{gamma}.
-
-At the end, the \py{SweepFrame} stores the fraction of students infected for each pair of parameters, \py{beta} and \py{gamma}.
-
-We can run \py{sweep_parameters} like this:
-
-\begin{python}
-frame = sweep_parameters(beta_array, gamma_array)
-\end{python}
-
-With the results in a \py{SweepFrame}, we can plot each column like this:
-
-\begin{python}
-for gamma in gamma_array:
-    label = 'gamma = ' + str(gamma)
-    plot(frame[gamma], label=label)
-\end{python}
-
-Alternatively, we can plot each row like this:
-
-\begin{python}
-for beta in [1.1, 0.9, 0.7, 0.5, 0.3]:
-    label = 'β = ' + str(beta)
-    plot(frame.row[beta], label=label)
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap13-fig03.pdf}}
-\caption{Fraction of students infected as a function of the parameter \py{gamma}, for several values of \py{beta}.}
-\label{chap13-fig03}
-\end{figure}
-
-Figure~\ref{chap13-fig03} shows the results.  This example demonstrates one use of a \py{SweepFrame}: we can run the analysis once, save the results, and then generate different visualizations.
-
-Another way to visualize the results of a two-dimensional sweep is a {\bf contour plot}, which shows the parameters on the axes and contour lines, that is, lines of constant value.  In this example, the value is the fraction of students infected.
-
-\index{contour plot}
-
-The ModSim library provides \py{contour}, which takes a \py{SweepFrame} as a parameter:
-
-\begin{python}
-contour(frame)
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap13-fig04.pdf}}
-\caption{Contour plot showing fraction of students infected as a function of the parameters \py{gamma} and \py{beta}.}
-\label{chap13-fig04}
-\end{figure}
-
-Figure~\ref{chap13-fig04} shows the result.  Infection rates are lowest in the lower right, where the contact rate is and the recovery rate is high.  They increase as we move to the upper left, where the contact rate is high and the recovery rate is low.
-
-This figure suggests that there might be a relationship between \py{beta} and \py{gamma} that determines the outcome of the model.  In fact, there is.  In the next chapter we'll explore it by running simulations, then derive it by analysis.
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap13.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-\chapter{Analysis} 
-\label{chap14}
-
-In the previous chapters we used simulation to predict the effect of an infectious disease in a susceptible population and to design interventions that would minimize the effect.
-
-In this chapter we use analysis to investigate the relationship between the parameters, \py{beta} and \py{gamma}, and the outcome of the simulation.
-
-\section{Nondimensionalization}
-\label{nondim}
-
-The figures in Section~\ref{sweepframe} suggest that there is a relationship between the parameters of the SIR model, \py{beta} and \py{gamma}, that determines the outcome of the simulation, the fraction of students infected.
-Let's think what that relationship might be.
-
-\begin{itemize}
-
-\item When \py{beta} exceeds \py{gamma}, that means there are more contacts (that is, potential infections) than recoveries during each day (or other unit of time).  The difference between \py{beta} and \py{gamma} might be called the ``excess contact rate", in units of contacts per day.
-
-\item As an alternative, we might consider the ratio \py{beta/gamma}, which is the number of contacts per recovery.  Because the numerator and denominator are in the same units, this ratio is {\bf dimensionless}, which means it has no units.
-\index{dimensionless}
-
-\end{itemize}
-
-Describing physical systems using dimensionless parameters is often a useful move in the modeling and simulation game.  It is so useful, in fact, that it has a name: {\bf nondimensionalization} (see \url{http://modsimpy.com/nondim}).
-
-\index{nondimensionalization}
-
-So we'll try the second option first.  In the notebook for this chapter, you can explore the first option as an exercise.
-
-
-\section{Exploring the results}
-
-Suppose we have a \py{SweepFrame} with one row for each value of \py{beta} and one column for each value of \py{gamma}.  Each element in the \py{SweepFrame} is the fraction of students infected in a simulation with a given pair of parameters.
-
-We can print the values in the \py{SweepFrame} like this:
-
-\begin{python}
-for gamma in frame.columns:
-    column = frame[gamma]
-    for beta in column.index:
-        frac_infected = column[beta]
-        print(beta, gamma, frac_infected)
-\end{python}
-
-This is the first example we've seen with one \py{for} loop inside another:
-\index{loop}
-\index{for loop}
-
-\begin{itemize}
-
-\item Each time the outer loop runs, it selects a value of \py{gamma} from the columns of the \py{DataFrame} and extracts the corresponding column.
-
-\item Each time the inner loop runs, it selects a value of \py{beta} from the column and selects the corresponding element, which is the fraction of students infected.
-
-\end{itemize}  
-
-In the example from the previous chapter, \py{frame} has 4 columns, one for each value of \py{gamma}, and 11 rows, one for each value of \py{beta}.  So these loops print 44 lines, one for each pair of parameters.
-
-The following function encapulates the previous loop and plots the fraction infected as a function of the ratio \py{beta/gamma}:
-
-\begin{python}
-def plot_sweep_frame(frame):
-    for gamma in frame.columns:
-        series = frame[gamma]
-        for beta in series.index:
-            frac_infected = series[beta]
-            plot(beta/gamma, frac_infected, 'ro')
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap14-fig01.pdf}}
-\caption{Total fraction infected as a function of contact number.}
-\label{chap14-fig01}
-\end{figure}
-
-Figure~\ref{chap14-fig01} shows that the results fall on a single curve, at least approximately.  That means that we can predict the fraction of students who will be infected based on a single parameter, the ratio \py{beta/gamma}.  We don't need to know the values of \py{beta} and \py{gamma} separately.
-
-
-\section{Contact number}
-\label{contact}
-
-From Section~\ref{sirmodel}, recall that the number of new infections in a given day is $\beta s i N$, and the number of recoveries is $\gamma i N$.  If we divide these quantities, the result is $\beta s / \gamma$, which is the number of new infections per recovery (as a fraction of the population).
-
-\index{contact number}
-\index{basic reproduction number}
-
-When a new disease is introduced to a susceptible population, $s$ is approximately 1, so the number of people infected by each sick person is $\beta / \gamma$.  This ratio is called the ``contact number" or ``basic reproduction number" (see \url{http://modsimpy.com/contact}).  By convention it is usually denoted $R_0$, but in the context of an SIR model, this notation is confusing, so we'll use $c$ instead.
-
-The results in the previous section suggest that there is a relationship between $c$ and the total number of infections.  We can derive this relationship by analyzing the differential equations from Section~\ref{sireqn}:
-%
-\begin{align*}
-\frac{ds}{dt} &= -\beta s i \\
-\frac{di}{dt} &= \beta s i - \gamma i\\
-\frac{dr}{dt} &= \gamma i
-\end{align*}
-%
-In the same way we divided the contact rate by the infection rate to get the dimensionless quantity $c$, now we'll divide $di/dt$ by $ds/dt$ to get a ratio of rates:
-%
-\[ \frac{di}{ds} = -1 + \frac{1}{cs} \]
-%
-Dividing one differential equation by another is not an obvious move, but in this case it is useful because it gives us a relationship between $i$, $s$ and $c$ that does not depend on time.  From that relationship, we can derive an equation that relates $c$ to the final value of $s$.  In theory, this equation makes it possible to infer $c$ by observing the course of an epidemic.
-
-Here's how the derivation goes.  We multiply both sides of the previous equation by $ds$:
-%
-\[ di = \left( -1 + \frac{1}{cs} \right) ds \]
-%
-And then integrate both sides:
-%
-\[ i = -s + \frac{1}{c} \log s + q \]
-%
-where $q$ is a constant of integration.  Rearranging terms yields:
-%
-\[ q = i + s - \frac{1}{c} \log s \]
-%
-Now let's see if we can figure out what $q$ is.  At the beginning of an epidemic, if the fraction infected is small and nearly everyone is susceptible, we can use the approximations $i(0) = 0$ and $s(0) = 1$ to compute $q$:
-%
-\[ q = 0 + 1 + \frac{1}{c} \log 1 \]
-%
-Since $\log 1 = 0$, we get $q = 1$.
-\index{integration}
-\index{constant of integration}
-
-\newcommand{\sinf}{s_{\infty}}
-
-Now, at the end of the epidemic, let's assume that $i(\infty) = 0$, and $s(\infty)$ is an unknown quantity, $\sinf$.  Now we have:
-%
-\[ q = 1 = 0 + \sinf - \frac{1}{c} \log \sinf \]
-%
-Solving for $c$, we get
-%
-\[ c = \frac{\log \sinf}{\sinf - 1} \]
-%
-By relating $c$ and $\sinf$, this equation makes it possible to estimate $c$ based on data, and possibly predict the behavior of future epidemics.
-
-\section{Analysis and simulation}
-
-Let's compare this analytic result to the results from simulation.
-I'll create an array of values for $\sinf$
-\index{linspace}
-
-\begin{python}
-s_inf_array = linspace(0.0001, 0.9999, 31)
-\end{python}
-
-And compute the corresponding values of $c$:
-
-\begin{python}
-c_array = log(s_inf_array) / (s_inf_array - 1)
-\end{python}
-
-To get the total infected, we compute the difference between $s(0)$ and $s(\infty)$, then store the results in a \py{Series}:
-\index{array}
-\index{series}
-
-\begin{python}
-frac_infected = 1 - s_inf_array
-frac_infected_series = Series(frac_infected, index=c_array)
-\end{python}
-
-Recall from Section~\ref{dataframe} that a \py{Series} object contains an index and a corresponding sequence of values.  In this case, the index is \py{c_array} and the values are from \py{frac_infected}.
-
-Now we can plot the results:
-
-\begin{python}
-plot(frac_infected_series)
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap14-fig02.pdf}}
-\caption{Total fraction infected as a function of contact number, showing results from simulation and analysis.}
-\label{chap14-fig02}
-\end{figure}
-
-Figure~\ref{chap14-fig02} compares the analytic results from this section with the simulation results from Section~\ref{nondim}.  When the contact number exceeds 1, analysis and simulation agree.
-When the contact number is less than 1, they do not: analysis indicates there should be no infections; in the simulations there are a small number of infections.
-\index{analysis}
-
-The reason for the discrepancy is that the simulation divides time into a discrete series of days, whereas the analysis treats time as a continuous quantity.  In other words, the two methods are actually based on different models.  So which model is better?
-
-Probably neither.  When the contact number is small, the early progress of the epidemic depends on details of the scenario.  If we are lucky, the original infected person, ``patient zero",  infects no one and there is no epidemic.  If we are unlucky, patient zero might have a large number of close friends, or might work in the dining hall (and fail to observe safe food handling procedures).
-\index{patient zero}
-
-For contact numbers near or less than 1, we might need a more detailed model.  But for higher contact numbers the SIR model might be good enough.
-
-\section{Estimating contact number}
-
-Figure~\ref{chap14-fig02} shows that if we know the contact number, we can compute the fraction infected.  But we can also read the figure the other way; that is, at the end of an epidemic, if we can estimate the fraction of the population that was ever infected, we can use it to estimate the contact number.
-
-Well, in theory we can.  In practice, it might not work very well, because of the shape of the curve.  When the contact number is near 2, the curve is quite steep, which means that small changes in $c$ yield big changes in the number of infections.  If we observe that the total fraction infected is anywhere from 20\% to 80\%, we would conclude that $c$ is near 2.
-
-On the other hand, for larger contact numbers, nearly the entire population is infected, so the curve is nearly flat.  In that case we would not be able to estimate $c$ precisely, because any value greater than 3 would yield effectively the same results.  Fortunately, this is unlikely to happen in the real world; very few epidemics affect anything close to 90\% of the population.
-
-So the SIR model has limitations; nevertheless, it provides insight into the behavior of infectious disease, especially the phenomenon of herd immunity.  As we saw in Chapter~\ref{chap12}, if we know the parameters of the model, we can use it to evaluate possible interventions.  And as we saw in this chapter, we might be able to use data from earlier outbreaks to estimate the parameters.
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap14.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-
-%\part{Modeling thermal systems}
-
-
-
-\chapter{Heat}
-\label{chap15}
-
-So far the systems we have studied have been physical in the sense that they exist in the world, but they have not been physics, in the sense of what physics classes are usually about.  In the next few chapters, we'll do some physics, starting with {\bf thermal systems}, that is, systems where the temperature of objects changes as heat transfers from one to another.
-
-\index{thermal system}
-
-\section{The coffee cooling problem}
-
-The coffee cooling problem was discussed by Jearl Walker in {\it Scientific American} in 1977\footnote{Walker, ``The Amateur Scientist", {\it Scientific American}, Volume 237, Issue 5, November 1977.}; since then it has become a standard example of modeling and simulation.
-
-\index{coffee cooling problem}
-\index{Walker, Jearl}
-
-Here is my version of the problem:
-
-\begin{quote}
-Suppose I stop on the way to work to pick up a cup of coffee, which I take with milk.  Assuming that I want the coffee to be as hot as possible when I arrive at work, should I add the milk at the coffee shop, wait until I get to work, or add the milk at some point in between?
-\end{quote}
-
-To help answer this question, I made a trial run with the milk and coffee in separate containers and took some measurements\footnote{This is fiction.  I usually drink tea and bike to work.}:
-
-\begin{itemize}
-
-\item When served, the temperature of the coffee is \SI{90}{\celsius}.  The volume is \SI{300}{mL}.
-
-\item The milk is at an initial temperature of \SI{5}{\celsius}, and I take about \SI{50}{mL}.
-
-\item The ambient temperature in my car is \SI{22}{\celsius}.
-
-\item The coffee is served in a well insulated cup.  When I arrive at work after 30 minutes, the temperature of the coffee has fallen to \SI{70}{\celsius}.
-
-\item The milk container is not as well insulated.  After 15 minutes, it warms up to \SI{20}{\celsius}, nearly the ambient temperature.
-
-\end{itemize}
-
-To use this data and answer the question, we have to know something about temperature and heat, and we have to make some modeling decisions.
-
-
-\section{Temperature and heat}
-
-To understand how coffee cools (and milk warms), we need a model of temperature and heat.  {\bf Temperature} is a property of an object or a system; in SI units it is measured in degrees Celsius (\si{\celsius}).  Temperature quantifies how hot or cold the object is, which is related to the average velocity of the particles that make up the object.
-
-\index{temperature}
-
-When particles in a hot object contact particles in a cold object, the hot object gets cooler and the cold object gets warmer as energy is transferred from one to the other.  
-The transferred energy is called {\bf heat}; in SI units it is measured in joules (\si{\joule}).
-
-\index{heat}
-
-Heat is related to temperature by the following equation (see \url{http://modsimpy.com/thermass}):
-%
-\[ Q = C~\Delta T \]
-%
-where $Q$ is the amount of heat transferred to an object, $\Delta T$ is resulting change in temperature, and $C$ is the {\bf thermal mass} of the object, which quantifies how much energy it takes to heat or cool it.  
-In SI units, thermal mass is measured in joules per degree Celsius (\si{\joule\per\celsius}).
-
-\index{thermal mass}
-
-For objects made primarily from one material, thermal mass can be computed like this:
-%
-\[ C = m c_p \]
-%
-where $m$ is the mass of the object and $c_p$ is the {\bf specific heat capacity} of the material (see \url{http://modsimpy.com/specheat}).
-
-\index{specific heat capacity}
-
-We can use these equations to estimate the thermal mass of a cup of coffee.  The specific heat capacity of coffee is probably close to that of water, which is \SI{4.2}{\joule\per\gram\per\celsius}.  Assuming that the density of coffee is close to that of water, which is \SI{1}{\gram\per\milli\liter}, the mass of \SI{300}{\milli\liter} of coffee is \SI{300}{\gram}, and the thermal mass is \SI{1260}{\joule\per\celsius}.
-
-\index{density}
-
-So when a cup of coffee cools from \SI{90}{\celsius} to \SI{70}{\celsius}, the change in temperature, $\Delta T$ is \SI{20}{\celsius}, which means that \SI{25200}{\joule} of heat energy was transferred from the coffee to the surrounding environment (the cup holder and air in my car).
-
-To give you a sense of how much energy that is, if you were able to harness all of that heat to do work (which you cannot\footnote{See \url{http://modsimpy.com/thermo}.}), you could use it to lift a cup of coffee from sea level to \SI{8571}{\meter}, just shy of the height of Mount Everest, \SI{8848}{\meter}.
-
-\index{Mount Everest}
-
-Assuming that the cup has less mass than the coffee, and is made from a material with lower specific heat, we can ignore the thermal mass of the cup.
-For a cup with substantial thermal mass, like a ceramic mug, we might consider a model that computes the temperature of coffee and cup separately.
-
-
-\section{Heat transfer}
-
-In a situation like the coffee cooling problem, there are three ways heat transfers from one object to another (see \url{http://modsimpy.com/transfer}):
-
-\index{heat transfer}
-\index{conduction}
-\index{convection}
-\index{radiation}
-
-\begin{itemize}
-
-\item Conduction: When objects at different temperatures come into contact, the faster-moving particles of the higher-temperature object transfer kinetic energy to the slower-moving particles of the lower-temperature object.
-
-\item Convection: When particles in a gas or liquid flow from place to place, they carry heat energy with them.  Fluid flows can be caused by external action, like stirring, or by internal differences in temperature.  For example, you might have heard that hot air rises, which is a form of ``natural convection".
-
-\index{fluid flow}
-
-\item Radiation: As the particles in an object move due to thermal energy, they emit electromagnetic radiation.  The energy carried by this radiation depends on the object's temperature and surface properties (see \url{http://modsimpy.com/thermrad}).
-
-\end{itemize}
-
-For objects like coffee in a car, the effect of radiation is much smaller than the effects of conduction and convection, so we will ignore it.
-
-Convection can be a complex topic, since it often depends on details of fluid flow in three dimensions.  But for this problem we will be able to get away with a simple model called ``Newton's law of cooling".
-
-\index{Newton's law of cooling}
-
-\section{Newton's law of cooling}
-
-Newton's law of cooling asserts that the temperature rate of change for an object is proportional to the difference in temperature between the object and the surrounding environment:
-%
-\[ \frac{dT}{dt} = -r (T - T_{env}) \]
-%
-where $T$, the temperature of the object, is a function of time, $t$; $T_{env}$ is the temperature of the environment, and $r$ is a constant that characterizes how quickly heat is transferred between the system and the environment.
-
-Newton's so-called ``law" is really a model: it is a good approximation in some conditions and less good in others.
-
-For example, if the primary mechanism of heat transfer is conduction, Newton's law is ``true", which is to say that $r$ is constant over a wide range of temperatures.  And sometimes we can estimate $r$ based on the material properties and shape of the object.
-
-When convection contributes a non-negligible fraction of heat transfer, $r$ depends on temperature, but Newton's law is often accurate enough, at least over a narrow range of temperatures.  In this case $r$ usually has to be estimated experimentally, since it depends on details of surface shape, air flow, evaporation, etc.
-
-When radiation makes up a substantial part of heat transfer, Newton's law is not a good model at all.  This is the case for objects in space or in a vacuum, and for objects at high temperatures (more than a few hundred degrees Celsius, say).
-
-\index{radiation}
-
-However, for a situation like the coffee cooling problem, we expect Newton's model to be quite good.
-
-
-\section{Implementation}
-\label{coffee_impl}
-
-To get started, let's forget about the milk temporarily and focus on the coffee.  I'll create a \py{State} object to represent the initial temperature:
-
-\begin{python}
-init = State(T=90)
-\end{python}
-
-And a \py{System} object to contain the parameters of the system:
-
-\index{State object}
-\index{System object}
-
-\begin{python}
-coffee = System(init=init,
-                volume=300,
-                r=0.01,
-                T_env=22,
-                t_0=0,
-                t_end=30,
-                dt=1)
-\end{python}
-
-The values of \py{volume}, \py{T_env}, and \py{t_end} come from the statement of the problem.  I chose the value of \py{r} arbitrarily for now; we will figure out how to estimate it soon.
-
-\index{time step}
-
-\py{dt} is the time step we use to simulate the cooling process.
-Strictly speaking, Newton's law is a differential equation, but over a short period of time we can approximate it with a difference equation:
-%
-\[ \Delta T = -r (T - T_{env}) dt \]
-%
-where $dt$ is a small time step and $\Delta T$ is the change in temperature during that time step.
-
-Note: I use $\Delta T$ to denote a change in temperature over time, but in the context of heat transfer, you might also see $\Delta T$ used to denote the difference in temperature between an object and its environment, $T - T_{env}$.  To minimize confusion, I avoid this second use.
-
-Now we can write an update function:
-
-\begin{python}
-def update_func(state, t, system):
-    r, T_env, dt = system.r, system.T_env, system.dt
-    
-    T = state.T
-    T += -r * (T - T_env) * dt
-    
-    return State(T=T)
-\end{python}
-
-Like previous update functions, this one takes a \py{State} object, a time, and a \py{System} object.
-
-Now if we run 
-
-\begin{python}
-update_func(init, 0, coffee)
-\end{python}
-
-we see that the temperature after one minute is \SI{89.3}{\celsius}, so the temperature drops by about \SI{0.7}{\celsius\per\minute}, at least for this value of \py{r}.
-
-Here's a version of \py{run_simulation} that simulates a series of time steps from \py{t_0} to \py{t_end}:
-
-\index{time step}
-\index{\py{run_simulation}}
-
-\begin{python}
-def run_simulation(system, update_func):
-    init = system.init
-    t_0, t_end, dt = system.t_0, system.t_end, system.dt
-    
-    frame = TimeFrame(columns=init.index)
-    frame.row[t_0] = init
-    ts = linrange(t_0, t_end, dt)
-    
-    for t in ts:
-        frame.row[t+dt] = update_func(frame.row[t], t, system)
-    
-    return frame
-\end{python}
-
-This function is similar to previous versions of \py{run_simulation}.
-
-One difference is that it uses \py{linrange} to make an array of values from \py{t_0} to \py{t_end} with time step \py{dt}.  The result does not include \py{t_end}, so the last value in the array is \py{t_end-dt}.
-
-\index{linrange}
-\index{NumPy}
-\index{array}
-
-We can run it like this:
-
-\begin{python}
-results = run_simulation(coffee, update_func)
-\end{python}
-
-The result is a \py{TimeFrame} object with one row per time step and just one column, \py{T}.  The temperature after 30 minutes is \SI{72.3}{\celsius}, which is a little higher than stated in the problem, \SI{70}{\celsius}.  We can adjust \py{r} and find the right value by trial and error, but we'll see a better way in the next chapter.
-
-\index{time step}
-\index{TimeFrame object}
-
-First I want to wrap what we have so far in a function:
-
-\begin{python}
-def make_system(T_init, r, volume, t_end):
-    init = State(T=T_init)
-                   
-    return System(init=init,
-                  r=r, 
-                  volume=volume,
-                  temp=T_init,
-                  t_0=0, 
-                  t_end=t_end, 
-                  dt=1,
-                  T_env=22)
-\end{python}
-
-\py{make_system} takes the system parameters and packs them into a \py{System} object.  Now we can simulate the system like this:
-
-\index{\py{make_system}}
-
-\begin{python}
-coffee = make_system(T_init=90, r=0.01, 
-                     volume=300, t_end=30)
-results = run_simulation(coffee, update_func)
-\end{python}
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap15.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-\chapter{Mixing}
-\label{chap16}
-
-In the previous chapter we wrote a simulation of a cooling cup of coffee.  Given the initial temperature of the coffee, the temperature of the atmosphere, and the rate parameter, \py{r}, we can predict how the temperature of the coffee will change over time.
-
-In general, we don't know the value of \py{r}, but we can use measurements to estimate it.  Given an initial temperature, a final temperature, and the time in between, we can find \py{r} by trial and error.
-
-In this chapter, we'll see a better way to find \py{r}, using a {\bf bisection search}.
-
-And then we'll get back to solving the coffee cooling problem.
-
-\section{Finding roots}
-\label{root_bisect}
-
-The ModSim library provides a method called \py{root_bisect} that finds the roots of non-linear equations.  As a simple example, suppose you want to find the roots of the polynomial
-%
-\[ f(x) = (x - 1)(x - 2)(x - 3) \]
-%
-where {\bf root} means a value of $x$ that makes $f(x)=0$.  Because of the way I wrote the polynomial, we can see that if $x=1$, the first factor is 0; if $x=2$, the second factor is 0; and if $x=3$, the third factor is 0, so those are the roots.
-
-\index{\py{root_bisect}}
-\index{root}
-
-I'll use this example to demonstrate \py{root_bisect}.  First, we have to write a function that evaluates $f$:
-
-\begin{python}
-def func(x):
-    return (x-1) * (x-2) * (x-3)
-\end{python}
-
-Now we call \py{root_bisect} like this:
-
-\begin{python}
-res = root_bisect(func, [1.5, 2.5])
-print(res.root)
-\end{python}
-
-The first argument is the function whose roots we want.  
-The second argument is an interval that contains a root.  
-The result is an object that contains several variables, including \py{root}, which stores the root that was found.
-
-So how can we use \py{root_bisect} to estimate \py{r}?  
-
-What we want is the value of \py{r} that yields a final temperature of \SI{70}{\celsius}.  To work with \py{root_bisect}, we need a function that takes \py{r} as a parameter and returns the difference between the final temperature and the goal:
-
-\begin{python}
-def error_func1(r):
-    system = make_system(T_init=90, r=r, volume=300, t_end=30)
-    results = run_simulation(system, update_func)
-    T_final = get_last_value(results.T)
-    return T_final - 70
-\end{python}
-
-I call a function like this an ``error function" because it returns the difference between what we got and what we wanted, that is, the error.  When we find the right value of \py{r}, this error will be 0.
-
-\index{error function}
-\index{function!error}
-
-We can test \py{error_func1} like this, using our initial guess for \py{r}:
-
-\begin{python}
-error_func1(r=0.01)
-\end{python}
-
-The result is an error of \SI{2.3}{\celsius}, because the final temperature with this value of \py{r} is too high.
-
-With \py{r=0.02}, the error is \SI{-10.9}{\celsius}, which means that the final temperature is too low.  
-So we know that the correct value must be in between.
-
-Now we can call \py{root_bisect} like this:
-
-\begin{python}
-res = root_bisect(error_func1, [0.01, 0.02])
-r_coffee = res.root
-\end{python}
-
-In this example, \py{r_coffee} turns out to be about \py{0.012}, in units of \si{\per\minute} (inverse minutes).
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap16-fig01.pdf}}
-\caption{Temperature of the coffee and milk over time.}
-\label{chap16-fig01}
-\end{figure}
-
-As one of the exercises for this chapter, you will use the same process to estimate \py{r_milk}.  
-
-With the correct values of \py{r_coffee} and \py{r_milk}, the simulation results should look like Figure~\ref{chap16-fig01}, which shows the temperature of the coffee and milk over time.
-
-
-\section{Mixing liquids}
-
-When we mix two liquids, the temperature of the mixture depends on the temperatures of the ingredients, but it might not be obvious how to compute it.
-
-\index{mixing}
-
-Assuming there are no chemical reactions that either produce or consume heat, the total thermal energy of the system is the same before and after mixing; in other words, thermal energy is {\bf conserved}.
-
-\index{conservation of energy}
-
-If the temperature of the first liquid is $T_1$, the temperature of the second liquid is $T_2$, and the final temperature of the mixture is $T$, the heat transfer into the first liquid is $C_1 (T - T_1)$ and the heat transfer into the second liquid is $C_2 (T - T_2)$, where $C_1$ and $C_2$ are the thermal masses of the liquids.
-
-In order to conserve energy, these heat transfers must add up to 0:
-%
-\[ C_1 (T - T_1) + C_2 (T - T_2) = 0 \]
-%
-We can solve this equation for T:
-%
-\[ T = \frac{C_1 T_1 + C_2 T_2}{C_1 + C_2} \]
-%
-For the coffee cooling problem, we have the volume of each liquid; if we also know the density, $\rho$, and the specific heat capacity, $c_p$, we can compute thermal mass:
-%
-\[ C = \rho V c_p \]
-%
-If the liquids have the same density and heat capacity, they drop out of the equation, and we can write:
-%
-\[ T = \frac{V_1 T_1 + V_2 T_2}{V_1 + V_2} \]
-%
-where $V_1$ and $V_2$ are the volumes of the liquids.  
-
-As an approximation, I'll assume that milk and coffee have the same density and specific heat.
-As an exercise, you can look up these quantities and see how good this assumption is.
-
-\index{volume}
-\index{density}
-\index{specific heat}
-
-The following function takes two \py{System} objects that represent the coffee and milk, and creates a new \py{System} to represent the mixture:
-
-\begin{python}
-def mix(system1, system2):
-    assert system1.t_end == system2.t_end
-    
-    V1, V2 = system1.volume, system2.volume
-    T1, T2 = system1.temp, system2.temp
-    
-    V_mix = V1 + V2
-    T_mix = (V1 * T1 + V2 * T2) / V_mix
-    
-    return make_system(T_init=T_mix,
-                       r=system1.r,
-                       volume=V_mix,
-                       t_end=30)
-\end{python}
-
-The first line is an \py{assert} statement, which is a way of checking for errors.  It compares \py{t_end} for the two systems to confirm that they have been cooling for the same time.  If not, \py{assert} displays an error message and stops the program.
-
-\index{assert statement}
-\index{statement!assert}
-
-The next two lines extract volume and temperature from the two \py{System} objects.  Then the following two lines compute the volume and temperature of the mixture.  Finally, \py{mix} makes a new \py{System} object and returns it.
-
-This function uses the value of \py{r} from \py{system1} as the value of \py{r} for the mixture.  If \py{system1} represents the coffee, and we are adding the milk to the coffee, this is probably a reasonable choice.  On the other hand, when we increase the amount of liquid in the coffee cup, that might change \py{r}.  So this is an assumption we might want to revisit.
-
-Notice that \py{mix} requires the \py{System} objects to have a variable named \py{temp}.
-To make sure this variable gets updated when we run a simulation, I use this function:
-
-\begin{python}
-def run_and_set(system):
-    results = run_simulation(system, update_func)
-    system.temp = get_last_value(results.T)
-    return results
-\end{python}
-
-Now we have everything we need to solve the problem.
-
-\section{Mix first or last?}
-
-First I'll create objects to represent the coffee and cream:
-
-\begin{python}
-coffee = make_system(T_init=90, r=r_coffee, 
-                     volume=300, t_end=30)
-
-milk = make_system(T_init=5, r=r_milk, volume=50, t_end=30)
-\end{python}
-
-Then I'll mix them and simulate 30 minutes:
-
-\begin{python}
-mix_first = mix(coffee, milk)
-mix_results = run_and_set(mix_first)
-\end{python}
-
-The final temperature is \SI{61.4}{\celsius} which is still warm enough to be enjoyable.  Would we do any better if we added the milk last?
-
-I'll simulate the coffee and milk separately, and then mix them:
-
-\begin{python}
-coffee_results = run_and_set(coffee)
-milk_results = run_and_set(milk)
-mix_last = mix(coffee, milk)
-\end{python}
-
-After mixing, the temperature is \SI{63.1}{\celsius}.  So it looks like adding the milk at the end is better by about \SI{1.7}{\celsius}.  But is that the best we can do?
-
-\section{Optimization}
-
-Adding the milk after 30 minutes is better than adding immediately, but maybe there's something in between that's even better.  To find out, I'll use the following function, which takes the time to add the milk, \py{t_add}, as a parameter:
-
-\index{optimization}
-
-\begin{python}
-def run_and_mix(t_add, t_total):
-    coffee = make_system(T_init=90, r=r_coffee, 
-                         volume=300, t_end=t_add)
-    coffee_results = run_and_set(coffee)
-    
-    milk = make_system(T_init=5, r=r_milk, 
-                       volume=50, t_end=t_add)
-    milk_results = run_and_set(milk)
-    
-    mixture = mix(coffee, milk)
-    mixture.t_end = t_total - t_add
-    results = run_and_set(mixture)
-
-    return mixture.temp
-\end{python}
-
-When \py{t_add=0}, we add the milk immediately; when \py{t_add=30}, we add it at the end.  Now we can sweep the range of values in between:
-
-\begin{python}
-sweep = SweepSeries()
-for t_add in linspace(0, 30, 11):
-    sweep[t_add] = run_and_mix(t_add, 30)
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap16-fig02.pdf}}
-\caption{Final temperature as a function of the time the milk is added.}
-\label{chap16-fig02}
-\end{figure}
-
-Figure~\ref{chap16-fig02} shows the result.  Again, note that this is a parameter sweep, not a time series.  The x-axis is the time when we add the milk, not the index of a \py{TimeSeries}.
-
-The final temperature is maximized when \py{t_add=30}, so adding the milk at the end is optimal.
-
-In the notebook for this chapter you will have a chance to explore this solution and try some variations.  For example, suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?
-
-
-\section{Analysis}
-
-Simulating Newton's law of cooling is almost silly, because we can solve the differential equation analytically.  If
-%
-\[ \frac{dT}{dt} = -r (T - T_{env}) \]
-%
-the general solution is
-%
-\[ T{\left (t \right )} = C_{1} \exp(-r t) + T_{env} \]
-%
-and the particular solution where $T(0) = T_{init}$ is
-%
-\[ T_{env} + \left(- T_{env} + T_{init}\right) \exp(-r t) \]
-%
-You can see how I got this solution using SymPy in \py{chap16sympy.ipynb} in the repository for this book.  If you would like to see it done by hand, you can watch this video: \url{http://modsimpy.com/khan3}.
-
-\index{analysis}
-\index{SymPy}
-
-Now we can use the observed data to estimate the parameter $r$.  If we observe $T(t_{end}) = T_{end}$, we can plug $t_{end}$ and $T_{end}$ into the particular solution and solve for $r$.  The result is:
-%
-\[ r = \frac{1}{t_{end}} \log{\left (\frac{T_{init} - T_{env}}{T_{end} - T_{env}} \right )} \]
-%
-Plugging in $t_{end}=30$ and $T_{end}=70$ (and again with $T_{init}=90$ and $T_{env}=22$), the estimate for $r$ is 0.0116.
-
-We can use the following function to compute the time series:
-
-\index{unpack}
-
-\begin{python}
-def run_analysis(system):
-    T_env, r = system.T_env, system.r
-    
-    T_init = system.init.T    
-    ts = linspace(0, system.t_end)
-    
-    T_array = T_env + (T_init - T_env) * exp(-r * ts)
-    
-    return TimeFrame(T_array, index=ts, columns=['T'])
-\end{python}
-
-This function is similar to \py{run_simulation}; it takes a \py{System} as a parameter and returns a \py{TimeFrame} as a result.
-
-Because \py{linrange} returns a NumPy array, \py{T_array} is also a NumPy array.  To be consistent with \py{run_simulation}, we have to put it into a \py{TimeFrame}.
-    
-We can run it like this:
-\index{\py{run_analysis}}
-
-\begin{python}
-r_coffee2 = 0.0116
-coffee2 = make_system(T_init=90, r=r_coffee2, 
-                      volume=300, t_end=30)
-results = run_analysis(coffee2)
-\end{python}
-
-The final temperature is \SI{70}{\celsius}, as it should be.  In fact, the results are identical to what we got by simulation, with a small difference due to rounding.
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap16.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-%%\part{Pharmacokinetics}
-
-\chapter{Pharmacokinetics}
-\label{chap17}
-
-{\bf Pharmacokinetics} is the study of how drugs and other substances move around the body, react, and are eliminated.  In this chapter, we implement one of the most widely used pharmacokinetic models: the so-called {\bf minimal model} of glucose and insulin in the blood stream.
-
-\index{pharmacokinetics}
-
-%We will use this model to fit data collected from a patient, and use the %parameters of the fitted model to quantify the patient's ability to %produce insulin and process glucose.
-
-\index{glucose}
-\index{insulin}
-
-My presentation in this chapter follows Bergman (2005) ``Minimal Model" (abstract at \url{http://modsimpy.com/bergman},
-PDF at \url{http://modsimpy.com/minmod}).
-
-
-\section{The glucose-insulin system}
-
-{\bf Glucose} is a form of sugar that circulates in the blood of animals; it is used as fuel for muscles, the brain, and other organs.  The concentration of blood sugar is controlled by the hormone system, and especially by {\bf insulin}, which is produced by the pancreas and has the effect of reducing blood sugar.
-
-\index{pancreas}
-
-In people with normal pancreatic function, the hormone system maintains {\bf homeostasis}; that is, it keeps the concentration of blood sugar in a range that is neither too high or too low.
-
-But if the pancreas does not produce enough insulin, or if the cells that should respond to insulin become insensitive, blood sugar can become elevated, a condition called {\bf hyperglycemia}.  Long term, severe hyperglycemia is the defining symptom of {\bf diabetes mellitus}, a serious disease that affects almost 10\% of the population in the U.S. (see \url{http://modsimpy.com/cdc}).
-
-\index{hyperglycemia}
-\index{diabetes}
-
-One of the most-used tests for hyperglycemia and diabetes is the frequently sampled intravenous glucose tolerance test (FSIGT), in which glucose is injected into the blood stream of a fasting subject (someone who has not eaten recently); then blood samples are collected at intervals of 2--10 minutes for 3 hours.  The samples are analyzed to measure the concentrations of glucose and insulin.
-
-\index{FSIGT}
-
-By analyzing these measurements, we can estimate several parameters of the subject's response; the most important is a parameter denoted $S_I$, which quantifies the effect of insulin on the rate of reduction in blood sugar.
-
-
-\section{The glucose minimal model}
-
-The ``minimal model" was proposed by Bergman, Ider, Bowden, and Cobelli\footnote{Bergman RN, Ider YZ, Bowden CR, Cobelli C., ``Quantitative estimation of insulin sensitivity", Am J Physiol. 1979 Jun;236(6):E667-77.  Abstract at \url{http://modsimpy.com/insulin}.}.
-It consists of two parts: the glucose model and the insulin model.  I will present an implementation of the glucose model; as a case study, you will have the chance to implement the insulin model.
-
-\index{minimal model}
-
-The original model was developed in the 1970s; since then, many variations and extensions have been proposed.  Bergman's comments on the development of the model provide insight into their process:
-
-\begin{quote}
-We applied the principle of Occam's Razor, i.e.~by asking
-what was the simplest model based upon known physiology
-that could account for the insulin-glucose relationship
-revealed in the data. Such a model must be simple
-enough to account totally for the measured glucose (given
-the insulin input), yet it must be possible, using mathematical
-techniques, to estimate all the characteristic parameters
-of the model from a single data set (thus avoiding
-unverifiable assumptions).
-\end{quote}
-
-The most useful models are the ones that achieve this balance: including enough realism to capture the essential features of the system without so much complexity that they are impractical.  In this example, the practical limit is the ability to estimate the parameters of the model using data, and to interpret the parameters meaningfully.
-
-\index{Occam's Razor}
-
-Bergman discusses the features he and his colleagues thought were essential:
-
-\begin{quote}
-(1) Glucose, once elevated by injection, returns to basal level due to
-two effects: the effect of glucose itself to normalize its own
-concentration [...] as well as the catalytic effect of insulin to allow
-glucose to self-normalize (2) Also, we discovered
-that the effect of insulin on net glucose disappearance
-must be sluggish --- that is, that insulin acts slowly because
-insulin must first move from plasma to a remote compartment [...] to exert its action on glucose disposal.
-\end{quote}
-
-To paraphrase the second point, the effect of insulin on glucose disposal, as seen in the data, happens more slowly than we would expect if it depended primarily on the concentration of insulin in the blood.  Bergman's group hypothesized that insulin must move relatively slowly from the blood to a ``remote compartment" where it has its effect.
-
-\index{compartment model}
-
-At the time, the remote compartment was a modeling abstraction that might, or might not, represent something physical.  Later, according to Bergman, it was ``shown to be interstitial fluid", that is, the fluid that surrounds tissue cells.  In the history of mathematical modeling, it is common for hypothetical entities, added to models to achieve particular effects, to be found later to correspond to physical entities.
-
-\index{interstitial fluid}
-
-The glucose model consists of two differential equations:
-%
-\[ \frac{dG}{dt} = -k_1 \left[ G(t) - G_b \right] - X(t) G(t)  \]
-%
-\[ \frac{dX}{dt} = k_3 \left[I(t) - I_b \right] - k_2 X(t) \]
-%
-where
-
-\begin{itemize}
-
-\item $G$ is the concentration of blood glucose as a function of time and $dG/dt$ is its rate of change.
-
-\item $X$ is the concentration of insulin in the tissue fluid as a function of time, and $dX/dt$ is its rate of change.
-
-\item $I$ is the concentration of insulin in the blood as a function of time, which is taken as an input into the model, based on measurements.
-
-\item $G_b$ is the basal concentration of blood glucose and $I_b$ is the basal concentration of blood insulin, that is, the concentrations at equilibrium.  Both are constants estimated from measurements at the beginning or end of the test.
-
-\item $k_1$, $k_2$, and $k_3$ are positive-valued parameters that control the rates of appearance and disappearance for glucose and insulin. 
-
-\end{itemize}
-
-We can interpret the terms in the equations one by one:
-
-\begin{itemize}
-
-\item $-k_1 \left[ G(t) - G_b \right]$ is the rate of glucose disappearance due to the effect of glucose itself.  When $G(t)$ is above basal level, $G_b$, this term is negative; when $G(t)$ is below basal level this term is positive.  So in the absence of insulin, this term tends to restore blood glucose to basal level.
-
-\item $-X(t) G(t)$ models the interaction of glucose and insulin in tissue fluid, so the rate increases as either $X$ or $G$ increases.  This term does not require a rate parameter because the units of $X$ are unspecified; we can consider $X$ to be in whatever units make the parameter of this term 1.
-
-\item $k_3 \left[ I(t) - I_b \right]$ is the rate at which insulin diffuses between blood and tissue fluid.  When $I(t)$ is above basal level, insulin diffuses from the blood into the tissue fluid.  When $I(t)$ is below basal level, insulin diffuses from tissue to the blood.
-
-\item $-k_2 X(t)$ is the rate of insulin disappearance in tissue fluid as it is consumed or broken down.
-
-\end{itemize}
-
-The initial state of the model is $X(0) = I_b$ and $G(0) = G_0$, where $G_0$ is a constant that represents the concentration of blood sugar immediately after the injection.  In theory we could estimate $G_0$ based on measurements, but in practice it takes time for the injected glucose to spread through the blood volume.  Since $G_0$ is not measurable, it is treated as a {\bf free parameter} of the model, which means that we are free to choose it to fit the data.
-
-\index{free parameter}
-
-
-\section{Data}
-
-To develop and test the model, I use data from Pacini and Bergman\footnote{``MINMOD: A computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test", {\em Computer Methods and Programs in Biomedicine} 23: 113-122, 1986.}.  The dataset is in a file in the repository for this book, which we can read into a \py{DataFrame}:
-
-\index{data}
-\index{DataFrame object}
-
-\begin{python}
-data = pd.read_csv('data/glucose_insulin.csv',
-                   index_col='time')
-\end{python}
-
-\py{data} has two columns: \py{glucose} is the concentration of blood glucose in \si{\milli\gram/\deci\liter}; \py{insulin} is concentration of insulin in the blood in \si{\micro U\per\milli\liter} (a medical ``unit", denoted \si{U}, is an amount defined by convention in context).  The index is time in \si{\minute}.
-
-\index{concentration}
-
-\begin{figure}
-\centerline{\includegraphics[width=3.5in]{figs/chap17-fig01.pdf}}
-\caption{Glucose and insulin concentrations measured by FSIGT.}
-\label{chap17-fig01}
-\end{figure}
-
-Figure~\ref{chap17-fig01} shows glucose and insulin concentrations over \SI{182}{\minute} for a subject with normal insulin production and sensitivity.
-
-
-\section{Interpolation}
-\label{interpolate}
-
-Before we are ready to implement the model, there's one problem we have to solve.  In the differential equations, $I$ is a function that can be evaluated at any time, $t$.  But in the \py{DataFrame}, we only have measurements at discrete times.  This is a job for interpolation!
-
-\index{interpolation}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap17-fig02.pdf}}
-\caption{Insulin concentrations measured by FSIGT and an interpolated function.}
-\label{chap17-fig02}
-\end{figure}
-
-The ModSim library provides a function named \py{interpolate}, which is a wrapper for the SciPy function \py{interp1d}.  It takes any kind of \py{Series} as a parameter, including \py{TimeSeries} and \py{SweepSeries}, and returns a function.  That's right, I said it returns a {\em function}.
-
-\index{function!as return value}
-\index{Series}
-\index{interpolate}
-\index{interp1d}
-\index{SciPy}
-
-So we can call \py{interpolate} like this:
-
-\begin{python}
-I = interpolate(data.insulin)
-\end{python}
-
-Then we can call the new function, \py{I}, like this:
-
-\begin{python}
-I(18)
-\end{python}
-
-The result is 31.66, which is a linear interpolation between the actual measurements at \py{t=16} and \py{t=19}.  We can also pass an array as an argument to \py{I}:
-
-\begin{python}
-ts = linrange(t_0, t_end, endpoint=True)
-I(ts)
-\end{python}
-
-The result is an array of interpolated values for equally-spaced values of \py{t}, shown in Figure~\ref{chap17-fig02}.
-
-\index{linrange}
-\index{NumPy}
-
-\py{interpolate} can take additional arguments, which it passes along to \py{interp1d}.  You can read about these options at \url{http://modsimpy.com/interp}.
-
-In the next chapter, we will use interpolated data to run the simulation of the glucose-insulin system.
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap17.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-\chapter{The glucose model}
-\label{chap18}
-
-In this chapter, we implement the glucose minimal model described in the previous chapter.  We'll start with \py{run_simulation}, which solves differential equations using discrete time steps.  This method works well enough for many applications, but it is not very accurate.  In this chapter we explore a better option: using an {\bf ODE solver}.
-
-
-\section{Implementation}
-\label{glucose}
-
-To get started, let's assume that the parameters of the model are known.  We'll implement the model and use it to generate time series for \py{G} and \py{X}.  Then we'll see how to find the parameters that generate the series that best fits the data.
-
-Taking advantage of estimates from prior work, we'll start with these values:
-
-\begin{python}
-params = Params(G0 = 290,
-                k1 = 0.03,
-                k2 = 0.02,
-                k3 = 1e-05)
-\end{python}
-
-A \py{Params} object is similar to a \py{System} or \py{State} object; it is useful for holding a collection of parameters.
-
-\index{State object}
-\index{System object}
-\index{Params object}
-
-% TODO: I introduce Params here because I use it with leastsq, but since we aren't
-% using leastsq here any more, we have the option of postponing Params
-
-We can pass \py{params} and \py{data} to \py{make_system}:
-
-\begin{python}
-def make_system(params, data):
-    G0, k1, k2, k3 = params
-    
-    Gb = data.glucose[0]
-    Ib = data.insulin[0]
-    I = interpolate(data.insulin)
-    
-    t_0 = get_first_label(data)
-    t_end = get_last_label(data)
-
-    init = State(G=G0, X=0)
-    
-    return System(params,
-                  init=init, Gb=Gb, Ib=Ib, I=I,
-                  t_0=t_0, t_end=t_end, dt=2)
-\end{python}
-
-\py{make_system} uses the measurements at \py{t=0} as the basal levels, \py{Gb} and \py{Ib}.
-It gets \py{t_0} and \py{t_end} from the data.
-And it uses the parameter \py{G0} as the initial value for \py{G}.
-Then it packs everything into a \py{System} object.
-
-Here's the update function:
-
-\index{update function}
-\index{function!update}
-
-\begin{python}
-def update_func(state, t, system):
-    G, X = state
-    k1, k2, k3 = system.k1, system.k2, system.k3 
-    I, Ib, Gb = system.I, system.Ib, system.Gb
-    dt = system.dt
-        
-    dGdt = -k1 * (G - Gb) - X*G
-    dXdt = k3 * (I(t) - Ib) - k2 * X
-    
-    G += dGdt * dt
-    X += dXdt * dt
-
-    return State(G=G, X=X)
-\end{python}
-
-As usual, the update function takes a \py{State} object, a time, and a \py{System} object as parameters.  The first line uses multiple assignment to extract the current values of \py{G} and \py{X}.  
-
-The following lines unpack the parameters we need from the \py{System} object.
-
-Computing the derivatives \py{dGdt} and \py{dXdt} is straightforward; we just translate the equations from math notation to Python.
-
-\index{derivative}
-
-Then, to perform the update, we multiply each derivative by the discrete time step \py{dt}, which is \SI{2}{\minute} in this example.  
-The return value is a \py{State} object with the new values of \py{G} and \py{X}.
-
-\index{time step}
-
-Before running the simulation, it is a good idea to run the update function with the initial conditions:
-
-\begin{python}
-update_func(system.init, system.t_0, system)
-\end{python}
-
-If it runs without errors and there is nothing obviously wrong with the results, we are ready to run the simulation.  We'll use this version of \py{run_simulation}, which is very similar to previous versions:
-
-\index{\py{run_simulation}}
-
-\begin{python}
-def run_simulation(system, update_func):
-    init = system.init
-    t_0, t_end, dt = system.t_0, system.t_end, system.dt
-    
-    frame = TimeFrame(columns=init.index)
-    frame.row[t_0] = init
-    ts = linrange(t_0, t_end, dt)
-    
-    for t in ts:
-        frame.row[t+dt] = update_func(frame.row[t], t, system)
-    
-    return frame
-\end{python}
-
-We can run it like this:
-
-\begin{python}
-results = run_simulation(system, update_func)
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[width=3.5in]{figs/chap18-fig01.pdf}}
-\caption{Results from simulation of the glucose minimal model.}
-\label{chap18-fig01}
-\end{figure}
-
-Figure~\ref{chap18-fig01} shows the results.  The top plot shows simulated glucose levels from the model along with the measured data.  The bottom plot shows simulated insulin levels in tissue fluid, which is in unspecified units, and not to be confused with measured insulin levels in the blood.
-
-With the parameters I chose, the model fits the data well, except for the first few data points, where we don't expect the model to be accurate.
-
-
-\section{Solving differential equations}
-\label{slopefunc}
-
-So far we have solved differential equations by rewriting them as difference equations.  In the current example, the differential equations are:
-%
-\[ \frac{dG}{dt} = -k_1 \left[ G(t) - G_b \right] - X(t) G(t)  \]
-%
-\[ \frac{dX}{dt} = k_3 \left[I(t) - I_b \right] - k_2 X(t) \]
-%
-If we multiply both sides by $dt$, we have:
-%
-\[ dG = \left[ -k_1 \left[ G(t) - G_b \right] - X(t) G(t) \right] dt  \]
-%
-\[ dX = \left[ k_3 \left[I(t) - I_b \right] - k_2 X(t) \right] dt \]
-%
-When $dt$ is very small, or more precisely {\bf infinitesimal}, this equation is exact.  But in our simulations, $dt$ is \SI{2}{\minute}, which is not very small.  In effect, the simulations assume that the derivatives $dG/dt$ and $dX/dt$ are constant during each \SI{2}{\minute} time step.
-
-\index{time step}
-
-This method, evaluating derivatives at discrete time steps and assuming that they are constant in between, is called {\bf Euler's method} (see \url{http://modsimpy.com/euler}).
-
-\index{Euler's method}
-
-Euler's method is good enough for some simple problems, but it is not very accurate.  Other methods are more accurate, but many of them are substantially more complicated.
-
-One of the best simple methods is called {\bf Ralston's method}.  The ModSim library provides a function called \py{run_ode_solver} that implements it.
-
-The ``ODE" in \py{run_ode_solver} stands for ``ordinary differential equation".  The equations we are solving are ``ordinary'' because all the derivatives are with respect to the same variable; in other words, there are no partial derivatives.  
-
-\index{ordinary differential equation}
-
-To use \py{run_ode_solver}, we have to provide a ``slope function", like this:
-
-\index{slope function}
-\index{function!slope}
-\index{unpack}
-
-\begin{python}
-def slope_func(state, t, system):
-    G, X = state
-    k1, k2, k3 = system.k1, system.k2, system.k3 
-    I, Ib, Gb = system.I, system.Ib, system.Gb
-    
-    dGdt = -k1 * (G - Gb) - X*G
-    dXdt = k3 * (I(t) - Ib) - k2 * X
-    
-    return dGdt, dXdt
-\end{python}
-
-\py{slope_func} is similar to \py{update_func}; in fact, it takes the same parameters in the same order.  But \py{slope_func} is simpler, because all we have to do is compute the derivatives, that is, the slopes.  We don't have to do the updates; \py{run_ode_solver} does them for us.
-
-\index{\py{run_ode_solver}}
-
-Now we can call \py{run_ode_solver} like this:
-
-\begin{python}
-results2, details = run_ode_solver(system, slope_func)
-\end{python}
-
-\py{run_ode_solver} is similar to \py{run_simulation}: it takes a \py{System} object and a slope function as parameters.  It returns two values: a \py{TimeFrame} with the solution and a \py{ModSimSeries} with additional information.
-
-A \py{ModSimSeries} is like a \py{System} or \py{State} object; it contains a set of variables and their values.  The \py{ModSimSeries} from \py{run_ode_solver}, which we assign to \py{details}, contains information about how the solver ran, including a success code and diagnostic message.  
-
-The \py{TimeFrame}, which we assign to \py{results}, has one row for each time step and one column for each state variable.  In this example, the rows are time from 0 to 182 minutes; the columns are the state variables, \py{G} and \py{X}.
- 
-\index{TimeFrame object}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap18-fig02.pdf}}
-\caption{Results from Euler's method (simulation) and Ralston's method (ODE solver).}
-\label{chap18-fig02}
-\end{figure}
-
-Figure~\ref{chap18-fig02} shows the results from \py{run_simulation} and \py{run_ode_solver}.  The difference between them is barely visible.
-
-We can compute the percentage differences like this:
-
-\begin{python}
-diff = results.G - results2.G
-percent_diff = diff / results2.G * 100
-\end{python}
-
-The largest percentage difference is less than 2\%, which is small enough that it probably doesn't matter in practice.  Later, we will see examples where Ralston's method is substantially more accurate.
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap18.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-\chapter{Case studies}
-\label{chap19}
-
-This chapter reviews the computational patterns we have seen so far and presents exercises where you can apply them.
-
-\section{Computational tools}
-
-In Chapter~\ref{chap11} we saw an update function that uses multiple assignment to unpack a \py{State} object and assign the state variables to local variables.
-
-\begin{python}
-def update_func(state, t, system):
-    s, i, r = state
-
-    infected = system.beta * i * s    
-    recovered = system.gamma * i
-    
-    s -= infected
-    i += infected - recovered
-    r += recovered
-    
-    return State(S=s, I=i, R=r)
-\end{python}
-
-And in \py{run_simulation} we used multiple assignment again to assign state variables to a row in a \py{TimeFrame}:
-
-\begin{python}
-def run_simulation(system, update_func):
-    frame = TimeFrame(columns=system.init.index)
-    frame.row[system.t_0] = system.init
-    
-    for t in linrange(system.t_0, system.t_end):
-        frame.row[t+1] = update_func(frame.row[t], system)
-    
-    return frame
-\end{python}
-
-In Chapter~\ref{chap12} we used the functions \py{max} and \py{idxmax} to compute metrics:
-
-\begin{python}
-largest_value = S.max()
-time_of_largest_value = S.idxmax()
-\end{python}
-
-And we saw the logistic function, a general function which is useful for modeling relationships between variables, like the effectiveness of an intervention as a function of expenditure.
-
-In Chapter~\ref{chap13} we used a \py{SweepFrame} object to sweep two parameters:
-
-\begin{python}
-def sweep_parameters(beta_array, gamma_array):
-    frame = SweepFrame(columns=gamma_array)
-    for gamma in gamma_array:
-        frame[gamma] = sweep_beta(beta_array, gamma)
-    return frame
-\end{python}
-
-And we used \py{contour} to generate a contour plot of a two-dimensional sweep.
-
-In Chapter~\ref{chap15} we used \py{linrange} to create an array of equally spaced values.  \py{linrange} is similar to \py{linspace}: the difference is that \py{linrange} lets you specify the space between values, and it computes the number of values; \py{linspace} lets you specify the number of values, and it computes the space between them.
-
-Here's a version of \py{run_simulation} that uses \py{linrange}:
-
-\begin{python}
-def run_simulation(system, update_func):
-    init = system.init
-    t_0, t_end, dt = system.t_0, system.t_end, system.dt
-    
-    frame = TimeFrame(columns=init.index)
-    frame.row[t_0] = init
-    ts = linrange(t_0, t_end, dt)
-    
-    for t in ts:
-        frame.row[t+dt] = update_func(frame.row[t], t, system)
-    
-    return frame
-\end{python}
-
-In Chapter~\ref{chap16} we used \py{root_bisect} to find the value of a parameter that yields a particular result.  We defined an error function:
-
-\index{\py{root_bisect}}
-
-\begin{python}
-    system = make_system(T_init=90, r=r, volume=300, t_end=30)
-    results = run_simulation(system, update_func)
-    T_final = get_last_value(results.T)
-    return T_final - 70
-\end{python}
-
-And passed it to \py{root_bisect} with an initial interval, like this:
-
-\begin{python}
-res = root_bisect(error_func1, [0.01, 0.02])
-r_coffee = res.root
-\end{python}
-
-In Chapter~\ref{chap17} we used \py{interpolate}, which returns a function:
-
-\begin{python}
-I = interpolate(data.insulin)
-\end{python}
-
-We can call \py{I} like any other function, passing as an argument either a single value or a NumPy array:
-
-\begin{python}
-I(18)
-
-ts = linrange(t_0, t_end)
-I(ts)
-\end{python}
-
-In Chapter~\ref{chap18} we used a \py{Params} object, which is a collection of parameters.
-
-\begin{python}
-params = Params(G0 = 290,
-                k1 = 0.03,
-                k2 = 0.02,
-                k3 = 1e-05)
-\end{python}
-
-Chapter~\ref{chap18} also introduces \py{run_ode_solver} which computes numerical solutions to differential equations.
-
-\py{run_ode_solver} uses a slope function, which is similar to an update function:
-
-\begin{python}
-    G, X = state
-    k1, k2, k3 = system.k1, system.k2, system.k3 
-    I, Ib, Gb = system.I, system.Ib, system.Gb
-    
-    dGdt = -k1 * (G - Gb) - X*G
-    dXdt = k3 * (I(t) - Ib) - k2 * X
-    
-    return dGdt, dXdt
-\end{python}
-
-And we can call it like this:
-
-\begin{python}
-results, details = run_ode_solver(system, slope_func)
-\end{python}
-
-The rest of this chapter presents case studies you can use to practice these tools.
-
-
-\section{The insulin minimal model}
-
-Along with the glucose minimal model in Chapter~\ref{chap17}, Berman et al.~developed an insulin minimal model, in which the concentration of insulin, $I$, is governed by this differential equation:
-%
-\[ \frac{dI}{dt} = -k I(t) + \gamma \left[ G(t) - G_T \right] t \]
-%
-where
-
-\begin{itemize}
-
-\item $k$ is a parameter that controls the rate of insulin disappearance independent of blood glucose.   
-
-\item $G(t)$ is the measured concentration of blood glucose at time $t$.
-
-\item $G_T$ is the glucose threshold; when blood glucose is above this level, it triggers an increase in blood insulin. 
-
-\item $\gamma$ is a parameter that controls the rate of increase (or decrease) in blood insulin when glucose is above (or below) $G_T$.
-
-% TODO: explain why t is there
-
-\end{itemize}
-
-The initial condition is $I(0) = I_0$.  As in the glucose minimal model, we treat the initial condition as a parameter which we'll choose to fit the data.
-
-\index{insulin minimal model}
-\index{differential equation}
-
-The parameters of this model can be used to estimate $\phi_1$ and $\phi_2$, which are values that ``describe the sensitivity to glucose of the first and second phase pancreatic responsivity".  They are related to the parameters as follows:
-%
-\[ \phi_1 = \frac{I_{max} - I_b}{k (G_0 - G_b)}\]
-%
-\[ \phi_2 = \gamma \times 10^4 \]
-%
-where $I_{max}$ is the maximum measured insulin level, and $I_b$ and $G_b$ are the basal levels of insulin and glucose.
-
-%TODO: Clarify whether G0 here is the parameter we estimated in the previous
-% model, or the maximum observed value of G.
-
-In the repository for this book, you will find a notebook, \py{insulin.ipynb}, which contains starter code for this case study.   Use it to implement the insulin model, find the parameters that best fit the data, and estimate these values.
-
-
-\section{Low-Pass Filter}
-
-The following circuit diagram\footnote{From \url{http://modsimpy.com/divider}} shows a low-pass filter built with one resistor and one capacitor.  
-
-\centerline{\includegraphics[height=1.2in]{figs/RC_Divider.pdf}}
-
-A ``filter" is a circuit takes a signal, $V_{in}$, as input and produces a signal, $V_{out}$, as output.  In this context, a ``signal" is a voltage that changes over time.
-
-A filter is ``low-pass" if it allows low-frequency signals to pass from $V_{in}$ to $V_{out}$ unchanged, but it reduces the amplitude of high-frequency signals.
-
-By applying the laws of circuit analysis, we can derive a differential equation that describes the behavior of this system.  By solving the differential equation, we can predict the effect of this circuit on any input signal.
-
-Suppose we are given $V_{in}$ and $V_{out}$ at a particular instant in time.  By Ohm's law, which is a simple model of the behavior of resistors, the instantaneous current through the resistor is:
-%
-\[ I_R = (V_{in} - V_{out}) / R \]
-%
-where $R$ is resistance in ohms (\si{\ohm}).
-
-Assuming that no current flows through the output of the circuit, Kirchhoff's current law implies that the current through the capacitor is:
-%
-\[ I_C = I_R \]
-%
-According to a simple model of the behavior of capacitors, current through the capacitor causes a change in the voltage across the capacitor:
-%
-\[ I_C = C \frac{d V_{out}}{dt} \]
-%
-where $C$ is capacitance in farads (\si{\farad}).  Combining these equations yields a differential equation for $V_{out}$:
-%
-\[ \frac{d V_{out}}{dt} = \frac{V_{in} - V_{out}}{R C} \]
-%
-In the repository for this book, you will find a notebook, \py{filter.ipynb}, which contains starter code for this case study.   Follow the instructions to simulate the low-pass filter for input signals like this:
-%
-\[ V_{in}(t) = A \cos (2 \pi f t) \]
-%
-where $A$ is the amplitude of the input signal, say \SI{5}{\volt}, and $f$ is the frequency of the signal in \si{\hertz}.
-
-In the repository for this book, you will find a notebook, \py{filter.ipynb}, which contains starter code for this case study.  Read the notebook, run the code, and work on the exercises.
-
-
-
-\section{Thermal behavior of a wall}
-
-This case study is based on a paper by Gori, et~al\footnote{Gori, Marincioni, Biddulph, Elwell, ``Inferring the thermal resistance and effective thermal mass distribution of a wall from in situ measurements to characterise heat transfer at both the interior and exterior surfaces", {\it Energy and Buildings}, Volume 135, pages 398-409, \url{http://modsimpy.com/wall2}.
-    
-The authors put their paper under a Creative Commons license, and make their data available at \url{http://modsimpy.com/wall }.  I thank them for their commitment to open, reproducible science, which made this case study possible.} that models the thermal behavior of a brick wall, with the goal of understanding the ``performance gap between the expected energy use of buildings and their measured energy use".
-
-The following figure shows the scenario and their model of the wall:
-
-\vspace{0.1in}
-\centerline{\includegraphics[height=1.4in]{figs/wall_model.pdf}}
-
-On the interior and exterior surfaces of the wall, they measure temperature and heat flux over a period of three days.  They model the wall using two thermal masses connected to the surfaces, and to each other, by thermal resistors.
-
-The primary methodology of the paper is a Bayesian method for inferring the parameters of the system (two thermal masses and three thermal resistances).
-
-The primary result is a comparison of two models: the one shown here with two thermal masses, and a simpler model with only one thermal mass.  They find that the two-mass model is able to reproduce the measured fluxes substantially better.
-
-For this case study we will implement their model and run it with the estimated parameters from the paper, and then use \py{fit_leastsq} to see if we can find parameters that yield lower errors.
-
-In the repository for this book, you will find a notebook, \py{wall.ipynb} with the code and results for this case study.
-
-
-\chapter{Projectiles}
-\label{chap20}
-
-So far the differential equations we've worked with have been {\bf first order}, which means they involve only first derivatives.   In this chapter, we turn our attention to second order ODEs, which can involve both first and second derivatives.  
-
-\index{first order ODE}
-\index{second order ODE}
-
-We'll revisit the falling penny example from Chapter~\ref{chap01}, and use \py{run_ode_solver} to find the position and velocity of the penny as it falls, with and without air resistance.
-
-
-\section{Newton's second law of motion}
-
-First order ODEs can be written
-%
-\[ \frac{dy}{dx} = G(x, y) \]
-%
-where $G$ is some function of $x$ and $y$ (see \url{http://modsimpy.com/ode}).  Second order ODEs can be written
-%
-\[ \frac{d^2y}{dx^2} = H(x, y, \frac{dy}{dt}) \]
-%
-where $H$ is a function of $x$, $y$, and $dy/dx$.
-
-In this chapter, we will work with one of the most famous and useful second order ODE, Newton's second law of motion:
-%
-\[ F = m a \]
-%
-where $F$ is a force or the total of a set of forces, $m$ is the mass of a moving object, and $a$ is its acceleration.
-
-\index{Newton's second law of motion}
-\index{differential equation}
-\index{acceleration}
-\index{velocity}
-\index{position}
-
-Newton's law might not look like a differential equation, until we realize that acceleration, $a$, is the second derivative of position, $y$, with respect to time, $t$.  With the substitution
-%
-\[ a = \frac{d^2y}{dt^2} \]
-%
-Newton's law can be written
-%
-\[ \frac{d^2y}{dt^2} = F / m \]
-%
-And that's definitely a second order ODE.  In general, $F$ can be a function of time, position, and velocity.
-
-Of course, this ``law" is really a model in the sense that it is a simplification of the real world.  Although it is often approximately true:
-
-\begin{itemize}
-
-\item It only applies if $m$ is constant.  If mass depends on time, position, or velocity, we have to use a more general form of Newton's law (see \url{http://modsimpy.com/varmass}).
-
-\index{variable mass}
-
-\item It is not a good model for very small things, which are better described by another model, quantum mechanics.
-
-\index{quantum mechanics}
-
-\item And it is not a good model for things moving very fast, which are better described by yet another model, relativistic mechanics.
-
-\index{relativity}
-
-\end{itemize}
-
-However, for medium-sized things with constant mass, moving at medium-sized speeds, Newton's model is extremely useful.  If we can quantify the forces that act on such an object, we can predict how it will move.
-
-
-\section{Dropping pennies}
-
-As a first example, let's get back to the penny falling from the Empire State Building, which we considered in Section~\ref{penny}.  We will implement two models of this system: first without air resistance, then with.
-
-\index{falling penny}
-\index{air resistance}
-
-Given that the Empire State Building is \SI{381}{\meter} high, and assuming that the penny is dropped from a standstill, the initial conditions are:
-
-\index{State object}
-
-\begin{python}
-init = State(y=381 * m, 
-             v=0 * m/s)
-\end{python}
-
-where \py{y} is height above the sidewalk and \py{v} is velocity.  The units \py{m} and \py{s} are from the \py{UNITS} object provided by Pint:
-
-\index{unit}
-\index{Pint}
-
-\begin{python}
-m = UNITS.meter
-s = UNITS.second
-\end{python}
-
-The only system parameter is the acceleration of gravity:
-
-\begin{python}
-g = 9.8 * m/s**2
-\end{python}
-
-In addition, we'll specify the duration of the simulation and the step size:
-
-\begin{python}
-t_end = 10 * s
-dt = 0.1 * s
-\end{python}
-
-With these parameters, the number of time steps is 100, which is good enough for many problems.  Once we have a solution, we will increase the number of steps and see what effect it has on the results.
-
-We need a \py{System} object to store the parameters:
-
-\index{System object}
-
-\begin{python}
-system = System(init=init, g=g, t_end=t_end, dt=dt)
-\end{python}
-
-Now we need a slope function, and here's where things get tricky.  As we have seen, \py{run_ode_solver} can solve systems of first order ODEs, but Newton's law is a second order ODE.  However, if we recognize that
-
-\index{slope function}
-\index{function!slope}
-
-\begin{enumerate}
-
-\item Velocity, $v$, is the derivative of position, $dy/dt$, and
-
-\item Acceleration, $a$, is the derivative of velocity, $dv/dt$,
-
-\end{enumerate}
-
-we can rewrite Newton's law as a system of first order ODEs:
-%
-\[ \frac{dy}{dt} = v \]
-%
-\[ \frac{dv}{dt} = a \]
-%
-And we can translate those equations into a slope function:
-
-\index{system of equations}
-\index{unpack}
-
-\begin{python}
-def slope_func(state, t, system):
-    y, v = state
-
-    dydt = v
-    dvdt = -system.g
-    
-    return dydt, dvdt
-\end{python}
-
-The first parameter, \py{state}, contains the position and velocity of the penny.  The last parameter, \py{system}, contains the system parameter \py{g}, which is the magnitude of acceleration due to gravity.
-
-\index{State object}
-
-The second parameter, \py{t}, is time.  It is not used in this slope function because none of the factors of the model are time dependent (see Section~\ref{glucose}).  I include it anyway because this function will be called by \py{run_ode_solver}, which always provides the same arguments, whether they are needed or not.
-
-\index{time dependent}
-
-The rest of the function is a straightforward translation of the differential equations, with the substitution $a = -g$, which indicates that acceleration due to gravity is in the direction of decreasing $y$.  \py{slope_func} returns a sequence containing the two derivatives.
-
-Before calling \py{run_ode_solver}, it is a good idea to test the slope function with the initial conditions:
-
-\begin{python}
-dydt, dvdt = slope_func(system.init, 0, system)
-\end{python}
-
-The result is \SI{0}{\meter\per\second} for velocity and \SI{9.8}{\meter\per\second\squared} for acceleration.  Now we call \py{run_ode_solver} like this:
-
-\begin{python}
-results, details = run_ode_solver(system, slope_func)
-\end{python}
-
-\py{results} is a \py{TimeFrame} with two columns: \py{y} contains the height of the penny; \py{v} contains its velocity.
- 
-\index{TimeFrame object}
-\index{\py{run_ode_solver}}
-
-We can plot the results like this:
-
-\begin{python}
-def plot_position(results):
-    plot(results.y)
-    decorate(xlabel='Time (s)',
-             ylabel='Position (m)')
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap20-fig01.pdf}}
-\caption{Height of the penny versus time, with no air resistance.}
-\label{chap20-fig01}
-\end{figure}
-
-Figure~\ref{chap20-fig01} shows the result.  Since acceleration is constant, velocity increases linearly and position decreases quadratically; as a result, the height curve is a parabola.
-
-\index{parabola}
-
-The last value of \py{results.y} is \SI{-109}{\meter}, which means we ran the simulation too long.  One way to solve this problem is to use the results to estimate the time when the penny hits the sidewalk.
-
-The ModSim library provides \py{crossings}, which takes a \py{TimeSeries} and a value, and returns a sequence of times when the series passes through the value.  We can find the time when the height of the penny is \py{0} like this:
-
-\begin{python}
-t_crossings = crossings(results.y, 0)
-\end{python}
-
-The result is an array with a single value, \SI{8.818}{s}.  Now, we could run the simulation again with \py{t_end = 8.818}, but there's a better way.
-
-\section{Events}
-\label{events}
-
-As an option, \py{run_ode_solver} can take an {\bf event function}, which detects an ``event", like the penny hitting the sidewalk, and ends the simulation.
-
-Event functions take the same parameters as slope functions, \py{state}, \py{t}, and \py{system}.  They should return a value that passes through \py{0} when the event occurs.  Here's an event function that detects the penny hitting the sidewalk:
-
-\begin{python}
-def event_func(state, t, system):
-    y, v = state
-    return y
-\end{python}
-
-The return value is the height of the penny, \py{y}, which passes through \py{0} when the penny hits the sidewalk.
-
-We pass the event function to \py{run_ode_solver} like this:
-
-\begin{python}
-results, details = run_ode_solver(system, slope_func,
-                                  events=event_func)
-\end{python}
-
-Then we can get the flight time and final velocity like this:
-
-\begin{code}
-t_sidewalk = get_last_label(results) * s
-v_sidewalk = get_last_value(results.v)
-\end{code}
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap20.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-
-\chapter{Air resistance}
-\label{chap21}
-
-In the previous chapter we simulated a penny falling in a vacuum, that is, without air resistance.  But the computational framework we used is very general; it is easy to add additional forces, including drag.
-
-In this chapter, I present a model of drag force and add it to the simulation.
-
-
-\section{Drag force}
-\label{drag}
-
-As an object moves through a fluid, like air, the object applies force to the air and, in accordance with Newton's third law of motion, the air applies an equal and opposite force to the object (see \url{http://modsimpy.com/newton}).
-
-\index{air resistance}
-\index{drag force}
-\index{force!drag}
-\index{drag equation}
-
-The direction of this {\bf drag force} is opposite the direction of travel, and its magnitude is given by the drag equation (see \url{http://modsimpy.com/drageq}):
-%
-\[ F_d = \frac{1}{2}~\rho~v^2~C_d~A \]
-%
-where
-
-\begin{itemize}
-
-\item $F_d$ is force due to drag, in newtons (\si{\newton}).
-
-\item $\rho$ is the density of the fluid in \si{\kg\per\meter\cubed}.
-\index{density}
-
-\item $v$ is the magnitude of velocity in \si{\meter\per\second}.
-\index{velocity}
-
-\item $A$ is the {\bf reference area} of the object, in \si{\meter\squared}.  In this context, the reference area is the projected frontal area, that is, the visible area of the object as seen from a point on its line of travel (and far away).
-
-\index{reference area}
-
-\item $C_d$ is the {\bf drag coefficient}, a dimensionless quantity that depends on the shape of the object (including length but not frontal area), its surface properties, and how it interacts with the fluid.
-
-\index{drag coefficient}
-
-\end{itemize}
-
-For objects moving at moderate speeds through air, typical drag coefficients are between 0.1 and 1.0, with blunt objects at the high end of the range and streamlined objects at the low end (see \url{http://modsimpy.com/dragco}).
-
-For simple geometric objects we can sometimes guess the drag coefficient with reasonable accuracy; for more complex objects we usually have to take measurements and estimate $C_d$ from data.
-
-Of course, the drag equation is itself a model, based on the assumption that $C_d$ does not depend on the other terms in the equation: density, velocity, and area.  For objects moving in air at moderate speeds (below 45 mph or \SI{20}{\meter\per\second}), this model might be good enough, but we should remember to revisit this assumption.
-
-For the falling penny, we can use measurements to estimate $C_d$.   In particular, we can measure {\bf terminal velocity}, $v_{term}$, which is the speed where drag force equals force due to gravity:
-%
-\[ \frac{1}{2}~\rho~v_{term}^2~C_d~A = m g \]
-%
-where $m$ is the mass of the object and $g$ is acceleration due to gravity.  Solving this equation for $C_d$ yields:
-%
-\[ C_d = \frac{2~m g}{\rho~v_{term}^2~A} \]
-%
-According to {\it Mythbusters}, the terminal velocity of a penny is between 35 and 65 mph (see \url{http://modsimpy.com/mythbust}).  Using the low end of their range, 40 mph or about \SI{18}{\meter\per\second}, the estimated value of $C_d$ is 0.44, which is close to the drag coefficient of a smooth sphere.
-
-\index{Mythbusters}
-\index{terminal velocity}
-
-Now we are ready to add air resistance to the model.
-
-
-\section{Implementation}
-\label{penny_drag}
-
-As the number of system parameters increases, and as we need to do more work to compute them, we will find it useful to define a \py{Params} object to contain the quantities we need to make a \py{System} object.  \py{Params} objects are similar to \py{System} and \py{State} objects; in fact, all three have the same capabilities.  I have given them different names to document the different roles they play.
-
-% TODO: This is not actually the first use of Params
-
-\index{Params object}
-
-Here's the \py{Params} object for the falling penny:
-
-\begin{python}
-params = Params(height = 381 * m,
-                v_init = 0 * m / s,
-                g = 9.8 * m/s**2,
-                mass = 2.5e-3 * kg,
-                diameter = 19e-3 * m,
-                rho = 1.2 * kg/m**3,
-                v_term = 18 * m / s)
-\end{python}
-
-The mass and diameter are from \url{http://modsimpy.com/penny}.  The density of air depends on temperature, barometric pressure (which depends on altitude), humidity, and composition (\url{http://modsimpy.com/density}).  I chose a value that might be typical in Boston, Massachusetts at \SI{20}{\celsius}.
-
-\index{System object}
-\index{\py{make_system}}
-
-Here's a version of \py{make_system} that takes a \py{Params} object and returns a \py{System}:
-
-\index{unpack}
-
-\begin{python}
-def make_system(params):
-    diameter, mass = params.diameter, params.mass
-    g, rho = params.g, params.rho, 
-    v_init, v_term = params.v_init, params.v_term
-    height = params.height
-    
-    area = np.pi * (diameter/2)**2
-    C_d = 2 * mass * g / (rho * area * v_term**2)
-    init = State(y=height, v=v_init)
-    t_end = 30 * s
-    dt = t_end / 100
-    
-    return System(params, area=area, C_d=C_d, 
-                  init=init, t_end=t_end, dt=dt)
-\end{python}
-
-The first argument of \py{System} is \py{params}, so the result contains all of the parameters in \py{params}, \py{area}, \py{C_d}, and the rest.
-
-It might not be obvious why we need \py{Params} objects, but they will turn out to be useful soon.
-
-We can make a \py{System} like this:
-
-\begin{python}
-system = make_system(params)
-\end{python}
-
-Now here's a version of the slope function that includes drag:
-
-\index{slope function}
-\index{function!slope}
-\index{unpack}
-
-\begin{python}
-def slope_func(state, t, system):
-    y, v = state
-    rho, C_d, g = system.rho, system.C_d, system.g
-    area, mass = system.area, system.mass
-    
-    f_drag = rho * v**2 * C_d * area / 2
-    a_drag = f_drag / mass
-    
-    dydt = v
-    dvdt = -g + a_drag
-    
-    return dydt, dvdt
-\end{python}
-
-\py{f_drag} is force due to drag, based on the drag equation.  \py{a_drag} is acceleration due to drag, based on Newton's second law.
-
-\index{gravity}
-
-To compute total acceleration, we add accelerations due to gravity and drag. \py{g} is negated because it is in the direction of decreasing \py{y}, and \py{a_drag} is positive because it is in the direction of increasing \py{y}.  In the next chapter we will use \py{Vector} objects to keep track of the direction of forces and add them up in a less error-prone way.
-
-To stop the simulation when the penny hits the sidewalk, we'll use the event function from Section~\ref{events}:
-
-\begin{python}
-def event_func(state, t, system):
-    y, v = state
-    return y
-\end{python}
-
-Now we can run the simulation like this:
-
-\index{\py{run_ode_solver}}
-
-\begin{python}
-results, details = run_ode_solver(system, slope_func,
-                                  events=event_func)
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap21-fig01.pdf}}
-\caption{Height of the penny versus time, with air resistance.}
-\label{chap21-fig01}
-\end{figure}
-
-Figure~\ref{chap21-fig01} shows the result.  It only takes a few seconds for the penny to accelerate up to terminal velocity; after that, velocity is constant, so height as a function of time is a straight line.
-
-\index{terminal velocity}
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap21.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-
-\chapter{Projectiles in 2-D}
-\label{chap22}
-
-In the previous chapter we modeled objects moving in one dimension, with and without drag.  Now let's move on to two dimensions, and baseball!
-
-In this chapter we model the flight of a baseball including the effect of air resistance.  In the next chapter we use this model to solve an optimization problem.
-
-
-\section{Baseball}
-\label{baseball}
-
-To model the flight of a baseball, we have to make some modeling decisions.  To get started, we ignore any spin that might be on the ball, and the resulting Magnus force (see \url{http://modsimpy.com/magnus}).  Under this assumption, the ball travels in a vertical plane, so we'll run simulations in two dimensions, rather than three.
-
-\index{Magnus force}
-
-Air resistance has a substantial effect on most projectiles in air, so we will include a drag force.
-
-\index{air resistance}
-
-To model air resistance, we'll need the mass, frontal area, and drag coefficient of a baseball.  Mass and diameter are easy to find (see \url{http://modsimpy.com/baseball}).  Drag coefficient is only a little harder; according to {\it The Physics of Baseball}\footnote{Adair, {\it The Physics of Baseball}, Third Edition, Perennial, 2002.}, the drag coefficient of a baseball is approximately 0.33 (with no units).
-
-\index{drag coefficient}
-
-However, this value {\em does} depend on velocity.  At low velocities it might be as high as 0.5, and at high velocities as low as 0.28.  Furthermore, the transition between these regimes typically happens exactly in the range of velocities we are interested in, between \SI{20}{\meter\per\second} and \SI{40}{\meter\per\second}.
-
-Nevertheless, we'll start with a simple model where the drag coefficient does not depend on velocity; as an exercise at the end of the chapter, you will have a chance to implement a more detailed model and see what effect is has on the results.
-
-But first we need a new computational tool, the \py{Vector} object.
-
-
-\section{Vectors}
-
-Now that we are working in two dimensions, we will find it useful to work with {\bf vector quantities}, that is, quantities that represent both a magnitude and a direction.  We will use vectors to represent positions, velocities, accelerations, and forces in two and three dimensions. 
-
-\index{Vector object}
-\index{array}
-\index{NumPy}
-
-The ModSim library provides a \py{Vector} object that represents a vector quantity.  A \py{Vector} object is a like a NumPy array; it contains elements that represent the {\bf components} of the vector.  For example, in a \py{Vector} that represents a position in space, the components are the $x$ and $y$ coordinates (and a $z$ coordinate in 3-D).  A \py{Vector} object can also have units, like the quantities we've seen in previous chapters.  
-
-\index{unit}
-
-You can create a \py{Vector} by specifying its components.  The following \py{Vector} represents a point \SI{3}{\meter} to the right (or east) and \SI{4}{\meter} up (or north) from an implicit origin:
-
-\index{component}
-
-\begin{python}
-A = Vector(3, 4) * m
-\end{python}
-
-You can access the components of a \py{Vector} by name using the dot operator: for example, \py{A.x} or \py{A.y}.  You can also access them by index using brackets: for example, \py{A[0]} or \py{A[1]}.
-
-Similarly, you can get the magnitude and angle using the dot operator, \py{A.mag} and \py{A.angle}.  {\bf Magnitude} is the length of the vector: if the \py{Vector} represents position, magnitude is the distance from the origin; if it represents velocity, magnitude is speed, that is, how fast the object is moving, regardless of direction.
-
-\index{angle}
-\index{magnitude}
-
-The {\bf angle} of a \py{Vector} is its direction, expressed as the angle in radians from the positive $x$ axis.  In the Cartesian plane, the angle \SI{0}{\radian} is due east, and the angle \SI{\pi}{\radian} is due west. 
-
-\index{radian}
-
-\py{Vector} objects support most mathematical operations, including addition and subtraction:
-
-\begin{python}
-B = Vector(1, 2) * m
-A + B
-A - B
-\end{python}
-
-For the definition and graphical interpretation of these operations, see \url{http://modsimpy.com/vecops}.
-
-\index{vector operation}
-
-When you add and subtract \py{Vector} objects, the ModSim library uses NumPy and Pint to check that the operands have the same number of dimensions and units.  The notebook for this chapter shows examples for working with \py{Vector} objects.
-
-\index{dimensions}
-
-One note on working with angles: in mathematics, we almost always represent angle in radians, and most Python functions expect angles in radians.  But people often think more naturally in degrees.  It can be awkward, and error-prone, to use both units in the same program.  Fortunately, Pint makes it possible to represent angles using quantities with units.
-
-\index{degree}
-
-As an example, I'll get the \py{degree} unit from \py{UNITS}, and create a quantity that represents 45 degrees:
-
-\begin{python}
-degree = UNITS.degree
-angle = 45 * degree
-\end{python}
-
-If we need to convert to radians we can use the \py{to} function
-\index{\py{to}}
-
-\begin{python}
-radian = UNITS.radian
-rads = angle.to(radian)
-\end{python}
-
-If you are given an angle and velocity, you can make a \py{Vector} using \py{pol2cart}, which converts from polar to Cartesian coordinates.  To demonstrate, I'll extract the angle and magnitude of \py{A}:
-
-\index{pol2cart}
-
-\begin{python}
-mag = A.mag
-angle = A.angle
-\end{python}
-
-And then make a new \py{Vector} with the same components:
-
-\begin{python}
-x, y = pol2cart(angle, mag)
-Vector(x, y)
-\end{python}
-
-Another way to represent the direction of \py{A} is a {\bf unit vector}, which is a vector with magnitude 1 that points in the same direction as \py{A}.  You can compute a unit vector by dividing a vector by its magnitude:
-
-\index{unit vector}
-\index{hat function}
-
-\begin{python}
-A / A.mag
-\end{python}
-
-We can do the same thing using the \py{hat} function, so named because unit vectors are conventionally decorated with a hat, like this: $\hat{A}$.
-
-\begin{python}
-A.hat()
-\end{python}
-
-Now let's get back to the game.
-
-
-\section{Simulating baseball flight}
-
-Let's simulate the flight of a baseball that is batted from home plate at an angle of \SI{45}{\degree} and initial speed \SI{40}{\meter \per \second}.
-Using the center of home plate as the origin, the x-axis is parallel to the ground; the y-axis is vertical.  The initial height is about \SI{1}{\meter}.
-
-As in Section~\ref{penny_drag}, I'll create a \py{Params} object that contains the parameters of the system:
-
-\index{Params object}
-
-\begin{python}
-t_end = 10 * s
-dt = t_end / 100
-
-params = Params(x = 0 * m, 
-                y = 1 * m,
-                g = 9.8 * m/s**2,
-                mass = 145e-3 * kg,
-                diameter = 73e-3 * m,
-                rho = 1.2 * kg/m**3,
-                C_d = 0.33,
-                angle = 45 * degree,
-                velocity = 40 * m / s,
-                t_end=t_end, dt=dt)
-\end{python}
-
-The mass, diameter, and drag coefficient of the baseball are from the sources in Section~\ref{baseball}.  The acceleration of gravity, \py{g}, is a well-known quantity, and the density of air, \py{rho}, is based on a temperature of \SI{20}{\celsius} at sea level (see \url{http://modsimpy.com/tempress}).
- I chose the value of \py{t_end} to run the simulation long enough for the ball to land on the ground.
-
-\index{density}
-
-The following function uses the \py{Params} object to make a \py{System} object.  This two-step process makes the code more readable and makes it easier to work with functions like \py{root_bisect}.
-
-\index{System object}
-\index{\py{make_system}}
-
-\begin{python}
-def make_system(params):
-    angle, velocity = params.angle, params.velocity
-    
-    # convert angle to degrees
-    theta = np.deg2rad(angle)
-    
-    # compute x and y components of velocity
-    vx, vy = pol2cart(theta, velocity)
-    
-    # make the initial state
-    R = Vector(params.x, params.y)
-    V = Vector(vx, vy)
-    init = State(R=R, V=V)
-    
-    # compute area from diameter
-    diameter = params.diameter
-    area = np.pi * (diameter/2)**2
-    
-    return System(params, init=init, area=area)
-\end{python}
-
-\py{make_system} uses \py{np.deg2rad} to convert \py{angle} to radians and \py{pol2cart} to compute the $x$ and $y$ components of the initial velocity.  
-
-Then it makes \py{Vector} objects to represent the initial position, \py{R}, and velocity \py{V}.  These vectors are stored as state variables in \py{init}.
-
-\py{make_system} also computes \py{area}, then creates a \py{System} object with all of the variables from \py{params} plus \py{init} and \py{area}.
-
-\index{deg2rad}
-\index{State object}
-
-Next we need a function to compute drag force:
-
-\begin{python}
-def drag_force(V, system):
-    rho, C_d, area = system.rho, system.C_d, system.area
-    
-    mag = -rho * V.mag**2 * C_d * area / 2
-    direction = V.hat()
-    f_drag = direction * mag
-    return f_drag
-\end{python}
-
-This function takes \py{V} as a \py{Vector} and returns \py{f_drag} as a \py{Vector}.
-It uses the drag equation to compute the magnitude of the drag force, and the \py{hat} function to compute the direction.  \py{-V.hat()} computes a unit vector pointing in the opposite direction of \py{V}.
-
-\index{unit vector}
-\index{slope function}
-\index{function!slope}
-
-Now we're ready for a slope function:
-
-\begin{python}
-def slope_func(state, t, system):
-    R, V = state
-    mass, g = system.mass, system.g
-    
-    a_drag = drag_force(V, system) / mass
-    a_grav = Vector(0, -g)
-    
-    A = a_grav + a_drag
-    
-    return V, A
-\end{python}
-
-As usual, the parameters of the slope function are a \py{State} object, time, and a \py{System} object.  In this example, we don't use \py{t}, but we can't leave it out because when \py{run_ode_solver} calls the slope function, it always provides the same arguments, whether they are needed or not.
-
-The \py{State} object contains two state variables, \py{R} and \py{V}, that represent position and velocity as \py{Vector} objects.
-
-\index{state variable}
-
-The return values from the slope function are the derivatives of these quantities.  The derivative of position is velocity, so the first return value is \py{V}, which we extracted from the \py{State} object.  The derivative of velocity is acceleration, and that's what we have to compute.
-
-\index{acceleration}
-\index{velocity}
-\index{position}
-
-The total acceleration of the baseball is the sum of accelerations due to gravity and drag.  These quantities have both magnitude and direction, so they are represented by \py{Vector} objects.
-
-We already saw how \py{a_drag} is computed.  \py{a_grav} is a \py{Vector} with magnitude \py{g} pointed in the negative \py{y} direction.
-
-Using vectors to represent forces and accelerations makes the code concise, readable, and less error-prone.  In particular, when we add \py{a_grav} and \py{a_drag}, the directions are likely to be correct, because they are encoded in the \py{Vector} objects.  And the units are certain to be correct, because otherwise Pint would report an error.
-
-\index{Pint}
-
-As always, we can test the slope function by running it with the initial conditions:
-
-\begin{python}
-slope_func(system.init, 0, system)
-\end{python}
-
-We can use an event function to stop the simulation when the ball hits the ground.
-
-\begin{python}
-def event_func(state, t, system):
-    R, V = state
-    return R.y
-\end{python}
-
-The event function takes the same parameters as the slope function, and returns the y coordinate of \py{R}.  When the y coordinate passes through 0, the simulation stops.
-
-Now we're ready to run the simulation:
-
-\begin{python}
-results, details = run_ode_solver(system, slope_func,
-                                   events=event_func)
-\end{python}
-
-\py{results} is a \py{TimeFrame} object with one column for each of the state variables, \py{R} and \py{V}.
-
-\index{TimeFrame object}
-
-We can get the flight time like this:
-
-\begin{python}
-flight_time = get_last_label(results) * s
-\end{python}
-
-And the final \py{x} coordinate like this:
-
-\begin{python}
-R_final = get_last_value(results.R)
-x_dist = R_final.x
-\end{python}
-
-
-\section{Trajectories}
-
-We can plot the $x$ and $y$ components of position like this:
-
-\begin{python}
-x = results.R.extract('x')
-y = results.R.extract('y')
-
-x.plot()
-y.plot()
-\end{python}
-
-The \py{extract} function loops through the \py{Vector} objects in \py{R} and extracts one coordinate from each.  The result is a \py{TimeSeries}.
-
-\index{extract function}
-\index{TimeSeries}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap22-fig01.pdf}}
-\caption{Simulated baseball flight, $x$ and $y$ components of position as a function of time.}
-\label{chap22-fig01}
-\end{figure}
-
-Figure~\ref{chap22-fig01} shows the result.  As expected, the $x$ component increases as the ball moves away from home plate.  The $y$ position climbs initially and then descends, falling to \SI{0}{\meter} near \SI{5.0}{\second}.
-
-\index{monotonic}
-
-Another way to view the same data is to plot the $x$ component on the x-axis and the $y$ component on the y-axis, so the plotted line follows the trajectory of the ball through the plane:
-
-\begin{python}
-x = results.R.extract('x')
-y = results.R.extract('y')
-plot(x, y, label='trajectory')
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap22-fig02.pdf}}
-\caption{Simulated baseball flight, trajectory plot.}
-\label{chap22-fig02}
-\end{figure}
-
-Figure~\ref{chap22-fig02} shows this way of visualizing the results, which is called a {\bf trajectory plot} (see \url{http://modsimpy.com/trajec}).
-
-\index{trajectory plot}
-
-A trajectory plot can be easier to interpret than a time series plot, because it shows what the motion of the projectile would look like (at least from one point of view).  Both plots can be useful, but don't get them mixed up!  If you are looking at a time series plot and interpreting it as a trajectory, you will be very confused.
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap22.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-\chapter{Optimization}
-\label{chap23}
-
-In the previous chapter we developed a model of the flight of a baseball, including gravity and a simple version of drag, but neglecting spin, Magnus force, and the dependence of the coefficient of drag on velocity.
-
-In this chapter we apply that model to an optimization problem.
-
-\section{The Manny Ramirez problem}
-\label{manny}
-
-Manny Ramirez is a former member of the Boston Red Sox (an American baseball team) who was notorious for his relaxed attitude and taste for practical jokes.  Our objective in this chapter is to solve the following Manny-inspired problem:
-
-{\it What is the minimum effort required to hit a home run in Fenway Park?}
-
-Fenway Park is a baseball stadium in Boston, Massachusetts.  One of its most famous features is the ``Green Monster", which is a wall in left field that is unusually close to home plate, only 310 feet away.  To compensate for the short distance, the wall is unusually high, at 37 feet (see \url{http://modsimpy.com/wally}).
-
-\index{Ramirez, Manny}
-\index{Fenway Park}
-\index{baseball}
-\index{Green Monster}
-\index{velocity}
-
-We want to find the minimum velocity at which a ball can leave home plate and still go over the Green Monster.  We'll proceed in the following steps:
-
-\begin{enumerate}
-
-\item For a given velocity, we'll find the optimal {\bf launch angle}, that is, the angle the ball should leave home plate to maximize its height when it reaches the wall.
-
-\index{launch angle}
-
-\item Then we'll find the minimal velocity that clears the wall, given that it has the optimal launch angle.
-
-\end{enumerate}
-
-We'll use the same model as in the previous chapter, with this \py{Params} object:
-
-\begin{python}
-t_end = 20 * s
-dt = t_end / 100
-
-params = Params(x = 0 * m, 
-                y = 1 * m,
-                g = 9.8 * m/s**2,
-                mass = 145e-3 * kg,
-                diameter = 73e-3 * m,
-                rho = 1.2 * kg/m**3,
-                C_d = 0.3,
-                angle = 45 * degree,
-                velocity = 40 * m / s,
-                t_end=t_end,
-                dt=dt)
-\end{python}
-  
-\index{Params object}
-
-This version of \py{make_system}:
-
-\begin{python}
-def make_system(params):
-    angle, velocity = params.angle, params.velocity
-    
-    # convert angle to degrees
-    theta = np.deg2rad(angle)
-    
-    # compute x and y components of velocity
-    vx, vy = pol2cart(theta, velocity)
-    
-    # make the initial state
-    R = Vector(params.x, params.y)
-    V = Vector(vx, vy)
-    init = State(R=R, V=V)
-    
-    # compute area from diameter
-    diameter = params.diameter
-    area = np.pi * (diameter/2)**2
-    
-    return System(params, init=init, area=area)
-\end{python}
-    
-This slope function:
-
-\begin{python}
-def slope_func(state, t, system):
-    R, V = state
-    mass, g = system.mass, system.g
-    
-    a_drag = drag_force(V, system) / mass
-    a_grav = Vector(0, -g)
-    
-    A = a_grav + a_drag
-    
-    return V, A
-\end{python}
-
-And this event function:
-
-\begin{python}
-def event_func(state, t, system):
-    R, V = state
-    return R.y
-\end{python}
-
-
-\section{Finding the range}
-
-Suppose we want to find the launch angle that maximizes {\bf range}, that is, the distance the ball travels in the air before landing.   We'll use a function in the ModSim library, \py{maximize}, which takes a function and finds its maximum.
-
-The function we pass to \py{maximize} should take launch angle and a \py{params} object, and return range:
-
-\begin{python}
-def range_func(angle, params):  
-    params = Params(params, angle=angle)
-    system = make_system(params)
-    results, details = run_ode_solver(system, slope_func,
-                                       events=event_func)
-    x_dist = get_last_value(results.R).x
-    print(angle, x_dist)
-    return x_dist
-\end{python}
-
-\py{range_func} makes a new \py{Params} object with the given value of \py{angle}.  Then it makes a \py{System} object, calls \py{run_ode_solver}, and returns the final value of \py{x} from the results.  
-
-We can call \py{range_func} directly like this:
-
-\begin{python}
-range_func(45, params)
-\end{python}
-
-And we can sweep a sequence of angles like this:
-
-\index{parameter sweep}
-\index{SweepSeries object}
-
-\begin{python}
-angles = linspace(20, 80, 21)
-sweep = SweepSeries()
-
-for angle in angles:
-    x_dist = range_func(angle, params)
-    print(angle, x_dist)
-    sweep[angle] = x_dist
-\end{python}
-
-\begin{figure}
-\centerline{\includegraphics[height=3in]{figs/chap23-fig01.pdf}}
-\caption{Distance from home plate as a function of launch angle, with fixed velocity.}
-\label{chap23-fig01}
-\end{figure}
-
-Figure~\ref{chap23-fig01} shows the results.  It looks like the optimal angle is between \SI{40}{\degree} and \SI{45}{\degree}.
-
-%TODO: Draw a vertical line to show this
-
-We can find the optimal angle more precisely and more efficiently using \py{maximize}, like this:
-
-\begin{python}
-res = maximize(range_func, [0, 90], params)
-\end{python}
-
-The first parameter is the function we want to maximize.  The second is the range of values we want to search; in this case it's the range of angles from \SI{0}{\degree} to \SI{90}{\degree}.  The third argument can be any object; it gets passed along as an argument when \py{maximize} calls \py{range_func}.
-
-The return value from \py{maximize} is an \py{ModSimSeries} that contains the results, including \py{x}, which is the angle that yielded the highest range, and \py{fun}, which is the value of \py{range_func} when it's evaluated at \py{x}, that is, range when the baseball is launched at the optimal angle.
-
-\index{ModSimSeries}
-
-For these parameters, the optimal angle is about \SI{42}{\degree}, which yields a range of \SI{103}{\meter}.
-
-\py{maximize} uses a golden section search, which you can read about at \url{http://modsimpy.com/minimize}).
-
-% TODO: update this link to point to Golden section search
-
-
-\section{Finishing off the problem}
-
-In the notebook for this chapter, \py{chap22.ipynb}, you'll have to chance to finish off the Manny Ramirez problem.  There are a few things you'll have to do:
-
-\begin{itemize}
-
-\item In the previous section the ``optimal" launch angle is the one that maximizes range, but that's not what we want.  Rather, we want the angle that maximizes the height of the ball when it gets to the wall (310 feet from home plate).  So you'll have to write a height function to compute it, and then use \py{maximize} to find the revised optimum.
-
-\item Once you can find the optimal angle for any velocity, you have to find the minimum velocity that gets the ball over the wall.  You'll write a function that takes a velocity as a parameter, computes the optimal angle for that velocity, and returns the height of the ball, at the wall, using the optimal angle.
-
-\item Finally, you'll use \py{root_bisect} to find the velocity that makes the optimal height at the wall just barely 37 feet.
-  
-\index{\py{root_bisect}}
-
-\end{itemize}
-
-The notebook provides some additional hints, but at this point you should have everything you need.  Good luck!
-
-If you enjoy this exercise, you might be interested in this paper: ``How to hit home runs: Optimum baseball bat swing parameters for maximum range trajectories", by Sawicki, Hubbard, and Stronge, at \url{http://modsimpy.com/runs}.
-
-
-\chapter{Rotation}
-\label{chap24}
-
-In this chapter we model systems that involve rotating objects.  In general, rotation is complicated:  in three dimensions, objects can rotate around three axes; objects are often easier to spin around some axes than others; and they may be stable when spinning around some axes but not others.
-
-\index{rotation}
-
-If the configuration of an object changes over time, it might become easier or harder to spin, which explains the surprising dynamics of gymnasts, divers, ice skaters, etc.
-
-And when you apply a twisting force to a rotating object, the effect is often contrary to intuition.  For an example, see this video on gyroscopic precession \url{http://modsimpy.com/precess}.
-
-\index{gyroscopic precession}
-
-In this chapter, we will not take on the physics of rotation in all its glory.  Rather, we will focus on simple scenarios where all rotation and all twisting forces are around a single axis.  In that case, we can treat some vector quantities as if they were scalars (in the same way that we sometimes treat velocity as a scalar with an implicit direction).
-
-\index{scalar}
-
-This approach makes it possible to simulate and analyze many interesting systems, but you will also encounter systems that would be better approached with the more general toolkit.
-
-The fundamental ideas in this chapter and the next are {\bf angular velocity}, {\bf angular acceleration}, {\bf torque}, and {\bf moment of inertia}.  If you are not already familiar with these concepts, I will define them as we go along, and I will point to additional reading.
-
-At the end of the next chapter, you will use these tools to simulate the behavior of a yo-yo (see \url{http://modsimpy.com/yoyo}).  But we'll work our way up to it gradually, starting with toilet paper.
-
-
-
-\section{The physics of toilet paper}
-\label{paper}
-
-As a simple example of a system with rotation, we'll simulate the manufacture of a roll of toilet paper.  Starting with a cardboard tube at the center, we will roll up \SI{47}{\meter} of paper, the typical length of a roll of toilet paper in the U.S. (see \url{http://modsimpy.com/paper}).
-
-\index{toilet paper}
-
-\begin{figure}
-\centerline{\includegraphics[height=2.5in]{figs/paper_roll.pdf}}
-\caption{Diagram of a roll of toilet paper, showing change in paper length as a result of a small rotation, $d\theta$.}
-\label{paper_roll}
-\end{figure}
-
-Figure~\ref{paper_roll} shows a diagram of the system: $r$ represents the radius of the roll at a point in time.  Initially, $r$ is the radius of the cardboard core, $R_{min}$.  When the roll is complete, $r$ is $R_{max}$.
-
-I'll use $\theta$ to represent the total rotation of the roll in radians.  In the diagram, $d\theta$ represents a small increase in $\theta$, which corresponds to a distance along the circumference of the roll of $r~d\theta$.
-
-\index{radian}
-
-Finally, I'll use $y$ to represent the total length of paper that's been rolled.  Initially, $\theta=0$ and $y=0$.  For each small increase in $\theta$, there is a corresponding increase in $y$:
-%
-\[ dy = r~d\theta \]
-%
-If we divide both sides by a small increase in time, $dt$, we get a differential equation for $y$ as a function of time.
-%
-\[ \frac{dy}{dt} = r \frac{d\theta}{dt} \]
-%
-As we roll up the paper, $r$ increases, too.  Assuming that $r$ increases by a fixed amount per revolution, we can write
-%
-\[ dr = k~d\theta \]
-%
-Where $k$ is an unknown constant we'll have to figure out.  Again, we can divide both sides by $dt$ to get a differential equation in time:
-%
-\[ \frac{dr}{dt} = k \frac{d\theta}{dt} \]
-%
-Finally, let's assume that $\theta$ increases at a constant rate of \SI{10}{\radian\per\second} (about 95 revolutions per minute):
-%
-\[ \frac{d\theta}{dt} = 10  \]
-%
-This rate of change is called an {\bf angular velocity}.  Now we have a system of three differential equations we can use to simulate the system.
-
-\index{angular velocity}
-\index{differential equation}
-
-
-\section{Implementation}
-\label{papersim}
-
-At this point we have a pretty standard process for writing simulations like this.  First, we'll get the units we need from Pint:
-\index{Pint}
-
-\begin{python}
-radian = UNITS.radian
-m = UNITS.meter
-s = UNITS.second
-\end{python}
-
-And create a \py{Params} object with the parameters of the system:
-
-\index{Params object}
-
-\begin{python}
-params = Params(Rmin = 0.02 * m,
-                Rmax = 0.055 * m,
-                L = 47 * m,
-                omega = 10 * radian / s,
-                t_end = 130 * s,
-                dt = 1*s)
-\end{python}
-
-\py{Rmin} and \py{Rmax} are the initial and final values for the radius, \py{r}.  \py{L} is the total length of the paper. \py{t_end} is the length of the simulation in time, and \py{dt} is the time step for the ODE solver.
-
-We use the \py{Params} object to make a \py{System} object:
-
-\index{System object}
-\index{\py{make_system}}
-
-\begin{python}
-def make_system(params):
-    init = State(theta = 0 * radian,
-                 y = 0 * m,
-                 r = params.Rmin)
-    
-    k = estimate_k(params)
-
-    return System(params, init=init, k=k)
-\end{python}
-
-The initial state contains three variables, \py{theta}, \py{y}, and \py{r}.
-
-\py{estimate_k} computes the parameter, \py{k}, that relates \py{theta} and \py{r}.  Here's how it works:
-
-\begin{python}
-def estimate_k(params):
-    Rmin, Rmax, L = params.Rmin, params.Rmax, params.L
-    
-    Ravg = (Rmax + Rmin) / 2
-    Cavg = 2 * pi * Ravg
-    revs = L / Cavg
-    rads = 2 * pi * revs
-    k = (Rmax - Rmin) / rads
-    return k
-\end{python}
-
-\py{Ravg} is the average radius, half way between \py{Rmin} and \py{Rmax}, so \py{Cavg} is the circumference of the roll when \py{r} is \py{Ravg}.
-
-\py{revs} is the total number of revolutions it would take to roll up length \py{L} if \py{r} were constant at \py{Ravg}.  And \py{rads} is just \py{revs} converted to radians.
-
-Finally, \py{k} is the change in \py{r} for each radian of revolution.  For these parameters, \py{k} is about \py{2.8e-5} \si{\meter\per\radian}.
-
-Now we can use the differential equations from Section~\ref{paper} to write a slope function:
-
-\index{slope function}
-\index{Function!slope}
-
-\begin{python}
-def slope_func(state, t, system):
-    theta, y, r = state
-    k, omega = system.k, system.omega
-    
-    dydt = r * omega
-    drdt = k * omega
-    
-    return omega, dydt, drdt
-\end{python}
-
-\begin{figure}[t]
-\centerline{\includegraphics[height=4.5in]{figs/chap24-fig01.pdf}}
-\caption{Results from paper rolling simulation, showing rotation, length, and radius over time.}
-\label{chap24-fig01}
-\end{figure}
-
-As usual, the slope function takes a \py{State} object, a time, and a \py{System} object.  The \py{State} object contains hypothetical values of \py{theta}, \py{y}, and \py{r} at time \py{t}.  The job of the slope function is to compute the time derivatives of these values.  The derivative of \py{theta} is angular velocity, which is often denoted \py{omega}.
-
-\index{State object}
-
-We'd like to stop the simulation when the length of paper on the roll is \py{L}.  We can do that with an event function that passes through 0 when \py{y} equals \py{L}:
-
-\begin{python}
-def event_func(state, t, system):
-    theta, y, r = state
-    return y - system.L
-\end{python}
-
-Now we can run the simulation like this:
-
-\begin{python}
-results, details = run_ode_solver(system, slope_func,
-                                   events=event_func)
-\end{python}
-
-
-Figure~\ref{chap24-fig01} shows the results.  \py{theta} grows linearly over time, as we should expect.  As a result, \py{r} also grows linearly.  But since the derivative of \py{y} depends on \py{r}, and \py{r} is increasing, \py{y} grows with increasing slope.
-
-Because this system is so simple, it is almost silly to simulate it.  As we'll see in the next section, it is easy enough to solve the differential equations analytically.  But it is often useful to start with a simple simulation as a way of exploring and checking assumptions.
-
-In order to get the simulation working, we have to get the units right, which can help catch conceptual errors early.  And by plugging in realistic parameters, we can detect errors that cause unrealistic results.  For example, in this system we can check:
-
-\begin{itemize}
-
-\item The total time for the simulation is about 2 minutes, which seems plausible for the time it would take to roll \SI{47}{\meter} of paper.
-
-\item The final value of \py{theta} is about \SI{1250}{\radian}, which corresponds to about 200 revolutions, which also seems plausible.
-
-\item The initial and final values for \py{r} are consistent with \py{Rmin} and \py{Rmax}, as we intended when we chose \py{k}.
-
-\end{itemize}
-
-But now that we have a working simulation, it is also useful to do some analysis.
-
-
-\section{Analysis}
-\label{paper_analysis}
-
-The differential equations in Section~\ref{paper} are simple enough that we can just solve them.  Since angular velocity is constant:
-%
-\[ \frac{d\theta}{dt} = \omega  \]
-%
-We can find $\theta$ as a function of time by integrating both sides:
-%
-\[ \theta(t) = \omega t + C_1 \]
-%
-With the initial condition $\theta(0)=0$,  we find $C_1=0$.  Similarly,
-%
-\begin{equation}
-\frac{dr}{dt} = k \omega                    \label{eqn1}
-\end{equation}
-%
-So
-%
-\[ r(t) = k \omega t + C_2 \]
-%
-With the initial condition $r(0)=R_{min}$,  we find $C_2=R_{min}$.  Then we can plug the solution for $r$ into the equation for $y$:
-%
-\begin{align}
-\frac{dy}{dt} & = r \omega                    \label{eqn2}   \\
-              & = \left[ k \omega t + R_{min} \right] \omega \nonumber
-\end{align}
-%
-%
-Integrating both sides yields:
-%
-\[ y(t) = \left[ k \omega t^2 / 2 + R_{min} t \right] \omega + C_3\]
-%
-So $y$ is a parabola, as you might have guessed.  With initial condition $y(0)=0$, we find $C_3=0$.
-
-\index{analysis}
-\index{integration}
-
-We can also use these equations to find the relationship between $y$ and $r$, independent of time, which we can use to compute $k$.  Using a move we saw in Section~\ref{contact}, I'll divide Equations~\ref{eqn1} and \ref{eqn2}, yielding
-%
-\[ \frac{dr}{dy} = \frac{k}{r}\]
-%
-Separating variables yields
-%
-\[ r~dr = k~dy\]
-%
-Integrating both sides yields
-%
-\[ r^2 / 2 = k y + C \]
-%
-When $y=0$, $r=R_{min}$, so
-%
-\[ C = \frac{1}{2} R_{min}^2 \]
-%
-Solving for $y$, we have
-%
-\begin{equation}
-y = \frac{1}{2k} (r^2 - R_{min}^2)                 \label{eqn3}
-\end{equation}
-%
-When $y=L$, $r=R_{max}$; substituting in those values yields
-%
-\[ L = \frac{1}{2k} (R_{max}^2 - R_{min}^2) \]
-%
-Solving for $k$ yields
-%
-\begin{equation}
-k =  \frac{1}{2L} (R_{max}^2 - R_{min}^2)           \label{eqn4}
-\end{equation}
-%
-Plugging in the values of the parameters yields \py{2.8e-5} \si{\meter\per\radian}, the same as the ``estimate" we computed in Section~\ref{papersim}.  In this case the estimate turns out to be exact.
-
-Before you go on, you might want to read the notebook for this chapter, \py{chap24.ipynb}, and work on the exercises.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-\chapter{Torque}
-\label{chap25}
-
-In the previous chapter we modeled a scenario with constant angular velocity.  In this chapter we make it more complex; we'll model a teapot, on a turntable, revolving with constant angular acceleration and deceleration.
-
-\section{Angular acceleration}
-
-\index{angular acceleration}
-\index{torque}
-
-Just as linear acceleration is the derivative of velocity, {\bf angular acceleration} is the derivative of angular velocity.  And just as linear acceleration is caused by force, angular acceleration is caused by the rotational version of force, {\bf torque}.  If you are not familiar with torque, you can read about it at \url{http://modsimpy.com/torque}.
-
-In general, torque is a vector quantity, defined as the {\bf cross product} of $\vec{r}$ and $\vec{F}$, where $\vec{r}$ is the {\bf lever arm}, a vector from the point of rotation to the point where the force is applied, and $\vec{F}$ is the vector that represents the magnitude and direction of the force.
-
-\index{vector}
-\index{lever arm}
-\index{cross product}
-
-However, for the problems in this chapter, we only need the {\em magnitude} of torque; we don't care about the direction.  In that case, we can compute
-%
-\[ \tau = r F \sin \theta \]
-%
-where $\tau$ is torque, $r$ is the length of the lever arm, $F$ is the magnitude of force, and $\theta$ is the angle between $\vec{r}$ and $\vec{F}$.  
-
-\index{magnitude}
-
-Since torque is the product of a length and a force, it is expressed in newton meters (\si{\newton\meter}).
-
-
-\section{Moment of inertia}
-
-In the same way that linear acceleration is related to force by Newton's second law of motion, $F=ma$, angular acceleration is related to torque by another form of Newton's law:
-%
-\[ \tau = I \alpha \]
-%
-Where $\alpha$ is angular acceleration and $I$ is {\bf moment of inertia}.  Just as mass is what makes it hard to accelerate an object\footnote{That might sound like a dumb way to describe mass, but its actually one of the fundamental definitions.}, moment of inertia is what makes it hard to spin an object.
-
-\index{mass}
-\index{moment of inertia}
-
-In the most general case, a 3-D object rotating around an arbitrary axis, moment of inertia is a tensor, which is a function that takes a vector as a parameter and returns a vector as a result.
-
-\index{tensor}
-
-Fortunately, in a system where all rotation and torque happens around a single axis, we don't have to deal with the most general case.  We can treat moment of inertia as a scalar quantity.
-
-\index{scalar}
-
-For a small object with mass $m$, rotating around a point at distance $r$, the moment of inertia is $I = m r^2$.  For more complex objects, we can compute $I$ by dividing the object into small masses, computing moments of inertia for each mass, and adding them up.
-
-However, for most simple shapes, people have already done the calculations; you can just look up the answers.  For example, see \url{http://modsimpy.com/moment}.
-
-
-\section{Teapots and turntables}
-
-Tables in Chinese restaurants often have a rotating tray or turntable
-that makes it easy for customers to share dishes. These turntables are
-supported by low-friction bearings that allow them to turn easily and
-glide. However, they can be heavy, especially when they are loaded with
-food, so they have a high moment of inertia.
-
-\index{teapot}
-\index{turntable}
-
-Suppose I am sitting at a table with a pot of tea on the turntable
-directly in front of me, and the person sitting directly opposite asks
-me to pass the tea. I push on the edge of the turntable with \SI{1}{\newton} of force until it has turned \SI{0.5}{\radian}, then let go. The turntable glides until it comes to a stop \SI{1.5}{\radian} from the starting position. How much force should I apply for a second push so the teapot glides to a
-stop directly opposite me?
-
-\index{force}
-\index{Newton}
-\index{friction}
-
-We'll answer this question in these steps:
-
-\begin{enumerate}
-
-\item
-  I'll use the results from the first push to estimate the coefficient
-  of friction for the turntable.
-
-\item
-  As an exercise, you'll use that coefficient of friction to estimate the force needed to rotate the turntable through the remaining angle.
-
-\end{enumerate}
-
-Our simulation will use the following parameters:
-
-\begin{enumerate}
-
-\item
-  The radius of the turntable is \SI{0.5}{\meter}, and its weight is \SI{7}{\kg}.
-  
-\item
-  The teapot weights \SI{0.3}{\kg}, and it sits \SI{0.4}{\meter} from the center of the turntable.
-
-\end{enumerate}
-
-\begin{figure}
-\centerline{\includegraphics[height=2.5in]{figs/teapot.pdf}}
-\caption{Diagram of a turntable with a teapot.}
-\label{teapot}
-\end{figure}
-
-Figure~\ref{teapot} shows the scenario, where $F$ is the force I apply to the turntable at the perimeter, perpendicular to the moment arm, $r$, and $\tau$ is the resulting torque. The blue circle near the bottom is the teapot.
-
-Here's a \py{Params} object with the parameters of the scenario:
-
-\begin{python}
-params = Params(radius_disk=0.5*m,
-                mass_disk=7*kg,
-                radius_pot=0.4*m,
-                mass_pot=0.3*kg,
-                force=1*N,
-                torque_friction=0.2*N*m,
-                theta_end=0.5*radian,
-                t_end=20*s)
-\end{python}
-
-\index{Params object}
-
-\py{make_system} creates the initial state, \py{init}, and
-computes the total moment of inertia for the turntable and the teapot.
-
-\begin{python}
-def make_system(params):
-    mass_disk, mass_pot = params.mass_disk, params.mass_pot
-    radius_disk, radius_pot = params.radius_disk, params.radius_pot
-    
-    init = State(theta=0*radian, omega=0*radian/s)
-    
-    I_disk = mass_disk * radius_disk**2 / 2
-    I_pot = mass_pot * radius_pot**2
-    
-    return System(params, init=init, I=I_disk+I_pot)
-\end{python}
-
-%\index{make_system}
-
-In the initial state,
-\py{theta} represents the angle of the table in \si{\radian}; \py{omega} represents the angular velocity in \si{\radian\per\second}.
-
-\py{I_disk} is the moment of inertia of the turntable, which is based on the moment of inertia for a horizontal disk revolving around a vertical axis through its center:
-%
-\[ I_{disk} = m r^2 / 2 \]
-%
-\py{I_pot} is the moment of inertia of the teapot, which I treat as a point mass with:
-%
-\[ I_{point} = m r^2 \]
-%
-In SI units, moment of inertia is expressed in \si{\kilogram\meter\squared}.  
-
-Now we can make a \py{System} object:
-
-\begin{python}
-system1 = make_system(params)
-\end{python}
-
-\index{System object}
-
-Here's a slope that takes the current state, which contains angle and angular velocity, and returns the derivatives, angular velocity and angular acceleration:
-
-\begin{python}
-def slope_func(state, t, system):
-    theta, omega = state
-    radius_disk, force = system.radius_disk, system.force
-    torque_friction, I = system.torque_friction, system.I
-    
-    torque = radius_disk * force - torque_friction
-    alpha = torque / I
-    
-    return omega, alpha 
-\end{python}
-
-\index{slope function}
-
-In this scenario, the force I apply to the turntable is always perpendicular to the lever arm, so $\sin \theta = 1$ and the torque due to force is $\tau = r F$.
-
-\py{torque_friction} represents the torque due to friction.  Because the turntable is rotating in the direction of positive \py{theta}, friction acts in the direction of negative \py{theta}.
-
-\index{friction}
-
-Now we are ready to run the simulation, but first there's a problem we have to address.
-
-When I stop pushing on the turntable, the angular acceleration changes
-abruptly. We could implement the slope function with an \py{if}
-statement that checks the value of \py{theta} and sets
-\py{force} accordingly. And for a coarse model like this one, that
-might be fine. But we will get more accurate results if we simulate the
-system in two phases:
-
-\begin{enumerate}
-\item
-  During the first phase, force is constant, and we run until
-  \py{theta} is 0.5 radians.
-\item
-  During the second phase, force is 0, and we run until \py{omega}
-  is 0.
-\end{enumerate}
-
-Then we can combine the results of the two phases into a single
-\py{TimeFrame}.
-
-\index{two-phase simulation}
-
-Here's the event function I'll use for Phase 1; it stops the simulation when \py{theta} reaches \py{theta_end}, which is when I stop pushing:
-
-\begin{python}
-def event_func1(state, t, system):
-    theta, omega = state
-    return theta - system.theta_end 
-\end{python}
-
-Now we can run the first phase.
-
-\begin{python}
-results1, details1 = run_ode_solver(system1, slope_func,
-                                      events=event_func1)
-\end{python}
-
-%\index{run_ode_solver}
-
-\begin{figure}
-\centerline{\includegraphics[height=4.0in]{figs/chap25-fig01.pdf}}
-\caption{Angle and angular velocity of a turntable with applied force and friction.}
-\label{chap25-fig01}
-\end{figure}
-
-Before we run the second phase, we have to extract the final time and
-state of the first phase.
-
-\begin{python}
-t_0 = get_last_label(results1) * s
-init2 = results1.last_row()
-\end{python}
-
-Now we can make a \py{System} object for Phase 2, with the initial state from Phase 1, and with \py{force=0}.
-
-%\index{get_last_label}
-%\index{get_last_value}
-
-\begin{python}
-system2 = System(system1, t_0=t_0, init=init2, force=0*N)
-\end{python}
-
-For the second phase, we need an event function that stops when the turntable stops; that is, when angular velocity is 0.
-
-\begin{python}
-def event_func2(state, t, system):
-    theta, omega = state
-    return omega
-\end{python}
-
-Now we can run the second phase.
-
-\begin{python}
-results2, details2 = run_ode_solver(system2, slope_func,
-                                      events=event_func2)
-\end{python}
-
-Pandas provides \py{combine_first}, which combines
-\py{results1} and \py{results2}.
-
-\index{Pandas}
-
-\begin{python}
-results = results1.combine_first(results2)
-\end{python}
-
-Figure~\ref{chap25-fig01} shows the results.  Angular velocity, \py{omega}, increases linearly while I am pushing, and decreases linearly after I let go.  The angle, \py{theta}, is the integral of angular velocity, so it forms a parabola during each phase.
-
-In the next section, we'll use this simulation to estimate the torque due to friction.
-
-
-\section{Estimating friction}
-
-Let's take the code from the previous section and wrap it in a function.
-
-\index{function}
-
-\begin{python}
-def run_two_phases(force, torque_friction, params):
-    # put the parameters into the Params object
-    params = Params(params, force=force, 
-                    torque_friction=torque_friction)
-
-    # run phase 1
-    system1 = make_system(params)
-    results1, details1 = run_ode_solver(system1, slope_func, 
-                                          events=event_func1)
-
-    # get the final state from phase 1
-    t_0 = results1.last_label() * s
-    init2 = results1.last_row()
-    
-    # run phase 2
-    system2 = System(system1, t_0=t_0, init=init2, force=0*N)
-    results2, details2 = run_ode_solver(system2, slope_func, 
-                                          events=event_func2)
-    
-    # combine and return the results
-    results = results1.combine_first(results2)
-    return TimeFrame(results)
-\end{python}
-
-We can use \py{run_two_phases} to write an error function we can use, with \py{root_bisect}, to find the torque due to friction that yields the observed results from the first push, a total rotation of \SI{1.5}{\radian}.
-
-\index{\py{root_bisect}}
-\index{error function}
-
-\begin{python}
-def error_func1(torque_friction, params):
-    force = 1 * N
-    results = run_two_phases(force, torque_friction, params)
-    theta_final = results.last_row().theta
-    print(torque_friction, theta_final)
-    return theta_final - 1.5 * radian
-\end{python}
-
-Now we can use \py{root_bisect} to estimate torque due to friction.
-
-\index{torque}
-\index{friction}
-\index{\py{root_bisect}}
-
-\begin{python}
-res = root_bisect(error_func1, [0.5, 2], params)
-force = res.root
-\end{python}
-
-The result is \SI{0.166}{\newton\meter}, a little less than the initial guess.
-
-Now that we know the torque due to friction, we can compute the force needed to rotate the turntable through the remaining angle, that is, from \SI{1.5}{\radian} to \SI{3.14}{\radian}.
-
-In the notebook for this chapter, \py{chap25.ipynb}, you will have a chance to finish off the exercise.  For instructions on downloading and running the code, see Section~\ref{code}.
-
-
-\chapter{Case studies}
-\label{chap26}
-
-\section{Computational tools}
-
-In Chapter~\ref{chap20} we rewrote a second order differential equation as a system of first order equations, and solved them using a slope function like this:
-
-\begin{python}
-def slope_func(state, t, system):
-    y, v = state
-    g = system.g    
-
-    dydt = v
-    dvdt = -g
-    
-    return dydt, dvdt
-\end{python}
-
-We used the \py{crossings} function to search for zero-crossings in the simulation results.
-
-Then we used an event function like this:
-
-\begin{python}
-def event_func(state, t, system):
-    y, v = state
-    return y
-\end{python}
-
-To stop the simulation when an event occurs.  Notice that the event function takes the same parameters as the slope function.
-
-In Chapter~\ref{chap21} we developed a model of air resistance and used a  \py{Params} object, which is a collection of parameters:
-
-\begin{python}
-params = Params(height = 381 * m,
-                v_init = 0 * m / s,
-                g = 9.8 * m/s**2,
-                mass = 2.5e-3 * kg,
-                diameter = 19e-3 * m,
-                rho = 1.2 * kg/m**3,
-                v_term = 18 * m / s)
-\end{python}
-
-And we saw a new way to create a \py{System} object, copying the variables from a \py{Params} object and adding or changing variables:
-
-\begin{python}
-    return System(params, area=area, C_d=C_d, 
-                  init=init, t_end=t_end)
-\end{python}
-
-We also used the \py{gradient} function to estimate acceleration, given velocity:
-
-\begin{python}
-a = gradient(results.v)
-\end{python}
-
-Chapter~\ref{chap22} introduces \py{Vector} objects, which can represent vector quantities, like position, velocity, force, and acceleration, in 2 or 3 dimensions.
-
-\begin{python}
-A = Vector(3, 4) * m
-\end{python}
-
-It also introduces trajectory plots, which show the path of an object in two dimensions:
-
-\begin{python}
-x = results.R.extract('x')
-y = results.R.extract('y')
-
-plot(x, y, label='trajectory')
-\end{python}
-
-In Chapter~\ref{chap23} we define a range function that computes the distance a baseball flies as a function of launch angle:
-
-\begin{python}
-def range_func(angle, params):  
-    params = Params(params, angle=angle)
-    system = make_system(params)
-    results, details = run_ode_solver(system, slope_func,
-                                       events=event_func)
-    x_dist = get_last_value(results.R).x
-    return x_dist
-\end{python}
-
-Then we use \py{maximize} to find the launch angle that maximizes range:
-
-\begin{python}
-bounds = [0, 90] * degree
-res = maximize(range_func, bounds, params)
-\end{python}
-
-With that, your toolkit is complete.  Chapter~\ref{chap24} and Chapter~\ref{chap25} introduce the physics of rotation, but no new computational tools.
-
-
-\section{Bungee jumping}
-\label{bungee}
-
-Suppose you want to set the world record for the highest ``bungee dunk", which is a stunt in which a bungee jumper dunks a cookie in a cup of tea at the lowest point of a jump.  An example is shown in this video: \url{http://modsimpy.com/dunk}.
-
-Since the record is \SI{70}{\meter}, let's design a jump for \SI{80}{\meter}.  We'll start with the following modeling assumptions:
-
-\begin{itemize}
-
-\item  Initially the bungee cord hangs from a crane with the attachment point \SI{80}{\meter} above a cup of tea.
-
-\item Until the cord is fully extended, it applies no force to the jumper.  It turns out this might not be a good assumption; we will revisit it.
-
-\item After the cord is fully extended, it obeys Hooke's Law; that is, it applies a force to the jumper proportional to the extension of the cord beyond its resting length.  See \url{http://modsimpy.com/hooke}. 
-
-\item The mass of the jumper is \SI{75}{\kilogram}.
-
-\item The jumper is subject to drag force so that their terminal velocity is \SI{60}{\meter \per \second}.
-
-\end{itemize}
-
-Our objective is to choose the length of the cord, \py{L}, and its spring constant, \py{k}, so that the jumper falls all the way to the tea cup, but no farther!
-
-In the repository for this book, you will find a notebook, \py{bungee.ipynb}, which contains starter code and exercises for this case study.
-
-
-\section{Bungee dunk revisited}
-
-In the previous case study, we assume that the cord applies no force to the jumper until it is stretched.
-It is tempting to say that the cord has no effect because it falls along with the jumper, but that intuition is incorrect.  As the cord falls, it transfers energy to the jumper.
-
-\index{bungee jump}
-\index{bungee cord}
-
-At \url{http://modsimpy.com/bungee} you'll find a paper\footnote{Heck, Uylings, and Kędzierska, ``Understanding the physics of bungee jumping", Physics Education, Volume 45, Number 1, 2010.} that explains this phenomenon and derives the acceleration of the jumper, $a$, as a function of position, $y$, and velocity, $v$:
-%
-\[ a = g + \frac{\mu v^2/2}{\mu(L+y) + 2L} \] 
-%
-where $g$ is acceleration due to gravity, $L$ is the length of the cord, and $\mu$ is the ratio of the mass of the cord, $m$, and the mass of the jumper, $M$.
-
-If you don't believe that their model is correct, this video might convince you: \url{http://modsimpy.com/drop}.
-
-In the repository for this book, you will find a notebook, \py{bungee2.ipynb}, which contains starter code and exercises for this case study.
-How does the behavior of the system change as we vary the mass of the cord?
-When the mass of the cord equals the mass of the jumper, what is the net effect on the lowest point in the jump?
-
-
-
-\section{Spider-Man}
-
-In this case study we'll develop a model of Spider-Man swinging from a
-springy cable of webbing attached to the top of the Empire State
-Building.  Initially, Spider-Man is at the top of a nearby building, as
-shown in Figure~\ref{spiderman}.
-
-\index{Spider-man}
-\index{Empire State Building}
-
-\begin{figure}
-\centerline{\includegraphics[height=2.8in]{figs/spiderman.pdf}}
-\caption{Diagram of the initial state for the Spider-Man case study.}
-\label{spiderman}
-\end{figure}
-
-The origin, \texttt{O}, is at the base of the Empire State Building. The
-vector \py{H} represents the position where the webbing is attached
-to the building, relative to \py{O}. The vector \py{P} is the
-position of Spider-Man relative to \py{O}. And \py{L} is the
-vector from the attachment point to Spider-Man.
-
-\index{vector}
-
-By following the arrows from \py{O}, along \py{H}, and along
-\py{L}, we can see that
-
-\begin{code}
-H + L = P
-\end{code}
-
-So we can compute \py{L} like this:
-
-\begin{code}
-L = P - H
-\end{code}
-
-The goals of this case study are:
-
-\begin{enumerate}
-
-\item
-  Implement a model of this scenario to predict Spider-Man's trajectory.
-\index{trajectory}
-
-\item
-  Choose the right time for Spider-Man to let go of the webbing in order
-  to maximize the distance he travels before landing.
-\index{range}
-
-\item
-  Choose the best angle for Spider-Man to jump off the building, and let
-  go of the webbing, to maximize range.
-\index{optimization}  
-  
-\end{enumerate}
-
-We'll use the following parameters:
-\index{parameter}
-
-\begin{enumerate}
-
-\item According to the Spider-Man Wiki\footnote{\url{http://modsimpy.com/spider}}, Spider-Man weighs \SI{76}{\kg}.
-
-\item
-  Let's assume his terminal velocity is \SI{60}{\meter\per\second}.
-\index{terminal velocity}
-
-\item
-  The length of the web is \SI{100}{\meter}.
-
-\item
-  The initial angle of the web is \SI{45}{\degree} to the left of straight
-  down.
-
-\item
-  The spring constant of the web is \SI{40}{\newton\per\meter} when the cord is stretched, and 0 when it's compressed.
-
-\end{enumerate}
-
-In the repository for this book, you will find a notebook, \py{spiderman.ipynb}, which contains starter code.   Read through the notebook and run the code.  It uses \py{minimize}, which is a SciPy function that can search for an optimal set of parameters (as contrasted with \py{minimize_scalar}, which can only search along a single axis).
-
-
-\section{Kittens}
-
-Let's simulate a kitten unrolling toilet paper.  As reference material, see this video: \url{http://modsimpy.com/kitten}.
-
-\index{kitten}
-
-The interactions of the kitten and the paper roll are complex.  To keep things simple, let's assume that the kitten pulls down on the free end of the roll with constant force.  Also, we will neglect the friction between the roll and the axle.  
-
-\begin{figure}
-\centerline{\includegraphics[height=2.5in]{figs/kitten.pdf}}
-\caption{Diagram of a roll of toilet paper, showing a force, lever arm, and the resulting torque.}
-\label{kitten}
-\end{figure}
-
-Figure~\ref{kitten} shows the paper roll with $r$, $F$, and $\tau$.  As a vector quantity, the direction of $\tau$ is into the page, but we only care about its magnitude for now.
-
-Here's the \py{Params} object with the parameters we'll need:
-
-\index{Params object}
-
-\begin{python}
-params = Params(Rmin = 0.02 * m,
-                Rmax = 0.055 * m,
-                Mcore = 15e-3 * kg,
-                Mroll = 215e-3 * kg,
-                L = 47 * m,
-                tension = 2e-4 * N,
-                t_end = 180 * s)
-\end{python}
-
-As before, \py{Rmin} is the minimum radius and \py{Rmax} is the maximum.  \py{L} is the length of the paper.  \py{Mcore} is the mass of the cardboard tube at the center of the roll; \py{Mroll} is the mass of the paper.  \py{tension} is the force applied by the kitten, in \si{\newton}.  I chose a value that yields plausible results.
-
-At \url{http://modsimpy.com/moment} you can find moments of inertia for simple geometric shapes.  I'll model the cardboard tube at the center of the roll as a ``thin cylindrical shell", and the paper roll as a ``thick-walled cylindrical tube with open ends".
-
-\index{cylinder}
-
-The moment of inertia for a thin shell is just $m r^2$, where $m$ is the mass and $r$ is the radius of the shell.
-
-For a thick-walled tube the moment of inertia is
-%
-\[ I = \frac{\pi \rho h}{2} (r_2^4 - r_1^4) \]
-%
-where $\rho$ is the density of the material, $h$ is the height of the tube, $r_2$ is the outer diameter, and $r_1$ is the inner diameter.
-
-Since the outer diameter changes as the kitten unrolls the paper, we have to compute the moment of inertia, at each point in time, as a function of the current radius, \py{r}.  Here's the function that does it:
-
-\index{unpack}
-
-\begin{python}
-def moment_of_inertia(r, system):
-    Mcore, Rmin = system.Mcore, system.Rmin
-    rho_h = system.rho_h
-    
-    Icore = Mcore * Rmin**2   
-    Iroll = pi * rho_h / 2 * (r**4 - Rmin**4)
-    return Icore + Iroll
-\end{python}
-
-\py{rho_h} is the product of density and height, $\rho h$, which is the mass per area.  \py{rho_h} is computed in \py{make_system}:
-
-\index{density}
-\index{\py{make_system}}
-
-\begin{python}
-def make_system(params):
-    L, Rmax, Rmin = params.L, params.Rmax, params.Rmin
-    Mroll = params.Mroll
-    
-    init = State(theta = 0 * radian,
-                 omega = 0 * radian/s,
-                 y = L)
-    
-    area = pi * (Rmax**2 - Rmin**2)
-    rho_h = Mroll / area
-    k = (Rmax**2 - Rmin**2) / 2 / L / radian    
-    
-    return System(params, init=init, area=area, 
-                  rho_h=rho_h, k=k)
-\end{python}
-
-\py{make_system} also computes \py{k} using Equation~\ref{eqn4}.
-
-In the repository for this book, you will find a notebook, \py{kitten.ipynb}, which contains starter code for this case study.   Use it to implement this model and check whether the results seem plausible.
-
-
-\section{Simulating a yo-yo}
-
-Suppose you are holding a yo-yo with a length of string wound around its axle, and you drop it while holding the end of the string stationary.  As gravity accelerates the yo-yo downward, tension in the string exerts a force upward.  Since this force acts on a point offset from the center of mass, it exerts a torque that causes the yo-yo to spin.
-
-\index{yo-yo}
-\index{torque}
-\index{lever arm}
-
-\begin{figure}
-\centerline{\includegraphics[height=2.5in]{figs/yoyo.pdf}}
-\caption{Diagram of a yo-yo showing forces due to gravity and tension in the string, the lever arm of tension, and the resulting torque.}
-\label{yoyo}
-\end{figure}
-
-Figure~\ref{yoyo} is a diagram of the forces on the yo-yo and the resulting torque.  The outer shaded area shows the body of the yo-yo.  The inner shaded area shows the rolled up string, the radius of which changes as the yo-yo unrolls.
-
-\index{system of equations}
-
-In this model, we can't figure out the linear and angular acceleration independently; we have to solve a system of equations:
-%
-\begin{align*}
-\sum F &= m a \\
-\sum \tau &= I \alpha
-\end{align*}
-%
-where the summations indicate that we are adding up forces and torques.
-
-As in the previous examples, linear and angular velocity are related because of the way the string unrolls:
-%
-\[ \frac{dy}{dt} = -r \frac{d \theta}{dt} \]
-%
-In this example, the linear and angular accelerations have opposite sign.  As the yo-yo rotates counter-clockwise, $\theta$ increases and $y$, which is the length of the rolled part of the string, decreases.
-
-Taking the derivative of both sides yields a similar relationship between linear and angular acceleration:
-%
-\[ \frac{d^2 y}{dt^2} = -r \frac{d^2 \theta}{dt^2} \]
-%
-Which we can write more concisely:
-%
-\[ a = -r \alpha \]
-%
-This relationship is not a general law of nature; it is specific to scenarios like this where one object rolls along another without stretching or slipping.
-
-\index{rolling}
-
-Because of the way we've set up the problem, $y$ actually has two meanings: it represents the length of the rolled string and the height of the yo-yo, which decreases as the yo-yo falls.  Similarly, $a$ represents acceleration in the length of the rolled string and the height of the yo-yo.
-
-We can compute the acceleration of the yo-yo by adding up the linear forces:
-%
-\[ \sum F = T - mg = ma \]
-%
-Where $T$ is positive because the tension force points up, and $mg$ is negative because gravity points down.
-
-Because gravity acts on the center of mass, it creates no torque, so the only torque is due to tension:
-%
-\[ \sum \tau = T r = I \alpha \]
-%
-Positive (upward) tension yields positive (counter-clockwise) angular acceleration.
-
-\index{SymPy}
-
-Now we have three equations in three unknowns, $T$, $a$, and $\alpha$, with $I$, $m$, $g$, and $r$ as known quantities.  It is simple enough to solve these equations by hand, but we can also get SymPy to do it for us:
-
-\begin{python}
-T, a, alpha, I, m, g, r = symbols('T a alpha I m g r')
-eq1 = Eq(a, -r * alpha)
-eq2 = Eq(T - m*g, m * a)
-eq3 = Eq(T * r, I * alpha)
-soln = solve([eq1, eq2, eq3], [T, a, alpha])
-\end{python}
-
-The results are
-%
-\begin{align*}
-T      &= m g I / I^*   \\
-a      &= -m g r^2 / I^* \\
-\alpha &= m g r / I^*    \\
-\end{align*}
-%
-where $I^*$ is the augmented moment of inertia, $I + m r^2$.
-To simulate the system, we don't really need $T$; we can plug $a$ and $\alpha$ directly into the slope function.
-
-In the repository for this book, you will find a notebook, \py{yoyo.ipynb}, which contains the derivation of these equations and starter code for this case study.   Use it to implement and test this model.
-
-
-%\section{Rigid pendulum}
-
-%\section{LRC circuit}
-
-% Pendulum:
-
-% Springy pendulum
-
-% Stiff problem as k increases
-
-% Add drag
-
-% Rigid pendulum: solve those constraints
-
-% Generalized coordinates
-
-
-\backmatter
-\printindex
-
-%\afterpage{\blankpage}
-
-
-\end{document}
-
-\end{itemize}
-
-
-\section{Under the hood}
-
-Throughout this book, we'll use functions defined in the ModSim library.  You don't have to know how they work, but you might be curious.  So at the end of some chapters I'll provide additional information.  If you are an experienced programmer, you might be interested by the design decisions I made.  If you are a beginner, and you feel like you have your hands full already, feel free to skip these sections.
-
-\index{modsim}
-
-Most of the functions in \py{modsim} are based on other Python libraries; the libraries we have used so far include:
-
-\begin{itemize}
-
-\item {\bf Pint}, which provides units like meters and seconds, as we saw in Section~\ref{penny}.
-
-\item {\bf NumPy}, which provides mathematical operations like \py{sqrt}, which we saw in Section~\ref{computation}.
-
-\item {\bf Pandas}, which provides the \py{Series} object, which is the basis of the \py{State} object in Section~\ref{modeling}.
-
-\item {\bf Pyplot}, which provides plotting functions, as we saw in Section~\ref{plotting}.
-
-\end{itemize}
-
-You could use these libraries directly, and when you have more experience, you probably will.  But the functions in \py{modsim} are meant to be easier to use; they provide some additional capabilities, including error checking; and by hiding details you don't need to know about, they let you focus on more important things.
-
-However, there are drawbacks.  One is that it can be hard to understand the error messages.  I'll have more to say about this in later chapters, but for now I have a suggestion.  When you are getting started, you should practice making errors.
-
-\index{debugging}
-
-For each new function you learn, you should deliberately make as many mistakes as possible so you can see what happens.  When you see what the errors messages are, you will understand what they mean.  And that should help later, when you make errors accidentally.
-
-
diff --git a/book/figs/RC_Divider.pdf b/book/figs/RC_Divider.pdf
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diff --git a/book/figs/chap03-fig04.pdf b/book/figs/chap03-fig04.pdf
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diff --git a/book/figs/chap05-fig06.pdf b/book/figs/chap05-fig06.pdf
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diff --git a/book/figs/chap08-fig02.pdf b/book/figs/chap08-fig02.pdf
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diff --git a/book/figs/chap13-fig02.pdf b/book/figs/chap13-fig02.pdf
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diff --git a/book/figs/chap22-fig02.pdf b/book/figs/chap22-fig02.pdf
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diff --git a/book/figs/image_sources.txt b/book/figs/image_sources.txt
deleted file mode 100644
index 7e2136df..00000000
--- a/book/figs/image_sources.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-globe, public domain, http://www.freestockphotos.biz/stockphoto/16911
-sphere, CC0, https://www.kissclipart.com/blue-sphere-clipart-clip-art-spxa3o/
-chart, CC0, https://openclipart.org/detail/215430/chart
-empire state building, public domain, https://freesvg.org/boort-art-deco-empire-state-building
-other building, public domain, https://freesvg.org/building-vector-silhouette
diff --git a/book/figs/kitten.odg b/book/figs/kitten.odg
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diff --git a/book/figs/modeling_framework2.pdf b/book/figs/modeling_framework2.pdf
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diff --git a/book/figs/penny.png b/book/figs/penny.png
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diff --git a/book/footer.html b/book/footer.html
deleted file mode 100644
index e1ca5b72..00000000
--- a/book/footer.html
+++ /dev/null
@@ -1,41 +0,0 @@
-</td>
-
-<td width=130 valign="top">
-
-<p>
-<h4>Are you using one of our books in a class?</h4>  We'd like to know
-about it.  Please consider filling out <a href="http://spreadsheets.google.com/viewform?formkey=dC0tNUZkMjBEdXVoRGljNm9FRmlTMHc6MA" onClick="javascript: pageTracker._trackPageview('/outbound/survey');">this short survey</a>.
-
-<p>
-<br>
-
-<p>
-<a href="http://www.amazon.com/gp/product/1449370780/ref=as_li_qf_sp_asin_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=1449370780&linkCode=as2&tag=greenteapre01-20">Think Bayes</a><img src="http://ir-na.amazon-adsystem.com/e/ir?t=greenteapre01-20&l=as2&o=1&a=1449370780" width="1" height="1" border="0" alt="" style="border:none !important; margin:0px !important;" />
-
-<p>
-<a href="http://www.amazon.com/gp/product/1449370780/ref=as_li_qf_sp_asin_il?ie=UTF8&camp=1789&creative=9325&creativeASIN=1449370780&linkCode=as2&tag=greenteapre01-20"><img border="0" src="http://ws-na.amazon-adsystem.com/widgets/q?_encoding=UTF8&amp;ASIN=1449370780&amp;Format=_SL160_&amp;ID=AsinImage&amp;MarketPlace=US&amp;ServiceVersion=20070822&amp;WS=1&amp;tag=greenteapre01-20" /></a><img src="http://ir-na.amazon-adsystem.com/e/ir?t=greenteapre01-20&l=as2&o=1&a=1449370780" width="1" height="1" border="0" alt="" style="border:none !important; margin:0px !important;" />
-
-<p>
-<a href="http://www.amazon.com/gp/product/144933072X/ref=as_li_tf_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=144933072X&linkCode=as2&tag=greenteapre01-20">Think Python</a><img src="http://www.assoc-amazon.com/e/ir?t=greenteapre01-20&l=as2&o=1&a=144933072X" width="1" height="1" border="0" alt="" style="border:none !important; margin:0px !important;" />
-
-<p>
-<a href="http://www.amazon.com/gp/product/144933072X/ref=as_li_tf_il?ie=UTF8&camp=1789&creative=9325&creativeASIN=144933072X&linkCode=as2&tag=greenteapre01-20"><img border="0" src="http://ws-na.amazon-adsystem.com/widgets/q?_encoding=UTF8&amp;ASIN=144933072X&amp;Format=_SL160_&amp;ID=AsinImage&amp;MarketPlace=US&amp;ServiceVersion=20070822&amp;WS=1&amp;tag=greenteapre01-20" /></a><img src="http://www.assoc-amazon.com/e/ir?t=greenteapre01-20&l=as2&o=1&a=144933072X" width="1" height="1" border="0" alt="" style="border:none !important; margin:0px !important;" />
-
-<p>
-<a href="http://www.amazon.com/gp/product/1491907339/ref=as_li_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=1491907339&linkCode=as2&tag=greenteapre01-20&linkId=O7WYM6H6YBYUFNWU">Think Stats</a><img src="http://ir-na.amazon-adsystem.com/e/ir?t=greenteapre01-20&l=as2&o=1&a=1491907339" width="1" height="1" border="0" alt="" style="border:none !important; margin:0px !important;" />
-
-<p>
-<a href="http://www.amazon.com/gp/product/1491907339/ref=as_li_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=1491907339&linkCode=as2&tag=greenteapre01-20&linkId=JVSYKQHYSUIEYRHL"><img border="0" src="http://ws-na.amazon-adsystem.com/widgets/q?_encoding=UTF8&amp;ASIN=1491907339&amp;Format=_SL160_&amp;ID=AsinImage&amp;MarketPlace=US&amp;ServiceVersion=20070822&amp;WS=1&amp;tag=greenteapre01-20" /></a><img src="http://ir-na.amazon-adsystem.com/e/ir?t=greenteapre01-20&l=as2&o=1&a=1491907339" width="1" height="1" border="0" alt="" style="border:none !important; margin:0px !important;" />
-
-<p>
-<a href="http://www.amazon.com/gp/product/1449314635/ref=as_li_tf_tl?ie=UTF8&tag=greenteapre01-20&linkCode=as2&camp=1789&creative=9325&creativeASIN=1449314635">Think Complexity</a><img src="http://www.assoc-amazon.com/e/ir?t=greenteapre01-20&l=as2&o=1&a=1449314635" width="1" height="1" border="0" alt="" style="border:none !important; margin:0px !important;" />
-
-<p>
-<a href="http://www.amazon.com/gp/product/1449314635/ref=as_li_tf_il?ie=UTF8&camp=1789&creative=9325&creativeASIN=1449314635&linkCode=as2&tag=greenteapre01-20"><img border="0" src="http://ws-na.amazon-adsystem.com/widgets/q?_encoding=UTF8&amp;ASIN=1449314635&amp;Format=_SL160_&amp;ID=AsinImage&amp;MarketPlace=US&amp;ServiceVersion=20070822&amp;WS=1&amp;tag=greenteapre01-20" /></a><img src="http://www.assoc-amazon.com/e/ir?t=greenteapre01-20&l=as2&o=1&a=1449314635" width="1" height="1" border="0" alt="" style="border:none !important; margin:0px !important;" />
-
-
-</td>
-</tr>
-</table>
-
-</body>
diff --git a/book/header.html b/book/header.html
deleted file mode 100644
index 09d20282..00000000
--- a/book/header.html
+++ /dev/null
@@ -1,15 +0,0 @@
-<table cellpadding=10>
-
-<tr>
-
-<td valign="top" width=100 bgcolor="#FF9933">
-</td>
-
-<td valign="top" width=600>
-
-<p>This HTML version of is provided for convenience, but it
-is not the best format for the book.  In particular, some of the
-symbols are not rendered correctly.
-
-<p>You might prefer to read
-the <a href="http://think-dsp.com/thinkdsp.pdf">PDF version</a>.
diff --git a/book/hevea.sty b/book/hevea.sty
deleted file mode 100644
index 17b4e546..00000000
--- a/book/hevea.sty
+++ /dev/null
@@ -1,90 +0,0 @@
-% hevea  : hevea.sty
-% This is a very basic style file for latex document to be processed
-% with hevea. It contains definitions of LaTeX environment which are
-% processed in a special way by the translator. 
-%  Mostly :
-%     - latexonly, not processed by hevea, processed by latex.
-%     - htmlonly , the reverse.
-%     - rawhtml,  to include raw HTML in hevea output.
-%     - toimage, to send text to the image file.
-% The package also provides hevea logos, html related commands (ahref
-% etc.), void cutting and image commands.
-\NeedsTeXFormat{LaTeX2e}
-\ProvidesPackage{hevea}[2002/01/11]
-\RequirePackage{comment}
-\newif\ifhevea\heveafalse
-\@ifundefined{ifimagen}{\newif\ifimagen\imagenfalse}
-\makeatletter%
-\newcommand{\heveasmup}[2]{%
-\raise #1\hbox{$\m@th$%
-  \csname S@\f@size\endcsname
-  \fontsize\sf@size 0%
-  \math@fontsfalse\selectfont
-#2%
-}}%
-\DeclareRobustCommand{\hevea}{H\kern-.15em\heveasmup{.2ex}{E}\kern-.15emV\kern-.15em\heveasmup{.2ex}{E}\kern-.15emA}%
-\DeclareRobustCommand{\hacha}{H\kern-.15em\heveasmup{.2ex}{A}\kern-.15emC\kern-.1em\heveasmup{.2ex}{H}\kern-.15emA}%
-\DeclareRobustCommand{\html}{\protect\heveasmup{0.ex}{HTML}}
-%%%%%%%%% Hyperlinks hevea style
-\newcommand{\ahref}[2]{{#2}}
-\newcommand{\ahrefloc}[2]{{#2}}
-\newcommand{\aname}[2]{{#2}}
-\newcommand{\ahrefurl}[1]{\texttt{#1}}
-\newcommand{\footahref}[2]{#2\footnote{\texttt{#1}}}
-\newcommand{\mailto}[1]{\texttt{#1}}
-\newcommand{\imgsrc}[2][]{}
-\newcommand{\home}[1]{\protect\raisebox{-.75ex}{\char126}#1}
-\AtBeginDocument
-{\@ifundefined{url}
-{%url package is not loaded
-\let\url\ahref\let\oneurl\ahrefurl\let\footurl\footahref}
-{}}
-%% Void cutting instructions
-\newcounter{cuttingdepth}
-\newcommand{\tocnumber}{}
-\newcommand{\notocnumber}{}
-\newcommand{\cuttingunit}{}
-\newcommand{\cutdef}[2][]{}
-\newcommand{\cuthere}[2]{}
-\newcommand{\cutend}{}
-\newcommand{\htmlhead}[1]{}
-\newcommand{\htmlfoot}[1]{}
-\newcommand{\htmlprefix}[1]{}
-\newenvironment{cutflow}[1]{}{}
-\newcommand{\cutname}[1]{}
-\newcommand{\toplinks}[3]{}
-\newcommand{\setlinkstext}[3]{}
-\newcommand{\flushdef}[1]{}
-\newcommand{\footnoteflush}[1]{}
-%%%% Html only
-\excludecomment{rawhtml}
-\newcommand{\rawhtmlinput}[1]{}
-\excludecomment{htmlonly}
-%%%% Latex only
-\newenvironment{latexonly}{}{}
-\newenvironment{verblatex}{}{}
-%%%% Image file stuff
-\def\toimage{\endgroup}
-\def\endtoimage{\begingroup\def\@currenvir{toimage}}
-\def\verbimage{\endgroup}
-\def\endverbimage{\begingroup\def\@currenvir{verbimage}}
-\newcommand{\imageflush}[1][]{}
-%%% Bgcolor definition
-\newsavebox{\@bgcolorbin}
-\newenvironment{bgcolor}[2][]
-  {\newcommand{\@mycolor}{#2}\begin{lrbox}{\@bgcolorbin}\vbox\bgroup}
-  {\egroup\end{lrbox}%
-   \begin{flushleft}%
-   \colorbox{\@mycolor}{\usebox{\@bgcolorbin}}%
-   \end{flushleft}}
-%%% Style sheets macros, defined as no-ops
-\newcommand{\newstyle}[2]{}
-\newcommand{\addstyle}[1]{}
-\newcommand{\setenvclass}[2]{}
-\newcommand{\getenvclass}[1]{}
-\newcommand{\loadcssfile}[1]{}
-\newenvironment{divstyle}[1]{}{}
-\newenvironment{cellstyle}[2]{}{}
-\newif\ifexternalcss
-%%% Postlude
-\makeatother
diff --git a/book/htmlonly b/book/htmlonly
deleted file mode 100644
index ea5fa884..00000000
--- a/book/htmlonly
+++ /dev/null
@@ -1,72 +0,0 @@
-\newcommand{\adjustpage}[1]{}
-
-\newcommand{\clearemptydoublepage}{}
-\newcommand{\blankpage}{}
-
-\newcommand{\spacing}{}
-\newcommand{\endspacing}{}
-
-\newcommand{\frontmatter}{}
-\newcommand{\mainmatter}{}
-\newcommand{\backmatter}{}
-
-\newcommand{\theoremstyle}[1]{}
-\newcommand{\newtheoremstyle}[1]{}
-
-\newcommand{\vfill}{}
-
-\newcommand{\href}{\ahref}
-
-\htmlhead{\rawhtmlinput{header.html}}
-
-\htmlfoot{\rawhtmlinput{footer.html}}
-
-% these styles get translated into CSS
-\newstyle{p+p}{margin-top:1em; margin-bottom:1em}
-\newstyle{img}{border: 0px;}
-
-% change the arrows
-\setlinkstext
-  {\imgsrc[ALT="Previous"]{back.png}}
-  {\imgsrc[ALT="Up"]{up.png}}
-  {\imgsrc[ALT="Next"]{next.png}}
-
-% colors for code listings and output
-\usepackage{color}
-\definecolor{bgcolor}{rgb}{0.982, 0.982, 0.982}
-\definecolor{comment}{rgb}{0.000, 0.488, 0.000}
-\definecolor{keyword}{rgb}{0.000, 0.000, 1.000}
-\definecolor{strings}{rgb}{0.700, 0.000, 0.000}
-
-% syntax highlighting in code listings
-\usepackage{listings}
-\lstset{
-    language=java,
-    basicstyle=\ttfamily,
-    backgroundcolor=\color{bgcolor},
-    commentstyle=\color{comment},
-    keywordstyle=\color{keyword},
-    stringstyle=\color{strings},
-    columns=fullflexible,
-    emph={label},  % keyword?
-    keepspaces=true,
-    showstringspaces=false
-}
-
-% code listing environments
-\lstnewenvironment{code}
-{}
-{}
-\lstnewenvironment{stdout}
-{\lstset{commentstyle=\relax,keywordstyle=\relax,stringstyle=\relax}}
-{}
-
-% inline syntax formatting
-\newcommand{\py}{\lstinline}%}
-
-% CSS for code listings
-\newstyle{.lstframe}{margin: 1em; width: calc(100\% - 1em);}
-\newstyle{.lstlisting}{padding: 3pt;}
-
-% make urls hyperlinks in html
-\input{urlhref.hva}
diff --git a/book/latexonly b/book/latexonly
deleted file mode 100644
index 09b602cf..00000000
--- a/book/latexonly
+++ /dev/null
@@ -1,129 +0,0 @@
-\usepackage{geometry}
-\geometry{
-    width=5.5in,
-    height=8.5in,
-    hmarginratio=3:2,
-    vmarginratio=1:1,
-    includehead=true,
-    headheight=15pt
-}
-
-% paragraph spacing
-\setlength{\parindent}{0pt}                      % 17.62482pt
-\setlength{\parskip}{12pt plus 4pt minus 4pt}    % 0.0pt plus 1.0pt
-\linespread{1.05}
-\def\arraystretch{1.5}
-
-% list spacing
-\setlength{\topsep}{5pt plus 2pt minus 3pt}      % 10.0pt plus 4.0pt minus 6.0pt
-\setlength{\partopsep}{-6pt plus 2pt minus 2pt}  %  3.0pt plus 2.0pt minus 2.0pt
-\setlength{\itemsep}{0pt}                        %  5.0pt plus 2.5pt minus 1.0pt
-
-% these are copied from tex/latex/base/book.cls
-% all I changed is afterskip
-\makeatletter
-\renewcommand{\section}{\@startsection{section}{1}{\z@}%
-    {-3.5ex \@plus -1ex \@minus -.2ex}%
-    {0.7ex \@plus.2ex}%
-    {\normalfont\Large\bfseries}}
-\renewcommand\subsection{\@startsection{subsection}{2}{\z@}%
-    {-3.25ex\@plus -1ex \@minus -.2ex}%
-    {0.3ex \@plus .2ex}%
-    {\normalfont\large\bfseries}}
-\renewcommand\subsubsection{\@startsection{subsubsection}{3}{\z@}%
-    {-3.25ex\@plus -1ex \@minus -.2ex}%
-    {0.3ex \@plus .2ex}%
-    {\normalfont\normalsize\bfseries}}
-\makeatother
-
-% table of contents vertical spacing
-\usepackage{tocloft}
-\setlength\cftparskip{8pt plus 4pt minus 4pt}
-
-% balanced index with TOC entry
-\usepackage[totoc]{idxlayout}
-
-% The following line adds a little extra space to the column
-% in which the Section numbers appear in the table of contents
-\makeatletter
-\renewcommand{\l@section}{\@dottedtocline{1}{1.5em}{3.0em}}
-\makeatother
-
-% customize page headers
-\usepackage{fancyhdr}
-\pagestyle{fancyplain}
-\renewcommand{\chaptermark}[1]{\markboth{Chapter \thechapter ~~ #1}{}}
-\renewcommand{\sectionmark}[1]{\markright{\thesection ~~ #1}}
-\lhead[\fancyplain{}{\bfseries\thepage}]%
-      {\fancyplain{}{\bfseries\rightmark}}
-\rhead[\fancyplain{}{\bfseries\leftmark}]%
-      {\fancyplain{}{\bfseries\thepage}}
-\cfoot{}
-%\rfoot{\textcolor{gray}{\tiny ThinkJava Draft \today}}
-
-%% tweak spacing of figures and captions
-%\usepackage{floatrow}
-%\usepackage{caption}
-%\captionsetup{
-%    font=small,
-%    labelformat=empty,
-%    justification=centering,
-%    skip=4pt
-%}
-
-% colors for code listings and output
-\usepackage{xcolor}
-\definecolor{bgcolor}{HTML}{FAFAFA}
-\definecolor{comment}{HTML}{007C00}
-\definecolor{keyword}{HTML}{0000FF}
-\definecolor{strings}{HTML}{B20000}
-
-% syntax highlighting in code listings
-\usepackage{textcomp}
-\usepackage{listings}
-\lstset{
-    language=python,
-    basicstyle=\ttfamily,
-    backgroundcolor=\color{bgcolor},
-    commentstyle=\color{comment},
-    keywordstyle=\color{keyword},
-    stringstyle=\color{strings},
-    columns=fullflexible,
-    emph={label},  % keyword?
-    keepspaces=true,
-    showstringspaces=false,
-    upquote=true,
-    xleftmargin=15pt,  % \parindent
-    framexleftmargin=3pt,
-    aboveskip=\parskip,
-    belowskip=\parskip
-}
-
-% code listing environments
-\lstnewenvironment{code}
-{\minipage{\linewidth}}
-{\endminipage}
-\lstnewenvironment{stdout}
-{\lstset{commentstyle=,keywordstyle=,stringstyle=}\minipage{\linewidth}}
-{\endminipage}
-
-% inline syntax formatting
-\newcommand{\py}[1]{\lstinline{#1}}%{
-
-% prevent hyphens in names
-\hyphenation{GitHub}
-
-% pdf hyperlinks, table of contents, and document properties
-\usepackage{hyperref}
-\hypersetup{%
-  pdftitle={\thetitle: \thesubtitle},
-  pdfauthor={\theauthors},
-  pdfsubject={Version \theversion},
-  pdfkeywords={},
-  bookmarksopen=false,
-  colorlinks=true,
-  citecolor=black,
-  filecolor=black,
-  linkcolor=black,
-  urlcolor=blue
-}
diff --git a/book/links/urls.csv b/book/links/urls.csv
deleted file mode 100644
index 98057499..00000000
--- a/book/links/urls.csv
+++ /dev/null
@@ -1,90 +0,0 @@
-zen,https://zen-of-python.info/namespaces-are-one-honking-great-idea--lets-do-more-of-those.html
-precess,https://www.youtube.com/watch?v=ty9QSiVC2g0
-myth,https://www.youtube.com/watch?v=PHxvMLoKRWg
-kitten,https://www.youtube.com/watch?v=1Z9YNmCIscs
-insulin,https://www.ncbi.nlm.nih.gov/pubmed/443421
-sir,https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease
-khan2,https://www.khanacademy.org/math/ap-calculus-bc/diff-equations-bc/logistic-diff-eq-bc/v/solving-logistic-differential-equation-part-1
-khan3,https://www.khanacademy.org/math/ap-calculus-ab/diff-equations-ab/exponential-models-ab/v/newtons-law-of-cooling
-khan1,https://www.khanacademy.org/math/ap-calculus-ab/diff-equations-ab/exponential-models-ab/v/exponential-solution-to-differential-equation
-bergman,https://www.karger.com/Article/Abstract/89312
-nasa,https://www.grc.nasa.gov/www/k-12/airplane/balldrag.html
-cdc,https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf
-loc,https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.loc.html
-series,https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html
-color,https://matplotlib.org/users/colors.html
-plot,https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot
-zip,https://github.com/AllenDowney/ModSimPy/archive/master.zip
-git,https://git-scm.com/download/linux
-yoyo,https://en.wikipedia.org/wiki/Yo-yo
-worldpop,https://en.wikipedia.org/wiki/World_population_estimates
-trajec,https://en.wikipedia.org/wiki/Trajectory
-torque,https://en.wikipedia.org/wiki/Torque
-paper,https://en.wikipedia.org/wiki/Toilet_paper
-timeser,https://en.wikipedia.org/wiki/Time_series
-thermrad,https://en.wikipedia.org/wiki/Thermal_radiation
-thermass,https://en.wikipedia.org/wiki/Thermal_mass
-popbomb,https://en.wikipedia.org/wiki/The_Population_Bomb
-stock,https://en.wikipedia.org/wiki/Stock_and_flow
-spline,https://en.wikipedia.org/wiki/Spline_(mathematics)
-singval,https://en.wikipedia.org/wiki/Single-valued_function
-thermo,https://en.wikipedia.org/wiki/Second_law_of_thermodynamics
-recur,https://en.wikipedia.org/wiki/Recurrence_relation
-quarter,https://en.wikipedia.org/wiki/Quarter_(United_States_coin)
-penny,https://en.wikipedia.org/wiki/Penny_(United_States_coin)
-ode,https://en.wikipedia.org/wiki/Ordinary_differential_equation#General_definition
-nondim,https://en.wikipedia.org/wiki/Nondimensionalization#First_order_system
-varmass,https://en.wikipedia.org/wiki/Newton's_laws_of_motion#Variable-mass_systems
-newton,https://en.wikipedia.org/wiki/Newton's_laws_of_motion
-mythbust,https://en.wikipedia.org/wiki/MythBusters_(2003_season)
-mortality,https://en.wikipedia.org/wiki/Mortality_rate
-magnus,https://en.wikipedia.org/wiki/Magnus_effect
-minpack,https://en.wikipedia.org/wiki/MINPACK
-logistic,https://en.wikipedia.org/wiki/Logistic_function
-moment,https://en.wikipedia.org/wiki/List_of_moments_of_inertia
-lmm,https://en.wikipedia.org/wiki/Linear_multistep_method
-levmarq,https://en.wikipedia.org/wiki/Levenberg-Marquardt_algorithm
-inverse,https://en.wikipedia.org/wiki/Inverse_problem
-herd,https://en.wikipedia.org/wiki/Herd_immunity
-transfer,https://en.wikipedia.org/wiki/Heat_transfer
-specheat,https://en.wikipedia.org/wiki/Heat_capacity#Specific_heat_capacity
-hardcode,https://en.wikipedia.org/wiki/Hard_coding
-wally,https://en.wikipedia.org/wiki/Green_Monster
-geom,https://en.wikipedia.org/wiki/Geometric_progression
-expo,https://en.wikipedia.org/wiki/Exponential_growth
-euler,https://en.wikipedia.org/wiki/Euler_method
-vecops,https://en.wikipedia.org/wiki/Euclidean_vector#Addition_and_subtraction
-drageq,https://en.wikipedia.org/wiki/Drag_equation
-dragco,https://en.wikipedia.org/wiki/Drag_coefficient
-diffeq,https://en.wikipedia.org/wiki/Differential_equation
-tempress,https://en.wikipedia.org/wiki/Density_of_air#Temperature_and_pressure
-density,https://en.wikipedia.org/wiki/Density_of_air
-boolean,https://en.wikipedia.org/wiki/Boolean_algebra
-contact,https://en.wikipedia.org/wiki/Basic_reproduction_number
-baseball,https://en.wikipedia.org/wiki/Baseball_(ball)
-plato,https://en.wikipedia.org/wiki/Allegory_of_the_Cave
-minimize,https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
-interp,https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html
-hindmarsh,http://history.siam.org/oralhistories/hindmarsh.htm
-minmod,http://faculty.washington.edu/gennari/teaching/591-BioSimSem/Bergman-minmodel05.pdf
-license,http://creativecommons.org/licenses/by-nc-sa/4.0/
-wall ,http://discovery.ucl.ac.uk/1526521
-runs,https://aapt.scitation.org/doi/abs/10.1119/1.1604384
-brent,https://en.wikipedia.org/wiki/Brents_method
-divider,https://en.wikipedia.org/wiki/File:RC_Divider.svg
-hooke,https://en.wikipedia.org/wiki/Hookes_law
-moment,https://en.wikipedia.org/wiki/List_of_moments_of_inertia
-runge,https://en.wikipedia.org/wiki/Runge-Kutta-Fehlberg_method
-getgit,https://git-scm.com/book/en/v2/Getting-Started-Installing-Git
-bungee,https://iopscience.iop.org/article/10.1088/0031-9120/45/1/007
-spider,https://spiderman.wikia.com/wiki/Peter_Parker_(Earth-616)
-trees,https://www.cfr.washington.edu/research.smc/working_papers/smc_working_paper_1.pdf
-wall2,https://www.sciencedirect.com/science/article/pii/S0378778816313056
-human,https://www.youtube.com/watch?v=PUwmA3Q0_OE
-dunk,https://www.youtube.com/watch?v=UBf7WC19lpw
-drop,https://www.youtube.com/watch?v=X-QFAB0gEtE
-ivp,https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html
-fsolve,https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fsolve.html
-sq,https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.leastsq.html
-lm ,https://en.wikipedia.org/wiki/Levenberg-Marquardt_algorithm
-powell,https://en.wikipedia.org/wiki/Powell's_method
diff --git a/book/localdef.py b/book/localdef.py
deleted file mode 100644
index 14f7a5bc..00000000
--- a/book/localdef.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import plasTeX.Base as Base
-
-def idgen():
-    """ Generate a unique ID """
-    i = 1
-    while 1:
-        yield 'a%.10d' % i
-        i += 1
-
-idgen = idgen()
-
-class Eqn(Base.Command):
-    args = 'self'
-
-class Anchor(Base.Command):
-    args = 'label:str'
-    def invoke(self, tex):
-        Base.Command.invoke(self, tex)
-        self.ownerDocument.context.label(self.attributes['label'], self)
-
-class exercise(Base.Environment):
-    counter = 'exercise'
-
-class index(Base.Command):
-    args = 'termstring'
-
-    def setEntry(self, s, seetype=0):
-    #    TYPE_NORMAL = 0
-    #    TYPE_SEE = 1
-    #    TYPE_SEEALSO = 2
-        if type(s) != type(''):
-            s = s.textContent
-        if s.count('!'):
-            priterm, secterm = s.split('!')
-            if priterm.count('@'):
-                prisort, primary = priterm.split('@')
-            else:
-                prisort, primary = None, priterm
-            if secterm.count('@'):
-                secsort, secondary = secterm.split('@')
-            else:
-                secsort, secondary = None, secterm
-        elif s.count('@'):
-            prisort, primary = s.split('@')
-            secsort, secondary = None, None
-        else:
-            prisort, primary = None, s
-            secsort, secondary = None, None
-
-#        if secondary:
-#            self.ownerDocument.userdata.setdefault('index', []).append(\
-#                Base.IndexEntry([primary, secondary], self, [prisort, secsort], None, type=seetype))
-#        else:
-#            self.ownerDocument.userdata.setdefault('index', []).append(\
-#                Base.IndexEntry([primary], self, [prisort], None, type=seetype))
-        return prisort, primary, secsort, secondary
-
-    def invoke(self, tex):
-        Base.Command.invoke(self, tex)
-        self.ownerDocument.context.label(idgen.next(), self)
-        p0,p1,s0,s1 = self.setEntry(self.attributes['termstring'])
-        if p0:
-            self.prisort = '%s' % p0
-        if p1:
-            self.primary = '%s' % p1
-        if s0:
-            self.secsort = '%s' % s0
-        if s1:
-            self.secondary = '%s' % s1
-
-class scriptN(Base.Command):
-       unicode = u'\U0001D4A9'
-
-class uxbar(Base.Command): pass
-class uybar(Base.Command): pass
-class unhat(Base.Command): pass
-class ule(Base.Command): pass
-class minus(Base.Command): pass
-class lowast(Base.Command): pass
-class Erdos(Base.Command): pass
-
diff --git a/book/next.png b/book/next.png
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From 3e66ab4de5483ac04359b321bd807b6506291b43 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 22 May 2025 14:51:26 -0400
Subject: [PATCH 130/144] Updating chapters

---
 chapters/chap11.py    | 2 +-
 chapters/chap22.ipynb | 4 ++--
 chapters/chap22.py    | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/chapters/chap11.py b/chapters/chap11.py
index d8569569..859f4dc4 100644
--- a/chapters/chap11.py
+++ b/chapters/chap11.py
@@ -26,7 +26,7 @@ def update_func(t, state, system):
 def plot_results(S, I, R):
     S.plot(style='--', label='Susceptible')
     I.plot(style='-', label='Infected')
-    R.plot(style=':', label='Resistant')
+    R.plot(style=':', label='Recovered')
     decorate(xlabel='Time (days)',
              ylabel='Fraction of population')
 
diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
index 85d7a56c..0a5f28ee 100644
--- a/chapters/chap22.ipynb
+++ b/chapters/chap22.ipynb
@@ -987,7 +987,7 @@
    "id": "sustained-slide",
    "metadata": {},
    "source": [
-    "Inside the draw function, should use `decorate` to set the limits of the $x$ and $y$ axes.\n",
+    "Inside the draw function, you should use `decorate` to set the limits of the $x$ and $y$ axes.\n",
     "Otherwise `matplotlib` auto-scales the axes, which is usually not what you want.\n",
     "\n",
     "Now we can run the animation like this:\n",
@@ -1447,7 +1447,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/chapters/chap22.py b/chapters/chap22.py
index 00fe5be2..a5b5f745 100644
--- a/chapters/chap22.py
+++ b/chapters/chap22.py
@@ -21,7 +21,7 @@
 
 def make_system(params):
     
-    # convert angle to degrees
+    # convert angle to radians
     theta = deg2rad(params.angle)
     
     # compute x and y components of velocity

From 1f7de1ab43e0a5f9c621318276cd92ecbc29e5ef Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 22 May 2025 14:51:27 -0400
Subject: [PATCH 131/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 244852 -> 278101 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index f83bc09bace305c1e18b22af42f7107aebc52c43..fcf026c40fd4f189ab1a8fbad75c90d788b3f326 100644
GIT binary patch
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From f9469c5e7c1b8775b54e30c0e7c50c3418ff0b6d Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 22 May 2025 15:53:48 -0400
Subject: [PATCH 132/144] Updating tests and docstrings, adding validation

---
 modsim/build.sh       |  33 +++
 modsim/modsim.py      | 584 ++++++++++++++++++++++++++++++----------
 modsim/test_modsim.py | 601 +++++++++++++-----------------------------
 3 files changed, 661 insertions(+), 557 deletions(-)
 create mode 100644 modsim/build.sh

diff --git a/modsim/build.sh b/modsim/build.sh
new file mode 100644
index 00000000..a3878b80
--- /dev/null
+++ b/modsim/build.sh
@@ -0,0 +1,33 @@
+#!/bin/sh
+
+# Exit on any error
+set -e
+
+# Get the directory where this script is located
+SCRIPT_DIR="$( cd "$( dirname "$0" )" && pwd )"
+CANONICAL_FILE="$SCRIPT_DIR/modsim.py"
+
+# Function to copy if destination exists
+copy_if_exists() {
+    dest="$1"
+    if [ -f "$dest" ]; then
+        echo "Copying to $dest"
+        cp "$CANONICAL_FILE" "$dest"
+    fi
+}
+
+# Copy to root directory
+copy_if_exists "$SCRIPT_DIR/../modsim.py"
+
+# Copy to chapters directory
+copy_if_exists "$SCRIPT_DIR/../chapters/modsim.py"
+
+# Copy to examples directory
+copy_if_exists "$SCRIPT_DIR/../examples/modsim.py"
+
+# Copy to ModSimPySolutions directory and its subdirectories
+copy_if_exists "$SCRIPT_DIR/../ModSimPySolutions/modsim.py"
+copy_if_exists "$SCRIPT_DIR/../ModSimPySolutions/examples/modsim.py"
+copy_if_exists "$SCRIPT_DIR/../ModSimPySolutions/chapters/modsim.py"
+
+echo "Done copying modsim.py to all locations" 
\ No newline at end of file
diff --git a/modsim/modsim.py b/modsim/modsim.py
index 0cd9034f..cce63298 100644
--- a/modsim/modsim.py
+++ b/modsim/modsim.py
@@ -38,13 +38,30 @@
 from types import SimpleNamespace
 from copy import copy
 
+# Input validation helpers
+def validate_numeric(value, name):
+    """Validate that a value is numeric."""
+    if not isinstance(value, (int, float)):
+        raise ValueError(f"{name} must be numeric, got {type(value)}")
+
+def validate_array_like(value, name):
+    """Validate that a value is array-like."""
+    if not isinstance(value, (list, tuple, np.ndarray, pd.Series)):
+        raise ValueError(f"{name} must be array-like, got {type(value)}")
+
+def validate_positive(value, name):
+    """Validate that a value is positive."""
+    if value <= 0:
+        raise ValueError(f"{name} must be positive, got {value}")
 
 def flip(p=0.5):
     """Flips a coin with the given probability.
 
-    p: float 0-1
+    Args:
+        p (float): Probability between 0 and 1.
 
-    returns: boolean (True or False)
+    Returns:
+        bool: True or False.
     """
     return np.random.random() < p
 
@@ -52,18 +69,27 @@ def flip(p=0.5):
 def cart2pol(x, y, z=None):
     """Convert Cartesian coordinates to polar.
 
-    x: number or sequence
-    y: number or sequence
-    z: number or sequence (optional)
+    Args:
+        x (number or sequence): x coordinate.
+        y (number or sequence): y coordinate.
+        z (number or sequence, optional): z coordinate. Defaults to None.
 
-    returns: theta, rho OR theta, rho, z
+    Returns:
+        tuple: (theta, rho) or (theta, rho, z).
+        
+    Raises:
+        ValueError: If x or y are not numeric or array-like, or if z is provided but not numeric or array-like
     """
+    if not isinstance(x, (int, float, list, tuple, np.ndarray, pd.Series)):
+        raise ValueError("x must be numeric or array-like")
+    if not isinstance(y, (int, float, list, tuple, np.ndarray, pd.Series)):
+        raise ValueError("y must be numeric or array-like")
+    if z is not None and not isinstance(z, (int, float, list, tuple, np.ndarray, pd.Series)):
+        raise ValueError("z must be numeric or array-like")
     x = np.asarray(x)
     y = np.asarray(y)
-
     rho = np.hypot(x, y)
     theta = np.arctan2(y, x)
-
     if z is None:
         return theta, rho
     else:
@@ -73,15 +99,25 @@ def cart2pol(x, y, z=None):
 def pol2cart(theta, rho, z=None):
     """Convert polar coordinates to Cartesian.
 
-    theta: number or sequence in radians
-    rho: number or sequence
-    z: number or sequence (optional)
+    Args:
+        theta (number or sequence): Angle in radians.
+        rho (number or sequence): Radius.
+        z (number or sequence, optional): z coordinate. Defaults to None.
 
-    returns: x, y OR x, y, z
+    Returns:
+        tuple: (x, y) or (x, y, z).
+        
+    Raises:
+        ValueError: If theta or rho are not numeric or array-like, or if z is provided but not numeric or array-like
     """
+    if not isinstance(theta, (int, float, list, tuple, np.ndarray, pd.Series)):
+        raise ValueError("theta must be numeric or array-like")
+    if not isinstance(rho, (int, float, list, tuple, np.ndarray, pd.Series)):
+        raise ValueError("rho must be numeric or array-like")
+    if z is not None and not isinstance(z, (int, float, list, tuple, np.ndarray, pd.Series)):
+        raise ValueError("z must be numeric or array-like")
     x = rho * np.cos(theta)
     y = rho * np.sin(theta)
-
     if z is None:
         return x, y
     else:
@@ -92,11 +128,13 @@ def pol2cart(theta, rho, z=None):
 def linrange(start, stop=None, step=1):
     """Make an array of equally spaced values.
 
-    start: first value
-    stop: last value (might be approximate)
-    step: difference between elements (should be consistent)
+    Args:
+        start (float): First value.
+        stop (float, optional): Last value (might be approximate). Defaults to None.
+        step (float, optional): Difference between elements. Defaults to 1.
 
-    returns: NumPy array
+    Returns:
+        np.ndarray: Array of equally spaced values.
     """
     if stop is None:
         stop = start
@@ -106,8 +144,18 @@ def linrange(start, stop=None, step=1):
 
 
 def __check_kwargs(kwargs, param_name, param_len, func, func_name):
-    """Check if `kwargs` has a parameter that is a sequence of a particular length
-       param_len: sequence enumerating possible lengths
+    """Check if `kwargs` has a parameter that is a sequence of a particular length.
+
+    Args:
+        kwargs (dict): Dictionary of keyword arguments.
+        param_name (str): Name of the parameter to check.
+        param_len (list): List of valid lengths for the parameter.
+        func (callable): Function to test the parameter value.
+        func_name (str): Name of the function for error messages.
+
+    Raises:
+        ValueError: If the parameter is missing or has an invalid length.
+        Exception: If the function call fails on the parameter value.
     """
     param_val = kwargs.get(param_name, None)
     if param_val is None or len(param_val) not in param_len:
@@ -129,14 +177,17 @@ def root_scalar(func, *args, **kwargs):
     Wrapper for
     https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root_scalar.html
 
-    func: computes the function to find a root of
-    bracket: sequence of two values, lower and upper bounds of the range to be searched
-    args: any additional positional arguments are passed to `func`
-    kwargs: any keyword arguments are passed to `root_scalar`
+    Args:
+        func (callable): Function to find a root of.
+        *args: Additional positional arguments passed to `func`.
+        **kwargs: Additional keyword arguments passed to `root_scalar`.
 
-    returns: RootResults object
-    """
+    Returns:
+        RootResults: Object containing the root and convergence information.
 
+    Raises:
+        ValueError: If the solver does not converge.
+    """
     underride(kwargs, rtol=1e-4)
 
     __check_kwargs(kwargs, 'bracket', [2], lambda x: func(x, *args), 'root_scalar')
@@ -157,15 +208,17 @@ def minimize_scalar(func, *args, **kwargs):
     Wrapper for
     https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
 
-    func: computes the function to be minimized
-    bracket: (`method` is `brent` or `golden`) sequence of two or three values, the range to be searched
-    bounds: (`method` is `bounded`) sequence of two values, the range to be searched
-    args: any additional positional arguments are passed to `func`
-    kwargs: any keyword arguments are passed to `minimize_scalar`
+    Args:
+        func (callable): Function to be minimized.
+        *args: Additional positional arguments passed to `func`.
+        **kwargs: Additional keyword arguments passed to `minimize_scalar`.
 
-    returns: OptimizeResult object
-    """
+    Returns:
+        OptimizeResult: Object containing the minimum and optimization details.
 
+    Raises:
+        Exception: If the optimization does not succeed.
+    """
     underride(kwargs, __func_name='minimize_scalar')
 
     method = kwargs.get('method', None)
@@ -199,13 +252,16 @@ def maximize_scalar(func, *args, **kwargs):
 
     Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
 
-    func: computes the function to be maximized
-    bracket: (`method` is `brent` or `golden`) sequence of two or three values, the range to be searched
-    bounds: (`method` is `bounded`) sequence of two values, the range to be searched
-    args: any additional positional arguments are passed to `func`
-    kwargs: any keyword arguments are passed as options to `minimize_scalar`
+    Args:
+        func (callable): Function to be maximized.
+        *args: Additional positional arguments passed to `func`.
+        **kwargs: Additional keyword arguments passed to `minimize_scalar`.
+
+    Returns:
+        OptimizeResult: Object containing the maximum and optimization details.
 
-    returns: OptimizeResult object
+    Raises:
+        Exception: If the optimization does not succeed.
     """
     def min_func(*args):
         return -func(*args)
@@ -220,21 +276,18 @@ def min_func(*args):
 
 
 def run_solve_ivp(system, slope_func, **options):
-    """Computes a numerical solution to a differential equation.
+    """Compute a numerical solution to a differential equation using solve_ivp.
 
-    `system` must contain `init` with initial conditions,
-    `t_end` with the end time.  Optionally, it can contain
-    `t_0` with the start time.
+    Args:
+        system (System): System object containing 'init', 't_end', and optionally 't_0'.
+        slope_func (callable): Function that computes slopes.
+        **options: Additional keyword arguments for scipy.integrate.solve_ivp.
 
-    It should contain any other parameters required by the
-    slope function.
+    Returns:
+        tuple: (TimeFrame of results, details from solve_ivp)
 
-    `options` can be any legal options of `scipy.integrate.solve_ivp`
-
-    system: System object
-    slope_func: function that computes slopes
-
-    returns: TimeFrame
+    Raises:
+        ValueError: If required system attributes are missing or if the solver fails.
     """
     system = remove_units(system)
 
@@ -330,20 +383,18 @@ def run_solve_ivp(system, slope_func, **options):
 
 
 def leastsq(error_func, x0, *args, **options):
-    """Find the parameters that yield the best fit for the data.
-
-    `x0` can be a sequence, array, Series, or Params
-
-    Positional arguments are passed along to `error_func`.
-
-    Keyword arguments are passed to `scipy.optimize.leastsq`
-
-    error_func: function that computes a sequence of errors
-    x0: initial guess for the best parameters
-    args: passed to error_func
-    options: passed to leastsq
-
-    :returns: Params object with best_params and ModSimSeries with details
+    """Find the parameters that yield the best fit for the data using least squares.
+
+    Args:
+        error_func (callable): Function that computes a sequence of errors.
+        x0 (array-like): Initial guess for the best parameters.
+        *args: Additional positional arguments passed to error_func.
+        **options: Additional keyword arguments passed to scipy.optimize.leastsq.
+
+    Returns:
+        tuple: (best_params, details)
+            best_params: Best-fit parameters (same type as x0 if possible).
+            details: SimpleNamespace with fit details and success flag.
     """
     # override `full_output` so we get a message if something goes wrong
     options["full_output"] = True
@@ -368,14 +419,14 @@ def leastsq(error_func, x0, *args, **options):
 
 
 def crossings(series, value):
-    """Find the labels where the series passes through value.
-
-    The labels in series must be increasing numerical values.
+    """Find the labels where the series passes through a given value.
 
-    series: Series
-    value: number
+    Args:
+        series (pd.Series): Series with increasing numerical index.
+        value (float): Value to find crossings for.
 
-    returns: sequence of labels
+    Returns:
+        np.ndarray: Array of labels where the series crosses the value.
     """
     values = series.values - value
     interp = InterpolatedUnivariateSpline(series.index, values)
@@ -383,30 +434,41 @@ def crossings(series, value):
 
 
 def has_nan(a):
-    """Checks whether the an array contains any NaNs.
+    """Check whether an array or Series contains any NaNs.
 
-    :param a: NumPy array or Pandas Series
-    :return: boolean
+    Args:
+        a (array-like): NumPy array or Pandas Series.
+
+    Returns:
+        bool: True if any NaNs are present, False otherwise.
     """
     return np.any(np.isnan(a))
 
 
 def is_strictly_increasing(a):
-    """Checks whether the elements of an array are strictly increasing.
+    """Check whether the elements of an array are strictly increasing.
+
+    Args:
+        a (array-like): NumPy array or Pandas Series.
 
-    :param a: NumPy array or Pandas Series
-    :return: boolean
+    Returns:
+        bool: True if strictly increasing, False otherwise.
     """
     return np.all(np.diff(a) > 0)
 
 
 def interpolate(series, **options):
-    """Creates an interpolation function.
+    """Create an interpolation function from a Series.
 
-    series: Series object
-    options: any legal options to scipy.interpolate.interp1d
+    Args:
+        series (pd.Series): Series object with strictly increasing index.
+        **options: Additional keyword arguments for scipy.interpolate.interp1d.
 
-    returns: function that maps from the index to the values
+    Returns:
+        callable: Function that maps from the index to the values.
+
+    Raises:
+        ValueError: If the index contains NaNs or is not strictly increasing.
     """
     if has_nan(series.index):
         msg = """The Series you passed to interpolate contains
@@ -434,11 +496,12 @@ def interpolate(series, **options):
 def interpolate_inverse(series, **options):
     """Interpolate the inverse function of a Series.
 
-    series: Series object, represents a mapping from `a` to `b`
-    options: any legal options to scipy.interpolate.interp1d
+    Args:
+        series (pd.Series): Series representing a mapping from a to b.
+        **options: Additional keyword arguments for scipy.interpolate.interp1d.
 
-    returns: interpolation object, can be used as a function
-             from `b` to `a`
+    Returns:
+        callable: Interpolation object, can be used as a function from b to a.
     """
     inverse = pd.Series(series.index, index=series.values)
     interp_func = interpolate(inverse, **options)
@@ -450,22 +513,29 @@ def gradient(series, **options):
 
     If the elements of series have units, they are dropped.
 
-    series: Series object
-    options: any legal options to np.gradient
+    Args:
+        series (pd.Series): Series object.
+        **options: Additional keyword arguments for np.gradient.
 
-    returns: Series, same subclass as series
+    Returns:
+        pd.Series: Series with the same subclass as the input.
+        
+    Raises:
+        ValueError: If series is not a pandas Series
     """
+    if not isinstance(series, pd.Series):
+        raise ValueError("series must be a pandas Series")
     x = series.index
     y = series.values
-
     a = np.gradient(y, x, **options)
     return series.__class__(a, series.index)
 
 
 def source_code(obj):
-    """Prints the source code for a given object.
+    """Print the source code for a given object.
 
-    obj: function or method object
+    Args:
+        obj (object): Function or method object to print source for.
     """
     print(inspect.getsource(obj))
 
@@ -475,8 +545,12 @@ def underride(d, **options):
 
     If d is None, create a new dictionary.
 
-    d: dictionary
-    options: keyword args to add to d
+    Args:
+        d (dict): Dictionary to update.
+        **options: Keyword arguments to add to d.
+
+    Returns:
+        dict: Updated dictionary.
     """
     if d is None:
         d = {}
@@ -495,8 +569,9 @@ def contour(df, **options):
 
     Note: columns and index must be numerical
 
-    df: DataFrame
-    options: passed to plt.contour
+    Args:
+        df (pd.DataFrame): DataFrame to plot.
+        **options: Additional keyword arguments for plt.contour.
     """
     fontsize = options.pop("fontsize", 12)
     underride(options, cmap="viridis")
@@ -514,7 +589,9 @@ def savefig(filename, **options):
 
     https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html
 
-    filename: string
+    Args:
+        filename (str): Name of the file to save the figure to.
+        **options: Additional keyword arguments for plt.savefig.
     """
     print("Saving figure to file", filename)
     plt.savefig(filename, **options)
@@ -530,6 +607,9 @@ def decorate(**options):
 
     The keyword arguments can be any of the axis properties
     https://matplotlib.org/api/axes_api.html
+
+    Args:
+        **options: Keyword arguments for axis properties.
     """
     ax = plt.gca()
     ax.set(**options)
@@ -542,9 +622,10 @@ def decorate(**options):
 
 
 def remove_from_legend(bad_labels):
-    """Removes some labels from the legend.
+    """Remove specified labels from the current plot legend.
 
-    bad_labels: sequence of strings
+    Args:
+        bad_labels (list): Sequence of label strings to remove from the legend.
     """
     ax = plt.gca()
     handles, labels = ax.get_legend_handles_labels()
@@ -564,6 +645,12 @@ class SettableNamespace(SimpleNamespace):
     Takes keyword arguments and stores them as attributes.
     """
     def __init__(self, namespace=None, **kwargs):
+        """Initialize a SettableNamespace.
+
+        Args:
+            namespace (SettableNamespace, optional): Namespace to copy. Defaults to None.
+            **kwargs: Keyword arguments to store as attributes.
+        """
         super().__init__()
         if namespace:
             self.__dict__.update(namespace.__dict__)
@@ -572,8 +659,12 @@ def __init__(self, namespace=None, **kwargs):
     def get(self, name, default=None):
         """Look up a variable.
 
-        name: string varname
-        default: value returned if `name` is not present
+        Args:
+            name (str): Name of the variable to look up.
+            default (any, optional): Value returned if `name` is not present. Defaults to None.
+
+        Returns:
+            any: Value of the variable or default.
         """
         try:
             return self.__getattribute__(name, default)
@@ -583,7 +674,11 @@ def get(self, name, default=None):
     def set(self, **variables):
         """Make a copy and update the given variables.
 
-        returns: Params
+        Args:
+            **variables: Keyword arguments to update.
+
+        Returns:
+            Params: New Params object with updated variables.
         """
         new = copy(self)
         new.__dict__.update(variables)
@@ -591,22 +686,25 @@ def set(self, **variables):
 
 
 def magnitude(x):
-    """Returns the magnitude of a Quantity or number.
+    """Return the magnitude of a Quantity or number.
 
-    x: Quantity or number
+    Args:
+        x (object): Quantity or number.
 
-    returns: number
+    Returns:
+        float: Magnitude as a plain number.
     """
     return x.magnitude if hasattr(x, 'magnitude') else x
 
 
 def remove_units(namespace):
-    """Removes units from the values in a Namespace.
+    """Remove units from the values in a Namespace (top-level only).
 
-    Only removes units from top-level values;
-    does not traverse nested values.
+    Args:
+        namespace (object): Namespace with attributes.
 
-    returns: new Namespace object
+    Returns:
+        object: New Namespace object with units removed from values.
     """
     res = copy(namespace)
     for label, value in res.__dict__.items():
@@ -617,12 +715,13 @@ def remove_units(namespace):
 
 
 def remove_units_series(series):
-    """Removes units from the values in a Series.
+    """Remove units from the values in a Series (top-level only).
 
-    Only removes units from top-level values;
-    does not traverse nested values.
+    Args:
+        series (pd.Series): Series with possible units.
 
-    returns: new Series object
+    Returns:
+        pd.Series: New Series object with units removed from values.
     """
     res = copy(series)
     for label, value in res.items():
@@ -647,18 +746,35 @@ class Params(SettableNamespace):
 
 
 def State(**variables):
-    """Contains the values of state variables."""
+    """Contains the values of state variables.
+
+    Args:
+        **variables: Keyword arguments to store as state variables.
+
+    Returns:
+        pd.Series: Series with the state variables.
+    """
     return pd.Series(variables, name='state')
 
 
 def make_series(x, y, **options):
     """Make a Pandas Series.
 
-    x: sequence used as the index
-    y: sequence used as the values
+    Args:
+        x (sequence): Sequence used as the index.
+        y (sequence): Sequence used as the values.
+        **options: Additional keyword arguments for pd.Series.
 
-    returns: Pandas Series
+    Returns:
+        pd.Series: Pandas Series.
+        
+    Raises:
+        ValueError: If x or y are not array-like or have different lengths
     """
+    validate_array_like(x, "x")
+    validate_array_like(y, "y")
+    if len(x) != len(y):
+        raise ValueError("x and y must have the same length")
     underride(options, name='values')
     if isinstance(y, pd.Series):
         y = y.values
@@ -669,6 +785,13 @@ def make_series(x, y, **options):
 
 def TimeSeries(*args, **kwargs):
     """Make a pd.Series object to represent a time series.
+
+    Args:
+        *args: Arguments passed to pd.Series.
+        **kwargs: Keyword arguments passed to pd.Series.
+
+    Returns:
+        pd.Series: Series with index name 'Time' and name 'Quantity'.
     """
     if args or kwargs:
         underride(kwargs, dtype=float)
@@ -684,6 +807,13 @@ def TimeSeries(*args, **kwargs):
 
 def SweepSeries(*args, **kwargs):
     """Make a pd.Series object to store results from a parameter sweep.
+
+    Args:
+        *args: Arguments passed to pd.Series.
+        **kwargs: Keyword arguments passed to pd.Series.
+
+    Returns:
+        pd.Series: Series with index name 'Parameter' and name 'Metric'.
     """
     if args or kwargs:
         underride(kwargs, dtype=float)
@@ -698,7 +828,14 @@ def SweepSeries(*args, **kwargs):
 
 
 def show(obj):
-    """Display a Series or Namespace as a DataFrame."""
+    """Display a Series or Namespace as a DataFrame.
+
+    Args:
+        obj (object): Series or Namespace to display.
+
+    Returns:
+        pd.DataFrame: DataFrame representation of the object.
+    """
     if isinstance(obj, pd.Series):
         df = pd.DataFrame(obj)
         return df
@@ -710,21 +847,44 @@ def show(obj):
 
 
 def TimeFrame(*args, **kwargs):
-    """DataFrame that maps from time to State.
+    """Create a DataFrame that maps from time to State.
+
+    Args:
+        *args: Arguments passed to pd.DataFrame.
+        **kwargs: Keyword arguments passed to pd.DataFrame.
+
+    Returns:
+        pd.DataFrame: DataFrame indexed by time.
     """
     underride(kwargs, dtype=float)
     return pd.DataFrame(*args, **kwargs)
 
 
 def SweepFrame(*args, **kwargs):
-    """DataFrame that maps from parameter value to SweepSeries.
+    """Create a DataFrame that maps from parameter value to SweepSeries.
+
+    Args:
+        *args: Arguments passed to pd.DataFrame.
+        **kwargs: Keyword arguments passed to pd.DataFrame.
+
+    Returns:
+        pd.DataFrame: DataFrame indexed by parameter value.
     """
     underride(kwargs, dtype=float)
     return pd.DataFrame(*args, **kwargs)
 
 
 def Vector(x, y, z=None, **options):
-    """
+    """Create a 2D or 3D vector as a pandas Series.
+
+    Args:
+        x (float): x component.
+        y (float): y component.
+        z (float, optional): z component. Defaults to None.
+        **options: Additional keyword arguments for pandas.Series.
+
+    Returns:
+        pd.Series: Series with keys 'x', 'y', and optionally 'z'.
     """
     underride(options, name='component')
     if z is None:
@@ -736,12 +896,38 @@ def Vector(x, y, z=None, **options):
 ## Vector functions (should work with any sequence)
 
 def vector_mag(v):
-    """Vector magnitude."""
+    """Vector magnitude.
+
+    Args:
+        v (array-like): Vector.
+
+    Returns:
+        float: Magnitude of the vector.
+        
+    Raises:
+        ValueError: If v is not array-like or is empty
+    """
+    validate_array_like(v, "v")
+    if len(v) == 0:
+        raise ValueError("Vector cannot be empty")
     return np.sqrt(np.dot(v, v))
 
 
 def vector_mag2(v):
-    """Vector magnitude squared."""
+    """Vector magnitude squared.
+
+    Args:
+        v (array-like): Vector.
+
+    Returns:
+        float: Magnitude squared of the vector.
+        
+    Raises:
+        ValueError: If v is not array-like or is empty
+    """
+    validate_array_like(v, "v")
+    if len(v) == 0:
+        raise ValueError("Vector cannot be empty")
     return np.dot(v, v)
 
 
@@ -750,9 +936,18 @@ def vector_angle(v):
 
     Only works with 2-D vectors.
 
-    returns: angle in radians
+    Args:
+        v (array-like): 2-D vector.
+
+    Returns:
+        float: Angle in radians.
+        
+    Raises:
+        ValueError: If v is not array-like or is not 2D
     """
-    assert len(v) == 2
+    validate_array_like(v, "v")
+    if len(v) != 2:
+        raise ValueError("vector_angle only works with 2D vectors")
     x, y = v
     return np.arctan2(y, x)
 
@@ -760,16 +955,32 @@ def vector_angle(v):
 def vector_polar(v):
     """Vector magnitude and angle.
 
-    returns: (number, angle in radians)
+    Args:
+        v (array-like): Vector.
+
+    Returns:
+        tuple: (magnitude, angle in radians).
+        
+    Raises:
+        ValueError: If v is not array-like
     """
+    validate_array_like(v, "v")
     return vector_mag(v), vector_angle(v)
 
 
 def vector_hat(v):
     """Unit vector in the direction of v.
 
-    returns: Vector or array
+    Args:
+        v (array-like): Vector.
+
+    Returns:
+        array-like: Unit vector in the direction of v.
+        
+    Raises:
+        ValueError: If v is not array-like
     """
+    validate_array_like(v, "v")
     # check if the magnitude of the Quantity is 0
     mag = vector_mag(v)
     if mag == 0:
@@ -783,9 +994,18 @@ def vector_perp(v):
 
     Only works with 2-D Vectors.
 
-    returns: Vector
+    Args:
+        v (array-like): 2-D vector.
+
+    Returns:
+        Vector: Perpendicular vector.
+        
+    Raises:
+        ValueError: If v is not array-like or is not 2D
     """
-    assert len(v) == 2
+    validate_array_like(v, "v")
+    if len(v) != 2:
+        raise ValueError("vector_perp only works with 2D vectors")
     x, y = v
     return Vector(-y, x)
 
@@ -793,18 +1013,43 @@ def vector_perp(v):
 def vector_dot(v, w):
     """Dot product of v and w.
 
-    returns: number or Quantity
+    Args:
+        v (array-like): First vector.
+        w (array-like): Second vector.
+
+    Returns:
+        float: Dot product of v and w.
+        
+    Raises:
+        ValueError: If v or w are not array-like or have different lengths
     """
+    validate_array_like(v, "v")
+    validate_array_like(w, "w")
+    if len(v) != len(w):
+        raise ValueError("Vectors must have the same length")
     return np.dot(v, w)
 
 
 def vector_cross(v, w):
     """Cross product of v and w.
 
-    returns: number or Quantity for 2-D, Vector for 3-D
+    Args:
+        v (array-like): First vector.
+        w (array-like): Second vector.
+
+    Returns:
+        array-like: Cross product of v and w.
+        
+    Raises:
+        ValueError: If v or w are not array-like, or not both 2D or 3D, or not same length
     """
+    validate_array_like(v, "v")
+    validate_array_like(w, "w")
+    if len(v) != len(w):
+        raise ValueError("Vectors must have the same length for cross product")
+    if len(v) not in (2, 3):
+        raise ValueError("Cross product only defined for 2D or 3D vectors")
     res = np.cross(v, w)
-
     if len(v) == 3:
         return Vector(*res)
     else:
@@ -814,8 +1059,20 @@ def vector_cross(v, w):
 def vector_proj(v, w):
     """Projection of v onto w.
 
-    returns: array or Vector with direction of w and units of v.
+    Args:
+        v (array-like): Vector to project.
+        w (array-like): Vector to project onto.
+
+    Returns:
+        array-like: Projection of v onto w.
+        
+    Raises:
+        ValueError: If v or w are not array-like or not same length
     """
+    validate_array_like(v, "v")
+    validate_array_like(w, "w")
+    if len(v) != len(w):
+        raise ValueError("Vectors must have the same length for projection")
     w_hat = vector_hat(w)
     return vector_dot(v, w_hat) * w_hat
 
@@ -825,13 +1082,33 @@ def scalar_proj(v, w):
 
     Which is the magnitude of the projection of v onto w.
 
-    returns: scalar with units of v.
+    Args:
+        v (array-like): Vector to project.
+        w (array-like): Vector to project onto.
+
+    Returns:
+        float: Scalar projection of v onto w.
     """
     return vector_dot(v, vector_hat(w))
 
 
 def vector_dist(v, w):
-    """Euclidean distance from v to w, with units."""
+    """Euclidean distance from v to w, with units.
+
+    Args:
+        v (array-like): First vector.
+        w (array-like): Second vector.
+
+    Returns:
+        float: Euclidean distance from v to w.
+        
+    Raises:
+        ValueError: If v or w are not array-like or not same length
+    """
+    validate_array_like(v, "v")
+    validate_array_like(w, "w")
+    if len(v) != len(w):
+        raise ValueError("Vectors must have the same length for distance calculation")
     if isinstance(v, list):
         v = np.asarray(v)
     return vector_mag(v - w)
@@ -839,7 +1116,22 @@ def vector_dist(v, w):
 
 def vector_diff_angle(v, w):
     """Angular difference between two vectors, in radians.
+
+    Args:
+        v (array-like): First vector.
+        w (array-like): Second vector.
+
+    Returns:
+        float: Angular difference in radians.
+
+    Raises:
+        ValueError: If v or w are not array-like or not same length
+        NotImplementedError: If the vectors are not 2-D.
     """
+    validate_array_like(v, "v")
+    validate_array_like(w, "w")
+    if len(v) != len(w):
+        raise ValueError("Vectors must have the same length for angle difference")
     if len(v) == 2:
         return vector_angle(v) - vector_angle(w)
     else:
@@ -855,9 +1147,16 @@ def plot_segment(A, B, **options):
 
     Additional options are passed along to plot().
 
-    A: Vector
-    B: Vector
+    Args:
+        A (Vector): First vector.
+        B (Vector): Second vector.
+        **options: Additional keyword arguments for plt.plot.
+        
+    Raises:
+        ValueError: If A or B are not Vector objects
     """
+    if not isinstance(A, pd.Series) or not isinstance(B, pd.Series):
+        raise ValueError("A and B must be Vector objects")
     xs = A.x, B.x
     ys = A.y, B.y
     plt.plot(xs, ys, **options)
@@ -869,10 +1168,19 @@ def plot_segment(A, B, **options):
 def animate(results, draw_func, *args, interval=None):
     """Animate results from a simulation.
 
-    results: TimeFrame
-    draw_func: function that draws state
-    interval: time between frames in seconds
+    Args:
+        results (TimeFrame): Results to animate.
+        draw_func (callable): Function that draws state.
+        *args: Additional positional arguments passed to draw_func.
+        interval (float, optional): Time between frames in seconds. Defaults to None.
+        
+    Raises:
+        ValueError: If results is not a TimeFrame or draw_func is not callable
     """
+    if not isinstance(results, pd.DataFrame):
+        raise ValueError("results must be a TimeFrame")
+    if not callable(draw_func):
+        raise ValueError("draw_func must be callable")
     plt.figure()
     try:
         for t, state in results.iterrows():
diff --git a/modsim/test_modsim.py b/modsim/test_modsim.py
index a287c955..2b719004 100644
--- a/modsim/test_modsim.py
+++ b/modsim/test_modsim.py
@@ -1,101 +1,12 @@
 import unittest
 from modsim import *
-
-import os
-
-import pint
-from pint.errors import UnitStrippedWarning
+import numpy as np
+import pandas as pd
 
 import warnings
-
 warnings.simplefilter("error", Warning)
 
 
-class TestModSimSeries(unittest.TestCase):
-    def test_constructor(self):
-        s = ModSimSeries([1, 2, 3])
-        self.assertEqual(s[0], 1)
-
-        q = Quantity(2, UNITS.meter)
-        s[q] = 4
-        self.assertEqual(s[q], 4)
-        self.assertEqual(s[2], 4)
-
-
-class TestModSimDataFrame(unittest.TestCase):
-    def test_constructor(self):
-        msf = ModSimDataFrame(columns=["A", "T", "dt"])
-        msf.row[1000] = [1, 2, np.nan]
-        msf.row["label"] = ["4", 5, 6.0]
-
-        col = msf.A
-        self.assertIsInstance(col, ModSimSeries)
-        self.assertEqual(col[1000], 1)
-
-        col = msf.T
-        self.assertIsInstance(col, ModSimSeries)
-        self.assertEqual(col[1000], 2)
-
-        col = msf.dt
-        self.assertIsInstance(col, ModSimSeries)
-        self.assertEqual(col["label"], 6.0)
-
-        row = msf.row[1000]
-        self.assertIsInstance(row, ModSimSeries)
-
-        self.assertEqual(row.A, 1)
-        self.assertEqual(row.T, 2)
-        self.assertTrue(np.isnan(row.dt))
-
-        self.assertEqual(row["A"], 1)
-        self.assertEqual(row["T"], 2)
-        self.assertTrue(np.isnan(row["dt"]))
-
-
-class TestTimeFrame(unittest.TestCase):
-    def test_constructor(self):
-        msf = TimeFrame(columns=["A", "T", "dt"])
-        msf.row[1000] = [1, 2, np.nan]
-        msf.row["label"] = ["4", 5, 6.0]
-
-        col = msf.A
-        self.assertIsInstance(col, TimeSeries)
-
-        col = msf.T
-        self.assertIsInstance(col, TimeSeries)
-
-        col = msf.dt
-        self.assertIsInstance(col, TimeSeries)
-
-        row = msf.row[1000]
-        self.assertIsInstance(row, State)
-
-        row = msf.row["label"]
-        self.assertIsInstance(row, State)
-
-
-class TestSweepFrame(unittest.TestCase):
-    def test_constructor(self):
-        msf = SweepFrame(columns=["A", "T", "dt"])
-        msf.row[1000] = [1, 2, np.nan]
-        msf.row["label"] = ["4", 5, 6.0]
-
-        col = msf.A
-        self.assertIsInstance(col, SweepSeries)
-
-        col = msf.T
-        self.assertIsInstance(col, SweepSeries)
-
-        col = msf.dt
-        self.assertIsInstance(col, SweepSeries)
-
-        row = msf.row[1000]
-        self.assertIsInstance(row, SweepSeries)
-
-        row = msf.row["label"]
-        self.assertIsInstance(row, SweepSeries)
-
-
 class TestCartPol(unittest.TestCase):
     def test_cart2pol(self):
         theta, r = cart2pol(3, 4)
@@ -114,7 +25,7 @@ def test_pol2cart(self):
         self.assertAlmostEqual(x, 3)
         self.assertAlmostEqual(y, 4)
 
-        angle = 45 * UNITS.degree
+        angle = np.pi/4  # 45 degrees in radians
         r = 2 * np.sqrt(2)
         z = 2
         x, y, z = pol2cart(angle, r, z)
@@ -140,105 +51,24 @@ def test_linspace(self):
 
     def test_linrange(self):
         array = linrange(0, 1, 0.1)
-        self.assertEqual(len(array), 10)
-        self.assertAlmostEqual(array[0], 0)
-        self.assertAlmostEqual(array[1], 0.1)
-        self.assertAlmostEqual(array[9], 0.9)
-
-        array = linrange(0, 1, 0.1, endpoint=True)
         self.assertEqual(len(array), 11)
         self.assertAlmostEqual(array[0], 0)
         self.assertAlmostEqual(array[1], 0.1)
-        self.assertAlmostEqual(array[10], 1.0)
-
-
-class TestAbsRelDiff(unittest.TestCase):
-    def test_abs_diff(self):
-        abs_diff = compute_abs_diff([1, 3, 7.5])
-        self.assertEqual(len(abs_diff), 3)
-        self.assertAlmostEqual(abs_diff[1], 4.5)
-
-        ts = linrange(1950, 1960, endpoint=True)
-        ps = linspace(3, 4, len(ts))
-        abs_diff = compute_abs_diff(ps)
-        self.assertEqual(len(abs_diff), 11)
-        self.assertAlmostEqual(abs_diff[1], 0.1)
-
-        # TODO: bring back this test when np.ediff1 is fixed
-        # self.assertTrue(np.isnan(abs_diff[-1]))
-
-        series = TimeSeries(ps, index=ts)
-        abs_diff = compute_abs_diff(series)
-        self.assertEqual(len(abs_diff), 11)
-        self.assertAlmostEqual(abs_diff[1950], 0.1)
-        # self.assertTrue(np.isnan(abs_diff[1960]))
-
-    def test_rel_diff(self):
-        rel_diff = compute_rel_diff([1, 3, 7.5])
-        self.assertEqual(len(rel_diff), 3)
-        self.assertAlmostEqual(rel_diff[1], 1.5)
-
-        ts = linrange(1950, 1960, endpoint=True)
-        ps = linspace(3, 4, len(ts))
-        rel_diff = compute_rel_diff(ps)
-        self.assertEqual(len(rel_diff), 11)
-        self.assertAlmostEqual(rel_diff[0], 0.0333333333)
-        # self.assertTrue(np.isnan(rel_diff[-1]))
-
-        series = TimeSeries(ps, index=ts)
-        rel_diff = compute_rel_diff(series)
-        self.assertEqual(len(rel_diff), 11)
-        self.assertAlmostEqual(rel_diff[1950], 0.0333333333)
-        # self.assertTrue(np.isnan(rel_diff[1960]))
+        self.assertAlmostEqual(array[9], 0.9)
 
 
 class TestOdeSolvers(unittest.TestCase):
-    def test_run_euler(self):
-        s = UNITS.second
-        m = UNITS.meter
-
-        init = State(y=2 * m)
-        system = System(init=init, t_0=1 * s, t_end=3 * s)
-
-        def slope_func(state, t, system):
-            [y] = state
-            dydt = y / s + t * m / s ** 2
-            return [dydt]
-
-        results, details = run_euler(system, slope_func)
-        y_end = get_last_value(results.y)
-        self.assertAlmostEqual(y_end, 24.9737147 * m)
-
-    def test_run_ralston(self):
-        s = UNITS.second
-        m = UNITS.meter
-
-        init = State(y=2 * m)
-        system = System(init=init, t_0=1 * s, t_end=3 * s)
-
-        def slope_func(state, t, system):
-            [y] = state
-            dydt = y / s + t * m / s ** 2
-            return [dydt]
-
-        results, details = run_ralston(system, slope_func)
-        y_end = get_last_value(results.y)
-        self.assertAlmostEqual(y_end, 25.8344700133 * m)
-
     def test_run_solve_ivp(self):
-        s = UNITS.second
-        m = UNITS.meter
-
-        init = State(y=2 * m)
-        system = System(init=init, t_0=1 * s, t_end=3 * s)
+        init = State(y=2)
+        system = System(init=init, t_0=1, t_end=3)
 
-        def slope_func(state, t, system):
-            [y] = state
+        def slope_func(t, state, system):
+            y = state[0] if isinstance(state, np.ndarray) else state.iloc[0]
             dydt = y + t
             return [dydt]
 
         results, details = run_solve_ivp(system, slope_func)
-        y_end = get_last_value(results.y)
+        y_end = results.y.iloc[-1]
         self.assertAlmostEqual(y_end, 25.5571533)
 
 
@@ -247,18 +77,36 @@ def test_root_scalar(self):
         def func(x):
             return (x - 1) * (x - 2) * (x - 3)
 
-        res = root_scalar(func, [0, 1.9])
-        self.assertAlmostEqual(res.root, 1.0)
+        res = root_scalar(func, bracket=[0, 1.9])
+        self.assertAlmostEqual(res.root, 1.0, places=5)
 
-    def test_root_secant(self):
+    def test_minimize_scalar(self):
         def func(x):
-            return (x - 1) * (x - 2) * (x - 3)
+            return (x - 2)**2 + 1
+
+        # Test with bracket
+        res = minimize_scalar(func, bracket=[0, 4])
+        self.assertAlmostEqual(res.x, 2.0, places=5)
+        self.assertAlmostEqual(res.fun, 1.0, places=5)
+
+        # Test with bounds
+        res = minimize_scalar(func, bounds=[0, 4])
+        self.assertAlmostEqual(res.x, 2.0, places=5)
+        self.assertAlmostEqual(res.fun, 1.0, places=5)
 
-        res = root_bisect(func, [0, 1.9])
-        self.assertAlmostEqual(res.root, 1.0)
+    def test_maximize_scalar(self):
+        def func(x):
+            return -(x - 2)**2 + 1
+
+        # Test with bracket
+        res = maximize_scalar(func, bracket=[0, 4])
+        self.assertAlmostEqual(res.x, 2.0, places=5)
+        self.assertAlmostEqual(res.fun, 1.0, places=5)
 
-        res = root_bisect(func, [0, 0.5])
-        self.assertFalse(res.converged)
+        # Test with bounds
+        res = maximize_scalar(func, bounds=[0, 4])
+        self.assertAlmostEqual(res.x, 2.0, places=5)
+        self.assertAlmostEqual(res.fun, 1.0, places=5)
 
 
 class TestRunInterpolate(unittest.TestCase):
@@ -289,137 +137,71 @@ def test_interpolate(self):
         i = interpolate(series)
         self.assertAlmostEqual(i(1.5), 2.0)
 
-    def test_interpolate_with_units(self):
-        index = [1, 2, 3]
-        values = np.array(index) * 2 - 1
-        series = pd.Series(values, index=index) * UNITS.meter
-        i = interpolate(series)
-        self.assertAlmostEqual(i(1.5), 2.0 * UNITS.meter)
-
 
 class TestGradient(unittest.TestCase):
     def test_gradient(self):
         a = [1, 2, 4]
         s = TimeSeries(a)
         r = gradient(s)
-        self.assertTrue(isinstance(r, TimeSeries))
+        self.assertTrue(isinstance(r, pd.Series))
         self.assertAlmostEqual(r[1], 1.5)
 
-    def test_gradient_with_units(self):
-        s = SweepSeries()
-        s[0] = 1 * UNITS.meter
-        s[1] = 2 * UNITS.meter
-        s[2] = 4 * UNITS.meter
-        r = gradient(s)
-        self.assertTrue(isinstance(r, SweepSeries))
-        self.assertAlmostEqual(r[1], 1.5 * UNITS.meter)
-
-
-class TestGolden(unittest.TestCase):
-    def test_minimize(self):
-        def min_func(x, system):
-            return (x - system.actual_min) ** 2
-
-        system = System(actual_min=2)
-        res = minimize_golden(min_func, [0, 5], system, rtol=1e-7)
-        self.assertAlmostEqual(res.x, 2)
-        self.assertAlmostEqual(res.fun, 0)
-
-    def test_maximize(self):
-        def max_func(x, system):
-            return -(x - system.actual_min) ** 2
-
-        system = System(actual_min=2)
-        res = maximize_golden(max_func, [0, 5], system, rtol=1e-7)
-        self.assertAlmostEqual(res.x, 2)
-        self.assertAlmostEqual(res.fun, 0)
-
 
 class TestVector(unittest.TestCase):
     def assertArrayEqual(self, res, ans):
-        self.assertTrue(isinstance(res, np.ndarray))
+        self.assertTrue(isinstance(res, (np.ndarray, pd.Series)))
         self.assertTrue((res == ans).all())
 
     def assertVectorEqual(self, res, ans):
-        self.assertTrue(isinstance(res, ModSimVector))
+        self.assertTrue(isinstance(res, (pd.Series, np.ndarray)))
         self.assertTrue((res == ans).all())
 
     def assertVectorAlmostEqual(self, res, ans):
-        [self.assertQuantityAlmostEqual(x, y) for x, y in zip(res, ans)]
-
-    def assertQuantityAlmostEqual(self, x, y):
-        self.assertEqual(get_units(x), get_units(y))
-        self.assertAlmostEqual(magnitude(x), magnitude(y))
+        for x, y in zip(res, ans):
+            self.assertAlmostEqual(x, y)
 
     def test_vector_mag(self):
-        warnings.simplefilter("error", Warning)
-        m = UNITS.meter
-
         v = [3, 4]
         self.assertEqual(vector_mag(v), 5)
         v = Vector(3, 4)
         self.assertEqual(vector_mag(v), 5)
-        v = Vector(3, 4) * m
-        self.assertEqual(vector_mag(v), 5 * m)
-        self.assertEqual(v.mag, 5 * m)
 
     def test_vector_mag2(self):
-        warnings.simplefilter("error", Warning)
-        m = UNITS.meter
 
         v = [3, 4]
         self.assertEqual(vector_mag2(v), 25)
         v = Vector(3, 4)
         self.assertEqual(vector_mag2(v), 25)
-        v = Vector(3, 4) * m
-        self.assertEqual(vector_mag2(v), 25 * m * m)
 
     def test_vector_angle(self):
-        warnings.simplefilter("error", Warning)
-        m = UNITS.meter
         ans = 0.927295218
         v = [3, 4]
         self.assertAlmostEqual(vector_angle(v), ans)
         v = Vector(3, 4)
         self.assertAlmostEqual(vector_angle(v), ans)
-        v = Vector(3, 4) * m
-        self.assertAlmostEqual(vector_angle(v), ans)
 
     def test_vector_hat(self):
-        warnings.simplefilter("error", Warning)
-        m = UNITS.meter
         v = [3, 4]
         ans = [0.6, 0.8]
-        self.assertArrayEqual(vector_hat(v), ans)
+        self.assertVectorAlmostEqual(vector_hat(v), ans)
 
         v = Vector(3, 4)
-        self.assertVectorEqual(vector_hat(v), ans)
-        v = Vector(3, 4) * m
-        self.assertVectorEqual(vector_hat(v), ans)
+        self.assertVectorAlmostEqual(vector_hat(v), ans)
 
         v = [0, 0]
         ans = [0, 0]
-        self.assertArrayEqual(vector_hat(v), ans)
+        self.assertVectorAlmostEqual(vector_hat(v), ans)
         v = Vector(0, 0)
-        self.assertVectorEqual(vector_hat(v), ans)
-        v = Vector(0, 0) * m
-        self.assertVectorEqual(vector_hat(v), ans)
+        self.assertVectorAlmostEqual(vector_hat(v), ans)
 
     def test_vector_perp(self):
-        warnings.simplefilter("error", Warning)
-        m = UNITS.meter
         v = [3, 4]
         ans = [-4, 3]
         self.assertTrue((vector_perp(v) == ans).all())
         v = Vector(3, 4)
         self.assertTrue((vector_perp(v) == ans).all())
-        v = Vector(3, 4) * m
-        self.assertTrue((vector_perp(v) == ans * m).all())
 
     def test_vector_dot(self):
-        warnings.simplefilter("error", Warning)
-        m = UNITS.meter
-        s = UNITS.second
         v = [3, 4]
         w = [5, 6]
         ans = 39
@@ -428,18 +210,7 @@ def test_vector_dot(self):
         self.assertAlmostEqual(vector_dot(v, w), ans)
         self.assertAlmostEqual(vector_dot(w, v), ans)
 
-        v = Vector(3, 4) * m
-        self.assertAlmostEqual(vector_dot(v, w), ans * m)
-        self.assertAlmostEqual(vector_dot(w, v), ans * m)
-
-        w = Vector(5, 6) / s
-        self.assertAlmostEqual(vector_dot(v, w), ans * m / s)
-        self.assertAlmostEqual(vector_dot(w, v), ans * m / s)
-
     def test_vector_cross_2D(self):
-        warnings.simplefilter("error", Warning)
-        m = UNITS.meter
-        s = UNITS.second
         ans = -2
 
         v = [3, 4]
@@ -451,41 +222,21 @@ def test_vector_cross_2D(self):
         self.assertAlmostEqual(vector_cross(v, w), ans)
         self.assertAlmostEqual(vector_cross(w, v), -ans)
 
-        v = Vector(3, 4) * m
-        self.assertAlmostEqual(vector_cross(v, w), ans * m)
-        self.assertAlmostEqual(vector_cross(w, v), -ans * m)
-
-        w = Vector(5, 6) / s
-        self.assertAlmostEqual(vector_cross(v, w), ans * m / s)
-        self.assertAlmostEqual(vector_cross(w, v), -ans * m / s)
-
     def test_vector_cross_3D(self):
-        warnings.simplefilter("error", Warning)
-        m = UNITS.meter
-        s = UNITS.second
         ans = [-2, 4, -2]
 
         v = [3, 4, 5]
         w = [5, 6, 7]
-        self.assertArrayEqual(vector_cross(v, w), ans)
-        self.assertArrayEqual(-vector_cross(w, v), ans)
+        res = vector_cross(v, w)
+        self.assertTrue(isinstance(res, (np.ndarray, pd.Series)))
+        self.assertTrue((res == ans).all())
+        self.assertTrue((-vector_cross(w, v) == ans).all())
 
         v = Vector(3, 4, 5)
         self.assertVectorEqual(vector_cross(v, w), ans)
         self.assertVectorEqual(-vector_cross(w, v), ans)
 
-        v = Vector(3, 4, 5) * m
-        self.assertVectorEqual(vector_cross(v, w), ans * m)
-        self.assertVectorEqual(-vector_cross(w, v), ans * m)
-
-        w = Vector(5, 6, 7) / s
-        self.assertVectorEqual(vector_cross(v, w), ans * m / s)
-        self.assertVectorEqual(-vector_cross(w, v), ans * m / s)
-
     def test_scalar_proj(self):
-        warnings.simplefilter("error", Warning)
-        m = UNITS.meter
-        s = UNITS.second
         ans = 4.9934383
         ans2 = 7.8
 
@@ -498,20 +249,10 @@ def test_scalar_proj(self):
         self.assertAlmostEqual(scalar_proj(v, w), ans)
         self.assertAlmostEqual(scalar_proj(w, v), ans2)
 
-        v = Vector(3, 4) * m
-        self.assertQuantityAlmostEqual(scalar_proj(v, w), ans * m)
-        self.assertAlmostEqual(scalar_proj(w, v), ans2)
-
-        w = Vector(5, 6) / s
-        self.assertQuantityAlmostEqual(scalar_proj(v, w), ans * m)
-        self.assertQuantityAlmostEqual(scalar_proj(w, v), ans2 / s)
-
     def test_vector_proj(self):
         warnings.simplefilter("error", Warning)
-        m = UNITS.meter
-        s = UNITS.second
         ans = [3.19672131, 3.83606557]
-        ans2 = Quantity([4.68, 6.24])
+        ans2 = [4.68, 6.24]
 
         v = [3, 4]
         w = [5, 6]
@@ -522,17 +263,7 @@ def test_vector_proj(self):
         self.assertVectorAlmostEqual(vector_proj(v, w), ans)
         self.assertVectorAlmostEqual(vector_proj(w, v), ans2)
 
-        v = Vector(3, 4) * m
-        self.assertVectorAlmostEqual(vector_proj(v, w), ans * m)
-        self.assertVectorAlmostEqual(vector_proj(w, v), ans2)
-
-        w = Vector(5, 6) / s
-        self.assertVectorAlmostEqual(vector_proj(v, w), ans * m)
-        self.assertVectorAlmostEqual(vector_proj(w, v), ans2 / s)
-
     def test_vector_dist(self):
-        warnings.simplefilter("error", Warning)
-        m = UNITS.meter
         v = [3, 4]
         w = [6, 8]
         ans = 5
@@ -543,14 +274,7 @@ def test_vector_dist(self):
         self.assertAlmostEqual(vector_dist(v, w), ans)
         self.assertAlmostEqual(vector_dist(w, v), ans)
 
-        v = Vector(3, 4) * m
-        w = Vector(6, 8) * m
-        self.assertAlmostEqual(vector_dist(v, w), ans * m)
-        self.assertAlmostEqual(vector_dist(w, v), ans * m)
-
     def test_vector_diff_angle(self):
-        warnings.simplefilter("error", Warning)
-        m = UNITS.meter
         v = [3, 4]
         w = [5, 6]
         ans = 0.0512371674
@@ -561,105 +285,144 @@ def test_vector_diff_angle(self):
         self.assertAlmostEqual(vector_diff_angle(v, w), ans)
         self.assertAlmostEqual(vector_diff_angle(w, v), -ans)
 
-        v = Vector(3, 4) * m
-        w = Vector(5, 6) * m
-        self.assertAlmostEqual(vector_diff_angle(v, w), ans)
-        self.assertAlmostEqual(vector_diff_angle(w, v), -ans)
-
 
 class TestSeriesCopy(unittest.TestCase):
     def test_series_copy(self):
         series = TimeSeries()
         res = series.copy()
-        self.assertTrue(isinstance(res, TimeSeries))
-
-
-class TestMagnitudeUnits(unittest.TestCase):
-    def test_magnitudes(self):
-        # scalar
-        x = 5
-        res = magnitudes(x)
-        self.assertEqual(res, 5)
-        res = magnitudes(x * UNITS.meter)
-        self.assertEqual(res, 5)
-
-        # list (result is NumPy array)
-        t = [1, 2, 3]
-        res = magnitudes(t)
-        self.assertEqual(res, [1, 2, 3])
-        res = magnitudes(t * UNITS.meter)
-        self.assertTrue((res == [1, 2, 3]).all())
-
-        # Series (result is list)
-        s = ModSimSeries([1, 2, 3])
-        res = magnitudes(s)
-        self.assertTrue((res == [1, 2, 3]).all())
-        res = magnitudes(s * UNITS.meter)
-        self.assertTrue((res == [1, 2, 3]).all())
-
-        # Quantity containing Series(result is Series)
-        res = magnitudes(UNITS.meter * s)
-        self.assertTrue((res == [1, 2, 3]).all())
-
-    def test_units(self):
-        # scalar
-        x = 5
-        res = get_units(x)
-        self.assertEqual(res, 1)
-        res = get_units(x * UNITS.meter)
-        self.assertEqual(res, UNITS.meter)
-
-        # list (result is list)
-        t = [1, 2, 3]
-        res = get_units(t)
-        self.assertEqual(res, [1, 1, 1])
-        res = get_units(t * UNITS.meter)
-        self.assertEqual(res, UNITS.meter)
-
-        # Series (result Series)
-        s = ModSimSeries([1, 2, 3])
-        res = get_units(s)
-        self.assertTrue((res == [1, 1, 1]).all())
-
-        # Series containing Quantities (result is a Series)
-        res = get_units(s * UNITS.meter)
-        self.assertTrue((res == [UNITS.meter] * 3).all())
-
-        # Quantity containing Series(result is a single Unit object)
-        res = get_units(UNITS.meter * s)
-        self.assertEqual(res, UNITS.meter)
-
-
-class TestPlot(unittest.TestCase):
-    def test_plot(self):
-        os.environ["QT_XKB_CONFIG_ROOT"] = "/usr/share/X11/xkb"
-
-        t = [1, 2, 3]
-        plot(t)
-
-        t = [1, 2, 3] * UNITS.meter
-        plot(t)
-
-        x = [4, 5, 6]
-        plot(x, t)
-
-        x = [4, 5, 6] * UNITS.second
-        plot(x, t)
-
-        a = np.array(t)
-        plot(a)
-
-        x = np.array(x)
-        plot(x, a)
-
-        s = TimeSeries(t)
-        plot(s)
-
-        s = TimeSeries(t, x)
-        plot(s)
-
-        s = TimeSeries(a, x)
-        plot(s)
+        self.assertTrue(isinstance(res, pd.Series))
+
+
+class TestLeastsq(unittest.TestCase):
+    def test_leastsq(self):
+        # Create noise-free test data: y = 2x + 1
+        x = np.array([0, 1, 2, 3, 4])
+        y = 2 * x + 1
+
+        def error_func(params, x, y):
+            m, b = params
+            return y - (m * x + b)
+
+        # Initial guess
+        x0 = [1, 0]  # m=1, b=0
+
+        # Run leastsq
+        best_params, details = leastsq(error_func, x0, x, y)
+
+        # Check results
+        self.assertAlmostEqual(best_params[0], 2.0, places=5)  # slope
+        self.assertAlmostEqual(best_params[1], 1.0, places=5)  # intercept
+        self.assertTrue(details.success)
+
+
+class TestCrossings(unittest.TestCase):
+    def test_crossings(self):
+        # Create a simple linear series from 0 to 10
+        index = np.linspace(0, 10, 11)
+        values = index.copy()
+        series = pd.Series(values, index=index)
+
+        # Find where the series crosses 5
+        result = crossings(series, 5)
+        # Should cross exactly at 5
+        self.assertEqual(len(result), 1)
+        self.assertAlmostEqual(result[0], 5.0, places=5)
+
+
+class TestDataStructures(unittest.TestCase):
+    def test_state(self):
+        s = State(a=1, b=2)
+        self.assertIsInstance(s, pd.Series)
+        self.assertEqual(s['a'], 1)
+        self.assertEqual(s['b'], 2)
+        self.assertEqual(s.name, 'state')
+
+    def test_timeseries(self):
+        ts = TimeSeries([1, 2, 3], index=[10, 20, 30])
+        self.assertIsInstance(ts, pd.Series)
+        self.assertEqual(list(ts), [1, 2, 3])
+        self.assertEqual(list(ts.index), [10, 20, 30])
+        self.assertEqual(ts.index.name, 'Time')
+        self.assertEqual(ts.name, 'Quantity')
+
+    def test_sweepseries(self):
+        ss = SweepSeries([4, 5, 6], index=[0.1, 0.2, 0.3])
+        self.assertIsInstance(ss, pd.Series)
+        self.assertEqual(list(ss), [4, 5, 6])
+        self.assertEqual(list(ss.index), [0.1, 0.2, 0.3])
+        self.assertEqual(ss.index.name, 'Parameter')
+        self.assertEqual(ss.name, 'Metric')
+
+    def test_timeframe(self):
+        df = TimeFrame([[1, 2], [3, 4]], columns=['a', 'b'], index=[0, 1])
+        self.assertIsInstance(df, pd.DataFrame)
+        self.assertEqual(df.shape, (2, 2))
+        self.assertEqual(list(df.columns), ['a', 'b'])
+        self.assertEqual(list(df.index), [0, 1])
+
+    def test_sweepframe(self):
+        df = SweepFrame([[5, 6], [7, 8]], columns=['x', 'y'], index=[0.1, 0.2])
+        self.assertIsInstance(df, pd.DataFrame)
+        self.assertEqual(df.shape, (2, 2))
+        self.assertEqual(list(df.columns), ['x', 'y'])
+        self.assertEqual(list(df.index), [0.1, 0.2])
+
+    def test_make_series(self):
+        s = make_series([10, 20, 30], [1, 2, 3])
+        self.assertIsInstance(s, pd.Series)
+        self.assertEqual(list(s.index), [10, 20, 30])
+        self.assertEqual(list(s.values), [1, 2, 3])
+        self.assertEqual(s.name, 'values')
+        self.assertEqual(s.index.name, 'index')
+
+    def test_vector_polar(self):
+        v = [3, 4]
+        mag, angle = vector_polar(v)
+        self.assertAlmostEqual(mag, 5.0, places=5)
+        self.assertAlmostEqual(angle, np.arctan2(4, 3), places=5)
+
+    def test_interpolate_inverse(self):
+        # y = 2x + 1, invert to get x from y
+        x = np.array([0, 1, 2, 3, 4])
+        y = 2 * x + 1
+        series = pd.Series(y, index=x)
+        inv = interpolate_inverse(series)
+        # y=5 -> x=2
+        self.assertAlmostEqual(inv(5), 2.0, places=5)
+
+    def test_gradient(self):
+        s = pd.Series([1, 4, 9], index=[0, 1, 2])
+        g = gradient(s)
+        self.assertIsInstance(g, pd.Series)
+        # Should be [3, 4, 5] for [1,4,9] at [0,1,2]
+        self.assertTrue(np.allclose(g, [3, 4, 5]))
+
+    def test_magnitude(self):
+        self.assertEqual(magnitude(5), 5)
+        class Dummy:
+            magnitude = 42
+        self.assertEqual(magnitude(Dummy()), 42)
+
+    def test_remove_units(self):
+        class Dummy:
+            def __init__(self):
+                self.x = 5
+                self.y = 10
+        d = Dummy()
+        res = remove_units(d)
+        self.assertEqual(res.x, 5)
+        self.assertEqual(res.y, 10)
+
+    def test_remove_units_series(self):
+        s = pd.Series({'a': 1, 'b': 2})
+        res = remove_units_series(s)
+        self.assertTrue((res == s).all())
+
+    def test_underride(self):
+        d = {'a': 1}
+        underride(d, a=2, b=3)
+        self.assertEqual(d['a'], 1)
+        self.assertEqual(d['b'], 3)
 
 
 if __name__ == "__main__":

From 0d11aff195de6f2db67d5bdac4fd70a95792514c Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 22 May 2025 16:02:16 -0400
Subject: [PATCH 133/144] Updating modsim.py

---
 modsim.py | 612 +++++++++++++++++++++++++++++++++++++++++-------------
 1 file changed, 472 insertions(+), 140 deletions(-)

diff --git a/modsim.py b/modsim.py
index 0cd9034f..ce765923 100644
--- a/modsim.py
+++ b/modsim.py
@@ -1,9 +1,33 @@
 """
+A collection of functions and classes for modeling and simulation in Python.
+
 Code from Modeling and Simulation in Python.
+Copyright 2020-2025 Allen Downey
+MIT License: https://opensource.org/licenses/MIT
 
-Copyright 2020 Allen Downey
+This module provides a simplified interface for modeling and simulation tasks,
+built on top of NumPy, SciPy, and Pandas. It is designed to be beginner-friendly
+and includes comprehensive input validation to help catch common errors.
 
-MIT License: https://opensource.org/licenses/MIT
+The module includes utilities for:
+
+    - Mathematical operations (coordinate conversions, vector operations)
+    - Numerical methods (root finding, optimization, integration)
+    - Data manipulation and analysis
+    - Visualization and plotting
+    - System modeling and simulation
+
+Key features:
+
+    - Vector operations in 2D and 3D
+    - Time series and sweep series data structures
+    - System and parameter management
+    - Numerical methods for solving equations and optimization
+    - Plotting and visualization utilities
+    - Unit handling and conversion
+
+This module is designed to be used in conjunction with the book "Modeling and 
+Simulation in Python" by Allen Downey.
 """
 
 import logging
@@ -38,13 +62,30 @@
 from types import SimpleNamespace
 from copy import copy
 
+# Input validation helpers
+def validate_numeric(value, name):
+    """Validate that a value is numeric."""
+    if not isinstance(value, (int, float)):
+        raise ValueError(f"{name} must be numeric, got {type(value)}")
+
+def validate_array_like(value, name):
+    """Validate that a value is array-like."""
+    if not isinstance(value, (list, tuple, np.ndarray, pd.Series)):
+        raise ValueError(f"{name} must be array-like, got {type(value)}")
+
+def validate_positive(value, name):
+    """Validate that a value is positive."""
+    if value <= 0:
+        raise ValueError(f"{name} must be positive, got {value}")
 
 def flip(p=0.5):
     """Flips a coin with the given probability.
 
-    p: float 0-1
+    Args:
+        p (float): Probability between 0 and 1.
 
-    returns: boolean (True or False)
+    Returns:
+        bool: True or False.
     """
     return np.random.random() < p
 
@@ -52,18 +93,27 @@ def flip(p=0.5):
 def cart2pol(x, y, z=None):
     """Convert Cartesian coordinates to polar.
 
-    x: number or sequence
-    y: number or sequence
-    z: number or sequence (optional)
+    Args:
+        x (number or sequence): x coordinate.
+        y (number or sequence): y coordinate.
+        z (number or sequence, optional): z coordinate. Defaults to None.
 
-    returns: theta, rho OR theta, rho, z
+    Returns:
+        tuple: (theta, rho) or (theta, rho, z).
+        
+    Raises:
+        ValueError: If x or y are not numeric or array-like, or if z is provided but not numeric or array-like
     """
+    if not isinstance(x, (int, float, list, tuple, np.ndarray, pd.Series)):
+        raise ValueError("x must be numeric or array-like")
+    if not isinstance(y, (int, float, list, tuple, np.ndarray, pd.Series)):
+        raise ValueError("y must be numeric or array-like")
+    if z is not None and not isinstance(z, (int, float, list, tuple, np.ndarray, pd.Series)):
+        raise ValueError("z must be numeric or array-like")
     x = np.asarray(x)
     y = np.asarray(y)
-
     rho = np.hypot(x, y)
     theta = np.arctan2(y, x)
-
     if z is None:
         return theta, rho
     else:
@@ -73,15 +123,25 @@ def cart2pol(x, y, z=None):
 def pol2cart(theta, rho, z=None):
     """Convert polar coordinates to Cartesian.
 
-    theta: number or sequence in radians
-    rho: number or sequence
-    z: number or sequence (optional)
+    Args:
+        theta (number or sequence): Angle in radians.
+        rho (number or sequence): Radius.
+        z (number or sequence, optional): z coordinate. Defaults to None.
 
-    returns: x, y OR x, y, z
+    Returns:
+        tuple: (x, y) or (x, y, z).
+        
+    Raises:
+        ValueError: If theta or rho are not numeric or array-like, or if z is provided but not numeric or array-like
     """
+    if not isinstance(theta, (int, float, list, tuple, np.ndarray, pd.Series)):
+        raise ValueError("theta must be numeric or array-like")
+    if not isinstance(rho, (int, float, list, tuple, np.ndarray, pd.Series)):
+        raise ValueError("rho must be numeric or array-like")
+    if z is not None and not isinstance(z, (int, float, list, tuple, np.ndarray, pd.Series)):
+        raise ValueError("z must be numeric or array-like")
     x = rho * np.cos(theta)
     y = rho * np.sin(theta)
-
     if z is None:
         return x, y
     else:
@@ -92,11 +152,13 @@ def pol2cart(theta, rho, z=None):
 def linrange(start, stop=None, step=1):
     """Make an array of equally spaced values.
 
-    start: first value
-    stop: last value (might be approximate)
-    step: difference between elements (should be consistent)
+    Args:
+        start (float): First value.
+        stop (float, optional): Last value (might be approximate). Defaults to None.
+        step (float, optional): Difference between elements. Defaults to 1.
 
-    returns: NumPy array
+    Returns:
+        np.ndarray: Array of equally spaced values.
     """
     if stop is None:
         stop = start
@@ -106,8 +168,18 @@ def linrange(start, stop=None, step=1):
 
 
 def __check_kwargs(kwargs, param_name, param_len, func, func_name):
-    """Check if `kwargs` has a parameter that is a sequence of a particular length
-       param_len: sequence enumerating possible lengths
+    """Check if `kwargs` has a parameter that is a sequence of a particular length.
+
+    Args:
+        kwargs (dict): Dictionary of keyword arguments.
+        param_name (str): Name of the parameter to check.
+        param_len (list): List of valid lengths for the parameter.
+        func (callable): Function to test the parameter value.
+        func_name (str): Name of the function for error messages.
+
+    Raises:
+        ValueError: If the parameter is missing or has an invalid length.
+        Exception: If the function call fails on the parameter value.
     """
     param_val = kwargs.get(param_name, None)
     if param_val is None or len(param_val) not in param_len:
@@ -129,14 +201,17 @@ def root_scalar(func, *args, **kwargs):
     Wrapper for
     https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root_scalar.html
 
-    func: computes the function to find a root of
-    bracket: sequence of two values, lower and upper bounds of the range to be searched
-    args: any additional positional arguments are passed to `func`
-    kwargs: any keyword arguments are passed to `root_scalar`
+    Args:
+        func (callable): Function to find a root of.
+        *args: Additional positional arguments passed to `func`.
+        **kwargs: Additional keyword arguments passed to `root_scalar`.
 
-    returns: RootResults object
-    """
+    Returns:
+        RootResults: Object containing the root and convergence information.
 
+    Raises:
+        ValueError: If the solver does not converge.
+    """
     underride(kwargs, rtol=1e-4)
 
     __check_kwargs(kwargs, 'bracket', [2], lambda x: func(x, *args), 'root_scalar')
@@ -157,15 +232,17 @@ def minimize_scalar(func, *args, **kwargs):
     Wrapper for
     https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
 
-    func: computes the function to be minimized
-    bracket: (`method` is `brent` or `golden`) sequence of two or three values, the range to be searched
-    bounds: (`method` is `bounded`) sequence of two values, the range to be searched
-    args: any additional positional arguments are passed to `func`
-    kwargs: any keyword arguments are passed to `minimize_scalar`
+    Args:
+        func (callable): Function to be minimized.
+        *args: Additional positional arguments passed to `func`.
+        **kwargs: Additional keyword arguments passed to `minimize_scalar`.
 
-    returns: OptimizeResult object
-    """
+    Returns:
+        OptimizeResult: Object containing the minimum and optimization details.
 
+    Raises:
+        Exception: If the optimization does not succeed.
+    """
     underride(kwargs, __func_name='minimize_scalar')
 
     method = kwargs.get('method', None)
@@ -199,13 +276,16 @@ def maximize_scalar(func, *args, **kwargs):
 
     Wrapper for https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize_scalar.html
 
-    func: computes the function to be maximized
-    bracket: (`method` is `brent` or `golden`) sequence of two or three values, the range to be searched
-    bounds: (`method` is `bounded`) sequence of two values, the range to be searched
-    args: any additional positional arguments are passed to `func`
-    kwargs: any keyword arguments are passed as options to `minimize_scalar`
+    Args:
+        func (callable): Function to be maximized.
+        *args: Additional positional arguments passed to `func`.
+        **kwargs: Additional keyword arguments passed to `minimize_scalar`.
+
+    Returns:
+        OptimizeResult: Object containing the maximum and optimization details.
 
-    returns: OptimizeResult object
+    Raises:
+        Exception: If the optimization does not succeed.
     """
     def min_func(*args):
         return -func(*args)
@@ -220,21 +300,18 @@ def min_func(*args):
 
 
 def run_solve_ivp(system, slope_func, **options):
-    """Computes a numerical solution to a differential equation.
-
-    `system` must contain `init` with initial conditions,
-    `t_end` with the end time.  Optionally, it can contain
-    `t_0` with the start time.
-
-    It should contain any other parameters required by the
-    slope function.
+    """Compute a numerical solution to a differential equation using solve_ivp.
 
-    `options` can be any legal options of `scipy.integrate.solve_ivp`
+    Args:
+        system (System): System object containing 'init', 't_end', and optionally 't_0'.
+        slope_func (callable): Function that computes slopes.
+        **options: Additional keyword arguments for scipy.integrate.solve_ivp.
 
-    system: System object
-    slope_func: function that computes slopes
+    Returns:
+        tuple: (TimeFrame of results, details from solve_ivp)
 
-    returns: TimeFrame
+    Raises:
+        ValueError: If required system attributes are missing or if the solver fails.
     """
     system = remove_units(system)
 
@@ -330,20 +407,18 @@ def run_solve_ivp(system, slope_func, **options):
 
 
 def leastsq(error_func, x0, *args, **options):
-    """Find the parameters that yield the best fit for the data.
-
-    `x0` can be a sequence, array, Series, or Params
-
-    Positional arguments are passed along to `error_func`.
-
-    Keyword arguments are passed to `scipy.optimize.leastsq`
-
-    error_func: function that computes a sequence of errors
-    x0: initial guess for the best parameters
-    args: passed to error_func
-    options: passed to leastsq
-
-    :returns: Params object with best_params and ModSimSeries with details
+    """Find the parameters that yield the best fit for the data using least squares.
+
+    Args:
+        error_func (callable): Function that computes a sequence of errors.
+        x0 (array-like): Initial guess for the best parameters.
+        *args: Additional positional arguments passed to error_func.
+        **options: Additional keyword arguments passed to scipy.optimize.leastsq.
+
+    Returns:
+        tuple: (best_params, details)
+            best_params: Best-fit parameters (same type as x0 if possible).
+            details: SimpleNamespace with fit details and success flag.
     """
     # override `full_output` so we get a message if something goes wrong
     options["full_output"] = True
@@ -368,14 +443,14 @@ def leastsq(error_func, x0, *args, **options):
 
 
 def crossings(series, value):
-    """Find the labels where the series passes through value.
+    """Find the labels where the series passes through a given value.
 
-    The labels in series must be increasing numerical values.
+    Args:
+        series (pd.Series): Series with increasing numerical index.
+        value (float): Value to find crossings for.
 
-    series: Series
-    value: number
-
-    returns: sequence of labels
+    Returns:
+        np.ndarray: Array of labels where the series crosses the value.
     """
     values = series.values - value
     interp = InterpolatedUnivariateSpline(series.index, values)
@@ -383,30 +458,41 @@ def crossings(series, value):
 
 
 def has_nan(a):
-    """Checks whether the an array contains any NaNs.
+    """Check whether an array or Series contains any NaNs.
+
+    Args:
+        a (array-like): NumPy array or Pandas Series.
 
-    :param a: NumPy array or Pandas Series
-    :return: boolean
+    Returns:
+        bool: True if any NaNs are present, False otherwise.
     """
     return np.any(np.isnan(a))
 
 
 def is_strictly_increasing(a):
-    """Checks whether the elements of an array are strictly increasing.
+    """Check whether the elements of an array are strictly increasing.
 
-    :param a: NumPy array or Pandas Series
-    :return: boolean
+    Args:
+        a (array-like): NumPy array or Pandas Series.
+
+    Returns:
+        bool: True if strictly increasing, False otherwise.
     """
     return np.all(np.diff(a) > 0)
 
 
 def interpolate(series, **options):
-    """Creates an interpolation function.
+    """Create an interpolation function from a Series.
+
+    Args:
+        series (pd.Series): Series object with strictly increasing index.
+        **options: Additional keyword arguments for scipy.interpolate.interp1d.
 
-    series: Series object
-    options: any legal options to scipy.interpolate.interp1d
+    Returns:
+        callable: Function that maps from the index to the values.
 
-    returns: function that maps from the index to the values
+    Raises:
+        ValueError: If the index contains NaNs or is not strictly increasing.
     """
     if has_nan(series.index):
         msg = """The Series you passed to interpolate contains
@@ -434,11 +520,12 @@ def interpolate(series, **options):
 def interpolate_inverse(series, **options):
     """Interpolate the inverse function of a Series.
 
-    series: Series object, represents a mapping from `a` to `b`
-    options: any legal options to scipy.interpolate.interp1d
+    Args:
+        series (pd.Series): Series representing a mapping from a to b.
+        **options: Additional keyword arguments for scipy.interpolate.interp1d.
 
-    returns: interpolation object, can be used as a function
-             from `b` to `a`
+    Returns:
+        callable: Interpolation object, can be used as a function from b to a.
     """
     inverse = pd.Series(series.index, index=series.values)
     interp_func = interpolate(inverse, **options)
@@ -450,22 +537,29 @@ def gradient(series, **options):
 
     If the elements of series have units, they are dropped.
 
-    series: Series object
-    options: any legal options to np.gradient
+    Args:
+        series (pd.Series): Series object.
+        **options: Additional keyword arguments for np.gradient.
 
-    returns: Series, same subclass as series
+    Returns:
+        pd.Series: Series with the same subclass as the input.
+        
+    Raises:
+        ValueError: If series is not a pandas Series
     """
+    if not isinstance(series, pd.Series):
+        raise ValueError("series must be a pandas Series")
     x = series.index
     y = series.values
-
     a = np.gradient(y, x, **options)
     return series.__class__(a, series.index)
 
 
 def source_code(obj):
-    """Prints the source code for a given object.
+    """Print the source code for a given object.
 
-    obj: function or method object
+    Args:
+        obj (object): Function or method object to print source for.
     """
     print(inspect.getsource(obj))
 
@@ -475,8 +569,12 @@ def underride(d, **options):
 
     If d is None, create a new dictionary.
 
-    d: dictionary
-    options: keyword args to add to d
+    Args:
+        d (dict): Dictionary to update.
+        **options: Keyword arguments to add to d.
+
+    Returns:
+        dict: Updated dictionary.
     """
     if d is None:
         d = {}
@@ -495,8 +593,9 @@ def contour(df, **options):
 
     Note: columns and index must be numerical
 
-    df: DataFrame
-    options: passed to plt.contour
+    Args:
+        df (pd.DataFrame): DataFrame to plot.
+        **options: Additional keyword arguments for plt.contour.
     """
     fontsize = options.pop("fontsize", 12)
     underride(options, cmap="viridis")
@@ -514,7 +613,9 @@ def savefig(filename, **options):
 
     https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html
 
-    filename: string
+    Args:
+        filename (str): Name of the file to save the figure to.
+        **options: Additional keyword arguments for plt.savefig.
     """
     print("Saving figure to file", filename)
     plt.savefig(filename, **options)
@@ -530,6 +631,9 @@ def decorate(**options):
 
     The keyword arguments can be any of the axis properties
     https://matplotlib.org/api/axes_api.html
+
+    Args:
+        **options: Keyword arguments for axis properties.
     """
     ax = plt.gca()
     ax.set(**options)
@@ -542,9 +646,10 @@ def decorate(**options):
 
 
 def remove_from_legend(bad_labels):
-    """Removes some labels from the legend.
+    """Remove specified labels from the current plot legend.
 
-    bad_labels: sequence of strings
+    Args:
+        bad_labels (list): Sequence of label strings to remove from the legend.
     """
     ax = plt.gca()
     handles, labels = ax.get_legend_handles_labels()
@@ -564,6 +669,12 @@ class SettableNamespace(SimpleNamespace):
     Takes keyword arguments and stores them as attributes.
     """
     def __init__(self, namespace=None, **kwargs):
+        """Initialize a SettableNamespace.
+
+        Args:
+            namespace (SettableNamespace, optional): Namespace to copy. Defaults to None.
+            **kwargs: Keyword arguments to store as attributes.
+        """
         super().__init__()
         if namespace:
             self.__dict__.update(namespace.__dict__)
@@ -572,8 +683,12 @@ def __init__(self, namespace=None, **kwargs):
     def get(self, name, default=None):
         """Look up a variable.
 
-        name: string varname
-        default: value returned if `name` is not present
+        Args:
+            name (str): Name of the variable to look up.
+            default (any, optional): Value returned if `name` is not present. Defaults to None.
+
+        Returns:
+            any: Value of the variable or default.
         """
         try:
             return self.__getattribute__(name, default)
@@ -583,7 +698,11 @@ def get(self, name, default=None):
     def set(self, **variables):
         """Make a copy and update the given variables.
 
-        returns: Params
+        Args:
+            **variables: Keyword arguments to update.
+
+        Returns:
+            Params: New Params object with updated variables.
         """
         new = copy(self)
         new.__dict__.update(variables)
@@ -591,22 +710,25 @@ def set(self, **variables):
 
 
 def magnitude(x):
-    """Returns the magnitude of a Quantity or number.
+    """Return the magnitude of a Quantity or number.
 
-    x: Quantity or number
+    Args:
+        x (object): Quantity or number.
 
-    returns: number
+    Returns:
+        float: Magnitude as a plain number.
     """
     return x.magnitude if hasattr(x, 'magnitude') else x
 
 
 def remove_units(namespace):
-    """Removes units from the values in a Namespace.
+    """Remove units from the values in a Namespace (top-level only).
 
-    Only removes units from top-level values;
-    does not traverse nested values.
+    Args:
+        namespace (object): Namespace with attributes.
 
-    returns: new Namespace object
+    Returns:
+        object: New Namespace object with units removed from values.
     """
     res = copy(namespace)
     for label, value in res.__dict__.items():
@@ -617,12 +739,13 @@ def remove_units(namespace):
 
 
 def remove_units_series(series):
-    """Removes units from the values in a Series.
+    """Remove units from the values in a Series (top-level only).
 
-    Only removes units from top-level values;
-    does not traverse nested values.
+    Args:
+        series (pd.Series): Series with possible units.
 
-    returns: new Series object
+    Returns:
+        pd.Series: New Series object with units removed from values.
     """
     res = copy(series)
     for label, value in res.items():
@@ -647,18 +770,35 @@ class Params(SettableNamespace):
 
 
 def State(**variables):
-    """Contains the values of state variables."""
+    """Contains the values of state variables.
+
+    Args:
+        **variables: Keyword arguments to store as state variables.
+
+    Returns:
+        pd.Series: Series with the state variables.
+    """
     return pd.Series(variables, name='state')
 
 
 def make_series(x, y, **options):
     """Make a Pandas Series.
 
-    x: sequence used as the index
-    y: sequence used as the values
+    Args:
+        x (sequence): Sequence used as the index.
+        y (sequence): Sequence used as the values.
+        **options: Additional keyword arguments for pd.Series.
 
-    returns: Pandas Series
+    Returns:
+        pd.Series: Pandas Series.
+        
+    Raises:
+        ValueError: If x or y are not array-like or have different lengths
     """
+    validate_array_like(x, "x")
+    validate_array_like(y, "y")
+    if len(x) != len(y):
+        raise ValueError("x and y must have the same length")
     underride(options, name='values')
     if isinstance(y, pd.Series):
         y = y.values
@@ -669,6 +809,13 @@ def make_series(x, y, **options):
 
 def TimeSeries(*args, **kwargs):
     """Make a pd.Series object to represent a time series.
+
+    Args:
+        *args: Arguments passed to pd.Series.
+        **kwargs: Keyword arguments passed to pd.Series.
+
+    Returns:
+        pd.Series: Series with index name 'Time' and name 'Quantity'.
     """
     if args or kwargs:
         underride(kwargs, dtype=float)
@@ -684,6 +831,13 @@ def TimeSeries(*args, **kwargs):
 
 def SweepSeries(*args, **kwargs):
     """Make a pd.Series object to store results from a parameter sweep.
+
+    Args:
+        *args: Arguments passed to pd.Series.
+        **kwargs: Keyword arguments passed to pd.Series.
+
+    Returns:
+        pd.Series: Series with index name 'Parameter' and name 'Metric'.
     """
     if args or kwargs:
         underride(kwargs, dtype=float)
@@ -698,7 +852,14 @@ def SweepSeries(*args, **kwargs):
 
 
 def show(obj):
-    """Display a Series or Namespace as a DataFrame."""
+    """Display a Series or Namespace as a DataFrame.
+
+    Args:
+        obj (object): Series or Namespace to display.
+
+    Returns:
+        pd.DataFrame: DataFrame representation of the object.
+    """
     if isinstance(obj, pd.Series):
         df = pd.DataFrame(obj)
         return df
@@ -710,21 +871,44 @@ def show(obj):
 
 
 def TimeFrame(*args, **kwargs):
-    """DataFrame that maps from time to State.
+    """Create a DataFrame that maps from time to State.
+
+    Args:
+        *args: Arguments passed to pd.DataFrame.
+        **kwargs: Keyword arguments passed to pd.DataFrame.
+
+    Returns:
+        pd.DataFrame: DataFrame indexed by time.
     """
     underride(kwargs, dtype=float)
     return pd.DataFrame(*args, **kwargs)
 
 
 def SweepFrame(*args, **kwargs):
-    """DataFrame that maps from parameter value to SweepSeries.
+    """Create a DataFrame that maps from parameter value to SweepSeries.
+
+    Args:
+        *args: Arguments passed to pd.DataFrame.
+        **kwargs: Keyword arguments passed to pd.DataFrame.
+
+    Returns:
+        pd.DataFrame: DataFrame indexed by parameter value.
     """
     underride(kwargs, dtype=float)
     return pd.DataFrame(*args, **kwargs)
 
 
 def Vector(x, y, z=None, **options):
-    """
+    """Create a 2D or 3D vector as a pandas Series.
+
+    Args:
+        x (float): x component.
+        y (float): y component.
+        z (float, optional): z component. Defaults to None.
+        **options: Additional keyword arguments for pandas.Series.
+
+    Returns:
+        pd.Series: Series with keys 'x', 'y', and optionally 'z'.
     """
     underride(options, name='component')
     if z is None:
@@ -736,12 +920,38 @@ def Vector(x, y, z=None, **options):
 ## Vector functions (should work with any sequence)
 
 def vector_mag(v):
-    """Vector magnitude."""
+    """Vector magnitude.
+
+    Args:
+        v (array-like): Vector.
+
+    Returns:
+        float: Magnitude of the vector.
+        
+    Raises:
+        ValueError: If v is not array-like or is empty
+    """
+    validate_array_like(v, "v")
+    if len(v) == 0:
+        raise ValueError("Vector cannot be empty")
     return np.sqrt(np.dot(v, v))
 
 
 def vector_mag2(v):
-    """Vector magnitude squared."""
+    """Vector magnitude squared.
+
+    Args:
+        v (array-like): Vector.
+
+    Returns:
+        float: Magnitude squared of the vector.
+        
+    Raises:
+        ValueError: If v is not array-like or is empty
+    """
+    validate_array_like(v, "v")
+    if len(v) == 0:
+        raise ValueError("Vector cannot be empty")
     return np.dot(v, v)
 
 
@@ -750,9 +960,18 @@ def vector_angle(v):
 
     Only works with 2-D vectors.
 
-    returns: angle in radians
+    Args:
+        v (array-like): 2-D vector.
+
+    Returns:
+        float: Angle in radians.
+        
+    Raises:
+        ValueError: If v is not array-like or is not 2D
     """
-    assert len(v) == 2
+    validate_array_like(v, "v")
+    if len(v) != 2:
+        raise ValueError("vector_angle only works with 2D vectors")
     x, y = v
     return np.arctan2(y, x)
 
@@ -760,16 +979,32 @@ def vector_angle(v):
 def vector_polar(v):
     """Vector magnitude and angle.
 
-    returns: (number, angle in radians)
+    Args:
+        v (array-like): Vector.
+
+    Returns:
+        tuple: (magnitude, angle in radians).
+        
+    Raises:
+        ValueError: If v is not array-like
     """
+    validate_array_like(v, "v")
     return vector_mag(v), vector_angle(v)
 
 
 def vector_hat(v):
     """Unit vector in the direction of v.
 
-    returns: Vector or array
+    Args:
+        v (array-like): Vector.
+
+    Returns:
+        array-like: Unit vector in the direction of v.
+        
+    Raises:
+        ValueError: If v is not array-like
     """
+    validate_array_like(v, "v")
     # check if the magnitude of the Quantity is 0
     mag = vector_mag(v)
     if mag == 0:
@@ -783,9 +1018,18 @@ def vector_perp(v):
 
     Only works with 2-D Vectors.
 
-    returns: Vector
+    Args:
+        v (array-like): 2-D vector.
+
+    Returns:
+        Vector: Perpendicular vector.
+        
+    Raises:
+        ValueError: If v is not array-like or is not 2D
     """
-    assert len(v) == 2
+    validate_array_like(v, "v")
+    if len(v) != 2:
+        raise ValueError("vector_perp only works with 2D vectors")
     x, y = v
     return Vector(-y, x)
 
@@ -793,18 +1037,43 @@ def vector_perp(v):
 def vector_dot(v, w):
     """Dot product of v and w.
 
-    returns: number or Quantity
+    Args:
+        v (array-like): First vector.
+        w (array-like): Second vector.
+
+    Returns:
+        float: Dot product of v and w.
+        
+    Raises:
+        ValueError: If v or w are not array-like or have different lengths
     """
+    validate_array_like(v, "v")
+    validate_array_like(w, "w")
+    if len(v) != len(w):
+        raise ValueError("Vectors must have the same length")
     return np.dot(v, w)
 
 
 def vector_cross(v, w):
     """Cross product of v and w.
 
-    returns: number or Quantity for 2-D, Vector for 3-D
+    Args:
+        v (array-like): First vector.
+        w (array-like): Second vector.
+
+    Returns:
+        array-like: Cross product of v and w.
+        
+    Raises:
+        ValueError: If v or w are not array-like, or not both 2D or 3D, or not same length
     """
+    validate_array_like(v, "v")
+    validate_array_like(w, "w")
+    if len(v) != len(w):
+        raise ValueError("Vectors must have the same length for cross product")
+    if len(v) not in (2, 3):
+        raise ValueError("Cross product only defined for 2D or 3D vectors")
     res = np.cross(v, w)
-
     if len(v) == 3:
         return Vector(*res)
     else:
@@ -814,8 +1083,20 @@ def vector_cross(v, w):
 def vector_proj(v, w):
     """Projection of v onto w.
 
-    returns: array or Vector with direction of w and units of v.
+    Args:
+        v (array-like): Vector to project.
+        w (array-like): Vector to project onto.
+
+    Returns:
+        array-like: Projection of v onto w.
+        
+    Raises:
+        ValueError: If v or w are not array-like or not same length
     """
+    validate_array_like(v, "v")
+    validate_array_like(w, "w")
+    if len(v) != len(w):
+        raise ValueError("Vectors must have the same length for projection")
     w_hat = vector_hat(w)
     return vector_dot(v, w_hat) * w_hat
 
@@ -825,13 +1106,33 @@ def scalar_proj(v, w):
 
     Which is the magnitude of the projection of v onto w.
 
-    returns: scalar with units of v.
+    Args:
+        v (array-like): Vector to project.
+        w (array-like): Vector to project onto.
+
+    Returns:
+        float: Scalar projection of v onto w.
     """
     return vector_dot(v, vector_hat(w))
 
 
 def vector_dist(v, w):
-    """Euclidean distance from v to w, with units."""
+    """Euclidean distance from v to w, with units.
+
+    Args:
+        v (array-like): First vector.
+        w (array-like): Second vector.
+
+    Returns:
+        float: Euclidean distance from v to w.
+        
+    Raises:
+        ValueError: If v or w are not array-like or not same length
+    """
+    validate_array_like(v, "v")
+    validate_array_like(w, "w")
+    if len(v) != len(w):
+        raise ValueError("Vectors must have the same length for distance calculation")
     if isinstance(v, list):
         v = np.asarray(v)
     return vector_mag(v - w)
@@ -839,7 +1140,22 @@ def vector_dist(v, w):
 
 def vector_diff_angle(v, w):
     """Angular difference between two vectors, in radians.
+
+    Args:
+        v (array-like): First vector.
+        w (array-like): Second vector.
+
+    Returns:
+        float: Angular difference in radians.
+
+    Raises:
+        ValueError: If v or w are not array-like or not same length
+        NotImplementedError: If the vectors are not 2-D.
     """
+    validate_array_like(v, "v")
+    validate_array_like(w, "w")
+    if len(v) != len(w):
+        raise ValueError("Vectors must have the same length for angle difference")
     if len(v) == 2:
         return vector_angle(v) - vector_angle(w)
     else:
@@ -855,9 +1171,16 @@ def plot_segment(A, B, **options):
 
     Additional options are passed along to plot().
 
-    A: Vector
-    B: Vector
+    Args:
+        A (Vector): First vector.
+        B (Vector): Second vector.
+        **options: Additional keyword arguments for plt.plot.
+        
+    Raises:
+        ValueError: If A or B are not Vector objects
     """
+    if not isinstance(A, pd.Series) or not isinstance(B, pd.Series):
+        raise ValueError("A and B must be Vector objects")
     xs = A.x, B.x
     ys = A.y, B.y
     plt.plot(xs, ys, **options)
@@ -869,10 +1192,19 @@ def plot_segment(A, B, **options):
 def animate(results, draw_func, *args, interval=None):
     """Animate results from a simulation.
 
-    results: TimeFrame
-    draw_func: function that draws state
-    interval: time between frames in seconds
+    Args:
+        results (TimeFrame): Results to animate.
+        draw_func (callable): Function that draws state.
+        *args: Additional positional arguments passed to draw_func.
+        interval (float, optional): Time between frames in seconds. Defaults to None.
+        
+    Raises:
+        ValueError: If results is not a TimeFrame or draw_func is not callable
     """
+    if not isinstance(results, pd.DataFrame):
+        raise ValueError("results must be a TimeFrame")
+    if not callable(draw_func):
+        raise ValueError("draw_func must be callable")
     plt.figure()
     try:
         for t, state in results.iterrows():

From cd0a1039e6a11e9ced7157248819742a06c15125 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 22 May 2025 16:04:13 -0400
Subject: [PATCH 134/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 278101 -> 278101 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index fcf026c40fd4f189ab1a8fbad75c90d788b3f326..f2312106c3e6ef299fd281bf98444e43f85d4eee 100644
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From 697c68b3a5143f9ffc583cf680a7f2d4003be69b Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 22 May 2025 16:05:12 -0400
Subject: [PATCH 135/144] Updating examples

---
 examples/bungee2.ipynb    |   4 +-
 examples/filter.ipynb     |  20 +++----
 examples/glucose.ipynb    |   4 +-
 examples/population.ipynb | 108 ++++++++++++++++++++++++++++++++++++++
 examples/wall.ipynb       |   4 +-
 5 files changed, 124 insertions(+), 16 deletions(-)
 create mode 100644 examples/population.ipynb

diff --git a/examples/bungee2.ipynb b/examples/bungee2.ipynb
index ff6dc9ad..d2c90373 100644
--- a/examples/bungee2.ipynb
+++ b/examples/bungee2.ipynb
@@ -429,7 +429,7 @@
    "source": [
     "## Phase 2\n",
     "\n",
-    "Once the jumper has falled more than the length of the cord, acceleration due to energy transfer from the cord stops abruptly.  As the cord stretches, it starts to exert a spring force.  So let's simulate this second phase."
+    "Once the jumper has fallen more than the length of the cord, acceleration due to energy transfer from the cord stops abruptly.  As the cord stretches, it starts to exert a spring force.  So let's simulate this second phase."
    ]
   },
   {
@@ -748,7 +748,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/examples/filter.ipynb b/examples/filter.ipynb
index a7aab5db..b5c1c8ae 100644
--- a/examples/filter.ipynb
+++ b/examples/filter.ipynb
@@ -758,7 +758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -777,7 +777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -810,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -826,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -835,7 +835,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -851,7 +851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -867,7 +867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -877,7 +877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -909,7 +909,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ModSimPy",
    "language": "python",
    "name": "python3"
   },
@@ -923,7 +923,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/examples/glucose.ipynb b/examples/glucose.ipynb
index e6fefbc2..08bae476 100644
--- a/examples/glucose.ipynb
+++ b/examples/glucose.ipynb
@@ -337,7 +337,7 @@
     "Each time `error_func` is called, it prints the parameters, so we can get a sense of how `leastsq` works.\n",
     "\n",
     "`leastsq` has two return values.\n",
-    "The is an array with the best parameters:"
+    "The first is an array with the best parameters:"
    ]
   },
   {
@@ -608,7 +608,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/examples/population.ipynb b/examples/population.ipynb
new file mode 100644
index 00000000..af67e35c
--- /dev/null
+++ b/examples/population.ipynb
@@ -0,0 +1,108 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# One Queue or Two"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# download modsim.py if necessary\n",
+    "\n",
+    "from os.path import basename, exists\n",
+    "\n",
+    "def download(url):\n",
+    "    filename = basename(url)\n",
+    "    if not exists(filename):\n",
+    "        from urllib.request import urlretrieve\n",
+    "        local, _ = urlretrieve(url, filename)\n",
+    "        print('Downloaded ' + local)\n",
+    "    \n",
+    "download('https://raw.githubusercontent.com/AllenDowney/' +\n",
+    "         'ModSimPy/master/data/World_population_estimates.html')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext nb_black"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import requests\n",
+    "\n",
+    "# Fetch the data.\n",
+    "df = pd.read_csv(\"https://ourworldindata.org/grapher/population.csv?v=1&csvType=filtered&useColumnShortNames=true&time=1700..latest&country=~OWID_WRL\", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})\n",
+    "\n",
+    "# Fetch the metadata\n",
+    "metadata = requests.get(\"https://ourworldindata.org/grapher/population.metadata.json?v=1&csvType=filtered&useColumnShortNames=true&time=1700..latest&country=~OWID_WRL\").json()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table2.loc[1950:1980].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Modeling and Simulation in Python*\n",
+    "\n",
+    "Copyright 2021 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/wall.ipynb b/examples/wall.ipynb
index 4814fe1d..d6583156 100644
--- a/examples/wall.ipynb
+++ b/examples/wall.ipynb
@@ -365,7 +365,7 @@
    "outputs": [],
    "source": [
     "def compute_flux(t, state, system):\n",
-    "    \"\"\"Compute the fluxes between the walls surfaces and the internal masses.\n",
+    "    \"\"\"Compute the fluxes between the wall's surfaces and the internal masses.\n",
     "    \n",
     "    state: State with T_C1 and T_C2\n",
     "    t: time in seconds\n",
@@ -621,7 +621,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

From fd52a3c6cf4f58eba6840ad6055c9d73134c419a Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 22 May 2025 19:59:36 -0400
Subject: [PATCH 136/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 278101 -> 246043 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index f2312106c3e6ef299fd281bf98444e43f85d4eee..b13fd484416704eafa504f829dac3adc006f70b4 100644
GIT binary patch
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From beaa438a92cbf2191a6c76497e37e6c1a6a3d592 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 3 Jul 2025 10:09:37 -0400
Subject: [PATCH 137/144] Updating modsim.py

---
 modsim.py | 156 ++++++++++++++++++++++++++++++------------------------
 1 file changed, 88 insertions(+), 68 deletions(-)

diff --git a/modsim.py b/modsim.py
index ce765923..84952767 100644
--- a/modsim.py
+++ b/modsim.py
@@ -41,43 +41,45 @@
     logger.warning("modsim.py depends on Python 3.6 features.")
 
 import inspect
+import numbers
 
 import matplotlib.pyplot as plt
 
-plt.rcParams['figure.dpi'] = 75
-plt.rcParams['savefig.dpi'] = 300
-plt.rcParams['figure.figsize'] = 6, 4
+plt.rcParams["figure.dpi"] = 75
+plt.rcParams["savefig.dpi"] = 300
+plt.rcParams["figure.figsize"] = 6, 4
+
+from copy import copy
+from types import SimpleNamespace
 
 import numpy as np
 import pandas as pd
 import scipy
-
 import scipy.optimize as spo
-
-from scipy.interpolate import interp1d
-from scipy.interpolate import InterpolatedUnivariateSpline
-
 from scipy.integrate import solve_ivp
-
-from types import SimpleNamespace
-from copy import copy
+from scipy.interpolate import InterpolatedUnivariateSpline, interp1d
 
 # Input validation helpers
+
+
 def validate_numeric(value, name):
     """Validate that a value is numeric."""
-    if not isinstance(value, (int, float)):
+    if not isinstance(value, numbers.Number):
         raise ValueError(f"{name} must be numeric, got {type(value)}")
 
+
 def validate_array_like(value, name):
-    """Validate that a value is array-like."""
-    if not isinstance(value, (list, tuple, np.ndarray, pd.Series)):
+    """Validate that a value is array-like by checking for __getitem__ and __iter__."""
+    if not (hasattr(value, "__getitem__") and hasattr(value, "__iter__")):
         raise ValueError(f"{name} must be array-like, got {type(value)}")
 
+
 def validate_positive(value, name):
     """Validate that a value is positive."""
     if value <= 0:
         raise ValueError(f"{name} must be positive, got {value}")
 
+
 def flip(p=0.5):
     """Flips a coin with the given probability.
 
@@ -108,7 +110,9 @@ def cart2pol(x, y, z=None):
         raise ValueError("x must be numeric or array-like")
     if not isinstance(y, (int, float, list, tuple, np.ndarray, pd.Series)):
         raise ValueError("y must be numeric or array-like")
-    if z is not None and not isinstance(z, (int, float, list, tuple, np.ndarray, pd.Series)):
+    if z is not None and not isinstance(
+        z, (int, float, list, tuple, np.ndarray, pd.Series)
+    ):
         raise ValueError("z must be numeric or array-like")
     x = np.asarray(x)
     y = np.asarray(y)
@@ -138,7 +142,9 @@ def pol2cart(theta, rho, z=None):
         raise ValueError("theta must be numeric or array-like")
     if not isinstance(rho, (int, float, list, tuple, np.ndarray, pd.Series)):
         raise ValueError("rho must be numeric or array-like")
-    if z is not None and not isinstance(z, (int, float, list, tuple, np.ndarray, pd.Series)):
+    if z is not None and not isinstance(
+        z, (int, float, list, tuple, np.ndarray, pd.Series)
+    ):
         raise ValueError("z must be numeric or array-like")
     x = rho * np.cos(theta)
     y = rho * np.sin(theta)
@@ -147,8 +153,10 @@ def pol2cart(theta, rho, z=None):
     else:
         return x, y, z
 
+
 from numpy import linspace
 
+
 def linrange(start, stop=None, step=1):
     """Make an array of equally spaced values.
 
@@ -163,8 +171,8 @@ def linrange(start, stop=None, step=1):
     if stop is None:
         stop = start
         start = 0
-    n = int(round((stop-start) / step))
-    return linspace(start, stop, n+1)
+    n = int(round((stop - start) / step))
+    return linspace(start, stop, n + 1)
 
 
 def __check_kwargs(kwargs, param_name, param_len, func, func_name):
@@ -183,18 +191,25 @@ def __check_kwargs(kwargs, param_name, param_len, func, func_name):
     """
     param_val = kwargs.get(param_name, None)
     if param_val is None or len(param_val) not in param_len:
-        msg = ("To run `{}`, you have to provide a "
-               "`{}` keyword argument with a sequence of length {}.")
-        raise ValueError(msg.format(func_name, param_name, ' or '.join(map(str, param_len))))
+        msg = (
+            "To run `{}`, you have to provide a "
+            "`{}` keyword argument with a sequence of length {}."
+        )
+        raise ValueError(
+            msg.format(func_name, param_name, " or ".join(map(str, param_len)))
+        )
 
     try:
         func(param_val[0])
     except Exception as e:
-        msg = ("In `{}` I tried running the function you provided "
-               "with `{}[0]`, and I got the following error:")
+        msg = (
+            "In `{}` I tried running the function you provided "
+            "with `{}[0]`, and I got the following error:"
+        )
         logger.error(msg.format(func_name, param_name))
         raise (e)
 
+
 def root_scalar(func, *args, **kwargs):
     """Find the input value that is a root of `func`.
 
@@ -214,13 +229,15 @@ def root_scalar(func, *args, **kwargs):
     """
     underride(kwargs, rtol=1e-4)
 
-    __check_kwargs(kwargs, 'bracket', [2], lambda x: func(x, *args), 'root_scalar')
+    __check_kwargs(kwargs, "bracket", [2], lambda x: func(x, *args), "root_scalar")
 
     res = spo.root_scalar(func, *args, **kwargs)
 
     if not res.converged:
-        msg = ("scipy.optimize.root_scalar did not converge. "
-               "The message it returned is:\n" + res.flag)
+        msg = (
+            "scipy.optimize.root_scalar did not converge. "
+            "The message it returned is:\n" + res.flag
+        )
         raise ValueError(msg)
 
     return res
@@ -243,29 +260,30 @@ def minimize_scalar(func, *args, **kwargs):
     Raises:
         Exception: If the optimization does not succeed.
     """
-    underride(kwargs, __func_name='minimize_scalar')
+    underride(kwargs, __func_name="minimize_scalar")
 
-    method = kwargs.get('method', None)
+    method = kwargs.get("method", None)
     if method is None:
-        method = 'bounded' if kwargs.get('bounds', None) else 'brent'
-        kwargs['method'] = method
+        method = "bounded" if kwargs.get("bounds", None) else "brent"
+        kwargs["method"] = method
 
-    if method == 'bounded':
-        param_name = 'bounds'
+    if method == "bounded":
+        param_name = "bounds"
         param_len = [2]
     else:
-        param_name = 'bracket'
+        param_name = "bracket"
         param_len = [2, 3]
 
-    func_name = kwargs.pop('__func_name')
+    func_name = kwargs.pop("__func_name")
     __check_kwargs(kwargs, param_name, param_len, lambda x: func(x, *args), func_name)
 
     res = spo.minimize_scalar(func, args=args, **kwargs)
 
     if not res.success:
-        msg = ("minimize_scalar did not succeed."
-               "The message it returned is: \n" +
-               res.message)
+        msg = (
+            "minimize_scalar did not succeed."
+            "The message it returned is: \n" + res.message
+        )
         raise Exception(msg)
 
     return res
@@ -287,10 +305,11 @@ def maximize_scalar(func, *args, **kwargs):
     Raises:
         Exception: If the optimization does not succeed.
     """
+
     def min_func(*args):
         return -func(*args)
 
-    underride(kwargs, __func_name='maximize_scalar')
+    underride(kwargs, __func_name="maximize_scalar")
 
     res = minimize_scalar(min_func, *args, **kwargs)
 
@@ -344,7 +363,7 @@ def run_solve_ivp(system, slope_func, **options):
         raise (e)
 
     # get the list of event functions
-    events = options.get('events', [])
+    events = options.get("events", [])
 
     # if there's only one event function, put it in a list
     try:
@@ -354,7 +373,7 @@ def run_solve_ivp(system, slope_func, **options):
 
     for event_func in events:
         # make events terminal unless otherwise specified
-        if not hasattr(event_func, 'terminal'):
+        if not hasattr(event_func, "terminal"):
             event_func.terminal = True
 
         # test the event function with the initial conditions
@@ -369,25 +388,26 @@ def run_solve_ivp(system, slope_func, **options):
             raise (e)
 
     # get dense output unless otherwise specified
-    if not 't_eval' in options:
+    if not "t_eval" in options:
         underride(options, dense_output=True)
 
     # run the solver
-    bunch = solve_ivp(slope_func, [t_0, system.t_end], system.init,
-                      args=[system], **options)
+    bunch = solve_ivp(
+        slope_func, [t_0, system.t_end], system.init, args=[system], **options
+    )
 
     # separate the results from the details
     y = bunch.pop("y")
     t = bunch.pop("t")
 
     # get the column names from `init`, if possible
-    if hasattr(system.init, 'index'):
+    if hasattr(system.init, "index"):
         columns = system.init.index
     else:
         columns = range(len(system.init))
 
     # evaluate the results at equally-spaced points
-    if options.get('dense_output', False):
+    if options.get("dense_output", False):
         try:
             num = system.num
         except AttributeError:
@@ -397,11 +417,9 @@ def run_solve_ivp(system, slope_func, **options):
         y_array = bunch.sol(t_array)
 
         # pack the results into a TimeFrame
-        results = TimeFrame(y_array.T, index=t_array,
-                        columns=columns)
+        results = TimeFrame(y_array.T, index=t_array, columns=columns)
     else:
-        results = TimeFrame(y.T, index=t,
-                        columns=columns)
+        results = TimeFrame(y.T, index=t, columns=columns)
 
     return results, bunch
 
@@ -428,11 +446,8 @@ def leastsq(error_func, x0, *args, **options):
     best_params, cov_x, infodict, mesg, ier = t
 
     # pack the results into a ModSimSeries object
-    details = SimpleNamespace(cov_x=cov_x,
-                              mesg=mesg,
-                              ier=ier,
-                              **infodict)
-    details.success = details.ier in [1,2,3,4]
+    details = SimpleNamespace(cov_x=cov_x, mesg=mesg, ier=ier, **infodict)
+    details.success = details.ier in [1, 2, 3, 4]
 
     # if we got a Params object, we should return a Params object
     if isinstance(x0, Params):
@@ -668,6 +683,7 @@ class SettableNamespace(SimpleNamespace):
 
     Takes keyword arguments and stores them as attributes.
     """
+
     def __init__(self, namespace=None, **kwargs):
         """Initialize a SettableNamespace.
 
@@ -718,7 +734,7 @@ def magnitude(x):
     Returns:
         float: Magnitude as a plain number.
     """
-    return x.magnitude if hasattr(x, 'magnitude') else x
+    return x.magnitude if hasattr(x, "magnitude") else x
 
 
 def remove_units(namespace):
@@ -758,6 +774,7 @@ class System(SettableNamespace):
 
     Takes keyword arguments and stores them as attributes.
     """
+
     pass
 
 
@@ -766,6 +783,7 @@ class Params(SettableNamespace):
 
     Takes keyword arguments and stores them as attributes.
     """
+
     pass
 
 
@@ -778,7 +796,7 @@ def State(**variables):
     Returns:
         pd.Series: Series with the state variables.
     """
-    return pd.Series(variables, name='state')
+    return pd.Series(variables, name="state")
 
 
 def make_series(x, y, **options):
@@ -799,11 +817,11 @@ def make_series(x, y, **options):
     validate_array_like(y, "y")
     if len(x) != len(y):
         raise ValueError("x and y must have the same length")
-    underride(options, name='values')
+    underride(options, name="values")
     if isinstance(y, pd.Series):
         y = y.values
     series = pd.Series(y, index=x, **options)
-    series.index.name = 'index'
+    series.index.name = "index"
     return series
 
 
@@ -823,9 +841,9 @@ def TimeSeries(*args, **kwargs):
     else:
         series = pd.Series([], dtype=float)
 
-    series.index.name = 'Time'
-    if 'name' not in kwargs:
-        series.name = 'Quantity'
+    series.index.name = "Time"
+    if "name" not in kwargs:
+        series.name = "Quantity"
     return series
 
 
@@ -845,9 +863,9 @@ def SweepSeries(*args, **kwargs):
     else:
         series = pd.Series([], dtype=np.float64)
 
-    series.index.name = 'Parameter'
-    if 'name' not in kwargs:
-        series.name = 'Metric'
+    series.index.name = "Parameter"
+    if "name" not in kwargs:
+        series.name = "Metric"
     return series
 
 
@@ -863,9 +881,8 @@ def show(obj):
     if isinstance(obj, pd.Series):
         df = pd.DataFrame(obj)
         return df
-    elif hasattr(obj, '__dict__'):
-        return pd.DataFrame(pd.Series(obj.__dict__),
-                            columns=['value'])
+    elif hasattr(obj, "__dict__"):
+        return pd.DataFrame(pd.Series(obj.__dict__), columns=["value"])
     else:
         return obj
 
@@ -910,7 +927,7 @@ def Vector(x, y, z=None, **options):
     Returns:
         pd.Series: Series with keys 'x', 'y', and optionally 'z'.
     """
-    underride(options, name='component')
+    underride(options, name="component")
     if z is None:
         return pd.Series(dict(x=x, y=y), **options)
     else:
@@ -919,6 +936,7 @@ def Vector(x, y, z=None, **options):
 
 ## Vector functions (should work with any sequence)
 
+
 def vector_mag(v):
     """Vector magnitude.
 
@@ -1187,8 +1205,10 @@ def plot_segment(A, B, **options):
 
 
 from time import sleep
+
 from IPython.display import clear_output
 
+
 def animate(results, draw_func, *args, interval=None):
     """Animate results from a simulation.
 

From 9446d67d64f315b858042e9df2f7a2903e57a6f2 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 3 Jul 2025 10:16:29 -0400
Subject: [PATCH 138/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 246043 -> 246318 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index b13fd484416704eafa504f829dac3adc006f70b4..2d5f007dc7f0a7b5771409298ffb4dc927f8eb84 100644
GIT binary patch
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From 5f4234adabd52287c5de7e0d738a4af54e1fbe1a Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Thu, 3 Jul 2025 10:17:36 -0400
Subject: [PATCH 139/144] Updating examples

---
 examples/filter.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/filter.ipynb b/examples/filter.ipynb
index b5c1c8ae..dd78c2a4 100644
--- a/examples/filter.ipynb
+++ b/examples/filter.ipynb
@@ -909,7 +909,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ModSimPy",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },

From 0706247d267182aef2aa022ea68986c977f9a0f0 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sun, 1 Feb 2026 15:01:56 -0500
Subject: [PATCH 140/144] Updating modsim.py

---
 .github/workflows/tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index d114f87b..e6c8423b 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -17,7 +17,7 @@ jobs:
       matrix:
         # don't run on macos-latest; it can't install pytables
         os: [ubuntu-latest, windows-latest]
-        python-version: ["3.10"]
+        python-version: ["3.12"]
 
     steps:
       - uses: actions/checkout@v2

From 2f3291e255f6ecdac44601b11948f24011e7d88c Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sun, 1 Feb 2026 15:01:59 -0500
Subject: [PATCH 141/144] Updating chapters

---
 chapters/chap22.ipynb | 152 +++++++++++++++++-------------------------
 1 file changed, 62 insertions(+), 90 deletions(-)

diff --git a/chapters/chap22.ipynb b/chapters/chap22.ipynb
index 0a5f28ee..fd0badd1 100644
--- a/chapters/chap22.ipynb
+++ b/chapters/chap22.ipynb
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "electoral-turkey",
    "metadata": {
     "tags": []
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "id": "formal-context",
    "metadata": {
     "tags": []
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "id": "progressive-typing",
    "metadata": {
     "tags": []
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "id": "handy-terrain",
    "metadata": {},
    "outputs": [],
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "id": "vocal-latino",
    "metadata": {},
    "outputs": [],
@@ -185,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "id": "controversial-shower",
    "metadata": {},
    "outputs": [],
@@ -193,34 +193,6 @@
     "A.y"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "earlier-contemporary",
-   "metadata": {},
-   "source": [
-    "You can also access them by index using brackets, like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "digital-channels",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "automated-drove",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A[1]"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "grave-burst",
@@ -232,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "id": "conditional-latitude",
    "metadata": {},
    "outputs": [],
@@ -251,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "id": "encouraging-cabinet",
    "metadata": {},
    "outputs": [],
@@ -278,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "id": "peripheral-tattoo",
    "metadata": {},
    "outputs": [],
@@ -303,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "id": "strange-cleaning",
    "metadata": {},
    "outputs": [],
@@ -324,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "id": "cellular-community",
    "metadata": {},
    "outputs": [],
@@ -348,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "id": "monetary-firmware",
    "metadata": {},
    "outputs": [],
@@ -371,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 20,
    "id": "explicit-piano",
    "metadata": {},
    "outputs": [],
@@ -389,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 21,
    "id": "relative-republic",
    "metadata": {},
    "outputs": [],
@@ -421,7 +393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 22,
    "id": "narrative-latest",
    "metadata": {
     "tags": []
@@ -459,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 23,
    "id": "bored-billy",
    "metadata": {
     "tags": []
@@ -509,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 24,
    "id": "ethical-donna",
    "metadata": {},
    "outputs": [],
@@ -527,7 +499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 25,
    "id": "legitimate-gossip",
    "metadata": {},
    "outputs": [],
@@ -547,7 +519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 26,
    "id": "legal-terminal",
    "metadata": {
     "tags": []
@@ -583,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 27,
    "id": "frank-chick",
    "metadata": {},
    "outputs": [],
@@ -606,7 +578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 28,
    "id": "suitable-salem",
    "metadata": {
     "tags": []
@@ -666,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 29,
    "id": "closing-simon",
    "metadata": {},
    "outputs": [],
@@ -696,7 +668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 30,
    "id": "brief-level",
    "metadata": {
     "tags": []
@@ -720,7 +692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 31,
    "id": "threatened-alberta",
    "metadata": {},
    "outputs": [],
@@ -738,7 +710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 32,
    "id": "special-background",
    "metadata": {},
    "outputs": [],
@@ -761,7 +733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 33,
    "id": "prospective-external",
    "metadata": {},
    "outputs": [],
@@ -779,7 +751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 34,
    "id": "medieval-calvin",
    "metadata": {},
    "outputs": [],
@@ -798,7 +770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 35,
    "id": "gorgeous-survey",
    "metadata": {},
    "outputs": [],
@@ -817,7 +789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 36,
    "id": "failing-bangkok",
    "metadata": {},
    "outputs": [],
@@ -836,7 +808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 37,
    "id": "copyrighted-highway",
    "metadata": {},
    "outputs": [],
@@ -855,7 +827,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 38,
    "id": "structured-adams",
    "metadata": {},
    "outputs": [],
@@ -883,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 39,
    "id": "spare-burst",
    "metadata": {},
    "outputs": [],
@@ -908,7 +880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 40,
    "id": "dated-browse",
    "metadata": {},
    "outputs": [],
@@ -941,7 +913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 41,
    "id": "resistant-vegetation",
    "metadata": {},
    "outputs": [],
@@ -964,7 +936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 42,
    "id": "starting-fabric",
    "metadata": {},
    "outputs": [],
@@ -1011,7 +983,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 43,
    "id": "prescription-boutique",
    "metadata": {
     "tags": []
@@ -1060,7 +1032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 44,
    "id": "optical-weather",
    "metadata": {},
    "outputs": [],
@@ -1072,7 +1044,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 45,
    "id": "acknowledged-belgium",
    "metadata": {},
    "outputs": [],
@@ -1082,7 +1054,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 46,
    "id": "spatial-ensemble",
    "metadata": {},
    "outputs": [],
@@ -1092,7 +1064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 47,
    "id": "domestic-apparatus",
    "metadata": {},
    "outputs": [],
@@ -1102,7 +1074,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 48,
    "id": "correct-pittsburgh",
    "metadata": {},
    "outputs": [],
@@ -1122,7 +1094,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 49,
    "id": "global-referral",
    "metadata": {},
    "outputs": [],
@@ -1134,7 +1106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 50,
    "id": "appointed-sugar",
    "metadata": {},
    "outputs": [],
@@ -1144,7 +1116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 51,
    "id": "specialized-mediterranean",
    "metadata": {},
    "outputs": [],
@@ -1178,7 +1150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 52,
    "id": "directed-moisture",
    "metadata": {
     "tags": []
@@ -1201,7 +1173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 53,
    "id": "fuzzy-register",
    "metadata": {
     "tags": []
@@ -1225,7 +1197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 54,
    "id": "returning-fellowship",
    "metadata": {
     "tags": []
@@ -1247,7 +1219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 55,
    "id": "reasonable-swaziland",
    "metadata": {
     "tags": []
@@ -1270,7 +1242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 56,
    "id": "attached-shower",
    "metadata": {
     "tags": []
@@ -1290,7 +1262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 57,
    "id": "christian-camcorder",
    "metadata": {},
    "outputs": [],
@@ -1312,7 +1284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 58,
    "id": "heated-belfast",
    "metadata": {
     "tags": []
@@ -1333,7 +1305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 59,
    "id": "engaged-provision",
    "metadata": {},
    "outputs": [],
@@ -1343,7 +1315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 60,
    "id": "directed-fiber",
    "metadata": {},
    "outputs": [],
@@ -1353,7 +1325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 61,
    "id": "framed-dealer",
    "metadata": {},
    "outputs": [],
@@ -1363,7 +1335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 62,
    "id": "accomplished-elizabeth",
    "metadata": {},
    "outputs": [],
@@ -1373,7 +1345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 63,
    "id": "going-techno",
    "metadata": {},
    "outputs": [],
@@ -1383,7 +1355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 64,
    "id": "brief-saying",
    "metadata": {},
    "outputs": [],
@@ -1393,7 +1365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 65,
    "id": "spare-pregnancy",
    "metadata": {},
    "outputs": [],
@@ -1403,7 +1375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 66,
    "id": "catholic-staff",
    "metadata": {},
    "outputs": [],
@@ -1413,7 +1385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 67,
    "id": "broad-sequence",
    "metadata": {},
    "outputs": [],
@@ -1433,7 +1405,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ModSimPy",
    "language": "python",
    "name": "python3"
   },
@@ -1447,7 +1419,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.13.11"
   }
  },
  "nbformat": 4,

From 6272977040fad4164865e63163e4d07149c973c1 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sun, 1 Feb 2026 15:01:59 -0500
Subject: [PATCH 142/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 246318 -> 245956 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
index 2d5f007dc7f0a7b5771409298ffb4dc927f8eb84..f68df00e3ad5463573123a13951d2c53d0016741 100644
GIT binary patch
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From 206d5193595755b3a1b70ec11089d41b2ad8b875 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sun, 1 Feb 2026 15:15:54 -0500
Subject: [PATCH 143/144] Updating the notebook zip file

---
 ModSimPyNotebooks.zip | Bin 245956 -> 245956 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/ModSimPyNotebooks.zip b/ModSimPyNotebooks.zip
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From 5ad7ae1318b5385244b7ed6c8fbafe518998a175 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Sun, 1 Feb 2026 15:18:12 -0500
Subject: [PATCH 144/144] Updating build system

---
 environment.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/environment.yml b/environment.yml
index 56d42fc4..628e539e 100644
--- a/environment.yml
+++ b/environment.yml
@@ -2,7 +2,8 @@ name: ModSimPy
 channels:
   - conda-forge
 dependencies:
-  - python
+  # Core dependencies for users
+  - python=3.13
   - jupyter
   - numpy
   - matplotlib
@@ -16,6 +17,5 @@ dependencies:
   - pytables
   - pip
 
-# pip install pytest nbmake
-# pip install jupyter-book ghp-import
-# pip install pypandoc fastdoc
+# For development dependencies, use environment-dev.yml
+# Install with: mamba env create -f environment-dev.yml